From 03938f9d3d655eb6936c64887336ec617a39e7cf Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Thu, 8 Jun 2017 15:09:43 +0800
Subject: [PATCH 01/16] Delete Your_first_neural_network.ipynb

---
 .../Your_first_neural_network.ipynb           | 555 ------------------
 1 file changed, 555 deletions(-)
 delete mode 100644 first-neural-network/Your_first_neural_network.ipynb

diff --git a/first-neural-network/Your_first_neural_network.ipynb b/first-neural-network/Your_first_neural_network.ipynb
deleted file mode 100644
index f7f7bae..0000000
--- a/first-neural-network/Your_first_neural_network.ipynb
+++ /dev/null
@@ -1,555 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 你的第一个神经网络\n",
-    "\n",
-    "在此项目中，你将构建你的第一个神经网络，并用该网络预测每日自行车租客人数。我们提供了一些代码，但是需要你来实现神经网络（大部分内容）。提交此项目后，欢迎进一步探索该数据和模型。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 加载和准备数据\n",
-    "\n",
-    "构建神经网络的关键一步是正确地准备数据。不同尺度级别的变量使网络难以高效地掌握正确的权重。我们在下方已经提供了加载和准备数据的代码。你很快将进一步学习这些代码！"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
-    "\n",
-    "rides = pd.read_csv(data_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "rides.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 数据简介\n",
-    "\n",
-    "此数据集包含的是从 2011 年 1 月 1 日到 2012 年 12 月 31 日期间每天每小时的骑车人数。骑车用户分成临时用户和注册用户，cnt 列是骑车用户数汇总列。你可以在上方看到前几行数据。\n",
-    "\n",
-    "下图展示的是数据集中前 10 天左右的骑车人数（某些天不一定是 24 个条目，所以不是精确的 10 天）。你可以在这里看到每小时租金。这些数据很复杂！周末的骑行人数少些，工作日上下班期间是骑行高峰期。我们还可以从上方的数据中看到温度、湿度和风速信息，所有这些信息都会影响骑行人数。你需要用你的模型展示所有这些数据。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "rides[:24*10].plot(x='dteday', y='cnt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 虚拟变量（哑变量）\n",
-    "\n",
-    "下面是一些分类变量，例如季节、天气、月份。要在我们的模型中包含这些数据，我们需要创建二进制虚拟变量。用 Pandas 库中的 `get_dummies()` 就可以轻松实现。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
-    "for each in dummy_fields:\n",
-    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
-    "    rides = pd.concat([rides, dummies], axis=1)\n",
-    "\n",
-    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
-    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
-    "data = rides.drop(fields_to_drop, axis=1)\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 调整目标变量\n",
-    "\n",
-    "为了更轻松地训练网络，我们将对每个连续变量标准化，即转换和调整变量，使它们的均值为 0，标准差为 1。\n",
-    "\n",
-    "我们会保存换算因子，以便当我们使用网络进行预测时可以还原数据。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
-    "# Store scalings in a dictionary so we can convert back later\n",
-    "scaled_features = {}\n",
-    "for each in quant_features:\n",
-    "    mean, std = data[each].mean(), data[each].std()\n",
-    "    scaled_features[each] = [mean, std]\n",
-    "    data.loc[:, each] = (data[each] - mean)/std"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 将数据拆分为训练、测试和验证数据集\n",
-    "\n",
-    "我们将大约最后 21 天的数据保存为测试数据集，这些数据集会在训练完网络后使用。我们将使用该数据集进行预测，并与实际的骑行人数进行对比。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Save data for approximately the last 21 days \n",
-    "test_data = data[-21*24:]\n",
-    "\n",
-    "# Now remove the test data from the data set \n",
-    "data = data[:-21*24]\n",
-    "\n",
-    "# Separate the data into features and targets\n",
-    "target_fields = ['cnt', 'casual', 'registered']\n",
-    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
-    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们将数据拆分为两个数据集，一个用作训练，一个在网络训练完后用来验证网络。因为数据是有时间序列特性的，所以我们用历史数据进行训练，然后尝试预测未来数据（验证数据集）。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Hold out the last 60 days or so of the remaining data as a validation set\n",
-    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
-    "val_features, val_targets = features[-60*24:], targets[-60*24:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 开始构建网络\n",
-    "\n",
-    "下面你将构建自己的网络。我们已经构建好结构和反向传递部分。你将实现网络的前向传递部分。还需要设置超参数：学习速率、隐藏单元的数量，以及训练传递数量。\n",
-    "\n",
-    "<img src=\"assets/neural_network.png\" width=300px>\n",
-    "\n",
-    "该网络有两个层级，一个隐藏层和一个输出层。隐藏层级将使用 S 型函数作为激活函数。输出层只有一个节点，用于递归，节点的输出和节点的输入相同。即激活函数是 $f(x)=x$。这种函数获得输入信号，并生成输出信号，但是会考虑阈值，称为激活函数。我们完成网络的每个层级，并计算每个神经元的输出。一个层级的所有输出变成下一层级神经元的输入。这一流程叫做前向传播（forward propagation）。\n",
-    "\n",
-    "我们在神经网络中使用权重将信号从输入层传播到输出层。我们还使用权重将错误从输出层传播回网络，以便更新权重。这叫做反向传播（backpropagation）。\n",
-    "\n",
-    "> **提示**：你需要为反向传播实现计算输出激活函数 ($f(x) = x$) 的导数。如果你不熟悉微积分，其实该函数就等同于等式 $y = x$。该等式的斜率是多少？也就是导数 $f(x)$。\n",
-    "\n",
-    "\n",
-    "你需要完成以下任务：\n",
-    "\n",
-    "1. 实现 S 型激活函数。将 `__init__` 中的 `self.activation_function`  设为你的 S 型函数。\n",
-    "2. 在 `train` 方法中实现前向传递。\n",
-    "3. 在 `train` 方法中实现反向传播算法，包括计算输出错误。\n",
-    "4. 在 `run` 方法中实现前向传递。\n",
-    "\n",
-    "  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class NeuralNetwork(object):\n",
-    "    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
-    "        # Set number of nodes in input, hidden and output layers.\n",
-    "        self.input_nodes = input_nodes\n",
-    "        self.hidden_nodes = hidden_nodes\n",
-    "        self.output_nodes = output_nodes\n",
-    "\n",
-    "        # Initialize weights\n",
-    "        self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, \n",
-    "                                       (self.input_nodes, self.hidden_nodes))\n",
-    "\n",
-    "        self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, \n",
-    "                                       (self.hidden_nodes, self.output_nodes))\n",
-    "        self.lr = learning_rate\n",
-    "        \n",
-    "        #### TODO: Set self.activation_function to your implemented sigmoid function ####\n",
-    "        #\n",
-    "        # Note: in Python, you can define a function with a lambda expression,\n",
-    "        # as shown below.\n",
-    "        self.activation_function = lambda x : 0  # Replace 0 with your sigmoid calculation.\n",
-    "        \n",
-    "        ### If the lambda code above is not something you're familiar with,\n",
-    "        # You can uncomment out the following three lines and put your \n",
-    "        # implementation there instead.\n",
-    "        #\n",
-    "        #def sigmoid(x):\n",
-    "        #    return 0  # Replace 0 with your sigmoid calculation here\n",
-    "        #self.activation_function = sigmoid\n",
-    "                    \n",
-    "    \n",
-    "    def train(self, features, targets):\n",
-    "        ''' Train the network on batch of features and targets. \n",
-    "        \n",
-    "            Arguments\n",
-    "            ---------\n",
-    "            \n",
-    "            features: 2D array, each row is one data record, each column is a feature\n",
-    "            targets: 1D array of target values\n",
-    "        \n",
-    "        '''\n",
-    "        n_records = features.shape[0]\n",
-    "        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n",
-    "        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n",
-    "        for X, y in zip(features, targets):\n",
-    "            #### Implement the forward pass here ####\n",
-    "            ### Forward pass ###\n",
-    "            # TODO: Hidden layer - Replace these values with your calculations.\n",
-    "            hidden_inputs = None # signals into hidden layer\n",
-    "            hidden_outputs = None # signals from hidden layer\n",
-    "\n",
-    "            # TODO: Output layer - Replace these values with your calculations.\n",
-    "            final_inputs = None # signals into final output layer\n",
-    "            final_outputs = None # signals from final output layer\n",
-    "            \n",
-    "            #### Implement the backward pass here ####\n",
-    "            ### Backward pass ###\n",
-    "\n",
-    "            # TODO: Output error - Replace this value with your calculations.\n",
-    "            error = None # Output layer error is the difference between desired target and actual output.\n",
-    "            \n",
-    "            # TODO: Calculate the hidden layer's contribution to the error\n",
-    "            hidden_error = None\n",
-    "            \n",
-    "            # TODO: Backpropagated error terms - Replace these values with your calculations.\n",
-    "            output_error_term = None\n",
-    "            hidden_error_term = None\n",
-    "\n",
-    "            # Weight step (input to hidden)\n",
-    "            delta_weights_i_h += None\n",
-    "            # Weight step (hidden to output)\n",
-    "            delta_weights_h_o += None\n",
-    "\n",
-    "        # TODO: Update the weights - Replace these values with your calculations.\n",
-    "        self.weights_hidden_to_output += None # update hidden-to-output weights with gradient descent step\n",
-    "        self.weights_input_to_hidden += None # update input-to-hidden weights with gradient descent step\n",
-    " \n",
-    "    def run(self, features):\n",
-    "        ''' Run a forward pass through the network with input features \n",
-    "        \n",
-    "            Arguments\n",
-    "            ---------\n",
-    "            features: 1D array of feature values\n",
-    "        '''\n",
-    "        \n",
-    "        #### Implement the forward pass here ####\n",
-    "        # TODO: Hidden layer - replace these values with the appropriate calculations.\n",
-    "        hidden_inputs = None # signals into hidden layer\n",
-    "        hidden_outputs = None # signals from hidden layer\n",
-    "        \n",
-    "        # TODO: Output layer - Replace these values with the appropriate calculations.\n",
-    "        final_inputs = None # signals into final output layer\n",
-    "        final_outputs = None # signals from final output layer \n",
-    "        \n",
-    "        return final_outputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def MSE(y, Y):\n",
-    "    return np.mean((y-Y)**2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 单元测试\n",
-    "\n",
-    "运行这些单元测试，检查你的网络实现是否正确。这样可以帮助你确保网络已正确实现，然后再开始训练网络。这些测试必须成功才能通过此项目。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import unittest\n",
-    "\n",
-    "inputs = np.array([[0.5, -0.2, 0.1]])\n",
-    "targets = np.array([[0.4]])\n",
-    "test_w_i_h = np.array([[0.1, -0.2],\n",
-    "                       [0.4, 0.5],\n",
-    "                       [-0.3, 0.2]])\n",
-    "test_w_h_o = np.array([[0.3],\n",
-    "                       [-0.1]])\n",
-    "\n",
-    "class TestMethods(unittest.TestCase):\n",
-    "    \n",
-    "    ##########\n",
-    "    # Unit tests for data loading\n",
-    "    ##########\n",
-    "    \n",
-    "    def test_data_path(self):\n",
-    "        # Test that file path to dataset has been unaltered\n",
-    "        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
-    "        \n",
-    "    def test_data_loaded(self):\n",
-    "        # Test that data frame loaded\n",
-    "        self.assertTrue(isinstance(rides, pd.DataFrame))\n",
-    "    \n",
-    "    ##########\n",
-    "    # Unit tests for network functionality\n",
-    "    ##########\n",
-    "\n",
-    "    def test_activation(self):\n",
-    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
-    "        # Test that the activation function is a sigmoid\n",
-    "        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
-    "\n",
-    "    def test_train(self):\n",
-    "        # Test that weights are updated correctly on training\n",
-    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
-    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
-    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
-    "        \n",
-    "        network.train(inputs, targets)\n",
-    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
-    "                                    np.array([[ 0.37275328], \n",
-    "                                              [-0.03172939]])))\n",
-    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
-    "                                    np.array([[ 0.10562014, -0.20185996], \n",
-    "                                              [0.39775194, 0.50074398], \n",
-    "                                              [-0.29887597, 0.19962801]])))\n",
-    "\n",
-    "    def test_run(self):\n",
-    "        # Test correctness of run method\n",
-    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
-    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
-    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
-    "\n",
-    "        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
-    "\n",
-    "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
-    "unittest.TextTestRunner().run(suite)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 训练网络\n",
-    "\n",
-    "现在你将设置网络的超参数。策略是设置的超参数使训练集上的错误很小但是数据不会过拟合。如果网络训练时间太长，或者有太多的隐藏节点，可能就会过于针对特定训练集，无法泛化到验证数据集。即当训练集的损失降低时，验证集的损失将开始增大。\n",
-    "\n",
-    "你还将采用随机梯度下降 (SGD) 方法训练网络。对于每次训练，都获取随机样本数据，而不是整个数据集。与普通梯度下降相比，训练次数要更多，但是每次时间更短。这样的话，网络训练效率更高。稍后你将详细了解 SGD。\n",
-    "\n",
-    "\n",
-    "### 选择迭代次数\n",
-    "\n",
-    "也就是训练网络时从训练数据中抽样的批次数量。迭代次数越多，模型就与数据越拟合。但是，如果迭代次数太多，模型就无法很好地泛化到其他数据，这叫做过拟合。你需要选择一个使训练损失很低并且验证损失保持中等水平的数字。当你开始过拟合时，你会发现训练损失继续下降，但是验证损失开始上升。\n",
-    "\n",
-    "### 选择学习速率\n",
-    "\n",
-    "速率可以调整权重更新幅度。如果速率太大，权重就会太大，导致网络无法与数据相拟合。建议从 0.1 开始。如果网络在与数据拟合时遇到问题，尝试降低学习速率。注意，学习速率越低，权重更新的步长就越小，神经网络收敛的时间就越长。\n",
-    "\n",
-    "\n",
-    "### 选择隐藏节点数量\n",
-    "\n",
-    "隐藏节点越多，模型的预测结果就越准确。尝试不同的隐藏节点的数量，看看对性能有何影响。你可以查看损失字典，寻找网络性能指标。如果隐藏单元的数量太少，那么模型就没有足够的空间进行学习，如果太多，则学习方向就有太多的选择。选择隐藏单元数量的技巧在于找到合适的平衡点。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "\n",
-    "### Set the hyperparameters here ###\n",
-    "iterations = 100\n",
-    "learning_rate = 0.1\n",
-    "hidden_nodes = 2\n",
-    "output_nodes = 1\n",
-    "\n",
-    "N_i = train_features.shape[1]\n",
-    "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
-    "\n",
-    "losses = {'train':[], 'validation':[]}\n",
-    "for ii in range(iterations):\n",
-    "    # Go through a random batch of 128 records from the training data set\n",
-    "    batch = np.random.choice(train_features.index, size=128)\n",
-    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
-    "                             \n",
-    "    network.train(X, y)\n",
-    "    \n",
-    "    # Printing out the training progress\n",
-    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
-    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
-    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
-    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
-    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
-    "    sys.stdout.flush()\n",
-    "    \n",
-    "    losses['train'].append(train_loss)\n",
-    "    losses['validation'].append(val_loss)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "plt.plot(losses['train'], label='Training loss')\n",
-    "plt.plot(losses['validation'], label='Validation loss')\n",
-    "plt.legend()\n",
-    "_ = plt.ylim()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 检查预测结果\n",
-    "\n",
-    "使用测试数据看看网络对数据建模的效果如何。如果完全错了，请确保网络中的每步都正确实现。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(8,4))\n",
-    "\n",
-    "mean, std = scaled_features['cnt']\n",
-    "predictions = network.run(test_features).T*std + mean\n",
-    "ax.plot(predictions[0], label='Prediction')\n",
-    "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
-    "ax.set_xlim(right=len(predictions))\n",
-    "ax.legend()\n",
-    "\n",
-    "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
-    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
-    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
-    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 可选：思考下你的结果（我们不会评估这道题的答案）\n",
-    "\n",
-    " \n",
-    "请针对你的结果回答以下问题。模型对数据的预测效果如何？哪里出现问题了？为何出现问题呢？\n",
-    "\n",
-    "> **注意**：你可以通过双击该单元编辑文本。如果想要预览文本，请按 Control + Enter\n",
-    "\n",
-    "#### 请将你的答案填写在下方\n"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 37f88d0fa37b9907f6c67763ba2e71f3d201c249 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Thu, 8 Jun 2017 15:14:38 +0800
Subject: [PATCH 02/16] Add files via upload

---
 .../Your_first_neural_network.html            | 18080 ++++++++++++++++
 .../Your_first_neural_network.ipynb           |  1111 +
 2 files changed, 19191 insertions(+)
 create mode 100644 first-neural-network/Your_first_neural_network.html
 create mode 100644 first-neural-network/Your_first_neural_network.ipynb

diff --git a/first-neural-network/Your_first_neural_network.html b/first-neural-network/Your_first_neural_network.html
new file mode 100644
index 0000000..1a63757
--- /dev/null
+++ b/first-neural-network/Your_first_neural_network.html
@@ -0,0 +1,18080 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>Your_first_neural_network</title>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.6 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: 1px dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    background: transparent !important;
+    color: #000 !important;
+    box-shadow: none !important;
+    text-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: 'Glyphicons Halflings';
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Glyphicons Halflings';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+  content: "\e156";
+}
+.glyphicon-unchecked:before {
+  content: "\e157";
+}
+.glyphicon-expand:before {
+  content: "\e158";
+}
+.glyphicon-collapse-down:before {
+  content: "\e159";
+}
+.glyphicon-collapse-up:before {
+  content: "\e160";
+}
+.glyphicon-log-in:before {
+  content: "\e161";
+}
+.glyphicon-flash:before {
+  content: "\e162";
+}
+.glyphicon-log-out:before {
+  content: "\e163";
+}
+.glyphicon-new-window:before {
+  content: "\e164";
+}
+.glyphicon-record:before {
+  content: "\e165";
+}
+.glyphicon-save:before {
+  content: "\e166";
+}
+.glyphicon-open:before {
+  content: "\e167";
+}
+.glyphicon-saved:before {
+  content: "\e168";
+}
+.glyphicon-import:before {
+  content: "\e169";
+}
+.glyphicon-export:before {
+  content: "\e170";
+}
+.glyphicon-send:before {
+  content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+  content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+  content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+  content: "\e174";
+}
+.glyphicon-floppy-save:before {
+  content: "\e175";
+}
+.glyphicon-floppy-open:before {
+  content: "\e176";
+}
+.glyphicon-credit-card:before {
+  content: "\e177";
+}
+.glyphicon-transfer:before {
+  content: "\e178";
+}
+.glyphicon-cutlery:before {
+  content: "\e179";
+}
+.glyphicon-header:before {
+  content: "\e180";
+}
+.glyphicon-compressed:before {
+  content: "\e181";
+}
+.glyphicon-earphone:before {
+  content: "\e182";
+}
+.glyphicon-phone-alt:before {
+  content: "\e183";
+}
+.glyphicon-tower:before {
+  content: "\e184";
+}
+.glyphicon-stats:before {
+  content: "\e185";
+}
+.glyphicon-sd-video:before {
+  content: "\e186";
+}
+.glyphicon-hd-video:before {
+  content: "\e187";
+}
+.glyphicon-subtitles:before {
+  content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+  content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+  content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+  content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+  content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+  content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
+.glyphicon-pawn:before {
+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
+.glyphicon-bitcoin:before {
+  content: "\e227";
+}
+.glyphicon-btc:before {
+  content: "\e227";
+}
+.glyphicon-xbt:before {
+  content: "\e227";
+}
+.glyphicon-yen:before {
+  content: "\00a5";
+}
+.glyphicon-jpy:before {
+  content: "\00a5";
+}
+.glyphicon-ruble:before {
+  content: "\20bd";
+}
+.glyphicon-rub:before {
+  content: "\20bd";
+}
+.glyphicon-scale:before {
+  content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+  content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+  content: "\e232";
+}
+.glyphicon-education:before {
+  content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+  content: "\e234";
+}
+.glyphicon-option-vertical:before {
+  content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+  content: "\e236";
+}
+.glyphicon-modal-window:before {
+  content: "\e237";
+}
+.glyphicon-oil:before {
+  content: "\e238";
+}
+.glyphicon-grain:before {
+  content: "\e239";
+}
+.glyphicon-sunglasses:before {
+  content: "\e240";
+}
+.glyphicon-text-size:before {
+  content: "\e241";
+}
+.glyphicon-text-color:before {
+  content: "\e242";
+}
+.glyphicon-text-background:before {
+  content: "\e243";
+}
+.glyphicon-object-align-top:before {
+  content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+  content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+  content: "\e246";
+}
+.glyphicon-object-align-left:before {
+  content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+  content: "\e248";
+}
+.glyphicon-object-align-right:before {
+  content: "\e249";
+}
+.glyphicon-triangle-right:before {
+  content: "\e250";
+}
+.glyphicon-triangle-left:before {
+  content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+  content: "\e252";
+}
+.glyphicon-triangle-top:before {
+  content: "\e253";
+}
+.glyphicon-console:before {
+  content: "\e254";
+}
+.glyphicon-superscript:before {
+  content: "\e255";
+}
+.glyphicon-subscript:before {
+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
+h5 small,
+h6 small,
+.h1 small,
+.h2 small,
+.h3 small,
+.h4 small,
+.h5 small,
+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 18px;
+  margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
+h2,
+.h2 {
+  font-size: 27px;
+}
+h3,
+.h3 {
+  font-size: 23px;
+}
+h4,
+.h4 {
+  font-size: 17px;
+}
+h5,
+.h5 {
+  font-size: 13px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
+address {
+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
+.col-xs-9 {
+  width: 75%;
+}
+.col-xs-8 {
+  width: 66.66666667%;
+}
+.col-xs-7 {
+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+  content: "\f044";
+}
+.fa-share-square-o:before {
+  content: "\f045";
+}
+.fa-check-square-o:before {
+  content: "\f046";
+}
+.fa-arrows:before {
+  content: "\f047";
+}
+.fa-step-backward:before {
+  content: "\f048";
+}
+.fa-fast-backward:before {
+  content: "\f049";
+}
+.fa-backward:before {
+  content: "\f04a";
+}
+.fa-play:before {
+  content: "\f04b";
+}
+.fa-pause:before {
+  content: "\f04c";
+}
+.fa-stop:before {
+  content: "\f04d";
+}
+.fa-forward:before {
+  content: "\f04e";
+}
+.fa-fast-forward:before {
+  content: "\f050";
+}
+.fa-step-forward:before {
+  content: "\f051";
+}
+.fa-eject:before {
+  content: "\f052";
+}
+.fa-chevron-left:before {
+  content: "\f053";
+}
+.fa-chevron-right:before {
+  content: "\f054";
+}
+.fa-plus-circle:before {
+  content: "\f055";
+}
+.fa-minus-circle:before {
+  content: "\f056";
+}
+.fa-times-circle:before {
+  content: "\f057";
+}
+.fa-check-circle:before {
+  content: "\f058";
+}
+.fa-question-circle:before {
+  content: "\f059";
+}
+.fa-info-circle:before {
+  content: "\f05a";
+}
+.fa-crosshairs:before {
+  content: "\f05b";
+}
+.fa-times-circle-o:before {
+  content: "\f05c";
+}
+.fa-check-circle-o:before {
+  content: "\f05d";
+}
+.fa-ban:before {
+  content: "\f05e";
+}
+.fa-arrow-left:before {
+  content: "\f060";
+}
+.fa-arrow-right:before {
+  content: "\f061";
+}
+.fa-arrow-up:before {
+  content: "\f062";
+}
+.fa-arrow-down:before {
+  content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+  content: "\f064";
+}
+.fa-expand:before {
+  content: "\f065";
+}
+.fa-compress:before {
+  content: "\f066";
+}
+.fa-plus:before {
+  content: "\f067";
+}
+.fa-minus:before {
+  content: "\f068";
+}
+.fa-asterisk:before {
+  content: "\f069";
+}
+.fa-exclamation-circle:before {
+  content: "\f06a";
+}
+.fa-gift:before {
+  content: "\f06b";
+}
+.fa-leaf:before {
+  content: "\f06c";
+}
+.fa-fire:before {
+  content: "\f06d";
+}
+.fa-eye:before {
+  content: "\f06e";
+}
+.fa-eye-slash:before {
+  content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+  content: "\f071";
+}
+.fa-plane:before {
+  content: "\f072";
+}
+.fa-calendar:before {
+  content: "\f073";
+}
+.fa-random:before {
+  content: "\f074";
+}
+.fa-comment:before {
+  content: "\f075";
+}
+.fa-magnet:before {
+  content: "\f076";
+}
+.fa-chevron-up:before {
+  content: "\f077";
+}
+.fa-chevron-down:before {
+  content: "\f078";
+}
+.fa-retweet:before {
+  content: "\f079";
+}
+.fa-shopping-cart:before {
+  content: "\f07a";
+}
+.fa-folder:before {
+  content: "\f07b";
+}
+.fa-folder-open:before {
+  content: "\f07c";
+}
+.fa-arrows-v:before {
+  content: "\f07d";
+}
+.fa-arrows-h:before {
+  content: "\f07e";
+}
+.fa-bar-chart-o:before,
+.fa-bar-chart:before {
+  content: "\f080";
+}
+.fa-twitter-square:before {
+  content: "\f081";
+}
+.fa-facebook-square:before {
+  content: "\f082";
+}
+.fa-camera-retro:before {
+  content: "\f083";
+}
+.fa-key:before {
+  content: "\f084";
+}
+.fa-gears:before,
+.fa-cogs:before {
+  content: "\f085";
+}
+.fa-comments:before {
+  content: "\f086";
+}
+.fa-thumbs-o-up:before {
+  content: "\f087";
+}
+.fa-thumbs-o-down:before {
+  content: "\f088";
+}
+.fa-star-half:before {
+  content: "\f089";
+}
+.fa-heart-o:before {
+  content: "\f08a";
+}
+.fa-sign-out:before {
+  content: "\f08b";
+}
+.fa-linkedin-square:before {
+  content: "\f08c";
+}
+.fa-thumb-tack:before {
+  content: "\f08d";
+}
+.fa-external-link:before {
+  content: "\f08e";
+}
+.fa-sign-in:before {
+  content: "\f090";
+}
+.fa-trophy:before {
+  content: "\f091";
+}
+.fa-github-square:before {
+  content: "\f092";
+}
+.fa-upload:before {
+  content: "\f093";
+}
+.fa-lemon-o:before {
+  content: "\f094";
+}
+.fa-phone:before {
+  content: "\f095";
+}
+.fa-square-o:before {
+  content: "\f096";
+}
+.fa-bookmark-o:before {
+  content: "\f097";
+}
+.fa-phone-square:before {
+  content: "\f098";
+}
+.fa-twitter:before {
+  content: "\f099";
+}
+.fa-facebook:before {
+  content: "\f09a";
+}
+.fa-github:before {
+  content: "\f09b";
+}
+.fa-unlock:before {
+  content: "\f09c";
+}
+.fa-credit-card:before {
+  content: "\f09d";
+}
+.fa-rss:before {
+  content: "\f09e";
+}
+.fa-hdd-o:before {
+  content: "\f0a0";
+}
+.fa-bullhorn:before {
+  content: "\f0a1";
+}
+.fa-bell:before {
+  content: "\f0f3";
+}
+.fa-certificate:before {
+  content: "\f0a3";
+}
+.fa-hand-o-right:before {
+  content: "\f0a4";
+}
+.fa-hand-o-left:before {
+  content: "\f0a5";
+}
+.fa-hand-o-up:before {
+  content: "\f0a6";
+}
+.fa-hand-o-down:before {
+  content: "\f0a7";
+}
+.fa-arrow-circle-left:before {
+  content: "\f0a8";
+}
+.fa-arrow-circle-right:before {
+  content: "\f0a9";
+}
+.fa-arrow-circle-up:before {
+  content: "\f0aa";
+}
+.fa-arrow-circle-down:before {
+  content: "\f0ab";
+}
+.fa-globe:before {
+  content: "\f0ac";
+}
+.fa-wrench:before {
+  content: "\f0ad";
+}
+.fa-tasks:before {
+  content: "\f0ae";
+}
+.fa-filter:before {
+  content: "\f0b0";
+}
+.fa-briefcase:before {
+  content: "\f0b1";
+}
+.fa-arrows-alt:before {
+  content: "\f0b2";
+}
+.fa-group:before,
+.fa-users:before {
+  content: "\f0c0";
+}
+.fa-chain:before,
+.fa-link:before {
+  content: "\f0c1";
+}
+.fa-cloud:before {
+  content: "\f0c2";
+}
+.fa-flask:before {
+  content: "\f0c3";
+}
+.fa-cut:before,
+.fa-scissors:before {
+  content: "\f0c4";
+}
+.fa-copy:before,
+.fa-files-o:before {
+  content: "\f0c5";
+}
+.fa-paperclip:before {
+  content: "\f0c6";
+}
+.fa-save:before,
+.fa-floppy-o:before {
+  content: "\f0c7";
+}
+.fa-square:before {
+  content: "\f0c8";
+}
+.fa-navicon:before,
+.fa-reorder:before,
+.fa-bars:before {
+  content: "\f0c9";
+}
+.fa-list-ul:before {
+  content: "\f0ca";
+}
+.fa-list-ol:before {
+  content: "\f0cb";
+}
+.fa-strikethrough:before {
+  content: "\f0cc";
+}
+.fa-underline:before {
+  content: "\f0cd";
+}
+.fa-table:before {
+  content: "\f0ce";
+}
+.fa-magic:before {
+  content: "\f0d0";
+}
+.fa-truck:before {
+  content: "\f0d1";
+}
+.fa-pinterest:before {
+  content: "\f0d2";
+}
+.fa-pinterest-square:before {
+  content: "\f0d3";
+}
+.fa-google-plus-square:before {
+  content: "\f0d4";
+}
+.fa-google-plus:before {
+  content: "\f0d5";
+}
+.fa-money:before {
+  content: "\f0d6";
+}
+.fa-caret-down:before {
+  content: "\f0d7";
+}
+.fa-caret-up:before {
+  content: "\f0d8";
+}
+.fa-caret-left:before {
+  content: "\f0d9";
+}
+.fa-caret-right:before {
+  content: "\f0da";
+}
+.fa-columns:before {
+  content: "\f0db";
+}
+.fa-unsorted:before,
+.fa-sort:before {
+  content: "\f0dc";
+}
+.fa-sort-down:before,
+.fa-sort-desc:before {
+  content: "\f0dd";
+}
+.fa-sort-up:before,
+.fa-sort-asc:before {
+  content: "\f0de";
+}
+.fa-envelope:before {
+  content: "\f0e0";
+}
+.fa-linkedin:before {
+  content: "\f0e1";
+}
+.fa-rotate-left:before,
+.fa-undo:before {
+  content: "\f0e2";
+}
+.fa-legal:before,
+.fa-gavel:before {
+  content: "\f0e3";
+}
+.fa-dashboard:before,
+.fa-tachometer:before {
+  content: "\f0e4";
+}
+.fa-comment-o:before {
+  content: "\f0e5";
+}
+.fa-comments-o:before {
+  content: "\f0e6";
+}
+.fa-flash:before,
+.fa-bolt:before {
+  content: "\f0e7";
+}
+.fa-sitemap:before {
+  content: "\f0e8";
+}
+.fa-umbrella:before {
+  content: "\f0e9";
+}
+.fa-paste:before,
+.fa-clipboard:before {
+  content: "\f0ea";
+}
+.fa-lightbulb-o:before {
+  content: "\f0eb";
+}
+.fa-exchange:before {
+  content: "\f0ec";
+}
+.fa-cloud-download:before {
+  content: "\f0ed";
+}
+.fa-cloud-upload:before {
+  content: "\f0ee";
+}
+.fa-user-md:before {
+  content: "\f0f0";
+}
+.fa-stethoscope:before {
+  content: "\f0f1";
+}
+.fa-suitcase:before {
+  content: "\f0f2";
+}
+.fa-bell-o:before {
+  content: "\f0a2";
+}
+.fa-coffee:before {
+  content: "\f0f4";
+}
+.fa-cutlery:before {
+  content: "\f0f5";
+}
+.fa-file-text-o:before {
+  content: "\f0f6";
+}
+.fa-building-o:before {
+  content: "\f0f7";
+}
+.fa-hospital-o:before {
+  content: "\f0f8";
+}
+.fa-ambulance:before {
+  content: "\f0f9";
+}
+.fa-medkit:before {
+  content: "\f0fa";
+}
+.fa-fighter-jet:before {
+  content: "\f0fb";
+}
+.fa-beer:before {
+  content: "\f0fc";
+}
+.fa-h-square:before {
+  content: "\f0fd";
+}
+.fa-plus-square:before {
+  content: "\f0fe";
+}
+.fa-angle-double-left:before {
+  content: "\f100";
+}
+.fa-angle-double-right:before {
+  content: "\f101";
+}
+.fa-angle-double-up:before {
+  content: "\f102";
+}
+.fa-angle-double-down:before {
+  content: "\f103";
+}
+.fa-angle-left:before {
+  content: "\f104";
+}
+.fa-angle-right:before {
+  content: "\f105";
+}
+.fa-angle-up:before {
+  content: "\f106";
+}
+.fa-angle-down:before {
+  content: "\f107";
+}
+.fa-desktop:before {
+  content: "\f108";
+}
+.fa-laptop:before {
+  content: "\f109";
+}
+.fa-tablet:before {
+  content: "\f10a";
+}
+.fa-mobile-phone:before,
+.fa-mobile:before {
+  content: "\f10b";
+}
+.fa-circle-o:before {
+  content: "\f10c";
+}
+.fa-quote-left:before {
+  content: "\f10d";
+}
+.fa-quote-right:before {
+  content: "\f10e";
+}
+.fa-spinner:before {
+  content: "\f110";
+}
+.fa-circle:before {
+  content: "\f111";
+}
+.fa-mail-reply:before,
+.fa-reply:before {
+  content: "\f112";
+}
+.fa-github-alt:before {
+  content: "\f113";
+}
+.fa-folder-o:before {
+  content: "\f114";
+}
+.fa-folder-open-o:before {
+  content: "\f115";
+}
+.fa-smile-o:before {
+  content: "\f118";
+}
+.fa-frown-o:before {
+  content: "\f119";
+}
+.fa-meh-o:before {
+  content: "\f11a";
+}
+.fa-gamepad:before {
+  content: "\f11b";
+}
+.fa-keyboard-o:before {
+  content: "\f11c";
+}
+.fa-flag-o:before {
+  content: "\f11d";
+}
+.fa-flag-checkered:before {
+  content: "\f11e";
+}
+.fa-terminal:before {
+  content: "\f120";
+}
+.fa-code:before {
+  content: "\f121";
+}
+.fa-mail-reply-all:before,
+.fa-reply-all:before {
+  content: "\f122";
+}
+.fa-star-half-empty:before,
+.fa-star-half-full:before,
+.fa-star-half-o:before {
+  content: "\f123";
+}
+.fa-location-arrow:before {
+  content: "\f124";
+}
+.fa-crop:before {
+  content: "\f125";
+}
+.fa-code-fork:before {
+  content: "\f126";
+}
+.fa-unlink:before,
+.fa-chain-broken:before {
+  content: "\f127";
+}
+.fa-question:before {
+  content: "\f128";
+}
+.fa-info:before {
+  content: "\f129";
+}
+.fa-exclamation:before {
+  content: "\f12a";
+}
+.fa-superscript:before {
+  content: "\f12b";
+}
+.fa-subscript:before {
+  content: "\f12c";
+}
+.fa-eraser:before {
+  content: "\f12d";
+}
+.fa-puzzle-piece:before {
+  content: "\f12e";
+}
+.fa-microphone:before {
+  content: "\f130";
+}
+.fa-microphone-slash:before {
+  content: "\f131";
+}
+.fa-shield:before {
+  content: "\f132";
+}
+.fa-calendar-o:before {
+  content: "\f133";
+}
+.fa-fire-extinguisher:before {
+  content: "\f134";
+}
+.fa-rocket:before {
+  content: "\f135";
+}
+.fa-maxcdn:before {
+  content: "\f136";
+}
+.fa-chevron-circle-left:before {
+  content: "\f137";
+}
+.fa-chevron-circle-right:before {
+  content: "\f138";
+}
+.fa-chevron-circle-up:before {
+  content: "\f139";
+}
+.fa-chevron-circle-down:before {
+  content: "\f13a";
+}
+.fa-html5:before {
+  content: "\f13b";
+}
+.fa-css3:before {
+  content: "\f13c";
+}
+.fa-anchor:before {
+  content: "\f13d";
+}
+.fa-unlock-alt:before {
+  content: "\f13e";
+}
+.fa-bullseye:before {
+  content: "\f140";
+}
+.fa-ellipsis-h:before {
+  content: "\f141";
+}
+.fa-ellipsis-v:before {
+  content: "\f142";
+}
+.fa-rss-square:before {
+  content: "\f143";
+}
+.fa-play-circle:before {
+  content: "\f144";
+}
+.fa-ticket:before {
+  content: "\f145";
+}
+.fa-minus-square:before {
+  content: "\f146";
+}
+.fa-minus-square-o:before {
+  content: "\f147";
+}
+.fa-level-up:before {
+  content: "\f148";
+}
+.fa-level-down:before {
+  content: "\f149";
+}
+.fa-check-square:before {
+  content: "\f14a";
+}
+.fa-pencil-square:before {
+  content: "\f14b";
+}
+.fa-external-link-square:before {
+  content: "\f14c";
+}
+.fa-share-square:before {
+  content: "\f14d";
+}
+.fa-compass:before {
+  content: "\f14e";
+}
+.fa-toggle-down:before,
+.fa-caret-square-o-down:before {
+  content: "\f150";
+}
+.fa-toggle-up:before,
+.fa-caret-square-o-up:before {
+  content: "\f151";
+}
+.fa-toggle-right:before,
+.fa-caret-square-o-right:before {
+  content: "\f152";
+}
+.fa-euro:before,
+.fa-eur:before {
+  content: "\f153";
+}
+.fa-gbp:before {
+  content: "\f154";
+}
+.fa-dollar:before,
+.fa-usd:before {
+  content: "\f155";
+}
+.fa-rupee:before,
+.fa-inr:before {
+  content: "\f156";
+}
+.fa-cny:before,
+.fa-rmb:before,
+.fa-yen:before,
+.fa-jpy:before {
+  content: "\f157";
+}
+.fa-ruble:before,
+.fa-rouble:before,
+.fa-rub:before {
+  content: "\f158";
+}
+.fa-won:before,
+.fa-krw:before {
+  content: "\f159";
+}
+.fa-bitcoin:before,
+.fa-btc:before {
+  content: "\f15a";
+}
+.fa-file:before {
+  content: "\f15b";
+}
+.fa-file-text:before {
+  content: "\f15c";
+}
+.fa-sort-alpha-asc:before {
+  content: "\f15d";
+}
+.fa-sort-alpha-desc:before {
+  content: "\f15e";
+}
+.fa-sort-amount-asc:before {
+  content: "\f160";
+}
+.fa-sort-amount-desc:before {
+  content: "\f161";
+}
+.fa-sort-numeric-asc:before {
+  content: "\f162";
+}
+.fa-sort-numeric-desc:before {
+  content: "\f163";
+}
+.fa-thumbs-up:before {
+  content: "\f164";
+}
+.fa-thumbs-down:before {
+  content: "\f165";
+}
+.fa-youtube-square:before {
+  content: "\f166";
+}
+.fa-youtube:before {
+  content: "\f167";
+}
+.fa-xing:before {
+  content: "\f168";
+}
+.fa-xing-square:before {
+  content: "\f169";
+}
+.fa-youtube-play:before {
+  content: "\f16a";
+}
+.fa-dropbox:before {
+  content: "\f16b";
+}
+.fa-stack-overflow:before {
+  content: "\f16c";
+}
+.fa-instagram:before {
+  content: "\f16d";
+}
+.fa-flickr:before {
+  content: "\f16e";
+}
+.fa-adn:before {
+  content: "\f170";
+}
+.fa-bitbucket:before {
+  content: "\f171";
+}
+.fa-bitbucket-square:before {
+  content: "\f172";
+}
+.fa-tumblr:before {
+  content: "\f173";
+}
+.fa-tumblr-square:before {
+  content: "\f174";
+}
+.fa-long-arrow-down:before {
+  content: "\f175";
+}
+.fa-long-arrow-up:before {
+  content: "\f176";
+}
+.fa-long-arrow-left:before {
+  content: "\f177";
+}
+.fa-long-arrow-right:before {
+  content: "\f178";
+}
+.fa-apple:before {
+  content: "\f179";
+}
+.fa-windows:before {
+  content: "\f17a";
+}
+.fa-android:before {
+  content: "\f17b";
+}
+.fa-linux:before {
+  content: "\f17c";
+}
+.fa-dribbble:before {
+  content: "\f17d";
+}
+.fa-skype:before {
+  content: "\f17e";
+}
+.fa-foursquare:before {
+  content: "\f180";
+}
+.fa-trello:before {
+  content: "\f181";
+}
+.fa-female:before {
+  content: "\f182";
+}
+.fa-male:before {
+  content: "\f183";
+}
+.fa-gittip:before {
+  content: "\f184";
+}
+.fa-sun-o:before {
+  content: "\f185";
+}
+.fa-moon-o:before {
+  content: "\f186";
+}
+.fa-archive:before {
+  content: "\f187";
+}
+.fa-bug:before {
+  content: "\f188";
+}
+.fa-vk:before {
+  content: "\f189";
+}
+.fa-weibo:before {
+  content: "\f18a";
+}
+.fa-renren:before {
+  content: "\f18b";
+}
+.fa-pagelines:before {
+  content: "\f18c";
+}
+.fa-stack-exchange:before {
+  content: "\f18d";
+}
+.fa-arrow-circle-o-right:before {
+  content: "\f18e";
+}
+.fa-arrow-circle-o-left:before {
+  content: "\f190";
+}
+.fa-toggle-left:before,
+.fa-caret-square-o-left:before {
+  content: "\f191";
+}
+.fa-dot-circle-o:before {
+  content: "\f192";
+}
+.fa-wheelchair:before {
+  content: "\f193";
+}
+.fa-vimeo-square:before {
+  content: "\f194";
+}
+.fa-turkish-lira:before,
+.fa-try:before {
+  content: "\f195";
+}
+.fa-plus-square-o:before {
+  content: "\f196";
+}
+.fa-space-shuttle:before {
+  content: "\f197";
+}
+.fa-slack:before {
+  content: "\f198";
+}
+.fa-envelope-square:before {
+  content: "\f199";
+}
+.fa-wordpress:before {
+  content: "\f19a";
+}
+.fa-openid:before {
+  content: "\f19b";
+}
+.fa-institution:before,
+.fa-bank:before,
+.fa-university:before {
+  content: "\f19c";
+}
+.fa-mortar-board:before,
+.fa-graduation-cap:before {
+  content: "\f19d";
+}
+.fa-yahoo:before {
+  content: "\f19e";
+}
+.fa-google:before {
+  content: "\f1a0";
+}
+.fa-reddit:before {
+  content: "\f1a1";
+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+ul#tabs {
+  margin-bottom: 4px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+@-moz-document url-prefix() {
+  div.inner_cell {
+    overflow-x: hidden;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 20ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
+    display: none;
+  }
+  span.autosave_status {
+    font-size: x-small;
+  }
+}
+.toolbar {
+  padding: 0px;
+  margin-left: -5px;
+  margin-top: 2px;
+  margin-bottom: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+  width: auto;
+  vertical-align: middle;
+  margin-right: 2px;
+  margin-bottom: 0px;
+  display: inline;
+  font-size: 92%;
+  margin-left: 0.3em;
+  margin-right: 0.3em;
+  padding: 0px;
+  padding-top: 3px;
+}
+.toolbar .btn {
+  padding: 2px 8px;
+}
+.toolbar .btn-group {
+  margin-top: 0px;
+  margin-left: 5px;
+}
+#maintoolbar {
+  margin-bottom: -3px;
+  margin-top: -8px;
+  border: 0px;
+  min-height: 27px;
+  margin-left: 0px;
+  padding-top: 11px;
+  padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+  float: none;
+  vertical-align: middle;
+  text-align: right;
+  margin-left: 5px;
+  margin-right: 0px;
+  margin-top: 0px;
+}
+.select-xs {
+  height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+  background-color: #F37626;
+  color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-webkit-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-moz-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+@-webkit-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+  overflow: auto;
+  height: 200px;
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+  text-overflow: ellipsis;
+  overflow: hidden;
+  height: 80px;
+}
+.tooltipbuttons {
+  position: absolute;
+  padding-right: 15px;
+  top: 0px;
+  right: 0px;
+}
+.tooltiptext {
+  /*avoid the button to overlap on some docstring*/
+  padding-right: 30px;
+}
+.ipython_tooltip {
+  max-width: 700px;
+  /*fade-in animation when inserted*/
+  -webkit-animation: fadeOut 400ms;
+  -moz-animation: fadeOut 400ms;
+  animation: fadeOut 400ms;
+  -webkit-animation: fadeIn 400ms;
+  -moz-animation: fadeIn 400ms;
+  animation: fadeIn 400ms;
+  vertical-align: middle;
+  background-color: #f7f7f7;
+  overflow: visible;
+  border: #ababab 1px solid;
+  outline: none;
+  padding: 3px;
+  margin: 0px;
+  padding-left: 7px;
+  font-family: monospace;
+  min-height: 50px;
+  -moz-box-shadow: 0px 6px 10px -1px #adadad;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  border-radius: 2px;
+  position: absolute;
+  z-index: 1000;
+}
+.ipython_tooltip a {
+  float: right;
+}
+.ipython_tooltip .tooltiptext pre {
+  border: 0;
+  border-radius: 0;
+  font-size: 100%;
+  background-color: #f7f7f7;
+}
+.pretooltiparrow {
+  left: 0px;
+  margin: 0px;
+  top: -16px;
+  width: 40px;
+  height: 16px;
+  overflow: hidden;
+  position: absolute;
+}
+.pretooltiparrow:before {
+  background-color: #f7f7f7;
+  border: 1px #ababab solid;
+  z-index: 11;
+  content: "";
+  position: absolute;
+  left: 15px;
+  top: 10px;
+  width: 25px;
+  height: 25px;
+  -webkit-transform: rotate(45deg);
+  -moz-transform: rotate(45deg);
+  -ms-transform: rotate(45deg);
+  -o-transform: rotate(45deg);
+}
+ul.typeahead-list i {
+  margin-left: -10px;
+  width: 18px;
+}
+ul.typeahead-list {
+  max-height: 80vh;
+  overflow: auto;
+}
+ul.typeahead-list > li > a {
+  /** Firefox bug **/
+  /* see https://github.com/jupyter/notebook/issues/559 */
+  white-space: normal;
+}
+.cmd-palette .modal-body {
+  padding: 7px;
+}
+.cmd-palette form {
+  background: white;
+}
+.cmd-palette input {
+  outline: none;
+}
+.no-shortcut {
+  display: none;
+}
+.command-shortcut:before {
+  content: "(command)";
+  padding-right: 3px;
+  color: #777777;
+}
+.edit-shortcut:before {
+  content: "(edit)";
+  padding-right: 3px;
+  color: #777777;
+}
+#find-and-replace #replace-preview .match,
+#find-and-replace #replace-preview .insert {
+  background-color: #BBDEFB;
+  border-color: #90CAF9;
+  border-style: solid;
+  border-width: 1px;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .match {
+  background-color: #FFCDD2;
+  border-color: #EF9A9A;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .insert {
+  background-color: #C8E6C9;
+  border-color: #A5D6A7;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview {
+  max-height: 60vh;
+  overflow: auto;
+}
+#find-and-replace #replace-preview pre {
+  padding: 5px 10px;
+}
+.terminal-app {
+  background: #EEE;
+}
+.terminal-app #header {
+  background: #fff;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.terminal-app .terminal {
+  float: left;
+  font-family: monospace;
+  color: white;
+  background: black;
+  padding: 0.4em;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+}
+.terminal-app .terminal,
+.terminal-app .terminal dummy-screen {
+  line-height: 1em;
+  font-size: 14px;
+}
+.terminal-app .terminal-cursor {
+  color: black;
+  background: white;
+}
+.terminal-app #terminado-container {
+  margin-top: 20px;
+}
+/*# sourceMappingURL=style.min.css.map */
+    </style>
+<style type="text/css">
+    .highlight .hll { background-color: #ffffcc }
+.highlight  { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #7D9029 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #A0A000 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+    </style>
+<style type="text/css">
+    
+/* Temporary definitions which will become obsolete with Notebook release 5.0 */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-bold { font-weight: bold; }
+
+    </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+  overflow: visible;
+  padding: 8px;
+}
+
+div#notebook {
+  overflow: visible;
+  border-top: none;
+}
+
+@media print {
+  div.cell {
+    display: block;
+    page-break-inside: avoid;
+  } 
+  div.output_wrapper { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+  div.output { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+    <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    MathJax.Hub.Config({
+        tex2jax: {
+            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+            processEscapes: true,
+            processEnvironments: true
+        },
+        // Center justify equations in code and markdown cells. Elsewhere
+        // we use CSS to left justify single line equations in code cells.
+        displayAlign: 'center',
+        "HTML-CSS": {
+            styles: {'.MathJax_Display': {"margin": 0}},
+            linebreaks: { automatic: true }
+        }
+    });
+    </script>
+    <!-- End of mathjax configuration --></head>
+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#20320;&#30340;&#31532;&#19968;&#20010;&#31070;&#32463;&#32593;&#32476;">&#20320;&#30340;&#31532;&#19968;&#20010;&#31070;&#32463;&#32593;&#32476;<a class="anchor-link" href="#&#20320;&#30340;&#31532;&#19968;&#20010;&#31070;&#32463;&#32593;&#32476;">&#182;</a></h1><p>在此项目中，你将构建你的第一个神经网络，并用该网络预测每日自行车租客人数。我们提供了一些代码，但是需要你来实现神经网络（大部分内容）。提交此项目后，欢迎进一步探索该数据和模型。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#21152;&#36733;&#21644;&#20934;&#22791;&#25968;&#25454;">&#21152;&#36733;&#21644;&#20934;&#22791;&#25968;&#25454;<a class="anchor-link" href="#&#21152;&#36733;&#21644;&#20934;&#22791;&#25968;&#25454;">&#182;</a></h2><p>构建神经网络的关键一步是正确地准备数据。不同尺度级别的变量使网络难以高效地掌握正确的权重。我们在下方已经提供了加载和准备数据的代码。你很快将进一步学习这些代码！</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_path</span> <span class="o">=</span> <span class="s1">&#39;Bike-Sharing-Dataset/hour.csv&#39;</span>
+
+<span class="n">rides</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rides</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[3]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>instant</th>
+      <th>dteday</th>
+      <th>season</th>
+      <th>yr</th>
+      <th>mnth</th>
+      <th>hr</th>
+      <th>holiday</th>
+      <th>weekday</th>
+      <th>workingday</th>
+      <th>weathersit</th>
+      <th>temp</th>
+      <th>atemp</th>
+      <th>hum</th>
+      <th>windspeed</th>
+      <th>casual</th>
+      <th>registered</th>
+      <th>cnt</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>2011-01-01</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>6</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0.24</td>
+      <td>0.2879</td>
+      <td>0.81</td>
+      <td>0.0000</td>
+      <td>3</td>
+      <td>13</td>
+      <td>16</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>2011-01-01</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>1</td>
+      <td>0</td>
+      <td>6</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0.22</td>
+      <td>0.2727</td>
+      <td>0.80</td>
+      <td>0.0000</td>
+      <td>8</td>
+      <td>32</td>
+      <td>40</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>2011-01-01</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>2</td>
+      <td>0</td>
+      <td>6</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0.22</td>
+      <td>0.2727</td>
+      <td>0.80</td>
+      <td>0.0000</td>
+      <td>5</td>
+      <td>27</td>
+      <td>32</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4</td>
+      <td>2011-01-01</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>3</td>
+      <td>0</td>
+      <td>6</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0.24</td>
+      <td>0.2879</td>
+      <td>0.75</td>
+      <td>0.0000</td>
+      <td>3</td>
+      <td>10</td>
+      <td>13</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>2011-01-01</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>4</td>
+      <td>0</td>
+      <td>6</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0.24</td>
+      <td>0.2879</td>
+      <td>0.75</td>
+      <td>0.0000</td>
+      <td>0</td>
+      <td>1</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>6</td>
+      <td>2011-01-01</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>5</td>
+      <td>0</td>
+      <td>6</td>
+      <td>0</td>
+      <td>2</td>
+      <td>0.24</td>
+      <td>0.2576</td>
+      <td>0.75</td>
+      <td>0.0896</td>
+      <td>0</td>
+      <td>1</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>7</td>
+      <td>2011-01-01</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>6</td>
+      <td>0</td>
+      <td>6</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0.22</td>
+      <td>0.2727</td>
+      <td>0.80</td>
+      <td>0.0000</td>
+      <td>2</td>
+      <td>0</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>8</td>
+      <td>2011-01-01</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>7</td>
+      <td>0</td>
+      <td>6</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0.20</td>
+      <td>0.2576</td>
+      <td>0.86</td>
+      <td>0.0000</td>
+      <td>1</td>
+      <td>2</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>9</td>
+      <td>2011-01-01</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>8</td>
+      <td>0</td>
+      <td>6</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0.24</td>
+      <td>0.2879</td>
+      <td>0.75</td>
+      <td>0.0000</td>
+      <td>1</td>
+      <td>7</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>10</td>
+      <td>2011-01-01</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>9</td>
+      <td>0</td>
+      <td>6</td>
+      <td>0</td>
+      <td>1</td>
+      <td>0.32</td>
+      <td>0.3485</td>
+      <td>0.76</td>
+      <td>0.0000</td>
+      <td>8</td>
+      <td>6</td>
+      <td>14</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#25968;&#25454;&#31616;&#20171;">&#25968;&#25454;&#31616;&#20171;<a class="anchor-link" href="#&#25968;&#25454;&#31616;&#20171;">&#182;</a></h2><p>此数据集包含的是从 2011 年 1 月 1 日到 2012 年 12 月 31 日期间每天每小时的骑车人数。骑车用户分成临时用户和注册用户，cnt 列是骑车用户数汇总列。你可以在上方看到前几行数据。</p>
+<p>下图展示的是数据集中前 10 天左右的骑车人数（某些天不一定是 24 个条目，所以不是精确的 10 天）。你可以在这里看到每小时租金。这些数据很复杂！周末的骑行人数少些，工作日上下班期间是骑行高峰期。我们还可以从上方的数据中看到温度、湿度和风速信息，所有这些信息都会影响骑行人数。你需要用你的模型展示所有这些数据。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rides</span><span class="p">[:</span><span class="mi">24</span><span class="o">*</span><span class="mi">10</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;dteday&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;cnt&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[4]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x2435e964278&gt;</pre>
+</div>
+
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABBoAAALzCAYAAAC/R2QvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAAewgAAHsIBbtB1PgAAIABJREFUeJzs3Xu0ZHV95/3P75zTTXfTzUVppaXVcGkvaJxwMcZLHhTR
+R8eExKWOhmRiZFRiMmYxurw/BEEzGSMTjeNEFC/gM6PELHXGDAyJDMo8CnJN1Ag4Cijdne7YCDR9
+pZtzfs8fu3bXr/bZe9dtX37fXe/XWmedOudU19lVXWfX3p/6fr8/570XAAAAAABAFeba3gAAAAAA
+ANAdBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAy
+BA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0A
+AAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAAAKAyBA0AAAAA
+AKAy0QcNzrnTnHMXOOf+1jm32Tm33zm3yzn3Q+fcZ51zzx/hNl7vnFsa8eN3R7i91c65dzrnbnbO
+/dw5t9s5d6dz7hLn3JOquecAAAAAANiz0PYGlHHO/W9JL+h96YMfrZB0kqRNkn7POfd5SW/y3h8c
+cpN+yM9H2aaTJF3d+/3h7T1F0lMlvdE599ve+6um/V0AAAAAAFgTddAgaYOSk/l/kvTXkv4/SfdJ
+mpf0XElvl3ScpN9Vcl9+Z4TbfKmkbSU/31L0A+fcWklXqR8yfErSX0naJ+lFkt4j6QhJVzrnnu+9
+/94I2wMAAAAAQGc476d+k782zrmvSbpC0ld8zoY65x4j6QYl1QRe0hne+2/lXO/1kj7Xu87x3vv7
+JtyeiyX9P73beYf3/s8zP3+upOuVBCHXe+/PnOT3AAAAAABgVdQzGrz3Z3vvv5wXMvR+/oCSqobU
+q+vaFufcgqS3KgkZ7syGDL3tuVHSZyQ5SWc4506ra3sAAAAAAIhR1EHDiL4RXD6xxt/zIklH9i5f
+UXK9y4PLr6xtawAAAAAAiFAXgobDgsuLNf6eFwSXry+53q2S9vYuD10RAwAAAACALulC0PDC4PKd
+I1z/cufcVufcI865Hc65G51zH3DOPWHIvzs5uHxX0ZW894uSfqykfeLpI2wPAAAAAACdYTpocM45
+Se8KvvWlEf7ZGZKOVbJKxWMk/bKk90n6sXPuzSX/bmPv8x7v/cNDfsfm3uf1zrkVI2wTAAAAAACd
+EPvylsO8TUlQ4CV92Xv/9yXXvVvSlyV9R/0g4ARJr1IyRHKVpE8455a895/O+ffrep93j7Bde4LL
+ayU9OMK/AQAAAADAvKiXtyzjnDtD0teVhCXbJT3Le39/wXXXee93ldzWv5T01d5t7ZV0ovf+Z5nr
+/FhJMHGf9/4XhmzbFZL+tZIA5Ine+38a9X4BAAAAAGCZydYJ59wzJH1FSTCwT9JrikIGSSoLGXo/
+v1rSxUrmKqyR9G9yrra/93nlCJsYDqjcN8L1AQAAAADoBHOtE8654yX9raSjJT0q6bXe+29XcNOf
+UhI2SMkchz/N/DwNK9aOcFuHB5dHabU4xDm3R0lQ4SU9MMI/WZS0NM7vAAAAAADMhDlJ8yNc7zFK
+3nh/xHt/+LArD2MqaOitDHGtpCcoObl+g/f+f1Rx2977Hc65n0t6rKTjcq6yRdJzJB3unDtiyEDI
+J/Y+7/DeHxxzUw5T/4nwuDH/LQAAAAAAkzps+FWGMxM0OOceq2Qmw/FK3u3/t977/1rxrykbWHGH
+ksGRkvQ0STfnXck5Ny/pxN5tjbLcZuE2rF+/fuiV5+fnNT8/SkAF2HHgwAHt2LFD69ev18qVo3Qr
+Ad3E3wLA3wGQ4m8Bk1hcXNTi4uLQ691///3qzW+spFreRNDgnDtC0t9JerqSE/F3ee8vrfh3HCPp
+mN6XecMbvxVcPkMFQYOk05W0TnhJk7R0PCDpcevXr9fPfvazoVcGuuj222/XaaedpmuuuUannnpq
+25sDtIa/BYC/AyDF3wLqtHHjRm3dulWSKjkJjX4YpHNutaSrJZ2i5OT9g977S2r4Vecp6UmRpOtz
+fv5NSTt7l19fcjtvCC5/dfrNAgAAAADAjqiDBufcCkn/TdLzlIQMH/XeXzjmbTzZOfdLQ67za5Iu
+6H25T9LnstfpzVr4mJIw4unOubfn3M5zJZ3b29Zveu9vG2dbAQAAAACwLvbWiSslvUTJift1kj7b
+W9qyyAHv/Y8y3/sFSd9wzt0o6W8kfVf9cpATJL1GyewF1/s9b/febyu4/Q9Leq2kp0j6sHNuU28b
+90k6U9J7lDymeyWdP/rdBAAAAACgG2IPGl7Z++wkvVjS94dc/ydKwoMsL+lXJD234N95SXskne+9
+/0zRjXvvdzvnXiHpKkmbJL259xHezk5J53jvh20rAAAAAACdE3vQULYKxKjXv03S7ygJGU6XtEHJ
+0McFSQ9K+oGk/yXp0977+4f+Au/vds6dIukPlVRDnCRppaTNSgKIj3nvN4+53QAAAAAAdELUQYP3
+fup1G733uyV9sfdRCe/9PkmX9D4AAAAAAEBP1MMgAQAAAACALQQNAAAAAACgMgQN8VmUpPn5qbtG
+ALM2bNigCy+8UBs2bGh7U4BW8bcA8HcApPhbgCXO+3HnLaJOzrktko477rjjtGXLlrY3BwAAAADQ
+cRs3btTWrVslaav3fuO0t0dFAwAAAAAAqAxBAwAAAAAAqAxBAwAAAAAAqMxC2xsAAAAAALE4/fTT
+tX379rY3Axjbscceq1tvvbXtzZBE0AAAAAAAh2zfvj0digdgQgQNAAAAAJAxNzfHUpIwYdu2bVpa
+Wmp7MwYQNAAAAABAxoYNG1huHiYES1NGg2GQAAAAAACgMgQNAAAAAACgMgQNAAAAAACgMgQNAAAA
+AACgMgQNAAAAAACgMgQNAAAAAACgMgQNAAAAAACgMgQNAAAAAACgMgQNAAAAAACgMgQNAAAAAACg
+MgQNAAAAAACgMgQNAAAAAACgMgQNAAAAAADU6IorrtDc3Jzm5uZ07rnntr05tSNoAAAAAACgAc65
+tjehEQQNAAAAAACgMgQNAAAAAACgMgQNAAAAAADUyHvf9iY0iqABAAAAANA5u3bt0sc//nGdffbZ
+Ov7447Vu3TqtWrVKxx13nM466yxdfPHFuuOOO5b9uze84Q2HBjd+/vOflyTt27dPf/mXf6lf/dVf
+1bHHHqtVq1bpSU96ks455xzdcMMNhdvwe7/3ewMDIL33uvzyyw/dfvhx5pln1vNAtGCh7Q0AAAAA
+AKBKl156qd73vvfpwQcflDQ4hHH79u3atm2brrvuOr3//e/XNddco5e+9KXLbiP9N3feeade9apX
+6a677hq4nS1btujKK6/UlVdeqQsvvFAXXnhh7m2k/yatapiFgZAEDQAAAACAzvijP/ojffzjHz90
+kj8/P69nP/vZ2rRpk1atWqUdO3boH/7hH/STn/xEkrR///7C29q6dave8573aPv27Tr66KMPVTTc
+f//9uu6667Rz505J0sUXX6yTTz5Zr3nNawb+/Ute8hKtW7dOd911l6699lo55/S0pz1NL37xi5f9
+rk2bNlX3ILSMoAEAAAAA0AmXXnrpoZBBkl772tfqwx/+sI477rhl173jjjt02WWXac2aNYW3d/HF
+F+vAgQN697vfrQsuuECrVq069LOHHnpIr371q3XdddfJOaf3vve9y4KGc845R+ecc46uuOIKXXvt
+tZKk5zznOfrYxz5Wxd2NFjMaAAAAAADmPfTQQ3rXu951KGR4y1veoi984Qu5IYMknXzyyfrIRz6i
+s846K/fn3nsdOHBA733ve/Unf/InAyGDJB111FH6whe+oMMPP1zee91zzz265ZZbqr1TRhE0AAAA
+AADM+9SnPqVdu3bJe68nP/nJ+shHPjL1ba5fv14XXHBB4c8f97jH6RWveMWhr2+++eapf2cX0DoB
+AAAAAA05/XRp+/a2t2Jyxx4r3Xpr21uR75prrpGUDFt805vepBUrVkx1e845/fqv/7pWrlxZer1T
+TjlFX/rSlyTp0NyHWUfQAAAAAAAN2b5d2rq17a3opptuuunQ5Re96EWV3OYv/uIvDr3OYx/72EOX
+H3744Up+r3UEDQAAAADQkGOPbXsLphPr9u/atUv79u079PUJJ5xQye0eeeSRQ68TVk4cPHiwkt9r
+HUEDAAAAADQk1rYD63bt2jXw9dq1ayu53XSwJMbDMEgAAAAAgGnr1q0b+Hr37t0tbQkkggYAAAAA
+gHHr1q3T6tWrD3197733trg1IGgAAAAAAJj3nOc859Dl6667rsUtWW7WWjAIGgAAAAAA5r385S8/
+dPmyyy6LajDjqlWrDl2OabvqQtAAAAAAADDvTW96k9auXSvvvX7605/q/PPPb3uTDgmXwNw6A+ub
+EjQAAAAAAMw76qij9KEPfUiS5L3XJz7xCb3uda8rPLH/wQ9+oPPPP1/XXntt7dv2zGc+89Dlm266
+SVu2bKn9d7aJ5S0BAAAAAJ3wlre8RT/4wQ/0iU98Qt57felLX9KXv/xlPfvZz9ZTnvIUrVq1Sjt2
+7NDf//3f6yc/+YmcczrzzDNr367HP/7xet7znqcbbrhB+/bt07Oe9Sy97GUv04YNGzQ3l7z/f+KJ
+J+r3f//3a9+WJhA0AAAAAAA64+Mf/7ie+tSn6o//+I/18MMPa2lpSd/5znf0ne9859B1nHOHPtas
+WdPIdv3FX/yFXvziF2vXrl3auXOnrrzyyoGfv/CFL+xM0EDrBAAAAACgU9761rfqnnvu0SWXXKKX
+vOQl2rhxo1atWqVVq1Zp48aNOuuss/SBD3xAP/zhD3XWWWct+/dpCDGq9Lpl/+a0007T9773Pb3t
+bW/TKaecoqOOOkoLCwsDoUdXOO9929uAgHNui6TjjjvuuM737QAAAACx2bhxo7Zu3SqOx2FFFc/Z
+9DYkbfXeb5x2m6hoAAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFo
+AAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFoAAAA
+AAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAAlSFoAAAAAAAA
+lVloewMAAACacuut0ve/L73uddLq1W1vDYCYbdu2TRs3bmx7M4Chtm3b1vYmLEPQAAAAZsLDD0sv
+fKG0Z4+0Y4f0zne2vUUAYra0tKStW7e2vRmASQQNAABgJtx3XxIySNIdd7S7LQDideyxx7a9CcBE
+YnruEjQAAICZsLjYv/zoo+1tB4C43XrrrW1vAmAewyABAMBMCIOGgwfb2w4AALqOoAEAAMyEsIqB
+igYAABIXXSTt3FntbRI0AACAmUDrBAAAy33609Lu3dXeJkEDAACYCQQNAAAsV8drIkEDAACYCQQN
+AAAsF74+VoWgAQAAzARmNAAAsBwVDQAAABOiogEAgOWoaAAAAJgQQQMAAMsRNAAAAEwoPJA6eLC9
+7QAAICa0TgAAAEyIGQ0AACxHRQMAAMCEaJ0AAGA5KhoAAAAmRNAAAMCgpaV6bpegAQAAzASCBgAA
+BtXRNiERNAAAjPFeevjhtrcCFjGjAQCAQXW9HhI0AADM8F56wQuk9eulr32t7a2BNVQ0AAAwiIoG
+AMDMu/de6YYbpAMHpC9+se2tgTUsbwkAwCAqGgAAM+/Agf7lnTvb2w7YREUDAACDqGgAAMy88MVw
+1672tgM2ETQAADCIoAEAMPMIGjANhkE2Z8eO+pZMAwBUh9YJAMDMC4MGVp7AuKhoaMYXvygde6z0
+/OcnA1wBAPGiogEAMPOoaMA0CBqa8dWvJtUM3/mOtGVL21sDAChDRQMAYOZR0YBpZIMG3m2vR7ii
+xyOPtLcdAIDhqGgAAMy8MHU/cGBwFQpgmOy7NnUdXM06lhEFADuoaAAAzLzsiSHtExhH9vlD+0Q9
+CBoAwA4qGgAAMy/7Ykj7BMZB0NCMbOURACBeBA0AgJlHRQOmQdDQDCoaAMAOWicAADOPoAHTyB5M
+ETTUg6ABAOygogEAMPNoncA0qGhoRvg40zoBAHGjogEAMPOoaMA0CBqaQUUDANhBRQMAYOZR0YBp
+ZJ8/nATXIwxweIwBIG4EDQCAmZd9B5qKBoyDGQ3NoKIBAOygdQIAMPNoncA0aJ1oBjMaAMCOma1o
+cM6d5py7wDn3t865zc65/c65Xc65HzrnPuuce/6Yt/dy59xXgtva3Pv6ZWPcxmrn3Dudczc7537u
+nNvtnLvTOXeJc+5J499LAMAoaJ3ANAgamkFFAwDYUddr4UI9N1sN59z/lvSC3pc++NEKSSdJ2iTp
+95xzn5f0Ju994cuZc85JukzSuZnbe4Kk35T0m865y7z35w3ZppMkXd37/eE2PUXSUyW90Tn32977
+q0a4iwCAMVDRgGkQNDSDoAEA7JjVioYNSk7mt0r6C0mvlvTLkp4r6W2StvR+/ruSPjfktv69kpDB
+S7pN0m/1buu3JN3e+/4bnXMfLLoB59xaSVepHzJ8StKLJT1P0vsk7ZJ0hKQrnXPPGvveAgBKUdGA
+aTCjoRnh40rrBADEra6gIeqKBkl3Snq3pK94733mZzc75/5fSTcoqSb4Lefcpd77b2VvxDm3SdLb
+lYQDt0g6w3v/SO/Htznn/kbS9ZJOl/QO59xnvff35GzPO5VUUXhJ7/De/3nws5ucc9f3bmeNpI9K
+OnOiew0AyEVFA6ZBRUMzqGgAADtmchik9/5s7/2Xc0KG9OcPKAkQUq8uuKl/p36o8tYgZEhvZ5+k
+t/a+XOhdf4BzbqF3HS/pzkzIkN7OjZI+I8lJOsM5d1rRfQMAjI+gAdNgectmEDQAgB2z2joxim8E
+l08suM7ZSgKCu7z3t+RdwXt/k6QfKgkJfiPnKi+SdGTv8hUl23N5cPmVJdcDAIyJ1glMg4qGZhA0
+AIAdM1nRMKLDgsvL8hjn3PFKBj5KSVtDmfTnxznnnpz52QtyrpfnVkl7e5fHWhEDAFCOigZMgxkN
+zWB5SwCwg4qGYi8MLt+Z8/OTg8t3Dbmt8OdPn+R2vPeLkn6spDIiexsAgClkTwwJGjAOKhqaQUUD
+ANhB0JCjt2Tlu4JvfSnnahuDy1uG3OTm4PITC25nj/d+WLFuejvrnXMrhlwXADAiWicwDYKGZoSP
+K0EDAMSN1ol8b1OyRKWX9GXv/d/nXGddcHn3kNvbE1xeW3A7w25j2O0AACaUPVHcv58TGYyOoKEZ
+tE4AgB1UNGQ4586Q9Ke9L/9Z0h8UXHVVcHnYy124GsXqgtsZ5SWz7HYAABPKezGkfQKjImhoBq0T
+AGAHFQ0B59wzJH1FyVKU+yS9xnt/f8HV9weXVw656XCw5L6C2xl2G8NuBwAwIYIGTCN7MMVJcD0I
+GgDAjroqGhbqudn69FaR+FtJR0t6VNJrvfffLvkn4SHosDaGw4PL2RaJ9HZGaYUou52RHDhwQLff
+fvvQ623YsEEbNmyY5FcAgDkEDZgGFQ3NIGgAgHhs27ZN27ZtK/z5T3+aXqq2181U0OCce4Kka5Us
+V7kk6Q3e+/8x5J+FAyA3Fl4rEQ6A3Jz52RZJz5F0uHPuiCEDIdPb2eG9n+gldseOHTrttNOGXu/C
+Cy/U+9///kl+BQCYkxc0MBASoyJoaEb4uDKjAQDa9clPflIXXXRR47/XTNDgnHuspK9LOl7J8Md/
+673/ryP80zuCy08bct3w59mlMu+Q9KrgejcXbOe8pBN725i33OZI1q9fr2uuuWbo9ahmADBLqGjA
+NAgamkFFAwDE47zzztPZZ59d+PPPflb6z/9Zkl4maUdlv9dE0OCcO0LS30l6upIT+Hd57y8d5d96
+7+91zv2TpA2Szhhy9f+r93mr9/6nmZ99K7h8hgqCBkmnK2md8JLKWjpKrVy5Uqeeeuqk/xwAOomK
+BkwjGywQNFRvaWnwa4IGAGjXsFb7q65KL40yinB00Q+DdM6tlnS1pFOUnLx/0Ht/yZg3898lOUlP
+c879csHv+RUllQpe0n/Luco3Je3sXX59ye96Q3D5q2NuJwCgRN6JIRUNGBUVDfXLPsa0TgBA3GZy
+eUvn3AolJ/3PUxIAfNR7f+EEN/VRSelD+J+cc+GSl+p9/bHel49K+ovsDfRmLXxMSWDxdOfc23O2
+97mSzu1t6ze997dNsK0AgAK0TmAaBA31yz7GVDQAQNxmddWJKyW9RMmJ+3WSPttb2rLIAe/9j7Lf
+9N7/yDn3YUnvlvRsSd92zn1I0t1K5im8S/2KiT/z3t9dcPsflvRaSU+R9GHn3KbeNu6TdKak9yh5
+TPdKOn/M+woAGILWCUyDk+D6sYQoANhSV+gee9Dwyt5nJ+nFkr4/5Po/kXRCwc/eJ2m9koqDX1IS
+EKR87+PT3vsLim7ce7/bOfcKSVdJ2iTpzb2P8HZ2SjrHez9sWwEAY6KiAdNgRkP9CHMAwJaZbJ1Q
+PwAY5yP/hhJvkvQKJTMbtkp6pPf5v0t6uff+vKEblFQ7nKKkCuIWSQ9K2iPpLkl/LulZ3vv/OcF9
+BQAMQdCAadA6UT9mNACALTNZ0eC9n6/hNq+RNHzdyPLb2Cfpkt4HAKAhtE5gGgQN9aOiAQBsmdWK
+BgAADqGiAdMgaKgfQQMA2ELQAACYeVQ0YBrMaKgfrRMAYEtdr4UEDQAAM7pc0fCtb0knnCD9/u+3
+vSXdRUVD/Vh1AgBsoaIBADDz8k4MuxI0fPKT0r33Jp+3b297a7qJsv768RgDgC1UNAAAZl54EjPX
+ewXrSuvEnj39y7t3t7cdXUZFQ/0IGgDAFioaAAAzL3wxPPLI5HNXKhrCk15OzurBjIb6MaMBAGwh
+aAAAzLzwxfDoo5PPe/fW9yLZpPA+EDTUg4qG+lHRAAC20DoBAJh5eUGD1I2qBioa6kfQUD+GQQKA
+LVQ0AABmXvhieNRR/csEDRgFQUP9aJ0AAFuoaAAAzLyiioYuDIQkaKgfQUP9so/x0lLyAQCIExUN
+AICZ1+WKBmY01GtpSfJ+8HsEDdXLO2Dl+QwA8aKiAQAw85jRgElxAtwMHmcAsIWKBgDAzAtPxsOK
+BlonMEzegRQVDdXLe0yZ0wAA8SJoAADMvC63ThA01IugoRlUNACALbROAABmXnoS45x05JH973eh
+oiGmGQ0PPST9h/8gXXddu9tRpbwDKYKG6hE0AIAtdVU0LNRzswAAVC99MZyfl444ov99Khqq9R//
+o/TBD0pr1khbtgzOw7CKioZm5D3OtE4AQLyoaAAAzLwwaFi3rv99goZq3X138nnvXunee9vdlqoQ
+NDSDigYAsIUZDQCAmVcUNHShdSKmoCHclp//vL3tqBJBQzMIGgDAFoIGAMDM63LrREwzGsIT8Pvv
+b287qpQXKrT9OHcRjzMA2ELrBABg5tE60QwqGjApZjQAqNP+/dKHPiR9/vNtb0l3hIO2q8QwSACA
+GemJYbaigdaJahE0YFK0TgCo0xe+IL373cnl006TnvGMdrenC6hoAADMvLCiYfVqaa73KkZFQ7UI
+GjCpGIOGhx+WvG93GwBU4yc/yb+MydVV0UDQAAAwI30xXFhIXhDT9okuVDTEOqOhK0FDXqhA0FC9
+2FonvvY1af166Vd/lbAB6IJwv80+vBoMgwQAzLywokHqt09Q0VCtLgYNVDQ0I7ZhkF/6UhJ0fPvb
+0j33tLcdAKpB0FA9WicAADMvGzSkFQ0EDdUiaMCkYmudCKsp9u9vbzsAVCOm6r+uoHUCADDziioa
+du+Wlpba2aaqEDTUK7YT4K6K7XEOn8usfgHYR0VD9ahoAADMvKKKBikJG6zyfjAoafsEuItBAzMa
+mhHbjAaCBqBbCBqqx4wGAMDMKwsaLA+EzL7IxxQ0PPxwN07QaJ1oRswVDY880t52AKgGrRPVo3UC
+ADDzilonJNtzGrInvG0fPGW354EH2tmOKhE0NCO2YZBUNADdQkVD9WidAADMvPTFMK+igaChOtnt
+6UL7RF7QsLjIkodVo3UCQJ3CfQxBQzWoaAAAzLyyigZaJ6ozK0FD2fcxmZhbJwgaAPuoaKgeFQ0A
+gJmXnsQsLCSfqWioRxeDhqIDKQ5UqxVz0MCMBsC+mFZo6gqGQQIAZlq4KgOtE/XqYtBQdCDV9mPd
+NTEHDVQ0APbROlGtpaV+CyGtEwCAmRQeXHStdYKgoX5FQQMHqtViRgOAOtE6Ua062wcJGgAAJuQF
+DV2paGBGQ/0IGpoR86oTtE4A9lHRUC2CBgDAzBsWNFDRUJ3s9tx/fzvbUSVmNDSD1gkAdWJGQ7XC
+x5PWCQDATMoLGlat6n/P8klE7EEDFQ0YFa0TAOpE60S1qGgAAMy8vKBh5cr+9yyfRBA01I+goRlU
+NACoE60T1SJoAADMvPCAIg0aVqzof6/tk/NpZF/o2z4hImjApGIOGpjRANhH60S1aJ0AAMw8Khqa
+ES51lepC0FAUKHCgWq2Yh0Fa3kcASNA6US0qGgAAMy98MVxYSD4TNFQv78DtgQeWhw/WhM+f8F0b
+DlSrxYwGAHWidaJaVDQAAFq1c6f0ve+1uw1UNDQj78BtcTF5DlgWPn8OO6x/mQPVasXcOmF5HwEg
+QUVDtahoAAC05sAB6RnPkP7Fv5Auv7y97cgLGro6oyGWE7OQ9fYJgoZmxBw0MKMBsC/cx1h+3Y8F
+QQMAoDU/+pG0dWty+RvfaG87qGhoRleDhvB+hcuiEjRUK+agwfI+AkCCioZq0ToBAGhNeJLQ5gkD
+QUMzig7c7r+/2e2oGhUNzch7PJnRAKAqMQUNDzwg/at/Jf3hHyaDlC2qs6Jhob6bBgB0QSxLSRE0
+NKOrFQ0EDc2IuaKB1gnAvpiGQX7xi9Jf/3Vy+VWvks48s93tmQQVDQCA1sRc0cCMhurNWtBg+XkT
+o5iChuyW5gG5AAAgAElEQVRSrZbDSACJWN78kJKKhtTmze1txzSY0QAAaE34Qh5LCXQaNDjXX+rS
+8klErBUNj31s/7L1oIEZDc2IaXnL7P+t5X0EgERMrRPh73/wwfa2YxoEDQCA1sRc0SD12ycsn0Rk
+D5YWFwffiW1rWx7/+P5l60FD+PwhaKhPTBUNBA1A98TUOhH+/rC6wRJaJwAArYmlTDE8uFgIJgx1
+MWiQ4jg562rQwIyG+sQcNDCjAbAvlmMSiYqGYQgaAAClYq9oSOc0tH3AMY1YT84IGjCu8PGc6x1l
+xvBclmyHkQASVDRUq87HkKABAFAq9qCBioZqhdty9NH96hHrQQMzGpqR16LCjAYAVWFGQ7XCfTat
+EwCARsVSpkjQ0IxwW1as6A+EvP/+dranKlQ0NCN8nFevTj7H8FyWbO8jACRiChrC/Z3VigZaJwAA
+rbFS0WC5dSLWoGFhQTrmmOSy9YoGlrdsRsxBAzMaAPvCfUzb++8uVDTQOgEAaE3sQUM6o8Hyu5Ux
+Bw1pRcO+fcmHVaw60QxaJwDUKaaKhi4EDbROAABaE0vQEL6gd611IqZhkNnVPdKgQbJd1UDQ0Iy8
+WRgxhGaS7X0EgETMwyDbWpZ6GlQ0AABaY2VGw8GDNl/kJRsVDZLtoCG8X8xoqE/6d+pc+21NtE4A
+3bK0lHyk2t5/h79/cVHavbu9bZkUFQ0AgNbEUtGQfac9lZ7MSO0fdEyKoKF+DINsRvo4LywMLj3b
+RghIRQPQLdnqv5hmNEg2B0JS0QAAaE2MQUPejAbJ7okEQUP9CBqakT7O8/ODf5ttPM7Z32m56gnA
+8qCh7f139vdbnNPAqhMAgNaEJ7xtnsgPa52Q7AYNMc1oIGhozh/8gfSMZ0i33dbeNlQtDBrCv802
+ns8xBXgAppf9m44taLBY0UDrBACgNVZmNEh2g4aYToi6GjTkDSmU2nucN2+WPvEJ6Y47pD/7s3a2
+oQ7p45ytaIglaGBOA2BXXpVSm7JvElisaKB1AgDQGkutE20fdEwq5qDhmGP6X99/f/PbU5XYVp3Y
+tat/+bvfbWcb6lDUOtFGCJj3f2s1jAQQf+uE9YqGqhE0AABKhSe82YnPTaKioRldrWiIrXUi/P/9
+0Y+k/fvb2Y6qhcMgY2ydsLqPABB/64T1igZaJwAAjYqlVLHLQQMzGuoXW9AQ/t6lJenOO9vZjqoV
+VTTEEjTQOgHYRUVD9ahoAAC0JnuCEMMJcNeChpgrGo4+uv+15aChaEZDDBUNkvT977ezHVWLPWiw
+uo8AsPxv2vt6T5SH6UJFA0EDAKA1sQQNzGhoRjbQWViQjjoq+dpy0BBbRUP2//cf/7Gd7agaMxoA
+1CXvb7rNqoYuBA20TgAAWhNj0LCw0L9MRUO1shUNUr99gqChOtnf25WKhnDVCWY0AKhS3rvvMQUN
+tE4MImgAAJRiRkP9Yp7RIPWDhoceardMdRqxBQ2zVtEQS9DAjAbArtgqGrq2vCUVDQCARsVY0dC1
+oCH2ioYjj0w+ey/t3t38NlWhaEZDW49z9vdu2WLzIDUrXHWC1gkAVYoplJeoaBiGoAEAUCr2oKHt
+d02rEHvQcPjh/e/t3dvs9lQlfP7EMAwy7/d2oaohrGigdQJAlWKraOjCjAaCBgBAa7InCG0dqFPR
+0IxhQcOePc1uT1Vib52Quhc0tB0CEjQA3RJ70LBzp732QlonAACtYUZD/WIqB80LGtas6X+PoKEa
+ef+/XRgIGQ6DjDFoYEYDYFdMr5VS/j7moYea345pUNEAAGhN7K0TXQgaqGioX3q/5uYGT4Bjap3o
+QtDA8pYA6hJ7RYNkb04DFQ0AgNbEEjSEL4bMaKhPV4OG8AQ4XB41poqGf/zHZOCmVd73t39hgRkN
+AKplIWiwNqeBigYAQGtonagfQUP9LAQNDz0kbd3a/LZUJfs32nYImPc7aZ0A7Mo7KY4taLBW0UDQ
+AABoTSwVDeGLYXii2IWgIaa+01kLGmJ4nDds6F+23D5RFjTQOgFgWjGF8lL+a7e1igZaJwAArYkx
+aKCioT5dX95yYSGOGQ3h/+8pp/QvW155ItveROsEgCpZqGiwFjRQ0QAAaE3sQUPb5dlViOmEqKsV
+DeFqCOHzJ4ag4dRT+5e7WtFA0ABgWhZmNFhrnajz8SNoAACUYkZD/WKvaOjS8pbz80l5aPocimHV
+iV/8xWQ1DMl2RUO2vSnGoIEZDYBdFoIGyxUNtE4AABoVe0VDF4IGZjTULwwapP59i6GiYe1aadOm
+5PIdd7R74DwNZjQAqFNMr5VSfx8TvkZaq2igdQIA0JrYg4a2T2aqEHtFQ5eChvQ+xRQ0rFiRVDVI
+yTvuP/5xO9s0rezfKDMaAFQppoqGpaXkQ5LWr+9/31pFA60TAIDWxB40tH0yUwWChvqFMxqk9oOG
+8PeGQYNkt32CGQ0A6hRT0BDu7445pn/ZckUDrRMAgEZlX8RjGFLYtdYJgob6FbVOtPU4h793YUF6
+5jP7X99xR/PbU4Xs32jb1UbMaAC6JabWiXBb1q6VVq9OLlPR0EfQAAAoZamiwWrQENPBU9eXt4yl
+oiHbOvH4x/e/3rmz+e2pQnYYZNvVRlQ0AN0SU0VD9rXyMY9JLlPR0EfQAAAoFWPQkJ4kSu2XZ1eB
+iob6ZYOG9HkTw0HqihXSqlX9r/fvb357qkDrBIA65YXyMezDFxako49OLluraGAYJACgNSxvWb/0
+MQ5PNmMKGrqwvGV6v2IcBrmwMPh/b7W830LQYPWxBWCjomHfPlthMa0TAIDWxFjRQNBQ/7ZI/ZPx
+ubn+tlkNGmJvneh6RUMsMxqs7iMAxF39l1Y0SLaqGmidAAC0hqChful9S4dJSfEcPKXS9gmChmp0
+sXUiOwySGQ0AqhRr68T8fL+iQbIVNFDRAABohffxt060XZ5dhfQxPuyw/vcIGqoVW9BQ1jphNWiw
+0DpB0ADYFXPrRFjRYGkgJDMaAACtiGk1hC5XNITzA9KTs7aDBueSlomU5aBhaSkJzaTlMxpiWN5y
+xYrBkKkLQUP4XJbiaZ1gRgNgV6wVDbRO5CNoAAAUyjsJI2ioXoxBQ1jNIA0GDelJuxV5z522Kxq6
+2DqRfZxpnQBQpZhmNGSD1bB1wlJFA60TAIBWxBQ0ZPshw8tpCm/1JCI9YLEQNHhv713hvKAhfZyX
+lpKPpmVbJxYW+tvWlaCB1gkAVbLSOmGxoqHqagaJoAEAUCLWdw/CoMG59k/Op2WpokGy1z5RVtGQ
+/XlTsq0TUr+qwWrQkA0DYwoa0oNoayEZgL6YWyesVzRkX/OrQNAAACgUU0VDtkwxlJZoW323Mn2h
+D0/OYgsa1qzpX7YWNOQNuAzvXxsHqtnWCakfNFg9GY55ecv0+Wt1HwEgrjc/ulbREL6BUxWCBgBA
+obwX8LYO1IsqGiTbQYP3g60T6X1p++Bplioa2ggasq0Tkv2KhmwYGNOMBoIGwL6YWyesVjSExx9V
+I2gAABSKtaKhS0FDOB+A1ol6xB40pNvUpaBhfn7w77TtoGH16uSzxX0EgESsrRPz83YrGsKKyqrV
+kF0AwGy67z7pfe+TNm/uf2/lSunNb5Ze/er2tmsaMZUphgcYc5mYvO2T82lk3xVp+77MYtDQ5knw
+ihX9+QFdCxrS+SkHD7bbOjE/318+1GpbCoC4KxqOOqr/taWgoc7WCYIGAKjIRz8q/Zf/svz7N90k
+/cZvDPYrWxFjRUPei6HlioaiAXoEDdWJcUZD+v8bbkd6MtyVoEFK/jYPHmw3zFlY6D+2FvcRABJ5
+FQ1tH5NI/VWDjjxS2rnTVusEwyABwIBt2/K///DD0u7dzW5LVQga6hdbRUNRv2YYNOzd29z2VKFs
+eUup3aAh3I60ouHRR9t7l24aeUvQtvl8Dg+gLe8jACRirmiQ+nMaqGhIEDQAQEXCA+m775bOPrv/
+tdV3KGMKGsr6CNOTGYsnEdl3RdL7sriYDIpsWhcrGmKc0RC2TqTSoEGyWeKftzJMbEHDo48OzkUB
+YEfsQUM6p+GBB9p5/Z4EFQ0AYEB4IH344YMnZvv2Nb89VYhxRkNZRUOXZjRI7Z+chSwvbxlj0JDX
+OhEGDRbDybLKkTZnNIStE21tC4DpxToMMhs0LC7aqWSlogEADAhPClessH/SIMVV0TBK0LC4mH8g
+ErOiGQ1SXEFDVyoaYpvR0NWKhnBGg9T+czlcapOgAbAppjc/ylonJDtzGmY6aHDOrXfOvcI5d5Fz
+7mrn3A7n3FLv47Mj3sbrg38z7ON3R7i91c65dzrnbnbO/dw5t9s5d6dz7hLn3JOmv9cALMoGDely
+apLdioYYg4a88r7wJMJaVYOVigbLQUPe7IC2g4ZhrRMWw8myioa2n8sEDYB9sbZOpPs7i0tc1tk6
+YWHViX/OfO17H5OYulvGOXeSpKslnZS5vadIeqqkNzrnftt7f9W0vwvAcN5L3/2udOKJ0rp17W5L
+FysaYnr3oCx1D0/WDhwYfOxjVzSjQWr+sfa++HG2HDTEuLxlF1snyoZBtt06EQYNFqtFAMTfOhG+
+wWRlP8Pylv0T+vsk3SXp/9bkocFLJRXMhpckbSn6gXNuraSr1A8ZPiXpryTtk/QiSe+RdISkK51z
+z/fef2/CbQQwoksvlf7gD6QTTpD+z/+pZ0c5Kioa6jVK64RERcM08loMUl0OGmJsnbAYNMTcOsGM
+BsC+mN78yAsaLAaas17RcJGkWyTd4r3f4Zx7sqR7p7i9H3nv75vw375T0iYlIcM7vPd/HvzsJufc
+9ZKul7RG0kclnTnFdgIYwXXXJZ/vuUf66U+TwKEt6Yudc8lBdhg0WDxpkGwGDdZOImKa0ZB34JRi
+ectqdb11ItZVJyR7+wgAiZgqGvL2d2GgaSVomOkZDd77i7z3V3vvd7S5Hc65BUlvVRIy3JkJGSRJ
+3vsbJX1GkpN0hnPutGa3Epg94QtM2+9kZ9+hDE8aulTR0NZB+iwEDW1XNIwaNFiraMi7X7FUNITb
+ER6kWg8asoHO0lLzg1oJGoBuiXVGQ17QYGU/M9NBQ0ReJOnI3uUrSq53eXD5lbVtDQBJgydibe/U
+s0FDFyoaYipTHGdGgyUxzWgoCxpY3rI64SyMrlY05FWOtPV8zrZOWHmnEcAgS0GDhf2M90kILNXT
+OkHQMLoXBJevL7nerZLSotLn17c5AKTBHX3bO/VZqWhoux+SGQ3NbUvIckVDbEFD+Pu6FDTkDYNs
+82+TigagW/Kqoto+JpHszmjIe22s0iwGDZc757Y65x7pLZV5o3PuA865Jwz5dycHl+8qupL3flHS
+j5W0Tzy9gu0FUMJKRQNBw/RmoXUi5hkNhx0mzfWOGggaphP+vxatOmHhIDUrpooG7wkagK6hoqFa
+Za/5VZjFoOEMSccqGYT5GEm/LOl9kn7snHtzyb/b2Pu8x3v/8JDfsbn3eb1zbkXpNQFMJdxJtn3w
+WFbRYPHdSSnOoCHvxdDySYSVigbn+lUN1oKGYTMamn6csyvUpKzvM8qGQUrN/m2m5cDptlh7pxHA
+cjENg8yr4LI2o6HuigYLq05U5W5JX5b0HfWDgBMkvUrSqyWtkvQJ59yS9/7TOf9+Xe/z7hF+V3gI
+tlbSgxNtMYChYg4aulDRwIyG+mVPzpzrfx1T0CAlQcOuXfaChtgqGrraOlG2vKXU7PM5+1y2dgIA
+YLnw73puLgkUYwgaaJ3INytBw1e893kDHG+T9NfOuX8p6atKHo+POOe+5r3/Wea66cv/KC9P4VNr
+tQgagNrE3Dph/aRByj8xePTRpCw5PCFuwqitE9ZnNMwFtYYxBg0Sy1tOa5TWCYv7jJhaJ7LPZctV
+TwAS4aymhYXkZL7tNz8kWieKzETrhPd+15CfXy3pYiVzFdZI+jc5V0tf8lfm/CwreJrJ6PuYgA1U
+NNSr6AW8jXcQmNHQ7LaUBQ1UNExnFlonCBoAVC1soUz3LTFVNFirnKKioTmfUhI2SMkchz/N/DwN
+K9aOcFvBbO6RWi2WOXDggG6//fah19uwYYM2bNgwya8AOoGKhnoVvYAfPDh4AlE375MPqdtBQ+yt
+E+kSl/v3JwcodRyY1GHYjIZYWifCg1SL+4xhq040+bdZFjRYeKcRwHLZiobwe21tixR/68S2bdu0
+bdu2Zd/fsaN/effu5PyzSgQNPd77Hc65n0t6rKTjcq6yRdJzJB3unDtiyEDIJ/Y+7/DeT3SYuGPH
+Dp122mlDr3fhhRfq/e9//yS/AugEKhrqVXSi2/QJ8LDUvUszGiy0TkhJ+8S6dcuvE6OYKxq62jqR
+NwySGQ0AphGuJJPuY2Ja3jLW1olPfvKTuuiii0qv881vVv97CRoG+ZKf3aFkcKQkPU3SzXlXcs7N
+Szqxd1t3Troh69ev1zXXXDP0elQzYNbFUtHgff8gu0sVDeHju2pV/37EFjR0aUZDeP9iDhr27CFo
+mBStE/WjdQLonrB1IsaKhlgDzfPOO09nn332su9v2yb92q8ll1/yEukf/uFl2hGWOUyJoKHHOXeM
+pGN6X/5TzlW+FVw+QwVBg6TTlbROeEnfnnR7Vq5cqVNPPXXSfw7MjFgqGvJOHLpW0bBmTXtBQ15J
+dsjySUTZjIam78u4QYMVw4KGNp/PRUFDTO+GjWpY0BBL64S1fQSARNg6EdOMhrxWsZj24UWt9nff
+3b+8fn1y/lmlmRgGOaLzlAyDlKTrc37+TUk7e5dfX3I7bwguf3X6zQJQJpaKhmFBg8V3J6XBF9K0
+P19q/rEep6LB2klE9oTIwjBIyVbQENuMhllonYh5ecuYTgAAjM5SRYOF/UzdwyA7HzQ4557snPul
+Idf5NUkX9L7cJ+lz2ev0Zi18TEkY8XTn3Ntzbue5ks5VUs3wTe/9bVNuPoAhwh19mzv1vKAhPGno
+QkVDGJy02TqRdwLc5sn5tLL3jaChejEvb9ml1om8d/honQBQlbxhkCxvObm6l7eMvnXCOfd8SScF
+3zomuHySc26gusB7f0XmJn5B0jecczdK+htJ35X0s97PTpD0GiWzF5ySgODt3vvlYzkTH5b0WklP
+kfRh59wmSVcqCSfOlPQeJY/pXknnj34vAUwqloqGondMFxaSn3UhaAgrGmKe0WDtJMJqRcPevfVv
+T1Vim9EwSuuExaAh5mGQlvcRABJ5wyBjrWiwsJ9heUvpjcpvVXCSXtD7SHlJ2aAh/f6vSHpuwe/w
+kvZIOt97/5miDfHe73bOvULSVZI2SXpz7yO8nZ2SzvHef7/odgBUJ+YZDVJy4rB7t82TBomgoQll
+MxpiCxrC54D1igZaJ6o3rHUilhkNFt5pBLBc2DoR04yG2Je3LDKsWnRaFoIGqXw1iGHXu03S7ygJ
+GU6XtEFJVcSCpAcl/UDS/5L0ae/9/UN/gfd3O+dOkfSHSqohTpK0UtJmJQHEx7z3m0fcXgBTiqWi
+oShoWL06CRqsVjQUzWiILWiwvLyl1YoGq0FDbDMaulrREFPrxIoV9t5pBLBcXutETEGD5daJmaxo
+8N6/QYMDFsf997slfbH3UdU27ZN0Se8DQIssVDRINk8apDhnNHRteUtmNNQv72AqxtaJ8HlscZ8R
+a9BA6wTQDXmtEwcPJkuMO1f87+rclnR7JHuBJsMgAaCA9/EHDenJudWKBlon6kdFQ/1iW96yqHXC
+uf6BqsWgYdgwyFhaJ6ztIwAk8lonJGlpqfltsbS8ZZG6h0ESNAAwKzx5kOIMGqxXNFhpnbB8EmFp
+RkNXg4ZYWiek/j7DwkFqFstbAqhTXutE+P02tkUabMmb651dW9jPUNEAAAWyLywxBg1pRcP+/UkF
+hjWxVDQM6yNkRkM925JF0FCNUYIGi+Ekq04AqFNY0dBmVZpU/HqZhpoW9jN1D4MkaABgVvaFJcag
+IRzuZiHdzoolaOjyjAarQYOl5S3z7lf4OLc5oyH7OHclaGBGA4AqLS3137CJoaKh6CQ93ddYOOar
+exgkQQMAsyxVNEg25zTEGDTknQBbPomwNAyS5S2rMQsVDTEvb2ltHwGgPJSPpXVC6lc0WAgaaJ0A
+gALZk7A2d+pdXa6OGQ31Y0ZD/QgamhFzRQMzGgDbsqF82xUNXQgaGAYJAAWoaKhf0fKWTT/Ww4IG
+ZjTUsy1ZBA3VGLV1wtpcl2GrTtA6AWBS2f1LrDMa0n2Nhf0MFQ0AUMDCjIbw5NziO5Tp/Zqba3cO
+AjMa2tmWLKtBQ9F08LyfN6GsosHaOuyhYcMg22ydmJ9Plg9tejsAVCPmiobwuMRSRQPDIAGggIWK
+hrB1wmJFQ/oYr1jR7glwl1snsi/0bYYmXQ0ahlU0NP04j9I6IdkLJ2Na3jL8XQsLSchgaUgbgEGx
+zmhwrr+kpWQraGAYJAAUsFbRYDFoSO8XQUN9LM1omJ/vH0R1KWiIsXVCsnGgGop5RoNka9k5AIPK
+WifaDBqy+/B0P3PwYPztb7ROAEABaxUN1t6dlPr3q+2SfmY0tLMtedKqBkvLWw47AY6pdcLyPiP2
+oMFS7zSAQWWtE220TKbbk32ttPTGB8MgAaAAFQ31i6WiYVh5X5vbNi1rQUO6+oiligZLMxosBw15
+f6exLG8ZbkvsB/8Aliur/ouxokGKvyqNigYAKEBFQ/2szGiYn+9/39pJRPZdmtiDhrSiwVLQYLV1
+wto+I32cw57lmCoaLPVOAxiU/ZuOvXVCiv94hGGQAFCAiob6hRUNsaw6UfRiaPXdSmsVDWHQEHv/
+aSq2oKGrFQ3p4xyGgTEFDVb3EQDia50oChrCY6XYQ02GQQJAASoa6mdlRoPU3z5rJxGWhkFK/aBh
+cdHOY03Q0IxhQQOtEwAmZW0YpBR/0EDrBAAUsBA0dKmiIfagIT2JsD6jYX4+KT2X4g4aJDvtE3n3
+K3wexfQ4hwepXQga2qqEKgsaYj/4B7BcWZthm0FD9pjEUtDAMEgAKJA9aG1zh97VigYrMxoku+9W
+5rWFpI91bEM3JZtBQ97zx7n+ZSoaqpF34B1T60R6ArC0NPicABA/KxUNlladoKIBAApQ0VA/Wifq
+l3dC1FbQMMosjDBosLLEZdHzJ32cYw0aYn83LMvKjAbJ3n4CmHVlwyBjmtFgqaKBYZAAUCD7wtLm
+u1SjBA3W3p30vrh1oumD9C5XNORVEcRQ0TBseUvJdkWD1L+PrDpRjbx15WNc3rLpbQEwvbJhkG1U
+NOTt7yRbQQPDIAGgQN4LS1sHj+EJYfiiE540WKtoWFrqX267dWKUF8OuzGiQ4g4arLdOhPerraCh
+q60TsVc0WDoBADCobHAyrROToXUCAArkHbTGEDR0pXUie59ib52wWtEQ64yGLgUNRUEVQUO18oKG
+cBZG20GDpRMAAINiq2joQusEwyABoEBMFQ3htnRlGGS2SiP2oCGc0eB9/dtUFSoa6kfrRDPyggap
+nfkpBA1AtzCjoXpUNABAASoa6hVrRUPRCXB4EtHGuxuTsjajoYtBQ9OPc1crGoqWe2ujrYmgAeiW
+mFonvB9tRkPs+xkqGgCgQEwVDV1c3jJbpRFL0DCsdUKyNaeBoKF+zGhoRtGBdxvPZ2Y0AN0SU+tE
+2TFJeCwS+36GigYAKEBFQ72y96nNE/lxg4bY30UIpfdtbi75kAgaqlY0o6Gt5S3LHufwZNhq0FDU
+OtF20GB1HwFg+X68zdaJUffhBA0AYBQVDfWyOqNBsnUSkdfnGXPQEC5vuXdvvdtTldhmNIxa0RD7
+QWpWUdDQxqBWggagW8pmNLRZ0WA5aKB1AgAKWKhoCF8MrVc0xB40WD2JyOtrTx/rxcVmB1t2taIh
+1qAhXJEhZTmctFTREPsJAIBB2ZP7Nmc0lL1WWjoWoaIBAArkvbC0dfA4yjuU1k4amNHQjLKKBqn9
+k7Msy0GDc8lHqu1VJ/IeY8tBQ9EwyFiCBktD2gAMKmudiClooKKhj6ABgFkWWiek/pwGyxUNCwuD
+MwTaLOnvWkVD3gA9goZqFZ3Yt13RkN1XSLaDhmHDIGmdADCpsmGQMbUZWgoaqGgAgAIWWickuxUN
+efeprdkBszqjQWovaCh6nC0GDUUl/eljvrSUfDSli0GD9/3HkOUtAVQtphkNtE6MhqABgFlUNNQr
+2zoRfm76cZ6F1om8GQ1SOydn8/ODLQahLgYNUrMHql1snQiDmqLWiUcfbW7mCMtbAt2SDcJjmdGQ
+3d9Z2s/QOgEABaxUNFgNGmKtaCh6MbT0LkIoxoqGsgOOLgUNbR2ollU0WF3ectR15Zt6nKloALql
+rHUipooGS0EDFQ0AUMBKRUP6DuUjjzS7gsC0sjMapDiCBmY01GeUoGHVqn61g5XlLYfNaAiv04Sy
+oGFhof8ctxQ0lL3D10ZbE0ED0C1lwyBjmtFgaT9DRQMAFIixoiGv5DytaJBsnTjEWtEwazMa2hig
+V3bA4Zy0Zk1y2XpFQ4ytE9JgOGlFWdVRGwfeBA1At2T/pttsnSjb31HR0EfQAMCsGCsaujTcrWxG
+Q4xBAzMaqtuWYe9spO0TBA2TKdtfSDYHyI7aOtHU85kZDUC30DpRvVHaUqdB0ADArBgrGvJOHMKK
+BktzGmidaIa1GQ0SQcO0Zi1oaLt1Il2W1+o+AoCd1glLQcMoK01Ng6ABgFlUNNQrr3WijWXqpG4H
+DdZmNEj91gkrwdkoMxpiepy7FjS02TqxsNBvZ7O6jwBgp6LB0n6G1gkAKEBFQ71imtEwSure1RkN
+MZ0Ap9Ln9N69NgacUtFQv1hbJ8L/Y0vvNAIYFOvyll2paKB1AgACeS8sbe3Uu1jRwIyG+nlvc0ZD
+GjR4byPUiWl5y6Wl5CP7+0MWg4ZYV50In8uW3mkEMCh7UhxLRUN2f2cpaKCiAQAKUNFQr7IZDd4P
+vkDVbZSBRRZPItITTslWRUPaOiHZeE7HVNEwyjtIadDw6KPNH0BPKtZVJwgagG4oa52IaUZD+HXs
++yJKv2UAACAASURBVBmGQQJAAWY01KusdSL787p1dXnLohf52IOGMDzbu7e+7anKKDMamjqhz/u7
+yrL0jljKQusEQQNgV9kwyJiWt3Suvw+Pff/NMEgAKGAlaLBa0VDWOiHFFzRYPIkoelfEUtBg4Tkd
+U0XDKEFDGE7GfqCainXVCWY0AN2QPbmPdUaDZCdooHUCAArE2DqR94ITnjRYOClLDatoaPKx7uqM
+hqJ3E2IPGmidmNw4rROSnSqomFedaHM7AFQj1hkNefvxdF8T+34mvR/O9ZcBrhJBAwCzLFY0WDlp
+kMpnNGR/XjcqGuoXDqbsWuvEKEFDU4/zuBUNVvYZZSW4tE4AmFZZ60RMMxokexUNdVQzSAQNAAyL
+paJhcbG/xF+XWieY0VC/mGY0FA2mzGPpOb201P/7tDKjwWLQUNazTOsEgGnROlG99DGtYxCkRNAA
+wLBYKhryZhmELJ40SMxoaEJMFQ3jrKdtqXWi7LlD60R1aJ0AUKeYhkEOG6JoJWjIW167SgQNAMyK
+paJh2DuUlt79DcXUOjHKZOQuzWhos9Rc6lbrxKhDCqlomA6rTgCoU/Y1KtzPxNY6YWVGA60TAFAg
+loqGYScOFk8apPz71dbJPBUN7W1LHkvhWWwVDbMYNMTSOmFxHwEgkW2dcK6/r4m5dSJt3YvRqHOZ
+JkXQAMAsKhrqFWvrRNELIjMapkPrRDPb09XWiVGHQbYZNMzP97ct9pJmAIPy9jHpa2XTQcOwY5Jw
+HkzMFZZUNABAgfSFJTzpaePgcZYqGmIIGqhoaHZb8lhqnSi7X7FWNFgcWlh24N10JZT3xUPOrJQ0
+AxiUt49JP8dW0WDleIRhkABQID1gDYMGKhqqE9OMhnGDhpjfQQgVvQtsKWiI/Tk9akUDy1tOJ6bW
+iVFCj5gP/gEsl/d6mf59xzajwUpYzDBIACiQ7iBXruzv6GMMGiyeNEhUNDTBakUDrROT62rrREyr
+TpQ9xgQNgE15f9exVjRYCRponQCAAukJ2MJCuwePXa1oiHVGQ9ELIjMaptPV1onYgoZZrGhoutqo
+7LlsZdk5AIPyXi/bmtEwbCUsK298MAwSAAqkO8gVK+IOGsKTBktBg+XWiZhf2ENWKxoshWdl94vl
+LasTU+sEFQ1A95S1TlDRMBkqGgCggMWKBisnDdLw1okmH+vwJGau4JWLGQ31bEseWicm19XWiZhW
+nSBoALqnrHWCGQ2TYRgkABSgoqFeMc1oSP+v5+aStbPzUNFQz7bkoXVicrNQ0dD2qhO0TgDdE9Oq
+E8OWtwz3eTHvaxgGCQAFwt6ymIOGhYX+C5GVkwYpzhkNZS+G4Yu9laChCzMaYg/PCBqaYbF1wvv6
+twVANfKqpmKY0TCsoiHm4xFaJwCggJXWCal/Yhb7SVkoxhkNZSfAzvW3L+YX9pDVigZLrRNl96uN
+5S1HeZytlN2GrK064X3zJycAJldW0UDrxGQYBgkAOZaW+u9Gxd46IfXfobTy7qQUV+vEqKl7+jxg
+RsN020LrRH1msaIhptaJMCSL/bkLoC/8u05nNTEMcnLeJ8fSEhUNADAg+2577EGDxYqGvNaJtgYu
+jho0UNFQ/bbkWbmyf6AX+3OaoKEZZcMgY2qdOPLI/uWdO+vfFgDVyHv3PWydaLIVatjrpYWZUWnI
+IFHRAAADsifB6U59cXHwxKIJs1DREEvrxKgVDbG+sGfFNKNh2HCrkHP98Cz2d4VHnR0QU6BjMWgY
+dRhk20HDEUf0Lz/8cP3bAqAaeS2U4eXwxLluw1ZpslDRMM5KU5MiaABgUvYkuM3BO12taEjvl3PL
+By+FP2/CLLROtB00jFPRINl5TpfdrzYe565WNMQU6BA0AN2Tt0JCG3N2wm3JbkPKQtBQts+uCkED
+AJOKKhqkOIOG9MThkUfsTDpP71d4n6wEDVYqGqzOaJD6ve6xBw0xnQBnf8+sBA0xVTTQOgHYNKyi
+ock5DV1onRj3NX8SBA0ATCqa0SDFGTSEw/OsnDikL0IxBA2jrvXMjIbqt6UIrROTmYXWiZiDBioa
+AJvKZjSEP2/CsFZDKhoSBA0ATLJa0SDF/w5wKlw+NEVFQ7VimtHQ1daJ2IKGUfYX4f7MStAw6jBI
+WicATGJY60RMFQ0WggYqGgCgQKwVDUU7a4sVDZZbJ5jRUN22FElbJw4ebH5psXGUvfMUa9Dg3GC7
+lQWjPs5tVzTQOgHYNKx1IqYZDeExaaz7cCoaAKAAFQ31GxY0NPk45x1g5EmfB48+2uwE6kkVvQs8
+P5+cbErxBg1heBbzc7rsnfY29hujPs7pO2JWgsmyg9a5uf59beL5XBb+UtEA2GS1oiHWCstxVpqa
+FEEDAJNirWhgRkM9Rq1oaGv7JjXKiggEDdOx2Doh2VsSd9i7Y03OT6F1AuiemGY0sLzlaAgaAJhk
+raLByklZyPKMBinedxFCo5Sbxxo0pK0TUtzPaYKGZgwLGpqcn0LrBNA9tE5Ui9YJACiQ3cnHHjRY
+nCJveUaDREVDlduSJwzPYl55IrYZDaM+zl0NGhgGCWAStE5Ui2GQAFAge3LfZtCQra7IY7GiIa91
+oq0T+a5WNIwyqT+2E+CUlef0qKshNPV86WpFw7DnD60TAKaRV9HA8paTo6IBAAqUVTQ0vVPvekWD
+pdaJtoZVTspyRUMXWifaCM4mCRq8r3ebqmCldWLt2v6gVVonADusVjRYCBqoaACAgOVhkDGflIVi
+aZ3wvr+CRJcrGoqChthWQ0hZbJ2IYUbDuK0Tko3nspXWibk5ad265DIVDYAdecMgLcxoiHX/zTBI
+ACiQbVdosx+uixUNi4v9d1HbDhrGKe+zNqPB8jBIK+FZbEHDuBUNUrzviIWsrDoh9dsnCBoAG7wf
+PgySiobx0DoBAAWoaKhX0dwJS0FDrO8ihGidqN+wd7fnekdCsQUN4YGqlXAyFXPrhNRfeYLWCcCG
+tKpRGty/tL28pXP915CQhaCBYZAAUMDa8pbhu5Mxn5SlskFOqu2gYdiLoeUZDZaHQVptnZCaPQGW
+JmudsBA0DCvDDVsn6p45MWxAb1rRsHdvsycnACZTdBzQdkVD0ZsfLG+ZIGgAYJLligYLJw1F96nt
+oIGKhna2JY+VKp1RS/pjq2iwFjQMCwTT+xqWQNdl1NYJSdq1q95tATC9or/ptmc0FL1WWljekmGQ
+AFCAioZ6dSFosD6jIb0vS0uDZaN16WrrBEFDM0atHJHq30eP2joh0T4BWFBUMdVWRUPevIiQhYoG
+hkECQAEqGupVdKAevhg19TjPakVD0/elq60Tw+5XrJUjXQ4a6n6sx6loYCAkEL+iUL7tGQ1F+3Dn
++tsWa9BA6wQAFKCioV5F9yl88YyxoqFLMxqaHiY1660TTT1fZrWiocm/TYIGoFtGqWiIqXVC6r+G
+x3oswjBIAChARUO9yu5TzEFDlyoaYg8autI6EQ4pbEJXg4ZRh0FK7QcNtE4AtowyoyGmigap/xpO
+RQMAGGOtosHKu7+psqntbZaaz+KMBinOoMFK60RsMxqGLYuWCoOGWA9UQ8MGi9E6AWBS1lonpP4+
+L9b9N8MgAaBA9kCyzcE747ZOWHh3smh5S6n5d4BnpXXCWkWDlfAsthkN6e8pq2aQBv//LewzrLZO
+UNEAxC+2YZDDlreUbLVOUNEAAIHsyT0VDdWKtXVi2Amw5dYJazMautI6EWvQYC2ctLrqBBUNQPyK
+jgMszGiwUNFA0AAAgeyBZJtrFocvbkU7a2snDbEGDcxoaGdb8nSxdcL7+rdnlANUyd4+g1UnANQl
+1ooGy0EDwyABoECMFQ0rViR913msrTpR9gJE0FCdsmqNpsOzrrZOjHMC3MSB6iQVDTE/vilaJwDU
+pehvuq0ZDen+btQZDU2E2OOiogEACpTNaGgzaCiysNB/QbJw0mC1oqHpKoBpdaWiIebn9KgzGqRm
+ntOjBg1WWlNSwx5nWicATGqU1olYKxq8H9z+WDAMEgAKxLi85bATh/TEzEIZtNWgoc2hoJOwPKNh
+bq6/jV1onZCaDRqGPcZWgpwUrRMA6jJK60SsMxqkOI9HGAaJznrgAWnPnra3ApbFuLzlqD3XFk4a
+YlrekoqGOIMGqX8yHPNzOragIX2cRw0mpbiDnJSl1omwooHWCSB+RX/TbVQ0eD/e8pZSnMcjtE6g
+k667Tnrc46RNm3gnAZOjoqFeZctbpvdzcVFaWqp/W7ocNIw6oyHWoCEt77ccNDS97+hq60RMj/Ow
+5/KaNUlFjsRxCGBB0WtlGzMawuOeUZa3lOKcGcUwSHTSBz6Q7DC2bZO+9a22twZWxVjRMOpwN2tB
+Q1FFQ/Z6dRmnvM9a0NCVioaY33Ef1odK60Q1LLVOONdvnyBoAOIXU+vEqK+VsR+PUNGAzvnRj6Rv
+frP/9T//c2ubAuMsVjTEvtRRKKagocsVDZZnNEg2WieGBVW0TlRjnMe57YoGqd8+QesEEL+YhkGO
++lppqXWCigZ0wmc+M/j19u3tbAfsy1Y0NHkQm9XFoGGUGQ1S80HDsBfD2EsVs9LH2bl+KXeqzaBh
+1Hc3wtaJGJfvkuKb0TALrRPZ57IUV+uEREUDYEnR61MbrROjHpPEfjzCMEh0ysGD0uWXD36PoAGT
+ylY0zM/3d5RNn8iPGzQsLTW7DNMkymY0NFkCLXW7oqFsLW5LFQ1SvC1BMQ0pXFzsBzJdbZ2Yn0+C
+s6wmA51xgob9++M8CQDQF9MwSFonRkfQgMZcddXyVglaJzCpvHfc0xPgpg8aRy2Fjv1FJ0TrRDPK
+Jlc3XXZZVl1RxMLJcKyzA7rWOhEGDXliq2gIV56gqgGI2yitE7HNaIi9dYJhkOiUyy5b/j0qGjCp
+vB1kG0GD96NXNMT+ohMiaGhG+jzOu19tVTSMc8Bhobx/2MFUk8/nsr+rrBUr+s+LWB/bkLWgIa1o
+kAgagNiNMgySiobxUNGAzti8WbrmmuTyk54kHX54cpmgAZPKO2BPd+pNBg3hjrpLFQ1lL6QEDdUp
+O7m3EDRYeNc9phkNZS1JWc7ZGLaZKgvNpHhbJySCBiB2MS1vOUnQEGN7FsMg0Rmf+1x/3dlzz5U2
+bEgu0zqBScVS0TDOO5SWToKpaGhGjDMaJg0aYj0ZjiloGKd1QrKxfGiq7LksxVfRELZOsPIEELdY
+ZzSUHZPEfjzCMEh0wuJif7UJ55Kg4dhjk68feijeAWKIW96JMEFDdWIKGsZ5MQy3LfbHWBq9oqGJ
+53RXWydiGgY5zv5CGlzVI3a0TgCoyyitE8xoGA+tE+iEb3xDuu++5PLLXiY98YnS4x/f//nPftbO
+dsE2KhrqFevylsNeDJ2zuYyo1RkNFlonhh0UNjkMcpzWCclmRUNMrRNlg00JGgA7RhkGSevEeBgG
+iU645Zb+5XPOST6nFQ0ScxowGSoa6lV2QhRz0CD1nwexP8ZSt2Y0xPquexdaJ2J9bEMxVjSUPZdp
+nQDsKKpoaGNGw6izDWI/5qOiAZ1wzz39yyefnHwmaMC0qGioV0ytE+MOLLJU0WB9RkPXWidiWnVC
+6j++Bw82dxA9KWtBAxUNgB2jzGigdWI8DIOU5Jxb75x7hXPuIufc1c65Hc65pd7HZye4vZc7577i
+nNvsnNvf+/wV59zLxriN1c65dzrnbnbO/dw5t9s5d6dz7hLn3JPG3aauu/fe/uXjj08+h60TDITE
+JPLecU936ouLgzvQprZjnKAhxjK60KhBQxP3Y9zU3VLQ0KWKhljL+2Od0TBO64QUb5CTGvb8aaNy
+hKAB6AZaJ6rXxDDImvKLSmVPQ33vYyzOOSfpMknnBrcjSU+Q9JuSftM5d5n3/rwht3OSpKslnZTZ
+jqdIeqqkNzrnftt7f9W429hVaUXDUUdJRx+dXKaiAdNKd5Bzc/0e3GyvdV07zlBXKxqsLm8p2Qwa
+YpjRMGzVgDwWToRjmtEwaeuElDy+69ZVv01VsVbRQOsEYEdMrROTBA0xHo9Q0dCXhgs/lfR3ktwE
+t/HvlYQMXtJtkn5L0i/3Pt/e+/4bnXMfLLoB59xaSVepHzJ8StKLJT1P0vsk7ZJ0hKQrnXPPmmAb
+O+fRR/uDINNqBmmwooGgAZNITwjCnWMbZWpdDRpibZ3oatBgtaKB1onxTNo6IcX7+KbGCRqoaAAw
+jlgrGsqOSWJvnaCiIXGRpFsk3eK93+Gce7Kke4f8mwHOuU2S3q4kHLhF0hne+/S//Dbn3N9Iul7S
+6ZLe4Zz7rPf+npybeqekTb3beYf3/s+Dn93knLu+dztrJH1U0pnjbGcXbd7c3zmccEL/+2FFA60T
+mES6gwwP1pt8xyw1i0FDkycM0uRBQ4ylilnWZzR0rXUi1lUnpHgf39Q4q07EUNFA0ADYYXF5y9iP
++RgGKcl7f5H3/mrv/Y4pbubfqR+qvDUIGdLfsU/SW3tfLvSuP8A5t9C7jpd0ZyZkSG/nRkmfUVJx
+cYZz7rQptrkT8uYzSFQ0YHrDKhoIGqbThYqGAwckP3ajXbPKTogWFpLl+aR4gwYL77inzx/n+o9n
+yMKqE1K8j2+K1gkAdSk6uQ/3NzG3TsT4xgetE9U5W0lAcJf3/pa8K3jvb5L0QyUhwW/kXOVFktKX
+pStKftflweVXjr2lHROuOBFWNBx2WDKzQSJowGSoaKhXTDMaxi3vi/3FPbW01A9C8u6Xc80u1dnV
+5S3HGVLY5DDIrrVODHucY2udWLWq/3MqGoC4FZ0UO9f/OrblLWmdmIGgwTl3vJKBj1LS1lAm/flx
+vRaN0AtyrpfnVklpgePzR9rIDiuqaJD67RO0TmASeQeSbZxgjnPiEPuLTqgLFQ1S3I/zKO+KNDVv
+Ymkp+SjbljwWSvtjmh1A60QihooG5/rtEwQNy+3bJ3396zw2iEPZSXHTQUMXWyeoaJjcycHlu4Zc
+N/z50ye5He/9oqQfK6mMyN7GzCmqaJD6QcPu3dKePc1tE7ohPWCnoqEeBA31G+VFvqmgYdIDDgvv
+uI9zAkzrxOSstU5I/fYJWieWO/dc6aUvlV428uLvQH3KTu7Tr2Oe0RBjdSUVDdXYGFzeMuS6m4PL
+Tyy4nT3e+2H5bno7651zIxxKdFda0eCc9ORMjUg4p4GqBowr70CSoKE6ZSdEbQYNo5wEW3mcY6po
+GPXAKcvCiXBMQUNXWyfSahgpjsd51KCBioZ8990n/dVfJZdvvFHatq3d7QHKjgOoaJgMwyCrEa46
+vXvIdcP31dcW3M6w2xh2OzMlrWh4whMG/+CkwZUnmNOAcVHRUK+yEm8qGqoxyrsJTa2gUUXQEGtp
+f9nKHlJ7Mxq61Doxyt/o/Lw01zvqjKWiIQ0aDhyQ9u+vd5ss+dznBgfp3nhje9sCSOWvl+k+PLag
+IfZ22Ulf98cxC0HDquDysJe28GmwOvOz9HZGeXksu52ZsXu3tKO3Vki2bUIiaMB0YqxoGLajtnIC
+LNE60YQuVDRYeMc9vW8xzGjoauvEuOvKxxI0hCtPUNWQWFyUPvOZwe8RNKBto1Q0tNE6Uba/i711
+goqGaoQZ9crCayXC99yzL+np7Qy7jWG3MzPKBkFKtE5gOlQ01Mty0BD7uwipmGY0zEJFQwwl/V1t
+nRh3Cnudj/M4g03TigaJoCH19a9LmzcPfu+GG9rZFiDFMMjqNTEMsqabjcqu4PKwNobDg8vZFon0
+dkZphSi7nZEcOHBAt99++9DrbdiwQRs2bJjkV9QuDBqoaEDVYqxo6FLQMOrylk08zlQ0JM+zpaV+
+6Xkb25JnxYrk/2RxMd4T4ViDhllrnZD6j3Wd+41xDp7DoIGBkIlPf7p/Of3bvu22ZF+abYEFmjLK
+MUlsQUNMb3ps27ZN2zLDVh58sH/5/2fvzMM0qcqzf1dvs/TsMMMMOzjAgKCyyOa+JJ/GfYtL4gqK
+Jm5JrlyaqEE+jIpK1Gg0KC7RGMyncTcSDbgCKgwIyD5sAzMDszALzHT39FLfH2eO56nqet/azvJU
+1fO7rrm6ptfq6npPnXOf+7mfG25QLX/3Wh6cuyA00ADIg3t+loIGQKb0XNwP4DQAo1EULcoJhNTf
+Z0scx5WmLVu2bMHJJ5+c+3nnnnsu3v/+91f5Ec6hHSfyHA0iNAhliGMzmezlaPA1qLdVaKC/V3rh
+4FvQ6YLQkJfRAKhrPXdu9ufZPJeyOxvz5wMPPyxCQxHaWjpR9DXqo3SizL0spRNJHnwQ+O531fHK
+lcDTngZccokaR6+7Djj99LDnJ3QXTmGQRcVMTnORiy66COedd17Pjz/hCW5+bheEhpvJ8Zqcz6Uf
+vyXj+7yEfN5vs75BFEWDAB4FIM74HoVZvnw5Lr300tzP4+pmAMo5GqR0QihDr4lkkxwNHOv1KLQ0
+JYqSH/P98CzbgonTw70fZRwNgPpdOAoN8+YpoYHrjntevX6oMMi2lk4UERpcCjpl7mUpnUjyla+Y
+6/e616luYZdcov5/1VUiNAjhKFI6wa295cCA+vjUVPg53znnnIPnP//5ife97nXAjTeq46uvVuf7
+rGc9C1t0wJ4FWi80xHF8dxRFGwGsAvCUnE9/8r63G+I4vjf1sV+R46egh9AA4BSo0okYwBUlT/cP
+jIyM4KSTTqr65SzIczQsX64WMHEsjgahHL3sx00SGjgvgIHsDAyNb8GkrY6GMhkNgNvfpa7QAPBd
+COc5GnyGQUrphHrLxdEgpROGOE6WTZx1FrCb9FC76irgr/7K/3kJAsDL0VBmjBkZUZ8fei6SVWpP
+ny+nnKLejowUiSIsThfCIAHguwAiAGuiKDo16xOiKDodyqkQA/hOxqf8DIB+DL22z896PTn+dukz
+bRHa0TBnDpBlvBgeBvbbTx2L0CCUoZf9WIQGe/TbBfb9e7RVaKjiaAh5Lr3Qu+5NFRqkdKI+ZWuW
+uQgNUjph+OUvgdtvV8dPexqwejVw/PHAgn3JZBIIKYSkie0tAX+BzlXQ47BlbSFBV4SGTwDQU9VP
+RVGUMJ/u+/8/7/vvFIBPpr/BvqyFf4YSLI6Nouhv0p8TRdEZAN4AJVb8LI7jtdZ+g4YRx0ZoOPzw
+3gFmunziwQeTPZsFoR+cHA1lFg5NWQADxR0NvoWGIovgppSolM1o4Co06MUw1x13TkKDlE6ot1I6
+wY+vftUcn322ejs4CJx2mjresGF2NwpB8EW/1zV1NPhYS5Qp59TPcI5zkX7zPFuwL52IougJAFaT
+d+1PjldHUZRwF8Rx/G/p7xHH8R1RFH0UwLsBPB7AFVEUXQDgTqg8hXcBOBFKIPhIHMd39jidjwJ4
+OYCjAXw0iqKjAHwdqoXl0wH8HdQ13QPgnSV/1VaxebOZdGblM2hWrgR+/3tgfFw95OnugiD0otfi
+PsQCs62OBq5CQ9ccDb4CTm0IDdPT6r5xOWmpQpmMBm5Cg5ROlEdKJ6pBy12f8xxzfMYZwGWXqeOr
+rgIOOQSC4J0ipRP681y1atSIo6E47IUGAGcju1QhAvDEff80MYBZQsM+3gNgOZTj4HFQAgH9uhjA
+xXEcv6/XicRx/EgURc8B8EMARwF4075/9PvsBPCqOI5v7PM7tZ68fAYN7Tzx4IMiNAjF4ORoKLNw
+4NTqKA+XpRO/+AVwzjnAi14EfPCD+Z/fVqGhLRkN6V13bkJDGUeD63Gj7HVuSulEWUfDzIz6miKv
+57JI6UQ1aB7DwoXm+MwzzfGVVwJ/+qf+zkkQNEXCIPXncRIa9JjHcS6i569SOmGEgCL/sr+B4o0A
+ngOV2bABwMS+t98F8Ow4js/JPRHldjgRygVxNYDtAHYDuBXAPwF4TBzHP6r0W7aIvI4TGtp5QnIa
+hKI0NaNhcNA8IDk+dCguHQ0XXgjceivw4Q8DO3bkf35bhYa2ZDRw33VvcukE7TLSJqEBcHetpXSi
+GlpomDcvWe5KO01cdZXfcxIETb/XNR1LfeQ0lCnn5Fw6oc+p06UTcRy/HsmAxbrf71IA+X0j+3+P
+MQAf2/dPyKCKo0GEBqEoTW1vCaiHzp49vBfAgFuhYcMG9TaOldCwZEn/z++C0FAko4GL3TwN5133
+mRlTs9vrGg8OqoXVzAw/oWFgQIkN4+M8RRxN0fsn7R5x0a5VSieqoYWG0dHk+5cuBdasUeLwddep
+1zh9zQuCD4qWTvgQGtpSOiGOBqGxVHE0PPigu/MR2kWvyXpThAaA50OH4lJooC2aH3kk//O7IDQ0
+2dHAObCw6M6Tvs99dp0oep25tw8FqjkaXI3RUjpRDS006C4TFF0+MTkJrO1szLkQkqKlE67H8PS5
+FC2dmJ5OjpMc8OFoEKFBcEJRR4OUTghVaLqjAeC9AAb6ZzTULQEpKzSUSXgGmiM0tCWjgXPpBKeQ
+QqD8eAHwbx8K8Cqd6JXhk8WcOeacRGhQb9OOBkAFQmqkfEIIQb/npe/SiSqOBoDffETaWwqNRTsa
+li7tH/AopRNCFYo4GnwN6G0VGvLaHlUNONq9O7lgogFkvSjraGhK6GZbHA2cSyc4tV1Mf/+iQgP3
+9qFAeUEH4OFoAEz5RJdLJ2Zm+gsN6UBIQfBNmTDIkOeShnO7bR/tLUVoEKwzOWl6LfcrmwCkdEKo
+hjga3DIzo/4BvX+nqr8HdTMAUjqhKZLRwFVo4Fw6UXRCKKUT9Whq6QRghIYuOxrovZUlNKxZYzpR
+3NjpnmpCKPq9rjmXTtAcGk5icRyb30McDUKjWL/eLFL6lU0AwH77mUmJOBqEokhGg1vK7LT7FhqK
+LByaKDS0xdHAaSIFJK8ZvZZpfAkNdUsn4p69tcJSVtABeHSdAJpRmuIa6izLEhoGBszG0Natfs5J
+ECj9xEzOYZDLlpnjhx5ycz5VoOOvCA1Co6D5DHmOhsFBYPlydSxCg1CUNjgauFnoKEV+p6pCw+bN
+yf932dHANaOhyDWmcC6doK+zpgoN9PqOj9s9H1sUFQM5OhqoY4SrkOOaPKEBUBtDgCox8bGYOWif
+QwAAIABJREFUEwSKvueiKNl+FfDbopieC5A/xujXDQBs2+bmfKpAx18pnRAaxe23m+PVq/M/X6vk
+mzcbJ4SQTVcnQWna4GiYmeE7WaPXrpfSLaUT9WmLo4Fz6URZR4PrcaNO6QTA7/pqmlw6Qa8v5/HC
+JWWEBoDXzqzQDfoFVPsWGsq4LOnrhpMbSBwNQmO55RZzfOyx+Z+vhYbJSWD7djfn1Ab++7+BFSuA
+N70p9JmEp9dEMkToTlWhAeA7qS2yOKvqzEgLDS7CIJtwjYH2ZDS0oXSCcxgkZyFHUyUMkkvpRBOE
+HNfQcTirvSUA7L+/Oea0Myt0Az3GZL2mfW8yiaOhOCI0CNa59VZzvGZN/uevWGGO07ZqwfCv/6rU
+0M9/Xq5Tr/ZlTXI0AHwXwWWEBu6OBs4lKm1xNHBeqLUho4GzkKNpi6OB2/3rCzoOF3E0cFowCd1A
+v66zxhff86q2CQ3iaBAahRYa9tsvqYD3YskSc9zl9lJ50AXapk3hzoMDdJBvaukE0A6hYXo6ucjI
+Q0onDEXsl75adXahdKLfZIoKDS5L1KR0whyL0MCHsqUTnCzgXWbzZuDCC4Ebbgh9Ju4RR4Ndqoje
+VSg5nRCE/jz8MLBhgzou4mYARGgoCq2JFEeDOaaDvI8e7f3OpYtCg/58utjsRxWhoWxQYROuMdBO
+RwO3HfeyYZCAuhauJl5tLZ0oev9I6QRPygoNnBZMXeav/xr42teAgw8G7r13dkhimyjqaPAtNOTN
+SbiWHImjQWgkt91mjosKDYsXm+MdO+yeT5ug+RVdFxp6ORoGB82gH0JoKDKp9bVDXYcqQkNRfDga
+6D3B9RoD7RQauC3UypZOAG7LJ8qOFwBvIUcjjoZmI0JDM/nlL9Xb++9XG31tpl8YpO95VdscDSI0
+CI2hbD4DII6GIsRx0tHw4IPhzoUD/QZ5PWD6FhqiqD277S6FhrRI5iIMMoqqZ0j4pGwYpMt7ugul
+E0XCIAG3QoO+zgMDxXcfm7AQbrLQQO9frkKOa0RoaB67dwPr15v/t11oaGrpxLJl5pjT60bCIIVG
+UkVoEEdDPo88kpzIdd3R0M9+rB84vhaY+lyKDtRdFxrqOBoGBpSIUIQmCA1FMhqa5mjgtlDj6mgo
+M7FrwkJYuk40myJdJ0Ro4AVtJQ+0X2jgVDpRpr3lyAiwcKE65vS6kdIJoZGUbW0JJIUGcTRkk+5Z
+3XWhgaOjQYSGfMbGZjsYyggNRdwMmiYIDVI64Z42CA2cr6+myY6GJlxf14ijoXnQjT0A2LUrzHn4
+oqijgVvpBGBeO5xeN77CIEVoEKyiB76REeDww4t9DS2dEEdDNjSfAZDSiSKOBhEaqlMkqb/Kgz3t
+ZgBEaNA0WWjgXDpRdNfGV5BsvzrjXjRhIVz0/vFRoiJCQ3mkvWXzSAsN4mhQcCudAMxr56GHgJkZ
+N+dUFnE0CI1jagq44w51fPTRxRcF4mjIRxwNScTR4BZXjgafQoPvEpoqlM1o4Co0tKF0wldGg5RO
+mGNxNPChiKNh7lzzMWlvGR7qIAba72jgGgZZJGtHCw0zM3w2VCUMUmgcd99tbtyi+QyAOBqKkHY0
+dF1oEEeDW9ogNDTB0dCWjIa5c80xt4WalE74QUonmk0RoQHgaQHvKl1zNHAMgxwcLJYbxbHFpYRB
+Co2jShAkII6GIqQdDQ8+qDpRdBVxNLjFp9CwZ0++lbCfZbIXTRAaOJVOFCmX6UUUmcUat4UaN6Gh
+raUTVYQGKZ3gQ1mh4aGHuj0HCc30tIRBUkKVThQdxzmWHYmjQWgcVYWGkRHzoBdHQzZpR8PERPsf
+Kv3o52jQD5wmCA2+zrEsPoUGIN8OXsfRMDnJpyYyTZEF0dCQ2TFxKTRQkXfRovJfr10N3BZqVYQG
+l69LKZ0wx+Jo4EORrhOAWTBNTbXfqs+Ze++d/Txo+9+DY+lEk4UGcTQIjYMKDUU7Tmi0q0EcDdmk
+hQag2+UTRRwN09PJya/rc2mTo4E+gGwKDfSe1e2egPzyiTpCA8BX0CmS0RBFftwZdOylLrOiNMHR
+UDQMUkonylMlDJKL0NAEIcc1VGig91sajgumLpIumwDavfkUx2bDgFPpRFuEBnE0CI2ABtMcfXS5
+r9U5DSI0ZJMunQC63XmiiKMBAMbH/Z1Lm4SGso6Gog926mg44ghzXFRoKGM3b8J1Lvqgb5LQ4OM1
+V4YiohngLwxSSifMsZRO8EGPwfPn9w+347hg6iJZQkObHQ1544vv533ZOQnH1420txQaRRybge+Q
+Q/pb77LQE9tdu/zsQjcNcTQk6TeR9N1qT4SGaqUTtP1tVx0NdFFOAxXT+BYaqpRONMHRwCGjQUon
+zDEXR4MIDcbR0C+fAeC5YOoiXXM05AUni6OhPOJoEBrFli1mMVwmn0FDO0+0ebCsSpajoctCA10I
+pAd6n5PGODYPQBEa8uklNFDbbhZ1hYYmX2fAT6tOvRu2YEG566yhGQ2cQuI4CQ1x3N7SCU5dJ6hw
+2a8MIOtzuF5f1xQVGjim53eRdGtLoN1z57wyQwmDLI+EQQqNomoQpIZadSUQcjZZjoYul07Qh056
+wu5z0ljFeuY7tKgKroWGpUvVP41rR0OTrzP9mA9HQ5WyCcC87uKYl4OEUxgkDSWV0gl3gs6GDeb4
+wAPzP78J19c1VRwNW7e6Ox+hP3rOTcfrNpdO5LmUuIdBUoGOy+tGwiCFRmFTaJCchtmIoyFJv4cO
+nTS6thlXERrasgCuIzQsX54sr+qq0FC2dMLlAt6W0ADwymngFAZZdWLnc0yrCqfSCSo0rFqV//ld
+FxpmZsx9lVf2ynFntmts3WoWqyedZLoStdnRwLV0ouichOPrRhwNQqOo03ECSJZOiKNhNtrRQBcj
+XRYa+i3wuTsamrAAdiE0TEyYHZfly5M7Z10VGrg4GqamzI6mDaGB02KNUxgk3XGkXVfyGB42k2tO
+15bCqevExo3q7YoVxSbQXO9dX9DfWTIa+HPbbeb4uOPMWNIVR0MTSydGR81YxOV1I44GoVHQejFx
+NNhletpcE9rNo8tCQ1FHgwgN1XAhNNB8hrKOhrK7B1XOLwRlhYbJyaT93hZ0glpVaKAiKKfFGqeM
+BloCR0uHisA1bFPDpXRiZgbYtEkdFymbAERooBk5IjTwJ+0g1kKDOBoUHEsnosi8dri8biQMUmgU
+euBbtAhYubL814ujoTf0ehx0kHmodDmjQRwNbimyOCv7YO8nNHQ1DJKWGBQRGgA3uzV1O04AfBdr
+nDIa6FheVmjQnSekdKI/mzebRcBBBxX7moEBc29wund9QYVeERr4kxYa9JjdZqEhz9Hgu3SiSstt
+bkKDtLcUGsOePcC996rjNWtMvVgZxNHQG5rPsHSpsoMC4mjQiKPBPtwcDVWEhiaFbg4P9+9d7/qe
+oWNumzMabDgaNm8G3vpW4AtfKH8u1NFAxfUitNHR4GJBQPMZigoNgLm+XIUcl5RxNCxaZJ65XBZM
+XSPtIKaOBheONw7klWb5nleVdTQARmgYH+cxzoijQWgMd91l2pkdc0y17yGOht7QyemyZUZoeOgh
+t/3eOdNvgU97zovQUI0iAXp1hIYVK4oLDXFsJk9lHupNus79FsDpjzdBaOC0GC4aBlnU0v/JTwL/
+8i/AG99oBPaiSOmE+9KJukID1+vrkjJCQxSpeQggQkMotKNhdDTpcgXy3YFNJa90gr6PY0YDwM8N
+JGGQQmOgL5gqZROAOBr60cvRACQXb11CHA1u8eFoKBoGSXdo2lo60QahgWtGg550Dg31d40UdTTc
+dZd6G8fAunXlzqWO0MC9dILel/06qLgundBBkIAIDUWhi9O8rhOAadXHYbHUBrZvLz6XGx8H7r5b
+HWsHMS13a2sgZF7pRBT56c4EqDmJ3lwtIzTQFpccXjsSBik0BjqwVa3vpY4GERqSpB0NBxxg/t/V
+8om2ZDT4UN6rwKl0ouhOad3zC4E+r34LM8D972IjDJJ76USemFNUaKCOuwceKHcudTIa9PWdnubp
+ZCvaqnVw0JRXui6dKBoGCYjQoMlzNABmZ3b3bl6v9SbywAPA4YerNqxr1+Z//rp1RnzXHd6oo6Gt
+OQ1FutroXXnXz/s80aMX3BwNUjohNAYbk1T6dVI6kaSfo6GrQkNRR4Pr3b+2Ohr0A2hwsPeD1JfQ
+QCfBeQvyOucXAimdcE8VoaHfApg+n8oG8trIaAB4XV9NUaEhisyklmvphN6t7ApVhQaAx4Kpyfz8
+52oOPT0N/Md/5H9+OggSSG7wtVVoyCudAMy44noDp2gr3zTcXjcSBik0BhuJ5eJo6E2vjAagu50n
+ODoaij5w2rIALuvMoKJYma4T9OuomyePtlzn9Me5Cg1cSyc4ORpslE4APMsnigoNgLnWHMMg45iv
+08wVZbpOAPwWTE2GXvtrr83//CyhgToaulo6AfgrnbAhNGzdau98quLL0VDiEglCNjZKJxYsUDsd
+cSyOhjTpySmdxIujoXkZDQMD6pynppq9AK7jaNh/f/Vg06/5fo4Geo9TkS2PJpSo6MVZ6NIJcTQU
+DykMXToB8Lq+mjJCg8udR53RMGeOCS0sQjpEOO9+aRPiaAhHWmiYmemfJZPuOAF0o3SijKPBZ+mE
+OBryEUeDUBsbpRMDA0akEEdDElo6kXY0dFVo0ANkFM1Wt7kLDYCZxHZRaFi0SH1tFJlJbT+hgbp2
+qgoNHK/zzIy5f8osgl0LDVXFYq4ZDXoxm7djU8XRUKd0om1CAz2noo4Gl6UTBx5YrtU29+vrEhEa
+wkGffbt2mbDZXmhHw8AAsHq1Ou5C6QQnR0MR0SMLbq8byWgQGoONSSpgyifE0ZAkPTmV0on+rYWa
+IDT4Ut6r4lJoWL7cvE+XTxR1NLSpdII+5NtQOsF1oWazdGJ8PCmiVC2dGBpK7qAXoUmlE0WFM9sL
+grExc43LlE0AfrN9uFG26wS3BVOTST/7+pVPzMwYoeHII83rrGulE+JosIO0txQag43SCcBMcMXR
+kETCIGfTT2hIW2Bd0mVHAw2KzPs9JieNgEjv37JCQ5scDWUWZq7LQCSjoVgYZPrZVNXRsHRpud12
+gK+Qo9H38/BwfhK7K6Ghaj4DwP/6ukQcDeFI5xP1Exo2bDAimO44AXTD0dCGMEhpbykIFbFROgEY
+R8PEBC/rbWj05HTePDWZX7bMTOS6KjToBX7W4NgER0MbhAb68bzfgwYfZTka+oVBtrV0gp6TZDS4
+YWrKtIKzkdFA3WWAcunQCXAeWmwrWzYB8Ly+lKJ5I4C70gkRGqpRVmigCyYOoXZNpoyjISsIEuie
+oyGvdGJ6uty47OJcsliyxAjMnISGrBJkm4jQINTGVukEneCKq8GgHQ16cjowYBZrUjox+2MiNNTH
+ttCQbm2p0ULDxETvRUdbSyfoOXEpnZg3r/rOBseMhjLXuEjpRLqsb2YmeW/3Y3raXOcqQkNTSieK
+CA2udh51ECQgQkMZpOtEOLKEhl7tVYsIDW11NJQpnQDcuhqqOhoGB83Yz+F1QzfsyjrsyiBCg1Ab
+raAOD5frc5+GtriUnAaD3kWjCdp6Z3fz5u71/AaKOxpcT8jbKDRU2QWuKjTQSW0vV0O6LWZRuAsN
+VUsn+v0uDz8MXHON+fsVRY/hdRxpHBdqZcKuqggNQHGxl4rn9FlXFI7Xl8JBaKCOhgMPLPe13K+v
+S6R0IhxpoWHbNmD9+uzPzeo4AUjphIa70ACY1w6H103RoOS6iNAg1EZPUhctqqeKiaNhNuPjZtJD
+d8H0zu7eve21yvWjn6NBt00EmuFo4CYUVdlpz1vI9xILaPBYr5wGvZBbtKickOm6U0NdbJdOTE8D
+J58MPP7xwIUXljsXPd7WERo4ZjRUdTT0mqRmCQ1FAyHrdJwA+C+Eq5ROTE+XF8X6IaUT1SgrNNBN
+Dw4LpiaT9dzrVT5BHQ3HHGOOpXRC4aultQ2hYceO5PcJgZ6/itAgsEdPUuuUTQDJXR4RGhR0cprl
+aAC6WT7Rz9EQRWbSyF1oiOPwD5s0LoSGvNIJoLfQoEWKMvkM9NyKnF8IbJdObNgA3HGHOv7GN4qf
+x8yM2QVrm6PBdekEUHz8pV8rpRPm2GZOQx2hwWeIMDeo0EBfx70YGjJjhQgN9chy8uUJDcuXJ10l
+4mhQ+NpcqNreEkj+3WjQewi0GOMyCBIQoUGwgA3bbfrrpXRCke44oel654l+jgbAn9BARYIqQgPA
+bxFMzydP6bYpNGRNuCYmjOhYJp+BnluR8wuBbaGBTjBvuKH4Au7hh42rxpbQ0MSMhiKLX3E0ZBPH
+1YUGmzuPNKNBSieKo8fe0VGVAVUEThbwJqMFduoGzhIadu4ENm1Sx7RsAki6UMTRoODuaADCv3bE
+0SA0gokJ84IWR4N9ijgauig09HM0AM1xNAD8FsGcHA1VW1vScytyfiGgi3EbpRP0+k1MADffXOw8
+bIX5clyo+XA0VBEa2pbRMDlpxKoypRP6a22hHQ377Vc+L8pntg83qNBQFL1g2r7dbcJ/29Hj9oEH
+GgEyS2i47TZzTFtbAkoc0s/StjoayoZBunzmt0VoEEeD0AhsTVIBcTRk0cvRQHd3uyg05DkatA3W
+xoT8jjt6WxltCA2uez6XhZ5PUaFhZqZ/CQh9oNLWaF0WGmw7Gsq0SaPYaG0JtCujQUonylFGNAPc
+OBri2DgayroZAN5CjmvqCA1xLPO1Ouhxe8EClbEDKPGSunOA3h0nNHr+3VahoS1hkHT+w0VoEEeD
+wBpq07JZOiGOBoVkNGTjy9Fw993AccepCcCPftT7PPqdSxacF8Eu2i7SBypV9OnENk9okNKJ2V9H
+CS00DAyYCQuXhVrVrhMSBlkODkLD1q3me5XNZwB4X1/X6LGjjNBAF0xbt9o9n64Qx0mh4aSTzMfS
+43evjhMaHQgppRMKX0JDr3PpBSdHQ9482hYiNAi1oIOazdIJUcgVktEwGxqgmJfRMDFRL9X8V78y
+P+vzn5/9cREazHG/B7t+oI6MJCezeY4GKqKVdTQUWTiGxHZ7y9BCA2Bed03MaBgcNHXSRRwN+nNF
+aCgvNLgonaA7wCI0FGdmxvy+VRwNQPgFU1Oh85M8oaGMo4FbJysbFBGNpXSiHOJoEBqBq9IJcTQo
+bGU0zMwAf//3wNOellTGmwi10OU5GoB6ix4dvgQAl14627IsQkP216XRD9T99kuGXrksnYii4hkS
+ISjT3rLIBCp9/X73u2K10y6EBi4LtTL3MmCucxGh4ZBD1NuijrK6GQ1SOtGfOh0ngO4KDfReqiM0
+TE8D73oX8NSnJvMEhN7QMbuo0DB3LnDoobO/l3Y0xHF2sHLToa/JXp1RmlA6wUVomJkx8wMRGgTW
+2CydEEfDbHrtgpUtnTj/fOBDHwJ+9jPggx+0dnpBKDLI2wr2okLD2Bjwk58kP37PPdk/Mw9fynsV
+bAsNcWystdRuC+R3nahTOkHPj9s1Buw7R9K1uXv2FJvw2xzD9SKTy0KtrNCgxcI8oWHRIrOY3bat
+2K583YwGzgvhOkKDLUeDCA3VoOMuHY/zSC+YzjsP+MhHgJ//HDj3XHvn12ao0DA6CjzqUWbDjgoN
+k5PAunXq+Oijs+36WmgA2pnTQF+TvcYYXxs4ttpbhhQaqm6SVUGEBqEWNksnxNEwG1o6QR0N8+aZ
+B0ueo+G//1tNAjRF0+i5UqSlpK1JIxUaAOA73zHHO3cC3/ymOl68GDjllOLft0uOhj17zMfoQxZw
+WzpBz4/bNQbcl04AxconpHTCoMeTvIyGJUuAlSvN+4u4yuqWTrTJ0eCirIkKDVXCIOn17arQUNXR
+8LWvqc0Mza9+Vf+8ukDa0TAwAJx4ovr/ffeZbk133WXmPemOExo6/26j0EDHGHE01KdMflFdRGgQ
+amGzdGLuXDMZFEeDot/kVC+8+jka7rwT+LM/S9bsrVvX7Bo+qsQWcTTYFBq+/33zkLnkEvO9//zP
+yzkauiQ09AqCBMqFQbZNaChTOtE0oYHLQq3sZKqoo2HJkqTDpkhOgx7Lo6jas5LzjruUTjQXG0LD
+//5v8mMbNqiFstCftNAAJMsntGCTl88AJB0NbQyELFI6ESIMso7QEDJEVRwNQmOwabul30McDQrq
+aEjX9eqJ7o4d2TuIe/YAL3nJbNFm1y6jlDeRIoO8rd2ptNCwbZt5+NNwyDe+sdz3FaFBUTSjYWio
+2i5wU4QGLo6GumKxngBOTfVvd+oLmxkN4+Pm+6UdDWWEhsWL1c5lWebMMfkm3BbCHEonbIZBcnOM
+uCRt3y9KeiwHkuP5VVdVP6eukCU0PPWp5n0f+pDaFMrrOAG039FQpHSiCWGQc+ea15k4GgShADYn
+qYBZTIvQoKCT03RdHrWHZk103/524Prr1fHRR6tdd42u92siRZRYW7tT6V7WgCqfuPZas4g75RTg
+sY8t933bIjQUebDbEBpWrEiGSBalq0KDnvxcd11+1xWbjgY6AeSwGLaZ0ZDOWKCOhiI5Ofrrqwhm
+gLr/9bjGbSFcxNZMcVk6MTw8OwumCOJoqCc0HHss8IUvmP9feWW98+oCWfkYz30ucMIJ6vjqq4Hv
+flccDQDfMMiy7S0B89qRjAZBKIDNjAYg6Wio05awLWhHQ9bkdNUqc5zeeR8bA778ZXU8Ogp8+9tJ
+S94dd1g9Ta+UDYOsOml8+GEzETjpJDMYf+c7wMUXm887++zy37stQoNNR0M6DHJmJik0VEFPPLhd
+Y6DcLnCR60x3sU4+Wb3dtUuVT/XDRekEwCOnwZXQUNbREMdGNK4qNAD8SlM0nEonVq2q5hgRoaGc
+0LB8uXlNLVwIfOtbwDOfaT4ujoZ8stwkAwPAP/6jef973wvcdJP5/9FHZ3+vtodBFhEzfZVO0O9d
+ZZGu89aoY9k34mgQGoPt0gntaIjj7B3OLkEnpzQIUtNPaNiwwSTjPve5wHHHAUcdZT4ujoZ86DVd
+s0a1BgWAe+81QsP8+cArX1n+e4vQoOjnaNixw4hKVYUGfX6Tk/yEyzLXeWjIODqKOBqe8hRznFc+
+4Upo4LBYsxkGmW5PSYWGPEfDI4+Y8bhKa0tNG4WGoqUTN90E/PCH2S1bJyZMvXOVsglALfD0eXG7
+vi6p2nVi3jzgn/4JePKTge99Tz0jly0zO+7XXdet61iFrNIJQM3ZTj9dHd90E3DNNer4sMOSZaGU
+LpVOFHE0uJxX0b8bFXiKor9mctJe6VhZ6M8VoUFgje3SCTrR7Xog5MMPm0lVWUdDVjDW6tXmfeJo
+yIde01WrgBe+0PxfD9Ivf3m1+16EBsXIiPkbpoWGuq0t0+fncoejCmWucxTll4Ho6xdFwBOfaN5f
+VGgYGSm2SOwHt9IJm2GQaUdDmTDIuq0tNXqRwbl0wkXXiS1bgFNPVQuwL35x9sfpWF1VaAD4Cjku
+qepoAIC/+AvVzpLmCpxxhno7OQmsXVv79FpNL6EhirLbkPfqOAF0q3SiSHtLl8/7uk5u+jrLauvt
+g7qujDKI0CDUwlXpBCA5DXQHrYqjQaOzHI480lhKu+RoqDopTwsNL3jB7M8pGwKpaYrQkLc4K/J7
+0GTlrNppPcFKCw11W1sWPb9QlGlvST8nT2gYHU22Ws0TGvQYbsOR1nRHAw2DTHfmqSM01G1tqeG6
+EHZdOrF2rRnHf/Ob2R+v23FCw/X6uqSO0JDFmWeaYymf6E8voQFQDkpaigL0zmcA2u9oKNve0uXz
+3qbQEMq5LaUTQmPQL7g5c4pN5PKgttKuOxpo/VZZR0NWAvfIiLLeAcrR0NQWl6EcDQceCJx2mnnf
+cccZe2NZOC+AfToagN5CQ93WlkXPLxRl2lsCxYWGBQvU/aqt/WvX9n+ta0HXttDQ5IwGYLZFPy00
+zJ9vdhHzSidsCw0TE7xKgVyXTtx/vzmm11JjS2jQjhERGqqjHQ2ACA155JWt0KwGoL/QII4Gf2GQ
+9PpWKZ3g4GiQMEihMehJqg03AyCOBkoZR0O6O0KviZcun9i1K2wP3zqEyGjQ1/rFLzbvO/vsap0Q
+AN6Wfno+XIQGG6UTnIUGm46GdD/27dtVrkgWcSyOBko/S39aaACMmFPG0VAno8FW217buC6duO8+
+c5wVoJY1Vlehi46Gqu0te3HssWYsufLK5m5o+KCfowFQ5UK0ZFN3o8ii7WGQ+jU5d27veZeUThRH
+HA1CY7A5SQXE0UDJczTst5/Z0S+S0QAkAyGbmtNQxNFgY0KeNXn9i78AXvpS4NWvBt7ylmrfF2jP
+AriM0BBF2YssPcHavTs5KZXSiSR5QoOeXKaFBqB3+cTu3Wbn3sYYzi2joY7QkN5p7yc07NzZ38Fh
+K6OBm5CjcV06QYWGLEcDfVZWaW2poe1Du7JAtu1oGBgwTr8HHwTuuaf+92wrRUSeT38aeM5zgLe9
+LekWSdP20gkqNPSiiaUT4mgQhD7Q3TBxNNgnz9EwMGAmukUyGoBkIGRTcxpCOhoWLAC+8Q3gK1+p
+F5zHeQFcVWjotWDQQsPSpdk9p/VDd3o6+bNtl05wc46ULZ3o16pz717zutA7W7rFJdBbaLAd5stt
+IVzGnQOUFxqo06Zf+YTt0gmAx/XV+CydyHI00PdluaaKoq9vHPMbL1xRtetEP+iC+Mor7XzPNpLn
+aADURtEPfgD88z/3d1C2vXRCjzG98hkAf6UTVMhpqtAgjgahEYyNmd1lW0KDOBoMeY4GwIgIW7Yk
+d/p1KcWyZcmJX1ccDTYm5Poazptn7/7WtFFoyHM09FoA9GpxKaUTSfTnZE2gsiasj3uced8NN2R/
+T5utLQHeGQ1FJlP9FsD9HA1A//IJW0IDdWpx6jzhs3Qiy9FAy7OyRPmicBVyXGLb0QBIIGRRiggN
+RaFf32ZHQz+hwXfpxPBwtWw6+rcSoUEQ+kBVU1ulE+JoMOQ5GgCz0x7HZkctjs0iOR0HeOcYAAAg
+AElEQVSMJY6G4mhHw6pV1bMYeuHL4lcFm0LD1JR5HZcVGugO8fLl/c+j6vmFRC/OhoZMN5h+6N9l
+cnJ2EGDWhPXQQ83xjTdmf0+XQgOHhZrr0oksR8PFFysHzv/9v+ZjtjIauF1fjcvSiThOCg2PPDL7
+b0NFeREayuFCaDjtNPPMFKGhNzav/eCg+R5tdDQUERp8l04sWlRtbsjB0SClE0IjsN3aEkhOwrou
+NNBdml67YFmdJ7ZuNZO3tNBwxBFmUSOOht6MjZmFRZ1wsV5wXgDbFBqK1E7nORoWL67e0aYJ17lo
+CU6/3ZosoWFgADj+eHV8zz3Zu1y2hYY2ZTT0CoOMIvO8SzsaHngAeMc7lMPs/PPNArztGQ1FEuEp
+ZUondu6cPRlPuxr0ODMwUO8+5np9XeJCaFi0CHj0o9Xx9deHW0xxR4/bc+f2nsuUQZdPtM3REMfF
+Mhp8OxqqdJwApL2lIBTGdn0vkJwkdL10gu7o0gktJUto6Nfqa84ctdMJKEdDEwOvqNBQxNFQxWJM
+bdAiNPQm7/egnU2KOBrohFQLDVXLJoqcX0j0+RQVUfr9Lr0suDSl/KabZn/PLpVO2HI0LF5sxNq0
+0PDBD5rxZmoK+P3v1bGUTiQpUzpB3QyadE4DzYEp4g7qRReFBj12RFH/3eKy6PKJ6Wng6qvtfd82
+oa+9TYEHaJ/QMDVlXHzcHA1VEEeDIBTERemEOBoMerEbRb13g8sKDYDJadi5s5ktLukA6crRQIMg
+aZimLZqwAAbqCw15rS2BbHV/fNyML1WDIIucX0hsCg10YkmFBu1oALLLJ2yP4dwWamXDIItkNNBn
+FBXBfvtb4F//Nfk1a9eqty5KJ5osNJQpncgSGno5GuqUTQB824e6RC905s+3WyJIAyF//Wt737dN
+pFsS10XvsO/a1cxNpF7Q12LoMMi9e83zt8lCgzgahEbgonSCWpG67mjQQsPy5b0X1FRo0LkM+i2Q
+vUhuek6Dj9IJW33Ze9GEBTCQvzjL20EoIjRklU7Y6DhR5PxCohdnth0NdAyljga9u07pUkZDEWty
+L0dDHGcLDdTR8MMfzhYndLcP/bWjo/V2j+hzlpMQ77LrBO04oaGOhulpc33rCg3c7l8f6IWOrcWu
+5jGPMcd33WX3e7cFV0LDzEy77t+ipVk+SifqdpwAeAgNdNwVoUFgi4vSicFBM1hymkj5hoY79iqb
+AOo5GoBm5jQUsXzV3ZkSoUHhw9HgUmhownWuktFQtHQiz9HQlYyGOXOK7db2svSPj5v/U6Eh695c
+vNj8LC006B34OmUTQNLZRl9boaFCQ90sjDR5jga6IVGntSXA1zHiEr3QsWXf1xxyiDnO+ht2nZkZ
+c4/ZEhroPLxNgZBVHA2unvc2Nli5dZ2Q0gmBLS5KJwAzkeuyo2HHDjMQ9KtRrys0tNXRMDJiJvsi
+NJRDn0+RbgiuhAaaTyIZDbM/r6jQsHy5uX433jjbTmtbLOaa0VD0GvdyNGR1nNDfNy0evOtdwNFH
+q+MbblDfx5bQQF9DnMre9N96ZKRYRkLd0gnqaLDV2hLotqPBttCw//7mdZflSuk6VMiy7WgA2pXT
+QJ8l/YSGsm1zq2BDaODgaJDSCaERuCidAIxo0WVHAw0j7OdoWLHCTOyKCg20dKKtjgYabCVCQznK
+LM5sCw36odt2R8PMjHnQuxQaAFM+sXVr8roC3SmdcCU0AMnxecUK4O1vB04+Wf1/716V06AnyjaF
+Bo6OhqLunKzrfOONwAUXJMdeIHuRSh0NtlpbAvzuX9dMT5vf07bQEEXAwQerY3E0zIaO2bbDIIF2
+CQ1FHQ1RZBbNTREaQnWdkDBIoRG4KJ0AzERubMxtixrOFN3RHRoyCzE9QdMZDcPD2SGStMVlWx0N
+QD2hgeZcuBAaBgbMuXNaAAPuhIYy7S3bLjTQcc1l6QSQzGlIl0+I0JCkV3ZAP6GBjs/vfa+aRJ50
+knnf5Zf3/tqytEVoSDsaxsaAP/oj4N3vBt7yluTn5jkaRGioDt1Vty00AKZ8YufOdi18bdBvzK4K
+dTS0tXQib4zRY4uP0gkb7S3F0SAIfXBVOtFWVbYMRR0NgFkIP/ig2inVjoZVq7JtrLTF5R13NC+d
+uKgSa8PRMDxcv+63F3rxw01M0+dT5OFjo71llrrf9tKJsm0X059XtOsE0D+noe0ZDWXuZaCao+GF
+L1RvH/tY4E1vUsdUaLjsMnMsjgZFWmj41rfMa/6nPzWt7OLYCA30a3o5GmxmNHC4f11DFzkuhQZA
+yifSuBYa2jR3Llo6AbifV7WldEIcDUIjcFU6QQfdNg2WZaALraJCw9SUEhn04i6rbEJDW1xymrAW
+oayjoUqolxYaVq602/KLoh+InBbAQNjSia44GsqG56U/r6qjId15QgsNg4PJANWqtCmjgU5U+7Wn
+fMc7lDPsyivNzznxRPPxK64wx3WFhoULzZjHadyuWzpx8cXm/7t2AXffrY63bzcL/uOOM5/jKqOh
+a+0t6SLHdtcJwJROAFI+kcbFtW/rJl3R0gnAvaPBRteJ4WEzBoqjQRD6YHs3TENV2VD1S6Ghjoa8
+HV1q7dd924H+QkOTcxpcOxomJ4EtW9Sxi7IJTRuEhsFB9Q/I3kHQi4DR0d7fz5fQwMk5Qv/mVXaB
+i7a3BNQCTYtlvRwNtFNCHbjtCPvIaACARz0quUhdskS9j54DUF9oiCIj2DVZaKD38i23AD/7WfLj
++jlGF6dUMJOMBjv4dDSI0JBESieKU0Vo4OxoAMzrjYOjQYQGgS02apWyEEdDtdIJALjmGnNcxNEA
+NC+noayjYWLCWHGLQN0kIjTk0+/30IuhfpbmfkLD8HC9unaujoa6pRPpSVS/SevoKHDkker4ppuS
+rwU9htsSioeGzGsy9EItjssHblYVGrKg5RNlv7YfOuuEi9AQx/WEhnvumf1x3RaULk6PPNLc25LR
+YAcRGsLhQmjogqMhb4xpQukEYP7mHBwNUjohsEW/4ObNs3ujiqOhWukEUFxo6IKjge4wlrFxu+44
+oXFt8auKLaEhjs0ioKjQsHs3cNdd5p5csaLeTntbhYYypROA2Q3es8fY0uM46WiwhZ4Ihl6o0YmU
+yzDIXmQJDXUdDYB5Le3ZE/4aA9WCTbMEYponpIUGWtd/yCHm+vnIaKhSctc0JKMhHC66TrTV0VAm
+o8FnGKQNR0OoNY6UTgiNwMUkFRBHA2AcDYOD+bs0vUonDjyw99doWy9gFh5NoayjASg3IfclNHB0
+NExNmR3vukLDrl3mb9VvAUAnWZs3Ay9+sZnk/8mfFDuHvHPLOr+QuMxoyMpayOo8MT5uFtM2x3D9
+ugud0VBFaHDtaLApNAA8XA3071xUaIii2SLxi15kyqTWrk0GQQKq3l8/Cx96yIQY28xoEEeDXSSj
+oTcSBlmcMqUTPh0NdZzcnEonxNEgsEW/4GwGQQLiaACM0HDAAdmdIyhUUKAp//0cDYcfbo7vuqv0
+6QWFCg1FMhoA/kIDl84fdXba04vfIq0tASWm6QXK734HXH+9Oj76aOBjHyt2DnnnlnV+IamS0VCk
+68ToaPZ4kdV5wlV74jrdXmxCr1GVrhN0okqFhqJigavSiTYIDcDsv8kb3wicfLI6fughYP365OKU
+OhomJ40YqR0NUVRfMOua0OBiV52ybJm5piI0JJHSieJUaW9JN01sYtvRMDUVJj9KHA0Ce+LYndDQ
+dUfDzIypUc8rmwB6L4b7CQ3z55vv3TRHA11AuBYa+rlC6qIXjnGcFE9C4kpoyLM0Z+UKfPvb9ceW
+JggNNh0NvSasWZ0nXIX5cimdqHKNbToa9t/ftBHWiKPBQK/1oYcCz3xmUpy59trZpRPUsaDLJ7TQ
+sHRpviifR9eEBteOhigy5RNSOpHERdeJtpZOVAmDBNws4G0LDUAYV4M4GgT27N5t1ELbpRNddzRs
+2wZMT6vjvI4TQG8xIm+RrAPiNm1q1qSq6E5A1Unjxo3m2IejAeCzCK6yOOtVE0ndNWWFhi99KdnO
+riocrzHgrnSil5Vz9Wrzd8pyNLSxdKLOvQxkCw0DA+UWBWlXgwgNBnqtzzpLOZvSQoPeBV+0SN3b
+9PppgaFIDkxRuiw0uGhvCZjyiYcfTo45XUccDcUpk9HgutMUva51/m6hhQZxNAjssaXqZdHWOrOi
+lOk4AahBIj3JWrw4f4fiiCPMcVbyN1eKTo6qBnv5Lp0A+CyCQzka6Gv+b/4GeNnLiv3sPPq1hAyJ
+7dKJPEfD8DBw7LHq+Pbb1de7FhrKdnuxjQtHw+LF5XbNRWjojf7cgQHg9a9Xx/R6XXON2QXXu+Jp
+R8P0tPnb1M1nAJL5Jl0TGlw4GgDpPNELF2UrdPwXR4ObZ76+rgsWmNbeVUiHYPtGhAaBPa7qe4Hs
+dnddgnacKOJoAGYviPuVTWi0owFoVk6Da0eDFhoGBkw4mQvaJjTMzCRLQMoIDa99rbreL3kJ8OEP
+F/u5Zc4N4HONAbulE3v3mklDv9eDLp+YngZ+8hPgttvMx1wIDUBYV0PdMMisjIayGQt04TwyUm4h
+3ou2CA2veIV6++Y3m8XoYYcZweAXvzDfW3887WjYscPk29gQGrrsaPAhNEj5hMGFo2FoyNzDbdqk
+q5LRALgtnai77qGvtxDrHCmdENhD1VKXpRNtGiyLUtbRAFQTGqijoUk5Db6EhhUr6inWeXBcBNtc
+AJcRGv7mb9Si4Zvf7N9JpCwcrzFgt3SiqMOHBkI+73nA295m/u8iowEIu1irGwapJ2JxXF1o0OGG
+gFok12nVqqHBqk0WGi64QIkFn/60eV8UGXGGutC0/T7taLDZ2hJQYqe+V9ouNOzcCfz2t+b/roQG
+6TyRjQuhATAL4DbNnat0nQDcCg11Ok4AUjohCLm4LJ3oehgkdTS4FBqa7mgYGOg/sa1ig52eNtff
+ZdkEwHMRHEpoAOo/uLOgC0cu1xiwWzpRtGb0CU/o/bFjjil2DkXg4miwVToxNmaOywoNK1eq7imA
+vWtMX0s0ByUUVYUGIFt8yerW0cvRQIUGG44GgE/XFJd8+9sqA+fyy837XAUfS+lENq7yMfQYtXUr
+n25WdSmT0eCydGJmxjxvbToaQoZBRpHbDTUAsLh3JHQJl6UTXQ+DpI4Gl6UTTXc0jI723yEs6mj4
+3veASy5Rtv/JSRPEKUJDsa+xJTS4IIrU+U1M8LnGgN3rXHRn7IlPBC66CLjiiuT7TzsNeMpTip1D
+EbjYz22FQVbpOEH5+tfVP51DUJe2lE70op/QkHY00N/fptCwc2e5XJ+msHu3KlP7r/8y7xsdBT7x
+iaTzwCZSOpGNHrejKH/xXIaDD1Zlcbt3q/vYRkvd0HBxNOzebcSbpgsN+tq4djMAIjQIFXFZOtF1
+R0OV0on0bkSR3YmDDlI7eJOTzXI06EE5bxegyIJnxw7g5S/P3nl12doScG/xq0IVO10RoYHavX3T
+JaEhzxXypjepfy5pstCQ5WioKzSceKL6Zwu6q89NaLCxYKLlJhq9CBZHQz0uvjgpMvzJnwCf+YzK
+xnCFlE5kQzdM6rZlpaQdJG0TGspkNNh+5tP1SNOFBv18c53PAEjphFARl6UTIyNmsOiio8FX6cTg
+oJlg3H13c2x2eQn7miILnt//PltkWLAAeM1rqp1fUbriaBgaclMWUZReXTFCYjOjwVWtb1W4ZDTY
+CoOsKzTYZmjInAc3ocGGo+HII2fPKXo5GmxnNADtFhroJsbHPgb84AduRQZA3at6USVCg4EKDTZp
+o7BTtXTC9gaOzXVPaKHBp6NBhAahEq5ao2n0hLnLjoaRkeLXtorQAJjyiYcf5jFpLYJNoeHWW83x
++eerB/N99ymx50lPqneeebRRaKAPdl0/vt9+dkLwqqLPj4trBKiW0dBrAsVNaOCY0VAnDJKb0ACY
+RTWHMdu20DAwMNsBEsrR0BTxvSi0HOQJT/AzLkeREYruv79917QqRecxZWljqYqevw0P5+cJuHSK
+2hQauLS3FKFBYItLRwNgdkC76GjQQsPKlcUnAlWFBhoI2YSchr17zQLAttBw2mlqQnvwwckgSVe4
+7vdcBReOhlD5DBqOjoYQGQ2+kNIJ9+jX1I4dJlMmFLaFBiCZ07BkibmvFy0yz0RXGQ167J+ZSbaA
+awP09ejjGafRi9/du5Ovpy5TtAS0LG0M39T3bZHxxeW8ypWjIWR7SymdENjiWmjoqqNhasrsBBcN
+ggSSQsPgoGrNWAQaCNmEnAY6IOdZDumCp1ewFxUa1qypfl5VaKOjQX/9+Li55iI0zMZm6UTRrhO+
+aLLQkBUGuX27eR/dUQ+JzjyJ4+T5hcCF0EBzGujCaWDA/A1cOxqA9pVP0OegT6GhjXb+OkxOmvHJ
+9pjdxmutX4dFMmB8lU60pb2lOBoEtrgundAv4j17wu/Y+GTLFmMtLJrPAKhJg95tW7WqeLuapjka
+yrSEKuNoGB11l7rdizYLDRw6Tmg4Cg0221tyczRwyWio62jQEzG6kHfxrKsCpxaXrh0Nhx6a/JgW
+GlxnNAAiNNiijbvsdXDV2hJoZ+mEHmOKCA1NKZ0ILTSIo0Fgj6/SCSDMizAUVTpOaN75TjUQv/Od
+xb+myY6GvAc0nUhlTRjHx424smaN/xwBERr8oM9vclLZoTlQ5ToPDZl7tGrXCR9wzGioUzqxYYN5
+n+uWt0Xh1OLShdCwZg3wkpeoMf6cc5If086FHTuMyBJF9kSgrggNNlsq5tHGxW8dyjgzy7J4sZkb
+tUXUqepokK4TvZH2lgJ7bFqIsqCLyEcecSNmcIR2nChTOgEA554LvOc9akFSlKY5GsoIDXkTxnXr
+zMLTd9kE0B6hIevBzqW1JTB7h8PWYqgOVUonokh97vg4b0cDl4Va3a4TWmigk3W6YAoJJ6GhTOu5
+okQR8M1vqlLC9PNMOxri2IjjS5faaxFYpOSuqUjpBA9cjtlRpK73rbeqax3HYcOYbVAmo0EcDcXQ
+zzcpnRDYoksnFiwobtMvAxUvupTTUMfRAJQTGQA1QdMDZtscDXkLnltuMcciNCja6GjgHrpZZnGW
+VQYiQkM2VbpOZGU06B3Y4eHi2Teu4SQ0uHA0aLKeZzSLQZdO2MpnAPjcvy7Qv8/AgJ8FhkZKJ5K4
+LJ0AzPUeG0uWFzWR6WkzFod2NLRFaJieNiXpUjohsEW/4Fw5Dejg2yWhgToaqggNZYki42pYv17t
+IHGmahhk1oQxZBAk0G6hgdaNhxYa2nKd6edyFhroYrMNpRN6YXTQQfZ2zevSFaEhi6xATptjTJuF
+Bu1omD/f7y63lE4kcT1mt+l60/GFUxhkk7tO0G464mgQ2OJaaKCOhi61uKSOhrKlE1XROQ1TU/wf
+SjbDIEVomE0bHQ0cr3OV0gnALOTo60C6TmRT5V4eHDQLsL171cJM7whyKZsAui00ZLkXxNFQDCo0
++GTRIjOnE0eDX6Gh6debvgY5hUHWLRkfHjaLfN+OBio0iKNBYMnMjJncukrh7qqjoW7pRBVoICT3
+nIYyD+g5c8yioZ/QMDAAHHWUnfMrA8cFsC2hYeNG877QAXptuc6A2lUHVHcaPTnh5mjgslCreo31
+xGtyMim8itCQDQdHg02hIS9EuMloocFnEKRGv350bkCXcRkGCbQrE6NsBkxTSicA87f3LTRQAUYc
+DQJLHnnEPCjE0WAX36UTQDIQkntOQ5lFVRSZB1M61GtmxggNRx5ZbiFii7YsgLN+D5rUrxfHoeB8
+nYeGymXcUEHszjvVW9eT1rJwERqqhEECSaGBTtJ9t7/tBw1YbWN7y35kiQpSOlEM/fv4djQARmgY
+H29+bkBdpHSiOFwdDU0WGsTRILDHR1/xrjsa5s/3tzvZVkcDYB5M6Qnjhg1GfAhRNgG4fSBWpcri
+rJ/QMDDgrwSoF5yFhrICFxUa1q1Tb/VrYnSUR4YAx4yGMrs2+nPTQoM4GrJpm6OhrUJDHIcrnQDa
+tcteFymdKE6djAZX7S2Hh+1sTomjQRB6QHdQli938zO66mjQQoMvNwPQrBaXZXdv9YQqPWEM3XEC
+4L0ABoo/gPoJDQccUL4Tim04Xmc9eSo7WVm92hzfcYd6q18THMomAD4LtbqlE3v38i2dmDfPXGdO
+QoMPZ5hkNFRjctIkzYd0NADNX/zWxXXXiTaJOnVKJ1w5GhYtshOmSoUGn+VEIjQ4IIqimYL/Li/w
+vZ4dRdG3oii6L4qi8X1vvxVF0bN8/C4coEIDtXDapIvtLScmjFvE5y7w4YebY+6lE2Uf0L0cDaGD
+IAGeC2AbpRNTU6YEKHTZBMDTOaKvc9kd4CxHgx4fRWhIYiOjgWvpBGBcDVyEBpqJ4xKfjoZ0yV2T
+ob9LaEcDLa3rIq4dDYsWGWu/lE7Yw3YIvv7bT035nZtI6YQ74oL/MokUFwP4IYAXAjgQwPC+ty8E
+8N9RFF3k8hfggg+hgQ6+XXE0bN5sjn06GubOBQ48UB03ydFgS2g49tj651UFlxa/qtQVGvbuVSLD
+zIz6Pzehgdt1FkeDO2wLDZwcDUBSaAgZrqeFBh9lE4BkNFSFCg0hwiDpXJGW33YRHwG+ery6//5m
+h2/6Kp3Yswe4+ur+Ld5tCw3UleuzfEIcDW75LIAT+vx7Q5+v/eC+j8cA1gJ4JYBT9729dt/7z46i
+6AOuTp4L4mhwQ4iOExqd0/Dgg/7rxcpQVWgYH08+bKnQcMwxds6tLJwXwEB1RwOnIEggeX4hMwMo
+VUsnFi825Wp33KEmDHrSwEVo4JLRUDUMkmY06N3AkRF3ZYJV0YvrycmwYrxvoUEyGqpBf5cQjoYl
+S8zxjh3+fz4nfAT4agfJ+Hj4wNg6lHU0VCmd2LIFeMxjgFNPBc4/P/tzJibM/Khua0tNKKFBHA1u
+2RzH8c19/t2b9UVRFB0F4G+gxISrATwxjuP/F8fx2jiO/x+AJ0GJDxGAv42i6Mis79MWfGQ0dNHR
+QDtO+A7QozkN99zj92eXoarQACQXPVpoWL7c7m5YGbogNGinTEjoa+mmm8KdB6Vq6QRgXA0bNyZd
+UFyEhpGR/m1lfVE1DDLL0XDwwX7KAsrAJRDSt9Awb97ssUnaW+YTunRChAaDT0cD0OychrIZDWVL
+J6amgFe8wnRx+vd/z/48uuEpjoZydE1oqMpfAdCRZm+L4zixLIjjeAzA2/b9d2jf57eWLVvMsTga
+7MHB0QDwzmkouxOQtTu1cyewaZM6DpXPAHRDaODgaHjqU83xZZcFO40/EMfmQV8lPI/mNFx/vTm2
+tctSlyjqXbLkE30vDw6WayGqhYbdu82CiFvZBMCnxaVvoSGKZrsaxNGQjwgNfHAdBgm0p8VlHUdD
+kXnVe98LXE7S+e66K7npp2mT0CCOBp48H8rNcGscx1dnfUIcx78BcBuUq+EFHs/NO74zGroiNNDd
+yRUr/P5sKjTcm+nr4YEejIeHiymxWcFet91m3idCQxJ9HkNDxVslph/sGzea/3MQGh71KODQQ9Xx
+FVeEL5+omh2goTkNv/udOebiaADMopOD0FD2GmdNvDgKDV11NABJYSGKkovYuojQ4AYRGgw+HA1t
+6TxRNqOhjKPhW98CLrhg9vuvumr2+3Q+A9B8oUEcDcyIougIqMBHAPh5zqfrjx8URdFh7s4qLFRo
+cGU7nz/fWFW7UjpBBRWbE6ci0BKY0Cnm/SgbfJc1aeTQ2hJIPhA3bgQuuUT9u/xyE6bomyqLM+6O
+higCnvEMdTw+Dlx5ZdjzqSs0UEfDddeZY05CA81GCUUXhYbpaeCnP026Dl0Sx/XKgKpCHQ1LlxYX
+RYvQBaEhRBjkwoVmTidCg3o7NORuodfG0gmbjoZbbwVe+1rz/2c/2xxnzRFEaKhO14SGP42i6KYo
+inZHUbQriqLboyj6chRFT+3zNceR41t7ftbsjwfKsnePFhrmz3enjEeRmTh3xdHgQ+XuBd0h4pwI
+XVZoyKq35dBxAlCT46F9BVl33AG86lXq3zOeAXwgUKSsbaGBQ0YDYIQGIHz5BJ381MloAPgLDSEX
+alXLU7ImXtxaWwLZQsP73w88/enAKaf0T0+3Rd17uSpUaLBZNgG0V2gIHQY5MKDCbAERGvQ8ZnTU
+XfZLG0sniowxRcMg3/xm83d41auAL37RfMyXoyFUFp2UTrjlWABrAMwFMArgUQBeA+DyKIq+FUVR
+1u1Dpxh5L1eqGzLcA7GDFhpclU1odM1xFx0NvhcNdOL20EN+f3YZbDgaqNAQ0tEAqAVBFr/8pd/z
+0NgUGubPNxPL0DztaeaYk9BQt3SC5qmI0JCka46GPXuAT31K/X/9+qTg5wrqWAlVOuFSaKAugKYT
+unQCME5NERrUW5djdltKJ1x0nYhj4Ne/VscHHQR87nMqF02Hol9zzeyvpUJD07tOiKPBDbsBXALg
+jVAdIk4E8McA/hHAVqj8hRcC+E4URenYKHpL5S156a3CaNpnjzj2LzSIo8E9TXA0xLFdoWHuXFO7
+H4rvfU895D75SeATnzDvD3XP2xQaDjqIT1L/gQca98rVV6tA0FDQxVkVoWHJkuyxl5PQoBed6bay
+PtH3ctmJVFOFhv/6r+R9TSfGrgglNFBh3Hb5ZlsdDdyEhlDjAgf0otLlmL1ggbneTRYaymY0RJEZ
+w3uVToyNmY8ddZRZ8J9xhvmZNP8IaFfphDga3HBQHMd/HsfxF+M4vjKO4xviOL4sjuN/APBoAPqW
+egqAt6S+lj4+85ql0Ns6QBWce3buVHWggHuhQQ/CjzzSjYdSSKGhCY4Gumgp2ns6PWkcGwPWrVP/
+P/rocmn0Lli+HHjjG4G3vx14xzvM79UkoYGm+m/bZs6dQz4DRZdPzMwAv/hFuPOwYTenOQ0aLl0n
+APO6ox02fGPT0dCE0omLL05+vM1Cg0tHw+CguWfatMnBSWjYu7ddIk4ZqmyYVG7QyHoAACAASURB
+VEULpBs2hMt9qkvZ0gnAiMu9nj10jkvH0TPPNMfp8ok2dZ0QR4MD4jju+ciN43gLgJcC0BrP21Kf
+QuOs8v4kdErTymHUR8cJjZ44T0+HT4r3QUihYXjY/EyujoYq1yctNFxzjald7lW2EBIqroWgal27
+/vx77jHv4yo0AGHLJ+qWTgDJ8gkNJ0cDh11hW0LD3LnuQo/rQM/pN7+ZLZ61WWhwmdEAmAXavfe2
+Z5MjdBgkkPy7dbV8YmLCbNa5HrO1QLp3r7+AWNuULZ0AzJjfS2igged0/NCOBmB2IKQ4Gqoz5P5H
+8CeO47ujKPoJgD8BsDqKopVxHD+w78NU084bFug+a62lwt69e3Httdfmft6qVauwatWqOj+qFD6F
+hnRQSqiHoy/04nLOHD8v/jRLl6pzaLPQQFVqql5zYeFC1cO5SY4G/fl79iQf7FyCIDVPfaoKJJuZ
+CSs01C2dALIdDZyFBt9ddKanzWS+bhjkIYfwKQGiLF6sdt+np5NZHRofY0gbHQ2Aave8bp26htu2
+uZ/r+CB0GCQwu8Ult2eED+g8pqgzsyrpzhMHHOD257mgitCgx/BepRPU0UDHjxNOUH+T3btnOxra
+IDRs2rQJmzZtwp13mvdt3Aikl5p7LdsQRWgw3AwlNADAQQC00EADIPMMlLSSs1ZV1JYtW3DyySfn
+ft65556L97///XV+VClCOBoA9cCnLRjbiC87XS+WLVMPo4ceUrs43CbXdYWGPXuSKjVVr7kQ0tEw
+NWXslVUdDRRujoYlS4CTT1YZDb//vRJ0Qky8bJROcHc00N8rhBuNzpPqOho4lk0ASjRbtqz3TmWb
+HQ1nnGFEwyc9yf7316FwAHD33e0QGjiVTgDddTT4dK6mO09wdHHmUTajAch3NPQSGoaGgFNPVS2C
+77tPXTM9/reh68RFF12E8847L/G+971P/XOJCA2GXga5m8lxXkY9/fgtdU5m+fLluPTSS3M/z6eb
+AQjraGg7egcq1IJB2xp1/WSoyUgvbDoaliwJ33EiCy2uTU6qBWnVHe8q0AVw2bq9JggNgCqfuPpq
+dXz55cArX+n/HGyUTjTN0eCbOvdyWmjgGASp2W+/pNCwcKF5jrRZaDjySOCGG9Tvetpp9r//EUeY
+47vuAh7/ePs/wzciNITjH/4B+MIXlJhP2876FBqaGghZJ6OhiKMhXRZ35plKaADUfPFlL1PHbeg6
+cc455+D5z38+vvxl06Hoox9VLZEpz3rWs7DFYq2NCA2G48jxRn2wr6xiI4BVUGGR/Xjyvrcb4ji+
+t87JjIyM4KSTTqrzLZwQ0tHQdjg4GjQPPcRPaKADcVHLIf0dbroJ2LxZHZ9+utoR40ZaXAslNLTR
+0QAooeHDH1bHl10WRmiwUTrB3dHASWio62jgLjRQzj4b+PjH1XGbhQYAePSj3X3vtKOhDYjQEIYt
+W4APfCA762PFCrc/uw0tLuuUTpTNaACSTtdeQkNTSyd0qf0Pf2jed+yxQHqpOWI5IZLhVNs/URQd
+AeCPoFwNd8ZxvCn1Kd8FEAFYE0XRqT2+x+lQjoYYwHccnm5QqMjlupSBTpzbLjRMT5uJQGhHA8Az
+p6Guo4HW5XMsmwDCimtdEBqe8ARzrqFyGmyUTmS1uOTYdQJontCQnmNxLZ0AkkLD4YcDL3qR+X/b
+hQaXpB0NbYBDGGQXhYatW43IsGCBureOOEKJ3uec4/Znt8HRoMeYgYHi2WVVSycAtQmloaW2dD5m
+a47elTDI1gsNURQ9N4qink3soig6AMB/wXSU+JeMT/sEgH3RUvhUFEWJR+q+///zvv9OAfhkrZNm
+TChHQ9tLJ+gkINSCgXuLy7pCAw3A4RgECYS9520LDStX1jsfF8ybZ/7299yT7JLhCxulE8BsVwMn
+R0PojIYuOhrOOiu5kPMtNLQprLmNjgaOYZBdgL4OX/96JVzddRfwv/+rxEGXUJF0/Xq3P8sV+r6d
+N694bpgWiycns9t69hMa9tsPOOYYdXzttWaM03/HBQvstUWX9pbt4dMA7o2i6JNRFL0iiqLToyh6
+bBRFz4ii6AMAbgTwOCgnwi8BfCb9DeI4vgPAR6FcDY8HcEUURX8aRdHJURT9KYArAJyy73t8JI7j
+O9Pfoy2Eymhou6MhZGtLDR1w2+ho0ESRCvzhSMh73uYu8IoVfh5gVaA7Frfe6v/n2yidAGbnNHAq
+dQrtaLAZBslZaHjyvmLNpUuBN7whaeltc9cJ1yxdaq5lGx0NIjT4g74OfW8izZ9vxIYbbjCdeJqE
+fn6UGV/omE937zX9MhoA43idnATWrlXHWmiwVTYBqPBJPU8SoaHZxFD5Cm8D8B8ArgRwHYCfAPh7
+APvt+5xvAnh+HMcZtyUA4D0AvrDvcx8H4OsArt73VgsVF8dx7Di/MyxUaHDdW7xLjgYOQkPbHQ2a
+E06w+7CwSVscDRzLJjR0F+neWkk61XDhaJg/394uiw1CCw02wyA5l0689rXK3nvddapVIB3XpHSi
+OlFkXA3r1ycD/JqKFhqiyG/2D6WLQoOLEMEy6EXzI4+obktNgzoaikLH/KzyCZrRQOe9Gup4/dWv
+1FsXQgNgXA0+53tSOmGf1wA4F8CPANwGYBuASQDbAdwA4CIAZ8Zx/PI4jns+mmPFGwE8ByqzYQOA
+iX1vvwvg2XEcO664Co8WGhYvdn+DdikM0kX9V1ma5GgoGgaZ9XDims8ANNfR0CSh4bDDzHFooaHO
+4ow6GjiVTQC8hIY6job587MnolyIIjWe6XuaPjNFaKiHzmmYmlJt7pqOFhrKWNBt00WhgT7HQ2xw
+0PkOzRxoCnqMqSo0ZHWe0Btp8+Zlf98nPtEcX3CBcjXpv6Ptv6F+drfZ0dD6rhNxHP8SqiTC1ve7
+FEB+38mWooUGH32lu9TeUhwN+dCBuI6jgWs+A9CeMMgDD6x/Pq449FBzHFposOVo4CY0NDmjgU68
+Djkk3KKsCkNDaswbGxOhoS7pnAbX9fSu0UJDyBKrrgsNIRwNdL5z1VXAW97i/xzqULd0IsvRoOe3
+6XwGzbHHqmDdb39bbbo973km0NP231BvmkkYpCBAKft6p9uH0NAlRwMVGkKFQTbJ0VB0YZU1qWqK
+o0FKJ9xAhYYQAVm2Mho4Cw1tcTRwzmfohd5x8yE0VOlx3xTa1nlC/61CCg0LFpi20l0RGly0RSzD
+iSeaMfCqq/z//DrEsX1HQxyb0ol+5d9f+pJ5xt58s3m/q9KJ3buzW6C6QDIaBLZs325eCOJosIs4
+GvKxkdGw//6z0/o50RZHA2ehYXTUjF+hHQ11FmdLlwJPepI6fvrT652TbUILDbbCIDnnM/TCp9DQ
+JUdD0+HgaBgYUGW3QHeEhtCOhpER4JRT1PG6dcDmzf7PoSpVu9r0y2gYGzPP4F6OBkDdp9/+9uzX
+iyuhYXq6dztO24ijQWCLz44TQHcdDZLRkE2VazRnTtL6fMYZvK3Q4mjwg65p37AhO5XaJbZKJwDg
+f/4H+M1vgAsvrPd9bEMnhU0rnWiTo8H1DlmbhYa2ORo4CA2AKZ/gOMdwQegwSCDp4vz1r8OcQxWq
+Cg39Sif6tbZMc/zxwBe+kHyfK6EB8Fc+IY4GgS2+hQZxNPhl0SKzCOfuaCgaBhlFyQkw57IJIKy4
+VmcXuKlCw8yMEht8Yqt0AlCTr1NPNXZkLtDXXOjSibITqRUrzLHup94k9ER4Zsb9tW+z0EAzGZru
+aJicNJ0zyizYXKCFhh07/FnFQxI6DBJIznuaVD5RtTSrX+lEGaEBAF7xCuCd7zT/ty0+07msr3WO
+hEEKbAkpNLTd0cCh68TAgLJjP/QQz90GqvYWFRoAE44G8A6CBNrT3pJzGCQwu/OEz6A3W6UTnAld
+OlHnXn7Oc4A3v1ktgl72Mrvn5YN0i0uXO9htFhrmzlXj2MaNzXc0aDcDwMfRMDWlzqvMs7yJhC6d
+AJrbeYI+O2w5Gmhry34ZDZSPfETNy++/HzjrrOLnUQQ63/flaPBdOiFCg1AY30LD0JB62I+Pi6PB
+F1po4OxomDcPGBws/nXz56vfZ3DQ1CpypQ3tLefMKbZTEJKQLS5tlk5wpclCw8gI8NnP2j0fn6SF
+hpUr3f2sNgsNgCqf2LhR1bXv3t3cRTF9DXIRGgDlamjqNS1K6DBIAFi1Sonp99wDXH21Wmj6WGDW
+parQYNPRAKhrdf75xX9+GaR0QhAIW7aYYx9CA2AU4LY7Gjh0nQDMwLtjh7LeckJfo7JCzB//sXr7
+0pfyn9RwcTSUffjQxdxBB/HOwQDCCg02Sye4Erq9ZZ0yoKZDxxDXgZBtFxraEgjJydFAQ6e7EAip
+565RFHb+oV0NY2PADTeEO48yuAiDrCI0uCSE0CBhkAJbqKNh+XI/P7OLQkNoRwOgbMM7d4Y7jyyq
+Cg2f/7x6sH71q/bPyTZtcDRwz2cAxNHgGjopvPPO5ELHB124xr1IOxpc0nahgQZCitBgh7Sjoe3o
+1+CCBWEFeFo22pTyiaoZDbbCIH0Q0tEwMFDOHVwVERqEwvgunQDMwktKJ/zAufNEVaFhYAA44YRm
+WAVHRowaL0KDOw491ByHFBrauDgD1GJCT2B+/WvgMY8BLrvM38+v485pOiI02IM6Gpqc00CFBi5h
+kEA3hAb9HA9VNqFpYiCki9KJKhkNLgnpaPA1JxahQShMCKFBOxrGx01qchvhEAYJJG2NnISG6Wnz
+0OFe/lCXUOJa3bp2DfcgSEAJavo+EkeDfUZHgU98wtwXd94JPPOZwOte58fd0IVr3Au6oHEtVlKh
+oY2CTlscDZwzGtqOfg2GLIkFlNir//ZNERqkdMIN+pr4GrNFaBAKo4WGgYHkw8IlXWlxWaV1owvo
+wMspEJIuTkIKMT4IVS5UZ3FGdzOb4GiIIlM+sX693zwSPXkaHPRjWwzFW98KXH898KQnmff9278B
+F1zg/meL0KDw5WiYO5d/LksVJKPBPl0SGmZm+DgahoeBxz9eHd9zD7BpU9DTKYSLrhOchQbf7S1F
+aBDYoYWGZcv8TZCpCtzmnAY9wFDrfAi4Ohq4lJb4oImOhmc8Q01gR0aA5z3P7nm5QgsNExMqVd4X
++jq30WqeZs0a4Gc/Az7+cfO+devc/9wuh0GGEhrayIEHmudxW0onRGjwB92hDu1oAJpXPlE1o8F2
+1wmXhGxvKaUTAju00OCrbALonqMh9MOIq6OhS0KDvgf27FElI76oIzQcfjhw333AAw8ARx1l9bSc
+ESoQUl/nriyABwaAV7/a/N+HgCmOBoUIDfUYGFBjG6AcDXEc9HQqI0JDGOjmWOi5HZAMhLziinDn
+URQXjgad0TBvXvi8EkBKJwThD0xMmEHTp9DQNUdD6EW0OBrCQ+95Xw8eoP7ibNmy5P3DHSo0rF/v
+7+fqxVmXFsB0ceFDwOxyGGSI9pZtFRoAk9Owe3eyxXeTkDDIMNDXX+jSCUA5GnSJ01e+wn9ObSOj
+oZejgYObAZAwSCEwa9cC550H3H9/6DNJJrWKo8E+HIUGTo4GOgB3JQwS8DsR6NoucGhHQ5sXZ2kG
+B4HFi9WxOBrcIo4Gu7Qhp0HCIMPAzdGw//7AK1+pjrduVYG9nLFROtEro6HLQoM4GoQ/8MIXAu9/
+vwrVCk2IjhNANxwNMzNmgAktNHBtb9lVR4NPca1ri7PQQkMXrjFFjy2+HQ1du84iNNiFdp5oak6D
+lE6EgZujAVCblzpj7WMfS24icsN26cTYmBmzRGgQR0PniWPjZPjpT/2momfBQWiwteiamjLWIQ7Q
+wSX0Ipqro6FLQkMoR0PXAvRCCQ1dLJ0AzNiyfbv7Wveu3csUX+0tZ2bMdW6z0NAGRwMnoWHBApV9
+AfDazHABN0cDAKxeDZx1ljretQv4yEfCnk8/qgoNvUonqKiy337Vz8smIbpO6PWPOBo6DhUWdu0K
+/4CjtYmhSidsTJo2bgQOOUQtMri096GDS+iHkTgawhPKxdO1XeBVq4yi70toiGOzOOvCNabosWV6
+2v193bV7mTJvntmxdOlooNe4zUIDdTTceWe486gDJ6Ehioyroe2OBo5CAwC8731mXPzUp9S8mCNV
+Mxp6ORq4dZwA/HedmJ4260sRGjpO2sGwdm2Y89BwcDTYmJz+53+qZPxNm4Dvf7/+97MBp0X0vHlm
+8BFHQxhC5ZJ0bXE2MAAcfLA69iU00ElPmxdnWfgMmu3avUyJIuNqcCk00EVAm+/l1auNcPOtbzVz
+ccwpDBLojtDAsXQCUM+9v/xLdTw2BvzjP4Y9n17YzmjgKDT4Lp2gbm4pneg4aaHh2mvDnIcmlNBg
+e9FFe7jT3ykknBbRUWQGYE6Ohi6FQXJwNHQlqV+XT+zcqf65pssLYJ9lWfQ6+5pMcUKEBnssWgS8
+5jXqeMcOVdfeNDiFQQJJoaGpLUOLwNXRAADvfreZb37uczzzR1yWTnARGgYHzVzAh9BAhRdxNHSc
+9ODLSWhYvtzfz7W96LrjDnPMZceek9AAmAUBl+sD8LtGLgkdBjk0ZGpo247vnAa6OOua0OCzLEvf
+yyMjpp1bl9BjiAgNdjj3XCNYfeITwIMPhj2fsnAqnQCM0DA97beFs2+4OhoANY//679Wx1NTwOc/
+H/Z8srAdBknntFwyGgCzeeZjvieOBuEPZDkaQiq/nBwNcVx995E6GrgspKmAwmERrRcEu3fPbg0U
+ii4JDaHbW3ZpAexbaOhKXXsWIUonunQvU/SiZmzMXfBxl4SGww4DzjlHHe/eDXzoQ2HPpyxchQag
+f/nE9u1KjGgqnB0NAPDqV5vj0DlwWVTNaOjlaOBYOgEAK1aot5s2+Q1KFkdDx0kLDdu2AffdF+Zc
+AD4ZDVNTwEtfqh5U/+f/lLN77d2bXExwaevDbRHtc0FQFG7XyCWhHQ1dWpyFFBq6dJ2B5MTOtcjb
+1cBNjY/OE10SGgDgPe8xi53PfhZYvz7s+ZShiULDl7+s5ppPfCKvLmFl4C40rFxpjjdvDncevehC
+RgOgAuoB9Tp1Peem10McDR0nq51lyEBILTQMD/sdMNOOhr//exXIBAA//jFw/PGqZnJqKv973X13
+8rpycTRw6joB8Ow80SWhQRwN/pDSCX+Io8EfIjTYZ+VK4B3vUMd79wLnnx/2fMpAhQYOf6siQsMX
+v6jma7/+NfClL/k5L9twLp0A1FxDC09tEhp6lU5wzGgAjNAAuN9QpqKdOBo6TpbQEDKnQQsN++/v
+t+aVLrx/9Svgox9NfnxsDPjbvwVOPTWZv5BF+uMchQYOi2ifoW1F6WoYpDga3CKlE/7w6WigGQ1d
+hC5qXOU0dE1oANRcY/FidfylLwG33x72fIqiF2zz5vHILKFzjCyhIY6BG280/z/vvOSisylwdzQA
+xrbPMXdEjzFz55a7b4uUTnDKaNDdrwDg/vvd/iwpnRD+AFehwfeLc84c01qKPpA+9CHg7W83g891
+1wHPfnb/ej+azwDwWURzExrE0RAWcTT4g+4k+LBCd7l0QhwN/hChwQ3LlimxAVDZARwD9LLQjgYO
+ZRNAvqNh48bk+zduBD7zGffnZRtu+VtZaKFh27ZizmCfaHGp7PhSJAxSHA1SOtF5aCCIVp1CCQ17
+9phJhc98BkAJCWkl+M/+DHjXu4BPfhK48krg6KPV+++8U7WhyhJpgNmOhm3beLRW4vYw4uho0EJD
+FPHoA+6SEO0t49i4RtruGKHMmQOsWqWOpXTCLb4cDV29lykiNLjjFa8wxyFzs8rQNKGBuhk0H/qQ
+2y4qLtDnu2AB305OBxyg3sYxn9wyDXXilIEuoLOEhrlzec0jfQoN4mgQ/gBdLJ9xhnr7wAMqldQ3
+oVVAuvB6zGNUz1/tZDj9dOB//sec1/e/D3zwg9nfJ+1omJzk0VqJ2249Z0fDggU8rJ8uyeq04pqJ
+CbObwdXi6YpDD1VvH3gguXhygTgaFC7HlfFxk1TftXtZQ39vERrsQjdbuC3MetFkoUFf723bgI9/
+3O152UZvFHAeh7SjAeBXPlFVaBgYUG26geQzV79eObkZAL+lE+JoEP6AFhpGR4HTTjPvDxEIGVpo
+OO449XbJEhUEmX5YHn44cMklZgH6D/8AXHrp7O+TleHAYceeWxgk564THIQY18yfb3Y/fDkauIld
+PqEPedcTrS5nNCxYYCZ/LsfdLt/LGt+OBk67g65ZtMjcx00TGrj8nfKEht//3hx/5jPmel94YbID
+Gnf0a49jEKSGCg3cAiH1GFPlvtVCfpajgVM+AyCOBiEQWmhYuhQ46STz/hDlE6GTWj/7WeADHwB+
+8xvgUY/K/pw//mP1OYCygL3qVcA995iPp1tbargJDRwmxj5D24rSJSt0FJn7wJejoQmhVa7w6eDp
+culEFBkR0+V17vK9rPHRdaJqInzTiSIzZjRBaJiaMruYTXM0DAwAz30ucNZZ6v8PPwxccIH787NB
+HJvnN+dxSJdOALyEhjiuntEAmEW0FvfHxszzl5ujYdEiM2aL0CB4QwsNS5YAJ59s3h9CaAid1HrE
+EaqHtc5i6MW73w284AXqePt24MMfNh9Lt7bUcJgocBMaxNEQHv17hnA0cJ4UuYBOen2FFALdExoA
+M7b4cjR07V7W+HY0dO1e1vMgDvOHPKggxFFoSI+509PAzTer46OOUrvZ732vuccuuohHtlYee/aY
+OWdTHA2cSicmJ831q+Jo0ItovbAO7czOQzsr77/f7f0tpRPCLJYuBY480gxUoYUGji9QzcAA8OUv
+mwHmxz82H6P5DPRhy2HHnk6MOezYc3M07N1rHhZdERr0AimEo6Er11iT12rNJl0unQDM2LJrl8lR
+sE2X72WND6Ghy84RLTTs3u0+16UuumwC4Ck0pMfcdevMOHn88ertwQcDz3iGOn74YZWnwx36uuP8
++uBaOkEFMhulE6Gd2Xno8onxcbcCpjgahFksXaoW0CeeqP5/333Ali1+z6EpQgOgHmBnnqmO775b
+/QOS+QynnGKOOSyk9YRtZIRH33efO7xFoIGdXVk4UEeDj92bLi8aQrRdBLq3Cwz4EXW6fC9rfAgN
+9Pty3rF1AXV2cnc1cBQaRkez25YDySDIE04wx0ceaY7vusvdudmiKeMQV6GhbgZMunQitDM7D185
+DeJoEGahJ2Yhcxrog5TjCzSNVr4B4LLL1FvqaKDhmhyEBm5lAcPD5sHI6foAfK6Ra/T1n5nxs2PW
+xWus8Sk00Ek/l2A2n/hwS3X5Xtb46DpBF1IiNPCF45gTRWZDIy000CBIKjQccYQ51htInGmKEEcz
+GjiVTtTNgEk7GrhvmPrqPCGOBmEWejAOKTRwf4GmyRIaqKOBCg0cJgkcA4N8hLYVpcuOBsBPTkNT
+dl9ckBdMZpMuX2fAj6jT9WsMiKPBNU1qcckxowHoLTRQR4MunQDE0eCK/fYzHds4ORrqlk70czRw
+XMf4cjRQoUEcDQKAbEfDDTf4PQfuL9A0p5xiFmqXX66s59rRsHAhcMwx5nM57dhzWkTT0LbQwUvc
+Mix8QCcmPoSGLu8C+3Q0dHlxBoijwRc+HA1NqUF3QVMdDVyFBjrH0ELD3LnJLmNNczQ0RWgYHDTC
+WRuFhslJdX81JaMB8Fc6IY4GAYCZBB92mHmf7yCcpgkNw8PAU56ijjdvBq67zrS6POqo5CQhtNAw
+M2N27DlNivXfeXIyOVHRxDFwyy3Jh4ErurhwoL+nj0DIpkyKXOBTaOjydQbE0eCLoSGzqHQlVIrQ
+oBChoRpaaJiZMc+4sTGzKXTccSbHAUgKDU1wNDRJVNblEw8+GH5jSUNLRuuUTgBqHss9o4GWTvhy
+NIjQIAAwE7PRUaPq+VYd9YN03jw+NX550PKJiy82bXJWr06KJaEnCXQSwGkRTRcEWWLMpz+tJgKn
+nOIuPV7TRaFBHA3+EEeDP8TR4A99f7l2NMydyyPE2CciNNQnq2Tt5pvNQpfmMwDqftbXXRwNdtGB
+kOPj/jpd5WHL0QAAX/tasxwNLjMaJAxSmAUdjJcvV29DdZ3g+OLsBRUa/v3fzfFRRymlU1vwQzsa
+uLZio3/rrMXXf/6nenvzzcC997o9ly4uHOjERBwNblm82BxLRoNbxNHgD19CQxcFsyYJDXUXbK7I
+Ehp6BUFqdE7D/fcnd2Y50iRRmWPnCZrNVUUgO/xwc/yGNwBf+Yr5P8e1zIIF5jUhjgbBK3RipgeD
+bdvMDr0Pmig0HH+8EWboxHP1avVW/y6hhQaui+h+joY4Tk4IXF9DCYN0//O4Cl4+GB42v7PP0omu
+XWcg3yllA65jqm+o0ODCDi1Cg4K70NAkR0OvIEiNLp+IY/cbHHVpkuDJsfMEFWroZkBRPvIR4OUv
+N/+nO/lc1zLa1XD//e7WeOJoEGZBJ2Z64Twz42+BPDZmaqW4vjizGBgAnv702e8/6ij1lgoNIWvS
+6KSY08OI/q3TD5777gN27jT/d30v0gd2F8MgfTgauN6HvvDVZUVPnmgf+S6R55SyQZMm+C6hLXKz
+cnbqEMfmOnddaNi6Ndx5FIGr0EA7d/zbv6m3VGjo52gA+JdPNGkc4uhooHPMKkLD0qXA178O/OAH
+ybKE0VFerwOKzmnYu9fduCKOBmEWWUID4K98gqr1HANU+kHLJzRaaNC/y969yR1z33DdfVuzxhxf
+f33yY9TNALgXGppkQbSFtLf0CxUaXAqP+jp38RoD/h0NXb3OgNsWl+PjwNTU7J/TFTjlPOXBVWh4
+6UtNKccXvqCytPTcYulSYNWq2V/TpEDIJs1bOAoNtq7fc54D3HQT8M53AitXAu95T/1zc4WPzhPS
+3lKYRVZGA+BPaGhaxwlKWmhYuNBcQx+hZEXgKjTQdqrXXpv8GN11ANxfv7rKdhMJ6WjgdB/6Qo+z
+e/cm065toydPXV0A+85o6IoDKgs6ObctVna54wSgJuj6WSRCQzVWrwY+f5nnKwAAIABJREFU9znz
+/7/8S2DjRnV8wglAFM3+GnE0uIF76URdoWbhQuDjHwc2bQL+7u/qfS+X+BAapL2lkGBkJBneI0JD
+OY48MhkIc9RR5uElQkN/DjnEWBuvvTa5y5t2NLieaHVRaAjlaJg7V7XG6xo+FsBdt5sD6v7SzzTX
+QsO8ed28lzUuHQ1N2q11hXZFchcauIZBAsCf/znw1reqY7rTmlU2ATTL0UCf29xfIxwdDV2c99EW
+l646T0jphJBg6dKkqkuFBl+DQZOFBiDpatBBkAAfoYFrOFwUGVfDli3Ahg3mY+JocE+o9pac7kGf
++BAaxsZMwBP3HS6XuA7i7fq9rBGhwS1aaNi+3X2L5zpwdTRoLrwQOPPM5PuygiAB4NBDVf4WwN/R
+QF8j3McijkJDF8cY344GKZ0QEmUTgGQ0VIEKDcccY465pEZzriem5RNr16q3k5PALbckP0+EBvvQ
+iYnP9pbc7kFf+BAaujhxysJ18GbX72WNCA1u0XOIOHbfFrcO3IWGkRHgG99Q9fOaxz42+3OHh81i
+rCmOhvnz+Qf/UqGhjaUTTcF3RoM4GoTE5BdIDgZSOlGMF78YeO5z1aL57LPN+7k4GriWTgDZOQ3r
+1s3uX+2rdCKKurN4EEeDX7JardmmSTW7LtFjL+1oZJOu38saERrcwmWzIg/uQgMAHHgg8N3vAo97
+HPDa1wKnn977c3VOw/btvAWeJrV/HR01eTZcHA1d3GDyUTohjgYhQVpokIyG8syZA3z/+2pH/tBD
+zftFaMjn5JPNsRYa0mUTgD9Hw8KFxjbZdnyGQU5NmTreri6AfYcUdvU6A26v9dSUES+6fI2B5O9v
+W2hoUv25K0RosMuppwLXXQd8+cvZQZAamtPAuXyiac4qvZHJRWjQY9bwsJrHd4H5883aRBwNghdE
+aHCHCA35HHGEUZK10JAOggT8CQ1dUbWBZFq+a0cD53vQF1I64Q+XLS7lXjaIo8EtVGhw1fPeBpzD
+IKvQBKGhicG/uvPEtm2mdW1IqCOkn/DUNnT5xIYNJtPJJiG6MonQwJh0RsOCBUbZk4yGenDZjeAa
+BgkkAyE3bgQeeCDpaNBqqGuhoUkWRFsMDZlJoWtHA+ecEF+Io8EfVOS1fa3lGhukvaVbdFcmoDmO
+hjYIDbTFJdechrExExDalNdHiNLsfnRxgwkw5ROTk27yMvRYFUWz15iuEKGBMWlHQxQZV4M4Guoh
+joZipHMatNAwfz5w3HHq+KGHku0vbTI5aSZKXXvg6PvBtaOBs9jlCx8ZDbILrBBHgx/E0eAWLpsV
+eejn59y57Sg9bIKjoYmCJ6fOE3HczQ0mIBkI6SKnQY9VS5f6CyltwbDTXtJCA2CEhq1b3dhq0uiJ
+IO1/3gZcTnbLwH03meY0/PKXZgfh0Y82OzrT0/Ynshr6fbsmNOj7QRwN7hFHgz/E0eAHERrc0k9o
++O1vTaem0GihgXM+Qxl6ORo2bQK+9z1gYsL/OaVp4uuDk9AwMWFCC5ty/WzhuvOEHqt8OtRFaGBM
+P6FhaspP4q5ehLfJzQAo4UQ/eLm0t/RVL1UG6mj4j/8wzoUTTkgOVK7Emi4mD2tCOBq6ujgTocEf
+LkVeucYGERrc0ktouPJK4LTTgFNO4SE2tE1oWLHC/C7a0bBnD/CEJwAveAHwjneEOzdNE8chndEA
+hG9x2eV5H+08YVtomJw011aEBgFAdv2M7zqqEOqXL/TvxMHRMDLiLwG2DEcdZRa869eb9x9/vJ/y
+ky4/cPQEhar7LhC7eXLxK6UTbnHpaJB72SBCg1t6CQ0//rE5/tGP/J1PL3QYZFscqVFkyifuuUc5
+e//lX4zo8ItfBDu1P9DEriycHA1dHl9clk7QeboIDQKA/o4GwL3QQPuct83RAJjfyWXGQB7ce74P
+DKje1mlOOCF5T7hyhXRZaKD3hMvyiSbuvthm7lwTtCuOBre4dI/INTbMnWtqcKW9pX16CQ233mqO
+s7o0+aZtjgbACA0TE8BttwEf/rD52Pr14eZzmiaGpXISGro873NZOhEq3F+EBsaEFhraGgSp0b/T
+3r3JZGaf6AkbV6EBSOY0aMTR4B46QXFZPiG7wAo93vpob9mUyacLXI4bci8bosiIAK66TgwMtGsB
+W4b58404SSfwt9xijmmXphBMT5vMgjb9nWhOw1vfmhxHdu8O61IFmil4ciqd6LKj4eCDTTtPKlra
+gI5TtGuOa0RoYExooYHelG0WGoBwOQ16Ysz5YURzGgA1QB1wgGQ0uIbeE+JocI9roUF2gRXiaPCH
+vs9clU4sXNitHveUKDKT9a1b1dvpaeD2283n3HZb2HBCXTYBtEtooJ0nLr989sfvvdffuWTRxIUy
+J0dDE6+fLebOBU48UR1ff70ZW2wgjgZhFlkZDaEcDW3OaADCKOAzM0p9B3jvvqWFhhNOUJMsKZ1w
+C70nXDoapL2lQi+Ad+92k4khi2AFfa6Jo8EtepK+Y4ddO3lXW8+l0XOIbdvU9V2/3pSbAkp4uO22
+MOcGJJ2abcloAJKOBg0N0w4tNDRxrF+2zLQ/DS00dHneBwDPeIY5/ulP7X1fERqEWWQNUFI6YQ8f
+1v9+0EkA50nxmjXJScrxx6u3Pq5fl5VtX44GaW+poAtgF4GQUjqhGBw0k0dxNLjl8MPV24kJ4Kab
+7H1fERoUerK+d68SKLOsziHLJ7rgaADULvD73mf+z0loaMprZHDQrC+kdCIsVGi47DJ731eEBiFB
+FBl1kUKFBteqowgNbmnK7tvQEPDYx5r/n3CCeiulE24RR4NfXLe41Nd53jz1muoy+lqLo8EtT3ua
+ObY1YZ2aMiJ5FxcBlHQgZJbQEDIQkm5mtFloeNvbgNNPN/8PLTQ0VVTW5RObN4cN1Oy60PDEJwLD
+w+pYhAbBGVkiAyAZDTYJndHQpEnxqaeaY10/JmGQbhFHg198CQ1dvsYaPXZs3253QiuOhiQudsbo
+eNHFRQCliNAQ0tHQVqFhwQIVmgeo1/m73gUcdpj5eGihoanjkBYaJibcbm7k0eV5H6DKgLRwtm5d
+srV8HWjegwgNQk+hYfFio3RJRkM9Qmc0NGkn+W//Fnjuc9UDXXehoAszyWiwTwhHQ5MmRbah97PL
+0omuL84Ac62np+3e2yKaJTn+eLM58fOfKzdCXbq+20jpJzTockNxNLjhk58Envxk4KtfVX+Hgw4y
+82ZOQkOTXiNcOk/IGONGJBZHg5Cgl9AQRWbiIBkN9eBUOsF9UnzwwcD3v6/6VeuU8eFhc97iaLBP
+CEcDd8HLJTSjwbajIY7F0UChY6/Na90k8dYHAwPA05+ujnftAq65pv73lEWAoZfQsGqVcf7de6/9
+rh9FaWsYJAC8+MVKPHvBC9T/h4eV2ACEFxqaXjoBhA2ElDFGhAbBA72EBiApNLisoxKhwS1tWODp
+wcqH0NC1B45vR8PgoArV6iouSyfGx81uctfu4yxcXWt9Lw8NAXPm2Pu+Tcb2hLWpiygX0Mn67f+/
+vfsOc6O6+gf+PWuvu8EGDO7YVJtijOnF2JTQIfQSQgmBNwkQfrwhgYQaIIHQISR5eAOBUEIghE6o
+BtPBEFMMGBuCMQYMuGJcd+3d8/vjzOReaVVG0ozq9/M8enZ2NRpdaedKM2fOPfdDd3I2YoQrmgxU
+LquhXotBZhMOn5g/383oVQm1miVYLYGGRr7AFNp2WzeTyjPPxHOuFwYaevYs77EeAw1VKkqgYeXK
+ZCPlrNGQrHoINITv4YIFyQS9wi+cnj0br4Cef4BSjkBDr14uW6URJRloqNUDz6QkFeQNP1MbfV/2
+JRloaPSg2VprueWXX3bLI0a4oslA5QIN9Tx0IhO/TkNc49qLEfaRWiv8W41DJxr1+7JLFxsaBABf
+fQV88EHp2wzPc8o9FJ6BhiqV6yCpXAUhwwPAbt3q80uq0hkN/nPW6gFb+B62tSUT9AoDDY0Y1fZT
++b/6KrnnCU/OGvULPZRkjQYGGlIlndHA99hZbz03zeUrr6Re5S4GAw2Of8D+2mtuOT3QUKmCkB9/
+7JYb4Tu0WgpC1urn0MCBbrmStUXCz5iuXRs7My3OILGqO+dgoIEARMtoAMoTaKjHbAYgNYBSiUDD
+J5+4Zf8LspYkHawJv3Aa4SAp3QYbWFQbAN5+O7nn8TMaGlmSNRp4cpaqHBkN5IQHrC0tqVfei8F9
+2fEP2P33pVqGTjz0kFseP74ybSinags01Fr/2HFHd8zx0EOVm+KykS8w+eIMNHz7rRu+yUADAYge
+aEhyHFW9BxqA1NT/cvMDDenzQteKJGfu8CvS19oXdhy6dHFXxaZPT2b4hCozGkIcOlE+ScxYw305
+uzgPWGu1on4Ssh2wjxxp9w0YYL+/+275T9o++wyYPNmWx4yp3YsZhaiGQEN7uws61drn0GqrAXvs
+Ycuff+72n3LjDE1m1Cg3POu550qbNahShSABBhqqVq5Ag1+wJamMhuXLXYplIwQa5s8v/4HAjBn2
+U6R2DwKSzGjwD2gbNbIdTiWqCrzzTvzbX7bM7feNfhU4yaETHHOaKkzlB4CpU+PZJvfl7MKZJ4B4
+royFGv1EoE+fjsdqPXu62Q/CrIb585Md/pbJgw+65YMOKu9zV0o1BBpmzXInhIMHV6YNpfD3FX8f
+KhfVxs5k9TU1AbvuasuLFgFvvln8tubNc8sMNBCAyg+d8E8ay71TllP42lpbUwsnlUOY0TB4sEtX
+qzVJFtRk5WG7EhUq5Usmm1qaYjVpvXrZzBtAshkNjX5yBtgJWFgkLa79mlkj2a2zjjvpnTy5tEAa
+Aw1OU1NqgBIANt7YHb9VsiBkIwYahg51y5UKNPj1OPz/f6048EBXI+6BB8r//MuWWTYrwM8XIL5s
+NGY0UAfVFGhohIwGoLzDJ7791nX89dYr3/PGLcn3j4GG5AMN/slZo18FFnF1Gjh0Illdu7oT3w8+
+iCfIWw+z+CQpzGpob7c03GIxOydV+kH7iBFuuVIFIRcsAJ5/3pbXXz+1XkQ969HDpZoz0FCcddax
+Wg2AZZt9+GF5n5+BzFQMNFBiKj3rRCMGGs47DzjzTOAXvwAmTkz2eeuhPgOQbI0GBhrsQCW8ys6M
+huSFVydZDDJ54bCg9nZgypTSt8dgTm5xHbByX07lT3EJpAYaKlUQ8tFH3VXhgw5qrKlew+ETs2fb
+FPDl5v+fazXA42fA+AVFy8H/fGnU4z7f+uu7TJ2XXwZWrChuO36gIf0zK2kMNFSpSmc0VDL6VU5+
+oOH224FrrgGuugrYZx/gyy+Te14/0FAvGQ0cOhG/bt2ATTe15alTS5+aLh1PzlKFgYZFi+wEOC58
+nzvys3XiKDrG9zi3cePccUVcGQ0MNHQ8Pho50i1vsok7yS9nRkMjDpsIhYGG9nYraFhu4f+5uRnY
+aKPyP38c/H2m3MMn/OM+fr7Y50cYJF6xwqYoLgYzGqiDXIGGPn3cVU5mNJTmwAPde+lraSm+Q0cR
+FoIEajujgUMnkhde+W1ri+fKr49DJ1KFQyf8glRx4ElwR3EPC+LQidxWX90FLadNK/5qL4dOpMo1
+dKJHD7siCQDvv28F2RYsiL/YrG/ZMuCJJ2x57bWBHXZI7rmqUSULQra2Wt8CrFZHrdbe2mADl43x
+2mvJXnRLx0BmR3FkozHQQAVpanKpLww0lGbHHS3q/dJLdrvySndfEqnqoXrJaODQieQlWaeBQydS
+JTXFJQ+eOho1ygXU49ivGczJL7zavmpVarC7EOH73L27XbVtdP53YFOTnaT5wnH6y5dbNuqaa9rn
+zF57xZs1FZowwWW+ZbuQUs/8QMOsWeV97g8/dDNO1GJ9Bl+Y1aAKPPJI+Z6XQyc6CmeeABhooDIK
+p7icMyeZaRkbJdAAAP37AzvtZLcjjnB/TzLQUC8ZDf6JWdyBBn7hmCQDDcxoSJVUoIEnwR316GGp
+5YCNa25pKW17zGjIz7/aHl55LRTnuE/lH7QPH27D3XzbbZf5cU89Fc+QoXR+qvvBB8e//WpXyYyG
+Wi8E6avUNJccOtHRwIEuSPzGG6nvUVQMNFDBwjoNLS2pB1hxaZQaDemGDHGvd/LkZII4gMto6NbN
+Ah21qrnZnTglWaOhkb9wttjCjfNlRkOy/EBDnOnNzGjILAyirVpV+hh2BnPyY6Ahfv7xkf/+hk45
+BfjpT4F997XbqFHuvrjrNqxa5a4+9+rlZhppJJUMNNRDIcjQmDF2PAzYVfQ4hxLmwu/KzMLhE+3t
+wAsvFP74efPsZ+fO5X9fGWioUUkXhGykjAafiBsTP3cu8MUX8T+Hqgs0DB9e+xWhw/2DQyeS0bOn
+O4B9910bBxoXZjSkCms0AMxoKIc4s3WY0ZBfqYEGv3YJTwKMX8HdLwQZ6t0b+P3vgX/9y27XXOPu
+izvQ8OqrLuC/zz4dsysaATMa4iHishpaW4Gnny7P8zKTNbNS6zSEnwtrrFH+cw4GGmoUAw3JSTJV
+HQC++spNUVPL9RlC4RWdBQvizQBhoMEJg18rV8Y7TRozGlIlPXSia1eOa/fF+VnLYE5+fhX8YgIN
+y5e7aRP5Hptdd7WhrM3NwFFH5V8/ySkvJ0xwy/vvH++2a0Xfvi7QWKlAQ69eqQGPWuVnxLz9dnme
+k5msmY0f72oalRJoKPfUlgADDTUr6UBDuFN27WpFnxpJ0oGGeqnPEAoDUW1tqQf7pWKgwUlqn2RG
+Q6qki0HywCnV6NHxDQtiRkN+PXu6E6APPig8MMy05o769gVmzrR6WWFAOJd11nHHb3FnNPgnIP4V
+0EYi4vbxWbOSKbiZyeLFth8AFkyq9UxVIDUrI+6gWDb8jMmsTx/3+fLee8DXX0d/7IoVNhsNUJmh
+8Aw01KgkAw2tre5kuH//+vjALETSgYZ6mXEi5Ge8xFmngYEGJ6l9khkNqZKq0RAGdPgep+rd211l
+nzKl+CkXAWY0RBUOn1i0qLCDVYAnAdl075467CqfMKvh66/jO35bsgSYNMmWN94YGDQonu3WoqFD
+7WdLiwWAyuH9991yrQ+bCA0fbsFJIP6gWDYcOpGdHzx89tnoj6t0zT0GGmpUkoGGSZOApUtteezY
+eLddC9Zbz33AJVEVul4zGoB46zSEgYZu3Wp3Puq4jB7tlpnRkJykajSEB088Ae4oDKK1tABTpxa/
+He7L0ZRSp8F/jxloKF4SV4pfeMFNrdio2QyhStRpqKdCkKGmJmDTTW15xgx3XpAkDp3Irtg6DQw0
+UFHC6S2Bwq9K5NPo6Xci7uB39myrqRAnP6OhHgIN/gdXEoEGRrXtPQjnZ3/nHXdAWSpeBU6VxNCJ
+lhZ3pZ4HTh3Fla3D7JxoSgk0MKMhHn6gIa4rxY1+3OarRKChngpB+sKgiWppgeCo/M8Yfo6n2mkn
+G84OMNBAZTB4sFueNSvebfMLK/Xg96234t02MxqiCb9wGGgw4fi8FStsfHUc/JOzMEWykSURaGAw
+J7e4Ag3++8x9OTsGGioviYKQ4XGbiBWOa2R+oCHu4+Ns/EBDvWQ0AMkExXIJP2N69GDh5HTduwM7
+7mjLM2emnkvkwkADFWXIEKBTJ1uOurNFsWQJ8Nprtrzhhm4e3UaTZJ2GMKNhzTXr42AtiRoN/jRq
+DDQYf5+Ma0hPeHLWo4f7PGlk/r4WV40GnpzltuWWbjmOjIaePV11burIn4KRgYbKCNPRgXhO3ubO
+tUw3wL4nGm2msHR+oOHRR+OtHZVNGDDyi33WAz9oUo5AQ5jJys+XzIoZPjFvnltmoIEia252QQA/
+Fb9UL77IcX5AMid1gKVRf/65LddDIUggmYyGJUtctWh+4Ri/ovkbb8SzzfDkjFfaTadObn9jRkN5
+9O3rPgvffttNn1goFtyMZu21XS2SQjOjmNYcj969gWHDbPm990qfGWHiRLfcyMdtoY02csHG55+3
+4Npdd8U7/bbPL+pZT8MmgPLPPMELTLntuqtbDou/5sOMBipamHa/cGF8V984bMJstJErKBZnRsOs
+We7Lrh6GTQDJ1GjgjBMdbbONmwHmlVfi2WZ4csbieU44fIKBhvIJA7vLlgHTpxe3jTBoxn05NxE3
+fGLWrMIKvDGjIT7hCdySJaWn9/O4LdVaawG33+6OHebOBY45Bth/f2D58vifr16HTQDJTsearr2d
+U0HnU0zgxw80rLVWvO2JgoGGGuZfEY8rq8Ef5+dHzhpNU5Or9P/pp/Gl3vnDXOoxoyGu94mBho5W
+W80dxEyZklpfoVjMaOgo3J8XLIjnoJQnZ/n5GWT//ndx22BGQ3R+nYYPP4z+OO7L8Ylz7Ht43Nbc
+bAXjyAILU6cChxzi/vbYY8BVV8X/XP4JX71lNADJTMeaydKl7kIcP18y693bXaSMmg3FjAYqmn9F
+PI46DfPmWeoqYCfZldghq0kSBSHrbcYJIJmhEww0ZBYWAmpvL334RGur3QBeBfaFNQPa2oAJE0rf
+HjMa8tt2W7cc1ggqRGurm9mD+3J+xRaE5PSW8YmrIOSnnwIff2zLO+zAQqi+gQOB++4D/vlP97c4
+PtPT1euME6FyDZ/wA5k87ssu/H8sXRptVhUGGqhocWc0cJxfqiQKQvr/p3rMaGCgIVk77OCWSx0+
+wekAMzvoILf84IOlb49XgfPbdls3pvrVVwt/PIM5hSm2ICT35fjEldHAYRP5HXqoq4nxxhsuKBmX
+8P8nAmyySbzbrgblmnnCP+7j50t2hRbo9AMNlSgUy0BDDYs7o8H/wtptt9K3V+uSKAhZb1NbApau
+GR7cM9CQrDCjASjuhMznn5zxKrCzxx7uquDDDxdfnDDEk+D8evd2B7NTpqS+Z1H4QTPuy/kVm9HA
+QEN8NtrITd/HQEPywiD98uVuho44fPqpG+618cb1mVFSrpkn+PkSTaGBnzDQsPrqQOfOybQpFwYa
+aljcGQ3PPms/O3cGxo4tfXu1buRIoFs3W447o6GpCRg6NJ5tVoMwSjpvXukVtAEGGrLZYANXzOfV
+V0uros0T4My6dwf23tuW580DXn65tO3xfY4mPBEoZlgQ3+PCDB/uTnIZaKiMLl3sxBSw/0E4jK0Q
+qu64rVev1CFIlCrOIL3v1lvd9/Axx8S33WriT8fKoROVV+hQlnB6y0oNh2egoYb16wf06GHLpWY0
+fPYZ8NFHtrz99rwqBFjAZYstbPk//yl8KrBMwv/TkCHuQK8ehCe/8+YB48cXPj97On7hZCZi/ROw
+7JFCCrml49CJ7OIcPsGTs2hKGRbEQENhmpstaAnYLB9Rs3bCfbmpyQJyVJrwhGHVquI+y6dNA776
+ypZ32aW+jini5n++xBVoaGsDbrnFlpuagBNOiGe71Sbu6Viz4dCJaArJhmprc7MSMtBABRNxWQ0z
+Z5bW+Zl+l5lfsfiCC0rb1jffuCnz6qU+Q+i449zyiy9agOaSS4q7SgMwoyGXuK7McOhEdvvt51IM
+H3yQmSPlUMp+zaEThQuHT7S0RCsoBqROPRdOtUvFK7UgpB+Q22WX0ttTz0aNcsGxuKaHfuopu0gH
+APvuCwweHM92q5E/HWvUz4tCMSgfTXOz+/yePj33cfbChe74hYEGKko4zr+1FZg9u/jtMNCQ2amn
+2jzCgFUuLmUIRT3OOBE6/XTg6addAKW11QIzxaYSMrKdXVwFIZnRkF3fvpaZA1i/nTKl+G35B098
+n7Nbf32XGfXaa4UFzhnMKVwxBSE5x328Si2y5wfk/EAdddTc7IaWfPppacfLoZtucssnnVT69qpZ
+OWaeYCZrdGGQctUqCzZk4xeCDL9fy42BhhoXR52GVauAxx+35V69gO22K71d9aJnT+C889zv/nKh
+/AJEG25Y/Haq1R572MHSWWcBnTrZ3/75T+CFFwrfFjMasttmG/f+MqMhOXENn+CUgNGIuJOlQocF
+MaOhcMUUhAz3Ze7H8Sg10BAGmjt3BrbeOp421bM4h0989RXwyCO2PGCAZcHVs3IUhOQFpuiifnZU
+empLgIGGmhfHzBMvveR2xn32sSJF5Jx8MrDuurb8+OM2NKAY/tXncJx9venRA7j8cuDPf3Z/O/fc
+wlPPGWjIrmdPVzvk/fdT36tCMKMht+9+1y3HFWjg+5xbsScCfI8LV2igYdUqYNkyW+ZJQDyGDnWB
+sUKvEi9c6OpGbbkla2ZEEWdByNtusz4BWG2GSlTzL6dyTHHJoRPRRc0wYaCBShZHRoN/EH3wwaW1
+px517QpceKH7/ZxzihuzHX6xdepkV6Xr2fHHuwPZl14CnniisMcz0JBbeEKmCkyaVNw2mNGQ2+DB
+rp++/Xbxn6/hwVOXLvZZQtn5JwKFDAtiRkPhwhkPgGjT/TEzJ35NTe5K8SefuBpOUfif+36AjrLz
+L/CUEmhQBW6+2f3+wx8Wv61a4Rcg5NCJyouaYcJAA5Ws1IwGVeCBB2y5udkK2lBHxx7rDsxeegl4
+8snCHr9okV19BuxqdD3Otezr1MmKQYbOPbewMddhoKG52U0xSk4cV2Z4FTg/P/B6/fVWh+Tpp+0g
+P2ql/vB95nuc39ZbuyuDzGhI1mqruToNkyenHuRnwquNyfCDBM89F/1xfiCOgYZo+vVzs638+99W
+CLUYzz9vM5EBwG67WX2ZehfHdKz5cOhEdOuu677rGGigRPmBhmKuuL39NjBrli3vuiujiNl07tzx
+xLmQrIZJk9z6jXJQcMghwJgxtvzWW8B990V/bPiFs/rqrG6eSRwFIXkVOD+/TsP11wN77mm37bcH
+fvGLaNtgoCG6Hj1ShwWF03Llw325OGHh57Y2O3nKhUVNk+EX3/aLcufDQpDFCd+r1lY7LinGX/7i
+lk8+ufQ21Qp/OtaLLio+UJMNMxqiE3FZDZ9+mj1QHE5/CzDQQEXq2RNYe21bLiajgcMmojv0UBsL
+CdjsE/ffH/2x/kFBowQampqA3/7W/X7++W5MYz5+oIE6GjbMzYaqSjt1AAAgAElEQVQyaVJxU9vy
+KnB+I0a4Pp/uxhujnQizUn9h/JOmqMOCuC8Xp5CTXGY0JGOXXVwWz4QJ0R7T1mYzswDAwIHAkCHJ
+tK0elRqkb2kBHnrIlvv0SQ1G1zv/vbv0UruQFNdUoUDqZwwDxvn5wyfCjGnf0qXAHXe43yuVecNA
+Qx0IsxpmzwZWrCjssX6g4cAD42tTPUo/cT7vvOjp0/6HcSNdfdhrL2DsWFuePj31Qy8bVfeFw0BD
+Zn6F/kWLXFGwQvAqcH4i9hl52WUWKDv/fEuVBYDly4G77sr9+NZWd9WHJ8DRFFMQkvVGijN+vH2v
+AfkDDazRkIzevd20i9OnA198kXr/ww9bYVr/GOL9993n9447MuuvEKXOPDFxousLBxzQWEM7f/IT
+4Oyz3axXU6cCO+9s34txCC8w9erlnoOyy1cQ8g9/AL7+2pYPPTQ1A76cGGgokogMFZGrReQDEVki
+IvNF5HUR+bmIlLX+r18QcubM6I+bMcPND7/ddhYZp9z23ts+WAEbp3bnnfkf097ursyts45djW4U
+IqnBmSjpdsuXu8wHBhqy8w+Y7r678MfzKnA0Q4cCv/wlcPHFdrv2WnffTTflHkLF97hwxRSE5Awq
+xenTB9hqK1t+7z13UJpJOCY9fBzFJ1tmyaJFwNFHW7Dh4IPdfs76DMXbbDMXjHzllcILe/sX5xop
+mwGwzJvf/c7qW4TTqaoCv/lN8UWpfbzAVJhcM4F8843NAAdYMPnii8vXrnQMNBRBRA4AMAXA/wLY
+CEB3AH0AbAXgCgBviUjZklSKrdPAYROFE7GUsdCFF+Y/cf7gAxepbcSrD2PH2rSpgI0lu+mm3Ouz
+IFA0hx/uqkBfey0wZ05hj+fJWXFGjXJXIN9+24ZRZcOrwIUbOtTmpQeiF91kRkPx/JPcZ5/Nvt5f
+/+qWx49PqjWNKVug4e9/d1OKzpkD/P73ttyIQzHj0qmTXVgDLAv4s8+iP7a93Q2b6NbNMjYb0ejR
+tg/+8pfub/4sHMXiMMPC5Jp54uqr3Sw2xx4LbLJJ+dqVjoGGAonIlgDuBtAbwGIA5wDYEcDuAG4C
+oAA2BPCoiBQzt0ATALRFzclHakZDIXUaGjkyW4qxYy2zAbAT53wfsLz6YBFvf3np0uzrWqDhSwC/
+RnPzlwm3rHYNG+YKUS1dalcaolq1yqXoNjdbRWmKzi8AlitwFkdGw5dffolf//rX+PLLxugL/rCg
+b78FXngh/2MWLLCfXbpwXy5UlDoNb71lM1MAlgExenTy7UpXz/1g++2B7kEe7DPPuKvs6ccWV1xh
+Jw9hoKFLF1dwmaLzs6YKmenj9dddcb0996zc7GHV0Bc6d7bhw+H32t//nvp9V6i2Nvd4BhqiWWst
+oH9/W373Xfe5MWeOy7xsbrYLopXEQEPhrodlMKwC8B1VvVxVJ6nqc6r6YwBnARBYpsOZRWy/E1BY
+oKGYjIY5c4CXX7blESNS59Sm/PwT50suyX3izOrQdjB02GG2/PXXwA03ZF/XBRouQqdO9XdQGafz
+znMHqH/6U/SrM+ec44KSlYx016ojj3QHmXfdlb3/x1FA78svv8RFF11UlydY2XznO2752GNzp/S/
++KKNFQYaY5q5uO20E9C1qy1nCzT4J7yVqrJfz/2ga1dXy+iLL4APP0wN7oQWLbIx8h99ZL9vtZX7
+31F0YZ0dALjyyui1tsKp4IHKXpyrlr7Qsyfwve/Z8tKlwD33FL8tP8OSQyeiC4dPzJ/vvicvu8wd
+k/zP/1SuNkOIgYYCiMg2AHaGZS3crKqvZ1jtGgAfwIIN/09EEi9pUkxGw223uUr1HDZRuK22suIq
+gHXuCy/MXvk/zGhobnbjYRvRxRe7wmOXX569Yr8/dIJp0LkNGACcdpott7SkTsGazT//aQdXgF2V
++OMfk2tfverdGzjqKFtevBj4xz8yr+efIHN4SnQnnmjV+AE78TrqqMwz1qha0CwUdcpRcrp3dwHw
+mTM7HkMsWwb87W+23KOH1Qyg+KVnlvjBnXPOcZk6fgZVo164KNW4cW74xHvvRatxpOoCDU1NwP77
+J9e+WpIru++RRyyAdvvtmR976aU2nfFmm7n/B8CMhkL4wyd22sl+D4/puncHzj23Mu3yMdBQGD+G
++ddMK6iqAgi7VR8AuybcJgwe7Cq05sto+Oor4IgjgLPOcn/77neTa1s980+cr77aikSmTzGzYIFV
+kgZsmrxGqlCcbuRI4LjjbPmbb4Crruq4jmrqHNUsOpbf2We7k9hbbnFXuzL54APgBz9wv19zjX05
+UeH8A6z0FOdVqyzN+fvfd3/r27c87aoHzc12dSwsUPzcc6njgUNPPgm89JItb7yxZT9Q4fwrvOlZ
+Dffd54K/RxzBk4Ck+P+DRx9NDe6cfTbw4x93fEyjDsUsVXqtrQsuAFauzP2YadPcd+vYsUC/fsm1
+r5aMGeOGUr3+uisw/8YblsX60kvACSfYZ7XvppvsJHjKFDtuDo+TARsSQNFssYVbnjHD3stwXz79
+dFfvqJIYaChMMN8AlgKYnGO9573lxA/jO3cG1l3XlmfMyFxFV9UOhkeOBO691/392GNdYTMqzCab
+pFZyffVVCyZccIGbZjSc6xrg1QfAMj/CAobXXdcxJfraa1OvDvsHX5TZmmsCP/+5Lbe1ZR+Pt3gx
+cMghLkXxmGNcNgQVbttt3dWEV16xoSt//7tdvdlmGzs5WL7c7h82zGVAUTT9+9t3VefO9vvVV6d+
+NrS3p2YzXHKJW5cKk6tOg3+VslLDJhrBllu6wPrjj7vgzpFHWnDnnHMs6OBjoKF4u+3m9vsZMyxI
+nwtrmmUm0jHoPm+efd+1ttrfVG2IRTgr3uuvpx579Oxp2au9egGbbpo5qEaZHX647cvh+xfexo8H
+fvWrSrcuoKq8RbwBmAOgDcCbedbrA6A9WPfuAp/jawDar18/LcTuu6tad1adPz/1vunTVceNc/cD
+qmutpXrnnart7QU9DWXw/POqG22U+v5uvLHqCy+onnuu+9s991S6pdXhtNPcezJqlOrrr9vfJ05U
+7dQpvG+yAtDJkydXtK214ttvrU+H7+spp6guWuTuf/FF1REj3P2bb666ZEnl2lsvrrsutd+n35qa
+VM84Q3Xx4uKfY/Lkxu4LN9zg3s/mZtXzz1ddvlz13nvd37fcUrWtrdItrV0rV6r27m3vZb9+7r2c
+Ns29xyNHVvZ4oRH6wcEHd/wMeflld/8557i/r7tuxZpZN157zb2fAweqLluWfd1ttnHrfvJJ2ZqY
+UbX1hYULVbt1s/emTx/V3XZz71WXLm55zBjVWbNUhwxxf/vpTyvdeko3aNAghZUI+FxjOHdmRkNE
+ItIVQJjQ83mudVX1G1jWAwAMSbJdoUx1GlautPSwUaOA570ci2OPtRTqY45pvKkWk7DLLsA771hh
+vvCK2vTp9nd//DszGsy557piP1OmWMXt006zKzdhUaYTT6xc+2pR796WSRP6058s4+aee+zqwNix
+lvoJ2Ht///2Vq5hdT449NvuQiFGjLKPp2mtZa6QUp57qhkSsXGmZC6NHW8ZI6Le/dcPYqHCdO9u4
+dQCYO9fGrQOpw9hOPpnHC0nzM0sA+wz3sxZ+/nOXCh0WV6bibbedGzo8e7Z9b2by+ec2FACwz55h
+w8rSvJrRp49dWQdsWGw4TW7//jYF9AYb2O9vvmkZC2HR6h13zDyEluoLEw2j80t5Lcm6lrMUQA8A
+ZTnE9KuK7r+/pdgtWWIHDf46N95o0/JQvLp1swPgI46wA7JJk+zvYcHDwYPtRvblM3Gi1Qp45x1L
+gfYDMnvuaSfH+VIZKdVpp1mg5txzrYBbWETPt+229r6GX/xUmjXWsCFTEyakVi4fONAOYMNhQlQ8
+ETvhHTrUisiuWpU6nnfnnd10w1S83Xe32gAAsMceFhybPdt+b25m/YtySA80nHRSanCnb1874Z08
+mcdxcbnkEuDhh+36+rnnZi6OvGyZW+awicxOPhm44w73e+fONtRt5Ei7sLH99vY+hlNYhkPjOB1x
+/eM1gOj8Mn6tEdZvgc080T2Z5qTaaCO3/PXXVhQyDDI0NVkk/N13+eWUtM03t2lDf//71KuYLLiX
+asst7YDpsstSC2Suu65NF9gp8bla6o8IcMYZVgwo/cSrZ0/g+uutlsCmm1amffVq443tqvvpp7vb
+YYcxyBCn5mabUvjNN1OrkwOWtccr7aXbYw+3PHeuHUO0tNjvBx/MAm3lsPHGwKBBttylS+bgzqBB
+wIEHNnZh6ThtvrmborGlxfb79JtfS4qBhsx23tn239BVV7kpWzffPLVgcqdOFoQIi/1SfWOgIboV
+3nKUGFxX2BiX5ck0J9UBB9jBwNprp952280Kr1x5JVOly6VTJ+CnP7W53Y8+2k6qM1VMb3TNzfa+
+vPuuFQ7acUebDmnNNSvdsto2bBjw2GNWtXzUKMuymTrVToAZwKFa5gdyN9nEil2FB7NUms02A848
+01Lz/WOIMWMsyEPJE7GhVptuaj8Z3CmPK66w44/042f/NmCAXbDzq/yTI2IZ06NG2TDi009Pvf/o
+o+08ZMQI4Lbb+LndSEQ1wxQF1EFQo2E5LHjwL1U9MM/6i2FDJ15T1cjXs0VkJYIhLf0izJ/TqVMn
+dOLZA9WZ1tZWzJ07F/369UMX5tZRA2NfIGI/IAqxL1Ax2tra0OaP8cxi3rx54eQEK1W15B2MgYYC
+iMhcAGsAeEdVx+RYrw+ABbCgxL2qelS2dTM8dhUARg6IiIiIiIio3NpUteRajiwGWZipAMYC2EBE
+mlS1Pct6I7zlDwp8jha4YRcLIqzfBptKk4iIiIiIiMjXhGgXsteA1RhsieNJGWgozEuwQENPAFsB
+eCPLeuO85ZcLeQJVZSUFIiIiIiIiqlksBlmYB73lH2RaQUQEwHHBr98AmJh0o4iIiIiIiIiqBQMN
+BVDVNwC8CEsp+aGIbJdhtZ8DGAkb+nCdquavvEFERERERERUJ1gMskAiMho2HKI7gCUALoVlLXQH
+cDSAk4NVpwHYRlWXVqKdRERERERERJXAQEMRRGQ/AHcCWA2W3eBTANMB7Keqn5S7bURERERERESV
+xEBDkURkCID/B2A/AIMBtAL4D4B/APijqq6oYPOIiIiIiIiIKoKBBiIiIiIiIiKKDYtBEhERERER
+EVFsGGggIiIiIiIiotgw0EBEREREREREsUk00CAiW4nI+SLypIh8JiIrRGSxiEwXkVtEZKcCt7eP
+iNzvbeuz4Pe9Izy2p4iMFZEzReQeEZkhIu3BbUYBbRgsIoeIyGUi8oyIfONt54JCXk8hROTo4H38
+UkSWi8hMEblDRLaP8NjVRWQPETlHRB4UkS+8Nj+bYJt3CNo4M2jzlyLyhIgcFeGxXURkOxE5TURu
+F5FpItIWtLktqTYnhX0hPg3YF3YRkV8G/9/3ROQrEWkJ3u8pIvInERmTVNvjxH4QnwbsB7d6bcx3
+G5rUa4gL+0J8GqkviMjxBfSD8HZLUq+jVOwH8WmkfpC2jZ2CbcwQkWUislBE3hSRC0VkzaTaThGp
+aiI3AC8AaA9ubRlu4X1/BdCcZ1sC4OYs2wv/9n95tjHRWzd9GzMivqahadtI384FCbyP3QD8K8v7
+2A5gVb7nBfBJjjY/m9D//9dB27L9vx4B0CXH42/N0uZ2AG1J7bfsC+wLVdgXPs+x77R5911f6X2d
+/YD9IMF+cGuW15x+WwVgaKX3d/YF9oUk+gKA4yP0gfTbWZXe59kP2A/i7AfB4zsDuCnHPtQO4EsA
+O1d6X2/kW5IZDQMAKIAvAFwP4DAA2wLYAcDPYAfPCuA42AFELpcCODFYfzKAo4NtHQ3gzeDvJ4nI
+b/JsR4PbfABPAVgK+3CKKlxXYTvwR7APykK2UahbAewTPOezAA6CvfYfwqbTbAJwoYiclGc74Wv/
+CsCjSLDNIvIjABcEz/Ef2P9uW1jbnw3asS+AW/JsKmzztwCeh7W9FrEvxKNR+8JiAI/DvpSPAbAb
+gK0B7A/gYrh+cZqIXBr7i4gP+0E8GrUfhGYD2AzA5lluo2D7WDVjX4hHI/aFB5B93/dv/wmeox3A
+ncm8mpKxH8SjEfsBAPwB9hoVwIcATgawDYCdg21/A2AdAA+JyAaJvBDKL6kIBoCHARyKYArNDPev
+AWAaXBQqY8QJwIYAWoN1XgPQNe3+7gBeD7bTAmC9LNs5CcCR/v1wEbyokco1APwKwO4AVg/+Ns57
+DbFGKmEnFOG2H0h/LwGsCWBmsM78sE0ZtvMzAAcDGOT9LdxurJFKAH0BLAy2/QmAvmn3C4CHvOff
+Jct2Dod9uYz0/hZGm2sto4F9ofT3sJH7QlOE5/mP93/vG0f7476xH7AflNgPbi3kf1PNN/YF9oVS
++kKE5xnhbeOpSu/vOdrJflD6e9iQ/QB2sSW8/00AvTKssynsQk0bgIcrvb836q2yTw7s5+0o12VZ
+50/eOttkWWc7b50bCnj+gj5AsmwjyQ+QMBWqBcDALOsc6T3/mQVsO6kPkF942z48yzqDAKwM1nmk
+gG3XZKAh4mtjX8i9bfaF3M91tvdc+yS1nyZ9Yz/Iu+2G7Qeoo0BDxPeNfSH3thu2L0R4nsu85zkm
+yf006Rv7Qd5tN2Q/AHCDt41dczzXJd56m5Zrv+XN3So968REb3n9LOscCEuLmaaqb2RaQVUnAZgO
+i4J9N9YWVoiI9IJFRBXABFWdnWXV+2FDCwCLRlbaQcHPb2HR1Q5U9QsAE2D/r91FpGeZ2lbN2Bey
+YF+IZLG33K3IbVQD9oMs2A8aDvtCFuwL2YmIAPhe8OsS2HtQy9gPsmjwfrB18HMFbGh1Nk94y4cW
+3lQqVaUDDV295bb0O0VkOICBwa+5diT//kEism4Mbau0bQB0CZazvnZVXQlLFRMA24hIpzK0LSMR
+aYaNsVIAr6rqqhyrh6+pK9wHRiNjX8iOfSH3cwmAI7w/TSt0G1WE/SA79oPGwr6QHftCdrsCGBI8
+z32qurzQtlYZ9oPsGrkfhLNJzFfV9hzb+Npb3qWQtlI8Kh1oGO8tf5Dh/k285XwHz/79I4ttUBUp
+5rV3ho1Vq5SNAIQfYI32/yrVeG+ZfSEV+0IaEWkSkYEish/sis8usC/up1U10/5TK8Z7y+wHqdgP
+zJoi8pyIzAumr5sdTId2qoh0L6m11WW8t8y+kIp9IbvjvOXbC3xsNRrvLbMfpGrkfrAk+Llanm2s
+7i1vknUtSkzFAg3BVbizvT/9I8Nqg73lz/Ns8jNveUix7aoitfjaa7HNFce+kFctvvZE2hzOaw2b
+Eupz2PRPYZBhMoATCm5plWA/yKsWX3sSbe4FYCysoFgzrKr4nrAxux+KyA5FtLOqsC/kVYuvPfE2
+i0gPAIfAvg8+U9XnIreuCrEf5FWLrz2uNodBp94iMjrHNsYFPwXAOiLSOX8TKU6VzGj4GVz6zH2q
++laGdXp7y0sy3O9b6i33KrFt1aAWX3sttrkasC/kVouvPak2a4bbUgA/AbCTqtbqFLAA+0E+tfja
+42yzAngVwLmwqdzGANgRwI8ATAruHwTgSRHZotgGVwn2hdxq8bWXo82HeOvXQzYD+0Futfja42rz
+w97yb4KgVAoRWQu2D2mW56cyqEigQUTGwariAjZ+5pQsq/pFzVrzbLbFW66H9MlafO212OaKYl+I
+pBZfe1JtDudIHw1gLwC/C7Z/NYDLazVaz34QSS2+9jjbfIaq7qSqv1PVp1T1HVWdpKo3q+oOAC4N
+1usJ4OYS2lxR7AuR1OJrL0ebj/WW7yjgcVWH/SCSWnztcbX5XgDvwDIV9gXwLxHZTkS6ikhvEfku
+gJcBDEh7nnr4v9eUsh+UisimsAqonQEsh01tMi/L6iu85S5Z1gn5BWOqsvhNcBKwcY5VpqlqWOym
+al67iGyYow2fq+qiYLlq2lwL2BfYFwKR26yqU71f3wUwQUT+BOAFAGcA2ERE9lFVzbiBKsR+wH4Q
+yNlmVf02/W9p958vItvDqrCPEZEdVPXVPM9ZVdgX2BcCBbdZRAbAzUAwSVU/ivK4asR+wH4QyNpm
+VW0XkYMBPAlgAwB7B7eU1QDcCCskuU3wt8WgsiproCGoDvskbGzlKgBHqurLOR7i7xD5Un38qU/y
+peNUyiDYyUE2wwDMCpar6bU/DWBolvtOgEvRq6Y2VzX2BfYFT0ltVtUvRORUAI8B+A6AH6JGruiy
+H7AfeOJo8//BTrYAG5tbM4EG9gX2BU8xbT4WlqWsAG6L+Jiqw37AfuDJ2WZVnSkiWwP4BWz/95//
+fQBXqOqdIhJeoGlTVQYayqxsQydEZCBsTtSBANoB/EBVH83zML9QyOCsaxm/UMhnWdeqvExjrBX2
+nviq6bW3o/baXLXYF/6LfcHE0ean4CL+hxW5jbJiP/gv9gMTR5v9jJ9BRW6j7NgX/ot9wRTT5u8H
+P1sB3BPxMVWF/eC/2A9M3jar6mJVvUBVhwPoB8sGWUtVRwVBhiYAw4O21PKMXDWrLBkNIrImLNoV
+/rNPU9W/RXiof9AwIs+6/v1VuTOp6qdw07rkk/7aH862ItxrXwUg9nQ5VV0v4qofwuY5bkId/L+S
+wL5g2BdSlPz/CtIIF8IO0Kp+fnD2A8N+kCKO/1fNDBkKsS8Y9oUUBf2/RGRLAJvB9p9HVfWbiG2q
+GuwHhv0gRUH/L1VdAGBB2p83gw3BUACvR2wjxSjxjAYRWQ12tW0k7B99tqreGOWxqvoJgNnBr+Ny
+rQub4g0Avgg6aq17A66ASdbXLiLNALaHvbdveGO3yk5VV8I6sgDYIU9huvA1tQD4d9JtqwbsC0Vj
+X8gjeO1rwV57taaEAmA/KAH7QX7+POmzs65VJdgXisa+kOp4b7nmZptgPyga+0F+R3jLNZnpU+sS
+DTSISHfYuOEtYTv4b1T1qgI38xBshxwhIttmeZ7tYZEvBfBg8S2uHqq6BMAzsNe+R5BSlsmhAFYL
+lu8vR9vyCN//1WBTLXUgIoMB7AH7f01Q1aWZ1qsn7AvFY1+I5CC4wkq5xndWFPtB8dgPIvmRt/x8
+kdsoC/aF4rEvpKzfCcBRwa/zYPtUzWA/KB77QW4i0g/AqcGv01V1QhFtpVKpaiI3AM2wgi7tsDSZ
+q4vczoYAVgbbmASgW9r93WDRsXZY1Gv9Arb9SfC4GSW8znHea7wg5vdwV2/bDwBoSrt/LQAzg3Xm
+A1i9gG2H23025jb3BbAwfF8B9E27vwmW3hU+/9gCtj0xfFxS+20SN/aFWN7DhuwLsMJ2Of+PsKu4
+s73tjE9yfy7h/WA/KP09bNR+sB2A/nme5zfBNtoBTE5yX47hPWFfKP09bMi+kGGb+3nrX1uufTim
+94P9oPT3sGH7AYABeZ7jVW8b45Laj3nLfUuyRsPdsAroCuBZALcEU9Zk06oZpuNR1Y9E5EoAv4RN
+T/KyiFwO4GMA6wM4Gy4SeoWqfpxp4yKyPoCd0/7cK3hcLxE5Pu2+x1V1Tobt7AWgv/cnfwzR6LTt
+LFHV+zK1JwpVnSgid8Oi1d8F8LSIXAc7qRgF4BxYlVUFcJa66WPS27wFgNFZnqZ/htd+r6ouK7LN
+C0XkbNiUMsMAvC4iv4VdZR0I4H8BjA/afJeqvpilzeug41Q1/b3709v8oqrOKKbNZcC+wL4wDMX1
+hZ0BPCEiz8AOyKbADhY6w2ox7AUrAtYt2M5fVPW5YtpbBuwH7AfDUFw/2BvAL0XkCdg47qkAvoGN
+vR0F4ERYMAIAlgI4uZi2lhH7AvvCMBR5fJTmOG+51oZNsB+wHwxD8f3gHBEZD+AfAF4DMBdAH9gQ
+mR/D/gcK4DxVreoMt7qWVAQD7spC1FvWaCEsLegmWFSqLe1x4d/+L097ji+wPbtk2c7EArZRdATU
+e75uAB7J8dpXAjg/zzYuLKDNbQCGxtDuC2FFZ9LbHD7HwwC65Hj8uALa3A7guKT2ZfYF9oVK9YXg
+sZleb/o2WgFcBkAqvb+zH7AfVKAfhPfNALB9pfd19gX2haT6Qtp2VgewLHjclErv1+wH7Afl7AcA
+bsjy2PDxiwGcWun9vNFvSWY0aFzrq+1RJ4vIfQD+BxaxXAs2Hu0NADeq6lMxtinfenFtJ/8GVFcA
+OEBEjoLNRbsFLGL3NYAXAPxRVSfF2JaS2wwAqnqRiDwJGx81FsA6sCtQ7wC4RVX/EWNbYmlzgtgX
+2BeK7QtXA5gMYDfYFdsBANaGpRYuBDAN9tpvVyuKVc3YD9gPiu0HtwD4CsAOsKt0awNYE3aQOg/A
+m7AD7btUtTXbRqoI+wL7QqnHRwBwOFxF/VrLZgDYDwpZL/sGGrcf3BisPw6WGdEPVgz7UwCPwjI8
+q3ka04Yg1jeJiIiIiIiIiEqX+PSWRERERERERNQ4GGggIiIiIiIiotgw0EBEREREREREsWGggYiI
+iIiIiIhiw0ADEREREREREcWGgQYiIiIiIiIiig0DDUREREREREQUGwYaiIiIiIiIiCg2DDQQERER
+ERERUWwYaCAiIiIiIiKi2DDQQERERERERESxYaCBiIiIiIiIiGLDQAMRERERERERxYaBBiIiIiIi
+IiKKDQMNRERERERERBQbBhqIiIiIiIiIKDYMNBARERERERFRbBhoICIiIiIiIqLYMNBARERERERE
+RLFhoIGIiIiKJiLHi0i7iLSJyNBKtycKEZkZtPmWSreFiIioHjHQQERERI1GgxsRERElgIEGIiIi
+SoSIPBdkDjxb6bYQERFR+TDQQERERElh5gAREVEDYqCBiIiIkiSVbgARERGVFwMNRERERERERBQb
+BhqIiIgoKxHpIyK/E5EPRGSZiHwtIk+LyGE5HvNXEWkHMC740/igVoN/+yTLY1cTkV+JyEsiMkdE
+WkRktog8LCKHRmzzPiLyr+DxS0VkuohcLSIDIz5+uIj8LJ3yuyIAAAhFSURBVHjOT4LXvSyYreJu
+Edkrx2MnB6/v/QjPs0bw+tpF5A9R2kZERFQLOle6AURERFSdRGQkgAkABsDVWugKYDcAu4vIrQBe
+yPBQvzaDIHOdhvYMz7c7gHsArJH2mHUA7A9gfxF5DMARqrosS5uvAXCG1w4A2ADA/wL4vojsm+lx
+3uOHAfg47fGhIQCGAjhCRO4EcIKqpr+OmwH8EcAIEdlWVV/P8XTfB9AcPM9fcrWLiIioljCjgYiI
+iDoQkd4AngTQH3YifDeAfQFsDeB7AN4AcAKAUzI8/BwAmwOYHPz+7+B3/5aSFSAiOwF4DEBfAF8B
+OA/AAQC2Cn7eEbRjHwC3ZWnzGbAggwL4AsBpALaDZVZcDmA1APcC6JHjpXcC0ALgIQCnA9gDwJjg
+5ykA3gu2fwyA8zM8/m8AlgfLP8jxPIC9fwAwRVXfyrMuERFRzRBVFoMmIiKiVCJyJYAzYSfVv1LV
+K9Lu7wTgXwD2DP6kAIar6ixvnYmwk/znVHW3HM/VGcBHsGyBxwEcpqorMqx3EoA/B8+1p6o+493X
+D8AnALoD+BTAdqo6N+3xu8KCJ52DbdymqiemrdMdwGqq+nWO9t4CCxIsATBIVRen3X8bgGMBfANg
+gKq2ZNjGaABvBu04Q1VvyPZ8REREtYYZDURERJRCRJoBnAg7CZ6SHmQAAFVtA/BDACtjeMqjAKwL
+YAWA4zIFGYLnvBlAOBThhLS7j4fLVPhZepAhePxEADflaoiqLs8VZAicCaANQE9YpkO6m4OfqwM4
+JMs2wgBHKywLgoiIqG4w0EBERETptoINYQCyDFMAAFX9AsBTMTzfgcHP51V1QZ51X4DVfdgh7e/h
+Cf9CAA/nePwthTRMRDqLyCARGSEim4rIpgAGAZgfrLJF+mNU9UUAHwa/dhg+EQRyjoYFch6O8JqJ
+iIhqCotBEhERUbrNveU38qz7OoD9Sny+rWEn3XsHs1VE0T/t982DbbyVoUCj721YFkFzthWCoRw/
+ghVr3BJAlyyrKoC1stz3F1hdiN1EZIiqfubddyCANYPH35qjrURERDWJGQ1ERESUbg1veU6edfMN
+M4hi7eCnFnDrlraNsM052xsM+ciaQSAifQG8BuAGANvCzQqR6QZYTYhMboMNKxHYsA5fOGziC1jN
+CCIiorrCjAYiIiLKpRxVozsFPx8HcFaJ2yq1vb+HzTKhAB6AZRxMATDHL+ooIp8CGAwLJHRshOoc
+EXkUwMGwehK/CR43AFZAMyxGyarcRERUdxhoICIionQLveV1APwnx7rrxPB88wEMANBFVacWuY2F
+QVtytieYLWONLPf1BnAELAhwp6qmZyL4+ua4L3QzLNAwXER2UdUXYNkNnYLn+GuEbRAREdUcDp0g
+IiKidO96y9vkWTfX/VGv1r8FywzYOqiPUIx3g22MFpFcxzdbIHvNhQ3hajf8I9sGRGRjAL0itOkJ
+AJ8Hy2FRyBNg78uLqvpxhG0QERHVHAYaiIiIKN1kuKyGY7OtJCKDYMMAsgmnqeya5/nCWSJWR4ZZ
+GiKaEPxcA8ABOdb7YY77/CBHzxzr/SRKg4JhEbfCAiCHicheADYK7v5LlG0QERHVIgYaiIiIKIWq
+tsKdII8WkZ+nrxMMQbgJOWZvAPBl8HO9PE95G4DPgue7SkTG5lpZRHYSkV0ybGN5sI1rRGTtDI8b
+B+BkZM+0+I93X8ZhEyJyAIBTc2wj3S3Buj3gZphYDOCfER9PRERUcxhoICIiokwuhqX9C4ArRORv
+IrKXiGwpIkcCeBXAXgD+nWMbrwQ/1xaRa0RkjIisH9yGhisFgY0jYBkQvQA8KyJ3iMihwWO2FpED
+ROTXIjIFwIsANvOfSFXnADg/+HU4gMkickrw2J1F5DK4oQzzMjVWVRcAeCx4zfuIyFMicnDQhr1F
+5GYA9wP4ONs2MmzzU1i2hcDqRyiAe1R1eZTHExER1SJhsWMiIiLKREQ2AfA0gP7oOLtCOCzgxeCn
+AhiuqrO8x/cE8A7sxD/98TNVNSXTQUS2hdVGGJJhff95AeB4Vb0zQ5uvBXB6+Gva3XMA7AfLJlgX
+wF9V9UR/BREZHLymoRkerwA+BbAvbIaMjNvI0KbDAdzjbWMnVX0t12OIiIhqGTMaiIiIKKNgBohN
+AVwB4ENYxsFcAM8COFpVTwpXRYahBKq6FMAOAK4HMBXAUm/dTOu/DivI+GMAjwL4AkALbEjELABP
+AjgXwIhMQYZgG/8LCyY8CZvNYjmAjwBcB2BLVZ2cp82fw6a3vBLA9OA1fwPgbQC/DrYxLdc2MngQ
+wJJg3WkMMhARUb1jRgMRERFRgkRkA1igRgGcpapXV7hJREREiWJGAxEREVGywpkuVgG4o5INISIi
+KgcGGoiIiIgSIiKrw8108UBQtJKIiKiudc6/ChERERFFJSL9AKwGYCCAiwCsAaAdwO8q2S4iIqJy
+YaCBiIiIKF5XAjjO+10B/FFV365Qe4iIiMqKgQYiIiKieIWzUbQC+BjAnwH8oaItIiIiKiPOOkFE
+REREREREsWExSCIiIiIiIiKKDQMNRERERERERBQbBhqIiIiIiIiIKDYMNBARERERERFRbBhoICIi
+IiIiIqLYMNBARERERERERLFhoIGIiIiIiIiIYsNAAxERERERERHFhoEGIiIiIiIiIooNAw1ERERE
+REREFBsGGoiIiIiIiIgoNgw0EBEREREREVFsGGggIiIiIiIiotgw0EBEREREREREsWGggYiIiIiI
+iIhiw0ADEREREREREcWGgQYiIiIiIiIiig0DDUREREREREQUGwYaiIiIiIiIiCg2/x+/PjMt5Ybk
+/wAAAABJRU5ErkJggg==
+"
+width=525
+height=377
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#34394;&#25311;&#21464;&#37327;&#65288;&#21713;&#21464;&#37327;&#65289;">&#34394;&#25311;&#21464;&#37327;&#65288;&#21713;&#21464;&#37327;&#65289;<a class="anchor-link" href="#&#34394;&#25311;&#21464;&#37327;&#65288;&#21713;&#21464;&#37327;&#65289;">&#182;</a></h3><p>下面是一些分类变量，例如季节、天气、月份。要在我们的模型中包含这些数据，我们需要创建二进制虚拟变量。用 Pandas 库中的 <code>get_dummies()</code> 就可以轻松实现。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dummy_fields</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;season&#39;</span><span class="p">,</span> <span class="s1">&#39;weathersit&#39;</span><span class="p">,</span> <span class="s1">&#39;mnth&#39;</span><span class="p">,</span> <span class="s1">&#39;hr&#39;</span><span class="p">,</span> <span class="s1">&#39;weekday&#39;</span><span class="p">]</span>
+<span class="k">for</span> <span class="n">each</span> <span class="ow">in</span> <span class="n">dummy_fields</span><span class="p">:</span>
+    <span class="n">dummies</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">rides</span><span class="p">[</span><span class="n">each</span><span class="p">],</span> <span class="n">prefix</span><span class="o">=</span><span class="n">each</span><span class="p">,</span> <span class="n">drop_first</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">rides</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">rides</span><span class="p">,</span> <span class="n">dummies</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="n">fields_to_drop</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;instant&#39;</span><span class="p">,</span> <span class="s1">&#39;dteday&#39;</span><span class="p">,</span> <span class="s1">&#39;season&#39;</span><span class="p">,</span> <span class="s1">&#39;weathersit&#39;</span><span class="p">,</span> 
+                  <span class="s1">&#39;weekday&#39;</span><span class="p">,</span> <span class="s1">&#39;atemp&#39;</span><span class="p">,</span> <span class="s1">&#39;mnth&#39;</span><span class="p">,</span> <span class="s1">&#39;workingday&#39;</span><span class="p">,</span> <span class="s1">&#39;hr&#39;</span><span class="p">]</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">rides</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">fields_to_drop</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[5]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>yr</th>
+      <th>holiday</th>
+      <th>temp</th>
+      <th>hum</th>
+      <th>windspeed</th>
+      <th>casual</th>
+      <th>registered</th>
+      <th>cnt</th>
+      <th>season_1</th>
+      <th>season_2</th>
+      <th>...</th>
+      <th>hr_21</th>
+      <th>hr_22</th>
+      <th>hr_23</th>
+      <th>weekday_0</th>
+      <th>weekday_1</th>
+      <th>weekday_2</th>
+      <th>weekday_3</th>
+      <th>weekday_4</th>
+      <th>weekday_5</th>
+      <th>weekday_6</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0.24</td>
+      <td>0.81</td>
+      <td>0.0</td>
+      <td>3</td>
+      <td>13</td>
+      <td>16</td>
+      <td>1.0</td>
+      <td>0.0</td>
+      <td>...</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0.22</td>
+      <td>0.80</td>
+      <td>0.0</td>
+      <td>8</td>
+      <td>32</td>
+      <td>40</td>
+      <td>1.0</td>
+      <td>0.0</td>
+      <td>...</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0.22</td>
+      <td>0.80</td>
+      <td>0.0</td>
+      <td>5</td>
+      <td>27</td>
+      <td>32</td>
+      <td>1.0</td>
+      <td>0.0</td>
+      <td>...</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0.24</td>
+      <td>0.75</td>
+      <td>0.0</td>
+      <td>3</td>
+      <td>10</td>
+      <td>13</td>
+      <td>1.0</td>
+      <td>0.0</td>
+      <td>...</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0.24</td>
+      <td>0.75</td>
+      <td>0.0</td>
+      <td>0</td>
+      <td>1</td>
+      <td>1</td>
+      <td>1.0</td>
+      <td>0.0</td>
+      <td>...</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>1.0</td>
+    </tr>
+  </tbody>
+</table>
+<p>5 rows × 59 columns</p>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#35843;&#25972;&#30446;&#26631;&#21464;&#37327;">&#35843;&#25972;&#30446;&#26631;&#21464;&#37327;<a class="anchor-link" href="#&#35843;&#25972;&#30446;&#26631;&#21464;&#37327;">&#182;</a></h3><p>为了更轻松地训练网络，我们将对每个连续变量标准化，即转换和调整变量，使它们的均值为 0，标准差为 1。</p>
+<p>我们会保存换算因子，以便当我们使用网络进行预测时可以还原数据。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">quant_features</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;casual&#39;</span><span class="p">,</span> <span class="s1">&#39;registered&#39;</span><span class="p">,</span> <span class="s1">&#39;cnt&#39;</span><span class="p">,</span> <span class="s1">&#39;temp&#39;</span><span class="p">,</span> <span class="s1">&#39;hum&#39;</span><span class="p">,</span> <span class="s1">&#39;windspeed&#39;</span><span class="p">]</span>
+<span class="c1"># Store scalings in a dictionary so we can convert back later</span>
+<span class="n">scaled_features</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">each</span> <span class="ow">in</span> <span class="n">quant_features</span><span class="p">:</span>
+    <span class="n">mean</span><span class="p">,</span> <span class="n">std</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">each</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">data</span><span class="p">[</span><span class="n">each</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
+    <span class="n">scaled_features</span><span class="p">[</span><span class="n">each</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">]</span>
+    <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">each</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">each</span><span class="p">]</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">/</span><span class="n">std</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#23558;&#25968;&#25454;&#25286;&#20998;&#20026;&#35757;&#32451;&#12289;&#27979;&#35797;&#21644;&#39564;&#35777;&#25968;&#25454;&#38598;">&#23558;&#25968;&#25454;&#25286;&#20998;&#20026;&#35757;&#32451;&#12289;&#27979;&#35797;&#21644;&#39564;&#35777;&#25968;&#25454;&#38598;<a class="anchor-link" href="#&#23558;&#25968;&#25454;&#25286;&#20998;&#20026;&#35757;&#32451;&#12289;&#27979;&#35797;&#21644;&#39564;&#35777;&#25968;&#25454;&#38598;">&#182;</a></h3><p>我们将大约最后 21 天的数据保存为测试数据集，这些数据集会在训练完网络后使用。我们将使用该数据集进行预测，并与实际的骑行人数进行对比。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Save data for approximately the last 21 days </span>
+<span class="n">test_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="mi">21</span><span class="o">*</span><span class="mi">24</span><span class="p">:]</span>
+
+<span class="c1"># Now remove the test data from the data set </span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="o">-</span><span class="mi">21</span><span class="o">*</span><span class="mi">24</span><span class="p">]</span>
+
+<span class="c1"># Separate the data into features and targets</span>
+<span class="n">target_fields</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;cnt&#39;</span><span class="p">,</span> <span class="s1">&#39;casual&#39;</span><span class="p">,</span> <span class="s1">&#39;registered&#39;</span><span class="p">]</span>
+<span class="n">features</span><span class="p">,</span> <span class="n">targets</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">target_fields</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">data</span><span class="p">[</span><span class="n">target_fields</span><span class="p">]</span>
+<span class="n">test_features</span><span class="p">,</span> <span class="n">test_targets</span> <span class="o">=</span> <span class="n">test_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">target_fields</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">test_data</span><span class="p">[</span><span class="n">target_fields</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>我们将数据拆分为两个数据集，一个用作训练，一个在网络训练完后用来验证网络。因为数据是有时间序列特性的，所以我们用历史数据进行训练，然后尝试预测未来数据（验证数据集）。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Hold out the last 60 days or so of the remaining data as a validation set</span>
+<span class="n">train_features</span><span class="p">,</span> <span class="n">train_targets</span> <span class="o">=</span> <span class="n">features</span><span class="p">[:</span><span class="o">-</span><span class="mi">60</span><span class="o">*</span><span class="mi">24</span><span class="p">],</span> <span class="n">targets</span><span class="p">[:</span><span class="o">-</span><span class="mi">60</span><span class="o">*</span><span class="mi">24</span><span class="p">]</span>
+<span class="n">val_features</span><span class="p">,</span> <span class="n">val_targets</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="o">-</span><span class="mi">60</span><span class="o">*</span><span class="mi">24</span><span class="p">:],</span> <span class="n">targets</span><span class="p">[</span><span class="o">-</span><span class="mi">60</span><span class="o">*</span><span class="mi">24</span><span class="p">:]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#24320;&#22987;&#26500;&#24314;&#32593;&#32476;">&#24320;&#22987;&#26500;&#24314;&#32593;&#32476;<a class="anchor-link" href="#&#24320;&#22987;&#26500;&#24314;&#32593;&#32476;">&#182;</a></h2><p>下面你将构建自己的网络。我们已经构建好结构和反向传递部分。你将实现网络的前向传递部分。还需要设置超参数：学习速率、隐藏单元的数量，以及训练传递数量。</p>
+<p>&lt;img src="assets/neural_network.png" width=300px&gt;</p>
+<p>该网络有两个层级，一个隐藏层和一个输出层。隐藏层级将使用 S 型函数作为激活函数。输出层只有一个节点，用于递归，节点的输出和节点的输入相同。即激活函数是 $f(x)=x$。这种函数获得输入信号，并生成输出信号，但是会考虑阈值，称为激活函数。我们完成网络的每个层级，并计算每个神经元的输出。一个层级的所有输出变成下一层级神经元的输入。这一流程叫做前向传播（forward propagation）。</p>
+<p>我们在神经网络中使用权重将信号从输入层传播到输出层。我们还使用权重将错误从输出层传播回网络，以便更新权重。这叫做反向传播（backpropagation）。</p>
+<blockquote><p><strong>提示</strong>：你需要为反向传播实现计算输出激活函数 ($f(x) = x$) 的导数。如果你不熟悉微积分，其实该函数就等同于等式 $y = x$。该等式的斜率是多少？也就是导数 $f(x)$。</p>
+</blockquote>
+<p>你需要完成以下任务：</p>
+<ol>
+<li>实现 S 型激活函数。将 <code>__init__</code> 中的 <code>self.activation_function</code>  设为你的 S 型函数。</li>
+<li>在 <code>train</code> 方法中实现前向传递。</li>
+<li>在 <code>train</code> 方法中实现反向传播算法，包括计算输出错误。</li>
+<li>在 <code>run</code> 方法中实现前向传递。</li>
+</ol>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">NeuralNetwork</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_nodes</span><span class="p">,</span> <span class="n">hidden_nodes</span><span class="p">,</span> <span class="n">output_nodes</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">):</span>
+        <span class="c1"># Set number of nodes in input, hidden and output layers.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_nodes</span> <span class="o">=</span> <span class="n">input_nodes</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_nodes</span> <span class="o">=</span> <span class="n">hidden_nodes</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_nodes</span> <span class="o">=</span> <span class="n">output_nodes</span>
+
+        <span class="c1"># Initialize weights</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights_input_to_hidden</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_nodes</span><span class="o">**-</span><span class="mf">0.5</span><span class="p">,</span> 
+                                       <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_nodes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_nodes</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_nodes</span><span class="o">**-</span><span class="mf">0.5</span><span class="p">,</span> 
+                                       <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_nodes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_nodes</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">learning_rate</span>
+        
+        <span class="c1">#### TODO: Set self.activation_function to your implemented sigmoid function ####</span>
+        <span class="c1">#</span>
+        <span class="c1"># Note: in Python, you can define a function with a lambda expression,</span>
+        <span class="c1"># as shown below.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">activation_function</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span> <span class="p">:</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))</span>  <span class="c1"># Replace 0 with your sigmoid calculation.</span>
+        
+        <span class="c1">### If the lambda code above is not something you&#39;re familiar with,</span>
+        <span class="c1"># You can uncomment out the following three lines and put your </span>
+        <span class="c1"># implementation there instead.</span>
+        <span class="c1">#</span>
+        <span class="c1">#def sigmoid(x):</span>
+        <span class="c1">#    return 0  # Replace 0 with your sigmoid calculation here</span>
+        <span class="c1">#self.activation_function = sigmoid</span>
+                    
+    
+    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
+        <span class="sd">&#39;&#39;&#39; Train the network on batch of features and targets. </span>
+<span class="sd">        </span>
+<span class="sd">            Arguments</span>
+<span class="sd">            ---------</span>
+<span class="sd">            </span>
+<span class="sd">            features: 2D array, each row is one data record, each column is a feature</span>
+<span class="sd">            targets: 1D array of target values</span>
+<span class="sd">        </span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">n_records</span> <span class="o">=</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">delta_weights_i_h</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights_input_to_hidden</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">delta_weights_h_o</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
+            <span class="c1">#### Implement the forward pass here ####</span>
+            <span class="c1">### Forward pass ###</span>
+            <span class="c1"># TODO: Hidden layer - Replace these values with your calculations.</span>
+            <span class="n">hidden_inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights_input_to_hidden</span><span class="p">)</span> <span class="c1"># signals into hidden layer</span>
+            <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_function</span><span class="p">(</span><span class="n">hidden_inputs</span><span class="p">)</span> <span class="c1"># signals from hidden layer</span>
+
+            <span class="c1"># TODO: Output layer - Replace these values with your calculations.</span>
+            <span class="n">final_inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">hidden_outputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span><span class="p">)</span> <span class="c1"># signals into final output layer</span>
+            <span class="n">final_outputs</span> <span class="o">=</span> <span class="n">final_inputs</span> <span class="c1"># signals from final output layer</span>
+            
+            <span class="c1">#### Implement the backward pass here ####</span>
+            <span class="c1">### Backward pass ###</span>
+
+            <span class="c1"># TODO: Output error - Replace this value with your calculations.</span>
+            <span class="n">error</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="n">final_outputs</span> <span class="c1"># Output layer error is the difference between desired target and actual output.</span>
+            
+            <span class="c1"># TODO: Backpropagated error terms - Replace these values with your calculations.</span>
+            <span class="n">output_error_term</span> <span class="o">=</span> <span class="n">error</span>
+            
+            <span class="c1"># TODO: Calculate the hidden layer&#39;s contribution to the error</span>
+            <span class="n">hidden_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">output_error_term</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+            <span class="n">hidden_error_term</span> <span class="o">=</span> <span class="n">hidden_error</span> <span class="o">*</span> <span class="n">hidden_outputs</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">hidden_outputs</span><span class="p">)</span>
+
+            <span class="c1"># Weight step (input to hidden)</span>
+            <span class="n">delta_weights_i_h</span> <span class="o">+=</span> <span class="n">hidden_error_term</span> <span class="o">*</span> <span class="n">X</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>
+            <span class="c1"># Weight step (hidden to output)</span>
+            <span class="n">delta_weights_h_o</span> <span class="o">+=</span> <span class="n">output_error_term</span> <span class="o">*</span> <span class="n">hidden_outputs</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>
+
+        <span class="c1"># TODO: Update the weights - Replace these values with your calculations.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">delta_weights_h_o</span> <span class="o">/</span> <span class="n">n_records</span> <span class="c1"># update hidden-to-output weights with gradient descent step</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights_input_to_hidden</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">delta_weights_i_h</span> <span class="o">/</span> <span class="n">n_records</span> <span class="c1"># update input-to-hidden weights with gradient descent step</span>
+ 
+    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">features</span><span class="p">):</span>
+        <span class="sd">&#39;&#39;&#39; Run a forward pass through the network with input features </span>
+<span class="sd">        </span>
+<span class="sd">            Arguments</span>
+<span class="sd">            ---------</span>
+<span class="sd">            features: 1D array of feature values</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        
+        <span class="c1">#### Implement the forward pass here ####</span>
+        <span class="c1"># TODO: Hidden layer - replace these values with the appropriate calculations.</span>
+        <span class="n">hidden_inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights_input_to_hidden</span><span class="p">)</span> <span class="c1"># signals into hidden layer</span>
+        <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_function</span><span class="p">(</span><span class="n">hidden_inputs</span><span class="p">)</span> <span class="c1"># signals from hidden layer</span>
+        
+        <span class="c1"># TODO: Output layer - Replace these values with the appropriate calculations.</span>
+        <span class="n">final_inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">hidden_outputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span><span class="p">)</span> <span class="c1"># signals into final output layer</span>
+        <span class="n">final_outputs</span> <span class="o">=</span> <span class="n">final_inputs</span> <span class="c1"># signals from final output layer </span>
+        
+        <span class="k">return</span> <span class="n">final_outputs</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">Y</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">y</span><span class="o">-</span><span class="n">Y</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#21333;&#20803;&#27979;&#35797;">&#21333;&#20803;&#27979;&#35797;<a class="anchor-link" href="#&#21333;&#20803;&#27979;&#35797;">&#182;</a></h2><p>运行这些单元测试，检查你的网络实现是否正确。这样可以帮助你确保网络已正确实现，然后再开始训练网络。这些测试必须成功才能通过此项目。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">unittest</span>
+
+<span class="n">inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span>
+<span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.4</span><span class="p">]])</span>
+<span class="n">test_w_i_h</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2</span><span class="p">],</span>
+                       <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span>
+                       <span class="p">[</span><span class="o">-</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]])</span>
+<span class="n">test_w_h_o</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">],</span>
+                       <span class="p">[</span><span class="o">-</span><span class="mf">0.1</span><span class="p">]])</span>
+
+<span class="k">class</span> <span class="nc">TestMethods</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
+    
+    <span class="c1">##########</span>
+    <span class="c1"># Unit tests for data loading</span>
+    <span class="c1">##########</span>
+    
+    <span class="k">def</span> <span class="nf">test_data_path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Test that file path to dataset has been unaltered</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">data_path</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;bike-sharing-dataset/hour.csv&#39;</span><span class="p">)</span>
+        
+    <span class="k">def</span> <span class="nf">test_data_loaded</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Test that data frame loaded</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">rides</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">))</span>
+    
+    <span class="c1">##########</span>
+    <span class="c1"># Unit tests for network functionality</span>
+    <span class="c1">##########</span>
+
+    <span class="k">def</span> <span class="nf">test_activation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">network</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
+        <span class="c1"># Test that the activation function is a sigmoid</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">activation_function</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">))))</span>
+
+    <span class="k">def</span> <span class="nf">test_train</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Test that weights are updated correctly on training</span>
+        <span class="n">network</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
+        <span class="n">network</span><span class="o">.</span><span class="n">weights_input_to_hidden</span> <span class="o">=</span> <span class="n">test_w_i_h</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">network</span><span class="o">.</span><span class="n">weights_hidden_to_output</span> <span class="o">=</span> <span class="n">test_w_h_o</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        
+        <span class="n">network</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">weights_hidden_to_output</span><span class="p">,</span> 
+                                    <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span> <span class="mf">0.37275328</span><span class="p">],</span> 
+                                              <span class="p">[</span><span class="o">-</span><span class="mf">0.03172939</span><span class="p">]])))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">weights_input_to_hidden</span><span class="p">,</span>
+                                    <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span> <span class="mf">0.10562014</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.20185996</span><span class="p">],</span> 
+                                              <span class="p">[</span><span class="mf">0.39775194</span><span class="p">,</span> <span class="mf">0.50074398</span><span class="p">],</span> 
+                                              <span class="p">[</span><span class="o">-</span><span class="mf">0.29887597</span><span class="p">,</span> <span class="mf">0.19962801</span><span class="p">]])))</span>
+
+    <span class="k">def</span> <span class="nf">test_run</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Test correctness of run method</span>
+        <span class="n">network</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
+        <span class="n">network</span><span class="o">.</span><span class="n">weights_input_to_hidden</span> <span class="o">=</span> <span class="n">test_w_i_h</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">network</span><span class="o">.</span><span class="n">weights_hidden_to_output</span> <span class="o">=</span> <span class="n">test_w_h_o</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">inputs</span><span class="p">),</span> <span class="mf">0.09998924</span><span class="p">))</span>
+
+<span class="n">suite</span> <span class="o">=</span> <span class="n">unittest</span><span class="o">.</span><span class="n">TestLoader</span><span class="p">()</span><span class="o">.</span><span class="n">loadTestsFromModule</span><span class="p">(</span><span class="n">TestMethods</span><span class="p">())</span>
+<span class="n">unittest</span><span class="o">.</span><span class="n">TextTestRunner</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">suite</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>.....
+----------------------------------------------------------------------
+Ran 5 tests in 0.016s
+
+OK
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt output_prompt">Out[12]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>&lt;unittest.runner.TextTestResult run=5 errors=0 failures=0&gt;</pre>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#35757;&#32451;&#32593;&#32476;">&#35757;&#32451;&#32593;&#32476;<a class="anchor-link" href="#&#35757;&#32451;&#32593;&#32476;">&#182;</a></h2><p>现在你将设置网络的超参数。策略是设置的超参数使训练集上的错误很小但是数据不会过拟合。如果网络训练时间太长，或者有太多的隐藏节点，可能就会过于针对特定训练集，无法泛化到验证数据集。即当训练集的损失降低时，验证集的损失将开始增大。</p>
+<p>你还将采用随机梯度下降 (SGD) 方法训练网络。对于每次训练，都获取随机样本数据，而不是整个数据集。与普通梯度下降相比，训练次数要更多，但是每次时间更短。这样的话，网络训练效率更高。稍后你将详细了解 SGD。</p>
+<h3 id="&#36873;&#25321;&#36845;&#20195;&#27425;&#25968;">&#36873;&#25321;&#36845;&#20195;&#27425;&#25968;<a class="anchor-link" href="#&#36873;&#25321;&#36845;&#20195;&#27425;&#25968;">&#182;</a></h3><p>也就是训练网络时从训练数据中抽样的批次数量。迭代次数越多，模型就与数据越拟合。但是，如果迭代次数太多，模型就无法很好地泛化到其他数据，这叫做过拟合。你需要选择一个使训练损失很低并且验证损失保持中等水平的数字。当你开始过拟合时，你会发现训练损失继续下降，但是验证损失开始上升。</p>
+<h3 id="&#36873;&#25321;&#23398;&#20064;&#36895;&#29575;">&#36873;&#25321;&#23398;&#20064;&#36895;&#29575;<a class="anchor-link" href="#&#36873;&#25321;&#23398;&#20064;&#36895;&#29575;">&#182;</a></h3><p>速率可以调整权重更新幅度。如果速率太大，权重就会太大，导致网络无法与数据相拟合。建议从 0.1 开始。如果网络在与数据拟合时遇到问题，尝试降低学习速率。注意，学习速率越低，权重更新的步长就越小，神经网络收敛的时间就越长。</p>
+<h3 id="&#36873;&#25321;&#38544;&#34255;&#33410;&#28857;&#25968;&#37327;">&#36873;&#25321;&#38544;&#34255;&#33410;&#28857;&#25968;&#37327;<a class="anchor-link" href="#&#36873;&#25321;&#38544;&#34255;&#33410;&#28857;&#25968;&#37327;">&#182;</a></h3><p>隐藏节点越多，模型的预测结果就越准确。尝试不同的隐藏节点的数量，看看对性能有何影响。你可以查看损失字典，寻找网络性能指标。如果隐藏单元的数量太少，那么模型就没有足够的空间进行学习，如果太多，则学习方向就有太多的选择。选择隐藏单元数量的技巧在于找到合适的平衡点。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[39]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
+
+<span class="c1">### Set the hyperparameters here ###</span>
+<span class="n">iterations</span> <span class="o">=</span> <span class="mi">8000</span>
+<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.5</span>
+<span class="n">hidden_nodes</span> <span class="o">=</span> <span class="mi">20</span>
+<span class="n">output_nodes</span> <span class="o">=</span> <span class="mi">1</span>
+
+<span class="n">N_i</span> <span class="o">=</span> <span class="n">train_features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">network</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="n">N_i</span><span class="p">,</span> <span class="n">hidden_nodes</span><span class="p">,</span> <span class="n">output_nodes</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">)</span>
+
+<span class="n">losses</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;train&#39;</span><span class="p">:[],</span> <span class="s1">&#39;validation&#39;</span><span class="p">:[]}</span>
+<span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">iterations</span><span class="p">):</span>
+    <span class="c1"># Go through a random batch of 128 records from the training data set</span>
+    <span class="n">batch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">train_features</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">train_features</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">train_targets</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">batch</span><span class="p">][</span><span class="s1">&#39;cnt&#39;</span><span class="p">]</span>
+                             
+    <span class="n">network</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+    
+    <span class="c1"># Printing out the training progress</span>
+    <span class="n">train_loss</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">train_features</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">[</span><span class="s1">&#39;cnt&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">val_features</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">val_targets</span><span class="p">[</span><span class="s1">&#39;cnt&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+    <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\r</span><span class="s2">Progress: </span><span class="si">{:2.1f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="p">(</span><span class="n">ii</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">iterations</span><span class="p">))</span> \
+                     <span class="o">+</span> <span class="s2">&quot;% ... Training loss: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">train_loss</span><span class="p">)[:</span><span class="mi">5</span><span class="p">]</span> \
+                     <span class="o">+</span> <span class="s2">&quot; ... Validation loss: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">val_loss</span><span class="p">)[:</span><span class="mi">5</span><span class="p">])</span>
+    <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
+    
+    <span class="n">losses</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">train_loss</span><span class="p">)</span>
+    <span class="n">losses</span><span class="p">[</span><span class="s1">&#39;validation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val_loss</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Progress: 100.0% ... Training loss: 0.051 ... Validation loss: 0.130</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[76]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">losses</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training loss&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">losses</span><span class="p">[</span><span class="s1">&#39;validation&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation loss&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">8000</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">1.6</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABCQAAALJCAYAAABsnPJvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAAewgAAHsIBbtB1PgAAIABJREFUeJzs3Xd4VFX+x/HPSQ8lJISSEIog6iJFCEiRJmBBpYnoShEE
+VBALCzYURVAssLqKiIqKCyhrF0RFpKP8YKlSFFEUiCS00EEgIcn5/THJ3YS0STKZIeH9ep775M6d
+7z3ne2ZCHuY7555rrLUCAAAAAADwJj9fJwAAAAAAAC48FCQAAAAAAIDXUZAAAAAAAABeR0ECAAAA
+AAB4HQUJAAAAAADgdRQkAAAAAACA11GQAAAAAAAAXkdBAgAAAAAAeB0FCQAAAAAA4HUUJAAAAAAA
+gNdRkAAAAAAAAF5HQQIAAAAAAHgdBQkAAAAAAOB1FCQAAAAAAIDXUZAAAAAAAABeR0ECAAAAAAB4
+HQUJAAAAAADgdRQkAAAAAACA11GQAAAAAAAAXkdBAgAAAAAAeF2xFiSMMZWNMTcZY8YZY+YZYxKN
+MWnp23vF2G9PY8wnxpgdxphTxphDxpitxpj3jTF3GmNMcfUNAAAAAADyZ6y1xde4MWnnHMrc2Qxr
+7SAP91dD0n8ktT6nLyck/XiEtfa4J/sGAAAAAADuC/BCHxmFgT8lbZN0vXIuFhSJMaa6pOWSLpKU
+Iul9SV9LipNrJkhtSddI6uHpvgEAAAAAQMEU9wyJpyWtlbTWWptojKklaadcBQmPzpAwxiyX1FbS
+YUk3WGvX5hLnZ609d+YGAAAAAADwomKdIWGtHVec7WcwxvSTqxhhJd2dWzEiPSeKEQAAAAAA+Fhp
+ucvGfek/f7XWzvZpJgAAAAAAIF/eWEOiWKUvZNlCrtkRX2U6HiApRlKqpH3W2hTfZAgAAAAAAM5V
+GmZItMi0v8UYU9UY829JR+Var+JPSUeNMV8YY67wSYYAAAAAACCL0lCQuDzTfqSkLZIGSAqVa9aE
+Td/vIWmNMaav1zMEAAAAAABZlIaCRMVM+y/IVZSYKamhpGBJ1SU9LilJUqCkacaYJt5OEgAAAAAA
+/E9pKEiUzbQfLGmatfZOa+1Wa22KtXavtXaipIHpMYGSxns9SwAAAAAA4CgNBYkz6T+NpBRJo3MK
+stZ+JGldetx1xpgw76QHAAAAAADOVeLvsiHpRPpPK2mjtTYxj9jvJDWTqxDTVNJSdzsxxvwl1wwM
+K+mwG6ekSkpzt30AAAAAwAXDT5K/G3EV5fpSPclaWza/4JKmNBQkdueyn19s5QL2E6z//cJUKeC5
+AAAAAAAUVrCvEygOpaEg8XOm/fwqTJmfTylgPzZjJ7hcOYWFhubdkb+//P3dKXjBF5KTk5WYmKjK
+lSsrKCjI1+nAA3hPSxfez9KF97N04f0sfXhPSxfez5IhNTVVqamp+cYlJjoXANi84kqq0lCQWCfp
+tKQQSXXyib04035CAfs5LKmKykhdxjynzx55sICn43yyYcMGNW3aVPPnz1dsbKyv04EH8J6WLryf
+pQvvZ+nC+1n68J6WLryfpUuVKlUyihLuLBtQ4pT4RS2ttackzZfrupr6xpiLc4ozxhhJ3dMfnpK0
+wTsZAgAAAACAc533BQljzABjTFr6NiaXsBczwiVNMcbkNPNjtFwzJKyk96y1Z4shXQAAAAAA4IZi
+vWTDGNNaUt1Mhypl2q9rjBmQOd5aOyOP5nK9ZsZau9YYM0XSfZKuk7TCGPOKpN/kWrzyDkl908P/
+lDTO7UEAAAAAAACPK+41JO6SNCCH40ZSm/Qtg5WUV0EiP8MllZPUX9KVkj4853krabukLtbaUnn9
+DQAAAAAAJYU3LtmwBdjyaiPvTqxNs9YOlHSDpM8lxUtKknRI0vdyFSwaWWt/L/RIAAAAAACARxTr
+DIn0AsHAIrYxQwWYOWGtXSBpQVH6BAAAAAAAxeu8X9QSAAAAAACUPhQkAAAAAACA11GQcF+qJNdy
+nCjxoqOj9fTTTys6OtrXqcBDeE9LF97P0oX3s3Th/Sx9eE9LF97P0sXf3z9jN9WXeRQXY22+60VC
+kjEmXlKMyku3PDVJnz3yoK9TAgAAAACUYtWrV1dCQoIkJVhrq/s6H09jhgQAAAAAAPA6ChIAAAAA
+AMDrKEgAAAAAAACvC/B1AgAAACi4Zs2aad++fb5OAwDghqioKK1bt87XaZx3KEgAAACUQPv27ctY
+6AwAgBKJggQAAEAJ5ufnx+39AOA8tXfvXqWlpfk6jfMWBQkAAIASLDo6WvHx8b5OAwCQg0y37UQO
+WNQSAAAAAAB4HQUJAAAAAADgdRQkAAAAAACA11GQAAAAAAAAXkdBAgAAAAAAeB0FCQAAAAAA4HUU
+JAAAAAAAgNdRkAAAAAAAAF5HQQIAAAAAAHgdBQkAAAAAAOB1FCQAAAAAFNmkSZPk5+cnPz8/jRw5
+0mv9Hjt2zOm3YsWKXuv3fNC4cWNn7Js3b/Z1OkCBUZAAAAAAillcXJzzwdFT2zPPPOPrYeXIGHNB
+9etLGWO+EMeO0iHA1wkAAAAAFwo+OBYfa62vUwBQQBQkAAAAgGIWFham+++/P8+YNWvWaM2aNTLG
+qFq1arr55pvzjG/evLknU/QIX8+OoOADlCwUJAAAAIBiFhERoddeey3PmHHjxmnNmjWSpEsuuSTf
++PPN8OHDNXz4cK/3W6FCBaWmpnq9XwBFxxoSAAAAAADA6yhIAAAAAAAAr6MgAQAAAJRgPXr0cO68
+MXfuXEnSoUOH9NJLL6l169aKiYlRYGCg/P39s527Z88evfvuu7rjjjvUuHFjRUREKCgoSBUrVlSD
+Bg10zz33aPny5W7l4c5tP7/88ksnpmfPns7x+fPnq1evXqpdu7ZCQ0NVpUoVderUSdOmTVNaWlqe
+/bp728/w8HAn7vjx45KkXbt26dFHH1XDhg1VoUIFhYWFqUGDBnr44Ye1b98+t8ad4auvvtItt9yi
+GjVqKDQ0VNWrV1fHjh317rvvKikpSZI0YsQIJwdfXJKzb98+jR8/Xm3atFF0dLSCg4NVpUoVNW/e
+XKNHj9aOHTvcbmvLli0aOXKkmjdvrkqVKikoKEihoaGKiopS8+bNdc899+g///mPTpw4kWsbx48f
+1+TJk9W5c2fVqFFDZcqUUVBQkMLDw1W/fn317NlT//znP7V9+3ZPDB/nI2stmxubpHhJVuVlb5k4
+yQIAAPhSTEyMlWRjYmJ8nQo8ZOzYsdYYY/38/GyHDh3cPq9Hjx7OeV9++aVdsGCBrVKlinMs85bZ
++PHjrb+/vzXG5BibcdwYY7t27WqPHz+eZx6vvvqq086IESNyjJkzZ44Tc/PNN9tTp07ZPn365JhD
+xrF27drZEydO5Nrv0aNHnXMjIiJyjQsPD3faPnbsmH3//fdtuXLlcu23QoUKdvny5XmO2VprT506
+Zbt3757l9Tq3rWbNmtm4uDj7j3/8w3l+0qSif6Zo3Lix096mTZvyjJ00aZIz3tzGHBQUZJ966ql8
++x05cqTbvzsPPPBAjm0sWrTIVq1aNdc2MrdTvnz5Qr0+54Oi/q3OOF9SvD0PPhd7emNRSwAAAKCU
+2LRpk1588UWdOXNGERERateunapWrapDhw5lm+kQHx8va62MMbrkkkv0t7/9zfmm++jRo9q4caN+
+/fVXSdLXX3+tLl26aNmyZR67k4W1Vv369dPs2bMVGBioVq1a6ZJLLtHZs2f1f//3f8639StWrNCQ
+IUM0a9asfNtzp09jjL744gsNHjxYklS3bl01b95cZcuW1e+//67vv/9eaWlpOn78uG6++WZt27ZN
+lStXzrG9tLQ0devWTYsXL3ZelypVqqh9+/aqUKGCdu3apeXLl2vDhg3q2rWrrrrqqoK8RB7z5JNP
+6vnnn5cxRsYYhYaGqkOHDqpWrZoOHjyopUuX6tixY0pJSdH48eOVkJCgadOm5djWuHHj9Morrzht
+Va1aVS1atFDVqlUlSYcPH9bWrVu1bdu2XN+TX3/9VV27dlVSUpKMMQoODlaLFi1Up04dhYSE6MSJ
+E9q5c6c2b96skydPFtvrgvOArysiJWUTMyQAAMB5hBkSpY8nZkgEBgZaPz8/+8QTT9ikpKQsccnJ
+yVkeT5kyxb7zzjt2//79uba9bt0626hRI6f9KVOm5Bpb0BkSISEh1hhjO3bsaOPi4rLFjh8/Psu3
+51u2bMmxzYwZEsYYt2dIhISE2IiICDtnzpxscevXr7eVK1d2+n3ooYdybfOll17K8u3+s88+a1NT
+U7PEJCQk2A4dOlhjjDNmb86QmD9/fpZZB3//+9/tkSNHssScPn3aDhs2LMtY/v3vf2dr69SpU7Zs
+2bLWz8/P+vv72zfffNOmpaXl2G9iYqJ9++23c/ydufPOO52+brrpJnvw4MEc20hJSbGLFy+2AwcO
+zOeVOH8xQyLvjTUkAAAAgFLAWqvU1FQ98sgjeu655xQUFJTl+cDAwCyPhw0bprvuuktVqlTJtc2m
+TZtq0aJFqlChgiRp8uTJHss3KSlJsbGxmj9/vmrWrJnt+dGjR+uaa65xHn/44Yce6Tfjdfr222/V
+vXv3bM/Hxsbq1VdfdWJz6/fMmTN67rnnnJkRo0aN0pNPPik/v6wfsapVq6avv/5a9erVU3JyskfG
+UBCPPvqos9+5c2d9+OGHCg8PzxITEhKiKVOmqH///hlfxmr06NFKSUnJErdhwwadOnVKknTjjTdq
+6NChuc6YqVSpku6++24NGzYs23MrVqyQJBljNGPGDEVGRubYhr+/vzp27Kj33nvPzdGipKEgAQAA
+AJQS4eHhGjdunEfbrFy5sm688UZZa/Xbb78pISGhyG1a67p04uWXX85WKMls0KBBzv6aNWuK3K/k
++hDct29ftWjRIteYW2+9VeXKlZPkWggypzF/8cUXOnr0qKy1qlSpksaMGZNre2XKlNGLL77ojNtb
+Vq9erS1btri+ifbz05QpU/Ls/5VXXlHZsmVlrdW+ffs0e/bsLM9nLAYqKdfLWNyR0U5AQIAiIiIK
+3Q5KPtaQAAAAQDbNmkkFvMnAeSUqSlq3ztdZeJcxRt26dVNwcHCBz927d69Wr16tbdu26ejRozp1
+6pTzTbkk/fzzz87+xo0bFRMTU+R8IyMj1b59+zxjmjRp4uzv2rWryH1mFAR69eqVZ1xgYKDq16+v
+1atXO32fO+Zly5ZJcr3uPXv2zPd1v/HGG1WxYkUdPnzYa0WJJUuWSHLl2K5dO1100UV5xkdERKhn
+z556//33JUlLly7Vrbfe6jxfo0YNZ3/evHnavXt3lmPuqlGjhhITE3X27Fm9++67uueeewrcBkoH
+ChKFYPNfLwcAAKBE27dP8sAX4fCypk2bFij+xx9/1GOPPaYlS5bke3vNDAcPHixMalkYY9SgQYN8
+4zJP5c/87XxRNWzYsMh9b9y40dnPa7ZFBn9/f8XGxmrRokVuZll0P/74o7Pv7oKarVu3dgoSGzZs
+yPJcgwYNVK9ePf3yyy/av3+/rrjiCg0YMEDdu3dXq1at3C6G3XbbbdqwYYOstRo6dKjmzp2r3r17
+q1OnToqKinJzdCgNKEgAAAAgm5L+maCk519YBZlG/9lnn6lPnz5KSUlx7piQl4wZEydOnChSjhky
+1qXIS8blHNZanT171iP9FrRvSTn2nZiY6Oy7O0ugevXqbsV5SuYca9Wq5dY5mWdR5FR8mjlzpq6/
+/nodOXJER48e1aRJkzRp0iQFBQUpNjZW7dq1U+fOndW+fftcf6dGjBihxYsXO8WZefPmad68eZKk
+OnXqqG3bturQoYO6d+/u1nuFkouCBAAAALK50C53KC1CQ0Pditu9e7f69++v1NRU57afQ4YMUevW
+rVW7dm1VqFAhy6KYI0aM0KRJkyTJ7ZkU+fHmWgrF0Xfm21GWKVPGrXMy1qXwlsw5li1b1q1zMuKs
+tTkWn5o2barNmzfrmWee0Ycffuj0cfbsWf33v//Vf//7X02cOFEXXXSRnnvuOfXu3TtbG4GBgfr2
+2281depUTZo0Sdu3b3ee27Fjh3bs2KEZM2YoODhYd999t55//nmvv3bwDha1BAAAAC4wkydP1pkz
+ZyRJbdq00aZNmzRixAg1b95clStXznaHDk/NiihNMn9AzrjzRH7++uuv4konR5lzdLfvjDhjjMqX
+L59jTLVq1fTWW2/pwIEDWrRokcaOHatrr71W5cqVc2bb7Nq1S3379tXYsWNzbMPPz0/33nuvtm3b
+pp9++klvvPGG+vXrp1q1ajltJCcn6/XXX1fr1q2zFFdQelCQKITDabt8nQIAAABQaBmLHUrS2LFj
+8732Py4urrhTKnEqVark7MfHx7t1jrtxnpL5Ep4///zTrXMyLx6aeYw5CQ4OVocOHfTUU09p/vz5
+OnTokL788ks1a9bMmYXy3HPPZZkBkZN69eppyJAhmjFjhnbu3KktW7Zo2LBhTmHip59+0sSJE93K
+HyULBYlCWJb0ij7f+rmv0wAAAAAKZc+ePc5+fotLJiUlac2aNT69xOJ81LhxY2c/424ceUlLS8u2
+SGRxy3yXkpUrV7p1Tua42NjYAvUXGBioLl26aMmSJc5aFGlpafrmm28K1M7ll1+uyZMna+TIkc7a
+JXPnzi1QGygZKEgUUq9P875VEAAAAHC+8vP738eA/C43+OCDD3TixIkstwGFdPXVV0tyrbXwxRdf
+KDk5Oc/4efPm6dChQ14t7HTs2FGSK8fly5fnO9Pl6NGj+uKLL7KdX1Bly5ZVhw4dnMf79+8vVDvd
+unWT5Mq/sG3g/EZBAgAAALjA1KlTx9nP65vn+Ph4Pf7448yOyMEtt9zi3AEiMTFRzz77bK6xp0+f
+1uOPPy5JXi3stGjRQo0aNZLkmqlw//335xk/cuRIZ62GqKgo9ejRI8vzR44ccbvv3bt3O/tVqlRx
+9lNTU92+hWvGZSbGmCxtoPSgIAEAAABcYLp27ersP/nkk5ozZ062mJUrV6pdu3Y6dOiQ23douJCE
+hIRo9OjRklxFhueff17PPfecUlNTs8Tt2bNHN910k37++WeFhIR4Pc+JEyfKGCNrrebNm6c+ffro
+6NGjWWJOnz6t+++/X9OnT5fkKgA8//zzCgjIelPGmTNnqn79+po8ebISEhJy7O/MmTN68cUXtXDh
+QudY586dnf2TJ0+qevXqGj58uFatWpVr3itWrNBjjz3mPL7xxhvdHjNKDm77CQAAAFxghg4dqsmT
+Jys+Pl4nTpxQz5491bBhQzVs2FABAQHavHmzNm7cKGOMrrrqKjVp0kSvv/66z/J1Z4aGL2ZxjBw5
+Ut9++62WLVsma62eeuopvf7662rfvr3CwsIUFxen5cuX6+zZs2rUqJGuuuoqvfXWW5KyXjZTnK67
+7jo98cQTev7552Wt1UcffaS5c+eqQ4cOqlatmg4dOqQlS5Y4RQpjjAYOHKgBAwbk2N4vv/yi4cOH
+a/jw4apdu7YaNmyoypUrKzU1Vfv27dOqVat07Ngxp637779f9erVy9LGyZMnNXnyZE2ePFnh4eFq
+0qSJqlevrtDQUCUmJmrr1q369ddfnfiaNWtmKU6g9KAgAQAAAJxHvDGlv3z58vr666/VpUsX55vu
+LVu2aMuWLZLk3N3guuuu0wcffKDx48d7tP+CjtGdeF+sceHn56evv/5avXv31ldffSXJtV7CJ598
+4sQYYxQbG6svvvhCEyZMcI6HhYV5Lc9nn31WlStX1ujRo3Xq1CmdPn06y0KTGe93YGCgHn30UT3z
+zDM5tpNxW88Mu3bt0s6dO7PEZLQVEBCgkSNH6oUXXsjyvL+/v8qWLeusXXLs2DEtXbo0W18Z/bRs
+2VIfffSRwsPDCzd4nNcoSAAAAADniYwPYQX9tj/jQ2BBNGrUSJs3b9Zrr72mL7/8Ur///rtSU1MV
+FRWlxo0bq2/fvrr55puz5eZO/u7EuJuvO/HuxhT0dXInPjQ0VHPmzNFXX32l6dOna82aNTp48KAi
+IyN16aWXqm/fvrrjjjsUFBSkw4cPO+d56gO2u2N68MEHddttt+ndd9/V/Pnz9ccff+jIkSMKCwtT
+rVq1dN1112nw4MFZ1hc51+DBg3XjjTfqu+++08qVK7Vlyxbt3LlTR48elTFG4eHhuuyyy9S+fXv1
+799fF198cbY2ypUrp0OHDmnp0qX64YcftG7dOm3fvl0HDhxQUlKSypYtq5o1a6pZs2a69dZbs1zu
+gdLHsFque4wx8ZJiVF7SQ65j9mleOwAA4BvVq1dXQkKCYmJiFB8f7+t0ALihQYMG2rp1q4wx2rZt
+my655BJfp4RiVtS/1RnnS0qw1lb3eII+xqKWAAAAAFDMfvrpJ23dulWSVKFCBYoRgChIAAAAAECx
+SktL04MPPijJdYlF7969fZwRcH6gIAEAAAAAhfTwww9r6tSpzp0lzvXbb7+pc+fOWrZsmSQpODhY
+w4cP92KGwPmLRS0BAAAAoJD++OMP/etf/9KDDz6oK664QpdddpnKly+vEydO6Oeff9bmzZuVlpYm
+yTU7YsKECbr00kt9nDVwfqAgAQAAAABFYIxRSkqK1q1bp3Xr1mV7zhijcuXK6eWXX9Zdd93loyyB
+8w8FCQAAAAAopGnTpmnOnDlaunSptm3bpsTERB08eFDWWlWsWFGXX365rrnmGg0ePFgVK1b0dbrA
+eYWCBAAAAAAUUsWKFTVo0CANGjTI16kAJQ6LWgIAAAAAAK8r1oKEMaayMeYmY8w4Y8w8Y0yiMSYt
+fXuvOPtO7z/KGHMkU59LirtPAAAAAACQv+K+ZGP/OY9t+uYtr0uq4OU+AQAAAABAPrxxyUZGESJO
+0gJJxgt9yhjTVVJPuYoiXukTAAAAAAC4p7gLEuMkdZUUZa2tLWloMfcnSTLGlJU0Ra5CyMPe6BMA
+AAAAALivWC/ZsNaOK8728/CCpOqSFltrZxlj3vdRHgAAAAAAIAel7i4bxpjmkoZJSpJ0r4/TAQAA
+AAAAOShVBQljjL+kd+RaM+JFa+3vxdnfmZQzxdk8AAAAAAClVqkqSEh6RFJDSdvlumyjWE1YMaG4
+uwAAAAAAoFQqNQUJY8zFkp6SayHL+6y1ycXd52e/fFbcXQAAAAAAUCqVmoKEpLckhUj6yFq72NfJ
+AAAAAACA3JWKgoQxpr+kTpKOSxrptX5lvNUVAAAAAAClSrHe9tMbjDGRkl6S61KNJ6y1+4u1w1RJ
+e1y7p8+c1oYNG3IMi46OVnR0dLGmAgAAAAAoefbu3au9e/fmG5ecXOwrEfhUiS9ISHpFUiVJa6y1
+bxZ7b6ckve3a/V2/q+mLTXMMe/rppzV27NhiTwcAAAAAULJMnTpV48aN83UaPleiCxLGmGhJ/eSa
+HbHUGPP3nMIy7VfJFLPTWrumwJ2WSe9RUt2KdfXxrR/nGMbsCAAAAABAToYMGaJu3brlG9e5c2cl
+JiZ6ISPfKNEFCUlB6T+NpMfyiTWSLpf0Yfrj6ZIKXpDwl1TNtRtaJVSxsbEFbgIAAAAAcOFy9xL/
+oKCgfGNKstKwqKV1Y8stFgAAAEAuevfuLT8/P/n5+emTTz7JMWbq1KlOzLBhwzzSb1JSktNmmTJl
+PNJmcXn88cedXCdOnOjrdLyiJL0/OL+V6IKEtTbOWuuf35YRLml5puODfZk7AAAALhyPPPKI8wGu
+Tp06hW7nyJEjCg4OdtqaOXOmB7PMnTH5313OnZji6Pd8UZJy9ZQLcczwrPO+IGGMGWCMSUvfxvg6
+n8z4BwgAAAB3DBgwQJLr/49xcXH6/vvvC9XOhx9+qLNnz8oYo3LlyqlXr16eTPO8Y613JzVfiLMd
+isLb7w9Kn2JdQ8IY01pS3UyHKmXar2uMGZA53lo7I4/m+G0HAABAidSgQQM1adJEP/74o4wxmjlz
+ptq1a1fgdt5//31nv1evXufNdPmML+pKyxd2BR1HaRk34G3FvajlXZIG5HDcSGqTvmWwkvIqSBQV
+fyUAAADgMwMGDNCPP/4oa60+//xzTZkyRcHBwW6fv337dq1evdp53L9//+JIs8CGDBmiIUOG+DoN
+n3nhhRf0wgsv+DoNoETyxiUb7iw6md8ik0WdHcFClgAAAPCpPn36KDAwUMYYHT9+XHPmzCnQ+TNm
+/O+7u5o1a+rqq6/2cIYA4F3FWpCw1g50Z9HJ9C3H2RrW2hmZYp4pZB4Z53cq2ogAAACAwqlUqZJu
+uOEG57r7gi5IOWvWLEmuywPOl9kRAFAU5/2iluczFnEBAABAQWQsbmmt1cKFC3XgwAG3zlu+fLni
+4uKcx3fccUeusVu3btW//vUv3XzzzbrssssUFhamoKAgValSRc2bN9cjjzyi3377rWgDyaSgt/08
+evSonn32WTVr1kwREREqX7686tWrp3vvvVebNm0qcP+7du3SG2+8odtvv10NGjRQeHi4goKCVKlS
+JTVu3Fj333+/1q1bl2cbLVu2lJ+fnyZMmCDJ9f6MGjXKGVfm7dwxFnQhzOTkZL3zzjvq3r27atWq
+pTJlyig8PFz16tXTPffco6VLl7o17qioKKffjN+j3bt364knnlCjRo0UHh6u8uXLq379+hoxYoQS
+EhLcare4bNmyRQ899JAaN26sSpUqKSQkRDExMerYsaNeeuklHT161O22Fi5cqIEDBzrvd2BgoMqW
+LauaNWuqffv2GjlypObPn6+UlJRc24iLi9PTTz+tdu3aqWrVqgoODlZISIgqV66sJk2a6I477tDb
+b7/t9r9RFJK1ls2NTVK8JKvyshrr2sJeCLMAAAC+EBMTYyXZmJgYX6eCAkhOTraRkZHWGGP9/Pzs
+K6+84tZ5AwcOdM5p3bp1rnFdu3a1xhhn8/Pzy7JlHPf397ePPfaYTUtLy7Pf22+/3Wnn448/zjHm
+rbfecmLuvffePNtbsmSJjYqKyjE/Y4wNCAiwEydOtGfOnHFiQkNDc23v/vvvd2u8xhg7YMAAe+bM
+mRzbadmyZbZzzm0rYzt3jKNGjXLiJ0yYkOf4f/jhB3vRRRflOv6M7aabbrJHjhzJs62oqCjn3P37
+99uPP/5JOiTUAAAgAElEQVTYhoWF5dpuuXLl7MKFC/Ns013uvj/WWnv27Fk7dOhQ6+/vn+e4IyMj
+7YcffphnW8ePH7edO3d2+z2fNWtWju289tprNjQ01K02rr322kK/TtYW/W91xvmS4u158LnY01tx
+L2pZqlnLDAkAAAC4LzAwUL1799aUKVMkuS7b+Mc//pHnOWfOnNHnn3/uPL7zzjtzjd29e7eMMQoM
+DNTll1+uunXrKiIiQsYYHThwQGvXrtWePXuUlpamiRMnKiUlRS+99JJHxpafVatWqWvXrjp9+rQk
+16UnLVq00OWXX66kpCStXLlSu3bt0qhRoxQaGupWm/Hx8TLGyM/PT5dddpkuu+wyVaxYUYGBgTp0
+6JDWr1+vnTt3SnK91idPntRnn32WrZ3bbrtNV155pVatWqX169fLGKNWrVopNjY2W2zbtm0LNf5F
+ixapW7duSkpKkjHGGX+9evWUlJSkVatWObnOmzdPbdu21YoVK1ShQoVc27TWyhijb775Rnfffbes
+tapdu7Zatmyp8uXL6/fff9f333+v1NRU/fXXX+rVq5e2bt2qatWqFWoMBZWWlqYuXbpowYIFzpgr
+Vaqk9u3bKyIiQnFxcVq2bJnOnj2rw4cPq2/fvjp+/LjuueeeHNu77bbb9N133zl3Nbn00kvVuHFj
+RUREKDk5WQcOHNCWLVv0559/5prTRx99pOHDhzv5VKhQQa1atVJMTIz8/f117Ngx/frrr/r55591
+9uzZYnldkImvKyIlZVMOMyTKP1/eAgAA+AIzJEqutWvXZvlm9qeffsozftasWVm+jT527FiusY89
+9pidPXu2PXnyZK4xs2fPtpUqVXJmSqxbty7XWE/NkDh9+rStW7eu8+3zxRdfbNevX58tbtq0aTY4
+ONgGBwc7sXl9A//CCy/YDz74wB4+fDjXmGXLltk6deo4OX7++ee5xhZktkNBzklMTMwyo6FBgwZ2
+y5Yt2eJmzJhhQ0NDnbi///3vufabub2QkBBboUIF++mnn2aL27Rpk42OjnZyvO+++9waV17cnSEx
+bty4LDMQxo0bZ1NSUrLE7Nmzx3bq1MmJCwkJsRs3bszW1urVq52YiIgIu3jx4lz7/eOPP+yzzz5r
+v/vuu2zP1atXz8nn0UcftUlJSTm2ceLECfvxxx/bp556Ktd+3MEMibw31pAAAAAAvKhZs2aqV6+e
+8zi/xS3ff/99Sa4ZBT169FBYWFiusS+++KJ69OihsmXL5hrTo0cPffHFF5JcX06+/vrrBUm/UN55
+5x398ccfstaqXLlyWrRoUY6zDwYNGqQ33nhDycnJbrU7atQo9e3bVxEREbnGtG/fXgsWLFBgYKAk
+afLkyYUbRBH885//1P79+2WtVeXKlbV48WI1aNAgW1z//v313nvvOR/WPv3003zXv7DWKiUlRXPn
+zlWvXr2yPd+oUSPnPbbW6qOPPvLMoPJx5MgRTZgwwZnN8NRTT2nMmDHy9/fPEhcdHa2vv/5ajRs3
+lrVWycnJGj16dLb2fvjhB2f/kUceUceOHXPtu06dOnryySd13XXXZTl+6NAhbdu2TZJUt25dTZgw
+QUFBQTm2Ua5cOd1222165plC3VcBbqIgUQQZ/7gAAACAgsi8uOV//vOfjBm52ezbt0+LFi1yHnvq
+7hpt27ZV7dq1Za3N0n5xmTZtmiTX/58feughXXTRRbnGDho0SE2bNvVo/xdffLHatGkja61Wrlyp
+pKQkj7afl7S0tCzjf+aZZ1SlSpVc42+//XZ16NDBefzmm2/m2b4xRr169VK7du1yjenRo4cqVqwo
+yVUo+OOPPwoyhEKZOXOmTp8+LWutatSooSeffDLX2JCQEE2aNEmS69/E/Pnzs112cfz4cWe/UqVK
+hcrJE23As1hDAgAAANk0e7uZ9p3c5+s0Ci2qXJTW3ZP3N8u+1K9fPz3xxBNKS0vTnj17tGjRIl17
+7bXZ4j744AOlpqZKct1V4frrr3e7j99++03r1q3Tjh07dOzYMSUlJWUpfJw6dUqStGfPHh06dEiR
+kZFFHFXODh8+rM2bNzuP87pDSIb+/ftr/fr1Bernzz//1Jo1a/Tbb7/p2LFjzofhDLt375YkpaSk
+6KeffvJ40SM3mzZt0uHDhyVJQUFB6tOnT77n3HXXXc7dNvK664a11ilI5MXPz08NGzbU8uXLJbnu
+THLxxRe7O4RCWbJkiSRXwaRv374KCMj7o2fbtm11ySWXaPv27bLWatmyZVkKcDVq1HD2p0+frv79
++ys4OLhAOUVFRSkgIECpqanasGGD1q5dqyuvvLJAbcCzKEgAAAAgm30n9ynhhG9vE1iaVatWTddc
+c40WLFggyXVZRk4FicyXa/Tr18+tGbpz5szR2LFjsxQB8nPw4MFiK0hs3LjR2a9UqZLq1KmT7zmt
+WrVyu/0ffvhBjz/+uFauXOn2OQcPHnQ7tqh+/PFHSa73sEGDBipXrly+57Ru3VqSq+AQFxeno0eP
+Kjw8PNf4hg0b5ttm5vc380yB4pIxbkm66qqr3DqndevW2r59uyRpw4YNWQoSXbt2VUhIiM6cOaNV
+q1apXr16GjRokG666SZdccUV8vPLf/J/aGiounTpojlz5igpKUnt2rVT7969dcstt6hdu3YqX758
+AUeJoqIgAQAAgGyiykX5OoUiKQn5DxgwQAsWLJC1VrNnz9apU6dUpkwZ5/mNGzdqy5YtzmN3LtcY
+NWqUJk6cKMm9y4szZhCcOHGioOm7LTEx0cmnZs2abp3jbtybb76p+++/35kpkB9vjPdcGeOXpFq1
+arl1Ts2aNeXn56e0tDRJrgJKXgWJvO7EkSFjDQ1JXrl7RGHGnflSnnOLRlWrVtXUqVN11113KSUl
+Rbt27dKYMWM0ZswYlS9fXi1btlT79u3VtWvXPAs0kydP1ubNm7Vz504lJSVp+vTpmj59uvz8/NSg
+QQO1a9dO1157rTp37pzlNUPxoCABAACAbM7nyx1Ki5tvvllhYWE6fvy4Tp06pc8++yxL0WHGjBmS
+XB/kmzRpovr16+fZ3ty5czVx4kTng3nbtm01YMAANWvWTDVq1FDZsmWzfMBq1aqVVq9eLUnOB9/i
+cPLkSWc/c8ElL3ktyplh06ZNeuCBByS5XqNGjRrp7rvvVsuWLVWrVi2VL18+y4KFvXv31scffyyp
+eMd7rszjd2dcGUJDQ/XXX39Jyr+Acr6tbZeampplnQ53x505Lqcx33HHHapfv76eeeYZffvtt0pJ
+SZHkeo0XLlyohQsX6sknn1SLFi30yiuvqGXLltnaiImJ0YYNGzRhwgRNmzbNKZxYa7V582Zt3rxZ
+r7/+uiIjI/X4449rxIgR593rW5qwqCUAAADgAyEhIbr11ludxxmXZ0iuD3SZ74Zw55135tveyy+/
+7Ozfd999WrZsmQYOHKiGDRsqPDw827e93polkPkShYx1K/KT8UE8Ly+//LJTWOjevbvWr1+vYcOG
+KTY2VpGRkdnunuDNWRGZZR6/O+PKcPr0aWe/pF1K4O/vn2V9B3fHnTkutzHHxsZqzpw52r9/v2bP
+nq2HHnpILVq0UGBgoIwxMsZo9erVateunb7++usc2wgLC9Nzzz2nPXv2aOXKlZowYYK6deumyMhI
+p41Dhw7p4YcfdmvNDxQeBQkAAADARzLfbWPZsmVKSHCt2/Hdd99p//79klxT7Xv37p1nO0lJSc4a
+CgEBARo/fnye8dZaxcfHFzV9t1SuXNnpM2Nhyfy4E5exaKIkjR8/Pt81BOLi4tzq29Myxi8p250j
+cvPnn39mmcVREu8IUZhx79q1y9nPb8zh4eHq1q2bJk6cqJUrVyoxMVHvvPOOYmJiZIxRamqqhgwZ
+4iwKmxM/Pz+1aNFCDz/8sGbPnq3ExEQtW7ZMN9xwgzMr4pNPPtG8efPcyh8FR0ECAAAA8JE2bdo4
+izympaXpgw8+kJT1co0bbrgh3wUn9+/fr9TUVBljVL16dYWFheUZv3HjRq8sbChJjRs3dvYTExO1
+c+fOfM9ZtWpVns9ba7Vvn+suMH5+frr88svzjD906JB++eWXfKfeF8fU/CZNmkhy5bxlyxa3Zgtk
+FJeMMapVq1ae60ecrzLGLcntBUczx8XGxhaov7CwMA0aNEgLFy6Uv7+/8zuydu3aArXTtm1bffXV
+V1luozp37twCtQH3UZAAAAAAfCjzuhHvv/++jh8/rq+++so5ljGLIi+ZZwe4c1nEG2+8UcAsC69i
+xYpq1KiR8zjzpSm5yS/GGOOMOS0tTWfOnMkz/q233lJaWlqW24DmJCQkxNn31MKPV1xxhVNQSk5O
+1ocffpjvOdOmTXP2O3bs6JE8vC1z3rNmzcpzpoIkrVixQr/99psk1/t79dVXF6rfyy67TJdeeqnz
+OGOmUUEYY3TTTTcVqQ24h4IEAAAA4EP9+/d3vpn/5Zdf9OijjzofsCtWrKguXbrk20ZUVJTKlCkj
+a60OHDiQ57fCS5Ys0b///W+vLtR31113SXLNEvjXv/6V5+UT06dP19q1a/PNL/MdGTIXcM61detW
+vfDCC26NN/NMlIzLZ4rKz89PgwcPluQa/5gxY7LcgeJcn3zyiRYvXuw8Hjp0qEfy8Lb+/fsrNDRU
+1lr9+eefev7553ONTUpK0vDhwyX9b1bQuXdaOXTokFv9nj17NksBoUqVKs7+iRMnnIUw85P5sqHM
+bcCzKEgAAAAAPnTRRRepbdu2zrf3b7/9tiTXB7M+ffooICD/G+MFBATo+uuvdx7369dPP/74Y7a4
+WbNmqXv37rLWFuiOD0U1ePBg59KUEydOqFOnTtqwYUO2uH//+98aOnRolgURc9O1a1dn/4EHHsiy
+pkSG+fPnq1OnTjp9+rRb423QoIGz/+233xZoEcq8PPzww86H2v3796tjx4766aefssXNnDlTd955
+p7Ow4m233aZmzZp5JAdvi4iI0KhRoyS5CjHjxo3T+PHjs82U2LNnj2666Sbn9zUoKCjHNVAeeOAB
+derUSbNmzcr1cqODBw9q4MCBTvEiMjJSV155pfP8ypUrVbt2bY0fP96ZjXGu1NRUffDBB5o6dapz
+7IYbbijAyFEQ3PYTAAAA8LEBAwbo+++/l+QqRGQUJzJfzpGfMWPG6JtvvlFycrK2b9+uK6+8Uq1a
+tdIll1ziLHoZFxcnY4weeOABrV692rntZ3ELDQ3VzJkzde211+r06dPasWOHrrzySrVo0UKXX365
+kpKStGrVKu3YsUPGGL322mvOLT1z89BDD2n69Ok6cuSIDhw4oGuuuUbNmjVTvXr1ZK3VunXrtG3b
+Nhlj1K1bNwUHB+uTTz7Js802bdqoatWq2r9/v+Li4vS3v/1N11xzjXP3Bcl1u9SePXsWaPyVKlXS
+rFmz1K1bN505c0Y///yzrrjiCrVq1Up/+9vfsoxfcv0O1K9fX2+99VaB+vG2/GadjB49WqtWrdJ3
+332ntLQ0jRkzRpMnT9bVV1+t8PBwxcXFadmyZUpOTpbkmk0yadIkXXHFFdnastZq6dKlWrp0qfz9
+/VWvXj3Vq1dPERER+uuvv5SQkKD/+7//cy61Mcbo1VdfzVbQS0hI0JgxYzRmzBhFR0ercePGqlq1
+qvz9/bV//36tW7fOWZ/EGKNrr71WPXr08MTLhZxYa9nc2CTFS7IqL6uxri3shTALAADgCzExMVaS
+jYmJ8XUq8IATJ07YsmXLWmOMs9WvX7/A7Xz66ae2TJky1s/Pz/r5+WVpL+PY8OHDbUpKim3ZsqVz
+fPXq1Tm2d/vttzsxH3/8cY4xb731lhNz77335pnf4sWLbVRUVK75BQQE2BdffNGeOXPGOR4aGppr
+e99//72NjIzMc7y33367PXnypFtjsdbazz//3AYFBeXYpjEm2xhHjRrltDthwoQ8x79ixQpbp06d
+XNvOON6lSxd75MiRPNuKiopyztm/f3+esda69166y933x1prz549a4cOHWoDAgLyfJ8iIyPtRx99
+lGs7d999txOb02uXua2IiAg7c+bMbG18//33NjAwMM92Mj/Xr18/e/r06SK9VkX9W51xvqR4ex58
+Lvb0xgwJAAAAwMfKlSunm2++Wf/5z3+cY3feeWeB2+nVq5diY2P1yiuvaMGCBYqPj1dgYKCqVaum
+Nm3aaODAgWrVqpUTn3FpQF7cjcn8MzcdO3bUL7/8otdff12zZ8/Wjh07lJKSopiYGF199dUaMmSI
+mjRpoqSkJLfabNu2rX7++We9+uqr+uabb5w7eERHR+vKK69U//79s1zK4s5YevbsqbVr12rKlCla
+uXKldu/erZMnT2Z8SZnj+e6ux9G6dWtt27ZNM2fO1Ny5c7Vx40YlJiYqKChI0dHRatu2rfr06eP2
+go7ujKco8fm1lflnbgICAvTmm29q2LBhmj59uhYvXqz4+HidPHlSkZGRuuyyy9SlSxcNHjxYFSpU
+yLWdt99+WyNGjNCiRYv03//+V1u3btWff/6pEydOKCgoSJGRkWrQoIGuv/569evXTxUrVszWRtu2
+bXXgwAEtXLhQK1as0MaNG/XHH3/o8OHDSk1NVVhYmOrWraurrrpK/fr1y3KnEBQPk/EPC3kzxsRL
+ilF5SQ+5jlUIrqCjo476Mi0AAHCBql69uhISEhQTE6P4+HhfpwMAyEFR/1ZnnC8pwVpb3eMJ+hiL
+WgIAAAAAAK+jIAEAAAAAALyOggQAAAAAAPA6ChIAAAAAAMDrKEgAAAAAAACvoyABAAAAAAC8joIE
+AAAAAADwOgoSAAAAAADA6yhIAAAAAAAAr6MgAQAAAAAAvI6CBAAAAAAA8DoKEgAAAAAAwOsoSAAA
+AAAAAK+jIAEAAAAAALyOggQAAAAAAPA6ChIAAAAAAMDrKEgUgTHG1ykAAAAAAFAiUZAAAAAAAABe
+F+DrBAAAAFB4e/fuVfXq1X2dBgAgB3v37vV1Cuc1ChJFkGbTfJ0CAAC4wKWlpSkhIcHXaQAAUGAU
+JIrgeNJxX6cAAAAuUFFRUb5OAQDgJv5m54yCBAAAQAm0bt06X6cAAECRsKglAAAAAADwOgoSAAAA
+AADA6yhIAAAAAAAAr6MgAQAAAAAAvI6CBAAAAAAA8DoKEgAAAAAAwOsoSBSRtdbXKQAAAAAAUOJQ
+kCiimZtm+joFAAAAAABKHAoSRXTnl3f6OgUAAAAAAEocChIAAAAAAMDrKEgAAAAAAACvoyABAAAA
+AAC8joIEAAAAAADwuoDibNwYU1lS8/TtyvQtMv3p6dbaQR7qJ0xSF0nXSIqVdJGkMpKOSvpJ0teS
+pllrj3miPwAAAAAAUDTFWpCQtP+cxzZ98xhjTGdJcyQFZeojQ6Sk9pKulvSwMaaPtXaZJ/sHAAAA
+AAAF541LNjKKEHGSFkgyHm4/Uq5iRKqk+ZJGSOoo10yJbpI+Tu8/StJXxphGHu4fAAAAAAAUUHEX
+JMZJ6iopylpbW9LQYujjrKS3JF1krb3RWvuatXa5tXaTtfYba20fSQ+mx5aR9C9PJzBj4wxPNwkA
+AAAAQKlmrPXoFRR5d2ZMLUk75ZqxMMNTa0i42fcaSc3kmklR1Vp7uIDnx0uKUXlJD2V/3j7tvdcR
+AAAAAFD6Va9eXQkJCZKUYK2t7ut8PO1CusvGsvSffpJq+zAPAAAAAAAueBdSQSI4036qz7IAAAAA
+AAAXVEGiffrPs5J+92UiAAAAAABc6C6IgoQx5iZJjeRau2K+tfakj1MCAAAAAOCCVuoLEsaYCEmv
+pz9MlTTGh+kAAAAAAACV8oKEMcZP0n8k1ZJrdsSz1trNvs0KAAAAAACU6oKEpDclXS9XMeIrSeN9
+mw4AAAAAAJCkAF8nUFyMMS9IuluuYsT3kv5urbVFbjhV0p7shzds2JDlcXR0tKKjo4vcHQAAAACg
+dNm7d6/27t2bb1xycrIXsvGdUlmQMMY8JukxuYoR6yV1tdYmeaTxU5Lezn646dtNszx++umnNXbs
+WI90CQAAAAAoPaZOnapx48b5Og2fK3UFCWPMMEkvyFWM2Cqps0fvqhFQThqUvbn1Q9ZneczsCAAA
+AABAToYMGaJu3brlG9e5c2clJiZ6ISPfKFUFCWPMHZImy1WM+EPStdbaw57tJECqlv1wbGysR7sB
+AAAAAJRO7l7iHxQU5IVsfKfULGppjOkp6b30h7sldbLW7vN8T0VfhgIAAAAAgAvdeV+QMMYMMMak
+pW9jcom5Tq7be/pJ2i/pGmvtbm/mCQAAAAAA3Fesl2wYY1pLqpvpUKVM+3WNMQMyx1trZ+TRXI5T
+E4wxLSR9ISlQ0llJIyUFG2Pq59FWvLX2WF65AwAAAACA4lPca0jcJWlADseNpDbpWwYrKa+CRG46
+SyqTvh8k10yJ/NwpaWYh+nJlDgAAAAAAisQbi1q6u+hCXnH5tVGQhR1YBAIAAAAAAB8z1vL53B3G
+mHhJMQqqID2R/WoP+zSvIwAAAADAc6pXr66EhARJSrDWVvd1Pp523i9qCQAAAAAASh8KEgAAAAAA
+wOsoSAAAAAAAAK+jIAEAAAAAALyOgkSBsXglAAAAAABFRUECAAAAAAB4HQUJAAAAAADgdRQkAAAA
+AACA11GQAAAAAAAAXkdBAgAAAAAAeB0FCQ/5K/kvX6cAAAAAAECJQUHCQyavmezrFAAAAAAAKDEo
+SHjI44sf93UKAAAAAACUGBQkCszm+syeE3u8mAcAAAAAACUXBYmCSg3O9akhXw/xYiIAAAAAAJRc
+FCQKKjVYAcq5KLF4x2IvJwMAAAAAQMlEQaIQ/hHwa47HjTFezgQAAAAAgJKJgkQh+Mk/x+NGFCQA
+AAAAAHAHBQkPYoYEAAAAAADuoSBRKDkXHpghAQAAAACAeyhIFEYud/5khgQAAAAAAO6hIFEINpeK
+BDMkAAAAAABwDwWJQkhTao7HmSEBAAAAAIB7KEgUglVajseZIQEAAAAAgHsoSBSCZYYEAAAAAABF
+QkGiEFJtLgUJZkgAAAAAAOAWChKFwAwJAAAAAACKhoJEIYQqIsfjzJAAAAAAAMA9FCQKobyJzvF4
+4qlEL2cCAAAAAEDJREECAAAAAAB4HQWJQrDW1xkAAAAAAFCyUZAAAAAAAABeR0ECAAAAAAB4HQUJ
+D2pTs42vUwAAAAAAoESgIFEIua0hUblMZe8mAgAAAABACUVBwoP8/fx9nQIAAAAAACUCBQkPMjK+
+TgEAAAAAgBKBgoQHfbr1U1+nAAAAAABAiUBBAgAAAAAAeB0FiULIbVFLAAAAAADgHgoShfT6Da/7
+OgUAAAAAAEosChKFdF/z+3ydAgAAAAAAJRYFCQAAAAAA4HUUJAAAAAAAgNdRkCgEFrUEAAAAAKBo
+KEgAAAAAAACvoyABAAAAAAC8joIEAAAAAADwOgoSAAAAAADA6yhIFAKLWgIAAAAAUDQUJAAAAAAA
+gNdRkPAwy/QJAAAAAADyRUHCw6woSAAAAAAAkB8KEoWQ1yQIZkgAAAAAAJA/ChIeNvTroVqyc4mv
+0wAAAAAA4LxWrAUJY0xlY8xNxphxxph5xphEY0xa+vZeMfXZ2xjznTFmrzHmtDFmlzHmfWNMy+Lo
+71zv/viuOs3spKSUJG90BwAAAABAiRRQzO3vP+exTd88zhgTIulzSTec00cNSX0l9TbGPGOtfaY4
++j/X8aTjqhxQ2RtdAQAAAABQ4njjko2MIkScpAWSTDH182/9rxixRFIPSc0lDZb0u1xjfdoYc5en
+OqxStoqnmgIAAAAA4IJS3DMkxklaK2mttTbRGFNL0k5Pd2KM6Sjp73IVI+ZK6mn/t7rkemPMV5LW
+S6opaYIx5lNr7bHC9pfRcoBfcb98AAAAAACUTsU6Q8JaO85aO89am1ic/Uh6KP1niqT77Dm3urDW
+HpL0WPrDcEkemSWR1x01jCmuiSAAAAAAAJR8Jf4uG8aYcpI6yTU7YpG1dk8uoV9IOp6+f7Mn+rbF
+sxwGAAAAAAClXokvSEi6UlJQ+v7y3IKstWcl/VeuNSyuNMb4F7XjvGZIAAAAAACA3JWGgsTlmfa3
+5ROb8XyApEuK2jEzJAAAAAAAKJzSUJConmk/Pp/Y3Zn2axS2QyZGAAAAAABQNKWhIFE+0/7JfGL/
+yrRfrqgdc8kGAAAAAACFUxoKEiGZ9pPziU3KtB9a1I471u6Y63NG3GUDAAAAAIDclIaCxJlM+0G5
+RrkEZ9o/XdSOX7vhtaI2AQAAAADABSnA1wl4wIlM+/ldhlE2035+l3fkIll7927Qhg3pD3O5yei+
+vfsUeXFk4boAAAAAAJRae/fu1d69e/ONS07O7yKAkq00FCQyL2RZXdKG3AKVdSHL3blG5SlR773X
+VO+9l3fUjEozNPG5iYXrAgAAAABQak2dOlXjxo3zdRo+VxoKElsz7f9N0tw8Yv+W/jNF0vbCdVdZ
+gwbN1333uR41ndo0x6gBgwYUrnkAAAAAQKk2ZMgQdevWLd+4zp07KzEx0QsZ+UZpKEislWsxy0BJ
+7SXlOC3BGBMoqaUkK2mttTa1cN0FKSoqVrGx6Q+r5RwVFR1VuOYBAAAAAKVadHS0oqOj840LCspv
+mcSSrcQvammtPSlpsSQj6RpjTC4lAt0iKSx9/wtv5AYAAAAAAHJ23hckjDEDjDFp6duYXMJeSv8Z
+IGmKMSbLuIwxlSS9mP7wqKRpRcnJWjdi5EYQAAAAAAAXqGK9ZMMY01pS3UyHKmXar2uMybLQgrV2
+Rh7N5foJ31q71BjzkaTbJXWXtNAY86pc98BoJOkJSTXT23jUWnusQAMBAAAAAAAe9f/s3Xd0VVXi
+9vFnJyQE6QIq0lQURcAC2AbQsTPYu1hAVAZ9dcQC6oAj4m907FhHYVTsYh0LjKCogChKGwUVkCJS
+JiAKBAjp2e8fJ+Xe3H7uOfcm4ftZKyvn7LvP3juAs+Y82cXvPSSulhRud0cjqW/FVyUrKVogEcuV
+kvgLo9EAACAASURBVJpKGiDpj5KOr9F2maS7rbVJzY6oqW2TtsrdEXpci41nGgUAAAAAALuoVCzZ
+sAl8RWsjeifWFlprz5B0qaRPJG2UVCRpjaRXJfW11v6f+x8jvNlXzva6SQAAAAAA6j1fZ0hYa4dI
+GpJkGy8qgZkT1tpJkiYl02ci9mu5n5pkN9GO4h3B42APCQAAAAAAIqr1m1rWRjVXY5Tb8vQMBAAA
+AACAOopAwgPhAomffv8pDSMBAAAAAKBuIJDwQLhAYvyC8WkYCQAAAAAAdQOBhAfGnToupIxlHAAA
+AAAAREYg4YGurbuGlBFIAAAAAAAQGYGEC/FsakkgAQAAAABAZAQSHgh3xCeBBAAAAAAAkRFIeKBD
+sw4hZbbmNAoAAAAAAFCFQMIDB7Y+MKSszJalYSQAAAAAANQNBBI+WbRxUbqHAAAAAABArUUg4UI8
+qzFWbVnl/0AAAAAAAKijCCQAAAAAAEDKEUgAAAAAAICUI5AAAAAAAAApRyABAAAAAABSjkDChXg2
+tQQAAAAAAJERSAAAAAAAgJQjkPDIPi32SfcQAAAAAACoMwgkAAAAAABAyhFIuMAeEgAAAAAAJIdA
+AgAAAAAApByBBAAAAAAASDkCCQAAAAAAkHIEEh45uM3BIWVbCrakYSQAAAAAANR+BBIuhNvUcvzp
+40PK9n1sX20r2paCEQEAAAAAULcQSHikfbP2IWV5RXl6au5TaRgNAAAAAAC1G4GEz4rKitI9BAAA
+AAAAah0CCQAAAAAAkHIEEgAAAAAAIOUIJFwIt6ll5LoJVAYAAAAAYBdBIAEAAAAAAFKOQMJn5bY8
+3UMAAAAAAKDWIZDwGadsAAAAAAAQikDCZ+whAQAAAABAKAIJF0pKwpe/dPZLIWUs2QAAAAAAIBSB
+hAtFEVZhdN69c0hZmS3zeTQAAAAAANQ9BBIuRAokdhTvCCkrKyeQAAAAAACgJgIJFwoLw5fvv/v+
+IWXMkAAAAAAAIBSBhAuRAon9Wu4XUsYMCQAAAAAAQhFIuBApkAiHGRIAAAAAAIQikHAh0h4S4XDK
+BgAAAAAAoQgkXGCGBAAAAAAAySGQcCGhQII9JAAAAAAACEEg4UIiSzaYIQEAAAAAQCgCCRcSmSFx
+VLuj/BsIAAAAAAB1FIGEC4kEEjkNcvwbCAAAAAAAdRSBhAucsgEAAAAAQHIIJFxIZIYEgQQAAAAA
+AKEIJFwoLJSsDf9ZowaNgu4JJAAAAAAACEUg4UJ5uVRaGv6zFjktgusSSAAAAAAAEIJAwqVI+0i8
+cf4bQfcEEgAAAAAAhCKQcCnSPhL9OvULuieQAAAAAAAgFIGES9E2tmy9W+uqawIJAAAAAABCEUi4
+FO3ozwmnT6i6Xr9tfQpGAwAAAABA3UIg4dIXX0T+7IdNP1RdPz738RSMBgAAAACAuoVAwqUhQyJ/
+1q1Nt9QNBAAAAACAOihlgYQxpqMx5mFjzBJjzA5jzO/GmLnGmBHGmEYe9dHNGPOEMWaRMSbPGFNk
+jPnVGPO5MeYmY0wTL/qJJXBjy1M7n5qKLgEAAAAAqFMapKITY8wZkl6W1EySrShuJKmXpN6SrjbG
+nGatXZlEH7dJ+rukzIA+JKmVpOMqvm40xpxprf3ObT/xaNawWdV1fkm+n10BAAAAAFAn+T5Dwhhz
+uKRJkppK2i5plKQ/SDpR0r/khAcHSJpsjGnsso+Bkv4h5+cpkvSIpNMkHSXpEklfVPTTQdJHxphm
+EZryRHZmtnIa5EiS8grz/OwKAAAAAIA6KRUzJB6TMxuiRNLJ1tq5AZ/NMMYsl/SgpC6SbpF0t4s+
+RgVcn2OtnRpwP1/SG8aYtyWdK2lPSVfLCS180yS7iQpLC7WjeIef3QAAAAAAUCf5OkPCGHOEpL5y
+Zic8WyOMqPSIpCWSjKThxpjMBPtoKqlbRR8La4QRgcYGXB+TSB9uNM5yJnuwZAMAAAAAgFB+L9k4
+O+D6hXAVrLVW0ksVty0kHZ9gH9kB16ui1AvcnyI7Yi2PNM6uCCSKCSQAAAAAAKjJ70Cib8X3fEkL
+otSbGXDdJ5EOrLW/S9pccbtflKqdA66XJdKHG40aOAeH5Jfky8lcAAAAAABAJb8Dia5yllKssNaW
+R6m3tMYziXpGzpKPnsaYSOds3lnxvVTSsy76SMiC3Or85b8b/ut3dwAAAAAA1Cm+BRLGmIaSWlfc
+rotW11q7Vc4sCsk5CSNR90qq3DviPWPMg8aY/saY3saYC40xn0s6T04YcZ219icXfbg26tNRsSsB
+AAAAALAL8XOGRNOA63iOmqgMJJok2pG1dqek0+WcnrFOzmkd/5E0V86Ro8dJekfSMdZa32dH1DRt
+5TQN/2i4Fm9cnOquAQAAAAColfwMJHICrovjqF8kZ9lFI5f9HS3pMjn7SNgwX6dIutoY08xl+0G6
+d0+s/uNzH9dh4w/zomsAAAAAAOo8PwOJwoDreE61aCgnOChItCNjzPmSPpX0R0nfyTndo1VFv50l
+jZKUKWmYpDnGmL0S7aO6L+d7SUn0erf+4daQsvKo22gAAAAAALDraOBj29sDruNZhtG44ns8yzuq
+GGP2kDRRTvjwvaQ+1trAUGO1pPuNMfMkfSLpIElPSLogkX6qFUtaqB07pIULI9fq3ay3u+YBAAAA
+APVabm6ucnNzY9YrLo5nsUHd5VsgYa0tMsb8Lml3Se2j1TXGtJATSFhJaxPs6uKAZ++tEUYEjucz
+Y8ynkk6SdLYxprm1Ni/BvmTtJkm9tH691KtX5HqDbhjk/OQAAAAAAAQYP368xo4dm+5hpJ2fMyQk
+6UdJ/STtb4zJiHL050EB10sS7CPwmNBY52sukBNIZEjqImlegn0pI6ONysunavfdpU8+iVxvZclK
+vTT1pUSbBwAAAADUc8OGDdOZZ54Zs17//v21adOmFIwoPfwOJGbLCSQaS+qlyAHAcQHXXybYR2nA
+dayfJyvCc3EzJltST0lSz56R621ZtcVN8wAAAACAeq5t27Zq27ZtzHrZ2fFsx1h3+bmppSS9F3A9
+JFwFY4yRNKjidqukzxPs4+eA674x6h5b8d3K2VsiYfFuamll3TQPAAAAAMAuwddAwlo7T9IXco7z
+vMoYc1SYaiPkLLuwkh611pYFfmiMGWyMKa/4ujPM81MqnpWkO4wxe4cbizHmz5Iqd5qcY611NYUh
+7kDCEkgAAAAAABCJ30s2JGm4nGUYjSR9Yoy5V84siEaSBkoaWlFvmaRHorQT9g3fWrvMGDNR0pVy
+Ns/8rzHmUTlByHZJHSr6GVjxSKmcY0CTEiuQ6Nqma/QKAAAAAADswnwPJKy13xpjLpT0iqRmku6t
+WUVOGHGatTbfZTf/T9Juki6S1FrSPeGGIilf0lBr7Rcu+6maIVFWJllbfV9T+2ZRDxYBAAAAAGCX
+5vceEpIka+0USYdIGicnfMiXtEXOJpe3Supprf05cgvRN2Sw1hZbay+RdIKklyr62CGpRNLvkr6S
+dLekg6y1byT301SLNUuiV9so54ICAAAAALALS8WSDUmStXatnP0iRiT43IuSXoyz7kxJMxMfXfwC
+Z0T89JPUvXvkutmZ9XtHVAAAAAAA3ErJDIn6pKio+vqii6LXbdigob+DAQAAAACgjiKQSMKPP0b/
+nBkSAAAAAACERyDhIwIJAAAAAADCI5DwEYEEAAAAAADhEUj4qFGDRukeAgAAAAAAtRKBhI8yTOgf
+r7VRTzAFAAAAAGCXQCDho3Xb1oWUldvyNIwEAAAAAIDahUDCR8aYkLKthVvTMBIAAAAAAGoXAgkf
+Dew+MKRs7MyxaRgJAAAAAAC1C4GEjzJNZkjZ2m1r0zASAAAAAABqFwIJHy3IXRBSVlpemoaRAAAA
+AABQuxBIJKhFi+rrPn2i172o20UhZQQSAAAAAAAQSCQsJ6f6um3b6HUP3evQkDICCQAAAAAACCSS
+UlYW/fOm2U1DyggkAAAAAAAgkEhY4EmesQKJcMd+EkgAAAAAAEAgkZRYgYQknX/w+cHPlMfxEAAA
+AAAA9RyBRBLKy2PXWZi7MOieGRIAAAAAABBIJCyRJRuSlGkyg+4JJAAAAAAAIJBISjyBxG5ZuwXd
+E0gAAAAAAEAgkZR4AolmDZsF3RNIAAAAAABAIJGwRJdsNM9pHnRPIAEAAAAAAIGEK5kV20IUFMSu
+e9iehwXdE0gAAAAAAEAg4UqTJs737dtj1x3Vb1TQfZnl2E8AAAAAAAgkXGja1PkeTyDRKKtR0D0z
+JAAAAAAAIJBwpXKGxI4diT9LIAEAAAAAAIGEK5UzJHbskKxN7NmycpZsAAAAAABAIOFCZSBRXi7t
+3JnYs8yQAAAAAACAQMKVyiUbUuLLNvbffX9vBwMAAAAAQB1EIOFCRsCf2ubNseu/eu6rVdd9OvTx
+YUQAAAAAANQtBBIuvPde9fX48bHrd2nVpeqaYz8BAAAAACCQcKVdu+rreI7+zDSZVddsagkAAAAA
+AIGEK3fdVX39/POx62dmBAQSzJAAAAAAAIBAwo3ATS3jwQwJAAAAAACCEUi4kOhRn8yQAAAAAAAg
+GIGEC+ecE3y/fHn0+kEzJAgkAAAAAAAgkHCj5pKNfv2i1w+cITFj9QzvBwQAAAAAQB1DIOFCgwbB
+9xs3Rq8fOENi3bZ1stb6MCoAAAAAAOoOAgkXjEms/p5N9gy6X5C7wMPRAAAAAABQ9xBIeGT9+sif
+5TTICbp/9OtHfR4NAAAAAAC1G4GESzU3trzzzuj1j2x3ZNV1jz16+DAiAAAAAADqDgIJl2ruI1Fc
+HL3+Hf3uqLq+/dPbfRgRAAAAAAB1B4GES5MnB9/H2lei5nGfs9fM9nhEAAAAAADUHQQSLhUUBN/H
+CiT2a7lf0P3S35Z6PCIAAAAAAOoOAgmXTj89+D5WIHHInocE3ReUFESoCQAAAABA/Ucg4dLQocH3
+U6Yk9ny5LfduMAAAAAAA1DEEEi717x98/9tviT1vZb0bDAAAAAAAdQyBhEvZ2ck9by2BBAAAAABg
+10UgkSbMkAAAAAAA7MoIJFKoXdN2VdeNsxqncSQAAAAAAKQXgUQK3XfSfVXXJeUlaRwJAAAAAADp
+RSCRhBtuSKx+dmb1xhPFZcUejwYAAAAAgLqDQCIJXbsmVj8rI6vqevJPkz0eDQAAAAAAdQeBRBIW
+LQq+X7cuev2FuQurrj9f/bkPIwIAAAAAoG4gkEjCNdcE3//5z9Hr5xXl+TcYAAAAAADqEAKJJHTr
+Fnw/c2b0+oMPHRx0/+OmHz0eEQAAAAAAdUPKAgljTEdjzMPGmCXGmB3GmN+NMXONMSOMMY087utc
+Y8ybxphVxpidFX39aIx52RhzhTHGeNFPZmb0+5oO2+uwoPvz3jzPi2EAAAAAAFDnNEhFJ8aYMyS9
+LKmZJFtR3EhSL0m9JV1tjDnNWrsyyX46SHpNUp+AfiSpoaQWkg6SdImkdyVtS6avcLZvj/55ZkZw
+YrH0t6VeDwEAAAAAgDrB90DCGHO4pEmSciRtl3SvpBlyAomLJQ2VdICkycaY3tbafJf9tJc0U9I+
+kkrlBCCTJf0iZybIvpJOknS2+58GAAAAAAB4IRUzJB6TEz6USDrZWjs34LMZxpjlkh6U1EXSLZLu
+dtnPq3LCiM2S/mStnVfj8/mS3jLGXGutLXfZR1TZ2YnVb7NbGz+GAQAAAABArefrHhLGmCMk9ZWz
+fOLZGmFEpUckLZFkJA03xsTYiSFsP5dJ6lfRz9AwYUQVr8OIWbOqr485JrFn+3bs6+VQAAAAAACo
+M/ze1DJwecQL4SpYa62klypuW0g63kU/11V8X2at/beL510rLq6+jnXKBgAAAAAAcPgdSFROAciX
+tCBKvcBX+T6JdFCxkeVRcmZHfBhQ3sAY08kY094Y49vSlMaN3T9bWl7q3UAAAAAAAKhD/A4kusoJ
+ClbEWCoReNxE1wT7OCrgerExZk9jzERJWyX9LGmNpK3GmHeNMYcm2HZMRx8dfF+ewIKQnSU7vR0M
+AAAAAAB1hG+BhDGmoaTWFbfrotW11m6VM4tCkjok2NXBAdetJC2WNFjORpq24quRnOUjc40xlybY
+fkLOPFN6/PH46uaXuDpQJK2W/bZM24o8PzEVAAAAALCL8XOGRNOA6x1x1K98O2+SYD+7B1z/Q04o
+8ZKkHpIaSmov6a+SiiRlSXqu4ihSz/QN2JtyyhRp+HBp2bLwdW8++uaq64aZDZPu21qrGz66Qce/
+eLxWbVmVdHvRvPXDWzroqYPU5YkuKiwt9LUvAAAAAED95mcgkRNwXRyxVrUiOSdtNEqwn8BdHBpK
+es5ae4W19kdrbam1Ntda+4CkIRV1siT9PcE+opo9O7Rs6dLQMkn6a7+/Vl0bY5Lu+9OfP9UTc5/Q
+jNUzdOFbFybdXjQXvu20vzF/o9764S1f+wIAAAAA1G9+BhKBv0LPjqN+QznLKwpc9mMklUoaHa6S
+tXaSpPkV9U4xxjRLsJ+ImoSZ05ER4U+29W6tq65nrJ6RdN8/bvqx6npBbrR9Q71VUl6Ssr4AAAAA
+APWPb6dPSNoecB3PMozKmQ7xLO8I14+V9K21dlOUutMk9ZYTxPSS9HmCfam4uFgLFy4MKjvpJOm9
+94LrbdnSVlLbmO2VlpeqQYb7vwbn1NTUS1e/AAAAAFDX5ebmKjc3N2a94uJ4FhvUXb4FEtbaImPM
+73L2eGgfra4xpoWcQMJKWptgV2sjXMeq2ybBfiRJmzZtUq9evWLWmzp1jAYNuitmvc0Fm7VH4z3c
+DEWSZOUuGFiYu1Dv/PiOrjz8SnXevbPr/gEAAAAAiRk/frzGjh2b7mGknZ8zJCTpR0n9JO1vjMmI
+cvTnQQHXSxLs44eA68wYdQM/L02wH0lSmzZtNHXq1KCyrVulE08MrjdgQOzZEZL0xS9f6LyDz3Mz
+FEnuZyr0muCEKi8teklrb0o0A3IfhAAAAADArm7YsGE688wzY9br37+/Nm2KtgigbvM7kJgtJ5Bo
+LGeJxLwI9Y4LuP4ywT7my9l3IkfSfjHqBk4FWJ9gP5Kk7Oxs9ezZM6isJMx2CrvvHloWzvlvna8V
+f1nhepZCssHAum1RT2SN3C9LNgAAAADAlbZt26pt29i/xM7Ojmc7xrrLz00tJSlwZ4Uh4SoY56iJ
+QRW3W5Xgvg7W2p2SpsrZrLKbMSbsm31FP2dV3O6UtDBcPTeyskLLIm1qKUnXHXFd0P3ds+523Xfa
+9pBghgQAAAAAIAm+BhLW2nmSvpATFlxljDkqTLURkrrK2T/iUWttWeCHxpjBxpjyiq87I3R1X2V1
+SU8ZY8LN/BgtZ4aElfS8tdbXYyKinejZvlnULTUSUh5xFYy/mCEBAAAAAEiG3zMkJGm4nCUVWZI+
+Mcbcbow5yhjzR2PMeEn3V9RbJumRKO1EfAOuCD6eUsWRnpJmG2MuMsYcbow5xRjzsqTKaQhrJPm+
+e0i0QKJ5w+bBdRVcOZGXfWYqAAAAAADqIt8DCWvtt5IulJQnZy+JeyXNkfSZpKFygoalkk6z1uYn
+0dVwSS9WtHeEpNclLZCznOPSivKfJJ1srd2cRD9xiZYptMhpEXS/tXBr1fWoT0epzYNtNOn7SXH2
+w5INAAAAAEDdk4oZErLWTpF0iKRxcmZC5EvaImeTy1sl9bTW/hytiTj6KLfWDpH0J0nvSFonqUjS
+75JmyQksDrHWrkjiR4nossuC78vKwteTpOY5wTMk3l/2viQnXPjH7H/o94LfNfCdgXH1m65ggCUb
+AAAAAIBk+H3KRhVr7Vo5+0WMSPC5F+XMfIi3/seSPk5sdMm77DLplVeq70ujHCqalRFmF0y5CxfS
+tocEMyQAAAAAAElIyQyJXUGXLsH30WZIlNnwH9acdfDR8o9i9stMBQAAAABAXUQg4ZE99gi+jxZI
+ZGeGP0u25myHAa8N0Nq8tVH7ZckGAAAAAKAuIpDwSOPGwffRAonjOh0Xtjzc8osv1nwRtV83wYAX
+YQJLNgAAAAAAySCQ8NAVV1RfRwskMjMy9Vj/x4LKSstLwwYSscKDdO07wQwJAAAAAEAyCCQ81Lt3
+9fXvv0eve3Cbg4PuOz/eWduKtiXcp6sZEsxuAAAAAACkGYGEhzIzq69vuCF63YNaHxR0vyZvjfpO
+7BtSL1Z44CZcYMkGAAAAACDdCCQ89OWX8ddt36x9SNmKzStCymKFB26WX3gRJrBkAwAAAACQDAIJ
+D7Vs6X2bMWdIsKklAAAAAKAOIpDw0I03et9mbd3UEgAAAACAZBBIeKhJk+D7H35Ivs3AwMFaq5mr
+Z+ql715SXmFeVVkybboeF0s2AAAAAABJIJDwUJs2wffdu0s7d0au/8b5byTU/qTvJ+mPL/5Rg98b
+rMPGHyZrrUrKSxIeJ2ECAAAAACDdCCQ8ZExo2TPPRK5/dPujY7ZprZW1VlN+mqJL3r2kqnz11tXa
+UrhFBSUFCY/TkxkS7CEBAAAAAEgCgYTP8vIifxbPTIVFGxfpmfnP6PTXTw/5zMho35b7JjwmTza1
+ZJYFAAAAACAJDdI9gPquPMn9Ix/95tHo7bvYoNKLTS2ZIQEAAAAASAYzJDx26aXB99EmEnRs3jHp
+/hINFwpKCggTAAAAAABpRyDhsZqBREmUPSdNuE0nElRWXhZ33Re+fUHN72uuQf8elHS/LNkAAAAA
+ACSDQMJjjRoF3xcWRq/fvGHzpPpLZIbEkPeHqKS8RB/+9GFSfUos2QAAAAAAJIdAwmPNmgXfFxVF
+r//J5Z8k1V+ZjX+GhJeYIQEAAAAASAaBhMcOOCD4PtYMiSPaHaHfRv7mur9IMyRGfzpaZ086W2vz
+1rpuOxpmSAAAAAAAksEpGx5r2jT4PtYMCUlqkdPCVV/LNy8P2UPi01Wf6qSXT6q631K4RTOvmOmq
+fQAAAAAA/MIMCZ81aSIVF0tffy2Vloavk5mR6arto549SoWlwVMwAsMISZr1yyxXbcfixdGhAAAA
+AIBdF4GED667rvq6WzfpwgulY46R/vxn7/v6afNP3jcah0RO9wAAAAAAoCYCCR8cf3z1dUmJ9P77
+zvXEid73la6ZCqXlEaZ7AAAAAAAQBwIJH2RlVV8XF8f3zEn7nRS7UhjxBhJen4pBIAEAAAAASAaB
+hA9atqy+3rw5vmdeOecVV33Fu3Ri6oqprtqPhEACAAAAAJAMAgkf7LVX9XVubnzP7NlkT1d9xTtD
+YsXmFa7aj4RAAgAAAACQDAIJH7RtW329YYO/fZXZ9Gwu+eXaL7Umb01a+gYAAAAA1H0EEj5o0qT6
++vPP438uKyMrdqUavDjt4tf8XxN+5pv136j3hN6ctgEAAAAAcIVAohY5sPWBCT9TUl6SdL/FZXHu
+vFnDpp2btDF/Y9L9AwAAAAB2PQQStcgb57+R8DNfrf0qrnpWkU/Z8OIEjvXb1uu4F47TwHcGMmsC
+AAAAABATgYRPcnLCl0d79z+4zcHKzsz2Z0BRfLDsg6jLNn7N/zViaFFZPvTDoZr1yyxN+n6SHv/m
+cS3euNiXsW7K36STXz5Z5795vkrKIs8OWbl5pcxYIzPW6LJ3L9O4OeM8P/oUAAAAAOAegYRPHnoo
+fPlbb0V/bshhQ7wfTAzXf3S9jp14bNgX9gkLJmivh/bSgNcGhH22cubFRys+qiq7+eObdcgzh+iV
+Re6OMpWkvMI8TVgwQUt/WxpUfuO0GzV91XS9s+QdPTn3yYjP7//E/lXXry5+VTd/fLPnR58CAAAA
+ANwjkPDJnhFO8fzgg+jPpeu3+Mt+X6ZtRdtCyodNHiYrG/FlPtp4L//35a7Hc/RzR2vY5GHq+lTX
+oJkQn6z8pOp6fu78hNr8YFmMP3wAAAAAQMoQSPikU6fw5bHyhmh7PfjNGONr+88tfE6jPh0VNvio
+KXBmxIYd1WenBo4xUhhSWFoYttzvnw8AAAAAED8CCZ907x6+PB0TIHK352r41OEx6xn598I+b/08
+Xf3h1frH7H9o1KejEnp2R/GOquvAMYYLb5ZsWqJG9zQK2048P983677R5f++XDNWz0hojAAAAACA
+xBBI+KRR+HfimIFEr7a9PB/L6a+f7nmbleKd0TFt5bSq66fmPaXtRdu1tXBrXEeOBs54yDDV/2TD
+zZDoO7FvxHbimSFx9HNH65VFr+j4F4/Xum3rYtYHAAAAALhDIJFisQKJq3pepUt7XOppnwtzF8ZV
+z81ykXJbHle9wCBBko569ii1fbitDnjiAO0s2Rl3H4GhQri+NxdsjthOojNA+r/SP6H6AAAAAID4
+EUikWHmM9/cGGQ30yrmv6Kajb0rNgALEGy64eaZmGLDktyUqLC3Umrw1emb+M3H3ETRDIsEAJdE9
+JH7Y9ENC9QEAAAAA8WuQ7gHsakpL46u3tXCrvwMJw80JH4s2Loq5SeVXa7/SqM8i7xuRV5gX9fmg
+GRKKvallJH7ukQEAAAAASAwzJFKsrCy+eumYITHyk5Exw4GaznnjHA1+b3DUOn2e7xP188D9JcKJ
+NAvD7xkSySotL03bMa4AAAAAUNsRSPioR4/QsnhnSPTYs4fuOeEebwcUw3P/fU63fnJrws/lF+cn
+1e83678Juq/5Eh9xyUYtniGx9Lel6jCugw595lAVlBSkrF8AAAAAqCsIJHx0332hZfHOkJCkUf1G
+6ZZjbvFuQHGYsHBCwqdLxNqUMlE1Z0T0ndhXX639StuLtuuXvF8i1qtNLnnnEm3YsUGLf12sR79+
+NN3DAQAAAIBahz0kfNS6dWhZIoGEJD10ykN66JSHZMam7rf7HcZ10PH7HK8DWx0YV/3cHbkRP7vg
+rQviamPJpiV6ev7TurDbhbrz8ztDPg+37CMwnNhetF3LNy+P2seWwi1RP/cy4Pj+1++rrjk+FAAA
+AABCEUj4qF270LJEA4l0+Xz15/p89edJt/P2j2/HVe/gfx4sSXpi7hNxt72lwAkYysrLdPj446wh
+PAAAIABJREFUw7Vyy8qo9WNtFFpSVhJ337EELi2pzTM5AAAAACBdWLLho913Dy2Ldw+JmrbetlV/
+7vlnfTjwQ13Y7cLkBlZPVG5quSB3QcwwQnI2mdxSsEXXTblOj8x5JOTzknLvAonADTQT3XwTAAAA
+AHYFzJDwUU5OaJnbQKJ5TnONP2O8JGnfFvvqzR/eTGJk9UPlppZl5fFNO7GyuuXjWzTx24mSpOKy
+Yn274VsN7TlUJ+53YtgZEh8t/0indD5FmRmZVX2O+HiEVuet1lMDntJeTfYK2xczJAAAAAAgOgIJ
+H4U7ZdJtIBGo2x7dkm+kHkj0Rf+MLmfo2inXVt3/9dO/SpLe+OEN2TFWxWXFIc8MeG2Aeu/dW19e
++aUaZDRQh3Ed9L/t/5PkzLh4/+L3q+pOXzVdN069UZf2uDToRA8CCQAAAAAIRSDhswMPlJYtq773
+ag+JDJOxy7/ort++Xl+t/Sru4zwbNWgU9fNISzbm/2++npz7pFo1alUVRkjSB8s+CKp38ssnS5JG
+fTZKDTKq/9OqnMlRWFqox75+THs12UuDDxsc15gBAAAAoL5iDwmfBYYRkneBxKi+o7xpqI7r83wf
+Xf3h1XHVjbWXQ7RNLW/5+BbNXjM77nGVlldPhSmXExw98OUDuv3T23XF+1foi1++iLstAAAAAKiP
+CCRSzKtAYnvxdtfP9mrby5tB1BI/bvoxrnpTlk+J+nm4JRtuZZrMquvKmSx3z7y7quytH9/yrC8A
+AAAAqIsIJHz2t78F33uxh4Qk9dijR/VNURPpLitNflqStF/L/VTytxLZMeFnBMz/83xvBlHHRDuC
+1Fob85SNnaU74+4r8JSNykCCjS4BAAAAoBqBhM8GDQq+92qGxKBDB+ncrueqfXk/6fEVTuH8YVp0
+zSJ9d813VXsYtNmtTdBzf+3rbOR4R787IrZ9audTvRlkHfLxyo/V4+keUeu8tvi1uNsLt6ll0FGg
+tjosKiot0n+W/0dbC7fG3T4AAAAA1HUEEj5r2DD43qsZElmZWXrnwnc0qHSWlL9nRalRjz17qEl2
+k6p6o/oF7zVx6J6HSpLWvFJj6kaFW465RVMuib60oT7q/2p/39quDB8izZAYPnW4TnvtNP3p1T/5
+NgYAAAAAqG0IJHy2227B917NkKgU7mjRQJ2adwq6P7r90ZKklyZmS7/0Dak/tOdQZWZkavXw1Wqa
+3TR8o/9+UU+236q9muwVsd+B3QdGH9guJK8oT+u3rY94FOj4BeMlSV+v+zrlYwMAAACAdCGQ8Fmr
+VsH3qQ4kzjzwTHVu2VmSNPyo4erUopOKiio+fGm6NPu2qrqtd2utfVrsI0nq1KKTtv11m5Zdv0z5
+o/J157F3OpU2dpe+G6TdMpor95bciP1ef+T1bn+kOuWuGXfFrPOf5f9Rx0c7qqC0oKos1okfAAAA
+AFDfEUikmFdLNirFCiQyMzK1/C/LlT8qX4/2f1SStGNHxYdlDaXp92nzrZv1WP/HNO2yaWrYIHiN
+SZdWXbRb1m4ae/xYZ+PMpxfHHNOVh12pP3T4g0b+YWTIZ52ad9IvN/4SVDb10ql1dkbF2JljlV+c
+H1Jec4PMmptYBu4hEU85AAAAANQ3DVLVkTGmo6ThkgZI6iCpSNJKSW9KespaWxDlcbd97iVpiaTm
+FUUzrLUneN1PIkqiH+SQsIw4IiVjjHbLql47UvOdt2WjlrrhqBtc9T/tsmk64/UzdGCrA7Vw2ELl
+F+ereY7zx/3AyQ/ogZMfUElZicpsmQpKCtS0YVNtK9oW1Map+5+qYzsdq9e/fz2ovGvrrlry2xJX
+40qlGatn6KDWByX0TKRTNqxs0NIOAAAAAKivUhJIGGPOkPSypGZS1Vz1RpJ6Seot6WpjzGnW2pUe
+d/2knDCi1vzaefNm6YwzpA8+iD27IR5u2vDyl/CndD5Fm0ZuUuOsxsrMyKwKIwJlZWYpS1nKaZAj
+SVXfA2VnZgfdT7lkigYcMEBTfpqi018/3bsB+8DN+NZtXxe23For8ggAAAAAuwLfl2wYYw6XNElS
+U0nbJY2S9AdJJ0r6l5yw4ABJk40xjT3s9wxJ50raqDS/4j3/fPD95MnSJ59407YXoUaymjVspsyM
+zLjrhwskMjMy1TjL+es/Yd8TNOCAAZKk07qcpuuOuM6bgdYiH6/8OGx54D4TicjdnqvSco/XAwEA
+AACAj1Kxh8RjcmZDlEo62Vp7v7X2G2vtDGvtNZJulRMYdJF0ixcdVgQbT8kJO0Z40WYyunQJLduw
+wZu241myUVO6tynIMBm67JDLZGT0yCmPVJXP//N8jTt1nF4797Wg+veeeK+G9hxadZ+VkaUJp08I
+abd5w+aadN4kLfzzQg0/arhG9xutTBN/UFIbvPTdS1XXzy58Vue/eb42F2yO+sybP7ypdo+0U8/x
+PSMuBQEAAACA2sbXJRvGmCMk9ZUTDDxrrZ0bptojkq6U1FXScGPMPdbaZM+i+Iek9pI+tda+aox5
+Ocn2klLzpA1JKvfovbEuBhKS9PI5L+vx/o+rZaOWVWUHtT4o7F4MzRo204QzJmj86eO1IHeBurXp
+pkZZjdRqt1Y6783zJEnX9r5WD5z8gJpkN5EkHd72cEnS30/4u8zYWjCNJIwdxTtCZovkFeapoKRA
+S39bqqEfOiHMO0vekR0T+S/torcvkiQt/nWxZv0yS3/c54++jRkAAAAAvOL3HhJnB1y/EK6CtdYa
+Y16SEyK0kHS8pOluOzTGHCnp/8nZNPNat+14ac89Q8u8CiRqw5INtwLDiHgYY9R7795V9+ccdI7u
+OeEebSnYorHHjw3auLMuuOOzO3T4XocHlY36bJRGfTZKLXMS+7OpVFRaFLsSAAAAANQCfi/Z6Fvx
+PV/Sgij1ZgZc93HbmTEmU86+FEbSfdbaFW7b8lKzZqFl6QwkasMMCS8YYzSq3yg9eMqDUcOIKw67
+Iu429999f71w1gvJDy4Oj33zmK54/4qwn20p3OKqTVPxD6KotEgL/rcgaAnHzpKdISecAAAAAEC6
++B1IdJWzXGOFtVEXty+t8YxbIyX1kLRczoyLWiEzzDYG6Vyysat57szndO8J9+qFs15Qv479qsov
+7n6xpl02LajuZ4M+03kHn5fqIXrq0ncvVc49Oer9r966ffrtkqRN+ZvUYVwH7f3w3lr629IYLQAA
+AACA/3xbsmGMaSiptZxAIvwZhxWstVuNMfmSdpPUwWV/nSX9raK/66y1xW7a8cvFF0uTJlXfezVL
+IV0zJOrSUpEMk6G/9vurJGnwYYOVV5inxtmN1SCjgay1mj1ktqatnKZTOp+iDs2df37zh87XrF9m
+qf/+/fXk3Cf1z/n/TOePoJWbV6rz7p1j1pu3fp5eW1y9KeiDXz2oB05+QKM/G121Oebg9wbrm6u/
+8W2sAAAAABAPP/eQaBpwvSOO+pWBRBOX/T0jKUfSJGvtpy7b8E2TGj/V9u3etJuuYKAuL/tontO8
+6toYoz4d+6hPx+CVQr327qVee/eSJD112lMa8YcR2u/x/ULaate0ndZvX+/vgCVtLdwaV71IJ3L8
+tvO3qusNOzw64gUAAAAAkuDnhP/A4wPima1QJGfvh0aJdmSMGSTpREnbJN2c6POpsMcewfejRnnT
+7q68h0Qq7dtyX5X+rVTld5arTwcnvLjlmFu09qa1Kr+zXDtH7dS22/3bn6G4LL4JP9mZ2THrGNWh
+6S0AAAAA6i0/A4nCgOvYb0lSQznLLQoS6cQY00rSQxXPjrLWbkzk+VQ5+ujg+5ISaVuY99dXXpFO
+PVX66qv42k3XsZ91acmGVzIzMmWM0RdDvtD2v27XQ6c8JGOMjDFqlNVITRs2jd2IS394/g9Vx3tG
+EymQsCKFAgAAAFC7+LlkI3BRQjzLMBpXfI9neUegcXL2qphrrX06wWcTVlxcrIULF8as17ZtW7Vt
+27bq/k9/Cq3zf/8nPfhgcNnllzvfP/44vuCAJRupZ4xRk+zw/6QvP+RyvbzoZV/6ffOHN3VRt4s0
+8pORuvyQy9WxeceQJRp3z7o7ZjtmV0yTAAAAgFokNzdXubm5MesVF9eqrRE951sgYa0tMsb8Lml3
+Se2j1TXGtJATSFhJa+PtwxjTVtJlFc99bowJ9yvkwLevPQLq/GytnRtvX5U2bdqkXr16xaw3ZswY
+3XXXXVX3DcL8Sb/9dnAg4eYlnyUbtctL57ykiWdN1PvL3td5b3p/Wkdlm2Nnjo37mRWbV8gG/KWv
+3rpaIz4eoXtOuEfZmdmas26OurTqota7tfZ8vAAAAABCjR8/XmPHxv//6esrP2dISNKPkvpJ2t8Y
+kxHl6M+DAq6XJNB+5fx0I+m2GHWNpIMlvV5x/4KkhAOJNm3aaOrUqTHrBc6OiKRx4+B7N0eBpuvY
+T37JHllmRqbO7Xquyu4sU4bJ0HEvHKdZv8ySJK28YaU6Px77tAwvHT7+cB3b6digsofnPKw9G++p
+RlmN9JeP/iJJ2nb7tpBlJ6XlpZq2YpoOan1QXKd8AAAAAIht2LBhOvPMM2PW69+/vzZt2pSCEaWH
+34HEbDmBRGNJvSTNi1DvuIDrLxPsI57f91e+PgfWdTVPIDs7Wz179nTzqAYOlF5/vfq+YcPgz93M
+XEjXHhLMsogtwzh/OTMGz9DWwq1qnN1Y2ZnZOv/g8/X2j2+nbBw7infoP8v/E1L+6uJX9d3G76ru
+m93XTOV3OqnYyE9G6tf8X9W5ZWfdNfMuNcluotxbciMuVQEAAAAQv5pL/CPJzo5nO8a6y+/fr78X
+cD0kXAXjLGgfVHG7VdLn8TZurf3FWpsZ66uyuqSZAeVXufmBkrHbbsH3WVnB97zk10/GGLVs1LJq
+w8l/DvinHjr5oZB6Nx51Y0rHlZmRGVK2Jm+N3vrxLT0852G9vOhl3TXzLklOqDFtxbSUjg8AAABA
+/eZrIGGtnSfpCzkzFK4yxhwVptoISV3lBAaPWmvLAj80xgw2xpRXfN3p53j9VjOQ+Oab4Hs3Szbc
+hBgEH+nVpnEb3fKHW7RxRPWBMPOHzte4/uNkx1iV/K1EX1/1tWYPme3rOBbmhm7O+tnPn2nm6plh
+65eWl/o6HgAAAAC7Fr+XbEjScDnLMBpJ+sQYc6+cWRCNJA2UNLSi3jJJj0Rpp86/RvfvLz3xRHBZ
+YaGUk+Nc1wwKSkpCZ1HUFm7CEwTbo/EesmNC/1k3yGigo9o72d2iaxbpkGcOSdmYrvzgSrXMaRn2
+s7LgrBAAAAAAkuL7lojW2m8lXSgpT85eEvdKmiPpMzlhhJW0VNJp1tp8H4eS9m0YTz01tCw/4Ceu
++ZJf895aadYs6eefg8sS5cUMCQKJ1OixZw/16dAnpX1uKdwStvzyf1+uu2fGPlYUAAAAAOKRkjMa
+rLVTJB0iaZycmRD5krbI2eTyVkk9rbU/R24h6dkRNuArbTJDl+wHnVZRMyioeZLF669Lxx0nde8u
+bd0a/pl4sKll3TL7ytk6ZM/UzZKIpNyWa8yMMfpk5SfpHgoAAACAeiBlh0Zaa9daa0dYa7taa5ta
+a1tZa4+y1j5srS2M8tyLARtRuvr1bMDzJ7r/CfwRONOg5kt+cXHw/aWXOt937pSefz78M/H4PO5t
+QyMrY/Z+Sv132H/1xZAv0j0MSdKcdXPSPQQAAAAA9UDKAgk47rsv+L40YJ/AmssgunUL/jyckpLE
+x/Daa4k/UxNLNlIrw2Sob8e+evHsF9M9FN3/5f3qN7Ff0KaYa/LWaOOOjVGeAgAAAIBgBBIpdttt
+wfeBMw1qznZYs0aaPDl8O5V1//WvxMcwfXriz0TqH6k16NBBsmOs7Bir8jvLNX/o/LD1/n7836uu
+x58+3tMx7CzZqdlrZqvXhF66ZvI1uuSdS7TvY/uq46Md9fW6r/Xqole1rWibp30CAAAAqH9SccoG
+ovjvf6V27ZzrcLMOpk6Vzj47tLwyEPjpJ//GFg1LNtLPGKNee/eSHWOVV5injo921Laibfr78X/X
+6GNH66yDztI+LfZRk+wmmrpiqv699N+ej2H8guqwo7isWMc8d0zV/YhjRuiOY+9Q85zmVWVr89aq
+bdO2apDB//QAAAAAuzpmSKTZGWdIq1Y51+FmHYxP8Jfbr70m7buv9PjjyY8tGpZs1C7Nc5pr7tVz
+9eb5b2pkn5GSpO57dFeT7CaSpHcverdqZsXW27amZEwPzXlIoz4dVXX/wrcvqOOjHXXkv46UZYoN
+AAAAsMsjkKgF7rrL+Z7IS37l+1z79sHll14qrV4tDR/uxcgia9zY3/aRuANbH6gLul2g7MzsqPWa
+5zRXyd9KNPOKmb6P6Z/z/1l1PeT9IZKk/274r77d8K3vfQMAAACo3QgkaoGXX3a+R/ul8d01zhep
+rHvGGf6MqTb4+WepMOL5K7Xf779LBQXpHkV4DTIa6NhOx6pgdHoGWFoeY7dWAAAAAPUegUQtYW3k
+QOLrr6UxYyI/lw5FRf62/8Yb0n77xXfSSG00b56zN0inTlJeXrpHE1lOg5y0HCdqjEl5nwAAAABq
+FwKJNHjyydCyjAxng8tw+vULLXvoIed7ugIJvze1vPhi5/uqVdK0ae7bmTJFOu886auvvBlXvM45
+xwltNm2SHnggtX0nqm/HvrJjrNbetDbdQwEAAACwCyGQSIPBg8OX9+8fvjzcDIFNm6SPP46+6aWf
+sxhSuallcbH7Z08/XXr3XalPH+/GE48NG6qva/MMiUDtm7VXjz16pKQvI2ZIAAAAALs6Aok0aNLE
+m3ZOPTX65zk50nffedNXTfX5lI0ZM6QrrnCWXbgVuCKhLh0osejaRRrdb7SuOvwq/TriVy2+dnHS
+ba7YvELFZcGpEks2AAAAADRI9wB2Va1aOZse+u2006Q5c5xlD+ec4/TrhWiBRGmptGWL1KaNN325
+5TY0Of545/uLL7oPE+pqICFJfz/h71XXbRq30erhq7XPY/u4bu+AJw5Qu6btgsoKS+vwbqUAAAAA
+PMEMiTS5777U9LN+vbNcYehQ6aKLvGs30h4SZWXSIYdIbdtKkyd705ebX6Zv3SodcIA3/e/qOrXo
+pHcufKfqflivYdqnxT4JtbF++/qg+z7PB6+heeKbJ3Tpu5dqTd4a1+MEAAAAULcwQyJNmjdPXV9r
+K/Yq/PRT6eijvWkz0uyDKVOkJUuc6zPOSN/sgL/9zdkQM13q8gyJcM7teq7sGKuSshJlZWapsLRQ
+ez+8t7YUbnHd5gfLPlDu9ly9uvhVfbHGOenj5y0/619n/Ev/2/4/nbjficowZKYAAABAfUUgkSY9
+e6an32++8aadSIFEfr437SfLbRjhVXhQ3wKJSlmZWZKc40LnDZ2nWb/M0uTlk/XukncTbuusSWeF
+lM1ZN0c9nu4hK6vXz3tdF3e/OOkxAwAAAKid+PVjmnTunO4RJCeVm1oGvtyXl0tvvy1Nnx65fnm5
+9NNP7vq64gp3z9VUXwOJQJ1376whhw/RC2e9oBP3PdGzdq2cP7CB7wz0rM1f83/VBW9doBEfj5Ct
+r38hAAAAQB1DIJFGTz+d7hG4F2kPCb+9+aZ0wQXSySdLCxeGr3PBBdKKFe7af+kl92MLtCsEEpWa
+Nmyq6YOmy46xmnD6BE/b3la0TfPWz9O6beuSaufaKdfq7R/f1sNzHtYHyz7waHQAAAAAkkEgkUZe
+bjKZauk69vMvf6m+HjcufJ13E189AI8MPmywp+01v6+5jnz2SHUY10GbCza7bidwScnc9XO9GBoA
+AACAJBFIpFHLlukegXvpCiQCeT3zwMv2dqUZEoGyM7N1/0n3q1Ujj86XDdDqAe/bBAAAAJA+BBJp
+NnJkukfgTm0IJMJJ5uXfy2Uou2ogIUm39rlVv936m+wYq+I7irX1tq2+9VVYWuhb2wAAAAD8RSCR
+Zv/4R7pHIG3alPgz998vPftsYs/k50vr1yfeV6DAF/1wkglKvAxZduVAIlBWZpaa5zTXnKvmeN72
++0vf1+73764zXz/T87YBAAAA+I9AIs0yM6V7703vGEpKwpfv2CGtXRv5uaFDpe3bg8siBQb5+dK+
++0odO0qffeZunDWFe9FPZpaDXxt17sqBRKWj2x8tO8bqg4u921Dy7DfOVkFpgT786UMt2bTEs3YB
+AAAApAaBRC0QuFFjOmSE+VeQny/tt5/UqVP0Izbz8uLr45lnnJkY5eXOCRmJCAw5Ys2QqC2BBDMk
+whtwwICknj/ttdN08ssn67mFzwWVX/ef65JqFwAAAEDqEUjUAk2aSM89F7ueX8K95E+Y4AQI1kYP
+EEpLg+8jvXzv3Fl9XV4urVwpHX64dM453i6VqI2BBKplZmTqtXNfc/38f5b/R9NXTdfVH14dVP75
+6s+VX5yf7PAkSRt2bNCv+b960hYAAACAyAgkaokrr0xf3+FenvNrvNutWhX+2cCX+NGjpUsuia/P
+iy+Wvv1Weu896bUY76eRXu7dLNkoLpYmTgy/bIQZEqkxsMdAPfGnJ3Rbn9v03JneJXFFZUVJt7Hs
+t2XqOK6jOozroJ+3/OzBqAAAAABEQiBRi3z1VXr6jeeFuXPn8OWVL/HPP5/YXhjz51dfr1gR/3PJ
+Ltl4/HEn/DnxRGn58sSeTQSBRHTXH3m97jvpPl15+JWyY6xePPvFpNssLiuOq56J8o9o2ORhKikv
+UXFZsW6YekPSYwIAAAAQGYFELXLMMc4pFOH2dPBT797SuHHuni0pcQKFq65y3388P29urvTAA9LG
+jdHrxQoVAo9Zff31xJ5NBEs2EjPo0EGyY5JLbvKL8zVuzjjdPv12jfh4hJb/vlzltlwzV8+Mu428
+oupNUbYXbY9SEwAAAECyGqR7AAi2997O8oh99kldn+vWSTffLF17rZST45TF+0JdVCRdl+R+grH6
+slY67zxpThwnRyYSKtScucAMifT7/trv1f3p7q6e3f+J/YPu/73037qtz20aNnlY3G0YVf/FWfEX
+BwAAAPiJGRK1UKdOUq9eqe/3+++rr+N9id66VZoZ/y+gw6o5Q6Jm32edFV8YIUmjRkX+7OcaWwL4
+GUhE6weRddujm1YPX60hhw1Juq1VW1ZFDSPKbbkGvjNQZqzR7dNvT7o/AAAAAIkhkKilvv5aatYs
+tX0ecYQ0eXJiz5x4YvL91pwhkcypGxMnRv6sZqiRjhkSBQX+BR/1RacWnfT8Wc9ry21btPamtSq/
+s1zXH3G95/28tvg1Tfp+kiTp/i/v14YdG6LuLwEAAADAWwQStVSDBlJennTPPant94wz/Gk32nve
+6NHB9/HOKMjNTW4MbgOJZ5+V7rhD2h5li4FwgcTixc6SnC5dgo9BRXgtclqofbP2MsboiQFPaMbg
+GZ60a63VrF9maeQnI4PKtxZuDakHAAAAwD8EErXcyJHShAmp7XP6dOn9971p66abpLVro89cqCne
+GRKtWiU2lszM0H5++KG6v3gCiTlzpKFDnaDozjsj1wsXwFxwgbPEZdUq6eGH4x83HMftc5wmD0xw
+Ck8Y/1r4Lx33wnHasGNDUHlZefA/APaQAAAAAPxFIFHLZWU5L8B5ebHreuXkk4OP5UzGo49KHTs6
+L+HxijeQ6NKl+tra2LMOKjfsrHTPPVL37tJll8Xf75Qp1dePPhq5XrgZEoHHm/76a+y+EOq0Lqdp
+2+3bNHngZB2/z/E6Yu8jEm5j085NYcvLbXnQppYAAAAA/EUgUUc0a+a82ForXX11ukfjr3gDicqX
+fmulAQNiz5iItMSi8vjPcDMkCgriG0s0r78uHXpocPusBnCvacOmOq3Lafps8GeaO3Su7Birh09x
+ppzs02If1+1O+n6SFuQuqLrfWcK6GgAAAMBPBBJ10IQJ0kcfpXsU3rJWWr5cKiyU+vdP7NkffpCm
+TnWejeauu6J/Hi6QaNMm+BSReIOEmjMgFi0Kvk9m406EuvmYm2XHWP08/OfYlSO4d/a9QfcLcxdq
+5ur4jpB5Zv4zGv3paO0o3uG6fwAAAGBXQyBRBxnjvLRXLlMYMSLdI0re4MHOEoxGjaQvvojvmUce
+cb7Hu0Fk4JKJcMIFEvn50h//WH1fWhpfX7EQSPjnxH09OPqlwh9f/GPMOrN+maVrp1yre2ffqzGf
+j/GsbwAAAKC+I5Co4xo1ksaOTfcokvfyy4k/U1goDRkivfaaN2OItanl9u3SAw940xdLNvzzzoXv
++Nr+is0rdOsnt+rrdV9Lkib/VL3R5iNfP+Jr3wAAAEB9QiBRD+y2m/OCu3mzc0LG6aene0Sp88IL
+0mOPJd+OtdEDid9+8y6MqOwP/mie01x2jNX2v27XgAMGeN7+n179kx786kEd89wxnrcNAAAA7EoI
+JOqRli2lE0+UPvxQ2rJF6t073SOqOwoKogcSN9wgbdwYWh64N8S8ec5Smuefj91f4JKNzz6Tbr5Z
++tn99gcIo0l2E025ZIqK7yhWjz16uG7nwrcu1JXvX6mVm1dKcmZIVKp5VCgAAACA+BFI1FMtWjgv
+yJW/+S8tlXJz0z2q2uvJJ6MHEpUncdR06KHV10ceKU2bJl11Vez+KmdIlJQ4IdK4cdKBB8Y/XsQv
+KzNLi65dpHtOuMfV82/9+JYmfjtR+z+xvxZtDN6dtKC0QLbGdJfpq6Zryk9TQsoBAAAABCOQ2AVk
+ZEiZmdJee0nffpvu0dROv/wSew8JL23b5oQSq1ZVl5WUBNeZPVuaOFEqKkrduOqzUf1G6crDrkyq
+jUOfOTTofmfJTk1ePjmo7OSXT9bpr5+uj1d+nFRfAAAAQH3XIN0DQGodeqiz9ODVV6W5c6UNG6Ql
+S8IvR9iVlJa6DyTKypzAJxFvv+0ERTVZ65yismaN1K+fU/a//0mjR7sbG4I9d9Zzatesnf5v1v95
+0t7Okp1a+tvSsJ/d/PHN+mH/HzzpBwAAAKiPDNOK42OMWSepXbt27bRu3bp0D8dzS5bODaMFAAAg
+AElEQVQ4swQ+/FD65z/TPZq65emnpWuucYKEZBUWSg0bOht13nhjdTn/mXrr/2b+n+6ccWfS7fzw
+/35Qt392i/i5HcNfHAAAANxr37691q9fL0nrrbXt0z0er7FkA5Kkrl2dDRmfesp5+a1cTrD77uke
+We137bXSiBHetFW5PIMAwl9/O+5vsmNs0oFBfnG+RyMCAAAAdj0EEoho332l33+vDigWLpQmT5ZG
+jnROnUC1hx/2pp2iImnTJn/2jSgvd5amINgb579Rdd2rba+Ejgp9+8e3o35+9QdXa/229a7HBgAA
+ANRnLNmIU31fsuFGWZmzxOP776Xrr5cWLJBOOindo6rbXnlFGjIkdIPLcP+ZrlvnzGjp31867rjq
+8uefl+bMke66S2rXzinLy5N69ZJ27pS++kraZx+/foK6q6y8TJkZmbLWKuNu77LaE/Y9QZ8O+tSz
+9gAAALDrqO9LNggk4kQgEb+tW53vzZtLU6Y492eeKd19t/Tee9KwYVK3btJpp6V3nHVJuP9Mjz5a
++uYb53rVKql9e2n9emdmi+SEFDNmONc33SQ9+qhzfeqp0tSpvg+5Tpu3fp7OmnSWWu3WSiP/MFK3
+Tb9NG3ZscN1e0+ym+mDgBzp0z0PVslFLD0cKAACA+oxAApIIJPywdq3zW/5ff3VOnDjzzHSPqPYK
+959pzU00u3eX7r8/OOipfO6ss6QPPnCu99tPWrkyen8ffugsHbn8cikry/2465OVm1dq/yf2T6qN
+ljktteamNWqS3STks62FW7V+23od3OZgGS92SAUAAECdV98DCY79RNp06OB8Varcp2LmTKljR+e4
+y2bNpDvucGZbDBzoXCO87793lmoEWrxY6tEjeO+IwCNKV62SbrvNOWK0cl+QhQurw6GCAum66/wd
+d13ReffOSbexpXCLXvj2BV1/5PVB5UWlRer6VFdt2LFB404dpxuPvjFCCwAAAED9waaWqFV69nSW
+F5x3nvSXv0iDBzszKb7/Xho92tlbIfCl+5pr0jfWVMrLi69eeXnw/QknON/LyqrLAgOJc8+V3n5b
+Gj68etbEc89Vf37zzYmPtT777prv1LF5x6TaeH/Z+yFlX6z5ompJyE3TbkqqfQAAAKCuIJBAndKg
+gbN3QuXJH08/7XwvKpKKi53r8nJn74rXX5eOPz7dI/7/7J13eBRV24fvSUjovUmRJtJBKYIoiggI
+iCAdFcSCSFUBX0GxIKAifkhRelOa9A6igDRBeidAaIEUAimk92TP98chW7I92U2Bc1/XXNmZOeWZ
+yWaz5zdPcQ0lSsj8EPHx0muhUyfL7by9TffDwuRPYw+JfEZ+UefOGV5fuyZ/GgsWqanyvjoqiDzs
+NCrfiNsjb3N75O1Mj7Hn5h7O3j1L+anl0SZobLq8ieL5i7vQSoVCoVAoFAqFIm+gBAnFQ4G3tyHX
+gabBq6/CG2/A3r0GkWLWLHl+4UKZT2HmzJyzNzNUrgyFC0OhQtaTUuazEoRlzUPCUhvj8zodPPEE
+VKgAJ086b/PDSpXiVRDjBXsH7M1U/8bzGxMSFwJAj7U9OHlH3VyFQqFQKBQKxaOHyiGheCTQNJkL
+IWM+hI8/hlu3ZC6FkBDpfXH8OBw9CtOn54ipbsFYkDh3TobANGhguU1GwSI9h+vrr0svDYWBNtXb
+sL73eu4n3Kd8kfIU9S7Ky8tednqcYX8OM9m/E3OHikUruspMt+IX4cfEgxNpV70d/Rr1y2lzFAqF
+QqFQKBR5CCVIKB55qlWTm/F+nz4wbZoMBfnhB1i+HPz8cshAJ8iYQyId45ANgKefhuvXTY+Fhcnw
+DGseFHfuwOXLULeuvC/582fd3oeBnvV6muzf+989yk8tn6Uxk1KTbJ6/FXmLSQcm0aZ6G/o36u/w
+uBEJERQvUBwPzXXOcV1Xd+ViyEV+P/s77Z9oT7nC5Vw2tkKhUCgUCoXi4UaFbCgUNsifHyZMkB4U
+QkgvAiFkToV79+DKlZy20BRrgoSxh0T6fuvWpscGDpTXe/y49fEbNpQVOUqWhI0bs2brw0q5wuW4
+P+Z+lsZI0aWYHUtOS+Zy6GWEEPRa24slZ5fw9qa3aTy/Mb8e+9XumBsvb6Tc1HK0WtIKV5Z7vhhy
+Uf86ICrAZeMqFAqFQqFQKB5+lCChUDiBx4O/mGLFoFw5qF3bkGAzKUkKAlFROZdv4dtvLR/PKEgA
++Ptbbrt/v/Xx09Lg0CGZWLNnT/Pzy5dD+/Zw+LA9Sx9uShYsya7+uzLdf9LBSTw29TG0CRrDdgwj
+ICqANkvbUG9OPaYcnsKp4FP6tmfvnuXjvz4mNC7U5pg91/YkVZfKkcAjHAs6lmnbFAqFQqFQKBQK
+V5FtgoSmaVU0TftZ07TLmqbFapoWrmnacU3T/qdpWsEsjl1M07S3NE1bomnaWU3TIjVNS9Y0LUTT
+tL2apo3WNE2lsVe4FW9vmauiWDFo2lSKFJs2ySSUGXNXZDcZQzbcxYABsGcPtGqVPfPlZto/0Z4j
+A4/Yb2iBFedXcC/uHgBzT86lyowq/BfwHwBf/POFxT7hCeEOj5+QkpApuxQKhUKhUCgUCleSLYKE
+pmldgPPAKKAWUBAoATQFfgLOaJr2RCbH7giEACuAd4GGQFHAEygNtAamApc1TXspK9ehUDhLt24Q
+FycrfKR7UggBq1dnrx3G5T3dhQujAB4anq38LBv6bNDvVyhSwW1zeXl4WT0XlxzntnkVCoVCoVAo
+FIrM4nZBQtO0xsBqpEgQA4wDngPaAgsBATwJbNc0rXAmpigNeANpwF9I0eNloAnQFVjzYI7HgG2a
+pjXKyvUoFK6gb1+DOHHjhkwm+cYb7plr7Fj3jAuyEsmmTfK1PUHC1xc++sh2SMjDSPc63Zn2yjQm
+t53M7ZG32TtgLzc/vknYZ2EuncfTw5CNVCdMk4mcuXvGZF/TNJfOrVAoFAqFQqFQZIbs8JCYifSI
+SAXaCyGmCCGOCSH2CyGGAGMADek58Wkmxk8B5gHVhBCvCiF+EUIcEEKcE0LsEEK8BXz8oG0hYFqW
+r0ihcCE1aoCXF6xaJRf1Fy7IkA9X8dNPrhsrI6NHQ48ecP689YSa6bzxhvQUadPGdtsDB2QuihTz
+vI55Ek3TGNVyFJ+3+hwvTy/aVG9D9ZLVKV2oNAlfJvDve//SpVYX3nv6vSzNky5CTP53MqWmlKL3
+ut78e/tfAKISo7J8HQqFQqFQKBQKhatxqyChadozQCukh8IiIYSl/P3TgMtIUeITTdOsFB20jBBi
+rRBimBAiyEab2cDJB3O01jStlDNzKBTZSYMGMimmELIU548/wqRJMllkbmXDBnORoVs36RER/iC1
+wdmzhnPhVtIdXLsGL70kc1HMnesWU3MVBfIVoFWVVmx9cyuLui7K0lhpujT239rPuL3jiEqKYv2l
+9bz4+4ucunOKiQcnmrS1VGXDL8KPXmt7Mf3I9CzZoVAoFAqFQqFQOIq7PSS6Gb3+3VIDIb8ZL3uw
+WwJo4yZb9j/46QFUd9McCoVLKV1ahlx89RXs2iVFil2ZL97gNnQ6c0FiyxbpETFihHn7xETL4yxf
+bnj9ySeusy8v4KF5sLFP5mup9tvYjzZLzT8+x+0dx9Xwq1b7xSbHsunyJjqt7MSGyxsYvWs0V8Iy
+V8/WkVAQIYRLy47mJEmpSey8tpOIhIicNkWhUCgUCoUiT+JuQSI9134ccMpGuwNGr593ky35jV5b
+KIKoUOQN2reXwkRqKly6BM2a5bRFEBtrXWSwlMAzYxlSIWSIxqOe2qBbnW4s6bqEUc+O4u6nd1nb
+ay0Xhl5wqO+JOycsHt91YxfJackmx6YcnsKLv73I6eDTDN4+mB5re+Ab7qs/7xPik/mLsEFoXCh1
+Zteh3px63E+475Y5spPRf4/m1T9epcOKDjltikKhUCgUCkWeJJ+bx6+LDNe4LoSwFWFu/Diurpts
+af3gZwpw3U1zKBTZhqcn1K0LJx6sQ0NDoVy5nLFlxgxY5ETEgbE3hRDwyivyOho3dr1tIENEPvsM
+qlaFb77JvcKHpmm819iQS6J3/d4A6L7Rsej0Irw9vWldrTV3Y+/ScnFLh8eNT4k32f/7xt8ANF1g
+OVlJUloSyWnJ/H39b5567CmqFK9icv5OzB3KFiqLl6d5ZY/bkbf5+8bf9KjbgzKFypicG/X3KL23
+xud7PmdBlwUOX0NOcS38GmP2jKF11daMfHakybk5J+cA1sUghUKhUCgUCoVt3CZIaJqWHyiDFCQC
+bbUVQkRqmhaHTDr5uBts6Qw0emDLX0KIWFfPoVDkNGXLmla6OHBA5mPILmKd+KsyFiSOHIE9e+Tr
+jBU40tKk8JJVRo6EFSvk6+bNoVOnrI+ZnWiaxqCmg/T71UpUo0C+AiSmWnFLcQFTDk3hm/3f8FiR
+x/Af6a8XH9b5rKPv+r7UK1uPc0PM68k+v+R5gmKCWH1xNXvf2Wty7lLoJf1rY4+M3EyHFR3wi/Rj
+85XNvFbrNWqWqmmxnRBCVS9RKBQKhUKhcBJ3hmwUNXrtyFIl7sHPIq40QtO0ksCsB7tpwDeuHF+h
+yK20bi0FipgYmcehatWctsjAVaOUBsnJ1tsZ55TICuliBMChQ64ZM6e5OPSi28b20Dz4Zr/8qLwb
+e5dz9wzCQ5/1fRAIfEJ92Hdrn0k/ndARFCPzC2c8ByAwKGYaeWPx7hfpp3/9zT7r/z7C4l1bxlWh
+UCgUCoXiUcCdgkQBo9c2lhx6kpBVMAq6ygBN0zyAP4CqSO+ISUKI864aX6HICxQpAr/+CrduSYFi
+7167XVxOcLDpfufO8md8PPzvf9b7jR3rPpvyOk+UeoLoz6PdMvZan7Um++mJKHUZIu/W+awz2X9m
+4TM2xzVOZpkXvQlWXVxFqi7V4rklZ5ZkszUKhUKhUCgUeR93ChLGvsTeDrTPjxQNElxow1ygw4Nx
+twHfuXBshSJP0qaNTIjp4wPPuyuFbAbeeMPy8e+/h1M20t2GhLjHnoeFovmL0rpqa/sNnWTTlU0m
++xMOTKDKjCp4TjSNn1lwOvM5INzhIREeH+52T4V5J+dZPJ5RrMlN3Iu9h29Y3giRUSgUCoVC8Wjh
+zqSWMUavHQnDKPzgp0vyO2iaNhkYhBQjDgJ9hQtqzSUnJ3P69Gm77SpUqECFChWyOp1C4RY8PaFe
+PRm+oNPBsmXw44/g66Y1y8GDlo8bh1JYY/9+qFQJnnzS9LgQcP481KwJhQtb7PpI8Hf/v/EJ9aFy
+scr8eOhHph+d7vI5dlzb4ZJxjEM2XM3NiJvUn1MfIQQ+w3x4otQTbpln/qn5jGhuXsvWndeWFULj
+Qqk+szoJqQnsHbCXNtXdVVlboVAoFAqFMwQHBxOc0Y3YAsm24psfAtwmSAghkjRNCwdKAZVttdU0
+rQRSkBBAQFbn1jRtLDD2wXingC5CiKSsjgsQGhpK06aWM9MbM378eL799ltXTKlQuBUPD3j3XblF
+RsKECbBggQyncDeOSIRt2kCBAhAQAGWMijbMmgUffwy1a8vypx7uLmKcS8mfLz9NKjQBYFqHafzU
+/idaLGrB6WD7wml242jIxrJzy5h4YCJjnx9rkswz41iH/A9RtURVqhSvwsi/RuqTfA7ZMYTdb+92
+rfF2yK0eElMOTyEhVToedlvTjajPo3LYIoVCoVAoFADz589nwoQJOW1GjuPusp+XgBeAmpqmedgo
+/VnH6PXlrEyoadowYDJSjLgEdHRlVY2yZcvy119/2W2nvCMUeZESJWD6dLklJUGzZnDRTbkTnfFX
+SkyE99+XP/v0gQ8+kGIESK+Os2ehSRP32JnXyOeRj1MfyjiYMbvH8H///V+O2ZKQkkBBL0NaIGMv
+gr1+e1l0ehE/HvqRwU0H07FmRwKjA+lYsyPvbH4HgA+3f2hVkFh5YSVvb3obgKYVmhIYbSjmdD/h
+vjsuxybJabnz6UVSqkGLz1j+VaFQKBQKRc4xePBgunbtarddx44dCQ0NzQaLcgZ3CxKHkIJEYaAp
+YK1Yu3EQ9OHMTqZp2tvAr0gx4gbQXgjh0m+m3t7eNFErH8UjQP78cOGCYX/NGuu5IDJDsWLOlQrd
+tk3+3L0b+vc3PZdqOc+gRfJgLsVM81P7n3jnqXe4HHaZdze/S4F8BQhPCM+2+WccncEXL3yBEILE
+1EQuhpiqW4O2SbFhzJ4xjNkzBoBfO/1qd1zfMF+9GAFwKtg0EUmaLi2rpjvNrOOzmNhmYrbPa4/c
+GkqiUCgUCsWjjqMh/t7ejqRjzLu4W5DYDHzx4PV7WBAkNOm3O+DBbiRgXivOATRN6wGkpzkPANoK
+Ie5mZiyFQmFO377QsyccPSpDKJ6xXVDBLs6IERmJizPdz3p2mIeX+uXqU79cfXrV66U/JoRg3619
+lC9cnvrl6nM86DgtFrVw+dzj9o7DJ9SH3Td3U6dMHfsdgI92fmSyrxM6PDTTeJw6s22PlRMeEhGJ
+Edk+p7PklVKrCoVCoVAoHh3cGnUthDgB/Iss5zlQ0zRL33j/B9RFejXMEEKYPNrSNO0dTdN0DzaL
+ReA1TXsFWd7TA7gHtBNCZDkXhUKhMCVfPmjVSoZy3L8Pv/+eM3YY55IA2GEj52KYhaILQUEQlclQ
++rt3Ydw4+PvvzPXPDWiaxsvVX6Z+ufoANK/UHDFeMO2VaS6fa+WFlYTEhXDwtpXMpnaITZbKlU+I
+DwM2DWDzlc12+wREW/74v3H/Bh/9+RG/n/09U7bY43jQcWzlTk5JS2HW8VmsPL+S2ORYFp9ezIV7
+F6y2dwUuyOWsUCgUCoVC4TY0d39Z0TTtaWQYRkFkBY0fkF4QBYE3kZUwAK4Azwgh4jL0fwf4DSlY
+TBBCTMxwvgXwz4PxUoB3AXvf8AKFEE4tRzRNCwQqVapUicDAQLvtFYpHBSFkJYzISOjRI+fsSEqC
+jB5tx4/DCy+AcXLiNm3gwAEoWhT8/KBkSefmadcO/vlHvo6MhOLFs2Z3bkYIQcvFLTkWdCzHbBjU
+ZBC3o26z68Yup/olf5WMl6eXybHqM6tzK/IWALM6zWJ48+Em529G3KTPuj5UL1mdNb3W4KF5oE0w
+9SpoUK4BF4bKfzEZzwGs7bWW3vV7W7Tp12O/8vFfMvlJ+cLluRd3D4C4cXEU8irk8LWl6dIY/fdo
+opOj+aXjLxTNX9Rq2+E7hjPn5BwAvDy8SP46d+a6UCgUCoVCYZnKlSsTFBQEECSEsFksIi/i9rz0
+QoizQB8gCplL4gfgCLAXQ1nOK0DnjGKEg3QECiG9MLyRnhIX7GyvZ/6KFAqFMZomF/ndu0txIjBQ
+ehBkN5aqgvToYSpGAOzbJ0udRkXB/2XI9xgXJ0NT3npLChyWSBcjQAoaDzOapnH0g6OI8YLkr5Jp
+Wbllttuw8PRCp8UIgMVnFutf/3HhDwp9X0gvRgCM2Gko3ZkuzL/424ucCj7F+kvrWXNxTabs7bO+
+j9Vz3+w3OPmlixEA5++dd2qOJWeW8MvxX/j97O+M3z/eeSMVCoVCocijbLmyhaHbh+IX8ZB/CXuE
+yJZCeUKIHUAjYDrgC8QBEcicEmOAJkIIW+8qe24cwoktd9ZmUygeEipVgu+/h5QU+Oyz7Js3zUIe
+QykmWydjLoqffoK1a2HVKpg82XW2PQx4eXrx38D/uPnxTQJHBZL8VTJhn4VxYegFNve1H0aR3Qzd
+Ib+sxKfE029jP33pS2MiEyNZdWEVHhM9qPVrLYJiDG+Y8/fOExIXYtZHQ+NK2JVMhX1Y80h0Ngnn
+rpsGgWatz1qH+9kqtapQKBQKRW4nJimGbmu6Me/UPF5b9VpOm6NwEdkiSAAIIQKEEP8TQtQVQhQV
+QpQWQrQQQvwshEi00W+pEMLzwWaWwlwIMcHovCNbPiHEMvderUKhyJdPLvB1OggPlzknmjZ133zG
+nhBCwAlrNX2MyOgFsXu34bWtvBSPMtVLVqdSsUp4eXpRulBpGpRrwOt1XmfvgL05bZoZNX6pQeEf
+Cls9v//Wft7a+BYA1+5fMzn34+EfKT+1vFmf+JR4mi5oyntb3nPIBp3QsfvGbk4HnyZNWBYeUnVO
+lInBVNjImPAT4EzwGRacWqDPv6FQABy8fZCtvlvRWa3ArlAoFLkbY+/CS6GXctAShStxd5UNhULx
+iKNpUKqUfH3yJMTEQNmy1kMiMovxeCtXwttvW2+bzvz5MHeuoRRoIaMw/gTzB+pmOPPA+eZNmYyz
+WDHH++Ql2lRvgxgviEyMZPbx2Wiaxung05wKPkVgdKDTi+7sIJ+H8/8Cb0TccKhdbHIs+T3zs/nK
+ZpthHIBVocIaxgvKjF4PscmxNFkgS1Ofv3fe6bKfJ4JOMGjbIF554hV+av+TU30VuZfz987T+ndZ
+YX1d73UmVXcUCoVCITl4+yDLzi1j2DPDaFKhSU6b88igBAmFQpGtFC0KiQ98olJSpNfEBRcUGkj3
+kEhLc0yMSOfCBWjUSL42FiF0LnyIuHMnvPqqFCRu3YLC1h/a53lKFCjBly9+afFclelVrFbAyAm6
+rOrilnH/uv4XnVZ2crj9p7s+pUrxKsztPJeKRSvabW8sMhh7SEQkRFBtZjX9/uwTsxnabKjDdgC0
++q0VyWnJnLt3jl03dtG0QlPmd5mPp+bJ6eDT1C1b124Czn1++1h8ZjEjmo/g2crPOjW/wj38dNgg
+Lg3dMVQJEopHBp3QIYTA08Mzp01R5AHShdvFZxYjxqsqVdlFtoVsKBQKRUa8vOD8eQgIgNeyGAqY
+nCwTTuZzUmZ95hmIjoYxY+C//7JmgzVefVX+DAuDxYttt32YOTvkLD+2/ZFRz44CoH+j/qR+ncqu
+/rvwHeGbw9a5DmfECICzd8+y1XerQ2Eg/lH+/HPTkFnVWJBYcX4F0UnRTs2dkeQ0Q+zTuXvnWHJ2
+CX3W9WHCgQk0W9iMF357wSwXhhBC3+9W5C1eXvYyKy+spOXilly/fz1L9ihcj4bKJaJ4NLifcJ8n
+f32S6jOrcyfmTk6bo8jlZAxnU2GP2YcSJBQKRY5TuTJs2wZ37kiBIDN06iRLcjpLcjJ8/bV5xQ13
+4epQlbxEqYKlGNtqLNM6TEOMFyzvvhxPD0/aP9GeWqVrcWX4lZw2MUfZdWOXzQSXscmxNJrbiJjk
+GP2xyMRIAELjQvUlRa2RnJbMhP0T2Oq71eS4Tuj4bNdnDNk+xGK/TVc2MeHABABOB58mND5Ufy5V
+l8qzi5+lws8V6La6G9VnVjfp+9ofKumYs+zz28f/dv3PpQsoY68aldxU8ajwxZ4vuBlxk4DoAEb8
+OcJ+B8Ujze4bu032pxyakkOWPHqokA2FQpFrqFABjh+Xr4WAmTNh1CjH+gYGZn7ec+csH/fzg+++
+k0LHm2+an9fpZIjIW2+Bv7/0fpg7V+bI+Ppr53JMKKB2mdr4j/Snyowq5PfMz8H3DtK8UnOS05Lx
+1DxJE2mExYdxN/YujR9rjKZpnL93noO3D9KvYT/+vPYn/Tf1z+nLyBLtlrfjs+c+o0G5BkzYPwEv
+Ty+mvjKVIt5F2HBpA1FJUSbt7yfcZ8qhKSw/v9zieAtOLTDZ//bAtwD4j/Tn8eKPA/D72d+ZemRq
+puxdc3ENx4PkH+0W3y1m533DnfN8EULw85GfiUuO44sXvsDb09uhfrHJsRTxLuLwPJGJkRT1Lsrt
+qNvcjb1Ly8otc8VCPTw+nJeXvQzAb2d/I3xMuEvGNfZqUR4SzpGmSyNVl0r+fPlz2hSFk9yMvKl/
+fTX8ag5aosgLpAv86RhX3lK4FyVIKBSKXImmwciRcgNZpaN0affMdeCA+bHLl6FGDfl6yRJo317m
+gDAmLU0m0Fz7oPJi/fqGc/XrQ8+e7rH3Yebx4o8jxgtSdan6pJPpi1JPPKlYtKJJnoVG5RvRqLxM
+AtKvUT/O3D3Dz0d+pmPNjvx1/a/sv4Assv/Wfvbf2m9ybP6p+bSv0Z5Xn3zVYp/P//nc6njWEmYe
+DTyqFyR2Xt+ZOWPB5SEZ6y+t57Pdsl5wIa9CfPa8/drBb296m1UXVjG381wGNR1kt/2em3vo/Edn
+iucvzv2E+6SJNDb22Uj3ut2zbH9WGffPOP3r+wn3EUKYCSVXwq5wL/YeL1Z9MVeIKA8LRwOP4h/l
+T/c63fHy9AIgOimap+c9TXRSNP8N/I9apWvlsJUKZ1Di28NHxpDBLVe28Hqd13PIGoWrUCEbCoUi
+T1CqlPSaCMohwTrAQi7GtDTw8bHc3lrZUGvrh7S0rHl5PGxkpgIGwNRXpqL7RsfOfjsJ/SyUL1p9
+wZxX5/BR8494stSTDGlqOSwht7P75m5G/e2gu5ADGFc9yWzZ0YkHJuo9LlzFqour9K9nnZhlt31i
+aiIrzq8gTaTx4fYPuR15226f9svbk5yWTGh8qF6w6bG2R+aNNiIpNQn/KH/9fnRSNPEp8Q73Nw6H
+AZh7cq7J/t3YuzSa24iXlr5k0SPFGu4M2dh1Yxff7PuG0LhQq23iU+L55+Y/JKXmzpi125G3abm4
+JX3X9zXxKpr872T8Iv0ITwjn7U1OZEt+SNl9Yzff7v+WsPiwnDbFIZytMqTIORJTEzPVr9uabi62
+xLUIIVh4aiE91/bk12O/OvX/4FFCeUgoFIo8RcWKUpgAOHECmjfPnnnjLfwPSUuzLjCkpDg3/ssv
+w8GDMGcODHWuMIIiA+kLrjKFyvBD2x/Mzs/uPJuUtBS9C3ZUYhRTDk/BL9KP1RdXZ6utOcXck3N5
+s6GMQzoTfMapvkcCjxCdFM34/eNdZs+WK1sYvWs0NyMMLtYZE4xlJCElwcyl9uVlL3Pj4xv4hvlS
+sWhFiuYv6rANlrwR0klJS2Hzlc3UKFmDphWbmvQ5HXyaMoXKULlYZZ6a9xS+4b50qtkJgeCv639R
+skBJLg2/xGNFHrNrg3GSUoDhfw5n2DPDAHk/ph+ZTopOfrh0X9PdLAv8meAz9FVN8zcAACAASURB
+VF7Xm6YVm7K652r99WQM2Vh8ejFbr25lUptJeg+jzHA/4T4dVnQAHiRnfXOrxXY91/bkr+t/8Xaj
+t1nWfVmm53MXy84ZbBqxcwTDmw8H4E6sIY/HtfBr2W5XdhKfEk/BfAUt/g3svLYTv0g/hv8p78v5
+e+fZ2HdjdpuoeEjZcGkD/Tf1p1PNTm59X8Ulx5GqS6V4geImx/f67WXPzT2ULVTW5XNu8d3Ch9s/
+BGDj5Y0ExQTxY7sfXT5PXkd5SCgUijzLM89IcSIpCY4ede9crVrBnj2mx9Ks5x/k7FnHxw4KkmIE
+wLBhztvmCoSAY8cg3DUh67kaD83DJB68eIHi/ND2B1b1XGXWtkfdHsx+dTbhY8LpWbcnHpoH41qN
+4/2n36dBuQY8Xuzx7DTdZfzr/y8tFrUgICqA21H2vQqM6b6mO+9sfsfh9of8D5m52Wak25puJmIE
+2BYkElISKPN/ZXjy1ydNjt+MuMnqi6upM7sOdWbXceqJ/J/X/gTAL8KPVkta8erKV/V2j/57NH3W
+96HZwmZoEzQ2XpZfmhecWkCzhc2oO7su6y6t0+fM2Hl9pz5kKCIxgrF7xtqdXwhh9ZqPBR6jws8V
++Om/nyyeT+eVFa9wI+IGa33W0m55O/31G/+Oo5Ki+GDbB2z13aovcZdZLode1r/ednWbPinr+kvr
+6bm2J6funALQ3wtruU5yK4+Ky//2q9sp81MZOqzoYPa3eizwGK/+8apejACZ6DYvYM1j6nLoZQZv
+G8yem3ssnncld2PvOu2F9rAjhMA/yl//Xuu1rheJqYlsurKJ1r+3Zp/fvkyPnZCSwMbLG/VJgdPv
+/d3Yu1SeXpmK0ypyNfyq/nhscixtl7Vl8qHJjN412mQsV3gzZPRym3JYJcq0hPKQUCgUeR5vb2jR
+QiaZDA6W3ga+bqgi2b696b4tQeLiRejSBbZafmBoQnKy/TbuZuFCGDxYJha9dUve00eRxC8TGbZj
+GM9XeZ73G79vcm59n/UkpyWbJFoMig6i8vTK2W2mSzgedJwqM6q4fZ4XfnuB12u/zuY3NuuPpepS
+mbB/AocCDvFJi08s9rMlSLRf3t7ql8U3N0jPjzsxd9jiu4U+9fs4ZGf/Tf0JGBVAu+Xt9OJI9zXd
+WdljpVn4SM+1pgliElIT9PNaYtm5ZQxrNoyOKzvSpEITdr+928QbIiIhgjZL23DunnmG3SthV3h2
+8bMOXYOxK/1ev710/qMz37/8PUcDDYqt8X3LmMQtq0w6OInCXoUZs2cMIJ8I/tLxF5fO4Q6+2f+N
+xeO53eU/IiGCiMQIapSskan+qy+uJjopmsHbBwMyNOxU8CmaVWymb/PzkZ9dYmtOcO2+wavFJ9QQ
+X/n8kueJSIxgwekFZl5GrmTDpQ30Wd+HemXrcW7IOTMPqNyATuhcZteuG7tY57OOkc+OpH65+lbb
+vbP5Hb04mfH+H7x9kJeXvWzx95KqS+XMXdsefWP3jOXX479So2QNCnsV5kLIBQAGPDVA/3lXe1Zt
+AF6r9Rr/1956ibU1PmuY0XEGRb2Lcv3+dUoWLEmV4o7/z4xOirYY3vTu5nf5vdvvJsd0QkeqLtXh
+RM4PG0qQUCgUDw2aJkM6rlyBiAgoVw5S3fhgwlbIBsD27TLpZUYbM2Ln4XG2MFh+HyU4GHbtgtce
+0WqN+fPlZ/Hri62ez/hloVKxSvgM8+FW5C2qlahGIa9CPF7scY4HHSc4Npg21dpQsmBJ9tzcQ/vl
+porWx80/ZmanmVwKvcSCUwuYeWymW64pN7DFdwsrz6+kb4O+VJ9ZncBoQ8KUjEk807ElSBwOOOzQ
+vH3X96V0wdK0rdHWbtvIxEiKTjYN8djiu4WDtw86NJc90kWFvX57+fXYr3zyrEGI+Xrf1xbFCIC6
+s+tmes5//P7hn8X/ZLq/NT7c9iELTy+kQL4CJsfTy8MaY6sc7Z/X/uSQ/yFGPjuScoXL6Y8npCSw
+49oOnq38LJWLWRb8klKTyOeRD08PT7v26oSO8PhwyhZ2vUt2ZvAJ8aFK8SpOhRRlJCoxiorTKupj
+74M/DXYoLCidfX77LIpoh/0PmwgSGcsEu5Ok1CROBZ+ieaXmmc4j5AgRiRH617ZCtUAKeO9veR9N
+01jSdQkFvQo6PE+vdb0AuBhykX1++xz6HDImKjGKovmLZlowSE5L5krYFRqWa6i/xpS0FH3S1plH
+Z/L1vq/58oUvGdvKvheXPdLDtzZc3sD9sffNzqfqUvEN8zXxlHLm8/WN9W+w4fIGm21+Pf4rgJnH
+3ZGAI2Ztt1/dTplCZcyOG1Ph5wom++eGnLMa5pamS9N/Ht2JuUOdWXVMynSns/TcUhNBIi45jiYL
+mnA/4T4H3z1I3bKZ/8zPq+Q+qU6hUChcQMmSMo+DEDIM4dtvXT9HWBiEhNhu87YDedBseVpY484d
+94ktmbHnUaZe2Xq8+uSr1Ctbj2olquHp4UnLx1vSo24PShYsCUC7Gu0Q44V+S/oqiZmdZur7z+g4
+AzFesO+dzLuq5nb6b+rPN/u+MREjbBEaF8q0I9OoOqMqjeY2IiUthdPBp/l2/7dOzdtueTuGbh/K
+gE0DTBJOOsqrf1iubpIVRv490sQ13vjprTPsvrE7y7ZsueJ4ckyQi5yFpxcCmUtEF5EgF4Nh8WF0
+/qMzkw9NZuDWgdyLvacPMRn3zzh6r+tNqyWt9GEgxviG+VJ5emVqz6pNbHIsY3ePpfMfnS3+foUQ
+tFvWjsd+fozfz/7usJ1CCO4nmC+qssrSs0tpMLcB9efUJyXNuWRDQgjO3j1LYmois47PMrn/GRdO
+AVEBRCdFWx3LmlfIyL9HmuwnpVkOexqy3TRBcEJKAp/+/SkT9k+wGaKVlJrEpsubLIZT9Fjbg+eX
+PM+wHe6JXQyOCTY7ZssLRgjB9we/Z43PGlZfXM2PhyzH/u+5uYdea3tx4JaFkl0PcPZvZZvvNsr+
+X1meW/yc3ZA3a7yy/BWemveUPt/P7OOzKfZjMcbuluLDyL9HEpMcY7NSkzXikuNM9o3/To0FH2Oa
+LWhGg7kNTI45EzZjT4ywhbG3jDHOfCYAvL/lfbNjQgi6rOpCvkn5mPzvZNZfWk+laZUsihGWmH50
+OlfDrxIWH0a9OfVMctqAFMtd7c2W21CChEKheOgpVQrGj5chHbdvw1tvuWbc4cNh//7M909f+Dub
+AHP5cqhcGVq2dI93hc52LkGFC7Dmltm6amueqfiMfr9gvoJcHXGV+HHxrOxh6m7TrGIzniz1ZMYh
+APSlUdvXaE/cuDiLbXKCyYcmO9xWIPh016f4R/lzIeQC3t9503RBU4tP4e0x79Q8lp9fTtUZVZ3u
+6y46rewEyKeg1rxE7PHKilf49/a/HLx9kE2XMxfX321NN5adW4ZfhJ/JcZ3QcTTwqD7E4+u9X/Px
+zo8dFpSs8f2/35OqS+XXY7/qj22/up1K0ypRa1YtElMTmXFsBiBzX9yLuwfIJ44hcVIBHrB5AGHx
+YdyIuEHRyUX56b+f+PPanxYrYdyMuMm+W/vQCR3vbXnP7PyVsCtmx0LiQqgzuw7br27XH3NF+IYQ
+gne3vAtAQHQAu27scqr/dwe/o/H8xrT+vTVxKeZ/1z4hPvRZ14cuq7pQZUYVSk0pxeqLq/nnprmX
+jK2qKIO2DuK3M7/xx4U/rLaZf2o+Uw5NITktmYiECAr9UIhpR6fx7YFv+ePCH3y7/1teX/26mfDw
+3cHv6LG2B88sfEYvyKS/x9LzuCw8vZAaM2tkKZeAJSpOq2gmSlgSvEAKZw3mNuCHQ4bEyHv8LC+e
+2y9vz4bLG3hp6UtW5zZ+/0QmRhIQFcCi04sYvmO4/n1tTNfVXUnRpXAs6BgtFrXgq71fWbV1m+82
+Fp1eREpaCkIIhu8YzjMLn+HAbSmQTDo4CZAJWxNTEy3movnyny+JSXJsAT3z6EyK/1jcRDhyJE+G
+JS8wW599qbpUpv43lV+O/ZJpUcbVnAo+ZbIvhMBjoof+s2LcXimm2sNYjAyKNk3O/M7mdzjkf4iY
+pBgWnFpAySklzQSghw0tt/yCczuapgUClSpVqkSgqs2nUDwUhIVBv34yRCE7ee01KF8eVq+GDz+E
+AQOgcWPDeXsfy8bepSdPQtOm1ts6ivGYGzZADyeqIAphO3RF4TwhcSEIIShfpLzJcSEEe/32UqZQ
+GZ567Cn9sVPBpyiWvxi7buyiS60uVC1huvDutbZXlp4uKdzD6GdHs8dvD+fvnc9pUwAY12ocnh6e
++EX6seL8CgDqlqlLUlqSmQu0u1jYZSGDtg3S7x8deJTC3oV5et7TeHp4sv+d/Ty35Dmr/TPGnl8K
+vUT9OfUtng+PD6fy9MpmT6/faviW2WK8WP5iRH0eBcCFexfY4ruFAU8NsBtTfjvyNpWLVcbTw5Pe
+63qz/tJ6/bnNfTfzep3XbfYH6REyaNsg/vX/V3+sT/0+rPVZa7dvOkcHHqVF5Rb6/fpz6nMp9JLD
+/TPLc48/x+H3DSFW2gTDP4szg89w/t55Ptj6Aa/Xed3k3qTTsWZHNvfdbJKI+Is9X/Dj4R/5qPlH
+/NLJeo4S47nSaVCuARdDLur3E75MMAk9+vKfLzkccBjfcF/uxt4169+uRjsGNx1Mr3q9OH/vPE/N
+e8rkvPH7y3j+qsWrsqHPBqqXrE7pn0qb9OlWpxub+poKipZsX9RlEQObDOTPa3/iF+HHwCYDuRx6
+mSYLmujbWHtfRH0eRfEfDdUlxHhhNsfIFiOZ3nG6ybFl55Yx/9R81vVepxe6jfut6rmKvvX7EpcS
+ZxLy9kHjDzhw+wD73tlHiQIleG7Jc059zonxgvkn5zNkh/TEWdNrDX3X97XY9sSgEzSr2Iwea3pk
+S8LVJV2X0LxSc+qXq8/B2wczlRx4abeldK3dldPBp2m7zIFQnp8BqRcFCSHyZuIqGyhBwkGUIKFQ
+PNwEBkJAAHzyiSwnmp0cPQrPGuWs0+lsL/CNz/37r6wAklWMx1y3Dnr1cqzfnj3Qvz907gyLrade
+AKRw8fff4OEBr7ySeVsVzpMe7tCkQhPuxNyh2sxqFts1qdCEmR1nss5nHRPbTCQuJY5i+YtRxLsI
+KWkpHA86TkGvgny19yt61evFotOLOBIoY3N71etlcUGhUDjDpDaT+Hrf15nuf+PjG9QoWYNUXSp9
+1vUxW6CkLxiDooMYvWu0U4v61K9TORp4lFa/yQ/dhuUacn6oYZGV/vS9QlEZPjHj6AxG/T2KVlVa
+Ma7VOLPwn3we+fAZ5kOt0rVszlt7Vm2uhl912E5LPFbkMRqWa8jum7t5+rGnuRt71+KC2x1YW6Sf
+GXyGxvMbW+piwpR2UxjzvEyUmqZLI98k0/wS54ecp2H5hvz8388cuH2Aqa9MpVbpWhYX9RmJGxdH
+Ia9CgCxd64g96ddkaXxr12qP4E+D6bW2F6ULlWZ97/V4f2fuRdepZiemd5hOndl1APm3ci/2nlnS
+XUv4j/Q3SWI86tlRTD863aydGC/4eu/XrL+8nrHPjzXxKur8ZGeaVGii97hIZ2e/nQghXBreNr3D
+dH4+8rPDHlltqrVh363sC3n00DyI/SKW5eeX65PCOkO5wuUsesZYRQkSClCChELxKHH2rKnHgruZ
+Nw+GGIXjJieDl5f19sbiwf790NoBcT4hQfYrUMDyeeMx166F3vY9Ds36Xb0KT1qOIACkeJFeqeTA
+AXjxRcfmyK1ER8OKFbLCiyu8VLITndCx+uJqCnsVpmvtrmiaRqouFU/N02aCN0sYJ4X748If9NvY
+z+R8leJVKFuorJmrq0LhDgrkK4DfJ35mORXSEeOFS6vjiPGyXGuLRS04eeckAL4jfHmi5BNmC2dL
+1CpdC98RtstCObOwzY3s7LeTjjU7AqbX8kmLTxxK5vtWw7f4rs13rLq4ijkn5hAUE2TWxu8TP6rP
+rK7fjx8XT6EfCtkdO/rzaAp6FaTDig7s9dvryOUAUKpgKYs5RsR4QVJqEpGJkTz2s+NJRnvU7aEv
+J9yofCOL3gQdnuhAqyqtTAQ7D83DZgLgdE4OOkmzhc3stgv7LIwy/2c70WNGMnqdPCps6ruJ7mu6
+Z89kSpBQgBIkFIpHESFkCdGs5InIDNHRULQo3L8P/v6QP7+0pV49ed54vfjPP9JGW4SEyBARgJEj
+Ybr5QxGTMVevhr6WPSNt9jtxAprZ+L7TpAmceVCxq3Xr7L+vrmbgQFiyRL6OiYEiRXLWntyETui4
+HXmb6iXlAiE+JZ5Rf41i8ZnFTG47mavhV1l0ZpFL5ipbqCwDGw/kX/9/Ha6+oXh0GdpsKHNPznXZ
+eNNemcbeW3tN8k0ATG47mS/++cKhMcR4wed7PmfK4SmUK1yOQl6FuBV5iw5PdGBDnw0UmZz3P1yW
+d1/Oa7Veo+SUkm4Zf13vdSax+5+2/NShkqUb+2wkLD6MD7d/6BI7vmvzHRMPTiQ5LRfU8zaiTKEy
+FktQZuTC0As0nNswGyxSOIUSJBSgBAmFQiFLePbv7/55QkLk4rZaNfMqHkeOyGSW6ezaZfA6sMaE
+CaZVRvz85NjGGAsLq1bBG284ZqszgsTTT8O5BzmtXnoJ9uXxghLG137sGDRvnnO25FWEEGz13Up4
+Qjhda3elVMFSnLxzkhaLWtjtW79sfQ6/f5jiBQxx0VMOTTHJGP9py0/pWLOjWclVe5wZfIZCXoW4
+Fn6NpeeWMrDxQDrU7IAQgjN3z5DPI59Z7LgxP7z8A39elyUtFQp7THhpgr4SQka8PLxI0TmZ+Vih
+UDxcKEFCAUqQUCgUBuLjoXBh943v7w///eeYKLBzJ3TsaLvN6NGmXhF79kDbDDmUjBfXf/wBb5qX
+p7eIM4LEU0/BeSMv1O7dYcwY0/wZeQlnrl3hHBEJEfx29jdaVGrB81We1x9PTE3EU/PEy9NGTJMF
+Dvsf1teHj0uO4/2t7+tLRC7puoT+jfrjF+nHnpt76FO/j93a9AB+EX7U+KUG5QuX5/bI23Rf053d
+N3fz2+u/0b9Rf4QQ/BfwH33X97XoYm6LkgVK4uXpRUhcCOULl+fw+4dJE2l0X9Od2qVrZ0viNoVC
+oVDkEh5yQcJ+cJtCoVAoTChUyFAJw9dXVuo45cLw+JgYSLJc+t2Muw7kJCtWzHTfXlnPzJb9vHDB
+9qI847ibNsktoy5+7pwMhRgwIO/kZvBQRbRdSsmCJRndcrTZceNs+M5gLGoA3B55m8TURHRCp09o
+V6t0LbvJBY2pXrK6SQK7HW/tIDopWu+xoWkaz1d5nsDR8iFGVGIUx4KOUaFIBWqXqc1vZ37DJ9SH
+X4/L8pdRn0cRHh9O2cJlKeJt2UXfZ5gPIEtVBscEM3THUHzDZf6BxV0X837j94lNjiVNl0aJKSUc
+vhZ3EfxpMFfCrhAUHcTQHUOJSXasrKBCoVAoHh2Uh4SDKA8JhULhCDduQM2aWR+ncGGIc6DsdIkS
+EBFheiw2Ft5/X1a0+PBDSE2FGTMM54cMgeeek0/4+/WTP42f9i9f7nhoSsb8h7b+pTRoAD4+5scz
+9vHykjbbGy+nMb72s2elB4hC4SxJqUl4e3o7nUwUZK4ODdkvY/+YpBgCowOpW7YuQggK/1CYhNQE
+/fl5nefpS+oB/NTuJzw0Dzw0D0bvMheDkr9KJjA6kGolqqFpGm2WtmH/rf368wMbD2TxmcUUy1+M
+iLEReGimKp0QAo+JpseGNRvGyeCTHA867vS1A8zsOJM21drQaF6jTPVXKBQK3xG+1J9Tn1Rdqsnx
+umXqcjnscg5ZlYGH3ENCCRIOogQJhULhDGlp8MMPMnHjXscTd2eKv/6CWrWgYEEYNAi2b7ffJ51J
+kyA4GObMMRxbulR6JzhCxjXUypWwaBF8+aVpWEhyskzOaYmM/4aMx8zpf1HR0dCnD+TLJ6uPFDJK
+2m5s5/nz0FDlAVPkctJ0afrQFYCh24cy79Q8ZnacycctPjZrfzzoONOOTGNKuylULVHV5JxO6Ph4
+58cERAewuOtih8JcQuJC2H9rPx1rdqRYfoPrVpoujWNBx/h639dEJkay/539JKclM//UfO7G3tV7
+kaQz5rkxTGk/xeSYEILjQcd5drFpDNiApwbwXZvvGLNnDKsvrtYfX9J1Cf0a9WPR6UUM/3O4Xdu3
+vbmNLqu62G2nUCjyDumfJemfhemkl4M9EXSC5osMCaL6NezHygsrHR7fWjUWY5pVbEbNUjX1n08T
+X5pI+SLlTcuJKkFCAUqQUCgUmSf9Y/bmTdd4T1ijaFEZ7pFVliyBbt2gpAPJ0G091DX+9/J//yfz
+Rdhrl3FMV/+LSkmBgACoUcOx9oMHw4IF8vXkyfC5IV+iiZ2bNsl7Zo+dO2VOj3fflaJPuXLg6Wm3
+m0LxSJOSloKXpxepulTyediPNhZC6Ntm9ByJToo2EUNAiivxKfFcCr1Es4rN+C/gP67fv06/hv3w
+0DxMRBwhBHv99jL8z+H6cBmAc0POUdS7KM0WNrO7ALky/AqxybEWyzA6WsYRZMWQJWeWkJSWRL+G
+/Xjv6fdot7wdXWp1YcnrSyjiXYSDtw/SYUUHk35fvfAVQTFBHLx9kIJeBWlYriGrLq6yOk/yV8ns
+ubmHZhWb4enhyYFbB+hQswMF8xWkxaIWnLhzwiF7jalQpAIfNv2Qq+FXzeauXqI6EYkRRCZGOj2u
+K6hRsgZHBh5hr99e3tzwJrVK12Jki5EADPtzmL7dkq5LeK/xe3y7/1smHJhgcSxnFrBzO89l6I6h
+dts9VuQx/Ef64/2dt/7Y9je389qq1xyax9Us776c/o36k6pLJSUthev3r9OgXAP6beyn/92+9/R7
+NK3QlDpl6tC2Rlt0QkdoXCjli8gSYGm6NGKSY+iyqos+GXCHJzpQNH9R1l9aD8DqnqvpWa8nYfFh
+hMSF0GttL67dvwbAjA4zGPn3SL1Nq3uu5k7MHRadWcSl0EtmNh989yAvVH1Bvx8WH0adWXVITE3k
++KDj1CtbT3/uWvg1PDQPnij1BClpKZScUpK4FFMX1kltJhEaF8rduLvMeXUOpQuV1nuwvbflPa7f
+v86KHiuoVqIaANfvX2ev31561etFqYKl9DYYC7tbrmyh2xr5xaLALwVIvJ8ISpB4tFGChEKhcBVC
+yCSI+/fD2LE5bY1lvLxg2zbo0MF2O1uCRHAwPPagDHvv3rB+veV2jggSqalw4IDMUVG8OJlCCFkJ
+4+RJmDtXhq7Yo0gRQ+hM797SS8KSnTVrwrVr9sfLeL+aN4ejR23fx4wIAYsXS++Njz6SvyuFQpEz
+pIslGRFCkJyWTP58+bkTc4eyhcqSmJpIEe8iepHkkP8hXvhNLooWvLaAQU0HmfS/n3Cfgl4FKeRV
+iKVnl/Lulnd5/vHn2f32bkLiQvReKzqhMwuRyWiLf5Q/VYpXsRkalKpL5eOdHxOdFE3Puj05HnSc
+wc0G6xdRlohJiuHM3TPUK1uPHVd38HL1l3m8+OOEx4dz7t45CuQrQERCBDqho0SBEjz92NMU9i5s
+Yu/rq19nq+9WhjYbyqxXZ+GheZCSlsJnuz/javhVdl7fqW+roeGheVChaAVW9lhJi0otOOR/iKYV
+m1KiQAl6ru3JxssbAZjeYTqftPgETdO4GHKRtT5r6Vq7K80qNuOZhc9w8s5J1vRaw4tVX2Tqf1Pp
+UbcHzz3+nMn1RSREUKJACZP7FhQdREJqAjVLmT9h2HF1ByUKlKCgV0GqFq9K8QLFqTGzBgHRAQDk
+88hHo/KN+KXjL2a5bTKSnJbM3BNzKV6gOG2qtWHTlU20r9Ge+uXqA5CQkkBgdCBPln7S5PdR7Ecp
+uLWr0Y6Pmn/E66tf15+/9797nL93nrbV25KiS+HLf75k6pGpNu2wxEfNP2Jw08HUK1vP6nsqKjGK
+zVc282TpJ2lZuaVTYWnG7+mElAQKehW02O5e7D3C4sOoX64+PiE+/HLsF/rU70PbGm0ttrdFYmoi
+yWnJZoJlRtIFT2eTK2cGndARmRhJoycbERQUBEqQeLRRgoRCoXAnERFysevIIjk7SUiAAkZ5BJOT
+YcsWmQ+ibl3bC+lXX4UdO+TrXr1gwwbL7d56S+aWKFFCljE1Du3Q6eQcn30GU6fK0qGnTzu3gE/H
+1xfq1DHsO/Lvz3ie8eNNy6dmtGHLFujc2bbHgyW7z5yR1+UoO3fKewvwyy9SlDBGCIiMdMzDRaFQ
+5DyOen48rOiEjqvhV6ldurbFRWuqLpXpR6ZTxLsIQ5oNyVS+lZxGJ3QIIUy8bdyFEAKd0OnnCosP
+4+Sdk7xc/WW8Pb3t9JZ5bVr/3ppjQcc49N4hroZfZeiOobzV8C1mdpyJt6c3+fNZicFUuIXKlSsr
+QUKhBAmFQpF9/POPLM158qT8mZN4ecmSoQULQliYFCi+/Ra8vSEkRIoItkj/F9OzJ2zcaH++fv1k
+Hop0UlJk/gbj758REbKixzPPmIol9vDxkUJKRttsYTzvvHkyhMPSuXQWL5YJRS1x7ZrM9ZGR//6D
+li3t25JOnz6wbp18XbEiBD2oKJmSAt9/DxMeeA5bEisUCoVCoXCWpNQkJULkIA+7IKEKlSkUCkUu
+o21bma9g9265aE5NlTkYcoKUFBgxAgYOlOEl6R4CycnOJc90VPtemSHUNjnZvM2HH8KLL0qR5LXX
+pBdFaipMnCgX46mp5n1cgSPlUAcOtH6uV6/Mj2uMtXu5Zo1BjAD42DxHodMsXgxdushSrM6QmJj1
+ud2FTie9bFJSXDeeQqFQPMwoMULhTpQgoVAoFLkcT0/43//kQlSnk5uvr/1+7saSWGCNzDrjhYeb
+H0v3DgAZElKoEHzxhSGk4oMPLI+VVS/fmTNl9ZTMcv685eNZWdAaX9PSpZkfxxIxMfJebt8Oz9sO
+dTZh3DiZYHXKFPttc4JPP4WmTR1LQmqPgwehQgUpNrnK4TQwELZudZ1gUUoMlQAAIABJREFUkk5U
+lMzrolAoFApFbkIJEgqFQpGH0DS51aolF0DpW1wcDB8OpUplny2OPgXfvh02b87cHN9/b79NUpLM
+L5HO0qWZe0Kfnmw0IsLyeV9fWL7c8fGiohyzw1mRw9rCt6CFnF86nQzb6NVLhtw4Q1SU4XVcnPV2
+GZk8WXqpGFckyU3MmCF//vln1kWE1q1l6NKGDXD8eNZtS0yEFi3g9delyOYq7t6FypWhShUZCqZQ
+PArEx0OnTtLrMDJnCnYoFAoHUIKEQqFQPAQUKgSzZkmPgtRUuHFDVoVwJ+kLO1t8+aV0+c8sR4/K
+p9DOct9C1T17T5znzpVVLxo1kiLH1avmbRwNnblwQeZ3ePxxy7YYk5WQDWMPCQ8L/9HXrJHviw0b
+YORI8/O2sDTew8a+fa4by5I3j7OcOQN37sjXP/+c9fHSGTcOYmPlZ8OIEa4bV6HIzUyeDH/9BXv3
+5l6BVKFQKEFCoVAoHjo8PaFGDVm1I92DwtdXLkwLF3bdPJYW7Bn54YeszXHunHwK7SyW8kh8+qnt
+PsOHy5+BgbJCRe3a5m3seTOkV9h45x35dC4sTFYjsZWcNCthIPYEif/+M7xes8b+eFevygSks2c/
+GoLE9es5bYEp7sozHhBgeB0d7Z45FIrchrHX0r//5pwdCoXCNo/A1w2FQqFQ1KolF9yxsXLRk5YG
+332X01a5D0uL/H/+cbx/QoLl4zqdvH+hoZbPez+oqJZe+QKkS3/79tbnclVSRFcsZjt3ltVQRoyQ
+wowt/voLqlVzbWhBdmPp3h87BkOHysSXzuCKSoTuEiSMrzPfo1tdUvGIYSyqquSzCkXuRQkSCoVC
+8Qji4SHDKYzzUKSlyRCJiROhXLmctjBr7Nhhv01SkvRgmDXL8SShiYnQpIn1+5MuSHg6UWreGQ+J
+y5dNy6emL4Jv3bKcp8P4ybgji11jjwF/f9ttO3WC27fhxx9lfLYQrslP4O8vS6RmB5bu/bPPyhKv
+TZtmjw3GZEcldlcIJwpFXsBYkMiKJ5pCoXAvSpBQKBQKBSC/vLVoAV9/DffuycVRSopctJ8/L6tY
+WApjyI189JEM93j6aVka1FKiys6dpbDw0UfQrJlj4wYEwNmz1s/nf1AZzZmn0M5UU6hXz3Q/fXE5
+aJDl9lu2OD52RpxZuA4fLj1unnnG9LizC+xbt2S4Ua1aMsGou3FXidjM4og9W7fCnDlSUHOU7BA6
+FIrchvKQUCjyBspxT6FQKBRWyZcPSpeWW8OGsqwmyAXOtWtw6ZJ8kvz443DqlOML++zg6aflz3Pn
+LFcfMQ7hiI11zZzpgoQzHhIDB8Lo0bBsmbR5xQr5s0UL03a2FqC2RJKMbNokPUJGjIAiReTvcvdu
+WYnBGGcEiT/+sHw8JgaKFXN8nHHjDE8y33lHvr/cSW5bpNgTJM6dkxU4QL5nx4xxbFxriVBdxbVr
+Ml9Jz57yPaVQ5AaUIPHwkpRk+H+ryPsoDwmFQqFQOE166dFu3aQYAVKYEEJ+8Uvfdu603D87y5Nm
+J97e0qPEGYEjPBxu3oRWreRibsgQGTYQEmLa7tYt877pi0tHSnqmpcFbb0GPHjLvw3ffyd9XjRrQ
+oYMUAIyZNct0v2tX+O03hy8LkGFBjhATIxfbxrk74uNt90lMzPqT/4xu3BkFEGeSXrpiwWNPkFi5
+0vB67NjMzxMRAfv3u8aNPSVF/u2/+679xLEKRXZiLEgoL6GHh759oUQJmbhb8XCgBAmFQqFQuBRN
+M2wdO5rmqUjfwsPl4m/NGvmkQwjHn/bmZm7cgLJlHRMI7FG+vLyHBQrIhXpmqo1kZNUqw+spU6Tr
+vyWhA8yTgG7bBu+/75zYklHUALnoTk423W/QQHqFGOfBsLWAOHRIhtu0aCHFnGXLbFeP8POD1avN
+RY7gYFi3TtqQlmZeGrV1a1kO9s8/5cI7JQXWr5chTBlZsMDw+vp16e1x6pR1myzhTPiOM2NkvJct
+W0KbNjBpUtbnCwqSghKY3oOHmdu35bW64u88Jzl5UpbEfFgX68aeahnFN+UxkTcJDJRCRGKiFCYU
+DwdKkFAoFApFjlC3LvTpY0gEOWWK/GKcnCy/LPr5yQWkry/075+ztuYkSUlQqJDM65ERT0/p9ZBZ
+Roxwvk+HDpmfLywMqleHqlXl3F26wNKl1hNo3r0rK178+qvp8Xbt5CL4xAl44gnp3fHxx4bzoaEG
+b4PUVCksvPmmeTLSadPke9DLS5Z63b3b9PydOzBsmMw3MmkSzJ8PvXvDU0+Ze7Bs3Wp4/corMHmy
+4yFMOp0Ub6ZNMz1+7pxj/dM5fFgKWS+/bLrgMl5wBgQYkrhOmABxcVJ0mTLFPGzHEby8nGt/927m
+5rFHYKBpdRtjQkJk6V1rHig6HfTqBY0bS1ExHSHk72TGDFMR7YUXYPBg6NfPdfaDfL/Nnm2ajNZd
+XLkic760bSvvTXpiWmsIIQW83bvzToJISyEbQsjQorJlYd8+631TUuTf+6ZN7rVR4Rz2POcUeRQh
+hNoc2IBAQFSqVEkoFAqFImfR6YSIihLizh0hDh8WIjFRiL/+EqJJE0v+GGpz9fbnn463NWbgQMf7
+Pf64EKNGGfZ/+EGImzeFePFF23Nt3SqEp6cQdeoIkZIixI0b2Xdf9u0TYtkyy9efmChEly5C9Ogh
+hI+PEOHhhnOrV1ser3x50/v3v/9Zv7dCCFGwoOHctm2G48b3rHRp0zHeecfwumVLx/7+Vq8W4rHH
+hPjqKyECA63bNG+eENWrC/Hbb0LExQlx/boQ3t7y9+Pj49hcjuDjI4SHhxz36lXTcykpQtSoIW37
+9lvL/detM9jfpInhuPG9+uUXw3FL15uaKsSqVUKMHSuEv7/5HHfuyN9JUpL162jcWI5Zt679a84q
+vXqZXke+fEI895z8bE3n5EkhFiwQomlTIWrVMrT96SfTsdLShNi5U7ZPJzVViMhI91+HLfr0Mdhc
+oYI8duKE7b+hdGbNMrQ5fjx77FXY5/Jlx35/DxuVKlUSgAAChcj5dbGrtxw3IK9sSpBQKBSKvEda
+mhDnzwvxxRdyMbJ4sRABAUKULSuybZGqNrkdOiTECy9kbQx7/fftM93fskWIAwdy9rpv3ZLvReOF
+PwhRpIhBlLAnsty9K9+/zZqZnps50/T9bnxu0SIhzp6VC0pjoa5cOdv22iM83LT9uXPm/e/dE2LJ
+EtPjnp7Oz2VMZKQUFzKSnGw6ZvfupucvXbI/55Qp5m0yjpt+XKezPN7SpabHk5OlaPrTT0Ls2mUQ
+RcaNM4wTHW1qR2buTXy8EOvXS2HIGbp1s/z7X7lSiIkThTh40PH3yYYNhuM3b8rfU4MGQuTPL8Se
+Pbbt0OmECAtzznZHeeMNg13p4t7ffzt2n43bDBniHvvyKjqdEEFBOTO3j0/mP0McYelSIT76SH7m
+phMXJ79D/PyzqWCXnShBQm3yRilBQqFQKB5qdDq5eElOFuLUKbmYnTZNLoSMv3Abb/PnWz6uttyx
+tW6d8zaA6ZNa423YMPnea9vWel9r3hPp28CB8mm0EKbHp07NnK326N/ffv82bRybq18/uWi8ccP2
+nIcPC1GggFzUJyTILSRELnznzTMds317Q7+kJCnK2Lu+mTPN28THm9v7779yTEfanjsnf7+WrnvB
+AsPrihUNdjjzexBCegOlt69VS35WLVggP8eEkJ9hb70l70FGunTJ2nvaGGOx6aOPTD1OQIqRmzYZ
+3qfGtG0rvVuWLnXsmp2hXz+DDWXLymOZEST69XO9be7gxg0hZs+WgqCrmDpVenXdvGk4NmiQvC+f
+fea6eRzlzBnn/04c5do1w7jPPWc4PmmS4fjWra6d01GUIKE2eaOUIKFQKBQKC6SkCHH7thAxMfKL
+95gxQrRqJRdQvXoJ8fbbQv9l5t13DU/Df/9dtsnKokBteX8bNUqIjh2zNsbnn8v3oivsefNNIQYM
+kE/yQYjChYXw85PjHz1qv/+YMc7P2bCh4e8pMVGI7dtlCMCkSTLUoUwZQ9t0u9K3p582Hy8wUC6M
+8+cXonNn03OWyChq3LsnRKdO5uM2aSL/zjOOl9EmkJ5Zjl5/ehiHNTt9fYV46SUhGjUS4oknhOjZ
+U4gLF8zH6dFD/syf31Q4KVzY/Jpffjlr7xNj8uc3HB8yRIjp0y33WblSvoc8PKQQdf26/d+NLRYv
+FqJFCxmuZ4n69Q1jlyolj2VGkGjXTh47dkwKPLt2OW+rEPL/w7hxlkN6XEHlytLel16yfD4yUv5N
+bdni2HjG4RHGoUxZ+Z1llX//NZ3fld41q1ZZvjbjz5+33nJ+3IAAw2doZlGChNrkjVKChEKhUCjc
+QEKCDC25dUt6Z8TESHfpu3eF2L9fiN69hcmXpOrVs76AVVvu2owXTpndrC0CXbHVqycXM+68B+3b
+CzFypBAjRpgfd+U8aWnmbteWwkksbQ0bSoHE+Jixt4PxljGUxdZWp465sGIcnpKeW8KZLaNgERcn
+85v4+wsRHJz1+zh/vsHzonBhw3HjXBP2ttmzTffTcTT/hKW+Z87IcS29X4Uw/31Zy+lh3Oall2zn
+SgkMlJ/fe/fK98etW/J9ZkxKimn/sDDZxlIYUmYxHn/4cCGefdY0V4vxeVteSatXS0+oDz+0fM3W
+7oOzWPodh4Zab//ll+a/0z59rLcPDRXi00+F+OMPuW/JQ8eYjN5oQsjf0eOPZ/6ab9yQny+aZtlT
+yRFGjhTC01MJEmoTQgkSCoVCocgzpKQIEREhv2yfOiWfJF67Jt1ut2+XwsfJk0L/Bat+fSHee0+Y
+fOkyTpCoNrU9LNuZM0JUqyZzaYSEmHtHuGrLaoLdu3flYiuroRWWtg8+cN1YGZ9YZ2VLSxPi/fcN
++7byT/j7m/aNjJTiroeH3LeUQFcImYzV+JhxclkhZLjL+vXmfTPmb0lnzhzL19Kpk6FNaqr1nBzl
+y5snYhVCCtOzZsn3qxDSW2HVKulBdPy4FCAHDJBhQX37ClGzpvX7mphoHlY0apRMsvriizIkMZ2o
+KOvjCGE9h0o6EREyX85nn1nPt6DTSWEdhPj+e8Px/2/v3uPlqsr7j3+enJOchCQQESIkgXD9AeEi
+F0OCBAigFERUREGkQCnlJbZSlHstglRqiwJVKwg/WsAqP/mJUEEQFUO4tQIpck8whFtuBwgJgSTk
+OufpH88eZ2eyZ87MyZk5e8bv+/Var9kze+21L8+cOTPPrFnr0ktLz88s1Y4rS/p/2tVXu2+6afRY
+rKQ8ITF7tvv48Rvur55xJNKDxxYTRPUkoebNK27f3gkJc/dmTObR8sxsPjB27NixzJ8/f6APR0RE
+pKkKhZg+cenSmJZw++3huuvg2GNhyhS49164807o7Iwp9R5+GKZNG+ijFpFWVvyYMmECzJpVud6Z
+Z8bUvxMmlB478MCYCrfoyCPhV79af7u5c2PK3/PPh6FD4YYbYirc3qxaFa91nZ2V66xeHfW+9CX4
+0Y8q1zvgALjlFthiC+jqiulft9++tH706NI0w8OGwcqVvR9f2sUXw557wgknVK7zyiuw3XYxJfCu
+u2bXWbIkpmC+9NLSY/vuG6/748bF/bPPhu99L5ZvvTX2uW5dHMNtt0V87r57/XZPPTWmf05Lfzx1
+B7MoWRYsgDFjYnntWvjNb2I/O+yQXf+RR2DxYpg0KaZJBnjsMfjc5+DVV7O3SVuxIqbihvi/+IlP
+wFNPxZTYJ54Yz4mFC+N4b7opptVNO+oo+OUv13/stdfif+uOO8KIEaXHn38e9tgDYBywAGCBu4/r
+/ShbixISNVJCQkREpLEKhXjz2dERb+a7uuCll+JN+JZbxpvF55+HSy6JN+U/+AFcdVW8GV2yJN4Y
+A+y0E8yZU2q3owP23x9+97vej2HkSPjiF+Fb32rIKYpIHa69FubPh29+s/e648fHB7t6nHtuvIb0
+xdZbQ3d35fXHHQe3315fm2PGxIfZgfLpT8Mdd2Sv22abSEaXO+ywSPTcf38kfYpOOik+8F99dd+O
+5ctfho9/PI5p+PDq13rGDJg4sf59nHVWJKO23bb2bRYujNj/7Gdw2mmwfHn9+121KhI0gwdHsmj3
+3UvrRo+GmTPjenZ0xPNICQkBlJAQERH5U7N2bSRIhgyJ5c7O0rd0s2fDoEHx5nSPPeL+L34BY8fC
+Bz8I99wTSZOtt4YLL8xuf8KEeOOZVp5MERGRfDFbvxdH4ykhISghISIiIs2zZk18e7ZuXdxmcYe3
+345kSVdXdPkdPBi22ip6kmy1VXwTN2JEdEufPRtefDF6nOy7b3R1Xr48vn0cPx6efDJ6mXzve9Fb
+pZrf/ha++lV4/PHSY5Mnw6OP9n5u6S7oIiLSGyUkBCUkRERERBqh+Fa0UIhvHleujOWuLnjvvUim
+mMHTT0fdXXeNde++C5tuGsmX7baLxEuhAA89FL8fd4fLL4czzogEyHHHRfLmwx+OhIw7XHNNdIse
+Mwbuuiu6pG+ySSRannsuer988pPw+c/H/r/whegePmlS/GSo3IgRG3bhPvHE6PnyjW80/FKKSFtS
+QkJQQkJERERE8mvdukiyFAdafOON0qB9CxbEOCydnZGAAejpgXfeicdGjozH1q6N5M/y5TBq1Prt
+u8eYLkuWREKopyfGbBgxIpI8m2wSjz39NOy8c/TWmT07kj3d3dHr5623YuC+RYuifnd3rF++PAbB
+NYtBHi+/HKZPh/e/PwYg3GefSEQtXAgPPhjH09EBhxwSv7VvhOnT4fTT4eWXG9O+SO2UkBCUkBAR
+ERERkdZTKEQCZ82aGBMn/XhPT4yH8/rrpdkqzCLBNWhQrIfYtph06ukpJb56eiKRNWRIqW5HRyTB
+3nwz1u28c/R0gujRtNlm0btp1apIOG29dbRXKMQMFAceGAm0FSti9pFlyyLRte22cWyjRsW2a9bE
+T9PWrYMXXoBddonE2dy5kczaYouo19UVj7/+OvzkJ7Hu3Xdjdqi99oqBOZ99NpY32SRu58+PmV2G
+DIHDD49ju/feSMgtXhwJv7FjY78QxzRxImy+eSTK5s2LXldTp8a5PPdcJO9mzoyBQ4uzb/zZn0Wv
+sIcegiuuiBlXnnkmBrscOzau14IF4+jpUULiT54SEiIiIiIiItJM48aNY8GC9k1IDBroAxARERER
+ERGRPz1KSIiIiIiIiIhI0ykhISIiIiIiIiJNp4SEiIiIiIiIiDRd0xISZratmV1lZrPMbLmZLTaz
+x83sPDMb1o/7OdHMfm1m3Wa20sxeNbMfmdnk/tqHiIiIiIiIiGycpiQkzOwY4BngK8D/AYYBo4D9
+gG8BT5rZjhu5j6Fmdg9wC/ARYDQwBNgGOAl4xMwu2Zh9iIiIiIiIiEj/aHhCwsz2AW4FRgLLgK8C
+HwYOB24AHNgZuNvMhm/Erm4Cjkraux/4FLA/cDowhzjXS83sr/rY/iCAQqGwEYcoedHd3c3Xv/51
+uru7B/pQpJ8opu1F8Wwvimd7UTzbj2LaXhTP9pL6/NmWwy0046S+S/SIWAd81N2vcPfH3P0Bdz8T
+uAAwoufEuX3ZgZkdBpxAJCPuAo5w91+4+xPufjNwADA32c8VZrZZH3bTAUpItIvu7m4uu+wyvVC3
+EcW0vSie7UXxbC+KZ/tRTNuL4tleUp8/OwbyOBqloQkJM5sITCESBf/m7o9nVLsamEUkC842s75c
+6GIiYx3wN+7u6ZXuvhi4MLk7CuhrLwkRERERERER6QeN7iHxqdTyzVkVkuTBfyR3RwGH1rMDMxtB
+/PzDgd+6+8IKVe8A3k2Wj61nHyIiIiIiIiLSvxqdkJiS3K4AnqhS78HU8oF17mMiMXhleTvrcfe1
+wKNET4yJfeyJISIiIiIiIiL9oNEJid2Ingtz3L2nSr0Xyrapx4QK7VTbTycxkKaIiIiIiIiIDICG
+JSTMrAvYIrk7v1pdd19K9KKAmKazHuNSy1X3A8xLLde7HxERERERERHpJ43sITEytby8hvrFhMSI
+Bu5nRWq53v2IiIiIiIiISD9pZEJiaGp5TQ31VxPjOwxr4H5Wp5br3Y+IiIiIiIiI9JNGJiRWpZaH
+VKxV0kWMN7GygfvpSi3Xux8RERERERER6SedDWx7WWq5lp9HDE9ua/l5R1/3Mzy1XO9+NgdYtGgR
+o0eP7rVyR0cHHR2ayCOv1qyJzjRHHnkkQ4bUki+TvFNM24vi2V4Uz/aieLYfxbS9KJ6toVAoUCgU
+eq23aNGi4uLmDT2gAdKwhIS7rzazxcSFG1etrpmNIpIFzvoDT9YiPZDlOOD3VeqmB7Ksdz9WXEg9
+KaTFKZbtRzFtL4pne1E824vi2X4U0/aieLYd671K62lkDwmAmcBBwE5mNqjK1J+7ppZn9WEf6Xbu
+qlK3uJ91wIt17mc1pZ+VLKmhfgGoNtWpiIiIiIiI/GkaBNTSpX5zIhmxureKrajRCYlHiITEcGA/
+YEaFeoeklv+rzn3MIAazHJy0862sSmY2GJhMJBRmuHvv/WNS3H1477VEREREREREpBaNHNQS4Oep
+5dOyKpiZAackd5cC0+vZgbsvB6YRWaOPmNmYClWPAzZNlu+oZx8iIiIiIiIi0r8ampBw9xnAw0Sy
+4HQzm5RR7TxgN6LnwnfKey6Y2alm1pOUSyrs6srkthO4xszWOy8z2wL45+TuUuDf+3RCIiIiIiIi
+ItIvGt1DAuBsYorNwcB9ZnaRmU0ys6lmdj1wRVLvD8DVVdrxiivcpwO3EomPTyb7OcbM9jOz04Df
+AdsmbVzg7u9s9FmJiIiIiIiISJ81egwJ3P0pMzse+DHxk4lvllchkhFHu/uKjdjVXwIjgY8BU4FD
+y/ZRAP7B3dU7QkRERERERGSANaOHBO5+D7AX8C9E8mEF8DYxIOUFwL7u/kq1JmrYxyp3PwY4CbgP
+eIMYiXQucAswxd2/sTHnISIiIiIiIiL9w9x7/awvIiIiIiIiItKvmtJDQkREREREREQkTQkJERER
+EREREWk6JSREREREREREpOmUkOiFmW1rZleZ2SwzW25mi83scTM7z8yGDfTxtTIz29LMjjazy8zs
+l2a2yMx6knJjH9o7yszuMLN5ZrYqub3DzI6so41hZnZBEuPFScxnmdmVZrZtHe3sbmbXm9kcM3vP
+zN40s4fM7Atm1lHvubWCZJrdr5nZr1MxWGZmfzCzG83swDrbUzwHkJmNNLMTkmv1gJm9aGZLzWy1
+mb1hZtPN7Hwz27zG9hTPnDKzK1KvvT1mdnAN2yieA6wsZtXK/TW0pXjmjJltY/H+aEZyDVaa2dzk
+WlxmZrv3sr1iOoCS/5u1/o32+tqreOaHmQ02s78ys1+Z2UIrvd99weL97gE1tqOYFrm7SoUCHAMs
+BXqIaUPTpQd4AdhxoI+zVUtyDdMlfX1vrKMdA/6tQjvFx66voZ2dgNlV4r2UmJ62t3bOAFZVaedR
+YPOBvv79HMuHKlz/8jjcDAxWPPNfgMOrxDN9/m8CRyierVmAvYE1ZdfhYMUz/6WGv89imaZ4tlYB
+zgKWVYlxD3C1YprfAkyv8e+zeB3WAlsrnvkuwLbAcxXikY7Jd6q0oZiWH8NABzavBdiHmJ60ALwD
+XAhMAqYC16UCNQsYPtDH24ql7In/CnBv6rF6EhL/lNpuBnA8sF9y+z+pfVxepY0RxJS0xXZ+kMR6
+EnBR8hzoId4g7FWlnY8B65I2FgJ/DXwIOAK4LXUsD5LMctMOBXgxOa95wNXAsUkM9gfOJqbfLb6w
+/VjxzH8hEhKvAjcCXwI+mcRzMvAZ4Fbig2wPsBLYU/FsrUK8KXo8Oefu1PWtlpBQPHNSUtfv+8CE
+KmW84tk6Bbg4dS1nAecABwF7AYcm9x8GrlRM81uA8b38XU4APpu6xr9SPPNdgE5KyYgC8HvgZOK9
+0eHApcC7qetwgWJa47Ud6ODmtVD6xnc1sH/G+nNTT4JLBvp4W7Ekf7gfA7ZM7o9PXdOaEhLAzpS+
+3XsU6CpbP4x4w12M5Q4V2vmH1L7PyVh/QGo/91dooxOYk7TzNrBdRp3vp/ZzykDHoB9jeRdwXKUX
+K2BzokdR8dynKJ75LrX84yGSFMXz/5ni2VoF+HJyvs8Dl6fOPTMhoXjmq7CR70EUz/wV1u+ZdiPQ
+UaVup2La2gW4InX+Jyqe+S7E+9zi+T1MxvskYN8kFgVgMTBIMa3h2g50cPNYgImpi39NhTpGvInr
+SZ5wFf9pqNR83fuSkLg2tc3ECnUmper8a8b6zuQPsQA8V2VfP0i1s1/G+nSm+/wKbQxLni8F4NmB
+vuZNju/RqeuT2ZVN8Wy9QnyD1wO8oXi2TgG2ofRNzkFEgri3hITimaOSOv++JiQUzxwV4n3lbErf
+vA7qQxuKaYuUJN7zkmv0DjBU8cx3Aa5KXYOKP4UAbk/V210x7b1oUMtsn0ot35xVwSNK/5HcHUV0
+o5Pm+wTgwAvuPiOrgrs/RnRrMuIb3XKHApslyz+ssq+bU8vHZqxPP28y23H3lcBPk2OZYGY7Vdlf
+u5meWt6xQh3Fs/UsS26HZqxTPPPrWmA4cLO7P1zjNopne1E88+UI4jfhAP/s7j19aEMxbR2HA2OJ
+eN3m7qsy6iie+TIktfxylXovVdgGFNNMSkhkm5LcrgCeqFLvwdRyXTMIyMYzs+2BMcndB6vVTa0f
+a2bjy9ZNyaiX5X+A95LlrHgX2/mDu79Zw7FUaqdddaWWC+UrFc/WY2a7EIMiOvGTnPQ6xTOnzOx4
+osfSEuD8GrdRPNuI4plLn01uHbin+KCZvc/MdjKz91XbWDFtOaekln9UvlLxzKU/pJZ3qFKv+KWb
+E2OsAYppNUpIZNuNeBLN6SVDnX4DvltjD0kyTEgtv1Cx1obry2NVUzvuXiB+a2XlbZjZcKIL9AYf
+zOo8lnY2NbU8K2O94tkCkimmdjKzc4AHiK6DAP9SVlXxzCEz2ww9IBOUAAALVElEQVT4LnEtLnD3
+JTVuqnjm1/Fm9ryZrTCzd81stpndbGZTq2yjeObP5OT2VXdfYWafN7Nnie7Ss4HFybSC55pZ+beu
+oJi2jOT6HEtcn9fcPetDpeKZPz8hfupowIVmtsHnaDPbh0j4O3CLuy9PrVZMK1BCooyZdQFbJHfn
+V6vr7kuJXhQQQZXmGpdarhor4nd6ReWxKrazwt3frbGdLc1scAOOpS2ZmREz1RT9NKOa4plTZnZq
+cZ504jVvNnAlMJr4Z/ZP7n5r2WaKZz59G/gA8Ii731THdopnfu0G7Er8bGo48e3cKcD9yZz2m2Zs
+o3jmSPI/clfi9fQtM/sO8GPig4enys7E3/D9GXFVTFvHccTfKmT0jkgonjnj7ouJWTVWED0FZpjZ
+yWY2ycwON7NLiS9qBhM97M8ra0IxrUAJiQ2NTC0vr1irpJiQGNGAY5Hq6onVitRyeayK7dQT7/J2
++utY2tU5xLRIDtzu7k9m1FE8880zylPELEQXZ9RXPHPGzA4CTifmuz+zzs0Vz/xZQXxjdwYxMOk+
+xDgE/wi8RfyNfgr4uZl1lG2reObLZpTek+8F/C0xBd9JxCxVmwCHEKPyOzGC/o1lbSimraPqzzUS
+imcOufsviCk6/534yeoPgd8B9xGDQ68gZrA62N0XlW2umFaghMSG0gOzramh/mqiK8ywxhyOVFFP
+rFanlstjVWyn1nhntdNfx9J2zOwQYs5lgDeIOY6zKJ759Z/AnknZHzgxeWxv4FYzOzpjG8UzR5Jv
+Rv5vcvdqd8/62VQ1imf+jHX3P3f3G939v939GXef5u6XALsTCUOID7JfLNtW8cyX4anlocQHgKnu
+fqu7v+Puq939EWIgxGeI953HmtnEsu2KFNOcMrOxxE9YHXjU3edUqKp45lDyv/QvKA02Wf5FzQeI
+XhQfzdhcMa1ACYkNpUe5zfqNXrku4gm4sjGHI1XUE6v0gIrlsSq2U2u8s9rpr2NpK2a2O3AHMc7A
+SuCz7v5WheqKZ065+7vuPjMpT7j7T939M8S3PDsQ38CeUraZ4pkvfw/sArxGzF9eL8UzZ6p11U2+
+mfsM0RsG4KyyKopnvqSvgQM3ZH1QTWZi+PvUQydUaEMxza+TKX3+urlKPcUzZ8xsE2AacBHwPuAK
+4idzXUQvpyOAR4APEe+LvlzWhGJagRISG1qWWq6lW0oxq11LtxnpX/XEKv3tQ3msiu3UE+/ydvrr
+WNpGMprwr4kX7XXACe7+X1U2UTxbjLvfAtwGdADfN7NRqdWKZ04ks6FcRHzQOSuZxqteimeLcfdX
+iG7EBuxkZlulViue+bKs7P59VepOI/6nAqR7SCimreHPk9vVZI+nVaR45s9lxMwUDvylu3/V3We7
++zp3X+7u04gpOacTr7vfNrM9U9srphUoIVHG3VcTIxrD+gN+bCB5810M0rxqdaUh0oOwVI0V6w/C
+Uh6rYjvDKwz+ldXOIndfm3p8QT8dS1swszHAb4npjXqA09z97l42Uzxb053J7XDgyNTjimd+fIX4
+BuRlYISZnVBegD1S9Q9PrSt2z1Q8W9PM1PLY1LLimSPuvgZYRHyIgSrnlrxPLfY03DK1SjHNOTPb
+j9JApXe7+ztVqiue+XMayVSe7v7jrArJ7IxfS+4OIn7eUaSYVqCERLaZlL5RqHaNdk0t1/t7XNl4
+6Tdau1asteH68ljV1E4yKNiOxIvRem0k0/rMI543G3MsLc/M3k98u7M9ca2+lHyT3hvFszWlB21K
+z5WteOZHsbvljsQgiFnluKSOEW+mfgL8P0ofeBTP1uQVHlc88+f51HL5IKTliuvXpR5TTPPv1NTy
+D3upq3jmiJl9gBhgFuD3vVR/IrWcvh6KaQVKSGR7JLkdToykWskhqeVqXdGlAZLuqAuTu4dUqwsc
+nNwucPfXytY9klqu1s6HKPWIyYp3sZ1dzGx0lXba9nmTZGp/Q/ymzoEL3f26WrZVPFtW+lvXP3bl
+UzxzJ2uWlPKSVTceUDxbVXq++mL8FM98eii1vEOlSmY2ktL09H/8llMxzTcz66Q05sci4N5q9RXP
+3Ekn/zp7qZueXvOP2ymmlSkhke3nqeXTsiokc0YXB3FbSvxeSJrvTpLsnpntn1XBzCZTmt/75xlV
+HgCK3eZOzVhflH4u/GfG+nTbf1HhWIYBxyfHMrPK6MotJzm3XxJTzzlwubtfWWczimfr+Wxq+dmy
+dYpnDrj7ae7eUa1QGujSidH9O9y9093npppSPFtIMo7PR4nzf8ndu8uqKJ75cntq+dgq9T5N6acd
+D5etU0zz6yiix5kDtyRd+3ujeObHEqA4kPDkXnrQT00tv1K2TjHN4u4qGQV4kPjt+2pgUsb685P1
+BeBrA3287VCI7t7Fa3pjjdvsTIwiXgAeA4aWrR8KPJ6K5Y4V2rkste9zM9YfQEyLUwCmVWijE5iT
+tPM2sH1GnWtS+zl5oK95P8ZuMDGAZfHcrupjO4pnTgrxT66rlzpfSc6/B3gRMMWzNQsxf3rx3A+u
+UEfxzEkBPg50VFn/AaJbcfH8z1Y881+Ae5JzXAscmrF+K2BuUmclsLVi2hqFGAC6eK5717iN4pmj
+AtySOr/Mz37EQO7Ppep9RDGt4doOdHDzWoC9iXmge4iM2EXAJCLrdT2lN+EzgeEDfbytWIADiQ89
+xXJu6kn/UNm6U6u0881UPJ4gMnn7JbdPpNr8RpU2RgAvpNq5Lon1JODvkudAD9Elfc8q7RxFdM/q
+AbqBvyFGwT4C+FnqWB6g7MNbKxfim53iud0H7N5L2VnxzHchsvpvJa93JwMfBvZK/m7PJL6ZK17f
+lWS8eVY8W6dQQ0JC8cxPAV4lBjb7LvA5YDLwQeBw4HLgzbLzH6x45r8QH1aWJOf6XhKfKUlM/ppS
+MiLzQ4hims8CjCL+TxaAp+vcVvHMSSGmzl6euo53Ej2W9iZeg79CvDYXr8OvFdMar+1ABzfPBTia
+yBgVUgHvSQVnJhnZJJWar+9NGde1UilUaceAG5KYlMeq+Nj1NRzPjskfd6V4vw0cVUM7p1P6x5PV
+zn8Dmw/09e/nWNYax2J5WfHMdyESElnnXB6LV4HDFM/WLtSekFA8c1Dq+Pv8/8CmimfrFCL5u7DK
+NVgHfF0xbZ1CJPGL53tOndsqnjkqwGHAGxXOPx2T3wCbKaY1XteBDmzeCzHVyZXEqKLLiClBHyO+
+zR860MfXyoVISBRqLOtqaO9I4A5i1NiVye0dwBF1HNMw4LwkxouTmM8Evg1sU0c7E4hs5YtET5s3
+iZ8BnQEMGuhr34BY1hrHYnlJ8cx3IWZJOYOYaeFJ4g3yamLMnNnE/Omn1Po6qHjmuxAJieKHnYoJ
+CcUzHwU4CLiY6OI/ixgkrzht+VPAtWT83FTxbI1CdPu+hPjZzdvJdZhDfIj5oGLaWoUYPLCQ/I1u
+1cc2FM+clOTv8zxgGvA6sIroiTCHmKHqGMW0vmLJQYiIiIiIiIiINI1m2RARERERERGRplNCQkRE
+RERERESaTgkJEREREREREWk6JSREREREREREpOmUkBARERERERGRplNCQkRERERERESaTgkJERER
+EREREWk6JSREREREREREpOmUkBARERERERGRplNCQkRERERERESaTgkJEREREREREWk6JSRERERE
+REREpOmUkBARERERERGRplNCQkRERERERESaTgkJEREREREREWk6JSREREREREREpOmUkBARERER
+ERGRplNCQkRERERERESaTgkJEREREREREWk6JSREREREREREpOmUkBARERERERGRplNCQkRERERE
+RESaTgkJEREREREREWk6JSREREREREREpOmUkBARERERERGRpvtfm26QBCO34nUAAAAASUVORK5C
+YII=
+"
+width=530
+height=356
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#26816;&#26597;&#39044;&#27979;&#32467;&#26524;">&#26816;&#26597;&#39044;&#27979;&#32467;&#26524;<a class="anchor-link" href="#&#26816;&#26597;&#39044;&#27979;&#32467;&#26524;">&#182;</a></h2><p>使用测试数据看看网络对数据建模的效果如何。如果完全错了，请确保网络中的每步都正确实现。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[102]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_loss</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">test_features</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">test_targets</span><span class="p">[</span><span class="s1">&#39;cnt&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+<span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;Test loss: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">test_loss</span><span class="p">)[:</span><span class="mi">5</span><span class="p">])</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
+
+<span class="n">mean</span><span class="p">,</span> <span class="n">std</span> <span class="o">=</span> <span class="n">scaled_features</span><span class="p">[</span><span class="s1">&#39;cnt&#39;</span><span class="p">]</span>
+<span class="n">predictions</span> <span class="o">=</span> <span class="n">network</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">test_features</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="o">*</span><span class="n">std</span> <span class="o">+</span> <span class="n">mean</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">predictions</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Prediction&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">((</span><span class="n">test_targets</span><span class="p">[</span><span class="s1">&#39;cnt&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">std</span> <span class="o">+</span> <span class="n">mean</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Data&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">right</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">predictions</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">rides</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">test_data</span><span class="o">.</span><span class="n">index</span><span class="p">][</span><span class="s1">&#39;dteday&#39;</span><span class="p">])</span>
+<span class="n">dates</span> <span class="o">=</span> <span class="n">dates</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">d</span><span class="p">:</span> <span class="n">d</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">&#39;%b </span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dates</span><span class="p">))[</span><span class="mi">12</span><span class="p">::</span><span class="mi">36</span><span class="p">])</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">dates</span><span class="p">[</span><span class="mi">12</span><span class="p">::</span><span class="mi">36</span><span class="p">],</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Test loss: 0.209</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABYMAAAMGCAYAAACgX5vSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAAewgAAHsIBbtB1PgAAIABJREFUeJzs3Xl4VOXd//HPnYSwyKIiCIIbSBXXCogiWpC6Umu1Vlut
+IlKXPq0LSlFrqYBWq6JWQXwU2rq1T12ex6WKxbogKqKC6E9QECqCBAKEJYSwZJm5f3/MZHJmciYz
+Sc7MnMl5v64rV05yztznPsM4cT7zne9trLUCAAAAAAAAALRuBbmeAAAAAAAAAAAg8wiDAQAAAAAA
+ACAACIMBAAAAAAAAIAAIgwEAAAAAAAAgAAiDAQAAAAAAACAACIMBAAAAAAAAIAAIgwEAAAAAAAAg
+AAiDAQAAAAAAACAACIMBAAAAAAAAIAAIgwEAAAAAAAAgAAiDAQAAAAAAACAACIMBAAAAAAAAIAAI
+gwEAAAAAAAAgAAiDAQAAAAAAACAACIMBAAAAAAAAIAAIgwEAAAAAAAAgAAiDAQAAAAAAACAACIMB
+AAAAAAAAIAAIgwEAAAAAAAAgAAiDAQAAAAAAACAA8j4MNsa0McZcYYyZbYxZZ4zZbYzZboxZZoz5
+qzFmSJrjnGWMecEYsyY6xproz2c2YS7tjTE3GWM+NsZsNsZUGmOWGmPuM8Yc0PyrBAAAAAAAAICW
+MdbaXM+h2aIB62uSDo/+KvFiTPT7VGvt2CRjGEkzJY1xGaPu9jOttVenmMsh0bkckmQeFZJ+bq2d
+1dg4AAAAAAAAAJAJeVsZbIwpUn0QbCV9Jmm0pCGSTpd0u6TK6L5rjTE3JRnqLkWCYCvpE0kXSRoc
+/b4o+vsrjDF/aGQuHSXNUn0QPEPS9yWdKOl3krZL6izpGWPM0c29ZgAAAAAAAABorrytDDbGnC/p
+eUXC1w8kfc8mXIwxZoCk+ZKKJJVL6matDTv295P0haRCSQskDbPWVjn2t5c0V9IgSTWS+ltrV7rM
+5XZJE6JzGW+tfSBh/5DoOIWS5lprR7Ts6gEAAAAAAACgafK2MliRqts6dycGwZJkrV0k6VVF2jTs
+Kal/wiE3KBIUS9K1ziA4evtdkq6N/lgUPT5OtEL5WkWC4KWJQXB0nPmS/hKdxzBjzMCUVwcAAAAA
+AAAAHsrnMLjYsd2gWtfh6yS3kaRzFAlxl1lrF7jd2Fr7kaSvFAlyf+RyyCmSukS3n2xkHk84ts9r
+5DgAAAAAAAAA8Fw+h8FfObb7NHJc3+h3K2lF3S+NMQdL2i/649wU56rb38sYc2DCvpNcjnOzUNLO
+6PbQFOcDAAAAAAAAAE/lcxj8D0kVilTs3myMaXAtxphjJf1AkSD479baSsfuwx3by1Kcy7k/sdVE
+WuNYa0OS/hOdb+IYAAAAAAAAAJBReRsGW2s3S7pU0g5FKm0XGGMuNcYcb4z5vjFmoqR3JLWR9Imk
+3yQM0duxXZLidGsc2/snGWeHtbYizXG6GWPapDgWAAAAAAAAADxTlPoQ/7LWvhJdjO03kn6hhj17
+10uaIGmmtXZ3wr5Oju1KNW6HY7tjknFSjeE2ztY0bgMAAAAAAAAALZa3lcGSFK2uHa36hd1swte+
+ilQPn+Zy83aO7eoUp6pybLdPMk6qMVKNAwAAAAAAAAAZk7dhsDGmg6S3JN0iaS9J9yjSi7etpC6S
+Tpf0vqRBkl4yxoxNGMJZKVyc4nRtHdu7koyTaoxU4wAAAAAAAABAxuRzm4jJkk5SpAJ4jLX2b459
+lZLeMsbMkfSGpFMkTTHGvGWtXRw9Zrvj+MTWD4n2SBjbqW6cVGOkGqdRxpgdioTJVtKWNG4SkhRu
+yjkAAAAAAACAPFQgqTCN4/aWZCRVWWv3SHVwa5TPYfDligSjKxKC4BhrbdgY83tFKoQLFGkpMS66
+27loXG81zrlo3JqEfSWSjpe0hzGmc4pF5OrGKbPW1qQ4Z6K2qn9Qd2/ibQEAAAAAAABEtE19SOuU
+l2GwMWZfRZJ8K2lRisM/cWwf5tj+Msnv3Tj3L03Y96Wk8x3Hfew2gDGmUFJfReacOEY6wpIKjTHa
+Z599Uh5cWFiowsJ03hBJrbq6WmVlZerWrZuKi9PphoGW4j7PPu7z7OM+zz7u8+zjPs8+7vPs4z7P
+Pu7z7OM+zz7u8+zjPs8+7nPvhEIhhUKhlMeVlZXVbdqMTsjH8jIMllTr2E51DW3cbmet/cYYs05S
+T0nDUozxvej3tdba1Qn73ndsD1OSMFiR3sV7KPJgm5fifG42Suq13377qaSkJOXBXlq0aJEGDhyo
+2bNna8CAAVk9d1Bxn2cf93n2cZ9nH/d59nGfZx/3efZxn2cf93n2cZ9nH/d59nGfZx/3efZ17969
+LhBOpwVrq5SvC8htkVTXjuEEY0xj1zHcsf1Nwr6XFekTcpgxZrDbjY0xJyhS8WslveRyyDuStkW3
+L2tkHpc7tl9s5DgAAAAAAAAA8FxehsHWWitpliJBbi9Jv3M7zhizl6S7Hb96NeGQBxVZaE2Sphlj
+2iXcvp2kqdEfayU95DKXmugxRlJ/Y8y4xGOMMUMkjVEkUH7HWvtJ4jEAAAAAAAAAkEl5GQZH3S5p
+pyIh7GRjzMvGmB8bY75rjDnBGHODpE8lHa5ICPumtfZN5wDW2hWSpkTHOE7SPGPMhcaYgcaYCxVp
+5zAoevt7rbVfJ5nLFEnLo+NMMcY8aowZbow53hjzW0mvK9LOYpeksZ7eCwAAAAAAAACQhnztGSxr
+7VfGmHMk/UPSPpJ+GP2KOyz69ZakC5MM9TtJ3RSp3P2upGdcbv9na+3vG5lLpTHmB4pUK/eTdFX0
+yznONkkXW2sXp3WBAAAAAAAAAOChfK4MlrX2bUX6+d4saY4ii6xVK1IxvFLSc5LOtdaebq3dlmQM
+a629UtIPFOkhvFZSVfT7y5LOstZencZcvpZ0bHQuCyRtlbRD0jJJD0g62lr7r+ZfLQAAAAAAAAA0
+X95WBtex1m6VdF/0qyXjzJY0u4Vj7PJiLgAAAAAAAADgtbyuDAYAAAAAAAAApIcwGAAAAAAAAAAC
+oHDSpEm5ngPSMHny5Bslde7cubNuvPHGrJ+/Y8eOGj58uDp16pT1cwcV93n2cZ9nH/d59nGfZx/3
+efZxn2cf93n2cZ9nH/d59nGfZx/3efZxn2fXAw88oMrKSkmqmDRp0v25nk8uGGttrueANBhjSiT1
+6tWrl0pKSnI9HQAAAAAAACCv9O7dW2vXrpWktdba3rmeTy7QJgIAAAAAAAAAAoAwGAAAAAAAAAAC
+gDAYAAAAAAAAAAKgKNcTAAAAAAAA+WPQoEFav359rqcBIMB69OihhQsX5noaeYkwGAAAAAAApG39
++vV1CzABAPIMYTAAAAAAAGiygoIC9ezZM9fTABAgpaWlCofDuZ5GXiMMBgAAAAAATdazZ0+VlJTk
+ehoAAqR37958MqGFWEAOAAAAAAAAAAKAMBgAAAAAAAAAAoAwGAAAAAAAAAACgDAYAAAAAAAAAAKA
+MBgAAAAAAAAAAoAwGAAAAAAAAAACgDAYAAAAAAAAAAKAMBgAAAAAAAAAAoAwGAAAAAAAAAACgDAY
+AAAAAAAAAAKAMBgAAAAAAACB8OSTT6qgoEAFBQUaM2aM6zFz586NHTNixIgszzA9l19+eWyOTz31
+VK6ngzxCGAwAAAAAABAAw4cPjwWIbl+dO3fWwQcfrHPPPVcPP/ywKioqcj3ljDHGeHJMruXDHOEv
+hMEAAAAAAAABYIxp9GvHjh1avXq1/vnPf+q6667TAQccoKeffjrX084Za21WzkOVL7KpKNcTAAAA
+AAAAQHZYa2WM0XHHHafBgwfH/b68vFwLFizQihUrJEkVFRW67LLLVFVVpSuuuCJXU86JuorbbFbe
+NvVcdSE+0BSEwQAAAAAAAAEzcuRI3Xbbba77Xn75ZV1++eXatm2brLW67rrrNHLkSO23335ZnmVu
+DBs2TKFQKNfTaNTjjz+uxx9/PNfTQB6iTQQAAAAAAABifvSjH+lvf/tbrIq4qqpKjzzySK6nBcAD
+hMEAAAAAAACIM3LkSB1zzDGxvrlvvvlmjmcEwAuEwQAAAAAAAGjgxBNPlBTpJ7xy5cq4fQcddFBs
+0bNvv/1WkrRy5UpNmDBBAwYMUPfu3VVYWKgBAwYkHb+kpER/+MMf9L3vfU+9evVSu3bt1LVrVw0Y
+MEDjx4+P9S5O16effqqrrrpKffv2VYcOHdS9e3cdf/zxmjJlirZu3Zr2OHPnzo1d24gRI9K6zcaN
+GzVlyhSdfvrpOvDAA9WhQwd16NBBBx54oEaOHKkpU6Zo9erVcbepuw+ffPJJSZH7efTo0bFzO79u
+v/32uNs2ddG5HTt2aOrUqTrzzDO1//77q3379tp777111FFH6dprr9XHH3+c1nXWnbOwsDD2u+XL
+l2vs2LE6/PDD1alTJ3Xp0kXf/e53deutt2rz5s1pjYvsoWcwAAAAAAAAGthrr71i2xUVFXH7Ehcv
+mzFjhsaOHavdu3enXNTMWquJEyfqvvvu0+7du2PjSVJ5ebm2bt2qzz77TA899JBuuukm/eEPf0g5
+1wkTJuiee+6J9fqta2+xefNmLViwQNOmTdPzzz+f3oU7rjEVa63uuOMO3Xvvvdq5c2eD25WUlGjN
+mjWaPXu2fvvb32rJkiU67LDDYsfVHVtXgd2cReRSefXVV3XVVVdp/fr1cbeprq5WeXm5vvjiC02f
+Pl0XX3yxZs6cqfbt26d9zkcffVQ33HCDqqqq4n7/+eef6/PPP9fMmTP1+uuvN/qmALKLMBgAAAAA
+AAANOKtpu3Tp0mB/XU/h5557TjfddJOMMerVq5eGDh2qLl26aN26ddqyZUvcbcLhsC688EK98MIL
+sTC0V69eGjx4sLp166bKykp99NFH+vrrr1VbW6u77rpLmzZt0qOPPpp0nrfeeqvuvvvu2HgdOnTQ
+iBEj1LNnT61fv15vv/221q5dq5EjR2rs2LGe3T/hcFg/+clP9NJLL8XOXVxcrCFDhuiggw5SmzZt
+tH79en3yyScqLS2VtVbV1dWx248ePVqbN2/Wm2++qWXLlskYo+9///uxsNhp8ODBzZrjs88+q0su
+uUThcFjGGBUWFuqkk07SIYccosrKSr333ntat26dJOl//ud/tGrVKr399tsqLi5OOmbdv/uTTz6p
+X/3qVzLG6LDDDtOgQYPUvn17LVu2TPPmzZO1Vps3b9Y555yjpUuXqlOnTs26BniLMBgAAAAAAAAN
+zJs3T1KkEvTggw9usL+uEvTWW29V27ZtNX36dI0ZMybumJqamrifJ02aFAuCe/TooUceeUQ/+tGP
+Goz9f//3f7ryyitVXl6umTNn6tRTT9VPfvKTBse9++67uueee2JzueCCC/TYY4/Fhdfbt2/XL3/5
+Sz3zzDO68847m3gvJHfrrbfGgmBJuvbaazVx4sS4iuo6Cxcu1PTp09WmTZvY7yZOnCgp0vJh2bJl
+kqRLLrlEo0aN8mR+K1eu1JVXXqlwOCxJOv744/X3v/+9wb/lgw8+qPHjxyscDmv+/Pm66aab9OCD
+DyYdt+56f/nLX6p79+56+umnddppp8Ud8/777+vss89WRUWFSktL9dBDD2nChAmeXBdahp7BAAAA
+AAAAiDNr1ix9/vnnseDv1FNPdT3OWqtQKKTHH3+8QRAsKS78XL16tf74xz/KGKO9995b8+bNcw2C
+Jen888/XCy+8EPt50qRJrsf99re/jbVYOPXUU/WPf/yjQRVzp06d9Le//U2nnXZaXGVuS6xYsUL3
+3Xdf7P65++679eCDD7oGwZI0aNAgPf744+rfv78n50/H5MmTVVlZKWutDjnkEL3++uuuof7YsWM1
+ZcoUWWtlrdX06dMb9DdOVFcd/NZbbzUIgiXppJNO0l133RX7+R//+EfLLwieIAwGAAAAAABAzEsv
+vaRLL71UxhhZa9W2bVv913/9l+uxxhgNHjxYP/vZz1KO++CDD8Z6+k6cOFEHHXRQo8cPHz5cZ5xx
+hqy1Wrp0qT777LO4/cuWLdP8+fNjP0+dOjVpD11jjKZNm9ag13FzPfDAA7GK2xNOOEHjx49v8Zhe
+2rZtm5577jlJkWufMmVKo20arr/+eh1xxBGSIu0vZsyY0ej4xhhdffXVsdu4GTVqlIqKimSt1Vdf
+faXKyspmXAm8RpsIAAAAAADgG4MGSdF1rvJSjx7SwoW5nkVqs2bNUllZWdzvysvL9fHHH2vFihWx
+3xlj9Kc//Um9evVqMEZddWg6QbAk/etf/4ptX3TRRWndZsSIEXr99dclRVoPfPe7343tmzNnTmyO
+AwcO1KGHHtroWP369dMJJ5yg+fPntzgQrpuTJF1zzTUtGisTPvjgA1VVVUmS9tlnH5199tmNHm+M
+0ZgxYzRu3DhJ9fdtY9zadjh17NhRffv21VdffSVrrVavXt1oeIzsIAwGAAAAAAC+sX69tHZtrmfR
++i1YsEALFixw3VcXlHbq1ElTp05N2cN24MCBKc+3ZcsWLV++PLbIWrK2D4m+/PLL2PaaNWvi9n36
+6aex7SFDhqQ13pAhQ+KqiZtj48aNWrVqVeznU045pUXjZULdfVNXuV1QkLo5wNChQyVFQn7nfZuo
+7k2Ao446KuWYXbt2jW1XVFSkPB6ZRxgMAAAAAAB8o0ePXM+gZfJl/m6VsR07dlTXrl119NFH69RT
+T9WoUaPUuXPnlGN169Yt5TGlpaWx7aqqKk2fPr1J87XWauvWrXG/c1Y2H3DAAWmNk+5xjdmwYUNs
+u23bturhw390531z4IEHpnUbZ9uO6upqVVZWqmPHjkmPT+ex4ewZnbiYIHKDMBgAAAAAAPhGPrRY
+aA0mTpyo2267zZOx2rdvn/KYbdu2xbab26Khrt9wHWcP2g4dOqQ1xh577NGscztt3749tt1YWJpL
+zvsm3WtOPG779u2+vT40H2EwAAAAAAAAMqouaLTWqkuXLg2qfJvDGVTu3Lkzrdvs2LGjxed1LsTm
+10XRnPdNuteceFxjC84hf6VuGAIAAAAAAAC0wL777hvbrqio0O7du1s8prM9xbfffpvWbRL7DjeH
+81qqqqri2kb4RXPuG2cf5OLiYqqCWynCYAAAAAAAAGRUjx49tP/++8d+/uCDD1o85rHHHhvb/vDD
+D9O6TUsXj5Ok7t27x/XXffvtt1s0XnPbZjSm7r6x1urjjz+WtTblber+TYwxcfctWhfCYMSx1ioU
+DqU+EAAAAAAAoAnOPvvs2PYjjzzS4vFOOeUUSZEsY+HChVq+fHmjx//nP//R/PnzPQlfzzrrrNh2
+UxfDS9SuXbvYtleLrJ144olq27atpMhicrNmzWr0eGutHn/88djPI0aM8GQe8B/CYMTsqN6hYx49
+Rn2n9tWq8lW5ng4AAAAAAGhFxo0bp8LCQllr9eKLL+rJJ59M+7ZurRgOO+wwnXjiibGfr7/++qS3
+t9bquuuuk7U2rSrZVMaOHauCggJZazV//nzde++9zR6ra9euse21a9e2eG6S1KVLF/30pz+N/Tx+
+/PhGewdPmzZNixcvliQVFBToqquu8mQe8B/CYMS89c1bWrxxsVZvW60Xl76Y6+kAAAAAAIBWpE+f
+PpowYYKkSDg7ZswYjR8/Xps3b3Y9PhQK6Y033tCll16atG3BnXfeKWOMrLX697//rYsuukjbtm2L
+O2b79u265JJLNHv27Fi1bEv169dP48aNi13LLbfcouuuuy7pwngff/yxLr/8ci1durTBviOPPDK2
+/fLLL3tWHXzbbbepY8eOstZq+fLlOv300/XNN9/EHWOt1UMPPRS7FmOMrrnmGh1wwAGezAH+U5Tr
+CcA/dtfWN2+vClXlcCYAAAAAAKA1mjhxolavXh2rCr7//vs1bdo0DRo0SH379lWHDh1UUVGhVatW
+6fPPP49Vs+6zzz6u4w0bNky/+c1vNGXKFFlr9eyzz+rVV1/ViBEj1KNHD23YsEFvv/22Kisrtffe
+e+v666/XxIkTPbmWu+66S1999ZVeeeUVWWv18MMPa8aMGRoyZIgOPvhgFRUVaf369frkk09UWloq
+Y4xuuOGGBuOcddZZat++vXbt2qVPP/1U/fv31/Dhw7XnnnvGWlqcccYZOvXUU5s0vz59+ujPf/6z
+LrnkEoVCIc2fP1+HHnqoTj75ZPXt21eVlZV67733YtXIxhgNGTJE99xzT8vvHPgWYTBinL2Cwzac
+w5kAAAAAAIDW6q9//asGDhyoiRMnauvWraqpqdEHH3zQYFE5Y0zs66STTko63j333KOioiLde++9
+CofD2rlzp1555ZW4cXr37q3nn39ey5YtS3ueqdpJFBYW6qWXXtLvf/973X///aqqqlJNTY3mzp2r
+uXPnNriOoqKiuP7AdTp37qwHHnhAv/71r2Wt1cqVK7Vy5cq4Yzp16tTkMFiSLrzwQnXs2FFXXHGF
+NmzYoFAopDlz5mjOnDlxc5Okiy++WDNnzlRxcXGTz4P8QZsIxIRsfRjMInIAAAAAALQ+Xiye5hyr
+ueP9+te/1urVqzV9+nSde+656tOnjzp16qQ2bdpo77331tFHH62f/exnevTRR7V69Wq98MILjY53
+55136uOPP9aYMWN00EEHqV27dtpnn300aNAg3XXXXfrss890/PHHx+bt/N7YtaVzfXfccYdWrFih
+22+/XSeffLL2228/FRcXq0OHDjrooIM0cuRI3X///Vq1apW+853vuI5x9dVX67333tOll16qQw89
+VB07dlRBQUGjc0h3fiNHjtR//vMfPfTQQzrttNPUq1cvtW3bVnvuuaf69++vX/3qV/rwww/19NNP
+u4bVzb1fmjpPZIfxoml2Lhhj3pH0vSbebLi19t0k450l6UpJx0nqJqlM0gJJM6y1s9OcU3tJ10r6
+iaS+ktpKWiNplqSp1tpvmzhf59glknr16tVLJSUlzR2mUU9+9qRGvzxakjRp2CRNHO7NxyYAAAAA
+AK1H7969tXbtWmXy9SkAuGnp80/d7SWttdb29nyCeSCf20TY6Fc6jKSQpBUNdkTempgpaYxjXEna
+T9K5ks41xsy01l7d6AmMOUTSa5IOSZjXdyQdKukKY8zPrbWz0pxz1jlbQzirhAEAAAAAAADkv3wO
+g0dL2iPFMUdIelaRcPYta22pyzF3KRIEW0mLJE2R9LUilb03STpWkSC3zFo7we0kxpiOilT/1gXB
+M6Ln3SXpFEm/ldRZ0jPGmKHW2s/Tv8zscQbA9AwGAAAAAAAAWpe8DYOttatTHWOMuczx45Mu+/tJ
+GqdIgLtA0jBrbVV09yfGmFckzZU0SNJ4Y8xfrbUrE8dRJDTuFx1nvLX2Ace+j4wxc6PjdJD0oKQR
+qeaeCywgBwAAAAAAALRerXYBuWj7h4ujP1ZKetHlsBtUH4hf6wiCJUnW2l2K9ABW9LgbXM5TFD3G
+SlqaEATXjTNf0l8UaVcxzBgzsMkXlAUsIAcAAAAAAAC0Xq02DJb0fUm9FAlpn7fW7nY55pzo/mXW
+2gVug1hrP5L0lSJB7o9cDjlFUpfodoPqY4cnHNvnNTrzHKEyGAAAAAAAAGi9WnMYPMqx/XTiTmPM
+wYosEidFWjg0pm5/L2PMgQn7TnI5zs1CSTuj20NTnC8nWEAOAAAAAAAAaL1aZRhsjNlDkepbK2m1
+tdYtpD3csb0sxZDO/f2bM461NiTpP4pUGCeO4QssIAcAAAAAAAC0Xq0yDJZ0vqQ9otsNqoKjeju2
+S1KMt8axvX+ScXZYayvSHKebMaZNimOzztkmgp7BAAAAAAAAQOvSWsPgRltERHVybFemGG+HY7tj
+knFSjZFqnJyjMhgAAAAAAABovVpdGGyM6SVpuCItIj601v4nyaHtHNvVKYatcmy3TzJOqjFSjZNz
+9AwGAAAAAAAAWq9WFwZLulT11/VEI8ftdmwXpxizrWN7V5JxUo2Rapycc7aGoDIYAAAAAAAAaF2K
+cj2BDLgk+r1K0nONHLfdsZ2qZcMeju3EdhB146TT9qGxcdJSXV2tRYsWpTyuZ8+e6tmzZ5PGdlYD
+0zMYAAAAAAAA+aC0tFSlpaUpj6uuTueD/a1bqwqDjTEDJR2uSIuIV6212xo53LloXO+kR0U4F41b
+k7CvRNLxkvYwxnROsYhc3Thl1tqaFOd0VVZWpoEDB6Y8buLEiZo0aVKTxo6rDBaVwQAAQJoxQ3rl
+Femuu6Sjjsr1bAAAAICGHnvsMU2ePDnX08gLrSoMlnSZY/vJFMd+6dg+LMWxzv1LXcY533Hcx24D
+GGMKJfVVJKhOHCNt3bp10+zZs1Me19SqYInKYAAAEG/nTum666SqKqlDB+nZZ3M9IwAAAKChq6++
+Wuecc07K484880yVlZVlYUb+1WrCYGNMkaSfRn8sk/Svxo631n5jjFknqaekYSmG/170+1pr7eqE
+fe87tocpSRgsaZAibSKspHkpzpdUcXGxBgwY0NybN8rZJ5iewQAAoLIyEgRL0qpVOZ0KAAAAkFS6
+7VKLi9NZ8qt1a00LyJ0lqZsiYevfrU0rzXxZkpF0mDFmsNsBxpgTFKn4tZJecjnkHUl17Sguc9lf
+53LH9otpzC3rWEAOAAA4hRwfFAp4AQUAAADQKrSmMHiUY/vpNG/zoKS6lznTjDHtnDujP0+N/lgr
+6aHEAaK9f6cqEir3N8aMSzzGGDNE0hhFAuV3rLWfpDm/rIprE2FpEwEAQNARBgMAAACtS6sIg40x
+e0o6W5GwdYm19rN0bmetXSFpiiJB7nGS5hljLjTGDDTGXKhIO4dB0XHvtdZ+nWSoKZKWR8eZYox5
+1Bgz3BhzvDHmt5JeV6Qlxy5JY5t9oRlGZTAAAHCqra3frqyUdu/O3VwAAAAAtFxr6Rn8M0ltFQlt
+Uy0cl+h3irSXGCPpu5Keceyz0a8/W2t/n2wAa22lMeYHkmZJ6ifpquiXc5xtki621i5u4vyyJsgL
+yG3ZtUW7a3drv0775XoqAAD4RijhfwfKyqT998/NXAAAAAC0XKuoDJZ0iSKBa62k/2nKDW3ElZJ+
+oEgP4bVuAyr+AAAgAElEQVSSqqLfX5Z0lrX26jTG+VrSsZJulrRA0lZJOyQtk/SApKOttY0uapdr
+QV1Abn3leu3/p/11wJ8O0KLSRbmeDgDAp0pLpX/+M1jVsW5hMAAAAID81Soqg621J3kwxmxJs1s4
+xi5J90W/8o6zGjhIPYPnfTtPO2t2SpLeXPmmBvQckOMZAQD8JhSShgyRVq+Wbr1VuvPOXM8oOwiD
+AQAAgNaltVQGwwPOADhIlcG14fqGiEG6bgBA+rZsiQTBkvTOOzmdSlYlhsGbNuVmHgAAAAC8QRiM
+mKD2DA5qCA4ASJ8zFF27NnfzyDbnAnISlcEAAABAviMMRowzAA5SKBrXHiNAITgAIH3OMHjdOikc
+kD+TtIkAAAAAWpdW0TMY3nAGwEHqGRzkymBrrcI2rMKCwlxPBQB8zVkhW1MTaZfQvXvu5pMthMEA
+ALQuw4cP17vvvuu6r23bturSpYs6d+6sfffdV8cee6wGDhyoESNGaP/998/yTAFkCpXBiAlqKBrU
+hfMqqyt19KNHq+/UvlpVvirX0wEAX0sMRf3WKmL55uX6ZN0nno9LGAwAQOtijEn6VV1drbKyMn39
+9deaN2+eHn74YV1++eU6+OCDdfbZZ+vf//53TuY8fPhwFRQUqKCgIGmQDSB9VAYjJqjtEoIagr/9
+zdtasnGJJOnFpS/qhiE35HhGANByu3ZJ1dVSly7ejpsYipaUSMce6+05mmt1+Wr1n95fYRvWO5e9
+o2EHDfNsbHoGAwDQ+lhrZYzRcccdp8GDB8d+Hw6HtW3bNpWXl+uLL77Q6ujqudZavfbaa3rttdc0
+evRoTZ06VR07dszafI0xcd8BtAxhMGKCGooGtVfyrppdse2qUFUOZwIA3ti8WTr00Egg/NFH0pFH
+ejd2Yijqp8rgBesWxP5+fbT2I0/DYCqDAQBovUaOHKnbbrst6f6NGzfq6aef1tSpU1VSUiJJeuKJ
+J/Tll19q7ty5atu2bbamCsBDtIlADD2Dg1sRHaTrBtB6zZ0bCYR37pRmz/Z2bD+3iagN1yfVXj+f
+J173pk2eDg8AAHyse/fuGjdunJYuXaoLLrggVlG8YMECjR49OtfTA9BMhMGICWqFbFCve/PW+ute
+s5YwGED+q66u366p8XZsP1cGZ/LvWGIYvGVLw/sCAAC0bh06dNAzzzyjH/zgB7LWylqr5557Tu+/
+/36upwagGQiDEbNzd/0rvsrK4ISiQW2Pseyr+lfzX68MznUDaL2cwWViiOnl2JLPwmCbuYVQ3e7H
+zZs9PQUAAMgTTz31lDp16hTr3XvnnXcmPXbRokW6++679cMf/lB9+/ZVp06d1LZtW/Xo0UNDhw7V
+hAkTtGbNmkbPV7do3Ny5cyVFehc7F5Nzfj311FMNbl9WVqYnnnhCo0eP1oABA9S1a1cVFxdrr732
+Uv/+/TVmzJicLYoH5BI9gxGzaUv9K77yiuBUisYtnBeg9hg1IdpEAGhdnBWrYY/f4/J1GJzBBWDd
+qoDLyqR99/X0NAAAIA/stddeGj16tKZNmyZJeuONN1ReXq4999wz7rjBgwdr4cKFsZ+dC7+VlZVp
+48aNmj9/vqZMmaI//OEPGj9+fNJz1t3WWttgrMZMmzZNN954o0LR/4lz3q6iokLbtm3TV199pSee
+eEIjRozQc889p7333jutsYF8RxiMGOcLSKvgVIoGtTK4NpTZEHzaNOmf/5TuvVc69ljPhweABpzB
+pdeVwb5uE5HBv2Nu9yOLyAEAEFwXXHBBLAy21ur999/X2WefHXfMmjVrZIxR27ZtdcQRR+iQQw5R
+ly5dZK1VaWmpPvroI23atEk1NTW6+eabZYzRb37zmwbnuuaaayRJL7zwgtatWydjjM4991z16tWr
+wbH9+/eP+3ndunUKh8MyxqhPnz7q37+/unXrpnbt2qm8vFyLFy/WF198IUl6++23ddppp+nDDz9U
+mzZtPLmfAD8jDEZMYBeQy2BFlZ/Vxl23t+HBjh3SuHGRnp133CG98IKnwwOAq0yGwYnjlZdHFqrr
+0MHb8zRHJj/hQhgMAACcBg4cqMLCQoWjryE//PDDBmHw+eefrx/+8IcaPny42rZt22AMa62efvpp
+XXPNNaqsrNSECRN0wQUX6MADD4w7burUqZKkxYsXa926dZKk66+/Xt/73vdSzvPQQw/VtGnTdN55
+56lnz56uxyxZskS/+MUvtGDBAn322WeaMmWKbr311tR3ApDn6BmMGCqDg1UZXBNyrD7vcXiwY0f9
+4k0rVng6NAAklc0wWPJPdXBcz2CP39QkDAYAAE7t27fX/vvvH2vbsGHDhgbHPPzwwzrjjDNcg2Ap
+0rJh1KhR+stf/iJJqqmp0aOPPurpPEePHq1f/epXSYNgSTryyCP1xhtvqEePHrLW6pFHHoldF9Ca
+EQYjJj4UDU6FbCZXYfez2iz1mEyxJgAAeCabbSIkqaTE23M0Vyb/jrld96ZNnp4CAADkmS5dusS2
+t27d2uxxzj//fHXs2FGS9Oabb7Z4Xs3RuXNnnXfeeZKk0tJSffnllzmZB5BNtIlAjDMADmplcJDa
+Yzh7Bnsd/jtDmG3bpO3bpU6dPD0FADQQCkna8xupuFKh0FHej53Al5XBtIkAALQCg2YM0vrK9bme
+RrP16NhDC69amPrAPFUX4ErS9u3bGz128eLF+vTTT7Vq1SpVVFSoqqoqbr8xRtZaLV68OCNzlSKL
+1n344YdaunSptm7dqh07dsRVADsXu/vss890xBFHZGwugB8QBiMmvrIoOKFoUCuD48ODzFaSrVkj
+HX64p6cAgAbKqr+VrusnFYS0Zvt7kk7ybGxfh8GOv2O1AWsTEQ5Hvor4P1oAaFXWV67X2u0++UOL
+BpwBcOfOnV2PefLJJ/XHP/5Ry5cvT2vMmpoabdu2La7quKW+/PJL3XzzzZo9e7ZCaX5sbBMfgUIA
+8L/OiHEGoWEqg1u9WkfP4ExWBkuEwQCyY3Xtx1JB5AlobcEH8jIMdmuX4JcweENZ/ZPut2u8/fvt
+5zC4slI67jipokJ67z2pT59czwgA4JUeHXvkegotku/zT2Xbtm2x7b333rvB/jFjxuiJJ56QFKn8
+TaWuSnf79u2ehcGvv/66zj33XFVVVckYk3IezjkArR1hMGJCcW0ighOKxlUGh4MTgtdmcfV5+gYD
+yIZMLozp58rgknX1k9uwMTiVwW+9JS1bFtl+6SXpxhtzOx8AgHdac4uFfLdz506VlJTEwtUePeKD
+7xkzZuiJJ56I7T/zzDN10UUXacCAAerdu7c6dOigIsdHeg4++GCtXr1aknevxzdt2qSf/exnqq6u
+ljFGBx54oH75y1/q5JNPVp8+fbTnnnvGLW43efJkTZ482dM5AH5GGIyYsCMADlJl8KYt9de9dl1w
+rrs2iwsOEQYDyIbacCi2NK7Xn3jwc2Wwswe81yG423X7JQzevbt+O6H9IAAAyJCFCxfGWi4YY3TC
+CSfE7b///vtj27fffrt+97vfNTpeJipxZ86cqW3btskYo2OOOUbvvvtuXJ/jbMwB8LOCXE8A/hG3
+gFyA2iVsdHy8dvPW4Fx3KIuVwSUlng4PAK7iQtEs9M71TRicwU+4uF33pk2RPr255gyq02wDCAAA
+Wui5556LbRcUFOikk+rbcpWUlGjFihWSpD333FO33HJLo2Nt375dW7du9XyOb731Vmx7woQJjQbB
+kmKVyUBQEAYjJi4MDlBlcCYrZP2s1mauZzCVwQByIZtvcklSaak/QshMVga7XV8oJDlaBeYMYTAA
+ANm1ZcsWPfXUU7EevGeddZY6deoU279u3TpJkYrhww47TIWFhY2O9/7778d69TYmnb7DTnXzkKSj
+jjqq0WPD4bDmzZvXpPGBfEcYjBhna4gghcGhgIbB8ddNz2AA+a82g89rbu0SQiFp40ZPT9Ms2eoB
+72it54tWEYTBAABk16hRo1RZWRkLcCdMmBC3v6CgPmLauXNnyvEeeeSRtM7brl272HZNTU3K45sy
+jxdffFHr169vcuAM5DPCYMQ4XziHA7SAXCbDAz/LZAjuVhmcxhu+ANAicc/nHr+p6QwbHWue+KIN
+Traez53rwxAGAwAQHDt27NBPf/pTvfbaa5IilbqjRo3S4MGD4447+OCDZYyRtVZLlizRqlWrko75
+7LPPatasWWmFsF27do1tr02jT1efPn1i2//85z+THldWVqYbb7wxNmcgKAiDEeMMgANVGWwDWhns
+bBPhcfif+KJ8xw6pvNzTUwBAA9lqE7HffvXbvghFs/RJj27d6rcz0N6vyQiDAQDIrA0bNui+++7T
+4Ycfrueff15SJAgeOnSoZsyY0eD4rl27xhaUC4fD+slPfqLly5fHHWOt1fTp0zVq1CgVFRXFVf0m
+c+SRR8a2//d//zfl8T/84Q9j23/84x/197//vcExixYt0rBhw1RSUqI99tgj5ZhAa1KU+hAERVzP
+YBOcV1WZDA/8LJNtItw+Tr1mjbTXXp6eBgDiZKtNRIcO9dvV1Z6eplmyFYI7rzuNT2hmnPPfxO3v
+DgAAaNysWbNU5nhnOxwOq6KiQuXl5fryyy/1zTffxPbVVfBeddVVeuCBB9SmTRvXMe+44w6dfvrp
+CofDWrRokY466igNHTpUffr0UWVlpd577z2VlpbKGKM777xTjz32WMoF3H784x/r1ltvlbVWr776
+qo4++mideOKJcf2KL7roIg0YMECSdNlll+n+++/X8uXLtXv3bl166aW66667dMwxx6hdu3ZasmSJ
+Fi5cKGOMjjnmGJ1xxhm65557mn0/AvmGMBgxlp7BwbruDFZEu1VorVkjHX20p6cBgDi14cwtjOl8
+Xmvfvn7bD2GwcwE5m8Hnc2fhjt/CYCqDAQBoGmutFixYoAULFrjur1skTpIKCws1cuRIjR07VsOH
+D2903BEjRuiRRx7Rtddeq9raWtXW1uqdd97RO++8Exu3sLBQv//973XLLbfoscceSznXfv366ZZb
+btHdd98tSVqyZImWLFkSd8xRRx0VC4OLi4v1yiuvaOTIkVq5cqUkaenSpVq6dGnctZ100kl65pln
+XKucgdaMMBgx8W0igvOqyhmKhgLVJiJzPaKTVQYDQCZlq0LWGQb7IhS1Ab1uwmAAAJolWZ/e4uJi
+de7cWV26dFGPHj107LHHauDAgTr11FO1n7NPVgpXXXWVhg4dqj/96U+aM2eO1q1bp/bt26tXr14a
+MWKExowZo2OOOSZuPql6B9955506+eST9fjjj+uTTz7Rhg0bYovDud22X79++vTTTzV9+nS98MIL
++uqrr1RdXa0ePXroqKOO0s9//nNdcMEFsduygByChDAYMTaoPYODuoCczU4FXR3CYACZFv+Jh8y9
+yeW3CtlQOHM94JNdty8qogmDAQBosjlz5mTlPEcccYT+/Oc/pzzO2YoilTPPPFNnnnlm2sd37NhR
+N998s26++eZGj5s4caImTpyY9rhAvmMBOcTEVQYHqWewzdzHa/0srkc0lcEAWoG4N/cyuDCm3ypk
+49/UzFybCL9dN2EwAAAA0HSEwYiJCwRNcELRTIYHfhbfJiI7PYMBIJMy+eaen3sGZ7Ii2s/XTRgM
+AAAANB1hMGISW0N4XV3kV5lcSM3PqAwG0Npk8s09f7eJyE5FtN+umzAYAAAAaDrCYMQkBoJBCUbj
+KsmC1CvZZq7HpNuL8pISyVpPTwMAcTK5MKafQ9FMVkT7OQQnDAYAAACajjAYMYl9gp2VRq1ZOIMf
+r/WzuB7RGQwP6lRVSZs2eXqaZluwdoHeXPmmLOk00Kpk8vnc+bzmt9652QrB/dwmwu3vDgAAAICG
+CIMRk+nK4NJS6ZVXIqGgn4Qz2DvXz8JZCg8KHM8yfmgVsWLzCg3+82Cd9vRpemPlG7meDgAPZesT
+D34LRbP1fO63EJzKYAAAAKDpCIMR06Ay2MOqqlBIGjJEOuccafJkz4b1REjBbBMR1zPYZC486NGj
+fru01NPTNMtn6z+LbX9a+mkOZwLAa6EM9kL3cyiai4Xz/HDdhMEAAABA0xEGQ5IiH5c38R+Z97Iy
+eMsWafXqyPZbb3k2rCeC2yYicxV0zhfoHTvWb/uhgs4Zmnj5hgeA3Mtkhayfe+fmoleyH57PCYMB
+AACApiMMhiT34NfLMNj5Iu2bbzwb1hPhgC4gF85gRbSfF1pyPq6D0hcbCIqgVgZnKwT323UTBgMA
+AABNRxgMSe4Vkl4GZc4XbGVlUmWlZ0O3WHwoGpxXk5m8bl9X0Dke17VhVhxC6xQOS+PGSb/4hbR9
+e65nkz3O5zWv+97nS8/gTL65RxgMAAAA5D/CYEhyD34zVRksSatWeTZ0iwV1ATmbwTDYz+EBbSIQ
+BHPnSg88IP31r9Jzz+V6NtmTyV7ofn6TK5ylimjaRAAAAAD5jzAYkpJUBnsYlNUmFGD6qVVEJtsl
++JmzZ7DX1+3njxU73/igTQRaq7Iy9+3WLhehqB+e1+IqogN03YTBAAAAQNMRBkNS5nsGJ4bB/qoM
+doaiwXk1GReCe1xBR2UwkFtBDcmyFYr67Xktk20ikr2554fKYOe/SeL/ZwAAAABwRxgMSe4Vkl5W
+TSaGEVQG515c8B2gj1PTMxhBENwwOHNv7vk5FM1kD3gqgwEAAIDWhTAYktwrJDNZGeynMDiTvXP9
+zJrMhSZ+rqCLqwzOQJuIvyz6i8579jwt2bjE87GBdAU1JMtkz2A/h6LZCoP99nwe1Mc5AAAA0BJF
+uZ4A/MG1MtjDj9DnTWWwCU5lcCavO1llsB8q6OJ6BnvcJmJnzU79+rVfqypUpT3a7KG//fhvno4P
+pCuoIVkm39zzcy/0TH7CJVkY7Ifn86A+zgEAAICWoDIYkrLfM/ibbyRrPRu+RSxtIgJVSeYMgL1u
+E7G9aruqQlWSpI07Nno6NtAUQQ3JchGK+uF5zQa0Ijqoj3MAAACgJQiDIcm9QtLLj9AnhsEVFdLW
+rZ4N3yLOF87BahNBz2Cv20TUhOsvsDrkg7I5BJYzGAtSSJaLhTH9UCGbyTYRfq6IJgwGAAAAmq7V
+tIkwxuwv6QpJIyUdKKmTpDJJqyTNkfSctfaLRm5/lqQrJR0nqVv0tgskzbDWzk5zDu0lXSvpJ5L6
+SmoraY2kWZKmWmu/bc61ZYNbKOZlZbDbi7RvvpH23tuzUzRbYBeQc/YM9rhNhJ8r6OJ6BnvcJqIm
+RBgMfwhqSJatNhF+e5PLZrDtT7LKYD+E4EF9nAOAn5SWlqp37965ngaAACktLc31FPJeqwiDjTHX
+SrpL0h6SnM0HekW/TlIkHL7R5bZG0kxJY6K/qrv9fpLOlXSuMWamtfbqFHM4RNJrkg5JmMN3JB0q
+6QpjzM+ttbOadnXZ4VoZ7GFQllgZLEXC4IEDPTtFs8UFBgHqGZytymDfhcEZ7BnsbDvhrBIGsi2o
+IZnNUmVwcbFkTKTdkR+e1zL5CRfndRcVRb5qa/1x3c7Hudv/ZwAAMi8cDmvt2rW5ngYAoAnyPgw2
+xkyQdLsiAexXigS7CyRtk9RV0rGSzpOSlnzepUgQbCUtkjRF0teKVPbeFL39FcaYMmvthCRz6KhI
+9W9dEDxD0rOSdkk6RdJvJXWW9IwxZqi19vOWXbX3clUZ7AeZbhOxeLH07rvSJZdIXbp4PnwLZP/j
+1H4IDzLZM5g2EfCLoIbBYWXuTa7EULRNm0h1rB8qZLPRHsOYyFebNv4Mg4P0OAcAP+jRo0eupwAg
+4Hgear68DoONMd9XfRD8pKQrrW1Q6jdH0gPGmAbXaozpJ2lc9PYLJA2z1lZFd39ijHlF0lxJgySN
+N8b81Vq70mUqN0nqFx1nvLX2Ace+j4wxc6PjdJD0oKQRzbrgDMr2AnKSj8LgDH68tqZG+v73pbIy
+ackS6b//29PhW4SewRnoGUybCPhEUEOy+PY3mXteKyysD4P98LzmvO7k7303T911FxZGvrdpI+3a
+5Y8QPKiPcwDwg4ULF+Z6CgCAZsrbBeSi7R3+W5EA9v9JusIlCI6x1rqVAN6g+kD8WkcQXHebXYr0
+AFb0uBtc5lEUPcZKWpoQBNeNM1/SXyQZScOMMT5ojhAv2wvIST4KgzMYipaXR4JgSfrXvzwduuUK
+HP8oAeoZ7HyTI5NtIrZWVOuII6QRI6SqqkZuBGRAUEOybLZLaNMmsu2H57VstMcoiv7fUnFx5Lsf
+rjuoj3MAAACgJfI2DJZ0uiJtGSTpbmubVcZ6jiIh7jJr7QK3A6y1HynSfsJI+pHLIadIqvvw/5ON
+nOsJx/Z5TZ5phtW6vIoKSpsIxYUHmamokqTVq6UNGzwdvkUyGYInW3DID+FB3AJyXlcGO9pEbCir
+0ZdfSnPmRNqEANkUxJDMWsU/l2XwTa7CQn+FonEheIaez52VwRKVwQAAAEC+yucw+ILod6tIv15J
+kjFmL2PMIcaYvRq7sTHmYEUWiZMiLRwaU7e/lzHmwIR9J7kc52ahpJ3R7aEpzpd1NS6vojK9gNyq
+VVLYB+u1ZXIBucTr/ugjT4dvGWdgUBCKBCkeSbaAnB/CA2cA7HXP4OpaRypUWH+xlZWengZIKYgh
+WSik+Df3stAmQvLH81om/44lC4P9EIIH8XEOAAAAtFQ+h8EnRL+vstbuMMZcbIxZLGmzpOWSNhtj
+lhljxhljil1uf7hje1mKczn392/OONEWFv9RpMI4cYycq67NfmVwVZW0fr1np2i2TFZU5U0YbMKe
+BvN5UxnscZuILeXOtKg+IfLDdSNYghiS1dZKKsj8Jx6MkQoK/BWKKguf9KgLg/1UEZ3px/mkSdIx
+x0jvv+/92AAAAECu5GUYHO0XfJgiVcGbjDEPSvqbIsGsdXz1kzRF0tvGmM4Jw/R2bJekOOUax/b+
+ScbZYa2tSHOcbsaYNimOzarqmoZJYKZ6Bhc4HnW+aBWRpY8VS/4Jg8NhJfQMDnn6QjqoC8jtqnav
+DPZD5SCCxfnfoNsnM1qj2lplNRT1UxicjTc163oG+6kiOpNh8Pbt0h13SJ9/Lt1zj7djAwAAALmU
+l2GwIj166+Z+tKTrJK2T9HNJe0vqIGmYpA8VCYWHSPprwhidHNupPsS9w7HdMck46XwQvLFxcqq6
+xm0BOe+CUecLtu7d67c3b/bsFM1irZUKHNeZ4crgBQv80RojFFJ8BV1BWLW13vWJ8PMCcs5q4JqQ
+tynZbmcYXFC/7YfQBMHi/G8wKJXBDZ/XQp4+3yaGon6qkI3/2xXMNhFev+mxc2f93+tVq7wdGwAA
+AMilfA2D93Bst1MkZB1urX3GWrvNWltlrX1f0vclfa5Ia4bzjDHHJdyuTqqopsqx3T5hX9046cQ9
+jY2TU25tItz6CDdXKCSpeLvU+0O171D/QjXXLyYbtMIwNhIQeyTxxWlFhbQsVVOSLEjsrSlJNbWZ
+Cf/9FgZvLAu5bnthd43jwouqFXkvyh/XjWAJbJuIhMrgTLS/8eNCajahB7yXgtomwjne2rXejg0A
+AADkUr6Gwbsd21bSTGvtfxIPstbulvQ7x69+mmQMt57CTm0d27uSzCXVGKnGySm3yuDakHevomtq
+rHTl8dIVQ7RjwB/rz5vjF9FuPWOtMhcGS/5oFVFTk1ARLW/DYD9XBm8ur59c5U6vw+CEC4y24sj1
+4xzBE9gw2Nn+piAz7W/8WCEbH4J7+6ZmshA8FMr9J10y+Th3jr11q7TLV//XBgAAADRfUa4n0Ezb
+E35+o5Fj35JUK6lQkrMy2DlGqpYNzkrkxHYQdeOk0/ahsXHSUl1drUWLFqU8rmfPnurZs2fa49Zk
+uDK4onaz1G2pJGlXj7dUl9Hn+kW0W8/YUDikgkJv3idJFgZffrknwzdblUv4H6kO96aVta97Bjse
+12GPF5CrSrzAwmop3MYX141gCW4YHN8D3strrxsrsXeutZF9dWFpTiRUA4dtWIXGmwklhuDFjre+
+a2qktm0b3iZbamyV9PNzpXblqn3pJUn7ejZ24t/vdeukvn09Gx4AAAAeKy0tVWlpacrjqqnWys8w
+2FpbbYwpk9RNkcrgNY0cW2WM2aTIK4Rujl3OReN6q3HOReMSz1Ui6XhJexhjOqdYRK5unDJrbbPi
+obKyMg0cODDlcRMnTtSkSZPSHrfGpQrYy8rgaserqnDbLfW/92FlcIPWES3g18pgtzDY7XfN5Qxg
+2rSJLBoYDvsjDK51LiAnb5tMVtckjFdYI9Xk/nGO4AlsGJzQJiITYXCyUDRXYXA4rAZtf0I2pEJ5
+M6FkIbgUeW7LaRjc+y2p3+zI9neel3SNZ2Mn/v1eu5YwGAAAwM8ee+wxTZ48OdfTyAt5GQZHfSFp
+eHQ71Sueuv3O/7X/0rF9WIrbO/cvTdj3paTzHcd97DaAMaZQUl9FwuvEMdLWrVs3zZ49O+VxTakK
+lpL0DHb5XXM5w+BQ8db63+c6DHapDPYyDHYLIhYvjixM06GDZ6dpMte2IBnqGVxUFAkPqqr8EQY7
+3wDwujK4QZuIwsgD3A/XjWAJbBicsIBcNtpESJG/Zc5PQWRTg4XzFP3b5lE4naxNhJT757ZQUf2H
+vMJFzfrAVfKxEx479A0GAADwt6uvvlrnnHNOyuPOPPNMlZWVZWFG/pXPYfC7qg+D+0j6f24HGWM6
+Sdon+mPsf+Wttd8YY9ZJ6ilpWIpzfa/u9tba1Qn73ndsD1OSMFjSIEXaRFhJ81KcL6ni4mINGDCg
+uTdPyi349bRncKg+magt8lEY7BIGuv2uudwqg0Mh6dNPpaFDPTtNk1UlVrDK/Q2B5nK+iC4srA+D
+c/3vLcW/AeB5m4ha9zDYD9eNYAliGNxgYcwMVQa7VcjmMhRtEIIrM29qJquIzpVIRXT9A92aWlkr
+GePN+G6VwQAAAPCvdNulFhens+RX65avC8hJ0v85ts9r5LgfS6p7afBewr6Xo/sOM8YMdruxMeYE
+RTEHEbEAACAASURBVCp+raSXXA55R9K26PZljczD2SX2xUaOy4lMh8HOyuDawu1SQeQVZM6rijJc
+GewWBkvS5s2enaJZ3CqDvawEd6sMlnL/7y3F/5t73iYi8R+cymDkSGwxtaLdgQmD3SqDvVzgzK8V
+stU1DS/Syzc1G7vuXL7R1bBHtLf/3oTBAAAAaK3yNgy21i6W9C9FwtyLjDGnJB5jjOkh6Y7oj9WS
+Hk845EFJda8kphlj4j7kGf15avTHWkkPucyjJnqMkdTfGDPOZR5DJI1RJFB+x1r7STrXmE01Li0C
+vFxAriac8KqqXbmk3FdMul1jrYfX7Xwx6WwLketwMPkCct5wqwyWcn/dkhRyhP1eVwZXJ1YGR9/0
+yPXjHMGzy26Trusr3dhb24uX53o6WZHpnsGpFlLLlarq7Pwd82dFtOOPbEFtRv696xAGAwAAoLXI
+2zA4aqykckU6480yxtxljDnJGDPQGPMrRVo29FYkhJ1grY1bVtBau0LSFEWC3OMkzTPGXBi9/YWK
+tHMYFL39vdbar5PMY4qk5dFxphhjHjXGDDfGHG+M+a2k1xVpybErOmffcQtFQ162iUh8VdU+sohc
+rkMytwDUy4po52W3b+84b65D8Cxdd0FB5CO7vgqDnW0i5G0YnKxNhB+uG8FStsfb0p7fSh02a1PX
+V3I9naxoWBkcVm2t9Wz8VAup5Yrbm3tub/A2l1/bRDQMgzPTFqQOYTAAAABai3zuGSxr7QpjzNmS
+/lfSvpJuiX7FDpEUlvQHa+39SYb5naRuilTuflfSMwm3t5L+bK39fSPzqDTG/EDSLEn9JF0V/XKO
+s03SxdGKZt9xCwc9rQwO1ca/9dA+0jc41yGZa7uELITBub7uKpf+FV62iUj2seJcX7cU//Fp63EY
+3KAymJ7ByJEaW/+gCykYD8AG4aCk2pBVfaeolvFvm4jMfdLD2siX5L82EZGF86gMBgAAAJoq3yuD
+Za39QNIRkiZL+kyR0HWXpJWS/ippoLV2UiO3t9baKyX9QJEewmslVUW/vyzpLGvt1WnM42tJx0q6
+WdICSVsl7ZC0TNIDko621v6reVeZee7tEjysDE5sE+GTymDXdgkuv2su593qpzDYPQTP3MeK6yrJ
+cn3dUnxrCK97BjsXSpREZTByJuR4zvW6HYpfNVhATt62v/Fru4RMtv1JbPkj+ee6G4T/GWoLUmfd
+OnnakxgAAADIlbyuDK5jrd0q6fboV3PHmC1pdgvnsUvSfdGvvOLeNsDDyuDa2vhHW7tIZXCuw+Bs
+tUuQfBYGu1y32yJEzZU3lcFe9wwOJVYG0zMYuVHreGwHJQxu0CZCdc/xbVyPb6rG2iXktE2EW89g
+j9pEOP+GBa1NRGIYXFMjbdokde/u3TkAAACAXMj7ymB4w601Qq2HJTC1DSqD/REGZ/LjtVJ+9Qz2
+sjI4WW9Nv4XBXvcMTtYmwg/XjWCJqwz2+HHuVw0WkFNmns/9ViFb5fbmXgYqg/3WKznTC8i5jbVu
+nXfjAwAAALlCGAxJUm2mK4OTtInIdUjm9iLaq4qqyFj1236qDM50z+BkoUmur1uSwo4F5KzJTpuI
+XIf/CJ4QlcGR33n0d8za+hYBfmsTkcm2P25tInxVGewM/zPcJkKibzAAAABaB8JgSMp8z+DaxJDM
+J20isrmAXIcOjnPkOBStcbluL8P/ZG0irHWvtsomZ5Wk1xWTNWEWkIM/UBkc4VWFrPODMn4LRd3+
+jnn1pmaqnsF+qwx2C3BbNH4CwmAAAAC0BoTBkOQeBNaGPawU9ekCcm7VsF4uIOfXNhFuAUkmQnC/
+faxYSugZ7HUYnNgzuCDyc67DfwRPECuDa2qsVBD/PObVJx7ceuf65XmtqqZhahnIBeQy3DNYIgwG
+AABA60AYDEnulcEhD8PBhm0i/FsZ7GVFtPNubdeufjvX4WCmFwxMVhks5f7awxkMgxu86UFlMHIk
+ZB2VwR63Q/Er1973Hj2fp+qdm8vntUz2gHcGonXX7ZeK6AZhsMdtItzGIgwGAABAa0AYDElSyGWx
+OC8rg0M+7RnsWiEbgJ7B1YltO+TtAnKNVQbn+tqdYbDXIVmyNhG5vmYETziAlcHVLhWyXlUG+7lC
+1u3vWFDbRFAZDAAAAKRGGAxJWegZnBgG+6VncAYrqiQf9wx2rQz2viLab6GJFN8/1fvK4PiLM0VU
+BiM3Qgpez+BMPp+7tYnwS4VsJq/bzyE4bSIAAACA5iEMhqQs9AxOHN8nbSIyHYr6tWewW1DgVQWd
+1LAy2C+hiZTQJsJ4HQbHpweFxfQMRm5ksh2KX7l/0sP7UNRvvdBdF0LNYEW0X57PM10ZTJsIAAAA
+tFaEwZDkHvy6tY5o9vg2n9pEZH4BuVxfdyZ7TEp+rwx2PK5NWNZaz8autfEXV1hMZTByI65ncEDC
+YNc39zx6UzP29NiuXOVd3lUoHPLN81om39T088J5kTDYce0e9wx2qwzeskXatcu7cwAAAAC5QBgM
+Se6VwV6GwQ16BhdVSUW7ch6S5aoyONeBaI1Lz2AvF5DLl57BkhTysJ9qYpuIgjb0DEZuZLIdil9l
+skK2tlaSCUtXHK953xmmO9+70zfPa9Vun/TIQJsIvz2f56JNhCStW+fdOQAAAIBcIAyGpCQLyHkZ
+DiZWBktS+y05D4Nd22NkoHeu5LcwOLg9gxODsZCXCyXaxDYRVAYjN8JBrAzOdCjafrO0z3JJ0pxV
+c3zTLsEtBPfq+dytTcRmu0LqFOmX4K8wuDZpgNvs8aO6davfplUEAAAA8h1hMCRlvk1Eg8pgSWq/
+NechWTYXkPNVz+AMfpxa8nllcGIY7GFlcCihTURBET2DkRtBrAzO5JtctbWSCuv/Qy7bUeabdgmu
+bX8y1DP45WUv64avD5Ou/f/svXm0LNdd3/vdNfSZrgbrWZMHZGNLWDwgtuWYGBOEw4Ilgpdxlhke
+vCAHJ8bwhxdT7DCY2CY2JDHPMWOeHQiwWAnYi0nkGZT1wEt+siOMhwRJnrBkhDXrSlf36p5zz+mu
+Yb8/qqrrt3ft6j6na1ft3dW/z1p33b6n+9Tpuj2d/a1vfX7XAace9kATQT5kLWsi6LauvLK+fO6c
+vZ/BMAzDMAzDMC7gMJgBYA4CrYaDLc1g1yGZeZAaayK6QI8h+OaYBJphsD70rQspzJoI1/vMbB45
+6ue17UGJvtKnCz3LoASPj1943JuDXL03oivCGV79/lcX3vXJBeBZf+ldM7gvTcT2dn3Z5s9gGIZh
+GIZhGBdwGMwAMAeBucVmcG4Kg7fdN4PNzuB+msG7u+TnOg6DjeF/DwOHfGwG96mJ0J/ngp3BjCOo
+G3tTmsEzgyPA1vt5EQbXL+THLzyOMKrfMzdhgNznT71Pu1b41Qzu0Rm8tVVf5jCYYRiGYRiGWXc4
+DGYAtDiDB2gGOw+De3bn+qqJMLqSLT3eJsek12GwTU2E3gyOuBnMuCEXtBlsUaTqMX3qb/TgMZMZ
+krD2BYy+GTw5j4/v/qx6ZZD41Qy2rIloC4NteokZhmEYhmEYxgUcBjMAzO1Im85gYzPYA2dw3wPk
+/NVE9Bce+N4MbjiDbQ6Q08JgUTqDXT/Pmc0izwGIzWsGmw/uWQxFQ/X1fSBPzy/75gy2ut8A8LJ3
+4zA4rV4ZpO7DYKpAsayJoNviZjDDMAzDMAwzJjgMZgC0hcE2B2uZNRGug8E+wwOgXDS+8LeA7/xO
+PJJ9tv65zve7+XhkPTgmq2bwZEJ+tutmsOZPtekMbmgiymZwnnOAwAyH7rdFkEFKZ3dnMEzv3Xad
+wXoY/Hj9czxrBts6qDnf75e9u3llkG6MJoI6g7kZzDAMwzAMw6w7kes7wPhBajhVPpP2GrLGMNgH
+TYTxtGJ7+32UXQBe+YNANMX77kkB/FHxcx0Hoqag39Z+08W4j83gPjURudCbwfUTPEnqcJxh+qTR
+mCxPn49G/omfZBmgvcZsHdxrBI8A9j0Jg81nuFjc7+2zwPZTzSt9aAZrA+RsBrXsDGYYhmEYhmHG
+CjeDGQADNIPRrolw2VjrWxNxmO0D0RQA8Jmzn5iHga5D8D6dwXQBvRbOYJsD5DRNBEI1DGaYIei7
+MekrQ2si9jNPNBG9N6JbElbfwmB2BjMMwzAMwzDMseAwmAEA5IZWaP/O4DPFz3EYUvStiaA6hkcO
+H0B0UbHProNBU/DbpybCqzC4T02EPqiLhEeuDwAwm0PfIZmvmA9y2dQlqK/vpzJ/m8G2Pr9NIfic
+kWsi2BnMMAzDMAzDjBUOgxkA5lPlrZ4+b2oGbz8JwG1IZgxFLYbgSaYuosNn3FV83UdnsKX99n2A
+nB4G23yeS24GMx5QhGR0sNZmhMF9Htwr/k/VF/G5pG4GO3UG9z1Abl2awZYHyLEzmGEYhmEYhhkr
+HAYzANo0ERadwUZNRNGS9S0MNi2sVyXRW6dXFmGw65aoKQC1pYlY1gx2ve+9aiI0ZzCCemdd7zez
+OWxqM9h8kKs/TcS5xI9mcGJ0wPcTBm+FpCLrWxg8kCZiE15LDMMwDMMwzLjhMJgBYA4Hc4sD5Mya
+CA+awT2eVlxsS91vefmdANy3RM2nFdt3BvvYDIZQH1+rzWCtOSgDbgYzw9MYILchzWDzYMz+GrI0
+DPbuc8yS+14PXLcjUpH1QhNBn+f9NYPZGcwwDMMwDMOMCQ6DGQAtmgirjcl69RSKsi66/SQgcv9c
+ixYHyKVaUy17uh9hsOmxNbXLVtp2tRmRYxY/CsCvMLhPZ7DUnMGSncGMAza3GdzzYEztYM+ZqR+a
+iD4Hguoh+E68U18ZJk73uxHQszOYYRiGYRiGYY4Fh8EMAHML2GozmGgiLt+7vLgQ5MDkvHeaCFuL
+aKCpiUiedrfzABwwB6CmIYIrbTsFAAnc/E34wDVX4dc/9etehcHQncEWH2+paSKk4GYwMzxFSNZf
+Y9JXzA3Z/prBTx75oYkwvZ/bDYPrnfOvGczOYIZhGIZhGIY5KRwGMwCAfMABcpfvXl5fsfOkd6fX
+2nQl681gGR8Al/6t85Zo787g3SeA594GAPj9z/w+JpP6epehiZQSEFL5ms3nud4clOwMZhzQDMk2
+oxnc58G9NEXDGfzEkb/NYFtnuDSawRFpBnvoDLYZ1LIzmGEYhmEYhhkrHAYzAMztSFtNUUANg6/Y
+u6K+YueM05DM6Ji0uNJrDJADgCvvdN4SNQUkVp3B0dH83/uzfW+aweYQ3E56kMu8aLvTr3EzmHFA
+wxlsOSTzlT4P7pmawedn54FwCsC/Qah29RjtzmCvwmDLBz3YGcwwDMMwDMOMFQ6DGQBAjn6bwZJs
+f66JAICdM45Pr+25GZwbds6DMNgU/FoNTcI6GfEqDO4zBDepNwQ3g5nh2dRmcJ/ue12XMGe3UEX4
+9znWjx5jkzQR7AxmGIZhGIZhxgqHwQwA8yLapjOYDtZ62vbT6iviA+8aVTbD4EyamsF3IcsAiz/m
+xKSG+2Ur/C9Op57O/32QHPgTBveox0iy5o5Rh7DrAwDM5lCEZGozeBMCLHMzuD9NBACIU+7D4N4P
+7rUNkPOhGSz6e56zM5hhGIZhGIYZKxwGMwDMwW9fzuCLJhfVV8SH/mkiLA4UMyoIrrwTgNvwIO+7
+SRbVYbDvzeDEUnpgeqyzkTaD3/9+4J3vBA4OXN8TxsSmNoP7H6TW3H50sfswuE9NRHGmR/sAOedh
+8EAD5LgZzDAMwzAMw4yJyPUdYPzApInoqxl8anKqviI68m8RbWnwTrF9Qxh82T1AfIAk2VMWmENi
+PJ3aajO4Tj4PZn43g2eJnZrXzNAMzjC+MPhznwO+53sAKYGdHeDHfsz1PWJ0+m5M+kqfGpg2TUR4
+0WkkcPv67n+/PdZExMM7gzfhtcQwDMMwDMOMG24GMwCA3KiJsOgMJmHwRVukGRz51wzOLIbgRk2E
+kMAVn3YcihparFadwWozOIrk/N++Pd62msEXpovD4LFoIj75ySIIBoC77nJ7Xxgzm9sMNhzcs3mQ
+y9AMDjzQRPSpO1o8QC7xqxls+aBHmzOYNREMwzDMqnzxi8Dp067vBcMwDIfBTIl02Ax2Gg72OHAI
+UJvByn6fesS7UNSqW5NoIiQk8vBo/m/vmsGpnf0+PDIMkEO9s2NpBn/hC/Xlhx5ydz+YdjbVGdx7
+Q9boDC5WdL7pb3prBoc+ayJyJIlsvf1K2y+hzuBNeC0xDMMw9rn9duB5zwOe8xwOhBmGcQ+HwQwA
+c/Brqxmc51AWbL47g/saIKeEweHUrTPYGP7bDE3UBzUVtVzWt9BkllgKg2fNHcuRzU/XH0sz+J57
+6sscBvtJw2+7Ic1g88E9m4PUDGHwrvtmcJ/u+2UD5JxrIrS2tq0zPebbL+FmMMMwDNOVD32o+PvC
+BeAv/9LtfWEYhuEwmAHQrzNYX7D55AzuuxmckcF5e/FefUXkNgw2O4P70UQAQCL268uenU6dZHZW
+9ocGTQSAeZuQm8HMUDScwZYHawHA4WFxqqNPmN67bR3katNEyN2i2uPbGS62Pr/1EHwnUsNgr5rB
+aPH0d9l+yWRSX96EAysMwzCMfejnCn+WMAzjGg6DGQBmTURm+Noq6M0iNQweezO4XinvTUgY7LgZ
+bHIGWw1NIjUMnsGPZnCSNh9bW5qIo7ZBdGVLeozN4DNngKOj9tsybujbpSolcOONxamOP/VT9rbb
+lT4P7rVpIuTOZjWDvRsgF6j7abMZXG0qCKAMQeVmMMMwDLMK9PODP0sYhnENh8EMAHMzWPbRDJYB
+duPd+kofncE9DZDTm8Eu99sU/Jr+L1bBpImYST+awabgN7UUHrQ2g4PxNIPPnCn+UB55xM19Ydpp
+hGSWNRFnzgAf/3hx+ed/HrjlFnvb7oJ5IGi/zeBsy4MwuMf388UD5Nw2g5NUNsJgW+/nQL1QjyIg
+DOuvc5uLYRiGWQX6mcmfJQzDuIbDYAZAvwPk6GJSyFB1Djp2BhtDUZuaCBoGe9QMNu231WawpomY
+Sj+awSY/8MzSoXmTMxjAqJrBtBVcwaoI/+i7Gaxv65/9M+Dv/s7e9lclNZzxYHeQWvNFnG17qomw
+6kpudwZL6W5BmxgO7tnS/gB1GByGRSCsf51hGIZhTgI3gxmG8QkOgxkAgIRhgFwPmgghI7VZ5KEz
+2NYiGgDy1maw2/3u0xGdZWhqIjwOg22dVjxdookYQzOYw+D1oOkM7jcMPnsW+O7vdt9y6f+Mh/rN
+KxDFr0/Z5HEAEmla6DNcMOR+681gwN17myn4taXHALgZzDAMw9iFroE4DGYYxjUcBjMAhtNEBIjU
+ATSuncGGBbPNxSQdIKe6kv1zBttq0BXNYPVBPcz91USY2mWrcDSiZvAnPwm89a3Al76kfp3D4PVg
+6GYwAHzsY8D/+B/2fsYqDKlLuGLvCgCADFJg+1x9Gwf07kpeoIkA3L23GcNgi83g6nkeRdwMZhiG
+YbrDA+QYhvGJaPlNmE3AqIkYqBnsmybCVkMWAPK2MDh07Azue2CgronI62awy/02NYNthf/tYfD6
+OYNf85ritP//+T+BP/mT+utf+ELzthwG+0ffzuC2kydOn7b3M1bB+H5uVZdQv8avOnUVHtkvhdm7
+jwNHlyJJ1EFjQ2E8w6UP5z+0MLh8b/MpDLY5QI6bwQzDMIxNWBPBMIxPcDOYAQDI6pTivF7x9LGY
+DBA1nMG+uXOzQTQR/jmDrTbBNU3EhXQfQhSXnTaDDat4W87gadt21qwZnCS1//X229VT37kZvB40
+m8ESaWrPYdAWhk2n5q8PhbEh28NBTQB4xkXPqK/cLYbIuTrgk/c4OK/hDI5UZzDgbr/T3NQMth8G
+szOYYRiGsQFrIhiG8QkOgxkApBmc1bUmW81gPQz2qRnc52nFAJCL+lPfpwFypia4Xcek+qAezA7m
+jTmn0+dNzWBrzuDFmoh1aQYfHtaXz55Vw96qGXzxxfXXOAz2jyyD6gyG3cZk2/Ey12GwuRlsU39T
+v8av3LuyvnLnDAB3722mwLsvTcRWtFVf6VwTYTrTw/4AOW4GMwzDMDZgTQTDMD7BYTADgAyQk1Hz
+ax3RNRGBCBCLSXGlY2dwn4PUpFQ1EXoz2O1+NxfMpiBlFYrQRE2F9mf7mJQPuW/OYFth8FicwRcu
+qP++++7i7yefBJ54orj84hcDO2VB8OGHh7tvzPFoNINhz40NtC9gjo6s/YiV6PPgnq6JUN7PHb/G
++9Qd6fsdBzGioPw9wXEYbPIDW3X+szOYYRiGsQhrIhiG8QkOg5mCskUmZDhXRZjao6ugN4MBYBKW
+7eARO4PzHEog41Mz2BiC2zydWtNEHCR+NIONA+QsDRyapvWORaDtufVyButh8F13FX/fe2/9tWuv
+BZ5RniXPzWD/aDiDAcx6GKwFAFvkqe5lM7gnXYL6fu5jGNzPfkdB1AiDXb23JSZNBDeDGYZhGE9h
+TQTDMD7BYTADgDiDZVD8ge1mURkGi2IRuR2WtUIvncH9TJ/3yRlsCvqtOoM91UQYw2BLjzd1D8fY
+ra9YM03EwYH676oZTIfHPf/5dRh89mwzQGbckiSyGQYbFCmrQjURu+Sp7jwMNpzxYKsZrGsidmOy
+444P+BgHgtrc7yVhsLNmsDEMZmcwwzAM4yesiWAYxic4DGaKAVFVMxg9NYPL7YdlM3jLl2Zwj5oI
+fRF9anKqvtJ1M1j03AzWNRHJvhdhcGoIgzNbzmDSDJ6IZhi87poIOjyOhsEAqyJ8I0mb72E2B2vR
+TdEw2LUmovczHsj7uRIGu24GG7U/9g/mAn41g82aCG4GMwzDMH7CzWCGYXxibcNgIUR+zD8fOsa2
+vlUI8YdCiPuFEEfl338ohLjpBPdnRwjxZiHEXwkhnhBC7AshPiuE+AUhxJd129t+KRZ7RBNRNoNt
+OYNNmoj5RHLHzmBT4D3IacWOncGyb2ewronwuRncwwC5LdE8hXxdmsF6GPzpTxfPZdoMvvZa4Oqr
+63+zKsIvZoYVRl8D5LxqBveo/Sk+x1qawc7D4OE+x+LQI2ewIfi1dWYPoDqDgwAQovy5vIBnGIZh
+VoCdwQzD+ES0/CZeI7vcRgghAPwnAK/TbvsMAK8G8GohxH+SUr5h0Q8QQjwfwJ8CeL72864D8BUA
+/oUQ4v+UUn7wGPd3cIrmbrFgFqjD4D4aVVUYvB150gw2DhzqpxmsDhzyTxOR2xwYqGki9md+NINN
+Q7RsNcmoe3gS7GL+31meWr6uzeCjI+CLXwQ++9n6a1/+5WozmMNgv0iyDAgNX7PEWjWDbYaibZoI
+x69xafocs3qmB/GhK81gt/udSUMz2OLznDaDgaIdnKbcDGYYhmFWg8NghmF8Yt3DYAD4jwB+bcH1
+Bwuu+zkUQbAE8CkA7wJwL4DnAXgzgBehCHJPSynfYtqAEOIUgA+iDoLfB+D9AA4BvALATwK4GMDv
+CSFeLqW88/i7NgxU4xAgBGSlibAfioalM3gnrprBs9JlGZq/uWf610SQ6fMTf5zBRk2E1dCkfYCc
+y/A/yfo7fX5GNBHbwXiawQBwyy3AJz5RXL7+emBvj8Ngn5mlaeMttS9NxM5OfdnLZrDNUHSyWBPh
+kzPYqgPeV02EoQWcGgLiVZBSdQYDRSicpryAZxiGYVaDrv34wCLDMK4ZQxj8mJTyMyf9JiHEtQB+
+HEWA+3EAN0opq6XsJ4UQ/w3AhwG8BMCbhBD/WUr5RcOm3gzg2nI7b5JSvptc9zEhxIfL7ewCeA+A
+f3TS+9o3dPK8QGBdE5GkORAU25o3g+Pt+fVH6RSgA7cGpE9NhL6I3gq3ECAsFu6Om8EQzdWsVUe0
+ponYn+1jy9tmsKUwOCOaiMCfU8hPiikMfuc768uvfW3xNzuD/SVJM2BL+9rINRHUfU+x+n6+XbyI
+QxFiEk7qKx03ZE3v3bYGyB3HGexTM9iWJoI+x2kzGOAFPMMwDLMa3AxmGMYn1tYZbIEfRR2Gv5EE
+wQAAKeUhgDeW/4zK2ysIIaLyNhLAZ7UguNrOHQB+A4AAcKMQ4gZre2CJRjO4GiBnWFivAp1iXzWD
+d+O6TnaUHlr5Oasw5AC5KIgQB2VC49wZ3PcAOXXnfHEGm9qRtjQRM6KJ2A79aQ2eFFMYfPZs8XcY
+AjffXFzmZrC/JIbndF/N4D1SgnepiaDue0ofuqMoiNUweIOcwcXnWPlm7qEz2Nb7OX2OV2Fw9Tcv
+4BmGYZhV4AFyDMP4xCaHwa9CEeJ+Tkr5cdMNpJQfA/B5FEHutxtu8goAl5SXf3vBz/otcvmfnPie
+9kySYO4MDoT9AXJT8mk3D4MndTN4mrlLEIZsBkdBhFiUYbBrZ7Ah6LeqBTFoIiZldiKlu2aVqR1p
+qxmckGbwTkQSMsetwZNiCoMrbrqpHhzHYbC/GBvwI28G04OalD6cwXEQ14Eo4N4ZbNJE2HTA0wFy
+QXOAnKsDXcZmsM3P7xJuBjPMsEynwO23r89BdIY5LvSzhT9LGIZxzUaGwUKI56IYEgcUCodFVNc/
+UwhxjXbd1xtuZ+ITAKqI5eXHupMDQjURqjPYlku1GQbvTepm8DR31ww2haJWB6lpYfCENIP9GyBn
+MTQxaCJikp242ndTGGzrtGIlDCbN4HCy/s3gite9rr580UXAqVPFZQ6D/YIOM6ywddADUBcw27sZ
+8NJfAf7397sPg4Pmftt6X6Pbj4LI2Ax28RqXsuVzzGozuG2AnH/NYFvv53TBTp3B+nUMw9jne78X
++IZvAL7/+13fE4axC2siGIbxiTGEwd8lhPi0EOJACPGUEOJvhBC/JYT4xgXf85Xk8ueWbJ9ef/0q
+25HFqO97UDSM9W04hzaqhLDvDJ4lzTB4O/K5GdyPJiIOY0zCuhnsKhyUEsbQxKozWNNEzLIZwkmd
+GDgLgw2NSWthMAkmdshwqWCyvs7gZz2rvvz0pwOvfKV626odzGGwX5jCYNPXVoWGwV+87D8Ce93j
+HAAAIABJREFU//iNwHf+Hzgd3GXtZ5wUelCTYmuQGg1FoyBGHNJmsLvXeJ7D3IjuIQQH/BogZ24G
+23meczOYYdxx++3q3wwzFlgTwTCMT4whDL4ewAsAbAPYA/A8ADcD+JAQ4g+FEBcbvodEHHhgyfbv
+J5ef3bKdAynlU8fczuVCiHjhLQem0Qy27QzOmmHwTuRxM7hHTYQPzeA2t6bV04rDZkUw2DqYX3bW
+JDOs4k1+1VWgzeDduNZErHMz+KUvrS9/3/dhrvqoqJQR588Xfxg/MOpQetJE3Lv7u/PLT4WmGavD
+0KqJsOpCr8LgyJsBcm0huK2Dmps6QG6RM5jDYIbplyok49caMzZYE8EwjE+scxh8AOB3AbwewD8E
+8CIA3wLgnQAeR+EDfjWAPxZChNr3XkQu7x/j51ScatnOsm0s245TlAFyIkT9tBimGZxIv5rBVt25
+2iJ6Kyz3OzpyF4jSJnheBxpWm8FRMwwWPoTBhqDAlmMyyeud2iNhcBCvrzP4da8DLr8cuOoq4Id/
+uHnbKgwGgEcf7f++McdjME3EqUfwUHDH/Ouz1N2TvO8BcvT9vOkMdnfAZ5AQXDvDpQ6Dc0DkDsNg
+0/u5/WZw1Qiu/uY2F8P0S/Ua49caMza4GcwwjE9Eru9AB57Z0sb9CyHELwO4FUVAfCOAHwLwK+Q2
+2+TysuUbTbV2tOuq7RxnCahv58ljfM8gFIvJcoAcQggZQsJiM5g6g4NiNbUT1/+VM+lbM9jmIDXV
+tTiJ6mbwLJEozCHDojTB8wmyoHj65pYe76JB13xJiIn7MLhPZ3BGGsZ7E6KJiNa3GXz99cD99xeN
+uFA/pAbgssvqy096847GmFrAvTSDr/tvgJDzr88yd0/ytlDU1kEuqomIQ90Z7F8z2O5+tzSDUVw3
+m02a39gzUgL5wAPkuBnMMMNQvZdyWMaMDXYGMwzjE2vbDF6kZZBSngbwHQCqpdkbtZvQKuqyVcwW
+uaynltV2jrMSWrQdpyjhoAjtO4MNmgilGZz71Qzu07W4FdZPg6OZm0SULu4D2VMz2KCJwKQu0Puk
+ibDnDKbNYBIGx+sbBu/uAltb5iAYAJ72tPoyh8H+MFgz+AV/rHzdeRjcYzOYvm/GQWwcIOcsDO5x
+EKrpoKYeBjtzJRvc932GwdwMZphh4GYwM1ZYE8EwjE+sczN4IVLKvxVC/L8A/jGA5wshrpJSPlJe
+Te2Wy5QNe+SyroOotnMc7cOi7Ryb2WyGT33qU0tvd/XVV+Nqeg73ApJEzttdAYJ5M9i0wFwF2gyu
+FpHUGZw4zMb7bAabGlXbUR0GT9MpjnccwS6KFkQJgy3ut0ETAdIMdhWMGpvBtk4rJmHwqa365S6i
+9R0gt7vbfjuAm8G+YmwG2w6DJ+eBL/9z5evOw+Aem8E0FI1DfwbItQ/O668ZrOy7ozBYP9haYWoL
+rwI7gxnGDVJyGMyMF9ZEMEz/PPzww3j44YeX3m62Lk2tHhltGFzyGRRhMAA8E0AVBtOhcc/CYujQ
+uPu16x4A8LUA9oQQFy8ZIldt57SUcuWl0+nTp3HDDTcsvd1b3/pWvO1tbzvWNqdJvbIJRAhRFsal
+sBMO0pZaFQbTZnAGN81gKdF/eKA3g2kYnE2h6quHQWmCkzDYVvifpNKoiZDxuJvBKQkhVE1EsbPr
+8nlzQOzmO7oYR4M2g8+c6ef+MCcnNQRi1jURz78ViNQnNT0gMjTDNINbNBGBu9d4azPYpvan/BwT
+EAhEYNBEWPlRJ6ItDO6jGczOYIYZDvpRxa81ZmywJoJh+ue9730v3v72t7u+G2vB2MNg2fL1z5DL
+L1iyDXr9Zw3beQ253V+ZNlAOsHteeX/0bZyIyy+/HLfeeuvS2x23FQwAs1QNgyHLFY+1cJAEoqUm
+gjqDE+GmGVycZtrz6bVksRqKUG0GZ4b27AAozWDyFmAtBM9SxSM6J3bvDO5zgFxGgrCLt9e/GRzH
+xZ9FsCbCT/o86AGUi3VNEQEAiXStiWiurGy9rxUHuUpNRGgeIOdXM9j+GS5VI9gHTURrMxj2B8hx
+M5hhhoPDMmbMcDOYYfrnDW94A171qlctvd1NN92E06dPD3CP/GXsYfBXkssPVRdKhcRDAK5GMWBu
+Ed9Q/v2glPLvtOs+Qi7fiJYwGMBLUGgiJICPLrvTi5hMJnjxi1/cZRMNkqReNIa0GdyDM9inZrDS
+qJJiHmDaWkTTxWqACEIIbMe6JmJ4FGcwIiAPgSCz1gSf5eb98qEZnOXNfbSmiSCF/1PbxK9QhsFp
+WrTRxfAzA09EFQYvU0QArInwlTRvPqdtaiKSLAWu+2Dz67m7MDjL0HKmh6VQlPz/RYE/A+SSBPP9
+FjKCFMVj38fgvOrz258wuL+De+wMZhg30PeTPC/+BGs74YZhVNgZzDD9c1xd6mQyvK7TN0b78SqE
+eC6Ab0YRwN4rpdTFIbcAEABeIIR4acs2/gGKxq8E0KxBAbcBOFdefu2Cu/P95PIfLb3zA6M3g6sw
+2LTQWgWTJoI6g/PgsFA2DAxdTApZt7z60ERUDdyd2JNmcLXfoOG/rUFq5kAoDz1oBhudwZZCExIq
+X7RVJ6mCKDPWoR18kjCYNRF+Ygp+bYbBT82eBLaLjz6B+uhG5rwZ3KP2hwTs3g2QK/c7pA54S2f2
+0M+xtjDYJ01EbtOVXKKHwVKWZxYxDGMd/WALB2bMWMhzKOtdPrDIMIxr1jIMFkK8slQvtF1/JYA/
+QD2d61cNN3sPMF8l/rIQYpteWf77l8p/pgB+Ud9A6f79JRSh8vVCiB833JeXAXgdikD5NinlJxfs
+mhNmijO4GCBX0IMzOGw2gxEdOZ/CTt25djURxY5VYTBtBrc1aPtG1UTUWhBb4QHdLxoU5ZEHYbAh
+ELMWHoBoInbIvMiw/vo6eINXDYO5GewPpmZwZvjaqsyy+jkdo36i5CJxtnBvHSBnS3dENDCNAXKe
+OIOFov2xr4nwrxlscgbb10RUIXBEdpsDKobpBz0g48CMGQv6ZyU/txmGcc26aiJ+BUAkhPgDAHcA
+uA/AIYCnA3gFgB8oL0sAtwP4NX0DUsovCCHeBeAnAPx9AB8VQvw7APei8Pv+KwAvKrfx76WU97bc
+l3cB+G4A1wF4lxDiWgC/V96ffwTgJ1H8P18A8CNdd7wPaDM4VJrBlkJRUzOYOIMRH2I2A4Zu6heL
+3CoMjudHBuyeXlvse2hoBs+cNoPLxrIIIWRYyLVtNclkvV+XbF2Ks9MiJUxDHzQRfTaD6526ZJeG
+wevZDN7bW3w7gDURvmJqwFvVRJBgdCvYwywvD/SEM0ynxzuQYJu+m8H09R0FEQIRIBRh8f7hSTM4
+QAjkARDklgfnLQqDE7+awTbP7CnRm8FA8f+yzKnOMMzJ4cCMGSvcemcYxjfWNQyWKHy/byz/mK6X
+AH4fwOvLBq+JnwZwOYrm7gtRhLj6Nn5dSvkzrXdEyn0hxLcB+CCAa1EE0T+gbeccgO+VUt61fNeG
+Z5roYXC94slljkB0K5An5NMuNjiDER25b1QpmogenMGi2QxOHDWDlRAcUd0EF7kVp21KThW/bOey
+eRicBX42g201yejgoot3SBpGwmDfm8F5DhyW8xyPE+jt7BQHcWYz1kT4hOk5bTMMnqUkDBZ7OF/9
+w3UY3GMzWNdEAEVDOEs9CIPnzeDqTI+8l4Oa1X5Xg2ABeNcMtjVAzqSJoM1gDqgYph84MGPGCrfe
+GYbxjbXURAC4GcBbAfwZgM8DeAJAAuBJAHcCeC+Ar5NSfreU8qm2jciC1wP4NhQO4QcBTMu/bwHw
+rVLKNyy7M2Vr+EUo2sQfL+/HAYDPAXg3gK+RUv7ZarvaP0lKBsgFpBmMIgzuvH2DJoI6gxEdOm9U
+Ka7FHsLgqhm8HbkPg5UmmQgxfxsQmRUPIt2vy3bq6mgauG8GD6WJuISEwTJYn2bwEZnleJxAT4ha
+FcHNYH8wHvSwOkBODYPnhDPlOTQkjWZwbteFnmrNYAC1N9jhALlGM7jS/tj8HAt9HSDXnzP4OM1g
+hmHsw4EZM1a49c4wjG+sZTNYSnk7Cv2Dre3dCuDWjts4BPAL5Z+1IlEGyFFncBEgKAu/FUjzFJU6
+1tdmcIB+B8iFotj+VkjCYOkmDJ4ldUgQIKwf7yBDlqkL3lVI0BIGi7oZ7Kohm2VZ4xCYrdOKcxIG
+721v1aeQB+vjDK4UEcDx252XXQY8+iiHwT5h8gNbDYM1TcScIMHUzdtaEc7Nz/QIIKuGrKVmMP0/
+rXzBVVO2aga71iUUYXAZgtvab4MmQvUlezZArgdNhMkZzIt4hukHDsyYscKtd4ZhfGNdm8GMRRRn
+8FDNYIMzeGiUZjB6bgaXp9VuRe4HyE2T+vEIRVRrQURu5ReTNK8fzP9ttw6DE+FBM9jQGrOniSh3
+Kg8QBsE8MFmnZvAqYXDVDN7f93//NgWzDqWnZnCgKlFchcEmFzpgcZAaOdhThcDzZnDguBks6jM9
+au3PUM5gh81gkxbEkiaCm8EM4wZuBjNjhQ90MAzjGxwGM0ozWHcG2wgQqGtxEq5DM7jHMJg0g1NH
+zWClCQ4S/ovMThh8jGawq9Aw71ETMXdV5uop5HmwPs7gLmEwwO1gXzB5U/tqBm8H/mkiis8wuw3Z
+NF+kifBjgJzoQRPhdRhsaAZnNl3JJewMZpjh4MCMGSt8oINhGN/gMJhRnMFREEKIDXQG0zDYZqOq
+dC2amsE0NB2SmaIFUTURNn4xoSH307brpDDxIAw2OoNtaSJE1QxWW4NjbwZfVuf9HAZ7gin4tdoM
+ppqIUA2DnTaD5wf3IvsNWRKw0wFyANyHwUozuD9NRLW/ehjskyaCm8EMs95wYMaMFdZEMAzjGxwG
+M2ozOAgRaM7grihT2H1rBgfDNoPpfmeuwuCkpQluqRmcoX4wd+KdefA/le41EaZAzJYjWpZhsNDC
+4HlIDG4GM8NgUp/0pYnYUZrB7pzBrYPUBmkGF9e5/hwLlWawTff9Gg2QY2cww6w1HAYzY4Vb7wzD
++AaHwQySjIbBgdIMTrPuwagpDC5C5zKAdeQM1gepzafP2woPyGK1Wjz7oInQncGBbWcw2a+tcAt7
+kyIsmsF9M9jcmLTkDBbFdoTUNBHYHE3EmTN27w+zGqbg16YmIlWawaoz2Kkmokd3LlVvtA2Qc/G+
+liRo2W9LBzWzHAiKbRnD4DBxst+0sUzhZjDDrDccmDFjhQ90MAzjGxwGM81mMHEGU6XAqpicwQAQ
+oWzJRkdOFpO0IRsI4pgcaIBcKtykJjT8DwRxBgeWmsGChMHRFk5NTgEApvmGNIOlGhTlgjURzLCY
+m8H2Vh1UE7HjiSaiCAfrZrCw3Az2eoBcUIfBtl3J9D2zrRnskybCVjN4HZzBjz8O3H+/63vBMHbh
+wIwZK3ygg2EY3+AwmFHCwSgg4SBUn/Cq0BAiJqupGKU3OHLUDNYH58lqv+2EwbMkB4Qstm9oBufC
+vTM4DOxrIlLShJ2EE+zFRVg0le6bwSYHtq3wQAZmTUQmNqcZzGGwHxibwRY1EakkYXDkRxjcaAbD
+sjOYfI41NBFBDojM+SDUsIdGtPL5HZidwT5pIiAySGlp+wBw5Z24fforOHt01qtm8COPAF/2ZcBz
+ngN84hNu7wvD2ITDYGassDOYYRjfiJbfhBk7CVFB6M3gpM9msKibwa7D4KJRZbdJNkubi2jaDM4w
+hZSAEFZ+3LFJtBDctiYih1kTMc0vFKcuy8ArTURu6bRiWWkioGsi6p31vRl8UOf1rIlYY3JDCzjv
+yRm8TTURQeJWExHQULR8Y7WkS6Cv47kmIqxd84UuIdS/rXf0ZnAVgts4w0VK9blUhcBzPQbguBls
+eE4HKfJcVTqsvv0E+L5vwfvPP4qrbrsHUfQe9XqHfOQjwOFhcfkv/gJ4yUvc3h+GsQW3J5mxwgc6
+GIbxDW4GM0hTrRlMnMGJbWdwVK/QJqJsBjtzBmu6hKoZbMu1SPY7Es1mMKKpk6PCujN4KE2EhASi
+YvXqqiHbpyaiOlW8cmHPm8GYASiqatwMZoZgyGbwJJggqI4ru24Gl03R4gBX9X6eIe/4li6lGgY3
+msEAEM6cayKUM1xMQekJyXPMh+MB6zFAztbnWJoC2HkSOPUoAODux+72qhmcJACe/VHg2j/FdGbn
+dxaG8QEOzJixwgc6GIbxDW4GM4UmolzkhEGgNINT25oI0gyOxXaRkblyBjc0EfXptXkOBB0PlczS
+FCizgvkAOdIMRjhFkqgewiFQBwba10RQLQLVRBRfOACSPYeaCEMYbKkJrofBSmswSIE89r4ZzM7g
+cWBqu9tsBqd5Ms9a4zBGJCaYydSzAXLlFWU42OX9PM+hBI9VM1ZpyDoKg+kg1OLgnj1NhD6kbR2c
+wQhSpCkwmTSvOglZhvlgQAC4kFzwyhn8pYPPA//86wEAn8/+FMC3ur1DDGMJPpWeGSv83GYYxje4
+Gcwo2oAoDBGQZrD1AXJkNTUJKmfwFEfT4ZsttBkcCtoks6NLmKXNRpXeDHbSqNLC4Hn4H2RWFri6
+JqJqBgMAJsUQOZ8GyNlwBtPQRNdEAJiHCmNvBrMmwg9Mz2nrYXBJHMaIRflc92SAnB6Kdn0/L4LB
+piZCeY0HiZPXt679EUT705W1DINtue9TNMJgn5rB9x3dOb/8UPbXDu8Jw9iF25PMWOHnNsMwvsFh
+MKM0ReNQ1USkFjQRtBmshsHb88sXHCQIajOY6BJshcEZWUSH5mawa1dyKMLSlwx7zuBA1UQozeC4
+kNJ61Qy24AxOEszDohCGoKi8bp2awXt77bejsCbCP0zN4L40EXEQIawasmHixQA5xYVuQRvQFoqq
+zmBHzWB9IKis9RhdB6np+13trx4Gu3jMB9FEBPUD6lszmB4EoAfcGWbdYU0EM1b4uc0wjG9wGMwo
+gW+kDZCz0QxuC4O3qmYwgIPZYeefc1IajSppLzwott8MD3xoBuv3y74zWNVE+NQMVsLgrAg2bGgi
+jqYZqvPSA2MYPN5m8NZWfVsOg92T5zAqAmw04CuUMFhrBvswQC4Imtqf7tumAbj5gI8PB/eExYGg
+euDa1gz2KwxOe2kGHyQHXjWDE3KwOck9P8rIMCeA25PMWGFNBMMwvsFhMIM0J4vJMFA0ETaawSkJ
+g7diEgaHdTP4MBk+QWg0qiw3g+liLV7gDB6aGdk5PfxPLDiipVA1EXsTzRkMh2EwDcSyIsixMUDu
+cFY/1tUwLd0nCqxXM/i4YTBQt4NZE+GetpCsL03EJIzrUNT1ADnSDBa2m8GGQWqTQD3g42Lfk8bn
+mO1G9LIBcm7a4H1rIvTHXG8Gu17E098vUg6DmRHB7UlmrOhrgDxH54PVDMMwXeAwmFEcsnFItAFQ
+F5qrkrc0g7dDz5rBpFFl45fPhJy6WZ1e60MzuOEMFnbDYF0TsRuTVDEu0kYvnMFVGGyhGXzhqN4h
+syZivM1goA6DuRnsHhqKUmyGwRm0ZnDgSRgc1GFwYLEh26ZL0DURLvZdd9/XBzV70GMIczM4TYdf
+0LY9z/tqBuvOYNcBFdV7sSaCGRMcBjNjxfRcdn1gkWGYzYbDYEYJB6MwRIBhnMFbEWkGz3xqBlvS
+RNBmsEfOYF0TQR/vxMKOy0DVROxEdeiPqHic/dBEVGFw95XGhSkJg0V7GDzWZvBllxV/Hx0Bh8Mf
+12EIrc1gC27s+c/QmsHzUDRIHGsiin20HYoeSxPhqCGrf471GYK3aSIADL7vQzuDZ9kMIqx/nusF
+PDeDmbHCmghmrJjWAK4/SxiG2Ww4DGYUTUQUhggtN4OzFk0EDQkPUx+awT1qIkKPnMG6JsLi4y0l
+IANVE7FNQn/XYbA0aSJsOIOJJiIs23N6UASMvxkMcDvYNbQhS7E5QI42gydRjC0fNBGZnHu7+9El
+GAbIBc1mcNehbSdFfz+v9ztHmna7M3rgamxE+xYGWzqYqzeDAUCGh+r1DlHCYMlhMDMeuBnMjBXT
+c5mf3wzDuITDYEZp/xaaCLvNYNrGVDQRJCQ8Soevk+m6BOsD5DJ6KjVpVElR/tDxhcF5DiBSNRE+
+hcEmZzBEDtkxwTmcEa9m2QxeZ2ewEMVguOPCYbA/tDeDLYbB2gC5SVSFwSkOj9wI8HR3bt2QlXZC
+0VDdZ6A5QA4Y/jXe3G9ypkdH7U+bK9nvZnBqZXGt7zsA5NGBer1DMnIQnx5wZ5h1h8NgZqxwGMww
+jG9wGMxozeBADQctrHgymJvBuzFpBifDN4NnehhsuRlMPX7V4lkIgRBlyha50URkWhOcPt4zK+GB
+qokwhcGuGrL0wITI6rSza2vy8JiaiHVpBu/uFoHwcak0EQCHwa5pC8lsDEqc/wwSBm9FMbai+rlO
+D4wMCdUlREFUN2S161ahbZCa7gwGhn+NtzaDAcwSC+/nm6yJ0JrBWXBBvd4h3AxmxgprIpixwpoI
+hmF8g8NgRgkHe2kGkzCYNoN3YtIMzoZvBiep1pC16FoE1MUa3e8I5X47awYvcAZ3DE2KBbS/mgil
+HZnXARZ9DawC1UREJk3EGobBACClxJ9/8c9x2323Lfw+bgb7Q5bBPECu12ZwHYoeOQqDGw1Ziwc1
+GwPkTM7g8jU+dCiq7HdIPsdgKwRftzDY4gC5QGsGh3UY7HoBTw82ZxwGMyOCm8HMWOFmMMMwvsFh
+MKPoEmLdGWxhxZOTUxgrXQIA7E7qZvA0c+AM1htVwu4AuaxlvyPSDHauibD8eGcZvNZESNQHNwQN
+gzs2g2kAFhmDIjenkJ8UGgZfSC7gn/7RP8U3/8434xW//Qp87IGPtX4fDYPPnOn5TjILadVEWHQG
+0wN8W1GMbaUZ7OaIh/K+JtRQ1M5BriWaiDI4HDwMVg7ukc8xdN/vtQyDe3QGp8KfZnCqaCI8/2Bh
+mBPAYTAzVjgMZhjGNzgMZpDldUAWRf02g+kikjaDpw6awanWJLM+QI40d2hzbh4GO2oGpwucwV0f
+b30BPQkn2CE6EERF6O9DMziw2QxODM5g5RTyIilZl2Zw9PT78PL//HL817v+6/y62790e+v3sSbC
+H9oGyPWliZhEMbbj+rV0lDgKg9ucwbDkzj3GADnARRjccoYLgMTG+7mhEa2EwaGbEJw+z2NBBOeW
+NBG68ggA0sAfZ3Ca0WYwpwnMeGBNBDNWTGsffn4zDOOSaPlNmLFDGyaTMEQQ0HDQrjOYLiL3tjxq
+BtPTa205B9s0EcIfZ/BcC1LOV7LSDC6DT5FHCESgNoNj181g4gzO6wCBnnK7CoeGZvBWSAKKNRgg
+JyVwUGYdD974Shw98mnl+vvO3tf6vayJ8IchBsjlIGFw6EcYrLjvgxBC1EPj7GgiSDN4Qft/8FB0
+URjckyvZt2ZwFMRI0hkgpF1NhDZAzidncEqa/hk8/mBhmBOiv7ZcH3hhGFuYPjc26fkt5cnmkTAM
+0z/cDGYaA+TCgZrBe1ukGZw7cAY3Bu/UzWAbCz0aME6IJmLeYoqO3GgiyP2Kw0jRRKQ2TqcuNRGB
+LPaThsHBxKMwWNrTRMySphJkKyJhcOR/MzhJ6jD/6OIiCH7adp3yHjcMZk2EW9IURmewzWYwDZ+2
+4lhpwR85enFTXYLuzrWtiVg0QM5pMzhUNRGb4gwORYig6jYMpIlwvYBXnMEcBjMjgpvBzFjZ5Gbw
+F78IPP/5wNd93fC/LzAM0w6HwYzaFI1Uh6yNMFgeIwxO5PDN4KYuwa4mgp5KHdNmcOCRJiLsYdBS
+uYAOyrBVCYMdN4MVTYSsgxyrmghjM7j4zcfnZnCliMD2ufnXXvbsl833Y1EYzJoIf2htBhsC4lWh
+zeCtKFYastPUvSZC19/YDkV9cgY33s9BD+4NoMfwIgyOIOZn9qR2DuYaBshlHjmDMxIG0wPuDLPu
+sDOYGSub7Az+wAeKQPiOO4APf9j1vWEYpoLDYKYRBgcBbQZbGCAnzGHwKaKJmLluBtNFtK1mkeIM
+rvd7UjWDgxxHs+F/C1Cb4HbD/6JNVaQCoaEZLFw3g6tALA8QyPox6aqJmCbNU8jXrRlch8Fn51+7
+bOcyXHPpNQCKMFjK4tT7/dk+7j93//x2rInwh3ZnsL33GtpEnEQxJgEJg11pIpRBqFGvugSzJsJR
+M3hBCN714F7DGRwanMGehMHzZnCPzuDEo2Yw/b0t52YwMyI4DGbGyiZrIg4OzJcZhnELh8GMMkBu
+7pAtSfP+NBGntuuQcOZBM3h+em1gpxmck6EuiiYiqEPCCw7OlWk4gwPL0+fL4DOEIQwum8GuQtH5
+qfIyrJtk6K6JmJo0EbQZHLkNwY+DKQy+dOtSPOfS5wAADpIDPHH4BM4encXzful5uOY91+DWe24F
+AFx0Ub2d/f2B7jBjpK0ZLC02g2kYvB2rzeBZ5qgZvGgwZsfmv/5/6tMAudTkgC/ZFE1EFET2D+Ya
+nMEJDtTrHZKS3y9YE8GMCdZEMGNlkzURdD99XgsxzKbBYTCjBGFhEGhN0e6rqjZNxEXbdTM4xfDN
+4MWn10rTt5xs+7IZEALAhITBh8nwYbDeWA5tN8kWaCKch8FlICak+nh31UQozeDQ0AwO17MZfOn2
+pXjOJc+Z//u+s/fhL774F3js4DFISNzyuVsAAJMJEJb/nXzE3y1DOIP1AXLUnTvL3PyWrx/cU97X
+bISiYfM17t0AOf1zrOOZHvp++xwGU01EX87gBB41g8nvFzZb/wzjGm4GM2NlkzURNADmMJhh/CFa
+fhNm7NAgLBQhwsDyADmiiaAL9FM7jp3Bmi6h2ajq9vLIlEFtdVgyCV2HwZomIqANOhvhwaJmcPE4
+uxoeQJvBAXl8OzeDUzUcA/Rm8Do5g9UweDfenf/7vrP34a5H75r/+4HzDwAopgOfOgWcO8fNYNfo
+bc6KvsLgrdgPZ3Dz/bx+L7OivzE1gz0YIKc3g62H4D43g8uDHqGIEFrWRJicwTQMdr0VXDbDAAAg
+AElEQVSAVzQRwuMPFoY5IRwGM2PFtAZwfWBxKDgMZhg/4TCY0ZrBZThYrp27nl4LAPIYzmAnzWBt
+ES1gNwSnzWC630ozeOZeExEKe47oNAXRRBQBEQ1Fq2bw0fAPNwDSDEagaCK6OoNnabMFPpZmcCMM
+foyEwU89ML/MYbAftDqDbQ6QE+2aiCT3JQyu/21lMOYyZ7AHA+TiKFIOatrZ7+OFwUO/t9H71kcz
+2OgM9rQZzGEwMyZYE8GMFW4GNy8zDOMW1kQwahgs1HAwsxCKtoXBO3HdGE2FC2cwuV+hNninY6MK
+UBdrdL9pSHjkoBmsDwxUmsEdV7izNJsHUVHZDBZC1O3g0p3rqhkMqokQFjURS5rBwWR9m8GVMxgo
+wuA7H71z/m8aBu/tFX9zGOyWVmdwX5qIKFJCURnMnCxu1AFyof0BcgZNhJ/OYMsDQekAucA0QM5R
+CK5oIkKEorxPVp3Bahg8k/44gxVNhNiQNIHZCLgZzIwVDoOblxmGcQuHwQxyogaYN4NLbDeD6bZ3
+oroZnAn3zWBlcJ6FELwtDKaaiKPUgSaCDrbTnMFdw+CjpF48V2EwgEYYnKZumlXzdqTuDO6oiZjR
+MDhuOoODeH2bwTQMvvuxu3Hvk/fO//34hcdxmBQHck6dKr52cADI7sptZkXanMEIMmuPy7yJmEWI
+IqGGokHipPlP2/2RrkuwccaDoSGrOoP9C4NHr4kwDZCz6gxWV6wz6U8zOCcHdyQ3g5kRwWEwM1ZY
+E9G8zDCMWzgMZpQgLBCBdWfwPAyWgRK4Upds5qQZrJ9WXC+iZ6kFVzKag3cAtTE6dRAGNzQRFp3B
+VHsRijooqR5rGdYpkZN28FwTQZpkAJKs22qDfv+k0kQYmsFrEQZvnZt/7dLtS3HlqSvn+/KRL32k
+8X0Pnn8QQB0Gp6nf+zl22prBthqTAHlvy2MEQTMUdfHa1gfI2Wz+H0sTUQaHg+sStDM97Gsi1mCA
+XBjVj3eQWQmPzM1gj5zBSjOYV9bMeGBNBDNWNrkZTPeTw2CG8QcOgxnkuiaip2awyFVF9U5cN4Pz
+wINmMHk59KmJ2I7cNoMb4UFgzxlMh0eZmsH0cR66PZjnAMqhUkJrBs86Pt5KMzhqNoPFGjeDAxHg
+mkuvAWBuUFeqiCoMBlgV4ZI2Z7CtwVoACZ+yGGHoSRisvK9FyudYX+5c3wbI6Y3orpqnY+23yzA4
+pM1gu5oIPQgHgKlPzWDyXixNB38YZk3hZjAzVkwh6KY8v7kZzDB+wmEwYxggR5zBHZuiAIAqDJZq
+GBwFEZAXC9cscNAMXhiKWtBEoCUMJq5kJ81gcr8mUYTIYmhCm8GRMITBDpvB9PR5AbunU8+y+jeb
+rTIMps33ddZEAFBUETpVGFw5g4FCFcG4YZBmsKibwaYw2IUmgh7kikQPmgiDM9iHAXLKfgfD6DF8
+aAYn5OydWGkG29REaM3g3J9mcA5uBjPjhMNgZqxscjOYBsA+r4UYZtPgMJhBLokzWG8Gd1xV5Tnm
+i0U9DAYAkRXtYBfN4IYuAfYW0QCQH6MZPM3cNoMnUaiEwV3Df9p0jgyaiJzoQIYOjIqmV7HvAcK6
+SYbuzeCE+Eq3YoMmogyDnQ3OOwamMPiS7UsAAM+55Dmt38fNYL+gBz1o+91WSAZQZ3ChiVAbsonz
+ZnCoayKsNGSbugTfBsiFQj2oabsRXT3ONHB2FgYTNU8cRLX2x1ID3uQMPsrro1zOm8EkDNYbzAyz
+zuitQdevNYaxhSn43ZTnNzeDGcZPOAxmlEEktpvBNIATaIbBQV65ZMfXDKaLNRoa7MR1SDhzEQZL
+bb+FPU0EHSAXm5rBwWyuanDaDJbqQY9Zx0PzCW0GGwbIVZoIF43J46KHwacmp+ah16Jm8P3n7i9u
+z2GwF9A2ZwjaXLXjUgVIE7HFGey6GRyKEKF1dy4ZhFqGoSZn8ODva5n2+T2AJkIIUf8cD8LgKIyU
++9NXM3jqVTOY7GSY8NBOZjRwM5gZK6yJaF5mGMYtHAYzinsuEIHVpigNJkzN4CAvAjMZzAZfzOjN
+YDUU7U8TsTNx3AyW7c3gro5otRlc7+dOVPuhEbppyVKXqoDmDE46NoMNmgjaDBZr0Ayeqx3KMLhS
+RADNMPirr/jq+eUHzrMmwifo8zwSMSBFcYVFTQR1BgvhnzNYbwZ3PchFW6IhYghR/J/64AzONOe/
+9QFyYbMRrVz2IQwOyPt5j87go8wnZzBtBttr/TOMa3iAHDNWWBPRvMwwjFs4DGbUZrDQm8H2nIOB
+KQxGuZgOksE/HPSGbGgxPAAA2eoMJs3gfPjUhC4iJ1GEMKThf9dmcL0/cdDURAAAoqI2OHR7UD99
+3uaAqdSkiaDN4Gg6vw++Ltrnbd5jhMHf8rxvmb9eWBPhF7TNGYiwPghnc4BcUDeDAT/C4EYz2KLu
+iP6fznUEaO434NYZXJzZ089+Ay1hsKNGtKKJiKgmIkeadj+ybGoG0zDY9QI+F6omghfXzFjgZjAz
+Vkzv076uCWxDX8f8ecUw/sBhMAOpaSJsNoPpYtKkiZifxuzg1OK+m8E5zI0qnzQRkeXHe5Yt1kQU
+P7R4oF03g0PyfOw6QC7J68d6e1IEZFQNIsN6Z31tB58/jyL8mBSBx6Iw+EVXvQjPuOgZADgM9g31
+oEcEYbkxCdTNYJE3n+sIEjeaCHLwTdcldD3joXjvqJvBFV4MkNOawep+2zuzB/CrGUwPwMVBhEDR
+/nR/opucwYeZP85gqWgiUsxm7IlgxgGHwcxY4WZw8zLDMG7hMJgxDJAjoWjPzeD5wjocPkDQdQlq
+M9iuM5guoncntBk8fGpCtSC6I9qmJmISLA6DnQyQa2kGd3UGpzQMLp3BQohaFUHCYF+9wfv7ALbO
+zf9Nw+ArT12paC+++sqvxrMufhYA4LGDxzBNpxwGe4LiDBYhhKxcqhvcDO74vkZ1CQFpBvswQE5v
+Bqvue8sD5Mj+ug6DVU1EhIg8LjYGwJqawUk+m++v6zBYGSAH4Gi2IfUyZvSwJoIZKxwGNy8zDOMW
+DoM3nDzHPCADCmew/QFya9AM1gapdW2KSglI0RIGb9WhWiJdaCLU0MSmM3iaHl8T4bIZHAjVKdq1
+SZYYNBFArYpYm2ZwqYgA1DA4EAGe+7TnAigCoRc8/QXzMBgAHjz/IDuDPYE+z0MRWXepSinnYbCQ
+fobBURCp72sWQ9FI1IGo6gx20wxuvJ9b1B3p3lz6OTbfd2fNYPXxpgdzaVC8KsUBgFnziqgYdut6
+AU9/vwCAwxmvrplxwM1gZqxssiaCw2CG8RMOgzecYrGnaSKoQ1Z2+5RSmsGmMFjQZvCwpznqzmC1
+UdXPFHZAbQanLsJgrbGsPN4d95tqIiZhSzM4LhbTLp3BAnbdmrO0/s3m1G4dEFVtWhmsSTOYhsFb
+lyrX/8TLfwJX7F2Bf33jv8YknChh8ANPPcDNYE9Q3nNFWGsiLDWD6fumaGkGO9FENM54sDxArgxF
+aRgciKAOIZ0NkNP0GIE9TcSxnMEeaCKiILI6MBBQH3OFuNDouF7AK5oIAIdTXl0z44DDYGascDO4
+eZlhGLc00zlmo6CnzgNVU9ReM3hZGByJOkTYP0wB4mPsG9qoikNtoJhFPUax/Xq/XDeDm6GJPU3E
+NKPNYL+cwUkigaB4PgcIlNOKu2oijkgr62kXkzC4bAbngf/N4EYYvK2Gwa994Wtx89+7GUIIAMCz
+L372/LoHnnoAz+Aw2AvoQY8osO8MTrL6uT53BmsNWRfPcb0hG4p+znChA+SAIgg/TA+dhcH6AFjV
+fW/3c8znMJjet740EQDmYbDrBbzeDD6abUiiwIwe1kQwY2WTw2AeIMcwfsLN4A2HnlIMGJrBNlyL
+C8PgOkQ4OBz206E5eIeEB302g7dcN4PbNRFdH+9ZSpvBddC/E+3UN3LkDJ6l9WMaaE7RruHBNKkf
+670dookIqzC43llfm8GLNBEVVRAMoNEMZk2EH/TdDKbDEts0Ed41gy06g0OhHrCc77sPA+S0z28r
++91yUNOnMFgfGGhDE2EaIAcAmBRvbs6bwUK9A0esiWBGAjeDmbHCmojmZYZh3MJh8IZTtMjaB8hl
+st9mMPXKHhwNHAbnC8KDjmHwokbVnusweEF40FULMsuPP0Bu8GYw8QIHQgsPOjqDp0QTMQkNzWCx
+/s1gHdZE+Ak9wBeJCMF8gFxqvxlcDgX1wRncaAb3pYkI1M+xeUDqqBksFx7cG0ATEboPgyMRIST3
+zYYmotUZ7EEzOM+hPC4AO4OZ8cBhMDNWhmgG/+ZvAm95S1nw8AgOgxnGT1gTseHomohABFabwUma
+A6JwAYemZjCZTr5/aFh49cii04q7Nqr0VhFdRO/EJAyGe2dwHNoLD6gmYqvNGewoDJ4m5HmOUHlM
+bDqDaXuu+j/I4LczOE2Bw0OsHAbf/9T9owiD73z0Tnzwbz6Im//ezXjmxc90fXdWonjvKZUGQWhf
+E7GsGRy40UQ0GrKBvYNcbQPkALLv5fv9bNiPscZ+q+57e41owBwGiyCFhINGtKaJUM/0sNQM9tQZ
+nCRohMGsiWDGAmsimLFiCkFtPr/vuQd43euKy5dcArzpTfa23RUOgxnGT0bZDBZC/DshRE7+fMMx
+vudbhRB/KIS4XwhxVP79h0KIm07wc3eEEG8WQvyVEOIJIcS+EOKzQohfEEJ8Wbe96gejJoIsJvOu
+4SA5fT4QpmawQ00E2hfRXTURi5rBVVsUADIxfGqiN8mUJnjX8D8zayJMYfDQoWiSaJoI0gyedQgP
+pFTbkqbHOhfpvIHvYzN4rnU4QRh89UVXz/2kY2kGv+YDr8FPfein8CP//Udc35WVoQf4QkEGa9nS
+RJicwQF1BrvRRNCDXI1msEVNBD2ACdT7LiL/msG2NRGmMFg60mOkUr1fkbCn/QH8dgZTL3jFEa+u
+mZGgv7Y25TR6ZvyYPjdsPr/vv7++fPfd9rZrAw6DGcZPRhcGCyFeCOBHAUjyZ9HthRDi1wF8EMCr
+ATwDxRSzZ5T//lMhxHuP8XOfD+CvAfxbADcAuBTADoDrAPwYgDuFEN+24m71hnGAnEVtAG2rBAgb
+19PA8ODIcTPY4iJ6YRhMGrOZk2bwMJoIGnr70Ayepe3P8y4NuoMDQIb1ztB9pY81ytv42Ayeh7cn
+CIOjIMLVp64GMA5ncJZnuOfMPQCATz/2acf3ZnXoe08YhLWep4dmcACzM9i5JkJvBnc+w0XW6o2g
+OUCu+KHDh8FSqmFwMUjN3pkexxogJ9xoIuhjqg+Qs6GJaHUGx+6dwaZm8JRX18xI4GYwM1b61kTQ
+M5MefNDedm1AX9dDn0HFMEw7owqDRTHd6H0AQgCPARCLvwMA8HMAXociNP4kgO8B8NLy70+VX/8X
+Qoh3LPi5p1CEyc8vb/8+AN8E4OsA/DSA8wAuBvB7QoivWWXf+sI8QM6eM3iW0raWoRlMTqk/nA67
+mNHDA0UTMVQz2HEYbFsToTiDWzURhwAcDJCjmgjdGdxhZX/uHOb7BKjD8uhjjaj4v/GxGTx3i50g
+DAZqVcSj+48C4Qxx+XJex2YwVZycm55zeE+6UQSXxes4CqL6IFwPzeCgZYDc0M/xwqHaHop2PbhH
+tQONZnD1GeagIVs0d3X3vb0zPRoD5ALTALkcELmDwXntmgjqE16FPC+Cdq+bwY0wmBMzZv3J8/L9
+nMBhMDMW+tZE0O0/9JC97dqA7icfu2QYfxhVGAzghwG8BMDnAPzGshsLIa4F8OMoAtyPA/h6KeUH
+pJSflFJ+AMA/RBEQCwBvEkJ8ecum3gzg2nI7b5JS/pCU8jYp5ceklP8WwE0AUgC7AN7TaQ8tUzSD
+1QFytDGZd1xMLtNEbEWkGTwduBm86PTajmHwokU0DUadaCKEejq1Ev53bUTn9WNIH1sfmsE08C32
+mzbJVv9t7Nw5ADEJg2MSBo+4GQzUYbCExKP7j85VEesYBh+l9QNz9ujsglv6DR2GGAVhHQYP5QwO
+k8Gf4/pp8039Tbf3czUMNjeDpYNmsHm/h9FEKGoQkTkPg202g4tvly3NYF+cwZomggfIMSPA9Lri
+MJgZC9wMbl5mGMYtowmDhRDPBvCzKALZHwRwnLeaH0U9RO+NUkplOSOlPATwxvKfUXl7/edG5W0k
+gM9KKd+t30ZKeQeKcFoAuFEIccNx9mkI9MVkMUDOXjOYLqJNzeCJT83gwPIAOU81EXIgTcT2Ek2E
+82Zw0E8zmD6+69wMvmT7kqXfd9HWRfPLB8nBXBWxjpoIGgZfSC5glq3neWxqGBxB9OgMrprB9AwP
+F81g0xkuoUVNhDIgMmgZIBckAOTwYXDD+W/vTI8iDK73nf6fKqF4kCLPhw1taBis73fSsRlsat7O
+8aAZbBwgx6trZgT03ZxkGJeYnt82DyzS7T/1lD/FDCm5GcwwvjKaMBjArwHYA/BbUsrbj/k9r0IR
+4n5OSvlx0w2klB8D8HkUQe63G27yCgBVavLbC37Wb5HL/+SY9693TKeZUm1A3jEMps3gEIZmcFw3
+yg4HlgjpzWCbTTKfB8jp+x1bDIMTaQ6DaVvWF2dwbKlBR5vBkdxGYaspUELwNWoGn5qcajQgTVAl
+xoXkwmiawQBw7mg9VRF0GGI4UDM4FCFEZWVyEAb37b6n+6yHwfN/i8IrPLgmYmEj2t5BzQDBfGAk
+0AyDgYEVGXozmJzpYWW/TYoIAGLivhls1ESkvLpm1p++m5MM4wqqQCEf0701gwF/VBH6PnIYzDD+
+MIowWAjxXQC+DcAZAG865vc8F8WQOAD48JKbV9c/UwhxjXbd1xtuZ+ITAC6Ul19+nPs4BKbTTNVm
+sD1NhKkZvOVVM5iEBz06g2mYkItp4SYcCCkxH/hT3S9VC9KxCU40EZMlmojBm8H6ADlLpxXTZnAs
+dpTrFE3EGjWDj6OIANQw+DA5VMLgIZ/XNmiEwWvqDaZnY8Q9O4PDcoCcEEIZpOZEE7GwIWvRGRy2
+DJADgCDBdDrcc9/s/O9HExFqB4dchsFSAjl0TYS9ZnCWQVFE0PdxsVWc9uC8GSzUx3bGiRkzAjgM
+ZsYK/f1rmyyJ+gyDfVFF6OEvh8EM4w9rHwYLIS4B8IsoGr5vllKeOea3fiW5/Lklt6XXX7/KdqSU
+GYB7UDSM9W04owiDiTM40J3B/bkWAWDbYTNYLmgG29FEkNOpSaNKCAGRl4vLaDpocGJqgtsM/9OW
+ZrAXzmASBgfa87xLeHD2LObN4K1gQRi8Rs3gY4fBpPF9mNZhcJ77uZ+LmKbqE3JdvcH0eR6GIQKi
+iUjT7iklbclWmggAiINal+BbM7j7ALkFzWBNkQEMt9Ax7rd1TUTx3hgJdb9dhsHFwMD2AXJZx6Me
+ejOYvh962wzm1TUzAlgTwYwV+tymYXBfmgjAn2Ywh8EM4y9rHwYDeBeAKwF8REr5myf4vmeRyw8s
+ue395PKzW7ZzIKV86pjbuVwIbWXlCD0c1J3BeVeHbLqkGRyTZvDAnw655s7tSxMRyEhRBwBAWIXB
+4RSHhxgMU3igakEsaiJizwfIKQMD7TSDtyItDF4TZ/D+Porwoww6jhsG78a788uHyeHcGQysnzdY
+bwavbRisNYNDQd3Y3d7XACAlB04CkDC4CkWdNYPbHbLdB2MuHyBX/OBhh8gZXckWB4IqYfCiZnDZ
+oh3qeK5+RlMURIjpQFDLB3OpPz3Y8sUZrDeDeXXNrD/cDGbGCn0eb1ozmDURDOMvax0GCyH+IYB/
+jmJY3A+e8NsvIpeXGS5prHGqZTvHsWQu2o4TTJoIm85gpaVmCIO3J/VCeuhmi+7OVRyTFhfRwuBK
+DlE3gy9caFzdG6bwwObjrTaDt42Xqxbt0IGROljL3uNdhMHFzuzoYfCaNIPPnwewVWsRVtFEUGcw
+sH7e4LGEwTQIi0OiiYA6RHFVlAFyJAzeIpoIJ83gQA0HrTqDyT7ToaeA1hQuQ9Gh9t94cC+wt99U
+l6B/frtsBps0TMrBPSuaCHMzuBog51szmDURzBjgMJgZK23NYJvPb24GMwxzUtY2DC6bte8r//lu
+KeVnT7gJ8laMZX0WusTZ0a6rtnOcTsyi7Tihb20AXaCYNBE7pBl8lAynichzqIvoHpvBIZol8FC6
+aQYbF9EWH+9E1oHa7qQlDPZggFzRgK+fj7RNeVLOnpPzgHs3XuNm8HYdfnbVRMy3uUaMZYBcqqh5
+iCYCajt+Vdo0EXNHuIMweJn7vqvuKJVEE6GFwb41g9WDXPY+xxY2gwcOg+nB1uq+WG8GE2ewqolw
+7wyezWSjGcwD5JgxwJoIZqy0NYNtHlj0tRnMYTDD+MvahsEAfhrAVwD4OwA/u8L305X/pPVWBSTR
+gR7dVdtZto1l23HC0kU0ui0ml2kidrbcNIP7di2qU9ib+x2Jqhl8NHwzWG+SRfaaZCl5We3Ei8Ng
+l81gvRHdpRn85LkZIAoX695kjZvBNAzeWm2A3KZoIu5+7G684rdfgXf8f+/o+26dGBr4xmGkvO/S
+18CqzAwD5AASiobJ4M9x40FNiw1Z2jSNlwyQA9w2g5WDmjaawdVBTe3zWwnFHTeDm5/f3dKjRc5g
+H5rB06T5+wlrIpgxYAp+s2z9BtIyjM4Qmgh9Gc1hMMMwy2imVGuAEOIrAPwEiqFxb5RSrhKsnieX
+lykbSMTR0EFU2zmO9mHRdo7FbDbDpz71qaW3u/rqq3H11VcvvV2xmFQHyNFwsLNDdukAuXpBOU2H
+awYbXYvWB8gdJwweVhNhCk1sPt4pKb/vEAUIDQ2DyRFyuHUGR1pY1OXxPnO+fvs5tb1ZzWDFGTyy
+ZvAd/+ssbjkNfPu3N2/7H+74D7jtvtvw4fs+jB+44Qdwxd4VA93L5Sh+21BtBndxY1fQg3ahoAPk
+amewH81ge2HwEdln6rkHdE2E+2awrYNcgBoG643oSHimibA4MLDhDN6qncFVGOyyrThNmj+8yxBU
+hvGFtpAoy4BoLVesDFNAn9s7ZKnQpzOYNRHMpvLwww/j4YcfXnq72VDDLjxmXT9afxRFE/deAKeE
+EN9tuM1XkcvfJISoktE/KcNjOjSODpMzQYfG3a9d9wCArwWwJ4S4eMkQuWo7p6WUK70Vnj59Gjfc
+cMPS2731rW/F2972tqW30xeTgQgQKwPkOjaDl4TBO1s0DB7u02FZI9pqM9jQiI6FQ02E0B9vi87g
+qhmcThBH9f8nDUXFxE0zOMvUgx4TsrLoEpqc3V8QBq9JM9iGJmJszuA/+H/O4g/+DPgv/wX43u9V
+b/vQfvEbtoTE2aOzXoXBzWawXU0EbSAGxmawe2ewrkvoepBrOqs/x7bi5QPkhvrd0niGi8X38+kU
+81DUJ02EKQymjW07ruT6QdyJdjAJJ5hlM0gfmsGzZnrAzWBmDLQFYxwGM+uOC03EQw8VWsTA8Xng
+evib58V+k19XGMYq733ve/H2t7/d9d1YC9b1o7VKWJ4H4HeX3FYA+JnysgTwXABfAvAZcpsXLNkG
+vV53E38GwGvI7f7KeCeECMv7Kw3bODaXX345br311qW3O04rGGgZQDNgM3iPaCJmAzaDl4UHnU8r
+Js7B0PAymwRVGJxi/yDHUMYWuogWsrhfihbEliYi3VZ+cS8G/ERI8xRBfIQMvjWDOziDD+oweG9r
+W7luXZrBDU3ECgPkDpNDXDWiMBjbhTP4LW8BvuM7AFJ0VxQSs8yvo8qq0iC0HgbTg3ZmTcRs8AMe
+ywahdm3I0mbwzkRrBofuBsiZncH0oGa3/T48BGkG+x0Gq65kG5oI1RO9G+8Wr/XIfTN4ZtC9UH0L
+w6wrba+rNAW2tszXMcw64GKAXJIATzwBXH65vZ+xCqZ9TBIOg5n+eMMb3oBXvepVS29300034fTp
+0wPcI39Z1zAYKELVZQjttvPvkVL+rRDiIQBXA7hxyXa+ofz7QSnl32nXfYRcvhEtYTCAl6DQREgA
+H13y81qZTCZ48YtfvOq3N6CLSQEBIYRVZ3CyZIDc7na9kB5yMWNsBlscILfItQgAcbCF6r/2/IUp
+hponqDzesvgUpqFojo6NqkoTkW01PuS3o23sz/adDZBTnMEWXclPkWo3DUcBrRnsyJV8HGwNkFtn
+Z/A0056Q5f/H3/4t8Bu/AfzQD9VX0eFy3oXBtBkcRQgDu5oIxRksDGGwkDiaZQCG+y3fqL+xeHCP
+BuDbsT8D5JY1gzvrMY4w/xybeBQG6493FETK/bMzQK5+XU/CCfbiPZw9OgsZH9T3wRFHhmZw2mEI
+KsP4Qtvp4zxEjll3hnAGm85KevBB92Gw6XWdJOr/A8PY5Li61MnkOCO/xs1aDpCTUn6/lDJc9Af1
+UDkJ4BvLr0dSyi+RTd2CIjB+gRDipaafJYT4BygavxLAHxtuchuAKhl47YK7/f3k8h8t3cmBoKGo
+KBfvNpvBSzUR5EWY5G6dwcrptT0PkKON0XMHw6WiNDyoHm8aFnU9rTgT5mYwQIbIxUV4OvgAObJ6
+D8PQmmPy/KIwOGpqIsbcDB6TJoL+f/ybfwPF7b0uzeAoUJ3BtjURtBlMG7JHybD/J8aBYjY1EcTR
+6tMAuWXO4K7v57QZPIk0Z7ASBrvY7wXNYGl3gNwknMz96HnoaTM452Yws/4sagYzzDrjYoAc4Ic3
+uC0MZhjGPWsZBq+AaPn6e4B5FfKXhRDKMary379U/jMF8Iv6Bkr37y+VP+N6IcSPN364EC8D8DoU
+gfJtUspPrrITfUAHyAVVGEyawbKrQzZrX0QDwFbkTzPY5hR2ulg1NYNpY/T84XDpIA0PqpA6EPY0
+EZko9yU1N4MBQIZumsE08I2CUGmSrXo6dZ4D+1MSBseLmsGeO4O3at35xVsXH+v76AC5C+m4nMHY
+Pjs/oPHww8Cv/mp91bmpx83gXG1MhrYHyKVLmsEolD9DTn/XG7KFQ5aGgx3d9+0kU04AACAASURB
+VKmqDKC4HCBHz0ABDO57i81g/53B9hrRxf8recyDeP5eJyMPnMGmAXKsiWBGAIfBzFhp00T06QwG
+imawazgMZhh/2ZQw2IiU8gsA3oUiyP37AD4qhPguIcQNQojvQqFzeAmKEPffSynvbdnUuwD8Tbmd
+dwkh/m8hxDcKIb5WCPGTAP47CiXHIYAf6XevToaqiSieDkozuKsmYkkzmAYI7pvB/QyQi0TcuJ42
+RvcHD4OrxnLZDBYWNRGkGayfeTFvBpMweMjAiGoiooYjerWVxv4+5vsDLG4Gi9jfZvD+PuYaC6AZ
+arehaCKS9dZENMLgrXP4uZ8DRHko8ed/HjhzpghdLiR1Tdi3MJg+l4uDXLQZ3H1FrWgiTM5gAAhn
+g/6ib3o/pwf3OjeDF+iO1P120JAV+ucYPcOlozP4KAeC4rPQ5zA4DELElgaCzrff0gyW4RQQmXdh
+MGsimDHAmghmrLhqBnMYzDDMIjY6DC75aQC/gSLwfSGA3wPw8fLvF5Zf/3Up5c+0bUBKuQ/g21AE
+whLADwD4EIA7ALwThSv4HIDvlFLe1duerABtVM2bwRF1BnccIEdPWTY0g2nLKh3wNEejM9ji6bVK
+6GpoBu/E9W8Cg4fBCzUR9sLgHS1PrMLgnISnpqPYfaE0JsMQk6i7JuLcOcy1F8DiZnC05WcYLGWp
+iSBh8Dy4X4KiiUjHp4l4/euBm28u/vnkk4UugraCAf/C4DRrbwZb10S0NIMRJoM24JcOQu34OaY0
+g4NFA+SGbQY3QnBhWRMxbQ/B3TuDFzWD+xkgV3/h0Gk4lRg0EQlrIpgRwM1gZqy4cgazJoJhmEWM
+PQyWWDJoTha8HkWYewuABwFMy79vAfCtUso3LP1BRWv4RQD+FYow+UkABwA+B+DdAL5GSvlnq+9K
+PyjaANF0BlvVRCxtBg8cBvc4hV1tBpvCYNIMHjAdpIN3TM1g2aEJnubpXDmCbAu7u+r1VXCYB0eo
+XpZDBkZJ1t4MXjUEP3cOQHQ8Z3A48VMTMZuVz9dVwuB4zM7gp3DRxRne8Q7MD2z86q8C/+tzZ5Wb
++RYGK83gIFRCOxuaCHo6Oj3rQdclDHnQY7k7t+NBzaxdE+HTALnCnWtREzFrD4OV/wcvNBH1/ev6
+eBsHyE3IaQ/xAbJs2DNbKMZmMIfBzAjgMJgZKzT8pGdOboImwvT65TCYYfygmVKNBCnl2wG8/QS3
+vxXArR1/5iGAXyj/rAW9N4OpJkKXyEINEFI5sCZCb5LRZlFHTUSSZoAoVoqhcXBeHRIeHLlpBhud
+wR0ebyVMS7cbU2LnAaPIi4V8Hg8bGGUZyqc4ojDEVkxCshWbZCdpBgcTP5vB58+XF7gZ3Pja+dl5
+POtZl+Jf/suiFZwkwDv/r3PAdfVtpqlfD2jfzuDjNYOHDYOXNYO7hqKzBZoIJQT3YYCcxUY0bQbr
+IbhPmogoiNTHG92So0XO4OILhSYmz9Fw4w/BzLCy5mYwMwZYE8GMFfocjmMgioqv8QA5hmFcMvZm
+MLOEIhwsgk9RhoL09PkuTVFAnWxvGiBHA4TBNRGLmsEWHZOmAXK7LsPgchFt1ER0CA9oKBbk23PX
+aoUSMJbB46CnkufcDDYxD21XCIPDIJy/hi8kF8blDAZw9qhoAb/5zcBVVxVfu+0Oz5vBOX3vUZ3B
+q+pQKLOWZrAeBg/5PO+zGSylGrTpmgifmsG2NRGLmsHOw+CFAwN7cAZHzTDYlTd4ZtBE0N+1GGZd
+4WYwM1Zo+BnH9YHEvjQRl11W/O1DM3iIMDhJSLmFYZhjw2HwhmPSBtBQtPPptTQMNjRkadsow3AT
+6PXFZCACbb+7Tp9fPDiPhsGHs6HD4AWaiA77TcO0EFuN65WAsWzTDt4Mrn685gxeVQty9iyO3Qz2
+dYBclzAYqANwfYDcujWDp1nzganC4FOngHe8o/zitt/OYBqERUGkhsGWNRGtzeAg8aoZ3OUgV5IA
+EB4PkFtwULPL53eWqQdofQqDzc5g2wPkFjmDL9S3c0Bi+MGZ5JoVs/5wM5gZK/Q5HEXFH8DuQUX6
++nnuc4u/T592v+7oOww+OgKuvx64/HLgox+1t12G2QQ4DN5waChahYJR0FMzODKEwdoptkP9wkcX
+0UJWIbi9RpUSBhuawXvbdUh4YcAwmC6iq8Yy1URIS5qICM0w0dQMHvIXlIQEBGEYYBJ1d0zqzWA9
+RKX/DmI/m8G6JiIUofEARhtVAH6YHiKOga3yqb1uYfBR0t4MBoBXvrK8sO13MzjVnMFDNYPdO4PV
+/aYHe7q8nx8doREMUlwOkFsagncIg4+O0AhcKc6bwdp9i5QzXGwMkFvgDJ4Upz24awYbnMEcBjMj
+QA/MTF9nmHWkLQzuqxl8zTX15cces/czVqHvMPiOO4B77y1+B/md37G3XYbZBDgM3nBMA+RshYOA
+FgYvGSA35KnFJleyTU2E2gyOG9fvbdVh8FHi1hlsTRNBmpXRsmawC00EHSCnN4NtOYMXaCKqZnCS
+FK5JX9CbwSdpBQOYN+YOk+L/oWoHr5sm4mDafDKeO6pbwFdcAVx8MYAtz5vBmjM4suwMTpS2qB+a
+CHqGS3G/IoSBnYasHor6pIlY6gzuEIIfHuL4YbCTRrSqQ6H3p+sZTVkGJQyOg1h9b3fdDDa8jlkT
+wYwB+pqicyc4DGbWnSE0EdXPiKLy99US17+P9x0GnyO/ln/hC/a2yzCbAIfBG44SihrD4J6bwUqr
+arhTi5VmcBUGB/Y0EckSTcSpHaKJGDoMLhfR1eOtaCIsNYNj4V8zOCUJbBzaOY284QxeoIlAVO+s
+61O2KHoz+KRhcBWSXEiKgKQaIrduzeB9QxhMm8FCANdeC++bwblUQzLbzeDkmM7gwZvBWkNWdd93
+DYPbdQkuB8iZncGWBoIuCcF9awbT5/mqB/fU7deP+SScqL+rlD/bp2ZwBm4GM+sPDYh2yK9Trl5r
+DGOLITQRVTN4MlFfPxcu2PsZq2AKvDkMZhg/4DB4w6GhaKUzsNUUBdRhRhNDGOyqTaY2ZA2D1Lq6
+krPFYTDVRBylbpzBofHx7tAkI6fZT4JmoKg0qxwPkIvDEJO4+8ChIgyud2JRMxihn2Fw12Yw1URI
+Kdc2DL4wWxwGA8B112HtnMFRn5qI1mZw4rQZ3GjIdg1FF2giTM3g2UBPCb0ZHIjAmibiRM1gD5zB
+9KBm199bTJoI0/66cwY394+dwcwY4GYwM1b0ZnCfmog4BnaJ5v7w0Hz7oei7GfzUU/Xl++93v78M
+s05wGLzh0EVV783gcLkzeNAwWGsG0/3u7AxeEgZvk5BwOmAYvPzxXn0Rff5CvR+T4HiaiGGbwWoY
+vBXT04pX+21s2QA5JSgizWCfvMG2msG5zJHkiaKJGGogpA0OlziDgSoM9rwZTHypYRAiDGkY3H3V
+oQwVo87g0LEzuKcBcsvcuS4HyNFmsEAAIYTqSu7w+a2H4Isb0S6awfpBD3uaCNMAOWX/y/9zV23F
+xPA67tqGZhgf4DCYGStDOIOrgFVvBrsOR03Br82D5ufUjgbuvdfethlm7HAYvOHQRXS12FG0AaJj
+M5gsyo7TDB50ManpMZRmUecBcnQh2dxvqg+YZsMlg0kim81gS4/3uQukGRweTxPhrBkcaQOmbGki
+tGZwIIJ5aCKDcTeDgcIbXDWDpXT/C+hJoJqTinNT9TfM665DwxlMXdk+sLAZbCG9os3geIEzeMjn
+uKkZPLE6SM28z4DbAXKK89/gvh9rM3iZJqLrADndGTwJJ8rnpOtmsFETwc3gVpIEeMtbgLe/3S9f
+P9OkTRPBYTCz7uhhcB/OYF81EUM2gwFWRTDMSTj+yHhmlNCmaBUODtoM1pzBLprBgaEZ3HWA3DJN
+BNUHzAYMk5K0fjxDgx6jy+NNm8HbS8PgIiV01QzWB8itGpqcOwfg4vZmMFA81sksgQz9bAbbGiAH
+FN7gU6cuUbZNT1XzGdPrUG8G++4MlrLpDKaDO604gz0cINe/M5h+ji3QRDh0BlcHNeNwmEa0b2Gw
+0gy2oYnQDgCY9tdVM9h0UIedwe3ccgvwzncWl7/qq4DXvMbt/WHa4WYwM1baNBE2P0eqn7Fpmgi9
+GXzPPfa2zTBjh5vBGw5dVFXNFyEEIAWAbotoAEhJMLG1rBk8tCZiweC8zgPkloXBpBk8y4dLRGcp
+bc+Zwv8OmojD+sFTXLklXjWDwxBx1H3Q0rJmMFA/1r42g8+fR/EeEBbP2VU1EUDhDa6awcB6eYNn
+eflknNY7YA6D/XUG5zkap88rg7UshMHpccLgYLhhoIC5GRyFDnQJXjSD7Rzc05vBPg2QW+4MtjBA
+bqEzOKtv5wD6+0VF130eM/SU4TvucHc/mOVwGMyMlSE0EW3N4LGHwdwMZpjV4TB4w6GLaGWxI4un
+RtdmMPXYmTQRyqmXQw+Q0xbR6gA5e2HwRGuSAWpYmsghw2C1NUj/BgCIbGXP6z558EyBomtnMG17
+x1GIOBZAXuz7qgvpc+ew0BkM1I91Jur/H++awaS13CkMTg7nzmCg8AavC4ksH5SDK+df08PgSy8F
+gl1/m8F6Y1IPRa00g1s0EXooOrgzOFDf22wd5FqmiTANkHPZDFYPam5OM9jmAFiTM5hu33kz2OQM
+5mZwK2fO1JfvvNPd/WCWQwMiDoOZMaE3g/vQRNBmsE+aCNM+9tkM5jCYYY4Ph8EbTpLmgCjSv1AJ
+g4tPqc5hMNVEGMJgIQRClIvpcLg22dJmcNdG9BI9Bm0GZ5gOtqikU8jnzmBlkZut7NTbP6ofvJ3Y
+vwFyujM4DFGHwSuGB2fPYt4Mpn5gSvVY5z43g6PFQf4iFGfwmjaDpZR1GHz4tPlrQ3cGA0Cw428z
+WNcl6M5g+83g+r3NpSaChqJA8VpUXOg96hJ01RHgthlsd7/bG9H03yJysd/aQQ9yf6Sw7ww2hd/u
+msHNxzUXHAa38cQT9eW//mt394NZDjeDmbHS1gy2uf6jzeBN0kRwM5hhVofD4A2HNkVpaCDKZjA6
+D5AjmojYrKgOUS6mHTWDQxgGqVlsBpvCYCVwC6eDfVArzeCgGYJD5Cv/YnIwrR+83YnfA+QmYVj8
+IiarZvDJdzrLyiC1bAZvR9uFYkVj3gyGx87gDmFw0xmsbXsNSPJkflAM6Q4uji8F0GwGSymRxZ6H
+wVpDljpkbYTB1BlMg1CXA+T0UFQIoQ0U6xiKhuZ9Btw2g2n4HxoPatrTRCwKg6P4/2fvzYNlueo7
+z+/JrO3WXd59q/RAElpBCJDAWswu0TZLmwabwWuH3YA9RriDjrG7wz09eIHu8NIee9oepu0ebMwS
+btvYDMYIG+QAgwBjgUBCSCAJ7et7enr7XWrLyjzzx8nM8ztZue9Vld+IFzfvrXp1M29mnpPnc77n
++xPvy7M6eJh8ncEsn/MtPz8kM9j+m1eXGewXE9HA4CBRZ/AzzwBPP13dvjQKV1NArtGiquiYCM5l
+H1w3Z3DZmcFPPVX9MTdqNC9qYPCSixYiaRXgDKZQtN/1h8EtZg+my84MDimcl3UwaUQ5g2mmbqs8
+GDym8J/NQnAwM/WDSRQMViIUahATIZzB4m+QJiZie9vesI/FLy8YkM5gk9XTGZwVBntjItbX5Wve
+2fq6ajQlDc+0hz09UQTPC4N3jV1wzwRZ/WCwxxlMYyIyLp8Xn2E/wXOmgGYVipbXlgP+cQllOYMr
+LyCneY5bK+a4vRCc/h30dgUxETMOeOIMzgMGe5zBSj9ZsTPYLyaCN5nBgaIwGGjcwXVW4wxutKgK
+iokwTaSO56Oik5PLnhkMNEXkGjWKqwYGL7kmAYXOmH1peMFHUlEYvLoS4Axm0hlcRQEav0JqWTOD
+pzRXMyImAvq4tBlMJSZCm4UH0NLD4AE5eSvdmheQa9nOYDsmIs117s5E2zERfnnBAHUGTwCIJ746
+OYPzjonYs0e+5p2tr6u8MHjvinAGnx2dBSdP6V44DNQRBgdnBucaE2G2oZEnCDUuoVpnMOCBohn6
+sajMYPo9a1fnDM4bgidxBpcNg30LyClO8HwLyLX1tm9MRFXOYMPnPs47JmIwEPm6eUCKqtXA4PlR
+A4MbLaqCnMEAUsfzUdGVOe32csVE+I01mqiIRo3iqYHBSy663FCBgrYzGCxHZ3AvyBnswOBqncEK
+PMgaE1FTZ7DhA/9VZ7CVeqnv0JAnb60bERNhRytU5QzuOM5g1wGfAQbbxxLlDAZQ+jLyOMrbGTzv
+MJiZPezrCxhschO7hqyCd3Y0e0B1gsHe7NxCM4OtNghnrjQmgh63X2HMsmIitJJhMHUG5x0T4T3u
+sKxkrQpncEhMRFZnsMgMlsc+mxlsyv2oQKafMzhHGMw58NKXAlddBfzO7+T2sZWpgcHzoyYmotGi
+yusMpjA4j+ubfr7XGVx1ZEKRBeQ493cGNzC4UaN4amDwkssIiIlwMoOzOoPpcsaVgJiItuYUkCs7
+M7i4mAiazdvxKZxXmTPY53yrmcFm6g56aEgSsNarX2YwLRLX0jXFGZzmfJ844XxYPGeweK/4Gy2q
+M3hgDOYSBo+n8trtaD1s9jbd76kb2M8ZPDbrQ/YjM4N59hGHGxPhcQZXWUBOcQb7xiVkhaLEJep1
+BlMoWmEhNT3nmIhEzuBWtTBYZ2oBOStjATnfmAhtNiaiusxgP2dwfrTs2DHg7rvF9l//dW4fW5lo
+ATlAOJ4b1VNBzuCq7rVGjfKS1xlMJ9PzgMHUyLNMMRE7O3IFy9698udNTESjRvHUwOAl1zTAwcqQ
+T2Zw0OdTtZzBdUWZwS0/GJzRGTyeUAjennndCwjLi4mIKCCnmbk4g1d78WIiqnIGtzQ1MzhN3uI9
+94j/iXa8zGAAgC4OeJGcwbSA3CLERHT1HvZ05UFQAHx2XG9nsJ9jUomJyCMzOI4zWDMqcwZrPg7Z
+LIVQRyMo94d30oceN2tV4Ay2j83pR1WHbEYIHhMGV+kM9isYmEtmcIwCcpVlBvtM6uTpDKYuq+9+
+d75dmcPh7OTrfffVqw9uJNXERDRaVIXFROQx2eF1Hi9LTATtr66+Wm43zuBGjeKpgcFLLiMwJsK+
+NHJ0BnsHk446rjO4PIAwMSyAialE3ScuIetgcmzI4+51op3B5cVEzDqDGWOuExwsPQymQG1jpX7O
+YJMAfl2zM4OdmIgU1/l3voNQSOSozs5gywJ2d7H0MREDMpHRa3cVZzCNhvBzBg/GdYPBai54m4w4
+comJIM5gCoMVx2wlmcEhk3tZYyLofe6Z9FEyg/UqncGzx51lZY/XGex1RCswmDiDy8iYpZnBDvwv
+qoCczloCNvsUkKvMGewXE6EVA4MnE+B738vto0uXNyICEOdXTOY2qpuamIhGi6qiYyLCnMFVx0QU
+CYPpOOO884BDh8R2A4MbNYqnBgYvuehyw7Y26wzOmhnsggMEw2B3mW2JS4tpjINfXEKWrEVAhcF+
+8RiVOYOVzGAyuHWaggyZwXSp/dpK/ZzBNCZCZ44zOH1m8He+AzcvGJhPZ/BgYMObJS8gd2qLQv3g
+mAi/zOD6wWC1baMxEVYezmAuncF1iYmg2bkahXaW04/lB4OVthtiMs3t22xncNo2NKkoFHUz4HNy
+yCZyBuvifZyXA2384L8X1maB0jQz2Klp4FdArrrMYJ/zmmNMxPa2+v08xypQGMyY3G5yg+upxhnc
+aFFVZkxEu708MRF08nLPHuCyy8T20aP2ysdGjRqFqoHBS66gQmfUKZppUKXJ4kt0OTmVCxF0A8NR
+OaWr/WBwXhmTgAqD/TKDVXBSrTMYEEttxUZ6Z/DYIs7gfjxncGUwWNMFyHJiIhLCIs4dZzCBwQHO
+YPW46+UMdh+UCnIGn5k10tZSJ8/K4+93VBh8enTa3faLiRhPawaDmTrp0c45JsIMcAZXWUCOHrcC
+BTMUiHQkYLC4z1us5Tup6Rw7K7lApG9mcE4xEcIZHDyZq9QYaMn3lXHs1AHvVwAWmpnJtUudwU5N
+A78CclU5g/2yv4tyBgPzDU4pDH7xi+X2PB9TVu3sAEeOVL0X/mpgcKNFFb2Gvc7gvGMiOh31/qkj
+DM5r0pyaTjY2gAsukN8fO5bP72jUaJHVwOAll+IUJSN7RpyiWTopCoNXO6u+76EFeIbjcp74VIes
+z/LarDER03BHtMY06LCPuypnsHK+pYMu7WzthBTS2rM6CxQV52zFBeR0pguXkBsTkey6e+op+wGE
+OIODIGqdncGuAyynzOCBMcDaGlzH6Nw4gwkMXu30sG9ln/v9yYGsPuRbQK5uMLjozOBYzuAKMoMd
+OEigIMsQA+OIOoO7uv+94UYokJiIsuMSfPuxrMcd0xnMdPm+8mCwGo/hde5mhsE2XHXOrV8BuTpl
+BkMzcrvmvDB4np3BtHjca14jt5cFBt9/v1pI6fhx4LnPBc4/H/iHf6huv4LUxEQ0WlTRa9ubGVyE
+M1jTJBCuOibC7/iKcgbTdqMuxptGjeqsBgYvuehyQ78CclkcNpwDVsuGwZwFL6NvSYgwHOfnbgmT
+4gz2yVoEszINrPycx161mQ0JS3QGB8WCaCQWJO1srUGcwXtWo2IixAGXCoygOoMBAosSwv/vfMfe
+CMkSdVTnzGD3ATHHmAjG4LqD5wYGb8vjX1vp4eDqQff744Pj7rZfTET9Csh5MoNzjolwXYnezGBd
+zQwu8xr3g4MAMmWCO1JgcMC9IVe3yGshr4FOmIwpBzTh/s17UtObGVwnGEwhuF/mP5iZaXCtOIN1
+P2dwtZnBll9MhDaFlW1Bk6tFdQZffjlw7rli+9vfLmfCpkp94xvimK+4ArjtNvGzD31ILJ+2LOBT
+n6p2//xE79tu1//njRrNo4qOifA6gwFZRK6OzuAiMoM3NlRHdF2MN40a1VkNDF5yTYNiInJwBo9G
+ANqCNmlmH4wGthF1WxIilJW/qcBa3W8wmc0RHQsGa/aTbonOYKWgn58TPENMxIQTGLw2C00U52AN
+nMEAUsMiCYPnOzPYfUDMKyZiKj5w3mDwGQKD11d6ONgnMHhXwuAzY+IM5qI9M2oHg4Mzg3ONibDq
+ExPh55AF5GRPXpnBQfe4c89YumwPSnHIBhQElQVg09NBrzNYgf3wFs6rzhnc8nUGZ4PBNDPYOU7l
+81n9YiKgG7kNrr2ZwUeOACdO5PPZZYvC4H37gKuukj+va1RCXvrLvxTA2zCA3/otsf2hD8nXn3yy
+un0LEr2Gm5iIRouksgvIAdIlu8gw2OsMpu1GXYw3jRrVWQ0MXnIFw0HpFE3bYA+HANrCGdyy/CMi
+gGqcwVExEWDZMgf9Pt+rjiadwdUUkPNzBqeHwVNuUwDOsN6fPWbGmAtNWLv6zGAAYNzJDE72JPbd
+79ob7ejM4KVyBhvzCYPP7soLcaPfw4H+Aff7QGfwQLzH4PWBwaaJ2czgVn7OYM45LMdtatangJwC
+B7XZSc0s2bnjMdz7fKXtf2+sddYAAJYuY5HKaNvU2B/qiHaOezGdwfR8OwUDvTEOWfII/TKDvQXq
+3PdVIBP+MRFFDK4dzWtUhBcGX3ml/H6eHc9xdMstcvumm4CPflTERjh64onSdylS9J5qYiIaLZK8
+zuAiM4Pb9lytcw9VHRNRlTO4LmOtRo3qrAYGL7mmZLlhR6dwUBaQSzuoGo0AdGwYzENgcFs6jEZG
+OWBFcUT7FJADszI9fFJnMHVQUXUcx2hrVElMBIX/zoA6izN4CrvXnfawuurvAvfC4FKdwZh1BtOs
+ZD/9wz8AP/uzwD33qD93ncHtBXEGx8g+DpI3MxiQMHgymY+Hsa0BjThRYyJODKQlTskMtmGwOwlS
+A0U5g7PCYMMiT+9hzmCtiszgWRiMvJ3BATDYycOfajsAxNrz0mGwjyM6czyGHq+AHL3myobBjmPZ
+69zNAoPHY7gwuOP3+RXHRJi+MREWxpN8ciIWGQY7zmBgsWHwqVPAnXfK7zkHbrxRfU/dYXDjDG60
+SAorIFeUM3gZYiIaZ3CjRtnUwOAllxkQE+HCwZycwW30A9/XIzB4OKkgM1j3cQZnWGZqGKrTNMgZ
+7ELCMgvIkfNN4T9jcllx2vNNYfCKPxeVoLFdfmawFZIZDM0C9wQIcg78zM8AH/4w8EM/BOzapj/L
+ks7gw+c3zuCwmAhgPtzB20N5/JtrXfRaPdftqTiDx+Jg+q01wBBtmon6OIO9mcEtraU4g30dhQlk
+mKRx8DiDlUmvkmMiFDhI4wtygKLDEXfvj6B7Y7W96vxC973lw+DZwnl5xkTUCQYr8J/5xDxldAYP
+BnALyHXbfpnB4nqqClBZAffxaJLPDvnB4HkFp7SA3P79ywODv/zl2Uxk7z1x8mT1kMgr5/mTMQm0
+gAYGN5p/eQvI5Z0Z7C0gB6gxEVVmpDfO4EaN6qsGBi+5isyQ3R1YQEfQpg6CncG9tnziK8sZTAfR
+bd+YiPSF1KKW1zpyIWGJBeSUgoFkWlrPISbCYjYFmHbR9jdDS3BoZ+2W2VFzn8xgJyYCACyugpPJ
+RFTeBoDHHgPe+16x/cgjcgD17AsXxBmcAQbT93tjIoD5gME75ELcuy6Ox8kNVjKDbWfwRmcPYIp2
+izPT36lXgbzO4LwLyIU5gystIGdyF9C1/AqhZnHITgwBeRF8bzgTBwDc1TCVxkSQlT1pi4oliYmo
+0hnc8lvZk+G5BQB2BxxoiQ/o2TFW3hgKoMrMYPmLNYu0vznFbHkzg4HFcQY/73kSMi4yDKYREc97
+XvD76pYb7ECxIpyTjRpVqTJjIryZwUC1YNQ5dmogaJzBjRrVQw0MXnLRQUWQMzjtoOrMjgRlHRYM
+gzsEIoyn5WcGtwMKyBUNg3vOkuPWRAw+S5ChOMHzjYkwNdHrMisYJjruWd6qiTMY8m9AJ0YA6QR2
+9Ad/AHzrWyQiAmmcweXHY4QpD2cwzYL2cwafOeP3v+qlnTGBwRs2DLaj36fRiAAAIABJREFUIk4N
+T7mw18kM3ruy6cJgwANJK9R0CgV8ipgI2f5kLSDndQZTGKwxTbZ1JTuDjelsITWAOv/N1K6YoSGv
+jaB73ImJAAB0dgCUBEXp5F7AcWcqAEsLyHnijlQ4Kq+L0mEwXdljF3UEMzMNNAdDEp/VCo6JqIMz
+WOcEBue0sooOrs87T3z97nfnE8g5MLjbFWCk3QZe8ALxs/vvr58zNi998YviK2PA//yf4qujG26Q
+23WLinCuMS8sm8drr1EjKq8zuMiYCMeQ0yeLcqts65xjp/vTOIMbNaqHGhi85PLLzgWISzaDU/Q0
+oWldLQwGS6gyzqt3iNDUJx7DW0AuGwwOzlp0tNKWkHBnWI4j2jT94b+eA/y3NEEBtDAY3KIwmFcH
+g72ZwZgFZTs76v83TeDnfg74u7+TPzt4OKEzuLV4zmBAHrs3MxiYD2fwgMDg/XtUZzAHx8nhSRim
+gV1DtGl7V/aAWbLdmpj1iIqYcQYzXYF2VoaCYsCsM1jzPEG4bblebmZwUD9GncGpHbLT6DxtNyYC
+cKORynYGt30d0VZqGBw1qakxze0zeQ2cwQAphJoxJmJ3JP+zc017M4mB6pzBFAa3uOx38o6J0DTg
+ZS8T2+OxWnxsXuTA4H37JBB1oiIsS53cXRSdOCGd3C95CXDNNcDP/7z4/rWvBd7yFvneujmDnSGA
+1xlc1b3WqFFeou5YTcs/JiLKGVxlEbkiYbDTXzEGrK01MLhRo6RqYPCSiw6iKTTQc8gMPkNgcC8E
+BtPlxZXEROg+y0yzZiXHcAZTGDwoiZwYAbEgFP6nPW5uO4M1qxv4HtdZxyxAN0rrqDkHOAFhzvFq
+BOgprkfMwmBAOIM/+EH5/b5zkjmDWc1gcB7OYEAWkZvXmAjq/jxgw+AD/QPuz47vHsfWWNrlNlc2
+0dbqCoNVlyxd8ZA5JiLEGQwQ96g+wWSC1AA2+X75xyVQh2zafRlNo++NqmIiTCvouGV7ns0ZHD6p
+6fyMZuSXAsGn3C1upzjBYW9njIkYjORxO88n3kxioEJnMI08Is7gUc7O4I0N6aIFgAcfzOXjSxWF
+wY6uvFJuz2v8RZi+/GW5/ZrXiK9/9EeioNzf/i1w/vny9cYZ3KhROaIRKED+kx1hmcFAPZzBdH/y
+dgavrwvI3sDgRo2SqYHBSy46qGj5OYM1C+NxuvW1Z4cSBq+0YjqDK4yJ8BaQKzomYqVDnMGjcugg
+Pd+dFj3f2WIiOOeu65UOTr1S3LOtISyrnId804RSTMkB/xon156pngMKg1/+chVwAmLmfW0zGhRR
+Z3CrV68Ccrk5g20Q7rgoNzfla/MAg0dTee4311RnMCCKyDl5wQCwp7unxjBYneCjk1w8Z2ewFwZL
+Z7D4e2YBckkU7AyWhTHTDrbGZvS9UQdncMfPGaylm9yzLHv/I/ox92clw2DTlG15uwBn8GAc4Qyu
+ODNYcQYj/5gIJzN4fV2FqPPQllONRnLCkx7HoheRcyIiABkJoeviuPv9BgY3qocsS6y0u/XWqvek
+HDl9sXNd5319+zmD6xYTQR3/eTuDnTFaA4MbNUqmBgYvuabcf7CnERfMeJIOBm8RGNwPgcE0i9Ao
+Caoo8KBVbAE5pbASEXUGDyflwOBpQGawC4xSxmNQGNZCDGcwALTLKyLnzVJ1XF4UBnuBHs0Mvv56
+4L77hLvm9a8XVcl/5VeAiRUjJoI4g/VuvZzBecdEzKsz2M/96WQGA8IZfHYsD2Szt6lA/rJWNETJ
+LzO4SGdwcEyE+HuU9SAe6AwmMRFpBluce2CwHgCDq8oMVvpvstKDxESkac/dfY/ox5y+2yoZBk8C
+zreWQyFUABiQQmzOMaowWNxHVQAqy4IK6UGdwfnGRGxszF9bTkWLx+3fL7cXHQY7xeM0DXjVq2Zf
+pzB4XmIiGhi8eLrpJuBNbwJe8Qrg4Yer3pviRSc6gPxjImifV9eYiHZbupbzdgZvbIivXTIEbWBw
+o0bRamDwksskg0kKDSgYHY3TAYStEYHB7ZjOYLMcZzCFos4g2ltAruiYCAoJy4qJUAoOtXyc4Cnh
+wfZQ9rh0cOqV1xkMlJkxOVtALgwGU2fw6ipw7rnAL/wCcPPNIpPv139dzRMNjInQZ2FwXR5Q8oqJ
+oM5gzvncAYSJ5QODiTP4xOAEju0cc7/ft7IP3ba8dk5v1QMGmyZKzQz2OoPdNs2GwWVNevhlwAPU
+KZoODk6nMv4GCCkgR/s3OyaiDFf0NCgzOGPmvztJFNMZXHpMhOKIlpBaY9ljIjgHhpNZZ7BaMK86
+Z/DMhA/pb/OouWCaciJ0kWAwdQbv3w88+9li+9vfRuriknWUZYlifwDwwhfOrmgCgIMHJZBpnMGN
+qtKdd4qvnC9mdrdXFIgCyxUTQe9rZ9/yeEaaTuU4pnEGN2qUTg0MXnKZln9MBB34jI10YYvbIzkN
+uRoCg6njqCxnsDKY9HUGFx8TQSHhoCRnMM2Y7BCS47rKUg6iz+wQGMxCYHBFzmDDgK8zWCcu5jAY
+vLYGXzlOWCCmM7hdb2ewzvTA6zVMTmYwIFy28wQQTBOYcnkROvel4gweHMdDpx9yv79478XoERh8
+4kw9TmhkZnBWGBzhDHbbtJKzsQNhMJNO0TT7MhrBbaeAmJnBJcZE0KKXyuReRgjutskxYbAFeV2U
+MrkXAMFpTERaLjqZAFyTfzTn+cSvgFwVgMowoJyXDiMF5HKAwU5EBLC4MBiQ7uCzZ4HHHy9vn4rW
+YCDh9oED/u/RNOC888R2A4MbVSXaV5QVKVWlvM7gZY2JyNMZvCVLebjO4AYGN2qUTA0MXnIp2XMU
+BhMwOp6kAwg7Y+kMXuvEcwZbMEp56CszJiIODKaZpUXKDMgMpjERaTros7ty/ztaSExEjZ3BY885
+oDERgTA4oTNY69TbGZzGFQyo53U4Hc4VQNjagq8zWskM3j2Oh0/LdYwX770YKx157Zw8U4+RTNWZ
+wdIZXO51PlUmufyz0FPD4Biu+cpiIqIc0Sn7MXfQqIcXkHPaPIPLdrDK49YdZ3CGydzBAErhvI5W
+r8xgLwymk69jI/vDE4XB6+uLD4OBxYqKiDOJDcioiNOn1WedqkWhUd7L6BvVS7SNzisyoM7yFpAr
+MibCzxlcVUwE5+qxFwWDG2dwo0bp1MDgJZcSE6FRp6gc+AzH6XqpnQmBwb14mcHQJyU5qmbhgbIM
+NGtMRMQgGlAdoyOjLBhMITjJDGbZ4MHZXdnjtsOcwa1ZZ3BpMNjXGUxhcHhMhJ8UGBzDGezA4Lo6
+g1PDYALCh4YKg8+c8fkPNdKZM/CHwR5nMIXBl+y9BP0uiYk4WyMYHJYZjGwjDq8zeAYGu87gCQBe
+uTNYRw7O4Dgw2CcmonQoSgvnZYyJcAdQOpnkI5O2jpwVARMuR5llDDiNKCd4hgJygwHcmBPx+eL5
+RImRsmFsFYDKO7HZYfnGRHidVrQtp6/Ng06elNteGHzllXJ7UWHw+nrw+xxnMFCv3ODGGbw8om30
+MjiDqyggV4eYCHpsecNgOkHZOIMbNUqnBgYvuWhBIQotW0wC2rQVqncNCYM3uvGcwdCNcgqKUSeZ
+nzM4Q+ZgGmew15ValPyOGwB0Z713yuPeHsj97wYUWQI87tlWyTERPs5gnZMifkbGmIg4zmASE1GH
+nMIinMEDYzBXbjIKgxnX3Pv1QF+usaUwuKN38Kz1Z6kweLseIxmvM1hjmtL+5O0MDiwgB5Q2sQd4
+MuB94aCF8Tj5DeeFwUETPlXFRNDj1v0KyKVsz91BYzs86smBwWNrAED8fctwGQbFROgs23EDDgwm
+zmDfzGBxH9XBGdzR8o2JCIPBdW/LvQoqIAeozuC77y5nf8pQUmcwUK+oCArMGhi82FpWZ7AfDC4q
+M7gOMRH03JYdE1EX402jRnVWA4OXXCZxilHnC83xHaWEwcOpHBWur/QD36dUKdcnpcBBxRls98je
+AnKFw2DiGDXZuJSHIVOB/z6ZwYxjPEkOTc4O5Enr6HFjIsT/qdQZzCS8GqWBwTGcwRSwspY82Do4
+IXJzBntiIlZX5RK4ugMECoNb6IExBgBY76y7IOiZ3WdcGHzh5oXQNR1rPXntnKlJATnqGtQxu+LB
+YvlmBgfGRABAa1xJTESL+UxyARjlAIPjxUTsyv9bsGg/psY8ZVvp4e47gcE0F9z7MwuW66YtAwbT
+SQn1uJ0Ccvk5g502QGMaGJj7+UCFzmByH3c0eU1Octgh7+DaGWAD9W/LvQqLibjsMll1/q67ytun
+ojXvMJguJ29g8GJr2TKDvQXk8o6JiHIGVxUTUSQMpn1SExPRqFE6NTB4yRU0mGxrEnQMU/bSFAbv
+WYkZE6EZlWUOegvIZYqJIJmDyvERUcco9HEpHbUVcL5b5KkkTUb09iAamADVFZALygxukZiIwThF
+ZnAcZ7AHkDmqw0NKXs5gCoqGxhCMSYhQd4AgYLA4LzR/kzHm5gY/cPIBd6XDxXsvBgCsrshr5+xO
+PUYy1BmsuRMexWUGBxaQAwB9XJorIxKKAhiOkh+7gMHRBeTUmAhBY8qAokEFYLW8YiJsGKwxLTQm
+gr63lOMOON+0YGBemcG0/3Z/V40ygykMziMmwpsZ3G5Ld1nd23KvwmBwqwW84AVi+4EHqgMleSsu
+DK5jTIRlyRVTjTM4XJYFfP7zwP33V70n6dU4g2dfyyL696xrTESr1RSQa9Sobmpg8JKLVpdXYDB1
+BqdssYemHBVuBoWuopqYCCU71xcGZ3QGt5NDwjI6ajMgFoQ66CaGlfhzd0YkJqJVvwJyhgFfZzCd
+9NgdJ88MHk1tR6nWihUHQmFwHZYvFZIZbLulnVn6ugME6gymYAWQucFjU56sS/ZeAgBYrysMtq9z
+xyWZawG5JM7gklZ5AB5nsNKuERg8TguDoye6/GIiaPtRlMyAArBaxrgEb0xEv913HfNUCgQvEQar
+mcF+sDb9ZG6QM1j5fPseqy4zWP7iLomJGJv5x0QA89OWexUGgwGZG2xZwD33lLNPRYvC/HlzBtN7
+ttVqCsiF6cMfBl77WuCaa4Bjx6rem3RaVhjswNCyYyLq4gx2QHXjDG7UqB5qYPCSywooIEfdMGlj
+IkaWHBXuDYHBVcRE+MGDfGFw+PJaoBpncFDBQLo9MVI4g4c0VzMYKCowpQbOYBppMRilj4kIg6gq
+IKuXM9iNNLH/NnllBgPA5qb4/uzZeuQjB+n0abjAz5t37TiDqRxn8MaqPK9bgxrB4BKdwYEF5ACg
+VQdncLZCqFliIsqAwYEQPLeYCNG+BfVhys/t4y6jHwuMBcmtgBxxBpPnE7efrNwZLH8xbbMmRv4x
+EcD8wuCwAnKAWkRuUaIi5jkmwltoijEJhKu41+qsr35VfN3eBr7whWr3Ja2WqYAc58U7g+taQC4o
+JmI6zT428OuvnLYDqMc4q1GjuquBwUsuK8BZ1G0FZ6nG1ZincAaXFBPhBw8YYyQTMGNMRCs6S7YK
+Z3BQwcAOKcIzSjGg3B3LHrffSVZArsrM4C659nZH6WMigs6x+B3+MLgOzuDBALFgV5QUZ7ChOoMN
+o94PZNQZ3PO42h1nMJULg9fktbNTExhsmpCZwX7O4Jwzg2diIlrVxERMuX+GrOL8HyU/RzMF5AJW
+efjFRJTiDA6KS9CyxUT4OYP9VFlMRJQTPGtMRJQzuMLM4JmYCAqDp8U6g7e35wvKOc7gTsd/dc+i
+w+D19eD3HTggM5PrEhPhXU5OvzbOYFXPPCO3b7utuv3IIvp8sOjOYNpuOtd03s73eSsgB2Q/bj9n
+MGPSHVznsUejRnVRA4OXXEExER3ihhmnHGBMCAzetxYzM7i0AnIBWYvI5qgCVGdwW+uoVciJVBfd
+qJzM4ICCgRT+j6fJD3yXPNWttGPGRJToDFbcVJy5S5475LgHk+QxEY4zOAgSAQJQOH9rXkdncA4w
+WMkM9sREAPV2lKkwONoZ7MRE7FmV1872sAZkH6ozWK+BM7isazww+z5ksieO4jqD1Sz08pzBysoe
+cp6lQ9bCOG3hPMDtx4Imu+qWGUxhbbGZweI+qgKMemMieuTcpH1Wo3JjBjYfxTeHH8fQGCpF5GgM
+Qd3lwOB9+6RTjGrRYXCYM5gxmRtcF2ewNyaCfm1gsCoKg7/+9er2I4uWyRnsdb0D+cdEzFsBOe9r
+aeQ3eQk0MLhRoyRqYPASy7IAMP/BZKdFYHDK1tqAHBXuX69vZjAdTLKMhXcANTO4pwdDQgUs6NVm
+Bnfb8hxMUsHg+XEGM9LsUQg+CMgMphlXXsVxBovfIyCZpdXHGcx5js7gVrAzGLCBa0118rTpLgv3
+XrsH+gdm3n/R3osAAJvr2VynRSgyM7hgZ7DallcfE9HVSAzMOKUzmOS/B90fGtOkO7jEmIjAeAxy
+zseT5DBY9EU8kTO4t14eDA6Kx2hpBIKnOG4g3BlMYyiACp3BTl/GNWXi3sgrM1gzgHe8Gu/9zo/j
+fbe8b24m9qg4lzERfhERAHDwIHDuuWL7rrvqHWcUV3FhMCCjIs6erQfkDwNmDQxWRWHwHXfMp7N2
+mTKDwyY6gOKcwXWOifC+lkZ+zmCggcGNGiVRA4OXWGJJcXRMRBqnKABMmZyGDIuJ8GYGVxUTAQCa
+c0swK1tMRMQgGpiNiSjdGUyAQY/A4LGZ/HwPJ/KkrfZCYLCPM7g8GCwK47nub6ixAMMAZ3DYgCqO
+MxiQjkmu1ccZ7P7d846JmDNn8Kmz8pys9cKdwYdWD7nFwvrdmsJgxxmsFe0MbtU+M1jJBJ8E74xh
+AO95D/Brv6Y6dOI6gwGSG1xmAbmggqAk/340SVk4TzdcB2wcGNwtEwYHxILQ7fEkeSFUYBYG+xeo
+qy4zWMkFR0uJeJqYOWUGbzwJ7BF20duP3j43bTnVkSPSCec4YP3kuINPngSOHi1+v4pWEhh8+LDc
+rkMRsiYmIp44V2HweAzcfXd1+5NWy+oMLiomws8ZXIeYCO+xN87gRo3qpbmEwYyxdcbYTzDGfo8x
+dgtj7AHG2BnG2Jgxdowx9kXG2C8zxgL8ADOf9y8ZY3/DGHuCMTayv/4NY+wNCfZphTH2HxljtzHG
+TjLGdhhj99r7eEH6oy1ONF8S8Ayic3AGT5k9Kpx20CYDFq+8mcFlNN5WEAzOWHgHUDODwyBhFQXk
+gjKDexnh/8CQJ221GxIT4eMMLuMBhcZEMPjHYwwn/pnBQQMq0zIxscF5lDPYgUhTJg+2amewe73l
+7Ax2CsjNC0A4cVYe/wwM9mQGO3nBgNpupXGdFiHfmAgaU6NNxYqQlFKch34xEZ7M4KpjIroxYfCn
+PgX89m8Dv/EbwM03y58ngsGOM7hbfWYwPeejcXJiGbcIqgKDV6vPDG4phVDTja69BeR8M4NtZ251
+mcGyL6PPapO8nMFrT7vfb0+256Ytp7rjDrn9kpcEv2/RoiKowzcKBtNM4TLu2yg1MRHxtLs7+9w8
+j1ERy5QZ7HXHAvnHREQ5gxcxJqJxBjdqlF1zCYMBXAfgLwH8EoBXAbgYwDqAFoADAF4N4HcA3McY
+e13QhzChDwL4ewA/AuBZANr21x8B8BnG2AeidoYxdimAbwP4rwCuBrAJYAXAcwH8ewB3McbemOpI
+C5Q3e44OILs5DDBMXTxdsmmwKxioJjM4qHCe5jiqtKwxEaLXXe3EdwaXUkAuIBaE7sskhTN4ZAQD
+NSo/Z3Ap1efJ8nmNHPdKh8Jgf2dwkKl9NI0uLOV93SQwuOqHFPd6yzsz2Ccmos4A4cwWzbsOdwY7
+ecGACom8105Vom16y25XafsGzcw06FCcwX4F5JQJrnJWeQDq5B51coZN9lA98oj/9kwBuZBJH8cx
+7jiDy1h2HdSPKTA4rTM4IQxu90uEwfA/3+2MhVABH2cweT5x/651cga38o2J2N6GAoN3Jjtz05ZT
+fetbcvv7vi/4fYsGg5M4g+mzTR1gcBMTEU/UFexoHovILZMzOAoG5x0T4ZcZXMeYiKzn3emPWi0J
+gAFZHLPqcVajRvOgeYXBAPA4gI8C+N8A/C8AXgbgFQB+AsDHAUwhwPCnGGMvCviM3wLwswA4gNsB
+/BQEaP4pAHfYP/9fGWO/EbQTjLE1CJh8qf3+PwbwAwBeDuBXAGwD2ADwMcbYlUGfU4W8MFhxiraz
+x0Q4MFiLgMGVZAYHwmDpDE47YzkYchd09ufEGUzPQRoYPDRoTEQyZ3AZDjpxrdswmMZEkGJ3I0Me
+N+fRMREKDI5wBjvQxMCCOoPnOCbizE7w8cd1BhvWpBbOlukUrqvRgVh00gcsIwxO4gyuQUxENyQG
+hoouNaTb6WIihgAzyykgF9CPUYA5miS/MNM4gx0YPBohk/s8joILBmZf0eQtIOfrDK46M9h1/7eU
+iXsjr5iIBYDB1Bm8rDCYOn/9RJ9tymivouTnDHb6mAYGS/nB4Hl0Bi9rZrADQ4uMiaDA2dmuIwzO
+et6DioQ6YHgyKf55pFGjede8wuAvcM4v5Jz/LOf8v3POP8U5v41z/jXO+f/HOf9JAD9mv7cD4L3e
+D2CMXQbgP0AA3G8AeCXn/K8557dzzv8awnF8OwAG4JcZYxd7P8PWfwRwmf05v8w5/wXO+S2c869z
+zv8rgDdAgOk+gD/I7S+Qg0wTrlsS8BYUy+4Mtlo2DDYjnMEVZAYHx0TIzODU1cjHY4CJSiRJMoNL
+cQYHZAZ7wVZSjQkY3egncwaXMQihRXcoDKbO4BGZ9BiP5QPE2hpwengaN3zkBrz2z16LnYnYYQd6
+AtHOYAmDBxBNRfUz1nk6g6MKyNUVIHAOnN0NgcH9YBjsdcHWoUgeneBzYbASE1GmM7i8mIggKEon
+e+iElaPfv/X3ccUfXoE7B3/n/mwWBkcXkANITAQAtAelw+Cg9jyNaz2uM5gec2tFvr/oic1g+E/a
+cyNdBx5WQE7CYHETVeEM9vZlHWIvy62A3ALB4PV14OKgp3cAl18uweOiweDGGbyYOn589mf33Tc/
+96ajxhksf5ZnTESrpYJRxx28iDERTpHQA55az9QlXLXxpgj94z8Cr3kN8Bd/UfWeNFoEzSUM5jy6
+5i/n/FMAvgcBc1/l85ZfgoiVAIB/xzlXmgvO+RDAv7O/bdnvV8QYa9nv4QDu5Zz/N5/9uBXAn9r7
+cT1j7OqofS9LMzERLN+YCNgwWOfBQBTwxERoRikdVrAz2IHB6WMiBkY8SOgFJ1U6yRQYnMIZPDbl
+7bOxEgKD6+AMJtd5n8DgMYFFdJ9WV4GP3/NxfOmxL+HzD38ef3rHnwKQ0BOIhqgKTLHha9UPKEU5
+g+cpM3g4BAwuj1+5JwHsXdkr2wQEx0TUCwaL9tq5v+n+g5mZHr5r6wwOgsEt6vxXd8a0TLznC+/B
+vSfuxa3d97k/T+sMdmMiAKCzC8MofoAbxxGdBorS3HsgnjNY68qOu2iwFAsGp1zR5M0MppPV7jNS
+XWIimCcmwsofBm+P1cxgen/UVSdOAE+I+nd4yUswM2lF1e0KIAwA9947/1DKeXZpt+VS8SDVzRnc
+FJCLJ+oMdoA+58Dtt1ezP2nVZAbLn+XpDPbe9w4MXjRn8HAonzX271dfozC4auNNEfpP/wm45Rbg
+F3+x6j1ptAiaSxicQE5qn98I7s0QEPc+zvk3/P4z5/zrkED5h33e8hoAzmPyR0P24yNk+y0h7ytV
+ooCc/6CKOiZTwcHpBNBth5qVLCaiSiiq51FAzoh2VAGz4KSM4+bECU7hvxITkcYZbMredr0fEhNR
+kTNYyQyO4Qz2umueOPuE+/3nHv4cAI8zOGZMBADXcVf1A0phzmD777K5KV+vKww+fRqhx68xDftX
+5FNmUEwE9In4rIplTLlsdwnEYtyBWOknuYBZZ7AXBqt/kxo4g5X7W4XBp4an3KiXs6374Tj26bVK
+YbDOdHUVi0duTATg5gYX2bZxDnAErOyhzmAfR3SU0mQGa11JgIuGwUEre6gTfFyoM7gmMRFoKau4
+plb2HfJmBhuWgf66/HvUtS2nipsX7OiKK8TX6RR4/PFi9qksRcVbUdUNBjcF5OKJwuDXvlZuz1tU
+xLI7g/OOiXD+hm3PY0rf7qargsHeSZ68YLDjCgaWzxn8wAPi6/HjTQxGo+xaWBjMGHsegBfDBr6e
+1y6CKBIHAF+K+Cjn9Wczxp7jee2VPu/z0zcBOCOrV0T8vtJE3ZKAd1CVrSjJyW05GmwhWUxEpTBY
+kwXk0nZScSGh1xlcdMEhAQ9iOINTwOCJJanP5lowUOzoHTDY65dKdAbTCuzUGUzzjSchMPjpHTk4
+vuXRW2CYhuIMjoLBKgQXTUHVDyiFFZDzyQyug2vWT6dOIfL4z9s4D4BYEn94/bD7cy/4rMMxTk3/
+9tyFwRlWPACzzuDQmIgSncFBcLAfAgef2ZUj6qm+DayKtbdBzuCOFn5vKDERHdGAFNm2WRYCJ3M7
+LbriIaUzOCEMZp1ynMGcAxaT12FQrYM02ffAbGYwXbnk/i5WXUyEd5VLl8ZEZHQGj8c2UCAwGADa
+q/JCngcYHDcv2NFBkgZUh0m9LHKeI+PA4CYmYj5FYfC/+ldy+ytfKX9fsmjZM4PLdgYvWkwEhcHL
+5Aze2lL74arOa6PF0ULBYMbYCmPsUsbYvwdwC2QMxO973noF2b4P4aKvPz/N53DOTQAPQjiMvZ9R
+mbzOYJo52OtkW3pIYXAnAgYrUEUzKoWieWQGj8x6OoMFPIguIGfy5AdukJSVPavB0IQxJiMFbGdw
+GYMQxRlMYyK6BJqQqAu6T6urwNO7cnC8a+zia09+Dbc+eav7s0Orh0J/v+oMFsdd1Sy9ozxjIta7
+slLNmZGgovMQExHlDAaA917/Xlx5zpX4vdf9nhK5UMeYCFpAqkOdwSjHGay0aXo5MDgMiq50SFyC
+xxlMYTAAYN+DAHxgsH2/Rt0bKgwu3hkctrJHicdIEZfgdQYHTXbyzXo6AAAgAElEQVQp7VpJMDjs
+fOcGgwOcwe4zkmYB4NU7g5nHGZwxM9i99j0wWF+RD2V1bcupKAx+yUui3793r9x2ChLNq+bZGdzE
+RMQThcGvfCVwyH78/OxngX/+52r2KY2W3RlcVGaw1xlMYyKiQzbzl/fYKazOAoNPnJDbYc7gRYPB
+Tzyhfl+Hibyi1RQCLFZzD4MZY29jjFmMMQvALoD7AfwegEMQruDf5px/zPPfziPbT0b8CnrbnR/w
+Obuc86gkNedzDjLGgtealihvZnBgTEQKp+hpAoO7LMIZrJXrDA4bTLqDvZQw2LIAg9fTGTyTER1U
+QC4NDCbO4I2QmAhA/k1Y2TERZADtaLXnDw/CnMGAiIr48J0fdr9/6xVvDf39fjERVXfgeTqD1zpr
+bmbq0e2jAOYJBktI6Hf8P3z5D+Pb7/o23nXNu5Sf1zImgsCglk6cwajAGaxPSnkID+/HiPPfVGHw
+8YGnCs/ehwAEx0R09fB7Q8kMLiEmIizzn0LRNM7gNDERaJUDg0PPd9t/ci+JwjKD6e/KWowxrbx9
+GV3FNeXZiJmAwXwGBmsr8+UMdmIiej2ZBxymffvkdh3a8bSyLHnvra+HvxeoHwwOi4mo4l6rqygM
+Pvdc4Nd/XX7/7nfPx99qOlXhzjI6g4uKifA6g52YCNOs5u9clDO4gcFCVY8li9Y3vwlceinwnOeo
+57xRfpp7GGyL+/y7E8B1nPNf9Xk/fUyKegSit5l3rt35nDiPUWGfU4lME65bEvAOorM5g0+T1qkT
+AYO9mcFlQ1E1M1gWkEvTScUdRAOzzuBSjjvgfNNzMOXJB9FTyN623wmHJo4zmHVKjomwB/gtMhdD
+YTCd9PDC4GM7x5TP+5M7/gR3HROlx1963ktx+YHwEWcdYXCezmAAeNa6SN45sn0EgHgAdR526woQ
+4jiDg1RHZzAt9kmdwVoVzuCSYiLC2vPVLomJMFM6g+3rY6Ud4Qzu1MgZ3A7OSo6jNDERFoHBRS5b
+9J5vOplMC8gV4gwmwB3atDpnsN2P60xHty3P+zRjTMT2NoDeGaCl/u1YZ35g8NaWzFO86irVfRck
+6gyeZxhMnX+LFhPBeeMMc+TA4E4H2NgAbrwRuPJK8bNvfQv44Aer27e48j6HLLszOM+YiCBnMFDN
+isQmJiJfLRMMPnIE+OEfFsf85JPAJz9Z9R4tphYBBn8SwIvsf9cB+Cn7Zy8G8DHG2Bt9/g8d1UV1
+QXQk5bV5Op8TpxsL+5xKFOosIjA4zQDjzK4cDfa0emUGh8LgjM5gMYgmzuB2fGdw0cc9EwsSUEDO
+YpPED91TcnkrQMhHrlu6xMxgcc79YDBxDhIYrMZE8BlnMP3+7Ve9PfL31xEG5+kMBiQM3p5sY3u8
+DcakO7iuACFPGFwHiEALSLX9nMHaND8YbPnAYL38mIgwKEpjYLxwcBYGC2dwMAwO77bLzgwOz/zP
+BkXTOIMtrRxncNj5Vla45ASDfTODIfahcmew1lKf1XgOMREeVzAAjPi2O7iua1vu6M475XaciAhg
+cWIivJPYUaqbMzgsJsL7+jLLgcGHDgGMib/R+98vX3/Pe+o/qeF9DllGZ3BRMRFBmcFA9TA4zwJy
+jTNYqA5tdxEaDoG3vEUAYUdHj1a3P4usuYfBnPMtzvk99r/bOed/zTn/UQD/BsDFAP6WMfZvPP+N
+Ng2eZnNGlGx5m1Hnc6I+I+pzKlEYFKUOm2mK2ICzAzkaXGmFw2Cd6bKoWAmZweEwWBaQSw+D4zmD
+dU2XQLYsZ3CMzGDok8QdtMnkLaUAIR85UIWXDYMdZzAZ3K+vUGewJFfKPq2cDnTHd/UufuKFPxH5
++/0KyFUNg4tyBgPA0R3RYztLVev6sLJozmAa49Bpyetcd+Lz84yJMH1iIjzO4KpjIqgzeGLFdwY7
+7rokzuCqYyLUuATiiE6RGRy3H6OAfFoSDA5/bvGf3EsibwE5ep8rMJiZFWYGS2cwjYkwrWw7FASD
+dyY7tZ/Yc+RERADxiscBixMTkRQG180ZHBYTATQwGBDu6ON2wtEhUqri+uuBn/xJsX3qFPDpT5e/
+b0nUOIPLKyDXJ913FcXGvI7/BgZn0+OPq9/Xoe0uQu9+N3DbberPnp59PGmUg2IsoJpPcc7/nDH2
+JgA/DuC/M8Zu4pw7w3WK3aIemSjJ9A7tnM+JE/sQ9jmxNZlMcAetjhGgw4cP4/Dhw6HvEQ4bfzhI
+3TBp3CZbo/gwmDGGtt4WDqYSnMFhziIXBjMrVSc1HMJ1vALhmcGAGLwOjEGtMoMdGNwNZ7qKTGaD
+FrOtfK6fnL8J18cAszAcajBNzLgM89Rkwn1h8CqBwdOAmIhpT/Y+B/sHlazRtzz/LdjsbUb+fjVb
+s7zCeWHK3Rm8JmHwke0jeO7+57qDzaqPNUheGBzlaqdSi6XVBAYHOIPdookZYyKUqAWzO3PPqu1I
+9c5g6vw3YsJgwxCDh5UVYDg23D5yrmIiOuU4gzWmYaW1guF0CJNVD4OVuKMsMJjEItBjV/q2ipzB
+tIBcS2upMREoxhm8M9nBxgZw7Fj9YfD998vtF70o3v9ZlJiIRXIGFwXM5l2nT0sXKYXBAPCv/zXw
+MbtCzsMPl7tfSeV9NlhGZ3CemcGcRxeQA6p3BjcxEdm1DDERDz0EfOhDYrvdltdJEhh89OhRHI1h
+JZ4s+kxUDC0sDLb1KQgYvArgDQCcQnK0aNx53v/kES0a57kF8SSA7wewyhjbiCgi53zOcc7Tr+U7
+fvw4rr766sj3vfe978X73ve+0PeEwUEa3ZAmJuLsULZOqxEwGBCDOAGDjQWIiYjnDAaEs3RgDIBW
+8TERcTODoU8SH7ulid5Ws6JhmrLcujUCjD52d0X2WVGaTMlxkwJy1BlMHfD0XIzbsvf50St+FH98
++x/D5OLz4kREAPWMiSjSGezkBlMYbFmYcZJWrVOnkPr4vYUvt6JKiJYg6mD3zQzO6AzenZCLdrIa
+XkCuDs7gEBg8U0Cuf1LkpY42sbUlBlADI/614RcTUeQEX9wCsF5HdBwl6cf67T6G0yEM1AsGpyl8
+a1n2wHFFZgXsW5G2UW9MRBVwamJYABPWdZ21lPoOZsaYiO1tRDqDt7bq2ZY7opNyXrdYkBYlJoK2
+N0mdwXWAwY0zOFq0eJwXBj/nOXL70UdL2Z3UapzB+cZE0P8f5gxeJBi8rM7gZYDBf/IncvtXfxV4
+73vFdhIY/IEPfAD/+T//53x3bEG16DCYjvZIN4l7yHZUrWH6+r2e1+4B8FbyPo+hXYgxpgO4BKKw
+nfczEungwYO4+eabI98X5QoG4mfvmbEikVVtE2ew4pgKkAtW9EmlMREaKSBXdGYwQABDCc7guJnB
+SWEw5wB3YXA0TFPc0q0hYPSxs1MwDDYNNxSHTnT0V3TA0gHNxJRc57RzHWiy97ls32V43SWvw2cf
+/Cwu2XsJfvDiH4z1++sIg4vKDAYkDKYD0uFQHXzWQVliIpyYF5ObgD4pxQUbpakp728aE5GXM3hg
+kDWGxmp4AbmS/iahmcFkVGR44o5mnMEAsPch4OjV2NoCzjkHGE/jXxtlx0TMtOdkMpfC4DTZucIZ
+LPuxKBh8cngSk5JgsDhuOYIMhMEp4q3cNtGGwavtVeWaVmGwWYkzeDI14czt6JqOlW5+MDjIGbw9
+3nZhMOcovL/OIupcdvY5SssaE6HrApqMRtU/jwBNZnAcHSej2oMH1dcoDH7ssXL2J62azOB8r236
+9wxzBlcRE1G0M1jXZ9v6RYXBnC9+ZvBkorqC3/UukYl+8mQyGHzjjTfizW9+c+T73vCGN+D48eOR
+71tkLToMfjbZdm8XzvkjjLEjAA4DuD7iM15tf32Kc+7tXv+JbF+PABgM4BoIdzIH8NWonQ5Tp9PB
+98UNQotQmFOUut7MFEsPd8YUBoe7YwEyiNMMDAYoNDYg1BnMpDM4dUxEEmewM9BsjTAei47R25Hn
+pdCsRV2FOEmAkcjVFNRH5zFgMAXk7SEwLL4zGxtTN7WbxkT0egDMDqANMeX+mcE7TPY+566di4/8
+yEfwiXs+gddf+vrISAxHylLjlQFMVD/4KgMGezMJawmD19Iff0fvYDgdAq1yIhGiRGEQdcBrTBe9
+T1ZnsGFftJYuImFqUEAurF3rkexcen8DATB4n4DBDlAaEhgcFflTdkxE2HH3O+mzczlP7gwGgLEl
+G7Q6OIO95zuO3IGyDYOpKxhQJ1ArcwZPpy4MbntiIkxefGYwIIDrIsHglRURizUeLw4MdvL6o7S2
+Jp7h6gAU/GIi8lxKvwgKcwbv2QNsbgp3/LzB4GV0Bud5bdO/X50LyHlhcJbz7jiD9+2bXamyqDD4
+5MnZ46l6LJm3PvlJOen1lreIdu7ccyUM5lwUzoxSnLhUQHC1ZVdNF3rlph8j23d7XvsUAAbgcsbY
+dX7/mTH2UgjHLwfwtz5vuQWA8+j5tpD9eAfZ/mTI+0pVmFOUuifTuE12yHLitTjOYOf32bmuVQ0m
+pTM4Q0xEksxgB57YMLXM6vNhmcFJjl0cs+iddMSIifA6g1H8QGQyldcwvbZdGAzVAU/3Z9tSYfCh
+1UP4hWt/ARfvvTj276cAvNOvR2ZwmTERQPXH66eZzOCI4odeufdNimiVIjRVMoNJAbm8ncGTVQCs
+FgXkTBNuvwEET3JRODgxJzgz8gl5JkXkgGTOYCUmoixncMBkbq/tXxgzjtzrIyEMHpny/UW6j7z9
+N73OVRic/EIX+81FZAhmYXA9YiLUZxfFGVxgZrAXBtdVzr71erNQJEyOO3ieYyKSOoPp++oAg5uY
+iGiFwWBAuoOfeCJ79ECRajKD842J8Pt8R1XHRHgd/3nHRPjFAdGaN4sEg72uYKCeY6ss+sAH5PaN
+N4qv554rvg6HxcavLavmEgYzxt7GGAsdtTPGfgnAD9nfPgzgK563/AEAp/n9fxhjykjP/v799rdT
+AP+393fY2b/vh4DKz2eM/Qef/XgZgJ+FAMq3cM5vD9vvMhXXYZMmJsJ1kAFY7yWLiQCqc1S5MDgl
+NEntDNbFk1FVGZN+BeTiisLgFhLGRLRLgsEmgcHEGdztApiKc2Ax/5iIM1MVBqcRvQ5aK+L6qGK5
+FlXezuDD63IG1g8G12Gw6dXp0wD6MnjMC3+iRGFwHZzBtNin2q7lnBlsiOu57s5gCqenkDtzfFcu
+CTvQImtrvTDYkiOnRDERdmZwVf1YFijqtgsJYfCUG258Qz2cwSlhcGfXnVwIhcHMrGSQOTHVCd0+
+gcFWRhgclBm8PdmeOxgc1xXsyMkNXhRncFwYXKcir01MRLTiwuDpFDhypJx9SqPGGVxcTESYM3hR
+YiJotI23eBywuM7gxx+f/Vkd2u68dP/9wBe/KLYvuwx4zWvE9rlk6J0kKqJRPM0lDAbwPgBPMcY+
+wBj7GcbYyxljVzLGXsEYexdj7CsA/i/7vWMA7+Scc/oBnPMHAPwuBMi9FsBXGWM/zhi7mjH24xBx
+DtdAQNz/k3P+UMC+/C6A++3P+V3G2P/LGLuBMfb9jLH/A8A/QMRxDAH8Yo5/g8zyOkWDYiIslry1
+HhAYvLESr4AcAHcwWRUUpQXk0sdExM8MVp3BvNDjVpxknEnwjRycwTbMbsVxBrdnncFFd2bUGUwL
+a3U6kM5g5h8TcWoiex4KPJNIiYno1SMzOG9ncL/dx2ZvE4B/ZnDVx+sV5/bAf12OmJKe39rBYMt/
+0iN3Z7Ah2vQoZ3DVmcHqpKbcGRoR8Rz9ZfLD9opu3gFKYzOBM7jkmIiw46ZQPmkhNXfgRGBwWD+m
+5qEX357HPW4TE6hPfdEaDBBYPA5QV9NAm1YywUWdwW29hU4rv5iIU6fgwmD6fOB1BtehWGaQssLg
+3d35BVNZnMFOkdcq5RcT0cBgVVEw+MIL5XadoyKazOB8YyLo589TTETa8+7kBQP+zuBFhcF+zuA6
+Gm3SihaOe+c7ZRxEA4OL1bzCYADYC+DnAXwUIrv3Tgj37x8BeDkExH0cwBs5518M+IxfAfCn9ntf
+DOBjAL5hf32x/fMPcs5/LWgnOOc7AN4IAYQ5gHcC+AKAWwH8JkRW8FkAP8Y590ZVVCrvoIo+/NOl
+l2ncJkNTjgb3xIDBMiaiJs5gxjGeJBxJIoMzmPHCB5f0uDWolr64MPiee4BrrgFe+lI5INzeNQFd
+fG6LzYEzmFzbjAGwxLFTZzDdn+ND0fN09S72dBOOMG0FweCksCJP5e0MBmRUxNGdo+Cc1zomYjCw
+H0TXjgIQy/zXOzGDFm3VLiaCR01ymZkArbviYyJOrNcZrLYjY0ynxS9VjZuFbpL7+/hAOoM3ppcA
+u3YlHuIMnk4BS6tvTIT3uIMKgmZ1ButMVyYWvCq7OGaSFS5Jrz0vDN6/olqOaA43tGklAG0yVY+d
+Tm6mmbinevqZKbAq7o0LNi5wfz4vMRGWJZ9LNjeT/d9FKCJHzQRJncFA9auVmpiIaMV1BgPzBYPr
+8PxUpKosIFd1TEQRMPiEXNC39DC4bmOrtJpOgT/7M7HdbgNvI+GrDQwuVvMKg/8FgHdBQNtvA3ga
+gAFgC8CDAD4BkdN7Oef8C0EfwoV+HgLmfgrAUxBO4qfs7/8l5/zGqJ2xXcMvAfC/Q8Dk0wB2AdwH
+4L8BuJJz/tlUR1qgvHCQkURuOqji2iTxgGc4la3TZj+BM9henlk5DAYwMZKP8lJnBgNAa1yCI1qM
+jpmndmQcGPzAA8AP/ABw++3A178O/NVfiZ9v7Uq61NESFpArKTPYMP2dwQCg2TCY+8Dgbhc4tit6
+nnPXzlXukSSiwETrihGXaVb7AOwM/FjbzntmuroMOoUOrwln7cAYYGu8VWsY7GZDrgsYfHj9cOLz
+WzdnMM13988Mnqa+5kzLxMjJ0DX6YGy2iIPGNHkN2TnoRf9dQp2iLeoU9XcG6+NDwKlLxTcbR4D2
+AFtbTmHM+AXkOnpH/p1LiImI64hOWkjN6wzut/uh90WdYXDSaz3KGaxmBpvgvHyAZhDC3dL1zBP3
+VEfOHhcT0wAu23+Z+/N5iYnY3pYTrGmdwcD8wuAszmCg+j66iYmIFoXBBw/Ovj4vMNj7XMB5vTOO
+s6rMzOC6x0TQ/cvDGRwVE1GHZ/O8tMgw+OabgWPHxPab3qS2bw0MLlbZRv4ViXP+CIA/sf/l8Xk3
+A7g542cMAfye/W8uRGMDNM+loDiBNAOGoQayR2lsDdyK15urSTKDDYi4hHTQLY5CYyKIu8qYWoDH
+QRul1M5gANDH2N6O+QSfQkmcwd4O+rHHBAimjfADD4ivR48nK8BVhTN4HFBADgA03oUFMenhyOlc
+++sGTgzEFHTavGBAPWbWkZMFu7vJ7qs85TgEWHsEjuyuYGC2iNzamhyR120p0+nTEPdqT5ANB2Qn
+kcz8rokz2PJv19ztDDERwymxlBirMxERjrp6V+yHvcpjPFadKXkrrjPY0vxhMNs9BJy6BDj/VvGD
+vQ9ja+uFMzA46v5gjGGts4az47Ng3V1w1CQzOCLz/2tfE3Ef19lldL3O4Kg+jDqiW/0BpqdLgMEB
+BQP9YHCSay9pTAQgznFc8JaHqDO4rbeU47eQjZYdH8gO/sLNC6ExDRa3hDOY/CnqCoPpfmWBwfNa
+RI62N+sxF7nQa3dnBzjnnHz3KYminMGLDAvjyoHB6+sq5HNEYfCjj5ayS6nk9xwymfgf0yKo6JiI
+MGfwIsZENM5gqbqNrdLqIx+R2+94h/paA4OL1bw6gxvloDA4qAAz3UgMEMYWcQbHgMHKIK7EuARv
+dq7qDDYTL+NPnRkMAK1xadXnvfDfu7zbe75/+qdnO6GH7BTt7z0ke9o9q/V3BndbXhhsO4P1MZxo
+cWd/VvYfB4f4WRYY7OeeA6qd0fU6g4uAwXV2Bp8+DTciAlD3Pa5q7QymmcFuTATHeJwum8QtHgcA
+Rn8mIsKRtyhm0Q/iXocsPW51hcvYbc8pDOY7xBkMAPsexNmzjjM4fgE5QOYGs25ZmcH+mf+qIzq4
+8/7854GXvQz4/u8HvvlN8bOkMJi+vrJevDM4riParx+LUjJnsITBZWpKiFhbb4lnFks8t/AMMRG7
+u8CoRfLx1w67RRHnJSYiCwxehJiILAXkgOr7aD93Y+MMVuXAYD9XMDA/zmC/tnmRc4P9YDBjsu5C
+kQXk6IRoFc5gr+O/yQxOL6eAXJ1WdOShkyeBm24S2+ecA7zhDerrDQwuVg0MXmLRQZXGwmMDkjbY
+Y9itE2fYjAEIVfg8Kb6QmgvB1eNWnD/MTOxE8DqDI2MiZpzByX5fElEIzhJkBhsG8E//JLaf/Wz5
+cP6giNfEQ49KCrZ3o57OYMMKiYmwYTAYh8lNZX+6+2WvkxsMbtUDBnszg5cSBq9LGJzGGSwLX5oY
+Taq3LZkBmcEt0q6NjXT76RaPA4DJajAMVopiFr9EL0lsgNOPURhsnDkEnLlQvm/jCd+YiFgw2HHJ
+VpAZHFw4L5iIvv/9ctup4OxWoE8Bg7s2DC5ywFmbmAh7YrXIPttPamawfRNaok/Lkhl87Bjc4nGA
+6O+cDPVlgMFNTET1DrOic1XnXZOJvDb98oIBAcYc+DdvMLgOq6uKkt+1DcjrO8+YiGVzBkfFRCwK
+DDZN4KmnxPall8qJhLqNrdLoL/5CXgs/8zNquw80MLhoNTB4iUUzZPWImIiknbTBZaGhfj868kEd
+xBklFlILOe4UjmiaGdxmHRUu+6iqzOBwZ7A6iD5zBsA53wZe/Ru4/Nqn3GrFDz0kcr4eelz2tAc3
+6+8M7nicwTrksU9MUYHe6Vxbm/nA4LbedkGCVRMY7AKbkmBw1QNNr7zO4MPrGWAwAMO+dqqUyfzj
+UCgMTgut3eJxAGD0g2MiKnYGU2DHGAOzHPf22AXTtIDc5PRBYIec+7Wn/TODI1Z5AHCdlLxdn8xg
+i/nT+KNHgc98Rn7vxP6Ir9xtmxPB4DVxjZSZGRzkBE8ziR0ZE8H8YyLKlGHKY+/YIybGxVfO0tMy
+PxjsXM/b4/nIDG5iIuR2A4MXTxSABcFgxqQ7+LHHqi1SHCa/SeJlcwYDMiqiSGfwosPgZXEGP/20
+nDQ4/3y5qmMRYDCNiHj722df37dP9gUNDM5fDQxeYqnOYBVaKg6YFFDUYHbrZKzGyoBSIWyxzuAw
+GJx1MEmdwb1WdFihAoNLdAZHZQbPwOCf/BHgX/wavvvct+GSS8TPd3fFkrVHn5BPdXvWooGiAh0r
+cAZ7YyJakOdgZIwxHMoHaH1PPjAYkNDE0quHwZzLh0KuFweD67yUSTiDj7jfZ3IGA6nai7xlcX9I
+RielxilhsOIMNoKdwe7fpAbOYADQuXQqO+2a4wxmYBie2g/skHt7/SiJiUjoDLZjIrhmAJqB0ag4
+gOE9bnqOvQVg/VxHf/ZnqhvJWenx4IMQec+aKKCaBAZ3+tUWkFNX2hRfQA4o3xnsLSAHAMx2BmeJ
+iQiDwTuTHWxsSKq0iDB4kWIiWq1ZIBSkOq3e8cs9bWCwFC0eFwSDAQmDRyP1/9RJjTNYyLm+s17b
+Yc7gqmMiioDByxgTQaMaKQyuehIvqx57DLjjDrF97bXAC14w+x5Nk3n2DQzOXw0MXmKFQVHGGDTu
+pNwnH1RNNekMjgOD1czg8pzBuicewxvbkMoZbAPOqIiImd9XYmaw97jDoNaJUwaw91EAwNP9f8Sh
+y2SPdO+9agG5XithTERJzuApgcFtz/oTncljH4wn6r6s5QeDneM2mVpArgpNJhJ4W1pBMHin3jER
+p05BiYnIlBkM1CI32IR/Ya1uS+7nKOXTtzczOKyAHADXGVz03yTMIQuQGBjiDHZg8P7+fmydaQHb
+s87g3V2kj4kAgE6xURFxC+f5TVJwDnzoQ+rPFGdwgiKo9PWWDYOn0+IG9sXHRMhRZtwCcmVKcQbr
+jjPYhsFaRhi8+aj7/blr52K9K2IiTG6ityZv5EWEwYvgDHYmJtbWhEM0jurqDG4yg2d17JjcDiv0
+Nw+5wU1msFBeMRHL7AxelpgICoMvuGBxnMGPPCK3X/3q4Pc5URHPPNMUE81bDQxeYk2nCISDACQM
+1ozEDbbpwGCjH88Z7ClYVxdncDoYLAbEK3PkDFad4OpxHzulTiWfffbH3e3Pfx7gHTkCc5xEYVKW
+W5flDA4pINciMHh7qMJgq5+/M3jKqncGu+4Abeo63PKAwdRdOxeZwTnGRKRpL/IWzQymbSrdz5GR
+js7Gzgxu0cxgXviDeGxnMIH1Dgw+tHoIW1sAhvsBy/5/a0extWU/eLeTFZBT2r9OsVERdHIPSAZF
+b70V+N731J89+aRoFx58EOlh8ErxbVv8AnJFO4OrKiBH7vGWBwZnjYl4lqgi2Nc28JzN5yjXs8G2
+XciwiDB4kZzB6+vx/0+dopyamIhwxYXBTpQbMF8wuOrnpyJVpjPYC4Orfg4vEgZrGrC5Oft6lwyt
+FwUG33qr3L7kEjmRV7exVVI9+aTcPu+84Pc5MNg0VWd4o+xqYPASKywmAgA0OM7gZDERFrdg6fYg
+2lidWbLiJ+8grqrM4ChHVZRoZnA/Rsak1xlcGgxms05wNy7BM4h+5rQKg+/V/8rdvvlmJC7CVYkz
+mECyrueCbDN5DnaGE6VjNbr5w2AD1cNgb/E4IB8Y3G11sX9FTNN7YyKqHmh6lWsBOaByZzDn6jJx
+Cq56bRqFkm7E5c0MjiwgxzigmZU7g3VIOD0eC4ezA7YP9Q8JgMQ1tMf26HpdwOBHH0XqmAgAhReR
+m4mJYP4xEX4rXD78YblNodldd9lL8GgR1Ih+TIHBveLbNnHc/td5njC4p6/MHHsdCsjRmIi2ExPh
+ZAZncAY/fOIpYEPE5lyx9xpoTFNgsIiKENvzAIP9AEGYFqmAXNy8YO97q4YKDQwO1yI5g5vMYCHn
++s567H4RK47oPb61le33pBG9b1stdf/STgA4MHDfPviuUvs3ppEAACAASURBVGNMAuFFgMGcAzfd
+JLZ1HfiBH5CQfzye77bRKYoHiCL1QWqKyBWnBgYvsehgsuXjDHYLayUcVFEHmWauhrxTyluwrqqY
+CGUw2UoeEzEYcqAjjn+1G+0MVgCDXmxMhFJAzgf+tzX/8338rAqDHxjcBmyKdR23347E7ko6wGYd
+QSWLHoRMwzKDqTN4oJ6DSUf2OOeshjx9x5AKg0VGQ+XO4JxhMCDjFo5sH0G/L3Mmqx5oekUzg3t6
+D5u9hPQA9XIGCyhK4lBIm9qjMRHTdHRWjYlYjS4gByjRDEUpyhksJ7nEvjiuYADYv3LIHYR1Dbvt
+6h/HmS1TLF2jBeRixP6UGROhTOZCByPrwsOyczkHPm4v7lhfB268Ub528832BnUGR6xwoTBYKw0G
+x3AGp+m/CQze09k383odCshNrdkCcnQVV1rdv/MNd/uaw9cCANY70mK6M9lxJw7KBuBxtcwxEZxn
+h8FVT9j6ATM66TjPwCMPpYHBjz5a2O5kUuMMFnJcvFmPPcwZrOvyPq9iIq9IZ7BfXrAjJypiEWDw
+ffeJou0A8KpXif6qasd3XqIwOI4zGGhgcN5qYPASSwwmQ2IiIAcYSToqCg1aVgoYXGIBuZnMYD1j
+ARpCPlY7MZzBennOYAoPdMyebxeKeo775JZPL/NC6Q5OWoSLQpXWSgUF5LzOYAIQdjwxEUNd9Dh7
+unsiHXJRcqAJB3eLay2aMxiQMHhiTjDW5ai6bg8rNCbi8PphBabFlbe9qNIZHAbJqDN4nBIGx46J
+8LRpRT+Ie53BSq4rSCa4DQePD467r+1pHXS3Vy37SVOzYPWewT33IGNmcLExEWnjjra35YDwuuuA
+q66Sr332s/ZGgpgI6obWOvL/FVWoJklmsHegeeQI8Od/HjwgpjB4b28WBtchJsIgMLitz8Jgy0r3
+uU9aEga/+hIBg6kzeHuy7TqDt7Zk5nyddOaM3E4Kg9ttCUzm0RlMC98mgcF1Agp+uaeNM1hqkZzB
+TWawkONezQqDw5zBgGwPq3AG5w2Dx2PZ7y4LDP70p+X2m98svtap7c4iGhPROIOrUQODl1hqbMDs
+yF5nMiYiSYNNlxPHhcHqIK48Z3D4IHqcuKPancQfRAOzLrrCIXhIRjR1BtPjPrU9O6pvvZjC4GRF
+uChU1bvlwGCTywPq6OqTUkeT53wwVmMidpnocbJGRACe5datchzRQSrDGQwAp40jbiGbuj2snDw7
+BvoC/KTJCwbqFRMxnQLQiTOYXOe9ttzP8TSfmIg6OoMZNGhM3TE3BkafYDTiijN4jcmS7OtMLSJ3
+991IfH8omcEFx0R4ncFUyuSqxyFLq8ufcw5w2WXy+284PDBlZjA65WcG0+s8ajL3da8DfvqngXe9
+y/+zt4dDoC3O+f5+FAyuJibCJDERjlOZOc8x2jR9dfaehMGvvOg6AAiMiTCM4gtDplEWZzAg3cHz
+CINpO7NIzuAGBktRGHzoUPD7Dh+Wf795gsHL7AzO2p6GFZADoEzklS3n2DVN/MsKg2lerF/xOEeL
+CoPf9CbxtU4RP1nkOIMZE21XkBoYXJwaGLzEooMq79JaAGjlEBPR4jGdwXo9nMGZMwcNWXAojpPU
+66IrLx5jFv4HOYNP78zC4OmBO4H9dgUiEhMRB5pSZ3BZMHjK/ZfPA0BHk+dgd0ScwZqBMcSFeKAf
+Mv0cUwo0sWFL5c7grnwyVFyNGURh8GNnH3Vnr6seaHp1apItLxioV0xEmGNyhTqDzRycwUawM9i7
+VL9MZ7B3cg8AWkxmGA9GUwUG9yFH1Jstcg2sHxX3ZitZATklM7jgmIiwDHhd08G4fYK8GfAEBh88
+CFx6qfzedXumhcHtamMiwgqhjsfAd78rtv/mb/zPy5YhVzLsX/WJidDqFRPhHK9O6jukgQqccwz2
+ChisD8/BeRtirWYQDAaqgQpRygsGnzpVT+dzmOgzcwODF1MODG631VgTrzQNuOACsf3YY/W8lhtn
+sFBezuCgz3fktN3b2/YzU4ly9s3Zrzgw2LKCVxc5ERGAdAZPrSne+BdvxKXvvxT3HL8HwOJkBp84
+AfzzP4vt5z9fPrMtmjP4nHP8r11HDQwuTg0MXmJFOUVbLHtMRAcpnMFlZgZ7ILi3oFvSDno4zeAM
+bo1KgOAhzmDdHwafGcjzubdLpmEv/JL4asdE7FvZpx5PgCgk1wgMLvKBlWYGt73OYJKnujsiQL4n
+R5Zp8mS98oMmRS2ljpL7e1cI/OiHTLEn0FXnyHXnX33iq+4DS50eVjgHzkyTOdr95AWfVbrlQjOD
+iVVkYqZ0Bk9UZ3CsmIiSncF+8Td0smdnNFZgcG8qYfC+DnnSdCa4ssRElOIMtttzn+PWgwqCEhh8
+6JCACjPumraE4ElgMG+VC4MZmOIEZ4wF1jqgoHAyAT73udnP3jZle3jABwbXIyZCjuRdGExWce3s
+zHak/+W/AOefD7z73cDRozMv456nHwR6ImNhY+daNzLHmxk8LzB4ZSV8UBmkffYpn0zIhOmciF6H
+6+vB7/OqTkAhCgaXDbHqJgcGHzoERKVaOVERW1tqfEpd5PdcsMzO4OkUqSN+gGhnMJ0cK73PSgiD
+LQt4xSsEwP63/3Y21ok6gx0Y/IVHvoDPPPAZPHT6IXz0zo8CWBxn8Gc+I68NxxUMqG131RN5aTWd
+SrAblhcMNDC4SDUweIllmgB0B4qGx0Qk6aTPjiTR7LB4FoUyM4MVR3SEM9jbUX3608BP/RRw553+
+nz2akkF0ROEdYBacFHncxtQCmBgo+p3vTkABua2hHOC/+Jxr5AubjwDgbkxEXHcldQYze0muaRa7
+7NREiDOYnPNdGhPRk0/Qe3opbEYeKddDXZzBBAbvW5mFH2l0/YXXu9u3PHpLLWHwzg5grebvDK48
+JiJg+Tx1Bk/ycAZPQgrIVZgZ7HXIAiT+BsDueIzjuzIzuD2RmcGHVtSYCABqAbkYKz2UmIgSM4P9
+Jvd07t+ee2EwoEZFAMD6vnTOYFOTN3kZMNjXCR4Ag71A5O/+bvazdykMXpmdHFNgMKsoJoI4g51+
+nO7X9o5KFEwT+M3fFO6bP/xD4JJLgF/9VdVl+YXvyYiIc83r3G0lM3i8PTcwOI0rGFDdlvMWFbEI
+MRFNZnCwTBM4bnddYXnBjuqeG9w4g4UouM0Cw8MKyAFQ2u6yi8g5961zL0fB4HvvBb72NXHN/4//
+Idywn/mMfJ06g52J7G8//W33Z05dCAqD6+iOj6ubbpLbTl4wUK+JvLR6+mkJusPygoEGBhepBgYv
+sYypHDR4oSgQnCEbpZM7cpTQY/GeyqvKDE5SQI5z4B3vAD72MeBtb/P/7JEpB9Fx4IHiNmuNMBwW
+534wpjRr0MdB55yDlpqVvD2Sx3TVuS+QL+x9WABTuxhaXHelrukSyJJl2EWec5oZ7HUGd4kzeDgh
+MRFd4gzuFuMMXkQYfGj1EK44eAUA4JtHvomVTUFLqh5oUtHicUB+mcGVx0SQzGC1gBxxBlv5ZAYH
+OoNbahtatPs9Coq2iTN4MJ5gayz7J2sk+6dz19SYCADJncElxkREQXC3cJ4enBnswGAaFQEAB56V
+DgZbermZwf4rmvyP2zsA/vu/n3ViDSAtR37toRKvVKOYCDrBeWZbfVg7e1aFDMOhgMM0g/DWxyUM
+vrh7rbs9rzERecDgU6eC31dHpYXBdXKXNTERwTp5UrZXcWDwhRfK7XmBwcvoDO6Sx6UsZoK4BeSA
+8tturzNY1+GaCfzYgrftPXoUeOtb5XX8+OPytYP2fP5dz9zl/uzMSMz89sgj2zxPNHz+8+Lr/v3A
+S18qf74ImcFOXjAQ7QxeW5P9VQOD81UDg5dYk+nsoILKjYlgHKNxfEJ5YluOulbYRsg7pRRAp4mC
+dUU9GEwMC9DEU5U3JsJbQI7uw2Agl6fcdRfwne+on8s5MLbiL68FgPUuWc9n57cW9UA+JufbLzNY
+xkQYGE/kNCpdHv7Cc64Ag70+be8jSvG4JEDNAeW8ChjscQb3WhQW0ZiIfJ3ByuRAuyYF5AqAwQBw
+w3NuAACY3IT57K8CEA+6dVnmefYs3HgTIKeYiLo5g8l1Tie5DCufzOBYzuCCVzsAHjjo4xSl+7M7
+GitQ29iRT9PP3qAxEaozmIHNtBt+Utr8VrF56FEZ8EFxCcelMTrQGbzvUHwYTFd6TFlJMRH2pIcv
+DA6YxPbC4GPHgNtvV382RHh7qOQTd6uBwSafjYmg7dCZHfX+pgPrPjmV3/qW3L7zmdvc7Sv2ytU/
+9Pmk7jDYsqRLOy0M3kdO+bI4g3VdQpOqgYK30BTQwGBH3sKfUWqcwfWSc2yMQZlIXwZnsBcG022/
+c07bXgf+jUbAzTeL7X/6J/n61VeLr3cdkzD49Eh8AIXB8xoVYZryfD3veeq1swjOYCcvGIh2BgPS
+HdzA4HzVwOAllmHOLjekapHB73ASv5c+tStHCX09jTNY9IhFQQTqkPU6or2utrDB5F/+pedzDYAT
+ZxQdJAdpT5f8fWwYXMZxeyE4AHTJORiR871LINDelb149obdYm8+ogC1JEvtnb+NpZcEgxHsDKau
+yeGERJSUkBlcJ2fwfp9l0Wl1w4U3yN916BZ3uy4PLNvbUCcyFqaAnL8zmO6nkTYzOI0zuFU8DKbH
+7b/igUz2TMbYmciGZrIjn6Yv2OeTGWxP2vRaPTdHNUxKm2/f40Udf5RDts3iZQYDs87gPQfiw+C2
+3nZBOYXBRTnCwwrnAcGFUP1yM6k7FgDGWnwY3O1VExMxNWcndTu6HPWe3lJHvRQGv+IVcvt7dv3X
+qTXFQwObDJ+6GBcdksVSlZiISb1jIra35VLgxhmc7P8676+LM5hCowYGCzl5wUByGPzoo7nvTmYt
+a2aw17W7jM5guh0Fg9/6Vrl9yy2ijf/KV8T3e/cCL3gBYJgG7j1+r/s+P2fwvMJgek3QawWo16qO
+tEriDAZkLMiZM9kythupamDwEkuBoj5wkGYtDsbxe+lTQwnRVvWYzmAlM1j0DkU1bmGOaG9BqLBl
+ph/7mJpDNBwiUeEdwOM4teFjUcdN4X/LLzOYwmBDHri3KN5FmxeJb1ZPAPsecF9LBINtl6yllQOD
+LfhDMsADg42JhAbUGdzNITOYXA+sUy0MLtoZTHODt/bd4m7XCgbnEBPhjZWplTOYTHpQQGvwdDup
+FJCbrMYuIFeqM9gHDtJJrsFYdQYPt+TT9P7NLtZ0mwZ5YiLiRP4A3gmfMp3B8aEohcHOEkuvM3ht
+b7JCqM57JrzcAnLu6iUit2BgSAE5R97c4Ike3h7SSfPuSn1iIugkxJldddRLi+1ce62Ea/fdJ77e
+c/weTLjdDx+5VgFN8xQTQc/vZsq523l2BtN2NikMrkuuv3O/BsHgRXaORikLDG6cwdUrCAbn5QyO
+KiA3r87gH/xB2Z596UvAnd8ZuX3aK18pVhB87+T3YJAi4aeHi+MMjguDq2670yqpM9jp2zmv3zPI
+PKuBwUssw2dQQUVhwijAGcw58KEPAR/9qASjZwayp1lr188ZPAmJSwjLDPZ2oA8/DNwmV1faMDhZ
+ZvBGl/TQBTuDleP2cwa3ZmHwZAJMIXuZfruPi/ZeJP/T+f/sbiZZau8MXk1WgTPYGxNBimuNJhN5
+nrvFOYO7a/VzBucJg2lu8Jn+N4FOvXKDhTNYuNp1tFO7omsXExGQGUz3c8rTjTjcmAjOgGkvMCbC
+O6FWjjM4BAYTED4yJgrUHm7Je3LPHuBA154UWHsaAIfWESOIOHnBgL/7v9DMYLuIWSgMbo2V69KB
+wb2eHGR5ncErG8n6MScruQwYHOmIDiiE6ucM/ta3pDPFMADei+8M7vTEPpTtDDZMusJHPL/Q6/PM
+zlB5P3W4nnOOPNf33y/+lk+cfUK+4cTlCmha78xPTAQ9v8teQG59Pfh9fqqbM5jCLApAFtk5GqWk
+MPi882TURgODq1ccZ3BeMRF1dQbTiR1nH/2Omba9Bw8K6AsAR69+F675xDpw7R8CAF71KvFzGhEB
+LI8zeNkygwF1orfsSY1FVgODl1jGNDwmgkKzUUAv/fd/D/zczwFvfzvwj/8ofnZ2JHua9U68p3Jv
+ZjBQoDPYjOkM1sehMRGAGhUxHEIpiBbLGazERIhfUFhMhEnjMWbPN4XBE3sp+ZkzUAD3antVOoMB
+7L1KwuA0mcFTAoOL7Mws+DsmAWCFjDxG07E8zzlnBtProbNaP2fw3pW9/m9OKSc3mDMTuEDkBtfl
+gYXGROzRDseKAPBTnWMiAjOD0zqDHUet0QfAYhaQq4EzmOzPkMRE9Nt9bG/JR6CNDeBQ327D2kOg
+u5UYBqu54MXC4NiF8/QJxmO5hMWBwYcOifxCQECw/WQ+xGmfgGTO4LFVtjM4Pgym/TctwvK5z4mv
+gwEiJ8cUGNwV/eloVN7ydc4Bk88+v/TJdbc1CI6J2LcPuPxysT0aiUI8z+wSq/jOOYHO4LrHRNDz
+28REJPu/zvt3d6tdeusHzCgAmVegk4eSwuB2Wzrt5gUGLzLsj+MMzismom7OYKd/pMfu5Nf7xUnR
+ib3NTeCGGyBMYlf/MSw2Ba75AADg1a8W7/HC4LPjszAtc+Fh8DI7gwH/Cf5G6dTA4CXW1JJwsO3j
+FA2KDaCiQe633iq+nhpQR2WamIjynMHhMDh6melf/ZUsijXjDI6RGdxr9eQ+FFxALqpgIIXB46k/
+DFZiIgCcxsPudqrMYJiFw38g3BmswGBDOoNba/KE5xETQa+H9kq1BeSkM1ist+q3+7GBV1zR3GBc
+eAuA+jywnNkygFVRSWtvO11EBFBDZ7Dmf4/n6gyeiCfQWAXkynYG+7RrK8T5PzRkTMRqe3UGIB1e
+9xSRa8nM4DiqqoBcaMwT4xhNRCdlWcCJE+LHTl6wI2dg9aIXqdm/SWDwyCwXBvudb7cQamuiFEKl
+A4c3vUlu33GH+BoHBtOVRO2evNfKatcMA773eL8jr8+4MBgQucEKDN49tBAxEctYQI62sxQSxBF9
+/3AY/L6i5QfMFgHo5KGkMBiQUREnTtTn2cuR37PSMjqDiyggd9J4Et946hvgJMewDs5geuxh0TS0
+7d27F7j+egCrxwBmH8/6U+j3ge/7PvHt3c/cPfMZW+OthWg7liUzeHMzXr9Fr+MGBuenBgYvsZQM
+Wd0PBkc7g++Vme24/37x9fi27Gkuvyh9TERh2bkhUNRb/CgIBjvLXZ5+Gvjyl8V2msxgxpgEjb1i
+ncFTn+WlVBSaKDC4ExITQZTGGQyg8GxNQM0M9jqD+x0VgjsdTGdD9jR5x0S0+vVyBucZEeGI5gbX
+DQYf3ZYjqwO9vGDwuFIYbJpQYiKCMoNNZMwMNsR1HM8ZPCl84EGdwX4ZsnSSa2iM3eNY7awq+7a+
+Dpy3Sa6F9aOwtITOYDLho3VLiIlwncF+K3vUwpiAAIOO888Lgz/wAfHvppsI+EdSGDwEmPgFpcBg
+v4KB5LjH5LmF9t833CC377xTfKUwWLO6vsdNnxfaXfkcUVZUxGQCNxoEkOd9rSuvu+1RcEzEvn2i
+Irmj++5TYXDHOKS4Sjt6xz3mZYDB8xwTkSUzmZ7zKqFCFAyusn+tWllgMFA/d3DjDBbKvYDcyim8
+8bOX47oPXodP3PsJ9/WqnMGc+zuDnTZnMJhdjeCFwVdfDfQOyhof6J/Cda8Yup/ndQYDwOnR6aWC
+wXUZWyUR59IZHMcVDDTO4KLUwOAlllqIxGcwSWDCOAAG33cfgI0ngfWn8IBdS+w0cQZfeXm88DIn
+cxAA0BFPo6XEROjhzuCgmIjXv15uf/Ob4uv2NhJnBgMkN7jwmAgalTA7iKaF1IKcwasdNSbC0Z7u
+nljQwJHimi7YQcc5wFmwM7jfVSG4c571PnEG5xwToffE33Q8ls7yMiXcP7xQGHxo9RDO3zhffLP5
+KID6zF6f3JVPEft66Y+9fjER0c5gExmdwYZoq+tSQC7KIbvSUe9vxxm81llz7/VeTzh0LthHYPDG
+k2JJIuKt8gDUe1zrFtuuGVMOaGIU5eeQ7SgFYMWIwq94HP3+ne8ELrwQGE6TTWr6OaKLhcGiPfc7
+353WLAQH1IHDRRcBF1wgtu+8UwxGKQzu8X2+0TEKDO7Ihrusdm08hu89vkpGvTvD4AJyXmfwffcB
+xwfH3e8P9A+CHjZjzM0NbmBwveVdWp1EdYHBzu0alBk8r0AnDzkwWNfVSJ8wzRsMbpzB2T8f534L
+g6nofL/82Jfd16tqu2mEkp8zGJhdjeCFwe028Pxrn1bec9UrBBw+PTyNJ7eehFdnRmcWHgbPe2bw
+qVPy+OLkBQMNDC5KDQxeYikZsjFjA6gmE+DB7buBX3wO8IsX4p5j94NzYGdi9zTjNVxxeQAx8Egp
+4NQXo5dSoKjXGRyzgNzLXy63narcDzyAxJnBAAGNBcdERJ1vCoODMoP77T6etf6sGaD6/7P35tGy
+ZHWd73dHzplnPvfceb41D1hQRZVAMRbwqCWCIlLSrqeIII3r9UKxkUe3vbS71UZFLXHZDSiC7y0f
+oG0LWDLK8HwMBUUNUEXdKmq6VXXne+49Q548mRk57PfHzoj925ERGRGZsSMiz8nvWnfdPOdkRkZk
+RuzY+7O/+/sL4woG4nUGD3JMAkC5QGMimvZNlZX1OYMtGAwkcxPf3IT4XrPie9YBg5Xtli4B4Knp
+sNCl1FPFYKDPTemLifDPDO6yZuhcSM45gcHiPPaMicjGHBPR4UAmWExEo123j6OSk85gCx7tnyUx
+Eb0JDCC4MziXydluTd3O4LZPe57P0HgMcZ1TGOx0BlNRZ3AQEK7c6/J6Vz2oTnCXfotHvJXTOXnD
+DeJxtSqKwdZqsGFwCe7tIV1RQ53BcQE00wRg9H/v06QN22h6O4MXF/udwec25Emxe7r/pLCiIqrN
+rZ8ZPM6DaydACaO0OMwmMRHesmDw0pL3vdepw4fl43GAwRNn8PDbtz87spLTKqQGqG1inM5gr8J2
+gyIOrLasUpGvOXTdGeU5R35M5Au4RUQAAhJvhbZjKzuDw+YFA5MCcro0gcHbWH5O0Xx2sDP48ceB
+7rG7hDsp00Z1x1dw8SLQgLhCWWumz33kpcUygcG9HFN9g+igzmDvmIibb5aPH31U/H/8OEJnBgPE
+GZw1gWwjFgjuFhOhFJDrUhgs7zLFbBEZI4PDc4eV14bJCwbidQYPgmQAUCnK496oky+859TOGtnA
+3+UgKa7BfLIwuF6Hko+pTMZEKBsGZ1pAbjM1HZZqXfawporDZyWn2RnsFRMxzH422g1w9PLazHDO
+4I0N4c7XpVZbkm03OFgmzuBaRw6OaEyEBbmUdmzuKfthmDxt6zpneb0w2C8Dnp6blkM2LAzOGbm+
+yTM30batNCNe61YYJgr5OcHpue4Gg3M5AZie+1z5mvvvB773QNMeSE9n3WEwfb9sTkLZWGMijP77
++HRJnp+1pndm8NycAIXWMvNHHwVOr/dOik4Wexf6Jz0tGLxhbqBUktf9VoTB+byED2lZxRJUlkuq
+UFABahClxRnsV0Buu8ZEcC7b7qAREYDqDD5xItJdGlmTzGChqJzBEgbLC5jC4KQm8uh3miW360Eg
+04LBdFJrxyHVGTx3QMBgGhFxbP6Y/Xg7OIPHPTPYygsGJs7gpDWBwdtYHRoT4TKyL1AY3O6/Sx8/
+DmBeFhBD+SIeeADoZMWdpoBZuKy0dJXqDBYVbhJxBg+AJnSwsX8/sHeveGw5gwUMHsIZTIuTFdb1
+HXebFAx0zYiWvZKWizO4wMowmGgynLnBoZ3B2ficwe02AjuDKQzu5MSdZq4457pkOKyoG5rl5XmS
+BCBdXYVvsaQoNF8ivbnSSmpgMF1KPVUqDHjmYKXOGUzOc6+YCGcWehBZ0QoAfJ3BzvfiXO85rmTf
+u8ZEyP2pdeU5n+lM2Z1Jq3CU0o7NjwaDrUmuzU09UTB+x03jEhqt/piIIDA46D3MDQbr+s5bbekE
+zxqDM6Lpiibru56bAxhTYfADDwB3/Ys8Nw7u9HAGk2zmbD5+Z7BXTMRsRd5bNlvuzuC5OQlyLXfw
+2bPAWQsGby5h967+i3q6IGMiAG5Dha0IgwEJRsdtcE3P77BKA1TgfOIM9tLKivxshoXBE2dwsgri
+DI4kJsIDBlcqss+WhpiIQasw3NoyNqM6gy+apwGoMPglh15iP94OmcH5vATsaRlbhdGozuAJDI5O
+Exi8jUVjA9zgYMElQ5bqkUegwuDSRdz1uQ5QEDRzKjvT9xovzRZnbdBoxUToi0vwdkQ7QYZXZvDs
+rMzeu3hR/OtzBofNDAaAwpo+KOqTEU2P3S0mopiRA35nbnBoZ3AuPmewqMDu7QyeJjCwRu687az4
+whVYP4IUsJJL1hkcGwwuEhhcXEnNAHuD9AxnysM7g52TR4nDYOoM9oiJGMYZbBePA/wzg7OqMxjQ
+65wcVBAUUDPBNyEDVE+fqNiO5Ve/Wvy/e0rGRBR2DQeDrbaNZ/Ve434w2C0u4YKMh40WBmfl84pT
+umEwcYL7xFu5OYMtUGjFRADAd74D/Ov3ZHt4dLe/MziTSygmgvXHRNA2rG66O4MXyCHJ3GCOi40e
+DK7txLFj6JPlDOYQUTETGJxOubnpgioN8RgUGlG35FYAOqNqmOJxgMxFB8YDBm9VZzDnckJ4kDM4
+kpiInHtMBGPSHRzn8novmOk1AdVsygxh2pad21Rh8Kn1/piIFx98sf14pb6yJfLGB8FgQLbd4wiD
+h3EG03v7BAZHpwkM3sbq8MExEdQZbHaCOYM/+wXZqs+VgvfIDWZId3BJc2ZwN2hMhLczeHZWzd67
+//5eZ2uYzOCYnMFtH/ivOIO74g60ugp76Wwp5w2D907vDbUvaXIG05iIetP6wjlMJr7wKPKCAfV8
+oKBI13LqQVpZQfwwOEXOYAr9Z0pbKSYigDM4E94ZpI3ePwAAIABJREFUTDNkLWdwoJiIbAwwuDsY
+ilZID7oBec4/+aiE2u94h/jdbGHWBr9mWY6ew8TEWNd5N6PX/a/C4P4vo+gSlzCogBxVWBhMC8AW
+pvXC4LZfIdRs/6Qm5/0w+OBBOdj8yleAOpbt1y2Wg8Dg+GMinM5gy6k8R5zBtPhftyshoSsMLq6B
+99oMo74Tv/RL/e9pwWBALSKXZhg8jDvW0jjC4HZbnoPDHHsaYiK8skUnMRHDw+BSST4/rTC4VOr/
+3VaT17kNaCgg5+EMBuS9L862mx4TPVavmAiv7POzG2pMxKnqKXDO8ciyWJp7cPYgDswesP++HWIi
+APk5jtP9ytLEGZweTWDwNpbqkB0cE2G6xEQ8/KgJzD4jf1G6iKdOyx75jungzmCA5AbH6AzOOwaT
+zrxLNxiczYoODIXBn/1s78EomcEAUFyLCYIPdga3eb8zmA74+2IiUuwM9ssMniq7OODzNfCeA8su
+8DeiKFjpZpJzBnOeUExEMT0weJO454q5rR8T4SzqNlJMRC8zOFABuYx4o7Q4gxtMjjTqa+I4fuqn
+ZEeUMWa7g+2MZIR0Bvfa/Y6xCfS2kYgz2GVlT9iYiKCrW2jblivLzOCwhQqDyPQ57qJy3OKC3NiQ
++2INJhhT3cGYPm0/9JrcpFn7iTmDXWIiKgV5fjbasm1bW5PH7QqDy9IqfsX+JVfQ5AWD6/V0Ofno
+wHAmXNdTEYXBOrPOoxSFO6PGRCR1jw4Cg8cV6Iwq2m6HgcGAjIo4fTo9ML3dlu0SnYhIU3sSpQbB
+4KgLyLGCuzMYSMYZTPuaXs7gIDD4TFV1Bp+unsby5rJ9jFcuXqmYT7ZDTAQgP8e0jK3C6LTsck1g
+cMKawOBtrA4f7BSlWYuWw8ZStws8cuYZUTzOUnkZKMhe6d6FcBDNdgYXqkDGjKWA3MCYiIzpGhMx
+OysGknKpJfDpT1sblK6coANpBTamxhksvu+VVW7D4Kn8gJiICDKDtS0rbsETkgHATLkfXqEo7zJR
+OYNzRs52ciUJg6vV3pK1slwyH48z+FJqZq/rLQKDQ4A+p1IHg3ugyEBGybke1cE8tDM4jpiI7mCn
+6FRR7k8zI895mGIU+qu/qj7/tiO39W+DADE/WWCUs47d7ui4xttdn5gnGpfQ7s8M9nIGc85Hygwu
+VPSuemh1vNtywAGDadxRT3SZIc0Nxoy0qeybdh+Z0PczskllBsvv3YLT9H7a7Mo+CC0et0jKMtgT
+2RV5Qrzkee6zA9P5aftx1awqoDUuR3QQWf2zcrkfuISRBac4l8uV0y4vgBJUaXYGMyYhyLgCnVFF
+ncGDJvHcRHODn302mv0ZVbT/Qc+9iTN4+PewXmsU5QVcNatKPKB172s04vusaZ+YHqtXNI1bW8Y5
+d3UGP3rxUfvnKxavUMwnTmdwWiZCwmorw2DaLwt63yqXZU5ynJMaW10TGLyN1fYZRBdy3jERp04B
+9eKT6gvKF4GCvDr37RjSGQwApYuxOGRzWYczOEABOeuGSp3BdifLKraWKcgMZB+pMRHxZAa7OcEV
+GNxzBq+smfbgc7pIYHCEzuB8OWZnsCMmYqqk5kQDUM7jqDKDGWP2cXdYcgXk7BvwNi4gV2/JHlZk
+MHgIx22Uoud5Buo5PmjFQxC5ZQYHcgb3riedyxL9HLI0BsbMEDpmVnDllcDLX64+/0Ov/RA++TOf
+xC8/95dxeO4wDs4exJuvf3Pg/VEAalbfRJdv4bwBzuCZGTWLk4o6S4eBwfkpSYB1fO+DMv8BoJSX
+5581ie0VIaDCYBlgt3/GPcBOhcHxx0R4OYNpG2Z25PdHYTB1Bh861BtYEhh8eMmdMnk5g4F0Dcac
+/bNhlYZiamFFB9XjGhNB70le7slxBTqjatiYCCCdReS8YPDEGTz8e1jXbbasdjbWm/ImTNvuuKIi
+gsRE0DbHDQZfql9Cq+tgEOun8OiyCoOpcWe7OIOt66fdHr/JFKvfVC57m0ucYkze4ybO4OjU35Oe
+aNuoo0BRl8xBctdqOWCwyAt+Qn1B6SJQlKODxcqQzmAAKF/ExkY4wBhUgxxVfXCnd5PyyhwsFh03
+md7gP+ggGnAWkFtHdcX7uaPID/67xUSsbMiB/VRB3r0XS4uYyk/1KoyPlhmcr9RhIsbMYEdMRLmg
+uiYBaHEGA+K82DA30GLJOYPtzhaBwcq1F6EUyJySmAjOgSYBXgooDSlnFm+Sg9VOB9IZzIJnoQeR
+4gw2BxeQc34mgF5Y5teu0ZiIdpbQsVYFv/qronNJlTWyuOO6O3DHdXcMtT/KipDcJtCc1eQMDgFF
+HTB4kLuMZs4OBYMrKgzeG+7W4Cu/46bOYDcYTGGhEhNBncEz7s5ga2UHABiZhJzBLgXk6Dlnwt0Z
+TGFwJgNcfz3wPS5h8M6KPwyuNquJAIUgigoGp6GYWliNCoPTFhNBoREg+tnr6+MLdEbVKDD48GH5
+OI0wmJ574wazgioOZ7DVFmdLG6Dd0NXGqt0Pd7bdO3YM/35BFTYmws0temZDjYgAgGanibtP3m3/
+fOXilYpxZ7tlBgOiH7Kgx9ejRda4YHp68POcmp0FlpcnMDhKTZzB21h+y0xpTIQVG2DpkUegFo8D
+gNKqApjCZq3uKJM7k0ZncHtAZnDWyEpHL4mJqNdltWNrsGEYwBVXODaeD5e1CDg+J42ZwfT7DpwZ
+XJMDezrgZ4zhup3XAQB2VXZhuhCuNVecwRW9zuBWC65uKkt0ObWEwdE7gwH5GbaQLhi8nQrI1esA
+z0QTE6E6blMQE5HxcAZHmRmctgJyftm5ZH/aOeoMnsKrXx39/igAVWNxTHrcGbcCcg4oapqy8xyk
+eBwwHAzOluS5osM5qjqD+/MA6HG3XGIiKCy76irikJ6WzmCvyU3l/EoABjudwRacpm1YmzfsrFsn
+DO7yLv7wm3+IP/7WH+POOzmuvskfBldycrS52dpMJQzudGQbEyUMHhdn8FaOiQDkNTpxBo/mDD5x
+IpLdGVn0e5w4g+XjUc5vq/0zimonm+YG07YxrlUdXjERQTKDrXu1MyLC0tdOfM1+fMXiFcgYGdtc
+tVLfHs7gNEzkDathYTB1Bo9Lrn/aNYHB21j+DhviDO66OYMdMBgA5k7YDxXHawD1O4NDvTyw2gNi
+IgAgb/TuWGQ5tVvm4Kn1Uzh0zTnltZnC6M7gOI7bCcHF7+SdugtTuKHr8u7iPKY7/7c78bPX/Cw+
++rqPht4XCgkzM+IzjCMmgvGskqUK9LsmAWh1BgOAybcJDHYUkEvD4LpahYwDwYgweETIGqVoZnBG
+pzM4TExECpzBdH94hhy4WQmdvxhEah66+Nx0XOMdX2cwzc5tYnlZ/i1I8Tgg+H2MAkOaWagfBvtk
+3/PBzuBsFrj1VvE4My+cwTsrO9XrhUiJicikJyZCOeeydTvr9iKJyF5YAD710Kfw3n95L/79l/89
+Lu24C7e9LgAMJoVja61aKmEw/fy3IwyO0hmcRhi83TODz/ZYGGODJ/LcRAsznXVnarFrkhksFYUz
+uNmU78EK6gVMYXDSMRGN/Ek8/y+fj9d94nUoluWH4pcZTIvH0fHyEytidXIhU8DB2YPiNb2x5XZ0
+Bo8TDOZ8dBjc7Y7PPTrtmsDgbawOp4OKfmdRYUBMxCOPAFhwxEQACiAO66iMKzNYgaIuMNiGBwSa
+OAeTXz/xdVz+55fjC1ceBXYcl3/sDf6VwZmP3DKDdVRh71AnuEssiBMY1WpAsyvBAB3wA8At+2/B
+3/3s3+EnrviJ0PtydP6o/bg7K86Zer231D1iDXJMAhBO8E7vPMi4ZAaHdLgPkg2Du5sAxJRm0pnB
+xWwxlJM9jNLoDBYwmMREZIePiVAiRxKOiVAyg5l6nhvMgMHlOT5aZnB6ncF+cJDK6FSGAid+Up3B
++mBwy6cgKI2JaHVNpXhcYBicDQaD6coQVpRfthYYHCLuyM8ZDAB/9VfAb/9OB5gWA06v4nGAw4Ft
+JBQTYfTHRCgTWtmGvT/OAnJfevJL9s/fevZbuLB5wf55qeJOmeh9v2amEwZ7wf5htB1hcBqiMYI4
+g8cV6IwqC+IuLcniSUFFncTnznk/L05NMoOlooDBSv8q7w2Dk3AG02N6JPf/4Hunv4d/+tE/4eHG
+V+zf+2UGU2fw8/Y8r+89Llu4zL43W+adlcYKCgVpGx3XtiNoZjAwXjC42ZSrrYeFwcAkKiIqTWDw
+NlaH9w8qqNxiAyw9fJx7OIOfsh+O5gxeRq2mCYrywTDYPu5s076J0xtnZbaOt//T21Fv10X263M/
+1vsLR8cY0RnciyfQMQgJ46BDpgcPcuFdYkF0aO6QHcfRmpLnkQ5oJGIixBdpuMBgAGBdyw2u1xls
+TRJ00bUBddLOYF2uYMDx2aUkM9gJg0dxBjPGJPxMkTM4y/rP8wyT5/hIMRFmGGeweCOdMNjPIeuV
+CT1bmvI8hlEUVwE536zkvBoTQWHws7v/B27+y5vx5Se+3Pe6alN+WUEniabzsjfPc/L1OmCh3/et
+OoPFSGoQLDx0CHjHu8/b/SGv4nGA2k/iBAYn5Qy2BsBqTnXdPt+cMRHfeOYb9s8/OP8DnK/5O4Pp
++ZzWmIjtDoNHjYmgg/GkigJ6FZoC1JiI7bYsmHMJg/cMUUaFOonpPSBJ0e+6XHb//VaS7pgIev/h
+We+YiCTabnpMLUM2LlVIt6+vM5hkBt+056a+97hyh6zmbq1GNDsmkJP9/K0Ag92K/qZhVccwoufs
+KDA4TUVsx1kTGLyNpTqD+wdV1PXW5vJuVq8D56sXgYLLCIg6g0M6KhVncPkiOAc2N72fP6zaQWGw
+hzP4wfnfx+OXHpe/uOzz4n8CmIbODC6s971fVKLO4IHHDRAY7B0TMYrymTwOzBwAANSLemEwdQYb
+3AMGcycM1psZDECra3CQ4oTBNMMLpUvpgcGZaGIiALqSIAXOYMsBz/qv7yyGh9ZqTMRgZ7Caux6D
+M5hEGPlOchEtTFdcfz+q4oqJ8C0gV1Az/20QYLTxlcxv4J7T9+A/fPU/9L3u4QsP248PzR7q+7ub
+qDO4k43PGewXd2TVOnCLeaI6uU6Kxw1wBtN+Upd37IFYvM7g/n5bEGdwp3RW6bf84JyEwaVsqW/l
+j6VxiInY7jA4Cmew1Z5TGBOnggCzble6ybaLLl2Sn83u3eFfn8vJolJpcQY73Y7W9z1xBg+3fdq/
+6mTT6wzuklodm5C5VWFg8I17b+x7jysWZPEeakBpQG5MB0uIQxRib6WYiKhg8MQZHI0mMHgbizps
+aJVsS7Q4S5sMuM+ehQJ9FYhIKnKP5AwuibA7HRCh4zOYHBgTsfQwvm38gfqCXQ8Bs8/YxYKAcOCU
+uqqseAIdDRyF4EFiIi5cgOIM9hosDisrKqKVXQGK4qatDQZ7LJ+3ZHQlKAOA3LTezGDxJgnC4Gzd
+Pl91wmCAREWU0pQZTGIiPJyjQUWdwYnDYMsZbPSf51kfZ/CG6f3lqDERoh3wgsGA+pkACWcGe3y/
+O2b0wOC4Csj5xR0pBeS6TdGeA0DlPFoQ+/XQ+YeUSUIAuO/MffZjt4GXm+i9vpPRC4PbfvCfFgx0
+yQx2g2WnqrJ43CBnMO0ntbttexATawE51r+iS41mcXcG/6jxTWVbJ9dP4skV0Y/bWdnZl6VvaRxi
+IkaFoVTbEQYzJsFLGmHwVsj+HFZnJAcbyhkMyFigNDqD83n5fW8HZ7DzVh21M7hjpNcZzA35w0ZH
+wmDaztK2zC0m4sY9LjB4UcJgGk3XzcuNjSs03KqZwaPAYDqpMa7fa9o0gcHbWB0MFxMhYLDMC75h
+9w3yRYbMdQjrqNxR3iF/KOuDwQoUHeQsyrjERLzm19CB+KUyaLzs8wo4DZMZnMvkJEDoOYN1NHA0
+FsTPUaU7JgIAjs0fkz/0Jhd0dFBaLQzMDAYAw+EMzk3pzQwWb5IMDF5dRSzF4yzZReSKK2i3eeId
+/ihjIgDVGZx8TMQAZzDz3s+3fuatmH3/LO68+07Xbbs5gwdFLNA2FEgWBntlBi/NanIG52IqIOeT
++V/MqlDUBgHTEnw22g08vfa08rp7z9wLAGBg6r19gOiEpsk0w2CaEe2zwsXqt4RyBs8Ecwa3u20b
+HMYVE+F0BltwmjGGLO+1Yx7O4O+vyIgIS/W2mBTwiogAtl9MxDguux01JgKQ7tG0w+AkJ1yTEC36
+NowzGJC5wbVaOoAR7X8UCtIdO3EGD7d9ef/hMJFiZ7Ah+91r7cHO4EJBXvdWAbmp/BSOzh/tM6/R
+mAhq3lk3V+xjpvfCcdJWzQyeOIPTpQkM3sZSnMEug0kaE9EhMRFnzkBxBj9/7/Ndtx/WGaxAqZic
+wW4Q3HbZOJ3BrAMc/RcAwIGZA/jEz3xCvujyz+FlrxrOGQyQz6oXT6CjQ64ct4utzxUG5/XERABq
+ETmdMDiQM9gBg43KFncGExisOPI1yL6uM20gX0u8wyJgcIQxEWlyBvcmPXIuzuCchzO40+3g4w98
+HF3exV/c8xeu23bLDB7oDM6qzmCd0MgvA94rJmLXwpTr70dVXNe4nzNYgaJdUzqDZ04pz6OxEGbH
+xIPnHwQgBlhT+WCfUTlXtqNBmlxzZjCdzPWZxO4goDN4PZgzmPYXOt1OMs5gl5gIAMix3iQEyQy+
+KLpRmJkBvnWyHwZbGgSDt3NMRNL3qqDym+wIIgsir63pKeTrp0GZwRSCTJzB4UULhqYhKmI7O4O1
+ZgZnTHCmXrxJO4MVGMzkAa42l2EtRnGDwXRSy3IG75nag4yRwe4pdVbEyxm80lixJ7m2Kgwex8lL
+YAKD06YJDN7G6sInM5jERFhuWCAYDM6wTGh4mMvkJBQti1lDLTDYJyuZFpBrmqJaxdoaBKhl4udr
+d16LFx54IXZVxJR76dp/we//iSS4YZzBAHFRx+QM9nOCI9vscwbTQWEUihUG+ziDM1BhMCOZwWEn
+NQYpruJSg+SEwbHFRABAMfmoiL6YCA9YGFQSfDYShcGdDmxQ5OYMzhnumcHrzXVwiHbtiUtPoN6q
+971WdQaPV0yEGxgHgD2LMWQGa7zGw2TAt9G0wSCmTyvPozD4h+d/KAqvwL1qt5cYYzY4rndlI64n
+JoJ+3/3frR8MnnFpzk9Wg2UG00lz6gxuteIBGcIZ7H4fzzFvZ/D8rhruP3M/AHen/FJlqe93lpSY
+iG0Gg5O+VwWV1V+cmupfhh5UFL4kMcCexES4K0pnMJCOqAjaT8rnJ85gSyM7g/P9DVbSMJh+1x0m
+L97l+rLd1g6CwfVWHWtN0cBbEJiu3lkoLSiriql5Z7WxqsTfjGPxyUlMRL8mBeSi1wQGb2OFgYPW
+oArodU4WZEzE8/f1w+CZwoxnBt0g2S5FjTERfjDYhjuMo9UWn5GAwapb1GAGbr/8dgBAvbOJ//bN
+37X/PrQzuLAOsK4eGOyTER17TMQCiYnonU/aYiJ6zuCshzM4A7mEHgB4QXwBU/kp13NkWFFQlJ9K
+hzM4VhhcWkm8wxJ5TEQmHTERZrtjT1a5OoON/hUPgDpY4OA4vny877WKMzhATITVhrIYYLCfM5gx
+JjPBLXUN7N052iSAl2JzBgeNO4KIS1i2VmVOq85g+n0recEu2XyDZEVFbLY1x0QEncwF0EETnPvD
+MuoMHhQTYTADDKJf0+EdBRzGERXhdAZTOF0weveWbB0bG6LYlgWD80e+Y/f33njNG/u2u7M8iYmw
+NM4weNiICOdrk4iK0O2eHFdF4QymMHjiDI5faYHBScdEdIgzeHlzua8Aa6slH7sVj9szLS6AvdN7
+7d9RVzBAYukArNSlM7jTiS/OKUpNYHC/Js7g6DWBwdtYdBDtWkDO8HcGz+UXVYdnT8PmrC6WezC4
+uAKwbrLOYADNjmiJ+2BwQbRGt192u/27f/rRP9mPr1m6JtQ+2Z8X40CupgcG+zjB3QvIbZGYiJ4z
+2HBxTAJA1nIGZ00AHJ2c6CmFzb32E/0Miz0YHHeV28QygyHeN+kOS7UKG/oDERSQszOD22h3uuh2
+Bz9fl8y2z/VtWO73FhpNuZOW68LSD8//sO+1bpnBgZzBPad9XM7gfNYnBsaSOYWdO8NPVgZRXAXk
+fCc1yXndgWk7g7ML3jERVl4wEM4ZDADTBdGj32hV7YkC3c7gIPexdlvuhxcotDKDp/PTvitBLABL
+C8gB8YBDrwJyAFDISGdwrSauOast6uyXERG3X3Z7X58tcEyEWVNg6QQGp0MWvB2leN4C6QakDQbH
+5QzmPH3uwSicwTQmIg3O4ElmsFSkMRH5/s510s5g+l23QZzBBAZbYwK/4nG7Kz1nMFm9c+WizAsG
++p3BtF0bx6gIPxhM23w7CmwMRO+tExicvCYweBsrTExEFy27k3T6TAeYEYOng7OHUcwWlaWEwPBL
+6+3lHkYXKK4m4wwmg2hryaybMxgAXnX0VX0g/bde/Fv45ef9cqh9Uj6v4pr2mAi3jGjnIPrpp6HG
+ROSiXVY9X5yXsFVrATluw2AvZ3CWkWM32mhlxBcQZV4woIKibekMLqbLGZxlWddrIYwUmJygO9hs
+yxFH1nX5vNzPhimfSwcLAPDQ+Yf6XlszxZeWN4oAF92GIM5gHkMBOb8MWQAwuKMX3apgyXtl/EhS
+CsjlNRaQC7HSowPpDM7O98dE8N7NnTqDn7v7uaH2x3IGb5gbmJ4h8UoRK8xkruWCt+6nbrCMc45T
+VQHIB7mCne9JYyKAeMChVwE5AChmZWbwxoY68K0tSBh868Fbcf3O65XtDoLBdDVLrVWDYcjB2wQG
+J69GQwLSUWBw0s5get9MAgY/8QRw8CDw/OcD9f6kpMQ0cQaPvwbBYLpSRbczuFiU4D0uZzCFmRQG
+r9RXUJ4S9zKrf+RWCNMqHgdIZzCFwX3OYEdmcNLt2qhyRqo4dYwssH3sMf37E5VGcQbTe/wEBkej
+CQzexuqGyZDNmHZRiVMXZXbunhkxorYdvT0N66hUilmVLibuDG51PZzBPUg4X5q3oyKm8lP4hzf9
+A/7rK/6rXUwnqJTPq7CupYHrhhxEP/44tMZEMMZkVMTsM4DR0vJ9N1v0PPeCwQQW5WtoM3Hcwzrc
+vaTA4HL8mcH1eq9zkZgzOF2ZwbabbgQpmcMJFpFrdYIX1qq35E6uNRzO4Av9zmArJqJoyAmhQM5g
+ow2wLppNfa4fP6coAGScMNjUB4PpNZ4raYTBIdrzNpoSDjoKyG2YGzi5fhLtbhvfP/d9AMBlC5eF
+bvusCU0OjukFccB6CsiRSY9BMU8AkDGxuSlXX7iBwrXmmu18H1Q8zvmenW7yMRH0+O24G6ODaq0t
+M6LBcal8NwCxvPbQ7CE8Z9dzlO0OgsGMMfuctj4ny2E2gcHJix57VDERSTjo6P1hUAE5XffXj3wE
+OHkSuPde4F//Vc97DCPLGVypqOdmGKWtgNwkM1iKMXl+j+wMzg12BgPxt92qM1geIAdHcU7Q2WZT
+rOCk497p+Qa+8cw38K1nv2X/zsoMfsGBF9i/e9nhlynvt1Wdwfk84Ja8ubgo2+4f/Si+/RpVtL8U
+tl2bOIOjV3RhmBONnagz2M0dp+ROGi2YppjFPLMqW9TFsmhpF0uLeGbtGfv3wzqDFRhcXka1evlQ
+2xkkP0e0CoMHO4MB4GOv/xg+++hn8Yojr8DhucND7ZMKg2NwBgfIDO52oSw7ihoGAyIq4r4z94mi
+OLPPYH39mP+LQkpZPu/hDM4Z5Ngrch2dTmdwrixBEefuN/qoZc+Mb3dn8JLoYY2aFwz0O4OTgsHU
+GeyWGUydwXXSQw/iDLYgUDFThjWGGASD1bakCbRLqFbVpchRya89B4AsYnQGEydltlhHC8nAYApF
+O9wEes1/u3Sq77nHl49jtbGKRltMkoTNCwZkTAQATC1WgSenUuEMpksn3UChFREBDC4eZ2+SJRcT
+ISby3Cfxy3l53q3W6rh0qbdz2QZahhh5Xb5wORhjoWAwIFYFbbY27RUCMzPAqVPphMFuBQLDqEy6
+OeMAg6nbbZydwUnHRPzgB/Jxmr53yxk8rCsYSF8BOS9ncKsVX184Tg06twHxGTSbwzuD7XbYxRlc
+Natod9v2vWJ2FlheTiYzuMXVizc7uwxAdMRqNdLuGC18auF6/I+PPa48f8+UuAheeuil+Ny/+Ryy
+RhYvPPBC5TlKZnBjBdckHH8zqqzxhFtEBCCulSuuAL7zHeDZZ8XEdzn6oXrkGsUZPDUlViZ2uxMY
+HJUmzuBtrDAxEcgIGNzpAMs1ApKKPRjsdAaPmhkMAOWLWgYbfsdNB9FmxwTng2HwjvIOvPW5bx0a
+BAPxxESEgeBW1qcSE5GPNiYCAI7OqbnBOr7vZos6yQLA4LKkB1FnBtMl5NmSGFhzHt+yxCRgsLL9
+lBWQU1yEQ8rpDE4sJoI6g11iIgpezmBHZvDTa0+j2lRtjhYEKhBncJCYCACA5iJygZzBzGE1M6e0
+gGlAnfDJFHU6g8Ot7AEA5GpoZ/tHgQ9feHikvGBAxkQAQHleNOSNRvRLf0eBwW6wjBaPC+MMTiIm
+YlABuXJOErPqZkO6oFz6LU4YvFQZPDNi3futFQIWcN3YgL1iLElZ/aVKxR22hJFhyMF00veqIKJ9
+xe0Ag3VNtj74oHysM5c4jOp1Ce2GzQsG0ucM9soMBno1PraY/GBwZM5gl8xgAFhvyoEVdQbHkY+t
+xERw9QAz08v2YwUG73wIl5gKgnNGDtfvEvFGjDHcfvnteNWxV/W9n9MZnPSKh1HlB4MBAYMtPf64
+9/PSpFFgsGHIif24JjW2uiYweBuri3CDSdMUM4q82A+SFEcvUh4T4QdFDdXV1ukMhsFRSIHn2mIi
+hoEH+mIigP4icjpgcCMsDNboDKbby07LLzmuwZd9XpXkGyrOXQ1SYiLS4gzuweBSbnRnsOIuTtAZ
+3OoEXz7fIDDY6QwG1KJinHPbGVwwCOgMEhNk30t3AAAgAElEQVQB2MX6dMHgYZzBeVQGwuxRRNtJ
+o6AvCiYsFAUATMu84CNzR+zHD194WMkLHsoZTGBwaVZ+2VF31tWMaDcHvHrc1AkXhTPYjong8cdE
+0MxgBqbEUVUKcqJxvV4nMFh+AVY/49j8McXBvlQeDIO9YiKAdLgo/QoEhpX1vabh2PzkVnRpGKW5
+gBwFITpA7cqKcLrrfI9hRIvHjeIMnpqSExxpdgY7/7ZVFMQZDOjJDAbUPp7VRrbb8ZhQ6DGZXfXC
+MqY8YDAZg73owIvw3he9F1/637+EvdN7fd+vlC3ZfYCV+sqWiYkICoPHJSpiFBgMyInPiTM4Go0t
+DGaM3cgY+0+MsS8yxp5ljDUYY1XG2KOMsb9mjL0o5PZuZ4z9L7KtZ3s/vybENkqMsd9kjH2XMXaR
+MbbBGDvOGPsAY+xg+KPUKyUmwiU2wC0m4swZACU7jM4TBg8dE+FwBusYYPll5zozB01TPwxWPq/C
+mpbOOB1E+8aCECeZJR0w2M4MBrTBYL/l84C6hJ52RKJ2BtPrhFVkR0jmO+qVfV6R71WH45tKgc2l
+lcQHI9UqbEAZeUxESjKD827O4KyEZI2W7KEvV/tpHY2KqDVM24Xa3hzeGaxrSbkfFAWAHFN70tTh
+HLWo+5/1CshZmXhRKtRKj953QPOCbztym/343jP34vOPf97++bl7whWPA9SYiMK0vHFH/b2HPW43
+Z/BaYw3/9q5/i3fe9U4FggcpIGfdO5OIiTBNAExci85jrxRkW7bR8HAGF8QHkDEyeNFB0UW+fOFy
+3xUSVvHYmlkD5zyRqvSDtJ1h8FaJiXACQirdMRHUFazrPYYRhcGjOIMBGRWRBmewV2YwsDVzg4PC
+4CidwbTfTWFw3G23GhPhOMCSFwyWN+03XfsmvP+V7+/LBvYSY8wenzszg7diTASwPWGwda9fXY3H
+4b7VNZaZwYyxfwVwa+9HehrkAFwG4HIAb2GM/V8A3s4597y9MMYYgL8E8FbH9vYC+CkAP8UY+0vO
++Tt89ukyAJ/rvT/dpysAXAngbYyxn+ec/3OAQ9SubhcACxcT0WpZMJhmBgu4taO8Q3ntsBBN2Y4m
+Z3DYweTaWm8gr9MZHEcBOR8nOGMMWeTRhtnnDDaYoUKviKQ4gxeewPojkb8FmkFgsJG3MzV1OoPp
++c2LkgDHD4OFJaCQKYQudhhWagG5S0o2XxKKPCbC4YJNytnS6pLzPNN/fRdz7s7gc2v9jQ0tIveV
+f5WrA86fHsYZLD4QXc5Jv5Ue4vfq91zODlmJJ4Co45KurKjVooNVgP/kntt3QJ3BVy9djf0z+3Fy
+/aQCRF+w/wVDRcdQZ3B+Oh5n8DCZwZxzvOUzb8GnH/l032tHiYmIY2BNncHO73y6JM+7aqOOZWuc
+Xeh3BgPAh1/7YXz8gY/jDVe/wfd9rQlDDo5Gu4GZGeJCThgGdzoS2m5HGKwjJiLpAnJxx0Q4YXBS
+E7pOWXnBwGjOYEBERTz1lPhuW63R41RG0cQZrMoCfaM6g/NTG7A2sW9mH1YaosPv5gwGxL151EkG
+P9FryeyosyzdooTBGxvuzmC/PHs3zRfncb52HiuNlcTbtVG11WEwYyLeKayse12rJRzu45CTnGaN
+qzN4DwRwPQXgzwC8EcDNAF4A4N0ATvb+/gsAPuazrd+HAMEcwL0A3tzb1psB3Nf7/dsYY7/rtQHG
+2BSAf4YEwR8BcBuAFwL4jwCqAGYAfJIx9hyv7cSpTgeiaFdPQWMizp6Fa96oMzM4ugJyQ21moHwz
+gx2DaHtQFZczuLiGtbUesI9Qfk5w8fved+6AweVcGUxDVYcDMwfkvujKDA4Cg7M0JoJkBg+Zfe0l
+ep2087IjFFcnRcJg+b3qljLRUVzBAw9of8uBWt/oABlxLUTiDM6mxRlMzvNs/3lezMlzvNkmzuCN
+wc7gJ09Kt4m5IXttqYmJCOQMVq1mOt3wGSMj751ZuQ4z6qiI4WIipDN43/Q+XLN0jfKaSq6Cv379
+Xw+1P9QZnK0kB4Od92+nM/iD3/mgKwgGQsZEdDtYJN2VOCb0aGZwnzM4L9uyTbOBEyd6P3j0W47O
+H8V/efl/wQ27b/B9X3qf2GxtpsoZTN8/ahjcaKQ/wzSqmIikncFJxkRsJ2cwAKVNTEJOGDxxBov/
+R4bBFTl7RSc20+IMNrtq55iOgWo10paNCIOt+9x6cx2zc5JzbFUYfNll8vG4weCpqeEKRtKJz0lU
+xOgaVxh8HMCbABzknL+bc/6PnPN7Oeff5Zz/GYAbADzWe+6bGWO3um2EMXY5gN+AALj3ALiVc/53
+vW39HYAXQwBiBuA9jLGjbtsB8JsQbmQO4D2c83dyzr/OOf8O5/z9AF4DoA2gDODOCI5/ZLXb8CxE
+Ysk7JiJAZnBEBeQSdwZnyDJTMqiKGhI6M4M5jx6e+GUGA0DWCYN7y450QcNcJodDc4fED/NPYG09
++vUeNCYi67J8HlCLa+mMiajkKjasaBjxx0TYN80epKJL2nUpY2Tk51hawYkTyd28u11gk9DayGMi
+EnQG00JqeRdncIk4g5sd+Rlc3CRfhimucwqDnz4jKeYoMRFJZgbnHc7gmaLeaBSrvexmVWdwlPJb
+6aGu7Ol3Bu+d3otrdqgw+MOv/TCu2nHVUPtDncGZkj4Y3MXgbOxBzuAL+e/iPV9+j/3z9Tuvtx8X
+MgXfQmqAnEhtd9vYQRYyLS97vCBCCRjsHhNB2/Jas44nnxSPMxXiDB7yfmbFRACiiFyaYDA9v6KG
+wUD6i8hFFRNRLktQlTYYvF1jIqJ2BltKOirCWUBu4gwW/48aE5Ety8aKTmx6weA4im/JYxKrSqjM
+nH9m8FDOYLIakd7/xi0mgnN5PQyCwVNTwL7e1z1uMHiYiAhgAoOj1ljCYM756zjn/8C5e1II5/wS
+BOS19EaPTf06ZFTGv+NcDbThnNcB/Lvej9ne8xUxxrK953AAxznnf+KyP98G8FEIqPxSxlj46iwR
+SziDBw+ild9lfGCwDmewhpiIbhe+x+05mOzB4GK2GAlEonJmBgPRN3CKM9gF/gP9MNgoCJhBB4NR
+69BsDwYX11FtbkSe/2MSx2TewxlMl9DrjIlgjNlREXUkGRMRnzMYIJ2zotiBpKIiNjZgR0QA+pzB
+9Trw1a/GO6ikzmC3zGAvZ/Bao9dZ7hrAGXFrOrNxBpfqop0/dU4CTbSCxUQ4J9QAjTERAZzBeUfE
+zWxZX0wEIK8rntHnDPZzRBvMAOO932f6M4P3zezDTXtvsn9+23Pfhp9/zs8PvT/0HsYKKckMdhSQ
+++jyL9txKu954XvwvV/5Ht51y7swlZ/Cb7zgNwJF5tCYiLhhsBIT4VjdQ9uyzVbDhsHze0Zf0USd
+9DVz68Ngumw17VERUcVEMCaLyKU5MzjqlTecAw89pP4uLTBYlzM46boNcWcGf/GLwM03A+97X/Tb
+DqKgzuButzc2DyHOZRuVLfs7g+OOTbCv60wLHOrgrmmoMRH2OCgiZzAANNmq/fmOmzPYOWkySFZU
+xMWL8Y0nR1GUMDiOSY2trrGEwQH1NfL4mMdzXgcBcR/hnN/j9gTO+XcAPAoBcl/v8pSXA7C6oH8z
+YH8+Th7/9IDnxaJ2G3YhEsA7Q9bgvTtXxkSrNSAmwukMHtKBUs6VpdNOgzM4CAR3FpBzxkREDQiB
+/sxgIHoYzH2cZACQMywY3OutxQAN6SxuO7MWeWdfKSDn5QymMRHT0o4RtQMckBMn1c4yrHjxpDKD
+lXxTjbIzSEsrAHhiURE0LxhAJDnYTmdwswn83M8Bt90G/MIvjLz5wKLO4FzWxRmcl/tJl+tVW72G
+pjkDnJNOyR+eF7nBp5YJDTEDOoMz6XYGzw8TUhZC1nXVZhKkRw2Vuj6Z/wCQ4b3jdomJ2Du9F3dc
+dwfe+6L34n23vg8fvP2DI+0PjYngBUkIo+yoc+7/fWeMDBjvnZwZU7rgKufxdF1Qn+t3Xo/fe8Xv
+IZ/J487X3Im1/3MNv3fb7wXaBzsmgqsxEbE5gz0KyNG2fKVatycfZna6ZwaHUTmb3pgI3c7gcYLB
+o8RE0NenLTNYZ0zEM8/0n8NpgcFROoMpDE6TM1h3ZvD//J/AT/4kcM89wPvfD5w8Ge32gyioMxgI
+P9lRq8kCWkbB3xm8RBa/xBEXImFw/4HVmeoMts53Y1rAYAbWxxaCiJqXNls1e5Jr3GAwPReCwmAA
+eOwx7+elQXQCY+IMToe2Mgyml07fXBtj7AhEkTgA+H99tmX9fR9j7JDjb7e6PM9N3wNgjQxf5PN+
+2uWEop4ZsujduTxiIiwwGpUzmDondTiDnfEYoaqRa4TBzsxgINoGzumI9vq+c4ozmKOb0Q+DVRC+
+FvngkjqD3QprAaprEgvyTnps3mseaXhZ53eLm3YMR7yZwdyGwbE5g63KxkYHyG+kBgbrcAabJvD1
+r4sfv/rVkTcfWLSAXN4lM7hE7DdmR464Njs9mtKYAy5ca//++PJxAMDpDTJ62pAj0oGZwVkVkAMJ
+w+CsajVbnI4nJoLC4LidwQCQgSP2pxcTsVBaQDFbRNbI4v2vfD9+/7bfHzkyhsZEdLN6YiKc9zGv
+yT2afW8vm9wp7X+3HblNeW2YIprWqpp2t41cTgLI2J3BhrczGBnZxpUXInYGO2IiknblTGIi5ONR
+nMGAhMEbG/Fnt8YZE7GxAXzkI8ADD7ivUkoLDLacwYYBZRXCMKIxEUk7g+PKDP6bvwHuuEPd5unT
+3s/XJee5bXZM/Nndf4ZPPvRJAOrxh4XhtF/FCnLmat9MOmCwBTSNQv9FVeMqDD7Vm6vOTIsdWywv
+eq5iHaSpvGzAawQGj1tMxLAwOO1REZubsibSsDCY3usnMHh0bWUY/DLy+LjL32lY3iM+26J/v3qY
+7XDOOwAeh3AYO7cRu4JAUQAwLBjsEhMxW5i1X2cD3J5GcVTaYLm8jEaDR1rAI8hxuxagMdpAb+mr
+Dhg8lZ8CQy9FXYMzOOj3ncsQeJBtAExMOessuOR0RUcNg/2WzwNqnqqVybhUXsKuqV2uzx9FziKJ
+QMyZwQSGxpEZDKjub5Qu4fvfj+Vt+1StQnEo6MgM3tyUTqOLF+MbVPtlBpepM7iXGcw5R5NZzuBZ
+4JKc/Hhq5SkAwHLrhNzI6mH7YfACcmJ0kyQMdjrAd8xqdgbnLGdwHZb7P3IYzIJM5lqF7JpiP3ow
+OEihtLCizuCOJhgc9D4mIXjTXnZbOvRD++/X7rzW5VXBRGMiAAlp4ssMdo+JUNrynIwnyc9EnBls
+1hIvNkY1cQaL/w1D3e9hRL/XuAfYQWFwFCvHPvAB4B3vAG66CfjQh/r/nhYYbDkld+0afL8NorQ6
+g3VlBp87B/zKr/QX4o6jnXbKeW5/6qFP4de++Gt48z+8GQ+ee1CBwWHPb9qv4vlwzuDY7lkA8qX+
+A6t25Q6cPSvb2k5JzFYMExEBeN+vNjbGK5M6DAy+8kr5OO0wmJ6zE2dwOrQlYTBjjAF4L/nV37k8
+bT957Ldw5Fny+IDHdmqccz+MZW1niTHmTqViknAGhysoZppqTIS99BvCFUS3MawzWNlu1gRy9Ugh
+QmhncKYpbphk2asOGMwYk59ZLzM4ykGWOG75fXvNtsqYCNOOiAD0OkidrujIncE0JsLFMQkARWdI
+HYDn7HpOtDvSkzJxUhIUONaYiJi+VyrbGQwApRU89FAyVaO1xEQozuBG30ArLhdO28cZTN3vra7o
+ETfaDXRZ73WNOeQ2j9jPeXLlKTQaQC13Qm6EwOD0FJAbXFCsb38A7F6IJzMYgH2+RV9ALggUJTER
+5WVxT4XqGopK1BncYnoyg4PEPAEuhVABzF4mncHX7bxu6H2w3rPLu+Cc2zB4ZQWRTly7qdmEfUzO
+c1qZ2CJtnFEa3RlMz+fN1mbsWcmDFFVmLtU4wuC5ucFtchAtyC597JCfQhrdMRGWG7jTAT73uf6/
+pwEGd7sS2o6aFwykyxkcR2bwQw/Jc6pMbsdxuGGdcsLgH12UtO7Ri48q5/cozmCeFY1VhmUUIwuF
+wbTtjjMmIlvsv6g2O+v2/cwGmLmavSJ1aBhMzEsb5kai7dooou3QVnIGT2Bw+rQlYTCAdwO4GcKS
+8w+c8/tdnkNPQb/uHh3GOUeR1naCdBkHbSdWOaGoFxykMREXLwKb9a4Ng2k0BGNqts8oMFgpVpbb
+jBQiDJMZfOEC7IgIQA8MBoibWkNMRFBHVd6CwVkZYQBojoko6o2JUJbPezmDE4LBxUUxko41JoK4
+xuLKDFZgcHEFpgk84rceQ4O0xEQ48nFp0RcAfT/rEnUGF9xgMGnXWr1aqXSQgOYsXnnTIYCLFQrH
+zzwlllTOnZDPGcoZLN5LV7ZokOzcogOc7VmMJzMYgH29RQmDu134Zv4DDig6LdfH7p3a6/r8UUSd
+wSaSdQbnmCMrGQAnMRHXLF3jfElgUUduh3eUwbXudtw0YbdfzrZLOeeyso3v5kbPDHbGRNBjTrpY
+zXZ3BltwIwoQHndxKSoKzAYVkIsC1G5uDv571HUrhtHysiwmNmpeMJBeZ7CuzOC6bAJxjKS9pcEZ
+3GjLk7jarAaOiXAD5XR83MmIxmoqP6WMU9MQE+HmDAZgG2JsgDli8TjAOyYCGC8YHMYZfPgwYJUK
+2W4wOOmoqq2gLQeDGWMvBfDfej+eA/CrHk+lPWm/2w9txZz0xNpOkFvYoO3EqqAOmwyTMRFPPw3h
+kDXEuhvqDAZEQRpALEVU3LUhpYDHiGHwMJnBZ89ChcEFPTBYOoOjj4nodKDAA69lxcqxF2QLqwD6
+iKUsXdXgDG51BkMyACjHCIPppEllKd6YiMScwSXVGQwgkagIAYMjjolw5OM64W9cA68OqDN4cLtm
+OYPXmvIaL/A53PbSAlAV7fiJtadEsRULBpsVYFOeu2lxBvMgMRE59fretRBPZjAA+3qLEioN5ZCd
+kcXjdDuDGzxZGNzvDOZYL4iYiIOzB0earKbv2e62YwWjzSbs68m5qsHLGdzoxcCUsqWh+2XOZbdx
+F84bpO0MgzmX/cRRi8c5txE3NAlaQC4KUOsGgw+RajBpcAbTfkQUzuD5eTmBmzYYrMMZTGHwwYPy
+cRqcwRQGb5gbgc7vf/xHcR686EVykgBQ+1VtQ8w4V/IVFLNF+55AYfDUlLyeknYGAwDK4qb5xBO9
+nykMLkcbEwGMVxE5ei4UfYYquRxw9Kh4/Nhj/fEoadLEGZw+bSkYzBi7FsD/ApAFUAfws5xzr64q
+bZn8esi01113/M3aTpBe9qDtxKp2GwGdRRYMNvHMM1CKxzlh8G+95Ldw9Y6r8buv+N2R9k3JvsvG
+HxPhzLt87DHE4wy2oGiuDhitZJzBpNDS4n65A3E6g6OGRkpmsAcMnqn0T7tev/P6aHekJ8UZvCA6
+QpcuyYrAutRq9dyJ2eSdwQASKSLXFxORjSAmIjXO4MEOeHqsbRdncCUzixtvBLAioiKq3Qt4/Nkq
+MPe0eMLqYcDKNcdgZ7BzQg3QA4OdBcW82rWS43ueLcYYE9GDwVE6g8PD4CYwTWCwhszgXCZnDz5r
+HTmjFzkMzvjHgvTB4JlTqHOxI6NERADqKqpOtxNbZEK323P/9+Ke+pzBLpnBe/YAVVMc9yh1HJwx
+EZWKBApbEQZXyFxRmmFwrSbBUNTO4DTBYF3OYMbk5/YzPxPte4wqKy8YiMYZbBgyKiLpmIi4ncEH
+SLhj6pzBpr8z+JvfBN78ZjFO+Na3gHvukX+j5pkWk85gQI5VaT+PMekOjhcGe1DuXt0UO2KpIncq
+ipgIpzN4XGGwnzMYkFERm5tq+5E2RQGDJwXkopV7T3oMxRg7AuCLAOYBtAHcwTn/5oCX0KGp38iQ
+2oicXUNrO0FGl4O2E0imaeK+++7zfd6ePXuwZ0APoi8mwsMpmjXyQBeA0eqHwUUVBr/h6jfgDVe/
+wXff/FTOpsgZnDHFa2KAwYpbKb+B1dUIrB49OTODPWEwOfbdh1dgGZ20wmDdBeQCxETs263O5RjM
+GGkp8SDReJX8rOwIra9HN5h1k33DTMAZTI8ZU4KOJucMjjgmIiXO4DYPPsnV5qKHfm5VkpTp3Byu
+vx7AylHg0DcAAN9+9m7ppCYREUDwmIhCuYkm9MDgoJNcxbzak9ZZEBNwX7IfJQwOGvOUYwURlpVV
+ncHWKp6oNZ2fRqPdwIZZRbkMpZhiFAocE2E4YDCJiLh2afjicc73dDqDdYKGVgtK8csgmcHHjgHf
+74GAYYvHAf2Da8ZE9uSpU5OYiCRFge24w+BBmcGDCsg1m8Df/q0AIbfeGuy9LFBYqQDHjwPf/S7w
+6lcDf/qnYlI+CRj86KPAzIwEv6dlqk8kMBgQURFnzggY3O2OnjE9rKzvMJMR/7adM7ijxkQMcgY/
+/jjw+terv6dtLu1XmbznDO45Y+eKczi7cVaNA4OAwSdPivuV7vPA2m/qDM5n8jA7VqCz46ZJnMFL
+lSUMI+oMHufM4LAweB+Z4z97Vv05TaL3VJ3O4DNnzuBMACpujlNVQU3aEjCYMbYXwL8A2AuBLn+J
+c36Xz8to0bj9ns8SokXjnnX87SSAWwBUGGMzPkXkrO1c4JwPdcu7cOECbrzxRt/n/fZv/zZ+53d+
+x/PvwZ1FjpiIAc7gqOR0uCRdQA5ALDCY5hzpgcH+8GC6JI9937FVWPXXdcZE6C4gRx2TBecow/p9
+VoXBVyxeoZ6HEYo6gzNTsld36ZJeGGx3gmhmsKZjdIoCmMLRe9D8/4QzmHPhVIhL1SoUoLKVMoM7
+5JaSc5n0oO2a5Qx+9oJs12aLs1hcBKbaR+yZyvtXvgZY8wUOGDxoAEE/13y5kTgMLuccMFhjeybe
+Lx3O4Jw1mQsAs0/bv9cREwGI3OALmxdQbVYxOytgcJTO4FDHDdgwuHLkh3bBhlGdwfQ9nZnBOmGw
+iIjwnshSYbBo448c6+AbprjwRum3OJfdAsDiooDBy8vxt+NUumFw1IUfoxQFG1HERCQJTQZlBg8q
+IPfhDwPvepd4zTPPqNm4XrKcweWyWHr/uteJn4tFARHjhsFf/zrw8pcDpZJYLr9nj7i2LEUFdSxn
+cLstAMqCniGcryz2Yn3Pup3B+8kIP43O4JKHM7jbFSDYOeFG3a12vypj2lFhTmdw1ayi3W3b9y7L
+GdzpiPYzirbDS9bxZArymPdO78WJ1RPihwEwOJLMYLOGo1sgJiIIDKZFIpOY9AiqKJzBMwQbeMHg
+D3/4w/jP//k/D/cG20xjD4MZY4sAvgzgCIQH5v/gnP9tgJc+TB5f5fNc+vfjLtv5GfK873rsZwbA
+sd4+OrcRWEtLS/jCF77g+7xBrmAguFM0a8iYiKefBnBMPwx2DqTjdhY5C8gBSAgGR7ftoLEgU2XZ
+K3nJq1fxpZ63fqsUkPPKDHZmKerKCwbUzGBOOkIXLwJHjmh7WwKD43cGX7njSswV57DaWAXfdzcA
+juVlhuVltaCFbvXFRGRGj4lQIEym2TfYiA8GB2/X2r2I+1MXJUlZKIt27dDsEXsS6NGmNwwe5Aym
+bVm+ImiKDhgcFA46C0Rqdwa7LNnX6Qz2haIAMP+U/VBHTAQgc4OrZhVHZ4ULTWdmcM5wb89zVnue
+aQOsi+mjD0UGg+kqqjidwaJ4HHEGO9ou1Y0u2rj9R6uix4loYyIAWZXeNIXTZ9hB3aii59fM8FHQ
+isbFGUwH/PQ8rJk13HvmXtyy75ZQUUhpKSDnnLPPZsX9ptPpB7XWQknTFC7KsDCYKkkYDIj3/vrX
+RSQAdQbvjWghB/1szp5NDwzW4QymudAzM2KiaG0tJc5gBwyeJcdPAeDx48DDPVKRzcooBVcYnJMd
+DKt/Q8eq6811e8xO24oLF/TBYM4pDJYHtn9mvw2DjallKPG2EcDg7RoTQWFw0lEwg0THAlNDJrZl
+MuK6Xl/3hsHveMc78Dprpm+AXvOa1+BCmul5DBprGMwYmwHwJQBXQ3R538s5/1CQ13LOn2KMnQaw
+B8BLfZ7+kt7/pzjnTzv+9g3y+KXwgMEAboKIieAABsVXDFQ+n8fznve8YV9uyzmI9nKK5q3BJOOo
+NzrxOIOzyWYGu+VdjjsM7vu+PWJBlKXkWWkNiS0mIilnsGNgrSsvGFCdwZ28CoN1yj6fEsgMNpiB
+W/bdgi8+8UWY+XOiKNnqEZw4kSwMjjwmItvsy36OrYAcdQa7QDLarnV6tUzPEZKyNCOuw+v2SRi8
+MUvC6UI4g2lblikLmrKxEb2DsA8OesXA7CkAJKN6SziDA0zuKTB47in7ucMuv/TTdEEQQbNjYnrO
+BJBHtSr2d9DkQVAFzr6nx50x0d0hYiIYGK7a4Tf3P1hJxUSEcgb3JiB2HVoFTohfjeQMdgyuAfQV
+zksaBlcqspr6qBoXGEzPN3offeX//UrcffJuvP15b8dHfvIjgbeX1sxgQIDaWq1/GT39DOoBK7FY
+oLDk6P5YcRRxw2B6TM/21p7qcAbTQnTnzgHX6ElC89UgZ7COmIhyWVwfaYHB9ZbcOWcBOeoMpvt6
+zTXAD34gHrvGRORlQ2X1v+jYarWxao/ZaVtx4YLMmo1a7bashZLJy4tq/4y0amdnl0HN4MXF83Yh
+pqFhsDMmgnjjtnJMxDjC4FH6DXNzAgZ7GQ784lIt5V0KyG83jW0BOcZYCcDnADwXArD+Luf8AyE3
+8xmIijhXMcZu9nifH4dw/HIAn3Z5ytcBWKfiLw54r18ij/8x5H5GrsBVuSlUyLRigcHOgXTSBeQA
+jD0MHiYzmOZMjbMzuE0gWdEDBsfpDJ7KT9nvZ2Zlr043DJYOgvidwQDwgv0vkD/svxsA8NRTHk/W
+JAGDNcZEZJp9f0+NM5jsZ6fX/b6wLq/x3bOiXbvlSmJPJ20GVkm5dQR3BmdKYpDS7Ua/5DqoM3iK
+9KTzmbwnNI5KbjA4SqgUGIrSc3NGJDEQDEAAACAASURBVGPtmdoDg+np+lnOYACoLMgbd1THHvS4
+1eiWBlZzwl51bOHYyG2eEhPR7WCRxKFrdwYPyAxWC++K4fTCHjlKGikzONcPg+M6bj9ZA8EoI5bG
+EQZbcL7equPuk+L++vEHPo7lzeBfThpgsGG4TzRaTbgT1NJ+UxAYzLl8ntMZ7PUeukXfzwmDDSOY
+2zmIKAyOq1/iJgtyWZ+3XwG1YUTPhVJJXh+rq9EB56Aa6AxueheQoy7Wyy93/72EwcQZ3Guv6f3Y
+ivcB+mGwLtFjMfLy3kVXJmWm1PYpNzd6ATklJqJVS3TFwyiawODBsnKDJwXkRtdYwmDGWA4CzL4Q
+AtLeyTn/7SE2dScAa7T754wxhQz0fv5g78c2gD9zbqCX/ftBCKh8NWPsN1z29wUA3trb169zzu8d
+Yl8jVWBnER00Z0wFBitFoSJUnJnBboPitGQGR9kZD5oZrMDgpjxmncuqS9mSdCprKCDXTllMBGPM
+joqoM9kR0t1JsQccCWQGA8CP7/9x+cOBbwNICgaTmIgQS2i95HQGOxUbDEbwzOAuEw7mizUJi/Yu
+Cpry4hv2Ah2X62TImAgLBgPRA4bAcJB8R7pdwUD8BeSCTO7BEIsxdeUFA9IZDAClWXnjjioqIij8
+z9MM+MUfwYQA8qNGRADqvTNRZ3DGPzN4Zmc0/ZZBMRFAsjDYGghOYLD4/8KmhCmtbgufePATgbeX
+hgJyXiYtL9duWGdwq9VrR+AeEwH0u491y80ZbMVE7NoVneM9LTA4bmdwqaQC0LiLXvrFRHgVkKP7
+6Q+D+53BtB+2Ycq/JwOD3Z3BrKLePNiUoJg5Izf0BKayksWsbZsCctsNBlv3/EYjmaKfW0ljCYMB
+fBLAqyDg6lcB/DVj7NoB/y532wjn/DEAfwQBcp8P4JuMsTcxxm5kjL0JIs7hpt77/CHn/AmP/fkj
+AD/qbeePGGMfYoy9jDF2C2PsfQC+CBHJUQfwa1F9CKOoDw56xAaog8mt5Qw2eBbMZc2yW2YwK8UP
+g9fXZad1VAXNDKbf90o9npgIxhhmrJu+jpgIHiAmgnzn0/lpHJo95Pq8qGRFRdS6F2GFOuruoEoY
+nIwz+Jb9t8gfEnIGr60h+pgIH2fw2lo8HZUwmcHINkURGeL+P7BDtGvXXZPpdwF3ymCNHcrvgsZE
+sII+GBwUDtLvSGlnNYleV6ygv4BcoPt3T3unIwqgdBF1IhVnZUMeFQwW92/ZngeC4Hvusx/SQpbD
+yhkTMT8vo09izQx2OoMdmcHT0wCKETmD8/0F5JwxEUmo3ZbX1VyE3bKxhsE1le58/PsfH7iNcxvn
+0OmKvmGpJIFoUpnBHl00T1AbFgbTLFkvGJx0TES7LeOlooqIANILg+N0BgPxT15RGJzNBncGDwuD
+rQnvpGEwPa9ZTv6wq7JL9lkcBeTaBUExlypLruPzIHLGRNB7w8QZnLyidgYD0dam2I4aVxj8073/
+GYDbADzo8++LA7b1HwF8FILI3AABmu/p/X9D7/d/xTn/T14b4JxvAPgJCCDMAfwKBKT+NoDfg8gK
+XgPws5zzB0MfrQYFdthk1JiI0sLWyQw2PCKz3TKDS/MSmoxSiGWQnDAYQGRgdBh4sNKIBwYDwJz1
+mRbWIi801VFgsP93fv2u64fuhASV5ao3ecOGs7HB4AQygwExiXL1jqvFD7vvB7L12GHwpUtQgK2O
+zGA3xZEb3EXwzGBkTJgmUG3JHtTBXeIaLBaBSkutZDjLD2NhXr0mgjqDkUveGUyPXXfxOEB13OfL
+yTmDCy4wWFfxOACYKcgKXvnp6J3BwZ3g8rjLR++3H0fhDFZiIngH2ax0VCaZGay0Q7k6jh0D1prR
+TGLT+0SaYiJo/yhKZ3A+L9u3cYPB52sqBbjvzH34wbkfuL7+Ew9+Anv+eA9u/qub0eVi5YB1LicV
+E+EFg90iHNptdYlwEBjshIRUFDg7s/91ygmDz54VsUrA9oDBcTuD484Nto4pmxUTh8M4g48ckRPw
+fgXk/JzBsRY97Ynl5DGXciXbENMu0hORo5ER7dewERGAuDcyiP5qrVVDJiPvD1sZBi8syHNku8Hg
+SVTEaBpXGMyH+Oe+IaG3Q8DczwA4BaDZ+/8zAG7nnL/Dd4eEa/i5AN4LAZNXANQAPALgTwA8h3P+
++SGOVYucGbJeGYLOmIi5vfLuNF/UU4JUpzOYQlEvGOyWGZydEi1NMVuMBCC5yQ0GR9XADQNNLtXl
+XVM3DLYBe3ENa+vR9sKDOIP3Te+zv/eXHvKrJzm6aBE5lMU1FV9MRDLOYIDkBmfawJ77cOJErG8v
+PmMaE5GJICbCxxkMxAWDBxdSc+6naQK1tmxgju6TNGVXQYXBS/nDfZXHBzmDi9mifU/paoTBgZ3B
+McdE0OsqV9blDPZf6eEWg6ITBlNncK6SDhic2y/n3+3JqBFEJ1LbXbEv1uA61sxgR9tlMAN5o/e7
+bEPA4AZxBo8wic0Ys8/pNMVE0PMqShjMmHQHjzsMBoC/eeBvXF//9w//PTg47jtzH55ceRJAemGw
+m2vX2WeKyhkMxBsVQY/pwgXgySflz3sjXMiRNhisMzOYfs9pcQZb5zaFwRvmRqDM4KUleW26wWAa
+x5WWmAjFGZxV710HZw8CAMzCaSWO0erHjgKDGWP2sVsrWaz+61aOiTAM+d1OYPBEYTSWMJhzngn5
+71iAbX6Bc/4GzvkBznmp9/8bOOdfCrFfdc75Bzjnt3DOFznn05zzazjn7+GcPzvaUUcrOqhiyHg6
+IemgCkYLuWlxF5rOT2srwhNXZnAgZ7CjgJyuiAggDhjsD//psT9+6XH7sU6AABBHWaaF9Vq0vXDF
+GeyRGTxfmsc//5t/xh+88g/wvlvfF+n7u2lHicJg0TONLyYimcxgoD83+MQJ6YDRLc77YXBczuA4
+Bl4dNnj5vJszuGHVPm0VsWdJHsexBRUG7586rDgBgcHOYNoZbxvJO4OTjImwYHASBeSU+3dPWmMi
+SGZwpixv3LpWuHj1QRZm5XG3Zh+1Hx+dPzryPjhjIgAJGtbXo4MZTomYiMFtVynX+12ujre8RY2B
+GbXvYk2iuMVEbDUYDEgYHHXRyyhlfe65nBxUu8Hgv33wb9Hq9Fsu6YR/tSmuVws4bW7GC0St68YP
+Brdass/g7DNRAOiloDA4zqgI5+f83e/Kx1E6g+fn5eebFAxut+X3F5czuFxOhzPYDQY7YyK8nMGL
+ixJousHg4gwpIJdPR0yEci903LuOzPf6mIwDs8+IxxXZdo0CgwH5GVgrWehnF6frfxSFhcGAjIo4
+fz69x2lPYGTUNjesJjER0WksYfBEo4sOqjIeUBRQYyJm5ltoGOIupCsiAogvM5ghQM5ib8bSNLYC
+DO4dN/eG//TYrQ7LVH7KnsXVJZpnuN6MtlXvUMeky/J5S7cdvQ2/+aLfVICGLinFF2OHwQk6gw+8
+QP6w/26YJnDmTDzvXa322r1stDERrgUnHYpj4OUXE+GE1qYp2zXDnFOcvj92UIXBl+0IB4MB2Z61
+WMqcwXHERJBl9dli9DERgY875xITobOAHHEGG8XknMEVMnLa5KLPsqO8I5K2XYmJ6GWtxpGfK2Ii
+vDODATm5d+BwA699LbDWjCYzGOgfXKchMzgOGDwOzuAdO2RuNYXB1iT+udo5fPGJ/rQ8BQab4npN
+qtiSBcy8Csi5LaV3TkJE6QxOEgbffbd8HKUzmDHpDk4KBlNAuN0ygy0YXG/LnWt1WzBInq5XZvDC
+grw2V1dlPRlrfFyYDu4Mnp+X/bckYHAhW8CROdLHnO/lxVXkzuwsjwiDe5OX1nFbk1ydDiKPItSl
+UWBws5ne47T2a3pa3reG0cQZHJ0mMHibihYU83LIAsB0WUKFl91m2p1HnTA4rszgDAvoDDbaaHCx
+E+MKgzsdkO/bm+K4FRy6dula7Rm6dAnrZnctssJ5gOoM1uVmDysaE1FZEj2+rZ4ZDIhl2jYw2v9t
+ADy23GB7YOvolI4qgxkSvpJtU7dL3DERQZ3BnZygKdmOSlJeeI0Kg6/dGy4mApDtmcmTdwYrmcEx
+x0QYRUEfWq3oXE9Bj7uUizkmgsLWQnIw2O0+pgw+R9CgmAhAH2gI4gy2ftfoiDY+SmewMyYiDZnB
+ccHgNDqsOJcgh55/5zclDH7XLe+yH3/yoU/2bYPCYCc0AZKBwX7OYED2ZYaBwYMygylwSQsMjtIZ
+DEgYfOFCrz2NWW4wWKczOJMR20+LM5hzrjiDAaCdkfdKN2dwpSLOTasPxrkcG1rj41yFOIN9CsgZ
+hmy/44qJcNbqUO7HcxYMjs4Z7BUTAYxPVMQoMBiI/zwPKgqDRxG9509g8GiawOBtKjU71xsOzk7J
+QdVb3nnJHvxsBWewFwTPGBk54Ms2gYJc4zquMDjIcQPug+goiu74SXEtRVxEruPjmExCiyU5ki4v
+ihHNdsgMzhgZ3LT3JvHDzGmgvBwbDLY/34hjIgAClYlzj1Z/jt0ZHCAzePlSx27bClDbtRddoy6n
+v+HI8M7gencDVmx/Us7g3VO77Tb90OyhaHfCRTR+xchL+hCVO7ivIKjh/mUUXZzBWmMiiDOY5+R9
+MxUweD4aGDwoJgLQB0abTQzMDAbk5J4FGxRn8IiFb2lMBOfcBhTA1obBnU68cQlBVavJ/aLn34Wa
+JAA//5yft2t7fObRz6DeUmnpoJgIIF0w2M0Z7JxAH1dnsPO9Tp2Sj3XBYDqZEKdo377Sm5fV6Qy2
+gH9anMHtbtsu1mipk5UfiltmsNX3okDz0iVxP7TOnWw5uDMYkHA8rgJyPKP2u5XIpp4zODsrYfBS
+hdD7IWStZGl2mmh3232f3ThoVBic1tzgqGDwxBkcnSYweJtKgYMeDllAhQpmTtrblGXuESuuzGAv
+ZzAA5KxCLBkTuw9H564ZJDcYHFVnnGYGD/q+k4LBtAo9imuRZUwC/pAsCVFncH5OjGhWV/U6NdKQ
+GQwAVyxeIX+Yfyp+GJyJNiYCIGCGbPtqUqtKNwzuduELyTJGBoz3bvkZE48/IxvWsqGSlKXKIjId
+2R5dtWt4Z3CHt+3sda3OYM48s9B3Vnbio6/7KN550zvx7he8O9qdcBGdZGF5SR+igsFBoejuJbU9
+n85Pa43BodtukwFupJnBmcHZ2IBeZ7ASE8H7YyLS4Ay2liFHmhncG1xzCGcbY/K4t3JMBJDOqAi3
+4nGAjIkwmIHdU7vx01f9NAABgz7/uKxhXW/VleXqVkwEnfSLEyYEzQwGRnMGp7GA3KD3ijImAki+
+iByFNtbEg05nsAWD0+IMdrqCAbWugnUdcC7bVS8YTMfGtICcX2YwINuMel1fLroKg9WJTGVytucM
+ntoVYWYwWQFWM2vKJFdS96uwGgYG0/M8jTC425Xn2wQGp0cTGLxNRauRD8oMpk7Ksxuy57BQjNcZ
+HNUyvaAwOG/0BpOZJg5eQQZUha3gDA4fE6FbqjN4PVIYrMREpMUZTCZTsjNyRKPTiZMGZzAAHJsn
+9Tznn0jUGezmrhtGbs7gq66Sf9cdEyHac//zPAO5n0+cko3LdFZt1xhj+LE94rqfL+zAjvKOoZ3B
+ACKf3LKkrnDxbs8B4Bdv+EX895/479gzvSfanXCR8x5mKSqoRGN/ADW6QNkPRwCnzrxgQJ3Uaxt6
+YyIYvOG/1pgII5mYiDCZwe1uG+1uG2sN8cEzsJELJ9Jz2hkVsbycTJQC7R9FDYMrJE1mHGHwUnkJ
+BjNwx3V32H+jURHUFQxIWHSUGPYeeyzCHR4gzuVEuFdmsBsMHtUZ7IyJSEtmsKVSSQUeUShpGEz7
+ANax6XAGW9+z9R1PT8v3SdIZ7A6D+2MiqlV5TXjBYDpOMgqS6Lo6g1vuzmBAHxyn53XXUCcyD84e
+BEMvfrDnDC4uRh8TAYic+7RDUjdtRWcwvZdOYHB6NIHB21SqMzgYHDxXk0QjzszgTie6jlnQuIRi
+TjqDn3NzPM5gJctSa2Zw+pzByhLWiGMiuizdzmBWliMancuX3DKDo3LGhpG6POxJnDgRz/tqjYlw
+cQbv2SM7K7oHXUGdohlYk1wmTpyVhG7GZQn5n7/2T/D6K1+Pj/3UX4ExlkoYHLQ9j1vOe5glHc7g
+QQVBncBQZ14woMZEtJheGBz2PjbuMRFhnMGAgA6WM3i2OOsJzoNKcVo5isiZZjLAdOIMFrK+B865
+DYMtmPKKI6+w+xt3/eguG/o6YbAVE3HllfJ3jzyiY8/7RVdEhYmJGDUzOC0xEV4weN++0QosuYnC
+4DhqGTiVlDOYrmSI2xlsnd9eMNhk/TERzuJx9H+g3xnMCsQZ7JMZDMQDgynY7xrqRGY+k8f+mf3i
+F/NPAgBys6SA3KjO4LzqDKYO+7iKVo+qrQiD6TkbJQyOqo+5XTWBwdtUQR1VFJ6d24gHBiuDnJ6r
+Kio4GNQZXMiKweT8kolXvjYeGJwxMhIi6HQGB4T/ADBfnMfuqd0ez45OijNYZ0xESpzBFAZ3i3JE
+o3P5ktMZXMwWRwYEw8gJg+N3BoselsEMT2gaVm7O4Pl5OfDSPehqt6Esn/ea9LCdwZkmTl6QjctC
+ub9de+GBF+LTP/dpvP6q14vnhI2JyMmBSHk+eWdwnKL3sG42+piIoMftbM915gUDakxEk6cMBuuI
+iejGFxMRJjMYENDBygxW7q9Dyjm4BtTjTmLpLT2vonZQjiMMrppVNDviHLFgStbI4o1XvxGAiA+5
+60d3AXCBwb2YiCtIitOjj+rY835RAJiWmIgkM4MtRR0RASTvDKZjGp3OYOtcoN8xzcmNcyWDnzPY
+RL8zmLanQWIieM7HGZw0DGb9E5n2BG35IpCvwqzIwUCUMREb5oZyLZ0+PdKmY9MEBg/WpIBcdJrA
+4G2qdhu2U3Rwdi6JiaiRmAiNMJgxJgc1vXzTqOBg4JiI3mDSyDVR68QDgwFy89YCg/1jQZwDzOt2
+XufpOotSSmZwIVoY3EmhM3g6P21DBTMXNwwW15TiXoxRThj87LPRuUEGyekMLmQKkZ3bNvzLqDB4
+1y7xeGNDL1AIWkgtS5zBZ1clSdkx5Q+LRnEGzyz23GgRO98pBE8TDKb3sK6h1xkcJvYnTmdwrV21
+wU6kmcHW/RvebbnzPsbAcHD2YCT7QCM53JzButrwsM7gequuOINHVTnrHRMBJFNELi5nsK5MzVFE
+P28L7FiuYECFKTQq4lM//BQA75iImRmxqgVILwzeagXkBjmDo1bSMNgtJiJqZ3CnI7dDo0DoSoYo
+Vx8OEucqDKY53Zaa6HcG076SGwy+eNEBg7P9BeRKuZIdxZAEDKbndcfon8hUJmh3/hAXsvcDAK5Z
+umbkCDvnSpZxhMG0DZrA4H5NYHB0msDgbSoVigaMiYjJGQyQfDqdzuABrkDL6Wd2zEiLsPhJLwwO
+7wyOIyICcMZERJsZzFPoDGaM2e7gpiFHNHE6g5PICwbEd71Ysnq3T6DbBZ59Vv/72oOQHlCJMiLD
+hk9ZE4CwnCwsxLckU1zf/ud5lkkH8/l12bjsmvVv15zO4DAwuEKcwVE6ctLqDAbk9dUxNDuDQxQE
+1Z0ZXM6V7dUGVbNqd9Z1OIODTOZa2jezzzVjdxglW0AuWGYwIIrHmR1BFqLotyjO4Fa/M3irwWDa
+3iUBzfzk5gy+UJNUZ6ksac+LD77YXuH15Se+DM65pzMYkHn3y8t6o6ssUQehV2YwhSFROYPTnhm8
+FWGwW0xE1M5geh7Q75gC0Ljaq46M9vd0Bje5BLVuEx1BYiI6GdEmMzC7b2sww263vQrIAZrvWdb+
+oWHvk3UPpTD4mjd9El2Ie/utB24d+b2VzGCzZk9wAeMDgyfO4MHK5WS2/wQGj6YJDN6mUhw2g5zB
+xEl5ZkMG7eiGwfagppe3GB0M5rZDNhtgMNnsNLcEDA76fTsH0XEUjwM0x0Qw/+rzScgCojUue2Jx
+ZgZTcBC3bHfwzEkg04wlKsL+bHvu3UhhMAUzGdEDpjERQBwwWDqDvRzwOSaz0M8RknJ4T3hnsG9M
+BOmMl+Y27P2M0mWnTnKl59oG5PXVYtEXkAsal+B0yOqOiWBMFipbb65rhcFhIHhUERGAewG52Vl5
+PegtIOfjDM7I39GCv5HEROTSFxMR5cDSKVpI7Yknot12FHKDwV7O4IyRsftytVYNa801T2cwoOYG
+x+EOHjYmYhhn8KDMYDfgHIfijImwVisBW9cZ7AWDaXsVV26w89x2h8GDM4OtvhftgzlhcMsQg6bp
+wrSy4s26HycdE9FG/4o8muO/vOcT9uNbD44Og52Tl+WyPN+ShMGXLgF///fBxvbDwOCpKdlWph0G
+09U3w8r6Tq3P8wMfAF78YuCee0bf9nbSBAaPmaKNSwgXE3G6KlvQqJZcekmXM9hs0wrs/oNos2Mq
+neb4YPAmwDrROoOtAnKpdwZHC4N5CmMiAJkbbPI60Mv70jmgtjsWCTuDAQKDGQfmno4XBlsxERE5
+BQEHdOu59xYW4ht4OTODPWMiDCsmogkUZONybK9/uzY1pQ7awjiDizNyIBJlbrAaG5AuGGxdX23o
+ionwj/2JOyYCkFER1abqDI7CEa44g0Mcd1TF4wD3AnKGIQfqWl1WfpnBZIKPwuConcFpiYmw+oWl
+EpCN+PK/7DL5+PHHo912FAoDgwF1IuhM9YxnATkg/iJyw8REtNv995IoYyK83LpRq9NR3aNUOpzB
+lYqcOJk4g/UrCAyud4LBYKczmELNOsT17DRqJQmDlZgI1m/CoJO0tO2KBAY7MoMBObly+nS8mdFU
+b34z8KY3Ab/wC/7PtT6/TMa/v22JMekOTiMMpufsztFioQFIGLy2Jq6Z974X+MY3gD/8w9G3vZ00
+gcFjJq9Ow1DbsRw2RnA4CAhHo24Y7MwMjg4G++dqAupxP7P2jP1YdzE1ClCQ29QSExHKGbxzvJ3B
+nAM8wPL5JKS49GZOAYghJoJ1gZzojCaVGQz05wafOKH/PZ0wWJ8zWPTg5ubiW5IZNCYiZ5A4iyJd
+8eDvHGRMHYyEcQYXpvTA4KAO2SRkwWCTb5+YCEAWkauaVcz0ouA7nWCgxk/0PA9zH4vSGexWQA6Q
+QC5RZ3BWnzOYTh6mJSbC6hdG4TByKu3OYApwLFg0CAbvmZLrpM9suMBg0x0Gp8UZ7HTtukUOjWNm
+8CDorAMGA7JfkjQM1uUM9ooCSaszuN7tLyDnlhlMi2ReugQ8/LD1E0etmz4YTMF+i/ebMNwmafdO
+78XhucMjv7czJgKQWeibm9EZ68LqW98S/3/zm/7Ptc6FoK5gSxZkXV4Gut1wr9UtOtY74tItO187
+j7d99m248+47A23PuiZqNXE9WMebRH9knDWBwWOmqC5sOogeFJfg5qS8ce+N2ouK2QOPbBNgndhh
+ML1hPbnypP14z/Qet6dHJgUG5zdQrfa+qxGlOsGDwf9dlV22e1W3lOOO0Bnc7UKFZClyBh+YOSB/
+mDkJIAYYTGBCks7gY/PH5A/zT+CZZ7yfG5UkDNYQE+FwBk9PC6canfnW2TlxxkR4tW05g0Cy3jkH
+IPB1Tp2AYZzB2UoMzuC0xUT0Jlva3LRXZegoIDdwhQu5jzEw7Krs8nxuVLKKgW6YG5iZlR2WKKIi
+ho07UiafRpRbATlAgoZaTQ9ICpQZnNXoDE5xTETUERGAcFBa0CyNMNi6n5TLEmoOhMGk73q6ehqX
+Gt4xEVZmMBAPDKbQKGhMhNv5FjYmIg2ZwRQGO2MhdMREAPK8XluLZpIujNxiIjIZObkctTOYAv+0
+OoNr7cHOYGsSPpORnxmFwUZpw86vny/OK9u2+mFmx7Qz5IF4wDg9t9u8v9+9d3pv37361oO3RsIX
+3DLu6fV05ozzFfplmjIq7NIl/9UH1t+LIYcq1rij240n8z2Mnn5aPj50qP/vf/rtP8VH7/8ofv2L
+v46vPfU13+3RWgH33Scf0wmhifw1gcFjpqiWNtDs3KAxEZZu3HNjNDsxQEqeabahBQYPKiBHb1BP
+rYo17DsrO12d0lHKCYOB5AbRcUVEACLTbirXG9EV1iPLmGy1oGappsgZvH9mv/yhB+Z0dlAFDJa9
+5FRkBgPA/JOxdMxFp4jrgcEOZ7BXwQ9dcsZEeE16FAyyn/Nykks5FwdoWBicKel3BqcNBitteVE0
+aFqcwQg4uTe1K5bJMOpCLc3LhjyKNj1oAdi4nMEUBs+TMbiOYiahncE14gwO4Pz3k1tMRFqcwTpg
+MCCjIs6di251WlSyPm/6HZzfDOgMrp7BxU2VptKYiIMHpRstbmewVwE5Z4SD2/k2js5g+j6XX67+
+TTcMBvTWMnCT1TYWCiqMtyYBtmNm8GZrAxb/dCsgR/tdVp/ywgXg+HHx+PBVsnPp5QwG5CSetS8W
+WI7TGUzvUQYzcGhWJYJRFI8DBsdEAMnkBjvHAH4xDqM6g4O8R9yizuCDLgvMHzj3gP34L+75C9/t
+Ubf8vffKxxMYHE4TGDxmisoZ3GzJDWVCxkQ8b8/zotmJAVJci7nNyDrizRZxzwXIDAbkwEd38R3A
+HQZHMbCkmcGDBtFH5o/Yg/nXXPaa0d84hGyIUFyLNh4jQJZqEqIALrvwLIAYnME5eYdMRWYwAMw/
+qd1V1mj0BgjUWeeSuTmsnM5gCwzFCoODOINpez4vJrmmctP20n4/DRsTYRTjcAanZ6IHcKwimRI2
+lLidwfT+HUdeMCCgs6XsnASSUcPg7IDv2+ma1ZUZbLmxAHVQEuU5binpzGC3mIgkM4NNU8IGXTD4
+GFnA8uST3s+LW92u7CsogKsmqc5SZUl5jZIZ7BMTkclIMPn449GsThukYWIi3PoMpukfpZc2GEzd
+gbt3y33asSM8BAqquOKr3GT17eccTZI1CbAdM4OrZtX+rp3OYMbUz8rqg62uynP00NXBYLAzKsKq
+aXH2rJ4MXRUGixPded9yrtqJqfYVywAAIABJREFUIi8YcIfgaYPBftfeVobBe/e6H9ePLv7/7J15
+lBxXfe+/t/dlejZpJGuzdsu2bGMsywtgDDGOMQZjE9ZA2BLiEHjEvBhIDhASII+8kANxFhwgkBBO
+gJCwJc8CYzuYsBgv8m5ZtnZrHY00+0zvXe+P6lv3V9VV3bXc6rqtme85OuqZ7q7qmq66de/nfu/3
+95zx+Pu7v48j00daX0REr41FZ7B/LcLgHpOsBnuuSCvPe4yJ6IYzmOaZJovSYPB8SRx3qk3FETsI
+3o3BdFgwuFrVgJg+AdAuJiKXzGHn7+7ED978A/zB5X8QfMceNJhtwuC0ZBjcjIlgjUTo8SZeRGFw
+eqRLzuAkcQZHmBm8un+1cGkP7wu9Y27AGQJTQouJiEcFg5vnuRZDjNnf2k2fM6ePNtYMuHMFA8AN
+N+j/b95sP6tPZc4/X3jOYNP9opkJLus+Vq9DTO65hMHdmMwEzA5E9InRjozoHz8QPBVPST12OnlO
+ncF0UNINZ7BdTESYmcF2MRH5vBjQdTsmYpZwjbCdwYBaURFTUwJ6mpzBzZiIdDxtFHLkopNTdjB4
+tjILjQwweG5wtYrQC7x6LSBXKjn3lTpBXAoJrMuvo4bB6TTwutfpj1/72vD2GSUM5vf/IXOawYJx
+Bherrfb1mcqMAcOtmcFDQ+ZVWMNm1gsAWLHBHwzmcHRuLpyVD+Lc1lBp2NfqoKt2CqkCLlx+oZR9
+d4qJiAIGW++RYcFgOumhEgwuFsVKBLuIiHKtjIOTB42f61odX9r5pbbbpP0u7pQHFmGwVy3C4B6T
+LGfwfFFMn6fawWDLsvqhzJCUcPdOCssZXCqT415AMLhSE8fdzhENABuHN+LGLTd2PV+XZ00iNY+J
+KQk9QjQ7YnEOydRyDq4ZEJnB8SGRGRzGDH2t1gQpijiD47G4aEeG9uPU6XBL+1qLxwEhxkQkREwE
+HfCECUuoQ5a1KaRm2655KCr2nvfoHa7HHvMWE9EICQarnBlsul8UdBgsK/7GLRRdO7DW+B6uXH2l
+nJ13EC2y2siJYD7ZcUduC8CuHVjrODniR1HFRNDM4GQsaXtMoWYG28REMBZ+4Twn0T6hlwJymqZh
+7/heVOud+xjUGbx3r4cPF7Lo39oOBi/LL2uZ+KaTNMdmjrXA4FqjhnJdkMlu5gZ7zQx2iokAOkMA
+DgqzWcDqDbC6j7shCoMzGeBrX9OzYL/UnoEEUlQwuFYT162TM7hTjqobOcHgKFYyuHIGl52dwVb4
+aweDl65pA4OTzjB4BZm3DSND17iuyerMdqt2rlxzpbTVm6bJywUGg1V1BtPaMOvWtT6/b2IfGpoZ
+cn35kS+bsq6tou0I5WOyVuEtFC3C4B6TLFBULJMlxQn3MRGXrLikK+5Kk2sxUZQ2iC5W3DmD7ZZg
+9nJMRIkcd7tYkChFcw0n5uVUkKOOyRjUgsHL8suMjk+joMNgWmBApowOdkINZzBAloel5jBdG5Pi
+CHGSHQy2c9b5lTkmomSAoUQC6O+3fIYQRONQ4m3Oc7tjdpsXzHXuua3La+1E27J6PBwYXKk2AKbf
+FFWDwab7RUEfeciChG4heCFdwM/e9TN87aav4dYrbpWz8w6i0KmalhsTMV/UgLg3GCwzIsK633rD
+PiYiPBjcrMget5/IohNcFPbJKARrFxMBCMBy6lQ4E5lOojDYrTN4bG4M1379Wmz+28248Vs3mpyw
+dqIwWCVnsB0MbmgNjM3rdkdrXjCgw3zuFj40ecj0HXJRWMSdwUD4MNhNZnC7mAjqhOuUG8xhsd09
+LOrM4HRaj18677zOMUxBFBUMpvcAKwzm0FZGQTs6IWDNJQ47J9cqtzERFIbXauIeQgE2YA+D+5eL
+TpVTATkgQhjcxoSxZYloaK46+ypp+6aTl/y46fGqHhOhaWceDKZ5wXYwmEZE8MnuE7Mn8L1nvue4
+zQGHRU/z893tj/S6FmFwj0nTOmdiuZEpLsFDTEQ38oKBVmewLJBCIbhnZ7AHB51fhQWD5+bdOaKj
+FF3KOlWaltKQU0gWU8wZHGMxwz1YyRw2fh+GY8EYcCjiDAa6mxssYHBIMREOBeQA0ZnvVmZwu0mP
+dBdXPNC2rMrCgcHVursM+ChE7xfxId0ZLDf+hsPg9pN7F591Md7+grd3rWAkXY5eSsp1Bru9j63u
+X21Mdl2x6orgOyaif++ux0Q0Y26cJrLsvuMNQxukLL21c1oBAsRVKnKiQNzKCQYfnDyI6//1enz4
+7g+bYO8jxx/BpV++FPceuBcA8KO9P8JPDravVq5qTIQdDB4vjhuuKjsYDIgJqkNTh2yfp0XkooLB
+fmIi1ohFVtJgsAyHqhtZYyK6oahgMG0XrTERMmGwkzMYEO2VSs7g2cqsyRlM+0idYHAsBqQGnJ3B
+tB6EU0wEEA4cFSYUYsKwGK1etflVeMsFb8H1m67H+7a/T9q+7TKDo4bBXpzBtZqAmQsFBj97Stxo
+fvuFv208/upjX3XcpnVSiatelxM5s1C0CIN7UDIiE4olMqhKuo+J6EZeMGAZ1CSL0gCCWxhsN9jq
+5ZiIWZoR3cYJHqUoDK4lpqR0CqtVKOsMBoQrs5I4bbh2w4CiAgYTZ3CX4JCTzDB4X5dgMHEoOLjr
+/MipgBwgOu/j4/JifqwyO+Cd27VMsnvtGp1sqGghOYNrdMWDYjCY/F0TTRgs69irtYbIgFfsuGlM
+xByTC4Nn593VOhjMDOKut92Fz1/3edz2otuC75jIKSaim87grMNElt0E14de9CEpS2/tYiIAYBVp
+Po60r/UiVU6ZwV946Av40d4f4bO//Cx+9vzPAOiDzJd89SV4fup50zb+4ud/0XYfw8Pie1U9JoJH
+RADOMNhU1NJGtIicajCYAhFrTMRqsrjFLQy2QkJAjczgbmi5qPEZGQxu5wwOagSh54AV+vPrZXKy
+O7DIbUwELaBHjQOdYiI2bgRmqv4yg7sXE+FswkjGk/jGb3wDO966A0NZywxBANlNXmYy4u8XxvF2
+khdncJB2QVUYfIjMQdplBlNn8C3bbsHyvN5Q7Ty203EVjxMMBhZzg71oEQb3oGS4L+ZNUNR9TMS2
+ld2BwVZnsKxBNI1LSLeB4HbO4KhiImQcuykjWlFnsJEZDEgrIqeyMxiwLNFvFplaKM7gjUNkDe7Q
+/lCdGqHHRFicwXYwuNEIp0gH4CUmorVd8xoT4VbxWNw4x4qN8J3BqsVELO9bbix1Y/1yYyKqHjLg
+uy0aEzGjyS0gRwvfdrqPXbX2Ktx6xa0mZ5QMmWIitO7FROgF5Do4gy3RP8vzy/HOi98pZf+mmIiK
+cAZTENdNGNzOGcx153N3AgC+uPOLKNZ0QnTF6iuMokV3778bO4/tbLsfHhVx+LA52zZK0XsldzqO
+zYl1744wuK89DKawaGBAbHv/fn+f0638OIPp5LEXGMyfVyUmwpoZ3A1FBYvovd/JGdxoBIe0bpzB
+QHeKXraDwTy2pdqoIpHRT4Ry2fy5rM5g68/nn2+OBPJTQA7oXkyEzH53OyXjScPMRo+bH/OxY92P
+EfDiDA4Cg+mEgcz+dlB1jIkYFzD4nCXnGCuaThdPmyY7qRZhsBwtwuAelIxBlVuHLI2J6E/3m918
+IcqaGSyrQSu7hMF2mcE9HRNRdAf/oxTNDEZGIgyOdYZkUWlNP1nf2K+PpEOFwSpmBgNdjIkIqYCc
+xRlMO2P0cVhREeaYCI/O4BDbNd6ezVVnDWATljNYVuERWUrEEoazoZ7XJ3pmZ5vfVUBVSVaUas7g
+/nS/cW1N1MJzBqedqFHIopn7UTmDndou6+9vveJWae1cNpEFg14vgsZE0CX6UcFgWkBudG7UeLxj
+7w5omobv7/4+AP2a3PGbO/DhF3/YeM3//cX/bbsfHhXRaJgHs1GK5p3aOYNHciOwkx0Mpqv/aEwE
+IKBh2NAsSAG5/n6Ryw+0h8G1mtiXKjDYmhncDSWTol/STRjsxhkMBI+KaAeDacFFfh1Vq8DDD8uJ
+YLSqBQbXxRc+khfXaSKnX3uVSnsYbHUGB4HBYccmiJgI4gyWuCKvk4z+J5m85DC4VArnPt1O3XIG
+Z7N6vRJAXtFiGaL3z7PPbn2ex0SsLKxEIV3ABSMXGM89dfIp220uwmA5WoTBPSgZF3exLO567aAo
+7ShesuISqVW528nqDJ6clLPEulT15wxOxVNYkl3i8Gp56gYMbnfcUYrGRCA9LeW4q1UIZ7BiDjrA
+6gzWc4PDjYlQxxlsOvbCsVCdwQaAbLNcLYio24ElyriERKvTzntYg2rTpAdzhmQjQ91zBgOiPZut
+zBpOoNAygxWDooAA7dX0qAHrZdy/y+Q+ppozmDFmQKfTZckF5Eitg3REK1yiiomgmcFObReN/ulP
+9+O9l75X2v4ZY8Y9g8ZEUFfm4cPWd4UnJ2fwiVlxzj118ins2LMDByYPAABetu5lGMoO4Z0Xv9OY
+qPmPXf+BPaf3OO6HFpFTJSrCb0yE3eq2NQOC5tOYCECAqGJRTpark4IUkFu61Ax2233OdvEBdvvo
+hqKIiQAE6I/KGRwVDKbOYH4dve1twPbtwG//NqTLCoOLVfHh6HUaz+rXnqYBo2I+qyMM3roVmCiR
+AnLZ3iogF6Z4tBGdvAw7J7mdrP1/+j1bFaRdYEwUVlMRBi9f3npdThQnjAKo5yw5BwCwddlW4/lF
+GByuFmFwD0qGM7hUpoNoZ6fohqENxkzjTVtuCr5jl7JmBstaYk2dwe2ykq0weGVhJRhjwT9AB4UF
+g+dNGdE94AyWGROhsDPYDIO74AxWKDN4ODuMBGteZyHDYNuYCBv3v1/RDu4ffqRsAgjddgbH2ziD
+CznzMSdjSSzNLXV4dXA5wWBZy/NMzmDFoChA4AtrAHm95y+jXaMre5IKxv7wbNLJ8rgBMKXD4Igm
+NU0xEY0uxkRU60BcP36npbYbhjYY1eRvu/I28z1VgvjgmkIFFZzBFAaPzppH2B/40QeMx7wPm0lk
+cOsVtwIANGj4yD0fcdwPbctVKSInMzP47AFh0bLCIgqiwnQHe42JmJ8X99IlS9yDRAoH7DKD43Hh
+qFsoMHhmJlzQT+WmgBwQ/PO0+56tzmBNA+7U02Rw993B9mundjER1MEfz4prj4LZTpnB1Bmcjqdb
+Vvy1g8GFglhVEQYYtcsM7lZMBCByg+lxR1lEztqGzs2Zs++pgrYLqsHgclmc13YREXvGxYTsOcM6
+DL5gmXAGPz32tO12B9p0cRZhsHstwuAelBQYXHHnqMoms3ji957APb91D95/2fuD79ilrM5gQI6j
+jDqDk22O23rD6kbxOCA8GFxUwFHVSabMYJkxES6yVKNSt2Cw0bFQyBnMGMNIugnLCsfOmJiIlWvM
+Zci7BoPjnZ3B1kmuVf2rQl3twduzUq2EgWG9DapW5XXSVI6JACz3jWYmuJzJPVpITb3JPVpEDgXd
+qdntzOCwFGf2MRH9/bojBwgHBpeqnVc15JI5PHrLo7jrbXfhYy/9mPTPwFdH0XzaqJzBdgXkSrUS
+psrm0e/+CRF4e+OWG43H7730vQY0/d7u7+E/n/1P2/3wmAhAHRhsV2Dq1LzoONDl51R2MRFrB0Ql
+H2tMhKow+PhxMaG4dKl7kNjJGUz3cyZnBgPR5AarEBNhdQZPT+tQTsZ+7dQOBtOJeJYR1x6Fwe2c
+wYzphR45DB7ODreYltrBYEDA0TCcwWLcEY0zmMZE8AJkUTqD7fr/TlERMmFwt7OR7fQ8qd1qB4N5
+RAQAbFmqVy89f+R843dOzuB02rndnJuz//2iWrUIg3tQcmAwKUDTYRC9qn8VrtlwjSkrL2xZM4MB
+OSDFtLy2zXHbQZNuiN64Y9lwnMHd/B69yBwTIQcGVyqaK0gWlegSTQ6DQ42JUCgzGADOyjd7Zrlx
+jJ4qt39xAPG2g6XCj4ko183HQTvzYcHgeh3CGdzGIWt1Q4c9yUXbs/5h0TOTFRXhNvs+Kpn+voVw
+YLCKx02hU365PtqR4gwui5F1Ih69M5jC4FhMZJeGkhnccLeqYe3gWvz6xl8PZSXT8j49WmGuOmfk
+MA4Pi8FY1M5gqyuY6tKVl5rutwOZAXz+us8bP79/x/ttYYmKMRG8/czlRKyCaal4ZsjmXZ2dwU4x
+EUC4MNhNZjCNjzhwQDz2AoPpJKQqMDiKzGAgGhjspoAcIBcGW79nqzP46FHxcxhOQrfO4BiBwRRS
+WmEw/butX68fH4XBVplgcLW1feNwdGbG2aXqV/y65sXxALkr8jqJr2Spa3VU6vqHUSkmAggfBtdq
+3XP+t1PH4nGnzcXjAN0gxu9PT489bQB9q5yiIhadwe61CIN7UDJgMI1LoE4XVRSWM7jiEgZbb1gr
++1qz1sIQvXEnJMLgYlltBx0QTgG5Sk0ETavoDF6eXy6uvwHdVhVuTIQ6zmAAWD0orqtjMyFYE5ri
+EDbfH05VY9pelGsROYObcSgJj87gMEXbs/ywGGnI+juoDkVNGZ0FfeSxEGAwdQZnR/TrWk6tg+jv
+Y3Qyta6ZKw7xQUkoMLgezkSWF/GcXUAUamNMREVElRnMlzrTvGCr7GLO3nLBW3DthmsBAIenD+MT
+P/lEy2tWrBCgShVnMD+/TNEkJXHSDWbsR8d2mcHUGaxCTIRTZjBjAorQQf6VV8qLiQAEDC6HNzdt
+UtQxEcDCdgZTIFguy6lNQ9UOBi/JkQss7c4ZnEwCN9+sP3772/X+Js9wt+YFA+6dwdb9yhA/txPp
+iDKDmzERgMgNpjA4DDe0k4pF+wkmvzD40OQhfPp/Pu3omKXxCSpERRw6JB6vXdv6/LOniTN4yRbj
+MY+KmC5P48i0/WwzbUvo32oRBrvXIgzuQXXbGRyFrJnBgCQY7HJZcVTO4FQ8ZRTti2VkwmC1v2/A
+vGQKfSekHHeZ9MTiMfVgcDwWFwO0bjiDFcoMBoC1w6JnNjof3jQ9h4/Z/pBiIto4g7sBgyvVBhDT
+RzFtncEWAL66EF7xOMA8EMkNiIGILGcwdYq2y4CPSqb7RjMmQsaxuy0AG5WoMzg1LNEZXIoeBjs5
+gwEzDJa9NLPcCGciy4tMMJi4cHlUxMyMnP6pG9k6g+fEZ6J5gwBw07mtMJgxhjtuuMO4F9z+wO2G
+y44rFtMdeIDuSJUNi/yI942oS5DCYKes6EKq0DIJvHawN2IiAPvlwK9+dW87gxcSDFahgFw7Z7CM
+fVvlBIMziYwpGk9Liv4RdVFaYTAAfOc7+sTbJz5hXhHQ0RlsA4PDhKOGMzgbzb2LO4MBcexROYOd
+2k8nGNxpxcB7/us9+PhPPo7X/dvrbN+vGgx26wxOxBJYNyhecMFI59xg2pacd554vAiD3WsRBveg
+pDiDXTpko1JozmCXMDiqzGCA3LzT+s1rbs7cofAj6qhS0QkO6H9jw9U4tF8OBK+QZcUKxkQAJDc4
+PwYkSgvLGdwvemanK+H0zGo10RnK9oUUExGxM9jcrjmf5y0xESFPchVSorJTJgQYTLPvVYSiYcVE
+qB6PQZejxwf0EWapZF4O7kemwrcRFQx0KiAHiEFJpSIfJlUbCjiD+wQMpi5cWkSuW+5gu8xgCqh/
+66LfMvo6m4Y3mfIHqTYOb8RbL3wrAN3pvef0npbXcBhcLjsP3rulSkUMcukgmEOhQqrg2LdljJkm
+ahKxhMktbF1GrhoMtkKRF7xAP/cWM4O9K2pnsLXwkwrOYEA+QGoHg2n/CCkxEcO/j7VrxaoHKsbE
+BBydvLKDwdQd28kZLBuOGjA4pHi2TqIgnMcanUXKGUQFg88WyTy+nMGVegU/PfRTAHrhtalSK+3t
+JRjc0BpGAbkNQxuQjIubwNZlW43HTi5oeh+88ELxeBEGu9ciDO5BBYXB1SrQAIGDCmbI2mUGS4HB
+dX/OYLvldWGJ38DoTHEQgKBpQKkHnMHxWBxrCuv0H4b2Y2IyuLWKOoNVhcGm3ODCMZw6Jd9Vpmpm
+ML2uprVwemb02snk3eVuepVbZ3BYA2oTDPYQE2EqYBiCaGc83RcCDC6rDYPDiomgMDidUu+47QrI
+AcH6Lppmhv90wNBNORWQAyzL9iVGRdTrQCMWTtvlRXYxEYC5iFy3coM7xUScu/RcfO66z+Hisy7G
+7a+8vW2G8sYhEQx8eLqVZnMYDJjzaqOQ01J77gx2iojgohM1w9lhE5Bq5wwOayITcJcZDLTC0te8
+Rv+/l53BUWUGLxeXctdhcKEAWOcwZcJg/j3H463nU1+fiCKJ2hlM+0eN5Iz1rdi+vfP2TTA40wqD
+47G40dePKiYinonm3mUXE5FKiQkBWtQsbNH283wyL+kHBj8x+oSRgQwAByZbb0oqw2AKwwHdFcyj
+TmhEBGBe4eMEg+k5fMkl4vEiDHavRRjcgwoKg4tFAExtOBiGM7heB+oNf5nB3YqJAARAacTlwOBe
+gP9cG4Y26A9Sczg5F7yH2gsw2LRUv/8wKhX5VVBVdQZTWFZMHEOt1ubFPkXbjVQ+pJiINs5gupQ3
+rAG1OQ5FnRUPdLCTzMuHwUUCBzMKwuDBzKCYdOmX5ww2QfCEeu05dR82cmKEGWRQUizCKJIIKBIT
+oXUHBlcqAOLRO4Mp5KcuXOoM7jYMzmYFWKKA+qy+s/CByz+AR295FK/a/Kq226JF1J6faiUEqsJg
+u5iITjCY3nOHs8MopAkMthSQ68ZEJuAuMxhohcGvfrX+fxiZwfU6QumPWKVCTMSoc91FqeL3fWvx
+OCAcZ7Ddd8yYgIFROIOLNf3DZRIZ07XXSEiAwTbOYED0w6KKiYhF5Ax2ckVv3qz/f/So/KJ5TqLt
+51ZhdvUFgx86+pDp5wMT6sNgDt6XLgXyefNzf/PA3xiPX3L2S0zPnbf0PDDoE7lOMREf/CBw9dXA
+n/0ZcO654veLMNi9FmFwDyrohV0qQYlBVTvZZQYHBSlejlsFZ3AtNgtAt4gGGVjOz0N5+M91zohw
+6IxV9wfeXonCYAUzgwGLO7OZGyw7KkLVzGCrczIMWEq3mcqGFBPRxhmcSgnnWmgwuOZu0iNKZ3A8
+F3JMhIIOWcaYmEiUGRNhcsiqd9zL8ssQY3r3spKWA4Pn56FEv8VNTAQgFwaXywASCjiD+zo7g7sV
+E8FhMF1GTZ3B1MXcSXR1zuEptZ3BdrmrpVrJcBzaFZGiohM1w9nhtpmiKsdELFsmYJnMmAi6j264
+g1WAwd12BlvzgoHuwWBA5AbbOYO7FRORTWRNrvy6BBjsdO23g8HdiImIJaMpIGcXEwGYgeGzz6Ir
+on3/c87RXeuAPxj84LEHTT/3gjOY3z9ouwPo9+yvPvpVAHrE0e9u+13T89lkFhuHdS6wa2wXGlpr
+aP+FFwL33Qf8yZ+Y2/VFGOxeizC4ByXFGUwGVSpmyIbhDPYLgwczg111URoxEaxhDAADw2DFv2+u
+jdwZDGAcwWGwW0gWpboLg8XdMSp3GZUVBoeRl0w7YYlMOIUsKJyh1aK5+KC6O5nB7lc80CXDYYh2
+xnlBTEAeDKbZ9xkFYTBAzvHMNJCalVMYU/ECsPFYHCM53X5VisuJiVAFBtOVNd2KiahUACSidwY7
+xURE6QwukNhN+pkouO4kkzN4unecwUbBQlI8rmNMhAUGJ2IJ43yyxkRE4Qx2GxNxww16cT8gnJgI
+oPswuJuZwQMD4m/dDRhcLIpj7RYMdvqOuTO4WgV277Z/ryy1zQwmzuB63AxqGQO2beu8/Yli+wJy
+QHTOYCMmgjiDoyogx2MiADMMfuaZ7nwW2n6OjAgouhCcwdWqfc49ANz+q9sNA83vXfp7tvcvHhUx
+X53HwcmDbfe1CIP9aREG96DkwGC1B5PpeNpYGiArM9gLDKY3rG4WjwPMAAUp/eYdZGA5NwclBtFu
+tIHA4JnEvsDbq1AYHFHGZCeZMoPX/wRgdekDL2tmcCaRMZx7Uao/3Y+E1rx7hwSDaXsZS4UUE9HG
+GQyIQfX4uPw8aMB8nifbOODpJNey/LIWp7BsmdoykoEuq3NKncEpBR2ygLWI3DE5MREV9dtzPtEw
+FzsBMN3NEeR7n5sDEKcrPaJ3BlthsGnZfpjO4C4OqKmW5YWth8ZEROEM5st7KQzmzuCB9ICn9n1V
+YZXR3+zkDN4ffI46kOxiIjzBYEtmMCAKfVpjIpJJoL9ff6wCDKZQhOcFA+HB4HLrrVy6osoMZkwA
+qW7AYKd4E64onMFA63g6TGdwPNEwsl6tmcG1mPna27JFXHvt5CUmYr4637KapVAQ14JMZ3C9DjSa
+Jk4WkTPYKSbivPPEa6yTAWGJtp9LlojM7pMnxd+JygkGz5RnsGtsl+m1qjuD6f5Nn6s0hS88/AUA
++tjk1itutX3/BSMiN/je/fe23Rdt12XHLZ7Jip4GLMqzpDuDFcyQZYyJZewRO4O7mRcMyIfBuqNK
+dABU/L65+HIQAChm9gcGZ6aYiIiqz3fS1pGt6E83e32b7gJueB/GxuQSQ6szWIW8YEC/zgdiTWtC
+4VgoA05T5z6k3M12mcGAgMG1WjgZZVW3WegEIoUdEQGY27JqTBy4LPcNdQarCkXNMPioHGdwDxw3
+z5dtoAZk9QHrGRcToXUnJkKVzOB0Im3ARurCHR4WEK0bzuBqVQyWTc7gJqD24goG9GKEHJLaZQYP
+DIg2PGpnsF1MhAkGp9vDYNoeLcnqS1baOQf5qpYwYbDbAnIbml6B/n7gFa8Qv987+xiQ14lmO5jn
+JTMYOLNjIgABg8fG7IGUTDkVPuSiECdsGMydwXYKEwZb23AaE1G1wGA3ERGANxgMwCjUxcWYcAfL
+dAbTa5rC4K4WkKPOYIeYiG45g+mqwOFh4Kxm/H61as82nNqFR44/Ag3m8WEvwWB67f/Dw/+A6bIO
+tN7xgnc4xnFeve5q4/Ef/vgPsXd8r+O+Fp3B/rQIg3tQsmGwqoNJXngnlpbjDPZy3CO5EcMpsnl4
+c7Ade1Q4MFj97xsA1g8CaVKeAAAgAElEQVQKC442uD9wp9DkmFTUGVxIF/Cvr/tXxNH8Xi79Ir52
+5KNS92HNDDaKWimg4SRfRj+FoyflT+XSc6gRD6dT6tYZDIQzqKaTHu3Oc9MkVxdWPNC2rAIBGmR1
+0igUVfX6NnVw++XAYOoMVnVyjy5HR58+yjwTYDCNWepuTET0mcGAiIqg+byMiaiIbjiDZwg34ZnB
+89V5w9lKC9251Zp+/QBG50ZtJ/S4O/jIEQvg6bI6OYM7ZQZftfYqXLLiEgxlhvDWC98KAMZydWtM
+BCBg8MREeMDQbQG5z3wG+PjHgTvvFJMAX3/867j6my8E3r8FSM0EzgxeiDC4VpPbXtmJjt/CdAbX
+auJ8cuMMtirMmAgtbnbImvpHzCcMLnmDwe1yg6emJPbNyHnNktFMZJoyg0lMxPr1op2Jyhl8FrlF
+2UVFOMXHPHTsoZbXHpg4AM3inFIVBtPP9Y2nvgEAYGD40Is+5Pj+a9Zfg7dd9DYA+uqVN/77G23j
++IBFGOxXizC4BzUzE6xTViyiJwqKcfciS3XfGbyisAKfv+7zeNPWN7VtpMJQKDCYfN8qZwYX0gWk
+qs1p+6H9gTuobpfPR61Xn/Nq3LbpXwBNn4C4p/QXHbORvEhVZzAALM8KWHZwXHJoGcwdgkYspJgI
+l85gIJzc4GKJxCUknNu1dYPrDBf6i9e8WP4HsYi2ZWUtXBis6n3MtLKkcExKXnLZZUZ0lDLB4IJ+
+XQeZyLbGHUXVnreLiQi3gFz0zmBAuG5nK7MmlxmPipiZCW5Y6CQKgzkUpLEVXorHcdHc4CPTrfZm
+DoMbDVEZPQrZOSxpbminmIhUPIWH3/MwTtx2AttW6qGk3KFYrpdRrZtJN4fBjUZ4wNBtTMTKlcAn
+Pwm8hBScv/2B2/UH2UngrMcXM4M9aDm5TMKOiujkDJYFg+l7VXMGNywwOB6LG33xCoI7g4cy7QvI
+AZ2LyMlyB1NncFQRRzQmgjqD43G9iBsA7NmjTyCELSdnMNAZBtNJogePiuJxfAKzWCuaVuoAasFg
+eu3zzzU6O4onRp8AAGxbuQ2blzib7hhjuOOGO7BlyRYAwKMnHsWHfmzPZfLiK1+EwR60CIN7VEGW
+GvdCATkAJCZCv7tPTgaD4F5gMAD8wRV/gG+9/ltYO7jW/059yA4GBwEIqjiq3GqgwdcCHsWJ08Gm
+6cs94Azmev2WtwAPvl//gWnYfUrelLU1M9i4thTQyn4Bg49MSi5nDAsMZuEAlXgsbrSjnZzBYcDg
++bI4z9NtRtN9qT788t2/xDd/45uO+VwyRduyUn0WrBkDL6uTVu0BKGqNiZiftwyUPKpWAxqa+sdt
+cmf26aOdM8EZbIqJaHQxJkKBzGDAUkRu1r6IXNjuYFsYTAbDfpzBFAYfnm6fGxxlVETHmIgOMBjQ
+B9d0lUg7WMRhMBBeVIRbGGzV3vG92Hl8p/hFeso1DFYlJoLuo50rOgxxZzAAjI46v06GVILB7ZzB
+YcLgOhMfjvc/jYkYiBm0RAK4+GJ32+cTQQwMA5kB29d0gsFhFJEzw+BoJjJpTIT1uHlucLXanRx4
+3nbm8zrcdYLBd98N/P7vA9/7nvgdhcHcGZxNZPGqza8yfm8tIqcSDLaLibj3gMj+vXbDtR230Zfq
+w7+/4d+N8+eOh+8w9T+46DW/CIPdaxEG96iCOC96JSaCz5g24voVrWnBGjWvMDgqhVNArjcygwFg
+CRO5wbuPHwy0rWo9eieZWy1dCuCUCLOiy3CDqlSCXsSpmd2lkjN47ZDoiR6fDdcZTDvcsv8GvJMS
+iTO4LM7zdLJ9u7Z12Va8+YI3dwUomQYh1VnDjSXNGdwDMNgMRfXOa5D7WK+s7KGFqs6omIhY92Mi
+ymUokRkMmM9nCmBpEbmwc4OpGYLDYHq/9OMM5i4rwD43WBUYHLSAnJ14TATQWkSuGzDYbWawVd9+
++tvmX2Tcw2AnZzAFL910BieTQKzLI3IKg8N2BncrJkJpZzBZmcaj2vj1WmyIm+OFF7p3iXNn8FB2
+yLEodBTOYFPxxZDi2TrJKSYC6H5uMO/38/aUwmB+Pzl8GLjhBuCOO4BdpEYcd7uOzY0ZK0a3rdyG
+TcObxDYsucG5nO6ABqKHwXbO4Lv332387hUbXgE3unD5hbj1ct3EUtfq+MaT32h5TSwmrp1FGOxe
+izC4RxUEButQVP3BJL9ZarGKMfgN4pBdqDBYlUG0Wy1LbTAeP3cq2JRthSx5TCV6AAbPih6CTBhs
+rUavUmbwhmUCBp8qh+sMnqrpI56hzJB0pziHq3bOYDqgDgcGi/M842U0HbKsgxCZMLheB+ouC+dF
+qSU58uVndZoSpD3vlclc2c5ga0yECs7ghZwZDDg7g8OGwR1jIjwWkAMszuCpVmfwBtEtUQYG2zmD
+nZaKtxMtZBW1M9iLO/bfnv438y86OINVzgzudl4w0F0YHIUz2Ok7jioz2C6mjGd8F7VpY5zrNiIC
+EDDYKS8Y8OYMPnrU/b7biU7waBFNZJpiIiwwmDuDgfBzgzVNtJ28Pd22TTx/b9Mku2NHax79r/2a
+cInTvODtK7ebauxYncGMCfAaNQy2OoM1TcPd+3QYnE1k8aI1L3K9rXde/E7j8T899k8tWckApJtO
+FoIWYXCvqXmzCOwsojERijpFTc69ZPAicr0SjxF2ZrCq8IBrVU6Mug5MBoPB5WpvZAYD+uxvohQO
+DC6VYOQFA2o5gzefJWwJE7VwYfBERR/xLMsvc3i1f3FAE4UzmBYUy6bUOc9pZ1w2DO6Vyb2B9IC4
+12T1L38hwGDZmcH6pKZoz1WAwXXNHBNRKMCIQjnTM4MBZ2dwN2MieAE5er/0VUBuoDecwbwPzBjQ
+39/8Xcl9ZrCdaJ/TWkRO1ZiIZ8aeMTInDaWnezYzOGhesF2/o5NUdQYH6R+o7Ayus1YYbLpeM/rA
+/sYb3W27oTWMiaB2k0CdYHAYqzrMMDicWh2dRGMirO1aN53BMzMil5iPBTZtEhOMv/iFvtrlhz8U
+77nzTn2scO+9YsXArjFhF37hWS/E+iECgydbb0qqwGCrM/jZ08/i6Iw+63DV2qs8nRNblm7Blauv
+BAA8efJJPHbisZbXLMJg71qEwb2mJhxcCDERplzTRHAYXCrB5K6JckDVTvTGnSrIdwarCsG51vWL
+mIjDs/sCbWuuJHpiuYw6kMxOjAHDqRBhcEL0klXKDF4zKGwJs5APg43BQaKEuZreIQwFBrdxBocP
+g0lmcEqd9jwZTxqQnMLgubk2b3KpXrmPMcaEaycn3xmsantucmfmg8djqLLChS7FtTqDYzExAFvM
+DA5PnTKDgxaQe366FQavXStAfzcyJp3Ez6uBAQEJAsdEpKKNifADg1tcwYCnmAg3mcFl73zVszhw
+9usMrjfq+M3v/Cb6PtOHv3/w7z2990x0Brv5joedTbTSARI9h+xgMIW4390xgXvu0aMC3GiqNAUN
+ujMyiDP4bNH04dAhd/vuJHrcjZj4oZv3ruHsMBj0Rts6ntqyRTwO2xlM+/y0Pb3uOv3/ahX48Y+F
+Q3hkBHjlK1snTQ5Nii9nw9AGszO4Awy2MdB2TVZnMHcFA+7ygq16xwveYTz+58f+GXOVOfzHrv/A
+3vG9ABZhsB8twuBeUzPeITAM7gGnqNkZrF/VgWFwSlAIOmuokuiNOy0NBqv/fXNtWiKcwcdLwUZd
+8xQGp9WGwQAwkhWD2IXiDKYOwmIiRGdwbsz4XZTO4DAG1CUymlbJGQyI9szqDA7aOe0VZzBAoiIW
+UExEX6rPcIazQnAYrEpMBN23FQYDZOm+bBisSGYwhfz0HrWK1Ek8Jr8ZNymMAnIjuRGjDbeLiUin
+xXJqFWIiTJEkEjODo4iJ8JoZrGmaPQxOT6FUcr638L5AKiXyNK3qtZiIT/3Pp/DNp76JWqOGz/3q
+c57eGxUMHrCpc5ZMiu8k7MzgZNLenRx033aibVU83d4ZvOacCVxzjftt84gIIBgMXr1aTHQ93zoP
+5kv0mubxGHEW7+p9OxVPYWVBb7QPTZkpdy6nT/ABujM4TFhK2006FnjlK8XjP/1TkYV/3XX2+eH0
+GNYOrsVQdggDaf1i2j/ROlbm11m12p22zEm03zcwANxz4B7jZ7d5wVRvuuBNxr36a49/DZv+dhPe
+8O9vwOX/eDnmKnNSTScLRYswuNfEGgDkOoNVdRaZck0lxEToQEy0DioBMSqTM7hPVgE59WNBuDaM
+rARqenDcqbo8GNyXVQuS2WnZUB4o6wOzY9OyYTBxBiuUGVxIFxCr6cdcyx5DrZWvBJIBg/NitDOS
+a7NO0KecnMG7T+3G4frDxs+hx0Sk1YKDdjC40bBUm/ahXoGiALAk2yQq6VkgXlkQMBgg4FBCZrDV
+GSw789uLeJ+p3qi3PEdhsKwBpjXzXZnMYAJglywRea+ycied1KmAnJ/JPsaYERVhFxMBiKiIsTHz
+Z+iWNE30gSkM5jERMRYzgV23ahcTEfaqFkA4g+Nxd0XU9k3sw+5Tup1vVYHMQjSX2TuBDw76nCIi
+gN6Cwffuvxef/OknjZ/3T+w3TQx0UjdhMIWidjAYEPA2bBgMmHODaWaubDchHauzZHtnsJfvDpAH
+g1MpUUROFgw2OYObjugoVrSsHdSJ78m5kyhWzScWj4qYngZOyBtutcjJGfzylwOJZvftySfF7ykk
+puIwOBFLGCYaHhVxeOpwy+Q0vc6ijIqg/d18fxU/OfATAPo47KLlF3ne3mBmEDefdzMAYKo8Zdz7
+x4vjeHz0caN9r1QgfTx5pmoRBveamCRncA8MJu2cwUE6o1Z3JM2zVEn0xp3I6T2oYtH/krVeywwe
+HooBk/oNbgL7bQPi3WqeFNbK9wAMpkXkFoozGACy1WZvvHAs0ISPnexgcNjOYH7O7hrbhYvuuAjX
+f287sEFfGhXGgLpcI4USI4RkdrKDwUDwQVev3McAy0Ate1pC9n1vtOfcoallJoB4+YyIiaD7bucM
+rlTkwSQ9JkI9ZzCFwYwJqBI2DG5XQG4wM+gbOqzp12HwTGUGU6XWk5XmBh886GsXgTQ/Lwa31NnI
+IdJAesAUY+JWqhSQcxsRQXMib9xCAlbT+sDICSbye86ZAINPzJ7AW7/7ViMmgOvR44+63kY6LYDR
+6Gj71wYVHbP29dm/ppswmOYGb9okHsuGwbytymaBSkN8OGsBOQCYKHrrGMiCwYCIijhxQs55Tyf7
+682YiCjuW2sH1hqPrZN8tIhcmLnBtN2k7WmhALz4xebXMgb8+q/bb4fHRKzpX2OYunhURF2rt6xo
+4ZnyQLQwmO57X/FhI4romg3X+LpfAcC7Ln6X7e93je1CnqAd2U7/M1WLMLjXJM0ZrP5g0uRelJUZ
+nFLfGUyXDcVy4oD9Nua9lhk8OAhgXM8NrseKgaBokcDgvpxakMxOy5fDgMEz1SmMjsu5k7VkBivk
+DAaAApoUIT2LQ8dn2r/Yo3jnPtYfMgxuAggNmgGJvvnkN1Ft6Odg/AX/DiAkGFxVA5LZiQ9E5ipz
+yOYaxu8XEgw2YiIAIHd64TiDaXZr/iRmZnRXuB+pGBNhLSAHWJbvS4qKaHEGR5gZnElk0J/WR5k0
+MxgQURHj4+GCtHYF5PxERHCZcoMVLCLnlLvKYbCfiAjA4gyOMDPYLQymheOuOvsq8URa7yR3gsHt
+IGE3YTBdIeO1gNxtP77NmIyh7tKdx3d62g53B3fLGZzPO0d0yIbB7aA/dQZTGCwbHvGxen8/UKq1
+j4lw6ww+NnMMX3/866ZYkCAF5AARmQDIKSJngsFoOoMjWNFCYbA1KqJbReScYiIAkRvMdeml9gUO
+p0pTmCrr7Rt3OwNomxusmjM4HgeOFvcav7981eW+t3nthmvx6Zd/Gr910W/hL1/xl8bvd43tkmo6
+WShahMG9JtYAYrVAMNiatahqbIDszOBiEWZnsKKZwXQJez0remh+j10lR5UbDQ4CmFxn/Hx42n81
+miIprJVXvIAcALz73UBsXgxmX3bDqJT8RdWdwUMJsU7viUNyqw/xzn1ysDvOYEBERezYu8P4HTv7
+5wDCgcEV4gyOcvm8nfhARIOGVF5Mxi0oGJwlRCU7vmBgsAnM9engYsbnXI9K9zHeZ2rnDAbkwWCV
+MoMB8b1SZzDQvdxgqzN4tjKLuaretvgpHsfFncGAfb9jgyhnEAkMpn1Afp5pmhYYBtNoCWtMRKEg
+ljKrCIO3r9qOVLyZT5JpD4PdxERQh27YMJgCMy/O4MdPPI5vPPkNALoj9Ntv+LbxnF8YPDUVbsE8
+PmYttEkxOZOdwYWCGQbzIs4U4vK4F9vtlGfwkbs/gq1f2IpVn1uFt3//7fjxvh8bz6/qX+X4Xi/O
+YEBOVIS5cF6EzmACTmkBNgA45xzxeO9ehCbabneCwddfb78NU14wAdw8JgIADkyoCYP5vgcGgNPz
+p4zfB7lXM8bw0Zd+FP9y87/gbRe9zfj9M6eeWYTBPtSzMJgxNsIYu4Ex9meMsR2MsTHGWKP576s+
+tnc9Y+y7jLHDjLFS8//vMsYc0ltst5FljH2YMfYgY+w0Y2yWMfYMY+yvGGNnd96CS+XGFkZmcHJh
+ZgZnk1lj2V4lJQZbwWCwcDCpCv+5+voAlMWgZrrs/2SnMDiVUBeacL3whcBNrxAAZfeRE7jttuDb
+Vd0Vv3FATNH/197vSN027wzECyEXkCNuvXKtjOMzx/HI8UeM39WGngFypzA+Lr9YhcrOYO4iBIBE
+XlzLQTtppRKAuLi+Vb2PAVYYLNcZrHJ7bnYGBysiNz8PZb5vfo1V69WW57riDI4wMxgQ3+t0edoE
+OGj2ZphREVYYTB3KNMbCqzo5gykwOSx3ztKV6PnEYyLmqnPGpARdcu5F7WIiGBPu4LALyLmFwU+e
+1AM2c8kcNgxtMIootXMGNxrqZQZTYOYFBv/xvX9sxEN87KqP4eq1VxttAu1zuBHNDR4bc35dUFGH
+rJNkwGDar3CbGbxunXAry4RHmibaqqDO4E/c9wn85S//ErvGdpl+n0vm8LaL3oZXn/Nqx/eaYHC1
+Mww+dMj2JZ5EJzpqiDAzuI0zeONG8XjfvvA+Ay1kZo1Iufhi88SEY17wpD0M3jAkZigfPWGOiFEF
+BvP71sAAcIrA4KW5pQ7v8Kaz+s4y7gGLzmB/6lkYDGAUwH8B+DiAVwIYBqA1/7kW0/WPAO4EcBOA
+lQCSzf9vArCDMfZFF9vZBOBxAH8BYBuAQQBZAOcA+N8AnmCM3eDlszkqfzI4DO4Bh6xsZzB1R8ZZ
+QrgJFBQf0JRiws3od2Cp0vJaN4rFgGzM2aniRSUCg5MxtRyTTtq2hbrpTuD++4Ntz1iKmBJ/Rwro
+VNDrNr4daOi3o3unvmjruPMrIyai0D1ncKlWwo/2/qj1RWt+iUoFePBBufuu1NU9z+m5Fs/Jg8HF
+IkzRJ6pNcFAt2JgICuYCFpGbm4My3zevNzBfbT2Jw4DBesyPOs5gU24wAbHdcgZbC8gdnDxo/Hx2
+v3/fBYXB1vxFAFgjjMNSllJ7lV1MBAVIYcREAOHDYO4MTrnoks+UZ7B/Qi8sfOGyCxFjMQxkOAx2
+zgymYLeXYfBPD/4UP9z7QwD6+fre7e9FMp40CjE9d/o5TwaKbhSRs0JRJ3F4W60C9dYEHldy6wx+
+2cv0/1MpPbeVnxMy4VGpJDK+rc5gL5nBs5VZfOXRrwAAGBguX3U5PnbVx3DfO+7D+IfH8fWbv972
+fkj7YDRnmIrGRMhwBptgsKaIM9gCg1etEm1OmDCYnlPWticWA97+dv3xuecC27fbb8PkDCbHdPmq
+y42/6z8/9s+m71cFGKxpYt+Dg+HAYMYYzh85H4A+ictrLQGLMNitehkGAwL+HgLwYwDMxzb+D4B3
+N7ezE8BbAFzW/P+R5u9/hzH2aacNMMb6oMPkTc3XfwnANQBeBOCjAGYA9AP4FmPMe+lEq/pGA13Y
+xSJMYIh2BFUSzTVlKbmZwbmEmgCci8OqEpsA4vpdNZAzmBSQU9lBx5WLExhsMzhxq7LCy+edZF5a
+fQJHj/rvGANkwJEWAwTVYPBlW84GnnsNAGCGHcV/Pvuf0rZtV0BuJG8TyhVQJmdwvWyKiDDUjIr4
+5Cdbn/IrTQOqNXXhoOHaAsCy4sYlBQanBBVS9T4GtBaQCwyDe6QgqF1MRCBnsCLfN28/7aBLGDB4
+chLKZAYDZsc3zfSnMLhbzuC+PmDfhBjJbxzeaPMOd1ozIGjv89OtRIQeXxQw2C4mwgSD0xJiItrA
+4Pn5cACpl5iIp04+ZTzmANToz2SmAGi2MNitY5TC4DBjEwDz39JNZrCmafije//I+PmTL/ukAYK2
+rdhm/J4W2OskCoPDmsCZnxdZ8W5iIgD/7mC3MPi664AHHgCeflp3xXJIJzMzmLZTrpzBZfsbxjee
+/IZxr3nXxe/Cr37nV/jUr30KV6+72tW9YCA9YFwj+8btqWd4MREaatB/iDwz2BITEY+LHPj9+/3X
+M+ikdjAYAD7zGeC//xv4+c9FJI9VdKUKPaYluSV498XvBqCvEvnCQ18wnlMBBheLYkJkYAA4VZQP
+gwEYMBgAivndxuNFGOxOvQyD/wzAawCcpWnaegC/53UDjLHNAP4QOsB9CMBLNE37tqZpOzVN+zaA
+q6ADYgbgQ4yxDQ6b+jCAzc3tfEjTtPdqmnafpmkPaJr2F9CdyzUAOQB/7fVztig/GtwZTAZVdImY
+SqIznbkB/YoOkrdJncHZhLouMsDiXMzpa7cCwWCyvFZleMCVJ+fkdABnsClLVTHHpJOsMLhaDVbp
+2RhwEBhMB38qaO1aAA/9vvHz3z/091K2q2miM9Bo5m/HWKxt5WW/oh3d2cqskedGoVVqkw6Dd+wA
+HnpIzn6rVUCLqTvpYbi2ALCMZGewInCwk0wxEQvJGZyX5wy2wuAoVzTxgXW5Xka5ZiZGQ2SlviwY
+PD4OIzM4GUv6rsAtS/R7pbnB3Y6JyGT0wTN3igLmZbNeZcoMtnEGZ7MCjEbtDObnGYXBfmMi6OTw
+6flW+2/YReS8wGAeEQHozmCATDjG6kBy3hbouS0sprIzeM/4HvzqyK8AAFtHtpqyMi9ZcYnxeOcx
+97nBmzeLx2EV0bJCUSd1EwYzBlx2mcgLDsMZTMfpjs7gTHtnsKZppv7w+y57n+fPwRjD5mH9iz40
+dQiVeqXlNdQZLDUmIi72FYUzOJ/KG/0vqzMYEFERpRJwwn+t8rbqBIOTSeDlLze3s1Y5OYMB4LYX
+3WaYvG5/4HZjxZIKMNi6moU6g00r5gKKwuCZjIhSoREdi3JWz8JgTdP+TNO0HZqmBUk5+iAAPpL6
+X5qmmXr1mqYVAfyv5o+J5utNYowlmq/RADyjadrnrK/RNO1+AF+BDpWvZoxts77Gk/okwOB0DziD
+SWZwthDcGazHY+gtA1/mqaqW5QgMbjoaA8HgHogFoeonMPj0rL+TvVIB6hCXdNRuKreywmAgWD6h
+gMHqxkT09QHDk68ATusd1v8+8N94Ziz4yKRcFvm89Yx+HY3kRkKBKRQG33fwPsPJ8ZpzXoMtS7bo
+n2H5w8ZS9099Ss5+dTio7qQHPde0VHjOYJXbNVOnV3IBOZVXepjasmZmsN9j12Gw6Nmr4AwGWt3B
+YTiDJyZgOINTEecFA+5iIroBg7nLUBYMLqQLhlPPLjMYAFav1v8/ejQ8J5mT7GIiKEDyGxMxlBky
+gIk1kxQIHwZ7yQymxeO4M5hOOCIzZXtv6QRkjLcrDIOfO/2c8fh1573OlBe/baUYVj5ywn1u8IUX
+isdPPun8uiCi41VVYLDTvmXCYGu2uR0MpvcSuwJyvzj8C+Ocv3zV5Sbo70Wbl+h964bWMLWXXAMD
+oj2VGhOhwIoWDk+PTh9tiaDrRm4wPafyPrup1NVMJy0BvYjcmy54EwAdtn71Ub1slgowmO6XZgbn
+kjmpUV/nLT3PeDyZFGPHRWewO/UsDJakG6FD3N2aptn6tDRNewDAs9BB7mttXvJyAPyS+1qbff0z
+eXyz509KJdkZrCoMpg1Fuk+/oqem/C+ZN8VEpNR2BpuyFmXA4B74vqkGsgIGn5r25wyenYUy8MCL
+7GBwkM6ZrTNYwdUA69fFTO7gOx6+I/A2xcBAQzWlX0dh5AUD5o7u93Z/z3j8qs2vwkvOfgkAoI4q
+Ri7WbzX/9V/AI95qvdhKdacojYlopCQXkOuR69taQC74pKYY8ZoKrSom6ZnBFP5HOKEbDQzWyVE2
+4rxgwJ0zOMzMYCcYHGMxU+6vH/H3H5k+gobWSns5DK5Ww8tYdVLHmAifMJgxhguX61Tw+OxxjM2Z
+PTbdcga7yQymMJh/ZnqPQXpaWkyEajC43aTH1pGtxkSwF2fwuefquaUA8NRT7V/rV1aHrJNkw+B2
+0N8qGhMhq8CvFYIXa+LDcRgcj8WN89eugJzJFbzduyuYizuDAWDP6T0tzzMmoiKefz7430DE00Vv
+POOxCnWtjqPT5lnKbsNgL+ckFXcGr+hbYQvVP/LijxiP/+qXf4Vao6YEDHZyBsuMiADMzuBTTExo
+LsJgd1qwMJgxth56kTgA+GmHl/PnVzHG1lqee4nN6+z0MAB+Wr7Y1Yd0UkBnsD6IVh8O0szgVF7c
+RP02asVS3RhQ9SnsIgMswKqZteg3IsM6iFb1+6YaypGCB7P+YPDMDHoiG9uqkdwIGI8/l+kMVriA
+HKBXdMZj7wCqeif5O898B1rAHqnREUjNohHTr/2wYDBdAnffwfsA6MU+rtt4nQGDAeCKN/7cePyV
+rwTfb7EIUwyMajER9FyrJxYzg5E7jVLJP2jopePOJXNi4qlPgjM4qQb8dwuDg8RaUY2Pw3BYqbDC
+xckZnMuJ4w/TGcwLyHGwxDODzx44O3BhYO66qjaqpmMzno+wiFynmAi/MBgALlomypnQKAYgXBis
+aSJTspMzWNM04+olHSQAACAASURBVLOt7l9ttKum/kx6yhYk0uXC7WAwhbJhw2CvmcEUBm8cMmdj
+pxNpA47vPrUbcxV366MzGREVsWtXsPoUTupmTIRb6G8Vh3RGwWUJcuMMBkS8izUmYt/4Pnxn13cA
+6PDsDVvf4PuzmGDweCsMBkRURLkcfKLL+BumRZ/Pb6Z5UJlygy1RETwmBOgODPZyTnKVaiUjm98a
+EcF10fKLcP2m6wHox3j/4fuVgMF0v/0DDSOGSDYMXjOwxjAIjDYWYbBXLVgYDOB88ni346tanz/P
+8pyr7WiaVgewF7rD2LoNb2o6g/1yElpALh1PKwcPuKgzOJkVV7Rvhyyp/N2XVtsZbAJWC9AZvLRf
+2AfG54I4g9XPxrYqGU+KG6UEZ7BdATnVMoOBJgwuDQGHrgYAHJs5ht2nOjXN7WVXPC40Z7DN8u0b
+t9yIkfyICQbPLREwWMZyPGtMhHLOYLKEtxYPJzM4hlgkxUncKpvMisnNrN4Z9j2paY3HUDzyyACH
+AZzBjYZ5EpuBRZI/yNUOBlN37MGDcvY3MQEjMzjK4+aiq1eoMxgQURFHj8pz2FFVq+KeVijoAIUD
+0SAREVzUWXx4unUWljuDgWhhsJ0zmOaPehWPXACAJ0e7B4NJ7dOOMPjI9BHjeHleMGBxBmfsYTB1
+qp91VuvzxtsVdgbTQol25/olZ+kRAho0T0XkLrhAfJ69e12/zbWicgb7iYkA5AGkdgXkqNmJT+JM
+liYNA4Smafj9Hb+PakPv292y7ZZAbT+PiQDsncGA3CJyBgzOyJmsCiIKUK1F5LrpDE4m3UXhWEXz
+6ynYtuotF7zFePzDvT9UAgbTe1aqfwp1TZ9tkg2DYyyG80Z0tDZW22/E8S3CYHdayDCYdOnQqUtH
+e4RrLM/x7cxpmtbJr8u3M8IY809g8yehaf6DselgUkUoxEWXv8YzEmBwrXdyc02FdwLA4Gq12dnu
+MRi8YliclxPzQZzBvXXcXMZgu+8EAE16ZrCKYHzduuaDfdcav7t7/92BtmkHg0dyI4G26STqlkvF
+U7j18lvxtZv05KCNQxsNCP3wyV8ATO8QjQVJvG9Kjw0QbZvMHC4ZouCsEpPsDG46RbOJPjDGgm0w
+ZBm5wTmdpvh1yNLjBtRv14y2LDMFJEq+jls4/PXj7ktF+31T+GSFwUuXAsNNI/izz8rZH80MVsIZ
+7BATAQgYXC4Hq/HgJApY+vosS+cHg8NgmsdolxusCgzmzmCaMxoEtnBXKWCOYgCAZWT+VPYxUxdm
+J1BilxcMWDKDHZzBdGLG6GvYiEZVqAaD+bmejqexorCi5XmaG/zoiUddfw6aGxxGVISKBeSsosv3
+/e7bKjcF5AAxiVNtVA3T0ree+pZRhHhN/xr80Uv+KNBnceMMlgmDjXNbBRjcxhm8fr0ekQGEB4M5
+qwkaEQG0h8HXbbrOeLxjzw709YkIGBWcwfE+UTxONgwGRFSEBg1YouerL8Jgd1rIMJjSkFnHV+mi
+2NU68uLb6bSNTttxr+ZyS79REbSAnMoDSVPhobS4ofheZloVf37VgIlV1L0YK+jft5+BFV0mz6W6
+kwwAVo2Iy3OqKMcZrPK5bpUBUBJlIDMlNTM4m8gquRrAGKDtf4Xxu3v23xNom910Br/1wrfid174
+O/jAZR/Ac+9/Dp9/5eeNQSpjDC9eo6cDTZenMbBJL3AgI3NSn9xTFw5ScFZm8mAwdYrmEmods52M
+3ODsOAAtGAzuoXbNOrEZDAbrxx31MbdzBgPAFr1eJA4fDl7NulhsnusJdZzB2WTWmFDky1e5wi4i
+Z116Td2SG4c32rzDm0zO4Kn2zuAgk7R+xPuAyaQAXbJiIraObDXiqZ44aYbBW7eKx7KLjFXFopaO
+mcE0vsIEg11kBh84IB63g8GMCXewSjBY0zQDBm8Y2mBbBLedu7uduDMYCAcG90IBOQrquuEMpu04
+vW4nS5OYKE7gg3eJevV/96q/C3zPW5JbYkDnTjERAHDokO1LXMvOGWyatOmiaJtudQan06JND9sZ
+7BsGk8/sFBMB6GOb7Su3AwAeH30cx2aOGtebCjBYyxEYnJUPg2kROYzoURGLMNidFjIMpj3qTglB
+5JYN6+2Fb8dNylC77bhX/iTAGr5gsKZZnMEKOgS56CCylBDuE7+NWqlOnMGKA1EKrBKD/p3B1kF0
+Kp5SEgRatWa56PjMVoPDYAamdKElq6xF5GRmBqu6GsAYoJ28EJmafv7fd/A+VOtVx/d0kjEw6AIM
+HsoO4cs3fhm3X3+7bYftytVXGo+z59wPQCYMVneyh4KzshZOTEQ+qTYQBYgzOF4FUrOmgaIX9TQM
+7jvh6/5tvY9FvbLHLQwGgOeeC7aviQkArGHkgqsSh8JXQlhzdWlMRhgweJbYLgqF9kW1/IiCAztn
+sAqZwYODws0mCwbnU3lsGtYDNJ8++TTqDREeu24dkG9ecrJhMAWunYCokzPYlBnsEBPh1hkMCBhM
+YW0Y8pIZfGL2hAETnc7zC5YJqmsF+u3UTRjcrZiIeNzbkvwwYiLaOYPpCg8a7zJRmsCf/+zPjRUX
+N597M27ccqOUz8OjIg5PHTZ9Fq5wnMEkM1iFmIipVsrNoyJOnw4HmgaGwS6dwYBesJqLRkWoEBPR
+SHfHGQxgEQZ71EKGwbQl7FRxgnZRrLcoA7W42Ge77bhXrA5kx31d3NUq0EDNqEau8kCSuk+KseAw
+uNzoHWfwUHYIcRYHALA+/zDYcCUlxfLaXtCK5XGgon9HczV/FngaE5FC3tZJoaqsMHh01P/AxOoM
+VrF4HEBcCVoMhVO6O3imMoMHjj7ge5vCGSzyGMKCwZ105RoBg7VVvwKgX58y4xKA6EGZVdQNUtTC
+KSCnGgC3k+EMBoDc6WAre3hWMosp4RRtJ1Nblh/15QxW7T7mBQYHjYqgecGAGs5gQED+qfKUCSxQ
+ZzDNaZUlqzNYNgxeMyBor11mMD2+qGDwEIkGNmUGZ/1nBgMCsBZrRZPjOhYTwHD/fvieyLKTFe63
+087jOwHopoZzlpxj/N5LTEQ2a469sBMH39Y+9+nTcnOwvTiDO+UFA3q7tH5wPQDgqZNPoaE1XH2O
+jRvF/mXDfqC7MRGnmrxp0CN3DCMmwqmAXCKWMNV2sDqDf3LwJwD0+/vfXP83cj4MRFSEBg37xltt
+sNQZfCZlBi/JLjHG/O1gMBCOO1gqDG7jDAbMMHjHnh2Rw2C630pyEQarqt6hI/JFuzOdRhZ0pGmN
+g+DbcTM6abcdd6oDOAYg9RM8+ugjeOQR+3/Hjx+3fbsVHKjqEuTi7pMZTQYM7h1ncIzFDGil5fRj
+n501L6lzI9WW17rVyAiAst5rLDUCOIObcSiZeG8cN5cVBgP+B546DNaMv4WqqwEKBVGopr5HTlRE
+N2MiOmnbim3GAGB++H7j90Fzg1WPiehL9RnLj4t1ec7guVIFiOvVhwpptY7ZTiYYnD0txRkcdXau
+G9Es7UDO4FgVSOgjzKjv3xQGT5VbD0gmDB4fh5EXDKiRGQyYv9eTc6J97XZMBIXBG4eCx0SsKqwy
+2is7Z3A+L2BsN2FwoyH6vhR08czgZCxpKkrlR7QomzU3+CJhxMXTTwfajUnWDGgnTZYm8dxp3WZ/
+8VkXIxUX/ptOBeQ0TcDgdeuEq9pJHA6dPCkA/B136Hngr3lN+/d6kRcY7HbSg2c/z1ZmcXDyoKvP
+kUgA5zc5yp498uMxuuUMrtVEm7O2PTNrURgxEdZ4jGJNPyjrhB6dxJkoThjf9dqBtVjdvxqy1Ck3
+eMUK3VENBI+JML4/BWAwY8xw1D4/9bxRpI8rTBjcaIi/hZSYiA7O4EtXXmqA1nv234PCoN5nqlTC
+j72xE538L8fDhcHrBtcZJjoMHQBwHIcPO3My/q9ScbOw/8zWQobBtBvXqbWlReOsVgG+nTxjrJPl
+jm9nTNM0f2uf5wF8CcDkG/He927Dtm32/774xS/avr1UgqmQlGrgwCruPplvTBnuGL9Zi2Wo656z
+E4dW1dRJAPrNy+ux9zQMrui9xioLXkAuF1cTgDrJDgb7nak38iaby4xVdQYDYvnmxE4Bg4MUkbMt
+IJcPp4BcJ2WTWVx81sUAgJnMM0YnOWhUhOoxETEWMyYdZ+vynMGzFXHMvQCDh7PD4oecPBisusxt
+mT9n8Pw8lJrw6LozOKGuMxgwR0V0Myair084Jgczg4GdsQCQjCeN4lx2MBgQGZNHjuiD/W5oelq4
+UikM5s7gwcxg4ImhdpmztMiYTPeoW2fww8ceNh5ftvIy03OdnMGjowKEdIqIAIBzzxWP+fX7pS/p
+/995p7zCiH5hcLtJj4uWie/QCvTbiTu/Gw1g927Xb3OlbjmDjxwB6s10ExVgsJMzuAUGk5iI/RP7
+jWtaxkoHKh4DAwB7TrfC4ERCTOYFdQaPjzcf0MzgdDSZwYBw1JZqJdPkJRAuDKYANqgzeCgz1NHA
+F2MxvHLTKwHoKyvrq35hPBeFO5jucx7hwuBELCFW9gweBPBF/OhHzpyM/xuTUcm7x7WQYfAu8vhc
+x1e1Pv+Mn+0wxuIANkKnetZtuFcOwO8C2Ph/8OlP78TOnfb/brnlFtu3W8GBqi5BLpOLrwl0/DRo
+jQZQQ+/ERADi2LVY1chd8toJnZ+HnjeYUmN5rVvl80Csqp+btfhMy0yuG9HM4F4oMEVlB4P95gbr
+hbZEr1Tl1QB8oKZNrsGGfp2oPHDkAVvg4kYqOYMB4IpVV4gfVj0IQBIMbq72YFAzNoDDs7mqRGdw
+VdzH+jPqX99GZjAAZOXERKgG/u1kzQz2HROhUE5yJxi8caOo4h0UrOgwmDiDVckMpjB4TsDgsGMi
+aEG+TK5qAFuZ4ITnBo/OjaJca81n4jC4UhHL0sMWvW7sYiJkuO4oDLZmzlIY/IR7xthRbp3BDx19
+yHi8fdV203OmCW6bAnI0L3j9+s6fyTqZU6sBz5BR2+nTnbfhRl4yg93ERABqFpHrVgE56mb1CoPD
+yAx2KiBn7aPRa5dHoQDyYTDPDAaAveN7bV/D/26nTgX7O/BrhGWjzwwGzI5avsKAK0wYTP+GfmDw
+bGXWKGTaKSKC61WbRFTE1LId4nEEMJjet2br4cJgQHcHAwCyE0DqLbjiCmdOxv+NjERjEFJJic4v
+OTOladoBxtgxACsAXN3h5S9t/n9U0zTr4omfk8dXA3jQYRuXQo+J0AD8wuE1nRUHsBLA0hxGRi7B
+JZd4e7s+kOw9ZzAAoG8UmF7jq0ErlwEkeycmArAsr82PAqVBfzA4UQSYDlNV/76pUijogdyxOkq1
+kucCcJMzFWBQd8P20nEDFhi84W7g9QfxhYMjeGPts55hn74aQPTGe8EZDAAv6LsW+6efRV2r4+59
+d+M3zv8Nz9uzwuB0PB3pBNiVa67E3z30d/oPq+8H9v26pJgIHZRlYnklYwMG0gM4giOYrshzBs/X
+SNxRRv323BwTMb4wncH5UZRK+v24kxOOan4eSuVid4LB6TSwYQOwd69eQE7TOi9Nd5KqmcH0e6XO
+4GXL9KXG9Xo4zmDabpTSzxu5qDIiIrjW9K/Br6Dnuh+ZPoKNw+ZtW4vIdcqglSHa9+PO4IbWwFRJ
+b1NluKLXD61HPpnHXHWuxVUatTP4oWMEBq80w+CWmAgLoPdSPA4wO4N379avY+riNVyPAeXXGbx+
+yJlo85gIwFsRubC+X8B9TETQ3N4gMDiMzGCnAnLWOBd67VIYLLNNAzrHRACtMT+bN9u+rKP4JFmy
+MAm+CD9KGLx1ZKvx+IN3fRC/ePcvjMilbsHgvI9uy/8c+h/UNd3ufvmqy12959qN1xqPp/Ki3YzS
+GZzNAuPlLsJgABgqI5HozMlSKTclv85sLWRnMAD8AAADcC5j7DK7FzDGroDu+NUAfN/mJfcB4JfY
+O9rs613k8fc8f1Kr+kZNDgm36jVncAsQhb8GTXdH9pgzONfqivYKg1VzVHlRholzc2LeOzmZmOut
+ZeRUJoCy8hHggn/DA9rf4Qe7f+B5W9ZoGJWvedqBX1+7wXj85Ue+7Gt7Rqe+ef0syy+LFJZesZo4
+g1frsEFOTITetmViap7nfBnvfHUeiZSe8xu4gFyNtGtJNY+byuQMDlBAbr4sspJ7oT03Zwb7u4fr
+MRHqfN90WbrTqgXuLpybCwZFWzKDVXEGk+/1xOwJ43E8rmdPAuHAYNrvnUq4c0t6FXcGA/ZF5FaT
+YLlu5QZThxWHwdPlaWjNCDEZoCXGYrhgmW4R3T+xHzNl0W9YskREgDzxhLxCam6dwQ8e1X02hVQB
+W5ZuMT3Xl+oTBYJtYiIOHBCP3cBg6gzevbvVKSvLGewHBp/Vd1bb8cum4U3GhJGfmAhAvjOYf8eM
+tQdiMp3Bbr5nqjBjIrJZPYLBjTN49ymxlES2M3goO2RMSruFwX7Fr5F4Xm+4GFikqxLfefE7jZiM
+ncd34kN3f8h4bnAQGG6meKnmDKY1U67dcG2bVwotzS01zqm5pLgo/EZsBhHv6w0MAKfmBQw29Ycl
+at3AOvHD4MHFAnIutdBh8F9DL8kGAH/LGDO10M2feSnPGoDbrRtoZv/+DXSofB5j7A+tr2GMXQng
+3dCB8n2apu20vsaz8qOmGXW3KhbRk5nBAIyBpJ8GrVSC2RncQ5nBAHzDYOsguhcc0Vy5hOg4HB71
+DoOnir21jJxqKDNkyhHjeuqk9156rzqD86PXGpWx79p3l23GWScZMSk5vRMSZUQEAKwfXI+RXHNJ
+0upfAawhNTM4m1Dz+qbnXHZQPxcDw+BGb01ySSsgV++t484kMsK514y88QqDrZOaUd+/s4msUaik
+EwwGguUG90RmMImJAAQ0PHnSe9HbTqLtxiTcFdXyqjX9wvprlxusCgzmERGAPNcdjRl4esxcKY4X
+kZuYkBcB4sYZfHzmOI7O6GTq0pWXCvDbFGNM3GMyUy1GGa/O4HXrAG4We/bZVqdst2HwfHXemHDp
+5BZNxBKGC3LP6T2Yr7q70a5erQMbAHjoIblZ2Hzis69PxOfY6UyLieDHXSgAmqa5ygzmKx0A+TAY
+EFERR6aP2J4bMmDw/DyJQGlmBven+1uu226qkC7g26//tjGZ+rcP/i2++8x3jee5O/jwYfN1GVRB
+YTCvmcLA8PL1L3f9Ph6LMcOOAEzHXHu8D6ECi9+3BgcFDO5P95sKgMqUyRm8CINdq2dhMGPsxYyx
+d/B/AF5Pnt5En2s+3yJN0/YA+Cx0kLsdwC8YY29kjG1jjL0RepzDpdAh7l9qmuY0Z/RZAM81t/NZ
+xtg/MMZexhi7nDH2xwDugh7JUQRwa/CjB9AXAAZTZ7DC+aGA2X3CCgGdwcnecgbbOar8wWB1Cu94
+USElANKhUe82OgqDB7K9c9yAPrj52k1fw5u3vhnxB/638Xun2fx2askMVtgZTAdqzx+M472Xvtf4
++Y6H7/C8vfl5AJkJIKZ3hqKGwYwxXLnmSv2H7CSw5DmpmcE5RWEwXcabGdQb8KCdtFJDHTjoRtYC
+cn6cwZoGlLXegsEAuZfl/U3oqnYfo/CpOzCYOIMT6jmDrTCYAwVNA06cgFTRduO05q6olleZnMFT
+7Z3BfrP8vcpaOA+AqRCSabIpgM5dKjISaDQBEE6UgBtncLuICC4DBqensXu3GWZ6zQyOx4FzztEf
+79kDPPaY+XlZMRFuM4Pp9+AGEPKoCA0ado3t6vBqXYwBL3uZ/nhsDHjgAVdvcyX+HbfLCwaCA1n6
+PasQE8GPu1AAao2aAXrbOYOprPE0MkSjIvaNtyINGTCY5qg3knp/L8qICK4Xrngh/vqVf238/Kf3
+/anxeEPzstK04MXzqILA4OMzxw0D0KUrLzX3ITuI5ws3UAMK+szdzuA2RE+q18U1QJ3BYUVEAIsw
+2K96FgYD+B0A/0T+fbb5ewbgJZbnvtpmOx8F8BXowPdiAN8C8FDz/4ubv/9HTdM+7rQBTdNmAdwA
+HQhr0Eu8/TeA+wH8OfSs4CkAb9A0TU4XKogzuEczg1NDQWFwb2UGh+EMVv37phrICmh57JR3G91M
+RbxnMNc7x831mi2vwTdf/02s2/cZoKE31b5hcI84g2kH/uBB4F0vfJcxk/9Pj/2Ta5cL1/w8TMXj
+RvLRFwowFZFbfX/gzODZYhVI6KlseUXjEug5l+mX4wyuaOrAQTeyFpDz4wzWJ3Z6rz037uOZaSBR
+9AmD1Vrh4gUGBykiNz4OJTODTc7gWXtnMCC/iBx1fY5VQ3IGD6jnDKbHzaHpsRnxx11VWAUZWt0v
+Du7otJkGhVFEzo0zmEdEAMBlq2wT/cSEY3oK09PAfsKxOSTM5/W4Czfi12+1Ctxzj/m5bjuDvcLg
+i5aRQoAeoiJe+1rx+AfeE8kcxSc+vcDgIM7gvj5zkUU3kh0ToWlmCM5dwYCNM9gm73soMxQKQO2U
+GywDBtPro5aQV+BShm7ZdgvOW3oeAD2So97QjSLUiEInFYKKttteYfC9B+41HruNiOCiBfPYkH4P
+e+QRb/sPKlPhyMEaJoo6wFiEweqpl2EwoINXt//sN6DrPdBh7g8AHAVQbv7/AwDXa5p2S8cPoruG
+XwjgI9Bh8gSAOQC7AXwOwEWapv3Q11ESGcssZDmDFXYJAmb3SWJAhzp+YDDN1QR6wxm80GHwUI7A
+4NM+YHBZHPdgTu3zvJ3Wrk4Bk+sA6Mv+NI9hfS2ZwQqvBujvF9ldBw/qnYY3X/BmAPqS2G8++U1P
+27PCYFMOd0QynMEAsOqhwM7gmbI6hbWcRJ3ByYIcZ3AFvdWuDWWGwNDMq/ZZQK5XYbApA71v1F9M
+RFIt+B+ZM1iRzOB8Km9AeaszeCkZ68kCZ1y03ZioCtvxqn45MBRQMzOYQgWeu0ph8MrCSsgQhco8
+moHrIsEYo3MGr7J3Bhs53skSEK8Y4KPREHBn3Tr3hRxpETnrvUp5GLzcHwx+9atFjIMsGNxomB2y
+7RQEBjcawtHp5Xu227cMgFQui4icQgGYKoubnvX+lUlkWgBxGBERgNltfGDiQMvzUmFwooQ6009y
+mrMfpRhjRlRGtVHF8dnjAMKDwUGcwTwiAgBeseEVnt5LYfDK8/RZkqeeMq9GCFu0n5cdnjDy7cOE
+wav6VxkRXhg4tAiDXapnYbCmae/SNC3u8l/CxfZ+pGna6zRNW6NpWrb5/+s0Tfuxh89U1DTtrzRN
+u1zTtCWaphU0TTtf07QPaZomZTGZAYPzJzEz672CgxUMqTCoaifqPuExEZOT3otX9H5msL+YiF4u
+ILeE9BxPTnonJ/NVOunRO8dt1Zo1AMb1zstMZca0NNSNymX0jDMYEJ2yI0eAWg143/b3Gc/9w85/
+8LQtHQYL660KzmDTcub8aGAYPEtgsKqFEuk5l+zTz8VSyX8uYaMB1GK91a7FY3HhjvEZE2GdzFXB
+IetGpuz//GhgZ7AK3zc/p4u1Iqr11mDc5cuFE+5MzAwGxGS91RncLRg8V9dPpHwyLzWDcCQ3YkB3
+O2dwoSDyVc84GEyg+pFp88Gde64eoQDIg8GdnMGapuGhozoMXpZfZspzpqITjkhPGTD4xAmgoi+c
+8VRUbMsW5+dkxUS4hcF0Ob+bOBQeEwEAT550/0WNjAAvfrH+ePfuYO0WFz1vw3QGj46K79lrRAQg
+PyaCTnL095sjXeziyqzO2bBgMN3uvonWmAgZqzqMmIiM/ExzGaJFxg5OHgRgjo850MrIfcsvDNY0
+zSgel01k8aI1L/K0Xx4TAQAjm3UYXKvJLw7ZTrSflx4S2SFhwuBELCFW9jSdwTLzz89U9SwMXqiK
+xZo9sUQZk0Xvo8leywzuS/UZg59GTh9w1Greb9a9mBm80J3BywbEuTk25QMG13rzuK06+2wAp9sv
+7Wona0yE6qsB+ICtXtcH2ttXbTeWdT16/FFPzuhiEaYOqV1Rvm7LtCQwO4GxsWCV2ecqtD1XEw5S
+V0g8L+wCfgddVodsr0BRIyrCZ0yE9f7dK+2aaRImP+bZGdwSE6HAZC6d4LBzBzMm3IXPP+//XB8f
+h5KZwYCA/BOlCZRrgmzRpfiyYTCFS7M1vW2X7TpjjBkDSjtnMCDcwUeOBGu/3YpC0zBhMN2O1Rmc
+TgtIumuXnOKAnZzB+yb2YaKkd3wvW3UZmIPl0zTJnZ428jG95gVzUWewVbLOabeZwQcmBZ1aP9T5
+IJbllxnX5pOj3qi97KgI01LxEGFwkOJxgPyYCHrchQIwNkdMCblWU4K1byozA52KwmBrJjigF04c
+aX68wM7gtLjRKwWDSZQAh8GqOYOfOfWM0b6/dO1LPd/3qTM4t0JcHN2MiqD9vESBwOBseDAYIN9v
+bhxIT3fVDd2rWoTBPaY4qcY5VRtt80p79VpmMGPM6NTUUuJ4vQ4mdXhAllP3ADzIJDKig7sAYfBZ
+wwJajs95JyfFRm8et1VbtsBwBgPA7jEfMJhc873iDAZEp4wPzOta3bTcrpPm56FchzSfzCMZS+o/
+ZCZQLsMXGOSaq4p2TdVCifSci2XFKMnvoIsWzQN65/o2ijxlJzE1U/P8/l49btPgNzfm2Rmsr3BR
+67g7wWBAgDNNA/bu9b4PTWve8xXMDAbM8R+mYmYhwmDaZkxXwsuj5FER0+VpTJVa7zm84FCp1B23
+Vbecwal4yjAiWJ3BgIiKqFaB554Lvj9677NzBj987GHjsVPxOMDiDM7ozmBNM7v8ZDmDux0Twa8t
+BmaO3GkjvhR+bH4Mxap7siobBnf6fqnSaRHv4BUGBykeB8iHwVZn8Nh8+xVq3XIGj+RGjPunHQwG
+RFTEsWP+XJXG9UGdweno+95cdjDYWq9ElvzCYO4KBrznBQNmZ3C9T8DgbhaRo5yG9XXHGQxYcoMX
+oyJcaREG95iMLBQA0zXvlYd6LTMYEEsRK4nTQEwfQPuCwT0WEwEQd3Cfv5gIFQvvuNWKJeLcnPDo
+gtc0oKydn4zJQwAAIABJREFUGTD45puBVRkBg79+Z0BnsOKrAexgMO088Iq0bjQ/D+WWqjHGhDs4
+q1/QQaIiinVxnvdn1by+6UCdZUTj7beT1qvZubQa9Fx9wvNA60xxBp8JBeToOe0Eg2n+op9rfG5O
+XwmlYmYwYCkiR3KDKQw+5b65diWjzYjVjImwMNp1Gkdg5w5+BYlwvPNO6btvUTsYnIqnPFWa7yRe
+RO74zHGjwBKX7CJydo5nqp3HBL24dOWljtsxucPTUxgf1x35FOx4gcH9/cCKFebfJZqBg92OiRgv
+6jsczAyKqMAOopMDdNKgkzZtAs4/X398//16/EIQeXEGMyYc0t12BsvODLY6gzvFRFiLyIUFgxlj
+xrYPTh5sub4Bcd+q1eCrwLFdTIQqmcGAPQzOZMT1rkJMBM36funal3re70huBNmEflJP4pAxydJN
+ZzDt52nZLsJgEgOyWETOnRZhcI8pFhNf2WzDe4+kWERPZQYDZMDBNCCn35m8Diapo4qBKTWgaidR
+hX0KiJcXlDN4zQhxXpW8WSfn5wEke/O4rcrlgL/+EwGDf/b0Hk83dGtOeC85g3kH33BUAjg9796W
+Y4XBqnRIjSWBWb0NDwSDazQzOHpIZifrEl6uQM5gxWID3MiIiQCA7LgJ8LhRz8JgizPYV0yEYo5o
+N85gWtHe670bINBJ8cxgwJwb3A1ncKJPnEQmV6gk0SJydoWWbrhBPP5//0/67lvUDgavLKx0jE/w
+I15Erq7VW4oDyi4ixx2UuZzII6Z65ITo7Fyy4hLH7VidwYAOPvzGRADmqIihIeEG77YzmMdkeAH+
+7QoBdtJNN+n/axqwY4ent7bICkU7iUPZIDDYC/TnCjszuFNMhHVCixZ6ky0Og6uNqq37P2gROVtn
+sAJGDC47GAyI8+bECTnnAOAfBtNr1uR0dSnGmHEPOzxzCJvP0bOMnnhCZGuHLdrPq6UicgYvwmBX
+WoTBPSY6Kzyv+YTBPZQZDFiKzzQdskGcwSmWl9pxDlPm3OAxfwXkFBtEu9VwXpybc1VvMHh2FiYA
+2gvneTu99uq1YJo+UtKG9+B97+vwBqJezQwGZDmD1YqJAIgLJD0DxGq+3Bdcxbr6BeQohNdSwZ3B
++n2s99o1U1Za/qTnInI9C4MDOoOthVBVgP/dgMHGexTPDAacncFhZQZnBsMFDVuWiJyAXWO7Wp7f
+uFHAwvvvl3+cVlEY3NcHlGtlnC7qO5UVEcHFncEAcHTaTIOoM1gGDObOYLu8YE3T8MhxHQavLKxs
+G5FgN+G4c6c5nsUrJKQw+IILxHk9PS0nL5lnWcbjwnVsVUNrYKIYEAZPeyN61PX++OOe3toiKxTt
+JBkw2I8zmGY2y46JKBTMMRG2zmCSGZyIJUzXoGxtGGyfGywPBqvX9wb0z8LbCzsYDOirCmSInkt2
+Kx+cxK/ZZCxpNhF4EI+KmK/O44Lt+pdSqeh5790Q5TSVxCIMVlmLMLjHFCNfWQl+YfCMsS2+jEBl
+UfcJz84NkhmcjqlfPI7LDINHMTPjrRPay85gCnCLdW8weGYGPXvcdkrGk9gw3LS1DO/Frx7QXJ8H
+1sxg1cG4XXaXXxhsLSCnSofUVCwkMxnIGUzjUFRYPm8nOlBvJOU7g3vl+jbBjL5Rz1nRvXrcZmfw
+KZ8F5NSC/12FwYoWv3VyBvf1AclmLHpYzuDMQLig4YJlFxiPnxqzDwXm7uBGA7jrLukfwSSrM/j4
+7HHjZ9kwuJ2r9OyzBdSTERPB20A71+iByQOYLOn373auYKA1JgIA/uVfgPvu03+1bJn5enQjmht8
+4YXmSQ4/17NV3BnczhU8VZqCBt3V5wUG+42JAIDzzhOPd+/29NYWeYmJAILD4HRa/669ijGx77Bj
+IjplBq8dWItEzGF2QIKo6zhMGMyy6vW9Ad01y4Hh81PPG1EZdOWArKgIv85guurDbTSMVbSI3NkX
+dr+IHI0AKrJFGKyyFmFwj4k2CpX4uOfMQZq1mEv09YRD1uQMzgd3BmdiagITO1kH0YC3iIyehsHE
+wVqNzZiW1HXS7Cx69riddE6zIAhSc0D+hOssN+oMziayoXYyZai/XwzaOAw2xUQUA8REhLCc2I9M
++XCZiYAwWC1IZif6d68lZDmDe+/6NsPgE4GdwSo4ZN1oODsMhmZfw0cBORXvY12NiSArO1RpwwBn
+ZzBjwNLmeC8sGJzqD9kZvHSLUaPj6ZNP276mm1ERHAbH40AqZSke1ycZBvcLGmRdRs6YcAc//7z3
+vrhV7ZzBNC9424ptbbdDr4vskP6hDpOo549+VBQnc6urrxaPr7kGGCYsVsZ57QYG87xgwKMzuN9/
+TMTy5cBA888ZFAb/f/bOOz6KMv/jn9mSbUk2jRBCCSV0aSJFBUUUQbHg2ct5p+eJ7X7eKWc9r4h6
+qFc87zzPU0/v7L2CooAiqCCgdAgQSAiQhDTSk23z++PZeZ5ndmd3Z7YkMyEfX76Y3Z3d7CQzzzzP
++/k8n6+WAnJAfDBYFFlfcdAgwBQn2ZBgXUpiIjhnMN+flcQbBFKVF6z0+amAwVJmML96Q0/3LYAB
+Q2/ASyfWlFYlJqp4YHCHr4OOc/jrWKt4GJw9pOuLyPEwuDXQdTC4f2Z/upIWWWVJmbjr6eqFwQaT
+bIbIUa/5psUPJl3W7h9QqZHMHRuMidA6mCQwmPSm7Wb9OGtiKXR5LaBtUKnHQbRayRysac2altL3
+RBg8PIflBiN3L46oNHvwmcF6zwuWJHXKKipIEYt4nMGiKIfBdotdN0usZc5gR0NCMREe6B8O8ued
+15TcAnIm0YI0c1oiX6/LFAqDjxdnsNlkZiDDpT0zmI+JECDoYkUTf043diofEA+D4yk6Re/13HJb
+veSeAyHO4JBsWclFmaqYCGtGakGD3WJHcU4xAGBX7S7FQkszZjC346efBov9pUjSwNrlIlCTh8H9
+MvpFeFd8ihYTAcijIrYrm6ZVyeNh+ZVKoFCKiAC0OYPzB8qvx7POAm67Tfv3mzgR+Pxz4M03SSHf
+ZMefaIXBsn5DDCWSGSwILCKjvDwxp2y8zuCODtKHU6P6etYuxJMXLEmCdalwBkuZwTmOHFjN1rD9
+eYPAsOzU5QUDchhc2lAa9nqynME2tz6dwYC8yJgUFcGfO8lyBvMrOtTCYNlEXwKrPqSYCACw5HU9
+DOaPvclPxm0ChLBiicmWxWRBXlqwAGxWGT75JKU/rkeoFwYbTKEwmJ95USO+gFy6zrNDJcljIuJz
+Bre1i0AaucM7LfoEJkqSzaAFncHHCwy2mW0wiUEXq61Zk3uyp8VEAMDwXA4G56iHwSQ/mfRM9R4R
+IUnqlPn9pDPKZ2aphcEeD1m+Ky0Z1VNnVObwSdAZ7IX+ncF2ix1WExkAeUxJiokITu6lCfo8ZiUl
+2xms17+3kujEZrzO4ODf25Wmj8x/Nc5g3kmYUEwE5wzW04Qe7wyuaqmSvSaBs/b25MAVgLTnNGfV
+lXrQIEVFdPg6FB10Viswdy7ZbmgA1q1LydcAwAbWocXjgNTGRBxqDi8wxcPgRKIi+PGLojO4Ur0z
+mL8usgrY9ZKVBbzwQvxu0bPOAi69lADSZMNg6Vy2R6kJKRWPA+KPidCaGQzI85L37NH8dqp4C8gB
+7PcTS4nmBUtKJgwOdQZLMRFKxeMAYGDmQLo9ps+YxL9AFBW5i+hKnWQ7gz0edux8kU899b8B5SJy
+fExEdzqD+badb4u1ii+CerSznMbebNyo3VAXj/j2vdFLxm3ZjuwuWZ06qmAw2XDW4433m5KS8d6T
+1QuDDSYZDHbWaYbBBIqSN+m12FCoklFArqWDWagdFuM4g+UwWLszOLTwjsPa/Y4qtRIEATYEO/i2
+poScwXrKWYxX8TiDm5qA2lqRZgbrCSREU+hyLf46UBsTQTthQWewnjqjoc7geGFwIAD4zfrPDBYE
+gTq3OpHcmAi7gWFwfM5g/cN/JdFBsK0Fja0dmiKu+ElNvZzj3RUToac2PD0tnd5b+cxgIDVF5PiV
+cGZn6kGDLDf4aPTcYABYujQlXwNA18LgWM7g8ePZdiJF5KJFCPDF4/Jd+TGPkXeH5/Un54YgAE8/
+DQxIUi0ufnInHqd/qFIZE+GwOmg/Q2tmMCCHwYlERcRbQA5QH9eQLBgcb16xkngIbnN2otlDfhFK
+ecEAMHvIbPxi6i9wzfhrcN2k6xL/AlFks9gw0E3gsxIMzs5mExRaYTDf1pucOnYGK8DggQNZlEx3
+wmC+zU0EBvMxEeWN5Zg3j2z7/WTFQ6rFO4PrOsggJ9UREZKG5Q6m2/X+cqxa1SU/1rDqhcEGkyAI
+LAslDmdwa2cHYCLL3TLtxnAJJsMZ3NLJWiW9LqVWklJmcLzOYJfVFXcQfXfJYQ6eo2nxO4NtQrrh
+jltJ8TiD9+0DqURvJutX9QQSoikUBvMZa2qdwW1tAAQ/YCe9cj1llsmWSTnq44bBfPwNoG84KJ17
+HWJyC8jZDJQBn+fMgyB1u5IQE6EXMKpGskGws0b1sft8waXkQQiul3OcX5YeCQZnZDBHYmLOYNLh
+sZgsuojIkCQIAp2sjxQTASQPBvODy64oTjS2z1i6HQkGz5nDtjdsSMnXANC1MDjDlkFrNoRmBgPA
+CYyRp8wZfLDxIJ34ndxvcszVAPz1aM1oxLp1wPr1wBVXxP/9QtXdMRFaYDDA8kaPNB+BqDZzIahk
+FZGLNyYCUA9l+SX9yYiJ8HgSj3zh72/eNOZkkcUecjKbzHjynCfx0kUvdck9ToqKqGuvQ2OHfEAt
+CMwdnAgMho210XobeyjBYJuNHXeyC8gJQvTrnBcf65JIZnD/zP409778WHmXTVxKou27tRWtXvJA
+ZohIofgYEOSU4rXXuuTHGlbGJyTHoexiECTEA4O97A2ZBnEGZ9uz6RLjeDODWzyMOhgJBicSE+H1
+kv/1NojWIpc1CIM1xkQQZzDpjdlNxjtuJQ1yD4JFCF4HKp3B+/aBxsIA8qJ8elYoDHZYHdSBVtem
+bhRGInG45aI6cibInMH2+DODQ52iem7bJBjfHmgEgtXR44fBIj1uh9k417fZZEaWJTgYTDAmwirY
+FLMH9SrZxKZLfVQEPUeCx62X+5gaZ7DJRJapA8mJici0ZeoiIoOXNFlf314vcwenAgbz7YXIFwZN
+UY4y7wzeUaNcRK6wkBTcAoDNm9XnnGoRnRABg6aphMEAcwcfbj4cBhKzskihLoBkBsd7zNGcwVry
+ggH59Xis4ximTQOmTInve0VSMs9pUUw9DJbOi05/p6bCu0BqnMFaYyLUwuB9+9h2cbG69yiJd24m
+6g7mj7vdxAYvkWIiulpDs9QVkTt2TFs/jb8u/FbSRrusLt31VZRgMMDGHjU18snHeCX97pxO9QUs
+k9W2W0wWCpPLG8tx2mlsMvGTT6BpdVY8okVP3axf0FUwONQ89d576mNnjkf1wmADyikEOwRxwOA2
+H7tDpRsEBguCwGZTXeSmqjkz2Mu754wTGSCDwRoLyOl1EK1FNOPW2o6qo+qn6nlnsNNivONWksVk
+QZE7GGqVvV8VDN67F7pdYhxNSlV9pWtBkzPYrs/MMrkzmMDgeDpmRnKKSueeHz7iVkf8MLi5vZOu
+cDHa9Z1rC3aGXdVoatb2R+f/3kY7bvkqF/VF5CorAZh8gIWQE71MeKiBwQCLikhGTIQe2+9TBpxC
+tx9e8zDdTjkMTku9M7g4p5gaESI5gwFgwgTyb10dVGf5axEPJaTBfGVLJQDAYXGkZNWLBBHavG2K
+BRKl3ODGRlLoNR5FcwbzMDhWXjBA+kcSaDjQkCRbX4iSGRPh9TKIHjUzuJ01HFoLL/FLzLVGRQwd
+CliC0Z56dwYnCwbH87MjiT/uNoHN9usGBmerg8GANndwLdc995r1V69DUpY9i95PlWAwII8fiVc8
+DFYrmTM4gZgIgEVF1LfXwyu04KyzyPNHj6a+kJzUvjv6MBgsi/1MoUbmjmQPcvegqQm9heSiqBcG
+G1AuU7BHYm9EU4u2tSztftb7MopLEOCiIlxHASEQBwxmo4h0uz4Gk2rksDoY4NHoDD4kre4zMAzO
+crBz9Eit+pkPPjPYZTBoEk0D3MGq4WmtOFQdm6Tt3QvqkAaMc83zuW8SDJaiIura61QteSQwWJ+Z
+ZaHOYL8/PljEF1ID9H2Ny9x7QUgfLwxubOfzwPV7zErq4wzCYLMPR5u1EQUjw+DQVS5qncGlpdDl
+Oe6yumgRHjUw+Ngx7RM+tE0IXi96irqR9OtTf01Xbfxr478oWEh1TITfmvqJPqvZilF5xCJZUlcC
+j9+juJ8EgwFgy5bkfw8lGCzBvcKMwpS4xWVF5BSiIpJRRC6aa1RWPK4wNgwGQP9W1a3VMoiaLCXz
+nJZcwUAMZ3BHAjER3N9QaxE5q5WB1ZKS+F2EEhQ1meSwNZIiAdkdO4C331b+HhIMdrvlfyOt4oFd
+okXkpHPbbgcaOmPHRHS1huUMo9vJhMH8ddEpkJt8qlZuJCJBEKg7+GDjQfgDxFzAF5FLRlREXDCY
+u1YTXfVRlMXlBh8rx7nnstdSHRUh3bfSsrseBsucwXklANAbFRFFvTDYgMqwsg7B0SZteQntAc4Z
+rJNBlRrRBsTkBxz1mmFwK+cMzrAZxxkMyKuwA+qhUUkJAJOXOqqM9PeWlONiI4SqBvUBm40tHsBC
+Bm4uAx53JPG5m4ePxc4WIDERxnMGu90MooQ6g30BX1T4IikUBusJpMgGdQ5yQceTGxzqDNZzoUTZ
+uRc8J+MdcDV1cG5oi3Em9wCgwMWWydW2V0fZM1wyGKxjF7iSZJnBGmIi9u+HLt3vgiDQczpaeyS5
+CUVR+4qmhgYA5k56D9dj+12QXoA7pt8BAPAGvHjgiwcAAHkc+0+FM9hn6Zq2XYqK8AV82Fu3V3Gf
+iRPZ9ubNyf8OvIPW5SLmhmMd5PhTEREBdE0RuWjO4K3VhDDnOHIwMHOgqs/j3WAldSXxfakoSiYM
+5kG4K0qTloyYCEDuNlQrKSqioyN+l6R0nJmZ6pbJK8Hg+npg5kzg0kuBhQvl+3s87LsVF6tfiq+k
+ZMJgCYJnZABHW7mYiAgF5LpavDO4tKE07PWEYbDJi06RjLv1ZMTgJcFgb8BLV1oorUpMRPHAYGmi
+z21zJ7wSakgWo9sflHzQpTBYat+t2VX0ua6Kici0ZdKfJeTtAQAsW9YbFRFJvTDYgHLzMFijs6gj
+YExnsGw21VWtOTO4w8fu7JkOfQwm1Yo6qhz1gOBXDYP37IFhK89Lystgg9+DVephcEMrD/+Nd9yR
+lOdgI+xjnhqZu0RJJCaCcwbbjHPNS52yQ4dIZmKuU1sROV07gx1yZzCAuHKD+cxgi+iA2WROwrdL
+jWTAxpaYM7iZKwhqlLgjSf3drDNc56mKsme42toDhs2AjzcmorQUur2PqYHB2dylrtX939AAWfut
+RxgMEHewtHLj1W2vYnPV5pTHRHjMpG1PM6fBbomyzj5BqSki19XO4MrmSvo4VTBYizM4XhgcyRns
+C/goEBmWPUy181lyBgPA7toEsg0iyOkE0tLIdqIxEZXsT4iCKHyEh8GyFUUqxBef0hoTASQnN1iC
+omoiIgBlGLxmDWs7n3sO+Owztk9ZGXMLJxIREfqzk+UMzswEalqPw5gInUa08eKLjElRETwMTtQZ
+LIrsPIo24SN/j0gnbhIpHifpkjGX0ALqi79aDI9rP53I27gRqNbmR1AtUWT3LVMG5wxO7xpnMMAm
+B0VXNWBrRGsrsHp1l/14Q6kXBhtQWXYGg2tbtfWyO0VjOoNluTmZh9DUpK1oRXuA9aYzHfp1zymJ
+dh5MAcDRoBEGc44qnWQtalEmBy/3VTSpHlTyy8gz7cY5z2NJ5ipw1sgGFKFqagq6TQ3oDAZYp8zn
+IzmMPAhXUwyFFJDTZ4fUYXEgzRwcVSbJGWyFvq9v2bmXYExEq4eb1DQYDB6QzUb+x7zaYHBLJ1s3
+a7Tj7mnOYICd00qZqpLihcGBgASD2WfrcbktQH4P98+8nz7+18Z/yWBwrbqY95jioahHYHmUqSyq
+xxeR21y1GesPrQ+DwiNHsqX+qXAGh8LgVBePA0KcwQqu0pEjSZQAEH9MRCRncFVLFcRgkVEtQCTV
+MFgQmDs40QkOvu/Wr1/k/aS4C5fVBZslSp6EghKJiQCSA4MlKKqmeBygDIPXr5fv8/Ofs89NVl4w
+kNwCcrwzuKZNfzERuY5cagjbWx++4oGHwYfC54Iiil4XOu178+KLyG08shEAadckLVuWWEFQrxfw
+k/QJ1c7gho4GdPiIfTXRvGAAGN93PP5v6v8BADp8Hbhl6S04dz47qMsuC5qGkiyPh4zdAADpXe8M
+BoARuSPYg1ziDv7ooy778YZSLww2oHK5pUL8rHEsiSLgAT+INpBLkGu0kVWGQACaiud1+hl1yLDp
+ZzCpRqFZi9u2sRtMNIXCYCPBf0myczStGV99pe59je1s0iPbaZzzPJZkrgJXTdRiNbSTbMDMYCB8
+uVZPcgYLgsBcPvYEYXAwT9Um6Pv6ljuDE4uJaPHy9zFjteeDclhnuEnUCIM9xp3kitcZvH8/dJkZ
+DDAY3OZtgy+gXL8hXhhcXR0ciPKTeWn6ncy74cQb6PbW6q0pdwZ3gLTtqW7XeRi85OslmP78dIx/
+ejw+3vMxfd5iAU4I7rZ3b3Kq0PPqDhjMQ1glZ7DVCoweTbZLShBzlZKSIjmDeXCpBYikGgYDyYPB
+fN+tMMqfUBrjaS0eByQvJgKIDwb7/ezcTcQZHAqDDx4E7rmHbKcKBifiDO7sJCAQIMetx5gIQRBo
+21Z2rCwsY3sYixTGxo3qP5deFzqNaOM1Np+t+rhj+R1YsnYJCgtFnHYaeW7XLuCbb+L/fP4cUguD
+U9G2P3jGg3Ryb3npcuSc9gZd4fDVV2SVx/PPJ+VHUfH3rICz6zODAXlskDmfwOCPP04M8PdU9cJg
+AyrXxWBwQ6d6GOzxQAaG9DSoiqVQGAxoy9+TsosAfedqKkk+iK5FXR3w/feR95dUUgI5DDZYoSUg
+BF7amvHll+re19zJjjvLabzjjqRQZ3A0GExnew3uDAaI84ifFIkHBuvNVUcHdw7ShicaE2ET9A1F
+5ZnBiTmD27gMeLfDWNd3YQaDwa3QGBPhNS4Mlk1qqnQGi2K4M1hP/Ra+TWnuVI4xihcGr1gR3DBI
++51hy8Ag9yAAwM6ancjKEml+Z9JhsBBAh9g1RfWGZA+BwyKvfCVCxANfPCArZCrlBosisF05TSJu
+8QPr9HR9OIMBYNT4ZiB3D3y++GBhJGdwvMc3yD2IRoakGgZ3dCTmHlXjDBZFkcJgrXnBAHGhmgUS
+HRVPTATvkozn78vD/nhhsN8PbNhAHufkMKj2z3+SPi4Pg4dzNaPiUbJgcOgkB+8MluJ09KDJ/Vhh
+xh+qfpC91rcvm+z57jv1420WE6FPIwavOUPn4JIxlwAgbfq9K+/FHcvvwA1sXhPPPRf/5/PttloY
+HO9EWDRl2DLwj3P+QR//efsv8foHDXSM1dlJsri1xIHEEt+2++wMBnelM553Bg+aTDLky8uTf3/u
+CeqFwQZUn3TWKWjyqofBocWGDOUSVIDBWnKDPRwM1tMyUzWSO4NJp2L58ujvqa8P3pR16qhSq1Bn
+8BdfqHufkR100RQ6MaAVBhtpNYA0Ow8Ab74pvw7q2mLTBQKD9btUjTqDbS2AyRuXM7i1jWXI2k36
+btdkMD5BZ3Cbj13fRoPB/DK5dos2GNzKHXemwWIibBYb0i1BIuBUB4Orq4PnCJcZrKe4Ix7ORsoN
+5mGwlpzRTz4JbnBtmJ5hMACM6TMGAInNONpeiaxgk5ssGEwH12nNNEYg1e26STDh+knXAyADWen6
+3Vy1GZ+VsvBSPjc42VER3eEMznPmwWoiORCl9aUy8A0QN/znw8cDvxgJTHwxrtzgiM7g5viAiEkw
+UTdYaUMpvH6v9i8VQzkck03kvFYDg9t97ej0E8t1PDDYbDKjXwb58HicwVlZLM941y7Nb4/4942m
+UBi8ezf7nNNPB+6+m73+6afyJe7JzAxubycAVMoj1qIm7lbAZwbnOHJgNVsT+5JJ1In9TqTbm45s
+Cnv9rLPIv36/+qxV6Zpw5ugfBguCgDcueQO/P/339Lknv3sSZ5/fBHewu/rGG9oLv0qKxxksa/uS
+kBks6cJRF2LBqAUAgOrWaiz33Yvt24GrriKv+/3ACy8k7cfJI52spJ+bbc/WHHWTiHgYnDV0D93+
++GOlvY9v9cJgA6rAzWYWm33qRxeHDkFWjMRIcFBynACIyxnsEVmrbDRncGhMBEA6QdFEO0g6dVSp
+VagzeNs2dfmDPDQx4nFHUmjupjoYrP8CREqaMIEtU1yzBvC39JyYCCC0iNyxuGBwUxuzJjks+j7P
++XPPmpGYM7jdzzn/Xfo+7lDxMNiTpg0G88dtxHYtR8r9dtaqun+XSkXOdXof42MbIsFgHh6pdQb7
+/WzC157FPldvqxtCNSZvDN3eVbMraUvqJdH2oosn+f5+zt9xdNFRVN5ZiafOfYo+v+TrJXQ7lUXk
+wmBwS+phsEkw0WXUJXUleHXbq7LXvyr/Cg1iGXkw4qO4YHAkZzDvjtN6fFJUhC/gQ2lDaYy9tStZ
+8Sd83y0SDOaX7scDgwH2+zvaehQev0fz+8cGV9LX1HATVCoVCkXViN9v7155RMS0acAFF7DHK1cy
+Z3B6OpCfoOmQB3a3305g+IIF2j8nFIJLMRF6KR4naXIhcwZ/XxW+3FSCwQC3UiWGpGvClatfIwYv
+k2DC72b9DleNI1Q0IAZQ66nANdeQ19vbgddei++z9RITIenJeU/S/tMzm57Blvpv8MgjoCt4nn8+
+vskPJfFte4eFOIO7sngcQIokSisjOtNL6PO9MDhcvTDYgOrnZp2CVr96GEyqchszM9hmsbGGMQ4Y
+7BWWYlAVAAAgAElEQVT06SxSIx4A5g0mM8zr1kV3Ru+RJsF0OohWKxm8DEacqMkNbu+pMNipPiaC
+Lp/jncEGWg0gCGzWGgA2rNZWQE7vMFg2uHM0xAWDj3GFEh1mfbdr/HJuiysxZ3BHgB13tlPfxx2q
+TFsmBD9Zyuy3V2nKL+vgYLDR7mMAkC/dy5z1qD+mnLHLa79U5FznBeQAdc5gtTD4u++Yi3jUeGPE
+RADMGQyQqIi8YJN97BhXTCYBMRjcte26IAjo4+oDk2DCglELqPv0y7Ivse7QOgCphcH8wNru9OGb
+ChZkmSoYDACLz1hMt3+1/FeyGiVrD65lO7oPxlVELpJzlIfdWt1xqc4N5mGwFqd/qCRnsMkUGWLy
+v+8ce3wwmHdWV7Vom3wESLE2SXfeybJw1YiHwWqdwTNnsmKML78s7+9PmwaMHw/arnzxBaknARBX
+cKJ1JHlgJ5lOPvpIewHMKu7X7MzsRLOHnOh6yQuWNDpvNGxm8stWcgaffjpgJixNFQz2+9k9zp6t
+/8xgXkOyhtDtg40HkxIVEZczOAUxEZIGugfioTMeoo8XfrwQhQO8OPts8risDFi1Kjk/i63iaaH8
+pSuLxwGA1WzF0OyhAICylj0YPYZ0tr/9Nr5Yvp6sXhhsQPXnrCZt0AqDjekMBrioiIwqwNKuCQb7
+BNYq62kwqUa8M3jgCNIr8fvJrHgklUiTYAaHwbIJiyDUVBMVYXQHXSSF5m6qcQbb3cZ0BgPAlVey
+7RUfanMGt7dD10UsaEwEANgb4uqcNLXz8Tf6Ps9lrsascgDxw2A+A95oMRGCIMDmDXaK06s0FZvq
+EI3drvXNYIPh6ubYEzrUGazTuKNUwWDegTd0tHFiIkb3GU23d9bslIEzLXnJkUSvlW5s102CCXed
+ehd9vGQtcQe73SznfsuW5DmsALkz+PvON1F2rAwAMHfY3JReD+eNOA8Xj74YAMk9vftztkZfDoMr
+UuYM1gpEUg2Dkx0TkZ9PChAqiYfB8RSQA+S/P/73qlaXXQacfDLZ3rULeOYZ9e89eJBtF6jkQDk5
+wCUkxhX19QQIAwSan3QS+Xf2bPJcUxObZAqNiBBFEXcuvxMXvn4hqluqoUZFRbGPQ434omNDTmAd
+u67MS1Ujq9mKCQVkFmtv/d6we5jbDUydSrZ37YqdKdvQwIpzpWXo14ihpIGZA+l2RVMFJk4k5xsA
+bNoUX/SPnmIiJN029TaaFb396Ha8tv01Gfh+9tnk/Bzatru6p3icpJF5ZOK2zduGWeeTAbMoxl5d
+fbypFwYbUIXZrDfSoRkGGzMzGAjJDXYfVJ0ZLIqA39QzCshlD2Adi2iNmZIz2IhOstCYCAAxi8j5
+fIDPZNzzPJqsZivrWEVxBjc2splPR5YxM4MB0sGfMoVs79gQRwG5YKEyi2DR3XUvg8FxOoObOo1z
+fRfnFFOY1V70LpC3KwEYzK9w0Q8cVCuHPzgydtWi7ph6q5XH4DA4n3NG7a+uiemKVnQG6+g87woY
+3G8IFxOhswmtUI3O42BwrRwGa3XXKam7nMGhunrc1RSyfVjyIc0ElYrItbZyExlJEIPBIt6pZtEU
+98y4J3k/JIL+Nu9vtA/13A/P4duKb+Hxe7D+MLd+P70ah6s6NTtlJWewxcLcoAADIk6rU/MESFc6
+g+OFwYEAc49GiogAQpzBCcZEAPHlBgsC8Ne/sse/+536dize4m4LF7Jtv5/8O3YsmzDg4wskhcLg
+TZWb8Jd1f8GHJR/i/lX3q/q5J58MPPUU8JvfANddx57XCoPXrOG+13jWsdNbTAQAnFjAcoN/qPwh
+7HX+dx3NgATIrwdLusFgsJuDwY0VAIDrr2evS5MSWpRITIRJMKVk8sBsMuORMx+hj9cfWo8LLgD6
+BE/N995Lzr2a3rPSGQzuamcwAIzIYbnBg09iURHxrGTpyeqFwQZUtsMNiGQ9TKdZfW+ktBSGzQwG
+gMHuwexBVplqZ7DHA8DKOYN1NJhUI94Nas6ohZ2sMMannyLiYFqCwSa7seEBDy9zC8m5u3179CUe
+zc0wtAM+luj54KqRFSHhxXfCrS72uzAiGKdREV4nLCI5+bXGRGSkZUFIdA1hkiXPDG5AXZ32pdQt
+nWySK0Pn57nT6sTdpwadZaYAcOb9ccNgL4zdrqWDdYrLa9TPAnigT4esWvGD4XZEbr8kMRisz+Pm
+QVVjp3KHRGsBuaNHgY0byfb48YBoNU5MRLYjG/3SCd0KdQYnIzeYthe27s2jtFlsNGNShIhVB8ja
+2kmT2D4bNiTv59GB9fBPsL+VWHCn9Z+G04tOT94PiaD+mf3x8OyH6eN/bPgHvq/8Hh2+DvmOmYc1
+u4Ml91h6unyJvwRE+mf013zf5osG6TUmoqaGQc7CKCkfyYDBvLswHmcwQOIZrr46+J3qgcceU/e+
+eIu7zZjB6kVIkhyqgDoYfKDhAN1+c8ebaPXEXoJjMgG33AIsXgzMmsWe1wKDPR6yDB0gTmNzhn6d
+wUBIbnBlYrnBvDnF5DQYDA5xBgPEFW8N1vt79VV2zapVJBgcWoyTlzRhU5BeAIspwpKBBHVS4Ul0
+e0fNDqSlAT/5CXns9QIvvZT4z6DO4HSWmdIdzmD+fuBzsyJye/YA/oDGP2gPVi8MNqDMJjOETtK4
++qzxO4P1NKhSI5kzWAMM7uiAbJmp3hyCsZTtyIZJIJdqQ2ctTg/2/w8dAnbsCN8/EGCdMHe+cf/e
+gBxe5hQwqBkt16isDIY+z2OJAhV7I441exSBGt8JF+wEJjitTphN5i74hsnVZZdxA8U2AsK1FpBz
+2/TXGZU7g0k7rhWY8DA43ab/Sa7bp91OYRFGvwdfwTpNGYSSjJwBDwCZJgaDy+rU5Tj6fEDAYux2
+LbQA5p49kfcFmLvS4dbncfPRJzy44ZWRwXIX1TjqpMJxAHDOOXLIrHcYDLDc4Nq2WthzGAhJBgxW
+jInopqJ6c4bOoduf7/8cADB9Ont93brk/Sx63DOYK/jeGfd22QTnjZNvpPer93a9h6V7lobvlKk9
+KkJyBvN5si2eFuqyj2eZtNPqRJGbrPffXbs7KniJR8mIieAnwaI5gxs6Ei8gNyBzAN1eW7E2yp7R
+9cc/AmlpZPuZZ9RFPPGmBC0wWBCAG2+UPzdtGtseMoT8zyv08/l85GZPM97b/Z76LwBgEFezvLxc
+/fs2bgyOOQGcdhqJV5GkS2dwP+YM3lQZnhs8fToDmStWRDYgAfI2z5TJHKG5zlyFvfUlmTM4CINz
+c4H588lzlZXa83RDYbAoilj02SLYHrJh8erFYft7/V4aaZLsvGBeOY4cumJg+9HtEEURP/0pe331
+6sR/hlJMRHc4g6WYCAA46i+Bw0G2N3heQsYfM/CT93/S5d9Jj+qFwQaV2UM6Bv40dTDY55MgGel9
+2cw2WM3WFH271CheGFxfD7kz2GCZwSbBhFwHuZnWtNZg3jz22t//Hr7/kSPsJuTO06ejSq14Z3BG
+LoPB774b+T179qBnw2AeqDhrFd11/BIYv4X83owAEpRUWAicGOyv+poIDK5rq4s5yGttE2nV+axu
+AgbRJHMGO8igT2tURKuXneeZdv2f5640F353+u/YE2fdg9ZW7YN1PgbGiNd3toV1ig8dUweD29th
++HYttABmSUnkfdva2FJqVzafja2f+7cEnQBgX/0+xX0EgbmD1cBgPgbpnHPk8RPdBT61iI+K6Mzc
+RbeT6gzWQWHQGYNm0OJLn+//HKIoytyL69dHeGMcam0F0O97oIisPx+dNxrnjzw/eT8ghngndLuv
+HX/+9s/hO7krNC+95Z3Bknj3arzF8aSoiMbORlS3qsuLVas8tlAP1XF+tFoYLMsMtseXGTx9wHTa
+7r69823FQmFqNHAgcPnlZLuhgTglY0mCwf36AS6Nzfa11zL4DMhhMBDuDo4GgwHgxc0vavr5PAzW
+4gzmIyJOOw042srFROisgBwAnJB/AtLM5BetBIPT0shxAOS8jVYccy031+C1EZuwzWyj41c9y21z
+0z6VFBMBAD/+MdtHq2OWh8EuF/DC5hfw52//DG/Aiwe/ehDlx+SzDC9sfgEiSH84FXnBvMb2GQuA
+rLKsbq3G6NGgoHTXrihvVCkWE8E5g9O71xm8t34PjaupHP4Q2n3t+N+W/8Ev9jqEe2GwQWX1ERgs
+2o6psrpXVASXIAcHk0bLDgXCYbDazOCqKsiWmTqsjqR+r66Q1ImobavFj38MZAa53vPPA7tDVsLx
+A2xXtrHhQXpaOgQE3S+OBrpE7+OPIzsTejwMDgEqSrnBLE9aRItInAnxOkv0oIHSpH07OQG8AS+t
+0BxJrZ5WwETaxmyH/pzBsr+HPT4Y3OZl7VqmQz+QLJqun3Q9XB3BHtng1dh8eKfmz/CbjX1959oZ
+DD7SpA4G19fD8O2arACmszaqM5hGRACwZ+jzuHnXSbQl6VpgMA+YxoyRw2AjTOhJzmAAaLKxazu5
+MLh7YyIA0o+cMWgGAFJ9fl/9PmRlseXtP/zAHIKJqrUVQOFG+njh5IV0tVhX6acTf0q3233t4Tto
+dAYHAgwG885gKSICiN8dl8rc4MGD2UqlWCsbIomHwamOiXBanfjNab+hj+9deW9cnwMAv/gF237y
+yegu0eZm1pZpcQVLys1lub3DhpG2kBcPgx2O8N9jKAxedWAVDjaqp7oDBrC/sxYY/NVXbHvmTNA8
+cUCfMRFp5jSMyx8HACipLUGLpyVsn/POY9v//a/y5wQCwNdfk+38fKCmk0zqFGYU6i6iTUmCIGCQ
+m8wAVDRVULPJ/PlAVvAW8+670FTwlx+j1pi24dZlt9LHvoCPTqp5/B7cuvRWLPyYhWXPHjw7ziNR
+pxPyT6DbO47ugMnE7l2lpUBnZ2Kfz2IiutcZ3C+9H+0zbq7ajOEjRCC7FMhljXenL8GD7QHqhcEG
+lS0Q7BgIImpbYltkaTGLYGawngZUaiU11AA0OYOrqkBjIqxwdHknOhmSBtGt3lY4M9txdzB60+8H
+7g+pjcB3UG2Z+hxEq5VJMNFJgD31JVhwESnR3dYmL7LD6/iCwbVhMPjIETIQBYAJ0xrRERy4xeuy
+0YNoR79NfRG5Zi+bLcpx6g8GhxaQA+KAwX52nrudxoDBVrMVw1qupY93Ho1iD1WQKMrjEvTkFFWr
+fCfrFFe3qIPBofE3Rjzu0JiIaM5gHgZbXfotICcBKzUwuLExdu5gE1eHLjOTxURYTVbqRNWzeBhc
+Z0ouDJYG4nrJozxrKCNSK/aTQE0pKsLrja8CvZJaWgBksQxUHnZ2lSb3m0zdZJJkg3t3BbZvJ1BI
+jXhQInMGc0XO4oXB/Dm4/lASLdogy72LggsCdu1iQPSll4A5c+TuyEji+2ypLiAHkMmDIVkkV+Hz
+/Z9j5f4YlcAiaMoUdn5v2xZ9OXm8ERG8nngC+PBD8ju1hMSnzuZY2fDh8sxpAKhqld9XRYh4aYt6
+a2daGvvbqIXBfr8ciI4Yof+YCIBFRYgQsbkqvNG68kpW4PGll4J1eEK0YwfomPzkme004iTVDtdk
+SsoN7vB10LokNhuJqgPI/ef999V/Hm3jrK34a8VlYTnrz33/HCqbK3HpW5finxv/SZ+/5aRbcNNJ
+N8V9HGrEw+DtR7cDYBMufr88ajAeUWjOxUR0R2awIAiYOWgmADLR6Bq5Dhguhwed/l4YbDwq1isA
+gENkyy4Oq6hiQGGw5Aw2YCEpm8WGwvQgFcoqU13RlsBg0irbTMbKC5bEO6pq22px++2so/Luu/Ks
+Jn6AbXUaH4qO60tmrVs8LZh5fhl9/u23lfcPhcFGdMFHUyhQCYXBy5ax7VPmsheNDIPpgKmNtXt1
+bdHpQouPy5W06W95dWgBOSB6YUQltfuZTSHbaZzrO0cYSrf3c4Ve1IjPgBcCaYaLOwKAfhxEqWlX
+B4PLy2H4Sa7QVQ1qncEmu34z/yUwV9deF3GCii8iF2sSW4LBNhv5X3IGu+1uQziseBBX6U1NTITZ
+pY+2XSk3mF/Onqzc4NZWANmsnRySPSTyzimSIAgydzAAXD72cvYgswItLcABlc15M7ewh3cGJyMm
+YvYQRgo/2RfBNZCAJGjS3AwcPkxcdAsXkkzVn/0sumMW6NrMYICMnRafwTJK71l5T9xZyv/3f2xb
+KaZOEg+DpaXZWmW3A+efDxQoGArz8oC77iIZzosWhb8uOYOF4H8A8OKWFzUdtxQVUVWlzim5bRtr
+32fOJIC6soX9sbtjmbwaTe7HisgpxYjk5AAXXUS26+qAjz4K/ww+HmPsycYcd/BF5HgXOR8V8dpr
+6j+PAtFJL6Cig0wWT+g7ATdNJqC33deOk549CR+WfAiAuLSfv+B5PDX/qZT3a/mJPQkGj2YJTwlH
+RSgVkOsuZzx/n6rMfR0Yvkz2eq8zuBcGG1ZOgXUMDjeohMGCH7ASl6ARB5IAMDh7MNnIqML2knZV
+1T2rq0FjIhwW/biKtIgfRNe21cLlAn7/e/b63XeTDqjfT2bSJQk2YzvJAGB8/ni67Rq6lRbw+Oij
+YI4mJ1EMh8F6gweJKhSolJXJX+dh8OipXKcs3TidslDF4wxuC3T/UuJoSoYzuCPAzvNsraF83ahC
+B4MZe45qg8EtLaDXt8VvzPtYoZuNbus88TmDjXgPD53I2r9f2WUEcBPYYE5wl9Wlu5U9apak8zA4
+1ty9BBOkKCgJBhshIgIgf2MpI/JAK3MG18au+RlTEgzWizN4Ur9JFNKtOrAK/oA/JUXkWltBncEC
+BFlWdVfq6nFXwyywIrQXj74YVlMQWrhJzqbaY27hVqPzzmBZTEScrsLinGIMzyEE8uuKr9HYoXIZ
+oUqFQpPt21lfdM8e4Lvvor9fa2awxWRJuL2/ctyVmNB3AgBg45GNeHtnBDdFDF18MfvO778PfP+9
+8n7JcAbH0qOPknaFh3WSJBhckF5AJwf21e/TNDnA5wZXVETeTxIfESHl7B5qOgSAgD69OoMnFzIY
+vOHIBsV9rr+ebf/nP+Gv8474QSck7u7vDsmKyHG5waecAvQJ/unWr4892SOJOoMLmNv6qXOfwn0z
+74PFRKzuUntnM9uw7KpluH7S9aEfkxLxk7Y7akglev+gL4CT/wykNWOn9vQ2mVhmMHEG5zpyu824
+sWDUApaL3fEGMPgL2etqolZ7uvTVq+6VarnMDAZXN6qEwQYfSALy3OBm4SB27Ij9Ht4Z7EozJhjk
+ncHSsqPrryfLkADSCfniCxKdIDkz5s4FOsUgNDFZaGNoNI3vy2DwrrptWLCAbLe28tm4RLW1IFnS
+wTgUPcKDRBUKVPiiLZ2dwOefB/frA2QUGnOGPlR0wNTOnMHRYLAoAo0d+gAGkeSwOtiy7zgzgzsD
+nDM43Tht+tQRDAbvrdUGg/ftA72X2QTjHDOvQTnMIXTMpxD6raCyMlBHNGDMe7jL6mKrkrIOwO+P
+7CTkC6n5TeS49RQRIUkNDM7hTH2xVjRJzuDMTFJ9XAJZRoHBABtoVrcdocXetLZtSgrNDDYJpm69
+DkyCCWcOORMAifO49v1rcfPGU2A5dxEAMWlF5HhncGFGIWyW7okL6ZfRDxeOuhAAmZSe2n8qA7aZ
+BJ7wDsFo4p3ByY6JAIBzis8BQLI5pQiPZCkUBm8KMVPGKjTFr+ZScr1KkmBwtj074VUBJsGEP575
+R/r4vlX3wev3orS+FHNfnotr37sW7V6FLOgQpaUBt91GtgMBsoReabVDV8BgIDweAgACYgDVLQRC
+FaQX4Lapt9HXlqxdovqztRaRU4LBElQckDlAtys7xvcdT8eHkWDwmWey38ennxJHPC8JBjudgLOv
+QWEw5wyuaGIw2GQCJk4k27W1UCzarSR6v8phmQsn5J+Age6BuHrc1fQ5s2DGG5e8gTOHnhn3d9eq
+DFsG5Snbj27Hvvp9eKhsLjB3ETDrD0lyBos0JqI7XfFuuxvziucBAOo91YCVxHUIAWMykVSoZ1GS
+40gZFja6qGqMvf6utBRABuuB6DHIXo0GuwezB1llNJ8pmiqrRDqIzrTrbzCpRqHOYIBkaPHu4N//
+HnjqKfb41ltBiwGkp6XrtiMSSzwM3np0Ky69lL321lvyfemy4zR23D1Noc7gzZvZTPWaNcxxc845
+QFVrz4DBSs5gKdNLSYcOAe2ivmEwwEVFOMigT2tMhEdkcDDDZpy2beakvoCXFPI80r4/xt5y7d4N
+en3rEQ6qUa7bDrSQznE91B0/7wwWIBiyEKogCBibH1yemF0GpLUo5gbv2UPcdgDJqJSysfXYnmt1
+BkeDwaIoh8Gd/k54A14A+oy6iSR+CWruScSFs359cGI+AUluI9FG2na3rfujM/ioiFe3vYp1h7+F
+b+qfgcFfoqxMXhAwXrV4WgAXuTkMzR4aY+/U6t/n/Rt/mvMnLL9mOWwWGwMoznrA2qoaBvPOYFlM
+BAeD+2VEsc3G0LnDz6Xby/Yui7KndvHFzJRg8OuvR17xADCY1KcPYI1ilpNgcLKK/84rnofTi04H
+QFyyi79ajDP/dyY+K/0ML219CYu/WhzjE4gWLSL5wQAZW95wQ7hbkofBw4Yl49urV11bHfwicfsV
+pBfggpEX0HZ6zcE1+PqgioEjWDY0EBsGHz7M4hOysoBx40gRYynqY0DmAG0H0YVKM6dhYgGhnXvq
+9uAYZ6SQZDKxgn6BAHHLjhtHXNnr1zPn9PTpwNE2Y447IjmDAWDCBLa9ZYu6z6MwOJfA4D7OPnDb
+yX38/pn3I8ueBavJiv8u+C+dZOtKSffpZk8z7lt5H+1rYPQ72LEzvhgZSa2tIP3VNPJL6I7icbxk
+kUZBuXbcprDn8aleGGxQZaWxzkFNS3RnsCgGYbCbNW78DJiRxDuDkVWGb76J/Z7Kox7ARKpauB3G
+hAcyZzBXnfayy1gF0DVrmFN28GDg3HPlMNioKs4pht1iBwBsrd6K2bNZddePP5Z3uo8HGMyfC3DV
+4Ngx1lHlIyLOPVe+5NJInbJQscxg5esgVNu3gzrSANAOmN5EoyLijInwCsZc7TFmjAAcGwwAaDaX
+acrx27lLpLE/bodxjplXRgaAemKXajNVKVbwDhWfGexKM+6KB1kRqj47FXOD33mHbV98MbuP6THq
+KJkwuKMD8PnIttsN2fJ2IzmD+YFt5mxSGMfvj1yJXo18PnavD6SRtl0Pk3zziucpF/YL5hImwx3c
+Yu3evGBeuc5c3HnKnZjUbxIAOUCBuwI7d6qLBInkDJb6LH2cfRJazXb64NPhsJAJs2X7lsWdkask
+3hm8c2c4DK6rC1+1JkkU2aRItIgIX8BHI2KSBYMFQcCSs5gzdvFXi1HeWE4fP/7N4zQ/NJrS0oA3
+32T98LffBv76V/k+UgGq/HwWedPubU/q3yGSqriirAXpBTAJJtx96t30uUe/flTV52hxBj/+OGuf
+Fi4EzGb5xIaeYTAATCmcQrc3HtmouM9Pf8q2Dx4k/eyXXyb5yJJmzgxx9xuwgBxAnMGVzZW46I2L
+8Lsvfhc/DE5rATLI7M/wXBaePTx3OPb/336U/7IcV4+/OsInpFZ8Ebm3dnLOquwylNTupn2ReNTS
+AhoRAXRP8TheF4y8gN4PAAB+K1o+uVf+3HEsY44meoUsO+sc1LZFh8E1NcELM5ODwe6eAYNVOYPr
+WK/TZTNmTAQfDcAvjzebgQceCN//5pvJa61eAk2MBIpCZTaZKUDYV78PPqEN8+eT15qbgW+/Zfse
+DzBYFhPhJEBUqlq+dCn512wmMSE9BQbn5QWrSTezYzjYFLl3vm0b6FJiQB/QQEl0kJfWCpi8ccBg
+5gzWIyiLJLsdSPcSqCGaO3CwQb1lcEdJOyCQAWVuhjGv78xMAHVsYLCvfl/knUFA2sGD6BHtGj8A
+Qf52RWcwD4MXXOSnVbj1eNz9M/rTay9RGMwvt87MZHnBgLFg8NnDzsawbGIHPGBaAeSSP/I/PlyD
+rw+qmMFXEKsPIMJv0Q8MHugeiA+v/BD3z7wfr/zoFTZJE4TBieYGe72AL52DwVndC4NDJTOWBMcY
+fH5oJCk5gwNigPZZEu2v2C12uuy6qqUKm6s2x3iHemVlsXiH7duD/Q0E+yhBRYqKqK9n0LAwyiHy
+7sxkwWAAmD5gOi4adZHsOWkywxfwYeHHCxEQAzE/Z/Bg4MUX2eM77wSeeYZst7Yy97NUPO793e8j
++9FsnPHfM+D1exM8iugKhcEAcNW4qyiQ/WjPR6qgNw+Dy8sj71ddDfz732Tb4QDuuINs8+7SARn6
+hsFT+0+l2xsOK0dFDB4M/Pa3ZKLS4WDnu5f7c86YEZL7baSYCLccBt+/6n68v/t9PPjVg7AMYoBc
+EwzOYX07KcdcUrYjO6HVD4lK1hcLkXfwMtXFQJXU2gpZ8bjudganp6Vj/oj57InymUBbHsZnn9J9
+X0pH6oXBBlUu1zmQlhIBZIb6yivls9K0EEsPdAYfOBA9v0cUger2Q/Rxv/Tua3gTkVJmsKTLLwdG
+jmSP7XaporHYI5zBAIuKCIgB7KzZiXnz2Gv8ub5nDwCTF7CQ6qAZNm79YQ+R0+pkRfFcDAbv28dg
++KmnkgEL3ynr7ptxIjKZgoOvBrZENhpAC3UG6wEaKInGRACAo0EzDPaZuAKRBotM6Odgf8uvtqrv
+de44wCbD+rr16fiOJeIMZgODvXV7I+8MkjHp84E5gw0E/kMlG4D02RHmDC4rY067SZOA/AHchIcO
+z3FBEKg7+MCxAxRc81ILg5sY+w2DwUaKiTAJJtx80s308YCLngZmPoxDZ5+GmS/MwNqDKmhhiGhB
+Gms7RBOxLOmlXT972Nl4aPZDuGrcVZg+IFhBLn8n4C6XTVbHI754HKBzGOxWnxus5Ayuaa2BL0D+
+tslwFJ5bzKIiPiz5EG3etqQVC5KiIni4+6MfsUJTH30UrF8RIj4vWE3xOCC5MBgAHjnzEVoIsBSg
+Fk8AACAASURBVH9Gf2y+aTMFVd9UfINnNz2r6nMuvFBuRrnpJuC55+TFP6W84Oe+fw6d/k6sLl+N
+T/dFsE0nSUowOM2chjtPvpM+f8XbV6C0vjTsvbzUOoP/8hc2WbVwIXFDA6x4HKB/AxbvDI6UGwwA
+f/gDOa/b2kg0hpSNDJB++rRpcmewkUwoTquTXmul9aV4d9e79LUqy7dICy5U0AaDWd8uFAZ3t2Sr
+tEI1fFlCReRaWkDzgoHudwYDkOU0o4SsXirCrO75MjpTLww2qPLSWefgWCfpNNTWAvPmkbyqH/+Y
+5PoA3I25BziDB7kHQUAwI64PSTiPFhXR2Ah4HcaH4EqZwZJC3cFXXAHk5gIdvg46w99TYDBAoiLm
+zmWFIz7higPv2QOZI9Toxx1J9HzgnMGSKxggEREAg8F5zrxuKzqTLBUWAvA6gSbSuYzWkSfOYAPA
+YDtHiZy1aGoiRQDVSiqsBRjvXB+ex6DGt7vVweCODqCihWXsdveS6XiVng4IHAzeEwMGU1dSD3AG
+ywYgCs7gd9n4CxdfLJ/Q0ms1dgkGB8SA4iQVD4Proyzk4mGw202KkkkykjMYAK6bdB2Nd6oa+DRw
+5m8AACJE/Hez9rwIVjxO3/E/PIDE8E+wbp22Nj1UfPE4QH9tnmwsoaGInJIzONmOwnOGn0O3f7/6
+93A94kL+n/Kx7lCCdm3IoyIkTZtGzDgA+ZvzfVNJvHlFLQyW9ROSoFF5o/DOZe/gtim34avrvsKo
+vFH413n/oq8v+Vp9kbU//AG46y72+MYbgcceY48lGLynjs36vbjlxXi/uiopwWAAuOHEGyic3FGz
+A1OenYLl+5ZH/JzsbDZREQkG19UB/yRJOLDZgF//mr3Gw2C9x0SMzBtJi7tGg8G88vOBFSuA228H
+XC6SJZ2RARxuIjA4255tuNoGEiOobKmU3X83VX9HJ4BKSviVKpHV1gaaFwzIYyL0oFF5o2RxY5P7
+TUa+NXh/KVqDzTubI7wztogzmIPB3VhATtKFIy/E43Mex0V59wIbbwIAZNSd3s3fSh/qhcEGVR8X
+g8FNHtJpuOMOltVVWwvsD46Ze5Iz2Gax0aB7FGwB0quiwuDqasiOe5B7UOSddaxcZy7drmwJt0Jf
+eSUBwldeCfzpT+Q53kFsJFeRksblj6PbW6u3ok8fYPJk8njLFuK2CASCOWXcspwidxF6omhUhLMO
+EPzYvFmeFzx/PnGGJ2vJpR5EB07BrNWathpZpqYkn48UdZFBA52e/7J2eNbvAIiqi8iJIhCwBAuK
+BSwJ5St2h04cwqDGtgp1MHjvXkDMYjC4u4spxSuTCRg/gA0M1u2NDoPLygCYPYCFWNCMDIML0guY
+0y1/O6qr5fEIfETEJZcABxr064qUFCs3OIcz9h0PMREAcTNeeQIhYz5RXk3rg90fanZoKsFgPU7y
+8QASw5ehoyOxqAgjOYOzikhf+/vv5bBXSUrO4GQ7CgdnDZb1HQECWW9eerOqKIRoUoLBJ54InH02
+e7xdIYlALQzmc1uT7QwGSK7338/9O72Hzh4yG7MGzwIAlB0rQ9mxMlWfIwjAkiUkJgIg/ZJXXmGv
+FxcDHr8H+xvYffujko9Q21aLVk8rHlj1AJ767qmkZglHgsHpaelYde0q2l43dDTggtcvkIHq0GOT
+3MEHD4YXyQOABx9k5/rPfiaP/jASDDYJJpxUeBIA8r0rm6MsueVktQJPPEEmMh99VD7uMFJesKRI
+jGDD4Q00NzgQUL62QxUrJqK75bA6UJxTTB/fOuVWnDEgOJlp9uKL8pVxf3ZLC2T3LT2syhYEAYtO
+WYQ/nPYI4Cfjpfa9U4HurUGrC/XCYIOqr5vNFDf6a7B8eXhGlZQjSseZwVl7h8WRks5FV4mvEozi
+T6PmBldVoUc4op1WJ71JrTu0Dgcb5dPUJhPplLz6KnEFA/Klx3yDb0TxzuBtR0lAGx8V8dlnpJpt
+ZyeAPDYY5wfpPUnUISeIgKMBZWXAF6RoOwYOBMaOBera62h12J4Ag2knu4GVpi5tCHcHl5YGzwOb
+/jODF560kH23sW8DU/6pOirC6wVgJc5gs994cHDmOAY1DhxTB4N37QKQbXwYDAA/v5i1yRv3q4DB
+aYyeGBkGC4LAoiIyjwD2BhoVUVrKVvqMHUvij/hzQ2+uSEmxYHAyYiKMBoMBMriUqZ38Imraj2p2
+Z9KYCL5dt+mvXZ9YMJEBqCErAUsHvvwy/s/jncEm0aq7eznfp07vT/rafn9sAK7kDJYchUDyskaf
+v+B5LBi1ALOHzKZAYnPVZryx/Y2EPldyCfKaNCm8uFyo+JiISJnBS/csxa+W/4o+liBdqjV78Gy6
+/WXZlwAIuB3414G46/O7IryLQNPHHweuvTb8teJiMqHnF9nkjzfgxavbXsV1H1yHh9Y8hNs+uQ3v
+7Hon/M1xqqo1clbpyLyRWH/Depw34jwABFS/uu3ViJ8lweCOjvDCiNu2AU89RbadTuCee+SvVzRx
+mcE6h8GA+qgIJZmCNKm+vR6dfrIUQm9tlRpFMsqV1JVgxHh271ETFREaE6HHcfhZQ84CQM7PK064
+ApefyPjKDs+ySG+LKq83GJ1TwH5J4/qOi/yGLlYx92co3WNTLgB7nKkXBhtU7gwL0EgarRr/Hiy8
+KXzKUmqsvvsOAETqkB3oHghBMO5UiAwGD1+G77+PvGSjqgo9whENANdPvB4AWYr6703/jrk/P9s9
+IndEyr5XV6iPqw/t1G2p2gJRFMNyg2n25PEAgxWKyElFHObPJ51zWfG4dON1ykJFB0717E6uFBUh
+FXORHGQCBN1mRw9yD8KLF77Inph7B745sCni/rza20EKzwEwB/SXpRpLU4czsFfjVQeDd++GDAZL
+RaqMqGuvSIfQQuBEtXcvg10KKi8HkMPOdaOucJEkj4rYgeefJ26bG25gT192Gfm3JziD44mJyMyE
+bOWDHiMRYmly4WRcNe4qCBBwWf9fA5/+lb72/u73NX0WdQY72C9Qj5N8JsGEc4qD7uC0NqDoKzpR
+G49aWkTqsMoUi2A2mZPwLZOnXEcujQMJpLO+dqyoCCVn8I6aHfS5ZBk3pvSfgvcufw8rr12JFxe8
+SJ//zRe/gcfvifzGGAp1BhcXk2iXoiJSXAsITl6GKJYz+Kvyr3DJW5fQ7OSfn/hz+ZgnhZKcwQCB
+waIoYtHni3Co6RAe/+bxsIg6XoJAiqjNmCF/vriYgLRQ3b/qfry18y36eMnaJUlzB0dyBkvKtGXi
+X/NZLEa0tihSETlRBH7xCzLxAQD33UeMGLwkZ7DVZEW+K1/DEXSP1BSRiyXe3W+k4nGSorU7tsHa
+isjxMREF6QW6HIc8fvbjeOOSN7D2urVwWB2YO3IWBB9pz+tylsHv135Nkr6sCBQQR2K+K18XzmBJ
+Dge7rktK0AuD0QuDDav0dAA1ZGraY2pEeS25+Z3A1WbZvJnkGe3dCwJGguDA6APJaf2nMWfzsM/g
+9XuDwDtcPcUZDAA3Tr4RFhMp3/rs98+i0xc9iI7vgBkdBgPMHVzXXoeqlipMm0aKpAHEGUxdGMcD
+DOazM13yXIH5wYKpPAzuzoq1yVJoTASgXESOLt8KwmC33S3LxdKbLhx1Ic6wB11AFg+e2ndn9DcE
+1dAAmiGbBuM5RbMcbli8hJJ5XPtVOaKJM5hAUQECirKMGwOTkQH0tZBlg6LrKF56synivmVl6FHt
+mqyIXP52PPMMcP75oA7KQYOAX/6SbBvBGVycU0zbGCUY7HIRuAsAGzZEdgeHZgYb3RkMAK/86BW0
+3NeCZy55DNhzHhAgMPO93e9pgj8UBo/6gD4nKyisI8kNC0vx7bfqMiaVVNlYB9hIO58j6O/8FwSB
+miwaxQoA5G+6alX09/EwWHIGf7KPhOxaTBacPODkZH9VzBk6B7OHEPfr/ob9eGTNI1h3aB1Kaks0
+g8i+fVn/E2CxZWYzK+i8bx8rLieJL67WX4GVLfx4IS1CefnYy/H0/Ke7zLwztf9UCva/LPsSW6q3
+yEwl26q3RXorAJKZ+957wNDggp1x48jviP8MqZ2UiltL2lS5CSsPxL8snZcEgx0WB83BDVX/zP4U
+fm6p3iKbdOQVqYjcm28Cq1eT7aFDWUwGLwkG98/sr+s+qKQp/Zkz+LsjEQbVMZTs3O+uVqhhjC86
+1pjOALkaGNzibaK5uXqLiJDktDpx2djLaF/aaXWiT+sZAAAx4zDe36B9UqC1FUBGJR2bTiyYqDsD
+osTKjh0DAt5eGKz/1qlXiuJhMACgz05YLMBbb5FBBEAaq/Xrg6/3EHcsAJhNZswdNpc8sDcCA7+l
+Af6h4p3B6eYsQy+v7ZfRDz8a/SMAwNHWozGXVfEdsJG5I1P63bpCobnBFgswZw553NBAZuYBUGhi
+t9gNP/ERSTIY7GQw2GYDziD3cVnmlxGXa4WKOYOjx0QQGCzSTliyi6+kQj8rWkKPq6RjDRrao6wl
+D6q6WqQxEXaT8ZzBAAc3MivwwxZvzP35mIj+Gf3pwNWomjKMDRCeeTtyVESPhsF9iBuQzzx/4QUG
+TyUYbBJMuu272C126lreXbs7DHQIAnDNNWS7tRV49lnlz+lJmcG8nFYnsrKAEQNzgXJSfr60oRQ7
+a9SXK29tBZngG0cCSTPSMmh/SG+aM3QOnbjH+JfhMR2LOzeYh1R5Fv3BYICZLFp9LSg+gZzEX38t
+j0QI1QGOvfXtS2LNpMndGYNmpMQJLwgClpzJiqP9YfUfcPLzJ2PUU6OwZK36omnks+RRERIMBtjz
+fj8X0wfiJpXGZLm54U7SyuZKOpk0oe8E/O+i/3WpE9xmseGUgacAAMoby8N+J1urt8b8jLw8coz/
++AcBw4B8LHLtBHmWBB9NoPVvEEkSDC5IL4gKoRaMXEC3Pyj5QHGfIm6+WYLBgQBw993s+SeeAOwh
+XZF2bzvq2usAGCMiAiBsQHIwf176OU5+/mQ89NVDYfezaOKjXow47uANY3nOPNx1KotH2XFsAwYE
+/5RbtypnSEvy+QBvhn7zgqNpuvtiun33W/+KsqeyWlpAXcEAMLHvxGR8raSKL3rZ2mSseiupUC8M
+NqiUYPCttwKjRoGGnFdUAEuXBl/P7DkwGAiPinjrrQjFGqoCQCaZne3nMv5x33LSLXT7nxsiEPCg
+JGdwelq64lIpo4kWDgQrrsFHRbS1ATB5qXNwZO5IQ8zGx6M8Zx7dFtIZDD7jDOJCA0JiIgzYKQsV
+dQZzmcFKzuBt2wBkHiYTRTAGOCvsmwbsvhAAICKAz0o/i/meyqMewEyWkjqtxpzkGpgRhBumAL7a
+UhF130AA2H2gCXCRpapDc4ybFyzplFFsgLD54F7FZcWBQHAQ2oNgMB8TUThJfuO+7TZgNouupDBs
+YOZAWM3WLvl+8UhyVbV6W3Hz0pvDnIa3304AEgA8+SSL9eEVFhPBVTPXaxFMLZo6FcBuBmC0REW0
+tQGY+CKJXgDwkwk/0eWyW4CsRrlq3FXkgbMeOPWxuKMiDjYzalpg0ykM5sYUZ/yIfF9RJO5JJYki
+668PHEjco8v2stmgc4tTF4swpf8UXDb2srDnH1n7CJ2EFUUR1S3VMYsc8jD4xBPZdqTc4L17yWpN
+AJg+nbUHkvgc7XnF87qlKOysoll0+40d8lxlNTAYIED41luBYcGuGr9K8ben/Za6daf2n4rVP11N
+s1RXHlgZdzyBpE5fJ+rbSZRMrHHPglGx2yLeGVxWRv5du5ZFRpx5JnDeeeHvM1LxOEmCIFDnvAgR
+6w6twwNfPIDFqxer/gxZTIQBC8iN6TOGxgZcM+4ajO0zlp6vfBG5xkZ5bEioQvOCh+caBwb/feEV
+EDpJf6PU8Ro++bJO0/tbWyGHwQX6g8Gnnw7cdBPZTmLtSsOqZ5KS40AuF2QwOG3ATvz2t2RbaqwA
+4OWXgxvunhGVIGnusLkQpBKQw5dBFIHFCverg3VHATMZdRVlG/+4Tys6jQ6kv674GluqlNeqdPo6
+aTXgEbkjdLdEIx5N6z+Nbq87TDrNF13EOmt5ecCcK0opIDM6MIkmPjM4r4jBYCkiAuh5MJg6gzuy
+YPWSKomhzuD2drI0E33ZoCW0mrgelZ8PYC8bAC/bF7tww/4q1kEz6oqHMf0Y3Ni4L3pucHk50Ons
+GXnBkkbwA4ScvVi0KLxjWlUVXGochME2sw1FbuPGYwBArjOXDtQ97u049VTy/KhRpDK9pMaORjR0
+EECj14gISQ/OepAOGl/e+jL+88N/ZK+PGEGiMADg8GHg7bfDP6MnxkTwmjIFdNILAN4vUQ+DW1oD
+wBQ2AX7zlJuT+dWSrgdnPYg0UxDmTX8Cn34dxSYbRUfaWLvY36nPCbAJfdmgI2sSy4d4/XXl/Ssq
+mAt+XPD2zN/zUp2R++z5z2LxGYvxy2m/xKkDSePT4mnB0xufhiiKuPrdq1Hw5wLk/ykfl751KV7Z
++opijMQttxCYfe65wKxZ7HkeEvMTfLw7/GSFFIxvD31Lt6cPmB7v4SUkPjc4VFuPqoPBoZKcwX1d
+fTEkewhWXLsCj895HMuvWQ6H1YG7TmE2vce+eSyunyHpaCvLm4oFg0fljaIRemsOrlHMRB7BJex9
+9BGZnH2DY+TXXx8O9YEQGJxhDBgMAE/MfQK/mv4rjOnDTuLlpctVv9/oMRF5zjx8cvUneHzO43j4
+zIdhNpkxuZDY/iuaKjBsAsuj3rw50qcETSm5+i4eF0mDClw4I+s68sDagRueeoFmY6tRmDNYhzAY
+AB59NHx1xvGqXhhsUBFnMJt+HjhpF3KCMboTuetOGly4+rOwo57gDO7j6sPyjfpuAzIrFN3Bh1sY
+BB+aa/zjFgQBN510E3388Z6PFffb37AfATEAoGdERADkZprrIBBw3aF1EEUR2dmkqNSBA8DRo8At
+D/Qc91w08Z2szCGko20yhcDglp4Fg/PyAEtw5a21mXSsDjUdQruXhTHu2kU668hn2XZS1rSelZ8P
+4OAMoJNA3U/2fkKv30jaXssmgoZmjI6yp3514jAG+L4/cCDqDD0fEQEAQ7P1CUa0SLZ0MHcvli0D
+3g/hY2VlICsecogLfkTuCN0VkYpHUlREbXstXv3gKD76iCwtd3GJJ7K8YJ0Wj5M0PHc4nj2f5T/c
+9slt2H5U3iG54w62/Ze/hIP/nhoTIWnqVACNRUAVgYebjmxSFYkDAFuaV9LB9VjXGTJYoUcVZRXh
+linBlVzWdmxKf5DlHmtQZSdr8+hKCp3pnOHn0O1NTcuoIWX9enkchKStHFMcPx5o9bTiy7IvAZCa
+Jqn+22baMvGb036Dv877K15c8CJdQfbEuifw8JqH8dr21wAA9e31eHvn27jmvWvw0taXwj5n0iQy
+Sbl0KckKlhTJGfwtY72KMJh3BncXDOZzg0O14+iOmG7pUDV1NtHYBgm8Tu0/FYtOWUQLQF474Voa
+T7B0z1LVhf1EUQz7PrGKx/ESBIFGRQTEgOJ4qqAAOPtssr1/P7B8OYljBEg0hDTBFyojOoMBoG96
+X/xl7l+w45YdFOJtrd4qK2YaTbwz2KjjjjOGnIFFpyyC0+oEII8ycRYz53qkWkUAsGIF5M5gA8VE
+AMA/fsomW48UPo1/Pxt9PMKLdwZb4NBtzaLMTOCZZ7r7W+hDvTDYoEpLA/Izs4FmcrNrsrEex0SF
+SZicwT3LGQyELCUbsVTRHXy0kx33oJ5y3Jxr4svyLxX36WnF4wDScZM6yLVttdQV6nAAgweT2Xm+
+eE9PhsHj+o6jS5n8/dbhZz8jOZtDuLGiNEMvQEBfV9/u+JpJlclEOuYAEKhlrlAeGNHJIM4ZbAQY
+nJMDWE02YP9ZAICathp8X/l91Pfsad5EtyfknxhlT/1qZB92wtb6DkTN1dy9Gz0OBg/L4dzNQdh7
+++1BZ0VQ5eUAsg/QFS49pV3joyL2Nm7DeeeBTmhL4vNS9Q6DAeDyEy7HzSeRQVSHrwOLPlske/20
+09hy8o0bEXa+R4uJ6AkweOLE4IRe2SwAZCky74aMpjWd/6DbF/a7NQXfLvm6/7T7YQ0Qt3hg4nO4
+7fVH0ebVRoRrfOwaGOzW5zUwMnckvT7XlK/BRZez6nBvvBG+/zauDtm4ccCqA6soADy3+NwuXclW
+nFOMS8dcCoDcdx/44gH6Gh/N8swmZWqg9FWLi9nENe8MlmCwyRScGOHk9Xtp/NngrMHdFu3G5wYD
+gNVkpW7hdl+7Yp2GaOLzgiONRWwWG+YVz6M/Q01UREAM4Jr3roF1sRVPrHuCPq8FBgPyqIj3dr+n
+uM/ChWz75puBmuBivPnzWfHDUPEw2Khj7hkDZwDQ1k5LmcFmwUwBv9ElFRoEgI5cdm5+803k96xY
+AcM6gwFgdP4ITMkJFuXJ2Y8nPlbvDq9pbKb92X6mcbo2L8ybJ5/IO17VC4MNKkEgERD9rGQGvaat
+hi5xGTMm/OS25vaszGAAOH8km5JNG0fC/995h92o/X6gUeQc0Qa9IYdqSNYQ+jf8+uDXirPoPa14
+nCTeLfFtRXjn5HiBwWnmNJzYj1CFsqZ9WPJkLa6V1+WgMDjfla/rrE0tknKDOypZx4rPDf7yy+BG
+EAZbTBaMzNP/+W82A5dcAmAfc1jxGYpKqvAxWHxy0eQoe+pXsqX/2fvxyiuR9w11BveEmAin1Uld
+Q9YCMnCoqAAefJDt09OKx0niJ2nWHFyjuM/+BuPB/7/M/QsGZw0GQJbXbq5iyyUFgeXUAeGDyVAY
+LDmDbWYbbBbjV7y224kTFAdn0OfWHlwb830bj2xECT4kD5oKMbvwghR9w+Qqz5mHH/X9NXlg8uOF
+insw/O/D8Xnp56o/oy4QvL91pqMwKzcF3zJxCYJATQregBcFp6ykr732Wvj+oc5gWV5wiiMilHT3
+qXeHPXfvjHtRd1cdXcHwTcU3sn51NFmtwPCgEbCkhIxFmpsZBB83Lri6k9PW6q1o95FVTicPULAN
+d6H43OCzh50te6w2N1hSSS0zpkQbi/A/Q3KJR9Mf1/wRr257FSJE3L3ibhxsJGM9rTB42oBpdL9l
+e5fJCi9LOv98ZkTgc2Ivvzzy51Y0sTG3kZzBvGYM0tZOA2zc0S+jn64hoBbxzuCdTetpUcENG5Sz
+/5ubgxO9QSBamFEIV5rxijzfP4dNupYW3SdbqRRNJce2AQJZ9lRk02dEhCRBCC/+eDyqFwYnWYIg
+DBIE4c+CIOwSBKFFEIQ6QRC+EwRhkSAIjmT+rDlzgItnsuVUu2rIFLTdLl+mBAAdaeTG5La5dVt0
+Q6smFUyiUNQ3aCVga4Lfz5bw1NUBYgbvDB6k9DGGkyAIspl6pVl0NbPxRhTfSeaX1EniYXBPOm4l
+RftdBMQA7dQadamWkmhucF04DPZ6g0vszR4Kz0bnje6WIizx6E9/AlyVDAa/+UN0GHzUEnQGd6bj
+pKHGWoImaXDWYJb9PuZtvLjzH/B4lLMiNm0CLQ4JGAcOxpK0fNBrrUVa5jEAwN/+RnJlAQITeiIM
+njtsLt2OVLxHFhOh88xgSXaLHb8+5df08aNfPwpfwIcbP7oRWUuyUJ7DsoT3hLAlCQbb7WT1lzT4
+6gmuYElTpgA4eCp9rAYy3LPiHvZg7b3ITDfO5Obz190D586FQIAMt440H8Elb12C5s7mGO8ENldt
+RqMpeA1UTUR6un5rP/AQd1PTMkwLlnjYupVAE14SFLVagREjRJoXnGZOowWsulKT+k2StUczBs3A
+g2c8CLPJjJ9O+Cl9/r+b/6v6M6Xc4M5OEpWxYUMwwgqkeFyo9JAXLOlHo38Es0BA3sLJC2UTd9uq
+t0V6m6LUjkX4rOJIKx4lrS5bjd9++Vv62OP34Pdf/h6AdhhsEky4biLJR/UFfHju++fC9rFagZ/9
+TP6cyyWPZQuVUWMieJ06SFs77fV7aWZzTxp3DHIPosezpnwNppxCJm3a2uQTW5JWrwZ8jiOAizjT
+jBYRIWn+iPlwtJOxlj9/M8596QJZLF8k7W1hE+DDXPqGwUAvDAZ6YXBSJQjC+QC2AvgVgBEAHACy
+AEwG8BiAHwRBSKqdaXQfRn131rCoCL6InMkcQK2X3Jh6ijsWCOY9BZf4BAQvUPwJAOZEqKqCvHBe
+D3FEAyEdJ4VZdD4mwkhVTGNpSv8pFB6FLlsSRZHC4CJ3Ec176qniBwyhMLimtQZ+kWSp9aROmeQM
+RgNrRkvrCSD84gugoQEEnAWLCBohIkJSYSHw8F0DgWpSUWfHse9Q2VijuG9tWy06bMFVD1WTkJdr
+zFu53WLHz0/8OXlg9qH19F9g/r9/FpYD2NERBAhBZ3B6WjrynHld/G1TI36gcPktxB3s8QCPP05c
+wq+/jh4Jg/tn9qfLL7dUb5FFQkgyUmYwr+smXoc+TlLk880db+LiNy/Gs98/i8bORjy551eAjcQ/
+lJTI3ydlBmcG2a+U0+i2u9FTNHUqgJZ+QD1pw787/B06fZ0R9/+89HOsPBB0mtYPBTbdKMuV1rtc
+DisemPQv4OmtQDlx2jV1Nilm0Ibqxc0vsgdbr9H1cc8aPItGVy3buwxXXskm9RYsCK5wAIGj0nk/
+ejTwxcHPqKtz1uBZ3eage2zOY+jj7IMxfcbgtYtfg8VEch6uHn81BaP/2/o/1Zm5obnBsYrH8X24
+7nYGj80fi003bsLa69bi/JHnY1xfVoRXaxG5PfXcKsUoq7QGZw2mhp1IKx4BUiDuyneupDUVpPHA
+f7f8FztrdmqGwQAB3lJu9DObnoEv4Avb5+c/l0eCXHAB4IwyxJBgsFkwGzambUDmALrKZf3h9TGz
+nCtbKiGCXPdGLB4XSYIg4JxiYtRo97Ujb/Jq+tq3CukZK1YAGMMqxJ5WdFqqv2JKZDFZML/5faCN
+5Hd9fXg1Ln3rUsXrg1d5B4PBo9z6h8E24y+6SljGHEHqUIIgTALwOoAMAM0A7gNwCoAzhnmgogAA
+IABJREFUATwLQAQwHMDHgiAkrbfDF1rgYTCfGzx2ag1txHsSEAXkeU8ZU4m7aO1aMoiuqgKQafyl
+OkqKNYsuzcb3S+/Xo1xFmbZMumxva/VWtHpa6WtVLVU0Y7GnAJNoOnkgGzCEgnG+om9PgsHUGVzP
+OYMbiDP4nXeCTxgsL5jXrbcC+Y1Bh5Ug4qE3lQtE8nnCjmOTYTLwnfyp+U/honzmpFxR9wJ+8+7z
+ePZZtiRz82bA5/cDWWUAiCu4K3MlUyl+sm7yOdvhCK4feuYZ4Be/IPAEuT0vAx4ALd4DAB+UfBD2
+ugSI7RZ7t2VoxiOH1YFfTv8lALJK48OSD+lrzZ4muGb9C0BkZ3BmJpnc7LHOYIBGRXT6O2lWaqgC
+YgD3rORcwV8sBvxpUSGMHnXjjYCzdSywjOUeP7XhKYhRKmZ6/B68si2Ym+OzATsu1zUMdlqdOGPI
+GQBIEanpF27DSSeR144cISsZq6pI9rsvyBLGjQOWfL2EfsaNJ97Y1V+banzf8aheVI1tN2+TjRUK
+0gtogbxDTYew6sAq+pooithavRUr96/Eyv0rse7QOgqLx3A18Hbtil08TurD2S12TCiYEL5DF2tC
+wQTqDB2aPZSaK+KNiTAJpqiredSseAyIAVzz7jWobCGr3s4aehYenv0wfe2O5Xdgew0r2qn2nlGU
+VYTzRpwHgJy7H+wOvxcVFZF8UUnRIiIAFhNRmFFo6LgEKSqiw9cRs44FH9nWk2AwIF/50JDHVu0p
+5QavWAFgLAtLv+KEK1L51VKqKUVjgZc/pcWtl+5div/88J+o7znkC8JgUcCYvHFR99WDeshQIiEZ
+eAipO/0NxAnsAzBHFMVHRVFcL4ril6Io3gTgLgACiGP4zmT9UBkMrmUw+ESuntCoaT3THQsAMwfN
+RLY9GwDgKVoKmInD5I035M7gDCG/R2TuSYqWG3ys4xhdqtOTwIEkyRHrF/3YeGQjXt/+/+zdd5gU
+VdbH8e+ZGXIQkSAioIAKIqIgKEpQxICorFkxR8SwrK6usr4qJky76OqaUFdXV13zimFxVYyYMCAq
+oCQlKxKUHGbu+8et7q5pZpgZ6K6e7v59nqefqe6q7rl9pqf61qlb5/6bWz+4lXd/TJytzYdk8PYN
+t493uD6d92mpESu5mgyOjwxe2ZRa5jsnM5bMYMMGeDGY+6OoZeJgpXOz6t8RCSsqgisGHRW//+Ks
+si9L/Wx+YvK4xmuzc/K4mKKCIv599m3UG5soGHzL+7dx3pBiBgzwl9ZOmAA0nBsf8Z0L9YJj9m65
+d3z5/YWvxierWbMGXnoJwGHNfAmoVg1bUb9m/Y1fJEuFT+Yml4pwzvHDsh+AoJxIlvXYL+h+AQ1q
+JkpyFVhBfBTbuq53QNEaFixIJICdSyxvtRUsX7c8fnVHLiWDd93VX2JdmbrBz09+Pp6AaLhqD/jG
+H1RnWzK4cWM480zgpy7xEhmTF03mvR/fK/c5r017LT4PCFOOgjWNqnUyGEpP6vzYtw/wxIuL6RB0
+xaZP9yOEv/oqsf1Wu30cv7Jt5212LrU/yAQzi48QDQuXihj18ShmLp3Jx3M/5oB/HkCX+7vQ//H+
+9H+8Pz0f7sm5L/srXcIjgydNSowM3mabRD3hmJ9X/hyvj96tRbdqV9qqwArifamZS2dWqsQJ+H14
+bGDKjo12rPB9VVQ3+Ob3b+aNmb7e9rb1t+VfR/2LYfsMi/dxX5/xeqn/qaqMyL2we6I+6j0T7ilz
+m1Gj/FW3J5+86RIRazasif/vZvvVuLFJ5MAfa27Ki1MSE/CFJ13LBf3b9o9fLTBh2avUruNP5CUn
+gxcsgG/nzobWfsVuzXYrlafJNu3aAfO7w9MvxB+7bfxt5V4hsWT1En4iKCWzeCeaNMyd/mouUzI4
+BcysO9ALP/r3Iefcp2VsNgqYgk8IDzOzlJwqbFq3KdvU8ZNKxGoGA+y/PwweDN26wQGDQsngLP9i
+SlajsEb8jO5alsMO70CdxTz2/M/MX7gB6vszyE1r5db73tRZ9FydPC4mfAndqS+eyknPn8SVb13J
+Sc+fFH88H5LBkEiMr1i3gm8XfRt/PFeTwfGRwRhrg0nkZi39gSde/y4+ceQ2u2bvyGCA8w7rAb/4
+z++CWu+WmkQrZsLcxAiNlpadk8eF1awJp+05GGbEZi+eAbs+z5Qp8MUXQTK4ce7VCwbYt9W+8ZIX
+Y6eP5cI/rKZm+Ji57i+42kuB3NuvdWjSIX7C8v3Z7yeSX8BPK3+KT6iUTSUiYhrVbsTFPS6O33/4
+yIc5dtdjAVhf6yfo4k/0TAsmHF+1yk80BX5k8P2f3R9/bttGufN5LywMBiuEk8FzNk4GO+cY+cHI
++P0dZ9wMzh+yVPekaFn+8IdgBNKnFSedIKlExMQzgOr/vmMjaAHu/exeOjzclJaXH8H27f3+65NP
+YMSIxPZf1r01vvynff9UbUdQHr7z4TSu4y+VHjt9LO3uakfPh3uWGoAQ88jER3h9+uvssktixNmT
+T8Ivwa5tn302HolWnUpElCd8Yj3c19yU+cvns3K9v3qvMhP5Jl/x6Jxj2uJpfLHgC5759pl4neAC
+K+DJo5+kef3m1K1RlxsOuGGj19q9+e5VGgDUv23/eMmmt394m2H/HcZr015jfXFihrAOHfxVSv/6
+lz9xX555v82LL2f7FamlJpErYz8dU1xSzHNTfGmEWoW1GNRhUNrbFqWGtRrSu3VvAGYum0nnvv6L
++8cf/ZUPMW+8AXR6Jn7/hE4VDCGv5trFxl3MPIjtVvcHYMbSGTw3+bmNtnXOcfp/TqfY1vgHZvfa
+aKJMqZ6UDE6N8OnsR8vawPnrwR4L7jYCDkjFLzaz+FmnecvnxWvMFRTAE0/AZ5/B+jq5OzIYSo8u
+KjzhRLiiCV8f1JK/fXgPFPi6Ui3r5977Lq9ucK5OHhcTrpUbnrE3LNeSJuUpbxK592YnRkdke2c0
+rHv30AHxL/6zXUIxZ3zaCQ4fAnV/Yd3W/qx04zqNszIRXr++0XrJGfH7945/bKNtPo+NDF5Xlzb1
+c+OEzyWXQP2JoUvCe90COMaMgU8/JV4vGHIrGVxYUMiROx8JwMr1K5m69i3OOiuxfptdcq9ecIyZ
+xUtFlLgSXvk+URYlXEM4G5PBANcdcB2jDx/NuNPGccYeZ3DFflckVu53GxRsiJeK+C00UXftrZdy
+8wc3A74m5iU9L4mw1enXvTvwyy6wyg9kGD97fLwGaMz/ZvyPiQv95aZ7bbcXteclJviqk9KpmKPR
+vj307g1MOQZW+BGLL059sdSJ25ifV/7Mq9NeBaDG6u1gZn8KCyl9kqgaat+4PQe1PSh+3+F4a84r
+7HbZpfHHZsX+rZtM4aOl/mqAlg1acsrup0TZ1CqpVVSLP+z9hzLXtW/cnuG9hnPGHmfEHxv66lBc
+0SralvE1dcghpe+v2bCGa9+5Nn4/XP6rOgmfWP9q4Veb2DIhfFxSmYEpyXWDez3Si53/vjPdRnfj
+hOdOiO8jru17bbwkCfga7U8c/QRX9b6Kq3pfxY0H3MiLJ7xY5u8oT4EVcEH3C+L37/r0LgY+OZBD
+/nVIhTVSk4Xfd+uG2T1xecemHeNX334w+4NyS9u89+N78XrNh+10WE5dzRITqxsMsNVe/40vf/SR
+v4Lt/vt9qbdwiYicSQYD20xN9M9vGX/LRp+FUR+NSvThVjaBd66r9icwxVMyODVip85WAp9vYrvw
+aeT9yt2qijo2SVyPNOWXKRutn/Nr7o4MBj8ree0iPx1kcU0/GzuFG1jYJVGNY4dtcu99l1c3OFaj
+C3IzGbxLk11oVLtR/H79mvU5ZfdTqFHgZxivXVQ7K0eEbo5wYjxWc+7HZT/y1Nd+FsXGdRpn7eQF
+ZWna1F9yecUV0GzGH2FFM7+ioBj2Gg3n78nSDf7gunOzzll3aXnMEa1Pic8+/9ikf5ZKlCxdvZQ5
+KxIzzDdvWj1HU1XVTjvBok8PoNu2QVHRFl9Cuzd48slgwqFtEie5cikZDBuXSxg+3H/WzeB35+Zu
+MhhKv/fnpzwfXy41edzW2ZkMLioo4txu58YTF92265ZIljWeCXs8Gp9MK5wMnt3qVpat8X2Z0/c4
+PV4nP1f06AFg8dHBS9csLXVlG5SuJXvlfleyaqXfl9euTdbWSO/cGSiuCZ/7UgIbSjZwzdvXlNrm
+pxU/cewzx8YTUA1mnQaukPr1s6O24Wsnv8bYk8dyWc/L4mVSxi58lL6nv1NquxqHJN73pT0vrfZl
+3K7uezUTzp3ATf1u4oAdDqDn9j25b+B9TL5gMiMPHMk/jvxHvK81a9ksbnzvRv7v/2DrrWHHHeHE
+E+Ghh+D88/3EkLHSbpe+fmn8pMfO2+xcqjZpdRLuU1/+xuXc8O4NrFi3YpPPufeze+PLlSkBknzF
+44dzNi7IeuCOB3JV76s2et7gzoO5sd+N3NjvRq7qc9Vm9RHO7XouA9oPKFUq5O0f3uaRLx+p9GsU
+lxRz+4e3x+8fs+sxVW5HdVJgBfHRwb+s+qX0FQshT3+bOwnQ8oT/NxdvnagbfO+9sN9+MHQorKgx
+A1r6Gvh7bts16ydwb9DA90UBFn/Wj+7b+f75xIUT+d+M/8W3+3jux6Xr+7/4OPy2vUYGZ4ks7VJV
+Ox3xJSKmO5c0vKG0qaHljuVuVUWdmnWKLyfX3VuyekmpnXRsZtBcUq9mvcSM9ICt9qNNKEjUtNmt
+Ve4lg8N1g9+e9TZfLfyKtRvWxi/VgdxMHhRYAYN3GwzATo134pNzPuHxox5n6kVTuX7/63n5pJfj
+l/Tluq4tusaT4LGRwX/96K/xWpMX97g4p2qMArRtC7fcAvMn9OCJvWfQ9JvrYW1Qm7Ph3Ph22XxC
+4Ij9W8KMgwFYtP6HUnXwSk3isaAbzZpF3br0qV3b+HOfUIdy4AXMKHgNevwd9rkj/nDscs5c0b9t
+//gEPWO+G0PL7Yv5+msYO2Ea39VOjAzPxf353tvvHa/t+Mr3r3DfhPuA3BgZXJZSiYyD/sSkGb6+
+fzwZ3GAeUxv9DfCX2163/3URtzD9kieRA7j2nWvjl1d/PHfjWrKrVvntsnmkUXxSsc/Opyb+//3h
+Lx/miUm+XvqHcz6k2+huvD/7fcBPylb0lZ9ULVved1FBEYe0P4TbD76d2w66Lf74nC5DqFXPz+nB
+Tq+yfiffT21at2mp/nt1ttd2e/Hn3n9m3Onj+PDsDzl/r/OpUej7X2bGA4c/EO+P3f7h7ex12Dcs
+XgwzZ8JTT8HZZ8PfP7uDxrc1Zutbt6bvo3257zO/v6tdVJtnj3s2PrCluunesnt8Qrbl65ZzzTvX
+0PWBrixZvaTM7ScunBhP5u7WbLf4JfYVCdcNBj/Y6YK9LuCCvS7g6j5X89zxz6WtnEi9mvV47eTX
++OXyX3joiIfij494dwSr1q+q1Gu89N1LfLfYn+Hr26ZvqQEb2eq4XY+LL5/z8jm8MOWFUuvXF6+P
+lw2oW6NuvHRjrtm16a7xkevfrnwHavgSKOPGJWqCs9cD8e1P3C03kuKx0cHz5xmX9Ej0z69/73qK
+S4pZu2EtZ750ZvwEZpvZw2G6n20xW7638p2SwVvIzGoBTYK7cze1rXNuGX70MEDKspO/6/C7eGH+
+uz65K37ZWax+S+xS+v132D+nDqrCRh0yiq+Hfs2iyxcx6YKvqFvYoNT61jk4ItrMGNLNzzZU7IoZ
+8soQbh1/K1N/8ecc9m65N+0bt89kE9Pm7sPuZuKQiXxzwTfxMiltt27L1X2vpn/b/hluXXTq1KjD
+HtvuAcDUX6Yy8v2RPPSF78TWrVGXi3pclMnmpVVhIQw+tj4L/n01d3eYQouSvUutz+Zk8L77gk06
+I37/zo/vZM2GNTjneGvWW4kNF3SNn7XPFb/r8LtE0rPxDDh5IBx2cXzyuBN3O5F2jXNnAjnw/8eH
+tved50WrFvH8lOe5dsL5HPZqx/jkWgVWkHMjRMG/r6v7XB2/f+FrFzL0laHc8XEi+Z+tI4PL0neH
+vpzY6WR/p85S3q11GQC//go0+gEGHx6vuXdh9wvjB5+5ZMcd/URazEpc6v38lOdpf3d7+jzShxOe
+SxxEx2rJLg/mrMq2yePC4sng5S3psyIxanLIK0M45plj2O8f+zFvuU+It2zQkhcHvc3P3/nPfuss
+/Bic1+28eDJs5m/fs/tlf4Q6S2Bgom7yqENG0aBWg/JeIqt0aNKB4b2GA37U9/mvnI8jMT5o9Oej
+ufR/l1LiSli1flWpk7x3D7i7WvdZ6taoyxfnfcGQbkMoDKa8mbZkGle8cUWZ2987IfH5vrD7hZW+
+Suv4TsfTp00fOjXtxMNHPsykoZO4Z+A93DPwHq4/4PpSVwWmy9Z1tubsrmczaBdf93b+8vnc/cnd
+vP/j+5z0/Elc8/Y1rN2wdqPnOee45YPQFQ29rtxom2x0yu6nxOvfl7gSTnr+JF79/tX4+nGzxrF4
+9WIAjtj5COrVzM0MoJnFJ8lcV7KOhoMvgKI18fXbDhwN+/lR4QVWwPGdjs9IO1MtXCqic9Hv4iVf
+PpzzITd/cDO3f3h7PO/Qo2UPmk++Pr59Nn9f5xMrr/6LVI6ZNQF+xo8Mfto5N7iC7RcCTYFvnHNd
+qvB75gItW7Zsydy5G+ecLxl7CXd+cicA53U9jweOeIC/fPgXLn/jcgCa1G3Cl0O+zKn6oZvy90//
+zsX/TUze8sGZH7Bf65RV5qg21m5Yyx4P7BHfEccUWiGfn/c5Xbat9EdMslT4/zxs2N7DuPPQOzPQ
+osxYu2EtF712EQ99+RC1Cmvx3UXf0aZRm0w3a7P12HcNE/puC7V9HfhWDVuxQ6Md4qPGALh3Es/d
+25ljsvtKxI1M/WUqxz5xKt8u+6zU42fufDmjTxgZn9U5lzz+1eOc9p/TylzXtG5T/nrwXzm1y6kR
+tyo6w98cXqo0QEyzes34YdgP1KmRhYViy/Hzyp9pcVMHSmr5ibVG9ruZGVPq8/CMEVDXH1Q3qduE
+KRdOiU8umGsGDICxY4Geo6h/+AhWrF++0TbbNdiOmb+fyZqVtWgU5ID22w8+KH8eo2pt0SLiV3Ic
+eCC0uvjMMi+77t26N88e9yyfjGvOoGAepj/+Ef7yl+jamipf//Q1XUd3jY8aKyypQ3GBnxjywB0P
+5I1T38jack5lWbNhDbvftzvTlvgJpkYfPppzu53LU18/xckvnIzDH3NvU2ebeALttC6n8eigR7Mm
+DlMWTWGfh/fht7X+cob3zniP3m0SI3+XrVlGy1EtWbV+FQ1qNmDepfOyMuE/edFkOt/XmRJXQlFB
+UanawT2378lzxz9Xal6KcbPGceBjBwLQpXkXvhzyZdb8TStS4ko466Wz+OdXfuLTAitgZL+RHLHL
+EZwz5px4mboXjn+BozoelcmmptVHcz5i33/sG79fd9le7OUuYJfu83ho+jXx/++R/UYyvPfwTDUz
+pa69Fq4P8rtjxkCDzu9w4GMHUuJKKLACigqKWFe8Lp53OOWgLnzzjU8Er1y56deuDrbffnvmzZsH
+MM85lx9JsiQaGbzlwtf0rKvE9msBA1J6ZHNVn6vi9bke/vJhDn/y8FIJosePejxvEsEAQ/caGq9t
+U2AFWV+3pzy1imrxwOEPbPT4JftcokRwnrhkn0u4fN/SyeCigiIu7XlpOc/ITbWKajH6iNF8cd4X
+TLlwSlYnggEO6F0b3rgNnD+YmPPbnNKJ4BkHwc+75VSZiJgOTTow6eJPafr2s/BTZ1jRnFovP8WD
+x9+Wk4lggIE7D4yPuIppULMB1+9/PTN+PyOnE8EAIw8cyfndzi/12ODOg/nknE9yKhEMPsG967zE
+JfR/HjechxdcHE8ENyvciXdOfydnE8EQqxsMfHQpd+wwk0v3uZR6NRIjyurXrM+dh9xJraJaTA2d
+6+6YsgJr0WvaFJoEf9LJk+HvA/4ev7IJ/OfirkPv4q3T3qJ5/eaMH5947n5ZOpahc/PO3Nr/Vgz/
+PRZLBNcqrMV9A+/LmWRZTO2i2tx/+P3x+396808c9fRRDH5hcDxRdPm+l7Po8kV8M/Qbxp02jkcG
+PZJVcejYtCMj+42M3x/yyhD+/c2/GfLyEI56+igGPDEgXlbh9C6nZ2UiGHxZgNO7nA6w0SRyH839
+iG6juzHqo1F8ueBLbh9/O8c+c2x8/ZW9rsyqv2lFCqyAh458KD7atcSVcOVbV9Lp3k7xRHCj2o0Y
+sNOATb1M1uvZqif/Pubf8bJeqxp9xntbn8WD068u9f+dK6PCofTI4Bkz/FXmI/qOAPznIFb/PJZ3
+WBGUEle94OyhkcFbqLqMDAa44d0buOadazZ6fHiv4Yw8cGQZz8htc36dwzXvXEPv1r05a8+zKn5C
+Fjv7pbP5x8R/ANB6q9ZMvmByzl6qI2V7+punOWvMWaxav4oh3YaUOiCR7PPaazBwILDtRNqceRU/
+1vITVuzUeCe2nXwj7z9wLLgCpkyBDrlXShaAYcPgrrsAHH36GO++W9EzstvRTx/Ni1NfpGZhTS7s
+fiHDew2nab0cqwOyCSWuhNvH386sZbM4f6/z4yVwctGwP5Rw16/9YIekD/X3hzH60Cc499T0Xw6d
+Sa+8Akcc4Zcvuwxuv91PvhRLuhQVFMVrgz76KJx5pt/2r3+FS7P4PGffvvBeUB1gyRJYXjCbm967
+iXaN2zF0r6GlEme9ehFPCC9cCM2bZ6DBKTJx4USuGncVr03z32O3HHgLV/Qqu8RALjj9P6fz2FeP
+bfT4uV3P5YHDH8j6RGFxSTH7/mNfPp336Sa3+/aCb0ud8Mg2s3+dTZf7u7BszTJaNWzFxT0u5u5P
+746XXyxLp6admHj+xJw8cV3iSrjh3RsY8e6IUo+3qN+Cx456LG/K9H218CuOevqoUhPdQu78f4eN
+H++/iwAuugjuvtv//x/6xKG8OfNNoHTeoWlT+OUXXw5q5swMNrySNDJYyeAtFtQMXo1PBr/qnDuy
+gu2XA3WBj51zlT7XH0sGN23alLFjx5a5zar1qxj01CBf0L8BtGjRguv2v45zup6TUzsm2djiVYs5
+4J8HMGvZLMacOCY+e7nklx+X/cjnCz5n4E4Dq/3s3LJpv/3mZyIvKYFOneCR1yewYMUCBrQfwBED
+a/D66367xYuhcY7Ol/j229Cvn18ePhxG5vg5zeVrl/P6jNfZu+XetMrBOveScN99cMElv8EuL3Hy
+Gav4bTm8/NgOMLM/L48p5PDcnIMn7qefYFs/HxV9+8I775S/7ZVXwq23+uXXXvMlJrLV0KFwf3Ce
+dvx4Xx++LGvXwlZb+Z/t28O0adG1MZ0mLpzI0tVLc76PumjlIjrc0yE+wVqL+i0Ysf8Izul6DgWW
+GxflTlw4kb1G7xWfsDjZOXuew4NHPhhxq1Jv1tJZTFsyjT5t+lC7qDaLVi5i8AuD44mwmAIr4LQu
+p3HzgTfHJ9vLVWO+G8PZY86muKSYP+33J36/9+/jo2XzxfK1y3nl+1fi5VJ2abILfdv0zbl8y8KF
+0KKFXx4wwH8Hgy93deBjB/LDsh9K5R3q1oXVq6FzZ5g0KUONBhYsWMCCBQsq3O7QQw9l0aJFoGSw
+bAkzWwQ0Br5yznXdxHaNgCX4xPGzzrkTq/A75gItK7t9/zP789IDL+XdzjmflbgSnHNpm2lXRKLV
+owdMmOCX582D7YLydF27wpdf+kn01q2Dgtw4ttyIc3DjjfD99zBqFDk3WZ7kr7fegv7BIKo//hHM
+EjVh330X+vTJXNui0qYNzJ7tZxz/9Ve/PyvLoEG+ViHArFmwww6RNTHl7r4bfv97v/zgg3DOOWVv
+9+GHidIQp5/uR0dLdvlozkfc9uFt9Ny+Jxf1uCgnj8ee/PpJ/jXpX3Ru1pn+bfuzW7PdMDNqFtak
+cZ0cPUuNnyxu8qLJvDnzTd6b/R6NazfmD/v8gU7NOmW6aZFZX7yeAivQMWeOcw4aNPD1f3feGb77
+LrGuuMSfCIp9BoqLoSgYEL/PPvDRR1G3NmHEiBFcd911VXlK3iaDc+8ahsyYDPQG2ptZgXOupJzt
+whfzTtmcX7SpkcFhLVq0yMmOh5SvwAogt05IiuS1Qw9NJIPHjoWzgmo3/iS2T47maiIYfILs6qsz
+3QqR1Ntll8Tyd98lRt6AHxGaD3r08MnglSth6lR/BURZpgS95bp1oXXr6NqXDruGrpifPLn87XKh
+XnC+69mqJy+e8GKmm5FWgzsPZnDnTVZHzElmRqdmnejUrBPD9hmW6eZkRI3CGplugkTADNq2ha+/
+9idji4sTJ26TTwSEJ4zLdM3gIUOGcOSRm7xYHyg1MjhvKRmcGh/gk8H1gG7AhHK26xtaHl/ONptU
+s2ZNunYtd/CxiIjkiAED4IYb/PJrr/lksHPw88/+MY2UFclO223nk5urVvmR7/VCJf4bNsxcu6LU
+vTs895xf/uSTspPBa9f6SWvAJ9Cz/eRXZZPBH36YWC6vlISIiEi6tWvnk8Hr18Pcuf6qnrJMn55Y
+Dp/gzoQWLVrQohKNqFmzZgStqd6yvFtVbfwntHxmWRuYLyJzWnB3GfB2uhslIiLZq0ePRD3gN97w
+HbHly31pCIBmzTLXNhHZfAUFiYkfp0+HH39MrMuXZPDeeyeWH3zQn+hKNm2ar5sO0LFjNO1Kp223
+hUbB3IDlJYOdS4wMbtQoN963iIhkp7ZtE8uxk7Nl+eKLxLLGLWYPJYNTwDk3AXgff5H+2Wa2dxmb
+XQZ0xNcLvtO5cirui4iI4C/FOuQQv/zbb360WGxUMGhksEg26xtcK1ZS4kfGxuRLMrhXr8Ro4I8/
+hhde2HibKaGCah06bLw+25gl3vOcOX6/nmz69EQpoH33zf7R0CIikr123jmx/Oab5W+nZHB2Uhcj
+dYYBq4EawBtmdqWZ7W1m+5vZA0AwFzLfAaMy1UgREckeAwYklv/730SSADQyWCT5SXfiAAAgAElE
+QVSbxSaQg8So2Dp1oEaelGIsLIRbbkncv/JKf/VD2NSpieVcGSEbLhURfn8xqhcsIiLVxZFHJvol
+o0fD6tVlbxdOBu+xR/rbJamhZHCKOOcmAscDv+JrB48EPgLGAefiRwRPBQY651aW9zoiIiIxhxzi
+R5OBrxuskcEiuaFPn8TM2zH5Mio4ZuDAxAjp6dP9gWZYeGRwLiaDyyoV8dlnieWePdPfHhERkfK0
+aAHHH++XFy+Gp57aeJsNG+Crr/zyTjvlX18mmykZnELOuVeB3YE78COAVwJL8RPK/Qno6pyblbkW
+iohINmnWDPbayy9//TV8+WVinZLBItmrfv2Nk335dgBlBrfdlrg/YkTpqx9iyeDCQn+AmQvCyeBv
+vtl4feyAGjS6SkREMu/3v08s/+1vG9f4nzoV1qzxyyoRkV2UDE4x59wc59xlzrmOzrkGzrltnHN7
+O+f+6pxbk+n2iYhIdgmXirjvvsSyykSIZLeDDip9f6utMtOOTOrRA044wS//8gucd54/0Cwpge++
+84+3awe5Mun37rsnlidOLL2upCSRDG7TBrbeOrp2iYiIlKVHj8Skr5MmwXvvlV6vesHZS8lgERGR
+auyooxKlIlQmQiR3hOsGQ/6NDI654w5o0sQv/+c/8PDD8OOPidqEuVIiAmDbbaF5c788cWLpEVY/
+/ADLl/vlLl0ib5qIiEiZhg1LLP/tb6XXKRmcvZQMFhERqcb22AMefHDjRJFGBotkt+7doUGDxP18
+TQa3aAEPPZS4P2wYXHFF4n4uJYMhUf5h8WKYNy/xeHiksEpEiIhIdXHMMf67GvxJ20mTEuvCyeA9
+94y2XbJllAwWERGp5s4+G77/Hs46y9fP7Ns3d2poiuSroiI44IDE/XwsExEzaJAvEQGwahU8+2xi
+XYcOmWlTuoQPlsMJYCWDRUSkOqpZEy6/3C87B1df7ZdLShLzmbRuDdtsk5n2yeZRMlhERCQLNG/u
+L59evhzGjUuUjhCR7BUuFZGvI4NjRo0qXVMX/ER7Bx+cmfakSzjRG54UNDx5nMpEiIhIdTJ0KLRs
+6ZfHjIGPP4bp02HFCv+YSkRkHyWDRUREskidOlCgb2+RnDBoENSt65e7d89sWzKtXj349FN/yen4
+8fDWW76ObuzS1FwRTgaXNTK4YUPYYYdImyQiIrJJtWvDNdck7l91Fbz/fuK+ksHZx1x45gKptsxs
+LtCyZcuWzJ07N9PNEREREZEU+PZbmD/fjxLWiP/cV1zsE76rVkHbtjBjBixZkri8tlev0gfYIiIi
+1cH69b6O/4wZG6975RUYODD6Nm2u7bffnnm+cP8859z2mW5PJmhskYiIiIhIhnTqBAcdpERwvigs
+TJTDmDkTfv219GQ8qhcsIiLVUY0acN11Gz9eqxb06BF9e2TLFGW6ASIiIiIiIvlizz19vUXwiWBN
+HiciItngpJPgu+/8FSwNGvjJb088EZo2zXTLpKqUDBYREREREYlI8iRymjxORESyQUEBXH99plsh
+qaAyESIiIiIiIhFJnkQuNjK4sNCXDRERERFJJ40MFhERERERichuu/nRVSUl8OyzsHKlf7xDB6hT
+J7NtExERkdynkcEiIiIiIiIRqVsXdtnFL69YAc755f33z1iTREREJI8oGSwiIiIiIhKhnj0Ty40a
+wYgR8Je/ZKw5IiIikkdUJkJERERERCRCI0b4UhHt2sHQoX5GdhEREZEoKBksIiIiIiISoVat4MEH
+M90KERERyUcqEyEiIiIiIiIiIiKSB5QMFhEREREREREREckDSgaLiIiIiIiIiIiI5AElg0VERERE
+RERERETygJLBIiIiIiIiIiIiInlAyWARERERERERERGRPKBksIiIiIiIiIiIiEgeUDJYRERERERE
+REREJA8oGSwiIiIiIiIiIiKSB5QMFhEREREREREREckDSgaLiIiIiIiIiIiI5AElg0VERERERERE
+RETygJLBIiIiIiIiIiIiInlAyWARERERERERERGRPKBksIiIiIiIiIiIiEgeUDJYRERERERERERE
+JA8oGSwiIiIiIiIiIiKSB5QMFhEREREREREREckDSgaLiIiIiIiIiIiI5AElg0VERERERERERETy
+gJLBIiIiIiIiIiIiInlAyWARERERERERERGRPKBksIiIiIiIiIiIiEgeUDJYREREREREREREJA8o
+GSwiIiIiIiIiIiKSB5QMFhEREREREREREckDSgaLiIiIiIiIiIiI5AElg0VERERERERERETygJLB
+IiIiIiIiIiIiInlAyWARERERERERERGRPKBksIiIiIiIiIiIiEgeUDJYREREREREREREJA9kbTLY
+zOqZWW8z+6OZPW1mM82sJLjN3IzX62RmD5jZdDNbZWY/m9l7ZjbEzAqr8DonmdnrZrbAzFab2Q9m
+9riZ7VPVNomIiIiIiIiIiIikSlGmG7AFXgH6hu674FZlZnYucDdQM/QatYD9gF7AmWZ2mHNuySZe
+ozbwPDAgqR2tgJOBk8zseufc9ZvTRhEREREREREREZEtkbUjgwOxBPBi4H/ASsCq8gJmdhhwH1AD
+WAhcDOyNT+q+ELx+d+BFM9vUaz9CIhE8Dvgd0AM4G5iOj/W1ZnZOVdpXHSxYsIARI0awYMGCTDcl
+byjm0VPMo6eYR08xj55iHj3FPHqKefQU8+gp5tFTzKOnmEdPMY9ecXFxbDHbc6KbLZvf+BPAYGAn
+51xT59wAfFK40sysCLgLH4ffgH2dc/c65z5zzv3POXccPlFs+BHCp5bzOv2AE/CJ4DHAwc65l51z
+nzvnHgV6ArOD17nVzLaq+tvNnAULFnDddddp5xQhxTx6inn0FPPoKebRU8yjp5hHTzGPnmIePcU8
+eop59BTz6Cnm0QslgytdEjbXZG0y2Dn3kHPuaedclesDhxwFtMUncUc6534oY5vLgaWh5bL8Mfi5
+AbjQOVeqXIVzbjFwRXC3EZB1o4NFREREREREREQku2VtMjhFfhda/mdZGzjnVgPP4Ef17mpm7cPr
+zaw+cCA+ofymc25+Ob/rBfzoY/BJaBEREREREREREZHI5HsyuFfw8zvn3M+b2O7d0PJ+Seu64yee
+S96uFOfceuBjfFK5u5nl7XB0ERERERERERERiV7eJoPNrB7QCj+id2oFm4fXd0xat2s5223qdYqA
+nSpqo4iIiIiIiIiIiEiq5G0yGNg+tDy3gm3nhJZbpel1RERERERERERERNImn5PBDULLKyrYdmVo
+uX6aXkdEREREREREREQkbfI5GVw7tLyugm3XhpbrpOl1RERERERERERERNImrclgMytJwe20NDVv
+TWi5ZrlbebVCy6vT9DoiIiIiIiIiIiIiaVOU5td3aX79LbE8tFxRyYZ6oeXkUhCpep2KNAOYP38+
+zZo1q3DjwsJCCgsLq/gryrZunR/wfOihh1KzZkX5bkkFxTx6inn0FPPoKebRU8yjp5hHTzGPnmIe
+PcU8eop59BTz6CnmqVNcXExxcXGF2y1atCi22DitDarG0p0M7piC11iQgtcoy7zQ8vblbuWFJ3ub
+k7QuPGnc9sAXm/k6FSkAcM6FP7iRytTvzWeKefQU8+gp5tFTzKOnmEdPMY+eYh49xTx6inn0FPPo
+KebRU8wzwjLdgExJazLYOfd9Ol9/SzjnVpjZHHyCtkMFm4fXT0laNzlpuzGVeJ0NwLTKtDNkLb7M
+hAOWVGL7YqCkir9DREREREREREQk2xQAlblEvjE+Eby2og1zVbpHBld3HwAnAbuYWTPn3M/lbNc3
+tDw+ad0E/MRxNYLtbivrBcysBrAPPpk7wTlX8dj1EOdcvYq3EhERERERERERESlbWieQywL/CS2f
+UdYGZlYHOB6fxJ3snJseXu+cWwG8hT+r0N/Mtivndx0DNAyWX9iCNouIiIiIiIiIiIhUWb4ng18E
+ZuITucPNbMcytvkLsHWwXOao32Ab8COt7zGzUnE1sybALcHdZcDDW9JoERERERERERERkaoy51ym
+27BZzKwd0Cvp4b/ga38sBi5PWvffsspAmNkA4GV8Yvwn4EbgU3wC+DzgaPyo4PeBA1w5ATOzJ4ET
+g7tvA3cC84HdgT8D7YLXOc85p2SwiIiIiIiIiIiIRCqbk8GnA49U4Sn7O+feK+e1zgb+DtRk49kE
+HfAJcLhzrtyJ28ysNvAscFjsoaTXKAGud87dUIU2i4iIiIiIiIiIiKREtpeJcJW8lWzyRfxI3W7A
+g8AMYDXwC3408PlAr00lgoPXWOOcOwI4GXgDP8p4LTAbeCJ4DSWCRUREREREREREJCOydmSwiIiI
+iIiIiIiIiFReto8MFhEREREREREREZFKUDJYREREREREREREJA8oGSwiIiIiIiIiIiKSB5QMFhER
+EREREREREckDSgaLiIhISphZ3Uy3QURERERERMqnZLCIiOQsM9P3XETM7CbgNDMrMDPLdHvygZkV
+hpYVcxFJCe1PJB+YWb3QcuGmtpXUUL9FpPrQQbJInijvC1dfxOllZoeaWatMtyOfmNlfzOxiAOdc
+Sabbkw/M7B5gOHAR0MA557RviUS8H+ecc5lsSL7SCSfJRcn7E33O00t9xeiZ2eHA1WZ2rZkVOueK
+M92mPKF+S0R07C8VMf0PSqaYmelLIFpmtgP+S7gh8I1zbkPweIGSZqlnZg8BZwFXAf90zs3PcJNy
+npn9DbgYWAns6ZybnuEm5Twz+ztwAVAMFAI3A/+n/Xv6mFlfoCswCFgMLAFuAuY759Zlsm25ysy2
+A3YEGuM/51OA6UogpJf6itEys9bArsDuwUOzgBedcxv0t0gP9RWjZ2Z/Bi7F788XA2c758ZktlW5
+Tf2W6AWl21oDNYB6+GP/FZltVe7Lpu/Kokw3QPKPmf0BeNs591U2/bNkMzM7EegDnBg8tBUw1sze
+B253zhUrIZxaZnYfvnMPcFnw2GPOuXmZa1VuM7O78CNTHb6TPzOzLcp9QcwvCO7GLv3riu90rtA+
+PvXM7GrgbHwHP6w3cJOZPeecWx19y3KXmV0GHAX0DD08G5gVlEf51jm3ICONy1HqK0bPzC4CjsPv
+S2KKgU/M7Fjn3EL1FVNLfcXomdko4A/4vuLTwFglgtNL/Zbomdn5wKHAIcAGfL/8IzObBYwAFjjn
+VmauhbknG/stGhkskTKzB4BzgbeBi51zk7PlnyVbBQeqlwI1geTLQpYDLwFnadRH6phZY+BVoAew
+GqgLLANuR6M+0iKUCAY4D3hEI/bSKynmp+BHkv0puH++c250RhqWw0IHsQC/AVOB3fAHtfWAacDJ
+zrnPtD9PDTO7HfhjcNcBc4FW+IOrImAO8B5wp3Puc8V9y6mvGD0z+wu+rxjzNdAZWIfvP34K9HbO
+rc9A83KS+orRM7OrgBuCu5cAz8RO5OlER3qo3xI9M7uN4ORSYCGwbej+NOAp4Cnn3HdRti1XZWu/
+RfWfJDJmdgv+nwRgH+BuM+uk2pLpE3wBDwdqAWPxHaBrgfuCTRoAxwM3Z8MOK1s455YAk/HJ9w+A
+r4BGwOXA6cHlxpIiSgRHLynmQ/Cja94BYgevJwWXG0uKmNkNJA6orgUOds7tA/QD/oa/7HIn4BpQ
+Lb5UCBIHsUTw9fhRNr2BY4CH8AdYrYCjgafM7ED1abaM+orRM7NbSSSC7wWOBboBRwBvAWvwCcvL
+M9LAHKW+YrTMrB+JfsulwH2hRHBhLBEcro+tWtlbRv2W6JnZzSQSwaPw/ZMD8FcHvwAsxcf8YuAu
+M9s7E+3MJVndb3HO6aZb2m/ACfgdfjGwCijBj0p9C+gUbGOZbmcu3YArgziXABcC2yatPzm0fgLQ
+UX+HlMS9IPh5URDb54DTgO+C+0vwCfrtMt3WXLgBd4U+x+cAhcl/i008V5/11MS8KLRuTPD4UmB/
+xTllMT8aP2KsBJ+cbJC0vg0+ObkBP4KvZqbbnO03oBPwbdBvuQKok7S+Pr4kypTQ/8NKYECwXp/7
+qsdcfcXoY3556PN7EdA4aX0/YG2w/u+Zbm+u3NRXzEjMLwu+I18D2oQeL0rablugXqbbm+039Vsy
+EvPj8FcZlOBLuNVLWt822K/8EmyzPujn9Ml027P1lu39Fp3tkrQzs7b4WnuN8J2b64Cf8JeG9MCf
+lcqOsydZwswOAc4P7l6NHym5MFhXBOCce4LEJd3d8GcNccFeSzaPS1xi9j7+AKo28DJwC/6yHI36
+SBEzu4fEKI/T8JdVFgfrilxilEeRme1pZj3N7BAza6uR8JsnmKAveRT2htDomZH4y+i3wteB20px
+3nyh78Te+Ik/JwEvOeeWh9c7537EJ+ILgOb4y41ly7QHOuL7LW+5oJ5h7LPunFvhnPsC/7d5FX8A
+Vgd41cwOUZ+matRXjJ6ZHUli5Pu1+P35kmBdrK84Dvgi2KaZmRUmvYb+FptBfcVomVkD/CCYAuDd
+4DsTAJeYTPsqM3sOn5CfZGbjzez3ZtYpWK/PeiWo3xK9UMx74a8Gfg143gU1gWP7befcTOAJ/Mmn
+9firEjoC/zSz/aJud7bLhX6LksEShUPwl5wZvjbNrfgh9D+TRf8s2SAUu/5AS3wdw/8451bFtklK
+3LyBnwSnBF8bTp2dFAji+wuwAjgIaIr/4r0Z+J7SnfyWoefVMrOa0bc4+5jZNcBQ/Gd3HL5zv968
+GqHO/RDgMfyog3HAf/Gf+9fMrKOZ1crMO8g+QSL4YnzMS5XjCB3Y/oivoQqwHdA9eG4hslnMrCl+
+5AHA18656bF1Sd+Zy/E1+B4CmphZLzM7zsx2DV5DqqZ98HMBvsZhbIboeE3J4NLiX4DT8fX3fwtW
+PWNm++tESJWorxihIMF4MrA18C/gSReaTCj0HboXsAN+3/Io0MLM9glOrDYhMXGoVJH6ipEqwF/N
+Af5qDmIxNLPtzOxFfCm9o/El9NrgJwy9DnjOzA7Sfqfy1G+Jnpk1JzFR/ETn3E+xdS5UOs85Nxu/
+nykkkQtsBdxpZntE1NxckfX9FiWDJa3MrC7+MoUC/MiZKyB+NrAXWfTPkg2C2HXD12cqBF51zn1b
+xnaxg9mvgXn4v09XM6ulg9ct55wrcX4m6Nfxf4eWwRnx/+BHfYQ7+aeZWdMgKXkMcG7whS6b9h98
+rb0C/ImMK8yshfPWA5jZ/cDf8Z2jQhIHrTviv8CfA44JRozIJpjZrsDhJC49e8SVUZfZ+fp7twR3
+2wCDg8dVw3kzBPvjFfhLzwDaxT6vodE1zszq4L9THXAS/oTHu/hazuOARzTqo8piyZY2+AOlja6c
+cc4VBwnhJfhyTK/jJ9xqAIwys90jbG/WUl8xI+oCA/GTID7hnJsRW5EU1374UXurgZvw37sf4k+s
+fgL8n5ntFFWjc4n6ipEqxJcjWIf/nsQ5ty5IyI8GBuHL/IwB7seP0l6Iv8ppF2CsmR2qY6SKqd8S
+vSDm6/CjfTcEt/gJj5hQ/N/El4eYg/+cF+A/50PMrFl0Lc9eudJvUTJY0ioYkXoy8ArwL+fc6mDk
+XmFwljBr/lmySHP8JE4T8B2cMkf7WmLW3NilUoX4M1uyhUIjr+fi97NHBSPKluGL94cvA7wMn0Q4
+Gz9B0d3AYH3+yxd8difha2NNxo+mOQ642swaBdv8Ez96dT3wJnAVflTrFcBnwK/4S6NGAAcHz1HM
+y+Gcm4yP3yDg4QqSux/j9/kOOM7MBkTQxFxWi+DgFdgG2MfMasYOSs2sBrAvcAp+f9MKqIE/0bcE
+aIY/+fGYme0bcduz2Sf42tc1gCPNl5vZaB8RJIQLnHNL8QcGHwSr2gFnmdlWkbU4S6mvGL0grucD
+I51zr4P/Dgz6KrF9y3EkTu79hP8+fRFfNuIn/InVocCVZrZDpG8gB6ivGJ3ghN1P+JN8x5tZvWDV
+LcBh+Ks6DgKOc85dgB8h3AOfoFyBPz76t5n1irrtWUr9lujVxMe9CNgTSp3wILgfi387/BUfs/D9
++tn4kfMD8MdGOiaqQM70W1w1KFysW+7eSEyQ0ISNi5gXBj/b489KZVXB7ep8A87EdxTrVGLba4PY
+fwVslem258It9rnFd3TW4+s2hddvFfyNpoY+97H/gYVAq0y/h+p+C+1bOgLfhGJ3F3BrKK7nAK2T
+nrsrfubiRcF2XxNMmqN9TvmxruJzYpMSbQCuVmw3O/axfckQEpM4fQCcij/Aah/sSyYH66YAR+CT
+NM3xE5x9RmKikHFA20y/r2y44UcE/xTE7j2gUfB4YTnbx/o0zYEZwfNmALuE/5a6lRk79RWryd8g
+dP8EEhPLPY5PlNUJ1rUkkUAowZdSGaa/RZVjrr5iNHGO7V/uD2L3WbA/KcCfvF4BHBxsUxT8rBH8
+3Bp4Ej851AZ8kn7rTL+n6nxTvyUzMQ8+z/8KPqurgYtD62Of59j36WH4kdvPBfePxicuS4D/JX8f
+6FZmzHOi35LxBuiWX7fkD342/bNkwy28847tmCqKH35kZAn+7GCTyr6+bpX6e+wcfCGvCHU8Y52k
+OvgRNfNJHHAtAboH68tMOOhWKr5lJYR/CWK+Cj+pXGFo+6LQ8vb4ESHLg+fdq31NSv4msc93QdCB
+j+3Td89027L5BuwEPAisCe0rluNn6o499i3QvIy/RRP8SL6SoLM/KLxetzLjHYvdBSRmh/5XaH1F
+CeGDQgdWj2X6/WTbTX3FjMe/OX6EXuy7cdvwvj203f74RHAsoVMvE+3N9pv6ipHFuSeJ5ORT+NGn
+8/Gjr7ctY/tYH7MR8GXwvHlA+0y/l2y4qd+SkZifFdpPfAGcV8Y2HUPfo7HBGi3wieT1wA/Azpl+
+L9l2y9Z+i8pESKRc8MkP3S+u5HD6AgAzq29mh5pZ68gbnwWccyWhekArg59l1rcKXaKwuqLXNbNW
+wd+pxDQRVKUE8Z2Gnym6DtDChSYewp+5XYf/cogpAPY2szZONVYrFHweC5xzU0iUjGiMj+N9wBhX
+etKEDaHlufgRHrH1O5b3vyKVF+yvYyMU3sF3LGsAJwaX2avfsRmcc9OAO/En75bhLwOsga8tuRY/
+yv0M59xPlpg12lligrPz8JfBNsHX5pNNCO0LPgQ+wu+nB5vZyGB9cVmf5dD+5htgYrDcRt+bVaO+
+YsYtxZcFugm4xjm3MPY3SerHTCCxD2oPdIu6odlOfcVoBHGeiJ9QeD1+5Ps/8bGc7ZxbmPyc2DGP
+82U7LscnclrgR1XqMvoKqN8SndCx/z/wMQfYA7jRzB4xs4HmJ+a7DF+XuRl+wMZNwfMW4I+JCoHW
+QJeI30K1Ff4/39T/fLb2W3RQJhlXiX+W3YIv5Ib4y0geAD4P6t1IksomtELbxUYvFQJ1knd05icG
+GQPMMbMa6nhWjgvgLxM2grq0wY6/AX720UuA7fBny2cBDfG14Aabn+lbKlBGQngqfiTfy0EHflPP
+/RR4O7i7t5k1U7JyywUf/Q3AI/ikQk3gUKBm+ISVVE6ok/+tc+4WoBPQHV/uJDYj9HRgWlBvMnwC
+JJa0XIgfdQb+IKzS3xW5rozvvPh959xEfMJgZfDQeWZ2VbCu3JOjwYHV2OBuL/zlrxLYVMzLo77i
+lqlKzJ1z64CXgOuCpEx5263En/iohd8PFaWmtbmhMjFXXzG1yot5EObV+OOZWOJ3f3xSbFczK3Mf
+Hfo+nUuiBm5snb5DKTvm6rekV1n7klB/5HJ8qUjwifTT8SdBngZuDB4bhx81XGK+fjPBY1ODZc11
+EAj2xduZWRN8neW4ivou2dBv0Ze2bDEza+ic+21LXiP8z2K+OP8H+C/oHsAdZvZn/GVqfyKY1ZvS
+Z8nzSipiHlIQ3GrhL6OPf9EGieB7SZwh3I7EhHN5paoxDzo3Dl+PbAjQOXi8LnA4fkKzjviaewPx
+s7jejL+s6g9AXTO7N0gq5KXKxjycEDazk/C1396p4LULg85nrAO6AD/qI687mqnatwR/jzlmdjtw
+A36EwpX4UWZ5HeNkFcU8aZ9swT5hQXB/f6AusMQ5tyw4GChOen6JmZXgk/LgJ0/Ma2a2J/7gtB/Q
+0My+Ar5yzo2JxTv4DJc45x4zsxb4/XNj4MJg/3F9qO9SHHrt2OSssX33aoKZvfNZZWJeEfUVq2ZL
+Yl7Rif/Q53x57Cn4E7F5raoxV19xy1Vxf/6yme0M3I4/7lkLNMBPKne385NClWU5fkQx5PE+JaaS
+MbcgkaZ+SwpUIubFocT6MDObgx9RvSe+9jX4k0kfARcGJ0dwzsU+1ytIHAPFEsR5zcyOAvYDBuM/
+o7XM7DF8reWPK9N3qfb9FlcNamzolr03fH2ZLyij1tJmvl5Z9VVW4EcexArNLwI6ZPq9Z3vMSdRl
+OjeI60+EJqPAdzTfJFGvSTHfjJgDuwef4clAB/wXyrdBXH8gmNwMP6nCmSRq31ZYwzmXb5sTczae
+AKeietlNgO+DeP870+8507dU78+D1+yLH81UAvw39pmu6G+TL7fNjTmJWoajgtiOT14Xul8Y7FvW
+4E/m7Z/PfwP8qJmJJOrqlZC4DPs6oGlZscSPxIttvxT4a9LrWvh5oe3Hp/P9ZMOtKjGv5Ouprxhx
+zMv5HQ3xlxaXAG9n+j1n+rYlMUd9xbTHfBP781h91QNJTLRlUGoOhJPwJ/amE0wKmq+3zf2co35L
+JDFP+py3wtfKHoQ/mdS2rO2C++3wZTlKgJMz/Z4zfQviujQp5rHbq0C/Kr5etey3ZDzQumXvDbgn
+9E8xltQlhGMzubYmMZP3ahKTQ3XM9HvPpZjjL0ErxidsYrOehxPBivlmxjzoTG4LzAyefwuJSSh+
+IEi+k+h8NgTOx9fh65Lp956NMa/i3+aY4It3PnB07PFMv/9cizlwa+i1T8v0e60ut1TEHH/5X+w1
+bgw9XiO03BE/EqEEf4nsNpl+7xmM+R1BHIqD77Yv8Af3a0JxvCW0vVF6Eq0KBsoAABoxSURBVMpr
+KX1A8Cz+AKpO0nM6AZ8G29yJvxIvX/ctVY55JV9XfcWIY17G7zkIX95gBXAZoQRavt22JOaorxhZ
+zJP25yOS9ucf4ZNmW4W2KcSXNng32OZ5oGGm33s2xbyM11C/Jc0xp4IJ35PX4/soQ/Ej5b/I53gn
+xbwEX8/9FuC24LMYe/yhzXjdatdvyXiwdcvOGzAs+OBuCH2IXyfFSRt8nayVwesvAXbN9HvPtZjj
+a9QU4zvzu+NnNVYiOIUxx9dOLSExK/0PJDr3hUnbNiTUEc23W4T7ll2DL/jY6zfL9HvPtZiTGAWy
+X9BxLQk699tn+j1n+pbCfUsfEnXf5wE3hdbVBw7BT+RXgh9d0y7T7z2DMR8Z6sRfDewL1MbXxruc
+xIFnCX5Sm/BzwwmEPwaxXk9iRNnD+Fqf/fATb40P1n0P7JDp956NMa/i71FfMfqYdyCRIHsfP/FZ
+xt9/Nscc9RUjiXnS/vxSfD3g2P58Kv4k35H4ieKuAj4J1s3Rd2hKPufqt0Qc80r8nt3wpWpKgNFA
+vUy/9wzGPHzSfzihKwHwNYOfDq3vugW/p1r0WzIecN2y74Y/sP8w+PDOxl+2sJYUJm2CL4NT8Ge9
+S4DFQKdMv/dcijmJy5+ODl5nET4x/DJKBKck5qEYnwJ8FupMltm5z/dbOvctob9F3eD3xDqaP+R5
+RzOK/XlhaL+yHjgg0+87l2KOP0AIj276HHgteN0FJA648vmS+TNIHOxfBNROWl+EL5k0P9jmzaAf
+Eh69F04gHIufBCc8MmcDidE7JfhRfnl7OXEqYl6J36G+YkQxJ3Fyrx4+mRNLBM8G2mf6vWdzzFFf
+MRMxD+/Pj8YneMran8duU7U/T92+BfVbIo950nNj+5wGQG8Sx0RTyO8T2OeQ6MMNC8c89B04DH/C
+bh1BMji0rjKxr1b9lowHXbfsuuELit9Cok7NMPxsrG+QuqRNXeBU/GgbJSXTHHP8aKa1wW2eYp76
+mOMnqRiKvxS8TfCYOvdpjHk5v2NH/FneSSRGHORzRzOKmMc6SF1IHGjtlOn3ngsxp3RduKvxE9zE
+XjfWmV2MT9rkc8zb4g8yi4EHgeZJ68MJgldJjNJoU0HMW+MvKf4Mf6l87KD2S+BRQrX58u2Wyphv
+4neorxh9zNvjL62P1TecmeffoSmNOeorRhrzpP15S/xI4PfxCbHY/vwj4G7yO0GWrpir3xJBzDfx
+O7oD9+FPdMSOifL5hEdX/FVdG/B1rcuMOf5qgpLgs7t98NhGNa/L+R3Vrt+S8cDrll03oBuJs1SP
+hx4fgD8jlYrkZIfQjm1Rpv9JMn1Ld8zxo9TWhL6MFyvmqYt56MujJsFlNwQ1g3RLT8zLeO3G+AOs
+WMH+EvzIzLwdzZTumJfxu1oBT2jfktqYU3p003FBB3YO/oTHi/gJWFJeczubbsDJQUxXAoM2FUf8
+pE2rg9uA4DFL2jb5fiOgOdAr+D6tD9TN9PvOpZiX83z1FSOKOdACuILEZKBrgXHk8QmPVMcc9RUj
+j3k59xviL8HfE38SuxColen3nWMxV78l4piX8dyGJEpMrMSPDM7nqySL8EneYvwJoL2S1of3z48E
+cXsyeM6T+BHto4FLw69Zxu+pdv2WIkSqpia+A1gf+FvsQefcf82sJLjbGz+pxD/N7HTn3MIq/o75
++H+qFsCpzrkpW97srBZFzJcBzfCzZvZWzFMXc+ecMzNzzq3DJ9xxzm1Ia+uzU9o+5865JWa2An85
+1Nf4pNsdzrm5qXwDWSiKfUvsNeeY2dnOuTVb2ugsl9KYO+eKzazAOVfinHsWeNbMrgHWBfucvGZm
+9fAHmwD/cc69VNZ2zrniYHEG/m9k+NpwuKAHH9o2fj/Yty8L7v6UupZnr3TEvBzqKwbSHXPn3AIz
+m48f1fQx/mD2H865BSl5A1ko1TFXX7Fi6d6fB/d/Cxa/DP3eEvJUmmKufssmRPEd6pz7zcyG4uc4
+mAQ8t7n9+1zgnNtgZqvwCfXHnXOfxdYF++VYPE/GT4AIcCJwAj6xW4ifdwkz6+ecOzx4zcLQ3wmq
+Yb9FyWCpEufcR2b2Z/womG8AQjv0180stulmJxCCHdTtwF+dc0tS/BayTrpj7pwbb2b340dO9nPO
+TU79u8guqY55JQ9s81oEn/PHzWwh8BvwrXNuRerfRXaJYn+e9PvyPRGclpg750qS7sc/20md2Hy0
+Gp90r4FPYsXjnbyhmRXir4z5FT/at8I+cp7HtjxpjXmM+oqlpD3mwXfobHziYZFzbm2K2p6tUh5z
+7U8qFMm+JVme/13SEnP1WzYp7Z/z4PW+NrPfAxt04gmcc/eb2WTn3HuQ+BzGPotm9jv8ZMHgS3h8
+DDwDNAX2Af6MH3R0mJk945w7PikRXC37LUoGS6WF/ik+D5/pcM6VhNa9bmaxHXiVEgjhnb9LjLTJ
+axHEvMg5t8E5N8LM7qouO6ZMivJzLl6En/M30vxWsoY+59HLRMzz/W8QxHY8fuTGpNhj5WxbHJww
+Wow/qGoK5R+ESdmijLn6il66Yx76Dn03LW8gC2nfEj3FPHqZiLn6LemPeWyd0yANoNSgjPfC90Pr
+BwIvBHcfAm4DfnTOrQe+B8ab2Szgr0Ab4GAzO8M592hy37y69VsKMt0AyR5JH+TkMx3OgiFNzrn/
+Abfji/CvJ3Ewu23ya5pZbTPbPvk1xIsg5hvMrEawnPeJYNDnPBMi+pzr+y5En/PoKeaZ4Zxb55x7
+1zm3tJLxiR0A1I69RPIGZlYzZQ3MQYp59NIZ8+A7VPuWJPqcR08xj55iHj3FPFrJifMyEum1gp8P
+AcOdc9Odc+vNKwie8wJwU7BdQ/wcIdX+5IYOjiVlkg5mX8efNUk+mG0R297M6gPHANeZ2amx14i8
+4VksRTFfH3nDs5g+59FLUcw1EqQK9DmPnmKefpuKj/nLLQvwl2iCn1h1o+eY2c7AFWbWN13tzCWK
+efTSEXPtWzZNn/PoKebRU8yjp5hnXpDo3QO4wjm3OPS4c34kdyyn+hiJeuN9ggEbhRE3t0pUJkJS
+KnYwG/xz/C90Nit2ueujZnYSfubKI/CzE+8GNDOzF53qeFaZYh49xTx6inn0FPPoKeaZ4/zllmtI
+HFQ1hNLlN8xsJ+ABoC/wiJl94nSZ5WZTzKOnmEdPMY+eYh49xTx6inn6hfrkk8rbxiXKva0zs9gg
+u7nZEGclgyXlkg5my5oQ5xngeeBc/EHsr/gzLTqI3UyKefQU8+gp5tFTzKOnmGeUw8/IDb7+Xlxw
+QHUf/oDqN/wEINW+o58FFPPoKebRU8yjp5hHTzGPnmKeRrGkemW2M7PWQLvgoZnpa1XqKBksaVHO
+wWwJfmfUD9gT2BpYAvR2zk3JXGtzg2IePcU8eop59BTz6Cnm0QuNwl4Z/Ixfbhk6oOqHj3kv59zU
+6FuZWxTz6Cnm0VPMo6eYR08xj55iXn2Yn4NpIFAPmIEvGVHtJ9RWMljSJtgRxWZnfN3MagMtgPb4
+g9il6CA2pRTz6Cnm0VPMo6eYR08xj57zE4KsCu4WQLzW3r0kDqh664AqdRTz6Cnm0VPMo6eYR08x
+j55iXm3sCpyBn8RvAjAdqn+9fSWDJa1cMGmTmdXB76Bq42dkXAb00UFs6inm0VPMo6eYR08xj55i
+Hp3YaGwSfeMmZrYL/oDqADQKO+UU8+gp5tFTzKOnmEdPMY+eYp45sRG/ZlYEdALuALoD04A/O+eW
+ZrSBlaRksKSd+RnPjwD+hK+jEhvNNDmjDcthinn0FPPoKebRU8yjp5hHJ+jYrw3ubgPcjQ6o0kox
+j55iHj3FPHqKefQU8+gp5pkRxL0JcDAwDJ8IXggc6Zz7IZNtqwolgyWtzKwQXz/levxBrHZMaaaY
+R08xj55iHj3FPHqKeUYsCX7uA9RAMY+CYh49xTx6inn0FPPoKebRU8wjZGbbAAcCFwAdgGbAl8CJ
+zrlpmWxbVRVkugFSPZhZQXjZzGoFy4Vb8rrOuWL8DqkdsAFfvFw7JhTzTFDMo6eYR08xj55iHr10
+xDy43DL2/NgBlcpxBBTz6Cnm0VPMo6eYR08xj55iHr1Uxzx4vfbARUAfYDXwOHB0tiWCQclgwf8z
+hOoSDgBuA/5nZm2DA9Et4px7AzgK2F3Fyz3FPHqKefQU8+gp5tFTzKOXrpgHE328BMwH1uIPqFSO
+A8U8ExTz6Cnm0VPMo6eYR08xj146Yh683ufAKOB2/OjgYc65H1PU7EiZq94T3EmaBf8kxcHyZcCV
+QCP8iYI7gMtj/0SSGop59BTz6Cnm0VPMo6eYRy/dMTezesCxwPvOuZkpaHLWU8yjp5hHTzGPnmIe
+PcU8eop59CKIeUEu9O+VDM5j4Q+xmY0ArglW/Rt4FXjaObchQ83LSYp59BTz6Cnm0VPMo6eYRy/d
+MTfzs0NvcUNziGIePcU8eop59BTz6Cnm0VPMo6f+eeUpGSyY2UXAXcHd4cBjzrkFwTrtYNJAMY+e
+Yh49xTx6inn0FPP/b+/OQm0t6ziOf5+9j51jzhN6SKMcTmpkA2miOVDehEHSjQVZEIiikdFIUTZ5
+4ZWEhoEUFURCUJLRBJlzFEcTs0QpBzKPdBzJNKezny7Ws3W1PMM+uffvXWs/3w/82eusyXd913pu
+nrV4zbN5ns3zbJ5n8zyb59k8z+Z5Nt+xNUMfgIZVSjme0QmwAS4CLqu1Pt1uc5GsAJvn2TzP5nk2
+z7N5ns3zbJ5n8zyb59k8z+Z5Ns+z+dL4P5DT0cDrgb8BP11cJPDiCcm1/GyeZ/M8m+fZPM/meTbP
+s3mezfNsnmfzPJvn2TzP5kvgZnDHSilrgQ8CuwB/qrXeOvAhrXo2z7N5ns3zbJ5n8zyb59k8z+Z5
+Ns+zeZ7N82yeZ/OlczO4bwvAbu3yf2B0wu2deYJSyvxyH9QqZ/M8m+fZPM/meTbPs3mezfNsnmfz
+PJvn2TzP5nk2XyI3g/s2DzzVLq9tf7f7s/lSSml/31VKOaTWumXxOi2JzfNsnmfzPJvn2TzP5nk2
+z7N5ns3zbJ5n8zyb59l8idwM7lit9RngzvbP95dSTt3ROVRqrbWUchBwOfD7Usq+nndl6WyeZ/M8
+m+fZPM/meTbPs3mezfNsnmfzPJvn2TzP5kvnZnAnJn8aP/ZNx2+ATYw+CxeUUt6wg+dZA7wHWA9s
+xs/QNtk8z+Z5Ns+zeZ7N82yeZ/M8m+fZPM/meTbPs3mezV+ZLl5kjyZ/1l5rXZj49+I3HdcAt7fL
+JwLnllIOH3+eiUV2JHAOsEd77JPLfOgzy+Z5Ns+zeZ7N82yeZ/M8m+fZPM/meTbPs3mezfNsvrxK
+B79+7k4pZb7WuqVdfh1wGHAc8ATwIHBNrfWpsfsfANwEHAE8DPwCuLzWunHsPrsDxwAXAacCfwZO
+r7U+sPKvaPrZPM/meTbPs3mezfNsnmfzPJvn2TzP5nk2z7N5ns2Xn5vBq8zEIjkP+Ahw7MTdfsXo
+G49LFr89KaUcDfwE2AA80+ZS4FHgIeAM4G2MvjXZDJxSa717xV/QDLB5ns3zbJ5n8zyb59k8z+Z5
+Ns+zeZ7N82yeZ/M8m6+QWquzSgaYG7v8ZWChzYPAdYzOnbJ43ZPAFROPeQ3wO0b/98WFsXm+/X0O
++COwYejXOi1jc5v3MDa3eQ9jc5v3MDa3eQ9jc5v3MDa3eQ9j8xVsO/QBOCvwpsInxj7kXwLePnbb
+cYx+Lr/44f9qu36+/d0d+CTwY+DxtjgeA64FvgAcPPTrm8axuc17GJvbvIexuc17GJvbvIexuc17
+GJvbvIex+Qo0HfoAnGV+Q+Fk4IHFRQC8mnY6kHb7ekbnTFkAbgaOGrttfuK5Xs/oJ/WHD/26pnls
+bvMexuY272FsbvMexuY272FsbvMexuY272FsvjKzBq02RwMHAr8FflBrfbqUMg9sKaVsAK4H9gNu
+BM6ptd61+MD60nlY5mqtC7XW+8afuJRSaltB+h82z7N5ns3zbJ5n8zyb59k8z+Z5Ns+zeZ7N82ye
+Z/MVMDf0AWj5lFLWAWcCa4A/1Fr/2j70W0ophzNaJAcCNzCxSNrj9wKotS5s7fl7XSTbY/M8m+fZ
+PM/meTbPs3mezfNsnmfzPJvn2TzP5nk2XzluBq8uFdi1Xb4FRh/6tkhu5KVFcu74IikjewPva9+s
+aOlsnmfzPJvn2TzP5nk2z7N5ns3zbJ5n8zyb59k8z+YrxM3gGVBKmSullMnLW1GBf7XL+7b7H8F2
+Fgm8+G3IkcD3gItLKfss+4uYMTbPs3mezfNsnmfzPJvn2TzP5nk2z7N5ns3zbJ5n8+G5GTzFFhdE
+HZ3bpE5enlRrfQ64s/3zzFLKaYwWx4GMFsvLFkn77+wLfKz9c12t9fHlfSWzw+Z5Ns+zeZ7N82ye
+Z/M8m+fZPM/meTbPs3mezfNsPj38H8hNsVprLaUcA5wGHA/sAswDvwbuqLXesHjfUsqaWusL7bYP
+AMcCVwG7AdcB529jkcwBJ7bZDHy3Xd/libRtnmfzPJvn2TzP5nk2z7N5ns3zbJ5n8zyb59k8z+ZT
+pNbqTOEAJwMXA88ALwALY/Ns+3sx8I6Jx+0B/Lzd/gLwd+C0dlth9GvwubH7vxG4qd3/KmD/oV+7
+zfsZm9u8h7G5zXsYm9u8h7G5zXsYm9u8h7G5zXufwQ/A2cqbAh8H7gC2tA/w/cA9wCbg4YlFcx2j
+n8aPP/6g9pgF4J/A5cCbJu6zZ1uM17b73QMcOvRrt3k/Y3Ob9zA2t3kPY3Ob9zA2t3kPY3Ob9zA2
+t7njZvDUDfD1sUVwE/ApRifKngf2Bg4GvgFsHLvfXcAXJ57nzcB97fYngX8AnwY+CpwB/BC4u92+
+CThy6Ndu837G5jbvYWxu8x7G5jbvYWxu8x7G5jbvYWxuc6f1HPoAnLE3Y7QAFj/8XwPeMnH7mvZ3
+DjgJ+M7Y/R8APjtx/0OB2xj9DH/8m5bn299/t8V4xNCv3eb9jM1t3sPY3OY9jM1t3sPY3OY9jM1t
+3sPY3ObOWMuhD8BpbwRcOvZBPh/Ya+y2uW1cPgC4ZOxxdwAfnnjefYDPAz8DHmN0jpWHgF+2/876
+oV+7zfsZm9u8h7G5zXsYm9u8h7G5zXsYm9u8h7G5zZ2J92foA3AqwDfHPuxnA/M78dh1E4vsatp5
+UYBdJu57CKNvUrpfHDa3eQ9jc5v3MDa3eQ9jc5v3MDa3eQ9jc5v3MDaf/hn8AHof4NtjH/Lz2nVl
+J5/jYODKsef5zMTtW32+nf3vrJaxuc17GJvbvIexuc17GJvbvIexuc17GJvbvIex+WzMHBpMKeV0
+Rie7BrgXWCilrKu11lLKzrw3DwI/as8BcEEpZcPijbWtiknbun41s3mezfNsnmfzPJvn2TzP5nk2
+z7N5ns3zbJ5n8zybzw43g4e1kdFJtGH00/azgbNKKbvWWhdKKWUpT9I+8FcDt7er9mB0rhW9nM3z
+bJ5n8zyb59k8z+Z5Ns+zeZ7N82yeZ/M8m+fZfEa4GTygWutm4DLgK+2qtwLnAh8a+/Zkh4ullDJX
+a90CXAE8y2ihnNBuW9Ji64XN82yeZ/M8m+fZPM/meTbPs3mezfNsnmfzPJvn2Xx2uBk8sFrro8Dl
+vHyxnLXUxVJrXWgXN49dvdBu82fyE2yeZ/M8m+fZPM/meTbPs3mezfNsnmfzPJvn2TzP5rPBzeAp
+UGt9hFe4WJo9gbXt8n3LfqCriM3zbJ5n8zyb59k8z+Z5Ns+zeZ7N82yeZ/M8m+fZfPq5GTwlXsli
+Gbt+8RwqTwCbVvBwVwWb59k8z+Z5Ns+zeZ7N82yeZ/M8m+fZPM/meTbPs/mUq7U6UzTA/sCFjH4C
+vwDcyuik2+va7WUbj1sP3Nwec+XQr2OWxuY272FsbvMexuY272FsbvMexuY272FsbvMexubTOf4y
+eMrU/+Pbk1LKq4D3AkcB9wLfb9f7/i6BzfNsnmfzPJvn2TzP5nk2z7N5ns3zbJ5n8zyb59l8Sg29
+G+1sfdjxtydzY/c9Dril3e9bwF5DH/8sjs1t3sPY3OY9jM1t3sPY3OY9jM1t3sPY3OY9jM2nawY/
+AGc7b862F8uuY/fZAFzfbt8IHDL0cc/y2NzmPYzNbd7D2NzmPYzNbd7D2NzmPYzNbd7D2Hx6ZvAD
+cHbwBm17sawFXgtc066/Hzhs6ONdDWNzm/cwNrd5D2Nzm/cwNrd5D2Nzm/cwNrd5D2Pz6ZjS3gxN
+sVLK/sB5vHSOlduAq4B3A6cAjwLvrLXePcgBrkI2z7N5ns3zbJ5n8zyb59k8z+Z5Ns+zeZ7N82ye
+Z/PhuRk8I7ayWB5h9I3Ko8BJtda7Bjq0VcvmeTbPs3mezfNsnmfzPJvn2TzP5nk2z7N5ns3zbD4s
+N4NnyNhiuRCYAx4GTnGRrByb59k8z+Z5Ns+zeZ7N82yeZ/M8m+fZPM/meTbPs/lw3AyeMaWU/YDP
+AecAJ9Ra/zLwIa16Ns+zeZ7N82yeZ/M8m+fZPM/meTbPs3mezfNsnmfzYbgZPINKKfsCc7XWR4Y+
+ll7YPM/meTbPs3mezfNsnmfzPJvn2TzP5nk2z7N5ns3z3AyWJEmSJEmSpA7MDX0AkiRJkiRJkqSV
+52awJEmSJEmSJHXAzWBJkiRJkiRJ6oCbwZIkSZIkSZLUATeDJUmSJEmSJKkDbgZLkiRJkiRJUgfc
+DJYkSZIkSZKkDrgZLEmSJEmSJEkdcDNYkiRJkiRJkjrgZrAkSZIkSZIkdcDNYEmSJEmSJEnqgJvB
+kiRJkiRJktQBN4MlSZIkSZIkqQNuBkuSJEmSJElSB9wMliRJkiRJkqQOuBksSZIkSZIkSR1wM1iS
+JEmSJEmSOuBmsCRJkiRJkiR1wM1gSZIkSZIkSeqAm8GSJEmSJEmS1AE3gyVJkiRJkiSpA24GS5Ik
+SZIkSVIH3AyWJEmSJEmSpA64GSxJkiRJkiRJHfgvEh1SRSKMeQgAAAAASUVORK5CYII=
+"
+width=705
+height=387
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#21487;&#36873;&#65306;&#24605;&#32771;&#19979;&#20320;&#30340;&#32467;&#26524;&#65288;&#25105;&#20204;&#19981;&#20250;&#35780;&#20272;&#36825;&#36947;&#39064;&#30340;&#31572;&#26696;&#65289;">&#21487;&#36873;&#65306;&#24605;&#32771;&#19979;&#20320;&#30340;&#32467;&#26524;&#65288;&#25105;&#20204;&#19981;&#20250;&#35780;&#20272;&#36825;&#36947;&#39064;&#30340;&#31572;&#26696;&#65289;<a class="anchor-link" href="#&#21487;&#36873;&#65306;&#24605;&#32771;&#19979;&#20320;&#30340;&#32467;&#26524;&#65288;&#25105;&#20204;&#19981;&#20250;&#35780;&#20272;&#36825;&#36947;&#39064;&#30340;&#31572;&#26696;&#65289;">&#182;</a></h2><p>请针对你的结果回答以下问题。模型对数据的预测效果如何？哪里出现问题了？为何出现问题呢？</p>
+<blockquote><p><strong>注意</strong>：你可以通过双击该单元编辑文本。如果想要预览文本，请按 Control + Enter</p>
+</blockquote>
+<h4 id="&#35831;&#23558;&#20320;&#30340;&#31572;&#26696;&#22635;&#20889;&#22312;&#19979;&#26041;">&#35831;&#23558;&#20320;&#30340;&#31572;&#26696;&#22635;&#20889;&#22312;&#19979;&#26041;<a class="anchor-link" href="#&#35831;&#23558;&#20320;&#30340;&#31572;&#26696;&#22635;&#20889;&#22312;&#19979;&#26041;">&#182;</a></h4><p>验证集损失最终降低到0.13左右，测试集损失接近0.2。</p>
+<p>问题：
+1、矩阵相乘时维度总是出错，有时需要调整顺序，有时需要转置，甚至有时dot报错，使用*反而可以，不知有何技巧？
+2、数据预处理时删除了几个特征不知是何用意？
+3、不太理解为何输出层激活函数要使用f(x) = x？</p>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+</html>
diff --git a/first-neural-network/Your_first_neural_network.ipynb b/first-neural-network/Your_first_neural_network.ipynb
new file mode 100644
index 0000000..f37fcab
--- /dev/null
+++ b/first-neural-network/Your_first_neural_network.ipynb
@@ -0,0 +1,1111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 你的第一个神经网络\n",
+    "\n",
+    "在此项目中，你将构建你的第一个神经网络，并用该网络预测每日自行车租客人数。我们提供了一些代码，但是需要你来实现神经网络（大部分内容）。提交此项目后，欢迎进一步探索该数据和模型。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 加载和准备数据\n",
+    "\n",
+    "构建神经网络的关键一步是正确地准备数据。不同尺度级别的变量使网络难以高效地掌握正确的权重。我们在下方已经提供了加载和准备数据的代码。你很快将进一步学习这些代码！"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
+    "\n",
+    "rides = pd.read_csv(data_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>instant</th>\n",
+       "      <th>dteday</th>\n",
+       "      <th>season</th>\n",
+       "      <th>yr</th>\n",
+       "      <th>mnth</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>workingday</th>\n",
+       "      <th>weathersit</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>atemp</th>\n",
+       "      <th>hum</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>casual</th>\n",
+       "      <th>registered</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.2879</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.2727</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>32</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.2727</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.2879</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.2879</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.2576</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.0896</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.2727</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.2576</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.2879</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>0.3485</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
+       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
+       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
+       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
+       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
+       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
+       "5        6  2011-01-01       1   0     1   5        0        6           0   \n",
+       "6        7  2011-01-01       1   0     1   6        0        6           0   \n",
+       "7        8  2011-01-01       1   0     1   7        0        6           0   \n",
+       "8        9  2011-01-01       1   0     1   8        0        6           0   \n",
+       "9       10  2011-01-01       1   0     1   9        0        6           0   \n",
+       "\n",
+       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
+       "0           1  0.24  0.2879  0.81     0.0000       3          13   16  \n",
+       "1           1  0.22  0.2727  0.80     0.0000       8          32   40  \n",
+       "2           1  0.22  0.2727  0.80     0.0000       5          27   32  \n",
+       "3           1  0.24  0.2879  0.75     0.0000       3          10   13  \n",
+       "4           1  0.24  0.2879  0.75     0.0000       0           1    1  \n",
+       "5           2  0.24  0.2576  0.75     0.0896       0           1    1  \n",
+       "6           1  0.22  0.2727  0.80     0.0000       2           0    2  \n",
+       "7           1  0.20  0.2576  0.86     0.0000       1           2    3  \n",
+       "8           1  0.24  0.2879  0.75     0.0000       1           7    8  \n",
+       "9           1  0.32  0.3485  0.76     0.0000       8           6   14  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rides.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数据简介\n",
+    "\n",
+    "此数据集包含的是从 2011 年 1 月 1 日到 2012 年 12 月 31 日期间每天每小时的骑车人数。骑车用户分成临时用户和注册用户，cnt 列是骑车用户数汇总列。你可以在上方看到前几行数据。\n",
+    "\n",
+    "下图展示的是数据集中前 10 天左右的骑车人数（某些天不一定是 24 个条目，所以不是精确的 10 天）。你可以在这里看到每小时租金。这些数据很复杂！周末的骑行人数少些，工作日上下班期间是骑行高峰期。我们还可以从上方的数据中看到温度、湿度和风速信息，所有这些信息都会影响骑行人数。你需要用你的模型展示所有这些数据。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2435e964278>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBoAAALzCAYAAAC/R2QvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2435e964cf8>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 377,
+       "width": 525
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rides[:24*10].plot(x='dteday', y='cnt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 虚拟变量（哑变量）\n",
+    "\n",
+    "下面是一些分类变量，例如季节、天气、月份。要在我们的模型中包含这些数据，我们需要创建二进制虚拟变量。用 Pandas 库中的 `get_dummies()` 就可以轻松实现。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>yr</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>hum</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>casual</th>\n",
+       "      <th>registered</th>\n",
+       "      <th>cnt</th>\n",
+       "      <th>season_1</th>\n",
+       "      <th>season_2</th>\n",
+       "      <th>...</th>\n",
+       "      <th>hr_21</th>\n",
+       "      <th>hr_22</th>\n",
+       "      <th>hr_23</th>\n",
+       "      <th>weekday_0</th>\n",
+       "      <th>weekday_1</th>\n",
+       "      <th>weekday_2</th>\n",
+       "      <th>weekday_3</th>\n",
+       "      <th>weekday_4</th>\n",
+       "      <th>weekday_5</th>\n",
+       "      <th>weekday_6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>32</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 59 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
+       "0   0        0  0.24  0.81        0.0       3          13   16       1.0   \n",
+       "1   0        0  0.22  0.80        0.0       8          32   40       1.0   \n",
+       "2   0        0  0.22  0.80        0.0       5          27   32       1.0   \n",
+       "3   0        0  0.24  0.75        0.0       3          10   13       1.0   \n",
+       "4   0        0  0.24  0.75        0.0       0           1    1       1.0   \n",
+       "\n",
+       "   season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
+       "0       0.0    ...        0.0    0.0    0.0        0.0        0.0        0.0   \n",
+       "1       0.0    ...        0.0    0.0    0.0        0.0        0.0        0.0   \n",
+       "2       0.0    ...        0.0    0.0    0.0        0.0        0.0        0.0   \n",
+       "3       0.0    ...        0.0    0.0    0.0        0.0        0.0        0.0   \n",
+       "4       0.0    ...        0.0    0.0    0.0        0.0        0.0        0.0   \n",
+       "\n",
+       "   weekday_3  weekday_4  weekday_5  weekday_6  \n",
+       "0        0.0        0.0        0.0        1.0  \n",
+       "1        0.0        0.0        0.0        1.0  \n",
+       "2        0.0        0.0        0.0        1.0  \n",
+       "3        0.0        0.0        0.0        1.0  \n",
+       "4        0.0        0.0        0.0        1.0  \n",
+       "\n",
+       "[5 rows x 59 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
+    "for each in dummy_fields:\n",
+    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
+    "    rides = pd.concat([rides, dummies], axis=1)\n",
+    "\n",
+    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
+    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
+    "data = rides.drop(fields_to_drop, axis=1)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 调整目标变量\n",
+    "\n",
+    "为了更轻松地训练网络，我们将对每个连续变量标准化，即转换和调整变量，使它们的均值为 0，标准差为 1。\n",
+    "\n",
+    "我们会保存换算因子，以便当我们使用网络进行预测时可以还原数据。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
+    "# Store scalings in a dictionary so we can convert back later\n",
+    "scaled_features = {}\n",
+    "for each in quant_features:\n",
+    "    mean, std = data[each].mean(), data[each].std()\n",
+    "    scaled_features[each] = [mean, std]\n",
+    "    data.loc[:, each] = (data[each] - mean)/std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 将数据拆分为训练、测试和验证数据集\n",
+    "\n",
+    "我们将大约最后 21 天的数据保存为测试数据集，这些数据集会在训练完网络后使用。我们将使用该数据集进行预测，并与实际的骑行人数进行对比。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Save data for approximately the last 21 days \n",
+    "test_data = data[-21*24:]\n",
+    "\n",
+    "# Now remove the test data from the data set \n",
+    "data = data[:-21*24]\n",
+    "\n",
+    "# Separate the data into features and targets\n",
+    "target_fields = ['cnt', 'casual', 'registered']\n",
+    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
+    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们将数据拆分为两个数据集，一个用作训练，一个在网络训练完后用来验证网络。因为数据是有时间序列特性的，所以我们用历史数据进行训练，然后尝试预测未来数据（验证数据集）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Hold out the last 60 days or so of the remaining data as a validation set\n",
+    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
+    "val_features, val_targets = features[-60*24:], targets[-60*24:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 开始构建网络\n",
+    "\n",
+    "下面你将构建自己的网络。我们已经构建好结构和反向传递部分。你将实现网络的前向传递部分。还需要设置超参数：学习速率、隐藏单元的数量，以及训练传递数量。\n",
+    "\n",
+    "<img src=\"assets/neural_network.png\" width=300px>\n",
+    "\n",
+    "该网络有两个层级，一个隐藏层和一个输出层。隐藏层级将使用 S 型函数作为激活函数。输出层只有一个节点，用于递归，节点的输出和节点的输入相同。即激活函数是 $f(x)=x$。这种函数获得输入信号，并生成输出信号，但是会考虑阈值，称为激活函数。我们完成网络的每个层级，并计算每个神经元的输出。一个层级的所有输出变成下一层级神经元的输入。这一流程叫做前向传播（forward propagation）。\n",
+    "\n",
+    "我们在神经网络中使用权重将信号从输入层传播到输出层。我们还使用权重将错误从输出层传播回网络，以便更新权重。这叫做反向传播（backpropagation）。\n",
+    "\n",
+    "> **提示**：你需要为反向传播实现计算输出激活函数 ($f(x) = x$) 的导数。如果你不熟悉微积分，其实该函数就等同于等式 $y = x$。该等式的斜率是多少？也就是导数 $f(x)$。\n",
+    "\n",
+    "\n",
+    "你需要完成以下任务：\n",
+    "\n",
+    "1. 实现 S 型激活函数。将 `__init__` 中的 `self.activation_function`  设为你的 S 型函数。\n",
+    "2. 在 `train` 方法中实现前向传递。\n",
+    "3. 在 `train` 方法中实现反向传播算法，包括计算输出错误。\n",
+    "4. 在 `run` 方法中实现前向传递。\n",
+    "\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class NeuralNetwork(object):\n",
+    "    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
+    "        # Set number of nodes in input, hidden and output layers.\n",
+    "        self.input_nodes = input_nodes\n",
+    "        self.hidden_nodes = hidden_nodes\n",
+    "        self.output_nodes = output_nodes\n",
+    "\n",
+    "        # Initialize weights\n",
+    "        self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, \n",
+    "                                       (self.input_nodes, self.hidden_nodes))\n",
+    "\n",
+    "        self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, \n",
+    "                                       (self.hidden_nodes, self.output_nodes))\n",
+    "        self.lr = learning_rate\n",
+    "        \n",
+    "        #### TODO: Set self.activation_function to your implemented sigmoid function ####\n",
+    "        #\n",
+    "        # Note: in Python, you can define a function with a lambda expression,\n",
+    "        # as shown below.\n",
+    "        self.activation_function = lambda x : 1/(1 + np.exp(-x))  # Replace 0 with your sigmoid calculation.\n",
+    "        \n",
+    "        ### If the lambda code above is not something you're familiar with,\n",
+    "        # You can uncomment out the following three lines and put your \n",
+    "        # implementation there instead.\n",
+    "        #\n",
+    "        #def sigmoid(x):\n",
+    "        #    return 0  # Replace 0 with your sigmoid calculation here\n",
+    "        #self.activation_function = sigmoid\n",
+    "                    \n",
+    "    \n",
+    "    def train(self, features, targets):\n",
+    "        ''' Train the network on batch of features and targets. \n",
+    "        \n",
+    "            Arguments\n",
+    "            ---------\n",
+    "            \n",
+    "            features: 2D array, each row is one data record, each column is a feature\n",
+    "            targets: 1D array of target values\n",
+    "        \n",
+    "        '''\n",
+    "        n_records = features.shape[0]\n",
+    "        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n",
+    "        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n",
+    "        for X, y in zip(features, targets):\n",
+    "            #### Implement the forward pass here ####\n",
+    "            ### Forward pass ###\n",
+    "            # TODO: Hidden layer - Replace these values with your calculations.\n",
+    "            hidden_inputs = np.dot(X, self.weights_input_to_hidden) # signals into hidden layer\n",
+    "            hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n",
+    "\n",
+    "            # TODO: Output layer - Replace these values with your calculations.\n",
+    "            final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n",
+    "            final_outputs = final_inputs # signals from final output layer\n",
+    "            \n",
+    "            #### Implement the backward pass here ####\n",
+    "            ### Backward pass ###\n",
+    "\n",
+    "            # TODO: Output error - Replace this value with your calculations.\n",
+    "            error = y - final_outputs # Output layer error is the difference between desired target and actual output.\n",
+    "            \n",
+    "            # TODO: Backpropagated error terms - Replace these values with your calculations.\n",
+    "            output_error_term = error\n",
+    "            \n",
+    "            # TODO: Calculate the hidden layer's contribution to the error\n",
+    "            hidden_error = np.dot(output_error_term, self.weights_hidden_to_output.T)\n",
+    "            hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)\n",
+    "\n",
+    "            # Weight step (input to hidden)\n",
+    "            delta_weights_i_h += hidden_error_term * X[:, None]\n",
+    "            # Weight step (hidden to output)\n",
+    "            delta_weights_h_o += output_error_term * hidden_outputs[:, None]\n",
+    "\n",
+    "        # TODO: Update the weights - Replace these values with your calculations.\n",
+    "        self.weights_hidden_to_output += self.lr * delta_weights_h_o / n_records # update hidden-to-output weights with gradient descent step\n",
+    "        self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records # update input-to-hidden weights with gradient descent step\n",
+    " \n",
+    "    def run(self, features):\n",
+    "        ''' Run a forward pass through the network with input features \n",
+    "        \n",
+    "            Arguments\n",
+    "            ---------\n",
+    "            features: 1D array of feature values\n",
+    "        '''\n",
+    "        \n",
+    "        #### Implement the forward pass here ####\n",
+    "        # TODO: Hidden layer - replace these values with the appropriate calculations.\n",
+    "        hidden_inputs = np.dot(features, self.weights_input_to_hidden) # signals into hidden layer\n",
+    "        hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n",
+    "        \n",
+    "        # TODO: Output layer - Replace these values with the appropriate calculations.\n",
+    "        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n",
+    "        final_outputs = final_inputs # signals from final output layer \n",
+    "        \n",
+    "        return final_outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def MSE(y, Y):\n",
+    "    return np.mean((y-Y)**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 单元测试\n",
+    "\n",
+    "运行这些单元测试，检查你的网络实现是否正确。这样可以帮助你确保网络已正确实现，然后再开始训练网络。这些测试必须成功才能通过此项目。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      ".....\n",
+      "----------------------------------------------------------------------\n",
+      "Ran 5 tests in 0.016s\n",
+      "\n",
+      "OK\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import unittest\n",
+    "\n",
+    "inputs = np.array([[0.5, -0.2, 0.1]])\n",
+    "targets = np.array([[0.4]])\n",
+    "test_w_i_h = np.array([[0.1, -0.2],\n",
+    "                       [0.4, 0.5],\n",
+    "                       [-0.3, 0.2]])\n",
+    "test_w_h_o = np.array([[0.3],\n",
+    "                       [-0.1]])\n",
+    "\n",
+    "class TestMethods(unittest.TestCase):\n",
+    "    \n",
+    "    ##########\n",
+    "    # Unit tests for data loading\n",
+    "    ##########\n",
+    "    \n",
+    "    def test_data_path(self):\n",
+    "        # Test that file path to dataset has been unaltered\n",
+    "        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
+    "        \n",
+    "    def test_data_loaded(self):\n",
+    "        # Test that data frame loaded\n",
+    "        self.assertTrue(isinstance(rides, pd.DataFrame))\n",
+    "    \n",
+    "    ##########\n",
+    "    # Unit tests for network functionality\n",
+    "    ##########\n",
+    "\n",
+    "    def test_activation(self):\n",
+    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
+    "        # Test that the activation function is a sigmoid\n",
+    "        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1 + np.exp(-0.5))))\n",
+    "\n",
+    "    def test_train(self):\n",
+    "        # Test that weights are updated correctly on training\n",
+    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
+    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
+    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
+    "        \n",
+    "        network.train(inputs, targets)\n",
+    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
+    "                                    np.array([[ 0.37275328], \n",
+    "                                              [-0.03172939]])))\n",
+    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
+    "                                    np.array([[ 0.10562014, -0.20185996], \n",
+    "                                              [0.39775194, 0.50074398], \n",
+    "                                              [-0.29887597, 0.19962801]])))\n",
+    "\n",
+    "    def test_run(self):\n",
+    "        # Test correctness of run method\n",
+    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
+    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
+    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
+    "\n",
+    "        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
+    "\n",
+    "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
+    "unittest.TextTestRunner().run(suite)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 训练网络\n",
+    "\n",
+    "现在你将设置网络的超参数。策略是设置的超参数使训练集上的错误很小但是数据不会过拟合。如果网络训练时间太长，或者有太多的隐藏节点，可能就会过于针对特定训练集，无法泛化到验证数据集。即当训练集的损失降低时，验证集的损失将开始增大。\n",
+    "\n",
+    "你还将采用随机梯度下降 (SGD) 方法训练网络。对于每次训练，都获取随机样本数据，而不是整个数据集。与普通梯度下降相比，训练次数要更多，但是每次时间更短。这样的话，网络训练效率更高。稍后你将详细了解 SGD。\n",
+    "\n",
+    "\n",
+    "### 选择迭代次数\n",
+    "\n",
+    "也就是训练网络时从训练数据中抽样的批次数量。迭代次数越多，模型就与数据越拟合。但是，如果迭代次数太多，模型就无法很好地泛化到其他数据，这叫做过拟合。你需要选择一个使训练损失很低并且验证损失保持中等水平的数字。当你开始过拟合时，你会发现训练损失继续下降，但是验证损失开始上升。\n",
+    "\n",
+    "### 选择学习速率\n",
+    "\n",
+    "速率可以调整权重更新幅度。如果速率太大，权重就会太大，导致网络无法与数据相拟合。建议从 0.1 开始。如果网络在与数据拟合时遇到问题，尝试降低学习速率。注意，学习速率越低，权重更新的步长就越小，神经网络收敛的时间就越长。\n",
+    "\n",
+    "\n",
+    "### 选择隐藏节点数量\n",
+    "\n",
+    "隐藏节点越多，模型的预测结果就越准确。尝试不同的隐藏节点的数量，看看对性能有何影响。你可以查看损失字典，寻找网络性能指标。如果隐藏单元的数量太少，那么模型就没有足够的空间进行学习，如果太多，则学习方向就有太多的选择。选择隐藏单元数量的技巧在于找到合适的平衡点。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Progress: 100.0% ... Training loss: 0.051 ... Validation loss: 0.130"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "\n",
+    "### Set the hyperparameters here ###\n",
+    "iterations = 8000\n",
+    "learning_rate = 0.5\n",
+    "hidden_nodes = 20\n",
+    "output_nodes = 1\n",
+    "\n",
+    "N_i = train_features.shape[1]\n",
+    "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
+    "\n",
+    "losses = {'train':[], 'validation':[]}\n",
+    "for ii in range(iterations):\n",
+    "    # Go through a random batch of 128 records from the training data set\n",
+    "    batch = np.random.choice(train_features.index, size=128)\n",
+    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
+    "                             \n",
+    "    network.train(X, y)\n",
+    "    \n",
+    "    # Printing out the training progress\n",
+    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
+    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
+    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * (ii + 1)/float(iterations)) \\\n",
+    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
+    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
+    "    sys.stdout.flush()\n",
+    "    \n",
+    "    losses['train'].append(train_loss)\n",
+    "    losses['validation'].append(val_loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABCQAAALJCAYAAABsnPJvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x24360f91be0>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 356,
+       "width": 530
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(losses['train'], label='Training loss')\n",
+    "plt.plot(losses['validation'], label='Validation loss')\n",
+    "plt.axis([0, 8000, 0, 1.6])\n",
+    "plt.legend()\n",
+    "_ = plt.ylim()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 检查预测结果\n",
+    "\n",
+    "使用测试数据看看网络对数据建模的效果如何。如果完全错了，请确保网络中的每步都正确实现。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.209"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABYMAAAMGCAYAAACgX5vSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x243639c3710>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 387,
+       "width": 705
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_loss = MSE(network.run(test_features).T, test_targets['cnt'].values)\n",
+    "sys.stdout.write(\"Test loss: \" + str(test_loss)[:5])\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,4))\n",
+    "\n",
+    "mean, std = scaled_features['cnt']\n",
+    "predictions = network.run(test_features).T*std + mean\n",
+    "ax.plot(predictions[0], label='Prediction')\n",
+    "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
+    "ax.set_xlim(right=len(predictions))\n",
+    "ax.legend()\n",
+    "\n",
+    "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
+    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
+    "ax.set_xticks(np.arange(len(dates))[12::36])\n",
+    "_ = ax.set_xticklabels(dates[12::36], rotation=45)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 可选：思考下你的结果（我们不会评估这道题的答案）\n",
+    "\n",
+    " \n",
+    "请针对你的结果回答以下问题。模型对数据的预测效果如何？哪里出现问题了？为何出现问题呢？\n",
+    "\n",
+    "> **注意**：你可以通过双击该单元编辑文本。如果想要预览文本，请按 Control + Enter\n",
+    "\n",
+    "#### 请将你的答案填写在下方\n",
+    "\n",
+    "验证集损失最终降低到0.13左右，测试集损失接近0.2。\n",
+    "\n",
+    "问题：\n",
+    "1、矩阵相乘时维度总是出错，有时需要调整顺序，有时需要转置，甚至有时dot报错，使用*反而可以，不知有何技巧？\n",
+    "2、数据预处理时删除了几个特征不知是何用意？\n",
+    "3、不太理解为何输出层激活函数要使用f(x) = x？"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 14f7ffa18dac06cc7b0e1d6012f5c62e46ddba76 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 9 Jul 2017 10:33:40 +0800
Subject: [PATCH 03/16] Delete dlnd_image_classification.ipynb

---
 .../dlnd_image_classification.ipynb           | 874 ------------------
 1 file changed, 874 deletions(-)
 delete mode 100644 image-classification/dlnd_image_classification.ipynb

diff --git a/image-classification/dlnd_image_classification.ipynb b/image-classification/dlnd_image_classification.ipynb
deleted file mode 100644
index dceff26..0000000
--- a/image-classification/dlnd_image_classification.ipynb
+++ /dev/null
@@ -1,874 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# 图像分类\n",
-    "\n",
-    "在此项目中，你将对 [CIFAR-10 数据集](https://www.cs.toronto.edu/~kriz/cifar.html) 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。\n",
-    "\n",
-    "\n",
-    "## 获取数据\n",
-    "\n",
-    "请运行以下单元，以下载 [CIFAR-10 数据集（Python版）](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "from urllib.request import urlretrieve\n",
-    "from os.path import isfile, isdir\n",
-    "from tqdm import tqdm\n",
-    "import problem_unittests as tests\n",
-    "import tarfile\n",
-    "\n",
-    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
-    "\n",
-    "# Use Floyd's cifar-10 dataset if present\n",
-    "floyd_cifar10_location = '/input/cifar-10/python.tar.gz'\n",
-    "if isfile(floyd_cifar10_location):\n",
-    "    tar_gz_path = floyd_cifar10_location\n",
-    "else:\n",
-    "    tar_gz_path = 'cifar-10-python.tar.gz'\n",
-    "\n",
-    "class DLProgress(tqdm):\n",
-    "    last_block = 0\n",
-    "\n",
-    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
-    "        self.total = total_size\n",
-    "        self.update((block_num - self.last_block) * block_size)\n",
-    "        self.last_block = block_num\n",
-    "\n",
-    "if not isfile(tar_gz_path):\n",
-    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
-    "        urlretrieve(\n",
-    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
-    "            tar_gz_path,\n",
-    "            pbar.hook)\n",
-    "\n",
-    "if not isdir(cifar10_dataset_folder_path):\n",
-    "    with tarfile.open(tar_gz_path) as tar:\n",
-    "        tar.extractall()\n",
-    "        tar.close()\n",
-    "\n",
-    "\n",
-    "tests.test_folder_path(cifar10_dataset_folder_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 探索数据\n",
-    "\n",
-    "该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 `data_batch_1`、`data_batch_2`，以此类推。每个部分都包含以下某个类别的标签和图片：\n",
-    "\n",
-    "* 飞机\n",
-    "* 汽车\n",
-    "* 鸟类\n",
-    "* 猫\n",
-    "* 鹿\n",
-    "* 狗\n",
-    "* 青蛙\n",
-    "* 马\n",
-    "* 船只\n",
-    "* 卡车\n",
-    "\n",
-    "了解数据集也是对数据进行预测的必经步骤。你可以通过更改 `batch_id` 和 `sample_id` 探索下面的代码单元。`batch_id` 是数据集一个部分的 ID（1 到 5）。`sample_id` 是该部分中图片和标签对（label pair）的 ID。\n",
-    "\n",
-    "问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "\n",
-    "import helper\n",
-    "import numpy as np\n",
-    "\n",
-    "# Explore the dataset\n",
-    "batch_id = 1\n",
-    "sample_id = 5\n",
-    "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 实现预处理函数\n",
-    "\n",
-    "### 标准化\n",
-    "\n",
-    "在下面的单元中，实现 `normalize` 函数，传入图片数据 `x`，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 `x` 的形状一样。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def normalize(x):\n",
-    "    \"\"\"\n",
-    "    Normalize a list of sample image data in the range of 0 to 1\n",
-    "    : x: List of image data.  The image shape is (32, 32, 3)\n",
-    "    : return: Numpy array of normalize data\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_normalize(normalize)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### One-hot 编码\n",
-    "\n",
-    "和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 `one_hot_encode` 函数。输入，也就是 `x`，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 `one_hot_encode` 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。\n",
-    "\n",
-    "提示：不要重复发明轮子。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def one_hot_encode(x):\n",
-    "    \"\"\"\n",
-    "    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n",
-    "    : x: List of sample Labels\n",
-    "    : return: Numpy array of one-hot encoded labels\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_one_hot_encode(one_hot_encode)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 随机化数据\n",
-    "\n",
-    "之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 预处理所有数据并保存\n",
-    "\n",
-    "运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "# Preprocess Training, Validation, and Testing Data\n",
-    "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 检查点\n",
-    "\n",
-    "这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import pickle\n",
-    "import problem_unittests as tests\n",
-    "import helper\n",
-    "\n",
-    "# Load the Preprocessed Validation data\n",
-    "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 构建网络\n",
-    "\n",
-    "对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。\n",
-    "\n",
-    ">**注意**：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。\n",
-    "\n",
-    ">但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 `conv2d` 类 [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d)，而不是 TF Layers 版本的 `conv2d` 类 [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d)。\n",
-    "\n",
-    "我们开始吧！\n",
-    "\n",
-    "\n",
-    "### 输入\n",
-    "\n",
-    "神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：\n",
-    "\n",
-    "* 实现 `neural_net_image_input`\n",
-    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
-    " * 使用 `image_shape` 设置形状，部分大小设为 `None`\n",
-    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"x\" 命名\n",
-    "* 实现 `neural_net_label_input`\n",
-    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
-    " * 使用 `n_classes` 设置形状，部分大小设为 `None`\n",
-    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"y\" 命名\n",
-    "* 实现 `neural_net_keep_prob_input`\n",
-    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)，用于丢弃保留概率\n",
-    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"keep_prob\" 命名\n",
-    "\n",
-    "这些名称将在项目结束时，用于加载保存的模型。\n",
-    "\n",
-    "注意：TensorFlow 中的 `None` 表示形状可以是动态大小。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "def neural_net_image_input(image_shape):\n",
-    "    \"\"\"\n",
-    "    Return a Tensor for a batch of image input\n",
-    "    : image_shape: Shape of the images\n",
-    "    : return: Tensor for image input.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "def neural_net_label_input(n_classes):\n",
-    "    \"\"\"\n",
-    "    Return a Tensor for a batch of label input\n",
-    "    : n_classes: Number of classes\n",
-    "    : return: Tensor for label input.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "def neural_net_keep_prob_input():\n",
-    "    \"\"\"\n",
-    "    Return a Tensor for keep probability\n",
-    "    : return: Tensor for keep probability.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tf.reset_default_graph()\n",
-    "tests.test_nn_image_inputs(neural_net_image_input)\n",
-    "tests.test_nn_label_inputs(neural_net_label_input)\n",
-    "tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 卷积和最大池化层\n",
-    "\n",
-    "卷积层级适合处理图片。对于此代码单元，你应该实现函数 `conv2d_maxpool` 以便应用卷积然后进行最大池化：\n",
-    "\n",
-    "* 使用 `conv_ksize`、`conv_num_outputs` 和 `x_tensor` 的形状创建权重（weight）和偏置（bias）。\n",
-    "* 使用权重和 `conv_strides` 对 `x_tensor` 应用卷积。\n",
-    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
-    "* 添加偏置\n",
-    "* 向卷积中添加非线性激活（nonlinear activation）\n",
-    "* 使用 `pool_ksize` 和 `pool_strides` 应用最大池化\n",
-    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
-    "\n",
-    "**注意**：对于**此层**，**请勿使用** [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers)，但是仍然可以使用 TensorFlow 的 [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) 包。对于所有**其他层**，你依然可以使用快捷方法。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n",
-    "    \"\"\"\n",
-    "    Apply convolution then max pooling to x_tensor\n",
-    "    :param x_tensor: TensorFlow Tensor\n",
-    "    :param conv_num_outputs: Number of outputs for the convolutional layer\n",
-    "    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n",
-    "    :param conv_strides: Stride 2-D Tuple for convolution\n",
-    "    :param pool_ksize: kernal size 2-D Tuple for pool\n",
-    "    :param pool_strides: Stride 2-D Tuple for pool\n",
-    "    : return: A tensor that represents convolution and max pooling of x_tensor\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None \n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_con_pool(conv2d_maxpool)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 扁平化层\n",
-    "\n",
-    "实现 `flatten` 函数，将 `x_tensor` 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（*部分大小（Batch Size）*，*扁平化图片大小（Flattened Image Size）*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def flatten(x_tensor):\n",
-    "    \"\"\"\n",
-    "    Flatten x_tensor to (Batch Size, Flattened Image Size)\n",
-    "    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n",
-    "    : return: A tensor of size (Batch Size, Flattened Image Size).\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_flatten(flatten)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 完全连接的层\n",
-    "\n",
-    "实现 `fully_conn` 函数，以向 `x_tensor` 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def fully_conn(x_tensor, num_outputs):\n",
-    "    \"\"\"\n",
-    "    Apply a fully connected layer to x_tensor using weight and bias\n",
-    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
-    "    : num_outputs: The number of output that the new tensor should be.\n",
-    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_fully_conn(fully_conn)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 输出层\n",
-    "\n",
-    "实现 `output` 函数，向 x_tensor 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n",
-    "\n",
-    "**注意**：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def output(x_tensor, num_outputs):\n",
-    "    \"\"\"\n",
-    "    Apply a output layer to x_tensor using weight and bias\n",
-    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
-    "    : num_outputs: The number of output that the new tensor should be.\n",
-    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_output(output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 创建卷积模型\n",
-    "\n",
-    "实现函数 `conv_net`， 创建卷积神经网络模型。该函数传入一批图片 `x`，并输出对数（logits）。使用你在上方创建的层创建此模型：\n",
-    "\n",
-    "* 应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）\n",
-    "* 应用一个扁平层（Flatten Layer）\n",
-    "* 应用 1、2 或 3 个完全连接层（Fully Connected Layers）\n",
-    "* 应用一个输出层（Output Layer）\n",
-    "* 返回输出\n",
-    "* 使用 `keep_prob` 向模型中的一个或多个层应用 [TensorFlow 的 Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def conv_net(x, keep_prob):\n",
-    "    \"\"\"\n",
-    "    Create a convolutional neural network model\n",
-    "    : x: Placeholder tensor that holds image data.\n",
-    "    : keep_prob: Placeholder tensor that hold dropout keep probability.\n",
-    "    : return: Tensor that represents logits\n",
-    "    \"\"\"\n",
-    "    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers\n",
-    "    #    Play around with different number of outputs, kernel size and stride\n",
-    "    # Function Definition from Above:\n",
-    "    #    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
-    "    \n",
-    "\n",
-    "    # TODO: Apply a Flatten Layer\n",
-    "    # Function Definition from Above:\n",
-    "    #   flatten(x_tensor)\n",
-    "    \n",
-    "\n",
-    "    # TODO: Apply 1, 2, or 3 Fully Connected Layers\n",
-    "    #    Play around with different number of outputs\n",
-    "    # Function Definition from Above:\n",
-    "    #   fully_conn(x_tensor, num_outputs)\n",
-    "    \n",
-    "    \n",
-    "    # TODO: Apply an Output Layer\n",
-    "    #    Set this to the number of classes\n",
-    "    # Function Definition from Above:\n",
-    "    #   output(x_tensor, num_outputs)\n",
-    "    \n",
-    "    \n",
-    "    # TODO: return output\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "\n",
-    "##############################\n",
-    "## Build the Neural Network ##\n",
-    "##############################\n",
-    "\n",
-    "# Remove previous weights, bias, inputs, etc..\n",
-    "tf.reset_default_graph()\n",
-    "\n",
-    "# Inputs\n",
-    "x = neural_net_image_input((32, 32, 3))\n",
-    "y = neural_net_label_input(10)\n",
-    "keep_prob = neural_net_keep_prob_input()\n",
-    "\n",
-    "# Model\n",
-    "logits = conv_net(x, keep_prob)\n",
-    "\n",
-    "# Name logits Tensor, so that is can be loaded from disk after training\n",
-    "logits = tf.identity(logits, name='logits')\n",
-    "\n",
-    "# Loss and Optimizer\n",
-    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
-    "optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
-    "\n",
-    "# Accuracy\n",
-    "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
-    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n",
-    "\n",
-    "tests.test_conv_net(conv_net)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 训练神经网络\n",
-    "\n",
-    "### 单次优化\n",
-    "\n",
-    "实现函数 `train_neural_network` 以进行单次优化（single optimization）。该优化应该使用 `optimizer` 优化 `session`，其中 `feed_dict` 具有以下参数：\n",
-    "\n",
-    "* `x` 表示图片输入\n",
-    "* `y` 表示标签\n",
-    "* `keep_prob` 表示丢弃的保留率\n",
-    "\n",
-    "每个部分都会调用该函数，所以 `tf.global_variables_initializer()` 已经被调用。\n",
-    "\n",
-    "注意：不需要返回任何内容。该函数只是用来优化神经网络。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n",
-    "    \"\"\"\n",
-    "    Optimize the session on a batch of images and labels\n",
-    "    : session: Current TensorFlow session\n",
-    "    : optimizer: TensorFlow optimizer function\n",
-    "    : keep_probability: keep probability\n",
-    "    : feature_batch: Batch of Numpy image data\n",
-    "    : label_batch: Batch of Numpy label data\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    pass\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_train_nn(train_neural_network)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 显示数据\n",
-    "\n",
-    "实现函数 `print_stats` 以输出损失和验证准确率。使用全局变量 `valid_features` 和 `valid_labels` 计算验证准确率。使用保留率 `1.0` 计算损失和验证准确率（loss and validation accuracy）。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def print_stats(session, feature_batch, label_batch, cost, accuracy):\n",
-    "    \"\"\"\n",
-    "    Print information about loss and validation accuracy\n",
-    "    : session: Current TensorFlow session\n",
-    "    : feature_batch: Batch of Numpy image data\n",
-    "    : label_batch: Batch of Numpy label data\n",
-    "    : cost: TensorFlow cost function\n",
-    "    : accuracy: TensorFlow accuracy function\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 超参数\n",
-    "\n",
-    "调试以下超参数：\n",
-    "* 设置 `epochs` 表示神经网络停止学习或开始过拟合的迭代次数\n",
-    "* 设置 `batch_size`，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：\n",
-    "\n",
-    " * 64\n",
-    " * 128\n",
-    " * 256\n",
-    " * ...\n",
-    "* 设置 `keep_probability` 表示使用丢弃时保留节点的概率"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# TODO: Tune Parameters\n",
-    "epochs = None\n",
-    "batch_size = None\n",
-    "keep_probability = None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 在单个 CIFAR-10 部分上训练\n",
-    "\n",
-    "我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "print('Checking the Training on a Single Batch...')\n",
-    "with tf.Session() as sess:\n",
-    "    # Initializing the variables\n",
-    "    sess.run(tf.global_variables_initializer())\n",
-    "    \n",
-    "    # Training cycle\n",
-    "    for epoch in range(epochs):\n",
-    "        batch_i = 1\n",
-    "        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
-    "            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
-    "        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
-    "        print_stats(sess, batch_features, batch_labels, cost, accuracy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 完全训练模型\n",
-    "\n",
-    "现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "save_model_path = './image_classification'\n",
-    "\n",
-    "print('Training...')\n",
-    "with tf.Session() as sess:\n",
-    "    # Initializing the variables\n",
-    "    sess.run(tf.global_variables_initializer())\n",
-    "    \n",
-    "    # Training cycle\n",
-    "    for epoch in range(epochs):\n",
-    "        # Loop over all batches\n",
-    "        n_batches = 5\n",
-    "        for batch_i in range(1, n_batches + 1):\n",
-    "            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
-    "                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
-    "            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
-    "            print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
-    "            \n",
-    "    # Save Model\n",
-    "    saver = tf.train.Saver()\n",
-    "    save_path = saver.save(sess, save_model_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 检查点\n",
-    "\n",
-    "模型已保存到本地。\n",
-    "\n",
-    "## 测试模型\n",
-    "\n",
-    "利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "import pickle\n",
-    "import helper\n",
-    "import random\n",
-    "\n",
-    "# Set batch size if not already set\n",
-    "try:\n",
-    "    if batch_size:\n",
-    "        pass\n",
-    "except NameError:\n",
-    "    batch_size = 64\n",
-    "\n",
-    "save_model_path = './image_classification'\n",
-    "n_samples = 4\n",
-    "top_n_predictions = 3\n",
-    "\n",
-    "def test_model():\n",
-    "    \"\"\"\n",
-    "    Test the saved model against the test dataset\n",
-    "    \"\"\"\n",
-    "\n",
-    "    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))\n",
-    "    loaded_graph = tf.Graph()\n",
-    "\n",
-    "    with tf.Session(graph=loaded_graph) as sess:\n",
-    "        # Load model\n",
-    "        loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
-    "        loader.restore(sess, save_model_path)\n",
-    "\n",
-    "        # Get Tensors from loaded model\n",
-    "        loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
-    "        loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
-    "        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
-    "        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
-    "        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
-    "        \n",
-    "        # Get accuracy in batches for memory limitations\n",
-    "        test_batch_acc_total = 0\n",
-    "        test_batch_count = 0\n",
-    "        \n",
-    "        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n",
-    "            test_batch_acc_total += sess.run(\n",
-    "                loaded_acc,\n",
-    "                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})\n",
-    "            test_batch_count += 1\n",
-    "\n",
-    "        print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
-    "\n",
-    "        # Print Random Samples\n",
-    "        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
-    "        random_test_predictions = sess.run(\n",
-    "            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n",
-    "            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n",
-    "        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
-    "\n",
-    "\n",
-    "test_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 为何准确率只有50-80%？\n",
-    "\n",
-    "你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率[远远超过 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130)。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。\n",
-    "\n",
-    "## 提交项目\n",
-    "\n",
-    "提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 \"File\" -> \"Download as\" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

From 2c21305dc6d76dc0f6173829529fc0e8d2eac543 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 9 Jul 2017 10:35:06 +0800
Subject: [PATCH 04/16] Add files via upload

---
 .../dlnd_image_classification.html            | 19516 ++++++++++++++++
 .../dlnd_image_classification.ipynb           |  1107 +
 2 files changed, 20623 insertions(+)
 create mode 100644 image-classification/dlnd_image_classification.html
 create mode 100644 image-classification/dlnd_image_classification.ipynb

diff --git a/image-classification/dlnd_image_classification.html b/image-classification/dlnd_image_classification.html
new file mode 100644
index 0000000..ad561b3
--- /dev/null
+++ b/image-classification/dlnd_image_classification.html
@@ -0,0 +1,19516 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>dlnd_image_classification</title>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.6 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: 1px dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    background: transparent !important;
+    color: #000 !important;
+    box-shadow: none !important;
+    text-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: 'Glyphicons Halflings';
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Glyphicons Halflings';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+  content: "\e156";
+}
+.glyphicon-unchecked:before {
+  content: "\e157";
+}
+.glyphicon-expand:before {
+  content: "\e158";
+}
+.glyphicon-collapse-down:before {
+  content: "\e159";
+}
+.glyphicon-collapse-up:before {
+  content: "\e160";
+}
+.glyphicon-log-in:before {
+  content: "\e161";
+}
+.glyphicon-flash:before {
+  content: "\e162";
+}
+.glyphicon-log-out:before {
+  content: "\e163";
+}
+.glyphicon-new-window:before {
+  content: "\e164";
+}
+.glyphicon-record:before {
+  content: "\e165";
+}
+.glyphicon-save:before {
+  content: "\e166";
+}
+.glyphicon-open:before {
+  content: "\e167";
+}
+.glyphicon-saved:before {
+  content: "\e168";
+}
+.glyphicon-import:before {
+  content: "\e169";
+}
+.glyphicon-export:before {
+  content: "\e170";
+}
+.glyphicon-send:before {
+  content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+  content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+  content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+  content: "\e174";
+}
+.glyphicon-floppy-save:before {
+  content: "\e175";
+}
+.glyphicon-floppy-open:before {
+  content: "\e176";
+}
+.glyphicon-credit-card:before {
+  content: "\e177";
+}
+.glyphicon-transfer:before {
+  content: "\e178";
+}
+.glyphicon-cutlery:before {
+  content: "\e179";
+}
+.glyphicon-header:before {
+  content: "\e180";
+}
+.glyphicon-compressed:before {
+  content: "\e181";
+}
+.glyphicon-earphone:before {
+  content: "\e182";
+}
+.glyphicon-phone-alt:before {
+  content: "\e183";
+}
+.glyphicon-tower:before {
+  content: "\e184";
+}
+.glyphicon-stats:before {
+  content: "\e185";
+}
+.glyphicon-sd-video:before {
+  content: "\e186";
+}
+.glyphicon-hd-video:before {
+  content: "\e187";
+}
+.glyphicon-subtitles:before {
+  content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+  content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+  content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+  content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+  content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+  content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
+.glyphicon-pawn:before {
+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
+.glyphicon-bitcoin:before {
+  content: "\e227";
+}
+.glyphicon-btc:before {
+  content: "\e227";
+}
+.glyphicon-xbt:before {
+  content: "\e227";
+}
+.glyphicon-yen:before {
+  content: "\00a5";
+}
+.glyphicon-jpy:before {
+  content: "\00a5";
+}
+.glyphicon-ruble:before {
+  content: "\20bd";
+}
+.glyphicon-rub:before {
+  content: "\20bd";
+}
+.glyphicon-scale:before {
+  content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+  content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+  content: "\e232";
+}
+.glyphicon-education:before {
+  content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+  content: "\e234";
+}
+.glyphicon-option-vertical:before {
+  content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+  content: "\e236";
+}
+.glyphicon-modal-window:before {
+  content: "\e237";
+}
+.glyphicon-oil:before {
+  content: "\e238";
+}
+.glyphicon-grain:before {
+  content: "\e239";
+}
+.glyphicon-sunglasses:before {
+  content: "\e240";
+}
+.glyphicon-text-size:before {
+  content: "\e241";
+}
+.glyphicon-text-color:before {
+  content: "\e242";
+}
+.glyphicon-text-background:before {
+  content: "\e243";
+}
+.glyphicon-object-align-top:before {
+  content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+  content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+  content: "\e246";
+}
+.glyphicon-object-align-left:before {
+  content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+  content: "\e248";
+}
+.glyphicon-object-align-right:before {
+  content: "\e249";
+}
+.glyphicon-triangle-right:before {
+  content: "\e250";
+}
+.glyphicon-triangle-left:before {
+  content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+  content: "\e252";
+}
+.glyphicon-triangle-top:before {
+  content: "\e253";
+}
+.glyphicon-console:before {
+  content: "\e254";
+}
+.glyphicon-superscript:before {
+  content: "\e255";
+}
+.glyphicon-subscript:before {
+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
+h5 small,
+h6 small,
+.h1 small,
+.h2 small,
+.h3 small,
+.h4 small,
+.h5 small,
+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 18px;
+  margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
+h2,
+.h2 {
+  font-size: 27px;
+}
+h3,
+.h3 {
+  font-size: 23px;
+}
+h4,
+.h4 {
+  font-size: 17px;
+}
+h5,
+.h5 {
+  font-size: 13px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
+address {
+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
+.col-xs-9 {
+  width: 75%;
+}
+.col-xs-8 {
+  width: 66.66666667%;
+}
+.col-xs-7 {
+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+  content: "\f044";
+}
+.fa-share-square-o:before {
+  content: "\f045";
+}
+.fa-check-square-o:before {
+  content: "\f046";
+}
+.fa-arrows:before {
+  content: "\f047";
+}
+.fa-step-backward:before {
+  content: "\f048";
+}
+.fa-fast-backward:before {
+  content: "\f049";
+}
+.fa-backward:before {
+  content: "\f04a";
+}
+.fa-play:before {
+  content: "\f04b";
+}
+.fa-pause:before {
+  content: "\f04c";
+}
+.fa-stop:before {
+  content: "\f04d";
+}
+.fa-forward:before {
+  content: "\f04e";
+}
+.fa-fast-forward:before {
+  content: "\f050";
+}
+.fa-step-forward:before {
+  content: "\f051";
+}
+.fa-eject:before {
+  content: "\f052";
+}
+.fa-chevron-left:before {
+  content: "\f053";
+}
+.fa-chevron-right:before {
+  content: "\f054";
+}
+.fa-plus-circle:before {
+  content: "\f055";
+}
+.fa-minus-circle:before {
+  content: "\f056";
+}
+.fa-times-circle:before {
+  content: "\f057";
+}
+.fa-check-circle:before {
+  content: "\f058";
+}
+.fa-question-circle:before {
+  content: "\f059";
+}
+.fa-info-circle:before {
+  content: "\f05a";
+}
+.fa-crosshairs:before {
+  content: "\f05b";
+}
+.fa-times-circle-o:before {
+  content: "\f05c";
+}
+.fa-check-circle-o:before {
+  content: "\f05d";
+}
+.fa-ban:before {
+  content: "\f05e";
+}
+.fa-arrow-left:before {
+  content: "\f060";
+}
+.fa-arrow-right:before {
+  content: "\f061";
+}
+.fa-arrow-up:before {
+  content: "\f062";
+}
+.fa-arrow-down:before {
+  content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+  content: "\f064";
+}
+.fa-expand:before {
+  content: "\f065";
+}
+.fa-compress:before {
+  content: "\f066";
+}
+.fa-plus:before {
+  content: "\f067";
+}
+.fa-minus:before {
+  content: "\f068";
+}
+.fa-asterisk:before {
+  content: "\f069";
+}
+.fa-exclamation-circle:before {
+  content: "\f06a";
+}
+.fa-gift:before {
+  content: "\f06b";
+}
+.fa-leaf:before {
+  content: "\f06c";
+}
+.fa-fire:before {
+  content: "\f06d";
+}
+.fa-eye:before {
+  content: "\f06e";
+}
+.fa-eye-slash:before {
+  content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+  content: "\f071";
+}
+.fa-plane:before {
+  content: "\f072";
+}
+.fa-calendar:before {
+  content: "\f073";
+}
+.fa-random:before {
+  content: "\f074";
+}
+.fa-comment:before {
+  content: "\f075";
+}
+.fa-magnet:before {
+  content: "\f076";
+}
+.fa-chevron-up:before {
+  content: "\f077";
+}
+.fa-chevron-down:before {
+  content: "\f078";
+}
+.fa-retweet:before {
+  content: "\f079";
+}
+.fa-shopping-cart:before {
+  content: "\f07a";
+}
+.fa-folder:before {
+  content: "\f07b";
+}
+.fa-folder-open:before {
+  content: "\f07c";
+}
+.fa-arrows-v:before {
+  content: "\f07d";
+}
+.fa-arrows-h:before {
+  content: "\f07e";
+}
+.fa-bar-chart-o:before,
+.fa-bar-chart:before {
+  content: "\f080";
+}
+.fa-twitter-square:before {
+  content: "\f081";
+}
+.fa-facebook-square:before {
+  content: "\f082";
+}
+.fa-camera-retro:before {
+  content: "\f083";
+}
+.fa-key:before {
+  content: "\f084";
+}
+.fa-gears:before,
+.fa-cogs:before {
+  content: "\f085";
+}
+.fa-comments:before {
+  content: "\f086";
+}
+.fa-thumbs-o-up:before {
+  content: "\f087";
+}
+.fa-thumbs-o-down:before {
+  content: "\f088";
+}
+.fa-star-half:before {
+  content: "\f089";
+}
+.fa-heart-o:before {
+  content: "\f08a";
+}
+.fa-sign-out:before {
+  content: "\f08b";
+}
+.fa-linkedin-square:before {
+  content: "\f08c";
+}
+.fa-thumb-tack:before {
+  content: "\f08d";
+}
+.fa-external-link:before {
+  content: "\f08e";
+}
+.fa-sign-in:before {
+  content: "\f090";
+}
+.fa-trophy:before {
+  content: "\f091";
+}
+.fa-github-square:before {
+  content: "\f092";
+}
+.fa-upload:before {
+  content: "\f093";
+}
+.fa-lemon-o:before {
+  content: "\f094";
+}
+.fa-phone:before {
+  content: "\f095";
+}
+.fa-square-o:before {
+  content: "\f096";
+}
+.fa-bookmark-o:before {
+  content: "\f097";
+}
+.fa-phone-square:before {
+  content: "\f098";
+}
+.fa-twitter:before {
+  content: "\f099";
+}
+.fa-facebook:before {
+  content: "\f09a";
+}
+.fa-github:before {
+  content: "\f09b";
+}
+.fa-unlock:before {
+  content: "\f09c";
+}
+.fa-credit-card:before {
+  content: "\f09d";
+}
+.fa-rss:before {
+  content: "\f09e";
+}
+.fa-hdd-o:before {
+  content: "\f0a0";
+}
+.fa-bullhorn:before {
+  content: "\f0a1";
+}
+.fa-bell:before {
+  content: "\f0f3";
+}
+.fa-certificate:before {
+  content: "\f0a3";
+}
+.fa-hand-o-right:before {
+  content: "\f0a4";
+}
+.fa-hand-o-left:before {
+  content: "\f0a5";
+}
+.fa-hand-o-up:before {
+  content: "\f0a6";
+}
+.fa-hand-o-down:before {
+  content: "\f0a7";
+}
+.fa-arrow-circle-left:before {
+  content: "\f0a8";
+}
+.fa-arrow-circle-right:before {
+  content: "\f0a9";
+}
+.fa-arrow-circle-up:before {
+  content: "\f0aa";
+}
+.fa-arrow-circle-down:before {
+  content: "\f0ab";
+}
+.fa-globe:before {
+  content: "\f0ac";
+}
+.fa-wrench:before {
+  content: "\f0ad";
+}
+.fa-tasks:before {
+  content: "\f0ae";
+}
+.fa-filter:before {
+  content: "\f0b0";
+}
+.fa-briefcase:before {
+  content: "\f0b1";
+}
+.fa-arrows-alt:before {
+  content: "\f0b2";
+}
+.fa-group:before,
+.fa-users:before {
+  content: "\f0c0";
+}
+.fa-chain:before,
+.fa-link:before {
+  content: "\f0c1";
+}
+.fa-cloud:before {
+  content: "\f0c2";
+}
+.fa-flask:before {
+  content: "\f0c3";
+}
+.fa-cut:before,
+.fa-scissors:before {
+  content: "\f0c4";
+}
+.fa-copy:before,
+.fa-files-o:before {
+  content: "\f0c5";
+}
+.fa-paperclip:before {
+  content: "\f0c6";
+}
+.fa-save:before,
+.fa-floppy-o:before {
+  content: "\f0c7";
+}
+.fa-square:before {
+  content: "\f0c8";
+}
+.fa-navicon:before,
+.fa-reorder:before,
+.fa-bars:before {
+  content: "\f0c9";
+}
+.fa-list-ul:before {
+  content: "\f0ca";
+}
+.fa-list-ol:before {
+  content: "\f0cb";
+}
+.fa-strikethrough:before {
+  content: "\f0cc";
+}
+.fa-underline:before {
+  content: "\f0cd";
+}
+.fa-table:before {
+  content: "\f0ce";
+}
+.fa-magic:before {
+  content: "\f0d0";
+}
+.fa-truck:before {
+  content: "\f0d1";
+}
+.fa-pinterest:before {
+  content: "\f0d2";
+}
+.fa-pinterest-square:before {
+  content: "\f0d3";
+}
+.fa-google-plus-square:before {
+  content: "\f0d4";
+}
+.fa-google-plus:before {
+  content: "\f0d5";
+}
+.fa-money:before {
+  content: "\f0d6";
+}
+.fa-caret-down:before {
+  content: "\f0d7";
+}
+.fa-caret-up:before {
+  content: "\f0d8";
+}
+.fa-caret-left:before {
+  content: "\f0d9";
+}
+.fa-caret-right:before {
+  content: "\f0da";
+}
+.fa-columns:before {
+  content: "\f0db";
+}
+.fa-unsorted:before,
+.fa-sort:before {
+  content: "\f0dc";
+}
+.fa-sort-down:before,
+.fa-sort-desc:before {
+  content: "\f0dd";
+}
+.fa-sort-up:before,
+.fa-sort-asc:before {
+  content: "\f0de";
+}
+.fa-envelope:before {
+  content: "\f0e0";
+}
+.fa-linkedin:before {
+  content: "\f0e1";
+}
+.fa-rotate-left:before,
+.fa-undo:before {
+  content: "\f0e2";
+}
+.fa-legal:before,
+.fa-gavel:before {
+  content: "\f0e3";
+}
+.fa-dashboard:before,
+.fa-tachometer:before {
+  content: "\f0e4";
+}
+.fa-comment-o:before {
+  content: "\f0e5";
+}
+.fa-comments-o:before {
+  content: "\f0e6";
+}
+.fa-flash:before,
+.fa-bolt:before {
+  content: "\f0e7";
+}
+.fa-sitemap:before {
+  content: "\f0e8";
+}
+.fa-umbrella:before {
+  content: "\f0e9";
+}
+.fa-paste:before,
+.fa-clipboard:before {
+  content: "\f0ea";
+}
+.fa-lightbulb-o:before {
+  content: "\f0eb";
+}
+.fa-exchange:before {
+  content: "\f0ec";
+}
+.fa-cloud-download:before {
+  content: "\f0ed";
+}
+.fa-cloud-upload:before {
+  content: "\f0ee";
+}
+.fa-user-md:before {
+  content: "\f0f0";
+}
+.fa-stethoscope:before {
+  content: "\f0f1";
+}
+.fa-suitcase:before {
+  content: "\f0f2";
+}
+.fa-bell-o:before {
+  content: "\f0a2";
+}
+.fa-coffee:before {
+  content: "\f0f4";
+}
+.fa-cutlery:before {
+  content: "\f0f5";
+}
+.fa-file-text-o:before {
+  content: "\f0f6";
+}
+.fa-building-o:before {
+  content: "\f0f7";
+}
+.fa-hospital-o:before {
+  content: "\f0f8";
+}
+.fa-ambulance:before {
+  content: "\f0f9";
+}
+.fa-medkit:before {
+  content: "\f0fa";
+}
+.fa-fighter-jet:before {
+  content: "\f0fb";
+}
+.fa-beer:before {
+  content: "\f0fc";
+}
+.fa-h-square:before {
+  content: "\f0fd";
+}
+.fa-plus-square:before {
+  content: "\f0fe";
+}
+.fa-angle-double-left:before {
+  content: "\f100";
+}
+.fa-angle-double-right:before {
+  content: "\f101";
+}
+.fa-angle-double-up:before {
+  content: "\f102";
+}
+.fa-angle-double-down:before {
+  content: "\f103";
+}
+.fa-angle-left:before {
+  content: "\f104";
+}
+.fa-angle-right:before {
+  content: "\f105";
+}
+.fa-angle-up:before {
+  content: "\f106";
+}
+.fa-angle-down:before {
+  content: "\f107";
+}
+.fa-desktop:before {
+  content: "\f108";
+}
+.fa-laptop:before {
+  content: "\f109";
+}
+.fa-tablet:before {
+  content: "\f10a";
+}
+.fa-mobile-phone:before,
+.fa-mobile:before {
+  content: "\f10b";
+}
+.fa-circle-o:before {
+  content: "\f10c";
+}
+.fa-quote-left:before {
+  content: "\f10d";
+}
+.fa-quote-right:before {
+  content: "\f10e";
+}
+.fa-spinner:before {
+  content: "\f110";
+}
+.fa-circle:before {
+  content: "\f111";
+}
+.fa-mail-reply:before,
+.fa-reply:before {
+  content: "\f112";
+}
+.fa-github-alt:before {
+  content: "\f113";
+}
+.fa-folder-o:before {
+  content: "\f114";
+}
+.fa-folder-open-o:before {
+  content: "\f115";
+}
+.fa-smile-o:before {
+  content: "\f118";
+}
+.fa-frown-o:before {
+  content: "\f119";
+}
+.fa-meh-o:before {
+  content: "\f11a";
+}
+.fa-gamepad:before {
+  content: "\f11b";
+}
+.fa-keyboard-o:before {
+  content: "\f11c";
+}
+.fa-flag-o:before {
+  content: "\f11d";
+}
+.fa-flag-checkered:before {
+  content: "\f11e";
+}
+.fa-terminal:before {
+  content: "\f120";
+}
+.fa-code:before {
+  content: "\f121";
+}
+.fa-mail-reply-all:before,
+.fa-reply-all:before {
+  content: "\f122";
+}
+.fa-star-half-empty:before,
+.fa-star-half-full:before,
+.fa-star-half-o:before {
+  content: "\f123";
+}
+.fa-location-arrow:before {
+  content: "\f124";
+}
+.fa-crop:before {
+  content: "\f125";
+}
+.fa-code-fork:before {
+  content: "\f126";
+}
+.fa-unlink:before,
+.fa-chain-broken:before {
+  content: "\f127";
+}
+.fa-question:before {
+  content: "\f128";
+}
+.fa-info:before {
+  content: "\f129";
+}
+.fa-exclamation:before {
+  content: "\f12a";
+}
+.fa-superscript:before {
+  content: "\f12b";
+}
+.fa-subscript:before {
+  content: "\f12c";
+}
+.fa-eraser:before {
+  content: "\f12d";
+}
+.fa-puzzle-piece:before {
+  content: "\f12e";
+}
+.fa-microphone:before {
+  content: "\f130";
+}
+.fa-microphone-slash:before {
+  content: "\f131";
+}
+.fa-shield:before {
+  content: "\f132";
+}
+.fa-calendar-o:before {
+  content: "\f133";
+}
+.fa-fire-extinguisher:before {
+  content: "\f134";
+}
+.fa-rocket:before {
+  content: "\f135";
+}
+.fa-maxcdn:before {
+  content: "\f136";
+}
+.fa-chevron-circle-left:before {
+  content: "\f137";
+}
+.fa-chevron-circle-right:before {
+  content: "\f138";
+}
+.fa-chevron-circle-up:before {
+  content: "\f139";
+}
+.fa-chevron-circle-down:before {
+  content: "\f13a";
+}
+.fa-html5:before {
+  content: "\f13b";
+}
+.fa-css3:before {
+  content: "\f13c";
+}
+.fa-anchor:before {
+  content: "\f13d";
+}
+.fa-unlock-alt:before {
+  content: "\f13e";
+}
+.fa-bullseye:before {
+  content: "\f140";
+}
+.fa-ellipsis-h:before {
+  content: "\f141";
+}
+.fa-ellipsis-v:before {
+  content: "\f142";
+}
+.fa-rss-square:before {
+  content: "\f143";
+}
+.fa-play-circle:before {
+  content: "\f144";
+}
+.fa-ticket:before {
+  content: "\f145";
+}
+.fa-minus-square:before {
+  content: "\f146";
+}
+.fa-minus-square-o:before {
+  content: "\f147";
+}
+.fa-level-up:before {
+  content: "\f148";
+}
+.fa-level-down:before {
+  content: "\f149";
+}
+.fa-check-square:before {
+  content: "\f14a";
+}
+.fa-pencil-square:before {
+  content: "\f14b";
+}
+.fa-external-link-square:before {
+  content: "\f14c";
+}
+.fa-share-square:before {
+  content: "\f14d";
+}
+.fa-compass:before {
+  content: "\f14e";
+}
+.fa-toggle-down:before,
+.fa-caret-square-o-down:before {
+  content: "\f150";
+}
+.fa-toggle-up:before,
+.fa-caret-square-o-up:before {
+  content: "\f151";
+}
+.fa-toggle-right:before,
+.fa-caret-square-o-right:before {
+  content: "\f152";
+}
+.fa-euro:before,
+.fa-eur:before {
+  content: "\f153";
+}
+.fa-gbp:before {
+  content: "\f154";
+}
+.fa-dollar:before,
+.fa-usd:before {
+  content: "\f155";
+}
+.fa-rupee:before,
+.fa-inr:before {
+  content: "\f156";
+}
+.fa-cny:before,
+.fa-rmb:before,
+.fa-yen:before,
+.fa-jpy:before {
+  content: "\f157";
+}
+.fa-ruble:before,
+.fa-rouble:before,
+.fa-rub:before {
+  content: "\f158";
+}
+.fa-won:before,
+.fa-krw:before {
+  content: "\f159";
+}
+.fa-bitcoin:before,
+.fa-btc:before {
+  content: "\f15a";
+}
+.fa-file:before {
+  content: "\f15b";
+}
+.fa-file-text:before {
+  content: "\f15c";
+}
+.fa-sort-alpha-asc:before {
+  content: "\f15d";
+}
+.fa-sort-alpha-desc:before {
+  content: "\f15e";
+}
+.fa-sort-amount-asc:before {
+  content: "\f160";
+}
+.fa-sort-amount-desc:before {
+  content: "\f161";
+}
+.fa-sort-numeric-asc:before {
+  content: "\f162";
+}
+.fa-sort-numeric-desc:before {
+  content: "\f163";
+}
+.fa-thumbs-up:before {
+  content: "\f164";
+}
+.fa-thumbs-down:before {
+  content: "\f165";
+}
+.fa-youtube-square:before {
+  content: "\f166";
+}
+.fa-youtube:before {
+  content: "\f167";
+}
+.fa-xing:before {
+  content: "\f168";
+}
+.fa-xing-square:before {
+  content: "\f169";
+}
+.fa-youtube-play:before {
+  content: "\f16a";
+}
+.fa-dropbox:before {
+  content: "\f16b";
+}
+.fa-stack-overflow:before {
+  content: "\f16c";
+}
+.fa-instagram:before {
+  content: "\f16d";
+}
+.fa-flickr:before {
+  content: "\f16e";
+}
+.fa-adn:before {
+  content: "\f170";
+}
+.fa-bitbucket:before {
+  content: "\f171";
+}
+.fa-bitbucket-square:before {
+  content: "\f172";
+}
+.fa-tumblr:before {
+  content: "\f173";
+}
+.fa-tumblr-square:before {
+  content: "\f174";
+}
+.fa-long-arrow-down:before {
+  content: "\f175";
+}
+.fa-long-arrow-up:before {
+  content: "\f176";
+}
+.fa-long-arrow-left:before {
+  content: "\f177";
+}
+.fa-long-arrow-right:before {
+  content: "\f178";
+}
+.fa-apple:before {
+  content: "\f179";
+}
+.fa-windows:before {
+  content: "\f17a";
+}
+.fa-android:before {
+  content: "\f17b";
+}
+.fa-linux:before {
+  content: "\f17c";
+}
+.fa-dribbble:before {
+  content: "\f17d";
+}
+.fa-skype:before {
+  content: "\f17e";
+}
+.fa-foursquare:before {
+  content: "\f180";
+}
+.fa-trello:before {
+  content: "\f181";
+}
+.fa-female:before {
+  content: "\f182";
+}
+.fa-male:before {
+  content: "\f183";
+}
+.fa-gittip:before {
+  content: "\f184";
+}
+.fa-sun-o:before {
+  content: "\f185";
+}
+.fa-moon-o:before {
+  content: "\f186";
+}
+.fa-archive:before {
+  content: "\f187";
+}
+.fa-bug:before {
+  content: "\f188";
+}
+.fa-vk:before {
+  content: "\f189";
+}
+.fa-weibo:before {
+  content: "\f18a";
+}
+.fa-renren:before {
+  content: "\f18b";
+}
+.fa-pagelines:before {
+  content: "\f18c";
+}
+.fa-stack-exchange:before {
+  content: "\f18d";
+}
+.fa-arrow-circle-o-right:before {
+  content: "\f18e";
+}
+.fa-arrow-circle-o-left:before {
+  content: "\f190";
+}
+.fa-toggle-left:before,
+.fa-caret-square-o-left:before {
+  content: "\f191";
+}
+.fa-dot-circle-o:before {
+  content: "\f192";
+}
+.fa-wheelchair:before {
+  content: "\f193";
+}
+.fa-vimeo-square:before {
+  content: "\f194";
+}
+.fa-turkish-lira:before,
+.fa-try:before {
+  content: "\f195";
+}
+.fa-plus-square-o:before {
+  content: "\f196";
+}
+.fa-space-shuttle:before {
+  content: "\f197";
+}
+.fa-slack:before {
+  content: "\f198";
+}
+.fa-envelope-square:before {
+  content: "\f199";
+}
+.fa-wordpress:before {
+  content: "\f19a";
+}
+.fa-openid:before {
+  content: "\f19b";
+}
+.fa-institution:before,
+.fa-bank:before,
+.fa-university:before {
+  content: "\f19c";
+}
+.fa-mortar-board:before,
+.fa-graduation-cap:before {
+  content: "\f19d";
+}
+.fa-yahoo:before {
+  content: "\f19e";
+}
+.fa-google:before {
+  content: "\f1a0";
+}
+.fa-reddit:before {
+  content: "\f1a1";
+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+ul#tabs {
+  margin-bottom: 4px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+@-moz-document url-prefix() {
+  div.inner_cell {
+    overflow-x: hidden;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 20ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
+    display: none;
+  }
+  span.autosave_status {
+    font-size: x-small;
+  }
+}
+.toolbar {
+  padding: 0px;
+  margin-left: -5px;
+  margin-top: 2px;
+  margin-bottom: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+  width: auto;
+  vertical-align: middle;
+  margin-right: 2px;
+  margin-bottom: 0px;
+  display: inline;
+  font-size: 92%;
+  margin-left: 0.3em;
+  margin-right: 0.3em;
+  padding: 0px;
+  padding-top: 3px;
+}
+.toolbar .btn {
+  padding: 2px 8px;
+}
+.toolbar .btn-group {
+  margin-top: 0px;
+  margin-left: 5px;
+}
+#maintoolbar {
+  margin-bottom: -3px;
+  margin-top: -8px;
+  border: 0px;
+  min-height: 27px;
+  margin-left: 0px;
+  padding-top: 11px;
+  padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+  float: none;
+  vertical-align: middle;
+  text-align: right;
+  margin-left: 5px;
+  margin-right: 0px;
+  margin-top: 0px;
+}
+.select-xs {
+  height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+  background-color: #F37626;
+  color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-webkit-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-moz-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+@-webkit-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+  overflow: auto;
+  height: 200px;
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+  text-overflow: ellipsis;
+  overflow: hidden;
+  height: 80px;
+}
+.tooltipbuttons {
+  position: absolute;
+  padding-right: 15px;
+  top: 0px;
+  right: 0px;
+}
+.tooltiptext {
+  /*avoid the button to overlap on some docstring*/
+  padding-right: 30px;
+}
+.ipython_tooltip {
+  max-width: 700px;
+  /*fade-in animation when inserted*/
+  -webkit-animation: fadeOut 400ms;
+  -moz-animation: fadeOut 400ms;
+  animation: fadeOut 400ms;
+  -webkit-animation: fadeIn 400ms;
+  -moz-animation: fadeIn 400ms;
+  animation: fadeIn 400ms;
+  vertical-align: middle;
+  background-color: #f7f7f7;
+  overflow: visible;
+  border: #ababab 1px solid;
+  outline: none;
+  padding: 3px;
+  margin: 0px;
+  padding-left: 7px;
+  font-family: monospace;
+  min-height: 50px;
+  -moz-box-shadow: 0px 6px 10px -1px #adadad;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  border-radius: 2px;
+  position: absolute;
+  z-index: 1000;
+}
+.ipython_tooltip a {
+  float: right;
+}
+.ipython_tooltip .tooltiptext pre {
+  border: 0;
+  border-radius: 0;
+  font-size: 100%;
+  background-color: #f7f7f7;
+}
+.pretooltiparrow {
+  left: 0px;
+  margin: 0px;
+  top: -16px;
+  width: 40px;
+  height: 16px;
+  overflow: hidden;
+  position: absolute;
+}
+.pretooltiparrow:before {
+  background-color: #f7f7f7;
+  border: 1px #ababab solid;
+  z-index: 11;
+  content: "";
+  position: absolute;
+  left: 15px;
+  top: 10px;
+  width: 25px;
+  height: 25px;
+  -webkit-transform: rotate(45deg);
+  -moz-transform: rotate(45deg);
+  -ms-transform: rotate(45deg);
+  -o-transform: rotate(45deg);
+}
+ul.typeahead-list i {
+  margin-left: -10px;
+  width: 18px;
+}
+ul.typeahead-list {
+  max-height: 80vh;
+  overflow: auto;
+}
+ul.typeahead-list > li > a {
+  /** Firefox bug **/
+  /* see https://github.com/jupyter/notebook/issues/559 */
+  white-space: normal;
+}
+.cmd-palette .modal-body {
+  padding: 7px;
+}
+.cmd-palette form {
+  background: white;
+}
+.cmd-palette input {
+  outline: none;
+}
+.no-shortcut {
+  display: none;
+}
+.command-shortcut:before {
+  content: "(command)";
+  padding-right: 3px;
+  color: #777777;
+}
+.edit-shortcut:before {
+  content: "(edit)";
+  padding-right: 3px;
+  color: #777777;
+}
+#find-and-replace #replace-preview .match,
+#find-and-replace #replace-preview .insert {
+  background-color: #BBDEFB;
+  border-color: #90CAF9;
+  border-style: solid;
+  border-width: 1px;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .match {
+  background-color: #FFCDD2;
+  border-color: #EF9A9A;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .insert {
+  background-color: #C8E6C9;
+  border-color: #A5D6A7;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview {
+  max-height: 60vh;
+  overflow: auto;
+}
+#find-and-replace #replace-preview pre {
+  padding: 5px 10px;
+}
+.terminal-app {
+  background: #EEE;
+}
+.terminal-app #header {
+  background: #fff;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.terminal-app .terminal {
+  float: left;
+  font-family: monospace;
+  color: white;
+  background: black;
+  padding: 0.4em;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+}
+.terminal-app .terminal,
+.terminal-app .terminal dummy-screen {
+  line-height: 1em;
+  font-size: 14px;
+}
+.terminal-app .terminal-cursor {
+  color: black;
+  background: white;
+}
+.terminal-app #terminado-container {
+  margin-top: 20px;
+}
+/*# sourceMappingURL=style.min.css.map */
+    </style>
+<style type="text/css">
+    .highlight .hll { background-color: #ffffcc }
+.highlight  { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #7D9029 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #A0A000 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+    </style>
+<style type="text/css">
+    
+/* Temporary definitions which will become obsolete with Notebook release 5.0 */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-bold { font-weight: bold; }
+
+    </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+  overflow: visible;
+  padding: 8px;
+}
+
+div#notebook {
+  overflow: visible;
+  border-top: none;
+}
+
+@media print {
+  div.cell {
+    display: block;
+    page-break-inside: avoid;
+  } 
+  div.output_wrapper { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+  div.output { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+    <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    MathJax.Hub.Config({
+        tex2jax: {
+            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+            processEscapes: true,
+            processEnvironments: true
+        },
+        // Center justify equations in code and markdown cells. Elsewhere
+        // we use CSS to left justify single line equations in code cells.
+        displayAlign: 'center',
+        "HTML-CSS": {
+            styles: {'.MathJax_Display': {"margin": 0}},
+            linebreaks: { automatic: true }
+        }
+    });
+    </script>
+    <!-- End of mathjax configuration --></head>
+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#22270;&#20687;&#20998;&#31867;">&#22270;&#20687;&#20998;&#31867;<a class="anchor-link" href="#&#22270;&#20687;&#20998;&#31867;">&#182;</a></h1><p>在此项目中，你将对 <a href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10 数据集</a> 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。</p>
+<h2 id="&#33719;&#21462;&#25968;&#25454;">&#33719;&#21462;&#25968;&#25454;<a class="anchor-link" href="#&#33719;&#21462;&#25968;&#25454;">&#182;</a></h2><p>请运行以下单元，以下载 <a href="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz">CIFAR-10 数据集（Python版）</a>。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">urllib.request</span> <span class="k">import</span> <span class="n">urlretrieve</span>
+<span class="kn">from</span> <span class="nn">os.path</span> <span class="k">import</span> <span class="n">isfile</span><span class="p">,</span> <span class="n">isdir</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+<span class="kn">import</span> <span class="nn">tarfile</span>
+
+<span class="n">cifar10_dataset_folder_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-batches-py&#39;</span>
+
+<span class="c1"># Use Floyd&#39;s cifar-10 dataset if present</span>
+<span class="n">floyd_cifar10_location</span> <span class="o">=</span> <span class="s1">&#39;/input/cifar-10/python.tar.gz&#39;</span>
+<span class="k">if</span> <span class="n">isfile</span><span class="p">(</span><span class="n">floyd_cifar10_location</span><span class="p">):</span>
+    <span class="n">tar_gz_path</span> <span class="o">=</span> <span class="n">floyd_cifar10_location</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">tar_gz_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-python.tar.gz&#39;</span>
+
+<span class="k">class</span> <span class="nc">DLProgress</span><span class="p">(</span><span class="n">tqdm</span><span class="p">):</span>
+    <span class="n">last_block</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="k">def</span> <span class="nf">hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block_num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">block_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">total_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">total</span> <span class="o">=</span> <span class="n">total_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">block_num</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span><span class="p">)</span> <span class="o">*</span> <span class="n">block_size</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span> <span class="o">=</span> <span class="n">block_num</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">isfile</span><span class="p">(</span><span class="n">tar_gz_path</span><span class="p">):</span>
+    <span class="k">with</span> <span class="n">DLProgress</span><span class="p">(</span><span class="n">unit</span><span class="o">=</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">unit_scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">miniters</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;CIFAR-10 Dataset&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pbar</span><span class="p">:</span>
+        <span class="n">urlretrieve</span><span class="p">(</span>
+            <span class="s1">&#39;https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz&#39;</span><span class="p">,</span>
+            <span class="n">tar_gz_path</span><span class="p">,</span>
+            <span class="n">pbar</span><span class="o">.</span><span class="n">hook</span><span class="p">)</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">isdir</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">):</span>
+    <span class="k">with</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">tar_gz_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">tar</span><span class="p">:</span>
+        <span class="n">tar</span><span class="o">.</span><span class="n">extractall</span><span class="p">()</span>
+        <span class="n">tar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+
+<span class="n">tests</span><span class="o">.</span><span class="n">test_folder_path</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>All files found!
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#25506;&#32034;&#25968;&#25454;">&#25506;&#32034;&#25968;&#25454;<a class="anchor-link" href="#&#25506;&#32034;&#25968;&#25454;">&#182;</a></h2><p>该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 <code>data_batch_1</code>、<code>data_batch_2</code>，以此类推。每个部分都包含以下某个类别的标签和图片：</p>
+<ul>
+<li>飞机</li>
+<li>汽车</li>
+<li>鸟类</li>
+<li>猫</li>
+<li>鹿</li>
+<li>狗</li>
+<li>青蛙</li>
+<li>马</li>
+<li>船只</li>
+<li>卡车</li>
+</ul>
+<p>了解数据集也是对数据进行预测的必经步骤。你可以通过更改 <code>batch_id</code> 和 <code>sample_id</code> 探索下面的代码单元。<code>batch_id</code> 是数据集一个部分的 ID（1 到 5）。<code>sample_id</code> 是该部分中图片和标签对（label pair）的 ID。</p>
+<p>问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
+
+<span class="kn">import</span> <span class="nn">helper</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># Explore the dataset</span>
+<span class="n">batch_id</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">sample_id</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">display_stats</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">batch_id</span><span class="p">,</span> <span class="n">sample_id</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Stats of batch 1:
+Samples: 10000
+Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}
+First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]
+
+Example of Image 5:
+Image - Min Value: 0 Max Value: 252
+Image - Shape: (32, 32, 3)
+Label - Label Id: 1 Name: automobile
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsIAAAK/CAYAAABjrqvGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAAewgAAHsIBbtB1PgAAIABJREFUeJzsvWu23LjOJbhBf2PtnlbPocdQa9Wg7iHqB/HYAKmIOE6n
+M2+ZsOOIL1F8gMAWBFGiqrh06dKlS5cuXbp06U+j8U834NKlS5cuXbp06dKlf4IuEL506dKlS5cu
+Xbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLhS5cuXbp0
+6dKlS38kXSB86dKlS5cuXbp06Y+kC4QvXbp06dKlS5cu/ZF0gfClS5cuXbp06dKlP5IuEL506dKl
+S5cuXbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLhS5cu
+Xbp06dKlS38kXSB86dKlS5cuXbp06Y+kC4QvXbp06dKlS5cu/ZF0gfClS5cuXbp06dKlP5IuEL50
+6dKlS5cuXbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLh
+S5cuXbp06dKlS38kXSB86dKlS5cuXbp06Y+k//mnG/Bvof/v//1/9JNyqgqs/4CqHQG1RLUE9do4
+z895dSUpB4tIz24FABFZ5YYfB8UHpff4qOdRngJQu7ZSvKbveTjEf2BVO0Twwy43APwQsXTgB1b4
+h5VbaRS2c1Qn9Gtizq84zjmhX+04vzC/7EhldU5MP+/rC//5mvb7st/Ef+bM8IfHrzmhxhNq8578
+YfEIJ/9oy4fnP7GIbCkv8og/fhEJX8iOAiHe9bBQkSyfUc+X7QcPjz3v1S/Oa03c4ytRKP9Udohg
+WBvGEIwxIu3pmGU9Lc/5nx8/8OPHD/z4MfDjxw+K/8D/WNqPHz/wP/+TZWq5EeGNXB5ZGMAWj7Qm
+hFQ1edbDwZ89jcIP5+TE57xkPPN8vjy8+IMnYcmQOaf9tBy/5sRUbXkW1nX8aud+fX3hP//5Wsev
+L4pP/OfrP/j6z4z0rex/6jlH6kvttPZOy7HL+aYLMluO+bQErbytA+T6fxWXrOYQPzRdfHbOXRXo
+WZ9h572Sc+BdrSeU+KZPtV2BZaq25MO5n9HHBQHEjGVYntO3tOOVcwwUdbxqXFPf8Dk0xqF/SDeF
+HmppfATlvR1HJR54U/ZTTfX//6///cuU2rUIX/rj6Hsi7FKnO37PdMfmQ7oDdenSTv9F6+K/qKlv
+6QLhS38c/Trb6J9Jd/ye6Y7Nh3QH6tKlnf6L1sV/UVPf0gXC/0L6exnsH2Lff82q6Q/zLn2Xfsf4
+/WvY5Zv0r+EtevT9r6R/zUBduvQvor9pXexy4K9Lhv+blvAFwv9C+nsZ7B9i33/NqvnXNOTSC7qz
+9Bep+1leunTpj6VdDlzJwHSB8KU/jv61VrL/Errj90x3bD6kO1CXLu30X7Qu/oua+pYuEP4X0nWN
++Dvpukb8VbquEc/0r+Gt6xpx6dJ/H13XiH+ELhD+F9J1jfg7Sf+94OC/hH7H+P1r2OWb9K/hrX+7
+a8S/ZqAuXfoX0d+0Lv4O14j/m5bwBcKX/jj614KD/xK64/dMd2w+pDtQly7t9F+0Lv6LmvqW7gc1
+jHTOz8rRn9x02qwvuVt1buJd8vYPbZQNswWVuzwrTqId03s5VQCDdtXmXcQnoKN2IMrNdd7007Ps
+2ix7tXJtxS0ZV10f01CBiuWJ5ZUjbNN8ACoQUUxZG7ar+Gb8FvbzuJnbbac87OpNO4CD0mk39dyg
+vQ9giz/c6tpW//bxhn6U+uEIqlZ943mVOqdlo/1Dflz4xYMtqann9IdzOeWbt/dS2iVUhQf2D2fU
+y7x+dP+4+TplPmYZb8KGuAxluMbottxqglgpxVT/kAvaB1PyOO28wem2lOacwBCIDkxViP2GKqYd
+Ve06EdZVl9J146MVyT4sj2LIDqx94v0iy+Br0fvEH9PoaZSO8wc4Yn5dBmiygirxi7VNhGSmf1TA
+q9GtA3Xm9JDGcuGYX4vWgVK8cqAKrm+8dWRm7+RHC2yVa0smruUp0vJ47fVz+aMa/rEGsfo8jFau
+fEyDz6f+7OtYSxebWKJ4HQtVpY/9BDN4Zu2861KxVdyKF/5h9nBZYG3p/FjWx2manlnhI+Iqt7kD
+tSMOxAcmxLiLsh03SdZq1/i7jmvMGVawrFTAdDu1SzNNmwr2vGig1Zlz8GIA+9L4i2P9s3SBsJFu
+2uOx5AaAEbKWgW79qlgFpvwFMS0LMunzFZnLZAIqUIylhSHGoQMwZYw5gDGBKSWu8DBWWQeqauBX
+TD3wUWtaYFT/spzlAbCvS9WjCDBNqMUX6kTXV+v8yL0uinH9lJFB+dHg98/kgOIP4yrl51/DQggl
+8X/9y2bH+dmvEGlNEJxFWmqS4xVKnhzzHs99KvzmhLjWQUFX4MuN4/SfIVOErh+Puee2agttJ7J2
+Qa7LCUlwOwHFxBwCu5tbJ4wRS6o0zNbDiIbOwitTdN0YDgbGS4EMRQPGBsbpF2x8GIAqzvhrliSz
+KJCY8QB2t6/JPYBgBsIx1ActZ0rSs9eSlsIXC+hrPbd1nL+ZVe5MCCxnt7OcRhlt5Ttp+QULSy/z
+wNCflqOynYUABk91jDKcALjm0Y271dPTPi7H7erzki0Aj6VUNFT65TpRhG45WBYWQ4EjLsm86Kzm
+JW29SamHLsxNoXgHzhttc6bH4KvztzH040GuP6WX+EsB6lzh5iuxo68b09W87mlog+NpaBcOaOq0
+laHCRT7FxMQ84jAvJoceRPTfTRcIG6l+ZhGGKYCKpVKZdCYoYLmU8z8SvFEv8+TLekpNrlIdcECc
+lt5Z8+ZS3p6nHqe8sNQ6SD0CYg6vdp3ySbpuQHgBAlusqmcwLEsoxl3n5BXcjj4pYQlqeS1+ugES
+Dokf2LKyQFAojBdA+LSuX6WdZUBTgKcSJbOVfHPuQ40fXKdXfrACR5GTKshzntuXI6LqAr7naqQ/
+6b1TCuvaWINKitHz5rpH9ILDlte0JQOsG0dxgExH4ThGgF8ZGtbhOWvcwW9aiStLB2s3nMAd2wBg
+DZ7L0Zr4KRBM6Z1E6vzFww9rQ7fqBQ/VjuWhXIPWNgnYBMosrPPH7aRb9yhfAIvVz2mdzamyMxWE
+VstnTlpIKyCqMsCfPnmZTK/nDuSTqpRX7QgQCH440hhkozTb8GLlHYeEq4HJYbtWCduCXE8/1twu
+8GxzpKQXDuCqgFwfZ44rjXNfR5/QG8Eq21/U+eKyVWRSeuWbjYu4Ay3k4+YtKGJDqvoEhddT37ze
+uZw01apRccE4flF/ehzxKJQ3wmWCfh9dIGz0qWvEKpyLEvD517qQCiCuLhGtYPyta+pphb1iErYE
+u5YWU0BpFS4W4zmsqFmMrZzKAwhugHhGGA8A2foiKC4Ec1S3Age/7IoR4Lr3+pUVuFt/1RUipRXF
++XpIt8eH4pZg7o9giGDKPoPaYic1saUp4rF3EaEPLCFboCY8JH+DDl+83yVxCOTannZm383gQfjn
+GjkVSAAnyvDkPL5PyrlfrgKSxS78EEVt2TgoHpPAMD1o4bZKiU98yYAMhUwtFuFh8WGAOF0mKmuz
+xSbajcbWPHylzF5YOQwQuMUzCI70szXYAeY26gJsejDATwPInibRMuqMy1ZlLVw6r2Wt918fwBNv
+ZB8YuAS3OTqMa7xeo8y7J55OGUPxuK4c0qWWbwB4NS/l7REAy7nM6Hle8YHJUkIzGuWZ5zBLNDL1
+lDtSCkOJIQ5hIEHTqQllQesR9AqFA4g90s8BNBaDLEl33mp8JrKnx3rYT9rcvto1o78gS7ENZcyQ
+ydNYFSRv/EY246v2LJeFlQfV5ASPb0ocBufc+t9LFwgbfcc1gnGVxzUTyh1SlR0dEL++ThOZB5JY
+yIvYEjyhAWyR7g8GkLsVWF3jyzov/HYLCKZFInXBTHnOC+spAWHRdU0HwJPyAxQL1h3kks40NNp+
+hzQfwbpqc7AO+s91W8RJ+qTCQSgJbMDYH16fodeT3nwqdxCDL4Ajtgx5XeptRXvyoaAc8kzCl9JH
+t5GHC4fUpCQXj8aDLjiLbvSRi0Mqhlo/XZr1K/Y0f6Cidh9ZAO9oYFgAIWCsExjhP79OEJmY010j
+BGMMzGlgmAGw1TtUMUGgGMnKJ5bmrvLNepFTLd3lWD5EYWCLFxbiloe0ZMf0auVnH9dWpNz4FcDi
+faKOb3PaQG/mnNa/h7sAyDTZ0lZbTha7E/vz2Y/L792yJJ48gmAheUXuCy+tvyIJclluwW/mT8A4
+3SUqg8VsoUyUZpqW8eVByf7xUj9ZgnNx97AzvQ+Olrr3cGW8XgVbiffGfkin4kUWU7ilBxA8uaIA
+gKjJMy3n7wUtIjwr2uJmKVaEa0pClnfxXCFa4mteQn5AYl2qCMTD3tYyVjkRKvpPGIQvEA760DVi
+B7/xZ3tEsMXpDyuCqBs7f28A48RAQf6yXLUKL6ZciljJ8qtsIWawPMcCow0ET7ilFq+B8CFfBglY
+B5AqkMEAGA340ohYl+itpfyB46hhb0GuWouelWAf9VACcNcHcoUIpYJQJN7MPjN7mos1aoKcz+1c
+cRSEpzO2Mu+07+f1bwWeTnqsLG8uXtS6SJMP8sVDE+ggQa8+jA8A+A11YxSzHiaWtXahUwwIprk9
+jGlPA9wVgvjEmWLVOyEGfN0SPOesYNgA8GYRNp4tYNi1GQ8TqP2gJRBlOiB00IFi2fX89xbiHQSX
+dpwZuvC645tyTp0ZbyXSvzfTIxzXPpXR6M85ny9bbVL84txa61k+X+Zt0lteLKEXLhJ+qMC2guCa
+RkAV1VXiFfgdh6db4wCCHRwniuJxs2OMeb44DX2261UTz2ks1Bf6kpFb2K/hYwmUu1m7k4psiaQt
+HN2Qdd5fwmDHKc3ExxspqWmZ7tLMSavMPISjj5QulFlG3grHClDQ+z2WpzzTbP3t5VKGZHwdy/sA
+muPfx1qh5uryl2bhp+gCYaOf2TUCxCRppvHsrqCyMNfRWf3hihl8B1B0iVEtVmExFwhzlWgvyKn4
+i3QCFctTcnsI/CiY1gx2gYi0SK9pEMGXC7NBQFLsRaQhZgG231Do9DhiwwsAC5V00Ft8hjXuSitq
+IKXflGcf5qOQYuXiR1cuZOketapY8G/TRCoIqTDhY0z7vtx+4juWOhd4cdbR+tvOk2PqzuOq9pa4
+C8pVWkVdd6VQDWTyIEif9G8Ls9VSRDGGLJ6egjGWRXfxMjZLsBl+Cfyu9g/LmHOuF0QJ/IaP8Ew3
+CR2LrR9dJLzdfIQrIupLCagPaZ7kPFcszgcA/AoER94BCPPkauXqrpS3vCb6igCNm+BIoE5zf1te
+4Y1T3qb2rQu1UyIaip3r01e8X7L0lHgARgx065HdI9hKnOB48V0Jn8BwB8ElLvSyJ2LMi7uZ5Kzp
+tkWD2uhlmVihingSAKUX5yKdFmOETWMqAnSLajUMZ6UHRkOmgaqkFv4a+kkAfCjb008AuKdl/Ssj
+X1rPYjunM/B9AsTpTQ/4UvO5R8qR0MVSjz74vA0FTZKD4d9NFwgbfe9luTjpgzRljtvTPE6hRzYI
+Rf9E5BpRrMKAhgtEukYE6IUc8tIRXrFbgwN7diCMtRNEVysOgKdKhN2vVqAYAYLFro8EwBPQQSNz
+sgizYD5ZhkuYOvZqrLEUjP+D1GPfMaLrwBMke0yj7tUyb4TCGVe+LJvRvyBwZAt8dmE5DXvRtCjz
+LBQ2CR+WAyxAotqs6zgDrg6Bnsq4jA7dye+XHjZfwQBkrrb6UwMOexIwMefAlyhkTszmFrHu6c4+
+wivNdpGgoelWYNf/uR5YNvmh8r5SegfAcJCLTNtAcAHCqGCc54P5u7XfIZK0k0Jelplrjed+FWFL
+6z16wXkkGwqdV5+gW4qt5AN4eZW4SXzit2AXS0vwyxbfFj6UGV4Xg+AAx2bt5bQNFHv6uob6uFm7
+3ZXBx3M9clcalBxxxqEAGpAltVbq9HHplmCtQJtlRAG+UtOAZLpSjZbL/rxYfD5R/G+b58iPSF0F
+0q3C/eQ4HpiQjrKlC60GvwGp7/XwUW0xRpjPg+QNcMgGKWGJfIRVOMl457DafxddIGyk89Pht8kq
+svmkdJSLb8onldV51whfvHveK4hUXSO6VVi7O4SD3mFg2bdNGyOskwvkLiE4gWIhzjwUMJzhBZIh
+MDcIA74UHmLWNgMaalh+eXWYhSXewqchWOggw48Wohj9La8DhD78MQkMNkPxpHUlAP0HmLVfqqd9
+CwRziW8I7/2cvwCITzVTdadV9XS1ks6MT8pO1USm0CO3OPvpZbkHKtdoDaC4ufwuEEwbsUyzFs9p
+PEC7D0JAVmK3FA58zXSPCIuw7tZgVywBgNFY3doYYoTaXfma3QV6vsaxLiMCuzik9XINBNfdGAiD
+cFikYhik6t2mghtXJqiBskiqeTXf6+H8p/p7XdmPCEvPpda/WFZ16eU5QvEOuhPHvAbB8eLcCeSe
+jtjTwy1ChlmEGfBaez1sIDV0nD3d8jY4f0HbdmnbmDEcorxiCSarewHNfl2A96Rm4EvF+JJVDPxi
+EMzz3OfTm/e+rNbEDeAmv8mhzNO5rrvdqAAh0NuOSi1ZY9bTHAxLygBFxmeu/6qLcy6T6z+W4L+M
+LhA2+tgiDBTt8wSIs5i28yzA4Tb3XRF8RoLy4psDXSxUuRgwXSPCTcLBsVtjadeIBLQa4bQEvwfC
+fgRk7RKhdjSrg8qKj7BAp1U4+4H45ocGIlFrCKEC/TTe56EuTboXh9jj96Vc1h+x9A0QNyD8GuC+
+KPPB5L/Mfsj8mJ/eofkPqPfpVX4pV+S9K1s6mOKrYNiU8L6Mfq7hecnQs2EBhluEdfExpLhDiANg
+0fAXVkmrGjAh09wjhh3nqIA4fu4W4eByZ2VW5DywJzynXEgBbRkbyIVfy8DPCQA/gODzi8cSf+2K
+G0j2VNfdx6Wg3OHVj2LpBXUgpFfL74NT1n9ruxAIFSxAZoCDMVY9p7b8yJN7x8BjFFi6gZ2MJwj2
+fAfBkS8o8onB7hEQPx6HzVXKS7cGr6PzkAFWNVcFzec0ZW2uxbzWMK35lH2KtWXayRJ8AsFUgV2X
+tyeIl7WoEXEpAsXHufyIXpwV8uwwt/3sE1iOsloLCOUd0hgcP4HiuOmQCmpPQDhXxkMZBXSyLFgD
+qtNupkemoUAtmj8sHvtLTyp/ki4QNvqOa0QNuxAolR3Ka1mEHhYq9lemP98nbS/M9RfkAISbhCxA
+rFEOUN41Aq8Bbmyfdsjr7hKiY1mB3RpsoHigWoZVRi4c+iBe1WUNEVTNXeOb8rO0plDrWNaIizCB
+pnJxoHwAwh+DYJr0d6D427whx+DPVXJow8PQuY47pp/ac6ynLIamBDsYtnxWmvI8tftCo3i0XWpr
+NwswBOFWHwDZtjsSsf2EEyxB1NIO4NeVx7RfWINNoaAD0Mba6LLnnKYlkazEmnGDOARutS4xBsAB
+gs95TN1xha2orvQyzkISyJXRO0RW3jhhL7PLgexlvVCeHy4Qzgc+hx5Eo+MCS8bq8uRUsmeVa5kM
+cheFtAB7uoNkKWXSqotwg3gCwWOMF2A5hXC8bEjWYAfIvkfsWoapkbh/SscyTgRs84WpPOPkPlFc
+JhQkH4B4V6AD3dYIbqOE0H2YqL+goB8B8EG2CryhLd3HpAHeHQhnGbEyFUBnrSeQ6yvglJbn1HNV
+sV6wn1V2WEG4P6WAdLtl9ZcB/gEX4QuEnb7rGlFlrm7Znq5b+ist/VepbYdGvsLpd+vMO6DkK+wv
+zCnMYgx9BLcdHFcrsWxlAZjFSzDHgBj4nViAeIzVhglg2Ac+FAKYANZiDQaKdi6W4fZzha/1nCqE
+F20vw4DUmBDoDUDs4FdDceQLhFlLgwARXwJgSecn4MyC8jv0maL+tB45JSZQL6SlDKcyru19PVRf
+TyzWJymWYZAePLVftVbaoitNXclYPulVKGwHQt8pIoHFHBq7RMSNkPkLKwEJFVlePUIg2AHx0AeL
+cFqGdzcF6heLI80OqXfMcotS2sqDwG9fRnr+4Tl/qjbe0epCoAyAtZRM6xSixA5my+yh3tRqKRer
+7QUorvXmkSFCp8h7sbbCheDN+su+a4JYykgQTGmbBbiBYP93ALfDbtaGjEP62EDw8IvTDRD4GE9l
+NI/QwKJxJIalZV3mu6zXkm8Z6u4VVrHwEZnnNTIwLkDdL0hHb3Ncr8zOu9k759KkvQXAFGGHyZJe
+QDBcPVGcgXHynkiNe6EdCH8jXShdsUCwgeFZbpbpJt8BQXyMKPujFpZdsfztdIGw0c+6RhQ52SeQ
+hbOe09u6/1nMYme6FViw3CHS2rtotwQHIIZknkzbDi1B7iMopvxjGUMUbgGeUIgOTH8TXwQTA3PM
+5SIB81GeWG1USbMbKfrTb/MTLPk8/o4kymQcx17iZ9LSrX7FGoxiEeZz9UW8CvpWpjPDp8r0E/rp
+unoHK7/HQ622NArkcVncpuR83aVkw1JjNw7xqNPOylX0oY+w61Rq22nx+SWHvQg3BZBBTkdTwn1d
+hqmHSaBYnF0cELlFmAExgV6PT2ZdzbVF7M0iKMcyB/XA3kcrcJxJ13v+0fVflivoIoI5Q3ndXDPu
+a0gvpAmXrWubbidpEOpa1zpaVI/XdRgkjxMjMwyI/vjvxGwF5OyM9ama79ilp/ERHQQLyr7AI6zD
+DoIJ7I4EvGLW4cz3dNDcuqw14GPjmYAXKIba6LM2HrA+2Zrm0sUqTGu/ukPQkSfjdPHDsVfrmLnS
+QTC8mrBD4gnsPgPgDBQw7H/sZol5kIFvAGW3KBdGQT03OLsD3gMAFinxXnaxgEJ1pvqljxCVjxF1
+AOzbEZJc/t10gbDRt74sB5aTrGm4wjadSoEPZvoby68JXAO9/uU4ArpKrhFsCXbf4MjDco3ogJbj
+4RKhnLaXjxvAMcgCvIThxBLAcU2hNjgYBuwbs1bZkKrwYhXSryjNHFGNfBrkXMX7nOXgRjBAL8gi
+8wCE39EOlKv9+NWJ37zUL67T1RnZy0pH9jXBnl9qQOP0UahIOW2mz2AYiLAIX4dbuIdLuyQjiiqI
+hQpOa/sA7F7SngIMuvWcai9NOo+sCsV8if3GbuhoFt9ZXpxjq+rZQosNeMSQ9TFXznArMZenvKdr
+gX+vwS/XEReKNrpCz3R/E90bxNYz6kCbNH7KAw6UcttaLz3ZBumQv1NiinSTOC2avYbKUdspxHPH
+epv1twPezG8yisHvZhUeGwh2ANzB8BiuU6qVL+KiJU+EwvBni7mEHcCKuGNCDlM3DDD4Dfl4Mula
+OTV5kKcdQPGRXsldmr9vCd48R/bUFwCY0zXmlwsn4HV+EUqnMDxMYDjurt4D4bT6nsoQKFZgyoyn
+ySA+Ucz6MaJ4UG010t3B+Wbk76cLhJ0COPYVc0o7yeiW0EBYiZOOWNF+j/whbQubtk1zK2r5EgW9
+IAckALW9hHMvX/+gRgO/muF11B0I6w6KAeALyxK8fCyXC8WE0Ic4zEo9qa0LMdtNiluF32hrX4QB
+iKlMG/+T8Av54gpE3fJrRzUR4GnKrhLPU/Oz8bd8QWDuM9prPF7j0wvzMglwQbYMMf7WLO7W9bLl
+qBN3WvN8sGXYpb+tubADu+IrDfuEdAsyDvd7y1w96xrTdoTIrQCdb8wnGMuX2MusXSPsc8pmGdY5
+DPQiALDHi1tEWGTJFtoxXUvb4y2R0gxi7v98LX0AfpXauw9tF3p0G5WMgsoQ+1xq/PX1zTnZizyB
+GtcHbDsyHcp8KKRPxeqzv71Q73IBthyWGvbVVFwlGiAeQHGJCFDsIPgAhoubxLCX5ZgXHOyatUKB
+3BaWRbIAosNmhQAxD7Ov5aO1d605PVmAC+h1EJwuVH7D3DV4ls3L8vHlRH5M3vZWHc1vucTLsqtR
+FcxSWU5rgLim5UXEAK5v8ahxJSlHCPY0L88gWXW9CAy7MZrLMrzwwMDoYNheKI6XjXXpg+1G6DfR
+BcJGehSU2NNOIKrN3Objoi1SQMFe9JP1dy5TQW9+lKJaftmFQh8sxtNedlA0H2B9AYQ5HPEF84eu
+7aUWAHZfYnPDQL6JurZum1BdL/mpAeBt+6PTjyWwD6ZSgExJqawPy66jUkpMa0uC3wDIcj79bdzk
+e89/zwcvpPabk+WTQueT6qUbAPaDEMDyx3EOiF3ovrZ/u7LLa2ixEtvFCRDHHEWL3ohUqrtE6RIi
+MDBrn0vugLdYhs3H0wAwmvsMMHfXCCWXCCWX9+mWYWJxVNBZFDeLJ80OKOXVOOdb/18srde/k2sE
+iNEtcIpvAJg0Id896ckdwvsRA0RARtvPi/Z6clCE02I+vS15EAocHn5UKsCnLJqtfKm7nZ/hQDJx
+2EGw5FMrqb+08p6ODQzTcTWY53ga8B01HTPWEW+16R2I5Uogz58WBW/G3rlmIvK00w4SkVeTy1Af
+fIlZyLoVcq33J8n7QiJvybLHTgBY9tJHKzGlO+ewi4Mcj2cQzMcAseIz08CuHNKiMRU087pSe6FH
+4V/ObGBYYNtOenvXpK0x0YeF9PfSBcJG33WNWCetP9LS9BDpZVrg7WWOS3DLEJRt0ygOPFl+5xae
+tkjCBUI7uPVw3VZtUrpSechaCBOKLx0Yqpg61jkGzqe9jqR09I2FQ9hOH9Nv/HwOWMd1HbjNRQKq
+fDnOhEgobQfBpoDsTrer6m5lkOOVavhzOdBre13yzEQPwv2jCrGuz19KiEGVIyDmb8kvi4S2eQDx
+tWuqve4aRxmK85yeae0+kXVUqFIb5S4SKf6l7BiRN0ZmmRL31UyL8JyaVmH1XSNmhtlnuFiG24+7
+v2M66l8dpwCplp5A+hO3h/c/d+nIa9haOoJiBsQgoMPx0twW6KvF1zut/Z4X8qAKggQo+0tyxRXC
+2utp/Qa205kDfTWcKXmsuTxsYalhAzQsl4aA3CHkEegWyzCl/7AwgDbXawzZMryGfeTNkM4i0/hW
+I6283G9FfiL+AAAgAElEQVQCxb7mA9nm2FTLL8BA97zlWgx5iKuCq4MfWUk8zM53xWUDu3JIAx4A
+sPTzqt45gd5yHA/pJquULlLB7qr4lPYIllXNZWzd5AP+BMC3Z4V9E8CeEk9vi8tKLF9x7vRvpAuE
+jb7/shzJ76wkikgrq/S3WxF4nT5SKJKKqur7SgyC8wU5t/Y+ukaQVTgstfBHsS9A7xEIa1qNlVwj
+FMUq7PU64D6DYaVx0xeuEe2HF2ldifYxpeGOgAFgBsRuAS4uEyHlQsSUml/Fi/A/lN2pQraflh3y
+GHlZVDngWNU7UEAqCzlX//zI0v9KrTvGwhcQaw/lpUYtO82jlnHdSbdLRX9MWbvi8a+V5+Snv3CC
+D44zIF7XGW4RVn45LgHk7hvsljYtT2YSKDQWtnGOP5GvyIPmsIIrfbWsvgeIfc5c3ar6PBF4oXAo
+ei8XPFShVEwzRYF05Sg84ANlP93qWWEplaLW4c32dKnpbLQ+0YntlP42JFjrBv2OoLeFAwSvbSmr
+f3CGz6A4QfAPBsTuNwzmgVkAb5XXk0bZDBovRwP5slxZpxyhl+cUCOuCZP6j1ZeqilOK8KI8zuIK
+viNcpRaWQ9YpraQ3LCgtjUHw0Ro8ki86+IVguW2Jy2KTvl6BY4UOejswLuXNNSJmW+H77AAzrcEY
+kDntBePavrAKn/be/A10gbDR59unAam+IwqgAwU9pgO56MrJx6t8F+Cc/IEzzoBYKW895nKrMMxd
+oVqAl07RSDsDYaU8B7rmGgE1q3C6RygSFLPgVPHHbf66qSszvAgffjaIDBS0p9M8ORWBpK5w1h8G
+wFBSRPI0Vwz3euoe5vjz3H+fMz4m2QIvMCQpIwsrEHf2S1+55UzCZcjD3ou+b2SMR8EKDKioVZan
+bQ5LPY9r7Kj24uKucz23uj+0sJ5BMIcB2iXCwbD/GAwXYLz6vH1EkZpfLbCVx2ucx1Db+br/2yyA
+BVe+BsILOa1LHIFvhiEO3iXHm9xg6uy1RU5teyoTIDnKNQFRZIDWekgxF2ssmL9eAd6dpJVSS+11
+b5WW658A8AoPb6sI5dWdInjnCLYIdxDscQCFF86gmMLuI1rkO4VNdOxjRHOvbvkF6kdKQnIgF6mf
+R2l2txhPoWjCdqtwDafMqVp9m5QPxPAJ7H4HAHOY9cwZCMshLAl+KW0HuyPjByBc80bJ0+m3QADc
+Eozc83Ri6c58l0IK+F0GA5cJ78f0V9MFwkblBY/3pV9Gc7W9vOA6niaedQb24+nCK29YykRafQEt
+cRNWshb+lBm7Ncx+nsmUbuVla7Fqy4cJychb7UyLr7dTlysGWxbCN5htwRplYWWhCB9KkFAGK+KP
+w9nmUJxkVSu68wVJmSR/ROdzw96wCRAX0BbDCBR/KwleSd9v8LF4675PDG6X3nGrADajzJl3W5NJ
+A52WU6QRWj3NS1GXRYc53Dgr3+f29dYftGkglzNFm9gSrJpuEBMNAHvcjs6zJZ8gIjGrRl89TkDv
+cA6DaO0/xIp4+IftF/31+gkEh2sRAV/YC6d5Q2MKG45SkA16OcL6WE56OYp3UNrDCTwpj+b7DOb6
+dV+vSokSFQktvFIBb4AhcQBcgS4D3wS/o7woF7tGlJ0hGBD7L0ExkHI2gXAHvv40D9AxMN2QEW6H
+uXdWGHWz5w/yglY0o2dp/BFgmCvf0zz4nPZiojpSPd2pxEH2Uu+A7osyDITDimoJPt+P4QFKR9wI
+hfNfgFyrmwEurQBIBb8MjFd6NcRNFXuKZuXtaXC+gG43KWLuh/bvpxTSX6QLhI2+4yEskLSqlHRP
+qmpVStE9vTC+cJrmAlA65yHNldOEtPDKc1eEc96hnKayC/1JIDcEFWlAzwti7Ui/JUglTljAVEKZ
+FT80VdtKzS07cwnhubaf0rnik+JnwHGKT3xZ/GsqvjTzvuicL/Pj5DT++XlqYwQeIxozHsMso23s
+zhbOZ/pW4UUEULQlvzzHA6UPqbLO6QTSqG/1vL0vpy8MaawvPJbjqjqsLh4WVNpLfSKD9dPCfX06
+e1sd6QaxW4OdN9l94mtOjDi6xzKBWQeD1Mi8wfM0nwuNU3xOEuDs1uDAmGUNn3+OYb3T8dDAhy2Q
+R6Y/vqP0AQUu9cADGD79SgEKO6A85h/qBl6vwn76U1nGDY4JEvAetkM7gWEGweMVyJUFhsMFwsDx
+SFcJzweA4gc8mzsEA19dMnrYVlrb1xIt7P68vt3awqVpRMjdHVociVlfSr4Aw+ew14VWb5mxDdQe
+kOvbc1qWp1NE/O8BawuXL2A3eSHD1koGxN5uLh/tYqC79y1BcmubXSeAcD4qzcUskq5jILDrVuBI
+VTrvn6ELhI0+ngLti6UkJKOcKtRugeBz8oUS4fQtTR+B8VKycgS9HSA/lWNAzEo7zgtwB4DStj5z
+2tZpSwygKwR4JV/AIPCrArMUm8MFfZqWXzrK+DQgMQkczy19dhA8Z4LhBnwDMHN+B9kOBH3sGARq
+jitoXDOMAJKvgfDPCwzmqdPkbBivMFgt9R4A1/QM0xj44HD1flE99FTirK3RSmVO4ERKWd2VDvb+
+b9cnfXrIquMqFAbMSr6Dy3Jz1sDw15wYOjcQ/PU1zcJ6AMExvgyAvYynl4V8BL3xw/n3RB3YRpDm
++ZN7COFxfAC4QSYTX9bb0a/sWXLgiVKF1LLcJD7v3Qo98V2FW27dZf/zBDIMeE8AOIFwBbrs9+th
+4ZflhlB8xKeX3UXBZfDa3WfJ3GngdyhCJisAHcxTezj4VDX3Iu7gOMYm9pBIt3HSjVpGkLjHQW+E
+l747h3G4ITuBWwle6GkdBDso3eb9wGdbuagy695B8DpxSyfg68xK8BduTfak6HbcgdXGZPkdNCvs
+qSb7ETZgrNFe2lnnBJJxeCL4G+gCYaP5rQ9cs+aVEj9ivhqIYLy3Seu3g2Deii3TKI4E12p3tjsI
+9pfTNMIfAWKlcq4QCUiwEkhdyyCI+ku/tCwxCBZg6hKe00TbkBSg0wUrCdVJgLgBiKfjyeqWIHg/
+duDbQfHU6uvJNwrF0mtjk3kEGA9jmmP7a8VCB3pHYsH4Yl9en+lsc5b5PkjmeveFVMtl5nHZautn
+ADIFRbPcAyA+rmUCdzWPtjQ6thn8wAP5AlxahIM/2RKsucvE15wYXxNfY0LmAQhbO7hN57xD3NuF
+w0/f/Fp57rsD4LZBgEmiZLTjeP8kSftF4mEpud7Oxp7DHUO/a+yJnz4pH9ch3PEEfl8BYDmCYN4u
+rYNishrLWG4UVHaRb42TVl/YLkDFQjwe/IY1P7hQgS/iZTjhNCDdr9q2Wg/TmYNd7sQ0B9Xy1sH0
+TABqs2wWsNhmszGLdOZp4Hg7fYsTyG3d6Dfc3f83AG6AzxPwBSTaUQFzXsSBM/ch80rjiDn5nLVR
+hMc1XhAWA9GDLMVK6fECus8o4ZvfSRcIG31r87TySaeMl9Q+mbvmXPOe8x/pZ8DraVWZx8Jy0CqA
+6m759fAs6XbOAzguSi4AnkZaBXkJNEpfWTNu2rWD4EQKYSmgY1gaGiitYHc+gOBmCY5fAt1nMDyP
+1uDTr48THsdNKS/Hvpf/VeAgKHhlRbb7b2Ji7fkdnHqDI9z6hYPl91Tm1Miq98r1yxmnIMltbyKv
+oQLSLH93mWjgjMr0cwEcAbm0PAaOk+K8PdqcOHx2mS3CCvmaGMNdI7jnPGgNkiraXBPzWTyt1f0H
+sJvUcSlr8kC5RJ8LHzuVAzj+SX5/hYpMN7MrN+GGl1XWch0MWFAOlz605RNgzMa4bCtbhp8B70sg
+HCB4FHcHBsXVCiwEiLtrRPcJ3i3EGwi2dz4USCux8VoCX6WX10z/ODCmMcxxazpXgf0xBJ2pWPUB
+sSAzvsqo+6vHZOS8M6zcwC7HN6Dc+aAy3g6M+RpN91uF5QaJ2lCtwxyWOJ/O2IFvNDjdJzZQXNLT
+dWKlKSBrRwnfcz2B78EKLL5b0Fqg1yL8L6DvbBoRzNok/0kRtEBTPigKqqwn9WtFYAPG+eZ1Cond
+EvzdsCk9fxRmjeygeO8Kg5vaxW2QzDdYS4UJit1fmN0jAFSLcLhCmL9whPXBDeIMnL8srwPdfjz+
+OtgOIExj0eMxftX6u5fxkf91dFL8oSo6WmG185DH8/8xAAYe8lqrpMQoULXGcXw0i24grOnKqNFZ
+EPs4lXKcToULYEY9QUBrCgge7j7CG2/1j29Q/Otr+U5yK7MN5/R6y6Gb5YUBbVnvLh+ovU+MGYC4
+jf0T6Fx5XOJVac//kJ4AsuzhAj5Pl9/Swoa188U7NK/vecyBSjxdBjZrsBjAfQLFK1xBcLpD9C3S
+2ApMgNi/RAfgY+DrfOKubSPzpipE55LzbhUuYNi4zfRdrONT+GlwaYHHXuYOhgEDXZYfQFejfJnu
+BnIrUOX0HQhLraRmE0lL3MtkXY9gN0tWHuJrOObk9PibIDcszJEm1OUdEOeYrDkdkmHnRUVzgwiL
+cryyh3/EFGx0gbDR2Xv3XDL1jIlCpTAoKU+pEdLs5YYLzgsatUeetvL2h8sHqBUrGVacBnbFAdsL
+QNwtQA38BmBrAK7p2hp2GeXK0oAvDAhqPDtGCFLEixYgi3C+LFf8hAmUxpe5DmB1c3342WMHy275
+oH4/AmMeU23xjYF+DfXvFwTZsFfxqAkMN2SXnan8wH3X5/5S/7o9ODi/918O5TuQI/SVig47INZc
+P572BIaj/zRoUcch7XQtYJWNm7iwstYX5vg3zfcyX5ZbVmHhX1faTW48xnkto1rVCgjW9itjTf1A
+/lDKHOBtgJNfRK/A7gdryMHvY2YvS4D56RpPIPexACoGKBY8cUAsBQz3XSB4B4iwBsvpRbn6Ipwc
+yokBZZE8b3VE4e4QW9i/nxuc4B9EquWG8Yw47ziwdT6K+NqVdq0bf6GKgDLzLQ90LGQb6OC3Kjd8
+e0cXEnQGGOimnDiBYQa9JyC8M1ACz54Wk99LkEyTuOwOiBvY7fl2fuAGArTRPe9zEyxctnbRz3d9
+YfsLm0/wMICiYDCMDHubDOBIpPx+ukDY6GPXCHaLOEl+Tn4ChQAIMQTQ5cUaDGurlJlj5VWLzqri
+DGqxpVe3CFB4CScuQ0ox6kERHvqq32181JFF2THCEYkDhLUv4ZKdQi9f8KO5CnTzk7RPPsLTXByq
+tfgd2N39gHH0Dfa8R8uutniMIZUn0Mh1dPqrgqLgusbO7KtVGyAdQZX2Kv3pfXkFjo/9FKqTGx0N
+xhErc2UFbFlyBa7y7Re6QllSGTyl2XpWqRco68n4PXlKCfjab7hrzlhfo2NALPVBYpURz+na0n20
+4wUmJFBnoItDWtzUEGt4WQf/zjrdTeIEkoPvXjA5g1f5zs9BBMUPAxTHct6pER/SEx/1QtymFRZq
+w9k/uIPgsAjTC3H1Zbj9pTkhqzAD5UFAGTaX4eNbPqKhbWeIvi3mnheW4LAAG8i1+HpZbgRP+pg8
+ycQyuLzwARz9npA37UJ/fPcKQoFAgGYHhAlKPa0D4eJ6EHPcQB4JYdnSQNfNvHCHkGhapHsaA+Nu
+wOVrMaCV0jfudzJ/Bc+5QNIarvbSHAPi5SahMAAsaRFGyC+yFAPvn6j8DXSBsNHnL8u5RiN1QqA2
+AYEnlMim+MXKbLL4AHQd1jJWiTS4ElqZHnbwChAIVmNY7BZhUDj2BUYqP1C8dJXauo2Bx12S6epf
+2T7N6yQQDAUBYtijtplWs8POEeH7e9gd4rQ12lfbOWKz+L4Dyn3XiBibOteepjSI7yzDT/RWGRxL
+VyHcwRCn5xMIT1v+XKcLr25p4YPvAeBuE8YuCQvo7IxHUdIa5VGql3HFJ7VNrsROAPEpXMpRpryI
+hxXMxoEtwlNBwJf4dE7IHBCZGbZdI0beQRdgiBZmcJUrnPpkkVjnsSYrKI4yXM5GssuIPmantHf5
+dbS5Dxr9Oq6VE2LqaTuLVeB5KncYZL7P+XRhvtY0bvnNC1TwiwCvvh9s+TBGWHLTR/hH9/097Bxx
+BsoJtH3c2c3hM+Db4u7qJtrK9LQZY8Uvmfa0Pq7nKVBXOfvEgedOadwZ4Nm8ENitrhAdCDtYLZU1
+jLtzwcmC7Gd18Ltbh/dwORfn/NhKTfIi/EQir3VOKy8LGtBVUQyYrzDUXpxLn+DcNxgZpg7el+X+
+Qfp47EPYH1YifwyhawROauC4yldTbLHos2zwirY0Us0F+BrYTMCRrhPe1gKSFWbBerIIE7CjbkT6
+aRADAGsAHKUTHPx6WZ0aT9hip4hp1xiWpu5fmdui5fHsw5sgeBr4nRUE6+H40kq8+xB/qYPfBvaO
+aQfwqH0cf6VEWHNahfGai55WAQOhJC8QZbIPSg0vYaD2n/JPPKNySqwNdIC7d5Gu4UJbs3wHrhmv
+ViBV0lPqde15apkRD2UqpW/Sn7BoguDhR9sxpViFxwK+c05MGQmMvwQiM6zNqZyQcRAAJgDB08fH
+tRa1AI4A7bZgOwCOMqApUq7Tt61CtQZrslWA8A4UStuy/W9XBPHmqyJ+fG3tlZbWgAVdRVrxaME3
+lnC0JQCJt7G6RbA1eIgD4hHgeNseTU4+wdKswg6uz6B5dSiEclpvN7/gZil+YTleT0y4Hh7XEXlg
+ULwNaRFWj0lb8oO1ocvItILu8bT6HoBwA8HZlBLZ4tLO4Y506+4jCAbnS+a3Icl6DIASuM4+0HUD
+JEucly/LLUWiqNbgsAhbnggBYLYOgyzCx5n5e+kCYaPPX5YzoFiwbE5fw7gUbCinScnYH5jyOuPu
+VmLNfHGFZcxoamS3CD+FQx2CLckMcjOeq3S7F2j9tmZv2ldLmC2pLm8lhGamIQWpvRjnbhHl2EBw
+cYVgYOwW4S1NN4DM7g+bNZh+BSAS6K1jo6WvPnIRbsDxUzphw05FcVPa4iGBA3SO5wV8wuhcBp8N
+HH8CgLd+EsqNHNkKtLXlbaOmGrgKblXYYzlqG4MznMeE0ysIRnWHsLwY37g+NVkIOAbPJyieqhC2
+Ck+FDONJ3zJtCr5kkGuEXa8rOrH2IYG6ULuibzzFsRY11mhZ/3Tz1vP8EGWljo+08evj/G06acyW
+JkiAwLtGPOCM4zV4TL2+KP5okuYyLVH3Zp/OEwIeDmjybftmJW6uEU+W3gDEDHIPZd0/WMgqbE0H
+vzC3yP2CDayOV5biHl9CXbxeqek+UNKvWeZZKOkwsqfBluc5EFsUQpNfrJXdKvwSCHeQW+M9vzCL
+UPnIqmA4MEMwJ/F34tVSfU1znqr9lAZ+IVTOeNHbwgYEmDXYAbDYi3OxHwRZih30ooV/Uhr8ZbpA
+2Gh+PAGu4Qw8FvMILbACFnAoo6E8PeXZCsz53Y2iLul0jfAXDAAtXxDbgW+m1fwEvqTg7JxIA+BW
+t024KLG1IwdGGGQJ5rFRB1AmX8MSHBbhGWB3B8O0NVr7aMbRLUKrm8MGgj+xBkdeTvZbgKs5phs4
+fKFcX9HLswTlJkpbfAMWW/ys9Fe7NcMW15K//vDa2MbBr3nqSRTtkp3LVAQUPsAAWYc1XIJ82dHX
+fW07paycQdoW7iCPusAW5x5mq2u4ROh6iWgq1tEtwqIQswbLVExZvsLLTSIBygJOGfa5dtkhunrl
+ffX20hllLtT++FOiXPfJm8TqJcxjdQK5r/J+FXEf3xbsURqjChBen/udxj2xOTciwPATIJaTW8Ru
+6a3uDs314QEwc74D4sW/nwHcs+VYif/38/ycUA38ueYYG1cAPFwPuucVvSgcW6oRCASBQCCB4A6G
+saXxiQlIO+NVjV55K+M7yO3A2MPp4rHlozYtwGizCLuTUL1WdZvIFzlhX1dO1wi3AMeewXETl9uk
+5TsOPpaabfrNdIGw0afYowNdLYlPoPe5PLTYV5NZgQaAa36Ei5IXAiEV2OYZSkfOP7hQwIVSbvvU
+x8KB8Tr5BQuztCLAl8A3wS58t4gTCA6LsJZjguAKjBkE84tyCYLZIowKcg9W4SdrsJfJvmVHtfT7
+GfTGOcwrr0f1W/QKfGiolFVItcZrYyTaWVmZ+rISMmzxCoZLLvEXtbI0uA1MBJvyUONqVwa2Z+0K
+rz8MeWNcGjD2AidA3IFvWIot7FPJ4ahPl20rvsTlINiswUI8JVPxJdUS/CUC+XKry2qFsALXBFKr
+Da58ECDe3TfCGoNcx8HGSsBXCfhqfaIR7N3Cfu8kAtRbjDM9geRPgC1bfQsbaS3DrBKAl0EEn8vH
+hzTJwePDZ7TxbLbHY26dYxBc3CKG1PTtQxnnfYTZ6isnwNwANSFUvHSNeADGou1FZ3kG09UXjuMA
+iI9dN31G31DwR344WIA/AsKN7x9Bb5d5JyYjECymsRnkUl6cVgDvkgVee/aB+E4qzz3Hqb/eAFFM
+49PhgBg7OOYPbTA4ppb+drpA2OjzD2rYPrcOJt0t4gB0tSUoaQ+Gpuhh2udQsqY9bBZWrmMHt6my
+exq/MrNeVsi0ahFmwPrqDtyQxHnYdhTRjwSuOjjmY2w5RW4PSi4O1UeYP6LRrb+zAN6jdZjDMy14
+uzV4haM7ZC7TGijW4kgufLKN6k/RPhM7AAQSMCmFSyWaW9/Uj8l0N4fWp4hmheUGoXdO6KRTfkj4
+vV+1nvRNXTy9rKIQULqNRZRBGZcM79bjV8A3mug3ppzuINMA5mYJNuArsnzZRWRZg4d9SEMmvr4k
+lA9bWeCKxRSkz1UARMnxWGe5vGHpsToULhDKaahpwDEt+Ajt6OPXj2289xl4opRCnWU5kdUr13q0
+8n5AXJ8D/cjgVr9YtMeshoMS/KLNdf3VvYH3D2VUcPvKRzhdJzKc5Va7+Z/77Y4tXQ9pKH7Czk+n
+c3oYqNbhEdfeBu40yprl9nHfU07W3nQFaED4lLadR+3soLcDXqAxZjsnAG++0yAMbgP4skxAMGzs
+0LDxGlmEOe4g2XjQrb/lnBiulR8AOPLpBTmOuww6yaN/4G25C4SNPnaNUAKOnBTA2BN35VDCdD5b
+3oQKVxeJFPou4buleDWjAfU4S7kU9EUa0HeTSLUTVtyeTrWUxc3aEHSMcPbXt28DkLtFABUMtzeL
+T9umVfeI7tqw3r5PEOxfl6Mvep1AsGoD2OQzTHnOENr6WEAdg0NCFfWcv069mgB3b2UNoQjefFhr
+dl6H59ATtU71Zi2OMw8NfgF4ge1GYUM1qmmFDKCr4f/nwOwMhvlvvXIHUwyCC7ALwLn6LSKRluto
+/QoItp+7R4i4b/BSIl+uTL4oHMqwKRYfQgdTmkfvgD8FYKDIVt8Yawctyd45dyTPUgacx+uXEfHf
+EQTvxXb+RQsT5khlX0GveOanbXTS56yeLls4YYNb2tgCvG2l1sDx0TLcQPCrcvESHkCWWxxdIaSl
+d0vv2U0iP7Sx7VE8/fEgsHyRJ43Oi9F0Hd2ydygs7RzEOQ4geR09p78Dwg/A9wH06kP5vKaDYiUr
+bQLfU1tXvkNdTbmAg0W48H0C3npDlnEE+PU9gquvsMq6dUlArHkuSwnP+wf2T7tA2Ojzl+VA2E1S
+4TMQUBzTgAoIUiMb02oDuz0sFNZTmbT6PoFbxBna8vY61Eol0CVR0k0h1KVtKAt6sINZsx38Rtvc
+PQJin11mMGyWatUCcsNNIkBwBaxfZhnmXSJOPsGbL/DE0fL7ZA2eNPnbOJ3SOLilvVLvPwcu+jS4
+oFR9H04NobVpEd1BqrYE7n+pYkNL2k5wknPHOzI24Cnia1MCsMKBKXLdFCu5psLpYO40ftvRztet
+0BLwCQDyN6diDIoTKF6uEWrKY4UhCnwphoCUivXC05SsL8qgzsCv5fu4qtC8KIHdDn5LvAJfzpOY
+A6tPXo/XX6KCeLGzqeWHcn/4gY5b/Z+kvWofWhufygkCXCQA4R+BYH/xzb8IN8b+6+D25CMsJxBc
+64gmygnoJgCWLa+/HLfcJGYAXgCqGLHbBFl+Y994u8aUsEMfJ8IZUPqNbNNVTb6qnQPky+gBOj2y
+AVwCviYkz0D4GQRnC6TkxzW3/MQADmLj6XHIgFrdBoLLexAJaKPdWB/C6BbfwQB5403BFGCK7xCx
+wPAk/+AhFkfKIHiY274t5t9DFwgbfTr0So+Gu1LY0xgUsbWYnBMSHZSFewTADkwOILn0gkAm4Jq5
+Wof9qIXxahqDX42Ubgm2utoAlqjUfDXzWQWGBLxdPhIYBgwMC+Avy6V7BLtAdICabhEOfL9Uwyrc
+rb9nQOwAGHjyDa4WYQKHjBR8vFpa5RMfjydi2OlztpP0CAGXtzr8CHxbuLeF+sJx7lPv1XZT2Fsv
+PUuxVXJCUWoW4djaTCnsHE5ADWeQeyQHdQzucokFq59dATT5u/3cAnz+Lb/gjopUUpGFdacoLPpZ
+28bWMWqkOn+ytZd4czs2qzDqsY9nP7Yp+zYgPqlM6ekuAg9lj+i3p3GjDuWFO/lBg3nJn4sQGEIH
+xWJzKMU3OL4AJ6+svQ3gnsq1MsJWYSDBLBT+DHV3g7A8wcE6nNbgYUaLAL/DAe86znkY8DEMMItd
+hfIaA1XdWAfeX5j1uBCzKAEy9qFdc4Ed6L6LVwRL18m5rvks+w754WpgbXcA7/X1F+WsGqFzEYAU
+9S/Lkgi7FbjJF0mAy2A5LMCC2CHCAfACw9UiXAGwA+RvCoJfQBcIG33ngxpL+AuBmnSVUMiuIBgU
+tDTkWZHoTBth9TDDn3N4B7isArrF+DkvW8QAOuMVtmuJb6pub17N4P2XCfy6yIUDYE/T3QrM1rUN
+GHP6Bozna7B7BMo4u0wUINxAW2MEPaRl2QSY77myD+6euufGsNedFQLkHs6O5CW8atMroCV4vGH6
+oxvFRqcCch6M002DkEX4BIY3ELsStr1suRwejgx+vcmPhUE3eWss2E84rMDl564RCYpdibgf3nDF
+gq5gZAPAE6jWYuRxjZEPK8/f4Sb+ERzTFLZxkTYeR0B8SDvREwjWlnEExk91tjyCMREuPyH26+Lv
+RdmtFWEAACAASURBVLtLQPe8AkriWrL9fFeH8oU5fvlNHkBwA8T+5bnqH5yfYXaXi7T+JshFsQJX
+6zAMstZ/5AMcewpjGTQA+JvSw63Bc2KyW8SQcJlYf09A03UiAWBJ/czuhzBgWIo5uIu5qKA4ALEh
+QL5ZcSC8wuPQNskJjqQOglmO1/Jp9WVQm093OgiWVi6PT6DX+Y54Dwl4e9kR/UbsFzwlfYUnBAMG
+gLG2T5shdxBXYR/hkzf3300XCBt9/rIcwiqsDQTz+urW4q5QHBDkpCvxf897Clc4+qyyEeEjQOZN
+P+GgQONKLkL0Ic4teKQQ/FXVObB1IO7iE3MtqgKAwyLsoLeC2unC+ASACRh/mcX3iz6xvFt/093h
+1S4R7kIRQNjnmQfkA8Bb0cSHY7oN7wka1Lp49tHCfooqyV5TDPnY8YA9tYLhmt/GIoJ66i614wCR
+ToVPZkRNv1wHw0v2kxuEauwrvI2HooDio8UXfKQTelk6x+tS4ykx8OuuEJE+G+iFAjKRmsg87sxi
+t6aIlZOSUstHoeEiYU1darquRQa6PHeFhduRsuxI49rHslzxOe1joheGN+5vvCom5hzMlKOXobTA
+JWgF3rbJjp8s3mi4Ax0YrzroSotc+AS7pVbcAiy55+8Y+LF9RpnB7w6I64c4zmUWjQS25V0NrbtC
+0A8Ha/AWnsAcE0NHTcNcVmCd9mnnuYDvADAlWjRj4JyM550BbaBXVH2Akfyd4Ks4DgTAjdmpaU9A
+WASCkYuNJ7szk1Ae9SNgYQf4difPx3R90HKJ4Gs5HXOlJuhdoDXWgDAAloe486yD33SPcFeJKd0a
+zPIq49GHn5cGP00XCBt9CjgqCH4CvHS0P3kk60oHtErhYFQOV9gZebGuSUULNeIDgLyr9lDxdCVt
+rUuwXMNETSkQXMprhkWYr5ml4iU5EXuCSxZhsgzP0/ZpBwDsL9Jl2C2/OADoZi1+yHNLcYDbA+Ct
+A9CA3QMQ/pTOp/FjJy7ZxI0hhk/TTqCjgl0t3czDG/BbKmylToD3RTl+Sa1YfAkAO58zaOOhegRt
+Du7ouG4Q3P847E71RDtUQMDAWG2/4GoJhkxgSgDj1aqJoWwVNtAbyijjQ2DbGlUAvGxzpXkxdp6Y
+87i7S8R5D8CYJUofz9N4Pw56oxP/9YxepoDiU/2vrrnldQDTZSldNMbqsdVRLm5YPCmw0W4Nzpfi
+2CpMFt4nEMwfzmg7Q7yyHAMJYOHA13q0fsNuOvefuIFCQWCZLL5jrK0EzVViAWDBOIDfHfgmEFy0
+GEgag6mmL23q7wSPgNj6tekNGbFKFfArBB0bAF7HEedwO3OuT3Frg8QVD2WRfPJkCY48TmOgbNbX
+aG+CXinhBLl7WFp4XTOB8HKHWHLH4mD/4G4BJv73NfOb6QJho/po/3VJhq8ei7TEdqk0OB+vpnnP
+YacDWta1vF+H76heKpSW+YQxtsxDWEKcWDRBVoj2ECB5LCaZV42oPcUSYhrKOIFFAtDMr2Vg6RpZ
+HKa6Telznmbl23lK162NPQGGXgY7mNsKOH3Ko1k62g1syqKaLfFx2uYGo3trlfuK2vUVPIyDbIFe
+Yevgc7kFgq0PhlYXcOX+AA5bgVp2DVHvu+S2aC09rdAMAbkdqZCV+GwaqBCV2DJtfUTDgO+UWGNu
+efEhShBsIJ8Uk0ZYSzkR2NvdDprNbULq+CXYVUquc1BjPm40lgYUvPIIRx88T/L6BQH2+fbxTOAh
+muMiDEzG2nN5yOrgnAYY/R0tUfNLRW5U4I/qQWub5QT4x+4ifTyY0alv3KGNnRvQLXHEmAn2YWEq
+653AYJFTQrKZ2xXCugw4hYlNzDpsW0WEewQsjV+aWzJTioV4+ktwtqbmNP9SXTd9qrL2Gp5rDNWA
+91RZFmIA03aZGPYi9YydJhRj2At5UwFMjGE7U8zleiFj2mPg5WCxlrvrLiHwS3oLUsYl54V1mK3z
+AMuI8a/lHtJdRq1KLFl4KuvcblvVNNkFynf5RfziejrD7RIWSn7pJVDj20I5KAdkcf45+3xXz/0K
+ukD4m8Syz+fM+a3jIG3hPOojb9TST1fOkLS8tEUxW/fjokd2OwKSFITa0+0YOAIpUJTKycvQfs3o
+FYELNWEBdWVN4+HKmtej/cmwp2vUEaBWs54ALMi8mE8/j8+lvGg9T+MBSABPnPBKcjyTtBk9Xc2F
+7dlhk8cZtvduMveKelpeoHDklsZZNXXDttoCpTsHbj3eQDD4XO3VphwE5JMeIBYGsLwPCWbz+poK
+5sWxgOXW+v3xsD/NWO4QDobhR8wGUGh9mDWPQXAFxx62R6mjPwil2vrYx0RqzS6RHJNYo4GpHESl
+pcznJtKiW9YSEeqi1BYa6FVoAODYq9TzkZbSiUmA2MD+mJhz7cssul4AE7WPBJNc0BJv+rwMD7nF
+8ZDtkb0MD3ywogPfESAsxsv62IFpETF0SVXNDxoYez6gnKyPwyVqc6cIkBay3tcKA2KTix528Atv
+l7orxXpc7k9SpgrUnnx4eIqsF+R0+QMvN4mFd5cVWQ37KsYE5koEpgH1kduwCfcFw/KANNgsEF1B
+sFSepTjzJutBfsqUN0KmqU9xatfiqZqmzmsknnw+VdTWBSBxI7omWGEW402eZXhPCenfEAOHtIyl
+K1wpi4WU4iHM+Hjj4ddq7m+hC4SNPh5702/tOxrl+DZNQAL0/Kh5ryFD8pD3YfO32hPk7rC0K18P
+iy9MpCApR8dWXl6AvIt2hXhq1aGBwtE1mkoryYEFHJj66R3MWhoD47D4+LllVVIdqmV956Tadb08
+SuDQGQ6e5u75JulhlD44k2to2rBsKiyUdshnf4DGk/3Sp3HQvUBrdevh+YTPALJI7NQA/8Sw9939
+Sgkw1z6tMtWCvDchLckS1+RjsQYToHSe8hfk3C0ijv4xjblAsEyBv4nPIGWoQscJ+LJF2MK2D7e7
+THB+AqzG/CX4iiu1KUUGtR4mEOdAN4RBSpUsW6fX5yJcT6xc+HkHEB6ArNdxxhiYZhX2x++APZJn
+KzAMVMXcsLzg7q/2hk6HT2sdq2RtLewZ9/MOJKxOpSEUGcUqXABZGfMadu6Km33+mb5RsyxGGvWj
+16vI15kir091JJCcUFsVJI9DXtsgLFm6gK/IwHTL8HRgLMCcJFP9KYCBYJ8vXX7EQ9c8uz/xNJQ8
+/EW7YZ97tjKY/oqd84q9jEf92kBvrBEGqckPiy9pjpT4HWz5FeODlmcAVind+x46OMTTGu8UzS5j
+DuGYOy3R0J0NTaSGaG5jQNlRw+cyaFeMdMx8N0yVBRTp21m/jS4Q/iadVEWdet3TjAk19eFWj7Qa
+zm9OdgbrZU3dxx6hFSQUVSflUGoNJS+dKWlRlkqknBdHyXiAX9ZxIq2JrDXQrm+LHzSGWgUuC93q
+KoEUwH6+rtErCoPO5XWd67bmFWWoNPN9jg9Wo/Oqfy0C/rKAYNNQCGOz3jCIe8qPcK3y1DI9NlYf
+0jP/mRpvvDingldX1iu8/Bi9vib+uTyn9yPVz/ql1pXjyV+Z63uwTrVHw6bhxUH4nNTtXKzOYwvE
+LctoAcEjQXABxugAeXV1GBAUmpic331sZUupYxkgNoCE5ROgSKDbgEZpAJWFyxFz8wgQYOcHfw6I
+pH/p9EfisI+WGA7qIDfWeaTxS8FJZc0HqEk5w2u6r/01xhFKUKwmv208+CMZ4SIBtuL7+O6zEbzh
+WoiFm49vqAUSsFL7pVx9zJ/Pbmoc8blSRexGYHJU7PrRDm+bjcGcE6ITU8wtyHgTYRVeaZjsImF9
+FCxAHLhW13EhY4yhOzB2XrD0WF9D1runEOjwOUqLfPBhswoXPiV9aJ67xDu20MR5Bjn/4HSXD1m3
+xvnO/yCrMDkriK9fobDPDc1HmT3mmaOUQwHDJOt2bOIMxMoyj2mc4r82n7SolOO/mS4QNvrO2PNc
+lbBjCJMNEBQmOJ0DNIC6xTvYDbtWaY+0UM9NX8i8hudGnGRlFuVFzxkV6DpwTlHpd8pZNq8jpZnC
+Fz+0PQUgjUqiWZKzCbQYGKdA9vlgQLtbe9Xqd+AcTYn8h3OKBgQ1jHv0xGmn9F8pEXjGn8Pxwogz
+71P4qXU+/j1Xj0FLIEX9SK/Gop2r5OqAB7eHzZfO67F+VslPx6YumvW3VifZtwBM/QmDrhc+7UWj
+BMR2vvk49m4uHl3bXBXAqxUMh0vErAB5mJsEzHeW16cSW+yzog957NZgMbL4BohwYBHpiHLFn7jM
+rWYZhYFhpB+mxNUDEGNMiAMgIVA8ATHA4zcB66aiuUjA51pqWh8NWuMpb1jZu5tR/KH7TY2Xo5yl
+GATzE7QcThsHGqEOJgKAuAXY+2W66SXFJFrfaf54YoQZvdw8N6BVZKCPTbpCrCcfCXzDKjyz0+ki
+IWENXp6+9mVGLH/gYS/eYbpPsLlIsHuE2NHKxc2TvYbnumt/kkHHCOd8uOFn9TJvXGI0GhgO8Kso
+Y6w+yJphtesQC+XEh/UX5iZh5wgIECPllosvapsnhRoVFnnEv8QA3AxSrMRsp2NLy7NL6LWx5O+h
+C4S/ScFDiqITA4CBGOptmFP4+HFL2vEEcIj3WwnWRXFGASS82F/HDXJYtINhAa31PPMgYE/ddOGd
+IDcXjFqgv+Dmi7OuxT2tIN03gNirReRXl4oq8w9z2dOiwtPM/2ppENIypdqb8BKA5/C75uoxonuZ
+YN9X/X2nveu5TyBYQulUt4elJywsvSm0yFmRlEUlpJTW0V9KSYB8ACXq4FcN8CrY1xJutWo9TYWl
+a+eI8BU+W389vtwGFhDEJP0+Zrx0dpITvkyjy97VKCv7FBGI4PwdDDcA3UGHZa7x9LkTe0xrylwA
+f7ztoNf3ol2PyfPx+Rij6GMGv6BwiJAY8woOw3Ugbr7paUe5gc6bcC+z2MT5xOSSsVH9UEb9+SCV
+oXb903mEZdJavNGTJbMkgPG28rblJiTTqbxb4z1na1gDS3yQCdEveyl0gWG4VXiK34OYD2/OBeDg
+18Cw+wubr7Afl/sD3RCNCooF645IzZfc9ZXPcedDvgmpR0pHzlGTSNaHBLWRxkC5gV4Gq/niHJpN
+wn2a6aLFL7iNfyOe/5hfrSKuyoIHOV2YDsfjo/W3hf8JukDY6O2dchRsyuhb4QRUr+db3x4rU5KV
+uFjXmI0rRYkjGD4URGYG6JUEuxUMV2uxbMquhV+MAEnOFPi+cCygpajGoitlT2norhGe9gEg9uul
+lltJLV7a9S7tufc/R3yyUOLmA/ydMHLSjo1Lhfey7d/q2KeFTQn52DpaO1mCWXNs6JfKeVCAcucb
+9Wuew2ndYqb0uPjxtx4BK97sa25859bg5StMYBjN+oux3CBkQseyrK385Ss7fIti7y/1siJgiqqD
+1ihI+QYSDqC3lH+TLyE0FH5zgQC+zoNCMiRl0iICw7RzwFCEbzA4jLrmWdZ3WZ7ld6CbR59vAOIu
+a4gbI7cIM7AvPsI8hjw+D1TklLiDRAO+3njZzy0Ul01h7X93W4n06jbqAGqK2NwtMAyyBkOwXCEc
+CAqAmVZgOPiFfSDGzMTrPt5uesDuMHlDJHEUcxEWcpUIxox+7zdmNB8EfHMMqzU4e07H4g9sYT52
+azDcF954yJqYgFgz3MFvW7sJqN2Xu0i0whqfhPNSrDDVdGyNP4NjL0/8+ZvpAuFvkrZwF5ArrHue
+cZq6j2LXvb+sdZVFxdqTAPawNiKXw5KP7YAqhNkibIpHLb0D4yxjuvFUZden3Egat8RYJuA9SV0h
+ndMYLG+PpkHnKc9JLuwCiMu59CPL0DYlOFlRD5P/kwyRTX5xvgEXjTNIrClQH2+ew7H/7pvrHIKU
++J0+Es99dBoVcqusCf91UNMNgr6ThJ+DMoeVCSvGNcVE1+FdDXIf42pxLjtFBNMZ+o03uOhnVmHd
+fro+QOCuEQyGwRbhZfXybcRkjtTnAQgGJMAZAhSH8oWD2xyOdIFCzYfn2xw4mNssxBJDngHKJ5AR
+rfBz7K/a/CWXVJcIT/OvlM25dqnVMda3zeyluUHjGi/NEUd1mZ885oq7A+C0fmk8ieCjt92fTKy0
+NQwNDMNBsctQBmONgjmz4cFKbSnteIOs3G38fVeSUlryvPjb5XiPBw/psnxP/8jCF9IneBWZ0WEQ
+KCYwrOtLdAJ3kZjLL9ytwe4CsRgd4f7gH6OJ/FouGZzGIHgTkfYMkE+iioGtlxGc3R8on8/TlSa2
+DhIEtwsyKDbZV0lLyE/v4aoh6j5RZ3LlTAr4MBonMd7Tfk4L/jW6QNjoO4NfhIiQoLGjaird/oKc
+K7G8ZmeY3drrqfKmHH9OtrIy6XhKDyUmvXSLNOC7QgSU2RpsShKUz0KSQfFG0fY6Ihwvj1BMyrtv
+biiflraKVksvIs3L7IA45qrdsXr6bjnW2tgTV7miPGe8oBf5/bKP5UhCFmnpZxqHafoJbz7DfMqb
+i+qb/NcnffdEKr6BYI31uRhQI+xAryiUUCB6iNuxKKBD2oNl2HnRP60cHxbQtYVatwW7n+fIFOO9
+AR0JhoeBbh3D3CVyf2G3ECvGsgqL+RHPYZ9epi/YxerNcYst8/jxEQ1ZgmDKD+suAd+jBViibJU/
+DDj4RSzkVQVIu2C2ar0Qx2O6zhnDb2DXmI9BN8g+dVq5rrzgRMCIX5Zb51UQzNuFQV2OWFw83W/I
+nH8M9PrHLtgy3MBWzoC1s+geknelLw8y9SXMYUDIIFfalNd8Bs4uP1jcrJ1RCADLl/Uf8ZJcjnvt
+7pT1FMMpXSQ0XSXMb1hscBabT4h9olmKq0S6fsSFnP/KuJcBqOkPv0dge0pXvxk5lcn++821K+6Q
+3F2sK+GGIq+sHkrqiCF0XM5cpNcYn0C1WTz19Sktf8UQ9U2x/yvoAuFv0ulFuM5Mn8VdyXxn3nU7
+rqZ0cSeP8arOyUosRc1naelhVlzm0wWkYKS04tcmKR5MOpVq9xZa60lPrOxUMLH2fAwdAPt5dplS
+1vMorYLfF4A4FnhN57Vex72RPqRzfx/SH+mhyLkeFnM+4T4okuEXeQrhdxY/atte/CCRC71SzMcK
+69kEghfA8DBQdo0oVmEgAXM/jy5Y8knz9DSA8jPNt7FK6zDCR7h/BAAAvdTj/Cb0aN8eqTMYBsg9
+woAv/DO5DoKH+Qj7a/eJU305uywoz5F4Z4BY9glY1v/0p6x7BXOaYLMQb2nMBlxPl29ocbfqrTFj
+ULx26RgxfqBv64VcJnm8QMm0tERewQ2uA+JG2SRChGuev8DETwvyRi1l5g5+LT26WvufOkDimn4D
+lXy2y6597Lh/BPxYcNtReE7L0Xim5Uv0A9EfBsCrq7Lwr9Trrk/0+pRquEk4VAx/YV3bpZnx1z6o
+Yi9KytpCT8yXIttoT1QkvllHfZdzuOhFbOEYv+YC4enPYNffI9jL+8S4uILPbYlTe1hGHuP5zoJn
+86/w05Z+6DgB3Xq0dra0YpSKxed5+O10gbDRd8d+MXMFMFryEyRxfo8vLuTU5+NJBcTiAFBfKd2p
+g93tKO5/V86iA2emsGMwbKowyufnbL2OXK0hTB10PJHCfN5ACierKu4PsZDcIkKPKYGS5uU88wiI
+cUqvbStmZpqrUuaRw07p+rbEp3VtNanPm4+X5LyEVPWBFRPs+TbySxT8nUX0sqy+jL47TePxR909
+whUAAJSX5/zEyNca9n4/5fU0t0gDAXjiZTkHJQKzDK8XfNaXsfxln7Xvaex3OmTtAbw6Z4/z3Sq8
+Pn+qcH9gGAiydAPFsDI7IHZXAVLz6so1+xO8Y8lHGRHAKcHDtj0aBfm0DDfAERlK+U/XZ2KHB3KT
+gAIjc4atA39pzvPWCiHwm1iktM3dW1w2pJI3OWB58aU1543gz/Ukz62W3RLcQXFIU0mQwt/GYdec
+bJe3nWRXAxwZJBAmNS1feqabHYBArgTfrJsqaWE+fkV5P87CEJXcJUKx3FeSUbE+Se43dGNiIHcH
+AdwvGBD/SE0DxLz3NPeXLf/JuO0XIPfEsyZfXPccAC6HvfwzKG7i+U08unKUnxrXZ9SgcD7peam3
+88AMFAqSjggerFZDblDlyX+KLhD+JsVctrge4u/SggkembVfuR2JqRbrdgGy2FiolDNyU+N0Jmm5
+opw83sJkEQAkFAZYCCKcJcKyUe4s365ejUZG763v+ejRi6UCyHJIxVCGrrpGkMQi8EsWX1AaKZn8
+kUQqpIdQi31DGsRcflNwlPPYjMBxt3CmZt3iZcsnYOO6Nw34NWVf5dukL0BghQXhw5vsJaiPZJLv
+0i+VriXABobjgx3pRrDOk2Q+SaCjAVKcV9cxfRxn7IGbL/G4byMS7KrYo/7qIqEKjPUZNajMFZ4W
+Nh9anetTw+A9VQN0GTCBW9tIQmwTTVbKNBEiQJMDBWlp2NOK3zDoclKuFHPBUyabe0Rp4rKYk2VU
+kaAKOqG2m4YOrvvgNhH9mckSBQRrkQ8AhVXy+gSGY3cTl8sj/arrRzVyMGLp5mgV0KKFbbXIM+4X
+kCwKa0VUKSUQ85tuLkigDlCbbTYdwAc/VUCc1uDWoR1LBuWWaQkP7ZYvtk5bng7LBWLMgSkg94dh
+O6bYThHmMy/2EqnMBKMbH+MZFOshLdMBLeGsv4e5R/t5jf99fdB6EJ98UJkuJ6Wew4C34gAmbUzn
+MtPXqytCfvKgnbmqims/NjZ9V6/9CrpAmIlkflV+e5o6IKA8PaT7IzFOZ7/h7dpB2o4ZkzdlksUZ
+bq4zw1gWl0z4nHVLY3ygLJFQcnu8+v5xXdxyVqx9oI8dpW66kmlgly29aILfwWwsNk7rbhDN5ksL
+/AR8y7nbAn5a0Q8C4q0E2HnhO+UXSctzAas5XzYO8iJ+avVRiH5XqL0q/2me8aFGn9BAsN0csq+w
+rdVVmBeoaZMGgPklMn2yFjcAvNqZlvgJtT1R/chvtedLPAqE28M6KkSGgeHVRvcFXtPzBIgBfmEo
+Pik7oqdYZyTZq0XmS+wDzTJDKO4gqYIIlgkBmDpw4LRiReYbeZ/bOu+5fRqtTtG0wpZVux6Xr100
+ENZ1UIkF/hPsaozOrHFJcNtBcMgkqfECnA2GxM0RCFwykOQxzj+VXBYKgjfrTb7layn+Iix5YBN+
+ad/e3nThOKR7eYhZhD3+dbiWtabwnR2VZ2aFl5/wAsMCmHuEWXz9BVF/OVTEXtYzSzDxXnL3+jHI
+FTQwzD9qdh1PL+Oz7fXiISw2ZxSn+XAAnABXGyimeet44qBi91+61UQ82sRcyz1dN3aN6ewiNF+x
+PjJMU3gcv99FFwgbpSWzHVsazzGwM9I5/dlFopwhOKGpVquBkmMxZ9tdUPIa6GDYa97D0sbDlVOL
+R1pVYF4mrcC+7PuVuDVtpfrYCY0jry0GxbzAUMErXCForYPnp4LaCnyjQduE14nQQwi9xMPcnVMe
+6unD9EnZkJZCk+xXEdr0H1SGLyR4dI3QykN0wc/oxXB995zYm9VA6To4w6T1NhirfH5Ukrk2ZcLn
+8nkeXiesKrws1efNpnW+9kTVeJQ7sLZ6KlYsd2UA4F9WG0Ohsh7s54tfYwfEWODZPyerQFiJMRQ6
+zVIs6/pCPR0+Br6+IicVMo8NA6UiC0paFu8ipdTGYobL85B7hu7t6VPn06C6ADPLY48PioPOX6Bk
+Un0rDqC6RrDV2QSKgtPSZWFtF51xJ9/veQPDfANR2sFS1Hak8Owi66jPvhTqiNNBKJ9ke/s97Xcc
+P9oTmb+YF9UKykS7NZkvu9MaS4aJ/nGU5e2wPpbhDzxEbPs0LIvvGkcDy0JPQsBufFk7PcsEwGC4
+t7GWA5VbquIQdx/i7EmJu7bkPAbAgMuDNkRlOzVPI6VP/K3S1Bl64AkheJ9DEaPqtqJgSbFSMdav
+mqe8lPl/E10g/FMkCZ5Q563jpMzXQxrwNOsKXwa9jNp/F1NazjjTyiuQU2rNR2jaNFUaBljJHdJc
+S3XLsF2giplyxWPLAaS1znvulhiQYPdx0Zq2WXTjyBbdtAXnulU8WYzLXLLya/14N8+fpL+WC/pc
+pku2x+dp5zIKF7wOrPgG7MxrlQtft/zlS3c/P1zGu2K81kCvM2D0SwnPEmh1tOc9Ou0/vIFoOqeE
+XQmtdFWqYwJzrM3//Y13wbQvwa2tv1yBq+p65GvWYAnXiKUs10tgDRArlssEsHyBzdVCAxQPcpEw
+dqA9Wof1mdgg+KKAT17qYGsmGsaix83NIvxkKQZQ7zlsKHcwbteJaRRIWGQ1w9CwerNszlaS5VcE
+a2AQbeR4WJ1PIPiQHmH/gMpqZciUvocwg0cGac/rT+t4acoqEKsWMEzprFE2Awe6tVq2HS7GBoDH
++SMhXx7+KjziE7z3TuHSd7BcUwXs/VJjXWDOtS3bkPQXXvjX6s01BfvC4nKR8OukZXgHwK5Hn4Bv
+gl1YOaXeqJ5Bb/cXznKgummW3bjE8pA+fFRBsMYcMyednkzr4Veleuprb1tUHsykrRLtFVYGJP3q
+4d9NFwgbfTr2vGsE8cSRgbjOXMYJpJ5bcco9p+Wy7SBkpdiD1EgpsCd0jgOdJ/EKArWk1aRUUuUz
+lS+iQtrABRjeu3gcI1cyFnbwWbZMizwb9ZLG5YEN6CrlxY/AbynX1/gTF+3p+pD+oueU+sytqSMe
+eGhpW0qycgyI/dSeJoujqqj/gNpeloUHj+X/SpoGEkos6jxraRTPhYA2ZgxovYwew/7CU7EW27kC
+A7A+dnrgEwXMMSIU8lLcEzqrNU1UoGO5RoyFduFW4fWYP9enwl6s4311fV/hQbtTGBgOS5o1Lz9h
+K8Wty5vs4GC5bxCIDfzUwG2zEoPOCSkRoKuKGrWEAsA5XMigSuwQgriRANbuHO4Q4XtGpORNkAwR
+2rEjAXIKuon5AHQLEJ4s9w30jgqSobCXFBtg9H/Sxrb1mO/fANdTKbPgfEfL++lYBz8vKCTv5YkD
+cQAAIABJREFUl8V3lHDwqd2s9fAogBjAFzW4HF06Vi0a8+UyHMvVRADIXK4u5gURaeH6MNgibOB9
+ABo7pvhewi7jHsCwr//tR3NRgHGmVXDr2jnTllxqaXR977VANgAMO5+vGOPZd76x9MSi1U+4o4k9
+jZECpR0twF23b0ozlfM/SBcIf5vybqj8ngCype9HrcxRq6c0ZpCjyDqwrJbUp17UWqUYYo4St4Pc
+lI4WNYHRlF4VFQalGlCTAi4aueWXIUQI9gZ0PQ0VqGaabmuUl3pdyMh160qOi8S/UsM2smd6Kv3+
+nOcSLhBfVOEFeI5LGqWrEtCQVEAERJKeeY0LHkRyTe2ZR354kxYgONuc7FUB7nokynwnDRRX0Ps6
+nHU7AF5VSLbp0Gz/iMNaJzPBj1uCOzhSA9/2ueAExKbUFAaK/VvKgC4HjAqMB1bBqZa+QPHyV17t
+HiarlmyTvM/AQcI8rH8Gw5vf8EG+CNXFiWtbPNmt0b0NPHdIS3CA4YnlN718UVAtxJqnTr5lYX4g
+i/DcLb8OjqFqdxJFiOTgzWzj4hsDOQSC864ox0KOHadWkpqIG3+P4xRvE3A80lyaeTVdIEY5ju24
+gOcwUFym9WVfflB4cbDfYAjWPtzLPLw4sd9IhrsEEE87hiBeoDutr1WT39jBwCX7CtN4+C/kSnVp
+WO2uANh1bYBd3QEvA+PuNlFsvS6ElR0OSeYAbS1YvmMTnABwlCJt45ingRs+h/h78/1VFN1b9KpH
+Ke2lCvyb6AJho0/H/hn07Pnvfj+l5bUHUrS1ByJBO+h9ymeAS3Hpi4+KAkXB7ZYfL+wAeLUiXqoo
+dw9ZtneTF5zGqiHB3yzCcSMai5PT7KSTNdirs1ZoH/vT3Wxb+Nvclml5xWl73jOffJJ3ypdMrmaF
+lHGlz66x6jjs3PZuBUmZ5ZqzaeRzlY9l2hyZQmMzWfhOFl9hHEDwyVXCrL1UZukTBtESQCb4yxBj
++Ap7AzWHWpC+jJt+FUBEdythKG1TLoOsmwaKfSgUQknmBes+GM60I7eYggHFMSTWwzTUOQyIOhjm
+GawWRLK8FRlCSDeA7JuyPH9vwTBxmN98CBaYYTCMtARjAprP2nO9qyD9gj1sIHhSmrtbECAWAsMJ
+AA4W4uHAGVHnAsJIa3BYhV2cHmRyrFsPk2yEBj9uqoPGTbcskt0xHWxRrSB4jJFWYTty2gqTpVgO
+/WjqK9dwynxVA8BIQCyqSHw97amIYM62ZtZ+gsU6HGspbj68Ic7n3YnPfXu53bRgYwyr9RdcLxgA
+2xqldPS0SG8Lz2RT/kXmnwCw54dhqeORGt7jBzAsgaqLPswntP7L/FIn6c8Chn8zXSD8F2jDQ9/4
+gY6n2EqpnkFP5d6DkKQTVD6Dk1Z4swJXhVeBLxdJQQ511wgEt0ux4JwbxqAmxk4RC82tv7zuUnjm
+Olz5ZC0GNgvx0S9Yta3l3RKckPnQl+iHnifg5fzpnqvx53t5lhpwoSAJDR1KKOV9+lb/sYMEJDmN
+FUdvZkvthV7FGQAbwI+xV7+yNt9hoDyR6HzZLY0bAAb6fsWbq0Q2CqERWhdS7ZrSFilguICQYgk2
+HpQR8QC/Y+ZT/UhTaDz2Xy/bQeG+ELRoxL6AJ/WpFtiSZePcwHC1ZnpyA7xFrlAVdkK+XKUJhkG3
+/YojIOaxD9cVA61jVPuvW8IzBSRvFuCNNhAIhgjmnEs+SAXDcMDLLhAGgKdbiM1PeEChYwbwWsOU
+QLiOae2n800yUA5IvrcAOuY6rMcKfLc0mq/kRbIEjxH+wGOM9dviaSV+EBO1XaoAfhAvI8cW+WVG
+Mf/2Yc2czHpkgA4f/PIBmVxb7ne7XJh8HMgiHOvd0kJ2VABMsBRoVl0FSJd0y3CzDh8swqxL8gaJ
+JHEBwKkHYsh1z2M+qGGNsnF9KbUiDFn9iYcVCX18ulAvjxL8rXSBsNOLhVlI1YTNqyNAPBR6k+Nh
+CAVi9qMJYbUqF66FCPB4kjz83pHpp/ormRmWfjGPRrwqv3Idwgm0FiMsW7iDhU+Wia2ybQjJuts1
+wcf1fp8+O4sGow2bBrPU4sf0yHt94ciSgDAtT0N6yikdmceXcXVQ05qyBqjOE59/L/6Ov5nFnqrk
+B4uRp5WFa4nzdXaMkspD2noQBxn26+e78nrqB1s3l4IjsArXMw7CMn2Swhoi9uSebv4MNOZ5hzjM
+xaAjUGr0Ph5NKrnl2IEFyY7uWyzW81THXcC2NHZTaWligHWBJHeJEED9IbqhpDnjvAWUBkTnOo6J
+r7n2Xx5jQKdZgO3mwq3Cbi2eDpBlrpfkmrV4mhU+bwJoPCycK7EBL+PTfJnYgQviRb45l3vIVA0V
+Vd9x6MaBnM7wpy+U87Q+RmG/kaB4jdNIEDzMNcKAcK1WofhRwa6lOeAF3No+IGo3D2r7Z5fZ91sk
+u5Egy68edFLXlmzOUBpnB72B5xQtTFZ1bedGmpfrFmEu8wSanTOsj9EX53XvDvfRtmCMsII/LpP9
+Rwuf0ixM47DnE32k+A4Y4ZPT/ga6QDjoU6CTrPs8aRuLtx9CNifqOzSl6JkGfAg1Bvt2MJv65jGP
+gW/8SAkVE80uiylcx0+8QQCqpW1J32y+LXnxoeDwKp9fulrCfVWtcXMR+RSP9N4mYBvuz4iV7qs4
+CSi+lpQYCmA85NUstjDoOZ1A6npkv6cfGnQmas8TkATIKtda/TNp2/XfDTVelGnjKX18CXBsDZHa
+p7CCcViA8HV1EOfAABS2hRWPuD0eZRlQYLf4otXNIFFgFre0yjnI6GWLcqSww42idB0Mw10vZI+b
+7o29FdxU7ItQ7at2pKMJkiZ+dtkE2fqYL1pJyglhv2vNa2qp/cAMDto1TvFt6dYLgr5GLHM6+LXH
+7iKQocv/dKht0eUAd2JOs07aVwHV4lPMYhnuE7K+gjbXB1QcCLuPtttQ1jRJ5VGaO+U/0f/VT79B
+WnJ19QEyl1/5XG0SA+pQD8MM1AbgYbswOFin+StgDnkjFjcLkiD4+ecfhbHr40cAW3GwixUe9jGU
+NQ9+I6KA2sttMtCtus5YBVCWttf+TKybhBlP/siSjnR1Cq3vZVxGkojVF/EKenVvi9I5vi4LECY+
+CD0gYBbJ/V58bdlYio+GLL6PMUPKGGQYUsMEDrbwWv4SI7QUL424ZFrMivh84vD7OQ39V+gC4SB9
+XwRAhR6HJfbCKpxHZ5h+aQKIh6Yd+UNdWOrOr6wHxZXKQeH0n50fYKoD4hwMasQhvyujEB3eVwZ8
+OWYnMOxhEQQYPjVHvpHOeb/qxzVrjA12KoCLeOrEAEruDIUJlO4xXCBabEs/k2yBw/VLC1HazTa6
+XvbztA8E37ZWDmmnyS1AIuPS+9vZV1o5AqwJjh+AKtDAKAjY+hrsZd4D4P3cfAEptqgiMAxqDyRt
+Zc6buSpNMYtUBewYF2jxD6zG5Erh+HhhiHiga+O+yyQYCBbvF5C+2HEjTYo3hIU1EP5Ql8OVDSRe
+GlyuCjLF3EPmqmcyEDYrpIHfORaQdUtvAcRzYpplWIZC/ctlqmtLLwejBoqXe0paZztrP62OVCV+
+Q5o31mLAWAwAR1987HSBcQfoDoALIGbtFmCMHtE/KRr6CVuI3X3ixwLKy5rrbg8K1R8B0MXDouu8
+AO3D8hMQi1ofQ0cNhGWkCDeJceN+TaX+BRj2sjamWrWVjznA47PG/DMQTPnHcjsI7jwMkxFnubzG
+IPBIAcILAA9oAZ5+/7CmNOM+xyJo48nXlow9KF5nlaqMfa40+DP49DfTBcJG3a39idjDqgJfLXf0
+aHkbGLYi6+LNutx5quhy0y4h/P8Pe+e6JSmuc9slR7//C58K6/zQbUk2kVm9d1fvb4wkB4kxhjC+
+SBMhm1TtA2b7H1A+VB9kVwPngoDYegdojZluSLyEjoxHV+ZyiUtSeMCw1C2WMvXLshVYJLKhxeRa
+6UDpOP67dX4unJnbPt39AFcAON82Si/CQmiPlHasFKZDVqTXSnXGz4zdlscDbenc6QrgcoW4i4n8
+81euwvxepPf4oz9JPySUiPXjUe7toF+mAy24fzHk3kAVl7gb+H4Bv2f6uFYNTgoL6gHB1aHz3sIi
+WzB1A95ygZguEY/7BwwD/StVLFNmviqvOS9tzlGLlKPi127W4JsrRCKFy0wKp51wudXRZ3ZIdw9l
+VwILrwDGhQwbBO8GwntvLLcMyzZ3CBFx94SwCNtUeVDb2kdUesPOZjjaevYf5UMd9sOCGVZtBNBv
+s8Kp+KeHEzzLVaO7TzgsUolG+0mzS6tHqr9l7hJsHbYBcy8DMXW3B4LgpS/EYDhN6HWL79oOwhvQ
+gGMD4gApzPYeeaL2l1utFhLwvzVchyp9lGdzmdAJxVZjHXI7zFYeCqS1xXUIrjz0tab2Kwwof2bq
+SqgdYRgOII6iwiw2kpMcz4JS6tqctybYWa4KHeOmAr4+/9739NB/c/kB4Vy+D0UBwOHLVMo7Xk3p
+SR/DUiwwZTMFHre5+r2evd5OJDHkS8Cl61UjfFK8npKUJ6jBatwTpyG1k/vcKbm8qIwmDBf0nmGh
+n+1wq80Vove9/qAhHPii2oUSsgyoVfL6s/sewNuAFoiG0LJADaCVI8GvSMY2CWT1kpKY4rX9hlxC
+f2d5ht8z7qs08xhAdTuXdjG5x8f56MezdbZo6ecKoXs2OuorJMk/W2zZSvwQ/9D/no/zNc65Wtkq
+DDq/+rNUOWsVXypoYReIh3192D9guJR8F1rUS497XGgfaYgPHoS8zcFgIT+ikXCrmmGXTJ7H6qtm
+Gdsr4MsyG7M+hJuArHptb+PcGIQNfnVvbDEA3ttm4VjiH3YQswpvt8xuEdjsE4KamcJgmtu7csch
+fdJUy2M8AAlQ3FW+KgS/aNbgkJ9RmjluEgxqBIgDgAOCsTy83G84YfiVQGwQ/KJyDRcIAuC5FbfO
+52t1vzf/VnaH4Wpzym2e7mV7OF0icguqkwh3n91oU2U9pj5FEMzHznLkcz6ni2ULfeQmuoF0Y4Nl
+13WWl0kwi5Vd6dkqKjmLrivROgiKo99LRxpyi5hhyTxxfn4swv9DSzT3u0iNfUsZ4sC7hD9xVxeJ
+dqgJaMklqgmSXN/zIUgqS8fxlvQCfycEPyhZyKOVOH9QpO4HpTgLhiNeEygKgOMFW8B6iqIOg0CD
+4ScATlh2ARBpuL/Cjx2gfCs7LrOHVUf4bBV8LbIxTyu/jLMk/3GwWlA1NipXKoSc/ovjkAJRuXHp
+ISb/s2VcLF7O9jhO9n0XinmNZNQq2Oe85AVvnUVaOceFex3N+nDlMFvFE+w+Auv3wfc5DRDW0QOE
+AxhnnMQ9l4aremE4OMH16hLB4YTjOPYAw/6DDaKEy1ogWJ6/Vffgr9XDnassv0FrpGgbGFPnp37R
+BYHErMqASEJwzTJToKgIC6nDsh/fGltzb9h7Y799Kw7Fsh3Ydt7XjkF4EJSXtYV3M71y/7V86ohX
+Ji3Qm6IIbMF7bev/YRFeZQVmdwieAzmPgSzE0RbiJ6LnNBiOD2ywr3DA7wvysu1aK38vrMDTKpzu
+EVcwDquwWYnDgh/9JvODMoE0V4ZYtUA3LcJbqeWwRVezDOJ6B+hGPYQe0Pl73m9IT3Tg/WxFhgh9
+BtzjCfJLX5Hya+Br4ZVxTTR0/Q+UcGwKlsMkM1I5X0R1VAnF8e+cvII/vvyAsC9P4yCPOhEk9Gqe
+ly80ukDOllktW4QEmGv4/A0dv33T+XyqU180/Nao44+f9GZj47SHMq5SiVcV6lBQvsPntjzW4t5K
+KeXDQRzLcnGxc4Hhds+8H309yzz7fT9GBdYePCjb4HRfLB2JJEVtQH9cs2HwrGcZv8hJSVpoBr18
+lBWQ1qnxqo5uUEhQtxv/Yvl+yl5+820AX+vvxPU8Se8L10wKjiKnk0TOdHLbZ2mdx2/9qKA11ju8
+XuLX96H4a1D+MEDu0CrWUvVS0vZaXBwqygq8vUPF/L0bScIQiVfJXoYZ7jKgFP8QGJfyazBMvs88
+E4NZas26mnOKZztx6y0KCiIHIYqjjle6b5QlUBwot1PMThB2Ke/bnG3OIVjeDrybV6FPCQvaNHgk
+RdxRImetgz9M0CgD+ud3EhDmhKW+U/sOhjssqL66e0T43p4rsiwK0uqNkkb7Eas/xVl/EGkD45qf
+sPsIi75ythJJCL5ZhTsA2/2YhR2+rTZP/sEzj7hAqZZVOCzDW7Wn/RJ8CQQ1ymceu6WfEFz1qQ/n
+5bn+UKfZtuv+JBVd6KZQhiGPdGyzmKprUlwTh6Q0A4DzuFK6eQ6fmyJJqO7KSvxjEf4/ssQLAAAk
+dI26ehgZLgms1GB0gK82nSUV3dtUNCKigVDopxJu7e+qxGNU9kwTF1baCkjntTSh+Hzr1tDosLVE
+eVDeLzAsUVyZBs2nt/YtIVuHozzaMSrPhOXLcumzx/qpi+ZxqWvVnUslorzU8XnT3EziXALtrADN
+tJoV5r+Yuz3+afld8fNdqP1P4mKvydkz0DIvR0c6O5aMSmrnoIT1QwfK9RPofgbiE2Bv5z/+xidA
+xhl/CgMvNlLO0UwMCgCIYrl1d4sJtUVW3602BVtzhSBLMK9VRQzFvRzrXv0jDQFUXCnNEhx1pBXf
+KnrGISVVpgl5JHXJtAIj4jTLquDELYR74x1WYLIMv9ka7JAMeeNxUZ/jVkFwL2U0cZgBgRo0yjvy
+Z+UQFt5yIZAGvjHDhYrSPMfI8+qv12GX5B2IbzB8+gi/yiIMc46oGSMWlKzA4SesCfIxRdsmQDbL
+MMjSzXnp7x2l8oxejzVjhI46RksLbwt9P8LDX5av086Vdv2q3W4NplpHDYjkYzKup2ADVipFQUHv
+qvDiqgPp3aH/gXM/FVPIkJYf75tjK3Mriprx4xStf3r5AWFffm+wXFgcyNKQzRFZ2dkD2NIZ1kv7
+Ufp9T0r5YF0cMe24Ujp81NelIFGQjKc09EQdHc2sRGg/WKBVcTUQ56aUomxwwLBEWSELAvEdee50
+HZD1sABPIOaf5/4V5zIX0KWueY+weJitwdUO8g56WXEZtcyB6pis7e1YWHyoJUg+jtH9ah+oqHRu
+KwxavNy/1/rrFBn78ftxhEvsTPd8rR73JBEv8TfpKXQVuv+WVLoVu+0LMKHy43oA8N9MEzD4zbTg
+fhvgfYFMLrfeos0yZkBiVuAVSllIaYsr7DlY7gbD+TedoqJgS85IlDvfD89H67LAgM+V/AHEfDf1
+i9mnfQ7hkNpNTsQ9gtwAIr86885xPkfv3tjyxt4b73fB71veKVOrLZLE4c7ulVA9sUsi9R/mGL7T
+Nj9xWFoZgLfPtCA+S8awApc7BMJ9s1xBLmstQ5EsISuwYM4akStZhFXdhWJagXekJfjd4fNcluH4
+YlwnKdsqPwR6QR4Wbw/HYLm8x2tbON0euE3MY7f9p7i7FbneM3K55/zXQ4+0naxMaUVja3ePAKKY
+tE6la1oaTXUThgMN/ST1s19t5bKdHPKnlx8QzuW7KDAgOIQ05qt/YAJwWhkCYjTVgm2p/o/2M+Eu
+0xccc58/Ffg8DpcVlzR+LBRGdl4h8ZwZsY7Gz6xhKShngakGNbRTu+Hst6MQmlWXOy/pxey0kZbd
+UWa54bJzqX55WJW2PX1i5xUAMx3rv5ExucWnP7CecegFxhasKBQr6of2zdJO89+xzFhW11/FyWPo
+03kDUB/icInhAha+v6Y3pO8f0FznVKBrk28D7kegtSnQns/9YDkmCI6+DES813yDTfvH1qVWvooP
+gEuWXgl4oDQHDHeIBP/e7IwE8mlBZGtinB1uETFDRLMEx379Vt2lrWEw5MZx9OeHPJPkavHb3SC2
+LLzfb4hs3wrwFohsAO+qA5ZGSr/pX0CLr81RLz4qqiy4+nGVrTlNmuSAwOEHzFZi8DX94egGbKMf
+FHiutt7mFO4+wivdIsyiu9w9wv2E13oA4wUMNwk58lIS+8x/h097uCPLcNQ1tYXDUvuNY32/Hgpn
+mjSfjbKesCzeTkL/Zv4lqISWFF/CRVFVFl/cC0swqm+wa0L2l/Ip8s1U1L2RMiPI2Jrs9P58cYv4
+cY34V5fvFf4Bv+1cBryAEeoxUcGk+eN8qajWxrit9TgCYG+I8TSVUItq4P2Yr+iADEpjcIvyC/Zs
+K/WFCXH9BU7cVZRJL6fMF7/6ozI6rcEVH79v51P5RFKyurPlB5m+13VeC3ENRtrnJRBD6k5T3TVB
+QGEdF4ginE/UGc9l4tLviAMKdKm+utWd3yXoLIKR0Q4U51JCkYr2chQf07SfbOfJ2J9n3/Ytrsnm
+WZhjX7gCEIK60ohHHg+S2V8scAXdEbda3AVu13P8enCjiLxZNiUz3SyQTQsJvbGhnhqv1oVcGoSV
+7IRhpbQFzuE+YYOzShRq/oT0NSGeAep278jX/kggrmuaTKbjHSce2xGXQ7XBOj7dmfoxK1PzD377
+wDjfH1BseXx7Hdh+5J2Bx4A4vjS364fi/innVidoLg02cC9A1l0itllMZS/L41pt0Nza8ZtAzYqB
+vP4Gx3kZCIWjrc26Oz6q8coBc7JeEGizCOtrHe4RDYQ3gbEDMHwgYPiUiGzEA1UK9KzAcI2g1qE0
+SC73vdjZMkztmMO1T289jmOzNV7Ov17T6wBdbudvRXqSX6kZe5evKsqqMnm2GgQPvZr6mBqh0C9J
+vP9TkhWC+kRi9dXcDtCdzy3MKX96+QFhX37HNUJoiwsY63zVfwXg6lmDgeoJKuNDZFO8zvNOsGV5
+8HysK1VunCHs5rzA4sqxehyDkdYNNBWk962XncxjdOmmPiV+jsov4lDxqdzG+XwnUkkeFy7jWu+g
+bHXfzwPFRV4jQZ4vlV+Oq1uIA5ppWxxAoFv3r1wPyvcaP6gPN28FejcisytQBwOhEDX1luarOL0c
+l+N3pqAc+wGB/V+VfRPqdmA0caSVdUppsLKPKO4/51oQfJ/lYX0FxCMurhcaRNo9BdjJuLc6zvMI
+81KK94MV+ALDWyzBYstwgBpCaQ8IEc7/LDOyCq+A5bAGO5Xx1ehmZvupe/M+q/7mLtsEta1LWY2G
+gwCASBPgu90N4v02C7CF336Nd123bQtklyp0Adhq8wqLfSHMjN/m5qAatYQCrrTyMgz7Z57dEhxW
+4R3hZXMnB3SnRVjrYxorQKyBXPRwyftv9VikZe0z3VteA4hXwq/whzTSZWM166+F3T1ie38gIDb/
+a/tdCH2r2vPVLLGoNrk5rOQnnG222o5S+zqhWGn/AxTTuRVXcDvz1/a91Ujri3aPSo0+HfeyCIax
+yyE4LcKkU9KDWvhKQLfQdv1ufSYyoPCZobvCvSjehHRIwrHgxyL8P7B8r/DTF9SB6xP8Gq91uIu5
+Kf1iILGC1vjqlNpHQVgq6dZgq/2dCjniY+3xXd+7kDuUV+/EXeP0DpIJohzihriMqFyA02dXuADa
+ZSktKcEA4tnvMqDe2W8UQOny97UEUHfx6Nd3PMq2MO8w46THRLkrJYoi7Odr1ocdjDZHJ0ZcKnGl
+ZwWKQwlZjr+Wx7jlUWvt3v4bcXIJn1jToeT5GO0LlbjwYelnHQBNl6C+FJ3lyULL1uD1AMDriOv7
+5/HLdQ5Qazd33Bff/2yfqaRlbqerhMXFDBMQMWhiQIYcgHCvF9KKWX48hdrK/RN+tSjyagkO6KX9
+Ue/1+15eIVNllOtIxwLTQPiNd+Z/HxA8bt66m7JFVx2Ggb024F+jA2yrEkKCgQMFWgHD8WEPh2GI
+pG/wWhvbPxGdHwdJCA7Lb0Egu0XkfMINDqksmLQCgh8+prHWgrwIfl/kF8wW4AbBEQ6LdvkGyxYD
+7j3cI3wt2VoPMAWjBf/Nqu5CuaBU855tX2p/lEmlZdeKsux2wFXMeYirXk9XCkh8+hrt7U09nFEb
+87oR4TBBsAOstfnBIMKl5RkKPUHtTyTCgua7yWKZulCkj/5b4Durbcr1f375AeFcnsjoXMoaOKzD
+bkVrz3cNNLhB0S56+8mtfDiWYalGSw3pkE8Z361Z+aSY8ZL7DL67KdlzqflsGUa4M4XMpLKos+na
+UTZ1bnbUkLuKKgAtWcyXDCjOi7R6oXSXlTlxZq+v3S7M53IZzssh9NrQt+we0X9aMv9KJwjIanvE
+ufAjpZktRnRUARVWbqhwFZeSs1RpgRixYBDjchlx574cxw6AOa408iCz0HvbFS7gkV4ovXjDEuqI
+V/DleHaJmB+GELfmHgPhlsPvmb6+HneCcO9rT2UTGcf9uHq704BfsupKKO8etxU+9dgJyDWQrvg0
+FXz7bdaQVX5o9+kQFb/tmr8Gzm18tcw2RhXr9ZcVO+ra9/OYpUvY8jR71xzBAsEbPE/wnCWCXms3
+CC4QWxvuZrGgatcSWJkiyiHvLUCuw3CsEB/Mt2orykBc8LcUZB32eKAswxiwJoRPWSZRPjFQLsLT
+RYJdIRZ0LewbCB9W4bGVlRDclRltU4NQ+ccadQD+spyXLLdbnhEijud+yasTiil82/cd7h/X63hc
+PozEKg7cKTNDQYY8DDnGMsq2iyzCcxt6uEtidyKi0eklr708DpnquY/Rl7hBL692/R+L8L+4fN81
+wv6bXOzgq0BVIluC7QdyN4FJ+EkMI6xnXLRRyP04BuTG8dYJbvEBTdQgPY/bwzGRd1M93im6svGy
+YADhcvAykryf/jSaZ8k4j8gtIFc4rfb7ymKntPn7rdzu9S6XtXKnI13Zg6ma6148QCwKaftV/oev
+sCuiVMRAWh1M8fjvjbhUl3OgXf4oFcqI01Eks4SOe/sQx/sYZXQ7r58jtJk1SGE54w+gPdLagX5I
+RjKPaVDkxw8YvlmFLxDcBr/xQLkOx2uka6Ac8xAfpTvL5gaglxavcFnWH37LRaJguKZMw6MVeIeb
+BOpa9jPU+EvYEFyuA4bDMn6H3gW0X8lcI9FFFRqP8eLqXADI6nU7t5Avj4kI3jkbw9tMipLPAAAg
+AElEQVTl75vuh1YVQN+WxVeA6wAbh09j3voanFmESe6Mc3igl60b22da2Ko2mC9nj6gv6bFbRMCw
+WYOnXzBDHLkaeB0qCoCrP8yBcgbAK3yElVwhXoqlGxouE3tZfi8QjLX882oCbK6b+oJelL0i8lfN
+nO+n2jjIMhwQzNbYast3KL5Yey8wy9A79/t5pVc5XbSN/FCNCqVjeRBtF1UeXlcNikHwG7UrZ9zz
+9mYGonWKqKZ4QzH7Gr8d1feHlx8QzuUORHMJeEoFPlwk8lri2wCyOGdYSau9dLeIa1ge4lEW4ExH
+ylowLcDP8QVXQ4BITLFUnTKOMkAw2JwvRrWHuRBavyEn/liFjtOPNNDNETpnH0xMIH3Cy/w9nQfj
+mD6n4/347XmcyzUhluWXbxUcF4Nq4h66P/AtLs/JwYd9v35UKeNy7Gvb11mDaPXvcXKk+V7clH8K
+tLYofJZgnEFhuaTJjVwuISMpdzT/1QG9wBP83iy5w0WCXR8Y+hh+F/sOLzSXigHCvc9VBJdpU2PK
+cdUeroPhwg1CACjNGzxhWNTnHuYvtRHw5e9PCSYAFuohnnyD08dUkhKan/Bt3XdgVti5ZnEadRmw
+fXugIbgD1WfA1/IZIkKeMgC39jwg6fVSKh+FbsUOEFgwQBSu5y7RGMbyOrtbhMXnNpa1cpq3tVYC
+s2w1rgwYRvkK27W11V8Xm5QnKps28HFOmfZ6GRC//GtyL3PlUF3Y61VgHKAefsEBxCt8hG+WYfuA
+SfMTlmpr+XZjlF1MmRbuEbald76KBN3Q8wmeWS4XFwe13vfkA4x4eOT4lLE3P2Oa0jDzRECss41U
+3TT4zentIgVbhJuUQP0KKEcytn6frpu0N1PPSijpeox6cokQUax1UdD/8PIDwrn8TuF36A09waY0
+dpeoyBMjnuF27ifakJgdwBhtH6DGX78hrVNw46t4eBxwgu9CATA/tTWLOKokJyjyfR/7cjl2Ka8s
+M+lFO+cFjms2cKUMTfVyq/0o85YmySLKv9Ic4BF1G5AaoCX9jhy1jjSZJw2f4Lq6BKhSuYkLSH7Y
+qqmm+n5en463QrgUylMPucPvCci4putlVuVoGe7X5ji+UhZ0XU84jDojyjfPiaR5oF8zISj6F/cV
+GX1qAHHbDph12D0hmQA4jlOYrcjRZqxeuTPA31RxG8JxHDgfbNKemhZgcoMQEASfsLziODp0nFno
+SjpBqq2LVsBg+faSfoDvsk/u6lbIOKZweUDwy+UccMtuKQy/wsDs5+y18losmetHbS9g8qVwAHxh
+pWW4rLTwcq37DnCvsuM6NdDSBDpzj9g1iG+HFdgtxNtgM10m1GTJVqR7RFqIR0nX6mDpa4P/ReuH
+6dP2K+A2rMPbZpQ4wHdsjzgpX+E2WM7yZXkktPObmNbugOBwM0Hcp1YvaQPhCHAn2B7WYOX+Vfk4
+4fhzmrAINxeJQsusgxRWXg6nno8Htw7Bt3AWVkqTZy3PeWX4bca/I5uej5xVAv/K8gPCvnzXNSKW
+/lKgw1wooakGCm6GStC7neTr/Q5q0eAhMuKiwT3EkSyLuGiR1gE1IXhu87bH9tsAfNnPDin9fvO+
+GwAD/dPMcR9ooNyv/XmJNHc47ivnuqz+VT8H7FEhCaTvR/uQeY5fjQR0FETCryVwKMoz/Zpx4WiY
+SUKHTEtwxmy9Z1go5u4rPNM9x82zVc64BqkZHK1PPoU9wD/en+gKY6jtWX+Svh0wfIJcrTzwrSy6
+A4JXB+TpF7wIhMNNAgiLk/XbqOKo54ThIEA/FG0sFa3SftsqQLDbP5gh/irf2lZ8jEMRabW9Uj7a
+D2lFhmEQAK+Ap/jNIhaU9Jm4trP+3IaG/v4KwHgQEa6T8VByT0cgvMuPt7bVJ1WBl9cRHIJf+oLG
+vL7rhb3NClZ+vIDYCMQE7JLeyOvWa31yj9i1FQkXCfcLDreI4R7B8wunfI+ipnDWYxOgXHdWf7G2
+j6Ksqk9Z/jGNtROI13pBdXeXiBkOa7CHzT2Cp967+ArTOm2f2ZygCcF7EwRHvWX60PoFyNZ/Ahk1
+ZW22yN+CYK3for4a8XFuPLRYH+z3p6B7F2o/rR27bIFb4A8I1sooCmS5n2nqENIjIi0NtRCqDrP6
+xiwRoaNatX2pof/7yw8I5/J7IBzijs9PJT8JTLlRUDo/bwIf0Pc/xzEMl9UrG1Z0goyTM46UUZMh
+COgVgt8O7crb7OB6HONy4jI8n2Vv9XB2qswnl23Ej0vka5gPVcx50CM+yphqj/gqPTKSsZ4BuBSl
+HYz6CgEbXNYG2YUAlrq60gUyV37S/OqcQMtnOwH4KUwF4GGd4bincY+/G3fb17rNvkhYP6nguRdM
+AXoD3HaqUBh1Za6nENzjGh/B97AGD3eI1a26FZ7HBgBfQDnbTAKtehVLtsDqj1JArNXeUm4NC699
+YQ40JRrBMGjKtMNaDOzhR2nbm6Km8k4A7uUX1vEGugvIT+vCfIBrcTCWTffPQEzlzsCbdbDIAl8u
+Gu0BhtJ3EEbvIww8Dl0vVejLynUpDIiXuQos/9zxEoXKMj0SyoHkRr9mWTEtvN01gl0iOvg2q++I
+qwcYLV9h8Dp8UpvCIR9hfoigWSPkZX7CWwcQx9fmMjwtwOID49z6TmFe0fJVi17X4frBYZKx+R4i
+3R+GuwOVUbMMQwmSKR96B95Wv3xN35YlmNwiuC549cGbCcNSMiVlE8gnWHvJSMsh5cTdRgTalFs+
+8vrPN08N6r+s35k1wi2iZnn5s8sPCPtyVb6X5fxCV4e0sxL16JQywqQPbk2a4oUaKKfpll0LR+MH
+HZORhuKiDFKgnHcY46EXhTl/SoP/GgTrratOWq1fmulmWVSRarvv2yUPa3E7BnYrPo9f4u3YAGP/
+cSHhmOcOsCJ+tTsIIAsBksc9vVvlMr0L1wTmwEOHHK1fc7ip/fjsatYJhQOGJfJ0g+SHZULtd+Nu
+gNxjvJZZr82A9LTcdhsc83kcxeXvsYcsSGVS1+f+1ZXNd+D4AgtyA2WyFF8gOdVL/Iu6qk2VdUKw
+3//FWjvVXiQ/fIK9obZZItR6o021pWS1+tR8UgtWhbV1ZdmU7y9ZgNeGbotrll8FIPzOKuRQyDqu
+m1VQ3KA3AI7SkM9yhPe7/0YDmVgXsJbitRT6Mvhbaxn4+nbHlGAOwAYpbrToDdbAowFqHyyXX4/b
+OwfEpV/wAwyHpbFcJC4zRjTwCtnVlAnC7QQBq+2hwiy/NnBOsV/hA/wy14jp/sAPHTlnsF83Bsqt
+1dpMvl3wdmW+wXLWkdfN5nCUnxd2WX39fhlyGWaV04w+9QFy/05c9j1XFGWBZi1JEIzV6iXfTLkL
+Efvs8n4IgO4eoehjTLz10cN1pyHqEb1aPD/hK1w+w0tohqo/uPyA8G8uZZ3IGN/e1Hqp/qeqLbgt
+6DvjOxDieiwU5GoNvvyBbnH0JC3SfjMmlxdXaqDfY5cI26/ySLBC9zG+eDH1ONfj0sqyl3LzXQp/
+olkdoA77AMEQrde3KGHPYkfzf4/HQ/rcplInKMub5H0pJqN9wBXM0WD8F4/7ISedEejFQnej+nnL
+6WnLac5FjrI+2/yIUebTUPeSgMo9JwE00kjb87L8DLUnJF/CI7e3B2SuxtxKqEW5xgmdK54XAdLi
+Ga4QtT2tkUsIzmRZEYay5u2Iw4yLdk9x4Lw8fewjOxwvZUXb8Om/FuyjDkugW2x6rL2xt2CLYO+F
+Le7Lunx/2b5ugcqyL6SJnSPiPsk+Ty67ScRX1OqLahQXadoxYJmvhUGgE1/MmgCtQWNQxd4G9pXG
+ZRtbU3d3Uejh4SaiVEfa6yPLE1w/E976cVzWagOVRi9rwq4Ch4tE3hvK3WKAdm0/tbnLfaBAjzYN
+pFiUsR2RQWte4xN09nvX3lcIgLPM4NA7r3ndZwguM1VYbg/tF8LBzaYCcX1baVIHa2p3kg/1Rill
+Az8oHzPWnK49IV9LWk3PfhLm9sWwbJuiesxCpOwPnANz3NUHp/vFch1cyl8LjJ9UzD+4/IBwLL/1
+EBKKTVvcdy4l7eiEw2f4ZSy4QjAwLFDje+8DfAuGWaFXvCm2iLN83IGYRJPqtMGQFdn8z+6rHTvv
+rcpgFOKkPFt5ZJxyd3bBF52PhSf5P1VXR4EfxfN99dDYP2Re0g/CgnghowOIMR+67sHHmBZLIBv3
+92n7CZa5fBr4PQmwbwm2gtu6fW6ZuMAxpx9w3OC2ypSSjL44j3FYL6m1ElDo1vfnsQbDgrSQpJIL
+GI1Xyq7gymfWtkDVxyfQLSgpaK3XwAXC7IpgYeATBDMU2DiCjQ2BJPAKtrsPPO77p38Dkt9hGXUA
+lm15mqALAuK27g3c4hOcga0by10reJDaAb3+JYkE4O2Q7O+oN107Zmpgf90GxFzmDMyYxyasUZ1R
+y+P/fT1xpgCP89IhsID4HDRXsM8wjITHHufXyd8cUMqyFyfgXmdYmGG9xCvfJ63cR8B9hKzBozzO
+PCjatGkA7Qv9PkGwHzsg2I/f4HeuM008pK58K1Hygt2ojmkaVwFwHwxqN10P8J9akcUGBIu3zQRk
+7TBsz+nRoAiAoSiYKEtwQvNGPmz+yeUHhH9zaapApnKM4wUJHxWjCKJd3C2mT91E72lWB1+QEmtW
+4Phja3AoZUSXEAi0W3/F2vmEXQt2q0ZuXdCsOP96n0/3RivlLadLg3c6qbxRhvK41k35kcLb2u/5
+bpODn3ebEv0GyAxrafnNTKJtC4wvcb+56Ie9yrZeofYpLsMz7vgJPX8youSW/lymZbfglnD3gON7
+er4eZwHynWOPGcSEYrnF+c1WTCka4XwzAJNlOIE31rQC1VogzJAzFP8lDsrHCwyibBugQ0aY2nHe
+WVVy+JRCFNv9UxNyY3/FvkPwJ0j2WQ8gQFqCnbTukLsvcdosyZZpA+ugv0foXWQlVlfiWvMk47BC
+M+iOcj6OeelxHEmlqh8q4zhvgh6tbcm0BMDZFuyvimG6SwQUszW4W4cThlt+uO2RdCTxwPmc94CR
+JsrhiOfrEbhC7+eecBxpOf/8cChe8nJcr+3nbzL8jjxx3vOB0kH3C03PadaqgbITettMMzllI78F
+HjAsAoZdGnFAd2Z1aGNOwhLskiysxF6QzU0qZokguC3LcICuFhAHCKvp8ue3jv/c8gPCvvwud8gM
+NyUq9zQeYIVZgPcd6P0ibXONGINPLmtc6fYX7TxS7aNb06JuMeY0WuIiMGCpW4AfgPjRKpywEQWn
+BcA36T8swxkP6oCCGhlP+ew4fwHkzMNDWYyMM+i28GLwvYerrXyjcX6n/WpXUvhG+OlYFEuWDzvy
+Fm94/fRKempJca/NsouzHMpNojpdg2EqjysAPxwPgAX9Zl1VMWuD49rdCefGt9JTzt63JhAvXhmA
+XxkGWHkTiOgIH+kGoHGeEoarrLI+uGDpdvMti9ogOlEpoH0EXoJk2g/XCD4OkRNuVc39YsZ9sR/k
+spekJRhboeTQrESbAYOn9VjMz7UB7rASJwzegLfHAf06JJZyc1tnxA2O81IEg82HG/EVue5HnNsG
+vwrdoPur+2llwb+vl7xkuAOjpY+eWG1NpwX2dm5VL22rLBsURz+YfUUH/OqZ5/PYEwSTliMA7pp8
+tWP0fjfjY99kxIDh1S3Ax5crecaTpv8Xlab2cIo1yWNQPSzBMYYAkIyzfHsJ+dOUTehh2yX+5iXm
+7BMkAIuq+Qj/gPC/uXyHJEAK89wCoTS+c0yo6T8AILgb9LVDY/gIB3D1Bn8D4jMd2qpwpahP+esv
+fjaLiwY/JmCS+2D9ZMkNfC+/RQWYClpnbXknPEa+ddEV846HcrLzQpBX3kM5ZP7bfj/GdzqXAN8q
+b/hXs6THr54u4+gu+4PaaKtyxNwPMCAAHRL+xn7dPCnuGScIaZnAJJ6mzYFMPaZ4K1GMbiWOM5QG
+sFF5tWISDp4lOMtv7M8+fbuO+a1LP2/GZfVe+pz79iX8utJbwpbgV4PiKL0TgAlGjmN3OPas9nsa
+Zd7L1NuDiLvie19QQEQfgHe4QqzTNSL2F7lXPIHw9wGY3SVQMKu4+wj7dGZWOGID8g7rcc3W0L5I
+dt0GGI+6YGBMuZOlS/XU64r7mt7W5Bmh8880nK+V27qntassVGNqtj7fbpy/SW4ycE+QtDYiLf/G
+Wd9wiaC+xeVwS9/K7rqt/nFataM9S7923ssA3whTmX+p2ScYD+g1udE1ZMqHOSf5YQEevv0Uj1Xx
+nk0gZ5kZq6rrx5IDBcHq8tzTiliZKcDTrdmYVYfgaGPwrcO0TfUHu+b6cY34d5c7STwmC6Uw1Pcl
+3rek2PnY90H33qUWX6NZg29AHB2M/zgfknnUIEp7V9ELYEokFkMu7AWg+ZH1hF+/9LNvcFfMvB+R
+8hX80sVap20pK+8KNDjLY23D/1shZF4TelGgA4edfAChcFiIz3C76gF3cf2+yCWOcjlg4WYBrtfo
+Z1qO9yCq3lGC0KfUSmGq1bKitMy6wPfB2OVlxj2M7l0qQT9rwvDYmXGzqEq1xV6pgbp8d3voA/lC
+Td7jZvvuUFyW4bXWYQ1+LZt6KqA4cvpo7f10TAla8pZ15PkoPgi1f/FrxlJxZhk+3CAWD5AT+5Su
+PLtGrOYaoa09fg96L+eA3R9wWnmhfWDccJHolmGk7/JnSzDGPj9cIvMVFutb/WSphyzK63QI5LqI
+a1U76ddm8I38xQc2pnV4j/tMV4lDRnB+O1pBz3hc0uat6ojPvPd7svKQ9hv92Gz/sx/0MoFrsNIC
+JRWOvLbp0aZvcC2t5ycEu04e8dNKHGH2DV45MI7GFIyBc3e/4BpEV/qwy7sDih16xWU5FDWzUINi
+9xNO2ecl6HNjQ93lRuwBNNqwCLLdRdyfXn5A2JcneLimzS0BD6r6jzhS6D1uAu19XXqH5eO8dI14
+sATztCqkfJsy9lxquy4g/tRY9wl00Ru7BUsgUbJgQjfgdwGfYXiUYdtPqJ1gQzNJBMN08Wr5yRGr
+rngIzjL//J+ecPlmub9maiLgJ+s7hqBi+M245Q8med+d5PjY3H8CwlQCD2uDi6/SAp4+gkpbcflp
+5WEbL2vJYm8L57n9b/dxKQ/qbxzby2b81qWvyyVhvGmpQzp++fyN3F5+n/MmUvIj4Tfqv0GwGAAT
+BL9eL7cIS4ctDCUf+0PZt31Kn6o7+41vtd8/93O2BgPVDgpuY7vH/sJ7b4hcLMXSB82FRfgj4DrI
+3eJbHGCuENCcmHVaeQuMzccxLFlCluLyox0AnH7DGIDM/Wn0xSg3kkWzvhgC55rQeDmOdq0zD+wP
+HABs1l/Qvd9njIjrxAC5WMtaTG1MZ55PgMw3RyAca9ZjP96sx6cbQysLjkPPZ+sPWhZ75G9Td7iV
+6czXJRz6NiVHWkWk7VvYzVo3UBaCXyEAZotvm2ucwHjAcE45d4PeXGTEt5ZZaRXZP9mIZJdw2dEq
+IuYm721DqL38G8sPCP/mEgqbgVAe9iP9tAbP/RsIdsB9TtfjA6yGk3xYgS9QVgPoPE8OxKbkCn4f
+y+IiGlS5o1gHCtcIg2H5eD9z7YAHUsxATLnS6ap3VhssZ2n6rBG8r3wGq/YmHpT+Y3b8yNIgn1bW
+E4Ln0zsJOsgF/nL3d8OsBP+DFbCR+VHPWXz1AGQsrKbEQkB6uhCvfUsWVuo7CZ0XQM6yiXQ3GK4o
+3rQ6uwPw3E81RsvXcfIQlzJEcPTFsO6E0lsEwWYRfiUMA13Rf4LchKA8dqZHEhXaln2AW/kFOIxz
+VTWtvuEeoevuD7yFLMUJxGpz4Ep3jeiQW3LmI/hqB2QoDleIbuWVBsZ53ka3FKtZkAsMCaZucQRc
+mnmmepn11oqc4fgEhoYneZK0uFb/o330qdMMSmKdPsK3VdVm04iVkanlHcD1AxMP4Xl/t3juA8B5
+rWyarfxH+x/9os7XLMczHzLiJpwLpTMNmzNAXLYMvTKB2D+O0eRCwDD5C8+vUXbXiYuPcHtLelt9
+Q9bfYxttWSTLy4sD0OblbbcSsif1RW/zUZ9/evkB4VieaO+SsCkzMKyWqm5w/CkdTiD8e/vRcdg5
+PiD4MosE/0nkMfLpqp/h0huxLdVSQ9ixkkx0DEUFpGuEwbAm3H+5ZrqCunpIrt+9QQ4E9Xq+H6k8
+Av6RgPMIv6LR454fYovnuqV9CY4HEHqSv4YlAK/D7ae4gnCCL4/8BLh7P0xHdVsTTAqEEjzymFsr
+RfzLeAIJOE5BWzh84K9QOAqWAR+ocric14/0ZaYrVfZhGTAtLS7u4au4vI0uI7yt5FygAcPhDhFW
+YYLh12uZwmWFjq5kWlwq/QFF0YVCERFclkaKevJ0uRvKS8EQCsDnASb3iL1s7tDV/YFXAnHEmevE
+ojQQnKBLeawt8tgBw3ne3UcYyu4Qw2VC7WPN0JEuIOCYPeLW13qZh8h8AuJKUIDGQAbc4gnO+NzZ
+Flp7sG0A8Bp5nh/hOPxpP61NPva8W/5PqX31FVYK8/3NsuBttFWc8Ud/yLWXP5dny09uJ9Rbz47b
+ivOHmQyuvHLbPnoxgTiNWOt8SB6WXv7YS3OXmEax/C1knvrjiheI1H7UQ4lsqUYmJPsvV3Xjs53q
+BZxq2esiEYLFzh9cfkDYl4sd6SlhOyPANgHtGs8gzBDc3eF/B4Cvx7LjTJeICwQTqEVOMw4YDbrH
+3EuqpLvSfjTy5SCdMBz5tv4zfJ3771ixakGFhgji6gjLMBVK9DSMPHk6/qgGkN29wtLEQArXut7D
+0mVMwTBZhRdbgGP0b/ssKdVNg9yC3WxL39ya0t4Xvz770pUOGD7SHbDsJaUh4DwsEXYohuTXxxiD
+qeoaWeb/A4brXlpakRnji/JlPlTVbPHxW2dbBNgnODNObfUbcRLyodoEf8yi5gvus0YkDL8WXutF
+rxcvCh4c/xAHPj/q7L4Fl1DbvwNnzBixchaI7vuroqc/cFiG3VIsnqYervz6IOiN+JZ/XOPjwTYt
+vujA++gygYufMNRnjcC9r1Af+QTI4LIHQUHUJXDEN8DziiiouKyz7rONnPkLX820CruLhG5t/tBt
+vUElcI/jfF/CGGE7LhlR8XLCMDXPIw8U7mmiH/DDimavBZCv/OdUapEPjHjViCvgZDeJ1P5S+/km
+F2SxRcErwMYScoUgF4nFg+TYsHKdRUJAd88SFr11BeTe4uqmzWcYQ9jGdGgFwymPs93TNWYj+IPL
+DwjH8hscnNAIhlyKy3BYhDoI8/YJbnlQGbs+fEqPeOqjDlTW4L6fgBx5ofyK+HRp8Slflx69iKoR
+64gvwe4WBs6vMhRTuSBEB+1HnqLzJNQoJfJOqvX7XbxyOtBr+i529QjNNB4jFa5i0IwpMC/4bQ8h
+0p/k87OtA4TXWnUNv/HcEuBW+zrTcNsDXMivRdMgFdhu1bT0JhzrsBT78b133ncpdD63hGycc7xd
+9ypp8zUH6HNE1vkNhim9tLOOEP/yRzB+PKgzZ/1a8nUcz9VZCq+3C/7SHA+UYxgOII4r/h4AX9JG
+3I66pDl7/YOnpqQ07yD6f4GO93mfs7d9QW6NuYSlBsxx3BquE0vKNQLKD17+Wwm7GPuRN0oX5wHH
+dGlPX5g7pkxTmgP1Ar/TLaIGzRVgdfg66wLzeN53l2yR9hqPiq+dCXx1/ZoUo2AwLMH8tTmbRxjt
+nhtM81/83jVv3T+Y88rpuoTmttpuq9J+49i1X8x96b8/yz+PCe+fQMzx5fcbT8Fh+hmD2EM3XGA4
+B9BKAXCmv4DuszXY0qs62odF95DQVV9xjKG4AbL28rDTLD5lqpJs5HY6K/pfWH5A+HeXpu06kJxQ
+fAJmbDvECoUH6EqPv53HoDyfKiPcO5qc8TOvEPCLjjsflFLgbbPaWOhu+XUx8VQWHR4KkeLp8rAK
+cy9qkKyZj1IzHiK3iFSgdK12bPyOCcPRc30EoDjJx0M/P8GnRTjnhq0w71ddgervFr7HVf16rhl6
+94aulUC8HZIDdBOGRQ5wlgSTsiR1K5fBcCnLUnrT3mACVKsWpeo5IgpOpf/PUyg9Sd3eZkkaj+UT
+GNt1JgBTfkecPMZ5uLUJn0klALhNoTbg92V+wbGu1wsADZYDLtvfBGExiI15YhUbqv6mgMu2QQ61
+g/hwhQPk48cyHuLeW7B8wNw72n76CKPLF29ztV+g/AmSAXTgbRZfS3MfGKfmMoEaQBfg3wfJPfWL
+D+Goq+gbcZxa6zUOZ5ylpRkU4vrUBrbLzpx0h8C3VmQ9PvsI18C4/Eob/+b4/cxzy2vEDR9bBa4D
+6uK+RtlUedD9znLLssexv1u6uh4Qst9cHvg+SldguEOUSac9xMtlnuA0lAwD1txioc8m08NXGP5g
+FTa9kD04JEnWhEY/ItDNewxrEgP0RYgSIrUCPZJSg/gsi/+55QeEfXm2BI10F8gN5V2wW8fWRxDu
+IHtae+8W4Gcojk5VT5oJxwzCmHDc8x3AksAZD4SXRce2x7mSURssFwPlltbguVo7HIcAiQcBK2PK
+0+w9zS2ClCBKYWfeBOUeoaCjoNdgdTddMOpTUXiKCUj08HGxANaT/iIwXmUd9roB1ROD7nGM61Pi
+wcFRbEIvhVeE9y743bt8fD2sagOZnhT+CcjoULxneVVJNdAVLkehw9+A4XZNHTFPjfmMr3YYP63t
+l25xeIhLnUfXrXpCTZIf6wWI28C5F0+fNrdfwHHsN2CJAWymCrd9bxg+fQBEzUWh+pWXF9V9wPBW
+8gO+zRUst7hlwOVxS+aX5TQJZVp+K6x3YE4itLzPj2LEsQJeuQyMU6yYcorSTuvvnuXR9gnMWl1F
+vql+snKf4tDj6lCDNK5rzPbAbQF1H3JZ44tydY+V9z6bxGhvoN+m/jBne8A1Tb+P47xxjRbWfu61
+P7S4un8QFGoqwHEfT1A846eW845/6IMPELx8vM+cQ7ilYVeqKxTzdUs3hKSaML0JsKAAACAASURB
+VDzXCcZ5z0PsTu3XQiWu29EmJ/HvLD8gzEvoQtaJM66orAHv4fLwCMTiltGCvAJbaa4Q9+0dntNH
+uMEuTZfGHS3jCIAR/V1c312A4RKlc8+Vkqp9kjS24QphQ3w8z8ruEewWEWXDooSgh6C3vpdeeZCZ
+pwHJ8Yq0hB1oH8d+XZDuVsc+FxPJvgavCcAdeI99h50b6Fb90T4B72FFZqHHILw3tgOwQZA9oGyH
+3RZ2q12cK3GtQ9GTZfD4c5G6jK2ORkTSsoIP4jLulyvnAGLuwLPu7tB7Lp4xOdOcAj3aq2RT47jj
+x1h+SK/fUmqu/HLatPIPfoVrBAMVHhR+bqdFDGTJU8gWbAjUP6Ku25gQYtOc2WdVxcOjftUAWHWT
+lffiCiFzPmF3n2gf0fCZI+jLcgm5qP4bD11BIffweZ66FXhOl8a+v1FAe+uYWzgLLOfWvQFvWRjH
+sdhH7GeWqQ79HrjfTLBDf0Dnes04rv9xfW430y9YN6CrW4mnL/STb/DGKI/IjwL8gQ/OY/SSORND
+Ha/0fL8cb+V4+SzELCP0vB0PhpQ+oTbjrC2az7L0fVC8dI1Wciq1W9uvAe5dRxToUrjNKPMMvc1f
+mAGY3jQqbFyNhkwH2tfiov+kK0T0J9ep0YarFv0aq981330eEY470//p5QeEYwmle9dwtY0KZPgI
+MEmAK2BZFL8yvkD5ANoGx3Rc71DM+wW8dqQgSlqeejzdA3XRLUB9WBxIaUAhzf/RQWzPPt5YnSZe
+sefruCgLXAC4xXGn0SxHKDJdr0OletIC4FCE7R48vxJ7Hq9jn9K3fRn7Sr8NKueEHNSTeX4J6LT8
+Cc0XawJsAC7V2WFZQKXFiEeAbMDvstfSsV2xFemW4bAKj62ShcjWsCYCupIrgAm9sDbeFZZGJSNK
+jyqVDkmL7ilH/82vJfHxXkdNzY6mxMttABy7c1TcRWxc4rKnkaxgS3C9JYiZI2ou4XSTWC9T1MVs
+D1vtwDu2Evv+4AOpotgItxCxqRSlA0joSQRYBBxu7b7B0oH3+MqcyJhuzeJE2Ee4ZMpj2O/3Mewk
+2QbIKQ2eG3MEo/kNxzGY2wTs2GkRfYDjLGfQMYazSgOuJwZ5vkZUAYWzTkZzPmCYfjPmD+5uAqd7
+RHxUY84e0e49IBMkX0d+5j4uxyyulG7Fy8N9EUCT4FaKS16j8jvrgx4upK7hP51qUD/tR1xaQxCa
+CwUQtZ7yeo3PIgfQrmb15Q9jtPmFb9A7rMHxhhgJwe2Ri9Yq51YGqAfhAGIqJv5ncSHn6FDq9wbE
+tf+nlx8QjuWbpd8qMmGY4Rf1yhsFmYuPoQD5E9h2a+8FmhGALBcQnsA7rcHS8ox2Dw6tl/uPTp9S
+ANVl0vbHUOyWg8jrCghWnkKNAJg7jdAztdQvSYKu55sHzIVm5/wOAAaUrqct/yzQJhSPUuj7N9rJ
+vIaQIqhNAbaGBbC+HHZMuYZxjasgvacDcMKvQ2/bSrcMiwM0bw2OzXewXo+SQvc1BiWFJVrhXHzt
+awWyB/QmNI60XsD9ckL1kVRcxxrJTTjm5L2OGYBl5pDiMr65R5QqLNkBahNVV80ton1euU+d9nqZ
+n993wVfG/tyGpV8AvDXkiyXY/qGaaFtH6w+QyFkFNpbSBzUW+QKzFbj5DK8DnvnLciFTmBSbBZgJ
+B9Nq7PVJcez3C+iA4++7TcyPS0wgPl0lNMsMqPPi1gogS4bWMZJGBHoN+oBxPQo/QmBBLVuAI4zL
+V+UaBB/X49/S9pvVaKxXlOzl+Iqb9x+9qclonWkvDxUN9fSS1yp3CNUJkGIiwlnmJDPibckcNBfa
+TF0mIwGZdLEDbDdsPEFwuEKccwe3eYTjgfrBGmxvj6tk2F8YXgboJZYF2sbVUL2EnilRTqAr9CgQ
+YRlpKPynlx8Q9uXbhZ8wSZV6VOwNfAmARdI1YkGu1t45EK7AeMCyRPgJcs840LFmDWYlrYI2T69M
+iAiBHmGlw2HhKPHUwbffZ/gMn1biyJNS2K4f9XVRyz1MU6XxzBGqmtNPkXo5oLhdOZRYL4GeqDJ2
+Ausi0KGJzvmrYbY12EmBFvX0AMHfWoFyiXDLcISVQGVahkWk/IRFINtnEVAXiLterSoIAmySVrIS
+L3/NvO/uEV5o0spRzmM9QR2VnjZriDWZJazwbDp8ulYwEh+wy4BLce0+jruAt42SHzx1mpCrTFiD
+wy0ipk6z9vEaINwthgv+ej7ivtiKKhnv7ea32sAwUQFCcbLyj98lQNp7k0X3CXh5EN0YNPeuuDd9
+Wa6DsG0ZdjMu61xxA+RMr+H3S/sJu5puEzFzQqQXmkaN23oNmOtl0lwlvLzaXLz585X3Bo63fdA+
+0Oo960Z7fMOarC8TiRtA+4CGwvy11+kjfLpIVN7jHjf93gm2X31Qo3rRPA4d9+w96iiLdv4Ac165
+7ICsl/b7LC4mEEciocGJUifGBzRCctwGyk0YNqBdJRdCHqTV95xDOAGZwxN+hxVZ4rVclmFrIbhZ
+idnQlfsSIB3l4sAfgAR64A/+iTiE0RDESi6i//DyA8KxfLPwS5FNeBQC3AijxS0G4AgjrKQDcrX7
+Dj8BcXONYMC9QS/HHRDc4ySdhUq6NqGE6h4cNp/gCitAoNvBt8TELTwBmMIadZEB9HmENaE3lWHm
+fYjeeLrVOj7vMeNkHuDr9kaS0Jadu4Mpj/qVw+pHM0h8AbiL9z+lBzr03sBXumVYwhLsYaX7SF9j
+kA+hC0mbkQLAXmYR3tumaFqwDyRgY0kY47RJPqH/Jww7YkqPaUcFqaHMh08rnVIY2nj5WK4w/Cms
+kbsjLtIWMPPDTa/H7h5xmT2CfIUZArpCt62M+K9AuIqjYM/eCpi/bsgOIEb1T4hwOHLXiEW+wTq/
+ICdkBWZ3CZpOTaS7RgBs3UVmwIJBK173TI3jvBUy4KOPcBVOvN0o1wmpWSMYiBlyox4mFGc2OK7L
+IKX89v3I0t0i1/cLKLOu+bbiupmP+sxyWIax40GIwHdrTqF2fEoZ2rc6fh+Vt4jjfILScpVm+nGc
+z8t7p4Sch1YW4Dzqmd+ojLAMC+WnmqKHLaKsw/UuVUkC5FflAoLhD5c8jqetiyBYas5gMqJMH+Gn
+cHOLWDVdG5fGxB++774EIlPbIUFp+qLktDgglQ4sIF5j3/I9peifWX5A+HeXaEOIh7qh1DI+Klhy
+WyAc4WfQTYsyCHb1MxDfoRfP0NvSRJ7zNjPEnyW2pcNixJSg6jC8vaPlJ5u/guD4eEajkQhHr9Ki
+C+WO3EQj7Yf48xhhe7Uec0eGsrpdEUD6CLcDjd8ky3NahTsQl3Br0PN65fHfAeB1SStrJQgn9BIQ
+J/hKuUXk9RxiheEYyLB9GEEh/jUuSWuzpkU4/JI34ABsBbPgfqla9TqRmAqUdmUkGYIz23Roq9mG
+uFIJlqeClapYadnh/FbFRzbLBsRZL4WT8iHXs+6OD2nkIMqaQm2C8G8B8DzH6MbB5uVhgezKE+tw
+uyPvUw2CabAcf1Bj0WA4ublLnNOpidSX5ZiOFLQf9Ii7hdg2fZ99fqFi7Rd9ujSzBCPhWKbrhArW
+7gPm7h/VKDl4xuMAtsNVgkFOSWYxWFK47Ue5UJtu9a8Fv3wPPJ1aWMGPTyrTfbQvzjEMRwsZ6uMm
+odndIR+0xrntvMsX6Kakb+VB99geCtqxKuNQPe03Oc7o9rAAFxQzDJ8QnA+VYkD8lXznmSPaYLiA
+YQ7nOJSnGSNi/FCVnLZS1Co0Op5/cqS0WiuFZ/rPIlPGMewm/wQL/YDw/8ry3cK/jgMfarr+buDZ
+AEmL56i7tDWahkjnP1D6yEMMRosem6+MvbXKAbVPS0gIG0ceEqPNPxmKXWOAHAsJV/sOtQvRAbzB
+gyzE9OCQ952dguLlLBu+f1zC19XzZ/va6rPYitwvqLxCvJldk+ICyP1e8iEl9qPz51bw8v2Xg89r
+cTz5gE0heZkKZ35n/nbe9jwrUO4RHpfHPD7TxeoKotIp9oqteYFnGlUoNrYuE5wEx3tt6F60JYDJ
+8v0CdCNO5hHe81pVzQo3pRiVf4HiS1z015jTu7sj1XY5ALNLU9hslw80e8HgfzmIvTKd5vrytjXj
+F/x8P3chII+hp+9/2kbb5X7Lai+7PJVmrK2vz7BUmupzPBVXyKmKSxpbNk2FikOFCLDfUFn9Bv1X
+03J32Sb+HecBiHmRY/HXEy1OFVuXj4BH+gNzvlUMovd727rf2G9b3++37e+3H3tYdZe7Un71cdNc
+3vsyMHVAHSqu6oMglM5Fu06HeHmIC9jPbaQ58sUPBudvT8u4znqhJayWh6EppwsTkx/S35hsH1+x
+1sZruc/5S/Ha263ZNaAz8s95FpHsbdETVVaFYVZczd4tvk/mHJGsh6a3hpLp800MmTfPQZFHXF3z
+hJAF4nVc4QP0t/v8e9zzFxHvbeTpmOlAk6tLYC4TKXfLoi+cZ+kGsIUaVP+nlx8Q9uW7Ra+A+8y5
+mCcn+Qd1PQKMygXDkeS4hn56PgrhL7mfej4Qz3V+4l48wvpgHlXPiWiGBTHYxkWquuqMJ0QGYph4
+SAUp9i9/xv/S/SNAkMMBFnwMBJK071nt0HwJryOeVq1ZOCJtwAEDboTDnWpzWoJ/r4ksg7q3vhr4
+CF4c5/f78pUBuSD6Zi0AbsAbDxo3y0JY2lTKIqdSFroE3YhnID7iCnRzoFwD3hsk2wcadm5d0bc2
+PBd52H3qF6EYCoL7o472NLdjBM+hxMrnH/ct5rYeAEO1Lij+cth9AXipHutSxUt3hmvdEN25LzmI
+rBohzfrVoTdh8Dnt3jZNnm4DrwCm0KTR9aEGsXEh1u3vqJdlbfgvSLbrgP4X6p4CKDMccxaLz1ks
+An0LJDLLkBv7AMGu/WuAdQPh9MuJajfhpKD7XXJAb+RRVozstz70dvDldb8HFPP+3gXIA5KVAJnB
+WONrf7l/gm3dpmbR5C36/3IF+AzGBwQxmDc4n3OKa0/31bUjzykDCiTbW7RtsKtrQdfLzl2K18vl
+zsvBlvy0QQ8H2fZT1xJgI97cLrzXG/XouRzi5iMpx3WcnT7QBcEWa287PVVaZAIPdWTU26OYZFYs
+2FcfV6nivW0OboWXt6T7mUD8u5AhK0PjKfqXQL8PvPd09lsaio/If7m+0QUqK84LiqW8L6bx7g8u
+PyD8u0voSvRVgITiiV7XP3uEujWL2t7o/BIXT2SKeppiNV8wHNc36M1UeTA6dNxXCFgtJRGdmMIJ
+k2IKNts3AEMBJXAoC1uCnpcHO9I3AJYOtHXtJxi+rXWcoSXgDyDwpfgzrM9wjAG/GGseM1B4eZq0
+AHvcS+KBgOGWwLfFE/SCwgOGQyhtkTY4LuBWx35ahyN93KMLXhvwplC27OJiDVYk8G74l+tgkKxr
+Y+91upk8QjGn+JRGvKYeQJe3KtTR6ljAcz2IoLdhINtxbjEBOFQmIEoPPDog2OF4EQy38PaVgXgb
+LJ7QS7f6JSAjAXG5Qostg3DZ71jFe4klu1gbiweuaMfZzgVXC7g9UG8zLqjYhzvUygvpH5ySrIPN
+AcJauVMqCO1xZtXbddwHAmpYgFUhuoD4KqX71icEx1YW3logvH8F+O4rFL/9mO3vtBi/93tYih3u
+wlqsZb3kz6IX1AIBufkg4PeWMtwfZpiTGYgPS/BWqDhkSXxym6yqBME5ZR7l7Tal3ASsG7CDdMB0
+IdMdILyguoDXAnRBXy/Lg8NwWKsPEK6fuILwkoVfbwdhlWqhWsBrD/V07EibGtIkSIxdcVmSOkwm
+NnuaAcDq/QP+VkQdeA1kDYhtRh672ibNuPPXpPhEzfilH0CYH2C+hmL+2igKhEuZu5U93vIAuurh
+AZGvrA8fnPuHlx8Q9kVnh3xKF/UNdB372wtZhekyE4Ifofi27wPcJgJoKHxPqN7g2tMhqhtWd6y/
+UjwdgHldIcUuWcvp4hJ+ub/INa5Z21KIRKl9D3onAK9L+mkFfobgT2F+pc3g2y28rwYEDL4MDkKu
+JNPaS0CcgBbK4ozLeHwDgp8AeLhRfAt0BxDX9GqeTnzQDX3g5LFtH8tXnW4Cb+CBXI61R8Yzjh/K
+olxBch4Bv9SGOQ4RV3BbVmFfA3wpzcth97U31tpYvp/rMmW4ubz0GXpVByQj0nodOnAjLcJm0Uul
+7GuTU9Qn3942t9jnkV8i+EvCIiwDgtGs3WV53e6T7IXtv6ER/gC3FqSGc0sbgV3X13h6Z/AVSeUu
+ewMEv9jbXs8vexDZWw1w9wTd2u9AvOlYWIcLim/W4QOKJ6AgILc/vES1Rfs/IQZ3uIlZP1Ssv6qU
+dXq6bGzv0w2eyGK8P4FV9MuqN2tXBagxi4LKbjAM92PHi8G3rst8eZqhanaGmqHBwq+3W1UhbauQ
+AmCNsDykrYfMdIVMGaKI6RbvQIzKuKo/sJlcTYNBdsedULvzQXJZfeyp5UKmrehUVVdfwPAdiPtx
+iX4q4jNSmDDMsSUIy7BAdwFx5EXg5zbDxJ9bfkA4l+8RbeLftP7+jjW44RzqDUnsP0LveILWHpQr
+BPPWIRjqHZYtyX2rKaLCwtA7qvj1GCa5LEOXxW+n9RLoYJHpuhWY07SyaiVLv0VxN9CdABxxNwDG
+h2Nh+WYIZvE13SECiF+QBsjN8psWNLYSD+h6gt8b+PLW/Y4BA+K90SzDDXrJ/UGBfO1m1t/tg568
+LMIaHC4OPlhOc2YKtddhzS9Y66tVlG6S77cNAo9Pr9zikeGAnI8wrAFe8UjID2dnuNqwuoodgKzV
+D5prhD6smC4RekBjuUZoB1+G3rhthmT0NBHOmRO2wS8PCjsg2Mu8+mNZugyCra2+AoIFzT0i/Jwl
+tgOCbdSlbwE0IRI3cglrBT6mS0G7BLoVIqtgOKxu7hah3o8CaBoEb4O0994OwcNFYu8GvtMKnND7
+ntbg+DpfWF43AeWHNdouFwFJcKX7T4tsAjNDsObD6d5mzVTZ7lM7LMNXYNrNKptW44d8tyrLFuXb
+JtMIhuOLPQ7Dr6VmDb5AcFuU2yzDdg1WfgcIKwx2HXCNsw04t7/diePi6UVj5h1J/ZE6RzRlRLlG
+lMblDDcrsSl2Dzv4Clt1V3XRDez41SXAAcNhBXYgPdrV/hJ4qy5PEA4IFrXfF7WBsGkRXjAIDouw
+EjshIJi1/Z9bfkDYl+8+g+SIbLn3tUyHC1pLP1ZVPiwtHE5AJmHOAEwA3cG228Duyt9vJC8o7Wjc
+Y6kZ76hS145Xv+EWAP/tTVCqIgS8BAZj36BCxn4/3svvEwDfoTggmMPfAWCA3CEUDYIZig+XCIZh
+sgyfFuIAZSmrcAAwLvCLCwyTgF9jG+J3C/LJ3CyyZRW+huHuCwstnJ9XJT/gb1uDdfoN43nR1tz7
+gbQUXo9S6Al6BekrP/uFL9mWojxRAJxtKdonlNpXtb+l1ZanO8T0D2aXCNvuvg6LcIJvygYLjBnA
+LulquzSmzNpmFdYdc3/VSic1T7/sm2H5sjYW7T3bPIAlfdBfWMkLiDfMPVguI1Ep0w3q8HgMT8fE
+v5knPpZBpNrCEkDtjYUddwBey5R0grqB8NYC3gDgtATvYRWmYwXFTzBMUBk+w+l3uxvAgmE44Jeh
+WKOsyAr7CNUOW+EaoWwZ7jNHdKuwP9xegL2lz3wi7yHyV3akLsd0LfePD/cAtwZDAZi/8CvuLRv8
+idf5JzSLE1uDZeG9dkJw355xVi72zBYwHE/xqf+kHpJT13unCeBl81PJpKIL1QBnb9aezG4z6ivg
+1sMwCG0w7IBa7hF30G0PL1sf0vUHnCVoEJwPMigrsFI48pH58j4oWiX1J5cfEPblozLmdLOOiiHP
+hGQh7nZgyRPiaF5ClcLjNyicD4qDFK4QrBQ+Ms2Y7F3VT86J0T0zSqcI1OcLtCXGn5jFlGDY4fEG
+whOCr/Db1mkFvlmKn6F3Wuwky/DZ9SHuLQB4poWYpS/E1tNAuRowJ7muWAXkU9mtxQnA4ClmQpjP
+/edjBwjDPmxgggnDQuzgmy4RYdEFhbe/prwMllMrj2419kEeFzCGKriJk/Zu/Yqj2zL4tbf+z9v2
+/kQDrmu/z/LS2+VzOw3XiN7OYqaIF4C/CIjXBGKwS8TqfsIrgNEHlHnDu1l7c55mhl46nnAMuFsE
+Q/AmoAhQLQhu/S7LpQAmZ0TxcqoBczTsKOBX4av4LBKbq53qBCTuLuD7nf3I9Q6fxQBeA11V8dfX
+cdzWLG8R1CdqDU4DetP6y9bgWzwPltu0Tz7C7BIxX1+nRS7+EoaZAauCwyLM4FkgjXbdOXNEfi47
+Z1rYbXvOcsGg9AxZBcB9aXqB5Jo7b+cTnugr+5UuBZiNs9mK66rxJ4IFdon4ZdZgtwhvBd4bBcAe
+fqtkmOMhqOl4FNa28j5ChmiGE3pdKndF/xR2+SnFyVaHy8dbGGBuSFqDF8xCfUDwEix/uLk+qFzA
+N9vgExgL6jfgQLuqzM2thWDYXTkyb2QN/gHh/yOLq4asvNNbtlck73XFGVinlMY7x8kB5TIxAFj5
+GGMAXaMpj7hqo/9ylYhDKUsU5wNAdEjy4wi3ATvPn3MFJBQ+gPB3jnPcES9jezvWIWYCb9xDswgL
+wQIB75YBH6ixPYevMK8yrMFSljNew7f4hFoM4D2312N4gF/YK0f+/LLBLlmHD8twh9kE47AK8zkp
+qPvgOnOPQH6COVtyMGgj4Gpj7QXGsQRQR6Ln7bdhWXp7axbhW9scbbY/eJEVGGgW4ZpWbeMV8Bvr
+1vIT1o21V855e4Jvlc9hFY4HlKFrrR2TZmcIJosdF7x4v7f+4q+PTf/655FP3/jWD5QeGNRcPdzj
+mWrerYdybwt5D7NBzLi53yqwgDgqW8dxOeIKim3WiF3W3rYltwmeRm1C8p6zR9R8zB08J4yUJbjk
+O/WViA9C9HKYADyhNwbK6RZ3k+AZI6Y18A67py/zJV3kpSom6wD0UJXTpvnHPdYi4oXipS8LvrQV
+Qcp9AqyyCJsV+Jf8cmvwG78ChDfw3tqA+L0VawPvRWDs05Bhe5v1TqgLWJv0HrVp0/UmX/j9a8S1
+ekop6NLKfdjDNULgMAtpD7RKALzE4dj3VRyC3W3tBsDPdTbqnc5f4vDr61quW/yONxQrrMB7ZUno
+6ufZ1BOPAv4fW35A2JemeL9K51T45MuY49ByM1AtOmbIX4S9rtInHDf4RXaSua/aoTmSNW44AwOi
+pXSGFAQ2PvB8g+4RqO4c8BzlE2Gh8pige433grzBRZapPENuL/Eef/gLM8yC3B7ktAKnfzCdw3ET
+dh8twwEHl7g8hgLgGkBIMIwBxgsfgdj8c8VnathpAW4Q7L7ABa5IkC0Avll3w10i4OzuJhGWqa0F
+zpqKWqhPURsXB9fcP5PEQNE48MlTnreZjtwk2n4q4i/gN9vp/bHY2p2UW4Te1gsEp1tEWIbdUuwW
+yj7zwwTfCm+SGze/4u2/dUIw+/HWGiW1qVzy64QBwCiXnxwsF/clYRE2qcPW4aL1VfV91Y16j5+w
+jDjfcZErE7Nie1ymBQ6ABoQswgTAHi7AddeJPYF5WoIHDKc7ArklHJbXuQZCdYjq4Dm2DXTsnsoC
+Yq4gBegKtljHfLxsOcxZJEbebgP9QHmMug5Y1QBgCfhdNq+xp47pO/Gi9kGv1gt8FxZsTvZf4Qss
+b7zXG69fC7+Wh98vrxsD3/dWvDy8GIb9mIiFMzPbZcpWn0GJrcHnFmklrrYpdSMobeRbh950zwyZ
+CfsgjX0oyZSWUDsOdwVVuKuJwfAeMPsZhsfxeZ4A0JVlvpNhTE8ZDC8LL4UG9AYk+L5ofMHyzy4/
+IPy7C+tbTE71Co0n2o5tfdsgjqmSkDwvzlBcwl85fuiEGmCagfx90gl1SLkT0n1KAW5bghscxtTv
+qSBY8vy4oYKFvo+ECx37QmXVy5CvVdOr9e30BX4CFF7SSuwF1XyBZ5yUzg7r8W2wXANc1HzBS6S7
+Sgi5SyAswmwB/iKMOwQnCHtdbkFahefWXCTgVtvdtuUOEXAcVmI9BsGV9ZEV8QMQeztqsJJ+N9Gw
+zTrVG3kwT38qsyhFe2Ib+zqPP22F2tgE4slR5AvYV4djpfmDcfEVPgB547UXXkvTN9im71IbsKVy
+gVpFG3+o1GYpTcGxH3fIWnubyYsgOOYh5nuKiRdqLvGA4u6ec/jM5xqgH1ZhM6XJ9vcx4Ws16pZq
++AGOuc1cj1RbSCsZWmVqCiHfB0aFO0xPEGYAzn23Ck+rL/sFZ/y7IFP1tAQ/WuoCfgOO4l7HMe6L
+X61sFQbDUHd5CFDP6dOug/ru1mB4XriuxOsj+hskpn1cWKL2wQzvTzn5h8KE6ljYAsyW5bAGr/iE
+sYPw+5dvHXRtRbklvdWswL6+y6enINibl31QAh1+Xa7E+7myAltHktaoz21KJnUZ5jJT87jYQ4sI
+1lLs9GU3xSwLZREWUB1S/R5g/GQhDteYAuF0h1TBVntoWQTAlsdVoJBKtLT9j2vEv7zcjAjXdAF6
+GUPq4SazM+r28h50hCzCAQaF1/f+QaA8MkX3ow65lITvtZ0zKMMVhNbn6vo9EUgwQqQyaVaWE3Tj
+OjEQoJ7kGZD1iLOn/lGSUmnmegPieD0bxag4XSRavFyswF6Y7EJxdYUguK1PbdfxBskyrMXo06jZ
+Oc+uEJ/2zXIt7vMcYOzWYUwrccHwblBc0Ntgdg6CIwsz+w2bpTgsyr51ZahhkVUAPiCzLLXwNKPN
+tlap1Fp6PMOtjv3btoadzrZ6gi4QbXse89x4ew0raHOPIPA9LcGrhUXZIly/MMGXp0ULzpjgG/Bb
+x0zZ1+eHzToc1vr87C6VSZRWfqJbxWAl263k8YBfsxCpDyAMi/CGBATHVGUpJD4oxavMfmwcfQlr
+b4T992zrSCIPcZR26ybrLgHwAbg9TaZ9zynTHE6mVXhPEBlQGZWNqvs2dZo9cAAAIABJREFUhiR1
+hPe1bB83AL6F+8C46RpxTsN1c6O4zSV8qRruaSKAw6/6G4Jo7ikKXnXe8cdyEB2CX+EX/Mu3KyzC
+ivdb8WvvBOC1HYD3thlb3gTB2FWeghw3kQ/IIRvIEhy5j15cmq/dmUkiUtohu0JKwQfJWS9SB06z
+EIeve8h++8JlWNpxracE332rw5sPsbUL+ADqt1oPj7xsibozP3xrS66FzcHb9LkpHLu3p1ft/+Dy
+A8K+fNc1opptPZG19puputrsypSPaB3RaOT0evUGrSTYVLULvJkWWofa8ZvCGNeIef4o5+lJLNGR
+66azMycEU5ivJOj7vM1jgTR0bnvtfCvTv7fm7SKErHYoFgIKDMuwVHVcLcIP6wG8cvoK908yg4T6
+Le60FK9Fg+VCGIIAeG2H2tXAVddKi29ahNOSS9A7fX0TkGtO4YBf/uqcDf4m14lU4jKAWDv4Slhy
+K06bS0Spi1unvB9Tgu1wwaj9aJNXK3C2oUrLfVog7l0R7VZ8ejQ0K3B8MrmA+ITg5iKxfRopWMNr
+1l5vpwXH1XCfXCLymG6fQs2oOeE3IdiuG/JJrUoMurzd2YNWtEPkw5tIPIQq4pPTS+M7XbBXoggY
+FjcvR13IvVb1VtPnY44XU7WEbEdVU2EFPraRHkhwDhiO8nsnAL/TOsxA/N4dkk+f4A7C4QKRQOJ1
+coVhh+B4KO3uBhRPfc3gKs77YA1WsX4Lodki+mC+6RpxwFTA85GGZSopHRLMEo1oC+Jb1z6ph59H
+DzPVW0s/uBx8S8wb/DaZSBD8Wi9zk3jVzB+/3opf7n70623++fJWiGzIXpC3jYwTsa0q8PLZb8ww
+UA+EpQq7JVhkwi/Orfdfa4s0D5NyK4eBqAun7bC9XEDtlP/WbvmrpDUv9ZMPOPukzzcB8xyY2xQM
+yG3cgFnxE4K3PczYw5VPW6jIexIH4mFz+yPLDwj/5qLcRgHi1ye8KkAssz8rzQG++UP8oxQgCOan
+++pDI4MH8F7S3o4JhgNgRachOKA38i0U8PNiqphIVdbf+uEO00pp/fg4X3KvgNiEy9hv2+kjLNfB
+crEcFuHKQgGFEFB4mUyLcJs/OOPQ4LVNMyXnHMNhBW6Qm8dC4J/H7q4RBMBeEQqQiwRZhNlqOwa6
+MfSaFZh8fg83CRpIl9cjFwr3dw2wA8h1QdWUHbXVaHrcXK0XDMClLSuOJDdKc55LVe5lH+02FS51
+j2qPBMUabdfbbYDvgOA2aE4vQOzgG4PmwnrqvcmhQttguY8uE6Rvl5+rgLlGOISttNjZVmAWn7Nk
+7Y3RUlPCC2FxKmmX0ySiYFiU3tQ484rPgSosd6TDTkDT7Tn+JjLvYpSBNvpyShvSzQOEub/A37A4
+FE7ofQ/A7TCszQ3iCsLpbtAHnHWL6i6YdSCL+uKHgGgIHYjhRpQ4ly194gPnwv0moHdDLzDevx7H
+A+VGfi+D/FpFVs+hjhWyaxUAK9+W0Dib9/HfZi7aCcRmEbaZcV7rZb7Bbgnea+O93vbGZW388v72
+670NgmXb6DkIIBt4u9uI5+XlZZcQHA/A4ncVbhKhw0qRUo96aLUs6/jN2SG7TFZuAl6e2lAjP4Kq
+781t6wGK90N8vDUSEADPrcMvFnGF90OlbcrnP0/CPyDsy+iPz4vMZnqCbyTsuDuPUOfQUGlDzbA5
+jBVY259wXHEFFiMt9yoXmke8d5ywCks4/IoVQIJvbuh+pV5rNRCWKIm6kV5yOkqqoIIz1kv2u6sr
+5dhqV87MCDxTBB7A95pGpuVXcLMCx7RSNaVUuEVMi/AFfL0M+/VnmvFKUOwpW33cRXOLiI9geBhs
+EcawDvuI3uYC4ZDFFqkaJEdKFzr8hUkQo6ybbK1rEOyb7BqjlfTwHYZLaTynyd/m9ifVCkk/c7P2
+U6r9yiVs1lAcU6YZ/Br0vlDW4BOGzSViybYR2BCy/LqrQ4Kvt0+SEzmY7jhuF1hhDSZFF9Np5dRa
+UMdVq8MYypNvG6AFwsEzR1/U7HcC0CQM8eq4VqWy/+8sXf5oQm7UFOlnjGMZX8fCejsh+O1uDXc4
+1tNK3AaZlQU4oTIgMuMIRsEPLXWnHoMAYFGggWg87Iz97XErZID3i7QK04C5mNu4uXZcLcAnRHXd
+g+zzEOTHFRDw5A+ApaYkZUPI9w3BG2/YXNYLIm/zKX4H+G63Am/s18u+8Lde2Gtjv2p+59f7jfXe
+WO+NX2tD1huyCoZF3h2Cs1zN/3apWJtGtH/N/sDK0+JDN/E7aS2xFNNQpEiasky8hsQfvL3fKYEw
+AIZh8bKe7g0dhm8g/AzDpvPNGzgBOK3BalNyircLd5HAXg2IReMN2n+zv39v+QFhX77LwSUwe9Ot
+JcR97UWIAbgfDeALnzlaPwFw7hfgpiBMAHY0oDTPsEzH+Il8rT6dAuANn5NeQMF3ZHXlk3f+WOhR
+ynzeDaCfrb8MuU3peplyGvrJtjRAFs3i2ZSVdJvw7dOUaeUCIQ2QY/o0GzjHYBw+xeRjeQHemz/w
+PY1nOQE4rMKwT5buneGwCEM7BJsvGFl8yT3iOxbiT8es7QblhotC7iLcFUIPNChO94gH0FUpd4vZ
+nuK6BxTHbxYX10T4sR/oFMoljvHAGD+qpvTqy3HIadNuH9SwTyzbNGkFw5r7sq11HpZfNctMlN15
+/MlyzGB0vn4XB2C7W23AGABsD0CSejutUa2v1YN/xqcxIOL9wTtIIpe/qyD5PCWYjXuQHoeKA2qK
+WOBM+2YQ1g65AcO7wfCMI6twA92AzttUZBeYZRjOjkN9yNNJ6AP6myCMsArn7/JUbgW/aeW9Qu8T
+BFebsoxkiHpgPQBZ17f3dwr/sIl6a1QBdJfsdwPMll1A/N7YbgHee2OJDfrd7429Xg7Fb+z3C/tl
+ftzr/Yb8MsuwyBviUG3zpL29DbytJyigL8XOWV3izZ3bkDDkhFCbb3eNlB/V8qJADIb1SK6VOvqL
+b1UKKqMvIsrUt/3T2FGf+xmI8yHtBGKI+MA4ezzY4rMKYfnc9Q7AAnflWHUvGixhV0ijW8pjUHjK
+8f/O8gPCuXxPyF5nUMjmO69ReCYjPhukzifC0PLjKp8AWEHgQAJmgO+E4es5vk1rsPrEiUvcod07
+l19rKjoWShDYpNq3JzyNm5oN/UhE993hgkH2+P2RhiGY4fhUgLUy8HJllIVYGwSHEeM2c8QxlRQB
+cIFytwaXe8R/ui0QblbhGJ8AFAADQLMIA9juH+wwC4LYTYDLfsMNlh+OsctEKWTvFIq0+HTo7V2A
+txY+25RS27kJ1rtbhIedcJsVeMAwwEBMYBy/pgq4Ykr4Rc0d3OK0+wXXdGrbAdgs/Gu5XVbLzzdu
+q6ZRUz9OjRqn5Titwr4ur48CFyRElYX/BEf2mY+yYZYtK3lZsKKMEgDIPztOvkkGKt0vlrjeWB6i
+7lDcv5eQqxIIExB3v2F1QO5W4jYYrllXvV8N+N032ATXT6viaHII2M2HFwDTCszhGDRpTkxWfgvj
+d78BvreBVfvym2d1hRakWl8mqERXNeZlIId8gCoQjgew/d5Q8bKO7VoOwQ73r50QvN8b79fG+vU2
+AF5vICBYAn5DA1kZb/+0817uG7xgMLykfSXctkr9IcCUXf88fjZOBVQKEA8ZlzLTHvjNGuypGIRp
+Hw6kDLsJuU/xCcRnPMScUezrkZpWYQNggmBRQFa2WQusErXpI3yX7mfcf2f5AWFfbn3yW+ddzfiM
+aBXHFuF6JQKw8k1LSdDVuGpKYJLGyZQeH1Axw3DF6P9aGK7sojDUhcoCaISC1u9n/jXdDfLVS25r
+bR08gOZaoBcwyZ4bkMGA+wF6EcBrx6Y7xK2WeGnTpE3lT/DL2wa9ggNoyzXCobhB8PmBja8svd9a
+lzTohwAhruAwrEADYISP8DYANisRbMTvgNjbYLkDfjXmGaZjNAAvrbYNhjWtweGHllZjifYaD6bR
+bqKTSMY2CNZqT985Fk0v34CQIsmN9HYUv8xvNKrdkTVYyVVCu0W4PqaxHXwrzj6FXIN1bbBcAVGf
+HeK0GrM7RIHzBX5pH7P9z1X6fki2ggDfutKUkCEaZWadSGKKvHyFEe21Srj32UsPFg5oXWcsd6D3
+MhpxO9NqO7a33iE4Ybgsqg18H+Ki7Pnztpvq5LAKeyXWg2QBJktS9bJny3Cr2wjveOCN61m/b/MH
+e95qVotyiTi/hFdzIE+3iNQ9VEGtlrmPhQRbdJ4L3XLFcf/ftAK7BXtve2CPAYqL4rfWfliE1y8D
+4LXcGuxTfuXj2jvrZTsEr6V47Y29AoYNfDP/oS/znvQiM1DtlcUSHVc1I1XIvjzHZZbxsORbTx1y
+i8OboPYrID4sxGk59vpcMGv72yA43CHSGryi/lev9vDx9lVGafyp5QeEf3OpOitFVFaQQqtbKPek
+YM7iopF2iV3giykv+k4KloCDEGKWLi3BWgITruiezgt4Ne4wCI65Pi0sOUq0Q2d0+D5jwSXXtO+F
+iADwApMGNy4ASphQmY71ad7gGT+nULutOViOhNPT9tESLNM9Ij6aEfMIk4uEECCDrLsJ1zKu8w0Y
+5iaU8Os+v2u7lRjNJSKOqaJZhaEn9N5dIQKmOhBbHExxLn/tF1Cr1QYZfJslmPnG+dVbeiWQ0XZa
+ZUXbDxKjiwcE07Rt2XxDNw+4A10V3H7BPu/lF9sswrjA77EfbhLhIyympKMc9YMvsJc3W4W7O0SB
+c8IRahtyosXNPhL1RDBMxVQPD7l4zahUOOKz6OicdnKXkofi5Kq8KlRvT6h0eR8OlubiQTCsBb8B
+xD5fQAfhsT2g+LLdtC3oxQHAXx1r7gaeN/H+lPUqYRlmAAYSoMP1ya8fAyb3tjbIoNRgaQJxnJcA
+xXEF2Vz4VwCu7gogHpIy03ZegnAMgrM3Jvxlvu0zbxjw6hn/qvj93li/FiC/IP+vm0vMVxpU5qj7
+3PYxC+ufyLchAD8MEiWkDutscGhIEk/q56nr3arvEoLqD5mW1uIrH9xbwkeY623jAOIDlAmKyW9Y
+FOQWYWtYg7csuK0ji1Pj1Wz6B1ed/0yf9i8uE9A+L6OiciDZTDMtl9K15wWAp5V4Ngl+egoBFnEJ
+EdFrQuighE8Ivq/irCevfHUuDMSegbAYM2CGEKiZDsT9ukqBMrwgfloIUwhyBH5fYIzpDxO/byGu
+7XfqvfsKuyB82ObcwQ18OxTHfMEBvekrjCeLcIffO/iOY+ucXcK9Qwt4wxIcA+XUFAPiuALNNzjb
+yxfQG8cyjo7ponmEq+3ltR2Cap8UdlhCqH00q3C0K/IZrt5FGoVheMTVOdQnx5OXwXDtMBxXMBq1
+P7hpvJWY8wUz/M5p06Tvh1uEmqUYkHSL6FbfDrh2vKA50s1zQ3ZEfYHDrlytK8ZRVH3FvM+Yj/N9
+if5dZX0JXy/Q6yB2tB29yWDeDaLwFpFlUfcTAzoTeJUhuI7FgNCtA4T1BsPuEqEdfPncCE+43RSu
+fqStnlmOt4cVF+PZyuPWs3twn9aqZ2885h4hWEv7ILiv/IL32I41puIrXRJ5FEDiYUiqTqEOwQXD
+cT4AwOfM1YBgsbcoNge60jRvu6agSyDW8sPeZhGWtWAuEb9SD1pbk2ozCIvwwmsvsyb7w2rM4nO8
+DcFlX3S2UlCBgMWZPfjGeyZu+SXJzH+angfHQyjv3aG3u0Gcg+Tu4Aw1F5qXaFqFGxDvAmPTNWEZ
+Xtnfg2P8PfQfXX5AOJfvPoUcaJr/yzl9Nr0JZWfchOKK7+GZA4bmXBguEm5DCN7gmLYwYbpQadsr
+tbhQStQOxgaY5a8Vk+qnr58LEol5W9Vf9cCszApFWNXqNbiEprZjWq4Y6ZaB+D2kewb75dbHLApG
+bbS7JMgGiKn6CPHcp3S4xJGv8Iuun6tcVszVrbsj/jr4DoqXSgG2FmzXp2wJwFXNu4Wax+PW76ni
+Nd1YKp7Lx21MUWZxLOOU4mJUPo/Ol+/ly9sl/JzMj5RSUKX801zEQaoaZBAkneFodzjOycX3W/RQ
+dLw0GM5Sc9cIUF0eQMwwTEC8xV/T7uZulIAL9gW2iKvltwpzzCKhftuJwF5MJROyPdCxiov2U+2k
+liOix8dGL8daocpw9z3rJ5UqH88qoHYDf40cbYEz4MeV4qtYq7y7ZVbLejanGNuXMMf5fly4rPL5
+o5Q3jtfMq92m8q4HSk6TxaT2tVbzL3U57zJNlO9lj/tSiv8CiscKoCyX6PdRrjP1diXkfcKwn29R
+AtkbippH12Sy+ayq1FcxV4Cxw/3m+vDp0sDAm33L3tL8teth5/Va2LvmJF5rpxFCBDlYjp/PssVK
+1/XzGe721vOAXmoWCb7taLXdMq8pHRqA+xGEKW5Yg1VtKsctNpxoCzKsghxmlN6VHgbFdQFym9D0
+n11+QNiX38HgCbM1H+Z0EyiLaT+vFCEyTtv1QWktrA/xpXTb039kOF8zaO468/V0uaQarDkmC2FS
+SOcrtnGtOJvnp2z9kjti3EBECZCvfNLhn18128KzJYh/N33Oy2ufeF7kqO8QJMufWCxu8X3UXeY+
+l0imUYyBcgK4RewlC39JfTb5LwheCvylgpcK/lL4fn1VLMJ/bXuifsk2izB8qizYaz+zBvqcsrIN
+dpcDsQ/UsCl0lg3MUN/KytdkJYk8vLsy5DWUYqRJJflJwYdgZQV4KMNS9qX4vU14g0q00oNPUpHm
+PvpxbmDVq2ZLJ6tvHmZ1UuH2YEsk3GDrSNklSkiGfDBTh2Bo7kvEhSuEqK/bH+R2tfv4DSq4ANsd
+BdkUzN0qXJZGLlcCXI0YVH3VUf/pEaNV1nf5clv0G0m0lX8/p2QogHrIyWS9DYjW7BkCJfeQmnVD
+4FZzTpfhsKjf+5FewkoF39oy/UaVaZVrydnzwWOuXhRZVMcbu7HtcSYBYxt6qbZxjuYajUe4LEAy
+JsNhOfQwlamCewt16Oh/VHWSD6TldiRxYMEGqIUVZLuzLoAVX8m0PQRsrRivgJjrFvnRDV5fa+G1
+BO+X4LXFp2ELABa8fN0ZXvYBIVTfRxrJKOcUV5qd41H3SIEnXjkfwKMkeZYKLt7S8TN9GMyOOCJX
+lulQ2of2/f/P3rUtSo6jSFDN//9xm30QAQFCzqye7pp9OKrKY92tC4IwRjLkwGp+vz6Pa4+f/Rbi
+T7sfIOzueyB80fAOILjm2UAZAJjLQuyeQNfI1kgpj7XyCW7PThFoEAmN7G0QjNKxnEJoctjr4oWk
+kouAwXKUIgZn7b5sBApZN4Fg8XFUfw221DazMpNfuuTRDSb3AewmZhUMwy/QFOCcTGpZ3Tlu0aoi
+lKJMBcj/EQfBDsgjLHuxQdP7H9mAeP9M/mN5jNYvB8RLGPySiYQ1AExj0MdkPxxsLUkHssf1qeED
+EPefa7HyjUEDwC18MMagFYk6GQgkMLBCPsjP8rKkXRyD20Z8UoGxL4QmdT4C4i6tJAU3/qo4CBZr
+4Bcb4fau/aVkD+yaMdVHlOqJDhcURJpeEXrwoHVM4Bcb55J/EOiVPtY9jeelw+Mo3Kchk95m62Bm
+PoLJeCIuhT/nG5lh+B5v2wa+Hm8D0I20DpA9Y1sTloRLcekPWmaQQWkBKKRUUcLo7fRTSdvmK/BV
+ilMGv12GNfnlXE8JCAUgZq4ZIDg/093X/151OZ5CbZ7oQgkNq0gcrxec2z8IpOggjvskMCxuurDH
+0VUgujXFj7d7mcr6tfaD6AMgrCM4XgGCl/yl/LU6B82kEj4QQ/CF4d1wYTN3ZHIDvTmaMqRZ8dsQ
+ro9ZHtsBsCS/OPIJ5vqp4gKksVJ8PCu1yKZ+QNVLn/8t9wOE3b0RXM1Xf8uJ+hMIvmuHWdObCz6W
+BcwASr58RZvx+HsCBO4ZL48URhY79ptUFYaFAXAJ7HYArJzi9eEV2MngSNBoCUVfpkU+aYHXUjGD
+NniDwV+HRti1wa45FTX5S1YR4CPAPcI5xj0c9r2StsAMgAF+oRlm8PsfhUZ4h5dYaAFTK+yv3WAa
+AVtgg022a4DNYid1fKhgArPtpy64P4JmCDX6CEMBxGYBgoXj4idFMAbYCiDgo07EzBgDiZ3WiUoa
+k69pcdwQ8sSCcE+AYS6T/vZoVvw6xleAEQDFJAGwg2BVgGETfTD/+bC9hB5/J3TkYzOGI98OLxQn
+8MUj1+FthOxM72WKi8m9uVZqzJr9zFHuGeeHHbZFrTRjXG38Qf826GogONrgoDC0wTKvK8m0NEvL
+vJXuvS0xHwAd9BBiFD9cUR0Uo9r9ktpfbXIp/Tb8aESdR3ziJ/kAjc11rhmWUy51Tn+GSesPmata
+97Es2ajKX72XsIjk5oenhJfvWxATUQK565cSIE6Am9fVwip/uZLm13J0N4LeHke9Vpbo00i8uGPd
+GPn0msIUBHpNesp0Y9qVys+txPk+EMgB2oQZJ3WYbnrQPQ2POSj+AcL/O/ft0Cfp6gCC9QDBsE/l
+fGy7WupUEWhnsBSCASkvGc9juWBwbuBpel8FTBUfxtkKyaMkA1sVIX8uikPWsgw22SD0cGcctONH
+axX995KPyrM22FsMepftr3Ox1hcb9cgkQhwsSjGNyOdrE8ABHgsNgQQ71NjIRXapAYLJPCJ+VsHw
+/rTuCYh/qe0vjAnMQLZWOD6csRL0Lluh9V0OfJfa/rpRAGPNsW2gtQPfM42vThPO3Kr9YAPIUd/T
+wh0MS4Iqy0BltklMTF/ywZ/EY1KOnTioD2n9ChLsgoootiw3LWVOQaYFAO9nMgutMMCwOghez+M8
+BVeJY5Gi7mPxCQEy7wmmHes1cBmtX6sgivnGN/E1xznCiLqmcaZPWUawm/HDo3OpPjXA+dloaIYx
+vttvJW/Gb+7Qz1+O3wCMrYWTSSY4FpFYZ/F1Rsyb5BUmGjb9NPtynJAjCXAnOaWX9EhzGp21wPg9
+w5i0I9MepxyaFj08HDwfQgHuVaSA3v3tCfpMPMIGedU0wmZi8mxlidvHbOCrxSzir5VxrPmdrzCT
+wEawE/xu72zkOIWZm5w8zrLMBGRu686VX/wQi1wVFBNIDj7MdEvxHQSXH2mIn72p8VnLT90g2+Kx
+E/+u+wHC7r7XCJNdcIDbtP2rzOU0o1hUB/9Ek9nmYrcEwE5ouN/+T+FC4xy4Qd9JkNiRwyBMZZM/
+nskzvgPjSUN8G8vuOQLRkvKKmTTB1q6/VDdQdXvgfUyLH8/l2lG+PvHBWBbspAX2TWBi5ubWrAE+
+yyUIzq/D/UdyI1uCYUsA7MAnQPDzeFlzc4gEwQpTiQjvh4GwlwYgJoAcQJjmhoVyB7y7M3Pa5p2c
+F8A4hV0ImKeGAwSTWA8/AbkKDEQygWBYZhfKMQR68B30Gmeu7ygbj5jBsU5p5MMaD1BiAMES2uBt
+HsEgWMj0SqOeaDsv23hgwRTmeEFYxR4pYbCFljLQpbC2cE8fwuls9N5y17wk5G+VEH883QCaLUEt
+01EHvu9+871n9uFH9MzhWAeSD32W8CP9BIZ5qhEnMv46WDwB7wmOCwA2CbOJBMCZF7JIIg1x2Vd+
+EJYAQvt3PCSymGq7T3c79OyP0oosGmCRY3dwMZUw0QDEInHKjStswuzhF5lH/FL59Sw/GaJrhmv4
+8fCjGjJjBMLBUW7pgyv8qaf11TSM7bW65MmpeQC/zizW8gSvV/CSCQQDAK8z7hF/Y+ua4R8g/L91
+3w59LEYAVJlMHpp5hPLr0ATSoRHGLtlIt5CtSmdt5sK3ODkBgLgy/Mb8rVwGR8BDUjhVbXAHt2f8
+BG42E8wFeYgzPcdej8momrewEV66v+bjgJdNIX755jgjTTDAr6jFkW6Pz0LqWjxkssF0GQ3krOYS
+OD5KTPYrMQLEGwBDG+xhswDD+LhCAcWyN8ytB5pfdbBbQXGmmQPkHqc5VqJXkCsU3zXFInftsdIc
+c12T9reA4CEe0r0CN4xvUm8BxpiBj/Q9u1No2HBFWs8LOp3TDj/vIbDUCKvBVtgBxcOblR7iJfA3
+UVmRUe4BoHGEJp+w2Z5rASlUMBuAWGmcLFdC5NUMcfkuXqt7n6wiyq2mdJfnjd4qG0CwNGArecwc
+k5ZNec1NJDieQC/ouNK1BdBNvig1H48XzwWDYcGSqHNlJR/9GgDe8uM0hZjMIxIYT/FJt6Ah70wd
+C8HGOIzBszXBRmscZd0ubi8nfxpN4ZYTFRPIAO8Ghj3A2mILtiKW50MK8zgxSXOIZ7IP1kEz7GBY
+lzzFZIKPAcNi6qAX0h3h3mEC9H0cMPSUFiui8ckoy+yVKMYKHdIDGcJEv6DZYjcc9CwDCF50fNoG
+vI/L6v3hE/GPb2y/6s/xaf8zN34G+JJv0vTm76YFriCY82MphF+TySvCosF4tka4mUf4CQsWzDqF
+BJ5KlVZBF1XGPhOccx1rZ/Q3ps3mEgyWY6mbECaeEDBHtdfOlLZ0P20DDG+t7f6lXRY2xKWfzSL2
+w4x/Bcd7BICArwUe4Bgz4oyaT4tAj/dJD2kagaPUFsCwuXYYfgfB69kfWfilfqYsbETVN7fQiQGL
+wG2G7Ux3TXEcYefMsW5+63GYqBoH0FvAsMwAOGzCJ8ArPX+2qwB1oisi0wJWOmjpgSYG5A526aog
+UlpDSuW11zf5O30rxU48gLRutk+NiA/WmIk+BIKNgIjzBHQ2x93v5uObcac2OMCwV8JCtQ59AmOq
+LsBwgzbfhSZh3XIzZioa+VtZ88bz3azGWE3addsJjlU+aYUlgF791fgDIANMBK6gvJIgBnMU1RWA
+MoDf9pvlE0Ax7IS7HLOBRvPRn7XB4ScweQBiMwEgxqkRqRGuawaKh0yzI098uRR+8c5gQhgMu+Gp
+0kkFsvbh6crHefn4bqC8QbCuyTxCyUwiT4sAGH4WnybhskhcTqBPAPgIW/Z4AsF2DoEPFsaMkmMd
+S+656UtEz7i+QkFfINARMFMaNMKlrBAIbqAYH+TYLwzxlVGRx7aBrmQ9AAAgAElEQVSZxHrkj7sf
+IOzuOxh8Yyz8b9qhexOA+Yu6VQRHAWGxA4gUkwjaNVtMI/ypP9glHSvEQg4CMF1lsQkFLUBP9wP8
+xV/DOrNY/PXOKcoM7T4mQKN9gv5TmxG3XNtrDnbLUWnLHBwv+U9oRvkoMQk/m0b0nksZOQLA0UuJ
+MdhZHARLmsqkiUSaR2wtMF/zS2K/nsfBs4X2dwK/FfBqmD/UuDSL0CCJKqzZVKKC2503/JQ3zCMi
+bASqWABajM0EAlBvATwEhqe4g6ZKUufyHr5oBiN5jJ/qSn/Fwzr7j3K05i1/C+ubwDDAL3iCDmWz
+/9SJmDcShAXwNtMJzoviMS4+0oQikR4jY5kecPgmhOWMPHnQW3a7Ziso3Rt68HQvl4BWw0/ss8bZ
+CyD28AH8JNdMsY0NPFEBBnfHbJtnLDsgCBUHb3/JI9LoZwK9/ddth62VOwFwAcG4c+lzntMrAKPP
+E8oGjKMwjSlkVuX9SsIy33b5bQGApfoV59SGVjjthQW8Sy029i4R0vyyiYQeG+aqltjTFEeobe2w
+GRouMXKFVrXHhboqx6bF8dDEmNE4YU0FGxOKRJwTd6eeMRw0fU8Dj7e40oMcmUY8YRKR5hDQBm/T
+CMjkP+t+gLC7b5XxVbv7ov1Vf61N6X137hLdwNUgWq3UJW4yEX4XWunHE/FmVtAELzF5YFIRQiqF
+JwusDgTqMx1gXjOHMIqzlk5MvtiVeptNKhxmhFs+ARlP+y0ssj8va5aA2LXCVfPr40J+xCcgTtMI
+hwQ0BgR2Yfowpft8YNwqENY0h8CcW4Lf/indX+anBcgjv0R845sEcJ9AMIPfDEvNh3IhqOXiT4Sk
+JMwnP4NmAWgKwd/CIiMQriAYTFUCNMQDBkjF04zDzRkT9qtDnsuVgySQq794mv9Mi3VtBE4AfEVD
+06uuxVL/AkY5Ns1kpzGCRHNjPnPsDgBs4AUQVLuCMsxaux63YoCr59SISHt+CIZzmQ2bLtf0Gq0t
+Ld86TVVMZeNR1nwTnHTAq0Nc8uewMW4PfxX41rTUDs/lYin4uggwgaraXL39RO6KlxPgfhMWwUNV
+0l/vs+2ROcZiA6KwGfZ6RVOBsiPLwvN51Wj4lq1gipAZnr+DYQwEm0Z43F4Xmg/ykrxqOiJtMotg
+M4iHNMJLAYhXsjQRiQ+6sFAWTZI0hv8VRBN2TdK3KBa0jyKFd5YK0tnxF3WQEiKGMSNy6okuiblY
+zDV9YTQA8TaTePQJDbCZyPOQacTBR/999wOE3X07+B38nkfPOBhpedbhZwCdoDVsgh24iEj4QyMc
+/mRWIhso7bMvtzZQBHU4CD0WA0lKSWIHJLTw0aJDFUb5AHBQAowl4jQqUAeV8cwf6NYvLn1DEUyv
+v4IJPk8Bv6biKhSyCV4qYrAL1gKen7C3NX94yH5y74NJhT8RwdYO54CiDL5g90sTDAco9maFdthw
+fJY4ADb5tSz8rBFWn9NVwtASS8sHcCyZD30qYDP9DHDF51QlGdvoFwg4HykAXBIqExgOPxgug3C6
+WgsXBEBX6/Hsjrg3kNzB8Duw/f00AsOSD7Kx2chM9JEEwevZYdC+iW/0WdS0Oi7x0CIJdnOZN82w
+8NBbgtuQ05p+Gja2HGHhfJ0GG2OL8L3NCQvkCnSboL/5hzTwXAa/qelN8KtCH9po+bMeybU0flSD
+6H4AxPnLNYV5iZMjIjn1deDP4+kRYLeJH/OnCYJFOiD+EiCbHf7gLQ34JgCqH9xxVc5ug5v1JUmD
++de3hlvmAQAnz8tJ1gKAFZ+QXy53AHqNFTUYq9xDsX6prL9IE/wsqadHrLAjTkCc2mKAYmyWM/SJ
+/dTNMJ3Q9FtQYlsnevHHLcCPZZD3EjKs/k3q6Y9ZrCCxEgZvST4e9DmBX9vA154NdvfRabI1wLql
+8KP6A4T/1+7boWemwtrhsBvOtdryZF4wltXqBGDFma8AfupcbdIIb43yvu/jQA9CVsTCVghaZ1MS
+gMXxooDQbNpe6XGp402gnAukgGMR+ptg+JwBgGAGcugzAT4/JgynRYhrhqVohjVMIf4is4htkG/7
+lUwwAfR+BsGZJpQOBGDBxAoA1jSJwDm/4bdto4ezY38tDf9aOE9WyqkPcRxa+J2OGjAu40TjKILX
+4Bb0xXbBbNubaeQXiYccIdDFILiDZImyBAqYPgCC2nUGwcyk7y5mrGSbAPBeP1ViNDAc+biYXvzx
+p/pbKwDo9jzt+wNUiIhrhPdp8/uTylvVFQ/Ky8vxUFTVUzF3wLxi7nlDHUAWmmvU7AJ4kQC/p1nr
+4jQzdiScuewt3Qb/FPelH59uD1AbIQa/WijiEbx1k5i71Ah7BxjoBl9p2nbiifUqcQWoANmXnFbD
+Z435Y3ALDvBuIlFBr5R8AL00mPwASwBp+x2il+PU8HW57K/GXbPNKQpSkCpdeRNwAGPckp2D4gTB
+2TRdDRh7uohvkmvmEF0T/PiX4/6KeKX4BMablzEIltyDUpZwaoNz5JmXadX0BogWkvGW69dyCIPn
+DmzuBMHpP9IC9GY4wbEEAIYsSEAMTbAfU2dPmEbsj2xsAGwPHhx+gPD/zP2+Rjj/5evrCn77pjho
+iZdUEI2pTygIIQlAHPpTF4ZWwHAIU02zCJwrrFa1wbEWgAE8MoCHuLA8FiazbHqilmYWYQ0c+4JA
+e3nBi9+NzSACR4DheaADPJhELMPAu2aYAPDWBPjDAW+Q8/RHRSxOEu29leQyPAImGVdMJvbY7XON
+00Y4NcJOE0bAWDoglgDBa/mGqdDsMiBmv9PkoRkWYQAdY23eE59vBsYiMoNhjxc5wa56PRwvVH8F
+w1KE4Jl3ujbH0p/z9LwRvgBgjlOROxjOywls38IXgEycJgGxr+3HZG+YNv9UrINgfQgAO0+IndU8
+BixZMW9VSHYwzBrkENbeXt7jw/FZuzT/mVZyXKaUE8b0aY57n1s+HeJw4bdcmA9wgdzGZa4BTvAL
+vgYQrFGhrwfWiAIoFA0p8iELAecA0Fnlrh7aNskrxWWfJDTBCBeZkpiSfvSWUbrmlwGxtTKSWmFD
+eu13/T3Ux/1RDVks1aihcgSqO0Ax5WdtsN8aIDgG5dHYPLfzaJqGOUo+bIQH0wj4z010FRifIFgk
+tL4pRpy+lGTxua4iUie/xRnSWbdl8bJMrFzrvRAPekwtcAHHBIajJtC3pBZ4X0WKlviRbQYBMKzQ
+FC95Hhgk/Vn3A4TdHW9Bb/n818FvxIe/geUjvf4E5g/QAAs9KysDXok8+RW6ZCkAwZtZpQYY5avm
+Bc6Gv6f2dwvTfHpPrUUulEmLnLdJpnswO2WmrTkh8fSfgI8/sQzge/tpA8CPLvnLkx81seCeWvqf
+XMavZrGrOSkh8+AEiaoNzrlPrTAAsX/+2LzMY/scYHxeV1Y89BQALHUsMF4V+NJYYTxjDuy4MjAW
+uYPhCPu8F+Bb6sw8iUBaHiFapLiUGLc0moLs1Og9Xc6f9jgI1HmBZAmAxFLJl4DYJWLiy6Ql+AGA
+8eAmbh6z+YMQXQOW5XxB9pXXo2InGJY9pwByIRj5gVRcmIa6yYeG4uopErQ2QrAPjsb3Po02Bcf5
+11talezl+kT9Qc3eNI3YtA8GL652xLhu1pcgN/wAEAR0++kqgSNaE832fZcEtiAOK+WHNvb4sBtt
+8inkjbDsSuAr5D9NKOqPH57BRyYwnDaj+cn1fBCToC2A8jp5SZMafUptsPJ5wYdcqevdBCBYmqlE
+hnOz3LkRrsQpnRzhx6b9Io0wrvHA4uvFfA33BxmMgbVWx5rW2hPuKjZx15db/ja4LDIrxSeqSr7d
+lhA/3EW+phEWi2mXAL5VG/z4RsnQCOMq6seoqejT+em/736AsLtvh34Cv6OWmNLnc4Yn0wjKA/ns
+BA5CL6YShnwJoAMEARBbLjSuT0WqEqwtDRENcHTXAru22QCOaYEYLzIIU9wMzK0xLge8wbwZBJN2
+c8UkqEh8OQ5xfCoEA+flFhM7HqA4mQB6r+O1aI2BMqJIpvHpDvkGgPzm9KASIFhd6C3XCG7/UwAw
+xqyDXsF8tzQ58krM5x76qpk9wbAEU4xHBBZ8XjaVDlZoiesS8htz5tHPEuItLqtXikpXiPtDOoM4
+SArNJC5ycAq9pk2v+ObSGGd2gGIqeNuTD30rH27sHGP2TxpgFoSFlAsT0jvwbVqupH8W1rUdXR5z
+wMZ4ztIk83979RbyZrit+Y0RklzzO4aBcWiJvc4KeDk+0w/NcAEep0YYcxS5OFz8zLfr75ApHhtv
+mMCHKb0CXwbEnsdy/e8w3dH67zn6nfbCkJJgp5u+wcpDRsiOiLVBD2vVRlhyGbdf1fxKMZWQ0Bhv
+OfaIjCD4V5g7nCYSfzkwZvOIv3RriUMTGiB4t4W1wR0Ad384SuANchy3h4vWOBj/IOurDyuR6BK0
+SXylaoHZ73Qu6I81+hepGmKyHX50m0wAJH+rlfwH3Q8QdvetOl4lNX3pT3A7Ad7ur1rj1LGGZrdr
+hwvoJTAbJhGI91YBECOOjmMri+NwSeAB/2gna5C85UKJq6/2zFsZfHmFFUsrgS+P8I4HAE6Gl4wP
+QN9BMT5l6ZvkdtoCZpBlW/sL8PtsbCHPkv2aLnr3d6/ebrNCH10jXGjAEDaK3+YRqvTGIYQX04fT
+jibdBugVvaQnKNpTQPQQgg0M1MNGc2VpH5ikYwRCrVyyXIk86e43gLHe8l6cFp/9nv9A140/NGZd
+QO8Bii+8xSTGtMeLz39IO80IDWYgx5gFZRJACjDsEd3PtxUAX1TPT/0AwOgfBPvR1wnc1j7O7MeG
+nBRRrlaRwm9cVaSYPSQIPuMybHKcIiGYnsrrgj8yOAYooD6l9izHI0HRHsOHummUzno8zHnfOIdZ
+YXl0ht8AsYU/P/IC3gC/2w478FXumMeFbfDzZNyzxNbWEu7n/A2MNOiMe+BtozXBChISTpxb0iQQ
+IBjXLdcwT+H3W64nN8v9etY+0eBZ8usv1/T+WvLEJjo/NeKXykNfnfu1lph/8ClIFUPi7d23tQaS
+a1fGrhENnSIp+9F5b6xKl/8mJrAhsSNfpbJUcqEvWS6VG3sONwlU0Ls3yolrgVUe8VMiRENTvLXC
+7eHmD7kfIOzu26HXy+8d8N7juM4tf2/gWFIhhE1ySIur+AJLoMiguMh6ZPYIfjJFSjdzSC2w+JP1
+kB6LKrXEYOo5hlpaELghmBz8FQAD+D667bPE3B+vi7EZrmqDjdKWSgJhtX3KxMxRRC/xwcUwqKRW
+y6PMEszGhxCEAK+cD1ALcxVlsx2sJYH2JP3S/KQ1KaNNMMWZJOaSZz43WlkAh2CjxrDHymWKqzMf
+N7vGXcEu35erIN4+S4t3ByrNWi5+GsRD06t9hKlplzQILc+UkSz/Qz2WbwXKK5Nyoxz3WG/kV6/v
+5hf1ftHaA2nnKzCl26ENOU7FPIKbN83LJ9ArRDvMlEzOONQHWpjyWM9GexkUMQx+8yrJXQt1xEa7
+pgktr5EJIAdooPjy5gwaNfyo+wgzAO4/aWGQiNJUafQm+Q9G45A5AgWMBf/KvOZrMvu6r48k+KXx
+iP766RHyiNra+WRtkFqEVLYlBpzfdKkECGZOHcXj5xHOrrfcUm+PRj8AkkX8Yxr4utzLmcLFFELX
+VrSUTywvARgPuoNfoeXXY87qvBIRDCIoZM+QXuxPsMaJhuBO4MvrhOiZ6DTKWbbS/E/Q6qEB1hp+
+Kgg2wYlOE8P4d90PEHb3Oxrht39dG9j9NY5ADRalkByylhaaYX+Kla41drDoT5kJnCUYgQ6Lhgkd
+xM5saK+hCnolctGCQTxrRaooLmt1Ht86zq9g2FGjOXI03zina+0vQ2kCXxw9hqd0c+3wFoK1J4Ut
+YWzBeQZmzaw4TnLg2TY6z9eFCzTBKgJFdgisJRZzmW3TYBAd6Ma40SvETUOUFm31/gWvmbWD/RW6
+CABGh7ZWLnm/CzOzSxqD4RaGP8FuvecnEHz2vwO2M41HrrWMiLeBXmkAtaR3EMzXYUSh1ikAGH6u
+O+emzEx7uMje5byWh2fnJcl8xNcW3csyLV9f8hXU0le39SkbAzfSSLIlujWp/hJnr+mPxkjsqyFs
+43WfN77XeL3y/SroBR+8haEwMCrLXWSwYiWefi1OROqrdsaQkv6MS95V+Egpk8A4fgaeaUA8MQZz
+GKdF5Mc1bOVmKlXkl+C3TOvcvvJQtpLfwnynL+s4KYJkKd5eYi9OmkmY5Ga5YaPcAIKPUyUKINYc
+ApXT3+YL815dXefhHcGvjSC4mKO1xVA0wU7LAYyJb6Q8T1pGXphEgI9UEkgwvE+I2I+YpltGPxHe
+cvnxEz3+tPsBwu6+HXql36cNcD3tLf+uWxvIlQMM49URXpWLWTADA9giUwoGwVgkwWcGl8ukgV7s
+dPW/AYydiScUrNphPG2jP9mM9ugBWY9XIwQMOxgW/9DEs0yWg1oAYtj/QhNsqnsTmi3XAvsRw+73
+3UlSWsbbb8WotY3z+NjvFe/zJNAG82MPzbXlhrmtZbGWbvlJZGUB5WJbQSlJjx0Ax98QhA00SWeO
+mNNkhjktKaA53+lqfUeeg974HhydbZTeFg5bCur7fV8IPSh2ytvKMRj0CLumTSOUiCTSIPAtHztx
+X2MQ7Gun2s1p2SFehSSNnwul0mLD2s78uHMADLpaC3dIZS3MY1dGvgXsiMt8xgHOZ91vl3iqqKf5
+dWt7B3B7gN76uWUVf7AW2iwHMMBaYXkLU/MtmxratJduvP2khc+Z6nyiXuvbxXo9+JV3ILXDUuyF
+44QI+rgG4iSuK9ICxB2tj8bF27JuNhfyDdmVxraEU5YWuRRodN/zvlHuBMH4yhx/VKN8dtmB9xUM
+Y35ZQ6wS4DLyyq7n2EdGIin2/OQrPcEKLadIYH1GHCu3cg6qplhiDhMTZPkoZRZR+Rzktr/m4Bdx
+wgB4uWnPQye3ZDc6J59l0N93P0DY3bcDqy+/bwDxbBqRzCbv4YtUJTa+iaRGePsTBIvZBnhGzAIa
+Urcjrq+35KAukLNJAtudrWqDO+hNxvcCmEUkz0JMplsHP8OxK9j7lx+JSMG8/Mg07+a2+fUxyc8u
+QytsceYwAPLjoBot6qYQ3ToaDx54ktA2gGGTLantL1fMjaXgyTxW071qBmtgD/zqSMl3zG2JrbMM
+mdNfkmnJlQw1gBXlflszX6VxJuumD6cpBIO5UryEO8t8SzvDTPW9jSYi1dQhM0wzIlp6ceY1GufW
+kgRBmC9oZ9s9yhylwMqcXjcq5FZ4/Vltri2+4kHzSFuoze/MmjzuJPWnO2v52HskseoM8TlQ36fF
+SGnMKbgavsiJ1f2oHfnVHATjQaZJfj5nNW5viRlCi9a0ar2LvSvxsyGO84JNeUzV9lYTiNt1BsMW
+D6Mhr7xBxUSiaYXzIQGnRkBLaOWXdN17VtdcgmE9tYcFPflA0IlLcRvSCLPcWko2wpdNc0/zAwTz
+BjqkwTY5wLC4aR7PoXoep4YnOiLUGe9StD+zmBMlW+pF5shvJS5BLtNOj/P5i5bNcQzag+bHH8wj
+8IDgdsMigmNMn5CIteeddQys5L9yP0DY3ToE3D0ffpAFE8DtcQF0KE6KfxMQ8ha/J3c/l8t4obJc
+P2uL+eoaUnFTA3FzAepj+TjDUWftS10Fe5HksU17kwSeAEIIB1UbXRqps3BnDQPdL+xLS307jb+4
+abJ3jMdntZ1hBrB1MBqdMilpMc7BdF1gBGg/x5/nWYUeYswK+GWQfIDb3Xl/Y9Digx4U2YIYOmUb
+jSeX5xHvoDMB2d2NafqSbi2tADgpdMCgt7BJo/xdaLy09XfyTHk7I57HWCpudu0v4hJuubxywRx/
+g8g8h9OaHXOac1dsrEmoVe23xzOdU85eQoe03R6ARlofBYlks7VHUO5JqsXxbtw0o7xm5G91mJ3l
+KCJ5l7fcJwufgec3KNusyYJf5HFqFvwDtwDP59N46svnc3h4JYc9KdW5rIbN6z/irG2WU5+TUB78
+/lUANHV/KGj/ZH80SPPko/0zOiZyn4uu5fdAFbg3zbXx0M2U3TVurV4O7PypfWfeYEIDRrQm0FRy
+XPe3K5ZffQ5UV8qQ34GyNg3yr7XkAQAmIJzg2I++MzKhEAtzDnN+8UR5X2Vm+dVBw1uLBKXY4B73
+djrOMnt1P0+OH8wH7fE3H4q9NCrqH7xQFX+TurGDOT6AsulXOWv580/H35I4Iv0Puh8g7O5bs5TK
+ABJIAuhUEMzgFwKQ4kgQ5dO259mX9ItI1ZoxQMgn9Z3fyE8Ai6+SABgfY5ClYm4DJbHQV9kA1gEx
+2pAgWAOolvc/0TPb1ghLJD4CP4Yjcrvn2adDoO8AvtOPJW0Xsh7mg9P61OvF3wFyARE0tjEmtKFx
+ugrCBRR7ephatLbgdd7URm1tHwBG5k+QdNBZTS1lbKjz9Rky6Pt3nJ3zJhJ0NM3ZVOAE7ycxHCDt
+qPNDy3uWD+EtyNyveUfGR+F3PmHK8dWoqPaR11m9J4rw/JV4NEjzqrzIh3pLPdHRve7r/XM+S3yr
+jM11Krjhgo02it/m+BbZITs/xHJ/eKSDzwaMzr4BFIOfLL7Ge/m8YY67pumabG00AzwTGUHvQ2PJ
++Y5TIwBmpV0Z6N6usWlhBdgLMCwivwSgeP9wTjq+PqciFQg/7ergWJ+XFYYJekQOcEyfkStjDGIx
+XIheOE1ExNhEZ8eHVjPmKGmm8vYBEOv50Q3YJG8t6G5PAF8BKJYBFPs7WEvg3MEvA2L1vAUQtzjs
+IwCwTmArYYUVZoWuLDNVsaX7c9UeXkv9yDP4n00HS+U/bhryC/SyPv9WB9C/Jyz+EfcDhN2tL0X1
+/lgCA2I/HktZAwyw6+DRFxkAcbHhAWPEgiOuv9dhE+ZFU8JCAcBQIj/aUsIq2642APBmeOZtW5B+
+zgzLJjXR7JNmndEu3vnLWuH4Ylb2LcGvL1IKR7oS+BXZEmCJ1M92guNZuSfi85Wd0Filq4ASyMPD
+N3+vo6X1X4lnsMtgWMgOD4B2uB+DlyP+1r4WNvp7QiopNFmH7NQuU1Wf3csSA6MuYUJPAC5nfqP8
+c6POtOHe3yYGcLl1GyuJWlHqoIDtPsL+F0BXgjJ2JrwBiIMaJHXCvWlw2vof8VSgzPcb6KW0eZzS
+3lKcvx1AFlXZUfKk27FMW8PdT7QyOyvJHQzvuAH86gyIl+JkCVco4K2TUvtr5eTXaDN4x+arbJVJ
+rCx+e1bxDYn+A/uMoXFmcWh6h7hIW35GNc6qdk1PaoRTM1y/ntm0w2YJjO2hK2uEh0/Bsbq3fzIZ
+TkUkvlwmsacmSIDpJSbdMqqnZyGphdhsSGKc6DmiaYWhOFryS7dpBGuDVUhLa5v2GfgmcJVibgC/
+uv+RLN/jVIxANfgk4nAfL/NImBXur71JgmNLv4V/g2J7HCRDceYPBP9x85Ar4NVZM/yrx/0A4f+d
+W4XiJ+61w1v7O4HfBJ55IgQDYguAUX4hBFILDGaLFnTt73seZ5fUbF/DpS0LuXHSBHLuXWhbnhEI
+3nUA2Hs95eoOqxdP6WA6TeuLhU0JnzXBMK0IhmnxK6/yKd6MAdNVSp6g8prmA4k5CEEmAZCKmQTG
+ngBOaIYN+WAfnH6RClpKG2yOF8n79PIlT9HQp09vcQVj3MdwbJA02nhx2XIPWwuLyCdwLJynpPEa
+OhvT63ppVbuDCSHUo8hkyHBwFSwTRVrSinAYXdejBRJzRZUzF+s9ynh1sEeRmm2pDUff0UG/WqO1
+6f6WiUbhj+AXf3n5TgBmoi07PDQ8sAoWGufkqxP4FZSgBxf8lpcKcwmehwMEZ+fVtXt/4UQD7uYF
+8AaL815wvCgPFUDu9k+a4PCvNcSlf6lr+xwIL3GNsAzmEZZyiE0kYqcUgLBSww+zCNL44geR8uRs
+YCa3h4iA6e2gGyK28JKsasR0yGyFQijHJj6kVDTCsINWAq5pBhFg1dPgxxdcWTP8kD81u1yvj1qA
+ZvNw1l00wmIEgDWOXLXuH4Cv6ZK1nn3iw9qfzN6mEbMmOACvnnEJfn+A8P8L96tw8jssgkY4AKCe
+T8R8xBlAEH/pjTWAEsKeBHUwErQgFy7nLRKlxYeGUfoCRvwGYqEBjtqXg1ER1RWMMEEwA2OvF7IB
+yAUcq7XRWl+laYFNxDXD7ZU1NMEoVLS/boNM2uFuD1nqonEGy1Ma3NH04Us/5rJqy6HxTZB6guGc
+jy2zKiAW4faf4Q54GVxMPIX1vKC3I67lyy5OiKNUTvlvSPxTWStRn8HxWY0eMff8SLmnfbzBa54Y
+Y5oYgLASBhkq2pOAq4QZYMk5HsjANM+zxsCvxA1ERivzKF+TSCtMQJHNWeDp85n1Jp878ncwwwWl
+p/UZP7oVsWmR3ey1RQTAvzxcUr3LGQBocmnee9GdogUOgmONg087IH6KRjhfnzM0i/AIkrXkCVAr
+ALgSfnyYaAbF3b/N46qdsINgbR8MMgBgaWYRrBVeDoxvKl/ZNaqJ2bOnwWTQEC8x8U+QE8EU0ih+
+m7xVjlpuBGtCd9QEY8NegGDljXVr0AALgdcEuw/me8gXIHbSBstpDlHAr1zKmREAfvIhQ2Eq4UA4
+bIHh9w+hPLvfBhMJBsIEeteatcK/biB44a30n3U/QNjd16YRmmAYAPgAnOXHINiREzP/WGsMfCVW
+aoIpoXVZ04pwsAEAHz/S8FJ4cQvAGJt5hOhZn2SpeAiINrGWF+cPraYFPkwgHolvDSMTbbJjzUKC
+YAmbYSO/c4j0C8Se+wnAcj8++kmadj/PI2gAsDAALj2U5HF4KfTDTzcdcMp21tPPF+fZxMkWOGlK
+JIXE9HpdS6i71sKbkfKtuFwwpk15rIXnIt9ph6f7vzRSKpg6G+GBiPP5Bwlq1pF3IiAWtFR7qq3n
+vKdgAsXcizrnKpUONJrbF/VNJm2TBudnXjjy9jcObX0ly2VZnv8AACAASURBVJreOLzk79rffiV3
+zHsrWseew9UGm9NE0iQCsZtv1tssJg7wF80HXSVAnEddpkYY9VSwm1RZflaHAXn3XHaQu8ivH/1h
+HrFwRm41j1gKe+E9BqEdNpFDG+wgWJ40jdh6ZZMD4WL8wjb4BMzoq4q6jXDPYG/BMf+u01qa84oi
+GrdSKABcXLFR7skNdADCwpvXJMwlKjgFgNWS7yG/crz3C+YQuAfAL+jjkVpu55MDAAfoXepnPTvQ
+XSrr2UecAQTvuDVqhP+jJyAuoFhV1pIEw1qBsWpdf3/C/QBhd9+q49kkIu2E+dWQuo1Ugl/+Mk/8
+IEME1yqcu3lDEVyXVQ2glTCP4gN4JSBbwXF2zY8uWX6+o6EMMUel0ng9FPWz0B8EGXfGHhFdZCIh
+0kwgRA7wi7BKAFs1E3vI3zTEJ6DpY4qxfvEzUH4BzUjvIDj9YNsJtDRiiD60gpSOJa9geAy7UAdI
+6to5akuP6znr81uDDx2RX4LdAXj14kmRc/jbPOhXH5OzjB05DvptEWa94XprRHEgX0aOdbzJJMLz
+QTAU8NhmjtffOb/ZyjLeUnt+vBxCnqFPdTjQly1RI39bK9HvbGppQ7cRZz4XaS/XUqanl3bfbay1
+hT4BY6xt8PPFN2RAHHEabaynxuzTKYqpA3WFf8J5tHY1f2S0FvxbCOR+AMCrpilrg6UC4PM3nBrh
+yovtr6dGhIYYNg9Pn48EwyZ1nkwacGrT3+OP6CZj8Vax8D5cNVdn2TxOmuF69rAl4BXSAAubNQjl
+mfyawJY3zEkzkfB4vFk6bIRFTk2x2wiHeYSf96sPtMFP0QqvZXuT3FKxAor3uf4JgKWaR7AWuGl/
+AYrrA8UPEP6fua+PT8OPXgktbSBXHGh2cwgCxHAQCHosvNl0IvNUwLmfChOtsGkEFDfcvq3ZkGCX
+aYW7ROWJRc6MUHzxC/W3tNdEau+83Y+IrWYp6SYQxRUTCM/UwbAsOeyDAXy9/xBz8cWgC0C7gkhN
+wXvMVUsPvwu9CnQ1NAmgie1XsgPmNwFbSPLYxn3jT7c4/dAXBye7pJW0FAKnfXCGT4FQoaQcwzu6
+U1Zl84726xE3YcwpT97qaOU4bjfsOoGiv+UIoKKOHH+P0z7ec74q7+tc4g63uHqPdKVObRm1XHqH
+ZrBRjvf7wh648UOcHMF543IFwV7mhQ4PPEp9scEEIlN7CMAYZRrLGsGvhPkYP/B2EPwcGuFmM0w/
+8ZaUsNU8BdxKB7tDeE3hrUU+TSOgFd7y75ckj1OjfhZA/ITCI4BvUfae9sGcZi6XgFf3m78VNDM6
+G73nBHWmhBHencicLhcLCA7w+2xlkj4RZvvgTxrgcmqEDKDYm5J2vgC/8j345bLqbVCYRwD8iuC8
+3zjjWFXMLE+MWBYguGiED22wVPvgGyhW+TGN+P/gvj267jCJcLmR9sFGJgfdTliCiR5A0hIoRRwB
+FAC6Uyixd7PA+klVvo+/0vHFoJrmEI8SLvUjHtQ7mBrhqGVop5Zwb55Y2wQn8gXwneKeA/zm1cdw
+c4gcGArnVp9ZczYCX4xFEdpaTEB6ecy50GjlXPC8QlOT89+B8M4nh7uD3x5XwUUHTLPNMMJWyvT7
+HM268bAPGU/QMQPdUsoZeWi9PYPJ/LAwx79rjV/dWXAEvmM+IcxEE4L5aoYLOxOAks5zMJqy6JBv
+iIs6D0KWuGeJN3H6x9g7Q3FpjX5VkEt9D/9vgGUbEm2YrQ9rpSefU2gC06WS6l5+Wb/hmfN3ITYF
+fuFAN80p7AqCMS8Bbguw1ZJG3c80rWkMgifg280lRoC80jzuAMAiTTO8bYP3m1E/OUKaZtismEYw
+8M2R9hHGArEEvfI8ZVZMkjfzRH25gsPlePYR5qFUGia3C1ZXKmkCOwvziDWYO9zBLptRxCY78hdt
+sPxN8EvAeWuDVUwdsOsTwDg+foG0pf4xlG0CYvYECLZFD0qkDT4BsBQzCIDiBMQSGw//tPsBwu5+
+XyMMf7UT7mB4MwKkqwhsQScAZS9AVwgolbJsMlHwwAzOnHOobOD7KGuDu30bNMD1tZqSxKztR3sq
+ZzKReRMcgdyMfwHDGGA3h+g/aMU3Q2jg2Md3cgFE7bs4FvCY0/h6URsT1gKfaRivXme95p3O9k9U
+e4uzlv4N2EVKhFumA2BcJVAK63r3Wn5ehTrnwbSKHQ8KN3B7v8fpeLzi7f+1DoqdMnjcCcIs/vIN
+GYTxSlKqpNoF1znMJdrs4ammDcoobia8177WFZ2dLHQR/MpozrIIz2chLxqUQndWrzrEHWVe+zPT
+fk3VcmUep5pKhP64n6BUQisMJQTM5vbr73oWsYiOZg+gjB4/pUVPJoArE/BdsVHuMJ1YWu2DpR+h
+Jg6CSQY2bbA+G8jqwls9n+P41DJ9NMPWNqeQDnz3G8syK9r6OzKhykOuLuxRcmTrssAbUgwNADG+
+OGdiDoJ/LSugdNIMHwBZOggG+JWLNtitrB0gB/htoDjAL5d/JD/t/BjZCW+bCTMLTXDRDkMbbBZm
+EtAIh3lEB8UdEDdTiLx+j8X+SfcDhN39lo2wAJMp+RPcBCMQIe2wiBAoFpmu1gBRBVbidRyOtZ0B
+CBvrJkYslqYR0G6sXnsgQYA9CUla7E65WQpB7XdtGwO3BMibBQQ4lMUWG+tsNTgQ21/zh12woSkO
+YASuWscttcJCcaef+ethKoE4SpvKHoD3kud+rcz9RqZTfI8rNRmH7UynMl1jzCDmWv/BzDoCurhE
+cJG9PwDsuHNVaPEwKJzK18Z0ehhaXkEmgb4rsjqGIAVXTat2qAHg9Owpv+tRSMejbUN7tc2rRxoR
+Aq/rXtlt2hQNJm1wjDibObTpL/bAZf1Yzcvrjhsx5BnbRjc4qTivpzEO30iPpCUS561CK7yc38Xb
+tcYKsRs/LY/xdytOAL1oCCQ2vgkD3u/iu6Z3KzQqCB41wWQSAYAMTd0GwRIgOD6mET9zW2mXgSYB
+hsVPjJAnv9NX5mncGFftg8ETy+wFz6iMuKqVhhVShVf4D/Mc3APDISJsG7xB8N5Qto8cAyCGPFIC
+tF+aQbC/lVeRUxtM/gMUy4tGOEAuzDgeAr9b2yvPBIBdK+y2wgtvDCYzCI8PGvK4slFOsVHuBwj/
+T933H9SoP32JU5EExPST63XSDtqYr7qUCsnaHRBqrW/J/lyimO0jg4klLd1PhDPqI4bD8ZxNRAoC
+P1z/khzitGh8zb84HnlVZJ80gQzWTowAo/UHgOL3gQlJ4eNJUQfDs3N+Shr1Tz+Um3DF6zXqOJkx
+u9+JTxbfwzand9AiM9gqU0w0scnrBp1uDfVeW94PDrarVaxx2+uqqFVPpg8ik5kEQbjBueQA3jsL
+DkVALDXzpNWtYp6ixOKDGyXtMicRp9QXbrP2PJVwrVdW6up3odYwGOaxfQXANIctH68lXr4dDB/z
+AMoYpvE7Dl8B8j1+X1mR0BUKKsm+Nkez+HztHi6VZfsYq/0pZadK7eB2t77HsbnEGKd+sw8A+GYS
+wT9sllt0ZfOIqhXe/LhsmHtMdDnfDoXKBHzfOBj6qXUmDrmESGbSjWs5rZ6SFKNsR1EazgKAFSB4
++c8SCJcPYwCsOo9LsCs136UM/PiCIEwhAHDzFAkqJ3SSBNX52B6jbQJh1Rxi2TZ5cCPiEQCbiT1L
+ZD27v3KC4NAIt7g4Qk21mEyEVvi7hfqPuh8g7O53NcLl58R3/7F5RP1Jub6A3rJiT+0is0OAvAOE
+OVqDeYSIJCP2Wh/JQ+EPLlBuSIxnr0qW6tEebsf2+2kRfkJaz3+C5NW8D63otBHeXM31IZaM4pCc
+ZUx6uHb7o8aX8ojJO4j2+5c0q/mmcgVUDK4DjiN+iPsW4JIoiXbwbUp+66U/NMZ6hgG0FXQ2CTXk
+tSGu3uTMca6RW9xXDmugl/5QIW+6nCE76Fg/Vt3nUUSq+YRSHiK2Yo/cb0/hY2apAZuerVRQNL0D
+SOU1YCXfVL7dvYOY414v7f7kzqGmmtpiFwfBuuVAfORMvf2uXVNNkwjzM4M3ywQY3vG4CyQBg1yE
+p7i53JYy5Y3eb/3yIxt5Tq7EsWmnmSDkoYNgkWoeATBsvnHORAQa4kBqaTaRvw2+dAiHsiMewjCB
+WONgKNNqabJNpBKdNyDktfpoappGwD5Yl4o+S3TtfsJWeDSLEBk0wGQWIacGOdMkTCRMNDW9JPfD
+HMO7gzegp1Y4zSIEZg/QBDvdisvXtBE2MR9/We5/tlJtBr9SP88NDTEDX7INhlb4T7sfIOzut2yE
+zarmV9M+aselHXAsIknGP5k8jEAp/DV/F/KISy0c2XR6otITcj8Lk7csrCYYmbccN2QXzCYLGad1
+m1/SDO+W9LOFJYFvjw/OAqYaKzxAcRGcAZTl6m5gVClDB8bjXIG3smCe5psGZ6xnaOtXgPiWRt0H
+/XDYWv4OqjJsJbxFA9VGsqXM/6W15o3TktehrdZWAUSpWCI7YTCcd+5i78w3uS8gU6m43WW6KVCR
+trgB1e4unWkx4rGYd3zMa5fnauf8TYAYcRPYHfjhGVX7tueHKSMrzLmj0rxGynKl9TLxkqF+vkdv
+Yoc/UbbEarmGDTUv/Ml5MZhHiGy/0b3ghxlEfrDAgYj6UVhsVhLlezhXyxQucZOm9wvwO2qL6dU2
+wHD8hMwiGDgSCMZZXYrv+Lo2WEVE1tooDeGLOUQPx2xBxS4igaiN/DxRWD8T84vFwiPZ0hRVVNMI
+/gqb4Yty6wZqb7bBu4dPiKsKluOYNEF9KQ4f6cAXJhhnXgOtwuRBLbS8YhyHN66kKTbzI0tJM7ys
+aoQHQBw0Q1rfxTTFYPhNmP1L7gcIu1ufs0Q+fJIYT73VPjhBcMZbMOTNvL8DvOmftYKzRhNCsLHR
+IhH3lgOTrcngvrmF8SwwSMB2V4QQgcFoDWmAC7tvoBenG0daB8MiBxCOn0iYRWxOkvFnw0lgOzPs
+c3LOA11Rpl0jT9RpRx3X+e8g4uLejol6KxskQHR2yIB2j0leACAYZYz0t7Zpv0t6t9yq6ZupA2hl
+Swy3JYBSU6nO0oATIJ81jDD1e9e0olxpAaG3u2AQqY4DtBGqrbW0davD/HbwS3kIY1/cxaBkE8Jo
+Ft7pSYQwCwWYdrSV5bU51R3NtyPL4ZJHMvC997tRndTFXumVN7uZSOzLeBynYTMSNHQmGnaaMEuz
+qLFSJcxjbulM+Tmf34Lft98Gxura4QS/DIBzs1+YR7hWeLn2V02DN2s5NYJNIzh8ppkD5T3yBIxD
++6BgGntuaC0az18IqDLBITJ4lDlr/boqhdcG+EsJBNsSbA4/Nr/Juz1waHybDXEFspQmJvx57vjS
+nKQMzi/LIb8E2A3wy8C3AOFMMzPXBEMzbHeNsDZwrNVWuIPfH9OI/wfue42w5e5Y1QTDmsAYuK6a
+RFTzCAZDgjiZ4qqgvgJibeGbC5S26wQgnn4SufB3Og+ii4RTQoX4vGl6S3CIVxH+Gl1+NW763Tpg
+2dPj9VcOS9H6BpilofvimvXlWNzmkDcHFdE2Cf/mxvRh6n8LAA/hOS0Zswx5AaoKRVttHOcV0WD2
+O44gi1GFdHfeLMajx+Ni0sb1Nc8n5zk/FWjpxXSD5PRbRfF2Z+RLDH9qzBj2JscQoimEH+baz+a9
+biqzzBH5LdMrAKZ7Ma0T+yjg+WjYXKak9/I9Xeb0Sklvk022lyLtK10Zr8LaXyGNG8pppHFbzjdz
+n7TFndI1f661/C0Q3GyEFzSgIn64BINhZ9NmqQwSMotwABymFmQaocUUYsXxaqLbTMIe9IzHg06P
+UDAbvrb5K9PY0to0G/0Vkf1mCmtFhEAwaYQdAC/TMBeQtT+vctr/ztrg0TZYehlJUCynGYT5uGda
+8tAYYnONMINb8mOf0Jw2hF0jnCYQUuyCyxuEASifbxrkj7sfIOzuaxthwyYBB8R62gjjE5NariJC
+fmj/DlAU/nrd6/VMD9dARgi9HilygIdg5orFQi/ASchkKQg1vIRBE7BAuS3wz+D20AB7/KGjj08x
+o5E2H6OG5U5xRTQPEjHmplxpDnyAWBHUAfNkT8x1hKgiYJCAgGihzWOZw0maD/0oznp6lQk2pCH8
+W2na0qznPBpKbcx8W5ZpjoMmLbJtuxDlVeByQpdJj9kaMJS5w5+a4ZLTZMawXF6EBHZ1KbNJWF/a
+8KntZdg0p0ZbOO/9iRm+jSjTdSHviKclQLyD7n/TEvNdjnVGOfgekT61VGhocwHbdQQqgFKqgzsV
+/FSEQHFyxgTDCXok/NnaXXd9sPisCa5l40eAGL8ExmQOsVVzQLqRrh0Mq29n9vGrm8RZG+zXxzag
+fExUc3vhbm3T/oLHP96fULVvMAhbYfPXmQogjMlULAqVERTzeHI0E2YbXVSbABjDtcdxX9c29TMT
+fCX1I8DFnA9gdy6zw7eNcSapEQ46JA0xwPGW4Vs+oo0FGK8Z+N7y75NEcjMcA2I+KaJstNQ8VaKD
+4T/tfoCwu69PjVBrwFc3KBZ+MqYnYuSDtgsAeNQIE1trsm+yKeVytcym9tN2sHrAL2Kpa2XiQkA3
+FlYI5iptGASzSYS0a2FFj3wGvVOayAiCi6aYeiGSzLM7/aeujaEWk5Qy19ymJuKYER+C/uIG5n0r
+YxRvLc+RZjXfUU7bHUuAar0ikEs+15Yp0sHtBSYRJgyG8Unfqa9vZhIZHojiW3fK1UjgkyE+Aebs
+/22WzbPUjkb1XnW5HcJahnanK4WHseO6b047BbUCpW2IoyWBfv+uljjv3+9VMyn5bn3B/NcHqqxz
+v2lrACou9WGA+aaJxFt6kwRbsQHJ40ILDK1gGcpOnTr6z7xKc+9oTckvL5rhVcOcL7Sf4mBQAILp
+HH1z+Wcu3+Lqm+Me3SAYCt9mCqGjWcT2G+UP2nLAnBvinKDBI4JXNOYMoh+IaosOB30tA9ZV1wjH
+1+X8a2wLgFgkNKzhF7QZYdCJpSiTs0wC3Z0W9GZuJhFh0Ob2PygTadwuy/5KBb1imacCYRnBMX9w
+ZRU/m0dUkHwzi/gBwv9D9/2pEQ58/YtA1xMjEA+GIPUagJjq3gvN40em3thja3M/uIFBjrZKgg84
+U9vaYI28YWBPWCSPSGKm0liML+SQZrj2zynDERg+wMojm3GuBobL43EDwJ6WO4w9T7CKc3zYr80P
+zP/xKg5UGoKsU3QXZRgnBgmXokO9vZ7ZFXpo1Wrzf8xnZxlzwgIjFpH+vJS5GfnEzYAYdl2xWY6O
+9NsmERBw2UpKjfiDno6wfUi/jbO+ZjrKfJtHuF+Tw2BqDqHM81bCSmEtVZR0blWfnmuLysMclbcz
+D9fJNFS6NsXJOV5Fg8zxZwtpXJTynwDYKO1cuaCtM02ptjgtovvRNlVKt2ClyS6zbuTPfn3j3+GI
+b2DXAAwPILwynbXEMI9gTbCKg2EAY/BN1wALyz6rYNgHJcwhnptmeJZ6DJ5j/tBm8IUCgoX8jaOF
+5hhRzNHq3WPY0N8GgPcD+dZw7pMvIH6s+EMkyQvYpXwiVYMcoBdxoDsP8+Y4dA1hHLuG+4tJBbs+
+Vl37OwJhqUAapqL1lAhpoLgC4EkT/AOE/8fu26HPc4EJ2EoDu8gD6tzUw9yucnJuxBDf12qAqI6V
+DmSiY8cimw4ACLwhAO/QBmnlaBFlu6i/nDa5RySeHKZdi88TTFrV35MR6D01w9LCNS9bRasgTjIs
+JEqsXYvoaVdHff1lJl91iIMfbSlpFzIp7jq09kWeWu8X2TzztGKsVVDpj6mJycH8YSxQgSIPbcAi
+AjT/qwdFumBo3S5AqS2Jnj70aIA9bc18LHNGXssXkP/Sqr1Io47oMq9pzXuUs4iV0pXCpXED75ho
+8YVoso86xFHYhri2zmqIME5vz9jcYZzQKm2UBFoUpSGmVa5Z/nZnhlzW/LtdScFCeVCa57IC4woO
+B+5R/ZrtToCLpmrW0eLQptSmbsebwkPuSQW72wziScAIBMbOTAAWt4Ikz9nAQZ5iK2WKreDh2LzF
+8dG3ilSzT4ef8vUyJmL2iNiz5c6hZKlyJDdn17HhGWHzi7TS0JAXCcg5bH7Bgt58IU94opUUvMWK
+wQmoAXNYy2ajOr1K+HNj5yYH38dhIrnpEwozTbtxv6YJCV9vfskHmk/s719wP0DYnVlfrbd84k9Y
+Rv52NYn0eP3NV3D/Ks+ENwuNAkMlQafMwMUkF96skfAmYNHjJAjjBUuv8LwvgXFKfwj4GJ5MnSl4
+OZwfCjeBitJj2Am3nwWv84NiCPDG6xmEr+neD287f4K5XrPteZ3ysS0WmKGlcCFQm/OYcTkKlmlt
+UiegYS+hcZytXK4l7SXtqD356+FKPS/5dl7Q4ZytrwNOCE1nu0e/5T2cM3dbdzXubCEAZk/hMXjj
+6+/jfLqpXbXvdSBh/pACP8cNQsfeGsg36wD9fbAgm+u3DcCbkMcHP94iRTm9vN2aOUgZx+kBgfIn
+UNx/Ip8yKNh5wmY9hkBbbUMDylPelH6W62/zUH8FJ/ddIpwWfSSAl/UMk201EA8GAfyU/CZxvhf9
+NigeZOiZNXny42/7lm0zB/888Y57ZH9WdPsNcfv7vNuva3/Qomi2s88iQxzmj8KKdIzh84j89exz
+c/969scl/npE/rLw76uH40dh87AYi4EI01AcebBApjwhxzE/wnUjjeQy5Y10vhfmQiTkZNcE48aQ
+q91EAu2o0q5KujmOfkrlf5cp/gPuBwi7s+dLICxygN0RFF/SdyUVmfAr2i5bIqy5OJDehS0vkuIH
+IC4g2IY8Lhh5YcRNLcrkVaQ0irSnJgmGxSr7TX8Xa95IZ6vq6Nc0md3W2NSFqLRQWftrSEObKW+E
+pbYV+DUAbWE21trKVyrT0lO5/lam1l3mf7YvGMbzbF31aI06WvNdOOrKKt+alyTyIW+n/byVz4yd
+WOxWzr4OY0zezSTycYXupVryc+qBDb8Am73OyU1jZ0O52EkflWuC4gAD4nHtnhOCFK5O5wHlTDTn
+/KACPlRAsqRO9hjjEqdnnNaU7+L7l8kqcN6nPCR9WBaLik1aa8ryHQBySbcznfwV8FJnoo3U3t7h
+CHUt8Oah43zj3gforX59SAssG/gq3zl3W9MPoNf8XLUGfh87ga4SGPaNe4+ubXaAjXyxoW/tzwAL
+ZIRU0DsBZMVHRlzeUbyJONA1kb+2VphB7wGQGfxOYZqjGOLABSlCM91KmUojnc68rlbPTquSyPqN
+JOVnke9NPjIoFjlBcME61ITpevxYHphGHf8Dy4gfIAz3WxphYYJNQkiCkkr9lMaAeQt2K/Q9Mfo8
+pSHzFIGhdMsAuxKCpixEejKrmzhkAw7U4SXiGhRt7cr9m9PRJ+X4cXBFgrNqtgM2oQDEAKr89Iow
+Fm3sPGewXMLo1wl+FfnIn0KyaY05PgfEy9ZR3JGWY4LxFqNJBXC+j1PnEzb4pnytJR7TANS17jNc
+mO8LQC0RBSPomObTPTrMkPZCMRvnGnoLM8TIMM/koAfW2vYOQfheiPp8EsMwXl87BrZa4xnsHiAY
+cVTHVHubtzbscZ/SCQK7wU/A7yR51c6jtRrwL5QRCmOOWYjS/WMZNQJSmrMpT67jBMEHpXkd50hp
+nTzmhQdDp86UcQymcNR9PgdTuzXHoOYD8NV5MZHZBDsXAylQ/AzZ/VU4xJkfcUafYjLxvRxPVLR5
+7ZI45tDD4sDYlm6w+zDo9evTwvrIs5asApYdELOS5AJ8U/ubea/aYtb0/mV3MAxNcdEON60wicMy
+rDTgASIbDXF+TphAdJHB5T4sp8/KIX+K5jf8FhWxUqxuqrN+6yR/p68uGffbilqOr3+fD/599wOE
+3b0Bj56vP9WBUk9w3OKQl4gXt534pTpCuwl05AliJqZ9EBjyHUDZzSBKOuk2Lcn41Gjn4sA1QaUE
+gNzxviyccx97uVkoRNiSybnX1KqmOZ5U942PMPoAAEzh8MsMeNG1CpBnwMyjjVmoy3/7GbZJSx+B
+cyaG7euY3obPu3Z1NvreQfGrA+0MrSkj0Im5uLPMkY3vYyTPiX5GUrqE57U1aHSRbwArfZx0CPzO
+WH6GzLd7Aw0CAE9At6fpcEMdfHTDNuZa0rjDFnwGTWDNvrU6jjDdq5Tnrko1oYAZgIjzOMq/76Gl
+zljHkpuGp/hzMNrIEA8MbfKkGZ6kf8+Lex/H/njQDu4ZHSxg2F/77z57+sl5qV0uBCBUzHki83do
+hdf2x7nvCgC8J9aWiQL0Up37fF3d2uFH02/q2mENswcZQO82hfDj3Mg/g+H6Kw8GBIRLedEEt6T5
+laYFlmYaIQUAW4Ln4Nn+o8EvgJZII+KueU0q7aSng+w6v60M5gZ0S3GT+UMo+0ieGmhENp2doLdd
+wR6iP6kJnpbFn3I/QNjd75pG7IDF5QS8/qdcOV8Fg4fg17xPF9oiJG94xYB/OSfv4Be2v3cQ7DtX
+NftncT3jslG9n840S/sYvYiIEUsmgRd+NcFmKVNzAK3RBzaP0JyEXTwW+Hs6+pGCj7W9uzGRxoAZ
+Dc1hl2reci7pBLkcz/P7DpxFcl7Z3QAb3/NT7ESFU8kbgyqM90sUxyAnvAMdBMlc6j3WzaUp/204
+2/oCEmMma4ZvtME7Yyv/Ww5ltYQZHKdmWCsIxvV64z5RUrXEPAm5vH2Ny7T0Sx2cNs05jyeOkMu4
+uumNh1qVNoN5AgA4g9wI0zDklU7SkSkd/MHrozEsPD6WvLZx4zdS2XfpfQl2o5HEShR+QIi8qlRP
+90upv649C1mgJFCwIXxvJNtAVR8/9izeNGDS1BXGW7iYblCsMIaGxvfRwSRCxfQRXWvbDKueJhOI
+g5/NGw67Yfq1I+JUVeyhEzJE0jTiqdpeIS1wgOFiCjFohWlIjckAJ0Zc0jmuzBEnSpapANguZbaU
+QX6A4BMAp2xF3JsdMSvr8pYDwMXDUGsaX+mAoD/qKszfOwAAIABJREFUfoAw3G+YRuxrBb7mRCZH
+uhMfOBxR/iTEjZhRlzORV5nWKwm+aoVRv8lnkEwdDuZ8gOBk8smYm8aWKgMTHLZ1ZAPVe9F2y0Y8
+LabD3KG0t5lDIH8DwEUjLAx8MZ4s9GjOrObnOeBBiXoOyWU0l8aVhv9mGtzlF+c607qr1DTlyV5/
+qutSvc75a5wmnVF+7YVa2U/pnKf34m+FI+K8c++P1uAl3z/jku60xqoksA0tWfqn9PAP9zhJofXT
+8rac9w3k8ql4R14Pp4IUe9clKwFrIDMPEWg/qS+6W5ubA/t4ZdvrOsc1zWVumzl5DLbfanx0OtOZ
+x9iRTmPI1Wm251gMRmmtb6kJlTJ+U/uD73I/yLZOH922vTjWMjTBfpqP0QePoFXW5QoNc9Br+0MY
+YR+sEb/9G+yqqth6RJduEF0AsRaziBH4kslEjAHuhbgFDfGWMfr4OJVNcFa0w/IMaR0oPyZ7s9wT
+w2s0dztMgLDweQLIKV7LXCHdbpmsZS7586ZFrhcAjEalZlgMMh3AWMifNN1/cQvCFT2N8+znrutK
++9fcDxB2961GOCb8SwDM1D7Gyx2ajMI9JOAJfgqBgfDAx5KfxcKEjWo8qUWZ7ang/QTBPcyN1tZP
+tUO0ZP5JEqEStFPx6lIvADfvF/bC3i5t7T20yd673gyRLrRa3JCPpZfmqJX5KvmH9NAeaysmvPEu
+nbbhzHqyHMecFNbjEZriz/sccVTtV2VpzkGr8UGKBhCM/AdQgyarFZOzmunWNYz2NNRwgYu1X3qm
+/9OsvdwrTCH4Tko/5FEpwLeB5G/amGO+J6ObpvwOyEVdHM75xxqntin4FvJojRMRbJgy9Nnj+KSM
+c/3OLGgCv51eMqF1HJlF6tukkn4u2v7wW8wgGjEXza9Rf1QSZGPgoB0uroWxuFQkji402+e5LxER
+1wCrOAZ2cGoEVKHSWyv9DnxN1b8sp66Z9U1yqq4VVpECem9hPcJhErFA16uES36AYAfFqpbtExm0
+vZamDr6J7tT+Dlc/Kxnyljl9itYen5McvI7KcFylHat5jcpMGmKv6B0Ap5xM7fEQX7S8k9TTxEWU
+LzTGxNc5z590P0DY3dc2wpJ2OGwGweEdB0beiDEIcAeDFWmNL2lC/JUyFGEPgKR5z5qm2+TBpJ4a
+MYDgHAtr7UUf5ms/TzhecThAiQ8h2PFSsoxwMFBvs3oYuiEfWdrwJgFusVi1zME9nIKtmz5ILOlZ
+YNoRJyKFrWWveM7sSGXpp8o0cJnra5xlvJ1553cQ9/js/d1Nq+Zovn7I95L2dveggyFXzusZ7nXX
+sI4AaG6znr5LwY/c5b/Wgmj1H9re/btuEkKW21lqvnR5ICNngCbUQWNLoA3hQp9K68tyGA5bYm9j
+gF/W+oYJRE5cgl/NPEPXjrWtFQyLnLRU47wj2fDMEeyRKJTlQCmT8UXz2xqhVMUBeK31RzHfNLWR
+tfXoWCCNgTzqYNh5jB9zuUkLWmGiJzyUOiDdwNr5uGqAY3UNcGiJ9WmA9w6KTVV0JeCNc+ancgS+
+FfcLrTBkjg/AkxreBMRdE/wFEGaNsGn6ffK2P7kYy2xII+P57vNF04R8RnR05JVWGYHaEwAPJhOS
+8d1sgunuuAJbTOnKS6JKqT/pfoCwu987NcIKgR5HngyAOAi2aQFADKG55HvBQ5KiC/eajrb5oiMC
+PIiSCTDiYDPsBFkYIl1LP+r1/LgG4rPV11MtiRnvZuyGhklEsAfkIe0u6qYHkB2WKDdrh6lsbQIJ
+xWxrX6Rz+smF1AchxpSlf9zzBM/amMic9wR9tzgOzRSnLT+PyLeu1cHzerTs3U19ueURkXpUl5xj
+cA+nLejn+56xM37Ulzr+eZebopTuSnGIP14j53zVturg8zAyJ7twYKZ1DXvNDIjB8wKQIZ/SuFuO
+6U4j7bUQsI2w5AbAA/xSGPXzvWgtHTzg8PeVpIW3FUabjIoKMIGlEFDRUGT0G8dYGLWdGtw3z0XW
+Ps/Cv8F1orfKESr4df4MTazpeT8yO4BJgjn4FFUykdjgVB4H7gUQqxRQvHo9g4b40B6zPbL6B5pW
+aoWXudmHb5ZrZg7STSSsgd0hbwXCfRNZakNBUTdAnFOjJMtJGXdmrJN3SY9yJgX8il92WgPGgjQb
+0lgS1n7WplA+xS2rPfGf4pfsfoCwu9/aLCcSQLAS5B0QM6isACEjKovdLoTMALF2BooEg9RsV48T
+ETKXSO0vg+BJI4zru2bYm9GKx65jCEpIPkbqLAAgXcmfZgwOjGl8Rap2GLn2/6YNprx5a4xBG86j
+aWRK0fOGv7Ev5XbW9GoGQ+XoiLVejocN4X7XAB8cF1PEbGZiO7+hBdYh7pafB0r6KEkBsAc9VFLY
+nr7pKOqtGuLewzNcTwWY8qdT+lv7c7rahlf3X2uDh7YAJE7gdwLDH8VPpt9BcMZztxgsdnyoXnVf
+cwmUJSoLsNvD3lf1eD4urmuLd5jWEvUueAnaR71OPqEHjeSbJeoIv7kDUE1mGJ3cILcJgMb+MLbW
+G9YXYmUY5xhy9ut0W87L4MBbFJWgLep9G8DwFjVpvsDa2AS/jwBUG9sBq0oFxdoA8lNAsa3HNcR0
+2kRoklfaBdOpFfF1OqjMGeBa1QAfoNfsVSuM0cK0VcC4tdGcnrMgc1yJsJZ4qURE7MAi7inaX6Fw
+TUv80tPyjWw2gfqoKFvBbtgD9+Zr7dafcj9A2N3XGmHJidr+Zh5BcTzRQvnykoBndEoEOKSJCGkj
+8v4iUjBmpDmKMr9pAuQhLm5r87WB31tebl/9ylzb3tGliz9lAkh6beX5mesrAFckFuf2B9uhMSHw
+DOFsg/BjIc2tJwF2CpvpBAkpubSkGFUylFWRLivP+56bK2dAdwy0fAK+XMbm6OE+L1VR4yb+/daa
+b/LhgW4CNDWsH9Lnu9ysB3r+T6P65iaau9/KAYTQhwKATkbwWzcSfQTCGBBLcCvlcUPYnD/nNsn3
+tAOWtrawLht4CwBIQHbzpxZmQDyBYc3KS/20Dvu6q/TDf3lgqHPJlAqR8DGSx+Bc46SMeyeEGFNn
+UodtsfC4aR2X0VltS+k3Z7MY8z22e4LSLAKE4CZGhfYkwTBpidleF+YSqRmm32H/e5pJCEBwHMGm
+Uk6beJbfw/bmOdPYtKfP2kCMgWzR/joYvsY1IExyK68Ag+63jBMFWKy89lj/k0iJe0kq53A9yqSM
+jAwN4GY4ah0AcN6ot5WbxbcREcIm1HeO/28Y5990P0DY3W+dIyyV0Dg+eCP9QV4pZSo0ORxLDIrK
+NC5LC04rPwVvY20wiDBthtG2zcisVxJMklbYLe3Iu5kpA4/tG3rdoxRPm1VEgU3gISFSGfBGM63l
+r+lps4u0HLMitEWqMPdCLMyjbs17985FW6KimidYwysCOk+r6OAh78gPI+XuIjSub1Cw96TE9ao+
+5ceIH+U0bhvpPgelOeSvK6hbClfd8NnjSpHdLviwPP7InM8MX3GUD9rgz3U0oQnAx4A4wO4JgBWg
++NKHW+zGO1qAr1KaUdciy1u8DlOMeAd0J7gFIBu0w2/5ZQLAyb/nVVLNsmZCZGaaCUUjfMTlDNd8
+OY6vYSx8pXTcg7WwRXhcFmtZABlZ+AjGz+KPhCZYcvy9AeGPr/dRm2DiIKud8buobAPE56a4DoBV
+AgCXcDub2EGxLt1nHj/7KirVBtisaYgJ7JpVUwhr9sQjEIacZnOCnPdCPu0aP7v7U/4OFRxxM/gt
+J0VwHrmnq4l/4a+C4ld9GfdJsy//C/cDhN39HdOIToxS/FYntvg7U23uAEh57xMg2eErQtGynDX/
+zqMC7av42b1BykTFvBktb2KXqxD4qisw2HDpwwX1xa1m2+iwBwbj9zafLDzzFoFmXdhJCmqaL35N
+qSIVELNwKL04+y0i8fAxpqscD2RstpFC+pRd3Odbmgg19gifaZ81xVT/haDfGNtbuan8ka01v59G
+QNh6LDJU4eUhvF8af2/VV6P2jzJ8AjnKxDiB4QaKAT7woZscj3PwMK7KAwa69PJxW0n2EetCyW+X
+eJFTOxzgVj6D3XVLZ9MKo3blTBwPxbQW62qwyA0+sqOx0ZIZLQ1cicsyUadJGdMAtz2cU3LwnM3O
+QQMaY5M0wCUl2hIPfkVdT3fC/amMuOIEm5kzQ173eJxhzFMxlcD80RFnExgu2mU/Xm0DWhy7tjY4
+XjiK7fG4R8xWaoP9Qx4Ih4nEpA0+AHCCXXvx+6z4VGuG4y0sODbsaV1BJRyP8aXJruLjQ9ylbNH8
+Ev2ZzABZMl8ByO2tuGQ12U+E9bhVxGFsRiXZv+x+gLC73zGNYJ61y25GacITOvkn84jmWIKMaaUl
+R1onNgnGaAR6pRKfUr86KuDXKH4DLR2wcq2CIVeGi+OLuw2IDT4GLlYAtwnfA9rkTNtlaVEaAVSU
+pXEJzZWR3wUZa4LrkGVLlTo/fYFu38OIgbQ8VcodjwssyPu0qZztaz2N8GwV+fvO5huWds0FRb5p
+wkQDn6qbw1Xbm9pIHfOf7j31azb+QRv8RQXVD5pk7ZxofT0tKsU0YiUgvrdGxznSTI01IaKfNcJR
+ZTuRhXhWpV8lMNzA7ZLUJAJY3cDwahVL6uiY03QesttQ16ZxXL5Sq+M1xlEnPX2zfG+/y5IDUVC7
+A6N63MwXJMakTFbxN+f8P85I17o3Q7hd6v3X2jDFfeN2sBHOPpuq6JPzFtpgAsH50NN+rAmOeWXN
+bwJggN+qCbbtPwDxCkZvXRtMmt8AxwF6SRMM8Mt2xd5fwRz7JAfl+SuPRlH1quTpDyd8fYkzSXEM
+vBK0ZR530w5HpjNfOTWCxHgq1lqzDCus4hC6y/f88x90P0DY3e/aCFvzi3RCEKedjDx4Y3f6ktgY
++JTGRDRpgxFfgLGYnEC5MnbeXAbhEJCB0rQPgrS4mytru+khh6IVutG9EZZsH/HtGgbAV6nKGdQV
+Y6ZVOaJNeA9NnIRr6ZDmmB55nAa0lSTWcQL2lq+Mg/HdG5Ip957Sbv539w0gnnj3UeSlKdCaIH6y
+3uS4tx6P4cAL3/X5W/fvMPlso/JraYGQdZDA2l/lODaPeL+DtlHB2gEeKrjLclyN4o3qAi+Z0yUo
+XkUIOO0b2iLQFBpDITDsFRII5tNnRCR5XFmJSaXoB1oCwBK0ZaAV9yBrZypBYMyMaeB4YZQz3Py1
+Mw9kGVQfN6ucU6MabdcLDaLNgYSjAVki+kB91dIIqjAW0B5d5RQVW1LNIVT3EW0qMwDGPI5a4QqA
+D782EEx+teWv9iEHfX54oxxA7wF4m/a3g+TQlKZWN7TBofUVCQAeFJY8q8t28+zlKnbGWc2fFaa/
+A9+qHc4Koo4JJB8aYdIAW70tH82KviV+on1KE33+y+4HCLvDa4yP+ei3Jz4nlonv4G2dKCz5xCYK
+u1NA4V5W4ykK5yHG0yUvIua32tqoUsBwAZdE5Eqdgl1Q7Sh30tN7n3o/bEg8ypwDc2pCu/lEC9s9
+nVj5DrsU2XLLQggVGbB51yF8om0xZ0bp6WcIXM4RJnpCWtGU2dBe/9tBBNIqPERqTfldd18tdLfe
+mOF+hRwaE/yd1sVQk/y1Hm511jDB5sAzf398Prrf1Aa/sYb0OPV43XWjEvzLgYZvOGLtcK3xDPGA
+BUHmuggMbiW5FuOlIVhD5xyVhzwHQcXOl+1KGVTxK3cA5aI13ncH1Cir7QDFtF5vaV9ofEuaoEP3
+ssV6yjCXVrLzGAfv0MuaY6Jmf6fvGHwRBsOFs0T9O/NoAlfuIS1+jz40wmkzLAMAlg9h0AK0v0pA
+uIFh1+rKL//UM2mD9dcGrrp8sxwDWwK3wmYR1swk7ATKAYTFaZPMISoYPqUYr/eUdNYjz+uYdil3
+A77+E6sRHfzeNMJxm6xSjNI5XDYQavKHP+1+gLC7bzXCIjyJ8yTLEC54sYW/BcEVPMlRBoyx3JvD
+hf/SQjQ70kRI84unOKtgmDuh5abVZEG9Hiz5Eg4/3SeKDitLeKFUcwiUfg+fYBN8O4ATj5eF7CQb
+VGZWFnnqdGSobqCrabVP+boohXJtJ/ehy7w+FlXYc04eh/fSn9yNbEueRo9fVcrYwZHW52+fQbDU
+25w00sPTprp/hx3vMfjn6uZNUPn6eYMEbJ7dr6wZCMIcooHhw+lJCpZZFR3CK3HwC800FI9x1SFO
+cq41q6xlymt0ib7kcVjSNMEEgAGaYRohQuurcGWJB/+KGPLYR2nXbuPbAXHR+Pbj0pLpbhCrsfaL
+UhYoAXICPLfwlDaWxU/9dk/hV2WBJDOzWHPDKid+/NtOYS5RQfAtDBo+0kDzBH73Z5nXBryHFnht
+ze+yDYBNA9jpcpAHrSXA7wUUB9BlbTHHMxBWFZhAQN5mHIY9F9YpHSqZjde/k+a/AnyLlvcEvgx+
+2bZ4k4MxeVZtL5kAxjIhuVCUdnfK+dfcDxB297ePT2thmcIUlxUBZWXGgwBYQgzxE8FMRIbwRJTc
+lHx9Q7eBwPB2FHA8dphAsFV/NnLwcz9ZkEjzE2jvQC95hbdZozR63sI9ncIAxASQxWAmkfMG8FHn
+wqi+i4kEnRUsIrSJruWbypI7BZ+N8VlHCnIrcTeX6Z9y3VtZMwZNll58dsZNdf+tZO9V70WEVYbw
+CaZv9/ht9wUI/r16GcTngFTgC0AB4Jvgd4NhAsIH7tHTZzV7gDzEYRmrELVl3xzrnXGtCdEUFeHX
+6OqANjW+2gCvSJpD+HWdm+WS3xROnfyN8wRqqHGhHBCtvDERjhRip7T8+FBj6L5pQ8Hr/NbqA2QE
+iJWKo3yfQs1eZUSnbgZidrELbiNyjbFbCvM/lSQY0OTgl0s8/EKb435pguJnVe3wL3NA/KuZUNhO
+M6PzhAnwNtBbgTGdJHEDx6EJbmuxgGGPLyPl88FkISl2i//legz9UVn+Jo3vpvEpTbxM0ou1tuYb
+BYl+5DLINE4fqOmPuB8g7O73To3QYwLLb4g7CCGY8OVGwYNsjK9tskhkXtp56xEWLBZoh5NA0TQw
+dpMEw9z0qybYkvWq8WkRTfiUOvu15jeqL4qiXhHhc31RNNi9Vu1x6R+FRbbQDcDtmfIzz3zPCq07
+xKlzl/6riQTnUxKU8gIo6N49bwhK8nPJqbYr/FM5WvrmTqZ2A4BT/LdxZ5s+ze8Z1tfwP6q9/cdq
+EuGeGLURADiOq2JAHFo0hJfAVCI0bBMJlDjNOA8q5dvYRAvtTbRZNL5DPmlp1TSCANEEhpdrjVkT
+TPbEtQMQ+Ihr/CniAF6opXxaQjJIwStwHDFX+ST18s1ezTy9L2SjcrjYlkmVd9zG8mUdHgXAMxMg
+ox4eo1KM0JcNcSXrof1F4+UAuxUEC4FKCRthe5boL3MtsKU2+NcSsQS+upYYgV8xizA0wqwFBjDu
+2tA7QCbtcYB5Ar0GMNyHfUeQtAzSGEXJp6sPvV2uHQhfNb4i+6xlrsNBcJShabZ278PP7fD+RXhi
+An/A/QBhd9+O/Z7EfJKJn30f9i9VvrTh3ppUKJx5wJZwsoGRVJvXCh9t4rAqJY8kkwZjTXvVqLVJ
+sZQNLBySkQJkxns9zT51GdPxWeyoJpfrZj77AF2Z0o5J0AqEMprivOms7tJS4SmBtIVHpoY+GCVg
+7I8yjeOM/jPPm83r55R+Heo7QCM2OdW6Egw1TbpmnlI1lx9veStztsdaFdCiFnoRPbvypft3ePhL
+Y8BUtIVDqDv9hVDTEGDKcZJgLcuQZtwQn6st4mUAu3XJHP5PcTWNgRB+GmBoIM2kRa1tq4HLbPV0
+a3nHdK86/Nij4Zy1PKwQXzIAQIsxD54Y460x/mC5EChxP5p3NLfyvE9vOeYU7lNZFODhwhkk+frF
+BZgmkBu1Mi+NMa/IKESHOgUSkIyiPlBbuq2d5uPD4xrgF7+FNbPD5foQMJx+T/qV1ha/VcTLiJSR
+bnwSwJiIdbrGuPz+9QaE7YvfN/kWwKw/kD44m1lFnqXut50utr8o6H7DGc5Cb1X+sPsBwu74abcw
+dMqDMMuah+Mp/Mgm9tdwbYH0oB0UURlvfZrcCTMITmHX79Y1xTtuRwb4lWFt2hQPZMgJWNzgYPVa
+wHEA8zxL8M5SeX7uJzkcIJWDxX8TBNrCNW8ycQdU0yB3fwufZezI8x4vFchQnZWxXDjsQUcdWRxn
+BRCwYLpilMngNEunpnLPWgGcHX0ec9TvMeU7V5W2tBMQYJPZKW/+N2z5S9cFpEkCfcv0LcxI2+vh
+AMRERKEVxtoP8EDWooaVynOZc8rNe/PzGL8tFYBffr1cSVSFgW8t+MFZ8xzrjFpj1PIiL2Yu1emd
++XKMZcxXbm4W8bXs84G55Hk1bxtvSE6Ql81nkNm73bnYsS6sJAff3wHN8RjAb9RFgDl4fVRb13K0
+wlTwxbo6/iKJLH0sTUXlac3VmLqk3+3RAHdrp8FeeJl/UMNntGmE8yctzKC35tFQ9OAouk2nAXwF
+NuNSbMJJHGY8j0FfQNys8Wpj3m+B8GdQ/Mjj2v1H8HZp0775ujU3WbIGgAV+f+jeSkYmODn9SSz/
+mPsBwuGM/srhRziIq4UDEA/pAX5tAsE2+M52jenJbTxIDIKYW2T3Bh7CqvGxFGpSNMB7XVtN5/YA
++EoriESgMqtBbkfnf1XQTCNk9batXbWtPibadZnTqjp1w6zpVr6R2w5HJ8BtsonFn8KJB93G/GGO
+MtSz52cul2NrVDVOwmgdo/QbgyFDl5KXmVbaqNJxV9IBgdJc0evQqV1v4TfQfLizX8xfOd+/yGv/
+PdcJPh42qx/HRMXiAyCWrV3D+gbQ4uPNxIVVkHnwAg2a5rXGTTv9WoT7PT/RVjGJEKk2pqg2w8wT
+IvzNOIb/tjYv/rzVQUN1eelBe8H7PLDHN8c1lj+BoATAFvGRd+ADM8/W90EZ2S01hjXCBIZNtfKl
+477V6ZHRhcBlE7k5Dat3uDyQmQge+vLBwMRsxdgkGPb7MEiGaUTJIwkCJeMS+Na4vQk8j2RTvJmB
+aFBqq8oJhj/NBYnDwLgRl2M2AuNI8/78V7/cUCiq+whogF6RDY6XbhMS/4y2LatgGPbT9Mlr4ye5
+qf9T+L90P0DY3WOnhlNkZiIMbguhtTRogU3sAxjuzipxHxlJB9EaeoLgHbb0zmsqJMep30Cfbwy1
+xDEgLBlA3HvFG576BVphtJ+zzozwtgqO9mnmrm2eR167jwryK/iCx4bXWQl0iZJui9p6nF38vZwd
+8QxaUFRbOStPGgMABXM+gC/79QC+CUw5Lq8MehVlRVJDNE1Jl9M9jzbPZUG9CWGuv+ZpZhu/yXjv
+a/sb9+FmE3OS9Mdyw1yT39yvAACPhVYmj+1ywGuIAyhLMCxSeUI054Jf2DTgIGmni6vc63bBeAuh
+LQ15o87iKXbJx52s+1tDXgAwj8EOV6B/pNObCW5hhI0Clk1TqxkPANzz9z6wG+JGdtt4Dj6yMYLg
+i1lEX3/B7/ddaxuiQG3MycuMdC57Ys1ka3ZhDiGs/R3A72PxiWXYFCMJqNG8vQ1FSkGgeMBEOYGm
+PpU0Xb6FuXATTm9pZT6oOdmubBY/NIUpZ0sHmP17gPgp4SXi4FY3ABZxQNy0wtLAMECybN5kj6XZ
+zMDbyvUfdD9AGM5mvWOPM/5Z9Z9aYQvga3KaR5TbH3euifGUdG0+mFPdivUmYE5hJeiBxME5nqnQ
+oDX6PCVBJGaVmpyMpGWWz5etIeRw78JfZyH0dj0KvDo9srFumO2Y62s+uQ/uR8KyErfrsqPMm3YY
+Hp6bGF/k6SqHzmS4XBmnOhoJNvJaQTHlozhoC3YRLivzvOgQDfl7a97FjbRgr8F622+QrY3e33Zd
+a5hrsD6klo1tvDjpNbtSXEmnSU7bYA3a2+TCtsOpMcYnfCe51FnBDQCLC8eSFnNbB5uP0Aqi6CYS
+7C9vIkpVAgjKS6nezE4/854OPmrF4SnjoHqES57DfIIyW8aX27b4aBrFx5Kne30T5p6Uhx1rjRjA
+78BKkp+xKVwfuqkgO9AkNxDN0CX7pfymnbQH9iP0AgyT/3FTCIDkx/bGOrzWD+Ccgwnb4xD4xS+h
+BUZ/oWFWUcEGOe1XXXUYb8CP+0zNkli7lTYY/HIzKyieTB4qwP0WCG9Mo2Ea8YgINuBa1wo7GIad
+8G4f+JJWBdgk5/4b5npxP0DY3bdje2yUs9nPGmYGw8iTYHi+s3UfSZoj7Vqug+C6qgwVui/z6pAK
+pqnHWhWRapuI2MLvwHHrjupgMnoy3ipUa1+7wJ3SjquefOUlcMQd4NgrO3baj4bXXq4I2nYtgraX
+te/ijfoIJlnaJdlgI4AXk0xIsxrwSh8B5A/bTQbAqKv5Nzip8RqD2LpGtymuAWQb87XxlxbE3A3V
+T4T1iT9o/PmuvrcMEzns4AgxZmLHFGOOQypqu5KWtwHivQ/AuZRV8wkjEH09bbZpPndc7UffSFnn
+ctb8xkNX0Fvzf+Fqts5leB32dTekqR7PmPsepBlW4pvFhMP7jfZbA7h0myMNTQnecnTnv/OXe1mi
+tUkD/JZGVXXpc+Vno7Nepd/6CZpS27aoIibx1bi1nNQ97rE8U9jfiOzPLGsKiQCMpBXGOJCf102J
+93US69BZXLmKiNkT9sN5css69831ccJYoTlxayv0AsDLTY9+fQlyx9/TNMLq9sGSphG2VJ7H4xgM
+G8wkvB3QDrdh/JPuBwi7sy9H30VHA71JrKkVtqIhZtMIkQ5Rp+V/F47JEW85VbpWRcQkjmy53jPj
+GAoXMByiYjpfQRp44iZBaKPtrh0QCZsyEwgPOp1Cs0jRGLdbMgaY4t7lo15CdUdzYD86eoDTlZn/
+m1D6mEbC1v3a84EJog2lLpvjd+NTYivo/g1AbJShLZyb5ABQOvilc1tVKZ+PE4XtuD9RL2G9sWkX
+mih9ehGwNnm0x7VbvtUz5dOXjLdaP/IjGhj/Ydx5AAAgAElEQVSdrphngNYdVsn4vEIDjHWNNwvq
+aRUY88Oq0O36Wis9wDyXMPXjGhaiH9CTZ2B/H0bQGtpxDPHACDH1DCREzjVd1ic6T2A42ifB37If
+oH/uS8bVpe/5G59gcs40PdOE5qXwkLnvUVNjP6UM0NQbCKaiTAs6qD5He9Cjs70xtQ+pdRWxBftg
+3R9ZMdjnmmw7YXMtrIk8GgA4vjSn/oq+D2bhy/UKaQvwy8eMlqFRaSBY5kWjjhZsjelBk0bDYM7L
+yzSljABYBrBH5Dvonc9SHjXC6rJbYBrhNsDQCjMYhp8A8W4LHgTepfW/4X6AsLvTMvaSD4QlBIYj
+Thz02gCMZ5A8tST/nvHh4wyKMsGFz745Y2AJllVUKAHBAWJGDpGE70qr0yh9CldOrNkBF44BghUM
+88IXSws7QB/4CcfpkM6g/Rg2vYSazysNDTHipmuJs3a95ynC+RDCaMIcH1qL3v7XAZtoKMFIVqUJ
+UpAWmrsd178OVUHx/vXbHStAazMb8VfAM43RRiOZg/sdYaIkJqraqIMubyx7qiLbMuWcMt7TX8/7
+Pa4pkY2BiANb0JkZVryGMM/1maZSihTrj/MMPJMXFLAqcmyQDCqn/lhZmxq8IumopiUR6TEuxzF9
+o+s5mO/9H3vftmW5iisbIv//i0+XtR9QSCGBZ2X16VXrJZ1jpjFgDEKXsCxjJJIo4EGN3693TG8+
+8UAHxNQZM9/1mtxZa7R7fq26wyNl86GDUg37qHjbaz0Gz5J/Il0vYH5zIw+GjHYtXv1x7bB2xs96
+9KSmzeLeXb4mxzjhBcbIu9ErHB/WeLY+ciABI9Pa/zOt+obj2jr56ukNckLSTUw5RHuAiHdu8+Fy
+7TieIHgDS5k+HgfNPPNevMDPS/5LfcpniwUGejgEyvNLQAwCYrfs73edkv/L7QcIx/ZHHuGYsPfw
+B+/1QHC3AXBTdtHq/eqf4LmUqdWN3xY0F8CBQuKqV18sOb1I9X+3p77BbuxND1rDWU6pNCppmtTS
+r6oWd97dQHVtPS3fifOOuucp95SLjfUCbQpEeAdr2vlv7l3/XfbdKA7DBaRCbEZP8lvyAEnUyL2s
+VscwHChVmSwB8O0H7A82YHiA7z/ldOUw7xk0+xe5kNVDWjz67Hs/05NwIhS0SvPU3pWj+RsP+S2n
+dUNbvMzvLJcq1uZSmrjNb/KdTPIlVjib8k3RAsY+jselZrfbNuPATchtbYgaNqBE7zdTslcy2Thv
+zMhHDd9kT497Hm84C6wMGQJQMd6Snzd8we85Bq7kErIwWdZROtc129rx9Azr+bubnbc6IEbx+1VP
+ucTXygtztH+8yeJNNwGyNHGkk3bnePN62lkhug89mTG3C6GjN+CtF/sWiDLT80tv8GM7NvhZddPe
+CMVd2fSDk4auaB+Qsrw03B70pdMCjLMJ1cHGvCcOVr+eC1lk7ryVCQ6JMgWbz7dA75n3XPKWAY/v
+MRlfmkPIwrJddgPE7NcTFAtg/Le3HyAc25+FRpAX30IkAvAiZR2PePfciUnv13T5/538XjaBytiG
+nuwG+Wb85dUdGk4+4lMVJ7q6rTiQXVAtLsqG5zmvFHSiIrj3rHXRx1VdyvRyL8Ecc8Cv+aXApfs2
+yiAdvxqVvrfv1JvWQpWxkpVGZOR3LTvGdRnySevBU9bTNkDtf/MzuXJd3485Jfqr/D7b2zB7zQXP
+zuHPV87kAlm9hxHZqKZkaWXeio7mWZJY7TCqr1eKrMscajBhtkvL+5bn0tbkV0rUBfwiPFx5DMUz
+qSnaqgiiCpjQuNkauoJl1SkKGoVOmmfHRa4q8KSuX8gseTf5o4wRcGTDykAlIq58zoKUmVEeBG16
+Knm2d/FWR7pX4/U63jr2Mj4atJnHk9qLli6K2fqx5rX+es6vVUfE2VJavA3CpeORN19Y2/zm4d19
+6rPbjgiTYKjdClpzPCtBMT/2gBEaIaRrxD/ENvLJbjUOroAgL8QZ6r7bsPtMhwHFku0L2fNOZ86V
+AJAGguENKDfQCUk/E9TewfDzqc4ToRGrgG7GC8MrTZCbHuA4znjhYI1vYrH/5fYDhGP749CI2D+o
+eNYNcCsswkFw7FKOfreb1z9Tf57/BuSajfuwqSL3VFRq8UuRnRaGxrAZs9mv1M5lRFmg6/t2ENwO
+rr0WX9Zh8GauhPhysEdPzxypniQxEGPlsFN3R2dvRud172OPblRuhlnPnUVybsaGypRmxZn3zka1
+JcjICLmY+/lb/Tg/ddvrKT+XLfB2OQQAQ6urAGrXp/GrMdVgG/uQAYMu6e0hYLx4rCZpioSdqTpv
+z3Nc2lLLp9vId80f4S4+qre5FevqUlFvjmgwJ+glRPE7i1R4RM1DE1HRAzniFLDIH2BYz2uA2XSv
+9JllU7jv29QRR2LKZyGMpBdk/G1seQVrgNWvcgA0+XChlVy638SXwp11txjINd2LD3OeK28DD9TB
+GLf5CAeZvzZkO/KS48RmpJiF8kxdn3Zx9yOP86ajrqsvr+U7bukZtjh56x5b4aE2hkOEfjALj/AO
+l2ihEUKEFzHuQ0eXa/72sLkaxeoPEDJNT/Ea+ZOSsbuCX88pwgS+sy6YT2/wOxh+PoBjepIff7Ds
+BoIlROLTb3kDxj8e4X9x++5NSPMCh3A23gxvsBWvljfYx/Fo+Uz9N/kfO/8HW5g2Pu/XW9mXppp9
+umaUUWWF8taWkOrpfZTvA7gatsy3VvYJ5F7PLB2euQWGI18b9jqvaXoxMi7pq+Gd+UBnUpdEqyOF
+t+ocxOjva56qdaNh1x/SiOdLKxMAp9Ff7bjq9BliXwmPlQeKJ2ZnUcdtreQXGlzzygJNcHN7p+fG
+cy1vnDvPJFhvPHooohNcAPXoeXqQqjOewOpIZ+c0jTsIxgUeewe9J6X7OJOPOET1jh7lF4/xDehq
++irH3zWm51ocmTqAL4guKl/CxfQ+o3eF/F/y0D3BIRNR966fTNhg1pB6CoIRvBcgKHWS6hbVNxeg
+v2ODd9nmNysavADjGxl2X4RLePPr5LvITnWWHUc3uOoVdgG4T4Y21NJkEQbhS+ZgP4a3xTqWa9dy
+TpIkNsiD+3Y8fNG5MZkHe4J+K73DOWYDNiBePVotifmUzJLUAogJfhUEVx4SEKtHGAJmb2D41Qv8
+zPI9LgLgBoKtA9y3X43jou7+wvYDhGP7r1eNEMZqx5lXddM7nMLOFm9C9pLvH+ovBSjrqPEqybpd
+wREF0BLxXc2MSWIYt9kNwo2J2a5GSRXGKxSJM2z0zfqwTft4a+ItMzstRijy0m67qHTVoHPvcY4Y
+HpcyaFmeO9q91tHGLnUUOU2N3ayX8lHfqjUx7kAHww0Er6q3rMDvGvXUGGbapXvVQcIy9qiCd0ij
+GBCNtgH+BoyTz70Amsu5kCbZvzYVduXZlkce8Xut33uIR9+dVECfN1rN2US3qHIcB6lTfgeClVWm
+d/7CRtrrFBIb+xhL0xejXrKjjf2H9J9sMs/HDQ+RBDNVPgdNj5ljn0JW9Keg+HwyYtJANSaap+VX
+90b9YBD3GFfYpldwfAXCUnbxAs/5/l1ZidsZQ5wc6UJ26eu+/pP9YJ4jAO2ql3M7KO5Ad3uCVUd5
+nx/pC9BlX8neRmed/6lqLcIw2k1xLPdmsSpEB8Txgpw2xDJwHnROSBuM9DswTkxyeIM/eIIjhOK5
+1M1PLF8AcC6fNn6UiQ6II4TkX0DCP0A4tj8LjRAgDG/Lp7HsYWUUE2fscCiZm3z5BDIfezfy+TLc
+0gPx4j3rio93J7PJwkv5iiu28lbVRrBwa6QZtbuaTEw9qlzHyT5J9cPAftjOKvZScBr22p9eYO1w
+xgo3Y1n7BC2XMo5PjU/TBf+foLjRVOJnTea8BmUv1s36jwAGBMM0JDcQvMrgrxWgeIRMRGu7O2d4
+hLWj4Rl2dGBGF8sEtMI4B+myzPvKCoMCANq9RJ4/as+zQ4SS9r0XNboOdnTy+rElFY5m+vynMEvB
+6IO7HlWkfoJgo+R/Br+NjE14uiQdL8QZiXPbR9pGug0Y0dsaw1H/t3pChNPRATFBh9JP5XcMPuVB
+9IxzTNMLrLKRN/F9XHOsfsmr8feBKgiuPg8wpWPSMspWAqzz177Y9jy4bVd1Ev248g5u14ovoD37
+OEGwbz9k9wR/Z1/geMcIM5/Dr3SR1TpvAyIb6JFnZhV15U/qxnaPLqBXPcTwB2areE/5V4Cu7r2l
+Jwh2uX+ovHvYw/D0SvjDHTDv9P4uxkKGR9jFOwz1EK/dJ5Sq3u3+hEb8q9ufrRpxD4dIg+y93qNK
+Jc/z1ubNQvvMyPxbnsjnWHUl7eTbumQUzqzPsAiIdBMgABoHkGAq99PIvTC1A29rSN7OmN22zP2d
+0NhI3urfAc8t5/AC87+P+t7HNgGyS3rvu6Ey5kHqHgfjOC855nmCOs4ndK6AA+Exz04a5s2VCRhu
+vwmCV3mB49jSQyz9Zvcyrd5ha08FEpi15RM4DlqX09Q65yenLS1WlaHijEsuSALrx+y/D9JNUrKu
+V0uzZc//l/K3WGf0asdFkwkt+9kqPxTzUY+awIvWvWQY/9avyTOjX6kf6noFci3mRsoVDDSgi+Cz
+avNVI3wK7XJNlOxeATFBMSf8eOlAx2woD3AdNxDMl7Rs9QaavhIddOQdhEXy0vAWph5qALjqML3Z
+dObH7wXwYq2P5ckjXmEW7RjSl0zrdXsfyiMsoFY9v0L/1x/0GEivvCnolXCdS1mfBsoFV9BYA+Du
+eSc/7fjhDnq3mfW7eXs854OA9tvHBxgmoL14gj94i5l+vDzFvgg76utyJwBW8LtBL/iyHMvVNv7F
+7QcIx/ZnHmEMMKzhD2GkR/4bKObVtf2ZV0ef8+q91XV9UWffPQ+XMDFDthTAwkeB0SPapdNHUzth
+lfFqlYCuXfyS+s520xazQ5cadpacrbyUES+EEmx60Hu9MqBybgLdyDrK53UGeFEekeu1m7mjDjNk
+bki7UVTbzboXSLE0BDQq9PJe9gmCT0BMV+n+Ty4c3l+O7VgzTPbtGeOFXDPtnrzayGtS2arMRhuj
+2mvd9sJUy7m1BOSNaHbWRt0+7wdzXjwqXkw0Tuj6o7zBux5vlzjf2v+tDiSk5Nad5DcFJjGrdubj
+Lb+VoepYjcK0/NKT1qsLbzTZGwyU4QS3uk0nqtKRvNuN4urp8iSPc4+xVB2/1rH6L6DoEyj2HKMM
+snkdDQlyCXgDGNnzdGlc6x0sh8wl78h51ItaVsA35DUAmoJhAwT87v32sBYgK6CMfJfhHTgXf9Iz
+3HhW50ds3b7WFv7tnQYs7zKX6Ift8dXjXGoSI4/0uc5JzR1ppADYfR4TDGveCYZ1JYnnCpYjLWCZ
+YZ8PVtK2geBnwfGUR/jZN34JfB8AFrT68Qj/e9u3PcLJf3cgPAHvGyDuXhk1xCNvHF3zmJnOtScW
+Cb/YSAww3Bq0AhFbu+xcvj1PY4TRLpsSPdHvuPU6csHWCGMeP81Dsz7XGi18Y/StQ5L3NjT/Yn4a
+BWhQJ50TxKo91e5n2sXIestXmplPEgqdeG7LrhO6sb/Q520ys8wwKVHgyHD3CjMWeAEm4RACiG0A
+4d0z8jn91WGkbYZFBARwyWcdR4d0AxhfyZSAajP6MNHgwxC2zTZM2jvwl1/KoGOlbHgr4yjt2mHO
+zUvZ6MNN7O5ahCJvwmvRi+xm9NrQqeM485QOeazAUCp8BMEKPqTuEMz2cZysO4lyjt1uuY1XvP2O
++FUb1SYTqCf4BogXvcLrnT645VMLDUCmdcPoNBCMDYYYPyxG7EPa91JjQHyu2DvQvQBfX+sEyHHT
+ZNht1hdFqU3YRVGY2Q/+ngLDzz72GH+B29JNBYbx7i3OugBBuvOYgPjDDVuaSo7NIvTCC0TzRThO
+neMB5NjCn5rqeROp1EPymdDkzTt8yVfwmzcVL57e7g0escDMGyETK2iUtHteQHDmrw24CYwlRvjH
+I/wvbt8l/h3UXkDxxQPcLkSv4OjE2Y0PeVN7+xKj9oRBr7vKMsyestys1gidoOx7WMLsr+PESdLM
+B/TYC2/jDVB0grhZs7RDgl/rtW52kKDn/gDVLqnZjr2WbxoX0GmAxQf9vNKsoi+0HPySeZMw3nnX
+B8V8UOQKDnCS9zZfMrnN2zINfB7LKhEDENtS77A+fve4UnmE6IksysqyVVavcxXfOBr49dbifdhp
+kHkNyoNlPfJ6pv3EQpUeMdh6xUCNdUUVxO+kf7d5Uzm1mdZ4PbdJlqE8d/SeBy+7rpjw1sMg2slO
+BSSO48MbLMpG9cv0CDfdY203xzgJ9DZVx2+b8eTDsPz4+BKxgtUEwPoT73AhIx3o5XgqVq1T8wT3
+5unlTaUYrJHWvSFj/lacHCD4AMQ3L7DenLjnMVd6SF2npoe8dQPBAb4UDPvzIF/tvIDezaeqk/Ys
+KWCePDdBb/+sd+W3NgP8cjDbKbBtMqdkxgTrl+M22C0vMBBy13gaGSN9zFV6gwV/HN7f4IHHg5wd
+7M6wiPli3DNCJjTG+AkarKB3eYMHCKYXmCD4WSNGOPjqL28/QDi274dGvIPe7h0egPmSd1x9aOGb
+2Z71Wq1QUKyy5SzuNAEJl/C224rB5bgVp/wzfrLpNwVCvK4ol8NTE+PeClBMZ9+1Tnyam5vxVcAy
+m7sdvVjLax07ci4tuNDac8hiTAVIcUK1bObzGnrjcDIClIjeE1LJZP82gBs91PAOg33zBHPubcVj
+wPAANxAc6TSECkXD+zYWQS0ADAEkRktSFjVvQm5SdG6kVwPDciOjqUxTLmROlbKZDl40niSlr2CY
+4DPHVvmVvgzkwxh/p+Xm+PJJEAI0cJwWlKLHK8Z2PnWx3FX4BHkGDXzcQLC1OlIvj6XJvKjUGd24
+jngY3Zs+madQvnlDcG46rikjml49vayfewxOyy7pAYYrFCLmcYBifZQ+6+aeciAguMUCz7SA4QSi
+QAHUAMS1asSuUVwdN/bsA+dIwXB4gvHEMTmXN+ZGni2vbstTuhkOYExvcHqFpW4C5cucmsx5rlNs
+AdQTEEtMMFBMlIwUY3EUcNaipEXM3TPyxBvc84oH/OoRpjdYQK8A4RYPPEDy4+URfmIeFkHw8whN
+pxd4oV6QC2+4c5m4v7v9AOHY/uuX5TJ98Q7f8iCPKKBK9wJieokWHnWyxgTDwyuZqr5Z82o3FUVs
+p1HnHTNfmOpl7dGlIR6rCmMvKyWsV1iQfK2PrURuMWcNZKsB7GkbdSc4v5ju76WbIUy/RFVUgKSY
+rBkaOW5l2o7Mdv07jm8AuGoeM/lyrNsEL3OPNBL9ZZX4XOmyvl5ngGIXcJzeMR0H0YZXOkMdJC+Z
+eDN61UsjIAacdH3o0bOzHTlOkG1W3ZpbWe+e7XeK7pb1pMoxAo5Rzt7onEpjr136b/Ibh1jtAcqx
+VqTHzVBAYe7ReQbzeNQZx+Wtq1MbEJZ9qjbcnrRByY0+1x04vK2OMH8JJCjcrtowLpRy/SZfn8r+
+pNqtgl+OlAieNO/yMHShsL8B8XniyNDwh7VBjWEF4Kb3U3RZgGA8j+gPF1vAHtSKR3ioN7jywv7x
+Jdn0pQR/9XsGL8bNYVOmfVzT4eLZ3wN34deiTQ+/8xvHDQ1r+6YSOGVcl05y5FwUFqxrET7nV2mz
+PquK/lSdN3icda+8Ps+dZeCNypAXHZvvMZcDkOPTeuyb6P4S4r++/QDh2N4MxVFvgtqW916mL9E9
+yRrDuDWmHsdaZ5zD/ymmvNtubhwUozJtqgCoJ7yUitBFIxZTQabNK7Az07kqgCMU3gLw5Off89uK
+T1RYkhc/8wc+lSowAK01e9uPrdfqFn2kxFbe8n+X7l1Mu7hJ4Hmcha0ulYscH0k/j1sz4zhBtCjv
+HOAHpWPjdxRXWITeGKVHxgQcrwLFOH70ipWBzjV/9cU4KstjhQgvPtY89/3uBQh8o84KAxJKnF/O
+qmOkck8v2SQb0zSsSqISn4tS6QZUx1ExwcNQunrLKu/W8m275b+dnfGbCSYG2D2A7up5R10UkGhp
+gotZ5y0fZS+lKLcGGrp4HaybRrd+JyDAYfxFo18outv04+ZLVn+53JClfuaP+I/91HHpnUjKgqGx
+CutpWV4T7di1rFMNze4k0uT/ESO+rLzjz4IvlRu/jPXZ4VAPPasKioEWx0uesqdCBPh7uqSkdJim
+h32wWa+dqsMvGh6TIekAcoYP9Z30pGzd2zKg3hNrere86ntJsngl3g1L3vh0lBqlCBk8j8+XTDwt
+enrUmTLkWr5VJqyWT4pO0s3u7368EFtuXnbdi6H5S9sPEI7tzzzCoRa5j/OPMueyajtSl8fL9XrB
+cBeF5L0g8pShm8qqs7JpsdqiIPORM6UH4zGnXP9Ih7D2F6MgXC+/jHuLjV5fl8dprWyFEqVRebK+
+qfGSfilo196fADgEsk48MJ5xoOjd1gW+vyOqhmPaTvuZNnUYKhbm3XM3Q/P424A4zxEr9tb5Rrva
+e5vjsG2yVz7onuDzJukAyukpMXQAfMnTgY/QiQ1+QioXkmcIcPOTsQJMbqAYWg/CG9b5oal59xfb
+mSZkFHrJJYBaLaKb+F2oYRTVzNtTxJs2O3mB/ylDLnGVyJvcCXZ3HOOQ9VteNp2W7r/PY29Nem+k
+GtAI41IvSS0GnQB3gl4Fq8FPPvOlrGQ3JiKBs8n5Nq5hxZNtAfqS79Z3HVvKgk6hoxweQre85uzy
+oRSq3o1pgK4MAdHjwH5a5zA8++l/krZ0eQLiGH+uNHIAXtvhAKJLDt0iJDm7q7pfjscYmsW4CdQL
+WG36k3RPCaI8j96ZHt/avI9l55mAY3nF3eNlPOyumm1MsYsK/O5f8X1F7uo152o9kKOzHntPPZj1
+bJDytpHkpv0bhf/S9gOEY3vTAUc9BxoI5h5AeX83023Q++I1fgE2FDKfF9U+tmNpZwXjk1NTuQY7
+U2GGpctD4xWtCWUT8xDIMplsRkEOUF8Q0180NN+l4AsX+SPwpe5c4Sn29rKGiszWEV2c5isrHQDv
+49kGaXgFwPSUDSah0si097K0i9L+YW1cK3o/RxRNTfM731TL/alB8wZn5YvmOhAFCphomeHiteng
+tuftMIgMjRhxwk5XBsp3u8fOfCWuxg73Bb/y5ZILyFUwoqAYku4gWOIpeflQ/uqJM2i5GMMUnk77
+8g1ppZJVWawMguLqfF7XYw4GEk47PPPG0e1xbsk0AYjI/bx5gcaAc77lK4IhSPrm/e74ACBvoHfW
+y7wpULyG9+oChlUYWnjNAXKpmTu/DKHs5+S1ZR4H4D0BN5LH6gtLHuGgF9k8xDeZcc+jKmyer3yp
+IhRjF5UztqHE2GQeT90K+MWJQceFylH7+MZFdzSd0mxRh00HOD70clP293xcq6ATW+QvCcCbXZVd
+0bFW59loU/zCnX7o5zhqfJRDvrdovsMsCvgiwbCBafUEb51V1OO16kmZaoMExIZ01ik1SkOx7ZOW
+5eEd+4PWehaT77X+ye0HCMd26IPXehcQzD2oc/e6eivzLnuv9iba2U0M89WQzR0ol4IH+CiES9Qk
+MktL52MPWd6GglvCVCrBkle798ga5/fwCEOFPXz6SfhDxHISABdgrueHU6V0PX3z+E4AXCKuTgDR
+ZTotScNrGzoHojQ1L3Oa5wnjvEgcnhuv4joqHc3ceY7yVgKJeij2UenMImo1zboCXxMeWFXOEAkt
+b2/LixGXvgI0+AUAugUUngfE6Pa0guJbuoVFRFhFznl2x3MuJ5NsvrGscxrpaRC1Uq/cZsYHbcSE
+zdY10THyNHnjHPYgp0Ot2OWnS98dezai7CJxj2mhx8UbEJa+2q3n91EUXThPU8iARIHBHz3OUWRP
+0aJLmVAzUYsAYJP0HRhfrnPoA/K6HCYtxrApD3LjlB8/Yj0f5yiznKxUzeU1aQRGRyiODniM8QDB
+qc9kvCHyHz2+yX/lJaYNapUv/f8tlLrI5VkocmdlEWvuSQOVZ3Zc3Q93nabhhkCnBWt2alvSn/dK
+i97hIOmKeVMwvP0MvvspoJhhY8jejpV5OPyoY0d/hAT4Bs1PgW/yTmtqv2/of779AGFufpGmWzVs
+xriCYZfQB8nbst9B8AOXuymXLrhkqUdQlP0LSOYb9enx0vOu4JchESNCUXSwqoh6sxaYX0pKr5EA
+neYZHnG//PVQiPg93pfr8Xj5wgH41wXgqsKZQqkllWeXUvXuUQnQBpRR1TOaGhN4EgZh2hjHmSEG
+0LWOKKBuqyYgrgqf64AaEr/bPAeoWk6tlHh+QQVulb+YXgV2JVQCS7zCfFue4zXtMQcQ+4t3eGd7
+egU3/25i/BbsXtLpJYbXEwHOEQ0K+5Iv4kxDyLqkF8fQDeF177Uv7/BtgvTQ7sV5/SOo4sQOVsMB
+IF7gkPUFJNidIDiPZb1oaTeTJob/sInFa9c6KV0FEZp+mzyTOvWSnzIn6QGOJzAu3TzqH3NY8/YG
+eu2SF0YhaZ/bvPmbYBfBh9oHs6ZXWqMy9EaWt00V4OTEzKa+E95V+yO0zKX4fhncft2fLP3SlRli
++Cbq0/Rn0r527pZXRaXOOkG8Bo2krZH7SMM9ThMBzzW2gZqr1hcreyC2FfCUs8ZF+TRGQtLYmscC
+I2G7F7vpnTRQz7Dmi/xkH7KnMwSicMpRNkZ5R8JlI+y2J/jtQ/zr2w8Qju13+qAqVmjDAYa1POtJ
+OvYP+GijhFVBTynxb9SpiqJ8ukEtxRhgLR6vtLdkEySVUc8Xn2Lopfes9hbif/UYye83m8CIF8/x
+fFnO5L+2Y+O41zQIQGktRTq1EelWSoXhJL0PckPjRbncUnvjzHPJ0BsBNbhZzTWrrjLq3PLqv2hb
+UfLH1ggiCljnMoFSqDMDpmeY6faEIEIhGELji4BZzQBXiBDD3gxxGdbdl27Vb95gTR8A+SF15FHh
+kvNySba4Rq0VBoF2ZY0SsMiQdHP6hDK3YYIAACAASURBVPuYr3snLWraqp0yrJLVL/Xb47KUDCFq
+L80FCG7ee/UI83PZ7dPZsjzU4C/VJ5mh/TkFOgpGz9vYPecmDX0DwFWe6QF0Kw1UDDHr9fLWTluT
+16o9lglAZj2f9Zpeq8tNLHXQVPUHrGQowbLWYxlGPs78zFbl32XQ9DK5L56tEL1znzeuv9BsxHGj
+FPzXPKel0PPQdf5ybNQdGGP2QT+rOWwb9aPejoYcWtnHfYnSUXva66L1khwvOHXZGEvIoHabnmK+
+8rDMwjx5ixFuYRJOUnn7lYywd+wnIKPsZWHzWp/aeW26rvtzpGi2pGqNCf5L2w8Qju1PFnHWON8J
+hlv5qJcvzrFuKIbUU6lwq60EuZJm/nleXb8eSTE/NJd6EUJxDueWGEYR2eTTycQvnD8BMe3J608f
+KTroVcDtB4pLF5g8ch6/lMeA6m6ZysEkjQaKNYyi1xua1vthlo58zPxhoPIm6KagMVjhm3lTv7im
+GiCxUV+0Wcxtjt4goDfKFACnd9hOEJwxw6H8pmVN4zH2DSDvvDJDMWjy/UjrShKkk4WF0XjGY83V
+fOHH5GU57W/JbpLOpK+HAQxAlKZy1pkT3z2igsXr0tf5LeHuLKYmrJ9T81jplONjv/JjKQWO14E1
+ju2Sd9adgjTpIvKRb7JTeIoXCABS4ATQ8man60q0Oi2/hLPKTHnAkk+K/4S3D56M/Y6lOwjSaSJt
+kB9Ubg/5fyOyElXHVnI/IyD6OIMHidDUgCidczfzvOzDL944C//KDXfOOPlQupVeVA83lMt4aLQi
+XU6lUafRbNKuyysdIS7zsI8pk2FNGCpBvdFsrzZ7hkcoCC5P8b7uA4uHZ54rTi5p2l728BpF2bvU
+DsfxCXKntujHr7zWNpud6GWX5N/cfoBwbNPsvG18tPMGhskWt1CIDJmI/Lx2Kt86H5J39xaf5/RH
+uaKgthsMTUkzLEKFVQTzNM1WYDn35QmsfYVH+KoXaQYVBVjoD+1nxwsn+3e2Zs3Le9yFpiLoinSm
+1YGTOL/lhTIUwWd6riyhtrhtbXyjygXwC5egTTnzIuNTXu8GO3DXOIdHjgOCzKPxt/PSGAThrx7h
+AxCvWk7N4hGAelqyr0F8RX3WlTNHmHkBYj1etCxl7fuz4/7ZY8zzD0DcKKrSgZZuuKX18WJcsR+r
+1stxehmOfbTnwJSCVA12sp2WIWlRc5n1RW407l9vXhIE8+MoclyhLvJ1LF5fiDLwx9nbrCsgIfSU
+TdI3IRoKRBwDdUw9o7oSlf+b8qv3kfxJuRVnQ93cS18bELYKBXvGfOSEetFiyqeC4kSwU35uBJ+8
+2+vPm6xil9GBNAhS0dvMJe1an6kfeC3qGO7E1kxlfvCP0jh1hbfLqlVLKRUwnC/ZtjIhggJ96nuT
+qOEMMdyddjDG9sOcjCE3+xp8wHsNnRd6f2MlyHcwXKRsrKEhmTdA3Kfcsx+b1KXjtJ6FTm6hDnrt
+PB7lpvufGOF/fXtXzGe9Q59hgN0BlhsIlnK4GPOmtNmh4fX1EuN7ngDiMOoU0puSLkBhBd6smDz3
+FEpUKER6ixIA2eBqMZrNI8y+DMKa9PEAx6OMp6DppsxTa9IES5RDFrL90A7He1jGO30pMAG+v2Mc
+l9/ISO9VU+RatZvE1qiQkErMpWKVSRuc/6GLr5sNg2iab8kDnH8FTQV49XjHlCYglrjS+qLWGYeX
+Y5gT3Uav51TZDm/YhJrLpt1jhbvsEBxDzju9fZsGdSN0mGnZDubKPDU/9lrv3lyZpka2RqWZvh0D
+SOAR07xXolGrlZ8EXiPNrwd2IFy917GcPbj25Tv1tsIL+stPwW8DwpB5G/upe1ue8FYmWSYoYwDf
+4rnRFnnokScSc4Bk6zbe4o3+MiH7rLoKI60Ne+nvTwpBQFi21h5HIGzGnFdLfds30kbC7tRzYQBD
+JHxeX2xTv9xT9Iw5SFvmdcnsrvsJ9FsXvRuNGSIh5dsxxvIKWQDiBle85fu4BR6WpDe7y9DDfb10
+vGOAYJrZ6M4RKxzOpnt4BK0G8rhLqGeOaT3hyYodHtuwGXYtm4bl391+gHBsf7SOcAIRF70nedGe
+Y39Io4FgVP627X5e/w0Uq6aUayabilJNZSuPdAsEMJ5MLGkIaQnG8AwLfkpvkQDiY83YI0aYIJJG
+ATgArl3ydCxp2Ib4UPCFPKlbQ2vSjqvU5jllUnY+lSjPF53I+he1JWCrhomsSdoj62hfXBMD8O98
+b/VqT77TtqSuJwmOrWq9GUFFReigOOcbRWzhgfIWWwHiiAemF9jzk8tW/Mw+qSE6wO9ON4AVmtmA
+BLU3wLuNZPBQ7OdSalVW7eQyfm0G4kfevQJWkbHrePQcBTg0QuT7KFedILa/UYfiIteqXqnH/eSB
+HIbItH4A5fwwSl8KTz+b7dKh/gh4xF32zox+ScYkrY43yTfmJYBAv5EugWk6doLeJkCit6bAkeB5
+o6nH6McTLFOv80tsMh8+r0Ea2sg7bsLKQ3z1rpFfbi/ZpU4TYT/a6Hmz+DUkTdL7hTiA4RGpP5gP
+iBKnjbnoMsP+uAb1x7xR5YhiHjKkizce2SGV65LPspVJtLCTDE9iPDRpGNyevIDtKR729U3nNk9w
+7jmPVuZaXpI7Y4M3AO+rRhS56jbjuwA4Oa9rKiON+nRcR/aBDyFj+9DCP7r9AOHY3nTsUc8LZiiw
+JUh1rzr1GeUePvG4x5KRnm1CjhMUUOm2OnWdQ0mnZKjipxR0YU6t4oDJW+XysEfUqigTK0bVx6ev
+3mB6/hJUBCHTYICSXUbK6rjFDYN5NR8ZB0XaUJ5EYo9jvynnEa3V2pI4znRjSJ7ELjbTnmP7wF1a
+ZwDg+RnuavetvbewiPAU6YTqIHnUFNNICyj2JEzMPzDALzK989fgk5UvyZXXmBfrhug1PV06Msnd
++8tjTzorOK5ylu28rH9ZxxqP0l/la/CPHpTl+Ma4OIE3kLPTLTREycFaFBftBiYIvghFnmMl6inP
+6F8F/FpoYRH8fX2BBrJz8BjjHNek2Q3hH0nqDJUdb7/6OI/UOQTlQ/6nOsmyXnVMzmvOCDnPHbLE
+UKUnGUQfV9YtfChZBtqxlPtjC30n4PcgufXat3Y0/6Y2YPaSj3pZDlSrCvR7u6fGG/o2aavhTDEd
+TAMD+Kl8VZsnb/KJqYJgPh3dNKYNzXdzaN/mChIEyMcVSoYPT3Da3WKnZRGKEeyW4RFRdYJg9Qbr
+PLjkvwNgPyhSZUhvdQ2rrwSRaebbOO6Mdr95+4e3HyAc2599WY5KvoNhSLqFP0jd+FgwGLdWursU
+qV+P8wJoIDmTChrZRx5bGYEQRKueIgVdNaGUaK4DzQMM09jhyyoBJpxNKUrQG5liKDIuOD3H0nca
+Ua/mVHc1LzAvOSR4mJQ+saLrLeegRm6h3KwNRq8z8vTIbz89qGpVNhvoKZpNYYVR7q2ecGcN+GVr
+scJ27ismeP/yoyY3j3B6hekN1qcGDI1gHyXtNw9Kn6hbrHAHuzwuudh85imbeXPF48eLTwYAdnjE
+cwodlVY3i/GxMNIH/1wakAn2mR1kOfL/IM3NmvWytLTdQzxCI74EDH+t0cmdyMfI2dn7+PQ0bz2c
+NBFYX4ITxzJvCoJTj6Dq6UXH8XRAdIK5KMTQTTb2EwBD+C/7Gcl2gzVoAQDisCANu3xcZMbiGgOQ
+Athy6lWvafom/1KiKMWqIssECyXAMfln81yxHcd1yS1dKfcy5ueNq55N++V5ukv1G/dn+BiQdGvz
+TBuUT586CM6lSJ32ghcdOldVGl9alx4lGI6OO2LVCCu2MuxrtxjhqyfYs02CYnZsapxdW8FxlZ31
++nE7sONAKk4eopo5efRvbT9A+A+39mW4UNROAwsBvSjPbwPBAowhe+QxmvJ0KXs/5jn75271gk8D
+w2jH/TF9xMKWjKQAWs9qMpwibG+/Wic2tdYBcpEg8+5Jseoo+4rekQS8ov/bi25Z1UZeqUvRqrvm
+lF8BxjQ3TX35+H3cbud4p4E0osPLm6OZj1JdjS10WDJx7y/GXXqqOk28vrusz7neAPm65Ms6tLnu
+dLv47uh8vN9HWmWW4xYD5jjigo+8p4wCqWZAeulua1ubeoiZtqemqw/hhQ9mRYzKYnwBdA9qP615
+gpUEcpniiB6ekOBDN9NfzIHGdY+wiPIMbwBsa8G+Vuo1voNQnXMZz8zvdWo5MOR8HZTMgYoktBvM
+B3Uj9PAEdN0rBJvsdasju+zEAEl7eKLLdNzkLZ6bURGnvM+u9PKa8OJ/4WTDeESPqkOQJI/T9GnB
+gVmaMoy2qQsjr6pIufEUq7JswsIsmVynLpekaxTxrJbkeFByyvlXuVWdqCI2m25Zg1+piwT4wiEg
+2GPqSd/N2xsYRxsin6WzhuRbjZkOJ9L2ieusqKP3Wc0L3I5dbF/RonMKdaCDOnCSqWuse73bD6Od
+960ZmN/W/l9vP0A4tv9m+TTgxSMsiq4D50rzM8yqbGeIRDs+FLd4jrMNPhaiIqCkMBZI2XmfU0Jw
+Mh8FjKtCTE/vqwd4xhSKt7Ahs3wLPPINrc9pKAnSaVzFmBnQAa/oLM2r9qeoVUjE0MWiOTVi7qYi
+eDyUjNdv3nSIThrt8Ihzfi/XnORGn/laZvikX67cT40aFHMTyuUjTUuCzsXxOyjuH17I+GAzeblK
+jHgeT4t1t2AN4iV9g78evT1g6x6fhhWeasC58giCPd/uf4o+AGBrX98cjqfxXrfY3Zzs3YWPmi4q
+vq/6QieKiVziCJM40rcQibCceVmdX6sPakww/CVgWAEx54EGM0GKEObCsI1UgNDCgvZCUEf1WUEQ
+JJ0OCnqFnztRlOYqQBh91PzsGvWUzpWOj7qndFArG9eq/8mp1/TsVp/X3Z95s2PUsdkWYuq7fvCo
+bFGuAPi2p30wvO9TVwCwX9GNX9H2L3awzMBtS/IBY84op3t+PT5G5wuwx/pUYesLZa3im6JdD2tg
+nPvOo/cXCYLD+STAuDzL0kxmdCXBfrE7W59Wmm25hbcZQ9UGay0rEVEQ3DzFqQeV37qqOrVSB8rH
+sG7zZa3r1Wcpk8RH+/RPbz9AOLYrEHipuPUYmUg8vJKmoLJueoe9Pr9M5ZfGV87pADfal3p+KbcG
+gmkELOsfZaJtirlLSPQmlmKT9axhpPpx8X2V0ryFpSGwGt9hRDzLLenQx0oaNC/wPKbgp66xccz6
+OvMvoFhdbcIIJmfxeL4A1I8uW9y8pG6c421XLBL0/3NfARC9ZvHoR1DcJrYnqaB3WjRdznkd8wap
+vTjJmFJ+mSy+Mndaiz89rpGm15ePmtcIfcibq5mHDKFo84In3u73XO/VH0c+7rQCxrYtb3NuftfU
+9GMSux+3m2GeIaSoovJaJVDuIi98IZlNljlvaHNXnmDrIFjBMHvbAJ9XeETmU+6lU44NMvJte0S4
+ipLJL/R1aUD2DQQP2VJaTmGatJZ0m5boS33KPpRL02+jPTbA9YMfbL75Jsjt6UaYKiMgEvlVXkkH
+xeALxc656gv4YrRVJTpI0NPUA/aaDj3ZEFFo0XzKJEO3IutN65lRHh/gWWSYXfxgO2Pyq30V0kAb
+cWpZMRIO8QBHjDDtS+RtEOxRvUBw2tz2DkHx9bQNipuTBDF3bhoasZ+WbLaKdNQhC8YzmZgmHWSB
+Y8/jsAsj3cvuGkvT+bM+fTWYSujTAtNc6/z3t7YfIByb/75K1iy9JsCXPB7pBnxRaZc2qKgrHW1G
+25BrsPzdS0ylL7+Wl1rkHLhXgr7PzfjDVIpQknHvnmF0w0nJcCANn0Higa36yCVfjrIao3n0MsaT
+L78luEZ5OFhnzLK1PQFwN45ZJy7Ga16i7Zqeg+cwjvzso6bZSreuozunup59UPNw2F3VYjZabMMp
+I3pcYwJczjMZw3qd5hFun+W1AlTM+y9B771OGDu+iCTg9hMgTvnSuvmhA3qEt7E122l/sMdAF9S+
+MOLLHUW3g5jN3IkcK/H1JCudcJ6OBmY0zbaHNN/T1V297LsneB0g2OKn+i3RV9NJNaY2VNd0lEXo
+gD+WvNsEmDydQxhCl8BIgfCg4zXtZ/7tGECGcigIfgX81vjq1t45P/PS+1p6PMGxxZOYwUVHWzXf
+ovunuCnCobwqGBbAvIssfwVwxtPBX8iynHZV7HLp1G5NBkLOHyAQ7z73efYN9vPsVZSeCrPiy26J
+rIVvGm0b7BNbRN0iefQIA5BQFO9E9EvT4yrMIHld1KIhQiPMhZbevME9DIKE85q+5BCPEXTJt5H+
+TtlkE90ay6jV1ITV3uqsD63+M9sPEI7tu6ERFEo+nnjzCLPNfMQdSn0vpyYe4zQYEKNA4VRv8cVz
+rOeq0TlAcNyZgl4uqZPGohtrk1Jl/FKwlnehVIRHeETzCM+GqYREaQQY5vqHGlun37FXpcLHUdTe
+lnlFK/UKUwdqh5oXeOivw0hFu2qHq00Ozy4nftjavCLBmEsbUiynlSJTPXsrh9OTcR/YtbepkE/l
+lMYvATBqvgcPuB7nmrOVZyM04jTXUzG+5THpwRve6HkDxLPcRl2H5xv9Fmk3x3YxBSC2Bz7AsBGk
+kMfn+JKNlfKHWYxD4Sfvdd9Cmmb6d3k1h5Hf5sfucys/Yz0C4qAdV+zIR9HtyU+XZXraxqOo8JiG
+vkjvKUqHJXmmThsvyblXWMuNWG+E+lhGC+6YwKr0Evuq/ZS0rpymTfu8ZPF98c4LGCbb2bP1YshY
+8/wemnCkBihrgFd5ATH/NxAs6Q6Ea3d9MRc1rX4lPMP9tGiHJhlWgGGLG1UTQFzzRJNTPES+kTlN
+jc6nMTHGsFl8uS7DHDVP+U554bKdkk/oqHQG3Ptr7k9NifwmQIaA5Nq3p78v/cj5ibZSP6CrtmSN
+yzzqINWS2Cz8F7cfIBzbFQjc6nmpoBke0dMMi9C0t3S2FBV/G/7QAHIv68aF7cgvqmgYYCsbhFCZ
+pf3Kl+JYTslQT4HVsRuN6MIEvdY8wDs/X5hrgFdAC9CMZ+Vbjc2qjhlfGoz6DTZMMxD0MWkbl+NJ
+qD/a9Fo6RzLvmn4592YWKn2GRfhIoeXJZrdRlaZLD4UC42PeBSiFR8qu5auDYr0WIIw6FeSbNbnR
+NvhAjv2g94dyhkMAFQphgK2NXPJxrC3YAYZtWPnd75PuSnTvRQeAvoy42B8Jfkb6xi8NVEks5BZp
+sVgJguz0CnPd4Bka8fWF2814HePQS3qcN4IEJPuFim5B9Xfc1Mv18KC8wfKy3CTIx2P7UEfmrIFg
+q2u1G5m3dG+qLqN8s48Z8uKtlkl577OB/Atg1Uz3mtZAjfbh7WXom1wn4F0fgLDZjhHmNX9Noqor
+YVAkPZwSdpPFIZfbdxo3T3sunMo9EKK5eIatXyavq/aJ51hRZffTsq4HL6Ydax4Vntf18Nym/JXO
+lfvBoWZVPGKK416xg98ucH3Y9Y6DxxgEKDvL9gl5nqHjCeZJX95GWfdDlf7NSf/o9gOE/3BTM3yk
+RcfTI5IGCrikO2M2uX7zFosxmcC5Gxc1Bpe0S+WLXN7GyIQjDCO5HhoSgYvS1BbHRfySlvK5/Bkz
+K99wGDchQXYRDn1hrC91Q8zi1/4w2fehAr2UdoVDSKjM8DpmedLXx16OpF6VodW75890PbqbRE4e
+0kEctTDOQc3z77bJD0DegmR7Vxe8yX8tswsr+ah5zpiP42/tm5XxHRYBA54Ftw54W6iI05TXCLJ8
+8le+7IWiJ9mweRStn8e5F9kiHV3mppfv9Fs5vffp+RNPcL70qmuD58c1JEyC8cOw0jPuyJfU2H/9
+YeS3eUUDAx8ZM88Zem724yOw1bwhM6/X5ry5zCGVx9BPb+nsloY4cKrJw5Xrcl11toi2Q4ZGrH2B
+7SlFHCt0Vp1aANG0rYv+TpadMj5WijGTT3FbxBvnsoRr32x6HW9dHWWpSFfVgcNbmQExNsqoD0ho
+Aoo3n6fSRxqa4K8Q4Zp+yshQPae5mnroVueSMX+zfuQ/MCzbuvwxB8MjuKbwYxUuwZfmbj4IUcV9
+PF7Xy7SM4wiVlPGd3IHkoYIBSsCeZnnGkv/l7QcIx/Zd4q/wZlqck2mgPY4A0Pk7H6EgFVvTkQnK
+ytgbwVtIZ63DGfWjzLUhZbRzlJdfbBcENYXhkxGyl5Rlys8a45pCiiOvQCljsEPdedA8wKehjjkX
+j2u9Om/5HqRFg/qyoT3+UvZUnVDEJultNm5p/CYNqT9rzBK0dOX5tTxzm8dTfnL3P8ucAIbe/Gc/
+fmwalfXTQ3rTusUNj/2K7FB8PplB2MLeyzoFNMtl4JU+QiRaXTlHb0KL8SKPvKxeppBBvo25FGYQ
+rBAgzYkp+rU4YdJc52R0qUJOAILYtrqD9eXP9lrAcixpWwb7+oJ9fQHrS0Dt+Olnsc22Fxw1x3yc
+22/sih7HjaJMA4e6y132wOTL9x9+/3uDJwcreZV/YkGFBk0Pq4KPvAZ+Q1YYtpBF4wY4+Q6/zW84
+2xb4kSWzFeDP4oVO8iyBaMyl0aMp9W0h19dV7+9a4VwQ3jdDxlHYihsr3/zlda7/+gV/Hviz93ge
++LP1zd6XTOxpVZrEmG0D3uR7EP2F23uFZxgRJx1Pb6iKNjh/4qEBZSRumRMpihxb8HGCSKYZSpIF
+2cd2o9CepiBkFRd9iSqXOhb9uoLfZfGCXNAEwFd4xz32X7wxsP0OAx9yPU+aATwpg6XSEWqdvGmZ
+EssuXc8b6UYLQwFjK/qrfZgo/S9uP0A4tpVxip83cw9bY1gJgg1tqRL+glMa8NMDAblU0AaJY4rq
+fOSCuA7MBEAj42f5V5I6ezS2D8o9u+q/q2uSsmv+e877tRN/iOGncG6qdeDCwwTAAn6BDpiZ/sV8
+Ab3mTwLfyqs0PFSNlqOXtzjseZyQtVlmjqgR+zOw/V55GUiTCY0+XECGW+979f/ZxmJJ3g34MpZT
+88vyqKbMX43iI0d8r4ya+Qp2o75OxeSj0fS8ch7HOAogeBnkJ2TUgPxYyGP5Ls9uyKoTXt1MqzLB
+L/uj4JjtD0CcIHXZpU7PMwHJ7ctw87PJpiBY0mOOnfOcw+o3cHtvlT8MbwPJ3D+eEQ6cq/kiftcF
+H37XydT8m578TXkbJedP2lXkMAOCgQLEZu00l5M1DKDXscycdN7Okliv2xxcrnDz7Ir5395V8rCt
+VY4V258/9wwLWI2/4PHyWYDeyre8ZobGKQg22+D3V4DeAMHwBx66pW6EQo9xvEHa9gTGRuhDgGJ7
+yhsed6epbWDIJ4AbkD3gajBtXXPr82BWaX2aYlFYsyRyRuCbYjJA31CRd4B8yVLw+2yaeNyEUxwW
+08Z3evZLhI7tUX98h0E8T6xs5Q9Xi9xh1qRehF07kLe5LezZZMzZV0tS9M4jcYoFwD+dJn93+wHC
+sf2xR3iCYNr/UPIiSnUNOOACFy9eYEDjlyyPAVQsn8ejKqOCyIvnNefvW1saYtnf6nwoL5pUzh5G
+GPBEKpd2DtRxxlEzX8GOh4LbGE4AsHvbGyQd506g20HxPX0tv1vlk7DiuUqA/ApjJ8k/eYXP2GCM
+ckBBOZLfJnAHng24FAzLGp1wg4Xxdjeka4HgN5/JRV6CYJUKUXoToHQmqFPeyuYRkcJBkEueFyDT
+vE9XKqAAdDAMMciolQ4WACxs73rwfwPqsbuAY+Thpax5kAgCzr3dymfdZRsAJwj+kp+EPNxenJtG
+vZEzdBdKVu/74LPjePDms39HvvD1K0jGnNsbU3Ey+moNvfx3GtVfD3fyAoazb3JdFy3gvVUdRj5t
+YPvCH7wZY9yqCwCG0aO7AXDxMkMZFCRvz6NzKUy9CYq6hyc46zoIgPlS3Qa/4gl+tgOivMGh71xA
+MIeWPDbAE/n8Qd2AMmzCng2M6RF9ADPPl1/TRq3OH22FJKWxlY1zKU+fVhrjU0YO/1R0MT9h/lLP
+2NzamGM92B7htblmBSB+lmWTXwTzDyEtwHd2Hn/4vZnd+IM93/5w4Y30dQBORzIS5ZjGcnPIAkIA
+tPhwrdNuArTOT2jEv7r9iUeYTLYnTT2PFwAaXFY3R6JIqbWMxrGYyIACyFGH3k3GdBWo9vPKTYo0
+9QfbDVWBYvC5zu+vKSLUNPz78R6+gBnnyCMsQsCt7icgBgZADgP7Cfy+Ad8bSO6g8rTGNgfZvFXN
+al/BrWfJzOt0ankW12EMsPbR/Dje3hkr8LGkDj1Yj2O/JBYG7zGcYDg0bDNY+xz9m1sf3dv2Xu6U
+rRvxjn2v18DEvFqKatDGUDHOcaNK24uHwIENrPLsnZPzWuaJGLVMLOPNu5sg2Orrfq0eer21YOsr
+fuv45Seypyd46BlNcw63GNzCJYKWPvd7Ejy+GKZilCB3eIjr1+vzSnxq00EpD25a6rtlfuT+lm0B
+VFDwlp0Mc3GhUdN7v0vbmR8A1XxJGI3DBACn1zcAcAe8XBXF9lrfVuENDJvIeHH1+C6GV+wyriZE
+YOxmAYAFDMuNT0untht0LtIlD/rCXkc4ljjcN+4bAJrHyroW+t42n+1+PrHWsKJClKVOc122tHNB
+v2naIDFSao6Pl00pexMYop/HdOCNBaSHdy1v3uANkOkZBlbcgH8BAHa4iz/PnppYBvKhqxcx/Lh5
+Jwhey/E8DM0IG+ZALkXnDdOivT+k4iOAN8d23MxkwV/dfoBwbN+9C0n74fQMK99WiITWNwAhg7E5
+L7p3qZ9dlCHvoAL4ioBagGCnqFq1P3mv9+ICO24G329t3Ov2a8w6ahybBXrdkg6zjhixZk7T8BGI
+Mi3njONez3N/B7/AjgvGvTwUdUVm9d/5gZPykQ3Eg/xi3kEP1rwACS3zmce0g64NJy0DUSS4tTI+
+hUgCDKP6376uFsbOQmN6AmBHB78oY4V6MYI8qR6tc+Qvucfryi+nqoL/UH7Nm71Ij9kuUADsUQ4g
+PMEAlw10OgBJVlQaKdvSR6F5ce/zNwAAIABJREFUCwGSsjTiAm758RLNn6AYqzxzPT9CIrjqg3qC
+7QsZEpFL3wUgnoYsNSDHFP3dhCo+jZsscvRnECz8N+XpmTLW6zOvwLFfDK3O8lvZob0jZZ+qghN7
+5VaebqOe6rlbWvlT+DezI20BUHdYhPXjAMD7JrgAcN7weABivhTpXmB6hEFYhgVFmYBiRJus67Z2
+kh5gfz6AYpTqiYEVH9U12gufCwnk4Pt5/o5l3jrKnhWyE/S2sg375rxkUaexZtraNM8pP8ryhdIO
+cm9e4aunWDEi9hNph5cIPtsjvIDtTV9WIhAd8fCEu6HWPX8cj0V8dCosvntj2zEey0Y+D3V2hMIA
+qX8t5j8HLeq+ADF/RZxaX9ou9a7o4x/dfoBwbGt9j/gJgKGe4a7PTkDqE34GOKZ4VVzS9AJvgLvz
+KjRC2pygOBjq7mdrV69NDG0K/A0gSP0pr2fbduSMBylSKl6cS7/8yKNx2/vb53HVCNolD+PcKxB+
+8RJXHq7n8Trq+c10ltUA+9u4XgNHqf45HT3+90OZ5PH6LQ5YvcEsbxboKSPMUDsNm3jUAxyVxPOb
+ANj2iyst/i454/Y05gU8NEa4s+eocjnusZjv9e3kP1aLrm9xjdmUp4+enwNG0Axdzi4hEp6Wqya0
++mnZSIIfK3Bb+9VXeBieYT4Cn15k4zJoLRRixAqbgmBr6QQmqXeKq70BYKGjsBnlWQEr5dkfbMB7
+9QAP2jrKa4xL/SI0iXjOrgAr1/wPGnUXC8+2S9i4JvlGKhM8tjm/0CsSv6+DjAumJ5cAePPDW2xw
+ABt+Bt3Ko1xPFxgzLLwwwiAQN1fevMgRk7zRWcUFTwAcnlldblTlkAs9pOzZmJsVwM6BFD4Pr7Bt
+Hb1xMfXftukZ+qWT533mj/TQDw0cZ//qN5ece1uCrh/vhg3o4PfZesbD8b1gzbxp/3dssG+dzAgt
+hktoOnTN8wC2PK9liHSEQ3ChgBbxqERogJhZCnTPMR9rTf/F7QcIx2b2zdAI+AC/9AIXOEYrP9Vn
+OrO0IDTj1oXBWdSWgUxzlQjJIycWE/XevkHi6+Nol74dhbe8Gu0cr9Kn6tQdvWrv1jSNm2aIoaw6
+JfHnS18Qz5HkP71OgtIDzOICek8g3PMRHt0nwewNFLcyUHFWfvdEuJIEaRR47D2/lTGXdSxoQhB8
+A8ACkPNxPMMi0hO8x+gEum0v2i95NLzAOdriB+aT5z5B3xduecm5bUSuesYlDrR5py9XtQ6itbrD
+6+0SyMtxQuZrGATQgTG4sz4/WT/yG5ClV3jhDJFYWSe9wQMQ15rAOzTi9sJcgmF6/gLcIG9w9py3
+uOAEwXWzXjGgbcg11EdkOGWWxyJTKdeVV/pg1FG57xMufKqT7SgenlxgufNbviOZ2rXodm3EExGr
+ygpi5rE+IfDXOmw6AJ7Vi277aSJDJcIBI6tCmFUZxmoTBaj1RsgFBPNX8cb9i6MG9xVDdcDVKxx7
+mb+2bn4OT20gvZOGQTLwEYytJ9RdeIKjXd4YWPvoCmWzpr2mslxNpLVdysfUQleIsIwDRj5MIZiY
+3mBr6Z1BdWJWDm97rHD/ilBt6WMOi5E4z44P3mFt4VnmR2bMUnftk2P/hLc8Xn60x7uTl0BX89hM
+qnsBvNAQiRMU/3iE/8Xtjz3C/D1+BYD9HJr9YpzSoqpwQ/lgp7ewezFk1LWR1z2tBL69F7e8e2+l
+qRvC+Ig6bOzvNbrBebmE168rmCNaLD1HJwg+DeisewfCPTTitRz9B6DXiwFYDGKGUiDbSJPYBx5j
+nNPhgw632gNnlQeq9D0OcKwAOMHayzFXkbCnvMHpHS5g7HjqDn+AJdO+XZjhyCG4OGpqfQ40T5LS
+TzJ+1pvX0eMOgKsNBzIMosAv0LzoUOBC49snrICxZLeQCdTN76qXk/iWvl+8xPl4vAHlFecJ2CUY
+TuAr3mBJu34sBQKKcxiXuGAx0ArszvW2T9mdwLcD3qJ10x2xt9kBkzlW/mtGOOhvkx9c6nW+ytCZ
+vFnhiQRw6OelXEZ4QeikJhIuXRSa6XByrKjzCTxPr68jQa94ffec+iWMwqAeY3p900MsL9TR64us
+W7zpCszdmxe4eYhTTgoM32KEU3MyDl9+lk3I4xjR3wWKw7MJguJBw6S/JwhO++w5dTm/YJ5WHl5e
+/fpqrc09we8lHbsNfE3SsfeeTv7QtKHra6Jxe8orzBEyJOIAwXxqsHmpMA2ywwS8BXKlTo7tBRRH
+xY5quv79pMn/m+0HCMf2Jx5hnS6+Sdny9Pdmf5tCdjSwa2QE5t+9wMkw+eJcXahM0jmC32/enSf+
+qe6njeMogziPGoprSa2jj0zLSGZaFaYYR3e0N8zVczwN7J6rz97fuqERIHsDzdG5bC/PQSpKtsHP
+z2pwS1Fugl1vNKq95Oe8XQC0gN70/oYyewPAmo/nycf/28gOMIxIb21bYCEVHBe7V9406Jvab6x2
+8xbf6jYqUrZ4nQFamvK+1bPzOi0tFuC1jrTpcpk0nhMYQ+ejLlTzUOdu59u6gGH9gMGSF3IKBPta
+PS9fiqufXcMjCG4KBDN21ISzdz/Z3xi/eNxyHH7bhyw/Jef5djuBzgiT8OkpFuCjfJzRZUlwayyR
+MqHzN/KqROd+8I+jAJ+ynld6A5PQ55TDIbTlFZXxC22FLUQfRtoIsEz6Urzh4R02AcCwJWEUwRfi
+3fUGoA0tHEJihOkBPj3GK0Fi19UODI8wAeikMIY9OV4xUDqT/I49LvIIda+AbkS55cVK3gwKfEOD
+Rf+yfc6Rsse6gWAkPqy9HfHEBxjeVw3c4RnJsSJcLd8NFJ7YQ4v5pIwwniPXVN76m7qKTgouvWaw
+WJGig2DTF6QQY2jHOWzkk+kGeDm+0h/8AItO5S39v95+gHBs3/cIIxeXTj59A8Nv+jNOVqHaEkvL
+qwY7mC4EtRphzDCgFnZ6fvU4c3Mxxt+P9wTEEx3YtR2lA4/PaooMuqFzqezH/0239rW2lueoeMOZ
+5wGOObju7V0Ero47GH4rwwUgM0/BcR4LfcJQ3zzCV/Drb/k377GXUYy+FQj24j+mCZRlQnLpNHDV
+AxevL0GwS3p6hsXLkBDYYhzxuPXgjckv0xhG7iG2NE1xDYs9apyNh9v5Wk55G9dNdreahyYC1utO
+nk4RrrhZgDxcIKrs8Es9Dw+NWQPDGIAXR14HxukNJuh5jQ3Wn/VfA8AMFnPJE770D/uUWVxvbE3A
+7hn+MOgcrDxFimEanJ8ituXcJJic3uE6SbhCeCvyckWGwazeUtbzxQ50wKtEEr04QLOXYpAhMY7T
+ct5subwMF3LfVoTwCqNYFvpC5nxxbJz/78UIV4yxF38cQHjoHtXvQv6Uv0ZTmZ6sKwKYABjg0xnq
+apd00hZAxhIHT9Tekt5c6tRSl8U5Rj6wIzTiBowP0LuqrICwpUTt0Ij9Ups/2OsJL2D57tvKoVjx
+CsnxZKIuGh/dACpUx62+ZFehlw4FwdTlXB4tuToPOIAah3qBc4wKkg/Q9M9vP0A4tj/yCK8Ki9js
+/xxgWHWdIfQDZI69ynBZGWJr8tTM6MZ8l5uPuulJxoWhXoy/Aw0Yq9LRaleNcx5bS80azReclz+u
+JQnVY81QsqsKciFgN4yo/ugd1mMqRwXC6cX1AXozr3t/e/4JgFv5rWxSSwjgLUu8aRh1xO5ePcQ0
+iJku4tanpcUb3NIPnK6Gx/PzqObPXic3gK89QIJQE89vKLq3Yx83oZMnfsczfqTI+157gtwGgqrI
+W7njpowrWiHqJo1NDPQ+t4EZdiHnyNLYZp2Uf+4IinTCu2cYZlgCgrlfw9OLK0heEivcwyOOsIjx
+c35gAwuOndZPrO/xaliE9WP1DKuMx+Bpo0tGe34C3EfPF+/eAFcJcjwvEuQtXsibjQP8TsA1FahM
+jwu/0QvHeRWA1toieNQbHsdIo/o+jt+AMceS3vq5bnA8FSBY3N5egto41rAK23LaXpxLYB2Tfvng
+hoIbX/Q2W80Hap6S+ScYRpeVpF5DXSJnDQDvcpM26VTqx/sqpjwSfFX9Asi320MsNpvlIusFcLtX
+OIFxgsOYI9NzIKC52NLyx49pbCdegl9nv6rbaD+GVgad+NU9vvcRQNhBj3Bcx9HA8MSv2rnuAWa+
+DGzkERjDBBz/5e0HCMf2Jx5hvmhZC0wHy9iTKlKfEpBhqg2XY0u9XBcowELAUMbZRQN0pbx5i8xV
+Hcm0m+j5HjihLWW6KR7rlSddssakI2MGxRJkvUtDrU85ctm72DpNz5/n4uxHWo5pQDvQxRX4rnZ8
+8Q7jHQCvtzJJr66xBhXOHLGBZ71rnjfjvL2ZNDxRi/nwCoXAqpfl8HClnd2OxSvIzwiJ0GMAnrHv
+VsfZudXGM8d9ln2u2xTuAYqB8miNRiH1fNc7+7CBgach4dW7MfZsTgGOFW1d2ksjvY8V7Dq0DeYJ
+eFkBhgMIr7XwLMPK2N4Im0jwOwCxvgAn4Jf1bIJhATsNENk3PMJe83YFvxxigtg4zhUjXMpKdis/
+pm+Ikbe8rukI2mv6hU8caCv1pHIXvaxPEDiXpkygvKObJ23akwcR2BYCBu9jkmMNn2jHMU993WAJ
+bXA5JuCNmN/jZbpFz64LoNt1C+xSdvYxw2fq4xrlXZw6ZzJFu+ErhVZ0HDcsegPOG4/sb9CYT9yo
+LwmAWx+YDjpulnDxDpPXCK7FC0yyCx/Pzyo3YJyhECNfQfFCO17UI74dYSv6ucSZleCXbBXnlHE0
+0SWysk+g7z38DYyX1/yZe8OzBeL9BDtZTo3Q8UkHxtbG/uMR/he3P/MIPw0MZ9oWLAx/3jEBaVtT
+OdLQsjCMayrcpnW97pBcuVuMOkzKqqd5pGD4tP73zV/Sl3pni4Q8Jilvsvl2uaaXRKmroXT++b6P
+nZ5f/p52XJ/vfAiKh0f4BoYV/D5abwLhqLcGgCYA5uO3BMTgcW1cbnZSpWjjoyToM+v4oGeCklDi
+yZDq9bXuJU7DXNbX+bkhnucSCpEgGLVsGHTPdcXEM8wrqziMcerY4vQX6mhKFSrHWsq5idfkSAHB
+lEeXaup1YvynS6PZJ7MmruPdt0hXprMvXqPXlSTqvPKsZjhEeHTXWnhse4SftQIM0/MXH06gN/fm
+DR5e4lpZQsIicsUIAUBi9er5hnqEZX4Yp5nHBRo6GEaL++0hEiijH2kj8Ii6PfaTIIbTFF4zk07k
+vAfPCxiuOzYN4ek6WnkgQYYJb/VdVZ1tic7LNKsIjZInctxFR8SOH7XIdYPzBsbB2OANWF1igQvQ
+6st0GgpRL8lV3eKT3UaLD848vpCndOmaqgAahcKrjHs9/QaaZCp2sguhzXmiPFH3OYR/SHcPgFu8
+1UGw9RuSNOnq6bUW8qD5uiR3AczLcdjSFfJl2G441T+LhxoW4ZVnBMNJU4JfwPFsgI3wDK/w2DrB
+MMMg6LlNzRV70bOpF1A8kmOwAYxJNJPW/t72A4Rj+65HeNv6tR81PU/OMYAAxRsMq/OJGthE+Xb5
+bRZ2nojpxSgpQztv1+ig9/OoLqX+B8c+WnD2oOcPqBH9mgJav6H2DlVIHZV2Eh30Hunnkp4eYXRQ
+yxCJBn6lTvMOS50HBYb3IyUBwDG2FXRbwP7WO8ob7I0YRWjahK66tdbF6xYHaWxNPZJlvMszjCz3
+WjQ48iJGOL+5SaPCL8vRM7zrOgy8KXThw0xHLJsV27Rx9bF9SvuZf3gbMIy5ghygLKYY2kIpr/1K
+b6IVLQEr0KvYqBneGH9a8zLWXoMoEAQkKFbPscYFL1sb/ArwdfkinGlIQwPBGiohYRQKgGW5NAiY
+qZAICwf7pnnjTWce90GDYsPiUQG8uVegm6EQJbcd/KJ0iMt5Xm3nNFvRkZk1n1EpjbnIkGo2ZZEs
+S1gl860a0ItGoteVF3KMkHQqPo5N0iTgqKuxvRXCEHMcsZ8EwLt+8EEDzAPAjhUhunfYBfDstgsU
+7/ZyaT+Rrb7v1G4KTQk/30KfRiZBGj3BdATMuVCOJV0F+EKArzvaC3cXEGw5B9G38OiW1xMRL1zH
+6Q2Wl+TKQ4yGD2lbdzOWbGbxW+R3DtOB8gaLYnLSJD62AXHPeNy4LMt7Gf1Q6O139rH6XjpYKyhN
+ClgXwP672w8Qju27xFcPMNbay4mIh5h3PJzvvS8QXE/SKHy8rjfhTVRzlEc6QWfUMQo9Ys+6tT9T
+YyNWUOU6yl+o8lZw1DpDJO7Np6pKpS8eI2xls73CnoD4CSVEIFz75wqCn1i/0oDDG5xAFxeAjACw
+Uq6eYNc9PNd2pDcYVK4sRwFo9QoLVJqmIWjTj+FlUH0cV30Bw1a1C3SJNzgt7YP4sCcKID+lUEcY
+RM02XvMVHPOm7jr/l7w3emT6VWOjRIo/7dl0TdtsO4AO5VTjQKE3FnWt5sF2aecFFPM4Z85FZlIk
+t+FdbTWI8ARnegkAVk/wBRDzWLx66R0e+T7zCYYJdg2VTvABAXBCh6THpTz33lZ+qSXUql4+/b4C
+5GSvqtMAFOeRvNHLHaM+KEOV7nUh/MDByTgxj6sNJ1HaeNB0XwHge72i7x54glgn6PB8iQ0WHk25
+wTkBMz3GAYgZ40swncB2vlBHhFY3TVjiLb5ShKS42YeZZ1LX5NzZWJWPW7SRViYhLYOH5OuFdcPF
+8ARHB8EkvaNelrN8UY40BUkugHjndfDbPcOUrE37bZd2DG/SW9/34fDZJ+z6FoB+j3b3MwFwAmGS
+YR+vmLv6uqDO14XsOQbeHoe+1/OnJ7gh67+7/QDh2Nb6XmgEHm/e4Dvw7cd5kMzpMtkiyF7cS9H9
+rWdY0hbXKh612ldn7tuMmbxr7Xb8JgOZst6b1tBN1+m11GBm9fL0po3ENgQPNvDN4wGIe/pJQEzD
+mYAXBUh7yMMue6TuLWxCvcDuBR9XAONclSJGFfdVuVh6qCaFq+O/GtwPef7mNaYhJ4HrSnUVgcKP
+b9wLz54m0OdC7A3sktFPcKxeYu3rji+dff1srvqIR/nwNpQnE59FAFpuPT+yGngLmSqaalsl48Ql
+rvWznQBNeuw6thlKIKDZNuDVFSJWAF0Pj7B6h31xfeEe95vHSS9pc3iC58c00gvMc5MOv4EdLnu/
+7Qt4wD1WjIB4iAWoJPitNg5PcNYHKpiT4Sve9R/B4UVfn2pLPMCc51are/7rjAuAzvHrWBKRHMfv
+4FjGl6ELERoTgKl5eQMA18t0AZiNTxfO+OC+rvAExYa2xnR4l/ULc4ccKq4Vch7p45h2b1aggaI+
+0hM9kzZm9PAA59fnev7myeLXBoJzOWJPL2/KVnp9CxCq93dPVeWbob9Yh8j3+MBG0HURG5BfeQee
+RsSKL1LHGL5cnkV6hUM8LmEQ4RmeAH3i2AL0w+YL8M1z5e/qtPjL2w8Qju2PPcJrbU/YAuzZb80V
+1lywkIbtPBKGdz89TxD/WPNIkKFHXtMaAXZVED5Z+wOYWm9erlJ9+F1belT/6wph7Hk8mnRJ+KVM
+jWTq+VD89AgT7OZPvL9P2z91HJ/yNHSPbj9GguPPYBkJgund9QA4GzoWCEacQ4S04hzA801ggT4F
+pHRWIrPRjvUls87lrAwwXGfJGeoTjoXW7YlHrQ7n+oF8HMqF6DNeuDiisdYAKnxhjnF+N57oef6h
+TPYCgLchB/JzzxMUt/tf6y1ajSFzreoWjUp2mXY5KW1PkwMaI86Zzs3vjxGPLiEeYQ13cPldX3hb
+5zn9BqIfX5dNk9CITY+9t3ZzoGXFy+SDvheg8SA+rSx16Jmjh/jxDJlo5R/BcenJjAV2IJ9KtNCI
+4ocOXjk23bzNOWDlmc3/ldB5bGUJcP1bx6TVFQyb3Nwseihl3WAFtS022CqsIuyVy5zbsqId5cwM
+Nl6cq5fkWEdvtpJMsQ9dMU2X3pDMcuoNlml7F3ta5zpwXMcry33bdve93Nv4gEseWx176kCUV4Os
+topOG9gWHthzU+kEm80zLGlYqCzHY9gvycHxWIQxCI/tPgR+CCVUTzcpj44vGDy+vLeD3eIlufg9
+BMQHKLaCHOj7xLMCgtsENV1z/v729gOEY/sTjzDWE2GSGxXvCZc00IQ9ga9pmuawK9eyFCM/hJeg
+Mq1rcr1Ve7yOtH8NhxBhPfL0+DjvbMsk7/QCf2MTo9EOvY7TjgEBOBUAv6UVHAsI9ge/qNhwhkPQ
+A7y9vDg8wRXbizznibICwfRc9xUhHMAXkOAX8KY7XQFPniF0mYDQP5S7GnECNQFYY7/BWXl+zzqy
+nnB4hHeLhlzhPT/TKd5gD4MwPMK0fjeWq/0c3/seQK5oUHetBHIISyEBKA/i7RJphUIsslHtjzAG
+0tNYFiUypg6ApT2vNugl/ngMNEC8BNCaeIEf8fT6AMWwc43gXC4NZZimoUogDPH+CgjKeg38jrHG
+Y9uZDwFwHvxDsFeAl2AkyCvnqbfXtQzIECSELDLsq8JXPMHWVqmO8g4TPLLDMq8CsIbqimO/lx3p
+UnBFD09g/+bt/QiAeRyhEYfn1qxig61AbXqJ4wW5bauWgOAInZAX52CWL4BNcDxjhBlwWp/lJgmj
+zCC0j3x4FFuZx7Rxld4yaP1UIGXZTGjd8np9Pslj3DT8Ca+r1xcNbes+5TP4Cl57kofzIjZDIyDg
+GODLcgSORcIaJ2m1bHfDLEIcjGB49+UJ1lyiuxjCZsFSJORebWLr8i8PMAzHevYcPfFy3HKEhzim
+6dLH+lnbt6Kccxv1ZICZ/rvbDxCO7c88wttDpt7gjZbkMQ0ZIZWDpreyLTWqhljSCWYj3z/UNxyg
+91Oc8HWTy+SjnlsdafF3bc6y6Q1mmy2PyhzyYwJoALP0/wmAfw0gXMcPnsfxK75k1L2+yBCHlmYd
+7OtMrzDra+yyH8flIa4wCo5R/zjUoooe583ArVxp6VWjA13NGXujZ6sAccUI1x4PjaLnC3I546bH
+zDYQ+if+cOSngKufc+/X/HtdJixA3tPT2x2zOyBfs6ORyH5STjvlam+GPiM2XvgTgGTjXNbPubK6
+xjwGwyLIE1HGxgKE1DJM7wDYAwD7AYK/Gii6GqXLsWMAZgLfsG81TinLMZzyDK+88gwjFcAMiVCP
+b4HfT2W8KJK/2Vmu/qHKrziidOypDuvGspWXqFVNYQCt53qijJ399Tew2z4KNOtGnpXnN2N0Cczc
+e2yw2V4fPMBveYHj/HjyUCA4ytLj+ylGeCVYrhdWgVx7OhFThFCoTQumcrnxqPtUS7bMuWS5mD4W
+86COIedL3vOIJ9gAe+rGwp9Amys/CQ2ELuNNdahNe4I+EhvM5dBAki0BhzfPsFWekGWrsMjYnmGI
+YrXGe9t2WYBg2jLAsfAFB59D0gu8IjRiucVLchLSYNLfQbZzq3lV4Hu01yZifWzxn9p+gHBsf7Jq
+xOZCS2dTMsUF/G6lg1Qom0ODmVQjiuFNReAl/Kq4+wPnSPMuNsGzXJ85qSAiT5nw2I2yI33POWun
+DxttyOz6MBYJIAkexw9+9/T+CoD765Hjh4A38sIjnHU0NAId7M70eznKi4wdUrG9wgglU6BY0E4O
+fxyAXyoKLhEYVkCoqhvQatyB4iT565YAiwfRfnohbeRHrDDqpRGLtYcz9OGJ8rXXH84WTPpiK9Pf
+21/Aqe4zbIle30v6ecIT/FKX5Xx0bUJnTqHJbBx5nG4feZJyHU2H/Dz3Or6QYRop5y/KefwQ4BAk
+aN3Lb4Le5uFVdDHVD/tEVuZdtMhsATzPZdF4E9XLnl7/eeD+FAC+hj9EGp6Ic+MWTzpnd+dNzpvH
+l7rXZr0ARzkPJc9Tl9XBWV7yrtney13zoiLHO/IK3AtAbtPkZa+WTts2aI4l9uzpXv+1NsiT8CIb
+vOIKePQlMXH8JPCmrRUbWSAJya9s10Y9h4AxXi7PyWnMOcoXwD+a+EapIu0xbSZKLLQ05TucZHx5
+3uMGc4YZNdoFHQ5b22TMk4aNFyKZoudJgjw9w+FrlrfecLo4uLdYHcLi3nrP16f9F1+g81rD/CuW
+cVzx8m7tK02amPWPAGm9v739AOHYvruOsHp/yz6cgt3AbzKxi2KA8L2F0gaSja3SLXwilXAIIfMJ
+kqfARb9415wKQ8Awr1TtiWe75edltPOSb+cZsvsWCAMK+OIOhnPlh0c8vc8GuL+cYLe8vgV8I1/y
+aCTTG4wBfoEMj+jAVz27CRfwFUD4y7dKXUCm99giTUMoYzev+yyygyeK8jpuYMlGmRJzUt3GftDf
+WkKaYV4YNDhMwA5Q6QTFz442czpm52WFjfmi8wEi3/Yv9XKkHur9eeKrdTfgaxX7TC9OGO9Mo0Ak
+dA6FtHX9mtAD9wiIPlP+zXydSQveKvDLLnuAhzLiCnZJ6wK3BDAJhFOp3baOJjbgaEgOqaMOMHtL
+O9wF+A6AnHXpeZN1hfnFuQ6GycICYVKOXHTsHocLmOnAAyULPhhV0nPYmlYZnuWTZFU3bn1bw0al
+cU6Fc2TsK9s5eUaCdsq86FATEGsUapz7aHy9XfmlgKn83OoFuvxt2hHcutioCYppp8ijHfx225YY
+MWygS7Mpk+xW2sqgQfBB0kRurvRF1amD+g1RXc+5vKo4p2w8dckwgasudqTTLC62l61spkA6H9d2
+nW+kPJCVaQ1M8uevwPC+0W4AeDF2eOHLPfZxnWUJgr8OELzyZ7bu4DjCu4xPt/7y9gOEY/t2gDa9
+RRYvxFE44SXosTe/5NPTECxrki43sWUs25bYYu8WJqGPi/J81B00bKRZU4Exz7t7gE1ytFavaeOs
+32+U8R42UIqnsNVZ3laAoJc3Yn4n6CUQTmAcnmCCZn8qrCH3TpOgeeMYwysMxmB5xv9+eXmFCYa/
+OK1loZNmT8zv0yhaj8kLHKkZQ1y9m1ZPzVc8pZNzGPHpMpl5rrwYfMy3qfWtapR3jm9d+w4yg9kj
+4DCuse8sxMiUEWuGh3s64zsBAAAgAElEQVSK0JEvj98f7Hjl8HL5ituLZ0W+wTOkaed7pi0tgnPx
+TC/Ka/97/7z1rfXPfOTVmWlUjytITRv1s93qLsEtDfYb+CV9uzcYBVQ4PNJzCnOqL46Xum7WI6B9
+XtIOuABdHj+XsvQE1/ntc8qlLMQzzC6yfHbPOxKKdM8HMkyC8nADxn1yr+mUXpF35RUt1ydBb6Fk
+jT9CmbqMlXr01BQytKQFChgatiyI57LFCF/ALkzCKtpvoUBv/xXPyR7jeOT7yKfN9pZXeuIExLyu
+1/QRYOt05k1cZy+lY94gy2ZAPA2z1H1IDyjpJ4A47XCc3dQt45T3vLZLvfFZHKvGdz12seVeTzEZ
+0eEWL8oFTQmCNwCOWOI4dl+5LOgXdiz6+qrfVwLi1TzEX18THNMjvLBsr4e+vuuU/B9uP0A4tm/f
+hYTrRe9O68545HGfFiW8uwmKgQZyFSSbnDO8wNlOas7IS0UD9NCIUigqeHpHmv+t0lrSpd7mWWcV
+zUotv7cGC7wnZxjEzFMwrCtEcBWIR0CuguD/KDiWNF9kM6C8vmFAmxf4WoYDRDMM4gue3t8Nfges
+a0qJ7ZMGAmwzLCEI2dxFCoCLyD6OrxPztjWeYr/JFI4GrJuVCLASoNfjhVKX99V8rQ2I4WFs2b0e
+Q0pxaRQTdr/WITADcAPAbtggWNMExFmP8YxPpC3pQMqKKA/ICnRALyS1ytG6VcFbjHFucZ6/TKPz
+FwaeoPixur3u4DdorQDZ+jgh53bPcINOyPm3PUCF6JQDBHDdQLbS6gkuL7B6fBUQa516aekMi0Bd
+K2SUtKsX5CgxMo58CavSmn+IV/MgS/pl+hrYFZ5RwMJMC94+0Y2cNK+V6sTzuD1dYNt95qqrKurQ
+uptOXPLs8ASPn+GW7x/PmeDXgh9nfttLfgPTQAHfaJ5tbRbd/Smwb8kzFatMwlSoye2JjE/GkHN5
+E2OArA4RNNIPnAgYbg20CfLrfDXuGKxCnhMt3W2N7PkjGDaUR9hQYRAVErHgazuFfNl+wQ4VGvGl
+3uAL4F2rQig6CN7trx+P8L+/Lfsm8adw8vbJ7SzT0AhVDukp5k7YliEPTUt9yAOa8DCuL4UNSIFL
+4GsCZFO/ZE61K2LTSofg43Kmnn3fSqIJGKm1eawvzVy9wfkLb3D+TuB7A8L/uQFhfwe5Exjf8rPv
+2GC4WbqLYSIb5M9VlU4rTCWMD2WqGefxZVb8zKr5IXNwHOTPMJME6fTCZToAZ3qCNzBOQAyU4XOU
+UQOB275UUs3YBZZ5NwoBpNNrowA4VonwOMZi+gZ6V/Rzy4ivlcYI2Se/4hFkH73yeI6ClTkt5tXI
+ZIqGTHzUsXxykOE0sIoVfgG8TCtAaS8tRTvvwqv6SwbXaABoKIPGABPo0subx1dA7OFFHnkKgC+e
+4KpTVOfIdpagP8qacy4k7eRLkQVOV6Zl/iaguaQJVOrYpMqIwp9t6KWaXkEb6x5i2Aer45vm6Nfe
+WqhWesB4+h90WKGtBCT7AMwNJH8CwmIzNTYY5FNA6spe8ltfE1DTFkpa800BsOfoQbrnTT5pSVqR
+93M2sb3eVWbRt/TqCg3SPl82Ff8xsV3fze2iLjYYLt5i21Q3eyWkmgLCmPIOl95gqIRHTPDXWilm
+fMKpAFc9wT1MYh3AeIPgAsAs/9vbDxCO7duhEQZZbsm4lokYhRCyZDHHhU2lOTEqAi5S0Fy8yASb
+XvV3C7xuVwwxsJJZUYwFfEsw9b8mlTIT+E7SMDHPaQ91m2IpDb+Fi4Bql+lqCx0M11Jov5o3mIC3
+e4L/c4DiB//5tdspYAu8Ad3PIFg8wjEml/EMaARzxy+hmWEvgdPOA9TaYvINj/0oG/UmWPm0KUA4
+gEFkKuoTMLQ5foMaLgZn8Tlm50sk9kgsXzeEbPYOhCvCsYFidlW8wRvkogDws1eJqGMDrAAyQbCv
+1Y53aASvX+Rphkn72YzUu+fYLBvq6kG3nEL/WKcvuWcf0vb79GGgP+lD8oXotsmeA7CqN7iX3WJ/
+o+wpr3GFRdSLcw3wTk8wmI758AIBOQYFupBJjrs1T6cFkA6MnL+b/uONbpXdQqgFLmVh5bnkDwZS
+8ueubq4dpAMz5CYggJn2rsQ9qMVjCwpKWEP7ohjvvIIW70B3nqu/6EnIf9sDRxjEe11rABiGBLjp
+ETbbc9nShlxKztDSTaCpvw8VyvCzmkfTvqfdrqfHBMEm/WoOqzHJBLNeV7uxgfCK9k6sg/CHOqrU
+E5x2yLZe4Iu2Zt5AsK9d/pXptfu5rIHer3WLEZ4xwQWCK/+NHv/s9gOEY7Nv34XQY4Qm1E1R+Gag
+5gW+pRvYUMAbbaR0ebUpClSVGIC62zSkEEYBNDyiwDHLKSDRzpHuqTqnHY62Rj67OuFC6mxPpXMD
+v+oNnvHBujwaPcIdBAv4fR78+vVknjcP8FY4suTjBQCPOiOvAMh4qNa1VikfH4+mQJDCE9Q4Wmuj
+lb2mdSY+KJhkIzH4e2KKb7ROGmkBG/C9vvYiBXac7n7ZBtsDGx/j2OubVoy95yUGEA4DnnkegIHL
+vPGcTDv4Bnz3Bm+wu3v7ZPwvlTmepzykyzKGWO9FBucWCTDLCaJGHkrkcopkSo7ZGXVmeY/3xTfT
+9poW1DQ6cduEB/aA0SjRXnzTMIiRHt5ffVHuBpyvnuCjDEhgLJ5h7bfrGKiHBej6oZ+rjkedmkxp
+ngcSP8zqStMUtUHpvMz1vJFBZcOE8iIBqgBlro2rbMejGiaPLY9neMQBdEmL3/x0VaPfAd3yBkud
+1BN25J/A9yV/0fdLL3DxQ84YaerncV5fbpj6+xVit53Dsuwr7fDpGa60Jxao+X4PmbHki5t14H/X
+lFd5I2P03QwtLMLjJoEg2H07Er4AcN0JM8P6sh4X/PXiAX7ZW8QHf3sFr//h9gOEY/u+R9gAGnQK
+Np8rNAHdxrruii8gxfZ5HfBSWKiQtEzBMI/ZMe/KJhWBVb+hj0BVTErpaBDECWj5v4PiK/C1M3/a
+ikymsCMNGI3aBMMFgmVNYO9xv/+JJdMUBP/nefCfXwWGEwg/LoB3e3Yzbcy7eIdfzvH8nXfwpxKK
+F+yyHYuv0gH1qK6brZOa301ftm6v6xJyM1aX9jGJp9eNeQhPsBnBJOVlE26zvSM/cgHx7loofWOL
+ks66BMGWXlo3wNN4b/Cr3t/dryeVO6jUE/CqJ7gM6Xxxr0aO7NdZHv9v5VbinGKndEaSpBvSMVeh
+OtqHrI8widxPwHwBw+h9kJ70kTWGEbmdm4DbOwiWvT9S78ENLOvLmL8Dw/3mrHg0b7ynd1cAzfFl
+OYIg4xyMc5VGN0Koqr/UU/ECuzbOyJQ24JmS80MGqU+jfzZuym6aIeEWZcgKAKfn8wZugdcy2hzK
+fV+ij3k8H1n3nlft9HMhdg8Fgrk+eZbvNrbcRN8zPGKPj8s+9rm7cndRbopI9Llj4+54MhmXzQbE
+vEPB+pXVZCYbT5nUq5qzs815k4CXOiOe8R26YoeP8VU7an3GCO8X5bpXuC+p1r3E1vaW3uK/vf0A
+4dj+DAhzb5fjUhq68H7dFXoa9DDzyc8lLAP0yt39VsJ1V4sUlOrH9AZ3QZzCWONOAVJSDGGdoqRk
++358MEDNnIEDBL/YeQmA0b3Bj+/YQqa5dFrGCktIxH+8g97j90uWTwvyTSCsXt8GjF3rFEiOkfVx
+Ck1+gcDFK+3MQyyJpQEjnHdJM98lnRb3kv86GR9mqQFitquXIsRQXuR190j0gxwmq1b6kyvOp7zQ
+A3QA4QQgAFcocGd+Ke7dI4/Hd9Vtv3h/HYh1UQGP5dMYyuSPKP5lJ+BIspWnTcu1To8tLJJn6JJT
+JCnHOjNSp5TDAC4p/QJ8MQwXxLhhgOIT8CdPvII678XHUwp2IkBsgtPp4e0vz2GC2eYxvoRFQOqq
+9xdhmOkJZtkxIEUW5OV7/k6+5E+Eks2f+RaMcZDWOY91ToVZnOStefDc5SUz7eJbCZsRclzxwhHC
+5wiwbGFGmKYdCp1yAbps+w6AL7ay5Ud/gFa3H0O8w1bHIQ+M+XXD/vQzAXE8urfk7wC+BKAEwas0
+GB7Pa7WZayBzJoDukOrl3aaOMEVJn1vZevVBHHbhdtFmc1pHWz27/BYMD8Mhmie465BjFWKTmOCv
+24oRlzAJs5Y2AcR/e/sBwrF9l/iOtRWzKgObcVL5gGnkWeSG8vDa1+MUb+WavwEtz0t7XOmhYMob
+HGXc6Z269jM3NtIFaaqIDoIvabn8zQwxUeYrwIMjH4d3rzDKI0xg/EhIRHqCn+M3QfD/i/CIx89Q
+iAS9rh7g7amd9cw7YFY4OKlpeVwR5A9KnZSio8JRxdXT9pL/Of2yBUtdbTrzVBtTebvOHFBQXurb
+XmrQ8wkKinEOg1qgDUbQxjCEuEFIA8h4YXqIeY5vMKugl0N50IAvfxknTI+HYXs9HksSkJSCBWXc
+cg2xRVqas5E3y0j5LL6oCxEucRkzy3Y6GPa4ZguBAJfiw8WjU/y16SieOh3IaVP7WB29wgRqDfT2
+n2lIhKwm4dN7TID8EPTJDz0MIh/5i/7QpxVkx2MgZGPl7Ze941LGhvyUyE6w2hppvXL4MZ0rPW/k
+jn8OoUmma1xcjYgWSJvU4+QxOlsSiFnZpivgxTfzej7lmnLP8ybQ5Tm9ftkvrlVMbzYBsfGm2Mo7
+TFuXIDi+BusLNZ3/x94bblmO4kqjIff7v/Cd09b9gUIKCdiZ2Wem5jtrJVU7DQJjECCFZRk3fvQ1
+ynXT5tJQeHOenYxsk2bj4g6g9uiOeaHzYKtS9cLe4r13XS+VZRgJgs3WxzXmEyXE+xT8Ip0D7UW3
+Bnr/urhCyH7CbW9hW+k/HX6BcIRvf2LZYuFtflOoBU4QGgovAXCmWRmwgeC8jVeLMPLuPvGrAuNc
+BCp4qj11V8ppzyKVribZMX4vtz3cGeVOYQAoAbmkU6BfP6jRLMH8mtybX5XjC3P/4+tFuQTACoZj
+b+Fm+bWD9deGBTjGI0EzquyNB3qP8n7jR1/hQlQFjlIwZu0qnm9gOFuC07i3kRFg3ACyHiHIYpiu
+lt5lL7B6tEw2qE8dkylUipZrg0ouBXCUpVXYxXpV8bAGs/wF9NJlYge+cW1xq6h2FHN2TCJw2Dor
+BlfW2qWyDvlgMZHoAqWsyTF3KvAUGeAX45Z7BEfekn8Kip15Jn0a4NjMxIg5OnqYHx2RbZ1Ft/j2
+l97q5bmDVXj6DqdbxBtzSyzHmlagzJFpYFjSctNUaIoyGDi5TnjO+VGmddp68lPI5WNb8dsqnudr
+fJ+jNT60t+j8VJi0W4AjDdFHXKfTMjxB7wS8qpMaDVBw+xHwAvnkIvWpyXlhtfaH89jKOoygWdBe
+rP6FC1SC4NeXBbmNQnEoVahSc43WOalWD3rxqiftnFs3b3MGmGZCdUNBXA40Z9kOiBccKfcIM1tb
+o6HkAg14S1ZwVx3E+xaxL3xsC8eX5e67RtQWaXrkS3L14tyVU/+x8AuEI/zky3Jr0Rk1EmjxTRAc
+i59vYtsGiGkTjAmqIDi0kaVQAijNzEqqNdeIFE7I+ru51mRBoIRR5nPZ7mKA56vQ1jzrfy55DLuC
+LRhRyutkBXZfy27fQq3A76sWYe8vyJ2swf/6+++yCJt1a6+J9deXnTOtxKQFyys+eSo/72k+WNLf
+EkADW7gKua4iu3CTpxBfq9ERDmWo11WByrGhPz2/CWgFwEA9SZHNhRPQUclhB8KxLtKPjZYqoIAx
+wmJMMMhfNCF/D3o+XryY4HiAxgOXWlxcSK5lglW8uV29XrwzfqUStWy5e4zBGvZKloNzpfor3/8Q
+UFxKLenaP2jcc84VCBzD2xM15tr/NTCH3R3iN3yEbfgCg59RVlBMoHsAvwVw1Srs2bQs2/5atT1l
+JSd5MNsJtAQMKWBWYCzn2+k65GsLXd7m0vo4ieroI98lv/sH88a5AyZRLdKatb44Twl8S8dY8eAr
+IJzxPc31oL6/jYYPeTHv67pYIDbcIdL4Y8tX15/SudTdRteoZ60/0qjSa2DCacW5Dru/LyReN6uR
+Zpk2P3qYM2IO6pTk5xqGYICXoSuNS2x3zUnqIUfpOe648SDczh7DQxkau//geZdcfeLswC71stz5
+q3J8Ee62r3AB5F+L8H8tfHsT580SrL8SEDdAnFO2ST4kCG5uEfn6cFl+y6JRLzYAqMVpcgET9wih
+ZTIbomX4pxZ3kwvyd+Nh+7uXySUbipIrOwGvSnjemeq/Iwgu63CB4L592v/EC3TTGvwvdY24AmGL
+tBfgdWtguO8esf4Q+Co3ycsHwN/WwbDz6NYEXj3QdOGrx/8VL767lNpF5jmo8q/kqUgBBAwwkE1C
+2rN5jgEJgE9KEsNKCZQ1AuORnEs62lCWYBcwt76QtFmCsQDvewC8/L2hKLP8AfcBHdOktXCWmXGL
+dR6WYMs32GXFOUFyjGLc9HIeJEvjmC/HkXdQANxdJGhBm486024aY8odL7qSXb1QsJWDzrWbeS6/
+w8tyQa+PY6gbhO4xTJ9g+vLHuZhpuSbEDYJp7FO6AeBpDYbygfNV4hMYn2TdZd7cQ3kJF+UjhgoZ
+mUI0+u5SS9w0EhjJ0tXhMilLw0udZymLO9h1ic+8qPUCghF1cQ3p+l9pzbeR3stDQTCtwWIVNnjq
+43ZcX4+oz8A/xfPUje1GaYpI7VuMIUFx6lWv7Hy8pmP6xUSJMje5Um3Sa9UTDKuLr5Rc85G1QUDM
+9/7d6RaxCnh4hOIB3AsA59HqZTnuGHHeTu1Jf2C1EBMQW7hH/OnwC4QjfJf5udhstwpz0SkCSjma
+d6SctCFMxOK3g+AQTOOuXFtaABkieErY6C/Pm+Xy6qmOqz2a4qIHFbeNvOx2C6q8Bzd73JGCnMB3
+txDvbhH5Jbl3viznR2vwv8Ia/K+/AwjHGK1PPCoQXuOQabNwiyAotg0Mz6BcfaxeM3jc8DcEDMe8
+SMtw8qxE74r1HIj6TJDcmH3mfBuC2fAJfBtNAIAVDulBdqf0ALq0DDuQ5hgBwlS0+ki/pS36SfBr
+y1rlASw9+JDuERY8Hb/3AcpyvAPmzTosLEn2CEuLfldWOhT5dvtjsHg0a0+Nb3tyEPy2ACQm9Rm5
+HPzhrhHdJULTFhbjExjmzXr0I5+l985qX/NAkMQbW8YD4Np8OW7bNm3RG+DVMrqvMIYlWNJrOnZA
+aNnUbjXeALBw1QcYbi4QLa4M0UW0L6izZmEbKu5xzbTaHicfsv9JOpRLizC4Tufj87rqDoAxzsMA
+hSYT9BDn2p56SOIOFMANek9zbkZrrGgAxDKM1McbCOYRahGOLRS5k6OCYBeRV8oymu3gjfj2oQ6I
+wclMaOxIcJ3yE+NCAk67YJE1Bbqc1RhD16m0tsY3bqTJ0PFIKfVSnPME7x+Z5jkWWDxCLM0FiKOC
+19KaW24Rh/2D6S/819lqXO4Rvxbh/1r40a4RRx/h+tFHuFmF4QmCyzJMQUWQC3wJgmMB8UUGiBCj
+QNpcI9juAz1hGleFUFsp0xS08EXQS96lQKisBH4d9M70wU/43S3D+lll/YLc/xyswWoRXsNqHfQi
+QHICXh/gd91xT1/h+pV1L0G0lH1MrMFuAYb5U/uQWousvfxSo9UtX8659eUIjQHJCkRwNx1fwtlv
+51OQSno1I/b0NeFSKChVfHl0rieHWi8hFuAEtbE+VhlPgLsBXdkVgkB5HXmNYTEO9jU15b3LneQX
+OrtLSxQSBNtr6T2ygHAA5BgDg9XDJtTq3NxAgj8LIKu1twOK6pt161oCnbIIst1DJxfAZEYCYS93
+BO8+vTZcH/hynIlV2AaA9vcd4FfSA+Amttjy+vTsMQEjEwBnfAwm5XICvznQ/yzo2j3eYZ4mFfvm
+HA2Xc8vVZUqTLi9484OUGWo9XGuggF5Zz1V3SDyBsMTRy0wXB29xS9Yf4wJE66W4OG7g9wn3iBi2
+512uSLAQwHH0Zz24evpIVNSqe9GW0p1Wejf6aUynzKz11NbRNqBMyroCUDvQ1NhVWDQLmUlMsW4U
+YyznTRy6ruJuRbQIl4V4WcyfV1r5xPzkzxyw204RB9cI664Qa/eIXx/h/yfCd10jjBau7/xE0dcd
+qcgQRuTRXIFgj3QWFPVncgzhlcAcJZjkV72z/mO05aEd9dwNXyvIatdBtq6HgzDIx5oknS3Bx08s
+JwD2ZhH+2/VDGt7dI/4WIPwS7B6AsC3L7QLACJcIr3IJiOsFuuLsEkjpduHcnWIB3he+wC9QAJiy
+jxaTHOf5V3nvMjv4+FxArIrM797sNXCAGpusE1BAvI8u2rkJfmnWIfjN+GpbB8JqARZaukCURbhA
+8brwG+q8uZwEdyZAVqD8OuDmCYRfKt4D8P067ht9dXfNDAJi82EJ1mPe+J7GqIBuAl/2U+ZPWsOl
+fH9hTuQZONYCm2QK9R4FqG0TV4Hx4WU5tQwPcJzAN9wh8gU5jXMUCXAT9FWcyj/HQrBMWa+DwTnH
+L/Hsv4JeylqJJ1/+iQK/nROML3bPiOR5JXIoPGoRGSFDbKMK6pDqhcs9sOooF92mfBH9A5lPG0BG
+s+r2GzSkPOhxyyGZblQwxJPZJ3T4WlO0DlucQ5NEuUcsy7BxEyi8azcEPsxq3NuHid2Zurat73U3
+228qQbbFOlODwxhv/UJgAWK9+BTAQYtxUreIsh7XGBrKFSLjICjmq84hv2kBXhaKFofTRziA7l/d
+Erx/Zrl2jlifVn66q8S39dS/L/wC4QjfZv4TM+JiFS4fpNL97YgCxVCw6/TjGhZhqPVXy1JEFQxS
+GNaT2sZZLITHUSCfgG3FZl7Jj3NtDKlkh0wnAKbCokBvIPitTyy7WoP5wpwXCE4A7H3HiATDf6+P
+cawtW+qFuAWCvVmFk4YAtXnO/lNLMN/EzbrhC1wD+MvCLcIR4DiAihO8kLOl1PSRZ418twaj0U4C
+E11vtzgV3SEtGpSPUDkv5xU8QYKJTqfiXPQCw0CBshHfrMI9TkGd8wcFgl/OHzMBt2oJnnkEx54g
+eYEDl37hh3EFlFYbZ8Sj2BaH3FA5ygrMI8rNxoqtOVfS/QEFMgh8+w2G5gdQiLLag26Z3LlQf71K
+BChWcNtAsFiDFfB2S3E/4n1rZBX4KviVvGNa+rFNfAEmDQDfQG/bMaKvvI12FIQ7sdY2IIzeA9ea
+C6FFCzStlIyqTsXRkoBlEbfqclJQ6/YEgAnAshxK50waau7pjS6AmqdRtc7XWI5oT5DipNwP2J/Y
+/cGqHwSnCAvxgzX/NO1Y8zC2f8yH80ddtinRDQdwJ4WlqynQOR9rrGg1bjZeAk0OmujEPVjWwy/S
+0RyyQHjMLX2KDIu1t5L5why6ZZjjdHRU4E0R+2UH0PvXBQBf9hQ2eXnuT4dfIBzh+eauEd40lyUg
+pjuE3hVvDvopPBjq8fURDAM5eXPbJRGy011i3pkOeJrX3BbxFig2OqXOx8gTyuzi7SqipFIwhOD1
++HP6oEbzE3b5qIYHGH7vluEJhv/1xq4RvDs269ZgHlHgtwFkWAFbE4DijK/z/gdxnpfF+S8Af7vj
+L7MEw25qveRIU8CrI0RZfjvYJTBECsJdel7GfIIBS+IBKLhoUFWrMYZixvQc61KAZXUsJXIFwrxp
+jN6WmwNdIgIY5zosgFvglnxdFuFmDfZVXsHwa1KPQIUb2G1pw7kcFR+s/IMRX1LyJwGxO8GwdTAM
+0IbV6l7At6xoCXzBOBJIlIWYskj5HrNJOjJVb1unDeCOdAPA80Mas5y8KHfaX5hpyJGjP9MCAhun
+HDvnFOC2OX/IVwDc1sBNfp4mQIU8a5y/3bT6HJOSldlrkZk9PW9rrM1kzevlYuS9tzVdZ1LA61HA
+4HZUfTjP0zm7yuc8Nku2N7eocc5Sjw/c6SLhIZVD5zrKVQL8ducCXQl+Y/05EP768cpv9rm3u41f
+gv2yAps98cTnqbkuq6mNsh8mCEejra8aodsZ2UxOWZ8gGM2uIaaI3K7TjLcC6zrPqNwV1AsYpkvD
+t0BvukfoRzX6y3N/OvwC4Qg/2TViOeILGJbFUGhI6UiLMKcfb6ITyKLuDnOLpQAgDQSrpTghqAio
+/Nnlh4GF7PAbvElaXM/2HLQSFfnE1VKsBMRecQW+I617B6s1eLpHtC/L/V0/ukX8f/my3AUAm+3g
+V90jDPVDvQRn8qY/wfL/gNZlw18egMyAv32B4teWn/ACw2UVLqBpMeYc+R0MpyVARW6zXv0gNKXX
+JCtSouo4ZryuV3bqaiWVXdFXfAfBtt5UHiC5gze6RFhsk1ZAubalC+uud4DbgC/sUs6S40dwe+q/
+XehxfGJm2POk353hXWmP4fJ1A5XL1Zd/+RviRnZnztHOF+as0hsw1nTwulnk2lTxpqM954Qo48Sn
+HdzSL3h7WW77iMYrrhG1vzDj9nrzEVbbPzBcJKCgruRIKy8AMDurS+M45yNOxqqsFYBaBKnwHN0n
+BbQdreK9ea39muHVPrkZANB8faVHFZ+gF7UynecruPVVege2oie2PLlCm5s1LyHxnT5AcsT5tAi+
+bijX0QscP5SX1JJ0ixCLMF74s3yJixm00NfYKH/SUqyGJzPAnrWLRYBhmMWXNPvI5hTLtMgY7z9d
+X00dZOhaAdJbBcENvJqJJTg4IXXzdqJ13uXa6coROKVZgYc1eLMM64c2uI/ws3j2/FqE/08EkQOg
+X17SFS/qRB1yM+vIfO8rZGpaozBDPYUKIoWUUVhvF791BEdgrLJ5RL4VTiDBZr73o8pvyiKV8U3P
+AqDvMIHOsqj6/nvrl0BZwDKtybvKDGEMH3ETsKWquWh/w/GXG/42Htdnlf/C7gZRvqzz2nXNJnQ2
+UdppRSEojrT1sSEcqPQ8dlVYYJBgaVOVIsa7JVHng3NAt7qVlwJbYmsjR2/j/beu/Q76Gyxcc4ZW
+YeZFWqzJZTVmuzyemMYAACAASURBVHrY+iSxDTyO8Oaj1xdvKN8nJv17jK91Xt/sY57JWllz2Zxr
+g+n10zgtr+a2QKq9YVYmEI1K+WLbALjtUW2Lo+/wQADrBYxrkRMoo2hAukpkvM08jkaBCB6mfFHM
+VQPRYIyAts/jtVeshbnaQLQoBb2t23b6rSx5L/WrhbddVwXkiS55em0FvSpJ1o3DxqVSTQNEZZtt
+HIEAkRjKUo7en1QwT8Ev42d6B8Kl99gbjfPI/XlMRo20ymP7AcAt3hJ7lz+Tc4uJ54kPcGC588RO
+NEtcLz44yl2BgqitF10r8tSkr6m6eWz+wqyX/M0dLS5jIiDY3MW6D3knoazDkPj2s3VT3l5eCCxk
+aTiycAWsJ6orn8MVxgBLVjc73X8j/ALhCLu6+1QyJmeexYlKZahq2OP/oMV5DGony7QA4P6Ye7ZV
+6qsZ+62+6GTeDIfG/vbraLp61KkFZj3z07LbIAZppQe2uNBEl3Z2Fqvlp/aicWfS+rHn7fFP+Yvp
+eU0R2szf8rSNQktwbAS0gFlZf0txzjZNCzFHt1Regs4NGP/k2B+sDhtbXrXpY5lnfZacQXDOS6uy
+bH+fOTPNPvZ0zi8qX4x5Fnmv93bQSqxhyomT1LitzrXICFjxdfwF0CHwij+A+xvbvL14X4PFh0HM
+Epr1lhMURf3+11p3f/lf8L/Wkwq82MBuMUuVNDZXB5dyBXQJwOWlNxkxlXnk68ROruugjYA+6t+g
+Zg8B3Cr3vK4VHH5WyS5loj3bk5teWxZpZfXqZJ23Xwk7lNBTQbi1CVK2J3UFOkq3tN5454BrgSFZ
+2lH8T5NO0KX5cXRHArIEt5HtCfDC8MCy1IFETKSHq1HJu4rXUV0mYja4Y91p8xi02ObRc7uJRfPM
+c7zmeF4DHsf7Gp7HgdfwPuuJxmMP3qdeDKUP/LyBrLx3y8s4z2ty0dpNRNFu6UKczqOL7BR5CBcY
+oaC40UvrWfB4PRXlMznPMkDdiJ8+ssP3AHLBL8H3R8MvEM5wUmmnUiXIN0AnqpxCq9EYF9Bs23W/
+kbY6pFX41J9v3WINBWI4nnNq1U5zkdvsP1J2Ny4ILUtysVMRsD6W387Zf7cOn9rbwxko18++jFsI
+oAJ445F0o4W4UBBsPe/Nmkq5JNw1dZCIMn5SVCNlxYecgTdg3Cy/83zNq7YVH8ue96ncyQ71NeCd
+1lodu24/pBKtuRRxC/0yxrk+cOKor9Z1vqBd7Ryucy3mMR/DUhSkhTTeYHc8+RaLtXLLpzE/If0a
+1tfx1vzjsTXEkQBnKde/1vp6FiB+/Al/SIyF9jnewC96mWV1jmc2TsXINU4Q1lcum21cQ2FFqvkr
+AD8NA33svxZ49/x503MocCB0OI5cMyzcgWG/Ql+jnc9exMH7JSO1GZ4HXRc280njcHH1ZJrX05bJ
+icdJfZA5CooTgXcrcQPBiJtVgjbSw3KMDTBHnWnZtNqLEuLuIKA3/YXd4gaNL9V5lPe1rtxiI10v
+y6kRvEe/CIDN8LwOPIb39fVY/13O/u/zwl7xEZ7glunthtKhO6tsvvhqQsXXaQW+laaeqqV7HNYR
+NjCMcNMiiyK/thEVAEyp7PVEavIG+YTq1zXivxauiutYTgCbUEoEFXUT9lfavTUFq3r6u609+wbP
+cpdMkZz6t8700ZvZ8+rjEciqDj3mDQ53nfAtcNytlmcr8ek8G2VmXOvf4gS1JQLkkZ4+mh8gGPpR
+Dct+7z7A/Ugd00MHxx3wUkDus5D0E22fsXer8HfL1coZrhFtrOYc1WsMC3DOlQKy+VIdZM4EGG6f
+t/ZVH90neh/28NUqnPkWDfW4WKbfGJXcpmg1clmJl3IoLLIez/q7WlrzpnwRhXEtQUvjXwTD7rmV
+0Q5qIW1UuizArWzR0iIM1huj6q1xg0FxCHnlpjOeMyFoFjTvFuN9zX4JcXNs7uPZ6/UtZtkWpc0a
+P84Xr/FZN2I0LHTDAGKOFp1DkZnV4oF0als1NqYkNFxWVDuN/VI+KD92edQE0gDGfPehvEFCP3GN
+Gmm6dlF1JkiOBr8Bfp91Y9jAL0fm4C8sPhcoAGygFdhzO4mVfn29HwILwfEYXnvxvOVC8ZrBnrAW
+xxt4+1OWHRB/i0Zu/xAMX8Hxl0iC9vU+uoblmgXop5O8rMG67pMW8yv78wEMP78W4f9a8A/ToRe8
+W4LpErFbiPWHQxotXoB3tMlQCyJmJm+i+ZGFXtOs+ytA/HWoninHSgDPfDaLACMFuB4hHPFxBETY
++16O12ATvKfvoXhVnNtBryzlLa67hLQPMGjZlLMT6Bo6WI48Y5lhDfMCwWoLbu4Sjeo5NyBt0ll1
+tARbZ1uoonzE6TjPMbVBeaN2penSXnWxaErVIKieLfhkFT7kHaxNCoBnnL7CfbWGFahYuIVvSo3q
+c1MIyKeA9qzPW9XcXRalrP8JSzDz3wWA31b/jNWi8Nfhf2Edn/Xrm93rXQ6fMsliRLX5cxnmvZtL
+RD4aHVzeHmmJ9p0W4Ql21zrU+fP1GBwHbVbhM5Nz91RG57M32i1+au3RJWLegKSQaydW/Yd2Z9z1
+lEooWC5AzYM3nrvEs87p/sB+ukseck17xN2RLg+MpxoTVwnqrd0ajLQY5/yw6QYRlt58E6xcINZu
+Ek88cVkv29EKbPYsoB3W3wV+bblEPAFy37VF2suP4thyicDzLkBs3SLsbfxk3VxA8BkIf3J/OKcR
+cnCmKf90lCv0OasPl8W5JLdZS4CsR0e+AFxuESgZ4O8mClwb9QfDLxCO8G0gnCW76lVajaqeoT/0
+eBw+35+5lJnKLgjccu0jIDYpf8n7cP1J6z2bwJYpARhDXi+aWDsgMkLj4zhHTDm70nY+/9i3OwC2
+Q7n2woaUY7y97TxBtAqhoBH8Mu+N+NjbPfuVUNYBBb9NQTVBNnmDBnjzaN8tewK4esZ0f7jnsa7F
+Uxe+ngDvLX0Dx1SwqCOQ1qQTMK6X6daxcYPMEF59Lwj4SYHv+Z6O+gbDl44FAA9wDGcZeWz4oIBT
+swwHOBb9mW4Qvup0f9rvfZ/6xLzHaqVYkUWrwHf1RfoElxerSvEpIFZJMVeihSXQbPLWmo/8mjk6
+5/fwSZIZsLvo+uqct7TE88rjnO2KJEr8Is6P5AGA+s45wsKaRDk2xzaJrDTv9LxeVqn1ePYCPg0e
+XNOTSQdgnGAYFQfAl73K8osGcnN9xmBxrZY1GN3K+WD5mMKWu8Mru0L4yk+rsPvaIYJ57vFtAMYN
+8ADBZqhNhiNuyyd4fQ446OEOQQuxvbF9Wi5kv/xwAMc9H6EbM/1N8HtOc2ywGWHKSszz1MwSNwpR
+V5pUoi46MvQvq3bTEY8JoqOPJ59h/HmD8C8QrnCRWFspF+HgNVGLEqWGwN8UQZW+i/OSKXsZT9kz
+9EbcgvWXSXq9Nspf8ki1T9w55UwloDcMJdT3o3fuXMpQLjT5gf23NdEpxE83CjuwJZ1lCYDt0zkN
+6NZd904rwEvaK+Vf6PgNKy+vZwd1NIajAeds32DLoB9Ytp3T4ykuofPHJQ+t9dWvJaQ/WXkrjYwD
+3nvdWzRfzEmwPKzDAoC3H/Mxg1wjwuf3UgsUOf84ygonc9lCCfgzyrXPm76Al7J3rPJvKo+QSc96
+VBnoN/rjAX7XTirPw9+LN3cSRckUR82dAy13somO9XP7F+Raus2m4nCbQRaMtT77aybNZyKfoHEf
+hx0MrxoM0bzhI7HNAcqhzLyA4C0tYHC0BwA2izC88xBlMHAf7dLJNVdplvUqx3EatO0cb1dJXmnc
+J53Aca5H4YG6P6z1dgC5dH9wNPCsPsNBAPfqJ+gzwi4CP9Ba3K3E6TP81F7E6RaRL+hZxfkU8H3w
+2rIqL9BLdwhDbgrePkVMXqqVF31NTNqg5+r4R2BY+Cp6J8sCJcjybtRzZyxdVwTEjy8/acBhbmn5
+fbyn+441dJOYIPgVy89HgfofCb9AOIJvC/5SrqnnRWlgWEGyqG20v+s3IYOGI/CNDHMpM+Wulg+h
+sReZUxsDEKOsZ0Ke8F450dwWEEKdZ/i0zpZA2NQizxMryPE4f74arXpWuawyvqWvHBEgZiVQEXUW
+b0zK1n0wbKe1r3pJ+xzlI/xqebnGCQbU4Mvx0w4TohSUB9+3BK9YvgneIXaV87r2DfzW+ZPzfcZF
+w8e5lbq7SHwBfj+C4X7+OdToe2+mhL7yCBQRypfVND/hBoKx9iANK1VMkFUrXSne9arcE2P1pgUs
+6nzWWnrc0+q29s5+8fqD533is6ZviIzqscqZBnJHesW95ZU1uFZA/rwUZh9nraB+3CJynxuTdgg2
+2hxoq0amzs/H7umzSrDVq+wgWCNjvbXxHxWdogIMpq/wBo44U7ebi73+JHg/ut/z8piHE+9Z1nJC
+OPtKXjaelPLaQbAXCOZ6FfeH6Ufc4mGhXf7B3OosfIETqMkX58IfmLRsb34tdn4wS0BxusNFXMDv
+yw9pmMGeF/ZSiXrxoAHcPuZzDpzSZQlffzbwC5Sc/6qshfxsPsS54MAbjq77UuoBZulWzas/ohGt
+RqGBYcT67yD4WX4Ub3zq+g+HXyAcYXv09bFkFz4dHHdBdfvdXhgZsO2QN2kueTsw4YRVF8Ckn2rk
+Ar+Gc7tXjh850HMOeV+AXp5zkh3aGj3qfU2jK0G4bZIuWk8TaBWvBRAbBQv6Y6cTLfNONO57a+Ia
+QWlzs4HtcLIFsc7oyOVnTQefVh5prDlA4ZhHE9jquVsZlzItX1fODdh+J0/iG/jlI9Z61Mqxe8EP
+mtRLcrp7xJwbveV2Tc4zPJhDKy9Br7MRAW4LGFcZvO9S8vky3aKtvUwfuMXOETGm5i7Kfq2x/Azr
+Gx8QeB3vs172MXnSsYCsxKUvExR3usn5b4DdYRmW1d9npNeFfdViIYtYrvw/99vAFr7yGT6AW7Be
+rzZ8DYIJJhUgjnTmbxXtIQEwppCs5CWv1euS8C+OYKWQm7PLsXVFebz3m3O9WYLzuiJ7lvDcQXBa
+ZiUeZcGyOWfK+ktZTPmccYOA4LAEB4pLQDwtv3SZaAB4lGm/F+lCQYA8Aa3GcywOeady5K4C0wZi
+T+ng/QUMl/OvCRCO/pEfOcYm96M6p9P+LiC4LMJw+hh3i/AEwe7vGov3z/tG/ALhCD8Bwqp+u2Dv
+EqoDZpVQM/1RbMucK+3UHoHnybR0nFSwHfzvPimMOz+qt73H1QwtUSet+U8rRwn2oneBn/U79vJS
+DpMmzdg53G8dgjPSt/JkAgBuZWYtv/pbX/AqcZDlrJdvtDjn3WirDv1qmKH7STYQ7C59OJXp/U/e
+DPyWRzvQxjkTdveZUte+A2Qp472MCte62hegN/OoUEe5uIw7Aa+CXzk3x2mn1arq62UDyrVEtxuC
+BSDFEheVW0u/cPEDjkkSAHi5ReA10OxLa/AbQDa/TvkE6LX1NUS3BSRfjz1O3xf5JSd7QlEJkM3f
+iXYvy9F8Qsm9KMtwumoEk2KTOGFYRKNCzkdeqW8ZqPMmKBsA7mXaS11Qq7AUY75VOS3CpFPooBF7
+R0b21q5ZTATd/cW5g+7RstvF+kr2kd6sk6jjBowPazPTZI71/MLQ1ofGTay+tLxbrlOCY9ZHlwi1
+2ufwrAWA3GEkJtACu0vp2eMBgp9BjzoTBPP31Kccx289IXzRPqfcfm/Gky8N+HaabfmXshBeCD83
+8KvxD8A4jTH6w+qjJUAW3uhQOs/vs2KB3XWzv76wSjAcfeGNLkEw/cCcLwurU+CfC79AOMK3gfBm
+1fCNXoIKB5pe53JNyu1LlpbZaEaoN63Di5YK+jtzTdG26PkJBYBNNJf6CcG1i+8dSFNwNu5NgAzs
+ZTCGRfLgGNdgvXLOsW/W0ps12GhlYFk+lqqyCYpDwOy+wrQAL1p+8SzKqUX4CnBT8UTr1HoDzUO2
+pfGntVNpYw7ZgVajDNc2SC5G2Q0gpw8v58QOevu1DnFHPvanTm5WYY1zPlzjS2Hyq4WzPzZoE/Yc
+IELj87QGI3RAB71iGVbLMQ0lfDnosfBVXIr3AQBbb7abh4IOAGABhI2b/pvhsfX4Nh/pRnsJitn2
+tPQIvX7nPP2a3RML7fU1uk8uPJUcca0EQmtgeAMIdIswJLYxepK6Dm9zGgQa4PwhSO0Ctj1hUmDY
+Kus0P9BamDSxCM8PK+wfYRD5daxYrsnoydLrLnlZUI5eLGr120gHDxw4WWl88oovJx7Br6xl8RN2
+BXNqGX4dZvG05H3FChwzUgGeu4DgJy3Ea64FQKZFFCcATJDIX/gDYy9jOZcg7wTsfM4t7U5jMMcs
+5P0GhjNuJcc3MDzL1a+BfSx+FO2wvMbiCo6DrhAAXSJC9qBcohZ7K50vRtA6TCFyEvl6/DeGXyCc
+4fucTeE90NeNnvUfrMMH+X2mHQb/VC4z5K3Pa+CE+xgIS87ij2s14QstvknXcp4Ckflfpw/HuLje
+aM72rLSlsC3ldOultTW35HG3/lYvdwBscj0Hyu1B6sj8A63vI6yWSLZtSgAFA44EyBRyKMAw9dTk
+g4+8lv/pnI2HGiuptVuHu62a5/zI9YF5aRly1GN0OY+uEIzjDojLJaJblzHmxgTDt5BGRSk9rcEK
+dK0B5LUn6no828v1r8LW49gXryjhAL7h85hpM1iAhbXn6fIPXk8dBNw2YNvznlOej/P8Datw+CSL
+ktdb2zaPGggm1pH5bScQ7GkJ3AfgFL1r0TWNKMvrpALB+oRLrZ3IvrWrzZvSrNQHjTIt6jwBYOFa
+nTGNLrd1qULS87gZcVL+Cj35EQMjvGh93TpD9KQtElCMtR7V5UHBL48NBDfLcdSbIDUsiQR2oKsE
+AfACvC3PfQfBCQDpaN8BbgOLeu0GfrUceV2876C4jmubwZ0OH08vUsZLfAJjsQTXefOcaONjqJcD
+te3PhkPbmFtJd8rI9BgJK7uZWohRwpZzMvtMF4kw/3xSOufl+78Kv0A4wvd5u6la+UmZFF6HvOM5
+PwDFElFfvg1cbGC4lHqj2bxKn/ap/K3SEwpMmss/S+XXxTfAdSGW4O+kVa6j0sVR6xYc0qei2nrL
+e9rJrZ5WIJsAOORff1wk1l9Dxb9B46Pu5H2gBB1BHV3twzab5S6q+FbC7CprcryrFVWuen8GvXvY
+11id85Xl9wiOFeRmPSjFyrKhSDXfA1jRQFsfManfm01QvmNrW3G0eKSjoFzqE7fHXekJfN/Yy9SA
++KJcAuIWXvjfwxo1QDHskP+KEscAuuP4RNxH3uO0ClsownijnBZhdDD8Yr0XM2WogUCHILgHWtgU
+BJfsaivlMAVLWOp2dbqCuGazvlwE55mbdJUrXdCIHPoEjqWwowwGOYMZXQ3vuw70vN5UBes14WrL
+LiAB8c1K3MzO5xvYCmN9tLZwYIUNwwLcQG6s2XUk/ZTvBebemNfiKpGg1wLk0j+Y5Y4g2ArRKahV
+cDstxAMwr/VUc0m3HuSNibVFDxkD5V+AYIJl1dUbsI2E6J+MH6zCBL9545HWcFpl46kT6mM+pqDD
+S+MgAC8xB9dnWoR9lbfBi3wc5gAtw/tq+8+HXyAc4bu7RkCFxlSdIjR2kDzLqIDZg0yxoolMVjBc
+tFIGdZIqC7b5AFTO2EUe/7NXHY6wF2zGKQ1QRiug7ee3+FZ2pEnzXv/US1MvpAuAdFq5UfFJL4B8
+cocoy1UBWgqmtEqzfNBy7+AbDbqPMK29YgXj1VISVq9SYOVc7P3iuGr5Gb/n13zymTnLHYAy2z7T
+NQfO8W2UHLi7VQDlFhE8dZlfpOt8goMb+i/eF8Cu931qfV2WyyombMl3TmIMi8E4W3lv8fj0MpX8
+ml9D+UIVMRL4EgwoGEYC2aIZgIeW4ogrEHYTUEzgHFa3xxBKb33K4H19gV1ahLE2e3sSaC1GvKhv
+HSTPrGZHLjDUeNfss+DtmB/zxt7a4XNoemBfV9uEp8yZiyGjOzhuckrXb1qZGS8LcXeP0LYMqzoz
+XdMuWZ5lKh61KfB1CGA+SYiSp6e87Pux0zhYeHmMa2/5Skc7z2D5cpqCXwT4JcBTlwnmTRDMl55P
+ll9Nr+KaFvBrUia7vdq/5rQnrYFCHb8gpBEpF0fw3YSPSk+2ExhDALPI7QD7/siOGGAc64YBD/zF
++tgPvfmNhq0FcHUOGKp/FuOTINggVmFg7VIjAPh96sb/eyv13xp+gXCEKU4+lWySxidNpZAKkiad
+JJ+g5Xat27SQCdgK7C4RBHB1ylTlnKlCPlz0AEcgMCJ9nBZoVRud9v5sDV7prmoaF0U+NG6OtN5U
+bzLmUDc7u/VNhEbSCTaEri/TTWGzuUKY0ND9gYumgJjtaVfoI2wBkqMfoiWyb2xt6lyceYJozyde
+eaEThERDA71uLX+C3jmxvMX67JKZlTNpxpUn/cZqNXbxfdyWepXtn1euXSOqvHDf97WGzvkWjj0/
+Tm6J3wBxXH+zCDvAm10qyAS3mk5w0BW4hdJcPsOWO08QENMK/HCem4UhjXlRr8d5sPUpWrUIC6Bb
+L8jVMVlh0qVcw9HelBlsr8x4eUFurdvDIGwjgjZz1sEO5b1jwGzfkum6BZ6cUpU423oqs6ddAKmL
+FaAAqrQ5eSrXqFYfLqOW5UhdrL8uwlavfeCExE5a6nbeujGFAfkpZcfXFmAIskxEhQS2CX4V7Ao4
+TauwGWw99thoKeOzLnS3CNQNYQPBCYCR64Sudfn4HzXGBIwucXKsfRb7xPsbGNb4LHMq/6wtaBIM
+k6+xEw2eN8EwHnFt4DgE0FW5TSyzRJJTRYDW4BiyOHKeAcsabPD3kTp3ndCP/77wC4QzfJexlCR+
+jndVfKFL/pCSJhHzRllkq3NGjt6SjfMEOom8v/ZY5Pichqe4Ts0ZVwTQrcHKoUOeD3rSkCxXOk7l
+sB/lNlloaAC2L+sCsbUMy5JbfbWs00NYrvOEhp2W23UNsMz3ogLCZA/mA/gJgqUrI7/Psq4462Ib
+v+TKaVk4BK1jJ8651oXaDexWhV9bjbXO3YXivPpym0/UThKsU90l2m1SKoIzF0xK5zGEvxvOQPYr
+mvoE8wggLaRU6IynLCgwXCCyFHbPK5C7ALGAYTnmRwkCACxjWwAJsarBlyWIgBjOHSJqlL2lhV/G
+NcZbnVCvfMFK0t8GwSr3yNMatgJYTOt5Kk98FBkLxrXQrEvJ7bzoh3vKvQK6EYfE5xNJFYJ6se16
+sioUWKuVflxj36f4OPFR4zmasYVYo7lALJdUfs6cN9cylz3ocGxAOW8EBayqP2/uQZwAmDT5pVU4
+2tgswEVLf3QBwLzBTABM3WAFClN65zzxVNUlY3JA2jC2dLTFNZ4qTeSzaRmcwbJ7guF8F+GRhYIC
+w0350io8dmmxqDMxNRelR4sT0/C4vsKXW8fwReGGTk4L6OME+0fhFwhH+GcWYRUQlU6b4fZIyQ/l
+TmFHEzrdTOaJwjp9NDIfW5SaicO21dBMn4OWUoVfcd7lKrxREew97gfONDBccrjRGJd045gL55wc
+Uo7ufereTR34Khyq9S3WYBNaAizLNnz0Ef4AltlCXseoNZIK4XwHzaex2+MiSLcycS27nbtzqlmH
+reip7W7lxO3gBHb1Wh5C+Gwp3i3D+8r8AiC385XXnAOO+uTofd1I73upKTbi6BPwusRfL7cIKu18
+2agsM/2lGVVqCoT3I90dCHg1TouYS/ohyJB4Wp5jwjwe/sDB8dcLGD8omaszIpZqW5Ml7HR1Au3m
+dY5FHkT76qjY20kyNvnU4yaes2C0/yRYJP5VPuIJhTwaS6tsSkwHmvve5iJBOTku0gw2PTst25lP
+ADxBsTb4A1OGnP1QKPi85IICWic/lJYCt8rmF+cStMb6gJV/cM5LZDwB8DPyXc7JOqV+VB197YRM
+aDeJQLoSBTfhUw543iBvfPWdNtxyB9C9gF4r+ZVxLUMFyg9YGJACKF/gjQ9dTCsU16KzcaWJ2MkV
+92RdW7bZtdgSMnQs4suXfzr8AuEI3wbCw/pbD8pXLU2wbPlo57aJLkuiHnvjYBWuvIZgQGUi9Wxg
+eATOvy9C9xNmKzr0QMYtgKyloKafE+V0B8B+oNW6a76dAyAnN1zO8QMdO/1jf1GWYMq7FRfgixJs
+tMY1kBt86zQrmgkt8l/ji1u6dRrbU1KEo5pzJh+xqYQ5W4tLDNcFrvp5K+Oo2VnC78zDEQ5TuOo7
+WXaB0+yaluIdPCstrqJzLejq/vApzpkukKv3Q5JzXhTNWu+y8gF6U4lIGX9cwG/P071uEwDn4UM8
+22w1tw8A+LEHznQ8Nnahb4A4Xqx5sNbq+lDeOsKXz3CCX89YjRXbwvHKpqoW7f2pNero/WxdbPTl
+nmhoj11Wo+RS1tpXQyPAdI5XHNv01qPmN5rnkXIODpSPbpUptwyp7AuhVrKP9e2At+1O4Z3mrVO3
+i2yRQ0oIBmxfiyPfGZ/WYpbNxWJilbTYRq0AboJfw0YzsRJv24cJkF1tvYNfAl+AN4yx3q3rCUg8
+d4cYbLHB55r1ykW1AK9rAWg3wJ5tVhmuBo+IP09vRz5tekqCPg5w/3JHuDvIj7vqdFQfZUe6d6qK
+a/fed5z4Z8IvEI7wE4uwWnxVuJiUUdVrkp75fZlo2JFDB8sgSl6LOukD/LboxUp8m3eNPi293UKq
+PTsBAgpul9qmRQ6NJnEtJ4qkWYNHmVJkvbfFiTo20DsY4KOfSxYTKO+guATQbt3dgfGJViCZYLh6
+soNbZHukhwcfYc84SlhufJH4tczp5uo06vuxLMzUbr2GsvLWbNP6PwHjaRFu8+H2G2VugNja39Hn
+88SBSdtqNMpPrjE1dE0HxlQwVmW5/3xzpfAOEHjBrHoHhycaBAg/D4HuC4+PbrgTCK8v1S0AHL6F
+8enatQfom6A9LcJyM6vWYAVoa8p6rhda3d1qvvvWZpmJqjwHEAbipZ2NFsNnMd+S4HdBpkHurjkU
+wCj7ozxao+qSywAAIABJREFURAvo0sqbYFRvIFSmakUni5rv0e7rfADFrrRD/w91Hxnlp2JLDpS1
+FwXmHGWxJAiOeZ5WzSyDBmB3UBvA9hvgOJHaBQDni3kzHyWDy194tVWnEFDSc06vzq/Owzb9lAek
+U+fIBD9ZjlMa5TTn/IrqHEuuqFJ3wLjHr/8FLhNtW7W7CFlfFqxJsPdXT78I1P9w+AXCDJx8O4ao
+kJOvZomCXE8agEGvTypPReij+ib2AzfUMjIpXfP+k+eoh3DIjrYr6QK7anVM6CslRfYu5VV8EFsP
+GtWFKgI/OTTirP9kJYaU7S2uSHHNzmUyr+7wOycIevVxV8hvAcUUOiqokmYnYFy06SPMOKWYSyu0
+9X3koyy/6LNN5tlv62nrPPH8O24qWM47lz6HKfopUYdqiEl1cnuo9mr+sCTrE4T58wt95M2t1LT1
+pzVyExk6Um0utccT6IoIa34vKzA2y7G9HtZMUYZy47OPX8Rtp/cxLRD8vG/F7cXjZQH2+ECHP8+6
+cTGD4wlgDNnVYl0gLcKSJq/5ZTnKBbaTO36IBCt+KutPGrnlf4qbACpH+Zqi4s7CAlCjH82H9sBU
+n0ye9JbnLU/dIs7XYtvkmActO2SxWnlZJ/SawwqMvfwWLuRbXplCLHi/1iz5ntbedI9YZW+uEqsq
+k5/v4NZKPivtlM4X3FIWyTFBpFh+gQ0Er+apNu/HKQk/B2FitCOnZVwzSyZAR+uDJ01khlwhJa1s
+nVx5lNUOC8twfSmuu3eWDJttBrpxZgR+we+JcfJa7ye5Opr4bwm/QHiGmybJtCf9NBgDWhzLKXgu
+r1TNq0WkC2jGt7yvZscP8k3ayTber19206S5HBURMHLgs+sikvx6jKrKYhbrIif/0loaR91H9aGw
+eupFoOepPDP09K3cUy8N6X6sZtImEar1OKs4pyB6uUiI7zF2AFwzZYpTX/tkjrINTtjOtwmK9uFR
+gZsNG9c+if17GVruUxFl/aqc0IIqAaWenJC2oPPbR+t4SZSxlXPIPsZlzqdl/lIudamdulatz8fA
+BAVdv9hYA4B/8POuSKdbK2O6zSKtvViPKd2Rm+47XuQ2c6T5G/FVL2/0CIBpES5A7NKPIU+bldWT
+BTkl/BZfPdJ3fRKbSxwA8L5xs8Gjw993Ab489vh2Dssqk9s4eaMr+X4OcLUK86j5X5bzDm4/gdzZ
+3MA6VxCcffIj/RiUD2Ih1I1m1rUNDfCqNGO5XKAHiOQjDS8RjM/nsFq9F8rm0R8fF7uuyxo6+RN+
+YMuhtu1IBpBfC9gKb4IfTRZ8sBZv9VdNKnpgOnCO7H075eHew9Z/GGnZ0zx9ulnb25NHOXZJ/2/D
+LxD+B+E0v9tEaoJ9ljsN6QTDEzoUEBpTZ0ufF59M9QOI2BECHyVaq79fd62KCdz3SyxJZxkfl05R
+qlKSWSJmd71yWAyiyAVsJDgdIFj3Tj3mEewm6EXSTmCYbwyb1YgqMK62UYgJJ61z1M3w4gMA9nPe
+OowxlTHyVk55qPlobVP+zvx23W0W78dUdEApDeYZH1miBwNd1Pbqtc1bP8+t0qaf1tU+378Gw1UH
+x32vO+O63ExbfBhPKp5Ufr1zp/Ux+z/Tvv0N/h5B7fryXPpCulW+29oKlDd0SbPwD14Klb7WD+PR
+CZWP7p4vj7PPT/TpAQT0et7EpAFQ+OqHPIKYrP91uL9x9A5y3wCHCoy3cpU/rbkn4dTzuiDzcY5L
+/mYZHsdmoZ5laBE+geDNMnygg3Q2rcvnLbRsP+a1pMcgYVh4h0zyRGRrQEt+8bxIC/jNTxtLPdqX
+FRlP0hrgXlbP0azeDXfwk9+JTMVK7WZXD5tPx6+CszsoHZI0HzSy5iDv25NN731s7fDqeMsTOvmR
+4FaBry9a28XjiQpeoTdQ/B1O/HvDLxD+YTgNUSkyv5Y72atqyNcUu6VtlL/Rvtv+ue3JPv3PIJj9
+2Ky/oz1Zh7NcCfeKD8E6F5U0zVvcxzm959luAhF55FVAVdPYgC1B8ATIdgK+Axg/hzYkHDJtLwWW
+zJ4DGC5esFwA4EQR53FU95gZ5ixVa0ErYwfaKLePfM9LxaVjNfkACCLc01nHkND8WlxpASrLvd/7
+ejzf5NXPenPafL/HIedv8XjKkPSmVazHnX2soRbs0JfL6Jta6T4sqxZfCokvqjzLP1iBrj/w5405
+WfGct1H2DeWXm144PTtWo7kBBtDdJnI6R+sVBHNECWxjC9gOiDPtR4DcNo44WITXJ63l2MDyAQwz
+rsxkwnsSl6OfzpG0B2EeP+bxcAK9M30CyxjlMq3z5TKRJOjTimNezOOSgWSAyctyowYXmUjZwKcL
+03Lc6lRt5VKvtdLt8b3UL9QuV9MCzI4QPKvc/lkYrGlH7dYSp1aXsSrT3CUyz/Zy7RrRdld6t3sj
+5tXWYvewCK+133fkWKtQP+dsL/MRoBj1efn/UvgFwhHOkOEb56i0c6lHVm97tMBzNQ6qe2vprmT7
+70ZrGV82/nO2KusJgI+wJwQnmrWSYWhv0oBaXBcL8KpSrR+zKhvRaKFpPxQEIwEtUPGPLhAnF4l5
+VHCtHOB1WjvLHtFdJQJAJHg6zQfREiKii5e8HhWDCnhrZwnSE3rn50ZraRW6x1kRzR20cdQ3ofX0
+zUIsL9TNUPNi8CSiponRGh0zg27rW4D4O6C3r4/DzWLogJoT0qKmWIPqpaa6fu0c8JHwWx7T3jkF
+s/IDRihzdYUIZapvxk8Xidff2F1ibZhWIBhlDeYsUbzhNXPci/f0J+b7O1wLZfUNdwgv/m4gOM8T
+Lgi4LQA8XCEuILiVE4uwoottiEaZz9Zjjzpcyg/5eMmvD2GQ9g3Qq9dj23w0XfKroXrwjT6igzEi
+k0JcidjaLcT0U2A1BkBpeS1r10mbp+mlLa+eKBHRP6tz3LU0W2x7nA1v+1B3Kf3T4zX07nUdojIz
+hN2e1/2Lz0EGNgXqLm23nS8OYDg/bR1Cb4Jik+0gHQGQn+P34//j4RcI/8NwmkuddlbXtUT4U/W8
++wzznK+A8XFufzXh0+rGNnWoUNe9+wlDypT6lya0aru2OFqokt4FtJw2Tpgt1sPJEowt3UCwPf8M
+BPPI64Ls7cComlszgQLKgQaGu/BmadUWhzwb5ZAwqvPK6qxJq7PGJWD7ObNfSssmflEuD9aOfih+
+FuSb7QJVuvfraAm2SVvp8hWu0dtfgvkOONb0vkZaW0WxborSd4U514O3Gv1DXic4PD420N0jbHOF
+WNZiSNzd1oeo1DLsYfGFbIMM3Ra5oAtQOCLt/7HW1V97AV4fFuDKN5MyQlN/8NXZt/sFn0DuN8Ew
+F2oCQhFx89h4fhB+LnkuhJOVWONVr++y8wqC0UGu47NFWNuDPVxv0LbCcm2udfcN3FZ1sRYc6O4L
+8UdvHD3WV6tj5VurNOyfTTRY8m+TIEPkRkl2YNXmvZ4WPurhPWzrnlV46wJcAPeU5970C5o4JlDO
+uj367fdrn2QPSHfETjEe1uAHBrECh67lS75mBjz8fDPSN9gfwH63T/u/EU4K7DRsV5pIhjVFTtbT
+oajld8471VCFD0vz0lhd3NU2xSjVDgJkT8RC5cSqEtI5dBX2INYOLaBJPT1k9IduFMjNRQgCYHQ3
+CK/PxG6g93Yk6H2eMzgGunuEMth6S6tL3RJcn16G/FHoY8cxu8cv3BrZfqDNchug/hj/Hi0Vlkw0
+3Rczr33ozuY7PPu9uQGNbBmnGyBmud6LkWcf8vQ8uYCFdtvBMddUqaOTgsrSA4TM9dGAUi+a6aXs
+Foi1sOrQLcKaiwSWQpP4a7ZcGaz7B3NXiNeVN3F09h0FeH0AXyAtT9xGmTKG+RMY67k1ruM26eQK
+4Z12Bb4Exknr/GwMdwjvb0c/lBFg5T1fd6k4W42VfnGLYPmNfvARTjpamO4SkwV6gh8KfXJ/WHPL
+dUFUlQY0f2Gp3FIW1ArhPvwEvszNdeWD1r5eONcT55HKWA21VrUV/8RX+NN6B0oW1tNEyRtlANto
+q13zlb/Yi8ejze7hKy1Sn09B5CZwTbqnPvTzhPwKC3GCYhAQP8D7rm0ZExhbfNYZ/5XwC4QjfIIM
+9zDvNLtqT9rUPtv11OK6x2c7b79ZzjVxBEZCG8BqKZiD9Uzb5afrC0Bu1xNv2CnBWGIKVi2q6w49
+vvO/4gU8xDoci3N9ShadPt0kbH1A4NtuExcwVTyaIw8oEGY6HyPbeV48rS7vcVc6oEJdH6cx3YO1
+JvqtzFfxj7tKRNzH9WjF2FDTYbYYVU7ngUv8FMrVJLgiysIyf7+JMS3Tzvheeq9LujzWXu+P61Ph
+VFgdQVwA7gQjUmguwYU/Ar5avCR3jGNZop5D3AyvG56nW34NZQ1uQ+sY/Fwt48txDtnWNMquHSC6
+AjcfwDf6l7tFeM/DcIVoYDh9gE9gOOjTIqygcIq3PPpIH8alWYBn2qVuGddbnsco39wgPoLg0SVp
+5Gne9M7MtbrPtaxTBnG6PzTampzRRj2Pz7pqVfD8pKV6q3VV4LeumSuKMknrA+RsZc5Jn/aUfeP4
+01C7RlQbFOC2lpjShnxlG2Lqj3uTrWs3qzCAAL8KhldJs2ddNwHxkiVr7Az2PNkG4IU7P+bxZ8Mv
+EP6HwQ5myQR6R5oP2g+u9c3fl5V8VcS4SE4vy7EPu5vECuVCIaTW/saaS3zJ4q5V8uHcydR1XDPV
+wtM/WoHnrhFztwgLq+/RDYIW4QGGTa5+btep6VXa22+KnYovK5vjOeR9+fnsCWwnOL6UmX25xvNy
+di+bym3Ub3u6WaqHwOfFQpVnfGnD3uayGCEEM2q8or0W5dY8Ye13gPvjtNW1K1x2ACEQivYkSMm6
+h6UulWOhGO9/tvLFNVtuDrClvMLlwS6AVy3GjOcnmeMa+v7LiwsgFuWqe/lr3FDWXyrNDnxXLzZf
+YhxcJYCyCAvIVUvvfInuZhGGbJ/WwN4F9CpS81ueT+A4y35IQ2RnyE0kuPVGa+CXx+zHyT1itAkS
+dN7tXd3LZ74gOk53dXWA0NJCbFUbz2NdcdOltGnV3cGv9TZanHNQNafO2TXv+4Fz/KujNFUuVX6/
+2zQDPgBkG/XL7bWT3d3UkDW4liEtPsWMpRcdBMSrcYsmO9DQAoz1AR5/n3CTeGsPxD8YfoHwPwgT
+NnQYIzQ/0LLc9L39EDfbXwA5/E4t2shb1j7ViTvqdwO/kn96bg0fn14cGmP6P0wBqE2MA5/KdGHA
+Rq+fBbNobZsW32b9BQ4vve1+wgscP+ft0wQwH8fmyJvevd0qrCzRMaJEX2lusfbgUu46F/Yxb2W/
+Sm91i8S1A/2rMiqwGwiWKkz4IsG31A7sFQQrUGrb2+W8L2Csf6UZjXJPn8+biVQmjQmybuRDA2U0
+874GhoYkoEmuKDKZyw9hV7Py8eWLLr6+4hEuErJzhLxIR79ixGeY+S0QoEDvTCtP9vQOeMkd7hjB
+UVZA7JCt1uDHa5RFeLfyzpfo7lZg77tHCOv34xiTrYxv56gF9uNYzroPluIJdBlPkAxPebpZiqMN
+rHtbd8MwUf3zns64zL0N4Xke1mDWZG98i/NcAXOKxALJCejiBIoU5pq20bhKvY/T7ESE9l7cLSIH
++3DsFe/XamGfKsgX4w5tyqOd6blF3LhIoznW2PterkUD8MI9Xngj+F28ze3aYtcIxwN73gWAAeB5
+yzXixR8Pv0A4wvfN8b2cTbofaLPcFIRfrQBiPJy3NTvi2y/q62FvQz1Cvl1Tods41+u8rP60Ugd5
+DoErqxzb2tMOmXkjNouwefMPVheIF8Mi/DwHF4iD9VfBr5R/DrwSnLf3UY6LTYur6hrxtJIKdCve
+/NYa8BbR+1Owe0wP2k/ipXUOZajcKmsVP8xsIZU6k2uY9HmonQTECqgYN84YxNw4gdjvAOJV2c6B
+XFQba7U31Y0oIHP7qKRVqfOPt6wDrddp8NgRotwjLACuBcDlC03+UPmGfzCtwW547cHzvB8BMOmT
+Rn06aSZ09/US3vkc3+pY6WkRLkCr7hHpMiHx5jcsVuK0Jrdx6HxtoOqQdzrXT7RNRn6PtgNbYLcG
+Y7MOd/cISWO/jsy83t/qzZi7TNjmJ5wj2JbtAlf5zgAzEziH7M+aLZaLi6ix7HtKyXwqQx7EOrbe
+5i1+5P0pLcz4oJxbV2fXx3G/ZN0gf5xWPNpeD9eUxU8B7y49y384FRZ40+pI1whofF3Y4sbZsHyE
+7V1vENgTT57edZNt/mws/RPhFwj/wzBU+Nc0v5Wb4HIAKevld0Day342PorUkYPPvGxLtMfmtS7g
+2AUcH99sKh5crcE+030x70aYLiaqVQV+FyghIO5uEMuP8GAJVlCcIPg57CDxhKuEukaI0hXB0sDP
+7Bf7ZvVImdaMV3r16Dh5xUOsxIX2sdR9JBtEm4hM09aIY+LuMO+fxU/XZ7xf1zO++tenGF/vMaFU
+NS5HuiZ4zG0elyIM7qSi1L96VP7GUV4a7SyzjZXXINrHx82dhsrqE6m9xHSzGOZSk3QIkOkGgbAS
+g7tFZBr5VjgfXy+A7HBuDooAu5v8WJecoLeUrjXZp3sJ86U6y/PLgtVdKwr8Qq9BATKtwOomcQDD
+vZycn2BSxu/A609509Whlz2McxvT/RylfwV6d8sxWrkGoNvlK9WfLMw2uiZbbHN7iIFsDgdtPUi6
+XYYFomgswLQPUwZbk4TR7nCFoIzYeDg6uY3nnfdg03v3/vFx8rEd7UL/cOz3Ib7xRj+ljA9l14Kr
+TQ+bSwQoT8gNtQyvsTM8yyLsz1pXXwrJf3/4BcI/DX4eJpuFvkErQb2v7gSb3hXJPNew533VgVj6
+hxNziRzrvVmA69xNQkmdM17JWaynYwVOTTOqYgspFgmAgQK/cGuW4fayHC28Cnqnu8SwCB/9haGP
+Z2XspoaQmI84f9x/VcdliYkAvQIKV0zHYMWPglSRWkY76ATPbhNgjvuYkXmhQ7ljXsUbe0yez5jk
+y6lz+JWj563U+lWpg4Fwg0gAXP3maxsmDVFgxVhu/TVZipNVeCTYv6ZVDp0bmtHZ15w8QyG3tJ/p
+kueGZQ1ON4juImGO2i0i7igqvTq/XpR78Nq7yabd7c+6Rdi0vOeaYbd3n+GD9dfLVQJCT5kQdRaI
+HVbgAYbnjhITAPdPLBePj2Bpiq/LuPV4AZBb/tE6GSd41L9ZhDWNr0FxK9ua1uXYqU2uf7WtNkGz
+JTIrwGuomm21owHnOmetY9fSC4DBu1sULNdAuUuERnTk3N70jFjJhQGN30rSMfgsjb4TVE4KR601
+rzf3iyPjVCFq6R3PF3eI0H4ux2cdn0c0MZqLBDdVnOB3uUiECei/sHPELxCO8E8naapsWQF7XWea
+TrOuBC6gGCrcPwDhnyHjQ9m5Z3HtAnG2Dkv7vNMJPlu3dVmKwJjW3lxjl3SXpAnvQStDukG4L6AT
+8b57xOFlOfEF3i3DT6dPcOzR9wmAVaZDBXj0NxQA+5b7sAYgK67cQW+KSNk1wvXiO1JDgjVJd/Ap
+kO8Gco9xSadSO9DHNU9V+TFdjG3ToC54qM9SKXI/4AUAS2AD61PaL6zAcWuS/HU03m35Uo7x097I
+2ouc1yYJmTsb9JhKePMnioOU2/Pi1iG2WVjgt3aKSLCbL88FYHYD7MH7vOvjNLQOH152eTfZwRvW
+WieL3q3BwNxSzbc9hjfrbyjz08t3J4tw+gAfLb7DTeJiEd5A0aC55h3RyTbA2xCf6tWypYJklkyL
+cKAezpXvguLe/DkLvV8fVUbb3tapo4PaDAS8kHVgGJYE9B0lOM7xl+RYnI6x8wrXvtCrEhZ0PbRx
+/vjU5ZTXu92AcT9a73LSpzU+Wn+ZDt89av2bhvE1dwocq+Y54RmiV62Jn40LuftSNlLaKfglKPZf
+H+H/a+G+c8SnMgSIVZ4TTJXoCeCe6Kdy18ZIG45A4YCT1lZFBzeI6Mf+IhgFZwcMzaK7s6RHpklh
+SgEVXrMygt1YbsslYgBgIAHvAx/W3w50P4JefVkuLcehzEO+myH3a+yAxsDXUbZfKAKKm9MLccta
+rOOo7g8l6lzOqQHp4LftykAeKos5Q7OKA2r1XrbGa9RdF0UHxz1r0rK6QZtK2edJRhUjCsCqnrQK
+iUV49d+2/mzg1jovGgtlju73H54sO/QYQ/OUFTc70S1zWX6A22JKj/vhvDXDwvXB4u3u/ELU7haR
+luHQ2rQOvwY8NVFSwbebw2IzzEu2AJQ3iPVTNy4PwTogVt8Oii04k/lcgzIrNovwCfi6d7/gm0uE
+vzLpBiA6ACV8ord4l5FHq+83zvU4OUHpd0AxJgiGnNvnrE6hRpNrH8/RpirQbWrJqi2J2AwTONOa
+y7YYlu+xLJWgDWkYAkDL6qsF2v51k5SMEDZL5734VmqsP5tq3WO3O+UYnUFGrxX9aoq1mxN0Vq62
+OQ4aozSL7z+1JMddcJwlMndZFtZNtAPwB5ZrJ95DIAj+dY34vxNOw1Rgtg6braqvYTmnA+DK//xy
+3PwdWzcr1cZ8KJ+K5nj9vqSVbl3DZ/9KuVFJDY0xF34urEh6nTIXvIqZbJu5CFiCYaRf8AK36+zr
+55UjbVZuEw0c011CfIWzn2rxpYVam3rqLhAgmC/MeYEK5AOl7Cu3uqvR6mPaPeIkx1B0BbeqYGgl
+7X8EwJme3mJfpgc43thxyxtV3jBBp0oLrQ9J8s0kHWjNqlBcWuJS7bae6Sdsg35tWQcHzfpDkMJx
+zik1AIoq4BHXz/M2DSjllkJ0Ab5ISzB3ikBahsOPTyzD6RZhy7LzmsgMebzNYbfgpxllBpLnCYwJ
+XPz8kQ3xSIwl5fmpZVxptHCFwlVAewS5vrlKpGU4rcQTvCifv6BzLLaymi+k44Q/yE9tEsFvXOsG
+ds+W4aJXtd1FYAdXMjdbCfaFyvELoJsLRFZElqVcX+npApEWR1faknm5W0SKIAoFj2rlhT25Scj2
+b+C4BqjvzCHnbXLLClhid5+4HRufB2+3o39dzqSsvgC3A+RqeU+jTsrVGBX6AzhiZwqCY1qAV3zV
+Rq224utJ1J8Pv0D4h0EnRafd0zea5lV+WTL3/AlI++9juw2XF+n2qb2u44DtILjaKPR8fFbgiF+O
+6sivBEW7vOZ5X1quwrMJnX4cjQfibWLjEev4YH35ioCY20a1l+WaZVhfhnvKUvycfYUfh1xXlG9I
+5HZzZOwffwu4OuSTtNb3ClYwvAHjMawueYjxbDziLKMuOIBfj3jmtKkyb/X6PLKbNfiaPuhzeSS6
+5TF+AMhNZGu3ZbHQt5UuETXJYz9cm7d8GreRxhUgazc8chXwFkCf6iribQ0c1oN7jbcLLZlxUOyq
+MSOpX5QrF4lglNEtgg2Ot/7j61HLGux4nGlevrtE1M4crsXqSc1aJJtF2LCswlS3jtjDH+IC4bvv
+sK65lFUHYNtejPsmGJ43FH0IfaQvdAB+oG2q4rt5x7q9xTkHNkvxpAcqyrkpMqV3a/oFy2y+zd0Y
+1POLcR3oxuRs9ZBG1MYVxXkArDmltFU1IbHSKAn7+jgC4kmX8T+BZMehK+1oF/pZp8tofjG1zuCX
+zYWsK777Q14VQHZZLxDA7NnHvoMEK1cn30IytQpRsbixTksxFd8fDr9AOMLP7kLmkNYCtZ3UQTAX
+acweXQY3wItj/OQ3PIBIbwYORQ7tt5QxvR3V3g6K97aTbnR6nQyZtNbIEjKN5LLetk5FS2n9BcIi
+KyDYRdmmMo4zZW/gDn5P1t91nC4UuWuEQcA3waAXSNKXQgY72D++EKeuEUcw7GrxpeAj6EXLS1FP
+lGsyPwYAdslnhG1US2qNjw1Sm/GQmkqBfTuwLlWmt+DbNfdkTeytyQmI+VMxHsW8VaGXHt1Sq49e
+r0CCtewOOBROeDtPQUYp3lwzUzHrovFRVs8Fck9gkz2ByZe1x3D48iXwffB4pJ917mvLX3gNc8AT
+VgMC2wDBHlwyhOtS8M3Jfq7hAYK9A16myW/yuadDZiYPJqB1Abwd+FoDxeNcHeMD+O2Pz3W+7GUr
+Dz2kzvh+2RX1agMpVwswqUHZwHFVXt09zUyMc9ByU6w3iy5AC3EPkdkQYtyA9VJxzZJ9eUpYePWG
+Cs75OYEqz5J+b+vpRBMeuriUHK00x5Zfg/oKoy5bbcwW6yUvlmKZC6vf7ANy+7SUBZD59jHdW7Fv
+fzblrWwUbFxC8VqyhbvE++eR8C8Q/odhqvKm9CJSgst7uZn/RfwEjvffD8BFR60jsG0mRUtMKAhu
+oNhJ263ZUm3V74PmdRhN6af6rKrDCdP4AMYFiMvnNvcRDstuA7g2/YHrpTl7+HvQPrYBlI9y8sTi
+OIPCHw8hv4SrmxUA9h0ME6wqXURcXLHiykcCDnHlBGy3/ma+gN/2SHH0pYVUbkJyk/rjHO+nXBWD
+4Zh7UtCb1N6aaAnAxqQetL5Qrj2O623A+HDSzTq0zjmqr+qfghNRvHmuH44jz1te0Vdba9cIxJZp
+sBfcOYJApIAv0oUiJ8Zj6RrBOVXxsgLTJ5suRAmIo0zu8ILxcpz70SK8wDB5PC3HJW9PFmH7wk3C
+T1bir16WuyGR75TFnjb0KpL64ZycIwp/vEkc7BZg5uwgWOdpNbmvwKtVsoHm0d4Ew8Anf+G+cOIk
+K74ssBs3Xy7VUYNFu1MsCXBuTcq1Umur0cijtp6E11J2rnNqAsrnRrdZbmdFTZnOT+Xzeerd3SP0
+uV7Sjpbg6isyzQoLvJZM86I3UMOX4hzrvYQo+j5xoT//ttwvEP5h0BfdiqbBB81HuV1ymSyPAp07
+vecXIEmd/fPOXAFx4YICticQDCkHsQBXX6W/7Q7ZN5o+csqzW/4uYPc2K+i04IuC4MWxBMFA9wdO
+y+6Tx5NFuO0koRZkj5fynEp9CWweT8GFdXxA9Xq4ccDjJXwboJd1KZi2rLBbiinwFfkWfzxoHNEC
+wDXB1+8tAAAgAElEQVSKCYbbfPmINq9l9JZgz6WK0PJFx0a/KNhby2IS5yP+ICwgdvjpuU0577QJ
+jDcaEABwWqJO8zn6tYHfYTlWkIue/nhs5/PGqqy91nyBLUFy8iWsw974BSRQtmGJs3oisXjNXSNi
+pRIQ04Uo3Is45c6gt976NxQPuH1hzjSnSu4WYVp68wtxJ5CrYHlzn/jaIlwD/0WZXS2wV4tvpwne
+iFFWxW3Uuz1b8CFJs8zJQizHGwDeQNYNACuYBjYrcMjBzJvW4IMMqC0yIWtKnjxAquHac7S5uM7r
+T9SSH1xbCQpjvXAe4Z7Htdq6kLIYe/iGENNpdATDYyzO46Pt0bQfaB0on849td/kb4saCucalhx5
+PQbOyyfwD4dfIBzBvjEJoyR0gLtlc+SLMMskLK/lIfjXxFKIG4uZ9aXQ6PWtVEzLWN0Ff9qya2dS
+YGRcqqxJ3q27+Uaph/9rLPp609RaGZajsM7dM9od5YGmwrrdkUu+12GTHpawJhd2puOG8/EH7/uG
+Dy6Wb6+C3ucAek9uEY8A4PQR9u4n3ECwIV/iO0g9skRBcYkhxqm4alw8RyzGwar2LoSBtNLlFLWK
+CxguwTWFmZVV2A9lvkmzjab8sDYX/jdhCvZJU2B8FO5ZrC0u4ecHGrSfTAsYiMHR8VHlzTXoyQud
++MO6ewS4XxxR6VornAMiICLd1hLdfSaHczK5gFpUJyl8guEe5dyJfSpda0WUvvR93ch6+dP7SMMD
+QKPAgAPbdmkNGHujlbXPYYNWPBe+J8+Ux0q/0HSqS535jOu0FGQiWeOPFvHqO+ToI63zSstu5x7G
+YtKzSyqhHa2riVCp37RTlnltHsi8yXOzPFkhDMg7+jGX9e4oWahImmsLOfaLjX7kWQHfnQ9z6m/j
+fw07iNz5zW5UzX3Mepk5PfH62jXDffnmwtEd81xqfaMfJvQ3+2bBwrxXdsDeB48CXPMd9J7iX/Lm
+3x9+gfBPQ84mQ20dFppUFmR3O698G3kei5O+mx7ltwUkAoR1bCCnNbFmUylWSQ+8wzMS4PhS2gl4
+Y7HX1+NCeWZ+L1PK5cnFaceXTuSH76V5t26jj+0ngFitv1zqz/OE0kX3B/7r2f2BD4C3f0hjAGEg
+AHEHw8Y0vN+hjBHInSNS8HXQW9aO/jJXF7yqIOq8VdEuZE+At02OzZostKlQLjQ7lcuy6s5BvVSr
+xbOM2sKVK/2yt/SN9ils5Wz0Rbvj45xTQ7xFMjuLOud3KT9zUWXMj/VGhZ15k96O0hjJ6yBX1pHn
+bmmVn2Xjy4zgUxRLuj5BqGjNqfQHFhHZ5txIuyyZdnQ9ejuSl/wwDYHxJnd+9Jvnoxqog5lHH+kL
+Tc63pkcO9WpJ30qhn+LJh15SgW7Pu1mSWY/O2ntXveiH/K07c2AveT4nAaMJblWO8YIiu5LPa27l
+fAEKV3vwawJKXiLHpkkswBzebtxs69Zo+pdCqJWPjm7AF5020xuN7accE56z1fkCneh2ypuihVyK
+43MAu+/rcdMcZW7rCnr8zJP/RPgFwhH2/XCvJftiIwAGara6lMl8i0lsrY6cpDk5Bz2OuYhinuiN
+9El2SCWrBtPkeAQiAkUj9YJc2RzLKowBgteG2EWrvQGXjHqz7CmOrOOzIqrHmKPNkVAFrp88fmIo
+Xo/HrI7s3WbVTRB8t/xW2QGKQ9nWwo89UBsY5rzQEanfkteefpQKepdwmjdKnnOkzwbvgFMZ9kMw
+nIqCNBVWP7b+9vO7BdtSaQsM7hbsVOqQcr0rp/R3V3i263KeaebMFyzX8kPLpp+0T/Di27USELNs
+oglRGFcg3PNqF5c9D851I3MUFm7ACn65lkgr0PtkCuLuUAxJnh3SCoaLVxhWetkBgOx3xTbySFr4
+UKBu0V6RW1MR+wcZRIV/BMZtbHQAB63leadF2XardwIEoWMUbp3LKUjzKk2eSB5509IJmk5USTsu
++V+woIRXyZhJc5Ffk8Z+2Dg512VP19zp/rkrdCnaQDDnkdyo7MMZL/C51Gx6zV1k4kL7KnAXlxyV
+7F/1gdJzH5NOa7IGaC/MuVOHIvt/Ar/2Fmh+LdJynMD3jXPSUnxaZz+W1P/78AuEfxpyVinARca9
+AeMOevt5rM760WMx5hpW0CN3mK7KpDctI9aoBQoG6LdRju1UoAsvcJeKVdLML0D7yMTuyucWP94h
+elmAm+JBlbPoQ+5TnABYLF2R/9jqv0e9DhTo3YCw9Ty7gGUFx8GbsgiLVdjLtjv9rkr3idVXhIKC
+XbcSsZwfnCH8++mJQRI2H2DktWMKZHvrnH6utr3RXYp/5ToBxA1eiHArQU+fvxLvAL9IpdTpZ3sC
+wP4h75SedW1pssVPZa2xLWXE57flwBdsVNuWQo75zjXhpMeR58n6LM3oI0/yY7YYSvk9El/0HRhv
+lmILX/sxX2YaKLCs82nKM87nle3ZheZbzfaHsl6sKnlE3rnwoG8LNX+40He5VDcYg8dtzMfY4HP5
+uwU4VmKc17K8l6nKuIoOx3atr0Fx76ZXvJU6d+12bO2fi8zRrb8kTKHGfpiVDOFEYkMCPC4WsWGy
+c4RTspI3coOhQ3eLt36tNe5i6NIlflr2PwHEJ8tv8dWkRKRdwTHg3sssWswrEd3N+mtlAFNA7K/D
+ng6Qp9W3AV+1FPvZOszr/enwC4QjfNci7AlsTWbvovXhK8C7Fo41WstTgEvh76GerC+cTQ7oOs+V
+pS3ZQchxorX+rwmbjhy+hNAdGIv1d1iG4e+qR8rO+EnBdDpykZSAP2kcQF+yeLgLQwgmJwgO4Ude
+n10gPgPi7wDhLiBijoVg0JHZxzcUt+VDu/y7LKJgDAmcoTQddamVYAPo1rZPbg9K35FKLzumXV7N
+D2VR09Sl/GYNNgLDgro6378Dan+Sdwp2iWc62Wd9Kh4qoeHIlScjtBs/WReqjHRdiJY7AuAE1kKv
+L17GWo65s6w4JgBYgHH8ChDTPaK7RTwbyI0/AwxD5uPG1VG2wEpZzlXcsTtG0NvAb9HIk89g2IVX
+d2uw3rzpGMhAynGWk0IbTYLrjW6vqvFLx7pd9mLp5fEClk9dmiW2tPRpdunYRW9NP4Jc0rzRhHCL
+H/MAIIxM2e/dQqw3TcojdDIv0mYkLbaKLC9dAserzWN8DpSJtezPbhGu5T+Nh4tIdtS6CJ3ZLMQQ
+a3CA4LW23qXzXgBm6Q7xfAF8fay1XMefhON/KPwC4R+GvlgCFKd8taSVnikAnEtNgLQD6Q+pkzRd
+0rkY8zH5SYnHgjeMElJQUUMDKN5ITDTLr1Xr6oU4XKzDSg/LMBl2fCHloJCwl9no1YFU0GkVTkUt
+li4p8/jip2OBw3pB7uQCoSD5Ao5tAuFlBc54PD7KGfBhl5gmKF3hrj4Om3NBLcNV004Lbk1AAnwN
+fDc0eAa/CX8PdJbfAHj0dZEE8LqoCgOsgeFSCKx9XtIvcTbvu2D4BoRtxG7sKjATS/8rQe/SYIn7
+tg5QRyDXH+llrSxFo9fW8tv6yXWkIFh8goFhIbbcgeVkAd7AsIYrUN7ZQk679C9BhSrSUOoUss2l
+6gZ8b3LnIrPaDYk2Eq1hku8HWh+ToouN8jRdhuGCa25rSqvjZukVKJV13K3C1c0TwPKWbnnKFo75
+WHzJvjNyHFbiPV+EJwC7/AXSYJLndEDb1lxrWO9b72M1SmD1VRZtE3xbG3E97909Wn7xyS0idJ1r
+eWx8dBq/YppbyKoEx7nuwvxC+hsvqVq3CL++pxUMc/lwTU63xz8ZfoFwhG9bhPOlNwG7CrXcZFEE
+9GnAV2FZyWXKBC5VXd+8lgPN+HYExCepIhp6CUwRAG0lCD9Csl5BLvMx6Ify7kD63r1iDW6KJ/KV
+JnGfdLZTFTsEBLuDG/qXH2NUEb68DovtUucLcl/4At9cI8yGJRjrLdoAvivNW5w3vsG++pMjp8Ng
+K6cecZX1YgLk3SlCQ9T+LeCLQ9kLXefQFF4XsGwH+tJDpYDS0SEAWRaNtF7qBHKlxx/jp3DK/wyE
+7UOZyJsNY4nZmZbUuS4KwjHS5Bkyba0cmpX4Vq6tGQe468kjoLiBYJRPMN0hlo+81RjLvNJu30Dv
+ViY5oTO/clfTq4+qSMuKi66E85xvWIVxpjcL8ZCbyvdOQ117o1XExPTZlpQKBRK8pSQy6xhOEH7K
+2x0lMvYB8Hba4ejnsjOehJigroadQ9ypNGMiGN3FRvxmMVY52W6u2Sr2+dBgJ49Tj4Zu317gWW2c
+8qePG6994EfKCV6XIwVMsPtzcNzYvbOY6yPeGVguW2t+ulqD3xf2PAV63/jEurhDvHYGw7Umo65c
+b9/DYv/O8AuEfxjiJqlSww+4pnX5C7ucnTQflj6vibmMzNNCHHEPtWXISb6B4Lba5NFaapEuPo0L
+W1wmUnla9OACchMU+3SLCHeJd23IXwB45cHDRUBB8OHX3CFysVTbdVyK82ggmLtFOAyPlejx4PVH
+y68Z7K+n7xUcAPhoHX4XH8onmHfN0Q/u5/RGq+RDO+4iIXPIYpbQgt3EdzlC9LkAoQ5BrFj2BIZb
+/ABYMj7Ab8rsHfz2eSh5AorzJQnjnCwwjABpHm3Wx5owz/mrCpeK5RMAPtFmaKzwA/0Y38FxU3U6
+GEM52bjRW1Nf1oSshRx0AVfTyqtAEZFWH2EqP74ot7b+W+Xjmxm5hopGMMy4+A8HGAb9vDkvdFo0
+8FtzrEklnXfedwZRfq0iMvLRLwXB/FFZ25AvJnz9CI5xox0a5ZJodA6qzI9W1Hv5VqbmS4KWVigR
+U69PjqfYvqPEft5+9ui2+97Nw7HKow2405Vgo48KFKvmIpeCfKSfJxEshyw8yIt5k7V1YozxaXiy
+cXEtukjM5gM+Xlw/yEt4Y0NmhxXbs+8lI136VE2d4LiO7n1NLn/7cInyeOoYsmfdJK+aKHv5pBMP
+2gtyMIe9b9h4LC3Cr7wgtwAwZJ26xNHXzh8Kv0A4Ajdvzxk4F14c1yKzOiogOLhG5ARNUNwBs5/i
+nA/WZEI0w7JewRGiSPNPdU7vnqO0ZXtLIlutKAHAlj5DbHkHwgF+0Wm6Y8Rq0prspj7CB8WSSuiq
+dDYRgrTBi0KlAjdJ88MU7U7aPr38dvMP/lDGHHhLUNhDgREAOT6kYzociUZqbNmf2kqtbMBlBdZ5
+sepI4HyCeAPUuvCszw8c4nahR2TKrjE57ZJXrnuWwpgnqCsEAbAqhJOOhNAmGJ553wpScL9v2AGv
+8t026urPe5DzrT053X30k2sBckQ9FeFkEICr+TbLKy3iGwj2Ar/NTxgKgCPfykeYN+sAuoGngWBc
+AfI61gtNE4AoiFRepfpXmv54Q3oDuzfZk3z1vW5pSwe3Mi6NHqN5pM90l+MbsJrvdpyuM2Id3J5p
+Ps5VoDuP/Rp7E655uhAjrmxTPVyA2GXSCGATsJvwT4Cj7uWefzV/kxafxo5RGwXYbuv9kdL7u0Qh
+Fdow2qhZYxPcVppXOVl+FSjrWkp2psAUnoJPKGgRxjh6lQugSyvv6+Uj3GixlurjI7WOnIN/YOl/
+OvwCYQ3fWc0N+CrNMq8mpG3HNdYdELcJC1mKlL/6ktMow+vVZG65AWLr7AmAy4IXrc5H1AMA+6BR
+MfBoulNE0N4XeB45f7f8NmD8USlBaK2L0e4iF9C03DaNH6txIF/m8TjxS5eHzQI88tTHOEHHavuy
+Cq9vqNv7rEdKr683C/QLO7AyatgSLrxD9+iDOkR0wNvBMXs6y3wCu8cdIiafdzTbknn+EGQNbAfj
+bZxHQW18nBh3eimIWXbTT5Zvfs/W+eWo4VPeDCfA+xkE72WA5Zaj7uF1bZe0KDO5SZwvmIiWE6C7
+zizXB/T12spSWoVLhId1J9IEus36i05L1wioZTj6Rn/2CxjO/nLafByEkmPUmY2J0ffjNmdvxZvV
+F4eyCqo3nvdz9Kai6XA/EQ9tPgGuTK+Z2TBSlhONcJj7DRC5zKX8e0p9ju3d+KEVGIdu6sKTuLe0
+p4Bq407gxpNUB/qg6Uo3gE9kSUsL7umNtZlutHkdkn0r0+e3iyxrI5z9tHmN5PdXbhFx9FOepMX4
+x/f7kn3wvHlw0Aghx9fhj8Nik24Fv3SFSOAbH+6AOzzA8Q6EUety7Cj0J8IvEM7wPebXhFD/3xU4
+SRP8ZhnVV/LaFOmy+PXHteVSbiyZJityAWUBnVS8u0OA2wsARoCPWBVp/TUqBtYreWwghntE8gvQ
+F+Wmi0Qqli+UEu8YdRFP/Zp7mIJgOLZMQ/kJ8/RndfwKfLt/8GeQTGtxWm3ijnntmrE+I2kBfo2f
+tBNAkKBXBHm5QhTQrfF1UCCqi0TRuzxp8+JLl4hWeF8aG5rcQXFFvweMJ8I1KzCMPMgcpQC/NQmf
+wbAfzvkqDPw24gWD1Q96QmNeefq293YXzDBREiUMSnkMrSdp1NrEHSRTdizXiALA+rKcphP8Jiju
+lmHuIAGInj0wrD2As3HUc2JtEPRkNTqnJO4HHigIPv081+pZ7tzOS+swL3hpU7tLO5bxTudcagBW
+GOg7NceyzZ5+1L/Fontslt3zetNPXbu6UoxJf3SBAOVhz2tuFCzLggTBg+YJchVUKk36lU3uEkIw
+7Dk0MHyQm9QNke9a4bimZT97nfRDppGjsjX9Ka+PQ+tT8jUWuxeP0pXTq7bcGvV9lzp7F6/LFeLF
+Gy+Nl1UYlUa5Q/CGs31R9g+GXyAcwb7JewcfG0Bm1AK/Z39hTswAyAGOdT+/1PdWE5W/tpa8l9kV
+uQoIbyC4tnQhOIl4A8DBC0EKdHlIACsAeLMMoyZ1AkBfn2z81m4RU/kcjzJgvhobXC+rMBU48wxt
+SyeHpB87vCjX059A8kzjFQs6Xtj7LL9gxu2NNq54YqZs37AEo4AuBdwOivsM2OlC2UDJTu9+xLP8
+IXEBxqc6TsB4zbMQvPI2d4FhKyWS2QWN2UPVRP9uEKzd2Fli497CWp6eQ0vwY7Y+UJqWKfELdIC7
+IvCh59Lr00Ipa7atQyTQ/ZRnmofwD/ZhBZ7p6zEsw+EjPEHwBLfRzQ0Et/wPyCMpX91A6w3CtArf
+zjvR0XnPN+ePiHA2+Ysyeze9ndJL+VZ/AmAfGVKTt1S7wrWM5py68zXNr+WoklqGzgGue6D6JX40
+KQLk3NINcr5XZpl/Ko8317trhF4IUvEXQZ7eboNIv/kkSYOzL3rB0gnVFAG14tqB6IWC4T7edUk1
+KHAckmVAvSyXIFjBb/nrr5fmgmsBfN0t/YGnhXhagRfwRfSBtMmDPxN+gXCG76nGfKnnBn45YeWl
+uAK+5R5Rk3pOUgxA7K1MA+yiUHIJZyFmrjgfOSdIMypgBcCWjbBYESYCpVt+Vz18UQ4+QXN1KK+Z
+dfRt1HZA/MXLcymzduHVQHAALDNNBwgm+EyL8DeArj1fgmMgeJKOoL5eGHzkQ8+vL0D8rJcJ2RaP
+PtE3ODiNafUlZOKHNU6uEp8g32egewG4t3hopBtGVu12A8ZrqsSc3MAwQliXUkubl8jzk078akV/
+XbZyeveFJkBvz9vPfTwAcDtHjrSyIFS3A7s7hMalqVx7kbZcex0wq5U4QTGXBH88fwPB5Qecu0Rg
+HTWPcoxsSLE15pFnvu9l9RwlphgqZd6PB9nxyhGH/En7ThlV2C0+2t/aLlbB0fy946RNBqgeaE40
+Wx3Mc6H4iB3L2Cy9s/lO+945nOgznWWYYXsacOSHodIK7CIUxpGgkXptlPGWPq16iHydoZeuoZE2
+tHHhwpwdQy6AfushSBVLJ7DEJ9eHnrdbiRkv0EuZI5rGKXEpM/rZFiotXwg3Dyv7DoLf4UfsLy4g
++cDi/3D4BcIR/tEHNdDmp9AL/NISTIG1dNXuI6zLMeW7reut/A6IWY6KZK0eK+WCKNAWY1RMEEyB
+kOlan/XSHdISDHjRHah9guPlOKNbBF+gW0fe5dn79SeUT8pn+5BGdnr1Kx/pktNW4PfJDin41eNn
+a/DZP/jkJ1xAuAu34OHLfdRe+mQsXNzas8bEKbhz3HewexfKH8IBzOZc+Q7ozbTd80NxfwTGUU4B
+YOmvuvnKLZK4WgoLx/kJF5F81j5JfB5PofKaxm0xEwIlgJbK6daUo/Irxs2rTwlSsw2hgJyKCX0N
+NEBcjbct7o3egC9m3Av8+m4FLjeIYQm2dWM5gXH3be888zH4HLmzVdhrctQwF0elzw7lTfBH3SLe
+sART6WLchDf+XmhbWtvhrU1bG2tItrJbvDHBNRVFDyAqM2v2u5wRS3MAol4u86TuLslONR/yDR/z
+My0LsnVH5wJZNV0iwJtmlEwYMkLnTG895cqpoLxQx8Zgj5Kgsqat+BRsJGRH6owUbNonpWnjqRuA
+/oW4u+sDUNO0WYw9VUxxhUvMxOziehXrVmDmyD7CcAG+Xr7CSifgLStwAeB1/KFe+zeEXyD806BC
+bgDd/I2BLL1V/sEdCNcdnIqwVGwNfJwEn+e6yTtd97TwWktHGfpMccp7AcpVVhouLhAAChg7ytfR
+D+4RpA+F8zFORTMUTrOKCRrJBzUGAcAVRyjoAsYsABb8whJ8shZ/sAibx/PvuFXOI+BpEX5xAuau
+NK/RvP6Mc8e+PDYQqNPzFv8q71bwVM53yD6FsHn5v+1KrfjRreL9klNtTX3YmnTt0jmnAWE70KRP
+jW3DKhw7DuE9AWCxFtajSKTC0LgC4tSxh/gEvuoj3AAyDLXlX5doz0gvmlqChWZlEe6MUMF5YpSA
+q43ZO6JpUlD6VMo0hW6CYadcutxsX1+gQ68vfYNTHqKHoCdZ8y9le37NYBO6o+ft9dksDNgAuY7G
+Rd9izP9sFdb4sXt+77bLHHDN3MBvAN1Zp84b99jlYAkMdaX6+phiRaaYPHECXZ5EJvAEmcIqicp7
+Y8xxlzpY5wTE2TmXqFzBcATAGq9rn8qMMfPieXvVOuRMsYVjRZcIBBiuGZlWXl9+wZjW31h/Pc11
+6XnjlfLtD4dfIBzhuxZhqgK6OrQ33eSoLhEKfjW9rBJY22OB+qtAcaYpgw1S7qAMJgI45HUQXEpw
+WapW2XSLIDBRYBzHsj7d3CMW6DN/s68T+J6VzcFqjHk01HK3lEsiT1Mxv7RUBcB8FQw/61ig9ruW
+4M+uEcV2TgYC37AIm8ElvqZRtMm84uhgVqT15fgp+AY8WrjklTL4/9l7ty3LdRRYNHCu///inWY/
+iIAAyc5ZvbtrnTNGqmqmdb8iCGMsP5QbTawsPdMGeoXhVuR6JEcbaWrH8+isMdwymaj4+W7JnJnT
+bJ1m7hRnx2tpgHXUljRpLe+ai7UXbNSlfsoBtUNd8bWHtq8wCUNQkMuy02Z4mk00E4iIo82w+X5S
+BP39pTmkiUTyKkPSr2qHm/1v+k38J2Goj2grC+coeZXMFePUNOJoH/wYZp3jZhy8cT/IbR/9P6af
+8+v7G1tWBUVb/Mo92T5BTa9vAGA7p2lZbTX9up4Pwzv6SbY20mTj+Sik9OIqq8kHoO4nPnniEHVV
+AOx6Ez8m19TmF3yiIbs9H3GQYQGU+SW0ZNBWE5Ozbhy8rsMBABsaSD6aRXjEE3cA4yauP2fUGdlm
+616FzVb31rGhi2FcNrTDcWqEmkGsbbTy0J8f3/r0ha3/ovsFwv+Jm7v/AHoxf2kjXLw5w8FwzqYQ
+ov0i9xBCqXysbWzuABUm4QLBHRCvzVx5l8LYUiAkMKZpBDeyR9sOIMwheKxKzdGu/f3RNngIp/ZJ
+U0FXOmKj3W9MO/HubRZ2vlZA8zKcTSPetMNPL9N1ILym6V6ANx8l3bFKcYMQ/VikQfooZlyLXnFN
+09tuoHreHfLJZGmKCiMbuZ/Q4BZtr+kq9Wb9OWMb+O12e2UG8dwlH3F+8J/ipv88xrjaD2EBc5Wn
+ar68tMG5z1rf6glKylAMEJZMo/aXAkONm+YSBMUNBIPaYIYPHMz1xIjDV+biBnPdW+oExMw2OhPA
+pndJor3s8cinBbVexRON7CLj5hwF+D2dHoEH/0/h9GN3PgIzD9fnkL/zepmAp7pQZm29WH9qQv7A
+rdjSvLVcOX8Cuv4Qbw/xc1hrm/c0nQulH23LhD/mJgk5NUwlNvth00af+CU5j1XdKUtxcLWTs2ar
+m+RmxiKAmPlqcGSIE3qW/7DN2/VP0tQV/3Px1xTO2Vo9KprhF+W4PxLw3rvJBH9TO1xg+I0T/2/c
+LxAO97FGmOYQx5fl1D/4MfhTDXG0jQA3vsiQgHiSXNahe2N2L/eabHwsAqXmN8dL0wgXMwoRNiu7
+F/U7tcYihHIT0y5Y03lSApp98EkgnTTFu21wxB0wnqGYjwMCiNf1Cu2vX1dqhe2ydWrEhxrfNzBM
+O2GeEMt1Wq9GLfjgXBK74ZeFOUAwPwHobpZH55zNHdqKc/SZtjM6YcA6YU/hnwDwaascBHT+fdha
+qTF1bGYRQJc/TDMJ+CigzBu9qleQ2wdxztmAbgtLrI18tue/YwzrpbJ1nJACTtfw1ERiaoMFHTgE
+6ApIyDi5cn5d2jEDAfAldTWziNQMUwscn1e20gRTQ5yaYLM6Mzn4kt4sAEhaB9RO2LMM50RiJcy1
+j8erG9PVq5dmGHfyk+3jPk/+w3psdH8MV6RJ0Fuep3psz+BFVzUD5zJNoxtr0tv3Vqzl95aadcwR
+ae90uL3ew9CyjVO9L2nOflsfbm6gIJo2Ry0TBUaPY3cjzbH2RclUyS5+VhUSNPd9U+ATvHOCRFvs
+WUkxPr4sDcpsR47BJY9LX4Ge56gVlvg2HCHlyTd3E33WcCO5g18Cass8AnbjctsAMG7HdZU2eGqJ
+/7b7BcL/gcsnGCKE0pEJzBfnUOYU/WU5BTAExEFupgC4b1by9tUhidQXCjKeOzmEUOSjJij5iSMF
+otbHKimRGjhWTY0CYKpBuVleAO+rluYUn2O2bdPCkNpgBOC9xRb4DlDsAYqfgfCu8f0RCF9XrIGj
+FWIAACAASURBVFVNVzHvOz5tt9bfVDMtHV9rzgFM+hDhlbRxBsZMm+5V66tSeqadwppgP2Rpwnvv
+A58+5AtyosFpH9VgYe917BZtu8zCCD/2dR/dutpL2KrlAsGaL9LCBtFkDPPX5IBLvtwGui+YT9BC
+3LBO8KvLW5rgc74ykTAxgai0BMFA2glPG+E7unSlP9YnJ96DHk2GoABAV53qAqEF1zlBPlpdfEaE
+KgGwvCz3RwD44C+N/RDac/tsyb7FzXJZWoTM3Dk+2z2USZ95i04eIhXXo3PpUt6MS5wEMtW4//ah
+jO4cX6Krern3Z1rtVu0zyONSDimgLB7iGjfhXoLmiltUxrRRt3Szysg16DnpmjScL82JxlO0067t
+BX9v/bKgbQLgiP8ZAKNd1d80vboujpQq2QPpjkm7HMZtvt59Wd9WxmUOt10DDN+1wPP3C4T/Tffp
+3B80wAlcTnbBI+4OZnPDwGNLbit7VwKYG8HMsbSDJGJv+VLu9ZdTczgHi58EvvSHgBOwnCBDXipo
+NsPCMa12RwrgLU/kewLBx5dU8ByftUr/i6EjtcCr4QV8Cwwb/Fo2un7Z+vJd0wDb4UMan5tHEJs0
+AeBYaIAM1ELINLtglA2lCAK9GVJGXUC3VrprSIXBEkQM5FdajIOgbXHs04TWo8JHZ3vWk+DniOyQ
+bngExLMnKtYYtlOao80szPZxp2z22gOyq07X8tvWN5OftpVglD/vz46aFhLcN8zL/skAJyFFnGlb
+7tLexs22n5pDNLMIrD1HzTD347LR57jJX4T9bH5vT3VINuknf2KPcg8ptysB3kAygn4dqDOaURp2
+FhI/bbQbqG75utAu3idLMOL2xKc4oUuYNDMKtieZmiZ7xTXVOxiNDJ1cXNJq/k/dbvXbiH8YjW/x
+I821L4cyuYn3MiEAqnKKtBP/CZmV9dA72ehkCrboP/knadYMCoJ99CMBcRCKi39hB4BaJ9c2XbZz
+dNKDwF2A9FkDXEo2+lOGGHex8MU292hgmUqxRpkeN7NhH0w74ZOcV3MJN7nGDezdsMDfdb9A+A+d
+h1Di9Za7Hce4ajrzA7XoKJ7qAL6CXDtD8VL8GISoNc9yKoQH7tp+vXSy29yotW0YoOQgZ6FXd0pK
+nMqTQdkUKBthg7c0hlNbowOE8qXJtdblCpvbpfGlmYEB1wK+Pq4giKWt8NdX/2Tyq1Z4tyFOIOy+
+2nHpgwD0eiFuXxUZudz9k9n19ff8+cgTcUa2N/iLyZICZZrAZEPdA21zXwLzlA70269zvgJFa86E
+zsuzX1H9PkrUIIsx1BT42o81B1P3IVU60l65DejBP280qnFplQBXtIqqXylq6P/yBU8XOkLHgnnv
+J1vjKW29H1CCruyYaxBJntkxgfZW11Y2wnljPq5aNA5YDH/xPdoQ3yb0YHeZO32vl0xXu3f2yfix
+mliQajKeUIV/fQr+jrHHh204D8rvwp+aaNlEmaXt2/+FAD8RlcQ3gp75FfgwSrWImk/jrJU6bbPK
+05vU8gAGAJ1Pr1DEsLUj49sLHMsrTWZ7SQRSTh7lVDuW/SWIXVtXwG08rbModw73fIg6iGybdtpQ
+2m5bo1ZtcdMcQ8Evsh6XvBmXV0u+d9IYAyWX+NQqTfVQ455zn1FyE3FlnhSAqw+O0vYS+JrDfX18
+w/2G3wa3O19oF2Oqv+Z+gXC4T29CXNc5rgVuxQ+U9tfPcfAd/LJORJyCoiZ4NV74wPqtjLYVevCr
+ZrXFoyRle+yELuDzomAqzhZuE1cA4AkYJxiWDZWaMaZP5rf9rK7rGW5pggP8EgzjQ4Dbrnbh+ro2
+wFxA2OFxfvJ9ceyW/co7cYZlODlV6E5BMGliAuMNBAMJpAtoeb0zcl7GHQSLJoD5lYoAg+LoCexW
+vndwHIinE/zTFUWWJ+zRqJzkKjZ6JRKgg6zKWIF59ftpG/2h0y20xkENrAsYQwFY8GmjhdbVcJGW
+mDfrEtJnvPe6No0XULggC6kwlAxMsvJndOtIVVOAuBrR8lfQf5pRWPG9LO8OfIc2PvfOXc3ZCmuX
+CLkqHADYwm8ChnkWupZQbR0ny0nHASIm8Z20sye58mncI5HZHnx4x4WzwJviFUdO75XH2q7o5Vp9
+e9szrurb+7HLL5My5/Gd6mv5ihAkyfZyRwA8+/UB6D1oftcS7GCYI8jzjmNDllZbtfZe8sCHHxhA
+N/i9xKm2uMmEYOjTHBPg3tG5kn2aE7TPfZlLxggJdlP+rd9FYES7qxMotnu9Jy9z+7fdLxAOd+RD
+p3zEbyhQqyBXP2CUYYywyweOAHxZCUcHyjaL4GLw1xQUw6kQZDH9XacyUrFu3JKXspmdvEQ3NaqT
+goaWDIlzcxMEywTCJZ7op+JW1QqKJU82qvCqIwDa4KZmWIBvaoS/rtIIX/woxk9Hph1MI+SLc+sJ
+wAW/eGZimWUgNMSmAPgoHFAMA2RcNXq0uBJm9fORp1bX+zLlVOaDNEsevC3rqbyK1KIUJOUUmTyD
+40UW3q6QK07Xlx1rkm3JHy+gkJ3vvU0XA/EWDkAsGpCZ/9mtERII8gVQAmDdRrXn+Asb9zDJMEfa
+vKu4UADMFi1vGEc6/ToUGYNhDNEkrmEKAcnBE0wLR3wDzRGXNsOSPwFwCMOkxRDwBL8FhGOGmlaY
+Ta04Nps8KfmR+DOfdjTCQifFaXY6zjlsbhJR+D4VNLp/tM5Dvrz4jPccwgS/y28tpgG3kaenswmV
+HtFQ42XSN/EU7xnhyHPSQp7q2fphleb0a5kTz9U+8ObSHkAuwzGfDfQKOCbhpyI4+6R8qPOl+bEQ
+fUKXL5M6Qpu6AqnxpZZY+b6A33zuZOQ/Pa7mI/as0HzjKrkpK7rtDpXvKcpdfkv7eyUovtc7q9zz
+9wLFv0D4X3Tnlw8O+dDWOrXAet3iD78U9sFneHf3BZH53Bdem4HtT1px9De8IX5DsXyD40qiVvC7
+0mbFHgI7/6jkZsMsO6fQgbCGHlpglHZ3aIdL+4sWD1SaTgD3ZgljNYcA7CowDALirytBMb5oFvHz
+i3A/5rML5h43O75AtzvgV7woQBBMhioMFyqsitcctbwZJzSBAzPM5SmBmOsX89ZA7+b0hqhfcfRb
++idgKH89Hs0XjjiKlL4dDDcbzu3qLWxA3rCxjtTeUCtis+3T0L1Anm6L2DdNLX50OiO9Xn3p7bRf
+l19OlRAt8O3xiXCZ6yrjAnCtgd3d7zlfBaJrTLx3y0Yongbi7uW0cEQLUC6GZAJ8aco0/Ch/rsU3
+zR/YOQLhAB7ZQNcOJ48KfrRMI4D8MuatoLgNOteR4KHMIepx889uUoHL31MVdojWvfVQb+PRSFr/
+Efwa9rjWx2nSoLQvfVUaAISAZlvP5bKdDSRbu/R2Dn06+F37Z4f+ZL4H0EtenTTHfiqNF0BuT/wI
+QJOP7OGaCxQ2yODarE4sEDJGTSQSJCswNjSFCul4moToEY9p29xuMA5OeCFt7UtkhzZYgXAAXXeE
+Bjj2FLXCQM7x33a/QDjcJ+xsZXSo6r/5I3y749vX9YZeUeEQ7l8hx0srXBvAXf1QHvc4BoJhlTvr
+UC9LjbC3MqviYvcvlZv3MKQhASQqgJKlKtgdYJhxFWYD/YWhbMMYlsas/+yiYLUFhlMbXCB40wg3
+zfCLeYQ9mFEYAW99Qee2C2Zemjwroe19tmRa+yNK955SAJezpJB45hH29478NmB8Dlev6VOxqdCA
+olvDjlk+Ws9l94qoIdUc6MBbOxWd/XV0wWMmANhGbgm2aQkQJvtPREYv8LqBRp2cK+83G7l3k7wt
+Pl9KMBxCLIB4kr+X1pnKJUu/5xYtzbBlW1xkxay5eioMM10Fv4zZRHYSBCQotkofcZVmAxDbslpw
+B75XQ9QAbzhoOI4Xci1zmjtU0gF+xT6ja4ZJQELhpeZTzLK71zirCJ9ZH0fTqtjBwj4hNXzdeStD
+8gbreapcN4vQsuc2tY9CmAm49vyzTj+W6eV81tPakno1n3F8FdW+WDfKdPA7zR64705A+ZC30Q0O
+4VgZAn/maWGAJhS8gXbaBQd/WvjDUutMsDvBr4Z17p5uCh7Xe7DO2lsDD8UJEpTz/WW5G27AxYOl
+Yn2v47Pr/637BcJ0ryChZ2vaYH/Q/s6wLxD8LeHcCLzDAxZBiNBtwvUHQWuHq37kt66hFc4q+eh3
+9UUFcx/8iKG0eZg7xc0dBLvscAG5/DnOtsROFq5/vfgd8YFqXKkJNkuNMEGwXxfs6wRuBeQe48cL
+dhJXfb0SDOO2BMRdW2DZ5ynskuFFzJihipO7/ZlHNcclQGNtQ2tQYRzCwsqV6TX6OGl+d3CMVubw
+7MFLCHcNsNBX0pOIUK9suwttcIDE/vlVLaMj8s7k8+pILfAxvdfVd4rMTNwsUzYQoqh9cIjZdQ1U
+u26q1jm9qRVOWeY556yjA+Cyy+UpL6odngDlhEfYr11G7iYRJeMlTUHvVXGL/hfg1ack+RTHSA/d
+JKIeA1nr504r5TcSyn3F23kDEIumfTVsxcQGIE4wcaK7U/SRPnXPPzH3GVd8+6c8WjOpRPcrrMBu
+5XEpaT1/m2iZpw1AdXpq/TLtCw7hzgcPBPcQPqSF/wh6te1RpmmAbbcNZh3TJKLy6pM+GU+7M6d3
+xWmflK4qOxUZHiYRpQVe2T1NH3KdM7z4nscTsQLL+5y/zmf2pPrNTUc+68CSeVgMbr4od4W9cD85
+YtkIXzn+v+9+gXC4d21Zz+d4sA9+uH6jwO+3xJOWvpTouVcO2uAIaW9a33hSBO+nTOIJfktO1Gsf
+q3rGSNU2vWOSDnPWthSRd2zsI7AdceulHvZq/UrkaZz3p9Mpw0JYhyBdTOnKj2csLXBoiF81wrvZ
+g74UV3m7CcX6ml78GgBeYKx+CpmsTxwZngQXrU1grI+8UHGelUieEnOi1Kq50wiV95xSL1GteHAl
+W8OP2vfK96w5liFXDvpzEnxkRrd1Ztvkz7mPpBM5zpnw5EJgjOX52G2g25LGuR4cB4cl8AIAT1ZY
+gsyiP5fJq11RiGO3FEzAGQAXU5HtEgK+GreWaDmcGppolHkZgILggY+LFQDjKiCBSwBw+kMbnMQa
+5b5NGyrnI75tniCGywC/lgqKAPhCmEYYP0GJIqKosxnGW4KKvlLe29Wu7VGHhGl+0Cb0IfxJngPY
+tR7uvKaHhWsc2iDNfJDW/D3/J5rgM6Dey/wJ6G16HdEKl7b3DHjL/KED5JOGGMmPR5t5419jKb6t
++YpnLvZRZg9l8wvkMWRcLwW++Y9rqzc8NX8b/Y21le6hRwZ/WR0KTTWaeUQHxXFCxM01u2I8Cxj/
+nhrxLzrfyeAhX2K47Urwq1pfguATGIZLuyRq4fsFgL21D2had8UHvI4nQoHkW/wTaBEMd8VZn5dT
+s6/sORQt1iZLTSF6nIaXsJ697A94lpyq++4SvigwHCCYL8rhKjD8qhEeIPdoJ2zWAHIC4dvje7q+
++nArCA4BSzS8jTDmmQwPdYWGfZY951GNsGK/vJnwWtcK7y/XsahlahejUjXqhQukT1lvsyd26XN2
+VMbXBqr0WOCjty3zZw6LR4Y8Ks3isWLd/Mn8RJkOgIPJW7RvtUc+cTlSTmTQOrX+1vKFX+5WCIav
+EJB3xK0yri1EfUVXdc63STqi86WJTsyrPW54g3ESKVpgAghWok9lEGAXBA2Xxlna8a8r+hOcCYQP
+M9uc+04i5CsZpj8A8WXwOztek7E6TKbSzCJ2p5xy5PFj7N7117H9EdcddcYuJeBqu7bCmn+C572N
+w1WAk9qc1rr1PAt8nfJFfBHfihtl8VCWabumt/K6FJ9ttaeKGg762F6mG4C57Qupuyko8oZU+1V8
+NeMz6IkXUtNL/EAzCfLQAMVcx9IgKwg+3Xihz9NwyiPJuEkfKasa+LVmJnHFl+bcr3aG8HWXffCq
++kPG+l90v0A43OcaYbmzQdn71k/NILyAb1wTGMem+Aqicve0E/4KvqQarU2RNV0Q0bSuqfeiV3vU
+ChMoWQurYFVYcZyJj6ITNKnG96AVbucGQ1+Mg5R10UxuDUEft0IFsYDftA2On8XLcj/aANsLGBYT
+igTCcfUrNrqAYTLTZn95nMYJgkum10wRQJ61xeSoyVSpGcQEuQdQnFcxl4mlrAetNYQ/Acdd/Or+
+GwD/BIJd6j35Hfl4vmmGM4ONgqj9xg7mHlwAmiS8YbGn/XhwHX6c55BunaxQHeK+XSBYBGaMseGD
+7Jc843EvLS/kpS+TF/YGBmSFimOAku/plwE2EG4QHLmAbntSc3UQvJkw0c+J/9aODNCk8yFgAh40
+pISm4De0wLw59ex0bYSTbPD8MyPR1mYW9s1zSPtjEPwc1k+1r2tRm7eYTo17fq13Lr7V2ke8H9JX
+DhOwZdLVaE+J7yMt8JO/l9d6j1phE/6VsmPFKcBd7OOgEU6gLOWblPLqQ7ExAbpIWtniw8OX4da7
+Q+WHKwCmLKg4qGbYKl2qbu4k2WfuTtbBW6gJhoBfR/9wxp3wHPmCHG/1k2d/yFD/i+4XCP+hU6Ul
+j0Jz6GkRXrbAwALDWKD4G/US3bcwZ8cEv97BL87ECQhveHF8F6QAkoDfJiBzlFs4Y7IjihokpkVT
+kqwxtZfhVAvMAiegDBQoHiy79dT0Z/HpZKTWqYPhMo2wV23v/jLcCTBvQDg+NbmOillC/boLmNM0
+AkBphwUkcupqjWoVuIYAGVrFM0XBMn8GJ1kVo328+/NaUCljwUStcjRw+yfguMqIQNbuTHV3kx7l
+6X0petC9Qy2part3+t01w71CFOKc20Pzi9ORG+dSaJv3fs2WfivffRdCwzMzO2DWx0UAnzPtMlNi
+P8xFLiCjgJbgIBOqDYIbywwdhJjhrBnm/oynMhFXT2+uBMSLeXXgr3slex1zmffTbRMIz7mWqsDj
+KOH8AnyaCTvqVABDInn6gxvVnvVGnmNJ2tqdQYSkPm3HVs0kvOew7pDisj1MWKL5d22w7Jh5V8T1
+RwFBpZNWhmCx1VP+bE/HmnXjsVwvw3YqSV+U89me1h/ZOtB9MYc45YGA6Kb1lXWp6Pyz9SvLyThc
+tcEiCzYATNCrmmLVBgP1rLXL0vT7U1rjkplm0f4FR5lIrPZ5lrCHNnhpjG/4vV7jdwCXXevlOVAm
+nnm7TMd/1f0C4XAfa4TR+eonR6Z9y49mEt9k3AZ8sXETAZ6cx9Pf5BzdK0XsBVj1/nPZHFV+Ebky
+nwlQJomWmKJgNfg67/5JCxyTmaYR0SOD92PUoi4vabeEv/Jm+Z2EL+TYNF6fAe5uA9zTOlA2W/aH
+HsemXZfDrxt2XzB+GQsCCjb1Yk0rH4Wl8ETX/CooFkVpS8PIy1yl1RWcZ961wUFvWXJf5g3cFtNS
+hr4DZz+UT0HtPaxzMNvXcLNhZrtpCqHzSWDn257f2Pt8OQ7ql/TNHSaL8V5JDbK0JyLLpQlTFLu1
+Dt2SOoDom2qClXnk7Lv08SdsMbFL7C2baRRgtgo3++D4lfb3SjBMkyWNX8D4SiBcYGgCve6fes5F
+B3WMIeJicOBWTZ+1/voGgPdp89H4tuJbvPb9THzzNcuB8F7S9rwKempXzLQe1vzewqd2BuMVgrCI
+Sy2oRdyBwN7AcdWhZSyD6vcWP+vp+d+0wifTB9JzM4eQPDjE1YvRMU4hmJxb7dNJtNLvKI0vmYit
+Qo8AeMYPf44/eaNQic6nd3pNeqpidRPuaAC4jlRzXDdl0TL0umgm4XxZTvjHX3a/QDjcxpie8hVu
+a9f9hIgyhdAX5BQUN9mc8knQCEqOsZfTTZLxQyKrpzCluYJL/GpTBU4I04yXSllAGdRPdxI5WQPw
+qhY4J6HnSR1M5HnCHounWAFf8audsIJgu76a+YOC2mkasacVUCYwXkLd823Yy651J2wFhpeIqNsE
+WIEd/lFxTrA7BX974pt5pna46jB0QZ41GQYIHqA4r33ui5UitR8qQi3zmfhd8sniuQpeNOY7SWuC
+3lNf2ZbBsjzH1jovuduAMi7WB6phHQ1p3/aoMVpu/PjpXKn0OxRtT3a29imIZLZNZkXtrNKmvtJK
+HlvHKgJkgCGkRNMnxbNw24/td21Xe4j3q6+qy7UtRU7rWke1wUZ+3TGutwF2raW9gqrv/ghcNcAc
+Y5pN6Goe2J5rBo1rnmeb4rYvHt1Ms83fAe3J9OFPNMV9zmvNRctLrkYQZUIDQAJJlk/KVzmiRNQ0
+uzbynPLrFLBNGY9xHJIu5ajR3l5+GzdLUyOMGZd5pb1ox8XT+F1Lj3gJ1AkTK6G0wgJyDRJ/AMD5
+Qh0yjZW7yHhVrFA+uA/6GLLH2gty7EecIHEML43wFaDY5foLhP9F96lGGBBCcw+Ay7OCH06PcAHA
+qKuja4OTuJLD+1nwHoTwzgbp9/bxUIeCYRUsZOBezCUlDHe09DWEbmtU5zDCGS0Cqnbaz6B4sx32
+0oXOp3T5Ik/8bGiCTUwjECdC4CtOhzgBXTkerdkJN3vhnu4EwtQMX7eYQ6h2DHWVFaOsTo2ty9qg
+g90Cij28aYIJMg2p5U14kctYa9uB5a5B5o1RM4sYmtyu+VX9A2P6qDt4aJy4u3c1bjs7uMOAoHUd
+x1MjwuQTob4dHXHYj9liTRq4kqZjGENt1WhHTfpOgXkspIPgJlQwKfGtrRMYtmy62f5Km7UHhZZz
+D0qm1Jpdh/14LdswPq2ZcfMmidcQruYAviiQUQCY1wTCDver6BoGv9epLg0Atx/nXqlVQNQGINHp
+R6OfSHfYRFa96p44/HMa63nW/p7shHvebWyzLVn/An8EjkgzE9JTmp1kVRshlbjZ4t7KzfjeRz/k
+edIKs59qKwxTM4gnc4iKg44TnR7IDlrczhIOeZSvU/OLAr8TGKMA8E3NrKRThk9TjTqtSqXAyAPN
+QzbpIcbDHCJflkNohyEAuORTB8O01/+77hcIh/sUCLt3gMuFTkCMenHu29U2eIHf/+MuGuGSyg7A
+zfEFbAJu1/5U8iY40SPJsg2W301y+VEkTEGzNgR1AERG4odUtnVht09soFcBb4uTXjhf6JF0YdfM
+15RS8dNHsdaArwjg8bLc/Fxy1/oujdUOgodWuAHhBYLdLtzXBbvufFmoH6H2vOmbhneA2zIheA6r
+kDPJ0xbLX0CxxJnWg84eiwzeNb9qD3wgHa78AUg8IYvRH9/jex/2Sp76UW14CUN2JY7Cyyo/5Ntt
++wTt5xZ57Fhom6Ifx6332Ifae22mbIRZ0xFvmKQVMKx8qkEuwJM3pfPpzEUwrIC3wiZPazLdeev2
+3TmAk5JiJA74V9CqI+0Wl4qKZ3wz9zKXyOMVzQf4WaCGwO3AebJdqF/2WFvWAwn7lumJFu3Bv6d5
+i/fRv777+lh+SpN5bpJHGK+EFRTPD1HsoLcRXDMXOObNIZ/KI9vqUxT9V9A3tMIVJ2YO8pTgdDrE
+fLHuGAdpc9BB+u0c37XCJ+BLnr+wR4ZN8vgBHKekKM7eTq1C3Bzm3tNr71vWElrhAr1ep4neXqZe
+1zKJsPjkcr4sh7rZ+NvuFwj/oQtM1p+0Sdw8Nq1MIuQUCdTLcrkJp3ZVd0yEU2gepOeJPRrqo6Fx
+r3UEw51B6kiD8J0CziHGo0ih6mRwZ8Bi6KB3e2mO6ai4smXkqyneuEKzGDSk4FUBbHEOKU+OoAbK
+QvNk+rP9pbiuJX43n6Df7wLACwTf64U5qy/LtRfkkmcvJtrXZIJgkeUjPNOV6eoac87U/CEWINZR
+Vn6s56mcCd3sml/g3SyiP95G88v+mHlc6wi/9z3Q+ydjcKn/B1fiXyO22Be3tR40zJu8CVVmmwYe
+9cZBLvtV7Apqr9I+Xpobqwr4/sLLEZsMnNGxDjVl7KeUt8qj+w8mdsFmYp4kpkpfHSDbdQl/6sIX
+spbuWCA4hL99BU9NIIxk0u4L+OLi+RuWcU0zrJMgGm/df3rlBFR4zvJOdScq7Hz4jdZ6vtrLJnFK
+7QpknjXFJ5viU186wIVof/nEh5y7a4Tr6QIBLG1pT0Qo8U37+xIXRXNc23TayMO+Vz1HjbBofylr
+NJ9qwesc4cHDopkWR1mKA01pH48aYQJQBb44gN+1rgWaY264zsEe6kt12hevvIx3SNhT/nObXaoN
+vrGUQ9yKN+C4ceGK+9N7vUyOAMVPbw//D90vEA63acse83UMRxD8k0lE2gzLzx1LA4w4NSIbievg
+gd9BrMKi84dRxA7+uhtEA8TVZP0tTTCZEv16xTMANgS4ZT1rotrLb7GjNC4/qMH8uvNAdq/s2ftY
+U0grAFaBfMG+QvP0iUZYP66xxcnJEhJHbfB9rVMj7qYJZv+ys52Bcz1qmDnipuV1rmdnSMwHrrfE
+pxiUhTd4v7c5xCW7bHGdbpSWmlmETXCsZhEDKBzISNMenff26QI7ZgUtT5uGUVIyuVlh3wmCRzDr
+bXELzGqd+hJi8vxtfAJe8yzkEjpNkI6O8EmK6ypRuxNqaZM2aq0KIJQtJ/f4yJPlpWnLAiAgYrie
+0lztRpTmEKkJVj+BMYW7gIU1xBK8BL7+JbzlK0ypeJShX7A439txLf5k17q5uwztBbk12AacXMbm
+2Ciqa+9kYuZTFqXBE1k/k/qJ01d/GsjLuvoaf6L9LU6r8kDa3fhVSSIL4NbsZ7nnqV3fQKzMcwOq
+f5ivDd+26Ur6MY53lc3xixA5gtyog7TcXprTOFQZur5fR5zNOOvpFIeGAXjPWt5T3J0+tHgAAoKR
+VlM8vWq1udNF62/I7GUSwbpEGwzREt/5kGd9WAMBgM0WMMZSHP1t9wuEw73J2ZaPxOdBXL5MIhyH
+UyMk7slGWP6Um3dEHUk8ugYI5Xe3sG4GESoHf9vGYVBvBzB86sfmmuq8ru2luFT31JbTNMs0PkTx
+5MuFKW34TbS/BnxZ1wQfNMIb4B1gdwfMlnm/LmqCV56bdsR2AMM6WRszpL+DWczwAfSu3JALjAAA
+IABJREFU6B0kA4mHGrjNOab3EGfB3FqydN/Nyi/snNqOXfxW/BwzI973pG/kl33dyvsGivdGH+K0
+Qgp0xjVCt1m4pVneONaa5ENJ36Ds6rPF48oAw4DV1sNhDfKvxlDqE9RXKddVsF4XMPGH5BHmUuD5
+YC6heazMlNoNqgBfvTnF9ZVhfSISQ0kWsswhFj9O4PtFNuFLBdU+cmMAos5rPaxdNxnFSDqQ6aCr
+aWuVZA7k0+OVm/oxf61L+fGBP/fslkfBC0Z4341PaZz7ztdrgWkGYVkmFz7nsoFIMm3gHeTqmGb+
+rdz04xjPuWqmLcJ7SQPzpbiPNcJRJhUvqLoXPRzihr+45+hf8v393wK6JzAcYndLU3kv4NYBnjqB
+zOPirz7yBo+0USYRZZZfD2NC63tdoiVGflp5+QMY/wLhf899qBBOAuCVdjm0D3Z31FnC/qgV/j/J
+XXxvYLqXRwWBqTa5bOPn2083Q9cwZb/ewK+ijtNV+rH2i2h+gTg+rYcJPDIt2Wptv8QkUXmOMf5Y
+MCgbgpc2vs0sghphtQM+guBuB3wCyWkaEdrjKz4KcJmdAXEKg77pleHFtNV0uqyfMKET6GU8c9WH
+JUTM2srTQFXYob3HeYKjzJPaao7orPktYakit7tHaj8kDHLDGCIs5yQqcC3nvXBjAkLn9DD8H/Hp
+3tMCwSeQocXWLOeX8AJwxhNM9jRbAMcFqzFKirausYUZDoA2wi2+d7F7ZFPqC6IdEMvNaQPBX2nD
+v+K+chR1n+yp/fXgFQS//hUDukQbLB+5Wfm91FJmcLsA2n3bBMH1+F/BSeOZk4x+im/8dt8DO5kf
+0V3VY+c8n4LcHs/yM+2B8BvYXHnyfF0IeAQqft5gbMD3JU3HuoHiSuKaleQY8yRFaJPMPCeNL3Q8
+B7CsWuTmx6CBtmd/AsXjyU9sgk0r3H5lJnHfOyh2CDCOO0kqUThfKVuM8meA4imHPJ5S2dpqTL/c
+UwNccWEXzLP1ed5+zO2vjfD/T1xTaMbvdHRa2QqPF+bcUytcj7rF/QR6x+9bE1/ynX5JzCJo2osK
+yQC9eUutaB1Yjb5ywgw2XpSjhFjX+RJdi+ekQ8KgRjjlbV3JjxsAPvy+BAg/gNoJhjc74Ydyl2qD
+04Y4tMYhGLTPGBs/GZOXeFItcALcyZDEX/GiTWsa3U5nNIeAV7oBElficJ4xrBCvwPFuLlEUFTls
+lHkm/Vens9dAne4vpdOH+KOfdG7Y7XKb85bhmC/2S26jMeDW98jFv8vE5KBBT7Cl0HbWVqvQc1rr
+hwLaYgN2js8nGxGRmCQzr2zyouoEwNNWH9cCwfbPV4FiNY1Q3ovOhwFP+2C/vGmJS03lCSRW2GDO
+r0EubVWqmQdTcZjwxprnRjI6+36I29as1gBbvncw4I3Qjpx3gKkd9Kr/JzOJ3haZ7P7rH5noYHjV
+Ed8+/QkM57Xo6TFN5mAcwHFM84jqc8j8sc4PwLbOBn4AvkOT3NdA16Wvs29pcx2RphFwX8dzPv4D
+bljY5ZZybi9T+wiQfWUj3OhC5I3sQ4NzG5XSxG19AMiRH7BJ04jQCNuNpYEetPO33S8QDud+/5xp
+ZUxmugBL999CePQXMd4Rf2eaQT+BnI0cWIwX6JV8myspG1fLcPL3p2uwq2ZKkfJdhHDkb2opNu+e
+b5vWo6Gub9h2vXS9/J0lNJZi0j8bV1DjShkmWikxYWBcgVjr5g9p8sB4AtnQDmd5a21MrW/2ieNj
+RwU87KNX10EwJazaCxNFEvhC4j29ns0myHV2pbSTrCnJRuquONU5TsjlOU6FXx00l0Tq4ldH/TIn
+B7LfBM0RgWAgF28FLKO80oW+batYXa1nO1JIulC7QZo4DmiE2w3M7GvrQdv6P9arYevxrb8p2Egn
+pUXiQ9WKU/HqyS/rd5e0fEq779gnNxnNowZM13C0vDvBVARieRTdplW0YxyBwmrDYj9Vgwpa2ixr
+uvX+NbLKNO2s9AmSnm3seTdtsEvfxo58AsOA0FjUqQC3tA7lnx+daGlSftUp/baHq+bbyqHxmVxL
+AE3L+1Ma+nqwXdtiDs1vefR2lfXr/FabPa33SW/iWz+JJcjvx+9+imv7Q+h4tDP3z1tY6Yo44YqA
+MbNZmpJdvp6gw5c5xB1XNwu/bzztb7lfIEz34Qo4brjfcT0D4nv4z787gTABLgGfxs3WtbvreB+s
+O6rwP7xOHvXvwCIBY5wMYR7nzTsKUKKI3aR6bvki+qrT2UlyDdVutn49XKOSEzOy+TNebQO/PSzm
+CWkqoRpgBb2V/xX4Yl4FjJvMoU7cZPQybr1T35wwM0YQJHOO16WDZNUVqimEWdXB7jyC5OxiLcqu
+WZSReE+TJc2/K7zT5JyTHvOWv4rVjYPUs6GPU3rP28CvjT3UEIy4pPn1Z5sb/St58JRndto13Kjh
+0J9nvnbKOXVXjCu6qBuyBL7hb9rWVBjcsNuA64Z7PBm67wWUrnvN7231wtG1vjLF/ZLaqfvG+ixr
+8NZb2nbhxQLE215qWFHBJRJMJT8VgMwXeQo8CJc2IG0pdcl1fr23/0iCdAliJWLwijLVGAMzSY+4
+5CqilFjLp7vRJOUBGAftK8DdPyBxAL9PaTHvep0mKG+guJ/yIHMpc+AzTcJ7/opTbrbLnyV4dzDM
+2bMW39e4gN4u9WSu/ZTOLUU6R+6/flqEHutacbUrCizrPJX06fl1brTvXrnBYwgt75gNl4WCz335
+Y0ovWPhLg3wCxH/b/QJhuk9vRaYGg8A4fwfNxSBYgmG4jxfZCgg/ivsgtu0RUGUQMuqb8lvetCGo
+/QaOcRdMNMYmsj2AXaQ1KV9daIziT8na5jX6BvPUWnKSjiCYZU5a2qbdpYZYtMM2AbFqjzsAfgTF
+CoiF8TfN8MHtoGcA3QRCuw7VR5ZTHDW9msGh9yoiggP5nkByawMqbkV4pABSW9QuXE5Q74XyDwP8
+Ib/OneefgVCiVQ269nZoeDSTSWqUaduB6T5HegC+3nMcGtx9KqFU0h3qOdWifbGR13LtuxbYKPiG
+VlZB6UKR1O46YHfcXd/BuwzmN3CH3+4489dSCew393P06b7Xz+8FpIPXYvBYCmbXdWqjJsNgcICu
+CTIbEiIIA6jh1C8VHrbgUfs4V6KtiOmO2QHvEQQnkJxxnIcDuGW/fMRj5temPgC4h7R83N3S9v73
+sQgAHuvhh3IONBCMFi5W4cf8eq3d0JY+99wEwSWQtDeNxx1ow6OuCkfrj3m51RXgKraYaQGIMTXH
+1uIw0jDy93T2w7c6aBVeU0BZsyZ9fSl94Z3lNwHBng+JwOtfdr9AOJwfDBTOGXdgm9oPUDtBjXEB
+Y9UGd+3FDoTVJWl5ZXJ4HrumyVqKwDHDktNg+A4Qm6DRgW/KCALjrW/IxxynGSxttGby9teboN6v
+i39LWNJLfvWbhQTBzLMB1hGX2l85DYJaYpOwncPTPIIA+Jpg2HQeRbg9TeE+o/VXNHFMTZDsIyz5
+9EU6Lk0CHVkuBckFfM4geT50UGaYq+OTAmrdFJTs83DigvaSww95vGfaEIe/+rPfJGOtS+/ElJZt
+j1OR6tvYB9DIWMnhPWVzqf1kuot3jOvNHYgxacQHGCatRfu1rxcPvN3jq1IOv+NzqdQMx4s8CYIv
+g90CjhMor/2KwNIAlkb49tAMUyssfgXBMpNtDynTAA4gGA1M7VphDO3w+uNR/I3kioMd0oS3dwlg
+rW9IAClxEwTrWLAYO9OOoPcRMKPlJfBXW9kCv3gFv4i883zdHdjqWLHlqzHJRNvsK7oZRNY14kZY
+40b1K9Wtda8/5+ora9XZMZe1p7s8lD74S37y8gl8If7g5QlUvVjB0w8tXPRwTl8JM70UY57rePm6
+/81BmcVpEr7ivTTBCZJd1/3vuV8gTPfhbYhjPaJb2uCD9nfT/N5hBnEfATHwMwBWBu6os4f1rmx9
+kY7MWxkeSxWzUVne/AC+5Y53AV9LoKymEgTFl/XqDxN28m6ieRafzEjkhGiBB0hPAIoApmj2v02r
+qy+wXbs2eAO7m2b4aqA3QgWMk0EKII6+UTDYNtJiO5Mpag7vWXeBP0Gz0DYB7wI2EWd/CpKFAR56
+P1+GU8Cr49HXdvba5qg/z1P3CjIrfkiPxIQFLpEqAUz2JwViFqyRBa9f+xACfK3PWfXWR/hpzC9p
+Ke1O5ZQ4GoVUlo7Oc/3Vr2AYKG0w+Z9qhAl60wzC71XebYHheATmJiA4TCH0GN/UL3F6QyPcrqmB
+livFtczLNpOGArnRFv0xwp3ZAAWcI77ZCI/Zn9es95SnkXS9DfEzMGR9Cg7JvGPFvFYzzSM8V1bK
+TzAcVwP4QZemAT4A401L3PKgAeRTn/1xvOiAOcZXc2iydmN+XwDvMb+4aqHfsHdDsA6K6113bzJ7
+bkVlUa0vfvaTD5cmGBsQ7nmq7oxrY7VKG3Mx+1bpvqdTPiA+q2wyuWH+cHOtOSCzOFatNME3DJd5
+m4+/6X6BMN2n+vgkPjLf0gBr2v6iXNgF424vywFrIwmLk7ZmxAPTdaStMGspFllEmNArTCQMqgVe
+JhGhiIE7QsPZweY1+nUzbvTZuCky2ofvSWTIVpXNU+ewMlxgWEFmA8FNq2tlF0ytrtgIZ752zNk1
+APThTGCcXphbHeONg67IZKonV/MTvpM2eM4ZGUiPagST4Iw82iKODLcBPsYNkGy90mnuoKkqFJRu
+BUoeR64595Af07aa/CHw6K9ak18noTtS0uZkMCyTFxWtUzU6wtSt3Ec5bwY+z7MB4CAZ74N/rFVB
+8OqfP/oJLI+AWIFw8D6LF95oRoHQAmfc3bW/S3uMikMBYwBlI5zgWq/Vv7wOfpKhlNPWSShBmIJg
+TqlohFGaYo6l6p8W1vN6BsLpf9PuPoLgKld9rjBlQvsoQgPJuZoChnU8sjGQDHbT/D6aQRzzaJ/Z
+dm+XFw3vQLnPg7f4ESdpJ4mjZXrPJg+qHAqUkTyVPZb1ya1ZfHoC3uyLv5ehHFhh0QD7OV7YwouW
+9xyHVsYP6Z5t2/Y1S3Aj18kR3EOh/S3Q7GUugY+fzf9X3S8QDvfp9HfNh4JiakKGnTD2l+fc62U5
+oECmOtLVadPy+sU8rfBieKVvjEyLzy3wi0WD31gCm2GD5VnHVxCpaoP5qKOBYiAfaQj/amI0uzfk
+8JOb8skO/gorWCdoFRCMw8tuPM6paYgHAI58m1kEOujdzCHyX4zchnZ4DPII54LTzOlKBhR/lNmx
+QDLFEVYNJcFsB8QoW1bjGk6QjGS2Kizqb4f5/jDOsx74NBPecvhL2p7kMldofsQ4WNof/dKCxV7y
+DKwK82UpzlXMcjD21K8JaDqN4ux7ct79nTDa+J4qfYDWmXGtsbcYBcQKOgt4zhMh7lX4XoBsnUct
+WmE32O1wWyDYeViEITTGwttctcFeV2mrXtx7GKNuvpM/eGkDu4e8CowV/G42qmPqi7Ie0hIId7AL
+aasuE1AOEGwWc1H0CQzziPxwgstYXK6sz7PunzTAvJvpnyg+mUoMKm7AWMdTc6/8ZJtLk20wrpre
+4h7KSjOZqRQAk8OZmAse0rk9hZ/vgHZPIw/f0rz4mvL2aucUN8fe1/dVK5ztzjp6mZLxLjQZhfJu
+lgxVjlXjfuJcWKGHv+l+gTDdxxphviAnAmADxfXy3DxO7ck0Qt0EwY/djTw8Qz5BsBsw7tAMhm9+
+HENSqFn9jnYXKK4eEdBdqGPVllbYcFsB4F18snwXTF20ThFRYWM4EIlxYrwAsfLV/IGAkyAYO+hN
+s4c6Km2z/T0B4zctMP+1OILhmFNZaOPkz3VtM4QCT3L1lkEZZ8WrZqGvTtWh/Ilz7TnNnPOKA1xA
+8hjL6H/Gsz+DynsZHdBMrbQzYGa5KUjRxr77Z2Z0HtAm1EYznFxLmqR/zWWYRhjntguLp33tM2Sn
++NHfpzE272HcLaX27WldToC4dnP4RUGgigILBYExK7XCt8PsXvQUgNcMiaWpOcybtjCH4Ity2kYH
+4xKv49CBDT8FsG/pBcYKHHdgzDNQdXqVWvcXsp6AsMl6vwBhiX+2tdV03hjT1GFosQ1DW4yeP/Od
+QC/bXr8/Asg6fmN75djmeauqrTTXZ8znG/DVONvTKnQAvTNczFFSI+yW7eRcDn5dpg21fSvsI4zk
+3RtQbvVoud5uxre5mGD4ARzX9hrlB+ZgQyJgDNxj7HPsJ/abe+mR2f1v3S8Qpvt4Bch8hzlE0wwX
++J1geF6D9wPYRXy1OK5ejJsg+Is5hCAp2ggcgR343vTDwkzCsj8O4KLNcNA1tcKX5MlrMIW0KWN3
+TtP7MN3bTYH2nf232mMN+IYApXmCaob3I9DknOCmBT4D4Hmk2uNP+oPoR2mEkQKlhNxpXvqKzxsI
+stV5I5HhA2j2bKrALIJpdkCMZOwzzntS77Ks0Sm+wwM0QTZLzhdReg2+xdsW79l4m06NcGx5GTTJ
+lgqKZPJxTaLewXAfeN02HEneHuIPXRuD6JlcBpxA51TBy8YzzmKXdqr5LXOIiuuANLTATTMcee7Q
+CocZhLuA4LQdljiQTryD4Lve0/D0h/CntJbdUWFrpJPgtwHKaQIRE0OwFnGpFVY6mwAPO+B7As1M
+L/+fgeACh1Iu6HMzj2ja4sUPykxiB0Gt7R9Ab77oaJb5T9ph5NqyndO8cM4Oc7nN6172Ne6lzC6H
+p4lEhIUvNICcL9YpaOU2OMRBAa1svS1v5+ceFVTcSJ8guI3zA03wY9hlW8vqzJflPajJLI9SK1ry
+SOfN5KJP0uDfdr9AONynH9TYNcEvR6eB/jtNIaZW+G3Ju8iyInIroncEGHYEk+LG5Ob1gGWhFc6w
+2AeDYDi+igeszTywGrXCeXWkVpjgOHHDuJ5ZzsN1IucY1isoDsBZcRP49hflCIK7TfAb2D2YSBTk
+LRAcdxp7HkJk/LjPk9nMeGGWzKiMsjFDZqL2V2IfX5KLQGoxEzdUXIGlt76XO+L8Q0YFv77nxiZs
+pPZTfgqJilD/3qF87M9IjjcmbAk+Rz4tcUeiORJoSFh+lnxqhWUo0kc8upekkUmk6lZ47q+fW9BZ
+LJ3WnlZSe9cIUwtMcwfNvs4ORm1ogmC+NIcAygCMxBovx8UhqXlV2+S8Acghq+iGgDkUCNOFEdA1
+8xIYV1wB1g4WOsjbAJ6NcLSn/WO9aPWPPqHSq95RxoYtsOEZDG/9cbki29xMI+xg9tDS8FBG25N5
+3+J0Lp+ue7nNbzOuxjfHTA5XtdroXYHgkxZ4niyzeLQ8Pwk+ndtH05OMBWzOMmyR+aL5HKtn7Lg/
+Hmvt3VTmGRi3Zz9tDtle3pcFP7TAKDeUAVryWWqAE2uwjeSpf9f9AuF0H06+16O5fgyaguKpFaa/
+TpDIs4QP3dDHOisqbGiCKV8oTTA3ybUhE93QtZmZMrXFlEfUDlO26zUJ3uvA7Etmbm4cgyVe0HRl
+VCdn7bezOTvku4IHT9DbrgmA+2eT25nCdop70QAzDyZILoAOyofDOJ+cMjQyPKaIaBfhLxwPylAl
+jm3OL8mxbwTNxeezva413kH6p8D4XGaAX7lbUPC7m1eIwHlr2GXGfgDHZNQlMoR4DbUwiXALDJed
+cOVtxTADZ3dMPk2wSsBBD1rLII29hQEG57DT54ffOgC4HZt2+lEbvE7T9zVXd1zhy04YpMUrwU92
+LWyCqRnmxzWO7UVfn+bR6RE6U5tfgr6pEe5xALWur+D3BJQbKNOyCmp7X/4TEJxAV22Bn8DwyJNk
+xd1uwc8noLUCwY9AeQDm/wTY1nye45/87+kuceU/bbUJilcvFQSbmEl0cNzAbvqBpuF9SCsw7LIm
+c0/7CO97fgvrfDi2+fkpPNOuNj/yvoSYS8QtbgoeKgscoXyzMX1/0f0CYboP70Im6EXT/AbgjX/t
+yDTJU8eqFQB9bE/u1ObvcuArwLEPwEsNsLBIXOb4PoBfBcDan0uud9RK4KsgODfzwAbUONZG4258
+YoHPjsruBMcqG8JfWln+OmBNkwiCYrMOhmFbmZ8AMMx6WxsgllWwHlOD+2kWuP6ek53MUUoeT5eQ
+/FPjS6EyX5LLBWU61xdlRrH38GXtHkc1fUG7XnEKfifw1fJq137SlGyd/AEQp5929WnnE1cIoW+a
+4dAGJ5yOvck7jNe5EGc/ZZD409hO+8yfq9orjJ+C3hkX8Y54wiW/tBG++8tw7mgmEet6LTEZGmFi
+v6S/m8A3zCDUPwDw6WZgm0bZc6UdFpAqWLTbBoeO3Oq66hHAO7W+LWytzVcgLHV30Is9DILRkfYT
+0DUxoUBdp6a4gC4Au8hw8/d4QoQA5fxlWzv5+kPcn6b95+ldhnZQG3xbtTvc58ErWtm0EVbt7tT0
+upCvgl/f87DFA7vaeB2e8pOmir+WsqVjDZ2XPewSrvLdVKR4pVkco4Z6WrZeFyh6S/n1L7hfIEz3
+oWkEIFpgj+V8+KIcbYQ7AO4vy90DJhyJzsmMZ9zaaBf4WcIgNBDUit/WaRCGMoOYvzuItWaikFAe
+ji3hPAPQOiiunihb+Wxu80oEDQFA1LhZB8NktAWGCXxRWmI9G1hAsB6LhqkFxpN2eNcSQ9ol8K2+
+1ByrgH2bASRL6glKH2R87R5OmGfW42TMsR4UwoxXPg40kwn2NfVese5b91842CfMTaGC2nI+mUyM
+W4kU5i3W88/Bv3cuPyIS9eUcJO7l4MtUgkDCBHSkGUrJhbz5+HEuPgG/gIBv7NdtDs51aVLBngY/
+9w1MgiMofgKiaSsMwK+Mz5fkfIBhAGbXAs0oQJpz1jTCrF+v0oc5ukmsQ+MKoTeX9AK37E83h1DN
+pcazLMfQtcBlBtHBM3KiU8NruhckLHE+0zIcAFeBrtykFUjWcQspZV8dmPVuGl/s4Bcd/J60w9zt
+2uaUfdji/BD3Q5k/qP/Jndh1AuVkngMAGkqzG31PkAuU3/EezznyR9a1yYQebw9xNZc5T29bOtB4
+AXQkDfH2hnd4dZza4ohL6bf4472YKQyeykDuk/YU7i+6XyAc7uN7EX1jGQp8eZ7wDoZv7KdH0E92
+T5+eyXuJ6PTtt2uKCwAHUKTflpBXDfENAt8Cv7fH55WpuRr0qGD3foif/am+v+kAyilOtEOYPDl/
+Nvz5O2mDrX06OTXCeAG5EKB7+sm/BoZTc8NOy2Qaxuh0FkhXEfLDvLmEI6MC35ak0ZRnwVw7IEYB
+XpbP/J79Js8/75bP9tCJzXlLqUdrOi/W5qs63WMPAn6TAt78C/i+DIMaoOLtaIFUc2o0oaUA49Nk
+0OlmaRGHPOzTJ9cPxb1jv7mo/LF7EwBbSUfz4ScAvtbxZkat8ALAi3ksjrHMI+41ZbaKrqlbjKlB
+8gTAbEOv62cqoVNSy9z1LdfshBXkrlF3sFma21IvEGQ2ja8VLy+gGvWzvmxfgLSA8x3ckpccNL6j
+n9kv5n8BwwmGOA4UzapE9OzOu2mEaCYK/D6UKcri/Avp5vyp3Ci5l35nDow80u9Y52O6H/LmKkjE
+1PR2ykTnTWS0nrkIatsnjycA5hWHOO991X72sD2k+TFNgeeTJlj9bU28KITsUSgYNzXBWMC3aYRR
+9LZUiQaz3v7fdr9AmO6kTTlmC9CLp5fjztphPT1iaYLVRvgshHJTWD+lYX2nuwhnvbhmcZbZcgWC
+PUwKCgRbaLT0BTQFlLfucTZq/XpZ9e2n60l+H2Y2t2pu2YZQvPoa/SVzbuAXPNsXHQCHX0+MuKaN
+MBT0nk0mGiCW+JaWfUyIvIVjkT52U64rI1Iw7MwtQiLv2pn9h5fkKJi2I9My/WERP+Riz9m49hTk
+ZO+kiBJT7D8Ft4Lh8j74peyTv5GeoT4Q00BxEHgTnx38YqQeXVYxN905akPNGwie+U/u50wEUQCx
+wOI4rnMwwXD0g2YRqhn2+0KeJRyMy+2C+V0gGFj7CJ3t4C6TCGua4fXTr92p2D5CA0Mzh9BpXSUP
+4BLomloCXgHOzdzBCDSQ/lWH+n8Cwh34giDcMNI6eEaATxCk61XBzwkU8zrAMfu7mTko6D1ecdQW
+H0EXZUbEci1aPvK6NpcDxPmo98l/AODlSgg23iICzdhYrt1K149LFMDsIPeO6+dAmMqRorNy+622
+mpQxZsb1nXL2fxb2/NYAkhuvOeCNA83vEhjDcBs5pYMnRii9/U33C4TpPjWNILNNJn86MaKfJFHm
+EPVVufsuIOwI0wan9oFxneAIePm25YWFfS8LoroDFF5C8rJrChgrOAsbYLfUEn9nWTxKb5LrT79G
+1DnejyT1aseQgH216+IXAJ/g9wSA1TxivTCXoDg0wlBNsJhEYIsrELxpifmvAWUUapfZK1Av8QOs
+pTZEbgY6AJa5lilNYeKVh0Am/RCeTqBMQe7BWhsg5hD8uGyfsa63XMU+k4nmMKdQKi1ciZsKc+zZ
+mnvzZ4tzKF5wgH72LctaMHUDymYYS8BnfGw7CkUKsNP4Wc9pfo4AGbKAUqZtqbG/3m7yFXG2UnV7
+4Ql2F1JZYNZrApMnBldKFdYN3FdqhZGAl/bAWGBYRadhfYAjuxa0IEC4QLbyYeXLcquQApjjFe5h
+HGsBV27MqcElCN60w45WT0+f5bXeU9zoBwFt9Nuz/w9a41HmEQw/gmLS7UZBxcMML6D3k6uM2yF9
+CFIyrlmBZdU81hf+pIx30JkAV8bwBI65TskzZB9Y5ur770krPEEwvPj16sMLAJbrG0iendw/QFH9
+aZ+oHq7NTQuXdrjPlx/y1u+K8TbwC8dtcWU4engDeXxrhnH4Uu1fcr9AOFw9Wv7J7V+OW0xlPy2C
+ZhF7/N00wh0iRX+w6OpSYCyaYMNiAG7ADc+jy1ZhAVpO3r4+Y5ggOPjm5WhflrNosz3qiYtJX6g1
+PgFi3Zi5aVxju//AmrLPnBQFwyKv2lgKlBIMaxzBr5wXfHxRLuyFFdAawfKD9hjqhRq6AAAgAElE
+QVQm5QEFwakRpv/Am7aoYJ7kzmSiklisaoBkSLxLfqZTfjpwfElugZESPDM/Bc3R+eZ5dDNHiUPr
+YSt9sIogMt0SOxToyPFuDTW/N7+KkxNAFmRVG2E7KHvsGa1zyisO1Q8JOulaS1ZP4Og9z1j/zX3A
+4/ZZFUDM+AaGo16T9gl6/Vo0dseHM/yK+OVf9sAACIw5HdCbotAg+tIA2wTDh5MjDKUd1hXhGhQN
+E2T2uOaPzevSt+XXsgU8XeJIv5r/bDv8bA7RgXAyOnSwzLLDH7Sq1wS68wqOrbRy7Rr86wRq38Dv
+SXuc45e261cUyLBHAX0KqiZQZfpgjVeWPJI8PuKHX6gF6o7g17lkMy0Z6eq/o7S9WGBw1xSfQDKB
+sed9XmMLm4u2VaMxaTzz6VzrWs858WMY2v/eTM4c+TSfAdwP10X/dWP2MRT7L7pfIJzus9n31EAE
+OSsYPnxeWc0iup3wOjUC6OLzGlfdYn270b6Gpzoo+C1gXEB4fSKZoOzyOC3CqBGWl+VC0KZkCp57
+5wavftzEBcFUyWwZrmmd7Kbrx56w1ep7CdwcY/FUJOjMOAXDQ2sr2mB9UW6B3/ExjRAoR+CbZaYG
+uINgpit7sNcR18xMivQRSAYlc0wNxAp2ZlX80XN9HfjBZAJNQ3zs2Ij8cCe1jKXhLUEIIMD3SEum
+O/Jm4y7z0v1bB5t/JFQn2j7YwXD0JvzGffM2J0aNt2/xmPG6WJAxbX09je+0Gm9rVVLNsZ4eeZg+
+mICq0sBS8JOZLX5n8YJc+m8P0be41aKvC/XVuaVdtThHqXoRfl8M6xEARx8KBG+wpfwJSiWxxRGE
+VvupiQ3/avEZIJ+0vQnGpPxa1h+AcKtz5LFeT4+bYBios2QfTCLy2gER2zcz+FVPxPJ3cXP0uM1O
++CIsEhDrvS3PX/Qz9lnOdwLg6Oss/xSfa1VttnFyS9eIW4jOWlpchcGWUnj18+ZYBeTe4l+k/QyA
+3T0tjLYuFVEjmQ+RqUmGw1BOc17+Mx045662nHCjtbK8jaUS6MYzGHaEqYQrTTzLxv+V+wXCdH9i
+GpEgdz9POO2H0bXAaiahGuMCRmhXxwDFIZBy63md1nDDcLnXqTYHjfBldWqEaoEvrw9r8GSI00xQ
+KHF78FFG95/uKM9XmdDtau1a7ZcSpgNiEPyig2BqaBX4XtZtg/lhjQTBWPF48Bf4rfwNHMsVMOkz
+UC/QWV/sw2wwdNLyTm3wdhctTL4vRoi6BnARfNIPJhMRf8if/Xvu/HATvM1ktawV6GsMS1o+Cq20
+HAMF59YvzzHUfHa/SVSDsQ6oicNKZCauaaW7VJjnab5QPp2lEGuRvZ+MYw1toeeYas7nzVVrxSou
+aybwFcG24lBAc2qFMa5+A65gl/7QDIuZxHo1+I5+TY1wTPcEwDfKnz8OKfoLoXmv5cplSRoLAaz+
+zFcaSvoR4K4BBIJlG+UeQfEZyLJs9m34N9MJ9bPdoKWudY11zLTKk8uW4yFHF9odmt0/sgse1wZE
+jUsnYHf4CdCXgkVA01jTGkfcwDGPrlOs/TH+4PK2LMGuC5NkHjruot7nm5ghxlPa3wPodUmPm79b
+O5cb1Xp7DQTXxCi70D6fwO5pPnKeI7LyCk9x1mg5XwWKkf4jCI7id5Z6Won/nfsFwuE+vQcxrMeC
+CcSCnBKcWY8nSxFWCL5RakQb2QNPElp7TQS7UbNlcf6e46I/6rjd1qkPLtpe+j1As+2AWPObv9no
+CDilkI/rHS/lqTaY2mKO66TpVGb4RP+KGdf+F0DJeCP4FRAMBcRdc1saYYNBbYGnphflZz9Yd7RO
+8K19re4RFO9j2Wf32b/jnQE8cADNLnMegEUxS66N15QGqQmwxMb3WzsPbl/p53zFp5P6a1fIeHkj
+UeBs9BklFLPU26Qeeqz+RpLZUZmM2XCmszBBjUhcvatA0RAEYKYTYGyHuBhhNpmrLesuUgqSseIy
+vYQn59p5c5I37J5PBzzHsOyAccWzqfsGrgC89/wWpY6k7ZToiohND+DrcTBk0HM/n5h5KJ2X3+6Z
+F6VJdofunTZ/IuR1qrgCDeCa0t8zwN3CLA807bGGG9gVf0qSVickDklf2U4Mxm1qh/X6cx72K3nj
+w2oqT1TeN/Pp8W1r7EC+MCUgVXlf5etpM530P2XLlEGtHOlBAWbY/NIMo4Ng3T8oeSjFV5WhOBKg
+e3PcDQRr+kMaym384ACCG6uxraSM/Q8AcfhmPlWM9V81vtfVOvavul8gHO6fDy2012cJDfdFwl2m
+BOsacVfF8TOGa5NbXuGOb7HXvSAAq3iggLwet0wf4i4rBa+De3Z1VgjS46i0kFs3gO8ls/B9Y2mT
+48B7u1aZr9jzvEaV8kln3ZriEiQg3iZdQX68w+XaNgeFSW6QWT/vMnWuBPTOnykjFuhEZmfB0q3K
+9L9oDCXjtXsJHqTHJWcXcxUtWTLcsUbl/PHqLbzPH9t0zRvhjuG8+kdMp/4odwbHVf7kesppDfeY
+yQ41nH6zc/xDeMX19lu7HU9C4B1kFh77vDccC7lNmhQ+aHz5ssu+lR76PtYlaQpoQLoAnqw1XvLC
+10ct7IZdYb9rF/y6M341vBiFGV94izK+8nKzmxv84jji6kAhGZpOOIzfab/61W+DXfz+spO43/0v
+INgdCyTfN/y+cYvf7/hA0k3gIeBDrroWqhFWENxMJcYvp0HjUN0vcFsAjC9e5gttSR4jDvPqxYPw
+H14HvdZqBt1KvG35eGszgXO/Ycu5UFpG3cjnPPmetwG4uT6tPsbp/I71ZR1yXrh7aDhPT2vURZGs
+hEA/6G8DufcI61glf46NHt37o33ybwWeFcxE4ac+5q1rYz1ze40t8ulcJNuTHzGLApeprLoynz65
+jeNb/7L7BcLhvj6c/PtaxPuPL63AfTn+cfLgAr63kdcTFMcd3hUE5wa7PejEdvCrAE3ilN6a4yYh
+UVN2hP8OP88Lzi/KeQBqAcG2VM4Bgtcm+Io2/DBNbcNFwAJd2eIn+UTz9t6XEggn8JFos4Ov0yTk
+3QJ3YOU1TmDMYM23SXWdYTP+OL8Ev0aA6D1dBfM+Sy28s+pdqM1SW05XcdAvKlBQXa/xkeeznuKX
+DRzrFJz69JP7KQ9XuYfDv/XBzmVGvl6/QTWz59X4tL99JprGJW5Gs0MYaXQUTt5WrijyeFhz7JG2
+10/g1kW46ZhduqFa3UiP02Y8+JKC4AWKsc79TSahd8/sdzKQnIMFRi2Od7yQqlUyKeNJE7YD42UP
+0MGsXPMOfwPH57gFQm74NwFwgN7by08ASD/CjzLK6s/50EBwGncxDno9/AI4rZYCQJlegTzqLMDZ
+zjm6Vk/X/ngj/nBVOmpXKx5MvqGPvTNeZuWsDS4zpwKzRbPkWTmvH+XBPvbGA2WMI8+RC5Oxxz5j
+XTUaqXwKifa0iCSo2l0EnVV8XXW8BYr7Gvb64V3cPfEt7eb80mE+beYQZIxaX/PbQ0JrrT8X2K6m
+L7T36992v0A43B9phC/Lu7kExGb45wrNr2iJXeIaKL7WayMJdtOvj+Bx9kP8Xo+MIXtFGcbt9XAy
+X4xDaYi/L3nUnycgOb4i0ln3yS99Cu7YHhGxXtUGbzJKfizoc3dZ2Ry1DcXNg/EzO+TVPLopTwxb
+Z7mY/2RC23UIEWWaKmw06SVixA+B1rKcGahWoeasun7mfkybvK7Okn3vbe+5CI4f8/YZdxST17Qn
+f4nkvd7nPp1o7bnLJTC8Nq10xDXT4fFpy39oRAH7qzjIqpW+dvpomt+mxUmCrO7dcSN0rXNo1pff
+JigGzG74dSUoNsMCyyBoNjiWHTAu2VUEwNSu+SWgw8CPcOQh5bfVi1kPQLeAsWp/57XGCgJe1QTH
+XXoDwNEMpNk29Y09mFx2EHyb8rgXQNyXtvpA8BtXBcVNKyygzbnKHDfpQ66VLrTxAIp595VgVofM
+q48wOg2X7SgS8OU4SYcufsa7jEfnx0dY+59hbO3MuC2cINhqn5N/HvhCnTGOomWujc/TIMp/H+I2
+gDzH1+bzHN/TB+gdOWqugw+SPSWRu5SdiqrRkVn/JpSRQKae4i4cpNrg3+PT/kX3dU0iObtF2AS+
+AxAn8KUGtOJaOGyh0mrOOhBLfwO/9hAPQMGw7GoH2osJBKN2L812fmr5DvtgQyp5dEMQELNOdcn7
+vcLJJL3CCbwxQbE8opHfVrlsTFaaj2NgKJQrnd02qPV0WJ/HjLFi7Ap01SWDtCbLV1ItgIc068KF
+ySpksojMQGeEs30VIK7FRGxQW0IevcCa19gOw3NJowCwrPFn95/kOQLbBOS2pW3+tj7CtMcjzdlu
+o7WhaXvrb8V7zxT02NajSawXsUWik/2r3Xts36ffpamTX6hM5yY1wli2kQ30ojTDBqxPvw17KtDP
+/bdA7NoDVgD3olaYxBXhBMECiG/u1xjbAyDeruhhAmb4BMLT7wWMD4K/wvoWCMrON4a6XgRSHoft
+N83DVlfX5lun7wQ9biAYG+gtjkEyHuvOgA+amQA482odHiPmyC1GBYnxRsI7X5WdfADB6RfaLC5W
+W2cDydXVLIdRD1r5GT7na0Kt+ffgvqNJ189A936Ib36ZH5I1n7TSVAydY3Y+vvWr59xAMEirnrw3
+63od72hAxTGsXwmC5zXTf4Hwv+r++VAd75fhnyRYE3vg97j76mHA6sMV2AVdwt6J6R7yl3AL0CBA
+djFd71rh22C0FdaSNMmL31eCUWnmMFWn2Ut75tzcJ8avTE+ALhTABNJEYV1ltNumM/rV5ETMSqzy
+UlhLTL0EqGPjuHX8HpklrYFgZbKNe3jnLDP54JQpnlODYbb5zI5USTJT1Fom/hq0VrN/YrlPvTi5
+khxPefT1N0cHtn3Kbffb3rfzLHUJdtLwVHD02Q55pNEWh9Kwa2LOpyJ9zfRCBLPvGTqCWgEzw690
+2fxA1/BeoeEF1mOjPJJmaXnLZMKizA13g10rnNpgmkSE9rcfwSYa4dsSBPvtMILgK54DCZhd96Ak
+9IfrS7q741YQ7KIJFm1c5Z9LQ7BQhJf70wbolfA90voTMm950eLW5vQAVgmU0W2Ea/U7PZ40wAp8
+f0rTvZXPXGg729Liqmw86ulmFCb97Hyha4aROTQ9yypfA0Y5nYOf80wgvWmFf3RCCMJQkyf7/FAG
+ks66XOzj0/ytf9IkSbGn99uULKKq/FGn1t3jAtRrQV/+nFkK17xIoAFekdUJhsVuGP2dnL/lfoFw
+uD83jRC7XxtxOLwsF+iywp1A6TLmk7331Mf40zWtPGEC+IanTPvu6HAxVtpsrL28GHADxodek+4d
+QuyxbawYP80yfPzY73xzOeuN7ReMt87StNpEualksyk4NoW6w1yiWmrXNpkytoZHBBg7GX8OqARp
+hoFNU7Ex6ceF9+aj8Dzm86qXzDW1CdGlIwB25HxrfuZpNwTHnr31/IA6D/l4czKb0fDZL2YRo6nz
+LO39aSHl9w95XCNsF1Lan1bOJRMONPeBU/pZYZXu/p6ODnoy1pAa4Hx0pKYPBL3o4dIKB7glWA5e
+2cAwwwQYNH8wAt9FeBnHJ3VtL8UYvQPjHl77o5lJIK7NNjiA8ADF+sIcF7PxK3ls3m2Cyeus8b00
+k4DmOfuhoDi1fwGIhD/O90CK5+sTidrPSpQKjita0hN59R3QwG4U2Xno2pWEu+3WVRhJ4/uD++U8
+aPgpn9BzLz/idOxbXLksqyD4yL6EEzmW7BxguIHeGc5faXwT9PqB5kaTEK//lNaYTIHUfDcnMp21
+xLt7shE2ChYC4GztAHxR/jre9PdluX/VffqyHE0f1otyC/CmhljAsL4s5+3OkOGlbxkysbyHjXfC
+PeebVYKg+Uiu4r8lb3e2Dq4P8Pu11W2HkB6rpnd968fj2Trj3zfdxoxiDgimAQubaAIm7URtuTR/
+aGtqveu0VZKe9m17Gi06s6GfHU/7WRGgNZofFAs+QlMEnPNuL1eIX1UNKfgeATDrwzZVg79uw/5s
+RNhK+UgB8Phi3OxH+m3vn7a19cFO6yD2caImntk2+lR//FEBoXv7ba4OO/AQOm3+aBPog/KXOI13
+39bAAdi95iKBsdk6SQKA4YbfBtUQ1xmxdbqEweJFLxPw60gQTHMIM4kjuLQCyLcAYUUG6ZfJVe0v
+yxw0xDeBb7suEEwksoGX7IMRZafZWd3oBL9V7I7gwzZPzOnAeL3vdwLA3OMBrLlmqRVmXVNzyiF7
+j8jpUaJ8uDFSMGyhARZN8AI3Hjza0OQAJg+dz3w6J5jczj+JH/sq03zEnfL5aL+VqTXOAXjzVN6m
+YUDJALeM2jS/Pn6QdI3LCjhmznExvd6bLrve0nKcEHpqtHLIs9XZqha/tEU5S/nNqFRiIbTAHSj/
+bfcLhMP9iUZ4AeA4LSLBL1+Cizynl+XEPAJu+L6E2C3I7bi5Sz7QL8VaPnLmYsKrXh5WbZna4V82
+PFFsUi74QfFWloCaVwNtjwVW2jgpwjH6V3yGd6b6gQJ9ecibxle0vXbucrM/yvzNEvjAvJdwVq3H
+EzJ09wJkoaJpgqYNUARLXF3rnEwW050o41B/RHLtG51I4EcArHHCeN9A8FN8pZ9LO4Y8we4n5XKN
+dtD80BfR7GwtH4GxUnkvdxK8Ld5H3pi847ycOrNFu6Ts5ZtmLy/jRkz8HQR1wZe49OIHLmS9m3mE
+AOTUCgPUDK+X5RTcBhhW0Ht5+W9JH37ud2IM7rfS8qJpgY/AWPaiw/vpEOOkCIYbIsGJX3ET7eud
+fA5hJ7yB4Odr+ykAhgCpoOfMZ2eeOtf4BIo7vSht1BxyjsmLLTYNaZKmEDF8iVPwi3ziVFD6oPzI
+63vaHnfs+p53sM2xBTIl8+XYOt965HO5l3S/CTlxnZ7CkLVv4eqP8nBu0CmeqvM7/yyw+nCjoaBY
+Ku2geGiMU9hHSxLen9iWzK6z/LtW+G+7XyAc7k9eljtpf28FxPpyHMQ2GB00x5dLk7jzERvbAlLQ
+1aMv7Uxd8nOtZlJ+tcvPLysgBpDa1UKORdiN2bPGOFbNrrIrXm7XAn+bJ8HDOwA+M//5KGZo6dJE
+gn2V3Z8aYPErd5Ds9VMwPBnGgYV4SyjOpOiNlyZAha3HYJnnyFZVYOXc7Nre/isNSUd1QgnKMElz
+4n8EwKMmLY9D+pPr6bNmE5BtGrv5M2ya1suc+tKFQ43mBFF9ZHXbsuiqbuVa2lHQPnXwOddp32fA
+9RptH+JmPm/pMjO3At14OW4Dv4aTVriBX5o2RDjzqHlE/NaNvoBhk/Jtcr37mcYbHQ9gDHRzCA8Y
+4OThXnx5gmH+uNcc2JbGpAtG/lV7MrtlFVd8zxoPfNQKo3j4qms1estNHU0ldp7A/jXG1OfykRY6
+XeRdhivLJRiuBPLInY9WvpJEUv3R/0Oe7civke7ncJU51K9lbPPkgLYb57wLs9YnXo5g16vf74BY
+15McrhpR3jcd8W7jZ1JD0ekLwOX1jLRbT7aGmzck7gTEYhpBbfAvEP4X3T8fTn57ES7A7y2mD7cb
+blicIuHYbIrFXIIM2oNru6Pd3QOR18ggsXiObMa1gZnbJH5tGn2cRkB8xw7UL3VVWzw2LTibYRyM
+73mEaMFID2BsBTgdeSoFN2PTButmz5pl03m/WswD4B3Uml4P9sECeXeTCdFXyC1sxvmKb6RBjilI
+LGY6+iusPAa5CZnmHJrsM+3VdVZezFMfs1FbFu0IF9QnUF5IdBuvzzg2bX/Ww2Oa9Tn35rdjvHT1
+WObTWezxIQxmcdvzU0zMNZsC5JT3/2nCTukuvfAe12zIVTV1jLPEphCcmqYOdkkcGjAuQHu1dDWF
+WC/RSQW5UQf4vUbaLU8BXPs8xzDn4DmtgeCjf+wf3VsAGuAZeyDzHQAwNcQT/G6KAQJb6+BaAW++
+ePcAgpMSW+d07WUSVWa85Dd+sYyZJvidYHlcp//ACfvfw97buz72+sMQ93K9wsdtpSknEKz5t6/i
+2C7vHFsctjg752N3rPZDw+l2mt+KIafVue1nUj8oo2QWWLbPXwfBue6xh7mdkTJaNMSRjy/K8Vzh
+v+1+gXC4f/5EI5wA2HFfiA9qdNMHNwkjCK6ZSyCOJOLmMAHBg7mxPmF60qPoVwQD+JLgHUvDEGZ/
+CwRv94Sr4cW8d3td3GELdjG3xfET/dB0M/laXoTvSFfg+3R0WoIRDRNckumetL/cOLGJcqNBQfH8
+dcOIyS4y5FtUCWUJrDfbc2ADhJQnGd+WdohooGwTBX/kTy3vGA/7Y4OJu6Bf08w2uvSwbR7kRU8b
+/TgD287AzargUxnW7Q3Vln8Du1ufJa/QZObxEZYcPX7mG40+TdJHkxeBbEAlvG9xBXJGPok7zxbx
+bNf6qklEgV95aS5/62hHbxpgjDwr7BY2wTOPDHGxA5pHFMcoM4k5L3u4NL5AB8EyT1M1l07AgdVm
+UOB7+u3HRsaLw3Y4Yx2iHWZc8maWfQDApt1entq/Z560hRvr0EDIgZAXFgxFWUcDvw0Yi/Z4MI0N
+zA7xdNoO8ynNBKiutE7fHP5P4RxIRR77snnCK8enrT7x5gXtuupV0OwtD5muo7YD+fns5uzfDow1
+dq3fy1aR6wMvbdXtHUqgC7UPDvkrAHieHPG33S8QDvf5y3K7ZrcAsOVb0vlSA9Uj1A5meJ2XR4aY
+J1BAGZ6LliDyDiHORywrEH+atqDXlX2x8i+Mbql1dtUGm8UXoaLxKyFJMLkllL6twt+8C4zxsGtT
+C3xm5NK/3Hc97qj9tYK2OTYFyuIvoBwlRBu8Sk4uK/5JJsKRTmYQqnWrtXIpCy11cL75NT/X9id/
+WeVZySIX3uUogGiVf6VZA8BtCgZDPo/hkHbYbk8a4PTb1tyxzM99GLlsF6RvFeyCYge8Gq4nLk8d
+OiS8SlyGXW7KOk11qaadlp61OE+zB4j2N1hVAs8JfHdwrPsNR8B7DiPBcD6F0bTZ/7YtehyfgDjQ
+ALIC5g42Ogieccq70gW/czsD0qPNr6Tlz5/BcKtH+vJU920934leelgnTYamtDHKN6D74i89fr3c
+pSD6jcU+xiufedoyL0N8LMu1P7Xrh7gPw3B0mRfyuGiM1zVPJ60wshzQuXJxQj3DXpNq23SAalmX
+Z3bmrhc9DdnRvN18GGdrQvcvlCCGOYSCX8jHNOxXI/xvuo9flgOgtsAbGAaANJcIoDnNI6IOw7qz
+X+f7Lo3w5XXY9jJlcHl0Zitd+pMggNw+EUsHwXwsVxtINNfiv+H5Hssy1qEphCWDTHsvB74JREJD
+8I0CwGYChIUhQPypFW5ghCgNtesItPVZnGiCmeVnTbC0EZtzn80Xf7sVH/64mVAu6lGmMzDkYP0Q
+JyVzDlTgEZRY4+iN4yYThmi/2vzlcKzmD8Mlg12tPrKnyYj35O4OwHmfUeuza+dZP5U5weEnLbAK
+hNY/WcaTAKg4b36gaJ0x3lIf+jETX/MwrICHe1M7Lj2fcQcNck6RPwNcQ2l3N+A7bzjjhtp1Y0L8
+c8Oi4px7M+KP2t85J0eQLB5vUyW8mPE97G1+pRz3ueFI+FzzO3j3jz+vvI6DdthGeKbT7z2s80Ae
+vXVUPNuTfaUbxFLQNCKUE3nDkn4cNMQ/8A91Y+9JJT3S9igNzPEfhvOaf3CPU1UPYa+/2d7Q9qJo
+mu8QtXDkW3H1FKA1YedpWa3t8foujJKs7vwi82KW/jLPD+w0rmKYOLZ7JOMCChALQP49Pu1fdJ+/
+LMeX4bqZgzsK7F6e5hIY5hH6At0CwmuT3ATAAn4NC/wqkMoPYiCEAnahwO/RL/5EEFyAuxiTbLZ4
+jLOEW9TIbzOHfx1yD9gV5UPwfoc/wa+HVtjLFENkSdvYx7DF3AGo0yPIb2XjoN9lHmGvESKNclDW
+/ASAu3tOifnSwYgGeGOST9z4UOv6ieZvqqYE9E5t8KQN1fgWM1XuvziWYv0as4zxAGSf+r+5E2M9
+lGugl2AItWajG1u44h8QMMe5dU4E2bG+HfhmvAgI1baopvFF2u7rujc+/N79XfpWHQ9aYhv5mhY4
+klLbFGnpl/iWhrjZSpAMpNTeNMUvcYh3AmwAqDHuJGcXsvZB/5JPp2xqT73Fo7WxmYsRsLRtP58E
+2JbGp3791wEzyyjAfTpz2KP8KW0n4jWYfT4B7pQeFyNdgmGVlc+Gm2yt5ke/dj95WSE510yzzzPu
+Cf31oWwJryx3ph/qetQY09+UGD2tmSqO7elSNF+sDz+SPq31kZr1XOPGq22fLtM+Wf5VWq3+Th7Y
+FQXOzS/1NaWNXrUD4waZ8njZBVu+LPd7asS/7D59WQ7XBMBWJ0Zg1xCvu8FRBktqLCAcWuHbkiEa
+wXBSft+YBMlJstxNVhnJfItQLcx6HTfv4G2B8Es+38mPPC2NMJbNXtgCUwPJj1ukPTBtg20dmH7H
+XuEjP+l6BwvCANjbnSmuylLYTayLIVOP17G4VjD4DIZxjtu46SyjYk/ihAMqvjnw8oh/4/IV0Nbs
+kNHz38iTz7rRwXF4jlvBgWVP9rBRZDqexvWWxnobAI4IP8VHmScwfG7nBeiKZPAZwT10WLtNuMnq
+dZv+0+PqIYGP0tjf01SqRpu2pddVwe8U4NxjKrfaC7oGpM0vM500xqEJ3gCuAuKMP4HgUWa6iVIe
+5+cc1ilRnrOvpaR7jyte20GC7kktv06K8NTu8sNC/H3jDHZvqasdQYnp3zXK7FrjDVvcit74h888
+LmBXX5iz9mSqYyDlJ5O3nHbo5NOSbW7sE22MKk/3kie6OLBWTC7sMqHblj2C4Ho6nPTgNRP9phnQ
+l9bKbxs9GoQHc69ubdshjinrr8f86XsU832I41gf6j25V7l8+F2Z9veR8C8QDvcnL8vdtA8OsNvO
+ExaAvOyFveyJSXgLfQJYYPe+Dfe1QKTdi9jXHZMnWPsOAZcyBvFzuSdzBOZ6IrEAACAASURBVJhd
+mfiN+tv5/W5PcwvH+qCH4qDbDRfLX54gGDxClCYSoR02X/V9O8vHV+ucQH6BYrBv5T1cQxOt+Wx4
+QsLXphJNcOLZmDFTzS9EG6wPiSYIHtcPdrvn7MXVkZpgeeZ6LHVgmy9NVjoZqz2mV6/Yj+xGoSQ0
+wZTDiMWMlzerDEuMiK0HL73/YIuluHwFwCd/CRnp7C4c3xI1JpIn89/CQ0BqnK7V+u+9cPqf4sXj
+Pbi6KPSVm8bHdf2xSQdRRk0pUiPM4obS7gYqNrPSBDsBshdy3swg5PoEjI9xVWYjTfTrkfDmZIlX
+j6msNe2PoF3yYeTLqqyXSxOzKMu4fmrEbi7xja4NJvidwLeuXbvcymhfx7xkz33finMeNS+ANIuA
+ITTEK6z6RxPP6QW6RYe2N575ii9tINhbRtQdWlbd3R+EH1h026+6b/rWHXlWpNDRotLtBisIiBrf
++uIob7orDdsVufe2rnNPjkne89b8bSyn7RMf5R78Ijp1qzO+mT88Xa20w3/b/QLhcOdPB+/Or7Pt
+L5J4yxZ4Faj8CGFCIjRYgsfbDfft+A4tbNphkRkJMwh820ndAT6v9M16fjFPapp5ddHiGjzMGmIj
+Ue0MhEmEx/mfpR2222AB4BMAm7wA4sD3EEinTaSb0ynxxFH5YpQ+CXZr09Wmkk2oefGQV3ewXI/a
+hDHfnQUGo0pOIuONoHvFi8SERj+znMYGW7oK4gZ+RSOpjzcLRXiQ7Yn4p771dSr6NDyO4Dlei3V7
+YHvwn4Fxr3EIqaNAXZ4SXWeAPPuuGmKd/w6CFRhJ5uaH0JL3pnViD/HkMTZAb78CDRyHl2WyDhbJ
+jeJxZCE3GfGHJ9gt7e9iSC0MHK6oilD1boD4VGabkx7VALKfAbNeCwwH/IqnHbUkBB66Pzrs022b
+gMc6LRDYFNC1BLJPNsPuXbub+b3imgY4413Sq3MKRGOoMorh38pofOc/JpM6Pz/fwK+EpinWGfPU
+jlaTi9rwpDdJ60N8DbeoaeMt+3eFfwC+CWCf8h4AbbH+LOvRlyP4ddJoFZKHv+mUHzKmxdlTvqJ3
+ZjiDYNvHPzpg7ByKR1DDe3pSOwHw75fl/mX3qUY4CfNk+0ttcIJj5rEicl/5aQhzh3b1O21wfbub
+ir0TOLQ+zSwyi10bgJmSLYRX9MXCLMJA8LoKZnyAJuNbcwGCJ/jN8lFu2TlbguBbZZ3O4IMwS2di
+/6QpspGbiYSmpea3bzwFxVUdGUW3q4IrC+l4ZJ9o29OCsy2AS2YiYEVlQft7mBgUmDLJW1UN2z4F
+vy0XBCFY635qeHQsiSgGCHnHJP9hWmj9Hpo6++0xz97OlKYPzNzOET6jpWzZfpZOX8OVLq1RGB81
+wSKpT3Eaf9L+HuIMcoOm4fBn9YEsLFTDyjrypTeCF4uRNlC88pUglDnfgG7+eU7LBX17DnFwJwk9
+kgluPdqhaRt5j7Ndy2nrb9RL/7vWj+u+FBJ87yPNG7ZfgeRv4AyCvYPfI/D1/YU7eB+55ahqInr6
+8M/05BlWx9Q9lW1+tmvHeK51+2iR14rnVJNIrV6YPKI6YCQ+pYtnS+8mThh+3b/+UiZpo7Js25v0
+ktzc501Z1CQ3DtrdnALZJDUtJSg5j/tUFON7nCbsU3pgmG0Pty0ege3ECPDo8F+N8P8n3OemEQV4
+p73wYobCOVuY0qMQiCFeNAsQfN8LUH5fjjy7lxyCdkNrn+TRawMH5m6giYQ+uueLeCEKY2t4N+cj
+X+AbeUCaQTQQnJph1A/0F6AmYH6d06cE2UmN3/EOk//E7KHKKgMYZhItr/W/fuhPY5rBwre4Ahdw
+MsJgLgP5D8j0NPpTB7JcAwaumkey3QJgiXO3+qzFmPtBOxy0p9ETID/29MO0hr9Hnz7yd7H6iStI
+8FzKR7Lrz3u45ck0rssZDK/raOCHOE1Sre588e0JHBuKHs3RaDMPnZkAmDYTcXGuf6ilTEBy2m41
+iTykMwY92Tk9qVRo7V3baw/xcm0AOAAq45JVF6/lEzYfNNbDseP4ElPeBHUwXEB1N5H4RrwwHXXe
+6KYRCohVQ9y0y96BsVK5zGy/eqV0oGx7/hRHtQhNY4vO6/d2PXOhpTVu1shga0PFIQC+a9OcD8/c
+4nPDau8/Ab0EvKoBfgTOKQWEZpRL924UPXnz9xnUPrNPmeXI1ULSY9t7kltXritl4qn2iBuTh20N
+o6l2tn/GrfkuLXAB4g8P8Pqvul8gHO7Tm5DrAi43XNc6yuy6DF98YQ6ex6atL8yhNMOrlcUceX6D
+XTDzdRd0x0kL5rjuBXS/DWEzXCcy2B0mFRCtLCw+u9nvuRMuKj5sV29CpjM9ATs0A+FLdQyTOfMH
+0Qrfju8A0BOG/YnbSpoyiyfgwUhHmgiMH+470u8l/NrGVoYYQhFoyMh1trzi3O/4eXy69d76gLyy
+ugmnUOEZfUifbJU+c84KwQsTjBlyDAUWTuv1adxP0PKU+7GqV6dF6qbuKe+D5BsppzZ43X6mdNdX
+sNIKRPn/Ze9rtyTXUWUD1/u/8Wlzf4iAAMmZWT1716y5q9SdZQnJ+kQQxtj2Ako597oWKJofaM2t
+BVqOD6+h4ryjE2vOu0CIPIu6PNwZEihTQxEkhmJKDcagt2PGOYhz+DAO5LAp4aPQnTSBVm8Zayrq
+U5EAJPGWHOR8haw25Px5rNNGH63VLlPg0+VRWYrR7hbceU4HwTzvbnU81Dn50M+cvV0YSF+7yeS7
+29Fqabnkpln2TN/OsVFupFWfDUDXLMqADu45PfLyDTWylyw6kB9yidM89phzPN7nF+A+fGZdlbs+
+OkbNU5cXvp07x7Gtm6tMr5+FHHFXD/biXIXiPW+fOgDr7Q9A//maz3WMh+EYj2Ma0YzT9RfK4B8I
+v0A4wqeeEZctMPzlwH0ZvrCu3L8IcqkA6ddDpRLxAsG8CjL8MV/13qv+CYgXDQmKJ0g2LPAJtjf+
+rtgZtOy0wYhpJXRAwPDt6+0Td1h973s9RPjHo19hUVbhqNXaIZbTJcmu2iw29gGYuN7+pQuKCyD1
+QOs3cBOo3qXE84pexViBhAaU09Je/XfSbwLh+LHtBMB3pdl71WANUmkoUUgstPrcxafDl3CWauf1
+vQra0ojWNeQLYTT1yt+FUX9N7WPLvVczNcEw+aFX4CPNcystKketPNG3HfyWVbE+YFNp7ijum2Jg
+giugbsPMNeg032hU1qs8LVoFdLmUMTsC8Pi53ATDQOzzcn0Als+vgpL86AaiTILgOocnlPvl1NJj
+kZuQ6OVe8QPDBHDuxfM28usc7tvQwpxfR16IeM4Rcj6R9dTt6+QRR8IHz/SkebPqLiuvAOM4J98W
+4XgGyKc2pA+cyjYPvs/Xy7l8UbZa6NFyRetLW7fHR3nhh+3cIjY+nKylHbSRnvlddNvI82qTxqCQ
+A0USUGx4CXRXXYt51jxOYNtn9QSI97honTluHR9o6GqMK+cGB/v5xwdsDwzdgmOB3S8bQBgLHBte
+gGAeNf5qLv+l8AuEI1wf2uO/sMDfFSC4QK8owdAKZRUpC3BZgtdry/7YsgQvMOzL2hzA+LaI3/zy
+SoHiBZI9me2PMqezXYaH+IYKxqYjCJ4WYQXDoWDX2yOW+8Sf9saJ5ToCULcPoZbbvhR+6+3BarTm
+OeZd9m7f0x0Mb5bh++5AOIatj4P4A53ghvPsQr/9xh11d0B8p3V464uqrm0d5xqV1C0lFVzHsQPJ
+iVSmJTRNwFGtB8+qZXFdJGmtz4IP2guN2c58DN6L9OpmqoPfUjJ9Er2d3//22J72h9/a38Nax1vt
+lAdxAZwAuVmEOZxOS8t9K9dpfKC0gWCv+qnokha+hTW1tHKyTMeoHrcyaeF1BPjNNCpNsDqswskX
+baltpDF4DK0jp3V5BGfbnuhTp2xVgBf8I2ta65KW4KjZc/CHPOUDcM0rvQBuAd4CuB75Vb77BVf5
+lSf1uORnuurRefkkfAxAyItW6QnRtBhQ4HcDs8kmVYleaJmcdKpvZ4SVzvwhT1rZY56VLKXFNy2/
+JV0SFGeVJQ0VHCfvTVGKEyCuzpXzoshp6xK7Nq7zIOPh3R5p1yB3aA1wvSu6DCilQLVC1XBCEkb7
+wkI5X76+0rtwzIIA80jAyzdUNTCM/074BcIRPrUIu62Pb6TCMw/LMK0DYhUGkLtZdq6F4qBFmMCX
+LhK8ovpDvWIL+BrqausPkp3TZSI7KCBiPhXb+Fv3SWZY+0sFoO4QNx/YC2FtcvwDwG7gD32M1deY
+lmVQ/4unrjh+lZy1hsfKq/kETKiA0EAwiX4T/IYlOD/fR1XClgkc7YGOnJ2MK53WZrFEbz/2OtG7
+DAIjPkOs05T98FypLEYg4NEW5z/HFErdRKAqsD27SbwPCjwaPZTLR2EqKSFPndLTXhmVglp3G/3Q
+15n38mfIfYEDbaVLXuSYQosWuCJNNq/cvldaKexFJODlOuZtXaw9ZULjxa1FHttdVYawSXAbnU1L
+cKUL2SDPIQhOi7CuzFz2Ex9YL+iHIjN0MCJEoXHaSIPuklgrFvRJg6xBiagHHim+ajSvvJRRkbf5
+9mZ5BO0ZFBeI7mm28RR0vnTeJm3b/dbL9UpND20tCwTPMgMMC088guFDvY8CQeJHtwmNM8919ChQ
+O48CfSc4PoXSpDb4Wu7kfASMtcP78JPWxlTttEG7w8Q4kxZi/qZlOPvbW+d97mWUO7hIQLBK9K0s
+wnxGiQaarl1/KvwC4QifPqnoCVrDXxhlGe67ax4vWFqDyz/4Nj8A4gMojpoSAKc+WtbXC7WHF9/H
+lqHw8tWXKcB5pauKwHiiPKRHHyne8l3AN3yUPV6fFg/T/YHV69ZuT4tRfpAjr/BXh3WmbDRd+EAU
+JCyVAQei1l9PbbOD0KKHRVhaLzCTk5GKk/MVWlIUas1oXjCINTjbalfbXoLF+Ye0uUJjvVrKOzZS
+aoy33yKuSc3XqVUtaSnW8U4viQ3PbL18Fd7tsUO+z+yphqddeD/1OKO5tvHXJZ71VguiHvafKcAR
+Vwjut7QIR22pNTkuLqDcadANwDpycxSNgJeW3bIUl/+/T1rbW1JOgYmtuy6W4DYyGgie6aCFPDmq
+66PWPhC383tI3hTKBorHvkDmMxLzbTK/qPlhRIGL7qFcf93KgFiDJ6g95REEd8B79A2Ottrr03zP
+F8kwp7RYTubpRNvmuuVNgTDOED5i+W4R7u42WsUzGLaqb9YxO/ouDmC5Eu150wocwhLpRpRHbCpS
+p6TJ3MOMCnyWeex5ExjbtlKyHmR+iMzWsc6LcFizACcAVstw1s+5Upp025cVmFZh9RE+WoVxco/w
+OL562uPfC79AOMKnrhEOS5eEL1AAqQ8wBREViIn+iK9SJBguf+A/AYgJim2kr5ZeX6Ir/5sFPlUg
+Z9zR+kY5rrqWGyP3j2TWwz4FgpeiL5/IO65m/8DLCgxkvPwT13ZWABzqNntIRc0rxHxgJ0NZ3uEY
+yqJW6eQaAYLUe1lrEa4R+lUfFV/IdrzikpeKFDLHt7pCOFxuPx2twwqKsyLV8DIyOaSw9eLAvBwT
+QMCelwIfAtblwYmc6U80Cl7Q/tmQLYzxZ5ho3WuV1nRKXNevqtyOk5Y/3QM56xE3BSMnizCQ2px7
+DGgWycX8QAGwYHACZ65V7JF8MC7GqYCX+7gpVgemfzCBNKKv+pGa2n9qFZZyExR3tPJs1X1leLCu
+5veV6cnV/yLrhWFau1mWa4DBCVbyMy3xheRKJgocOXVnyiMHig/cC8S60OO8CXDPaSQv7fVPWchZ
+PEGoCW7f7/Q6SzMVuLI1Tp3pFA6LsALbMxjerb/P57wfiA+6AsMaSr1vvcBwSUxxf9hPHbTiEmoN
+XYdzno+aVj0tT0y9J060cxVdvjChINjvBMDcHVs6TmcXZOXPABhlDd6ALyQeR4hs+unwC4QjfMc1
+4jLDl3l3kwBQQnIx26oyLMErGwa6RPCNEcMKbL6sqzi7QVR6bRCTdArC4HU+sNeFY5Vpz+5kz2si
+FDwlGMZSggS/hmUJvi+PPlpZg+HwOz5WYgDfWazHNSee88JN4blJCBJUKfULjhzP9iug2R6a83hg
+7uaujtGPW9x9ZgKwvMwrSzPbme4RKYDkuNpmWhapMd5B/Y7iFmMuUByAiVUT+MhFCcUwhZDGyc9/
+I5ZUSRTls/C+zWEFds6iofMyoGuUNN/z+jk13XP/LP6vOjYwDAggJp+qRTgqN+Rcr04faLn/av3W
++UqL9T0AXvUdLl9gBz+kg5kXYNbBB+a4cam5IOmGcKQsz5P5ToTzclGrflkHxnVlu373XIuGd7zW
+lsCY0q0Ar2VDZSVefXBtBN1ajDpN1v5wTB5BtwxHnO8Erteq7Q/L3VoH6560li5an8Hov8kwscdr
+Xvf4HiynUevf4nIBxTsMNvrTge0on0Ufzn3Z+b5nXpdZ/VA3ogLFKHf+2D/lZ4/tobld/iEbPINe
+5fIqeypDPk5a9Ftfa1cViaSUu0x0d1AQzLcMWTDU2tFLeSj4zWWV4xd2AMwH5K6YeuafwDDbA9M/
+HH6BcISPgfCFsAQvi/C69W9lBQWS8UqPhAX4Bi5cMCcAXpbg/7NKJxNZB8f6sFwxWoFhCvgUlMHz
+WxwH4dx2nwKHUD5iMVUQfOeGRFh/qxzuVe+XhbK1AYJDHGj/20/kkG4c4AEEr67vllYCYHlzRIHT
+G/wGNBV2x6JdKWfaqx96LoD+kNx9AMbZP3Y4Kxy1+STpymG+ygcSzyOVeM5WtyBW3LPFM/QtAXoK
+z0L/EB416+sNuJ82wHDSqjxQU+tbvhddFNhe7uFnuocUDAtfWs08a/VQWqUxhZYDPVuPO0hehSfQ
+LUVyAMG0cuncydImzdAemEuQDFqFE73g6Bphfd88hgeN93yeKP5ZmHggIjFl4Fp2H+Gi1+apMXgW
+9BonkBceVYWu+ZCvGSdQLavwE/3s8tBp8zPLG1CW9D5znLsOuJT12pSM4bZMjOWznkFZ3clJLRA7
+rL/JVoJyp4V4sw4zbMIwCCchWQzTywj4LVAM1EXjQ1PIqe0ehTN/TOWe5zEPaifuJcfqoSSM1KGA
+N5VB2+UN+M64AmDqZDzEDXQRrd90g2CcwPji+dTvEv+GNvnHwi8QjvCxawQBXpRPQKwLSEVxS1Lc
+IZIZ4kccXSDYBBh7Y7j8GeOejLie//IEvV1Iyq21oN+6k9E3bykCilAqgxUnCL51c91xsgJi1BYu
+QGtoX7cLN4gLc2Owg3q0nBEdC3SsHA/ng6BYgSkfmBM4P63B7YXq2K3F2i7Tt9cDed6Ab8URQmbG
+c8ZeyAFV6qL+UpCpJZgzVuvQhWyOihZCWOkBoPkR//dCqZxdMZ/AsOS29fOxXn1edlrP6z91exg0
+n+W0DhPtF736wA94t0Z6bM3gSbq3+HCNSFWK5vfo6XYUsyfMoq4R+cDcg2W4v0rN6se0zON3wrtz
+jlgmcmgUW3leYMS5S1FzrCHmzzXNmlU+7r1olP4bVuIXILjTKa9PYHcv90SboWQB5UD0m8svw5mj
+62fI3FiPl/VxtwJr/CUY1rKoso02yrYO5/6yBzogV39RMZnJYs+VhbXuoHAP+wC7pRu0yn0231mD
+d9DbzuN+bfmirzULIr9FqHcWXhLqBII3cAyhs90YuwG7fzDkzRHA+V3CEm/44G+Exn8YfoFwhM8f
+lgP8wgLEly8QDARPxiaVhTRDve+X7hB3WYCXdXQB3gS/NsCvyRXVw+8P4jaaWwpOyvuFAQlcKWTj
+7WbS/ZIXAj48odUGAm7Zec0LLR6Q87AGF+AP4BtKvFu0VzsJhmMer5xWCsXq7waCBfTWmxsg6XCH
+SMswH5ZTIFTuLUpTJenbOYgRYADgiuPQtwLDOqiqESM6wwQEnB9agnOlnOB4SRmOhx9iqFV8ldpa
+bT1pHmtbkXd7a88/tXIOOxj27W8/+iGv4p7zOfP7b1qBg2YCTFpZLm0oV1XS5C0rWlrvCCipxCln
+DN26zzWYYNgBurxsILj5CRNkWDVLAAwBxZQ4Am6eQLDO8XdCqvfc7E+1eGbTEnxOu+yTIA7EkvN/
+6rjtRH19GmURa2+vThNeUlp+Wc7lvcL+yi/49AaJknsnWpvDMRSV8To8dZ19DIe9nX9tFJhg1io+
+wXCdWlbfDRSfgHBXXjiDYC+FyHaybNApPBMMI3znC+Y2QMy/UddgqS09Z63nlbSd9mAO8giCt7FH
+X0401Niq2dIPNo9Rfzu2eEn9C0/uEfIsUzR/BMDwXMvTK1P/7fALhCN8xzXC87Vgtj0YtgqtQ4Jg
+2+PpL0OmMbEQIxjHzgC4vZJEGLQw3gLFl9MHbTHtHZ3z0eWVXwCgBtF3Uwn54fsIxx9McNDjNzy/
+NLPGXABGLcGQMcIW2E5jvVs8eGQPIDjSzvRKuKN92CK/+HbfyM+rBsDYQdKB1nyFR5n5MQ1aoQcY
+znVwr1+rtUJjTd/lOx7SnB/Kd/UR7m8bIB2KxUY4U1+FR0XQqvpenU99W3RR8z5zCUpqLTn/GR/H
+szVYfzsY5rnxVMBeDycYwMktYn40I3kkNG73M34AtRME06cRh/LpOxxzayghhbNVuIFiExr03D6f
+3wnnHXCGDSSrJbjW3/kIAJp1WKt9avRoltK1q9N0fbW6R6tv5ndLsH4uuQPok7tEnbfxpXM/OPwB
+2apkP9E17HvOOiH5YyXy+il2pPoF7yC48goM7y4S78EzOxq9nUJyCrZkGKEPSzCfGbGIq8tSAmKC
+N1ScXVKplE2MWVU3Jc+xaOlpKdZpP9QcQ2186tTeWn4VNifgVRAcuOJAezLG5XuEsb85wmDNTzhF
+TMZ5Z+q72uCfC79AOMJ3XCOIHhsoFiHZrqIMyzfY+itE/jjfBHE3IEzG0ngyzimOxeQGSwBsHgrv
+LsvyLWOgNZhxk23GflMwAEPh2wK1tWtXmc3NP4QEf2ZU1DyLV4qr3osCNOuqjbPAsOUUE4Rweyco
+zjhBcbe+QnyDCYztvnMEHGMODx0Ev6RRDhH8bq9QO1uEgTZreBlcIzy/1KuNdFqCc8ZegOAmjq2U
+x0ENcm3e9PYY+nmvxV613Ptw7hFyXfoh/iqP4Cm+g+L3P5P4AM62l0Eqa0DdIvgAI5Guy/5DgFoq
+SYLnl6DW0UFwuE+0ddbCbCfcHaIJvAPFuaEzbdvi5Lg/DKeitebs++QPDsPLz9B32qNxeWMoO3fk
+gWVzzb2kQ9GKP/hwnNLcT9ZesfK61N/OGXTv/Le6u2bJi9B6vTkWcfvPIVvLHpXFypieY+3Esgh/
+F+R+Ao5Hx1rct/2TzJ1zUjS9a9L3qde1kcmWCX6c87WzjmdO3oEBt/spT8HvkMPNRUKz4uw5/pwk
+nuJS1mtOsAPjBK5vwHB7h7DvD82l4Y/lnaJiya2sywB73KT/XvgFwhE+tQg3SzA3gwheM8MfWn4d
+CYL/3OH368v14ct9fSHOLvwf1CWCvsHLprS9hgSa9mRCw/rQxe1hEb7XJ6BXH9TVYm32P8KUalVu
+YMJkUzoVeggHW8B7Fa0PbNAlwuJBodvnl/JCQV+1HXkNYuO4T3V6Ku2AxKkwFASHchCrbPoG0084
+6y1hRvBCayGkHUjbOND8vpefsADeBYY7MOavv19YKs7Kt5GurBTEVbaJDxeBzCWlUkAHvynz7Sh2
+p/Z7wgIPCuDTjfUu9E4c9XpkuCT6lA6gewAOcxlOs7/9rHjmXVnYsNIFz1Ixp2J05N0KLtBaS5N1
+UoVmqUTmA3FpEEtLsYebktCAfkuSoDj22wTFBLz9K3QFdlSMnBdqX7cJKE5FZjXJu0yVTh+AeHbo
+A878gHUpezIutLowoiws+USgq8C4PrXc3yyx1zFAsLSl4Fk3MGHW88b59tAPhWT9Ebom+SbSwBnc
+PgLfD4FzDTX3hwozy31lIuC4cKTlRkk/8tU2+SraE+GrFxhaHapIk6nZSTEylN6dklfcI6SGbnRK
+IdIuRMgA1f9+TkoqLxxRFuBFuzLPB94ozECL7xdoDQ5A7BO7WILoBMQuoJrrOMfxQ+EXCEf4GAjD
+1ieDHWENLuFijvWZYYLhWNT7XsDwdiQwLoC4HAd2a/BV1mKIjsmf+teybYJgw58LBYDjwxYAUhZc
+EQc8X/3CTVChBACtSVTid2yOOzcVty+tjUgLEo3mBLQXLHysuzcJ4wsAr5HxnFvObwogfgQUGwgm
+4FTXCLoq3Hf216UfVBtsp9F90oeFWB7GKwB8tgbnw3KzFdfaJesQXPJKuInq9KIpQEqZjwJPCYyD
+h5GUczgD3w/Di7dQzPHZTPlTvtIFVDVQEqQNBA9r7sYTh58956nvfSt3sgKDFqvSpOoCoQ/LTaBc
+wBcFcMWqtVn8ZeH7e4RjLsUqXMAGAwRzbzM9yg100gDAE8PoPIyVRICS7XyXgwsgdsotT0FhQt/7
+FV145Ed7TDV+cYn3rmz81j6uIfx5fF1alHmyAO/0LhfY4RNgfN5B7+YipL2NdDZmuabMIX/QrUaP
+n1iFdz9h6fcYS1p0c79JPPeaVdzRwCknMF+bBkMBYiRPWk7yE2v3+VVD9MzfrcCVn3C4+T0cho86
+teuO3g4nghbkhSE889UyTBBMYEwrsVqG55fk0l8YuzEvQbQJhmEfvs2P/0z4BcIRro+dhEMiYa2u
+RdpoffUFSA3xEgUPq6Yv3EUQfLunj7DhaoC4rL/1DuLGQCgGKiZefVkg2OMzx1afXuYGqj0uAHiy
+XilkNuKx+z0f2Flg1VLJQG65c8NHfihOWoEdAXpvS1rzTIn5WkJ+TdIFtQbXliFUpAKAY4Bgiafb
+gtd7hEHXiBQLqcbYVzzS6xzS7+kj7P3XQGoq6qq78dlT8JmQ3vm5nGWrBZAOduEXDU4B/Vk4i+3v
+hcSL2ZfewpD/rYyCjMrdQS9a+oOfvcq3x/y2n0KRFwg+u0D0MigwEI/dRAAAIABJREFUncAXG+A9
+uU00vX/Ia2A3+lquEpZ4VD/FXMIpIxuLFCvveZPP6wJAztH1xKjCK+JRX0oECgXuL9kgp8u7zmd7
+I9m1bnob8iCbTN5qcdfjkk3rYTmxGIOyr+pqD8355N9zu9lj4ak2wqft7DhcFIgf+J7VlEjqpuSP
+ArukvwbBdWyg+QEQb+OR/fQyrzZDzU97dRKw/IRZLsbHjUCeUxadp2u29Kk7o2lfd6nMBSlNh1bP
+Fl7wcbms1H7gnZPCGAF+EwSjgV+9O73u6Hq+IaJbhgubvALEJUJKdv10+AXCET63CEdhDwaKe/fr
+dSOWgPSP0S0hbn95uAa443bffIQXM1zpEmEvadb9kIOZzeO1Zl5feUtrcD0Vl5vXKQM8mFA2RBOm
+cbAQ2lS+3DBAPYhnTos2LYurX/mmDYMAYk9f5VWNCO7o44Xq5xFwMO78EXSujG6BFZ9dPjAXk6GK
+CtIGRdam7J7y0hrswzp8AMSp+LwGUEOvcBQMHOBTuT5THkLegQaa2qvTgI+twU8hhf6TktXRvSrj
+e/IVSNGYv8jfAYPkeZut51/jRXugn89ZS2Y5UU6tlQp3dYRlnCcZoC4V84G404czTu8R3vzDm6tF
++QY3qzD7dQDKJ3eJuY8T3CZBlqeVlQsWG+V0RZuil8WU/FxQBGdLWluaPJWfmR6hdSes6koreSk8
+lM3qw25e+ah3BzercJZFpNVyfPg98G3bNRnlSgOzSJuHw9jtNGFQIte/qLblvQfBlW9yWgC/4DU9
+t41Dx5M0h9z+QjH8kolZBmHoUTCWd2lqGHyvfpucMTWbDEp+OXWQmmTQjTxadx313L4U1ZG2NyRl
+pIWgSSMWUBbgGYenZbfHIfFwjXDb3CGegO+8y736RcZ52Pj/YvgFwhG+BYQzGHB1AFzxAr98iK1+
+AYgNWEBX3SPifcMQhoE1xqwra4DfQ08ADgtTtMEuxx++ISy667GR10cukFdrDmXKkhepdRzyxHsx
+6nSNSGUa59E6/MWHC4Fwi7BZVQojyH7o/peW219v/+kdz9R3A3TSV5ivTfPr3lwjulKjqJnKRfrj
+3sqvfpUvsr4tAvQNhvRndj7n4DmUO8WYu9HzXidCFnv1HR0EhRqAiKXQm15g5RBe4JW/Cy8qm6rg
+XTXzdrSCBS15BBdvf8Mf2A5l7OAznHrM0+Xh2QrsiT5YJi2bpv77JmCYe08Br9ASDItKDcXXrL1c
+cwLblzTRaKTpmHLx5PyYgwzRgaKU3DlxmaaSHwp5SkFPecCOKDSQ6a7yWqeCLW1z8Bak6TO/iKU3
+zlWf4fn6tCw76py+wbN8G+rqaI1GtvI+m88XvX0+UAgmQZyebVJmlaP/r4nmevIJ1vcMv3ShUGC8
+ddCx+f3qSBzYQDDvmD5Yeg3cR1Bim0gf6ZqvPenYTsfizbqQ3YBxxmeV7PuJVZ/W1mUYclcXhTOu
+Q/rpeaX5urSnX8Mx+pDc+P10+AXCET59a0QgScBNgG8HwPnaMvl5WGvvO45uuMMdIt0j+IBcuEQU
+4yjDqDVYmCnjBMqOP3fs3MuWBdQWAJYhxM/7BlezaPoTBp0ak9nQq8rVHsHvErzhs3gBX+IKUQ+v
+qCWM9YTQj7Zo+ZqgOJVPdE2Nqpt/cLxPrgPipVpUGFX9KzUVGjLfJS2/fDDv7g/peY93jSUd3/it
+Iioat0Jq7fJJpyqu22vuvH1fN+iw0Z7Cpnn+mfAwBU/FXvbgOJ8l/Cc4+Z41uAPc27SMSbn9PPaL
+F5WL9z3iBbrqwUak4l7x7j/Md3PzrRD64YwCvPF3WJHBC+IBXlJJ0y3CbCvzEVhmhPJjrEGhsgIW
+tWwnVCE0mdwj9miIVPeZNu/ZfEE6DMaKi4mR3WWG8kzV2a3CPZ3uDX5+WG6V6TxUD8I98Ogon/NU
+bBUXVLV/TcromD7a3Vx3TW5nK9+ggdgGghXkwqBAV8tsgDgHF0w2UaYOXK3CNAIMEGzjwoFDWBeV
+MkdPoHdOEZts0rt1DGlKsue83SYsc5x3Ph6Cv1hZyUuM4QJ+gQaApzW4fILljREnMGwdPHfgK77C
+sd9+OvwC4QjfsQivjbrArILhyy3f26tg2DVuy4p60yJsF64/N/4PAYDTVUKAsYmvcLuKss5IdzGW
+SgK/HV9xkusvbtlNpmyBlivVBPDWAgRgscSSJ0uzGdZG8cvLMhwuJfnid9nlZY1eQq8+e0Gr8K4A
+VlfV/SAUw8kqfMnX5XJECnAeQG7S7ECL33SNGAA4+wIv624NoB9b76SxjEdvG11GMZcyFTGVSolY
+AuNmKxRryitw/KwTTuWnIH5ZwcvwKN6HpT7X1CftUAYDdLz72eAd0/zdV1jBrb4z2HPKo+9iuuPt
+2rIcY4BaTiF97AiGF9hLizEOILi5RsSR4He4QXRgzIm3AwhWJa3jjIoSuEgdHlKl8QIH9oY5DqA3
+rcC6Q8VUGqJJJFcBY6gizn2ClEciqtr+53ECY13/e+Q9fzL5wIuOnSb0ybM6Pz7W5GQZ1j3kI/0y
+pPIIcBpg1zTeCk6Ae7AKs8wJEI8yzx0OYmNu8v6al3z4TEF0MolUMydpsmToK8044TnRymjA2DBA
+72xoSt+uE+yQdaZ70jvoHb9Bb69Bw/lX/sG7hZh3nrW+U7t7p38m/ALhb4YlCD0ZhTRxwV0g2PSN
+CAHjwk0CFMAX8OVLwXwFKPzyAojuiw735UoQZtHFz7F5LyB9B0UapNK0EIK2FNKXWoVt+Sm7Lev0
+pTSODT1yZNYAy1S0AOp2bEzQ7WvO7hBMdSGB9Gu+oy7e7uKbNhDgn3n5EGIc3T2/nJfgFwqIBwiV
+OIZ1RcOJPq3Bs1y1G5QD2M31AxL4vAu7nK9bzkl36VfSV1/Wrdyy+DrH4uMBCgHB+bGRAFjVoBWD
+mTQo6i7HaN2XEtCHQOTU/zBQx7Ey33JPZ0RMAEZb73do4HFzROKQLqDLP2tfIvpQQFlvz9Ji5LK/
+LPZD7P+0cmmndT2xr3GbC/bXEpx4A7lAPjRHy7ECYwIhIqGc29EHba9toqdyMU+5X5E/15ejY44b
+de5h/ctAKPdJnF0X6SfVLsv7iikg9uC7XJutNQ3aH3Uro45gndbOyKFEA3XPSvp3aH8tS1/v7SEz
+GyCrXehoD7TUJ3HpixV7aN6QMIOOvOCjPDqV6Q1+tAg1rFFO+9sncvLm6MAmjGs9XivPQ5ogHxzn
+Zp6qpg4sb4dxnVqy8btwtgBvb3uYP+8PwvXfWtlTfScwTNpPh18g/N3QzW8bjbcwioljm4csT1lg
+xVi06FwBSM1W/GqAFQvEBnjGtUDnlwPOV7hZCEvDesVbbKCvEDRf0ZevFD9oNAK5y+tGTN0SPU9H
+l6miQCRFDZNAEQD9lBeeD6Acc5Sfg6bSCcs13NZbMC6PW588YpUJa6gC5HYbMQFy7632vwEhGXTR
+fdBOoKKGrccn6eR6PBQpkFeEqQhqvaoOj4LKix2EpOZPrl3TWUpnXfSpph3S35U/DuppA8xV/I2u
+eht21usr51v+IbzsxEndvksHrWkcATW04mLcaTF02ZIXG5DBhNDwKuNeYLgsvRCfYEAfhmzXMFy+
+uNBMK7DZ7i/89BNgXMJMxqu8r/gUY7wNQ4yyBMG8vUZAbJMWe3sA5G1fRRennN5B2hluqSuKnxhI
+l5EAl6mwwlusUfnPSksBLOZQ8k06sg/5MDFfZETadqdvtLO1u6WrQpZvyorxbeh9zlLmCIp345yE
+ngne9bkgyrCyH7SFjDdeUgE/4g/82EYiwiONBH4o3NRdzFUMNXVO1JFD0XRWUUB3bfORnkDYx9TX
+JJzFsEsXpaILwJcdgO+Jhg5ijWkvwHsFtyv4tXFefk334cj4T4dfIJzhM7Vc+6mzqsbXnvV9L0ua
+zJnW5GSCBRBvA64Au/e1HNX9sgC+ZS12X24H3Ndf0pN36a+w5NJK/GUI67Q3i8S3XXbGU59pRXWU
+b2QIPr6CzbEeIlynC/CFJyCGc6N7uKQE+HWC30rXOzrVQkt5FnCcytNKVelKdiWXIn0ELTWlEzqX
+vGCxo2H4gSZyL4ulNUgEfWKmzAurMGL+GXfxRWs+chGf0rVMZ0VPpfAAHlQXkvYwxFN4ZEHfInv9
+bxpZ5UYhXbhP+N8kcogTUtRFMcFnrVN+7CKbFc/AxAHWLMhpb1+Vlb+/jdusAbzEIxwNDLP90EbT
+6jvjTz+9nc0LsT6nVO0YeX4qBvIxbo9xO+B3+RBEWd544Rc/iSCzLrIudH7laD3dOi0uEVXGj9ir
+VnvwU8g5utUlGEa/g+gY4Jdp2fTLUU4my6Rc0BKwmOBWWG5bTTOf/ax8JoN/oC4OQCt4nLuiawle
+SLS7GI9HJO8/bcMl/x4WQ3muCUMp4L3v1U7tNR2vtYv/7MB2NlO552ykR3526w0QFs3cOp537Vpl
+IdNH9+iq8AnwfQTE7s0FIn2KYQ0c8yNalxEoYz9a9MOKR38y/ALhCB/eoUbfSJuU3+JqIUYoQZ6r
+OiQtALYUUlmDAxzTXeJCuU9ggVkCEd08X5hpCh+Ui8S1W4RZjn5974Xbe5plnTGGAALNTzlAgFtY
+gk0twfGHwvAGbFiFCwyv8SQgBsRgJBZhlSFRP3VpEzWJ8bo4fwLOtKjuszKtzefgW6THOZ+63lQC
+pSxKSednlx/B61/ST/VlX+tBLc3/BAx/LAM/3K/tFvI8+aM6nnYANnq/wTz5g1owRryhUZ5mJYyi
+/D4CmbXYS7Qwl89rOmS1OU6F7IfmzdbrFo+A+DqD4KuXU4twswaPISw2mQT2cYAP9wD4vnz8CTxi
+w3tYhf2+ExESDHvMtYpr5zSzqy+YzmIua7L2VZhGEQVSIOiNPdHAMOjHXSAYvluDr9g30zp898YG
+FpMHkEyA7QaGcbAW6wiljShwAio+6HpflA9Rny5BXoPi0cg4XfUqWaI6MNbCz25jYxT7wNi0RW2O
+1I35HAzrNB337Pa8mBrgF2U1Bkq/uNSi462adYyoSWjimXzrNXYb4PddOn/ewXD8EvRK2giCcQC/
+XXy0Cze6lP5k+AXCGT7UrLk/d6SSQjPjSEUD1P7WeBNUwRh0h3BDAN8Q5kJzlItE79fD0eW86OcX
+wr3CIP7DwNflaZ3d69sFVFOySpexJgC1uP1OtwhYgF/ZwLAEyFkBXD4vR/Cr8bIE0zpcvsMTAJeX
+rKMsozWeLjQphLieVWpXfZ07zjxFa/Uzy/W5TycFr5wUit7nv5fbIfkrq/Ck51lPrhFPFmPmHTSm
+pSJpcOF7YZu3F3vXqw0/0LUP/pD3FDpeoB+v5Ix46tLNGhyZCYCtNu0UJLmn6k7KKm5p8VJ//VK+
+taarbakugc6VQkitwAl0r8h/AMTLGnypBn7AGnIL95DX0wj3D4f7DT78t159yDs9d0zdlWDYbyT4
+cqt50emde2mHMDO4LMnrkpY/AXeCTgxIEJxgwuNNOREu0ChQaRdasyC7AuiSGu/AcPbXpOxTeeHn
+dJU4zMOcx00/OB/05Hx4Z8gHnj/VrZnTasz9keksLnD1UQ6stgtXxqono1Dv+j4PIhZz7GaD56Z+
+Vflxzp+hUaQfpoNjX3I+kXLmc/C7A+Berj6oUeX0ncIFkpfVd6ajrJV1+KfDLxCOMIXh3Lw8+tyV
+A2r0c2WTyqYiY6YeQVgIqFOgrhJIAPxVT9+V5Kydk60RoNG6+2UI628pQ4LRr2v1usAxoC8M3+ZF
+ry5JP+q7nk/uLoswQkkREMc4PQR5VmgFEKJCgmAeaf1tgJhjFNeIBMDOPCR4WGu0C6AaRdDcWtd0
+rH0C1E50yD/RvR0eQxeoLjJPFbXEY4Id9M+TWjj+A50XCQ3onso/AOVuAa483SPfDtuJL2p6VHKf
+1P+6ZGLVxwLrj0mcvJQGddStVwPyAbjqLMETu9Nv1fKT2Sqs0gLs7ONnluFp0fXLALUCqxOfAOYT
+rT3JrxO7Cwm0XXBa29jn9WGQmw8PrM7fcXV833Ghb8sSbMB6bSRCxqzBEi+oTONFwjv9O2U75YPK
+3fN5wwIMWoUrvkDt+kqX4wRuq5HLDtZh2We8dW94B3CR1uA2SI2YZllV8kH5PleWfeMbG2Qj1Aly
+gfcWEI/TW6ONNvVykje4qaGaN/C9+PQNX3cpTERjCLsgbH7ANvef1Tg4S23uZs+6fG7tSr4pLfrD
+vlfjVm4NVqDWsI5fEj+C4ElzLNcHX/zUrb/iO2xCt4e0LWD80+EXCDM04CLkAy3BgNIFrCUQEeZT
+eUEZoBbhi9ZeINwhxDUiLLkee62+zGZJo6sE+5QeE/LQnIN+weFfPGnZXggsoHxrocpVBoLa/7Xf
+ulBhPQSaOQ50q3C5SdSDcpsCvS3dIuqHDnjFEpxuhfoTFaaKTBVzNi9zp5zgOnDJH1xR9Gdt/6xB
+Ja8EptAoyL3np1yOQlPkrnnfrcKrIiq0vXyjn6zB3s9rim+M6S2QfDEXp6BNvK13K1Cq5nhum3yW
+0xYLBEw45ZYZqwgvpJpvApJB0lKWDUWvpibPjpr4Hqq/calMl6o6nrCuSFMbBQBuT7AMa/A1QfIA
+x6d5nntgykbUPLRz3AHjJ9FRgjNAsONeavm+Abvgdq/nKOI96vn+dLKpDINNzBvPT6r4BKgQc9/H
+27nJQPcIxgMkeMkXBbjTX3hZhz07PX2HV3rtX9Zj/BnjJ+swRp8O+SiLsOox5fuahxq5RX8Wf07X
+CKRCoTtb5/3SL8+8/xBebBfVU5T1T2utQ82HSnkx1VC47eVHGg8WYU1bm8chR6Q2a1RLedr6E7I4
+jVCiFNif6wCGGzA2VJn2O9H2MgYcQLB3sEwREuNX8fGT4RcIfzs8SvdGa2vJh1tSp1Hp1RU/N8m0
+AnsIoA6ApWlxqKFf8LLqSlxpaRlGukYo7QoLcr3vskTG1nbt4gKOosi0PPXZAl7sX7hFiBJZAp8W
+krCOiNJf4Or0w/ZTK3D+c2+gOK/0s68iZhxcsFxVXe10t4jF9KRqIW/lZ3gEXuiCU7FDF4SldFJv
+8JY5p5XnOueUGq0EaaiivI3clFWU39VGMPWsa3OhGKdFEPY50vc5+YgobHjIn0DrfXV73Y95wz3C
+SK0SZQF+ZQ2eICDsj1t8Ag8FxQ9Hq/mJ3VSKnpbd1E4Ffm0Du1dzh7CMG4xfJ5prcBSdfqBpmgDW
+kC9Kv7UQ/aXu3J8Og9+WY9LXRfJhxKyeirfxfe9Ermv0hxCmS7pTCPDb0gSXahWu3xngrnSzBHvd
+OSTBci+WWsi6WzuvAHH1tU0U4wdrsA9y3/piHU0rqhe/NvA24mzvFMfO26eg3hafnqNN2eCZNdag
+Gd2PKAd7rclJQw54S0l6iMxd0k+KCa3LbMvNXhNg40r4HZith9+mG4S6SJQrBMtbpJvLBes0sT6b
+HK2D4Z8Ov0A4wtPt662cE9Sd/DxPdRTNNOJDABkBcAlnpiGg2PMkNBDcWnMkaObY3A3XJcDYFyhd
+tLWhL1v+wauOenjNczcXGkuLXtMd8zZnn4+0BgNpEb69rB+bv3CE9dogy2/AbSAY63gNq/BmCXb2
+sH5rLaWPppxgPT8fIOw7dQ3VD4LsBMR4fM1vJQjPQCGBzDjyzoaBSihGGetdYrNbhVVVlPuHi4Zb
+ZeriQOiigKVoJvg0c2tHom/l3mdbs5U/zFy2dazugzYaIEpiuT/MnZ4zxAKijLin8rVRUdcrMKzq
+cFnQzv2cyj5Bh6btAAoIfA+g1wX8gmB3Wou1zKMo9JF+UwZYr0xsiG3uYoLgC+53GBEGACb8Sssc
+/2lD3DNzYj33mPI9585byVZd/TVKEw6jP6B25bnLUqcPx6nrw8lv+I6L1QmUq92ha1p6jHXm55Dl
+ITuma3jZZMkd2/grZcpwrysQieLz3FS1B+riLfrwds/uG6Tx+6ch9l8C3lSAKgcjZOXksui7tKh8
+0fpik/a+l+kKIVe49dnpojf3iJhue7IKy89a2o9xy7Rt59XRAxAPC7EeDb9vjfjfCY/SG8Bk3c58
+xZwogZMMgAGAVwGn4L+eW9l6QrAZv3zlWpxbVmCklZa+wgTEBsdtluBx1WUSB9ZtLx/jk06g5NjS
++6LAfH1cI0GwlWV43hbEbfDLy8px+/oFEL5uh1/qDnECxQ6i8GYRFsjqgmbKMsr+mwAWtY6X8Mri
+vnL1bFED24K9k+dnYDMVzyjr3o7MebIKlwW16FkrrcIx9lJQUse0iByUxEe66zT472W8rWvrx19U
+xVB1WRKa8hsgQm+xrrUXBQa8tAy7LvwD0Eg+sHPe9BVuA6HwodvDCeA+HrX82qn1FL+09ig+n8v4
+5cAfK2FJyTDFoN/AFaDXrQAxys0sb2sb1+gE+s593Dw+PkFUVkWO4NdK3h3dId7Etzzn2yQ824Pt
+Y2QcG20HuIDMi1Y4M4XW+a6s+imvlPdTCFKGoLODkGcL5yWw+OsH/pc9InvhtIyVtzrAd3U76DMc
+JUT2qb85hwTW0XpvkOyaVxndM3Op9b96n7ORoLfkTA0m+o7uCkF+PIHilXcAwaE/aRFuwNmqnAHt
+1WlpIZaj6fFh1P9m+AXCDG8sdL3sFmlAZEnNlt3KMK8JJyCviGBIa8YVHOLN6csf7x8QZF4ooEv/
+4SPoFUB8ZR5w32tT30DeUry18xP7NECnQLFANPdk5gmgVMsw2ykB7yXgY/gL7K5+8y0RSQPCa1Cd
+ItBSSWGnrfq6mqS1VGc2BJjiPVQF1ca+HtsaHVdvBP/escAvtuNaHy/rJPsqFpjmKxzjpBUNStcO
+0uLAdo5W36rP2ry9QRHf2JKn8m0LPtb1iMxeAJ1SYvPsshpbpqvGdcbCtzppeOEfKXEVHNqwsGQq
+bsa3PFHCmpcuEAvMuoBaewWAFfzG+fbSItwixySADqJj/JuvOTh/An6X8IwvcRIML4sq9/OUuW/D
+4INyqy7f+dk3bjPTNNY6b3I/+sGtclnd/XqyBmc8NpXmmXBBtbso2iZezMXWL9Q+3wZ1nKoBCLH6
+tVnVPfiRg891p3JhBVKTlWzbg+yRjTrdE5rwnzWAoDFLabtxgWXTADDqKVG4t9GZqtJ+yt/OnSV3
+hbymNGgKimNupwXYTAGwWnz9mQ4BxDatybbTbD/mtXYeP9mU/2z4BcIRPte5fow+1aJLqj6BzCMj
+LCZcFtZmFQ4Jlz58vFy64xbINdpwlPVY4uo37AAuL9C7ALEJIAZwFUhawHI90ayWiGYcTAXf58Ml
+prgt+wd6+ZnEUW4QMrU5b+7xwHhYhsNa7Fi3Ca9wi4iP0AUQ7K4SbJtWg3oYjP2jEN4BsQdd8d6R
+g/xlbi/qrfhzOZS7AwX2fvRqNSstC+8aL7Vm+AZLR7LmEM5Vl6iRzTWCzIC+aNNtIgL1Hk85DPTl
+LHxa3lqW73R/Vf6ZLqoGBB287U5yug8FCAnEGbSQBbFpPE9wOdlba2uvdUU/VGEDt4g+TjC8qp+u
+Ekvo+AH8ulqIk94BsU0r8pixXO9T8C3SounzvlWxBFyuY8isco0gCJb0CUF9rHe9T67uxDMWQvIG
+ClTmT0DxFdX1h+MWLQ0CIZuPluHckvHBDRf9Ir09p+2xTBJ5EN4GRmXVlQaGa6ZYtO5O8oxyXWWd
+LotdZ5d1eN/ZtNwaZBs1uBt76AiIH5jAUGAdniCtPk0v54aVuPUs5+5Uv43oqScjdejqyTpt7SHb
+6daBcI1AvPasgC558ewecQDBSQ+LL+sd5xrKAtzA8APtp8MvEM7wDqpEqa3Y+TzdaPn0ZmSQb21l
+dbcIlrHFoOscWy+Ih8d71SCAeOkitvXlBTTdyyeY1t+LZa7UHfGFurBAX4uW4kosVTdooSgYIHdh
+NmTBGMebyowyzuoUhwsI3o+4S7/yrRF8bdoFiQd9c42Qf+DRIXf91V/Yqm8cha81qbG5nLv7jqpN
+PGlD4X/GcXWGTUJU6kAqEn1ALkGO1zkJ8HnLWBYhwZgosOq44cjE0p8EfiffOa3r4DKxDfYb4Xwz
+R0CYf7vK750wFVPCAMt0+Ql7rA3nGfjMAlxxd2srlK9xQoFa5Re3AQis+tZRGkFtWYQtAK/blfEF
+diU+wLFRIB3n9DCxB2zaMLHfxcuapVePbnC/6gLeLT5CZOlv6yKDNlCKh1uyuoHk7gfztodLJTSA
+me1VHQsc1FoRLBzdIfxkGS7QewLMkLECfazaB5zS0mdIHW0C+vbPevuMiKxzG94+XtfWysGNRWQP
+VHWjjV60EUzdxiyqin3U4GmvraqSOsQwZASeqsTU0s39tnXwIZ1r9Fym5jPaJsBt8nhYqZ0PzvWB
+kd953EFvgdxG80nzcTx8WtkewDB5btIeVMe/GX6BMMPHis+T7xmmoi2+U2V1wBEIYeMhEAMw88ty
+S7KtAnn7iO8Ojq+r1UYsELJuE/q6PYgOfGkx1rdITBcJTyv06iitwcC6ZSf4vIGtAftytgpgIoVD
+6i/kcDbwSx0z5e7+eWX1C0b73bFxmyWYR4TXs6Pmlyu8pF2Hs95dC8BxyUN07nXGzlLeqa94TubL
+tKwXrYEd0KrC9fH0gS6ssM7wHGOI1vEQHRdoguYskzK3hG8JXav9ocrr4fbh94NvyVmrPyZ6eATI
+PuKjgdzfNtItb2XWF/ao4mI/B3+d/YHjluyxYu2a+B6DyjlZN6VPXqDAjnEDFpgOYOt8FzDj1wK5
+bhVXQKwA2QiQD2H385Q57pFMEoBsp4agWnx5rX3n3R3CzcQaXGBYDQ7VuZI9DemiCMnOSY49FBu0
+3rGOps1pdWUl820R+t5U+WZQfna5gd7oVgLmFqeVsg2rdUll6XcAMmZ6zN0pTJ0oEkUK2V5aG2/K
+7bmdBvaw5rjeuc19UOtJ2fgKdWlOVRMQV+p1Oz1geZinJ5D7QZlTLzea02JNvuSDyrHLm6U4HpRL
+/BEg9ykNtQYXIC4/YQLg+RBdgGM7gGGNG0Gw/VqE/5vhsEXcxFR/AAAgAElEQVRfFX4oXbergdJt
+udUNDQwDBL7FfLmjUypR0vl6RRE/LwwrQOrcngvl0fdTQWgDvqRdOy0twpfl08jAAsNkciCAqpfQ
+7NiBwqsmykc5+gSn8sEOghsglvMVCF/uuO+yBCfNxUfYcbYMt3Xs/mtrPjwbrgfHIG+WqLKQ2MRR
+ah32UWDy0a4oelTVhlqBNZ9tGKi0PXPXkkTcJA4Wllv32lpzi6ix70CxP4LykS/wywn4ftnJj0fa
+XIi/6kCqRRDq8K5BWyfyVHbCZPGoqFY8P5edDfT8dgvGZPppWYy1afHUtxI3623Y8qulm4PHK9EW
+GD5Zg9UivH7lTnE9r7j3iG30Q7j9IGc8j05Q7A7PI0Fx+AcTHLNKAj7rluCSR51v886y9IDvyHbd
+HsdAIKavLRMwPFwk6hkJvUu2Rt8swxJn82oVRrbFMr6B370MHsuM4Wxx175omSE+CKbJ6fvepFzd
+HwKuuP49BNG11TY7KMC13aF6qm2+L4YT1pT5XsdW1YTLdizTY1KXz1LeAa80nz7DAXoLvCPnhvy2
+W4KHFbjlM89bevkR7z7BVeYAhm3ELcD0f8Ek/AuEM3ymALfbsA8aN6GA6C5ggOHITzCMEsxuYYUl
+MA4wnJf/giIt3CMI+vh1OIJbdYNorhGOdJcoFwmkRfgEKQiIKST7j7eQCnoVEOxzV5apcrlgXBVC
+E8bxK0sw0heu/IH5ev3pGoGcDzgSEJqLtR3IspTmiZcU/DoPJWmnIpxuEC+Dj2ML+xock756S+C7
+zooec8xAswpTkDKuX53jbcQEsiZxDnBYHzoyA2VwdfJF/BGXPoWXZbuf4AcnfLsYlVrqFuHnui3a
+nx5Pn8Ik+AaG860Rn7hLsMt0jZhgmDo0gbi4UKTFes2WheU0j/QNTsBrDfSmBZhpdY3ge4S3GXsz
+v31YcmK91byEGn9LaCUIjjtZahV2W5bUtAjjAfzaSPcZB3ld2X7z+xrnFfg9geB+TJHuO9hdoHgR
+V7nVIJu83MNljVtRPNgTS+2uGjls6p9Bk4NEfKR7/LzE/qKAtdi3jFKncLy4nDLqsGDZlYPMbQKr
+06qWvc63qQ302TlmWqK3Y208cUdQ7tSZypHYQ4bd+st9UcDXj+DXJt0dV+CVlV+fT646o6ztb5fg
+HlA3ip8Ov0CY4T/ce0Btn1fHWTCd1oEEFDeWa8SN5QN0pfKL42XxkNhiclqHnX5xCGALAr6mMxCu
+dPi6FoDkJ5jLIrzcKtb9ueowx7B+ssFeTqhnVMEiLcIsc3NTMl/amqD4vssirBZgTadbRI7fS2Gq
+Uo32y6oToCGFCxIUTvCbAE7+CuSWOeigrOe8D8k7LnHW4RSMqAsulouFr5VYZxMs69Pa9SBdTkQK
+1xqdSaMC2BIMzw6P+Ivwqsg2V59M3qFMqjc/0N7Vy/36shitwlJ3WojLcgwCKK2tgV0caLEHrfb0
+KhLrlmAYua68TT5Bb8tjRrg9EPx6+gpP+rAOpzW4H9njbQ5l9s75c0az40tWfi2O5N2v3Lj3VVrd
+LT6oUVbgdJVA2RLURaKD3+4jr/yv6z8eN4Tug84nZIgJii3BgfahPRuhNBcLcDSpNgvu/dQdY56f
+wC/TFhXvtLkoSni3Gc/5g6vf1LGfWJJpTHaLn2SUyDHjmu5C6lkezRxvefZUbOYn6WAlPsVs0pju
+42mWbtCHOM50eXTRwoKLsv6albFsWomP8dA9GbdpNT74C7MdK35UQPzrI/xfDt/ZjAkstlP2FaRi
+pO5LsRmR9B9zb4yhoNfzaYglCQ1L7sORD8slEEYHvdfVLcDNVxieD9ddUvam9RlIS7QDCcg5jrRi
+z3lIYVSPpm2TZ1mk5s3KwptuGI22frcDduMAfssVJGnoVmGuG8e/HhK07JsPPzJql9XXAg8uA8lx
+jOWfoMu3zAP9RPA9aVJ5u+NAuCXjtRh/9tU68PW2GLQkjhuTAxxvGCA7MoCBHg9z9DwJFRokEUX4
+LnQw8r6db9XT8vcHY9T1IOPNUoUSBJvrw05zITULMkpx8ELOYnOyTYuj5ifP02pt9gh+sf0shMUp
+73q2CL+Z+8d8u4Of187jBvZL9jbfGEEAfFX85i/miGAYWA/+TtcsziuDx5rUg/f1Crxcl8bXDW42
+f2BesKyv8IXsF2txiPy3wLf7CIfxZJSP5e7ANkWtVw9bmerzHE0ZBnSoh92R4nQIOdvO7uENf7Rp
+9gGGZxj7q+7GWE0mq/KH8R6CbQXsENsrsaeyNnNOpYF0bziBXl6kUYZzn6e8XJNBOcDJSwDKeJSc
+FuJ1fLAG529Yg1nWrEBulrMBgE3cI2pv/mT4BcLfDL5Feu5U/5B4lxlL+rRn4rDA5h2CNoFnAlOD
+XQvkwa1cIhw7EB5HXrXRwX25QRjuA1D+CslAsecXcN31pHqzLHgOZUN+eb4SFFvlpj1MFiyswMMN
+w4E73hpBJViAGB0YDxCcH9agQs1+eVmDU3YQZNYtZs+xGE8T5dDg2jF+JpwCR9tPUz0iTbdq1V+Y
+FoDOjwKCVYPbAL4DHFfjowxB8rzVeNRPD0rr0yDjnbX4Y6IHnYe9gk8Wx3I6Use0utUdQrylBXiq
+NdgbTZspmh9omWabgmjKAhxp9nkhZPBVjFSMHnl0IygwvLSSxvFlZ+D7NUCzzMlpusfUbWUmCGg+
+TV/jGB/SWG+KUGswH5ZD/tbdtt0Fi8q3+cwnhfOcsy3XfCorq+8lzvSCpOac8Sv6SACyWYHHEQLa
+TkCZvqEKJszYb5lbsURaK5fS7RH0GTxl4ikc9xdC3mi/HI/z9lBFz/CSKOnaQlmUrwyLopJnUkbv
+enWpO2JjvIdp2fnZJq1bf7kODy32sjZrAlSeymV3YGHniai1L/kRIiAwQdx5FvC7A2Ch+Q6K66ty
+4Q+c5SsdN5420Jtvj+A5/4GK+NvwC4S/Gz7UlRPjZdyEiUWoLQajUBYliXjgKKQkBbWBgDBen3MD
+X5cA4QFu19HLfcK7tfhybxZhPkymktZzDCv/8hqPPvRnMfizhUdVjA0A7BJHjpSbhKC4u0Mg3xW8
++QSTDqAelJu0wkBuU+nN1Tus8yn+VOZVucycldYE9Ru2feoaaHKBtK4jWevPeLvY4bhfWI6nP/H+
+/koAG03y2MFpmflkT+23X1oowNXv7ehOemrnrUKedGEJk+Q851XeKnB4OO5Vu0+ZbEc1NgEutbFZ
+B8RWADldI+Lop3i+T/gCjA/TCfAN+qNrxBPI3YdyGOy93lPsstmvO5xnDbgvuK1PK7PPBL4wyisF
+wwWcfDTZjl5vQSEf8QG5xerdr77xOfr6c2nU4soLE0MAYqil14sWgLf5C0dbjK/z1u3uWybSsi/W
+gFkWsQJNmWd5hqR1X5WjT575xty/3XF13UZ9Dxwv8OUcKA2IPV+7hNZfnw+Std1orZ4TT9qINMls
+h7Jt7ftJtucIva/NYzm521ZzpGMeaaAB39lfw3Rl+A6NLhH1sFwB4O4a8fT55Uv7YWodfpIQ/174
+BcLfDS+10sdFHjVtYgns27YEVGd28/UaFFoWlpANq/JVbg+XW6a/GjhGfIkJYREGcAHWpDDSXOFC
+8zRThNgNmisN5fahtwFFB9fEvZqodE1QT1zvIFfpkp41btOvkqxrgDWCtLIBKVpHOQDxZg+ro4CM
+VHwCNJg+ayntnD/GecHwEr7LKd3q9QIce9UPWHMb6conFCHR2OlWZOuy5MFr/t6A3WPwLbKFE9DN
+M7zTSDKJuAKiAfTXkNdc8QKQ8QmCs12OUzpgg0YgRlpaeA7nVu1D6SkTyDTPPIv9nleOd+2gNSaH
+3w7L7zzWdtG9lHHH4MYx/o1mjb6xP10htH/ef7levvps0ql8iBeleOtXQImQd25Diz6Y8qnI4pUk
+QI51d02bbI9qY4nRei1a8wWOtlfcjxZi32hsXTnBz+OB8kPJM67ELg4t8/RZipIcIgOF2dgbhXUO
+77wYjCTdqZyNWXzD3AbLO6ipV0D/1LK+n2hX5kHK9TZttmYtu0Wez939g5WiovXsNdzXcs93VX5L
+nvgDTWQO5U4e4S1e+0doKPCb58ReyHk0BAax1PFqEc74ZfKsraXn1U+HXyD8N+Ek5b8T5FxCjaYw
+bYJgggue0zcFP8mptxrom3M3mufdTbcOjue7hk+S2du9O5cyJm+aQDygshTBxc1CQSEbXUGwzXcx
+KhLJuYm/HlBhWwMfUW9nei9RaZPZ7BpBOqjKTqxCKhjN1kOMdifYVdDbALD18adiTVAs6vr0poHB
+hIp5iqsepkWmp8a/avAEYtEWax2381MXTlDL+g1Ci5niLe6N2T/YTKfBPISsXsAi5ya5QRXvqPXR
+uBUWQAParfHFA3XhkHwhO7vhfram1voBcE3zZRxq4Vc+eKU7tjxZ1lzifOdWak3Q7cDvPn8mJe1A
+e+qDS2pbD8w1klJ3gOAGgIEdDHurIEGNpJ+ONsonYPU1SUsG93heAJnIcFdQqXVUuua9ZN5nQPd1
+3qQRQFk2uIu1pDWRZ1veYVVQrm0FktdfOV9kSOVKflW702STFs02mkUPeExwKy4nCc5Cxl6Sv9KV
+X31HCzUX+546p+0hb8yv5vmL9lWJyTatjac7jKFrvlzKEwiWPWTN0tvzi77Wt8oKAM71QJUzGyAY
+glmsQPL5EYN/NfwC4b8NXbLP5H9cpSoYAO2uG7eXfvLYUMB3MZQnAHbr4DjfGhHxtATDMw5gPZjX
+pLAn8AVQ1l9KpAvhn7eEpKcVObYJhYgcN6WUOzhG78RaC3kQqK3/ww0CaO4O55+4Q1i1VJ2wdjQg
+bg9DQK+NsiiLodm6jRuffk5gHIvTwDHWYm20jSkoZgZ3JMMYGkBtgypCuT8EJf3jiNKAQkbhRkP1
+ZUUD4pawgFrj4hEhDjeXtXYdOqkP3z8aXGekh9Nt19XNDqJcy8uwAJSF2Avce67PspxvRvDWKBup
+xnr7le+Dpg9JPoWpXLsgQbGt0I39Wbdz6oI3IVZZiPkiL9W/NDQ5N+MAU6qii6t6f+edluTYJysw
+Nz3H4qqs1bJVv+C8RgMgYHXKJPqUapz9smQoy7sDxXdFYz8s+0uAWPYD8hJKrnv1Re0O7rtI9hGf
+c7sDPIGrKoeFecQGULNhfSU5+S4ncN5GKxuw2/mvD1pBofJv9Xutv759QwHvs0W4eOAa+cd5GN3e
+gOxD2Vfn7WUPeX7Oe6q/FCH3B3Jf7Hnk0QCxBMEhi5oF+EDLvUGaoQNgK1oDufHTh+QSDIdV+Pc9
+wv9j4VNjVgsHgKKKYsZ5Cq3AFLYsQYZMK0Aw1W31JOYtjPYVmOcKl4jLxE3CLOPLiY6KpgQ4LcTU
+l77uSYX/8hKUnu4RNna0CpqSgFP49CeBazYWjiKQHe4RTbmOSZ6LZFXKheaZR43IDln2uT15H0ct
+Z9cNi6uRaRVWy3DmIySBaoSgL7ApHGHAtLxOmHFSgkr3AVIKFCd3FWA1TXM9eauXa1Tgr5DAZ64R
+XibS7O1RBH53j43xd+zoW762c957Eg+CgmHYmg/PsR+xZ++DdMq1gy0ut/xP+a+CdoBjOnZK42pb
+rJ1l+dZaFr1wAsV1LFXJu1vRAygI5oqXTCvgrzZGT4Dum1WYt3eXwpY5yi1DaxXqTQrox52mgJfC
+gq4RwdMxoQS7S2SUWwSEliMVRqh6azuK95lYeledtTL7p+j9Ia5j2uO1Pknb4t1FQs/zHJBSk6NL
+ZuaZ5WK1tVeLvuo6bELTiAhtGlMU8Ja7wzOtQDJFbtH6eB/mBz1s82yVel3WHvO2/KVg0R7NfCkK
+CFTnL+oe4JfHDSTjGQQzn301hDtE8LjcAB0gWI8WX2y3tBT/dPgFwhmOKvgh/KVmhu5jf6APBd72
+pSXD6nkF1Ah8fQPABL1lDbb0B3YLMByNukpZBb6Gco/Y3CZCV11WZUNwTUvPLlzxdvqblSUtZQoO
+VrqpcN9FwCYOonGXecxdGzSCXsYByyftc+JzDa51ZRyAeAHgC+ouoWCYIJiAOkX3JhHtLQhOhmkA
+S0oK+EkrsFiAQQuwlZW4p2WqQYtu1HF0jWDVwbfDilwde9oVn+zKpxLFG7PEtnvnfprZjhyr51Pn
+eLAII+NtiId+Md6svO/iDvg+guf++4GotBlvVtc7kJmC4wmKSy1z9PWcuXbsNCfx18ir3caY++sm
+CL5H//rPoMo7fmOqbPyKtta3gZ2cG7pDOOpiPkCwqSHD5PnPAgfcNzaBY5TlZUV+Dh5y8y3KnUEy
+V0TjlnXI1P9VfLPMZac5dzETWcwbvfLIKTN4zoFeGKSeIM8nU5SeqwEW4JruDgbb3CKulg+kWwSq
+XB/BSV+JRp56bJ53TM9ZOpW1kebfodSAnn7xKwtx7Jmo6xUI5ppyj0zazOc+t9APPHLedxAM8Kvs
+CwT/Piz3PxFKgAO5SZ/1kpzDMIFLL5dkYqFRvAsn77fKAiutV/J0twgCYAW99Av+QqVxSZPT9ACk
+hG6gN10kVp882qOjWorBKVEkKaJTpItYH82SsvayWoaHO8SQFU/Lk9e5TSINjRB95pP2CxT2NMsa
+LcKX4bov3AGI1wdQBAzzX1qKo402GVa07dbDicZzBk2Fpeia0PsrTayV6VXYm0WYVt9o49B8+QAz
+s/ozDdtrDI4d3PfxKHDahtoGKBmPt2lUieupbzbwqGIDwzGWhfHXeAoGeU6FZQVVmWsfPozbU39l
+jc95Md/T3yP3l7pBLEi1LoLV6rs2dq1urGbqVqsxyV7KsqbnxV/r85YfCUmLuB9dI7q/MISRhQ7u
+pJqaTIuo6Yqd8H7JHvaH5wBiHQ4Bl9Ip67NaNxsA0elh1rlbXd0ewa4/A99pz9ewga4parCLvlN8
+yQFNN+cH6LCFO1oHQppw4ZPY1IMXoU2TV2EL4V2Sclp/8UjbLMJC66Mdc/WGfizXZ+ixLPyh/Zdh
+arpX+6T/Fj9TUj2AYNmDxnJ5FD9hoCzBxgsQ5PEJ/C7gG26bSXs76H88/AJhhvcct0KTMK+U+OHE
+D4varNZExzXZWZ2+4OIfXCDYbQDi+NE/OC3C1IOAuEbYAxgmzasLPP8OP2T2+0J8EASHOT5MepJK
+1XpKVwVZKyf1IK3EbeK8xeavt1t94WuYco7H/Z0Ep9zhYdVd5AV4YRZvjqj09sOkoegazc7ynBMv
+veBFKc5pzIsWuilwrvPNHDjMueUr5tpnS9PV4TClhzdE5PtEp/Lb+MG3WLMcPQ32Ff3T7Yrs7oo6
+YGXuKzAMpFuIwSQ++y2PxHo10B4M+iSeh9of29H7ujxOh57GQfJdjArBeEvpvqMH6z0HGfe7niuA
+tbWiHGBjeXdFQbD4oqfvNesyDBA8rcLY0tw2u1tEhyUmP0i5DeDo2if49RxhAUOx9AI59+II0QT5
+FfNzC12eOV4yPeO5Gm/jWaeOAU3E1UxYL9PKaV47V/blwXpnY2NPlakgOOdLeRAA/aWz7eD5lClC
+L2vvOloeXz8U92QlbsMd82ODuOft58xymRa5vs/i4Zxg8aMye1Rsz8GiMss4ziBYwG+Pjz0Gr7nO
+uKexR98isfkLXwWG+bCcarSp3U7z9Z+EXyD83bDhjTMw6cW+w53YV9z7JmtVSkZ/WG7Ji2YZlrdC
+NDAs3aTFON0egJSyy+pr4gYR9TWrsC9QjZR1cquD0uPRppVhzy8FtLYjQbC3MpkHtPhTG30puTtr
+TmnpDUmJHcDqebQIX8sS/MJXOH8IoTzVsmmavwKQNUeS9yKILhFLb0wpAN7eV99dvv0D9H8lg3BO
+QynZALq0MNK3uKaz8hRYg0/aH8cwtff3xGGW+MYW1LJyMyJ91Ot2t8f2j95bAVsFe63KdrWgcuMc
+t0nPwysHicN47IMjHLi4mWkJLgswjC/6qpGZY7lLkeqXuOAkbKi3bQBrT7BZ4yWCxfYOxxIrwOTu
+62L6ybrVAG+8YWJMs+4S2WXZP5VQZSkusEv+pnLX2jpbdmCcf1Neb4I779SpG8QJ6ALDRcKfylm+
+rWf0Qpel98JG3hCD/QTDlpWgf2+gUr4ThfeWWLIHOrABYO4/qBxdx2vQTg/FnWh5X06aepyrp+k5
+xEmZ8ugkwSbtuU6d80Fuvxf7hvMIPIPgLDPKQ8pYja+D37NVWA12RqvwBcz3CL+ez38u/ALhfySU
+0vuOvn27sFrh1KibICnmIgDWtJvla9MSwCLcIYSeoBgoAAzA7wDI8GYlpoXZIfEAxcsybENI7kNj
+8BbrmtolTRCcpQmOucePdQ/3iZeTvyavADBq9w6L7gTJV/oDd7/g00Nz0xpcwkNV82nS9Dr8E44r
+n2pacAoEB9ho/JSZ69wArgWUo49h2dVb4enGIhZjWp0JSPp4uL4QBaOldKH7Y5RHxar5iYdezZGo
+7odiCoYJgOudskxre2u87fqYdXTG7f3wPe4VAVq8FNK78KlkMt53J6S6LgHDDlw3/F4b3+LK2Hnn
+CFiWXb2t2RCFJdGMj2XSx9ZqDikv5C0M9A329elIsRDH6BzIdwxL0+al3Mk3ahXWXdZ33LL2W2er
+4JHia0Nd7ClKmmCnM77U2GT5sBDD+/uEo+mT5ffJKqxtTWnysDSSrsFr2dWzuZGrwG4glovmeVLu
+q7Xeyqb1RhmhG9JyWbKD4ndaf9dKdCvxuVyzEvehb3NX49slVZvTh/Nz3JN2KPdKJqmi2yzFGxKu
+U1RRLp6nXJYH4j61BIc868D4ZBUWAKzxCwKIw1J8/bpG/JfDiTVPwfuGxTOvZvlP6QJ04h5iba4m
+NFGSXeKKq9QNYv28XCDip6D4awpoukkkAEb5/IpPMF0q6mG6ii8gHGBJVNBpRjw2o3rTsVyCGd5C
+JUCDPhA3LGQ+gO9hxjk3J+Nr0frEnkCstbiC4PmQ3AXQbUJAb31cA9mBNX7RTBadhc2RjkGdk41P
+HWhgV3xGCygvLeWc6wTDAK29YtcDUV/zBaayEotxvlkhwTKEj/VxMwbVLHMTyIBEKfrMG8EO2f6U
+z2IuSwR0a3fOE891+SvDm74jB1Cc57fFQk8flv+VDMqpk+Xykc4HkxRxRbo+phH8S29Wm6DYciz6
+OsHVBK28cgTL8YG5AMnz4cqjS0Qo57QAh0J3XoDUfFof6vHHWUwwlJM6rcBB0zsgGDzDuUzi4KiN
+jWUvYn8wziHguKVfuUbst9ttJHq6W0JV9MwKtpq57Do4GftsN4vl7RPLC8lkVgHGq1/B92Zt/sxr
+zV5ZhD+mWe9vi9sDXSJP52baD7RZzvcS61wvMHuou/Qktp8nYK2Tm2tEnLc9EDfLoMo0P2GISoQj
+3SCSXq9RW/ik3mpF1Vi006z8u+EXCH87yO7cGPYzgJIP0Aw6axGJgCFvNau16sAAvvG7iv3pEqF+
+wsXFXnHxCXag3s5O0DzSfh8AscVDefEUH8er+ld1POVbWdGkJG89o17aVLrPpa7Dg3MopQhJizjI
+rdzdE+S3gd2Kb3mXrXch6i8A8P6+4FLDBq0T/TgB1JtwKq5uEH6gJQgeYBeG5irBi5GICpBSTeZ1
+fo6wspBDoluFZkbfUiGVspTRRH2dtqt+XeXT/PiW1886n2eSwbnT7TnPnctBpdYbVRjhchIVVE/P
+vm6qQ+e5vTcNJU+4tXLZbiAsvu0F4najXZQh1juswe4eflfBTBf3Dc7HqGfxkB69H8mTzQocvxtB
+AxIE576OW7je58cknmvhstWUDgHFctVgjS/pNnFQBZMbG2P0tSfMdnRrcKXLDYK0t2nWPQdtOk4b
+6TE/1st4o++Ky/rZOCW9lUVXCFGvCQheR8+TrNGQfNStvwVqy/pbVt/HcrJmnReKoHO3lXkTf0UD
+NrY4lt1kiig6cx9iJbRdXBw+uhSlnH7tH0y3CKZbXh7VIkyV1n2Dyz3C6qE5Q75H+Nci/D8SaM0Y
+xF3jfZ7dyxSW6GBRCzXhEeW52eN3hTWnuSwAycIKik6hWYOZNmwAWC3BM32FcGObOaSwAjgHxzas
+KzAOrm7AqG8wJ2e+NSKhXps/R8dTpKlwOVqHM1KgtwPjev/Lbg3eQfCTm8Quhk9icKrsom8yUAeZ
+9MVYBEh8ENFDuRwtwvkgXD0MVgDdwNXoiqoIdI3YwTI6INYxe90+z4GoRuJYNL29KLdOm3yuM+gH
+WuX5RifPHuuQ5hv20bIb01faqkA7wbWwjMnG6LYpAXJT+Zwe7jWtIr/coKA4Nr0A5HxIig8fZDm2
+R+RQRwMa8OW6+zwGCKaAePq0sqGswWlcSIvwVNp9V7Vd5ON27pikRi8/IJlEtPrbWrDtlmmtBq3y
+inFMi6/jyUoM8NPKpDF/b7FSbSuNpdLOKpCedbSteG5oo2U0H+bktuV+NZla8Rt2PW/R8g0euUaW
+8ffvEsZOs8pr/T2s76nMU/7TtDS6H2iDnmlHuUPMgmSouDj0lFOhPSW/7w1P2bNZheEy1zwn4uLy
+1n4GXBaW4ZzbVHX9C7ilPn8twv/18Onctx3/AkZSyTxXsDVf8rBY1yRT33y0gLI15XaFslO/Xb7K
+ZFqKmwXYjHfdy5TA/og7BF+zloDYAL980cUSfG3uEWtMickOCrnABS2Ti9BsbQ4BxNzz/QE57bv+
+dO47bcyzbucEqpC4HtcOnhZhu579hI9WZFqZDKjX453U9oRWRfeNthajqW2d61yLmu/+Jgmg3u5g
+UsGaC75Sbb5L2HM2y282+TfOPfsRk++lx4aeFmDRECfnSvN13CONMWNIWi+juuUkm7fyMkUsYaOs
+9tNGuqJVOiXBqCBdNbKP1uucbaKm08fUtYuXO94SoWD4Cnq6RhjcDuCXm10twq+Ary0erU8V13G1
+EYpWAHC5QdSvfCTFH5gXUl5D6zuq3//RAmMXjRm1KCO+zfsyDnnyEE4MF7QGdqPipI0yLmWfXp8G
+lF6ZXVrd7Hmmg1fmPw2HW/URzR1obn3csb9LZjgmKOkZUS0AACAASURBVIbLGIi+vKy63doLSQNn
+669Yk5Mm3T0Mu+VLwRN9m4J5h0yH/wl9iIEUyQF6j4veQLFqz6qw+QdjB8Elrw4AGQiLb6QT/MoF
+iiEs808/G3dSD+P4l8MvEP5PwtSyW5iZfiQ39Z8AxPobkFIgWJfqWgeZyksneWiCAsGUDEgLcZPI
+dHuIfOZ+3d3HmOde8UlltQRfAYL5YY+2/Xz1kft3Yp/ttn4CXQLp+Bsbu/VfaAWM66/+lBb6uCsK
+KrEEqyvdwPDVAe4CwAF+JY8f1YC8R7gBYLZDsRxrZHqB8qydz/LvIU2g1cBvswQDZT1kmRCkTFMg
+cr1SITkZGA0sK+LKNgUn+Iq0B6ZCAKd9Tqy9BDqltXe3CFGXjaY8MbdvvfGh15HzcqCTI895M7fa
+aDmCoGyk869r3aURj33Vtkyqq+kSMEz0gdr7afkF3AiASTcA8UrA5FvEelvco7+Kf2FbnOcRFCP2
+nltdWC25V/y03hpxb1+W4y3eBMHuBYJlKre1Pv1yjlXAegKdUv6VZ96hRV4XHNak1rbktfaHQ7rl
+vOnyAJytxE/0asJahzI60F5uP5WFvaJzetb1VG7qLbH85yRwcow87NhBsdw/C/4J6YngUEkfaBMY
+W+XpVGl3T/Gna4TzsPf5UXkAiW4llW+YPvC1guJ6DSH3Bc/zBMXkaQBoQNfR8p9AcKW5DhK37gpx
+feP30+EXCGf4cPabmXdjwwPlVVDtVFXbyC0wbE1vFb6g9SSswrbOubDASxprLEBN+v4q5wL5urSo
+mr7Ablh+viOvALAvAAzU2yJs3eJYlml1XbCumKms+27Gth4s4CauEVV60lrew8zvK4cCxNzVOFmC
+CWxJuwr4XpVv1z1ooy70uIlUJXx8UNmHEfWxzbSCH8fpSObrSin9glsa4/a159wRCxPcvnKNqE8S
+k8GBfFAKdX6rHNg3QKKd53mps3kL8AVYeUgrnX+7X9/5McYcj4qO7Xyp1aWfmT0At4dlda8xaTKL
+Lc12p2W4vyd8vSHCaA0OALwqo2vE4n1uevJxXryNfcQ49xf9gWEd+LZ4jP35t/L50JzKUBdlrvPw
+avfUDhOLlj/NZPHnnEp2e2vLZqSs2KTtgNYf6EpbF5JK10bP/VjUtm2mZXiUfUB4r2lPZQ4g2Ea6
+FCDze5r8ZMAOaqeVGHVUC7ACZVh110Z3G81e5evfXSKc+S/OKQFQwWu4cIw7zV5M0ZRo7Y2ir4Lu
+SB/gV5bgl1bil2n52Td+V/kJ/3T4BcLfDqKQN8qeV2GeQ6sFFZrsf8pXrW5agglaJDQAmvopwEvQ
+AMjDb+MGsRp/Zs/1IbgAvvlFOljzDyY45nuK/UZuq3tWbCUPOyjuykfBbgfWLnShZflqav6AAgNN
+4KdUs9zNHQRbWYOvAsRpEb7KJQJB2x6W0zjQwXCuqz1zVJSbeU/pPD4oGbrU7OAXYhFm2hKIcdH0
+XcJ5m74xtbDs4O3k9yCWdX7VyX1S5jbZCMqsXqQG+jLtLW/GWcWs0jQxZrjB0wQ+hzJC2oGx7EJv
+Zy36Q7tPFuz0OGEbnCqZb6ZdB3fH5udzcmr1Nb4pYm1yC2HjvOepV9v5BNqCGJQ/vBME4X+my2Ls
+uQeWXIhJusUafCvwnQ8eSx6VPvo6bsoaVPYnyXdgsMEt86G8PMv2GmZd47Kvy0hHfjWOv6NbhNd5
+u2tEyTPrf3rHKIeUbtYnDYf4kWZ7GXuZxATFZ0uxtf0h99OaRXexcHd9MGB8OMPSkjzdKdoUHPr8
+zgrcaafJqo1rBzKpc99vssurDpkWUXwhXTzm0zHcI8iDO+h9DZDnUd0gvP+YH2KkWX6v8TNLn+Gf
+Dr9A+G9CyPoU6uk7iQfUMsNHhQqnKIjg6VabxUN6kdHcwp8sFNCy6Pp6g4MtIFPKLpTNFVu2dF19
+ihk8lptCtwgHOLYdjNNNoglqX8ZoAA0EnwAKoE/CijU5lN3ROjzmudOQ0qdAgJXSEmlnEIKt+VUl
+vrtFCF3dJE4AmAADpFe/Cnywsy2zB6u8yVWPafKMA/3ht52e6dbG4HcDtlde2dk1YrMOR/m0bpLp
+5ZxsbCI8VFs1wj5PqyaX+Nb17cwj/BHFknnTAtNIkjd8A5NfHfus5tQOLpbbnBjnWBtlHweAAXiZ
+FhCXkxG3c+5ryR2REX7dML6cPEHr+qocLkt/3/bGfOXjBoy9pREyy1jHSK8Life/fGAOgp1yjmT8
+3/rpw1hCc5l3ioji9BcAWPjQBy1OoIxUCy8X9OQXzKAPzT1rGOsxE9qQh5mynTaq+iA92pjAF6hJ
+O4FgEFKKjIg8Bbzv3ghBePxoEZYuTp6ZU/BY7jAVPX+TLtv5HRSXcix/+MFHA/x2v2HKJrpHvHaN
+yLS9c4cYAFrujsiWB+GGioYOjC1fncbXqP10+AXCfxVUpX7zrONpqs5q05XRzkpQAKX4pXC6UQVj
+OfjMCgW13LLkk+EJklGfS77EChlfikPop/ZRDpvgd13JLdDsm5uEfINjDWEIwASh/jyztKz1B+MK
+EKgKKF/hoqsCaRCGMlpAbx37zt3BroXVN9IHALyAcp1PcNBew0ZxzLUGZIIqPeGQ5vW5qkjeoiV2
+1DIJhLrbQwfBQHvrQJbRexlV3kw6QD5uWybAjVn5+g4XiLxt3syZ2XgbctWxz8g7EHyiYcQnQ/Y7
+FkkoRf2QrzzbrblyftK1zlWH9pVlTv1l82pAm2BYTzA9mQ/JwfKrPG5XySRuCQCwOwGwCRhevu26
+Z4ACz9wD9EtXGgSgW7UXQNfDGtwAMMQqPMABh9RkJxQAnXbNYX9trhF7efJa4y3pz8ky2LDerDYo
+E/g6nv2CYzZQAHq22vdNJjTN9WI5O5wzhzKBy5Y/oslMkImKQk65IHnsYwPI3ENlyRUpugPdKDM/
+p3wCyq3LMj3z+B1aDG6L5gr5npd1lGBuPLYpMqEVZ0ZCbpuoa0S59nRTBy/2AHkgDr1sB8GWtHp4
+rliIoHqCYL5WLd0iIv7T4RcIj3BSjqe8v6lvhdNmINhAv81mAmKAumWsBeR8+jjRIrzicashHnZb
+Fp84KXyC7bZ0KrPbl1KLDpL9840RcIkvIZLgN98UYWWZpjJbXcjmFXunsDmMPbevAAf2o7lDpHW4
+ztN532SGFa3rBasjN+kAwB3QdlDcfIK3B+Z4S84SdO8uEXKMjuZDc9p5HY/tY53xNo8mR80nWBJQ
+nM05FwXlw4mw7rnh7BpBBhbrcHPirGpXPPK1g3yDgIfgTURddbfBkpHGbG1D0eIPtD6Z3uM5mTWx
+3vIeysHbHl998lZMVzf77Z2mfTyNAyilmXQuEcd6mhSxAiP2Lj8X7sL3CmpP1uAGhoGdlgA55q7t
+KyRvpn/u6V3CIiD0wxr8CECfu2fZbe1HJW/9xTrHGhYPBrfv/DMYqa9by8x+qkzb3MginNwievn+
+ieVTc2aDuIFZzWPRQcPTWG0f3lPa1S9YhBIZlHKyuUbITJrelj+/N3ixtV78TNoA03NsEv8bWuXN
+XavR2rUtqJBX2eKjEJlhpCk3CX4zHeX0obgls5n27I+B89/LtnyICwTTCohr28sDdPVp5XygLj63
+/NPhFwhn2EXdFHt+pL4Oa99+co6qsF1xVzwARZzjck4yZ+AHG/GLgINM51Ug92IAjWX5XUyplt+v
+AGVKJ/j9yng/vyyu5eOmM6IAOXWxKCHKVRfaFD7ccBj0uV7pr/lySR4UhILXCXDf/AAF0RBQjcyv
+Juq86madSwv/rpjsyL8BJYcIk+OG/vKkAZTHw3KFisvlIcBBgl/rVt+Ta0R+pc7KpxNAWZgzXXWp
+RTn3B/t2GP8+K98I0wWigdzeUnv47aFcTbdnsZp+AUxa19anOLKgPvnWJkIkCC9MIHQVMED2X7lL
+XxzRaEEwuYhWjdfveLBrvUz+/IHOPL8xvy5nQXdahsE8iF9kbXYCyDaOx18BYjyW9fG3L49K5j30
+8rZR1EXi4efALWdNgLzhLjX9b2lutHM6L8Ta5pQ2Tvyo4unUnzlyufq2V+UkzjsHF+LCxXhEHSG/
+4EFLn0PLNPXIXIinFXwfBk+0febHYj4Lyn7khR5dhTIda+Ox8JnGTHuU82qDDylv2oHLzwkxlOWX
+Fx9xDDemBWy7EWgB3YiH2wPzl/WXbhEWINh+H5b7/yq8Bb9NcnRSJihKO+D1F3nz5nkKdBEMl2qD
+kFkKigEI2MUDIN7pLnR9aA7XEk63V5+atWMMXQExIF4baMaqDRATMD87EEygQi1D8HAOddsHBW5h
+Pf0dUKxAGgTGaECZA6IVrUAx0YcMeqy4zmceQ/EJZGv4KeGC0oJPmvWYvOO+geG8dafAxoM/Tee+
+MNp8KYQNkDvL5q4h7+Y8+Ul/fHh8t1c5GVK7C/jJiZmKrPI3oORPPPqmOz5kAKc5slNhwqqc1YIu
+2v6X58qb0xpY5LMDFhuR8M9gwD0u8gRxUkkWqLUzGOYCb7ToxH3Dboffdz04Fz/TdADf9WjfWLMc
+j1h9xzjbz1C3kEf5TfbUkA/U96TswKdqI4J+PIPvHOZeLl90mccEQLanhY9XX04dyopaf9OtLBmR
+m9PaKYyblj0t06DZUzlgAN4AZ2Fp5ieYiyGXnFIw927idV173NuZj7UMvd7uCLUxjbNVnsmdkQLA
+HxyhwLnaaHdTqS+bGinpUHNbd+w2+viaKgEt75QWGFbAey1gnOdcee5Ph18g/JfBT2z/Roh542yN
+nLYZCoFo3lSEQLOKae/aVfHT0ZBuFJCjPgj3OSBm+XpjxHqjRClaA3Cf1IX39oFl7Qi9G2lRUAkk
+nxWTzrUBDQS7xOzhzFan9VZaHxLQnq3ApwfllgW0yiU4lvgmnRIgBL3pHnWfOI+ns9yBd42cc2Bl
+YcWM8pY2xMKCaQlGaoitrJ9B7wnkZtmqro00y7a1/d7xHJo2kjYVeHse2hPZk98cT7MbS3LqSUia
+Q1bOQ/4R0NEWTEHv6mM+YOsucxftybIdj3eA2+uhzA3wi1IEYYt1Q+i49XTjexFMpMET/CboPViI
+ax1Wmg8GzTnjcT4IV3T5ka2zfCn/RXxxQZOr0DZq39JQNtn7yiHwZvWrH99+6a1OhN818AiIlTm9
++GTjU17ggsvCORj1yanlMjUHO5oYPJf9/UDHUrYn+2CwUubT8usyFmnj1D9ptmbl2Td/zpy6NI3D
+WKRDgxkvvtYPymzAl7C20eO87Lmmo+7JwHNTp1wJWY3a3zm/A+zaAL7rgxl8lkZpCpoLPP90+AXC
+/0B4rUjPBY9AIyMme1LTtdVO6S6oH+LmZRF+cVxWXR8A9wR8pUw+LBd+xfJmCcPweRszQL/hTrbM
+O49LBN2RVmVVyaTOVPClGkXxhPYmFXoo7xcg+PjDfh5GGWh9sHBjocqO7plYhw9Xz/5wZLzRRU/l
+bHQMBdh+TlN4FspBwHCBZIh/38rjGykshlBNrvM9FCDzFU/X+QJ6ow7VY++OLdiLPFFkFoRXAJiK
+pqpW7df9RJPvXkmQiUUa6SAPvMsOmUmYyaXEoV3WpA/JFL3GT2DXvqlhSGDb3X4UEIs1mP5ZgmIs
+aa6Vrn7eLq9QGxbg409ePeYJEXLGrI2HaZWlUoZdOpxHYfEEiBahMXAPugRW7RPDzIJcWxVhc087
+e7ftY/KiCDj3uoDmRYvKAPZju7Cdmy7OPQHiMTE2xz3nYgosOda5IhjQ2EjiBXzTZQJdFjXL9WHG
+T0tGGaDDOtWgF+XbeHX/HfMO+Qlu7xeAGNXqo5UYue7KS60jyvjgvBL40hWi3gbRQLCCXcaFdilN
+4vr76fALhP/T8EKHrfxXO77K8CZN5p8e8XaJgwJzbtcSmIauV2gxuJO2hNs1mB4Wr+Gx8vG95MG3
+Do6tXrN2V1l9p/BZmuzE+Hjrpoj672AJfrtvZL7ltnWCFncUEtbZzRZhD+kOgscr1Ao5v6SlRQyS
+jyrj1B4mk6mDVivxGHHTKaoYT+UGm/mc16ZYwyrIgqZATJUNsH04IxSPAtpVDqlb+3nSR2HTqaBT
+nz3MwUm/7onDlj5ps2kdzmxvq3DCQIpJHgWIv8gT+mfqWwAQqo8cgUkZBSmTq14fXeoKOi+ADGUZ
+DAVa/pncDwQmg9cnEHZPMGwutPlLBNfBhPbZtvTZDQLoAAvj3JfhZHGT/kzG2NezaOtnrVwHMpru
+DTR2kw3kZsWL5jVt2biNizXZjQbwwuv8JGasNfmgdWR0WrrwXO4wO951wAaGR3+W/FC3iCxwiI+m
+cu1F3vQZge7/xy2umGDLG8QhwCewfeUasbiAvaqJTVqKirP8XNMl+xVUXWvN89lY4Ogasd6i9Ax8
+N2AsVuGfDr9A+G/Ck35qCvF753dbEQv1tB22oEGYnEIhz+6gkiCCTMwmbojfsCCNejVafxguj/FB
+jv1jGtbKeTiy2T0V0GmidiVO2a3npv6M8nb47U2IlPWZdzyhBKlVmz1+tvx2X2KrcpKn1t8ECKj6
+az0Qa2/SIYhlWHqfA695fJK10xdY40P+Fq2V9RiDKsG4cege47GsoCzBhgLEFMAEtHSXUH29mJW+
+x9m3Vs1KNP87VN5Jr2r8ectm7aG/qiSfxtZ0lnAFxIuLpzfK3PWKUV4KkY2naw+oMmbPX9NTu69D
+7Iu07g+eb7yZcfoBz7SlZS75mzxD8JtgGFXhBMZwsQATADtcXCNMgAFi/juq03Wrox3SK+6HvNqj
+CbK4htwTco7nLI8wRTv7OYqu6ZpwrXw7uZb1jH+JtRqt114XS3AZRD0rIhDKV9dtyqw2nOkoTYQD
+gXOfhKzGlH46VsPnLTDK5/o4ZG1c1kofhONDuuxrjHXMsDbQ9+SizKWqs72lt316FMSa9pf5r8Dv
+Oa7nYKfZrgbJA03VGBL85haFbNXIv9QfOB+Ws+b/uwHjBMNXA8s/HX6B8L8Y/EVqBt2CtcnFGykE
+TbMc86oblDeeRxrkyLCXO18dXG9lsHJHUOsfdY9aftU9Io//j71vXXdjB5WEzrz/I5v5IQFVgLrb
+SXbOnPmWEi9LCN0lKGNaFj3yxK/LXZK/nnFtYfDxIdUNz8LHlY//OMhkLfYDi1ZinNNxCXCiqyQ4
+lHeoEf8C+EoHwocXgWFh62/N97ZM832N1X2BlXt5tArzLLQpGPKQgHojPiCZpLCy7WVNX7vuXeh7
+NizHqz/xIx6+mzHbh1J8glE/irfXyuSeYuFOsOA83tsA3FafEGA/3vZxFSzWBJ6OHTn0DIAJq+/6
+MO2pLoVkbUNjHP5gHObkuPSB5q4QIuwTbLG/CQw7kNrxtBDvGhEIm60H5vbXw/4Lc/4AUQfD+w+s
+l/cWT0uNn/Ny7r2L5ycMvLGy9iOK2mfbcsfnOk7rrTSWtPGt/sSwoR3akZ5nayDoJSAi+YFVK6/z
+7MguHx/6YG1DX3k8XKbK+KFbLgpOeS2UWybisxOmZe1D35O+x1U5HX2vjVvtMp9tiTxrxek73qHq
+2s4531n2vh+BsMxAOSoz6rmVJtq3fz4wP4KSxzT1Xp6D5hoBgBiBb9IY+KKv8I9F+H9zeNKqowIq
+0mqQEgSGN4/G0cu/LhxTZiUkCJ1iefOC77V6t6/awq2m299XJ4C7f3WuWYLzXa7d8kdCIjnEi0ed
+P3XSdo8HBbIAu4bsTbjICkta/CRhXPhYtNktdhLg0/+mYKiA2F9XebElOK1hxTrsI2o8Er62IeXb
+e9tVFCddglsO4t0qDN9u6FTWNeXKsLA2WVpZdh/DLw0AbKjwNcBcJ9BHuv8mcM5hG9SBIAHDaU6e
+8s6hgJLitsEWYU6HspTDPjv1BEjkc4j9cMaYk57vgIn733mkxO/P1Trs1Qq8trGJA2CtYNiFUZiY
+/cOSpHbFeHV7+CTotY+JAjhgMGzjGPUQf8q/Kyd7frsKV2TgxUdx74ItEll6jUQhDnveEhR7H7B0
+nGs/u75sQbdGr73Q+LuZyCrsNAmZQD9wA8BYsRoeZqdFHhSYyklZl5ATuafQ5x0/wM8nBftEpyvq
+ZR4RK7QqB3qVZQ+U+pi/8iL4nX2FN9fu3PzwHHDkrp2Uqa++H1V4Dz9hle4aUazDCIpr/BpulvjX
+4QcIfx2qOppCObgyHTM81SgsS1pkWwpY5LogwqPqyi7EE+IRT3vtlrdFRElT+aj/PPOqe/ILvgXB
+/sDcp7hGfLKfObAypj18tzar7Ade9hj802fE3QpczvGKszKJhzhcEHnSOm+8a5k7tNYSmHWhUCy7
+D5bhBA04ngTEYQkWb3uvVZMTXS3Tfjvsw65XwLIE23ByjbCdodEf17QST6nT147qe0z2/PkC+K4V
+VqJiBNgVaaHsvArX7LNKm8bszDNv35fThGZb7JKxZ4FxjnWQBBiFFeyDeKExWqXxBwPHIsmjrY70
+AUUOHAvEHeOA0mxW4bD47j2hQsB4rbNKAOAQVot2AsL+oxnx4xkfpiX4hReqe+OH5aS8n+J3+Sd5
+ljNihaC8OC6Lyr6uq5Hf9iXN4UwWZRcJPM++LKKxPHFkdc9/YFZfYz/SljXmnABD+PrDQGO9aTJo
+2HX62pSOPN5xzAMjBbWRt4KkmKoAWMRdqno/Jq3N8mAytJTpyo7WI34nqEJWlv7Y/qjTLML+INxE
+98qt9d9gMawukiqf82oJvpB2JZB1C3CxEhMALvTK+6/DDxD+w3Cvs4ZcPA/HwkVyiAvCrkpD/Jin
+BThNwFAXpclbQbZVWEJnyUeWFc4kgTC5RsgNCP50uojmzzrTYfMDuEdoXr+ly4Zo0MJdQpUFnr/r
+QMvJyQkHwcDpKXhbaBHW7IdbcyvgfaRtYZJShl8huVdaRdgyTPl7/iIbFWUG3HdsCfan64X0dC0D
+qA7aACVI4HbnhVUGtL8W39oACNX/N1W83dByb/OH1Pp1X9crJ4HLnH6mfIhW+ADDZA8MAU32t+MC
+6x170acT6Zl+yISJR6/UCtBRR2mlq0iC2/Qbjv2CwCn2PAAn38tHi7BAvAPjeJ/GVoCLd1cO729p
+eNZoTfssybyGug2mNS8OG+wZjf2E6awZJb/kOS/nGvPCY8HYnQ7HlOcMP9xk5WEZjg81We+YrtNR
+3+UbHs2/0Pc0juS5TVkuMUta+9TablKDSFYHZIV37POJFSTLWIYfhGuAVyAeAt3pEufHbcGrSW0y
+X2TPFKmmBMXL/1e2NXjHCezy/cAEhlXjIToCyw6IN6D+1+EHCP9JeKXA3jAOCCQOUW4Kxc1b+Mrn
+cSizqSr7kvmVDO8ESXAb6d2EGVh1FazCO3/9ypzJpTqD4H2bxLpHWKFjW2yDEhFJC6A/Ge0/FW2y
+AJDuMbiupHd4yfCOU+px+ppqELRR1oG3rnlWoD36AwO4JReJCpQl8xILa4BEtAxn57rAOOoS2GI5
+1AO48nXAeIF/cSMA5lZAvMHtzpJQ3aFZFy+BYteapChcATMNLTkG5DoZVIayePzvjnMCqTUPWRfo
+/U3ptKzivjUuYzJFsR9CfQG6eRz6bb1srbPF6QivhPoE+IcdQFdu6U0/4fQV9wfl/KGsVLoqDJQB
+PW0lnmB3f1PUXCIkwEDw13m6eb/LO/GYjz3Sml33fvp59TmDOY3zoPFYqFQLsK8hpnFV65luvvJ7
+aeJdZIGofUQRBIdnE9JivJmi22FwjZ/S2LGn9ykAD54T2rc20Hy2UIQ+Co7SEUKo9VwOeVZqqNU1
+hqEM1E83QpRvPgL8BtiVOA/pCCFhOd6nSKzs5ADA3jYo22UFltRTGxTjA3INAFc3iQC93VL884Ma
+/x+Eo36zGjnt9GkDMD0FoDJLEYlV6HtAa7CnDV4qun6Raeskk/WpDx+Uc99hB8G/4JaJyX0iPnZv
+F4kQ0mARNkvL77WF9SUaFp4rhLKmIIszq3lw4QCfv+6q68DSF2ebBSmA4BcvKWAXrb2ez5bgbEk2
+ODYaWA48rEF10II+g7z6LKZBccMWa3rI2EWBP4NBQVf6IsEUD8I5AEA4Fu4TXjj3kA+s+w07DMgi
+5Aiwq+VlZwV30q+d3hWjNj4j7gQuFkrWKM9K+vwe9dtUfwF2MObJIt15Up97f2lu6yz0Ze7KMtwh
+sutoPSQrMYBigXcNACz87vWLK/6VVnO3qa38TfoDc7BOuG8qePrd95yzvl+YMzcDAWUywSqVnyzA
+K9flZn7wZOkPXHBm0TXCv/2bQHBziVCR/BEcyeMLIJfuClelzzDJn+kxVOET73HYYU5hTXHblFmf
+aLj+Sbf2d1ToExBufE0IQVG7ZzN6KzTc2+wfLHJjLRbhPK9zWpQyMfztZ3nfrhGX+nsBtMXaO4Le
+ASTrlSeKZBZ37a+GHyD8bThp0t9jowLlOXmoxBqNL9UpB5jye08usQC502sJVw3rr/+S3C+V9RBc
+8Q9eND2AYNluEbuf7oshAhZhtgaHJdj2AROJr+6iylALSZP2bhRHQZifxNMH+bRoLghkz4sDYgK7
+AvckEvhdPT0C5lPekkLiwNm1uCqMTHHkPeCaMs1CQcYuaQoTdlQo8KzMYDKVCVA2C0WuA58q8HUr
+elA0McbydTf6BB+qjUqf/Pkrf74dNdkBlNpIn1p57kyv602Z1lTVppB2EBRxXCMEGjGQFaGrsfYm
+CquviAQQQp9gB8MiyedW5PqpVvJbEEKuvvlM0kdYZAPf3X+0CHtfLN3FcHTRBSu0L985MGDN+cAS
+xkDTreRw/jYF/nqdMyD2B+ac03YleLbx/Pq3feJy16cXjhXqAqxbC1WhhyLllojqI2w85/k+wZyy
+n2FJK70eV16bw4e7kTfLtHNFjZ957MhzqPKRVuSwmMRDcmKxxxMkS9CZx7N2WtYzQab+nmsuIrzR
+0SKsDILDTQIfkhvuBk5gfA0Py7HfMFqEn9fq74QfIPw3wqSpXuk+VpciQuJurrDQQznrwDtUc+if
+fxkXB0JlW2WFLL0maSF2v+AFem20BHs+OuWLamBkPAAAIABJREFUbqslACbPX4DMH9RL39UFiB20
+auhJjjMoXuNCgTpIm0kSYR0ANhO4IgiWB8vwU36CYUHwq27tgT6EctMEFNHZ5DmFZvlFBRmzkPOF
+2DbL4nxin8AyhnMa7QFQyhnd/QdBven5MB2WN8E3HL+RMk3QMc6DINtwbl6f3QqG6/sAjm069a1n
+NzwZyC3DePQOdDIuFK9jzfXAVRi6h1dR4WEzEXSDWPs3kY/6Pq5WYbIo7lbru8h2cchBxq0RsgYV
+WzOUPwwlPkhtyerbDpuR/s40O/B4BWu1k1724gCIk1WFFjCk8Y6rYCrHAaNSqDc+gGqZBi8HZ7tb
+h8s3CtC16P6Wx2hGpp9S9v7sShMce1nJ817lcJPLHJ7OxOLphadvB332xjACWjyfuF6Tjqkybai2
+0Sz/DqpqReD6NDFpQPhEk/zwSGPgA7yisckznT7CWzepgCsEu0aQdXcD39lKfLElGV7/OvwA4S/D
+Kx15KmMTtSAOcfkxqEI6IXWzsARZQms4rJICLo8IyClbYswc2DqvavgS/9pPraFfsKk2S7BdJr/i
+8mIT+TAA9oOWADzdIxwQ+y/ffUw2YATLsB/Iw4w0hdYkjIuCQWJJ6vnVBggJtAS/BLls+ZUQKGn1
+leQTtyYD0oi4loEhGumjR0UoMduYwumwqCqmSoHTct8o0CKVk7x7oKfNv78NSAWSliXZwNbrTRpW
+H7u8+EJgPYArSqiDPIUOWrx2lV7yzvVhCsXB6XVoD+y1BT0B4Il+Hj9Zf5vOhBHyU1cDwFUuQ+8C
+CteR0u4dAke08uI4fQ1D/E18+V5lRZMRwkDqmd/3mlI6c8s9HTFtBWHiHMbu1gBcbAV2q3lIL8EF
+il1oEg8949n16THN/WAi8RkmegsbLY8rjtPllu8BofUWYRBcH5gLcPrwPlmBQUhkMF6fKdzfzsK6
+gehWJGftFOYXudLUDgsx7ob1bhn+mUCvVLcI7OKmI5/T3CA1Sak4khoqJvTXJetsO4DdefGAHILh
+C/k4r7tEJDDOTtgQhw7+pfADhP8g3KnQzuTiatjpw0achWopY5m3YnD4QM9QvYoHK2WwQS88zhZg
+owflFkgGy/AWuPlwnZIvsVt9TSwUn5fJr2Zs93sB3rzGzQKEx68EDS8Z0oMUSoEhklrgKagkSJXE
+r7fuDeWVQLYDY/IhjsEALQY3jRqGVl6cbzdWYRDoWK1ZSbdmYS6FMqPO6D4UbNYJ59tgCjO3hnZ4
+gJu3+RhzF6h72Zm7Ba+DPQWLszSJa3yXiJ+/6zlN6VOgtpsy3pEKJIBGZUq9j217x3GwO66OsML6
+KymUzK3FqyV9AsS7zwmcGJDwEbcsWsHMMLaUGz1/4pvyc/14xVdq4GrzIbGXoxduSY/e+e5XyfXb
+dwnp1JM8DzhttifHIp06wZ/HcBnv/Wk+5ND3OH8q84eeuhe8hyB+BdLTvs9SPdPXXpnSGcZQz0ql
+ZafM5rxmGabzZMcmxp058ForeAOEPQ3xMoyoUwX7Cbt/VKZb66HOgx/O0EvY/YHyEAxfj37D/nBd
+htMp/LvhBwj/bpjO1nSgnjRaPQUupSqTFSAxNIrKGMWXSwqXRyIg/CR0k6A33RKG64C420NaiBO4
+xgN0+ym89Bu2tA67Sdef1LtE7GNRPwPitA6HX/AGxv7NK+JFeh3OyfzQ3AwAoNRMckFQrLe3AHi/
+0oKNwFi80gDZwb8bNJdA2KeStEiXjBzuMX2yBPtXqZmOAqGWo/vHlrLgGXBVFLD3YrsSArGRQj+o
+ssORu81s+a7rMddq3O7y27PYFCbaU+hQAOZsd4jOOMxPAF5Xgk2xZ6+qHyh1FrN8+QHw7gMrZCGM
+DYIPyYlUQNz8iClufenQzaaAEZq3g9U7ZCMM76R2OW6Uynqyr2QhNoH58drKbto8+LAj5iuVSyuw
+7nOAc4wji8/5CnI9p1/8w7HX7N/qFE8NIUt/tJBKJdbNRNBNQrPLdJnMNK8UALRhmt91XttRD3v0
+qAyA24jXaidQd5iXsFZ/c0WoXRnolNe2cgG88H6iH4GzbN9g2LZVloYlONKoxxzkCvkEs6tD/qBU
+c484XJ/GFuF/F36A8F8KJ/X6ezWdNgKepJlnCThj8FfkrivCxbutCiG7wG7lwlPd0pvAOB+a08A6
+bjWWSwEEy/oJ1Ap6VcU2WPY+BABW8BXeFskPguEAoMIvmV85dyY8hzi1LiSSrod/XnMHvddLWhEo
+8Ik7AXEOyOVCAOJiFTbto62jpvReXheJMXwojuBYokxDIvHGcEIgNZwMrbmhrYOiOhXlX5mbKp78
+AycIWXspUsYrUs7YGUzF0QKFMoHgu5P9bSDAK6iYvbE9j660DfgMy9vxg2LrrNPCurc3DdKsgF0H
+uJZx73R8sA8+GF0g07oJ+zycgM84rhjWtGPfgV8qQ+iuxE3KRvb5ETGyBgO/SM4f1Jk+8Hs+266C
+A7ytsdP5bjRJ2a1Ey3PI0+4f1nMfBQCOjaU0bHKdkKTXd8abe64O/OPSNqIVXjwjhXlwhYhZgI7x
+B0j4IH4AxMReu0p06OWNilp9i1WL+BEI3+W5vm0tXVI/reQ3l/jKbzPxZ5Prw3JxhVpxj6CH5eD2
+iB8f4f8vgk3yehbWRz4UqAe+yiMEYfM9wOOWRZYPjJhIe3DCAZJsfrQA/wJgbKLpErF9hUX94Tij
+2yTSYuwKVNhdorhOfHSDXtmHzNxPeAGdwILfBMOXC4MS33wNUgIgRSHAlt7TSxpNSl6tBy3CCH5V
+h61wmAcfaqaLb7AvvvQ6CVRFm6lUHPvMrZ5SsE8nRUSsCtYU1KgcjRhYjnutDIK5XOmHVa4+gugV
+rEVw924GxjPhWp/ib0K04+fb26evRGFS6IMeWP0OFtPHTqEAIaFjCY6ikQR+inPvgkmK1dNlkg7f
+g1nv0tTFM83G/J6e9s2pboMSA1AFa3B8WzCBYeelhoqV3kGxljbwwCqf+SX28Nu+1BchEqPvkqC5
+7I0EuAh2wdWjDMXvlsauEriG9wn0nk5g8q7K2g5mEVb2OOpoPhORwPQJ5IIOsVLOqFzpVhkbdqXx
+NrpJBcI20JhvmO/M2X8Z+G7lC/oo09Ul4qo/kDH8qAb9tDKCZi2gecf/dfgBwju8f2zlRmmISFW8
+d9zPiq+q2qlSPlWkj2BUo8wtBVKHLeHyURE1Xbc36KoD/XZN8+eQHdCy64RbjzVdIQIog4UYLcLR
+nm4rxop/zN0w1nVq+FJ6Gb1COIzTa03Qp/qAcoPi9anrCjIcHIDSObAGt25HesseFzwGo1PZFuCp
+8RgB51XhOgrbnTDlvTsCZhDM70XW3ZnQVJoEBADkFnr6mhsM2KDG3bNiFiSHhbauMwCO5ufhNL7W
+G+vg+BT3qt/ERaRZfF0p+4eNhb8spgl/aty/CYr6vGAVFndAucx7mYYm5LQSo7MKcaihtd2B8ddp
+vct/e93WIEAlgV+cmekhOHyAo2756F8BvVNnDIlwHnaFnqq++iLo+jDIPzx2d2sLo67zphDLNU/+
++pCj4R8AbRXEMqBLXv+BldbnwdJL4/ZzAvlBwbyDVfgRDFObRfZiNwd6k9Pxx2QEw1ioVZgvxwVI
+JSXqYaerMUeqH7Dubz4R2KqSBbjxXyJ43egFbfy4RvyvCc/3k04B1TQ+zJDU20JvKoY0/dQACAwQ
+4VsAk6JFYLxla/rm6v6xi3RdiFseAuTytWpIE7hKbVmRV8VGdJNrW5GX68S+VxiA87UBuFtVL50A
+cY5XbQEAumzfPhG3z2eBy+tKGvJKSZswD0mfansdwiBsHAxG3NO+Tk1Q1dGCuhlBcN+zuENyFCrS
+OM/7vcOU3wlL4eetE0r0NSWFjuihfbKDEewJ6QDGU4wsWAbDnB6U1gSQx+M4rsm7+D3flOp/a8la
+boVvofiX8H7LjAA3yvmrRgDAOx9bTT7ufVnF+3yQee7+dV8f+3tjr9lNRCRuQxnAblpyc+u2Dxol
+CWSe1jHkmqSTUN24OPjDLos9zP2KvMD2S+4vYK9kqLb4QKUpwFwYx1eQ0A6KUOyrwHygMrMaxXHi
+7jYYFvNAC9SmlUbPZwrytuwiFzLvh85dD3qRGaYDr9OnI4Zj3a8jb2zVvVeqTtlGlvUG6+d8DdBe
+/EAcWIGnbz/pwfFN21af1cTvKZA/Dj9A+I9CBw2h9NCn6156ZdnCxso7hQcX4nhXfr2nBzXV4u6W
+sMAwuCXI3tjCADUsw5fJr/1QnP9ms4kUv2HpgHgLhl9bYOY1bv7jHrbBd36KvOAAjdbh8mlYANBa
+IPVTmgHwSvtCWc6o04yymGdYj4oaOwbTR2QZ+023ElbeNaf4Ke9t+tStyvterrnirC1sGBJKfNix
+26dnTVnd2V4H92qeJVkWvONoYL6D/8xdq3o4uo9xSiNgcqUOWpWV+SilhpxJCnwZdyCAa2lS1k24
+HOYHsJSsK8IoEYf0G3ALW43yjdM6lcc5U+iz8fAIGSYoOoKZI7hRxNBD6Cdy1gM52MlKPPJ6yoeo
+K7E+0GgcW2dS1W0F30Bn8zoYXuXQt7Y2iXu55vNhs4kue2ylgXoeaA1vzgnPYz0rsvRUWUP8QguG
+RLLDbmhIZxopF2qz7ymTfrn4rkeF7QoiCXadFwHx0ao7uAJONN8rgXmB5s0J/fnn4QcI/5Uwg10+
+UsnTOefA59vWJ+/nUsfkCQCLHwxJkVpfl/qFD/kAWwBVB8Dibg6SPmbFIiwiYf39VS3EYvvmCRO7
+lAGyg2tdYPzXrs6twftqw2Uplu0asRUHWoN1W4NtA16y9H5M7IJ0e0kpk2kCzS6sJmF+ClsI0I0U
+e47DRUIxDoszVObuFCg2Te6twtnVRWORm7RvQuV/3MGxQVFSQ9wgXniW/K+AyznugXH+HUaohWo+
+b73bCJywUNP3Na33fAe8IAGwEESUEkZad1DmrTcnoCupRU0C4AXogw8haUXt5d0/NerY45C939M3
+twLf+923eN6C5V7nlO8uBjCCvvJh1U2EQQ+bDmAELcFW8nIfaivnJTSI9UQZF6AODOm6UWt+fRjQ
+Qe6OxnV4WzaFRZSswZsGYLgjxKErhVC9HCJixAUxK0OD+T6BYsizoWPmc4I0zX5kWnoamp2svp1m
+ndaALmyacZ/NPK6fSYeETlF453gA4wvcIfClm34Aw/jQnUCeEDD+92D4Bwj/RsAN2RXqvbB+x3Nq
+F8spG0tqfmvCxtZJ8KccTxCpIpf5Xb5+pZkmwNT9i28OgMElQj67YrAIm5r8ujao+uj+cQ4Azyr9
+vuJiHQ5rsCZIR1yYLxNByOIgFYCvXAmKR7eIwTIsApZhco0Y5n5UNHuOff5RENHCaOiRsYKDwJj4
+k7bnXns371Rnpc2g4Dmc9l7mFiQ5WYBJw0BPFPKgBROmT8C4lqT88VihEoT8m61QATXFn7bQUH4C
+AGgVXrFB4VfF3lYT18EA6Ja8p7hJAqliIc6H4HrbFg9XGVRbXSSkpXEeTjzP6ZSTM4+1Hq/xlOYr
+MJEEy+3mkyKUa7HMy570sRvMca3y7mQ/pNFFYIOYXFeNNgMAgxtEtQZvpZGTZaXFwyEYRSi5NvTI
+dKLbzx/XElDHae7yLA5n7ASIs7sjsMUzGbS6HxQqaaB3S+8jGF48BnTcria5b0LeAgCOB+N8La8N
+XOF+4Le3JC2Y6+9d9f17CLzCDxD+OpyBLKra81F6rueh8p2un2Jnvg17Gi2LVYCw4wUMuwxzFwTb
+ysxd7y8Hq1tgith6SM6k/NqchBX48rSge0Rag00T/AbYVr81gsHw6pc/JLc6uPq8hYTth+dGS+8C
+w90NIsuuN0yDL7AJ1yfwHjwlXdYjPgUjOo7Jd0EiaQkupaeHPV2l1/3XVd+kNoEWuqvv2VrXe0F2
+2pVFxR8twJUXNEvUOPduUpIncNxSWtKkzJX5cRilyhM0acAa42N5y6E/fEX8JJHafIrIvC6HeLEO
+m8j2H53ra/kQ9/nMorlqWua1rnjdg3e7Budl5uHd0mWnZt9lC8iSJgBWj9CkBnCPWR8P93Eecbdj
+y5C+y+snVGTLQAK2Iu43oQXshuWXAPIgaw7dqGeM1qJ3bfXYOM2sd+ej8vd5avUAOLVKd04fyxEA
+G+VPbhVdEo2Df8gbRgDoc6UZBLvBSwDIHt0jriv8hf1hugC/B1BMAJneb4bwH4UfIPzb4Q2QdZ4v
+Qa98yX7LW2EOh2nPrR6r6L6vUs0i7a4RYQmWvJQ7jqH7AHuFV+ata9Zku0JI+gqrhLXYb5j45bdH
+bLeIsAiLhH+wg2H2D3Y1kGB4AsFmH5HPtX7+GZ/ss/uH5tDqloA55w68Jg7zjooRkK3mCNZ/LbuH
+89sLauZ3JRr19dyzkeZl7pRzDO029y4coAtpwDPAylIdIDClANfSP/ySPvgKgZLD7Rq9VlZCTd0O
+ZUhpUZ9A8eMerJ0kgEClpQeYa4W0TPHdsXADmEB00ezFQrwkyFSnJJiu6zisgcJemPYn19N9iJMH
+5zFHgeUqDcykCRTrQk5TI8L7aY+husDNs5C91UJjnlqmDO6BN/6GG0zZG+QqAWkAyM0a7C4Utb3D
+eRnp4zm0QqsCuHIN/Ic+HfMH0Et1V0CrkvpDJD4vRVHNeqz+peNkvEHpyFn2rR3FfCCY5MpeplQv
+vmYJij3dAPHJL/hKNwhXbS0N3cP3fx1+gPCXoSmovcPeqXiGpKiWZ6jyohMvGSehP3ZNJV0Ahc5C
+uEggCF7XqXk8LRF27fo2IA6LsIPg7SbBgFfEPttSjHmyQPCvsAgXa3DpZ4BhQhZ7UNWqW8Bu8/Ud
+LMPNoiwylPtyrbSPIxKnAo2kdRIicDdmX+EnGu73N2He6WOHJAEFMjykSZIXK+LYSAW2OSLkydyT
+ypRUcFibMUNJsuwYFfmOT+sWW2ro0wkE16+PKWZcuXfymIa4CQAim8vtPBONGwaObSCQRl4CyzAs
+6WsWVQgVpTDVE3uTwMcT+MW1cOSi0O/sV7oQeOsFoADruQ9Jn6UB5ES09qVrrndpkJ8Igj2t0q3E
+kQfrGTdLGG/wqatP9E2cznLGeqkRyDbOed5ObVF6kAlVTLkLidU8VBnodkF5Ze8U0FvduWd+mId9
+PklXkP5AEDxYcQ83RTzeob8bUIG6QG7/+Aj/bwhHHKBx4N+4RTTQUZQ61Ty2d9NA/7g99TYjAzsB
+TAe5mnFX9Ca2fgnmg6VBMG9AvI8rWYHRXSLplr9WF6A53SJ+IdbbYBitws0ybBIPy8WjY+6v4cj7
+c+2H5NIaPPkJs2UY5rmA4pyDO0kOq6CVpAEKVST97FBI0SpxM2kPr3TqVe3lI21KP4usLcKhgmMZ
+dIPwsprjmVuttC8fhBOo35KnnUSYc6OM09yYDNU2fjr2hZf4tK8zas9m/TKYh+JTUf2GOX6CnH2e
+78GxCLlIIBIooNdvBdE7sFwC99Q6zSCdf0ByCc3zVFdtufkP41ijkOaY6ppM4n0W+dFetwP3H+to
+/W8PVc/rbTd5KwkcrieObg8rHjdGhGU8hbUNrhGt1ZtzVboldfJOtU6xU6k39U28x/IIgEUC5OKc
+etzP8JhnWHe2ies+X5lmDQzT5zXUJaGOUteQu0K4QUhxiTgB4ivKVPeIAMreunrL/x4Ei/wA4f8w
+HBHz1+EkKEDXTpz3zZfuacljIOxb1Dauhfg+fabLVQGbxXeyCLsLhOeRdRjjbg2W/JGNfTiXVXoG
+v5cYpVPj2eG1ALAN6WYtFkgLWoGF24gJwPaHNWhpnPyMhx6S7fhBi6OnytpaYH63kLynTcpiFGGj
+gu9lurKqKPIAfvVQjsrWbmiJ9U42G7b1mqiUDrVYr8cm/oKXWEH5MErrhLOsVTb3stv8WxjdHGAt
+qgXYxLUY1DekT1Zf2kvuE9x56w7InvX5bTvFAPwCqCAQIZVW6HjEo7c+BQrzUhZzcpPwM3VQEbX/
+eozJyNnjMsRL+slVwtehWn1tW3mLRZiAMt4RDpbik3p6pENk5rX5PI6y4HCej72AvBcCz/cf3ixB
+/sDeTz/fDQAzXcS3VKe3Pgfo5b3WDPKhPlivEAiu1mBlwDu7R0iniQNsb88twtnu/0T4AcI7fPfL
+cm+svh53oJgS+O6ILYb3IBoV4dsRoAJoGXDGHfxOYNhVy+XXBUX5LSxN6OXRX07yH94YAHC4RsRD
+cxJuEXgrD/6wxukX5kRsuD7Ndj/hxoiPiVwmtt+blRfcItJVYqcj/wvFUtekOE35Z+OY/ebyUFax
+47aoj/ahyPHGiKnHJGNhWae9RvrfKmUOJwjSW5n43Pd14nPu+55mN8/guM0LVdktxFPp2s1ipGXc
+vyO5btIUO/oZnoGBcZ6VPIhjWw1ujeD4Jn2w+uqRN+P+rVOAKBj4G3eH0vNoK2hlrqb6pPHWGeJR
+S6AbQiqZFSXtVMFt/3s/TydwqumUtpJ90mKQLiDYyhqRj3CAYZXJUnySDPX0Nlrd8sxK+rDXPRD1
+mDP26DztNzW4Xtzl4xwOQNcKHfnhe4ZcB99TsfVYD+W2tL4FK/DcgJdNtRJg9fiwXL1KrfAxiE5d
+52B59RtcJvQ4yf9Z+AHCfxTeA9Ze5mXZk3waFGHIpZuWp8pO2y7Ar64DmNZgGQCxiH/EnbqAFuEa
+DzB8rcP+yyHsh4Hxr7AIOxBehyfuEJY827rnIudjC43BGny8MaKmo7xkeZEQcovM4ONxiV3x45yj
+74fiTgHJVcDwovUbIpzXZOrK2QLcFdJMG0MZ9ry/JmU7QZqswQZar6vu8nkNOkC2HjNJkFbpWP9U
+CynmTBC3nrdKgu30J+x5Xo46lMygQFsPoWEeOd7iO0Ey6Hh1ZbgFzLYx0AyAsVz/+h/HCnAd5vge
+DNstn0gB03agV/4YnnKO8bjP4Dfzp5luaZvyc814vabz5dGbvGF/RGwCwW7txXjw7DV2q3E8KDfJ
+nB7mb6L6GeMh9ZoseIc8LPcV/jrIvqEO9PgKK/A+px0AZ93eQqQCIFu2tfm11INrTF1SP27721Zn
+KC/y3wVwzC4SZ4A8ukKILLq4ldnxhc59/YfhBwj/jRDW3gS5byzGfxbmmiZh3884qWeW14BvAo9Z
+WoYvKrRYrwGcRK5/Dbbl4K+4QWLfQCGzldhBb43HTysDANbdBwfsZA2W2RocVmC1uDXizX3C/inc
+HIAQQHaRheAFFcu98PSvoIK4BYWhNZgKRCGoa1ppLJdCdlY+c5hVp7IKs85RydoZS5kBeMYc3oNf
+VB88C128Gq0LchZe6+q4FdMOmSbrqxG/SOsCgmeDMWnhs2luevoIkIF4gA2S4CRXOpL1GrsJAJsU
+oLvHY/7UOgiaoZx/gJmsQy5hsfgk97qHbR8d0e1AR/5p2yDwHVw+qvNmPqzEjQyzcQbHXymS8z7g
+PTKf8tqBBoLd+kv+w0aA2azkU7XTLrw5syKpVw79xXMzjntCXN1nhsMXdFpazW9jAvg24Fp4VaAM
+73UcS7ZjTXd39wmLvFHm1AE4WI0XAFwEw/U+4dEqLBR3QJyqDKzDh2n+L8MPEP6NYC/iKyQwnnIr
+5fUGaN+r3scrtKjtTOm4BlN8325L8FYWCUAPPffyux8BgAPs5nv8aIa6P7CMANhdKPxw4h3C3s9L
+0ZVDQgDE9Wn73TYwvn0obkwL1COhsCcXinG9TnOPQCIswQoCW3NdCHT0NCuaVO0GL863Ql+Lx/wP
+e9jGXTDwlz3YdG2FA1NrFU5jJRPUTkXyCiAfl0ubAjX+M7TtCg7Wpx5f7fR4spy7nyB67sTYn6lX
+HXzcwi5aF57hUsakAGDnSX7zv+VHNnKOsu3J9SHr4KFRFdDr007K8n3taIx2oJc+VWRD5+ZuO9/0
+rVrH0cXEDNeizx01YiXd8g77xfPC5IiAViRdICzlTtxG4O/uE53vvTeJgqzk3INigpx0npps6Y7e
+VAXJsrfKuIrhHUGZ60vCYLhagwEsY3+JBkLMtuQmsTxZhncRiPsOCV9lsvrmSw+081Vps0sEg2jH
+EjsuCIjdOvx28v9e+AHCXwfcXdIO27uybhXgCmYFd8o8b5dZefQyFC+HBn9M4/L260m/a8Q/1vrB
+tNklAm+IcDCcANl2HH2IJYDwCQyrSNwSEXcK2+4TgdqPqF1idHfwwTLsg5ADWBYfszeWwvq0RZRW
+QSO6xpDCwcdrKCQUy0oHaCfLMH2N67G+D8c+bz3Ylr4I8DdiDEHffIx6bfNJqa3ZofMDhCkfUCbA
+1dqtuKL1qCp0bouONCjsXB9XntA2dKx+694A7WTVI/1f56cKmdJY2WfeIvvxetGKQHCAvp9tJ/e3
+Ca1crVcH32BD7jb81eW+b2odDlJOdWfesPM2EPG/yXxQChWZ1PpyVsUleJmVoU7MmeJ1s5a1Hule
+L+fZbsvuQHC8Ix/yp7WYzov6mLEr+KETZ6mfd+p9mWI6P1XoeP6wQXBqj/JMOVplbpxRfJc9l5pt
+ND/h0J9IM+gs/jaAATDu8kDbLRX8uwL9wWt+BXA9gd/rkosswcUqXF4OduNbIUVA/D8XfoDwbwff
+Tn37vYn3eirTQaCiUEBBhgK9n/c5DgdoyndALBBH0HmN48kasIxd2bVf9L4sG2klxofjZlocJuH+
+5ItvjehXp5nEz9vB9Wn2+Yhcp+vTBNIuvFI4+Y0S4SPcLMPz6rdZw8EgGJ4AsA/eW6jgN8AYgwJ/
+TYEUVFHT40+zDA92YqqBjNZwUYpDj8Z8m/JPdcj+hcFT2PBj6NuZkmtQR88K3am4TlwZKms80w0o
+V0VNDUzgK1Rl6/2cRpoyzaSAS99ZhU9EZktvrWN4gM6tjUMdWbTtqOmj0FpvHejDUKcH4aYPLgr0
+zEPZXc9CP0Mt1D5Ct7G/GYd5sxMPDW5onc8huwkNGmtqxN1dDmDYXSHoXXaeCO2D7I7mOcBNr9k0
+z0QJMN10XmJLD7qzbt9BfCQZMks0boXbEBmZAAAgAElEQVSIPJcP1dXBCgjGPJH8gCEwL5bjMW8P
+19DSO3O3yVekWZRzeRXeTZO4bJbgTbv4RT+njD+zXKzCzTK82wi/Yck2ikr7Z+EHCH8ZBjGxg5/C
+ASw8hi/K3Omul/xa3iMOeshpi65xXaRKAuD9q8gMiP2w6sr3UN0dyD3iWsLdf4PjMueV5i5x7U7l
+J1UeD70cjIqJWN4pfOf6UPPqQ3PkFkFgGOf6TvmBBIbO8uFXeNNtDa50ZG/qj+oxeCWd+zj/oMZp
+DDmtTzKLYEGrqBJybmxkq/M6tW430QEZ9daG8pNLRAUadX6HHitjjaqsM803UZwsx4s2rZnJYQLH
+uZzDCSnsDjk42chNib7br5beVod/aHAL0cCHc2l7ZoZlTBKAJFD+bVTDBwfzRqQ2YW1KtaScz0qa
+eYb0sAwBVJjSGaO/0zxjF4rWGvaGPfFFp/IhODuCYM9nl4jKG1VXUKy854z2ks9yGSdXOU433sTA
+4+Kp4m2rwFYXa5DHLLo74PVh+vK7xTa2Q5zgHGuMrZgiCPDaEShTX/Y46OYgUppKtNur0C6V9kMa
+p1+Zqy8EvyqSFuFAHf80/ADh3wmvcCv4Q5lJ+TLjuzAprwM4ON0aUcHvcHb5XWU9JAfAVm0B0gqA
+PxvEYn3++xpeziRfv/bBvxAMI0h2zwT1+4Ol+Ahr1g1A0pXpIrlK2sICQaxYpgPgzncHE69I5rvQ
+ngB0rIZLKV6Uqpw9gdZg9KMKayIJqqoIkg4tFx5OHvmCPtwqgVY7KP8ouuDr0DteBCMTt51Sln2e
+S9Z6a5vHUlj5MF8DjNY2a7t5UOYjAPZ0ajDsG4LxeSgzyOFkLXhQ7i1/AMRAZ6Awg96MZwnn87VL
+S64WgQbtDF0y4ij0ATf1WrkofQ8xydrjPE/KYVYY0xJWSHuyEJ8rKVx1owxuD21fTO41IrCOeCPE
+CQQvnXfKW4P1D3wPaRjXmB5AbzszseBwRhwA1ykTCVg2rqUmZ5PPsqfF16GCYe+TSQfBlD5ZhVMX
+1fH4h0SYPipPX8z4CKoecv0j/GCbXB0MixYAfAOC0zLsdVTwm33+9zD4Bwj/lTCIlpvgp/Tm9L5p
+yAnfNT72xt8TAK+Ig1inOQi+Nuj6SIJhBCHxoJ2mVfgCwGuXLreIfegZDO/0tZ44/lXy42Ex5b6v
+d4t3V3D1vVl+PyZy3d8YYVM5BMMiLJzMIjvWpKyNtniOIoQwLQ4KiwMYToZosnYhX7zfKs8UsHz9
+wFV38jEzkoO339iwwV+o3SpHT1X9Xns3f2g0emMq9Fh7z7hNZZrHKrh1VjzKAQhKW1W5Y72TbKDk
+NNg72gMgDm3qmlUBdEJHB9Ab9Qygl39lDvvA66qlqpWc/Qx97rR2QQyjHUhb44J269zNwOmtOxE2
+qTVmdTXYPYLnt1RG4xmUReM5l4luVwDpcnynEwS7a4Qs6/B+F5ECiNcfw/0gOO+62+BxPlqGcZpx
+eRyo1m0J6NAU5dO0DxGxZb9VYfZgbqL/cFT5Pd0fRh9ilBEqIoZ3ChvgYQt9bRCPdiIvh4U6VUQS
+AK/EKhNgVtjyW35JroNgofdAui0P9Nr/QPgBwh7eLgKe/iOovSn+tj+vGZ/r6IAx38Pii3n7NKsg
++BX52LrT9yPLfQE/sechU/lIgmC0CC++dcLD8gvl3VIcoHjnL9CcfsR4kER56XIcfvqn69M+Ih8V
+k886fHaJfGz/zPLOLw/N1buEm9uECNBwAUI8FUV0CDA+ESmDG/boy307XhdEPTwobRnuFX70hVzx
+yZKG6WP2iQxK+jSTJmsPnitkQHVbmYhMSnDEDgNUac2DlkRFRApOhAACtjX6FvdWSnLimkvWEcSb
+77E1uYcSZW5UhSzEo6VXuU6wHONPFCuAozoCpcT9tw7TN+OYYYU28THPMSG0uA8Buh9pwpwb1PiM
+JkzUkK3tDuF25JBQZVS2weS+l8KyuxGVmQPeNeb1lXuC4OoagS4SPk0WlhMRiTIi/kHKytj4vM3f
+vlQQfATHZdpCIUbWlBauJJSOcRoU69IP0BfyHTboY47HW2WJtUfv5XwY6AcsaU2OmyVEwh3IP5+S
+DELrb7wPLhHhH8wA+LpULnSVmNwm9vxNluCOTv5t+AHC3wY/xPCu8fVQYMjFKrn54z5xkP8RmuQD
+okFFVSHFji4SvmqDaEwbDfXcjG209223m91iMeybHNP+qe9Sk8+1gPRHN8DWZWX+XMvC7MAb3SfQ
+RSI/mULnI419AMHhcTOwWlWA++kAl0DvR8wfrKP8jxj8MEemsXxNwwv+kTVaYG0hjoLSHITTq9Nk
+b5dGH8JbHg9ddKlUBXUUb6FwZ6BTCXewIsY5tPzb4hW6NrXd8MaXPL8fV7mfjTu+uSyBIEXeQwCR
+1JZw77cOOlCuFRnnhbZgcT9CBwjjyBT6eJoO5WhWVwr8Nm2aoyKEaf9WAe15/PigmrvU5YmKjxcO
+cgpUqx8CTRbgItlgEN91kcxo6d2PDb4C0Hqv3NoLI8v684YkTuf4O9DlTVXnzCAPcx4POeSPn5V9
+DxLc3WktaeptVma+BthW2y+nE50zYS3tegy6G2B357gV2/Yu2WcDryrU6L/Jpbqfe/OH1DR/qVXz
+V1sZIyvEk34Mfs6P3w75xPqmtIcK/5vwA4QjvJv89QlrSWf1T8fb+uGCQeO6mAEYexx0QAt8Gnhz
+1I1lpREBbFjl7SS7QUB7Ne4awftVh64w2EWALCJyybYOSwJe3QesguG8KzgubiDf4Autw/sE0g0L
+sXxGfcjDZ3vNJOLp3uBA9ZLqK2zbOiwEdPfr8xHbbhX2sR3fgPdj8qkgWDowFqBFt+ifZLkpHqOr
+1hwOIYuVae1VAc2Bt2AeCnewgPKtc4wX/9nhnNRqjofJlRHX/XjiAyUc7g/+Kl0Byp/E/52iaC3R
+4i8Q1ORL03Mor4AnEF3KL3XZJiIG8WOnGsgY+NA3WxJK9WLTRjvQqHwdOPN2CHMuZ7u9tP7muYiv
+uyXzYzeYwyHItyIfHGtArzro5bQYlBd+/sBl9AR0wyWC2uM8kTvQiyAZ56iUURz1EL4Ax0g7bmUt
+aRhb6vR6H8r9qUagm11gEKzEDztKU8+HLgbl5yBYNffF3k0BgvFdVfKHqrbOxx+ucv0dYLio4HxZ
+gObscwW9mnGVtS/M4ssjnuMCieTvhh8g7OG1bkELsIU1NOT5XqngkfyEWBdWJAExAdoDEFl5qFSw
+zD58E+KY5PR00sdyrOhivNF3LV1mPz0/cGjlDUvw5TdEICDe7xv4XiJiliB5veAEYhf9FIqJW3zz
+BQO3nY+uEpbXpqEFWALkIoAtFmAHxJ+V/jS+G0swWKQD+rqcCB7IDR2MCjrjTfmVWWDlNYdWZhA9
+YJsay4eOOuVLz+9KQ4WGeajnfeCWDUmHPk3t3KV/l/d9HGd9Ugn3NL3l6yHlTiE+pgMVnIGxbKUd
+QlPSehbguGh5kaXYT71XGBmej9tBDvlmbVjjhpHGIDMwK7ue9nXmMdzr1l+voQLmPKUK/CAHXKZA
+WZIRkPYeTSB5rZGFVdjHGlIW5EtYjCsPlKsbhIGxSAXKIiJ2a+ERArLBw0qph7LQ8W1E9BQdfWo6
++44jwTPWZdscx7VdWRaq3vNB7a13GiKA3xiHcd7uZwXBEzBGq/ClGxRLU787zeDXEW06Quxx4ddI
+JusbhS0zQjXfzNL3cv85/ADhHebHLHqwLaTDWLGFjjv/u8N3l/lgKZbcRPOi7kIAduPrfKAbgODb
+it/QGk/u9LUxIS0i0saC486My7ZPsRaLMABef+jOv2JD0Os87j/s1mCLE1lefSZ3xE/Y8F6sww6O
+rViGGfyiBdgB8GeBYrAKH0HwARBTWiRAcg4GwTIC5aK0JJVbm49QVvOLdFMpa5DN22kCy5jfK+vK
+obA0gAIA9ljfVGJgvsmZK//OKvw270nQc1xHeg16w/SdEoEB1yl7mdaIb0LImw0muqBMuQcyhzve
+gQ9nHWQ51XWanLKHbdh1N5PIe7r3o1s4ay6A3b3/EeyKCLk/oBlCof/+HeXZCgzyAs6iTfnQOxOF
+tMQHa9dHBmPgduG6NaBlTd0FYubDcwB5R3Bb94qW/KkMb+fQ36TjkgNjhmuBD8dZzr0TaY23PA96
+mRF8ENzfGyDGeAGqviMQlF6XFuCbrhGhUh0E7xrSBpUuEn5M69SytFKYKN9ggHFgo02fS//r8AOE
+PbzDwQvMrsi29O7NbwJGDGPLqZTjXWT+KYSSOLxrpYsLQ6h4QiN3tAMgjjFAx/uHh3WN2gcr2v3U
+7f9rwtZhAr7XEnuXpICNX5m7llvE6sLqED3t6nNPJ9DaawmbnXaAK9W3d7D2ovvDpn32Q3ZyfUQ+
+H7kuXdZgcouo9e6XzKBYJN8F0ulL7HKjxg0ECKovISuxCCrjDKTcxlcHulN5EdiDQ/6km+7qYvqc
+06kFMFZd2Hv0WGnUoz37FM90B/BTvNFAf3jEhvbrCN7rkZs1fcAYyTNMSsiQLRtTeKS2xDTuWxWQ
+MQZ5Qy9GkIxwZOaLbrfB9Lpi11ntgRFvc1eBjT67shzy3AKIgBgWPtI7VcEv1uM5KCukpFEyCtFA
+ZjhNpVmFfbarfCFrsEnc+IBAOTa08piylq6cclRKcxxhfKCYJhhoZYNjUrN97hGe5eTIU2CUNuCW
+L+KMGdD2TioveNEqnMNB8IvWWgM/YD1YhHdcBl9hf2ntCwJ0nruwDvtG0kVDjLP2iY53fP/X4QcI
+R7iDpMzmXz344VY46GSc3dZc1Bd83PcWx1MD79ij+GTslZ18hN0XaNLzXdbw8FEXoKAQEf86stH9
+aFnmacm7JK3BqhL+vnEf8Qa11QrswrVetWb7FKKLhClOwxblWr6qsu3BFg/KfdaHGbQK0wt/enml
+yUqMLhGXW4AZ8H4m1wjhuATIrRbhau1lQByAvuwTVnLFSuwvn0+o4Y34YV5U2Z2H94Ed8uaytTOn
+vtmYed8Kt3G6dGtuuzb1vYrrcewPWp2TV2/XqNN0oCbtnaRLLpqvabGfaJhull4HJgqNFZmGvVAY
+CeWX3TbJvalLJ6U70P3Mzfu97DnDZjmvnlgqB/XpBpESaThtlgAJtQkI783GVmHXJCgfvM0qM6Sm
+AcjifkyYlkahuDUiADDud9oQZVda6EyaG3XeqrxO0kcO+cyrTZ/1mkG1ZZ5iXv4ViPUrKu8lgRae
+/lihg06L/hAwDZ4NgNW1cYJg72t3g9i3OWA61Wv4D9NLhpdjH0lArPs4w6HLuHnSlVH7GPtPwg8Q
+3uHtg4rmgkZBUA1xVGpa4nGgrLfLSmcXVAHDyxIwDQTXywGxvRik8PkrMoUe7KxMg8BAycB3AGr8
+NdFwjXAQGy4SFwhlyKvAOB6gE39pgF/vQpwzoPvg/JN8QEJyN9hpB72CVtzqGnHtK9jqw3MOfDXd
+Ier7ZP09uEmIwbz4P9Qku985H8ibCg3XFgpWAlGpPhVuA0T1hDNOKkkH4qRspu4dYMor/urFdwp1
+RLcYCs/vwPNVHOTCmbeDJ+5TPdSd6R4qHMo741S40TZx3BRlwkIRspyCT9Ip2zxxuyCQccv3UMnj
+hjIi0d2sQwGr/RbfZ7UvWcLzEisYUrbOSBr2KNzXsC7Ds7tbuaVlD4+0vV2W/WDpH6YnkOF2Bmtw
+GcMtOI46B8kR0wnzCvI/5lU7W8w96K8EuezqKNBj9gcGyp4L9QnbDGHwQnpmRjy3Ps5A8qNqm0Bo
+5jMYdmDqPsPL0ovuEO4DnAAYLcMqy6AVLhLensp6EA/dKaiP01nb+ttFwz5QrorvpfV/E36AcIR3
+079wZwpy9UvDd1xGcNwV/ggAVMKKjGQGvHDINNtaT4ZCR0tDaSHuErwB4EmweNxSMMeEVB5hQc2H
+BoGuhttD3C4hKXDx9QvTu90AwvsVa9ME6ASGwzNZqqVX6eG5yVXiSiDr6Xigzq3ANw/JVcuwWAi/
+UECOfG1PiFSrcfJ1P+KM3r58nqUVfQw4y0iTO/od/hg6MPXn1Mcjb/x5Y/fdamdsBBQ8DL6otCE+
+W3OneKfpI98UzsB3Br19XiagNhC0ECd/L59/LRM2XZuD9wkjDXhyBKe2DqFuvtPGOw12LD6dAsiz
+7KCNHNMYqpUXQdQ9LfuK+iM/HDtHt/gCbfM2mrdZAG3G0VLscec/03gCMC/TNGfK6XkDdFArwvOK
+0Zw7jS2pO8P7WzWKj0eBCJTS88PJPwDimB2b6T6uDoIR/NY0WITB5SGvS6suEiL8oxj+SuBcgW+C
+X3j3iaB347Tw+zd66G+FHyDs4QuTcD5RuoR/B8dV/udXRgyWBSy9u3r/o1hetp7ZbQAtgLjigRHe
+ZEGUrqxGHhmIAuNOuhYeFxrIc6mM4JbcJTCPrL8pyH8h8BUJIK0OqHd+KArxZ603JawpFg9ABPgV
+E7cMm12rXfuIOiCOh+MSEEuktxvER4sVGN0j7oHx6ssaKF2r5uPfc0BfaYIgjfyYMy47LGyWgxx7
+fOXz67nG4BcGirxaAybVNXXtJAgb/SuhidaoVXaGMLMcqPL6TfzPaPqKD0McX/PUPDt1hLmaw9jP
+UzLkBSJoDdloEXCBpUDbkUQihwFYKTMwTRiJSDZ+ONsdPtBrhAdbS/UbDvp5xJ/e5pqUuqEjP98W
+wbSUCYvKsqTS/DyFfIHyvqXCigfnKeUHguEzjY0VOVaRbLcro+mWCc5PMtqdi76ja8Q2D+XxY7/R
+K9WxR7Mfce1v7eu7+PQxmIFn+gcHrcUHcKzSLMIJfrdVWBQswAisZwDsxzWmEnreaCZSvyaP/TjK
+5f8+/ADhHd5Ofnwi3YB4dHkQf5AODk3kI3AogtNQJ0CFtQ0jxuJ/I/UUzzSXfKWMCQB5qCBhjdJf
+KxRuBMpf/eD41Wn+08nzSwdwrACGhazBK897tZWJ7IbCSg9iuVl/t/sDWH4XIAbQu/OizOeSz/WR
+yy75fMA/uLlHPPgLB/A958UskNJKBYNWHNxcpTQpP9HG/hA6yHrccnbD+4BpJnrHKah2TtbfM2i5
+O//H9su8Ters92h6ziM5s0J80DsNIPjW35nzZgYq9qh5VBwIUx5aK0P2DGAmNid28d1GXaLXXm1o
+bGb+GveQqKzxq3bKLHAYKZ/WkFfTrNL3Ck/0GGbZzXCmThbgyJ94Nz3kBJVLQ84ycKx1pdsjyG3C
+810e78odPUF/TkqMwTOXbfykfrieqKFYoGpN7APsPSh15GiEXCVMRNsnrOnUV7g886RUuwHBijc7
+VICcD7pdkPdLJX4RLkExXJW29Wq9Z7jmd+swjg/ecSm2L41FPM/LsyT7++EHCHt4aRGOa042f0sL
+CIVX6QwkyuIEusU3mVRdIYI02ugV5QM+Udywi+7De5I/yIhxTVAMIy4iAgXMyscfzuiW33yYwgVz
+B8RQTiQBsiQtTiJ1YVe2B2ougOPUrRdZfwEQo1uEFr/gBMsfkc+1LcYIXicrMP+4hltQZ5oLBVBf
+FTBDGQyoxGhO5YvXoQxuk3pqJvqZNy1yk+g7icN+rVrl3UoLtn7n1kbZlQsC6Uk1fUv7jv/eHeKb
+ecrTd+Z7knpWI0+AOHhgjptg20xDmbRm9/Y0vm0YAgi+4k3R+SizcI4F7yrcHGWiqiWyrkK1AnM+
+S1P8lgXZ6sNyWRHX9gR0BeghFoE/61FatgDFJS5UtrhGoBEH6DyAns5e4HhnaaPxN7PrA23iKfcB
+Bn3RbnxC3U36Dnu7xtHoOKEivBhA55sSznEGuPni+33d6msElNe7xg9kLAB8Ar0aLhPkHiGHl+Ls
+puW4TBLPgfLUBJSRfx9+gPC3YW+0tYCWaXEF7WARZBLjWK8m08hjcCiJbuLX1Sx3jPUeINxEGqpw
+5XCSHyJda9AuTIEMz4DGvs6vi2YwnC4iGj+R3H2OhofjLoZ/a/h5Cj9bUC2QloD4g11nURb9YP8k
+20rG9kNwtgGvxws43r8Akr9CZ2Kfj3yuSy7/+eVqDf5df2HL0Vt2leYFWBq9zl8wTVLmRD+EqghE
+evGpyhPt1Eal2JxxqCN7VvOPX4ff1InKvPLU99/P6/7EkwX4tvugY068+OXvi6kY658wbuMBoDHn
+nSoykIEog0qoeOk0GoUI8hV2GtrJNYLKFXBFQ8k22p7RyjWfivahYKMGlLlIp04AOfduDj/lA8uY
+iGMZ0Em2O+Z32PvtEO8Asp8fT91qxQSpp3xUmnsv9Z0NNHd9aOukZYucfH0dPIuMoDd6irdH9ZOr
+MtNXFPcmrit/XEBwS7/0Rmm4Axh4AgirHl6pl8ONQqDu+trzQncLYz9xHOOZd8vwNNJ/E36A8A5a
+BfYhmG9QTVDof+gJU/NDUz6FFsWmWK8OPIU+gWTvUxxNkA/ZSN2Mnp9CbLbOYZ0OeGcwzJULnQj8
+dZrJ2lt/OQ7zRUV+XatnCBRsz+1nNxv3FENXQDSt8tFxn1CT/FllcIGQBYLD+mv+gFxahSUA8XqP
+q9LiGrWTJXiwCsN7tfYiLdSUuZrpvKjKpJf86lVD1jpYG41WP7ebDTRMHNpoMTvx3QVUZViuKMCb
+Msd5qACh8N+6OPwR7xQUapl57vK+DiTUzlks2KY6SkWtkwxr+gY7yO1pIz5uONwZpyKVAFZq6x+6
+EAzh1Wc8nmLZ5QncLHU/2mH8RuVJEkDW2TKcvTHMM2HXBskPhdFGAci3vJGqC9XTXR7cAGgHWrHl
++MMrpTXZjYtJgGHlXmW8W4CF8uT4gXv+OD3HY9aMwW68lN/xQTYEyBQHWrUGo3FKBX9uuQLffFWV
+T3l1TQ1WNMh7z/i8zdP2n4YfIPxlWOdchdYSrozxDYs+SC4LUW4FregIlMn5YJ2JOwIrWYG3Mi/t
+c0egF+OptXqKIa0lQ+JrIwLFSiOFQW74ubuK/rwXujps4NtBsp9+5lNR+ex24e6HDYBZ7KFg856l
+f9tn9REswfiOYNfdJBT8hKW4RayfY/7Aj2l8YQm2VE2ZXxWHzJZh4ZD0tOhQ5heSZmrHq8htk6m6
+lTptABuFTwrfkNnG3Ggka0+DLkryoLhq/af3NzxzGf2NMhzqUcac6bGdCpxP4WFKzkyk+zaDDa0R
+CQegM19ryzpPkL8cnc05obiHcdY9a4AMDBhiBZRnnQ3Pw+kxKaunpVHsdB9vBb9eAuVCnu3Zaoyt
+pOzErqxzhPGsrVuDuysEjrlKC1dgz2V4p6fuWfPOUoqupGtp1uFa6RFnC/ARNOOEHk9xxmfnqGxf
+SzxeAEIDyAZ4VbLwukWYb4xAP+HqFqHsfqHcnqd7vxX6Wrys23z4/vj34QcIR3iPDvxc4lc2fPMP
+flZMoYEOBONRNrcqb2ER5UBgOug0kHs2fMq6RdiY1pIW4assFFh8GycgRZ4UG9JA9HpYzgK0+gyp
+CNCWX5MBj0Cez/eHhPB6d0AcP+e8c6IXOF84H/SwnDXA26zCAYAtfITF3SbUQTC6QyAYTkuwFDAs
+whbdEOjWednGC2zCLxxnzWuvPT9jHQ9hUN+kEEaadaVRa2xy8tAnazEd8xQ3zdArFsmoxAdAcNOP
+795LG8Pebu9lL5N8EKZPfZ2OwbfhsdzYaLF83lV0vO7sAJA3D4sym/nu2o38sZasCs5V1+swzrLl
+LJk6aMYWp/GOwAFJkOcPI0FOxKF6qzw4riiHZ6EA3XjeZQDAJmQQsuB5lhjWeN5ImUxX/+mwBLsu
+835FGmonQOuj2SnlHjGPt1R7XT5w2p4tXJAYnTEjtEEAWOEGBwe94gC3AF/P15rPt0ZUd4iwDov7
+FSsDYmibX+AmgWOir7NxxKzzx4dW/+PwA4Q9vHSNwEX033xHYUgK1LjqQYQBSN6lzSBd8zMtUqzD
+0D2Qr3nIvH8jQLY8/DSYAmbpIQINQa63vJ42udyiq2z9FbAOS3OL4Dw/fSrpE4wgGHth8b57YkjL
++Ym7g2WBWtk/p6zXzkPAa9cGwAyMP7ZujSDg+3Ff4UXrP7tcrcMClugOfI36vRXawAMs46vpj5tw
+rINWO3lFigXSOs8ZiBXwccJCjVZz5gHWL97upuCtKK4+vN+9PwPtp3cGfpn6hn5ej3fhVRnjBJfR
+vhjHSlme9T1S3F0OfL2dA8NAbnuuuETgg1eJcZT0hMGfpOvQnpGC4e4OJ7GMB8987p034Df7Zlsx
+hC/w/oYyeVJ5jNZgd5kAOrd4ehiu0Lbei7mq6bEOhFYMs/jht/13BLp7lIqlJh4eSf2mrJ92jist
+xOw+cQSbCGBHEFx+OGO/funsKxx8UHcCZGUAPIDh7DrMkPLM+z5cBooi+/9x+AHCO7yFwbidGfz6
+/tXMIrl2shKvgmRJjvRWVVvIOMgwyFz5udk0KtwsFYWX/NZRz0dICYKGwHB1nVAuU32Jq/tDukdo
+0EUlbojwU2Ylz2cKV83BsJgDYvwEffN4ELk/5ENwKuz/K2AtRn/hj11yARi2zwLA9jkB3wd3CVc+
+oYQs3CHIOhx8dURGZXq+ULmn130Yn1un3TOmjdO1b22JTnxt5FzJyQO48vtNLk9837w/8+hI/6ad
+iAPhhPsmuksqQ3lQwrdKqfE/CNZYx9uGlN7mhiDjd5H9Le9dHx3MsgFk0YhDAjQXzGu0kDAAa5F5
+r9rw4cKy3XFPGtOcc8zfOuhkDb4DvVnvBIazfB/0HW1Ibz213BdN0jXPDvRdg6+F61OoOXWdlNHM
+N0QwkMZ7qs9STKBeirscs6TjEzmqFpbhdFuYfymOrb7508rpGlEejgO3CPoBjRsAjLOhsoB9YgRY
+YxtmOFw9RbC2fxV+gLCH1xZhXyYDoXcCv8mbgoKbi6Uvujh/Mdnvvs0K42ueai4OYU2nMSvE/lK+
+0R4d3RsKGEa6HeghfCwP5hEAX2tGTdbhZQHNM6qGN0TkRH7ED+f+tByC3IIzrOhR/74lQtwybILA
+WMrVaQFGN/hFK7GoDoD3AHxjZD3/N0AAACAASURBVGDLHflwFhj8oo3YJLo2zJmUchKuEI2p0CaX
+iYn1d9JCNDxPnY9pnXrmg7MZDWjhy3AL0KHOBiqGus48Mwg+vu8uT21gQqNuI5oU+gksvwnflJnW
+86t6teQe60p5pgL69FTnmz7dVgLdQnmPYBRAcdJy7xrI4dqS1ULYHkdI/3DXjTjj7FqlFT4r/PUd
+5AZ2h2lgPYbzVi3JPL4uEd7RfE+zLVZKHB+Sm+Kp0weAqxnP1q3UfgLDO3fPe1p9J0nR46lRU2oo
+9DEAaXnlncL1F+TgirSLf0b5Uv7FuQDEkvQEwApxiSPYgXHq49hACjMJLjxx//T/QPgBwr8dNDZA
+XTo+oiklAEuOx7n9KIcD4DhYaQ1W5DM4zlChX/VCwjLSu7RKukuIJAJX73O6QNAANN9jdCc+9R/P
+sIUd98FyYXuJiX0kXCfEwXFYhpVoMPg1BssDqYbXuKxP0ypuSYdFAikeYBjcEpTifnXaAsaK4Ncf
+mAM/4/Vrcl9Ygd2CCyonhXoFwDu2EW+4R4CVmEeGcLkHe3idyrBS4CmNtA35NgHksjalPh7RE8/c
+W+TjLzfv+abHyUaAUN7PYPdLEDzWLW3gE757T2PqW1X0n/M9FUSgbPPYFt8EJmWeyFcd47xYG2wm
+Nj98aLLKZ0Cr6LI3QHvRCg/RhjZJojC7T8Mxj973OdkKC+8pqdeoZbm/bQ2eaNbiCbGSzq4RIMuK
+esTlOLeQ48O63FLeRzwJOb8VJOtZ/Uhejp9fl+s9GUCvA+RG18Z7+mllBL4zjT9GKM7K3i8xazHh
+Hn8rJf6b8AOEd3h9fRr/WWVlXkYHiKeHpfPeYThiBodVC58IoOXi+0Tg0LUCnO5dd6aT3xQPI/b3
+BtxO7wf+IO0+6h4HHrJqLV7pTve2+GG7nGCfqbhwQhMcMweIeIOXqwOgOZQksCz7OWkAzGjR5bQQ
+jfkk2q1WYM9CtwjJN6nAV7AcvXabWuhbBqEC7LXNLw8xkyY0w7n6b9L7DNjcj7u+nWjjaXYZXEin
+cAIDd3n3YOIPfIJBd5zGXcfSaYOkMs57q45GvknP/0l9GKaFmoaiA+v4letNi990WnmNvJlK43t4
+J9qqjJpu/XDgrJVcornJH/el3eR5Obc0wB7EtggMj/H5DHFNz7tXBOU48+FfbF+Bh6mFFsh1j8qm
+s5NRHXJwtOnOIFs25prw0zOgHzcgdK2pWybq/lp1BrIz6D3T9/sl8ktVfl06+AmnNbhbguFdip+y
++dzsjeLAeE9m+gKnvBeRMMaZ2L5+7+0B/HvhBwj/TihAsoe3C+kbYlssB7C7uDT9nMzVlTYAnTJA
+o3rS/Ns6kT7rngfAOyR1BbU4dmFazZtoyi8UFNHMPmVKIFmKFXkJrUvXOC3qWHNzKdIkDMhiDorz
+MDt4C2GJ0/YETWz6ktzEV4dBavKg0uH6kOTaKbk5xuUnlwnL7FR2Wz4Rb6W94ZHvQ0AwF+qtHtgU
+kn2utbxyixhkqWKkAYekV+XXV78od03aCVDc0vyoY95Ac94JDCvx2UDbfMOc8nR0oHi71kPmN3vj
+q30UMuuZL9c0mXuxE5h86FU/fm1N1vr120Byv5S+FlUSIrg2RX3kRqdeP+3BkAtTnmw5Mp0VAV1V
+9mrnPffv90IZu8qSJ+HEuxdEZRkuCl25oIiI1G9EnCLZUkqCoruQ+6QmXQ0HDQBvGocswHDYl3Y3
+nU9lcnMo1tyrA2L2CU7aAsEqv/SK+HqAThk4e7tbB18+H27JBQPbGquDeFC8aITDs/z/gDVY5AcI
+/1lQjqbQeJLWm8ufxPWDSXewwSnATVdoeC0MbrD8BApAdks/i6Ywz7JpPOKIILS8r0H0IVcpUrM2
+ElW4J9gBa3zlEnEXGBKfiNco/CEBFm0qUFd7d4G5uOPilpBWCH1iwoa0r5OFtTaWxCqfA4oZGCdX
+VUNTu8lbwW9tQaSC4wRZ0zVpjzyFduhehBT1HfSyCsKxwUaFvcOgNkvfis/SaK2i3xmsqdBL3gQU
+Gvi5if8WDeab8k3IKod5CgmiTXyF1sPAdZiXb8I3ZSZeWrcmXw4TI33OtOTfVDp0hgu6u1q1+rqI
+pTU05qu0w1QP9Cw08U77qrU55sHZIh6XsIf+vaE/qMWevdv0puv7VIcJgWECyWIBjN3aiiAtAa2n
+oV+6863QB36vK40t5eqz4LCSBnVq+SAclmsg99LQi7euEFe3FAcAvtIa/CveAfiq8JVpApZgHJ8D
+fB93jB/nWXN9YFclJvmfCT9A+C+F8aA/Liyc6vKzcWEFRgm5d5bGdT2w2+CrHQK5qNl9x8UbPNVa
+XSsAYDbwC91PciBJfsdp0ByqE9D668Oh16Win3UYRSWvXzOfJclDGe8MihFoRxsG/QmBVFWDx+++
+5NsrQW4VImHV9TzLurxcpk7tyk2aaaG4whVDyG+YfIXxaQ4vrNm76P4N7dyn+xBb0BpWJS4C9biX
+4qD1ko2iwv7vWH+pQjm7VRYrVIGN1FX9Lj/WTQ/03R8qBwCFlzE7DVLjFW0M0c7Xgu03ON/xU74l
+UHgCWaf1vC167AyXwHWZ1uoEhhttav7U753Z1qfUM+5HqzT+GF77E/3ADaS9v0+h8p92lSJhXBzI
+CLnuEaeZIBiWAKYueBx+7lbHr+RTTvo3pVW9VWuwineD3SPA5kNlnd+1bX5jqYUX7gm+ujW4WYiv
+k28wuEaERdhfA59mP+mnmiUBcU6hgXDXXJPJGgxTjAtvxzX/b8MPEP5LYVTNinkTMtyrTu4RwuBW
+8hCnr/D6tLjOZoLmEQDrpqGUAWc2K7zrDEPHKwguNBqzDnsYTzL0Qf00ubBSadZf8knyA72fsjPZ
+/sOWQsJnx90k8CumBoL9ELdFqe87itrjjhdUz/pqEXhAy6TluNeRtt3Dg3JQMvJtoN2lQRlHeVXm
+qzylx9jtsrxDHPa3PMtHpNY9hm0/KeO3gLfJZz+CUKiusr+/iSNt5PHjPvHsjX2q02PTbobuP9Ie
+J7OV5vDew7iW+5M2M6L3rI/t6/vOjOXrWt3RQhxorwMrrt2xwpQnRKVkHffZolnPazyc97fxybQf
+o33FERXd6TpMuUdawLCowb35ezJV0/3P9Y9UTeYPwhtZKRXzJMGgiDebVtLMnwDw6aUFaHK5SxIE
+n8DtyWVi5XEZB8BHS7CwVXgEwQJWbXeJEAW3FHgHrENWYV38+dTT395pz+EHCP9x+I1FCy3EG2Ll
+5abRvUlII5P/cB7ycPbHQ04AGKQqCosiEMx5C/BViNOQld8N8nWamjJUcl1Qhfi2AvswVNav030Y
++F5xdOCrGj+wloJFXEaSdUCyM1LjmSZLMVl3y7tbY6WCAo4HCA5Ng0rJhlIcM+xHaNoaSmlYUju9
+6IE6PfCk8n4CDYhBOb4hsUq7zo57X1BszSs+xbcd2TyVTQ9jwX1cV3IEGH8hXgEv5e9EdU9h15I+
+5EaHryQn3hoelrgwv5OFX9WJ4aF6FHVvy2BnvlLBWK6cKzE5WnxH3r2Xg7/tRYgftzw/SNelk7HI
+Ke+17SkPZ+jtPDU+kOl1j1Zeoo2LAxbJOMdwd60J6MvNE5++NRcgxAz4EIN4QYMJukH4LU2ud/ZK
+EmCO1wHg1m8xu9UV6zj4/R78gvUaALGK6AbT/2e7RcQLwbDyD2tQ/+K1cYePXXM6fU6rm4RA3ije
+Z5H/n4cfIBzhtQj8e+0RCFY4qLB7APBWn2L162vwkPsuRK3gAhKEhXdhfrAOaVs0KNdBV7Uh4oVo
+2894AEo8hMUhjqB49Ue3e0SC4QWOla3DBvVY1rcO7J5HEGBxwINW1QbMEzzUxpZfyBd0TSAPPK5L
+RFgD1tnr0qFai7vvsIECXHvn/YNxYGkeLMW/G5o8jD15LnPOUvH5PSI/zBqEbKg1yJuqWmPXRs+8
+N/H+FXRLg94+5hf6NNY39BdTfx9eFPwTndbGMHX+qVxb03s5byeuu/05rZmLAJdXj/Tz0wkGhCOP
+JbXuDWp3yKviZ8rrYbrB4QBmtXNUOSBDutQydSGALtpuJmNHxrO1vidgs2xeBV7veQLTNakKnKhD
+RDZ4FdA94vro9FPFQFesr15xxu4PCXJfWor91gi90jUCwHC2xZbh6I+5brUcf30Gp1p/i9Ev9gZm
+91X+J+EHCP/noR98zMuH3dZuCNW+N1Raz3QfOrQSg9UYn5AVSa2B/qDt03DyhTVBJeoiAEGCiK9Y
+Y74SR8AL79UaHHi/5u/uLmvw+orHTAIEn/yCUYi4sPLh+bxymKBFVxuzmpluhwWtMrpVZHyx3LXr
+SfD/bbzYMoBhw3R1idBXD8alpVhhZLPImvb5HS1OB+3Dp9IPArMoPeKNr0YP5YY+1JU5AYVTnn2R
+3np7xfWBj7vd+nqmh5T5LvzHWuqpehrDtKFaSKLdsf1uh5DNDuvlul9K3kCfJIDvhUp/yqf2rPRp
+qsvOeTR3eWhpdvtFZXOYQG9dG6xj8IAIhuzPJhiqqj0g+vZzg1cRQQOQ4oFXgecKDPrlfw2+ZUTd
+47z80Nu1dY0qWFOtpLX64FYwrOkfrOgewQ/NtQflqi9xPFgHt0YQ+C0v0f3LdGeXjVgDARC8XR1o
+gds7Kn3bM4hX2/3b8AOE/3r4StRK3yHCm6S6QpDkKHlBEzroEwC2Jz5vBqSQEprM7luQm4Qch1iB
+bx54iUMnRFtCQtTip5RXce2+wR43rkMk/cUU584/queIhQ/iF7SNUlOxFeCMD9DJ9LCcSEdhlg/A
+tTaxHVaO7KZRXqG03eqrHQyPZZao6mKqU57AL3zE49wBvU2XrZ1aiqNxU2SyQtOWHcpOQMVV3zmP
+57CmT7Sg4/pp58f2pnHc030VbsJ/rI/+pHoem/bKDgOzU/ZXnUk5gudPTM5geBegNbZ5bQXyateI
+ZgOt8g31tH1kA+1Qrw5xUkmSZ/wu4BGc4j3syR2Y46YIJxioLYM9DvqPf2xqUbJttgA7v+snrAP1
+zmrCEuyK6y+Ty/L6M6dfUPbaddNPJYvrPig7PCjXbo6gB+XwF+QYEFdL8C+VeHWLsMct+ohz7RZh
+hCxxi0QDAJys7/+x2BnDDxD+K+Hp2N/xWQG+Eo7j7Y5hTIOP03pwzqUigNGDBdge+bIv2XX+kY/g
+55GU/DL0MtRM+7i3IPGu7bqu/AVj8AnmB+SQjsKpfyUV0yDkGoFSPUdTwqBqwESbisWEAW2Jk5/x
+VGfGrebteL0sTYQBsxW++xdaijWVtx54X+/3Dn6neU1qySf01sveCUzfi6m0bjo31aV5NOoKndL2
+RfqJ3vKs5MPxbeOWnjfTi4fxSw30fvV7278fbiFS55gaBRAFsOlY5/TV/8RHP5Ti6+TfcAivYQXD
+ftRjTWEZJgmC/Lc83l6hTzyNNvB5SJnZ553OuvI+o3xUR8prYaUeDklteSZp7RXljvtZKZZfunVE
+rAFjH2+0uAVC0zEuvfa3TRrlNkA1112rjAPJy/PjHQFwAcL7vd0ScT0D4qBVn+Fya0SCZZVf3pYk
+GNbjKw+UFleITMPCwgpXK7Dvj38dfoCwh9+afG0pu8lvIaRC7KLcKPUqEuF0gmWJMyziLK79UeJU
+AGxNGlkIg5Ri6vnUbR0AbxKMk32CYFi2nzTFZhkIa8QXIDb6eeZuFT6/3CViXhX/yuukWiBOPsBO
+tS1oobxbiPfcNiswbRZUnMY4mlrBYjbSSMk6DR6EE7lzifDyA0+kvZauKJ8Cb4FqFZ5rDdlJhPsy
+NWvKjb091sftVkt4Weljuu6gN/RXvJa03vOedzd9B2hxDIet+e/Coaun8Y4MkvvwseKhDt9xJjJa
+6237/DZAbFmG6DKvKdaL+/jp3Xmn/IjbQIv38vAeuMqlPGUg2+ezBzypRR1wWSCQewQmkI6dB7+Z
+1U9g0uz7jVQovVwge4Hc7g7hc6PGFmK8bzdArW0gaxpXk+WPVPgNDW4BhndFEDv4/t4C4gKgL781
+IsGw+wzzr8tp9IFcOfxl7nu9dXgDwbi4mmvnzzLt/IQ/v6NR/k74AcK/Fd4c95lK1rQNWlXKncHD
+fXzBI7mhVODhuQ2WV1GQss0FwqWpAkjwjanZL+FiIi50JsCrPGRsXovMoWH5A3/C153tiF4i12e7
+Q6is92sBNHfWv0wyHfWksPIm0wNizWHmd7Vz+9iKVXqxkcZg78pBHoDnBmkLsLbyN5RntUhLsQSD
+lkMge+8Ssd0gogwoclxfOYcKdifFN4u9U45vHqbOHxoG3/Vyq4G1fE46qe6GKV2BxonnxPs7r9p9
+KfRhaK/zToH3+d8J31Y1+njXPXHOopzjHra7/a3IVj5klnWKM1YelPTzqrye7d0O9Bf8E8/MZ40P
++4mDpW/VpMdtiAcDhJMsmN57gJwdJfXTDkGVz9il/TdES7X4GoijKpe27ywYWNYLrcGWgNbYuhoA
+WBMABzh2IOv5t64QB79gtAjXh+gKGL5UhlsjukX4KmNce2IrBf+woHsNBtCba9NlPBb51+EHCH8d
+nlT/75SBo7+RIz0EJ77J8MG5Rc+bI1IiLKzrGxDAbfMzto23mcf3YX7aXmUsozCshI1ahwrKAff9
+BILTCK3xFZfXp7JO4vUR+QhbhFHwXq50LGllCHGIcV3wl3+Sd1Al5n+S7v13QBtuBhaprKO5S8DN
+DFKDQYwdIUyMCkFNBSRtSzD22gSUtu003AoB+QGGy3w3PSOzEpvDoExG7vlatbH2A6JrpUkQH6qW
+WRDXcU9zUWl3czbN6bev0zj1N/NqfuvwocxT2b+t1/j0PjfSP98DiJI7wFX4D32JD4wiR5cIEwbE
+dB5FGBCDPLh7J9qhDEuq7M9tXbVvm4Jgb80Kf8N2N494ar8DvUPlJnCQUcmgzoL6bKAF4bSxs8ds
+4YX01sm3eVvmXGIJbIVvYiArsPSfN2YgDNemVYALIJiuVCNA3O8XRreIuEeYwHB9mK/o0rAIC4Bf
+BMEV42AyAEtb5n8ZfoBwhD+Z/m8BcFFH6OrQT7yghVhVxazTyfQ6AmBpPBY8loIUvusKUZOSL3qF
+X5UFvYHkw7BhiAmCF8H86xbZNF1C5LPLd19gB8HZ/vgqwtAPcVjPBW9+sOBjVcIXaC15DKrCsDxo
+JpSylmUNiXbgp8DQsFmZgCcVsPOdXR5kW7QC/lO+dcuxb6fWoxKKUjopv9j3N1U9B0C5N/WcrmvT
+mjhUZTe0pOtAw/X4vdcn4jzrWkZAeeUc1r7XY3q3BZ8k5B8t32+EPgcyU8pavpP0Zy4EdW2dTi4R
+AuuvU7k8S/1M39DsBQ/S7Pf4fNz+qumkp9dnnUGct0kOyIE2BvO9XYwYd/t2qtAquQgIMFESIFb3
+Bx4AsGBeuXHBAGhKB8MMgtlv9wRw+SE6sBA36zCC5ORn/2AJ8Hv+QQ3/AJTva8H2XA0gOK3ESWur
+hN9W/+PwA4T/ONwhvk5vWwC/C8BNorIswIObxBEcbzCTUiQ3qAMe51nVuIaA69CsSAZF4RUIVXbt
+o6INFwqciqKdl1vHHgo2DVJLQUpdovK5RAR8hPEGiSMI3lWgMML+sMC+uSmWEILPW8bDdlr56AG2
+tBiHtqntlDRbgrMvNvC4QsU2sdbsQ7fyek1vwfDbMIm8An+ZTIOdS5/ppZ6m5GoV02r3fTvdlLHm
+4XxjRAUZ374+I33y/4Z9ESOAcZWOH4HvDc/v8P5rZZYy6h3vNA/6ugbeN+MDqfvbuw56Nb+lKXny
+EA+aDbSJr9Ls9/nEpFkDTTjNgHgISsfq+KBchC2DSGZXGQHporZ6sId8YkK3h6K5w13xDI7Rikqv
+CQCLwkvWg2qiHRijNfjJ4ot+wY22y0sBvxUM7zq7FbicAJM9doN32dby9CPm55IGmtf473HwDxD+
+s/ACBOsNHz3FioA40Wccv34OWeFjdftrinyYrtZrBATqA3YGmxVwafSj0miIEw3jZciLtAULViwL
+8H7AYnuJiF0q9rEQMiGM62G1QreSH2fQhHzIpILhQWWYLMt15OSDcgs09tsb+vftVQAU7VZ4fdnE
++wzKNzjMqE3KNynAVg4uEWtGZxDWgV8N1R+Y86TlacutGeWcvAo6FNXOMezJU1UiQms6rOaY1+fx
+/vUGAONrkixWcip2uBniax6nIlA6883hN1f1lqHvq9+svXVurtEEz1FdJxX+tUagC8uI8dq1HTlI
+oSNtjNuc/w2fy85L8mv/23CTj7LA24DrgG/Xj9TKwybqKsge8lesus+tVWOf4BM9gLEkEMSH4ioA
+/iUViNa7fR3w1ruCy00QF4LkAn7Dr7gAYgTBIqX+E3Df4zOJsZP+CmxhA+CFeMhnzp9d5f7b8AOE
+fzvUo7rSgGEHnkVLcMuINvfPpvl1aVoeirPZRWJlOc0bMe5U1CV7I7oGcwBsrPwlq3O6iZAQjOoA
+vPD1Ocbjw2Bs+a0w6bpM7CPyAcHjD8x9PgXYit/hWIBxlawwF+zhVhkm4VlUBPrq0vndeVg20hOt
+Ap/y1LYlj1CJTjOKu5/w5OLgSrpagU8Pylko/T5n5zDfDTH5AE/wWQ5IbobaTYgewK7d8NTmYj5L
+htX8Q7m3rzsQHHnG7dF0UNRk2r0D+2P+4qlC4QTC/7sw7ZYTg93xCc6ODjTkO90yk2XS5WECxGkB
+/ogMoPi8rn8tbhm/5bU534ODO5+n+yu1Vj4HbfvUIO7t3canRSqk867f+ccsy/IA5tZ4FPLdLcCB
+IPsKuzXY3SQug4fNlMFwPJgmSpbg+iMXv5r/bwfBBIxHK/EAlEUIDEcfPU+U8lCnNp275yKAr4ic
+QLB/H9iW8qAC/uvwA4R/K1StMwPe5/Q+1eQbgzR4F0vgHF/NgOtEfMpytGrZN5OyGSWl3gbJ+HAd
+CSevDmWYggAr+vEOMIsMQkhrwnr8Urk+4OO6D9ClCSD8U3cKIAbBeVglHfvLOBFEHb/0Nkz7mMFe
+2sCvMC3e0G/3pg3D3NqnTQM2K5z8IN7JEgzWXpNXYPhNmKEqrnCu5etSDwjuKEN12HuNR3n6S4OG
+L5XywecAcIb+Ta8TCP6IEEhCfuph2XIZ04o/Cs80UoIE4qKhBqzj3Y74u4Hk1Nd8NzdGPNZa94TR
+WVvnCz/A6Aue7OcU/908389IH3ntPn8CulLSFfh+BloAWy3pmi+wr6fNOy4PH4IOjplPB94E0Ql2
+Xf6468Na0dQ1bsyZ3CTwF+IuWTKoguHlDuH+umApbtbhyZrL4Lf9gMYBIF9Xgt54R2BcaTKA4bZG
+ey5Cv84g+DYut0+d/GfhBwh/HapWeQN4e3ofLcldZHtPFL9gOISGjuh7AxG/yn7oTkKpxynf+Q6c
+mgU43CQSCVRLb+xbGEqcAc0yNT/KTtNB88KCB+nLD2KN/foogF+LrI8IXJ9mHQTv+fFq17QxXBm9
+QY3TAd4GgBtxsP6SApRloWX++vgTV1VhS7hGmHcv28Ey1cJjVEN1f+iuDyd/4qlPE3w95Z/ib4O2
+yLtK+EG5LDwe5zJ3p47w2s78J8By+zIbwXEFTSJz/2hMdQtvhhkc56G9m947YP2vQXHIoNd870Du
+m7y1FsUSbIv2iTu5nQeAL+4ZnfeAfJEe8+zMRzR7yN8hQJ2lTkAQ/IE4zo+Heu5lSDut7t869Xhp
+RMri0+rlRDzlLyZNPeGAuPgFi9QH5pJvzRGCZaEf1chr1MASrGkR9l92q0D41xH8SnebGK5Rq0C5
+Al//FvVyveq6E8Gw+Jq7DnS96ovhc+nWYVjghzh6U/zr8AOEI7yc/r8AgpkEwLaqm8kC7FKB7u6D
+r5knACwi/AlMBJ/qtCkf+hJyCLt/in8VylfZKsJfmficeGoDM3X/YZFmBQ7gu+dGQHDD1Gk2GXXn
+OCaoUdbGwBfWtpozcEMQVES7vJU6K5C2wmvInTXt5oHqpdwFwouz1Tletiblzgr88W0is0uF4yVU
+aG0bjMQaJijMNC00m/iiI1OD5evt0mRtnevLN37BBwQ/whA6/xm09PWZfYInYHya3np6p7Xps66V
+BfLm+qf2v3bpfgpKb2Po/TlCnhvQ1a3EKI0yzeV5TyDY7Q/MjR9uzPdRyvGn/fLEI0N8or3Nx/E7
+0P1QfLkESCkzxSdAjGd72mvNvW1qoPTzCI5DztY+eH72bumF1L+LisC4WoGTdoFrhIPhsMDKwWdY
+wRos/pPH04NzDmYL7XTH8PQQXQO6BqA49Wp3gWEQ7IuWxqdNtC1TFOZ0G/R0x8PlDz58/G3x8Sb8
+AOFvg9WEwkmqX/TW4+5iDngC8KQFLyWR0TtZDSvvXw97Mw/C5xW+edGxFIgO7HGm8oQ5WA6QvM0R
+Wg7fArxoCWba+uSqWS5QsIsuAZpEu0EKEyysy+PrM9JtfH0aTQ7vCXDBnxi7Jv2dlC/Mv8fxJZTu
+K07W9uC3VlZ8/od6vQzuh7dhhCbjlhtuahlb0iHVr0Gr8/gEUOilcNyP5WYQ3MrsD892Gg6FtycW
+9/sMim8B8X+swd6M4t1IM5AMovQdr6eZUtcKqt30XkNvrXwCe9WPQzrF6rG8iswP9JeeaK0g3lxG
+vwlQ07BQJkKW3sZ2KN6qsq6JqZHT3KK7YCkUVmDQ9z7+0EcjzVIXeTn1OrOWKBdt0t0vOUh4D/AI
+BrN1/agDTPdPB5BpLot061vLafF9AMC17SXYKNmMF3K+Pfs+TrqVSshWN5X55vz+rfADhP9GID08
+ieITQJY4tKnprLyXTfO3lU2TijNLCyiUDGSHWbpHNCl23weyeMOM+R23CMjw5x39IDnQDYDWaP4E
+r8BTzztPuTsRPyLLrfIAnOoEbD+F9ln0AMWfrKOnOzD2GylW0xZdcAv1E5gyQWvvBIjR8oFzwfs6
+Qe4BAEfcgO/Ecw7aUjbku0SeS/c6Ol8/sck+uT7gdliKJflk4J34busrfSGeaGevSQEPc5jk0h0n
+9uC0C5L4qgt/IbwfxZ+HgjcrNwAAIABJREFUgzikvGm9PLy5bhD3WW+hcd326a6jUznCVY5jDGil
+J/XcTj1l2ekE3qekHySzaAq8H2VejlgVy5SRUjdOs2c8x3UsNbguYl5wjXCaZl7j197X5AG9o5yf
+82nSQHDIwvXNqQJrWl3TKotX+q6oUf82e7pcei8iz8DFMNcYjL0SIFpE4sH+HTdY/PWt6r863Rx+
+gPBfCoejB3RWLHkmUXsavVvNv2nt+/BGpM4881Y1jmohl2pqzZPgtf0Vi/8GuTuHqMj+1Ctg7dU4
+vHG1C3y9g+A3rMR1XCClOI89ZdEy7AAYLbby+Ygcwe5H5ACOOV2twkJt5BVtDIDrlJ9AlkmC4A+M
+F4U2LqegsAO+yV8w4wCQjRVDgGSpcy1H2pgHSOIE3jI11/q0y9H1Ye+AeW4LyBXpc25jfX39jmu3
+F4PlimZ0Hj7UrCeWgd/5Bnlj84x+K4t+R+1VOTHl/069d/ugyqfz+qY7xFxrwo3Y/TdPcW4JCK3d
+93nsZwM7/T3y9vk0E2q1GQrgzJ/DtBKdFm3pwAWdo5LGfJk3nAwrhWp3qhyighW8sqEgwaySHBQp
+oNj5SNbueko57gP0zCfKpBmb0Bhlnm97PwJAjofNLW3BPo4EvTyLdR91EIxAN+cwLb85P4FpwCJt
+wbj08sGW/5+GHyD8F8IoxuITVIVUpUSgjFLOCh/xFBJWV4pkmMX8+3y50TDW804y8KHluAlD4NOr
++FdNcEjA2ptW3koDQGwJfleepj8TmCurpaGBrlivfC3BsuNHKy7nNXBceStYHsCvCAJkKcDYcJvk
+SzkdYBjeRUC4t9XiXb3mXQoYNogLxdOlgq3I43yPtCVNT5D2VP6uxAQvKvSogKfSIq2+DnO5N69v
+2jsGzGybeBFr+TtgKVLxGkuyt1+On+qewr9Th72lE/DtZUru0OnTGid/1nEvhc+A+K7sBHCntTd8
+DxDTz3DtTU9bNhgkEBKgG0hN7E7VolFcuFztg2uHqPOwX7m9acYK2JUuDxsoVuA7WYURBKu308fR
+Ro+DL1bgpQPy2rLQlRv0Bn1fm+rA069NrXshvNQb6E3ZXUGwVwCuwYKWX/PFgLunDRaCXClgTe7O
+4N+WDT9A+I+D74ZGHWJaSRJgRkRGFOsbwwp9lbzp1wtg+6dhHwhO13yP7PfSJVQB65BKCF/bp0/F
+D25ezZLgFuqtgDcswEbgF32GqSeqclbpoMZIo5mkpXb2B04r8Q04/lh3i9g08hGWahVO4CuSW2gE
+VACCq0uEr4VQvC5oV0AEcCXnnvIobcB7UqzGaav52nmGMSTtPQieoIbP3Vr6erOG9LTyOoyvxnPv
+zpJiwKi9rOFGBo2DfF7bWq7lU14y3yr1L8KT4ptHfR/+VCpOyrmerbZuoxr/vhd118Mj0scWWkuw
+wRv4Hco4IEYQWgGxPKXbQtmwmUDilDwqnqK+VF8EHwVrdZyY51P0DIrj5ghFAGwk4zq47m36LLR+
+4CIMYPgWBEv5VhXyDRY81l1zDXh/8Rlv++pg+Y26CDSDG4TuvQxujVLqryv1J2d4Cj9A+E+CzQti
+R54hFhoUd16Jlx2BLH89VEn/R2GeID5EAXmh2S3iVeJGDN2n08C9wU/YHeB1qbnAGuRrCrRRstMY
+Vj0aEw+vI/A9WIILOG7AGMrJ8V0CAItIfLoPkGRFEY9Kel1Bh8oN76al8Tv4M2n3fybY9bWQIvxx
+eo3TW/jSBxJ7q2RTXM5gunMifQIOtTbkWWDHrxxEkFNBTwIjqesg0zrMdLmhV56k3oDhkRhaifKe
+jv/bfExNa/BNmEd3pr8JE6jluD7yVADcOR868G2ZsQJr1FpTA7sOcK3QpY/xeL600qbVKDRIgggX
+MqgAr/f1/7J3rUuO66wW8r3/I084PyRgcZPtdM/sOlVRddoyQuiOlglWSnY77lNBVNNGIgpftIci
+LvoaLNjYvs4CzIkPj1FTeniXAvUjtK2CX466Eeu8G5gt5STkFnXov7UnsAHQ/BC1izP9u/aNve8p
+sNX05l7L1TqEF+K0r5Llt3OLwN8x+JfhC4Q/DcOiMufvjm0TykaBO5zeYNxue2UxF3Yv7RMVbEKz
+7nNNdVk+E4WnRGJ9Wo1xZniqZbLVXy28W/mAdiguEuSA+CVM76BFbrZXdhmGOMU+2bf3ChyjFdjB
+8OBeQRTibiVWutZSrMvtA5tP9+lBcB68CBKzC4Qpd+nA8MBLcQ+cQe2Ull4usTRueJ1fAiXCtQwv
+5MN4BsMhLXya8WrzxO+A6pLKi3HiywyZI/boHcB7tYK6uuZx+c3Q9kRbxL1yI0CcN+tpfZUTIzjP
+QqzLtTbu6yMjz1FQU2QHKrOqZOp7z2cPx4FA94gcdCtpQe++Zyxv7w3HBkq4FHpHboGpA2CnNzR2
+q7DS9peMAfjOfVbLjfVj0hfM1kvITOqvoH7AwmD95Q14RY+B860R13arCxvQG10j+nuxTN4nk+V3
+JS9+0eaO5+T93fAFwp+EaR1JTJQUmdajgppIl5ihS/9ZdT18joSHAhXADrU4Kt34X7++8UVeAS36
+BNNFevcSnWmsrbTc06WDHUIKgKuFt95nn2Hl6azAy7XiDXkXrViDKfkLJ+uwkFUx13y7RERr8OVw
+2kfMKhxBbnO/C52OTss0v0qg3Y/PsCpvYT3A9v95KWiNhCiAW007xjmOBaadTpCQLK/hI6p1jVvc
+vWUdc9Rcfjd7AufN9E7oSulHKPLn1OPW+YM99Uotarq9IHfBb/PnsgTthbPzW5evy9OB2nJluJea
+bsAFPl2tDbgW3R8RbnEhxrw0pMMEQIltmzqjkXF3JN+MGTauoEegcRHYCqFf8MoXX5bz/JDelFH6
+rrvfmVY7AbFqx5LYCREKmtUvWGdINh+k7KFvtY7Wc4kXE/NpESor+xJHni3E+J/N+t8IXyD8NEgT
+7cZNhlvJ94PAsHN2PPfreT+kVUC65C8aGBbrRD/VKZahv6K3Fsz2OSI/jzAc3I0a8gLw9oDYNW7Y
+aIPeVvkC5e0PAl+q/r+9tbjxJW5ejssuEfH4tL316lQRss0Y21LBE9tB+Eg/WYSzDBzWCdiGQ9eJ
+7DnjlCeCIRrjPwHJeTav+x4E+z3H/rwAw4Wm80u6vqwAqutv/N/x1+CjdFIFnZwZO0aQ1cOFvrxn
+4BjnwQ+R7AdZ8izEeZHnSB4LpyVIakrlGjCnTKQz9GmeTJW71wEUMwobSh2DkOluQ3FDRlTpGfQS
+kX/hB3WNTfZZdJyZwN/Xf0koeoU17iVY/xSrsIT3sRlzMBXZrQ7DBiMKZWinjhOv1mQQrPfqLS0w
+sPmsaZtrCgcUuAZ44GeY4zHCi3+XYd3je7e2h4MVeEtDnn8cvkBYw+0RmBZTpAVlKTHRJkCRIVVm
++2Tb8fW1fRruK9wUgvZK9GarKAp4Lza7kj65ujpaa99WDrwYN7wkR8OpEZi+Wy1bWYVqY11NGSma
+kV1RsfgCpvoByy688GbHp3XAtz1WbQLGCqzUIoyuERUw6Utelr43GAXACowREGv+V7rXcarAVtI9
+8NzIg9fforUbGcTPIDgCydx/uM5DWkOjnWcaozpec5rJTGCirj7dKOdt/op+0op5jQQiIoYHMqt8
+HCGnZxmBdnsz7cf+TOvzdGNkdO772mf+lcaNPHjXxln1J1E+/7WTerwGsBZBX1zDgIRsQgoo1U3P
++4SsPh09JlTtwhwPEiXT+1Vwfpmu5qjfMHkJDnp3XIGcGV6UDrRUQn1ROFYslx77DcAwaR9r/0rK
+5qcv0d5R3U84dkm2CkNJFnFQvJSfzRHhwKfy3PIriUff+xl4/nH4AuHHAUY6rudKziBWmnXYZhyY
+y07403BHCTd1mDaatDEH4mUxXpewuHgrc95rr3vxTagCYtlpPPEu+WgRVs2Wla3XTkwhWRwswURS
+zwQOFuH00lxwk+h9hmkDYrsnB74KiLWe9jGaRLq2abcRLcPzyRGeF63Ifi5wfAEOQWmhiW8ci6e6
+TeSyn6RFnrx953gPkPN2LnBfrluJH3nSFceBSEb3CAp5mnFE9ZD1UFqfnR337qpvl/RULvJqJRt0
+WmtzXYcItg7hjsAHodOQPQh2qhDZjwDF0b4rcaqJSxmTC90RpEOhuQan6+Tfehlg6Pr55JO5A97I
+As1p6iKlzossxzxTfRlvQt5F69KN1gBg60N4EGCq0scVmhGqRtD6q0DXADDqlP5lOdK9FQQfXSUy
+KFYxmodIN2vby1Uf6Pn/I8/XR/j/U0gzgCjNW1jYkOYblxT2Lq+Vlne8vEaGtfwoPMLEHWOzKzYW
+YltwuIDIVUv+OtHA716U67fcyRbUWlwOcm1x8QZaQtVnOAFiUXlWDwpxtQYv0QLoRyhYgkmoO0JN
+X4Dr3SQ8T/UZHlwjoFyzCCeQnN0kTBkmEJwBMLb9lbbusjFuxvq13wCE9SPx7ekrYNtfz0C2B7+e
+C6d7jisf9t94BVVwhz9/Jjqmq0qY8mLIa6vcwteptzecpJKucgVeW1C9hNzvJ5kIhtvt8sH+eYaj
+VdCJ38aCI480eeb7uwrYVyi8cN/O53ANwIlM094Fxb8S7Cs/F1rk4zYCRzHiNDJ7bZAhgacVWCt0
+UV8q77lw1juqw0AZZt1n4HdnVBmxX/PM45LmdACznPMg0LXvBXzdQxqQvYRtRAol6mk5iMNV7yGA
+hePPzGpMvndbQSce0XX+b8MXCH8YgsLDxQu7Bm5Khd5FuvG3bM4zKaYq9y+FW9pRhnocVD8TWIEX
+r1uCdc3EjYAV2e2O8Z9OlvklOQTExC4TUFTZSHRb0+/qtluEAlCS965G59LwLnR1k5D3O7lOOHCu
+lmVyvI1x7RGMw1WgTTmNCEFxfYFD2/+i2ifGK6DwIX8EwtVybC/UKU/KH6880PtrpfX5M61+EU+7
+9oerbgpAn3jdAlytvP2neaCRvpxb9yGv1PSh/Sf5J37UgT6+eWPvKL2sDA8SvpvTi/C5hR041JSc
+VtZZijtEwzxTCXPJxmHruOe9ksxEZq12DCOhr6ruq+vZvoHjmhbDsFk0A17qoGuKswRfbJ10p8lQ
++tDHgdlvum8jrP3Ai6DYXpbTOPXg1/sNAHZyb4i9EmpBiEZj/4FlmBY49pmH/1FaWou69+JsDhbd
+xezWY9HJVV6IM3Dd8iwhjHL/cfgC4U/CtAvBBPZo9AkWSLQ5MJYhDS3sK1d6cw6jzvWE43wsSiMn
+NkoqW9GH6piyhq9N7KgWom1RvHZ7UE3K9qTaAGItUGXbhposJkHLCJlvMK14/rGLzvobae9IC77E
+biH20yOoWoUJAXfdiPPmnEdLf1QDQfCL4o9tvCCu6bgxQvcl14d7LhNIx7UQ0jiCs9MV55vmwnRp
+8vg4c6Fhf536cvIZzvm6D12kZ74s9xS/uq9ypdDP+1LaPE/8vgcCTx6RjpLr9+FO+TBbBoNdmtZI
+NWZ+cKEmXiWc6aYPW17XVac2TLQlW0GxhHR8Ua4stkNYepaD66pfRRWEyz4PeGgAguK8RjPIvBRG
+tkVAvYnc2IJp+qklxYeBnR72Ri4y1hXldCtIUnVThzKWoCPo32WZzcZo/t4NUdR3xER6FCfx3jOJ
+wGWBHBTD3rzyYXqsXgbHSwfsDOL7rc4LnSL/OnyB8C8G6SK4iFugDFsPAIFecCJP/GOYlO+95FoB
+yHfFOMitSplgEXlcF9yK70UoQme3B3eXWPzxJTk89FwrEPW2A871VC37KVvIQDABCFZ6sP6e3R7K
+5x39iKeTJCr4FaitD88EuDAo8O3AqvZHBsRR8Sea6lPOvFV+puerbpQhja/z4TbTXeXA24GfE7gx
+mm4KBz6YKeP40AXfnTGd0k58U+i26JMEp+RXjsg6t6qOOxRfl4F6pX8O6VXlzcyfzg21vZ3Goa+w
+xNumLt1dpnT17tP9PGLVv/2aP4R2aBKxmVFbTa84xx4I8Ts8vqM6/XKvjL1Z673jQadVq3BIZ7AK
+q2JUBVbKTWM+0jxNj0PTuVXBML5wrv+jicetsj7Ya+xxz86+vkT5l+EKXZOEAvAlJgDUnPb6vZdM
+b07+xfAFwk/DuMPkHVD0r6RLjPTirxYuFMcN/d+F3I5Ugexw1JM8zSSkJ06i8JLVWkebMALiCo5V
+MqYR87aKOLSzxRnqlJqHWlahS2v9Tffb+lstxsmFIlmDnZcSbVdr0/TeNmPoQ6IIfF+pUZiGH98M
+71t6nwBiauI2Vjkd1kZ0x+CU9+oKY324ariK635CFPs7giIC94j5QzmPfvLYPqjfKX435DxX4CKs
+5YM8DpT7G+Ezbg01xzTmd/JQuLp9tpMTaQ5i+iLn2nT1u3PuMErs4hE0zf2b127lPNyHQY81FiK3
+RpLrfJS2eAb6sQcE+Co917W2MbUFEoKOA7cJ/wCNNw+T71umV/M9EY4UWnzzqMW+6KzAvI02mEaQ
+I8e2vAR6OzcHHIT40pyOlaRfkdv7197kESv/FybhLxD+YRD7hzSJNHH3CFNV0izIImeFE8/9Sk4h
+Kdt9+3xz6Qu+Y7U2Z3y7J8IuBLy7qwhuETu/dygC4s4nOKflyqxPtjh4zD/mH0w6rkIR8Eb3BwS8
+2TpcXCimF+asNLQAi80xSxes8bxFTsC3gtTGx7fwDGmiG8Q5Dz2IG21r2+yffL7yTT4Pj0EmLCsE
+S3esvMiHIfPkch/XcYg/WfvdrOqAg0BK92J41HWRoYdUFYy04UNFFiHGfKXm2tE6v2Dvk9qL0Rd4
+kpBzc+DDtCdxA01BFpsezn3KlRRD7jiN6voowygpyjEfTfGoqUeZt+hR0qj7EPRyBrv9fC73nFtC
+4d61FXSYaXoK6cE1wuaD63/dK4XYNtRFykebrQTbl/dLdER4HjARA4LF/KqTBZq2Tn9iyEs7n9iE
+Z23ePw5fIPxpkHgjSNP5KjlNIE2QlYgGZeJZRmVjc+1qTd8M9ZUQTOvpWRGNQVROVPRZDZjuE+fD
+b0wCmKX4YtxaVw3oJfQR9rhqte7XjPwmqpgKVzYw3e4Q0p4jvH45LoBfS08uFDdcI0goWYBT7YTK
+2bX9uNUPWbwecTbzXqQJbiB9XnoU97mj+jjz+PXsKpHBQ7cCJuA4prEve720nz1GbRrQc8hg64p2
+J727h+bcCrNOgw1aN8Ihb7R6dcAm5unA0e0KD7W9G3KfZleIiR7LBcU41m3Wr1GfSkP/JF7thnmt
+5jrEkEdNqpJVtiQkjqlEgwV3Jxc7QyxVSi36gi+qjkRODMjHmRf3H9Wn0epbR4DivaTRCfWKFt9l
+EIlWY6X43FtxtvRYXnyJjSgecebt7o5P8+Q11vWluV1bnPJM2zoMg/+PwxcI/zQkbRcBLiIQsNJl
+038CKncU/lSNT0NQn2edezNExXQpL61xW7CoNwX6AKzA/vYyvKRBDeglpvqjGkS4IxedrFZf1qdq
+GIWdhv/x+LQAXt8TmF150F1iAr+rSADB5NDXRCEN2qO1zgAoA4gIDiXdx/gEYG+l77HGAzs63jmN
+a9rWpZqC/PmqfYHXLk37BsP5nksf+3QBazDreHWfu1bjWod8fUK7Ch3viNtSHm6o0wkdysEpVtJP
+hY9pVzW+zpPnDh2uvayhJ5vjJrt6XL8cF8vIMOtpvOjhkIbBDjbT2yZfHM+ig9KE6XQUoi9hBH2d
+9Clc9XUtedZvydjCvrfYB1whsvzqa6wdIDbRJ2CrCsbAMFp8Cay4MGO9VC8PjyMVlaP7cXaNIKFs
+QTb+UK3Fx0S9fzF0hYFhwj76d+ELhHf4dbcUQbndFqSJvzPovYfYnn04CYHP536jnIN/2tq1fduq
+nXXck6TLoXU5tacWEo/cWsCWAg2AUgC9ECeC+7kB/pQqNkHM6stvEn6R0JtEXvueNqB90/utR6Pt
+OIBd5emOSJO3ECF/C4wVENMGyatmq8677tgOov2VVNwiPT75F+a3jOumlEHqlIbqOXxsevpGo5tn
+BqURrKIXHKSp8gaFn+VIkkVUy8I0bVMOmSaJKikSQKzcA7rZxeJUjzt1nGifhk7jTHy+UQ9pJoRb
++i0ZrbXQ/+U5gPCgp9drV3a85pkwp13Ru9Kux28+oC0HcNscdENUkLnnu/GOtN1zYVBAYTcShChZ
+gJt0uzm3st9hYx7ON00mv21n1+3SZbvphd4WJmGFqgFJGl1ovdcSW+B7HBFTNrDxzo9NDpreqrBp
+hliJfA4pAJYNJRQAi/EGf9/2aDSi+BPLZPu2GDPikd/UUPfCFwh/EE5fNQNXm6dmahZLoVwF9ktT
+KZufEigxbS+0rk2++fQN5xBLi/FiTluOqd7n7KkODAsWAM1eiOtpPQIwfPp0xb9X8V7NSxHhVvUm
+odcCq3vjlW0xfv+JILj7vOHsYHkLvREcZ//hAQyrwjD3G4HNFIcpdGD8qjNu7vl9YgLqMEApaN48
+d+/QA+AVTEuAl3Oag5gOyGKbKFx74OP5PgCWcs03fUJIhQeePTVPgOnfbyODvmKPjPqM51QOkYHr
+lLbT5ZQeSyKie2AYh0hC3rha7qTNdLzeAcBV1tOAADhOQ18hGRRySN4AKWBF1akUuxoVVBzsGFQf
+XGyKUfzvrIJOb12XXhuq72kw8Ajxdl8D1wVZPW/AWODsIuwjtZ5St603ho29l60z46OG91mTRrz4
+NRDpy3aLJFtXD+4UDEOMP9QBSpyJAogOWPwfhi8Q/klQwPFgzeGmmZfLZ4HrreCUhskdHu4aDmZf
+FkLEaWF4GRt+SJKfKpJ9y6Zuup2WdWasjoMqa6fXIZwkQRRcJrKOFgTBtjK3MpJtCVYQLMsq/OYF
+yxTo4kfkHejqQyz5PrlQ5J9kRpeIDIK1f6bNNPeqwAy4AsN5JLp5e6LlvY8TXcgBb5blaVvhwtx2
+GfEt7QxYKF11NPs0rptu6sRprt6hS/pM9PDJg9EsmEsV9Jc2l1nskJIAT+HStXuqMN+XfwqTFTjy
++GrpQDDyZbCagWyl5RxdHSPX+XpvIzpPp37VF50b+PNo3dUQKZmILk3BQJ4lnvrhTpq7OfTg9kkc
+rvaAsOeTAsA9w2R/c7k4eH/zqGAx7tnWB0E5sPWfY5KUR7AHWMGAy7ZLthJvjnQkhOz2WB5rsri4
+A/AlIgfReO7/Pw5fIPyLofj+3s9JP9mpOMQkzH2lhydEnZDEtmLicvIFRXmhGYDwRXSuU6S17F2V
+7+TTugKo0bbqXhnAMEUAjG9CK7+pLcFKpQ1O3kQIgmkdv/YmXsD2T7UAv9EVIoBicJcA94jWCpxd
+I8hBsFuJ97+mw6JqnsGwc98biTvbXgSuPU3hRga6HndAUnn8+CFsxTNwrJufT//T3HPuPnRpQrqp
+RRDTfUI+9nxasefa5vc2mNuSdO9vckQ8+wwYc3szg+MIUSrMbmDLyiqVlkubwKnHJ4Bc4esnwPaT
+mXDK3wHldY2r+jQHOL/8ZICJzxkD2B1AsVRSTXyW9jehV+w1BMH720QS+wEoBcCKExUAM6N+XzVm
+kvjTxdCWaM2FPCyVTKlnGN0sVr2Y9AQJR7T24pvl21bf5G/DwaBUgW94qa6M+78JXyD8MNwZotkN
+AuX8xjZWNwfJSXnhZH5w3o+4FkC1gs0RG2UYfVY4HwHehO0QQBnopQhgFvDdgFctwaQAWAq/wiwH
+w1qab0v2EXIQTOxXBbl/AOwGYAyuEJJ8hTUv+hOjm0QAxKsSCwwLVBPA8W5ZN9ccDDtHBAKz33Du
+kzO4ne8zmNXadi/CRTC8xzDlD0A25UMoNYHknEaQ3gc53HXcbHwnIKybTwbMWpnAl8q9E8fQtu+A
+Cp4Bhsauy3hJ+qtHtzSQ59pwnZcTbwTIdT74j0rorAM+W2cS5OEVU+N4XIPgnK+T/VnoV3UHOyuU
+6sDw7pvS6UDIYJgy71BFjgQfhciDa/+zEPXGPf48c6b4bjNnELw1gvkIsx0xpuDYfIStrb4Xh70a
+CFlf4Ogu14hIK8CX4jehpnXTXh+sw2j1VaOM8lv1xIqNP9Usm7b74lb//274AuGnIU+2O9tkjQQe
+TrGfBZ+oNm+t6LrtBW4G2LT5w7RsQC/h5H5Uw6TMurSxZZGGV601SnFAjB+2o9U0h294EFDh2BPw
+doOgBIKJNuiV1iL8fkvvP4y+wgZ800t0QslHWMEwmaIUmjZPfEmovvyGoDf2sfdjv3VSSMkzONxL
+vNd4v33EN5srGI48KGcGt1LSIk+EZsiL4QlorPniQwqOUbzfcwsqYOmpUqP2uQCzTzebWdxFB5Rk
+AMdXoJYHwHyrCpUYXFdBXw3QxaSgjkYXCVJgp/8l9usMkKWhxTxdWmrNDZ7Ij2HSpRONE62Xn8ZJ
+7F9KwU2Uxylk2UuhiShAG6dj1GpduG7XaY/u+XDGoJnF6ELLR1j1uIFGLnRsYgSqFBKk0GIrG3OY
+7RM+N7UiKT28NLfbyeQLqrP6KpYwmvObpdkm2lPN9DvhC4R3eNb9rvEe5fvVMb63A0elDROvwB2/
+YyLCry3C9tAsQG6kxLzPQsmV1vWUQwGvtsJdIBDogGWYOb2YtT9CSVnQBp+q3Fb6+hoLQDBxALzB
+JSKB43AqRPAbTu4R4Ui1Vbgdu0YS4gSX3G/YVxMY7udEBbwofvIjR52G/Yv3Oa3Go4Wekhy21uh4
+U8sr6UrttbqJXMAvb0CNjgHByylu97p5YP7rqjwIZz0ypt5QP9zFBvDLRWZvTZ7X/8M68gBdpKc7
+9BKIJxCruAz0a0jf/yuNQko3jpJ4uiCHuy48AcN4JQN3Z3qsS+pwTLMOPQwYdrpJFIj3fJF4Jfxp
+aBvUprVzCn2AFVpuK7BiRWF9id1PSLLaBtXs+3PrHwzxbltGV4oIcrVM14rLmCsmKPgGA0ao5wqv
+PTcAX5LNx9iEfx6+QPin4RIQN6rrFwFxnjQdqCl5WHm8MtXdN83KtNjaX2R70C5d6Mf0Q1p3xXRL
+40gzgExEr72WQ9tiLT8/AAAgAElEQVRtE2OIx4uowkrx6CMsySIs5v4gIgEsRz9heEHueHya91+I
+Qzty/7kyno5Fi64StVfnESnyoD73QO8JDPsWa+mSed1HuAOz5cqR58TrtZ/CfdV9Ar9jvAPDqUbd
+WB/rd1Hl39mMJuTK3SXGGsA81enJsaOX0IUrn/Z9DyuacUj+nAKp05hdu0pMsakln4c7ujfoSOxQ
+9A0GtpEf2bCjp1E6DWDiW+P5SV9czpKbaVm7iL8Qhy/Oifru6sty20+Y/YU50+faN8mHl6Drin9w
+GtDuzRDf3ut3g6xn6DfWYda6qlwTLRv4bpBr8hPw3YUbyP8PzhAm+gLhj0K7tACU/Ezyb00EmMhU
+6xxTifQ8wggIgdt+YlFJ+Lg3S5/rdo/rqj8dtDjAnWgFDG8dYldyKFiRJbn7gR5powBYlkX4LRyA
+rx+lJpFeXCLe9Eaw+85uEQvpuksE3Ks1mGDzTSA4xxEM53EwhdbOnastUi5B8BQvAFgyQK1AdwLO
+BPIzn6gMOYDkqaUjnryGDRoy0D3RMI9iiAqumvIeqpA8/s9y3q2CftPUUpu8kfH5/pi8WTM2S/3o
+8zGbCCI4JqLGaDCA3aBGehAr5X/k6dKntd3JuQrTii59kBMo9jAOT/Yj9lvVrVVWCDrRuZUW8p93
+TLmxpd7prZOQmKbfFHYpUUv5C2gIgvVlOZs7Bhq33O6pjAj8a3P6juz0OBMtt2puyg9xOS2/NLf8
+frmCWcGixV05lIaWZlDQ0xGufzt8gbCGiARqXO+bLJ2wyzczfzscd+7eRhyVXVJodq6YxAzFWemy
+En2YRDR1PCnqKe6A2I/CYSLwFV70F5EZMfzFOfXf8t0SX4A0ECzrXMYVX/V8/wFLrwFgdItIcelo
+aB2mCoYby3B4ea52c9f1BkI6QKyQs1OamqcCW+8z3ATiFtDEZU6nQosWYkwngjHkXg6Rbq0dveYJ
+6+K0ptt9sl9xGdDg9ZRGROmMzR6Gf6JnfvPXnDjfcdc93IDbG2D5XqEWJgjT/Zy6/wrXomQwqMVI
+zmhRqWTedCnsVg7Sr9Kn8BP4cNLBXd8dQS8RdCaMnLElFDsVYqxyCxAPmUe+fuXcDbkmqpmidrS4
+nanrb2PEU3uAqu4S6iIhFL+1RICLR5SFtHITgr8wF1vgcZWtwBm1r4+HnxrhYJgUzIZ6qrjdCDhV
+wk6xQKD8H4QvEM7hhCKkn1pHVdWSk1Z8PPhlRYd4XQK6meenvYkfqNnyOwLumjbsF1NJNcg5PTfd
+XpKD9RQBMfoM14/JxE7agJcAANMGpwsELyD8lqVgDMz+QeuvuHtEsAYnMCwYR5qWvcEwKWhy6IQg
+qpuRqJhT91q/lQ2/hQKRA4NufR1Ixq3DaILAVvNWQNrRSPDltwqKryy+gRevnHmm1V03/wajUmEi
+79dL0DteXfidrdw2yblivxJi+wcIxQ2vbpaNtCt8/pPWFDgT6oZv0se5izR/Gblbc/l/lnHtKjHn
+vdh3UjiBXU2/ktn7BZMDoEJLPRxue0BcwK6xyjyHwz76Kbi9Cj34vXe/49onBnJ5g1xxX+ACgtf+
+G4GlAxF3PWg2S8yTdXXa1wvY3eWwrQPcI3acPY9bMzhWEcCv29Z0LDlBDNn3f09HTeELhD8JeWf6
+YZ7sVvUbYQK3QXkDR8tfFlTm3ovlRj+4CG774I6iPoJh28R0VXVgd1uHWUoaiYIedn0qRGbq3TQ/
+wYHMEvzetPXryBEAS7q+BV6m68Bw6xdM4Bax6pTTrJ4Sq4xBeyUfUIPKrYe+c+8j6M0sGcgaTWpa
+VMfprGDJfFluPRECt+0pH9bLrlLzhG6A1mXgJjG53bcdaDh/pxqmtCsQbPteX+ln4Zh1AEUd56Tc
+uJPRgeU+f0ttiBVciV064GVqD8cNOjuvEbOSSaQHOMtRRrbgxrGXgZ7jvwH66ko/Se76vLUKq5AO
+DAdBQChpnTzXExUQ+6JraxSI09lELfNl6LlRQ8UzI1B3dpZgEger7lPrij24QgRFlZAw+vHY5r9/
+YQ4G+zjPYD066E76zor1wrpTIsJLdBRpq80UF8o/Cl8g/GEIiqgowMzXxDKzrZkHC/DDPS6A0iIO
+lfdeiLagds4r1Hoo7ZQtQLGGMYMXrKZaehdf8hfmBIgluUuET19DESJZR0MAEHZQ/Nbr+03yx10i
+KjCO7hDvbAUO4LgHwgKgFy1RwS0ibLqx57sTIjQ2b7AnVwmzIexrAshQpQ4E6xVV7NKb3PJR4Q97
+oI1jmy+BrCqvnj6BDUBadFM4AGPNx3EUsmUY4z2tAcEFY/xUd9xXKvdwcuLiztGBKxulgXpSdhMi
+xlLZMNulysQxgOPWRwgbj7trNL9u9u2uEOk1bUo58fWUOyHMc7uPqyL215Cmiz7nKAsMFdZh7EGU
+OyAEZXf+hj3U56cha7mqNdBJAgGk/eDIrs+6OAjWF+XQ4qs6pAXA+ILc8GMadd7ofp6tvkETR7q5
+vOi3y25Y6V+Wa45H27qdDCjrpsAOM/5x+ALhHe4vjYNK+q31dSecFruzjFXCpZlTwvY8CskJHWRp
+pJeN5H6dcyldBge02kGDOwQ70FKXiRBkKysDnAQgWAwYv422LcN/ZLAI97TJIqxWXzXv6i/HlTOF
+N83qiA049rXPgG4O9DmnWTOPWqMDBzVL5QU53GYKKAbgUkAxxbRwLWXkPL7h4PpSAJW7GOdhXA3N
+XdyvoRe8ghOoOVuCp8c3CrJr+GDHuaF3UDbHfzEv97QWKFvsZ0q2wjXXcwJMnNIFsxhPXTto0WpU
+SpVnaZL4cr6O87TqarjSrVOIa2g4Im0v4GZEodNj7zuIq8KWuMNks7x1P64/7NFlHIo+hk6wpB7p
+eTIARv1nVlJC8BhBMKW4tx8BMMU+1cU/WIXDPGKPFPBL7sVcNLO9xEfg78tBUZv1OihabKvzrur+
+eyT8BcIfhCtlYg9j8FR2SwHBGprX8u2dSLn7sjlcejCkMzR47OfFmBbaZW2uFfJdcQXcpsQFegDs
+svO9AJQxrTd2DTALft2PrhAOgO1+g+G3CL3flPyC0TcYrcQSfj3u/U4WYdH0pXLUHcLQlJZNUCeC
+ewJcvDtyPlLvPuj1OemK9AQqw1UO6RLvcWydR0IakY9tyAd7Ai6VLI+uruL3Li9voo3VdzPHPb7u
++CcwczethYUNzugSw6jf1CfcxG4wxxuO95xoISXpqNtlh1B7IY5ZM/858rSlw9v1BagmkXKZNu8T
+OeVaL545rnTvveDQL6+ZFKnVmqzDiWQJtseMVenTCl3GtLLOj8Lv0Orqz04RRAvY8u4v2aBw0cW3
+3y0Gt14TXeKXOyvVGYXAVtLcd/CL2h6/G7S4nRzh9cdTItSF0stUcR1Q/vkMfRq+QPhpGMZoVmVz
+nn8V8vE/LQ/EfYPE1QiM99daDHKPDevztOuYKB2HRvAzy0gDt4gAelBhil8lXg0MvyVd088o/8F7
+tAR3wFdSegK8ehWK9cq78dhpT8FwTXsbNefJShZsCA9AcL4qwOx5JADfa/DdbN4JLGdZoWUS8/lm
+sP6HvR7WXJxbnYd2BbTxvktvUWIT+Hh7J0+b5RYWjnXMlkROoBiIVQYRfaZIJ5REsOm7nivcSREV
+OLonRwG0BfDWUR8kNvfdtzb3eLpw6JHLPHkut4IGMGxkotuA2FaHAr0D6C0lhrqcQwbD5+ndtf2C
+BgAvA2D9FkQB5ErawJAkgmAEvfUoiSG9b3H9FiKCYjjcwYFyAMUUXCoU/Lq/L9Zvl7qVfj5Ozd41
+UgvxPw5fIPxpOM0xZ+kJp4yDfnkcGo1+BT6NUxEKCmtWYzm4+1mF+vrIDZ6pBNi0AghGsMQKfnkd
+nUZiCzcowGWGDdZYMQAsIW6WYKF9drC0FuEMhsu9+QTjOcKUgHCKU8NDPizVElx7NEKArrc9zzQW
+BcAe0pbeczVcQKw0/MB7Asd0vHpLZ7CcZHHsHwTFDogXwXuQSzoCiO41nVO/Ftrlm7UAnH8AlD9X
+QxEA97RcUILJFy4SNTzYPbuf1tpKYFRnl8ujWR+mHhPggEl0BYCnIju+n4RbY12Y4sy2NbPJvt4G
+wGzMSXijRBwQ92BYXW5Lrmk/beiZjSfGUcSs/aoOWCXwrry5RQMABmwZ997i6gDXFgD3oFkgHR/q
+JdAUHBPE2TnCW24oFfBGPjZN9+R9fnKr/P9x+ALhh6EHbnniPZJg1M+UUZc+M2HKcbNUkHuwCnMm
+hEWTwtA/cUvq691BOQS5GRShTtCM8F5CBMTE5grhIGvDSUeeBogNAON5v3AfQa8UC/FEz77D9qty
+WgXCOJE40t1XvI+Krm625129H8FubPNLcc6DQFNLmsBmAJ7S802gVUvF+Zjng115yHu6WoMlgGLA
+MgkQOwBYdWZLi1vQfTBs6ce1zcBzFfZGxtMMuJf/lCHMCU735H0W7rVSTX4iqnroqhInpeMotRcz
+FdVY2YQEBzdhkB5czxbiiaPj7WJ/L/Rr5OAzbEyH3U2QB6SnW2f3Y8WK/iiyMsNvhkloR9dVn89V
+Btuq6QmpOqX1DQa6Eo4AOJJXMUz+X6uFWsq1JbZAmnR3n/S2kdBy9xCtqni9tUnNj3D8B54RXyD8
+GwHH7cpA+lfH+MkZbByXa1uvgiKEWmsK8lKACbfai6DixHMlLwAhTqA3rcHuBTqDiIIVElNO5Uct
+3ssa7NZfKaD3nS3Asiy/vfW3+RA5+CVQagCItbroKdH1cO275/Q6qmd41wFetPhSQ+vAaAtsqQfH
+I9CVga5X9rkQgavXkYj2L0Ap3V+Ew83L26zuG3wAqbHnYr+nhVrCSe61nNzGmFKID0IuL8vjlJQK
+6nRZq98OGmH65QuQF9ZUVGKz+JAGKyKpxit3iF78HQB8JeP3w3kaDKgVk3CBHLMnWSPe3P0evjmQ
+mf9H4QnodXpJRcsvKAqbQUGHMHSF+AUAsG7JxBl3DKA4ajPcShItxu2X4Si5Rey4DdvwstyqDns9
+Wwtxcpf4x+ELhH87DDjxiiGPfXvfnru57/nG+9SHCcbIsCdweOLDDcImsVBrMQap8fWAc/m3QHNe
+3zuvVYcoPEAzcwDEL1l+rm4R3i/RsXavkB6rRrJBmKHM/uMAmQqwpRzf1l7aABnzU5eHYscg8A0d
+oYRCO/dx1+eoDHu+5qs0aeQnGoLJy0qkNJyFHXCOVxnoFyGXxabmI7gVX4rxi0LYmEp5mXJ3x+5d
+KXaR98IJn2Ta3vB+vBd1An5COxbUT6DybW3lSCKa1bHThIb09m5aN4syL4GhHQfK1LS7s+tOwOcP
+vnn91UpBvlKepd3ZPW6G1N6qAZBtapQErkBmgJnG5tJUkTDmIXJXxK2czDVB/IWzo1VYf5wDxTJ+
+A9i8BEe0QXm0H+N/0xvmHyxuL8OvYoWOFmJ3l2i68y+HLxB+HABl7Ukdzv/rJhNMUv1ap3U3OG54
+U1oFx2UetVkbIpL2L93YGYOIMDuAbAsuX72a7V4DBV+6HOc1nmqg9/rRKigIdt+1TUdgTEQvYnox
+0ZuJmAXim5ect/905xJHZWpuF0oTUIbWQUKmZrDTBORBX6BrRxoRq1OnljPEijzS0GpfZ5kx7iXP
+PORKMKUZoZsTlyCnDxNALlfWe0ktqX3s2wW3PF44IOipYvWmZfRpIcB6kI2g74RGNu9hmZb8LQxw
+87n3494x7agoor2h09KfW9qa29LUdRjs2tE9X0jNyiTnuUjvfII7muXu6zTlmFswl9FxHkb6mNgN
+dbfeJyGBl3UFVRn1Wvnms7/Z9Tmdzob/7BPbwwPd9XGeqvjDayHsNE600NeXg7cZguLSfRfnR9qL
+cV8nPckhngXshoSoATOu0ZEyswN8PWdH3wF2QPCrL8YhSNZ3H47N/kvhC4SfhrDDpS0xPXnZjkrw
+BIVgGKZbLmJFLibGlSsEjzeX5MqiYF/b2bS3QApM2wvoQwDT8WewAd1twFerp8AXldCiMViEwTpM
+zYfTB9Ko44fKm5W5ybO0wAa/GwebFVr5dDqR/9M2GFcAyvCcj1iJfFNOeruFHHm4xrn6OF5/4a6T
+ezk98ySgB2C3u6a9ZRWBav8EiHH76HmIsLqSqENrTwAaBTJKnmRBhjv6AVOkRPyukcVEZBYo+wpb
+LT7w5jyRAWI7lmzv5qyTupkq9yFh4msArjetAcThtgOtB5Db8vd355Y8A8CX4c52cFx88Zcby5Wd
+48hn/L28HgQzlJE/ERD/JEQdfo/+qcx2/RelLFVBub8EtFg2EM37NcpQxEqW19Yn9RD4BIzBb2yD
+WsQGq54R/K664Iu/djrIExfPXwpfIPwwMFH704f6ZGX/TzyMT2JJvq7wUx0uJsqaaxeTqU1OUCi5
+P/S+v9zy7k6gvJpNwrC5nQJ+hZMqEQBNiEP1zCK871/k6+7F0dqbrcFV4VIFxcC36jQDYAXHZMok
+D0kEwdbrAHZN92weP6A91jPCrZMV2MvhIb2lBaAdAS7ms6ktDf0ULuZKeCa7EHUH/GJxOOMni2+M
+96DZK1BBrbRpqTWntCyIg1Qk9vx6M3TeLUgRsu8ewwd5dbVirY0D4vXngJiI1ks0WjaC9+tqtOBz
+4ITbHgBH7H8AyZJTJSfnSna1aOv5RE3+XRA8zwNd2zriqPPmK6d88VppMJ/4qorKK73+vkFDSVU/
+H2gFwDb17ZawWkvDum/4krYyTKvAuAXBDjJl6xL8BU8EuDV+DYxX1DXeGiMOWKB1h0Dwu9sht9bv
+74YvENZw9ynEBgl29NY6SmUihCNM9AlJRVFaADEJUERT9YYHt8yeeZYXJzW209M4LbSgrkN7dYV6
+ZWy5dEgh0IYFIbHjrGra9YMeQFD8kg10BVwemCq43Z9LYKxl0Fa+VL+io5RGKY02MFbloL94Z62F
+YfC+jD5VKk+HSgEyAQ1Li/cVJOd7SrRuI5jjTCOsgmnShm6udAXDVON8TQW3PBTbiX3wxE0i98xc
+/Qx4120rJ9x2o5GSQnJXetMhUx151BYmp2RXQwD0oKkCA8myMaMWoC5BqhSbSl3ph7YFGfhOMjq+
+ytNnlyZ2UY+x5OdgoMvR4qgmzNtC3E/K9c5+kiQFGdzL9rSL8i8/1WXibuj4W5rkfmjq1wLfiXZY
+90lZLUx7Ar/rGg93XAKGn8age8A45kN8YydAJHxU3CFI0yi4X/4HBuEvEH4cOof1lUD4E4P+1TS8
+dSnAYzt/XOi5KEqpPYi42qIINtfDLMvIJYP3vAphofkkTy/ZKY+JOCv4CCZ2aLL48qTd3wD+UnNM
+qUq+Mr1YCsAtwJji50WV7nncvYJCPrUAx3wGfkXgxbxNR4Ar3i8OuH0q1nsJFuMTCDa5qbtP9yZL
+YlqOn9OSe8SuaLsWsCFdPZTG836Tr1MaDbRFv3aToL05jZbhIzrpR8rWRGngBcA9JktPbu7SdKSS
+mLJpy9XHXb/p8j6Cb86gAHef2mvlrAbOdRkzFQ3jdwOwnWUP0DaI6xvxU+D7Ua5m3oXd5Zi+79MY
+t/q2XLnSuOflXcgojys/I0NbeV+FWZ9PH8+Z/IMF6xB6LyzfUp0LMGxlIFDURKFUgAPSXrvFfdit
+xaErQIbHMzCOcc2e/hcfZIJ939NkSGMEyN+fWP7/EbqX4CJYJGoBs8YRDNt/jKB6QVom3JgwoCHK
+hlyE5g0YygluD/vaQQFwxlceKbKA2lbqvnrXJa89ZrViQpxuRasS0ydPJgoAOFh+B5rSkaZ1CR/W
+IYrWYW8jgF+gOShVcAC9jICkpMVzPvRTQS+Z/B52dbz1PsfDvQHauAG1vE3aGHJl4PawxxTaCQxn
+WimWzy/G+f2EeE+9Tr7G2gAbxdhp0/rhOZmfAbPxXAkEgAwv1GzQSOx5F2nPDYYZaet18iKPaPJa
+W5w4JItr+Dv0WmXWpDs9+oT6w9AMWdiBxnTa4zYL4vZawSwRJSCNV0/o5VV9m+tw/qxVO32jV+o5
+lBHu01IN6TeUEjcyIh/OPY6K6nDFo9UcYK6GumvEkh/AsH3b24NkogkqUznVIn4DTrS+IWKvT2iT
+YgaCF2f/XfgC4U9C2vmWFUPsyUZAuZcTF3ac28EOamku+6iSer7MLwce47Sns2alhduBVy9M0RCM
+/fEo4KYV+8DfXPWisYk6DKtZi+9Fe+GT9GBXaRQVZlGkLP6hmOagFukZAJP1hSpGxcTYS9VNwoFs
+SCMqlmLtOoQWpmu5B3xhrnRpYQ3EMN9z6JPMy9Dgy3UwTJ+r/ecu8J3ii3/lyNWo98PxZxNGbtMH
+4JwLvgyH9eYK4UKnKPsNoGz7oCSaL1DbjuHri0K7HaSJdSxd6oEmtyQHxpYrrfd/v91j4Pu3XKnc
+8aXUzFMBY2P15SbfmOaW2k739s+Ss+IIejrVNeurk77r0gOvxLYQ0S3QXJPqvuzvIzFlFwn10WX7
+5tY1mL2vxCCDVFPn/5oH8u99dN1qmUSdq6idF6ydkcDwf4CDv0DYwkMf4QhyVQaVwUc+lmRJ7qox
+EEYLzCdt4SotzD1rEyyy8rVKXIDOA7yQtrokIqfYH0NdbWPJDFFLpOXsD6KwBjVBX4xTkP5iBsAr
+9eU5pgb8bl46HdeTLcGKcLEbQIYQ6ekRrnYWLUyzBvRq3PSJ8UsYuq737CEhp/EZEOf4nftKawb/
+NB86QQA+kMQDLafTDd6uWhnk1nQfg7qCfSurJe774AbRjWC+Tb19vm2CwP8uuUqYZObj0DLN1WCc
+VZyAco8ODuECgdbkJsODw679GLg71foPoS/Xm7GLA2vkndfzXetvXC/hyg2tpPXl1NXq1Kq3+ai3
+Y14usnKJdp+Uhr03wo38u8A30CKwDUrLSJmQwTEZ4Cxglojw/Z98RBrGiZI9OL3/VCy95HFz07AN
+BnWdYAtGff7bK+kLhJ+GYN7cQ7I0OpFPi3RWsPON4A/3PCRSR6eefgMAj0mQvuZzBr7kN6OrRCor
+pa2HAqLS8Mtp3W82IxDZa9Ebhve+CROns4E5Ka3m3gFyf9Qa2X1WtN09NKj0y75PU+Vk+dVzIVFh
+XILgFCe8l4YPdJeHVWLZGH5yf1PbCaVp3eEamtvbKdpc/Ng/kLNZzkfQXCrQEOQSDMdQHAnG21bZ
+DDIPArPMlLDmSzwOjYjIT4QgA9dqTUJezd8VeAvQjqEKmYHqJHeScc13JXkKN4fsx1Js1xlYOfPl
+tJR1euEt0LihWdpwtBrkdb06uz/UVvQjkPm7+ztpFqZl+wQMn4RPR6QRuD2Ul9nigWlEbtE19wia
+PYajbK0fU3xbHWkQt43EsZMAMLZ37SiOUEf7rfAFwjvcVTIAdW0QieIxaZ60J5ymdWA4lN0v1+uK
+X3AdAXJVeK629MijtNi65zLti9ZFAhcDUXloOFa+AYe4qVodd1Gavu9xjaJCMQC8q6cgl0l/VAOA
+K1MFyDTf+1fB2pfxPh8vtnx7Jd6LUNYCMCKu77BZAHpxBDiKsfs7gPgEjiml2X3j9nLOk16a0/Rh
+cpzgIOJG45P77Y5x/4/1yvc+KsADwjxPrnkGum2LDCxaY4YM9zcI11HnAG2/UjEtNIV1CoBWoD3o
+g4gT2fpMUn9dhMJx6V4xA90R2B5FSr27rPbEwEeuu3vWMUfR/WVTsmi3T1lay9/wwTXnZciDaVd5
+s46bFAeXzxk0Z7lTvKTdBbhTuOCNJ0VgFgTDuh07KFb3iPCDGlb7DWwF1iMNbhA7j++9uifnvV5p
+lNJ3jcFfmEv6vw1fIPxJYKL8AhxOnWi2YwfD5BNjgeHz2jAw0KTcWlMPAXAseU1KDi/FadvcJ7o+
+BTZXAMOuorCfYp8N+AeoseewJ0M+bWJGc5v2otWOF7ul1/2CmewEiJ316C5h9/mYnnqvIGTVDUCx
+SNq4o1uDXVM/MVHLl63BP43nMnOYaXFEs/00bn3Jbpor0IQGXsY2cKI3/RJrKSHudY64LLYCt4qJ
+Z6j9pdVXNxpowO1d1evRlHwRJESrDO44IQlOIklg3schLtKJfq7eZQ9fUgpdenoDc58UDEnnvFWb
+1d4/p+ZEviDBnpLmeHfX7U1c/keWQkuW3px+BwTn8v1Tj01rXRSGFaoyujZY+QJ1EGofBkKYlnaX
+YVQDSXNla7BB4nSS0wbDSFM+srYC36YWNwhxo4VXnb0DhneiSjppfOs1SP8PDo34AmELN32E7Vi0
+PcABDCdg57fsIBAmw6nESQHVuyH3yHQzrZu8EjghLS8sZYZFivRrdFADAkRdOI0mPryDmFDMir+I
+6a3KEhUlr48elTYeqZaUa6Rnuegekf2LU8WlguDc/wiMVVakBZX56/HcvcbQ8AQan/jq5GBkfDpv
+6AyMEeDpDMZiPM2ldFVh47raYu+A2AswTDRM9FyfmPse/1y3+4CS9rcCuORkpov3bE+/Ku1mjVuc
+e5J6AzBPlAFI3w05B5cZWfnnkbuab316NydmFwZOnDld42xy+vRzOTnPrHuxRn3/t3kPbWAi39Yu
+2pzlW9o0UIcl3zIfrMGiL8QRGQhmAKB6asQCvOtqcBj2cew/c4NghckpL05PPLJCwXvAEwRxeIL4
+viz3/ygcvuYnqucEH8FwFf6hykr1m3KOmbkKL/qDqbX0Ek5wfyOVYrfAaWvVyT4cCRZCR72AaNrM
+EyAGUUtxbusvUQC6L2J6s5Sj0oq/Lyje+dOAXoGKCPDZ3Ig7d+rx1o27tSBvOlHQn4+Ab9BzTVdO
+XTzRzrzz5hUyJWCTk6b7AClQL5PEtFSLyVUi8i0eJpjzl0HXE/Jf7IypezrOKUz78DEne+ROk/Al
+MgbdE18u4/hQQq4I/Di1W6Ucq97luynxufAbJ13cfyipeU7fI7YpPNMxwoUeb3prZxfr4tHa3PEy
+xrmh5Ty8pJZZkhp6qaPZVutcn2PbUrwoz0YuYL8QpiVvAriXjy+a7WNBFVQuFgeggm4IpIDX2y/c
+H5OmshUGe83wdlwAACAASURBVLoCZJcZgS8lYCzQFgfk2aL9r8MXCH8Q8Ig0oj0VwBocwTCBFZkT
+GAaZd8q9rtics03Kmy/SK8gN9BYUY/acluDUjH6bkBBPWixVd7imCQqqaSYqwxe4QoSj01CJIi3Q
+t4XXaB6PhbtiLC/MIRAAcFyA7UC3EzKgXwIIzvHN9AT4dmpq6mMud7gpcMkw8Y8hVWQ8/eLO/X6p
+C/snF5PdIjKf0+uLcfHuAHJDUgXeyJDbMoVpb73DWzPd+1KfPUp4NjB+Hb42V8zmaXic2q2Kl+o+
+YD7y3vgtuCdFPajNhJXIwEuffpp5dErzxRBT2v2hJtX4/g/bDI+8dODlSOOafj6951qX1LrFNXar
+7g1txHUS9487/MXKSvBo3gDJahkmO1YNtUgAtKJQt7EWNy/QrWPY2BuUX5azOPJ4B3ABz/Q9R/j/
+V9CBpTTgOU424BUMn9VULm26O+ZqWTlcerndBMbJnrIJ0QkwMxzjErqtCDmFmt69jd+CnaTZtTQh
+/NEMphdJ9QXG6614doGAuIK14BusygxqLahynK+AYr2KqbJAL8A3xwE85hN5pnyTrvbevUGH4Z5l
+1ZfoNLR14ETHJQj56r2EPiCJsiMo7sCpy3R6BKz3VPsMhju+CpBjXfqUnmfmPY1OF8T/my4EfoE5
+DQ/uvDOIMe7I2HEXPTokyynxjtw0wp+HPjfCLyl0zD2D4ctQFWJIzOuUG/oc98G+4mfgV/155nW5
+mb7e6SBq+5V3egbLnPKDHshldPUxWrNEsyvvuNigzccl34YIgLM1eLHIXkZ+bNk681fnkJbuL74p
+zQDy4QU6rz+nzYQoWntVtykPE/ouI8/XNeK/DDd9hP0oMEonQZBNJFJQcgTD3XZa6/ChqqOiibK0
+gkpStHuaQ56rEyI6UHy8xuh1MK8lqJbAf+Sc+nEB3+D2gFdVoI3bRFSis4IlpHc0OzdY5eiciUAZ
+AVzovQCOez/hXidzpUvi5Zpv6suqvHww5zk8A13P92hSEFHz8CO13t6fFRq1z3q6r6TcqZhSE9aD
+7Oda9vRQxASGa5jrEtON56JrbXyaqsIMPeffjJJ17DYIeHNDJ1P/o0N36n1/vlwD42fyfgceV5B7
+mgEdffXrpPU43VNaMFkwV1KJu7I48XBLp5K305VaRCcjljFZhyPYbcNQxkTL7cw0u78BhkP+acCD
+VXgT0RqMfsJERMx+ygQRHI22SsvW3UKj/AKd6kal6c6jpK2vR1cJduwzAuXjCP2V8AXCT8PkI6zj
+7ZG17BSR7LzhZbvD7vKjqdBOpEZLtYpP+QB9odxbLhJpMZj/8IyNQ3HaZ7rj5WvY7n1BZrAIujls
+LZZOWzki0MXrXRqmgRImitZgb57Gffs3v+CtJFiI9KxH7CrI1nVjC2h7/YtP/lGG9azEfpx0ebf3
+9/M3KtTYqFP+nnGsT07bkfpte/UJ1rw6JwOt1E83jhq8H3GjutsCoFsFJ9iTy6uiSttSrErkynUB
+PNtaceobMCJoORHUSlyzrihDWX0NjtW75uaYfg1nfwZ4uxDHCcFILDFSHu4U49bAMZlz6Z0IDlsN
+tzxbxzZpqjstfpLDPb3oZCtPsFltG6ZP7ou23L2mQ5ruZVNZvwCGHdQyYEawBpNs+tZQe98lPaVq
+K3V7gW7r1rXU4m6ja9P3Cd+sAw32fS6gFzZ4YsMDoo3RNDuGNrqd/qvwBcKfhOTecAbDBOZ/IvWR
+6wb7tHBDOM2TCQQXcrfy0pK/A3IRIgX+tnJRxuOAABLVMVD4anvg0NGoSNcJEXC2JHtaVbb66UE0
+AbgOgNfAcQTMOws54M9tpgBwM+hfc6paXPrHBqTFL7xaPp+6I6a4M217njoX7shKxY/7S0jbkZP7
+aUdTYpvWzMWYXnlqLW/sjgEQD+Ul1NrXJebr0wfdNNxNZXTAF8iEKN03f7YHkYPkQ+G1528JuGLk
+Z17HT+Yw1KLkxRNcke90/7xmPY0nDp5nA85MHtPiXtfxMPBwR4f4yYBY9TXo95ZbShm5jl1dQ5C5
+Th+B4amQPAvCV3j7tAhL0iML/Sxht/RuHazYBWl8ZTWGf+LuFQEMA1jmAI5rU/4jg/AXCOfQgYwu
+7WQZNk2ub4wmcLyyQ/5PKpnDfiKMdXdN43WPrw6sPHFjdRnxKRBB8To2brWhbYqlwXEt6bt6+2GR
+y9BtzM0vdYFiLWCu6TMlvRhcJHbVX+y+w0pb1xvW4/EjseyNXsxv2FokRj8CX6UZn7R8Kjl3Qewj
+CXwtgNZ65oVxCJc6zZfJDUkz42NALAdrMNZtSks1u+KxE1X6RJprzpWUYPhUn7IkD/W8Gqcn8LJ8
+rd8AX+PbafmYtfCA9ctWoo90ztXTxDn3ZejGhoGS1+8VGG5nVDP/2jk50JCaOXAHmdNcT1qcDnG+
+wZPu/QPf9NXGED4s5ny53tOViHwvO6SV0iewd/d52eIJ/FK0Blf/4D2ThHw/hj2+ukwsnuj+sOL5
+JAlsfPGPtrR4dFsAx9aWZ491vxW+QHgHVAinBR8QX3Z1sIFVtrjgMhhuV8NDDcopEhQSZ15I5Y5K
+e1FbA2zOlkIllxWBMsIs/LrDwTXKqoVE8gyC/ACYw5Xn9BXiaREv3mcLM21QzJsu9D9mktfqHnkt
+GQJD+pZ1/wa60pBXhOmdaMb32l97vZLPL5VniZVvd/sbaTv+bsB0vkYawuKoZ0O+tOt+orry3P3N
+cALXCq6W8h3qdJJ9M0e2hN6VeCvkQbmQdCrh6ozaJ9VZYUIAmfe86bH9k6CHfgaJezeOKnPWOT8Z
+rqe8nCh57mW57J2W5HSz9szn0zdad7t4OdkB0xjvD3yhDQ098Ti98Qne+5jO7Uj3tErvePOH6zeA
+1Fyt36a9K7ZpSl9BoPdiOTlPpfk6C/IGzGPrsgXsDpCJuBao2z9RxbTluLS913TuE/84fIHww1B9
+WADaMdksyEeomSvFtIO5uBoeb9bNBC27ULdRcYxnH56ji0QqKqMvlGn95EDPiilCaloWr+Ja+nD1
+bkrKcMtB8Ku/PPe/11IpLyF6vYj+t+zIoflv2TR6Ecl7g9f1cxxCLxKjqXrUNCL99S3Zct9vpjct
+cPqm1XVvjj8PLSoB+sgA8R6eNwzDvWu0tp8A9OrHRJFJ+f8rBbc6gw9F2raATJLTL8rIsWnN3ZRz
+Ky2TdBl+UILONg9nYHpH5inDmIePtyWhe8j56cy6bA8w5LIe98WNvHl2VQAEHKi2k0Sc51GGE7Cs
+6vPr/xHoMZTVpan4jq/wEhVAOca5owOo5ViOgeFC32k8geiufhzKP19Lb4e9JoeJ3qVN+TWCde7y
+hPhhXfZvj+QAPOmlN3MJJab8U8/15TjAFv+Bb8QXCH8SJreIyETF+lvy5HAGxyHHpPnLJOoydztP
+U5+hWS3ItfYmhuwSQUT26FD6juEBoi++DWnBh+XLeI9f88zK084QZgTEq6oLEC+ZQkT/kxfJa8FP
+Iaa3IOB1oOyWX6U1aRsc6wPUH2yH6g/y7nzxsvjiL+NhV7+xT2Aqnq4eV0jMtU9D5zcjdHrLrIS/
+C45tORyKsU2rO0TYeCB2qae5ic08OdQHjYuShoVyqua59Jr6fGtyOcd9DcxGhe2EDLCYgfXHM6v9
+OuxZXzzttwqfujQuKpvLf+AsW8AAeHkuA8FYPv3B0rjna3mbPIGO/G0dUpxBn6e8qsGmtNYyrGVa
+/TiUO32wb3O7bKE2bUaOSpedVtdmb+2N8jTCifhcPyh4vcjcZDO4o5sZ6WbFzsB7V/5ahP9/BF9W
+roXZXvIQIGc3gOxK0Unuy7tHHJi4oYXoacfZk/bWUWlNVrtF8JtYm+ygM4Znifq8WkVkdwiE3DFO
+TG4R3h8Evxg3QLzdJP5HREQvEhIAs3p9JXBMROAqQQqO5e2WY3EA+iahNzG9BKzAvNu2u17nnvIz
+LYCsSvy989wBwnq1uJ0hqT2KoVNYzTxQ+g399nsqMM6IO4DYtrvWr2JecHyD564sIq3rk50G2A/A
+sNKnzvjBGbW5rBu77ef6LfFNU68J4zQomVpFfZ3tB/w8xNc9t/321PVhynvKx8AT4pzuIX/h3ZGW
+nuNFbrzP5Z/cHQjTOMrr+P3DNV37v1zry8oE99zQc5szfQqnOZLXb8fLwBt30TtzuTGLXADcSMes
+kSf8CMg/DF8grOFu5z+0BBdXCuadLP2MG8jPAzeCuIlyw8KpnY2YlqZtx0WSgfKiLRIK8n4LDxAp
+b3m9juOSDKC4Tdv/99NnqxBpuRsI8XJFeOn9Ar16pdQNC2wOFl+6D46zRfgl7vqwfuzDz6ddL16I
+WYKZeLtGLHcK7X6Nt2C3uVqcMU087RPEqlPtkDdP2Z8B4zpR7wLiri5X/JdcfDo1+bm8vgwa2xal
+yiFN0z+rh6uWg4ygnhLfp83HfBcdzePN74YnoiNgaYAM13nJ5X/KH/q5WnkrDbg55kMQpXkQMIY0
+wrTIq3q8yNtltvQc50gPH640GuiLf7IKcymrKxP7bdpPPMS2x5Q6XzLvVXxOv16LfMVHZPtJpHVE
+ugmOoTg9Wu1rEf7/ENZgMine24O7x1YtZ9UtgihYqGh4ReVXHOC4mctpNWcGTrTHPsJKhyiQAj2X
+K33SKZtVseX1J2K5pEUFqhbfNy83jRcRWIIX+Pwf03phjhwQq+Dg4oAuEo2rBBGC4/xC3Up/0bbu
+0gbEGwSvbtf6O/jVtrxB4aIlWdvfgl+5SMexwOkjJZJCfXjxDhuyAOvPwTBVKRyr8ansdsuY1l6X
+1GT+yTNGuJEm7ZSvhHtg+F4fQNK1yCsRRHTrWWZmvlkHZz6PyhNx1+PBka9R5zijKvjZ/1GdH2iY
+b5QFZaquPPoIBz4vr48TgM0BIGuce7rr8MZFwmSfXpBDP97ZQIIfKnmx32LgQF/ziQ+8J9oUv8ub
+af187ExLN2Y5At6OP4Hj+Mtyi+E3vo16Gr5A+GlgomAJtnsiYolgmIgqaCbCM4b7Aq5IEi6BcdqE
+250Sb4dyB+A6pyUGsOhm3+BwGmH49QYJzxFEQUwoOpYmpOrZupoatwjOtH1ChLgyfbGYO4FsP2Fh
+WSc6ENH/3rRQKlbwLW7ptZfoojXYfk+dIuCl9JId0YvUqqtuEW9xEPwSWWBdiNiOsIuK3n4mWpZF
+megC8HKlncCxtx0oBdvG1VACX+NQX0kPQrc2Jil5iVwW1K2zge924CYWQ/sweCGyb9PFmJRSI+ek
+Yq7yT2om8N3t2otix+H/WG5SeDcG6U6RRyDDcVZUUMORX7mKynerb5uPXbrT6tf8yhFdDoa48Zxd
+Kiq94U/HoHXxCmqrG4TJ716QM16O+ZorAW/un1Iv7FOQgyHznGi6TmJapeUyc0In/zIIEdu3PA5m
+qYBZTd6mpg4cbxpbPpVHdPM8zV8NXyD8SWBKQA4SEMRtvgiGnY+IHu7uWAG6MZM7Pm6iSRDW0zYo
+oRHkFusxylCZygNyE0/NMn9NMluDyZRwBcv1vym53YwS34rzxezm2dfuLwTDOpySXCOKNdivlNwj
+1GfYa6cAGOok0VViHQ7P+/g0bdNW9ML0DmdZPwe8wYrcTtneVWV3BnDl4BQeeWLI86MLpmpHoZy4
+h4KGhHubx/0tpv+S9KdSG17rjyclalYHqZ+Eu/l/0zXwF0WB0HmmTqo08FxMXoRVZ8ttpuqc58RT
+wRrGGf4FWgPwjEcBYUq3OEf+lo8P+S9k6YrJPOWTLMMmW3U6VatxF6dONtaJPU/bv027NHfn9hCn
+T12r3fTqxlZfPIvhc2trrdspwA5rChkAb/lJ6Aiq/wPPiC8Q/jgwORgOO/Qe5OaEiMjLnh5CMal9
+XsFpN+SGZrfKMwBaukPbN83LdPnFOKblrxseEA74pDtsA4v2uL4m53EiMt9gIl+uHK7bDUJouULQ
++vpGNvrc+NVcI+yIPDucfL2u1p0GoSbmlk5U3CPUR5jFP+ukiG0ZNhDsLhKrBky2Yezuz0eoZeA7
+Ad4Mik25kUPgTkEGH3ipPN0Q8yHtTjCdm4QyMrQlnmrV8U3hySbD8P9eLX6jBgFsTuusFdL27v26
+HLKy/Xtexl8PWT3a/+tRmtRvuE1iKqCq86QAY45U7jjZJUValN9ZlLNbRQSEEGdu6XbPTZ7HPOi3
+e7Lsuj7UPop8qj+b/FmO1ok1X20bQZ7Ydw0PxcApMqYf6AyEjr/wHhi6vmhDWa57DwzPvQpykRdp
+yWq8v9381+ELhHe4+6ZitVBupZh0Y3zhS9O0jJMS5X7mHR6TqsI9LKW86sJtXi46o9PTXAtyMz9U
+PYjE/kqdRqUbgW2lYHob59jLucdjHvv9HLe26vpk3udALKCuP4Yi5KBY36BjKGyB2GXlJT1LeLIS
+lxMmOABlJrcIv0XoLbxAMO2v90RBcGPhID8+zdCr3AO+OQ1HF3F/nmUCsZC2+9QZzqdp11nxS2Gu
+sG2yvyN4SubUD/ek3emLq5rf2fyuwz2gWvXRBd9BpKml+fn4V0NRg2fOC8qNtAvwMwEY7LueF2Y0
+N7RAgflf5GW3igyQ+BK8VheI5mW5wtPEWznkuo9j3Xhz6C+mBTrvNMyPMoos6Cto71hn1tpB2Yf1
+9uQ+xC/W8Clf7avpsMzmGn4FTu+JgtNhOO/TaRJo5GcMfy3C/5+CT26NmuUx/eqDHakGQJmJ9k8T
+Ez1S7TDjLdaB8mkX4oYWbptlBE29RQtp2mD2I+M2LXtJ8AZ/6DstwJSLxPscp3DvPwzRuUUYCNbP
+HjMmMUD8EjLLML14ue8S+TESb4+zULTq0rpf1thqJbafxjCgHI9V+0MUXCLKhxAEZwux28NpyzGf
+591JJ+BLkFb4KSpg/PIjwuE4ZqooMwD9DTB8D6I1hXSCngi4VWhauynPlSrIRZR+vVVyX8jjPmt6
++ijDlZXdnFSR8nFO77q66bMnw3eux8P8D9OntAhcan9ZzzB2Td4b4D/7XQRCUXbnftFZgpWuOlPl
+wzB5PKRlazEnYCs1/50yTNbphzH2vlPoF/mxn7o6cJXnfej9G+mRJ+joA2+8745Hi+uylVPWUAa4
+h3CXJfDhzf455uCyEcGyvjj3wVL8cfgC4YdBX0paIQFc8kFdzOQmNCKKQA/oQXs/mAiXVuxu9+Am
+mpiKzy9ccfGM1mCmbBUuPhHeOVPNves61DvxErpE4DKksAjtyXWPFxP5CRGb/0XLFeL1hl9ae2VE
+uZr6R7afrhB1vsEk+ntwaiVO1mBiMr+LzbPfv6O3EP0JIFitwR0tzx91l9j9oICWAfB2oDgBX+JI
+wyC73zUh932hadkS+bLcbrjPs+YXwq3Fd3eF+hZ1yQUinwLjT3g+3Wy6TfduISdV1JI+aIROxVvh
+eRM+5p3SOjo3aQqrrlwnAkDiSGNIiGW41bfjRx6kHa24rEBvSst0hrgMPE2cPf98IsSWv08BQhlt
+fq17kdHJ9eByYr/lPb3LR8P9k7SaLjZITv+tUxlg3xc/jtTSCsDd9aG9kagxkBUg73R84e4fhi8Q
+/iA4ptsToezOToj4zyfHrPD3TQLHH9Ry3lW4oRkJtNtUbJfW0nzSxx8Syb7V64ncrcSh01Y6xZey
+YMkRkR/CXQHwtjClbmVMbZSbg+HFIJY79hkr4wakZukNLhGdz7Bag7Vi1ZJMQsRvr5P+uMbyHc4u
+ETgArnztJAytJgOYTaA4WIMnGt5baWLp1rctKJZ4z+lZieL9RLsMv4iUo6i7GpoPd5PsxK/T4mFb
+5hrKRfon8oZUnm4hz8iz+X7iKPgPs078Jzl38rTW4NJ1nV8wJdo9S7B3d5LJsS6WxrEclDf6Eo95
+Mo8Dp5mnlnG27Cr3lWV4ypva09KaftIeTSdfUBM/pV2FO3lLeXxP9goR+LYZw3nCi9/+JzcJQdcI
+A8hEhDjgH4YvEP402A7tAx9IYDWuuE4RwF2zj+6Ken+aKd0k7TafbifKS2VP0I98hK03kkjNT9hZ
+FGN06z6IDDy+4a7/vYuEptfPUsUv5XrtJiKK3MAX48sIDuBXS5R8njCeHQz+w7IPPNtlsYB7RPr8
+ESJ+45mXWtoGx9q38Kq6VVdBKvu9No/wXnkE1GC4j7Jx9qSTAm3kPhnjq1AgWaukw+V2wFl0l/tu
+OaG/Jh7YVy5W/aPyfoPP1/tDOXzBc78CvxIe98upvc0gTfI7egdcFMTFtDozL0HtdM8p/1CuAz4E
+bsnqC9OBxzx0mUclq/5maFcnh03ObBk2mcfj05r4gYb1RBo9iMcxeJ6/xPmCJ028ru4B+J5WyAav
+gUdxrWLdkox+xESKIfRdoO/Lcv9luNv76Leqg6z5JYNhjWhyB+NKAYc6NvnCEQwH1coNbZKJpDsm
+OpzsJY2hz6Jdt/ZV5Cem7Wfd5DlUSVsiQIi06mOlig6PBtZcQpkOhaKLApGD4A7gEu1JAy/LyaqN
+W4rBh3jLDK4Rb687v2i5Qry9rgZOu2EHwJsBcAHIIxj285URXOY+t3sWw+IlLXVl7trp/m4Iebr+
+KIzXOqCvR91Zekk8Alrkb8tgak5cvOdGdcXz0b5jmeIudylrb5ojX5J7kvfJnCjF3OUZMhRynmfS
+Jh/lBEDKSOkBjnJHtwlOPBzmZedXnMvoXCRa6zDUtdA58gRrb0qb5eaH/coTQSqbfM78DOltXhoB
+MQUZW07o86Y8SO0t3Eu71XGVlD/Hfd05vfMd9jp39Bw4yehDBMlRA+TdAOJCJMxbBQjZC3b2rfGi
+i1yV//vhC4SfBrDmlg16pxl0Swxsb0TKYefLk+BC3R8BfLcj5xXR5P81H2HogARwtYOyS4S/WNdU
+27KDLTJtPBEk4PLMG6u/wGcKT3rFGPzKlG8/uYajzYgiCKboCpFPinC/YXbECfFsEf7zJgO/9FaF
+Tna+MXvTSM/RtHt6CIADrxS3CJ06qtO07+M4wPnZm28CuGUtfRi6B6IYuvk+yziz9iXU/HHN4Tg9
+AsW67A716+XMLXq05YxdJ31ik/fIdXOzfsLzNPB4c11mm5bG+8RbQA83NHItFkBW6N/G8hv6FvIz
+JV6l99bjEbx2dE55Q5kovwGI3JSxF0BXRvg0NCq0HiyPVuFQH/+P6dDBQz/Efv438QvAjJMTGsEp
+ad0c1nnj97uswU5fsMDLiXsygurf2AmehS8Q/jjoROp2d9Q6MT2+bEc07mytlerBBCkAudtlOk3P
+fs0WbKb4MNiltfwuq/hXd1WnhKEH0cQDnWr35cWGtLVAIyB+NX0UvwGgCIZptesdrLyNKwR1fsM9
+YNap8UeE+M3EL3WJIKJgDablq7x/Po5NaZGPFwGw5c8AcOcjjOOA5wtz5tn5tWoTAPxtYOzS+nAF
+RCd598BylFTyXPQHSgjz+aJjrut/j6djPPfkkMpdT/SCL/n+QuDxZiRdpre0MpiVFwEKR0oBYUtm
+um/kZt/iSr+S6XoFZaCcSGerf6Bzk3fzqy4e/Yuh5as8KXymv0FmZ3l2PuAJead49CGmTi7UmUrc
+F+/M83n8ach1nXjKJhKUe12jCGmVUsxSCSjLlsWKF/5x+ALhTwJsRkxwLh6kO/5l4M9gUIVhkJ5M
+FAE28ubKlbzdLtNtRINKf/SrcgM/YVRlwCUB75XkDKE/myZJ2jRXc7rnS/cWzj2BIPgta0N4Qdpb
+327ddX4reBY1yqrF309+WJz6KxyqTJiiNRh5dzq9iN9vswj/oX0qxJvoz4uChZje3t684WA/ZRA8
+xonW2ckNANY4+ai64uMMiMPoh+E3HUhRVst/iM9hVqZyi6vjr3OmK/cK1OY62JKVJi3lxeUzyT6F
+W3zcRm+EuinOexrwdirrHwQukSbtlO8GvaUNE4FTegd4wpU7nvyy2+wX3PJf3Jc4L45A54afh/yQ
+pmWNLhBBru8QDOnhnojyL8y1PsyZJ3x4oMf65RD7Z2h34r+KV+K8hphCktOLQH/PpIbTg2iFu4Uf
+QC7bD3Th/aq0yH5YQVeJfxy+QPjTAJvRGkMOYJeYojFYB5oWT2dwdUYyvjFJCaHMgyrmhmbRYbIX
+14cYDTImWkhTdVd/jS94AltfRaGhC5viNFTgWzPh1zKrCxbQfLHCWAZZbMcFM/EyvG6grMDP3f/B
+qkvRwnu0BlP0D9ZymbYVmLZleFeKN25mWhUL9z7NQv9P1t0YPwNgIf/gWRpYnE5L0319dQ5r4LPg
+Zc1qfU6tfBpO2wRy1VgvL/PEeR/7a8onifcU7vAo423eMex1dVPQNd9pM/4sHFXeKc+DtNu0MNg3
+XBxAUOsHjPcHy+6Ynwd5BuicdnaHADp7HPmQp7bz+gU540vyNZ/fVz4qfNmCzFXOgzhBGad4HM+e
+964sItXJVQuWeFOAk+Kai6w7LSh2IXSLwFMlGlsw3MvOuvLKzvsfGIS/QFjD/V+Ws5htXC6EIlLb
+pv680dlENVbpd70yWxumUas3qwtvuKEZib2hWmem7dKhAFLAigtMozW4kak32b1kM1h3ToB5CHlY
+WkvVpuNrCqHWu1qqxNePyS3e1+YWctCs5b2Y6cVCrxfT/2SdrygvALwYFybRX+4gJv/9ZlVkr20J
+RsswuVWY3FXC7reVGK8KlCOglQBq/cMeF6E3axzympLzXjSwDT2u+tJGoOEx/dmMVAHO0M91jHva
+k/TIeQ6nrQJ5KPOJp7TAHFg6qSHPcTM7h5/sNWPeURc1+QZ9W9XVb++KV6N2Thv7fVLNF7Sshpmm
+qzNwpNR7PqSf8nPDn+6RVuLc0Lnh5ws5u7T8i3BtHMrkgQaNILrMi8aRlKGpx/GzzJylXbqKvc9c
+s+HsNBrUI1apmcvMtdqp7k13XNL7B9JKcwOJG4kUE/ieimcHr1xC+LLcWIm/Fr5A+GmweQyzK7lF
+RJBHYfOL6YmPKCZ2fHPiIC/no7g6Ap1j/PAinGSLbdcuy8qhj+KSj6AAeavF7QSCve74rHJY2VBy
+ZcLmAAM47wAAGqBJREFUMAEwJm+SgZWd/bUfjF+8XSUYgDEzvV6ywTGTvMROiVg4Ek6L2GcMq7J+
+vxfUXj+vLPRmSW4TcCW0IG+F/CZ6vxDQrp9rLq4RGSAzrwcAArrxQT/qV1rWrzB+wOsAmkJ6odE9
+Gh9omV9Dn84jzzTf8BWPKaQtD4RzbPw5c6jLYTrnki7DE94p8/Ub5g/5+YHMH4ZTKScA1fKmSTPy
+XdAiYIL7BohRx8tNXr3nhl/v+VS232N9sC5T/Cp9jN8CwfeePs6/OjfQunRo+6k8JI79wZmzGVtu
+ADI38U5WVjk5b6KvABrGoj3NHAwNIgicCEHb3QF1pPv/oquEfpPOHF9e/9fhC4Qt3O1+0HKGlBg2
+LambGBNlS/G8Bya+pvha32RbCk0ZVkRgGdpufsDUI4oAco1Ao1WYlAw0zR6E/gIIRsLF0GKp2eoY
+ROhXN5CR0zg7AF4gWBQEs9D/9F6txa8NIvdV9KftiIjoRczvBWJpdfOf7Qu8AO6LmIXe7wWY3wp+
+dzUzONZfqJP9EKOGaAXBb9EX47iAYt49IxSBMpEPfQSt7CCY43jeAsdp2AJNahoP/DiGma4pXZ7K
+helc0u/MyzZJLkq/qtztUj+u5YGp9sM5H1fSE56/EXgu5wm9a0+gycDX0HRWBlA0+flC/g74RBkD
+HfLeyTfFr9If8SYQXPg4W1kPn09A8Cntki+6VLj2xD5taMiX5k/bTzDgvnzuWYPx5s46c90Kyp5j
+av1PGxwTcecqsfcdPUXCzxy+/sb3b4QvENZwV/NOgHbNYwozD60+TLAXcU2/VanB1CB3Z3vKzA3N
+6pbgxJ1j1EIboYzywDDZ6e7SO55DUAQFvC1gDhLFn1aRbXeBBvUpVhnM21WCZYFNBcGv9Ws6/3sp
+SPTrlkTLCgzW4O108d6A+EVEf5jo9Xag+zZALMQUQbFdaYHnlwD43df3/irqtZVRB4pfG9Cun492
+1wl8OMGrg+DVm8Yr3ocBSKf8eUjCleN9niHdc4/WJ9sa7gHmlXqaiXXa8yGtYUvfrpQ8usxOstrS
+z7U85muFXK+1+kxd81S10/Dc1cc/CFMRT+i3aAhsip7J9/CffeY5GEr3kx/wD9NXWqQYEOMEyh7Q
+zj6/ZMB15DmBYM73H4JgvuBv+RoQarxr7U0v+xHI6AEyBet3GDNGXk3HelQGTpF+HnaalCBF8QC5
+9XfvE2b5JaquEGAcsW/VRN+Z4rhW/mH4AuGnYc1Vv7F7QEwBHAMx5KU4ky9BMfCGQhL5NOO7cgOd
+YzyfCqGLA81yeceezhGmxrtXJ396WBC8Ka2FeqRw7D0bo26ZRUswlhTrlLpuE/X8B2K3CKuLhITr
+Apb/237D/xMienm5AqCYwTXiz3aN+IMuEUxuLRa1/C7VYgBY/YQ3j8hSSO89pMJCrw123VqspS+Y
+q8BWe8jvV4NbUGwgGEAx7dNVlE/BOPJRHEPk63gy+O2AsalbzvSOd9PF25ZDmfJQyh2Q2gbTE9qT
+zexm8o3lgdjH9WjzHiQNSe0XnEV+p5cuZPximKTP9O4R6V7+ooql8l25OGCPHP2ATcbTdErynTby
+fUhr478Ggm/8ctxE63ggLXRKc4N8sW1du9aar8BVSr68GnyOOGOX3tfyQXA0i4SYvGPu5wvg2Hhk
+A+XIo/Uu7hL/OHyBsIUHna8jF+5xNifkpKasLm8oPtXh6DV+Ud9OHhH1QJAHcbvyrcVXbxJPZDJe
+Kectw4+LQAgn0BZRWGYMt/HB9mXqMi/pjfx9okQJQFJrq7pFyHaDCAD4tYGOMP2PdyaC7VWPY1sS
+zTWCt9wFfJcF+M92ibAf3CCiP5ROmaCVh18LFCsAXj/8AX7CQvRSv2FSgLxeDowuEsk9QhUa+bjJ
+nicGkDcoRrAv5DwGiONQ+MziMtPiVSqdKG4bV+DXaRwIHa/Sw4OZdPSJfwi6Xq/eFLklLGWBzfRZ
+Zob/hXzMY7FRr1ynXxb1aUjjO5eeaTXFKNLQTvKQCPPHVfGHL8NBOpbdnQAR04FrTKcA6PR6cq24
+ot2OJ+supjl4PfxaXPe54OkG7qkc77P4qDz1s3ISZ8swyor04CbR1TkKLzJvBQOvZO/HhBMiADDb
+DppcIzA1uELwbrOK+x6f9h+GuzPDdloG5deARLtvAN3oIpDrlCsl1/sYl8ggC+rZChiAaLb4dv7O
+TIGngOAq2PM0DYw/z1DDk2Wzun2CRgcbVKlCAsw7qtbfBf54A+PtamCAuOZnIaKX2ZaJiOjPdo1g
+fi/rLiHwZT2F2F+Koxe96b1dJpBf6A9Rsv5u8C7qHrGdMlhf4ltKawFj3lZmtQgzobVYW7OAsYPl
+CoL1eXHnByBtMoCXkvz2yjEvg0AHxWnoAo0bWgx1VnKk8YkXQjflM489UB7C410Msx1rOBfSqYqJ
+v4+2CVfpTU1+NQxa8TkN29OM8+28p6vxcUqHe75OP8oIdTlYjJtroPEhraGdXo67enHueYfHey8n
+pnNKa+MUT9Moy6YZ29g/6YW4MD7ZdYIq3+QmAfMgl4/tq+sQ96VzvP7faQH4Lpr/gAYt9wkiwpMk
+0F3i38PgLxCGcFPdHq29UndbuZFm4QbKDVm6Hb9px+VjIuTDlYKuESeLb+GZWuN9Uff66WC0pjwI
+j0Hwo5DAKlE/TZI1CBWmW4f3cKv5NpsRXpo/ukUsS/A+Ro3ei/ZefH+ULwBfDi/JMRH92b9KJ5vv
+pS4SROYecYyT/rTJGqP3rqkCXn2mK5ZfJhvT4C4hWxZ7mnZjB3DDVVzpdlfMyymv3cOazTxKOwLf
+kW8KuUJXQFd3jFvCPwhXZ16U3bFLvcWLpc3yrmXcSP4odDJ/TEuT6qm861MdmmsAVgNfAFknGV0d
+cnq9HusXyp/zXaWVeOcHnD98vsdK3spPkAkGsssX+2HDvNKPkR7HQNqtOo5OLAcn4T1rsFCLEQ4h
+wOLgI0ykbhDU0NQ1Qt3kdgujS8WjmvxO+ALhx2HPQt39g9JLaZt0TMPwCTDu5FjyoG453RcZWtF0
+DbdM1Q/Y80u2HGt7OOMA7ZceBE/vkP4eRohgF2o1llnSoAvNGswKfPfn5cwI+IIM3kqXl/sDv2VZ
+eGkB6j+8fm1OQS/zy45SMxq96C37pTmjLYuuiFuD3wKAmFdcXSUsTv4Sw2sDYPSmFlJwvO60Xdll
+IrhLBEC8LcN6D/2s/tKBRqZbY5r4KGa7RRwvNhnU8YRn3Fg+yjmDZqXVTcr4Lqy+qDL+rnkk218u
+9EnOe8nHFyK5qqMh/+1qPQhBXlDJ+THoIu9Ew+rLgS/Q0stpj192y9c8Bh1QruN0+3rId0fGaqO0
+eZiITn7B2V2Cd30e3V/xcC7T+2zKR4Evt6ceiXZ2ndBRk8Dr8VyveMWQ6zbxTQFPfAhnA2Mq6H6+
+Q5NNYyLaVuO/qvKG8AXCn4Y9cH4vcVedXpLLaVlmJgSWm1PkqRsENzRib5PVXyNwNR6d1Rv4hEZD
+cwRuoE1l62Gi/qcW7/ykRg13c+CijjSUVPvXeoSJmDm4SLzwdzL084pS3M83KtY3L19hPTrN3B92
+ngV2OVqAt6+x09iOT1OQqy/1idJo/6qeqLrzcyFFlde25DOMgYFfagAxKSh2V4hgFWYAyrANCFEF
+x7C8DBRDnxtN6ijhtpNnZbjXFzdgqJtZPMh4sqXsSiPyrgxeAW/EXwjcXQ7s/dy/4quq55B+wzr1
+sLevZYT2X9SdqPVjvAU+5MT305fdrl+Gw/jMMx/PVuitzHt83NyH+MEv+CMQ3H1u5tHCJznYwV2d
+Mb9qUKLcrwCGSx/yDHw5lmE5Gv7ARte0oEkDoI1p9rIbuf+v08hcI1oayFvpv7G6n4UvENZwt/PH
+XXLPxvLraTobM1CGtCoc6tXcjOCYm9k80CbZSrO62uzVx7cEjomQWEHqTuMMgr2fioV43PVV/o1+
+u52apeN24PlR0eQaYI15W0/xtAgiAMGvneON97R+Ill/HY62QtZj0ra7hLk/sL9Ih+kGfN9Mb5RF
+TH/e+6g32b+SJ+gGQXaqhILcF/PioXWYG8Dj3V4AwwZqabtETPTVm/EHPFZjFchaPuj7AnbJ50wG
+xdUtgssv2pUpDmo9jCenewzdy5MQTrM4cB3PFH8s8J6cT9i63TSzXPD8NH3M9zjHnPcuQCibdQNw
++3zO3/EZ+HlsCYZ7bvgDV6xL5GloRV497/by2pS1ZPQWX4xf5cmV5+nDz+97mfXbkE4OUQS8ZSzA
+Em5XsBiHNOw/kBXGgLXM1DmFJwnPDWmhroaEhu2tt5Vk/r+ENFq7hYLchmawiWkwgP3d8AXCT4Mp
+Mp2ZaSPDF+HC5pU0YEjLPBR5j2w+4Vr6XVqIMs0/igG0ZDF2di6gGf0yexAMaYHe5CthTr+3pOpy
+Dy1mMgDe6Q4MdlTaFmIvhzH5OWssxhAswaIgmPe5wMsveAHgF/3hNwBk9yVWqzDG/QU6sdMk3gI/
++CHZQqxW4e0WIRX4BjgstHyfSfvGQan9Sh43p0sgCIalEoAwQ5yogF7kUV2cZymq49ZnWHCbasYc
+4jru7TLP/I91+F5HkNdnYyr1ahkksb8RboFT4Gk5T+kcI3P6dfikyfk4uhEbXNCuAG6hRXUX0zvQ
+FMrpLcGet/Zlf2pE5Gtph/J8laWtI9G662V8Asin0yNSPTA8thaDzsj8WMSU5mPCVufQB9avAIZh
+XLR/UWg/N/oG9zx9uORBsKv1InhXY/sI4y5qZwOT+wRrm4SIGNwqlHflu+Ny9fvhC4QtPOl9BLk2
+mlFUB5SR3pkQPgXGhZWb5nDPF2hpmWfXiPaluJUozkRU4AS0GcS3FuLSSk+7ZWEb5dwPodZsnlEh
+eHrKJwsM64PBS78CRw2ogPglxPtFNiY9/WFZYs0qzAiAwS2ClqWWKZ4hbPE30Z/XUjj8WsW9FfSq
+KwQJvZkNDPP2Gc72X/XhwpfyJHyi6wOCUdnH4zgIrjRil4Wg2GYTz6DXpiXhs5ePVzf7lky2OGk9
+CGdQA34v7oNADHcnYivwBt/f2DiSTpjUyVQBPqTHdXM/7W54kivWMzm4wJy4kt9VuTvzuetHxrIM
+IEV+BEExjUNan9f1fKE1fNiW3rJM8IDUgLy71+bcYNW5uZ2ZHtI45j9ZdrHNpzw0pFHDi+KZXRf7
+uKR+8uEgoukFOqL8AxthbpnsLYMjnbo8HEnnZRaNQ9E/eKcLVJpckTvAJbN620kSCh9INkDmJK57
+kf7vhy8Q1nBXeyr4JaIIchk2p7RL6azQrCgDBU91CIDxxMAN24Fm0STX6og3gbhvmcoxasaus57q
+xC59AOB1SAsijgtlCbi3lnaFGVuZfGSnIobwAi0jxNv0SvYTdPzyM4ntAImtHPjN9GZZp0KYVfhN
+Lzgh4h0swRdx2f7CGxSrFVhfmjM3CXJsbnF2FwoGC7FajmX3cgTDVAAxgmSdC5J8hhXcivUbgGlJ
+95DOkMbEATBrXpxFumm04NiWL5dn18AH8Xzfp3GcL2kx+Fbn+oErWw139dXTMOkJvTukI4m79KB2
+Tnk/b9yTnJWXS/MQCugl5+vKDH01AOpAS213NXID6DZ5vSwuAKiTEWh5e8hXzvkk1aOvX07L8Y/S
+7pwakT95WjKm+36QP4uXIw3abPmhsTiORNL3EbhD5P61MU1uFNiIyW2ijjE2OhOkI5YQtniHxwZ6
+l16GSiSAq24Q5hssAjZDjtj6H4YvELZws/uDK0TatXSGZDqRj7xq0iempENSYcia9VPadF6wWYO9
+ERIExIblTX4RueT07LCC2tB2HpR3NwhcGa5ziVo9zmkceXgv+BdBn5HA7zGvuP7YBQvFOPM6MYKX
+Iqx+wBg/W4X5xeaNUQCwgK/wBrzBOkyq9BsLMW2Qi9ZdpWm6AV12Nwn9KC2A5vUP+dA3GL1t9Jfq
+dJl1YBSfTZm4sfpiHCzAMJ7dhOpnIB/SkI29kSepWJkHs/rjUKZ+Rgs5OqcroVMxP0u7H+5mvXKN
+4BzLG3UDjIsc2+iv6xhA1y6sgCK9h281WvCTHuSVu9KUP9NK64e8a46eeeq1jT+xEv/GqRF5Xl+k
+Yx0yL/ZfOyalf13G1lBpvDFffVDBMinH0/34kEKnkPfEfV+sDZzYAfQSxd/dUJcJdIEwsW4l/tfh
+C4QfBxtRIHEPcpG+s46+xVNZJRzAcZdQVstdmjaEoa6bFnyAmSawLMhHZG1v7bXmMF/T7FnY+p1b
+vlr3KUi9TQovPdQWmjF2t6EbvR+ZhFgBMJEDX9lWWACqb/bzgpnf+2ebMb4UCfoKqy9wjLP9why+
+n/cmtfAiXba7hWypYjIUAivtTQn4Guglt/gaTwLNgdeBrE4xBL6EMlCxAh1dI4KOtng8Ng1l6AbT
+AmlypRxmVANonkFVXFNntq68p6HF3MciMzoYuA5pGuXfTnsQ+lyxN7qXnvLzyFEmX+cxWhrPSbYB
+I073kP70ZbnCM5RV65FoEx+f+XPa1Y9kMN6fjlDDwCmdI+/VPTXpnbxcB+xzvK/9cLL8LqZ1f3gZ
+EeV26R3f0E+5z44BQC5ZLVcs+Ah3oJeSnzAoYNn9hlZiVfxMvV6+Vd+H4QuENdzt2acg1+jZHIWa
+A4b4I3CsGbOWSrxPaAVvMtXNO09Pp0mhUUPXZD1Cq020vH0l+9Q5zJzTwtMh5MzcxJci3NZg7bOt
+AN8q9bUsrrwtr6830xtoapVdoHN589qPZ1C0EDOtn0KOaRh3K7G6OiyLr1uFWa3CbyKmaPtdstwS
+zEQGlgPIJYrgN1l89fziApiTRZkQ5KoSpQiWsyW4LEny56/w0lwZT068OPZDvh0Js+9ToFqmdree
+ZvIUykp5tGvwIU/WLzna76p8TJt34p+4SNS69RS/iwDF0puHnvYexrKrddEdDZ+DGU4gx3tw1S+7
+NkRr4cn/N9CgsVWdHSzHLQ1XzYkvXkO8sQZP8QykOfFR5n/64Vy3NA4cy8plhzqHKa6uLqmv8niP
+L9h1/Ok+L6luMNqQ3oZRwJoyLctt6gAzeHFjjQCLRgbMsuWxinC5uPSyTvuhbaCELxC2cFPpMrmp
+isnBL+Fuyme+U9kWfQKOOxncJGda02ZbRThTbcZSfmFOtG3A5ydERIRRm7H66QSCz96+n50pPAf8
+uQhYy3nN4z2nYRd4F46I1CKMZy8w4Y9dEAlahsVdF/5s5bv49EW5zi3CeeY0cIl4qz8wWoW3tVoo
+uEcs67WfHsFw11l93wUgC0yXBY5bUAx9aMAanrvCC3cEUzHTQhq8iEE4FaMVWAdzLVnf7MaHohzP
+ywrqHehYJt7FhDYEa8rEo5JvqrK+hhTaw4c05OrUi2XgIWlM22PwcTs6aUNamgR5yw91OIBXJHTj
+dIdvAjIc0jil+f30oh3SovwyukVmSOeGluhd+kTL8b69vUtEzjdZe3PBV/xtXfjOPY/p5Wr8g+U3
+yLsx6Y4T/KIut8LSnvHwiKW8s1tg3OkcB1crMaWTI8heovt1lHsjfIHwJ6F1hdAZItd8OvLnQtro
+AVV6mUXMJ7Q4vSMtZ2xoyZ1CSvpqRN8Nqx4nkNsCig+C6H9At3ho2FJJsMwZ7hn6Z/e7ugGra8FL
+JfJ6ilYL7HoBjcx3N4Bg2j67ekzaPjWiAl61Ci+LMe0naqbkGiFE/BJ6v7e/8Estz2Ah3tbgJXHF
+mXi5b7z98DiGnkFL8JtUmUUXiHdxk9hAWvQHPdx/WJWt8bIDXW5oCPyQFkeMfMnRVr5p5vgM4vJF
+z90QZiEu3aNeh1ymPvJp1g1fEoht/FEYBfABlMaCI/iorfg8z8/CJOcMdJNNmjcw2bydXM6RBscc
++ZK1t7o8xGwKvCjz2jU+pOAYZG2fqX1604YpvRnjtv7N/Zj2AAR393ST/1S33PfdWFgb85iFe6aT
+ZdgqC/qgPCSd7ossiiHPiQeLbUPjsG+aMiUiNZJp7SMYJgO9Ts9A+9+GLxDWcHcEOitvOT0iPear
+a8S+tHz3KxouiZqIDfUmDTCst2GwCnsNepAcQSvRjA58RfU9w0NqV3YX7vd3Vg4FbKRdQG//r727
+XU4UBsAwSvb+r1n3BwaSANaW1bq+58zsdlCQjh/haQb1UvoZ4bpd/Trh+bZus8JlG8TLpzg0p0aM
+wTsPxkPw3j4mbTw3eKqX149pq9F9qecDzwNanQ2+XvqZ33lwb+P4NmhN7cxumeo7htdzh9fZgPoN
+dt0s8O1OXd5MNzVPrWnaDeKuBYf12sehDefuJTf1MxDLbU075wkv65VmvR9onifzJ2/0rv0qj+1o
+eU6WdvGcezcyHtyXheONjr4d6ruXf3Xdd43fCDfe98vyYej24++XH5FWHlhn2HcbSn3g7ETy7nZ1
+m+3zo2z+7y/fXtbuf2c/3d1RX00Ht7HcVh+z88Lxm+HGdcu47bCPu/+2D+ju9Y8u16W9328af46P
+ValbrjPD3a+27KsMy+P1w852HG27tRmR1uidys7Vx2+MW9ddR9ZlTF5iuG5zbS7/d6/3R/15+R75
+PO3B/qk7eu6tv71+Mgrg1xiHMrw+S19PCHOeEREAPk7C4V0Ic17Cn4wAECbh8C6EOW84Jfp5El6S
+d4znhAH8EuNQBjPC8E3OEX4i5wgDb8I4xKcQwpxnagAAPk7C4V0Ic55TI17DqRHAmzAOZUiY+RfC
+nOfj017DqRHAmzAOZUj4g0cIc54REQA+TsLhvVx/9O1mAADwfzMjDABAJCEMAEAkIQwAQCQhDABA
+JCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQh
+DABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwA
+QCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAk
+IQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEM
+AEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABApL+Uk2g8s+79bwAAAABJ
+RU5ErkJggg==
+"
+width=353
+height=351
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;<a class="anchor-link" href="#&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#182;</a></h2><h3 id="&#26631;&#20934;&#21270;">&#26631;&#20934;&#21270;<a class="anchor-link" href="#&#26631;&#20934;&#21270;">&#182;</a></h3><p>在下面的单元中，实现 <code>normalize</code> 函数，传入图片数据 <code>x</code>，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 <code>x</code> 的形状一样。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Normalize a list of sample image data in the range of 0 to 1</span>
+<span class="sd">    : x: List of image data.  The image shape is (32, 32, 3)</span>
+<span class="sd">    : return: Numpy array of normalize data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="mi">255</span><span class="p">)</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_normalize</span><span class="p">(</span><span class="n">normalize</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="One-hot-&#32534;&#30721;">One-hot &#32534;&#30721;<a class="anchor-link" href="#One-hot-&#32534;&#30721;">&#182;</a></h3><p>和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 <code>one_hot_encode</code> 函数。输入，也就是 <code>x</code>，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 <code>one_hot_encode</code> 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。</p>
+<p>提示：不要重复发明轮子。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[28]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">one_hot_encode</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.</span>
+<span class="sd">    : x: List of sample Labels</span>
+<span class="sd">    : return: Numpy array of one-hot encoded labels</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="kn">from</span> <span class="nn">tflearn.data_utils</span> <span class="k">import</span> <span class="n">to_categorical</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_one_hot_encode</span><span class="p">(</span><span class="n">one_hot_encode</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#38543;&#26426;&#21270;&#25968;&#25454;">&#38543;&#26426;&#21270;&#25968;&#25454;<a class="anchor-link" href="#&#38543;&#26426;&#21270;&#25968;&#25454;">&#182;</a></h3><p>之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;<a class="anchor-link" href="#&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#182;</a></h2><p>运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[29]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="c1"># Preprocess Training, Validation, and Testing Data</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">preprocess_and_save_data</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">normalize</span><span class="p">,</span> <span class="n">one_hot_encode</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[30]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+
+<span class="c1"># Load the Preprocessed Validation data</span>
+<span class="n">valid_features</span><span class="p">,</span> <span class="n">valid_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_validation.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#26500;&#24314;&#32593;&#32476;">&#26500;&#24314;&#32593;&#32476;<a class="anchor-link" href="#&#26500;&#24314;&#32593;&#32476;">&#182;</a></h2><p>对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。</p>
+<blockquote><p><strong>注意</strong>：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。</p>
+<p>但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 <code>conv2d</code> 类 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/conv2d">tf.nn.conv2d</a>，而不是 TF Layers 版本的 <code>conv2d</code> 类 <a href="https://www.tensorflow.org/api_docs/python/tf/layers/conv2d">tf.layers.conv2d</a>。</p>
+</blockquote>
+<p>我们开始吧！</p>
+<h3 id="&#36755;&#20837;">&#36755;&#20837;<a class="anchor-link" href="#&#36755;&#20837;">&#182;</a></h3><p>神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：</p>
+<ul>
+<li>实现 <code>neural_net_image_input</code><ul>
+<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
+<li>使用 <code>image_shape</code> 设置形状，部分大小设为 <code>None</code></li>
+<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "x" 命名</li>
+</ul>
+</li>
+<li>实现 <code>neural_net_label_input</code><ul>
+<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
+<li>使用 <code>n_classes</code> 设置形状，部分大小设为 <code>None</code></li>
+<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "y" 命名</li>
+</ul>
+</li>
+<li>实现 <code>neural_net_keep_prob_input</code><ul>
+<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a>，用于丢弃保留概率</li>
+<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "keep_prob" 命名</li>
+</ul>
+</li>
+</ul>
+<p>这些名称将在项目结束时，用于加载保存的模型。</p>
+<p>注意：TensorFlow 中的 <code>None</code> 表示形状可以是动态大小。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[39]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+
+<span class="k">def</span> <span class="nf">neural_net_image_input</span><span class="p">(</span><span class="n">image_shape</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return a Tensor for a batch of image input</span>
+<span class="sd">    : image_shape: Shape of the images</span>
+<span class="sd">    : return: Tensor for image input.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">neural_net_label_input</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return a Tensor for a batch of label input</span>
+<span class="sd">    : n_classes: Number of classes</span>
+<span class="sd">    : return: Tensor for label input.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">neural_net_keep_prob_input</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return a Tensor for keep probability</span>
+<span class="sd">    : return: Tensor for keep probability.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;keep_prob&#39;</span><span class="p">)</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_image_inputs</span><span class="p">(</span><span class="n">neural_net_image_input</span><span class="p">)</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_label_inputs</span><span class="p">(</span><span class="n">neural_net_label_input</span><span class="p">)</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_keep_prob_inputs</span><span class="p">(</span><span class="n">neural_net_keep_prob_input</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Image Input Tests Passed.
+Label Input Tests Passed.
+Keep Prob Tests Passed.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;">&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;<a class="anchor-link" href="#&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;">&#182;</a></h3><p>卷积层级适合处理图片。对于此代码单元，你应该实现函数 <code>conv2d_maxpool</code> 以便应用卷积然后进行最大池化：</p>
+<ul>
+<li>使用 <code>conv_ksize</code>、<code>conv_num_outputs</code> 和 <code>x_tensor</code> 的形状创建权重（weight）和偏置（bias）。</li>
+<li>使用权重和 <code>conv_strides</code> 对 <code>x_tensor</code> 应用卷积。<ul>
+<li>建议使用我们建议的间距（padding），当然也可以使用任何其他间距。</li>
+</ul>
+</li>
+<li>添加偏置</li>
+<li>向卷积中添加非线性激活（nonlinear activation）</li>
+<li>使用 <code>pool_ksize</code> 和 <code>pool_strides</code> 应用最大池化<ul>
+<li>建议使用我们建议的间距（padding），当然也可以使用任何其他间距。</li>
+</ul>
+</li>
+</ul>
+<p><strong>注意</strong>：对于<strong>此层</strong>，<strong>请勿使用</strong> <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a>，但是仍然可以使用 TensorFlow 的 <a href="https://www.tensorflow.org/api_docs/python/tf/nn">Neural Network</a> 包。对于所有<strong>其他层</strong>，你依然可以使用快捷方法。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[64]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv2d_maxpool</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">conv_num_outputs</span><span class="p">,</span> <span class="n">conv_ksize</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Apply convolution then max pooling to x_tensor</span>
+<span class="sd">    :param x_tensor: TensorFlow Tensor</span>
+<span class="sd">    :param conv_num_outputs: Number of outputs for the convolutional layer</span>
+<span class="sd">    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer</span>
+<span class="sd">    :param conv_strides: Stride 2-D Tuple for convolution</span>
+<span class="sd">    :param pool_ksize: kernal size 2-D Tuple for pool</span>
+<span class="sd">    :param pool_strides: Stride 2-D Tuple for pool</span>
+<span class="sd">    : return: A tensor that represents convolution and max pooling of x_tensor</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_ksize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conv_ksize</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">3</span><span class="p">],</span> <span class="n">conv_num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
+    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_num_outputs</span><span class="p">]))</span>
+    <span class="n">conv</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x_tensor</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conv_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span>
+    <span class="n">activate</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv</span><span class="p">)</span>
+    <span class="n">pool</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="n">activate</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pool_ksize</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pool_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">pool</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_con_pool</span><span class="p">(</span><span class="n">conv2d_maxpool</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#25153;&#24179;&#21270;&#23618;">&#25153;&#24179;&#21270;&#23618;<a class="anchor-link" href="#&#25153;&#24179;&#21270;&#23618;">&#182;</a></h3><p>实现 <code>flatten</code> 函数，将 <code>x_tensor</code> 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（<em>部分大小（Batch Size）</em>，<em>扁平化图片大小（Flattened Image Size）</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[68]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">flatten</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Flatten x_tensor to (Batch Size, Flattened Image Size)</span>
+<span class="sd">    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.</span>
+<span class="sd">    : return: A tensor of size (Batch Size, Flattened Image Size).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">layer_shape</span> <span class="o">=</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span>
+    <span class="n">num_features</span> <span class="o">=</span> <span class="n">layer_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">num_elements</span><span class="p">()</span>
+    <span class="n">layer_flat</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_features</span><span class="p">])</span>
+    
+    <span class="k">return</span> <span class="n">layer_flat</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_flatten</span><span class="p">(</span><span class="n">flatten</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;">&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;<a class="anchor-link" href="#&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;">&#182;</a></h3><p>实现 <code>fully_conn</code> 函数，以向 <code>x_tensor</code> 应用完全连接的层级，形状为（<em>部分大小（Batch Size）</em>，<em>num_outputs</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[72]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fully_conn</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Apply a fully connected layer to x_tensor using weight and bias</span>
+<span class="sd">    : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
+<span class="sd">    : num_outputs: The number of output that the new tensor should be.</span>
+<span class="sd">    : return: A 2-D tensor where the second dimension is num_outputs.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
+    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
+    <span class="n">fc</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">fc</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_fully_conn</span><span class="p">(</span><span class="n">fully_conn</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#36755;&#20986;&#23618;">&#36755;&#20986;&#23618;<a class="anchor-link" href="#&#36755;&#20986;&#23618;">&#182;</a></h3><p>实现 <code>output</code> 函数，向 x_tensor 应用完全连接的层级，形状为（<em>部分大小（Batch Size）</em>，<em>num_outputs</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
+<p><strong>注意</strong>：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[74]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">output</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Apply a output layer to x_tensor using weight and bias</span>
+<span class="sd">    : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
+<span class="sd">    : num_outputs: The number of output that the new tensor should be.</span>
+<span class="sd">    : return: A 2-D tensor where the second dimension is num_outputs.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
+    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
+    <span class="n">output</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span>
+    
+    <span class="k">return</span> <span class="n">output</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_output</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;">&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;<a class="anchor-link" href="#&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;">&#182;</a></h3><p>实现函数 <code>conv_net</code>， 创建卷积神经网络模型。该函数传入一批图片 <code>x</code>，并输出对数（logits）。使用你在上方创建的层创建此模型：</p>
+<ul>
+<li>应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）</li>
+<li>应用一个扁平层（Flatten Layer）</li>
+<li>应用 1、2 或 3 个完全连接层（Fully Connected Layers）</li>
+<li>应用一个输出层（Output Layer）</li>
+<li>返回输出</li>
+<li>使用 <code>keep_prob</code> 向模型中的一个或多个层应用 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/dropout">TensorFlow 的 Dropout</a></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[123]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a convolutional neural network model</span>
+<span class="sd">    : x: Placeholder tensor that holds image data.</span>
+<span class="sd">    : keep_prob: Placeholder tensor that hold dropout keep probability.</span>
+<span class="sd">    : return: Tensor that represents logits</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Apply 1, 2, or 3 Convolution and Max Pool layers</span>
+    <span class="c1">#    Play around with different number of outputs, kernel size and stride</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)</span>
+    <span class="n">conv_pool_1</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+    <span class="n">norm_layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">lrn</span><span class="p">(</span><span class="n">conv_pool_1</span><span class="p">,</span> <span class="mi">4</span> <span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.001</span> <span class="o">/</span> <span class="mf">9.0</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
+    <span class="n">conv_pool_2</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">norm_layer</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+
+    <span class="c1"># TODO: Apply a Flatten Layer</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#   flatten(x_tensor)</span>
+    <span class="n">flat_layer</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">conv_pool_2</span><span class="p">)</span>
+
+    <span class="c1"># TODO: Apply 1, 2, or 3 Fully Connected Layers</span>
+    <span class="c1">#    Play around with different number of outputs</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#   fully_conn(x_tensor, num_outputs)</span>
+    <span class="n">fc_layer1</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">flat_layer</span><span class="p">,</span> <span class="mi">384</span><span class="p">)</span>
+    <span class="n">dropout_layer_1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc_layer1</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+    <span class="n">fc_layer2</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">dropout_layer_1</span><span class="p">,</span> <span class="mi">192</span><span class="p">)</span>
+    <span class="n">dropout_layer_2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc_layer2</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+    
+    <span class="c1"># TODO: Apply an Output Layer</span>
+    <span class="c1">#    Set this to the number of classes</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#   output(x_tensor, num_outputs)</span>
+    <span class="n">logits</span> <span class="o">=</span> <span class="n">output</span><span class="p">(</span><span class="n">dropout_layer_2</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+    
+    <span class="c1"># TODO: return output</span>
+    <span class="k">return</span> <span class="n">logits</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="c1">##############################</span>
+<span class="c1">## Build the Neural Network ##</span>
+<span class="c1">##############################</span>
+
+<span class="c1"># Remove previous weights, bias, inputs, etc..</span>
+<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
+
+<span class="c1"># Inputs</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">neural_net_image_input</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">neural_net_label_input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">keep_prob</span> <span class="o">=</span> <span class="n">neural_net_keep_prob_input</span><span class="p">()</span>
+
+<span class="c1"># Model</span>
+<span class="n">logits</span> <span class="o">=</span> <span class="n">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+
+<span class="c1"># Name logits Tensor, so that is can be loaded from disk after training</span>
+<span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;logits&#39;</span><span class="p">)</span>
+
+<span class="c1"># Loss and Optimizer</span>
+<span class="n">cost</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">))</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">()</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
+
+<span class="c1"># Accuracy</span>
+<span class="n">correct_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">accuracy</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">correct_pred</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">)</span>
+
+<span class="n">tests</span><span class="o">.</span><span class="n">test_conv_net</span><span class="p">(</span><span class="n">conv_net</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Neural Network Built!
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;<a class="anchor-link" href="#&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#182;</a></h2><h3 id="&#21333;&#27425;&#20248;&#21270;">&#21333;&#27425;&#20248;&#21270;<a class="anchor-link" href="#&#21333;&#27425;&#20248;&#21270;">&#182;</a></h3><p>实现函数 <code>train_neural_network</code> 以进行单次优化（single optimization）。该优化应该使用 <code>optimizer</code> 优化 <code>session</code>，其中 <code>feed_dict</code> 具有以下参数：</p>
+<ul>
+<li><code>x</code> 表示图片输入</li>
+<li><code>y</code> 表示标签</li>
+<li><code>keep_prob</code> 表示丢弃的保留率</li>
+</ul>
+<p>每个部分都会调用该函数，所以 <code>tf.global_variables_initializer()</code> 已经被调用。</p>
+<p>注意：不需要返回任何内容。该函数只是用来优化神经网络。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[124]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train_neural_network</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Optimize the session on a batch of images and labels</span>
+<span class="sd">    : session: Current TensorFlow session</span>
+<span class="sd">    : optimizer: TensorFlow optimizer function</span>
+<span class="sd">    : keep_probability: keep probability</span>
+<span class="sd">    : feature_batch: Batch of Numpy image data</span>
+<span class="sd">    : label_batch: Batch of Numpy label data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">keep_prob</span><span class="p">:</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">label_batch</span><span class="p">})</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_train_nn</span><span class="p">(</span><span class="n">train_neural_network</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#26174;&#31034;&#25968;&#25454;">&#26174;&#31034;&#25968;&#25454;<a class="anchor-link" href="#&#26174;&#31034;&#25968;&#25454;">&#182;</a></h3><p>实现函数 <code>print_stats</code> 以输出损失和验证准确率。使用全局变量 <code>valid_features</code> 和 <code>valid_labels</code> 计算验证准确率。使用保留率 <code>1.0</code> 计算损失和验证准确率（loss and validation accuracy）。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[125]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">print_stats</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Print information about loss and validation accuracy</span>
+<span class="sd">    : session: Current TensorFlow session</span>
+<span class="sd">    : feature_batch: Batch of Numpy image data</span>
+<span class="sd">    : label_batch: Batch of Numpy label data</span>
+<span class="sd">    : cost: TensorFlow cost function</span>
+<span class="sd">    : accuracy: TensorFlow accuracy function</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Loss: &#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">label_batch</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;, Accuracy: &#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">label_batch</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#36229;&#21442;&#25968;">&#36229;&#21442;&#25968;<a class="anchor-link" href="#&#36229;&#21442;&#25968;">&#182;</a></h3><p>调试以下超参数：</p>
+<ul>
+<li>设置 <code>epochs</code> 表示神经网络停止学习或开始过拟合的迭代次数</li>
+<li><p>设置 <code>batch_size</code>，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：</p>
+<ul>
+<li>64</li>
+<li>128</li>
+<li>256</li>
+<li>...</li>
+</ul>
+</li>
+<li>设置 <code>keep_probability</code> 表示使用丢弃时保留节点的概率</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[130]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Tune Parameters</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">128</span>
+<span class="n">keep_probability</span> <span class="o">=</span> <span class="mf">0.75</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#22312;&#21333;&#20010;-CIFAR-10-&#37096;&#20998;&#19978;&#35757;&#32451;">&#22312;&#21333;&#20010; CIFAR-10 &#37096;&#20998;&#19978;&#35757;&#32451;<a class="anchor-link" href="#&#22312;&#21333;&#20010;-CIFAR-10-&#37096;&#20998;&#19978;&#35757;&#32451;">&#182;</a></h3><p>我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[131]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Checking the Training on a Single Batch...&#39;</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+    <span class="c1"># Initializing the variables</span>
+    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
+    
+    <span class="c1"># Training cycle</span>
+    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
+        <span class="n">batch_i</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+            <span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">:  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+        <span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Checking the Training on a Single Batch...
+Epoch  1, CIFAR-10 Batch 1:  Loss: 1.95307, Accuracy: 0.35
+Epoch  2, CIFAR-10 Batch 1:  Loss: 1.71162, Accuracy: 0.5
+Epoch  3, CIFAR-10 Batch 1:  Loss: 1.59222, Accuracy: 0.525
+Epoch  4, CIFAR-10 Batch 1:  Loss: 1.33961, Accuracy: 0.65
+Epoch  5, CIFAR-10 Batch 1:  Loss: 1.22308, Accuracy: 0.625
+Epoch  6, CIFAR-10 Batch 1:  Loss: 1.02561, Accuracy: 0.65
+Epoch  7, CIFAR-10 Batch 1:  Loss: 0.918526, Accuracy: 0.725
+Epoch  8, CIFAR-10 Batch 1:  Loss: 0.763063, Accuracy: 0.775
+Epoch  9, CIFAR-10 Batch 1:  Loss: 0.656814, Accuracy: 0.8
+Epoch 10, CIFAR-10 Batch 1:  Loss: 0.574128, Accuracy: 0.825
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;">&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;<a class="anchor-link" href="#&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;">&#182;</a></h3><p>现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[134]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">8</span>
+<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./model/image_classification&#39;</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training...&#39;</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+    <span class="c1"># Initializing the variables</span>
+    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
+    
+    <span class="c1"># Training cycle</span>
+    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
+        <span class="c1"># Loop over all batches</span>
+        <span class="n">n_batches</span> <span class="o">=</span> <span class="mi">5</span>
+        <span class="k">for</span> <span class="n">batch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_batches</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+                <span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">:  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+            <span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
+            
+    <span class="c1"># Save Model</span>
+    <span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
+    <span class="n">save_path</span> <span class="o">=</span> <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Training...
+Epoch  1, CIFAR-10 Batch 1:  Loss: 1.95822, Accuracy: 0.35
+Epoch  1, CIFAR-10 Batch 2:  Loss: 1.64322, Accuracy: 0.4
+Epoch  1, CIFAR-10 Batch 3:  Loss: 1.36831, Accuracy: 0.55
+Epoch  1, CIFAR-10 Batch 4:  Loss: 1.41689, Accuracy: 0.45
+Epoch  1, CIFAR-10 Batch 5:  Loss: 1.59784, Accuracy: 0.425
+Epoch  2, CIFAR-10 Batch 1:  Loss: 1.36398, Accuracy: 0.475
+Epoch  2, CIFAR-10 Batch 2:  Loss: 1.1802, Accuracy: 0.475
+Epoch  2, CIFAR-10 Batch 3:  Loss: 1.07384, Accuracy: 0.6
+Epoch  2, CIFAR-10 Batch 4:  Loss: 0.988241, Accuracy: 0.675
+Epoch  2, CIFAR-10 Batch 5:  Loss: 1.24307, Accuracy: 0.55
+Epoch  3, CIFAR-10 Batch 1:  Loss: 1.05733, Accuracy: 0.625
+Epoch  3, CIFAR-10 Batch 2:  Loss: 0.952706, Accuracy: 0.675
+Epoch  3, CIFAR-10 Batch 3:  Loss: 0.922446, Accuracy: 0.65
+Epoch  3, CIFAR-10 Batch 4:  Loss: 0.753417, Accuracy: 0.8
+Epoch  3, CIFAR-10 Batch 5:  Loss: 0.917541, Accuracy: 0.7
+Epoch  4, CIFAR-10 Batch 1:  Loss: 0.868109, Accuracy: 0.725
+Epoch  4, CIFAR-10 Batch 2:  Loss: 0.818949, Accuracy: 0.7
+Epoch  4, CIFAR-10 Batch 3:  Loss: 0.680601, Accuracy: 0.725
+Epoch  4, CIFAR-10 Batch 4:  Loss: 0.577342, Accuracy: 0.825
+Epoch  4, CIFAR-10 Batch 5:  Loss: 0.650067, Accuracy: 0.8
+Epoch  5, CIFAR-10 Batch 1:  Loss: 0.748057, Accuracy: 0.8
+Epoch  5, CIFAR-10 Batch 2:  Loss: 0.633852, Accuracy: 0.8
+Epoch  5, CIFAR-10 Batch 3:  Loss: 0.480863, Accuracy: 0.95
+Epoch  5, CIFAR-10 Batch 4:  Loss: 0.522334, Accuracy: 0.85
+Epoch  5, CIFAR-10 Batch 5:  Loss: 0.571857, Accuracy: 0.85
+Epoch  6, CIFAR-10 Batch 1:  Loss: 0.642935, Accuracy: 0.8
+Epoch  6, CIFAR-10 Batch 2:  Loss: 0.585723, Accuracy: 0.825
+Epoch  6, CIFAR-10 Batch 3:  Loss: 0.395464, Accuracy: 0.9
+Epoch  6, CIFAR-10 Batch 4:  Loss: 0.397977, Accuracy: 0.875
+Epoch  6, CIFAR-10 Batch 5:  Loss: 0.392235, Accuracy: 0.925
+Epoch  7, CIFAR-10 Batch 1:  Loss: 0.489782, Accuracy: 0.85
+Epoch  7, CIFAR-10 Batch 2:  Loss: 0.459161, Accuracy: 0.825
+Epoch  7, CIFAR-10 Batch 3:  Loss: 0.273993, Accuracy: 0.95
+Epoch  7, CIFAR-10 Batch 4:  Loss: 0.319732, Accuracy: 0.925
+Epoch  7, CIFAR-10 Batch 5:  Loss: 0.30099, Accuracy: 0.95
+Epoch  8, CIFAR-10 Batch 1:  Loss: 0.327477, Accuracy: 0.9
+Epoch  8, CIFAR-10 Batch 2:  Loss: 0.365161, Accuracy: 0.925
+Epoch  8, CIFAR-10 Batch 3:  Loss: 0.260866, Accuracy: 0.9
+Epoch  8, CIFAR-10 Batch 4:  Loss: 0.2765, Accuracy: 0.95
+Epoch  8, CIFAR-10 Batch 5:  Loss: 0.264591, Accuracy: 1.0
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>模型已保存到本地。</p>
+<h2 id="&#27979;&#35797;&#27169;&#22411;">&#27979;&#35797;&#27169;&#22411;<a class="anchor-link" href="#&#27979;&#35797;&#27169;&#22411;">&#182;</a></h2><p>利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[136]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
+
+<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+<span class="kn">import</span> <span class="nn">random</span>
+
+<span class="c1"># Set batch size if not already set</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">batch_size</span><span class="p">:</span>
+        <span class="k">pass</span>
+<span class="k">except</span> <span class="ne">NameError</span><span class="p">:</span>
+    <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
+
+<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./model/image_classification&#39;</span>
+<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">top_n_predictions</span> <span class="o">=</span> <span class="mi">3</span>
+
+<span class="k">def</span> <span class="nf">test_model</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Test the saved model against the test dataset</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_test.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
+    <span class="n">loaded_graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
+
+    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">loaded_graph</span><span class="p">)</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+        <span class="c1"># Load model</span>
+        <span class="n">loader</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">import_meta_graph</span><span class="p">(</span><span class="n">save_model_path</span> <span class="o">+</span> <span class="s1">&#39;.meta&#39;</span><span class="p">)</span>
+        <span class="n">loader</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
+
+        <span class="c1"># Get Tensors from loaded model</span>
+        <span class="n">loaded_x</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;x:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_y</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;y:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_keep_prob</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;keep_prob:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_logits</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;logits:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_acc</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;accuracy:0&#39;</span><span class="p">)</span>
+        
+        <span class="c1"># Get accuracy in batches for memory limitations</span>
+        <span class="n">test_batch_acc_total</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">test_batch_count</span> <span class="o">=</span> <span class="mi">0</span>
+        
+        <span class="k">for</span> <span class="n">test_feature_batch</span><span class="p">,</span> <span class="n">test_label_batch</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">batch_features_labels</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+            <span class="n">test_batch_acc_total</span> <span class="o">+=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+                <span class="n">loaded_acc</span><span class="p">,</span>
+                <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">test_feature_batch</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">test_label_batch</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
+            <span class="n">test_batch_count</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Testing Accuracy: </span><span class="si">{}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_batch_acc_total</span><span class="o">/</span><span class="n">test_batch_count</span><span class="p">))</span>
+
+        <span class="c1"># Print Random Samples</span>
+        <span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)),</span> <span class="n">n_samples</span><span class="p">)))</span>
+        <span class="n">random_test_predictions</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+            <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">top_k</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">loaded_logits</span><span class="p">),</span> <span class="n">top_n_predictions</span><span class="p">),</span>
+            <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">random_test_features</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
+        <span class="n">helper</span><span class="o">.</span><span class="n">display_image_predictions</span><span class="p">(</span><span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">random_test_predictions</span><span class="p">)</span>
+
+
+<span class="n">test_model</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Testing Accuracy: 0.684434335443038
+
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA9oAAAN6CAYAAABxCzGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAAewgAAHsIBbtB1PgAAIABJREFUeJzs3XeALFWZ9/Hvr3vuvWSQLKDoKoiCKEFRMee8hlURWTNi
+WNesr6uromtYVBZd16ywLmZd4wqKORNEXRHFNaBIkozEO9P1vH+cU901NdXTPT090zN3fh9tpm9P
+1anT1TXV9dQ55zmKCMzMzMzMzMxsPFqTroCZmZmZmZnZpsSBtpmZmZmZmdkYOdA2MzMzMzMzGyMH
+2mZmZmZmZmZj5EDbzMzMzMzMbIwcaJuZmZmZmZmNkQNtMzMzMzMzszFyoG1mZmZmZmY2Rg60zczM
+zMzMzMbIgbaZmZmZmZnZGDnQNjMzMzMzMxsjB9pmZmZmZmZmY+RA28zMzMzMzGyMHGibmZmZmZmZ
+jZEDbTMzMzMzM7MxcqBtZmZmZmZmNkYOtM3MzMzMzMzGyIG2mZmZmZmZ2Rg50DYzMzMzMzMbIwfa
+Zg0kbSbp7yV9SNLPJV0o6XpJRe3x35Ouq5mtfpKe13B+eeeQ6+7bsO7/LnWdVypJD2vYH1+cdL3M
+zGxtmZp0BWxlkbQtcCCwK7BdfhTANflxPvAH4E8RMT2pei4lSc8BXg/skF+Kyq9j7hpmZmOzmHOM
+z0+zeX+YmdnEONA2JO0LHAk8BLg1oCFW60g6GzgDOB34ekT8dulquTwkvRt4NukCrbxIG2Z/mK0I
+kt4AvGqBq10HXAVcSbqRdgZwGunveuN4a2hDEKMHiT5fzbaYfWlmZjYyB9prmKTbAu8G7pVfitrP
++bSA/YDbA0/L5f0BODYi/mPMVV0Wkp5KL8juvswCLtIkbQ/cqfbypRHxk0VX0GxhFhJcbJ4fuwK3
+BR6aX79M0gnAcRFx/nirZ0skWCXBtqQdgYNqL/8lIn46xs04yDYzs4lwoL1GSXop8AZgA3MDy6pB
+rbrVdW9BCtpXXaAtqQW8lrn74irgeOD7wF+Aeuve5bV/HwCcVHvt68ADx1ZZs+GNEnBV/wa2B14C
+PFPS8yPixPFUy5bIqgiwKw4BvlR77cvAI8dU/mrbH2ZmtglxoL0GSToa+Geau0eX/74B+B0p0Lwe
+2JZ00b07KTintl513dXogcCezN4fVwB3iojfj1Deat4XtulZyPFYPxcEsA3wEUl7RcRrx1ozG5f6
+Z7yazkFLUdfVvD/MzGwT4EB7jZH0DHpBdvfl/O8/AR8APgf8OiLmXJhIapO6jB9MGtP9YFKX07Kc
+1eoelefl/viPEYPsejlmk1J2I+4Ah9L/b3QrYEfS3/XDgH3oHb/V4/jVks6LiA8uZaVtYSLil0B7
+0vVYpLGdLyPif1j9+8PMzFY5B9prSB4//K80B9lvBl4/KPFRRHSAn+fHhyRtThrP+RzgPktR72Vy
+QMNr31n2WpgtkYg4fYjFPgW8XNJjgfeQgu9qy7aAt0v6XERctjQ1NTMzM1v9PI/22vJiUvfvUhlk
+vywiXj1KduGIuD4iPhsR9yclRvvceKq67HZseO3Py14LsxUgIj5LGj/7l4Zfb8XCs5qbmZmZrSkO
+tNeWxzB7DHIAp0bEseMoPCLOjoiPj6OsCdiWud0Wr59ERcxWgoj4A/Bk5uZhEPDEiVTKzMzMbJVw
+oL1GSLo5adxl3YeWuy4r1PpJV8BspYmIrwE/Yu7Y7p0l7T+BKpmZmZmtCh6jvXbcvM/rP1vWWiyA
+pF1IY6dvQWpxngKuBc4HfgX8silh24RNPCGcpHWkuWlvC+wArCNlUD8bOD0iFtRSn8f2HwTcmvQ5
+XA9cBPw8In49xqpXt9kCbkm6OXRTUtbrzUhZ8C/P2z89Iq5Ziu3bLJ8H7trw+iHA/y62cEkC9icd
+r7sCW5Km0bsU+FxEXDlCmTcjDWXZk3TsQOoG/xfSeePcxdZ7wPa3AO4C3AbYjpSM7nLgHOCMhf4N
+rgSSbgHsC9yMtE/XAdcAV5JmqPjVCOP2J36+HIWkfYDbAXuQhlIUwNXAH4GfTWLOeUl7kM7Te+Y6
+XQ1cApwdEb9Yom1uTvrb3Yv03bANqdfLdaRj4zzgXOAPETGzFHUwM1vRIsKPNfAAHku6GOjkR/n8
+VpOuW62eOwCvBs7KdZzvcRnwYeDgBW7j+CHKHubx5Fzen8dUXgHs1qfOb2hY9p9qy+yT39tV85R/
+NfBuYOch9tPDSXOAT89T3jnAUYDG8NnfmZQR/xukC7VB+2oaOAN4JbDNiNt8U5+yH7+I9/GoPmW+
+Y5n+hspjpfq3vnER5d2nobwO8Kp51nlzw/t/eW2ZfUmzHFzWZ391gDsvoJ57AW8H/jDEsfPLXMed
+xrzvDwY+TZoesd+2ryMlnbtTbd3nVd53+fOdQ25334bt/O8Y3s9dgPcBFwyxTwvSzbx3AIfOU+al
+Q5Y1zGP7Ptt4WMOyX1zkvrg98F7SzZpB9foN8Hpgl0Vsb+BnSsqs/nTgJwPqcz4pEep2YzgmtgKe
+DXwfmBnyc7oO+F7+m7vTYuvghx9++LFaHu46vnb0+6x3XtZa9CGpJellpLvfrye1bsWAx3bAU4HT
+JH1W0m4L2GRTecMs07T8YsupZnVeSL27JL2OlAn+KaQLoX7b2ZJ0kXSOpAc0bUDSLpK+BnwRuC/p
+2OlX3q1J2al/kFu+F0zSayT9DvgxcDQpsNswzzbLRws4EHgj8CdJrxhh868GvttQ9vsl3WqE93JL
+0s2fenmnAS8ZoX4rwSV9Xm9KIFg353iVNCXp30i9aZ5B+jse9DfUl6SdJP0nqZfLi0i9dwYdO/sA
+rwB+K+mVedrCkUnaTNJ7gFNJNzXXzbPtDcDfAT+W9K7cA2VcRtqHdZIOkPQd4IfAkcAuDN6nQWrB
+fz7wPUln5Zb9+eo4kfPlQkjaUdJHScfrs0g3gwfV71akc8sf8vltMZ9xv3P+fqQA+4PAHQfUZ1fg
+ZcDvJD1k1IpIejzwa9LN2rvRy/Uy6LGBNL3gK4BTJR0zah3MzFYTB9prx6V9Xp/4lFyStgW+Srrj
+vgW9CwrVHvXXoPdF/mjgTEl3G3azfcoftMwwyy6knH7rDKp7epJ8HHgNvaEg9TKb9tm2wJcl3XdW
+wdJepFbi+zP3c+j3PoPU8vV1SduwcM8hDQ+oXkz22z/118t1tgbenG+4NF3cN4qIAjic9PdRfY9b
+A5+WNPTY/Xwx/SnSvq3W90pSC/lq7Tq52O691eN1M+BrwAvoff80fcbDFSzdG/gF8PfMnod50LFD
+XnYr0o2aL+RusAsmaTvgm6SeHWW5g+pQei5wkqQNo2y7X5VYxGeWb3ieCtyd0f8mId0s3WxAHZf1
+fLlQkg4kBdhl8r+FHl8bgNcB35Q0zI2pvlWpbkvS/YAfkFrZo2HbTfsrgJsAn5f06AVXQHoV8AnS
+UJ7yuJjvc+l3rg76HxdmZpsUj9FeO35T+3f5Jfl8Se+NiMsnUKdyLOPXgDsx+4599YLtIlL37OuA
+3UhB2RRzg+2dgZMlPSgifjRg000tHPULhYW0gtTrPko5o7S6HAc8gd7nGaTxrb8jdXHcjDTWuey5
+UL3gWQd8UtI+EXGZpJ1JAUO1Z0CQxpb+kTTOe3vSBfQGZn9GAHcgdd09coT3Uf8sIXVL/CMpUL2K
+9JlvR2pF36LPeo8mdZ9/wrAbjogLJB0BnETvPSm/n3eSegAM41jSGMnqxXgAT4uIPw1bnxWoX4DQ
+7+bdfD4K3JvZxyukniwXAzcCu5PGvs4bfEr6W+CT9FqPYe7x8HtSi/z1pL+BW9M7dqvLPZR0o+g+
+sYBpDvONmC+RbjT1O3/9OT9m8nu7BbMDk/uQekH8cNjtLhVJ7yIF/03ns/K1C0jnlstJPWR2IJ1j
+2rXl5zOp8+XQJB1AGsZSjjuG2fuiIB23F5COwT1In2+5XLXl/VDgG5LuGRFXLbJeB5HyJmzB7PP5
+JaTj7ApSQL0X6UZSPdBdB3xY0mkx5FhySYeRhqT0O8avI/2tXUkaNrEVab/tSTpGmtYxM9vkOdBe
+IyLifEnnAHvXfrUz6QLzsIioB+PL4Z00B9kd0ni4D0bEz6sr5C7KjwdeS6p/9SJiK1LwuH/0T6L0
+elJ356rPk7pHltsvg7aL5qn77/LPh9MLCu6c31M1kDgd+Md5yqlqmre4n4eSuu+V++6XpNaTkyLi
+uuqCku4KHJOXr9qetD+eRwpaygvFAjgReFdEnFEra3NSC+Kb8vrV1o2nSfpARJy2gPdBXv9GUqD/
+JVI38rMjYrppYUm3B55EakXchtkXcH8n6VkR8f6hNx5xiqQ3kbp7Vt/PkZK+HRGfmG99SY8l7cN6
+kH1cRHxx2HqsUHfq83q/LuX9/D1p3Gm5jy4E/oWU8GzW31k+xp5An78HSfsCHyMFDd2Xc9lnkG74
+nBIRVzSU+2DS38l+zD5u7gK8ldTaPqzXkoKo+vlrGvg34EMR8X+1OuxO2hf/RApCBBxG729vIvLw
+k6Ygu7xh8XbgK003jXJPhTuSxkY/gdR1up8H0vvc7g68jdnnyx8ALx2y2gtOlDeMfAP4U/QS6UGv
+fleQgs5PRMTFtfX2AZ5J6j5fvQEE6Xh7Pwu4CVgTuT6fphe83kjqxn1C1BKeSZoCHgK8hbkzjmxD
+Gkv/d4M2mv9m/o25x8UNpPH7J0bET+ZZ/xak78VH5PrcZNA2zcw2GZMeJO7H8j1I46OqSY2qyY1u
+JAVW9wemlqk+j2qoT0G6uD5kiPVvApxcK6N8/skF1uUPDWXcfIT3dL/K+uXPr41hX72hVm49adSx
+MH9CMlKL0xcb9tc1wP+rlHc5cK8h6rQfKZFVfb8dv8D39iPghcBWI+yXm5KC8/p7ughYt8CyWn3K
+ugrYa571bkW64K8fxz9crr+leY6VcSRD+0Gfv7E7zLPOm2k+XssyPg1sMWJ9NifdVKrv7+uAZw5Z
+hkiBY9P7esCQZdyB1HukXo/zgf2HWH9PUtb2+n5aTDK0ajlDJ0Mj5WJo+l6YyeeGBR3HwAOAbwE3
+GbDcwxre86KSlo2jXNK456Zj41sMl0jyAHpJMutlPHkRn2n1378Gbj1EOVuQ8lDU6zJNn+SbtfWb
+EqleBRw4wueynpS87bmL/Yz98MMPP1bDw2O015Z3kQKQpq56U6Sxql8DLpP0VUmvl/QISTcdd0Xy
+lD5vZ+5d8r8CD4qIUweVEam16tGkQKDapU+kVs27j7XSK0u1W+K7IuLFETFvl7yI6JAucupdFzcn
+tU5DChweEBHfGViBiLNIrXJN+37o8a4RcdeIOC5GmKorIi4kXVSfzuzjeifS8byQsgrSWMyLa2Vt
+RRqvPacrc+46/CnSmO7uy6SbFU+I1TsuGwBJDyJN7VU/ti6NWk+TAarrf4k0Zv26fgsP8BrS8IWS
+SMftwyLig0NVJnkJ8O/MPR8ePWQ93sbsXmEi/W09MCIGTnsWEX8EHgT8iQl2qc2J4N5bf5kUZB8e
+EW9Z6HEcEadExH2i1qNgNchJxp7O3O7iPwEeEREDex1FxE9Jn231/Zfnx2NGTI5W7WnzR+BuEfHb
+IepyHanHxF9rv2oBTx5iuw+qPC+P06Mj4sxhKl2ry8aI+HBEvHuh65qZrUYOtNeQiLiWdHf6RpqT
+l1S7Xz+A1I32C8D5ks6X9N+SXijpwBwoL8YjSOP6SuUX+KvzRcpQIs1HewRpDGbd8xdVw5WrvNiC
+lGl52G6WRMSlpJ4LTZ9fAK+LeboBNvgwc7tvbkHKBr4s8jFwVMOvnjRCWRfn9YryJdK+uj3pRlXd
+caTWq1J5HD81Is5b6PZXkpxB/T9p7kr88QUUVV3/CuDIQTeF5qlTObVQvYv+yyLiWyMU+VLSlFRl
+PQUcIqlfd/myHrcitQLX6/GqiPjlsBvPN4qes9BKj9lTSGPXS+V7eVNEfGoyVZqopmE+G4EjFnIz
+MB8HL2XuuXYnUuA7ivKzeVIsIK9KRFxAOlfX63LoEKvfrOG1/xl222Zma5kD7TUmUpKwh5HGV5YX
+lv2CrupjV1JX72NJYyD/KOnNOUv1KJ7b8NpvgP9YaEG5ZehtzG1ZfdRStMavIAH8a/QZxzyP6pjh
+asBzFWl8+fAVSNs+mbnH0LIF2rkePyNln64mM7vziGV9kzRuvX48PV1SN3jPU900BX1vj4gvj7Lt
+lSKPOT+NFBTUXUuvB8TQRZL2zX9ExELHdlcdRS+re/n5nMMI5w3oHr//ytzj9+kDVj2yYZ3/Y27u
+h2HqcBKpJ9GkWrWryf6qN/DeMIG6TFSeNeFw5v5NvztGyGESEceTWsLrx8o/LLSoSl1OiYhREudV
+b5qU5R00xHpNM0lcPcL2zczWHAfaa1AOJA4gJb8qmB1w9wu8YXbgvTtpzPfZkj6wkIA2d7e9N3Mv
+Zj6Uu++O4v30WiFLU6Qx05uq60nTrSxUvVtruf8/O2J33qZusvWke8vh9Nq/t85Js0bxBlLG4Xqw
+/R5Jt8k3mD7A3O6lPwJeOeI2l5SkQ+Z53FfS4yUdI+ls0hjq+rzo5XHyimG6z/bx4cW8B+YmkiqD
+91HPG5ACkDLTePk532PAOtW5iMv9csKoLfXAh0Zcb1Ek7QkczOwAP4B35KEma8096M1oUPWBRZRZ
+TcpYHl8HSxo1Kdiof0M/Ze535C6StmxauKIp4dxINzHNzNYaB9prVERcEBGHA/uTLgQuY3YgPWzg
+3QKeAZwl6f5Dbv5AUlKUuk8O/QbqFUnTlHyXuXW966hlrmDdbOYjtGaX3aObpjD6/oj1ObfhtW0b
+XltqTcHffNmP+8oB0+GkzNjVY2pL4DOkY3Xr2mqXkcZlr6QApaz7FOkmQL/H10k3bV4K3Ia53cXL
+Y+6YiFhwq232x9z7ZCQ5IDigVjeAz41aJkBE3MjcMf77SGo8hnM9qtnTS59ZRDW+SMrivNwe0PDa
+RtI0bGtR9fuiPB7OiohfLaLMT5LGu9fdZcTyvjfKShFxA2kqsrpB5+r6UAgB/yppl6aFzcysx4H2
+GhcRZ0fEc0jZmx9Kmt7mVNLF1rCBd5AygJ+Uu9MO0nSBcfEYxrT+uPZvsWkG2qXFXPzVE+Msprym
+skYOtCWtl/RwSUfnvABnSTpP0lWSOpKKpgcpO3LddqPWI3dxPpyUaRd6gdW+pKmMqq3ZBSmb8FDz
+0k5AfSjIfI/633qQPuMjI2KU1vqyjIWM/W9yKL25mksX5fGni3Vu7d/luPwmBzH3u/OqYRJT9ZOD
+/bPof1NzqVS7Dpef008Xkahutat/NwXp+3BkEXE1KUP4OG4CX53H9Y+qqcv3oHN1dTx2eY7YG/iF
+pJdI2mER9TEz26R5Hm0DIGeV/Wp+kLOi7k9qfT6Y1KXuNnnx6gV4twjSRfCHJf0mj5ntZ8/K8+7F
+3RjeRrWMMmC4+RjKXamGTobToKn1bNTymsrabKGF5G6s/0xK2Fe9+Ku3HM7XPbd+MTtyoA0QEd+R
+9FrSfM/VcZLV7QXwtjzWdqVaSABXfX9XAh8Bjh3DjbA/L3L9/SrPy/1+saRDFlkuwJyM8sztOl+6
+RUM9BmYZH8LPSefa5dSUY2NRgeUqtydzzy/j+m7ar/baKN9NiznnQxpuVD+HzXuuzufAHwJ3q6wX
+wA6kG/NvkfR94BRSa/vpufXczGzNc6BtjXKX5J/kxwcAclexxwHPIrXqVRNPlV/AmwPvJn0p99M0
+Nm0xd+lLo3SLW83G3eo0zvIW1DIn6ZWkLPeb02s1GamsmkHjDweKiDdJugdpmpvq8V7+/AFpmrOV
+bNDY4etJyfCuIrXunkFKhnZKbm0dh/q0cgtVbzkTqWfBjxZZbj/9Au2m89eoY9bHXcZC7c7cY2Ox
+N0RWs+X8blrIGO3yHHjtGOrSr+z5PIl0A2anyvLleboF3Cs/AGYk/RT4NvBN4FsR0TRUycxsk+eu
+4za0iLg4It4VEfuTpoS5irlBRzk9znzjtZsuMMaRxbTpQr6VM8naCiXp3cAbSS0r1e7Y9Qu6YR5z
+ih9TNU+Y53dHLjIZ11Iq90knItrzPLaKiN0j4nYR8dCIeE1EfHmMQTYs/kZOU+C7kGNjocdPv5t0
+Tb0klur8tdS2anitKfnVWtH0mS/VZztqMrRll3MrHEK68V4/R5ff/eWjDdwJeBlwEnCRpPfn+cnN
+zNYUt2jbSCLiREk/IyXQ2pq5F6mPJSVYarJ5w2tN82AvVL8ytsTTkaxIkl7E7CmyoHfhNg2cSWpJ
++SOppe2vpK7qNzD3mHs28NQlqOMewL9Xtlff7quAJ497uzZHU4C7lGOa6+PBS03dzMfRYjfOmxrD
+anqP4zgXrzp5Now2c/++l+K7SYyht81yysH2nSUdAbyENLSs1JS3pbQt8EzgGZI+AfxDRFyxpJU1
+M1shHGjbyCLiLEkvB97L7CBEwH3nWbUp6K1ncB5FvzIm0VJkA+ShCK9jbpB9LXA08IGIGPqzk/To
+sVYwldkGPg7syOyWnGrisCdJ+k5ETGSKpjWkKXPzqNNpLUbTMTmO89cket5cCdSzR6/JHkARsVHS
+RmBd7VdL8d0UrNLvpYg4EThR0v7Aw4H7kBK7VW+gVwPvam+RJwJ3l3TviPjDctTXzGySHGjbYh0P
+vI25d+dvOc86TXezx3Fx19Ttb3oNZ9Bd6Z7C7N4QAq4BDo2IX4xQ3lJ0xXwjKdt1PciuB9vvkPTj
+iKhPhWPjU79BF8A3I6Jpiqql1NS1ehy5ICaRT+Jy5ibHWlQCwVXuCmDn2mtL9d202MRmExUR/0tK
+AvimfEPyIFLS1HuQgu9yWEL1+ArgZsBXJN1xzENTzMxWHI/RtkXJSdNOY27XsbakfoFPU6D9N2Oo
+TtOcye6itnI9svK8vBh77YhBNqRW57GR9FDSOMN6kH0ic+fX3hz4tKQtxlkHm6We9VzMTZC2HC6t
+PC9vtDRl716ovcdQxkJd2vDaON7LauXvphFERCciTouIt0fEo0jn4ocBnydNfVi/Ptgb+IdlrqaZ
+2bJzoG3jcHGf1/uNcTy78ry8UL3DGOpxQOV5GRSd3WdZm7yDmNv198RFlHdgQ3kjyeOy/7PhV2eS
+xhs+iXQBCb1j+DakYRS2NOrnDUitY8vt5w2v3VpSU+6JhajOzb5c6tMwirlzSa8lZzM3KDygacEF
+OoC5n+0m+90UEdMRcXJEPBa4N3N7gQj4+2WvmJnZMnOgbePQ1LWuExFNrSXQPB3PVpIOXGQ97s3c
+i5mlmvqnn0mMGV11cib4elKpKyLikhHL253Z8xuPLHeD/ASzW0tFGlP5+HwR+W3SOPJqZvRyvPYz
+xlEPm+NU5v59bZ/Hii6biPgtc7v9tpg/L8W8JO0F7LGYeo3oO5Xn5b69Tb7RtBxW2vmy+n1R/k3f
+fTEFSroFaX7u+ba1yYqI7wMvZPa5EuD2ksYx/t3MbMVyoG3jcBvmXjD1C7KJiHNoHp92xKgVyEH6
+vg2/Wu6LmaYxZ/XkOtY8ZvGaRZT3zEWsW/dm0jzw9S7jz6wl8PkXUmb9erD9DklNx6ItQkRcTJrf
+u97i+JgJVOdHDfV40iLKm1TW+h8xN2O6gKOWafsr7Xz5w4bXdpM08k0UUi6Kuo2kqbLWik/T6wFU
+VU/EZ2a2SXGgbYsi6QDg1tWXSAFH0wVL1WeZG6D8vaRRE/G8pOG1K4BvjFjeqP5a+/eqm8ZlmTRl
+3N1J0oLPSZK2IgUGi24dk/Qw0rFUD7LfHRGfrS4bEUG6OXQRHq+9XP6r8rw8b/xj7iEx6Xo8WtKt
++yzfl6RtgWcxgdbdiLgB+CRzz8VHSRprzoM+6udLmOz58lTm5gIAeNEohUnaEjiSueeTL+b8JmtC
+RFwPNCUl7Te8zMxsk+BAe42QdJCk40e5EJynTAHv6PPrLwxY/d8bXtseeNMI9bgXadqQ+sXMB/KF
+5HKqJrgp63OLZa7DihcRVzP3wmszUrbahXonsGt+PvK8ypJuBpzQ8KszgRc3rRMRfwEOp3m89ntG
+rYv1dQJwWe21bYEPLnM9PgfUhzmsJx2LC/VmYKf8fCnnBe/nGOYG+TuwPPu0fr4UEzxfRkRB+rut
+33h4aE6OuFCvA3ZreL3p+2+TJWlnelnIq85f7rqYmS0nB9prxxSpC9uvJH1kseOhJa0DPkoav1a/
+SLuMAYF2RJxFam1uakkZuhtwHtv4iYY6bATePWw54xIR5zG3lWYHSU1ZZ9e67zE3sDh6Ia3aeR73
+p9I7fkaSx2V/krnjsq8mj8vut24er/065h7LR0h6+qh1srki4hrgNczd14+V9G5Ji5qyUlJL0mMk
+7TegHtOkoLpejwdJOnYB2/tH4Nks8vhdjDwl3eeZ+14eIel9o/QyAZC0Q/67ms/vgfrN0N2XcYx4
+kw8A11b+Xe6P/5R022ELkfQk0g26+nfTT/K45VVB0gck3W6Rxbyw4bX/y3/PZmabLAfaa0+L1N31
+DElnSXq5pPnmvJ4lX4j+LSnz7mHMvogoW5JfkVssB3kOzRc075X0qkEXaZLuRwrWq/OelnV4dQ56
+J+GnzL1o/udJVGSF+3zlefnZ3xX42KBu15K2lfQe4C2VdWcWUZe3kLItDxqX3c8bgVOYG6y80+O1
+xysi3sPcfQ0pYP2hpAVnzZa0j6RXkQK/TwM3H2K1Y4BfMvczf6GkT87X9VrSFpLeCfxbZb1r+y2/
+DI4CLmDuPj0S+OpCjmFJt8o3G/7IgLnBI6JDmot5xZwvI+IyUoBcrVOQbsJ9Q9JD5ls/f0e+FDi+
+/ivSmPTVlizxqcBZkr4i6bA8VGcoSl4CvJzmaRLNzDZpi7r7b6tW+YV3W1KA8RZJF5HGVZ9OGnN6
+Galb3wZSVvFbkabgehCpm2PQHGT/d0TULzCaKxHxW0kvBt5XqVd50fkG4HBJJwAnAX8Grid1wzuY
+dLPgEX3q8K2IePswdVginwHumZ+X7+fJ+WL1U6RpXa4CmlpJfxIRiwkYV5PjgVeRsi1Xj6fHA3eR
+9G7gq8DvSK1eO5Hm+P1b0udfHociHbNfYIQkTpIezuyWp/I4ek9EfGaYMiIiJB1Bmi7pppWyNgc+
+JengPE4GDJbzAAAgAElEQVTRxuMwUo+IsqWtPH4OJgXbpwNfBH4A/JY0vdD1wNak4G93YH/SOe2+
+zJ47eqix0hExnTPMf4+UwKt6DD+O1N34C6SEeecDHdL56x7AY0lDZcrj91JSD5zXLGAfjE1EXCrp
+MOCbpOuC8n0EcD/g55K+DPwP6XviYtL3w5akAHQ/0r5/GGmaPRhyP5LOl3eurCPgSEl3IN30+BXp
+fNl0Xjw9d/ceq4j4QM7X8Ehm74tdgf+RdDIpUPwxcCHp898DeADwNNKx1fTd9OqI+MW467sMAnhw
+ftwg6RvAaaRhNb8hHQtXksZcb0f6e7onaQqvpmSp59F/2JmZ2SbDgfbaU79LX34B7kLK3jsog291
+nXpZn2KBmXfzBc0+zJ3+I4B9gH/Nj351KZUXMr8EnrCQOiyBE0ldicsL6bKeB+VHP0GaF/iCpazc
+Ehs6oVNEbJR0FPAlUk+L6gXtzcg3gebZTnlRfgMpOH/QQis7z7jsn7LABEgRcYmkw0mBVfl+RDqO
+30tz9mEbQURcIek+wFdIgV29JfZO+TFUcTSf04apx2k52D6eXmKnsrwtSOP3D59nmyINc3k8zbMm
+jGKkbugR8f0cbH+UNN68PKeW5T0yP8a9/RNIN9y2ZfZncWd6AXi/7exE8wwW9eVG8WRSz4mDmb0v
+oBd0Dtpm9bj80CJuAI9zaMGo+6NcbwPphsrDhlynvi+uBY6IiKZEeGZmmxR3HV87LiZlVC2YffFU
+fcQQj+ry5NcuBY6KiCeO0roQES8BXkFqsahuoyx/mLoE8DXgnrnr38RExJWkVo1OfmmY/TuscY7j
+rO7ncZY3dJkRcRLwD6R91XRsDfrsrwIeWRvzONT2K+Oyt69t+2rgcaNkBY6I79CbX7usRzlee7m7
+jI778x21Dksiz7l+D9JNjPK8tpBzWb/zCDT3NulXjxNJLezX9Cmv6W+9/P01wKPzOH8qv6v+XIhF
+feYR8d/AvUndvod5L/3e20K2eSmpi3r53THu8+VI+yQPf7ovqafMQvdFdZsd4PUR8ayF1qH2HsZl
+1PIWeiw07bOLgPutpjHqZmaL4UB7jYiIcyPirqQuk88mXTxcQv8LwKYHteXPAv4J2DsiFpWhNiLe
+RmqB+i7NX9JNj3K584FnRcRDcpA7cRHxJVIG7f9juP07VLG1shZdzTGWOfLFcES8l9Q6dB7Dffbl
+9k4BDo6Ir/epxyBvAQ6prVMw/Ljsft5IuulTr8tyzq+9mOBkKeqwNBuIuCEinkf6HE+mF3DD4PNH
+/Zx2AXAscMeIOGWB9fgsqWX9JGa/7/m2d1Le1knVohh9v43lM4+IU0mt6/9C6hI87Pm4WofzSL16
+mqbxa9rmZ0jdrn9fq/u4zpcj7Y+IuDYiHkNq3T6X4fdFudwPgUMj4uiFbntc72GIMgd5MWlIwUaG
++2ya9sP1wHHAbSLitDG8BzOzVUFpKlhbqyTdhpQEah9gb9JY7O1JYxm3JHXL/Suple8i4Bek5DU/
+ytlql6JOB5K6oN+PNAazKSnahcD3SfNxf36xc5JKOp7Ufb4UwFNzq9miSLon8FDSeNC9SV0ktyKN
+66sK4OYRsZq7jo8sZ4x+Mml866HMnU83SONtvw58pH7BJqmpu/CPI+LMpanxyiRpN9J40aqIiNMn
+UZ/lJOkWwN+Rzh0HMTuLfNWNpGPpbFIw9I1xnc9yxvInksao7k0as9ohBa3nkM5bn14NY3VzUsLH
+kLqM35XmqaogvbdfkW6UfhX4Xox4cZGHBDyEdL7ci5QjpN/5cueIGNR1fCxy9vVHkI6ve5CGt9QV
+pM/4m8DHI+JHy1G35ZCToB1KOg4OIY29vhnNDTZBujHxU9Lwjs+4q7iZrUUOtG1Fy917b04KTluk
+uZfP95f2pi1f1O5CCpSmSF1sz4uIGydaMVtVJG1DSk63Beni/6/5ccmogeBalgPv3UmBb7k/r1iu
+YHclkbQl6YbWlqQA+2rSOWpRN31Xk3xzdFfSjfktSDfmrwIuc/JHMzMH2mZmZmZmZmZj5THaZmZm
+ZmZmZmPkQNvMzMzMzMxsjBxom5mZmZmZmY2RA20zMzMzMzOzMXKgbWZmZmZmZjZGDrTNzMzMzMzM
+xsiBtpmZmZmZmdkYOdA2MzMzMzMzGyMH2mZmZmZmZmZj5EDbzMzMzMzMbIwcaJuZmZmZmZmNkQNt
+MzMzMzMzszFyoG1mZmZmZmY2Rg60zczMzMzMzMbIgbaZmZmZmZnZGDnQNjMzMzMzMxsjB9pmZmZm
+ZmZmY+RA28zMzMzMzGyMHGibmZmZmZmZjZEDbTMzMzMzM7MxcqBtZmZmZmZmNkYOtM3MzMzMzMzG
+yIG2mZmZmZmZ2Rg50DYzMzMzMzMbIwfaZmZmZmZmZmPkQNvMzMzMzMxsjBxom5mZmZmZmY2RA20z
+MzMzMzOzMXKgbWZmZmZmZjZGDrTNzMzMzMzMxsiBtpmZmZmZmdkYOdA2MzMzMzMzGyMH2mZmZmZm
+ZmZj5EDbzMzMzMzMbIwcaJuZmZmZmZmNkQNtMzMzMzMzszFyoG1mZmZmZmY2Rg60zczMzMzMzMbI
+gbaZmZmZmZnZGDnQtomQdK6kIj9uPqYyX1sp8zV9lrlXZZlvjmO7ZmZmZmZmVVOTroCtWZEfS1X2
+OJYxMzMzMzNbMLdoz0PSUyqtnx+edH02QZp0BczMzMzMzMbNgfZw3Pq5evizMjMzMzOziXLXcdtk
+RMTRwNGTroeZmZmZma1tbtE2MzMzMzMzGyMH2vPzGGIzMzMzMzNbkIkG2pJuLunZkj4m6ReSrpS0
+UdKlkv5X0rslHTJkWd+uJC675xDL950KStIJkgqgTIAm4KmV5YthpoiStKWkf5R0sqTzJF0v6fL8
+Xv9d0p2HfG/ltjqV1+6Q98+vJf01P34s6TmS2g1lHCTpeElnS7om7+NvSjp8mDpUypmS9DRJn8tT
+dF0n6apcjw9Kuv9CyquVfRtJ/ybpl7nMqyT9XNIbJO0yxPoDp/casV57SHq1pO9KOl/SDZIuk3Sm
+pLdK2mtc2zIzMzMzs9VvYmO0Jb0VeDG9VuNqEqubANsD+wHPlvQJ4BkRcf08RUbt57Calq9OPdVU
+v4EkPRx4P7Brbf31wHbAvsDzJH0MOHLAe5u1fUkvB94ItGv1unN+PELS30bEtKQW8C7g2bVyNgfu
+Ddxb0sOAIyJi3veYb3p8FPibWlkbgL3z4+mSTgEOj4jLBrynatlHAu/MZVXrcfv8eK6kp0bEl4Yo
+biwJ0SSJNOb7pcBmtbK3Ix2ndwReIOmYiHj1OLZrZmZmZmar2ySToe2RfxbAOflxGTAN7AAcANwq
+L3MYsDXwiGWq2ynAX4F9gPuTgqtfA99oWPb/6i9IegJwIqnHQAAd4PvAb4GtgHsAu+XFDwduIem+
+EbFxUMUkPQt4Sy7358DPcvmHALfLiz2IFLQ+B3gPcGRe5nTgV7le9wBumZc/LJd1zDzbvSfwFVKA
+Xt6IOA04m3Tz4C70Pq8HAN+XdPchg+1HAcflMv9M2lfXkAL3Q3N9bwJ8WtIjIuKUIcpclHyD4lPA
+Y+i93/NJ7/kS0ud4COk9TwH/JGnHiHh2c4lmZmZmZrZWTDLQPgM4CfhyRFzetICkQ0ndt/cCHirp
+8Ij42FJXLG/jY5KeQgq0AU6NiH8ctK6kvwE+QK9b/qnAkyLiD7XlXgi8NS93V1KQ+8IhqvcO4ELg
+sIj4Xq3MFwNvIwWFT5d0DinI/iXwxIg4q7Ks8rIvyi+9StK/N7WsS9qO1JK9OamF/5xc3s9qyz0x
+v/fNSUHyh0hBdD9l6/AxpBsBL42Id9TK3IcU8O4HrANOkHS7iLhqnnLH4XX0guyLgOdGxBfqC0l6
+LOk9bwccKenrEfGZJa6bmZmZmZmtYBMbox0Rb4+Ij/QLsvMyPwAeCNyQX3r+slRucV5Lau0UqQX7
+QfUgGyAijgNelpcTqRv5ngPKFqkHwP3qQXYu81jg63m5KeBY4GLg3tUgOy8befvn5Je2Ah7WZ7sv
+AnbP5V6et/+z+kIR8XHgiMp7eoSkuw/xntYBr6wH2bnMX5NayC/Ny+5K7+bAksifwytJQfblwKFN
+QXau32dJAXnpdUtZNzMzMzMzW/lWfNbxiPgj8C1SkHUnSVtNuEp9SdoWeHz+ZwAvi4i/zrPKO0it
+zZA+i2cN2EQA783BZz8fL6uTl39jv+7bEVGQWotL/ZKzHVnZ/usj4oK+FYz4PKmnQuk589S19AfS
+TYF+ZV4MvD7/U8AzhihzMV5IGv8OcHREnDvfwhHxbeCrpLrdVtIdl7R2ZmZmZma2ok2y63iXpJuR
+gry9SV1wyy7KpXIssYA7AD9Y1goO726kZF6QWmC/PN/CERGSPgy8Pb90nyG28dkBv//FApevtnTf
+sv5LSbell9CtA/zXgPIAPgg8hPR53Xue5cqbAR/LQf98TgT+jRQA7yZp74j4zRB1GcVDKs8/3nep
+2b5JGhsPcHfS2HkzMzMzM1uDJhpoS7orKbHX3Rl+zuodl65Gi3ZA/hnAaUMEj9C7aaDK+k3KoPSs
+eZYBuKLy/KqIuHDA8tWu+9s0/L76ns6JiCsalqmr3gjZVdKuEXHRPMv/aFCBEXFlHnNeJnw7ABh7
+oC1pe9INnwA2Aq9Lw9kHul3l+c3GXS8zMzMzM1s9Jjm919NJSaTKAHLQlExltLP1UtZrkXaqPP/j
+kOucW3m+XtJWEXFNv4Uj4uoB5c2UiwLDJAybqTxf1/D7Bb+niPiLpBvoTYm1IymhWD9/GqbcvFwZ
+0O4034KLcNPK8w3A8xa4vkgZ0s3MzMzMbI2ayBjt3B35vfmfQRqn/AJS9/FdgM0jol0+gI9UVl/J
+48qr48evHXKd+nLjvJEwjvmkR3lP9WUHvafrlqDMUW1beR4jPtqYmZmZmdmaNakW7RflbQdwMvC3
+ETEzz/JLEVQtRcBebYnecsh16svNlzxtEkZ5T/VlB72nLZagzFGVwbxIXe/dOm1mZmZmZgsyqdbh
++1aev3pAkA0waNorgOnK82FuIGw7eJEFu6Ty/OZDrnOLyvON83Ubn5AFvydJO9HrNg4pMdx8ht1X
+1bHPg8oc1cWV59tI2qzvkmZmZmZmZg0mFWjvVnk+b3IvSdsA+zO4G3R17PIOQ9Th9kMss9Cu1z/N
+PwXcWcNl0bpbZVs/nW/BCam+p30kbTfEOodWnl80IBEawF0GFZinTtun8tKZQ9RjwXJdz6u8dLd+
+y5qZmZmZmTWZVKBdzcY9qNvwkaQkXYOC1nMrz+edx1jSTUmZzgcF0jdUnjclCqv7IXBjfr4T8LAB
+9RDwtMpL3xxiG8sqIn5FL5FZGzhiiNXKea6DNAd63+JJn+sTh7gpcQS9sc8XLuHUXjB7WrbnLuF2
+zMzMzMxsEzSpQPv3leeP7LeQpL2A1zBcy/KpledPlDRfYHwcwwXvl1We7z6oAhFxFfDJyktvlTTf
+uObn02tZL4D3D9rGhJT1EvCafKOikaRHMvsGw3v7LVtxK9K4/X5l7gL8M71kYx8coszFeDtpznAB
+j5b0lGFXzHU1MzMzM7M1bFKB9pcqz4+V9MD6ApLuR2oN3Yrhsl1/mZQgS6Qx3R+sj6+VdBNJHwEe
+x+zW6n6q3doPkbTHEOu8npRATKT5mL8m6Za1ekjSC0gBHaTg8V0RMew0V8vtOOD8/HwH4JuS7lBf
+SNJhwMfoBcRfjIjvDyi7nK/6GEnPr7ds5wz1p5B6CIg0hvq4RbyXgSLi98C/lFUAPizprZIahyRI
+akt6gKT/YmV2/zczMzMzs2U0qazjxwHPJAVPOwAnSzoTOJsUeB0I7JuffxX4C/Dk+QqMiOslvQE4
+hhQc/T3wIEnfIo3fvhlwT1JX9V/kcl86oMyLJf2QNE53c+B/JZ0MXEiv+/vvIuK9lXV+L+mZwImk
+rs53Bc6R9D3gd6QbB/eg10IewI+AV8xXl0mKiCslHQ58hbT/bgOcKelU0me2njTO+tblKsBvSJ/x
+fMo51F8OvCM/Xibp+6SbFXuTuviXN4SmgadFxJVjemt9RcTRkvYEytbslwDPl3QG6XO8DtiGlMxu
+f3oZ0ZcqSZuZmZmZma0SEwm0I+ISSX8LfAHYMb98YH5Ar0X0c6QxzO8csui3A3vRC/B2Bp5Q3TRp
+HPXjgGcNWeYLgG+QphjbFjis9vtvU+seHRGfknQNqYvzLqSA+z75Udaj7A7/MeDIiNg4ZH2GNUwi
+tqGXj4jv5V4GHwX+Jr98F2YnMivf0ynAkyKi2vV+Pl8gtWofR7oBUd3H5b66khRkf3XIMgcZuH8i
+4umSfgIcDdyENNzgbsxNkFadQ3tQC76ZmZmZmW3iJtWiTUT8WNK+wAuBR9AL3i4EfgKcGBH/A5B7
+E1eD035lBnCUpM+RAulDSC3mlwG/Av4rl9uplDmonj+RtD9pPPV9cj23opeYq7GMiPiKpFsDTwce
+Tmqh3xG4HriA1C3+IxFx+qA6VLYxbBb0JVk+Ik7LXbmPAB5FSjq3M6ml+SJSkPnxiPj6ArYbuez3
+Sfou8Gzg/kDZTf9c4IukrvUXNxXS570Ms8wwn/9/SDqB1EPiAcAdSD0xNiMNVfgz8EvSDZevRMT5
+zSWZmZmZmdlaoRSbmpmZmZmZmdk4TCoZmpmZmZmZmdkmyYG2mZmZmZmZ2Rg50DYzMzMzMzMbIwfa
+ZmZmZmZmZmPkQNvMzMzMzMxsjBxom5mZmZmZmY2RA20zMzMzMzOzMXKgbWZmZmZmZjZGDrTNzMzM
+zMzMxsiBtpmZmZmZmdkYOdA2MzMzMzMzGyMH2mZmZmZmZmZjNDXpCpiZma12kq4FNgAF8JcJV8fM
+zGxTtjOpwfjGiNhy0pXpRxEx6TqsCL8447QAENBCtBACOlHQKQqKCDpFQSc6dIoCCNa1WqxriXUS
+SGllpaus6aJgpghmImiplR6tFm21mWq1abfatNUiCAqCgoKIICL/LCIXl+qh6BaPgHYrl9duESKX
+k+o50ymYmUk/AyFaqSS1mBK0JaYEREEUHYgORVEwDcwQTAOtSvmtVos26WhuARQdotOBoiCKAoDy
+OCrUIlr5oUqHCc3e3+XyEeXzSP/IP1ON089yX7VbbaS8B9RK+xz4mwMPqpVuZra8JM0A7UnXw8zM
+bA3pRMSKbThesRVbbur+TIEtAmL277q/zwFeGVsjpZgvB9wCWi1oEbQiUtCubskEkYJpBZE3IrqF
+pQ218vbLR72yYlbFymA1ohKwdheJvP3IW4rub/JbQS3RikpYLpByWUWR65pLjaK3rtQtHyCkHPjn
+OqlcR71tKe2PiMiri4gyYE9FBunmQkgE6v5MQXb+cHyPyMxWjnSzVmK33XabdF3MbAgbN27kkksu
+YaeddmL9+vWTro6ZDemCCy4oG+1WdDTgQDtTK7W+qiG47i4z68Ve0JeCUqUycqAdoVRYUQmkS5GD
+7SIqxaQlCloo/TYF4dELxsnBuMpgU5WgkxzYFgVEICIHynTr1A2ecyV6z3NLvkQbCPVeg4Iydi9y
+cJtDXyBIu03dusWs30Zat1t10erWOb3nFFAHRCu15hfdt0OUNyfU6rZgx6wPYUX/bZnZ2nI5sPOO
+O+7In//850nXxcyGcOaZZ3LQQQdx8sknc+CBB066OmY2pJ133plLLrkE0nfviuVAO2tVAjhB2Qzb
+fUWznqcgumyZTUF272dItIscJJeNr93W6aD6v17gnMpuVbZKkFqS87rdKs4J+INuNFxp0a42kEvR
+C/YrwXaOnGkJCkRL0M6hfllW5BsDRFBApcW73G/q3iwoKu+zEjMTuQt95BsSs29kBFEURFEG6+Wr
+OWCvtGrn2xgOsc3MzMzMbMVyoF1TaSCuqL1QRrC1gFdKLbYhES0RRdAmBZwpwK72Bk9jqkUrd0Gv
+lwd0g+zeuirLV6XVuKxjJcBOLdT5JZVdxcsx0GWrdeQAPK3VotL1u6h0/46gKFvJI3cT795koBtk
+d29IlC32Ue35ru7P3o4mh/RlK3f5r/Tv8t0ELQJRVMp3sG1mZmZmZiuVA+2smhRO1Qguer8vhwLM
+SuSVW4Lb3Tbq7gDlyoNuy7C6Aa9ya3dQRC8oTtupJEUjtYrnoczdOhRlfaOotGLnod2CVp65LZR6
+r5dllcuFcsIxpTC227ecyvD0XBeKotKOHJUAuNtpPb+3Sv3L5myV4XegXnN3bScDRe8GQY9ygJ3r
+UhQ50C7fy1AfrZnZsrn00kvZY489Jl0NMxvCxo0bAXjwgx/sMdrLaNddd+WMM86YdDXMlpwD7awM
+nlUL9aAaz0Ut4C4DziBCKXYsu5zXAu1ZLdPqtdyWgWrRDeiLXrBaCSbLOhSUXdtz0FuIbpqwsvt5
+NypPQbYiKKLo1rtsKQ9F6iZeecu95Ol5+0V5YyF6wXbZ7N/NBle2YEcaIl6k8efdmw5li3f3BkT3
+XfS2291HZYHleGxV9k2kPTArS7mZrVSS9gT+kP/51Ij4yIjlPAU4nnQWuWVE/Kn2++OBpwDnRsTf
+LKLKixYRnH/++ZOsgpktUB7raWY2Vg60a7pdl+e8Xm2oju6SZeBdZhCPsrW615M7NylTCdCj2/W7
+249bZSt5zHpQtjZX6gFQ5KBdEbQUtMoAWakLOBLKzdlFrlcRvam4ylbs7tjy+hj1tGA38O92AFfZ
+XV3d/ZUXrdS/u6MqreSVseT1OFu95Xs998twv7yRkdvQuy341RHgZrbCrbE/1t0nXQEzG8pG4BJg
+J8At2kvvQtJFoNna4EC7QeRprma1nFa6ZwPdjGndbtTVwLrbKlx/9ILzokjhryRaLUFRHaPdmwqr
+rA/Uumkrz2lNCrp73alT63inCDoBReVn5ORsre4Ya7rjvXtjoqkE0XnsdJTjqFMNClKA34noJW+r
+7ZpySrPuIG5Si7+K3KJeJlArd2UarZ6ToeW9Va5b3qDohfW9z8VswiR9G7gn8O2IuO+Eq7Mp693x
+W9EEOOu42epwJnAQcDLgrONLbw/APX5s7XCg3cfsluXZV3fdtGLd+DvNid1t680LS6RW5VRiJWDM
+wXPkIJtWniarOlS6DHarq3dDbVqRxi6X04JFkVt5i4KZTsFMUTDTidyYnlvPJdqtMkN4K/Uuz4F2
+mtc6bbcbbPcync2qS0S6L0AU3aC8FxBrdjbyvN1ynHa3H3i1pb6cIi0H55Gzk8+aJgxyr/FqgL0K
+rrltLVglAeDqFRH/CfznpOthZmZmNiwH2g164ezsqa26TbfqjeXuLlN29c4t2lBpye42BveWRbnb
+eSVQbbVa3fHMkiiKolKnVHaRyylyHVqU2y2g0yGKDhunO0zPpEeBUKsNrVae57uV1hPQaqVm8VZv
+fupqpvAyOKcy6VhELYt6pQW/91/RVqu3I/JrKSlbUdtP6nVhbwnl+bKLHGgXAgrVMpiDW7NthZk7
+3sTMzMzM1iwH2ll3sqhulFkJrOsxXQ6+yyzgZVfqVlRHL9NttFWlvIhei3aQW5aj16V81ljpbjCe
+E43lR0Ce8CqnQSs6FDMdYmaGYmaGjTMdNk7PsHF6Blpt1A5aU1O5a3buuq2CAIpIQaxaZZXL1Gqt
+sg94pdt8bl9WdN9zKFCod0OBFCyrDLTL1n3odmsvewnMHqadt5pb2Fv0etNXW/JnFehY28xWjs6k
+K2BmC3VT4LX5p5mtFu12u3y6or97W4MXWRuKoug+ImfoLgM8lVmzqxm2CYoiPTpFkbN6V1q+q0Fg
+twv67Izi1ZbjXpfx6mplJu80HroTRXoUndQ9vNNhptNh48wMN27cyPU33sh1N9zAddffwHU33Mj1
+N27kho3TbJyZYaZT0AmY7hTcMD3D9TdOc/2NG7l+Y3rcOD3NxpmCmSIHw1IK0ltT6dGegtwyHmpR
+IDqITuSfSvNcF600hzhlC3U7/aQcB14mgctjsaPbw7zcv5WdF3ne7vK1WQPelVvkfQivZZL2lfQq
+SSdLOk/SDZL+Kuk3kk6QdMg86x4vqZD0+wHbeEperiPp5pXXT5BUAPfKL907L1d9/KFPmftJel+u
+57WSrpZ0lqRjc6bufnXZs1L2k/Nrj5H0NUkXS7pG0s8k/YOkqdq6h0v6dl7uWkk/kXTUfO99sfXt
+U9bjJH091+M6Sb+S9CZJ286zTuNnsFCStpH0Sknfl/QXSTdKukDSFyU9dtRys9wFyZ0bzFaPmwKv
+w4G22epSCbRXdHY9t2hnRVGkWE45kVd9gTJpNmX36RQApm7UUEgU3WmzmNX/utu1vJuQLIeOqrSk
+dzfSkzKFl48iBfZRdOfQLluQOzMzTG+cZvrGG9PPmV7X8dZUsE4taKftd6fHioJ2W6xrt5hqial2
+m/YUtBHttrrjqtP/000CQkQo3TAoXyNo5URmIdGWUvd0tXrj07st+amFO3KCNXUTn9GdK7z67iuj
+wunmHy9verg1e82TdC/gW/mf1SNiHXAr4NbAkyW9OSJetQRVqB6k/fpYzPkCkPRK4A1Ux2QktwVu
+BzxH0rMi4r8GbBtJ7waeXStnf+CdwL0kPZ60Pz4KPLa23AHAeyQdEBHP7rehMdW3LOtDwNNq5ewN
+/D/SZ3W/iDhnUDmjkHQ/4JPA9rXt7wI8HHi4pK8Aj4+I65aiDmZmZrZ2ONDOyvHQitydm0rgV47J
+FpTZvSOKWWOVWy0R0eqN587X3dVprXqt2ZXW3D4BYy8JW95WUdDJ2crLQLs0MzPDxumN3HjjxhRs
+zxTp0ekwFYKpKVrr8hjvoqDodCiKGdotMdNuMdVusW4qWIdYp3ZqhVYrjxVP19ZRFBCdFBEXEBSp
+2zm9aCMN985BeivfrijHpJdTeqnMLF7uV2Y35ZeBPWWAn/dnzjDXvQWipknYbI2ZAq4BvkwKuH8N
+XA3sDOwL/COwJ/D/JP0mJ9Qap38C3gqcABwMnEEKIqs2Vv8h6bnAG0l/An8B3gL8EGgD9wdeBmwF
+HM6gXSMAACAASURBVC/pkog4eZ7tPwc4hPT+PwT8EbgZ8ErgLsBjgKcDd8jPTwQ+TppfZS9SM85t
+gSMl/XdEfK2+gTHX93nAnYAfA8cB/0f6rJ4KPJ7UpHSypP0i4tp5ylkwSYcCXyEdMxcB/w78HLgA
+2A14AnAE8BBS0rXHjXP7ZmZmtvY40J5D3Ti5bL2GXjKycpleA1Y30xl5wfwzj8kuZo/xVqULdbl4
+2XJNMXuYQacMsHNwXQbp1YRpaUz3OqKTmtLbrTZTBawvoBNBe9161m3YwLoNm9GemmJmZpqZmWli
+RqgFkROhFYiCVv6ZWqgL1Ev6JhG0UrfyFrkJukVlNnAKoCN1E5u1uvspLaBCqF2gooVyQrhuoF12
+Ky+D7jxftgikyDdAZn9Otub9FNgjIq5u+N0pkt4F/A/wAOC1kj4SUbtLtQgRcSFwoaQyKLw2Is7u
+t7ykHYFjSH8uFwCHRMQFlUV+JOlLwPeALYD3S7plRPQbf3Rn4NiIeGnltZ9J+jpwNnBzUmB8E+AF
+EfGu2nLfBX5DCpSfA8wKtJegvnci3RR4VKTuMKWvSjqL1Gp+c+CfSS3cY5G70J9I+r47Cfi7iLih
+ssjPgK9I+h7wfuAxuWX9G+Oqg5mZma09HuDalYLCMnwrM2N3g+3uPNW9pSvpttM69MZv98ZrRx5r
+nANjcotv2QU6l18UBZ1OQSePu56ZmaEz06HTKceN5+3nVmG1WrTabdpTU0ytW8/6DRvYsNlmbNh8
+C7bYciu22nprtt52O7baelu22GprNttiS9ZvtjnrNmygvW49ranamOscbAet/FDlZ+V1tUDtnGSt
+jVptopXW76TG7tyNXhTtFtEWTLVgXRutn0Lrp2itn0Ib1nX/rXVTMNWGvHy0qIzdLoNtaFVvMLR6
+D1ubIuLyPkF2+fsZUosrpJbtOy5Lxfp7GikgBXhRLWgFICJ+BryZdPTvDjxqnvLOA17RUMb1pFZZ
+kbpJ/7gWZJfLXQx8Li93jyWur4AbgGfVguzSm4Cz8nLPqI8vX6TDSJ//DcCTa0F2V0R8EDgt//Op
+Y9y+mZmZrUEOtOfoBW5lYD0r6XZ1uaYYr7tsJdjurqFuK27Zsg3l+O3cep2D7OmZGWY6HTpFJ3cX
+L3qDQZWmAusF2utYt34D6zdszmY50N5y623YZtvt2Hqbbdlyq63ZfIst2bDZ5kyt34z2uvUpuVm7
+TajdC7K7LduzW7eDVjd4TnNctyAnSaM9BWpTqEWH1KLdERStnBhtqkVMtWFdG9blwHr9Olr5wfp1
+sH4K1rXTsuVUY2WQrWqQnX62WmWwnacsMwMkrZd0M0m3zUnS9mX2Oe4Ok6pbdv/880pSgNvPBxvW
+afLf87Qe/7zy/FPzlFEudxNJ29R+N876BvC1iLio8Zepp0HZtX974MB5trdQj8w/vxMRlw9Y9ruk
+s89dx7h9MzMzW4PcdbymOzo4Kv+uDtamnL26KDtVp//mbuKhXuKwXuu3yqHJqcN5pVt6mU2cSqt1
+OZa71U6BZCs/6GbzbtFut5lqT6WsewHFVEGsD6ITOUN4mSm81W25Lsqe7rkiUXRSF+3yvSi3YAcU
+xew7C2lMeqqjWsrBbhq/XUTq3p5a32foFCkjervdTo+pNlOtqbzhNu2phjsUEeS5v6AooKM8L3jv
+k1G3o3rqGRBqutNha4mkLYAXkMbY7ksaO9zPjstSqf72I/1RnTlPgExE/EXSuaRW2P3mKe838/zu
+yhGW25o0xr007vqePs/voNeaDHD72r8X42DS+3hwzhI/jF1H31xBGno+SJv5D1czs03NhZOugK1w
+F154IRdeOPg42bhx48BlVgIH2lk3kze91Lqi958c2uVgOefKztOAld3KUyiaE4nl0lJSsdx1vJyq
+qpy2K4JOZ4bOTHqU04qlAc1i/fr1rN/Qpr1uHe2pqRR4t1u0Wu2cJXyKdnsKBUQnypg5B9a5W7da
+3fHPnUitwe12m6l1U3Rmpik6HTqdGYgCtdqgHJAXKct5OSVZynaeHlNTbaamppiaagOiUwTT0x2m
+p6e7ycwkmGq3Wb9hPevXr6dYn/5dBt/KE3eX3ednBdqdDtHJ84J3Wt2p01T2MOgG3LaW5WmlvgXc
+gkp2hKZF88/Nl6Fa89k+//zLEMteRHpf28+zzHyZsasB5bDL1aO+cdd3UDkXN2x7HMqodyGnjc0W
+t8lLFre6mZnZGvS+972Po48+etLVGBsH2lk1R1JOrE03XC7HRQcoWqgoA+1e63dRpAm/CorcxblN
+K48nTlNfBRFKAX10Umt2UTA9PcPGjTcyPb2RotPpdieXhNpt1km5a/h62lPtXnfxdpt2Owfa5AqX
+TeXlOGq1ulOJFYhOFLTbLabWtVk3PcX09DQz0xu72y6Tl0UR3W7snU6nkpCtQxEF69evB1q02lNA
+MNMJpmc63LhxmiI6RJ6LfGqqzWZFzs6ex3O3Wy3aU+totdt5zHrewWWQHUF0OhTT0xRl9/ooUplF
+MXsSJUfba92JpOCuAD5MmrrpV8AlETENoHQnp2yNXSldIFbbkTuu+k7qfZc3EE4CXr7UG5PEjjsO
+7jxR3nQ0M1trdt11EZ2GbJN21FFH8chHPnLgcv+fvXcPtm3L77o+vzHGfKzH3vuc07c7fW86UqBB
+q4wikEgwFokiIRCJPAxIWZiAmIhKqQVWKiolKbAqliQBJVARo6KUYEUqFBLLUIFKipdAeKQqoiaW
+PNJNQ9/HOWevteZjPP3jN9ba+17uvae7vd3ndN/xuTVqrn3nXHPNue8+5+7v/P1+3+/XfM3X8Oqr
+L/5D7Sa0K+eKthG5cxo/V7PPkVXng+Wy815FW0V0yYVitIX87A6uB57fp83puYrZECM+BJZ1Jad0
+MfsyxjLkjIjB9Wp2pvPYDlcr2ef2caFmVpdz3d1c+sPPRe4EpKxC20Zbz7HiRR8URAKisdi1qJyJ
+sZqypaSt4SXVGDQV2S7rPaaUCUGFdkqRmHTrnCXV6rwapxlc6aDOlxujedvGvFlo55SquZpGjJET
+JWWKpJoFXu61HDTej4jIPwp8BfqT8J+WUv6Tdzj03Sqj52ruswb9d5/i5b0Tb6AtyV/wSRz7YfTe
+njVT/Jnkvb7eZ53n/v738r5fR6PD+ndzhX+veOWVV/joRz/6mf6YRqPRaDQ+73j55Zd5+eWXn3mc
+Fv1efJrQrpT7s9YXgXz+d7q9COmz0dmFKo7lPFN9bo2u1eSciXWFnAk51ddJBX6t8p7Fs7EGZx2b
+7Y5xs2UYxiq071qvjVGhytnJvM6On9vcVWhr/Ja2rFedn7VinGJd56p1yph6DhFtLz/foohAipRU
+W8hzJoaIr0ZkwUdCUIf0GJIauUVP8EJOGb8Gpmliu92y3ezY7raMw0jf9wxDj7EdmDvdbMSQatt9
+shZiBJMgJSQnfUiRtGreeN/yj997/W5mX1/6LvsOdfvgGZ/1M5+x/5Ot1P44Kvh+joiYd3DfRkQ+
+iM47n9/zvHivr/fLnvF59/e/l/f919Cs7C8VEVfd6BuNRqPRaDQ+ozTL5spZPJ//gVI7sXX6upAv
+c9U5l5prDWeRLWeRXSu3crbJRvOwY0r4EPDeE0KoYjSRC4io0O6GgX4c2Wx2bHf7KrQ3DONGxXY/
+0HU9ruux1t0JetHKMObetVTBfPmnVt45Z3MnjQ+LNUIsJZ0PF6jmayroz9Vz/Sy9n5wLMSa8D/jV
+433Q80St0Acf8GtgnhcOt0cev/GY1z7xGm+89gZPHj/m9slTTscTfl1V4IvFGouprfDWdXqP/YCr
+y/Y9tuswtkOMrbFe93K6G+837j8kfLeK8296l31/s26vROSL3+4AEemAX/WMaznHRQ3POO6H6vYB
+8Cvf5bjfyN0P9g+9y3Gfad7L6xXgq0XkbavatcX/G+qXj4G/+qld6rvyx+v2Bo0sazQajUaj0fiM
+04R25SK0S+2fPq+z2C5nY7B8Mee6K2qf271VoJqz2ZfUee+ciUlbxHVFQtD4LnXxNtga0TWMG8bt
+lu1ux2a7ZdxsGccN/Vlo9wPOnc3RbDVbO2dzn0X3XWb3eQ76bOJWsrZm369mp3iOEKtt3kbvxdY5
+cOfcm6r0Z6Ed1sC6Br2X+xVtH1lXzzLNHA5HnrzxhFdffY3XX3+Dx2884emTW07HE+viq9Cuwt5Y
+rO2wrsd2vYrsYcD2A7YbsE7zv++upfE+5ifvvf7GtztARH4TGu30ThXnH7n3+re8wzHfheZDvxtn
+e8yf8Yzj/lvUmEyA7xCRV956gIj8LOBb65cfBf7YM875meS9vN6CPoj4Hnn7P7zfijqNF+B7zzP2
+7xF/EM0cF+B3icjbZYZfEJGvEJFf8B5+fqPRaDQajfchrXW8Ymq19lyI1nXXIl6LwfcEeRWu3GVi
+n92wc9HW5lwjr3yMhCpscyk6m2wMrkZ1nb+2ztH3HX2nTt3jONL1w5152Lkt/Mw5guw8m335ulRD
+Mog5EbN+dkiR1Qe814p6SgmKVrDP5JyJQE5Jc7zvrUvkmNyLG9NvQH0gUY3hSiGnQoxaOY8xkXPC
+iCUnCCGSUgYE5zq6bqgu5g7n5G6uvX5PjS2YXCg2Y3ImyzsmDTXeJ5RS/pqI/DgaJ/Vvisgj4H9A
+Re9HgF+HVqL/LPDP8jZiu5Ty10XkL6CZyd8kIgMqyp4CXwx8M/BVwJ9D58HfiT+PVko/JCLfiZq0
+Pa37Qinl79TPe01E/gPgu4EvAv6KiHx7fb8DfhHwW4E9Oj/+Te8Wq/WZ5jNwvT+KPvj4cyLyXejD
+kg+hD0p+TT3mp4Df+R7fhxeRX4061O+BPy0ifwR9KPA30QfOLwM/F63cfwnw76CZ2o1Go9FoNBqf
+Fk1oV6zVUC/DJa6as+nWWViXOqNNvj/LfXeOc+U7laLt4UHbqOM5LzsXxGr12vUdtuvVFKyuu9is
+Duccfd/Tdf2lFRzO5mtvcwOXInypmdY6dx1ixMdaRY+BWAV0TFGNzUTUzTwbFdklUUIk1up7CEFF
+8jnP29m718ZUR3KLpLvKOvceOKRUSElN1aZpJsbEPC+EkNTozXU41zGOI+Nmow8VRNQ4PYOYcjFM
+y+fqPXcPPBrva34d8KeAh8CvrutMAX4M+HrePbjzNwA/jAq+b+Cuffl8jv8cdTJ/N6H9R9CK7E8H
+/r26zvwt7lW6Sym/X0RugN9RP/O73nKugrai/xullB98l8/8rPAeX+93A1+JCus/8jbn+bvALy6l
+HHiPKaX8RRH5KnSe/4uAf7Wuf+DQum7fZl+j0Wg0Go3GJ00T2hVnz4ZiBSlgqppNdTb7YoJWq9pn
+13Eu1WxqjBaUlNVJfFlZlqXGbGkFWOeeO8Zxw7Dd1Jnkuu4ZnVn7ZuMzLhXrdxCXdZ86hldDshBZ
+vWf1K4v3+BAuDwNyvXRt23YUWyghEEMmhIBfV5Z1ZV1XSs4Mw0A/DAzOqci2tRKfMzalu9n08/ei
+dgCknIlRXcljXJjnFRGtauvDhIFhGDlXtxnQ8wBIubTGn8X2/Yq+OqA33q+UUn5MRP4pVOT+EtTw
+6gD8P2jU1++r1cx3O8f/LSI/B/iPgF+KVjafotXX/7KU8oMi8g3cCbC3O8dJRH5+vY6vRo3Btufd
+b3P8t4vIn0Crpv98ve4M/B3gB4Hfc66Cv9Nlv9O1fCaOey+vt5Tyr4vInwS+CW0V3wN/G/h+4D8r
+pTx9x7M8+36edR9/qc7ifyPwy4CfDbxU7+VV9IHKjwB/tJTyk+90nkaj0Wg0Go1PBmlVQeXH/tKf
+rdZmBSlZa7KX6nDdFkilELP+Nmeq+DPVcdwawYohxcQ8zczzzDTNKqK7Dus6+s2G7X7PZr9js9/j
+3iK0z27i2s59NjfTa7ybC3/Lf7OajEVW8bl6j69r8Z51XfXfxajnq/3x9wUzpdTjVFwvy8wyLyzL
+QilFHcN3W7bb7b2HAYaUM+uysi4Ly7owTxPzPLFME8uy4FcV+n71d3ncKXFzc8PLr7zMKy+/wodf
+/jAPbm64eXDDzc0NzjlyPud3J1IIpBhJMRLWFb/MrOtC8B6AL/uyn98c0RqNxnNFRD4KfOEXfuEX
+tnivRqPRaDQ+g3zkIx/hYx/7GMDHSikfed7X8060ivaZfB4tvGeEVmOxVPNKLVfrPHK+l+OcKeQU
+iVEr3ynEi7gNMaqjeNczbDYa1zWO9N2gGdjmzmDs/Lkide76fnD3xeG8UIpc8rgp59Zsbc8OIbIs
+C/MysywLMd25pcP9QrzoDHbwdR47Vjf0QAj+TnSHVefRvQVrKCJ3Le7WkUvGB4+POvcdgs6Arz6w
+rP4i1tflbMyshJjqDHd1Pc+5urmfM8b1vnI+G7ap0I4xEGu1PoaW0tNoNBqNRqPRaDRePJrQrtxF
+xJ4tzc4t5Hct1iI6N0wBKefoL600xxRJoYpBXyOuvM5pjxvBdR3b7Y5xu6UbBlyvEV3nlvCSzyK7
+qLDnPIv81ggrqUI8X1rZY0ysq6+V6IXTNDFNE6fpBKKf7boO62zN2NZTxhRZ1pVlXfDea+RXFd0+
+aEU8eG03xwoYveeu6+g6h+s6KKWKbF06D+7VdXxZmWat7M/zjL3MdlsVyjERa7RYSiquSy5kU9QF
+Pec35X3fF9gxBGJ4L42JG41Go9FoNBqNRuO9oQntM2+qaKNKGmo8llG1fXbYLmgbea5xXxTNya5t
+2sEHFYdBRWJB6LqezWbLdrfXiKrOYaxTcVmzuUvJGGPvuYdr67imbqnYPs8oazVbXcxjzKyrr+J6
+4nA8cjgeOByPuM6x2W7Zyhbj7Js0e0iJeZk5Ho/My0K+RJflKpzjnZg16rGeSqbPva6SEYQQ/EVo
+31W0Pcu6Ms8Lp5MK/+5s9NY5dWG/l+GtbeJaeTe5XBzbz47nMcZ761zVbkK70Wg0Go1Go9FovHg0
+oV3JSYX2uV3bSLkXM3WvT/ySqcUl0gpQ1/C+BzF0XU/JQNaZ6pubB+yurthst3R9r3PeZyFeW6ZT
+zljrqqg2OHdXydborjtDNs2xVsEZYrxUspfaqp5LwVhL1/fYmrWdsrZ456xiOefCsixM88S0LPjg
+1enbCGINBoutNXt1Uk+sfiXEwCaNQMFaU1vQa9t58EzzzOF44PbpLcfDUavZ08y8zAz9wDBo50DK
+SUV9/abrAwPN3w6GWrH22sY+zyzzzLrMhHUlBk/0gRSb0G40Go1Go9FoNBovHk1oV1KtaJ+7tkWg
+iNQB7dpOfje6/SZ72yKC7dTQbBhBipqjGTEYDNvdTg3QtlvEWGLw1Q08VLM1nVHWKC/BOW0pP8ts
+oLqJ31V41+oIvvq15mLrijlhrKEfBsRqJJgYQy53JmlrCGqWFrTNOwS9DtdZnLHY3qnYtgbjTK08
+R9ag7eWFe0JejArtqNX8aTpxe3vLG2+8wfF4VKO0dcV7X+PEwFijQpuiXxshZ62iL+uClIL3K8Gv
+rOvC6XDgeDhwOhyIwUPOlJyguY43Go1Go9FoNBqNF5AmtCvnijbV5TtXxa1ZznJpJS/lbnEW4wLW
+qDmYO2+to7PnPOyRvsZjpZQp3uN94Hg6XVqnU8oMQ6Sr0V+Xa9EXlCrIY4x47+9mn6eJkLSKfW5j
+F2Poh55+HC4z1zElfDi3l89M80RMqUZ96XtMZxBrcH1HySqyTbIE74lzwIeVeZ4xxtD3PSklxKpp
+WajifZomFdqP3+B4ONZKt7Z6AxhjcJ0jZf1sRL+/uWis2LIslJxYlwW/6v09ffyYJ3XlGKu7u9Ss
+80aj0Wg0Go1Go9F4sWhCu3KpTlO0HfzSGo46fJdCztQ2b4ilkICEbvtOcE4wnaPrBm2T7nqGbsC6
+DusczjkKUXO3a3RYvqzaSl3uO49Xt/BSCGcn87rmZWGZ58tstdqjS31QIGB0m1PUTO91YX6TUdpU
+q8tW296timzTWbrekbNBoj50SMlc3MXneaZzjr7r6LsOayzTdOJ0OjGdTszziWWZ8etKjGqkZq3B
+2J5+7Bk2A+NmZBgHXOe0mo2aya1+JZdMCoFlnlnmiel05I3XX+f1117jjddeo+RE7xy9czhnn88P
+S6PRaDQajUaj0Wi8C01oV4xV0WYAU6vaUmewcwFyIaaMT5mQCj4lfZ0TPifGYWQzJjYZQDBiscbR
+uYKUguRCyupSbq1jGEZ2uagBWTX/6vuecRxq6zjqAl4NwJZ1ZZ5mpmlScV0FughYYzQmzGqJN8RI
+WCMhBuZ55jSruJ7muRqWRXwMOOewxtD1Hf3QMwwDfd/rHHkMWkUXqjFZjQ/znuk0IQVSCAgwnSam
+UxXbVcBvd1uGjd6Lc47OOba7Hbvtlu12x4MHD7i63mOdJQRPzpEQPMZYgl+ZTkfm44nj4ZbXX3uN
+1199lddfew0pmbHvGYee3nXP4Sel0Wg0Go1Go9FoNN6dJrQrxui3QovC5b45N6VmO8eUCSGxxsQa
+IksMLCGwxIDfRFIG0Pgqazq6LpMLSC6IZCRlKGg7+TCAMbXlW5dzTiu9zmr7etZ273VZmaqb+PF4
+ZJqmi3t35xzGWW39tpYshTUElnVlmu87kB+Y55nLEwQRjLMYq0J7GAdtNx96uq4DyqXdu5RMSury
+7b1XAR4D6zyTU2aeTiq2p6mauRW2uw1d17HZbNhsN7qta7vZqAP7dot1hhA9IUId2WaZF06HW06H
+A4enT3nt1Vd57dVXef3VVzHAdjOyHUeGvv+s/ow0Go1Go9FoNBqNxidDE9qVS0VbwFQvcaG8KX4r
+pUyIEe8ji/ec1pVpXTj5lZASoGLXuo7O9cSk8VtSFzmDCNZahnFUl3KoadxamXZd96aKtvcr8zJx
+PB24vX3K06e3nE6nS3W46xzW2iqaLZlSjc9WDscjT54+4enTpzx5+pRpnuiHga7vtXI99JeZ7GEc
+6Meevu/o+o5SEqEaweWiFW2thnuiVydwg5BiZDpNzLUlfbvdstlu2e027K+uuL6+5ubmhuuba4Zh
+YBxGhmGobuj6OMOHVSv0Sdd0OnF48pTD06c8ffxYhfYnPsFrr76KFeFqt8Vvt2zG8bP9Y9JoNBqN
+RqPRaDQaz6QJ7UqRu3zqjIrtUvT1eYWYWFbPcZo5zDO308RhOnGYJ8Zh5PH2CdvNjqvdnpv9NTdX
+19xc3TAM453A7Lr6WUKpRmBGDKZuRYDaTh6CZ1kXpunEPE+E4CklYYy2tiNqgKbiNxNzxsfA7e2B
+p7e33B4OHI8n5nUhpHiJ0jLWXGbGnetwrsNYdTrXmK3qSu5rbFiN11qq+ZpBsMZgxSDA0Hf07pqr
+/Z7dfsf+as9uv2e32+oDgV2tXlt9KGCtAPfzs3N1Ntds7XnW+11mnfWmFIa+52q/x4poNXsY6Frr
+eKPRaDQajUaj0XgBaUK7cm4Wz6VcgrWgto3XOK+QMvO6cjhNPDkceHw48OR4y+Pjgc52DLVSvN/u
+eXTzkEcPHvLo5sjV/or9/or9fs8wjhjrLhVoQTBGLpnUaoKmpmgheNZl5jSdmBedr1ZzMdHUMQoF
+deueqkHaaVk4Ho8cTieOxyPzsrCuqzqMV8M0Y63GkXXdJZbM1ApzzokYssaArQvLsqi7eRXZ8zRh
+xdBZjQLru46xHxjqjPfV9RVX19dc3Vyx2W7oz9XzvrvkgJeiTuUxx5oHnkihvg6ReZpYlpllmVlX
+jyAMw8jNNVgR+k7N0LpmhtZoNBqNRqPRaDReQJrQrrxlKpusGzKXFG1CSsyL53A68fjpLa8/fczr
+T5/w+pMniAjWWKwx7DY7PvjoJY6PjswfmHn48CE+aEU5F3B9T0ePM0bN14yprdRUIaot1FrRnpkm
+dfJOKQEZY82bKto+eE6nI08OB24PR05zjfCaJs3WTpmUk45mm3N7u3vLskCtLudM8Dobvi61mj1N
+LJNunbVk21Gco7OWcei5ubnR9eCGm4c3XD+4YdwMiBikOqCHoLFmIQRyTKSkr33wRK8mbdGHGl02
+scwLfl0RYBwGxr7HClgxl4ivRqPRaDQajUaj0XjRaEK7krPOSctlPhsoEJPGcMWUmZeV0zRxezjW
+1uwTx9PEaV4oOdeAbZiGmZIKOZ1bojMgWOvICH2M9CnRl0SXuyrrNerqXPHNKVFK4hz1JefYLtFM
+75QT3ntSzkzLwnE6cTydOE6nSxXbh0DKOicOVWSLVLFtQDQ/PASPSCGXRMmJXBJTdRCfp5l1XohB
+XciNMXSuYzMMbPqRq/2eBw8e8PDhAx49fMj++or99Z791a7mcefqrJ71AUKOxOjVAC3cLe8DfvWE
+1bMuC8F7UooIqCu66+idU7O6UqBk3TYajUaj0Wg0Go3GC0YT2pUYE6DxXjWGWtu3Y8KHgI+R4zTx
+9Hjk8dNbntwemNeVlAuu64gx1iptYvWe0zTR2Q7JqAu5c7iu18ztcaD3uoahq6vXLGsR1IKsIEbo
++57dfouxwrqu5LVmToeV2S/kUtSYbV7w0auoFlGTs84hSWo+d0Yus+AAhRQD8wIxBawx5KziPud4
+mcdepgm/LlAKfd9xtdux3+243l1xtd9zfXXF1dUVV1d7rq+v6MYeaw0pBdIade46J2LSBwN+9Xjv
+CUGr2TEEYogEv2oFfV7wqydnnUXv+55NdRnfjiOmPhwoKVLU5r3RaDQajUaj0Wg0Xiia0K6kKrSL
+6Mw0F6EdWbzGZR1OWs1+8vSWp4cDIWdSKVjXqSs5KipXXzhNE2SIPmBsh+t6un7QirYf6IeBfhwY
+Q88m9qQ0VAdxoy3lVRT3Q8eOLWKEQsHHSCazeq8rBHwIrCHhYySRQcCchbaRmrmdVMRXIzUBYgzE
+GFhmoBRyVpFdcsKvK+uy4BetZkspDF3HYB0Pb7R6/YGHj7i+vtK4ru3IZrOhSKEYfRiQYlJBHWN1
+LA+E2joeo+5PUfO5vdc29XmeST5SkrbUu8Gy22643u+52u8x9bpT8KQaP9ZoNBqNRqPRaDQa1yeg
+OAAAIABJREFULxJNaFdS1OpoMdo4rgK1XJzGT/PC8TTx9HDkye2Bp4cj4izitFodY6IAMWVIkVMR
+Ukis80rXjwzjhnGzBWPpg6cLnj6shDiQ0kjOkWHo6FxH6RzO2lrR7bDOUgR88MgipJxY/MppnjlN
+EyElchF1Ry9cDM9cbRPPKZISaBu6ADrbnUImViGcYtSKdk6UlFSEB08KAUqhc45uGOhdx6NHD/mC
+D36QD33wQ1rF7jr6ztH1HWtc8WFljZ41eM0B97qNIRKq4VlKiZwyJWl02Nl8bZ4nSio4cWq25nq2
+2w1X11c8vLlBKIR1xfuVFPzz+nFpNBqNRqPRaDQajXekCe2KqdvzbHbJ6oy9rCvH01Edxg8HjtPE
+tC6sPmBywdSM7BgTKaqJGbmQS6lu5RrjVURFfBYIJVNSIAXIkgk5sPiZ7Tiw224wovnY5LvKekqJ
+NQSmZeEwTyzBs6ZIFMjWAHX+WgSTCzZnSi4qpNdMikHnsdeFkhMp+FpJViGcYqyRXRrd1RnDOG6x
+W0PnnJqRjQPjMHB9dcXN9TWbzYi1hkLGR0/IAR/93QoeH7XirqZsiZgiMWuLfY6JnNRxPKwev6z4
+eUWK4Pqa7913tdJfndZLQaQgojZ1jUaj0Wg0Go1Go/Gi0YR2xVTXcanW4MWg4nZdOJxOvPHkCU9u
+bzmcJuZlZfUBWwqGgi064x2T5l+TS42yUimoYlvIIiSBUhIpaVZ3zJ7VC86A9xtEoO97trW/W98P
+MWfWGJjWhcM8E1Ii5EQUKMZgjEWMxYqppmz6AMALpKhV6RQCJSdi8Hhjak62ZmXnlBi6nrHvGboe
+t9mwG7Zstxt2W91uax62RnnpssaQco3oyrGK7EA4b1OsreN1Xrs6oOvrSAqJWI3QzjPazlhwHdZY
+FdrO4pzBGDWKwxT9DyVtRrvRaDQajUaj0Wi8eDShXTlXtC9F0qzO3zqbfeKNp095crhVV+91ZQ0B
+B2golgrtlDIpZaRmb59rrll0FaPbVDIlFcgJ8QkpCVMSMXqGoWe/3yGmOoxTK9o5a0V7XTjOE0n0
+/MnoLDPWYqzDGquGbqgeLTnjVwNFq9oxaPY2pbAsizqLzzM5ZXabLbLZ0W0N3dayGzc8unnIzYMb
+rq72XF3v2V9dcZb/hUJMgeC1or36RWexYyAkFdkxpcs2JXUfv4jtmNQMrQptX4U2XU8ZC84a+r6j
+6yzWCmJB8rmiXcV2o9FoNBqNRqPRaLxgNKFdsaJSO1NqHJW6jWuk14nbwy3Hk0ZneR/UPE3kUnFW
+J+wMhToHrWiBvJBKJqSEj+EivDMFUkR7yDXK6vpqJaYEyEVgh1Sdz0Ng9YE1BLIxFCMUY0AEZw3W
+WZxzmKIPDkyBYOQisv26anxXjdoKIUBOWBG6zrHbqOnY9dU1Dx884NGDhzx6+JDrm2utZu82bMaR
+mKPOcCc1OVMzNs+6rlVUn0W2CuxYXcdVYFehHRKpiuzoA8FrtFdYPVb75VVod53Oq4sgRVvyqS7q
+lFbRbjQaLxYf//jH+chHPvK8L+OF5cMf/jA/+qM/+rwvo9FoNBqNzzhNaFectQB1fjhf4qiWZeE0
+TRxOJ6Z5Zl19NQ7LSHUqB61+UzJGwIioMKx7c87EGhNmvVcxTyFRIAZKCpQY6K1VEZ8yBUjVjO3O
+XTxqy3iNtSpiKKVoBdsYrHN0rrvMjZuile0ck+ZTzzOlJErOlJwRIwxdp/PX/cDDBw95ePOQRzeP
+uL655ub6Wl3Fd9vqYG6IKd7NX/tqfOZ1+Sq+Y430Olf4Y8rErJniMWklO4VE8oHkqxN5dSMPIdA7
+h1CwxtBXJ3aNW9Prvr9tNBovFiLyRcB/DPxC4AuBoe765aWUP/7cLuyzRM6Zj33sY8/7MhqNRqPR
+aDxnmtCunIV2KplcCjFG1tUzV6F9PB6ZppnVa8U5p1yFtLaA68uiItuoKdlZaeecVaCGgFk9iUwq
+mUShBE+OnhI8Q+fw3pNyBuROoPtQnbsjvs6CI2hruRRKdRe31tJ1Tturc0GyXmNOkbCurPOkIrWo
+YdswDgzDwGa74Wp/xQcevcRLj17ipQ98kP1ux263Zbvb0vc9uT4eCCnoAwi/sK6LCuygudg+BHKp
+IjvnywOLlFRgx6iz3DEmko8kH8hvqmprZTt1PRQV2p1zOGMQpM6+58vKuQntRuNFoorsvwp8gLtB
+nHLv9fuEL3zeF/AC8nF04KnRaDQajfcHTWhXjNHWcUGFcYgqbpdlYZ4XTtPMsi4Er/nPJav0pGSo
+wlpqFVvdv/Vchdr+HSOr92AtsSRSycSSKSFQoqdEz7puCDGpoZoIuRRCdRv352ispCKWLGAKUg3X
+EMFai7UWSEhWQ7SSMjlGYvBE7+s9FgTorGW72XBzfcPDhw956QMv8cGXPsgHP/Ahxs3IMAwM44Ax
+Uh3EV0IIrGFlXVfmZWENKzFqRFiMQWewS53FzmeBXd3GYyKGapwWIilEcog6p31vpRi14g7Y85x6
+zjUSLGqbftE580aj8ULx21CRHYD/EPgzwLHu+9vP66I+uxjgo8/7Il5APgK0Sn+j0Wg03j80oV3J
+VbSlnO5Etl/xQWOp8qVd+c7R2yAYVAyKxm8jCEbQeeKsztqhzi8vy0KiEHO+zC5bMpaCLYIRgxWD
+qS7iWtUutQocSTlTsrqYX/4RQYpWe3NMJImkdSWtnrR65uOR6D2mwND3DH1HX7f76ytubm64vrnR
+7fUNu/2ObugQK6SSWP1CoWgedlhrLvaq1exaydYHACqgzwI7V2EcaiU7pEhONf4sZci6LtXplOoD
+gUDwK+uysEwz0+lE11mis8TOQcnkFCHfzdU3Go0Xhl+IPvv7Y6WU73jeF9NoNBqNRqPxvGhCu1Jq
+Z2NKd9XnddUKbkw6k52LxnbxFrFt4dIqftcxrgLyYhhWq+OxZEJUJ+6QEoMzDNbgnMWKxZjzVr3D
+81mUR23Fzpcq7llqm7tIspTIgJ8X/DSzThPT8URcPQIMXcfVbs9+v2N/tef6+prrm2uub27YX12x
+2WzZbLa4oQN0Xj3kUDO8V7z3+Cq2dUbbX/Kx08VZXCvuOeW7dvEUibVKXfLZkv1unYV2ipEUtH3c
+LyvLPFWh7eg7S3SutuVXV3VjP7s/JI1G41mce6Z/4rleRaPRaDQajcZzpgntylnAxktFe9WKdlSH
+8XwWibVlWarRmAGtYFOlr6CxU6WQS4JUiEHP1y0LNiV8jDVrOsIw4MZe47nMXTXbyFsq2imSajY3
+Re7LbBX2WdvEUy74ZWE6HpkOB/w014p2Yeh6rvY7Hj16xKNHj7h5cM3VzQ3X19dsdzuscxjnsK7T
+KnQ1J7szPtNq9rlVPMRQK+3nXGydH1cTtPp1PS7FqI7s53Dxs9Cu1e1yrmjHoMZttaI9jxOx02p2
+1zk6Z3Vu21lsE9qNxotGj/4JD8/7QhqNRqPRaDSeJ633tnKOzzpXalevFdxSCq5ztdqrc8t919FZ
+ixXBcBaLiZLTPaOupFXaexXtdV20Su69zidXQzVnLEM/MPQDneuwxiIi5JQJIdY5cW2nXhedj16X
+81pY5ypKTxPLaSKsnhxTbRfXKvYHHj3iwx/6EB966YN88KUP8IEPPOLm+obddkPfdxgjNRc71ocM
+i65V18VZvH6fQgh13joSQyScZ8jD3dcxqMO4ZoZrBjbVpI2UqhHaSlgXUvDkFJGcKSkS/coya+v4
+PM2sy4JfV2KIpJT0YQcXv7lGo/GcEJFvEJEsIhkV2QL89vO/q+u/qcf+cP36T9evv1hEfq+I/ISI
+nOq+f+htPuOXicj3ichPicgiIq+JyJ8XkW8Rkd0ncY0bEfltIvJjInKs7/8zIvLr6/6vvHetv+A9
+/QY1Go1Go9F4X9Iq2hXvVwDW1bOsK8uqohKBvu+5utrjjGORFVsWAgGKRnrlHJFSncaLaL41WeO3
+EGLw6vrdddga3ZWLVsJVZPdsx5HNMNJ3PdYaKOiMs/fM88I8TUzTzDxPLPOMWIupKzpLcpZkLZ0x
+5BCQUuicox8GjfDqOzbjwPX1FVdXV1xd7+n6Htvpj0CMgYRW9jNcKtY6gx0ulemY7lrBQ4x3LePx
+XlZ2SqSYa153QTJaea8t91rJTuTgieuCnyeiXykxIEUfWsTgWeeJqXPkNEKp5mjG4Kyt3QXP66el
+0Wi8hbuZljd//db9FwdyEfk64H8ENm9zHPWYAfjDwC9/y/6HwM8Dvhz4zSLytaWUH3u7CxORjwB/
+GvhH7r1/A/wzwFeIyK8A/ot3uO5Go9FoNBqNT4smtCtrdeQ+V27XVVulQeiHgX0Bg8UUgySQBCmp
+AVmOiWJAjDZzU4SMkCVTEEIIWL9ilg6XC2IMUlvEnbUMXc9m3LAZR/quu7REp5rlPc+ziuxpYp5m
+5mnGWItYgxhLZ00V2obOGJzo3HjvHPvNhqv9jpurPVe7HZvtlu1uw2a7ATnHmanLesyadx1yuojp
+WJ3OY4y1HTy+ae46nd3E64x1TJlU58lLrhnforPs+oQBrWjHRPaecBHaCyWp0C45Eb1nWWaNXSsZ
+EbB1lj05p6Z0jUbjReD7gb9cX/84+if99wO/794xj9/ynp8G/CHUkfy3A38WSMCXcedSDvDfoyK7
+AH8d+E7g/wQeAf8K8I3AK8APicg/WUr5+P0PEREH/AB3IvtPAP81agv+EeCbgK8FPvhp3Hej0Wg0
+Go3GO9KEdiUEHSn04ew47vEhUIDOObYbg2SQJEgEkwvBQ8iZTKh1mprzLEIGFdsIqc47m1UdvJ3r
+sM5hraW3lqHr2I4j22FkcB3WGCiFFCPrsjKdJk6nY22jnlim6VLNNtZC57DJ4TqdX7Zdx1jXzf6K
+BzfXPHxww/V+Tz+o63g/dISU8MHXFvCATxGf1KjtEsmV7uav1UU913bxcBHil2p2TKRU1LQt1Xix
+8/y6QEmJEiMpBuK6ENdVt36FFDEUemdwVjBSIGc1SKufnXO5COymsxuNF4NSyi3wN6CaQiqfKKX8
+jXd4iwA/Hc16+vJSyv3Mp798OUjka4GvR/92/SHga0sp8d6xPyQifwH4A6jw/k7g177ls/5t4J+o
+5/iuUspvvbfvrwH/i4j8HuA3f3J322g0Go1Go/HJ0YR2JeWs25QIMVZnbW2nRgTrLH0/wAZcMQzW
+scwTi9E57VQyCa0Ol1Iod/bjKhJjIAaLEdHZbmtwAr01bPue/TiyHQd6Z7FAiRG/LszziePhlsPt
+LcfDgdPxwHyacH1H1/V0fYftHGPfsd9u2G9GdsNYhfvAfrtlv9uy324ZxwGps9ghBEKKdw7iMRDS
+2Q29VrFrBTvdbwlP6d5sdqji+rw/U4p6wZn6C7fkXNvrM2FdVFgvC2Gd8etC9is2J8QKbuwpnaNz
+HcO4Zaxrs9kwDCP90ONcp9V8Y1S9NxqNz0UK8C1vEdlv5d+q2wD8+reIbD1JKd8rIr8G+BeAXyki
+X1BK+fv3Dvnmuv0o8K3v8DnfAvwqtDLeaDQajUaj8Z7QhHblLLTjRWirMVoxDqzFWsvQC65YRtPj
+XY8zRt3ksrZek6O2k5d8bxARSq3MivFYEYqzmOLoBAZn2fRdFcgDg3MYIKdIWFeW6Sy0n3I8HJiO
+B6ZpZkwjUgrOCBZRR/HdjgdXV1zttlxtt1xtd2yGnqHvGYceZy25pJoVnvUe4z2hneNFbKcqtGO8
+V9FOWavXMVSzs7Mje9F9OasPuhgV2ucktJQpMRLmmeV0YjkdSX6h5EhJAVMSzhps5zDG0HcDfb+h
+H7YM44ZhHOnHga7vcV2HbUK70fhcxwP/8zvtFBELfCX61+ifLKX83Xc51x9AhbYDvgr4n+o5XgH+
+sXqO7yulvK0TeillEZHvA/7dT/02Go1Go9FoNN6eJrQrby+0A7YTjNNZahkcYoEeYjdgAWq1GiCF
+AiWScwFzdtXRLOyUInhwItB1mJJxCIO1l4r2bhzfVNEOfmWeJg6HpxwOTzkdj5yOJ+Z5QShYY8iu
+wwqMfcfVdsvDBzfcXF3x4OqKm6sreuewRqvoUFj9SsqRUCvZanimQltzs++q2fdF9qVyHWtFO6jY
+TjFRStG27lwwBowRjSeTmj1eHcbDvLAcjxyfPiGHFWfAmoKz+n3o+55h6On6ka7f3Fs93TDQV6Ft
+ap52o9H4nOUnSyn+Xfb/DGCL/jX6F59xrvv7v4QqtOvrM3/lGef40Wfs/xTIwIc+ieNsXe8XPv7s
+QxqNRqPxvubjH/84H//4s/9/4f27/Qrx4tCEdsUYTTozVrOszxXTmBNlXSkhIkkwCUwUckiUAp3r
+2W53dOPImKMaiZVEzKmaiyWt7MJFlJcUIEYkRxyF3ho2fc/YOTpjMGRyiirGjTB0jt1mxIgw9D0+
+Jvb7Pfvdnt1+z4Obax49uOHhgxuurvZsN+fILgNyzgjX2LGY1C383CLuY6iZ3oFYRXbIb567jrEK
+7VhbyeM5titBzjVPXECEkgoxBshBc7H9SvIraV1Zp9OlVdwZGDpL3xndViHdDwOuG7HdiO0GrOtw
+tT1eLgZwpr5+P/2S2mh8XvFWc7S38uje608849i/9w7ve3jv9avPOMez9n+KvMenazQajUbjfcD3
+fM/38G3f9m3P+zLeM5rQrhh7FtoW42xtTxZiSvgUCCmrCVoSbBIkoxnbTluZixGKhWKEWFLNolYH
+c82c1qisHKHEAEmXvQhtjeDqrKjzdjUH65xhM/Rc73bsthtS1vnvq6trjem6uuZqt2O301ns7XZk
+6HqscyCFlBNCIZVCyUmFdQoXgX0W2SF6fThwie+q89jxroqtojtSkraKa8RWQTAa32VEI79CIPhI
+XD1hnYmLzmaXsFLCiiPTWcu279iMurq+p+8HuqHHuAFxA9gecT3WdRhnL+L6Irhti4FvND5HSZ/C
+sZ9T1ociwksvvfTM42wdSXq/8eEPf/h5X0Kj0Wg0XlC++Zu/ma/7uq975nFf8zVfw6uvvvgPtZvQ
+rpj6C4+xBmsNxmnlNPrAsq5Mi0ci2CS4ZLBY+r6j6zo1JOsddtBtJHGaTrpmdQovORHWRC6ZHHuN
+skoBS74T2p1WoU3J5JQxAr2zbIaeXLZY5zBOP/P6+oab6xuub27YjCNd3+n1OKtma9X0rFQndIq2
+r4dUhfW9rU8en8K9qK46o312FA+RGGp8V0iag12H0AVBi//aCRCyJ66BZZ7x80yYJ/w8E5dZBTYZ
+R2F0lt3Qsd8O7Lajzl/3PV3fg+3JtqeYnmI6cA6xWtHG3MWaNaHdaHze8sa911/wjGPvK7f777tf
+NX9WfNd7Fu/1yiuv8NGPfvS9Ol2j0Wg0Gu8bXn75ZV5++eVnHtf3/Wfhav7/04T2WxBjsM5phXXo
+WUOkFIgxgs/EACGAFUspmontSo+xDtf19ONAL0WNuoyom7Yx1bSsYChsh45N5xitYbBCJwVLwZKR
+6totRUX2brPh4YMbNmGrs8pdTz+MWs3eX3F1dUXXdZrhbUTjdUrWeLCsmdQ5JXLSCK95XVjWhcUv
+l+q2Cu54z2H83B4e39w2HhMl5YvAlqJz3zkmhExG8MvCMk3Mpyqw15m0LCS/0DlD1xlG17EfO/bj
+wNVmZL8dsV13WcV2JHEk40hiwZ6XubSOc942Go3PR/5fYAI2wM97xrH/9L3XP37v9f9x7/XPBf7w
+u5zjSz+lq2s0Go1Go9F4Bk1oV2K662R0rmOz2eBTImbwKeNDJMRAyoHoIyRIKRNSYg2RIQWGHBlK
+xnYWEMZhpOsdm2Fgvx0JVztMKWz7nm3XsRsGtp3DlUxeZ+LcY/se02ur9HYcePTwhiyFkBK25m87
+1zGOo0ZeOYeIUEompXPOdEGKVp1T1Lb1GDzer5zmqa4TqSRSyWTUKT1XUZ6yiuyzuM4pQy4Y9EGE
+FKAIAuSUNSrMqznaPM0s08w8zyTvMbUF3jnHdujYDR27wbHf9Oy2A7tNz2bo1eDMaZU6G533zlIf
+HNSHCBiDMaLXIPp1o9H4/KOUkkTkR4BfAvwiEXnlXZzHf2PdRuCH753jYyLyE8DPBL5eRL717ZzH
+RWRA87objUaj0Wg03jOa0K7cCW3BdR3jZkukEFJmDZFlDaRFDc5W78lezcTWEOhWr0ZoJZMo9JsB
+11mGccB1W8p2Q447cgzYkhnEMJjqOH4W2stC7HuEgrGCdYbtZuCRPKDfjKRSEGOrUZvF1tfGWHLJ
+hHPOdU71LrQqHkLArwvrsrIsM4fTQdfxQJZSZ51VyOacKLkK7ksVO1JywYrFYLAXoQ3UOfWwrkyn
+mek0sS4Ly7KyLgukxGCE3ghj59iOA1ebgesqsLejY7Pp2AyOYrRijTEkETQoDLIAUqv11aTu/LrF
+ezUan9d8Nyq0e+B7ReSXvTVLW0R+A/DV6N9If/QtGdoA3wN8B/AR4NuB3/I2n/O7aBnajUaj0Wg0
+3mOa0K7EqL+/FcB1jo3ZUIywhsi8evp5xduVnAvBe/y0YkLEdh6zdviSiBSyUUM0120ZhpHdfoMp
+GVMSpiR13M5ZV8lsO4srSSvai8Nagd5h6NiOI/1m5IobsghVPgPUFm/NtfaxtopXR/Gz/BTU/n5Z
+VuZ5YjqdeHq45entE54cniJGcEN19XYWcqaUOs8dz2I7aiu7BWsNrgrtkgEp6qy+eubjkdunt6zr
+il+1eu7Q89u+Z3COzdCz3264udqy23SMvWUcLH1vKQIZUWGNZoMntGouwqWyfXGEP7fJNxqNzzU+
+KXOzUsr/WvOtvx74xcD/LiLfCfxfqKP4rwV+fT38dd5eRP/eesyXAP++iHwxmrv9UVR8fzPwS9GI
+sHOL+ueU+Vqj0Wg0Go0Xkya0K/OyABBzJuRIKCpkne3YbfcgFofDZI2wEmNIpZAo+LiS50IokWWd
+mecN67onhB0pB3oLvRV6K9icwK+wevCeEiNp9YRpIUwz9uEDLJnkDMkYotFtAkqh5lXnmnUdiTES
+zq9rRbvUY8iF0/HE7e1Tbp/e6vZ44HC45fZ4wHaWfhwYNgNdjQOztT2bok7ixloM0InBieCA6D1x
+8YR1ZZlmlsMRfziRpiOSC46C6Xo6a9mM2hq+GXvGzYZ+HFR8dx2mM0htF6cauBkR9MfSIej3nPMx
+VrToLVzm3RuNxuccn8oTsn8NDZv+FcDPBv7QW/YXVDR/bSnlHwjeLKUEEfla4E8B/zDwL9Z1//3/
+G/C76xZg+RSur9FoNBqNRuNtaUK7Ms36u1UqmVgyqS5nO3Y7yzBusMVAKtpSjbaQBx9YgyfkwLLO
+WGsZp4EQVlIOZBK7sUMGSzc4JCdknZHThJwm8upJ00o8ToRlVVO0zmI3A8k5knNEcaRSq9hRW8TP
+BmYhhYu4zqWQcyaHfGn9Ph6OPH79Ma+99jqPHz/meDpwPB05no50fc9mt2Gz2zBsRnVQ73q6rsMZ
+wYgKb5W94ARsKSq0T0fmw4HpdGKZZsI0k6YZbIdzHeLUBX2zGdiMA5vNwLAd6MYB1/fY3mE6QZwa
+m5lzldoYBIf6ujsSNS/bqNAWIxg51/ab0G40Pgc5D588+8BSVuBfrmL5G4EvB14CTsBPAN8PfHcp
+ZXqXc/yUiPwstOL99ajgXtHK+B8spfxXIvIv3XvL00/5jhqNRqPRaDTeQhPalXle6m9/WqVOqHO4
+7Ry967CuQ4oQY2L1njUFwjGTwsoSV0qqjtypMAw9ManIxhQkj3QysOlBcoR1huMBnt5SpoXUT4R+
+Q1gDrrPE7Yi93hFLTwKSMYRCjdnS5ZPHR8+avLqfIxgEqkN69GradjwceOONx/z9v/cJXnv1VU7T
+xDSfmKaJYTOwW/YEH9iExDhuyKMKXes0T7y3jk4EWxI2Z2zJLN4TTgfmx28wHQ4sq8evnrSuuHGH
+c07n3MeRzaau7cgwdvSbvraTW4wTxKkzu1idNxdrMLgqsy0ZpyK7CvFL97yUJrMbjReMUsq7OhSW
+Uv65T/O8PwD8wKd1Ufr+Gfiddb0dX1K3Efhbn+7nNBqNRqPRaJxpQruScr5sQ07EHCkIfRkwYjGd
+4DrHuBnYXW0JKZClEIn47ImrvjfnSApCDF5nub3He0PohRCELkXEr5Rlhmmi+ER2geRWkrXEmz3h
+dMIsMyFnQikEUKHtgwptH/BZs69D9pQCRgSDgQLrurLOC+u0crg9cjycOB1PTKeZZVnwiyesQXOv
+e4+vlexse6QHK07vGYNkKGRSjOToiTHgp4k4z2S/QPTYkhgM2M7heocberpNzzAOjEPPMHR0ncN1
+DlN7v4voyiIkEawYirEqto1FsBgcRSwi5k5si6Egum0z2o1G473h19TtXy+l+Od6JY1Go9FoND4v
+aEK74px+K5JficEzrysxJ/oYGGIkpkiKAWst2/2WIgXjBNMJtjP4aSXMK2FasMbSO4e1WtzJOeFj
+YFnBpEBZV4z3dMFTEuRQyCaTxokwTZhpgmnGp0zIhZALsVa0k4+ab10iqURKyVqJL0KmUGJmmWdO
+tyeOt2pQtswLOWZEDM44OtuR+6yVerHqIp4KRgyd7Rj6AScgpbbJx0BeZ9Iykxa9xug9hsLYdQxd
+B+gMuRm32GGDGTd0w8DYd3TOYq0goi7lKWdiFshGbcWNACqipbqqUwW2MbrVSC89hrPQ/pRGPRuN
+xvsREflpwEdLKekd9v8OtKJdgP/us3hpjUaj0Wg0Po9pQrtirQX0N60QAvM0sQbPEINmSqdIAY3d
+2m1xvcU4g+kMtjOs3cRqLUsRpEDnHLbmPKesWdOLzZjgMetK5z3FB4pAkUwmEscRM03INJGnCf//
+sXfvcZZmdX3vP7+1nufZVd3DgDrADI4YFY+ioBFE8YqKiRNRYzTJwRMiAvFgJEaPYrwl4ng0ejRe
+OF6OIygC6vHCQY9RxEskokRg8IZ38S5DCwM4TF/23s/zrPXLH2s9u3bXVFVXdVd3VXeomHjSAAAg
+AElEQVR93/N6Zu+q/dSz1q7pPbu/e631WznTT0EbIw8jeah7XFsiWyKTASe7YRhpTCwWC86dPct9
+f/8uzt5/lsWFBWnMdXuuSNu04NA0kcbKqDXZiQTa2LDRzrCcyONQ1novlvTnz7M8f5b+/FkYB2wc
+iEDbNcQYibHcWrcB7QzrNghtS9tG2iaWAms4GS/r4LNBttWe3NOccLOtbcssREIMhPXAXYN22dVb
++2iLyCV9HvAMM/tR4DXAW4AWeHR97En1vD8AXngE/RMREZEbkIJ2NQVtA9I4slgsmC/mdRutUtW7
+7Uq17I3Njm6jgeCEBmJrzENkYYEmgyenm3U0TUMIAXdnTIlln4hDCdlpKBXHszvJE4mRcT6HC3Py
+/AJpfoHenT5D72WrKx8TPiZySuSQ8eAQysrynB2yMw4ji/mcc+fOcd9993Hh3Jx+2YND27QYECzQ
+xBJimxAJGJad4EZjgTY05JxJY9m6q5/PmZ8/z/zsWRb330cXoKv7Y8+ahm42Y9Z1dF2HNx3etHjs
+sKaG5hgI0WrYhuROcC/bhq2OusrcQhnNjoEQSyG0qShbsFBXok8hW0FbRPblvYCv3OUxB/4I+DR3
+H65dl0RERORGpqBddW35VbRNpImRGAzDScPAwjNDv6TtOtpZR9t1WDA8JZoQOLUxo82wGRpuajfI
+2YkbHXFjRtzsaEIihoyTSA4jRk9gaYHgRsCxnEk50YwDTd/TLJeMFkmhITdtmTKdneCUwmcW8eA0
+MZQq5GmsIXvBYj5nPp+zmM/JOdHNOjY2NsFhGHrGoWcce7KX0XDIRMDHgWE+Z97cz9gvWF64wHJ+
+gX5xgbQo08W9bvnVdi2bbcPGrGXWdcxmHbOug9isDovTlO9SLdzqPthhtR92mSKeiSQCuOEZcigf
+HKSQiW6rtdhmde9sXw/cIiJ7eiFwH/CPgUcBDwVOAe8Efhd4OfAidx+PrIciIiJyw1HQrraCdkMT
+Qwna7oxDT+7L1lntrBT46jZmNG2D47Qx0MQZOTR4u0HeTLg73kZoYrn1AfKAe2Z0SnEzsxK0jTp9
+umzb1YwDbd/TLBblmk1LbkcsNFguo8ABA7OyXDlamV6eMsOyL/ta15A9X8yJ1nDq1CanNk/TtTOG
+fsnQL+mHshZ9qKEbHB8H+vkF5u4sF3Pm588xP3+OYbnAciLksYyIx6asv97c4NRU8Kxr2Zh1WKgj
+0aviZVZnhk+Budyur8XOFoGAT0E7QwoQ3IleqotbXco9he1Q/xER2Yu73wM8vx4iIiIi14SCdnXx
+iHYgWsCAcRjo+57l0NPNOsZhk5QGZhuzUkm7KxW7Q2fYJoQMnmEMMFo9xgVpKNO6kxtDHdEubTi4
+4w5jHmnHkbGOaFvTQtth40iIhrnVEfBAqGE1xEAeE0xBez5fOxZsbp5i1nU85MEP5vTpm+iXi3L0
+CxbzC8zn51nMM+MwlBHthcPQM79wnnNnz3L+3FnGfknXRLoY6ZpAjA1dN2Pj1GlOndpgY9axOStB
+OwSjFBYvHwaUMfNym4Dk5X62ACHiFnAC7qW4Wa5r3KeQndwxnAhEs/K862i2graIiIiIiBxHCtrV
+VCG8aSJdW0LjouvIKTEAnkbGwejrlHLPiTzrcM8YmdYamtCUtc8hkkIgRyPFwDg0jEPD0Jc127Ns
+dDnQEIgpE5JjybHTp2BjBl0LMUIdGS6HYblW2fa6JnvMJId+sWB+Yc75c+c4d/Yci/mCcRxxd5oY
+mW1scPqm0zz4QTezXLQs28hyEbA8Mi7nLFIi9Ut8HMgWGENguZgzLhfkcYCcMSIxBpqmJTYNoWmw
+aQ11XU8dY6CJRhugiWXkOVO28MoYo28d2QLZQgnaFiFM90MZsSfUgme1UFothBZCYP0fERERERGR
+40ZBuwp1HXATI13XsrmxwdD35JwYxp7lMkDOpGFg6U5KZZutNA6ksSW3M6yZEVsjxIbYtVg90tgx
+DjPGcQObzWibrhxtR5syXXK6lGlPnyLefBPx9Cni5gybddisxdqm7C2dHFKZxp5SLkXRSJw/f4Fz
+Z89x9v6zpcr4fEEaEzFG2rZjYzbj1KlTnL7pFE2AaE4g0y8i5k4eevr5hTpaXEaNx3HA00hjBm1L
+V/fa7rqW2LRYCGWU2kt/smfcE4FAEwIbEULYCtSZwEBg8EAkMBJIBBJWR7fLhwmY1eriddzaysh/
+XBVEK8XRgtZoi4iIiIjIMaWgXU0VsZsmMus6Njc3GMeBYRzolwtisFKJe8hlH+shkFMJ2WlssQ2n
+2TC6piHEQNu1tJubtKc2GceBlHrSOMBsg9h05WhntCnR5kyTMs3mJvFBD6I5vUnY2CDMZljXEtqm
+7hudy77ZOTOmkSENjKnn/PnznDs3Be376ZcjOeWyn3fXsrGxwalTm9x0+vQqZJsn5rEheCYNPcNi
+Dl52pra6LhogBiPGMsrftS1t1xGbqT+shexyBDPa4MyiEaORQyDXddiRSPBIIGIeMKwUQCOUAuJ1
+IbYFq2ux2Rq7tnBR9fHpeyIiIiIiIseNgnZl4YEj2mkcWS6XLNqOtmkYhoGUy3ZfGGX6eBpJaSBi
+tCEyNh05J8zKdPS2bWliIOdI9g6ahhAaYmwJ7ayEbM+0ORNmM8KDbyKcPoVtdFjXQIx4MLwG2pwT
+KY30Q08/LFkOyzKife48586e4/zZ86WomFMDcqkIvrGxwcbGBp4GfBjIw7JM78bxYWBcLjG2gnaI
+kdA0xNjQtC1d3b6rbWdl27IY6xpsp8TycgQyjRldhKahTBsPgRQieIMTcW/quuxpTXYZyaYWTTMz
+gkEEGjOixTqqHVeB2+rou4iIiIiIyHGjoL1St5AKkaZpS7GvjcSpzZ5hGBjHkcWiVOvuvSflsp/1
+OIB7ZmGRQMAzpJzpU2I2jszGkRBKhXCz8gtv2o7upkC3sUmD0+I0gLUNvjkjb26Qm4YRL6Pmi0xK
+ThpGUj+ShpFh6BnGUjX8/LSd12JB3/erIN80DU3bEGMs08KNWngt4TmVtdc5YziNQYxNWWcdAqFp
+yhT42BCbpnxg0La0TUvTNjRNuW4TIk0oVdobKwXLYg3JAUq1caZQXKuO16JnXjY2A2xVTdyCEc1o
+gM6MFqMl0hLLmvYpXju4+Q7/HUVERERERI6Wgnblq6AdiE1LNytrj/thYBhH0pjqVGUnjWVUO6eM
+u1MGuA0ypDEx1IA9G0e6YaBtG9quKVXKY0PsOmYbG2xaoA1GE0oBMY+BIQaGJpYiatnpx4E+9wxD
+IvUDYz8yDkM5xnJcuHCB+WJRPghY9nRdoG2MtinBOMZYp8bXkeec8VTCtnkuFb4DpbJ4V0avYyxB
+eytwx/K9JpYAP+03Hst07iZYCdsBYt2KK9YB52ysRp/dS9Aulchr2F5fl21GY0ZrRmswM6O1SEug
+mQJ7DdmunC0iIiIiIseQgvZKCYIhRJq2xaxUvB6HErJzynh20jjSL5aYg6dcvm+OZyePmWEY6YeR
+ZQ3Zs75nY3ODzc0NjA28Bu1utsHpjQ2aGGibciQcPJFyCcHjcmAxDsz7nn45MPTlGPuhFmIrx4X5
+gvl8wbIG7SZ2mBlt29LWkWeb1jO713XeCXICzwQy0YyuiWzOOjY2N8vU9hqyLTQXVT/fCtllK7Sy
+Hdo0or01qh0oITvUUG1ro9m5jmb7VFGcWlXcyoh2a0YHdFgN2eWgbhXmGtEWEREREZFjSkG7SvU2
+Y6tRVywQQkPTdHTdBl030LY9bdszjomUR5KDp0S2zGgJGLb2jc5ezhsGxr5nWC7Jmz30A35qxHMq
++3Y3gbaNJJxFGlfHfDmw6EvQHoaRcUikYWQcR/JYpq7ncVxNbR/HUgTNYFUIre3aGorL1HH3XKe8
+D3hKRIO2adjoWmazjtlsxmzWEWILIZaQXYN6qGG7bSNdG5l1kY02MGuNWRuYNYE2GjGW0IwFqIE6
+Y6RShq1OG69F0Oo5tlbsLNa9uGOogX215VcZzZ7mjmtEW0REREREjiMF7Srnclu2qypfu5ewHWOz
+tR1XO6NtS/AlGT5Sqm5nSCkDCWcguzOmXEJw39MvF/TzjmExZ1ycYlgu6JeLst65jTRtJOMsx5E+
+lRHx5TCyHAaWw8g4ltHzlMttHlMJ+CmVML8adc+YGU1smLUdXdvSNLGE5FXQHhmHHs+JYMasbbDZ
+jNlU8KzrsNDU/a3LHtcxlGuEKWh3DRs1aG+0xkZrzFqjbco08hCsViYvW3ulWvgsew3bW6XX6vrs
+GrKtjI6HOv082Nb67lINvc5HVx00ERERERE5phS0q1RHR3Mu97OX6ckWIiG2tG1ejWaXY8QdsmXw
+kZwcyLiXkeyQEmEYCXFJ3za084ZF27CczxhOLVn2Cxb9kqaLJWx3DRkvU8/HcgzjSD8mhjGRc63t
+7eDueMr4mPGUGesa8jLKXUqPlerpHV3blSrhdY2252l7sr6OaBuztiV6ppvN6GYlbLtFcg3K1JAd
+63Txro3M2oaNNrLR1aDdGBsttKFs62XB8LWgneue2cnLkTHcDNamjD9gRLtOO1/VF59Gs73cVdgW
+EREREZHjSEG7Srkk7Zy9VPhOmTF5GdnGIIQSukMkhLJFV7ARCGUvaPeyxjonzDKWDLMRMyMNpbjZ
+ECOp70l1D+x+HGi6Whm8K/8pyhTwxJhGxpRLP3Kmdq9MuXbIKeFjGdnup6nldTo57pgZsa6pngJ2
+TolUC6iNfU/OZep41zQ05jRdS9OUtdlep3TnOr07xIZY12Y3TaRrG2ZdU6aON9A10EWIoe5JboYT
+t0K2B7KHWmk8lOvXUuy2VnU81MOAus12ed4Oud7x6UEREREREZFjSEG7GmuSTSnXddWJsRY2G1bH
+FL4dn6aWu4EHnIwnXxXoCqGGxmBkd1IOWM4M5lgoU7jHPBLbpgTctsHM8Dpv3ach9eyEMoxNXk1r
+z6VIWz8yDiPL83P6+YKh7+todS2UlspzGPqexXKB58SyTlkf6oi21TXaHqwWPytVwKfRaKcWQYuB
+EGPZ6quJtE1TqpQ3gTY4jU2bdZVR6rIuexrJLke2WNdll82/zLb2z542/nLy2pruMqLtQJzWZNtW
+wNY+2iIiIiIichwpaFdjmoK2M4yJcVwP2KkcY2JMTsqUgl5ew3YtoJY9kz0DjtdFxpaNHDKWjZQD
+eNkSbMwjy9QT27aOaLdl2rQbpQZ3YZQ9qclOrgE8pcSw7BkWpRr5cj5nuShB28e8CtkpJcZxoO97
+losFnsZy3nJZRrRTIgCxjZgHvG3wGGvQrgXLzGq18UiIkSaWrb3aJjJrIl20ErRDJloZSWc1XTzW
+I5Rp6B7Kuu/pGVpde82Un8t/gzIJvxSos1q93LES+53VVmDroVtEREREROS4UNCupqnjQ8oMY6Yf
+Uq30vRa4x0RKeduIdigj2rl8P3nGPdOEsk7ZgmEZcjBI4Ckw5hHGAH0kti2xa2naliY2dBZoQ6Sz
+WKZTB9sa6Z6Knw2JYb5kuViymC/o50v6xZJh2UP2VQXyErRH+r5nsViQxshysaDvlwz9EksjrVHW
+cBukGBlDIJuRV2G7rNG2uo/2NKLdrQ5oLdOYE9naI/vitdmRxNqab+IqJJeQbXXcGsAvqlIOdTQb
+iA5uRqwj54rZItcvM6slKPk6d//6y7zGk4BX1S8/wd1ffSiduwJnzpzh9ttvP+pucOutt/KGN7zh
+qLshIiJyYilor0yLoL0UG/NcpminzDhV9q6jxDmXMI2vNqkiuJOy4znhnkm5zCrPBtlsVeDLc6Bs
+IhYxcwgBUoAYMcu12JfVUd1a9cudIWWW41D26B4Glsue5aKE7WFZ1lyncQSnTBW/cIHzZ88SzCEN
+kHq6tqFfXMDTQBNL8bHGnIayXNprVfFci585ESwSYiDErX20CYYHW418l+foZAMPEbeITz9PIBBW
+IXyaJx63/e7LmWXEmlyun6f7tShaMiN6JnkguBM0oi1yvTusTfqOzWZ/OWfuueeeo+6GiIiIHDEF
+7Wo9sjllHXbKZTutlKbiZCM5l/2v8QSeMc8Ed8wdyxlSxnMiT6uNa9AuA99GaMJUDqwG7TJabDmz
+GuCxulo51a28UqIfRhbDwHLoWfQD/bJn2Zep4+MwkOsWX8GMfrlgceE859oGfMTHHh+XbMxavC9T
+yNtopZK4eRkhNiOHUIu9lSnebnFVAK6s0y7TyAmlkFkOgRwgWyAbJKtbeq1+vhRUCxixFlWrUbt8
+fsDWv2xap12/djdynQkQoBaXM1KoIdu9fIggIifd6n8nx8d7HmHbZ6ilI0VEROQIKWg/QBnRzl7D
+9hSyx5GUxjqiXUatzXMd0a5B29eCdh3xds943BoFjh4Bh1oUjZggZyzX7+GUvbiNXIudjcPIsh9Y
+LJcs+p5539P3A/0w0PdDqUBeC6e5GcNyyfzCBWIwyCOMS3xckDY6Gs805BK0KUE71OnaMURyCNi0
+f7aVgB1C3HVEu0wzz6QQiMbW3tsWVkHb6oi21ZHpYKuB+tLvVeL21V+X3a38VTEbyepKbjOCl721
+g9cPKkTkxHL3X2X7BJkjF4A3H2H7twMaURcRETlqCtrVFNrsosERXzsyZmUUNYayxdRUjyvVqdBp
++vm8NfU857SaCu3B8Jwpow1lJXI2I4caWGMmrRVXG5YDQz8w9mMZve771e1Q11+ncQr90/Owulf2
+UIqeNYGxMVIbSNFpYqCJxizGMlLMFO4NQsStqVO/Yw3Noe6hHQjRyocDdUp8MmM0K8XJ6jVsOsFC
+3Se71iGvI9bBnGilgrhPe2JPM+TrWHe26Tc+7R1eQrd7LY5m9YMNBW0RERERETmGFLSrqRhXCXL1
+CEaMZX11Ew2PAZqApUBIRnYjuRGz4RlygnFal53L3tWpbMRdiqcZeK51xHPCckNysOxYyniI5Xpl
+cJuxH+v667LNWD8OjONITiOec6lIHg33UIqDOaVyeSyjy2Htg4E2QBcDsyYwayMbbailxkrSdayM
+YFvEaOr071CnjwdiCGV/7FBDNjC4Y3V/62RGW0uUhdqX8gstTyaQpz28yhZoVvYfn4qirfYHp4Tq
+bGVNdqk+blsj3F4+2FAlNBE5ZupmEfoAUOR6cebMGe666y6e/exnc9tttx11d0Rkn1JK092w13lH
+7Vh37lq6qF62lWXIIWyF7CYG2mi00eiaEli7xsr2VhGaYOUwq1PJKftn1325y3ZhZYR6WA6My560
+WDLOF4zzOcOFC/QXLtCfP8/i/HkW584zP3dudX954QL9YlG25RrGMt0crx8GlHDdNJGmibXwWqnO
+Hc1pDJoAXTRmbWTWNWzMOmYbHbNZSzdraWctXdfStS1d19A10xFpm0ATS9C2unV4Agagd1hiLDEW
+BHoCPcZQ98HO01i1ZYJloiVaMp0lupCZRWcWjS4YXQx0sVRdb0IgWinYNtUkz26MDkOGPkGfnD7p
+L7UiR83MbjOzbzaz3zSz+8ysN7O/M7M3mtmPmtnTzeymS1zjCWb2/5rZ35rZwszebGYvMbMP3ONn
+nmRmuR4fv8PjL6qP/UX9+hFm9u1m9idmdt7M3mZmP2tmn3Llv4XjNoVdRC7lzJkz3HnnnZw5c+ao
+uyIiB7AWtI/1e69GtKs6XlqmJa9GtCEGI0fDo2GNEVIg1T2xUw6M0QjJSKHs2DWNhoNDzqsK5V7X
+cmNgORNzIKeE1WJrnkaCBchgGTw7Yz9uHTkzupOmqdWxrJcOMWAW6kpoI4YSimMwQqCGbKMN5UOB
+rpmCdltGl2uV9eTUkmWRULflKlXFy1TwstVYnZpep8sPPm3FVUJwskAEGowIq8Onyuzk0kfz1V7Y
+VjfRzl6qjGeM4LZVMM3r4LWzmkGwNfleRI6amX0c8F+Bm7n4ZfnQejwGeCpwL/CKXa7xhcB3cvEb
+5m3A04DPMrM73P3X9+jGJf93YGaPr+3fsvbtDeBTgU81s29z9y+/1HVERERE9kNBe2VrjXY0pwng
+0QiNEXMkeSTRkHwkERk9MJSS2/hIDaGsgqOFQIiR6F43uCqVshujjJBbKKPfboTsxCFhVkbCQ10W
+XiprU0arA4zAaM5Yg7bX6exmgWixbNcVIhuzjs1ZW0atNzeYzTrariO2LaFpCDHW7cTA3KcV2rVo
+WSROddGnfbRXJcFLMI41yFsoP1X2vN5aW15XtJfwbJQ12QYxTBXHbeuSrC5dgreXkzJWt02rs88p
+H0DY1vzza/znQ0S2M7MO+DHgQcD9wPcC/x14G9AB7wN8NPDP9rjMHcBHAL8LPB/4fWCz/swX1/sv
+NbP3d/fxMrt6CvjJ2s9vAn4eWAIfCXwV8AjgS83sb9z9uy6zDREREZEVBe2JT9uh1HAbDKIRm0D2
+QLaGxFgDd2TIEUuGj0aKRrAy6julxhCsBG1KYI7rQfuiaeZgyQmeViO+RqmuHdkaUR+Jq2ngY4Ac
+Qz2MECIxRBpraGLD5qxjc6NjYzZjY2ODbmNWgnbTEpsGiw0WYvlwYO1XECjrsnMdfy5Bu3x64NOe
+1VYCtoUyIj0VMcs1ZMe1ad7ZKKPU07psynT8UIer19fD5/q8rbYZ10bKQ67F6gyYzlutqheRI/Qx
+lJFnBz7H3X9+2+OvB37czP4PStjdyROBnwU+a1uQfo2ZvRP4BuCRwFOA//8y+/kwoAee7O6vWfv+
+G8zs5cDrKOW6v9HMftTd33GZ7YiIiIgACtprpmJopYBYU9doexOAiIdMIq6CdpMDPgZStDJSWw9s
+mmZdio1N06ensN0AjRlNndZt0x7cKROsBMzpNlPOyTGsAva4Np09xUAKRowlYDehpW3KSPbGxozN
+2YyNzRndbEbbtTRtS2gioZlGtG0VdjHKXtd1BNtr5XCwGrgp23lh60PQZdp3Xd1uNWwHL6PTJWSX
+qelet+iK1MHotdAcanVytylAhzqiXa/FdJQR/9UFlLRFjtqta/d/bbeT3D0D53Z4yIA58MxdRqv/
+b+BrgRb4OC4/aDvwfdtC9tS3M2b2ZcCPA6eBpwPffpntiIiIiAAK2ivuNWjX6dqEskbbnLpqsIxc
+p8ZIybBoDNFYhvXMOY32ltrbJWhPo9Nebq2um7Yyqh2m/bitrms2Wx3U9dBg5ABjMMZA6Ucwxnob
+YkMMDU1saWJbCpx1LW3XEJuGEMuTmfa9Tm6M7qUy+TRpvAbhUJ8Hq0Bdfz9ToDZbjXTn1Uh3mMrI
+raaNmxtet+cqv9/y3JNDnLb08vXt03xtOrjXf9vaz9ctwVYbcF+tPwkicgDrFYSeARx02rUDv+Tu
+b9/xQfdzZvYm4IOA9728Lq780B6P/RRwH/Bg4JNR0BYREZErpKA9qVPHp7gYzPGw2p0KcycFVkXP
+coAYfKvwWQCCYTFgORIs13CaV2uNy5rrWhHcjDZMZccywfMqhMd6joVQpqSHstd2CdglaI+hhu1g
+ECIhRII1xBjp2kjTlP2vLZQwnIDRS4E2spETRN/qS0MghrKTdSTXafBT4La61VfdC5xQ9v22ULfj
+Kmum10e2Vxum+VS2zBndiFPRs/p7zT6tE1//p045p+wrnmoFd18F9K3gLSJH6teBv6CE4Oeb2dMo
+ofXVwN3uPuzjGn98icffSfnI70FX0M+esgZ8R+4+mtlvA58IPPYK2hEREREBFLS35JravARts3qE
+rQJlKThjcEKEHHy1VzXTiPa0djl6Gd2ua65XIdunEF0qgzchEMlbh21VCW8MQt22K8QAsUxTz9No
+9jTCbeAWt/bADpGmbWhjJMayB7bXadxjdjw7njKJWpTNAx4oe13XddNxej5Ma6PLOVPQXoVsCyRC
+2ds6l+Jn6xO9p32va3Qm1tCccpmubl6qq6/+McfJZX03YRXIs3vZp7zez9O+5BrWFjlSNaB+GvAy
+4NHAhwNPqA/PzezVwEuAH6/Tx3dy4RLNTD93JVt4vNP9kh/NvbXevvsVtEP5YPFh+zhvWlh02LRN
+kYiIXJ/OnDmzr+32hmE/n+MfPQXtla2q42UidN1L2+uWX8FX67BttYZ7GvSdKnuthW0y5mG1p/YU
+1s2MEMLWNlwEmhq0p/2u21DCdmwCTYzEJkI0cmOrAmiDwViPbIFabg2sWe2rHWIt0EbZz3r0GrQt
+1wJlgbLHNYTsq5nbkbVl2NPvpo7wE8o2XslKtXMnkHM9GXCfppHbqlr4VEM8OVvbc02j0kzj3b71
+tYN7LkXoarDO69PNV9POReSoufsfm9ljgU+vx8cDj6JsnfUp9fhSM/snu00RvxbdvLbN3XttmxMR
+EbkB3HXXXdx5551H3Y1Do6C9H9fNTlJH3dGjbl9EjkIdLf6ZemBmD6ds2/Uc4PHA44C7gM8+oi6+
+h5nZJUa1H15v33mZbaxGwqcPOC9lv+ddjre97W3cfvvtV+36IjeCvu8BuOOOO+i67oh7IyIpJR76
+0Ide8rx77119oH2Fs9CuLgXt6gu+8EuUEkVEDoG7vxV4sZn9CPBaStD+NDObufvyCLrUAR8K/M5O
+D5pZBP4hZeT79y+zjdV7yH5n3FzNmTk5Z+65556rdn2RG8naX9pF5PpyrPObgraIiFwVdQ33r1KC
+dgM8hK210Nfa09klaAOfBbwbJWj/8mVefwnMKGvK33aZ1xAREZFLexilFPVRfHi/bwraIiJyWczs
+Y4Ez7v7nuzzeAk+qX57j6BYvG/Bvzewn3f1/XPSA2a3At9YvLwAvvpwG3P30lXVRREREbiQK2iIi
+crmeDPwnM/s14OeAN1LC9CbwvwBfQBnNduCFe1Qev5QrnWP9NkqI/mUz+w7gFZRPwT8S+CrgEbWN
+/3iEBdtERETkBqKgLSIiV8IolcaftMNj08YCPw189RW2cSUuAP8c+HlKsP6qtcemPj7f3Z9/he2I
+iIiIAAraIiJy+b4V+F3gk4EPo4wMT5tI/x3weuDF7v7KK2xnCsMHfWzrJPffMsqxLmEAACAASURB
+VLPHAc8FngK8J3AeuJsSsn/xCvsoIiIismLaj1hERG5EZvYiShG0v3L39z3q/oiIiMjJEY66AyIi
+IiIiIiI3EgVtERERERERkUOkoC0iIiIiIiJyiBS0RURERERERA6RgraIiNzI9lWVXEREROQwqeq4
+iIiIiIiIyCHSiLaIiJx4ZvZIM/s2M/sjMztnZu8ws9eb2XPNbPMQ2/kcM/sFMztjZnMz+ysze6mZ
+PfGw2hA5Sa7ma9fMnmdmeZ/Hxx/WcxK5UZnZQ83sKWZ2p5m9wszuXXsN/eBVavPI3nc1oi0iIiea
+mX068FLgZh44zdyAPwWe4u5/fgVtbAD/H/BPdmkjA1/v7l9/uW2InDRX+7VrZs8DnrfDtbdz4JPc
+/dWX047ISWFmedu31l9bL3b3Zx5iW0f+vqsRbRERObHM7MOAHwMeBJwFvhr4aODJwAsob87vD/ys
+mZ2+gqZexNab/a8Anwl8BPAs4M8o78fPM7N/cwVtiJwY1/C1O3kM8Nhdjg8B7j6ENkROgql2yl8D
+v0gJvVfDkb/vakRbREROLDN7NfCxwAB8nLu/ftvjXwZ8K+WN+s7L+eTbzD4J+OV6jZ8BPsvX3nzN
+7D2A3wQeCfw98L7u/q7Le0YiJ8M1eu2uRrTdPV55r0VOtvqauhu4293vNbP3Bv6S8jo9tBHt4/K+
+qxFtERE5kczsCZS/qDvwwu1/Ua++HfgjyifuX2xml/OX7S+rtyPwHN/2Cbe7vwP4ivrlQwCNaovs
+4Rq+dkXkELn7ne7+Cne/9yo3dSzedxW0RUTkpPrMtfs/tNMJ9c35JfXLhwCfeJAGzOwmylRWB37Z
+3d+yy6kvB+6v9//ZQdoQOYGu+mtXRK5Px+l9V0FbREROqo+tt+cpU8h286tr9z/mgG08Aeh2uM5F
+3H0AXksZfXuCRt9E9nQtXrsicn06Nu+7CtoiInJSPZryifefufv2Sqjr/njbzxzEB+1ynb3aaShF
+nERkZ9fitXuRuj3QW81sWW9fZWZfYWYPuZLrisihOzbvuwraIiJy4pjZDLilfvnmvc519/soI2cA
+73XApm5fu79nO8Dfrt0/aDsiJ8I1fO1u98m13abefjzwTcBfmNlnXOG1ReTwHJv33eawLygiInId
+eNDa/XP7OP88cAq46Sq2c37t/kHbETkprtVrd/JG4KeB1wNvAVrgA4B/Bfxjyvrvl5nZp7v7L1xm
+GyJyeI7N+66CtoiInEQba/f7fZy/pKzj2ryK7SzX7h+0HZGT4lq9dgG+w93v3OH7dwM/bGb/O/B9
+QAReaGbv5+776ZOIXD3H5n1XU8dFROQkWqzd73Y9a8uMsiZ0fhXbma3dP2g7IifFtXrt4u73X+Lx
+7wd+gBLkHwF89kHbEJFDd2zedxW0RUTkJDq7dn8/08VO19v9TFW93HZOr90/aDsiJ8W1eu3u111r
+9590ldoQkf07Nu+7CtoiInLiuPsSeEf98va9zq1Vhac347/d69wdrBdi2bMdLi7EctB2RE6Ea/ja
+3a8/XLv/nlepDRHZv2PzvqugLSIiJ9UfUqZ8PsrM9no//MC1+390GW3sdJ292hmBNx2wHZGT5Fq8
+dvfLr9J1ReTyHJv3XQVtERE5qX693p4GHr/HeevTQV9zwDbuZqsYy67TSs2sBZ5I+Uv73e6eDtiO
+yElyLV67+7W+Z+9brlIbIrJ/x+Z9V0FbREROqp9eu/+MnU4wMwM+t355H/CqgzTg7ueA/0YZfftk
+M3vELqd+NnBzvf/yg7QhcgJd9dfuAXzB2v1fvUptiMg+Haf3XQVtERE5kdz9buDXKG/GzzKzj9zh
+tOcCj6Z84v2d2z/xNrOnm1mux9fu0tR/qbcN8D3bp7qa2S3AN9cv76NUMRaRXVyL166ZPcbM3m+v
+ftTtvZ5Vv/w74KcO/mxE5CCup/dd7aMtIiIn2RdTppRuAr9kZv+ZMvK1CXwO8Pn1vD8Bvn2P6+y6
+TtPdX2VmPwY8FfintZ3vpEwz/RDgq4FH1mv8B3d/1xU9I5GT4Wq/dh9P2Rv7VcDPA79HKcLWUNZ1
+Pg34R/XcEfh8d9e2fCJ7MLOPAR619q1b1u4/ysyevn6+u794j8sd+/ddBW0RETmx3P13zOxfAj9M
+mUL2n7efQvmL+lPc/fwVNPVM4EHApwKfAHzitjYS8PXurtFskX24Rq/dADwZ+OTdukEJ389091dc
+ZhsiJ8m/AZ6+w/cN+Nh6TBzYK2hfypG/7ypoi4jIiebuP2dmH0IZIXsKZTuQHvgz4CeA73H3xV6X
+2EcbC+DTzeypwOcBHwo8BHgr8Oraxuuu5HmInDRX+bX7c5Rp4R8FfBjwcOA9KIHgncDvAq8Efqiu
+CRWR/dlvpf69zrsu3nfNXbsSiIiIiIiIiBwWFUMTEREREREROUQK2iIiIiIiIiKHSEFbRERERERE
+5BApaIuIiIiIiIgcIgVtERERERERkUOkoC0iIiIiIiJyiBS0RURERERERA6RgraIiIiIiIjIIVLQ
+vobMLNcjHeI1X7R23c89rOteZl+et9aXrz3KvoiIiIiIiBwVBe1rz6+z616O49QXERERERGRa0pB
++9qzo+6AiIiIiIiIXD0K2jcGR6PIIiIiIiIix0Jz1B2QK+PuzwCecdT9EBERERERkUIj2iIiIiIi
+IiKHSEFbRERERERE5BApaB8xM/twM3uBmf2JmZ0zs3eY2evM7CvN7EH7+PlLbu+107ZbZrZhZs8y
+s18ws782s2V9/EN2ucYnmtmPmNlfmdnczN5iZq82s39rZptX9lsQERERERG5cWiN9hEys68D/iPl
+A4+pmNkm8IR6PMfM/oW7v3Yfl9tPMTSv7X4g8DLgg7b97AOuYWYR+H4uXgfuwMOBW4GPrf38rH20
+LyIiIiIicsNT0L72prD7RcDX1q/fBLwO6IHHAh9ez31P4OfN7Enu/sZDav8W4JXAewFz4NeBvwZu
+Ap64w/kvBZ7KVgi/D3gV8A7gkcAnAI8GXgH8zCH1UURERERE5LqloH10vpUSdJ/l7j+2/oCZfRTw
+48DtwM3AS8zs8e6eDqHdLwAi8JPAc9z9Hdvajmv3/zUXh+zvAr7C3Zdr5zwc+GHgycAXHkL/RERE
+RERErmtao300DGiBp28P2QDu/hvAHcCynvtY4F8fUtsR+AV3f+r2kF3bTgBmZsA3sBWyX+TuX7Ie
+suv5bwU+HXhjfU4iIiIiIiInmoL20XDg19z9Zbue4P6HwPesfevzD6Fdq7dfso9zP4UyvdwoI+9f
+vtuJ7r4AnlvP3c9acRERERERkRuWgvbReck+znlxvTXgCYdQ3duBN7r7n+7j3E9c+5lXuPvf73lh
+918G7mErzIuIiIiIiJxICtrX3hREf+NSJ7r77wHn6pcR2HHrrQP6zX2e92Fr9y/Z1+p1B+yLiIiI
+iIjIDUdB++j8zT7Pe/Pa/YceQrv37vO89bb229f9niciIiIiInLDUtA+Ohf2ed75tfsPOoR25/s8
+76a1+5fTVxERERERkRNJQfvonNrneafX7p+9Gh3Zxbm1+5fTVxERERERkRNJQfvoPHKf573n2v23
+X42O7GJ9ivl++/peV6MjIiIiIiIi1xMF7Wtv2v7qiZc60cwew9Z08QT87tXq1A5+e+3+JftafeTV
+6IiIiIiIiMj1REH76DxtH+c8vd46cLe773d99WF4Vb014FPN7CF7nWxmTwZuR/toi4iIiIjICaeg
+fTQM+AQz+6xdTzB7NPActoLrC65Fx9b8IvC39f4p4Ft2O9HMZsC3TV9e5X6JiIiIiIgcawraR8OB
+HnipmT11+4Nm9lHAK4EZJbj+PvDD17SD7hn4T1OXgGeZ2XfUUL3e11uBn6Xs8b28ln0UERERERE5
+jhS0j85/ADaBHzWzPzGzl5jZD5jZ64DXUAqLGaXS+NPdfbzWHXT3lwA/QflgwIAvBt5iZi8zs7vM
+7BXAXwJPBv4C+N5r3UcREREREZHjpjnqDpxABri7f5eZ3QJ8DfAo4P3Xzpmmi98D/Et3/51r3Md1
+/4qyj/a0XvzdgPUp7w78Yf3e51zbromIiIiIiBw/GtG+tnztwN2fB3w08CLgTcB54D7gN4GvBj7Y
+3V97gOte6pyDd9g9ufszKaPWP05Zt70E/g74deDfAx/h7n96Je2IiIiIiIjcKMxduUhERERERETk
+sGhEW0REREREROQQKWiLiIiIiIiIHCIFbREREREREZFDpKAtIiIiIiIicogUtEVEREREREQOkYK2
+iIiIiIiIyCFS0BYRERERERE5RAraIiIiIiIiIodIQVtERERERETkECloi4iIiIiIiBwiBW0RERER
+ERGRQ6SgLSIiIiIiInKImqPugIiIyPXOzM4DMyADbzvi7oiIiNzIHkYZMF66++mj7sxuzN2Pug/H
+wjO+5bUOUH4fO/9O7BDaecCV137/e/2XWG97z/P2eHB7/y86ddufg/3/qfCLbn2/P7nH817/M7m6
+v7qtz8PB6jN66dd90mH8pxERuWxmNgLxqPshIiJygiR3P7YDx8e2Y9feDgHxssPnHnZNzH6JBL32
+g3t8ODI9snX21r3D+1Dl6n44Y1z8PB4QxJmelT4kEpFjwwHMjEc84hFH3RcR2Ye+77n33nt56EMf
+Std1R90dEdmnt7zlLVOuOdZhQEF7m1Ww00j/ARz+72qngC0icoy9E3jYLbfcwpvf/Oaj7ouI7MNv
+/dZv8fjHP55XvvKVPO5xjzvq7ojIPj3sYQ/j3nvvhfLee2ypGNoDHEW8U6TciW27FRERERERuR5o
+RLuawtw0LflqRd/9rrXe8xpm+54Gfrnt7Tfcbl+Tvf+f2/1nLtVPBW8RERERETnONKJ9LJToaHbx
+UuzyPcO2f7N+f++rXXzv6tnexuG2OT3PrVsuuhURERERETluNKL9ANN49rVeJbwWj/cZIncN29d8
+Jvr239Xh/u7W/4tc9F2FbTkhzOzpwIsoL4X3cfe/OeIuYWbPA54HuLur2nb19re/ndtvv/2ouyEi
++9D3PQB33HGHiqGJXCO33norb3jDG466G9eEgvYuzA6/HtrFudhqG1chFe+Sc8uUc3Z+8DIb2One
+np3Yz9W3T403w9xxs7J92fo8fxGRY8Tdueeee466GyJyALWokojIoVLQXnlgets+YrzvddEHmNd8
+kHP35RJ9LM0drM2dn/fW78t2+vYlunKpdebT72XPczSkLSeLo4+XrhPvedQdEJF96YF7gYcCGtEW
+ubrOAPmoO3FNKWjLIfEDT+XeGmGvP7+/Vkozihtygrj7i4EXH3U/ZD8M0PZeIteH3wIeD7wS0PZe
+IlfX7cDJmvGlYmhyeC4j/G4N6Bt7JXXlahERERERuV5oRPsADn2a97Hiu3+1ttzat08VX509fX9r
+xTZra9B3Whs+fct2Wc+9vQCawraIHGPpqDsgIgd1G6Wm421H3REROYAYVzVYj/V7r0a0t7uRs/QV
+cHPc1qeHT+E6E8hEMpFEQ6Il0ZFoGWl9IOYlMc8JaYGlBTbOYZxjwwLGBYzzervE0hJLPZYHgo8E
+TwTPBJwABDOMnbc8EznOzOyDzexrzOyVZva3ZrYws7Nm9qdm9kNm9pF7/OzTzSybWTKzR+7w+H+v
+j/9K/fr9zey767XP18ceWR97Uv06m9nHW/H5ZvYaM3uHmZ0zs98xs680s9kVPN/WzD7NzL7LzF5v
+Zu80s97M3m5mrzWz55nZe1ziGn9V+/mD9esPMLMXmNlf1t/f35nZy/f63W273vuZ2XeY2RvN7D4z
+u2Bmf25mLzKzx1/uc63qwjP9v0nk+nEb8HUoaItcX9aC9rFe9K0R7RvVFe2w5atr7HwJB/c62XsK
+wdNtCcRmDp5JJHJOpJxwd9wh1591t61bYzWP3CxACJhFCAEIOBG30iGfKsLr77NynTCzJwGvql+u
+v6xa4P2ARwGfa2bf5O5fcxlNrAqlmdlnAD8KbG57fKefmQGvAD5l2zmPBT4EeJqZfZK7v+0y+vQC
+4HN3aPvdgCcAHwH8OzP7p+7+P3a5xvrz+kzgR4CNtccfCnwm8Olm9r+5+0/u1hkzey7wjZTf+Xqf
+/gHwPpTf/ze4+/P29/REREREdqegfUOqKfuywvalQjZgjuGY+ypkx9XIthPNCTVojz6Q8ghpIE8h
+O3uZFu6GY5iXPbHNrGzlZREjYiGDN7UvofyNu4bt9WnpIteBBjgH/CwlcP8xcD/wMOCDgX8PvDfw
+lWb2p7X42eV4b+CHa1tfB/w6ZVrVE+r3tvsG4MMplYC+D/hb4L2ALwT+EfBo4L+a2RP94C+4CPw5
+8HLgbuBvgLH28ZOBZwLvAbzczB7j7m/f41ofAjwVeAvwX4DfpPwf7lOAr6SE7+83s19x93ds/2Ez
++3Lg/6L83+N36nN9E3Af8AHAvwM+CviPZnavu3/3AZ+riIiIyEUUtCd1dNR8CnMP/Dvl1Z6ufDjB
+0bbdPvCRrW9stbfHCu2t3830/ZwgJzyP5HrrecR8LFPMg9MYQIacsDwSc8JTxlMip8yYEjk5KWVS
+zmWd9tROaLDY1NsWix2EGRY7LDQQ4tYhcn34beB2d79/h8d+ycy+G/g5Srh9npm95DKCrVFGZu8B
+nuju66U9797lZz4cuMvdv3BbX3/GzF4APKue82xKOD2Ir3X3v9zh+78F/JSZfS/wG5RR6S+iLJTc
+zeMoz+HJ7r7+gcHrzezPKR8u3Aw8DXj++g+a2aMpHyg48HXu/n9uu/ZvAz9mZi+pP/+NZvZSd3/X
+Pp+niIiIyAMoaD+AYbgKb21Txp7L4T7iqS+j1GOPpSVp7LHU48HxAITVGDQBxzyThgEfRsZxYOgH
++qHcjuO49fv2GrSbErBD7AjtJqHdJDabhHaD0M6I7QxrtOelXB/c/Z2XeHyso66/Qxnx/YeUAHjg
+poCv2Bay9/JW4Et3eexLgM8AbqGMcB8oaO8Sstcf/wMze2Ft5zPZPWhPc3OeuS1kT9f5UTP7Fsoi
+y49jW9AGnkuZLv76HUL2ui8C/gVwE/DPgR/Yq/8iIiIie1HQ3kXZ4/mkxe3dnq9fFLTxEc89eVzg
+wwLv5zAs8GFeQnZ0CBADBIMQjGBgwxJf9qTlkn65ZLlYsFgsWC6X9Xdd1nBbaAhNGcEOzQbN7Caa
+2U3E2U00s9O0m6chGDGqlp9cn8ysAx5OCXXTH+T1P9AfyuUF7R542QHO/wl3X+z0gLufN7OfAJ4D
+fLCZPewy12oDYGYPAd6dMs17mmBzX739IDOL7r5T9VAHfs/d/2CPy/828AjgfXd47NPqNV6+V//c
+/V1m9nuUTXU/CgVtERERuQIK2pWtNqfaCpvXurL1enuHFfL3ego7TI5n/Tcwjeybb1UXNzI59TDO
+yf15cn+BtDxPXl4gLy+QIuRo5AhtNGKMWBMIIcC4hHGBD0vycs64uMAwn9PP57hncnbcM1gDscPC
+jNDMaDbmtJtL2mGgywmCYU1LaNpD+R2JXAtmdgr4YuB/pazL3mvtwy2X2cyb3L0/wPm7TSmfvJ4S
+tKEUSPtvB+mMmT2GMmJ+B3DrHqcGSpG03dZp//ElmppmDDxoW/uPpExNd+CbzeybL9Xnaq++ioiI
+iFySgrZU6yF7q4pa2dHLMU8ERoKP5PECuT9LWtzPuDjHMD/HuDjPuDhHFwNDE+iaSNdGuq7Fcwtt
+g+cl+EiwkRgSMSSaMNLEkTQm3BM5JbJbCeO2AOtoUy5rucdUiqc1LXG2Scwbuz8dkWPEzN6bUgTt
+H7D1GddOn6ZNH41t7vDYfvz9Ac+/1Aj1W9fuv/tBLmxmzwL+H8r7zKp6+E6n1tu9nvOFSzQ3be+x
+/cOLh63dP8inl6cOcO4OXXnYJc8qXVWdCREROSnOXPqMM2c4c+bS5/X9QcYUjo6Ctqz4alS//M3X
+vGzjFTyX/awZCD5gwxxfniMt3sVw4V0sL5ylv3CW5fwcsybStQ2pbchdh+cZ5jOCdXgawEeMRLBE
+MwXtMIINZB/xNJASpBwYcyDTMKbEmBIpJYgNcbZJN/Z4PtZ71Ius+2FKyM7ADwI/DvwRcK+7DwBW
+prRMf6gvdzrNQV8UV2V9jJl9ACVkR0pY/xbKBw1/BZydpoib2TPYmqJ9NaYQrSfZrwd23f5rm/NX
+1uy9V/bjIiIiJ9Bdd93FnXfeedTdODQK2gKsD7Gt7QnmELwE7uCJkAeCL2G8gPdnSYv7GOZ/z/L8
+/SzO38/83P2MXcvYdaS2I2/MgFNl9Do6OY/gqY5oj0QbaUKijSN5HDBfQurJQ2YYoB9hzIFxLCE7
+pURoO7pTDyIpaMt1oobOj6G8sL5xj32aDzRifEgefoDH9yzots3nUd5fRuDj3f1Nu5x3tZ/z+lZf
+g7v/4VVuDzPjllsuPfM/xkiMGtEWEZGT5dZbd1+d9exnP5vP+IzPuOQ17rjjDu699/h/qK2gfaNZ
+y8mXGh6yrQ27tq3OruG6rssOjEQf6tETc4/lJZaXkJaQ+7LFl6cyEp0SFkYYAr5Ykhz6MZPdyV5u
+xxGSNdBsED3Q0JNtRrYebxzrjDBC8obZ5s10p26m27yZjZsezGzzNE03I0T98ZXrwgev3f+JPc77
+8KvdkR08AfiRSzw++f0DXHd6zr+7R8iGq/+c/wJ4F2Xrr4+5ym0B8IhHPII3v/nN16IpERGRG8pt
+t93Gbbfddsnzuu762HlISeVGZBdtkX2pUy+aO1oKoJXq4iVkJyKJyEikBO3gS0Iu23qth2zHydkZ
+U8It4IwkX9KnzLwfwawUfLOplQaaQAwzmjDgYcTDgCWIOdBkw2mYnXows1M3Mzt1M91N78bs1E10
+3QZN8z/Zu/N42bKyvv+fZ+2hqs5whx6gu2lRUREVpxciDqighqAEojH6E4OBQBDUKFEwRpMIbV4Z
+XqKI0fijDSqiPyUGExOHxIA4J4EmKEpQInEItNee7nSmGvbez++PtatqV52qOnXOPdPt833z2l27
+qlattXZd6tZ96lmD/u8rN4Xm/1FXF5T7+qPuyAxfaWbf7u696Sfqxdu+ivhXxPvd/YFdr55veM1z
+r9fM7iRuH3Zk3L0ys18Gng88y8w+3t0/cJRtioiIiMDkdjLyKGGj/+yj/NRLrF5hPKGKQbbHrHbq
+/RhsVz2o4lBvqgF4iXtF6RVFWTEoSrqDgq1un42tLtc2t9nY7rK502enV9AvoCTF0k69bdc50vZ5
+ss4FWisXaa9epLN+KyvnbmP1/G2snb+NtQu3s7p+kfbKOlmrTZJmJFp5XE6/Zkb3RbMKmNnXE4PO
+495T8A7g++Y89/2MV/X64X3WO7zmjzOzz5p+0sw6wE8Tt/o6av+COHc9AG81s8fNK2hmwcy+xszu
+OoZ+iYiIyKOYUoIzTed5T6AHB91abDI9vTDenijqPloEbbhvdrAYZKce51OPDgrMC6jKOpNdv8JS
+CCme5HiS4UmKW6CygHkg8ZSkLmNJhqUZlmUkSQZpBVlFKCsqNyDBSbCQ01pZj0dnjaS1SshXCGmO
+aX6j3ATc/XfN7H3Ak4GXm9ktwE8Sl9+8G/ha4CuA3waezsH/8jnI694NfIOZPQF4A/Ah4COAbwCe
+VZd5D3DvPuv9SeCbiIuR/bKZvZZ4fV3icPFvAT4G+B3iNR8Zd3+fmb0KeB1xSPv7zOxHgHcQF2pr
+Exeq+2zgK4k/PjwZ+Iuj7JeIiIg8uinQlpGJIJsqDhm3uDJ4HDpeklISrAKDyuPq4E6KhxakCZa1
+SfI2Wd4hJBmEBCxgFhf+CUlCCAlpmo4y0kmaYRWEyonrmwWwFLMES1LS1gppq0PS6hDSNpbmEFI0
+IENuIl9L3IP6InE49lc1nnPgvcQgb+89LeY7yK9z/wh4JfBXiXtdNzlxZfTnuns1/cJF3P3dZvZq
+4DXAeeCfzaj7e4H3s3egfcOrkbv7D5jZJvB64nztb6uPXUWJPwZ0b7RNEREROdsUaC/LDPxks9xH
+q7E0mhMDbatIKUiJq4SnFCRWkVgMxt2h8kBlGR4CJBkhXyVtr5K110iyFoQEswRCSgiBYCHeJinJ
+MNhOMhIM9xD7YQkWEsxSQpJiaU5IM0IWA2wLKYQEP2jWX+SYuft7zezTgO8AvgS4C9gAPkjc6uuH
+3b2/x0iWRXtRL/P8LH3gS4GvA/428CQgB/4P8Bbg+2fN316mTXf/p2Z2H/AK4qJqq8R9u98JvMHd
+32FmL1yi38te18Jy7v6jZvafgJcRs/UfD1wAesD9wB8AbwN+zt33s8K6iIiIyC7mj+rgcXkvee07
+x1HmcAz11FvjBx7R2axjt2FTtrBks9ScDo5eNi5jc/tsM6opMa+AipweLevSsi4ZXRLvkvoOiXe5
+cuUKj1y+zCOXL7OxtUNvAN3C6BXQWjlHe/Uc7ZVzpHlnFBhbSDEzjLggWkgSkiQjpBlJErPXYRhc
+h4QQ6iA7JDGIt4CHgNvwimy07/dPfuczFXGLLMnMvoC4p7UDz3T33zzhLj0qmNmHgcc97nGP06rj
+IiIiR+juu+/m/vvvB7jf3e8+6f7Mo4z2NJ+6HTrCaduzq51ucLpjczpjNDLveyWJALfR/eGQcaMi
+sbI+4rDx4AVGDMJjoJyRZB3SVgp5SvCUrErrudTr5J110rwFltbDx5uBNjGoDsOh5AGzgFHfWoKF
+AJbE1cvNGgF2vEj9PCQiIiIiIqeVAu1lHXtkN6/BJTqy548C1igyzqUHvD4aW3pZvDUrMS+BCkLA
+0pwk65B5m8TaZKFFFdrk7TWyzip5e42QtcCS8VG3a/U2X2aGhVAv/GZ1L+I5Fupbw42pQFtERERE
+ROT0UqB9as3LaM+7P3zNrJc3c8HNYeXjYNsmFkGLGe3UyjgvmxIowUrwCgsxox2yFVILWLaKZWtY
+tkraWiVrr5C2VglpjlNnpgnjULq5GrrV/RvNroxzv5uTLb0etO80fxY4vMgtDwAAIABJREFUhBWS
+REREREREjoAC7X2YDuwOkmGdiH/nVBhjTx9No54s7FMbZfvk81PbezXD1fHd4cJuzbDbxwug1UH2
+cOh48BKnwt2pHLCUkAbSVkaWZoR8jZCvY/kaad4myTskeRsLWZxbXefJxwHysNXx4e7gMcgeDX33
+4az4uszuixMRERERETl1FGjvsvxk7OlQd69yTT4nTjTARsEwM+LnOQH2TI7XC6LtHng9vk5z4rzs
+UJFaQW4FWb3aeKAkUFK6425UVYCQELJA1jbc8zqjHY+QZFjIYiYbq2PmySB5nJ8eXsdkkD1/+TZr
+nIvIDTjICuUiIiIisiQF2rUbyVYvG/RNry6+qI1RnR7nKO9+zZxaxi9stOijYdnNQqPMeZ1zTojZ
+7CzUgbaXJF7Pza4D4dIDWEaS5WSWAW1IO6PDQhIXPyOMhoDHlr0O+5sazzYy2bN+Etj9A4hCbZGD
+cPffAJKT7oeIiIjIo5kC7dowWzrMp84O4w4Y3NXDwHEw86lwd3g2HsptTLXkNtozehwcz+6T25y5
+3Oaj4eLNp6wOxIPFedlZKMlDHWQTh43jVR1oG5UHPOSE0CFNV8A6kLTwpI0nLcY1NsNinxFEN87m
+bTE32rs8vj82OZY+Pi8iIiIiInLKKNA+JjbMKDcTzY3s83Ce9GTgPA67bWIY+fycuNVj0mNg7sS1
+xOu6hpV43GYLqxpzs8dzslMrCBSjPbWHueiqPtxSnAxChlmGh3S0SvhBxqOa2fxgu2miiIJsERER
+ERE5nRRoH4NZq2RPbHfdeGyczd49H3lYfs+ZynWM7aPgenpe9LCFcZCd1gF2aiVJKLGqxChxr6jc
+h7trU1nAyXDLwHKwjNF+1xjzZ1gvtmewrdmkIiIiIiJyk1CgfUysOT95zjpmuwNoq5cym1l8Vwuj
+6uvF1OLIa4/ZbavGc6Tr7Hrc09rHK42HccANJRUVTkXl1IF2oCKlCilYVq8qnhGnezYy57Oi4iXW
+mIvBNnsXFBEREREROcUUaB+b4cDw3Qt+NdffbqwFPvXs/toy4pzq0XBzr7PbPgz6nWBOFpw8OK36
+NgtOFmIW2ysfDRevSChJKS2lsozKUtxShntk+ygXv3u5tlkP7Dkte4/rG16liIiIiIjIaaNA+xg0
+19qePN+9NJhjmDW39preCmtX5TQz4OPbegExB6jA4l7VVs+2DjhpqMhTp5NAOzEyg9wgMyjcqEKg
+sECFUZJQWUpJFrPallIxDLIDk2H18lukLWvpedwiIiIiIiInTIH2sbCZWepZWe1RWTOgis/Ui5ZN
+lByNJ58OrutMrw+HnY/3qa6XMiMMB4EHp5U4nQzaqZFiZGakxAx4URpYoDKoSGNGmzxmtUmpLKlD
+9omB8UdMWWwRERERETndFGjvgzW2k9qdXJ21cRXszjIPz2dvdzURkBsMZ0c7zZx3o6rRlle7Nwaz
+Ufm4bjjmJF7VW3lVcch46nRyo5MaASNxSNwoS8NCqMNyqwPrjIqsHjpeZ7MtTF6nH3Y2uzGYXtt5
+iYiIiIjITUCB9kxHESw2gmDzRsgdM85OVd8fh8fm1WjV8HFNs7b/Gp+PWrJ6UHcCSWL1EQhekmAE
+N1YyZ60Fay1oJQZlAWUKVUoIQDA8BCozKnIqckpyKlK8HjY+ztYfjflzthV0i4iIiIjI6aRA+zjY
+5MloILk51HtVj3egHi6aNtqjK5YbmsheT4TWo3uhXugs4GSJkedGKzeyzEjcCF4S3OhkzmrLWMkh
+T4xqkFIOUsoixRIwC7gleDCqKqfyfJTR9tEiaNMOP+zW/GwREREREbmZKNCe6yiy2uPTUahsHjPX
+9XzscZDdXKK7+frJrb+mg2yAYE5iTrCKVhpYacFKO6GdhzrIhsShnRGHjedGFgL9UDCgoF8NCMEg
+VBAqKgtUIY/BNnlccZyksdL4qJNT16vgWEREREREzh4F2lMWTwNuLFk2VW5xwnWYuTbcK9wHeFVA
+Vd96gVeDRiWNBdGsGUQ35kFbwCxgBDCj+T+SCoJjwQlZRitpsd5OWesk9bDxQEJCnkArgzw1jAKv
+EopBPS/boSJQulN6qDPZKRUZXu+bvfvN8omHDpqEnpwLP78STdkWEREREZHTSIH2XPvJ0M7bP3qc
+7XUqcMfLgmrQpSx2KAddqqJPVfYoi94oGI8vrbPFdbA9DrTrM0sIlsRgu3EEC2QJZCl4CtZaoWWB
+tVaLi6spqcVcdGpOEuIRAlQlDPoBCJSVUVaBojIKh9LjHtqjudkWy01nsxX3ioiIiIiIKNDeh0Xp
+2VnP2WiIuFeOeYlXFRQ9iv4Wg+4m/e4m5WCHYrBD2d/BqxKwOlNrEGwqqz0eNh5CSggx2A4hGQXe
+IQSqLOBZgDzAqpOHFustuLiakoW4T3YaYr/dK5yK/sDZSRIgbutVVEZZGUUV4h7aw0DbUqZXN2++
+C8cXbB9vayIiIiIiIstSoD0ye1fr5V41K+DzcSzoBVUxwMsBRX+b/vY1etvX6G5fp+hvUfS2GfS3
+8bKIcfVoEncYB9vNQNuMZBhoh5Rgw/OEJKRUrYQqT/BBSrUGSdmhFdZZzaGVBvIk0EoClTtFVVJU
+cXXzNEnAEioPlG4UHig9UAwz2pbUw8ZnrTV+fDtpN9sUERERERE5bRRoLzSeM737sanHJ4o47mXM
+UHvJoLdFv7sdb3c26G4NA+1rVINuPZS8i1dlI5hmnM0229VUsAQbZrQbWe0kpLRaKa08o9VKWcv6
+XFuBa6sJaxlUnTah06aVtglJICUh4FQhkLUK0rzAsgEUxNXGzag84PU+W0Y5432IvXKWnzetlcRF
+Hn3M7NXAqwF39+SAdfw68PnAr7v7Fx5i9xa1ecP9Hrp06RJ33333UmXvuOMO3v3ud99IcyIiInJK
+KdAeGgWIdSZ6QYZ7uAHXOJs9GV06FV6VVGUfL/v0dq6zvXmFnc2rdLeu0tu6Ngq2vexDOcCrfj1H
+u1FPPYTcR0009t+u52MHCxhJfT9muFutjHYrp5VnrGUF11YC11cz1lsJwc/RSlNsxQhJShIMEsOT
+hGynIMkHhKwPfcdD3HisBCo3HMesGC1yNrz+YQbe9rk62X6CbQXmImfGTf1Br6qK+++//6S7ISIi
+IidMgfYs83as2hV8zwgsh9tfe0FV9imLHXo719jeeJiNKw+yvfEI3WGgvXWNQIl5gXk5CrS90QIG
+bs1H4hHMMEIMc0fBdsxut1steq2cdqvFWl5xfTXl2lrOuU5OK01Y63QAI0kTQppgaYInGVlrQJL3
+sawPaYUHpzSo6m3Hhqun28RvEYbXq5/7aH75Pt5qBdsistvwL7ub1OP2eP4ScUtHERERebRSoF0b
+BojezGYvChp9vA641y90KqhKyqJPv7vBoLtBv7vB5tUH2bz6IFvXHmRn8zKD7ibFziZVfxOnwqgw
+H/+7cpi39omts70Od+MR1ycf/i+uAm6EOIScEvcBVTVgY6PNlSsdVto5aRKovIx7dyfGyuoKeadN
+3m7VQbKRJAlZlpImAwIllAOqfkFVVVReUlUl1MPULSQQ0nhrVme0m1ugzX8DmwHzogB6+jlt6SXy
+6ObuzzzpPtyYAHx4jzJ3A8p6i4iIPJop0N6XOgCG8fZb9axq9xIv6wXPelt0Ny+zs3GZ7c3LbF9/
+mO3rD7Oz8Qj9netUgy4UXRIKwKeC7JildhsPyaYOYI2qLleNovDpUe5V5RRFSZxS7WxsbXL56hWS
+xKi8YFD2GZQDinLAufPnWTt3jvVz5whZBlVFGqCVJ2ShT/AeDHYouz2KYkBZDCiKASFrkWQtkrRN
+yHIgjwu3jd6j/SeilK0WEREREZFHCwXa+zC5wriNZmebQekllH2qQZdBd5Pu5hU2rj7IxpUH6G5e
+prd1me7WFcr+FuYl5iUJZWNKuOOERmAdYka4uRiaV1Dnsoevia+3+jYG7WVZ4lSUVcnm9hbJVaOs
+BvQGPQbFgKIsKMqC7qBP6U6SpbRYxb0kCdDOErLESbyPD7aoulsU/R79fo9Bv0faWiFrr2HtOIQ9
+ro5+4/9XmhdsKwgXEREREZGbiQLtBeaHdsMQu85EO3g5oBx0KXpb9Lev0d24zPa1h9i4/JcMdq7R
+37nGoHsNL3qkwQgBEgO3OLcZC7jFodhY3GbLQsAsTAXaJe4eA0/3uDd3nMg97lswCFCZ0S9KtrZ3
+cK8oyiImns2pqoLKK0ISyFstIFAOSoyKLDXSUGJVDwablN3rFN0ug94OvW4XLwcEM9I0gzSHKqtX
+JF8+l90cVj49jHziz0ABtshNy8zOA98KfAXwkUAfeC/wI+7+ljmv+XXmrDpuZh8J/Gl990Xu/mYz
++xvA3wU+FXgM8FszXvc44DuBZwN3AZeBdwP/yt1/9RAuVURERGSCAu1pC6NFaxyAV3i9iFnR26K3
+dZXe9lW2rz9Cd+MRBttXKXvX8cEOwQckBiQJSQiEYIQQsJBiIYOQEpKckNZHksdAO8R5114vROZe
+4l7h7lRVFQPt0app8TbWbVhiZKmRp0aSGR4Sev0B165fj/PCQ5yTnaYpRVFSYpSEOHi96uGDbcre
+dYqdqwx6PQbdHoNejyTN8XZRb/Ud52ab7fnmicgZYmYfBbwdeALjvxhWgGcAzzCzLwO+xt2nVwVb
+ZiG0uJug2ZuBFywqb2afB/wCcK5R7g7grwHPNbPXLHVBIiIiIvugQHvf6qHcw2xyVeDVgKIbA+3t
+6w+xdf1huhuP0N+6QtndwMseVhWkBoSEkNT7XoeEJG2RZG1C2iLN26RZhyTvkObtUbmQxG1dKy/x
+elEyryqqMgbao6yvx+HsIQRCYlgIJFYQKAgMcCvp9gfYxnX6vS3MIEtT8izDK0iynCTPCVmOVX28
+2BoF2kVvQNHrM+gNyForeFlgOKHOuA/3/x7OJlfALXLm/VtiFvuHgZ8DrgGfAnw78ETgK4krgr1y
+xmuXWfbwW+r6fgN4A/C/gQvAR40qMfsIYpC9DpTAvVN9+YfAa4jZbREREZFDo0B7X5oZ7TrDXA6g
+7FH0NultXWXr2sNsXXuQ7tZl+ttXKbt19hgnGHWWOsWSFAspab5Cmq/W855Xydtr5O1V8vZq3Oc6
+SQhpCsQMdlXFlb+rcnhbDZdKH8W2IQSSJBBCwKseVbGFF1t4sUO336ff22KjGmBAnma0shyzQGd1
+hTardLIwymhX3esU3asUvYJBfZSddaqqiO9Icx756D1SkC1yxhnwGcDz3f1nG4+/x8z+HfDbxKHe
+32xmP+ru7z9AG58MvMndX7ygzOsYZ7L/1oK+fMYB2hcRERGZS4H20MTe2bOCxeFj9T7SZUHR36bo
+blL0Ntm6/ghb1x+J+2RvXqHf3aQcdHEvsDp7HUJCkmQkeZs0a5NkbbL2Gllrjay9StZarW9XyFor
+o2z2KKNdDTPaMZM9vN8MtIdrqRlGMCj7Wwx6MKgKKu9TlEBRQtlje3ub6xsbrF69SpJm9IsBg6qk
+wtne2aLX3aHfjyuOx6ngAUvzeCTZ6CAk+AH33dIcbJFHJQd+YSqwjU+4b5nZ1wHvJO6F9XLgm/dZ
+vwFXgG+aW8DsscCX7aMvIiIiIodGgfaUZohtE48Sn6m34vKyP1pdvLd5mc2rccj4zsZlutvXKAdd
+yqJfvzzB6vnXaWuFvLNGq7MWs9eddbJ2PE9bK3HoeBYD8eEcbULcOss9BtU+HLbu1eixcT7ZoXKs
+ihn3fkjqLcd2qDzBS8NLx4uSbq/H1tYW165dIyQJ/bJgUJUUOJtbW+x0d+j1+xRlRWUppHWGvbVC
+yNpY2oIkqxdwC4iINLxp3hPufp+Z/S/gk4AvPkDdw+B5a0GZZwJJXXbZvoiIiIgcCgXauywY+uyO
+eR3gFj0G3U12Nq6wdfVBtq49yNb1h9neuEx/5zruRVwoDY8Z7bRFyFdI2+u01i6ysnaBlfUL5O11
+8k48krxTL4jWIiRZY0h2c7g6jFc7r/fVdh8tTG4eg2gvCyhKzJ2it4PZBl4lVJVRlU41qOh1+2xt
+bZNn1yAYA68ocUqDza0ttrtd+oMBRVnhScCSnCS0Yz/zzjjQtmRGoK0h5CJn3H17PP8uYnD7RDNL
+3b3YZ/2/v8fzn3yAvoiIiIgcCgXateFiXo4P1zqbej5mlKtqAPWw8d72dXY2HmHjyoPsbDzMzuYV
+ulvXKAbbWBiu/p0QshZpa5WsvU5r9SKd9VtYOXcrq+duJe+sjTLaSdaeWIU89qfZg2Z/vN5Nu3Fb
+Z7irXp+y36Mq+qPAuigqBoOCalDGoyjp9fts72yTpjEjXdazyUuMzc0tut0e/UFB5cRV0bM2Sb5G
+0lrBsnbc2itkTKzEPjyz3X1uOviQ8UY7BxutLiLH48E9nn+gvjXgIvDQPuu/ssfztxygL4egIu4y
+tsjDh9eciIjIo8SlS5e4dOnSnuX6/f4x9ObGKdBeQgxkjaocUPS2KXrbdDevsHXtYTavPcTmtQfp
+bV+jv7NJUfSoqpIkyQlpRpLl5Cvn6azdQnv1FjprF+msXWRl/QLttQskWYd0mB0OGT7cN7vBR1ls
+aw5ir/sGlVdUZRGPok9/e7M+NtjZvFz/CPAwve2rUO7EoxrQ76f0egO6eZ8062JpSmWBgXsdaPcp
+KyBkJGmb0F4j6VwgbfwogBnuzf42FmZTICxylh31kJZyH2WPeXjNfn8zEBERkXvvvZd77rnnpLtx
+aBRozzXeqCrec6piwKC7Pdore+vaw2xefYjNqw8y6G1TDnYoiy4AqYW4ZVdrhdbqeTrrt7J2/jF1
+wH2e1so52ivnGouK5fVc7BBbnf5n4eTC4hMDs6uyoigGFIMeg94O2xtX2Ll+mZ3rl+luXaG/fYXe
+9hUG3eujrb6CDxikKf3+gG6vT5L0YpBdQb8o2N7u0+3Vi6eFjJB14sJtq+fJOuskeRuSNPZ11BkN
+FReRkccSt+9a9DzEvzj2yk4fRLPOZftyw8yM2267bc9ySZJwxx13HFazIiIiN72XvexlPO95z9uz
+3LOf/Wweeuj0/6itQHsOa0Syw2C7KgcMelvsbF5l6/rDbF17iK1rD7F59SGqsod7AV6QpBmMAu1V
+WivnWTl3G2sX72Bl/dZ6EbQ4ZNzrwNppznGeiqpH5+M0sdfZbTcoK2cwKOj3evR2ttjcuMLG1QfY
+vPwAve2rFN3rDLobVINt0uCjo99P6WUD0l6fEFIG7vQGJTu9Hv2B0+tWlJWDxWHjaXuNfPUCab4a
+F0MbLoJWLxAnItLwVBYHt0+tb//4APOzl/EHB+jLDbvrrrv48Ic/fFjViYiInBl33nknd955557l
+8jw/ht7cOAXatXlzob0q8arAq5L+zgY7m1fq4PpBtjev0NvZpBjsAB634gopebtDe+0CK+dupXPu
+FlbP387K+kXaK+fi6uL1omdYEtvz5hznYZA9a5L4MMs+vPX6qfE87bgqeYVXJZUXVFVBWZ+XVQnu
+VJVTmhOSgjQrSNICCwU5CW4lBKeqEkKak1uAdCUu2NZeJc07JFlOSNK4h/bEu+ZT4bbGjoucYS8E
+fn7WE2b2VODJxL/w3n5E7f8acXh5WLIvIiIiIodGezLVvA5X6x2osWE4WxaUgz6D3ja9retsX7/M
+5pUH4wJow/2yiwEAIcnI2qu0VmKQvXbxMZy/9S7WLz6GlfVbaK2cI2t1SNIcC2mc2+zjHgwPmzrC
+8LBx/tsYrjQ+7HXsuZlhoT4MLDgEr3cAdwqvGJQl/UFBb1DS6xf0egW9fkl/UDEYQFEYWE6SrdBa
+vUBn7SKtlfNkrbW4/VjSIoQktueT4bRCaxEh/lXwPDP7m7ueMFsF3lDfrYB7j6ID7v6XwH9coi/3
+oiE5IiIicsiU0R6ZXtXbRpnhctCj6A1XGb/MxtUH2bjyAP2dDfrdLcqiT5KmJGlG1lqltXqelXO3
+sn7hsZy/7U5anfO0OufJO+ukWQcswUlGSWsfNd+YE96Y9zzRs+GS6DZcpG24C5gR6t3AgsUR3Ran
+fI+S5hUOVbwmyhILMZudJCUhKSA4lkCSGVmakearpK01kvY6Sfscob1KyNuYxSHjw8z6ruz7ft51
+sxtYgVxETikH3g38jJk9A3grcB34FODbgY+vy/yQu79vzusPwyuBvwKsz+jLpwL/EPjYuq+HNnxc
+RERERIH2yPR63nGf6mrQZ9Ddor9znZ3NK+xsXmF74zI7W1cpBz3KqsBCIMlb5J01OmsXWD13K6vn
+bmP1/G2snr+dLF8lzVfJ8hUsyepMtlGN2vbG9OvGkHBr/FuzmfmenkBuhoVASGKwn2Yt8laHVmcV
+90FdX4UFqIq4OrkXRb3SeQu3DLccLIfQAmtj6Qppa428c460cw7L1yDvxEXb6oEQ1li0bXrQ+L7e
+eQXbIo9GXwX8KvD1wDdMPefEgPeVc157KINj3P3Pzex5xMz2et2PZl8cuKduT4G2iIiIHBoF2rN4
+BVR42WfQ26S7dYXt6w+zff0RdrauxuHigz6YkWUtsrzF6votrF24nbULt7F24TGsnb+d9upFstY6
+SdbCkjxu3TUc+G1g3sxmN5qP+fTJXPbodwCr71p96pglhCQjzWJ9K+t9gkGWZfR2ztHf2aDX3WTQ
+26Ic9KmKAeVgQJ5ltPMOrbxNu7VCq92h1Vkhb6+QddZJ2+dIWuuEfBXq7cdGKfLYNJMdVLAtInEO
+TB3kPgV4FfDlwEcCA+C9wL3u/pa96jjAc7sLu/+GmX0S8B3AlwJ3Elckvw/4QXd/u5m9er/1ioiI
+iCyiQHtktLZ4DLS9oKoD7Z2tK2xee4CtjYfpbl2j392iKPqkeYs0a5HmOSvnbmH94u2cu+UO1i48
+hvbaRTprF8jbaxBSzFLcwkTw7AtyNj4vobPrYYMQCGmGhQRLEoI5WZrR7qzQ727S726OAu1i0ItD
+4fs9siQlT1vkWU6etclbHfJ2h7zVIW2vxf2yW6uEbAVPWnjIcML0cnE0g+wb+Veqgm2Rm5u730PM
+EA/vXwP+SX0sW8czFzz350BygH7dD/y9Bc9P9FtERETkRinQnuaAl7gXVGUvZrQ3L7N59QG2rz/M
+ztZVesNAu9Umzdu0V9ZZPXcL6xcew4Xb7orDxTvr5O31uIWXGxVGtSiyPkA3R0IghDhnOvWcPMuo
+Oqt4cZFBfzsG2r0YaA/6XYr6CBbIQkqaZKRJTp63yVptsrxN2lohaa2StFawtE1lKW4JcZ9vsLlb
+etVBty1xrcP55hMPKdgWEREREZGbmwLt2nD59cpLqnJAVXTpd7fodbfodrfo7mxRlAUhzWivnqe1
+ss7K+gVW1y+wsnaB9YuPYfX8bbRXz5O3V0myNhYy3OssdnOM+OTNlGWC8dkBrhMDVSyNi6GlgQTI
+MEhSkqxNNuhTFj3Kok8gkISExFKSJCVNc9I0I81yQtbC0pjFxpJ62HsYTyWfGCnuoz6Mn1xstDXY
+VGCtIFtERERERG52CrRrowSsl5RFP64yXgfYw2C7rCqSvE0nzUmzjLVzt7J2/hbWzt1CZ/0WVupt
+sNJ8tV5obBhoH/HEPx/+p97sywKEFAiE1EgtwdKcNO/EhdDKgqoq40ZmlhAIhJAQQiBJEkKSYEkG
+SQYhpbLhpmLNBptH43086msVERERERE55RRo10ZxdlVSFj0G/Z16bnMMtrvdbcwCWd4iy9t0VtZZ
+v3Ab5y7ezrkLt9XDxFfJ22uErA2W4qRTw8V9uYT1Pk0GwAaEep+vJO53nWQk3sZH88+9XvBtvGc4
+2HhrMIjzyS3gIaG5e/euN813nR78OpTNFhERERGRRwEF2rVRvtYdryqqsqAsyrhltSWEJCfLW7RX
+1uOc7LXzrF+4jfXzt7F24VaSrEOStgjDIeME3BsrdNdriR9OvnePeixuHxaHkScEUrBGTtqGIXO9
+wfZoaHu9Ddhosy5rbjY2rn9U1uPS6RO9OoJfEkRERERERG4iCrSn2CjLmxKSFu32Omvrt0MFWatN
+ezUG2p3Vc3RWz9FaPU+SrxGSPA61tnQqyJ7KaO/ar/vgPR3WE7fVHg/tHm8A1mhyah+xUfBcB9rD
+LcXic16H0TZRI1Ov3n0No/D9QFe3r4XQFM+LiIiIiMgppUB7pBHgWYJZShJatFrnYB2yJCdrd2Kg
+vbZOq7NG1loha62Q5B3MkrhoGMNFw5qB9mEG2NPGK6uZTzwyPq+Hr8dY2xv7ihnu9VDzUc8CwyHu
+k/OyhyWmg+zpazuaCHgiCFeQLSIiIiIip5gC7SmjjLalJEmLVvscWZLT6ayTd1Zor67TWlsnb69i
+SYqFDEuyGIJ6vVvVcDj2VDbbplrCZs9LthlbYy0sNxz1PifYnnjEmyHyMMhuZq7HWe2pHsw4nw62
+x60eRVZ71/ti7NoeTERERERE5KQp0K5ZHbBZCCRpRpa341TnLKMqW3g5IG21ydodkrQdV/W2BCyM
+Y71d210NQ8/5mdhZQfXM/i1RbmLbrVHmenoO9XSOvaoD7smM++S94UDyydtpfgQZ7T2Hky/5/omI
+iIiIiBwXBdpDw1jYAmmaYThJkuBe4lUJVUlIM0KWEdIcC2kMspnMKk/Pgh6fHfkmXxOGm31NPjJ7
+IPvEjwETV+CN5+Yd7GrpsO1r7raIiIiIiMgJU6A9JYSApSkhBDyr6jnOwxW2A4TGQT38em7CupEd
+tt2DsY+G705dT2kOEB8H0s1w26aei7e+K8BmooSIiIiIiIgo0G5oBMUhYMGAZDx72awRZs5YJKy5
+ltrMmmcsh3bEEepwofH5g6tnzPue+bxPhuI2fmzRqw96fc1h8osy2Ro0LiIiIiIip5EC7WnDPbE8
+ZnDdhrtfN3PA47Bz1wrfi6qsJ1Ef1m7aCzUaWa6tWSujN5dNiz8xzJ7xLSIiIiIiIkMKtEeawaXj
+Nh4qPdoNqxFc2pzodVbYOb3eeLOmozLMZo9aW5BGb87Knjf8fXh2j1JeAAAgAElEQVS2e3m14a0C
+bhEREREREVCgPTZK5taZ7DrYnuR1PnrWy+dvbWX1smITW1gzIzTdvR7Z7udm9Xmqh3M6ONm7Rn3D
+RcpHqfeZjU8OmN8dbM/qkGZvi4iIiIjI2aNAe0qdr2YUjNr04/Oz2c065pkOtme+YNn4dDKaH93M
+GgQ+r86J2HpGfc07w8HzPje4PthQcq0qLiIiIiIijyYKtHdpDA+H0dzq4YxsmxyTPXx0fy0cRUw5
+o87ZYa8vcXfewHZrPD/LwYePK9gWEREREZFHCwXaM00uWDYaVj0jyIZhjvf0zFFeuic+83TXvfjI
+dK2n53pFREREREROEwXaC0wEz7tiT1t4d4IfX1g6d/T38NmJ4HrOvHKbulybXatRbzE+R3ObrkWU
+yRYRERERkUeTcNIdkGN0yuPZZQNzERERERGR00yB9lxnL+iz0X9ERERERETkoBRoL2QLjtNpPz1e
+eDUTTx7f+6CstsjNz8xeaGaVmZVm9viT7s9xunTpEnfffTd33303n/EZn3HS3REREZETojna0+Yt
+uC3HQvO1ReRmVlUV999//0l3Q0RERE6YMtoypmSyiMgh0FeriIjIWad/DRyLmyCCPQVdVDZbRG5+
+AbjzpDshIiIiJ0xDx4eGgaYDZjP3rZqcPjwdmc7dlLpR9Gj2ol42QJ2e/zz3Vbbw2cV1KlgWERER
+EZEzThntKXYaUrsnwObcHmsftBCaiNy86u9T/dgocrO4dOkSr3nNa7h06dJJd0VE9qEsy+HpqY5l
+T3XnTkpcaHsy6LvxGPAmCSJt5qmIyIiZXTCzf2lmf2hm22b2gJm9zcz+5j7qaJnZ3zOzt5vZJTPr
+Nep5sZklR1mHmf1ZvTL6j9X3n2JmbzKzPzGzrplVy15Lbc/+isjpcunSJe655x4F2iI3mUagfaq/
+ezV0vNYcOT68741I09kr270gQj3iBEfzR4AbGbk9XHC9ufD6fhdhX74vs580s6WHnytvJHL8zOwT
+gLcTJyIPP4Yt4AuBLzKzHwd+c486PhX4j8Djmfwo3zasB3iZmT3X3R88ojp8+Dozexnwg0x+Ye83
+0BYREREZUaA9x+EHccMQVhaZDLIVSoucJma2DvwKcAfxA/oW4M3Ag8ATgW8FXgQ8eUEdHwv8OnAO
+uAb8EHAf8CHgVuB5wMuApwI/b2af5+7lYdfR8JnA1wJ/Dnwv8D+J342ft8x7IiIiIjKLAu1dfOr2
+7PDhf07s94B5QfbZ+7MQOaW+C7ib+KH8Dnf/nsZzv2tmbwV+CXjWgjp+AjhPDGif5e5Xpp5/u5n9
+Ul3P04iB+48eQR1Dnwi8F/gCd7/eePy/L7gGERERkYU0R/tYnd6A8eR75o2h5iffGxGZZGYZ8GLi
+B/T3p4JsAOqs8UuAwZw6ng58dl3HC2cEyMN6fgV4K/Fnvxcddh3N6up6vnEqyBYRERG5IQq0a+PJ
+evNy2jYqc5AwML5uso79HEszJjLSy9Q5Uf+MHcgOuhCcsas7c8r57LY5jEXoROSQPAW4WJ//xLxC
+7n4/8F/nPP28+vYD7v7+PdobzvN+qpk1v6sOo46mD7n7f9ujHhEREZF90dDxkcZSYOZ4vRLaoYyk
+ttF/ji9Zu0ynm33ZY4tvG/4AMaf/B32Pprvgc1Zf27W1uQJwkeP2yY3z+/Yo+y7gOTMe/4z69kn7
+WNU7A24BHj7EOoYc+P0l6xARERFZmgLtoToYHs9T9lHAd+PB9uSK5LNi1WONG4fXNavRBR2ZXol9
+OhG915rs09c9WuHcJt7uXUvA71qJXEG2yEm4pXE+cyXwhgfmPP4Y9v9zowMrh1xH08yh5wfnwF8A
+0O/3ec973jOz1J133smdd955uE2LiIjcxC5durTUdnuDwcwZaqeOAu2h4Rhl9/pfcLZrCPaSm3vt
+MjlMe3bJ/QTzu3cPW7L15p5de7XRGLPtjTpsup5dwXbzR4Xx66avz30q+LZm/eM7zQB71zByjSsX
+OSkHHZsz3D7rvcAL9vG6+w+5jqZ5q5HfgPj2PPTQQzzlKU+ZWeLVr341r3nNaw6/aRERkZvUvffe
+yz333HPS3Tg0CrTl0CjsFXlUa2Z+Hwt8cEHZx855/BHiXxVrS8yvnucw6jgKo4z/8IfKJEm4ePHi
+zML33nsvb3zjG4+nZyIyU7/fB+DZz342eZ6fcG9EpCxLbr/99j3LPfTQQ8PTWxaVO2kKtGv/+hVP
+VZwoIjLfHzTOnwr8zoKyT53z+O8CnwM8wcwe4+57DUE/qjqOwug7ZDgSpyiK5j8GROSU0udU5KZ1
+quM3BdoiIrKM/0nMal8AvhZ4/axCZvY45u+j/Z+AbyR+Mb4C+EcH6Mdh1HEUekALqNh7DruIiIgc
+3GOIu2f1TrojiyjQFhGRPbl738x+HPhW4NPM7FXu/r3NMmaWAP+GuMr3rDreZmbvAj4T+DYz+113
+f+u8Ns3sycBHufsvHmYdR8HdV4+yfhEREbm52MRqziIiInOY2TngfcDd9UM/A7yZmMF9IvBK4n7b
+7yYOH3fgo939/zbqeALwTuK8KgN+Afi3wB8TFyZ7DPDpxP2ynwZ8r7v/g6l+HEYdfwo8HvgJd3/x
+jb0zIiIiIpOU0RYRkaW4+3UzezbwNuAO4Pn1MSoC/DjwW/XtrDr+xMw+G/g54MnAXwOeO6tofVw7
+ijpqp3pul4iIiNy8FGiLiMjS3P39ZvZJwLcDX07MCm8QF0v7EXf/WTN7IeMgd1YdHzSzTwO+CvgK
+Yvb7duLWXY8AHwB+G/gP7v57R1XHoj6KiIiI3AgNHRcRERERERE5ROGkOyAiIiIiIiLyaKJAW0RE
+REREROQQKdAWEREREREROUQKtEVEREREREQOkQJtERERERERkUOkQFtERM48M3u8mX2fmf2hmW2a
+2SNm9i4ze5WZdQ6xneeb2a+Y2SUz2zGzPzOznzSzzzqsNkTOkqP87JrZq82sWvL4/MO6JpFHKzO7
+3cyeY2b3mNkvm9lDjc/Qjx1Rmyf2vavtvURE5Ewzs+cCPwmcY/e+2gb8b+A57v5/bqCNNvBzwJfM
+aaMCvtvdv/ugbYicNUf92TWzVwOvnlH3NAe+0N1/8yDtiJwVZlZNPdT8bP2Eu7/4ENs68e9dZbRF
+ROTMMrNPB94CrAMbwHcCnwN8EfBviF/OHwf8opmt3kBTP874y/4dwJcBnwm8BPgg8fv41Wb2d2+g
+DZEz4xg/u0NPBj55zvEpwH2H0IbIWeD18efAfyUGvUfhxL93ldEWEZEzy8x+E3g6MAA+z93fNfX8
+K4HXEr+o7znIL99m9oXA2+s6/hPwN7zx5WtmtwL/E3g8cAV4grtfO9gViZwNx/TZHWW03T258V6L
+nG31Z+o+4D53f8jMPhL4U+Ln9NAy2qfle1cZbREROZPM7KnEf6g78Mbpf6jXXgf8IfEX91eY2UH+
+sf3K+rYAvtGnfuF290eAb6/vXgCU1RZZ4Bg/uyJyiNz9Hnf/ZXd/6IibOhXfuwq0RUTkrPqyxvmb
+ZhWov5zfXN+9ADxzPw2Y2RpxKKsDb3f3v5hT9N8D1+vzL99PGyJn0JF/dkXk5nSavncVaIuIyFn1
+9Pp2iziEbJ7faJx/7j7beCqQz6hngrsPgP9BzL49Vdk3kYWO47MrIjenU/O9q0BbRETOqk8g/uL9
+QXefXgm16Y+mXrMfnzinnkXtpMRFnERktuP47E6otwd6wMx69e2vmdm3m9mFG6lXRA7dqfneVaAt
+IiJnjpm1gNvqux9eVNbdrxIzZwAfsc+m7m6cL2wH+FDjfL/tiJwJx/jZnfbFdbtpffv5wL8A/sTM
+nneDdYvI4Tk137vpYVcoIiJyE1hvnG8uUX4LWAHWjrCdrcb5ftsROSuO67M79PvAzwPvAv4CyICP
+B/4W8Czi/O+3mtlz3f1XDtiGiByeU/O9q0BbRETOonbjvL9E+R5xHlfnCNvpNc73247IWXFcn12A
+73f3e2Y8fh/wU2b2dcAbgAR4o5l9jLsv0ycROTqn5ntXQ8dFROQs6jbO87mlxlrEOaE7R9hOq3G+
+33ZEzorj+uzi7tf3eP5HgB8lBvJ3AV+x3zZE5NCdmu9dBdoiInIWbTTOlxkutlrfLjNU9aDtrDbO
+99uOyFlxXJ/dZd3bOP+CI2pDRJZ3ar53FWiLiMiZ4+494JH67t2LytarCg+/jD+0qOwMzYVYFrbD
+5EIs+21H5Ew4xs/ust7fOH/cEbUhIss7Nd+7CrRFROSsej9xyOfHmtmi78MnNc7/8ABtzKpnUTsF
+8Mf7bEfkLDmOz+6y/IjqFZGDOTXfuwq0RUTkrPrt+nYVeMqCcs3hoL+zzzbuY7wYy9xhpWaWAZ9F
+/Ef7fe5e7rMdkbPkOD67y2ru2fsXR9SGiCzv1HzvKtAWEZGz6ucb539nVgEzM+Bv13evAr+2nwbc
+fRP4VWL27YvN7K45Rb8COFef//v9tCFyBh35Z3cfXt44/40jakNElnSavncVaIuIyJnk7vcBv0X8
+Mn6JmT1tRrFXAZ9A/MX79dO/eJvZC82sqo/vmtPU99a3KfCvp4e6mtltwL+s714lrmIsInMcx2fX
+zJ5sZh+zqB/19l4vqe/+JfAf9n81IrIfN9P3rvbRFhGRs+wVxCGlHeBtZvbPiZmvDvB84KV1uQ8A
+r1tQz9x5mu7+a2b2FuCrgb9et/N64jDTTwG+E3h8Xcc/cPdrN3RFImfDUX92n0LcG/vXgP8M/AFx
+EbaUOK/zBcBfqcsWwEvdXdvyiSxgZp8LfGzjodsa5x9rZi9slnf3n1hQ3an/3lWgLSIiZ5a7/56Z
+fRXwU8QhZP98ugjxH+rPcfetG2jqxcA68KXAM4BnTrVRAt/t7spmiyzhmD67Afgi4IvndYMYfL/Y
+3X/5gG2InCV/F3jhjMcNeHp9DDmwKNDey4l/7yrQFhGRM83df8nMPoWYIXsOcTuQPvBB4GeBf+3u
+3UVVLNFGF3iumX018CLgU4ELwAPAb9ZtvPNGrkPkrDniz+4vEYeFfzbw6cBjgVuJAcFl4L3AfwHe
+VM8JFZHlLLtS/6JyN8X3rrlrVwIRERERERGRw6LF0EREREREREQOkQJtERERERERkUOkQFtERERE
+RETkECnQFhERERERETlECrRFREREREREDpECbREREREREZFDpEBbRERERERE5BAp0BYRERERERE5
+RAq0RURERERERA6RAm05EWb2Z2ZW1cfjD6nOVzfq/K45Zb6gUeYdh9GuiIiIiIhIU3rSHZAzy+vj
+qOo+jDIiIiIiIiL7poz2Amb2wkb288dOuj+PQnbSHRARERERETlsCrSXo+znzUN/ViIiIiIicqI0
+dFweNdz9HuCek+6HiIiIiIicbcpoi4iIiIiIiBwiBdqLaQ6xiIiIiIiI7MuJBtpm9ngze7mZ/bSZ
+/YGZXTWzvpk9bGa/b2Y/bGZPW7KuX28sXPb5S5SfuxWUmb3JzCpguACaAS9qlK+W2SLKzFbN7JvN
+7L+Y2YfMbMfMLtfX+oNm9plLXtuwrbLx2KfW788fmdlGffwPM/t6M0tm1PEUM/txM3u/mW3W7/E7
+zOxrlulDo57UzP6Omf2HeouubTO7VvfjjWb2xfupb6rujzez7zez/1XXec3M3mtm/9TMHrvE6/fc
+3uuA/brbzP6xmf2mmd1vZl0ze8TM3mNmrzWzjzustkRERERE5OZ3YnO0zey1wLcyzho3F7G6CNwC
+PBl4uZm9BXiJu+8sqNKnbpc1q3xz66lZ/duTmf014EeAO6ZenwMXgE8CvtHMfhp46R7XNtG+mf0D
+4J8ByVS/PrM+nmtmf93dB2YWgB8CXj5VTwd4BvAMM3sO8AJ3X3iN9Y8e/x/whKm6WsAT6+PFZvY2
+4Gvc/ZE9rqlZ90uBf1XX1ezHJ9fHN5jZi9z9F5ao7lAWRDMzI875fhXQnqr7AvH/p58GvMLMvsfd
+//FhtCsiIiIiIje3k1wM7e76tgI+UB+PAAPgVuDTgY+py3w1sA4895j69jZgA3gS8MXE4OqPgF+d
+UfaPpx8ws/8H+CniiAEHSuC3gQ8Ca8DnAXfVxb8G+Cgz+0J37+/VMTP7OuBf1vW+F/i9uv6nAZ9Y
+F/urxKD164H/F3hpXeY+4A/rfn0e8NF1+a+u6/qeBe1+PvDLxAB9+EPEu4D3E388+CzGf15/Bfht
+M3v6ksH2lwGvr+v8MPG92iQG7p9b9/ci8O/M7Lnu/rYl6rwh9Q8UPwv8DcbXez/xmh8i/jk+jXjN
+KfCdZnabu798do0iIiIiInJWnGSg/W7gPwO/6O6XZxUws88lDt/+OOBLzexr3P2nj7pjdRs/bWYv
+JAbaAO9092/e67Vm9gTg3zAelv9O4G+5+59Olfv7wGvrcp9NDHL//hLd+wHgEvDV7v5bU3V+K/C9
+xKDwxWb2AWKQ/b+A57v7+xplrS77LfVD/8jMfnBWZt3MLhAz2R1ihv8DdX2/N1Xu+fW1d4hB8o8S
+g+h5htnh7yH+EPAqd/+BqTqfRAx4nwxkwJvM7BPd/dqCeg/DaxgH2X8JfIO7/8fpQmb2FcRrvgC8
+1Mze7u5vPeK+iYiIiIjIKXZic7Td/fvc/c3zguy6zO8AzwK69UPfdCyduzGvJmY7jZjB/qvTQTaA
+u78e+La6nBGHkX/kHnUbcQTAF00H2XWdrwPeXpdLgdcBDwDPaAbZdVmv2/9A/dAa8Jw57X4L8Li6
+3st1+783XcjdfwZ4QeOanmtmT1/imjLgO6aD7LrOPyJmyB+uy97B+MeBI1H/OXwHMci+DHzurCC7
+7t/PEQPyodccZd9EREREROT0O/Wrjrv7nwO/RgyynmpmayfcpbnM7DzwVfVdB77N3TcWvOQHiNlm
+iH8WX7dHEw68oQ4+5/mZYXfq8v9s3vBtd6+I2eKheYuzvbTR/ne7+1/M7aD7zxNHKgx9/YK+Dv0p
+8UeBeXU+AHx3fdeAlyxR5434+8T57wD3uPufLSrs7r8O/Aqxb59gZp92pL0TEREREZFT7SSHjo+Y
+2UcQg7wnEofgDocoDw3nEhvwqcDvHGsHl/c5xMW8IGZgf3FRYXd3M/sx4Pvqh565RBs/t8fzf7DP
+8s1M90dPP2lmn8B4QbcS+Mk96gN4I/AlxD+vZywoN/wx4KfroH+RnwK+nxgA32VmT3T3/71EXw7i
+SxrnPzO31KR3EOfGAzydOHdeRERERETOoBMNtM3ss4kLez2d5fesvu3oenTDPr2+deBdSwSPMP7R
+wBqvn2UYlL5vQRmAK43za+5+aY/yzaH752Y837ymD7j7lRllpjV/CLnDzO5w979cUP6/71Whu1+t
+55wPF3z7dODQA20zu4X4g48DfeA1cTr7nj6xcf4Rh90vERERERG5eZzk9l4vJi4iNQwg99qSaRjt
+rB9lv27Q7Y3zP1/yNX/WOM/NbM3dN+cVdvfre9RXDIsCyywYVjTOsxnP7/ua3P1BM+sy3hLrNuKC
+YvP832XqrcsNA9rbFxW8AXc2zlvAN+7z9UZcIV1ERERERM6oE5mjXQ9HfkN914nzlF9BHD7+WKDj
+7snwAN7cePlpnlfenD++teRrpssd5g8Jh7Gf9EGuabrsXte0fQR1HtT5xrkf8EgQEREREZEz66Qy
+2t9St+3AfwH+ursXC8ofRVB1FAF7MxO9uuRrpsstWjztJBzkmqbL7nVNK0dQ50ENg3kjDr1XdlpE
+RERERPblpLLDX9g4/8d7BNkAe217BTBonC/zA8L5vYvs20ON88cv+ZqPapz3Fw0bPyH7viYzu53x
+sHGIC8Mtsux71Zz7vFedB/VA4/ycmbXnlhQREREREZnhpALtuxrnCxf3MrNzwKew9zDo5tzlW5fo
+wycvUWa/Q69/t7414DNtuVW0PqfR1u8uKnhCmtf0JDO7sMRrPrdx/pd7LIQG8Fl7VVhvnfakxkPv
+WaIf+1b39UONhz5nXlkREREREZFZTirQbq7Gvdew4ZcSF+naK2j9s8b5wn2MzexO4krnewXS3cb5
+rIXCpv03oFef3w48Z49+GPB3Gg+9Y4k2jpW7/yHjhcwS4AVLvGy4z7UT90CfWz3xz/X5S/wo8QLG
+c58vHeHWXjC5Lds3HGE7IiIiIiLyKHRSgfafNM6fN6+QmX0c8F0sl1l+Z+P8+Wa2KDB+PcsF7480
+zh+3Vwfc/RrwbxsPvdbMFs1r/ibGmfUK+JG92jghw34Z8F31DxUzmdnzmPyB4Q3zyjZ8DHHe/rw6
+Hwv8E8aLjb1xiTpvxPcR9ww34MvN7IXLvrDuq4iIiIiInGEnFWj/QuP8dWb2rOkCZvZFxGzoGsut
+dv2LxAWyjDin+43T82vN7KKZvRn4Siaz1fM0h7U/zczuXuI1301cQMyI+zH/VzP76Kl+mJm9ghjQ
+QQwef8jdl93m6ri9Hri/Pr8VeMf/z967R1u25XV9n9+cc639OK+qU6fq1u2+gBDAQZqANE2QEAUV
+FBNIGBAeQ4UGhAYSDWYIKiEO4vCFEAEfaDAiTxUdiUSibTeP8BQQmwYSBMEICP2g+166763HOXvv
+tebvlz9+c+19bnW9urtuV13q97lj3rXP2WvPNdc6596q7/z+HiLyobeeJCKfCfwDdoL4u83sR+8x
+99Sv+qtF5I/f6my3CvXfi0cICJ5D/fXvxr3cEzP7ZeAvTEsA/p6IfI2I3DYlQUSyiHy8iHw7j2b4
+fxAEQRAEQRAE70EeVtXxrwc+HxdPl4DXiMjrgZ/HhdfLgZe1168F3gp89t0mNLMzEfnzwFfj4uiz
+gD8gIj+A52+/F/C78VD1/7fN+6X3mPMtIvJjeJ7uAvh/ROQ1wJvZhb//ezP7X8995pdF5POB78BD
+nT8K+EUR+RHg3+MbB7+LnUNuwI8Df/pua3mYmNmzIvKHgFfjz++3A68XkX+F/8x6PM/6/aePAL+E
+/4zvxtRD/U8Bf62NLxORH8U3Kz4QD/GfNoQG4HPN7NkHdGt3xMz+nIi8DzC52X8S+OMi8jr853gK
+HOLF7D6EXUX0F6pIWxAEQRAEQRAELxIeitA2s6dF5L8G/ilw0r798jZg54h+F57D/Nfvc+q/CnwA
+O4F3BfiM85fG86g/DXjVfc75JcD34y3GjoDPvOX9H+SW8Ggz+8cicgMPcX4CF9y/p41pHVM4/D8A
+vsDMNve5nvvlfgqx3ff5ZvYjLcrg7wPv1779O3l+IbPpnr4X+MNmdj70/m78U9zV/np8A+L8M56e
+1bO4yH7tfc55L+75fMzs80Tkp4A/B1zE0w3+M96xQNr5Htr3cvCDIAiCIAiCIPgtzsNytDGznxCR
+lwF/AvgkduLtzcBPAd9hZv8coEUTnxend5rTgC8Uke/ChfRH4o75bwK/AHx7m7eem/Ne6/wpEfkQ
+PJ/697R17rMrzHXbOczs1SLy/sDnAZ+IO/QnwBnwJjws/tvM7F/faw3nrnG/VdBfkPPN7CdbKPcf
+AT4ZLzp3BXeafwMXmf/QzL7vnbiutbm/UUR+GPgi4OOAKUz/V4HvxkPr33K7Se5wL/dzzv38/L9B
+RL4Fj5D4eOBD8UiMOZ6q8Abg3+AbLq82szfefqYgCIIgCIIgCB4XxLVpEARBEARBEARBEAQPgodV
+DC0IgiAIgiAIgiAIfksSQjsIgiAIgiAIgiAIHiAhtIMgCIIgCIIgCILgARJCOwiCIAiCIAiCIAge
+ICG0gyAIgiAIgiAIguABEkI7CIIgCIIgCIIgCB4gIbSDIAiCIAiCIAiC4AESQjsIgiAIgiAIgiAI
+HiAhtIMgCIIgCIIgCILgARJCOwiCIAiCIAiCIAgeICG0gyAIgiAIgiAIguABEkI7CIIgCIIgCIIg
+CB4g5WEvIAiCIAhe7IjITWAGKPDWh7ycIAiCIPitzBXcMF6b2d7DXsydEDN72Gt4JPiaV3yEAaxM
+WWGcoWwAKQnJPlbjyNlm4HQYuDkMPDtUnh0rzw2V3HV0fU/X9cy6wl6BvSIsCxx1cFTgsMA8GQkj
+mSGmPHNjzdM3VjxzY821jXKqmVNLrCyTSiZ3mVwS8z6zP8vszxJ7vdDZpo01SUByIuWMJeFsGFmN
+ldU4crqpnG5GzjaV1aCMClWFUcE0YSYwHU38iGAGt/5uyPYoIH4eAoq1AWKGmJHa59UUNaO291WE
+2uYQhWSQ1CgYxXwkjASIGEkgdSAFUoHcQ5kZZWbkztfzutcPQhAEwUNEREYgP+x1BEEQBMFjRDWz
+R9Y4fmQX9p5mKymlaTYTRIwmJxHMh+DCdhrsxjTJVvUJiIh/Jgk5Q0nWxKgiBin5fLs5rX3eEDHA
+2rQG0q6fIJv/8DpLZDEQQQBTKCYU82OH0ImwSZCTYQiKISaYGBiYGK6pZfscnnfc/QsxMPEjggvy
+9o82gYyB+hsogrZPm4A1cT496mTt/s3vKRtbkS3mQlsMEtKepW1/REEQBI8Q/n9REV7ykpc87LUE
+QXAfbDYbnn76aS5fvkzf9w97OUEQ3CdvetObJkPwkXaMQ2g3VDxd3ZosFGQnpieBK0ZKhiQXzufF
+NpwT2JiLQvwvXTkJOQulJEoysIqogYqL5tQEd2oiU82FrMtTtiK3iW8RyAKdCDPELRTbudBVQU0w
+ElWUIUGXYTSwalTExT5gJpjtxPzuSu1OblHeZtPadmeZ2E5s+35AmxtUPI5S21YFApLExbMYSWy7
+aVCADne0t5sW5j+LaSMipd0mByG4gyB4dHgbcOXk5IQ3vOEND3stQRDcB69//ev58A//cF7zmtfw
+8pe//GEvJwiC++TKlSs8/fTT4H/2PrKE0G6YnJPLtnNOJzc7bUWuudhrYjvtPtWOk1SVW9zsvBPa
+aiAKTAJbts72TkTa1iZ3ITw52n79ItCLMJNENsOqu9laoVoT2iaMCENztMfsojebMU76valmDxl3
+x3t3PGdm7/Q+mLX7lO0b079tu3Q/YwoXN8Qd7XM7EyK+sX0T6VoAACAASURBVJAwMh5zWabbni6o
+TWjjz3xytUNkB0EQBEEQBEHwqBJCu6Ht6ILQ5fWtYeJTiPfkPj8vfNyayH6eUNy52ikJKSd3xMWV
+uuFu960ieyu2wUPCZXLPxQW+CCUJXUrMUiKrO8laXcNXFdR8dAhFhC7BhhaanTxUW3VauMdnb8X2
+5HJbc7SbSz252TIJ6q3zbC0sfHK73V33B9FC2pvDPz2dKQ97K7JbfnbG12ZT2PwUUZD83iWde+aT
+tR4EQRAEQRAEQfAIEUK7UVuMdJXJkW5u6zmxnRMujK0J563QPh/e3TBgK1bdlcZsUoiklICMpOzq
+8Xy2t/nVRaaRSZJIksmSyCmRE3RZ6LOQq6HVUIHRjM6EqjCqkc1Dy1MLMd9uFJg76VMRNExQnULF
+dyJbleZgN/N7F9X9vKSIxBRw3/Kpweds3zea4y4u0rMZWZWsRlElmxeIS60Y2rQsyUAWJE+v28XC
+0Q6C4BHkmWee4amnnnrYywiC4D7YbDYAfMInfELkaAfBi4SrV68+7CXcNyG0G5PQVqy5qecc7JYT
+PTZnOjeRKrd0Id+a0FNicxOrU+j3JEw9RzkhUkjJxfQkVWmB0v5vF9cpZVJK5HZMIpTsQnuWEwlF
+E1S8yFo13B1W9SJj50R2bqHsySBltiLbTFqBNDC1rchW3bn0U6Ezk13QOG3F240J8I0IfG4PH2/5
+42ItZ9sFtYtsP2bbDZEmtBNYE9dSgCLItlra+Vj2IAgeRUTkfYBfaV9+jpl927s4zyuBb8b/o39f
+M/u1W97/ZuCVwK+a2fu9G0t+tzEz3vjGNz7MJQRB8E7Scj2DIAgeKCG0G7UddRKSzVyexGlOtBBn
+d2yTCqmFdO9ylqcj59xstm6251lLK+7lYlpSbkJ75yxvxXZztNNWYDdXO7XQ8Zzoi7vBVYwRRUzc
+yVZ1N7s52hkhizw//N385qwJbdPt0r011zmhPTnWu0rj28hwpkPz6P35THna0zNoRdHq1P4LI1df
+Z2kCe3K0RbyImk8o7cGL/7aGox0EL0Yes12xlz7sBQRBcF9sgKeBy0A42kHwaPNmdsm+Lw5CaDfq
+pDKbW5tabnROu5DxvG03ZR4Hfa7l1y7UXHbFzdI5Mb4tbLYrEratzI2cq8qdtipfzoeLS24h44ks
+qc2r21xlSUZOguXk1btFqCJsDIop2SrZEjkZBRhh62JLaeXGzDA1VKZNA1+zq+opw3oqlubtvHzv
+QLZV2l1se8i62M7FN/N5sxi1Cep3ENgtGdySu/4UgQ7oXGTbrSI7esAHjwAi8oPA7wZ+0Mx+70Ne
+zm9lXiRhLAJE1fEgeHHweuDDgdcAUXU8CB5tngJeXBFjIbQb2oS2SHNkJXl177QT24MJqQIYpgqm
+W3fWneJdYTMX27sxCdWpBRZ49HOd3GQEk9QKkqWto+052ZmS8lZk5yk/vM1Iq8SdMuRCK36W0Jzo
+K3RWKTq6e4wx4mKYJvC1ueumhqbz2eJe3GxbebwJXJvuxHY9radibQlr4eot1JypR7ehk9gGxHQb
+Kj6JbJmKnyVary9BOrCp91dpk07nvwj+yh08FrxIBOCLFzP7VuBbH/Y6giAIgiAI7pcQ2o1JaKet
+wPWK4EWEMrnamlrucXO0twJxW+Ns52jLTmRPPbW3H53EtnevagXDUsviTs8LL09bce3HsnW0pyJs
+Lag7JS8Wpi601RKmmS4ZpY4UMsUqoxg5udil5YFr8utpFurova11e18713qHi+bJ0U+kbRTAJLIz
+5mLeppv2OVW8U7mcK3yWWl72tuJaauHiTWxPjnZT6O/YbiwIHj6RzBAEQRAEQRBsCaHd0KbaJnfV
+tZ60FGHZtp0SA/RcFXGeVy+c55Xlnt68RRlqa5Nl6mHUav69aZgaaoppxTRhljGvIuYR660tl4l/
+Lk3XyR4KnsxzybMKJWWKZArZQ8dRskJuwlaTeUvvNBVK27Uys+fdi52/od19CUiybX/r57VBY+dU
+00LNtd2/T2G7c2R6wExKHSktfDxPYeO7kPZkk2MeBEHwSFDvfUoQBI8WTwJf2Y5BELxYyDlPLx/p
+P3tDq2yx541kTfdpK789KlT1183NbinFW4FtqmhVVOt2mOq26vjk2BpGNaVapZoyWmW0StVK1YGx
+DozjhmHcMA5rxs2Zj2HFuFn798aBWkefB0OTYdmgmwZIZ6QOchFKTnS5hZ236uRi1Y9UDyIX3YbB
+nw8fP1fVDdMKTRhvHfvkYptkSFJICqmCKJIUyUoqRspGStbE/FRd3HOyrRU+s8nB7gQpXm1csvn6
+rK1v2ghpI3h8EZGXichXiMhrROTXRWQlItdF5JdE5FtE5CPv8tlvFhEVkV++xzVe2c6rIvLe577/
+LSKiwMe0b31sO+/8+JU7zPnBIvKNbZ03ReSaiPyciHxtq9R9p7W8z7m5P7t971NE5HtE5C0ickNE
+fkZE/piIlFs++4dE5AfbeTdF5KdE5Avvdu/v7nrvMNenicj3tXWcisgviMhfEpGju3zmtj+DdxYR
+ORSRLxeRHxWRt4rIWkTeJCLfLSKf+q7O29gV+giC4EXCk8D/TAjtIHhxcU5oP9LV0cLR3rJLnBbb
+hTaLTj2krYnu3ZgqjE/mrlfqVmqtaBI0J1SnHOWpiJi0QmKKTmJbK7WJ7bHCWIVRBRkUsREsM0hh
+yJlxGghjEupU/Tx5ODmZtgHg95EGyCYUSz60kqQJbXYh7ZOoTmKtqNsUPj7laU/l2NvmQmqVy9OU
+n00L//bhBdr8e9v6ZWaYnuvM1YZhWBasNBe7E6RrRdpya/PVNiikOfheiDz+Qvs4IyIfA/xA+/J8
+HEkH/EfA+wOfLSJ/2cy+4gVYwvkEhvMd787zDn8AiMiXA3+eXU+/iQ8C/mPgi0XkVWb27fe4NiLy
+t4AvumWeDwH+OvAxIvLp+PP4+8Cn3nLehwF/W0Q+zMy+6E4XekDrneb6JuBzb5nnA4E/g/+sfp+Z
+/eK95nlXEJHfB/wj4PiW6z8BfCLwiSLyauDTzez0hVhDEARBEASPDyG0J2QXzrwdZqRJUBvN0W5C
+u/0V2x1taSJS0VrRRBPZoCqYJQ81B7YVu7eOdt0621VHqtLENlOjLMQyo2TGXHZDEjUnas6knEii
+SE5bQTvdUmIS2kLRRK5Cxrz3tk1Ce9cH23Otz4XCn+sJvi1sNuWgZy/+tq18LlN5ta2CdmHeqpGj
+bRpt/crZdRe3BFbkeTnZk6M9iXdMd726W7uy4LGmADeAf4YL7n8LXAOuAC8D/nvgfYA/IyK/1Apq
+PUj+R+BrgG8BXgG8DheR59mc/0JE/lvgL+L/KbwV+Crgx/C9o48DvgzYB75ZRJ42s9fc5fpfDHwk
+fv/fBPwH4L2ALwd+J/ApwOcBH9pefwfwD/H+GB+A2zgfBHyBiPwTM/ueWy/wgNf73wEfAfwE8PXA
+v8N/Vp8DfDpuKb1GRD7YzG7eZZ53GhH5aODV+O/MbwB/A/hZ4E3AS4DPAP4I8Afxomuf9iCvHwRB
+EATB40cI7UZKTXJOvaZpoeOyE6yTqJ76XpsZVSvjWKE5rcncFR5TRjNYyRjeLzvlQu4SyZRkRjZl
+rsKeJQ4ko7ORMhh5NLrRyK3qeUkw6zL9LNH1iVKEXAQpCYpgyftOmygIjC33e2wh5VKETKanY8QY
+xagCg0JVoarfjxbophxwm3LChXpLG60kshu7bmTNxW4tw87lum/dbhWoLfJevcq5Wct3bw62dcmd
+7Va2/FxnNGBqpy2tX3fwmPPTwFNmdu02732viPxN4J8DHw98pYh8m9ktv8zvBmb2ZuDNIjKJwptm
+9vN3Ol9EToCvxn+b3wR8pJm96dwpPy4i/xfwI8AS+Dsi8r5mdqf8o/8U+Foz+9Jz3/sZEfk+4OeB
+98aF8UXgS8zsb95y3g8Dv4QL5S8Gnie0X4D1fgS+KfDJZnbe6X+tiPwc7pq/N/BncYf7gdBC6L8D
+//PuXwD/jZmtzp3yM8CrReRHgL8DfEpz1r//Qa0hCIIgCILHj9AqjW0rrklANiHnqceT4ytN+Hkl
+8V2o+Mg4jgzDwNCOtY5UVS/8JbSq4IXcdZRZT7+YMVvOWewtWB4s2T/a5+Boz8fhkv2DBft7c5bL
+jvm8MJ8n+j7RdUJuI7UkZcug2RizMqTKmKofZUSzQoHSZfq+0M86Zn0bXaErmVISOQtlGmV6PbU3
+k9ZS7NxzEQ/d3rY0S4JkIZVE6oTUC2mWkLnANGZt9Ak6z8fW4sMmkd35a7JsW4pPoeLFfLRaaa1I
+Xbjajytm9rY7iOzp/RF3XMGd7d/xHlnYnflcXJAC/A+3iFYAzOxngL+M7y+9FPjku8z368Cfvs0c
+Z7grK3iY9E/cIrKn894CfFc773e9wOsVYAW86haRPfGXgJ9r5/3RW/PL300+E//5r4DPvkVkbzGz
+vwv8ZPvycx7g9YMgCIIgeAwJod1IKe1GE5NTxHIy21YcT+zadZmBVmUcK+O4E9vjODLWVgzNPC8b
+SaScyaWj9DO6+Yx+uWC+v2R5sMf+0R6HF/Y5OFpycLjk4HDB3v6M5bJnscjMZplZE9qlCKm4A+xC
+W6jZqEnPiezKINUd7SxNaE8iu2feBHffFUpOzxPZXRG69rW76rvCY5PITs35z9MGRfYhRZBJZM8E
+OS+2J5HdC9YnrEtoJz5K2gruqdI4MlUm9yrqZTsmR1taY7EgABHpReS9ROSDWpG0l/H8/8d96MNa
+W+Pj2vFZXODeib97m8/cjn9yF/f4Z8+9/sd3mWM676KIHN7y3oNcrwHfY2a/cds3PdJgCu0/Bl5+
+l+u9s/xX7fhDZva2e5z7w7jY/6gHeP0gCIIgCB5DInS8MfWJnvKUtznL53p3eTS1Fwmr28rhRlWF
+1kNaEFLKdN2M2dwd672DPfaOXEzvL3t3fYs7wHm1oVttmK823FiPnG5GzjaVs03F6tjGQJdg2QmL
+Tlh2wqxk5l1i1iVE8OJqKNV0m1ueVD2d3AQzoTejDAPdMDIbBjajshmNoRrDYIxDO26M9Wr0UUY2
+m4pWo1avqu4bElOP8OnZ7FpzSYu9l2m3Ykr6nkquyZQEri3mHf9NnJztDJbEC6AJYLr9eSSbnPUU
+IjtARJbAl+A5ti8D8l1OP3mPLOrOfDD+2/76uwhkzOytIvKruAv7wXeZ75fu8t6z78J5B3iO+8SD
+Xu+/vst7sHOTAf6TW75+d3gFfh+f0KrE3w9X3/XLKZ56fi+m2JwgCIIgCJzKnTt2PQPAZrO5w/uP
+HiG0G+fLgm3bWk25xcn7OKsZoxqDVTY6MpoLWwWSJFIulNIz6xcs9w44PDrk4sUDjo+PuHR8xKWL
+Rxzszcld8lGEg/WaC+s1N9drTtcDp5uR03XlbDNSx4E6jtRxJKPMMtvRZ6Evib64aFWbenK3Y8vT
+dkcdMD9nM45sxspQK2NVxgpDNcZRGTaVYePHs9M1pzdWnN5cszrbMGxGhmFk3FT/u+G0CyHWcsOn
+sma+ITFZ39ZamiHi4jlP5cxadXL1QnLS8rKlJXWbgIn4s/Xu214pXVoOeAjtx57WVuoHgN/Gror0
+7XKwp1+UxXtgWXfjuB3feh/n/gZ+X8d3OedulbHPC8r7Pe9W1feg13uved5ym2s/CCbV+87k58/f
+vUs+/e59PAiCIAiC2/L00y+eP2NDaDcmR3trYJttTVhrScKqRkXZWGVjlcGq97DGWq5ypisd/WzB
+cu+QgwvHXDw55uLJJY5Pjrl0+RKH+0tKnyldonSJ1WZ1bmw4XY+crgfONmMTtwObzYhYpROliPkx
+e3h3l73Gd23tvmsT1NPwiMxW39uUWttQpaq11uDGWJXNemSzGdisB25eP+X67Caz7iY3yxnr1caH
+Dd6zW8yrq297dYk/ialseW7dwNpDdHe6iW2SO+CqNI2ObOPS2+clodJy4Wm9y9FWkG7ys0NoP+Z8
+By7uFPh7eOumXwCeNrMBQPw/7Glr9FH5hXlgBdneQzyo9T6s+542EP4F8Kde6IuJCCcn9w6eyDmf
+7wMaBEEQBI89tVZqvWMQHQAnJyc888wzLwrBHUJ7Yvor+GT/tq+3wlGgCowo4ySym3ts23Dxntls
+wXK5z8HBERcunnDp8hUuXbnMpStXOL58mcPDfbo+0fWZrktshjOGzRmb4YzV5GqvNpyuB9brkfVm
+YL0ZsVpJVLJVEvq8/GlDWvVwQVXOiWxcpJ4b20rfOr0PakKtyma9Yb3esFlvuDa7Tl+uUVJPSR1n
+eUWRFaJnjCgVZcQwqedCwdnF3E+j9Q0XE3+OLTjfQ86nonLNzc7bEuWYJERyy9BWkEpqvb8T4Wg/
+7ojIbwc+Gv8V/otm9pV3OPVuzujk5t6rVsXeO7m8O/E2PCT5ifs49yp+b/fKKX4hedDrvdc8599/
+kPf9m3jrsP5uVeEfFC95yUt4wxve8EJfJgiCIAgeW5566qmHvYT7IoT2ROuj7cLTnWEwTGX6gqGq
+t85Sw9Sdi5IzXUnsLfe4cOGYo6OLnFy6xJNPXuHJJ69w9eplLl065ujiMftHF1jsLd3NbtW+lcRY
+gU2l1pHNesPp6ciNmyvPoR58oF7lO5PJklAVck5odvGqmlCbjnhF9O2mwU5oT622JJmXfBO361WF
+lEZyrpQyovU6XtD5Grlco3TXSeU6pOts6sBGB0wHzCA1h9tEKQVyYtsiDVpYvkz5660FeeuxjXi/
+ckm7VmCpiewkhSSZYkrRTE5KoT0HSVHJ7/HmZede363Y1yvu8t71drxwj2t94D3ev1+n9udwwfdy
+EUl3qL6NiFzG852nzzwsHvR6P+Ie1zv//oO875/Ge2W/QkRKq0YfBEEQBEHwghJCeyI1oa0utMfm
+9pq2kGdgrMZYm9BuIcw5JfoC+3v7HF885srlqzxx9QmuXr3M1Scvc/XqZQ6Pjjg4OGTv8JD5Yk7J
+qYUNCqMaaVMxNtRxxXptnN0cuHF97UXKRlyIA0XERWaCapliGSUhZNQKZhmzhKk0sQ3nhba0Mupp
+ErWpkFKHSIeRycUondF1BlxH5DlyukYpz5LK2yH3Hs49rLBhRR1c0Hv8t0IScrImtM1F/dZ1difa
+wMW2bVO3/dhaeU3t05JkUurIUsimFJRium3rNVU+Dx5bzv+/626O8xff5b1faccDEfkAM/t3t54g
+Ih3wqfdYy9QuanaP874P7+l9AfgU4H+/w3mfj/+nYO0zD4sHuV4Bfr+IPNHaij3/TQ/xf2X78u3A
+69+Ndd/KdwP/JXCEtyz73x7g3EEQBEEQBLclhPbE5GjLLqFTtYlt3B0etrnNTSiK95/uu8T+co/j
+Cxd58upVXvKSl/LEkyc8cfUyV568zHK5x2K5x2K5pO97UkpkSUhKlE0l5Q1wRq2Zzdo4Ox25cW3F
+aImqwmgeLF2SUJK34lIrGBmTgkjBrIOt2PYq46rSHGNPhBaUnHD3OBs5z3ykOUjHWLx92NgLkq6T
+0nOUco3c7UPuMRGqVWydqSu8fVgdEcmQvFCZt/wyF9r+YF1st4Rta+425k52MkNwUc4ktsWFdpZC
+Tj1F1d1sdaGd5Fwh8+Bx5bwo/hxuU6FaRL4Yb+10J8f5h869/pPAF93mnK/D+0PfjTe34/vd47xv
+Br4S7039V0Xkx27tTS0iHwp8efvyDcD/eY85X0ge5HoN34j4RhH5lNu441+OVxo34JumHPsHxLcC
+/xPwXsD/IiL/1sx+5E4ni8hHA9nMfvgBriEIgiAIgseMENoTrSbNFDpuurNorH1dTTDz3OAihXnf
+UfqOOR2XLl7k8skJT1y5wpUrlzk+vsjh0RF7e3vM5gv62YzSdaRSWmh0cke25SGrJWqFYais1yNn
+qw1qGSVTzcOpXcWat8RqydCGz9XKdLujbaAqzdH2awiGpOz52dbKo1lBrUNzh0hPVb+eSSGVRDfP
+mMzQ1FElYTlDKZSza5Sza6TTns3QY7ZqQxA8l3xbuZ1zCpppTUxFx1tLtMn5nnKv/Z/c/ikiFBJZ
+zO+49dcOQ/vxxcx+WkR+Dm8n9UUicgx8Oy56nwI+C3eifxT4z7mN2DaznxGRH8d7Jr9KRGa4KHsO
++ADgC4GPBf4lng9+J34Md0qviMjX4kXanmvvDWb2a+16z4jIlwHfgIu+nxKRr2qfL7h7/KXAPp7r
+8aq7tdV6oXkB1vs6fOPjX4rI1+GbJVfwjZLPaOf8OvAXHvB9bETk0/EK9fvA/y0i34lvCvwKnuXy
+JPDhuHP/wcAfw3tqB0EQBEEQvEuE0G5IEab62WatCBrshCFedEzwPOm+JPpugfRL6JZcPrnE5ZMT
+Ll++zKVLlzg8OmCxXFC6jlwyKSdvXQW7vtxMgre151L16t/D6AXQpvUgaJIW155cbGO7NYm/whSs
+okobzVNuRceSgGahFiOr50u7u+1h5ao0oZ5RmZGK0EnPIvVYKkg/pyz26G++ne7m2ynzOavVc4zD
+DeqYGQcQG5roH9satw21n/88zZCt2G4iWyahnckkF9ckMkLGtkKbrch+sRVvDh4wnwV8P3AR+PQ2
+Jgz4WeDT2DnOt+PzgB/EBd8r2YUvT3N8DV7J/G5C+ztxR/Z9gT/RxsSvcs7pNrO/LSJHwJ9v1/y6
+W+YyPBT9C8zstXe55nuEB7zebwA+BhfW33mbed4E/AEzu84Dxsz+lYh8LJ7P/17AH27jHU5t49pt
+3guCIAiCILhvQmhPlNQiml1wqti5BrOCmItQwcO++5ToFwu65SHd3iGXT064crkJ7ZNLLPbmLBYz
+cldIJbf+0OyqmU9CeWrH1YqsDWNlGL3NlrXK3IiSVJCUvJCZnmvZ1U5xBas+X22tvuokshNJsott
+9d7VKbcc6ia2RfxurRVHE+ZI19P1S9JsifQzynKP+f4h/Y19ynxBnnX0Nzs2q8JmBRsbsclJV235
+2638uEyJ2VPcfUvtft4/7flu3exEIVMwihiZySXX7f0Hjy9m9rMi8jtwkfsH8YJX14H/D2/19bea
+m3m3OX5RRF4OfAXwX+DO5nO4+/o3zOy1IvJKdgLsdnPcFJGPauv4/XhhsOX09m3O/yoR+We4a/p7
+27oV+DXgtcBfm1zwOy37Tmt5Ic57kOs1sz8qIt8DvAoPFd8H/gPwXcBfMbPn7jjLve/nXvfxkyLy
+AbjQ/yTgw4CTdi9P4xsqPwT8H7fL1w+CIAiCIHhnELMQKwB/9pNeYQB1MOraqBuoY/Owm0Ze6chp
+HTjTgVoKywuXWF44YXl0zEvf+7fxXu/9fjz1Pu/L8ckJ3bzQzQrdvFC6zkfpyLmQZBKWcO3Zt3Pt
+2bfx3LNv45mnn+FNv/E0b37L07zlrb+JpA6kR1JHlkxJaXcsPbl05NJ5jvTWJhcX7dUFN7jITpJd
+qKfWSitZm7+D5KHjIjOQGZJmXqyteNE2kcpYTxnHM8Z6yvXrT3PjxtNcv/5Wbt54mtWNZ1jdfIbV
+jWfQuvIxrlDTbZUzk0Q1RTGq6dZ9F1MfdCQKYh1Zekqa0aW5txczo+C7QoI2ie1iG+Af/fi/iSDy
+IAgeKiLyBuClL33pS6O9VxAEQRC8gDz11FO88Y1vBHijmT2yvb7C0W7ouWMVGMRQOd9ySpCc6LvO
+c55ncw6PL3B4conDk6tcunyJ/aMD+tmMVAqS20jFhbCr21YQzFtsGWCimCgkRZNSpTJaZaMVIZNS
+RSy702wtbNrAVNGqKNVdboFteLa2PtotdFvJmGVE0849VoFkSKpgIyIDyAZkBdqh9Bg9xgyRhFqH
+iJBzz2xmmGVS6unLnNPU05HJKoybG4zDzVbIzV15Q7ZH0K3tJCIkphDygtAhVkjSkZLnsvumRHOy
+bXePnlYZm0RBEARBEARBEDx6hNBuaBNwFWMERnZCO7ew75QzXc7kIvR7exxfusDxE5e5ePUqR8cn
+7B8e0M1nSCmkXJCUYRoeo91yqlvIN14N3FIbolSUwSobG0mW21C8SK94EbAWap7wKugpneuPLeYF
+z7ZyNoG1ommWoAluUkKsgiXPq95a3RmTjLLAWKJAzjOggMzIKTGfFXKe0/d7zLsFHZmiQqrKJnWs
+SUhVhDWGojI50I5hmOQWLN7Cx61sh/fPbkJ7ykE3/FltZwkTOwiCIAiCIAiCR5MQ2o1JBlaEKsoo
+RhWjtBRjEqQu0/WF1GeWh/tcvHSBkydOuPLSqyz2L7LYO6Cfz1xk54zkAqk0of18R9t7W7u49h7U
+LrYrymiVQStJKskqCc93lqllloGoIaqI1FbUTL2lVtJtvraIbSuRY1OudEHIoGWXPp1p4d3TAOOg
+HQtGT04dKc/JyUc/2wM7ZN0vyAppVNhsOLME1bBhAAWVilBR6lb661RWTry6uEhGLDeh3cqgtXD3
+tBXjTWI/z9UOsR0EQRAEQRAEwaNHCO3GOLrUrmpUM1QUS6AZUhIsC2VRmC0X9MsFBxcvcnR8kQvH
+Fzg6vkg32yf3cyRnL2KWBElCzkISaX2l3Y0W8eMu9LmV+DJBDUYVhoqLTHMxasnd6Ml5FhJTkack
+RkqGZCWpIpPIRlt78NbH2hIiilFa8bPmFqu56G6Vvw1BbECmHGr1omaiBeh3ofSpQK/UxRnsD2RV
+utyREUQrSRKjrhl1jVlr+WVG2trS0lR+wfur+dGLok0i2jBTtG0eYIaF0A6CIAiCIAiC4BEmhHZj
+rJPQVi/aNYV054Q1DZjnHfODJftHhxxdusTRpYscXLzA/tEhUhaY9CDJPysgSUhZWp63IVN/aaMJ
+YdtpbfOw8mqpCW05J7Qzph6CPuV7T5nLIE1oQ9ImuMVIoi6mbeeCe/X06fsFRFz472Y696qSTP19
+azndJMCdcN88SEiuLObHZFVmOZNTQkzROgCGDIKNlTqut32zzay1TxMg+waCTSI7t+ucy83GMCpV
+W2X08+XbgyAIgiAIgiAIHjFCaDfGsQK4m22KiaHJ0VIWaQAAIABJREFUo76tgHVCWfQsDvY4PL7A
+hZNjjo4vcnjxAvsXjlArDDUxjF4Xm9Y6K+fkkedinpPtydm0U1qRL3eRzRKqwliFoSYX2TmRLGOp
+hVdPhdWgudS4yE4usnMyUlJU1MV2q849FRMTUZJ5A2tRti2+krFtxeW9rCsZP9eLlQmivuMgMm0A
+FHIW8txFts7miCk2DoybFVoHzCq1rpuT7fnjqbnSPhI2hbNbhtbEq5V1Y3K0fdS2YTDF8wdBEARB
+EARBEDx6hNBu1OpC25rl7CJZKH2m6zu6Wcf8YI+9C4ccHh9z4dIlDi5cYHlwyHyxZKiCboxR1d1s
+8SJlSTzHOuEh2IJ5b+vJkdUKVj0k2kBVUBVvz2UtiNo89Fu2vbTleR1jk7rYzi263L9u9jHaRLa2
+a/tnBPXSask7bSECmv1cmTKpp7VVF7qpCXfBi6khpDSj9HtIJ6R5zzicMmxOGTY3UF2BbFA7pdbc
+8tN9KIlqhWqJartO2mxfm/va0oqnTRsgJluXOwiCIAiCIAiC4FEkhPaEN51GilBKQrpE7guzxZJ+
+uWC2XHLh5ISjkyscnTzBwcXLLPYv0PULLzC2FdcuAl1gG9kGxMZzw5ojnb1I2XgKdYPUEdHqBc7M
+ne+pTReiXuwsKaMokmRXFA08xN3ATMjWCpqlhMkk6ltbMAyRimEtV9tFb27524jnaCNgqWJpwNKa
+lM/I2dcsKftsBmr4feZETj1FYLF/yGY4YqzHICtytyHnM0o6JWclZyNnZVThbONjtTFQBRVQmXQ8
+yZ8SWGtX1sLOkRDaQRAEQRAEQRA8uoTQbpi50M7i7btKn+kWPYvDJfODQxYHhxydXObCyWWOLl3h
+4OJl5ntHlH6BSAG86ncSQUTJMuJNtQyxDaJr0I0XFqMAHZBhPIO6RnTciWw1r/uFt8WaKpOnpKRU
+ScltaWk9plPLeTYTTJOHkKthTTgLLUR8K7S9WrnZLlR8m8MtqbUcUzQNpLQm6RngYetmGTNBm8tu
+CJKF0vXMup75cMA4XkD1JimdkfMZmRtkrjProe9g1sNmVK7dbBXJR0XVtwJUtysiJ8jij+N5Ke2t
+1VcQBEEQBEEQBMGjSAjtiSa0RRJdEfpZZrbs2TtYsnfxiOWFYxfZJ1e4cPIEB0eXyN2C3C3wvOXq
+RciSh2gnGclUko2InkE9g7ryOG3rgd6P4xppQhut7uw2u9hwJ9dzxpvQzoJMfbNb1XATIaUmtEUw
+9bZhXrBMzolsbb22AYxs7hmLFe/+NfXpbo62pBFNa7J5T/CUM8kSatkrmGvGciblQul7+nlhMR6i
+dhPkjJTOSNwg2XNkm7NcJJZzYbkQ1ptKkjXjsGa9qlQxqlVMp9ZfQjIhGaTmapt5grtJU9xTHHwQ
+BEEQBEEQBMEjRAjtxnzmxbVmy57l0ZLl4R7LowOWFy+zd/Eyy+MTji5c4eDoEou9I/rZPpI6d7PV
+86cTQvF+WUhdUzenDOMZjDdgvAnjDaxW1DpUe1Q7Ts8q6zNlWFV0fRMbriPjTZKdUceRWjcM9Qyh
+MKbEIImSpjBv15opCUm84neWRM5GyVASvgHAADYCI0bFGMGqu92TGGcS3VOI+JyUF0iek8uSbrZH
+3+/RzZaU0m1H33eozXwwY6g3QUa6WWK+7NGhh3FGqnMO9oT9pXCwFFabkVqVcVRqVTYr2CBsqqeW
+l5ajnVqeepaEpqnfuW8ohNAOgiAIgiAIguBRJIR2Yz73St57+z0HR3scHh+xf3yRvePLLI+fYHn8
+BHv7l9jfP2axPKLv9zBLXim8CmLenEqSuCNdV+h4nY09C8M1GJ6D4Ro6DoxjR62FsRbONonVkBg2
+ibo+w4ZrUG+S9BQbM+MmsR4yqJBFWIsfvVKYF21LKZOlo6RCToW+K/QloyWTqJgNYBvMBqptUB2o
+uoHmFPvAW4xZAhNSniFlRso9pVvQz5a70c/o+zldP2NW51RbUG3BaAvQm0gaKH1irh029Mg4I+uM
+w33hcA+O9oX1OlHHJrTHypkYZ2owGFQ71+irCW4RcusdboiHyofQDoIgCIIgCILgESSEdmO+aEJ7
+r+foaMnxyRGHJ5dYXrrM8vgqy+MnmS8uMJ8dMp8dktOCWpU6KlTF+1m3xlSmWF1Th2uM42/C5m2w
+fjts3k4d1myGwjAUNmNmU2estWeoPXU9YJtryHijCW1hXMNmJVR10en51q2ieDJMjJw6ujyjpBld
+nlHHHutnYD1CxXSD6RrVDWNdMeiKsa5QrWitmFZMteV44w59LuTSk0pH6WbM5gv6+ZLZfMF8vmQ+
+WzCbLal1j6p7VNtjtD36PNLnkW4mZOlg7Em1p7MZh/twcV+4cACbTWIcK8NQGYdKqRU2lTEZpkah
+3SKToy1o09WWmtCOXtpBEARBEARBEDyChNBuLJYdAHsHCw4vHnDh0kUuXD5hfvES8wsXWRxdpOsP
+6PIepcxJdJhVTCracqAhIckwElXVQ79XK2xzBuub2PoG4+aM9Sax3girTaKyYJQlygJRpUuVRSfs
+z7ttrraqMY627YkN6iI7GyQlJyg50+VCX5RZD7M+seiLZ12rYlpRrQw1UaowVKhV0TpS64DWsbX5
+cvGeUvEhhSwdiVOS3kDqHMYFlpeQl9iwRNMeo+yRbI/cA72RE0g6w/IGupE8U/quhbSLMTKQGciM
+ZKvef1sVHSt19D7bVVxojwJVhBEX256LHo52EARBEARBEASPJiG0G4uDGQDLoyX7F444vHSJw0sn
+dPsX6Jb7lNmcnDtSyp4abLbNj8454QI4A4JqBgqqmXHM2DCNxLARVuvK2Uo522grrV0g93Q5sZz3
+XDhIjDpnOVduLIzlmTJUbQ2vFUMhu9gmKzl39GW+HfNuzrz3Y0Ix24D1mM4YtaNqodbOXe26po4J
+rQlTr3huqogkb7GVjJSVXAZvzVVGum6gKxu6dEbhJqXeIG3mYHNkatGlgtgpSd9G4TqkM0xHhk3l
+JpXVauTmjYHTGwNnNwdObxqnp8rpyhgHawXcWntvAU2CiudmaxPZIbSDIAiCIAiCIHgUCaHd2Art
+wz32L05C+zJpfoE0PyD18+bsJlr3ahfaWUCm9lj4e1qAgtZCHQs6ZmzM2JgYBlitK6erDaerkdIX
+ct9T0khJM/bmM8aDOSkLN86U5Uq5uVKGUTEbUepOaGeFbHSlo+9m9GXOrGtCu9sJbRgQGzBbo1pQ
+K1T1PPE6JuoIdaT1slbQuq387WJXQTaIDICQs/fWTqkjWU+qfpSxB83I1DbM1qT6LJlrkE9BN2w2
+AwwbTk9Hbl5XTm9UTm8op6dwegY3z2AYz/XzEoMkWBI/yjRShI4HQRAEQRAEQfBIEkK7Md+fhPaS
+vQuHHFw85uD4EpR9rOxhpUesIJqQVkBMBEhCFmj9sbxzdnWhbebOsdYOa8dhzGyGDetN5Wy9ppcZ
+szJQqHRFWM5npDxjPp+xt6rcWCk3V5WhVtRG1CrKiGSD4mK76zpm3ezcmDMvM2ZlTp6ENgNY532w
+8X7YdUyMI9RBqdW8vVit7khbax9mYKaez62KqvrGghQQr1COZqgZrJDICIVEIaWBojcQuUHK3t5s
+HFdUXXN6c+TsFFY3YX0KqzNhdSacrYXN6P3DFQ9jlwySvF+3SWpOdgpHOwiCIAiCIAiCR5IQ2o35
+cg7AbNbTlY6cMgnBTMEG0A1ihpgipkxh4rtWU5OpLVA68nyPTo89l3iYYZs9bHNIWj3HuLhOnV9H
+z27QdUtKv6D0BUk9HYfscUS1A1aDsRpgNRhjncRu9TWlaRglC10pdF2my5kiUJJQBF+rjWAb0A1G
+bVXGU3Pbe+q4QKuAVkxH0NHvFbabClUNrUZVWqa4eqstM28VZgmxDX1JzEpilhJJRiyvUDaYjL7e
+iseCd4nVzFgvlM3aGFUYqrCqYGsYqqLVGKuBCZKA6sLbvW7B7B1/jkEQBEEQBEEQBA+bENqN+cKF
+dj+f0XUdOWeSCIahNoKuvRiZ1TYKkFrYeDu23s5CR5rt0wmkrseGPWw8wsZj0uo56vzt6Pzt2Onb
+yblsRyk9uT+kdJfJ5TKDio8qqJo7ytWLhm2LoomRknn+dIacFLG1r1c3rRDa6CJb14jVFpGd0epC
+W6uhNYGOoIMLbXxDQTDMKrWaV1mvUM2oplQ1zBQxI/lS6DJ0yUdCIQ0gQxPx6hsPmpBO2cyMzQKG
+wRjUWI9wNsBoUDcu7IeK5423Z2umqBmGhdAOgiAIgiAIguCRJIR2Y7FYADCbzei6Qk7ZW3WhJBtR
+Yyu0pRUlc1e7uJstuxxipCOnPVLXUeb7WD2Eegr1jLx+ljrfR+czbFYQUQQlidH3Pcu9Q5Z7V1gs
+XopKRsmoZExpgtiwap5LbVPhskpKA5JHhA11vEYdrqHDgFZFxxGtLrTTlPZsHuLu8wlWM9iAqR/F
+ahPbLszHqoyjUCuMagzVGFtKd7JKUiWpksUoKBkjoYh4tXTJvnZNuHvep63QHquxHo2zAW5uYF2N
+TfWyb0Nt7jXWXGxzZ78J7iAIgiAIgiAIgkeNENqNXFy05TQitsbqDWwzw1LeDujAOrAeKCgFa4NU
+sFwgd+5qiyE5Qe5dk2sBnWE5MxOjJsOKNHE7gA10/ZLZcsl8ucdibx+TDCljkr2lVVWsGla9Ojiq
+/pph56hbZRxgTJVRNtS6wdIGrQOmI8kyyTtT46q3g+rtvzzEfAAbESrC2Nz7gbEO1NHnc5HdRHCt
+iA6kOiCqJK1kqy6+Tb10nEFiqpcuKAktsJlDNSB5Ky/NhhXIMyPfALJ/cByNsfrRnX22Dn8QBMGj
+xJvf/Gaeeuoprl69yute97qHvZwgCIIgCB4SIbQbKQ3+ws6w+hy27tHV2ER2QlNGrHeR3YadF9ql
+99H1SC7YVJ1cpBVNy5C8CninG+ZUJAuqa6yuUV1Ryj6pm0PpMO+t5Z9PgrRwaQ8Xd2FstYJUzAbM
+Nl5V3FaYnWGcYnIT5AxLa8wGoLogJyN07tibIOZFz5hC46mIjcDon7EBqRtSHch1Q6pKrkpRRccB
+xjNkXEFLw05VSWokteaeg5iQRFzbW2ZWhP15QnIm94r0Ql5Atw/z68b8OaObGaU3VmfeDm2liqls
+c7SJquNBENwHIvIxwA+0Lz/WzH74hbqWqvLGN77xhZo+CIIgCIIXCSG0G966CsROYXwOXQu1rLCU
+XGznBDZDdAY2d6FtBaNDKUg3R2ZzBH9PkrvcUCAlhAIIWYSOihQh9x11PKWOZ9TxlJyX5G6BZBfa
+kppQb226BfNWW1qxOmLixcumsHCzNaYrdCu020gbd86lgnTN/e48l1wKSQoiGcFar27DRXYbNpDq
+hqwDWjfkWil1pNaKjmsYshvhm0qqiowJr33WcrdVSGqoQJLkKe1dQopR5jBToyyFfiXM18J8z+hn
+Su6UlJXreQQbGTdGFRfZiiCkh/XrEgTBi5PINwmCIAiC4D1CCO2G2QYArWfUTWJcKaOcQk4uslMG
+m4HOEJ1j1mPWuaNtHWm2AJvGHEkzDxvPM0SKC9tUSKmj6xakDKUrjMOMcej/f/beP2iy9Lrv+pzz
+PPd29/vu7ErKytaqQookhCIJghDFGPwbMGhtKyb+ATipVBRRUKqQAioBVzlFBUmOCaGMU45TMd7C
+5STGP2LywxXbYCCpAis2FRJbsY2LcmJinFjJ2Po5uzPv233vfZ5z+OM8t7tntLO72hntzEbPZ/Xo
+dr99+/bt3t6Z93u/53wPpWREtuiwRfJwdLNRQFvaORWRAlJwCs6CU8An3CbwA+4HoC3ZIzpHCTgW
+v2KKxgUAGRDdoLoh6QaVIZx3mgPfRLYTZe1mUeLudabWgtUFqwUre3wSbHY8VWRxSIZoQWo43OtW
+TcORNkERVGFMwlaEtAjjImyLMO6MlA1px1EUKzAdDDMHB3OnF453Og8XEXkX8GeJPy1+vbv/w0d8
+Sp1Op9PpdDqvS7rQbty+vY8b1UjuZDdkmZAsaBY0t35mG6GOrVc7g0cgms9bfG6u9rCBtEXSJoS2
+jqhuQDchnmmiGSH5AHqB5hjvldMFKgNRFh2jtUJRFtynGDNmc4heW/C6QF0Qm1FrY8ioiDiqikvG
+RHEdcBdEt4hsQOKcREdoS0QRSWjM0uIktgvJlihRt3a7hvi2ZYPnhI0DPo+w7KEcoOzbeXlbhleN
+4DVrbrQqqglVpbYqda2CDwapkjaFcVsYhgOqB9yV21cz+0PBJqdafRRflU6n0+l0Op1Op9N5SbrQ
+bpyEdiW7MVpBl4E8SFu0QLMMZQDLIRY9xXbYwLzBp82Z0I5F2uJpB2kbglYzognVhMiASsLzNgR5
+2pF0dZchnGjDfUbsAHbA7QA2t7W0sVwVsYp6ONgqjifBPB1FtnvCNUS2S4hs0fNtlJEnHYh+7lVs
+R1Cae5Rwh8ifcZuxssGGAV9GbNhA2eNtYQti9XhuXgWrghfBSYgOuGayZtwcdcgOuq2ksTDuCpvd
+gmjGTChLG+sFzNWYS/e0O51Op9PpdDqdzuNHF9qNVWhLKYxWGOtMKplxBNsAI4gplIRXRWqM/1JX
+cMGHEaYRxgHyBsm7ENp5h6ZLPF8g6RLyBeQLJO8gh5NNSkhKkFbBuzra1nqma0sDn3C/Brs6Cl2v
+M9QarrETyeFScfU2dzr6yGObMRmOKwT2CGdiW1NzuiVeVyTC0Y7LK3jrCbcZrwdsGPFlg407vOyx
+2sT2XRcCFrwKUlahHQ4+ssF1QHEGcTY4w0WI7IsnFnaXM95E9nSoLNVYqnE96dnFiE6n0+l0Op1O
+p9N5fOhCu/GJT0wAlEOF2WCq2CGx28HFBegukrOpAlXBQmQ70W8cpeQDYi2BPG1x3bTy8Qu8LdIF
+NoTwtuECHTbIMKLDBvImAs4iuhuhhNCVivoB9T1iB9SmKBkvc6x13Fd1sIIxYT61baTgmlesDdgy
+qZgURCuq0fedUiGnSs4FckU05nOLVuRcaNNEs89NSM9NfBOBb7pBHTwlXOfmgkfJOTUq7T0LERLX
+LizIgLijOOpOtsIwLIzjwnaTuLgoPHFZefLJymF2DrMx7hfSoSvtTudhcE8qN8SVvl+ST76a9SXu
+/gER+XPA7wN+yd1/g4i8BfhDwFcAvw544mzfV5z4LXGFD+B97v6NL7Hf5wHvBr4QeAbYAh8Gfhr4
+UeB73f35l3/ndx3zDe25nwsswO939+//VI7R6XQ6nU6ns9KFduOjH4swtHlbsb1h+0q5zDx1A8Rg
+aLlkNINZHEIaCr7OpSYjnpGacR0RGaNsW7eI7jDZQtpRhx2WL6jDjrS5QDc7dBNOt3GByQ6THdqE
+sGolMZPYk3yKcVtlgVLwUqEYmMU87LpQ6kSxPaVeUw2q5eMyyTgxm1skHG2VkZRGxmHDOEYJeEpG
+SgVNq6tdWvBajeC4NlIsEtA9xH6lKekRJEaIrcFtpIIrePIw6UlHJx0doc0H9+q4FJAMRL/4OFQu
+LoynnnT2B+fOdfRtp/Roviudzj+hrInccs/9ex+/67aIfC7wI8Cb7rPvS/3spc7jkxCRLfBdwNe9
+yL5vbesrgKeB+wr1FznuW4H/FfitwDXwte7+v7zS53c6nU6n0+ncSxfajY99LBzteZuxg+F7ww7l
+KLJ3AyRZw8nafGgREoqLIKRWRp7wmhAyLrltNyAbhBFLW2reUvOOknek3RPkeoPkT+D5CSoThQWj
+klJtgrfiuiBMKBMQIptSkcXwWqBWvBpWZupyYF4OzMs1S3VKzZQaQttFMVJcHJABlRGVgZxG6maD
+bUfYbPBc8RwOt+gqsqNPe00id1/APVx8T0gLh1vHbrm0yPE1KV0dd8fMY5yYrqnsAywxI9zN47VI
+EcymyjhWLnbGkzfg6rpw6/aBcUhdaHc6D4+/BbwN+F3ANxEC9t8Cbt6z3/93z/0ngL8MjO15f50Q
+qm97kec+EBL2+g8BX9rO7xeAbwd+sr3mM8DnAf/up3jc3wj8NeCfBp4H3unuP/HQTrzT6XQ6nc5n
+JF1oN56/XQCwxUnV0eIkV7YjXG6jQtvVQmi7NR87RLaKIK5oWyIJVpfbEzAcl+lIzRtK3lDyFrMb
+VH+SZE9S9YqpXHGoV8x2yTA4w2AMo7PJxjZXdrmStOJW8dpWqVhZsLJQl5l5PnCY9hymPXNx5iUx
+l0SpCSREdgjZAZUZZSDngWV7oGwH6nZkGCvDUMhDlJW7ny2W6AVnARfER4QNygbQ9hopRL0ILmAi
+VDfMjepRcK8o6oIgLAZzVebFWZbEUjKlgJmT1NhuhRs3MrevCxe3Dmw2dxhyn6Pd6TwM3H0P/D8i
+8jlnP/6FVzDe62ngNvD57v5zZz//qYd9jsB/zElk/xXg97j7cvb4Wjb+R0XkmVdyQBF5G+FkvwX4
+VeBZd/+Zh3rWnU6n0+l0PiPpQruxuLQtTGYcrLCvylSduThLcTwZ4Ig7IudDugQ1DfHoioq2zm1t
+yeQLEC63aaYyUH3AfMDkCq+38el59mXHrastn7iz5YX9lu1Oj+vJy8wbb4y86cbIcDHgHv3Wrk6l
+sNSJMh+Ypz376Yr9Yc9hmthPxmFWDpMwF0V1QCTmeQuGYiiVnAqb7cRmo2y2yjgWxrEwjCG08YIf
+w9AWaA63AOIhsoUx3GnJiA6tyr5SvVIomDvVjYqBKJpmtCWdTzPME8zrdnLmyZmmRLEtKQ/sLi+4
+vHQuLiYutnfYjdeP7gvT6XQgRO9/c4/Ifug0N/s/b6/3IeBd94jsu0/K/WXd9Nbn/cPAG4B/APyb
+7v7/Ppwz7nQ6nU6n85lOF9qN0oT27M7sxmTOwSpTNeaWdO14CEs5NTGCgAsqq0PbhLaHzD6K7bZ1
+U6onzBQzpdQdddpR9IJb1yP/+KOZmx/LfPgTmcsbA5c3Ri5vDLz5TZfYZz3FNj/FE7sUQ67EQIzi
+hbkeOMxXHA532O+vuT7s2R9mrveVq4NztYd5EZKOpLQhqaFiqIfQTrqw2TjjxtlsjHFT2GwWxs1C
+ToVIQF+FdomgNi/tHYbIVjbHJHPRDYawWKVYZbFKxZrQjkR0TQlJUQEwz8I8KfMkLLOGqz0nSkmo
+DqRBGTaJyyvn8uKKi+0tLjbjI/mudDqdu/i+1+A1fhvwawmh/d+7+wNdZRORdxAl7zvg5wmR/Y8f
++Cw7nU6n0+l0Gl1oN1ZrZDGayDYO1ZlqZa6VpVTAo0y8VSw7grtgAqmVjSfRkNR+ktjS/oFQ6NW9
+pYHDMm+YPNbHPpH4Rx+CX/xH8Mu/Ijz5xh1PvmHLk2/cMe3fxCZX3nBjpNpFG/vlIOFoT2ViP19x
+vb/N9f7A1f7A9X7iznXh9pVx+8o4zDCkLUN2coLU3Gz1SlJhHBeGsTBuFjabhc12Zrtdok/bY9SY
+uJ9Etpd4jxIiWxmRtEPTFk0L1ZW5VuZSmatRsKPYdgVNiiYBEeYpMc8aDvYyUMuGUhS3xHa3ZTtu
+2O62XD5Rubi4xcV2y64L7U7nUXPH3X/pNXidf+ns9t94wGP9O8B/SPTz/BTwZe7+sQc85icxzzMf
+/OAH7/v4M888wzPPvKIK906n0+l0PiO4efMmN2++fMTLPM+vwdk8OF1oN6qFei7u0S9swmzObMZi
+QjFibJVElNgplldgTSD3KMZu+eSA4H5ywAXw1rOMhPSuizIvwvUCd55Xnr9l3Pq48bGPOXOdmcrE
+oUxsxsSvecMld+5csz9cNpFsJDewVfguCIWkMOaEjRuWOXFIC0kWpAWRlVpwX6jEexIgq0CqiFWS
+G8UMLYbMRjWP0WamiK/vRRCP96CktjU0V1IuJCuYKKUYpTq1OrZeYPCwpVwdk/iAShHqQszYtiiL
+RwySI0nRPJDyhnHccrHd8eTlJdMTl6/dF6TT6bwYt16j13n67PaDhqz9wbY9AF/16RDZAB/5yEd4
++9vfft/H3/ve9/K+973v0/HSnU6n0+m8Lnnuued4//vf/6hP46HRhXbDWul4daE4LHcJbaOYkDz6
+sx1DxMDPZsy6NLGtx2Nx8rFBmjhXwBRvfdy2FKa9cr1X7jwPt29Vnr9VuHWrRkm4LUx15nI38vzT
+VyG09wcGdUZxVP0otJVKVmPMMddbJVPKzGEShuwkLYC3udrhRotHUbujJHcqjklMMSsmSBHMBTGN
+9+uKeEWOHeptkrgL4GQ3Mkam4uIxecy9ZchJjEYzxd1xwOI6BWYOxnGetovHKDBxdBA0ZyRvGIYt
+l7sLnrq8xG7ceI2+HZ1O5z7UR30Cr4K/DHw1MXv7fxSRd7j7nYd1cBHh6aef5umnn+Z7vud77rtf
+d7M7nU6n07mb97znPXzlV37ly+737LPP8pGPfOQ1OKMHowvtxlFom9zlaC+WQmi7kM1RNRKVUIXr
+PG1wj+Rx9+jZjlzy1dt2RDy2SMyY9kgkr7My7wvXt5U7zzu3n595/tbCrVuF2QpTWTgsM09cbrj1
+/B3uXF2z3+/xLGiCnAWxgnpBKSSpjCmhZHJKLIuyH5wxFbJUqjvVKtbed4twwyVE8oDjArW1gFPj
+M8ESYqmN7zJO77AlsbftgDNgmFREY8R3teZi1xDa6wp3uznd4tEDLzGZXLR9ZupIFiRlNI0MzdF+
+6vISufHEa/sl6XQ6rwY7u33fUQEicvESx/jo2e1ngL/3AOfzp4G/CXwz8K8C/5OIfLm7Xz3AMY+8
+9a1v5UMf+tDDOFSn0+l0Op9RvNK2qnF8fbSPdqHdMPO2lQgpa+XixYxqSjGlupJ8DUWLmdoOrZxa
+4r7rXU63ISDR24xECbY0MS6u1OosszEdKoe9M03GshhlqcxTQQcgOVd3Dlzf2XN155rrqyt0TAyb
+CBJTX0iEkDa1GCwmSjaYMmxH2I3OPBq1vaditZ1drKxKTs6QnTE7KVVUKkoNF98q1vLQZH3/bblF
+SFokoYPjuFREEmZCdYnnFvDiIbSPDnaslBIvMEy6AAAgAElEQVSaHDRjalQpVFVIiTEv5DSTZGZI
+ld0INy4yeuP18R9Zp/M6wj8Nx7x9dvuNL7HfP/sSj503O38R8GMPckLu/i0iMgB/HPhC4Eea2N4/
+yHE7nU6n0+l0VrrQbniJCkhPzXm1hJtgFj3KtXorb7Y2M9sxQJvYhigZjC2tlPz4QAhyiR5tAdQj
+SC2Er1BqyF5NiXGE3UViHJVBU5R2F2M5TBzuXHH1/AsMlwM7xuhdpmBNZJMi1bt4RZjZpImL4cCy
+nRCb4+JBjdfFM0IGZnJKXGzhYge7naCyICwgC25GdaFalJGHgw3gUQJuhlXDzOIDtIzXhKi2Cxet
+YqA60txsIQaexYehaBqQPKBDlIybGqYV10rKQlYj+cIgV2yGKy62C3r56dAEnc5nNIez25uHdMxf
+Orv9O4AfvM9+v+cljvEzwC8D/xTwH4jItzxo8ri7/wkRUeCbgC8GflhE3unuh5d5aqfT6XQ6nc7L
+0oV24yi0s+CWcFPMFXfDVqFdDU/1FALWysabGR4C+yxdHJombT+Pm/G4tRC16lFavVj0h2vKDJvE
+buekAXKKPmorleUwsb9zxdULIxdssVxJO0eb0HaN8LDiFbFwqked2Q0Ttp1IzFSDWp1SvV1VCLGd
+UuJiq+y2ysVOiTnZc4Sm1cpizlKdxaLEu/2vTf0yanFqtbhAURUrCip4E9newuTEQM0REVS1rURO
+G/Jg5FEgO6YVSwXXpY0xW8AOZA5s8hW+Xci1C+1O5yFzHjT2G4FfeNADuvstEflZ4F8A3i0i3+zu
+d4WoicgXAP8J93HU3d1F5JuBbyPGfH23iPzuF5ul3WZuv+WVzNJ29z8uIhl4H/CvAz/UxPbrI860
+0+l0Op3OY0sX2g2v0UZoxXGLnmt3xSyHODXCsbXaHG05imzFz9zrs2Me70u7J83dDvEM4RBHiXo4
+2pKUcRR2FxL9yhqC2RdjOcwhtJ9PPJUrtnOSKVkLLhVSqHbVBZEFpDDqzMUwo7uFMS3UapRVFLs2
+BzqRcma3TVxsYwuFagtmM2UpaKk4leo1gs3WtHWLz8wWp5YoLbcqWImLDe6nlRySx3sXFTQnsiaS
+ZjbJGAdhHBOanWgHF0yFYgvVD1S7wqSwHfbobmaULrQ7nYfM3yFc7Q3wx0SkAP+AU5/1h9x9ehXH
+/TPAc8BnAz8uIn8M+LvAm4B3An8A+NvA53P/8vU/A/xO4EuJMLP/W0S+HfhJ4Bp4C9Fz/XXA9wLf
++EpOzN2/UUQS8Efbsf+qiHzli4n4TqfT6XQ6nVdKF9qNnIe2zahmVBMqSvx+KadqaWnjunStoG7l
+0xK3j/u33Y+GNkQzMspxRhbNHZZIMo9QNQ+Xu4KqoNJSwd0py8J82HO4Ug7bwrydmLfXkCtW9lAO
+aD2gVskUkBIO/BCJ5IMa1ayVwdvxV2dxSOpstpXtWNgkASouC6aFSmXjxk6MOa192IG5UCsxnquC
+JlAl+q1lvWBxEtranO0ksY8mI6VCHhaGYSInkKRxDUDBFKoMFB+oOqCjIbWAL0h+PQYedzqPL+5+
+R0S+Dfh64LcD/9s9u3wJ8IFXcejvBJ4Ffhfwm4HvP39Z4GeBrwF+5SXOzUXk3wb+PPC1wG8CvvXF
+dv1UT87d39uc7T8CvAP4QRH5qi62O51Op9PpvFq60G7knI/blBKqioge+6797Fe3VWjLGra9Otpi
+bcJ2e849ryHCMVnb19gwuVtsRxu4UyuIhNoUFCyE9nQ4sL9yDruJaXfNcjGguWI24xZ92MkNcUOk
+Islj3rY6JVskfFsI7TWIDCCJMY4wDDAmWupZwb3iUini1OSUcXXq4+KCe4THrUs0HostpxJzl1Y2
+frrOIBop7qJO0gVNQkqGqODquBguUCRTyBSGdn2ipZGP3dHudB427v4NIvL3gN8H/FbgKSDxyX+k
++Yv87H7HdBH5WuA9wO8Hfkt76O8DfwH4Vnef1j9vX+I4B+DfE5EvBt4NfAHhZCfgV4GfBn64HfOT
+nv5S5+vu/0Vztr8e+DLgL4nI17h7eSXvsdPpdDqdTuecLrQbw3BytFMKR1tUmtA+ZWyHyBZUJDxo
+jfLpo9g+iu7zo6892015Ht3vNaHbcIkC9BDC4TqrZpI20W5OXQrzfs/+amHaKfNOWa4SaaxAARbU
+S0zFllgpQVZhzEL1cMzNHPcYzyXuCEaSELk5GVkdqAiRJo63Uu4xUtld2vtuDv063uzYjy6cFDyt
+zBwiCM1Aavtw2siz+HiWNtqrtIC06Dl3cYqkENuS0GaZe1ao950U1Ol0HgB3/y7gu17i8XcTQvdT
+OaYD39HW/fZ5Rf9Ru/uP8Skkj7f90yvY7xuAb3ilx+10Op1Op9O5H11oN1KOgF1NehTaKtLUYeEu
+5Sxnd1f1vd59Mb9EQoGuY8HcPdztVlK9utjmcpTrIpFiLiohLiUS0JfFmKfCMgllgjoLTkUoIVKp
+Ld38LP3cE07M+Db3SEx3RxxUrM3SBnVDagU3hIoSW3BSS0lHJJzs1dEWO3vf0pz5M6u8DQOLOnI5
+/iycbj9doPDof3dPzfKusdRwifM3SVTP1JopNlD8ZX9v7nQ6nU6n0+l0Op3XnC60G9qEdsqK5oSm
+hCQQbSXUTSy6yJnQFnzt416Tx4FjvfS5cXt6MPZYRXboS2qJvmxcEYGUYrZ0SjnORQT3cLqXxSmL
+Ywv4EqJUWJrYrnGuQihtU8wTxRKLKeZOtSa0IZxsEVQ5zuE2PYns2HoTyS0xvWlmE4/Pg3DKz3vO
+4UxXr5+dASZg0XO+OviRYh6hbHirUG1C28VYUGLYmHKwgb0NHMyYvH99O51Op9PpdDqdzuNHVyqN
+NDRHO2sT2Yqqt/TuqGb0c6Hc3OKoBlfWAmk/G+KF+1GQx5Pu2hyFdq3EHO0aJdiqQs5KyiGyNaUI
+XyPCzEqp1GLY0uaCSQFKSFGvuAiGgihuiVqNpTpzVWoT2sU8RLZCUiErmFSSVnIT2utqOentfSkm
+vurlGFPW3re73RPytn5ackwpD5EtLfSt9Yy7R+qZNbHdhLZIxTFml1gIByvsvbJ3Z/I1CLnT6XQ6
+nU6n0+l0Hh+60G6sjrZmbWJbkWSIZkS09VCHox2u9iqyo1xaCFd37eY+FkkfxbWcbUN4mslJaBen
+VsU9QthS0ghlS60vWSIorVZjWQp1qVipeDHQBfHwfMUrSELQKBevmVrCBZ9rojSRXQySxpzunISq
+TpZKkkpdhbavYjvmXguKiGFAlQgtNzm5005ccFj7rteLDuH800R2fGbWhHasJrRrim0Uip8JbZgM
+ZpOjyL5GmF60Tr/T6XQ6nU6n0+l0Hi1daDdEh5CFKohqpI6rgWZcM07CJUdPsthRaIvEuCpfS6xD
+QscxTxFq0G65K0bCPUVJdxGWBabJmRfHTJrQzq1c3Cm1UIqxlMqyxBzsUqHWEOtu2kRuJLOZxXzu
+6sqywGFyrmZnvzSx3kaIpQQ5OUMSkkZ42hqkFo3jbR3L4uPGWgG+DjM7utlwV8BZ9GufjTo7G/Vl
+DoZRWR1tmnqXCHFTJymoCLPB7MJiwt6EaxOuzZmsC+1Op9PpdDqdTqfz+NGF9kqK0VGSQmhH6bgh
+MoAMR5EdIWCOS4R2ncztVWhbCxNrirLN1Q6ipNs8hLabUishtGdnnp1qAkRv9rGHuRpLMcpSKYOx
+FD/2dK+iWo+BY4qbUE2pJsyTcNjD1d65nuwokJ0mtDOUFMJWmmyGKGO3Er3jdia28XZJ4ew44Wi3
+PvU2Myyq7dsQMz/fN8S20crPaRPETaAaYkISGDLkFGXtpYns4sK+KlcFriscahfanU6n0+l0Op1O
+5/GjC+2GaG7bENly7mhLxiQ3SeitV7seM9FUAK9NqLZtE9nSptUcA8pcEU+4JcwSpYaTPc/GvIRL
+HY52olqNUV9WKVpZyiq4nVL9WHoejnYraXfBTbAq1Coscwjt6zvG7YOcTlggZRiyh6BNTTC74x7i
+f1mgLFBKe2x9W5yFoNGC0JxwtdURjQsQiJ9EtnvIeKddklj7vP1YVi5VwIxBhXFwNlkYslOayK6m
+7ItwXYSrxTmULrQ7nU6n0+l0Op3O40cX2g3VGBUVs7NDaLuAkSieWCyzuIfgc8UkRCUQs6jFUK+I
+W/RJu7WRVacy85bzjVpCSEDGqVSrLMUpJXZ3BFWN0nQVEsowKDlXNAmikcZtOKUtAFoF9uIwm7NU
+ZyrGVGBahHkBzYK2VHONvDQkAeqtJD22h0Os/cFZ5jZ6zKLkW1jni0OkiXtUmHs42XHcKB+3Jtwj
+XXytRA9xbdpGhCmIGeKCOGyycLFtJeoqrdw90s4LUMxZijOXHobW6XQ6nU6n0+l0Hj+60G6oattG
+6TgSQru20VhTycwVFk8sZAYsepq19TW7oVTSKrStgldcahPaCiKIJ6pkVJrQloXqwlLDubUYTo0I
+5JQQTYgKu6GyGQvjUEh5QVLFtFKApXnt1jqiF48AscVgclqPMxQXBgmxnUcYhigdzwOAs3iEsh0K
+3Jmc21fOnTvOYWpjyIwoU5fTYh1RZo4ZaGpLaUJ7fd6p3Tvy0BxP4OqgtOi2CF672CgkIY1CTqc+
+8Lha4Vgxqhjl0XxVOp1Op9PpdDqdTucl6UK7oSmEtqiAaqSL0wLFLDHXzFyVxZziThWDJrZDIFYS
+RiKcbazgVs5EdrN6PZEsI5YRMiYSzrRVShXqOoJbhJwSeUiknNgNle24MAxCyuFCmxhFnBknCtvD
+XZ8dZjNmcyaLxO7FoDhkFTQLw0YYBic3se0OFCg4hxpC+9aV84kXnOvrUy94NSWJkDXmbwuRhF4r
+WHU0QcqCpnDxaxPh1Y5Dv45CmwSeY5vESOIkUYpD2ggblI2ezS2X+HBscaraycnvdDqdTqfT6XQ6
+nceILrQb2kqhRdd0s4g3W0w4zFHSnLMwDMIwgGhiUCOLkdVAFKGiEuFn5zO3Q2Sn6Pv2THzsLc1c
+HKNiXokQ7YSIogg5J4YhM4yZzZAYMqRsaDLQiielilBVgLg4IK4UE5YKi0KRmJjl6khqfdmjM26E
+YfRwtHOkkMsc511MOBS4muD2tXD76lxoC1mUrEIWQXBKEWp1anFSjgsBKcfYs9rGl9VWEh9C26NP
+vIlsMmSN8LOszjA6swtVgWFNgo/54klAC8jiSBE6nU6n0+l0Op1O53GjC+0jNTZrKrYrSzWuysLk
+Ey+wZ3/tXF8LV9fwxE7YZGczONvBGdQYxRjVSNTWc+yIeSSZJ0FTokpittx6vjOGgRqaIQ8CjMCI
+yEBKioogHvXX3mqwPeaQISmRxoG0UZJmkmYEIy1OHhybnVFgJ2DJGWfY7ZzdNlYeQJOTcowLG0cY
+xriYMGYYh8SQnSEL5opbJKarQBJIrZQ7aUsW1yjB1yaKoy/dTqnka193XBc4utmSnZyUIStDEjY7
+ZbsTdjvh4kLRJGiKrYyJmmLJqI/mq9LpdDqdTqfT6XQ6L0EX2g1ZhTaKewRweTWmuVCniTrvubN1
+7lwJt+8oNy7gYitcbmO7ScZGYw1iKCG01Vsp9aAkoj979ia2PVMlepRT1tYrPSIMqAxo8kg0xyLR
+2+2YDC4qaE7oKKRtIiUjpQERJ81GGow8GKM6pkB2touz3RibjbHdGClVJDmqTqrOZlLGQRmH5txn
+YcgwrkLbE+baJnZHtBst7Exd8Caw1+Vr1bcbSutR1yiLJ9Hc7BDb49CE9qBstsJ2J2wvYHchpKxt
+CbpJ1JQpKSNjeu2/KJ1Op9PpdDqdTqfzMnShfWR1tB0nyqTNjf2hsL89s7+z5/bGubNTXrhQnrwU
+blwqN9p2l51tMrZpdbUt+o5ZtaSSSVhKLJ4jVM0zBpAEzUoaBBgQMioZkco6l/tcZHN0tBUdHd2A
+DuFMizppqOS5YrkyJofspAGsGJtjqJq34DdAHC3OODZXexDGQRizMmZhyIr7Ka5M3NsCM2vC2jDl
+WOKtqhF4xjpH285EuB6FtufYDoMyjolxULZbYbtzdhdwcQlpUHJbOmdKGig5QxfanU6n0+l0Op1O
+5zGkC+2GhuRtY6sMF8HMmZbK1WHh9p2Z/d7Y74Xb18rtK+WpJxLXh8xhgt3g7LKxS+EiJ8LVTlgT
+ipU8RF/1IsICzMDV5MxFcMkRyCYZkQEjx+xpq1gtFF8oXlisMEtlWZRiieoJEyWpwBAl6kkqrhVS
+RZOTk1MG8MUYUmFIlUELogWXAhQ8OZshcbFJ1ItMXRSvShLlxqW2xPCWih5DsyMB3JzaRoLVaq3M
+u/Vo41S32Mctkspbabmo4ImoQc8SZepDYhwSTz4Bb7jhPLFztqOTh3Rc7plpzMxLwmoX2p1Op9Pp
+dDqdTufxowvthmqbybymhOOYO3OpXB8qL1wtJKlc7aN/+WKjXB9GDjNMs3AxOLvsXGRnVItxXx4J
+5GlQ8lhIQzi5VZ2iRhHj6lqZSkhySSlKxnXAJVFKwapRa0HKwpwKSQsTlXkZWZZEqSPVM6klnemg
+JDUkGZorOTueHRscL0aikKgkCs7SpnAvuBibIVO3CfGEemJMictN4jBFKb21z8RpzjonoV0r1Oox
+QztF7/f6GVrr01Zp/duirI3ergJJGXJmyJmcM5c7uPFE5YmdsRuMPGRyHsg54zUxpVgl9R7tTqfT
+6XQ6nU6n8/jRhXZD5CS0XQxQqjtzMa6nwgvXC15LCwFztqNymEJkT0vmcnAuhlVoO2IhssUqaRDy
+oKShIBksO56Mmow7+4F5UUwSogOSMqIZNGM24e7UUnBbSLWgWhgw5nlkKYmljlQfMRnxNCJDJiVD
+s5Gr4YPD4DDGjC+xitQCteC+YCyYz6DGJidkkxg0h8jeZp66zCxFwpl2i3T0NrPbsRDgNZLFzUDE
+QaOEHbE20iv+UdFYKKKKq+Apxp7lNJDTSE4D29HZjoXdprAdKjmP5GEk5wGriV1WpiTULrQ7nU6n
+0+l0Op3OY0gX2g1tQtsQ3CqGUGphXgrTXNhPhbrU6E3GOcwtgCzH2K86QMlQsjCoICasLdZpgJSd
+lA3JFR9Wse1cz8p+TizVqYAS5dUSKWghtGvFamWmglSyOdd74/a188KVoKOyIbGVzGgD4gZrH3Vp
+qxpijpfSTrRilqlkzBPVjFoTxaLoXXMme2armVylCWyjeqV51NgqtA3cBKuAWohtMZAmsVuvtiJI
++/91rri3bdKRlAayRgicI5QiTK6UOpBKJqWB/awc9so0CcvUx3t1Op1Op9PpdDqdx48utI80oW2w
+mFG8cJgK8zyzlEq1EI2IIAgiCSQDAzCGIDZjKRaOeI0B1l4LWnOUhacEWbEkWI6RW4elcpgLhyXm
+Sw8jDBLzos2iv9kqeCF6oj1KwD/+QmXz0YWUJz5xBZsLZ7Mzhm1B28UAcVBztHrMnq4eA61rjAkz
+J0LfSG1GtlItZmWXVgpezWKG9rFsHEwij81ordoGHrXl0MaQI+unKm1/j33ccItHPJLYQBRVR9VQ
+qSR1shSSLiQxRAsiGZEDU0lcHRJXk3KYu6Pd6XQ6nU6n0+l0Hj+60D7iABE8VmEqzmG/ME9z9Eqv
+Y6ykKclzoe0DZlAwZqtUDGqIbDeFoqApHFwNoV2TUBPMxZjKwlwMUcdFEU2ophDZ5phFebZVWMxZ
+xPj4CwXNC5XExW0Yt8a4NYaxoBLjt1QgxZju2FbC6W6i2L0JYRKGUF3btvVkewjouwrAm8h2iNve
+BHZbIt6W4ED435HpbsWxGstbuBqE2BYxRGqkrcdZIBQEwz32cxfmmphK5rAk5tqFdqfT6XQ6nU6n
+03n86EK7IbRRVFYpizFNlcNhZpoXlhqOduy3lj2vQjsDI+aRur2QqF6xGqndVgvepk67KFVCYBeF
+mmCpRjFjqbWN50qknMk5haNdDWup3lYcLx5zqV+oFF+4noXt1hg2lbypDEOOPnKBJBJTtFxIFltx
+EG9l6dBGdoHJupUmpgWX8K0daE+kPTW2LYAcAzGPanHxuA5BON/VQ2RXoCxOWYyyGGbtIC64Sxtl
+psDSZnNX3CpuRm2ufjWnWKJ4pthA9Z463um8XpFjMAbvc/dvfJXH+GLgf293v8TdP/BQTq7T6XQ6
+nU7nAelCu1Gm+J1vmSvzVJgOhel6ps4FipMI9zRF4Ti4U2plKoWraSKLk1dv2CtWDa9t2zxaRzAR
+qkJVMIVip2ruYTCGlCnDgA0DANIcbtEM0nqkq3J1MKov7KdKHmfSkEhDJmUNgS0t2Nub0I6MsqPI
+FpfWO+2r3sXbk1z1mL4uRwefVhbu7XYrTwfEHbV2/HV3iUsXixvFnOLGPBfmqTLPhVq9iWxaMUFz
+ttG45OGGm+EeotzMqQYuGXTE1dqFjk6n8zrGH7PjPDB37tx51KfQ6XQ6nU7nMaArlUadQ2iXqbLs
+C/N+Zt7P2FSRCrkJbZGI8wIotXKYZ1IWUvOG1Q3x5kSb4dVOItsVl9a+vS5vvdLmbDbOOIxsN+Hk
+hpOsqGZEK+t07mJCOVSu54LeMSQJmhRJEaK2imyVk8DWo9BurjxAE9gouEo8PyuSosxdNIR2bFft
+LcfbUaJ+Onby9vmIIBql54tVFjMWq+wPC4e2SrHTOG5rfd2rmgdWBe5rX3rrKdc0kIZKGgzN42v2
+/eh0Oo8tx8t1jwO3b99+1KfQ6XQ6nU7nMaAL7UaZa2ynynJYmK9nlv1MnStUyJ5w8ZMUbGng07Lg
+B1BCYK+J396CzLx6CG2X1g/NXYFifiYocZg3W0qpWLsvomgKR9ulUkksVlhmo5SFpS64+6mU+1wA
+y0lcn0Q2qyd/2kmApOiQ0CHHVlfhrqhqE8/SbkdZuko4/Il1GyJbRVCVGI9W63Fd7Seuryeu9zPL
+UprIjn5t1s9V1iRxP25ibnd8VnncsNk6my0MXWd3Op/RuPuPAb2HpNPpdDqdzmNHF9oNr37aVj+K
+0yzKqIoMuaWOh6hVDWEYSeMR2tXmXIGdyp2tCUlDMJejuD5ZMHZ0b5MqyzzHWhZqrbh7lI9L88xd
+WAymxZnmSCyvVtvxWh+5nPTzUWD76SLBJwntttKQ0aGE0E5NbK9CW0M8qyhJpZWnx8pNZGeJx7UJ
+cnNnMWOuzlSNq71xdTCu9oVlruH4t8/oqK85d7fv+XcEbLxCMnSM99XpdDqdTqfT6XQ6jxtdaDdW
+WZdEGHJiM2TcnKzCmIQlK+an/G3EkRRl1BHe5c3J9qN4tOoR5HUco9VKpR2OLvZpUBbLsjDPM4dp
+ZjxMMQbLo/HZW0hZ9Thmcad4CxtrAv6sInx92jpx67wo++x22OrehG6pdnTDpfpxnvcqnqVt7xLZ
+ImRRsihVFFVIqiR1TKBYXGCABJIQaf3mCrhgYlgbrXZ+fudCe+0TFwHNmTQM5DwwdEu70+k8Piis
+VUqdTuf1wM2bN3nuued4z3vewzPPPPOoT6fT6bxCaq3rzcd6BNFjfXKvJav4TCoMSdkOid1m4HIb
+68Zu5IntwOVm4GLMbIfEkJQk4Ra7+9HBrubU6hQzSj1bxVhKpZR63JalUJZCXQplXpinhekwsd9P
+4WqbR+SYtNJzh9KCwaq1MLU2Yat6DMWqHoOxStsuCIvE9ngfYXGYndgazNWYinGYC9NcOEwLhym2
++8Ny2q5rfXwuTHNlWirzUtvIMmMpTjFab3oIbTS1cLeEqx6D4uxYWt/eA6f73lLeRXP0aOeBPAyM
+Y6xOp/NoEZFnRORPiMhPicgtEZlF5FdE5GdF5PtE5F0i8sTLHONzROT7ReSXReQgIh8Ske8WkX/u
+JZ7zxSJibX3Rizz+Z9tjv9juv1VE/qSI/F0RuRKRD4vIj4jIOx78U+gl7J3O642bN2/y/ve/n5s3
+bz7qU+l0Op8CZ0L7sf67tzvajfWKQ1JhzAlo5c9ZqCXGdFVLFKtUM6oZxZ1KuMur2Ma8VY/7KS3b
+z0LPnFZivjrara8bp6TCMi9M00zOB4ZhIGcYsh5nVq+Odj1ztM1PVvY6ceveaKAoF19HlK34aV9r
+PeFem3tszcG2CERr/deyOtm6bpVBE1WdQRMpQTIhKYhq9KKrHEeJwTpTPK4QhJ99t4e9loSHSy+t
+dD5C2iRlUsrkHK52p9N5tIjIFwI/DDzJ3X/yvLmtfx74OuAjwP98n2P8R8C3cvdfmM8Avxf4ahF5
+1t1//CVO42VtZBF5e3v9p89+vAW+HPhyEfkWd//6lztOp9PpdDqdziuhC+1GGuOjcHPIYRenmqhV
+sRJzsc2MWqO3uJq1su2YNG3VY+61GbWVjNf1tocDbb72bMe+vs6LdsOtkjThbizLwuFwYFkKOS/k
+lFlKZX+YmOeFUkr0b9vdJdfxBtr2k1uc2zCuu38fdY993dsscROk9aGLWLsfQt2b4HaJedsqQhGh
+aCWLsmj0dSddA9xOJe8uMM2FeSnUUo+fw9qsLnK2vff9+Kni4K6LBL1Es9N5pIjICPwF4AbwAvDt
+wP8BfBgYgV8PfB7wVS9xmGeBfxn4GeBPAT8H7Npz/tN2+38Qkd/k7uVVnuoF8Bfbef7XwI8CE/C5
+wB8B3gr8YRH5h+7+p1/la3Q6nU6n0+kc6UK7kTbtozBHzNEmlK1qm4etMbKrCW2rFuFmzaVeS8aj
+L9ua0PYzob32a7fjrqLcKmYFs+iHNotebXcnpURKCdVErcZhmpnmmWUV2h6Dw5pWZr3xYo726e76
+jLP97xXb7c46J3sdCGYiR7GrTXyrQBGNkLRjGJoe+7u99Ve7xDi0pdTjRYK1R309d/nk6wBHe1uO
+/9DGgnWh3ek8Bnw+4Tw78Lvd/UfvefxvAT8gIn+IELsvxr8C/Ajw1fcI6Z8QkY8D3wT8OuArgL/6
+Ks/zs4AZ+Dfc/SfOfv6TIvJXgP8L+LXAfyUi3+fuH3uVr9PpdDqdTqcDdKF9RJujHWnjTnLaHGzF
+reJVsVrxmrBqLfQsxnbhUOtJbIfAPu3UAmcAACAASURBVBPbq7vtTql2Kj03o9ZCrUqpBSfE57KE
+a31K/Q6hvSyFeS6UWqjtHFa395wX06sr9/58Fdnn2nudtyVNsR/F9vG1zmV7zOtW2tK11DvGgZ0i
+0OMigrWLDmvi+Hqo8wsF957w3SFp8cMutDudx4K3nN3+G/fbyd0NuPMiDwmwB/79+7jV3wb8l8AA
+fCGvXmg78B33iOz13G6KyH8G/ABwCbwL+JOv8nU6nU6n0+l0gC60j+gYZou3UVi6uqZW8Fqa2F6X
+RS92G4jtHm3XR9f6LoHtR2G9Ot2llaBXsyaaQzybWySbu2O+9kaD+zp921uqtyAoJoaKnjztpkQ9
+hk8f5fC5HPUzx9rX499HlgvnQt7vGqe1PmcV5muomXiUnosIYnJmf8txFnYY2eGYK/eUix/P6Xi3
+ze0OQb/OMPf2GXY6nUfKeYLQu4FPtezagb/m7h990Qfd74jILwC/BfgNr+4Uj/y5l3jsB4FbwFPA
+l9KFdqfT6XQ6nQekC+2G5hsApznX3gSuVTyF0KaJbaz1R59N56oe5eapCe7kZ6XiTVTX5mYPbq18
+3ENkW2m937GqR1k5tHFgCFbt5B6L4GaYR9849zi7x/NvcvisFfp0/0yU+7lX3KxlcY6iF+52lc+P
+deJstpjcI92P48xOT5Km4I8iW84C0c6Ut0hcWIgFiuPeLlSUV9uu2el0HhI/DvwiIYL/lIj8XkK0
+fgD42+6+vIJj/PzLPP5x4o+GGw9wnjPRA/6iuHsRkb8D/GvA2x7gdTqdTqfT6XSALrSPyBC/w8mZ
+QKWFlGEVvMT2bK2zs/EmsJu4TmvC+LFUOvqS1yA1czuGga2PmZXTflaptURqeZvNXbU2dzdEp7ce
+bzc5KWn85BrjuEs83+8KFsckHrtLfB8/Cb9L9N6VBH4U50TI2T0l5GdHaNvTed1jq5/Czdo8bWjz
+stvBhDajvL1fVWnGeLjZViulC+1O55HSBOo7gb8E/GbgdwCf0x7ei8gHgO8GfqCVj78Y1y/zMuvz
+HmSEx8f95XtNfrVt3/QAr4O788EPfvBl93vmmWf63N5Op9PpdM64efPmKxq3tyyv5Dr+o6cL7Ybm
+S4B7Arr8JLB9FdgFvCJWwS0WIbDFHVnTxVsPsUPrw17F85o2vs7dtpOTba3/uhRK1QhSqyGo1yAw
+JUqp3aQtTkJ7fU2nBbWdRor5mo6OoBBjtzzuxzs+FmsfN9I097nIhlVo08Zy3b8b3M+fcPfRoQnp
+KB2/uzFbpCWdt3Fi0fe9BrDF+1zD5DqdzqPF3X9eRN4G/M62vgj4Z4jRWe9o6w+LyJfdr0T8tTjN
+1/LF3v72t7/sPu9973t53/ve9+k/mU6n0+l0Xic899xzvP/973/Up/HQ6EK70+l0Og9Ec4t/qC1E
+5LOJsV1/EHg78NuB54CveUSn+GtERF7G1f7stv34q3yNoxP+5je/+WV3fu655/jO7/zOV/lSnU7n
+YTDPMwDPPvss4zg+4rPpdDq11lf0d+hHPvKR9eYDVaF9uulCu/Hffcd/+yKTpzudTqfzqeLuvwr8
+eRH5XuBvEkL7nSKycffpEZzSCPyLwE+/2IMikoDfRjjfP/cqX+P4d8jZLwCdTud1QP9vttN53fJY
+67cutDudTqfzaaH1cP8YIbQz8AZOvdCvNe/iPkIb+GrgjYTQ/uuv8vgTsCF6yj/8Ko/R6XQ6nU7n
+5fksogP1UVy8f8V0od3pdDqdV4WIfAFw093//n0eH4AvbnfvAI/KNhLgD4jIX3T3//OuB0TeAnzz
+/8/enQdalpX13f8+a5/hDjX0UA09AYoagyBEGgQEIQoir4QhaohTQCEKaozG4OtAXhuM0xtHXk2U
+CDKIvsYgGiMIxoBBEaURBA2IiCFC00LT0F1Vdzhn772e/LHW3mefc8+5U92qe6vu7wOnz7TP3muf
+6tu3fvtZQ366DrxyPwdw99ULa6KIiIhcSRS0RURkvx4P/D9m9gfA64D3kML0MvD3gOeRqtkOvHSb
+mcd3cqGTmX2cFKJ/z8x+Cng96Sr4I4DvBW7Mx/g3hzhhm4iIiFxBFLRFRORCGGmm8cfNea9ZPfA3
+ge+7wGNciHXgK4HfIQXr7+2817Txxe7+4gs8joiIiAigoC0iIvv3Y8C7gScAn0eqDN8rv/d3wNuB
+V7r7Gy7wOE0Y3ut7k43c32lmDwWeDzwZuAlYA24jhezfvcA2ioiIiLRs+9VORERELk9m9nLSJGgf
+cvf7H3Z7RERE5PgIh90AERERERERkSuJgraIiIiIiIjIAVLQFhERERERETlACtoiIiIiIiIiB0hB
+W0RErmS7mpVcRERE5CBp1nERERERERGRA6SKtoiIHHtmdl8z+wkze5+ZnTezu8zs7Wb2fDNbPsDj
+fLWZvdHM7jCzDTP7kJn9kpk98qCOIXKcXMyfXTO71cziLm+PPahzErlSmdl1ZvZkM3uRmb3ezO7s
+/Az94kU65qH93lVFW0REjjUzewrwS8AptnYzN+CvgCe7+wcv4BhLwK8D/9eCY0TgB9z9B/Z7DJHj
+5mL/7JrZrcCtc/Y9y4Evdve37Oc4IseFmcWZl7o/W69092cf4LEO/feuKtoiInJsmdnnAb8KnATO
+Ad8HfAHweOAXSL+cPwv4bTNbvYBDvZzJL/s3AU8HPh94DvDXpN/Ht5rZP7+AY4gcG5fwZ7fxIOBz
+F9weDNx2AMcQOQ6auVP+N/C7pNB7MRz6711VtEVE5Ngys7cAjwFK4Avd/e0z7/9r4MdIv6hftJ8r
+32b2xcDv5X38FvDl3vnla2bXAn8K3Bf4FHB/d79nf2ckcjxcop/dtqLt7sWFt1rkeMs/U7cBt7n7
+nWZ2P+B/kX5OD6yifVR+76qiLSIix5KZPZz0F3UHXjr7F/XsJ4H3ka64f7uZ7ecv2/8631fAt/rM
+FW53vwv47vz0KkBVbZFtXMKfXRE5QO7+Ind/vbvfeZEPdSR+7ypoi4jIcfX0zuNXzNsg/3J+VX56
+FfBFezmAmZ0gdWV14Pfc/aMLNn0tcDY//sd7OYbIMXTRf3ZF5PJ0lH7vKmiLiMhx9Zh8v0bqQrbI
+/+g8fvQej/FwYDBnP1PcvQT+mFR9e7iqbyLbuhQ/uyJyeToyv3cVtEVE5Lh6AOmK91+7++xMqF1/
+OfOZvficBfvZ7jg90iROIjLfpfjZnZKXB/qYmY3y/ZvN7LvN7KoL2a+IHLgj83tXQVtERI4dMxsC
+Z/LTj2y3rbvfTaqcAdxnj4e6ufN42+MAH+483utxRI6FS/izO+sJ+bi9fP9Y4EeAvzGzp17gvkXk
+4ByZ37u9g96hiIjIZeBk5/H5XWy/BqwAJy7icdY6j/d6HJHj4lL97DbeA/wm8Hbgo0Af+Gzga4En
+ksZ/v8bMnuLub9znMUTk4ByZ37sK2iIichwtdR6Pd7H9iDSOa/kiHmfUebzX44gcF5fqZxfgp9z9
+RXNevw14tZl9E/DzQAG81Mw+w9130yYRuXiOzO9ddR0XEZHjaLPzeLBwq4khaUzoxkU8zrDzeK/H
+ETkuLtXPLu5+dof3/yPwMlKQvxH4ir0eQ0QO3JH5vaugLSIix9G5zuPddBdbzfe76aq63+Osdh7v
+9Tgix8Wl+tndrZd0Hj/uIh1DRHbvyPzeVdAWEZFjx91HwF356c3bbZtnFW5+GX94u23n6E7Esu1x
+mJ6IZa/HETkWLuHP7m69t/P4pot0DBHZvSPze1dBW0REjqv3krp8fqaZbff78O93Hr9vH8eYt5/t
+jlMBH9jjcUSOk0vxs7tbfpH2KyL7c2R+7ypoi4jIcfWH+X4VuGWb7brdQd+6x2PcxmQyloXdSs2s
+DzyS9Jf229y93uNxRI6TS/Gzu1vdNXs/epGOISK7d2R+7ypoi4jIcfWbncffMG8DMzPgmfnp3cCb
+93IAdz8P/HdS9e0JZnbjgk2/AjiVH792L8cQOYYu+s/uHjyv8/h/XKRjiMguHaXfuwraIiJyLLn7
+bcAfkH4ZP8fMHjFns+cDDyBd8f7p2SveZvYsM4v59v0LDvXj+b4H/PvZrq5mdgb40fz0btIsxiKy
+wKX42TWzB5nZZ2zXjry813Py078DfmPvZyMie3E5/d7VOtoiInKcfTupS+ky8N/M7IdJla9l4KuB
+b8zbvR/4yW32s3Ccpru/2cx+Ffgq4Gn5OD9N6mb6YOD7gPvmffzf7n7PBZ2RyPFwsX92byGtjf1m
+4HeAPydNwtYjjev8OuBL8rYV8I3urmX5RLZhZo8GPrPz0pnO4880s2d1t3f3V26zuyP/e1dBW0RE
+ji13/zMzewbwalIXsh+e3YT0F/Unu/vaBRzq2cBJ4MuAfwh80cwxauAH3F3VbJFduEQ/uwF4PPCE
+Rc0ghe9nu/vr93kMkePknwPPmvO6AY/Jt4YD2wXtnRz6710FbREROdbc/XVm9mBShezJpOVAxsBf
+A78G/Ht339xuF7s4xibwFDP7KuDrgYcAVwEfA96Sj/EnF3IeIsfNRf7ZfR2pW/ijgM8D7g1cSwoE
+nwTeDbwBeEUeEyoiu7Pbmfq32+6y+L1r7lqVQEREREREROSgaDI0ERERERERkQOkoC0iIiIiIiJy
+gBS0RURERERERA6QgraIiIiIiIjIAVLQFhERERERETlACtoiIiIiIiIiB0hBW0REREREROQAKWiL
+iIiIiIiIHCAF7T0ys1vNLObb9x92e3ZiZo/rtPdNh90eERERERGRK52C9v75YTdgjy639oqIiIiI
+iFyWFLRFREREREREDpCC9v6oOiwiIiIiIiJz9Q67AZcbd38R8KLDboeIiIiIiIgcTapoi4iIiIiI
+iBwgBW0RERERERGRA3QsgraZ3dfMnmdmv2Jmf25md5vZ2Mw+YWbvMbP/YGaP2OW+dlzey8ye1dnm
+F/Nrwcz+qZn9ppl90MzW8/tP7Xzu5Z3PPTO/do2ZfbeZ/YmZfTx/7q/N7CVm9g8O4vvpHP/vm9l3
+mNmvm9lfmtnZ/D193MxuM7OfNLMH7HJfv985l8fm167O5/J2M7szn8sHzeylZvbAfbT36Wb2CjN7
+f/4z3TCzvzWz3zCzZ5pZsdd9ioiIiIiIXKgrfoy2mf0Y8J2A5Ze6E5ldDVwDPAh4npn9KvAcd9/Y
+xa53MyGa5zbcAPwa8OiZzy7aR/O5RwK/Dtwws+398+3ZZvaDedz4BTGzXwO+crYN2bXAGeAW4NvN
+7MXA8909brPLqXM0s0cD/wm4cWbfn55vzzKzb3b3l+6irQ8GXgk8ZE5bbwJuBp4GfK+Zfbm7v2+n
+fYqIiIiIiByUKz5ok0IXQATen293ASUpQH4e8Bl5m68CTgJPOcDjLwG/RQqpJfBHwAeBIfDQbT73
+acBPAVcB54A3AR8jBdUvAlZIPRJuNTNz9xdeYDvvQwqsFfBe4APA3UAN3At4OCnEAnwHMAD+xS73
+/bnAjwCr+Rz+gPRncBPwxcAyUAA/Z2bvcfe3L9pRro7/FnAqt7cEbsvtLUnf22NI3/tnA281s0e5
++/t32VYREREREZELchyC9juA3wF+290/OW+DXG39ReCzgC8zs69x9185oON/JSlEvhn4enf/8Myx
++ws+931AH3g18K3ufr7zmdPAS4GvyC+9wMze4O5/fAHtfBPw48Abu8eaaeuTgZeRgvc3m9mvuPsf
+7WLfP076Dr4T+JluJdzMbiL9+TyIdOHgh4EnLDj+vUk9A06SQvYrge9194/NbHcd8HPAlwOngf9k
+Zp/n7lqWTURERERELrorfoy2u/+Eu79qUcjO27wVeCKwmV/6tgNsQgG8B/iy2ZCdj10u+FwfeJ27
+P2s2+Lr7PaTq++/nlwLwoxfSSHd/gbv/+qKQnbd5HdPV/t18T0aqfn+zu794tru5u98OfDUpOBvw
+D3OgnueHSSEf4MXu/uzZkJ33eSfwDNLFAyNV1L9ydjsREREREZGL4YoP2rvl7v+bVHU24OFmduIA
+dtuMC/9udx/t8XMO/MtFG7h73XnfgC80s8/aVyv3wN1vA96Xj/n43XwE+HN3f9k2+/yfpO7f5P0+
+bHYbMzsDfG3e398B37NDOyPwgs5LX7uLtoqIiIiIiFyw49B1vGVm9wE+H/h7pLHPy0zCMKRJuciv
+PQR46wEc9lPAf9vjZxz4I3f/0LYbuf+Fmb2LNM4c0tjtD+y5hTNyYH8Yaez6adJ48u73dDrfX2tm
+N+Wq9Hb+8y4O+y7Snw2kcdaznkCqjDvwWncf77RDd/8TM1sjjQ1/zC7aICIiIiIicsGORdA2s0eR
+ulY/hunAuJ0zB3BoB/5sn2OD37aH7Zqg/XnbbbiTPAb7B/a4nzPATkH7z3exn7s6j0/Nef9RnccP
+MbOf2cU+u642s+VdzigvIiIiIiKyb1d80DazZwO/wKQ79k6htwniJw+oCXfu83N/u4/trtvnsTCz
+FwLNuuC7uTCwl+/pnl1s0x2rPm+CuBs7jx/D/irUVwMK2iIiIiIiclFd0WO0zewBwM/npw78T+Db
+SV2U7w0su3vR3IBXdT5+UN/NfoPd+i63W+s83tfFATP7ElLIbi5EvA34JlJl+wywNPM9vaXz8d18
+Twcx2/fpzmPf5+2Kv7AkIiIiIiKH70oPHv+KdI4OvAF4mrtX22x/UFXsg7Cyy+1WO4/P7fNY39V5
+/DJ3/6Ydtj+M76l7QeE73f3Fh9AGERERERGRHV3RFW3gizuP/80OIRvgfhezMXt0311ud5/O40/s
+9SBmFoDH5qeRtH73TnbbtoPUXcbr+kM4voiIiIiIyK5c6UG7O673L7bb0MxOAQ/mYLo5H4RH7nK7
+7iRh79zHcc4wmc374+6+bVjP3fEPYqK4vfqTzuNHH8LxRUREREREduVKD9qx83inrtjfSJqEa7ez
+kl9MBjzazLatsJvZA4GHdl76/X0cq/mOjLTc2U6+ZR/HOAhvBCpSO7/AzD73kNohIiIiIiKyrSs9
+aP9N5/FTF22U141uJgM7CpwUKBeOQ85dvv+/zkt/4O5/tY9j3cVkVvDTZvaF2xzz0cDzOITvyd0/
+Cry6aQrwKjPb1VhxSw6jCi8iIiIiIsfQlR60/2vn8U+a2RNnNzCzxwNvBk4wPeHWYRsDTzWzV5jZ
+ie4bZnYV8KvAF+WXIvC9+zlIXuP79Z2XXmFmD5/dzsyeAbyO9O/MYX1PLwDuIAXthwBvzzOmz2Vm
+N5nZvwLeDzzj0jRRRERERESOuyt91vGfBv45aX3pa4E3mNk7gfeSqrIPBR6YH78R+DjwzMNp6hY/
+QlqK7JnAPzazN5Hadz1pkrdmtnEHftjd33YBx/pB4OmkruOfDvyxmb0N+CvS+O1H5dedtCb5ZwOP
+u4Dj7Yu732FmTyMF/jO5HW80s9uBt5PWLO/n9x6U2wxHp6eCiIiIiIgcA1d00Hb3O3Mw+y9MJvB6
+KJNxzc36yr8BfAPTXbEP24eAJwOvAW4AntZ5r2l3DfyIu9+6i/0tHHvu7u8zs68GfpnJWPYvyLfu
+8V5CCv+/u+uzOGDu/g4zexjwMuDx+eUbgX88uymTgP13wAcuTQtFREREROS4u6KDNoC7/3GeNOw7
+gKcA989v3QH8KfBqd38dgJnBdEBbuNvdHHoP287fQWr7Q4BvIgXJTyN1cf8o8N+Bn3P3PzuItrj7
+b5nZg4DvBJ5IWsKrysd6K/AKd/9D2PP3tJfz39W27v5h4Ilm9gjgn5CWJ7sPcHVu812kYP0O0kWB
+33f3uGB3IiIiIiIiB8rSEF05Cszs5cCzSIHzG9z9VYfcJBEREREREdmjK30yNBEREREREZFLSkFb
+RERERERE5AApaIuIiIiIiIgcIAVtERERERERkQOkoC0iIiIiIiJygBS0j569LoklIiIiIiIiR4iW
+9xIRERERERE5QKpoi4iIiIiIiBwgBW0RERERERGRA6SgLSIiIiIiInKAFLRFREREREREDpCCtoiI
+iIiIiMgB6h12A0RERC53ZrYGDIEIfPyQmyMiInIluxepYDxy99XDbswiWt4re8Ev/tGWL8K2eW42
+/a5ZvuX3mhtAaJ53Ptd9v7uPRvPH4u5pYe32fvIYbxbcnvNnaFvb2baBSTtDp63BIIR03ysCRTCK
+EOgFI4T0PJjlbdK9mVEEw0J63gtGUaTPhTC97aQN09/H7PfQlf799PY8o+fvIE7O+gs+59rFOxAR
+uQTMrAKKw26HiIjIMVK7+5EtHB/Zhh2WeYnNuo9sZps2XG95kt5uEnjzGMuhvLvNnCNZJz573rXT
+/COFbCMn8m6LHHKIZe4xOu3E8PzZ7j/T65Pn3t1/Pod2h5078/ZRakMO1d3zb0O2kd/rNmnrt2+W
+TtHy1xBI5SICc68viIgckvRfQDNuvPHGw26LiOzCeDzmzjvv5LrrrmMwGBx2c0Rklz760Y/mYtzR
+TgMK2lmwZrj64j8vY1FwbV5rAuR0Vds6wddy0Ka9nx+026bY9NPpLWdD9mzDbG47m5BN57iew3Vz
+Pxu2u6F8av9TNfLuq53APaeC3X4fnYsOUxc0pk7JcE9Hj0wytquOLSJHxyeBe505c4aPfOQjh90W
+EdmFd77zndxyyy284Q1v4KEPfehhN0dEdule97oXd955J6TfvUeWgnY2qTDbpN82k5dma75bnhjT
+AXYqZE8C5bygPX38rhSkp+L0pKC99Rym2rQghU4F90lI3q6iPf28qVAbPlPVJgfq2XOfVMHT9zKp
+cE8uTkw+392lNWV73FJ7gzvuEJ0jfg1LRERERESOKwXtebZUWJvXt/3Q5K4TJpvXFoXs7cYnN/3H
+zRzPgbPpS21GCqEG82rd8xprne3mdDifqmi7WxoLzZxb7rZuTgrbbTtmmtKG+Ob77NS622o27Xcy
+Oe1J34F0upM/D8cmVW1VtEVERERE5AhS0J6n2+V/uyA886HZkQJNxdc8v5vDsdkk5rr74rDdzog2
+s+OtjV3w+nT6bYZ4O5Oxz+0LGI7ncJ26akeHGGmjbbo3PBjEACF9PALmNgnhueJsnivRnrt/063P
+N+22qfbO7RJvnWbS+SNRRVtERERERI4gBe2GL3jivkPY9slmTKqs3WCYCr42Sbo5yDYBuxl/PHv0
+Nri2s26zy/A9s233pVx8Tu313H28CeKGR8dDE7Chbj+ZQzYGBCxEzEMK1G07c1uj48Hb0M3MuU+H
+7EmotpmwbZ3Xmwp685F23jWRY8DMngW8nPSv/qe7+98ecpMws1uBWwF3d822nX3iE5/g5ptvPuxm
+iMgujMdjAJ70pCdpMjSRQ3T99dfzjne847CbceAUtLeYF17nTDo2Z7tJgdgngTuH6qaKPBuytzvq
+VFDeEpono6ZnY+viHdKGVGuz7yTEt13Hm3NwiLEJvClgp+pyCtnm3l5Q8LxTd0+3mL8DN3xqpvTp
+ec7nNnBOP/S2d3mn/apoi8hR4+7cfvvth90MEdmDPKmSiMiBUtDeohkJPHk6M4S4sx20XcYXmKpc
+z9vNlr1taUp+aO2RunXgqX3msc27WRp9+nPd1yfdx6OnkN1MPGae+ombGcHJodryRGWd582ePAVv
+fHJxYTJae+v5W+cc0sPOt9IJ2O2s4zufpsiVRP/aXzZuOuwGiMiujIE7gesAVbRFLr07yAv3XpEU
+tBtTQ4InCXcq/M1JyT4bGtt5wWzmA+l5t3P0nENPArelcc9tFTfNPJYnRuuOee62ZZfmHRByR3Jr
+/zbvDjEnd/NJ4A4O0Z3QTJjWfKBTpe/2cJ9tV/NddqdKa9fVbnfT+c5zmN8SztV1XI4Jd38l8MrD
+bofshgFa3kvk8vBO4BbgDYCW9xK59G4GrtxeYAraMzpzX6fIaZMgOL8e3YmRtmjs8NZQvPV40y+0
+473zB61Z5mr2IkCnFVOV7t10rZ4TuCf78XZSNLdU3U5VbWtDdpoizdvzaz/tk3HouE321Z5MDtjW
+BOxJ4G7em1ywmP/Fqee4iIiIiIgcVQramc2dyroTsm1rHbqpAHc/tXOR1eY8atow86Q7RDuH/nZp
+L3bXRXxxKxa31HOXcbfcmaMN+5NKdnTymtbdID2pYM/O2ea+uON8+903oXv2e+h8sjs3nQraInKE
+1DtvIiJHyw2kOR1vOOyGiMgeFEU7B+uR/t0bDrsBR9FUp++5oW4y0ti6n+lWaNvu5rNx0Kertc3n
+F3Tntk74nO2Mvq1tQvh2Ibv7cc9V7KZ7eDdET8Zye+467pNx2sy+PkndTTdznznWLpsuctkyswea
+2QvM7A1m9mEz2zSzc2b2V2b2CjN7xDaffZaZRTOrzey+c97//fz+m/LzzzKzn837Xsvv3Te/97j8
+PJrZYy35RjN7q5ndZWbnzezPzOx7zGx4AefbN7N/ZGY/Y2ZvN7NPmtnYzD5hZn9sZrea2bU77OND
+uZ2/mJ9/tpn9gpn9r/z9/Z2ZvXa7725mf59hZj9lZu8xs7vNbN3MPmhmLzezW/Z7rlkeZKZLgCKX
+jxuAF6KgLXJ56QTtIz3AWxXtbF4envdmd7bwpgP37CtT+926sx1DdjfktxXcdvburYfxBffzLFyz
+e2pvk07oTUhOmTmNyY7k8dnMVLSbqrVP9uG5P/0kgOc9O/iWXgJbW7G1/Ts0X+QIMrPHAW/OT7s/
+on3gM4DPBJ5pZj/i7i/YxyHa6RDM7KnArwDLM+/P+8wQeD3wpTPbfC7wYODrzOyL3f3j+2jTLwDP
+nHPsq4GHA58P/Asze5q7/9GCfXTP6+nALwNLnfevA54OPMXMvsbd//OixpjZ84EfIn3n3TZ9GvDp
+pO//B9391t2dnoiIiMhiCtqLzKkgz4bUrZ3Nu5N5bWfS/7m77eznZoeFL8jZ03verpK9oP1bW9eJ
+uc0x84Rsqbo9E7J9ErJToLa2At7sjfxap7idw/bW77H7XLlarhA94Dzw26TA/ZfAWeBewAOBfwnc
+D/geM/urPPnZftwPeHU+1guBKJ5k2QAAIABJREFUPyR1q3p4fm3WDwIPI80E9PPAh4H7AN8CfAnw
+AOC/mtkj3fc8WKUAPgi8FrgN+Fugym18AvBs4FrgtWb2IHf/xDb7ejDwVcBHgR8H/pT0n4cvBb6H
+FL7/o5m9yd3vmv2wmX0X8P+S/rPyZ/lcPwDcDXw28C+ARwH/xszudPef3eO5ioiIiExR0M6mMuhM
+NXu2C/hsVXteRXuqVrugctuONZ4N2J0x2N1Jv6YmO2uP6DNdsRdE021C9pYK++zMaM2xcjBul/Hy
+Jng7sRm/3YZtbyvgza6a+6ZK73mnnhvRzLLeHfu+yM6VeZEj5V3Aze5+ds57/83MfhZ4HSnc3mpm
+r9pHsDVSZfZ24JHu3p3G87YFn3kY8BJ3/5aZtv6Wmf0C8Jy8zXNJ4XQvvt/d/9ec198J/IaZ/Qfg
+baSq9LeRBkou8lDSOTze3bsXDN5uZh8kXVw4BXwd8OLuB83sAaQLCg680N3/7cy+3wX8qpm9Kn/+
+h8zsl9z9nl2ep4iIiMgWGqM9j3VuMJNKtwbWyW3yv+bNyWzaNtUt3ObsrhnjPXuo3Y7NXvi38pmg
+b1v+OXtGs697O9bau2Ox3fGYAnbzPE51Efc2WHfbN/V5bz4/qX3vRCFbLjfu/skFIbt5vwK+Kz+9
+H/AP9nso4LtnQvZ2PgZ854L3voO0wCykCvfeGjI/ZHff/5/AS0n/uXn6Nps21xafPROym/38CqnS
+DfCFcz7/fFJ38dvmhOyubwNGwAngK7dru4iIiMhOFLS3YW0ynl96ti23zv9seqq0NiwvSM2LsqN1
+k/mWSdS8889FJ7Goku3bVrXb17qTl/l0SI5NsJ4KzJNbt43poU8F7KYa3tynrbcP2wrZciUws4GZ
+3cfMHpAnSXsg0/89fsg+dz0GXrOH7X/N3TfnveHua8Cvkf5T8EAzu9c+2wSAmV1lZvc3s8/pnPPd
++e3PMbNiwUcd+PMczBd5V27n/ee894/yPl67XftyBfvP89NHbbetiIiIyE7UdTyb5Lf5wXR2Salu
+4Nt25LNtDdHtOtEz+X1LiMzB1pplvZiMad6xU6ltfbhTRp09j6me47nbeNN/vBuUo1ueII02gIfm
+Pu9ndnWv2fHYzWvbdRnfcmFAoVsuI2a2Anw78E9J47IXBUuAM/s8zAfcfbyH7Rd1KW+8HfjW/Phz
+gf++l8aY2YNIFfMnAddvs2kgTZK2aJz2X+5wqE/m+5Mzx78vqWu6Az9qZj+6U5uz7doqIiIisiMF
+7V3Y2t3b5r6+KPgZTbdwm7Ov6X1232tn5c4h1TpjttugmQdyuy8+9jZNWzhYe3Ys+FS72lnGuxVu
+y+O06ay37cTohHbIt+U6+rzvb1L1b76lRRczbPYFkSPOzO5HmgTt09h+gYDm3+zlOe/txqf2uP1O
+s4l/rPP4mr3s2MyeA/wc6fdMZyrErZvm++3OeX2HwzXLe8xevOhW4fcy5n1lD9vOacpuiv8F219r
+ERERudLdMf3sjju44447Fmw7MR7vpaZweBS0s21zqC0OrGZzarBbtpm8aGZTz9Ojzj5mQnyTeN1T
+0m7CNtZUtW3yMZ9fKd4Sshec7E6zfLeTmDVdvg2iGyEHa3PL906MRjQINukePv1X7W68zhcipkL4
+nJA950KFyGXi1aSQHYFfBP4T8D7gTncvASxdWarz9vv9V7zeeZMpe51wbVfM7LNJIbsghfV/R7rQ
+8CHgnLvXebtvAF7WfOwiNKWbZH8AWLj814y1CzvsnTtvIiIiIlNe8pKX8KIXveiwm3FgFLS3Mb/6
+3AnMW97bqlnaymzmb5HW2d+CruPdPG6ANWtp+8z7zUu56j21jwUV6y12W83uvNntOm4OgfQ4RCda
+qmZ78LmToXUHo85WrueG7Obg6i4ul5kcOh9N+lf/h7ZZp3lPFeMDcu89vP/JhVtt9fWk3y8V8Fh3
+/8CC7S72OXeX+ird/b0X+XiYGWfO7NzzvygKikIVbRERkeuvTyO2nvvc5/LUpz51x+2f9KQnceed
+R/+itoJ2FmbX92pLw5NZw7cE387jJkzPmsrS3c82n9mxZc2WjhmEnNAdT30lu2XoeSXp2YDfbdSi
+YlbnA7MXCJoWtwXqPFu4uxEjBJuEbe+E7RgjZkb0kLqW53Np9tPtOL61EfMq9PO/b5Ej6IGdx7+2
+zXYPu9gNmePhwC/v8H7jL/aw3+ac371NyIaLf85/A9xDWvrr0Rf5WADceOONfOQjH7kUhxIREbmi
+3HDDDdxwww07bjcYDC5Bay6cZh3PmqW1morq1PPcBbqpYgfrvN+5Te3H2LKPbsCeOvbuWpi7nRsh
+5HvrvEbTxq3Hpn3eNHTREacT7fQEcXPqzN4J253lvrozj6fXIu4xz0oeJ7OSt/3Jp49iTPcWmGqh
+QrZcfroXNFe32e6bL3ZD5vgnZjac90aevO0ZpB/S97r7x+Ztt0BzzgvP18xuAHa+bH0B3D0Cryf9
+5+SJuXeBiIiIyEWnoJ219dQ2oKb/dQP2omDNTLBdtO2WAzXPd9O+pk02G7Kb3TWvzw/czYZb2jP7
+Jcw87l506H5wsk72nOW6YrrF2FkGLDaB24lN2Ga2pm7Tt5kJ3hSy5TLVreh+/bwNzOybSaHzooyZ
+3sb1wE8seO+nmMzq9R/2uN/mnD/LzB45+6aZLQO/Aiztcb/78SOksesBeI2Z3bRoQzMLZvY1Znbj
+JWiXiIiIXMHUdTybHh/tW163JqjO9APvfmxuFXaHmci7psZEz36snXm8W1f23B6f2mzSOqafmbXr
+Vc9OyNZ9NpmcbbobdxPopz7RTNZGs5Y2hDwDebPEV3ft7GbStBS0u6PBm/3OPp5u3dT3rcQtlwF3
+f5eZ/QXwIOB5ZnYN8EukqTZvBv4Z8BXAHwKPYf9hez+fewfwLWZ2f+DngQ8D9wG+BXhi3uadwEv2
+uN9fAr6NNBnZ683sx0jnt0nqLv6vgM8A3ko654vG3f/CzJ4P/CSpS/tfmNl/BN5EmqhtCfg00trZ
+/4R08eFBwEcvZrtERETkyqagnYXZ0NbpNt2+1RmvvWUzZmLpohDY9s6eBMrtZ/ve7l2jWe3aF0Ts
+pt2Th0237/krC20pvreTvk2fc3f+b8/NdOtWuifrZ0+W/UozlE8vbjQJ296exfQ5z/uWFLLlMvPP
+SGtQX03qjv2MznsOvJsU8nZe02Kx/fxQvAD418CXkta67nLSzOhPyV2wd83d32FmtwIvBE4DPzRn
+3z8OvJedg/YF/7C7+4vN7Dzw06Tx2t+Vb1s2JV0M2LzQY4qIiMjxpq7jWdP1evbWjIduumUv6kbe
+jIFub3P3OVUMv9AW77yv3LAmGE+Nf55q6/S46EnX+e7zrZXubvjudiWP+XETsGP7/pzFdL376Tjp
+jj71qa3nLnI5cfd3A/+AVDX+EDAmzYj9J6Sg+4hdjIHebi3q3bw/zxj4MlIF+22kdbjXgPeQQvgt
+7v53+zmmu/9b4MnA75JmLB+RKuavAb7E3b97l+3e7Xltu527vwy4P3Arqbp+J1AC54H353Y9D7jZ
+3f9mF8cTERERWch8bmXz+PmJ//yuzhcxb4Gr6SrqvIpqt+t1Z8Mt+1m0j+n9T2/rTZfxZiw0dMZG
+T8Ktz+xgdums6Z3ObRaQrsBMXzCY7GtqfLgZIUzGhReFUQSjCIFeCPSK5lbQKyzfAkV+XASjKAJm
+TE3u1nyP8y5cNKfXPP7M65eUvEV2ycweR1rT2oEvcve3HHKTrghm9hHgpptuukmzjouIiFxEN998
+M7fffjvA7e5+82G3ZxF1Hd/CZ+6nu4IvqiJPdxvf+q51U+3cjbbrHj4zpnqm97djKWhPTTBm0zm6
+Hds92w7a2cOn27hgbHlT5p7T6qnJ4Npjxc7U5JbW9cqV9tS9POAYhIBbwEJYcPzZixDK1iIiIiIi
+cjQpaC9gM0+asdCdecXmbm9TL3Srydv28V74ejdkN7ucHS+dwrVNOlvnLttN8J6Jw/k+j4aeGQLe
+PY/pCwzTK11PZiLvfNy7W3h63QHqHLLzW3HyYScQCQQCHop88SBgUzue5r7wOoCIiIiIiMihU9Ce
+sW2deWaM9Zb42g3BC4J4ox2avE0Ab7tLdx5DCpmpa3fqct0EbWcy8ViMTh1tm97hzXttWXy6dZ0K
+/mwVe/7erP1+uhVta8rlTX/3TthuqtlOIP2raGCBNIv64kngFLJFREREROQoU9DOto6v9vaNbnXV
+Jol66nPdT06vgDVdfZ500576cLNQ11SYD3m97JDHQwcma1o3rzXtiZ6r2g51dKocVpt82zmj7kHB
+pqvaNjfgTk52dh3uycN0II/eToRm0akt4sFxi0RzYnBqc0LI5xaMEAK9/pBef0gfJ9CHsHWG9/ZI
+jrqPi4iIiIjIkaWgnYWwfWWZTjV54TZMB++tj5jqat1067b2n5NJxcxoJxUrmkDaBO6Z+1TJNmqH
+GKGscyfydh1r2kDdLsXVtqDbmsnj6S7wW89j0nW82TYHbY/UscaJRGpqrzHSLXUSrwkWMSAE8mRq
+gaXlVZaWVoET9LE0HX4Rpir6InJg9jNDuYiIiIjskoJ21l1He/Zvn93q6XaF1HkjobfTHHKyaNZk
+Bu8QmMzaHQJFsE4VuNOl3FINuo5Q52p22nEkEtNcZPmv1M3frGNzksYkebdP5iTrOSfTXdasnQod
+J3qJxYroFcQSYoV5mR57nW8VhufJ05yiCNRVCQ5Fr08RCswChLbOP/96gIjsmbv/D6A47HaIiIiI
+XMkUtLPJMlhTI5e3Zs15oXMm/c1OVjb/gNCUo5uYHWwSsguDXmEMikC/V9BrgnZo1vaehO3UXRyq
+CFXtuEeiO4Wn9z1XupsTa07Vc8j2qZDdTPrmU22fjNO2yQWCHJTTjiLuEa8rqEd4Pd56iyUxVnhd
+ApFm3vGiCIDR6/UZLq3Q7w0IRW9y3MZMyNbSdCIiIiIichQpaGf1eCPP2RVxr4kewWOecCzkbtp5
++anm8dTU27n6arYlcm+Jg806WJCOVUc81hhQFFAEsGCEYki/CCwPCnq9Incrz93FPa+bnScaS5Og
+OXXuLj7pGJpaMDWu2qcz65ZKfKd7u9n0JYemqzoeU3j3iNclHqsUpKsRXm3i1YhYjfEcumM1JtZj
+vC6J9RiI7RjtXq/H0tIJqrJM3//0l9X5BjutVmVbRERERESOKAXtrCo3AIixItYVdZ1CXwjF9K0o
+sNAjhABtF+e0HFXbAbyp/ObnjTZA2iTQxlhT1yWxHGPENAlYAV4YYRgYFAOWhwX9Xq/9nNPMKu7E
+GKlJoTvGfMvjsidjmzvJOdevZ3tiN9tPNk0XD6BzLcEmG6eQ73isqKsxsRwRq01iuUldbhLLjRS2
+6zFejYn1iLps7keYOUVRUBQF/f6A1dUNqqokxpj23Xx1M1cq3NMFDTfHXElbRERERESOHgXtrBqv
+AxDrkqoaU1djPNYURY9Q9PJ9n6LoE3p9rOilinfo5WSaBld3Z+XeMo3XljKy4zESqzHleAPzGooU
+tIteIMQB/WKF5WHBoN9rJzNzd6o6Qh1xT0nUm4p2dDx2J1rzNjPHzgUAc88znU/mRJqqes/NsJ2L
+Bp67qNc1VTmmGq9TjdeJ4w3q8Qb1eD2H71zZrkZU1SZ1OaIuNzFzer0+/V6ferjEaLROWY7TRYLd
+/IGpoi0iIiIiIkeUgnZWjtYAqMsxVblBVW4S65IipKAdQkHRG1D0+ilw9/qE0MOKHqFI1W4LBSEE
+LBRp3mwLbXdy75aEO2G8Gq8z3jjPeGMNYkndC9SF4f2CemWAxVX6BfR7oV0fOzpNaiaSxmh7s362
+O9FjWmYrxlzdnizz5Uza0sxjBrTV61mTwJ27qdNU/etU+a9GVKO1dBufzyF7gzjeSBXuKle0q1TJ
+rnLFOxSBAHivh4XQ9hZovsfZdcSa8diWH5uZxmiLiIiIiMiRpKCdjTfPA1CPNylHa5SjNWI5SiHQ
+Unhuqtohh+2Qq90piOfAnR9bmIRGsyJ1L2/vJ5OvjTfOsrl2D5vn78FjyaAo6PcC9aDHaHWJujqF
+kdad9pjKuDFPblbHyWzjTdj2JozXFXVVp67Y7rlLtuUQW0DosWh1n9Qyb7vAt0t3kfZVlWUK2OWY
+arxBPT5PNTpPPVojlhvEMnUhj1WeFK1Kk6HVubod6zEhDCiKgsFwmeWVUwyXVhkMlun1h+l7DmlS
+ZG9OKj3JXcqt09ddRERERETkaFHQzsoctMvRGuXGOcYb56jHG3ncchqBHZqgXfRy2O5NvVb0ctfy
+tpt5et9Ces2KXg7elidVy0H7/KdYP/tJvB5T9nr0ewXVYMD49ClilcYzh5DWyXbzTvU6Be0mdLdh
+O0bquqauylR5jjEHbQi9AaEwQpgXsre+NpmhPHUVx526KhmPNilHG1TjNerR+XQbn8erTWI5wqtR
+ngitbGcdj1WZgnZVQq/IQXuJpdWTDJdX6Q+X6fUH6TsMaSZy97ilaeaOz51lTkRERERE5PApaGfV
+OE2GVo3WKTfPU66fpRqtpSpqXiM6Val7WFO5LnrYTMhuupen57mreW+QA24a2526l6dZy8uNs4zW
+7mbj/F3Ecky/36Pf6xGHS4w2rqUqN9OM3l7j0Yl1Wiu7jpGqjtR1um8mQkvb1MSqpCpH1HXq5l3X
+KbAWDj0LBO9PZhCfNadSnCY+i8RYU1VjyvEm4801ytE5Yg7Z9eh8WtqrGkMO19QlHsc5cFdp+a9Y
+Eczo9dNyXsurpxgun2AwXKLXT0t7NRX/2e7hbb5WyBYRERERkSNKQTtbWj0NDr1en17Ro98fUI1W
+UxW2Lol1RYw17k5dl9R1mSvdKTBPdSMvem1luyj6OYz3sWZ8d1FQ9HoURWC0djfV5j14uQZVSaRH
+HXuU1Gys3c3ZT93JXasrLC2vUsVAHY0qGlWEsnbKOq2f3XQjL6vIaGONzY01RhtrqZt3XVPVNRBY
+Wj3F0uppQjFIVXXIw8anZ0dPy3rlsd3NzOhVOu9ytE49XqMu14jlOrHawKsNqDdhS8iu8FiBp4nl
+iv4As8DyiatYOXktq6euZeXkNQyXT1D0l4CQurpjhEUznpl6jYuIiIiIyNGloJ0tr5wGoB4MqfoD
+quEy1WidarxBWW5SjTeoylGqElcpdDbjh9PkXGFqMrRQ9CjyvYUUti13Oe/3063X6zHeOEe1eQ4v
+1/C6InqPuu5ResX6+bs5e/cKw6UBg6VVnILoPSKB2gM1gejpcXQjulHVkdHGOTbO38PG+bOMxyOq
+qqaqKswKojuhN2CwfCJP1AbmNllOC/JM5LTjsmOcVMircpNqvJZnGF8jjtfwch2vNqEZk103YTtV
+4okVeCT0B/T6S/QGy6yeuobVU9ewcupaVk9dw3D5JL3+ECxMJl2DHLaZWtoMV9IWEREREZGjS0E7
+W17NQbtaph4sUy9tUI03GG+cp9g8xzikSmtVjYh1yXi8kYJxlbtDt7Nk22QW7XxP6KdlwEKPXn/A
+YDBgMBzSHwyox+vU43V8vIF7xOuCOvQglmycv4ezwzSmerC0CtYH6+PWh9DDQw+3Hm4FTsC9oKrr
+HLTv5vw9dzEejSjLkrKsUmW9N2C4fAKPNd7O7k0buhs+tVZ2qmhX5SblaJ2qqWg3IbvcgKai3VSy
+67Q8GjGmsI1TFD0GSysMV06zcuoaVnPIXj11DYPhMkVvSKpoQ9M3PNJtVtPWvE74/DXIRERERERE
+DpWCdra0fAJI62j7YEislqnKTcbDJcabS4w3l9NkXRtL9PpL9Efr1ONRXhd6nKq+MU88lpe+almB
+W5rtu+j1qYZDqnLIcDhIM3Pn5a88RqIVYCV1VbK+dg+hMGKs6A2WwXoQ+pP70GtDvOXn7rB57lOM
+1j5FuXEP5WhEWdVUZYUVgzR52XiNerwOXoPlZcgsgDntROOxxj2dU12NqcsN6nKDqtygLteJZeou
+7tUmXm+2lex28rNYgnuetX1ACAXD5ZMsn7ia5ZPXsnrqDMsnrma4fJL+YCUvm9bDyZXs7kBsI1ex
+HXPwZmkyDdQWEREREZEjSEE7W1paBsDjAI9DiBWxXqEcL1OOT1CONxhvrjPaXGO0ucZ4cz3Nuj1a
+pxxvUI03qcoR5XiTqkpjuuu6oq4qIhXuRiRQFL085rvE60Eav1yXuXu149Sksm1JOB+IsaYcbxKK
+flqWi1S9JlfIsR6hN0jLYvWGEApG6+cYr5+j3jyXZviu0/TkZg5l6vJdbZ6FuJSW+wpFOzkbAGb5
+okFFjFUO2us5YK+n8djVJlaPsDjCY+oqTizxWOKexmWbBULRo9dfougvsXTiGlZOXcfq6evSuOzV
+0/SHq4Rervhb6iju7kSD0NSyPY0Z9zR4PHdtVzVbRERERESOJgXtrAnaaWByTN2T8wzbdVVSV2PG
+o03Gow1Go03Go3XGG+cZb55jvHGe0cZ5RhvnUnDdXGccI3V0yqqkjp6X4HJCKIj1IIftPuZOIGLE
+PN47LdMFEGPNeLTB+tpZzAKOgVu6tyJVtq2gN1iiP1xmMFyh6A+oRhuUow3q0QZe1xDBYg6r1QZx
+fJ56dA7zcTt2vFl2LJhhZnhMVfk6ltTlOHVxz0E7lht43XQVH6VbHKfAHasUtmPqql70+vQGKwyW
+T02C9lXXs3LiKpaWV+gPV2aW86LtOt7Wq3NF24z8ZqpuK2yLiIiIiMhRpKCdLQ2XgMlQazNLa0bH
+Oq9FXTMejynHI8bliPHmBqONs4w2zjHaOEv//N0UvT4WUlW2dseqktojVV1TV5GqqtN+Y1riyus+
+hUEwCIGU8T0v0+VOWY7YtICF3FE6v+4OTpG7fRf0h8ssLa8yXDnBYLBErHIX7jxhm3vA3LDoeLlO
+NTpHuT4kVkuT2dBzVTvkNb7rXJGvYkVVjqnG69TjDerxBl6uQ5m6jaegnUJ2qmbX4BFwQggU/SH9
+pRMMV69m+cS1LJ+8lpVTZ1heOcVgMKDXzzOyW/rym4p2MyVb/mNIwbqtaIuIiIiIiBxdCtrZoB/a
+x9aZFyzUgRqnNuj3e4BDSFVfC0bR79MfLtMfrjBYPslw9TTDtXtYP3c3xblPgRVpQjIb4T5u16Iu
+y/Q4BAhmhGYuNZ+MPLZomMXcpXsSwFPQrnEM90B0n8zFFkuIdRp/HWNbIXY3YuWMN88SzoLX4xRw
+Z7qOhxzsozvRY7rFiliNqesxsRpDPcKqzbai3Qb7ukozrvd7mBX0hydYOnEtSyevY/nEGYar19Ab
+niQUw9ztPbSzjM+f16wz6VmngO04itsiR5eZPQt4Oekn99Pd/W8PuUmXzB133MHNN9+88P3rr7+e
+d7zjHZewRSIiInIYFLSzQX9qbmua9a4COXjXk/HLFAVWFClkV8vUVZlD9hrLm2sMz99N0V8GC9R1
+jdka7qQqsdepS3oZqcuKEFJ37ZDDO7mabrlymx43YdnbqnZ08pJeED2NvzaLuI8J5Co5jru1Qds9
+Um6cw+uScrSWJkCjmVxscvFgktrTHGRArlSnCdJCLLE4JsRmXHZFrCs81mmd8N4SRX/IYOUqhieu
+ZfnUdaycujfDlVP0lk4QesMU8K2AqcDs+Tvw5lmzntfUDOMK2SJyVMUYuf322w+7GSIiInLIFLSz
+QW9S0ca7S0gFrLYUSkPAigKLNUV/kGYYj050Z1iO8oRomwxWTqU1q+uacrSJu6du2OUmsSqJdd0G
+ZmtCdghT96ETtlObmmp2qmzX0akj+XGNWbrhY4oQ6IUCipDGdLdBu2JcjylH51M1udlnjLlXdhP2
+LZ1nsyZ4SAE8hLRNoKLwGqfC8oWDdIvYIFD0hvSXTjJcuZqlk9eyfOpeLJ++N/3BEv3BMlYM8/jy
+NC47n17O9lOl66kqdhqn7fnlzp+XiMiRctOc1+4grakgIiIix4GCduadB03Idme6K3dT9bWCEDzd
+FynEBgsUIc0q7rGmGm1Ql2kt6f5whcFwhf5gmdHmGuV4RJXHeze9oiFNlmYeidHa7uvWrHGdt2q7
+jnsKnE6zznVFXY6pgqcB36HA6zTBWRu0m1N0crfzmEJ2jGlcdGdNbSsKQlEQih6hCGm8dWF50rKI
+UQM17V4tYEWgGKwwWD7N8MQ1LJ28juHqNfSXT9Efrqbx2L1erprTKWZPV6y3/sk0Xceb5J221Tra
+InL0BOAjc16/GVClW0RE5LhQ0M7qunnkbXdld9qKdfRJyM0JNwVSUtfrJtxS9BgMllhZPQ0x0uv1
+WVo9zcbaWZZPnGVj7Swba+fYWD8L6+dwj3ksdXNLXQ9px12n8dtAW21usmaAtqs57sS6pi6BEHCr
+8e6SXVhqJ7lLNuk4acx4Dtp5f2AEL3AvcpfxFKLdQ6qSE/F8w8jreBdY6NEfnmS4eg3Lp69n+cS1
+DFeuzkt4pbW0m+7q5p5ubdl63rhrzSwuIiIiIiKXHwXtrK6988zbJaZS0O5Wkkkh2yfjmFM375AC
+Z88xX8JWT1P0+gyXT7C0fi6F67VzrJ37FL3BJyAE6jy2ubml+ctqYh3xOqa5wpqJ0kJzjM5Ybsjd
+zIEY03JcxDR7dzCipVnEabqhW7oo4PnDMfpU2E5nnP4ZvKDwHniFeQ/zQCgCeeQ37pFIzLOVGxb6
+hN6Q3tJJhieuYeXUvVk6cS39pdW0hFevP2k7U1crpkwVujt/HgrcIiIiIiJyuVDQzurYREyahZxz
+oKSdfKytZmfNZGWQu1R7wOgR+hCsoD9Yol45xXDpBMPlkyytrNEfrmBFgRvUXlOXo9TluxxRlyUV
+eXIxj2k4X3AieR3s4HndbWsb0MRPbyraHvG8HnZaNmwyk3gz0VkTtNPFgzSzeOqK7pPKNk4wx/Ot
+mSG8Hdud1iLL1XHDQi8F7cEK/eXTabbxE9dQ9AYUvQEh9PLM6k09fXKceT3ArRuuuzm7+7Jv/ZyI
+XHxmdhXwPcDTgPsB54D0cbdgAAAgAElEQVT3AC9x99fsch9D4BuBpwMPBK4B7s77+f+BV7p7vXgP
+F7YPM/sQcF/gFe7+bDO7Bfg24LHAjcDA3TUZhIiIiOyLgnZW1bOT1HjTgzyHym7Gtk44bLo/O8GM
+GIxAAT3SxGZFL1WLzdrqbygC/eESS6unKEcbjEfrlKN1xpsblBsbjDc3GI82wCPudepentvjMVWS
+m27rmEGcjF12oDBop0vPun9btOYcgGABgqUwDW16LYqCoijo9QqKIlCYUTQzoeftvGkTlpYIK9Ka
+2OnWIxQ9LITuNO5b5QsZ3S7u08G6mSWts7b2wj9FEbnYzOwBwO8BNzD5wR4CXww83sxeDrxlh308
+BPgvpKDb/Y/DmWY/wHPN7Cnu/vGLtI/20qmZPRf4GaDovK+Zy0RERGTfFLSzpqI96Tw9YVMZsQnZ
+np9NV2YDlsdGB0KRu6CbEYqCoten1x+0IfvkVdexuXGO0fp5NtfPpdv5s2yunSWEQKzLNMlZXRJj
+Whc7esSjT5YA6yzFRVN1L0jrcnlqu89k1+Y8gqVpzYL51AZGCtq9IuTA3amQm09Ctqfx64UFzHpY
+0Z8O26EgTI0Tz/u3mbY0f93Nedq69/MsXnhbRC4iMzsJvBG4nvRT+6vAq4CPA38P+E7g64EHbbOP
+zwR+HzgF3AP8LHAb8GHgWuCpwHOBhwO/aWZfOFuVPoh9dHw+8M+A/w38OPCnpN+NX7ib70RERERk
+HgXtbKqibdMPmy7i3nktTd01GWtsTRo3wzwwWYs6EIqCXq/PYLDEcHmV5eo0ZVVSVSUb5+9mvbmd
++xT93iB3Q49U5YiyHEHpeJXGU8c6jadOy4LlEBsMj0Y0S93KLYD5pOrdtnm6mk3uYt6eV2ft7iIY
+vV4O2yGda+50TmzCtjdl/jCpaPf6eS3tVNVuLwa0Fybamd22/iHM5ufukl9ueGd28ukquIhcIt9P
+mj7bge9193/Xee9dZvYa4HXAE7fZxyuB06RA+0R3/9TM+79nZq/L+3kEKbi/7CLso/E5wLuBx7n7
+2c7rb9vmHERERES2paCdxe6A35xI2xiXZ/Vu3prekJxiJ0tjpf93wrYXWC+Pl65zZbuu0/rXkKvf
+PYqiT783YDBcZmn5ROpOPtpgPN6gHG1SjkdpabBynENuGmPtbVOcGFMQDqGpOFtaMswNYp5QzTx3
+Jbd2ZnOaSdcsxfOiMIqU4WcmKEvhOnpejsycsqrxsiSOxozH6QJCXaUqvIVA6Ewc59BOKjd7a3qN
+0x4vjQE37zTA26+6M55cRC42M+sDzyb9mL5nJmQD4O61mT0H+BugP2cfjwEelffxrDkBudnPG3No
+fwYzIfkg9tFtUt7Pt86EbBEREZELoqCdNUHbuoGOPOv3vEDX7Y7dhuzOm1hbcQ0hVbbNeilUx5iC
+dR2JS3XepqDXGzAcrrC8eprxqTVGm2uMN88z2kiPRxvrjDbWGG+uU1dNoK3SOO7cldvc8WCTdbY9
+hW8H3DzPYA4Q0mzlueIc8ozgTdgucrW7qWJ3u9RHn9y8drwsqW1EGddZ2dxgPNqkLMf0qzGh6ANG
+EQJNAbxpV3cm9+6tuW7RfJPTcnV7UiQXkUvjFuBq0k/eKxdt5O63m9nvAk+e8/ZT8/373f29Oxzv
+LaSQ/HAzC54mqziofXR92N3/aIf9iIiIiOyJgnbWjrSelIenJrre0qc5d2vudsduK9r5+eR1oygC
+7qm7d3QIDrFIxwmhoOgNGAxXqFZOUVdjqvGI0frZNG574xyba2kN7s3BgI1ej/FoEzY3iNHx2sHr
+VOG1VMV2n4TtNGu5p7HalidGC2BeTJYIC6EN2Wksdqp2p4q2T41cdzxXtCFapC4rzEdYbWy2QXtE
+VZVpArVQdBf0ymuFgwfa2dw977eZXG02YHeGcE/mVlPPcZFL6XM7j2/bYdu3Mz9oPyzf/30z2+1k
+Y33SbOKfOMB9NJw0Q7mIiIjIgVLQzrpdx7sVXADvdB1vt7HOnFzdGbOtE7Cn/jmZqdyaoJ2nArdQ
+UBR96sEyMdZ4rIl1xeb6SZaasL20ymC4zKA/pNcfsrF2nhB6gFGVI2JekzvN92OTZcny8bA0KzrB
+J5O7NV3Fu8uBmdFMFB7aIJsDdtM9PXcbryPUHiFWeJ3Gko+aoD3epCrHmAVi0SN4PwX9ttJOp42p
+Te6TUe/d7vtT4Rra2C8il9Q1ncdzZwLv+NiC1+/F3vuiOLBywPvomtv1fP8iqYmzUsYfj8e8853v
+5IYbbuCGG2442EOLiIhcxu644w7uuOOOHbcbj8eXoDUXTkE7m+Tozt/f0kDgFOxsusrq3gTnqenF
+ultAp7o9FVa7h8iTmhW9PhZCDtqRWPQYuqcu5f0lBsMVBkurLK+cYmX9LGvn72H9/FkG5+9hvLlG
+Od6kHG9QV2PMUvW6rtMY6nYcdkizojfHTLc08VkI0+OxuzOrt1Xo3NW7rp26dso6UrtBLPNa25Fy
+tMl4tM54c51yeSON0e71879pnSXB2qp7rrw3IduZ+r6bixne/TpF5LDt92pXs3zWu4Gv28Pnbj/g
+fXRtu1b3/ty5+J077+SWW27h1ltv5YUvfOHBH1pEROQy9ZKXvIQXvehFh92MA6OgnTVrZTfprrN0
+83Qx27ZG6pkB2vN2nkc6N12jaWfsNtL62mAplMaC6E7w2Ibs4dIJyuWTLK2cpBytM9o8z/CeTzJc
++hT9wRKba/ekZcI2jPEmuNfEmCdb63QHB4MiHycUFCFQhEBo1sjO3cStE7jb9uex3tGdKjpV7VR1
+pIrNtxdxq9Lkbe3a4BuEok+vvzQZQ95E7SZkE9pw3VyEcEsToDmTRkwNyZ53UURELrZu5ffewF9v
+s+29F7x+F+nH+cQuxlcvchD7uGjMjDNnzix8/8yZM7z61a9WNVtERGTGc5/7XJ76f9q79yjJ1rO+
+79/n3beq6u65nIukIxQSA3aMIzCyIsDcZIwSywgRA1lZkJAogB2c8AcrgMOCXIS0EtvLYAzJcpaP
+rUQIHAcwBoJB2IZEQUCWkcTVXlwCJk4QDHDQ5ZyZ7qrae7/vkz/evXft7um598z0Uf8+Z9XUbfd+
+d1WfmppnP+/7PJ//+Xfc7vWvfz3PPXfrk9rnhQLtwRRMjzfm0fUs6LYhaN5ls4+XQLvd2mHHdi2x
+ht3nILjIRdKGYDONWfRqkYNxd2LsiN2G2OeMcb3Yo2oWFFVFWZWEIrcE89TTdx0pJWLKyxdDCHiY
+H/ssoz208gpDYfB5Dn533D4UPxumiw9BdhcTMfpQjC1nqbt2naulb3LF9LJakGKcZgY4KQfbU5Cd
+dsH2kNUee39Pb/18gfbdvNEi8jD8s9nt1wA/fZttX3OLx38e+DTgY8zsJe5+pynoD2sfD83LX/5y
+3v/+9z/uwxAREXnRudtlVXVdP4KjeXAKtAdjq6hdV+mh/PU4NXy8PWa0bbp7Uz/nk0XKpyrbzMuJ
+7cZNKZGSTwH2WJHbQiBY7lFdjOOFAFaw2MvB9NinOxQFFgwLOdhtt2vYGpCGdddGGcLQG3voj12E
+qYVXmE0Vt+kogbRr5TUG2DmTPfb09t2aaiD1HX27oRsqppdlTVHVFGWdp5inHmJPWRY0iwV1sxje
+25DfSw/DNHxnKsM2zB3fvc85UFdGW+SR+llyVvsK8B8C33baRmb2Udy6j/YPAV9F/hv0q4H/8j6O
+4yz2ISIiIvJQKdAejdHxvOoW40PzYJsh2LZT1mXftNNcVZvZdPFjz0JKToyJvu9JKQ0BeQ60i6KE
+MmerzQIWSkJpFBZolpdwjFBUhLIghCFQDcb26AYWhmWMHqdAughGVRZDsB2OZ7JPK3zmY4Xw4Rij
+0/WJPkZiTMTkQ7C/yy2n2NF3G9rtIdv1DYoiB9lFURG77XQpq5K9/gBwijIA5XBCIOQ+4NNU+3nV
+ufHAbN7aXEQeAXdvzeztwNcAn2RmX+fu3zLfxswK4O9wSg/tYR8/ZmbvAT4Z+Etm9vPu/n23GtPM
+Xgn8a+7+w2e5DxEREZGHTYH2xHdXNo/g5pOp87RrG9dkH6s4fsrefBdgz6/nAXryHLj2XU+MMU+l
+HoPtCrCAhZzdtmA5ux1K6iWEoqSql4SyxGzM/uap4uCk1EPsCEP/7CJAVRZURaAqiymLbcNl9y74
+Lsh2mzLafZ/o+kgfEzGmnIl3Z3b+IQfa7ZZ2c0i7zhntsqopinKYTp6nldd1jeEUZUHV1JhBYQEP
+jo+vBcsVycdZBEPA7cPtkzMJROSheyu5L/UrgL9mZq8CvpNchfyPAF9L7rf9Pm49ffzfB36GXMX8
+e83sHwLfA/w6uTDZS4BXkftlfwrwLcDJIPks9iEiIiLy0CjQvsnJbPbNt473y77FT94iyJ6qag/6
+PtJut2zXG/q+H6aNJxxoFvnaxn5bWM5sF4GiavJxFAUxtnTbI7p2Td9vSLEj9i2x2+DRMJxgkcI4
+ls0eC77ZcMDuuzXRY/Gz6EPhsz7RxThNHY8pkYaTAsC0rxxor2nXN9hUDSEM2XZgs77B9uiQzfqQ
+ZrGkqiqa5ZLY9xQhEEOi8NzqK0+K9/nbOZ3UsDHgVkpb5JFy9xfM7PXAjwEvA75kuEybAG8HfnK4
+Pm0fv2lmfxL4B8Argc8D3njapsPl+Yexj4HO1omIiMhDoUD7JrsqXDbLWs9beY3FuWxanGzHfjQ/
+ekq352H7sSBacme72XDjhevcuH6Dtm2H4DHvc7m/YtX3OE6V6qFaeMDMiH1H33X03XYofpaDzyJU
+lGVFXdWkpsF7MI+5LzaJMkCYTRnfHVqeiz32t46JIaj2Ycp4pB+mjafkeEpMG8/ek77raLdrNkfX
+8/T1oUBb7NZsN2u26zWbzRGx32e5WtG1l4ixJ8aCskxTkA9jsG27t27+K3JuNZlARB4id/9lM/s3
+gK8HvgD4aOA6uVja33b37zWzN3Gs7ONN+/gNM/skcnb8i8jZ76fJrbs+APwa8FPAD7j7Lzysfdzu
+GEVEREQehALtk45V356vxD5ej/t4JWyfBd7Hu0+dLIQ2BpKenJQim82GG9dv8KEPfojtZrsLz824
+FHsAiqoAnKIooCixEIixp+9bunZL388Lo5WUZUVV13hX4yFBMkhOwCmCUYTZa5yfQGCWyR5aeLUn
+povHGPO686Fg25SfHwP0PgfaoajyY7HD45bYHrLdbthsNvl1pp7t5jJduyX2PamodmvUZ//szTXJ
+d1XI84GO5d8VaYs8Du7+YeAbhstpz78DeMcd9uHk6d7f8wDHcd/7cPc/dL/jioiIiNyJAu0TptDN
+8nrsXVr72LPZGFhPbb+OP3VyKnm+3gXZMcac0b5+nQ998EOsj44Ya36HEHCcUBbUi4oQwD3XFyo8
+EGNH380CbU9DT+4caHtVYU2NFwnvgRgxD0M2e+yrPXtNw9r08WRAHAqftV2k6yMxDYF2Ssdf2Tyj
+DfR9h23W4EaKPSluSf0R/XbBdtuy2XZsty3gbNY36NpN3i7FqfK6eQ6u529dcpsem/c4FxERERER
+OW8UaA+mwlpTnbN5FDd/5MTj4/anBH02rHvePemzaehZiomu72m3LZv1NlfyHitrFwVYILmz2tvQ
+1DVNU1GVJV17RLddD32rD4nbI2K3xvsNHltIPUYCEmbj4uvduGMhb98dWc5kDwF210farqdtc6Cd
+3G9ekz3tayye5oQ+EkJHF7aEYPSFEQuIRX7/qqoihIrl3j7NYkVVNYSiytPMhwPyU05a4LvMeRpn
+HWjCp4iIiIiInEMKtAdjoD3OpB5Da/N5YHwisz0Ezcd+9lg0u9vXmClOQyG1sZJ28kTsI23bsdm0
+9DHR9bmat1sgOvR9z/poyWpZs1w2LOqSvl8Tuw2xW9O3a2K3JnZHpH6N91tIHZZ68BxsjxXJ58fo
+nh/wISSPyemGQHvbRdo2sm07+n738wxVwI933MpZencnxoj1PVibC5yVgVgFvA+EYkFTN1i5YG//
+CsvlPlWzpCgqQiiwqfzZfH27kdwZZ8CPAfbxlmQiIiIiIiLnhwLtwbxX9u3jt2Eq+RhgzzLUu3Db
+p213WW0fxnB8NoYnp+8jXdez2ba0bc4kd30aWmrlbPfe+oh2b0G/1xCXFanf4HFLihtSv5mC7lxp
+fAupxTyCR6bS4j5bXz4d6hBsOzmoj07bR9ousu162ranjzGfGDCwwHDbjgXayXOBtBgjZj1gFMGI
+VSD1+VJVS6pmQbW8zP7BZRar/aE9WY2Fcjph4dMC+J1ErjY+Bthh/jpERERERETOEQXaA5tP72Y+
+WXwX8O2KjDvmhpPyVObjWwyrrI9H7Hn7Wabc5pcAFnCMmJy2i7RtTygCRl7Pnfotqd3QtzXtuiR4
+i9Fi3uJpi/dbPI7XLcQOPE8f97HXtVnuxz2MZ6HYXZLTe4vFLd5BIpG8Jw6tvcIYYLsRghOCkdIQ
+GA/Z7FzoLZFijwGxCKQYc4VyoCwrmuUei4MrLPev0iz3qeoFoSgJIU+TnxUyn715nKw+RxpOYoiI
+iIiIiJw3CrRvYZzCbMOfs7rcMATZKeaMsY8FwoZK3Dnzm/tHBwu7gmrDlOsxGx5CQVFV1IuGxWpJ
+s2mJyWj7BF2cMsR917JZ96R+Q7s2Diuoi0RVRqoiEuhz4O0dltpc6Tv1eIqQxmA0YAFCWROKilBU
+FFVDKBuKqsmbHR6RikOiHdL7IV3vWOiAMYgeTjA4eDJs6MU9dSSbWoQlUurB8/9eFkqKsqFs9mlW
+l1kdPMVy/wnqxQFFtSBYkfuDn1i/Pv0eTia4jyfoRUREREREzhUF2ndyLJjz3RrilEixw2NH6jvc
+03DJa5iLoqQoK4qi3GWQLTBmZXOgHXIrrqahWa5YLFu63im2eY1zXvPc07UJ750tPYdECousFsay
+geXCqItIQU9hPYEeUi6G5ilO6WEzg1AQipqiXlLWS8p6RbnYo2z2cAKpfJ5oz9N5Qdk5YdthYT0V
+OptOPji47davG+MU+mFqvCc8pTy+GVaUhGpB1ezRrK6wPHiS5cFV6sUeZdkMhdDG92a2inxcFj6b
+4j5NdUftvURERERE5HxSoH0HY9HwqTzX2DvaI6lv6bstsdviKQ5VuRPBAmVV457APRf6CgUEzwH3
+EJiGIlBWFXWzyBntdct62xPK7VBwzIl9T+/Qe0ds81psTy2X9gr6/RLrS6idqkiEImIhQZpltGEK
+8C0UhLIZAux96uUl6tVl6tVlnIJoDX0qaPvEdttSrNfDz45Tw32cOT8E2kwnDMI0Vd5hOOHgKWJA
+CBWhbKgWezSryywPnmSxf4WqqijKOk9dP7Y43jgWR89njR8v4C4iIiIiInLuKNAejOuCpwwqx+M6
+M8vBdOxJqadvt2yOXmB7dJ3t+gVi3w19oyMYlGVNUVaUZT2sQS4JRYGFklCUQ+BdcPT8dbr1C1h/
+SMmGOrQsyp6+SkDEPNH3idRth1ZeR6R+CzEQ20C7DiwbY1EbiwqaCiwYwUqKOgeyRZWnh5f1kqrZ
+p1rs764Xe1TNPm4BQkWollTLA+rFPlW9oqwajq5/mLbd0g0XH/9LYOa54FtwwlBJ3QzCULQseaLv
+O9ptS9d1xNhP72M3nqxoc7BehEBR5Eso8vtVFMXu9zNfq32iXZmIiIiIiMh5oUB74CfvjH2ax/XV
+GO6JGDti19JubnB0/YMcvvAH3Hj+A0NmuyX2ecr3bup4NV2HYgx863xd1hwdrenXGyxupkC7KTv6
+OuZsduyJfUfXbmjXR7SbQ/p2TWqNdg3rQ2O5COwtClbLguWipG4amqahbBZUi70cNI/Bc7NPvdin
+bvbzFPJqQVEvcALFEGQv969SNznILsqKqqo5OrzO0eELxNiTUhrWYaehCrgPjbl8CrJDMCzkrHbX
+dUOg3tH3PSnFfGLCEz15entZFBRlDqzLqqaqaiproCiGX8fYXHuW2lZGW0REREREziEF2rcyBdm7
+KuGenBR7+m7Ddn2Dw+sf5PkPXOP5D1yj3R7Rt1u6doOnSChKiiGLnbPbDWVVU1aL3OKqXlI2S7ou
+0rUR+khJlzPaVU+qIxvPQX3ftbTbIzabG2yPbtBuj2g3ThWcsnBWy5J2r6bramJs2As11aKiqFfU
+e1dY7D/BYv8qzSqvi66bHGjPC6NhRr08YNlv6buWetbfuijy1PcYe7abo9zOK0EaMtrBHA/D+2YQ
+AhQhP5dSpO87ttstXdcOmf9I6jtibEl9i6eOsqyGGQAl9dBOrCir6QzIdCIkn/1QjC0iIiIiIueW
+Au2Bz3tKzZOlN7d0nm3j00ZjcbTYbYl9l4uADRvmrHY5XDdDoL2grBcktylo9S5isaXwjtJaQtqS
+4jYH8Nsjuu2art3QtRuSOTE4RXDMaqqqoO6h9xKKJaG5RL3/BM3+kywOnsjB9t5VqnpF1ayo6yUh
+lFN7L4CirEhpiaecdU4xr/EOIeAWiJ7oupZ2u6Vtt6S0xT3mXPZUevx4b/GUctV0225oN0ds14ds
+j67jsaPvNvTdhti3VFVDVS+o6gZPKZ+gqJpZR/Jdf+1dxXHNHRcRERERkfNHgfZpThTfcveh+Fag
+KCqoF3jap7/0JLhTlRWbo+ts1jfYrm/Qbdd5innfkfo+7zJG+phyJrfPgXNRlDhG8txCrI+Jro3E
+LuJ9JHVbYrul327p2w2p3+CpA09TkW4LIWelqyWhPqBYXKbae4rFpadZXX4Jq4OrNKtLNKvLVM0+
+ZVXn12DFrpqY5z7XBgQzPBTUzYrVpScwC4SyglBORdWObrzA0Y3rpJjo+1138OS53pu74RjuRkwR
++hbfHrE5ep6j63/A9eWSqmrySYPtmth3NIs96uUezXIPB4qqpl70u+JrQ6u1sSydzWYciIiIiIiI
+nCcKtAfzjLYN/a6HblU5b5p2gXYY+keTEmVRslisWB++wPrwedZHL7A5uk63PaLd5EuKcbj0jDu2
+sR3W7JISdNGJEbxPpK7dBdrb3TRrIwK5XZiFgBUVNgXaV3KgffAyVleeYbV3mWqxol7sUdULwtDf
+OwfNMPb/njLwQxuyullhZjnz3iymINuBUFTE5Gw3a/rY5ceH3fhw0gALuJNft2+J7qyPnqe5vszV
+xouKdrum227ou5bF/mUW7WX6vseKknqxIsY4FKkbfwlDRlvxtYiIiIiInGMKtO9gV+zap+yuUVIU
+JWVR0ixWxP0rHB1+mMPre9Q3llT1gs3h8xiW13T7hthHYp/XKHuKecp1SliwHCwHy72s3Uge8GSk
+fpfRjl1P6iOe+pzRtjD9bChrimpJ0RxQLK9Sr56kOXgpqysfxXK1T1k1+VKW5DB4nObtu3LrQ2/v
+MaCumxVlvWCxd5lmuc8umHZiTGw3Gw6r57F2M71PY2Y+B+R5lBTz6yT2bNcvcHS9oghGsILtdkO7
+WdO3XV6/HSMJo2wWrPYuEYcTE2MxtKko2vSniIiIiIjI+aNA+64MQegURg69qYsKq5wiBHzol13V
+CxbLA7b7V9heusF2fZ12s6bdHtFtjujaDX2XC4713RYfem87aUgsBwoChRtdUxJjfr7aBvo+0vcl
+yWGxWrFY7rFY7XFw+SqXrz7FpSee4vLVp7j8xNOs9i9TVrlHdZ7+nkgpMlYGn9aX23hlu+zx+JqH
+WyEU1M2S5f5l+i4XNeuHS1mWpNiRYov33TAVPmfnfeo+7mDQtS3bzRFVdZ2irPAYKQqjWDYslkuW
+q3329i6xXO5T1Q0hlLOA2o6F18pqi4iIiIjIeaVA+1amiuMj3wWllqdGB0qCBbysCKGkahYsVgfD
+2uOjqYDZZn2DzVFev73d3GC7PmK7OaTdHA5ruVti7KasuYUCx1gOQTY4dRXp+kTXJaIHDi5fZf/K
+ExxcfoJLV57k0tWnuHTlKfYvP8Fq/4DV3j5F1WBFXovtJFJialtmw+sIJ1/rLMM9vtZQlNTNktX+
+ZQyn7zv6vqXvW0JhtMNraWOc1pzHNK6jHoPtXBSt3azZhIKqaXILtKKkquoh0N5jtXfAYrWfp7kX
+xclm5gqwRURERETk3FOgfTtj+vqUuzkArbByyLXWS/Cxv3SuPp57a29ZH77A0biGe7g+OnyeUBR0
+7ZquDaStY54IRSAURQ7ghyC7CLCtCtreaTsnecHlq1e5+vTLuPL0M1y++jSXrjzFwZUn2Tu4Slnl
+Ht5lVQ4nBYaMtqdhLbZNQbaPa57dZgXH0vQaHaMYMto4lGVF3w8nB/oWM+fwhTxFvt2sGaeYJwfz
+WUabnNEuwlGeBu49i8WKsq5oFg3LIaO92r/EYrlPVTWEUByPs4c/zU78YkRERERERM4RBdp3NE61
+hrHvVw7zhmB1KCw2bmNjUFsvhinVXV4fXS+oF0vqxYqqWVIO7b22Q6XyUOQp2CHYVGxtV+3cKYpE
+2UNVOskq9g9WHFw64PKVq1y68gT7l6+yd3CF1f6lHDhPxdbyWmx3cPOxDfVQX8xwG5LYNj68mzI+
+FYiz3KKsahwLgdX+Fbp2Q4z9NI3e3en7duhTli9pOFEwZslTjMS+o+8Cqa6xoqBqliz3LrHYO2Cx
+t89iuU/TLCmrmhDCeKBD1n2X0R4z8iIiIiIiIueNAu1bOq3clu8C7jFX646Rhkrbuy3NAqHIGeV6
+scIsDL2il7mP9WKfZnWZ9eGHWR8+T1k19N1mCFIjeBz6azvuccigQ1kaWMlqUbFY1DRNQ13XlGWZ
+p1pbGILrXaC+i5yHCuc+ZLmHADzBkN2ebTr/c6xKHgqKsqJe7LE6eCKvKQ9FbhXmTor9LpPftsP4
+u6y2u+eCafkHKasFzeoSy4MnWe5dYbE6oFnuUTULyrIiWJhVaR8PbhZpi4iIiIiInEMKtG9rXK98
++4B7VxXbpmDWQsDcIBSYFZRVQxP3aJb7NMt9FqvLLNaH1Nf3KOsFoShpN4d4zG28UsyBqqccdIcQ
+qZIRk2GhYrWsWV9f5GwAABcpSURBVC5qFosh0K4qiqLEgk2tsPJJgNmrsKkE2vTqphXZvluDPgXZ
+Q1A8vsIQSrCw63Vd1hRlnbdLuar6dn2D7ZHhfU+Ku65cPhzPGGxbKCjrJc3qMqtLT7LYv8JidSln
+/OsmzxQIYwG0/F7bsVuKtEVERERE5HxSoD2wWeh8PJBmyKrOq1+PfHreh8dzsth27beAsqynac4x
+9tTbA5rVmmazHqZI54JlZVUR2zV9uyG2IWe3UwTvKcKw9nkIeBdNyaKuqKuSqiopigILIWe0SXia
+Z5NtN/Xa8zGOM7KnbLPZTSntnJFOeZthenywgmqxRyhqmkVuHRZjT9+3dN06TyOPib7dYrGHBE7a
+vWtDsI0FiqqhXh6w3L86tRFrFiuKsjrWgsym0wV2PLyenzUQkRcVM0vDzW9y97fe5z5eC7xruPun
+3P3dZ3JwD+DatWu84hWv4GUvexnve9/7HvfhiIiIyGOiQPsm43TpeQusE9nTYQqzjYHfuB56eGq3
+DjpPe56vdfYhCA9FSVnV1M0ey70rJPc8fXy7pm9ztXJbv0CiIMRESi3JEykmPLX07Zp2c4Pt0fMU
+ZUMoqrzuOy2G8U5kgv3m/Pyweptdl+rpkKdp3+OGftO0+FyorKwXLFaX2L/0JCl2VNWCul5Q1Q3t
+5jBPI++3xL6lKEtCkaeDe0qzi0+V0EMwimC7swFTa7UTU8bHAxWRF7OzOl12bk67pZT47d/+7cd9
+GCIiIvKYKdC+DT+RTd0ZMsSzomMnm4HtAttZ9bHxp8MQaJdOvVjlomGhoKqXdNsj2u0R5fYIt0Af
+E227xfu464Xtkb5d021u0B4+T1nlwmrVcp+Uem6Zn59ls7HddHcb1mpPh368w9fx1w1Dtr6ksEBV
+L1nuHZBSj5lRVTnIruqGzdELdJtD2s0Nuu1RzvAPUX/uHz5e56JpwXKQHcKwNnt2nsNmJzPmvx8R
+ufDmZxZFREREzgUF2rfk09ri49nUYX3wyaz2yWnN417GXtK+W29sFiiKEjBq38OsoCgbqmZFu8lB
+9nZzSN9HttstVhyCbXHv8BRJMR7LaJfNknq5T+xbUooYYYxOb5robvOAevhzKuQ2TjUfA9zpD6bX
+bAAhDO3HnKp2mtWlaep7WddUdU1VNdRNzeawIrfyjjl7PQTWYzabtJvmnqem2zRNfixYPrYoO/Hb
+0SptkQvO3X8CKB73cYiIiIicpEB7cjyl64zrsu1YsbNxkrX5zcHftBs7niHObalysJ3XOw/Tx0Ne
+vw2GFSWhqPKlrAllTd919H1P33V5m1AMa5w3pBTp2w3t+jrVYo9ue0RsN6S+y2u+Q4FZOLHM3HdT
+wGfB9LGCaLPn3I4H5tMPTJltG6bAN7inPB5GEfK0+Kquc/XwIq/v7me9xT1FUt/lx9o1sW/x1LPL
+v4Pb6ScxxhZkrhyWiIiIiIicQwq0B8enIftsXbMPrbBsmg4+rxvmwwJms1nu+2QA6FNoeKwCeAiB
+oiiG+0M/7hAIRUVR1qQYh+DXKKqG4kY1BJh5pNS3tJsbVJvrdNtDYrcm9VsoKoLlYxqGZzysaf34
+OK18rFA+33A6bpte79Rse9rT7lYI5a76uHsOvpsFVb2Y1mUHM7ZH19kCsWtJsafvtrSbI7ZHN+hW
+B/RdO2S958dhx4539pZqibaInCfhcR+AiNyba9eu8eyzz/KVX/mVPPPMM4/7cETkLsUYx5vn+rv3
+XB/cozS2nsqX2X04VkhsbFPFbNv88+Oz43YnLr77uZzhHoui5d7UZVVTNQuaxR6L1SWW+1dZHTzJ
+/uWXsH/1GfYvv4Tl3lXqodI3QIwt7eY67foG/faQvt2Qum3ODHvctcOaFxAbD25st4WTxuJkU/ut
+4T0Y//Pda0hpWD49vRTDioKirIfCaAesDp7g4OpLOXjyZRxcfSn7V55m79KTNKuDXIEdjgfa6xt0
+2zWx70gp4rP38eT09+O/M2W1Rc4DM3vGzP6qmf2smX3YzFoz+10z+yUz+3tm9iYz27/DPl5jZv+r
+mf2WmW3M7P1m9p1m9kdv8zOvNbM0XD7rlOffPjz3m8P9l5vZt5rZr5nZoZn9vpn9sJn9mQd/FzSF
+XeTF5tq1a7zlLW/h2rVrj/tQROQezALtc/3dq4z2wE+5Nd712frlsRf1vIK3u88KdY3Z61P3Nq7w
+Jk8FtyEbDqGA4E5RJoqUKMdstgWsqDAzUuzou00OqFMkpUjsO7r2KD/Wb0ixI8QSL6s8+ph+n58R
+sHl18dmBHXvZs1Ljx2u5nXhlhllBKIY+21UzjOtUTUOus54wT6S+pdscEkKRg/a+y9PfN4d03YbY
+d3nd9lSlPRxbIi4i54+ZfSbwD4FLHP+b4unh8krgi4HngHfeYh//GfBtHP/CfAb4UuALzez17v5T
+tzmMO55yM7NXD+M/NXt4AXwu8Llm9tfd/S/daT8iIiIid0OB9p0ci/RuEe3ZWN37+Ba3/pffbn13
+DtBnoblZ7mkdAlVVkxarvK/Uk2KLp4iFQN9u6Iee20VZEYpimH6eDydgBAu79mJDsGzTuDcd/qmv
+6aZq6ydf59Rj3KYTEeOPl2VDs9jH9zvMndi1dG3OYhtOUTUQAslzQbQxw76bFn78HVWsLXK+mFkN
+fDdwALwA/I/A/wn8PlADfwj4NOALbrOb1wOfDPwi8O3APweWw8989XD7u8zsD7t7f5+HugL+/nCc
+fwX4UWALfArwDcDLga8xs//P3f+H+xxDREREZKJA+3ZOiex8qDM+b9p1WgOw04Ps472gd0F2LrEW
+xgA85ExuVdUAhKLMIw7TwYuyyOudj15ga0ZR1nmb4efGVlhj/2kfk9pT1fPxIPx4kG12m7D2tNZl
+x5/fteHK12VZ0yz2CDhlUdK3W9rtmnZ9g5R6iqrBLJBSysH2fCo+8/Xkpx2Nwm6Rc+DTyZlnB77E
+3X/0xPPvAb7HzP5zcrB7mk8Ffhj4whOB9E+b2QeB/xb4aOANwP92n8f5EqAFPsfdf3r2+PvM7PuB
+nwFeAfx3Zvb33P0D9zmOiIiICKA12jfxYwHcbbLYs+ec3Wa3buh6vEXYlPk9EaCGkFtchSGj3SxW
+LPcusTp4gr3LT3Fw9SUcPPEMq0tP0uxdoqwXhCmjvWvBNbbK2gXcu8enIzp2ewhdx5T49AOzYz/l
+3Tr+duTp8BYKQiimQHu5f5X9y3md9mr/Kov9K7u15kOgPWa0x4Jqt5oOMG+oJiKP3ctmt3/yVhu5
+e3L3G6c8ZcAa+PJbZKv/e3KADPCZ932U+W+Uv3UiyB6P7RrwtcPdPeBNDzCOiIiICKCM9mRe6XoK
+nH3sNG1D5XGmtdrTdlM+ehdIG34iTtz10PaxQvnYH3ra1mdbD2dALEDIRdNS1dAs9vAUmVZ5W8Cs
+oGpWNKtLVM2SsshBd5ivbx4qp594xXf5xjA7xvnjx08p7LLku4DdQiCUVe4bHgKL1SVWB1dpt3lN
+eVlVlFVFXS8omwWhKPNU92H/jmNu07r4kzG/Am6Rx25eQejLgHuddu3Aj7n7H5z6pPsNM/t14I8B
+H3N/hzj5jts89wPAh4HLwOuAb33AsUREROSCU6A9SGk3rToxC3bJ90OCFPLa5zEwHq/TscnkJ82D
+6cF8vvmxZ3z2mE8/nWeTB8qyoW72GDPiRVFT1UvKqmGxf5VmuU9ZNxRFRQjzIz2+z+PHMWtJdmrR
+s9kxTld+7NjN7KatzcfnAlaAU1Mv91geXCXGjti3FEVBUeSe24vlAWW9AAtTxfO8pnwMtsl9td13
+AfZNTb5F5BH7KeA3yUHwt5vZl5KD1ncD73X37i728at3eP6D5L+dDh7gOFvyGvBTuXtvZj8PfDbw
+CQ8wjoiIiAigQHvinoYbu0JliZuDbQ+7x+fPz+Ps3c1bRq7Ddn5Kotmn+HEeoodQUFY1mOU+20VN
+Va9olgcURUW1WFEv9iirmhB267XzGCdrqs9Kjc8bUg8Lo+1EiD4e1+7KT/S6zvsKs4UIeVaAYYSp
+R3i12GO1fxUzI8aWIoShl3iZW39VzZDR3gXyY+bfyEG2mw2VzPPsABF5fIYA9fOA7wM+Hvg3gdcM
+T6/N7N3AdwLf49Nfsjc5usMw4889SAuPD/rNf2md9HvD9RMPMM6kbVt+7ud+7pbPP/PMM+rbKyIi
+MnPt2rW7arfXdXdzHv/xU6A98Fm2dooXzXbB9DBlfLfdLAyeB6swTXWeTy6fjTQlg+1YCLzbflio
+fGyZdBiKi1koKcp66ltdL/axEPL9qqYoK3JbrF01c5+y2X4i9B+yw/PDHw5unqT2KcjeXc//zTpm
+tI+/Dfl0Qw6yDaOgqpf4KmKFkWJPGNamFyFQNyuKsp4y2vNg3sgZ8mkd+e7XIyKPmbv/qpl9AvDG
+4fJZwMeRW2f9meHyNWb2Z281RfxRHOajHvC5557j1a9+9S2ff/Ob38w3fdM3PboDEhEROeeeffZZ
+3vKWtzzuwzgzCrTvxYs0sLt9Xv0sB3qAN+hF+t6KCAzZ4h8aLpjZS8ltu74KeDXwJ4BngS96TIf4
+pJnZHbLaLx2uP3ifY0yZ8Ly0p+Dq1au33PjZZ5/lbW97230OJSJnoW1zrcXXv/711HX9mI9GRGKM
+PP3003fc7rnnnhtvnskstIdFgfbgKz//kxTqiYicAXf/PeAdZva/AP+UHGh/npk17r59DIdUA38c
++IXTnjSzAvgk8jnJf36fY+zm87jT9/38HwIico7psyryonWu4zcF2iIi8lAMa7h/ghxol8AVdmuh
+H7U3cYtAG/hC4Co50P7x+9z/FmjIa8p//z73ISIiInf2EvLq3sdx8v6uKdAWEZH7YmafAVxz939x
+i+cr4LXD3RvA40obGfCfmtnfd/f/69gTZi8Dvnm4ewS8434GcPe9BztEERER+UiiQFtERO7X5wD/
+tZn9JPAjwC+Rg+kl8EeAv0jOZjvwtttUHr+TBy0z8fvkIPrHzexvAO8knwX/FOAbgJcPY/xXj7Fg
+m4iIiHwEUaAtIiIPwsiVxl97ynNjy4MfBL7xAcd4EEfAvwv8KDmw/obZc+Mxfru7f/sDjiMiIiIC
+KNAWEZH7983ALwKvA15Fzgy/ZHjud4H3AO9w93/0gOOMwfC9PrfbyP3nzOxPAF8HvAH4KOAQeC85
+yP4nD3iMIiIiIhO7fbcTERGRFyczezu5CNq/dPePedzHIyIiIhdHeNwHICIiIiIiIvKRRIG2iIiI
+iIiIyBlSoC0iIiIiIiJyhhRoi4iIiIiIiJwhBdoiIvKR7K6qkouIiIicJQXaIiLyEcndv8zdC3f/
+2Dtta2YfbWZ/3cx+xcxumNkHzOw9ZvZ1ZrY8q2Mysy8xs39sZtfMbG1m/9LMvsvMPvWsxhC5SB7m
+Z9fM3mxm6S4vn3VWr0nkI5WZPW1mbzCzt5jZO83sudln6H9+SGM+tu9dtfcSEZELzczeCHwXcImb
+s98G/N/AG9z9XzzAGAvgHwB/9hZjJOCt7v7W+x1D5KJ52J9dM3sz8OZT9n2SA3/a3d99P+OIXBRm
+lk48NP9svcPdv/wMx3rs37vKaIuIyIVlZq8Cvhs4AK4D3wh8GvA5wN8hfzn/YeCHzWzvAYZ6O7sv
++/8D+HPAJwNfAfwG+fv4zWb25x9gDJEL4xF+dkevBD7hFpdPBN57BmOIXATjkq7/F/gn5KD3YXjs
+37vKaIuIyIVlZu8GPgPogM909/eceP5rgW8mf1G/5X7OfJvZnwZ+fNjHDwFf6LMvXzN7EvhZ4KOB
+DwEf4+7P398rErkYHtFnd8pou3vx4EctcrENn6n3Au919+fM7F8F/h/y5/TMMtrn5XtXGW0REbmQ
+zOw15H+oO/C2k/9QH3wr8CvkM+5fbWb384/trx2ue+Cr/MQZbnf/APD1w90rgLLaIrfxCD+7InKG
+3P0t7v5Od3/uIQ91Lr53FWiLiMhF9edmt7/jtA2GL+fvHO5eAT77XgYws33yVFYHftzdf+cWm34/
+8MJw+wvuZQyRC+ihf3ZF5MXpPH3vKtAWEZGL6jOG60PyFLJb+YnZ7U+/xzFeA9Sn7OcYd++Af0rO
+vr1G2TeR23oUn10ReXE6N9+7CrRFROSi+njyGe/fcPeTlVDnfvXEz9yLP3aL/dxunJJcxElETvco
+PrvHDO2Bfs/MtsP1u8zs683syoPsV0TO3Ln53lWgLSIiF46ZNcBTw933325bd/8wOXMG8K/c41Cv
+mN2+7TjAb81u3+s4IhfCI/zsnvS6YdxyuP4s4K8Av2lmn/+A+xaRs3NuvnfLs96hiIjIi8DB7PaN
+u9j+EFgB+w9xnMPZ7XsdR+SieFSf3dEvAT8IvAf4HaAC/nXgPwD+bfL67+8zsze6+z++zzFE5Oyc
+m+9dBdoiInIRLWa327vYfktex7V8iONsZ7fvdRyRi+JRfXYB/oa7v+WUx98L/F0z+0+AvwUUwNvM
+7GPd/W6OSUQennPzvaup4yIichFtZrfrW26105DXhK4f4jjN7Pa9jiNyUTyqzy7u/sIdnv/bwP9E
+DuRfDnzRvY4hImfu3HzvKtAWEZGL6Prs9t1MF9sbru9mqur9jrM3u32v44hcFI/qs3u3np3dfu1D
+GkNE7t65+d5VoC0iIheOu2+BDwx3X3G7bYeqwuOX8W/dbttTzAux3HYcjhdiuddxRC6ER/jZvVu/
+PLv9UQ9pDBG5e+fme1eBtoiIXFS/TJ7y+XFmdrvvwz86u/0r9zHGafu53Tg98Ov3OI7IRfIoPrt3
+yx/SfkXk/pyb710F2iIiclH91HC9B7z6NtvNp4P+9D2O8V52xVhuOa3UzCrgU8n/aH+vu8d7HEfk
+InkUn927Ne/Z+zsPaQwRuXvn5ntXgbaIiFxUPzi7/WWnbWBmBvxHw90PA++6lwHc/Qbwv5Ozb68z
+s5ffYtMvAi4Nt7//XsYQuYAe+mf3HvzF2e2feEhjiMhdOk/fuwq0RUTkQnL39wI/Sf4y/goz+5RT
+Nvs64OPJZ7y/7eQZbzN7k5ml4fLf3GKobxmuS+BvnpzqamZPAX91uPthchVjEbmFR/HZNbNXmtnH
+3u44hvZeXzHc/V3gB+791YjIvXgxfe+qj7aIiFxkX02eUroEfszM/jI587UEvgT4C8N2vwZ86232
+c8t1mu7+LjP7buCLgX9nGOfbyNNMPxH4RuCjh338F+7+/AO9IpGL4WF/dl9N7o39LuBHgX9GLsJW
+ktd1finwbw3b9sBfcHe15RO5DTP7dODjZg89Nbv9cWb2pvn27v6O2+zu3H/vKtAWEZELy91/wcz+
+PeDvkqeQ/eWTm5D/of4Gdz98gKG+HDgAPhf4U8BnnxgjAm91d2WzRe7CI/rsBuBzgNfd6jDIwfeX
+u/s773MMkYvkzwNvOuVxAz5juIwcuF2gfSeP/XtXgbaIiFxo7v4jZvaJ5AzZG8jtQFrgN4DvBf6m
+u29ut4u7GGMDvNHMvhj4j4E/DlwBfg949zDGzzzI6xC5aB7yZ/dHyNPC/yTwKuClwJPkgOCDwC8C
+/wj4jmFNqIjcnbut1H+77V4U37vmrq4EIiIiIiIiImdFxdBEREREREREzpACbREREREREZEzpEBb
+RERERERE5Awp0BYRERERERE5Qwq0RURERERERM6QAm0RERERERGRM6RAW0REREREROQMKdAWERER
+EREROUMKtEVERERERETOkAJtERERERERkTOkQFtERERERETkDCnQFhERERERETlDCrRFRERERERE
+zpACbREREREREZEzpEBbRERERERE5Awp0BYRERERERE5Qwq0RURERERERM6QAm0RERERERGRM6RA
+W0REREREROQMKdAWEREREREROUMKtEVERERERETOkAJtERERERERkTOkQFtERERERETkDCnQFhER
+ERERETlDCrRFREREREREztD/D+iauRi3tIFfAAAAAElFTkSuQmCC
+"
+width=493
+height=445
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;">&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;<a class="anchor-link" href="#&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;">&#182;</a></h2><p>你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率<a href="http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130">远远超过 80%</a>。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。</p>
+<h2 id="&#25552;&#20132;&#39033;&#30446;">&#25552;&#20132;&#39033;&#30446;<a class="anchor-link" href="#&#25552;&#20132;&#39033;&#30446;">&#182;</a></h2><p>提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 "File" -&gt; "Download as" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。</p>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+</html>
diff --git a/image-classification/dlnd_image_classification.ipynb b/image-classification/dlnd_image_classification.ipynb
new file mode 100644
index 0000000..314c0bc
--- /dev/null
+++ b/image-classification/dlnd_image_classification.ipynb
@@ -0,0 +1,1107 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# 图像分类\n",
+    "\n",
+    "在此项目中，你将对 [CIFAR-10 数据集](https://www.cs.toronto.edu/~kriz/cifar.html) 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。\n",
+    "\n",
+    "\n",
+    "## 获取数据\n",
+    "\n",
+    "请运行以下单元，以下载 [CIFAR-10 数据集（Python版）](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All files found!\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "from urllib.request import urlretrieve\n",
+    "from os.path import isfile, isdir\n",
+    "from tqdm import tqdm\n",
+    "import problem_unittests as tests\n",
+    "import tarfile\n",
+    "\n",
+    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
+    "\n",
+    "# Use Floyd's cifar-10 dataset if present\n",
+    "floyd_cifar10_location = '/input/cifar-10/python.tar.gz'\n",
+    "if isfile(floyd_cifar10_location):\n",
+    "    tar_gz_path = floyd_cifar10_location\n",
+    "else:\n",
+    "    tar_gz_path = 'cifar-10-python.tar.gz'\n",
+    "\n",
+    "class DLProgress(tqdm):\n",
+    "    last_block = 0\n",
+    "\n",
+    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
+    "        self.total = total_size\n",
+    "        self.update((block_num - self.last_block) * block_size)\n",
+    "        self.last_block = block_num\n",
+    "\n",
+    "if not isfile(tar_gz_path):\n",
+    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
+    "        urlretrieve(\n",
+    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
+    "            tar_gz_path,\n",
+    "            pbar.hook)\n",
+    "\n",
+    "if not isdir(cifar10_dataset_folder_path):\n",
+    "    with tarfile.open(tar_gz_path) as tar:\n",
+    "        tar.extractall()\n",
+    "        tar.close()\n",
+    "\n",
+    "\n",
+    "tests.test_folder_path(cifar10_dataset_folder_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 探索数据\n",
+    "\n",
+    "该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 `data_batch_1`、`data_batch_2`，以此类推。每个部分都包含以下某个类别的标签和图片：\n",
+    "\n",
+    "* 飞机\n",
+    "* 汽车\n",
+    "* 鸟类\n",
+    "* 猫\n",
+    "* 鹿\n",
+    "* 狗\n",
+    "* 青蛙\n",
+    "* 马\n",
+    "* 船只\n",
+    "* 卡车\n",
+    "\n",
+    "了解数据集也是对数据进行预测的必经步骤。你可以通过更改 `batch_id` 和 `sample_id` 探索下面的代码单元。`batch_id` 是数据集一个部分的 ID（1 到 5）。`sample_id` 是该部分中图片和标签对（label pair）的 ID。\n",
+    "\n",
+    "问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Stats of batch 1:\n",
+      "Samples: 10000\n",
+      "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n",
+      "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n",
+      "\n",
+      "Example of Image 5:\n",
+      "Image - Min Value: 0 Max Value: 252\n",
+      "Image - Shape: (32, 32, 3)\n",
+      "Label - Label Id: 1 Name: automobile\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAK/CAYAAABjrqvGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x263597db048>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 351,
+       "width": 353
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "import helper\n",
+    "import numpy as np\n",
+    "\n",
+    "# Explore the dataset\n",
+    "batch_id = 1\n",
+    "sample_id = 5\n",
+    "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 实现预处理函数\n",
+    "\n",
+    "### 标准化\n",
+    "\n",
+    "在下面的单元中，实现 `normalize` 函数，传入图片数据 `x`，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 `x` 的形状一样。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def normalize(x):\n",
+    "    \"\"\"\n",
+    "    Normalize a list of sample image data in the range of 0 to 1\n",
+    "    : x: List of image data.  The image shape is (32, 32, 3)\n",
+    "    : return: Numpy array of normalize data\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return np.array(x/255)\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_normalize(normalize)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### One-hot 编码\n",
+    "\n",
+    "和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 `one_hot_encode` 函数。输入，也就是 `x`，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 `one_hot_encode` 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。\n",
+    "\n",
+    "提示：不要重复发明轮子。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def one_hot_encode(x):\n",
+    "    \"\"\"\n",
+    "    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n",
+    "    : x: List of sample Labels\n",
+    "    : return: Numpy array of one-hot encoded labels\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    from tflearn.data_utils import to_categorical\n",
+    "    return np.array(to_categorical(x, 10))\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_one_hot_encode(one_hot_encode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 随机化数据\n",
+    "\n",
+    "之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 预处理所有数据并保存\n",
+    "\n",
+    "运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "# Preprocess Training, Validation, and Testing Data\n",
+    "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 检查点\n",
+    "\n",
+    "这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import pickle\n",
+    "import problem_unittests as tests\n",
+    "import helper\n",
+    "\n",
+    "# Load the Preprocessed Validation data\n",
+    "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 构建网络\n",
+    "\n",
+    "对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。\n",
+    "\n",
+    ">**注意**：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。\n",
+    "\n",
+    ">但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 `conv2d` 类 [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d)，而不是 TF Layers 版本的 `conv2d` 类 [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d)。\n",
+    "\n",
+    "我们开始吧！\n",
+    "\n",
+    "\n",
+    "### 输入\n",
+    "\n",
+    "神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：\n",
+    "\n",
+    "* 实现 `neural_net_image_input`\n",
+    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
+    " * 使用 `image_shape` 设置形状，部分大小设为 `None`\n",
+    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"x\" 命名\n",
+    "* 实现 `neural_net_label_input`\n",
+    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
+    " * 使用 `n_classes` 设置形状，部分大小设为 `None`\n",
+    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"y\" 命名\n",
+    "* 实现 `neural_net_keep_prob_input`\n",
+    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)，用于丢弃保留概率\n",
+    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"keep_prob\" 命名\n",
+    "\n",
+    "这些名称将在项目结束时，用于加载保存的模型。\n",
+    "\n",
+    "注意：TensorFlow 中的 `None` 表示形状可以是动态大小。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image Input Tests Passed.\n",
+      "Label Input Tests Passed.\n",
+      "Keep Prob Tests Passed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "def neural_net_image_input(image_shape):\n",
+    "    \"\"\"\n",
+    "    Return a Tensor for a batch of image input\n",
+    "    : image_shape: Shape of the images\n",
+    "    : return: Tensor for image input.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], name='x')\n",
+    "\n",
+    "\n",
+    "def neural_net_label_input(n_classes):\n",
+    "    \"\"\"\n",
+    "    Return a Tensor for a batch of label input\n",
+    "    : n_classes: Number of classes\n",
+    "    : return: Tensor for label input.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return tf.placeholder(tf.int32, [None, n_classes], name='y')\n",
+    "\n",
+    "\n",
+    "def neural_net_keep_prob_input():\n",
+    "    \"\"\"\n",
+    "    Return a Tensor for keep probability\n",
+    "    : return: Tensor for keep probability.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return tf.placeholder(tf.float32, name='keep_prob')\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tf.reset_default_graph()\n",
+    "tests.test_nn_image_inputs(neural_net_image_input)\n",
+    "tests.test_nn_label_inputs(neural_net_label_input)\n",
+    "tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 卷积和最大池化层\n",
+    "\n",
+    "卷积层级适合处理图片。对于此代码单元，你应该实现函数 `conv2d_maxpool` 以便应用卷积然后进行最大池化：\n",
+    "\n",
+    "* 使用 `conv_ksize`、`conv_num_outputs` 和 `x_tensor` 的形状创建权重（weight）和偏置（bias）。\n",
+    "* 使用权重和 `conv_strides` 对 `x_tensor` 应用卷积。\n",
+    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
+    "* 添加偏置\n",
+    "* 向卷积中添加非线性激活（nonlinear activation）\n",
+    "* 使用 `pool_ksize` 和 `pool_strides` 应用最大池化\n",
+    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
+    "\n",
+    "**注意**：对于**此层**，**请勿使用** [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers)，但是仍然可以使用 TensorFlow 的 [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) 包。对于所有**其他层**，你依然可以使用快捷方法。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n",
+    "    \"\"\"\n",
+    "    Apply convolution then max pooling to x_tensor\n",
+    "    :param x_tensor: TensorFlow Tensor\n",
+    "    :param conv_num_outputs: Number of outputs for the convolutional layer\n",
+    "    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n",
+    "    :param conv_strides: Stride 2-D Tuple for convolution\n",
+    "    :param pool_ksize: kernal size 2-D Tuple for pool\n",
+    "    :param pool_strides: Stride 2-D Tuple for pool\n",
+    "    : return: A tensor that represents convolution and max pooling of x_tensor\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    weights = tf.Variable(tf.truncated_normal(shape=[conv_ksize[0], conv_ksize[1], x_tensor.get_shape().as_list()[3], conv_num_outputs], stddev=0.1))\n",
+    "    bias = tf.Variable(tf.constant(0.1, shape=[conv_num_outputs]))\n",
+    "    conv = tf.nn.conv2d(input=x_tensor, filter=weights, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME') + bias\n",
+    "    activate = tf.nn.relu(conv)\n",
+    "    pool = tf.nn.max_pool(value=activate, ksize=[1, pool_ksize[0], pool_ksize[1], 1], strides=[1, pool_strides[0], pool_strides[1], 1], padding='SAME')\n",
+    "    \n",
+    "    return pool\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_con_pool(conv2d_maxpool)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 扁平化层\n",
+    "\n",
+    "实现 `flatten` 函数，将 `x_tensor` 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（*部分大小（Batch Size）*，*扁平化图片大小（Flattened Image Size）*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def flatten(x_tensor):\n",
+    "    \"\"\"\n",
+    "    Flatten x_tensor to (Batch Size, Flattened Image Size)\n",
+    "    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n",
+    "    : return: A tensor of size (Batch Size, Flattened Image Size).\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    layer_shape = x_tensor.get_shape()\n",
+    "    num_features = layer_shape[1:4].num_elements()\n",
+    "    layer_flat = tf.reshape(x_tensor, [-1, num_features])\n",
+    "    \n",
+    "    return layer_flat\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_flatten(flatten)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 完全连接的层\n",
+    "\n",
+    "实现 `fully_conn` 函数，以向 `x_tensor` 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def fully_conn(x_tensor, num_outputs):\n",
+    "    \"\"\"\n",
+    "    Apply a fully connected layer to x_tensor using weight and bias\n",
+    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
+    "    : num_outputs: The number of output that the new tensor should be.\n",
+    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n",
+    "    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n",
+    "    fc = tf.nn.relu(tf.matmul(x_tensor, weights) + bias)\n",
+    "    \n",
+    "    return fc\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_fully_conn(fully_conn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 输出层\n",
+    "\n",
+    "实现 `output` 函数，向 x_tensor 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n",
+    "\n",
+    "**注意**：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def output(x_tensor, num_outputs):\n",
+    "    \"\"\"\n",
+    "    Apply a output layer to x_tensor using weight and bias\n",
+    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
+    "    : num_outputs: The number of output that the new tensor should be.\n",
+    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n",
+    "    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n",
+    "    output = tf.matmul(x_tensor, weights) + bias\n",
+    "    \n",
+    "    return output\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_output(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 创建卷积模型\n",
+    "\n",
+    "实现函数 `conv_net`， 创建卷积神经网络模型。该函数传入一批图片 `x`，并输出对数（logits）。使用你在上方创建的层创建此模型：\n",
+    "\n",
+    "* 应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）\n",
+    "* 应用一个扁平层（Flatten Layer）\n",
+    "* 应用 1、2 或 3 个完全连接层（Fully Connected Layers）\n",
+    "* 应用一个输出层（Output Layer）\n",
+    "* 返回输出\n",
+    "* 使用 `keep_prob` 向模型中的一个或多个层应用 [TensorFlow 的 Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Network Built!\n"
+     ]
+    }
+   ],
+   "source": [
+    "def conv_net(x, keep_prob):\n",
+    "    \"\"\"\n",
+    "    Create a convolutional neural network model\n",
+    "    : x: Placeholder tensor that holds image data.\n",
+    "    : keep_prob: Placeholder tensor that hold dropout keep probability.\n",
+    "    : return: Tensor that represents logits\n",
+    "    \"\"\"\n",
+    "    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers\n",
+    "    #    Play around with different number of outputs, kernel size and stride\n",
+    "    # Function Definition from Above:\n",
+    "    #    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
+    "    conv_pool_1 = conv2d_maxpool(x, 64, [5, 5], [1, 1], [3, 3], [2, 2])\n",
+    "    norm_layer = tf.nn.lrn(conv_pool_1, 4 , bias=1.0, alpha=0.001 / 9.0, beta=0.75)\n",
+    "    conv_pool_2 = conv2d_maxpool(norm_layer, 64, [5, 5], [1, 1], [3, 3], [2, 2])\n",
+    "\n",
+    "    # TODO: Apply a Flatten Layer\n",
+    "    # Function Definition from Above:\n",
+    "    #   flatten(x_tensor)\n",
+    "    flat_layer = flatten(conv_pool_2)\n",
+    "\n",
+    "    # TODO: Apply 1, 2, or 3 Fully Connected Layers\n",
+    "    #    Play around with different number of outputs\n",
+    "    # Function Definition from Above:\n",
+    "    #   fully_conn(x_tensor, num_outputs)\n",
+    "    fc_layer1 = fully_conn(flat_layer, 384)\n",
+    "    dropout_layer_1 = tf.nn.dropout(fc_layer1, keep_prob)\n",
+    "    fc_layer2 = fully_conn(dropout_layer_1, 192)\n",
+    "    dropout_layer_2 = tf.nn.dropout(fc_layer2, keep_prob)\n",
+    "    \n",
+    "    # TODO: Apply an Output Layer\n",
+    "    #    Set this to the number of classes\n",
+    "    # Function Definition from Above:\n",
+    "    #   output(x_tensor, num_outputs)\n",
+    "    logits = output(dropout_layer_2, 10)\n",
+    "    \n",
+    "    # TODO: return output\n",
+    "    return logits\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "\n",
+    "##############################\n",
+    "## Build the Neural Network ##\n",
+    "##############################\n",
+    "\n",
+    "# Remove previous weights, bias, inputs, etc..\n",
+    "tf.reset_default_graph()\n",
+    "\n",
+    "# Inputs\n",
+    "x = neural_net_image_input((32, 32, 3))\n",
+    "y = neural_net_label_input(10)\n",
+    "keep_prob = neural_net_keep_prob_input()\n",
+    "\n",
+    "# Model\n",
+    "logits = conv_net(x, keep_prob)\n",
+    "\n",
+    "# Name logits Tensor, so that is can be loaded from disk after training\n",
+    "logits = tf.identity(logits, name='logits')\n",
+    "\n",
+    "# Loss and Optimizer\n",
+    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
+    "optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
+    "\n",
+    "# Accuracy\n",
+    "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n",
+    "\n",
+    "tests.test_conv_net(conv_net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 训练神经网络\n",
+    "\n",
+    "### 单次优化\n",
+    "\n",
+    "实现函数 `train_neural_network` 以进行单次优化（single optimization）。该优化应该使用 `optimizer` 优化 `session`，其中 `feed_dict` 具有以下参数：\n",
+    "\n",
+    "* `x` 表示图片输入\n",
+    "* `y` 表示标签\n",
+    "* `keep_prob` 表示丢弃的保留率\n",
+    "\n",
+    "每个部分都会调用该函数，所以 `tf.global_variables_initializer()` 已经被调用。\n",
+    "\n",
+    "注意：不需要返回任何内容。该函数只是用来优化神经网络。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n",
+    "    \"\"\"\n",
+    "    Optimize the session on a batch of images and labels\n",
+    "    : session: Current TensorFlow session\n",
+    "    : optimizer: TensorFlow optimizer function\n",
+    "    : keep_probability: keep probability\n",
+    "    : feature_batch: Batch of Numpy image data\n",
+    "    : label_batch: Batch of Numpy label data\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    session.run(optimizer, feed_dict = {keep_prob: keep_probability, x: feature_batch, y: label_batch})\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_train_nn(train_neural_network)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 显示数据\n",
+    "\n",
+    "实现函数 `print_stats` 以输出损失和验证准确率。使用全局变量 `valid_features` 和 `valid_labels` 计算验证准确率。使用保留率 `1.0` 计算损失和验证准确率（loss and validation accuracy）。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def print_stats(session, feature_batch, label_batch, cost, accuracy):\n",
+    "    \"\"\"\n",
+    "    Print information about loss and validation accuracy\n",
+    "    : session: Current TensorFlow session\n",
+    "    : feature_batch: Batch of Numpy image data\n",
+    "    : label_batch: Batch of Numpy label data\n",
+    "    : cost: TensorFlow cost function\n",
+    "    : accuracy: TensorFlow accuracy function\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    print('Loss: ', end='')\n",
+    "    print(session.run(cost, feed_dict = {x: feature_batch, y: label_batch, keep_prob: 1.0}), end='')\n",
+    "    print(', Accuracy: ', end='')\n",
+    "    print(session.run(accuracy, feed_dict = {x: feature_batch, y: label_batch, keep_prob: 1.0}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 超参数\n",
+    "\n",
+    "调试以下超参数：\n",
+    "* 设置 `epochs` 表示神经网络停止学习或开始过拟合的迭代次数\n",
+    "* 设置 `batch_size`，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：\n",
+    "\n",
+    " * 64\n",
+    " * 128\n",
+    " * 256\n",
+    " * ...\n",
+    "* 设置 `keep_probability` 表示使用丢弃时保留节点的概率"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# TODO: Tune Parameters\n",
+    "epochs = 10\n",
+    "batch_size = 128\n",
+    "keep_probability = 0.75"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 在单个 CIFAR-10 部分上训练\n",
+    "\n",
+    "我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking the Training on a Single Batch...\n",
+      "Epoch  1, CIFAR-10 Batch 1:  Loss: 1.95307, Accuracy: 0.35\n",
+      "Epoch  2, CIFAR-10 Batch 1:  Loss: 1.71162, Accuracy: 0.5\n",
+      "Epoch  3, CIFAR-10 Batch 1:  Loss: 1.59222, Accuracy: 0.525\n",
+      "Epoch  4, CIFAR-10 Batch 1:  Loss: 1.33961, Accuracy: 0.65\n",
+      "Epoch  5, CIFAR-10 Batch 1:  Loss: 1.22308, Accuracy: 0.625\n",
+      "Epoch  6, CIFAR-10 Batch 1:  Loss: 1.02561, Accuracy: 0.65\n",
+      "Epoch  7, CIFAR-10 Batch 1:  Loss: 0.918526, Accuracy: 0.725\n",
+      "Epoch  8, CIFAR-10 Batch 1:  Loss: 0.763063, Accuracy: 0.775\n",
+      "Epoch  9, CIFAR-10 Batch 1:  Loss: 0.656814, Accuracy: 0.8\n",
+      "Epoch 10, CIFAR-10 Batch 1:  Loss: 0.574128, Accuracy: 0.825\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "print('Checking the Training on a Single Batch...')\n",
+    "with tf.Session() as sess:\n",
+    "    # Initializing the variables\n",
+    "    sess.run(tf.global_variables_initializer())\n",
+    "    \n",
+    "    # Training cycle\n",
+    "    for epoch in range(epochs):\n",
+    "        batch_i = 1\n",
+    "        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
+    "            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
+    "        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
+    "        print_stats(sess, batch_features, batch_labels, cost, accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 完全训练模型\n",
+    "\n",
+    "现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training...\n",
+      "Epoch  1, CIFAR-10 Batch 1:  Loss: 1.95822, Accuracy: 0.35\n",
+      "Epoch  1, CIFAR-10 Batch 2:  Loss: 1.64322, Accuracy: 0.4\n",
+      "Epoch  1, CIFAR-10 Batch 3:  Loss: 1.36831, Accuracy: 0.55\n",
+      "Epoch  1, CIFAR-10 Batch 4:  Loss: 1.41689, Accuracy: 0.45\n",
+      "Epoch  1, CIFAR-10 Batch 5:  Loss: 1.59784, Accuracy: 0.425\n",
+      "Epoch  2, CIFAR-10 Batch 1:  Loss: 1.36398, Accuracy: 0.475\n",
+      "Epoch  2, CIFAR-10 Batch 2:  Loss: 1.1802, Accuracy: 0.475\n",
+      "Epoch  2, CIFAR-10 Batch 3:  Loss: 1.07384, Accuracy: 0.6\n",
+      "Epoch  2, CIFAR-10 Batch 4:  Loss: 0.988241, Accuracy: 0.675\n",
+      "Epoch  2, CIFAR-10 Batch 5:  Loss: 1.24307, Accuracy: 0.55\n",
+      "Epoch  3, CIFAR-10 Batch 1:  Loss: 1.05733, Accuracy: 0.625\n",
+      "Epoch  3, CIFAR-10 Batch 2:  Loss: 0.952706, Accuracy: 0.675\n",
+      "Epoch  3, CIFAR-10 Batch 3:  Loss: 0.922446, Accuracy: 0.65\n",
+      "Epoch  3, CIFAR-10 Batch 4:  Loss: 0.753417, Accuracy: 0.8\n",
+      "Epoch  3, CIFAR-10 Batch 5:  Loss: 0.917541, Accuracy: 0.7\n",
+      "Epoch  4, CIFAR-10 Batch 1:  Loss: 0.868109, Accuracy: 0.725\n",
+      "Epoch  4, CIFAR-10 Batch 2:  Loss: 0.818949, Accuracy: 0.7\n",
+      "Epoch  4, CIFAR-10 Batch 3:  Loss: 0.680601, Accuracy: 0.725\n",
+      "Epoch  4, CIFAR-10 Batch 4:  Loss: 0.577342, Accuracy: 0.825\n",
+      "Epoch  4, CIFAR-10 Batch 5:  Loss: 0.650067, Accuracy: 0.8\n",
+      "Epoch  5, CIFAR-10 Batch 1:  Loss: 0.748057, Accuracy: 0.8\n",
+      "Epoch  5, CIFAR-10 Batch 2:  Loss: 0.633852, Accuracy: 0.8\n",
+      "Epoch  5, CIFAR-10 Batch 3:  Loss: 0.480863, Accuracy: 0.95\n",
+      "Epoch  5, CIFAR-10 Batch 4:  Loss: 0.522334, Accuracy: 0.85\n",
+      "Epoch  5, CIFAR-10 Batch 5:  Loss: 0.571857, Accuracy: 0.85\n",
+      "Epoch  6, CIFAR-10 Batch 1:  Loss: 0.642935, Accuracy: 0.8\n",
+      "Epoch  6, CIFAR-10 Batch 2:  Loss: 0.585723, Accuracy: 0.825\n",
+      "Epoch  6, CIFAR-10 Batch 3:  Loss: 0.395464, Accuracy: 0.9\n",
+      "Epoch  6, CIFAR-10 Batch 4:  Loss: 0.397977, Accuracy: 0.875\n",
+      "Epoch  6, CIFAR-10 Batch 5:  Loss: 0.392235, Accuracy: 0.925\n",
+      "Epoch  7, CIFAR-10 Batch 1:  Loss: 0.489782, Accuracy: 0.85\n",
+      "Epoch  7, CIFAR-10 Batch 2:  Loss: 0.459161, Accuracy: 0.825\n",
+      "Epoch  7, CIFAR-10 Batch 3:  Loss: 0.273993, Accuracy: 0.95\n",
+      "Epoch  7, CIFAR-10 Batch 4:  Loss: 0.319732, Accuracy: 0.925\n",
+      "Epoch  7, CIFAR-10 Batch 5:  Loss: 0.30099, Accuracy: 0.95\n",
+      "Epoch  8, CIFAR-10 Batch 1:  Loss: 0.327477, Accuracy: 0.9\n",
+      "Epoch  8, CIFAR-10 Batch 2:  Loss: 0.365161, Accuracy: 0.925\n",
+      "Epoch  8, CIFAR-10 Batch 3:  Loss: 0.260866, Accuracy: 0.9\n",
+      "Epoch  8, CIFAR-10 Batch 4:  Loss: 0.2765, Accuracy: 0.95\n",
+      "Epoch  8, CIFAR-10 Batch 5:  Loss: 0.264591, Accuracy: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "epochs = 8\n",
+    "save_model_path = './model/image_classification'\n",
+    "\n",
+    "print('Training...')\n",
+    "with tf.Session() as sess:\n",
+    "    # Initializing the variables\n",
+    "    sess.run(tf.global_variables_initializer())\n",
+    "    \n",
+    "    # Training cycle\n",
+    "    for epoch in range(epochs):\n",
+    "        # Loop over all batches\n",
+    "        n_batches = 5\n",
+    "        for batch_i in range(1, n_batches + 1):\n",
+    "            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
+    "                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
+    "            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
+    "            print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
+    "            \n",
+    "    # Save Model\n",
+    "    saver = tf.train.Saver()\n",
+    "    save_path = saver.save(sess, save_model_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 检查点\n",
+    "\n",
+    "模型已保存到本地。\n",
+    "\n",
+    "## 测试模型\n",
+    "\n",
+    "利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing Accuracy: 0.684434335443038\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9oAAAN6CAYAAABxCzGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAewgAAHsIBbtB1PgAAIABJREFUeJzs3XeALFWZ9/Hvr3vuvWSQLKDoKoiCKEFRMee8hlURWTNi\nWNesr6uromtYVBZd16ywLmZd4wqKORNEXRHFNaBIkozEO9P1vH+cU901NdXTPT090zN3fh9tpm9P\n1anT1TXV9dQ55zmKCMzMzMzMzMxsPFqTroCZmZmZmZnZpsSBtpmZmZmZmdkYOdA2MzMzMzMzGyMH\n2mZmZmZmZmZj5EDbzMzMzMzMbIwcaJuZmZmZmZmNkQNtMzMzMzMzszFyoG1mZmZmZmY2Rg60zczM\nzMzMzMbIgbaZmZmZmZnZGDnQNjMzMzMzMxsjB9pmZmZmZmZmY+RA28zMzMzMzGyMHGibmZmZmZmZ\njZEDbTMzMzMzM7MxcqBtZmZmZmZmNkYOtM3MzMzMzMzGyIG2mZmZmZmZ2Rg50DYzMzMzMzMbIwfa\nZg0kbSbp7yV9SNLPJV0o6XpJRe3x35Ouq5mtfpKe13B+eeeQ6+7bsO7/LnWdVypJD2vYH1+cdL3M\nzGxtmZp0BWxlkbQtcCCwK7BdfhTANflxPvAH4E8RMT2pei4lSc8BXg/skF+Kyq9j7hpmZmOzmHOM\nz0+zeX+YmdnEONA2JO0LHAk8BLg1oCFW60g6GzgDOB34ekT8dulquTwkvRt4NukCrbxIG2Z/mK0I\nkt4AvGqBq10HXAVcSbqRdgZwGunveuN4a2hDEKMHiT5fzbaYfWlmZjYyB9prmKTbAu8G7pVfitrP\n+bSA/YDbA0/L5f0BODYi/mPMVV0Wkp5KL8juvswCLtIkbQ/cqfbypRHxk0VX0GxhFhJcbJ4fuwK3\nBR6aX79M0gnAcRFx/nirZ0skWCXBtqQdgYNqL/8lIn46xs04yDYzs4lwoL1GSXop8AZgA3MDy6pB\nrbrVdW9BCtpXXaAtqQW8lrn74irgeOD7wF+Aeuve5bV/HwCcVHvt68ADx1ZZs+GNEnBV/wa2B14C\nPFPS8yPixPFUy5bIqgiwKw4BvlR77cvAI8dU/mrbH2ZmtglxoL0GSToa+Geau0eX/74B+B0p0Lwe\n2JZ00b07KTintl513dXogcCezN4fVwB3iojfj1Deat4XtulZyPFYPxcEsA3wEUl7RcRrx1ozG5f6\nZ7yazkFLUdfVvD/MzGwT4EB7jZH0DHpBdvfl/O8/AR8APgf8OiLmXJhIapO6jB9MGtP9YFKX07Kc\n1eoelefl/viPEYPsejlmk1J2I+4Ah9L/b3QrYEfS3/XDgH3oHb/V4/jVks6LiA8uZaVtYSLil0B7\n0vVYpLGdLyPif1j9+8PMzFY5B9prSB4//K80B9lvBl4/KPFRRHSAn+fHhyRtThrP+RzgPktR72Vy\nQMNr31n2WpgtkYg4fYjFPgW8XNJjgfeQgu9qy7aAt0v6XERctjQ1NTMzM1v9PI/22vJiUvfvUhlk\nvywiXj1KduGIuD4iPhsR9yclRvvceKq67HZseO3Py14LsxUgIj5LGj/7l4Zfb8XCs5qbmZmZrSkO\ntNeWxzB7DHIAp0bEseMoPCLOjoiPj6OsCdiWud0Wr59ERcxWgoj4A/Bk5uZhEPDEiVTKzMzMbJVw\noL1GSLo5adxl3YeWuy4r1PpJV8BspYmIrwE/Yu7Y7p0l7T+BKpmZmZmtCh6jvXbcvM/rP1vWWiyA\npF1IY6dvQWpxngKuBc4HfgX8silh24RNPCGcpHWkuWlvC+wArCNlUD8bOD0iFtRSn8f2HwTcmvQ5\nXA9cBPw8In49xqpXt9kCbkm6OXRTUtbrzUhZ8C/P2z89Iq5Ziu3bLJ8H7trw+iHA/y62cEkC9icd\nr7sCW5Km0bsU+FxEXDlCmTcjDWXZk3TsQOoG/xfSeePcxdZ7wPa3AO4C3AbYjpSM7nLgHOCMhf4N\nrgSSbgHsC9yMtE/XAdcAV5JmqPjVCOP2J36+HIWkfYDbAXuQhlIUwNXAH4GfTWLOeUl7kM7Te+Y6\nXQ1cApwdEb9Yom1uTvrb3Yv03bANqdfLdaRj4zzgXOAPETGzFHUwM1vRIsKPNfAAHku6GOjkR/n8\nVpOuW62eOwCvBs7KdZzvcRnwYeDgBW7j+CHKHubx5Fzen8dUXgHs1qfOb2hY9p9qy+yT39tV85R/\nNfBuYOch9tPDSXOAT89T3jnAUYDG8NnfmZQR/xukC7VB+2oaOAN4JbDNiNt8U5+yH7+I9/GoPmW+\nY5n+hspjpfq3vnER5d2nobwO8Kp51nlzw/t/eW2ZfUmzHFzWZ391gDsvoJ57AW8H/jDEsfPLXMed\nxrzvDwY+TZoesd+2ryMlnbtTbd3nVd53+fOdQ25334bt/O8Y3s9dgPcBFwyxTwvSzbx3AIfOU+al\nQ5Y1zGP7Ptt4WMOyX1zkvrg98F7SzZpB9foN8Hpgl0Vsb+BnSsqs/nTgJwPqcz4pEep2YzgmtgKe\nDXwfmBnyc7oO+F7+m7vTYuvghx9++LFaHu46vnb0+6x3XtZa9CGpJellpLvfrye1bsWAx3bAU4HT\nJH1W0m4L2GRTecMs07T8YsupZnVeSL27JL2OlAn+KaQLoX7b2ZJ0kXSOpAc0bUDSLpK+BnwRuC/p\n2OlX3q1J2al/kFu+F0zSayT9DvgxcDQpsNswzzbLRws4EHgj8CdJrxhh868GvttQ9vsl3WqE93JL\n0s2fenmnAS8ZoX4rwSV9Xm9KIFg353iVNCXp30i9aZ5B+jse9DfUl6SdJP0nqZfLi0i9dwYdO/sA\nrwB+K+mVedrCkUnaTNJ7gFNJNzXXzbPtDcDfAT+W9K7cA2VcRtqHdZIOkPQd4IfAkcAuDN6nQWrB\nfz7wPUln5Zb9+eo4kfPlQkjaUdJHScfrs0g3gwfV71akc8sf8vltMZ9xv3P+fqQA+4PAHQfUZ1fg\nZcDvJD1k1IpIejzwa9LN2rvRy/Uy6LGBNL3gK4BTJR0zah3MzFYTB9prx6V9Xp/4lFyStgW+Srrj\nvgW9CwrVHvXXoPdF/mjgTEl3G3azfcoftMwwyy6knH7rDKp7epJ8HHgNvaEg9TKb9tm2wJcl3XdW\nwdJepFbi+zP3c+j3PoPU8vV1SduwcM8hDQ+oXkz22z/118t1tgbenG+4NF3cN4qIAjic9PdRfY9b\nA5+WNPTY/Xwx/SnSvq3W90pSC/lq7Tq52O691eN1M+BrwAvoff80fcbDFSzdG/gF8PfMnod50LFD\nXnYr0o2aL+RusAsmaTvgm6SeHWW5g+pQei5wkqQNo2y7X5VYxGeWb3ieCtyd0f8mId0s3WxAHZf1\nfLlQkg4kBdhl8r+FHl8bgNcB35Q0zI2pvlWpbkvS/YAfkFrZo2HbTfsrgJsAn5f06AVXQHoV8AnS\nUJ7yuJjvc+l3rg76HxdmZpsUj9FeO35T+3f5Jfl8Se+NiMsnUKdyLOPXgDsx+4599YLtIlL37OuA\n3UhB2RRzg+2dgZMlPSgifjRg000tHPULhYW0gtTrPko5o7S6HAc8gd7nGaTxrb8jdXHcjDTWuey5\nUL3gWQd8UtI+EXGZpJ1JAUO1Z0CQxpb+kTTOe3vSBfQGZn9GAHcgdd09coT3Uf8sIXVL/CMpUL2K\n9JlvR2pF36LPeo8mdZ9/wrAbjogLJB0BnETvPSm/n3eSegAM41jSGMnqxXgAT4uIPw1bnxWoX4DQ\n7+bdfD4K3JvZxyukniwXAzcCu5PGvs4bfEr6W+CT9FqPYe7x8HtSi/z1pL+BW9M7dqvLPZR0o+g+\nsYBpDvONmC+RbjT1O3/9OT9m8nu7BbMDk/uQekH8cNjtLhVJ7yIF/03ns/K1C0jnlstJPWR2IJ1j\n2rXl5zOp8+XQJB1AGsZSjjuG2fuiIB23F5COwT1In2+5XLXl/VDgG5LuGRFXLbJeB5HyJmzB7PP5\nJaTj7ApSQL0X6UZSPdBdB3xY0mkx5FhySYeRhqT0O8avI/2tXUkaNrEVab/tSTpGmtYxM9vkOdBe\nIyLifEnnAHvXfrUz6QLzsIioB+PL4Z00B9kd0ni4D0bEz6sr5C7KjwdeS6p/9SJiK1LwuH/0T6L0\nelJ356rPk7pHltsvg7aL5qn77/LPh9MLCu6c31M1kDgd+Md5yqlqmre4n4eSuu+V++6XpNaTkyLi\nuuqCku4KHJOXr9qetD+eRwpaygvFAjgReFdEnFEra3NSC+Kb8vrV1o2nSfpARJy2gPdBXv9GUqD/\nJVI38rMjYrppYUm3B55EakXchtkXcH8n6VkR8f6hNx5xiqQ3kbp7Vt/PkZK+HRGfmG99SY8l7cN6\nkH1cRHxx2HqsUHfq83q/LuX9/D1p3Gm5jy4E/oWU8GzW31k+xp5An78HSfsCHyMFDd2Xc9lnkG74\nnBIRVzSU+2DS38l+zD5u7gK8ldTaPqzXkoKo+vlrGvg34EMR8X+1OuxO2hf/RApCBBxG729vIvLw\nk6Ygu7xh8XbgK003jXJPhTuSxkY/gdR1up8H0vvc7g68jdnnyx8ALx2y2gtOlDeMfAP4U/QS6UGv\nfleQgs5PRMTFtfX2AZ5J6j5fvQEE6Xh7Pwu4CVgTuT6fphe83kjqxn1C1BKeSZoCHgK8hbkzjmxD\nGkv/d4M2mv9m/o25x8UNpPH7J0bET+ZZ/xak78VH5PrcZNA2zcw2GZMeJO7H8j1I46OqSY2qyY1u\nJAVW9wemlqk+j2qoT0G6uD5kiPVvApxcK6N8/skF1uUPDWXcfIT3dL/K+uXPr41hX72hVm49adSx\nMH9CMlKL0xcb9tc1wP+rlHc5cK8h6rQfKZFVfb8dv8D39iPghcBWI+yXm5KC8/p7ughYt8CyWn3K\nugrYa571bkW64K8fxz9crr+leY6VcSRD+0Gfv7E7zLPOm2k+XssyPg1sMWJ9NifdVKrv7+uAZw5Z\nhkiBY9P7esCQZdyB1HukXo/zgf2HWH9PUtb2+n5aTDK0ajlDJ0Mj5WJo+l6YyeeGBR3HwAOAbwE3\nGbDcwxre86KSlo2jXNK456Zj41sMl0jyAHpJMutlPHkRn2n1378Gbj1EOVuQ8lDU6zJNn+SbtfWb\nEqleBRw4wueynpS87bmL/Yz98MMPP1bDw2O015Z3kQKQpq56U6Sxql8DLpP0VUmvl/QISTcdd0Xy\nlD5vZ+5d8r8CD4qIUweVEam16tGkQKDapU+kVs27j7XSK0u1W+K7IuLFETFvl7yI6JAucupdFzcn\ntU5DChweEBHfGViBiLNIrXJN+37o8a4RcdeIOC5GmKorIi4kXVSfzuzjeifS8byQsgrSWMyLa2Vt\nRRqvPacrc+46/CnSmO7uy6SbFU+I1TsuGwBJDyJN7VU/ti6NWk+TAarrf4k0Zv26fgsP8BrS8IWS\nSMftwyLig0NVJnkJ8O/MPR8ePWQ93sbsXmEi/W09MCIGTnsWEX8EHgT8iQl2qc2J4N5bf5kUZB8e\nEW9Z6HEcEadExH2i1qNgNchJxp7O3O7iPwEeEREDex1FxE9Jn231/Zfnx2NGTI5W7WnzR+BuEfHb\nIepyHanHxF9rv2oBTx5iuw+qPC+P06Mj4sxhKl2ry8aI+HBEvHuh65qZrUYOtNeQiLiWdHf6RpqT\nl1S7Xz+A1I32C8D5ks6X9N+SXijpwBwoL8YjSOP6SuUX+KvzRcpQIs1HewRpDGbd8xdVw5WrvNiC\nlGl52G6WRMSlpJ4LTZ9fAK+LeboBNvgwc7tvbkHKBr4s8jFwVMOvnjRCWRfn9YryJdK+uj3pRlXd\ncaTWq1J5HD81Is5b6PZXkpxB/T9p7kr88QUUVV3/CuDIQTeF5qlTObVQvYv+yyLiWyMU+VLSlFRl\nPQUcIqlfd/myHrcitQLX6/GqiPjlsBvPN4qes9BKj9lTSGPXS+V7eVNEfGoyVZqopmE+G4EjFnIz\nMB8HL2XuuXYnUuA7ivKzeVIsIK9KRFxAOlfX63LoEKvfrOG1/xl222Zma5kD7TUmUpKwh5HGV5YX\nlv2CrupjV1JX72NJYyD/KOnNOUv1KJ7b8NpvgP9YaEG5ZehtzG1ZfdRStMavIAH8a/QZxzyP6pjh\nasBzFWl8+fAVSNs+mbnH0LIF2rkePyNln64mM7vziGV9kzRuvX48PV1SN3jPU900BX1vj4gvj7Lt\nlSKPOT+NFBTUXUuvB8TQRZL2zX9ExELHdlcdRS+re/n5nMMI5w3oHr//ytzj9+kDVj2yYZ3/Y27u\nh2HqcBKpJ9GkWrWryf6qN/DeMIG6TFSeNeFw5v5NvztGyGESEceTWsLrx8o/LLSoSl1OiYhREudV\nb5qU5R00xHpNM0lcPcL2zczWHAfaa1AOJA4gJb8qmB1w9wu8YXbgvTtpzPfZkj6wkIA2d7e9N3Mv\nZj6Uu++O4v30WiFLU6Qx05uq60nTrSxUvVtruf8/O2J33qZusvWke8vh9Nq/t85Js0bxBlLG4Xqw\n/R5Jt8k3mD7A3O6lPwJeOeI2l5SkQ+Z53FfS4yUdI+ls0hjq+rzo5XHyimG6z/bx4cW8B+YmkiqD\n91HPG5ACkDLTePk532PAOtW5iMv9csKoLfXAh0Zcb1Ek7QkczOwAP4B35KEma8096M1oUPWBRZRZ\nTcpYHl8HSxo1Kdiof0M/Ze535C6StmxauKIp4dxINzHNzNYaB9prVERcEBGHA/uTLgQuY3YgPWzg\n3QKeAZwl6f5Dbv5AUlKUuk8O/QbqFUnTlHyXuXW966hlrmDdbOYjtGaX3aObpjD6/oj1ObfhtW0b\nXltqTcHffNmP+8oB0+GkzNjVY2pL4DOkY3Xr2mqXkcZlr6QApaz7FOkmQL/H10k3bV4K3Ia53cXL\nY+6YiFhwq232x9z7ZCQ5IDigVjeAz41aJkBE3MjcMf77SGo8hnM9qtnTS59ZRDW+SMrivNwe0PDa\nRtI0bGtR9fuiPB7OiohfLaLMT5LGu9fdZcTyvjfKShFxA2kqsrpB5+r6UAgB/yppl6aFzcysx4H2\nGhcRZ0fEc0jZmx9Kmt7mVNLF1rCBd5AygJ+Uu9MO0nSBcfEYxrT+uPZvsWkG2qXFXPzVE+Msprym\nskYOtCWtl/RwSUfnvABnSTpP0lWSOpKKpgcpO3LddqPWI3dxPpyUaRd6gdW+pKmMqq3ZBSmb8FDz\n0k5AfSjIfI/633qQPuMjI2KU1vqyjIWM/W9yKL25mksX5fGni3Vu7d/luPwmBzH3u/OqYRJT9ZOD\n/bPof1NzqVS7Dpef008Xkahutat/NwXp+3BkEXE1KUP4OG4CX53H9Y+qqcv3oHN1dTx2eY7YG/iF\npJdI2mER9TEz26R5Hm0DIGeV/Wp+kLOi7k9qfT6Y1KXuNnnx6gV4twjSRfCHJf0mj5ntZ8/K8+7F\n3RjeRrWMMmC4+RjKXamGTobToKn1bNTymsrabKGF5G6s/0xK2Fe9+Ku3HM7XPbd+MTtyoA0QEd+R\n9FrSfM/VcZLV7QXwtjzWdqVaSABXfX9XAh8Bjh3DjbA/L3L9/SrPy/1+saRDFlkuwJyM8sztOl+6\nRUM9BmYZH8LPSefa5dSUY2NRgeUqtydzzy/j+m7ar/baKN9NiznnQxpuVD+HzXuuzufAHwJ3q6wX\nwA6kG/NvkfR94BRSa/vpufXczGzNc6BtjXKX5J/kxwcAclexxwHPIrXqVRNPlV/AmwPvJn0p99M0\nNm0xd+lLo3SLW83G3eo0zvIW1DIn6ZWkLPeb02s1GamsmkHjDweKiDdJugdpmpvq8V7+/AFpmrOV\nbNDY4etJyfCuIrXunkFKhnZKbm0dh/q0cgtVbzkTqWfBjxZZbj/9Au2m89eoY9bHXcZC7c7cY2Ox\nN0RWs+X8blrIGO3yHHjtGOrSr+z5PIl0A2anyvLleboF3Cs/AGYk/RT4NvBN4FsR0TRUycxsk+eu\n4za0iLg4It4VEfuTpoS5irlBRzk9znzjtZsuMMaRxbTpQr6VM8naCiXp3cAbSS0r1e7Y9Qu6YR5z\nih9TNU+Y53dHLjIZ11Iq90knItrzPLaKiN0j4nYR8dCIeE1EfHmMQTYs/kZOU+C7kGNjocdPv5t0\nTb0klur8tdS2anitKfnVWtH0mS/VZztqMrRll3MrHEK68V4/R5ff/eWjDdwJeBlwEnCRpPfn+cnN\nzNYUt2jbSCLiREk/IyXQ2pq5F6mPJSVYarJ5w2tN82AvVL8ytsTTkaxIkl7E7CmyoHfhNg2cSWpJ\n+SOppe2vpK7qNzD3mHs28NQlqOMewL9Xtlff7quAJ497uzZHU4C7lGOa6+PBS03dzMfRYjfOmxrD\nanqP4zgXrzp5Now2c/++l+K7SYyht81yysH2nSUdAbyENLSs1JS3pbQt8EzgGZI+AfxDRFyxpJU1\nM1shHGjbyCLiLEkvB97L7CBEwH3nWbUp6K1ncB5FvzIm0VJkA+ShCK9jbpB9LXA08IGIGPqzk/To\nsVYwldkGPg7syOyWnGrisCdJ+k5ETGSKpjWkKXPzqNNpLUbTMTmO89cket5cCdSzR6/JHkARsVHS\nRmBd7VdL8d0UrNLvpYg4EThR0v7Aw4H7kBK7VW+gVwPvam+RJwJ3l3TviPjDctTXzGySHGjbYh0P\nvI25d+dvOc86TXezx3Fx19Ttb3oNZ9Bd6Z7C7N4QAq4BDo2IX4xQ3lJ0xXwjKdt1PciuB9vvkPTj\niKhPhWPjU79BF8A3I6Jpiqql1NS1ehy5ICaRT+Jy5ibHWlQCwVXuCmDn2mtL9d202MRmExUR/0tK\nAvimfEPyIFLS1HuQgu9yWEL1+ArgZsBXJN1xzENTzMxWHI/RtkXJSdNOY27XsbakfoFPU6D9N2Oo\nTtOcye6itnI9svK8vBh77YhBNqRW57GR9FDSOMN6kH0ic+fX3hz4tKQtxlkHm6We9VzMTZC2HC6t\nPC9vtDRl716ovcdQxkJd2vDaON7LauXvphFERCciTouIt0fEo0jn4ocBnydNfVi/Ptgb+IdlrqaZ\n2bJzoG3jcHGf1/uNcTy78ry8UL3DGOpxQOV5GRSd3WdZm7yDmNv198RFlHdgQ3kjyeOy/7PhV2eS\nxhs+iXQBCb1j+DakYRS2NOrnDUitY8vt5w2v3VpSU+6JhajOzb5c6tMwirlzSa8lZzM3KDygacEF\nOoC5n+0m+90UEdMRcXJEPBa4N3N7gQj4+2WvmJnZMnOgbePQ1LWuExFNrSXQPB3PVpIOXGQ97s3c\ni5mlmvqnn0mMGV11cib4elKpKyLikhHL253Z8xuPLHeD/ASzW0tFGlP5+HwR+W3SOPJqZvRyvPYz\nxlEPm+NU5v59bZ/Hii6biPgtc7v9tpg/L8W8JO0F7LGYeo3oO5Xn5b69Tb7RtBxW2vmy+n1R/k3f\nfTEFSroFaX7u+ba1yYqI7wMvZPa5EuD2ksYx/t3MbMVyoG3jcBvmXjD1C7KJiHNoHp92xKgVyEH6\nvg2/Wu6LmaYxZ/XkOtY8ZvGaRZT3zEWsW/dm0jzw9S7jz6wl8PkXUmb9erD9DklNx6ItQkRcTJrf\nu97i+JgJVOdHDfV40iLKm1TW+h8xN2O6gKOWafsr7Xz5w4bXdpM08k0UUi6Kuo2kqbLWik/T6wFU\nVU/EZ2a2SXGgbYsi6QDg1tWXSAFH0wVL1WeZG6D8vaRRE/G8pOG1K4BvjFjeqP5a+/eqm8ZlmTRl\n3N1J0oLPSZK2IgUGi24dk/Qw0rFUD7LfHRGfrS4bEUG6OXQRHq+9XP6r8rw8b/xj7iEx6Xo8WtKt\n+yzfl6RtgWcxgdbdiLgB+CRzz8VHSRprzoM+6udLmOz58lTm5gIAeNEohUnaEjiSueeTL+b8JmtC\nRFwPNCUl7Te8zMxsk+BAe42QdJCk40e5EJynTAHv6PPrLwxY/d8bXtseeNMI9bgXadqQ+sXMB/KF\n5HKqJrgp63OLZa7DihcRVzP3wmszUrbahXonsGt+PvK8ypJuBpzQ8KszgRc3rRMRfwEOp3m89ntG\nrYv1dQJwWe21bYEPLnM9PgfUhzmsJx2LC/VmYKf8fCnnBe/nGOYG+TuwPPu0fr4UEzxfRkRB+rut\n33h4aE6OuFCvA3ZreL3p+2+TJWlnelnIq85f7rqYmS0nB9prxxSpC9uvJH1kseOhJa0DPkoav1a/\nSLuMAYF2RJxFam1uakkZuhtwHtv4iYY6bATePWw54xIR5zG3lWYHSU1ZZ9e67zE3sDh6Ia3aeR73\np9I7fkaSx2V/krnjsq8mj8vut24er/065h7LR0h6+qh1srki4hrgNczd14+V9G5Ji5qyUlJL0mMk\n7TegHtOkoLpejwdJOnYB2/tH4Nks8vhdjDwl3eeZ+14eIel9o/QyAZC0Q/67ms/vgfrN0N2XcYx4\nkw8A11b+Xe6P/5R022ELkfQk0g26+nfTT/K45VVB0gck3W6Rxbyw4bX/y3/PZmabLAfaa0+L1N31\nDElnSXq5pPnmvJ4lX4j+LSnz7mHMvogoW5JfkVssB3kOzRc075X0qkEXaZLuRwrWq/OelnV4dQ56\nJ+GnzL1o/udJVGSF+3zlefnZ3xX42KBu15K2lfQe4C2VdWcWUZe3kLItDxqX3c8bgVOYG6y80+O1\nxysi3sPcfQ0pYP2hpAVnzZa0j6RXkQK/TwM3H2K1Y4BfMvczf6GkT87X9VrSFpLeCfxbZb1r+y2/\nDI4CLmDuPj0S+OpCjmFJt8o3G/7IgLnBI6JDmot5xZwvI+IyUoBcrVOQbsJ9Q9JD5ls/f0e+FDi+\n/ivSmPTVlizxqcBZkr4i6bA8VGcoSl4CvJzmaRLNzDZpi7r7b6tW+YV3W1KA8RZJF5HGVZ9OGnN6\nGalb3wZSVvFbkabgehCpm2PQHGT/d0TULzCaKxHxW0kvBt5XqVd50fkG4HBJJwAnAX8Grid1wzuY\ndLPgEX3q8K2IePswdVginwHumZ+X7+fJ+WL1U6RpXa4CmlpJfxIRiwkYV5PjgVeRsi1Xj6fHA3eR\n9G7gq8DvSK1eO5Hm+P1b0udfHociHbNfYIQkTpIezuyWp/I4ek9EfGaYMiIiJB1Bmi7pppWyNgc+\nJengPE4GDJbzAAAgAElEQVTRxuMwUo+IsqWtPH4OJgXbpwNfBH4A/JY0vdD1wNak4G93YH/SOe2+\nzJ47eqix0hExnTPMf4+UwKt6DD+O1N34C6SEeecDHdL56x7AY0lDZcrj91JSD5zXLGAfjE1EXCrp\nMOCbpOuC8n0EcD/g55K+DPwP6XviYtL3w5akAHQ/0r5/GGmaPRhyP5LOl3eurCPgSEl3IN30+BXp\nfNl0Xjw9d/ceq4j4QM7X8Ehm74tdgf+RdDIpUPwxcCHp898DeADwNNKx1fTd9OqI+MW467sMAnhw\nftwg6RvAaaRhNb8hHQtXksZcb0f6e7onaQqvpmSp59F/2JmZ2SbDgfbaU79LX34B7kLK3jsog291\nnXpZn2KBmXfzBc0+zJ3+I4B9gH/Nj351KZUXMr8EnrCQOiyBE0ldicsL6bKeB+VHP0GaF/iCpazc\nEhs6oVNEbJR0FPAlUk+L6gXtzcg3gebZTnlRfgMpOH/QQis7z7jsn7LABEgRcYmkw0mBVfl+RDqO\n30tz9mEbQURcIek+wFdIgV29JfZO+TFUcTSf04apx2k52D6eXmKnsrwtSOP3D59nmyINc3k8zbMm\njGKkbugR8f0cbH+UNN68PKeW5T0yP8a9/RNIN9y2ZfZncWd6AXi/7exE8wwW9eVG8WRSz4mDmb0v\noBd0Dtpm9bj80CJuAI9zaMGo+6NcbwPphsrDhlynvi+uBY6IiKZEeGZmmxR3HV87LiZlVC2YffFU\nfcQQj+ry5NcuBY6KiCeO0roQES8BXkFqsahuoyx/mLoE8DXgnrnr38RExJWkVo1OfmmY/TuscY7j\nrO7ncZY3dJkRcRLwD6R91XRsDfrsrwIeWRvzONT2K+Oyt69t+2rgcaNkBY6I79CbX7usRzlee7m7\njI778x21Dksiz7l+D9JNjPK8tpBzWb/zCDT3NulXjxNJLezX9Cmv6W+9/P01wKPzOH8qv6v+XIhF\nfeYR8d/AvUndvod5L/3e20K2eSmpi3r53THu8+VI+yQPf7ovqafMQvdFdZsd4PUR8ayF1qH2HsZl\n1PIWeiw07bOLgPutpjHqZmaL4UB7jYiIcyPirqQuk88mXTxcQv8LwKYHteXPAv4J2DsiFpWhNiLe\nRmqB+i7NX9JNj3K584FnRcRDcpA7cRHxJVIG7f9juP07VLG1shZdzTGWOfLFcES8l9Q6dB7Dffbl\n9k4BDo6Ir/epxyBvAQ6prVMw/Ljsft5IuulTr8tyzq+9mOBkKeqwNBuIuCEinkf6HE+mF3DD4PNH\n/Zx2AXAscMeIOGWB9fgsqWX9JGa/7/m2d1Le1knVohh9v43lM4+IU0mt6/9C6hI87Pm4WofzSL16\nmqbxa9rmZ0jdrn9fq/u4zpcj7Y+IuDYiHkNq3T6X4fdFudwPgUMj4uiFbntc72GIMgd5MWlIwUaG\n+2ya9sP1wHHAbSLitDG8BzOzVUFpKlhbqyTdhpQEah9gb9JY7O1JYxm3JHXL/Suple8i4Bek5DU/\nytlql6JOB5K6oN+PNAazKSnahcD3SfNxf36xc5JKOp7Ufb4UwFNzq9miSLon8FDSeNC9SV0ktyKN\n66sK4OYRsZq7jo8sZ4x+Mml866HMnU83SONtvw58pH7BJqmpu/CPI+LMpanxyiRpN9J40aqIiNMn\nUZ/lJOkWwN+Rzh0HMTuLfNWNpGPpbFIw9I1xnc9yxvInksao7k0as9ohBa3nkM5bn14NY3VzUsLH\nkLqM35XmqaogvbdfkW6UfhX4Xox4cZGHBDyEdL7ci5QjpN/5cueIGNR1fCxy9vVHkI6ve5CGt9QV\npM/4m8DHI+JHy1G35ZCToB1KOg4OIY29vhnNDTZBujHxU9Lwjs+4q7iZrUUOtG1Fy917b04KTluk\nuZfP95f2pi1f1O5CCpSmSF1sz4uIGydaMVtVJG1DSk63Beni/6/5ccmogeBalgPv3UmBb7k/r1iu\nYHclkbQl6YbWlqQA+2rSOWpRN31Xk3xzdFfSjfktSDfmrwIuc/JHMzMH2mZmZmZmZmZj5THaZmZm\nZmZmZmPkQNvMzMzMzMxsjBxom5mZmZmZmY2RA20zMzMzMzOzMXKgbWZmZmZmZjZGDrTNzMzMzMzM\nxsiBtpmZmZmZmdkYOdA2MzMzMzMzGyMH2mZmZmZmZmZj5EDbzMzMzMzMbIwcaJuZmZmZmZmNkQNt\nMzMzMzMzszFyoG1mZmZmZmY2Rg60zczMzMzMzMbIgbaZmZmZmZnZGDnQNjMzMzMzMxsjB9pmZmZm\nZmZmY+RA28zMzMzMzGyMHGibmZmZmZmZjZEDbTMzMzMzM7MxcqBtZmZmZmZmNkYOtM3MzMzMzMzG\nyIG2mZmZmZmZ2Rg50DYzMzMzMzMbIwfaZmZmZmZmZmPkQNvMzMzMzMxsjBxom5mZmZmZmY2RA20z\nMzMzMzOzMXKgbWZmZmZmZjZGDrTNzMzMzMzMxsiBtpmZmZmZmdkYOdA2MzMzMzMzGyMH2mZmZmZm\nZmZj5EDbzMzMzMzMbIwcaJuZmZmZmZmNkQNtMzMzMzMzszFyoG1mZmZmZmY2Rg60zczMzMzMzMbI\ngbaZmZmZmZnZGDnQtomQdK6kIj9uPqYyX1sp8zV9lrlXZZlvjmO7ZmZmZmZmVVOTroCtWZEfS1X2\nOJYxMzMzMzNbMLdoz0PSUyqtnx+edH02QZp0BczMzMzMzMbNgfZw3Pq5evizMjMzMzOziXLXcdtk\nRMTRwNGTroeZmZmZma1tbtE2MzMzMzMzGyMH2vPzGGIzMzMzMzNbkIkG2pJuLunZkj4m6ReSrpS0\nUdKlkv5X0rslHTJkWd+uJC675xDL950KStIJkgqgTIAm4KmV5YthpoiStKWkf5R0sqTzJF0v6fL8\nXv9d0p2HfG/ltjqV1+6Q98+vJf01P34s6TmS2g1lHCTpeElnS7om7+NvSjp8mDpUypmS9DRJn8tT\ndF0n6apcjw9Kuv9CyquVfRtJ/ybpl7nMqyT9XNIbJO0yxPoDp/casV57SHq1pO9KOl/SDZIuk3Sm\npLdK2mtc2zIzMzMzs9VvYmO0Jb0VeDG9VuNqEqubANsD+wHPlvQJ4BkRcf08RUbt57Calq9OPdVU\nv4EkPRx4P7Brbf31wHbAvsDzJH0MOHLAe5u1fUkvB94ItGv1unN+PELS30bEtKQW8C7g2bVyNgfu\nDdxb0sOAIyJi3veYb3p8FPibWlkbgL3z4+mSTgEOj4jLBrynatlHAu/MZVXrcfv8eK6kp0bEl4Yo\nbiwJ0SSJNOb7pcBmtbK3Ix2ndwReIOmYiHj1OLZrZmZmZmar2ySToe2RfxbAOflxGTAN7AAcANwq\nL3MYsDXwiGWq2ynAX4F9gPuTgqtfA99oWPb/6i9IegJwIqnHQAAd4PvAb4GtgHsAu+XFDwduIem+\nEbFxUMUkPQt4Sy7358DPcvmHALfLiz2IFLQ+B3gPcGRe5nTgV7le9wBumZc/LJd1zDzbvSfwFVKA\nXt6IOA04m3Tz4C70Pq8HAN+XdPchg+1HAcflMv9M2lfXkAL3Q3N9bwJ8WtIjIuKUIcpclHyD4lPA\nY+i93/NJ7/kS0ud4COk9TwH/JGnHiHh2c4lmZmZmZrZWTDLQPgM4CfhyRFzetICkQ0ndt/cCHirp\n8Ij42FJXLG/jY5KeQgq0AU6NiH8ctK6kvwE+QK9b/qnAkyLiD7XlXgi8NS93V1KQ+8IhqvcO4ELg\nsIj4Xq3MFwNvIwWFT5d0DinI/iXwxIg4q7Ks8rIvyi+9StK/N7WsS9qO1JK9OamF/5xc3s9qyz0x\nv/fNSUHyh0hBdD9l6/AxpBsBL42Id9TK3IcU8O4HrANOkHS7iLhqnnLH4XX0guyLgOdGxBfqC0l6\nLOk9bwccKenrEfGZJa6bmZmZmZmtYBMbox0Rb4+Ij/QLsvMyPwAeCNyQX3r+slRucV5Lau0UqQX7\nQfUgGyAijgNelpcTqRv5ngPKFqkHwP3qQXYu81jg63m5KeBY4GLg3tUgOy8befvn5Je2Ah7WZ7sv\nAnbP5V6et/+z+kIR8XHgiMp7eoSkuw/xntYBr6wH2bnMX5NayC/Ny+5K7+bAksifwytJQfblwKFN\nQXau32dJAXnpdUtZNzMzMzMzW/lWfNbxiPgj8C1SkHUnSVtNuEp9SdoWeHz+ZwAvi4i/zrPKO0it\nzZA+i2cN2EQA783BZz8fL6uTl39jv+7bEVGQWotL/ZKzHVnZ/usj4oK+FYz4PKmnQuk589S19AfS\nTYF+ZV4MvD7/U8AzhihzMV5IGv8OcHREnDvfwhHxbeCrpLrdVtIdl7R2ZmZmZma2ok2y63iXpJuR\ngry9SV1wyy7KpXIssYA7AD9Y1goO726kZF6QWmC/PN/CERGSPgy8Pb90nyG28dkBv//FApevtnTf\nsv5LSbell9CtA/zXgPIAPgg8hPR53Xue5cqbAR/LQf98TgT+jRQA7yZp74j4zRB1GcVDKs8/3nep\n2b5JGhsPcHfS2HkzMzMzM1uDJhpoS7orKbHX3Rl+zuodl65Gi3ZA/hnAaUMEj9C7aaDK+k3KoPSs\neZYBuKLy/KqIuHDA8tWu+9s0/L76ns6JiCsalqmr3gjZVdKuEXHRPMv/aFCBEXFlHnNeJnw7ABh7\noC1pe9INnwA2Aq9Lw9kHul3l+c3GXS8zMzMzM1s9Jjm919NJSaTKAHLQlExltLP1UtZrkXaqPP/j\nkOucW3m+XtJWEXFNv4Uj4uoB5c2UiwLDJAybqTxf1/D7Bb+niPiLpBvoTYm1IymhWD9/GqbcvFwZ\n0O4034KLcNPK8w3A8xa4vkgZ0s3MzMzMbI2ayBjt3B35vfmfQRqn/AJS9/FdgM0jol0+gI9UVl/J\n48qr48evHXKd+nLjvJEwjvmkR3lP9WUHvafrlqDMUW1beR4jPtqYmZmZmdmaNakW7RflbQdwMvC3\nETEzz/JLEVQtRcBebYnecsh16svNlzxtEkZ5T/VlB72nLZagzFGVwbxIXe/dOm1mZmZmZgsyqdbh\n+1aev3pAkA0waNorgOnK82FuIGw7eJEFu6Ty/OZDrnOLyvON83Ubn5AFvydJO9HrNg4pMdx8ht1X\n1bHPg8oc1cWV59tI2qzvkmZmZmZmZg0mFWjvVnk+b3IvSdsA+zO4G3R17PIOQ9Th9kMss9Cu1z/N\nPwXcWcNl0bpbZVs/nW/BCam+p30kbTfEOodWnl80IBEawF0GFZinTtun8tKZQ9RjwXJdz6u8dLd+\ny5qZmZmZmTWZVKBdzcY9qNvwkaQkXYOC1nMrz+edx1jSTUmZzgcF0jdUnjclCqv7IXBjfr4T8LAB\n9RDwtMpL3xxiG8sqIn5FL5FZGzhiiNXKea6DNAd63+JJn+sTh7gpcQS9sc8XLuHUXjB7WrbnLuF2\nzMzMzMxsEzSpQPv3leeP7LeQpL2A1zBcy/KpledPlDRfYHwcwwXvl1We7z6oAhFxFfDJyktvlTTf\nuObn02tZL4D3D9rGhJT1EvCafKOikaRHMvsGw3v7LVtxK9K4/X5l7gL8M71kYx8coszFeDtpznAB\nj5b0lGFXzHU1MzMzM7M1bFKB9pcqz4+V9MD6ApLuR2oN3Yrhsl1/mZQgS6Qx3R+sj6+VdBNJHwEe\nx+zW6n6q3doPkbTHEOu8npRATKT5mL8m6Za1ekjSC0gBHaTg8V0RMew0V8vtOOD8/HwH4JuS7lBf\nSNJhwMfoBcRfjIjvDyi7nK/6GEnPr7ds5wz1p5B6CIg0hvq4RbyXgSLi98C/lFUAPizprZIahyRI\nakt6gKT/YmV2/zczMzMzs2U0qazjxwHPJAVPOwAnSzoTOJsUeB0I7JuffxX4C/Dk+QqMiOslvQE4\nhhQc/T3wIEnfIo3fvhlwT1JX9V/kcl86oMyLJf2QNE53c+B/JZ0MXEiv+/vvIuK9lXV+L+mZwImk\nrs53Bc6R9D3gd6QbB/eg10IewI+AV8xXl0mKiCslHQ58hbT/bgOcKelU0me2njTO+tblKsBvSJ/x\nfMo51F8OvCM/Xibp+6SbFXuTuviXN4SmgadFxJVjemt9RcTRkvYEytbslwDPl3QG6XO8DtiGlMxu\nf3oZ0ZcqSZuZmZmZma0SEwm0I+ISSX8LfAHYMb98YH5Ar0X0c6QxzO8csui3A3vRC/B2Bp5Q3TRp\nHPXjgGcNWeYLgG+QphjbFjis9vtvU+seHRGfknQNqYvzLqSA+z75Udaj7A7/MeDIiNg4ZH2GNUwi\ntqGXj4jv5V4GHwX+Jr98F2YnMivf0ynAkyKi2vV+Pl8gtWofR7oBUd3H5b66khRkf3XIMgcZuH8i\n4umSfgIcDdyENNzgbsxNkFadQ3tQC76ZmZmZmW3iJtWiTUT8WNK+wAuBR9AL3i4EfgKcGBH/A5B7\nE1eD035lBnCUpM+RAulDSC3mlwG/Av4rl9uplDmonj+RtD9pPPV9cj23opeYq7GMiPiKpFsDTwce\nTmqh3xG4HriA1C3+IxFx+qA6VLYxbBb0JVk+Ik7LXbmPAB5FSjq3M6ml+SJSkPnxiPj6ArYbuez3\nSfou8Gzg/kDZTf9c4IukrvUXNxXS570Ms8wwn/9/SDqB1EPiAcAdSD0xNiMNVfgz8EvSDZevRMT5\nzSWZmZmZmdlaoRSbmpmZmZmZmdk4TCoZmpmZmZmZmdkmyYG2mZmZmZmZ2Rg50DYzMzMzMzMbIwfa\nZmZmZmZmZmPkQNvMzMzMzMxsjBxom5mZmZmZmY2RA20zMzMzMzOzMXKgbWZmZmZmZjZGDrTNzMzM\nzMzMxsiBtpmZmZmZmdkYOdA2MzMzMzMzGyMH2mZmZmZmZmZjNDXpCpiZma12kq4FNgAF8JcJV8fM\nzGxTtjOpwfjGiNhy0pXpRxEx6TqsCL8447QAENBCtBACOlHQKQqKCDpFQSc6dIoCCNa1WqxriXUS\nSGllpaus6aJgpghmImiplR6tFm21mWq1abfatNUiCAqCgoKIICL/LCIXl+qh6BaPgHYrl9duESKX\nk+o50ymYmUk/AyFaqSS1mBK0JaYEREEUHYgORVEwDcwQTAOtSvmtVos26WhuARQdotOBoiCKAoDy\nOCrUIlr5oUqHCc3e3+XyEeXzSP/IP1ON089yX7VbbaS8B9RK+xz4mwMPqpVuZra8JM0A7UnXw8zM\nbA3pRMSKbThesRVbbur+TIEtAmL277q/zwFeGVsjpZgvB9wCWi1oEbQiUtCubskEkYJpBZE3IrqF\npQ218vbLR72yYlbFymA1ohKwdheJvP3IW4rub/JbQS3RikpYLpByWUWR65pLjaK3rtQtHyCkHPjn\nOqlcR71tKe2PiMiri4gyYE9FBunmQkgE6v5MQXb+cHyPyMxWjnSzVmK33XabdF3MbAgbN27kkksu\nYaeddmL9+vWTro6ZDemCCy4oG+1WdDTgQDtTK7W+qiG47i4z68Ve0JeCUqUycqAdoVRYUQmkS5GD\n7SIqxaQlCloo/TYF4dELxsnBuMpgU5WgkxzYFgVEICIHynTr1A2ecyV6z3NLvkQbCPVeg4Iydi9y\ncJtDXyBIu03dusWs30Zat1t10erWOb3nFFAHRCu15hfdt0OUNyfU6rZgx6wPYUX/bZnZ2nI5sPOO\nO+7In//850nXxcyGcOaZZ3LQQQdx8sknc+CBB066OmY2pJ133plLLrkE0nfviuVAO2tVAjhB2Qzb\nfUWznqcgumyZTUF272dItIscJJeNr93W6aD6v17gnMpuVbZKkFqS87rdKs4J+INuNFxp0a42kEvR\nC/YrwXaOnGkJCkRL0M6hfllW5BsDRFBApcW73G/q3iwoKu+zEjMTuQt95BsSs29kBFEURFEG6+Wr\nOWCvtGrn2xgOsc3MzMzMbMVyoF1TaSCuqL1QRrC1gFdKLbYhES0RRdAmBZwpwK72Bk9jqkUrd0Gv\nlwd0g+zeuirLV6XVuKxjJcBOLdT5JZVdxcsx0GWrdeQAPK3VotL1u6h0/46gKFvJI3cT795koBtk\nd29IlC32Ue35ru7P3o4mh/RlK3f5r/Tv8t0ELQJRVMp3sG1mZmZmZiuVA+2smhRO1Qguer8vhwLM\nSuSVW4Lb3Tbq7gDlyoNuy7C6Aa9ya3dQRC8oTtupJEUjtYrnoczdOhRlfaOotGLnod2CVp65LZR6\nr5dllcuFcsIxpTC227ecyvD0XBeKotKOHJUAuNtpPb+3Sv3L5myV4XegXnN3bScDRe8GQY9ygJ3r\nUhQ50C7fy1AfrZnZsrn00kvZY489Jl0NMxvCxo0bAXjwgx/sMdrLaNddd+WMM86YdDXMlpwD7awM\nnlUL9aAaz0Ut4C4DziBCKXYsu5zXAu1ZLdPqtdyWgWrRDeiLXrBaCSbLOhSUXdtz0FuIbpqwsvt5\nNypPQbYiKKLo1rtsKQ9F6iZeecu95Ol5+0V5YyF6wXbZ7N/NBle2YEcaIl6k8efdmw5li3f3BkT3\nXfS2291HZYHleGxV9k2kPTArS7mZrVSS9gT+kP/51Ij4yIjlPAU4nnQWuWVE/Kn2++OBpwDnRsTf\nLKLKixYRnH/++ZOsgpktUB7raWY2Vg60a7pdl+e8Xm2oju6SZeBdZhCPsrW615M7NylTCdCj2/W7\n249bZSt5zHpQtjZX6gFQ5KBdEbQUtMoAWakLOBLKzdlFrlcRvam4ylbs7tjy+hj1tGA38O92AFfZ\nXV3d/ZUXrdS/u6MqreSVseT1OFu95Xs998twv7yRkdvQuy341RHgZrbCrbE/1t0nXQEzG8pG4BJg\nJ8At2kvvQtJFoNna4EC7QeRprma1nFa6ZwPdjGndbtTVwLrbKlx/9ILzokjhryRaLUFRHaPdmwqr\nrA/Uumkrz2lNCrp73alT63inCDoBReVn5ORsre4Ya7rjvXtjoqkE0XnsdJTjqFMNClKA34noJW+r\n7ZpySrPuIG5Si7+K3KJeJlArd2UarZ6ToeW9Va5b3qDohfW9z8VswiR9G7gn8O2IuO+Eq7Mp693x\nW9EEOOu42epwJnAQcDLgrONLbw/APX5s7XCg3cfsluXZV3fdtGLd+DvNid1t680LS6RW5VRiJWDM\nwXPkIJtWniarOlS6DHarq3dDbVqRxi6X04JFkVt5i4KZTsFMUTDTidyYnlvPJdqtMkN4K/Uuz4F2\nmtc6bbcbbPcync2qS0S6L0AU3aC8FxBrdjbyvN1ynHa3H3i1pb6cIi0H55Gzk8+aJgxyr/FqgL0K\nrrltLVglAeDqFRH/CfznpOthZmZmNiwH2g164ezsqa26TbfqjeXuLlN29c4t2lBpye42BveWRbnb\neSVQbbVa3fHMkiiKolKnVHaRyylyHVqU2y2g0yGKDhunO0zPpEeBUKsNrVae57uV1hPQaqVm8VZv\nfupqpvAyOKcy6VhELYt6pQW/91/RVqu3I/JrKSlbUdtP6nVhbwnl+bKLHGgXAgrVMpiDW7NthZk7\n3sTMzMzM1iwH2ll3sqhulFkJrOsxXQ6+yyzgZVfqVlRHL9NttFWlvIhei3aQW5aj16V81ljpbjCe\nE43lR0Ce8CqnQSs6FDMdYmaGYmaGjTMdNk7PsHF6Blpt1A5aU1O5a3buuq2CAIpIQaxaZZXL1Gqt\nsg94pdt8bl9WdN9zKFCod0OBFCyrDLTL1n3odmsvewnMHqadt5pb2Fv0etNXW/JnFehY28xWjs6k\nK2BmC3VT4LX5p5mtFu12u3y6or97W4MXWRuKoug+ImfoLgM8lVmzqxm2CYoiPTpFkbN6V1q+q0Fg\ntwv67Izi1ZbjXpfx6mplJu80HroTRXoUndQ9vNNhptNh48wMN27cyPU33sh1N9zAddffwHU33Mj1\nN27kho3TbJyZYaZT0AmY7hTcMD3D9TdOc/2NG7l+Y3rcOD3NxpmCmSIHw1IK0ltT6dGegtwyHmpR\nIDqITuSfSvNcF600hzhlC3U7/aQcB14mgctjsaPbw7zcv5WdF3ne7vK1WQPelVvkfQivZZL2lfQq\nSSdLOk/SDZL+Kuk3kk6QdMg86x4vqZD0+wHbeEperiPp5pXXT5BUAPfKL907L1d9/KFPmftJel+u\n57WSrpZ0lqRjc6bufnXZs1L2k/Nrj5H0NUkXS7pG0s8k/YOkqdq6h0v6dl7uWkk/kXTUfO99sfXt\nU9bjJH091+M6Sb+S9CZJ286zTuNnsFCStpH0Sknfl/QXSTdKukDSFyU9dtRys9wFyZ0bzFaPmwKv\nw4G22epSCbRXdHY9t2hnRVGkWE45kVd9gTJpNmX36RQApm7UUEgU3WmzmNX/utu1vJuQLIeOqrSk\ndzfSkzKFl48iBfZRdOfQLluQOzMzTG+cZvrGG9PPmV7X8dZUsE4taKftd6fHioJ2W6xrt5hqial2\nm/YUtBHttrrjqtP/000CQkQo3TAoXyNo5URmIdGWUvd0tXrj07st+amFO3KCNXUTn9GdK7z67iuj\nwunmHy9verg1e82TdC/gW/mf1SNiHXAr4NbAkyW9OSJetQRVqB6k/fpYzPkCkPRK4A1Ux2QktwVu\nBzxH0rMi4r8GbBtJ7waeXStnf+CdwL0kPZ60Pz4KPLa23AHAeyQdEBHP7rehMdW3LOtDwNNq5ewN\n/D/SZ3W/iDhnUDmjkHQ/4JPA9rXt7wI8HHi4pK8Aj4+I65aiDmZmZrZ2ONDOyvHQitydm0rgV47J\nFpTZvSOKWWOVWy0R0eqN587X3dVprXqt2ZXW3D4BYy8JW95WUdDJ2crLQLs0MzPDxumN3HjjxhRs\nzxTp0ekwFYKpKVrr8hjvoqDodCiKGdotMdNuMdVusW4qWIdYp3ZqhVYrjxVP19ZRFBCdFBEXEBSp\n2zm9aCMN985BeivfrijHpJdTeqnMLF7uV2Y35ZeBPWWAn/dnzjDXvQWipknYbI2ZAq4BvkwKuH8N\nXA3sDOwL/COwJ/D/JP0mJ9Qap38C3gqcABwMnEEKIqs2Vv8h6bnAG0l/An8B3gL8EGgD9wdeBmwF\nHM6gXSMAACAASURBVC/pkog4eZ7tPwc4hPT+PwT8EbgZ8ErgLsBjgKcDd8jPTwQ+TppfZS9SM85t\ngSMl/XdEfK2+gTHX93nAnYAfA8cB/0f6rJ4KPJ7UpHSypP0i4tp5ylkwSYcCXyEdMxcB/w78HLgA\n2A14AnAE8BBS0rXHjXP7ZmZmtvY40J5D3Ti5bL2GXjKycpleA1Y30xl5wfwzj8kuZo/xVqULdbl4\n2XJNMXuYQacMsHNwXQbp1YRpaUz3OqKTmtLbrTZTBawvoBNBe9161m3YwLoNm9GemmJmZpqZmWli\nRqgFkROhFYiCVv6ZWqgL1Ev6JhG0UrfyFrkJukVlNnAKoCN1E5u1uvspLaBCqF2gooVyQrhuoF12\nKy+D7jxftgikyDdAZn9Otub9FNgjIq5u+N0pkt4F/A/wAOC1kj4SUbtLtQgRcSFwoaQyKLw2Is7u\nt7ykHYFjSH8uFwCHRMQFlUV+JOlLwPeALYD3S7plRPQbf3Rn4NiIeGnltZ9J+jpwNnBzUmB8E+AF\nEfGu2nLfBX5DCpSfA8wKtJegvnci3RR4VKTuMKWvSjqL1Gp+c+CfSS3cY5G70J9I+r47Cfi7iLih\nssjPgK9I+h7wfuAxuWX9G+Oqg5mZma09HuDalYLCMnwrM2N3g+3uPNW9pSvpttM69MZv98ZrRx5r\nnANjcotv2QU6l18UBZ1OQSePu56ZmaEz06HTKceN5+3nVmG1WrTabdpTU0ytW8/6DRvYsNlmbNh8\nC7bYciu22nprtt52O7baelu22GprNttiS9ZvtjnrNmygvW49ranamOscbAet/FDlZ+V1tUDtnGSt\njVptopXW76TG7tyNXhTtFtEWTLVgXRutn0Lrp2itn0Ib1nX/rXVTMNWGvHy0qIzdLoNtaFVvMLR6\nD1ubIuLyPkF2+fsZUosrpJbtOy5Lxfp7GikgBXhRLWgFICJ+BryZdPTvDjxqnvLOA17RUMb1pFZZ\nkbpJ/7gWZJfLXQx8Li93jyWur4AbgGfVguzSm4Cz8nLPqI8vX6TDSJ//DcCTa0F2V0R8EDgt//Op\nY9y+mZmZrUEOtOfoBW5lYD0r6XZ1uaYYr7tsJdjurqFuK27Zsg3l+O3cep2D7OmZGWY6HTpFJ3cX\nL3qDQZWmAusF2utYt34D6zdszmY50N5y623YZtvt2Hqbbdlyq63ZfIst2bDZ5kyt34z2uvUpuVm7\nTajdC7K7LduzW7eDVjd4TnNctyAnSaM9BWpTqEWH1KLdERStnBhtqkVMtWFdG9blwHr9Olr5wfp1\nsH4K1rXTsuVUY2WQrWqQnX62WmWwnacsMwMkrZd0M0m3zUnS9mX2Oe4Ok6pbdv/880pSgNvPBxvW\nafLf87Qe/7zy/FPzlFEudxNJ29R+N876BvC1iLio8Zepp0HZtX974MB5trdQj8w/vxMRlw9Y9ruk\ns89dx7h9MzMzW4PcdbymOzo4Kv+uDtamnL26KDtVp//mbuKhXuKwXuu3yqHJqcN5pVt6mU2cSqt1\nOZa71U6BZCs/6GbzbtFut5lqT6WsewHFVEGsD6ITOUN4mSm81W25Lsqe7rkiUXRSF+3yvSi3YAcU\nxew7C2lMeqqjWsrBbhq/XUTq3p5a32foFCkjervdTo+pNlOtqbzhNu2phjsUEeS5v6AooKM8L3jv\nk1G3o3rqGRBqutNha4mkLYAXkMbY7ksaO9zPjstSqf72I/1RnTlPgExE/EXSuaRW2P3mKe838/zu\nyhGW25o0xr007vqePs/voNeaDHD72r8X42DS+3hwzhI/jF1H31xBGno+SJv5D1czs03NhZOugK1w\nF154IRdeOPg42bhx48BlVgIH2lk3kze91Lqi958c2uVgOefKztOAld3KUyiaE4nl0lJSsdx1vJyq\nqpy2K4JOZ4bOTHqU04qlAc1i/fr1rN/Qpr1uHe2pqRR4t1u0Wu2cJXyKdnsKBUQnypg5B9a5W7da\n3fHPnUitwe12m6l1U3Rmpik6HTqdGYgCtdqgHJAXKct5OSVZynaeHlNTbaamppiaagOiUwTT0x2m\np6e7ycwkmGq3Wb9hPevXr6dYn/5dBt/KE3eX3ednBdqdDtHJ84J3Wt2p01T2MOgG3LaW5WmlvgXc\ngkp2hKZF88/Nl6Fa89k+//zLEMteRHpf28+zzHyZsasB5bDL1aO+cdd3UDkXN2x7HMqodyGnjc0W\nt8lLFre6mZnZGvS+972Po48+etLVGBsH2lk1R1JOrE03XC7HRQcoWqgoA+1e63dRpAm/CorcxblN\nK48nTlNfBRFKAX10Umt2UTA9PcPGjTcyPb2RotPpdieXhNpt1km5a/h62lPtXnfxdpt2Owfa5AqX\nTeXlOGq1ulOJFYhOFLTbLabWtVk3PcX09DQz0xu72y6Tl0UR3W7snU6nkpCtQxEF69evB1q02lNA\nMNMJpmc63LhxmiI6RJ6LfGqqzWZFzs6ex3O3Wy3aU+totdt5zHrewWWQHUF0OhTT0xRl9/ooUplF\nMXsSJUfba92JpOCuAD5MmrrpV8AlETENoHQnp2yNXSldIFbbkTuu+k7qfZc3EE4CXr7UG5PEjjsO\n7jxR3nQ0M1trdt11EZ2GbJN21FFH8chHPnLgcv+fvXcPtm3L77o+vzHGfKzH3vuc07c7fW86UqBB\nq4wikEgwFokiIRCJPAxIWZiAmIhKqQVWKiolKbAqliQBJVARo6KUYEUqFBLLUIFKipdAeKQqoiaW\nPNJNQ9/HOWevteZjPP3jN9ba+17uvae7vd3ndN/xuTVqrn3nXHPNue8+5+7v/P1+3+/XfM3X8Oqr\nL/5D7Sa0K+eKthG5cxo/V7PPkVXng+Wy815FW0V0yYVitIX87A6uB57fp83puYrZECM+BJZ1Jad0\nMfsyxjLkjIjB9Wp2pvPYDlcr2ef2caFmVpdz3d1c+sPPRe4EpKxC20Zbz7HiRR8URAKisdi1qJyJ\nsZqypaSt4SXVGDQV2S7rPaaUCUGFdkqRmHTrnCXV6rwapxlc6aDOlxujedvGvFlo55SquZpGjJET\nJWWKpJoFXu61HDTej4jIPwp8BfqT8J+WUv6Tdzj03Sqj52ruswb9d5/i5b0Tb6AtyV/wSRz7YfTe\nnjVT/Jnkvb7eZ53n/v738r5fR6PD+ndzhX+veOWVV/joRz/6mf6YRqPRaDQ+73j55Zd5+eWXn3mc\nFv1efJrQrpT7s9YXgXz+d7q9COmz0dmFKo7lPFN9bo2u1eSciXWFnAk51ddJBX6t8p7Fs7EGZx2b\n7Y5xs2UYxiq071qvjVGhytnJvM6On9vcVWhr/Ja2rFedn7VinGJd56p1yph6DhFtLz/foohAipRU\nW8hzJoaIr0ZkwUdCUIf0GJIauUVP8EJOGb8Gpmliu92y3ezY7raMw0jf9wxDj7EdmDvdbMSQatt9\nshZiBJMgJSQnfUiRtGreeN/yj997/W5mX1/6LvsOdfvgGZ/1M5+x/5Ot1P44Kvh+joiYd3DfRkQ+\niM47n9/zvHivr/fLnvF59/e/l/f919Cs7C8VEVfd6BuNRqPRaDQ+ozTL5spZPJ//gVI7sXX6upAv\nc9U5l5prDWeRLWeRXSu3crbJRvOwY0r4EPDeE0KoYjSRC4io0O6GgX4c2Wx2bHf7KrQ3DONGxXY/\n0HU9ruux1t0JetHKMObetVTBfPmnVt45Z3MnjQ+LNUIsJZ0PF6jmayroz9Vz/Sy9n5wLMSa8D/jV\n433Q80St0Acf8GtgnhcOt0cev/GY1z7xGm+89gZPHj/m9slTTscTfl1V4IvFGouprfDWdXqP/YCr\ny/Y9tuswtkOMrbFe93K6G+837j8kfLeK8296l31/s26vROSL3+4AEemAX/WMaznHRQ3POO6H6vYB\n8Cvf5bjfyN0P9g+9y3Gfad7L6xXgq0XkbavatcX/G+qXj4G/+qld6rvyx+v2Bo0sazQajUaj0fiM\n04R25SK0S+2fPq+z2C5nY7B8Mee6K2qf271VoJqz2ZfUee+ciUlbxHVFQtD4LnXxNtga0TWMG8bt\nlu1ux2a7ZdxsGccN/Vlo9wPOnc3RbDVbO2dzn0X3XWb3eQ76bOJWsrZm369mp3iOEKtt3kbvxdY5\ncOfcm6r0Z6Ed1sC6Br2X+xVtH1lXzzLNHA5HnrzxhFdffY3XX3+Dx2884emTW07HE+viq9Cuwt5Y\nrO2wrsd2vYrsYcD2A7YbsE7zv++upfE+5ifvvf7GtztARH4TGu30ThXnH7n3+re8wzHfheZDvxtn\ne8yf8Yzj/lvUmEyA7xCRV956gIj8LOBb65cfBf7YM875meS9vN6CPoj4Hnn7P7zfijqNF+B7zzP2\n7xF/EM0cF+B3icjbZYZfEJGvEJFf8B5+fqPRaDQajfchrXW8Ymq19lyI1nXXIl6LwfcEeRWu3GVi\nn92wc9HW5lwjr3yMhCpscyk6m2wMrkZ1nb+2ztH3HX2nTt3jONL1w5152Lkt/Mw5guw8m335ulRD\nMog5EbN+dkiR1Qe814p6SgmKVrDP5JyJQE5Jc7zvrUvkmNyLG9NvQH0gUY3hSiGnQoxaOY8xkXPC\niCUnCCGSUgYE5zq6bqgu5g7n5G6uvX5PjS2YXCg2Y3ImyzsmDTXeJ5RS/pqI/DgaJ/Vvisgj4H9A\nRe9HgF+HVqL/LPDP8jZiu5Ty10XkL6CZyd8kIgMqyp4CXwx8M/BVwJ9D58HfiT+PVko/JCLfiZq0\nPa37Qinl79TPe01E/gPgu4EvAv6KiHx7fb8DfhHwW4E9Oj/+Te8Wq/WZ5jNwvT+KPvj4cyLyXejD\nkg+hD0p+TT3mp4Df+R7fhxeRX4061O+BPy0ifwR9KPA30QfOLwM/F63cfwnw76CZ2o1Go9FoNBqf\nFk1oV6zVUC/DJa6as+nWWViXOqNNvj/LfXeOc+U7laLt4UHbqOM5LzsXxGr12vUdtuvVFKyuu9is\nDuccfd/Tdf2lFRzO5mtvcwOXInypmdY6dx1ixMdaRY+BWAV0TFGNzUTUzTwbFdklUUIk1up7CEFF\n8jnP29m718ZUR3KLpLvKOvceOKRUSElN1aZpJsbEPC+EkNTozXU41zGOI+Nmow8VRNQ4PYOYcjFM\ny+fqPXcPPBrva34d8KeAh8CvrutMAX4M+HrePbjzNwA/jAq+b+Cuffl8jv8cdTJ/N6H9R9CK7E8H\n/r26zvwt7lW6Sym/X0RugN9RP/O73nKugrai/xullB98l8/8rPAeX+93A1+JCus/8jbn+bvALy6l\nHHiPKaX8RRH5KnSe/4uAf7Wuf+DQum7fZl+j0Wg0Go3GJ00T2hVnz4ZiBSlgqppNdTb7YoJWq9pn\n13Eu1WxqjBaUlNVJfFlZlqXGbGkFWOeeO8Zxw7Dd1Jnkuu4ZnVn7ZuMzLhXrdxCXdZ86hldDshBZ\nvWf1K4v3+BAuDwNyvXRt23YUWyghEEMmhIBfV5Z1ZV1XSs4Mw0A/DAzOqci2tRKfMzalu9n08/ei\ndgCknIlRXcljXJjnFRGtauvDhIFhGDlXtxnQ8wBIubTGn8X2/Yq+OqA33q+UUn5MRP4pVOT+EtTw\n6gD8P2jU1++r1cx3O8f/LSI/B/iPgF+KVjafotXX/7KU8oMi8g3cCbC3O8dJRH5+vY6vRo3Btufd\nb3P8t4vIn0Crpv98ve4M/B3gB4Hfc66Cv9Nlv9O1fCaOey+vt5Tyr4vInwS+CW0V3wN/G/h+4D8r\npTx9x7M8+36edR9/qc7ifyPwy4CfDbxU7+VV9IHKjwB/tJTyk+90nkaj0Wg0Go1PBmlVQeXH/tKf\nrdZmBSlZa7KX6nDdFkilELP+Nmeq+DPVcdwawYohxcQ8zczzzDTNKqK7Dus6+s2G7X7PZr9js9/j\n3iK0z27i2s59NjfTa7ybC3/Lf7OajEVW8bl6j69r8Z51XfXfxajnq/3x9wUzpdTjVFwvy8wyLyzL\nQilFHcN3W7bb7b2HAYaUM+uysi4Ly7owTxPzPLFME8uy4FcV+n71d3ncKXFzc8PLr7zMKy+/wodf\n/jAPbm64eXDDzc0NzjlyPud3J1IIpBhJMRLWFb/MrOtC8B6AL/uyn98c0RqNxnNFRD4KfOEXfuEX\ntnivRqPRaDQ+g3zkIx/hYx/7GMDHSikfed7X8060ivaZfB4tvGeEVmOxVPNKLVfrPHK+l+OcKeQU\niVEr3ynEi7gNMaqjeNczbDYa1zWO9N2gGdjmzmDs/Lkide76fnD3xeG8UIpc8rgp59Zsbc8OIbIs\nC/MysywLMd25pcP9QrzoDHbwdR47Vjf0QAj+TnSHVefRvQVrKCJ3Le7WkUvGB4+POvcdgs6Arz6w\nrP4i1tflbMyshJjqDHd1Pc+5urmfM8b1vnI+G7ap0I4xEGu1PoaW0tNoNBqNRqPRaDRePJrQrtxF\nxJ4tzc4t5Hct1iI6N0wBKefoL600xxRJoYpBXyOuvM5pjxvBdR3b7Y5xu6UbBlyvEV3nlvCSzyK7\nqLDnPIv81ggrqUI8X1rZY0ysq6+V6IXTNDFNE6fpBKKf7boO62zN2NZTxhRZ1pVlXfDea+RXFd0+\naEU8eG03xwoYveeu6+g6h+s6KKWKbF06D+7VdXxZmWat7M/zjL3MdlsVyjERa7RYSiquSy5kU9QF\nPec35X3fF9gxBGJ4L42JG41Go9FoNBqNRuO9oQntM2+qaKNKGmo8llG1fXbYLmgbea5xXxTNya5t\n2sEHFYdBRWJB6LqezWbLdrfXiKrOYaxTcVmzuUvJGGPvuYdr67imbqnYPs8oazVbXcxjzKyrr+J6\n4nA8cjgeOByPuM6x2W7Zyhbj7Js0e0iJeZk5Ho/My0K+RJflKpzjnZg16rGeSqbPva6SEYQQ/EVo\n31W0Pcu6Ms8Lp5MK/+5s9NY5dWG/l+GtbeJaeTe5XBzbz47nMcZ761zVbkK70Wg0Go1Go9FovHg0\noV3JSYX2uV3bSLkXM3WvT/ySqcUl0gpQ1/C+BzF0XU/JQNaZ6pubB+yurthst3R9r3PeZyFeW6ZT\nzljrqqg2OHdXydborjtDNs2xVsEZYrxUspfaqp5LwVhL1/fYmrWdsrZ456xiOefCsixM88S0LPjg\n1enbCGINBoutNXt1Uk+sfiXEwCaNQMFaU1vQa9t58EzzzOF44PbpLcfDUavZ08y8zAz9wDBo50DK\nSUV9/abrAwPN3w6GWrH22sY+zyzzzLrMhHUlBk/0gRSb0G40Go1Go9FoNBovHk1oV1KtaJ+7tkWg\niNQB7dpOfje6/SZ72yKC7dTQbBhBipqjGTEYDNvdTg3QtlvEWGLw1Q08VLM1nVHWKC/BOW0pP8ts\noLqJ31V41+oIvvq15mLrijlhrKEfBsRqJJgYQy53JmlrCGqWFrTNOwS9DtdZnLHY3qnYtgbjTK08\nR9ag7eWFe0JejArtqNX8aTpxe3vLG2+8wfF4VKO0dcV7X+PEwFijQpuiXxshZ62iL+uClIL3K8Gv\nrOvC6XDgeDhwOhyIwUPOlJyguY43Go1Go9FoNBqNF5AmtCvnijbV5TtXxa1ZznJpJS/lbnEW4wLW\nqDmYO2+to7PnPOyRvsZjpZQp3uN94Hg6XVqnU8oMQ6Sr0V+Xa9EXlCrIY4x47+9mn6eJkLSKfW5j\nF2Poh55+HC4z1zElfDi3l89M80RMqUZ96XtMZxBrcH1HySqyTbIE74lzwIeVeZ4xxtD3PSklxKpp\nWajifZomFdqP3+B4ONZKt7Z6AxhjcJ0jZf1sRL+/uWis2LIslJxYlwW/6v09ffyYJ3XlGKu7u9Ss\n80aj0Wg0Go1Go9F4sWhCu3KpTlO0HfzSGo46fJdCztQ2b4ilkICEbvtOcE4wnaPrBm2T7nqGbsC6\nDusczjkKUXO3a3RYvqzaSl3uO49Xt/BSCGcn87rmZWGZ58tstdqjS31QIGB0m1PUTO91YX6TUdpU\nq8tW296timzTWbrekbNBoj50SMlc3MXneaZzjr7r6LsOayzTdOJ0OjGdTszziWWZ8etKjGqkZq3B\n2J5+7Bk2A+NmZBgHXOe0mo2aya1+JZdMCoFlnlnmiel05I3XX+f1117jjddeo+RE7xy9czhnn88P\nS6PRaDQajUaj0Wi8C01oV4xV0WYAU6vaUmewcwFyIaaMT5mQCj4lfZ0TPifGYWQzJjYZQDBiscbR\nuYKUguRCyupSbq1jGEZ2uagBWTX/6vuecRxq6zjqAl4NwJZ1ZZ5mpmlScV0FughYYzQmzGqJN8RI\nWCMhBuZ55jSruJ7muRqWRXwMOOewxtD1Hf3QMwwDfd/rHHkMWkUXqjFZjQ/znuk0IQVSCAgwnSam\nUxXbVcBvd1uGjd6Lc47OOba7Hbvtlu12x4MHD7i63mOdJQRPzpEQPMZYgl+ZTkfm44nj4ZbXX3uN\n1199lddfew0pmbHvGYee3nXP4Sel0Wg0Go1Go9FoNN6dJrQrxui3QovC5b45N6VmO8eUCSGxxsQa\nIksMLCGwxIDfRFIG0Pgqazq6LpMLSC6IZCRlKGg7+TCAMbXlW5dzTiu9zmr7etZ273VZmaqb+PF4\nZJqmi3t35xzGWW39tpYshTUElnVlmu87kB+Y55nLEwQRjLMYq0J7GAdtNx96uq4DyqXdu5RMSury\n7b1XAR4D6zyTU2aeTiq2p6mauRW2uw1d17HZbNhsN7qta7vZqAP7dot1hhA9IUId2WaZF06HW06H\nA4enT3nt1Vd57dVXef3VVzHAdjOyHUeGvv+s/ow0Go1Go9FoNBqNxidDE9qVS0VbwFQvcaG8KX4r\npUyIEe8ji/ec1pVpXTj5lZASoGLXuo7O9cSk8VtSFzmDCNZahnFUl3KoadxamXZd96aKtvcr8zJx\nPB24vX3K06e3nE6nS3W46xzW2iqaLZlSjc9WDscjT54+4enTpzx5+pRpnuiHga7vtXI99JeZ7GEc\n6Meevu/o+o5SEqEaweWiFW2thnuiVydwg5BiZDpNzLUlfbvdstlu2e027K+uuL6+5ubmhuuba4Zh\nYBxGhmGobuj6OMOHVSv0Sdd0OnF48pTD06c8ffxYhfYnPsFrr76KFeFqt8Vvt2zG8bP9Y9JoNBqN\nRqPRaDQaz6QJ7UqRu3zqjIrtUvT1eYWYWFbPcZo5zDO308RhOnGYJ8Zh5PH2CdvNjqvdnpv9NTdX\n19xc3TAM453A7Lr6WUKpRmBGDKZuRYDaTh6CZ1kXpunEPE+E4CklYYy2tiNqgKbiNxNzxsfA7e2B\np7e33B4OHI8n5nUhpHiJ0jLWXGbGnetwrsNYdTrXmK3qSu5rbFiN11qq+ZpBsMZgxSDA0Hf07pqr\n/Z7dfsf+as9uv2e32+oDgV2tXlt9KGCtAPfzs3N1Ntds7XnW+11mnfWmFIa+52q/x4poNXsY6Frr\neKPRaDQajUaj0XgBaUK7cm4Wz6VcgrWgto3XOK+QMvO6cjhNPDkceHw48OR4y+Pjgc52DLVSvN/u\neXTzkEcPHvLo5sjV/or9/or9fs8wjhjrLhVoQTBGLpnUaoKmpmgheNZl5jSdmBedr1ZzMdHUMQoF\ndeueqkHaaVk4Ho8cTieOxyPzsrCuqzqMV8M0Y63GkXXdJZbM1ApzzokYssaArQvLsqi7eRXZ8zRh\nxdBZjQLru46xHxjqjPfV9RVX19dc3Vyx2W7oz9XzvrvkgJeiTuUxx5oHnkihvg6ReZpYlpllmVlX\njyAMw8jNNVgR+k7N0LpmhtZoNBqNRqPRaDReQJrQrrxlKpusGzKXFG1CSsyL53A68fjpLa8/fczr\nT5/w+pMniAjWWKwx7DY7PvjoJY6PjswfmHn48CE+aEU5F3B9T0ePM0bN14yprdRUIaot1FrRnpkm\ndfJOKQEZY82bKto+eE6nI08OB24PR05zjfCaJs3WTpmUk45mm3N7u3vLskCtLudM8Dobvi61mj1N\nLJNunbVk21Gco7OWcei5ubnR9eCGm4c3XD+4YdwMiBikOqCHoLFmIQRyTKSkr33wRK8mbdGHGl02\nscwLfl0RYBwGxr7HClgxl4ivRqPRaDQajUaj0XjRaEK7krPOSctlPhsoEJPGcMWUmZeV0zRxezjW\n1uwTx9PEaV4oOdeAbZiGmZIKOZ1bojMgWOvICH2M9CnRl0SXuyrrNerqXPHNKVFK4hz1JefYLtFM\n75QT3ntSzkzLwnE6cTydOE6nSxXbh0DKOicOVWSLVLFtQDQ/PASPSCGXRMmJXBJTdRCfp5l1XohB\nXciNMXSuYzMMbPqRq/2eBw8e8PDhAx49fMj++or99Z791a7mcefqrJ71AUKOxOjVAC3cLe8DfvWE\n1bMuC8F7UooIqCu66+idU7O6UqBk3TYajUaj0Wg0Go3GC0YT2pUYE6DxXjWGWtu3Y8KHgI+R4zTx\n9Hjk8dNbntwemNeVlAuu64gx1iptYvWe0zTR2Q7JqAu5c7iu18ztcaD3uoahq6vXLGsR1IKsIEbo\n+57dfouxwrqu5LVmToeV2S/kUtSYbV7w0auoFlGTs84hSWo+d0Yus+AAhRQD8wIxBawx5KziPud4\nmcdepgm/LlAKfd9xtdux3+243l1xtd9zfXXF1dUVV1d7rq+v6MYeaw0pBdIade46J2LSBwN+9Xjv\nCUGr2TEEYogEv2oFfV7wqydnnUXv+55NdRnfjiOmPhwoKVLU5r3RaDQajUaj0Wg0Xiia0K6kKrSL\n6Mw0F6EdWbzGZR1OWs1+8vSWp4cDIWdSKVjXqSs5KipXXzhNE2SIPmBsh+t6un7QirYf6IeBfhwY\nQ88m9qQ0VAdxoy3lVRT3Q8eOLWKEQsHHSCazeq8rBHwIrCHhYySRQcCchbaRmrmdVMRXIzUBYgzE\nGFhmoBRyVpFdcsKvK+uy4BetZkspDF3HYB0Pb7R6/YGHj7i+vtK4ru3IZrOhSKEYfRiQYlJBHWN1\nLA+E2joeo+5PUfO5vdc29XmeST5SkrbUu8Gy22643u+52u8x9bpT8KQaP9ZoNBqNRqPRaDQa1yeg\nOAAAIABJREFULxJNaFdS1OpoMdo4rgK1XJzGT/PC8TTx9HDkye2Bp4cj4izitFodY6IAMWVIkVMR\nUkis80rXjwzjhnGzBWPpg6cLnj6shDiQ0kjOkWHo6FxH6RzO2lrR7bDOUgR88MgipJxY/MppnjlN\nEyElchF1Ry9cDM9cbRPPKZISaBu6ADrbnUImViGcYtSKdk6UlFSEB08KAUqhc45uGOhdx6NHD/mC\nD36QD33wQ1rF7jr6ztH1HWtc8WFljZ41eM0B97qNIRKq4VlKiZwyJWl02Nl8bZ4nSio4cWq25nq2\n2w1X11c8vLlBKIR1xfuVFPzz+nFpNBqNRqPRaDQajXekCe2KqdvzbHbJ6oy9rCvH01Edxg8HjtPE\ntC6sPmBywdSM7BgTKaqJGbmQS6lu5RrjVURFfBYIJVNSIAXIkgk5sPiZ7Tiw224wovnY5LvKekqJ\nNQSmZeEwTyzBs6ZIFMjWAHX+WgSTCzZnSi4qpNdMikHnsdeFkhMp+FpJViGcYqyRXRrd1RnDOG6x\nW0PnnJqRjQPjMHB9dcXN9TWbzYi1hkLGR0/IAR/93QoeH7XirqZsiZgiMWuLfY6JnNRxPKwev6z4\neUWK4Pqa7913tdJfndZLQaQgojZ1jUaj0Wg0Go1Go/Gi0YR2xVTXcanW4MWg4nZdOJxOvPHkCU9u\nbzmcJuZlZfUBWwqGgi064x2T5l+TS42yUimoYlvIIiSBUhIpaVZ3zJ7VC86A9xtEoO97trW/W98P\nMWfWGJjWhcM8E1Ii5EQUKMZgjEWMxYqppmz6AMALpKhV6RQCJSdi8Hhjak62ZmXnlBi6nrHvGboe\nt9mwG7Zstxt2W91uax62RnnpssaQco3oyrGK7EA4b1OsreN1Xrs6oOvrSAqJWI3QzjPazlhwHdZY\nFdrO4pzBGDWKwxT9DyVtRrvRaDQajUaj0Wi8eDShXTlXtC9F0qzO3zqbfeKNp095crhVV+91ZQ0B\nB2golgrtlDIpZaRmb59rrll0FaPbVDIlFcgJ8QkpCVMSMXqGoWe/3yGmOoxTK9o5a0V7XTjOE0n0\n/MnoLDPWYqzDGquGbqgeLTnjVwNFq9oxaPY2pbAsizqLzzM5ZXabLbLZ0W0N3dayGzc8unnIzYMb\nrq72XF3v2V9dcZb/hUJMgeC1or36RWexYyAkFdkxpcs2JXUfv4jtmNQMrQptX4U2XU8ZC84a+r6j\n6yzWCmJB8rmiXcV2o9FoNBqNRqPRaLxgNKFdsaJSO1NqHJW6jWuk14nbwy3Hk0ZneR/UPE3kUnFW\nJ+wMhToHrWiBvJBKJqSEj+EivDMFUkR7yDXK6vpqJaYEyEVgh1Sdz0Ng9YE1BLIxFCMUY0AEZw3W\nWZxzmKIPDkyBYOQisv26anxXjdoKIUBOWBG6zrHbqOnY9dU1Dx884NGDhzx6+JDrm2utZu82bMaR\nmKPOcCc1OVMzNs+6rlVUn0W2CuxYXcdVYFehHRKpiuzoA8FrtFdYPVb75VVod53Oq4sgRVvyqS7q\nlFbRbjQaLxYf//jH+chHPvK8L+OF5cMf/jA/+qM/+rwvo9FoNBqNzzhNaFectQB1fjhf4qiWZeE0\nTRxOJ6Z5Zl19NQ7LSHUqB61+UzJGwIioMKx7c87EGhNmvVcxTyFRIAZKCpQY6K1VEZ8yBUjVjO3O\nXTxqy3iNtSpiKKVoBdsYrHN0rrvMjZuile0ck+ZTzzOlJErOlJwRIwxdp/PX/cDDBw95ePOQRzeP\nuL655ub6Wl3Fd9vqYG6IKd7NX/tqfOZ1+Sq+Y430Olf4Y8rErJniMWklO4VE8oHkqxN5dSMPIdA7\nh1CwxtBXJ3aNW9Prvr9tNBovFiLyRcB/DPxC4AuBoe765aWUP/7cLuyzRM6Zj33sY8/7MhqNRqPR\naDxnmtCunIV2KplcCjFG1tUzV6F9PB6ZppnVa8U5p1yFtLaA68uiItuoKdlZaeecVaCGgFk9iUwq\nmUShBE+OnhI8Q+fw3pNyBuROoPtQnbsjvs6CI2hruRRKdRe31tJ1Tturc0GyXmNOkbCurPOkIrWo\nYdswDgzDwGa74Wp/xQcevcRLj17ipQ98kP1ux263Zbvb0vc9uT4eCCnoAwi/sK6LCuygudg+BHKp\nIjvnywOLlFRgx6iz3DEmko8kH8hvqmprZTt1PRQV2p1zOGMQpM6+58vKuQntRuNFoorsvwp8gLtB\nnHLv9fuEL3zeF/AC8nF04KnRaDQajfcHTWhXjNHWcUGFcYgqbpdlYZ4XTtPMsi4Er/nPJav0pGSo\nwlpqFVvdv/Vchdr+HSOr92AtsSRSycSSKSFQoqdEz7puCDGpoZoIuRRCdRv352ispCKWLGAKUg3X\nEMFai7UWSEhWQ7SSMjlGYvBE7+s9FgTorGW72XBzfcPDhw956QMv8cGXPsgHP/Ahxs3IMAwM44Ax\nUh3EV0IIrGFlXVfmZWENKzFqRFiMQWewS53FzmeBXd3GYyKGapwWIilEcog6p31vpRi14g7Y85x6\nzjUSLGqbftE580aj8ULx21CRHYD/EPgzwLHu+9vP66I+uxjgo8/7Il5APgK0Sn+j0Wg03j80oV3J\nVbSlnO5Etl/xQWOp8qVd+c7R2yAYVAyKxm8jCEbQeeKsztqhzi8vy0KiEHO+zC5bMpaCLYIRgxWD\nqS7iWtUutQocSTlTsrqYX/4RQYpWe3NMJImkdSWtnrR65uOR6D2mwND3DH1HX7f76ytubm64vrnR\n7fUNu/2ObugQK6SSWP1CoWgedlhrLvaq1exaydYHACqgzwI7V2EcaiU7pEhONf4sZci6LtXplOoD\ngUDwK+uysEwz0+lE11mis8TOQcnkFCHfzdU3Go0Xhl+IPvv7Y6WU73jeF9NoNBqNRqPxvGhCu1Jq\nZ2NKd9XnddUKbkw6k52LxnbxFrFt4dIqftcxrgLyYhhWq+OxZEJUJ+6QEoMzDNbgnMWKxZjzVr3D\n81mUR23Fzpcq7llqm7tIspTIgJ8X/DSzThPT8URcPQIMXcfVbs9+v2N/tef6+prrm2uub27YX12x\n2WzZbLa4oQN0Xj3kUDO8V7z3+Cq2dUbbX/Kx08VZXCvuOeW7dvEUibVKXfLZkv1unYV2ipEUtH3c\nLyvLPFWh7eg7S3SutuVXV3VjP7s/JI1G41mce6Z/4rleRaPRaDQajcZzpgntylnAxktFe9WKdlSH\n8XwWibVlWarRmAGtYFOlr6CxU6WQS4JUiEHP1y0LNiV8jDVrOsIw4MZe47nMXTXbyFsq2imSajY3\nRe7LbBX2WdvEUy74ZWE6HpkOB/w014p2Yeh6rvY7Hj16xKNHj7h5cM3VzQ3X19dsdzuscxjnsK7T\nKnQ1J7szPtNq9rlVPMRQK+3nXGydH1cTtPp1PS7FqI7s53Dxs9Cu1e1yrmjHoMZttaI9jxOx02p2\n1zk6Z3Vu21lsE9qNxotGj/4JD8/7QhqNRqPRaDSeJ633tnKOzzpXalevFdxSCq5ztdqrc8t919FZ\nixXBcBaLiZLTPaOupFXaexXtdV20Su69zidXQzVnLEM/MPQDneuwxiIi5JQJIdY5cW2nXhedj16X\n81pY5ypKTxPLaSKsnhxTbRfXKvYHHj3iwx/6EB966YN88KUP8IEPPOLm+obddkPfdxgjNRc71ocM\ni65V18VZvH6fQgh13joSQyScZ8jD3dcxqMO4ZoZrBjbVpI2UqhHaSlgXUvDkFJGcKSkS/coya+v4\nPM2sy4JfV2KIpJT0YQcXv7lGo/GcEJFvEJEsIhkV2QL89vO/q+u/qcf+cP36T9evv1hEfq+I/ISI\nnOq+f+htPuOXicj3ichPicgiIq+JyJ8XkW8Rkd0ncY0bEfltIvJjInKs7/8zIvLr6/6vvHetv+A9\n/QY1Go1Go9F4X9Iq2hXvVwDW1bOsK8uqohKBvu+5utrjjGORFVsWAgGKRnrlHJFSncaLaL41WeO3\nEGLw6vrdddga3ZWLVsJVZPdsx5HNMNJ3PdYaKOiMs/fM88I8TUzTzDxPLPOMWIupKzpLcpZkLZ0x\n5BCQUuicox8GjfDqOzbjwPX1FVdXV1xd7+n6Htvpj0CMgYRW9jNcKtY6gx0ulemY7lrBQ4x3LePx\nXlZ2SqSYa153QTJaea8t91rJTuTgieuCnyeiXykxIEUfWsTgWeeJqXPkNEKp5mjG4Kyt3QXP66el\n0Wi8hbuZljd//db9FwdyEfk64H8ENm9zHPWYAfjDwC9/y/6HwM8Dvhz4zSLytaWUH3u7CxORjwB/\nGvhH7r1/A/wzwFeIyK8A/ot3uO5Go9FoNBqNT4smtCtrdeQ+V27XVVulQeiHgX0Bg8UUgySQBCmp\nAVmOiWJAjDZzU4SMkCVTEEIIWL9ilg6XC2IMUlvEnbUMXc9m3LAZR/quu7REp5rlPc+ziuxpYp5m\n5mnGWItYgxhLZ00V2obOGJzo3HjvHPvNhqv9jpurPVe7HZvtlu1uw2a7ATnHmanLesyadx1yuojp\nWJ3OY4y1HTy+ae46nd3E64x1TJlU58lLrhnforPs+oQBrWjHRPaecBHaCyWp0C45Eb1nWWaNXSsZ\nEbB1lj05p6Z0jUbjReD7gb9cX/84+if99wO/794xj9/ynp8G/CHUkfy3A38WSMCXcedSDvDfoyK7\nAH8d+E7g/wQeAf8K8I3AK8APicg/WUr5+P0PEREH/AB3IvtPAP81agv+EeCbgK8FPvhp3Hej0Wg0\nGo3GO9KEdiUEHSn04ew47vEhUIDOObYbg2SQJEgEkwvBQ8iZTKh1mprzLEIGFdsIqc47m1UdvJ3r\nsM5hraW3lqHr2I4j22FkcB3WGCiFFCPrsjKdJk6nY22jnlim6VLNNtZC57DJ4TqdX7Zdx1jXzf6K\nBzfXPHxww/V+Tz+o63g/dISU8MHXFvCATxGf1KjtEsmV7uav1UU913bxcBHil2p2TKRU1LQt1Xix\n8/y6QEmJEiMpBuK6ENdVt36FFDEUemdwVjBSIGc1SKufnXO5COymsxuNF4NSyi3wN6CaQiqfKKX8\njXd4iwA/Hc16+vJSyv3Mp798OUjka4GvR/92/SHga0sp8d6xPyQifwH4A6jw/k7g177ls/5t4J+o\n5/iuUspvvbfvrwH/i4j8HuA3f3J322g0Go1Go/HJ0YR2JeWs25QIMVZnbW2nRgTrLH0/wAZcMQzW\nscwTi9E57VQyCa0Ol1Iod/bjKhJjIAaLEdHZbmtwAr01bPue/TiyHQd6Z7FAiRG/LszziePhlsPt\nLcfDgdPxwHyacH1H1/V0fYftHGPfsd9u2G9GdsNYhfvAfrtlv9uy324ZxwGps9ghBEKKdw7iMRDS\n2Q29VrFrBTvdbwlP6d5sdqji+rw/U4p6wZn6C7fkXNvrM2FdVFgvC2Gd8etC9is2J8QKbuwpnaNz\nHcO4Zaxrs9kwDCP90ONcp9V8Y1S9NxqNz0UK8C1vEdlv5d+q2wD8+reIbD1JKd8rIr8G+BeAXyki\nX1BK+fv3Dvnmuv0o8K3v8DnfAvwqtDLeaDQajUaj8Z7QhHblLLTjRWirMVoxDqzFWsvQC65YRtPj\nXY8zRt3ksrZek6O2k5d8bxARSq3MivFYEYqzmOLoBAZn2fRdFcgDg3MYIKdIWFeW6Sy0n3I8HJiO\nB6ZpZkwjUgrOCBZRR/HdjgdXV1zttlxtt1xtd2yGnqHvGYceZy25pJoVnvUe4z2hneNFbKcqtGO8\nV9FOWavXMVSzs7Mje9F9OasPuhgV2ucktJQpMRLmmeV0YjkdSX6h5EhJAVMSzhps5zDG0HcDfb+h\nH7YM44ZhHOnHga7vcV2HbUK70fhcxwP/8zvtFBELfCX61+ifLKX83Xc51x9AhbYDvgr4n+o5XgH+\nsXqO7yulvK0TeillEZHvA/7dT/02Go1Go9FoNN6eJrQrby+0A7YTjNNZahkcYoEeYjdgAWq1GiCF\nAiWScwFzdtXRLOyUInhwItB1mJJxCIO1l4r2bhzfVNEOfmWeJg6HpxwOTzkdj5yOJ+Z5QShYY8iu\nwwqMfcfVdsvDBzfcXF3x4OqKm6sreuewRqvoUFj9SsqRUCvZanimQltzs++q2fdF9qVyHWtFO6jY\nTjFRStG27lwwBowRjSeTmj1eHcbDvLAcjxyfPiGHFWfAmoKz+n3o+55h6On6ka7f3Fs93TDQV6Ft\nap52o9H4nOUnSyn+Xfb/DGCL/jX6F59xrvv7v4QqtOvrM3/lGef40Wfs/xTIwIc+ieNsXe8XPv7s\nQxqNRqPxvubjH/84H//4s/9/4f27/Qrx4tCEdsUYTTozVrOszxXTmBNlXSkhIkkwCUwUckiUAp3r\n2W53dOPImKMaiZVEzKmaiyWt7MJFlJcUIEYkRxyF3ho2fc/YOTpjMGRyiirGjTB0jt1mxIgw9D0+\nJvb7Pfvdnt1+z4Obax49uOHhgxuurvZsN+fILgNyzgjX2LGY1C383CLuY6iZ3oFYRXbIb567jrEK\n7VhbyeM5titBzjVPXECEkgoxBshBc7H9SvIraV1Zp9OlVdwZGDpL3xndViHdDwOuG7HdiO0GrOtw\ntT1eLgZwpr5+P/2S2mh8XvFWc7S38uje608849i/9w7ve3jv9avPOMez9n+KvMenazQajUbjfcD3\nfM/38G3f9m3P+zLeM5rQrhh7FtoW42xtTxZiSvgUCCmrCVoSbBIkoxnbTluZixGKhWKEWFLNolYH\nc82c1qisHKHEAEmXvQhtjeDqrKjzdjUH65xhM/Rc73bsthtS1vnvq6trjem6uuZqt2O301ns7XZk\n6HqscyCFlBNCIZVCyUmFdQoXgX0W2SF6fThwie+q89jxroqtojtSkraKa8RWQTAa32VEI79CIPhI\nXD1hnYmLzmaXsFLCiiPTWcu279iMurq+p+8HuqHHuAFxA9gecT3WdRhnL+L6Irhti4FvND5HSZ/C\nsZ9T1ociwksvvfTM42wdSXq/8eEPf/h5X0Kj0Wg0XlC++Zu/ma/7uq975nFf8zVfw6uvvvgPtZvQ\nrpj6C4+xBmsNxmnlNPrAsq5Mi0ci2CS4ZLBY+r6j6zo1JOsddtBtJHGaTrpmdQovORHWRC6ZHHuN\nskoBS74T2p1WoU3J5JQxAr2zbIaeXLZY5zBOP/P6+oab6xuub27YjCNd3+n1OKtma9X0rFQndIq2\nr4dUhfW9rU8en8K9qK46o312FA+RGGp8V0iag12H0AVBi//aCRCyJ66BZZ7x80yYJ/w8E5dZBTYZ\nR2F0lt3Qsd8O7Lajzl/3PV3fg+3JtqeYnmI6cA6xWtHG3MWaNaHdaHze8sa911/wjGPvK7f777tf\nNX9WfNd7Fu/1yiuv8NGPfvS9Ol2j0Wg0Gu8bXn75ZV5++eVnHtf3/Wfhav7/04T2WxBjsM5phXXo\nWUOkFIgxgs/EACGAFUspmontSo+xDtf19ONAL0WNuoyom7Yx1bSsYChsh45N5xitYbBCJwVLwZKR\n6totRUX2brPh4YMbNmGrs8pdTz+MWs3eX3F1dUXXdZrhbUTjdUrWeLCsmdQ5JXLSCK95XVjWhcUv\nl+q2Cu54z2H83B4e39w2HhMl5YvAlqJz3zkmhExG8MvCMk3Mpyqw15m0LCS/0DlD1xlG17EfO/bj\nwNVmZL8dsV13WcV2JHEk40hiwZ6XubSOc942Go3PR/5fYAI2wM97xrH/9L3XP37v9f9x7/XPBf7w\nu5zjSz+lq2s0Go1Go9F4Bk1oV2K662R0rmOz2eBTImbwKeNDJMRAyoHoIyRIKRNSYg2RIQWGHBlK\nxnYWEMZhpOsdm2Fgvx0JVztMKWz7nm3XsRsGtp3DlUxeZ+LcY/se02ur9HYcePTwhiyFkBK25m87\n1zGOo0ZeOYeIUEompXPOdEGKVp1T1Lb1GDzer5zmqa4TqSRSyWTUKT1XUZ6yiuyzuM4pQy4Y9EGE\nFKAIAuSUNSrMqznaPM0s08w8zyTvMbUF3jnHdujYDR27wbHf9Oy2A7tNz2bo1eDMaZU6G533zlIf\nHNSHCBiDMaLXIPp1o9H4/KOUkkTkR4BfAvwiEXnlXZzHf2PdRuCH753jYyLyE8DPBL5eRL717ZzH\nRWRA87objUaj0Wg03jOa0K7cCW3BdR3jZkukEFJmDZFlDaRFDc5W78lezcTWEOhWr0ZoJZMo9JsB\n11mGccB1W8p2Q447cgzYkhnEMJjqOH4W2stC7HuEgrGCdYbtZuCRPKDfjKRSEGOrUZvF1tfGWHLJ\nhHPOdU71LrQqHkLArwvrsrIsM4fTQdfxQJZSZ51VyOacKLkK7ksVO1JywYrFYLAXoQ3UOfWwrkyn\nmek0sS4Ly7KyLgukxGCE3ghj59iOA1ebgesqsLejY7Pp2AyOYrRijTEkETQoDLIAUqv11aTu/LrF\nezUan9d8Nyq0e+B7ReSXvTVLW0R+A/DV6N9If/QtGdoA3wN8B/AR4NuB3/I2n/O7aBnajUaj0Wg0\n3mOa0K7EqL+/FcB1jo3ZUIywhsi8evp5xduVnAvBe/y0YkLEdh6zdviSiBSyUUM0120ZhpHdfoMp\nGVMSpiR13M5ZV8lsO4srSSvai8Nagd5h6NiOI/1m5IobsghVPgPUFm/NtfaxtopXR/Gz/BTU/n5Z\nVuZ5YjqdeHq45entE54cniJGcEN19XYWcqaUOs8dz2I7aiu7BWsNrgrtkgEp6qy+eubjkdunt6zr\nil+1eu7Q89u+Z3COzdCz3264udqy23SMvWUcLH1vKQIZUWGNZoMntGouwqWyfXGEP7fJNxqNzzU+\nKXOzUsr/WvOtvx74xcD/LiLfCfxfqKP4rwV+fT38dd5eRP/eesyXAP++iHwxmrv9UVR8fzPwS9GI\nsHOL+ueU+Vqj0Wg0Go0Xkya0K/OyABBzJuRIKCpkne3YbfcgFofDZI2wEmNIpZAo+LiS50IokWWd\nmecN67onhB0pB3oLvRV6K9icwK+wevCeEiNp9YRpIUwz9uEDLJnkDMkYotFtAkqh5lXnmnUdiTES\nzq9rRbvUY8iF0/HE7e1Tbp/e6vZ44HC45fZ4wHaWfhwYNgNdjQOztT2bok7ixloM0InBieCA6D1x\n8YR1ZZlmlsMRfziRpiOSC46C6Xo6a9mM2hq+GXvGzYZ+HFR8dx2mM0htF6cauBkR9MfSIej3nPMx\nVrToLVzm3RuNxuccn8oTsn8NDZv+FcDPBv7QW/YXVDR/bSnlHwjeLKUEEfla4E8B/zDwL9Z1//3/\nG/C76xZg+RSur9FoNBqNRuNtaUK7Ms36u1UqmVgyqS5nO3Y7yzBusMVAKtpSjbaQBx9YgyfkwLLO\nWGsZp4EQVlIOZBK7sUMGSzc4JCdknZHThJwm8upJ00o8ToRlVVO0zmI3A8k5knNEcaRSq9hRW8TP\nBmYhhYu4zqWQcyaHfGn9Ph6OPH79Ma+99jqPHz/meDpwPB05no50fc9mt2Gz2zBsRnVQ73q6rsMZ\nwYgKb5W94ARsKSq0T0fmw4HpdGKZZsI0k6YZbIdzHeLUBX2zGdiMA5vNwLAd6MYB1/fY3mE6QZwa\nm5lzldoYBIf6ujsSNS/bqNAWIxg51/ab0G40Pgc5D588+8BSVuBfrmL5G4EvB14CTsBPAN8PfHcp\nZXqXc/yUiPwstOL99ajgXtHK+B8spfxXIvIv3XvL00/5jhqNRqPRaDTeQhPalXle6m9/WqVOqHO4\n7Ry967CuQ4oQY2L1njUFwjGTwsoSV0qqjtypMAw9ManIxhQkj3QysOlBcoR1huMBnt5SpoXUT4R+\nQ1gDrrPE7Yi93hFLTwKSMYRCjdnS5ZPHR8+avLqfIxgEqkN69GradjwceOONx/z9v/cJXnv1VU7T\nxDSfmKaJYTOwW/YEH9iExDhuyKMKXes0T7y3jk4EWxI2Z2zJLN4TTgfmx28wHQ4sq8evnrSuuHGH\nc07n3MeRzaau7cgwdvSbvraTW4wTxKkzu1idNxdrMLgqsy0ZpyK7CvFL97yUJrMbjReMUsq7OhSW\nUv65T/O8PwD8wKd1Ufr+Gfiddb0dX1K3Efhbn+7nNBqNRqPRaJxpQruScr5sQ07EHCkIfRkwYjGd\n4DrHuBnYXW0JKZClEIn47ImrvjfnSApCDF5nub3He0PohRCELkXEr5Rlhmmi+ER2geRWkrXEmz3h\ndMIsMyFnQikEUKHtgwptH/BZs69D9pQCRgSDgQLrurLOC+u0crg9cjycOB1PTKeZZVnwiyesQXOv\ne4+vlexse6QHK07vGYNkKGRSjOToiTHgp4k4z2S/QPTYkhgM2M7heocberpNzzAOjEPPMHR0ncN1\nDlN7v4voyiIkEawYirEqto1FsBgcRSwi5k5si6Egum0z2o1G473h19TtXy+l+Od6JY1Go9FoND4v\naEK74px+K5JficEzrysxJ/oYGGIkpkiKAWst2/2WIgXjBNMJtjP4aSXMK2FasMbSO4e1WtzJOeFj\nYFnBpEBZV4z3dMFTEuRQyCaTxokwTZhpgmnGp0zIhZALsVa0k4+ab10iqURKyVqJL0KmUGJmmWdO\ntyeOt2pQtswLOWZEDM44OtuR+6yVerHqIp4KRgyd7Rj6AScgpbbJx0BeZ9Iykxa9xug9hsLYdQxd\nB+gMuRm32GGDGTd0w8DYd3TOYq0goi7lKWdiFshGbcWNACqipbqqUwW2MbrVSC89hrPQ/pRGPRuN\nxvsREflpwEdLKekd9v8OtKJdgP/us3hpjUaj0Wg0Po9pQrtirQX0N60QAvM0sQbPEINmSqdIAY3d\n2m1xvcU4g+kMtjOs3cRqLUsRpEDnHLbmPKesWdOLzZjgMetK5z3FB4pAkUwmEscRM03INJGnCf//\nsXfvcZZmdX3vP7+1nufZVd3DgDrADI4YFY+ioBFE8YqKiRNRYzTJwRMiAvFgJEaPYrwl4ng0ejRe\nOF6OIygC6vHCQY9RxEskokRg8IZ38S5DCwM4TF/23s/zrPXLH2s9u3bXVFVXdVd3VXeomHjSAAAg\nAElEQVR93/N6Zu+q/dSz1q7pPbu/e631WznTT0EbIw8jeah7XFsiWyKTASe7YRhpTCwWC86dPct9\nf/8uzt5/lsWFBWnMdXuuSNu04NA0kcbKqDXZiQTa2LDRzrCcyONQ1novlvTnz7M8f5b+/FkYB2wc\niEDbNcQYibHcWrcB7QzrNghtS9tG2iaWAms4GS/r4LNBttWe3NOccLOtbcssREIMhPXAXYN22dVb\n+2iLyCV9HvAMM/tR4DXAW4AWeHR97En1vD8AXngE/RMREZEbkIJ2NQVtA9I4slgsmC/mdRutUtW7\n7Uq17I3Njm6jgeCEBmJrzENkYYEmgyenm3U0TUMIAXdnTIlln4hDCdlpKBXHszvJE4mRcT6HC3Py\n/AJpfoHenT5D72WrKx8TPiZySuSQ8eAQysrynB2yMw4ji/mcc+fOcd9993Hh3Jx+2YND27QYECzQ\nxBJimxAJGJad4EZjgTY05JxJY9m6q5/PmZ8/z/zsWRb330cXoKv7Y8+ahm42Y9Z1dF2HNx3etHjs\nsKaG5hgI0WrYhuROcC/bhq2OusrcQhnNjoEQSyG0qShbsFBXok8hW0FbRPblvYCv3OUxB/4I+DR3\nH65dl0RERORGpqBddW35VbRNpImRGAzDScPAwjNDv6TtOtpZR9t1WDA8JZoQOLUxo82wGRpuajfI\n2YkbHXFjRtzsaEIihoyTSA4jRk9gaYHgRsCxnEk50YwDTd/TLJeMFkmhITdtmTKdneCUwmcW8eA0\nMZQq5GmsIXvBYj5nPp+zmM/JOdHNOjY2NsFhGHrGoWcce7KX0XDIRMDHgWE+Z97cz9gvWF64wHJ+\ngX5xgbQo08W9bvnVdi2bbcPGrGXWdcxmHbOug9isDovTlO9SLdzqPthhtR92mSKeiSQCuOEZcigf\nHKSQiW6rtdhmde9sXw/cIiJ7eiFwH/CPgUcBDwVOAe8Efhd4OfAidx+PrIciIiJyw1HQrraCdkMT\nQwna7oxDT+7L1lntrBT46jZmNG2D47Qx0MQZOTR4u0HeTLg73kZoYrn1AfKAe2Z0SnEzsxK0jTp9\numzb1YwDbd/TLBblmk1LbkcsNFguo8ABA7OyXDlamV6eMsOyL/ta15A9X8yJ1nDq1CanNk/TtTOG\nfsnQL+mHshZ9qKEbHB8H+vkF5u4sF3Pm588xP3+OYbnAciLksYyIx6asv97c4NRU8Kxr2Zh1WKgj\n0aviZVZnhk+Budyur8XOFoGAT0E7QwoQ3IleqotbXco9he1Q/xER2Yu73wM8vx4iIiIi14SCdnXx\niHYgWsCAcRjo+57l0NPNOsZhk5QGZhuzUkm7KxW7Q2fYJoQMnmEMMFo9xgVpKNO6kxtDHdEubTi4\n4w5jHmnHkbGOaFvTQtth40iIhrnVEfBAqGE1xEAeE0xBez5fOxZsbp5i1nU85MEP5vTpm+iXi3L0\nCxbzC8zn51nMM+MwlBHthcPQM79wnnNnz3L+3FnGfknXRLoY6ZpAjA1dN2Pj1GlOndpgY9axOStB\nOwSjFBYvHwaUMfNym4Dk5X62ACHiFnAC7qW4Wa5r3KeQndwxnAhEs/K862i2graIiIiIiBxHCtrV\nVCG8aSJdW0LjouvIKTEAnkbGwejrlHLPiTzrcM8YmdYamtCUtc8hkkIgRyPFwDg0jEPD0Jc127Ns\ndDnQEIgpE5JjybHTp2BjBl0LMUIdGS6HYblW2fa6JnvMJId+sWB+Yc75c+c4d/Yci/mCcRxxd5oY\nmW1scPqm0zz4QTezXLQs28hyEbA8Mi7nLFIi9Ut8HMgWGENguZgzLhfkcYCcMSIxBpqmJTYNoWmw\naQ11XU8dY6CJRhugiWXkOVO28MoYo28d2QLZQgnaFiFM90MZsSfUgme1UFothBZCYP0fERERERGR\n40ZBuwp1HXATI13XsrmxwdD35JwYxp7lMkDOpGFg6U5KZZutNA6ksSW3M6yZEVsjxIbYtVg90tgx\nDjPGcQObzWibrhxtR5syXXK6lGlPnyLefBPx9Cni5gybddisxdqm7C2dHFKZxp5SLkXRSJw/f4Fz\nZ89x9v6zpcr4fEEaEzFG2rZjYzbj1KlTnL7pFE2AaE4g0y8i5k4eevr5hTpaXEaNx3HA00hjBm1L\nV/fa7rqW2LRYCGWU2kt/smfcE4FAEwIbEULYCtSZwEBg8EAkMBJIBBJWR7fLhwmY1eriddzaysh/\nXBVEK8XRgtZoi4iIiIjIMaWgXU0VsZsmMus6Njc3GMeBYRzolwtisFKJe8hlH+shkFMJ2WlssQ2n\n2TC6piHEQNu1tJubtKc2GceBlHrSOMBsg9h05WhntCnR5kyTMs3mJvFBD6I5vUnY2CDMZljXEtqm\n7hudy77ZOTOmkSENjKnn/PnznDs3Be376ZcjOeWyn3fXsrGxwalTm9x0+vQqZJsn5rEheCYNPcNi\nDl52pra6LhogBiPGMsrftS1t1xGbqT+shexyBDPa4MyiEaORQyDXddiRSPBIIGIeMKwUQCOUAuJ1\nIbYFq2ux2Rq7tnBR9fHpeyIiIiIiIseNgnZl4YEj2mkcWS6XLNqOtmkYhoGUy3ZfGGX6eBpJaSBi\ntCEyNh05J8zKdPS2bWliIOdI9g6ahhAaYmwJ7ayEbM+0ORNmM8KDbyKcPoVtdFjXQIx4MLwG2pwT\nKY30Q08/LFkOyzKife48586e4/zZ86WomFMDcqkIvrGxwcbGBp4GfBjIw7JM78bxYWBcLjG2gnaI\nkdA0xNjQtC1d3b6rbWdl27IY6xpsp8TycgQyjRldhKahTBsPgRQieIMTcW/quuxpTXYZyaYWTTMz\ngkEEGjOixTqqHVeB2+rou4iIiIiIyHGjoL1St5AKkaZpS7GvjcSpzZ5hGBjHkcWiVOvuvSflsp/1\nOIB7ZmGRQMAzpJzpU2I2jszGkRBKhXCz8gtv2o7upkC3sUmD0+I0gLUNvjkjb26Qm4YRL6Pmi0xK\nThpGUj+ShpFh6BnGUjX8/LSd12JB3/erIN80DU3bEGMs08KNWngt4TmVtdc5YziNQYxNWWcdAqFp\nyhT42BCbpnxg0La0TUvTNjRNuW4TIk0oVdobKwXLYg3JAUq1caZQXKuO16JnXjY2A2xVTdyCEc1o\ngM6MFqMl0hLLmvYpXju4+Q7/HUVERERERI6Wgnblq6AdiE1LNytrj/thYBhH0pjqVGUnjWVUO6eM\nu1MGuA0ypDEx1IA9G0e6YaBtG9quKVXKY0PsOmYbG2xaoA1GE0oBMY+BIQaGJpYiatnpx4E+9wxD\nIvUDYz8yDkM5xnJcuHCB+WJRPghY9nRdoG2MtinBOMZYp8bXkeec8VTCtnkuFb4DpbJ4V0avYyxB\neytwx/K9JpYAP+03Hst07iZYCdsBYt2KK9YB52ysRp/dS9Aulchr2F5fl21GY0ZrRmswM6O1SEug\nmQJ7DdmunC0iIiIiIseQgvZKCYIhRJq2xaxUvB6HErJzynh20jjSL5aYg6dcvm+OZyePmWEY6YeR\nZQ3Zs75nY3ODzc0NjA28Bu1utsHpjQ2aGGibciQcPJFyCcHjcmAxDsz7nn45MPTlGPuhFmIrx4X5\ngvl8wbIG7SZ2mBlt29LWkWeb1jO713XeCXICzwQy0YyuiWzOOjY2N8vU9hqyLTQXVT/fCtllK7Sy\nHdo0or01qh0oITvUUG1ro9m5jmb7VFGcWlXcyoh2a0YHdFgN2eWgbhXmGtEWEREREZFjSkG7SvU2\nY6tRVywQQkPTdHTdBl030LY9bdszjomUR5KDp0S2zGgJGLb2jc5ezhsGxr5nWC7Jmz30A35qxHMq\n+3Y3gbaNJJxFGlfHfDmw6EvQHoaRcUikYWQcR/JYpq7ncVxNbR/HUgTNYFUIre3aGorL1HH3XKe8\nD3hKRIO2adjoWmazjtlsxmzWEWILIZaQXYN6qGG7bSNdG5l1kY02MGuNWRuYNYE2GjGW0IwFqIE6\nY6RShq1OG69F0Oo5tlbsLNa9uGOogX215VcZzZ7mjmtEW0REREREjiMF7Srnclu2qypfu5ewHWOz\ntR1XO6NtS/AlGT5Sqm5nSCkDCWcguzOmXEJw39MvF/TzjmExZ1ycYlgu6JeLst65jTRtJOMsx5E+\nlRHx5TCyHAaWw8g4ltHzlMttHlMJ+CmVML8adc+YGU1smLUdXdvSNLGE5FXQHhmHHs+JYMasbbDZ\njNlU8KzrsNDU/a3LHtcxlGuEKWh3DRs1aG+0xkZrzFqjbco08hCsViYvW3ulWvgsew3bW6XX6vrs\nGrKtjI6HOv082Nb67lINvc5HVx00ERERERE5phS0q1RHR3Mu97OX6ckWIiG2tG1ejWaXY8QdsmXw\nkZwcyLiXkeyQEmEYCXFJ3za084ZF27CczxhOLVn2Cxb9kqaLJWx3DRkvU8/HcgzjSD8mhjGRc63t\n7eDueMr4mPGUGesa8jLKXUqPlerpHV3blSrhdY2252l7sr6OaBuztiV6ppvN6GYlbLtFcg3K1JAd\n63Txro3M2oaNNrLR1aDdGBsttKFs62XB8LWgneue2cnLkTHcDNamjD9gRLtOO1/VF59Gs73cVdgW\nEREREZHjSEG7Srkk7Zy9VPhOmTF5GdnGIIQSukMkhLJFV7ARCGUvaPeyxjonzDKWDLMRMyMNpbjZ\nECOp70l1D+x+HGi6Whm8K/8pyhTwxJhGxpRLP3Kmdq9MuXbIKeFjGdnup6nldTo57pgZsa6pngJ2\nTolUC6iNfU/OZep41zQ05jRdS9OUtdlep3TnOr07xIZY12Y3TaRrG2ZdU6aON9A10EWIoe5JboYT\nt0K2B7KHWmk8lOvXUuy2VnU81MOAus12ed4Oud7x6UEREREREZFjSEG7GmuSTSnXddWJsRY2G1bH\nFL4dn6aWu4EHnIwnXxXoCqGGxmBkd1IOWM4M5lgoU7jHPBLbpgTctsHM8Dpv3ach9eyEMoxNXk1r\nz6VIWz8yDiPL83P6+YKh7+todS2UlspzGPqexXKB58SyTlkf6oi21TXaHqwWPytVwKfRaKcWQYuB\nEGPZ6quJtE1TqpQ3gTY4jU2bdZVR6rIuexrJLke2WNdll82/zLb2z542/nLy2pruMqLtQJzWZNtW\nwNY+2iIiIiIichwpaFdjmoK2M4yJcVwP2KkcY2JMTsqUgl5ew3YtoJY9kz0DjtdFxpaNHDKWjZQD\neNkSbMwjy9QT27aOaLdl2rQbpQZ3YZQ9qclOrgE8pcSw7BkWpRr5cj5nuShB28e8CtkpJcZxoO97\nlosFnsZy3nJZRrRTIgCxjZgHvG3wGGvQrgXLzGq18UiIkSaWrb3aJjJrIl20ErRDJloZSWc1XTzW\nI5Rp6B7Kuu/pGVpde82Un8t/gzIJvxSos1q93LES+53VVmDroVtEREREROS4UNCupqnjQ8oMY6Yf\nUq30vRa4x0RKeduIdigj2rl8P3nGPdOEsk7ZgmEZcjBI4Ckw5hHGAH0kti2xa2naliY2dBZoQ6Sz\nWKZTB9sa6Z6Knw2JYb5kuViymC/o50v6xZJh2UP2VQXyErRH+r5nsViQxshysaDvlwz9EksjrVHW\ncBukGBlDIJuRV2G7rNG2uo/2NKLdrQ5oLdOYE9naI/vitdmRxNqab+IqJJeQbXXcGsAvqlIOdTQb\niA5uRqwj54rZItcvM6slKPk6d//6y7zGk4BX1S8/wd1ffSiduwJnzpzh9ttvP+pucOutt/KGN7zh\nqLshIiJyYilor0yLoL0UG/NcpminzDhV9q6jxDmXMI2vNqkiuJOy4znhnkm5zCrPBtlsVeDLc6Bs\nIhYxcwgBUoAYMcu12JfVUd1a9cudIWWW41D26B4Glsue5aKE7WFZ1lyncQSnTBW/cIHzZ88SzCEN\nkHq6tqFfXMDTQBNL8bHGnIayXNprVfFci585ESwSYiDErX20CYYHW418l+foZAMPEbeITz9PIBBW\nIXyaJx63/e7LmWXEmlyun6f7tShaMiN6JnkguBM0oi1yvTusTfqOzWZ/OWfuueeeo+6GiIiIHDEF\n7Wo9sjllHXbKZTutlKbiZCM5l/2v8QSeMc8Ed8wdyxlSxnMiT6uNa9AuA99GaMJUDqwG7TJabDmz\nGuCxulo51a28UqIfRhbDwHLoWfQD/bJn2Zep4+MwkOsWX8GMfrlgceE859oGfMTHHh+XbMxavC9T\nyNtopZK4eRkhNiOHUIu9lSnebnFVAK6s0y7TyAmlkFkOgRwgWyAbJKtbeq1+vhRUCxixFlWrUbt8\nfsDWv2xap12/djdynQkQoBaXM1KoIdu9fIggIifd6n8nx8d7HmHbZ6ilI0VEROQIKWg/QBnRzl7D\n9hSyx5GUxjqiXUatzXMd0a5B29eCdh3xds943BoFjh4Bh1oUjZggZyzX7+GUvbiNXIudjcPIsh9Y\nLJcs+p5539P3A/0w0PdDqUBeC6e5GcNyyfzCBWIwyCOMS3xckDY6Gs805BK0KUE71OnaMURyCNi0\nf7aVgB1C3HVEu0wzz6QQiMbW3tsWVkHb6oi21ZHpYKuB+tLvVeL21V+X3a38VTEbyepKbjOCl721\ng9cPKkTkxHL3X2X7BJkjF4A3H2H7twMaURcRETlqCtrVFNrsosERXzsyZmUUNYayxdRUjyvVqdBp\n+vm8NfU857SaCu3B8Jwpow1lJXI2I4caWGMmrRVXG5YDQz8w9mMZve771e1Q11+ncQr90/Owulf2\nUIqeNYGxMVIbSNFpYqCJxizGMlLMFO4NQsStqVO/Yw3Noe6hHQjRyocDdUp8MmM0K8XJ6jVsOsFC\n3Se71iGvI9bBnGilgrhPe2JPM+TrWHe26Tc+7R1eQrd7LY5m9YMNBW0RERERETmGFLSrqRhXCXL1\nCEaMZX11Ew2PAZqApUBIRnYjuRGz4RlygnFal53L3tWpbMRdiqcZeK51xHPCckNysOxYyniI5Xpl\ncJuxH+v667LNWD8OjONITiOec6lIHg33UIqDOaVyeSyjy2Htg4E2QBcDsyYwayMbbailxkrSdayM\nYFvEaOr071CnjwdiCGV/7FBDNjC4Y3V/62RGW0uUhdqX8gstTyaQpz28yhZoVvYfn4qirfYHp4Tq\nbGVNdqk+blsj3F4+2FAlNBE5ZupmEfoAUOR6cebMGe666y6e/exnc9tttx11d0Rkn1JK092w13lH\n7Vh37lq6qF62lWXIIWyF7CYG2mi00eiaEli7xsr2VhGaYOUwq1PJKftn1325y3ZhZYR6WA6My560\nWDLOF4zzOcOFC/QXLtCfP8/i/HkW584zP3dudX954QL9YlG25RrGMt0crx8GlHDdNJGmibXwWqnO\nHc1pDJoAXTRmbWTWNWzMOmYbHbNZSzdraWctXdfStS1d19A10xFpm0ATS9C2unV4Agagd1hiLDEW\nBHoCPcZQ98HO01i1ZYJloiVaMp0lupCZRWcWjS4YXQx0sVRdb0IgWinYNtUkz26MDkOGPkGfnD7p\nL7UiR83MbjOzbzaz3zSz+8ysN7O/M7M3mtmPmtnTzeymS1zjCWb2/5rZ35rZwszebGYvMbMP3ONn\nnmRmuR4fv8PjL6qP/UX9+hFm9u1m9idmdt7M3mZmP2tmn3Llv4XjNoVdRC7lzJkz3HnnnZw5c+ao\nuyIiB7AWtI/1e69GtKs6XlqmJa9GtCEGI0fDo2GNEVIg1T2xUw6M0QjJSKHs2DWNhoNDzqsK5V7X\ncmNgORNzIKeE1WJrnkaCBchgGTw7Yz9uHTkzupOmqdWxrJcOMWAW6kpoI4YSimMwQqCGbKMN5UOB\nrpmCdltGl2uV9eTUkmWRULflKlXFy1TwstVYnZpep8sPPm3FVUJwskAEGowIq8Onyuzk0kfz1V7Y\nVjfRzl6qjGeM4LZVMM3r4LWzmkGwNfleRI6amX0c8F+Bm7n4ZfnQejwGeCpwL/CKXa7xhcB3cvEb\n5m3A04DPMrM73P3X9+jGJf93YGaPr+3fsvbtDeBTgU81s29z9y+/1HVERERE9kNBe2VrjXY0pwng\n0QiNEXMkeSTRkHwkERk9MJSS2/hIDaGsgqOFQIiR6F43uCqVshujjJBbKKPfboTsxCFhVkbCQ10W\nXiprU0arA4zAaM5Yg7bX6exmgWixbNcVIhuzjs1ZW0atNzeYzTrariO2LaFpCDHW7cTA3KcV2rVo\nWSROddGnfbRXJcFLMI41yFsoP1X2vN5aW15XtJfwbJQ12QYxTBXHbeuSrC5dgreXkzJWt02rs88p\nH0DY1vzza/znQ0S2M7MO+DHgQcD9wPcC/x14G9AB7wN8NPDP9rjMHcBHAL8LPB/4fWCz/swX1/sv\nNbP3d/fxMrt6CvjJ2s9vAn4eWAIfCXwV8AjgS83sb9z9uy6zDREREZEVBe2JT9uh1HAbDKIRm0D2\nQLaGxFgDd2TIEUuGj0aKRrAy6julxhCsBG1KYI7rQfuiaeZgyQmeViO+RqmuHdkaUR+Jq2ngY4Ac\nQz2MECIxRBpraGLD5qxjc6NjYzZjY2ODbmNWgnbTEpsGiw0WYvlwYO1XECjrsnMdfy5Bu3x64NOe\n1VYCtoUyIj0VMcs1ZMe1ad7ZKKPU07psynT8UIer19fD5/q8rbYZ10bKQ67F6gyYzlutqheRI/Qx\nlJFnBz7H3X9+2+OvB37czP4PStjdyROBnwU+a1uQfo2ZvRP4BuCRwFOA//8y+/kwoAee7O6vWfv+\nG8zs5cDrKOW6v9HMftTd33GZ7YiIiIgACtprpmJopYBYU9doexOAiIdMIq6CdpMDPgZStDJSWw9s\nmmZdio1N06ensN0AjRlNndZt0x7cKROsBMzpNlPOyTGsAva4Np09xUAKRowlYDehpW3KSPbGxozN\n2YyNzRndbEbbtTRtS2gioZlGtG0VdjHKXtd1BNtr5XCwGrgp23lh60PQZdp3Xd1uNWwHL6PTJWSX\nqelet+iK1MHotdAcanVytylAhzqiXa/FdJQR/9UFlLRFjtqta/d/bbeT3D0D53Z4yIA58MxdRqv/\nb+BrgRb4OC4/aDvwfdtC9tS3M2b2ZcCPA6eBpwPffpntiIiIiAAK2ivuNWjX6dqEskbbnLpqsIxc\np8ZIybBoDNFYhvXMOY32ltrbJWhPo9Nebq2um7Yyqh2m/bitrms2Wx3U9dBg5ABjMMZA6Ucwxnob\nYkMMDU1saWJbCpx1LW3XEJuGEMuTmfa9Tm6M7qUy+TRpvAbhUJ8Hq0Bdfz9ToDZbjXTn1Uh3mMrI\nraaNmxtet+cqv9/y3JNDnLb08vXt03xtOrjXf9vaz9ctwVYbcF+tPwkicgDrFYSeARx02rUDv+Tu\nb9/xQfdzZvYm4IOA9728Lq780B6P/RRwH/Bg4JNR0BYREZErpKA9qVPHp7gYzPGw2p0KcycFVkXP\ncoAYfKvwWQCCYTFgORIs13CaV2uNy5rrWhHcjDZMZccywfMqhMd6joVQpqSHstd2CdglaI+hhu1g\nECIhRII1xBjp2kjTlP2vLZQwnIDRS4E2spETRN/qS0MghrKTdSTXafBT4La61VfdC5xQ9v22ULfj\nKmum10e2Vxum+VS2zBndiFPRs/p7zT6tE1//p045p+wrnmoFd18F9K3gLSJH6teBv6CE4Oeb2dMo\nofXVwN3uPuzjGn98icffSfnI70FX0M+esgZ8R+4+mtlvA58IPPYK2hEREREBFLS35JravARts3qE\nrQJlKThjcEKEHHy1VzXTiPa0djl6Gd2ua65XIdunEF0qgzchEMlbh21VCW8MQt22K8QAsUxTz9No\n9jTCbeAWt/bADpGmbWhjJMayB7bXadxjdjw7njKJWpTNAx4oe13XddNxej5Ma6PLOVPQXoVsCyRC\n2ds6l+Jn6xO9p32va3Qm1tCccpmubl6qq6/+McfJZX03YRXIs3vZp7zez9O+5BrWFjlSNaB+GvAy\n4NHAhwNPqA/PzezVwEuAH6/Tx3dy4RLNTD93JVt4vNP9kh/NvbXevvsVtEP5YPFh+zhvWlh02LRN\nkYiIXJ/OnDmzr+32hmE/n+MfPQXtla2q42UidN1L2+uWX8FX67BttYZ7GvSdKnuthW0y5mG1p/YU\n1s2MEMLWNlwEmhq0p/2u21DCdmwCTYzEJkI0cmOrAmiDwViPbIFabg2sWe2rHWIt0EbZz3r0GrQt\n1wJlgbLHNYTsq5nbkbVl2NPvpo7wE8o2XslKtXMnkHM9GXCfppHbqlr4VEM8OVvbc02j0kzj3b71\ntYN7LkXoarDO69PNV9POReSoufsfm9ljgU+vx8cDj6JsnfUp9fhSM/snu00RvxbdvLbN3XttmxMR\nEbkB3HXXXdx5551H3Y1Do6C9H9fNTlJH3dGjbl9EjkIdLf6ZemBmD6ds2/Uc4PHA44C7gM8+oi6+\nh5nZJUa1H15v33mZbaxGwqcPOC9lv+ddjre97W3cfvvtV+36IjeCvu8BuOOOO+i67oh7IyIpJR76\n0Ide8rx77119oH2Fs9CuLgXt6gu+8EuUEkVEDoG7vxV4sZn9CPBaStD+NDObufvyCLrUAR8K/M5O\nD5pZBP4hZeT79y+zjdV7yH5n3FzNmTk5Z+65556rdn2RG8naX9pF5PpyrPObgraIiFwVdQ33r1KC\ndgM8hK210Nfa09klaAOfBbwbJWj/8mVefwnMKGvK33aZ1xAREZFLexilFPVRfHi/bwraIiJyWczs\nY4Ez7v7nuzzeAk+qX57j6BYvG/Bvzewn3f1/XPSA2a3At9YvLwAvvpwG3P30lXVRREREbiQK2iIi\ncrmeDPwnM/s14OeAN1LC9CbwvwBfQBnNduCFe1Qev5QrnWP9NkqI/mUz+w7gFZRPwT8S+CrgEbWN\n/3iEBdtERETkBqKgLSIiV8IolcaftMNj08YCPw189RW2cSUuAP8c+HlKsP6qtcemPj7f3Z9/he2I\niIiIAAraIiJy+b4V+F3gk4EPo4wMT5tI/x3weuDF7v7KK2xnCsMHfWzrJPffMsqxLmEAACAASURB\nVLPHAc8FngK8J3AeuJsSsn/xCvsoIiIismLaj1hERG5EZvYiShG0v3L39z3q/oiIiMjJEY66AyIi\nIiIiIiI3EgVtERERERERkUOkoC0iIiIiIiJyiBS0RURERERERA6RgraIiNzI9lWVXEREROQwqeq4\niIiIiIiIyCHSiLaIiJx4ZvZIM/s2M/sjMztnZu8ws9eb2XPNbPMQ2/kcM/sFMztjZnMz+ysze6mZ\nPfGw2hA5Sa7ma9fMnmdmeZ/Hxx/WcxK5UZnZQ83sKWZ2p5m9wszuXXsN/eBVavPI3nc1oi0iIiea\nmX068FLgZh44zdyAPwWe4u5/fgVtbAD/H/BPdmkjA1/v7l9/uW2InDRX+7VrZs8DnrfDtbdz4JPc\n/dWX047ISWFmedu31l9bL3b3Zx5iW0f+vqsRbRERObHM7MOAHwMeBJwFvhr4aODJwAsob87vD/ys\nmZ2+gqZexNab/a8Anwl8BPAs4M8o78fPM7N/cwVtiJwY1/C1O3kM8Nhdjg8B7j6ENkROgql2yl8D\nv0gJvVfDkb/vakRbREROLDN7NfCxwAB8nLu/ftvjXwZ8K+WN+s7L+eTbzD4J+OV6jZ8BPsvX3nzN\n7D2A3wQeCfw98L7u/q7Le0YiJ8M1eu2uRrTdPV55r0VOtvqauhu4293vNbP3Bv6S8jo9tBHt4/K+\nqxFtERE5kczsCZS/qDvwwu1/Ua++HfgjyifuX2xml/OX7S+rtyPwHN/2Cbe7vwP4ivrlQwCNaovs\n4Rq+dkXkELn7ne7+Cne/9yo3dSzedxW0RUTkpPrMtfs/tNMJ9c35JfXLhwCfeJAGzOwmylRWB37Z\n3d+yy6kvB+6v9//ZQdoQOYGu+mtXRK5Px+l9V0FbREROqo+tt+cpU8h286tr9z/mgG08Aeh2uM5F\n3H0AXksZfXuCRt9E9nQtXrsicn06Nu+7CtoiInJSPZryifefufv2Sqjr/njbzxzEB+1ynb3aaShF\nnERkZ9fitXuRuj3QW81sWW9fZWZfYWYPuZLrisihOzbvuwraIiJy4pjZDLilfvnmvc519/soI2cA\n73XApm5fu79nO8Dfrt0/aDsiJ8I1fO1u98m13abefjzwTcBfmNlnXOG1ReTwHJv33eawLygiInId\neNDa/XP7OP88cAq46Sq2c37t/kHbETkprtVrd/JG4KeB1wNvAVrgA4B/Bfxjyvrvl5nZp7v7L1xm\nGyJyeI7N+66CtoiInEQba/f7fZy/pKzj2ryK7SzX7h+0HZGT4lq9dgG+w93v3OH7dwM/bGb/O/B9\nQAReaGbv5+776ZOIXD3H5n1XU8dFROQkWqzd73Y9a8uMsiZ0fhXbma3dP2g7IifFtXrt4u73X+Lx\n7wd+gBLkHwF89kHbEJFDd2zedxW0RUTkJDq7dn8/08VO19v9TFW93HZOr90/aDsiJ8W1eu3u111r\n9590ldoQkf07Nu+7CtoiInLiuPsSeEf98va9zq1Vhac347/d69wdrBdi2bMdLi7EctB2RE6Ea/ja\n3a8/XLv/nlepDRHZv2PzvqugLSIiJ9UfUqZ8PsrM9no//MC1+390GW3sdJ292hmBNx2wHZGT5Fq8\ndvfLr9J1ReTyHJv3XQVtERE5qX693p4GHr/HeevTQV9zwDbuZqsYy67TSs2sBZ5I+Uv73e6eDtiO\nyElyLV67+7W+Z+9brlIbIrJ/x+Z9V0FbREROqp9eu/+MnU4wMwM+t355H/CqgzTg7ueA/0YZfftk\nM3vELqd+NnBzvf/yg7QhcgJd9dfuAXzB2v1fvUptiMg+Haf3XQVtERE5kdz9buDXKG/GzzKzj9zh\ntOcCj6Z84v2d2z/xNrOnm1mux9fu0tR/qbcN8D3bp7qa2S3AN9cv76NUMRaRXVyL166ZPcbM3m+v\nftTtvZ5Vv/w74KcO/mxE5CCup/dd7aMtIiIn2RdTppRuAr9kZv+ZMvK1CXwO8Pn1vD8Bvn2P6+y6\nTtPdX2VmPwY8FfintZ3vpEwz/RDgq4FH1mv8B3d/1xU9I5GT4Wq/dh9P2Rv7VcDPA79HKcLWUNZ1\nPg34R/XcEfh8d9e2fCJ7MLOPAR619q1b1u4/ysyevn6+u794j8sd+/ddBW0RETmx3P13zOxfAj9M\nmUL2n7efQvmL+lPc/fwVNPVM4EHApwKfAHzitjYS8PXurtFskX24Rq/dADwZ+OTdukEJ389091dc\nZhsiJ8m/AZ6+w/cN+Nh6TBzYK2hfypG/7ypoi4jIiebuP2dmH0IZIXsKZTuQHvgz4CeA73H3xV6X\n2EcbC+DTzeypwOcBHwo8BHgr8Oraxuuu5HmInDRX+bX7c5Rp4R8FfBjwcOA9KIHgncDvAq8Efqiu\nCRWR/dlvpf69zrsu3nfNXbsSiIiIiIiIiBwWFUMTEREREREROUQK2iIiIiIiIiKHSEFbRERERERE\n5BApaIuIiIiIiIgcIgVtERERERERkUOkoC0iIiIiIiJyiBS0RURERERERA6RgraIiIiIiIjIIVLQ\nvobMLNcjHeI1X7R23c89rOteZl+et9aXrz3KvoiIiIiIiBwVBe1rz6+z616O49QXERERERGRa0pB\n+9qzo+6AiIiIiIiIXD0K2jcGR6PIIiIiIiIix0Jz1B2QK+PuzwCecdT9EBERERERkUIj2iIiIiIi\nIiKHSEFbRERERERE5BApaB8xM/twM3uBmf2JmZ0zs3eY2evM7CvN7EH7+PlLbu+107ZbZrZhZs8y\ns18ws782s2V9/EN2ucYnmtmPmNlfmdnczN5iZq82s39rZptX9lsQERERERG5cWiN9hEys68D/iPl\nA4+pmNkm8IR6PMfM/oW7v3Yfl9tPMTSv7X4g8DLgg7b97AOuYWYR+H4uXgfuwMOBW4GPrf38rH20\nLyIiIiIicsNT0L72prD7RcDX1q/fBLwO6IHHAh9ez31P4OfN7Enu/sZDav8W4JXAewFz4NeBvwZu\nAp64w/kvBZ7KVgi/D3gV8A7gkcAnAI8GXgH8zCH1UURERERE5LqloH10vpUSdJ/l7j+2/oCZfRTw\n48DtwM3AS8zs8e6eDqHdLwAi8JPAc9z9Hdvajmv3/zUXh+zvAr7C3Zdr5zwc+GHgycAXHkL/RERE\nRERErmtao300DGiBp28P2QDu/hvAHcCynvtY4F8fUtsR+AV3f+r2kF3bTgBmZsA3sBWyX+TuX7Ie\nsuv5bwU+HXhjfU4iIiIiIiInmoL20XDg19z9Zbue4P6HwPesfevzD6Fdq7dfso9zP4UyvdwoI+9f\nvtuJ7r4AnlvP3c9acRERERERkRuWgvbReck+znlxvTXgCYdQ3duBN7r7n+7j3E9c+5lXuPvf73lh\n918G7mErzIuIiIiIiJxICtrX3hREf+NSJ7r77wHn6pcR2HHrrQP6zX2e92Fr9y/Z1+p1B+yLiIiI\niIjIDUdB++j8zT7Pe/Pa/YceQrv37vO89bb229f9niciIiIiInLDUtA+Ohf2ed75tfsPOoR25/s8\n76a1+5fTVxERERERkRNJQfvonNrneafX7p+9Gh3Zxbm1+5fTVxERERERkRNJQfvoPHKf573n2v23\nX42O7GJ9ivl++/peV6MjIiIiIiIi1xMF7Wtv2v7qiZc60cwew9Z08QT87tXq1A5+e+3+JftafeTV\n6IiIiIiIiMj1REH76DxtH+c8vd46cLe773d99WF4Vb014FPN7CF7nWxmTwZuR/toi4iIiIjICaeg\nfTQM+AQz+6xdTzB7NPActoLrC65Fx9b8IvC39f4p4Ft2O9HMZsC3TV9e5X6JiIiIiIgcawraR8OB\nHnipmT11+4Nm9lHAK4EZJbj+PvDD17SD7hn4T1OXgGeZ2XfUUL3e11uBn6Xs8b28ln0UERERERE5\njhS0j85/ADaBHzWzPzGzl5jZD5jZ64DXUAqLGaXS+NPdfbzWHXT3lwA/QflgwIAvBt5iZi8zs7vM\n7BXAXwJPBv4C+N5r3UcREREREZHjpjnqDpxABri7f5eZ3QJ8DfAo4P3Xzpmmi98D/Et3/51r3Md1\n/4qyj/a0XvzdgPUp7w78Yf3e51zbromIiIiIiBw/GtG+tnztwN2fB3w08CLgTcB54D7gN4GvBj7Y\n3V97gOte6pyDd9g9ufszKaPWP05Zt70E/g74deDfAx/h7n96Je2IiIiIiIjcKMxduUhERERERETk\nsGhEW0REREREROQQKWiLiIiIiIiIHCIFbREREREREZFDpKAtIiIiIiIicogUtEVEREREREQOkYK2\niIiIiIiIyCFS0BYRERERERE5RAraIiIiIiIiIodIQVtERERERETkECloi4iIiIiIiBwiBW0RERER\nERGRQ6SgLSIiIiIiInKImqPugIiIyPXOzM4DMyADbzvi7oiIiNzIHkYZMF66++mj7sxuzN2Pug/H\nwjO+5bUOUH4fO/9O7BDaecCV137/e/2XWG97z/P2eHB7/y86ddufg/3/qfCLbn2/P7nH817/M7m6\nv7qtz8PB6jN66dd90mH8pxERuWxmNgLxqPshIiJygiR3P7YDx8e2Y9feDgHxssPnHnZNzH6JBL32\ng3t8ODI9snX21r3D+1Dl6n44Y1z8PB4QxJmelT4kEpFjwwHMjEc84hFH3RcR2Ye+77n33nt56EMf\nStd1R90dEdmnt7zlLVOuOdZhQEF7m1Ww00j/ARz+72qngC0icoy9E3jYLbfcwpvf/Oaj7ouI7MNv\n/dZv8fjHP55XvvKVPO5xjzvq7ojIPj3sYQ/j3nvvhfLee2ypGNoDHEW8U6TciW27FRERERERuR5o\nRLuawtw0LflqRd/9rrXe8xpm+54Gfrnt7Tfcbl+Tvf+f2/1nLtVPBW8RERERETnONKJ9LJToaHbx\nUuzyPcO2f7N+f++rXXzv6tnexuG2OT3PrVsuuhURERERETluNKL9ANN49rVeJbwWj/cZIncN29d8\nJvr239Xh/u7W/4tc9F2FbTkhzOzpwIsoL4X3cfe/OeIuYWbPA54HuLur2nb19re/ndtvv/2ouyEi\n+9D3PQB33HGHiqGJXCO33norb3jDG466G9eEgvYuzA6/HtrFudhqG1chFe+Sc8uUc3Z+8DIb2One\nnp3Yz9W3T403w9xxs7J92fo8fxGRY8Tdueeee466GyJyALWokojIoVLQXnlgets+YrzvddEHmNd8\nkHP35RJ9LM0drM2dn/fW78t2+vYlunKpdebT72XPczSkLSeLo4+XrhPvedQdEJF96YF7gYcCGtEW\nubrOAPmoO3FNKWjLIfEDT+XeGmGvP7+/Vkozihtygrj7i4EXH3U/ZD8M0PZeIteH3wIeD7wS0PZe\nIlfX7cDJmvGlYmhyeC4j/G4N6Bt7JXXlahERERERuV5oRPsADn2a97Hiu3+1ttzat08VX509fX9r\nxTZra9B3Whs+fct2Wc+9vQCawraIHGPpqDsgIgd1G6Wm421H3REROYAYVzVYj/V7r0a0t7uRs/QV\ncHPc1qeHT+E6E8hEMpFEQ6Il0ZFoGWl9IOYlMc8JaYGlBTbOYZxjwwLGBYzzervE0hJLPZYHgo8E\nTwTPBJwABDOMnbc8EznOzOyDzexrzOyVZva3ZrYws7Nm9qdm9kNm9pF7/OzTzSybWTKzR+7w+H+v\nj/9K/fr9zey767XP18ceWR97Uv06m9nHW/H5ZvYaM3uHmZ0zs98xs680s9kVPN/WzD7NzL7LzF5v\nZu80s97M3m5mrzWz55nZe1ziGn9V+/mD9esPMLMXmNlf1t/f35nZy/f63W273vuZ2XeY2RvN7D4z\nu2Bmf25mLzKzx1/uc63qwjP9v0nk+nEb8HUoaItcX9aC9rFe9K0R7RvVFe2w5atr7HwJB/c62XsK\nwdNtCcRmDp5JJHJOpJxwd9wh1591t61bYzWP3CxACJhFCAEIOBG30iGfKsLr77NynTCzJwGvql+u\nv6xa4P2ARwGfa2bf5O5fcxlNrAqlmdlnAD8KbG57fKefmQGvAD5l2zmPBT4EeJqZfZK7v+0y+vQC\n4HN3aPvdgCcAHwH8OzP7p+7+P3a5xvrz+kzgR4CNtccfCnwm8Olm9r+5+0/u1hkzey7wjZTf+Xqf\n/gHwPpTf/ze4+/P29/REREREdqegfUOqKfuywvalQjZgjuGY+ypkx9XIthPNCTVojz6Q8ghpIE8h\nO3uZFu6GY5iXPbHNrGzlZREjYiGDN7UvofyNu4bt9WnpIteBBjgH/CwlcP8xcD/wMOCDgX8PvDfw\nlWb2p7X42eV4b+CHa1tfB/w6ZVrVE+r3tvsG4MMplYC+D/hb4L2ALwT+EfBo4L+a2RP94C+4CPw5\n8HLgbuBvgLH28ZOBZwLvAbzczB7j7m/f41ofAjwVeAvwX4DfpPwf7lOAr6SE7+83s19x93ds/2Ez\n+3Lg/6L83+N36nN9E3Af8AHAvwM+CviPZnavu3/3AZ+riIiIyEUUtCd1dNR8CnMP/Dvl1Z6ufDjB\n0bbdPvCRrW9stbfHCu2t3830/ZwgJzyP5HrrecR8LFPMg9MYQIacsDwSc8JTxlMip8yYEjk5KWVS\nzmWd9tROaLDY1NsWix2EGRY7LDQQ4tYhcn34beB2d79/h8d+ycy+G/g5Srh9npm95DKCrVFGZu8B\nnuju66U9797lZz4cuMvdv3BbX3/GzF4APKue82xKOD2Ir3X3v9zh+78F/JSZfS/wG5RR6S+iLJTc\nzeMoz+HJ7r7+gcHrzezPKR8u3Aw8DXj++g+a2aMpHyg48HXu/n9uu/ZvAz9mZi+pP/+NZvZSd3/X\nPp+niIiIyAMoaD+AYbgKb21Txp7L4T7iqS+j1GOPpSVp7LHU48HxAITVGDQBxzyThgEfRsZxYOgH\n+qHcjuO49fv2GrSbErBD7AjtJqHdJDabhHaD0M6I7QxrtOelXB/c/Z2XeHyso66/Qxnx/YeUAHjg\npoCv2Bay9/JW4Et3eexLgM8AbqGMcB8oaO8Sstcf/wMze2Ft5zPZPWhPc3OeuS1kT9f5UTP7Fsoi\ny49jW9AGnkuZLv76HUL2ui8C/gVwE/DPgR/Yq/8iIiIie1HQ3kXZ4/mkxe3dnq9fFLTxEc89eVzg\nwwLv5zAs8GFeQnZ0CBADBIMQjGBgwxJf9qTlkn65ZLlYsFgsWC6X9Xdd1nBbaAhNGcEOzQbN7Caa\n2U3E2U00s9O0m6chGDGqlp9cn8ysAx5OCXXTH+T1P9AfyuUF7R542QHO/wl3X+z0gLufN7OfAJ4D\nfLCZPewy12oDYGYPAd6dMs17mmBzX739IDOL7r5T9VAHfs/d/2CPy/828AjgfXd47NPqNV6+V//c\n/V1m9nuUTXU/CgVtERERuQIK2pWtNqfaCpvXurL1enuHFfL3ego7TI5n/Tcwjeybb1UXNzI59TDO\nyf15cn+BtDxPXl4gLy+QIuRo5AhtNGKMWBMIIcC4hHGBD0vycs64uMAwn9PP57hncnbcM1gDscPC\njNDMaDbmtJtL2mGgywmCYU1LaNpD+R2JXAtmdgr4YuB/pazL3mvtwy2X2cyb3L0/wPm7TSmfvJ4S\ntKEUSPtvB+mMmT2GMmJ+B3DrHqcGSpG03dZp//ElmppmDDxoW/uPpExNd+CbzeybL9Xnaq++ioiI\niFySgrZU6yF7q4pa2dHLMU8ERoKP5PECuT9LWtzPuDjHMD/HuDjPuDhHFwNDE+iaSNdGuq7Fcwtt\ng+cl+EiwkRgSMSSaMNLEkTQm3BM5JbJbCeO2AOtoUy5rucdUiqc1LXG2Scwbuz8dkWPEzN6bUgTt\nH7D1GddOn6ZNH41t7vDYfvz9Ac+/1Aj1W9fuv/tBLmxmzwL+H8r7zKp6+E6n1tu9nvOFSzQ3be+x\n/cOLh63dP8inl6cOcO4OXXnYJc8qXVWdCREROSnOXPqMM2c4c+bS5/X9QcYUjo6Ctqz4alS//M3X\nvGzjFTyX/awZCD5gwxxfniMt3sVw4V0sL5ylv3CW5fwcsybStQ2pbchdh+cZ5jOCdXgawEeMRLBE\nMwXtMIINZB/xNJASpBwYcyDTMKbEmBIpJYgNcbZJN/Z4PtZ71Ius+2FKyM7ADwI/DvwRcK+7DwBW\nprRMf6gvdzrNQV8UV2V9jJl9ACVkR0pY/xbKBw1/BZydpoib2TPYmqJ9NaYQrSfZrwd23f5rm/NX\n1uy9V/bjIiIiJ9Bdd93FnXfeedTdODQK2gKsD7Gt7QnmELwE7uCJkAeCL2G8gPdnSYv7GOZ/z/L8\n/SzO38/83P2MXcvYdaS2I2/MgFNl9Do6OY/gqY5oj0QbaUKijSN5HDBfQurJQ2YYoB9hzIFxLCE7\npURoO7pTDyIpaMt1oobOj6G8sL5xj32aDzRifEgefoDH9yzots3nUd5fRuDj3f1Nu5x3tZ/z+lZf\ng7v/4VVuDzPjllsuPfM/xkiMGtEWEZGT5dZbd1+d9exnP5vP+IzPuOQ17rjjDu699/h/qK2gfaNZ\ny8mXGh6yrQ27tq3OruG6rssOjEQf6tETc4/lJZaXkJaQ+7LFl6cyEp0SFkYYAr5Ykhz6MZPdyV5u\nxxGSNdBsED3Q0JNtRrYebxzrjDBC8obZ5s10p26m27yZjZsezGzzNE03I0T98ZXrwgev3f+JPc77\n8KvdkR08AfiRSzw++f0DXHd6zr+7R8iGq/+c/wJ4F2Xrr4+5ym0B8IhHPII3v/nN16IpERGRG8pt\nt93Gbbfddsnzuu762HlISeVGZBdtkX2pUy+aO1oKoJXq4iVkJyKJyEikBO3gS0Iu23qth2zHydkZ\nU8It4IwkX9KnzLwfwawUfLOplQaaQAwzmjDgYcTDgCWIOdBkw2mYnXows1M3Mzt1M91N78bs1E10\n3QZN8z/Zu/N42bKyvv+fZ+2hqs5whx6gu2lRUREVpxciDqighqAEojH6E4OBQBDUKFEwRpMIbV4Z\nXqKI0fijDSqiPyUGExOHxIA4J4EmKEpQInEItNee7nSmGvbez++PtatqV52qOnXOPdPt833z2l27\nqlattXZd6tZ96lmD/u8rN4Xm/1FXF5T7+qPuyAxfaWbf7u696Sfqxdu+ivhXxPvd/YFdr55veM1z\nr9fM7iRuH3Zk3L0ys18Gng88y8w+3t0/cJRtioiIiMDkdjLyKGGj/+yj/NRLrF5hPKGKQbbHrHbq\n/RhsVz2o4lBvqgF4iXtF6RVFWTEoSrqDgq1un42tLtc2t9nY7rK502enV9AvoCTF0k69bdc50vZ5\nss4FWisXaa9epLN+KyvnbmP1/G2snb+NtQu3s7p+kfbKOlmrTZJmJFp5XE6/Zkb3RbMKmNnXE4PO\n495T8A7g++Y89/2MV/X64X3WO7zmjzOzz5p+0sw6wE8Tt/o6av+COHc9AG81s8fNK2hmwcy+xszu\nOoZ+iYiIyKOYUoIzTed5T6AHB91abDI9vTDenijqPloEbbhvdrAYZKce51OPDgrMC6jKOpNdv8JS\nCCme5HiS4UmKW6CygHkg8ZSkLmNJhqUZlmUkSQZpBVlFKCsqNyDBSbCQ01pZj0dnjaS1SshXCGmO\naX6j3ATc/XfN7H3Ak4GXm9ktwE8Sl9+8G/ha4CuA3waezsH/8jnI694NfIOZPQF4A/Ah4COAbwCe\nVZd5D3DvPuv9SeCbiIuR/bKZvZZ4fV3icPFvAT4G+B3iNR8Zd3+fmb0KeB1xSPv7zOxHgHcQF2pr\nExeq+2zgK4k/PjwZ+Iuj7JeIiIg8uinQlpGJIJsqDhm3uDJ4HDpeklISrAKDyuPq4E6KhxakCZa1\nSfI2Wd4hJBmEBCxgFhf+CUlCCAlpmo4y0kmaYRWEyonrmwWwFLMES1LS1gppq0PS6hDSNpbmEFI0\nIENuIl9L3IP6InE49lc1nnPgvcQgb+89LeY7yK9z/wh4JfBXiXtdNzlxZfTnuns1/cJF3P3dZvZq\n4DXAeeCfzaj7e4H3s3egfcOrkbv7D5jZJvB64nztb6uPXUWJPwZ0b7RNEREROdsUaC/LDPxks9xH\nq7E0mhMDbatIKUiJq4SnFCRWkVgMxt2h8kBlGR4CJBkhXyVtr5K110iyFoQEswRCSgiBYCHeJinJ\nMNhOMhIM9xD7YQkWEsxSQpJiaU5IM0IWA2wLKYQEP2jWX+SYuft7zezTgO8AvgS4C9gAPkjc6uuH\n3b2/x0iWRXtRL/P8LH3gS4GvA/428CQgB/4P8Bbg+2fN316mTXf/p2Z2H/AK4qJqq8R9u98JvMHd\n32FmL1yi38te18Jy7v6jZvafgJcRs/UfD1wAesD9wB8AbwN+zt33s8K6iIiIyC7mj+rgcXkvee07\nx1HmcAz11FvjBx7R2axjt2FTtrBks9ScDo5eNi5jc/tsM6opMa+AipweLevSsi4ZXRLvkvoOiXe5\ncuUKj1y+zCOXL7OxtUNvAN3C6BXQWjlHe/Uc7ZVzpHlnFBhbSDEzjLggWkgSkiQjpBlJErPXYRhc\nh4QQ6iA7JDGIt4CHgNvwimy07/dPfuczFXGLLMnMvoC4p7UDz3T33zzhLj0qmNmHgcc97nGP06rj\nIiIiR+juu+/m/vvvB7jf3e8+6f7Mo4z2NJ+6HTrCaduzq51ucLpjczpjNDLveyWJALfR/eGQcaMi\nsbI+4rDx4AVGDMJjoJyRZB3SVgp5SvCUrErrudTr5J110rwFltbDx5uBNjGoDsOh5AGzgFHfWoKF\nAJbE1cvNGgF2vEj9PCQiIiIiIqeVAu1lHXtkN6/BJTqy548C1igyzqUHvD4aW3pZvDUrMS+BCkLA\n0pwk65B5m8TaZKFFFdrk7TWyzip5e42QtcCS8VG3a/U2X2aGhVAv/GZ1L+I5Fupbw42pQFtERERE\nROT0UqB9as3LaM+7P3zNrJc3c8HNYeXjYNsmFkGLGe3UyjgvmxIowUrwCgsxox2yFVILWLaKZWtY\ntkraWiVrr5C2VglpjlNnpgnjULq5GrrV/RvNroxzv5uTLb0etO80fxY4vMgtDwAAIABJREFUhBWS\nREREREREjoAC7X2YDuwOkmGdiH/nVBhjTx9No54s7FMbZfvk81PbezXD1fHd4cJuzbDbxwug1UH2\ncOh48BKnwt2pHLCUkAbSVkaWZoR8jZCvY/kaad4myTskeRsLWZxbXefJxwHysNXx4e7gMcgeDX33\n4az4uszuixMRERERETl1FGjvsvxk7OlQd69yTT4nTjTARsEwM+LnOQH2TI7XC6LtHng9vk5z4rzs\nUJFaQW4FWb3aeKAkUFK6425UVYCQELJA1jbc8zqjHY+QZFjIYiYbq2PmySB5nJ8eXsdkkD1/+TZr\nnIvIDTjICuUiIiIisiQF2rUbyVYvG/RNry6+qI1RnR7nKO9+zZxaxi9stOijYdnNQqPMeZ1zTojZ\n7CzUgbaXJF7Pza4D4dIDWEaS5WSWAW1IO6PDQhIXPyOMhoDHlr0O+5sazzYy2bN+Etj9A4hCbZGD\ncPffAJKT7oeIiIjIo5kC7dowWzrMp84O4w4Y3NXDwHEw86lwd3g2HsptTLXkNtozehwcz+6T25y5\n3Oaj4eLNp6wOxIPFedlZKMlDHWQTh43jVR1oG5UHPOSE0CFNV8A6kLTwpI0nLcY1NsNinxFEN87m\nbTE32rs8vj82OZY+Pi8iIiIiInLKKNA+JjbMKDcTzY3s83Ce9GTgPA67bWIY+fycuNVj0mNg7sS1\nxOu6hpV43GYLqxpzs8dzslMrCBSjPbWHueiqPtxSnAxChlmGh3S0SvhBxqOa2fxgu2miiIJsERER\nERE5nRRoH4NZq2RPbHfdeGyczd49H3lYfs+ZynWM7aPgenpe9LCFcZCd1gF2aiVJKLGqxChxr6jc\nh7trU1nAyXDLwHKwjNF+1xjzZ1gvtmewrdmkIiIiIiJyk1CgfUysOT95zjpmuwNoq5cym1l8Vwuj\n6uvF1OLIa4/ZbavGc6Tr7Hrc09rHK42HccANJRUVTkXl1IF2oCKlCilYVq8qnhGnezYy57Oi4iXW\nmIvBNnsXFBEREREROcUUaB+b4cDw3Qt+NdffbqwFPvXs/toy4pzq0XBzr7PbPgz6nWBOFpw8OK36\nNgtOFmIW2ysfDRevSChJKS2lsozKUtxShntk+ygXv3u5tlkP7Dkte4/rG16liIiIiIjIaaNA+xg0\n19qePN+9NJhjmDW39preCmtX5TQz4OPbegExB6jA4l7VVs+2DjhpqMhTp5NAOzEyg9wgMyjcqEKg\nsECFUZJQWUpJFrPallIxDLIDk2H18lukLWvpedwiIiIiIiInTIH2sbCZWepZWe1RWTOgis/Ui5ZN\nlByNJ58OrutMrw+HnY/3qa6XMiMMB4EHp5U4nQzaqZFiZGakxAx4URpYoDKoSGNGmzxmtUmpLKlD\n9omB8UdMWWwRERERETndFGjvgzW2k9qdXJ21cRXszjIPz2dvdzURkBsMZ0c7zZx3o6rRlle7Nwaz\nUfm4bjjmJF7VW3lVcch46nRyo5MaASNxSNwoS8NCqMNyqwPrjIqsHjpeZ7MtTF6nH3Y2uzGYXtt5\niYiIiIjITUCB9kxHESw2gmDzRsgdM85OVd8fh8fm1WjV8HFNs7b/Gp+PWrJ6UHcCSWL1EQhekmAE\nN1YyZ60Fay1oJQZlAWUKVUoIQDA8BCozKnIqckpyKlK8HjY+ztYfjflzthV0i4iIiIjI6aRA+zjY\n5MloILk51HtVj3egHi6aNtqjK5YbmsheT4TWo3uhXugs4GSJkedGKzeyzEjcCF4S3OhkzmrLWMkh\nT4xqkFIOUsoixRIwC7gleDCqKqfyfJTR9tEiaNMOP+zW/GwREREREbmZKNCe6yiy2uPTUahsHjPX\n9XzscZDdXKK7+frJrb+mg2yAYE5iTrCKVhpYacFKO6GdhzrIhsShnRGHjedGFgL9UDCgoF8NCMEg\nVBAqKgtUIY/BNnlccZyksdL4qJNT16vgWEREREREzh4F2lMWTwNuLFk2VW5xwnWYuTbcK9wHeFVA\nVd96gVeDRiWNBdGsGUQ35kFbwCxgBDCj+T+SCoJjwQlZRitpsd5OWesk9bDxQEJCnkArgzw1jAKv\nEopBPS/boSJQulN6qDPZKRUZXu+bvfvN8omHDpqEnpwLP78STdkWEREREZHTSIH2XPvJ0M7bP3qc\n7XUqcMfLgmrQpSx2KAddqqJPVfYoi94oGI8vrbPFdbA9DrTrM0sIlsRgu3EEC2QJZCl4CtZaoWWB\ntVaLi6spqcVcdGpOEuIRAlQlDPoBCJSVUVaBojIKh9LjHtqjudkWy01nsxX3ioiIiIiIKNDeh0Xp\n2VnP2WiIuFeOeYlXFRQ9iv4Wg+4m/e4m5WCHYrBD2d/BqxKwOlNrEGwqqz0eNh5CSggx2A4hGQXe\nIQSqLOBZgDzAqpOHFustuLiakoW4T3YaYr/dK5yK/sDZSRIgbutVVEZZGUUV4h7aw0DbUqZXN2++\nC8cXbB9vayIiIiIiIstSoD0ye1fr5V41K+DzcSzoBVUxwMsBRX+b/vY1etvX6G5fp+hvUfS2GfS3\n8bKIcfVoEncYB9vNQNuMZBhoh5Rgw/OEJKRUrYQqT/BBSrUGSdmhFdZZzaGVBvIk0EoClTtFVVJU\ncXXzNEnAEioPlG4UHig9UAwz2pbUw8ZnrTV+fDtpN9sUERERERE5bRRoLzSeM737sanHJ4o47mXM\nUHvJoLdFv7sdb3c26G4NA+1rVINuPZS8i1dlI5hmnM0229VUsAQbZrQbWe0kpLRaKa08o9VKWcv6\nXFuBa6sJaxlUnTah06aVtglJICUh4FQhkLUK0rzAsgEUxNXGzag84PU+W0Y5432IvXKWnzetlcRF\nHn3M7NXAqwF39+SAdfw68PnAr7v7Fx5i9xa1ecP9Hrp06RJ33333UmXvuOMO3v3ud99IcyIiInJK\nKdAeGgWIdSZ6QYZ7uAHXOJs9GV06FV6VVGUfL/v0dq6zvXmFnc2rdLeu0tu6Ngq2vexDOcCrfj1H\nu1FPPYTcR0009t+u52MHCxhJfT9muFutjHYrp5VnrGUF11YC11cz1lsJwc/RSlNsxQhJShIMEsOT\nhGynIMkHhKwPfcdD3HisBCo3HMesGC1yNrz+YQbe9rk62X6CbQXmImfGTf1Br6qK+++//6S7ISIi\nIidMgfYs83as2hV8zwgsh9tfe0FV9imLHXo719jeeJiNKw+yvfEI3WGgvXWNQIl5gXk5CrS90QIG\nbs1H4hHMMEIMc0fBdsxut1steq2cdqvFWl5xfTXl2lrOuU5OK01Y63QAI0kTQppgaYInGVlrQJL3\nsawPaYUHpzSo6m3Hhqun28RvEYbXq5/7aH75Pt5qBdsistvwL7ub1OP2eP4ScUtHERERebRSoF0b\nBojezGYvChp9vA641y90KqhKyqJPv7vBoLtBv7vB5tUH2bz6IFvXHmRn8zKD7ibFziZVfxOnwqgw\nH/+7cpi39omts70Od+MR1ycf/i+uAm6EOIScEvcBVTVgY6PNlSsdVto5aRKovIx7dyfGyuoKeadN\n3m7VQbKRJAlZlpImAwIllAOqfkFVVVReUlUl1MPULSQQ0nhrVme0m1ugzX8DmwHzogB6+jlt6SXy\n6ObuzzzpPtyYAHx4jzJ3A8p6i4iIPJop0N6XOgCG8fZb9axq9xIv6wXPelt0Ny+zs3GZ7c3LbF9/\nmO3rD7Oz8Qj9netUgy4UXRIKwKeC7JildhsPyaYOYI2qLleNovDpUe5V5RRFSZxS7WxsbXL56hWS\nxKi8YFD2GZQDinLAufPnWTt3jvVz5whZBlVFGqCVJ2ShT/AeDHYouz2KYkBZDCiKASFrkWQtkrRN\nyHIgjwu3jd6j/SeilK0WEREREZFHCwXa+zC5wriNZmebQekllH2qQZdBd5Pu5hU2rj7IxpUH6G5e\nprd1me7WFcr+FuYl5iUJZWNKuOOERmAdYka4uRiaV1Dnsoevia+3+jYG7WVZ4lSUVcnm9hbJVaOs\nBvQGPQbFgKIsKMqC7qBP6U6SpbRYxb0kCdDOErLESbyPD7aoulsU/R79fo9Bv0faWiFrr2HtOIQ9\nro5+4/9XmhdsKwgXEREREZGbiQLtBeaHdsMQu85EO3g5oBx0KXpb9Lev0d24zPa1h9i4/JcMdq7R\n37nGoHsNL3qkwQgBEgO3OLcZC7jFodhY3GbLQsAsTAXaJe4eA0/3uDd3nMg97lswCFCZ0S9KtrZ3\ncK8oyiImns2pqoLKK0ISyFstIFAOSoyKLDXSUGJVDwablN3rFN0ug94OvW4XLwcEM9I0gzSHKqtX\nJF8+l90cVj49jHziz0ABtshNy8zOA98KfAXwkUAfeC/wI+7+ljmv+XXmrDpuZh8J/Gl990Xu/mYz\n+xvA3wU+FXgM8FszXvc44DuBZwN3AZeBdwP/yt1/9RAuVURERGSCAu1pC6NFaxyAV3i9iFnR26K3\ndZXe9lW2rz9Cd+MRBttXKXvX8cEOwQckBiQJSQiEYIQQsJBiIYOQEpKckNZHksdAO8R5114vROZe\n4l7h7lRVFQPt0app8TbWbVhiZKmRp0aSGR4Sev0B165fj/PCQ5yTnaYpRVFSYpSEOHi96uGDbcre\ndYqdqwx6PQbdHoNejyTN8XZRb/Ud52ab7fnmicgZYmYfBbwdeALjvxhWgGcAzzCzLwO+xt2nVwVb\nZiG0uJug2ZuBFywqb2afB/wCcK5R7g7grwHPNbPXLHVBIiIiIvugQHvf6qHcw2xyVeDVgKIbA+3t\n6w+xdf1huhuP0N+6QtndwMseVhWkBoSEkNT7XoeEJG2RZG1C2iLN26RZhyTvkObtUbmQxG1dKy/x\nelEyryqqMgbao6yvx+HsIQRCYlgIJFYQKAgMcCvp9gfYxnX6vS3MIEtT8izDK0iynCTPCVmOVX28\n2BoF2kVvQNHrM+gNyForeFlgOKHOuA/3/x7OJlfALXLm/VtiFvuHgZ8DrgGfAnw78ETgK4krgr1y\nxmuXWfbwW+r6fgN4A/C/gQvAR40qMfsIYpC9DpTAvVN9+YfAa4jZbREREZFDo0B7X5oZ7TrDXA6g\n7FH0NultXWXr2sNsXXuQ7tZl+ttXKbt19hgnGHWWOsWSFAspab5Cmq/W855Xydtr5O1V8vZq3Oc6\nSQhpCsQMdlXFlb+rcnhbDZdKH8W2IQSSJBBCwKseVbGFF1t4sUO336ff22KjGmBAnma0shyzQGd1\nhTardLIwymhX3esU3asUvYJBfZSddaqqiO9Icx756D1SkC1yxhnwGcDz3f1nG4+/x8z+HfDbxKHe\n32xmP+ru7z9AG58MvMndX7ygzOsYZ7L/1oK+fMYB2hcRERGZS4H20MTe2bOCxeFj9T7SZUHR36bo\nblL0Ntm6/ghb1x+J+2RvXqHf3aQcdHEvsDp7HUJCkmQkeZs0a5NkbbL2Gllrjay9StZarW9XyFor\no2z2KKNdDTPaMZM9vN8MtIdrqRlGMCj7Wwx6MKgKKu9TlEBRQtlje3ub6xsbrF69SpJm9IsBg6qk\nwtne2aLX3aHfjyuOx6ngAUvzeCTZ6CAk+AH33dIcbJFHJQd+YSqwjU+4b5nZ1wHvJO6F9XLgm/dZ\nvwFXgG+aW8DsscCX7aMvIiIiIodGgfaUZohtE48Sn6m34vKyP1pdvLd5mc2rccj4zsZlutvXKAdd\nyqJfvzzB6vnXaWuFvLNGq7MWs9eddbJ2PE9bK3HoeBYD8eEcbULcOss9BtU+HLbu1eixcT7ZoXKs\nihn3fkjqLcd2qDzBS8NLx4uSbq/H1tYW165dIyQJ/bJgUJUUOJtbW+x0d+j1+xRlRWUppHWGvbVC\nyNpY2oIkqxdwC4iINLxp3hPufp+Z/S/gk4AvPkDdw+B5a0GZZwJJXXbZvoiIiIgcCgXauywY+uyO\neR3gFj0G3U12Nq6wdfVBtq49yNb1h9neuEx/5zruRVwoDY8Z7bRFyFdI2+u01i6ysnaBlfUL5O11\n8k48krxTL4jWIiRZY0h2c7g6jFc7r/fVdh8tTG4eg2gvCyhKzJ2it4PZBl4lVJVRlU41qOh1+2xt\nbZNn1yAYA68ocUqDza0ttrtd+oMBRVnhScCSnCS0Yz/zzjjQtmRGoK0h5CJn3H17PP8uYnD7RDNL\n3b3YZ/2/v8fzn3yAvoiIiIgcCgXateFiXo4P1zqbej5mlKtqAPWw8d72dXY2HmHjyoPsbDzMzuYV\nulvXKAbbWBiu/p0QshZpa5WsvU5r9SKd9VtYOXcrq+duJe+sjTLaSdaeWIU89qfZg2Z/vN5Nu3Fb\nZ7irXp+y36Mq+qPAuigqBoOCalDGoyjp9fts72yTpjEjXdazyUuMzc0tut0e/UFB5cRV0bM2Sb5G\n0lrBsnbc2itkTKzEPjyz3X1uOviQ8UY7BxutLiLH48E9nn+gvjXgIvDQPuu/ssfztxygL4egIu4y\ntsjDh9eciIjIo8SlS5e4dOnSnuX6/f4x9ObGKdBeQgxkjaocUPS2KXrbdDevsHXtYTavPcTmtQfp\nbV+jv7NJUfSoqpIkyQlpRpLl5Cvn6azdQnv1FjprF+msXWRl/QLttQskWYd0mB0OGT7cN7vBR1ls\naw5ir/sGlVdUZRGPok9/e7M+NtjZvFz/CPAwve2rUO7EoxrQ76f0egO6eZ8062JpSmWBgXsdaPcp\nKyBkJGmb0F4j6VwgbfwogBnuzf42FmZTICxylh31kJZyH2WPeXjNfn8zEBERkXvvvZd77rnnpLtx\naBRozzXeqCrec6piwKC7Pdore+vaw2xefYjNqw8y6G1TDnYoiy4AqYW4ZVdrhdbqeTrrt7J2/jF1\nwH2e1so52ivnGouK5fVc7BBbnf5n4eTC4hMDs6uyoigGFIMeg94O2xtX2Ll+mZ3rl+luXaG/fYXe\n9hUG3eujrb6CDxikKf3+gG6vT5L0YpBdQb8o2N7u0+3Vi6eFjJB14sJtq+fJOuskeRuSNPZ11BkN\nFReRkccSt+9a9DzEvzj2yk4fRLPOZftyw8yM2267bc9ySZJwxx13HFazIiIiN72XvexlPO95z9uz\n3LOf/Wweeuj0/6itQHsOa0Syw2C7KgcMelvsbF5l6/rDbF17iK1rD7F59SGqsod7AV6QpBmMAu1V\nWivnWTl3G2sX72Bl/dZ6EbQ4ZNzrwNppznGeiqpH5+M0sdfZbTcoK2cwKOj3evR2ttjcuMLG1QfY\nvPwAve2rFN3rDLobVINt0uCjo99P6WUD0l6fEFIG7vQGJTu9Hv2B0+tWlJWDxWHjaXuNfPUCab4a\nF0MbLoJWLxAnItLwVBYHt0+tb//4APOzl/EHB+jLDbvrrrv48Ic/fFjViYiInBl33nknd955557l\n8jw/ht7cOAXatXlzob0q8arAq5L+zgY7m1fq4PpBtjev0NvZpBjsAB634gopebtDe+0CK+dupXPu\nFlbP387K+kXaK+fi6uL1omdYEtvz5hznYZA9a5L4MMs+vPX6qfE87bgqeYVXJZUXVFVBWZ+XVQnu\nVJVTmhOSgjQrSNICCwU5CW4lBKeqEkKak1uAdCUu2NZeJc07JFlOSNK4h/bEu+ZT4bbGjoucYS8E\nfn7WE2b2VODJxL/w3n5E7f8acXh5WLIvIiIiIodGezLVvA5X6x2osWE4WxaUgz6D3ja9retsX7/M\n5pUH4wJow/2yiwEAIcnI2qu0VmKQvXbxMZy/9S7WLz6GlfVbaK2cI2t1SNIcC2mc2+zjHgwPmzrC\n8LBx/tsYrjQ+7HXsuZlhoT4MLDgEr3cAdwqvGJQl/UFBb1DS6xf0egW9fkl/UDEYQFEYWE6SrdBa\nvUBn7SKtlfNkrbW4/VjSIoQktueT4bRCaxEh/lXwPDP7m7ueMFsF3lDfrYB7j6ID7v6XwH9coi/3\noiE5IiIicsiU0R6ZXtXbRpnhctCj6A1XGb/MxtUH2bjyAP2dDfrdLcqiT5KmJGlG1lqltXqelXO3\nsn7hsZy/7U5anfO0OufJO+ukWQcswUlGSWsfNd+YE96Y9zzRs+GS6DZcpG24C5gR6t3AgsUR3Ran\nfI+S5hUOVbwmyhILMZudJCUhKSA4lkCSGVmakearpK01kvY6Sfscob1KyNuYxSHjw8z6ruz7ft51\nsxtYgVxETikH3g38jJk9A3grcB34FODbgY+vy/yQu79vzusPwyuBvwKsz+jLpwL/EPjYuq+HNnxc\nRERERIH2yPR63nGf6mrQZ9Ddor9znZ3NK+xsXmF74zI7W1cpBz3KqsBCIMlb5J01OmsXWD13K6vn\nbmP1/G2snr+dLF8lzVfJ8hUsyepMtlGN2vbG9OvGkHBr/FuzmfmenkBuhoVASGKwn2Yt8laHVmcV\n90FdX4UFqIq4OrkXRb3SeQu3DLccLIfQAmtj6Qppa428c460cw7L1yDvxEXb6oEQ1li0bXrQ+L7e\neQXbIo9GXwX8KvD1wDdMPefEgPeVc157KINj3P3Pzex5xMz2et2PZl8cuKduT4G2iIiIHBoF2rN4\nBVR42WfQ26S7dYXt6w+zff0RdrauxuHigz6YkWUtsrzF6votrF24nbULt7F24TGsnb+d9upFstY6\nSdbCkjxu3TUc+G1g3sxmN5qP+fTJXPbodwCr71p96pglhCQjzWJ9K+t9gkGWZfR2ztHf2aDX3WTQ\n26Ic9KmKAeVgQJ5ltPMOrbxNu7VCq92h1Vkhb6+QddZJ2+dIWuuEfBXq7cdGKfLYNJMdVLAtInEO\nTB3kPgV4FfDlwEcCA+C9wL3u/pa96jjAc7sLu/+GmX0S8B3AlwJ3Elckvw/4QXd/u5m9er/1ioiI\niCyiQHtktLZ4DLS9oKoD7Z2tK2xee4CtjYfpbl2j392iKPqkeYs0a5HmOSvnbmH94u2cu+UO1i48\nhvbaRTprF8jbaxBSzFLcwkTw7AtyNj4vobPrYYMQCGmGhQRLEoI5WZrR7qzQ727S726OAu1i0ItD\n4fs9siQlT1vkWU6etclbHfJ2h7zVIW2vxf2yW6uEbAVPWnjIcML0cnE0g+wb+Veqgm2Rm5u730PM\nEA/vXwP+SX0sW8czFzz350BygH7dD/y9Bc9P9FtERETkRinQnuaAl7gXVGUvZrQ3L7N59QG2rz/M\nztZVesNAu9Umzdu0V9ZZPXcL6xcew4Xb7orDxTvr5O31uIWXGxVGtSiyPkA3R0IghDhnOvWcPMuo\nOqt4cZFBfzsG2r0YaA/6XYr6CBbIQkqaZKRJTp63yVptsrxN2lohaa2StFawtE1lKW4JcZ9vsLlb\netVBty1xrcP55hMPKdgWEREREZGbmwLt2nD59cpLqnJAVXTpd7fodbfodrfo7mxRlAUhzWivnqe1\nss7K+gVW1y+wsnaB9YuPYfX8bbRXz5O3V0myNhYy3OssdnOM+OTNlGWC8dkBrhMDVSyNi6GlgQTI\nMEhSkqxNNuhTFj3Kok8gkISExFKSJCVNc9I0I81yQtbC0pjFxpJ62HsYTyWfGCnuoz6Mn1xstDXY\nVGCtIFtERERERG52CrRrowSsl5RFP64yXgfYw2C7rCqSvE0nzUmzjLVzt7J2/hbWzt1CZ/0WVupt\nsNJ8tV5obBhoH/HEPx/+p97sywKEFAiE1EgtwdKcNO/EhdDKgqoq40ZmlhAIhJAQQiBJEkKSYEkG\nSQYhpbLhpmLNBptH43086msVERERERE55RRo10ZxdlVSFj0G/Z16bnMMtrvdbcwCWd4iy9t0VtZZ\nv3Ab5y7ezrkLt9XDxFfJ22uErA2W4qRTw8V9uYT1Pk0GwAaEep+vJO53nWQk3sZH88+9XvBtvGc4\n2HhrMIjzyS3gIaG5e/euN813nR78OpTNFhERERGRRwEF2rVRvtYdryqqsqAsyrhltSWEJCfLW7RX\n1uOc7LXzrF+4jfXzt7F24VaSrEOStgjDIeME3BsrdNdriR9OvnePeixuHxaHkScEUrBGTtqGIXO9\nwfZoaHu9Ddhosy5rbjY2rn9U1uPS6RO9OoJfEkRERERERG4iCrSn2CjLmxKSFu32Omvrt0MFWatN\nezUG2p3Vc3RWz9FaPU+SrxGSPA61tnQqyJ7KaO/ar/vgPR3WE7fVHg/tHm8A1mhyah+xUfBcB9rD\nLcXic16H0TZRI1Ov3n0No/D9QFe3r4XQFM+LiIiIiMgppUB7pBHgWYJZShJatFrnYB2yJCdrd2Kg\nvbZOq7NG1loha62Q5B3MkrhoGMNFw5qB9mEG2NPGK6uZTzwyPq+Hr8dY2xv7ihnu9VDzUc8CwyHu\nk/OyhyWmg+zpazuaCHgiCFeQLSIiIiIip5gC7SmjjLalJEmLVvscWZLT6ayTd1Zor67TWlsnb69i\nSYqFDEuyGIJ6vVvVcDj2VDbbplrCZs9LthlbYy0sNxz1PifYnnjEmyHyMMhuZq7HWe2pHsw4nw62\nx60eRVZ71/ti7NoeTERERERE5KQp0K5ZHbBZCCRpRpa341TnLKMqW3g5IG21ydodkrQdV/W2BCyM\nY71d210NQ8/5mdhZQfXM/i1RbmLbrVHmenoO9XSOvaoD7smM++S94UDyydtpfgQZ7T2Hky/5/omI\niIiIiBwXBdpDw1jYAmmaYThJkuBe4lUJVUlIM0KWEdIcC2kMspnMKk/Pgh6fHfkmXxOGm31NPjJ7\nIPvEjwETV+CN5+Yd7GrpsO1r7raIiIiIiMgJU6A9JYSApSkhBDyr6jnOwxW2A4TGQT38em7CupEd\ntt2DsY+G705dT2kOEB8H0s1w26aei7e+K8BmooSIiIiIiIgo0G5oBMUhYMGAZDx72awRZs5YJKy5\nltrMmmcsh3bEEepwofH5g6tnzPue+bxPhuI2fmzRqw96fc1h8osy2Ro0LiIiIiIip5EC7WnDPbE8\nZnDdhrtfN3PA47Bz1wrfi6qsJ1Ef1m7aCzUaWa6tWSujN5dNiz8xzJ7xLSIiIiIiIkMKtEeawaXj\nNh4qPdoNqxFc2pzodVbYOb3eeLOmozLMZo9aW5BGb87Knjf8fXh2j1JeAAAgAElEQVS2e3m14a0C\nbhEREREREVCgPTZK5taZ7DrYnuR1PnrWy+dvbWX1smITW1gzIzTdvR7Z7udm9Xmqh3M6ONm7Rn3D\nRcpHqfeZjU8OmN8dbM/qkGZvi4iIiIjI2aNAe0qdr2YUjNr04/Oz2c065pkOtme+YNn4dDKaH93M\nGgQ+r86J2HpGfc07w8HzPje4PthQcq0qLiIiIiIijyYKtHdpDA+H0dzq4YxsmxyTPXx0fy0cRUw5\no87ZYa8vcXfewHZrPD/LwYePK9gWEREREZFHCwXaM00uWDYaVj0jyIZhjvf0zFFeuic+83TXvfjI\ndK2n53pFREREREROEwXaC0wEz7tiT1t4d4IfX1g6d/T38NmJ4HrOvHKbulybXatRbzE+R3ObrkWU\nyRYRERERkUeTcNIdkGN0yuPZZQNzERERERGR00yB9lxnL+iz0X9ERERERETkoBRoL2QLjtNpPz1e\neDUTTx7f+6CstsjNz8xeaGaVmZVm9viT7s9xunTpEnfffTd33303n/EZn3HS3REREZETojna0+Yt\nuC3HQvO1ReRmVlUV999//0l3Q0RERE6YMtoypmSyiMgh0FeriIjIWad/DRyLmyCCPQVdVDZbRG5+\nAbjzpDshIiIiJ0xDx4eGgaYDZjP3rZqcPjwdmc7dlLpR9Gj2ol42QJ2e/zz3Vbbw2cV1KlgWERER\nEZEzThntKXYaUrsnwObcHmsftBCaiNy86u9T/dgocrO4dOkSr3nNa7h06dJJd0VE9qEsy+HpqY5l\nT3XnTkpcaHsy6LvxGPAmCSJt5qmIyIiZXTCzf2lmf2hm22b2gJm9zcz+5j7qaJnZ3zOzt5vZJTPr\nNep5sZklR1mHmf1ZvTL6j9X3n2JmbzKzPzGzrplVy15Lbc/+isjpcunSJe655x4F2iI3mUagfaq/\nezV0vNYcOT68741I09kr270gQj3iBEfzR4AbGbk9XHC9ufD6fhdhX74vs580s6WHnytvJHL8zOwT\ngLcTJyIPP4Yt4AuBLzKzHwd+c486PhX4j8Djmfwo3zasB3iZmT3X3R88ojp8+Dozexnwg0x+Ye83\n0BYREREZUaA9x+EHccMQVhaZDLIVSoucJma2DvwKcAfxA/oW4M3Ag8ATgW8FXgQ8eUEdHwv8OnAO\nuAb8EHAf8CHgVuB5wMuApwI/b2af5+7lYdfR8JnA1wJ/Dnwv8D+J342ft8x7IiIiIjKLAu1dfOr2\n7PDhf07s94B5QfbZ+7MQOaW+C7ib+KH8Dnf/nsZzv2tmbwV+CXjWgjp+AjhPDGif5e5Xpp5/u5n9\nUl3P04iB+48eQR1Dnwi8F/gCd7/eePy/L7gGERERkYU0R/tYnd6A8eR75o2h5iffGxGZZGYZ8GLi\nB/T3p4JsAOqs8UuAwZw6ng58dl3HC2cEyMN6fgV4K/Fnvxcddh3N6up6vnEqyBYRERG5IQq0a+PJ\nevNy2jYqc5AwML5uso79HEszJjLSy9Q5Uf+MHcgOuhCcsas7c8r57LY5jEXoROSQPAW4WJ//xLxC\n7n4/8F/nPP28+vYD7v7+PdobzvN+qpk1v6sOo46mD7n7f9ujHhEREZF90dDxkcZSYOZ4vRLaoYyk\nttF/ji9Zu0ynm33ZY4tvG/4AMaf/B32Pprvgc1Zf27W1uQJwkeP2yY3z+/Yo+y7gOTMe/4z69kn7\nWNU7A24BHj7EOoYc+P0l6xARERFZmgLtoToYHs9T9lHAd+PB9uSK5LNi1WONG4fXNavRBR2ZXol9\nOhG915rs09c9WuHcJt7uXUvA71qJXEG2yEm4pXE+cyXwhgfmPP4Y9v9zowMrh1xH08yh5wfnwF8A\n0O/3ec973jOz1J133smdd955uE2LiIjcxC5durTUdnuDwcwZaqeOAu2h4Rhl9/pfcLZrCPaSm3vt\nMjlMe3bJ/QTzu3cPW7L15p5de7XRGLPtjTpsup5dwXbzR4Xx66avz30q+LZm/eM7zQB71zByjSsX\nOSkHHZsz3D7rvcAL9vG6+w+5jqZ5q5HfgPj2PPTQQzzlKU+ZWeLVr341r3nNaw6/aRERkZvUvffe\nyz333HPS3Tg0CrTl0CjsFXlUa2Z+Hwt8cEHZx855/BHiXxVrS8yvnucw6jgKo4z/8IfKJEm4ePHi\nzML33nsvb3zjG4+nZyIyU7/fB+DZz342eZ6fcG9EpCxLbr/99j3LPfTQQ8PTWxaVO2kKtGv/+hVP\nVZwoIjLfHzTOnwr8zoKyT53z+O8CnwM8wcwe4+57DUE/qjqOwug7ZDgSpyiK5j8GROSU0udU5KZ1\nquM3BdoiIrKM/0nMal8AvhZ4/axCZvY45u+j/Z+AbyR+Mb4C+EcH6Mdh1HEUekALqNh7DruIiIgc\n3GOIu2f1TrojiyjQFhGRPbl738x+HPhW4NPM7FXu/r3NMmaWAP+GuMr3rDreZmbvAj4T+DYz+113\nf+u8Ns3sycBHufsvHmYdR8HdV4+yfhEREbm52MRqziIiInOY2TngfcDd9UM/A7yZmMF9IvBK4n7b\n7yYOH3fgo939/zbqeALwTuK8KgN+Afi3wB8TFyZ7DPDpxP2ynwZ8r7v/g6l+HEYdfwo8HvgJd3/x\njb0zIiIiIpOU0RYRkaW4+3UzezbwNuAO4Pn1MSoC/DjwW/XtrDr+xMw+G/g54MnAXwOeO6tofVw7\nijpqp3pul4iIiNy8FGiLiMjS3P39ZvZJwLcDX07MCm8QF0v7EXf/WTN7IeMgd1YdHzSzTwO+CvgK\nYvb7duLWXY8AHwB+G/gP7v57R1XHoj6KiIiI3AgNHRcRERERERE5ROGkOyAiIiIiIiLyaKJAW0RE\nREREROQQKdAWEREREREROUQKtEVEREREREQOkQJtERERERERkUOkQFtERM48M3u8mX2fmf2hmW2a\n2SNm9i4ze5WZdQ6xneeb2a+Y2SUz2zGzPzOznzSzzzqsNkTOkqP87JrZq82sWvL4/MO6JpFHKzO7\n3cyeY2b3mNkvm9lDjc/Qjx1Rmyf2vavtvURE5Ewzs+cCPwmcY/e+2gb8b+A57v5/bqCNNvBzwJfM\naaMCvtvdv/ugbYicNUf92TWzVwOvnlH3NAe+0N1/8yDtiJwVZlZNPdT8bP2Eu7/4ENs68e9dZbRF\nROTMMrNPB94CrAMbwHcCnwN8EfBviF/OHwf8opmt3kBTP874y/4dwJcBnwm8BPgg8fv41Wb2d2+g\nDZEz4xg/u0NPBj55zvEpwH2H0IbIWeD18efAfyUGvUfhxL93ldEWEZEzy8x+E3g6MAA+z93fNfX8\nK4HXEr+o7znIL99m9oXA2+s6/hPwN7zx5WtmtwL/E3g8cAV4grtfO9gViZwNx/TZHWW03T258V6L\nnG31Z+o+4D53f8jMPhL4U+Ln9NAy2qfle1cZbREROZPM7KnEf6g78Mbpf6jXXgf8IfEX91eY2UH+\nsf3K+rYAvtGnfuF290eAb6/vXgCU1RZZ4Bg/uyJyiNz9Hnf/ZXd/6IibOhXfuwq0RUTkrPqyxvmb\nZhWov5zfXN+9ADxzPw2Y2RpxKKsDb3f3v5hT9N8D1+vzL99PGyJn0JF/dkXk5nSavncVaIuIyFn1\n9Pp2iziEbJ7faJx/7j7beCqQz6hngrsPgP9BzL49Vdk3kYWO47MrIjenU/O9q0BbRETOqk8g/uL9\nQXefXgm16Y+mXrMfnzinnkXtpMRFnERktuP47E6otwd6wMx69e2vmdm3m9mFG6lXRA7dqfneVaAt\nIiJnjpm1gNvqux9eVNbdrxIzZwAfsc+m7m6cL2wH+FDjfL/tiJwJx/jZnfbFdbtpffv5wL8A/sTM\nnneDdYvI4Tk137vpYVcoIiJyE1hvnG8uUX4LWAHWjrCdrcb5ftsROSuO67M79PvAzwPvAv4CyICP\nB/4W8Czi/O+3mtlz3f1XDtiGiByeU/O9q0BbRETOonbjvL9E+R5xHlfnCNvpNc73247IWXFcn12A\n73f3e2Y8fh/wU2b2dcAbgAR4o5l9jLsv0ycROTqn5ntXQ8dFROQs6jbO87mlxlrEOaE7R9hOq3G+\n33ZEzorj+uzi7tf3eP5HgB8lBvJ3AV+x3zZE5NCdmu9dBdoiInIWbTTOlxkutlrfLjNU9aDtrDbO\n99uOyFlxXJ/dZd3bOP+CI2pDRJZ3ar53FWiLiMiZ4+494JH67t2LytarCg+/jD+0qOwMzYVYFrbD\n5EIs+21H5Ew4xs/ust7fOH/cEbUhIss7Nd+7CrRFROSsej9xyOfHmtmi78MnNc7/8ABtzKpnUTsF\n8Mf7bEfkLDmOz+6y/IjqFZGDOTXfuwq0RUTkrPrt+nYVeMqCcs3hoL+zzzbuY7wYy9xhpWaWAZ9F\n/Ef7fe5e7rMdkbPkOD67y2ru2fsXR9SGiCzv1HzvKtAWEZGz6ucb539nVgEzM+Bv13evAr+2nwbc\nfRP4VWL27YvN7K45Rb8COFef//v9tCFyBh35Z3cfXt44/40jakNElnSavncVaIuIyJnk7vcBv0X8\nMn6JmT1tRrFXAZ9A/MX79dO/eJvZC82sqo/vmtPU99a3KfCvp4e6mtltwL+s714lrmIsInMcx2fX\nzJ5sZh+zqB/19l4vqe/+JfAf9n81IrIfN9P3rvbRFhGRs+wVxCGlHeBtZvbPiZmvDvB84KV1uQ8A\nr1tQz9x5mu7+a2b2FuCrgb9et/N64jDTTwG+E3h8Xcc/cPdrN3RFImfDUX92n0LcG/vXgP8M/AFx\nEbaUOK/zBcBfqcsWwEvdXdvyiSxgZp8LfGzjodsa5x9rZi9slnf3n1hQ3an/3lWgLSIiZ5a7/56Z\nfRXwU8QhZP98ugjxH+rPcfetG2jqxcA68KXAM4BnTrVRAt/t7spmiyzhmD67Afgi4IvndYMYfL/Y\n3X/5gG2InCV/F3jhjMcNeHp9DDmwKNDey4l/7yrQFhGRM83df8nMPoWYIXsOcTuQPvBB4GeBf+3u\n3UVVLNFGF3iumX018CLgU4ELwAPAb9ZtvPNGrkPkrDniz+4vEYeFfzbw6cBjgVuJAcFl4L3AfwHe\nVM8JFZHlLLtS/6JyN8X3rrlrVwIRERERERGRw6LF0EREREREREQOkQJtERERERERkUOkQFtERERE\nRETkECnQFhERERERETlECrRFREREREREDpECbREREREREZFDpEBbRERERERE5BAp0BYRERERERE5\nRAq0RURERERERA6RAm05EWb2Z2ZW1cfjD6nOVzfq/K45Zb6gUeYdh9GuiIiIiIhIU3rSHZAzy+vj\nqOo+jDIiIiIiIiL7poz2Amb2wkb288dOuj+PQnbSHRARERERETlsCrSXo+znzUN/ViIiIiIicqI0\ndFweNdz9HuCek+6HiIiIiIicbcpoi4iIiIiIiBwiBdqLaQ6xiIiIiIiI7MuJBtpm9ngze7mZ/bSZ\n/YGZXTWzvpk9bGa/b2Y/bGZPW7KuX28sXPb5S5SfuxWUmb3JzCpguACaAS9qlK+W2SLKzFbN7JvN\n7L+Y2YfMbMfMLtfX+oNm9plLXtuwrbLx2KfW788fmdlGffwPM/t6M0tm1PEUM/txM3u/mW3W7/E7\nzOxrlulDo57UzP6Omf2HeouubTO7VvfjjWb2xfupb6rujzez7zez/1XXec3M3mtm/9TMHrvE6/fc\n3uuA/brbzP6xmf2mmd1vZl0ze8TM3mNmrzWzjzustkRERERE5OZ3YnO0zey1wLcyzho3F7G6CNwC\nPBl4uZm9BXiJu+8sqNKnbpc1q3xz66lZ/duTmf014EeAO6ZenwMXgE8CvtHMfhp46R7XNtG+mf0D\n4J8ByVS/PrM+nmtmf93dB2YWgB8CXj5VTwd4BvAMM3sO8AJ3X3iN9Y8e/x/whKm6WsAT6+PFZvY2\n4Gvc/ZE9rqlZ90uBf1XX1ezHJ9fHN5jZi9z9F5ao7lAWRDMzI875fhXQnqr7AvH/p58GvMLMvsfd\n//FhtCsiIiIiIje3k1wM7e76tgI+UB+PAAPgVuDTgY+py3w1sA4895j69jZgA3gS8MXE4OqPgF+d\nUfaPpx8ws/8H+CniiAEHSuC3gQ8Ca8DnAXfVxb8G+Cgz+0J37+/VMTP7OuBf1vW+F/i9uv6nAZ9Y\nF/urxKD164H/F3hpXeY+4A/rfn0e8NF1+a+u6/qeBe1+PvDLxAB9+EPEu4D3E388+CzGf15/Bfht\nM3v6ksH2lwGvr+v8MPG92iQG7p9b9/ci8O/M7Lnu/rYl6rwh9Q8UPwv8DcbXez/xmh8i/jk+jXjN\nKfCdZnabu798do0iIiIiInJWnGSg/W7gPwO/6O6XZxUws88lDt/+OOBLzexr3P2nj7pjdRs/bWYv\nJAbaAO9092/e67Vm9gTg3zAelv9O4G+5+59Olfv7wGvrcp9NDHL//hLd+wHgEvDV7v5bU3V+K/C9\nxKDwxWb2AWKQ/b+A57v7+xplrS77LfVD/8jMfnBWZt3MLhAz2R1ihv8DdX2/N1Xu+fW1d4hB8o8S\ng+h5htnh7yH+EPAqd/+BqTqfRAx4nwxkwJvM7BPd/dqCeg/DaxgH2X8JfIO7/8fpQmb2FcRrvgC8\n1Mze7u5vPeK+iYiIiIjIKXZic7Td/fvc/c3zguy6zO8AzwK69UPfdCyduzGvJmY7jZjB/qvTQTaA\nu78e+La6nBGHkX/kHnUbcQTAF00H2XWdrwPeXpdLgdcBDwDPaAbZdVmv2/9A/dAa8Jw57X4L8Li6\n3st1+783XcjdfwZ4QeOanmtmT1/imjLgO6aD7LrOPyJmyB+uy97B+MeBI1H/OXwHMci+DHzurCC7\n7t/PEQPyodccZd9EREREROT0O/Wrjrv7nwO/RgyynmpmayfcpbnM7DzwVfVdB77N3TcWvOQHiNlm\niH8WX7dHEw68oQ4+5/mZYXfq8v9s3vBtd6+I2eKheYuzvbTR/ne7+1/M7aD7zxNHKgx9/YK+Dv0p\n8UeBeXU+AHx3fdeAlyxR5434+8T57wD3uPufLSrs7r8O/Aqxb59gZp92pL0TEREREZFT7SSHjo+Y\n2UcQg7wnEofgDocoDw3nEhvwqcDvHGsHl/c5xMW8IGZgf3FRYXd3M/sx4Pvqh565RBs/t8fzf7DP\n8s1M90dPP2lmn8B4QbcS+Mk96gN4I/AlxD+vZywoN/wx4KfroH+RnwK+nxgA32VmT3T3/71EXw7i\nSxrnPzO31KR3EOfGAzydOHdeRERERETOoBMNtM3ss4kLez2d5fesvu3oenTDPr2+deBdSwSPMP7R\nwBqvn2UYlL5vQRmAK43za+5+aY/yzaH752Y837ymD7j7lRllpjV/CLnDzO5w979cUP6/71Whu1+t\n55wPF3z7dODQA20zu4X4g48DfeA1cTr7nj6xcf4Rh90vERERERG5eZzk9l4vJi4iNQwg99qSaRjt\nrB9lv27Q7Y3zP1/yNX/WOM/NbM3dN+cVdvfre9RXDIsCyywYVjTOsxnP7/ua3P1BM+sy3hLrNuKC\nYvP832XqrcsNA9rbFxW8AXc2zlvAN+7z9UZcIV1ERERERM6oE5mjXQ9HfkN914nzlF9BHD7+WKDj\n7snwAN7cePlpnlfenD++teRrpssd5g8Jh7Gf9EGuabrsXte0fQR1HtT5xrkf8EgQEREREZEz66Qy\n2t9St+3AfwH+ursXC8ofRVB1FAF7MxO9uuRrpsstWjztJBzkmqbL7nVNK0dQ50ENg3kjDr1XdlpE\nRERERPblpLLDX9g4/8d7BNkAe217BTBonC/zA8L5vYvs20ON88cv+ZqPapz3Fw0bPyH7viYzu53x\nsHGIC8Mtsux71Zz7vFedB/VA4/ycmbXnlhQREREREZnhpALtuxrnCxf3MrNzwKew9zDo5tzlW5fo\nwycvUWa/Q69/t7414DNtuVW0PqfR1u8uKnhCmtf0JDO7sMRrPrdx/pd7LIQG8Fl7VVhvnfakxkPv\nWaIf+1b39UONhz5nXlkREREREZFZTirQbq7Gvdew4ZcSF+naK2j9s8b5wn2MzexO4krnewXS3cb5\nrIXCpv03oFef3w48Z49+GPB3Gg+9Y4k2jpW7/yHjhcwS4AVLvGy4z7UT90CfWz3xz/X5S/wo8QLG\nc58vHeHWXjC5Lds3HGE7IiIiIiLyKHRSgfafNM6fN6+QmX0c8F0sl1l+Z+P8+Wa2KDB+PcsF7480\nzh+3Vwfc/RrwbxsPvdbMFs1r/ibGmfUK+JG92jghw34Z8F31DxUzmdnzmPyB4Q3zyjZ8DHHe/rw6\nHwv8E8aLjb1xiTpvxPcR9ww34MvN7IXLvrDuq4iIiIiInGEnFWj/QuP8dWb2rOkCZvZFxGzoGsut\ndv2LxAWyjDin+43T82vN7KKZvRn4Siaz1fM0h7U/zczuXuI1301cQMyI+zH/VzP76Kl+mJm9ghjQ\nQQwef8jdl93m6ri9Hri/Pr8VeMf/z967R1u25XV9n9+cc639OK+qU6fq1u2+gBDAQZqANE2QEAUV\nFBNIGBAeQ4UGhAYSDWYIKiEO4vCFEAEfaDAiTxUdiUSibTeP8BQQmwYSBMEICP2g+166763HOXvv\ntebvlz9+c+19bnW9urtuV13q97lj3rXP2WvPNdc6596q7/z+HiLyobeeJCKfCfwDdoL4u83sR+8x\n99Sv+qtF5I/f6my3CvXfi0cICJ5D/fXvxr3cEzP7ZeAvTEsA/p6IfI2I3DYlQUSyiHy8iHw7j2b4\nfxAEQRAEQRAE70EeVtXxrwc+HxdPl4DXiMjrgZ/HhdfLgZe1168F3gp89t0mNLMzEfnzwFfj4uiz\ngD8gIj+A52+/F/C78VD1/7fN+6X3mPMtIvJjeJ7uAvh/ROQ1wJvZhb//ezP7X8995pdF5POB78BD\nnT8K+EUR+RHg3+MbB7+LnUNuwI8Df/pua3mYmNmzIvKHgFfjz++3A68XkX+F/8x6PM/6/aePAL+E\n/4zvxtRD/U8Bf62NLxORH8U3Kz4QD/GfNoQG4HPN7NkHdGt3xMz+nIi8DzC52X8S+OMi8jr853gK\nHOLF7D6EXUX0F6pIWxAEQRAEQRAELxIeitA2s6dF5L8G/ilw0r798jZg54h+F57D/Nfvc+q/CnwA\nO4F3BfiM85fG86g/DXjVfc75JcD34y3GjoDPvOX9H+SW8Ggz+8cicgMPcX4CF9y/p41pHVM4/D8A\nvsDMNve5nvvlfgqx3ff5ZvYjLcrg7wPv1779O3l+IbPpnr4X+MNmdj70/m78U9zV/np8A+L8M56e\n1bO4yH7tfc55L+75fMzs80Tkp4A/B1zE0w3+M96xQNr5Htr3cvCDIAiCIAiCIPgtzsNytDGznxCR\nlwF/AvgkduLtzcBPAd9hZv8coEUTnxend5rTgC8Uke/ChfRH4o75bwK/AHx7m7eem/Ne6/wpEfkQ\nPJ/697R17rMrzHXbOczs1SLy/sDnAZ+IO/QnwBnwJjws/tvM7F/faw3nrnG/VdBfkPPN7CdbKPcf\nAT4ZLzp3BXeafwMXmf/QzL7vnbiutbm/UUR+GPgi4OOAKUz/V4HvxkPr33K7Se5wL/dzzv38/L9B\nRL4Fj5D4eOBD8UiMOZ6q8Abg3+AbLq82szfefqYgCIIgCIIgCB4XxLVpEARBEARBEARBEAQPgodV\nDC0IgiAIgiAIgiAIfksSQjsIgiAIgiAIgiAIHiAhtIMgCIIgCIIgCILgARJCOwiCIAiCIAiCIAge\nICG0gyAIgiAIgiAIguABEkI7CIIgCIIgCIIgCB4gIbSDIAiCIAiCIAiC4AESQjsIgiAIgiAIgiAI\nHiAhtIMgCIIgCIIgCILgARJCOwiCIAiCIAiCIAgeICG0gyAIgiAIgiAIguABEkI7CIIgCIIgCIIg\nCB4g5WEvIAiCIAhe7IjITWAGKPDWh7ycIAiCIPitzBXcMF6b2d7DXsydEDN72Gt4JPiaV3yEAaxM\nWWGcoWwAKQnJPlbjyNlm4HQYuDkMPDtUnh0rzw2V3HV0fU/X9cy6wl6BvSIsCxx1cFTgsMA8GQkj\nmSGmPHNjzdM3VjxzY821jXKqmVNLrCyTSiZ3mVwS8z6zP8vszxJ7vdDZpo01SUByIuWMJeFsGFmN\nldU4crqpnG5GzjaV1aCMClWFUcE0YSYwHU38iGAGt/5uyPYoIH4eAoq1AWKGmJHa59UUNaO291WE\n2uYQhWSQ1CgYxXwkjASIGEkgdSAFUoHcQ5kZZWbkztfzutcPQhAEwUNEREYgP+x1BEEQBMFjRDWz\nR9Y4fmQX9p5mKymlaTYTRIwmJxHMh+DCdhrsxjTJVvUJiIh/Jgk5Q0nWxKgiBin5fLs5rX3eEDHA\n2rQG0q6fIJv/8DpLZDEQQQBTKCYU82OH0ImwSZCTYQiKISaYGBiYGK6pZfscnnfc/QsxMPEjggvy\n9o82gYyB+hsogrZPm4A1cT496mTt/s3vKRtbkS3mQlsMEtKepW1/REEQBI8Q/n9REV7ykpc87LUE\nQXAfbDYbnn76aS5fvkzf9w97OUEQ3CdvetObJkPwkXaMQ2g3VDxd3ZosFGQnpieBK0ZKhiQXzufF\nNpwT2JiLQvwvXTkJOQulJEoysIqogYqL5tQEd2oiU82FrMtTtiK3iW8RyAKdCDPELRTbudBVQU0w\nElWUIUGXYTSwalTExT5gJpjtxPzuSu1OblHeZtPadmeZ2E5s+35AmxtUPI5S21YFApLExbMYSWy7\naVCADne0t5sW5j+LaSMipd0mByG4gyB4dHgbcOXk5IQ3vOEND3stQRDcB69//ev58A//cF7zmtfw\n8pe//GEvJwiC++TKlSs8/fTT4H/2PrKE0G6YnJPLtnNOJzc7bUWuudhrYjvtPtWOk1SVW9zsvBPa\naiAKTAJbts72TkTa1iZ3ITw52n79ItCLMJNENsOqu9laoVoT2iaMCENztMfsojebMU76valmDxl3\nx3t3PGdm7/Q+mLX7lO0b079tu3Q/YwoXN8Qd7XM7EyK+sX0T6VoAACAASURBVJAwMh5zWabbni6o\nTWjjz3xytUNkB0EQBEEQBEHwqBJCu6Ht6ILQ5fWtYeJTiPfkPj8vfNyayH6eUNy52ikJKSd3xMWV\nuuFu960ieyu2wUPCZXLPxQW+CCUJXUrMUiKrO8laXcNXFdR8dAhFhC7BhhaanTxUW3VauMdnb8X2\n5HJbc7SbSz252TIJ6q3zbC0sfHK73V33B9FC2pvDPz2dKQ97K7JbfnbG12ZT2PwUUZD83iWde+aT\ntR4EQRAEQRAEQfAIEUK7UVuMdJXJkW5u6zmxnRMujK0J563QPh/e3TBgK1bdlcZsUoiklICMpOzq\n8Xy2t/nVRaaRSZJIksmSyCmRE3RZ6LOQq6HVUIHRjM6EqjCqkc1Dy1MLMd9uFJg76VMRNExQnULF\ndyJbleZgN/N7F9X9vKSIxBRw3/Kpweds3zea4y4u0rMZWZWsRlElmxeIS60Y2rQsyUAWJE+v28XC\n0Q6C4BHkmWee4amnnnrYywiC4D7YbDYAfMInfELkaAfBi4SrV68+7CXcNyG0G5PQVqy5qecc7JYT\nPTZnOjeRKrd0Id+a0FNicxOrU+j3JEw9RzkhUkjJxfQkVWmB0v5vF9cpZVJK5HZMIpTsQnuWEwlF\nE1S8yFo13B1W9SJj50R2bqHsySBltiLbTFqBNDC1rchW3bn0U6Ezk13QOG3F240J8I0IfG4PH2/5\n42ItZ9sFtYtsP2bbDZEmtBNYE9dSgCLItlra+Vj2IAgeRUTkfYBfaV9+jpl927s4zyuBb8b/o39f\nM/u1W97/ZuCVwK+a2fu9G0t+tzEz3vjGNz7MJQRB8E7Scj2DIAgeKCG0G7UddRKSzVyexGlOtBBn\nd2yTCqmFdO9ylqcj59xstm6251lLK+7lYlpSbkJ75yxvxXZztNNWYDdXO7XQ8Zzoi7vBVYwRRUzc\nyVZ1N7s52hkhizw//N385qwJbdPt0r011zmhPTnWu0rj28hwpkPz6P35THna0zNoRdHq1P4LI1df\nZ2kCe3K0RbyImk8o7cGL/7aGox0EL0Yes12xlz7sBQRBcF9sgKeBy0A42kHwaPNmdsm+Lw5CaDfq\npDKbW5tabnROu5DxvG03ZR4Hfa7l1y7UXHbFzdI5Mb4tbLYrEratzI2cq8qdtipfzoeLS24h44ks\nqc2r21xlSUZOguXk1btFqCJsDIop2SrZEjkZBRhh62JLaeXGzDA1VKZNA1+zq+opw3oqlubtvHzv\nQLZV2l1se8i62M7FN/N5sxi1Cep3ENgtGdySu/4UgQ7oXGTbrSI7esAHjwAi8oPA7wZ+0Mx+70Ne\nzm9lXiRhLAJE1fEgeHHweuDDgdcAUXU8CB5tngJeXBFjIbQb2oS2SHNkJXl177QT24MJqQIYpgqm\nW3fWneJdYTMX27sxCdWpBRZ49HOd3GQEk9QKkqWto+052ZmS8lZk5yk/vM1Iq8SdMuRCK36W0Jzo\nK3RWKTq6e4wx4mKYJvC1ueumhqbz2eJe3GxbebwJXJvuxHY9radibQlr4eot1JypR7ehk9gGxHQb\nKj6JbJmKnyVary9BOrCp91dpk07nvwj+yh08FrxIBOCLFzP7VuBbH/Y6giAIgiAI7pcQ2o1JaKet\nwPWK4EWEMrnamlrucXO0twJxW+Ns52jLTmRPPbW3H53EtnevagXDUsviTs8LL09bce3HsnW0pyJs\nLag7JS8Wpi601RKmmS4ZpY4UMsUqoxg5udil5YFr8utpFurova11e18713qHi+bJ0U+kbRTAJLIz\n5mLeppv2OVW8U7mcK3yWWl72tuJaauHiTWxPjnZT6O/YbiwIHj6RzBAEQRAEQRBsCaHd0KbaJnfV\ntZ60FGHZtp0SA/RcFXGeVy+c55Xlnt68RRlqa5Nl6mHUav69aZgaaoppxTRhljGvIuYR660tl4l/\nLk3XyR4KnsxzybMKJWWKZArZQ8dRskJuwlaTeUvvNBVK27Uys+fdi52/od19CUiybX/r57VBY+dU\n00LNtd2/T2G7c2R6wExKHSktfDxPYeO7kPZkk2MeBEHwSFDvfUoQBI8WTwJf2Y5BELxYyDlPLx/p\nP3tDq2yx541kTfdpK789KlT1183NbinFW4FtqmhVVOt2mOq26vjk2BpGNaVapZoyWmW0StVK1YGx\nDozjhmHcMA5rxs2Zj2HFuFn798aBWkefB0OTYdmgmwZIZ6QOchFKTnS5hZ236uRi1Y9UDyIX3YbB\nnw8fP1fVDdMKTRhvHfvkYptkSFJICqmCKJIUyUoqRspGStbE/FRd3HOyrRU+s8nB7gQpXm1csvn6\nrK1v2ghpI3h8EZGXichXiMhrROTXRWQlItdF5JdE5FtE5CPv8tlvFhEVkV++xzVe2c6rIvLe577/\nLSKiwMe0b31sO+/8+JU7zPnBIvKNbZ03ReSaiPyciHxtq9R9p7W8z7m5P7t971NE5HtE5C0ickNE\nfkZE/piIlFs++4dE5AfbeTdF5KdE5Avvdu/v7nrvMNenicj3tXWcisgviMhfEpGju3zmtj+DdxYR\nORSRLxeRHxWRt4rIWkTeJCLfLSKf+q7O29gV+giC4EXCk8D/TAjtIHhxcU5oP9LV0cLR3rJLnBbb\nhTaLTj2krYnu3ZgqjE/mrlfqVmqtaBI0J1SnHOWpiJi0QmKKTmJbK7WJ7bHCWIVRBRkUsREsM0hh\nyJlxGghjEupU/Tx5ODmZtgHg95EGyCYUSz60kqQJbXYh7ZOoTmKtqNsUPj7laU/l2NvmQmqVy9OU\nn00L//bhBdr8e9v6ZWaYnuvM1YZhWBasNBe7E6RrRdpya/PVNiikOfheiDz+Qvs4IyIfA/xA+/J8\nHEkH/EfA+wOfLSJ/2cy+4gVYwvkEhvMd787zDn8AiMiXA3+eXU+/iQ8C/mPgi0XkVWb27fe4NiLy\nt4AvumWeDwH+OvAxIvLp+PP4+8Cn3nLehwF/W0Q+zMy+6E4XekDrneb6JuBzb5nnA4E/g/+sfp+Z\n/eK95nlXEJHfB/wj4PiW6z8BfCLwiSLyauDTzez0hVhDEARBEASPDyG0J2QXzrwdZqRJUBvN0W5C\nu/0V2x1taSJS0VrRRBPZoCqYJQ81B7YVu7eOdt0621VHqtLENlOjLMQyo2TGXHZDEjUnas6knEii\nSE5bQTvdUmIS2kLRRK5Cxrz3tk1Ce9cH23Otz4XCn+sJvi1sNuWgZy/+tq18LlN5ta2CdmHeqpGj\nbRpt/crZdRe3BFbkeTnZk6M9iXdMd726W7uy4LGmADeAf4YL7n8LXAOuAC8D/nvgfYA/IyK/1Apq\nPUj+R+BrgG8BXgG8DheR59mc/0JE/lvgL+L/KbwV+Crgx/C9o48DvgzYB75ZRJ42s9fc5fpfDHwk\nfv/fBPwH4L2ALwd+J/ApwOcBH9pefwfwD/H+GB+A2zgfBHyBiPwTM/ueWy/wgNf73wEfAfwE8PXA\nv8N/Vp8DfDpuKb1GRD7YzG7eZZ53GhH5aODV+O/MbwB/A/hZ4E3AS4DPAP4I8Afxomuf9iCvHwRB\nEATB40cI7UZKTXJOvaZpoeOyE6yTqJ76XpsZVSvjWKE5rcncFR5TRjNYyRjeLzvlQu4SyZRkRjZl\nrsKeJQ4ko7ORMhh5NLrRyK3qeUkw6zL9LNH1iVKEXAQpCYpgyftOmygIjC33e2wh5VKETKanY8QY\nxagCg0JVoarfjxbophxwm3LChXpLG60kshu7bmTNxW4tw87lum/dbhWoLfJevcq5Wct3bw62dcmd\n7Va2/FxnNGBqpy2tX3fwmPPTwFNmdu02732viPxN4J8DHw98pYh8m9ktv8zvBmb2ZuDNIjKJwptm\n9vN3Ol9EToCvxn+b3wR8pJm96dwpPy4i/xfwI8AS+Dsi8r5mdqf8o/8U+Foz+9Jz3/sZEfk+4OeB\n98aF8UXgS8zsb95y3g8Dv4QL5S8Gnie0X4D1fgS+KfDJZnbe6X+tiPwc7pq/N/BncYf7gdBC6L8D\n//PuXwD/jZmtzp3yM8CrReRHgL8DfEpz1r//Qa0hCIIgCILHj9AqjW0rrklANiHnqceT4ytN+Hkl\n8V2o+Mg4jgzDwNCOtY5UVS/8JbSq4IXcdZRZT7+YMVvOWewtWB4s2T/a5+Boz8fhkv2DBft7c5bL\njvm8MJ8n+j7RdUJuI7UkZcug2RizMqTKmKofZUSzQoHSZfq+0M86Zn0bXaErmVISOQtlGmV6PbU3\nk9ZS7NxzEQ/d3rY0S4JkIZVE6oTUC2mWkLnANGZt9Ak6z8fW4sMmkd35a7JsW4pPoeLFfLRaaa1I\nXbjajytm9rY7iOzp/RF3XMGd7d/xHlnYnflcXJAC/A+3iFYAzOxngL+M7y+9FPjku8z368Cfvs0c\nZ7grK3iY9E/cIrKn894CfFc773e9wOsVYAW86haRPfGXgJ9r5/3RW/PL300+E//5r4DPvkVkbzGz\nvwv8ZPvycx7g9YMgCIIgeAwJod1IKe1GE5NTxHIy21YcT+zadZmBVmUcK+O4E9vjODLWVgzNPC8b\nSaScyaWj9DO6+Yx+uWC+v2R5sMf+0R6HF/Y5OFpycLjk4HDB3v6M5bJnscjMZplZE9qlCKm4A+xC\nW6jZqEnPiezKINUd7SxNaE8iu2feBHffFUpOzxPZXRG69rW76rvCY5PITs35z9MGRfYhRZBJZM8E\nOS+2J5HdC9YnrEtoJz5K2gruqdI4MlUm9yrqZTsmR1taY7EgABHpReS9ROSDWpG0l/H8/8d96MNa\nW+Pj2vFZXODeib97m8/cjn9yF/f4Z8+9/sd3mWM676KIHN7y3oNcrwHfY2a/cds3PdJgCu0/Bl5+\nl+u9s/xX7fhDZva2e5z7w7jY/6gHeP0gCIIgCB5DInS8MfWJnvKUtznL53p3eTS1Fwmr28rhRlWF\n1kNaEFLKdN2M2dwd672DPfaOXEzvL3t3fYs7wHm1oVttmK823FiPnG5GzjaVs03F6tjGQJdg2QmL\nTlh2wqxk5l1i1iVE8OJqKNV0m1ueVD2d3AQzoTejDAPdMDIbBjajshmNoRrDYIxDO26M9Wr0UUY2\nm4pWo1avqu4bElOP8OnZ7FpzSYu9l2m3Ykr6nkquyZQEri3mHf9NnJztDJbEC6AJYLr9eSSbnPUU\nIjtARJbAl+A5ti8D8l1OP3mPLOrOfDD+2/76uwhkzOytIvKruAv7wXeZ75fu8t6z78J5B3iO+8SD\nXu+/vst7sHOTAf6TW75+d3gFfh+f0KrE3w9X3/XLKZ56fi+m2JwgCIIgCJzKnTt2PQPAZrO5w/uP\nHiG0G+fLgm3bWk25xcn7OKsZoxqDVTY6MpoLWwWSJFIulNIz6xcs9w44PDrk4sUDjo+PuHR8xKWL\nRxzszcld8lGEg/WaC+s1N9drTtcDp5uR03XlbDNSx4E6jtRxJKPMMtvRZ6Evib64aFWbenK3Y8vT\ndkcdMD9nM45sxspQK2NVxgpDNcZRGTaVYePHs9M1pzdWnN5cszrbMGxGhmFk3FT/u+G0CyHWcsOn\nsma+ITFZ39ZamiHi4jlP5cxadXL1QnLS8rKlJXWbgIn4s/Xu214pXVoOeAjtx57WVuoHgN/Gror0\n7XKwp1+UxXtgWXfjuB3feh/n/gZ+X8d3OedulbHPC8r7Pe9W1feg13uved5ym2s/CCbV+87k58/f\nvUs+/e59PAiCIAiC2/L00y+eP2NDaDcmR3trYJttTVhrScKqRkXZWGVjlcGq97DGWq5ypisd/WzB\ncu+QgwvHXDw55uLJJY5Pjrl0+RKH+0tKnyldonSJ1WZ1bmw4XY+crgfONmMTtwObzYhYpROliPkx\ne3h3l73Gd23tvmsT1NPwiMxW39uUWttQpaq11uDGWJXNemSzGdisB25eP+X67Caz7iY3yxnr1caH\nDd6zW8yrq297dYk/ialseW7dwNpDdHe6iW2SO+CqNI2ObOPS2+clodJy4Wm9y9FWkG7ys0NoP+Z8\nBy7uFPh7eOumXwCeNrMBQPw/7Glr9FH5hXlgBdneQzyo9T6s+542EP4F8Kde6IuJCCcn9w6eyDmf\n7wMaBEEQBI89tVZqvWMQHQAnJyc888wzLwrBHUJ7Yvor+GT/tq+3wlGgCowo4ySym3ts23Dxntls\nwXK5z8HBERcunnDp8hUuXbnMpStXOL58mcPDfbo+0fWZrktshjOGzRmb4YzV5GqvNpyuB9brkfVm\nYL0ZsVpJVLJVEvq8/GlDWvVwQVXOiWxcpJ4b20rfOr0PakKtyma9Yb3esFlvuDa7Tl+uUVJPSR1n\neUWRFaJnjCgVZcQwqedCwdnF3E+j9Q0XE3+OLTjfQ86nonLNzc7bEuWYJERyy9BWkEpqvb8T4Wg/\n7ojIbwc+Gv8V/otm9pV3OPVuzujk5t6rVsXeO7m8O/E2PCT5ifs49yp+b/fKKX4hedDrvdc8599/\nkPf9m3jrsP5uVeEfFC95yUt4wxve8EJfJgiCIAgeW5566qmHvYT7IoT2ROuj7cLTnWEwTGX6gqGq\nt85Sw9Sdi5IzXUnsLfe4cOGYo6OLnFy6xJNPXuHJJ69w9eplLl065ujiMftHF1jsLd3NbtW+lcRY\ngU2l1pHNesPp6ciNmyvPoR58oF7lO5PJklAVck5odvGqmlCbjnhF9O2mwU5oT622JJmXfBO361WF\nlEZyrpQyovU6XtD5Grlco3TXSeU6pOts6sBGB0wHzCA1h9tEKQVyYtsiDVpYvkz5660FeeuxjXi/\nckm7VmCpiewkhSSZYkrRTE5KoT0HSVHJ7/HmZede363Y1yvu8t71drxwj2t94D3ev1+n9udwwfdy\nEUl3qL6NiFzG852nzzwsHvR6P+Ie1zv//oO875/Ge2W/QkRKq0YfBEEQBEHwghJCeyI1oa0utMfm\n9pq2kGdgrMZYm9BuIcw5JfoC+3v7HF885srlqzxx9QmuXr3M1Scvc/XqZQ6Pjjg4OGTv8JD5Yk7J\nqYUNCqMaaVMxNtRxxXptnN0cuHF97UXKRlyIA0XERWaCapliGSUhZNQKZhmzhKk0sQ3nhba0Mupp\nErWpkFKHSIeRycUondF1BlxH5DlyukYpz5LK2yH3Hs49rLBhRR1c0Hv8t0IScrImtM1F/dZ1difa\nwMW2bVO3/dhaeU3t05JkUurIUsimFJRium3rNVU+Dx5bzv+/626O8xff5b1faccDEfkAM/t3t54g\nIh3wqfdYy9QuanaP874P7+l9AfgU4H+/w3mfj/+nYO0zD4sHuV4Bfr+IPNHaij3/TQ/xf2X78u3A\n69+Ndd/KdwP/JXCEtyz73x7g3EEQBEEQBLclhPbE5GjLLqFTtYlt3B0etrnNTSiK95/uu8T+co/j\nCxd58upVXvKSl/LEkyc8cfUyV568zHK5x2K5x2K5pO97UkpkSUhKlE0l5Q1wRq2Zzdo4Ox25cW3F\naImqwmgeLF2SUJK34lIrGBmTgkjBrIOt2PYq46rSHGNPhBaUnHD3OBs5z3ykOUjHWLx92NgLkq6T\n0nOUco3c7UPuMRGqVWydqSu8fVgdEcmQvFCZt/wyF9r+YF1st4Rta+425k52MkNwUc4ktsWFdpZC\nTj1F1d1sdaGd5Fwh8+Bx5bwo/hxuU6FaRL4Yb+10J8f5h869/pPAF93mnK/D+0PfjTe34/vd47xv\nBr4S7039V0Xkx27tTS0iHwp8efvyDcD/eY85X0ge5HoN34j4RhH5lNu441+OVxo34JumHPsHxLcC\n/xPwXsD/IiL/1sx+5E4ni8hHA9nMfvgBriEIgiAIgseMENoTrSbNFDpuurNorH1dTTDz3OAihXnf\nUfqOOR2XLl7k8skJT1y5wpUrlzk+vsjh0RF7e3vM5gv62YzSdaRSWmh0cke25SGrJWqFYais1yNn\nqw1qGSVTzcOpXcWat8RqydCGz9XKdLujbaAqzdH2awiGpOz52dbKo1lBrUNzh0hPVb+eSSGVRDfP\nmMzQ1FElYTlDKZSza5Sza6TTns3QY7ZqQxA8l3xbuZ1zCpppTUxFx1tLtMn5nnKv/Z/c/ikiFBJZ\nzO+49dcOQ/vxxcx+WkR+Dm8n9UUicgx8Oy56nwI+C3eifxT4z7mN2DaznxGRH8d7Jr9KRGa4KHsO\n+ADgC4GPBf4lng9+J34Md0qviMjX4kXanmvvDWb2a+16z4jIlwHfgIu+nxKRr2qfL7h7/KXAPp7r\n8aq7tdV6oXkB1vs6fOPjX4rI1+GbJVfwjZLPaOf8OvAXHvB9bETk0/EK9fvA/y0i34lvCvwKnuXy\nJPDhuHP/wcAfw3tqB0EQBEEQvEuE0G5IEab62WatCBrshCFedEzwPOm+JPpugfRL6JZcPrnE5ZMT\nLl++zKVLlzg8OmCxXFC6jlwyKSdvXQW7vtxMgre151L16t/D6AXQpvUgaJIW155cbGO7NYm/whSs\nokobzVNuRceSgGahFiOr50u7u+1h5ao0oZ5RmZGK0EnPIvVYKkg/pyz26G++ne7m2ynzOavVc4zD\nDeqYGQcQG5roH9satw21n/88zZCt2G4iWyahnckkF9ckMkLGtkKbrch+sRVvDh4wnwV8P3AR+PQ2\nJgz4WeDT2DnOt+PzgB/EBd8r2YUvT3N8DV7J/G5C+ztxR/Z9gT/RxsSvcs7pNrO/LSJHwJ9v1/y6\nW+YyPBT9C8zstXe55nuEB7zebwA+BhfW33mbed4E/AEzu84Dxsz+lYh8LJ7P/17AH27jHU5t49pt\n3guCIAiCILhvQmhPlNQiml1wqti5BrOCmItQwcO++5ToFwu65SHd3iGXT064crkJ7ZNLLPbmLBYz\ncldIJbf+0OyqmU9CeWrH1YqsDWNlGL3NlrXK3IiSVJCUvJCZnmvZ1U5xBas+X22tvuokshNJsott\n9d7VKbcc6ia2RfxurRVHE+ZI19P1S9JsifQzynKP+f4h/Y19ynxBnnX0Nzs2q8JmBRsbsclJV235\n2638uEyJ2VPcfUvtft4/7flu3exEIVMwihiZySXX7f0Hjy9m9rMi8jtwkfsH8YJX14H/D2/19bea\nm3m3OX5RRF4OfAXwX+DO5nO4+/o3zOy1IvJKdgLsdnPcFJGPauv4/XhhsOX09m3O/yoR+We4a/p7\n27oV+DXgtcBfm1zwOy37Tmt5Ic57kOs1sz8qIt8DvAoPFd8H/gPwXcBfMbPn7jjLve/nXvfxkyLy\nAbjQ/yTgw4CTdi9P4xsqPwT8H7fL1w+CIAiCIHhnELMQKwB/9pNeYQB1MOraqBuoY/Owm0Ze6chp\nHTjTgVoKywuXWF44YXl0zEvf+7fxXu/9fjz1Pu/L8ckJ3bzQzQrdvFC6zkfpyLmQZBKWcO3Zt3Pt\n2bfx3LNv45mnn+FNv/E0b37L07zlrb+JpA6kR1JHlkxJaXcsPbl05NJ5jvTWJhcX7dUFN7jITpJd\nqKfWSitZm7+D5KHjIjOQGZJmXqyteNE2kcpYTxnHM8Z6yvXrT3PjxtNcv/5Wbt54mtWNZ1jdfIbV\njWfQuvIxrlDTbZUzk0Q1RTGq6dZ9F1MfdCQKYh1Zekqa0aW5txczo+C7QoI2ie1iG+Af/fi/iSDy\nIAgeKiLyBuClL33pS6O9VxAEQRC8gDz11FO88Y1vBHijmT2yvb7C0W7ouWMVGMRQOd9ySpCc6LvO\nc55ncw6PL3B4conDk6tcunyJ/aMD+tmMVAqS20jFhbCr21YQzFtsGWCimCgkRZNSpTJaZaMVIZNS\nRSy702wtbNrAVNGqKNVdboFteLa2PtotdFvJmGVE0849VoFkSKpgIyIDyAZkBdqh9Bg9xgyRhFqH\niJBzz2xmmGVS6unLnNPU05HJKoybG4zDzVbIzV15Q7ZH0K3tJCIkphDygtAhVkjSkZLnsvumRHOy\nbXePnlYZm0RBEARBEARBEDx6hNBuaBNwFWMERnZCO7ew75QzXc7kIvR7exxfusDxE5e5ePUqR8cn\n7B8e0M1nSCmkXJCUYRoeo91yqlvIN14N3FIbolSUwSobG0mW21C8SK94EbAWap7wKugpneuPLeYF\nz7ZyNoG1ommWoAluUkKsgiXPq95a3RmTjLLAWKJAzjOggMzIKTGfFXKe0/d7zLsFHZmiQqrKJnWs\nSUhVhDWGojI50I5hmOQWLN7Cx61sh/fPbkJ7ykE3/FltZwkTOwiCIAiCIAiCR5MQ2o1JBlaEKsoo\nRhWjtBRjEqQu0/WF1GeWh/tcvHSBkydOuPLSqyz2L7LYO6Cfz1xk54zkAqk0of18R9t7W7u49h7U\nLrYrymiVQStJKskqCc93lqllloGoIaqI1FbUTL2lVtJtvraIbSuRY1OudEHIoGWXPp1p4d3TAOOg\nHQtGT04dKc/JyUc/2wM7ZN0vyAppVNhsOLME1bBhAAWVilBR6lb661RWTry6uEhGLDeh3cqgtXD3\ntBXjTWI/z9UOsR0EQRAEQRAEwaNHCO3GOLrUrmpUM1QUS6AZUhIsC2VRmC0X9MsFBxcvcnR8kQvH\nFzg6vkg32yf3cyRnL2KWBElCzkISaX2l3Y0W8eMu9LmV+DJBDUYVhoqLTHMxasnd6Ml5FhJTkack\nRkqGZCWpIpPIRlt78NbH2hIiilFa8bPmFqu56G6Vvw1BbECmHGr1omaiBeh3ofSpQK/UxRnsD2RV\nutyREUQrSRKjrhl1jVlr+WVG2trS0lR+wfur+dGLok0i2jBTtG0eYIaF0A6CIAiCIAiC4BEmhHZj\nrJPQVi/aNYV054Q1DZjnHfODJftHhxxdusTRpYscXLzA/tEhUhaY9CDJPysgSUhZWp63IVN/aaMJ\nYdtpbfOw8mqpCW05J7Qzph6CPuV7T5nLIE1oQ9ImuMVIoi6mbeeCe/X06fsFRFz472Y696qSTP19\nazndJMCdcN88SEiuLObHZFVmOZNTQkzROgCGDIKNlTqut32zzay1TxMg+waCTSI7t+ucy83GMCpV\nW2X08+XbgyAIgiAIgiAIHjFCaDfGsQK4m22KiaHJ0VIWaQAAIABJREFUo76tgHVCWfQsDvY4PL7A\nhZNjjo4vcnjxAvsXjlArDDUxjF4Xm9Y6K+fkkedinpPtydm0U1qRL3eRzRKqwliFoSYX2TmRLGOp\nhVdPhdWgudS4yE4usnMyUlJU1MV2q849FRMTUZJ5A2tRti2+krFtxeW9rCsZP9eLlQmivuMgMm0A\nFHIW8txFts7miCk2DoybFVoHzCq1rpuT7fnjqbnSPhI2hbNbhtbEq5V1Y3K0fdS2YTDF8wdBEARB\nEARBEDx6hNBu1OpC25rl7CJZKH2m6zu6Wcf8YI+9C4ccHh9z4dIlDi5cYHlwyHyxZKiCboxR1d1s\n8SJlSTzHOuEh2IJ5b+vJkdUKVj0k2kBVUBVvz2UtiNo89Fu2vbTleR1jk7rYzi263L9u9jHaRLa2\na/tnBPXSask7bSECmv1cmTKpp7VVF7qpCXfBi6khpDSj9HtIJ6R5zzicMmxOGTY3UF2BbFA7pdbc\n8tN9KIlqhWqJartO2mxfm/va0oqnTRsgJluXOwiCIAiCIAiC4FEkhPaEN51GilBKQrpE7guzxZJ+\nuWC2XHLh5ISjkyscnTzBwcXLLPYv0PULLzC2FdcuAl1gG9kGxMZzw5ojnb1I2XgKdYPUEdHqBc7M\nne+pTReiXuwsKaMokmRXFA08xN3ATMjWCpqlhMkk6ltbMAyRimEtV9tFb27524jnaCNgqWJpwNKa\nlM/I2dcsKftsBmr4feZETj1FYLF/yGY4YqzHICtytyHnM0o6JWclZyNnZVThbONjtTFQBRVQmXQ8\nyZ8SWGtX1sLOkRDaQRAEQRAEQRA8uoTQbpi50M7i7btKn+kWPYvDJfODQxYHhxydXObCyWWOLl3h\n4OJl5ntHlH6BSAG86ncSQUTJMuJNtQyxDaJr0I0XFqMAHZBhPIO6RnTciWw1r/uFt8WaKpOnpKRU\nScltaWk9plPLeTYTTJOHkKthTTgLLUR8K7S9WrnZLlR8m8MtqbUcUzQNpLQm6RngYetmGTNBm8tu\nCJKF0vXMup75cMA4XkD1JimdkfMZmRtkrjProe9g1sNmVK7dbBXJR0XVtwJUtysiJ8jij+N5Ke2t\n1VcQBEEQBEEQBMGjSAjtiSa0RRJdEfpZZrbs2TtYsnfxiOWFYxfZJ1e4cPIEB0eXyN2C3C3wvOXq\nRciSh2gnGclUko2InkE9g7ryOG3rgd6P4xppQhut7uw2u9hwJ9dzxpvQzoJMfbNb1XATIaUmtEUw\n9bZhXrBMzolsbb22AYxs7hmLFe/+NfXpbo62pBFNa7J5T/CUM8kSatkrmGvGciblQul7+nlhMR6i\ndhPkjJTOSNwg2XNkm7NcJJZzYbkQ1ptKkjXjsGa9qlQxqlVMp9ZfQjIhGaTmapt5grtJU9xTHHwQ\nBEEQBEEQBMEjRAjtxnzmxbVmy57l0ZLl4R7LowOWFy+zd/Eyy+MTji5c4eDoEou9I/rZPpI6d7PV\n86cTQvF+WUhdUzenDOMZjDdgvAnjDaxW1DpUe1Q7Ts8q6zNlWFV0fRMbriPjTZKdUceRWjcM9Qyh\nMKbEIImSpjBv15opCUm84neWRM5GyVASvgHAADYCI0bFGMGqu92TGGcS3VOI+JyUF0iek8uSbrZH\n3+/RzZaU0m1H33eozXwwY6g3QUa6WWK+7NGhh3FGqnMO9oT9pXCwFFabkVqVcVRqVTYr2CBsqqeW\nl5ajnVqeepaEpqnfuW8ohNAOgiAIgiAIguBRJIR2Yz73St57+z0HR3scHh+xf3yRvePLLI+fYHn8\nBHv7l9jfP2axPKLv9zBLXim8CmLenEqSuCNdV+h4nY09C8M1GJ6D4Ro6DoxjR62FsRbONonVkBg2\nibo+w4ZrUG+S9BQbM+MmsR4yqJBFWIsfvVKYF21LKZOlo6RCToW+K/QloyWTqJgNYBvMBqptUB2o\nuoHmFPvAW4xZAhNSniFlRso9pVvQz5a70c/o+zldP2NW51RbUG3BaAvQm0gaKH1irh029Mg4I+uM\nw33hcA+O9oX1OlHHJrTHypkYZ2owGFQ71+irCW4RcusdboiHyofQDoIgCIIgCILgESSEdmO+aEJ7\nr+foaMnxyRGHJ5dYXrrM8vgqy+MnmS8uMJ8dMp8dktOCWpU6KlTF+1m3xlSmWF1Th2uM42/C5m2w\nfjts3k4d1myGwjAUNmNmU2estWeoPXU9YJtryHijCW1hXMNmJVR10en51q2ieDJMjJw6ujyjpBld\nnlHHHutnYD1CxXSD6RrVDWNdMeiKsa5QrWitmFZMteV44w59LuTSk0pH6WbM5gv6+ZLZfMF8vmQ+\nWzCbLal1j6p7VNtjtD36PNLnkW4mZOlg7Em1p7MZh/twcV+4cACbTWIcK8NQGYdKqRU2lTEZpkah\n3SKToy1o09WWmtCOXtpBEARBEARBEDyChNBuLJYdAHsHCw4vHnDh0kUuXD5hfvES8wsXWRxdpOsP\n6PIepcxJdJhVTCracqAhIckwElXVQ79XK2xzBuub2PoG4+aM9Sax3girTaKyYJQlygJRpUuVRSfs\nz7ttrraqMY627YkN6iI7GyQlJyg50+VCX5RZD7M+seiLZ12rYlpRrQw1UaowVKhV0TpS64DWsbX5\ncvGeUvEhhSwdiVOS3kDqHMYFlpeQl9iwRNMeo+yRbI/cA72RE0g6w/IGupE8U/quhbSLMTKQGciM\nZKvef1sVHSt19D7bVVxojwJVhBEX256LHo52EARBEARBEASPJiG0G4uDGQDLoyX7F444vHSJw0sn\ndPsX6Jb7lNmcnDtSyp4abLbNj8454QI4A4JqBgqqmXHM2DCNxLARVuvK2Uo522grrV0g93Q5sZz3\nXDhIjDpnOVduLIzlmTJUbQ2vFUMhu9gmKzl39GW+HfNuzrz3Y0Ix24D1mM4YtaNqodbOXe26po4J\nrQlTr3huqogkb7GVjJSVXAZvzVVGum6gKxu6dEbhJqXeIG3mYHNkatGlgtgpSd9G4TqkM0xHhk3l\nJpXVauTmjYHTGwNnNwdObxqnp8rpyhgHawXcWntvAU2CiudmaxPZIbSDIAiCIAiCIHgUCaHd2Art\nwz32L05C+zJpfoE0PyD18+bsJlr3ahfaWUCm9lj4e1qAgtZCHQs6ZmzM2JgYBlitK6erDaerkdIX\nct9T0khJM/bmM8aDOSkLN86U5Uq5uVKGUTEbUepOaGeFbHSlo+9m9GXOrGtCu9sJbRgQGzBbo1pQ\nK1T1PPE6JuoIdaT1slbQuq387WJXQTaIDICQs/fWTqkjWU+qfpSxB83I1DbM1qT6LJlrkE9BN2w2\nAwwbTk9Hbl5XTm9UTm8op6dwegY3z2AYz/XzEoMkWBI/yjRShI4HQRAEQRAEQfBIEkK7Md+fhPaS\nvQuHHFw85uD4EpR9rOxhpUesIJqQVkBMBEhCFmj9sbxzdnWhbebOsdYOa8dhzGyGDetN5Wy9ppcZ\nszJQqHRFWM5npDxjPp+xt6rcWCk3V5WhVtRG1CrKiGSD4mK76zpm3ezcmDMvM2ZlTp6ENgNY532w\n8X7YdUyMI9RBqdW8vVit7khbax9mYKaez62KqvrGghQQr1COZqgZrJDICIVEIaWBojcQuUHK3t5s\nHFdUXXN6c+TsFFY3YX0KqzNhdSacrYXN6P3DFQ9jlwySvF+3SWpOdgpHOwiCIAiCIAiCR5IQ2o35\ncg7AbNbTlY6cMgnBTMEG0A1ihpgipkxh4rtWU5OpLVA68nyPTo89l3iYYZs9bHNIWj3HuLhOnV9H\nz27QdUtKv6D0BUk9HYfscUS1A1aDsRpgNRhjncRu9TWlaRglC10pdF2my5kiUJJQBF+rjWAb0A1G\nbVXGU3Pbe+q4QKuAVkxH0NHvFbabClUNrUZVWqa4eqstM28VZgmxDX1JzEpilhJJRiyvUDaYjL7e\niseCd4nVzFgvlM3aGFUYqrCqYGsYqqLVGKuBCZKA6sLbvW7B7B1/jkEQBEEQBEEQBA+bENqN+cKF\ndj+f0XUdOWeSCIahNoKuvRiZ1TYKkFrYeDu23s5CR5rt0wmkrseGPWw8wsZj0uo56vzt6Pzt2Onb\nyblsRyk9uT+kdJfJ5TKDio8qqJo7ytWLhm2LoomRknn+dIacFLG1r1c3rRDa6CJb14jVFpGd0epC\nW6uhNYGOoIMLbXxDQTDMKrWaV1mvUM2oplQ1zBQxI/lS6DJ0yUdCIQ0gQxPx6hsPmpBO2cyMzQKG\nwRjUWI9wNsBoUDcu7IeK5423Z2umqBmGhdAOgiAIgiAIguCRJIR2Y7FYADCbzei6Qk7ZW3WhJBtR\nYyu0pRUlc1e7uJstuxxipCOnPVLXUeb7WD2Eegr1jLx+ljrfR+czbFYQUQQlidH3Pcu9Q5Z7V1gs\nXopKRsmoZExpgtiwap5LbVPhskpKA5JHhA11vEYdrqHDgFZFxxGtLrTTlPZsHuLu8wlWM9iAqR/F\nahPbLszHqoyjUCuMagzVGFtKd7JKUiWpksUoKBkjoYh4tXTJvnZNuHvep63QHquxHo2zAW5uYF2N\nTfWyb0Nt7jXWXGxzZ78J7iAIgiAIgiAIgkeNENqNXFy05TQitsbqDWwzw1LeDujAOrAeKCgFa4NU\nsFwgd+5qiyE5Qe5dk2sBnWE5MxOjJsOKNHE7gA10/ZLZcsl8ucdibx+TDCljkr2lVVWsGla9Ojiq\n/pph56hbZRxgTJVRNtS6wdIGrQOmI8kyyTtT46q3g+rtvzzEfAAbESrC2Nz7gbEO1NHnc5HdRHCt\niA6kOiCqJK1kqy6+Tb10nEFiqpcuKAktsJlDNSB5Ky/NhhXIMyPfALJ/cByNsfrRnX22Dn8QBMGj\nxJvf/Gaeeuoprl69yute97qHvZwgCIIgCB4SIbQbKQ3+ws6w+hy27tHV2ER2QlNGrHeR3YadF9ql\n99H1SC7YVJ1cpBVNy5C8CninG+ZUJAuqa6yuUV1Ryj6pm0PpMO+t5Z9PgrRwaQ8Xd2FstYJUzAbM\nNl5V3FaYnWGcYnIT5AxLa8wGoLogJyN07tibIOZFz5hC46mIjcDon7EBqRtSHch1Q6pKrkpRRccB\nxjNkXEFLw05VSWokteaeg5iQRFzbW2ZWhP15QnIm94r0Ql5Atw/z68b8OaObGaU3VmfeDm2liqls\nc7SJquNBENwHIvIxwA+0Lz/WzH74hbqWqvLGN77xhZo+CIIgCIIXCSG0G966CsROYXwOXQu1rLCU\nXGznBDZDdAY2d6FtBaNDKUg3R2ZzBH9PkrvcUCAlhAIIWYSOihQh9x11PKWOZ9TxlJyX5G6BZBfa\nkppQb226BfNWW1qxOmLixcumsHCzNaYrdCu020gbd86lgnTN/e48l1wKSQoiGcFar27DRXYbNpDq\nhqwDWjfkWil1pNaKjmsYshvhm0qqiowJr33WcrdVSGqoQJLkKe1dQopR5jBToyyFfiXM18J8z+hn\nSu6UlJXreQQbGTdGFRfZiiCkh/XrEgTBi5PINwmCIAiC4D1CCO2G2QYArWfUTWJcKaOcQk4uslMG\nm4HOEJ1j1mPWuaNtHWm2AJvGHEkzDxvPM0SKC9tUSKmj6xakDKUrjMOMcej/f/beP2iy9Lrv+pzz\nPPd29/vu7ErKytaqQookhCIJghDFGPwbMGhtKyb+ATipVBRRUKqQAioBVzlFBUmOCaGMU45TMd7C\n5STGP2LywxXbYCCpAis2FRJbsY2LcmJinFjJ2Po5uzPv233vfZ5z+OM8t7tntLO72hntzEbPZ/Xo\ndr99+/bt3t6Z93u/53wPpWREtuiwRfJwdLNRQFvaORWRAlJwCs6CU8An3CbwA+4HoC3ZIzpHCTgW\nv2KKxgUAGRDdoLoh6QaVIZx3mgPfRLYTZe1mUeLudabWgtUFqwUre3wSbHY8VWRxSIZoQWo43OtW\nTcORNkERVGFMwlaEtAjjImyLMO6MlA1px1EUKzAdDDMHB3OnF453Og8XEXkX8GeJPy1+vbv/w0d8\nSp1Op9PpdDqvS7rQbty+vY8b1UjuZDdkmZAsaBY0t35mG6GOrVc7g0cgms9bfG6u9rCBtEXSJoS2\njqhuQDchnmmiGSH5AHqB5hjvldMFKgNRFh2jtUJRFtynGDNmc4heW/C6QF0Qm1FrY8ioiDiqikvG\nRHEdcBdEt4hsQOKcREdoS0QRSWjM0uIktgvJlihRt3a7hvi2ZYPnhI0DPo+w7KEcoOzbeXlbhleN\n4DVrbrQqqglVpbYqda2CDwapkjaFcVsYhgOqB9yV21cz+0PBJqdafRRflU6n0+l0Op1Op9N5SbrQ\nbpyEdiW7MVpBl4E8SFu0QLMMZQDLIRY9xXbYwLzBp82Z0I5F2uJpB2kbglYzognVhMiASsLzNgR5\n2pF0dZchnGjDfUbsAHbA7QA2t7W0sVwVsYp6ONgqjifBPB1FtnvCNUS2S4hs0fNtlJEnHYh+7lVs\nR1Cae5Rwh8ifcZuxssGGAV9GbNhA2eNtYQti9XhuXgWrghfBSYgOuGayZtwcdcgOuq2ksTDuCpvd\ngmjGTChLG+sFzNWYS/e0O51Op9PpdDqdzuNHF9qNVWhLKYxWGOtMKplxBNsAI4gplIRXRWqM/1JX\ncMGHEaYRxgHyBsm7ENp5h6ZLPF8g6RLyBeQLJO8gh5NNSkhKkFbBuzra1nqma0sDn3C/Brs6Cl2v\nM9QarrETyeFScfU2dzr6yGObMRmOKwT2CGdiW1NzuiVeVyTC0Y7LK3jrCbcZrwdsGPFlg407vOyx\n2sT2XRcCFrwKUlahHQ4+ssF1QHEGcTY4w0WI7IsnFnaXM95E9nSoLNVYqnE96dnFiE6n0+l0Op1O\np9N5fOhCu/GJT0wAlEOF2WCq2CGx28HFBegukrOpAlXBQmQ70W8cpeQDYi2BPG1x3bTy8Qu8LdIF\nNoTwtuECHTbIMKLDBvImAs4iuhuhhNCVivoB9T1iB9SmKBkvc6x13Fd1sIIxYT61baTgmlesDdgy\nqZgURCuq0fedUiGnSs4FckU05nOLVuRcaNNEs89NSM9NfBOBb7pBHTwlXOfmgkfJOTUq7T0LERLX\nLizIgLijOOpOtsIwLIzjwnaTuLgoPHFZefLJymF2DrMx7hfSoSvtTudhcE8qN8SVvl+ST76a9SXu\n/gER+XPA7wN+yd1/g4i8BfhDwFcAvw544mzfV5z4LXGFD+B97v6NL7Hf5wHvBr4QeAbYAh8Gfhr4\nUeB73f35l3/ndx3zDe25nwsswO939+//VI7R6XQ6nU6ns9KFduOjH4swtHlbsb1h+0q5zDx1A8Rg\naLlkNINZHEIaCr7OpSYjnpGacR0RGaNsW7eI7jDZQtpRhx2WL6jDjrS5QDc7dBNOt3GByQ6THdqE\nsGolMZPYk3yKcVtlgVLwUqEYmMU87LpQ6kSxPaVeUw2q5eMyyTgxm1skHG2VkZRGxmHDOEYJeEpG\nSgVNq6tdWvBajeC4NlIsEtA9xH6lKekRJEaIrcFtpIIrePIw6UlHJx0doc0H9+q4FJAMRL/4OFQu\nLoynnnT2B+fOdfRtp/Roviudzj+hrInccs/9ex+/67aIfC7wI8Cb7rPvS/3spc7jkxCRLfBdwNe9\nyL5vbesrgKeB+wr1FznuW4H/FfitwDXwte7+v7zS53c6nU6n0+ncSxfajY99LBzteZuxg+F7ww7l\nKLJ3AyRZw8nafGgREoqLIKRWRp7wmhAyLrltNyAbhBFLW2reUvOOknek3RPkeoPkT+D5CSoThQWj\nklJtgrfiuiBMKBMQIptSkcXwWqBWvBpWZupyYF4OzMs1S3VKzZQaQttFMVJcHJABlRGVgZxG6maD\nbUfYbPBc8RwOt+gqsqNPe00id1/APVx8T0gLh1vHbrm0yPE1KV0dd8fMY5yYrqnsAywxI9zN47VI\nEcymyjhWLnbGkzfg6rpw6/aBcUhdaHc6D4+/BbwN+F3ANxEC9t8Cbt6z3/93z/0ngL8MjO15f50Q\nqm97kec+EBL2+g8BX9rO7xeAbwd+sr3mM8DnAf/up3jc3wj8NeCfBp4H3unuP/HQTrzT6XQ6nc5n\nJF1oN56/XQCwxUnV0eIkV7YjXG6jQtvVQmi7NR87RLaKIK5oWyIJVpfbEzAcl+lIzRtK3lDyFrMb\nVH+SZE9S9YqpXHGoV8x2yTA4w2AMo7PJxjZXdrmStOJW8dpWqVhZsLJQl5l5PnCY9hymPXNx5iUx\nl0SpCSREdgjZAZUZZSDngWV7oGwH6nZkGCvDUMhDlJW7ny2W6AVnARfER4QNygbQ9hopRL0ILmAi\nVDfMjepRcK8o6oIgLAZzVebFWZbEUjKlgJmT1NhuhRs3MrevCxe3Dmw2dxhyn6Pd6TwM3H0P/D8i\n8jlnP/6FVzDe62ngNvD57v5zZz//qYd9jsB/zElk/xXg97j7cvb4Wjb+R0XkmVdyQBF5G+FkvwX4\nVeBZd/+Zh3rWnU6n0+l0PiPpQruxuLQtTGYcrLCvylSduThLcTwZ4Ig7IudDugQ1DfHoioq2zm1t\nyeQLEC63aaYyUH3AfMDkCq+38el59mXHrastn7iz5YX9lu1Oj+vJy8wbb4y86cbIcDHgHv3Wrk6l\nsNSJMh+Ypz376Yr9Yc9hmthPxmFWDpMwF0V1QCTmeQuGYiiVnAqb7cRmo2y2yjgWxrEwjCG08YIf\nw9AWaA63AOIhsoUx3GnJiA6tyr5SvVIomDvVjYqBKJpmtCWdTzPME8zrdnLmyZmmRLEtKQ/sLi+4\nvHQuLiYutnfYjdeP7gvT6XQgRO9/c4/Ifug0N/s/b6/3IeBd94jsu0/K/WXd9Nbn/cPAG4B/APyb\n7v7/Ppwz7nQ6nU6n85lOF9qN0oT27M7sxmTOwSpTNeaWdO14CEs5NTGCgAsqq0PbhLaHzD6K7bZ1\nU6onzBQzpdQdddpR9IJb1yP/+KOZmx/LfPgTmcsbA5c3Ri5vDLz5TZfYZz3FNj/FE7sUQ67EQIzi\nhbkeOMxXHA532O+vuT7s2R9mrveVq4NztYd5EZKOpLQhqaFiqIfQTrqw2TjjxtlsjHFT2GwWxs1C\nToVIQF+FdomgNi/tHYbIVjbHJHPRDYawWKVYZbFKxZrQjkR0TQlJUQEwz8I8KfMkLLOGqz0nSkmo\nDqRBGTaJyyvn8uKKi+0tLjbjI/mudDqdu/i+1+A1fhvwawmh/d+7+wNdZRORdxAl7zvg5wmR/Y8f\n+Cw7nU6n0+l0Gl1oN1ZrZDGayDYO1ZlqZa6VpVTAo0y8VSw7grtgAqmVjSfRkNR+ktjS/oFQ6NW9\npYHDMm+YPNbHPpH4Rx+CX/xH8Mu/Ijz5xh1PvmHLk2/cMe3fxCZX3nBjpNpFG/vlIOFoT2ViP19x\nvb/N9f7A1f7A9X7iznXh9pVx+8o4zDCkLUN2coLU3Gz1SlJhHBeGsTBuFjabhc12Zrtdok/bY9SY\nuJ9Etpd4jxIiWxmRtEPTFk0L1ZW5VuZSmatRsKPYdgVNiiYBEeYpMc8aDvYyUMuGUhS3xHa3ZTtu\n2O62XD5Rubi4xcV2y64L7U7nUXPH3X/pNXidf+ns9t94wGP9O8B/SPTz/BTwZe7+sQc85icxzzMf\n/OAH7/v4M888wzPPvKIK906n0+l0PiO4efMmN2++fMTLPM+vwdk8OF1oN6qFei7u0S9swmzObMZi\nQjFibJVElNgplldgTSD3KMZu+eSA4H5ywAXw1rOMhPSuizIvwvUCd55Xnr9l3Pq48bGPOXOdmcrE\noUxsxsSvecMld+5csz9cNpFsJDewVfguCIWkMOaEjRuWOXFIC0kWpAWRlVpwX6jEexIgq0CqiFWS\nG8UMLYbMRjWP0WamiK/vRRCP96CktjU0V1IuJCuYKKUYpTq1OrZeYPCwpVwdk/iAShHqQszYtiiL\nRwySI0nRPJDyhnHccrHd8eTlJdMTl6/dF6TT6bwYt16j13n67PaDhqz9wbY9AF/16RDZAB/5yEd4\n+9vfft/H3/ve9/K+973v0/HSnU6n0+m8Lnnuued4//vf/6hP46HRhXbDWul4daE4LHcJbaOYkDz6\nsx1DxMDPZsy6NLGtx2Nx8rFBmjhXwBRvfdy2FKa9cr1X7jwPt29Vnr9VuHWrRkm4LUx15nI38vzT\nVyG09wcGdUZxVP0otJVKVmPMMddbJVPKzGEShuwkLYC3udrhRotHUbujJHcqjklMMSsmSBHMBTGN\n9+uKeEWOHeptkrgL4GQ3Mkam4uIxecy9ZchJjEYzxd1xwOI6BWYOxnGetovHKDBxdBA0ZyRvGIYt\nl7sLnrq8xG7ceI2+HZ1O5z7UR30Cr4K/DHw1MXv7fxSRd7j7nYd1cBHh6aef5umnn+Z7vud77rtf\nd7M7nU6n07mb97znPXzlV37ly+737LPP8pGPfOQ1OKMHowvtxlFom9zlaC+WQmi7kM1RNRKVUIXr\nPG1wj+Rx9+jZjlzy1dt2RDy2SMyY9kgkr7My7wvXt5U7zzu3n595/tbCrVuF2QpTWTgsM09cbrj1\n/B3uXF2z3+/xLGiCnAWxgnpBKSSpjCmhZHJKLIuyH5wxFbJUqjvVKtbed4twwyVE8oDjArW1gFPj\nM8ESYqmN7zJO77AlsbftgDNgmFREY8R3teZi1xDa6wp3uznd4tEDLzGZXLR9ZupIFiRlNI0MzdF+\n6vISufHEa/sl6XQ6rwY7u33fUQEicvESx/jo2e1ngL/3AOfzp4G/CXwz8K8C/5OIfLm7Xz3AMY+8\n9a1v5UMf+tDDOFSn0+l0Op9RvNK2qnF8fbSPdqHdMPO2lQgpa+XixYxqSjGlupJ8DUWLmdoOrZxa\n4r7rXU63ISDR24xECbY0MS6u1OosszEdKoe9M03GshhlqcxTQQcgOVd3Dlzf2XN155rrqyt0TAyb\nCBJTX0iEkDa1GCwmSjaYMmxH2I3OPBq1vaditZ1drKxKTs6QnTE7KVVUKkoNF98q1vLQZH3/bblF\nSFokoYPjuFREEmZCdYnnFvDiIbSPDnaslBIvMEy6AAAgAElEQVSaHDRjalQpVFVIiTEv5DSTZGZI\nld0INy4yeuP18R9Zp/M6wj8Nx7x9dvuNL7HfP/sSj503O38R8GMPckLu/i0iMgB/HPhC4Eea2N4/\nyHE7nU6n0+l0VrrQbniJCkhPzXm1hJtgFj3KtXorb7Y2M9sxQJvYhigZjC2tlPz4QAhyiR5tAdQj\nSC2Er1BqyF5NiXGE3UViHJVBU5R2F2M5TBzuXHH1/AsMlwM7xuhdpmBNZJMi1bt4RZjZpImL4cCy\nnRCb4+JBjdfFM0IGZnJKXGzhYge7naCyICwgC25GdaFalJGHgw3gUQJuhlXDzOIDtIzXhKi2Cxet\nYqA60txsIQaexYehaBqQPKBDlIybGqYV10rKQlYj+cIgV2yGKy62C3r56dAEnc5nNIez25uHdMxf\nOrv9O4AfvM9+v+cljvEzwC8D/xTwH4jItzxo8ri7/wkRUeCbgC8GflhE3unuh5d5aqfT6XQ6nc7L\n0oV24yi0s+CWcFPMFXfDVqFdDU/1FALWysabGR4C+yxdHJombT+Pm/G4tRC16lFavVj0h2vKDJvE\nbuekAXKKPmorleUwsb9zxdULIxdssVxJO0eb0HaN8LDiFbFwqked2Q0Ttp1IzFSDWp1SvV1VCLGd\nUuJiq+y2ysVOiTnZc4Sm1cpizlKdxaLEu/2vTf0yanFqtbhAURUrCip4E9newuTEQM0REVS1rURO\nG/Jg5FEgO6YVSwXXpY0xW8AOZA5s8hW+Xci1C+1O5yFzHjT2G4FfeNADuvstEflZ4F8A3i0i3+zu\nd4WoicgXAP8J93HU3d1F5JuBbyPGfH23iPzuF5ul3WZuv+WVzNJ29z8uIhl4H/CvAz/UxPbrI860\n0+l0Op3OY0sX2g2v0UZoxXGLnmt3xSyHODXCsbXaHG05imzFz9zrs2Me70u7J83dDvEM4RBHiXo4\n2pKUcRR2FxL9yhqC2RdjOcwhtJ9PPJUrtnOSKVkLLhVSqHbVBZEFpDDqzMUwo7uFMS3UapRVFLs2\nBzqRcma3TVxsYwuFagtmM2UpaKk4leo1gs3WtHWLz8wWp5YoLbcqWImLDe6nlRySx3sXFTQnsiaS\nZjbJGAdhHBOanWgHF0yFYgvVD1S7wqSwHfbobmaULrQ7nYfM3yFc7Q3wx0SkAP+AU5/1h9x9ehXH\n/TPAc8BnAz8uIn8M+LvAm4B3An8A+NvA53P/8vU/A/xO4EuJMLP/W0S+HfhJ4Bp4C9Fz/XXA9wLf\n+EpOzN2/UUQS8Efbsf+qiHzli4n4TqfT6XQ6nVdKF9qNnIe2zahmVBMqSvx+KadqaWnjunStoG7l\n0xK3j/u33Y+GNkQzMspxRhbNHZZIMo9QNQ+Xu4KqoNJSwd0py8J82HO4Ug7bwrydmLfXkCtW9lAO\naD2gVskUkBIO/BCJ5IMa1ayVwdvxV2dxSOpstpXtWNgkASouC6aFSmXjxk6MOa192IG5UCsxnquC\nJlAl+q1lvWBxEtranO0ksY8mI6VCHhaGYSInkKRxDUDBFKoMFB+oOqCjIbWAL0h+PQYedzqPL+5+\nR0S+Dfh64LcD/9s9u3wJ8IFXcejvBJ4Ffhfwm4HvP39Z4GeBrwF+5SXOzUXk3wb+PPC1wG8CvvXF\ndv1UT87d39uc7T8CvAP4QRH5qi62O51Op9PpvFq60G7knI/blBKqioge+6797Fe3VWjLGra9Otpi\nbcJ2e849ryHCMVnb19gwuVtsRxu4UyuIhNoUFCyE9nQ4sL9yDruJaXfNcjGguWI24xZ92MkNcUOk\nIslj3rY6JVskfFsI7TWIDCCJMY4wDDAmWupZwb3iUini1OSUcXXq4+KCe4THrUs0HostpxJzl1Y2\nfrrOIBop7qJO0gVNQkqGqODquBguUCRTyBSGdn2ipZGP3dHudB427v4NIvL3gN8H/FbgKSDxyX+k\n+Yv87H7HdBH5WuA9wO8Hfkt76O8DfwH4Vnef1j9vX+I4B+DfE5EvBt4NfAHhZCfgV4GfBn64HfOT\nnv5S5+vu/0Vztr8e+DLgL4nI17h7eSXvsdPpdDqdTuecLrQbw3BytFMKR1tUmtA+ZWyHyBZUJDxo\njfLpo9g+iu7zo6892015Ht3vNaHbcIkC9BDC4TqrZpI20W5OXQrzfs/+amHaKfNOWa4SaaxAARbU\nS0zFllgpQVZhzEL1cMzNHPcYzyXuCEaSELk5GVkdqAiRJo63Uu4xUtld2vtuDv063uzYjy6cFDyt\nzBwiCM1Aavtw2siz+HiWNtqrtIC06Dl3cYqkENuS0GaZe1ao950U1Ol0HgB3/y7gu17i8XcTQvdT\nOaYD39HW/fZ5Rf9Ru/uP8Skkj7f90yvY7xuAb3ilx+10Op1Op9O5H11oN1KOgF1NehTaKtLUYeEu\n5Sxnd1f1vd59Mb9EQoGuY8HcPdztVlK9utjmcpTrIpFiLiohLiUS0JfFmKfCMgllgjoLTkUoIVKp\nLd38LP3cE07M+Db3SEx3RxxUrM3SBnVDagU3hIoSW3BSS0lHJJzs1dEWO3vf0pz5M6u8DQOLOnI5\n/iycbj9doPDof3dPzfKusdRwifM3SVTP1JopNlD8ZX9v7nQ6nU6n0+l0Op3XnC60G9qEdsqK5oSm\nhCQQbSXUTSy6yJnQFnzt416Tx4FjvfS5cXt6MPZYRXboS2qJvmxcEYGUYrZ0SjnORQT3cLqXxSmL\nYwv4EqJUWJrYrnGuQihtU8wTxRKLKeZOtSa0IZxsEVQ5zuE2PYns2HoTyS0xvWlmE4/Pg3DKz3vO\n4UxXr5+dASZg0XO+OviRYh6hbHirUG1C28VYUGLYmHKwgb0NHMyYvH99O51Op9PpdDqdzuNHVyqN\nNDRHO2sT2Yqqt/TuqGb0c6Hc3OKoBlfWAmk/G+KF+1GQx5Pu2hyFdq3EHO0aJdiqQs5KyiGyNaUI\nXyPCzEqp1GLY0uaCSQFKSFGvuAiGgihuiVqNpTpzVWoT2sU8RLZCUiErmFSSVnIT2utqOentfSkm\nvurlGFPW3re73RPytn5ackwpD5EtLfSt9Yy7R+qZNbHdhLZIxTFml1gIByvsvbJ3Z/I1CLnT6XQ6\nnU6n0+l0Hh+60G6sjrZmbWJbkWSIZkS09VCHox2u9iqyo1xaCFd37eY+FkkfxbWcbUN4mslJaBen\nVsU9QthS0ghlS60vWSIorVZjWQp1qVipeDHQBfHwfMUrSELQKBevmVrCBZ9rojSRXQySxpzunISq\nTpZKkkpdhbavYjvmXguKiGFAlQgtNzm5005ccFj7rteLDuH800R2fGbWhHasJrRrim0Uip8JbZgM\nZpOjyL5GmF60Tr/T6XQ6nU6n0+l0Hi1daDdEh5CFKohqpI6rgWZcM07CJUdPsthRaIvEuCpfS6xD\nQscxTxFq0G65K0bCPUVJdxGWBabJmRfHTJrQzq1c3Cm1UIqxlMqyxBzsUqHWEOtu2kRuJLOZxXzu\n6sqywGFyrmZnvzSx3kaIpQQ5OUMSkkZ42hqkFo3jbR3L4uPGWgG+DjM7utlwV8BZ9GufjTo7G/Vl\nDoZRWR1tmnqXCHFTJymoCLPB7MJiwt6EaxOuzZmsC+1Op9PpdDqdTqfz+NGF9kqK0VGSQmhH6bgh\nMoAMR5EdIWCOS4R2ncztVWhbCxNrirLN1Q6ipNs8hLabUishtGdnnp1qAkRv9rGHuRpLMcpSKYOx\nFD/2dK+iWo+BY4qbUE2pJsyTcNjD1d65nuwokJ0mtDOUFMJWmmyGKGO3Er3jdia28XZJ4ew44Wi3\nPvU2Myyq7dsQMz/fN8S20crPaRPETaAaYkISGDLkFGXtpYns4sK+KlcFriscahfanU6n0+l0Op1O\n5/GjC+2GaG7bENly7mhLxiQ3SeitV7seM9FUAK9NqLZtE9nSptUcA8pcEU+4JcwSpYaTPc/GvIRL\nHY52olqNUV9WKVpZyiq4nVL9WHoejnYraXfBTbAq1Coscwjt6zvG7YOcTlggZRiyh6BNTTC74x7i\nf1mgLFBKe2x9W5yFoNGC0JxwtdURjQsQiJ9EtnvIeKddklj7vP1YVi5VwIxBhXFwNlkYslOayK6m\n7ItwXYSrxTmULrQ7nU6n0+l0Op3O40cX2g3VGBUVs7NDaLuAkSieWCyzuIfgc8UkRCUQs6jFUK+I\nW/RJu7WRVacy85bzjVpCSEDGqVSrLMUpJXZ3BFWN0nQVEsowKDlXNAmikcZtOKUtAFoF9uIwm7NU\nZyrGVGBahHkBzYK2VHONvDQkAeqtJD22h0Os/cFZ5jZ6zKLkW1jni0OkiXtUmHs42XHcKB+3Jtwj\nXXytRA9xbdpGhCmIGeKCOGyycLFtJeoqrdw90s4LUMxZijOXHobW6XQ6nU6n0+l0Hj+60G6oattG\n6TgSQru20VhTycwVFk8sZAYsepq19TW7oVTSKrStgldcahPaCiKIJ6pkVJrQloXqwlLDubUYTo0I\n5JQQTYgKu6GyGQvjUEh5QVLFtFKApXnt1jqiF48AscVgclqPMxQXBgmxnUcYhigdzwOAs3iEsh0K\n3Jmc21fOnTvOYWpjyIwoU5fTYh1RZo4ZaGpLaUJ7fd6p3Tvy0BxP4OqgtOi2CF672CgkIY1CTqc+\n8Lha4Vgxqhjl0XxVOp1Op9PpdDqdTucl6UK7oSmEtqiAaqSL0wLFLDHXzFyVxZziThWDJrZDIFYS\nRiKcbazgVs5EdrN6PZEsI5YRMiYSzrRVShXqOoJbhJwSeUiknNgNle24MAxCyuFCmxhFnBknCtvD\nXZ8dZjNmcyaLxO7FoDhkFTQLw0YYBic3se0OFCg4hxpC+9aV84kXnOvrUy94NSWJkDXmbwuRhF4r\nWHU0QcqCpnDxaxPh1Y5Dv45CmwSeY5vESOIkUYpD2ggblI2ezS2X+HBscaraycnvdDqdTqfT6XQ6\nnceILrQb2kqhRdd0s4g3W0w4zFHSnLMwDMIwgGhiUCOLkdVAFKGiEuFn5zO3Q2Sn6Pv2THzsLc1c\nHKNiXokQ7YSIogg5J4YhM4yZzZAYMqRsaDLQiielilBVgLg4IK4UE5YKi0KRmJjl6khqfdmjM26E\nYfRwtHOkkMsc511MOBS4muD2tXD76lxoC1mUrEIWQXBKEWp1anFSjgsBKcfYs9rGl9VWEh9C26NP\nvIlsMmSN8LOszjA6swtVgWFNgo/54klAC8jiSBE6nU6n0+l0Op1O53GjC+0jNTZrKrYrSzWuysLk\nEy+wZ3/tXF8LV9fwxE7YZGczONvBGdQYxRjVSNTWc+yIeSSZJ0FTokpittx6vjOGgRqaIQ8CjMCI\nyEBKioogHvXX3mqwPeaQISmRxoG0UZJmkmYEIy1OHhybnVFgJ2DJGWfY7ZzdNlYeQJOTcowLG0cY\nxriYMGYYh8SQnSEL5opbJKarQBJIrZQ7aUsW1yjB1yaKoy/dTqnka193XBc4utmSnZyUIStDEjY7\nZbsTdjvh4kLRJGiKrYyJmmLJqI/mq9LpdDqdTqfT6XQ6L0EX2g1ZhTaKewRweTWmuVCniTrvubN1\n7lwJt+8oNy7gYitcbmO7ScZGYw1iKCG01Vsp9aAkoj979ia2PVMlepRT1tYrPSIMqAxo8kg0xyLR\n2+2YDC4qaE7oKKRtIiUjpQERJ81GGow8GKM6pkB2touz3RibjbHdGClVJDmqTqrOZlLGQRmH5txn\nYcgwrkLbE+baJnZHtBst7Exd8Caw1+Vr1bcbSutR1yiLJ9Hc7BDb49CE9qBstsJ2J2wvYHchpKxt\nCbpJ1JQpKSNjeu2/KJ1Op9PpdDqdTqfzMnShfWR1tB0nyqTNjf2hsL89s7+z5/bGubNTXrhQnrwU\nblwqN9p2l51tMrZpdbUt+o5ZtaSSSVhKLJ4jVM0zBpAEzUoaBBgQMioZkco6l/tcZHN0tBUdHd2A\nDuFMizppqOS5YrkyJofspAGsGJtjqJq34DdAHC3OODZXexDGQRizMmZhyIr7Ka5M3NsCM2vC2jDl\nWOKtqhF4xjpH285EuB6FtufYDoMyjolxULZbYbtzdhdwcQlpUHJbOmdKGig5QxfanU6n0+l0Op1O\n5zGkC+2GhuRtY6sMF8HMmZbK1WHh9p2Z/d7Y74Xb18rtK+WpJxLXh8xhgt3g7LKxS+EiJ8LVTlgT\nipU8RF/1IsICzMDV5MxFcMkRyCYZkQEjx+xpq1gtFF8oXlisMEtlWZRiieoJEyWpwBAl6kkqrhVS\nRZOTk1MG8MUYUmFIlUELogWXAhQ8OZshcbFJ1ItMXRSvShLlxqW2xPCWih5DsyMB3JzaRoLVaq3M\nu/Vo41S32Mctkspbabmo4ImoQc8SZepDYhwSTz4Bb7jhPLFztqOTh3Rc7plpzMxLwmoX2p1Op9Pp\ndDqdTufxowvthmqbybymhOOYO3OpXB8qL1wtJKlc7aN/+WKjXB9GDjNMs3AxOLvsXGRnVItxXx4J\n5GlQ8lhIQzi5VZ2iRhHj6lqZSkhySSlKxnXAJVFKwapRa0HKwpwKSQsTlXkZWZZEqSPVM6klnemg\nJDUkGZorOTueHRscL0aikKgkCs7SpnAvuBibIVO3CfGEemJMictN4jBFKb21z8RpzjonoV0r1Oox\nQztF7/f6GVrr01Zp/duirI3ergJJGXJmyJmcM5c7uPFE5YmdsRuMPGRyHsg54zUxpVgl9R7tTqfT\n6XQ6nU6n8/jRhXZD5CS0XQxQqjtzMa6nwgvXC15LCwFztqNymEJkT0vmcnAuhlVoO2IhssUqaRDy\noKShIBksO56Mmow7+4F5UUwSogOSMqIZNGM24e7UUnBbSLWgWhgw5nlkKYmljlQfMRnxNCJDJiVD\ns5Gr4YPD4DDGjC+xitQCteC+YCyYz6DGJidkkxg0h8jeZp66zCxFwpl2i3T0NrPbsRDgNZLFzUDE\nQaOEHbE20iv+UdFYKKKKq+Apxp7lNJDTSE4D29HZjoXdprAdKjmP5GEk5wGriV1WpiTULrQ7nU6n\n0+l0Op3OY0gX2g1tQtsQ3CqGUGphXgrTXNhPhbrU6E3GOcwtgCzH2K86QMlQsjCoICasLdZpgJSd\nlA3JFR9Wse1cz8p+TizVqYAS5dUSKWghtGvFamWmglSyOdd74/a188KVoKOyIbGVzGgD4gZrH3Vp\nqxpijpfSTrRilqlkzBPVjFoTxaLoXXMme2armVylCWyjeqV51NgqtA3cBKuAWohtMZAmsVuvtiJI\n+/91rri3bdKRlAayRgicI5QiTK6UOpBKJqWB/awc9so0CcvUx3t1Op1Op9PpdDqdx48utI80oW2w\nmFG8cJgK8zyzlEq1EI2IIAgiCSQDAzCGIDZjKRaOeI0B1l4LWnOUhacEWbEkWI6RW4elcpgLhyXm\nSw8jDBLzos2iv9kqeCF6oj1KwD/+QmXz0YWUJz5xBZsLZ7Mzhm1B28UAcVBztHrMnq4eA61rjAkz\nJ0LfSG1GtlItZmWXVgpezWKG9rFsHEwij81ordoGHrXl0MaQI+unKm1/j33ccItHPJLYQBRVR9VQ\nqSR1shSSLiQxRAsiGZEDU0lcHRJXk3KYu6Pd6XQ6nU6n0+l0Hj+60D7iABE8VmEqzmG/ME9z9Eqv\nY6ykKclzoe0DZlAwZqtUDGqIbDeFoqApHFwNoV2TUBPMxZjKwlwMUcdFEU2ophDZ5phFebZVWMxZ\nxPj4CwXNC5XExW0Yt8a4NYaxoBLjt1QgxZju2FbC6W6i2L0JYRKGUF3btvVkewjouwrAm8h2iNve\nBHZbIt6W4ED435HpbsWxGstbuBqE2BYxRGqkrcdZIBQEwz32cxfmmphK5rAk5tqFdqfT6XQ6nU6n\n03n86EK7IbRRVFYpizFNlcNhZpoXlhqOduy3lj2vQjsDI+aRur2QqF6xGqndVgvepk67KFVCYBeF\nmmCpRjFjqbWN50qknMk5haNdDWup3lYcLx5zqV+oFF+4noXt1hg2lbypDEOOPnKBJBJTtFxIFltx\nEG9l6dBGdoHJupUmpgWX8K0daE+kPTW2LYAcAzGPanHxuA5BON/VQ2RXoCxOWYyyGGbtIC64Sxtl\npsDSZnNX3CpuRm2ufjWnWKJ4pthA9Z463um8XpFjMAbvc/dvfJXH+GLgf293v8TdP/BQTq7T6XQ6\nnU7nAelCu1Gm+J1vmSvzVJgOhel6ps4FipMI9zRF4Ti4U2plKoWraSKLk1dv2CtWDa9t2zxaRzAR\nqkJVMIVip2ruYTCGlCnDgA0DANIcbtEM0nqkq3J1MKov7KdKHmfSkEhDJmUNgS0t2Nub0I6MsqPI\nFpfWO+2r3sXbk1z1mL4uRwefVhbu7XYrTwfEHbV2/HV3iUsXixvFnOLGPBfmqTLPhVq9iWxaMUFz\nttG45OGGm+EeotzMqQYuGXTE1dqFjk6n8zrGH7PjPDB37tx51KfQ6XQ6nU7nMaArlUadQ2iXqbLs\nC/N+Zt7P2FSRCrkJbZGI8wIotXKYZ1IWUvOG1Q3x5kSb4dVOItsVl9a+vS5vvdLmbDbOOIxsN+Hk\nhpOsqGZEK+t07mJCOVSu54LeMSQJmhRJEaK2imyVk8DWo9BurjxAE9gouEo8PyuSosxdNIR2bFft\nLcfbUaJ+Onby9vmIIBql54tVFjMWq+wPC4e2SrHTOG5rfd2rmgdWBe5rX3rrKdc0kIZKGgzN42v2\n/eh0Oo8tx8t1jwO3b99+1KfQ6XQ6nU7nMaAL7UaZa2ynynJYmK9nlv1MnStUyJ5w8ZMUbGng07Lg\nB1BCYK+J396CzLx6CG2X1g/NXYFifiYocZg3W0qpWLsvomgKR9ulUkksVlhmo5SFpS64+6mU+1wA\ny0lcn0Q2qyd/2kmApOiQ0CHHVlfhrqhqE8/SbkdZuko4/Il1GyJbRVCVGI9W63Fd7Seuryeu9zPL\nUprIjn5t1s9V1iRxP25ibnd8VnncsNk6my0MXWd3Op/RuPuPAb2HpNPpdDqdzmNHF9oNr37aVj+K\n0yzKqIoMuaWOh6hVDWEYSeMR2tXmXIGdyp2tCUlDMJejuD5ZMHZ0b5MqyzzHWhZqrbh7lI9L88xd\nWAymxZnmSCyvVtvxWh+5nPTzUWD76SLBJwntttKQ0aGE0E5NbK9CW0M8qyhJpZWnx8pNZGeJx7UJ\ncnNnMWOuzlSNq71xdTCu9oVlruH4t8/oqK85d7fv+XcEbLxCMnSM99XpdDqdTqfT6XQ6jxtdaDdW\nWZdEGHJiM2TcnKzCmIQlK+an/G3EkRRl1BHe5c3J9qN4tOoR5HUco9VKpR2OLvZpUBbLsjDPM4dp\nZjxMMQbLo/HZW0hZ9Thmcad4CxtrAv6sInx92jpx67wo++x22OrehG6pdnTDpfpxnvcqnqVt7xLZ\nImRRsihVFFVIqiR1TKBYXGCABJIQaf3mCrhgYlgbrXZ+fudCe+0TFwHNmTQM5DwwdEu70+k8Piis\nVUqdTuf1wM2bN3nuued4z3vewzPPPPOoT6fT6bxCaq3rzcd6BNFjfXKvJav4TCoMSdkOid1m4HIb\n68Zu5IntwOVm4GLMbIfEkJQk4Ra7+9HBrubU6hQzSj1bxVhKpZR63JalUJZCXQplXpinhekwsd9P\n4WqbR+SYtNJzh9KCwaq1MLU2Yat6DMWqHoOxStsuCIvE9ngfYXGYndgazNWYinGYC9NcOEwLhym2\n+8Ny2q5rfXwuTHNlWirzUtvIMmMpTjFab3oIbTS1cLeEqx6D4uxYWt/eA6f73lLeRXP0aOeBPAyM\nY6xOp/NoEZFnRORPiMhPicgtEZlF5FdE5GdF5PtE5F0i8sTLHONzROT7ReSXReQgIh8Ske8WkX/u\nJZ7zxSJibX3Rizz+Z9tjv9juv1VE/qSI/F0RuRKRD4vIj4jIOx78U+gl7J3O642bN2/y/ve/n5s3\nbz7qU+l0Op8CZ0L7sf67tzvajfWKQ1JhzAlo5c9ZqCXGdFVLFKtUM6oZxZ1KuMur2Ma8VY/7KS3b\nz0LPnFZivjrara8bp6TCMi9M00zOB4ZhIGcYsh5nVq+Odj1ztM1PVvY6ceveaKAoF19HlK34aV9r\nPeFem3tszcG2CERr/deyOtm6bpVBE1WdQRMpQTIhKYhq9KKrHEeJwTpTPK4QhJ99t4e9loSHSy+t\ndD5C2iRlUsrkHK52p9N5tIjIFwI/DDzJ3X/yvLmtfx74OuAjwP98n2P8R8C3cvdfmM8Avxf4ahF5\n1t1//CVO42VtZBF5e3v9p89+vAW+HPhyEfkWd//6lztOp9PpdDqdziuhC+1GGuOjcHPIYRenmqhV\nsRJzsc2MWqO3uJq1su2YNG3VY+61GbWVjNf1tocDbb72bMe+vs6LdsOtkjThbizLwuFwYFkKOS/k\nlFlKZX+YmOeFUkr0b9vdJdfxBtr2k1uc2zCuu38fdY993dsscROk9aGLWLsfQt2b4HaJedsqQhGh\naCWLsmj0dSddA9xOJe8uMM2FeSnUUo+fw9qsLnK2vff9+Kni4K6LBL1Es9N5pIjICPwF4AbwAvDt\nwP8BfBgYgV8PfB7wVS9xmGeBfxn4GeBPAT8H7Npz/tN2+38Qkd/k7uVVnuoF8Bfbef7XwI8CE/C5\nwB8B3gr8YRH5h+7+p1/la3Q6nU6n0+kc6UK7kTbtozBHzNEmlK1qm4etMbKrCW2rFuFmzaVeS8aj\nL9ua0PYzob32a7fjrqLcKmYFs+iHNotebXcnpURKCdVErcZhmpnmmWUV2h6Dw5pWZr3xYo726e76\njLP97xXb7c46J3sdCGYiR7GrTXyrQBGNkLRjGJoe+7u99Ve7xDi0pdTjRYK1R309d/nk6wBHe1uO\n/9DGgnWh3ek8Bnw+4Tw78Lvd/UfvefxvAT8gIn+IELsvxr8C/Ajw1fcI6Z8QkY8D3wT8OuArgL/6\nKs/zs4AZ+Dfc/SfOfv6TIvJXgP8L+LXAfyUi3+fuH3uVr9PpdDqdTqcDdKF9RJujHWnjTnLaHGzF\nreJVsVrxmrBqLfQsxnbhUOtJbIfAPu3UAmcAACAASURBVBPbq7vtTql2Kj03o9ZCrUqpBSfE57KE\na31K/Q6hvSyFeS6UWqjtHFa395wX06sr9/58Fdnn2nudtyVNsR/F9vG1zmV7zOtW2tK11DvGgZ0i\n0OMigrWLDmvi+Hqo8wsF957w3SFp8cMutDudx4K3nN3+G/fbyd0NuPMiDwmwB/79+7jV3wb8l8AA\nfCGvXmg78B33iOz13G6KyH8G/ABwCbwL+JOv8nU6nU6n0+l0gC60j+gYZou3UVi6uqZW8Fqa2F6X\nRS92G4jtHm3XR9f6LoHtR2G9Ot2llaBXsyaaQzybWySbu2O+9kaD+zp921uqtyAoJoaKnjztpkQ9\nhk8f5fC5HPUzx9rX499HlgvnQt7vGqe1PmcV5muomXiUnosIYnJmf8txFnYY2eGYK/eUix/P6Xi3\nze0OQb/OMPf2GXY6nUfKeYLQu4FPtezagb/m7h990Qfd74jILwC/BfgNr+4Uj/y5l3jsB4FbwFPA\nl9KFdqfT6XQ6nQekC+2G5hsApznX3gSuVTyF0KaJbaz1R59N56oe5eapCe7kZ6XiTVTX5mYPbq18\n3ENkW2m937GqR1k5tHFgCFbt5B6L4GaYR9849zi7x/NvcvisFfp0/0yU+7lX3KxlcY6iF+52lc+P\ndeJstpjcI92P48xOT5Km4I8iW84C0c6Ut0hcWIgFiuPeLlSUV9uu2el0HhI/DvwiIYL/lIj8XkK0\nfgD42+6+vIJj/PzLPP5x4o+GGw9wnjPRA/6iuHsRkb8D/GvA2x7gdTqdTqfT6XSALrSPyBC/w8mZ\nQKWFlGEVvMT2bK2zs/EmsJu4TmvC+LFUOvqS1yA1czuGga2PmZXTflaptURqeZvNXbU2dzdEp7ce\nbzc5KWn85BrjuEs83+8KFsckHrtLfB8/Cb9L9N6VBH4U50TI2T0l5GdHaNvTed1jq5/Czdo8bWjz\nstvBhDajvL1fVWnGeLjZViulC+1O55HSBOo7gb8E/GbgdwCf0x7ei8gHgO8GfqCVj78Y1y/zMuvz\nHmSEx8f95XtNfrVt3/QAr4O788EPfvBl93vmmWf63N5Op9PpdM64efPmKxq3tyyv5Dr+o6cL7Ybm\nS4B7Arr8JLB9FdgFvCJWwS0WIbDFHVnTxVsPsUPrw17F85o2vs7dtpOTba3/uhRK1QhSqyGo1yAw\nJUqp3aQtTkJ7fU2nBbWdRor5mo6OoBBjtzzuxzs+FmsfN9I097nIhlVo08Zy3b8b3M+fcPfRoQnp\nKB2/uzFbpCWdt3Fi0fe9BrDF+1zD5DqdzqPF3X9eRN4G/M62vgj4Z4jRWe9o6w+LyJfdr0T8tTjN\n1/LF3v72t7/sPu9973t53/ve9+k/mU6n0+l0Xic899xzvP/973/Up/HQ6EK70+l0Og9Ec4t/qC1E\n5LOJsV1/EHg78NuB54CveUSn+GtERF7G1f7stv34q3yNoxP+5je/+WV3fu655/jO7/zOV/lSnU7n\nYTDPMwDPPvss4zg+4rPpdDq11lf0d+hHPvKR9eYDVaF9uulCu/Hffcd/+yKTpzudTqfzqeLuvwr8\neRH5XuBvEkL7nSKycffpEZzSCPyLwE+/2IMikoDfRjjfP/cqX+P4d8jZLwCdTud1QP9vttN53fJY\n67cutDudTqfzaaH1cP8YIbQz8AZOvdCvNe/iPkIb+GrgjYTQ/uuv8vgTsCF6yj/8Ko/R6XQ6nU7n\n5fksogP1UVy8f8V0od3pdDqdV4WIfAFw093//n0eH4AvbnfvAI/KNhLgD4jIX3T3//OuB0TeAnzz\n/8/enQdalpX13f8+a5/hDjX0UA09AYoagyBEGgQEIQoir4QhaohTQCEKaozG4OtAXhuM0xtHXk2U\nCDKIvsYgGiMIxoBBEaURBA2IiCFC00LT0F1Vdzhn772e/LHW3mefc8+5U92qe6vu7wOnz7TP3muf\n6tu3fvtZQ366DrxyPwdw99ULa6KIiIhcSRS0RURkvx4P/D9m9gfA64D3kML0MvD3gOeRqtkOvHSb\nmcd3cqGTmX2cFKJ/z8x+Cng96Sr4I4DvBW7Mx/g3hzhhm4iIiFxBFLRFRORCGGmm8cfNea9ZPfA3\nge+7wGNciHXgK4HfIQXr7+2817Txxe7+4gs8joiIiAigoC0iIvv3Y8C7gScAn0eqDN8rv/d3wNuB\nV7r7Gy7wOE0Y3ut7k43c32lmDwWeDzwZuAlYA24jhezfvcA2ioiIiLRs+9VORERELk9m9nLSJGgf\ncvf7H3Z7RERE5PgIh90AERERERERkSuJgraIiIiIiIjIAVLQFhERERERETlACtoiIiIiIiIiB0hB\nW0RErmS7mpVcRERE5CBp1nERERERERGRA6SKtoiIHHtmdl8z+wkze5+ZnTezu8zs7Wb2fDNbPsDj\nfLWZvdHM7jCzDTP7kJn9kpk98qCOIXKcXMyfXTO71cziLm+PPahzErlSmdl1ZvZkM3uRmb3ezO7s\n/Az94kU65qH93lVFW0REjjUzewrwS8AptnYzN+CvgCe7+wcv4BhLwK8D/9eCY0TgB9z9B/Z7DJHj\n5mL/7JrZrcCtc/Y9y4Evdve37Oc4IseFmcWZl7o/W69092cf4LEO/feuKtoiInJsmdnnAb8KnATO\nAd8HfAHweOAXSL+cPwv4bTNbvYBDvZzJL/s3AU8HPh94DvDXpN/Ht5rZP7+AY4gcG5fwZ7fxIOBz\nF9weDNx2AMcQOQ6auVP+N/C7pNB7MRz6711VtEVE5Ngys7cAjwFK4Avd/e0z7/9r4MdIv6hftJ8r\n32b2xcDv5X38FvDl3vnla2bXAn8K3Bf4FHB/d79nf2ckcjxcop/dtqLt7sWFt1rkeMs/U7cBt7n7\nnWZ2P+B/kX5OD6yifVR+76qiLSIix5KZPZz0F3UHXjr7F/XsJ4H3ka64f7uZ7ecv2/8631fAt/rM\nFW53vwv47vz0KkBVbZFtXMKfXRE5QO7+Ind/vbvfeZEPdSR+7ypoi4jIcfX0zuNXzNsg/3J+VX56\nFfBFezmAmZ0gdWV14Pfc/aMLNn0tcDY//sd7OYbIMXTRf3ZF5PJ0lH7vKmiLiMhx9Zh8v0bqQrbI\n/+g8fvQej/FwYDBnP1PcvQT+mFR9e7iqbyLbuhQ/uyJyeToyv3cVtEVE5Lh6AOmK91+7++xMqF1/\nOfOZvficBfvZ7jg90iROIjLfpfjZnZKXB/qYmY3y/ZvN7LvN7KoL2a+IHLgj83tXQVtERI4dMxsC\nZ/LTj2y3rbvfTaqcAdxnj4e6ufN42+MAH+483utxRI6FS/izO+sJ+bi9fP9Y4EeAvzGzp17gvkXk\n4ByZ37u9g96hiIjIZeBk5/H5XWy/BqwAJy7icdY6j/d6HJHj4lL97DbeA/wm8Hbgo0Af+Gzga4En\nksZ/v8bMnuLub9znMUTk4ByZ37sK2iIichwtdR6Pd7H9iDSOa/kiHmfUebzX44gcF5fqZxfgp9z9\nRXNevw14tZl9E/DzQAG81Mw+w9130yYRuXiOzO9ddR0XEZHjaLPzeLBwq4khaUzoxkU8zrDzeK/H\nETkuLtXPLu5+dof3/yPwMlKQvxH4ir0eQ0QO3JH5vaugLSIix9G5zuPddBdbzfe76aq63+Osdh7v\n9Tgix8Wl+tndrZd0Hj/uIh1DRHbvyPzeVdAWEZFjx91HwF356c3bbZtnFW5+GX94u23n6E7Esu1x\nmJ6IZa/HETkWLuHP7m69t/P4pot0DBHZvSPze1dBW0REjqv3krp8fqaZbff78O93Hr9vH8eYt5/t\njlMBH9jjcUSOk0vxs7tbfpH2KyL7c2R+7ypoi4jIcfWH+X4VuGWb7brdQd+6x2PcxmQyloXdSs2s\nDzyS9Jf229y93uNxRI6TS/Gzu1vdNXs/epGOISK7d2R+7ypoi4jIcfWbncffMG8DMzPgmfnp3cCb\n93IAdz8P/HdS9e0JZnbjgk2/AjiVH792L8cQOYYu+s/uHjyv8/h/XKRjiMguHaXfuwraIiJyLLn7\nbcAfkH4ZP8fMHjFns+cDDyBd8f7p2SveZvYsM4v59v0LDvXj+b4H/PvZrq5mdgb40fz0btIsxiKy\nwKX42TWzB5nZZ2zXjry813Py078DfmPvZyMie3E5/d7VOtoiInKcfTupS+ky8N/M7IdJla9l4KuB\nb8zbvR/4yW32s3Ccpru/2cx+Ffgq4Gn5OD9N6mb6YOD7gPvmffzf7n7PBZ2RyPFwsX92byGtjf1m\n4HeAPydNwtYjjev8OuBL8rYV8I3urmX5RLZhZo8GPrPz0pnO4880s2d1t3f3V26zuyP/e1dBW0RE\nji13/zMzewbwalIXsh+e3YT0F/Unu/vaBRzq2cBJ4MuAfwh80cwxauAH3F3VbJFduEQ/uwF4PPCE\nRc0ghe9nu/vr93kMkePknwPPmvO6AY/Jt4YD2wXtnRz6710FbREROdbc/XVm9mBShezJpOVAxsBf\nA78G/Ht339xuF7s4xibwFDP7KuDrgYcAVwEfA96Sj/EnF3IeIsfNRf7ZfR2pW/ijgM8D7g1cSwoE\nnwTeDbwBeEUeEyoiu7Pbmfq32+6y+L1r7lqVQEREREREROSgaDI0ERERERERkQOkoC0iIiIiIiJy\ngBS0RURERERERA6QgraIiIiIiIjIAVLQFhERERERETlACtoiIiIiIiIiB0hBW0REREREROQAKWiL\niIiIiIiIHCAF7T0ys1vNLObb9x92e3ZiZo/rtPdNh90eERERERGRK52C9v75YTdgjy639oqIiIiI\niFyWFLRFREREREREDpCC9v6oOiwiIiIiIiJz9Q67AZcbd38R8KLDboeIiIiIiIgcTapoi4iIiIiI\niBwgBW0RERERERGRA3QsgraZ3dfMnmdmv2Jmf25md5vZ2Mw+YWbvMbP/YGaP2OW+dlzey8ye1dnm\nF/Nrwcz+qZn9ppl90MzW8/tP7Xzu5Z3PPTO/do2ZfbeZ/YmZfTx/7q/N7CVm9g8O4vvpHP/vm9l3\nmNmvm9lfmtnZ/D193MxuM7OfNLMH7HJfv985l8fm167O5/J2M7szn8sHzeylZvbAfbT36Wb2CjN7\nf/4z3TCzvzWz3zCzZ5pZsdd9ioiIiIiIXKgrfoy2mf0Y8J2A5Ze6E5ldDVwDPAh4npn9KvAcd9/Y\nxa53MyGa5zbcAPwa8OiZzy7aR/O5RwK/Dtwws+398+3ZZvaDedz4BTGzXwO+crYN2bXAGeAW4NvN\n7MXA8909brPLqXM0s0cD/wm4cWbfn55vzzKzb3b3l+6irQ8GXgk8ZE5bbwJuBp4GfK+Zfbm7v2+n\nfYqIiIiIiByUKz5ok0IXQATen293ASUpQH4e8Bl5m68CTgJPOcDjLwG/RQqpJfBHwAeBIfDQbT73\nacBPAVcB54A3AR8jBdUvAlZIPRJuNTNz9xdeYDvvQwqsFfBe4APA3UAN3At4OCnEAnwHMAD+xS73\n/bnAjwCr+Rz+gPRncBPwxcAyUAA/Z2bvcfe3L9pRro7/FnAqt7cEbsvtLUnf22NI3/tnA281s0e5\n+/t32VYREREREZELchyC9juA3wF+290/OW+DXG39ReCzgC8zs69x9185oON/JSlEvhn4enf/8Myx\n+ws+931AH3g18K3ufr7zmdPAS4GvyC+9wMze4O5/fAHtfBPw48Abu8eaaeuTgZeRgvc3m9mvuPsf\n7WLfP076Dr4T+JluJdzMbiL9+TyIdOHgh4EnLDj+vUk9A06SQvYrge9194/NbHcd8HPAlwOngf9k\nZp/n7lqWTURERERELrorfoy2u/+Eu79qUcjO27wVeCKwmV/6tgNsQgG8B/iy2ZCdj10u+FwfeJ27\nP2s2+Lr7PaTq++/nlwLwoxfSSHd/gbv/+qKQnbd5HdPV/t18T0aqfn+zu794tru5u98OfDUpOBvw\nD3OgnueHSSEf4MXu/uzZkJ33eSfwDNLFAyNV1L9ydjsREREREZGL4YoP2rvl7v+bVHU24OFmduIA\ndtuMC/9udx/t8XMO/MtFG7h73XnfgC80s8/aVyv3wN1vA96Xj/n43XwE+HN3f9k2+/yfpO7f5P0+\nbHYbMzsDfG3e398B37NDOyPwgs5LX7uLtoqIiIiIiFyw49B1vGVm9wE+H/h7pLHPy0zCMKRJuciv\nPQR46wEc9lPAf9vjZxz4I3f/0LYbuf+Fmb2LNM4c0tjtD+y5hTNyYH8Yaez6adJ48u73dDrfX2tm\nN+Wq9Hb+8y4O+y7Snw2kcdaznkCqjDvwWncf77RDd/8TM1sjjQ1/zC7aICIiIiIicsGORdA2s0eR\nulY/hunAuJ0zB3BoB/5sn2OD37aH7Zqg/XnbbbiTPAb7B/a4nzPATkH7z3exn7s6j0/Nef9RnccP\nMbOf2cU+u642s+VdzigvIiIiIiKyb1d80DazZwO/wKQ79k6htwniJw+oCXfu83N/u4/trtvnsTCz\nFwLNuuC7uTCwl+/pnl1s0x2rPm+CuBs7jx/D/irUVwMK2iIiIiIiclFd0WO0zewBwM/npw78T+Db\nSV2U7w0su3vR3IBXdT5+UN/NfoPd+i63W+s83tfFATP7ElLIbi5EvA34JlJl+wywNPM9vaXz8d18\nTwcx2/fpzmPf5+2Kv7AkIiIiIiKH70oPHv+KdI4OvAF4mrtX22x/UFXsg7Cyy+1WO4/P7fNY39V5\n/DJ3/6Ydtj+M76l7QeE73f3Fh9AGERERERGRHV3RFW3gizuP/80OIRvgfhezMXt0311ud5/O40/s\n9SBmFoDH5qeRtH73TnbbtoPUXcbr+kM4voiIiIiIyK5c6UG7O673L7bb0MxOAQ/mYLo5H4RH7nK7\n7iRh79zHcc4wmc374+6+bVjP3fEPYqK4vfqTzuNHH8LxRUREREREduVKD9qx83inrtjfSJqEa7ez\nkl9MBjzazLatsJvZA4GHdl76/X0cq/mOjLTc2U6+ZR/HOAhvBCpSO7/AzD73kNohIiIiIiKyrSs9\naP9N5/FTF22U141uJgM7CpwUKBeOQ85dvv+/zkt/4O5/tY9j3cVkVvDTZvaF2xzz0cDzOITvyd0/\nCry6aQrwKjPb1VhxSw6jCi8iIiIiIsfQlR60/2vn8U+a2RNnNzCzxwNvBk4wPeHWYRsDTzWzV5jZ\nie4bZnYV8KvAF+WXIvC9+zlIXuP79Z2XXmFmD5/dzsyeAbyO9O/MYX1PLwDuIAXthwBvzzOmz2Vm\nN5nZvwLeDzzj0jRRRERERESOuyt91vGfBv45aX3pa4E3mNk7gfeSqrIPBR6YH78R+DjwzMNp6hY/\nQlqK7JnAPzazN5Hadz1pkrdmtnEHftjd33YBx/pB4OmkruOfDvyxmb0N+CvS+O1H5dedtCb5ZwOP\nu4Dj7Yu732FmTyMF/jO5HW80s9uBt5PWLO/n9x6U2wxHp6eCiIiIiIgcA1d00Hb3O3Mw+y9MJvB6\nKJNxzc36yr8BfAPTXbEP24eAJwOvAW4AntZ5r2l3DfyIu9+6i/0tHHvu7u8zs68GfpnJWPYvyLfu\n8V5CCv+/u+uzOGDu/g4zexjwMuDx+eUbgX88uymTgP13wAcuTQtFREREROS4u6KDNoC7/3GeNOw7\ngKcA989v3QH8KfBqd38dgJnBdEBbuNvdHHoP287fQWr7Q4BvIgXJTyN1cf8o8N+Bn3P3PzuItrj7\nb5nZg4DvBJ5IWsKrysd6K/AKd/9D2PP3tJfz39W27v5h4Ilm9gjgn5CWJ7sPcHVu812kYP0O0kWB\n33f3uGB3IiIiIiIiB8rSEF05Cszs5cCzSIHzG9z9VYfcJBEREREREdmjK30yNBEREREREZFLSkFb\nRERERERE5AApaIuIiIiIiIgcIAVtERERERERkQOkoC0iIiIiIiJygBS0j569LoklIiIiIiIiR4iW\n9xIRERERERE5QKpoi4iIiIiIiBwgBW0RERERERGRA6SgLSIiIiIiInKAFLRFREREREREDpCCtoiI\niIiIiMgB6h12A0RERC53ZrYGDIEIfPyQmyMiInIluxepYDxy99XDbswiWt4re8Ev/tGWL8K2eW42\n/a5ZvuX3mhtAaJ53Ptd9v7uPRvPH4u5pYe32fvIYbxbcnvNnaFvb2baBSTtDp63BIIR03ysCRTCK\nEOgFI4T0PJjlbdK9mVEEw0J63gtGUaTPhTC97aQN09/H7PfQlf799PY8o+fvIE7O+gs+59rFOxAR\nuQTMrAKKw26HiIjIMVK7+5EtHB/Zhh2WeYnNuo9sZps2XG95kt5uEnjzGMuhvLvNnCNZJz573rXT\n/COFbCMn8m6LHHKIZe4xOu3E8PzZ7j/T65Pn3t1/Pod2h5078/ZRakMO1d3zb0O2kd/rNmnrt2+W\nTtHy1xBI5SICc68viIgckvRfQDNuvPHGw26LiOzCeDzmzjvv5LrrrmMwGBx2c0Rklz760Y/mYtzR\nTgMK2lmwZrj64j8vY1FwbV5rAuR0Vds6wddy0Ka9nx+026bY9NPpLWdD9mzDbG47m5BN57iew3Vz\nPxu2u6F8av9TNfLuq53APaeC3X4fnYsOUxc0pk7JcE9Hj0wytquOLSJHxyeBe505c4aPfOQjh90W\nEdmFd77zndxyyy284Q1v4KEPfehhN0dEdule97oXd955J6TfvUeWgnY2qTDbpN82k5dma75bnhjT\nAXYqZE8C5bygPX38rhSkp+L0pKC99Rym2rQghU4F90lI3q6iPf28qVAbPlPVJgfq2XOfVMHT9zKp\ncE8uTkw+392lNWV73FJ7gzvuEJ0jfg1LRERERESOKwXtebZUWJvXt/3Q5K4TJpvXFoXs7cYnN/3H\nzRzPgbPpS21GCqEG82rd8xprne3mdDifqmi7WxoLzZxb7rZuTgrbbTtmmtKG+Ob77NS622o27Xcy\nOe1J34F0upM/D8cmVW1VtEVERERE5AhS0J6n2+V/uyA886HZkQJNxdc8v5vDsdkk5rr74rDdzog2\ns+OtjV3w+nT6bYZ4O5Oxz+0LGI7ncJ26akeHGGmjbbo3PBjEACF9PALmNgnhueJsnivRnrt/063P\nN+22qfbO7RJvnWbS+SNRRVtERERERI4gBe2GL3jivkPY9slmTKqs3WCYCr42Sbo5yDYBuxl/PHv0\nNri2s26zy/A9s233pVx8Tu313H28CeKGR8dDE7Chbj+ZQzYGBCxEzEMK1G07c1uj48Hb0M3MuU+H\n7EmotpmwbZ3Xmwp685F23jWRY8DMngW8nPSv/qe7+98ecpMws1uBWwF3d822nX3iE5/g5ptvPuxm\niMgujMdjAJ70pCdpMjSRQ3T99dfzjne847CbceAUtLeYF17nTDo2Z7tJgdgngTuH6qaKPBuytzvq\nVFDeEpono6ZnY+viHdKGVGuz7yTEt13Hm3NwiLEJvClgp+pyCtnm3l5Q8LxTd0+3mL8DN3xqpvTp\nec7nNnBOP/S2d3mn/apoi8hR4+7cfvvth90MEdmDPKmSiMiBUtDeohkJPHk6M4S4sx20XcYXmKpc\nz9vNlr1taUp+aO2RunXgqX3msc27WRp9+nPd1yfdx6OnkN1MPGae+ombGcHJodryRGWd582ePAVv\nfHJxYTJae+v5W+cc0sPOt9IJ2O2s4zufpsiVRP/aXzZuOuwGiMiujIE7gesAVbRFLr07yAv3XpEU\ntBtTQ4InCXcq/M1JyT4bGtt5wWzmA+l5t3P0nENPArelcc9tFTfNPJYnRuuOee62ZZfmHRByR3Jr\n/zbvDjEnd/NJ4A4O0Z3QTJjWfKBTpe/2cJ9tV/NddqdKa9fVbnfT+c5zmN8SztV1XI4Jd38l8MrD\nbofshgFa3kvk8vBO4BbgDYCW9xK59G4GrtxeYAraMzpzX6fIaZMgOL8e3YmRtmjs8NZQvPV40y+0\n473zB61Z5mr2IkCnFVOV7t10rZ4TuCf78XZSNLdU3U5VbWtDdpoizdvzaz/tk3HouE321Z5MDtjW\nBOxJ4G7em1ywmP/Fqee4iIiIiIgcVQramc2dyroTsm1rHbqpAHc/tXOR1eY8atow86Q7RDuH/nZp\nL3bXRXxxKxa31HOXcbfcmaMN+5NKdnTymtbdID2pYM/O2ea+uON8+903oXv2e+h8sjs3nQraInKE\n1DtvIiJHyw2kOR1vOOyGiMgeFEU7B+uR/t0bDrsBR9FUp++5oW4y0ti6n+lWaNvu5rNx0Kertc3n\nF3Tntk74nO2Mvq1tQvh2Ibv7cc9V7KZ7eDdET8Zye+467pNx2sy+PkndTTdznznWLpsuctkyswea\n2QvM7A1m9mEz2zSzc2b2V2b2CjN7xDaffZaZRTOrzey+c97//fz+m/LzzzKzn837Xsvv3Te/97j8\nPJrZYy35RjN7q5ndZWbnzezPzOx7zGx4AefbN7N/ZGY/Y2ZvN7NPmtnYzD5hZn9sZrea2bU77OND\nuZ2/mJ9/tpn9gpn9r/z9/Z2ZvXa7725mf59hZj9lZu8xs7vNbN3MPmhmLzezW/Z7rlkeZKZLgCKX\njxuAF6KgLXJ56QTtIz3AWxXtbF4envdmd7bwpgP37CtT+926sx1DdjfktxXcdvburYfxBffzLFyz\ne2pvk07oTUhOmTmNyY7k8dnMVLSbqrVP9uG5P/0kgOc9O/iWXgJbW7G1/Ts0X+QIMrPHAW/OT7s/\non3gM4DPBJ5pZj/i7i/YxyHa6RDM7KnArwDLM+/P+8wQeD3wpTPbfC7wYODrzOyL3f3j+2jTLwDP\nnHPsq4GHA58P/Asze5q7/9GCfXTP6+nALwNLnfevA54OPMXMvsbd//OixpjZ84EfIn3n3TZ9GvDp\npO//B9391t2dnoiIiMhiCtqLzKkgz4bUrZ3Nu5N5bWfS/7m77eznZoeFL8jZ03verpK9oP1bW9eJ\nuc0x84Rsqbo9E7J9ErJToLa2At7sjfxap7idw/bW77H7XLlarhA94Dzw26TA/ZfAWeBewAOBfwnc\nD/geM/urPPnZftwPeHU+1guBKJ5k2QAAIABJREFUPyR1q3p4fm3WDwIPI80E9PPAh4H7AN8CfAnw\nAOC/mtkj3fc8WKUAPgi8FrgN+Fugym18AvBs4FrgtWb2IHf/xDb7ejDwVcBHgR8H/pT0n4cvBb6H\nFL7/o5m9yd3vmv2wmX0X8P+S/rPyZ/lcPwDcDXw28C+ARwH/xszudPef3eO5ioiIiExR0M6mMuhM\nNXu2C/hsVXteRXuqVrugctuONZ4N2J0x2N1Jv6YmO2uP6DNdsRdE021C9pYK++zMaM2xcjBul/Hy\nJng7sRm/3YZtbyvgza6a+6ZK73mnnhvRzLLeHfu+yM6VeZEj5V3Aze5+ds57/83MfhZ4HSnc3mpm\nr9pHsDVSZfZ24JHu3p3G87YFn3kY8BJ3/5aZtv6Wmf0C8Jy8zXNJ4XQvvt/d/9ec198J/IaZ/Qfg\nbaSq9LeRBkou8lDSOTze3bsXDN5uZh8kXVw4BXwd8OLuB83sAaQLCg680N3/7cy+3wX8qpm9Kn/+\nh8zsl9z9nl2ep4iIiMgWGqM9j3VuMJNKtwbWyW3yv+bNyWzaNtUt3ObsrhnjPXuo3Y7NXvi38pmg\nb1v+OXtGs697O9bau2Ox3fGYAnbzPE51Efc2WHfbN/V5bz4/qX3vRCFbLjfu/skFIbt5vwK+Kz+9\nH/AP9nso4LtnQvZ2PgZ854L3voO0wCykCvfeGjI/ZHff/5/AS0n/uXn6Nps21xafPROym/38CqnS\nDfCFcz7/fFJ38dvmhOyubwNGwAngK7dru4iIiMhOFLS3YW0ynl96ti23zv9seqq0NiwvSM2LsqN1\nk/mWSdS8889FJ7Goku3bVrXb17qTl/l0SI5NsJ4KzJNbt43poU8F7KYa3tynrbcP2wrZciUws4GZ\n3cfMHpAnSXsg0/89fsg+dz0GXrOH7X/N3TfnveHua8Cvkf5T8EAzu9c+2wSAmV1lZvc3s8/pnPPd\n+e3PMbNiwUcd+PMczBd5V27n/ee894/yPl67XftyBfvP89NHbbetiIiIyE7UdTyb5Lf5wXR2Salu\n4Nt25LNtDdHtOtEz+X1LiMzB1pplvZiMad6xU6ltfbhTRp09j6me47nbeNN/vBuUo1ueII02gIfm\nPu9ndnWv2fHYzWvbdRnfcmFAoVsuI2a2Anw78E9J47IXBUuAM/s8zAfcfbyH7Rd1KW+8HfjW/Phz\ngf++l8aY2YNIFfMnAddvs2kgTZK2aJz2X+5wqE/m+5Mzx78vqWu6Az9qZj+6U5uz7doqIiIisiMF\n7V3Y2t3b5r6+KPgZTbdwm7Ov6X1232tn5c4h1TpjttugmQdyuy8+9jZNWzhYe3Ys+FS72lnGuxVu\ny+O06ay37cTohHbIt+U6+rzvb1L1b76lRRczbPYFkSPOzO5HmgTt09h+gYDm3+zlOe/txqf2uP1O\ns4l/rPP4mr3s2MyeA/wc6fdMZyrErZvm++3OeX2HwzXLe8xevOhW4fcy5n1lD9vOacpuiv8F219r\nERERudLdMf3sjju44447Fmw7MR7vpaZweBS0s21zqC0OrGZzarBbtpm8aGZTz9Ojzj5mQnyTeN1T\n0m7CNtZUtW3yMZ9fKd4Sshec7E6zfLeTmDVdvg2iGyEHa3PL906MRjQINukePv1X7W68zhcipkL4\nnJA950KFyGXi1aSQHYFfBP4T8D7gTncvASxdWarz9vv9V7zeeZMpe51wbVfM7LNJIbsghfV/R7rQ\n8CHgnLvXebtvAF7WfOwiNKWbZH8AWLj814y1CzvsnTtvIiIiIlNe8pKX8KIXveiwm3FgFLS3Mb/6\n3AnMW97bqlnaymzmb5HW2d+CruPdPG6ANWtp+8z7zUu56j21jwUV6y12W83uvNntOm4OgfQ4RCda\nqmZ78LmToXUHo85WrueG7Obg6i4ul5kcOh9N+lf/h7ZZp3lPFeMDcu89vP/JhVtt9fWk3y8V8Fh3\n/8CC7S72OXeX+ird/b0X+XiYGWfO7NzzvygKikIVbRERkeuvTyO2nvvc5/LUpz51x+2f9KQnceed\nR/+itoJ2FmbX92pLw5NZw7cE387jJkzPmsrS3c82n9mxZc2WjhmEnNAdT30lu2XoeSXp2YDfbdSi\nYlbnA7MXCJoWtwXqPFu4uxEjBJuEbe+E7RgjZkb0kLqW53Np9tPtOL61EfMq9PO/b5Ej6IGdx7+2\nzXYPu9gNmePhwC/v8H7jL/aw3+ac371NyIaLf85/A9xDWvrr0Rf5WADceOONfOQjH7kUhxIREbmi\n3HDDDdxwww07bjcYDC5Bay6cZh3PmqW1morq1PPcBbqpYgfrvN+5Te3H2LKPbsCeOvbuWpi7nRsh\n5HvrvEbTxq3Hpn3eNHTREacT7fQEcXPqzN4J253lvrozj6fXIu4xz0oeJ7OSt/3Jp49iTPcWmGqh\nQrZcfroXNFe32e6bL3ZD5vgnZjac90aevO0ZpB/S97r7x+Ztt0BzzgvP18xuAHa+bH0B3D0Cryf9\n5+SJuXeBiIiIyEWnoJ219dQ2oKb/dQP2omDNTLBdtO2WAzXPd9O+pk02G7Kb3TWvzw/czYZb2jP7\nJcw87l506H5wsk72nOW6YrrF2FkGLDaB24lN2Ga2pm7Tt5kJ3hSy5TLVreh+/bwNzOybSaHzooyZ\n3sb1wE8seO+nmMzq9R/2uN/mnD/LzB45+6aZLQO/Aiztcb/78SOksesBeI2Z3bRoQzMLZvY1Znbj\nJWiXiIiIXMHUdTybHh/tW163JqjO9APvfmxuFXaHmci7psZEz36snXm8W1f23B6f2mzSOqafmbXr\nVc9OyNZ9NpmcbbobdxPopz7RTNZGs5Y2hDwDebPEV3ft7GbStBS0u6PBm/3OPp5u3dT3rcQtlwF3\nf5eZ/QXwIOB5ZnYN8EukqTZvBv4Z8BXAHwKPYf9hez+fewfwLWZ2f+DngQ8D9wG+BXhi3uadwEv2\nuN9fAr6NNBnZ683sx0jnt0nqLv6vgM8A3ko654vG3f/CzJ4P/CSpS/tfmNl/BN5EmqhtCfg00trZ\n/4R08eFBwEcvZrtERETkyqagnYXZ0NbpNt2+1RmvvWUzZmLpohDY9s6eBMrtZ/ve7l2jWe3aF0Ts\npt2Th0237/krC20pvreTvk2fc3f+b8/NdOtWuifrZ0+W/UozlE8vbjQJ296exfQ5z/uWFLLlMvPP\nSGtQX03qjv2MznsOvJsU8nZe02Kx/fxQvAD418CXkta67nLSzOhPyV2wd83d32FmtwIvBE4DPzRn\n3z8OvJedg/YF/7C7+4vN7Dzw06Tx2t+Vb1s2JV0M2LzQY4qIiMjxpq7jWdP1evbWjIduumUv6kbe\njIFub3P3OVUMv9AW77yv3LAmGE+Nf55q6/S46EnX+e7zrZXubvjudiWP+XETsGP7/pzFdL376Tjp\njj71qa3nLnI5cfd3A/+AVDX+EDAmzYj9J6Sg+4hdjIHebi3q3bw/zxj4MlIF+22kdbjXgPeQQvgt\n7v53+zmmu/9b4MnA75JmLB+RKuavAb7E3b97l+3e7Xltu527vwy4P3Arqbp+J1AC54H353Y9D7jZ\n3f9mF8cTERERWch8bmXz+PmJ//yuzhcxb4Gr6SrqvIpqt+t1Z8Mt+1m0j+n9T2/rTZfxZiw0dMZG\nT8Ktz+xgdums6Z3ObRaQrsBMXzCY7GtqfLgZIUzGhReFUQSjCIFeCPSK5lbQKyzfAkV+XASjKAJm\nTE3u1nyP8y5cNKfXPP7M65eUvEV2ycweR1rT2oEvcve3HHKTrghm9hHgpptuukmzjouIiFxEN998\nM7fffjvA7e5+82G3ZxF1Hd/CZ+6nu4IvqiJPdxvf+q51U+3cjbbrHj4zpnqm97djKWhPTTBm0zm6\nHds92w7a2cOn27hgbHlT5p7T6qnJ4Npjxc7U5JbW9cqV9tS9POAYhIBbwEJYcPzZixDK1iIiIiIi\ncjQpaC9gM0+asdCdecXmbm9TL3Srydv28V74ejdkN7ucHS+dwrVNOlvnLttN8J6Jw/k+j4aeGQLe\nPY/pCwzTK11PZiLvfNy7W3h63QHqHLLzW3HyYScQCQQCHop88SBgUzue5r7wOoCIiIiIiMihU9Ce\nsW2deWaM9Zb42g3BC4J4ox2avE0Ab7tLdx5DCpmpa3fqct0EbWcy8ViMTh1tm97hzXttWXy6dZ0K\n/mwVe/7erP1+uhVta8rlTX/3TthuqtlOIP2raGCBNIv64kngFLJFREREROQoU9DOto6v9vaNbnXV\nJol66nPdT06vgDVdfZ500576cLNQ11SYD3m97JDHQwcma1o3rzXtiZ6r2g51dKocVpt82zmj7kHB\npqvaNjfgTk52dh3uycN0II/eToRm0akt4sFxi0RzYnBqc0LI5xaMEAK9/pBef0gfJ9CHsHWG9/ZI\njrqPi4iIiIjIkaWgnYWwfWWZTjV54TZMB++tj5jqat1067b2n5NJxcxoJxUrmkDaBO6Z+1TJNmqH\nGKGscyfydh1r2kDdLsXVtqDbmsnj6S7wW89j0nW82TYHbY/UscaJRGpqrzHSLXUSrwkWMSAE8mRq\ngaXlVZaWVoET9LE0HX4Rpir6InJg9jNDuYiIiIjskoJ21l1He/Zvn93q6XaF1HkjobfTHHKyaNZk\nBu8QmMzaHQJFsE4VuNOl3FINuo5Q52p22nEkEtNcZPmv1M3frGNzksYkebdP5iTrOSfTXdasnQod\nJ3qJxYroFcQSYoV5mR57nW8VhufJ05yiCNRVCQ5Fr08RCswChLbOP/96gIjsmbv/D6A47HaIiIiI\nXMkUtLPJMlhTI5e3Zs15oXMm/c1OVjb/gNCUo5uYHWwSsguDXmEMikC/V9BrgnZo1vaehO3UXRyq\nCFXtuEeiO4Wn9z1XupsTa07Vc8j2qZDdTPrmU22fjNO2yQWCHJTTjiLuEa8rqEd4Pd56iyUxVnhd\nApFm3vGiCIDR6/UZLq3Q7w0IRW9y3MZMyNbSdCIiIiIichQpaGf1eCPP2RVxr4kewWOecCzkbtp5\n+anm8dTU27n6arYlcm+Jg806WJCOVUc81hhQFFAEsGCEYki/CCwPCnq9Incrz93FPa+bnScaS5Og\nOXXuLj7pGJpaMDWu2qcz65ZKfKd7u9n0JYemqzoeU3j3iNclHqsUpKsRXm3i1YhYjfEcumM1JtZj\nvC6J9RiI7RjtXq/H0tIJqrJM3//0l9X5BjutVmVbRERERESOKAXtrCo3AIixItYVdZ1CXwjF9K0o\nsNAjhABtF+e0HFXbAbyp/ObnjTZA2iTQxlhT1yWxHGPENAlYAV4YYRgYFAOWhwX9Xq/9nNPMKu7E\nGKlJoTvGfMvjsidjmzvJOdevZ3tiN9tPNk0XD6BzLcEmG6eQ73isqKsxsRwRq01iuUldbhLLjRS2\n6zFejYn1iLps7keYOUVRUBQF/f6A1dUNqqokxpj23Xx1M1cq3NMFDTfHXElbRERERESOHgXtrBqv\nAxDrkqoaU1djPNYURY9Q9PJ9n6LoE3p9rOilinfo5WSaBld3Z+XeMo3XljKy4zESqzHleAPzGooU\ntIteIMQB/WKF5WHBoN9rJzNzd6o6Qh1xT0nUm4p2dDx2J1rzNjPHzgUAc88znU/mRJqqes/NsJ2L\nBp67qNc1VTmmGq9TjdeJ4w3q8Qb1eD2H71zZrkZU1SZ1OaIuNzFzer0+/V6ferjEaLROWY7TRYLd\n/IGpoi0iIiIiIkeUgnZWjtYAqMsxVblBVW4S65IipKAdQkHRG1D0+ilw9/qE0MOKHqFI1W4LBSEE\nLBRp3mwLbXdy75aEO2G8Gq8z3jjPeGMNYkndC9SF4f2CemWAxVX6BfR7oV0fOzpNaiaSxmh7s362\nO9FjWmYrxlzdnizz5Uza0sxjBrTV61mTwJ27qdNU/etU+a9GVKO1dBufzyF7gzjeSBXuKle0q1TJ\nrnLFOxSBAHivh4XQ9hZovsfZdcSa8diWH5uZxmiLiIiIiMiRpKCdjTfPA1CPNylHa5SjNWI5SiHQ\nUnhuqtohh+2Qq90piOfAnR9bmIRGsyJ1L2/vJ5OvjTfOsrl2D5vn78FjyaAo6PcC9aDHaHWJujqF\nkdad9pjKuDFPblbHyWzjTdj2JozXFXVVp67Y7rlLtuUQW0DosWh1n9Qyb7vAt0t3kfZVlWUK2OWY\narxBPT5PNTpPPVojlhvEMnUhj1WeFK1Kk6HVubod6zEhDCiKgsFwmeWVUwyXVhkMlun1h+l7DmlS\nZG9OKj3JXcqt09ddRERERETkaFHQzsoctMvRGuXGOcYb56jHG3ncchqBHZqgXfRy2O5NvVb0ctfy\ntpt5et9Ces2KXg7elidVy0H7/KdYP/tJvB5T9nr0ewXVYMD49ClilcYzh5DWyXbzTvU6Be0mdLdh\nO0bquqauylR5jjEHbQi9AaEwQpgXsre+NpmhPHUVx526KhmPNilHG1TjNerR+XQbn8erTWI5wqtR\nngitbGcdj1WZgnZVQq/IQXuJpdWTDJdX6Q+X6fUH6TsMaSZy97ilaeaOz51lTkRERERE5PApaGfV\nOE2GVo3WKTfPU66fpRqtpSpqXiM6Val7WFO5LnrYTMhuupen57mreW+QA24a2526l6dZy8uNs4zW\n7mbj/F3Ecky/36Pf6xGHS4w2rqUqN9OM3l7j0Yl1Wiu7jpGqjtR1um8mQkvb1MSqpCpH1HXq5l3X\nKbAWDj0LBO9PZhCfNadSnCY+i8RYU1VjyvEm4801ytE5Yg7Z9eh8WtqrGkMO19QlHsc5cFdp+a9Y\nEczo9dNyXsurpxgun2AwXKLXT0t7NRX/2e7hbb5WyBYRERERkSNKQTtbWj0NDr1en17Ro98fUI1W\nUxW2Lol1RYw17k5dl9R1mSvdKTBPdSMvem1luyj6OYz3sWZ8d1FQ9HoURWC0djfV5j14uQZVSaRH\nHXuU1Gys3c3ZT93JXasrLC2vUsVAHY0qGlWEsnbKOq2f3XQjL6vIaGONzY01RhtrqZt3XVPVNRBY\nWj3F0uppQjFIVXXIw8anZ0dPy3rlsd3NzOhVOu9ytE49XqMu14jlOrHawKsNqDdhS8iu8FiBp4nl\niv4As8DyiatYOXktq6euZeXkNQyXT1D0l4CQurpjhEUznpl6jYuIiIiIyNGloJ0tr5wGoB4MqfoD\nquEy1WidarxBWW5SjTeoylGqElcpdDbjh9PkXGFqMrRQ9CjyvYUUti13Oe/3063X6zHeOEe1eQ4v\n1/C6InqPuu5ResX6+bs5e/cKw6UBg6VVnILoPSKB2gM1gejpcXQjulHVkdHGOTbO38PG+bOMxyOq\nqqaqKswKojuhN2CwfCJP1AbmNllOC/JM5LTjsmOcVMircpNqvJZnGF8jjtfwch2vNqEZk103YTtV\n4okVeCT0B/T6S/QGy6yeuobVU9ewcupaVk9dw3D5JL3+ECxMJl2DHLaZWtoMV9IWEREREZGjS0E7\nW17NQbtaph4sUy9tUI03GG+cp9g8xzikSmtVjYh1yXi8kYJxlbtDt7Nk22QW7XxP6KdlwEKPXn/A\nYDBgMBzSHwyox+vU43V8vIF7xOuCOvQglmycv4ezwzSmerC0CtYH6+PWh9DDQw+3Hm4FTsC9oKrr\nHLTv5vw9dzEejSjLkrKsUmW9N2C4fAKPNd7O7k0buhs+tVZ2qmhX5SblaJ2qqWg3IbvcgKai3VSy\n67Q8GjGmsI1TFD0GSysMV06zcuoaVnPIXj11DYPhMkVvSKpoQ9M3PNJtVtPWvE74/DXIRERERERE\nDpWCdra0fAJI62j7YEislqnKTcbDJcabS4w3l9NkXRtL9PpL9Efr1ONRXhd6nKq+MU88lpe+almB\nW5rtu+j1qYZDqnLIcDhIM3Pn5a88RqIVYCV1VbK+dg+hMGKs6A2WwXoQ+pP70GtDvOXn7rB57lOM\n1j5FuXEP5WhEWdVUZYUVgzR52XiNerwOXoPlZcgsgDntROOxxj2dU12NqcsN6nKDqtygLteJZeou\n7tUmXm+2lex28rNYgnuetX1ACAXD5ZMsn7ia5ZPXsnrqDMsnrma4fJL+YCUvm9bDyZXs7kBsI1ex\nHXPwZmkyDdQWEREREZEjSEE7W1paBsDjAI9DiBWxXqEcL1OOT1CONxhvrjPaXGO0ucZ4cz3Nuj1a\npxxvUI03qcoR5XiTqkpjuuu6oq4qIhXuRiRQFL085rvE60Eav1yXuXu149Sksm1JOB+IsaYcbxKK\nflqWi1S9JlfIsR6hN0jLYvWGEApG6+cYr5+j3jyXZviu0/TkZg5l6vJdbZ6FuJSW+wpFOzkbAGb5\nokFFjFUO2us5YK+n8djVJlaPsDjCY+oqTizxWOKexmWbBULRo9dfougvsXTiGlZOXcfq6evSuOzV\n0/SHq4Rervhb6iju7kSD0NSyPY0Z9zR4PHdtVzVbRERERESOJgXtrAnaaWByTN2T8wzbdVVSV2PG\no03Gow1Go03Go3XGG+cZb55jvHGe0cZ5RhvnUnDdXGccI3V0yqqkjp6X4HJCKIj1IIftPuZOIGLE\nPN47LdMFEGPNeLTB+tpZzAKOgVu6tyJVtq2gN1iiP1xmMFyh6A+oRhuUow3q0QZe1xDBYg6r1QZx\nfJ56dA7zcTt2vFl2LJhhZnhMVfk6ltTlOHVxz0E7lht43XQVH6VbHKfAHasUtmPqql70+vQGKwyW\nT02C9lXXs3LiKpaWV+gPV2aW86LtOt7Wq3NF24z8ZqpuK2yLiIiIiMhRpKCdLQ2XgMlQazNLa0bH\nOq9FXTMejynHI8bliPHmBqONs4w2zjHaOEv//N0UvT4WUlW2dseqktojVV1TV5GqqtN+Y1riyus+\nhUEwCIGU8T0v0+VOWY7YtICF3FE6v+4OTpG7fRf0h8ssLa8yXDnBYLBErHIX7jxhm3vA3LDoeLlO\nNTpHuT4kVkuT2dBzVTvkNb7rXJGvYkVVjqnG69TjDerxBl6uQ5m6jaegnUJ2qmbX4BFwQggU/SH9\npRMMV69m+cS1LJ+8lpVTZ1heOcVgMKDXzzOyW/rym4p2MyVb/mNIwbqtaIuIiIiIiBxdCtrZoB/a\nx9aZFyzUgRqnNuj3e4BDSFVfC0bR79MfLtMfrjBYPslw9TTDtXtYP3c3xblPgRVpQjIb4T5u16Iu\ny/Q4BAhmhGYuNZ+MPLZomMXcpXsSwFPQrnEM90B0n8zFFkuIdRp/HWNbIXY3YuWMN88SzoLX4xRw\nZ7qOhxzsozvRY7rFiliNqesxsRpDPcKqzbai3Qb7ukozrvd7mBX0hydYOnEtSyevY/nEGYar19Ab\nniQUw9ztPbSzjM+f16wz6VmngO04itsiR5eZPQt4Oekn99Pd/W8PuUmXzB133MHNN9+88P3rr7+e\nd7zjHZewRSIiInIYFLSzQX9qbmua9a4COXjXk/HLFAVWFClkV8vUVZlD9hrLm2sMz99N0V8GC9R1\njdka7qQqsdepS3oZqcuKEFJ37ZDDO7mabrlymx43YdnbqnZ08pJeED2NvzaLuI8J5Co5jru1Qds9\nUm6cw+uScrSWJkCjmVxscvFgktrTHGRArlSnCdJCLLE4JsRmXHZFrCs81mmd8N4SRX/IYOUqhieu\nZfnUdaycujfDlVP0lk4QesMU8K2AqcDs+Tvw5lmzntfUDOMK2SJyVMUYuf322w+7GSIiInLIFLSz\nQW9S0ca7S0gFrLYUSkPAigKLNUV/kGYYj050Z1iO8oRomwxWTqU1q+uacrSJu6du2OUmsSqJdd0G\nZmtCdghT96ETtlObmmp2qmzX0akj+XGNWbrhY4oQ6IUCipDGdLdBu2JcjylH51M1udlnjLlXdhP2\nLZ1nsyZ4SAE8hLRNoKLwGqfC8oWDdIvYIFD0hvSXTjJcuZqlk9eyfOpeLJ++N/3BEv3BMlYM8/jy\nNC47n17O9lOl66kqdhqn7fnlzp+XiMiRctOc1+4grakgIiIix4GCduadB03Idme6K3dT9bWCEDzd\nFynEBgsUIc0q7rGmGm1Ql2kt6f5whcFwhf5gmdHmGuV4RJXHeze9oiFNlmYeidHa7uvWrHGdt2q7\njnsKnE6zznVFXY6pgqcB36HA6zTBWRu0m1N0crfzmEJ2jGlcdGdNbSsKQlEQih6hCGm8dWF50rKI\nUQM17V4tYEWgGKwwWD7N8MQ1LJ28juHqNfSXT9Efrqbx2L1erprTKWZPV6y3/sk0Xceb5J221Tra\nInL0BOAjc16/GVClW0RE5LhQ0M7qunnkbXdld9qKdfRJyM0JNwVSUtfrJtxS9BgMllhZPQ0x0uv1\nWVo9zcbaWZZPnGVj7Swba+fYWD8L6+dwj3ksdXNLXQ9px12n8dtAW21usmaAtqs57sS6pi6BEHCr\n8e6SXVhqJ7lLNuk4acx4Dtp5f2AEL3AvcpfxFKLdQ6qSE/F8w8jreBdY6NEfnmS4eg3Lp69n+cS1\nDFeuzkt4pbW0m+7q5p5ubdl63rhrzSwuIiIiIiKXHwXtrK6988zbJaZS0O5Wkkkh2yfjmFM375AC\nZ88xX8JWT1P0+gyXT7C0fi6F67VzrJ37FL3BJyAE6jy2ubml+ctqYh3xOqa5wpqJ0kJzjM5Ybsjd\nzIEY03JcxDR7dzCipVnEabqhW7oo4PnDMfpU2E5nnP4ZvKDwHniFeQ/zQCgCeeQ37pFIzLOVGxb6\nhN6Q3tJJhieuYeXUvVk6cS39pdW0hFevP2k7U1crpkwVujt/HgrcIiIiIiJyuVDQzurYREyahZxz\noKSdfKytZmfNZGWQu1R7wOgR+hCsoD9Yol45xXDpBMPlkyytrNEfrmBFgRvUXlOXo9TluxxRlyUV\neXIxj2k4X3AieR3s4HndbWsb0MRPbyraHvG8HnZaNmwyk3gz0VkTtNPFgzSzeOqK7pPKNk4wx/Ot\nmSG8Hdud1iLL1XHDQi8F7cEK/eXTabbxE9dQ9AYUvQEh9PLM6k09fXKceT3ArRuuuzm7+7Jv/ZyI\nXHxmdhXwPcDTgPsB54D0cbdgAAAgAElEQVT3AC9x99fsch9D4BuBpwMPBK4B7s77+f+BV7p7vXgP\nF7YPM/sQcF/gFe7+bDO7Bfg24LHAjcDA3TUZhIiIiOyLgnZW1bOT1HjTgzyHym7Gtk44bLo/O8GM\nGIxAAT3SxGZFL1WLzdrqbygC/eESS6unKEcbjEfrlKN1xpsblBsbjDc3GI82wCPudepentvjMVWS\nm27rmEGcjF12oDBop0vPun9btOYcgGABgqUwDW16LYqCoijo9QqKIlCYUTQzoeftvGkTlpYIK9Ka\n2OnWIxQ9LITuNO5b5QsZ3S7u08G6mSWts7b2wj9FEbnYzOwBwO8BNzD5wR4CXww83sxeDrxlh308\nBPgvpKDb/Y/DmWY/wHPN7Cnu/vGLtI/20qmZPRf4GaDovK+Zy0RERGTfFLSzpqI96Tw9YVMZsQnZ\nnp9NV2YDlsdGB0KRu6CbEYqCoten1x+0IfvkVdexuXGO0fp5NtfPpdv5s2yunSWEQKzLNMlZXRJj\nWhc7esSjT5YA6yzFRVN1L0jrcnlqu89k1+Y8gqVpzYL51AZGCtq9IuTA3amQm09Ctqfx64UFzHpY\n0Z8O26EgTI0Tz/u3mbY0f93Nedq69/MsXnhbRC4iMzsJvBG4nvRT+6vAq4CPA38P+E7g64EHbbOP\nzwR+HzgF3AP8LHAb8GHgWuCpwHOBhwO/aWZfOFuVPoh9dHw+8M+A/w38OPCnpN+NX7ib70RERERk\nHgXtbKqibdMPmy7i3nktTd01GWtsTRo3wzwwWYs6EIqCXq/PYLDEcHmV5eo0ZVVSVSUb5+9mvbmd\n+xT93iB3Q49U5YiyHEHpeJXGU8c6jadOy4LlEBsMj0Y0S93KLYD5pOrdtnm6mk3uYt6eV2ft7iIY\nvV4O2yGda+50TmzCtjdl/jCpaPf6eS3tVNVuLwa0Fybamd22/iHM5ufukl9ueGd28ukquIhcIt9P\nmj7bge9193/Xee9dZvYa4HXAE7fZxyuB06RA+0R3/9TM+79nZq/L+3kEKbi/7CLso/E5wLuBx7n7\n2c7rb9vmHERERES2paCdxe6A35xI2xiXZ/Vu3prekJxiJ0tjpf93wrYXWC+Pl65zZbuu0/rXkKvf\nPYqiT783YDBcZmn5ROpOPtpgPN6gHG1SjkdpabBynENuGmPtbVOcGFMQDqGpOFtaMswNYp5QzTx3\nJbd2ZnOaSdcsxfOiMIqU4WcmKEvhOnpejsycsqrxsiSOxozH6QJCXaUqvIVA6Ewc59BOKjd7a3qN\n0x4vjQE37zTA26+6M55cRC42M+sDzyb9mL5nJmQD4O61mT0H+BugP2cfjwEelffxrDkBudnPG3No\nfwYzIfkg9tFtUt7Pt86EbBEREZELoqCdNUHbuoGOPOv3vEDX7Y7dhuzOm1hbcQ0hVbbNeilUx5iC\ndR2JS3XepqDXGzAcrrC8eprxqTVGm2uMN88z2kiPRxvrjDbWGG+uU1dNoK3SOO7cldvc8WCTdbY9\nhW8H3DzPYA4Q0mzlueIc8ozgTdgucrW7qWJ3u9RHn9y8drwsqW1EGddZ2dxgPNqkLMf0qzGh6ANG\nEQJNAbxpV3cm9+6tuW7RfJPTcnV7UiQXkUvjFuBq0k/eKxdt5O63m9nvAk+e8/ZT8/373f29Oxzv\nLaSQ/HAzC54mqziofXR92N3/aIf9iIiIiOyJgnbWjrSelIenJrre0qc5d2vudsduK9r5+eR1oygC\n7qm7d3QIDrFIxwmhoOgNGAxXqFZOUVdjqvGI0frZNG574xyba2kN7s3BgI1ej/FoEzY3iNHx2sHr\nVOG1VMV2n4TtNGu5p7HalidGC2BeTJYIC6EN2Wksdqp2p4q2T41cdzxXtCFapC4rzEdYbWy2QXtE\nVZVpArVQdBf0ymuFgwfa2dw977eZXG02YHeGcE/mVlPPcZFL6XM7j2/bYdu3Mz9oPyzf/30z2+1k\nY33SbOKfOMB9NJw0Q7mIiIjIgVLQzrpdx7sVXADvdB1vt7HOnFzdGbOtE7Cn/jmZqdyaoJ2nArdQ\nUBR96sEyMdZ4rIl1xeb6SZaasL20ymC4zKA/pNcfsrF2nhB6gFGVI2JekzvN92OTZcny8bA0KzrB\nJ5O7NV3Fu8uBmdFMFB7aIJsDdtM9PXcbryPUHiFWeJ3Gko+aoD3epCrHmAVi0SN4PwX9ttJOp42p\nTe6TUe/d7vtT4Rra2C8il9Q1ncdzZwLv+NiC1+/F3vuiOLBywPvomtv1fP8iqYmzUsYfj8e8853v\n5IYbbuCGG2442EOLiIhcxu644w7uuOOOHbcbj8eXoDUXTkE7m+Tozt/f0kDgFOxsusrq3gTnqenF\nultAp7o9FVa7h8iTmhW9PhZCDtqRWPQYuqcu5f0lBsMVBkurLK+cYmX9LGvn72H9/FkG5+9hvLlG\nOd6kHG9QV2PMUvW6rtMY6nYcdkizojfHTLc08VkI0+OxuzOrt1Xo3NW7rp26dso6UrtBLPNa25Fy\ntMl4tM54c51yeSON0e71879pnSXB2qp7rrw3IduZ+r6bixne/TpF5LDt92pXs3zWu4Gv28Pnbj/g\nfXRtu1b3/ty5+J077+SWW27h1ltv5YUvfOHBH1pEROQy9ZKXvIQXvehFh92MA6OgnTVrZTfprrN0\n83Qx27ZG6pkB2vN2nkc6N12jaWfsNtL62mAplMaC6E7w2Ibs4dIJyuWTLK2cpBytM9o8z/CeTzJc\n+hT9wRKba/ekZcI2jPEmuNfEmCdb63QHB4MiHycUFCFQhEBo1sjO3cStE7jb9uex3tGdKjpV7VR1\npIrNtxdxq9Lkbe3a4BuEok+vvzQZQ95E7SZkE9pw3VyEcEsToDmTRkwNyZ53UURELrZu5ffewF9v\ns+29F7x+F+nH+cQuxlcvchD7uGjMjDNnzix8/8yZM7z61a9WNVtERGTGc5/7XJ76f9q79yjJ1rO+\n79/n3beq6u65nIukIxQSA3aMIzCyIsDcZIwSywgRA1lZkJAogB2c8AcrgMOCXIS0EtvLYAzJcpaP\nrUQIHAcwBoJB2IZEQUCWkcTVXlwCJk4QDHDQ5ZyZ7qrae7/vkz/evXft7um598z0Uf8+Z9XUbfd+\nd1WfmppnP+/7PJ//+Xfc7vWvfz3PPXfrk9rnhQLtwRRMjzfm0fUs6LYhaN5ls4+XQLvd2mHHdi2x\nht3nILjIRdKGYDONWfRqkYNxd2LsiN2G2OeMcb3Yo2oWFFVFWZWEIrcE89TTdx0pJWLKyxdDCHiY\nH/ssoz208gpDYfB5Dn533D4UPxumiw9BdhcTMfpQjC1nqbt2naulb3LF9LJakGKcZgY4KQfbU5Cd\ndsH2kNUee39Pb/18gfbdvNEi8jD8s9nt1wA/fZttX3OLx38e+DTgY8zsJe5+pynoD2sfD83LX/5y\n3v/+9z/uwxAREXnRudtlVXVdP4KjeXAKtAdjq6hdV+mh/PU4NXy8PWa0bbp7Uz/nk0XKpyrbzMuJ\n7cZNKZGSTwH2WJHbQiBY7lFdjOOFAFaw2MvB9NinOxQFFgwLOdhtt2vYGpCGdddGGcLQG3voj12E\nqYVXmE0Vt+kogbRr5TUG2DmTPfb09t2aaiD1HX27oRsqppdlTVHVFGWdp5inHmJPWRY0iwV1sxje\n25DfSw/DNHxnKsM2zB3fvc85UFdGW+SR+llyVvsK8B8C33baRmb2Udy6j/YPAV9F/hv0q4H/8j6O\n4yz2ISIiIvJQKdAejdHxvOoW40PzYJsh2LZT1mXftNNcVZvZdPFjz0JKToyJvu9JKQ0BeQ60i6KE\nMmerzQIWSkJpFBZolpdwjFBUhLIghCFQDcb26AYWhmWMHqdAughGVRZDsB2OZ7JPK3zmY4Xw4Rij\n0/WJPkZiTMTkQ7C/yy2n2NF3G9rtIdv1DYoiB9lFURG77XQpq5K9/gBwijIA5XBCIOQ+4NNU+3nV\nufHAbN7aXEQeAXdvzeztwNcAn2RmX+fu3zLfxswK4O9wSg/tYR8/ZmbvAT4Z+Etm9vPu/n23GtPM\nXgn8a+7+w2e5DxEREZGHTYH2xHdXNo/g5pOp87RrG9dkH6s4fsrefBdgz6/nAXryHLj2XU+MMU+l\nHoPtCrCAhZzdtmA5ux1K6iWEoqSql4SyxGzM/uap4uCk1EPsCEP/7CJAVRZURaAqiymLbcNl9y74\nLsh2mzLafZ/o+kgfEzGmnIl3Z3b+IQfa7ZZ2c0i7zhntsqopinKYTp6nldd1jeEUZUHV1JhBYQEP\njo+vBcsVycdZBEPA7cPtkzMJROSheyu5L/UrgL9mZq8CvpNchfyPAF9L7rf9Pm49ffzfB36GXMX8\ne83sHwLfA/w6uTDZS4BXkftlfwrwLcDJIPks9iEiIiLy0CjQvsnJbPbNt473y77FT94iyJ6qag/6\nPtJut2zXG/q+H6aNJxxoFvnaxn5bWM5sF4GiavJxFAUxtnTbI7p2Td9vSLEj9i2x2+DRMJxgkcI4\nls0eC77ZcMDuuzXRY/Gz6EPhsz7RxThNHY8pkYaTAsC0rxxor2nXN9hUDSEM2XZgs77B9uiQzfqQ\nZrGkqiqa5ZLY9xQhEEOi8NzqK0+K9/nbOZ3UsDHgVkpb5JFy9xfM7PXAjwEvA75kuEybAG8HfnK4\nPm0fv2lmfxL4B8Argc8D3njapsPl+Yexj4HO1omIiMhDoUD7JrsqXDbLWs9beY3FuWxanGzHfjQ/\nekq352H7sSBacme72XDjhevcuH6Dtm2H4DHvc7m/YtX3OE6V6qFaeMDMiH1H33X03XYofpaDzyJU\nlGVFXdWkpsF7MI+5LzaJMkCYTRnfHVqeiz32t46JIaj2Ycp4pB+mjafkeEpMG8/ek77raLdrNkfX\n8/T1oUBb7NZsN2u26zWbzRGx32e5WtG1l4ixJ8aCskxTkA9jsG27t27+K3JuNZlARB4id/9lM/s3\ngK8HvgD4aOA6uVja33b37zWzN3Gs7ONN+/gNM/skcnb8i8jZ76fJrbs+APwa8FPAD7j7Lzysfdzu\nGEVEREQehALtk45V356vxD5ej/t4JWyfBd7Hu0+dLIQ2BpKenJQim82GG9dv8KEPfojtZrsLz824\nFHsAiqoAnKIooCixEIixp+9bunZL388Lo5WUZUVV13hX4yFBMkhOwCmCUYTZa5yfQGCWyR5aeLUn\npovHGPO686Fg25SfHwP0PgfaoajyY7HD45bYHrLdbthsNvl1pp7t5jJduyX2PamodmvUZ//szTXJ\nd1XI84GO5d8VaYs8Du7+YeAbhstpz78DeMcd9uHk6d7f8wDHcd/7cPc/dL/jioiIiNyJAu0TptDN\n8nrsXVr72LPZGFhPbb+OP3VyKnm+3gXZMcac0b5+nQ998EOsj44Ya36HEHCcUBbUi4oQwD3XFyo8\nEGNH380CbU9DT+4caHtVYU2NFwnvgRgxD0M2e+yrPXtNw9r08WRAHAqftV2k6yMxDYF2Ssdf2Tyj\nDfR9h23W4EaKPSluSf0R/XbBdtuy2XZsty3gbNY36NpN3i7FqfK6eQ6u529dcpsem/c4FxERERER\nOW8UaA+mwlpTnbN5FDd/5MTj4/anBH02rHvePemzaehZiomu72m3LZv1NlfyHitrFwVYILmz2tvQ\n1DVNU1GVJV17RLddD32rD4nbI2K3xvsNHltIPUYCEmbj4uvduGMhb98dWc5kDwF210farqdtc6Cd\n3G9ekz3tayye5oQ+EkJHF7aEYPSFEQuIRX7/qqoihIrl3j7NYkVVNYSiytPMhwPyU05a4LvMeRpn\nHWjCp4iIiIiInEMKtAdjoD3OpB5Da/N5YHwisz0Ezcd+9lg0u9vXmClOQyG1sZJ28kTsI23bsdm0\n9DHR9bmat1sgOvR9z/poyWpZs1w2LOqSvl8Tuw2xW9O3a2K3JnZHpH6N91tIHZZ68BxsjxXJ58fo\nnh/wISSPyemGQHvbRdo2sm07+n738wxVwI933MpZencnxoj1PVibC5yVgVgFvA+EYkFTN1i5YG//\nCsvlPlWzpCgqQiiwqfzZfH27kdwZZ8CPAfbxlmQiIiIiIiLnhwLtwbxX9u3jt2Eq+RhgzzLUu3Db\np213WW0fxnB8NoYnp+8jXdez2ba0bc4kd30aWmrlbPfe+oh2b0G/1xCXFanf4HFLihtSv5mC7lxp\nfAupxTyCR6bS4j5bXz4d6hBsOzmoj07bR9ousu162ranjzGfGDCwwHDbjgXayXOBtBgjZj1gFMGI\nVSD1+VJVS6pmQbW8zP7BZRar/aE9WY2Fcjph4dMC+J1ErjY+Bthh/jpERERERETOEQXaA5tP72Y+\nWXwX8O2KjDvmhpPyVObjWwyrrI9H7Hn7Wabc5pcAFnCMmJy2i7RtTygCRl7Pnfotqd3QtzXtuiR4\ni9Fi3uJpi/dbPI7XLcQOPE8f97HXtVnuxz2MZ6HYXZLTe4vFLd5BIpG8Jw6tvcIYYLsRghOCkdIQ\nGA/Z7FzoLZFijwGxCKQYc4VyoCwrmuUei4MrLPev0iz3qeoFoSgJIU+TnxUyn715nKw+RxpOYoiI\niIiIiJw3CrRvYZzCbMOfs7rcMATZKeaMsY8FwoZK3Dnzm/tHBwu7gmrDlOsxGx5CQVFV1IuGxWpJ\ns2mJyWj7BF2cMsR917JZ96R+Q7s2Diuoi0RVRqoiEuhz4O0dltpc6Tv1eIqQxmA0YAFCWROKilBU\nFFVDKBuKqsmbHR6RikOiHdL7IV3vWOiAMYgeTjA4eDJs6MU9dSSbWoQlUurB8/9eFkqKsqFs9mlW\nl1kdPMVy/wnqxQFFtSBYkfuDn1i/Pv0eTia4jyfoRUREREREzhUF2ndyLJjz3RrilEixw2NH6jvc\n03DJa5iLoqQoK4qi3GWQLTBmZXOgHXIrrqahWa5YLFu63im2eY1zXvPc07UJ750tPYdECousFsay\ngeXCqItIQU9hPYEeUi6G5ilO6WEzg1AQipqiXlLWS8p6RbnYo2z2cAKpfJ5oz9N5Qdk5YdthYT0V\nOptOPji47davG+MU+mFqvCc8pTy+GVaUhGpB1ezRrK6wPHiS5cFV6sUeZdkMhdDG92a2inxcFj6b\n4j5NdUftvURERERE5HxSoH0HY9HwqTzX2DvaI6lv6bstsdviKQ5VuRPBAmVV457APRf6CgUEzwH3\nEJiGIlBWFXWzyBntdct62xPK7VBwzIl9T+/Qe0ds81psTy2X9gr6/RLrS6idqkiEImIhQZpltGEK\n8C0UhLIZAux96uUl6tVl6tVlnIJoDX0qaPvEdttSrNfDz45Tw32cOT8E2kwnDMI0Vd5hOOHgKWJA\nCBWhbKgWezSryywPnmSxf4WqqijKOk9dP7Y43jgWR89njR8v4C4iIiIiInLuKNAejOuCpwwqx+M6\nM8vBdOxJqadvt2yOXmB7dJ3t+gVi3w19oyMYlGVNUVaUZT2sQS4JRYGFklCUQ+BdcPT8dbr1C1h/\nSMmGOrQsyp6+SkDEPNH3idRth1ZeR6R+CzEQ20C7DiwbY1EbiwqaCiwYwUqKOgeyRZWnh5f1kqrZ\np1rs764Xe1TNPm4BQkWollTLA+rFPlW9oqwajq5/mLbd0g0XH/9LYOa54FtwwlBJ3QzCULQseaLv\nO9ptS9d1xNhP72M3nqxoc7BehEBR5Eso8vtVFMXu9zNfq32iXZmIiIiIiMh5oUB74CfvjH2ax/XV\nGO6JGDti19JubnB0/YMcvvAH3Hj+A0NmuyX2ecr3bup4NV2HYgx863xd1hwdrenXGyxupkC7KTv6\nOuZsduyJfUfXbmjXR7SbQ/p2TWqNdg3rQ2O5COwtClbLguWipG4amqahbBZUi70cNI/Bc7NPvdin\nbvbzFPJqQVEvcALFEGQv969SNznILsqKqqo5OrzO0eELxNiTUhrWYaehCrgPjbl8CrJDMCzkrHbX\ndUOg3tH3PSnFfGLCEz15entZFBRlDqzLqqaqaiproCiGX8fYXHuW2lZGW0REREREziEF2rcyBdm7\nKuGenBR7+m7Ddn2Dw+sf5PkPXOP5D1yj3R7Rt1u6doOnSChKiiGLnbPbDWVVU1aL3OKqXlI2S7ou\n0rUR+khJlzPaVU+qIxvPQX3ftbTbIzabG2yPbtBuj2g3ThWcsnBWy5J2r6bramJs2As11aKiqFfU\ne1dY7D/BYv8qzSqvi66bHGjPC6NhRr08YNlv6buWetbfuijy1PcYe7abo9zOK0EaMtrBHA/D+2YQ\nAhQhP5dSpO87ttstXdcOmf9I6jtibEl9i6eOsqyGGQAl9dBOrCir6QzIdCIkn/1QjC0iIiIiIueW\nAu2Bz3tKzZOlN7d0nm3j00ZjcbTYbYl9l4uADRvmrHY5XDdDoL2grBcktylo9S5isaXwjtJaQtqS\n4jYH8Nsjuu2art3QtRuSOTE4RXDMaqqqoO6h9xKKJaG5RL3/BM3+kywOnsjB9t5VqnpF1ayo6yUh\nlFN7L4CirEhpiaecdU4xr/EOIeAWiJ7oupZ2u6Vtt6S0xT3mXPZUevx4b/GUctV0225oN0ds14ds\nj67jsaPvNvTdhti3VFVDVS+o6gZPKZ+gqJpZR/Jdf+1dxXHNHRcRERERkfNHgfZpThTfcveh+Fag\nKCqoF3jap7/0JLhTlRWbo+ts1jfYrm/Qbdd5innfkfo+7zJG+phyJrfPgXNRlDhG8txCrI+Jro3E\nLuJ9JHVbYrul327p2w2p3+CpA09TkW4LIWelqyWhPqBYXKbae4rFpadZXX4Jq4OrNKtLNKvLVM0+\nZVXn12DFrpqY5z7XBgQzPBTUzYrVpScwC4SyglBORdWObrzA0Y3rpJjo+1138OS53pu74RjuRkwR\n+hbfHrE5ep6j63/A9eWSqmrySYPtmth3NIs96uUezXIPB4qqpl70u+JrQ6u1sSydzWYciIiIiIiI\nnCcKtAfzjLYN/a6HblU5b5p2gXYY+keTEmVRslisWB++wPrwedZHL7A5uk63PaLd5EuKcbj0jDu2\nsR3W7JISdNGJEbxPpK7dBdrb3TRrIwK5XZiFgBUVNgXaV3KgffAyVleeYbV3mWqxol7sUdULwtDf\nOwfNMPb/njLwQxuyullhZjnz3iymINuBUFTE5Gw3a/rY5ceH3fhw0gALuJNft2+J7qyPnqe5vszV\nxouKdrum227ou5bF/mUW7WX6vseKknqxIsY4FKkbfwlDRlvxtYiIiIiInGMKtO9gV+zap+yuUVIU\nJWVR0ixWxP0rHB1+mMPre9Q3llT1gs3h8xiW13T7hthHYp/XKHuKecp1SliwHCwHy72s3Uge8GSk\nfpfRjl1P6iOe+pzRtjD9bChrimpJ0RxQLK9Sr56kOXgpqysfxXK1T1k1+VKW5DB4nObtu3LrQ2/v\nMaCumxVlvWCxd5lmuc8umHZiTGw3Gw6r57F2M71PY2Y+B+R5lBTz6yT2bNcvcHS9oghGsILtdkO7\nWdO3XV6/HSMJo2wWrPYuEYcTE2MxtKko2vSniIiIiIjI+aNA+64MQegURg69qYsKq5wiBHzol13V\nCxbLA7b7V9heusF2fZ12s6bdHtFtjujaDX2XC4713RYfem87aUgsBwoChRtdUxJjfr7aBvo+0vcl\nyWGxWrFY7rFY7XFw+SqXrz7FpSee4vLVp7j8xNOs9i9TVrlHdZ7+nkgpMlYGn9aX23hlu+zx+JqH\nWyEU1M2S5f5l+i4XNeuHS1mWpNiRYov33TAVPmfnfeo+7mDQtS3bzRFVdZ2irPAYKQqjWDYslkuW\nq3329i6xXO5T1Q0hlLOA2o6F18pqi4iIiIjIeaVA+1amiuMj3wWllqdGB0qCBbysCKGkahYsVgfD\n2uOjqYDZZn2DzVFev73d3GC7PmK7OaTdHA5ruVti7KasuYUCx1gOQTY4dRXp+kTXJaIHDi5fZf/K\nExxcfoJLV57k0tWnuHTlKfYvP8Fq/4DV3j5F1WBFXovtJFJialtmw+sIJ1/rLMM9vtZQlNTNktX+\nZQyn7zv6vqXvW0JhtMNraWOc1pzHNK6jHoPtXBSt3azZhIKqaXILtKKkquoh0N5jtXfAYrWfp7kX\nxclm5gqwRURERETk3FOgfTtj+vqUuzkArbByyLXWS/Cxv3SuPp57a29ZH77A0biGe7g+OnyeUBR0\n7ZquDaStY54IRSAURQ7ghyC7CLCtCtreaTsnecHlq1e5+vTLuPL0M1y++jSXrjzFwZUn2Tu4Slnl\nHt5lVQ4nBYaMtqdhLbZNQbaPa57dZgXH0vQaHaMYMto4lGVF3w8nB/oWM+fwhTxFvt2sGaeYJwfz\nWUabnNEuwlGeBu49i8WKsq5oFg3LIaO92r/EYrlPVTWEUByPs4c/zU78YkRERERERM4RBdp3NE61\nhrHvVw7zhmB1KCw2bmNjUFsvhinVXV4fXS+oF0vqxYqqWVIO7b22Q6XyUOQp2CHYVGxtV+3cKYpE\n2UNVOskq9g9WHFw64PKVq1y68gT7l6+yd3CF1f6lHDhPxdbyWmx3cPOxDfVQX8xwG5LYNj68mzI+\nFYiz3KKsahwLgdX+Fbp2Q4z9NI3e3en7duhTli9pOFEwZslTjMS+o+8Cqa6xoqBqliz3LrHYO2Cx\nt89iuU/TLCmrmhDCeKBD1n2X0R4z8iIiIiIiIueNAu1bOq3clu8C7jFX646Rhkrbuy3NAqHIGeV6\nscIsDL2il7mP9WKfZnWZ9eGHWR8+T1k19N1mCFIjeBz6azvuccigQ1kaWMlqUbFY1DRNQ13XlGWZ\np1pbGILrXaC+i5yHCuc+ZLmHADzBkN2ebTr/c6xKHgqKsqJe7LE6eCKvKQ9FbhXmTor9LpPftsP4\nu6y2u+eCafkHKasFzeoSy4MnWe5dYbE6oFnuUTULyrIiWJhVaR8PbhZpi4iIiIiInEMKtG9rXK98\n+4B7VxXbpmDWQsDcIBSYFZRVQxP3aJb7NMt9FqvLLNaH1Nf3KOsFoShpN4d4zG28UsyBqqccdIcQ\nqZIRk2GhYrWsWV9f5GwAABcpSURBVC5qFosh0K4qiqLEgk2tsPJJgNmrsKkE2vTqphXZvluDPgXZ\nQ1A8vsIQSrCw63Vd1hRlnbdLuar6dn2D7ZHhfU+Ku65cPhzPGGxbKCjrJc3qMqtLT7LYv8JidSln\n/OsmzxQIYwG0/F7bsVuKtEVERERE5HxSoD2wWeh8PJBmyKrOq1+PfHreh8dzsth27beAsqynac4x\n9tTbA5rVmmazHqZI54JlZVUR2zV9uyG2IWe3UwTvKcKw9nkIeBdNyaKuqKuSqiopigILIWe0SXia\nZ5NtN/Xa8zGOM7KnbLPZTSntnJFOeZthenywgmqxRyhqmkVuHRZjT9+3dN06TyOPib7dYrGHBE7a\nvWtDsI0FiqqhXh6w3L86tRFrFiuKsjrWgsym0wV2PLyenzUQkRcVM0vDzW9y97fe5z5eC7xruPun\n3P3dZ3JwD+DatWu84hWv4GUvexnve9/7HvfhiIiIyGOiQPsm43TpeQusE9nTYQqzjYHfuB56eGq3\nDjpPe56vdfYhCA9FSVnV1M0ey70rJPc8fXy7pm9ztXJbv0CiIMRESi3JEykmPLX07Zp2c4Pt0fMU\nZUMoqrzuOy2G8U5kgv3m/Pyweptdl+rpkKdp3+OGftO0+FyorKwXLFaX2L/0JCl2VNWCul5Q1Q3t\n5jBPI++3xL6lKEtCkaeDe0qzi0+V0EMwimC7swFTa7UTU8bHAxWRF7OzOl12bk67pZT47d/+7cd9\nGCIiIvKYKdC+DT+RTd0ZMsSzomMnm4HtAttZ9bHxp8MQaJdOvVjlomGhoKqXdNsj2u0R5fYIt0Af\nE227xfu464Xtkb5d021u0B4+T1nlwmrVcp+Uem6Zn59ls7HddHcb1mpPh368w9fx1w1Dtr6ksEBV\nL1nuHZBSj5lRVTnIruqGzdELdJtD2s0Nuu1RzvAPUX/uHz5e56JpwXKQHcKwNnt2nsNmJzPmvx8R\nufDmZxZFREREzgUF2rfk09ri49nUYX3wyaz2yWnN417GXtK+W29sFiiKEjBq38OsoCgbqmZFu8lB\n9nZzSN9HttstVhyCbXHv8BRJMR7LaJfNknq5T+xbUooYYYxOb5robvOAevhzKuQ2TjUfA9zpD6bX\nbAAhDO3HnKp2mtWlaep7WddUdU1VNdRNzeawIrfyjjl7PQTWYzabtJvmnqem2zRNfixYPrYoO/Hb\n0SptkQvO3X8CKB73cYiIiIicpEB7cjyl64zrsu1YsbNxkrX5zcHftBs7niHObalysJ3XOw/Tx0Ne\nvw2GFSWhqPKlrAllTd919H1P33V5m1AMa5w3pBTp2w3t+jrVYo9ue0RsN6S+y2u+Q4FZOLHM3HdT\nwGfB9LGCaLPn3I4H5tMPTJltG6bAN7inPB5GEfK0+Kquc/XwIq/v7me9xT1FUt/lx9o1sW/x1LPL\nv4Pb6ScxxhZkrhyWiIiIiIicQwq0B8enIftsXbMPrbBsmg4+rxvmwwJms1nu+2QA6FNoeKwCeAiB\noiiG+0M/7hAIRUVR1qQYh+DXKKqG4kY1BJh5pNS3tJsbVJvrdNtDYrcm9VsoKoLlYxqGZzysaf34\nOK18rFA+33A6bpte79Rse9rT7lYI5a76uHsOvpsFVb2Y1mUHM7ZH19kCsWtJsafvtrSbI7ZHN+hW\nB/RdO2S958dhx4539pZqibaInCfhcR+AiNyba9eu8eyzz/KVX/mVPPPMM4/7cETkLsUYx5vn+rv3\nXB/cozS2nsqX2X04VkhsbFPFbNv88+Oz43YnLr77uZzhHoui5d7UZVVTNQuaxR6L1SWW+1dZHTzJ\n/uWXsH/1GfYvv4Tl3lXqodI3QIwt7eY67foG/faQvt2Qum3ODHvctcOaFxAbD25st4WTxuJkU/ut\n4T0Y//Pda0hpWD49vRTDioKirIfCaAesDp7g4OpLOXjyZRxcfSn7V55m79KTNKuDXIEdjgfa6xt0\n2zWx70gp4rP38eT09+O/M2W1Rc4DM3vGzP6qmf2smX3YzFoz+10z+yUz+3tm9iYz27/DPl5jZv+r\nmf2WmW3M7P1m9p1m9kdv8zOvNbM0XD7rlOffPjz3m8P9l5vZt5rZr5nZoZn9vpn9sJn9mQd/FzSF\nXeTF5tq1a7zlLW/h2rVrj/tQROQezALtc/3dq4z2wE+5Nd712frlsRf1vIK3u88KdY3Z61P3Nq7w\nJk8FtyEbDqGA4E5RJoqUKMdstgWsqDAzUuzou00OqFMkpUjsO7r2KD/Wb0ixI8QSL6s8+ph+n58R\nsHl18dmBHXvZs1Ljx2u5nXhlhllBKIY+21UzjOtUTUOus54wT6S+pdscEkKRg/a+y9PfN4d03YbY\nd3nd9lSlPRxbIi4i54+ZfSbwD4FLHP+b4unh8krgi4HngHfeYh//GfBtHP/CfAb4UuALzez17v5T\ntzmMO55yM7NXD+M/NXt4AXwu8Llm9tfd/S/daT8iIiIid0OB9p0ci/RuEe3ZWN37+Ba3/pffbn13\nDtBnoblZ7mkdAlVVkxarvK/Uk2KLp4iFQN9u6Iee20VZEYpimH6eDydgBAu79mJDsGzTuDcd/qmv\n6aZq6ydf59Rj3KYTEeOPl2VDs9jH9zvMndi1dG3OYhtOUTUQAslzQbQxw76bFn78HVWsLXK+mFkN\nfDdwALwA/I/A/wn8PlADfwj4NOALbrOb1wOfDPwi8O3APweWw8989XD7u8zsD7t7f5+HugL+/nCc\nfwX4UWALfArwDcDLga8xs//P3f+H+xxDREREZKJA+3ZOiex8qDM+b9p1WgOw04Ps472gd0F2LrEW\nxgA85ExuVdUAhKLMIw7TwYuyyOudj15ga0ZR1nmb4efGVlhj/2kfk9pT1fPxIPx4kG12m7D2tNZl\nx5/fteHK12VZ0yz2CDhlUdK3W9rtmnZ9g5R6iqrBLJBSysH2fCo+8/Xkpx2Nwm6Rc+DTyZlnB77E\n3X/0xPPvAb7HzP5zcrB7mk8Ffhj4whOB9E+b2QeB/xb4aOANwP92n8f5EqAFPsfdf3r2+PvM7PuB\nnwFeAfx3Zvb33P0D9zmOiIiICKA12jfxYwHcbbLYs+ec3Wa3buh6vEXYlPk9EaCGkFtchSGj3SxW\nLPcusTp4gr3LT3Fw9SUcPPEMq0tP0uxdoqwXhCmjvWvBNbbK2gXcu8enIzp2ewhdx5T49AOzYz/l\n3Tr+duTp8BYKQiimQHu5f5X9y3md9mr/Kov9K7u15kOgPWa0x4Jqt5oOMG+oJiKP3ctmt3/yVhu5\ne3L3G6c8ZcAa+PJbZKv/e3KADPCZ932U+W+Uv3UiyB6P7RrwtcPdPeBNDzCOiIiICKCM9mRe6XoK\nnH3sNG1D5XGmtdrTdlM+ehdIG34iTtz10PaxQvnYH3ra1mdbD2dALEDIRdNS1dAs9vAUmVZ5W8Cs\noGpWNKtLVM2SsshBd5ivbx4qp594xXf5xjA7xvnjx08p7LLku4DdQiCUVe4bHgKL1SVWB1dpt3lN\neVlVlFVFXS8omwWhKPNU92H/jmNu07r4kzG/Am6Rx25eQejLgHuddu3Aj7n7H5z6pPsNM/t14I8B\nH3N/hzj5jts89wPAh4HLwOuAb33AsUREROSCU6A9SGk3rToxC3bJ90OCFPLa5zEwHq/TscnkJ82D\n6cF8vvmxZ3z2mE8/nWeTB8qyoW72GDPiRVFT1UvKqmGxf5VmuU9ZNxRFRQjzIz2+z+PHMWtJdmrR\ns9kxTld+7NjN7KatzcfnAlaAU1Mv91geXCXGjti3FEVBUeSe24vlAWW9AAtTxfO8pnwMtsl9td13\nAfZNTb5F5BH7KeA3yUHwt5vZl5KD1ncD73X37i728at3eP6D5L+dDh7gOFvyGvBTuXtvZj8PfDbw\nCQ8wjoiIiAigQHvinoYbu0JliZuDbQ+7x+fPz+Ps3c1bRq7Ddn5Kotmn+HEeoodQUFY1mOU+20VN\nVa9olgcURUW1WFEv9iirmhB267XzGCdrqs9Kjc8bUg8Lo+1EiD4e1+7KT/S6zvsKs4UIeVaAYYSp\nR3i12GO1fxUzI8aWIoShl3iZW39VzZDR3gXyY+bfyEG2mw2VzPPsABF5fIYA9fOA7wM+Hvg3gdcM\nT6/N7N3AdwLf49Nfsjc5usMw4889SAuPD/rNf2md9HvD9RMPMM6kbVt+7ud+7pbPP/PMM+rbKyIi\nMnPt2rW7arfXdXdzHv/xU6A98Fm2dooXzXbB9DBlfLfdLAyeB6swTXWeTy6fjTQlg+1YCLzbflio\nfGyZdBiKi1koKcp66ltdL/axEPL9qqYoK3JbrF01c5+y2X4i9B+yw/PDHw5unqT2KcjeXc//zTpm\ntI+/Dfl0Qw6yDaOgqpf4KmKFkWJPGNamFyFQNyuKsp4y2vNg3sgZ8mkd+e7XIyKPmbv/qpl9AvDG\n4fJZwMeRW2f9meHyNWb2Z281RfxRHOajHvC5557j1a9+9S2ff/Ob38w3fdM3PboDEhEROeeeffZZ\n3vKWtzzuwzgzCrTvxYs0sLt9Xv0sB3qAN+hF+t6KCAzZ4h8aLpjZS8ltu74KeDXwJ4BngS96TIf4\npJnZHbLaLx2uP3ifY0yZ8Ly0p+Dq1au33PjZZ5/lbW97230OJSJnoW1zrcXXv/711HX9mI9GRGKM\nPP3003fc7rnnnhtvnskstIdFgfbgKz//kxTqiYicAXf/PeAdZva/AP+UHGh/npk17r59DIdUA38c\n+IXTnjSzAvgk8jnJf36fY+zm87jT9/38HwIico7psyryonWu4zcF2iIi8lAMa7h/ghxol8AVdmuh\nH7U3cYtAG/hC4Co50P7x+9z/FmjIa8p//z73ISIiInf2EvLq3sdx8v6uKdAWEZH7YmafAVxz939x\ni+cr4LXD3RvA40obGfCfmtnfd/f/69gTZi8Dvnm4ewS8434GcPe9BztEERER+UiiQFtERO7X5wD/\ntZn9JPAjwC+Rg+kl8EeAv0jOZjvwtttUHr+TBy0z8fvkIPrHzexvAO8knwX/FOAbgJcPY/xXj7Fg\nm4iIiHwEUaAtIiIPwsiVxl97ynNjy4MfBL7xAcd4EEfAvwv8KDmw/obZc+Mxfru7f/sDjiMiIiIC\nKNAWEZH7983ALwKvA15Fzgy/ZHjud4H3AO9w93/0gOOMwfC9PrfbyP3nzOxPAF8HvAH4KOAQeC85\nyP4nD3iMIiIiIhO7fbcTERGRFyczezu5CNq/dPePedzHIyIiIhdHeNwHICIiIiIiIvKRRIG2iIiI\niIiIyBlSoC0iIiIiIiJyhhRoi4iIiIiIiJwhBdoiIvKR7K6qkouIiIicJQXaIiLyEcndv8zdC3f/\n2Dtta2YfbWZ/3cx+xcxumNkHzOw9ZvZ1ZrY8q2Mysy8xs39sZtfMbG1m/9LMvsvMPvWsxhC5SB7m\nZ9fM3mxm6S4vn3VWr0nkI5WZPW1mbzCzt5jZO83sudln6H9+SGM+tu9dtfcSEZELzczeCHwXcImb\ns98G/N/AG9z9XzzAGAvgHwB/9hZjJOCt7v7W+x1D5KJ52J9dM3sz8OZT9n2SA3/a3d99P+OIXBRm\nlk48NP9svcPdv/wMx3rs37vKaIuIyIVlZq8Cvhs4AK4D3wh8GvA5wN8hfzn/YeCHzWzvAYZ6O7sv\n+/8D+HPAJwNfAfwG+fv4zWb25x9gDJEL4xF+dkevBD7hFpdPBN57BmOIXATjkq7/F/gn5KD3YXjs\n37vKaIuIyIVlZu8GPgPogM909/eceP5rgW8mf1G/5X7OfJvZnwZ+fNjHDwFf6LMvXzN7EvhZ4KOB\nDwEf4+7P398rErkYHtFnd8pou3vx4EctcrENn6n3Au919+fM7F8F/h/y5/TMMtrn5XtXGW0REbmQ\nzOw15H+oO/C2k/9QH3wr8CvkM+5fbWb384/trx2ue+Cr/MQZbnf/APD1w90rgLLaIrfxCD+7InKG\n3P0t7v5Od3/uIQ91Lr53FWiLiMhF9edmt7/jtA2GL+fvHO5eAT77XgYws33yVFYHftzdf+cWm34/\n8MJw+wvuZQyRC+ihf3ZF5MXpPH3vKtAWEZGL6jOG60PyFLJb+YnZ7U+/xzFeA9Sn7OcYd++Af0rO\nvr1G2TeR23oUn10ReXE6N9+7CrRFROSi+njyGe/fcPeTlVDnfvXEz9yLP3aL/dxunJJcxElETvco\nPrvHDO2Bfs/MtsP1u8zs683syoPsV0TO3Ln53lWgLSIiF46ZNcBTw933325bd/8wOXMG8K/c41Cv\nmN2+7TjAb81u3+s4IhfCI/zsnvS6YdxyuP4s4K8Av2lmn/+A+xaRs3NuvnfLs96hiIjIi8DB7PaN\nu9j+EFgB+w9xnMPZ7XsdR+SieFSf3dEvAT8IvAf4HaAC/nXgPwD+bfL67+8zsze6+z++zzFE5Oyc\nm+9dBdoiInIRLWa327vYfktex7V8iONsZ7fvdRyRi+JRfXYB/oa7v+WUx98L/F0z+0+AvwUUwNvM\n7GPd/W6OSUQennPzvaup4yIichFtZrfrW26105DXhK4f4jjN7Pa9jiNyUTyqzy7u/sIdnv/bwP9E\nDuRfDnzRvY4hImfu3HzvKtAWEZGL6Prs9t1MF9sbru9mqur9jrM3u32v44hcFI/qs3u3np3dfu1D\nGkNE7t65+d5VoC0iIheOu2+BDwx3X3G7bYeqwuOX8W/dbttTzAux3HYcjhdiuddxRC6ER/jZvVu/\nPLv9UQ9pDBG5e+fme1eBtoiIXFS/TJ7y+XFmdrvvwz86u/0r9zHGafu53Tg98Ov3OI7IRfIoPrt3\nyx/SfkXk/pyb710F2iIiclH91HC9B7z6NtvNp4P+9D2O8V52xVhuOa3UzCrgU8n/aH+vu8d7HEfk\nInkUn927Ne/Z+zsPaQwRuXvn5ntXgbaIiFxUPzi7/WWnbWBmBvxHw90PA++6lwHc/Qbwv5Ozb68z\ns5ffYtMvAi4Nt7//XsYQuYAe+mf3HvzF2e2feEhjiMhdOk/fuwq0RUTkQnL39wI/Sf4y/goz+5RT\nNvs64OPJZ7y/7eQZbzN7k5ml4fLf3GKobxmuS+BvnpzqamZPAX91uPthchVjEbmFR/HZNbNXmtnH\n3u44hvZeXzHc/V3gB+791YjIvXgxfe+qj7aIiFxkX02eUroEfszM/jI587UEvgT4C8N2vwZ86232\nc8t1mu7+LjP7buCLgX9nGOfbyNNMPxH4RuCjh338F+7+/AO9IpGL4WF/dl9N7o39LuBHgX9GLsJW\nktd1finwbw3b9sBfcHe15RO5DTP7dODjZg89Nbv9cWb2pvn27v6O2+zu3H/vKtAWEZELy91/wcz+\nPeDvkqeQ/eWTm5D/of4Gdz98gKG+HDgAPhf4U8BnnxgjAm91d2WzRe7CI/rsBuBzgNfd6jDIwfeX\nu/s773MMkYvkzwNvOuVxAz5juIwcuF2gfSeP/XtXgbaIiFxo7v4jZvaJ5AzZG8jtQFrgN4DvBf6m\nu29ut4u7GGMDvNHMvhj4j4E/DlwBfg949zDGzzzI6xC5aB7yZ/dHyNPC/yTwKuClwJPkgOCDwC8C\n/wj4jmFNqIjcnbut1H+77V4U37vmrq4EIiIiIiIiImdFxdBEREREREREzpACbREREREREZEzpEBb\nRERERERE5Awp0BYRERERERE5Qwq0RURERERERM6QAm0RERERERGRM6RAW0REREREROQMKdAWERER\nEREROUMKtEVERERERETOkAJtERERERERkTOkQFtERERERETkDCnQFhERERERETlDCrRFRERERERE\nzpACbREREREREZEzpEBbRERERERE5Awp0BYRERERERE5Qwq0RURERERERM6QAm0RERERERGRM6RA\nW0REREREROQMKdAWEREREREROUMKtEVERERERETOkAJtERERERERkTOkQFtERERERETkDCnQFhER\nERERETlDCrRFREREREREztD/D+iauRi3tIFfAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2635fd7a2b0>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 445,
+       "width": 493
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "import pickle\n",
+    "import helper\n",
+    "import random\n",
+    "\n",
+    "# Set batch size if not already set\n",
+    "try:\n",
+    "    if batch_size:\n",
+    "        pass\n",
+    "except NameError:\n",
+    "    batch_size = 64\n",
+    "\n",
+    "save_model_path = './model/image_classification'\n",
+    "n_samples = 4\n",
+    "top_n_predictions = 3\n",
+    "\n",
+    "def test_model():\n",
+    "    \"\"\"\n",
+    "    Test the saved model against the test dataset\n",
+    "    \"\"\"\n",
+    "\n",
+    "    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))\n",
+    "    loaded_graph = tf.Graph()\n",
+    "\n",
+    "    with tf.Session(graph=loaded_graph) as sess:\n",
+    "        # Load model\n",
+    "        loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
+    "        loader.restore(sess, save_model_path)\n",
+    "\n",
+    "        # Get Tensors from loaded model\n",
+    "        loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
+    "        loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
+    "        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
+    "        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
+    "        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
+    "        \n",
+    "        # Get accuracy in batches for memory limitations\n",
+    "        test_batch_acc_total = 0\n",
+    "        test_batch_count = 0\n",
+    "        \n",
+    "        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n",
+    "            test_batch_acc_total += sess.run(\n",
+    "                loaded_acc,\n",
+    "                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})\n",
+    "            test_batch_count += 1\n",
+    "\n",
+    "        print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
+    "\n",
+    "        # Print Random Samples\n",
+    "        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
+    "        random_test_predictions = sess.run(\n",
+    "            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n",
+    "            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n",
+    "        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
+    "\n",
+    "\n",
+    "test_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 为何准确率只有50-80%？\n",
+    "\n",
+    "你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率[远远超过 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130)。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。\n",
+    "\n",
+    "## 提交项目\n",
+    "\n",
+    "提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 \"File\" -> \"Download as\" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From 7b06c3e724be3c13ad77b2cd12fb726fe658c7c0 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 9 Jul 2017 17:08:37 +0800
Subject: [PATCH 05/16] Delete dlnd_image_classification.html

---
 .../dlnd_image_classification.html            | 19516 ----------------
 1 file changed, 19516 deletions(-)
 delete mode 100644 image-classification/dlnd_image_classification.html

diff --git a/image-classification/dlnd_image_classification.html b/image-classification/dlnd_image_classification.html
deleted file mode 100644
index ad561b3..0000000
--- a/image-classification/dlnd_image_classification.html
+++ /dev/null
@@ -1,19516 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head><meta charset="utf-8" />
-<title>dlnd_image_classification</title>
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
-
-<style type="text/css">
-    /*!
-*
-* Twitter Bootstrap
-*
-*/
-/*!
- * Bootstrap v3.3.6 (http://getbootstrap.com)
- * Copyright 2011-2015 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- */
-/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
-html {
-  font-family: sans-serif;
-  -ms-text-size-adjust: 100%;
-  -webkit-text-size-adjust: 100%;
-}
-body {
-  margin: 0;
-}
-article,
-aside,
-details,
-figcaption,
-figure,
-footer,
-header,
-hgroup,
-main,
-menu,
-nav,
-section,
-summary {
-  display: block;
-}
-audio,
-canvas,
-progress,
-video {
-  display: inline-block;
-  vertical-align: baseline;
-}
-audio:not([controls]) {
-  display: none;
-  height: 0;
-}
-[hidden],
-template {
-  display: none;
-}
-a {
-  background-color: transparent;
-}
-a:active,
-a:hover {
-  outline: 0;
-}
-abbr[title] {
-  border-bottom: 1px dotted;
-}
-b,
-strong {
-  font-weight: bold;
-}
-dfn {
-  font-style: italic;
-}
-h1 {
-  font-size: 2em;
-  margin: 0.67em 0;
-}
-mark {
-  background: #ff0;
-  color: #000;
-}
-small {
-  font-size: 80%;
-}
-sub,
-sup {
-  font-size: 75%;
-  line-height: 0;
-  position: relative;
-  vertical-align: baseline;
-}
-sup {
-  top: -0.5em;
-}
-sub {
-  bottom: -0.25em;
-}
-img {
-  border: 0;
-}
-svg:not(:root) {
-  overflow: hidden;
-}
-figure {
-  margin: 1em 40px;
-}
-hr {
-  box-sizing: content-box;
-  height: 0;
-}
-pre {
-  overflow: auto;
-}
-code,
-kbd,
-pre,
-samp {
-  font-family: monospace, monospace;
-  font-size: 1em;
-}
-button,
-input,
-optgroup,
-select,
-textarea {
-  color: inherit;
-  font: inherit;
-  margin: 0;
-}
-button {
-  overflow: visible;
-}
-button,
-select {
-  text-transform: none;
-}
-button,
-html input[type="button"],
-input[type="reset"],
-input[type="submit"] {
-  -webkit-appearance: button;
-  cursor: pointer;
-}
-button[disabled],
-html input[disabled] {
-  cursor: default;
-}
-button::-moz-focus-inner,
-input::-moz-focus-inner {
-  border: 0;
-  padding: 0;
-}
-input {
-  line-height: normal;
-}
-input[type="checkbox"],
-input[type="radio"] {
-  box-sizing: border-box;
-  padding: 0;
-}
-input[type="number"]::-webkit-inner-spin-button,
-input[type="number"]::-webkit-outer-spin-button {
-  height: auto;
-}
-input[type="search"] {
-  -webkit-appearance: textfield;
-  box-sizing: content-box;
-}
-input[type="search"]::-webkit-search-cancel-button,
-input[type="search"]::-webkit-search-decoration {
-  -webkit-appearance: none;
-}
-fieldset {
-  border: 1px solid #c0c0c0;
-  margin: 0 2px;
-  padding: 0.35em 0.625em 0.75em;
-}
-legend {
-  border: 0;
-  padding: 0;
-}
-textarea {
-  overflow: auto;
-}
-optgroup {
-  font-weight: bold;
-}
-table {
-  border-collapse: collapse;
-  border-spacing: 0;
-}
-td,
-th {
-  padding: 0;
-}
-/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
-@media print {
-  *,
-  *:before,
-  *:after {
-    background: transparent !important;
-    color: #000 !important;
-    box-shadow: none !important;
-    text-shadow: none !important;
-  }
-  a,
-  a:visited {
-    text-decoration: underline;
-  }
-  a[href]:after {
-    content: " (" attr(href) ")";
-  }
-  abbr[title]:after {
-    content: " (" attr(title) ")";
-  }
-  a[href^="#"]:after,
-  a[href^="javascript:"]:after {
-    content: "";
-  }
-  pre,
-  blockquote {
-    border: 1px solid #999;
-    page-break-inside: avoid;
-  }
-  thead {
-    display: table-header-group;
-  }
-  tr,
-  img {
-    page-break-inside: avoid;
-  }
-  img {
-    max-width: 100% !important;
-  }
-  p,
-  h2,
-  h3 {
-    orphans: 3;
-    widows: 3;
-  }
-  h2,
-  h3 {
-    page-break-after: avoid;
-  }
-  .navbar {
-    display: none;
-  }
-  .btn > .caret,
-  .dropup > .btn > .caret {
-    border-top-color: #000 !important;
-  }
-  .label {
-    border: 1px solid #000;
-  }
-  .table {
-    border-collapse: collapse !important;
-  }
-  .table td,
-  .table th {
-    background-color: #fff !important;
-  }
-  .table-bordered th,
-  .table-bordered td {
-    border: 1px solid #ddd !important;
-  }
-}
-@font-face {
-  font-family: 'Glyphicons Halflings';
-  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
-  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
-}
-.glyphicon {
-  position: relative;
-  top: 1px;
-  display: inline-block;
-  font-family: 'Glyphicons Halflings';
-  font-style: normal;
-  font-weight: normal;
-  line-height: 1;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-.glyphicon-asterisk:before {
-  content: "\002a";
-}
-.glyphicon-plus:before {
-  content: "\002b";
-}
-.glyphicon-euro:before,
-.glyphicon-eur:before {
-  content: "\20ac";
-}
-.glyphicon-minus:before {
-  content: "\2212";
-}
-.glyphicon-cloud:before {
-  content: "\2601";
-}
-.glyphicon-envelope:before {
-  content: "\2709";
-}
-.glyphicon-pencil:before {
-  content: "\270f";
-}
-.glyphicon-glass:before {
-  content: "\e001";
-}
-.glyphicon-music:before {
-  content: "\e002";
-}
-.glyphicon-search:before {
-  content: "\e003";
-}
-.glyphicon-heart:before {
-  content: "\e005";
-}
-.glyphicon-star:before {
-  content: "\e006";
-}
-.glyphicon-star-empty:before {
-  content: "\e007";
-}
-.glyphicon-user:before {
-  content: "\e008";
-}
-.glyphicon-film:before {
-  content: "\e009";
-}
-.glyphicon-th-large:before {
-  content: "\e010";
-}
-.glyphicon-th:before {
-  content: "\e011";
-}
-.glyphicon-th-list:before {
-  content: "\e012";
-}
-.glyphicon-ok:before {
-  content: "\e013";
-}
-.glyphicon-remove:before {
-  content: "\e014";
-}
-.glyphicon-zoom-in:before {
-  content: "\e015";
-}
-.glyphicon-zoom-out:before {
-  content: "\e016";
-}
-.glyphicon-off:before {
-  content: "\e017";
-}
-.glyphicon-signal:before {
-  content: "\e018";
-}
-.glyphicon-cog:before {
-  content: "\e019";
-}
-.glyphicon-trash:before {
-  content: "\e020";
-}
-.glyphicon-home:before {
-  content: "\e021";
-}
-.glyphicon-file:before {
-  content: "\e022";
-}
-.glyphicon-time:before {
-  content: "\e023";
-}
-.glyphicon-road:before {
-  content: "\e024";
-}
-.glyphicon-download-alt:before {
-  content: "\e025";
-}
-.glyphicon-download:before {
-  content: "\e026";
-}
-.glyphicon-upload:before {
-  content: "\e027";
-}
-.glyphicon-inbox:before {
-  content: "\e028";
-}
-.glyphicon-play-circle:before {
-  content: "\e029";
-}
-.glyphicon-repeat:before {
-  content: "\e030";
-}
-.glyphicon-refresh:before {
-  content: "\e031";
-}
-.glyphicon-list-alt:before {
-  content: "\e032";
-}
-.glyphicon-lock:before {
-  content: "\e033";
-}
-.glyphicon-flag:before {
-  content: "\e034";
-}
-.glyphicon-headphones:before {
-  content: "\e035";
-}
-.glyphicon-volume-off:before {
-  content: "\e036";
-}
-.glyphicon-volume-down:before {
-  content: "\e037";
-}
-.glyphicon-volume-up:before {
-  content: "\e038";
-}
-.glyphicon-qrcode:before {
-  content: "\e039";
-}
-.glyphicon-barcode:before {
-  content: "\e040";
-}
-.glyphicon-tag:before {
-  content: "\e041";
-}
-.glyphicon-tags:before {
-  content: "\e042";
-}
-.glyphicon-book:before {
-  content: "\e043";
-}
-.glyphicon-bookmark:before {
-  content: "\e044";
-}
-.glyphicon-print:before {
-  content: "\e045";
-}
-.glyphicon-camera:before {
-  content: "\e046";
-}
-.glyphicon-font:before {
-  content: "\e047";
-}
-.glyphicon-bold:before {
-  content: "\e048";
-}
-.glyphicon-italic:before {
-  content: "\e049";
-}
-.glyphicon-text-height:before {
-  content: "\e050";
-}
-.glyphicon-text-width:before {
-  content: "\e051";
-}
-.glyphicon-align-left:before {
-  content: "\e052";
-}
-.glyphicon-align-center:before {
-  content: "\e053";
-}
-.glyphicon-align-right:before {
-  content: "\e054";
-}
-.glyphicon-align-justify:before {
-  content: "\e055";
-}
-.glyphicon-list:before {
-  content: "\e056";
-}
-.glyphicon-indent-left:before {
-  content: "\e057";
-}
-.glyphicon-indent-right:before {
-  content: "\e058";
-}
-.glyphicon-facetime-video:before {
-  content: "\e059";
-}
-.glyphicon-picture:before {
-  content: "\e060";
-}
-.glyphicon-map-marker:before {
-  content: "\e062";
-}
-.glyphicon-adjust:before {
-  content: "\e063";
-}
-.glyphicon-tint:before {
-  content: "\e064";
-}
-.glyphicon-edit:before {
-  content: "\e065";
-}
-.glyphicon-share:before {
-  content: "\e066";
-}
-.glyphicon-check:before {
-  content: "\e067";
-}
-.glyphicon-move:before {
-  content: "\e068";
-}
-.glyphicon-step-backward:before {
-  content: "\e069";
-}
-.glyphicon-fast-backward:before {
-  content: "\e070";
-}
-.glyphicon-backward:before {
-  content: "\e071";
-}
-.glyphicon-play:before {
-  content: "\e072";
-}
-.glyphicon-pause:before {
-  content: "\e073";
-}
-.glyphicon-stop:before {
-  content: "\e074";
-}
-.glyphicon-forward:before {
-  content: "\e075";
-}
-.glyphicon-fast-forward:before {
-  content: "\e076";
-}
-.glyphicon-step-forward:before {
-  content: "\e077";
-}
-.glyphicon-eject:before {
-  content: "\e078";
-}
-.glyphicon-chevron-left:before {
-  content: "\e079";
-}
-.glyphicon-chevron-right:before {
-  content: "\e080";
-}
-.glyphicon-plus-sign:before {
-  content: "\e081";
-}
-.glyphicon-minus-sign:before {
-  content: "\e082";
-}
-.glyphicon-remove-sign:before {
-  content: "\e083";
-}
-.glyphicon-ok-sign:before {
-  content: "\e084";
-}
-.glyphicon-question-sign:before {
-  content: "\e085";
-}
-.glyphicon-info-sign:before {
-  content: "\e086";
-}
-.glyphicon-screenshot:before {
-  content: "\e087";
-}
-.glyphicon-remove-circle:before {
-  content: "\e088";
-}
-.glyphicon-ok-circle:before {
-  content: "\e089";
-}
-.glyphicon-ban-circle:before {
-  content: "\e090";
-}
-.glyphicon-arrow-left:before {
-  content: "\e091";
-}
-.glyphicon-arrow-right:before {
-  content: "\e092";
-}
-.glyphicon-arrow-up:before {
-  content: "\e093";
-}
-.glyphicon-arrow-down:before {
-  content: "\e094";
-}
-.glyphicon-share-alt:before {
-  content: "\e095";
-}
-.glyphicon-resize-full:before {
-  content: "\e096";
-}
-.glyphicon-resize-small:before {
-  content: "\e097";
-}
-.glyphicon-exclamation-sign:before {
-  content: "\e101";
-}
-.glyphicon-gift:before {
-  content: "\e102";
-}
-.glyphicon-leaf:before {
-  content: "\e103";
-}
-.glyphicon-fire:before {
-  content: "\e104";
-}
-.glyphicon-eye-open:before {
-  content: "\e105";
-}
-.glyphicon-eye-close:before {
-  content: "\e106";
-}
-.glyphicon-warning-sign:before {
-  content: "\e107";
-}
-.glyphicon-plane:before {
-  content: "\e108";
-}
-.glyphicon-calendar:before {
-  content: "\e109";
-}
-.glyphicon-random:before {
-  content: "\e110";
-}
-.glyphicon-comment:before {
-  content: "\e111";
-}
-.glyphicon-magnet:before {
-  content: "\e112";
-}
-.glyphicon-chevron-up:before {
-  content: "\e113";
-}
-.glyphicon-chevron-down:before {
-  content: "\e114";
-}
-.glyphicon-retweet:before {
-  content: "\e115";
-}
-.glyphicon-shopping-cart:before {
-  content: "\e116";
-}
-.glyphicon-folder-close:before {
-  content: "\e117";
-}
-.glyphicon-folder-open:before {
-  content: "\e118";
-}
-.glyphicon-resize-vertical:before {
-  content: "\e119";
-}
-.glyphicon-resize-horizontal:before {
-  content: "\e120";
-}
-.glyphicon-hdd:before {
-  content: "\e121";
-}
-.glyphicon-bullhorn:before {
-  content: "\e122";
-}
-.glyphicon-bell:before {
-  content: "\e123";
-}
-.glyphicon-certificate:before {
-  content: "\e124";
-}
-.glyphicon-thumbs-up:before {
-  content: "\e125";
-}
-.glyphicon-thumbs-down:before {
-  content: "\e126";
-}
-.glyphicon-hand-right:before {
-  content: "\e127";
-}
-.glyphicon-hand-left:before {
-  content: "\e128";
-}
-.glyphicon-hand-up:before {
-  content: "\e129";
-}
-.glyphicon-hand-down:before {
-  content: "\e130";
-}
-.glyphicon-circle-arrow-right:before {
-  content: "\e131";
-}
-.glyphicon-circle-arrow-left:before {
-  content: "\e132";
-}
-.glyphicon-circle-arrow-up:before {
-  content: "\e133";
-}
-.glyphicon-circle-arrow-down:before {
-  content: "\e134";
-}
-.glyphicon-globe:before {
-  content: "\e135";
-}
-.glyphicon-wrench:before {
-  content: "\e136";
-}
-.glyphicon-tasks:before {
-  content: "\e137";
-}
-.glyphicon-filter:before {
-  content: "\e138";
-}
-.glyphicon-briefcase:before {
-  content: "\e139";
-}
-.glyphicon-fullscreen:before {
-  content: "\e140";
-}
-.glyphicon-dashboard:before {
-  content: "\e141";
-}
-.glyphicon-paperclip:before {
-  content: "\e142";
-}
-.glyphicon-heart-empty:before {
-  content: "\e143";
-}
-.glyphicon-link:before {
-  content: "\e144";
-}
-.glyphicon-phone:before {
-  content: "\e145";
-}
-.glyphicon-pushpin:before {
-  content: "\e146";
-}
-.glyphicon-usd:before {
-  content: "\e148";
-}
-.glyphicon-gbp:before {
-  content: "\e149";
-}
-.glyphicon-sort:before {
-  content: "\e150";
-}
-.glyphicon-sort-by-alphabet:before {
-  content: "\e151";
-}
-.glyphicon-sort-by-alphabet-alt:before {
-  content: "\e152";
-}
-.glyphicon-sort-by-order:before {
-  content: "\e153";
-}
-.glyphicon-sort-by-order-alt:before {
-  content: "\e154";
-}
-.glyphicon-sort-by-attributes:before {
-  content: "\e155";
-}
-.glyphicon-sort-by-attributes-alt:before {
-  content: "\e156";
-}
-.glyphicon-unchecked:before {
-  content: "\e157";
-}
-.glyphicon-expand:before {
-  content: "\e158";
-}
-.glyphicon-collapse-down:before {
-  content: "\e159";
-}
-.glyphicon-collapse-up:before {
-  content: "\e160";
-}
-.glyphicon-log-in:before {
-  content: "\e161";
-}
-.glyphicon-flash:before {
-  content: "\e162";
-}
-.glyphicon-log-out:before {
-  content: "\e163";
-}
-.glyphicon-new-window:before {
-  content: "\e164";
-}
-.glyphicon-record:before {
-  content: "\e165";
-}
-.glyphicon-save:before {
-  content: "\e166";
-}
-.glyphicon-open:before {
-  content: "\e167";
-}
-.glyphicon-saved:before {
-  content: "\e168";
-}
-.glyphicon-import:before {
-  content: "\e169";
-}
-.glyphicon-export:before {
-  content: "\e170";
-}
-.glyphicon-send:before {
-  content: "\e171";
-}
-.glyphicon-floppy-disk:before {
-  content: "\e172";
-}
-.glyphicon-floppy-saved:before {
-  content: "\e173";
-}
-.glyphicon-floppy-remove:before {
-  content: "\e174";
-}
-.glyphicon-floppy-save:before {
-  content: "\e175";
-}
-.glyphicon-floppy-open:before {
-  content: "\e176";
-}
-.glyphicon-credit-card:before {
-  content: "\e177";
-}
-.glyphicon-transfer:before {
-  content: "\e178";
-}
-.glyphicon-cutlery:before {
-  content: "\e179";
-}
-.glyphicon-header:before {
-  content: "\e180";
-}
-.glyphicon-compressed:before {
-  content: "\e181";
-}
-.glyphicon-earphone:before {
-  content: "\e182";
-}
-.glyphicon-phone-alt:before {
-  content: "\e183";
-}
-.glyphicon-tower:before {
-  content: "\e184";
-}
-.glyphicon-stats:before {
-  content: "\e185";
-}
-.glyphicon-sd-video:before {
-  content: "\e186";
-}
-.glyphicon-hd-video:before {
-  content: "\e187";
-}
-.glyphicon-subtitles:before {
-  content: "\e188";
-}
-.glyphicon-sound-stereo:before {
-  content: "\e189";
-}
-.glyphicon-sound-dolby:before {
-  content: "\e190";
-}
-.glyphicon-sound-5-1:before {
-  content: "\e191";
-}
-.glyphicon-sound-6-1:before {
-  content: "\e192";
-}
-.glyphicon-sound-7-1:before {
-  content: "\e193";
-}
-.glyphicon-copyright-mark:before {
-  content: "\e194";
-}
-.glyphicon-registration-mark:before {
-  content: "\e195";
-}
-.glyphicon-cloud-download:before {
-  content: "\e197";
-}
-.glyphicon-cloud-upload:before {
-  content: "\e198";
-}
-.glyphicon-tree-conifer:before {
-  content: "\e199";
-}
-.glyphicon-tree-deciduous:before {
-  content: "\e200";
-}
-.glyphicon-cd:before {
-  content: "\e201";
-}
-.glyphicon-save-file:before {
-  content: "\e202";
-}
-.glyphicon-open-file:before {
-  content: "\e203";
-}
-.glyphicon-level-up:before {
-  content: "\e204";
-}
-.glyphicon-copy:before {
-  content: "\e205";
-}
-.glyphicon-paste:before {
-  content: "\e206";
-}
-.glyphicon-alert:before {
-  content: "\e209";
-}
-.glyphicon-equalizer:before {
-  content: "\e210";
-}
-.glyphicon-king:before {
-  content: "\e211";
-}
-.glyphicon-queen:before {
-  content: "\e212";
-}
-.glyphicon-pawn:before {
-  content: "\e213";
-}
-.glyphicon-bishop:before {
-  content: "\e214";
-}
-.glyphicon-knight:before {
-  content: "\e215";
-}
-.glyphicon-baby-formula:before {
-  content: "\e216";
-}
-.glyphicon-tent:before {
-  content: "\26fa";
-}
-.glyphicon-blackboard:before {
-  content: "\e218";
-}
-.glyphicon-bed:before {
-  content: "\e219";
-}
-.glyphicon-apple:before {
-  content: "\f8ff";
-}
-.glyphicon-erase:before {
-  content: "\e221";
-}
-.glyphicon-hourglass:before {
-  content: "\231b";
-}
-.glyphicon-lamp:before {
-  content: "\e223";
-}
-.glyphicon-duplicate:before {
-  content: "\e224";
-}
-.glyphicon-piggy-bank:before {
-  content: "\e225";
-}
-.glyphicon-scissors:before {
-  content: "\e226";
-}
-.glyphicon-bitcoin:before {
-  content: "\e227";
-}
-.glyphicon-btc:before {
-  content: "\e227";
-}
-.glyphicon-xbt:before {
-  content: "\e227";
-}
-.glyphicon-yen:before {
-  content: "\00a5";
-}
-.glyphicon-jpy:before {
-  content: "\00a5";
-}
-.glyphicon-ruble:before {
-  content: "\20bd";
-}
-.glyphicon-rub:before {
-  content: "\20bd";
-}
-.glyphicon-scale:before {
-  content: "\e230";
-}
-.glyphicon-ice-lolly:before {
-  content: "\e231";
-}
-.glyphicon-ice-lolly-tasted:before {
-  content: "\e232";
-}
-.glyphicon-education:before {
-  content: "\e233";
-}
-.glyphicon-option-horizontal:before {
-  content: "\e234";
-}
-.glyphicon-option-vertical:before {
-  content: "\e235";
-}
-.glyphicon-menu-hamburger:before {
-  content: "\e236";
-}
-.glyphicon-modal-window:before {
-  content: "\e237";
-}
-.glyphicon-oil:before {
-  content: "\e238";
-}
-.glyphicon-grain:before {
-  content: "\e239";
-}
-.glyphicon-sunglasses:before {
-  content: "\e240";
-}
-.glyphicon-text-size:before {
-  content: "\e241";
-}
-.glyphicon-text-color:before {
-  content: "\e242";
-}
-.glyphicon-text-background:before {
-  content: "\e243";
-}
-.glyphicon-object-align-top:before {
-  content: "\e244";
-}
-.glyphicon-object-align-bottom:before {
-  content: "\e245";
-}
-.glyphicon-object-align-horizontal:before {
-  content: "\e246";
-}
-.glyphicon-object-align-left:before {
-  content: "\e247";
-}
-.glyphicon-object-align-vertical:before {
-  content: "\e248";
-}
-.glyphicon-object-align-right:before {
-  content: "\e249";
-}
-.glyphicon-triangle-right:before {
-  content: "\e250";
-}
-.glyphicon-triangle-left:before {
-  content: "\e251";
-}
-.glyphicon-triangle-bottom:before {
-  content: "\e252";
-}
-.glyphicon-triangle-top:before {
-  content: "\e253";
-}
-.glyphicon-console:before {
-  content: "\e254";
-}
-.glyphicon-superscript:before {
-  content: "\e255";
-}
-.glyphicon-subscript:before {
-  content: "\e256";
-}
-.glyphicon-menu-left:before {
-  content: "\e257";
-}
-.glyphicon-menu-right:before {
-  content: "\e258";
-}
-.glyphicon-menu-down:before {
-  content: "\e259";
-}
-.glyphicon-menu-up:before {
-  content: "\e260";
-}
-* {
-  -webkit-box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  box-sizing: border-box;
-}
-*:before,
-*:after {
-  -webkit-box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  box-sizing: border-box;
-}
-html {
-  font-size: 10px;
-  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
-}
-body {
-  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
-  font-size: 13px;
-  line-height: 1.42857143;
-  color: #000;
-  background-color: #fff;
-}
-input,
-button,
-select,
-textarea {
-  font-family: inherit;
-  font-size: inherit;
-  line-height: inherit;
-}
-a {
-  color: #337ab7;
-  text-decoration: none;
-}
-a:hover,
-a:focus {
-  color: #23527c;
-  text-decoration: underline;
-}
-a:focus {
-  outline: thin dotted;
-  outline: 5px auto -webkit-focus-ring-color;
-  outline-offset: -2px;
-}
-figure {
-  margin: 0;
-}
-img {
-  vertical-align: middle;
-}
-.img-responsive,
-.thumbnail > img,
-.thumbnail a > img,
-.carousel-inner > .item > img,
-.carousel-inner > .item > a > img {
-  display: block;
-  max-width: 100%;
-  height: auto;
-}
-.img-rounded {
-  border-radius: 3px;
-}
-.img-thumbnail {
-  padding: 4px;
-  line-height: 1.42857143;
-  background-color: #fff;
-  border: 1px solid #ddd;
-  border-radius: 2px;
-  -webkit-transition: all 0.2s ease-in-out;
-  -o-transition: all 0.2s ease-in-out;
-  transition: all 0.2s ease-in-out;
-  display: inline-block;
-  max-width: 100%;
-  height: auto;
-}
-.img-circle {
-  border-radius: 50%;
-}
-hr {
-  margin-top: 18px;
-  margin-bottom: 18px;
-  border: 0;
-  border-top: 1px solid #eeeeee;
-}
-.sr-only {
-  position: absolute;
-  width: 1px;
-  height: 1px;
-  margin: -1px;
-  padding: 0;
-  overflow: hidden;
-  clip: rect(0, 0, 0, 0);
-  border: 0;
-}
-.sr-only-focusable:active,
-.sr-only-focusable:focus {
-  position: static;
-  width: auto;
-  height: auto;
-  margin: 0;
-  overflow: visible;
-  clip: auto;
-}
-[role="button"] {
-  cursor: pointer;
-}
-h1,
-h2,
-h3,
-h4,
-h5,
-h6,
-.h1,
-.h2,
-.h3,
-.h4,
-.h5,
-.h6 {
-  font-family: inherit;
-  font-weight: 500;
-  line-height: 1.1;
-  color: inherit;
-}
-h1 small,
-h2 small,
-h3 small,
-h4 small,
-h5 small,
-h6 small,
-.h1 small,
-.h2 small,
-.h3 small,
-.h4 small,
-.h5 small,
-.h6 small,
-h1 .small,
-h2 .small,
-h3 .small,
-h4 .small,
-h5 .small,
-h6 .small,
-.h1 .small,
-.h2 .small,
-.h3 .small,
-.h4 .small,
-.h5 .small,
-.h6 .small {
-  font-weight: normal;
-  line-height: 1;
-  color: #777777;
-}
-h1,
-.h1,
-h2,
-.h2,
-h3,
-.h3 {
-  margin-top: 18px;
-  margin-bottom: 9px;
-}
-h1 small,
-.h1 small,
-h2 small,
-.h2 small,
-h3 small,
-.h3 small,
-h1 .small,
-.h1 .small,
-h2 .small,
-.h2 .small,
-h3 .small,
-.h3 .small {
-  font-size: 65%;
-}
-h4,
-.h4,
-h5,
-.h5,
-h6,
-.h6 {
-  margin-top: 9px;
-  margin-bottom: 9px;
-}
-h4 small,
-.h4 small,
-h5 small,
-.h5 small,
-h6 small,
-.h6 small,
-h4 .small,
-.h4 .small,
-h5 .small,
-.h5 .small,
-h6 .small,
-.h6 .small {
-  font-size: 75%;
-}
-h1,
-.h1 {
-  font-size: 33px;
-}
-h2,
-.h2 {
-  font-size: 27px;
-}
-h3,
-.h3 {
-  font-size: 23px;
-}
-h4,
-.h4 {
-  font-size: 17px;
-}
-h5,
-.h5 {
-  font-size: 13px;
-}
-h6,
-.h6 {
-  font-size: 12px;
-}
-p {
-  margin: 0 0 9px;
-}
-.lead {
-  margin-bottom: 18px;
-  font-size: 14px;
-  font-weight: 300;
-  line-height: 1.4;
-}
-@media (min-width: 768px) {
-  .lead {
-    font-size: 19.5px;
-  }
-}
-small,
-.small {
-  font-size: 92%;
-}
-mark,
-.mark {
-  background-color: #fcf8e3;
-  padding: .2em;
-}
-.text-left {
-  text-align: left;
-}
-.text-right {
-  text-align: right;
-}
-.text-center {
-  text-align: center;
-}
-.text-justify {
-  text-align: justify;
-}
-.text-nowrap {
-  white-space: nowrap;
-}
-.text-lowercase {
-  text-transform: lowercase;
-}
-.text-uppercase {
-  text-transform: uppercase;
-}
-.text-capitalize {
-  text-transform: capitalize;
-}
-.text-muted {
-  color: #777777;
-}
-.text-primary {
-  color: #337ab7;
-}
-a.text-primary:hover,
-a.text-primary:focus {
-  color: #286090;
-}
-.text-success {
-  color: #3c763d;
-}
-a.text-success:hover,
-a.text-success:focus {
-  color: #2b542c;
-}
-.text-info {
-  color: #31708f;
-}
-a.text-info:hover,
-a.text-info:focus {
-  color: #245269;
-}
-.text-warning {
-  color: #8a6d3b;
-}
-a.text-warning:hover,
-a.text-warning:focus {
-  color: #66512c;
-}
-.text-danger {
-  color: #a94442;
-}
-a.text-danger:hover,
-a.text-danger:focus {
-  color: #843534;
-}
-.bg-primary {
-  color: #fff;
-  background-color: #337ab7;
-}
-a.bg-primary:hover,
-a.bg-primary:focus {
-  background-color: #286090;
-}
-.bg-success {
-  background-color: #dff0d8;
-}
-a.bg-success:hover,
-a.bg-success:focus {
-  background-color: #c1e2b3;
-}
-.bg-info {
-  background-color: #d9edf7;
-}
-a.bg-info:hover,
-a.bg-info:focus {
-  background-color: #afd9ee;
-}
-.bg-warning {
-  background-color: #fcf8e3;
-}
-a.bg-warning:hover,
-a.bg-warning:focus {
-  background-color: #f7ecb5;
-}
-.bg-danger {
-  background-color: #f2dede;
-}
-a.bg-danger:hover,
-a.bg-danger:focus {
-  background-color: #e4b9b9;
-}
-.page-header {
-  padding-bottom: 8px;
-  margin: 36px 0 18px;
-  border-bottom: 1px solid #eeeeee;
-}
-ul,
-ol {
-  margin-top: 0;
-  margin-bottom: 9px;
-}
-ul ul,
-ol ul,
-ul ol,
-ol ol {
-  margin-bottom: 0;
-}
-.list-unstyled {
-  padding-left: 0;
-  list-style: none;
-}
-.list-inline {
-  padding-left: 0;
-  list-style: none;
-  margin-left: -5px;
-}
-.list-inline > li {
-  display: inline-block;
-  padding-left: 5px;
-  padding-right: 5px;
-}
-dl {
-  margin-top: 0;
-  margin-bottom: 18px;
-}
-dt,
-dd {
-  line-height: 1.42857143;
-}
-dt {
-  font-weight: bold;
-}
-dd {
-  margin-left: 0;
-}
-@media (min-width: 541px) {
-  .dl-horizontal dt {
-    float: left;
-    width: 160px;
-    clear: left;
-    text-align: right;
-    overflow: hidden;
-    text-overflow: ellipsis;
-    white-space: nowrap;
-  }
-  .dl-horizontal dd {
-    margin-left: 180px;
-  }
-}
-abbr[title],
-abbr[data-original-title] {
-  cursor: help;
-  border-bottom: 1px dotted #777777;
-}
-.initialism {
-  font-size: 90%;
-  text-transform: uppercase;
-}
-blockquote {
-  padding: 9px 18px;
-  margin: 0 0 18px;
-  font-size: inherit;
-  border-left: 5px solid #eeeeee;
-}
-blockquote p:last-child,
-blockquote ul:last-child,
-blockquote ol:last-child {
-  margin-bottom: 0;
-}
-blockquote footer,
-blockquote small,
-blockquote .small {
-  display: block;
-  font-size: 80%;
-  line-height: 1.42857143;
-  color: #777777;
-}
-blockquote footer:before,
-blockquote small:before,
-blockquote .small:before {
-  content: '\2014 \00A0';
-}
-.blockquote-reverse,
-blockquote.pull-right {
-  padding-right: 15px;
-  padding-left: 0;
-  border-right: 5px solid #eeeeee;
-  border-left: 0;
-  text-align: right;
-}
-.blockquote-reverse footer:before,
-blockquote.pull-right footer:before,
-.blockquote-reverse small:before,
-blockquote.pull-right small:before,
-.blockquote-reverse .small:before,
-blockquote.pull-right .small:before {
-  content: '';
-}
-.blockquote-reverse footer:after,
-blockquote.pull-right footer:after,
-.blockquote-reverse small:after,
-blockquote.pull-right small:after,
-.blockquote-reverse .small:after,
-blockquote.pull-right .small:after {
-  content: '\00A0 \2014';
-}
-address {
-  margin-bottom: 18px;
-  font-style: normal;
-  line-height: 1.42857143;
-}
-code,
-kbd,
-pre,
-samp {
-  font-family: monospace;
-}
-code {
-  padding: 2px 4px;
-  font-size: 90%;
-  color: #c7254e;
-  background-color: #f9f2f4;
-  border-radius: 2px;
-}
-kbd {
-  padding: 2px 4px;
-  font-size: 90%;
-  color: #888;
-  background-color: transparent;
-  border-radius: 1px;
-  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
-}
-kbd kbd {
-  padding: 0;
-  font-size: 100%;
-  font-weight: bold;
-  box-shadow: none;
-}
-pre {
-  display: block;
-  padding: 8.5px;
-  margin: 0 0 9px;
-  font-size: 12px;
-  line-height: 1.42857143;
-  word-break: break-all;
-  word-wrap: break-word;
-  color: #333333;
-  background-color: #f5f5f5;
-  border: 1px solid #ccc;
-  border-radius: 2px;
-}
-pre code {
-  padding: 0;
-  font-size: inherit;
-  color: inherit;
-  white-space: pre-wrap;
-  background-color: transparent;
-  border-radius: 0;
-}
-.pre-scrollable {
-  max-height: 340px;
-  overflow-y: scroll;
-}
-.container {
-  margin-right: auto;
-  margin-left: auto;
-  padding-left: 0px;
-  padding-right: 0px;
-}
-@media (min-width: 768px) {
-  .container {
-    width: 768px;
-  }
-}
-@media (min-width: 992px) {
-  .container {
-    width: 940px;
-  }
-}
-@media (min-width: 1200px) {
-  .container {
-    width: 1140px;
-  }
-}
-.container-fluid {
-  margin-right: auto;
-  margin-left: auto;
-  padding-left: 0px;
-  padding-right: 0px;
-}
-.row {
-  margin-left: 0px;
-  margin-right: 0px;
-}
-.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
-  position: relative;
-  min-height: 1px;
-  padding-left: 0px;
-  padding-right: 0px;
-}
-.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
-  float: left;
-}
-.col-xs-12 {
-  width: 100%;
-}
-.col-xs-11 {
-  width: 91.66666667%;
-}
-.col-xs-10 {
-  width: 83.33333333%;
-}
-.col-xs-9 {
-  width: 75%;
-}
-.col-xs-8 {
-  width: 66.66666667%;
-}
-.col-xs-7 {
-  width: 58.33333333%;
-}
-.col-xs-6 {
-  width: 50%;
-}
-.col-xs-5 {
-  width: 41.66666667%;
-}
-.col-xs-4 {
-  width: 33.33333333%;
-}
-.col-xs-3 {
-  width: 25%;
-}
-.col-xs-2 {
-  width: 16.66666667%;
-}
-.col-xs-1 {
-  width: 8.33333333%;
-}
-.col-xs-pull-12 {
-  right: 100%;
-}
-.col-xs-pull-11 {
-  right: 91.66666667%;
-}
-.col-xs-pull-10 {
-  right: 83.33333333%;
-}
-.col-xs-pull-9 {
-  right: 75%;
-}
-.col-xs-pull-8 {
-  right: 66.66666667%;
-}
-.col-xs-pull-7 {
-  right: 58.33333333%;
-}
-.col-xs-pull-6 {
-  right: 50%;
-}
-.col-xs-pull-5 {
-  right: 41.66666667%;
-}
-.col-xs-pull-4 {
-  right: 33.33333333%;
-}
-.col-xs-pull-3 {
-  right: 25%;
-}
-.col-xs-pull-2 {
-  right: 16.66666667%;
-}
-.col-xs-pull-1 {
-  right: 8.33333333%;
-}
-.col-xs-pull-0 {
-  right: auto;
-}
-.col-xs-push-12 {
-  left: 100%;
-}
-.col-xs-push-11 {
-  left: 91.66666667%;
-}
-.col-xs-push-10 {
-  left: 83.33333333%;
-}
-.col-xs-push-9 {
-  left: 75%;
-}
-.col-xs-push-8 {
-  left: 66.66666667%;
-}
-.col-xs-push-7 {
-  left: 58.33333333%;
-}
-.col-xs-push-6 {
-  left: 50%;
-}
-.col-xs-push-5 {
-  left: 41.66666667%;
-}
-.col-xs-push-4 {
-  left: 33.33333333%;
-}
-.col-xs-push-3 {
-  left: 25%;
-}
-.col-xs-push-2 {
-  left: 16.66666667%;
-}
-.col-xs-push-1 {
-  left: 8.33333333%;
-}
-.col-xs-push-0 {
-  left: auto;
-}
-.col-xs-offset-12 {
-  margin-left: 100%;
-}
-.col-xs-offset-11 {
-  margin-left: 91.66666667%;
-}
-.col-xs-offset-10 {
-  margin-left: 83.33333333%;
-}
-.col-xs-offset-9 {
-  margin-left: 75%;
-}
-.col-xs-offset-8 {
-  margin-left: 66.66666667%;
-}
-.col-xs-offset-7 {
-  margin-left: 58.33333333%;
-}
-.col-xs-offset-6 {
-  margin-left: 50%;
-}
-.col-xs-offset-5 {
-  margin-left: 41.66666667%;
-}
-.col-xs-offset-4 {
-  margin-left: 33.33333333%;
-}
-.col-xs-offset-3 {
-  margin-left: 25%;
-}
-.col-xs-offset-2 {
-  margin-left: 16.66666667%;
-}
-.col-xs-offset-1 {
-  margin-left: 8.33333333%;
-}
-.col-xs-offset-0 {
-  margin-left: 0%;
-}
-@media (min-width: 768px) {
-  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
-    float: left;
-  }
-  .col-sm-12 {
-    width: 100%;
-  }
-  .col-sm-11 {
-    width: 91.66666667%;
-  }
-  .col-sm-10 {
-    width: 83.33333333%;
-  }
-  .col-sm-9 {
-    width: 75%;
-  }
-  .col-sm-8 {
-    width: 66.66666667%;
-  }
-  .col-sm-7 {
-    width: 58.33333333%;
-  }
-  .col-sm-6 {
-    width: 50%;
-  }
-  .col-sm-5 {
-    width: 41.66666667%;
-  }
-  .col-sm-4 {
-    width: 33.33333333%;
-  }
-  .col-sm-3 {
-    width: 25%;
-  }
-  .col-sm-2 {
-    width: 16.66666667%;
-  }
-  .col-sm-1 {
-    width: 8.33333333%;
-  }
-  .col-sm-pull-12 {
-    right: 100%;
-  }
-  .col-sm-pull-11 {
-    right: 91.66666667%;
-  }
-  .col-sm-pull-10 {
-    right: 83.33333333%;
-  }
-  .col-sm-pull-9 {
-    right: 75%;
-  }
-  .col-sm-pull-8 {
-    right: 66.66666667%;
-  }
-  .col-sm-pull-7 {
-    right: 58.33333333%;
-  }
-  .col-sm-pull-6 {
-    right: 50%;
-  }
-  .col-sm-pull-5 {
-    right: 41.66666667%;
-  }
-  .col-sm-pull-4 {
-    right: 33.33333333%;
-  }
-  .col-sm-pull-3 {
-    right: 25%;
-  }
-  .col-sm-pull-2 {
-    right: 16.66666667%;
-  }
-  .col-sm-pull-1 {
-    right: 8.33333333%;
-  }
-  .col-sm-pull-0 {
-    right: auto;
-  }
-  .col-sm-push-12 {
-    left: 100%;
-  }
-  .col-sm-push-11 {
-    left: 91.66666667%;
-  }
-  .col-sm-push-10 {
-    left: 83.33333333%;
-  }
-  .col-sm-push-9 {
-    left: 75%;
-  }
-  .col-sm-push-8 {
-    left: 66.66666667%;
-  }
-  .col-sm-push-7 {
-    left: 58.33333333%;
-  }
-  .col-sm-push-6 {
-    left: 50%;
-  }
-  .col-sm-push-5 {
-    left: 41.66666667%;
-  }
-  .col-sm-push-4 {
-    left: 33.33333333%;
-  }
-  .col-sm-push-3 {
-    left: 25%;
-  }
-  .col-sm-push-2 {
-    left: 16.66666667%;
-  }
-  .col-sm-push-1 {
-    left: 8.33333333%;
-  }
-  .col-sm-push-0 {
-    left: auto;
-  }
-  .col-sm-offset-12 {
-    margin-left: 100%;
-  }
-  .col-sm-offset-11 {
-    margin-left: 91.66666667%;
-  }
-  .col-sm-offset-10 {
-    margin-left: 83.33333333%;
-  }
-  .col-sm-offset-9 {
-    margin-left: 75%;
-  }
-  .col-sm-offset-8 {
-    margin-left: 66.66666667%;
-  }
-  .col-sm-offset-7 {
-    margin-left: 58.33333333%;
-  }
-  .col-sm-offset-6 {
-    margin-left: 50%;
-  }
-  .col-sm-offset-5 {
-    margin-left: 41.66666667%;
-  }
-  .col-sm-offset-4 {
-    margin-left: 33.33333333%;
-  }
-  .col-sm-offset-3 {
-    margin-left: 25%;
-  }
-  .col-sm-offset-2 {
-    margin-left: 16.66666667%;
-  }
-  .col-sm-offset-1 {
-    margin-left: 8.33333333%;
-  }
-  .col-sm-offset-0 {
-    margin-left: 0%;
-  }
-}
-@media (min-width: 992px) {
-  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
-    float: left;
-  }
-  .col-md-12 {
-    width: 100%;
-  }
-  .col-md-11 {
-    width: 91.66666667%;
-  }
-  .col-md-10 {
-    width: 83.33333333%;
-  }
-  .col-md-9 {
-    width: 75%;
-  }
-  .col-md-8 {
-    width: 66.66666667%;
-  }
-  .col-md-7 {
-    width: 58.33333333%;
-  }
-  .col-md-6 {
-    width: 50%;
-  }
-  .col-md-5 {
-    width: 41.66666667%;
-  }
-  .col-md-4 {
-    width: 33.33333333%;
-  }
-  .col-md-3 {
-    width: 25%;
-  }
-  .col-md-2 {
-    width: 16.66666667%;
-  }
-  .col-md-1 {
-    width: 8.33333333%;
-  }
-  .col-md-pull-12 {
-    right: 100%;
-  }
-  .col-md-pull-11 {
-    right: 91.66666667%;
-  }
-  .col-md-pull-10 {
-    right: 83.33333333%;
-  }
-  .col-md-pull-9 {
-    right: 75%;
-  }
-  .col-md-pull-8 {
-    right: 66.66666667%;
-  }
-  .col-md-pull-7 {
-    right: 58.33333333%;
-  }
-  .col-md-pull-6 {
-    right: 50%;
-  }
-  .col-md-pull-5 {
-    right: 41.66666667%;
-  }
-  .col-md-pull-4 {
-    right: 33.33333333%;
-  }
-  .col-md-pull-3 {
-    right: 25%;
-  }
-  .col-md-pull-2 {
-    right: 16.66666667%;
-  }
-  .col-md-pull-1 {
-    right: 8.33333333%;
-  }
-  .col-md-pull-0 {
-    right: auto;
-  }
-  .col-md-push-12 {
-    left: 100%;
-  }
-  .col-md-push-11 {
-    left: 91.66666667%;
-  }
-  .col-md-push-10 {
-    left: 83.33333333%;
-  }
-  .col-md-push-9 {
-    left: 75%;
-  }
-  .col-md-push-8 {
-    left: 66.66666667%;
-  }
-  .col-md-push-7 {
-    left: 58.33333333%;
-  }
-  .col-md-push-6 {
-    left: 50%;
-  }
-  .col-md-push-5 {
-    left: 41.66666667%;
-  }
-  .col-md-push-4 {
-    left: 33.33333333%;
-  }
-  .col-md-push-3 {
-    left: 25%;
-  }
-  .col-md-push-2 {
-    left: 16.66666667%;
-  }
-  .col-md-push-1 {
-    left: 8.33333333%;
-  }
-  .col-md-push-0 {
-    left: auto;
-  }
-  .col-md-offset-12 {
-    margin-left: 100%;
-  }
-  .col-md-offset-11 {
-    margin-left: 91.66666667%;
-  }
-  .col-md-offset-10 {
-    margin-left: 83.33333333%;
-  }
-  .col-md-offset-9 {
-    margin-left: 75%;
-  }
-  .col-md-offset-8 {
-    margin-left: 66.66666667%;
-  }
-  .col-md-offset-7 {
-    margin-left: 58.33333333%;
-  }
-  .col-md-offset-6 {
-    margin-left: 50%;
-  }
-  .col-md-offset-5 {
-    margin-left: 41.66666667%;
-  }
-  .col-md-offset-4 {
-    margin-left: 33.33333333%;
-  }
-  .col-md-offset-3 {
-    margin-left: 25%;
-  }
-  .col-md-offset-2 {
-    margin-left: 16.66666667%;
-  }
-  .col-md-offset-1 {
-    margin-left: 8.33333333%;
-  }
-  .col-md-offset-0 {
-    margin-left: 0%;
-  }
-}
-@media (min-width: 1200px) {
-  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
-    float: left;
-  }
-  .col-lg-12 {
-    width: 100%;
-  }
-  .col-lg-11 {
-    width: 91.66666667%;
-  }
-  .col-lg-10 {
-    width: 83.33333333%;
-  }
-  .col-lg-9 {
-    width: 75%;
-  }
-  .col-lg-8 {
-    width: 66.66666667%;
-  }
-  .col-lg-7 {
-    width: 58.33333333%;
-  }
-  .col-lg-6 {
-    width: 50%;
-  }
-  .col-lg-5 {
-    width: 41.66666667%;
-  }
-  .col-lg-4 {
-    width: 33.33333333%;
-  }
-  .col-lg-3 {
-    width: 25%;
-  }
-  .col-lg-2 {
-    width: 16.66666667%;
-  }
-  .col-lg-1 {
-    width: 8.33333333%;
-  }
-  .col-lg-pull-12 {
-    right: 100%;
-  }
-  .col-lg-pull-11 {
-    right: 91.66666667%;
-  }
-  .col-lg-pull-10 {
-    right: 83.33333333%;
-  }
-  .col-lg-pull-9 {
-    right: 75%;
-  }
-  .col-lg-pull-8 {
-    right: 66.66666667%;
-  }
-  .col-lg-pull-7 {
-    right: 58.33333333%;
-  }
-  .col-lg-pull-6 {
-    right: 50%;
-  }
-  .col-lg-pull-5 {
-    right: 41.66666667%;
-  }
-  .col-lg-pull-4 {
-    right: 33.33333333%;
-  }
-  .col-lg-pull-3 {
-    right: 25%;
-  }
-  .col-lg-pull-2 {
-    right: 16.66666667%;
-  }
-  .col-lg-pull-1 {
-    right: 8.33333333%;
-  }
-  .col-lg-pull-0 {
-    right: auto;
-  }
-  .col-lg-push-12 {
-    left: 100%;
-  }
-  .col-lg-push-11 {
-    left: 91.66666667%;
-  }
-  .col-lg-push-10 {
-    left: 83.33333333%;
-  }
-  .col-lg-push-9 {
-    left: 75%;
-  }
-  .col-lg-push-8 {
-    left: 66.66666667%;
-  }
-  .col-lg-push-7 {
-    left: 58.33333333%;
-  }
-  .col-lg-push-6 {
-    left: 50%;
-  }
-  .col-lg-push-5 {
-    left: 41.66666667%;
-  }
-  .col-lg-push-4 {
-    left: 33.33333333%;
-  }
-  .col-lg-push-3 {
-    left: 25%;
-  }
-  .col-lg-push-2 {
-    left: 16.66666667%;
-  }
-  .col-lg-push-1 {
-    left: 8.33333333%;
-  }
-  .col-lg-push-0 {
-    left: auto;
-  }
-  .col-lg-offset-12 {
-    margin-left: 100%;
-  }
-  .col-lg-offset-11 {
-    margin-left: 91.66666667%;
-  }
-  .col-lg-offset-10 {
-    margin-left: 83.33333333%;
-  }
-  .col-lg-offset-9 {
-    margin-left: 75%;
-  }
-  .col-lg-offset-8 {
-    margin-left: 66.66666667%;
-  }
-  .col-lg-offset-7 {
-    margin-left: 58.33333333%;
-  }
-  .col-lg-offset-6 {
-    margin-left: 50%;
-  }
-  .col-lg-offset-5 {
-    margin-left: 41.66666667%;
-  }
-  .col-lg-offset-4 {
-    margin-left: 33.33333333%;
-  }
-  .col-lg-offset-3 {
-    margin-left: 25%;
-  }
-  .col-lg-offset-2 {
-    margin-left: 16.66666667%;
-  }
-  .col-lg-offset-1 {
-    margin-left: 8.33333333%;
-  }
-  .col-lg-offset-0 {
-    margin-left: 0%;
-  }
-}
-table {
-  background-color: transparent;
-}
-caption {
-  padding-top: 8px;
-  padding-bottom: 8px;
-  color: #777777;
-  text-align: left;
-}
-th {
-  text-align: left;
-}
-.table {
-  width: 100%;
-  max-width: 100%;
-  margin-bottom: 18px;
-}
-.table > thead > tr > th,
-.table > tbody > tr > th,
-.table > tfoot > tr > th,
-.table > thead > tr > td,
-.table > tbody > tr > td,
-.table > tfoot > tr > td {
-  padding: 8px;
-  line-height: 1.42857143;
-  vertical-align: top;
-  border-top: 1px solid #ddd;
-}
-.table > thead > tr > th {
-  vertical-align: bottom;
-  border-bottom: 2px solid #ddd;
-}
-.table > caption + thead > tr:first-child > th,
-.table > colgroup + thead > tr:first-child > th,
-.table > thead:first-child > tr:first-child > th,
-.table > caption + thead > tr:first-child > td,
-.table > colgroup + thead > tr:first-child > td,
-.table > thead:first-child > tr:first-child > td {
-  border-top: 0;
-}
-.table > tbody + tbody {
-  border-top: 2px solid #ddd;
-}
-.table .table {
-  background-color: #fff;
-}
-.table-condensed > thead > tr > th,
-.table-condensed > tbody > tr > th,
-.table-condensed > tfoot > tr > th,
-.table-condensed > thead > tr > td,
-.table-condensed > tbody > tr > td,
-.table-condensed > tfoot > tr > td {
-  padding: 5px;
-}
-.table-bordered {
-  border: 1px solid #ddd;
-}
-.table-bordered > thead > tr > th,
-.table-bordered > tbody > tr > th,
-.table-bordered > tfoot > tr > th,
-.table-bordered > thead > tr > td,
-.table-bordered > tbody > tr > td,
-.table-bordered > tfoot > tr > td {
-  border: 1px solid #ddd;
-}
-.table-bordered > thead > tr > th,
-.table-bordered > thead > tr > td {
-  border-bottom-width: 2px;
-}
-.table-striped > tbody > tr:nth-of-type(odd) {
-  background-color: #f9f9f9;
-}
-.table-hover > tbody > tr:hover {
-  background-color: #f5f5f5;
-}
-table col[class*="col-"] {
-  position: static;
-  float: none;
-  display: table-column;
-}
-table td[class*="col-"],
-table th[class*="col-"] {
-  position: static;
-  float: none;
-  display: table-cell;
-}
-.table > thead > tr > td.active,
-.table > tbody > tr > td.active,
-.table > tfoot > tr > td.active,
-.table > thead > tr > th.active,
-.table > tbody > tr > th.active,
-.table > tfoot > tr > th.active,
-.table > thead > tr.active > td,
-.table > tbody > tr.active > td,
-.table > tfoot > tr.active > td,
-.table > thead > tr.active > th,
-.table > tbody > tr.active > th,
-.table > tfoot > tr.active > th {
-  background-color: #f5f5f5;
-}
-.table-hover > tbody > tr > td.active:hover,
-.table-hover > tbody > tr > th.active:hover,
-.table-hover > tbody > tr.active:hover > td,
-.table-hover > tbody > tr:hover > .active,
-.table-hover > tbody > tr.active:hover > th {
-  background-color: #e8e8e8;
-}
-.table > thead > tr > td.success,
-.table > tbody > tr > td.success,
-.table > tfoot > tr > td.success,
-.table > thead > tr > th.success,
-.table > tbody > tr > th.success,
-.table > tfoot > tr > th.success,
-.table > thead > tr.success > td,
-.table > tbody > tr.success > td,
-.table > tfoot > tr.success > td,
-.table > thead > tr.success > th,
-.table > tbody > tr.success > th,
-.table > tfoot > tr.success > th {
-  background-color: #dff0d8;
-}
-.table-hover > tbody > tr > td.success:hover,
-.table-hover > tbody > tr > th.success:hover,
-.table-hover > tbody > tr.success:hover > td,
-.table-hover > tbody > tr:hover > .success,
-.table-hover > tbody > tr.success:hover > th {
-  background-color: #d0e9c6;
-}
-.table > thead > tr > td.info,
-.table > tbody > tr > td.info,
-.table > tfoot > tr > td.info,
-.table > thead > tr > th.info,
-.table > tbody > tr > th.info,
-.table > tfoot > tr > th.info,
-.table > thead > tr.info > td,
-.table > tbody > tr.info > td,
-.table > tfoot > tr.info > td,
-.table > thead > tr.info > th,
-.table > tbody > tr.info > th,
-.table > tfoot > tr.info > th {
-  background-color: #d9edf7;
-}
-.table-hover > tbody > tr > td.info:hover,
-.table-hover > tbody > tr > th.info:hover,
-.table-hover > tbody > tr.info:hover > td,
-.table-hover > tbody > tr:hover > .info,
-.table-hover > tbody > tr.info:hover > th {
-  background-color: #c4e3f3;
-}
-.table > thead > tr > td.warning,
-.table > tbody > tr > td.warning,
-.table > tfoot > tr > td.warning,
-.table > thead > tr > th.warning,
-.table > tbody > tr > th.warning,
-.table > tfoot > tr > th.warning,
-.table > thead > tr.warning > td,
-.table > tbody > tr.warning > td,
-.table > tfoot > tr.warning > td,
-.table > thead > tr.warning > th,
-.table > tbody > tr.warning > th,
-.table > tfoot > tr.warning > th {
-  background-color: #fcf8e3;
-}
-.table-hover > tbody > tr > td.warning:hover,
-.table-hover > tbody > tr > th.warning:hover,
-.table-hover > tbody > tr.warning:hover > td,
-.table-hover > tbody > tr:hover > .warning,
-.table-hover > tbody > tr.warning:hover > th {
-  background-color: #faf2cc;
-}
-.table > thead > tr > td.danger,
-.table > tbody > tr > td.danger,
-.table > tfoot > tr > td.danger,
-.table > thead > tr > th.danger,
-.table > tbody > tr > th.danger,
-.table > tfoot > tr > th.danger,
-.table > thead > tr.danger > td,
-.table > tbody > tr.danger > td,
-.table > tfoot > tr.danger > td,
-.table > thead > tr.danger > th,
-.table > tbody > tr.danger > th,
-.table > tfoot > tr.danger > th {
-  background-color: #f2dede;
-}
-.table-hover > tbody > tr > td.danger:hover,
-.table-hover > tbody > tr > th.danger:hover,
-.table-hover > tbody > tr.danger:hover > td,
-.table-hover > tbody > tr:hover > .danger,
-.table-hover > tbody > tr.danger:hover > th {
-  background-color: #ebcccc;
-}
-.table-responsive {
-  overflow-x: auto;
-  min-height: 0.01%;
-}
-@media screen and (max-width: 767px) {
-  .table-responsive {
-    width: 100%;
-    margin-bottom: 13.5px;
-    overflow-y: hidden;
-    -ms-overflow-style: -ms-autohiding-scrollbar;
-    border: 1px solid #ddd;
-  }
-  .table-responsive > .table {
-    margin-bottom: 0;
-  }
-  .table-responsive > .table > thead > tr > th,
-  .table-responsive > .table > tbody > tr > th,
-  .table-responsive > .table > tfoot > tr > th,
-  .table-responsive > .table > thead > tr > td,
-  .table-responsive > .table > tbody > tr > td,
-  .table-responsive > .table > tfoot > tr > td {
-    white-space: nowrap;
-  }
-  .table-responsive > .table-bordered {
-    border: 0;
-  }
-  .table-responsive > .table-bordered > thead > tr > th:first-child,
-  .table-responsive > .table-bordered > tbody > tr > th:first-child,
-  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
-  .table-responsive > .table-bordered > thead > tr > td:first-child,
-  .table-responsive > .table-bordered > tbody > tr > td:first-child,
-  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
-    border-left: 0;
-  }
-  .table-responsive > .table-bordered > thead > tr > th:last-child,
-  .table-responsive > .table-bordered > tbody > tr > th:last-child,
-  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
-  .table-responsive > .table-bordered > thead > tr > td:last-child,
-  .table-responsive > .table-bordered > tbody > tr > td:last-child,
-  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
-    border-right: 0;
-  }
-  .table-responsive > .table-bordered > tbody > tr:last-child > th,
-  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
-  .table-responsive > .table-bordered > tbody > tr:last-child > td,
-  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
-    border-bottom: 0;
-  }
-}
-fieldset {
-  padding: 0;
-  margin: 0;
-  border: 0;
-  min-width: 0;
-}
-legend {
-  display: block;
-  width: 100%;
-  padding: 0;
-  margin-bottom: 18px;
-  font-size: 19.5px;
-  line-height: inherit;
-  color: #333333;
-  border: 0;
-  border-bottom: 1px solid #e5e5e5;
-}
-label {
-  display: inline-block;
-  max-width: 100%;
-  margin-bottom: 5px;
-  font-weight: bold;
-}
-input[type="search"] {
-  -webkit-box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  box-sizing: border-box;
-}
-input[type="radio"],
-input[type="checkbox"] {
-  margin: 4px 0 0;
-  margin-top: 1px \9;
-  line-height: normal;
-}
-input[type="file"] {
-  display: block;
-}
-input[type="range"] {
-  display: block;
-  width: 100%;
-}
-select[multiple],
-select[size] {
-  height: auto;
-}
-input[type="file"]:focus,
-input[type="radio"]:focus,
-input[type="checkbox"]:focus {
-  outline: thin dotted;
-  outline: 5px auto -webkit-focus-ring-color;
-  outline-offset: -2px;
-}
-output {
-  display: block;
-  padding-top: 7px;
-  font-size: 13px;
-  line-height: 1.42857143;
-  color: #555555;
-}
-.form-control {
-  display: block;
-  width: 100%;
-  height: 32px;
-  padding: 6px 12px;
-  font-size: 13px;
-  line-height: 1.42857143;
-  color: #555555;
-  background-color: #fff;
-  background-image: none;
-  border: 1px solid #ccc;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-}
-.form-control:focus {
-  border-color: #66afe9;
-  outline: 0;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
-  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
-}
-.form-control::-moz-placeholder {
-  color: #999;
-  opacity: 1;
-}
-.form-control:-ms-input-placeholder {
-  color: #999;
-}
-.form-control::-webkit-input-placeholder {
-  color: #999;
-}
-.form-control::-ms-expand {
-  border: 0;
-  background-color: transparent;
-}
-.form-control[disabled],
-.form-control[readonly],
-fieldset[disabled] .form-control {
-  background-color: #eeeeee;
-  opacity: 1;
-}
-.form-control[disabled],
-fieldset[disabled] .form-control {
-  cursor: not-allowed;
-}
-textarea.form-control {
-  height: auto;
-}
-input[type="search"] {
-  -webkit-appearance: none;
-}
-@media screen and (-webkit-min-device-pixel-ratio: 0) {
-  input[type="date"].form-control,
-  input[type="time"].form-control,
-  input[type="datetime-local"].form-control,
-  input[type="month"].form-control {
-    line-height: 32px;
-  }
-  input[type="date"].input-sm,
-  input[type="time"].input-sm,
-  input[type="datetime-local"].input-sm,
-  input[type="month"].input-sm,
-  .input-group-sm input[type="date"],
-  .input-group-sm input[type="time"],
-  .input-group-sm input[type="datetime-local"],
-  .input-group-sm input[type="month"] {
-    line-height: 30px;
-  }
-  input[type="date"].input-lg,
-  input[type="time"].input-lg,
-  input[type="datetime-local"].input-lg,
-  input[type="month"].input-lg,
-  .input-group-lg input[type="date"],
-  .input-group-lg input[type="time"],
-  .input-group-lg input[type="datetime-local"],
-  .input-group-lg input[type="month"] {
-    line-height: 45px;
-  }
-}
-.form-group {
-  margin-bottom: 15px;
-}
-.radio,
-.checkbox {
-  position: relative;
-  display: block;
-  margin-top: 10px;
-  margin-bottom: 10px;
-}
-.radio label,
-.checkbox label {
-  min-height: 18px;
-  padding-left: 20px;
-  margin-bottom: 0;
-  font-weight: normal;
-  cursor: pointer;
-}
-.radio input[type="radio"],
-.radio-inline input[type="radio"],
-.checkbox input[type="checkbox"],
-.checkbox-inline input[type="checkbox"] {
-  position: absolute;
-  margin-left: -20px;
-  margin-top: 4px \9;
-}
-.radio + .radio,
-.checkbox + .checkbox {
-  margin-top: -5px;
-}
-.radio-inline,
-.checkbox-inline {
-  position: relative;
-  display: inline-block;
-  padding-left: 20px;
-  margin-bottom: 0;
-  vertical-align: middle;
-  font-weight: normal;
-  cursor: pointer;
-}
-.radio-inline + .radio-inline,
-.checkbox-inline + .checkbox-inline {
-  margin-top: 0;
-  margin-left: 10px;
-}
-input[type="radio"][disabled],
-input[type="checkbox"][disabled],
-input[type="radio"].disabled,
-input[type="checkbox"].disabled,
-fieldset[disabled] input[type="radio"],
-fieldset[disabled] input[type="checkbox"] {
-  cursor: not-allowed;
-}
-.radio-inline.disabled,
-.checkbox-inline.disabled,
-fieldset[disabled] .radio-inline,
-fieldset[disabled] .checkbox-inline {
-  cursor: not-allowed;
-}
-.radio.disabled label,
-.checkbox.disabled label,
-fieldset[disabled] .radio label,
-fieldset[disabled] .checkbox label {
-  cursor: not-allowed;
-}
-.form-control-static {
-  padding-top: 7px;
-  padding-bottom: 7px;
-  margin-bottom: 0;
-  min-height: 31px;
-}
-.form-control-static.input-lg,
-.form-control-static.input-sm {
-  padding-left: 0;
-  padding-right: 0;
-}
-.input-sm {
-  height: 30px;
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-}
-select.input-sm {
-  height: 30px;
-  line-height: 30px;
-}
-textarea.input-sm,
-select[multiple].input-sm {
-  height: auto;
-}
-.form-group-sm .form-control {
-  height: 30px;
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-}
-.form-group-sm select.form-control {
-  height: 30px;
-  line-height: 30px;
-}
-.form-group-sm textarea.form-control,
-.form-group-sm select[multiple].form-control {
-  height: auto;
-}
-.form-group-sm .form-control-static {
-  height: 30px;
-  min-height: 30px;
-  padding: 6px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-}
-.input-lg {
-  height: 45px;
-  padding: 10px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-  border-radius: 3px;
-}
-select.input-lg {
-  height: 45px;
-  line-height: 45px;
-}
-textarea.input-lg,
-select[multiple].input-lg {
-  height: auto;
-}
-.form-group-lg .form-control {
-  height: 45px;
-  padding: 10px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-  border-radius: 3px;
-}
-.form-group-lg select.form-control {
-  height: 45px;
-  line-height: 45px;
-}
-.form-group-lg textarea.form-control,
-.form-group-lg select[multiple].form-control {
-  height: auto;
-}
-.form-group-lg .form-control-static {
-  height: 45px;
-  min-height: 35px;
-  padding: 11px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-}
-.has-feedback {
-  position: relative;
-}
-.has-feedback .form-control {
-  padding-right: 40px;
-}
-.form-control-feedback {
-  position: absolute;
-  top: 0;
-  right: 0;
-  z-index: 2;
-  display: block;
-  width: 32px;
-  height: 32px;
-  line-height: 32px;
-  text-align: center;
-  pointer-events: none;
-}
-.input-lg + .form-control-feedback,
-.input-group-lg + .form-control-feedback,
-.form-group-lg .form-control + .form-control-feedback {
-  width: 45px;
-  height: 45px;
-  line-height: 45px;
-}
-.input-sm + .form-control-feedback,
-.input-group-sm + .form-control-feedback,
-.form-group-sm .form-control + .form-control-feedback {
-  width: 30px;
-  height: 30px;
-  line-height: 30px;
-}
-.has-success .help-block,
-.has-success .control-label,
-.has-success .radio,
-.has-success .checkbox,
-.has-success .radio-inline,
-.has-success .checkbox-inline,
-.has-success.radio label,
-.has-success.checkbox label,
-.has-success.radio-inline label,
-.has-success.checkbox-inline label {
-  color: #3c763d;
-}
-.has-success .form-control {
-  border-color: #3c763d;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-}
-.has-success .form-control:focus {
-  border-color: #2b542c;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
-}
-.has-success .input-group-addon {
-  color: #3c763d;
-  border-color: #3c763d;
-  background-color: #dff0d8;
-}
-.has-success .form-control-feedback {
-  color: #3c763d;
-}
-.has-warning .help-block,
-.has-warning .control-label,
-.has-warning .radio,
-.has-warning .checkbox,
-.has-warning .radio-inline,
-.has-warning .checkbox-inline,
-.has-warning.radio label,
-.has-warning.checkbox label,
-.has-warning.radio-inline label,
-.has-warning.checkbox-inline label {
-  color: #8a6d3b;
-}
-.has-warning .form-control {
-  border-color: #8a6d3b;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-}
-.has-warning .form-control:focus {
-  border-color: #66512c;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
-}
-.has-warning .input-group-addon {
-  color: #8a6d3b;
-  border-color: #8a6d3b;
-  background-color: #fcf8e3;
-}
-.has-warning .form-control-feedback {
-  color: #8a6d3b;
-}
-.has-error .help-block,
-.has-error .control-label,
-.has-error .radio,
-.has-error .checkbox,
-.has-error .radio-inline,
-.has-error .checkbox-inline,
-.has-error.radio label,
-.has-error.checkbox label,
-.has-error.radio-inline label,
-.has-error.checkbox-inline label {
-  color: #a94442;
-}
-.has-error .form-control {
-  border-color: #a94442;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-}
-.has-error .form-control:focus {
-  border-color: #843534;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
-}
-.has-error .input-group-addon {
-  color: #a94442;
-  border-color: #a94442;
-  background-color: #f2dede;
-}
-.has-error .form-control-feedback {
-  color: #a94442;
-}
-.has-feedback label ~ .form-control-feedback {
-  top: 23px;
-}
-.has-feedback label.sr-only ~ .form-control-feedback {
-  top: 0;
-}
-.help-block {
-  display: block;
-  margin-top: 5px;
-  margin-bottom: 10px;
-  color: #404040;
-}
-@media (min-width: 768px) {
-  .form-inline .form-group {
-    display: inline-block;
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .form-inline .form-control {
-    display: inline-block;
-    width: auto;
-    vertical-align: middle;
-  }
-  .form-inline .form-control-static {
-    display: inline-block;
-  }
-  .form-inline .input-group {
-    display: inline-table;
-    vertical-align: middle;
-  }
-  .form-inline .input-group .input-group-addon,
-  .form-inline .input-group .input-group-btn,
-  .form-inline .input-group .form-control {
-    width: auto;
-  }
-  .form-inline .input-group > .form-control {
-    width: 100%;
-  }
-  .form-inline .control-label {
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .form-inline .radio,
-  .form-inline .checkbox {
-    display: inline-block;
-    margin-top: 0;
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .form-inline .radio label,
-  .form-inline .checkbox label {
-    padding-left: 0;
-  }
-  .form-inline .radio input[type="radio"],
-  .form-inline .checkbox input[type="checkbox"] {
-    position: relative;
-    margin-left: 0;
-  }
-  .form-inline .has-feedback .form-control-feedback {
-    top: 0;
-  }
-}
-.form-horizontal .radio,
-.form-horizontal .checkbox,
-.form-horizontal .radio-inline,
-.form-horizontal .checkbox-inline {
-  margin-top: 0;
-  margin-bottom: 0;
-  padding-top: 7px;
-}
-.form-horizontal .radio,
-.form-horizontal .checkbox {
-  min-height: 25px;
-}
-.form-horizontal .form-group {
-  margin-left: 0px;
-  margin-right: 0px;
-}
-@media (min-width: 768px) {
-  .form-horizontal .control-label {
-    text-align: right;
-    margin-bottom: 0;
-    padding-top: 7px;
-  }
-}
-.form-horizontal .has-feedback .form-control-feedback {
-  right: 0px;
-}
-@media (min-width: 768px) {
-  .form-horizontal .form-group-lg .control-label {
-    padding-top: 11px;
-    font-size: 17px;
-  }
-}
-@media (min-width: 768px) {
-  .form-horizontal .form-group-sm .control-label {
-    padding-top: 6px;
-    font-size: 12px;
-  }
-}
-.btn {
-  display: inline-block;
-  margin-bottom: 0;
-  font-weight: normal;
-  text-align: center;
-  vertical-align: middle;
-  touch-action: manipulation;
-  cursor: pointer;
-  background-image: none;
-  border: 1px solid transparent;
-  white-space: nowrap;
-  padding: 6px 12px;
-  font-size: 13px;
-  line-height: 1.42857143;
-  border-radius: 2px;
-  -webkit-user-select: none;
-  -moz-user-select: none;
-  -ms-user-select: none;
-  user-select: none;
-}
-.btn:focus,
-.btn:active:focus,
-.btn.active:focus,
-.btn.focus,
-.btn:active.focus,
-.btn.active.focus {
-  outline: thin dotted;
-  outline: 5px auto -webkit-focus-ring-color;
-  outline-offset: -2px;
-}
-.btn:hover,
-.btn:focus,
-.btn.focus {
-  color: #333;
-  text-decoration: none;
-}
-.btn:active,
-.btn.active {
-  outline: 0;
-  background-image: none;
-  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
-  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
-}
-.btn.disabled,
-.btn[disabled],
-fieldset[disabled] .btn {
-  cursor: not-allowed;
-  opacity: 0.65;
-  filter: alpha(opacity=65);
-  -webkit-box-shadow: none;
-  box-shadow: none;
-}
-a.btn.disabled,
-fieldset[disabled] a.btn {
-  pointer-events: none;
-}
-.btn-default {
-  color: #333;
-  background-color: #fff;
-  border-color: #ccc;
-}
-.btn-default:focus,
-.btn-default.focus {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #8c8c8c;
-}
-.btn-default:hover {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-.btn-default:active,
-.btn-default.active,
-.open > .dropdown-toggle.btn-default {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-.btn-default:active:hover,
-.btn-default.active:hover,
-.open > .dropdown-toggle.btn-default:hover,
-.btn-default:active:focus,
-.btn-default.active:focus,
-.open > .dropdown-toggle.btn-default:focus,
-.btn-default:active.focus,
-.btn-default.active.focus,
-.open > .dropdown-toggle.btn-default.focus {
-  color: #333;
-  background-color: #d4d4d4;
-  border-color: #8c8c8c;
-}
-.btn-default:active,
-.btn-default.active,
-.open > .dropdown-toggle.btn-default {
-  background-image: none;
-}
-.btn-default.disabled:hover,
-.btn-default[disabled]:hover,
-fieldset[disabled] .btn-default:hover,
-.btn-default.disabled:focus,
-.btn-default[disabled]:focus,
-fieldset[disabled] .btn-default:focus,
-.btn-default.disabled.focus,
-.btn-default[disabled].focus,
-fieldset[disabled] .btn-default.focus {
-  background-color: #fff;
-  border-color: #ccc;
-}
-.btn-default .badge {
-  color: #fff;
-  background-color: #333;
-}
-.btn-primary {
-  color: #fff;
-  background-color: #337ab7;
-  border-color: #2e6da4;
-}
-.btn-primary:focus,
-.btn-primary.focus {
-  color: #fff;
-  background-color: #286090;
-  border-color: #122b40;
-}
-.btn-primary:hover {
-  color: #fff;
-  background-color: #286090;
-  border-color: #204d74;
-}
-.btn-primary:active,
-.btn-primary.active,
-.open > .dropdown-toggle.btn-primary {
-  color: #fff;
-  background-color: #286090;
-  border-color: #204d74;
-}
-.btn-primary:active:hover,
-.btn-primary.active:hover,
-.open > .dropdown-toggle.btn-primary:hover,
-.btn-primary:active:focus,
-.btn-primary.active:focus,
-.open > .dropdown-toggle.btn-primary:focus,
-.btn-primary:active.focus,
-.btn-primary.active.focus,
-.open > .dropdown-toggle.btn-primary.focus {
-  color: #fff;
-  background-color: #204d74;
-  border-color: #122b40;
-}
-.btn-primary:active,
-.btn-primary.active,
-.open > .dropdown-toggle.btn-primary {
-  background-image: none;
-}
-.btn-primary.disabled:hover,
-.btn-primary[disabled]:hover,
-fieldset[disabled] .btn-primary:hover,
-.btn-primary.disabled:focus,
-.btn-primary[disabled]:focus,
-fieldset[disabled] .btn-primary:focus,
-.btn-primary.disabled.focus,
-.btn-primary[disabled].focus,
-fieldset[disabled] .btn-primary.focus {
-  background-color: #337ab7;
-  border-color: #2e6da4;
-}
-.btn-primary .badge {
-  color: #337ab7;
-  background-color: #fff;
-}
-.btn-success {
-  color: #fff;
-  background-color: #5cb85c;
-  border-color: #4cae4c;
-}
-.btn-success:focus,
-.btn-success.focus {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #255625;
-}
-.btn-success:hover {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #398439;
-}
-.btn-success:active,
-.btn-success.active,
-.open > .dropdown-toggle.btn-success {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #398439;
-}
-.btn-success:active:hover,
-.btn-success.active:hover,
-.open > .dropdown-toggle.btn-success:hover,
-.btn-success:active:focus,
-.btn-success.active:focus,
-.open > .dropdown-toggle.btn-success:focus,
-.btn-success:active.focus,
-.btn-success.active.focus,
-.open > .dropdown-toggle.btn-success.focus {
-  color: #fff;
-  background-color: #398439;
-  border-color: #255625;
-}
-.btn-success:active,
-.btn-success.active,
-.open > .dropdown-toggle.btn-success {
-  background-image: none;
-}
-.btn-success.disabled:hover,
-.btn-success[disabled]:hover,
-fieldset[disabled] .btn-success:hover,
-.btn-success.disabled:focus,
-.btn-success[disabled]:focus,
-fieldset[disabled] .btn-success:focus,
-.btn-success.disabled.focus,
-.btn-success[disabled].focus,
-fieldset[disabled] .btn-success.focus {
-  background-color: #5cb85c;
-  border-color: #4cae4c;
-}
-.btn-success .badge {
-  color: #5cb85c;
-  background-color: #fff;
-}
-.btn-info {
-  color: #fff;
-  background-color: #5bc0de;
-  border-color: #46b8da;
-}
-.btn-info:focus,
-.btn-info.focus {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #1b6d85;
-}
-.btn-info:hover {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #269abc;
-}
-.btn-info:active,
-.btn-info.active,
-.open > .dropdown-toggle.btn-info {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #269abc;
-}
-.btn-info:active:hover,
-.btn-info.active:hover,
-.open > .dropdown-toggle.btn-info:hover,
-.btn-info:active:focus,
-.btn-info.active:focus,
-.open > .dropdown-toggle.btn-info:focus,
-.btn-info:active.focus,
-.btn-info.active.focus,
-.open > .dropdown-toggle.btn-info.focus {
-  color: #fff;
-  background-color: #269abc;
-  border-color: #1b6d85;
-}
-.btn-info:active,
-.btn-info.active,
-.open > .dropdown-toggle.btn-info {
-  background-image: none;
-}
-.btn-info.disabled:hover,
-.btn-info[disabled]:hover,
-fieldset[disabled] .btn-info:hover,
-.btn-info.disabled:focus,
-.btn-info[disabled]:focus,
-fieldset[disabled] .btn-info:focus,
-.btn-info.disabled.focus,
-.btn-info[disabled].focus,
-fieldset[disabled] .btn-info.focus {
-  background-color: #5bc0de;
-  border-color: #46b8da;
-}
-.btn-info .badge {
-  color: #5bc0de;
-  background-color: #fff;
-}
-.btn-warning {
-  color: #fff;
-  background-color: #f0ad4e;
-  border-color: #eea236;
-}
-.btn-warning:focus,
-.btn-warning.focus {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #985f0d;
-}
-.btn-warning:hover {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #d58512;
-}
-.btn-warning:active,
-.btn-warning.active,
-.open > .dropdown-toggle.btn-warning {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #d58512;
-}
-.btn-warning:active:hover,
-.btn-warning.active:hover,
-.open > .dropdown-toggle.btn-warning:hover,
-.btn-warning:active:focus,
-.btn-warning.active:focus,
-.open > .dropdown-toggle.btn-warning:focus,
-.btn-warning:active.focus,
-.btn-warning.active.focus,
-.open > .dropdown-toggle.btn-warning.focus {
-  color: #fff;
-  background-color: #d58512;
-  border-color: #985f0d;
-}
-.btn-warning:active,
-.btn-warning.active,
-.open > .dropdown-toggle.btn-warning {
-  background-image: none;
-}
-.btn-warning.disabled:hover,
-.btn-warning[disabled]:hover,
-fieldset[disabled] .btn-warning:hover,
-.btn-warning.disabled:focus,
-.btn-warning[disabled]:focus,
-fieldset[disabled] .btn-warning:focus,
-.btn-warning.disabled.focus,
-.btn-warning[disabled].focus,
-fieldset[disabled] .btn-warning.focus {
-  background-color: #f0ad4e;
-  border-color: #eea236;
-}
-.btn-warning .badge {
-  color: #f0ad4e;
-  background-color: #fff;
-}
-.btn-danger {
-  color: #fff;
-  background-color: #d9534f;
-  border-color: #d43f3a;
-}
-.btn-danger:focus,
-.btn-danger.focus {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #761c19;
-}
-.btn-danger:hover {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #ac2925;
-}
-.btn-danger:active,
-.btn-danger.active,
-.open > .dropdown-toggle.btn-danger {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #ac2925;
-}
-.btn-danger:active:hover,
-.btn-danger.active:hover,
-.open > .dropdown-toggle.btn-danger:hover,
-.btn-danger:active:focus,
-.btn-danger.active:focus,
-.open > .dropdown-toggle.btn-danger:focus,
-.btn-danger:active.focus,
-.btn-danger.active.focus,
-.open > .dropdown-toggle.btn-danger.focus {
-  color: #fff;
-  background-color: #ac2925;
-  border-color: #761c19;
-}
-.btn-danger:active,
-.btn-danger.active,
-.open > .dropdown-toggle.btn-danger {
-  background-image: none;
-}
-.btn-danger.disabled:hover,
-.btn-danger[disabled]:hover,
-fieldset[disabled] .btn-danger:hover,
-.btn-danger.disabled:focus,
-.btn-danger[disabled]:focus,
-fieldset[disabled] .btn-danger:focus,
-.btn-danger.disabled.focus,
-.btn-danger[disabled].focus,
-fieldset[disabled] .btn-danger.focus {
-  background-color: #d9534f;
-  border-color: #d43f3a;
-}
-.btn-danger .badge {
-  color: #d9534f;
-  background-color: #fff;
-}
-.btn-link {
-  color: #337ab7;
-  font-weight: normal;
-  border-radius: 0;
-}
-.btn-link,
-.btn-link:active,
-.btn-link.active,
-.btn-link[disabled],
-fieldset[disabled] .btn-link {
-  background-color: transparent;
-  -webkit-box-shadow: none;
-  box-shadow: none;
-}
-.btn-link,
-.btn-link:hover,
-.btn-link:focus,
-.btn-link:active {
-  border-color: transparent;
-}
-.btn-link:hover,
-.btn-link:focus {
-  color: #23527c;
-  text-decoration: underline;
-  background-color: transparent;
-}
-.btn-link[disabled]:hover,
-fieldset[disabled] .btn-link:hover,
-.btn-link[disabled]:focus,
-fieldset[disabled] .btn-link:focus {
-  color: #777777;
-  text-decoration: none;
-}
-.btn-lg,
-.btn-group-lg > .btn {
-  padding: 10px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-  border-radius: 3px;
-}
-.btn-sm,
-.btn-group-sm > .btn {
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-}
-.btn-xs,
-.btn-group-xs > .btn {
-  padding: 1px 5px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-}
-.btn-block {
-  display: block;
-  width: 100%;
-}
-.btn-block + .btn-block {
-  margin-top: 5px;
-}
-input[type="submit"].btn-block,
-input[type="reset"].btn-block,
-input[type="button"].btn-block {
-  width: 100%;
-}
-.fade {
-  opacity: 0;
-  -webkit-transition: opacity 0.15s linear;
-  -o-transition: opacity 0.15s linear;
-  transition: opacity 0.15s linear;
-}
-.fade.in {
-  opacity: 1;
-}
-.collapse {
-  display: none;
-}
-.collapse.in {
-  display: block;
-}
-tr.collapse.in {
-  display: table-row;
-}
-tbody.collapse.in {
-  display: table-row-group;
-}
-.collapsing {
-  position: relative;
-  height: 0;
-  overflow: hidden;
-  -webkit-transition-property: height, visibility;
-  transition-property: height, visibility;
-  -webkit-transition-duration: 0.35s;
-  transition-duration: 0.35s;
-  -webkit-transition-timing-function: ease;
-  transition-timing-function: ease;
-}
-.caret {
-  display: inline-block;
-  width: 0;
-  height: 0;
-  margin-left: 2px;
-  vertical-align: middle;
-  border-top: 4px dashed;
-  border-top: 4px solid \9;
-  border-right: 4px solid transparent;
-  border-left: 4px solid transparent;
-}
-.dropup,
-.dropdown {
-  position: relative;
-}
-.dropdown-toggle:focus {
-  outline: 0;
-}
-.dropdown-menu {
-  position: absolute;
-  top: 100%;
-  left: 0;
-  z-index: 1000;
-  display: none;
-  float: left;
-  min-width: 160px;
-  padding: 5px 0;
-  margin: 2px 0 0;
-  list-style: none;
-  font-size: 13px;
-  text-align: left;
-  background-color: #fff;
-  border: 1px solid #ccc;
-  border: 1px solid rgba(0, 0, 0, 0.15);
-  border-radius: 2px;
-  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
-  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
-  background-clip: padding-box;
-}
-.dropdown-menu.pull-right {
-  right: 0;
-  left: auto;
-}
-.dropdown-menu .divider {
-  height: 1px;
-  margin: 8px 0;
-  overflow: hidden;
-  background-color: #e5e5e5;
-}
-.dropdown-menu > li > a {
-  display: block;
-  padding: 3px 20px;
-  clear: both;
-  font-weight: normal;
-  line-height: 1.42857143;
-  color: #333333;
-  white-space: nowrap;
-}
-.dropdown-menu > li > a:hover,
-.dropdown-menu > li > a:focus {
-  text-decoration: none;
-  color: #262626;
-  background-color: #f5f5f5;
-}
-.dropdown-menu > .active > a,
-.dropdown-menu > .active > a:hover,
-.dropdown-menu > .active > a:focus {
-  color: #fff;
-  text-decoration: none;
-  outline: 0;
-  background-color: #337ab7;
-}
-.dropdown-menu > .disabled > a,
-.dropdown-menu > .disabled > a:hover,
-.dropdown-menu > .disabled > a:focus {
-  color: #777777;
-}
-.dropdown-menu > .disabled > a:hover,
-.dropdown-menu > .disabled > a:focus {
-  text-decoration: none;
-  background-color: transparent;
-  background-image: none;
-  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
-  cursor: not-allowed;
-}
-.open > .dropdown-menu {
-  display: block;
-}
-.open > a {
-  outline: 0;
-}
-.dropdown-menu-right {
-  left: auto;
-  right: 0;
-}
-.dropdown-menu-left {
-  left: 0;
-  right: auto;
-}
-.dropdown-header {
-  display: block;
-  padding: 3px 20px;
-  font-size: 12px;
-  line-height: 1.42857143;
-  color: #777777;
-  white-space: nowrap;
-}
-.dropdown-backdrop {
-  position: fixed;
-  left: 0;
-  right: 0;
-  bottom: 0;
-  top: 0;
-  z-index: 990;
-}
-.pull-right > .dropdown-menu {
-  right: 0;
-  left: auto;
-}
-.dropup .caret,
-.navbar-fixed-bottom .dropdown .caret {
-  border-top: 0;
-  border-bottom: 4px dashed;
-  border-bottom: 4px solid \9;
-  content: "";
-}
-.dropup .dropdown-menu,
-.navbar-fixed-bottom .dropdown .dropdown-menu {
-  top: auto;
-  bottom: 100%;
-  margin-bottom: 2px;
-}
-@media (min-width: 541px) {
-  .navbar-right .dropdown-menu {
-    left: auto;
-    right: 0;
-  }
-  .navbar-right .dropdown-menu-left {
-    left: 0;
-    right: auto;
-  }
-}
-.btn-group,
-.btn-group-vertical {
-  position: relative;
-  display: inline-block;
-  vertical-align: middle;
-}
-.btn-group > .btn,
-.btn-group-vertical > .btn {
-  position: relative;
-  float: left;
-}
-.btn-group > .btn:hover,
-.btn-group-vertical > .btn:hover,
-.btn-group > .btn:focus,
-.btn-group-vertical > .btn:focus,
-.btn-group > .btn:active,
-.btn-group-vertical > .btn:active,
-.btn-group > .btn.active,
-.btn-group-vertical > .btn.active {
-  z-index: 2;
-}
-.btn-group .btn + .btn,
-.btn-group .btn + .btn-group,
-.btn-group .btn-group + .btn,
-.btn-group .btn-group + .btn-group {
-  margin-left: -1px;
-}
-.btn-toolbar {
-  margin-left: -5px;
-}
-.btn-toolbar .btn,
-.btn-toolbar .btn-group,
-.btn-toolbar .input-group {
-  float: left;
-}
-.btn-toolbar > .btn,
-.btn-toolbar > .btn-group,
-.btn-toolbar > .input-group {
-  margin-left: 5px;
-}
-.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
-  border-radius: 0;
-}
-.btn-group > .btn:first-child {
-  margin-left: 0;
-}
-.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
-  border-bottom-right-radius: 0;
-  border-top-right-radius: 0;
-}
-.btn-group > .btn:last-child:not(:first-child),
-.btn-group > .dropdown-toggle:not(:first-child) {
-  border-bottom-left-radius: 0;
-  border-top-left-radius: 0;
-}
-.btn-group > .btn-group {
-  float: left;
-}
-.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
-  border-radius: 0;
-}
-.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
-.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
-  border-bottom-right-radius: 0;
-  border-top-right-radius: 0;
-}
-.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
-  border-bottom-left-radius: 0;
-  border-top-left-radius: 0;
-}
-.btn-group .dropdown-toggle:active,
-.btn-group.open .dropdown-toggle {
-  outline: 0;
-}
-.btn-group > .btn + .dropdown-toggle {
-  padding-left: 8px;
-  padding-right: 8px;
-}
-.btn-group > .btn-lg + .dropdown-toggle {
-  padding-left: 12px;
-  padding-right: 12px;
-}
-.btn-group.open .dropdown-toggle {
-  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
-  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
-}
-.btn-group.open .dropdown-toggle.btn-link {
-  -webkit-box-shadow: none;
-  box-shadow: none;
-}
-.btn .caret {
-  margin-left: 0;
-}
-.btn-lg .caret {
-  border-width: 5px 5px 0;
-  border-bottom-width: 0;
-}
-.dropup .btn-lg .caret {
-  border-width: 0 5px 5px;
-}
-.btn-group-vertical > .btn,
-.btn-group-vertical > .btn-group,
-.btn-group-vertical > .btn-group > .btn {
-  display: block;
-  float: none;
-  width: 100%;
-  max-width: 100%;
-}
-.btn-group-vertical > .btn-group > .btn {
-  float: none;
-}
-.btn-group-vertical > .btn + .btn,
-.btn-group-vertical > .btn + .btn-group,
-.btn-group-vertical > .btn-group + .btn,
-.btn-group-vertical > .btn-group + .btn-group {
-  margin-top: -1px;
-  margin-left: 0;
-}
-.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
-  border-radius: 0;
-}
-.btn-group-vertical > .btn:first-child:not(:last-child) {
-  border-top-right-radius: 2px;
-  border-top-left-radius: 2px;
-  border-bottom-right-radius: 0;
-  border-bottom-left-radius: 0;
-}
-.btn-group-vertical > .btn:last-child:not(:first-child) {
-  border-top-right-radius: 0;
-  border-top-left-radius: 0;
-  border-bottom-right-radius: 2px;
-  border-bottom-left-radius: 2px;
-}
-.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
-  border-radius: 0;
-}
-.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
-.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
-  border-bottom-right-radius: 0;
-  border-bottom-left-radius: 0;
-}
-.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
-  border-top-right-radius: 0;
-  border-top-left-radius: 0;
-}
-.btn-group-justified {
-  display: table;
-  width: 100%;
-  table-layout: fixed;
-  border-collapse: separate;
-}
-.btn-group-justified > .btn,
-.btn-group-justified > .btn-group {
-  float: none;
-  display: table-cell;
-  width: 1%;
-}
-.btn-group-justified > .btn-group .btn {
-  width: 100%;
-}
-.btn-group-justified > .btn-group .dropdown-menu {
-  left: auto;
-}
-[data-toggle="buttons"] > .btn input[type="radio"],
-[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
-[data-toggle="buttons"] > .btn input[type="checkbox"],
-[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
-  position: absolute;
-  clip: rect(0, 0, 0, 0);
-  pointer-events: none;
-}
-.input-group {
-  position: relative;
-  display: table;
-  border-collapse: separate;
-}
-.input-group[class*="col-"] {
-  float: none;
-  padding-left: 0;
-  padding-right: 0;
-}
-.input-group .form-control {
-  position: relative;
-  z-index: 2;
-  float: left;
-  width: 100%;
-  margin-bottom: 0;
-}
-.input-group .form-control:focus {
-  z-index: 3;
-}
-.input-group-lg > .form-control,
-.input-group-lg > .input-group-addon,
-.input-group-lg > .input-group-btn > .btn {
-  height: 45px;
-  padding: 10px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-  border-radius: 3px;
-}
-select.input-group-lg > .form-control,
-select.input-group-lg > .input-group-addon,
-select.input-group-lg > .input-group-btn > .btn {
-  height: 45px;
-  line-height: 45px;
-}
-textarea.input-group-lg > .form-control,
-textarea.input-group-lg > .input-group-addon,
-textarea.input-group-lg > .input-group-btn > .btn,
-select[multiple].input-group-lg > .form-control,
-select[multiple].input-group-lg > .input-group-addon,
-select[multiple].input-group-lg > .input-group-btn > .btn {
-  height: auto;
-}
-.input-group-sm > .form-control,
-.input-group-sm > .input-group-addon,
-.input-group-sm > .input-group-btn > .btn {
-  height: 30px;
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-}
-select.input-group-sm > .form-control,
-select.input-group-sm > .input-group-addon,
-select.input-group-sm > .input-group-btn > .btn {
-  height: 30px;
-  line-height: 30px;
-}
-textarea.input-group-sm > .form-control,
-textarea.input-group-sm > .input-group-addon,
-textarea.input-group-sm > .input-group-btn > .btn,
-select[multiple].input-group-sm > .form-control,
-select[multiple].input-group-sm > .input-group-addon,
-select[multiple].input-group-sm > .input-group-btn > .btn {
-  height: auto;
-}
-.input-group-addon,
-.input-group-btn,
-.input-group .form-control {
-  display: table-cell;
-}
-.input-group-addon:not(:first-child):not(:last-child),
-.input-group-btn:not(:first-child):not(:last-child),
-.input-group .form-control:not(:first-child):not(:last-child) {
-  border-radius: 0;
-}
-.input-group-addon,
-.input-group-btn {
-  width: 1%;
-  white-space: nowrap;
-  vertical-align: middle;
-}
-.input-group-addon {
-  padding: 6px 12px;
-  font-size: 13px;
-  font-weight: normal;
-  line-height: 1;
-  color: #555555;
-  text-align: center;
-  background-color: #eeeeee;
-  border: 1px solid #ccc;
-  border-radius: 2px;
-}
-.input-group-addon.input-sm {
-  padding: 5px 10px;
-  font-size: 12px;
-  border-radius: 1px;
-}
-.input-group-addon.input-lg {
-  padding: 10px 16px;
-  font-size: 17px;
-  border-radius: 3px;
-}
-.input-group-addon input[type="radio"],
-.input-group-addon input[type="checkbox"] {
-  margin-top: 0;
-}
-.input-group .form-control:first-child,
-.input-group-addon:first-child,
-.input-group-btn:first-child > .btn,
-.input-group-btn:first-child > .btn-group > .btn,
-.input-group-btn:first-child > .dropdown-toggle,
-.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
-.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
-  border-bottom-right-radius: 0;
-  border-top-right-radius: 0;
-}
-.input-group-addon:first-child {
-  border-right: 0;
-}
-.input-group .form-control:last-child,
-.input-group-addon:last-child,
-.input-group-btn:last-child > .btn,
-.input-group-btn:last-child > .btn-group > .btn,
-.input-group-btn:last-child > .dropdown-toggle,
-.input-group-btn:first-child > .btn:not(:first-child),
-.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
-  border-bottom-left-radius: 0;
-  border-top-left-radius: 0;
-}
-.input-group-addon:last-child {
-  border-left: 0;
-}
-.input-group-btn {
-  position: relative;
-  font-size: 0;
-  white-space: nowrap;
-}
-.input-group-btn > .btn {
-  position: relative;
-}
-.input-group-btn > .btn + .btn {
-  margin-left: -1px;
-}
-.input-group-btn > .btn:hover,
-.input-group-btn > .btn:focus,
-.input-group-btn > .btn:active {
-  z-index: 2;
-}
-.input-group-btn:first-child > .btn,
-.input-group-btn:first-child > .btn-group {
-  margin-right: -1px;
-}
-.input-group-btn:last-child > .btn,
-.input-group-btn:last-child > .btn-group {
-  z-index: 2;
-  margin-left: -1px;
-}
-.nav {
-  margin-bottom: 0;
-  padding-left: 0;
-  list-style: none;
-}
-.nav > li {
-  position: relative;
-  display: block;
-}
-.nav > li > a {
-  position: relative;
-  display: block;
-  padding: 10px 15px;
-}
-.nav > li > a:hover,
-.nav > li > a:focus {
-  text-decoration: none;
-  background-color: #eeeeee;
-}
-.nav > li.disabled > a {
-  color: #777777;
-}
-.nav > li.disabled > a:hover,
-.nav > li.disabled > a:focus {
-  color: #777777;
-  text-decoration: none;
-  background-color: transparent;
-  cursor: not-allowed;
-}
-.nav .open > a,
-.nav .open > a:hover,
-.nav .open > a:focus {
-  background-color: #eeeeee;
-  border-color: #337ab7;
-}
-.nav .nav-divider {
-  height: 1px;
-  margin: 8px 0;
-  overflow: hidden;
-  background-color: #e5e5e5;
-}
-.nav > li > a > img {
-  max-width: none;
-}
-.nav-tabs {
-  border-bottom: 1px solid #ddd;
-}
-.nav-tabs > li {
-  float: left;
-  margin-bottom: -1px;
-}
-.nav-tabs > li > a {
-  margin-right: 2px;
-  line-height: 1.42857143;
-  border: 1px solid transparent;
-  border-radius: 2px 2px 0 0;
-}
-.nav-tabs > li > a:hover {
-  border-color: #eeeeee #eeeeee #ddd;
-}
-.nav-tabs > li.active > a,
-.nav-tabs > li.active > a:hover,
-.nav-tabs > li.active > a:focus {
-  color: #555555;
-  background-color: #fff;
-  border: 1px solid #ddd;
-  border-bottom-color: transparent;
-  cursor: default;
-}
-.nav-tabs.nav-justified {
-  width: 100%;
-  border-bottom: 0;
-}
-.nav-tabs.nav-justified > li {
-  float: none;
-}
-.nav-tabs.nav-justified > li > a {
-  text-align: center;
-  margin-bottom: 5px;
-}
-.nav-tabs.nav-justified > .dropdown .dropdown-menu {
-  top: auto;
-  left: auto;
-}
-@media (min-width: 768px) {
-  .nav-tabs.nav-justified > li {
-    display: table-cell;
-    width: 1%;
-  }
-  .nav-tabs.nav-justified > li > a {
-    margin-bottom: 0;
-  }
-}
-.nav-tabs.nav-justified > li > a {
-  margin-right: 0;
-  border-radius: 2px;
-}
-.nav-tabs.nav-justified > .active > a,
-.nav-tabs.nav-justified > .active > a:hover,
-.nav-tabs.nav-justified > .active > a:focus {
-  border: 1px solid #ddd;
-}
-@media (min-width: 768px) {
-  .nav-tabs.nav-justified > li > a {
-    border-bottom: 1px solid #ddd;
-    border-radius: 2px 2px 0 0;
-  }
-  .nav-tabs.nav-justified > .active > a,
-  .nav-tabs.nav-justified > .active > a:hover,
-  .nav-tabs.nav-justified > .active > a:focus {
-    border-bottom-color: #fff;
-  }
-}
-.nav-pills > li {
-  float: left;
-}
-.nav-pills > li > a {
-  border-radius: 2px;
-}
-.nav-pills > li + li {
-  margin-left: 2px;
-}
-.nav-pills > li.active > a,
-.nav-pills > li.active > a:hover,
-.nav-pills > li.active > a:focus {
-  color: #fff;
-  background-color: #337ab7;
-}
-.nav-stacked > li {
-  float: none;
-}
-.nav-stacked > li + li {
-  margin-top: 2px;
-  margin-left: 0;
-}
-.nav-justified {
-  width: 100%;
-}
-.nav-justified > li {
-  float: none;
-}
-.nav-justified > li > a {
-  text-align: center;
-  margin-bottom: 5px;
-}
-.nav-justified > .dropdown .dropdown-menu {
-  top: auto;
-  left: auto;
-}
-@media (min-width: 768px) {
-  .nav-justified > li {
-    display: table-cell;
-    width: 1%;
-  }
-  .nav-justified > li > a {
-    margin-bottom: 0;
-  }
-}
-.nav-tabs-justified {
-  border-bottom: 0;
-}
-.nav-tabs-justified > li > a {
-  margin-right: 0;
-  border-radius: 2px;
-}
-.nav-tabs-justified > .active > a,
-.nav-tabs-justified > .active > a:hover,
-.nav-tabs-justified > .active > a:focus {
-  border: 1px solid #ddd;
-}
-@media (min-width: 768px) {
-  .nav-tabs-justified > li > a {
-    border-bottom: 1px solid #ddd;
-    border-radius: 2px 2px 0 0;
-  }
-  .nav-tabs-justified > .active > a,
-  .nav-tabs-justified > .active > a:hover,
-  .nav-tabs-justified > .active > a:focus {
-    border-bottom-color: #fff;
-  }
-}
-.tab-content > .tab-pane {
-  display: none;
-}
-.tab-content > .active {
-  display: block;
-}
-.nav-tabs .dropdown-menu {
-  margin-top: -1px;
-  border-top-right-radius: 0;
-  border-top-left-radius: 0;
-}
-.navbar {
-  position: relative;
-  min-height: 30px;
-  margin-bottom: 18px;
-  border: 1px solid transparent;
-}
-@media (min-width: 541px) {
-  .navbar {
-    border-radius: 2px;
-  }
-}
-@media (min-width: 541px) {
-  .navbar-header {
-    float: left;
-  }
-}
-.navbar-collapse {
-  overflow-x: visible;
-  padding-right: 0px;
-  padding-left: 0px;
-  border-top: 1px solid transparent;
-  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
-  -webkit-overflow-scrolling: touch;
-}
-.navbar-collapse.in {
-  overflow-y: auto;
-}
-@media (min-width: 541px) {
-  .navbar-collapse {
-    width: auto;
-    border-top: 0;
-    box-shadow: none;
-  }
-  .navbar-collapse.collapse {
-    display: block !important;
-    height: auto !important;
-    padding-bottom: 0;
-    overflow: visible !important;
-  }
-  .navbar-collapse.in {
-    overflow-y: visible;
-  }
-  .navbar-fixed-top .navbar-collapse,
-  .navbar-static-top .navbar-collapse,
-  .navbar-fixed-bottom .navbar-collapse {
-    padding-left: 0;
-    padding-right: 0;
-  }
-}
-.navbar-fixed-top .navbar-collapse,
-.navbar-fixed-bottom .navbar-collapse {
-  max-height: 340px;
-}
-@media (max-device-width: 540px) and (orientation: landscape) {
-  .navbar-fixed-top .navbar-collapse,
-  .navbar-fixed-bottom .navbar-collapse {
-    max-height: 200px;
-  }
-}
-.container > .navbar-header,
-.container-fluid > .navbar-header,
-.container > .navbar-collapse,
-.container-fluid > .navbar-collapse {
-  margin-right: 0px;
-  margin-left: 0px;
-}
-@media (min-width: 541px) {
-  .container > .navbar-header,
-  .container-fluid > .navbar-header,
-  .container > .navbar-collapse,
-  .container-fluid > .navbar-collapse {
-    margin-right: 0;
-    margin-left: 0;
-  }
-}
-.navbar-static-top {
-  z-index: 1000;
-  border-width: 0 0 1px;
-}
-@media (min-width: 541px) {
-  .navbar-static-top {
-    border-radius: 0;
-  }
-}
-.navbar-fixed-top,
-.navbar-fixed-bottom {
-  position: fixed;
-  right: 0;
-  left: 0;
-  z-index: 1030;
-}
-@media (min-width: 541px) {
-  .navbar-fixed-top,
-  .navbar-fixed-bottom {
-    border-radius: 0;
-  }
-}
-.navbar-fixed-top {
-  top: 0;
-  border-width: 0 0 1px;
-}
-.navbar-fixed-bottom {
-  bottom: 0;
-  margin-bottom: 0;
-  border-width: 1px 0 0;
-}
-.navbar-brand {
-  float: left;
-  padding: 6px 0px;
-  font-size: 17px;
-  line-height: 18px;
-  height: 30px;
-}
-.navbar-brand:hover,
-.navbar-brand:focus {
-  text-decoration: none;
-}
-.navbar-brand > img {
-  display: block;
-}
-@media (min-width: 541px) {
-  .navbar > .container .navbar-brand,
-  .navbar > .container-fluid .navbar-brand {
-    margin-left: 0px;
-  }
-}
-.navbar-toggle {
-  position: relative;
-  float: right;
-  margin-right: 0px;
-  padding: 9px 10px;
-  margin-top: -2px;
-  margin-bottom: -2px;
-  background-color: transparent;
-  background-image: none;
-  border: 1px solid transparent;
-  border-radius: 2px;
-}
-.navbar-toggle:focus {
-  outline: 0;
-}
-.navbar-toggle .icon-bar {
-  display: block;
-  width: 22px;
-  height: 2px;
-  border-radius: 1px;
-}
-.navbar-toggle .icon-bar + .icon-bar {
-  margin-top: 4px;
-}
-@media (min-width: 541px) {
-  .navbar-toggle {
-    display: none;
-  }
-}
-.navbar-nav {
-  margin: 3px 0px;
-}
-.navbar-nav > li > a {
-  padding-top: 10px;
-  padding-bottom: 10px;
-  line-height: 18px;
-}
-@media (max-width: 540px) {
-  .navbar-nav .open .dropdown-menu {
-    position: static;
-    float: none;
-    width: auto;
-    margin-top: 0;
-    background-color: transparent;
-    border: 0;
-    box-shadow: none;
-  }
-  .navbar-nav .open .dropdown-menu > li > a,
-  .navbar-nav .open .dropdown-menu .dropdown-header {
-    padding: 5px 15px 5px 25px;
-  }
-  .navbar-nav .open .dropdown-menu > li > a {
-    line-height: 18px;
-  }
-  .navbar-nav .open .dropdown-menu > li > a:hover,
-  .navbar-nav .open .dropdown-menu > li > a:focus {
-    background-image: none;
-  }
-}
-@media (min-width: 541px) {
-  .navbar-nav {
-    float: left;
-    margin: 0;
-  }
-  .navbar-nav > li {
-    float: left;
-  }
-  .navbar-nav > li > a {
-    padding-top: 6px;
-    padding-bottom: 6px;
-  }
-}
-.navbar-form {
-  margin-left: 0px;
-  margin-right: 0px;
-  padding: 10px 0px;
-  border-top: 1px solid transparent;
-  border-bottom: 1px solid transparent;
-  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
-  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
-  margin-top: -1px;
-  margin-bottom: -1px;
-}
-@media (min-width: 768px) {
-  .navbar-form .form-group {
-    display: inline-block;
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .navbar-form .form-control {
-    display: inline-block;
-    width: auto;
-    vertical-align: middle;
-  }
-  .navbar-form .form-control-static {
-    display: inline-block;
-  }
-  .navbar-form .input-group {
-    display: inline-table;
-    vertical-align: middle;
-  }
-  .navbar-form .input-group .input-group-addon,
-  .navbar-form .input-group .input-group-btn,
-  .navbar-form .input-group .form-control {
-    width: auto;
-  }
-  .navbar-form .input-group > .form-control {
-    width: 100%;
-  }
-  .navbar-form .control-label {
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .navbar-form .radio,
-  .navbar-form .checkbox {
-    display: inline-block;
-    margin-top: 0;
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .navbar-form .radio label,
-  .navbar-form .checkbox label {
-    padding-left: 0;
-  }
-  .navbar-form .radio input[type="radio"],
-  .navbar-form .checkbox input[type="checkbox"] {
-    position: relative;
-    margin-left: 0;
-  }
-  .navbar-form .has-feedback .form-control-feedback {
-    top: 0;
-  }
-}
-@media (max-width: 540px) {
-  .navbar-form .form-group {
-    margin-bottom: 5px;
-  }
-  .navbar-form .form-group:last-child {
-    margin-bottom: 0;
-  }
-}
-@media (min-width: 541px) {
-  .navbar-form {
-    width: auto;
-    border: 0;
-    margin-left: 0;
-    margin-right: 0;
-    padding-top: 0;
-    padding-bottom: 0;
-    -webkit-box-shadow: none;
-    box-shadow: none;
-  }
-}
-.navbar-nav > li > .dropdown-menu {
-  margin-top: 0;
-  border-top-right-radius: 0;
-  border-top-left-radius: 0;
-}
-.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
-  margin-bottom: 0;
-  border-top-right-radius: 2px;
-  border-top-left-radius: 2px;
-  border-bottom-right-radius: 0;
-  border-bottom-left-radius: 0;
-}
-.navbar-btn {
-  margin-top: -1px;
-  margin-bottom: -1px;
-}
-.navbar-btn.btn-sm {
-  margin-top: 0px;
-  margin-bottom: 0px;
-}
-.navbar-btn.btn-xs {
-  margin-top: 4px;
-  margin-bottom: 4px;
-}
-.navbar-text {
-  margin-top: 6px;
-  margin-bottom: 6px;
-}
-@media (min-width: 541px) {
-  .navbar-text {
-    float: left;
-    margin-left: 0px;
-    margin-right: 0px;
-  }
-}
-@media (min-width: 541px) {
-  .navbar-left {
-    float: left !important;
-    float: left;
-  }
-  .navbar-right {
-    float: right !important;
-    float: right;
-    margin-right: 0px;
-  }
-  .navbar-right ~ .navbar-right {
-    margin-right: 0;
-  }
-}
-.navbar-default {
-  background-color: #f8f8f8;
-  border-color: #e7e7e7;
-}
-.navbar-default .navbar-brand {
-  color: #777;
-}
-.navbar-default .navbar-brand:hover,
-.navbar-default .navbar-brand:focus {
-  color: #5e5e5e;
-  background-color: transparent;
-}
-.navbar-default .navbar-text {
-  color: #777;
-}
-.navbar-default .navbar-nav > li > a {
-  color: #777;
-}
-.navbar-default .navbar-nav > li > a:hover,
-.navbar-default .navbar-nav > li > a:focus {
-  color: #333;
-  background-color: transparent;
-}
-.navbar-default .navbar-nav > .active > a,
-.navbar-default .navbar-nav > .active > a:hover,
-.navbar-default .navbar-nav > .active > a:focus {
-  color: #555;
-  background-color: #e7e7e7;
-}
-.navbar-default .navbar-nav > .disabled > a,
-.navbar-default .navbar-nav > .disabled > a:hover,
-.navbar-default .navbar-nav > .disabled > a:focus {
-  color: #ccc;
-  background-color: transparent;
-}
-.navbar-default .navbar-toggle {
-  border-color: #ddd;
-}
-.navbar-default .navbar-toggle:hover,
-.navbar-default .navbar-toggle:focus {
-  background-color: #ddd;
-}
-.navbar-default .navbar-toggle .icon-bar {
-  background-color: #888;
-}
-.navbar-default .navbar-collapse,
-.navbar-default .navbar-form {
-  border-color: #e7e7e7;
-}
-.navbar-default .navbar-nav > .open > a,
-.navbar-default .navbar-nav > .open > a:hover,
-.navbar-default .navbar-nav > .open > a:focus {
-  background-color: #e7e7e7;
-  color: #555;
-}
-@media (max-width: 540px) {
-  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
-    color: #777;
-  }
-  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
-  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
-    color: #333;
-    background-color: transparent;
-  }
-  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
-  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
-  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
-    color: #555;
-    background-color: #e7e7e7;
-  }
-  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
-  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
-  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
-    color: #ccc;
-    background-color: transparent;
-  }
-}
-.navbar-default .navbar-link {
-  color: #777;
-}
-.navbar-default .navbar-link:hover {
-  color: #333;
-}
-.navbar-default .btn-link {
-  color: #777;
-}
-.navbar-default .btn-link:hover,
-.navbar-default .btn-link:focus {
-  color: #333;
-}
-.navbar-default .btn-link[disabled]:hover,
-fieldset[disabled] .navbar-default .btn-link:hover,
-.navbar-default .btn-link[disabled]:focus,
-fieldset[disabled] .navbar-default .btn-link:focus {
-  color: #ccc;
-}
-.navbar-inverse {
-  background-color: #222;
-  border-color: #080808;
-}
-.navbar-inverse .navbar-brand {
-  color: #9d9d9d;
-}
-.navbar-inverse .navbar-brand:hover,
-.navbar-inverse .navbar-brand:focus {
-  color: #fff;
-  background-color: transparent;
-}
-.navbar-inverse .navbar-text {
-  color: #9d9d9d;
-}
-.navbar-inverse .navbar-nav > li > a {
-  color: #9d9d9d;
-}
-.navbar-inverse .navbar-nav > li > a:hover,
-.navbar-inverse .navbar-nav > li > a:focus {
-  color: #fff;
-  background-color: transparent;
-}
-.navbar-inverse .navbar-nav > .active > a,
-.navbar-inverse .navbar-nav > .active > a:hover,
-.navbar-inverse .navbar-nav > .active > a:focus {
-  color: #fff;
-  background-color: #080808;
-}
-.navbar-inverse .navbar-nav > .disabled > a,
-.navbar-inverse .navbar-nav > .disabled > a:hover,
-.navbar-inverse .navbar-nav > .disabled > a:focus {
-  color: #444;
-  background-color: transparent;
-}
-.navbar-inverse .navbar-toggle {
-  border-color: #333;
-}
-.navbar-inverse .navbar-toggle:hover,
-.navbar-inverse .navbar-toggle:focus {
-  background-color: #333;
-}
-.navbar-inverse .navbar-toggle .icon-bar {
-  background-color: #fff;
-}
-.navbar-inverse .navbar-collapse,
-.navbar-inverse .navbar-form {
-  border-color: #101010;
-}
-.navbar-inverse .navbar-nav > .open > a,
-.navbar-inverse .navbar-nav > .open > a:hover,
-.navbar-inverse .navbar-nav > .open > a:focus {
-  background-color: #080808;
-  color: #fff;
-}
-@media (max-width: 540px) {
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
-    border-color: #080808;
-  }
-  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
-    background-color: #080808;
-  }
-  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
-    color: #9d9d9d;
-  }
-  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
-  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
-    color: #fff;
-    background-color: transparent;
-  }
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
-    color: #fff;
-    background-color: #080808;
-  }
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
-    color: #444;
-    background-color: transparent;
-  }
-}
-.navbar-inverse .navbar-link {
-  color: #9d9d9d;
-}
-.navbar-inverse .navbar-link:hover {
-  color: #fff;
-}
-.navbar-inverse .btn-link {
-  color: #9d9d9d;
-}
-.navbar-inverse .btn-link:hover,
-.navbar-inverse .btn-link:focus {
-  color: #fff;
-}
-.navbar-inverse .btn-link[disabled]:hover,
-fieldset[disabled] .navbar-inverse .btn-link:hover,
-.navbar-inverse .btn-link[disabled]:focus,
-fieldset[disabled] .navbar-inverse .btn-link:focus {
-  color: #444;
-}
-.breadcrumb {
-  padding: 8px 15px;
-  margin-bottom: 18px;
-  list-style: none;
-  background-color: #f5f5f5;
-  border-radius: 2px;
-}
-.breadcrumb > li {
-  display: inline-block;
-}
-.breadcrumb > li + li:before {
-  content: "/\00a0";
-  padding: 0 5px;
-  color: #5e5e5e;
-}
-.breadcrumb > .active {
-  color: #777777;
-}
-.pagination {
-  display: inline-block;
-  padding-left: 0;
-  margin: 18px 0;
-  border-radius: 2px;
-}
-.pagination > li {
-  display: inline;
-}
-.pagination > li > a,
-.pagination > li > span {
-  position: relative;
-  float: left;
-  padding: 6px 12px;
-  line-height: 1.42857143;
-  text-decoration: none;
-  color: #337ab7;
-  background-color: #fff;
-  border: 1px solid #ddd;
-  margin-left: -1px;
-}
-.pagination > li:first-child > a,
-.pagination > li:first-child > span {
-  margin-left: 0;
-  border-bottom-left-radius: 2px;
-  border-top-left-radius: 2px;
-}
-.pagination > li:last-child > a,
-.pagination > li:last-child > span {
-  border-bottom-right-radius: 2px;
-  border-top-right-radius: 2px;
-}
-.pagination > li > a:hover,
-.pagination > li > span:hover,
-.pagination > li > a:focus,
-.pagination > li > span:focus {
-  z-index: 2;
-  color: #23527c;
-  background-color: #eeeeee;
-  border-color: #ddd;
-}
-.pagination > .active > a,
-.pagination > .active > span,
-.pagination > .active > a:hover,
-.pagination > .active > span:hover,
-.pagination > .active > a:focus,
-.pagination > .active > span:focus {
-  z-index: 3;
-  color: #fff;
-  background-color: #337ab7;
-  border-color: #337ab7;
-  cursor: default;
-}
-.pagination > .disabled > span,
-.pagination > .disabled > span:hover,
-.pagination > .disabled > span:focus,
-.pagination > .disabled > a,
-.pagination > .disabled > a:hover,
-.pagination > .disabled > a:focus {
-  color: #777777;
-  background-color: #fff;
-  border-color: #ddd;
-  cursor: not-allowed;
-}
-.pagination-lg > li > a,
-.pagination-lg > li > span {
-  padding: 10px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-}
-.pagination-lg > li:first-child > a,
-.pagination-lg > li:first-child > span {
-  border-bottom-left-radius: 3px;
-  border-top-left-radius: 3px;
-}
-.pagination-lg > li:last-child > a,
-.pagination-lg > li:last-child > span {
-  border-bottom-right-radius: 3px;
-  border-top-right-radius: 3px;
-}
-.pagination-sm > li > a,
-.pagination-sm > li > span {
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-}
-.pagination-sm > li:first-child > a,
-.pagination-sm > li:first-child > span {
-  border-bottom-left-radius: 1px;
-  border-top-left-radius: 1px;
-}
-.pagination-sm > li:last-child > a,
-.pagination-sm > li:last-child > span {
-  border-bottom-right-radius: 1px;
-  border-top-right-radius: 1px;
-}
-.pager {
-  padding-left: 0;
-  margin: 18px 0;
-  list-style: none;
-  text-align: center;
-}
-.pager li {
-  display: inline;
-}
-.pager li > a,
-.pager li > span {
-  display: inline-block;
-  padding: 5px 14px;
-  background-color: #fff;
-  border: 1px solid #ddd;
-  border-radius: 15px;
-}
-.pager li > a:hover,
-.pager li > a:focus {
-  text-decoration: none;
-  background-color: #eeeeee;
-}
-.pager .next > a,
-.pager .next > span {
-  float: right;
-}
-.pager .previous > a,
-.pager .previous > span {
-  float: left;
-}
-.pager .disabled > a,
-.pager .disabled > a:hover,
-.pager .disabled > a:focus,
-.pager .disabled > span {
-  color: #777777;
-  background-color: #fff;
-  cursor: not-allowed;
-}
-.label {
-  display: inline;
-  padding: .2em .6em .3em;
-  font-size: 75%;
-  font-weight: bold;
-  line-height: 1;
-  color: #fff;
-  text-align: center;
-  white-space: nowrap;
-  vertical-align: baseline;
-  border-radius: .25em;
-}
-a.label:hover,
-a.label:focus {
-  color: #fff;
-  text-decoration: none;
-  cursor: pointer;
-}
-.label:empty {
-  display: none;
-}
-.btn .label {
-  position: relative;
-  top: -1px;
-}
-.label-default {
-  background-color: #777777;
-}
-.label-default[href]:hover,
-.label-default[href]:focus {
-  background-color: #5e5e5e;
-}
-.label-primary {
-  background-color: #337ab7;
-}
-.label-primary[href]:hover,
-.label-primary[href]:focus {
-  background-color: #286090;
-}
-.label-success {
-  background-color: #5cb85c;
-}
-.label-success[href]:hover,
-.label-success[href]:focus {
-  background-color: #449d44;
-}
-.label-info {
-  background-color: #5bc0de;
-}
-.label-info[href]:hover,
-.label-info[href]:focus {
-  background-color: #31b0d5;
-}
-.label-warning {
-  background-color: #f0ad4e;
-}
-.label-warning[href]:hover,
-.label-warning[href]:focus {
-  background-color: #ec971f;
-}
-.label-danger {
-  background-color: #d9534f;
-}
-.label-danger[href]:hover,
-.label-danger[href]:focus {
-  background-color: #c9302c;
-}
-.badge {
-  display: inline-block;
-  min-width: 10px;
-  padding: 3px 7px;
-  font-size: 12px;
-  font-weight: bold;
-  color: #fff;
-  line-height: 1;
-  vertical-align: middle;
-  white-space: nowrap;
-  text-align: center;
-  background-color: #777777;
-  border-radius: 10px;
-}
-.badge:empty {
-  display: none;
-}
-.btn .badge {
-  position: relative;
-  top: -1px;
-}
-.btn-xs .badge,
-.btn-group-xs > .btn .badge {
-  top: 0;
-  padding: 1px 5px;
-}
-a.badge:hover,
-a.badge:focus {
-  color: #fff;
-  text-decoration: none;
-  cursor: pointer;
-}
-.list-group-item.active > .badge,
-.nav-pills > .active > a > .badge {
-  color: #337ab7;
-  background-color: #fff;
-}
-.list-group-item > .badge {
-  float: right;
-}
-.list-group-item > .badge + .badge {
-  margin-right: 5px;
-}
-.nav-pills > li > a > .badge {
-  margin-left: 3px;
-}
-.jumbotron {
-  padding-top: 30px;
-  padding-bottom: 30px;
-  margin-bottom: 30px;
-  color: inherit;
-  background-color: #eeeeee;
-}
-.jumbotron h1,
-.jumbotron .h1 {
-  color: inherit;
-}
-.jumbotron p {
-  margin-bottom: 15px;
-  font-size: 20px;
-  font-weight: 200;
-}
-.jumbotron > hr {
-  border-top-color: #d5d5d5;
-}
-.container .jumbotron,
-.container-fluid .jumbotron {
-  border-radius: 3px;
-  padding-left: 0px;
-  padding-right: 0px;
-}
-.jumbotron .container {
-  max-width: 100%;
-}
-@media screen and (min-width: 768px) {
-  .jumbotron {
-    padding-top: 48px;
-    padding-bottom: 48px;
-  }
-  .container .jumbotron,
-  .container-fluid .jumbotron {
-    padding-left: 60px;
-    padding-right: 60px;
-  }
-  .jumbotron h1,
-  .jumbotron .h1 {
-    font-size: 59px;
-  }
-}
-.thumbnail {
-  display: block;
-  padding: 4px;
-  margin-bottom: 18px;
-  line-height: 1.42857143;
-  background-color: #fff;
-  border: 1px solid #ddd;
-  border-radius: 2px;
-  -webkit-transition: border 0.2s ease-in-out;
-  -o-transition: border 0.2s ease-in-out;
-  transition: border 0.2s ease-in-out;
-}
-.thumbnail > img,
-.thumbnail a > img {
-  margin-left: auto;
-  margin-right: auto;
-}
-a.thumbnail:hover,
-a.thumbnail:focus,
-a.thumbnail.active {
-  border-color: #337ab7;
-}
-.thumbnail .caption {
-  padding: 9px;
-  color: #000;
-}
-.alert {
-  padding: 15px;
-  margin-bottom: 18px;
-  border: 1px solid transparent;
-  border-radius: 2px;
-}
-.alert h4 {
-  margin-top: 0;
-  color: inherit;
-}
-.alert .alert-link {
-  font-weight: bold;
-}
-.alert > p,
-.alert > ul {
-  margin-bottom: 0;
-}
-.alert > p + p {
-  margin-top: 5px;
-}
-.alert-dismissable,
-.alert-dismissible {
-  padding-right: 35px;
-}
-.alert-dismissable .close,
-.alert-dismissible .close {
-  position: relative;
-  top: -2px;
-  right: -21px;
-  color: inherit;
-}
-.alert-success {
-  background-color: #dff0d8;
-  border-color: #d6e9c6;
-  color: #3c763d;
-}
-.alert-success hr {
-  border-top-color: #c9e2b3;
-}
-.alert-success .alert-link {
-  color: #2b542c;
-}
-.alert-info {
-  background-color: #d9edf7;
-  border-color: #bce8f1;
-  color: #31708f;
-}
-.alert-info hr {
-  border-top-color: #a6e1ec;
-}
-.alert-info .alert-link {
-  color: #245269;
-}
-.alert-warning {
-  background-color: #fcf8e3;
-  border-color: #faebcc;
-  color: #8a6d3b;
-}
-.alert-warning hr {
-  border-top-color: #f7e1b5;
-}
-.alert-warning .alert-link {
-  color: #66512c;
-}
-.alert-danger {
-  background-color: #f2dede;
-  border-color: #ebccd1;
-  color: #a94442;
-}
-.alert-danger hr {
-  border-top-color: #e4b9c0;
-}
-.alert-danger .alert-link {
-  color: #843534;
-}
-@-webkit-keyframes progress-bar-stripes {
-  from {
-    background-position: 40px 0;
-  }
-  to {
-    background-position: 0 0;
-  }
-}
-@keyframes progress-bar-stripes {
-  from {
-    background-position: 40px 0;
-  }
-  to {
-    background-position: 0 0;
-  }
-}
-.progress {
-  overflow: hidden;
-  height: 18px;
-  margin-bottom: 18px;
-  background-color: #f5f5f5;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
-  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
-}
-.progress-bar {
-  float: left;
-  width: 0%;
-  height: 100%;
-  font-size: 12px;
-  line-height: 18px;
-  color: #fff;
-  text-align: center;
-  background-color: #337ab7;
-  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
-  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
-  -webkit-transition: width 0.6s ease;
-  -o-transition: width 0.6s ease;
-  transition: width 0.6s ease;
-}
-.progress-striped .progress-bar,
-.progress-bar-striped {
-  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-size: 40px 40px;
-}
-.progress.active .progress-bar,
-.progress-bar.active {
-  -webkit-animation: progress-bar-stripes 2s linear infinite;
-  -o-animation: progress-bar-stripes 2s linear infinite;
-  animation: progress-bar-stripes 2s linear infinite;
-}
-.progress-bar-success {
-  background-color: #5cb85c;
-}
-.progress-striped .progress-bar-success {
-  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-}
-.progress-bar-info {
-  background-color: #5bc0de;
-}
-.progress-striped .progress-bar-info {
-  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-}
-.progress-bar-warning {
-  background-color: #f0ad4e;
-}
-.progress-striped .progress-bar-warning {
-  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-}
-.progress-bar-danger {
-  background-color: #d9534f;
-}
-.progress-striped .progress-bar-danger {
-  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-}
-.media {
-  margin-top: 15px;
-}
-.media:first-child {
-  margin-top: 0;
-}
-.media,
-.media-body {
-  zoom: 1;
-  overflow: hidden;
-}
-.media-body {
-  width: 10000px;
-}
-.media-object {
-  display: block;
-}
-.media-object.img-thumbnail {
-  max-width: none;
-}
-.media-right,
-.media > .pull-right {
-  padding-left: 10px;
-}
-.media-left,
-.media > .pull-left {
-  padding-right: 10px;
-}
-.media-left,
-.media-right,
-.media-body {
-  display: table-cell;
-  vertical-align: top;
-}
-.media-middle {
-  vertical-align: middle;
-}
-.media-bottom {
-  vertical-align: bottom;
-}
-.media-heading {
-  margin-top: 0;
-  margin-bottom: 5px;
-}
-.media-list {
-  padding-left: 0;
-  list-style: none;
-}
-.list-group {
-  margin-bottom: 20px;
-  padding-left: 0;
-}
-.list-group-item {
-  position: relative;
-  display: block;
-  padding: 10px 15px;
-  margin-bottom: -1px;
-  background-color: #fff;
-  border: 1px solid #ddd;
-}
-.list-group-item:first-child {
-  border-top-right-radius: 2px;
-  border-top-left-radius: 2px;
-}
-.list-group-item:last-child {
-  margin-bottom: 0;
-  border-bottom-right-radius: 2px;
-  border-bottom-left-radius: 2px;
-}
-a.list-group-item,
-button.list-group-item {
-  color: #555;
-}
-a.list-group-item .list-group-item-heading,
-button.list-group-item .list-group-item-heading {
-  color: #333;
-}
-a.list-group-item:hover,
-button.list-group-item:hover,
-a.list-group-item:focus,
-button.list-group-item:focus {
-  text-decoration: none;
-  color: #555;
-  background-color: #f5f5f5;
-}
-button.list-group-item {
-  width: 100%;
-  text-align: left;
-}
-.list-group-item.disabled,
-.list-group-item.disabled:hover,
-.list-group-item.disabled:focus {
-  background-color: #eeeeee;
-  color: #777777;
-  cursor: not-allowed;
-}
-.list-group-item.disabled .list-group-item-heading,
-.list-group-item.disabled:hover .list-group-item-heading,
-.list-group-item.disabled:focus .list-group-item-heading {
-  color: inherit;
-}
-.list-group-item.disabled .list-group-item-text,
-.list-group-item.disabled:hover .list-group-item-text,
-.list-group-item.disabled:focus .list-group-item-text {
-  color: #777777;
-}
-.list-group-item.active,
-.list-group-item.active:hover,
-.list-group-item.active:focus {
-  z-index: 2;
-  color: #fff;
-  background-color: #337ab7;
-  border-color: #337ab7;
-}
-.list-group-item.active .list-group-item-heading,
-.list-group-item.active:hover .list-group-item-heading,
-.list-group-item.active:focus .list-group-item-heading,
-.list-group-item.active .list-group-item-heading > small,
-.list-group-item.active:hover .list-group-item-heading > small,
-.list-group-item.active:focus .list-group-item-heading > small,
-.list-group-item.active .list-group-item-heading > .small,
-.list-group-item.active:hover .list-group-item-heading > .small,
-.list-group-item.active:focus .list-group-item-heading > .small {
-  color: inherit;
-}
-.list-group-item.active .list-group-item-text,
-.list-group-item.active:hover .list-group-item-text,
-.list-group-item.active:focus .list-group-item-text {
-  color: #c7ddef;
-}
-.list-group-item-success {
-  color: #3c763d;
-  background-color: #dff0d8;
-}
-a.list-group-item-success,
-button.list-group-item-success {
-  color: #3c763d;
-}
-a.list-group-item-success .list-group-item-heading,
-button.list-group-item-success .list-group-item-heading {
-  color: inherit;
-}
-a.list-group-item-success:hover,
-button.list-group-item-success:hover,
-a.list-group-item-success:focus,
-button.list-group-item-success:focus {
-  color: #3c763d;
-  background-color: #d0e9c6;
-}
-a.list-group-item-success.active,
-button.list-group-item-success.active,
-a.list-group-item-success.active:hover,
-button.list-group-item-success.active:hover,
-a.list-group-item-success.active:focus,
-button.list-group-item-success.active:focus {
-  color: #fff;
-  background-color: #3c763d;
-  border-color: #3c763d;
-}
-.list-group-item-info {
-  color: #31708f;
-  background-color: #d9edf7;
-}
-a.list-group-item-info,
-button.list-group-item-info {
-  color: #31708f;
-}
-a.list-group-item-info .list-group-item-heading,
-button.list-group-item-info .list-group-item-heading {
-  color: inherit;
-}
-a.list-group-item-info:hover,
-button.list-group-item-info:hover,
-a.list-group-item-info:focus,
-button.list-group-item-info:focus {
-  color: #31708f;
-  background-color: #c4e3f3;
-}
-a.list-group-item-info.active,
-button.list-group-item-info.active,
-a.list-group-item-info.active:hover,
-button.list-group-item-info.active:hover,
-a.list-group-item-info.active:focus,
-button.list-group-item-info.active:focus {
-  color: #fff;
-  background-color: #31708f;
-  border-color: #31708f;
-}
-.list-group-item-warning {
-  color: #8a6d3b;
-  background-color: #fcf8e3;
-}
-a.list-group-item-warning,
-button.list-group-item-warning {
-  color: #8a6d3b;
-}
-a.list-group-item-warning .list-group-item-heading,
-button.list-group-item-warning .list-group-item-heading {
-  color: inherit;
-}
-a.list-group-item-warning:hover,
-button.list-group-item-warning:hover,
-a.list-group-item-warning:focus,
-button.list-group-item-warning:focus {
-  color: #8a6d3b;
-  background-color: #faf2cc;
-}
-a.list-group-item-warning.active,
-button.list-group-item-warning.active,
-a.list-group-item-warning.active:hover,
-button.list-group-item-warning.active:hover,
-a.list-group-item-warning.active:focus,
-button.list-group-item-warning.active:focus {
-  color: #fff;
-  background-color: #8a6d3b;
-  border-color: #8a6d3b;
-}
-.list-group-item-danger {
-  color: #a94442;
-  background-color: #f2dede;
-}
-a.list-group-item-danger,
-button.list-group-item-danger {
-  color: #a94442;
-}
-a.list-group-item-danger .list-group-item-heading,
-button.list-group-item-danger .list-group-item-heading {
-  color: inherit;
-}
-a.list-group-item-danger:hover,
-button.list-group-item-danger:hover,
-a.list-group-item-danger:focus,
-button.list-group-item-danger:focus {
-  color: #a94442;
-  background-color: #ebcccc;
-}
-a.list-group-item-danger.active,
-button.list-group-item-danger.active,
-a.list-group-item-danger.active:hover,
-button.list-group-item-danger.active:hover,
-a.list-group-item-danger.active:focus,
-button.list-group-item-danger.active:focus {
-  color: #fff;
-  background-color: #a94442;
-  border-color: #a94442;
-}
-.list-group-item-heading {
-  margin-top: 0;
-  margin-bottom: 5px;
-}
-.list-group-item-text {
-  margin-bottom: 0;
-  line-height: 1.3;
-}
-.panel {
-  margin-bottom: 18px;
-  background-color: #fff;
-  border: 1px solid transparent;
-  border-radius: 2px;
-  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
-  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
-}
-.panel-body {
-  padding: 15px;
-}
-.panel-heading {
-  padding: 10px 15px;
-  border-bottom: 1px solid transparent;
-  border-top-right-radius: 1px;
-  border-top-left-radius: 1px;
-}
-.panel-heading > .dropdown .dropdown-toggle {
-  color: inherit;
-}
-.panel-title {
-  margin-top: 0;
-  margin-bottom: 0;
-  font-size: 15px;
-  color: inherit;
-}
-.panel-title > a,
-.panel-title > small,
-.panel-title > .small,
-.panel-title > small > a,
-.panel-title > .small > a {
-  color: inherit;
-}
-.panel-footer {
-  padding: 10px 15px;
-  background-color: #f5f5f5;
-  border-top: 1px solid #ddd;
-  border-bottom-right-radius: 1px;
-  border-bottom-left-radius: 1px;
-}
-.panel > .list-group,
-.panel > .panel-collapse > .list-group {
-  margin-bottom: 0;
-}
-.panel > .list-group .list-group-item,
-.panel > .panel-collapse > .list-group .list-group-item {
-  border-width: 1px 0;
-  border-radius: 0;
-}
-.panel > .list-group:first-child .list-group-item:first-child,
-.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
-  border-top: 0;
-  border-top-right-radius: 1px;
-  border-top-left-radius: 1px;
-}
-.panel > .list-group:last-child .list-group-item:last-child,
-.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
-  border-bottom: 0;
-  border-bottom-right-radius: 1px;
-  border-bottom-left-radius: 1px;
-}
-.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
-  border-top-right-radius: 0;
-  border-top-left-radius: 0;
-}
-.panel-heading + .list-group .list-group-item:first-child {
-  border-top-width: 0;
-}
-.list-group + .panel-footer {
-  border-top-width: 0;
-}
-.panel > .table,
-.panel > .table-responsive > .table,
-.panel > .panel-collapse > .table {
-  margin-bottom: 0;
-}
-.panel > .table caption,
-.panel > .table-responsive > .table caption,
-.panel > .panel-collapse > .table caption {
-  padding-left: 15px;
-  padding-right: 15px;
-}
-.panel > .table:first-child,
-.panel > .table-responsive:first-child > .table:first-child {
-  border-top-right-radius: 1px;
-  border-top-left-radius: 1px;
-}
-.panel > .table:first-child > thead:first-child > tr:first-child,
-.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
-.panel > .table:first-child > tbody:first-child > tr:first-child,
-.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
-  border-top-left-radius: 1px;
-  border-top-right-radius: 1px;
-}
-.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
-.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
-.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
-.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
-.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
-.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
-.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
-.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
-  border-top-left-radius: 1px;
-}
-.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
-.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
-.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
-.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
-.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
-.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
-.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
-.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
-  border-top-right-radius: 1px;
-}
-.panel > .table:last-child,
-.panel > .table-responsive:last-child > .table:last-child {
-  border-bottom-right-radius: 1px;
-  border-bottom-left-radius: 1px;
-}
-.panel > .table:last-child > tbody:last-child > tr:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
-.panel > .table:last-child > tfoot:last-child > tr:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
-  border-bottom-left-radius: 1px;
-  border-bottom-right-radius: 1px;
-}
-.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
-.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
-.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
-.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
-.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
-.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
-.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
-.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
-  border-bottom-left-radius: 1px;
-}
-.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
-.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
-.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
-.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
-  border-bottom-right-radius: 1px;
-}
-.panel > .panel-body + .table,
-.panel > .panel-body + .table-responsive,
-.panel > .table + .panel-body,
-.panel > .table-responsive + .panel-body {
-  border-top: 1px solid #ddd;
-}
-.panel > .table > tbody:first-child > tr:first-child th,
-.panel > .table > tbody:first-child > tr:first-child td {
-  border-top: 0;
-}
-.panel > .table-bordered,
-.panel > .table-responsive > .table-bordered {
-  border: 0;
-}
-.panel > .table-bordered > thead > tr > th:first-child,
-.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
-.panel > .table-bordered > tbody > tr > th:first-child,
-.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
-.panel > .table-bordered > tfoot > tr > th:first-child,
-.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
-.panel > .table-bordered > thead > tr > td:first-child,
-.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
-.panel > .table-bordered > tbody > tr > td:first-child,
-.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
-.panel > .table-bordered > tfoot > tr > td:first-child,
-.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
-  border-left: 0;
-}
-.panel > .table-bordered > thead > tr > th:last-child,
-.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
-.panel > .table-bordered > tbody > tr > th:last-child,
-.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
-.panel > .table-bordered > tfoot > tr > th:last-child,
-.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
-.panel > .table-bordered > thead > tr > td:last-child,
-.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
-.panel > .table-bordered > tbody > tr > td:last-child,
-.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
-.panel > .table-bordered > tfoot > tr > td:last-child,
-.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
-  border-right: 0;
-}
-.panel > .table-bordered > thead > tr:first-child > td,
-.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
-.panel > .table-bordered > tbody > tr:first-child > td,
-.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
-.panel > .table-bordered > thead > tr:first-child > th,
-.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
-.panel > .table-bordered > tbody > tr:first-child > th,
-.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
-  border-bottom: 0;
-}
-.panel > .table-bordered > tbody > tr:last-child > td,
-.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
-.panel > .table-bordered > tfoot > tr:last-child > td,
-.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
-.panel > .table-bordered > tbody > tr:last-child > th,
-.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
-.panel > .table-bordered > tfoot > tr:last-child > th,
-.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
-  border-bottom: 0;
-}
-.panel > .table-responsive {
-  border: 0;
-  margin-bottom: 0;
-}
-.panel-group {
-  margin-bottom: 18px;
-}
-.panel-group .panel {
-  margin-bottom: 0;
-  border-radius: 2px;
-}
-.panel-group .panel + .panel {
-  margin-top: 5px;
-}
-.panel-group .panel-heading {
-  border-bottom: 0;
-}
-.panel-group .panel-heading + .panel-collapse > .panel-body,
-.panel-group .panel-heading + .panel-collapse > .list-group {
-  border-top: 1px solid #ddd;
-}
-.panel-group .panel-footer {
-  border-top: 0;
-}
-.panel-group .panel-footer + .panel-collapse .panel-body {
-  border-bottom: 1px solid #ddd;
-}
-.panel-default {
-  border-color: #ddd;
-}
-.panel-default > .panel-heading {
-  color: #333333;
-  background-color: #f5f5f5;
-  border-color: #ddd;
-}
-.panel-default > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #ddd;
-}
-.panel-default > .panel-heading .badge {
-  color: #f5f5f5;
-  background-color: #333333;
-}
-.panel-default > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #ddd;
-}
-.panel-primary {
-  border-color: #337ab7;
-}
-.panel-primary > .panel-heading {
-  color: #fff;
-  background-color: #337ab7;
-  border-color: #337ab7;
-}
-.panel-primary > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #337ab7;
-}
-.panel-primary > .panel-heading .badge {
-  color: #337ab7;
-  background-color: #fff;
-}
-.panel-primary > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #337ab7;
-}
-.panel-success {
-  border-color: #d6e9c6;
-}
-.panel-success > .panel-heading {
-  color: #3c763d;
-  background-color: #dff0d8;
-  border-color: #d6e9c6;
-}
-.panel-success > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #d6e9c6;
-}
-.panel-success > .panel-heading .badge {
-  color: #dff0d8;
-  background-color: #3c763d;
-}
-.panel-success > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #d6e9c6;
-}
-.panel-info {
-  border-color: #bce8f1;
-}
-.panel-info > .panel-heading {
-  color: #31708f;
-  background-color: #d9edf7;
-  border-color: #bce8f1;
-}
-.panel-info > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #bce8f1;
-}
-.panel-info > .panel-heading .badge {
-  color: #d9edf7;
-  background-color: #31708f;
-}
-.panel-info > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #bce8f1;
-}
-.panel-warning {
-  border-color: #faebcc;
-}
-.panel-warning > .panel-heading {
-  color: #8a6d3b;
-  background-color: #fcf8e3;
-  border-color: #faebcc;
-}
-.panel-warning > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #faebcc;
-}
-.panel-warning > .panel-heading .badge {
-  color: #fcf8e3;
-  background-color: #8a6d3b;
-}
-.panel-warning > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #faebcc;
-}
-.panel-danger {
-  border-color: #ebccd1;
-}
-.panel-danger > .panel-heading {
-  color: #a94442;
-  background-color: #f2dede;
-  border-color: #ebccd1;
-}
-.panel-danger > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #ebccd1;
-}
-.panel-danger > .panel-heading .badge {
-  color: #f2dede;
-  background-color: #a94442;
-}
-.panel-danger > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #ebccd1;
-}
-.embed-responsive {
-  position: relative;
-  display: block;
-  height: 0;
-  padding: 0;
-  overflow: hidden;
-}
-.embed-responsive .embed-responsive-item,
-.embed-responsive iframe,
-.embed-responsive embed,
-.embed-responsive object,
-.embed-responsive video {
-  position: absolute;
-  top: 0;
-  left: 0;
-  bottom: 0;
-  height: 100%;
-  width: 100%;
-  border: 0;
-}
-.embed-responsive-16by9 {
-  padding-bottom: 56.25%;
-}
-.embed-responsive-4by3 {
-  padding-bottom: 75%;
-}
-.well {
-  min-height: 20px;
-  padding: 19px;
-  margin-bottom: 20px;
-  background-color: #f5f5f5;
-  border: 1px solid #e3e3e3;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
-}
-.well blockquote {
-  border-color: #ddd;
-  border-color: rgba(0, 0, 0, 0.15);
-}
-.well-lg {
-  padding: 24px;
-  border-radius: 3px;
-}
-.well-sm {
-  padding: 9px;
-  border-radius: 1px;
-}
-.close {
-  float: right;
-  font-size: 19.5px;
-  font-weight: bold;
-  line-height: 1;
-  color: #000;
-  text-shadow: 0 1px 0 #fff;
-  opacity: 0.2;
-  filter: alpha(opacity=20);
-}
-.close:hover,
-.close:focus {
-  color: #000;
-  text-decoration: none;
-  cursor: pointer;
-  opacity: 0.5;
-  filter: alpha(opacity=50);
-}
-button.close {
-  padding: 0;
-  cursor: pointer;
-  background: transparent;
-  border: 0;
-  -webkit-appearance: none;
-}
-.modal-open {
-  overflow: hidden;
-}
-.modal {
-  display: none;
-  overflow: hidden;
-  position: fixed;
-  top: 0;
-  right: 0;
-  bottom: 0;
-  left: 0;
-  z-index: 1050;
-  -webkit-overflow-scrolling: touch;
-  outline: 0;
-}
-.modal.fade .modal-dialog {
-  -webkit-transform: translate(0, -25%);
-  -ms-transform: translate(0, -25%);
-  -o-transform: translate(0, -25%);
-  transform: translate(0, -25%);
-  -webkit-transition: -webkit-transform 0.3s ease-out;
-  -moz-transition: -moz-transform 0.3s ease-out;
-  -o-transition: -o-transform 0.3s ease-out;
-  transition: transform 0.3s ease-out;
-}
-.modal.in .modal-dialog {
-  -webkit-transform: translate(0, 0);
-  -ms-transform: translate(0, 0);
-  -o-transform: translate(0, 0);
-  transform: translate(0, 0);
-}
-.modal-open .modal {
-  overflow-x: hidden;
-  overflow-y: auto;
-}
-.modal-dialog {
-  position: relative;
-  width: auto;
-  margin: 10px;
-}
-.modal-content {
-  position: relative;
-  background-color: #fff;
-  border: 1px solid #999;
-  border: 1px solid rgba(0, 0, 0, 0.2);
-  border-radius: 3px;
-  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
-  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
-  background-clip: padding-box;
-  outline: 0;
-}
-.modal-backdrop {
-  position: fixed;
-  top: 0;
-  right: 0;
-  bottom: 0;
-  left: 0;
-  z-index: 1040;
-  background-color: #000;
-}
-.modal-backdrop.fade {
-  opacity: 0;
-  filter: alpha(opacity=0);
-}
-.modal-backdrop.in {
-  opacity: 0.5;
-  filter: alpha(opacity=50);
-}
-.modal-header {
-  padding: 15px;
-  border-bottom: 1px solid #e5e5e5;
-}
-.modal-header .close {
-  margin-top: -2px;
-}
-.modal-title {
-  margin: 0;
-  line-height: 1.42857143;
-}
-.modal-body {
-  position: relative;
-  padding: 15px;
-}
-.modal-footer {
-  padding: 15px;
-  text-align: right;
-  border-top: 1px solid #e5e5e5;
-}
-.modal-footer .btn + .btn {
-  margin-left: 5px;
-  margin-bottom: 0;
-}
-.modal-footer .btn-group .btn + .btn {
-  margin-left: -1px;
-}
-.modal-footer .btn-block + .btn-block {
-  margin-left: 0;
-}
-.modal-scrollbar-measure {
-  position: absolute;
-  top: -9999px;
-  width: 50px;
-  height: 50px;
-  overflow: scroll;
-}
-@media (min-width: 768px) {
-  .modal-dialog {
-    width: 600px;
-    margin: 30px auto;
-  }
-  .modal-content {
-    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
-    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
-  }
-  .modal-sm {
-    width: 300px;
-  }
-}
-@media (min-width: 992px) {
-  .modal-lg {
-    width: 900px;
-  }
-}
-.tooltip {
-  position: absolute;
-  z-index: 1070;
-  display: block;
-  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
-  font-style: normal;
-  font-weight: normal;
-  letter-spacing: normal;
-  line-break: auto;
-  line-height: 1.42857143;
-  text-align: left;
-  text-align: start;
-  text-decoration: none;
-  text-shadow: none;
-  text-transform: none;
-  white-space: normal;
-  word-break: normal;
-  word-spacing: normal;
-  word-wrap: normal;
-  font-size: 12px;
-  opacity: 0;
-  filter: alpha(opacity=0);
-}
-.tooltip.in {
-  opacity: 0.9;
-  filter: alpha(opacity=90);
-}
-.tooltip.top {
-  margin-top: -3px;
-  padding: 5px 0;
-}
-.tooltip.right {
-  margin-left: 3px;
-  padding: 0 5px;
-}
-.tooltip.bottom {
-  margin-top: 3px;
-  padding: 5px 0;
-}
-.tooltip.left {
-  margin-left: -3px;
-  padding: 0 5px;
-}
-.tooltip-inner {
-  max-width: 200px;
-  padding: 3px 8px;
-  color: #fff;
-  text-align: center;
-  background-color: #000;
-  border-radius: 2px;
-}
-.tooltip-arrow {
-  position: absolute;
-  width: 0;
-  height: 0;
-  border-color: transparent;
-  border-style: solid;
-}
-.tooltip.top .tooltip-arrow {
-  bottom: 0;
-  left: 50%;
-  margin-left: -5px;
-  border-width: 5px 5px 0;
-  border-top-color: #000;
-}
-.tooltip.top-left .tooltip-arrow {
-  bottom: 0;
-  right: 5px;
-  margin-bottom: -5px;
-  border-width: 5px 5px 0;
-  border-top-color: #000;
-}
-.tooltip.top-right .tooltip-arrow {
-  bottom: 0;
-  left: 5px;
-  margin-bottom: -5px;
-  border-width: 5px 5px 0;
-  border-top-color: #000;
-}
-.tooltip.right .tooltip-arrow {
-  top: 50%;
-  left: 0;
-  margin-top: -5px;
-  border-width: 5px 5px 5px 0;
-  border-right-color: #000;
-}
-.tooltip.left .tooltip-arrow {
-  top: 50%;
-  right: 0;
-  margin-top: -5px;
-  border-width: 5px 0 5px 5px;
-  border-left-color: #000;
-}
-.tooltip.bottom .tooltip-arrow {
-  top: 0;
-  left: 50%;
-  margin-left: -5px;
-  border-width: 0 5px 5px;
-  border-bottom-color: #000;
-}
-.tooltip.bottom-left .tooltip-arrow {
-  top: 0;
-  right: 5px;
-  margin-top: -5px;
-  border-width: 0 5px 5px;
-  border-bottom-color: #000;
-}
-.tooltip.bottom-right .tooltip-arrow {
-  top: 0;
-  left: 5px;
-  margin-top: -5px;
-  border-width: 0 5px 5px;
-  border-bottom-color: #000;
-}
-.popover {
-  position: absolute;
-  top: 0;
-  left: 0;
-  z-index: 1060;
-  display: none;
-  max-width: 276px;
-  padding: 1px;
-  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
-  font-style: normal;
-  font-weight: normal;
-  letter-spacing: normal;
-  line-break: auto;
-  line-height: 1.42857143;
-  text-align: left;
-  text-align: start;
-  text-decoration: none;
-  text-shadow: none;
-  text-transform: none;
-  white-space: normal;
-  word-break: normal;
-  word-spacing: normal;
-  word-wrap: normal;
-  font-size: 13px;
-  background-color: #fff;
-  background-clip: padding-box;
-  border: 1px solid #ccc;
-  border: 1px solid rgba(0, 0, 0, 0.2);
-  border-radius: 3px;
-  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
-  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
-}
-.popover.top {
-  margin-top: -10px;
-}
-.popover.right {
-  margin-left: 10px;
-}
-.popover.bottom {
-  margin-top: 10px;
-}
-.popover.left {
-  margin-left: -10px;
-}
-.popover-title {
-  margin: 0;
-  padding: 8px 14px;
-  font-size: 13px;
-  background-color: #f7f7f7;
-  border-bottom: 1px solid #ebebeb;
-  border-radius: 2px 2px 0 0;
-}
-.popover-content {
-  padding: 9px 14px;
-}
-.popover > .arrow,
-.popover > .arrow:after {
-  position: absolute;
-  display: block;
-  width: 0;
-  height: 0;
-  border-color: transparent;
-  border-style: solid;
-}
-.popover > .arrow {
-  border-width: 11px;
-}
-.popover > .arrow:after {
-  border-width: 10px;
-  content: "";
-}
-.popover.top > .arrow {
-  left: 50%;
-  margin-left: -11px;
-  border-bottom-width: 0;
-  border-top-color: #999999;
-  border-top-color: rgba(0, 0, 0, 0.25);
-  bottom: -11px;
-}
-.popover.top > .arrow:after {
-  content: " ";
-  bottom: 1px;
-  margin-left: -10px;
-  border-bottom-width: 0;
-  border-top-color: #fff;
-}
-.popover.right > .arrow {
-  top: 50%;
-  left: -11px;
-  margin-top: -11px;
-  border-left-width: 0;
-  border-right-color: #999999;
-  border-right-color: rgba(0, 0, 0, 0.25);
-}
-.popover.right > .arrow:after {
-  content: " ";
-  left: 1px;
-  bottom: -10px;
-  border-left-width: 0;
-  border-right-color: #fff;
-}
-.popover.bottom > .arrow {
-  left: 50%;
-  margin-left: -11px;
-  border-top-width: 0;
-  border-bottom-color: #999999;
-  border-bottom-color: rgba(0, 0, 0, 0.25);
-  top: -11px;
-}
-.popover.bottom > .arrow:after {
-  content: " ";
-  top: 1px;
-  margin-left: -10px;
-  border-top-width: 0;
-  border-bottom-color: #fff;
-}
-.popover.left > .arrow {
-  top: 50%;
-  right: -11px;
-  margin-top: -11px;
-  border-right-width: 0;
-  border-left-color: #999999;
-  border-left-color: rgba(0, 0, 0, 0.25);
-}
-.popover.left > .arrow:after {
-  content: " ";
-  right: 1px;
-  border-right-width: 0;
-  border-left-color: #fff;
-  bottom: -10px;
-}
-.carousel {
-  position: relative;
-}
-.carousel-inner {
-  position: relative;
-  overflow: hidden;
-  width: 100%;
-}
-.carousel-inner > .item {
-  display: none;
-  position: relative;
-  -webkit-transition: 0.6s ease-in-out left;
-  -o-transition: 0.6s ease-in-out left;
-  transition: 0.6s ease-in-out left;
-}
-.carousel-inner > .item > img,
-.carousel-inner > .item > a > img {
-  line-height: 1;
-}
-@media all and (transform-3d), (-webkit-transform-3d) {
-  .carousel-inner > .item {
-    -webkit-transition: -webkit-transform 0.6s ease-in-out;
-    -moz-transition: -moz-transform 0.6s ease-in-out;
-    -o-transition: -o-transform 0.6s ease-in-out;
-    transition: transform 0.6s ease-in-out;
-    -webkit-backface-visibility: hidden;
-    -moz-backface-visibility: hidden;
-    backface-visibility: hidden;
-    -webkit-perspective: 1000px;
-    -moz-perspective: 1000px;
-    perspective: 1000px;
-  }
-  .carousel-inner > .item.next,
-  .carousel-inner > .item.active.right {
-    -webkit-transform: translate3d(100%, 0, 0);
-    transform: translate3d(100%, 0, 0);
-    left: 0;
-  }
-  .carousel-inner > .item.prev,
-  .carousel-inner > .item.active.left {
-    -webkit-transform: translate3d(-100%, 0, 0);
-    transform: translate3d(-100%, 0, 0);
-    left: 0;
-  }
-  .carousel-inner > .item.next.left,
-  .carousel-inner > .item.prev.right,
-  .carousel-inner > .item.active {
-    -webkit-transform: translate3d(0, 0, 0);
-    transform: translate3d(0, 0, 0);
-    left: 0;
-  }
-}
-.carousel-inner > .active,
-.carousel-inner > .next,
-.carousel-inner > .prev {
-  display: block;
-}
-.carousel-inner > .active {
-  left: 0;
-}
-.carousel-inner > .next,
-.carousel-inner > .prev {
-  position: absolute;
-  top: 0;
-  width: 100%;
-}
-.carousel-inner > .next {
-  left: 100%;
-}
-.carousel-inner > .prev {
-  left: -100%;
-}
-.carousel-inner > .next.left,
-.carousel-inner > .prev.right {
-  left: 0;
-}
-.carousel-inner > .active.left {
-  left: -100%;
-}
-.carousel-inner > .active.right {
-  left: 100%;
-}
-.carousel-control {
-  position: absolute;
-  top: 0;
-  left: 0;
-  bottom: 0;
-  width: 15%;
-  opacity: 0.5;
-  filter: alpha(opacity=50);
-  font-size: 20px;
-  color: #fff;
-  text-align: center;
-  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
-  background-color: rgba(0, 0, 0, 0);
-}
-.carousel-control.left {
-  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
-  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
-  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
-  background-repeat: repeat-x;
-  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
-}
-.carousel-control.right {
-  left: auto;
-  right: 0;
-  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
-  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
-  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
-  background-repeat: repeat-x;
-  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
-}
-.carousel-control:hover,
-.carousel-control:focus {
-  outline: 0;
-  color: #fff;
-  text-decoration: none;
-  opacity: 0.9;
-  filter: alpha(opacity=90);
-}
-.carousel-control .icon-prev,
-.carousel-control .icon-next,
-.carousel-control .glyphicon-chevron-left,
-.carousel-control .glyphicon-chevron-right {
-  position: absolute;
-  top: 50%;
-  margin-top: -10px;
-  z-index: 5;
-  display: inline-block;
-}
-.carousel-control .icon-prev,
-.carousel-control .glyphicon-chevron-left {
-  left: 50%;
-  margin-left: -10px;
-}
-.carousel-control .icon-next,
-.carousel-control .glyphicon-chevron-right {
-  right: 50%;
-  margin-right: -10px;
-}
-.carousel-control .icon-prev,
-.carousel-control .icon-next {
-  width: 20px;
-  height: 20px;
-  line-height: 1;
-  font-family: serif;
-}
-.carousel-control .icon-prev:before {
-  content: '\2039';
-}
-.carousel-control .icon-next:before {
-  content: '\203a';
-}
-.carousel-indicators {
-  position: absolute;
-  bottom: 10px;
-  left: 50%;
-  z-index: 15;
-  width: 60%;
-  margin-left: -30%;
-  padding-left: 0;
-  list-style: none;
-  text-align: center;
-}
-.carousel-indicators li {
-  display: inline-block;
-  width: 10px;
-  height: 10px;
-  margin: 1px;
-  text-indent: -999px;
-  border: 1px solid #fff;
-  border-radius: 10px;
-  cursor: pointer;
-  background-color: #000 \9;
-  background-color: rgba(0, 0, 0, 0);
-}
-.carousel-indicators .active {
-  margin: 0;
-  width: 12px;
-  height: 12px;
-  background-color: #fff;
-}
-.carousel-caption {
-  position: absolute;
-  left: 15%;
-  right: 15%;
-  bottom: 20px;
-  z-index: 10;
-  padding-top: 20px;
-  padding-bottom: 20px;
-  color: #fff;
-  text-align: center;
-  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
-}
-.carousel-caption .btn {
-  text-shadow: none;
-}
-@media screen and (min-width: 768px) {
-  .carousel-control .glyphicon-chevron-left,
-  .carousel-control .glyphicon-chevron-right,
-  .carousel-control .icon-prev,
-  .carousel-control .icon-next {
-    width: 30px;
-    height: 30px;
-    margin-top: -10px;
-    font-size: 30px;
-  }
-  .carousel-control .glyphicon-chevron-left,
-  .carousel-control .icon-prev {
-    margin-left: -10px;
-  }
-  .carousel-control .glyphicon-chevron-right,
-  .carousel-control .icon-next {
-    margin-right: -10px;
-  }
-  .carousel-caption {
-    left: 20%;
-    right: 20%;
-    padding-bottom: 30px;
-  }
-  .carousel-indicators {
-    bottom: 20px;
-  }
-}
-.clearfix:before,
-.clearfix:after,
-.dl-horizontal dd:before,
-.dl-horizontal dd:after,
-.container:before,
-.container:after,
-.container-fluid:before,
-.container-fluid:after,
-.row:before,
-.row:after,
-.form-horizontal .form-group:before,
-.form-horizontal .form-group:after,
-.btn-toolbar:before,
-.btn-toolbar:after,
-.btn-group-vertical > .btn-group:before,
-.btn-group-vertical > .btn-group:after,
-.nav:before,
-.nav:after,
-.navbar:before,
-.navbar:after,
-.navbar-header:before,
-.navbar-header:after,
-.navbar-collapse:before,
-.navbar-collapse:after,
-.pager:before,
-.pager:after,
-.panel-body:before,
-.panel-body:after,
-.modal-header:before,
-.modal-header:after,
-.modal-footer:before,
-.modal-footer:after,
-.item_buttons:before,
-.item_buttons:after {
-  content: " ";
-  display: table;
-}
-.clearfix:after,
-.dl-horizontal dd:after,
-.container:after,
-.container-fluid:after,
-.row:after,
-.form-horizontal .form-group:after,
-.btn-toolbar:after,
-.btn-group-vertical > .btn-group:after,
-.nav:after,
-.navbar:after,
-.navbar-header:after,
-.navbar-collapse:after,
-.pager:after,
-.panel-body:after,
-.modal-header:after,
-.modal-footer:after,
-.item_buttons:after {
-  clear: both;
-}
-.center-block {
-  display: block;
-  margin-left: auto;
-  margin-right: auto;
-}
-.pull-right {
-  float: right !important;
-}
-.pull-left {
-  float: left !important;
-}
-.hide {
-  display: none !important;
-}
-.show {
-  display: block !important;
-}
-.invisible {
-  visibility: hidden;
-}
-.text-hide {
-  font: 0/0 a;
-  color: transparent;
-  text-shadow: none;
-  background-color: transparent;
-  border: 0;
-}
-.hidden {
-  display: none !important;
-}
-.affix {
-  position: fixed;
-}
-@-ms-viewport {
-  width: device-width;
-}
-.visible-xs,
-.visible-sm,
-.visible-md,
-.visible-lg {
-  display: none !important;
-}
-.visible-xs-block,
-.visible-xs-inline,
-.visible-xs-inline-block,
-.visible-sm-block,
-.visible-sm-inline,
-.visible-sm-inline-block,
-.visible-md-block,
-.visible-md-inline,
-.visible-md-inline-block,
-.visible-lg-block,
-.visible-lg-inline,
-.visible-lg-inline-block {
-  display: none !important;
-}
-@media (max-width: 767px) {
-  .visible-xs {
-    display: block !important;
-  }
-  table.visible-xs {
-    display: table !important;
-  }
-  tr.visible-xs {
-    display: table-row !important;
-  }
-  th.visible-xs,
-  td.visible-xs {
-    display: table-cell !important;
-  }
-}
-@media (max-width: 767px) {
-  .visible-xs-block {
-    display: block !important;
-  }
-}
-@media (max-width: 767px) {
-  .visible-xs-inline {
-    display: inline !important;
-  }
-}
-@media (max-width: 767px) {
-  .visible-xs-inline-block {
-    display: inline-block !important;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  .visible-sm {
-    display: block !important;
-  }
-  table.visible-sm {
-    display: table !important;
-  }
-  tr.visible-sm {
-    display: table-row !important;
-  }
-  th.visible-sm,
-  td.visible-sm {
-    display: table-cell !important;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  .visible-sm-block {
-    display: block !important;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  .visible-sm-inline {
-    display: inline !important;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  .visible-sm-inline-block {
-    display: inline-block !important;
-  }
-}
-@media (min-width: 992px) and (max-width: 1199px) {
-  .visible-md {
-    display: block !important;
-  }
-  table.visible-md {
-    display: table !important;
-  }
-  tr.visible-md {
-    display: table-row !important;
-  }
-  th.visible-md,
-  td.visible-md {
-    display: table-cell !important;
-  }
-}
-@media (min-width: 992px) and (max-width: 1199px) {
-  .visible-md-block {
-    display: block !important;
-  }
-}
-@media (min-width: 992px) and (max-width: 1199px) {
-  .visible-md-inline {
-    display: inline !important;
-  }
-}
-@media (min-width: 992px) and (max-width: 1199px) {
-  .visible-md-inline-block {
-    display: inline-block !important;
-  }
-}
-@media (min-width: 1200px) {
-  .visible-lg {
-    display: block !important;
-  }
-  table.visible-lg {
-    display: table !important;
-  }
-  tr.visible-lg {
-    display: table-row !important;
-  }
-  th.visible-lg,
-  td.visible-lg {
-    display: table-cell !important;
-  }
-}
-@media (min-width: 1200px) {
-  .visible-lg-block {
-    display: block !important;
-  }
-}
-@media (min-width: 1200px) {
-  .visible-lg-inline {
-    display: inline !important;
-  }
-}
-@media (min-width: 1200px) {
-  .visible-lg-inline-block {
-    display: inline-block !important;
-  }
-}
-@media (max-width: 767px) {
-  .hidden-xs {
-    display: none !important;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  .hidden-sm {
-    display: none !important;
-  }
-}
-@media (min-width: 992px) and (max-width: 1199px) {
-  .hidden-md {
-    display: none !important;
-  }
-}
-@media (min-width: 1200px) {
-  .hidden-lg {
-    display: none !important;
-  }
-}
-.visible-print {
-  display: none !important;
-}
-@media print {
-  .visible-print {
-    display: block !important;
-  }
-  table.visible-print {
-    display: table !important;
-  }
-  tr.visible-print {
-    display: table-row !important;
-  }
-  th.visible-print,
-  td.visible-print {
-    display: table-cell !important;
-  }
-}
-.visible-print-block {
-  display: none !important;
-}
-@media print {
-  .visible-print-block {
-    display: block !important;
-  }
-}
-.visible-print-inline {
-  display: none !important;
-}
-@media print {
-  .visible-print-inline {
-    display: inline !important;
-  }
-}
-.visible-print-inline-block {
-  display: none !important;
-}
-@media print {
-  .visible-print-inline-block {
-    display: inline-block !important;
-  }
-}
-@media print {
-  .hidden-print {
-    display: none !important;
-  }
-}
-/*!
-*
-* Font Awesome
-*
-*/
-/*!
- *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
- *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
- */
-/* FONT PATH
- * -------------------------- */
-@font-face {
-  font-family: 'FontAwesome';
-  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
-  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
-  font-weight: normal;
-  font-style: normal;
-}
-.fa {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-/* makes the font 33% larger relative to the icon container */
-.fa-lg {
-  font-size: 1.33333333em;
-  line-height: 0.75em;
-  vertical-align: -15%;
-}
-.fa-2x {
-  font-size: 2em;
-}
-.fa-3x {
-  font-size: 3em;
-}
-.fa-4x {
-  font-size: 4em;
-}
-.fa-5x {
-  font-size: 5em;
-}
-.fa-fw {
-  width: 1.28571429em;
-  text-align: center;
-}
-.fa-ul {
-  padding-left: 0;
-  margin-left: 2.14285714em;
-  list-style-type: none;
-}
-.fa-ul > li {
-  position: relative;
-}
-.fa-li {
-  position: absolute;
-  left: -2.14285714em;
-  width: 2.14285714em;
-  top: 0.14285714em;
-  text-align: center;
-}
-.fa-li.fa-lg {
-  left: -1.85714286em;
-}
-.fa-border {
-  padding: .2em .25em .15em;
-  border: solid 0.08em #eee;
-  border-radius: .1em;
-}
-.pull-right {
-  float: right;
-}
-.pull-left {
-  float: left;
-}
-.fa.pull-left {
-  margin-right: .3em;
-}
-.fa.pull-right {
-  margin-left: .3em;
-}
-.fa-spin {
-  -webkit-animation: fa-spin 2s infinite linear;
-  animation: fa-spin 2s infinite linear;
-}
-@-webkit-keyframes fa-spin {
-  0% {
-    -webkit-transform: rotate(0deg);
-    transform: rotate(0deg);
-  }
-  100% {
-    -webkit-transform: rotate(359deg);
-    transform: rotate(359deg);
-  }
-}
-@keyframes fa-spin {
-  0% {
-    -webkit-transform: rotate(0deg);
-    transform: rotate(0deg);
-  }
-  100% {
-    -webkit-transform: rotate(359deg);
-    transform: rotate(359deg);
-  }
-}
-.fa-rotate-90 {
-  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
-  -webkit-transform: rotate(90deg);
-  -ms-transform: rotate(90deg);
-  transform: rotate(90deg);
-}
-.fa-rotate-180 {
-  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
-  -webkit-transform: rotate(180deg);
-  -ms-transform: rotate(180deg);
-  transform: rotate(180deg);
-}
-.fa-rotate-270 {
-  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
-  -webkit-transform: rotate(270deg);
-  -ms-transform: rotate(270deg);
-  transform: rotate(270deg);
-}
-.fa-flip-horizontal {
-  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
-  -webkit-transform: scale(-1, 1);
-  -ms-transform: scale(-1, 1);
-  transform: scale(-1, 1);
-}
-.fa-flip-vertical {
-  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
-  -webkit-transform: scale(1, -1);
-  -ms-transform: scale(1, -1);
-  transform: scale(1, -1);
-}
-:root .fa-rotate-90,
-:root .fa-rotate-180,
-:root .fa-rotate-270,
-:root .fa-flip-horizontal,
-:root .fa-flip-vertical {
-  filter: none;
-}
-.fa-stack {
-  position: relative;
-  display: inline-block;
-  width: 2em;
-  height: 2em;
-  line-height: 2em;
-  vertical-align: middle;
-}
-.fa-stack-1x,
-.fa-stack-2x {
-  position: absolute;
-  left: 0;
-  width: 100%;
-  text-align: center;
-}
-.fa-stack-1x {
-  line-height: inherit;
-}
-.fa-stack-2x {
-  font-size: 2em;
-}
-.fa-inverse {
-  color: #fff;
-}
-/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
-   readers do not read off random characters that represent icons */
-.fa-glass:before {
-  content: "\f000";
-}
-.fa-music:before {
-  content: "\f001";
-}
-.fa-search:before {
-  content: "\f002";
-}
-.fa-envelope-o:before {
-  content: "\f003";
-}
-.fa-heart:before {
-  content: "\f004";
-}
-.fa-star:before {
-  content: "\f005";
-}
-.fa-star-o:before {
-  content: "\f006";
-}
-.fa-user:before {
-  content: "\f007";
-}
-.fa-film:before {
-  content: "\f008";
-}
-.fa-th-large:before {
-  content: "\f009";
-}
-.fa-th:before {
-  content: "\f00a";
-}
-.fa-th-list:before {
-  content: "\f00b";
-}
-.fa-check:before {
-  content: "\f00c";
-}
-.fa-remove:before,
-.fa-close:before,
-.fa-times:before {
-  content: "\f00d";
-}
-.fa-search-plus:before {
-  content: "\f00e";
-}
-.fa-search-minus:before {
-  content: "\f010";
-}
-.fa-power-off:before {
-  content: "\f011";
-}
-.fa-signal:before {
-  content: "\f012";
-}
-.fa-gear:before,
-.fa-cog:before {
-  content: "\f013";
-}
-.fa-trash-o:before {
-  content: "\f014";
-}
-.fa-home:before {
-  content: "\f015";
-}
-.fa-file-o:before {
-  content: "\f016";
-}
-.fa-clock-o:before {
-  content: "\f017";
-}
-.fa-road:before {
-  content: "\f018";
-}
-.fa-download:before {
-  content: "\f019";
-}
-.fa-arrow-circle-o-down:before {
-  content: "\f01a";
-}
-.fa-arrow-circle-o-up:before {
-  content: "\f01b";
-}
-.fa-inbox:before {
-  content: "\f01c";
-}
-.fa-play-circle-o:before {
-  content: "\f01d";
-}
-.fa-rotate-right:before,
-.fa-repeat:before {
-  content: "\f01e";
-}
-.fa-refresh:before {
-  content: "\f021";
-}
-.fa-list-alt:before {
-  content: "\f022";
-}
-.fa-lock:before {
-  content: "\f023";
-}
-.fa-flag:before {
-  content: "\f024";
-}
-.fa-headphones:before {
-  content: "\f025";
-}
-.fa-volume-off:before {
-  content: "\f026";
-}
-.fa-volume-down:before {
-  content: "\f027";
-}
-.fa-volume-up:before {
-  content: "\f028";
-}
-.fa-qrcode:before {
-  content: "\f029";
-}
-.fa-barcode:before {
-  content: "\f02a";
-}
-.fa-tag:before {
-  content: "\f02b";
-}
-.fa-tags:before {
-  content: "\f02c";
-}
-.fa-book:before {
-  content: "\f02d";
-}
-.fa-bookmark:before {
-  content: "\f02e";
-}
-.fa-print:before {
-  content: "\f02f";
-}
-.fa-camera:before {
-  content: "\f030";
-}
-.fa-font:before {
-  content: "\f031";
-}
-.fa-bold:before {
-  content: "\f032";
-}
-.fa-italic:before {
-  content: "\f033";
-}
-.fa-text-height:before {
-  content: "\f034";
-}
-.fa-text-width:before {
-  content: "\f035";
-}
-.fa-align-left:before {
-  content: "\f036";
-}
-.fa-align-center:before {
-  content: "\f037";
-}
-.fa-align-right:before {
-  content: "\f038";
-}
-.fa-align-justify:before {
-  content: "\f039";
-}
-.fa-list:before {
-  content: "\f03a";
-}
-.fa-dedent:before,
-.fa-outdent:before {
-  content: "\f03b";
-}
-.fa-indent:before {
-  content: "\f03c";
-}
-.fa-video-camera:before {
-  content: "\f03d";
-}
-.fa-photo:before,
-.fa-image:before,
-.fa-picture-o:before {
-  content: "\f03e";
-}
-.fa-pencil:before {
-  content: "\f040";
-}
-.fa-map-marker:before {
-  content: "\f041";
-}
-.fa-adjust:before {
-  content: "\f042";
-}
-.fa-tint:before {
-  content: "\f043";
-}
-.fa-edit:before,
-.fa-pencil-square-o:before {
-  content: "\f044";
-}
-.fa-share-square-o:before {
-  content: "\f045";
-}
-.fa-check-square-o:before {
-  content: "\f046";
-}
-.fa-arrows:before {
-  content: "\f047";
-}
-.fa-step-backward:before {
-  content: "\f048";
-}
-.fa-fast-backward:before {
-  content: "\f049";
-}
-.fa-backward:before {
-  content: "\f04a";
-}
-.fa-play:before {
-  content: "\f04b";
-}
-.fa-pause:before {
-  content: "\f04c";
-}
-.fa-stop:before {
-  content: "\f04d";
-}
-.fa-forward:before {
-  content: "\f04e";
-}
-.fa-fast-forward:before {
-  content: "\f050";
-}
-.fa-step-forward:before {
-  content: "\f051";
-}
-.fa-eject:before {
-  content: "\f052";
-}
-.fa-chevron-left:before {
-  content: "\f053";
-}
-.fa-chevron-right:before {
-  content: "\f054";
-}
-.fa-plus-circle:before {
-  content: "\f055";
-}
-.fa-minus-circle:before {
-  content: "\f056";
-}
-.fa-times-circle:before {
-  content: "\f057";
-}
-.fa-check-circle:before {
-  content: "\f058";
-}
-.fa-question-circle:before {
-  content: "\f059";
-}
-.fa-info-circle:before {
-  content: "\f05a";
-}
-.fa-crosshairs:before {
-  content: "\f05b";
-}
-.fa-times-circle-o:before {
-  content: "\f05c";
-}
-.fa-check-circle-o:before {
-  content: "\f05d";
-}
-.fa-ban:before {
-  content: "\f05e";
-}
-.fa-arrow-left:before {
-  content: "\f060";
-}
-.fa-arrow-right:before {
-  content: "\f061";
-}
-.fa-arrow-up:before {
-  content: "\f062";
-}
-.fa-arrow-down:before {
-  content: "\f063";
-}
-.fa-mail-forward:before,
-.fa-share:before {
-  content: "\f064";
-}
-.fa-expand:before {
-  content: "\f065";
-}
-.fa-compress:before {
-  content: "\f066";
-}
-.fa-plus:before {
-  content: "\f067";
-}
-.fa-minus:before {
-  content: "\f068";
-}
-.fa-asterisk:before {
-  content: "\f069";
-}
-.fa-exclamation-circle:before {
-  content: "\f06a";
-}
-.fa-gift:before {
-  content: "\f06b";
-}
-.fa-leaf:before {
-  content: "\f06c";
-}
-.fa-fire:before {
-  content: "\f06d";
-}
-.fa-eye:before {
-  content: "\f06e";
-}
-.fa-eye-slash:before {
-  content: "\f070";
-}
-.fa-warning:before,
-.fa-exclamation-triangle:before {
-  content: "\f071";
-}
-.fa-plane:before {
-  content: "\f072";
-}
-.fa-calendar:before {
-  content: "\f073";
-}
-.fa-random:before {
-  content: "\f074";
-}
-.fa-comment:before {
-  content: "\f075";
-}
-.fa-magnet:before {
-  content: "\f076";
-}
-.fa-chevron-up:before {
-  content: "\f077";
-}
-.fa-chevron-down:before {
-  content: "\f078";
-}
-.fa-retweet:before {
-  content: "\f079";
-}
-.fa-shopping-cart:before {
-  content: "\f07a";
-}
-.fa-folder:before {
-  content: "\f07b";
-}
-.fa-folder-open:before {
-  content: "\f07c";
-}
-.fa-arrows-v:before {
-  content: "\f07d";
-}
-.fa-arrows-h:before {
-  content: "\f07e";
-}
-.fa-bar-chart-o:before,
-.fa-bar-chart:before {
-  content: "\f080";
-}
-.fa-twitter-square:before {
-  content: "\f081";
-}
-.fa-facebook-square:before {
-  content: "\f082";
-}
-.fa-camera-retro:before {
-  content: "\f083";
-}
-.fa-key:before {
-  content: "\f084";
-}
-.fa-gears:before,
-.fa-cogs:before {
-  content: "\f085";
-}
-.fa-comments:before {
-  content: "\f086";
-}
-.fa-thumbs-o-up:before {
-  content: "\f087";
-}
-.fa-thumbs-o-down:before {
-  content: "\f088";
-}
-.fa-star-half:before {
-  content: "\f089";
-}
-.fa-heart-o:before {
-  content: "\f08a";
-}
-.fa-sign-out:before {
-  content: "\f08b";
-}
-.fa-linkedin-square:before {
-  content: "\f08c";
-}
-.fa-thumb-tack:before {
-  content: "\f08d";
-}
-.fa-external-link:before {
-  content: "\f08e";
-}
-.fa-sign-in:before {
-  content: "\f090";
-}
-.fa-trophy:before {
-  content: "\f091";
-}
-.fa-github-square:before {
-  content: "\f092";
-}
-.fa-upload:before {
-  content: "\f093";
-}
-.fa-lemon-o:before {
-  content: "\f094";
-}
-.fa-phone:before {
-  content: "\f095";
-}
-.fa-square-o:before {
-  content: "\f096";
-}
-.fa-bookmark-o:before {
-  content: "\f097";
-}
-.fa-phone-square:before {
-  content: "\f098";
-}
-.fa-twitter:before {
-  content: "\f099";
-}
-.fa-facebook:before {
-  content: "\f09a";
-}
-.fa-github:before {
-  content: "\f09b";
-}
-.fa-unlock:before {
-  content: "\f09c";
-}
-.fa-credit-card:before {
-  content: "\f09d";
-}
-.fa-rss:before {
-  content: "\f09e";
-}
-.fa-hdd-o:before {
-  content: "\f0a0";
-}
-.fa-bullhorn:before {
-  content: "\f0a1";
-}
-.fa-bell:before {
-  content: "\f0f3";
-}
-.fa-certificate:before {
-  content: "\f0a3";
-}
-.fa-hand-o-right:before {
-  content: "\f0a4";
-}
-.fa-hand-o-left:before {
-  content: "\f0a5";
-}
-.fa-hand-o-up:before {
-  content: "\f0a6";
-}
-.fa-hand-o-down:before {
-  content: "\f0a7";
-}
-.fa-arrow-circle-left:before {
-  content: "\f0a8";
-}
-.fa-arrow-circle-right:before {
-  content: "\f0a9";
-}
-.fa-arrow-circle-up:before {
-  content: "\f0aa";
-}
-.fa-arrow-circle-down:before {
-  content: "\f0ab";
-}
-.fa-globe:before {
-  content: "\f0ac";
-}
-.fa-wrench:before {
-  content: "\f0ad";
-}
-.fa-tasks:before {
-  content: "\f0ae";
-}
-.fa-filter:before {
-  content: "\f0b0";
-}
-.fa-briefcase:before {
-  content: "\f0b1";
-}
-.fa-arrows-alt:before {
-  content: "\f0b2";
-}
-.fa-group:before,
-.fa-users:before {
-  content: "\f0c0";
-}
-.fa-chain:before,
-.fa-link:before {
-  content: "\f0c1";
-}
-.fa-cloud:before {
-  content: "\f0c2";
-}
-.fa-flask:before {
-  content: "\f0c3";
-}
-.fa-cut:before,
-.fa-scissors:before {
-  content: "\f0c4";
-}
-.fa-copy:before,
-.fa-files-o:before {
-  content: "\f0c5";
-}
-.fa-paperclip:before {
-  content: "\f0c6";
-}
-.fa-save:before,
-.fa-floppy-o:before {
-  content: "\f0c7";
-}
-.fa-square:before {
-  content: "\f0c8";
-}
-.fa-navicon:before,
-.fa-reorder:before,
-.fa-bars:before {
-  content: "\f0c9";
-}
-.fa-list-ul:before {
-  content: "\f0ca";
-}
-.fa-list-ol:before {
-  content: "\f0cb";
-}
-.fa-strikethrough:before {
-  content: "\f0cc";
-}
-.fa-underline:before {
-  content: "\f0cd";
-}
-.fa-table:before {
-  content: "\f0ce";
-}
-.fa-magic:before {
-  content: "\f0d0";
-}
-.fa-truck:before {
-  content: "\f0d1";
-}
-.fa-pinterest:before {
-  content: "\f0d2";
-}
-.fa-pinterest-square:before {
-  content: "\f0d3";
-}
-.fa-google-plus-square:before {
-  content: "\f0d4";
-}
-.fa-google-plus:before {
-  content: "\f0d5";
-}
-.fa-money:before {
-  content: "\f0d6";
-}
-.fa-caret-down:before {
-  content: "\f0d7";
-}
-.fa-caret-up:before {
-  content: "\f0d8";
-}
-.fa-caret-left:before {
-  content: "\f0d9";
-}
-.fa-caret-right:before {
-  content: "\f0da";
-}
-.fa-columns:before {
-  content: "\f0db";
-}
-.fa-unsorted:before,
-.fa-sort:before {
-  content: "\f0dc";
-}
-.fa-sort-down:before,
-.fa-sort-desc:before {
-  content: "\f0dd";
-}
-.fa-sort-up:before,
-.fa-sort-asc:before {
-  content: "\f0de";
-}
-.fa-envelope:before {
-  content: "\f0e0";
-}
-.fa-linkedin:before {
-  content: "\f0e1";
-}
-.fa-rotate-left:before,
-.fa-undo:before {
-  content: "\f0e2";
-}
-.fa-legal:before,
-.fa-gavel:before {
-  content: "\f0e3";
-}
-.fa-dashboard:before,
-.fa-tachometer:before {
-  content: "\f0e4";
-}
-.fa-comment-o:before {
-  content: "\f0e5";
-}
-.fa-comments-o:before {
-  content: "\f0e6";
-}
-.fa-flash:before,
-.fa-bolt:before {
-  content: "\f0e7";
-}
-.fa-sitemap:before {
-  content: "\f0e8";
-}
-.fa-umbrella:before {
-  content: "\f0e9";
-}
-.fa-paste:before,
-.fa-clipboard:before {
-  content: "\f0ea";
-}
-.fa-lightbulb-o:before {
-  content: "\f0eb";
-}
-.fa-exchange:before {
-  content: "\f0ec";
-}
-.fa-cloud-download:before {
-  content: "\f0ed";
-}
-.fa-cloud-upload:before {
-  content: "\f0ee";
-}
-.fa-user-md:before {
-  content: "\f0f0";
-}
-.fa-stethoscope:before {
-  content: "\f0f1";
-}
-.fa-suitcase:before {
-  content: "\f0f2";
-}
-.fa-bell-o:before {
-  content: "\f0a2";
-}
-.fa-coffee:before {
-  content: "\f0f4";
-}
-.fa-cutlery:before {
-  content: "\f0f5";
-}
-.fa-file-text-o:before {
-  content: "\f0f6";
-}
-.fa-building-o:before {
-  content: "\f0f7";
-}
-.fa-hospital-o:before {
-  content: "\f0f8";
-}
-.fa-ambulance:before {
-  content: "\f0f9";
-}
-.fa-medkit:before {
-  content: "\f0fa";
-}
-.fa-fighter-jet:before {
-  content: "\f0fb";
-}
-.fa-beer:before {
-  content: "\f0fc";
-}
-.fa-h-square:before {
-  content: "\f0fd";
-}
-.fa-plus-square:before {
-  content: "\f0fe";
-}
-.fa-angle-double-left:before {
-  content: "\f100";
-}
-.fa-angle-double-right:before {
-  content: "\f101";
-}
-.fa-angle-double-up:before {
-  content: "\f102";
-}
-.fa-angle-double-down:before {
-  content: "\f103";
-}
-.fa-angle-left:before {
-  content: "\f104";
-}
-.fa-angle-right:before {
-  content: "\f105";
-}
-.fa-angle-up:before {
-  content: "\f106";
-}
-.fa-angle-down:before {
-  content: "\f107";
-}
-.fa-desktop:before {
-  content: "\f108";
-}
-.fa-laptop:before {
-  content: "\f109";
-}
-.fa-tablet:before {
-  content: "\f10a";
-}
-.fa-mobile-phone:before,
-.fa-mobile:before {
-  content: "\f10b";
-}
-.fa-circle-o:before {
-  content: "\f10c";
-}
-.fa-quote-left:before {
-  content: "\f10d";
-}
-.fa-quote-right:before {
-  content: "\f10e";
-}
-.fa-spinner:before {
-  content: "\f110";
-}
-.fa-circle:before {
-  content: "\f111";
-}
-.fa-mail-reply:before,
-.fa-reply:before {
-  content: "\f112";
-}
-.fa-github-alt:before {
-  content: "\f113";
-}
-.fa-folder-o:before {
-  content: "\f114";
-}
-.fa-folder-open-o:before {
-  content: "\f115";
-}
-.fa-smile-o:before {
-  content: "\f118";
-}
-.fa-frown-o:before {
-  content: "\f119";
-}
-.fa-meh-o:before {
-  content: "\f11a";
-}
-.fa-gamepad:before {
-  content: "\f11b";
-}
-.fa-keyboard-o:before {
-  content: "\f11c";
-}
-.fa-flag-o:before {
-  content: "\f11d";
-}
-.fa-flag-checkered:before {
-  content: "\f11e";
-}
-.fa-terminal:before {
-  content: "\f120";
-}
-.fa-code:before {
-  content: "\f121";
-}
-.fa-mail-reply-all:before,
-.fa-reply-all:before {
-  content: "\f122";
-}
-.fa-star-half-empty:before,
-.fa-star-half-full:before,
-.fa-star-half-o:before {
-  content: "\f123";
-}
-.fa-location-arrow:before {
-  content: "\f124";
-}
-.fa-crop:before {
-  content: "\f125";
-}
-.fa-code-fork:before {
-  content: "\f126";
-}
-.fa-unlink:before,
-.fa-chain-broken:before {
-  content: "\f127";
-}
-.fa-question:before {
-  content: "\f128";
-}
-.fa-info:before {
-  content: "\f129";
-}
-.fa-exclamation:before {
-  content: "\f12a";
-}
-.fa-superscript:before {
-  content: "\f12b";
-}
-.fa-subscript:before {
-  content: "\f12c";
-}
-.fa-eraser:before {
-  content: "\f12d";
-}
-.fa-puzzle-piece:before {
-  content: "\f12e";
-}
-.fa-microphone:before {
-  content: "\f130";
-}
-.fa-microphone-slash:before {
-  content: "\f131";
-}
-.fa-shield:before {
-  content: "\f132";
-}
-.fa-calendar-o:before {
-  content: "\f133";
-}
-.fa-fire-extinguisher:before {
-  content: "\f134";
-}
-.fa-rocket:before {
-  content: "\f135";
-}
-.fa-maxcdn:before {
-  content: "\f136";
-}
-.fa-chevron-circle-left:before {
-  content: "\f137";
-}
-.fa-chevron-circle-right:before {
-  content: "\f138";
-}
-.fa-chevron-circle-up:before {
-  content: "\f139";
-}
-.fa-chevron-circle-down:before {
-  content: "\f13a";
-}
-.fa-html5:before {
-  content: "\f13b";
-}
-.fa-css3:before {
-  content: "\f13c";
-}
-.fa-anchor:before {
-  content: "\f13d";
-}
-.fa-unlock-alt:before {
-  content: "\f13e";
-}
-.fa-bullseye:before {
-  content: "\f140";
-}
-.fa-ellipsis-h:before {
-  content: "\f141";
-}
-.fa-ellipsis-v:before {
-  content: "\f142";
-}
-.fa-rss-square:before {
-  content: "\f143";
-}
-.fa-play-circle:before {
-  content: "\f144";
-}
-.fa-ticket:before {
-  content: "\f145";
-}
-.fa-minus-square:before {
-  content: "\f146";
-}
-.fa-minus-square-o:before {
-  content: "\f147";
-}
-.fa-level-up:before {
-  content: "\f148";
-}
-.fa-level-down:before {
-  content: "\f149";
-}
-.fa-check-square:before {
-  content: "\f14a";
-}
-.fa-pencil-square:before {
-  content: "\f14b";
-}
-.fa-external-link-square:before {
-  content: "\f14c";
-}
-.fa-share-square:before {
-  content: "\f14d";
-}
-.fa-compass:before {
-  content: "\f14e";
-}
-.fa-toggle-down:before,
-.fa-caret-square-o-down:before {
-  content: "\f150";
-}
-.fa-toggle-up:before,
-.fa-caret-square-o-up:before {
-  content: "\f151";
-}
-.fa-toggle-right:before,
-.fa-caret-square-o-right:before {
-  content: "\f152";
-}
-.fa-euro:before,
-.fa-eur:before {
-  content: "\f153";
-}
-.fa-gbp:before {
-  content: "\f154";
-}
-.fa-dollar:before,
-.fa-usd:before {
-  content: "\f155";
-}
-.fa-rupee:before,
-.fa-inr:before {
-  content: "\f156";
-}
-.fa-cny:before,
-.fa-rmb:before,
-.fa-yen:before,
-.fa-jpy:before {
-  content: "\f157";
-}
-.fa-ruble:before,
-.fa-rouble:before,
-.fa-rub:before {
-  content: "\f158";
-}
-.fa-won:before,
-.fa-krw:before {
-  content: "\f159";
-}
-.fa-bitcoin:before,
-.fa-btc:before {
-  content: "\f15a";
-}
-.fa-file:before {
-  content: "\f15b";
-}
-.fa-file-text:before {
-  content: "\f15c";
-}
-.fa-sort-alpha-asc:before {
-  content: "\f15d";
-}
-.fa-sort-alpha-desc:before {
-  content: "\f15e";
-}
-.fa-sort-amount-asc:before {
-  content: "\f160";
-}
-.fa-sort-amount-desc:before {
-  content: "\f161";
-}
-.fa-sort-numeric-asc:before {
-  content: "\f162";
-}
-.fa-sort-numeric-desc:before {
-  content: "\f163";
-}
-.fa-thumbs-up:before {
-  content: "\f164";
-}
-.fa-thumbs-down:before {
-  content: "\f165";
-}
-.fa-youtube-square:before {
-  content: "\f166";
-}
-.fa-youtube:before {
-  content: "\f167";
-}
-.fa-xing:before {
-  content: "\f168";
-}
-.fa-xing-square:before {
-  content: "\f169";
-}
-.fa-youtube-play:before {
-  content: "\f16a";
-}
-.fa-dropbox:before {
-  content: "\f16b";
-}
-.fa-stack-overflow:before {
-  content: "\f16c";
-}
-.fa-instagram:before {
-  content: "\f16d";
-}
-.fa-flickr:before {
-  content: "\f16e";
-}
-.fa-adn:before {
-  content: "\f170";
-}
-.fa-bitbucket:before {
-  content: "\f171";
-}
-.fa-bitbucket-square:before {
-  content: "\f172";
-}
-.fa-tumblr:before {
-  content: "\f173";
-}
-.fa-tumblr-square:before {
-  content: "\f174";
-}
-.fa-long-arrow-down:before {
-  content: "\f175";
-}
-.fa-long-arrow-up:before {
-  content: "\f176";
-}
-.fa-long-arrow-left:before {
-  content: "\f177";
-}
-.fa-long-arrow-right:before {
-  content: "\f178";
-}
-.fa-apple:before {
-  content: "\f179";
-}
-.fa-windows:before {
-  content: "\f17a";
-}
-.fa-android:before {
-  content: "\f17b";
-}
-.fa-linux:before {
-  content: "\f17c";
-}
-.fa-dribbble:before {
-  content: "\f17d";
-}
-.fa-skype:before {
-  content: "\f17e";
-}
-.fa-foursquare:before {
-  content: "\f180";
-}
-.fa-trello:before {
-  content: "\f181";
-}
-.fa-female:before {
-  content: "\f182";
-}
-.fa-male:before {
-  content: "\f183";
-}
-.fa-gittip:before {
-  content: "\f184";
-}
-.fa-sun-o:before {
-  content: "\f185";
-}
-.fa-moon-o:before {
-  content: "\f186";
-}
-.fa-archive:before {
-  content: "\f187";
-}
-.fa-bug:before {
-  content: "\f188";
-}
-.fa-vk:before {
-  content: "\f189";
-}
-.fa-weibo:before {
-  content: "\f18a";
-}
-.fa-renren:before {
-  content: "\f18b";
-}
-.fa-pagelines:before {
-  content: "\f18c";
-}
-.fa-stack-exchange:before {
-  content: "\f18d";
-}
-.fa-arrow-circle-o-right:before {
-  content: "\f18e";
-}
-.fa-arrow-circle-o-left:before {
-  content: "\f190";
-}
-.fa-toggle-left:before,
-.fa-caret-square-o-left:before {
-  content: "\f191";
-}
-.fa-dot-circle-o:before {
-  content: "\f192";
-}
-.fa-wheelchair:before {
-  content: "\f193";
-}
-.fa-vimeo-square:before {
-  content: "\f194";
-}
-.fa-turkish-lira:before,
-.fa-try:before {
-  content: "\f195";
-}
-.fa-plus-square-o:before {
-  content: "\f196";
-}
-.fa-space-shuttle:before {
-  content: "\f197";
-}
-.fa-slack:before {
-  content: "\f198";
-}
-.fa-envelope-square:before {
-  content: "\f199";
-}
-.fa-wordpress:before {
-  content: "\f19a";
-}
-.fa-openid:before {
-  content: "\f19b";
-}
-.fa-institution:before,
-.fa-bank:before,
-.fa-university:before {
-  content: "\f19c";
-}
-.fa-mortar-board:before,
-.fa-graduation-cap:before {
-  content: "\f19d";
-}
-.fa-yahoo:before {
-  content: "\f19e";
-}
-.fa-google:before {
-  content: "\f1a0";
-}
-.fa-reddit:before {
-  content: "\f1a1";
-}
-.fa-reddit-square:before {
-  content: "\f1a2";
-}
-.fa-stumbleupon-circle:before {
-  content: "\f1a3";
-}
-.fa-stumbleupon:before {
-  content: "\f1a4";
-}
-.fa-delicious:before {
-  content: "\f1a5";
-}
-.fa-digg:before {
-  content: "\f1a6";
-}
-.fa-pied-piper:before {
-  content: "\f1a7";
-}
-.fa-pied-piper-alt:before {
-  content: "\f1a8";
-}
-.fa-drupal:before {
-  content: "\f1a9";
-}
-.fa-joomla:before {
-  content: "\f1aa";
-}
-.fa-language:before {
-  content: "\f1ab";
-}
-.fa-fax:before {
-  content: "\f1ac";
-}
-.fa-building:before {
-  content: "\f1ad";
-}
-.fa-child:before {
-  content: "\f1ae";
-}
-.fa-paw:before {
-  content: "\f1b0";
-}
-.fa-spoon:before {
-  content: "\f1b1";
-}
-.fa-cube:before {
-  content: "\f1b2";
-}
-.fa-cubes:before {
-  content: "\f1b3";
-}
-.fa-behance:before {
-  content: "\f1b4";
-}
-.fa-behance-square:before {
-  content: "\f1b5";
-}
-.fa-steam:before {
-  content: "\f1b6";
-}
-.fa-steam-square:before {
-  content: "\f1b7";
-}
-.fa-recycle:before {
-  content: "\f1b8";
-}
-.fa-automobile:before,
-.fa-car:before {
-  content: "\f1b9";
-}
-.fa-cab:before,
-.fa-taxi:before {
-  content: "\f1ba";
-}
-.fa-tree:before {
-  content: "\f1bb";
-}
-.fa-spotify:before {
-  content: "\f1bc";
-}
-.fa-deviantart:before {
-  content: "\f1bd";
-}
-.fa-soundcloud:before {
-  content: "\f1be";
-}
-.fa-database:before {
-  content: "\f1c0";
-}
-.fa-file-pdf-o:before {
-  content: "\f1c1";
-}
-.fa-file-word-o:before {
-  content: "\f1c2";
-}
-.fa-file-excel-o:before {
-  content: "\f1c3";
-}
-.fa-file-powerpoint-o:before {
-  content: "\f1c4";
-}
-.fa-file-photo-o:before,
-.fa-file-picture-o:before,
-.fa-file-image-o:before {
-  content: "\f1c5";
-}
-.fa-file-zip-o:before,
-.fa-file-archive-o:before {
-  content: "\f1c6";
-}
-.fa-file-sound-o:before,
-.fa-file-audio-o:before {
-  content: "\f1c7";
-}
-.fa-file-movie-o:before,
-.fa-file-video-o:before {
-  content: "\f1c8";
-}
-.fa-file-code-o:before {
-  content: "\f1c9";
-}
-.fa-vine:before {
-  content: "\f1ca";
-}
-.fa-codepen:before {
-  content: "\f1cb";
-}
-.fa-jsfiddle:before {
-  content: "\f1cc";
-}
-.fa-life-bouy:before,
-.fa-life-buoy:before,
-.fa-life-saver:before,
-.fa-support:before,
-.fa-life-ring:before {
-  content: "\f1cd";
-}
-.fa-circle-o-notch:before {
-  content: "\f1ce";
-}
-.fa-ra:before,
-.fa-rebel:before {
-  content: "\f1d0";
-}
-.fa-ge:before,
-.fa-empire:before {
-  content: "\f1d1";
-}
-.fa-git-square:before {
-  content: "\f1d2";
-}
-.fa-git:before {
-  content: "\f1d3";
-}
-.fa-hacker-news:before {
-  content: "\f1d4";
-}
-.fa-tencent-weibo:before {
-  content: "\f1d5";
-}
-.fa-qq:before {
-  content: "\f1d6";
-}
-.fa-wechat:before,
-.fa-weixin:before {
-  content: "\f1d7";
-}
-.fa-send:before,
-.fa-paper-plane:before {
-  content: "\f1d8";
-}
-.fa-send-o:before,
-.fa-paper-plane-o:before {
-  content: "\f1d9";
-}
-.fa-history:before {
-  content: "\f1da";
-}
-.fa-circle-thin:before {
-  content: "\f1db";
-}
-.fa-header:before {
-  content: "\f1dc";
-}
-.fa-paragraph:before {
-  content: "\f1dd";
-}
-.fa-sliders:before {
-  content: "\f1de";
-}
-.fa-share-alt:before {
-  content: "\f1e0";
-}
-.fa-share-alt-square:before {
-  content: "\f1e1";
-}
-.fa-bomb:before {
-  content: "\f1e2";
-}
-.fa-soccer-ball-o:before,
-.fa-futbol-o:before {
-  content: "\f1e3";
-}
-.fa-tty:before {
-  content: "\f1e4";
-}
-.fa-binoculars:before {
-  content: "\f1e5";
-}
-.fa-plug:before {
-  content: "\f1e6";
-}
-.fa-slideshare:before {
-  content: "\f1e7";
-}
-.fa-twitch:before {
-  content: "\f1e8";
-}
-.fa-yelp:before {
-  content: "\f1e9";
-}
-.fa-newspaper-o:before {
-  content: "\f1ea";
-}
-.fa-wifi:before {
-  content: "\f1eb";
-}
-.fa-calculator:before {
-  content: "\f1ec";
-}
-.fa-paypal:before {
-  content: "\f1ed";
-}
-.fa-google-wallet:before {
-  content: "\f1ee";
-}
-.fa-cc-visa:before {
-  content: "\f1f0";
-}
-.fa-cc-mastercard:before {
-  content: "\f1f1";
-}
-.fa-cc-discover:before {
-  content: "\f1f2";
-}
-.fa-cc-amex:before {
-  content: "\f1f3";
-}
-.fa-cc-paypal:before {
-  content: "\f1f4";
-}
-.fa-cc-stripe:before {
-  content: "\f1f5";
-}
-.fa-bell-slash:before {
-  content: "\f1f6";
-}
-.fa-bell-slash-o:before {
-  content: "\f1f7";
-}
-.fa-trash:before {
-  content: "\f1f8";
-}
-.fa-copyright:before {
-  content: "\f1f9";
-}
-.fa-at:before {
-  content: "\f1fa";
-}
-.fa-eyedropper:before {
-  content: "\f1fb";
-}
-.fa-paint-brush:before {
-  content: "\f1fc";
-}
-.fa-birthday-cake:before {
-  content: "\f1fd";
-}
-.fa-area-chart:before {
-  content: "\f1fe";
-}
-.fa-pie-chart:before {
-  content: "\f200";
-}
-.fa-line-chart:before {
-  content: "\f201";
-}
-.fa-lastfm:before {
-  content: "\f202";
-}
-.fa-lastfm-square:before {
-  content: "\f203";
-}
-.fa-toggle-off:before {
-  content: "\f204";
-}
-.fa-toggle-on:before {
-  content: "\f205";
-}
-.fa-bicycle:before {
-  content: "\f206";
-}
-.fa-bus:before {
-  content: "\f207";
-}
-.fa-ioxhost:before {
-  content: "\f208";
-}
-.fa-angellist:before {
-  content: "\f209";
-}
-.fa-cc:before {
-  content: "\f20a";
-}
-.fa-shekel:before,
-.fa-sheqel:before,
-.fa-ils:before {
-  content: "\f20b";
-}
-.fa-meanpath:before {
-  content: "\f20c";
-}
-/*!
-*
-* IPython base
-*
-*/
-.modal.fade .modal-dialog {
-  -webkit-transform: translate(0, 0);
-  -ms-transform: translate(0, 0);
-  -o-transform: translate(0, 0);
-  transform: translate(0, 0);
-}
-code {
-  color: #000;
-}
-pre {
-  font-size: inherit;
-  line-height: inherit;
-}
-label {
-  font-weight: normal;
-}
-/* Make the page background atleast 100% the height of the view port */
-/* Make the page itself atleast 70% the height of the view port */
-.border-box-sizing {
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-.corner-all {
-  border-radius: 2px;
-}
-.no-padding {
-  padding: 0px;
-}
-/* Flexible box model classes */
-/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
-/* This file is a compatability layer.  It allows the usage of flexible box 
-model layouts accross multiple browsers, including older browsers.  The newest,
-universal implementation of the flexible box model is used when available (see
-`Modern browsers` comments below).  Browsers that are known to implement this 
-new spec completely include:
-
-    Firefox 28.0+
-    Chrome 29.0+
-    Internet Explorer 11+ 
-    Opera 17.0+
-
-Browsers not listed, including Safari, are supported via the styling under the
-`Old browsers` comments below.
-*/
-.hbox {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-}
-.hbox > * {
-  /* Old browsers */
-  -webkit-box-flex: 0;
-  -moz-box-flex: 0;
-  box-flex: 0;
-  /* Modern browsers */
-  flex: none;
-}
-.vbox {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-}
-.vbox > * {
-  /* Old browsers */
-  -webkit-box-flex: 0;
-  -moz-box-flex: 0;
-  box-flex: 0;
-  /* Modern browsers */
-  flex: none;
-}
-.hbox.reverse,
-.vbox.reverse,
-.reverse {
-  /* Old browsers */
-  -webkit-box-direction: reverse;
-  -moz-box-direction: reverse;
-  box-direction: reverse;
-  /* Modern browsers */
-  flex-direction: row-reverse;
-}
-.hbox.box-flex0,
-.vbox.box-flex0,
-.box-flex0 {
-  /* Old browsers */
-  -webkit-box-flex: 0;
-  -moz-box-flex: 0;
-  box-flex: 0;
-  /* Modern browsers */
-  flex: none;
-  width: auto;
-}
-.hbox.box-flex1,
-.vbox.box-flex1,
-.box-flex1 {
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-}
-.hbox.box-flex,
-.vbox.box-flex,
-.box-flex {
-  /* Old browsers */
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-}
-.hbox.box-flex2,
-.vbox.box-flex2,
-.box-flex2 {
-  /* Old browsers */
-  -webkit-box-flex: 2;
-  -moz-box-flex: 2;
-  box-flex: 2;
-  /* Modern browsers */
-  flex: 2;
-}
-.box-group1 {
-  /*  Deprecated */
-  -webkit-box-flex-group: 1;
-  -moz-box-flex-group: 1;
-  box-flex-group: 1;
-}
-.box-group2 {
-  /* Deprecated */
-  -webkit-box-flex-group: 2;
-  -moz-box-flex-group: 2;
-  box-flex-group: 2;
-}
-.hbox.start,
-.vbox.start,
-.start {
-  /* Old browsers */
-  -webkit-box-pack: start;
-  -moz-box-pack: start;
-  box-pack: start;
-  /* Modern browsers */
-  justify-content: flex-start;
-}
-.hbox.end,
-.vbox.end,
-.end {
-  /* Old browsers */
-  -webkit-box-pack: end;
-  -moz-box-pack: end;
-  box-pack: end;
-  /* Modern browsers */
-  justify-content: flex-end;
-}
-.hbox.center,
-.vbox.center,
-.center {
-  /* Old browsers */
-  -webkit-box-pack: center;
-  -moz-box-pack: center;
-  box-pack: center;
-  /* Modern browsers */
-  justify-content: center;
-}
-.hbox.baseline,
-.vbox.baseline,
-.baseline {
-  /* Old browsers */
-  -webkit-box-pack: baseline;
-  -moz-box-pack: baseline;
-  box-pack: baseline;
-  /* Modern browsers */
-  justify-content: baseline;
-}
-.hbox.stretch,
-.vbox.stretch,
-.stretch {
-  /* Old browsers */
-  -webkit-box-pack: stretch;
-  -moz-box-pack: stretch;
-  box-pack: stretch;
-  /* Modern browsers */
-  justify-content: stretch;
-}
-.hbox.align-start,
-.vbox.align-start,
-.align-start {
-  /* Old browsers */
-  -webkit-box-align: start;
-  -moz-box-align: start;
-  box-align: start;
-  /* Modern browsers */
-  align-items: flex-start;
-}
-.hbox.align-end,
-.vbox.align-end,
-.align-end {
-  /* Old browsers */
-  -webkit-box-align: end;
-  -moz-box-align: end;
-  box-align: end;
-  /* Modern browsers */
-  align-items: flex-end;
-}
-.hbox.align-center,
-.vbox.align-center,
-.align-center {
-  /* Old browsers */
-  -webkit-box-align: center;
-  -moz-box-align: center;
-  box-align: center;
-  /* Modern browsers */
-  align-items: center;
-}
-.hbox.align-baseline,
-.vbox.align-baseline,
-.align-baseline {
-  /* Old browsers */
-  -webkit-box-align: baseline;
-  -moz-box-align: baseline;
-  box-align: baseline;
-  /* Modern browsers */
-  align-items: baseline;
-}
-.hbox.align-stretch,
-.vbox.align-stretch,
-.align-stretch {
-  /* Old browsers */
-  -webkit-box-align: stretch;
-  -moz-box-align: stretch;
-  box-align: stretch;
-  /* Modern browsers */
-  align-items: stretch;
-}
-div.error {
-  margin: 2em;
-  text-align: center;
-}
-div.error > h1 {
-  font-size: 500%;
-  line-height: normal;
-}
-div.error > p {
-  font-size: 200%;
-  line-height: normal;
-}
-div.traceback-wrapper {
-  text-align: left;
-  max-width: 800px;
-  margin: auto;
-}
-/**
- * Primary styles
- *
- * Author: Jupyter Development Team
- */
-body {
-  background-color: #fff;
-  /* This makes sure that the body covers the entire window and needs to
-       be in a different element than the display: box in wrapper below */
-  position: absolute;
-  left: 0px;
-  right: 0px;
-  top: 0px;
-  bottom: 0px;
-  overflow: visible;
-}
-body > #header {
-  /* Initially hidden to prevent FLOUC */
-  display: none;
-  background-color: #fff;
-  /* Display over codemirror */
-  position: relative;
-  z-index: 100;
-}
-body > #header #header-container {
-  padding-bottom: 5px;
-  padding-top: 5px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-body > #header .header-bar {
-  width: 100%;
-  height: 1px;
-  background: #e7e7e7;
-  margin-bottom: -1px;
-}
-@media print {
-  body > #header {
-    display: none !important;
-  }
-}
-#header-spacer {
-  width: 100%;
-  visibility: hidden;
-}
-@media print {
-  #header-spacer {
-    display: none;
-  }
-}
-#ipython_notebook {
-  padding-left: 0px;
-  padding-top: 1px;
-  padding-bottom: 1px;
-}
-@media (max-width: 991px) {
-  #ipython_notebook {
-    margin-left: 10px;
-  }
-}
-#noscript {
-  width: auto;
-  padding-top: 16px;
-  padding-bottom: 16px;
-  text-align: center;
-  font-size: 22px;
-  color: red;
-  font-weight: bold;
-}
-#ipython_notebook img {
-  height: 28px;
-}
-#site {
-  width: 100%;
-  display: none;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  overflow: auto;
-}
-@media print {
-  #site {
-    height: auto !important;
-  }
-}
-/* Smaller buttons */
-.ui-button .ui-button-text {
-  padding: 0.2em 0.8em;
-  font-size: 77%;
-}
-input.ui-button {
-  padding: 0.3em 0.9em;
-}
-span#login_widget {
-  float: right;
-}
-span#login_widget > .button,
-#logout {
-  color: #333;
-  background-color: #fff;
-  border-color: #ccc;
-}
-span#login_widget > .button:focus,
-#logout:focus,
-span#login_widget > .button.focus,
-#logout.focus {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #8c8c8c;
-}
-span#login_widget > .button:hover,
-#logout:hover {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-span#login_widget > .button:active,
-#logout:active,
-span#login_widget > .button.active,
-#logout.active,
-.open > .dropdown-togglespan#login_widget > .button,
-.open > .dropdown-toggle#logout {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-span#login_widget > .button:active:hover,
-#logout:active:hover,
-span#login_widget > .button.active:hover,
-#logout.active:hover,
-.open > .dropdown-togglespan#login_widget > .button:hover,
-.open > .dropdown-toggle#logout:hover,
-span#login_widget > .button:active:focus,
-#logout:active:focus,
-span#login_widget > .button.active:focus,
-#logout.active:focus,
-.open > .dropdown-togglespan#login_widget > .button:focus,
-.open > .dropdown-toggle#logout:focus,
-span#login_widget > .button:active.focus,
-#logout:active.focus,
-span#login_widget > .button.active.focus,
-#logout.active.focus,
-.open > .dropdown-togglespan#login_widget > .button.focus,
-.open > .dropdown-toggle#logout.focus {
-  color: #333;
-  background-color: #d4d4d4;
-  border-color: #8c8c8c;
-}
-span#login_widget > .button:active,
-#logout:active,
-span#login_widget > .button.active,
-#logout.active,
-.open > .dropdown-togglespan#login_widget > .button,
-.open > .dropdown-toggle#logout {
-  background-image: none;
-}
-span#login_widget > .button.disabled:hover,
-#logout.disabled:hover,
-span#login_widget > .button[disabled]:hover,
-#logout[disabled]:hover,
-fieldset[disabled] span#login_widget > .button:hover,
-fieldset[disabled] #logout:hover,
-span#login_widget > .button.disabled:focus,
-#logout.disabled:focus,
-span#login_widget > .button[disabled]:focus,
-#logout[disabled]:focus,
-fieldset[disabled] span#login_widget > .button:focus,
-fieldset[disabled] #logout:focus,
-span#login_widget > .button.disabled.focus,
-#logout.disabled.focus,
-span#login_widget > .button[disabled].focus,
-#logout[disabled].focus,
-fieldset[disabled] span#login_widget > .button.focus,
-fieldset[disabled] #logout.focus {
-  background-color: #fff;
-  border-color: #ccc;
-}
-span#login_widget > .button .badge,
-#logout .badge {
-  color: #fff;
-  background-color: #333;
-}
-.nav-header {
-  text-transform: none;
-}
-#header > span {
-  margin-top: 10px;
-}
-.modal_stretch .modal-dialog {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  min-height: 80vh;
-}
-.modal_stretch .modal-dialog .modal-body {
-  max-height: calc(100vh - 200px);
-  overflow: auto;
-  flex: 1;
-}
-@media (min-width: 768px) {
-  .modal .modal-dialog {
-    width: 700px;
-  }
-}
-@media (min-width: 768px) {
-  select.form-control {
-    margin-left: 12px;
-    margin-right: 12px;
-  }
-}
-/*!
-*
-* IPython auth
-*
-*/
-.center-nav {
-  display: inline-block;
-  margin-bottom: -4px;
-}
-/*!
-*
-* IPython tree view
-*
-*/
-/* We need an invisible input field on top of the sentense*/
-/* "Drag file onto the list ..." */
-.alternate_upload {
-  background-color: none;
-  display: inline;
-}
-.alternate_upload.form {
-  padding: 0;
-  margin: 0;
-}
-.alternate_upload input.fileinput {
-  text-align: center;
-  vertical-align: middle;
-  display: inline;
-  opacity: 0;
-  z-index: 2;
-  width: 12ex;
-  margin-right: -12ex;
-}
-.alternate_upload .btn-upload {
-  height: 22px;
-}
-/**
- * Primary styles
- *
- * Author: Jupyter Development Team
- */
-ul#tabs {
-  margin-bottom: 4px;
-}
-ul#tabs a {
-  padding-top: 6px;
-  padding-bottom: 4px;
-}
-ul.breadcrumb a:focus,
-ul.breadcrumb a:hover {
-  text-decoration: none;
-}
-ul.breadcrumb i.icon-home {
-  font-size: 16px;
-  margin-right: 4px;
-}
-ul.breadcrumb span {
-  color: #5e5e5e;
-}
-.list_toolbar {
-  padding: 4px 0 4px 0;
-  vertical-align: middle;
-}
-.list_toolbar .tree-buttons {
-  padding-top: 1px;
-}
-.dynamic-buttons {
-  padding-top: 3px;
-  display: inline-block;
-}
-.list_toolbar [class*="span"] {
-  min-height: 24px;
-}
-.list_header {
-  font-weight: bold;
-  background-color: #EEE;
-}
-.list_placeholder {
-  font-weight: bold;
-  padding-top: 4px;
-  padding-bottom: 4px;
-  padding-left: 7px;
-  padding-right: 7px;
-}
-.list_container {
-  margin-top: 4px;
-  margin-bottom: 20px;
-  border: 1px solid #ddd;
-  border-radius: 2px;
-}
-.list_container > div {
-  border-bottom: 1px solid #ddd;
-}
-.list_container > div:hover .list-item {
-  background-color: red;
-}
-.list_container > div:last-child {
-  border: none;
-}
-.list_item:hover .list_item {
-  background-color: #ddd;
-}
-.list_item a {
-  text-decoration: none;
-}
-.list_item:hover {
-  background-color: #fafafa;
-}
-.list_header > div,
-.list_item > div {
-  padding-top: 4px;
-  padding-bottom: 4px;
-  padding-left: 7px;
-  padding-right: 7px;
-  line-height: 22px;
-}
-.list_header > div input,
-.list_item > div input {
-  margin-right: 7px;
-  margin-left: 14px;
-  vertical-align: baseline;
-  line-height: 22px;
-  position: relative;
-  top: -1px;
-}
-.list_header > div .item_link,
-.list_item > div .item_link {
-  margin-left: -1px;
-  vertical-align: baseline;
-  line-height: 22px;
-}
-.new-file input[type=checkbox] {
-  visibility: hidden;
-}
-.item_name {
-  line-height: 22px;
-  height: 24px;
-}
-.item_icon {
-  font-size: 14px;
-  color: #5e5e5e;
-  margin-right: 7px;
-  margin-left: 7px;
-  line-height: 22px;
-  vertical-align: baseline;
-}
-.item_buttons {
-  line-height: 1em;
-  margin-left: -5px;
-}
-.item_buttons .btn,
-.item_buttons .btn-group,
-.item_buttons .input-group {
-  float: left;
-}
-.item_buttons > .btn,
-.item_buttons > .btn-group,
-.item_buttons > .input-group {
-  margin-left: 5px;
-}
-.item_buttons .btn {
-  min-width: 13ex;
-}
-.item_buttons .running-indicator {
-  padding-top: 4px;
-  color: #5cb85c;
-}
-.item_buttons .kernel-name {
-  padding-top: 4px;
-  color: #5bc0de;
-  margin-right: 7px;
-  float: left;
-}
-.toolbar_info {
-  height: 24px;
-  line-height: 24px;
-}
-.list_item input:not([type=checkbox]) {
-  padding-top: 3px;
-  padding-bottom: 3px;
-  height: 22px;
-  line-height: 14px;
-  margin: 0px;
-}
-.highlight_text {
-  color: blue;
-}
-#project_name {
-  display: inline-block;
-  padding-left: 7px;
-  margin-left: -2px;
-}
-#project_name > .breadcrumb {
-  padding: 0px;
-  margin-bottom: 0px;
-  background-color: transparent;
-  font-weight: bold;
-}
-#tree-selector {
-  padding-right: 0px;
-}
-#button-select-all {
-  min-width: 50px;
-}
-#select-all {
-  margin-left: 7px;
-  margin-right: 2px;
-}
-.menu_icon {
-  margin-right: 2px;
-}
-.tab-content .row {
-  margin-left: 0px;
-  margin-right: 0px;
-}
-.folder_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f114";
-}
-.folder_icon:before.pull-left {
-  margin-right: .3em;
-}
-.folder_icon:before.pull-right {
-  margin-left: .3em;
-}
-.notebook_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f02d";
-  position: relative;
-  top: -1px;
-}
-.notebook_icon:before.pull-left {
-  margin-right: .3em;
-}
-.notebook_icon:before.pull-right {
-  margin-left: .3em;
-}
-.running_notebook_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f02d";
-  position: relative;
-  top: -1px;
-  color: #5cb85c;
-}
-.running_notebook_icon:before.pull-left {
-  margin-right: .3em;
-}
-.running_notebook_icon:before.pull-right {
-  margin-left: .3em;
-}
-.file_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f016";
-  position: relative;
-  top: -2px;
-}
-.file_icon:before.pull-left {
-  margin-right: .3em;
-}
-.file_icon:before.pull-right {
-  margin-left: .3em;
-}
-#notebook_toolbar .pull-right {
-  padding-top: 0px;
-  margin-right: -1px;
-}
-ul#new-menu {
-  left: auto;
-  right: 0;
-}
-.kernel-menu-icon {
-  padding-right: 12px;
-  width: 24px;
-  content: "\f096";
-}
-.kernel-menu-icon:before {
-  content: "\f096";
-}
-.kernel-menu-icon-current:before {
-  content: "\f00c";
-}
-#tab_content {
-  padding-top: 20px;
-}
-#running .panel-group .panel {
-  margin-top: 3px;
-  margin-bottom: 1em;
-}
-#running .panel-group .panel .panel-heading {
-  background-color: #EEE;
-  padding-top: 4px;
-  padding-bottom: 4px;
-  padding-left: 7px;
-  padding-right: 7px;
-  line-height: 22px;
-}
-#running .panel-group .panel .panel-heading a:focus,
-#running .panel-group .panel .panel-heading a:hover {
-  text-decoration: none;
-}
-#running .panel-group .panel .panel-body {
-  padding: 0px;
-}
-#running .panel-group .panel .panel-body .list_container {
-  margin-top: 0px;
-  margin-bottom: 0px;
-  border: 0px;
-  border-radius: 0px;
-}
-#running .panel-group .panel .panel-body .list_container .list_item {
-  border-bottom: 1px solid #ddd;
-}
-#running .panel-group .panel .panel-body .list_container .list_item:last-child {
-  border-bottom: 0px;
-}
-.delete-button {
-  display: none;
-}
-.duplicate-button {
-  display: none;
-}
-.rename-button {
-  display: none;
-}
-.shutdown-button {
-  display: none;
-}
-.dynamic-instructions {
-  display: inline-block;
-  padding-top: 4px;
-}
-/*!
-*
-* IPython text editor webapp
-*
-*/
-.selected-keymap i.fa {
-  padding: 0px 5px;
-}
-.selected-keymap i.fa:before {
-  content: "\f00c";
-}
-#mode-menu {
-  overflow: auto;
-  max-height: 20em;
-}
-.edit_app #header {
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-}
-.edit_app #menubar .navbar {
-  /* Use a negative 1 bottom margin, so the border overlaps the border of the
-    header */
-  margin-bottom: -1px;
-}
-.dirty-indicator {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  width: 20px;
-}
-.dirty-indicator.pull-left {
-  margin-right: .3em;
-}
-.dirty-indicator.pull-right {
-  margin-left: .3em;
-}
-.dirty-indicator-dirty {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  width: 20px;
-}
-.dirty-indicator-dirty.pull-left {
-  margin-right: .3em;
-}
-.dirty-indicator-dirty.pull-right {
-  margin-left: .3em;
-}
-.dirty-indicator-clean {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  width: 20px;
-}
-.dirty-indicator-clean.pull-left {
-  margin-right: .3em;
-}
-.dirty-indicator-clean.pull-right {
-  margin-left: .3em;
-}
-.dirty-indicator-clean:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f00c";
-}
-.dirty-indicator-clean:before.pull-left {
-  margin-right: .3em;
-}
-.dirty-indicator-clean:before.pull-right {
-  margin-left: .3em;
-}
-#filename {
-  font-size: 16pt;
-  display: table;
-  padding: 0px 5px;
-}
-#current-mode {
-  padding-left: 5px;
-  padding-right: 5px;
-}
-#texteditor-backdrop {
-  padding-top: 20px;
-  padding-bottom: 20px;
-}
-@media not print {
-  #texteditor-backdrop {
-    background-color: #EEE;
-  }
-}
-@media print {
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
-    background-color: #fff;
-  }
-}
-@media not print {
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
-    background-color: #fff;
-  }
-}
-@media not print {
-  #texteditor-backdrop #texteditor-container {
-    padding: 0px;
-    background-color: #fff;
-    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  }
-}
-/*!
-*
-* IPython notebook
-*
-*/
-/* CSS font colors for translated ANSI colors. */
-.ansibold {
-  font-weight: bold;
-}
-/* use dark versions for foreground, to improve visibility */
-.ansiblack {
-  color: black;
-}
-.ansired {
-  color: darkred;
-}
-.ansigreen {
-  color: darkgreen;
-}
-.ansiyellow {
-  color: #c4a000;
-}
-.ansiblue {
-  color: darkblue;
-}
-.ansipurple {
-  color: darkviolet;
-}
-.ansicyan {
-  color: steelblue;
-}
-.ansigray {
-  color: gray;
-}
-/* and light for background, for the same reason */
-.ansibgblack {
-  background-color: black;
-}
-.ansibgred {
-  background-color: red;
-}
-.ansibggreen {
-  background-color: green;
-}
-.ansibgyellow {
-  background-color: yellow;
-}
-.ansibgblue {
-  background-color: blue;
-}
-.ansibgpurple {
-  background-color: magenta;
-}
-.ansibgcyan {
-  background-color: cyan;
-}
-.ansibggray {
-  background-color: gray;
-}
-div.cell {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  border-radius: 2px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  border-width: 1px;
-  border-style: solid;
-  border-color: transparent;
-  width: 100%;
-  padding: 5px;
-  /* This acts as a spacer between cells, that is outside the border */
-  margin: 0px;
-  outline: none;
-  border-left-width: 1px;
-  padding-left: 5px;
-  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
-}
-div.cell.jupyter-soft-selected {
-  border-left-color: #90CAF9;
-  border-left-color: #E3F2FD;
-  border-left-width: 1px;
-  padding-left: 5px;
-  border-right-color: #E3F2FD;
-  border-right-width: 1px;
-  background: #E3F2FD;
-}
-@media print {
-  div.cell.jupyter-soft-selected {
-    border-color: transparent;
-  }
-}
-div.cell.selected {
-  border-color: #ababab;
-  border-left-width: 0px;
-  padding-left: 6px;
-  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
-}
-@media print {
-  div.cell.selected {
-    border-color: transparent;
-  }
-}
-div.cell.selected.jupyter-soft-selected {
-  border-left-width: 0;
-  padding-left: 6px;
-  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
-}
-.edit_mode div.cell.selected {
-  border-color: #66BB6A;
-  border-left-width: 0px;
-  padding-left: 6px;
-  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
-}
-@media print {
-  .edit_mode div.cell.selected {
-    border-color: transparent;
-  }
-}
-.prompt {
-  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
-  min-width: 14ex;
-  /* This padding is tuned to match the padding on the CodeMirror editor. */
-  padding: 0.4em;
-  margin: 0px;
-  font-family: monospace;
-  text-align: right;
-  /* This has to match that of the the CodeMirror class line-height below */
-  line-height: 1.21429em;
-  /* Don't highlight prompt number selection */
-  -webkit-touch-callout: none;
-  -webkit-user-select: none;
-  -khtml-user-select: none;
-  -moz-user-select: none;
-  -ms-user-select: none;
-  user-select: none;
-  /* Use default cursor */
-  cursor: default;
-}
-@media (max-width: 540px) {
-  .prompt {
-    text-align: left;
-  }
-}
-div.inner_cell {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-}
-@-moz-document url-prefix() {
-  div.inner_cell {
-    overflow-x: hidden;
-  }
-}
-/* input_area and input_prompt must match in top border and margin for alignment */
-div.input_area {
-  border: 1px solid #cfcfcf;
-  border-radius: 2px;
-  background: #f7f7f7;
-  line-height: 1.21429em;
-}
-/* This is needed so that empty prompt areas can collapse to zero height when there
-   is no content in the output_subarea and the prompt. The main purpose of this is
-   to make sure that empty JavaScript output_subareas have no height. */
-div.prompt:empty {
-  padding-top: 0;
-  padding-bottom: 0;
-}
-div.unrecognized_cell {
-  padding: 5px 5px 5px 0px;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-}
-div.unrecognized_cell .inner_cell {
-  border-radius: 2px;
-  padding: 5px;
-  font-weight: bold;
-  color: red;
-  border: 1px solid #cfcfcf;
-  background: #eaeaea;
-}
-div.unrecognized_cell .inner_cell a {
-  color: inherit;
-  text-decoration: none;
-}
-div.unrecognized_cell .inner_cell a:hover {
-  color: inherit;
-  text-decoration: none;
-}
-@media (max-width: 540px) {
-  div.unrecognized_cell > div.prompt {
-    display: none;
-  }
-}
-div.code_cell {
-  /* avoid page breaking on code cells when printing */
-}
-@media print {
-  div.code_cell {
-    page-break-inside: avoid;
-  }
-}
-/* any special styling for code cells that are currently running goes here */
-div.input {
-  page-break-inside: avoid;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-}
-@media (max-width: 540px) {
-  div.input {
-    /* Old browsers */
-    display: -webkit-box;
-    -webkit-box-orient: vertical;
-    -webkit-box-align: stretch;
-    display: -moz-box;
-    -moz-box-orient: vertical;
-    -moz-box-align: stretch;
-    display: box;
-    box-orient: vertical;
-    box-align: stretch;
-    /* Modern browsers */
-    display: flex;
-    flex-direction: column;
-    align-items: stretch;
-  }
-}
-/* input_area and input_prompt must match in top border and margin for alignment */
-div.input_prompt {
-  color: #303F9F;
-  border-top: 1px solid transparent;
-}
-div.input_area > div.highlight {
-  margin: 0.4em;
-  border: none;
-  padding: 0px;
-  background-color: transparent;
-}
-div.input_area > div.highlight > pre {
-  margin: 0px;
-  border: none;
-  padding: 0px;
-  background-color: transparent;
-}
-/* The following gets added to the <head> if it is detected that the user has a
- * monospace font with inconsistent normal/bold/italic height.  See
- * notebookmain.js.  Such fonts will have keywords vertically offset with
- * respect to the rest of the text.  The user should select a better font.
- * See: https://github.com/ipython/ipython/issues/1503
- *
- * .CodeMirror span {
- *      vertical-align: bottom;
- * }
- */
-.CodeMirror {
-  line-height: 1.21429em;
-  /* Changed from 1em to our global default */
-  font-size: 14px;
-  height: auto;
-  /* Changed to auto to autogrow */
-  background: none;
-  /* Changed from white to allow our bg to show through */
-}
-.CodeMirror-scroll {
-  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
-  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
-  overflow-y: hidden;
-  overflow-x: auto;
-}
-.CodeMirror-lines {
-  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
-  /* we have set a different line-height and want this to scale with that. */
-  padding: 0.4em;
-}
-.CodeMirror-linenumber {
-  padding: 0 8px 0 4px;
-}
-.CodeMirror-gutters {
-  border-bottom-left-radius: 2px;
-  border-top-left-radius: 2px;
-}
-.CodeMirror pre {
-  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
-  /* .CodeMirror-lines */
-  padding: 0;
-  border: 0;
-  border-radius: 0;
-}
-/*
-
-Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
-Adapted from GitHub theme
-
-*/
-.highlight-base {
-  color: #000;
-}
-.highlight-variable {
-  color: #000;
-}
-.highlight-variable-2 {
-  color: #1a1a1a;
-}
-.highlight-variable-3 {
-  color: #333333;
-}
-.highlight-string {
-  color: #BA2121;
-}
-.highlight-comment {
-  color: #408080;
-  font-style: italic;
-}
-.highlight-number {
-  color: #080;
-}
-.highlight-atom {
-  color: #88F;
-}
-.highlight-keyword {
-  color: #008000;
-  font-weight: bold;
-}
-.highlight-builtin {
-  color: #008000;
-}
-.highlight-error {
-  color: #f00;
-}
-.highlight-operator {
-  color: #AA22FF;
-  font-weight: bold;
-}
-.highlight-meta {
-  color: #AA22FF;
-}
-/* previously not defined, copying from default codemirror */
-.highlight-def {
-  color: #00f;
-}
-.highlight-string-2 {
-  color: #f50;
-}
-.highlight-qualifier {
-  color: #555;
-}
-.highlight-bracket {
-  color: #997;
-}
-.highlight-tag {
-  color: #170;
-}
-.highlight-attribute {
-  color: #00c;
-}
-.highlight-header {
-  color: blue;
-}
-.highlight-quote {
-  color: #090;
-}
-.highlight-link {
-  color: #00c;
-}
-/* apply the same style to codemirror */
-.cm-s-ipython span.cm-keyword {
-  color: #008000;
-  font-weight: bold;
-}
-.cm-s-ipython span.cm-atom {
-  color: #88F;
-}
-.cm-s-ipython span.cm-number {
-  color: #080;
-}
-.cm-s-ipython span.cm-def {
-  color: #00f;
-}
-.cm-s-ipython span.cm-variable {
-  color: #000;
-}
-.cm-s-ipython span.cm-operator {
-  color: #AA22FF;
-  font-weight: bold;
-}
-.cm-s-ipython span.cm-variable-2 {
-  color: #1a1a1a;
-}
-.cm-s-ipython span.cm-variable-3 {
-  color: #333333;
-}
-.cm-s-ipython span.cm-comment {
-  color: #408080;
-  font-style: italic;
-}
-.cm-s-ipython span.cm-string {
-  color: #BA2121;
-}
-.cm-s-ipython span.cm-string-2 {
-  color: #f50;
-}
-.cm-s-ipython span.cm-meta {
-  color: #AA22FF;
-}
-.cm-s-ipython span.cm-qualifier {
-  color: #555;
-}
-.cm-s-ipython span.cm-builtin {
-  color: #008000;
-}
-.cm-s-ipython span.cm-bracket {
-  color: #997;
-}
-.cm-s-ipython span.cm-tag {
-  color: #170;
-}
-.cm-s-ipython span.cm-attribute {
-  color: #00c;
-}
-.cm-s-ipython span.cm-header {
-  color: blue;
-}
-.cm-s-ipython span.cm-quote {
-  color: #090;
-}
-.cm-s-ipython span.cm-link {
-  color: #00c;
-}
-.cm-s-ipython span.cm-error {
-  color: #f00;
-}
-.cm-s-ipython span.cm-tab {
-  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
-  background-position: right;
-  background-repeat: no-repeat;
-}
-div.output_wrapper {
-  /* this position must be relative to enable descendents to be absolute within it */
-  position: relative;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  z-index: 1;
-}
-/* class for the output area when it should be height-limited */
-div.output_scroll {
-  /* ideally, this would be max-height, but FF barfs all over that */
-  height: 24em;
-  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
-  width: 100%;
-  overflow: auto;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
-  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
-  display: block;
-}
-/* output div while it is collapsed */
-div.output_collapsed {
-  margin: 0px;
-  padding: 0px;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-}
-div.out_prompt_overlay {
-  height: 100%;
-  padding: 0px 0.4em;
-  position: absolute;
-  border-radius: 2px;
-}
-div.out_prompt_overlay:hover {
-  /* use inner shadow to get border that is computed the same on WebKit/FF */
-  -webkit-box-shadow: inset 0 0 1px #000;
-  box-shadow: inset 0 0 1px #000;
-  background: rgba(240, 240, 240, 0.5);
-}
-div.output_prompt {
-  color: #D84315;
-}
-/* This class is the outer container of all output sections. */
-div.output_area {
-  padding: 0px;
-  page-break-inside: avoid;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-}
-div.output_area .MathJax_Display {
-  text-align: left !important;
-}
-div.output_area .rendered_html table {
-  margin-left: 0;
-  margin-right: 0;
-}
-div.output_area .rendered_html img {
-  margin-left: 0;
-  margin-right: 0;
-}
-div.output_area img,
-div.output_area svg {
-  max-width: 100%;
-  height: auto;
-}
-div.output_area img.unconfined,
-div.output_area svg.unconfined {
-  max-width: none;
-}
-/* This is needed to protect the pre formating from global settings such
-   as that of bootstrap */
-.output {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-}
-@media (max-width: 540px) {
-  div.output_area {
-    /* Old browsers */
-    display: -webkit-box;
-    -webkit-box-orient: vertical;
-    -webkit-box-align: stretch;
-    display: -moz-box;
-    -moz-box-orient: vertical;
-    -moz-box-align: stretch;
-    display: box;
-    box-orient: vertical;
-    box-align: stretch;
-    /* Modern browsers */
-    display: flex;
-    flex-direction: column;
-    align-items: stretch;
-  }
-}
-div.output_area pre {
-  margin: 0;
-  padding: 0;
-  border: 0;
-  vertical-align: baseline;
-  color: black;
-  background-color: transparent;
-  border-radius: 0;
-}
-/* This class is for the output subarea inside the output_area and after
-   the prompt div. */
-div.output_subarea {
-  overflow-x: auto;
-  padding: 0.4em;
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-  max-width: calc(100% - 14ex);
-}
-div.output_scroll div.output_subarea {
-  overflow-x: visible;
-}
-/* The rest of the output_* classes are for special styling of the different
-   output types */
-/* all text output has this class: */
-div.output_text {
-  text-align: left;
-  color: #000;
-  /* This has to match that of the the CodeMirror class line-height below */
-  line-height: 1.21429em;
-}
-/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
-div.output_stderr {
-  background: #fdd;
-  /* very light red background for stderr */
-}
-div.output_latex {
-  text-align: left;
-}
-/* Empty output_javascript divs should have no height */
-div.output_javascript:empty {
-  padding: 0;
-}
-.js-error {
-  color: darkred;
-}
-/* raw_input styles */
-div.raw_input_container {
-  line-height: 1.21429em;
-  padding-top: 5px;
-}
-pre.raw_input_prompt {
-  /* nothing needed here. */
-}
-input.raw_input {
-  font-family: monospace;
-  font-size: inherit;
-  color: inherit;
-  width: auto;
-  /* make sure input baseline aligns with prompt */
-  vertical-align: baseline;
-  /* padding + margin = 0.5em between prompt and cursor */
-  padding: 0em 0.25em;
-  margin: 0em 0.25em;
-}
-input.raw_input:focus {
-  box-shadow: none;
-}
-p.p-space {
-  margin-bottom: 10px;
-}
-div.output_unrecognized {
-  padding: 5px;
-  font-weight: bold;
-  color: red;
-}
-div.output_unrecognized a {
-  color: inherit;
-  text-decoration: none;
-}
-div.output_unrecognized a:hover {
-  color: inherit;
-  text-decoration: none;
-}
-.rendered_html {
-  color: #000;
-  /* any extras will just be numbers: */
-}
-.rendered_html em {
-  font-style: italic;
-}
-.rendered_html strong {
-  font-weight: bold;
-}
-.rendered_html u {
-  text-decoration: underline;
-}
-.rendered_html :link {
-  text-decoration: underline;
-}
-.rendered_html :visited {
-  text-decoration: underline;
-}
-.rendered_html h1 {
-  font-size: 185.7%;
-  margin: 1.08em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-}
-.rendered_html h2 {
-  font-size: 157.1%;
-  margin: 1.27em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-}
-.rendered_html h3 {
-  font-size: 128.6%;
-  margin: 1.55em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-}
-.rendered_html h4 {
-  font-size: 100%;
-  margin: 2em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-}
-.rendered_html h5 {
-  font-size: 100%;
-  margin: 2em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-  font-style: italic;
-}
-.rendered_html h6 {
-  font-size: 100%;
-  margin: 2em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-  font-style: italic;
-}
-.rendered_html h1:first-child {
-  margin-top: 0.538em;
-}
-.rendered_html h2:first-child {
-  margin-top: 0.636em;
-}
-.rendered_html h3:first-child {
-  margin-top: 0.777em;
-}
-.rendered_html h4:first-child {
-  margin-top: 1em;
-}
-.rendered_html h5:first-child {
-  margin-top: 1em;
-}
-.rendered_html h6:first-child {
-  margin-top: 1em;
-}
-.rendered_html ul {
-  list-style: disc;
-  margin: 0em 2em;
-  padding-left: 0px;
-}
-.rendered_html ul ul {
-  list-style: square;
-  margin: 0em 2em;
-}
-.rendered_html ul ul ul {
-  list-style: circle;
-  margin: 0em 2em;
-}
-.rendered_html ol {
-  list-style: decimal;
-  margin: 0em 2em;
-  padding-left: 0px;
-}
-.rendered_html ol ol {
-  list-style: upper-alpha;
-  margin: 0em 2em;
-}
-.rendered_html ol ol ol {
-  list-style: lower-alpha;
-  margin: 0em 2em;
-}
-.rendered_html ol ol ol ol {
-  list-style: lower-roman;
-  margin: 0em 2em;
-}
-.rendered_html ol ol ol ol ol {
-  list-style: decimal;
-  margin: 0em 2em;
-}
-.rendered_html * + ul {
-  margin-top: 1em;
-}
-.rendered_html * + ol {
-  margin-top: 1em;
-}
-.rendered_html hr {
-  color: black;
-  background-color: black;
-}
-.rendered_html pre {
-  margin: 1em 2em;
-}
-.rendered_html pre,
-.rendered_html code {
-  border: 0;
-  background-color: #fff;
-  color: #000;
-  font-size: 100%;
-  padding: 0px;
-}
-.rendered_html blockquote {
-  margin: 1em 2em;
-}
-.rendered_html table {
-  margin-left: auto;
-  margin-right: auto;
-  border: 1px solid black;
-  border-collapse: collapse;
-}
-.rendered_html tr,
-.rendered_html th,
-.rendered_html td {
-  border: 1px solid black;
-  border-collapse: collapse;
-  margin: 1em 2em;
-}
-.rendered_html td,
-.rendered_html th {
-  text-align: left;
-  vertical-align: middle;
-  padding: 4px;
-}
-.rendered_html th {
-  font-weight: bold;
-}
-.rendered_html * + table {
-  margin-top: 1em;
-}
-.rendered_html p {
-  text-align: left;
-}
-.rendered_html * + p {
-  margin-top: 1em;
-}
-.rendered_html img {
-  display: block;
-  margin-left: auto;
-  margin-right: auto;
-}
-.rendered_html * + img {
-  margin-top: 1em;
-}
-.rendered_html img,
-.rendered_html svg {
-  max-width: 100%;
-  height: auto;
-}
-.rendered_html img.unconfined,
-.rendered_html svg.unconfined {
-  max-width: none;
-}
-div.text_cell {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-}
-@media (max-width: 540px) {
-  div.text_cell > div.prompt {
-    display: none;
-  }
-}
-div.text_cell_render {
-  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
-  outline: none;
-  resize: none;
-  width: inherit;
-  border-style: none;
-  padding: 0.5em 0.5em 0.5em 0.4em;
-  color: #000;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-a.anchor-link:link {
-  text-decoration: none;
-  padding: 0px 20px;
-  visibility: hidden;
-}
-h1:hover .anchor-link,
-h2:hover .anchor-link,
-h3:hover .anchor-link,
-h4:hover .anchor-link,
-h5:hover .anchor-link,
-h6:hover .anchor-link {
-  visibility: visible;
-}
-.text_cell.rendered .input_area {
-  display: none;
-}
-.text_cell.rendered .rendered_html {
-  overflow-x: auto;
-  overflow-y: hidden;
-}
-.text_cell.unrendered .text_cell_render {
-  display: none;
-}
-.cm-header-1,
-.cm-header-2,
-.cm-header-3,
-.cm-header-4,
-.cm-header-5,
-.cm-header-6 {
-  font-weight: bold;
-  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
-}
-.cm-header-1 {
-  font-size: 185.7%;
-}
-.cm-header-2 {
-  font-size: 157.1%;
-}
-.cm-header-3 {
-  font-size: 128.6%;
-}
-.cm-header-4 {
-  font-size: 110%;
-}
-.cm-header-5 {
-  font-size: 100%;
-  font-style: italic;
-}
-.cm-header-6 {
-  font-size: 100%;
-  font-style: italic;
-}
-/*!
-*
-* IPython notebook webapp
-*
-*/
-@media (max-width: 767px) {
-  .notebook_app {
-    padding-left: 0px;
-    padding-right: 0px;
-  }
-}
-#ipython-main-app {
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  height: 100%;
-}
-div#notebook_panel {
-  margin: 0px;
-  padding: 0px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  height: 100%;
-}
-div#notebook {
-  font-size: 14px;
-  line-height: 20px;
-  overflow-y: hidden;
-  overflow-x: auto;
-  width: 100%;
-  /* This spaces the page away from the edge of the notebook area */
-  padding-top: 20px;
-  margin: 0px;
-  outline: none;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  min-height: 100%;
-}
-@media not print {
-  #notebook-container {
-    padding: 15px;
-    background-color: #fff;
-    min-height: 0;
-    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  }
-}
-@media print {
-  #notebook-container {
-    width: 100%;
-  }
-}
-div.ui-widget-content {
-  border: 1px solid #ababab;
-  outline: none;
-}
-pre.dialog {
-  background-color: #f7f7f7;
-  border: 1px solid #ddd;
-  border-radius: 2px;
-  padding: 0.4em;
-  padding-left: 2em;
-}
-p.dialog {
-  padding: 0.2em;
-}
-/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
-   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
- */
-pre,
-code,
-kbd,
-samp {
-  white-space: pre-wrap;
-}
-#fonttest {
-  font-family: monospace;
-}
-p {
-  margin-bottom: 0;
-}
-.end_space {
-  min-height: 100px;
-  transition: height .2s ease;
-}
-.notebook_app > #header {
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-}
-@media not print {
-  .notebook_app {
-    background-color: #EEE;
-  }
-}
-kbd {
-  border-style: solid;
-  border-width: 1px;
-  box-shadow: none;
-  margin: 2px;
-  padding-left: 2px;
-  padding-right: 2px;
-  padding-top: 1px;
-  padding-bottom: 1px;
-}
-/* CSS for the cell toolbar */
-.celltoolbar {
-  border: thin solid #CFCFCF;
-  border-bottom: none;
-  background: #EEE;
-  border-radius: 2px 2px 0px 0px;
-  width: 100%;
-  height: 29px;
-  padding-right: 4px;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-  /* Old browsers */
-  -webkit-box-pack: end;
-  -moz-box-pack: end;
-  box-pack: end;
-  /* Modern browsers */
-  justify-content: flex-end;
-  display: -webkit-flex;
-}
-@media print {
-  .celltoolbar {
-    display: none;
-  }
-}
-.ctb_hideshow {
-  display: none;
-  vertical-align: bottom;
-}
-/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
-   Cell toolbars are only shown when the ctb_global_show class is also set.
-*/
-.ctb_global_show .ctb_show.ctb_hideshow {
-  display: block;
-}
-.ctb_global_show .ctb_show + .input_area,
-.ctb_global_show .ctb_show + div.text_cell_input,
-.ctb_global_show .ctb_show ~ div.text_cell_render {
-  border-top-right-radius: 0px;
-  border-top-left-radius: 0px;
-}
-.ctb_global_show .ctb_show ~ div.text_cell_render {
-  border: 1px solid #cfcfcf;
-}
-.celltoolbar {
-  font-size: 87%;
-  padding-top: 3px;
-}
-.celltoolbar select {
-  display: block;
-  width: 100%;
-  height: 32px;
-  padding: 6px 12px;
-  font-size: 13px;
-  line-height: 1.42857143;
-  color: #555555;
-  background-color: #fff;
-  background-image: none;
-  border: 1px solid #ccc;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-  height: 30px;
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-  width: inherit;
-  font-size: inherit;
-  height: 22px;
-  padding: 0px;
-  display: inline-block;
-}
-.celltoolbar select:focus {
-  border-color: #66afe9;
-  outline: 0;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
-  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
-}
-.celltoolbar select::-moz-placeholder {
-  color: #999;
-  opacity: 1;
-}
-.celltoolbar select:-ms-input-placeholder {
-  color: #999;
-}
-.celltoolbar select::-webkit-input-placeholder {
-  color: #999;
-}
-.celltoolbar select::-ms-expand {
-  border: 0;
-  background-color: transparent;
-}
-.celltoolbar select[disabled],
-.celltoolbar select[readonly],
-fieldset[disabled] .celltoolbar select {
-  background-color: #eeeeee;
-  opacity: 1;
-}
-.celltoolbar select[disabled],
-fieldset[disabled] .celltoolbar select {
-  cursor: not-allowed;
-}
-textarea.celltoolbar select {
-  height: auto;
-}
-select.celltoolbar select {
-  height: 30px;
-  line-height: 30px;
-}
-textarea.celltoolbar select,
-select[multiple].celltoolbar select {
-  height: auto;
-}
-.celltoolbar label {
-  margin-left: 5px;
-  margin-right: 5px;
-}
-.completions {
-  position: absolute;
-  z-index: 110;
-  overflow: hidden;
-  border: 1px solid #ababab;
-  border-radius: 2px;
-  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
-  box-shadow: 0px 6px 10px -1px #adadad;
-  line-height: 1;
-}
-.completions select {
-  background: white;
-  outline: none;
-  border: none;
-  padding: 0px;
-  margin: 0px;
-  overflow: auto;
-  font-family: monospace;
-  font-size: 110%;
-  color: #000;
-  width: auto;
-}
-.completions select option.context {
-  color: #286090;
-}
-#kernel_logo_widget {
-  float: right !important;
-  float: right;
-}
-#kernel_logo_widget .current_kernel_logo {
-  display: none;
-  margin-top: -1px;
-  margin-bottom: -1px;
-  width: 32px;
-  height: 32px;
-}
-#menubar {
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  margin-top: 1px;
-}
-#menubar .navbar {
-  border-top: 1px;
-  border-radius: 0px 0px 2px 2px;
-  margin-bottom: 0px;
-}
-#menubar .navbar-toggle {
-  float: left;
-  padding-top: 7px;
-  padding-bottom: 7px;
-  border: none;
-}
-#menubar .navbar-collapse {
-  clear: left;
-}
-.nav-wrapper {
-  border-bottom: 1px solid #e7e7e7;
-}
-i.menu-icon {
-  padding-top: 4px;
-}
-ul#help_menu li a {
-  overflow: hidden;
-  padding-right: 2.2em;
-}
-ul#help_menu li a i {
-  margin-right: -1.2em;
-}
-.dropdown-submenu {
-  position: relative;
-}
-.dropdown-submenu > .dropdown-menu {
-  top: 0;
-  left: 100%;
-  margin-top: -6px;
-  margin-left: -1px;
-}
-.dropdown-submenu:hover > .dropdown-menu {
-  display: block;
-}
-.dropdown-submenu > a:after {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  display: block;
-  content: "\f0da";
-  float: right;
-  color: #333333;
-  margin-top: 2px;
-  margin-right: -10px;
-}
-.dropdown-submenu > a:after.pull-left {
-  margin-right: .3em;
-}
-.dropdown-submenu > a:after.pull-right {
-  margin-left: .3em;
-}
-.dropdown-submenu:hover > a:after {
-  color: #262626;
-}
-.dropdown-submenu.pull-left {
-  float: none;
-}
-.dropdown-submenu.pull-left > .dropdown-menu {
-  left: -100%;
-  margin-left: 10px;
-}
-#notification_area {
-  float: right !important;
-  float: right;
-  z-index: 10;
-}
-.indicator_area {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
-}
-#kernel_indicator {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
-  border-left: 1px solid;
-}
-#kernel_indicator .kernel_indicator_name {
-  padding-left: 5px;
-  padding-right: 5px;
-}
-#modal_indicator {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
-}
-#readonly-indicator {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
-  margin-top: 2px;
-  margin-bottom: 0px;
-  margin-left: 0px;
-  margin-right: 0px;
-  display: none;
-}
-.modal_indicator:before {
-  width: 1.28571429em;
-  text-align: center;
-}
-.edit_mode .modal_indicator:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f040";
-}
-.edit_mode .modal_indicator:before.pull-left {
-  margin-right: .3em;
-}
-.edit_mode .modal_indicator:before.pull-right {
-  margin-left: .3em;
-}
-.command_mode .modal_indicator:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: ' ';
-}
-.command_mode .modal_indicator:before.pull-left {
-  margin-right: .3em;
-}
-.command_mode .modal_indicator:before.pull-right {
-  margin-left: .3em;
-}
-.kernel_idle_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f10c";
-}
-.kernel_idle_icon:before.pull-left {
-  margin-right: .3em;
-}
-.kernel_idle_icon:before.pull-right {
-  margin-left: .3em;
-}
-.kernel_busy_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f111";
-}
-.kernel_busy_icon:before.pull-left {
-  margin-right: .3em;
-}
-.kernel_busy_icon:before.pull-right {
-  margin-left: .3em;
-}
-.kernel_dead_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f1e2";
-}
-.kernel_dead_icon:before.pull-left {
-  margin-right: .3em;
-}
-.kernel_dead_icon:before.pull-right {
-  margin-left: .3em;
-}
-.kernel_disconnected_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f127";
-}
-.kernel_disconnected_icon:before.pull-left {
-  margin-right: .3em;
-}
-.kernel_disconnected_icon:before.pull-right {
-  margin-left: .3em;
-}
-.notification_widget {
-  color: #777;
-  z-index: 10;
-  background: rgba(240, 240, 240, 0.5);
-  margin-right: 4px;
-  color: #333;
-  background-color: #fff;
-  border-color: #ccc;
-}
-.notification_widget:focus,
-.notification_widget.focus {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #8c8c8c;
-}
-.notification_widget:hover {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-.notification_widget:active,
-.notification_widget.active,
-.open > .dropdown-toggle.notification_widget {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-.notification_widget:active:hover,
-.notification_widget.active:hover,
-.open > .dropdown-toggle.notification_widget:hover,
-.notification_widget:active:focus,
-.notification_widget.active:focus,
-.open > .dropdown-toggle.notification_widget:focus,
-.notification_widget:active.focus,
-.notification_widget.active.focus,
-.open > .dropdown-toggle.notification_widget.focus {
-  color: #333;
-  background-color: #d4d4d4;
-  border-color: #8c8c8c;
-}
-.notification_widget:active,
-.notification_widget.active,
-.open > .dropdown-toggle.notification_widget {
-  background-image: none;
-}
-.notification_widget.disabled:hover,
-.notification_widget[disabled]:hover,
-fieldset[disabled] .notification_widget:hover,
-.notification_widget.disabled:focus,
-.notification_widget[disabled]:focus,
-fieldset[disabled] .notification_widget:focus,
-.notification_widget.disabled.focus,
-.notification_widget[disabled].focus,
-fieldset[disabled] .notification_widget.focus {
-  background-color: #fff;
-  border-color: #ccc;
-}
-.notification_widget .badge {
-  color: #fff;
-  background-color: #333;
-}
-.notification_widget.warning {
-  color: #fff;
-  background-color: #f0ad4e;
-  border-color: #eea236;
-}
-.notification_widget.warning:focus,
-.notification_widget.warning.focus {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #985f0d;
-}
-.notification_widget.warning:hover {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #d58512;
-}
-.notification_widget.warning:active,
-.notification_widget.warning.active,
-.open > .dropdown-toggle.notification_widget.warning {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #d58512;
-}
-.notification_widget.warning:active:hover,
-.notification_widget.warning.active:hover,
-.open > .dropdown-toggle.notification_widget.warning:hover,
-.notification_widget.warning:active:focus,
-.notification_widget.warning.active:focus,
-.open > .dropdown-toggle.notification_widget.warning:focus,
-.notification_widget.warning:active.focus,
-.notification_widget.warning.active.focus,
-.open > .dropdown-toggle.notification_widget.warning.focus {
-  color: #fff;
-  background-color: #d58512;
-  border-color: #985f0d;
-}
-.notification_widget.warning:active,
-.notification_widget.warning.active,
-.open > .dropdown-toggle.notification_widget.warning {
-  background-image: none;
-}
-.notification_widget.warning.disabled:hover,
-.notification_widget.warning[disabled]:hover,
-fieldset[disabled] .notification_widget.warning:hover,
-.notification_widget.warning.disabled:focus,
-.notification_widget.warning[disabled]:focus,
-fieldset[disabled] .notification_widget.warning:focus,
-.notification_widget.warning.disabled.focus,
-.notification_widget.warning[disabled].focus,
-fieldset[disabled] .notification_widget.warning.focus {
-  background-color: #f0ad4e;
-  border-color: #eea236;
-}
-.notification_widget.warning .badge {
-  color: #f0ad4e;
-  background-color: #fff;
-}
-.notification_widget.success {
-  color: #fff;
-  background-color: #5cb85c;
-  border-color: #4cae4c;
-}
-.notification_widget.success:focus,
-.notification_widget.success.focus {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #255625;
-}
-.notification_widget.success:hover {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #398439;
-}
-.notification_widget.success:active,
-.notification_widget.success.active,
-.open > .dropdown-toggle.notification_widget.success {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #398439;
-}
-.notification_widget.success:active:hover,
-.notification_widget.success.active:hover,
-.open > .dropdown-toggle.notification_widget.success:hover,
-.notification_widget.success:active:focus,
-.notification_widget.success.active:focus,
-.open > .dropdown-toggle.notification_widget.success:focus,
-.notification_widget.success:active.focus,
-.notification_widget.success.active.focus,
-.open > .dropdown-toggle.notification_widget.success.focus {
-  color: #fff;
-  background-color: #398439;
-  border-color: #255625;
-}
-.notification_widget.success:active,
-.notification_widget.success.active,
-.open > .dropdown-toggle.notification_widget.success {
-  background-image: none;
-}
-.notification_widget.success.disabled:hover,
-.notification_widget.success[disabled]:hover,
-fieldset[disabled] .notification_widget.success:hover,
-.notification_widget.success.disabled:focus,
-.notification_widget.success[disabled]:focus,
-fieldset[disabled] .notification_widget.success:focus,
-.notification_widget.success.disabled.focus,
-.notification_widget.success[disabled].focus,
-fieldset[disabled] .notification_widget.success.focus {
-  background-color: #5cb85c;
-  border-color: #4cae4c;
-}
-.notification_widget.success .badge {
-  color: #5cb85c;
-  background-color: #fff;
-}
-.notification_widget.info {
-  color: #fff;
-  background-color: #5bc0de;
-  border-color: #46b8da;
-}
-.notification_widget.info:focus,
-.notification_widget.info.focus {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #1b6d85;
-}
-.notification_widget.info:hover {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #269abc;
-}
-.notification_widget.info:active,
-.notification_widget.info.active,
-.open > .dropdown-toggle.notification_widget.info {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #269abc;
-}
-.notification_widget.info:active:hover,
-.notification_widget.info.active:hover,
-.open > .dropdown-toggle.notification_widget.info:hover,
-.notification_widget.info:active:focus,
-.notification_widget.info.active:focus,
-.open > .dropdown-toggle.notification_widget.info:focus,
-.notification_widget.info:active.focus,
-.notification_widget.info.active.focus,
-.open > .dropdown-toggle.notification_widget.info.focus {
-  color: #fff;
-  background-color: #269abc;
-  border-color: #1b6d85;
-}
-.notification_widget.info:active,
-.notification_widget.info.active,
-.open > .dropdown-toggle.notification_widget.info {
-  background-image: none;
-}
-.notification_widget.info.disabled:hover,
-.notification_widget.info[disabled]:hover,
-fieldset[disabled] .notification_widget.info:hover,
-.notification_widget.info.disabled:focus,
-.notification_widget.info[disabled]:focus,
-fieldset[disabled] .notification_widget.info:focus,
-.notification_widget.info.disabled.focus,
-.notification_widget.info[disabled].focus,
-fieldset[disabled] .notification_widget.info.focus {
-  background-color: #5bc0de;
-  border-color: #46b8da;
-}
-.notification_widget.info .badge {
-  color: #5bc0de;
-  background-color: #fff;
-}
-.notification_widget.danger {
-  color: #fff;
-  background-color: #d9534f;
-  border-color: #d43f3a;
-}
-.notification_widget.danger:focus,
-.notification_widget.danger.focus {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #761c19;
-}
-.notification_widget.danger:hover {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #ac2925;
-}
-.notification_widget.danger:active,
-.notification_widget.danger.active,
-.open > .dropdown-toggle.notification_widget.danger {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #ac2925;
-}
-.notification_widget.danger:active:hover,
-.notification_widget.danger.active:hover,
-.open > .dropdown-toggle.notification_widget.danger:hover,
-.notification_widget.danger:active:focus,
-.notification_widget.danger.active:focus,
-.open > .dropdown-toggle.notification_widget.danger:focus,
-.notification_widget.danger:active.focus,
-.notification_widget.danger.active.focus,
-.open > .dropdown-toggle.notification_widget.danger.focus {
-  color: #fff;
-  background-color: #ac2925;
-  border-color: #761c19;
-}
-.notification_widget.danger:active,
-.notification_widget.danger.active,
-.open > .dropdown-toggle.notification_widget.danger {
-  background-image: none;
-}
-.notification_widget.danger.disabled:hover,
-.notification_widget.danger[disabled]:hover,
-fieldset[disabled] .notification_widget.danger:hover,
-.notification_widget.danger.disabled:focus,
-.notification_widget.danger[disabled]:focus,
-fieldset[disabled] .notification_widget.danger:focus,
-.notification_widget.danger.disabled.focus,
-.notification_widget.danger[disabled].focus,
-fieldset[disabled] .notification_widget.danger.focus {
-  background-color: #d9534f;
-  border-color: #d43f3a;
-}
-.notification_widget.danger .badge {
-  color: #d9534f;
-  background-color: #fff;
-}
-div#pager {
-  background-color: #fff;
-  font-size: 14px;
-  line-height: 20px;
-  overflow: hidden;
-  display: none;
-  position: fixed;
-  bottom: 0px;
-  width: 100%;
-  max-height: 50%;
-  padding-top: 8px;
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  /* Display over codemirror */
-  z-index: 100;
-  /* Hack which prevents jquery ui resizable from changing top. */
-  top: auto !important;
-}
-div#pager pre {
-  line-height: 1.21429em;
-  color: #000;
-  background-color: #f7f7f7;
-  padding: 0.4em;
-}
-div#pager #pager-button-area {
-  position: absolute;
-  top: 8px;
-  right: 20px;
-}
-div#pager #pager-contents {
-  position: relative;
-  overflow: auto;
-  width: 100%;
-  height: 100%;
-}
-div#pager #pager-contents #pager-container {
-  position: relative;
-  padding: 15px 0px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-div#pager .ui-resizable-handle {
-  top: 0px;
-  height: 8px;
-  background: #f7f7f7;
-  border-top: 1px solid #cfcfcf;
-  border-bottom: 1px solid #cfcfcf;
-  /* This injects handle bars (a short, wide = symbol) for 
-        the resize handle. */
-}
-div#pager .ui-resizable-handle::after {
-  content: '';
-  top: 2px;
-  left: 50%;
-  height: 3px;
-  width: 30px;
-  margin-left: -15px;
-  position: absolute;
-  border-top: 1px solid #cfcfcf;
-}
-.quickhelp {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-  line-height: 1.8em;
-}
-.shortcut_key {
-  display: inline-block;
-  width: 20ex;
-  text-align: right;
-  font-family: monospace;
-}
-.shortcut_descr {
-  display: inline-block;
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-}
-span.save_widget {
-  margin-top: 6px;
-}
-span.save_widget span.filename {
-  height: 1em;
-  line-height: 1em;
-  padding: 3px;
-  margin-left: 16px;
-  border: none;
-  font-size: 146.5%;
-  border-radius: 2px;
-}
-span.save_widget span.filename:hover {
-  background-color: #e6e6e6;
-}
-span.checkpoint_status,
-span.autosave_status {
-  font-size: small;
-}
-@media (max-width: 767px) {
-  span.save_widget {
-    font-size: small;
-  }
-  span.checkpoint_status,
-  span.autosave_status {
-    display: none;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  span.checkpoint_status {
-    display: none;
-  }
-  span.autosave_status {
-    font-size: x-small;
-  }
-}
-.toolbar {
-  padding: 0px;
-  margin-left: -5px;
-  margin-top: 2px;
-  margin-bottom: 5px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-.toolbar select,
-.toolbar label {
-  width: auto;
-  vertical-align: middle;
-  margin-right: 2px;
-  margin-bottom: 0px;
-  display: inline;
-  font-size: 92%;
-  margin-left: 0.3em;
-  margin-right: 0.3em;
-  padding: 0px;
-  padding-top: 3px;
-}
-.toolbar .btn {
-  padding: 2px 8px;
-}
-.toolbar .btn-group {
-  margin-top: 0px;
-  margin-left: 5px;
-}
-#maintoolbar {
-  margin-bottom: -3px;
-  margin-top: -8px;
-  border: 0px;
-  min-height: 27px;
-  margin-left: 0px;
-  padding-top: 11px;
-  padding-bottom: 3px;
-}
-#maintoolbar .navbar-text {
-  float: none;
-  vertical-align: middle;
-  text-align: right;
-  margin-left: 5px;
-  margin-right: 0px;
-  margin-top: 0px;
-}
-.select-xs {
-  height: 24px;
-}
-.pulse,
-.dropdown-menu > li > a.pulse,
-li.pulse > a.dropdown-toggle,
-li.pulse.open > a.dropdown-toggle {
-  background-color: #F37626;
-  color: white;
-}
-/**
- * Primary styles
- *
- * Author: Jupyter Development Team
- */
-/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
- * of chance of beeing generated from the ../less/[samename].less file, you can
- * try to get back the less file by reverting somme commit in history
- **/
-/*
- * We'll try to get something pretty, so we
- * have some strange css to have the scroll bar on
- * the left with fix button on the top right of the tooltip
- */
-@-moz-keyframes fadeOut {
-  from {
-    opacity: 1;
-  }
-  to {
-    opacity: 0;
-  }
-}
-@-webkit-keyframes fadeOut {
-  from {
-    opacity: 1;
-  }
-  to {
-    opacity: 0;
-  }
-}
-@-moz-keyframes fadeIn {
-  from {
-    opacity: 0;
-  }
-  to {
-    opacity: 1;
-  }
-}
-@-webkit-keyframes fadeIn {
-  from {
-    opacity: 0;
-  }
-  to {
-    opacity: 1;
-  }
-}
-/*properties of tooltip after "expand"*/
-.bigtooltip {
-  overflow: auto;
-  height: 200px;
-  -webkit-transition-property: height;
-  -webkit-transition-duration: 500ms;
-  -moz-transition-property: height;
-  -moz-transition-duration: 500ms;
-  transition-property: height;
-  transition-duration: 500ms;
-}
-/*properties of tooltip before "expand"*/
-.smalltooltip {
-  -webkit-transition-property: height;
-  -webkit-transition-duration: 500ms;
-  -moz-transition-property: height;
-  -moz-transition-duration: 500ms;
-  transition-property: height;
-  transition-duration: 500ms;
-  text-overflow: ellipsis;
-  overflow: hidden;
-  height: 80px;
-}
-.tooltipbuttons {
-  position: absolute;
-  padding-right: 15px;
-  top: 0px;
-  right: 0px;
-}
-.tooltiptext {
-  /*avoid the button to overlap on some docstring*/
-  padding-right: 30px;
-}
-.ipython_tooltip {
-  max-width: 700px;
-  /*fade-in animation when inserted*/
-  -webkit-animation: fadeOut 400ms;
-  -moz-animation: fadeOut 400ms;
-  animation: fadeOut 400ms;
-  -webkit-animation: fadeIn 400ms;
-  -moz-animation: fadeIn 400ms;
-  animation: fadeIn 400ms;
-  vertical-align: middle;
-  background-color: #f7f7f7;
-  overflow: visible;
-  border: #ababab 1px solid;
-  outline: none;
-  padding: 3px;
-  margin: 0px;
-  padding-left: 7px;
-  font-family: monospace;
-  min-height: 50px;
-  -moz-box-shadow: 0px 6px 10px -1px #adadad;
-  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
-  box-shadow: 0px 6px 10px -1px #adadad;
-  border-radius: 2px;
-  position: absolute;
-  z-index: 1000;
-}
-.ipython_tooltip a {
-  float: right;
-}
-.ipython_tooltip .tooltiptext pre {
-  border: 0;
-  border-radius: 0;
-  font-size: 100%;
-  background-color: #f7f7f7;
-}
-.pretooltiparrow {
-  left: 0px;
-  margin: 0px;
-  top: -16px;
-  width: 40px;
-  height: 16px;
-  overflow: hidden;
-  position: absolute;
-}
-.pretooltiparrow:before {
-  background-color: #f7f7f7;
-  border: 1px #ababab solid;
-  z-index: 11;
-  content: "";
-  position: absolute;
-  left: 15px;
-  top: 10px;
-  width: 25px;
-  height: 25px;
-  -webkit-transform: rotate(45deg);
-  -moz-transform: rotate(45deg);
-  -ms-transform: rotate(45deg);
-  -o-transform: rotate(45deg);
-}
-ul.typeahead-list i {
-  margin-left: -10px;
-  width: 18px;
-}
-ul.typeahead-list {
-  max-height: 80vh;
-  overflow: auto;
-}
-ul.typeahead-list > li > a {
-  /** Firefox bug **/
-  /* see https://github.com/jupyter/notebook/issues/559 */
-  white-space: normal;
-}
-.cmd-palette .modal-body {
-  padding: 7px;
-}
-.cmd-palette form {
-  background: white;
-}
-.cmd-palette input {
-  outline: none;
-}
-.no-shortcut {
-  display: none;
-}
-.command-shortcut:before {
-  content: "(command)";
-  padding-right: 3px;
-  color: #777777;
-}
-.edit-shortcut:before {
-  content: "(edit)";
-  padding-right: 3px;
-  color: #777777;
-}
-#find-and-replace #replace-preview .match,
-#find-and-replace #replace-preview .insert {
-  background-color: #BBDEFB;
-  border-color: #90CAF9;
-  border-style: solid;
-  border-width: 1px;
-  border-radius: 0px;
-}
-#find-and-replace #replace-preview .replace .match {
-  background-color: #FFCDD2;
-  border-color: #EF9A9A;
-  border-radius: 0px;
-}
-#find-and-replace #replace-preview .replace .insert {
-  background-color: #C8E6C9;
-  border-color: #A5D6A7;
-  border-radius: 0px;
-}
-#find-and-replace #replace-preview {
-  max-height: 60vh;
-  overflow: auto;
-}
-#find-and-replace #replace-preview pre {
-  padding: 5px 10px;
-}
-.terminal-app {
-  background: #EEE;
-}
-.terminal-app #header {
-  background: #fff;
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-}
-.terminal-app .terminal {
-  float: left;
-  font-family: monospace;
-  color: white;
-  background: black;
-  padding: 0.4em;
-  border-radius: 2px;
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
-}
-.terminal-app .terminal,
-.terminal-app .terminal dummy-screen {
-  line-height: 1em;
-  font-size: 14px;
-}
-.terminal-app .terminal-cursor {
-  color: black;
-  background: white;
-}
-.terminal-app #terminado-container {
-  margin-top: 20px;
-}
-/*# sourceMappingURL=style.min.css.map */
-    </style>
-<style type="text/css">
-    .highlight .hll { background-color: #ffffcc }
-.highlight  { background: #f8f8f8; }
-.highlight .c { color: #408080; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
-.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #A00000 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gr { color: #FF0000 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #00A000 } /* Generic.Inserted */
-.highlight .go { color: #888888 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
-.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: #008000 } /* Keyword.Pseudo */
-.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: #B00040 } /* Keyword.Type */
-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
-.highlight .na { color: #7D9029 } /* Name.Attribute */
-.highlight .nb { color: #008000 } /* Name.Builtin */
-.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
-.highlight .no { color: #880000 } /* Name.Constant */
-.highlight .nd { color: #AA22FF } /* Name.Decorator */
-.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
-.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
-.highlight .nf { color: #0000FF } /* Name.Function */
-.highlight .nl { color: #A0A000 } /* Name.Label */
-.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
-.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #19177C } /* Name.Variable */
-.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mb { color: #666666 } /* Literal.Number.Bin */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
-    </style>
-<style type="text/css">
-    
-/* Temporary definitions which will become obsolete with Notebook release 5.0 */
-.ansi-black-fg { color: #3E424D; }
-.ansi-black-bg { background-color: #3E424D; }
-.ansi-black-intense-fg { color: #282C36; }
-.ansi-black-intense-bg { background-color: #282C36; }
-.ansi-red-fg { color: #E75C58; }
-.ansi-red-bg { background-color: #E75C58; }
-.ansi-red-intense-fg { color: #B22B31; }
-.ansi-red-intense-bg { background-color: #B22B31; }
-.ansi-green-fg { color: #00A250; }
-.ansi-green-bg { background-color: #00A250; }
-.ansi-green-intense-fg { color: #007427; }
-.ansi-green-intense-bg { background-color: #007427; }
-.ansi-yellow-fg { color: #DDB62B; }
-.ansi-yellow-bg { background-color: #DDB62B; }
-.ansi-yellow-intense-fg { color: #B27D12; }
-.ansi-yellow-intense-bg { background-color: #B27D12; }
-.ansi-blue-fg { color: #208FFB; }
-.ansi-blue-bg { background-color: #208FFB; }
-.ansi-blue-intense-fg { color: #0065CA; }
-.ansi-blue-intense-bg { background-color: #0065CA; }
-.ansi-magenta-fg { color: #D160C4; }
-.ansi-magenta-bg { background-color: #D160C4; }
-.ansi-magenta-intense-fg { color: #A03196; }
-.ansi-magenta-intense-bg { background-color: #A03196; }
-.ansi-cyan-fg { color: #60C6C8; }
-.ansi-cyan-bg { background-color: #60C6C8; }
-.ansi-cyan-intense-fg { color: #258F8F; }
-.ansi-cyan-intense-bg { background-color: #258F8F; }
-.ansi-white-fg { color: #C5C1B4; }
-.ansi-white-bg { background-color: #C5C1B4; }
-.ansi-white-intense-fg { color: #A1A6B2; }
-.ansi-white-intense-bg { background-color: #A1A6B2; }
-
-.ansi-bold { font-weight: bold; }
-
-    </style>
-
-
-<style type="text/css">
-/* Overrides of notebook CSS for static HTML export */
-body {
-  overflow: visible;
-  padding: 8px;
-}
-
-div#notebook {
-  overflow: visible;
-  border-top: none;
-}
-
-@media print {
-  div.cell {
-    display: block;
-    page-break-inside: avoid;
-  } 
-  div.output_wrapper { 
-    display: block;
-    page-break-inside: avoid; 
-  }
-  div.output { 
-    display: block;
-    page-break-inside: avoid; 
-  }
-}
-</style>
-
-<!-- Custom stylesheet, it must be in the same directory as the html file -->
-<link rel="stylesheet" href="custom.css">
-
-<!-- Loading mathjax macro -->
-<!-- Load mathjax -->
-    <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
-    <!-- MathJax configuration -->
-    <script type="text/x-mathjax-config">
-    MathJax.Hub.Config({
-        tex2jax: {
-            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
-            processEscapes: true,
-            processEnvironments: true
-        },
-        // Center justify equations in code and markdown cells. Elsewhere
-        // we use CSS to left justify single line equations in code cells.
-        displayAlign: 'center',
-        "HTML-CSS": {
-            styles: {'.MathJax_Display': {"margin": 0}},
-            linebreaks: { automatic: true }
-        }
-    });
-    </script>
-    <!-- End of mathjax configuration --></head>
-<body>
-  <div tabindex="-1" id="notebook" class="border-box-sizing">
-    <div class="container" id="notebook-container">
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="&#22270;&#20687;&#20998;&#31867;">&#22270;&#20687;&#20998;&#31867;<a class="anchor-link" href="#&#22270;&#20687;&#20998;&#31867;">&#182;</a></h1><p>在此项目中，你将对 <a href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10 数据集</a> 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。</p>
-<h2 id="&#33719;&#21462;&#25968;&#25454;">&#33719;&#21462;&#25968;&#25454;<a class="anchor-link" href="#&#33719;&#21462;&#25968;&#25454;">&#182;</a></h2><p>请运行以下单元，以下载 <a href="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz">CIFAR-10 数据集（Python版）</a>。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[4]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">from</span> <span class="nn">urllib.request</span> <span class="k">import</span> <span class="n">urlretrieve</span>
-<span class="kn">from</span> <span class="nn">os.path</span> <span class="k">import</span> <span class="n">isfile</span><span class="p">,</span> <span class="n">isdir</span>
-<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
-<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
-<span class="kn">import</span> <span class="nn">tarfile</span>
-
-<span class="n">cifar10_dataset_folder_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-batches-py&#39;</span>
-
-<span class="c1"># Use Floyd&#39;s cifar-10 dataset if present</span>
-<span class="n">floyd_cifar10_location</span> <span class="o">=</span> <span class="s1">&#39;/input/cifar-10/python.tar.gz&#39;</span>
-<span class="k">if</span> <span class="n">isfile</span><span class="p">(</span><span class="n">floyd_cifar10_location</span><span class="p">):</span>
-    <span class="n">tar_gz_path</span> <span class="o">=</span> <span class="n">floyd_cifar10_location</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="n">tar_gz_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-python.tar.gz&#39;</span>
-
-<span class="k">class</span> <span class="nc">DLProgress</span><span class="p">(</span><span class="n">tqdm</span><span class="p">):</span>
-    <span class="n">last_block</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="k">def</span> <span class="nf">hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block_num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">block_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">total_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">total</span> <span class="o">=</span> <span class="n">total_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">block_num</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span><span class="p">)</span> <span class="o">*</span> <span class="n">block_size</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span> <span class="o">=</span> <span class="n">block_num</span>
-
-<span class="k">if</span> <span class="ow">not</span> <span class="n">isfile</span><span class="p">(</span><span class="n">tar_gz_path</span><span class="p">):</span>
-    <span class="k">with</span> <span class="n">DLProgress</span><span class="p">(</span><span class="n">unit</span><span class="o">=</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">unit_scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">miniters</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;CIFAR-10 Dataset&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pbar</span><span class="p">:</span>
-        <span class="n">urlretrieve</span><span class="p">(</span>
-            <span class="s1">&#39;https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz&#39;</span><span class="p">,</span>
-            <span class="n">tar_gz_path</span><span class="p">,</span>
-            <span class="n">pbar</span><span class="o">.</span><span class="n">hook</span><span class="p">)</span>
-
-<span class="k">if</span> <span class="ow">not</span> <span class="n">isdir</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">):</span>
-    <span class="k">with</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">tar_gz_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">tar</span><span class="p">:</span>
-        <span class="n">tar</span><span class="o">.</span><span class="n">extractall</span><span class="p">()</span>
-        <span class="n">tar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-
-<span class="n">tests</span><span class="o">.</span><span class="n">test_folder_path</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>All files found!
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#25506;&#32034;&#25968;&#25454;">&#25506;&#32034;&#25968;&#25454;<a class="anchor-link" href="#&#25506;&#32034;&#25968;&#25454;">&#182;</a></h2><p>该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 <code>data_batch_1</code>、<code>data_batch_2</code>，以此类推。每个部分都包含以下某个类别的标签和图片：</p>
-<ul>
-<li>飞机</li>
-<li>汽车</li>
-<li>鸟类</li>
-<li>猫</li>
-<li>鹿</li>
-<li>狗</li>
-<li>青蛙</li>
-<li>马</li>
-<li>船只</li>
-<li>卡车</li>
-</ul>
-<p>了解数据集也是对数据进行预测的必经步骤。你可以通过更改 <code>batch_id</code> 和 <code>sample_id</code> 探索下面的代码单元。<code>batch_id</code> 是数据集一个部分的 ID（1 到 5）。<code>sample_id</code> 是该部分中图片和标签对（label pair）的 ID。</p>
-<p>问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[5]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
-
-<span class="kn">import</span> <span class="nn">helper</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1"># Explore the dataset</span>
-<span class="n">batch_id</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">sample_id</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">helper</span><span class="o">.</span><span class="n">display_stats</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">batch_id</span><span class="p">,</span> <span class="n">sample_id</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Stats of batch 1:
-Samples: 10000
-Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}
-First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]
-
-Example of Image 5:
-Image - Min Value: 0 Max Value: 252
-Image - Shape: (32, 32, 3)
-Label - Label Id: 1 Name: automobile
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt"></div>
-
-
-<div class="output_png output_subarea ">
-<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsIAAAK/CAYAAABjrqvGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
-AAAewgAAHsIBbtB1PgAAIABJREFUeJzsvWu23LjOJbhBf2PtnlbPocdQa9Wg7iHqB/HYAKmIOE6n
-M2+ZsOOIL1F8gMAWBFGiqrh06dKlS5cuXbp06U+j8U834NKlS5cuXbp06dKlf4IuEL506dKlS5cu
-Xbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLhS5cuXbp0
-6dKlS38kXSB86dKlS5cuXbp06Y+kC4QvXbp06dKlS5cu/ZF0gfClS5cuXbp06dKlP5IuEL506dKl
-S5cuXbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLhS5cu
-Xbp06dKlS38kXSB86dKlS5cuXbp06Y+kC4QvXbp06dKlS5cu/ZF0gfClS5cuXbp06dKlP5IuEL50
-6dKlS5cuXbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLh
-S5cuXbp06dKlS38kXSB86dKlS5cuXbp06Y+k//mnG/Bvof/v//1/9JNyqgqs/4CqHQG1RLUE9do4
-z895dSUpB4tIz24FABFZ5YYfB8UHpff4qOdRngJQu7ZSvKbveTjEf2BVO0Twwy43APwQsXTgB1b4
-h5VbaRS2c1Qn9Gtizq84zjmhX+04vzC/7EhldU5MP+/rC//5mvb7st/Ef+bM8IfHrzmhxhNq8578
-YfEIJ/9oy4fnP7GIbCkv8og/fhEJX8iOAiHe9bBQkSyfUc+X7QcPjz3v1S/Oa03c4ytRKP9Udohg
-WBvGEIwxIu3pmGU9Lc/5nx8/8OPHD/z4MfDjxw+K/8D/WNqPHz/wP/+TZWq5EeGNXB5ZGMAWj7Qm
-hFQ1edbDwZ89jcIP5+TE57xkPPN8vjy8+IMnYcmQOaf9tBy/5sRUbXkW1nX8aud+fX3hP//5Wsev
-L4pP/OfrP/j6z4z0rex/6jlH6kvttPZOy7HL+aYLMluO+bQErbytA+T6fxWXrOYQPzRdfHbOXRXo
-WZ9h572Sc+BdrSeU+KZPtV2BZaq25MO5n9HHBQHEjGVYntO3tOOVcwwUdbxqXFPf8Dk0xqF/SDeF
-HmppfATlvR1HJR54U/ZTTfX//6///cuU2rUIX/rj6Hsi7FKnO37PdMfmQ7oDdenSTv9F6+K/qKlv
-6QLhS38c/Trb6J9Jd/ye6Y7Nh3QH6tKlnf6L1sV/UVPf0gXC/0L6exnsH2Lff82q6Q/zLn2Xfsf4
-/WvY5Zv0r+EtevT9r6R/zUBduvQvor9pXexy4K9Lhv+blvAFwv9C+nsZ7B9i33/NqvnXNOTSC7qz
-9Bep+1leunTpj6VdDlzJwHSB8KU/jv61VrL/Errj90x3bD6kO1CXLu30X7Qu/oua+pYuEP4X0nWN
-+Dvpukb8VbquEc/0r+Gt6xpx6dJ/H13XiH+ELhD+F9J1jfg7Sf+94OC/hH7H+P1r2OWb9K/hrX+7
-a8S/ZqAuXfoX0d+0Lv4O14j/m5bwBcKX/jj614KD/xK64/dMd2w+pDtQly7t9F+0Lv6LmvqW7gc1
-jHTOz8rRn9x02qwvuVt1buJd8vYPbZQNswWVuzwrTqId03s5VQCDdtXmXcQnoKN2IMrNdd7007Ps
-2ix7tXJtxS0ZV10f01CBiuWJ5ZUjbNN8ACoQUUxZG7ar+Gb8FvbzuJnbbac87OpNO4CD0mk39dyg
-vQ9giz/c6tpW//bxhn6U+uEIqlZ943mVOqdlo/1Dflz4xYMtqann9IdzOeWbt/dS2iVUhQf2D2fU
-y7x+dP+4+TplPmYZb8KGuAxluMbottxqglgpxVT/kAvaB1PyOO28wem2lOacwBCIDkxViP2GKqYd
-Ve06EdZVl9J146MVyT4sj2LIDqx94v0iy+Br0fvEH9PoaZSO8wc4Yn5dBmiygirxi7VNhGSmf1TA
-q9GtA3Xm9JDGcuGYX4vWgVK8cqAKrm+8dWRm7+RHC2yVa0smruUp0vJ47fVz+aMa/rEGsfo8jFau
-fEyDz6f+7OtYSxebWKJ4HQtVpY/9BDN4Zu2861KxVdyKF/5h9nBZYG3p/FjWx2manlnhI+Iqt7kD
-tSMOxAcmxLiLsh03SdZq1/i7jmvMGVawrFTAdDu1SzNNmwr2vGig1Zlz8GIA+9L4i2P9s3SBsJFu
-2uOx5AaAEbKWgW79qlgFpvwFMS0LMunzFZnLZAIqUIylhSHGoQMwZYw5gDGBKSWu8DBWWQeqauBX
-TD3wUWtaYFT/spzlAbCvS9WjCDBNqMUX6kTXV+v8yL0uinH9lJFB+dHg98/kgOIP4yrl51/DQggl
-8X/9y2bH+dmvEGlNEJxFWmqS4xVKnhzzHs99KvzmhLjWQUFX4MuN4/SfIVOErh+Puee2agttJ7J2
-Qa7LCUlwOwHFxBwCu5tbJ4wRS6o0zNbDiIbOwitTdN0YDgbGS4EMRQPGBsbpF2x8GIAqzvhrliSz
-KJCY8QB2t6/JPYBgBsIx1ActZ0rSs9eSlsIXC+hrPbd1nL+ZVe5MCCxnt7OcRhlt5Ttp+QULSy/z
-wNCflqOynYUABk91jDKcALjm0Y271dPTPi7H7erzki0Aj6VUNFT65TpRhG45WBYWQ4EjLsm86Kzm
-JW29SamHLsxNoXgHzhttc6bH4KvztzH040GuP6WX+EsB6lzh5iuxo68b09W87mlog+NpaBcOaOq0
-laHCRT7FxMQ84jAvJoceRPTfTRcIG6l+ZhGGKYCKpVKZdCYoYLmU8z8SvFEv8+TLekpNrlIdcECc
-lt5Z8+ZS3p6nHqe8sNQ6SD0CYg6vdp3ySbpuQHgBAlusqmcwLEsoxl3n5BXcjj4pYQlqeS1+ugES
-Dokf2LKyQFAojBdA+LSuX6WdZUBTgKcSJbOVfHPuQ40fXKdXfrACR5GTKshzntuXI6LqAr7naqQ/
-6b1TCuvaWINKitHz5rpH9ILDlte0JQOsG0dxgExH4ThGgF8ZGtbhOWvcwW9aiStLB2s3nMAd2wBg
-DZ7L0Zr4KRBM6Z1E6vzFww9rQ7fqBQ/VjuWhXIPWNgnYBMosrPPH7aRb9yhfAIvVz2mdzamyMxWE
-VstnTlpIKyCqMsCfPnmZTK/nDuSTqpRX7QgQCH440hhkozTb8GLlHYeEq4HJYbtWCduCXE8/1twu
-8GxzpKQXDuCqgFwfZ44rjXNfR5/QG8Eq21/U+eKyVWRSeuWbjYu4Ay3k4+YtKGJDqvoEhddT37ze
-uZw01apRccE4flF/ehzxKJQ3wmWCfh9dIGz0qWvEKpyLEvD517qQCiCuLhGtYPyta+pphb1iErYE
-u5YWU0BpFS4W4zmsqFmMrZzKAwhugHhGGA8A2foiKC4Ec1S3Age/7IoR4Lr3+pUVuFt/1RUipRXF
-+XpIt8eH4pZg7o9giGDKPoPaYic1saUp4rF3EaEPLCFboCY8JH+DDl+83yVxCOTannZm383gQfjn
-GjkVSAAnyvDkPL5PyrlfrgKSxS78EEVt2TgoHpPAMD1o4bZKiU98yYAMhUwtFuFh8WGAOF0mKmuz
-xSbajcbWPHylzF5YOQwQuMUzCI70szXYAeY26gJsejDATwPInibRMuqMy1ZlLVw6r2Wt918fwBNv
-ZB8YuAS3OTqMa7xeo8y7J55OGUPxuK4c0qWWbwB4NS/l7REAy7nM6Hle8YHJUkIzGuWZ5zBLNDL1
-lDtSCkOJIQ5hIEHTqQllQesR9AqFA4g90s8BNBaDLEl33mp8JrKnx3rYT9rcvto1o78gS7ENZcyQ
-ydNYFSRv/EY246v2LJeFlQfV5ASPb0ocBufc+t9LFwgbfcc1gnGVxzUTyh1SlR0dEL++ThOZB5JY
-yIvYEjyhAWyR7g8GkLsVWF3jyzov/HYLCKZFInXBTHnOC+spAWHRdU0HwJPyAxQL1h3kks40NNp+
-hzQfwbpqc7AO+s91W8RJ+qTCQSgJbMDYH16fodeT3nwqdxCDL4Ajtgx5XeptRXvyoaAc8kzCl9JH
-t5GHC4fUpCQXj8aDLjiLbvSRi0Mqhlo/XZr1K/Y0f6Cidh9ZAO9oYFgAIWCsExjhP79OEJmY010j
-BGMMzGlgmAGw1TtUMUGgGMnKJ5bmrvLNepFTLd3lWD5EYWCLFxbiloe0ZMf0auVnH9dWpNz4FcDi
-faKOb3PaQG/mnNa/h7sAyDTZ0lZbTha7E/vz2Y/L792yJJ48gmAheUXuCy+tvyIJclluwW/mT8A4
-3SUqg8VsoUyUZpqW8eVByf7xUj9ZgnNx97AzvQ+Olrr3cGW8XgVbiffGfkin4kUWU7ilBxA8uaIA
-gKjJMy3n7wUtIjwr2uJmKVaEa0pClnfxXCFa4mteQn5AYl2qCMTD3tYyVjkRKvpPGIQvEA760DVi
-B7/xZ3tEsMXpDyuCqBs7f28A48RAQf6yXLUKL6ZciljJ8qtsIWawPMcCow0ET7ilFq+B8CFfBglY
-B5AqkMEAGA340ohYl+itpfyB46hhb0GuWouelWAf9VACcNcHcoUIpYJQJN7MPjN7mos1aoKcz+1c
-cRSEpzO2Mu+07+f1bwWeTnqsLG8uXtS6SJMP8sVDE+ggQa8+jA8A+A11YxSzHiaWtXahUwwIprk9
-jGlPA9wVgvjEmWLVOyEGfN0SPOesYNgA8GYRNp4tYNi1GQ8TqP2gJRBlOiB00IFi2fX89xbiHQSX
-dpwZuvC645tyTp0ZbyXSvzfTIxzXPpXR6M85ny9bbVL84txa61k+X+Zt0lteLKEXLhJ+qMC2guCa
-RkAV1VXiFfgdh6db4wCCHRwniuJxs2OMeb44DX2261UTz2ks1Bf6kpFb2K/hYwmUu1m7k4psiaQt
-HN2Qdd5fwmDHKc3ExxspqWmZ7tLMSavMPISjj5QulFlG3grHClDQ+z2WpzzTbP3t5VKGZHwdy/sA
-muPfx1qh5uryl2bhp+gCYaOf2TUCxCRppvHsrqCyMNfRWf3hihl8B1B0iVEtVmExFwhzlWgvyKn4
-i3QCFctTcnsI/CiY1gx2gYi0SK9pEMGXC7NBQFLsRaQhZgG231Do9DhiwwsAC5V00Ft8hjXuSitq
-IKXflGcf5qOQYuXiR1cuZOketapY8G/TRCoIqTDhY0z7vtx+4juWOhd4cdbR+tvOk2PqzuOq9pa4
-C8pVWkVdd6VQDWTyIEif9G8Ls9VSRDGGLJ6egjGWRXfxMjZLsBl+Cfyu9g/LmHOuF0QJ/IaP8Ew3
-CR2LrR9dJLzdfIQrIupLCagPaZ7kPFcszgcA/AoER94BCPPkauXqrpS3vCb6igCNm+BIoE5zf1te
-4Y1T3qb2rQu1UyIaip3r01e8X7L0lHgARgx065HdI9hKnOB48V0Jn8BwB8ElLvSyJ2LMi7uZ5Kzp
-tkWD2uhlmVihingSAKUX5yKdFmOETWMqAnSLajUMZ6UHRkOmgaqkFv4a+kkAfCjb008AuKdl/Ssj
-X1rPYjunM/B9AsTpTQ/4UvO5R8qR0MVSjz74vA0FTZKD4d9NFwgbfe9luTjpgzRljtvTPE6hRzYI
-Rf9E5BpRrMKAhgtEukYE6IUc8tIRXrFbgwN7diCMtRNEVysOgKdKhN2vVqAYAYLFro8EwBPQQSNz
-sgizYD5ZhkuYOvZqrLEUjP+D1GPfMaLrwBMke0yj7tUyb4TCGVe+LJvRvyBwZAt8dmE5DXvRtCjz
-LBQ2CR+WAyxAotqs6zgDrg6Bnsq4jA7dye+XHjZfwQBkrrb6UwMOexIwMefAlyhkTszmFrHu6c4+
-wivNdpGgoelWYNf/uR5YNvmh8r5SegfAcJCLTNtAcAHCqGCc54P5u7XfIZK0k0Jelplrjed+FWFL
-6z16wXkkGwqdV5+gW4qt5AN4eZW4SXzit2AXS0vwyxbfFj6UGV4Xg+AAx2bt5bQNFHv6uob6uFm7
-3ZXBx3M9clcalBxxxqEAGpAltVbq9HHplmCtQJtlRAG+UtOAZLpSjZbL/rxYfD5R/G+b58iPSF0F
-0q3C/eQ4HpiQjrKlC60GvwGp7/XwUW0xRpjPg+QNcMgGKWGJfIRVOMl457DafxddIGyk89Pht8kq
-svmkdJSLb8onldV51whfvHveK4hUXSO6VVi7O4SD3mFg2bdNGyOskwvkLiE4gWIhzjwUMJzhBZIh
-MDcIA74UHmLWNgMaalh+eXWYhSXewqchWOggw48Wohj9La8DhD78MQkMNkPxpHUlAP0HmLVfqqd9
-CwRziW8I7/2cvwCITzVTdadV9XS1ks6MT8pO1USm0CO3OPvpZbkHKtdoDaC4ufwuEEwbsUyzFs9p
-PEC7D0JAVmK3FA58zXSPCIuw7tZgVywBgNFY3doYYoTaXfma3QV6vsaxLiMCuzik9XINBNfdGAiD
-cFikYhik6t2mghtXJqiBskiqeTXf6+H8p/p7XdmPCEvPpda/WFZ16eU5QvEOuhPHvAbB8eLcCeSe
-jtjTwy1ChlmEGfBaez1sIDV0nD3d8jY4f0HbdmnbmDEcorxiCSarewHNfl2A96Rm4EvF+JJVDPxi
-EMzz3OfTm/e+rNbEDeAmv8mhzNO5rrvdqAAh0NuOSi1ZY9bTHAxLygBFxmeu/6qLcy6T6z+W4L+M
-LhA2+tgiDBTt8wSIs5i28yzA4Tb3XRF8RoLy4psDXSxUuRgwXSPCTcLBsVtjadeIBLQa4bQEvwfC
-fgRk7RKhdjSrg8qKj7BAp1U4+4H45ocGIlFrCKEC/TTe56EuTboXh9jj96Vc1h+x9A0QNyD8GuC+
-KPPB5L/Mfsj8mJ/eofkPqPfpVX4pV+S9K1s6mOKrYNiU8L6Mfq7hecnQs2EBhluEdfExpLhDiANg
-0fAXVkmrGjAh09wjhh3nqIA4fu4W4eByZ2VW5DywJzynXEgBbRkbyIVfy8DPCQA/gODzi8cSf+2K
-G0j2VNfdx6Wg3OHVj2LpBXUgpFfL74NT1n9ruxAIFSxAZoCDMVY9p7b8yJN7x8BjFFi6gZ2MJwj2
-fAfBkS8o8onB7hEQPx6HzVXKS7cGr6PzkAFWNVcFzec0ZW2uxbzWMK35lH2KtWXayRJ8AsFUgV2X
-tyeIl7WoEXEpAsXHufyIXpwV8uwwt/3sE1iOsloLCOUd0hgcP4HiuOmQCmpPQDhXxkMZBXSyLFgD
-qtNupkemoUAtmj8sHvtLTyp/ki4QNvqOa0QNuxAolR3Ka1mEHhYq9lemP98nbS/M9RfkAISbhCxA
-rFEOUN41Aq8Bbmyfdsjr7hKiY1mB3RpsoHigWoZVRi4c+iBe1WUNEVTNXeOb8rO0plDrWNaIizCB
-pnJxoHwAwh+DYJr0d6D427whx+DPVXJow8PQuY47pp/ac6ynLIamBDsYtnxWmvI8tftCo3i0XWpr
-NwswBOFWHwDZtjsSsf2EEyxB1NIO4NeVx7RfWINNoaAD0Mba6LLnnKYlkazEmnGDOARutS4xBsAB
-gs95TN1xha2orvQyzkISyJXRO0RW3jhhL7PLgexlvVCeHy4Qzgc+hx5Eo+MCS8bq8uRUsmeVa5kM
-cheFtAB7uoNkKWXSqotwg3gCwWOMF2A5hXC8bEjWYAfIvkfsWoapkbh/SscyTgRs84WpPOPkPlFc
-JhQkH4B4V6AD3dYIbqOE0H2YqL+goB8B8EG2CryhLd3HpAHeHQhnGbEyFUBnrSeQ6yvglJbn1HNV
-sV6wn1V2WEG4P6WAdLtl9ZcB/gEX4QuEnb7rGlFlrm7Znq5b+ist/VepbYdGvsLpd+vMO6DkK+wv
-zCnMYgx9BLcdHFcrsWxlAZjFSzDHgBj4nViAeIzVhglg2Ac+FAKYANZiDQaKdi6W4fZzha/1nCqE
-F20vw4DUmBDoDUDs4FdDceQLhFlLgwARXwJgSecn4MyC8jv0maL+tB45JSZQL6SlDKcyru19PVRf
-TyzWJymWYZAePLVftVbaoitNXclYPulVKGwHQt8pIoHFHBq7RMSNkPkLKwEJFVlePUIg2AHx0AeL
-cFqGdzcF6heLI80OqXfMcotS2sqDwG9fRnr+4Tl/qjbe0epCoAyAtZRM6xSixA5my+yh3tRqKRer
-7QUorvXmkSFCp8h7sbbCheDN+su+a4JYykgQTGmbBbiBYP93ALfDbtaGjEP62EDw8IvTDRD4GE9l
-NI/QwKJxJIalZV3mu6zXkm8Z6u4VVrHwEZnnNTIwLkDdL0hHb3Ncr8zOu9k759KkvQXAFGGHyZJe
-QDBcPVGcgXHynkiNe6EdCH8jXShdsUCwgeFZbpbpJt8BQXyMKPujFpZdsfztdIGw0c+6RhQ52SeQ
-hbOe09u6/1nMYme6FViw3CHS2rtotwQHIIZknkzbDi1B7iMopvxjGUMUbgGeUIgOTH8TXwQTA3PM
-5SIB81GeWG1USbMbKfrTb/MTLPk8/o4kymQcx17iZ9LSrX7FGoxiEeZz9UW8CvpWpjPDp8r0E/rp
-unoHK7/HQ622NArkcVncpuR83aVkw1JjNw7xqNPOylX0oY+w61Rq22nx+SWHvQg3BZBBTkdTwn1d
-hqmHSaBYnF0cELlFmAExgV6PT2ZdzbVF7M0iKMcyB/XA3kcrcJxJ13v+0fVflivoIoI5Q3ndXDPu
-a0gvpAmXrWubbidpEOpa1zpaVI/XdRgkjxMjMwyI/vjvxGwF5OyM9ama79ilp/ERHQQLyr7AI6zD
-DoIJ7I4EvGLW4cz3dNDcuqw14GPjmYAXKIba6LM2HrA+2Zrm0sUqTGu/ukPQkSfjdPHDsVfrmLnS
-QTC8mrBD4gnsPgPgDBQw7H/sZol5kIFvAGW3KBdGQT03OLsD3gMAFinxXnaxgEJ1pvqljxCVjxF1
-AOzbEZJc/t10gbDRt74sB5aTrGm4wjadSoEPZvoby68JXAO9/uU4ArpKrhFsCXbf4MjDco3ogJbj
-4RKhnLaXjxvAMcgCvIThxBLAcU2hNjgYBuwbs1bZkKrwYhXSryjNHFGNfBrkXMX7nOXgRjBAL8gi
-8wCE39EOlKv9+NWJ37zUL67T1RnZy0pH9jXBnl9qQOP0UahIOW2mz2AYiLAIX4dbuIdLuyQjiiqI
-hQpOa/sA7F7SngIMuvWcai9NOo+sCsV8if3GbuhoFt9ZXpxjq+rZQosNeMSQ9TFXznArMZenvKdr
-gX+vwS/XEReKNrpCz3R/E90bxNYz6kCbNH7KAw6UcttaLz3ZBumQv1NiinSTOC2avYbKUdspxHPH
-epv1twPezG8yisHvZhUeGwh2ANzB8BiuU6qVL+KiJU+EwvBni7mEHcCKuGNCDlM3DDD4Dfl4Mula
-OTV5kKcdQPGRXsldmr9vCd48R/bUFwCY0zXmlwsn4HV+EUqnMDxMYDjurt4D4bT6nsoQKFZgyoyn
-ySA+Ucz6MaJ4UG010t3B+Wbk76cLhJ0COPYVc0o7yeiW0EBYiZOOWNF+j/whbQubtk1zK2r5EgW9
-IAckALW9hHMvX/+gRgO/muF11B0I6w6KAeALyxK8fCyXC8WE0Ic4zEo9qa0LMdtNiluF32hrX4QB
-iKlMG/+T8Av54gpE3fJrRzUR4GnKrhLPU/Oz8bd8QWDuM9prPF7j0wvzMglwQbYMMf7WLO7W9bLl
-qBN3WvN8sGXYpb+tubADu+IrDfuEdAsyDvd7y1w96xrTdoTIrQCdb8wnGMuX2MusXSPsc8pmGdY5
-DPQiALDHi1tEWGTJFtoxXUvb4y2R0gxi7v98LX0AfpXauw9tF3p0G5WMgsoQ+1xq/PX1zTnZizyB
-GtcHbDsyHcp8KKRPxeqzv71Q73IBthyWGvbVVFwlGiAeQHGJCFDsIPgAhoubxLCX5ZgXHOyatUKB
-3BaWRbIAosNmhQAxD7Ov5aO1d605PVmAC+h1EJwuVH7D3DV4ls3L8vHlRH5M3vZWHc1vucTLsqtR
-FcxSWU5rgLim5UXEAK5v8ahxJSlHCPY0L88gWXW9CAy7MZrLMrzwwMDoYNheKI6XjXXpg+1G6DfR
-BcJGehSU2NNOIKrN3Objoi1SQMFe9JP1dy5TQW9+lKJaftmFQh8sxtNedlA0H2B9AYQ5HPEF84eu
-7aUWAHZfYnPDQL6JurZum1BdL/mpAeBt+6PTjyWwD6ZSgExJqawPy66jUkpMa0uC3wDIcj79bdzk
-e89/zwcvpPabk+WTQueT6qUbAPaDEMDyx3EOiF3ovrZ/u7LLa2ixEtvFCRDHHEWL3ohUqrtE6RIi
-MDBrn0vugLdYhs3H0wAwmvsMMHfXCCWXCCWX9+mWYWJxVNBZFDeLJ80OKOXVOOdb/18srde/k2sE
-iNEtcIpvAJg0Id896ckdwvsRA0RARtvPi/Z6clCE02I+vS15EAocHn5UKsCnLJqtfKm7nZ/hQDJx
-2EGw5FMrqb+08p6ODQzTcTWY53ga8B01HTPWEW+16R2I5Uogz58WBW/G3rlmIvK00w4SkVeTy1Af
-fIlZyLoVcq33J8n7QiJvybLHTgBY9tJHKzGlO+ewi4Mcj2cQzMcAseIz08CuHNKiMRU087pSe6FH
-4V/ObGBYYNtOenvXpK0x0YeF9PfSBcJG33WNWCetP9LS9BDpZVrg7WWOS3DLEJRt0ygOPFl+5xae
-tkjCBUI7uPVw3VZtUrpSechaCBOKLx0Yqpg61jkGzqe9jqR09I2FQ9hOH9Nv/HwOWMd1HbjNRQKq
-fDnOhEgobQfBpoDsTrer6m5lkOOVavhzOdBre13yzEQPwv2jCrGuz19KiEGVIyDmb8kvi4S2eQDx
-tWuqve4aRxmK85yeae0+kXVUqFIb5S4SKf6l7BiRN0ZmmRL31UyL8JyaVmH1XSNmhtlnuFiG24+7
-v2M66l8dpwCplp5A+hO3h/c/d+nIa9haOoJiBsQgoMPx0twW6KvF1zut/Z4X8qAKggQo+0tyxRXC
-2utp/Qa205kDfTWcKXmsuTxsYalhAzQsl4aA3CHkEegWyzCl/7AwgDbXawzZMryGfeTNkM4i0/hW
-I6283G9FfiL+AAAgAElEQVQCxb7mA9nm2FTLL8BA97zlWgx5iKuCq4MfWUk8zM53xWUDu3JIAx4A
-sPTzqt45gd5yHA/pJquULlLB7qr4lPYIllXNZWzd5AP+BMC3Z4V9E8CeEk9vi8tKLF9x7vRvpAuE
-jb7/shzJ76wkikgrq/S3WxF4nT5SKJKKqur7SgyC8wU5t/Y+ukaQVTgstfBHsS9A7xEIa1qNlVwj
-FMUq7PU64D6DYaVx0xeuEe2HF2ldifYxpeGOgAFgBsRuAS4uEyHlQsSUml/Fi/A/lN2pQraflh3y
-GHlZVDngWNU7UEAqCzlX//zI0v9KrTvGwhcQaw/lpUYtO82jlnHdSbdLRX9MWbvi8a+V5+Snv3CC
-D44zIF7XGW4RVn45LgHk7hvsljYtT2YSKDQWtnGOP5GvyIPmsIIrfbWsvgeIfc5c3ar6PBF4oXAo
-ei8XPFShVEwzRYF05Sg84ANlP93qWWEplaLW4c32dKnpbLQ+0YntlP42JFjrBv2OoLeFAwSvbSmr
-f3CGz6A4QfAPBsTuNwzmgVkAb5XXk0bZDBovRwP5slxZpxyhl+cUCOuCZP6j1ZeqilOK8KI8zuIK
-viNcpRaWQ9YpraQ3LCgtjUHw0Ro8ki86+IVguW2Jy2KTvl6BY4UOejswLuXNNSJmW+H77AAzrcEY
-kDntBePavrAKn/be/A10gbDR59unAam+IwqgAwU9pgO56MrJx6t8F+Cc/IEzzoBYKW895nKrMMxd
-oVqAl07RSDsDYaU8B7rmGgE1q3C6RygSFLPgVPHHbf66qSszvAgffjaIDBS0p9M8ORWBpK5w1h8G
-wFBSRPI0Vwz3euoe5vjz3H+fMz4m2QIvMCQpIwsrEHf2S1+55UzCZcjD3ou+b2SMR8EKDKioVZan
-bQ5LPY9r7Kj24uKucz23uj+0sJ5BMIcB2iXCwbD/GAwXYLz6vH1EkZpfLbCVx2ucx1Db+br/2yyA
-BVe+BsILOa1LHIFvhiEO3iXHm9xg6uy1RU5teyoTIDnKNQFRZIDWekgxF2ssmL9eAd6dpJVSS+11
-b5WW658A8AoPb6sI5dWdInjnCLYIdxDscQCFF86gmMLuI1rkO4VNdOxjRHOvbvkF6kdKQnIgF6mf
-R2l2txhPoWjCdqtwDafMqVp9m5QPxPAJ7H4HAHOY9cwZCMshLAl+KW0HuyPjByBc80bJ0+m3QADc
-Eozc83Ri6c58l0IK+F0GA5cJ78f0V9MFwkblBY/3pV9Gc7W9vOA6niaedQb24+nCK29YykRafQEt
-cRNWshb+lBm7Ncx+nsmUbuVla7Fqy4cJychb7UyLr7dTlysGWxbCN5htwRplYWWhCB9KkFAGK+KP
-w9nmUJxkVSu68wVJmSR/ROdzw96wCRAX0BbDCBR/KwleSd9v8LF4675PDG6X3nGrADajzJl3W5NJ
-A52WU6QRWj3NS1GXRYc53Dgr3+f29dYftGkglzNFm9gSrJpuEBMNAHvcjs6zJZ8gIjGrRl89TkDv
-cA6DaO0/xIp4+IftF/31+gkEh2sRAV/YC6d5Q2MKG45SkA16OcL6WE56OYp3UNrDCTwpj+b7DOb6
-dV+vSokSFQktvFIBb4AhcQBcgS4D3wS/o7woF7tGlJ0hGBD7L0ExkHI2gXAHvv40D9AxMN2QEW6H
-uXdWGHWz5w/yglY0o2dp/BFgmCvf0zz4nPZiojpSPd2pxEH2Uu+A7osyDITDimoJPt+P4QFKR9wI
-hfNfgFyrmwEurQBIBb8MjFd6NcRNFXuKZuXtaXC+gG43KWLuh/bvpxTSX6QLhI2+4yEskLSqlHRP
-qmpVStE9vTC+cJrmAlA65yHNldOEtPDKc1eEc96hnKayC/1JIDcEFWlAzwti7Ui/JUglTljAVEKZ
-FT80VdtKzS07cwnhubaf0rnik+JnwHGKT3xZ/GsqvjTzvuicL/Pj5DT++XlqYwQeIxozHsMso23s
-zhbOZ/pW4UUEULQlvzzHA6UPqbLO6QTSqG/1vL0vpy8MaawvPJbjqjqsLh4WVNpLfSKD9dPCfX06
-e1sd6QaxW4OdN9l94mtOjDi6xzKBWQeD1Mi8wfM0nwuNU3xOEuDs1uDAmGUNn3+OYb3T8dDAhy2Q
-R6Y/vqP0AQUu9cADGD79SgEKO6A85h/qBl6vwn76U1nGDY4JEvAetkM7gWEGweMVyJUFhsMFwsDx
-SFcJzweA4gc8mzsEA19dMnrYVlrb1xIt7P68vt3awqVpRMjdHVociVlfSr4Aw+ew14VWb5mxDdQe
-kOvbc1qWp1NE/O8BawuXL2A3eSHD1koGxN5uLh/tYqC79y1BcmubXSeAcD4qzcUskq5jILDrVuBI
-VTrvn6ELhI0+ngLti6UkJKOcKtRugeBz8oUS4fQtTR+B8VKycgS9HSA/lWNAzEo7zgtwB4DStj5z
-2tZpSwygKwR4JV/AIPCrArMUm8MFfZqWXzrK+DQgMQkczy19dhA8Z4LhBnwDMHN+B9kOBH3sGARq
-jitoXDOMAJKvgfDPCwzmqdPkbBivMFgt9R4A1/QM0xj44HD1flE99FTirK3RSmVO4ERKWd2VDvb+
-b9cnfXrIquMqFAbMSr6Dy3Jz1sDw15wYOjcQ/PU1zcJ6AMExvgyAvYynl4V8BL3xw/n3RB3YRpDm
-+ZN7COFxfAC4QSYTX9bb0a/sWXLgiVKF1LLcJD7v3Qo98V2FW27dZf/zBDIMeE8AOIFwBbrs9+th
-4ZflhlB8xKeX3UXBZfDa3WfJ3GngdyhCJisAHcxTezj4VDX3Iu7gOMYm9pBIt3HSjVpGkLjHQW+E
-l747h3G4ITuBWwle6GkdBDso3eb9wGdbuagy695B8DpxSyfg68xK8BduTfak6HbcgdXGZPkdNCvs
-qSb7ETZgrNFe2lnnBJJxeCL4G+gCYaP5rQ9cs+aVEj9ivhqIYLy3Seu3g2Deii3TKI4E12p3tjsI
-9pfTNMIfAWKlcq4QCUiwEkhdyyCI+ku/tCwxCBZg6hKe00TbkBSg0wUrCdVJgLgBiKfjyeqWIHg/
-duDbQfHU6uvJNwrF0mtjk3kEGA9jmmP7a8VCB3pHYsH4Yl9en+lsc5b5PkjmeveFVMtl5nHZautn
-ADIFRbPcAyA+rmUCdzWPtjQ6thn8wAP5AlxahIM/2RKsucvE15wYXxNfY0LmAQhbO7hN57xD3NuF
-w0/f/Fp57rsD4LZBgEmiZLTjeP8kSftF4mEpud7Oxp7DHUO/a+yJnz4pH9ch3PEEfl8BYDmCYN4u
-rYNishrLWG4UVHaRb42TVl/YLkDFQjwe/IY1P7hQgS/iZTjhNCDdr9q2Wg/TmYNd7sQ0B9Xy1sH0
-TABqs2wWsNhmszGLdOZp4Hg7fYsTyG3d6Dfc3f83AG6AzxPwBSTaUQFzXsSBM/ch80rjiDn5nLVR
-hMc1XhAWA9GDLMVK6fECus8o4ZvfSRcIG31r87TySaeMl9Q+mbvmXPOe8x/pZ8DraVWZx8Jy0CqA
-6m759fAs6XbOAzguSi4AnkZaBXkJNEpfWTNu2rWD4EQKYSmgY1gaGiitYHc+gOBmCY5fAt1nMDyP
-1uDTr48THsdNKS/Hvpf/VeAgKHhlRbb7b2Ji7fkdnHqDI9z6hYPl91Tm1Miq98r1yxmnIMltbyKv
-oQLSLH93mWjgjMr0cwEcAbm0PAaOk+K8PdqcOHx2mS3CCvmaGMNdI7jnPGgNkiraXBPzWTyt1f0H
-sJvUcSlr8kC5RJ8LHzuVAzj+SX5/hYpMN7MrN+GGl1XWch0MWFAOlz605RNgzMa4bCtbhp8B70sg
-HCB4FHcHBsXVCiwEiLtrRPcJ3i3EGwi2dz4USCux8VoCX6WX10z/ODCmMcxxazpXgf0xBJ2pWPUB
-sSAzvsqo+6vHZOS8M6zcwC7HN6Dc+aAy3g6M+RpN91uF5QaJ2lCtwxyWOJ/O2IFvNDjdJzZQXNLT
-dWKlKSBrRwnfcz2B78EKLL5b0Fqg1yL8L6DvbBoRzNok/0kRtEBTPigKqqwn9WtFYAPG+eZ1Cond
-EvzdsCk9fxRmjeygeO8Kg5vaxW2QzDdYS4UJit1fmN0jAFSLcLhCmL9whPXBDeIMnL8srwPdfjz+
-OtgOIExj0eMxftX6u5fxkf91dFL8oSo6WmG185DH8/8xAAYe8lqrpMQoULXGcXw0i24grOnKqNFZ
-EPs4lXKcToULYEY9QUBrCgge7j7CG2/1j29Q/Otr+U5yK7MN5/R6y6Gb5YUBbVnvLh+ovU+MGYC4
-jf0T6Fx5XOJVac//kJ4AsuzhAj5Pl9/Swoa188U7NK/vecyBSjxdBjZrsBjAfQLFK1xBcLpD9C3S
-2ApMgNi/RAfgY+DrfOKubSPzpipE55LzbhUuYNi4zfRdrONT+GlwaYHHXuYOhgEDXZYfQFejfJnu
-BnIrUOX0HQhLraRmE0lL3MtkXY9gN0tWHuJrOObk9PibIDcszJEm1OUdEOeYrDkdkmHnRUVzgwiL
-cryyh3/EFGx0gbDR2Xv3XDL1jIlCpTAoKU+pEdLs5YYLzgsatUeetvL2h8sHqBUrGVacBnbFAdsL
-QNwtQA38BmBrAK7p2hp2GeXK0oAvDAhqPDtGCFLEixYgi3C+LFf8hAmUxpe5DmB1c3342WMHy275
-oH4/AmMeU23xjYF+DfXvFwTZsFfxqAkMN2SXnan8wH3X5/5S/7o9ODi/918O5TuQI/SVig47INZc
-P572BIaj/zRoUcch7XQtYJWNm7iwstYX5vg3zfcyX5ZbVmHhX1faTW48xnkto1rVCgjW9itjTf1A
-/lDKHOBtgJNfRK/A7gdryMHvY2YvS4D56RpPIPexACoGKBY8cUAsBQz3XSB4B4iwBsvpRbn6Ipwc
-yokBZZE8b3VE4e4QW9i/nxuc4B9EquWG8Yw47ziwdT6K+NqVdq0bf6GKgDLzLQ90LGQb6OC3Kjd8
-e0cXEnQGGOimnDiBYQa9JyC8M1ACz54Wk99LkEyTuOwOiBvY7fl2fuAGArTRPe9zEyxctnbRz3d9
-YfsLm0/wMICiYDCMDHubDOBIpPx+ukDY6GPXCHaLOEl+Tn4ChQAIMQTQ5cUaDGurlJlj5VWLzqri
-DGqxpVe3CFB4CScuQ0ox6kERHvqq32181JFF2THCEYkDhLUv4ZKdQi9f8KO5CnTzk7RPPsLTXByq
-tfgd2N39gHH0Dfa8R8uutniMIZUn0Mh1dPqrgqLgusbO7KtVGyAdQZX2Kv3pfXkFjo/9FKqTGx0N
-xhErc2UFbFlyBa7y7Re6QllSGTyl2XpWqRco68n4PXlKCfjab7hrzlhfo2NALPVBYpURz+na0n20
-4wUmJFBnoItDWtzUEGt4WQf/zjrdTeIEkoPvXjA5g1f5zs9BBMUPAxTHct6pER/SEx/1QtymFRZq
-w9k/uIPgsAjTC3H1Zbj9pTkhqzAD5UFAGTaX4eNbPqKhbWeIvi3mnheW4LAAG8i1+HpZbgRP+pg8
-ycQyuLzwARz9npA37UJ/fPcKQoFAgGYHhAlKPa0D4eJ6EHPcQB4JYdnSQNfNvHCHkGhapHsaA+Nu
-wOVrMaCV0jfudzJ/Bc+5QNIarvbSHAPi5SahMAAsaRFGyC+yFAPvn6j8DXSBsNHnL8u5RiN1QqA2
-AYEnlMim+MXKbLL4AHQd1jJWiTS4ElqZHnbwChAIVmNY7BZhUDj2BUYqP1C8dJXauo2Bx12S6epf
-2T7N6yQQDAUBYtijtplWs8POEeH7e9gd4rQ12lfbOWKz+L4Dyn3XiBibOteepjSI7yzDT/RWGRxL
-VyHcwRCn5xMIT1v+XKcLr25p4YPvAeBuE8YuCQvo7IxHUdIa5VGql3HFJ7VNrsROAPEpXMpRpryI
-hxXMxoEtwlNBwJf4dE7IHBCZGbZdI0beQRdgiBZmcJUrnPpkkVjnsSYrKI4yXM5GssuIPmantHf5
-dbS5Dxr9Oq6VE2LqaTuLVeB5KncYZL7P+XRhvtY0bvnNC1TwiwCvvh9s+TBGWHLTR/hH9/097Bxx
-BsoJtH3c2c3hM+Db4u7qJtrK9LQZY8Uvmfa0Pq7nKVBXOfvEgedOadwZ4Nm8ENitrhAdCDtYLZU1
-jLtzwcmC7Gd18Ltbh/dwORfn/NhKTfIi/EQir3VOKy8LGtBVUQyYrzDUXpxLn+DcNxgZpg7el+X+
-Qfp47EPYH1YifwyhawROauC4yldTbLHos2zwirY0Us0F+BrYTMCRrhPe1gKSFWbBerIIE7CjbkT6
-aRADAGsAHKUTHPx6WZ0aT9hip4hp1xiWpu5fmdui5fHsw5sgeBr4nRUE6+H40kq8+xB/qYPfBvaO
-aQfwqH0cf6VEWHNahfGai55WAQOhJC8QZbIPSg0vYaD2n/JPPKNySqwNdIC7d5Gu4UJbs3wHrhmv
-ViBV0lPqde15apkRD2UqpW/Sn7BoguDhR9sxpViFxwK+c05MGQmMvwQiM6zNqZyQcRAAJgDB08fH
-tRa1AI4A7bZgOwCOMqApUq7Tt61CtQZrslWA8A4UStuy/W9XBPHmqyJ+fG3tlZbWgAVdRVrxaME3
-lnC0JQCJt7G6RbA1eIgD4hHgeNseTU4+wdKswg6uz6B5dSiEclpvN7/gZil+YTleT0y4Hh7XEXlg
-ULwNaRFWj0lb8oO1ocvItILu8bT6HoBwA8HZlBLZ4tLO4Y506+4jCAbnS+a3Icl6DIASuM4+0HUD
-JEucly/LLUWiqNbgsAhbnggBYLYOgyzCx5n5e+kCYaPPX5YzoFiwbE5fw7gUbCinScnYH5jyOuPu
-VmLNfHGFZcxoamS3CD+FQx2CLckMcjOeq3S7F2j9tmZv2ldLmC2pLm8lhGamIQWpvRjnbhHl2EBw
-cYVgYOwW4S1NN4DM7g+bNZh+BSAS6K1jo6WvPnIRbsDxUzphw05FcVPa4iGBA3SO5wV8wuhcBp8N
-HH8CgLd+EsqNHNkKtLXlbaOmGrgKblXYYzlqG4MznMeE0ysIRnWHsLwY37g+NVkIOAbPJyieqhC2
-Ck+FDONJ3zJtCr5kkGuEXa8rOrH2IYG6ULuibzzFsRY11mhZ/3Tz1vP8EGWljo+08evj/G06acyW
-JkiAwLtGPOCM4zV4TL2+KP5okuYyLVH3Zp/OEwIeDmjybftmJW6uEU+W3gDEDHIPZd0/WMgqbE0H
-vzC3yP2CDayOV5biHl9CXbxeqek+UNKvWeZZKOkwsqfBluc5EFsUQpNfrJXdKvwSCHeQW+M9vzCL
-UPnIqmA4MEMwJ/F34tVSfU1znqr9lAZ+IVTOeNHbwgYEmDXYAbDYi3OxHwRZih30ooV/Uhr8ZbpA
-2Gh+PAGu4Qw8FvMILbACFnAoo6E8PeXZCsz53Y2iLul0jfAXDAAtXxDbgW+m1fwEvqTg7JxIA+BW
-t024KLG1IwdGGGQJ5rFRB1AmX8MSHBbhGWB3B8O0NVr7aMbRLUKrm8MGgj+xBkdeTvZbgKs5phs4
-fKFcX9HLswTlJkpbfAMWW/ys9Fe7NcMW15K//vDa2MbBr3nqSRTtkp3LVAQUPsAAWYc1XIJ82dHX
-fW07paycQdoW7iCPusAW5x5mq2u4ROh6iWgq1tEtwqIQswbLVExZvsLLTSIBygJOGfa5dtkhunrl
-ffX20hllLtT++FOiXPfJm8TqJcxjdQK5r/J+FXEf3xbsURqjChBen/udxj2xOTciwPATIJaTW8Ru
-6a3uDs314QEwc74D4sW/nwHcs+VYif/38/ycUA38ueYYG1cAPFwPuucVvSgcW6oRCASBQCCB4A6G
-saXxiQlIO+NVjV55K+M7yO3A2MPp4rHlozYtwGizCLuTUL1WdZvIFzlhX1dO1wi3AMeewXETl9uk
-5TsOPpaabfrNdIGw0afYowNdLYlPoPe5PLTYV5NZgQaAa36Ei5IXAiEV2OYZSkfOP7hQwIVSbvvU
-x8KB8Tr5BQuztCLAl8A3wS58t4gTCA6LsJZjguAKjBkE84tyCYLZIowKcg9W4SdrsJfJvmVHtfT7
-GfTGOcwrr0f1W/QKfGiolFVItcZrYyTaWVmZ+rISMmzxCoZLLvEXtbI0uA1MBJvyUONqVwa2Z+0K
-rz8MeWNcGjD2AidA3IFvWIot7FPJ4ahPl20rvsTlINiswUI8JVPxJdUS/CUC+XKry2qFsALXBFKr
-Da58ECDe3TfCGoNcx8HGSsBXCfhqfaIR7N3Cfu8kAtRbjDM9geRPgC1bfQsbaS3DrBKAl0EEn8vH
-hzTJwePDZ7TxbLbHY26dYxBc3CKG1PTtQxnnfYTZ6isnwNwANSFUvHSNeADGou1FZ3kG09UXjuMA
-iI9dN31G31DwR344WIA/AsKN7x9Bb5d5JyYjECymsRnkUl6cVgDvkgVee/aB+E4qzz3Hqb/eAFFM
-49PhgBg7OOYPbTA4ppb+drpA2OjzD2rYPrcOJt0t4gB0tSUoaQ+Gpuhh2udQsqY9bBZWrmMHt6my
-exq/MrNeVsi0ahFmwPrqDtyQxHnYdhTRjwSuOjjmY2w5RW4PSi4O1UeYP6LRrb+zAN6jdZjDMy14
-uzV4haM7ZC7TGijW4kgufLKN6k/RPhM7AAQSMCmFSyWaW9/Uj8l0N4fWp4hmheUGoXdO6KRTfkj4
-vV+1nvRNXTy9rKIQULqNRZRBGZcM79bjV8A3mug3ppzuINMA5mYJNuArsnzZRWRZg4d9SEMmvr4k
-lA9bWeCKxRSkz1UARMnxWGe5vGHpsToULhDKaahpwDEt+Ajt6OPXj2289xl4opRCnWU5kdUr13q0
-8n5AXJ8D/cjgVr9YtMeshoMS/KLNdf3VvYH3D2VUcPvKRzhdJzKc5Va7+Z/77Y4tXQ9pKH7Czk+n
-c3oYqNbhEdfeBu40yprl9nHfU07W3nQFaED4lLadR+3soLcDXqAxZjsnAG++0yAMbgP4skxAMGzs
-0LDxGlmEOe4g2XjQrb/lnBiulR8AOPLpBTmOuww6yaN/4G25C4SNPnaNUAKOnBTA2BN35VDCdD5b
-3oQKVxeJFPou4buleDWjAfU4S7kU9EUa0HeTSLUTVtyeTrWUxc3aEHSMcPbXt28DkLtFABUMtzeL
-T9umVfeI7tqw3r5PEOxfl6Mvep1AsGoD2OQzTHnOENr6WEAdg0NCFfWcv069mgB3b2UNoQjefFhr
-dl6H59ATtU71Zi2OMw8NfgF4ge1GYUM1qmmFDKCr4f/nwOwMhvlvvXIHUwyCC7ALwLn6LSKRluto
-/QoItp+7R4i4b/BSIl+uTL4oHMqwKRYfQgdTmkfvgD8FYKDIVt8Yawctyd45dyTPUgacx+uXEfHf
-EQTvxXb+RQsT5khlX0GveOanbXTS56yeLls4YYNb2tgCvG2l1sDx0TLcQPCrcvESHkCWWxxdIaSl
-d0vv2U0iP7Sx7VE8/fEgsHyRJ43Oi9F0Hd2ydygs7RzEOQ4geR09p78Dwg/A9wH06kP5vKaDYiUr
-bQLfU1tXvkNdTbmAg0W48H0C3npDlnEE+PU9gquvsMq6dUlArHkuSwnP+wf2T7tA2Ojzl+VA2E1S
-4TMQUBzTgAoIUiMb02oDuz0sFNZTmbT6PoFbxBna8vY61Eol0CVR0k0h1KVtKAt6sINZsx38Rtvc
-PQJin11mMGyWatUCcsNNIkBwBaxfZhnmXSJOPsGbL/DE0fL7ZA2eNPnbOJ3SOLilvVLvPwcu+jS4
-oFR9H04NobVpEd1BqrYE7n+pYkNL2k5wknPHOzI24Cnia1MCsMKBKXLdFCu5psLpYO40ftvRztet
-0BLwCQDyN6diDIoTKF6uEWrKY4UhCnwphoCUivXC05SsL8qgzsCv5fu4qtC8KIHdDn5LvAJfzpOY
-A6tPXo/XX6KCeLGzqeWHcn/4gY5b/Z+kvWofWhufygkCXCQA4R+BYH/xzb8IN8b+6+D25CMsJxBc
-64gmygnoJgCWLa+/HLfcJGYAXgCqGLHbBFl+Y994u8aUsEMfJ8IZUPqNbNNVTb6qnQPky+gBOj2y
-AVwCviYkz0D4GQRnC6TkxzW3/MQADmLj6XHIgFrdBoLLexAJaKPdWB/C6BbfwQB5403BFGCK7xCx
-wPAk/+AhFkfKIHiY274t5t9DFwgbfTr0So+Gu1LY0xgUsbWYnBMSHZSFewTADkwOILn0gkAm4Jq5
-Wof9qIXxahqDX42Ubgm2utoAlqjUfDXzWQWGBLxdPhIYBgwMC+Avy6V7BLtAdICabhEOfL9Uwyrc
-rb9nQOwAGHjyDa4WYQKHjBR8vFpa5RMfjydi2OlztpP0CAGXtzr8CHxbuLeF+sJx7lPv1XZT2Fsv
-PUuxVXJCUWoW4djaTCnsHE5ADWeQeyQHdQzucokFq59dATT5u/3cAnz+Lb/gjopUUpGFdacoLPpZ
-28bWMWqkOn+ytZd4czs2qzDqsY9nP7Yp+zYgPqlM6ekuAg9lj+i3p3GjDuWFO/lBg3nJn4sQGEIH
-xWJzKMU3OL4AJ6+svQ3gnsq1MsJWYSDBLBT+DHV3g7A8wcE6nNbgYUaLAL/DAe86znkY8DEMMItd
-hfIaA1XdWAfeX5j1uBCzKAEy9qFdc4Ed6L6LVwRL18m5rvks+w754WpgbXcA7/X1F+WsGqFzEYAU
-9S/Lkgi7FbjJF0mAy2A5LMCC2CHCAfACw9UiXAGwA+RvCoJfQBcIG33ngxpL+AuBmnSVUMiuIBgU
-tDTkWZHoTBth9TDDn3N4B7isArrF+DkvW8QAOuMVtmuJb6pub17N4P2XCfy6yIUDYE/T3QrM1rUN
-GHP6Bozna7B7BMo4u0wUINxAW2MEPaRl2QSY77myD+6euufGsNedFQLkHs6O5CW8atMroCV4vGH6
-oxvFRqcCch6M002DkEX4BIY3ELsStr1suRwejgx+vcmPhUE3eWss2E84rMDl564RCYpdibgf3nDF
-gq5gZAPAE6jWYuRxjZEPK8/f4Sb+ERzTFLZxkTYeR0B8SDvREwjWlnEExk91tjyCMREuPyH26+Lv
-RdmtFWEAACAASURBVLtLQPe8AkriWrL9fFeH8oU5fvlNHkBwA8T+5bnqH5yfYXaXi7T+JshFsQJX
-6zAMstZ/5AMcewpjGTQA+JvSw63Bc2KyW8SQcJlYf09A03UiAWBJ/czuhzBgWIo5uIu5qKA4ALEh
-QL5ZcSC8wuPQNskJjqQOglmO1/Jp9WVQm093OgiWVi6PT6DX+Y54Dwl4e9kR/UbsFzwlfYUnBAMG
-gLG2T5shdxBXYR/hkzf3300XCBt9/rIcwiqsDQTz+urW4q5QHBDkpCvxf897Clc4+qyyEeEjQOZN
-P+GgQONKLkL0Ic4teKQQ/FXVObB1IO7iE3MtqgKAwyLsoLeC2unC+ASACRh/mcX3iz6xvFt/093h
-1S4R7kIRQNjnmQfkA8Bb0cSHY7oN7wka1Lp49tHCfooqyV5TDPnY8YA9tYLhmt/GIoJ66i614wCR
-ToVPZkRNv1wHw0v2kxuEauwrvI2HooDio8UXfKQTelk6x+tS4ykx8OuuEJE+G+iFAjKRmsg87sxi
-t6aIlZOSUstHoeEiYU1darquRQa6PHeFhduRsuxI49rHslzxOe1joheGN+5vvCom5hzMlKOXobTA
-JWgF3rbJjp8s3mi4Ax0YrzroSotc+AS7pVbcAiy55+8Y+LF9RpnB7w6I64c4zmUWjQS25V0NrbtC
-0A8Ha/AWnsAcE0NHTcNcVmCd9mnnuYDvADAlWjRj4JyM550BbaBXVH2Akfyd4Ks4DgTAjdmpaU9A
-WASCkYuNJ7szk1Ae9SNgYQf4difPx3R90HKJ4Gs5HXOlJuhdoDXWgDAAloe486yD33SPcFeJKd0a
-zPIq49GHn5cGP00XCBt9CjgqCH4CvHS0P3kk60oHtErhYFQOV9gZebGuSUULNeIDgLyr9lDxdCVt
-rUuwXMNETSkQXMprhkWYr5ml4iU5EXuCSxZhsgzP0/ZpBwDsL9Jl2C2/OADoZi1+yHNLcYDbA+Ct
-A9CA3QMQ/pTOp/FjJy7ZxI0hhk/TTqCjgl0t3czDG/BbKmylToD3RTl+Sa1YfAkAO58zaOOhegRt
-Du7ouG4Q3P847E71RDtUQMDAWG2/4GoJhkxgSgDj1aqJoWwVNtAbyijjQ2DbGlUAvGxzpXkxdp6Y
-87i7S8R5D8CYJUofz9N4Pw56oxP/9YxepoDiU/2vrrnldQDTZSldNMbqsdVRLm5YPCmw0W4Nzpfi
-2CpMFt4nEMwfzmg7Q7yyHAMJYOHA13q0fsNuOvefuIFCQWCZLL5jrK0EzVViAWDBOIDfHfgmEFy0
-GEgag6mmL23q7wSPgNj6tekNGbFKFfArBB0bAF7HEedwO3OuT3Frg8QVD2WRfPJkCY48TmOgbNbX
-aG+CXinhBLl7WFp4XTOB8HKHWHLH4mD/4G4BJv73NfOb6QJho/po/3VJhq8ei7TEdqk0OB+vpnnP
-YacDWta1vF+H76heKpSW+YQxtsxDWEKcWDRBVoj2ECB5LCaZV42oPcUSYhrKOIFFAtDMr2Vg6RpZ
-HKa6Telznmbl23lK162NPQGGXgY7mNsKOH3Ko1k62g1syqKaLfFx2uYGo3trlfuK2vUVPIyDbIFe
-Yevgc7kFgq0PhlYXcOX+AA5bgVp2DVHvu+S2aC09rdAMAbkdqZCV+GwaqBCV2DJtfUTDgO+UWGNu
-efEhShBsIJ8Uk0ZYSzkR2NvdDprNbULq+CXYVUquc1BjPm40lgYUvPIIRx88T/L6BQH2+fbxTOAh
-muMiDEzG2nN5yOrgnAYY/R0tUfNLRW5U4I/qQWub5QT4x+4ifTyY0alv3KGNnRvQLXHEmAn2YWEq
-653AYJFTQrKZ2xXCugw4hYlNzDpsW0WEewQsjV+aWzJTioV4+ktwtqbmNP9SXTd9qrL2Gp5rDNWA
-91RZFmIA03aZGPYi9YydJhRj2At5UwFMjGE7U8zleiFj2mPg5WCxlrvrLiHwS3oLUsYl54V1mK3z
-AMuI8a/lHtJdRq1KLFl4KuvcblvVNNkFynf5RfziejrD7RIWSn7pJVDj20I5KAdkcf45+3xXz/0K
-ukD4m8Syz+fM+a3jIG3hPOojb9TST1fOkLS8tEUxW/fjokd2OwKSFITa0+0YOAIpUJTKycvQfs3o
-FYELNWEBdWVN4+HKmtej/cmwp2vUEaBWs54ALMi8mE8/j8+lvGg9T+MBSABPnPBKcjyTtBk9Xc2F
-7dlhk8cZtvduMveKelpeoHDklsZZNXXDttoCpTsHbj3eQDD4XO3VphwE5JMeIBYGsLwPCWbz+poK
-5sWxgOXW+v3xsD/NWO4QDobhR8wGUGh9mDWPQXAFxx62R6mjPwil2vrYx0RqzS6RHJNYo4GpHESl
-pcznJtKiW9YSEeqi1BYa6FVoAODYq9TzkZbSiUmA2MD+mJhz7cssul4AE7WPBJNc0BJv+rwMD7nF
-8ZDtkb0MD3ywogPfESAsxsv62IFpETF0SVXNDxoYez6gnKyPwyVqc6cIkBay3tcKA2KTix528Atv
-l7orxXpc7k9SpgrUnnx4eIqsF+R0+QMvN4mFd5cVWQ37KsYE5koEpgH1kduwCfcFw/KANNgsEF1B
-sFSepTjzJutBfsqUN0KmqU9xatfiqZqmzmsknnw+VdTWBSBxI7omWGEW402eZXhPCenfEAOHtIyl
-K1wpi4WU4iHM+Hjj4ddq7m+hC4SNPh5702/tOxrl+DZNQAL0/Kh5ryFD8pD3YfO32hPk7rC0K18P
-iy9MpCApR8dWXl6AvIt2hXhq1aGBwtE1mkoryYEFHJj66R3MWhoD47D4+LllVVIdqmV956Tadb08
-SuDQGQ6e5u75JulhlD44k2to2rBsKiyUdshnf4DGk/3Sp3HQvUBrdevh+YTPALJI7NQA/8Sw9939
-Sgkw1z6tMtWCvDchLckS1+RjsQYToHSe8hfk3C0ijv4xjblAsEyBv4nPIGWoQscJ+LJF2MK2D7e7
-THB+AqzG/CX4iiu1KUUGtR4mEOdAN4RBSpUsW6fX5yJcT6xc+HkHEB6ArNdxxhiYZhX2x++APZJn
-KzAMVMXcsLzg7q/2hk6HT2sdq2RtLewZ9/MOJKxOpSEUGcUqXABZGfMadu6Km33+mb5RsyxGGvWj
-16vI15kir091JJCcUFsVJI9DXtsgLFm6gK/IwHTL8HRgLMCcJFP9KYCBYJ8vXX7EQ9c8uz/xNJQ8
-/EW7YZ97tjKY/oqd84q9jEf92kBvrBEGqckPiy9pjpT4HWz5FeODlmcAVind+x46OMTTGu8UzS5j
-DuGYOy3R0J0NTaSGaG5jQNlRw+cyaFeMdMx8N0yVBRTp21m/jS4Q/iadVEWdet3TjAk19eFWj7Qa
-zm9OdgbrZU3dxx6hFSQUVSflUGoNJS+dKWlRlkqknBdHyXiAX9ZxIq2JrDXQrm+LHzSGWgUuC93q
-KoEUwH6+rtErCoPO5XWd67bmFWWoNPN9jg9Wo/Oqfy0C/rKAYNNQCGOz3jCIe8qPcK3y1DI9NlYf
-0jP/mRpvvDingldX1iu8/Bi9vib+uTyn9yPVz/ql1pXjyV+Z63uwTrVHw6bhxUH4nNTtXKzOYwvE
-LctoAcEjQXABxugAeXV1GBAUmpic331sZUupYxkgNoCE5ROgSKDbgEZpAJWFyxFz8wgQYOcHfw6I
-pH/p9EfisI+WGA7qIDfWeaTxS8FJZc0HqEk5w2u6r/01xhFKUKwmv208+CMZ4SIBtuL7+O6zEbzh
-WoiFm49vqAUSsFL7pVx9zJ/Pbmoc8blSRexGYHJU7PrRDm+bjcGcE6ITU8wtyHgTYRVeaZjsImF9
-FCxAHLhW13EhY4yhOzB2XrD0WF9D1runEOjwOUqLfPBhswoXPiV9aJ67xDu20MR5Bjn/4HSXD1m3
-xvnO/yCrMDkriK9fobDPDc1HmT3mmaOUQwHDJOt2bOIMxMoyj2mc4r82n7SolOO/mS4QNvrO2PNc
-lbBjCJMNEBQmOJ0DNIC6xTvYDbtWaY+0UM9NX8i8hudGnGRlFuVFzxkV6DpwTlHpd8pZNq8jpZnC
-Fz+0PQUgjUqiWZKzCbQYGKdA9vlgQLtbe9Xqd+AcTYn8h3OKBgQ1jHv0xGmn9F8pEXjGn8Pxwogz
-71P4qXU+/j1Xj0FLIEX9SK/Gop2r5OqAB7eHzZfO67F+VslPx6YumvW3VifZtwBM/QmDrhc+7UWj
-BMR2vvk49m4uHl3bXBXAqxUMh0vErAB5mJsEzHeW16cSW+yzog957NZgMbL4BohwYBHpiHLFn7jM
-rWYZhYFhpB+mxNUDEGNMiAMgIVA8ATHA4zcB66aiuUjA51pqWh8NWuMpb1jZu5tR/KH7TY2Xo5yl
-GATzE7QcThsHGqEOJgKAuAXY+2W66SXFJFrfaf54YoQZvdw8N6BVZKCPTbpCrCcfCXzDKjyz0+ki
-IWENXp6+9mVGLH/gYS/eYbpPsLlIsHuE2NHKxc2TvYbnumt/kkHHCOd8uOFn9TJvXGI0GhgO8Kso
-Y6w+yJphtesQC+XEh/UX5iZh5wgIECPllosvapsnhRoVFnnEv8QA3AxSrMRsp2NLy7NL6LWx5O+h
-C4S/ScFDiqITA4CBGOptmFP4+HFL2vEEcIj3WwnWRXFGASS82F/HDXJYtINhAa31PPMgYE/ddOGd
-IDcXjFqgv+Dmi7OuxT2tIN03gNirReRXl4oq8w9z2dOiwtPM/2ppENIypdqb8BKA5/C75uoxonuZ
-YN9X/X2nveu5TyBYQulUt4elJywsvSm0yFmRlEUlpJTW0V9KSYB8ACXq4FcN8CrY1xJutWo9TYWl
-a+eI8BU+W389vtwGFhDEJP0+Zrx0dpITvkyjy97VKCv7FBGI4PwdDDcA3UGHZa7x9LkTe0xrylwA
-f7ztoNf3ol2PyfPx+Rij6GMGv6BwiJAY8woOw3Ugbr7paUe5gc6bcC+z2MT5xOSSsVH9UEb9+SCV
-oXb903mEZdJavNGTJbMkgPG28rblJiTTqbxb4z1na1gDS3yQCdEveyl0gWG4VXiK34OYD2/OBeDg
-18Cw+wubr7Afl/sD3RCNCooF645IzZfc9ZXPcedDvgmpR0pHzlGTSNaHBLWRxkC5gV4Gq/niHJpN
-wn2a6aLFL7iNfyOe/5hfrSKuyoIHOV2YDsfjo/W3hf8JukDY6O2dchRsyuhb4QRUr+db3x4rU5KV
-uFjXmI0rRYkjGD4URGYG6JUEuxUMV2uxbMquhV+MAEnOFPi+cCygpajGoitlT2norhGe9gEg9uul
-lltJLV7a9S7tufc/R3yyUOLmA/ydMHLSjo1Lhfey7d/q2KeFTQn52DpaO1mCWXNs6JfKeVCAcucb
-9Wuew2ndYqb0uPjxtx4BK97sa25859bg5StMYBjN+oux3CBkQseyrK385Ss7fIti7y/1siJgiqqD
-1ihI+QYSDqC3lH+TLyE0FH5zgQC+zoNCMiRl0iICw7RzwFCEbzA4jLrmWdZ3WZ7ld6CbR59vAOIu
-a4gbI7cIM7AvPsI8hjw+D1TklLiDRAO+3njZzy0Ul01h7X93W4n06jbqAGqK2NwtMAyyBkOwXCEc
-CAqAmVZgOPiFfSDGzMTrPt5uesDuMHlDJHEUcxEWcpUIxox+7zdmNB8EfHMMqzU4e07H4g9sYT52
-azDcF954yJqYgFgz3MFvW7sJqN2Xu0i0whqfhPNSrDDVdGyNP4NjL0/8+ZvpAuFvkrZwF5ArrHue
-cZq6j2LXvb+sdZVFxdqTAPawNiKXw5KP7YAqhNkibIpHLb0D4yxjuvFUZden3Egat8RYJuA9SV0h
-ndMYLG+PpkHnKc9JLuwCiMu59CPL0DYlOFlRD5P/kwyRTX5xvgEXjTNIrClQH2+ew7H/7pvrHIKU
-+J0+Es99dBoVcqusCf91UNMNgr6ThJ+DMoeVCSvGNcVE1+FdDXIf42pxLjtFBNMZ+o03uOhnVmHd
-fro+QOCuEQyGwRbhZfXybcRkjtTnAQgGJMAZAhSH8oWD2xyOdIFCzYfn2xw4mNssxBJDngHKJ5AR
-rfBz7K/a/CWXVJcIT/OvlM25dqnVMda3zeyluUHjGi/NEUd1mZ885oq7A+C0fmk8ieCjt92fTKy0
-NQwNDMNBsctQBmONgjmz4cFKbSnteIOs3G38fVeSUlryvPjb5XiPBw/psnxP/8jCF9IneBWZ0WEQ
-KCYwrOtLdAJ3kZjLL9ytwe4CsRgd4f7gH6OJ/FouGZzGIHgTkfYMkE+iioGtlxGc3R8on8/TlSa2
-DhIEtwsyKDbZV0lLyE/v4aoh6j5RZ3LlTAr4MBonMd7Tfk4L/jW6QNjoO4NfhIiQoLGjaird/oKc
-K7G8ZmeY3drrqfKmHH9OtrIy6XhKDyUmvXSLNOC7QgSU2RpsShKUz0KSQfFG0fY6Ihwvj1BMyrtv
-biiflraKVksvIs3L7IA45qrdsXr6bjnW2tgTV7miPGe8oBf5/bKP5UhCFmnpZxqHafoJbz7DfMqb
-i+qb/NcnffdEKr6BYI31uRhQI+xAryiUUCB6iNuxKKBD2oNl2HnRP60cHxbQtYVatwW7n+fIFOO9
-AR0JhoeBbh3D3CVyf2G3ECvGsgqL+RHPYZ9epi/YxerNcYst8/jxEQ1ZgmDKD+suAd+jBViibJU/
-DDj4RSzkVQVIu2C2ar0Qx2O6zhnDb2DXmI9BN8g+dVq5rrzgRMCIX5Zb51UQzNuFQV2OWFw83W/I
-nH8M9PrHLtgy3MBWzoC1s+geknelLw8y9SXMYUDIIFfalNd8Bs4uP1jcrJ1RCADLl/Uf8ZJcjnvt
-7pT1FMMpXSQ0XSXMb1hscBabT4h9olmKq0S6fsSFnP/KuJcBqOkPv0dge0pXvxk5lcn++821K+6Q
-3F2sK+GGIq+sHkrqiCF0XM5cpNcYn0C1WTz19Sktf8UQ9U2x/yvoAuFv0ulFuM5Mn8VdyXxn3nU7
-rqZ0cSeP8arOyUosRc1naelhVlzm0wWkYKS04tcmKR5MOpVq9xZa60lPrOxUMLH2fAwdAPt5dplS
-1vMorYLfF4A4FnhN57Vex72RPqRzfx/SH+mhyLkeFnM+4T4okuEXeQrhdxY/atte/CCRC71SzMcK
-69kEghfA8DBQdo0oVmEgAXM/jy5Y8knz9DSA8jPNt7FK6zDCR7h/BAAAvdTj/Cb0aN8eqTMYBsg9
-woAv/DO5DoKH+Qj7a/eJU305uywoz5F4Z4BY9glY1v/0p6x7BXOaYLMQb2nMBlxPl29ocbfqrTFj
-ULx26RgxfqBv64VcJnm8QMm0tERewQ2uA+JG2SRChGuev8DETwvyRi1l5g5+LT26WvufOkDimn4D
-lXy2y6597Lh/BPxYcNtReE7L0Xim5Uv0A9EfBsCrq7Lwr9Trrk/0+pRquEk4VAx/YV3bpZnx1z6o
-Yi9KytpCT8yXIttoT1QkvllHfZdzuOhFbOEYv+YC4enPYNffI9jL+8S4uILPbYlTe1hGHuP5zoJn
-86/w05Z+6DgB3Xq0dra0YpSKxed5+O10gbDRd8d+MXMFMFryEyRxfo8vLuTU5+NJBcTiAFBfKd2p
-g93tKO5/V86iA2emsGMwbKowyufnbL2OXK0hTB10PJHCfN5ACierKu4PsZDcIkKPKYGS5uU88wiI
-cUqvbStmZpqrUuaRw07p+rbEp3VtNanPm4+X5LyEVPWBFRPs+TbySxT8nUX0sqy+jL47TePxR909
-whUAAJSX5/zEyNca9n4/5fU0t0gDAXjiZTkHJQKzDK8XfNaXsfxln7Xvaex3OmTtAbw6Z4/z3Sq8
-Pn+qcH9gGAiydAPFsDI7IHZXAVLz6so1+xO8Y8lHGRHAKcHDtj0aBfm0DDfAERlK+U/XZ2KHB3KT
-gAIjc4atA39pzvPWCiHwm1iktM3dW1w2pJI3OWB58aU1543gz/Ukz62W3RLcQXFIU0mQwt/GYdec
-bJe3nWRXAxwZJBAmNS1feqabHYBArgTfrJsqaWE+fkV5P87CEJXcJUKx3FeSUbE+Se43dGNiIHcH
-AdwvGBD/SE0DxLz3NPeXLf/JuO0XIPfEsyZfXPccAC6HvfwzKG7i+U08unKUnxrXZ9SgcD7peam3
-88AMFAqSjggerFZDblDlyX+KLhD+JsVctrge4u/SggkembVfuR2JqRbrdgGy2FiolDNyU+N0Jmm5
-opw83sJkEQAkFAZYCCKcJcKyUe4s365ejUZG763v+ejRi6UCyHJIxVCGrrpGkMQi8EsWX1AaKZn8
-kUQqpIdQi31DGsRcflNwlPPYjMBxt3CmZt3iZcsnYOO6Nw34NWVf5dukL0BghQXhw5vsJaiPZJLv
-0i+VriXABobjgx3pRrDOk2Q+SaCjAVKcV9cxfRxn7IGbL/G4byMS7KrYo/7qIqEKjPUZNajMFZ4W
-Nh9anetTw+A9VQN0GTCBW9tIQmwTTVbKNBEiQJMDBWlp2NOK3zDoclKuFHPBUyabe0Rp4rKYk2VU
-kaAKOqG2m4YOrvvgNhH9mckSBQRrkQ8AhVXy+gSGY3cTl8sj/arrRzVyMGLp5mgV0KKFbbXIM+4X
-kCwKa0VUKSUQ85tuLkigDlCbbTYdwAc/VUCc1uDWoR1LBuWWaQkP7ZYvtk5bng7LBWLMgSkg94dh
-O6bYThHmMy/2EqnMBKMbH+MZFOshLdMBLeGsv4e5R/t5jf99fdB6EJ98UJkuJ6Wew4C34gAmbUzn
-MtPXqytCfvKgnbmqims/NjZ9V6/9CrpAmIlkflV+e5o6IKA8PaT7IzFOZ7/h7dpB2o4ZkzdlksUZ
-bq4zw1gWl0z4nHVLY3ygLJFQcnu8+v5xXdxyVqx9oI8dpW66kmlgly29aILfwWwsNk7rbhDN5ksL
-/AR8y7nbAn5a0Q8C4q0E2HnhO+UXSctzAas5XzYO8iJ+avVRiH5XqL0q/2me8aFGn9BAsN0csq+w
-rdVVmBeoaZMGgPklMn2yFjcAvNqZlvgJtT1R/chvtedLPAqE28M6KkSGgeHVRvcFXtPzBIgBfmEo
-Pik7oqdYZyTZq0XmS+wDzTJDKO4gqYIIlgkBmDpw4LRiReYbeZ/bOu+5fRqtTtG0wpZVux6Xr100
-ENZ1UIkF/hPsaozOrHFJcNtBcMgkqfECnA2GxM0RCFwykOQxzj+VXBYKgjfrTb7layn+Iix5YBN+
-ad/e3nThOKR7eYhZhD3+dbiWtabwnR2VZ2aFl5/wAsMCmHuEWXz9BVF/OVTEXtYzSzDxXnL3+jHI
-FTQwzD9qdh1PL+Oz7fXiISw2ZxSn+XAAnABXGyimeet44qBi91+61UQ82sRcyz1dN3aN6ewiNF+x
-PjJMU3gcv99FFwgbpSWzHVsazzGwM9I5/dlFopwhOKGpVquBkmMxZ9tdUPIa6GDYa97D0sbDlVOL
-R1pVYF4mrcC+7PuVuDVtpfrYCY0jry0GxbzAUMErXCForYPnp4LaCnyjQduE14nQQwi9xMPcnVMe
-6unD9EnZkJZCk+xXEdr0H1SGLyR4dI3QykN0wc/oxXB995zYm9VA6To4w6T1NhirfH5Ukrk2ZcLn
-8nkeXiesKrws1efNpnW+9kTVeJQ7sLZ6KlYsd2UA4F9WG0Ohsh7s54tfYwfEWODZPyerQFiJMRQ6
-zVIs6/pCPR0+Br6+IicVMo8NA6UiC0paFu8ipdTGYobL85B7hu7t6VPn06C6ADPLY48PioPOX6Bk
-Un0rDqC6RrDV2QSKgtPSZWFtF51xJ9/veQPDfANR2sFS1Hak8Owi66jPvhTqiNNBKJ9ke/s97Xcc
-P9oTmb+YF9UKykS7NZkvu9MaS4aJ/nGU5e2wPpbhDzxEbPs0LIvvGkcDy0JPQsBufFk7PcsEwGC4
-t7GWA5VbquIQdx/i7EmJu7bkPAbAgMuDNkRlOzVPI6VP/K3S1Bl64AkheJ9DEaPqtqJgSbFSMdav
-mqe8lPl/E10g/FMkCZ5Q563jpMzXQxrwNOsKXwa9jNp/F1NazjjTyiuQU2rNR2jaNFUaBljJHdJc
-S3XLsF2giplyxWPLAaS1znvulhiQYPdx0Zq2WXTjyBbdtAXnulU8WYzLXLLya/14N8+fpL+WC/pc
-pku2x+dp5zIKF7wOrPgG7MxrlQtft/zlS3c/P1zGu2K81kCvM2D0SwnPEmh1tOc9Ou0/vIFoOqeE
-XQmtdFWqYwJzrM3//Y13wbQvwa2tv1yBq+p65GvWYAnXiKUs10tgDRArlssEsHyBzdVCAxQPcpEw
-dqA9Wof1mdgg+KKAT17qYGsmGsaix83NIvxkKQZQ7zlsKHcwbteJaRRIWGQ1w9CwerNszlaS5VcE
-a2AQbeR4WJ1PIPiQHmH/gMpqZciUvocwg0cGac/rT+t4acoqEKsWMEzprFE2Awe6tVq2HS7GBoDH
-+SMhXx7+KjziE7z3TuHSd7BcUwXs/VJjXWDOtS3bkPQXXvjX6s01BfvC4nKR8OukZXgHwK5Hn4Bv
-gl1YOaXeqJ5Bb/cXznKgummW3bjE8pA+fFRBsMYcMyednkzr4Veleuprb1tUHsykrRLtFVYGJP3q
-4d9NFwgbfTr2vGsE8cSRgbjOXMYJpJ5bcco9p+Wy7SBkpdiD1EgpsCd0jgOdJ/EKArWk1aRUUuUz
-lS+iQtrABRjeu3gcI1cyFnbwWbZMizwb9ZLG5YEN6CrlxY/AbynX1/gTF+3p+pD+oueU+sytqSMe
-eGhpW0qycgyI/dSeJoujqqj/gNpeloUHj+X/SpoGEkos6jxraRTPhYA2ZgxovYwew/7CU7EW27kC
-A7A+dnrgEwXMMSIU8lLcEzqrNU1UoGO5RoyFduFW4fWYP9enwl6s4311fV/hQbtTGBgOS5o1Lz9h
-K8Wty5vs4GC5bxCIDfzUwG2zEoPOCSkRoKuKGrWEAsA5XMigSuwQgriRANbuHO4Q4XtGpORNkAwR
-2rEjAXIKuon5AHQLEJ4s9w30jgqSobCXFBtg9H/Sxrb1mO/fANdTKbPgfEfL++lYBz8vKCTv5YkD
-cQAAIABJREFUl8V3lHDwqd2s9fAogBjAFzW4HF06Vi0a8+UyHMvVRADIXK4u5gURaeH6MNgibOB9
-ABo7pvhewi7jHsCwr//tR3NRgHGmVXDr2jnTllxqaXR977VANgAMO5+vGOPZd76x9MSi1U+4o4k9
-jZECpR0twF23b0ozlfM/SBcIf5vybqj8ngCype9HrcxRq6c0ZpCjyDqwrJbUp17UWqUYYo4St4Pc
-lI4WNYHRlF4VFQalGlCTAi4aueWXIUQI9gZ0PQ0VqGaabmuUl3pdyMh160qOi8S/UsM2smd6Kv3+
-nOcSLhBfVOEFeI5LGqWrEtCQVEAERJKeeY0LHkRyTe2ZR354kxYgONuc7FUB7nokynwnDRRX0Ps6
-nHU7AF5VSLbp0Gz/iMNaJzPBj1uCOzhSA9/2ueAExKbUFAaK/VvKgC4HjAqMB1bBqZa+QPHyV17t
-HiarlmyTvM/AQcI8rH8Gw5vf8EG+CNXFiWtbPNmt0b0NPHdIS3CA4YnlN718UVAtxJqnTr5lYX4g
-i/DcLb8OjqFqdxJFiOTgzWzj4hsDOQSC864ox0KOHadWkpqIG3+P4xRvE3A80lyaeTVdIEY5ju24
-gOcwUFym9WVfflB4cbDfYAjWPtzLPLw4sd9IhrsEEE87hiBeoDutr1WT39jBwCX7CtN4+C/kSnVp
-WO2uANh1bYBd3QEvA+PuNlFsvS6ElR0OSeYAbS1YvmMTnABwlCJt45ingRs+h/h78/1VFN1b9KpH
-Ke2lCvyb6AJho0/H/hn07Pnvfj+l5bUHUrS1ByJBO+h9ymeAS3Hpi4+KAkXB7ZYfL+wAeLUiXqoo
-dw9ZtneTF5zGqiHB3yzCcSMai5PT7KSTNdirs1ZoH/vT3Wxb+Nvclml5xWl73jOffJJ3ypdMrmaF
-lHGlz66x6jjs3PZuBUmZ5ZqzaeRzlY9l2hyZQmMzWfhOFl9hHEDwyVXCrL1UZukTBtESQCb4yxBj
-+Ap7AzWHWpC+jJt+FUBEdythKG1TLoOsmwaKfSgUQknmBes+GM60I7eYggHFMSTWwzTUOQyIOhjm
-GawWRLK8FRlCSDeA7JuyPH9vwTBxmN98CBaYYTCMtARjAprP2nO9qyD9gj1sIHhSmrtbECAWAsMJ
-AA4W4uHAGVHnAsJIa3BYhV2cHmRyrFsPk2yEBj9uqoPGTbcskt0xHWxRrSB4jJFWYTty2gqTpVgO
-/WjqK9dwynxVA8BIQCyqSHw97amIYM62ZtZ+gsU6HGspbj68Ic7n3YnPfXu53bRgYwyr9RdcLxgA
-2xqldPS0SG8Lz2RT/kXmnwCw54dhqeORGt7jBzAsgaqLPswntP7L/FIn6c8Chn8zXSD8F2jDQ9/4
-gY6n2EqpnkFP5d6DkKQTVD6Dk1Z4swJXhVeBLxdJQQ511wgEt0ux4JwbxqAmxk4RC82tv7zuUnjm
-Olz5ZC0GNgvx0S9Yta3l3RKckPnQl+iHnifg5fzpnqvx53t5lhpwoSAJDR1KKOV9+lb/sYMEJDmN
-FUdvZkvthV7FGQAbwI+xV7+yNt9hoDyR6HzZLY0bAAb6fsWbq0Q2CqERWhdS7ZrSFilguICQYgk2
-HpQR8QC/Y+ZT/UhTaDz2Xy/bQeG+ELRoxL6AJ/WpFtiSZePcwHC1ZnpyA7xFrlAVdkK+XKUJhkG3
-/YojIOaxD9cVA61jVPuvW8IzBSRvFuCNNhAIhgjmnEs+SAXDcMDLLhAGgKdbiM1PeEChYwbwWsOU
-QLiOae2n800yUA5IvrcAOuY6rMcKfLc0mq/kRbIEjxH+wGOM9dviaSV+EBO1XaoAfhAvI8cW+WVG
-Mf/2Yc2czHpkgA4f/PIBmVxb7ne7XJh8HMgiHOvd0kJ2VABMsBRoVl0FSJd0y3CzDh8swqxL8gaJ
-JHEBwKkHYsh1z2M+qGGNsnF9KbUiDFn9iYcVCX18ulAvjxL8rXSBsNOLhVlI1YTNqyNAPBR6k+Nh
-CAVi9qMJYbUqF66FCPB4kjz83pHpp/ormRmWfjGPRrwqv3Idwgm0FiMsW7iDhU+Wia2ybQjJuts1
-wcf1fp8+O4sGow2bBrPU4sf0yHt94ciSgDAtT0N6yikdmceXcXVQ05qyBqjOE59/L/6Ov5nFnqrk
-B4uRp5WFa4nzdXaMkspD2noQBxn26+e78nrqB1s3l4IjsArXMw7CMn2Swhoi9uSebv4MNOZ5hzjM
-xaAjUGr0Ph5NKrnl2IEFyY7uWyzW81THXcC2NHZTaWligHWBJHeJEED9IbqhpDnjvAWUBkTnOo6J
-r7n2Xx5jQKdZgO3mwq3Cbi2eDpBlrpfkmrV4mhU+bwJoPCycK7EBL+PTfJnYgQviRb45l3vIVA0V
-Vd9x6MaBnM7wpy+U87Q+RmG/kaB4jdNIEDzMNcKAcK1WofhRwa6lOeAF3No+IGo3D2r7Z5fZ91sk
-u5Egy68edFLXlmzOUBpnB72B5xQtTFZ1bedGmpfrFmEu8wSanTOsj9EX53XvDvfRtmCMsII/LpP9
-Rwuf0ixM47DnE32k+A4Y4ZPT/ga6QDjoU6CTrPs8aRuLtx9CNifqOzSl6JkGfAg1Bvt2MJv65jGP
-gW/8SAkVE80uiylcx0+8QQCqpW1J32y+LXnxoeDwKp9fulrCfVWtcXMR+RSP9N4mYBvuz4iV7qs4
-CSi+lpQYCmA85NUstjDoOZ1A6npkv6cfGnQmas8TkATIKtda/TNp2/XfDTVelGnjKX18CXBsDZHa
-p7CCcViA8HV1EOfAABS2hRWPuD0eZRlQYLf4otXNIFFgFre0yjnI6GWLcqSww42idB0Mw10vZI+b
-7o29FdxU7ItQ7at2pKMJkiZ+dtkE2fqYL1pJyglhv2vNa2qp/cAMDto1TvFt6dYLgr5GLHM6+LXH
-7iKQocv/dKht0eUAd2JOs07aVwHV4lPMYhnuE7K+gjbXB1QcCLuPtttQ1jRJ5VGaO+U/0f/VT79B
-WnJ19QEyl1/5XG0SA+pQD8MM1AbgYbswOFin+StgDnkjFjcLkiD4+ecfhbHr40cAW3GwixUe9jGU
-NQ9+I6KA2sttMtCtus5YBVCWttf+TKybhBlP/siSjnR1Cq3vZVxGkojVF/EKenVvi9I5vi4LECY+
-CD0gYBbJ/V58bdlYio+GLL6PMUPKGGQYUsMEDrbwWv4SI7QUL424ZFrMivh84vD7OQ39V+gC4SB9
-XwRAhR6HJfbCKpxHZ5h+aQKIh6Yd+UNdWOrOr6wHxZXKQeH0n50fYKoD4hwMasQhvyujEB3eVwZ8
-OWYnMOxhEQQYPjVHvpHOeb/qxzVrjA12KoCLeOrEAEruDIUJlO4xXCBabEs/k2yBw/VLC1HazTa6
-XvbztA8E37ZWDmmnyS1AIuPS+9vZV1o5AqwJjh+AKtDAKAjY+hrsZd4D4P3cfAEptqgiMAxqDyRt
-Zc6buSpNMYtUBewYF2jxD6zG5Erh+HhhiHiga+O+yyQYCBbvF5C+2HEjTYo3hIU1EP5Ql8OVDSRe
-GlyuCjLF3EPmqmcyEDYrpIHfORaQdUtvAcRzYpplWIZC/ctlqmtLLwejBoqXe0paZztrP62OVCV+
-Q5o31mLAWAwAR1987HSBcQfoDoALIGbtFmCMHtE/KRr6CVuI3X3ixwLKy5rrbg8K1R8B0MXDouu8
-AO3D8hMQi1ofQ0cNhGWkCDeJceN+TaX+BRj2sjamWrWVjznA47PG/DMQTPnHcjsI7jwMkxFnubzG
-IPBIAcILAA9oAZ5+/7CmNOM+xyJo48nXlow9KF5nlaqMfa40+DP49DfTBcJG3a39idjDqgJfLXf0
-aHkbGLYi6+LNutx5quhy0y4h/P8Pe+e6JSmuc9slR7//C58K6/zQbUk2kVm9d1fvb4wkB4kxhjC+
-SBMhm1TtA2b7H1A+VB9kVwPngoDYegdojZluSLyEjoxHV+ZyiUtSeMCw1C2WMvXLshVYJLKhxeRa
-6UDpOP67dX4unJnbPt39AFcAON82Si/CQmiPlHasFKZDVqTXSnXGz4zdlscDbenc6QrgcoW4i4n8
-81euwvxepPf4oz9JPySUiPXjUe7toF+mAy24fzHk3kAVl7gb+H4Bv2f6uFYNTgoL6gHB1aHz3sIi
-WzB1A95ygZguEY/7BwwD/StVLFNmviqvOS9tzlGLlKPi127W4JsrRCKFy0wKp51wudXRZ3ZIdw9l
-VwILrwDGhQwbBO8GwntvLLcMyzZ3CBFx94SwCNtUeVDb2kdUesPOZjjaevYf5UMd9sOCGVZtBNBv
-s8Kp+KeHEzzLVaO7TzgsUolG+0mzS6tHqr9l7hJsHbYBcy8DMXW3B4LgpS/EYDhN6HWL79oOwhvQ
-gGMD4gApzPYeeaL2l1utFhLwvzVchyp9lGdzmdAJxVZjHXI7zFYeCqS1xXUIrjz0tab2Kwwof2bq
-SqgdYRgOII6iwiw2kpMcz4JS6tqctybYWa4KHeOmAr4+/9739NB/c/kB4Vy+D0UBwOHLVMo7Xk3p
-SR/DUiwwZTMFHre5+r2evd5OJDHkS8Cl61UjfFK8npKUJ6jBatwTpyG1k/vcKbm8qIwmDBf0nmGh
-n+1wq80Vove9/qAhHPii2oUSsgyoVfL6s/sewNuAFoiG0LJADaCVI8GvSMY2CWT1kpKY4rX9hlxC
-f2d5ht8z7qs08xhAdTuXdjG5x8f56MezdbZo6ecKoXs2OuorJMk/W2zZSvwQ/9D/no/zNc65Wtkq
-DDq/+rNUOWsVXypoYReIh3192D9guJR8F1rUS497XGgfaYgPHoS8zcFgIT+ikXCrmmGXTJ7H6qtm
-Gdsr4MsyG7M+hJuArHptb+PcGIQNfnVvbDEA3ttm4VjiH3YQswpvt8xuEdjsE4KamcJgmtu7csch
-fdJUy2M8AAlQ3FW+KgS/aNbgkJ9RmjluEgxqBIgDgAOCsTy83G84YfiVQGwQ/KJyDRcIAuC5FbfO
-52t1vzf/VnaH4Wpzym2e7mV7OF0icguqkwh3n91oU2U9pj5FEMzHznLkcz6ni2ULfeQmuoF0Y4Nl
-13WWl0kwi5Vd6dkqKjmLrivROgiKo99LRxpyi5hhyTxxfn4swv9DSzT3u0iNfUsZ4sC7hD9xVxeJ
-dqgJaMklqgmSXN/zIUgqS8fxlvQCfycEPyhZyKOVOH9QpO4HpTgLhiNeEygKgOMFW8B6iqIOg0CD
-4ScATlh2ARBpuL/Cjx2gfCs7LrOHVUf4bBV8LbIxTyu/jLMk/3GwWlA1NipXKoSc/ovjkAJRuXHp
-ISb/s2VcLF7O9jhO9n0XinmNZNQq2Oe85AVvnUVaOceFex3N+nDlMFvFE+w+Auv3wfc5DRDW0QOE
-AxhnnMQ9l4aremE4OMH16hLB4YTjOPYAw/6DDaKEy1ogWJ6/Vffgr9XDnassv0FrpGgbGFPnp37R
-BYHErMqASEJwzTJToKgIC6nDsh/fGltzb9h7Y799Kw7Fsh3Ydt7XjkF4EJSXtYV3M71y/7V86ohX
-Ji3Qm6IIbMF7bev/YRFeZQVmdwieAzmPgSzE0RbiJ6LnNBiOD2ywr3DA7wvysu1aK38vrMDTKpzu
-EVcwDquwWYnDgh/9JvODMoE0V4ZYtUA3LcJbqeWwRVezDOJ6B+hGPYQe0Pl73m9IT3Tg/WxFhgh9
-BtzjCfJLX5Hya+Br4ZVxTTR0/Q+UcGwKlsMkM1I5X0R1VAnF8e+cvII/vvyAsC9P4yCPOhEk9Gqe
-ly80ukDOllktW4QEmGv4/A0dv33T+XyqU180/Nao44+f9GZj47SHMq5SiVcV6lBQvsPntjzW4t5K
-KeXDQRzLcnGxc4Hhds+8H309yzz7fT9GBdYePCjb4HRfLB2JJEVtQH9cs2HwrGcZv8hJSVpoBr18
-lBWQ1qnxqo5uUEhQtxv/Yvl+yl5+820AX+vvxPU8Se8L10wKjiKnk0TOdHLbZ2mdx2/9qKA11ju8
-XuLX96H4a1D+MEDu0CrWUvVS0vZaXBwqygq8vUPF/L0bScIQiVfJXoYZ7jKgFP8QGJfyazBMvs88
-E4NZas26mnOKZztx6y0KCiIHIYqjjle6b5QlUBwot1PMThB2Ke/bnG3OIVjeDrybV6FPCQvaNHgk
-RdxRImetgz9M0CgD+ud3EhDmhKW+U/sOhjssqL66e0T43p4rsiwK0uqNkkb7Eas/xVl/EGkD45qf
-sPsIi75ythJJCL5ZhTsA2/2YhR2+rTZP/sEzj7hAqZZVOCzDW7Wn/RJ8CQQ1ymceu6WfEFz1qQ/n
-5bn+UKfZtuv+JBVd6KZQhiGPdGyzmKprUlwTh6Q0A4DzuFK6eQ6fmyJJqO7KSvxjEf4/ssQLAAAk
-dI26ehgZLgms1GB0gK82nSUV3dtUNCKigVDopxJu7e+qxGNU9kwTF1baCkjntTSh+Hzr1tDosLVE
-eVDeLzAsUVyZBs2nt/YtIVuHozzaMSrPhOXLcumzx/qpi+ZxqWvVnUslorzU8XnT3EziXALtrADN
-tJoV5r+Yuz3+afld8fNdqP1P4mKvydkz0DIvR0c6O5aMSmrnoIT1QwfK9RPofgbiE2Bv5z/+xidA
-xhl/CgMvNlLO0UwMCgCIYrl1d4sJtUVW3602BVtzhSBLMK9VRQzFvRzrXv0jDQFUXCnNEhx1pBXf
-KnrGISVVpgl5JHXJtAIj4jTLquDELYR74x1WYLIMv9ka7JAMeeNxUZ/jVkFwL2U0cZgBgRo0yjvy
-Z+UQFt5yIZAGvjHDhYrSPMfI8+qv12GX5B2IbzB8+gi/yiIMc46oGSMWlKzA4SesCfIxRdsmQDbL
-MMjSzXnp7x2l8oxejzVjhI46RksLbwt9P8LDX5av086Vdv2q3W4NplpHDYjkYzKup2ADVipFQUHv
-qvDiqgPp3aH/gXM/FVPIkJYf75tjK3Mriprx4xStf3r5AWFffm+wXFgcyNKQzRFZ2dkD2NIZ1kv7
-Ufp9T0r5YF0cMe24Ujp81NelIFGQjKc09EQdHc2sRGg/WKBVcTUQ56aUomxwwLBEWSELAvEdee50
-HZD1sABPIOaf5/4V5zIX0KWueY+weJitwdUO8g56WXEZtcyB6pis7e1YWHyoJUg+jtH9ah+oqHRu
-KwxavNy/1/rrFBn78ftxhEvsTPd8rR73JBEv8TfpKXQVuv+WVLoVu+0LMKHy43oA8N9MEzD4zbTg
-fhvgfYFMLrfeos0yZkBiVuAVSllIaYsr7DlY7gbD+TedoqJgS85IlDvfD89H67LAgM+V/AHEfDf1
-i9mnfQ7hkNpNTsQ9gtwAIr86885xPkfv3tjyxt4b73fB71veKVOrLZLE4c7ulVA9sUsi9R/mGL7T
-Nj9xWFoZgLfPtCA+S8awApc7BMJ9s1xBLmstQ5EsISuwYM4akStZhFXdhWJagXekJfjd4fNcluH4
-YlwnKdsqPwR6QR4Wbw/HYLm8x2tbON0euE3MY7f9p7i7FbneM3K55/zXQ4+0naxMaUVja3ePAKKY
-tE6la1oaTXUThgMN/ST1s19t5bKdHPKnlx8QzuW7KDAgOIQ05qt/YAJwWhkCYjTVgm2p/o/2M+Eu
-0xccc58/Ffg8DpcVlzR+LBRGdl4h8ZwZsY7Gz6xhKShngakGNbRTu+Hst6MQmlWXOy/pxey0kZbd
-UWa54bJzqX55WJW2PX1i5xUAMx3rv5ExucWnP7CecegFxhasKBQr6of2zdJO89+xzFhW11/FyWPo
-03kDUB/icInhAha+v6Y3pO8f0FznVKBrk28D7kegtSnQns/9YDkmCI6+DES813yDTfvH1qVWvooP
-gEuWXgl4oDQHDHeIBP/e7IwE8mlBZGtinB1uETFDRLMEx379Vt2lrWEw5MZx9OeHPJPkavHb3SC2
-LLzfb4hs3wrwFohsAO+qA5ZGSr/pX0CLr81RLz4qqiy4+nGVrTlNmuSAwOEHzFZi8DX94egGbKMf
-FHiutt7mFO4+wivdIsyiu9w9wv2E13oA4wUMNwk58lIS+8x/h097uCPLcNQ1tYXDUvuNY32/Hgpn
-mjSfjbKesCzeTkL/Zv4lqISWFF/CRVFVFl/cC0swqm+wa0L2l/Ip8s1U1L2RMiPI2Jrs9P58cYv4
-cY34V5fvFf4Bv+1cBryAEeoxUcGk+eN8qajWxrit9TgCYG+I8TSVUItq4P2Yr+iADEpjcIvyC/Zs
-K/WFCXH9BU7cVZRJL6fMF7/6ozI6rcEVH79v51P5RFKyurPlB5m+13VeC3ENRtrnJRBD6k5T3TVB
-QGEdF4ginE/UGc9l4tLviAMKdKm+utWd3yXoLIKR0Q4U51JCkYr2chQf07SfbOfJ2J9n3/Ytrsnm
-WZhjX7gCEIK60ohHHg+S2V8scAXdEbda3AVu13P8enCjiLxZNiUz3SyQTQsJvbGhnhqv1oVcGoSV
-7IRhpbQFzuE+YYOzShRq/oT0NSGeAep278jX/kggrmuaTKbjHSce2xGXQ7XBOj7dmfoxK1PzD377
-wDjfH1BseXx7Hdh+5J2Bx4A4vjS364fi/innVidoLg02cC9A1l0itllMZS/L41pt0Nza8ZtAzYqB
-vP4Gx3kZCIWjrc26Oz6q8coBc7JeEGizCOtrHe4RDYQ3gbEDMHwgYPiUiGzEA1UK9KzAcI2g1qE0
-SC73vdjZMkztmMO1T289jmOzNV7Ov17T6wBdbudvRXqSX6kZe5evKsqqMnm2GgQPvZr6mBqh0C9J
-vP9TkhWC+kRi9dXcDtCdzy3MKX96+QFhX37HNUJoiwsY63zVfwXg6lmDgeoJKuNDZFO8zvNOsGV5
-8HysK1VunCHs5rzA4sqxehyDkdYNNBWk962XncxjdOmmPiV+jsov4lDxqdzG+XwnUkkeFy7jWu+g
-bHXfzwPFRV4jQZ4vlV+Oq1uIA5ppWxxAoFv3r1wPyvcaP6gPN28FejcisytQBwOhEDX1luarOL0c
-l+N3pqAc+wGB/V+VfRPqdmA0caSVdUppsLKPKO4/51oQfJ/lYX0FxCMurhcaRNo9BdjJuLc6zvMI
-81KK94MV+ALDWyzBYstwgBpCaQ8IEc7/LDOyCq+A5bAGO5Xx1ehmZvupe/M+q/7mLtsEta1LWY2G
-gwCASBPgu90N4v02C7CF336Nd123bQtklyp0Adhq8wqLfSHMjN/m5qAatYQCrrTyMgz7Z57dEhxW
-4R3hZXMnB3SnRVjrYxorQKyBXPRwyftv9VikZe0z3VteA4hXwq/whzTSZWM166+F3T1ie38gIDb/
-a/tdCH2r2vPVLLGoNrk5rOQnnG222o5S+zqhWGn/AxTTuRVXcDvz1/a91Ujri3aPSo0+HfeyCIax
-yyE4LcKkU9KDWvhKQLfQdv1ufSYyoPCZobvCvSjehHRIwrHgxyL8P7B8r/DTF9SB6xP8Gq91uIu5
-Kf1iILGC1vjqlNpHQVgq6dZgq/2dCjniY+3xXd+7kDuUV+/EXeP0DpIJohzihriMqFyA02dXuADa
-ZSktKcEA4tnvMqDe2W8UQOny97UEUHfx6Nd3PMq2MO8w46THRLkrJYoi7Odr1ocdjDZHJ0ZcKnGl
-ZwWKQwlZjr+Wx7jlUWvt3v4bcXIJn1jToeT5GO0LlbjwYelnHQBNl6C+FJ3lyULL1uD1AMDriOv7
-5/HLdQ5Qazd33Bff/2yfqaRlbqerhMXFDBMQMWhiQIYcgHCvF9KKWX48hdrK/RN+tSjyagkO6KX9
-Ue/1+15eIVNllOtIxwLTQPiNd+Z/HxA8bt66m7JFVx2Ggb024F+jA2yrEkKCgQMFWgHD8WEPh2GI
-pG/wWhvbPxGdHwdJCA7Lb0Egu0XkfMINDqksmLQCgh8+prHWgrwIfl/kF8wW4AbBEQ6LdvkGyxYD
-7j3cI3wt2VoPMAWjBf/Nqu5CuaBU855tX2p/lEmlZdeKsux2wFXMeYirXk9XCkh8+hrt7U09nFEb
-87oR4TBBsAOstfnBIMKl5RkKPUHtTyTCgua7yWKZulCkj/5b4Durbcr1f375AeFcnsjoXMoaOKzD
-bkVrz3cNNLhB0S56+8mtfDiWYalGSw3pkE8Z361Z+aSY8ZL7DL67KdlzqflsGUa4M4XMpLKos+na
-UTZ1bnbUkLuKKgAtWcyXDCjOi7R6oXSXlTlxZq+v3S7M53IZzssh9NrQt+we0X9aMv9KJwjIanvE
-ufAjpZktRnRUARVWbqhwFZeSs1RpgRixYBDjchlx574cxw6AOa408iCz0HvbFS7gkV4ovXjDEuqI
-V/DleHaJmB+GELfmHgPhlsPvmb6+HneCcO9rT2UTGcf9uHq704BfsupKKO8etxU+9dgJyDWQrvg0
-FXz7bdaQVX5o9+kQFb/tmr8Gzm18tcw2RhXr9ZcVO+ra9/OYpUvY8jR71xzBAsEbPE/wnCWCXms3
-CC4QWxvuZrGgatcSWJkiyiHvLUCuw3CsEB/Mt2orykBc8LcUZB32eKAswxiwJoRPWSZRPjFQLsLT
-RYJdIRZ0LewbCB9W4bGVlRDclRltU4NQ+ccadQD+spyXLLdbnhEijud+yasTiil82/cd7h/X63hc
-PozEKg7cKTNDQYY8DDnGMsq2iyzCcxt6uEtidyKi0eklr708DpnquY/Rl7hBL692/R+L8L+4fN81
-wv6bXOzgq0BVIluC7QdyN4FJ+EkMI6xnXLRRyP04BuTG8dYJbvEBTdQgPY/bwzGRd1M93im6svGy
-YADhcvAykryf/jSaZ8k4j8gtIFc4rfb7ymKntPn7rdzu9S6XtXKnI13Zg6ma6148QCwKaftV/oev
-sCuiVMRAWh1M8fjvjbhUl3OgXf4oFcqI01Eks4SOe/sQx/sYZXQ7r58jtJk1SGE54w+gPdLagX5I
-RjKPaVDkxw8YvlmFLxDcBr/xQLkOx2uka6Ac8xAfpTvL5gaglxavcFnWH37LRaJguKZMw6MVeIeb
-BOpa9jPU+EvYEFyuA4bDMn6H3gW0X8lcI9FFFRqP8eLqXADI6nU7t5Avj4kI3jkbw9tMipLPAAAg
-AElEQVTl75vuh1YVQN+WxVeA6wAbh09j3voanFmESe6Mc3igl60b22da2Ko2mC9nj6gv6bFbRMCw
-WYOnXzBDHLkaeB0qCoCrP8yBcgbAK3yElVwhXoqlGxouE3tZfi8QjLX882oCbK6b+oJelL0i8lfN
-nO+n2jjIMhwQzNbYast3KL5Yey8wy9A79/t5pVc5XbSN/FCNCqVjeRBtF1UeXlcNikHwG7UrZ9zz
-9mYGonWKqKZ4QzH7Gr8d1feHlx8QzuUORHMJeEoFPlwk8lri2wCyOGdYSau9dLeIa1ge4lEW4ExH
-ylowLcDP8QVXQ4BITLFUnTKOMkAw2JwvRrWHuRBavyEn/liFjtOPNNDNETpnH0xMIH3Cy/w9nQfj
-mD6n4/347XmcyzUhluWXbxUcF4Nq4h66P/AtLs/JwYd9v35UKeNy7Gvb11mDaPXvcXKk+V7clH8K
-tLYofJZgnEFhuaTJjVwuISMpdzT/1QG9wBP83iy5w0WCXR8Y+hh+F/sOLzSXigHCvc9VBJdpU2PK
-cdUeroPhwg1CACjNGzxhWNTnHuYvtRHw5e9PCSYAFuohnnyD08dUkhKan/Bt3XdgVti5ZnEadRmw
-fXugIbgD1WfA1/IZIkKeMgC39jwg6fVSKh+FbsUOEFgwQBSu5y7RGMbyOrtbhMXnNpa1cpq3tVYC
-s2w1rgwYRvkK27W11V8Xm5QnKps28HFOmfZ6GRC//GtyL3PlUF3Y61VgHKAefsEBxCt8hG+WYfuA
-SfMTlmpr+XZjlF1MmRbuEbald76KBN3Q8wmeWS4XFwe13vfkA4x4eOT4lLE3P2Oa0jDzRECss41U
-3TT4zentIgVbhJuUQP0KKEcytn6frpu0N1PPSijpeox6cokQUax1UdD/8PIDwrn8TuF36A09waY0
-dpeoyBMjnuF27ifakJgdwBhtH6DGX78hrVNw46t4eBxwgu9CATA/tTWLOKokJyjyfR/7cjl2Ka8s
-M+lFO+cFjms2cKUMTfVyq/0o85YmySLKv9Ic4BF1G5AaoCX9jhy1jjSZJw2f4Lq6BKhSuYkLSH7Y
-qqmm+n5en463QrgUylMPucPvCci4putlVuVoGe7X5ji+UhZ0XU84jDojyjfPiaR5oF8zISj6F/cV
-GX1qAHHbDph12D0hmQA4jlOYrcjRZqxeuTPA31RxG8JxHDgfbNKemhZgcoMQEASfsLziODp0nFno
-SjpBqq2LVsBg+faSfoDvsk/u6lbIOKZweUDwy+UccMtuKQy/wsDs5+y18losmetHbS9g8qVwAHxh
-pWW4rLTwcq37DnCvsuM6NdDSBDpzj9g1iG+HFdgtxNtgM10m1GTJVqR7RFqIR0nX6mDpa4P/ReuH
-6dP2K+A2rMPbZpQ4wHdsjzgpX+E2WM7yZXkktPObmNbugOBwM0Hcp1YvaQPhCHAn2B7WYOX+Vfk4
-4fhzmrAINxeJQsusgxRWXg6nno8Htw7Bt3AWVkqTZy3PeWX4bca/I5uej5xVAv/K8gPCvnzXNSKW
-/lKgw1wooakGCm6GStC7neTr/Q5q0eAhMuKiwT3EkSyLuGiR1gE1IXhu87bH9tsAfNnPDin9fvO+
-GwAD/dPMcR9ooNyv/XmJNHc47ivnuqz+VT8H7FEhCaTvR/uQeY5fjQR0FETCryVwKMoz/Zpx4WiY
-SUKHTEtwxmy9Z1go5u4rPNM9x82zVc64BqkZHK1PPoU9wD/en+gKY6jtWX+Svh0wfIJcrTzwrSy6
-A4JXB+TpF7wIhMNNAgiLk/XbqOKo54ThIEA/FG0sFa3SftsqQLDbP5gh/irf2lZ8jEMRabW9Uj7a
-D2lFhmEQAK+Ap/jNIhaU9Jm4trP+3IaG/v4KwHgQEa6T8VByT0cgvMuPt7bVJ1WBl9cRHIJf+oLG
-vL7rhb3NClZ+vIDYCMQE7JLeyOvWa31yj9i1FQkXCfcLDreI4R7B8wunfI+ipnDWYxOgXHdWf7G2
-j6Ksqk9Z/jGNtROI13pBdXeXiBkOa7CHzT2Cp967+ArTOm2f2ZygCcF7EwRHvWX60PoFyNZ/Ahk1
-ZW22yN+CYK3for4a8XFuPLRYH+z3p6B7F2o/rR27bIFb4A8I1sooCmS5n2nqENIjIi0NtRCqDrP6
-xiwRoaNatX2pof/7yw8I5/J7IBzijs9PJT8JTLlRUDo/bwIf0Pc/xzEMl9UrG1Z0goyTM46UUZMh
-COgVgt8O7crb7OB6HONy4jI8n2Vv9XB2qswnl23Ej0vka5gPVcx50CM+yphqj/gqPTKSsZ4BuBSl
-HYz6CgEbXNYG2YUAlrq60gUyV37S/OqcQMtnOwH4KUwF4GGd4bincY+/G3fb17rNvkhYP6nguRdM
-AXoD3HaqUBh1Za6nENzjGh/B97AGD3eI1a26FZ7HBgBfQDnbTAKtehVLtsDqj1JArNXeUm4NC699
-YQ40JRrBMGjKtMNaDOzhR2nbm6Km8k4A7uUX1vEGugvIT+vCfIBrcTCWTffPQEzlzsCbdbDIAl8u
-Gu0BhtJ3EEbvIww8Dl0vVejLynUpDIiXuQos/9zxEoXKMj0SyoHkRr9mWTEtvN01gl0iOvg2q++I
-qwcYLV9h8Dp8UpvCIR9hfoigWSPkZX7CWwcQx9fmMjwtwOID49z6TmFe0fJVi17X4frBYZKx+R4i
-3R+GuwOVUbMMQwmSKR96B95Wv3xN35YlmNwiuC549cGbCcNSMiVlE8gnWHvJSMsh5cTdRgTalFs+
-8vrPN08N6r+s35k1wi2iZnn5s8sPCPtyVb6X5fxCV4e0sxL16JQywqQPbk2a4oUaKKfpll0LR+MH
-HZORhuKiDFKgnHcY46EXhTl/SoP/GgTrratOWq1fmulmWVSRarvv2yUPa3E7BnYrPo9f4u3YAGP/
-cSHhmOcOsCJ+tTsIIAsBksc9vVvlMr0L1wTmwEOHHK1fc7ip/fjsatYJhQOGJfJ0g+SHZULtd+Nu
-gNxjvJZZr82A9LTcdhsc83kcxeXvsYcsSGVS1+f+1ZXNd+D4AgtyA2WyFF8gOdVL/Iu6qk2VdUKw
-3//FWjvVXiQ/fIK9obZZItR6o021pWS1+tR8UgtWhbV1ZdmU7y9ZgNeGbotrll8FIPzOKuRQyDqu
-m1VQ3KA3AI7SkM9yhPe7/0YDmVgXsJbitRT6Mvhbaxn4+nbHlGAOwAYpbrToDdbAowFqHyyXX4/b
-OwfEpV/wAwyHpbFcJC4zRjTwCtnVlAnC7QQBq+2hwiy/NnBOsV/hA/wy14jp/sAPHTlnsF83Bsqt
-1dpMvl3wdmW+wXLWkdfN5nCUnxd2WX39fhlyGWaV04w+9QFy/05c9j1XFGWBZi1JEIzV6iXfTLkL
-Efvs8n4IgO4eoehjTLz10cN1pyHqEb1aPD/hK1w+w0tohqo/uPyA8G8uZZ3IGN/e1Hqp/qeqLbgt
-6DvjOxDieiwU5GoNvvyBbnH0JC3SfjMmlxdXaqDfY5cI26/ySLBC9zG+eDH1ONfj0sqyl3LzXQp/
-olkdoA77AMEQrde3KGHPYkfzf4/HQ/rcplInKMub5H0pJqN9wBXM0WD8F4/7ISedEejFQnej+nnL
-6WnLac5FjrI+2/yIUebTUPeSgMo9JwE00kjb87L8DLUnJF/CI7e3B2SuxtxKqEW5xgmdK54XAdLi
-Ga4QtT2tkUsIzmRZEYay5u2Iw4yLdk9x4Lw8fewjOxwvZUXb8Om/FuyjDkugW2x6rL2xt2CLYO+F
-Le7Lunx/2b5ugcqyL6SJnSPiPsk+Ty67ScRX1OqLahQXadoxYJmvhUGgE1/MmgCtQWNQxd4G9pXG
-ZRtbU3d3Uejh4SaiVEfa6yPLE1w/E976cVzWagOVRi9rwq4Ch4tE3hvK3WKAdm0/tbnLfaBAjzYN
-pFiUsR2RQWte4xN09nvX3lcIgLPM4NA7r3ndZwguM1VYbg/tF8LBzaYCcX1baVIHa2p3kg/1Rill
-Az8oHzPWnK49IV9LWk3PfhLm9sWwbJuiesxCpOwPnANz3NUHp/vFch1cyl8LjJ9UzD+4/IBwLL/1
-EBKKTVvcdy4l7eiEw2f4ZSy4QjAwLFDje+8DfAuGWaFXvCm2iLN83IGYRJPqtMGQFdn8z+6rHTvv
-rcpgFOKkPFt5ZJxyd3bBF52PhSf5P1VXR4EfxfN99dDYP2Re0g/CgnghowOIMR+67sHHmBZLIBv3
-92n7CZa5fBr4PQmwbwm2gtu6fW6ZuMAxpx9w3OC2ypSSjL44j3FYL6m1ElDo1vfnsQbDgrSQpJIL
-GI1Xyq7gymfWtkDVxyfQLSgpaK3XwAXC7IpgYeATBDMU2DiCjQ2BJPAKtrsPPO77p38Dkt9hGXUA
-lm15mqALAuK27g3c4hOcga0by10reJDaAb3+JYkE4O2Q7O+oN107Zmpgf90GxFzmDMyYxyasUZ1R
-y+P/fT1xpgCP89IhsID4HDRXsM8wjITHHufXyd8cUMqyFyfgXmdYmGG9xCvfJ63cR8B9hKzBozzO
-PCjatGkA7Qv9PkGwHzsg2I/f4HeuM008pK58K1Hygt2ojmkaVwFwHwxqN10P8J9akcUGBIu3zQRk
-7TBsz+nRoAiAoSiYKEtwQvNGPmz+yeUHhH9zaapApnKM4wUJHxWjCKJd3C2mT91E72lWB1+QEmtW
-4Phja3AoZUSXEAi0W3/F2vmEXQt2q0ZuXdCsOP96n0/3RivlLadLg3c6qbxRhvK41k35kcLb2u/5
-bpODn3ebEv0GyAxrafnNTKJtC4wvcb+56Ie9yrZeofYpLsMz7vgJPX8youSW/lymZbfglnD3gON7
-er4eZwHynWOPGcSEYrnF+c1WTCka4XwzAJNlOIE31rQC1VogzJAzFP8lDsrHCwyibBugQ0aY2nHe
-WVVy+JRCFNv9UxNyY3/FvkPwJ0j2WQ8gQFqCnbTukLsvcdosyZZpA+ugv0foXWQlVlfiWvMk47BC
-M+iOcj6OeelxHEmlqh8q4zhvgh6tbcm0BMDZFuyvimG6SwQUszW4W4cThlt+uO2RdCTxwPmc94CR
-JsrhiOfrEbhC7+eecBxpOf/8cChe8nJcr+3nbzL8jjxx3vOB0kH3C03PadaqgbITettMMzllI78F
-HjAsAoZdGnFAd2Z1aGNOwhLskiysxF6QzU0qZokguC3LcICuFhAHCKvp8ue3jv/c8gPCvvwud8gM
-NyUq9zQeYIVZgPcd6P0ibXONGINPLmtc6fYX7TxS7aNb06JuMeY0WuIiMGCpW4AfgPjRKpywEQWn
-BcA36T8swxkP6oCCGhlP+ew4fwHkzMNDWYyMM+i28GLwvYerrXyjcX6n/WpXUvhG+OlYFEuWDzvy
-Fm94/fRKempJca/NsouzHMpNojpdg2EqjysAPxwPgAX9Zl1VMWuD49rdCefGt9JTzt63JhAvXhmA
-XxkGWHkTiOgIH+kGoHGeEoarrLI+uGDpdvMti9ogOlEpoH0EXoJk2g/XCD4OkRNuVc39YsZ9sR/k
-spekJRhboeTQrESbAYOn9VjMz7UB7rASJwzegLfHAf06JJZyc1tnxA2O81IEg82HG/EVue5HnNsG
-vwrdoPur+2llwb+vl7xkuAOjpY+eWG1NpwX2dm5VL22rLBsURz+YfUUH/OqZ5/PYEwSTliMA7pp8
-tWP0fjfjY99kxIDh1S3Ax5crecaTpv8Xlab2cIo1yWNQPSzBMYYAkIyzfHsJ+dOUTehh2yX+5iXm
-7BMkAIuq+Qj/gPC/uXyHJEAK89wCoTS+c0yo6T8AILgb9LVDY/gIB3D1Bn8D4jMd2qpwpahP+esv
-fjaLiwY/JmCS+2D9ZMkNfC+/RQWYClpnbXknPEa+ddEV846HcrLzQpBX3kM5ZP7bfj/GdzqXAN8q
-b/hXs6THr54u4+gu+4PaaKtyxNwPMCAAHRL+xn7dPCnuGScIaZnAJJ6mzYFMPaZ4K1GMbiWOM5QG
-sFF5tWISDp4lOMtv7M8+fbuO+a1LP2/GZfVe+pz79iX8utJbwpbgV4PiKL0TgAlGjmN3OPas9nsa
-Zd7L1NuDiLvie19QQEQfgHe4QqzTNSL2F7lXPIHw9wGY3SVQMKu4+wj7dGZWOGID8g7rcc3W0L5I
-dt0GGI+6YGBMuZOlS/XU64r7mt7W5Bmh8880nK+V27qntassVGNqtj7fbpy/SW4ycE+QtDYiLf/G
-Wd9wiaC+xeVwS9/K7rqt/nFataM9S7923ssA3whTmX+p2ScYD+g1udE1ZMqHOSf5YQEevv0Uj1Xx
-nk0gZ5kZq6rrx5IDBcHq8tzTiliZKcDTrdmYVYfgaGPwrcO0TfUHu+b6cY34d5c7STwmC6Uw1Pcl
-3rek2PnY90H33qUWX6NZg29AHB2M/zgfknnUIEp7V9ELYEokFkMu7AWg+ZH1hF+/9LNvcFfMvB+R
-8hX80sVap20pK+8KNDjLY23D/1shZF4TelGgA4edfAChcFiIz3C76gF3cf2+yCWOcjlg4WYBrtfo
-Z1qO9yCq3lGC0KfUSmGq1bKitMy6wPfB2OVlxj2M7l0qQT9rwvDYmXGzqEq1xV6pgbp8d3voA/lC
-Td7jZvvuUFyW4bXWYQ1+LZt6KqA4cvpo7f10TAla8pZ15PkoPgi1f/FrxlJxZhk+3CAWD5AT+5Su
-PLtGrOYaoa09fg96L+eA3R9wWnmhfWDccJHolmGk7/JnSzDGPj9cIvMVFutb/WSphyzK63QI5LqI
-a1U76ddm8I38xQc2pnV4j/tMV4lDRnB+O1pBz3hc0uat6ojPvPd7svKQ9hv92Gz/sx/0MoFrsNIC
-JRWOvLbp0aZvcC2t5ycEu04e8dNKHGH2DV45MI7GFIyBc3e/4BpEV/qwy7sDih16xWU5FDWzUINi
-9xNO2ecl6HNjQ93lRuwBNNqwCLLdRdyfXn5A2JcneLimzS0BD6r6jzhS6D1uAu19XXqH5eO8dI14
-sATztCqkfJsy9lxquy4g/tRY9wl00Ru7BUsgUbJgQjfgdwGfYXiUYdtPqJ1gQzNJBMN08Wr5yRGr
-rngIzjL//J+ecPlmub9maiLgJ+s7hqBi+M245Q8med+d5PjY3H8CwlQCD2uDi6/SAp4+gkpbcflp
-5WEbL2vJYm8L57n9b/dxKQ/qbxzby2b81qWvyyVhvGmpQzp++fyN3F5+n/MmUvIj4Tfqv0GwGAAT
-BL9eL7cIS4ctDCUf+0PZt31Kn6o7+41vtd8/93O2BgPVDgpuY7vH/sJ7b4hcLMXSB82FRfgj4DrI
-3eJbHGCuENCcmHVaeQuMzccxLFlCluLyox0AnH7DGIDM/Wn0xSg3kkWzvhgC55rQeDmOdq0zD+wP
-HABs1l/Qvd9njIjrxAC5WMtaTG1MZ55PgMw3RyAca9ZjP96sx6cbQysLjkPPZ+sPWhZ75G9Td7iV
-6czXJRz6NiVHWkWk7VvYzVo3UBaCXyEAZotvm2ucwHjAcE45d4PeXGTEt5ZZaRXZP9mIZJdw2dEq
-IuYm721DqL38G8sPCP/mEgqbgVAe9iP9tAbP/RsIdsB9TtfjA6yGk3xYgS9QVgPoPE8OxKbkCn4f
-y+IiGlS5o1gHCtcIg2H5eD9z7YAHUsxATLnS6ap3VhssZ2n6rBG8r3wGq/YmHpT+Y3b8yNIgn1bW
-E4Ln0zsJOsgF/nL3d8OsBP+DFbCR+VHPWXz1AGQsrKbEQkB6uhCvfUsWVuo7CZ0XQM6yiXQ3GK4o
-3rQ6uwPw3E81RsvXcfIQlzJEcPTFsO6E0lsEwWYRfiUMA13Rf4LchKA8dqZHEhXaln2AW/kFOIxz
-VTWtvuEeoevuD7yFLMUJxGpz4Ep3jeiQW3LmI/hqB2QoDleIbuWVBsZ53ka3FKtZkAsMCaZucQRc
-mnmmepn11oqc4fgEhoYneZK0uFb/o330qdMMSmKdPsK3VdVm04iVkanlHcD1AxMP4Xl/t3juA8B5
-rWyarfxH+x/9os7XLMczHzLiJpwLpTMNmzNAXLYMvTKB2D+O0eRCwDD5C8+vUXbXiYuPcHtLelt9
-Q9bfYxttWSTLy4sD0OblbbcSsif1RW/zUZ9/evkB4VieaO+SsCkzMKyWqm5w/CkdTiD8e/vRcdg5
-PiD4MosE/0nkMfLpqp/h0huxLdVSQ9ixkkx0DEUFpGuEwbAm3H+5ZrqCunpIrt+9QQ4E9Xq+H6k8
-Av6RgPMIv6LR454fYovnuqV9CY4HEHqSv4YlAK/D7ae4gnCCL4/8BLh7P0xHdVsTTAqEEjzymFsr
-RfzLeAIJOE5BWzh84K9QOAqWAR+ocric14/0ZaYrVfZhGTAtLS7u4au4vI0uI7yt5FygAcPhDhFW
-YYLh12uZwmWFjq5kWlwq/QFF0YVCERFclkaKevJ0uRvKS8EQCsDnASb3iL1s7tDV/YFXAnHEmevE
-ojQQnKBLeawt8tgBw3ne3UcYyu4Qw2VC7WPN0JEuIOCYPeLW13qZh8h8AuJKUIDGQAbc4gnO+NzZ
-Flp7sG0A8Bp5nh/hOPxpP61NPva8W/5PqX31FVYK8/3NsuBttFWc8Ud/yLWXP5dny09uJ9Rbz47b
-ivOHmQyuvHLbPnoxgTiNWOt8SB6WXv7YS3OXmEax/C1knvrjiheI1H7UQ4lsqUYmJPsvV3Xjs53q
-BZxq2esiEYLFzh9cfkDYl4sd6SlhOyPANgHtGs8gzBDc3eF/B4Cvx7LjTJeICwQTqEVOMw4YDbrH
-3EuqpLvSfjTy5SCdMBz5tv4zfJ3771ixakGFhgji6gjLMBVK9DSMPHk6/qgGkN29wtLEQArXut7D
-0mVMwTBZhRdbgGP0b/ssKdVNg9yC3WxL39ya0t4Xvz770pUOGD7SHbDsJaUh4DwsEXYohuTXxxiD
-qeoaWeb/A4brXlpakRnji/JlPlTVbPHxW2dbBNgnODNObfUbcRLyodoEf8yi5gvus0YkDL8WXutF
-rxcvCh4c/xAHPj/q7L4Fl1DbvwNnzBixchaI7vuroqc/cFiG3VIsnqYervz6IOiN+JZ/XOPjwTYt
-vujA++gygYufMNRnjcC9r1Af+QTI4LIHQUHUJXDEN8DziiiouKyz7rONnPkLX820CruLhG5t/tBt
-vUElcI/jfF/CGGE7LhlR8XLCMDXPIw8U7mmiH/DDimavBZCv/OdUapEPjHjViCvgZDeJ1P5S+/km
-F2SxRcErwMYScoUgF4nFg+TYsHKdRUJAd88SFr11BeTe4uqmzWcYQ9jGdGgFwymPs93TNWYj+IPL
-DwjH8hscnNAIhlyKy3BYhDoI8/YJbnlQGbs+fEqPeOqjDlTW4L6fgBx5ofyK+HRp8Slflx69iKoR
-64gvwe4WBs6vMhRTuSBEB+1HnqLzJNQoJfJOqvX7XbxyOtBr+i529QjNNB4jFa5i0IwpMC/4bQ8h
-0p/k87OtA4TXWnUNv/HcEuBW+zrTcNsDXMivRdMgFdhu1bT0JhzrsBT78b133ncpdD63hGycc7xd
-9ypp8zUH6HNE1vkNhim9tLOOEP/yRzB+PKgzZ/1a8nUcz9VZCq+3C/7SHA+UYxgOII4r/h4AX9JG
-3I66pDl7/YOnpqQ07yD6f4GO93mfs7d9QW6NuYSlBsxx3BquE0vKNQLKD17+Wwm7GPuRN0oX5wHH
-dGlPX5g7pkxTmgP1Ar/TLaIGzRVgdfg66wLzeN53l2yR9hqPiq+dCXx1/ZoUo2AwLMH8tTmbRxjt
-nhtM81/83jVv3T+Y88rpuoTmttpuq9J+49i1X8x96b8/yz+PCe+fQMzx5fcbT8Fh+hmD2EM3XGA4
-B9BKAXCmv4DuszXY0qs62odF95DQVV9xjKG4AbL28rDTLD5lqpJs5HY6K/pfWH5A+HeXpu06kJxQ
-fAJmbDvECoUH6EqPv53HoDyfKiPcO5qc8TOvEPCLjjsflFLgbbPaWOhu+XUx8VQWHR4KkeLp8rAK
-cy9qkKyZj1IzHiK3iFSgdK12bPyOCcPRc30EoDjJx0M/P8GnRTjnhq0w71ddgervFr7HVf16rhl6
-94aulUC8HZIDdBOGRQ5wlgSTsiR1K5fBcCnLUnrT3mACVKsWpeo5IgpOpf/PUyg9Sd3eZkkaj+UT
-GNt1JgBTfkecPMZ5uLUJn0klALhNoTbg92V+wbGu1wsADZYDLtvfBGExiI15YhUbqv6mgMu2QQ61
-g/hwhQPk48cyHuLeW7B8wNw72n76CKPLF29ztV+g/AmSAXTgbRZfS3MfGKfmMoEaQBfg3wfJPfWL
-D+Goq+gbcZxa6zUOZ5ylpRkU4vrUBrbLzpx0h8C3VmQ9PvsI18C4/Eob/+b4/cxzy2vEDR9bBa4D
-6uK+RtlUedD9znLLssexv1u6uh4Qst9cHvg+SldguEOUSac9xMtlnuA0lAwD1txioc8m08NXGP5g
-FTa9kD04JEnWhEY/ItDNewxrEgP0RYgSIrUCPZJSg/gsi/+55QeEfXm2BI10F8gN5V2wW8fWRxDu
-IHtae+8W4Gcojk5VT5oJxwzCmHDc8x3AksAZD4SXRce2x7mSURssFwPlltbguVo7HIcAiQcBK2PK
-0+w9zS2ClCBKYWfeBOUeoaCjoNdgdTddMOpTUXiKCUj08HGxANaT/iIwXmUd9roB1ROD7nGM61Pi
-wcFRbEIvhVeE9y743bt8fD2sagOZnhT+CcjoULxneVVJNdAVLkehw9+A4XZNHTFPjfmMr3YYP63t
-l25xeIhLnUfXrXpCTZIf6wWI28C5F0+fNrdfwHHsN2CJAWymCrd9bxg+fQBEzUWh+pWXF9V9wPBW
-8gO+zRUst7hlwOVxS+aX5TQJZVp+K6x3YE4itLzPj2LEsQJeuQyMU6yYcorSTuvvnuXR9gnMWl1F
-vql+snKf4tDj6lCDNK5rzPbAbQF1H3JZ44tydY+V9z6bxGhvoN+m/jBne8A1Tb+P47xxjRbWfu61
-P7S4un8QFGoqwHEfT1A846eW845/6IMPELx8vM+cQ7ilYVeqKxTzdUs3hKSaML0JsKAAACAASURB
-VDzXCcZ5z0PsTu3XQiWu29EmJ/HvLD8gzEvoQtaJM66orAHv4fLwCMTiltGCvAJbaa4Q9+0dntNH
-uMEuTZfGHS3jCIAR/V1c312A4RKlc8+Vkqp9kjS24QphQ3w8z8ruEewWEWXDooSgh6C3vpdeeZCZ
-pwHJ8Yq0hB1oH8d+XZDuVsc+FxPJvgavCcAdeI99h50b6Fb90T4B72FFZqHHILw3tgOwQZA9oGyH
-3RZ2q12cK3GtQ9GTZfD4c5G6jK2ORkTSsoIP4jLulyvnAGLuwLPu7tB7Lp4xOdOcAj3aq2RT47jj
-x1h+SK/fUmqu/HLatPIPfoVrBAMVHhR+bqdFDGTJU8gWbAjUP6Ku25gQYtOc2WdVxcOjftUAWHWT
-lffiCiFzPmF3n2gf0fCZI+jLcgm5qP4bD11BIffweZ66FXhOl8a+v1FAe+uYWzgLLOfWvQFvWRjH
-sdhH7GeWqQ79HrjfTLBDf0Dnes04rv9xfW430y9YN6CrW4mnL/STb/DGKI/IjwL8gQ/OY/SSORND
-Ha/0fL8cb+V4+SzELCP0vB0PhpQ+oTbjrC2az7L0fVC8dI1Wciq1W9uvAe5dRxToUrjNKPMMvc1f
-mAGY3jQqbFyNhkwH2tfiov+kK0T0J9ep0YarFv0aq981330eEY470//p5QeEYwmle9dwtY0KZPgI
-MEmAK2BZFL8yvkD5ANoGx3Rc71DM+wW8dqQgSlqeejzdA3XRLUB9WBxIaUAhzf/RQWzPPt5YnSZe
-sefruCgLXAC4xXGn0SxHKDJdr0OletIC4FCE7R48vxJ7Hq9jn9K3fRn7Sr8NKueEHNSTeX4J6LT8
-Cc0XawJsAC7V2WFZQKXFiEeAbMDvstfSsV2xFemW4bAKj62ShcjWsCYCupIrgAm9sDbeFZZGJSNK
-jyqVDkmL7ilH/82vJfHxXkdNzY6mxMttABy7c1TcRWxc4rKnkaxgS3C9JYiZI2ou4XSTWC9T1MVs
-D1vtwDu2Evv+4AOpotgItxCxqRSlA0joSQRYBBxu7b7B0oH3+MqcyJhuzeJE2Ee4ZMpj2O/3Mewk
-2QbIKQ2eG3MEo/kNxzGY2wTs2GkRfYDjLGfQMYazSgOuJwZ5vkZUAYWzTkZzPmCYfjPmD+5uAqd7
-RHxUY84e0e49IBMkX0d+5j4uxyyulG7Fy8N9EUCT4FaKS16j8jvrgx4upK7hP51qUD/tR1xaQxCa
-CwUQtZ7yeo3PIgfQrmb15Q9jtPmFb9A7rMHxhhgJwe2Ri9Yq51YGqAfhAGIqJv5ncSHn6FDq9wbE
-tf+nlx8QjuWbpd8qMmGY4Rf1yhsFmYuPoQD5E9h2a+8FmhGALBcQnsA7rcHS8ox2Dw6tl/uPTp9S
-ANVl0vbHUOyWg8jrCghWnkKNAJg7jdAztdQvSYKu55sHzIVm5/wOAAaUrqct/yzQJhSPUuj7N9rJ
-vIaQIqhNAbaGBbC+HHZMuYZxjasgvacDcMKvQ2/bSrcMiwM0bw2OzXewXo+SQvc1BiWFJVrhXHzt
-awWyB/QmNI60XsD9ckL1kVRcxxrJTTjm5L2OGYBl5pDiMr65R5QqLNkBahNVV80ton1euU+d9nqZ
-n993wVfG/tyGpV8AvDXkiyXY/qGaaFtH6w+QyFkFNpbSBzUW+QKzFbj5DK8DnvnLciFTmBSbBZgJ
-B9Nq7PVJcez3C+iA4++7TcyPS0wgPl0lNMsMqPPi1gogS4bWMZJGBHoN+oBxPQo/QmBBLVuAI4zL
-V+UaBB/X49/S9pvVaKxXlOzl+Iqb9x+9qclonWkvDxUN9fSS1yp3CNUJkGIiwlnmJDPibckcNBfa
-TF0mIwGZdLEDbDdsPEFwuEKccwe3eYTjgfrBGmxvj6tk2F8YXgboJZYF2sbVUL2EnilRTqAr9CgQ
-YRlpKPynlx8Q9uXbhZ8wSZV6VOwNfAmARdI1YkGu1t45EK7AeMCyRPgJcs840LFmDWYlrYI2T69M
-iAiBHmGlw2HhKPHUwbffZ/gMn1biyJNS2K4f9XVRyz1MU6XxzBGqmtNPkXo5oLhdOZRYL4GeqDJ2
-Ausi0KGJzvmrYbY12EmBFvX0AMHfWoFyiXDLcISVQGVahkWk/IRFINtnEVAXiLterSoIAmySVrIS
-L3/NvO/uEV5o0spRzmM9QR2VnjZriDWZJazwbDp8ulYwEh+wy4BLce0+jruAt42SHzx1mpCrTFiD
-wy0ipk6z9vEaINwthgv+ej7ivtiKKhnv7ea32sAwUQFCcbLyj98lQNp7k0X3CXh5EN0YNPeuuDd9
-Wa6DsG0ZdjMu61xxA+RMr+H3S/sJu5puEzFzQqQXmkaN23oNmOtl0lwlvLzaXLz585X3Bo63fdA+
-0Oo960Z7fMOarC8TiRtA+4CGwvy11+kjfLpIVN7jHjf93gm2X31Qo3rRPA4d9+w96iiLdv4Ac165
-7ICsl/b7LC4mEEciocGJUifGBzRCctwGyk0YNqBdJRdCHqTV95xDOAGZwxN+hxVZ4rVclmFrIbhZ
-idnQlfsSIB3l4sAfgAR64A/+iTiE0RDESi6i//DyA8KxfLPwS5FNeBQC3AijxS0G4AgjrKQDcrX7
-Dj8BcXONYMC9QS/HHRDc4ySdhUq6NqGE6h4cNp/gCitAoNvBt8TELTwBmMIadZEB9HmENaE3lWHm
-fYjeeLrVOj7vMeNkHuDr9kaS0Jadu4Mpj/qVw+pHM0h8AbiL9z+lBzr03sBXumVYwhLsYaX7SF9j
-kA+hC0mbkQLAXmYR3tumaFqwDyRgY0kY47RJPqH/Jww7YkqPaUcFqaHMh08rnVIY2nj5WK4w/Cms
-kbsjLtIWMPPDTa/H7h5xmT2CfIUZArpCt62M+K9AuIqjYM/eCpi/bsgOIEb1T4hwOHLXiEW+wTq/
-ICdkBWZ3CZpOTaS7RgBs3UVmwIJBK173TI3jvBUy4KOPcBVOvN0o1wmpWSMYiBlyox4mFGc2OK7L
-IKX89v3I0t0i1/cLKLOu+bbiupmP+sxyWIax40GIwHdrTqF2fEoZ2rc6fh+Vt4jjfILScpVm+nGc
-z8t7p4Sch1YW4Dzqmd+ojLAMC+WnmqKHLaKsw/UuVUkC5FflAoLhD5c8jqetiyBYas5gMqJMH+Gn
-cHOLWDVdG5fGxB++774EIlPbIUFp+qLktDgglQ4sIF5j3/I9peifWX5A+HeXaEOIh7qh1DI+Klhy
-WyAc4WfQTYsyCHb1MxDfoRfP0NvSRJ7zNjPEnyW2pcNixJSg6jC8vaPlJ5u/guD4eEajkQhHr9Ki
-C+WO3EQj7Yf48xhhe7Uec0eGsrpdEUD6CLcDjd8ky3NahTsQl3Br0PN65fHfAeB1SStrJQgn9BIQ
-J/hKuUXk9RxiheEYyLB9GEEh/jUuSWuzpkU4/JI34ABsBbPgfqla9TqRmAqUdmUkGYIz23Roq9mG
-uFIJlqeClapYadnh/FbFRzbLBsRZL4WT8iHXs+6OD2nkIMqaQm2C8G8B8DzH6MbB5uVhgezKE+tw
-uyPvUw2CabAcf1Bj0WA4ublLnNOpidSX5ZiOFLQf9Ii7hdg2fZ99fqFi7Rd9ujSzBCPhWKbrhArW
-7gPm7h/VKDl4xuMAtsNVgkFOSWYxWFK47Ue5UJtu9a8Fv3wPPJ1aWMGPTyrTfbQvzjEMRwsZ6uMm
-odndIR+0xrntvMsX6Kakb+VB99geCtqxKuNQPe03Oc7o9rAAFxQzDJ8QnA+VYkD8lXznmSPaYLiA
-YQ7nOJSnGSNi/FCVnLZS1Co0Op5/cqS0WiuFZ/rPIlPGMewm/wQL/YDw/8ry3cK/jgMfarr+buDZ
-AEmL56i7tDWahkjnP1D6yEMMRosem6+MvbXKAbVPS0gIG0ceEqPNPxmKXWOAHAsJV/sOtQvRAbzB
-gyzE9OCQ952dguLlLBu+f1zC19XzZ/va6rPYitwvqLxCvJldk+ICyP1e8iEl9qPz51bw8v2Xg89r
-cTz5gE0heZkKZ35n/nbe9jwrUO4RHpfHPD7TxeoKotIp9oqteYFnGlUoNrYuE5wEx3tt6F60JYDJ
-8v0CdCNO5hHe81pVzQo3pRiVf4HiS1z015jTu7sj1XY5ALNLU9hslw80e8HgfzmIvTKd5vrytjXj
-F/x8P3chII+hp+9/2kbb5X7Lai+7PJVmrK2vz7BUmupzPBVXyKmKSxpbNk2FikOFCLDfUFn9Bv1X
-03J32Sb+HecBiHmRY/HXEy1OFVuXj4BH+gNzvlUMovd727rf2G9b3++37e+3H3tYdZe7Un71cdNc
-3vsyMHVAHSqu6oMglM5Fu06HeHmIC9jPbaQ58sUPBudvT8u4znqhJayWh6EppwsTkx/S35hsH1+x
-1sZruc/5S/Ha263ZNaAz8s95FpHsbdETVVaFYVZczd4tvk/mHJGsh6a3hpLp800MmTfPQZFHXF3z
-hJAF4nVc4QP0t/v8e9zzFxHvbeTpmOlAk6tLYC4TKXfLoi+cZ+kGsIUaVP+nlx8Q9uW7Ra+A+8y5
-mCcn+Qd1PQKMygXDkeS4hn56PgrhL7mfej4Qz3V+4l48wvpgHlXPiWiGBTHYxkWquuqMJ0QGYph4
-SAUp9i9/xv/S/SNAkMMBFnwMBJK071nt0HwJryOeVq1ZOCJtwAEDboTDnWpzWoJ/r4ksg7q3vhr4
-CF4c5/f78pUBuSD6Zi0AbsAbDxo3y0JY2lTKIqdSFroE3YhnID7iCnRzoFwD3hsk2wcadm5d0bc2
-PBd52H3qF6EYCoL7o472NLdjBM+hxMrnH/ct5rYeAEO1Lij+cth9AXipHutSxUt3hmvdEN25LzmI
-rBohzfrVoTdh8Dnt3jZNnm4DrwCm0KTR9aEGsXEh1u3vqJdlbfgvSLbrgP4X6p4CKDMccxaLz1ks
-An0LJDLLkBv7AMGu/WuAdQPh9MuJajfhpKD7XXJAb+RRVozstz70dvDldb8HFPP+3gXIA5KVAJnB
-WONrf7l/gm3dpmbR5C36/3IF+AzGBwQxmDc4n3OKa0/31bUjzykDCiTbW7RtsKtrQdfLzl2K18vl
-zsvBlvy0QQ8H2fZT1xJgI97cLrzXG/XouRzi5iMpx3WcnT7QBcEWa287PVVaZAIPdWTU26OYZFYs
-2FcfV6nivW0OboWXt6T7mUD8u5AhK0PjKfqXQL8PvPd09lsaio/If7m+0QUqK84LiqW8L6bx7g8u
-PyD8u0voSvRVgITiiV7XP3uEujWL2t7o/BIXT2SKeppiNV8wHNc36M1UeTA6dNxXCFgtJRGdmMIJ
-k2IKNts3AEMBJXAoC1uCnpcHO9I3AJYOtHXtJxi+rXWcoSXgDyDwpfgzrM9wjAG/GGseM1B4eZq0
-AHvcS+KBgOGWwLfFE/SCwgOGQyhtkTY4LuBWx35ahyN93KMLXhvwplC27OJiDVYk8G74l+tgkKxr
-Y+91upk8QjGn+JRGvKYeQJe3KtTR6ljAcz2IoLdhINtxbjEBOFQmIEoPPDog2OF4EQy38PaVgXgb
-LJ7QS7f6JSAjAXG5Qostg3DZ71jFe4klu1gbiweuaMfZzgVXC7g9UG8zLqjYhzvUygvpH5ySrIPN
-AcJauVMqCO1xZtXbddwHAmpYgFUhuoD4KqX71icEx1YW3logvH8F+O4rFL/9mO3vtBi/93tYih3u
-wlqsZb3kz6IX1AIBufkg4PeWMtwfZpiTGYgPS/BWqDhkSXxym6yqBME5ZR7l7Tal3ASsG7CDdMB0
-IdMdILyguoDXAnRBXy/Lg8NwWKsPEK6fuILwkoVfbwdhlWqhWsBrD/V07EibGtIkSIxdcVmSOkwm
-NnuaAcDq/QP+VkQdeA1kDYhtRh672ibNuPPXpPhEzfilH0CYH2C+hmL+2igKhEuZu5U93vIAuurh
-AZGvrA8fnPuHlx8Q9kVnh3xKF/UNdB372wtZhekyE4Ifofi27wPcJgJoKHxPqN7g2tMhqhtWd6y/
-UjwdgHldIcUuWcvp4hJ+ub/INa5Z21KIRKl9D3onAK9L+mkFfobgT2F+pc3g2y28rwYEDL4MDkKu
-JNPaS0CcgBbK4ozLeHwDgp8AeLhRfAt0BxDX9GqeTnzQDX3g5LFtH8tXnW4Cb+CBXI61R8Yzjh/K
-olxBch4Bv9SGOQ4RV3BbVmFfA3wpzcth97U31tpYvp/rMmW4ubz0GXpVByQj0nodOnAjLcJm0Uul
-7GuTU9Qn3942t9jnkV8i+EvCIiwDgtGs3WV53e6T7IXtv6ER/gC3FqSGc0sbgV3X13h6Z/AVSeUu
-ewMEv9jbXs8vexDZWw1w9wTd2u9AvOlYWIcLim/W4QOKJ6AgILc/vES1Rfs/IQZ3uIlZP1Ssv6qU
-dXq6bGzv0w2eyGK8P4FV9MuqN2tXBagxi4LKbjAM92PHi8G3rst8eZqhanaGmqHBwq+3W1UhbauQ
-AmCNsDykrYfMdIVMGaKI6RbvQIzKuKo/sJlcTYNBdsedULvzQXJZfeyp5UKmrehUVVdfwPAdiPtx
-iX4q4jNSmDDMsSUIy7BAdwFx5EXg5zbDxJ9bfkA4l+8RbeLftP7+jjW44RzqDUnsP0LveILWHpQr
-BPPWIRjqHZYtyX2rKaLCwtA7qvj1GCa5LEOXxW+n9RLoYJHpuhWY07SyaiVLv0VxN9CdABxxNwDG
-h2Nh+WYIZvE13SECiF+QBsjN8psWNLYSD+h6gt8b+PLW/Y4BA+K90SzDDXrJ/UGBfO1m1t/tg568
-LMIaHC4OPlhOc2YKtddhzS9Y66tVlG6S77cNAo9Pr9zikeGAnI8wrAFe8UjID2dnuNqwuoodgKzV
-D5prhD6smC4RekBjuUZoB1+G3rhthmT0NBHOmRO2wS8PCjsg2Mu8+mNZugyCra2+AoIFzT0i/Jwl
-tgOCbdSlbwE0IRI3cglrBT6mS0G7BLoVIqtgOKxu7hah3o8CaBoEb4O0994OwcNFYu8GvtMKnND7
-ntbg+DpfWF43AeWHNdouFwFJcKX7T4tsAjNDsObD6d5mzVTZ7lM7LMNXYNrNKptW44d8tyrLFuXb
-JtMIhuOLPQ7Dr6VmDb5AcFuU2yzDdg1WfgcIKwx2HXCNsw04t7/diePi6UVj5h1J/ZE6RzRlRLlG
-lMblDDcrsSl2Dzv4Clt1V3XRDez41SXAAcNhBXYgPdrV/hJ4qy5PEA4IFrXfF7WBsGkRXjAIDouw
-EjshIJi1/Z9bfkDYl+8+g+SIbLn3tUyHC1pLP1ZVPiwtHE5AJmHOAEwA3cG228Duyt9vJC8o7Wjc
-Y6kZ76hS145Xv+EWAP/tTVCqIgS8BAZj36BCxn4/3svvEwDfoTggmMPfAWCA3CEUDYIZig+XCIZh
-sgyfFuIAZSmrcAAwLvCLCwyTgF9jG+J3C/LJ3CyyZRW+huHuCwstnJ9XJT/gb1uDdfoN43nR1tz7
-gbQUXo9S6Al6BekrP/uFL9mWojxRAJxtKdonlNpXtb+l1ZanO8T0D2aXCNvuvg6LcIJvygYLjBnA
-LulquzSmzNpmFdYdc3/VSic1T7/sm2H5sjYW7T3bPIAlfdBfWMkLiDfMPVguI1Ep0w3q8HgMT8fE
-v5knPpZBpNrCEkDtjYUddwBey5R0grqB8NYC3gDgtATvYRWmYwXFTzBMUBk+w+l3uxvAgmE44Jeh
-WKOsyAr7CNUOW+EaoWwZ7jNHdKuwP9xegL2lz3wi7yHyV3akLsd0LfePD/cAtwZDAZi/8CvuLRv8
-idf5JzSLE1uDZeG9dkJw355xVi72zBYwHE/xqf+kHpJT13unCeBl81PJpKIL1QBnb9aezG4z6ivg
-1sMwCG0w7IBa7hF30G0PL1sf0vUHnCVoEJwPMigrsFI48pH58j4oWiX1J5cfEPblozLmdLOOiiHP
-hGQh7nZgyRPiaF5ClcLjNyicD4qDFK4QrBQ+Ms2Y7F3VT86J0T0zSqcI1OcLtCXGn5jFlGDY4fEG
-whOCr/Db1mkFvlmKn6F3Wuwky/DZ9SHuLQB4poWYpS/E1tNAuRowJ7muWAXkU9mtxQnA4ClmQpjP
-/edjBwjDPmxgggnDQuzgmy4RYdEFhbe/prwMllMrj2419kEeFzCGKriJk/Zu/Yqj2zL4tbf+z9v2
-/kQDrmu/z/LS2+VzOw3XiN7OYqaIF4C/CIjXBGKwS8TqfsIrgNEHlHnDu1l7c55mhl46nnAMuFsE
-Q/AmoAhQLQhu/S7LpQAmZ0TxcqoBczTsKOBX4av4LBKbq53qBCTuLuD7nf3I9Q6fxQBeA11V8dfX
-cdzWLG8R1CdqDU4DetP6y9bgWzwPltu0Tz7C7BIxX1+nRS7+EoaZAauCwyLM4FkgjXbdOXNEfi47
-Z1rYbXvOcsGg9AxZBcB9aXqB5Jo7b+cTnugr+5UuBZiNs9mK66rxJ4IFdon4ZdZgtwhvBd4bBcAe
-fqtkmOMhqOl4FNa28j5ChmiGE3pdKndF/xR2+SnFyVaHy8dbGGBuSFqDF8xCfUDwEix/uLk+qFzA
-N9vgExgL6jfgQLuqzM2thWDYXTkyb2QN/gHh/yOLq4asvNNbtlck73XFGVinlMY7x8kB5TIxAFj5
-GGMAXaMpj7hqo/9ylYhDKUsU5wNAdEjy4wi3ATvPn3MFJBQ+gPB3jnPcES9jezvWIWYCb9xDswgL
-wQIB75YBH6ixPYevMK8yrMFSljNew7f4hFoM4D2312N4gF/YK0f+/LLBLlmHD8twh9kE47AK8zkp
-qPvgOnOPQH6COVtyMGgj4Gpj7QXGsQRQR6Ln7bdhWXp7axbhW9scbbY/eJEVGGgW4ZpWbeMV8Bvr
-1vIT1o21V855e4Jvlc9hFY4HlKFrrR2TZmcIJosdF7x4v7f+4q+PTf/655FP3/jWD5QeGNRcPdzj
-mWrerYdybwt5D7NBzLi53yqwgDgqW8dxOeIKim3WiF3W3rYltwmeRm1C8p6zR9R8zB08J4yUJbjk
-O/WViA9C9HKYADyhNwbK6RZ3k+AZI6Y18A67py/zJV3kpSom6wD0UJXTpvnHPdYi4oXipS8LvrQV
-Qcp9AqyyCJsV+Jf8cmvwG78ChDfw3tqA+L0VawPvRWDs05Bhe5v1TqgLWJv0HrVp0/UmX/j9a8S1
-ekop6NLKfdjDNULgMAtpD7RKALzE4dj3VRyC3W3tBsDPdTbqnc5f4vDr61quW/yONxQrrMB7ZUno
-6ufZ1BOPAv4fW35A2JemeL9K51T45MuY49ByM1AtOmbIX4S9rtInHDf4RXaSua/aoTmSNW44AwOi
-pXSGFAQ2PvB8g+4RqO4c8BzlE2Gh8pige433grzBRZapPENuL/Eef/gLM8yC3B7ktAKnfzCdw3ET
-dh8twwEHl7g8hgLgGkBIMIwBxgsfgdj8c8VnathpAW4Q7L7ABa5IkC0Avll3w10i4OzuJhGWqa0F
-zpqKWqhPURsXB9fcP5PEQNE48MlTnreZjtwk2n4q4i/gN9vp/bHY2p2UW4Te1gsEp1tEWIbdUuwW
-yj7zwwTfCm+SGze/4u2/dUIw+/HWGiW1qVzy64QBwCiXnxwsF/clYRE2qcPW4aL1VfV91Y16j5+w
-jDjfcZErE7Nie1ymBQ6ABoQswgTAHi7AddeJPYF5WoIHDKc7ArklHJbXuQZCdYjq4Dm2DXTsnsoC
-Yq4gBegKtljHfLxsOcxZJEbebgP9QHmMug5Y1QBgCfhdNq+xp47pO/Gi9kGv1gt8FxZsTvZf4Qss
-b7zXG69fC7+Wh98vrxsD3/dWvDy8GIb9mIiFMzPbZcpWn0GJrcHnFmklrrYpdSMobeRbh950zwyZ
-CfsgjX0oyZSWUDsOdwVVuKuJwfAeMPsZhsfxeZ4A0JVlvpNhTE8ZDC8LL4UG9AYk+L5ofMHyzy4/
-IPy7C+tbTE71Co0n2o5tfdsgjqmSkDwvzlBcwl85fuiEGmCagfx90gl1SLkT0n1KAW5bghscxtTv
-qSBY8vy4oYKFvo+ECx37QmXVy5CvVdOr9e30BX4CFF7SSuwF1XyBZ5yUzg7r8W2wXANc1HzBS6S7
-Sgi5SyAswmwB/iKMOwQnCHtdbkFahefWXCTgVtvdtuUOEXAcVmI9BsGV9ZEV8QMQeztqsJJ+N9Gw
-zTrVG3kwT38qsyhFe2Ib+zqPP22F2tgE4slR5AvYV4djpfmDcfEVPgB547UXXkvTN9im71IbsKVy
-gVpFG3+o1GYpTcGxH3fIWnubyYsgOOYh5nuKiRdqLvGA4u6ec/jM5xqgH1ZhM6XJ9vcx4Ws16pZq
-+AGOuc1cj1RbSCsZWmVqCiHfB0aFO0xPEGYAzn23Ck+rL/sFZ/y7IFP1tAQ/WuoCfgOO4l7HMe6L
-X61sFQbDUHd5CFDP6dOug/ru1mB4XriuxOsj+hskpn1cWKL2wQzvTzn5h8KE6ljYAsyW5bAGr/iE
-sYPw+5dvHXRtRbklvdWswL6+y6enINibl31QAh1+Xa7E+7myAltHktaoz21KJnUZ5jJT87jYQ4sI
-1lLs9GU3xSwLZREWUB1S/R5g/GQhDteYAuF0h1TBVntoWQTAlsdVoJBKtLT9j2vEv7zcjAjXdAF6
-GUPq4SazM+r28h50hCzCAQaF1/f+QaA8MkX3ow65lITvtZ0zKMMVhNbn6vo9EUgwQqQyaVaWE3Tj
-OjEQoJ7kGZD1iLOn/lGSUmnmegPieD0bxag4XSRavFyswF6Y7EJxdYUguK1PbdfxBskyrMXo06jZ
-Oc+uEJ/2zXIt7vMcYOzWYUwrccHwblBc0Ntgdg6CIwsz+w2bpTgsyr51ZahhkVUAPiCzLLXwNKPN
-tlap1Fp6PMOtjv3btoadzrZ6gi4QbXse89x4ew0raHOPIPA9LcGrhUXZIly/MMGXp0ULzpjgG/Bb
-x0zZ1+eHzToc1vr87C6VSZRWfqJbxWAl263k8YBfsxCpDyAMi/CGBATHVGUpJD4oxavMfmwcfQlr
-b4T992zrSCIPcZR26ybrLgHwAbg9TaZ9zynTHE6mVXhPEBlQGZWNqvs2dZo9cAAAIABJREFUhiR1
-hPe1bB83AL6F+8C46RpxTsN1c6O4zSV8qRruaSKAw6/6G4Jo7ikKXnXe8cdyEB2CX+EX/Mu3KyzC
-ivdb8WvvBOC1HYD3thlb3gTB2FWeghw3kQ/IIRvIEhy5j15cmq/dmUkiUtohu0JKwQfJWS9SB06z
-EIeve8h++8JlWNpxracE332rw5sPsbUL+ADqt1oPj7xsibozP3xrS66FzcHb9LkpHLu3p1ft/+Dy
-A8K+fNc1opptPZG19puputrsypSPaB3RaOT0evUGrSTYVLULvJkWWofa8ZvCGNeIef4o5+lJLNGR
-66azMycEU5ivJOj7vM1jgTR0bnvtfCvTv7fm7SKErHYoFgIKDMuwVHVcLcIP6wG8cvoK908yg4T6
-Le60FK9Fg+VCGIIAeG2H2tXAVddKi29ahNOSS9A7fX0TkGtO4YBf/uqcDf4m14lU4jKAWDv4Slhy
-K06bS0Spi1unvB9Tgu1wwaj9aJNXK3C2oUrLfVog7l0R7VZ8ejQ0K3B8MrmA+ITg5iKxfRopWMNr
-1l5vpwXH1XCfXCLymG6fQs2oOeE3IdiuG/JJrUoMurzd2YNWtEPkw5tIPIQq4pPTS+M7XbBXoggY
-FjcvR13IvVb1VtPnY44XU7WEbEdVU2EFPraRHkhwDhiO8nsnAL/TOsxA/N4dkk+f4A7C4QKRQOJ1
-coVhh+B4KO3uBhRPfc3gKs77YA1WsX4Lodki+mC+6RpxwFTA85GGZSopHRLMEo1oC+Jb1z6ph59H
-DzPVW0s/uBx8S8wb/DaZSBD8Wi9zk3jVzB+/3opf7n70623++fJWiGzIXpC3jYwTsa0q8PLZb8ww
-UA+EpQq7JVhkwi/Orfdfa4s0D5NyK4eBqAun7bC9XEDtlP/WbvmrpDUv9ZMPOPukzzcB8xyY2xQM
-yG3cgFnxE4K3PczYw5VPW6jIexIH4mFz+yPLDwj/5qLcRgHi1ye8KkAssz8rzQG++UP8oxQgCOan
-++pDI4MH8F7S3o4JhgNgRachOKA38i0U8PNiqphIVdbf+uEO00pp/fg4X3KvgNiEy9hv2+kjLNfB
-crEcFuHKQgGFEFB4mUyLcJs/OOPQ4LVNMyXnHMNhBW6Qm8dC4J/H7q4RBMBeEQqQiwRZhNlqOwa6
-MfSaFZh8fg83CRpIl9cjFwr3dw2wA8h1QdWUHbXVaHrcXK0XDMClLSuOJDdKc55LVe5lH+02FS51
-j2qPBMUabdfbbYDvgOA2aE4vQOzgG4PmwnrqvcmhQttguY8uE6Rvl5+rgLlGOISttNjZVmAWn7Nk
-7Y3RUlPCC2FxKmmX0ySiYFiU3tQ484rPgSosd6TDTkDT7Tn+JjLvYpSBNvpyShvSzQOEub/A37A4
-FE7ofQ/A7TCszQ3iCsLpbtAHnHWL6i6YdSCL+uKHgGgIHYjhRpQ4ly194gPnwv0moHdDLzDevx7H
-A+VGfi+D/FpFVs+hjhWyaxUAK9+W0Dib9/HfZi7aCcRmEbaZcV7rZb7Bbgnea+O93vbGZW388v72
-670NgmXb6DkIIBt4u9uI5+XlZZcQHA/A4ncVbhKhw0qRUo96aLUs6/jN2SG7TFZuAl6e2lAjP4Kq
-781t6wGK90N8vDUSEADPrcMvFnGF90OlbcrnP0/CPyDsy+iPz4vMZnqCbyTsuDuPUOfQUGlDzbA5
-jBVY259wXHEFFiMt9yoXmke8d5ywCks4/IoVQIJvbuh+pV5rNRCWKIm6kV5yOkqqoIIz1kv2u6sr
-5dhqV87MCDxTBB7A95pGpuVXcLMCx7RSNaVUuEVMi/AFfL0M+/VnmvFKUOwpW33cRXOLiI9geBhs
-EcawDvuI3uYC4ZDFFqkaJEdKFzr8hUkQo6ybbK1rEOyb7BqjlfTwHYZLaTynyd/m9ifVCkk/c7P2
-U6r9yiVs1lAcU6YZ/Br0vlDW4BOGzSViybYR2BCy/LqrQ4Kvt0+SEzmY7jhuF1hhDSZFF9Np5dRa
-UMdVq8MYypNvG6AFwsEzR1/U7HcC0CQM8eq4VqWy/+8sXf5oQm7UFOlnjGMZX8fCejsh+O1uDXc4
-1tNK3AaZlQU4oTIgMuMIRsEPLXWnHoMAYFGggWg87Iz97XErZID3i7QK04C5mNu4uXZcLcAnRHXd
-g+zzEOTHFRDw5A+ApaYkZUPI9w3BG2/YXNYLIm/zKX4H+G63Am/s18u+8Lde2Gtjv2p+59f7jfXe
-WO+NX2tD1huyCoZF3h2Cs1zN/3apWJtGtH/N/sDK0+JDN/E7aS2xFNNQpEiasky8hsQfvL3fKYEw
-AIZh8bKe7g0dhm8g/AzDpvPNGzgBOK3BalNyircLd5HAXg2IReMN2n+zv39v+QFhX77LwSUwe9Ot
-JcR97UWIAbgfDeALnzlaPwFw7hfgpiBMAHY0oDTPsEzH+Il8rT6dAuANn5NeQMF3ZHXlk3f+WOhR
-ynzeDaCfrb8MuU3peplyGvrJtjRAFs3i2ZSVdJvw7dOUaeUCIQ2QY/o0GzjHYBw+xeRjeQHemz/w
-PY1nOQE4rMKwT5buneGwCEM7BJsvGFl8yT3iOxbiT8es7QblhotC7iLcFUIPNChO94gH0FUpd4vZ
-nuK6BxTHbxYX10T4sR/oFMoljvHAGD+qpvTqy3HIadNuH9SwTyzbNGkFw5r7sq11HpZfNctMlN15
-/MlyzGB0vn4XB2C7W23AGABsD0CSejutUa2v1YN/xqcxIOL9wTtIIpe/qyD5PCWYjXuQHoeKA2qK
-WOBM+2YQ1g65AcO7wfCMI6twA92AzttUZBeYZRjOjkN9yNNJ6AP6myCMsArn7/JUbgW/aeW9Qu8T
-BFebsoxkiHpgPQBZ17f3dwr/sIl6a1QBdJfsdwPMll1A/N7YbgHee2OJDfrd7429Xg7Fb+z3C/tl
-ftzr/Yb8MsuwyBviUG3zpL29DbytJyigL8XOWV3izZ3bkDDkhFCbb3eNlB/V8qJADIb1SK6VOvqL
-b1UKKqMvIsrUt/3T2FGf+xmI8yHtBGKI+MA4ezzY4rMKYfnc9Q7AAnflWHUvGixhV0ijW8pjUHjK
-8f/O8gPCuXxPyF5nUMjmO69ReCYjPhukzifC0PLjKp8AWEHgQAJmgO+E4es5vk1rsPrEiUvcod07
-l19rKjoWShDYpNq3JzyNm5oN/UhE993hgkH2+P2RhiGY4fhUgLUy8HJllIVYGwSHEeM2c8QxlRQB
-cIFytwaXe8R/ui0QblbhGJ8AFAADQLMIA9juH+wwC4LYTYDLfsMNlh+OsctEKWTvFIq0+HTo7V2A
-txY+25RS27kJ1rtbhIedcJsVeMAwwEBMYBy/pgq4Ykr4Rc0d3OK0+wXXdGrbAdgs/Gu5XVbLzzdu
-q6ZRUz9OjRqn5Titwr4ur48CFyRElYX/BEf2mY+yYZYtK3lZsKKMEgDIPztOvkkGKt0vlrjeWB6i
-7lDcv5eQqxIIExB3v2F1QO5W4jYYrllXvV8N+N032ATXT6viaHII2M2HFwDTCszhGDRpTkxWfgvj
-d78BvreBVfvym2d1hRakWl8mqERXNeZlIId8gCoQjgew/d5Q8bKO7VoOwQ73r50QvN8b79fG+vU2
-AF5vICBYAn5DA1kZb/+0817uG7xgMLykfSXctkr9IcCUXf88fjZOBVQKEA8ZlzLTHvjNGuypGIRp
-Hw6kDLsJuU/xCcRnPMScUezrkZpWYQNggmBRQFa2WQusErXpI3yX7mfcf2f5AWFfbn3yW+ddzfiM
-aBXHFuF6JQKw8k1LSdDVuGpKYJLGyZQeH1Axw3DF6P9aGK7sojDUhcoCaISC1u9n/jXdDfLVS25r
-bR08gOZaoBcwyZ4bkMGA+wF6EcBrx6Y7xK2WeGnTpE3lT/DL2wa9ggNoyzXCobhB8PmBja8svd9a
-lzTohwAhruAwrEADYISP8DYANisRbMTvgNjbYLkDfjXmGaZjNAAvrbYNhjWtweGHllZjifYaD6bR
-bqKTSMY2CNZqT985Fk0v34CQIsmN9HYUv8xvNKrdkTVYyVVCu0W4PqaxHXwrzj6FXIN1bbBcAVGf
-HeK0GrM7RIHzBX5pH7P9z1X6fki2ggDfutKUkCEaZWadSGKKvHyFEe21Srj32UsPFg5oXWcsd6D3
-MhpxO9NqO7a33iE4Ybgsqg18H+Ki7Pnztpvq5LAKeyXWg2QBJktS9bJny3Cr2wjveOCN61m/b/MH
-e95qVotyiTi/hFdzIE+3iNQ9VEGtlrmPhQRbdJ4L3XLFcf/ftAK7BXtve2CPAYqL4rfWfliE1y8D
-4LXcGuxTfuXj2jvrZTsEr6V47Y29AoYNfDP/oS/znvQiM1DtlcUSHVc1I1XIvjzHZZbxsORbTx1y
-i8OboPYrID4sxGk59vpcMGv72yA43CHSGryi/lev9vDx9lVGafyp5QeEf3OpOitFVFaQQqtbKPek
-YM7iopF2iV3giykv+k4KloCDEGKWLi3BWgITruiezgt4Ne4wCI65Pi0sOUq0Q2d0+D5jwSXXtO+F
-iADwApMGNy4ASphQmY71ad7gGT+nULutOViOhNPT9tESLNM9Ij6aEfMIk4uEECCDrLsJ1zKu8w0Y
-5iaU8Os+v2u7lRjNJSKOqaJZhaEn9N5dIQKmOhBbHExxLn/tF1Cr1QYZfJslmPnG+dVbeiWQ0XZa
-ZUXbDxKjiwcE07Rt2XxDNw+4A10V3H7BPu/lF9sswrjA77EfbhLhIyympKMc9YMvsJc3W4W7O0SB
-c8IRahtyosXNPhL1RDBMxVQPD7l4zahUOOKz6OicdnKXkofi5Kq8KlRvT6h0eR8OlubiQTCsBb8B
-xD5fQAfhsT2g+LLdtC3oxQHAXx1r7gaeN/H+lPUqYRlmAAYSoMP1ya8fAyb3tjbIoNRgaQJxnJcA
-xXEF2Vz4VwCu7gogHpIy03ZegnAMgrM3Jvxlvu0zbxjw6hn/qvj93li/FiC/IP+vm0vMVxpU5qj7
-3PYxC+ufyLchAD8MEiWkDutscGhIEk/q56nr3arvEoLqD5mW1uIrH9xbwkeY623jAOIDlAmKyW9Y
-FOQWYWtYg7csuK0ji1Pj1Wz6B1ed/0yf9i8uE9A+L6OiciDZTDMtl9K15wWAp5V4Ngl+egoBFnEJ
-EdFrQuighE8Ivq/irCevfHUuDMSegbAYM2CGEKiZDsT9ukqBMrwgfloIUwhyBH5fYIzpDxO/byGu
-7XfqvfsKuyB82ObcwQ18OxTHfMEBvekrjCeLcIffO/iOY+ucXcK9Qwt4wxIcA+XUFAPiuALNNzjb
-yxfQG8cyjo7ponmEq+3ltR2Cap8UdlhCqH00q3C0K/IZrt5FGoVheMTVOdQnx5OXwXDtMBxXMBq1
-P7hpvJWY8wUz/M5p06Tvh1uEmqUYkHSL6FbfDrh2vKA50s1zQ3ZEfYHDrlytK8ZRVH3FvM+Yj/N9
-if5dZX0JXy/Q6yB2tB29yWDeDaLwFpFlUfcTAzoTeJUhuI7FgNCtA4T1BsPuEqEdfPncCE+43RSu
-fqStnlmOt4cVF+PZyuPWs3twn9aqZ2885h4hWEv7ILiv/IL32I41puIrXRJ5FEDiYUiqTqEOwQXD
-cT4AwOfM1YBgsbcoNge60jRvu6agSyDW8sPeZhGWtWAuEb9SD1pbk2ozCIvwwmsvsyb7w2rM4nO8
-DcFlX3S2UlCBgMWZPfjGeyZu+SXJzH+angfHQyjv3aG3u0Gcg+Tu4Aw1F5qXaFqFGxDvAmPTNWEZ
-Xtnfg2P8PfQfXX5AOJfvPoUcaJr/yzl9Nr0JZWfchOKK7+GZA4bmXBguEm5DCN7gmLYwYbpQadsr
-tbhQStQOxgaY5a8Vk+qnr58LEol5W9Vf9cCszApFWNXqNbiEprZjWq4Y6ZaB+D2kewb75dbHLApG
-bbS7JMgGiKn6CPHcp3S4xJGv8Iuun6tcVszVrbsj/jr4DoqXSgG2FmzXp2wJwFXNu4Wax+PW76ni
-Nd1YKp7Lx21MUWZxLOOU4mJUPo/Ol+/ly9sl/JzMj5RSUKX801zEQaoaZBAkneFodzjOycX3W/RQ
-dLw0GM5Sc9cIUF0eQMwwTEC8xV/T7uZulIAL9gW2iKvltwpzzCKhftuJwF5MJROyPdCxiov2U+2k
-liOix8dGL8daocpw9z3rJ5UqH88qoHYDf40cbYEz4MeV4qtYq7y7ZVbLejanGNuXMMf5fly4rPL5
-o5Q3jtfMq92m8q4HSk6TxaT2tVbzL3U57zJNlO9lj/tSiv8CiscKoCyX6PdRrjP1diXkfcKwn29R
-AtkbippH12Sy+ayq1FcxV4Cxw/3m+vDp0sDAm33L3tL8teth5/Va2LvmJF5rpxFCBDlYjp/PssVK
-1/XzGe721vOAXmoWCb7taLXdMq8pHRqA+xGEKW5Yg1VtKsctNpxoCzKsghxmlN6VHgbFdQFym9D0
-n11+QNiX38HgCbM1H+Z0EyiLaT+vFCEyTtv1QWktrA/xpXTb039kOF8zaO468/V0uaQarDkmC2FS
-SOcrtnGtOJvnp2z9kjti3EBECZCvfNLhn18128KzJYh/N33Oy2ufeF7kqO8QJMufWCxu8X3UXeY+
-l0imUYyBcgK4RewlC39JfTb5LwheCvylgpcK/lL4fn1VLMJ/bXuifsk2izB8qizYaz+zBvqcsrIN
-dpcDsQ/UsCl0lg3MUN/KytdkJYk8vLsy5DWUYqRJJflJwYdgZQV4KMNS9qX4vU14g0q00oNPUpHm
-PvpxbmDVq2ZLJ6tvHmZ1UuH2YEsk3GDrSNklSkiGfDBTh2Bo7kvEhSuEqK/bH+R2tfv4DSq4ANsd
-BdkUzN0qXJZGLlcCXI0YVH3VUf/pEaNV1nf5clv0G0m0lX8/p2QogHrIyWS9DYjW7BkCJfeQmnVD
-4FZzTpfhsKjf+5FewkoF39oy/UaVaZVrydnzwWOuXhRZVMcbu7HtcSYBYxt6qbZxjuYajUe4LEAy
-JsNhOfQwlamCewt16Oh/VHWSD6TldiRxYMEGqIUVZLuzLoAVX8m0PQRsrRivgJjrFvnRDV5fa+G1
-BO+X4LXFp2ELABa8fN0ZXvYBIVTfRxrJKOcUV5qd41H3SIEnXjkfwKMkeZYKLt7S8TN9GMyOOCJX
-lulQ2of2/f/P3rUtSo6jSFDN//9xm30QAQFCzqye7pp9OKrKY92tC4IwRjLkwGp+vz6Pa4+f/Rbi
-T7sfIOzueyB80fAOILjm2UAZAJjLQuyeQNfI1kgpj7XyCW7PThFoEAmN7G0QjNKxnEJoctjr4oWk
-kouAwXKUIgZn7b5sBApZN4Fg8XFUfw221DazMpNfuuTRDSb3AewmZhUMwy/QFOCcTGpZ3Tlu0aoi
-lKJMBcj/EQfBDsgjLHuxQdP7H9mAeP9M/mN5jNYvB8RLGPySiYQ1AExj0MdkPxxsLUkHssf1qeED
-EPefa7HyjUEDwC18MMagFYk6GQgkMLBCPsjP8rKkXRyD20Z8UoGxL4QmdT4C4i6tJAU3/qo4CBZr
-4Bcb4fau/aVkD+yaMdVHlOqJDhcURJpeEXrwoHVM4Bcb55J/EOiVPtY9jeelw+Mo3Kchk95m62Bm
-PoLJeCIuhT/nG5lh+B5v2wa+Hm8D0I20DpA9Y1sTloRLcekPWmaQQWkBKKRUUcLo7fRTSdvmK/BV
-ilMGv12GNfnlXE8JCAUgZq4ZIDg/093X/151OZ5CbZ7oQgkNq0gcrxec2z8IpOggjvskMCxuurDH
-0VUgujXFj7d7mcr6tfaD6AMgrCM4XgGCl/yl/LU6B82kEj4QQ/CF4d1wYTN3ZHIDvTmaMqRZ8dsQ
-ro9ZHtsBsCS/OPIJ5vqp4gKksVJ8PCu1yKZ+QNVLn/8t9wOE3b0RXM1Xf8uJ+hMIvmuHWdObCz6W
-BcwASr58RZvx+HsCBO4ZL48URhY79ptUFYaFAXAJ7HYArJzi9eEV2MngSNBoCUVfpkU+aYHXUjGD
-NniDwV+HRti1wa45FTX5S1YR4CPAPcI5xj0c9r2StsAMgAF+oRlm8PsfhUZ4h5dYaAFTK+yv3WAa
-AVtgg022a4DNYid1fKhgArPtpy64P4JmCDX6CEMBxGYBgoXj4idFMAbYCiDgo07EzBgDiZ3WiUoa
-k69pcdwQ8sSCcE+AYS6T/vZoVvw6xleAEQDFJAGwg2BVgGETfTD/+bC9hB5/J3TkYzOGI98OLxQn
-8MUj1+FthOxM72WKi8m9uVZqzJr9zFHuGeeHHbZFrTRjXG38Qf826GogONrgoDC0wTKvK8m0NEvL
-vJXuvS0xHwAd9BBiFD9cUR0Uo9r9ktpfbXIp/Tb8aESdR3ziJ/kAjc11rhmWUy51Tn+GSesPmata
-97Es2ajKX72XsIjk5oenhJfvWxATUQK565cSIE6Am9fVwip/uZLm13J0N4LeHke9Vpbo00i8uGPd
-GPn0msIUBHpNesp0Y9qVys+txPk+EMgB2oQZJ3WYbnrQPQ2POSj+AcL/O/ft0Cfp6gCC9QDBsE/l
-fGy7WupUEWhnsBSCASkvGc9juWBwbuBpel8FTBUfxtkKyaMkA1sVIX8uikPWsgw22SD0cGcctONH
-axX995KPyrM22FsMepftr3Ox1hcb9cgkQhwsSjGNyOdrE8ABHgsNgQQ71NjIRXapAYLJPCJ+VsHw
-/rTuCYh/qe0vjAnMQLZWOD6csRL0Lluh9V0OfJfa/rpRAGPNsW2gtQPfM42vThPO3Kr9YAPIUd/T
-wh0MS4Iqy0BltklMTF/ywZ/EY1KOnTioD2n9ChLsgoootiw3LWVOQaYFAO9nMgutMMCwOghez+M8
-BVeJY5Gi7mPxCQEy7wmmHes1cBmtX6sgivnGN/E1xznCiLqmcaZPWUawm/HDo3OpPjXA+dloaIYx
-vttvJW/Gb+7Qz1+O3wCMrYWTSSY4FpFYZ/F1Rsyb5BUmGjb9NPtynJAjCXAnOaWX9EhzGp21wPg9
-w5i0I9MepxyaFj08HDwfQgHuVaSA3v3tCfpMPMIGedU0wmZi8mxlidvHbOCrxSzir5VxrPmdrzCT
-wEawE/xu72zkOIWZm5w8zrLMBGRu686VX/wQi1wVFBNIDj7MdEvxHQSXH2mIn72p8VnLT90g2+Kx
-E/+u+wHC7r7XCJNdcIDbtP2rzOU0o1hUB/9Ek9nmYrcEwE5ouN/+T+FC4xy4Qd9JkNiRwyBMZZM/
-nskzvgPjSUN8G8vuOQLRkvKKmTTB1q6/VDdQdXvgfUyLH8/l2lG+PvHBWBbspAX2TWBi5ubWrAE+
-yyUIzq/D/UdyI1uCYUsA7MAnQPDzeFlzc4gEwQpTiQjvh4GwlwYgJoAcQJjmhoVyB7y7M3Pa5p2c
-F8A4hV0ImKeGAwSTWA8/AbkKDEQygWBYZhfKMQR68B30Gmeu7ygbj5jBsU5p5MMaD1BiAMES2uBt
-HsEgWMj0SqOeaDsv23hgwRTmeEFYxR4pYbCFljLQpbC2cE8fwuls9N5y17wk5G+VEH883QCaLUEt
-01EHvu9+871n9uFH9MzhWAeSD32W8CP9BIZ5qhEnMv46WDwB7wmOCwA2CbOJBMCZF7JIIg1x2Vd+
-EJYAQvt3PCSymGq7T3c79OyP0oosGmCRY3dwMZUw0QDEInHKjStswuzhF5lH/FL59Sw/GaJrhmv4
-8fCjGjJjBMLBUW7pgyv8qaf11TSM7bW65MmpeQC/zizW8gSvV/CSCQQDAK8z7hF/Y+ua4R8g/L91
-3w59LEYAVJlMHpp5hPLr0ATSoRHGLtlIt5CtSmdt5sK3ODkBgLgy/Mb8rVwGR8BDUjhVbXAHt2f8
-BG42E8wFeYgzPcdej8momrewEV66v+bjgJdNIX755jgjTTDAr6jFkW6Pz0LqWjxkssF0GQ3krOYS
-OD5KTPYrMQLEGwBDG+xhswDD+LhCAcWyN8ytB5pfdbBbQXGmmQPkHqc5VqJXkCsU3zXFInftsdIc
-c12T9reA4CEe0r0CN4xvUm8BxpiBj/Q9u1No2HBFWs8LOp3TDj/vIbDUCKvBVtgBxcOblR7iJfA3
-UVmRUe4BoHGEJp+w2Z5rASlUMBuAWGmcLFdC5NUMcfkuXqt7n6wiyq2mdJfnjd4qG0CwNGArecwc
-k5ZNec1NJDieQC/ouNK1BdBNvig1H48XzwWDYcGSqHNlJR/9GgDe8uM0hZjMIxIYT/FJt6Ah70wd
-C8HGOIzBszXBRmscZd0ubi8nfxpN4ZYTFRPIAO8Ghj3A2mILtiKW50MK8zgxSXOIZ7IP1kEz7GBY
-lzzFZIKPAcNi6qAX0h3h3mEC9H0cMPSUFiui8ckoy+yVKMYKHdIDGcJEv6DZYjcc9CwDCF50fNoG
-vI/L6v3hE/GPb2y/6s/xaf8zN34G+JJv0vTm76YFriCY82MphF+TySvCosF4tka4mUf4CQsWzDqF
-BJ5KlVZBF1XGPhOccx1rZ/Q3ps3mEgyWY6mbECaeEDBHtdfOlLZ0P20DDG+t7f6lXRY2xKWfzSL2
-w4x/Bcd7BICArwUe4Bgz4oyaT4tAj/dJD2kagaPUFsCwuXYYfgfB69kfWfilfqYsbETVN7fQiQGL
-wG2G7Ux3TXEcYefMsW5+63GYqBoH0FvAsMwAOGzCJ8ArPX+2qwB1oisi0wJWOmjpgSYG5A526aog
-UlpDSuW11zf5O30rxU48gLRutk+NiA/WmIk+BIKNgIjzBHQ2x93v5uObcac2OMCwV8JCtQ59AmOq
-LsBwgzbfhSZh3XIzZioa+VtZ88bz3azGWE3addsJjlU+aYUlgF791fgDIANMBK6gvJIgBnMU1RWA
-MoDf9pvlE0Ax7IS7HLOBRvPRn7XB4ScweQBiMwEgxqkRqRGuawaKh0yzI098uRR+8c5gQhgMu+Gp
-0kkFsvbh6crHefn4bqC8QbCuyTxCyUwiT4sAGH4WnybhskhcTqBPAPgIW/Z4AsF2DoEPFsaMkmMd
-S+656UtEz7i+QkFfINARMFMaNMKlrBAIbqAYH+TYLwzxlVGRx7aBrmQ9AAAgAElEQVSZxHrkj7sf
-IOzuOxh8Yyz8b9qhexOA+Yu6VQRHAWGxA4gUkwjaNVtMI/ypP9glHSvEQg4CMF1lsQkFLUBP9wP8
-xV/DOrNY/PXOKcoM7T4mQKN9gv5TmxG3XNtrDnbLUWnLHBwv+U9oRvkoMQk/m0b0nksZOQLA0UuJ
-MdhZHARLmsqkiUSaR2wtMF/zS2K/nsfBs4X2dwK/FfBqmD/UuDSL0CCJKqzZVKKC2503/JQ3zCMi
-bASqWABajM0EAlBvATwEhqe4g6ZKUufyHr5oBiN5jJ/qSn/Fwzr7j3K05i1/C+ubwDDAL3iCDmWz
-/9SJmDcShAXwNtMJzoviMS4+0oQikR4jY5kecPgmhOWMPHnQW3a7Ziso3Rt68HQvl4BWw0/ss8bZ
-CyD28AH8JNdMsY0NPFEBBnfHbJtnLDsgCBUHb3/JI9LoZwK9/ddth62VOwFwAcG4c+lzntMrAKPP
-E8oGjKMwjSlkVuX9SsIy33b5bQGApfoV59SGVjjthQW8Sy029i4R0vyyiYQeG+aqltjTFEeobe2w
-GRouMXKFVrXHhboqx6bF8dDEmNE4YU0FGxOKRJwTd6eeMRw0fU8Dj7e40oMcmUY8YRKR5hDQBm/T
-CMjkP+t+gLC7b5XxVbv7ov1Vf61N6X137hLdwNUgWq3UJW4yEX4XWunHE/FmVtAELzF5YFIRQiqF
-JwusDgTqMx1gXjOHMIqzlk5MvtiVeptNKhxmhFs+ARlP+y0ssj8va5aA2LXCVfPr40J+xCcgTtMI
-hwQ0BgR2Yfowpft8YNwqENY0h8CcW4Lf/indX+anBcgjv0R845sEcJ9AMIPfDEvNh3IhqOXiT4Sk
-JMwnP4NmAWgKwd/CIiMQriAYTFUCNMQDBkjF04zDzRkT9qtDnsuVgySQq794mv9Mi3VtBE4AfEVD
-06uuxVL/AkY5Ns1kpzGCRHNjPnPsDgBs4AUQVLuCMsxaux63YoCr59SISHt+CIZzmQ2bLtf0Gq0t
-Ld86TVVMZeNR1nwTnHTAq0Nc8uewMW4PfxX41rTUDs/lYin4uggwgaraXL39RO6KlxPgfhMWwUNV
-0l/vs+2ROcZiA6KwGfZ6RVOBsiPLwvN51Wj4lq1gipAZnr+DYQwEm0Z43F4Xmg/ykrxqOiJtMotg
-M4iHNMJLAYhXsjQRiQ+6sFAWTZI0hv8VRBN2TdK3KBa0jyKFd5YK0tnxF3WQEiKGMSNy6okuiblY
-zDV9YTQA8TaTePQJDbCZyPOQacTBR/999wOE3X07+B38nkfPOBhpedbhZwCdoDVsgh24iEj4QyMc
-/mRWIhso7bMvtzZQBHU4CD0WA0lKSWIHJLTw0aJDFUb5AHBQAowl4jQqUAeV8cwf6NYvLn1DEUyv
-v4IJPk8Bv6biKhSyCV4qYrAL1gKen7C3NX94yH5y74NJhT8RwdYO54CiDL5g90sTDAco9maFdthw
-fJY4ADb5tSz8rBFWn9NVwtASS8sHcCyZD30qYDP9DHDF51QlGdvoFwg4HykAXBIqExgOPxgug3C6
-WgsXBEBX6/Hsjrg3kNzB8Duw/f00AsOSD7Kx2chM9JEEwevZYdC+iW/0WdS0Oi7x0CIJdnOZN82w
-8NBbgtuQ05p+Gja2HGHhfJ0GG2OL8L3NCQvkCnSboL/5hzTwXAa/qelN8KtCH9po+bMeybU0flSD
-6H4AxPnLNYV5iZMjIjn1deDP4+kRYLeJH/OnCYJFOiD+EiCbHf7gLQ34JgCqH9xxVc5ug5v1JUmD
-+de3hlvmAQAnz8tJ1gKAFZ+QXy53AHqNFTUYq9xDsX6prL9IE/wsqadHrLAjTkCc2mKAYmyWM/SJ
-/dTNMJ3Q9FtQYlsnevHHLcCPZZD3EjKs/k3q6Y9ZrCCxEgZvST4e9DmBX9vA154NdvfRabI1wLql
-8KP6A4T/1+7boWemwtrhsBvOtdryZF4wltXqBGDFma8AfupcbdIIb43yvu/jQA9CVsTCVghaZ1MS
-gMXxooDQbNpe6XGp402gnAukgGMR+ptg+JwBgGAGcugzAT4/JgynRYhrhqVohjVMIf4is4htkG/7
-lUwwAfR+BsGZJpQOBGDBxAoA1jSJwDm/4bdto4ezY38tDf9aOE9WyqkPcRxa+J2OGjAu40TjKILX
-4Bb0xXbBbNubaeQXiYccIdDFILiDZImyBAqYPgCC2nUGwcyk7y5mrGSbAPBeP1ViNDAc+biYXvzx
-p/pbKwDo9jzt+wNUiIhrhPdp8/uTylvVFQ/Ky8vxUFTVUzF3wLxi7nlDHUAWmmvU7AJ4kQC/p1nr
-4jQzdiScuewt3Qb/FPelH59uD1AbIQa/WijiEbx1k5i71Ah7BxjoBl9p2nbiifUqcQWoANmXnFbD
-Z435Y3ALDvBuIlFBr5R8AL00mPwASwBp+x2il+PU8HW57K/GXbPNKQpSkCpdeRNwAGPckp2D4gTB
-2TRdDRh7uohvkmvmEF0T/PiX4/6KeKX4BMablzEIltyDUpZwaoNz5JmXadX0BogWkvGW69dyCIPn
-DmzuBMHpP9IC9GY4wbEEAIYsSEAMTbAfU2dPmEbsj2xsAGwPHhx+gPD/zP2+Rjj/5evrCn77pjho
-iZdUEI2pTygIIQlAHPpTF4ZWwHAIU02zCJwrrFa1wbEWgAE8MoCHuLA8FiazbHqilmYWYQ0c+4JA
-e3nBi9+NzSACR4DheaADPJhELMPAu2aYAPDWBPjDAW+Q8/RHRSxOEu29leQyPAImGVdMJvbY7XON
-00Y4NcJOE0bAWDoglgDBa/mGqdDsMiBmv9PkoRkWYQAdY23eE59vBsYiMoNhjxc5wa56PRwvVH8F
-w1KE4Jl3ujbH0p/z9LwRvgBgjlOROxjOywls38IXgEycJgGxr+3HZG+YNv9UrINgfQgAO0+IndU8
-BixZMW9VSHYwzBrkENbeXt7jw/FZuzT/mVZyXKaUE8b0aY57n1s+HeJw4bdcmA9wgdzGZa4BTvAL
-vgYQrFGhrwfWiAIoFA0p8iELAecA0Fnlrh7aNskrxWWfJDTBCBeZkpiSfvSWUbrmlwGxtTKSWmFD
-eu13/T3Ux/1RDVks1aihcgSqO0Ax5WdtsN8aIDgG5dHYPLfzaJqGOUo+bIQH0wj4z010FRifIFgk
-tL4pRpy+lGTxua4iUie/xRnSWbdl8bJMrFzrvRAPekwtcAHHBIajJtC3pBZ4X0WKlviRbQYBMKzQ
-FC95Hhgk/Vn3A4TdHW9Bb/n818FvxIe/geUjvf4E5g/QAAs9KysDXok8+RW6ZCkAwZtZpQYY5avm
-Bc6Gv6f2dwvTfHpPrUUulEmLnLdJpnswO2WmrTkh8fSfgI8/sQzge/tpA8CPLvnLkx81seCeWvqf
-XMavZrGrOSkh8+AEiaoNzrlPrTAAsX/+2LzMY/scYHxeV1Y89BQALHUsMF4V+NJYYTxjDuy4MjAW
-uYPhCPu8F+Bb6sw8iUBaHiFapLiUGLc0moLs1Og9Xc6f9jgI1HmBZAmAxFLJl4DYJWLiy6Ql+AGA
-8eAmbh6z+YMQXQOW5XxB9pXXo2InGJY9pwByIRj5gVRcmIa6yYeG4uopErQ2QrAPjsb3Po02Bcf5
-11talezl+kT9Qc3eNI3YtA8GL652xLhu1pcgN/wAEAR0++kqgSNaE832fZcEtiAOK+WHNvb4sBtt
-8inkjbDsSuAr5D9NKOqPH57BRyYwnDaj+cn1fBCToC2A8jp5SZMafUptsPJ5wYdcqevdBCBYmqlE
-hnOz3LkRrsQpnRzhx6b9Io0wrvHA4uvFfA33BxmMgbVWx5rW2hPuKjZx15db/ja4LDIrxSeqSr7d
-lhA/3EW+phEWi2mXAL5VG/z4RsnQCOMq6seoqejT+em/736AsLtvh34Cv6OWmNLnc4Yn0wjKA/ns
-BA5CL6YShnwJoAMEARBbLjSuT0WqEqwtDRENcHTXAru22QCOaYEYLzIIU9wMzK0xLge8wbwZBJN2
-c8UkqEh8OQ5xfCoEA+flFhM7HqA4mQB6r+O1aI2BMqJIpvHpDvkGgPzm9KASIFhd6C3XCG7/UwAw
-xqyDXsF8tzQ58krM5x76qpk9wbAEU4xHBBZ8XjaVDlZoiesS8htz5tHPEuItLqtXikpXiPtDOoM4
-SArNJC5ycAq9pk2v+ObSGGd2gGIqeNuTD30rH27sHGP2TxpgFoSFlAsT0jvwbVqupH8W1rUdXR5z
-wMZ4ztIk83979RbyZrit+Y0RklzzO4aBcWiJvc4KeDk+0w/NcAEep0YYcxS5OFz8zLfr75ApHhtv
-mMCHKb0CXwbEnsdy/e8w3dH67zn6nfbCkJJgp5u+wcpDRsiOiLVBD2vVRlhyGbdf1fxKMZWQ0Bhv
-OfaIjCD4V5g7nCYSfzkwZvOIv3RriUMTGiB4t4W1wR0Ad384SuANchy3h4vWOBj/IOurDyuR6BK0
-SXylaoHZ73Qu6I81+hepGmKyHX50m0wAJH+rlfwH3Q8QdvetOl4lNX3pT3A7Ad7ur1rj1LGGZrdr
-hwvoJTAbJhGI91YBECOOjmMri+NwSeAB/2gna5C85UKJq6/2zFsZfHmFFUsrgS+P8I4HAE6Gl4wP
-QN9BMT5l6ZvkdtoCZpBlW/sL8PtsbCHPkv2aLnr3d6/ebrNCH10jXGjAEDaK3+YRqvTGIYQX04fT
-jibdBugVvaQnKNpTQPQQgg0M1MNGc2VpH5ikYwRCrVyyXIk86e43gLHe8l6cFp/9nv9A140/NGZd
-QO8Bii+8xSTGtMeLz39IO80IDWYgx5gFZRJACjDsEd3PtxUAX1TPT/0AwOgfBPvR1wnc1j7O7MeG
-nBRRrlaRwm9cVaSYPSQIPuMybHKcIiGYnsrrgj8yOAYooD6l9izHI0HRHsOHummUzno8zHnfOIdZ
-YXl0ht8AsYU/P/IC3gC/2w478FXumMeFbfDzZNyzxNbWEu7n/A2MNOiMe+BtozXBChISTpxb0iQQ
-IBjXLdcwT+H3W64nN8v9etY+0eBZ8usv1/T+WvLEJjo/NeKXykNfnfu1lph/8ClIFUPi7d23tQaS
-a1fGrhENnSIp+9F5b6xKl/8mJrAhsSNfpbJUcqEvWS6VG3sONwlU0Ls3yolrgVUe8VMiRENTvLXC
-7eHmD7kfIOzu26HXy+8d8N7juM4tf2/gWFIhhE1ySIur+AJLoMiguMh6ZPYIfjJFSjdzSC2w+JP1
-kB6LKrXEYOo5hlpaELghmBz8FQAD+D667bPE3B+vi7EZrmqDjdKWSgJhtX3KxMxRRC/xwcUwqKRW
-y6PMEszGhxCEAK+cD1ALcxVlsx2sJYH2JP3S/KQ1KaNNMMWZJOaSZz43WlkAh2CjxrDHymWKqzMf
-N7vGXcEu35erIN4+S4t3ByrNWi5+GsRD06t9hKlplzQILc+UkSz/Qz2WbwXKK5Nyoxz3WG/kV6/v
-5hf1ftHaA2nnKzCl26ENOU7FPIKbN83LJ9ArRDvMlEzOONQHWpjyWM9GexkUMQx+8yrJXQt1xEa7
-pgktr5EJIAdooPjy5gwaNfyo+wgzAO4/aWGQiNJUafQm+Q9G45A5AgWMBf/KvOZrMvu6r48k+KXx
-iP766RHyiNra+WRtkFqEVLYlBpzfdKkECGZOHcXj5xHOrrfcUm+PRj8AkkX8Yxr4utzLmcLFFELX
-VrSUTywvARgPuoNfoeXXY87qvBIRDCIoZM+QXuxPsMaJhuBO4MvrhOiZ6DTKWbbS/E/Q6qEB1hp+
-Kgg2wYlOE8P4d90PEHb3Oxrht39dG9j9NY5ADRalkByylhaaYX+Kla41drDoT5kJnCUYgQ6Lhgkd
-xM5saK+hCnolctGCQTxrRaooLmt1Ht86zq9g2FGjOXI03zina+0vQ2kCXxw9hqd0c+3wFoK1J4Ut
-YWzBeQZmzaw4TnLg2TY6z9eFCzTBKgJFdgisJRZzmW3TYBAd6Ma40SvETUOUFm31/gWvmbWD/RW6
-CABGh7ZWLnm/CzOzSxqD4RaGP8FuvecnEHz2vwO2M41HrrWMiLeBXmkAtaR3EMzXYUSh1ikAGH6u
-O+emzEx7uMje5byWh2fnJcl8xNcW3csyLV9f8hXU0le39SkbAzfSSLIlujWp/hJnr+mPxkjsqyFs
-43WfN77XeL3y/SroBR+8haEwMCrLXWSwYiWefi1OROqrdsaQkv6MS95V+Egpk8A4fgaeaUA8MQZz
-GKdF5Mc1bOVmKlXkl+C3TOvcvvJQtpLfwnynL+s4KYJkKd5eYi9OmkmY5Ga5YaPcAIKPUyUKINYc
-ApXT3+YL815dXefhHcGvjSC4mKO1xVA0wU7LAYyJb6Q8T1pGXphEgI9UEkgwvE+I2I+YpltGPxHe
-cvnxEz3+tPsBwu6+HXql36cNcD3tLf+uWxvIlQMM49URXpWLWTADA9giUwoGwVgkwWcGl8ukgV7s
-dPW/AYydiScUrNphPG2jP9mM9ugBWY9XIwQMOxgW/9DEs0yWg1oAYtj/QhNsqnsTmi3XAvsRw+73
-3UlSWsbbb8WotY3z+NjvFe/zJNAG82MPzbXlhrmtZbGWbvlJZGUB5WJbQSlJjx0Ax98QhA00SWeO
-mNNkhjktKaA53+lqfUeeg974HhydbZTeFg5bCur7fV8IPSh2ytvKMRj0CLumTSOUiCTSIPAtHztx
-X2MQ7Gun2s1p2SFehSSNnwul0mLD2s78uHMADLpaC3dIZS3MY1dGvgXsiMt8xgHOZ91vl3iqqKf5
-dWt7B3B7gN76uWUVf7AW2iwHMMBaYXkLU/MtmxratJduvP2khc+Z6nyiXuvbxXo9+JV3ILXDUuyF
-44QI+rgG4iSuK9ICxB2tj8bF27JuNhfyDdmVxraEU5YWuRRodN/zvlHuBMH4yhx/VKN8dtmB9xUM
-Y35ZQ6wS4DLyyq7n2EdGIin2/OQrPcEKLadIYH1GHCu3cg6qplhiDhMTZPkoZRZR+Rzktr/m4Bdx
-wgB4uWnPQye3ZDc6J59l0N93P0DY3bcDqy+/bwDxbBqRzCbv4YtUJTa+iaRGePsTBIvZBnhGzAIa
-Urcjrq+35KAukLNJAtudrWqDO+hNxvcCmEUkz0JMplsHP8OxK9j7lx+JSMG8/Mg07+a2+fUxyc8u
-QytsceYwAPLjoBot6qYQ3ToaDx54ktA2gGGTLantL1fMjaXgyTxW071qBmtgD/zqSMl3zG2JrbMM
-mdNfkmnJlQw1gBXlflszX6VxJuumD6cpBIO5UryEO8t8SzvDTPW9jSYi1dQhM0wzIlp6ceY1GufW
-kgRBmC9oZ9s9yhylwMqcXjcq5FZ4/Vltri2+4kHzSFuoze/MmjzuJPWnO2v52HskseoM8TlQ36fF
-SGnMKbgavsiJ1f2oHfnVHATjQaZJfj5nNW5viRlCi9a0ar2LvSvxsyGO84JNeUzV9lYTiNt1BsMW
-D6Mhr7xBxUSiaYXzIQGnRkBLaOWXdN17VtdcgmE9tYcFPflA0IlLcRvSCLPcWko2wpdNc0/zAwTz
-BjqkwTY5wLC4aR7PoXoep4YnOiLUGe9StD+zmBMlW+pF5shvJS5BLtNOj/P5i5bNcQzag+bHH8wj
-8IDgdsMigmNMn5CIteeddQys5L9yP0DY3ToE3D0ffpAFE8DtcQF0KE6KfxMQ8ha/J3c/l8t4obJc
-P2uL+eoaUnFTA3FzAepj+TjDUWftS10Fe5HksU17kwSeAEIIB1UbXRqps3BnDQPdL+xLS307jb+4
-abJ3jMdntZ1hBrB1MBqdMilpMc7BdF1gBGg/x5/nWYUeYswK+GWQfIDb3Xl/Y9Digx4U2YIYOmUb
-jSeX5xHvoDMB2d2NafqSbi2tADgpdMCgt7BJo/xdaLy09XfyTHk7I57HWCpudu0v4hJuubxywRx/
-g8g8h9OaHXOac1dsrEmoVe23xzOdU85eQoe03R6ARlofBYlks7VHUO5JqsXxbtw0o7xm5G91mJ3l
-KCJ5l7fcJwufgec3KNusyYJf5HFqFvwDtwDP59N46svnc3h4JYc9KdW5rIbN6z/irG2WU5+TUB78
-/lUANHV/KGj/ZH80SPPko/0zOiZyn4uu5fdAFbg3zbXx0M2U3TVurV4O7PypfWfeYEIDRrQm0FRy
-XPe3K5ZffQ5UV8qQ34GyNg3yr7XkAQAmIJzg2I++MzKhEAtzDnN+8UR5X2Vm+dVBw1uLBKXY4B73
-djrOMnt1P0+OH8wH7fE3H4q9NCrqH7xQFX+TurGDOT6AsulXOWv580/H35I4Iv0Puh8g7O5bs5TK
-ABJIAuhUEMzgFwKQ4kgQ5dO259mX9ItI1ZoxQMgn9Z3fyE8Ai6+SABgfY5ClYm4DJbHQV9kA1gEx
-2pAgWAOolvc/0TPb1ghLJD4CP4Yjcrvn2adDoO8AvtOPJW0Xsh7mg9P61OvF3wFyARE0tjEmtKFx
-ugrCBRR7ephatLbgdd7URm1tHwBG5k+QdNBZTS1lbKjz9Rky6Pt3nJ3zJhJ0NM3ZVOAE7ycxHCDt
-qPNDy3uWD+EtyNyveUfGR+F3PmHK8dWoqPaR11m9J4rw/JV4NEjzqrzIh3pLPdHRve7r/XM+S3yr
-jM11Krjhgo02it/m+BbZITs/xHJ/eKSDzwaMzr4BFIOfLL7Ge/m8YY67pumabG00AzwTGUHvQ2PJ
-+Y5TIwBmpV0Z6N6usWlhBdgLMCwivwSgeP9wTjq+PqciFQg/7ergWJ+XFYYJekQOcEyfkStjDGIx
-XIheOE1ExNhEZ8eHVjPmKGmm8vYBEOv50Q3YJG8t6G5PAF8BKJYBFPs7WEvg3MEvA2L1vAUQtzjs
-IwCwTmArYYUVZoWuLDNVsaX7c9UeXkv9yDP4n00HS+U/bhryC/SyPv9WB9C/Jyz+EfcDhN2tL0X1
-/lgCA2I/HktZAwyw6+DRFxkAcbHhAWPEgiOuv9dhE+ZFU8JCAcBQIj/aUsIq2642APBmeOZtW5B+
-zgzLJjXR7JNmndEu3vnLWuH4Ylb2LcGvL1IKR7oS+BXZEmCJ1M92guNZuSfi85Wd0Filq4ASyMPD
-N3+vo6X1X4lnsMtgWMgOD4B2uB+DlyP+1r4WNvp7QiopNFmH7NQuU1Wf3csSA6MuYUJPAC5nfqP8
-c6POtOHe3yYGcLl1GyuJWlHqoIDtPsL+F0BXgjJ2JrwBiIMaJHXCvWlw2vof8VSgzPcb6KW0eZzS
-3lKcvx1AFlXZUfKk27FMW8PdT7QyOyvJHQzvuAH86gyIl+JkCVco4K2TUvtr5eTXaDN4x+arbJVJ
-rCx+e1bxDYn+A/uMoXFmcWh6h7hIW35GNc6qdk1PaoRTM1y/ntm0w2YJjO2hK2uEh0/Bsbq3fzIZ
-TkUkvlwmsacmSIDpJSbdMqqnZyGphdhsSGKc6DmiaYWhOFryS7dpBGuDVUhLa5v2GfgmcJVibgC/
-uv+RLN/jVIxANfgk4nAfL/NImBXur71JgmNLv4V/g2J7HCRDceYPBP9x85Ar4NVZM/yrx/0A4f+d
-W4XiJ+61w1v7O4HfBJ55IgQDYguAUX4hBFILDGaLFnTt73seZ5fUbF/DpS0LuXHSBHLuXWhbnhEI
-3nUA2Hs95eoOqxdP6WA6TeuLhU0JnzXBMK0IhmnxK6/yKd6MAdNVSp6g8prmA4k5CEEmAZCKmQTG
-ngBOaIYN+WAfnH6RClpKG2yOF8n79PIlT9HQp09vcQVj3MdwbJA02nhx2XIPWwuLyCdwLJynpPEa
-OhvT63ppVbuDCSHUo8hkyHBwFSwTRVrSinAYXdejBRJzRZUzF+s9ynh1sEeRmm2pDUff0UG/WqO1
-6f6WiUbhj+AXf3n5TgBmoi07PDQ8sAoWGufkqxP4FZSgBxf8lpcKcwmehwMEZ+fVtXt/4UQD7uYF
-8AaL815wvCgPFUDu9k+a4PCvNcSlf6lr+xwIL3GNsAzmEZZyiE0kYqcUgLBSww+zCNL44geR8uRs
-YCa3h4iA6e2gGyK28JKsasR0yGyFQijHJj6kVDTCsINWAq5pBhFg1dPgxxdcWTP8kD81u1yvj1qA
-ZvNw1l00wmIEgDWOXLXuH4Cv6ZK1nn3iw9qfzN6mEbMmOACvnnEJfn+A8P8L96tw8jssgkY4AKCe
-T8R8xBlAEH/pjTWAEsKeBHUwErQgFy7nLRKlxYeGUfoCRvwGYqEBjtqXg1ER1RWMMEEwA2OvF7IB
-yAUcq7XRWl+laYFNxDXD7ZU1NMEoVLS/boNM2uFuD1nqonEGy1Ma3NH04Us/5rJqy6HxTZB6guGc
-jy2zKiAW4faf4Q54GVxMPIX1vKC3I67lyy5OiKNUTvlvSPxTWStRn8HxWY0eMff8SLmnfbzBa54Y
-Y5oYgLASBhkq2pOAq4QZYMk5HsjANM+zxsCvxA1ERivzKF+TSCtMQJHNWeDp85n1Jp878ncwwwWl
-p/UZP7oVsWmR3ey1RQTAvzxcUr3LGQBocmnee9GdogUOgmONg087IH6KRjhfnzM0i/AIkrXkCVAr
-ALgSfnyYaAbF3b/N46qdsINgbR8MMgBgaWYRrBVeDoxvKl/ZNaqJ2bOnwWTQEC8x8U+QE8EU0ih+
-m7xVjlpuBGtCd9QEY8NegGDljXVr0AALgdcEuw/me8gXIHbSBstpDlHAr1zKmREAfvIhQ2Eq4UA4
-bIHh9w+hPLvfBhMJBsIEeteatcK/biB44a30n3U/QNjd16YRmmAYAPgAnOXHINiREzP/WGsMfCVW
-aoIpoXVZ04pwsAEAHz/S8FJ4cQvAGJt5hOhZn2SpeAiINrGWF+cPraYFPkwgHolvDSMTbbJjzUKC
-YAmbYSO/c4j0C8Se+wnAcj8++kmadj/PI2gAsDAALj2U5HF4KfTDTzcdcMp21tPPF+fZxMkWOGlK
-JIXE9HpdS6i71sKbkfKtuFwwpk15rIXnIt9ph6f7vzRSKpg6G+GBiPP5Bwlq1pF3IiAWtFR7qq3n
-vKdgAsXcizrnKpUONJrbF/VNJm2TBudnXjjy9jcObX0ly2VZnv8AACAASURBVJreOLzk79rffiV3
-zHsrWseew9UGm9NE0iQCsZtv1tssJg7wF80HXSVAnEddpkYY9VSwm1RZflaHAXn3XHaQu8ivH/1h
-HrFwRm41j1gKe+E9BqEdNpFDG+wgWJ40jdh6ZZMD4WL8wjb4BMzoq4q6jXDPYG/BMf+u01qa84oi
-GrdSKABcXLFR7skNdADCwpvXJMwlKjgFgNWS7yG/crz3C+YQuAfAL+jjkVpu55MDAAfoXepnPTvQ
-XSrr2UecAQTvuDVqhP+jJyAuoFhV1pIEw1qBsWpdf3/C/QBhd9+q49kkIu2E+dWQuo1Ugl/+Mk/8
-IEME1yqcu3lDEVyXVQ2glTCP4gN4JSBbwXF2zY8uWX6+o6EMMUel0ng9FPWz0B8EGXfGHhFdZCIh
-0kwgRA7wi7BKAFs1E3vI3zTEJ6DpY4qxfvEzUH4BzUjvIDj9YNsJtDRiiD60gpSOJa9geAy7UAdI
-6to5akuP6znr81uDDx2RX4LdAXj14kmRc/jbPOhXH5OzjB05DvptEWa94XprRHEgX0aOdbzJJMLz
-QTAU8NhmjtffOb/ZyjLeUnt+vBxCnqFPdTjQly1RI39bK9HvbGppQ7cRZz4XaS/XUqanl3bfbay1
-hT4BY6xt8PPFN2RAHHEabaynxuzTKYqpA3WFf8J5tHY1f2S0FvxbCOR+AMCrpilrg6UC4PM3nBrh
-yovtr6dGhIYYNg9Pn48EwyZ1nkwacGrT3+OP6CZj8Vax8D5cNVdn2TxOmuF69rAl4BXSAAubNQjl
-mfyawJY3zEkzkfB4vFk6bIRFTk2x2wiHeYSf96sPtMFP0QqvZXuT3FKxAor3uf4JgKWaR7AWuGl/
-AYrrA8UPEP6fua+PT8OPXgktbSBXHGh2cwgCxHAQCHosvNl0IvNUwLmfChOtsGkEFDfcvq3ZkGCX
-aYW7ROWJRc6MUHzxC/W3tNdEau+83Y+IrWYp6SYQxRUTCM/UwbAsOeyDAXy9/xBz8cWgC0C7gkhN
-wXvMVUsPvwu9CnQ1NAmgie1XsgPmNwFbSPLYxn3jT7c4/dAXBye7pJW0FAKnfXCGT4FQoaQcwzu6
-U1Zl84726xE3YcwpT97qaOU4bjfsOoGiv+UIoKKOHH+P0z7ec74q7+tc4g63uHqPdKVObRm1XHqH
-ZrBRjvf7wh648UOcHMF543IFwV7mhQ4PPEp9scEEIlN7CMAYZRrLGsGvhPkYP/B2EPwcGuFmM0w/
-8ZaUsNU8BdxKB7tDeE3hrUU+TSOgFd7y75ckj1OjfhZA/ITCI4BvUfae9sGcZi6XgFf3m78VNDM6
-G73nBHWmhBHencicLhcLCA7w+2xlkj4RZvvgTxrgcmqEDKDYm5J2vgC/8j345bLqbVCYRwD8iuC8
-3zjjWFXMLE+MWBYguGiED22wVPvgGyhW+TGN+P/gvj267jCJcLmR9sFGJgfdTliCiR5A0hIoRRwB
-FAC6Uyixd7PA+klVvo+/0vHFoJrmEI8SLvUjHtQ7mBrhqGVop5Zwb55Y2wQn8gXwneKeA/zm1cdw
-c4gcGArnVp9ZczYCX4xFEdpaTEB6ecy50GjlXPC8QlOT89+B8M4nh7uD3x5XwUUHTLPNMMJWyvT7
-HM268bAPGU/QMQPdUsoZeWi9PYPJ/LAwx79rjV/dWXAEvmM+IcxEE4L5aoYLOxOAks5zMJqy6JBv
-iIs6D0KWuGeJN3H6x9g7Q3FpjX5VkEt9D/9vgGUbEm2YrQ9rpSefU2gC06WS6l5+Wb/hmfN3ITYF
-fuFAN80p7AqCMS8Bbguw1ZJG3c80rWkMgifg280lRoC80jzuAMAiTTO8bYP3m1E/OUKaZtismEYw
-8M2R9hHGArEEvfI8ZVZMkjfzRH25gsPlePYR5qFUGia3C1ZXKmkCOwvziDWYO9zBLptRxCY78hdt
-sPxN8EvAeWuDVUwdsOsTwDg+foG0pf4xlG0CYvYECLZFD0qkDT4BsBQzCIDiBMQSGw//tPsBwu5+
-XyMMf7UT7mB4MwKkqwhsQScAZS9AVwgolbJsMlHwwAzOnHOobOD7KGuDu30bNMD1tZqSxKztR3sq
-ZzKReRMcgdyMfwHDGGA3h+g/aMU3Q2jg2Md3cgFE7bs4FvCY0/h6URsT1gKfaRivXme95p3O9k9U
-e4uzlv4N2EVKhFumA2BcJVAK63r3Wn5ehTrnwbSKHQ8KN3B7v8fpeLzi7f+1DoqdMnjcCcIs/vIN
-GYTxSlKqpNoF1znMJdrs4ammDcoobia8177WFZ2dLHQR/MpozrIIz2chLxqUQndWrzrEHWVe+zPT
-fk3VcmUep5pKhP64n6BUQisMJQTM5vbr73oWsYiOZg+gjB4/pUVPJoArE/BdsVHuMJ1YWu2DpR+h
-Jg6CSQY2bbA+G8jqwls9n+P41DJ9NMPWNqeQDnz3G8syK9r6OzKhykOuLuxRcmTrssAbUgwNADG+
-OGdiDoJ/LSugdNIMHwBZOggG+JWLNtitrB0gB/htoDjAL5d/JD/t/BjZCW+bCTMLTXDRDkMbbBZm
-EtAIh3lEB8UdEDdTiLx+j8X+SfcDhN39lo2wAJMp+RPcBCMQIe2wiBAoFpmu1gBRBVbidRyOtZ0B
-CBvrJkYslqYR0G6sXnsgQYA9CUla7E65WQpB7XdtGwO3BMibBQQ4lMUWG+tsNTgQ21/zh12woSkO
-YASuWscttcJCcaef+ethKoE4SpvKHoD3kud+rcz9RqZTfI8rNRmH7UynMl1jzCDmWv/BzDoCurhE
-cJG9PwDsuHNVaPEwKJzK18Z0ehhaXkEmgb4rsjqGIAVXTat2qAHg9Owpv+tRSMejbUN7tc2rRxoR
-Aq/rXtlt2hQNJm1wjDibObTpL/bAZf1Yzcvrjhsx5BnbRjc4qTivpzEO30iPpCUS561CK7yc38Xb
-tcYKsRs/LY/xdytOAL1oCCQ2vgkD3u/iu6Z3KzQqCB41wWQSAYAMTd0GwRIgOD6mET9zW2mXgSYB
-hsVPjJAnv9NX5mncGFftg8ETy+wFz6iMuKqVhhVShVf4D/Mc3APDISJsG7xB8N5Qto8cAyCGPFIC
-tF+aQbC/lVeRUxtM/gMUy4tGOEAuzDgeAr9b2yvPBIBdK+y2wgtvDCYzCI8PGvK4slFOsVHuBwj/
-T933H9SoP32JU5EExPST63XSDtqYr7qUCsnaHRBqrW/J/lyimO0jg4klLd1PhDPqI4bD8ZxNRAoC
-P1z/khzitGh8zb84HnlVZJ80gQzWTowAo/UHgOL3gQlJ4eNJUQfDs3N+Shr1Tz+Um3DF6zXqOJkx
-u9+JTxbfwzand9AiM9gqU0w0scnrBp1uDfVeW94PDrarVaxx2+uqqFVPpg8ik5kEQbjBueQA3jsL
-DkVALDXzpNWtYp6ixOKDGyXtMicRp9QXbrP2PJVwrVdW6up3odYwGOaxfQXANIctH68lXr4dDB/z
-AMoYpvE7Dl8B8j1+X1mR0BUKKsm+Nkez+HztHi6VZfsYq/0pZadK7eB2t77HsbnEGKd+sw8A+GYS
-wT9sllt0ZfOIqhXe/LhsmHtMdDnfDoXKBHzfOBj6qXUmDrmESGbSjWs5rZ6SFKNsR1EazgKAFSB4
-+c8SCJcPYwCsOo9LsCs136UM/PiCIEwhAHDzFAkqJ3SSBNX52B6jbQJh1Rxi2TZ5cCPiEQCbiT1L
-ZD27v3KC4NAIt7g4Qk21mEyEVvi7hfqPuh8g7O53NcLl58R3/7F5RP1Jub6A3rJiT+0is0OAvAOE
-OVqDeYSIJCP2Wh/JQ+EPLlBuSIxnr0qW6tEebsf2+2kRfkJaz3+C5NW8D63otBHeXM31IZaM4pCc
-ZUx6uHb7o8aX8ojJO4j2+5c0q/mmcgVUDK4DjiN+iPsW4JIoiXbwbUp+66U/NMZ6hgG0FXQ2CTXk
-tSGu3uTMca6RW9xXDmugl/5QIW+6nCE76Fg/Vt3nUUSq+YRSHiK2Yo/cb0/hY2apAZuerVRQNL0D
-SOU1YCXfVL7dvYOY414v7f7kzqGmmtpiFwfBuuVAfORMvf2uXVNNkwjzM4M3ywQY3vG4CyQBg1yE
-p7i53JYy5Y3eb/3yIxt5Tq7EsWmnmSDkoYNgkWoeATBsvnHORAQa4kBqaTaRvw2+dAiHsiMewjCB
-WONgKNNqabJNpBKdNyDktfpoappGwD5Yl4o+S3TtfsJWeDSLEBk0wGQWIacGOdMkTCRMNDW9JPfD
-HMO7gzegp1Y4zSIEZg/QBDvdisvXtBE2MR9/We5/tlJtBr9SP88NDTEDX7INhlb4T7sfIOzut2yE
-zarmV9M+aselHXAsIknGP5k8jEAp/DV/F/KISy0c2XR6otITcj8Lk7csrCYYmbccN2QXzCYLGad1
-m1/SDO+W9LOFJYFvjw/OAqYaKzxAcRGcAZTl6m5gVClDB8bjXIG3smCe5psGZ6xnaOtXgPiWRt0H
-/XDYWv4OqjJsJbxFA9VGsqXM/6W15o3TktehrdZWAUSpWCI7YTCcd+5i78w3uS8gU6m43WW6KVCR
-trgB1e4unWkx4rGYd3zMa5fnauf8TYAYcRPYHfjhGVX7tueHKSMrzLmj0rxGynKl9TLxkqF+vkdv
-Yoc/UbbEarmGDTUv/Ml5MZhHiGy/0b3ghxlEfrDAgYj6UVhsVhLlezhXyxQucZOm9wvwO2qL6dU2
-wHD8hMwiGDgSCMZZXYrv+Lo2WEVE1tooDeGLOUQPx2xBxS4igaiN/DxRWD8T84vFwiPZ0hRVVNMI
-/gqb4Yty6wZqb7bBu4dPiKsKluOYNEF9KQ4f6cAXJhhnXgOtwuRBLbS8YhyHN66kKTbzI0tJM7ys
-aoQHQBw0Q1rfxTTFYPhNmP1L7gcIu1ufs0Q+fJIYT73VPjhBcMZbMOTNvL8DvOmftYKzRhNCsLHR
-IhH3lgOTrcngvrmF8SwwSMB2V4QQgcFoDWmAC7tvoBenG0daB8MiBxCOn0iYRWxOkvFnw0lgOzPs
-c3LOA11Rpl0jT9RpRx3X+e8g4uLejol6KxskQHR2yIB2j0leACAYZYz0t7Zpv0t6t9yq6ZupA2hl
-Swy3JYBSU6nO0oATIJ81jDD1e9e0olxpAaG3u2AQqY4DtBGqrbW0davD/HbwS3kIY1/cxaBkE8Jo
-Ft7pSYQwCwWYdrSV5bU51R3NtyPL4ZJHMvC997tRndTFXumVN7uZSOzLeBynYTMSNHQmGnaaMEuz
-qLFSJcxjbulM+Tmf34Lft98Gxura4QS/DIBzs1+YR7hWeLn2V02DN2s5NYJNIzh8ppkD5T3yBIxD
-+6BgGntuaC0az18IqDLBITJ4lDlr/boqhdcG+EsJBNsSbA4/Nr/Juz1waHybDXEFspQmJvx57vjS
-nKQMzi/LIb8E2A3wy8C3AOFMMzPXBEMzbHeNsDZwrNVWuIPfH9OI/wfue42w5e5Y1QTDmsAYuK6a
-RFTzCAZDgjiZ4qqgvgJibeGbC5S26wQgnn4SufB3Og+ii4RTQoX4vGl6S3CIVxH+Gl1+NW763Tpg
-2dPj9VcOS9H6BpilofvimvXlWNzmkDcHFdE2Cf/mxvRh6n8LAA/hOS0Zswx5AaoKRVttHOcV0WD2
-O44gi1GFdHfeLMajx+Ni0sb1Nc8n5zk/FWjpxXSD5PRbRfF2Z+RLDH9qzBj2JscQoimEH+baz+a9
-biqzzBH5LdMrAKZ7Ma0T+yjg+WjYXKak9/I9Xeb0Sklvk022lyLtK10Zr8LaXyGNG8pppHFbzjdz
-n7TFndI1f661/C0Q3GyEFzSgIn64BINhZ9NmqQwSMotwABymFmQaocUUYsXxaqLbTMIe9IzHg06P
-UDAbvrb5K9PY0to0G/0Vkf1mCmtFhEAwaYQdAC/TMBeQtT+vctr/ztrg0TZYehlJUCynGYT5uGda
-8tAYYnONMINb8mOf0Jw2hF0jnCYQUuyCyxuEASifbxrkj7sfIOzuaxthwyYBB8R62gjjE5NariJC
-fmj/DlAU/nrd6/VMD9dARgi9HilygIdg5orFQi/ASchkKQg1vIRBE7BAuS3wz+D20AB7/KGjj08x
-o5E2H6OG5U5xRTQPEjHmplxpDnyAWBHUAfNkT8x1hKgiYJCAgGihzWOZw0maD/0oznp6lQk2pCH8
-W2na0qznPBpKbcx8W5ZpjoMmLbJtuxDlVeByQpdJj9kaMJS5w5+a4ZLTZMawXF6EBHZ1KbNJWF/a
-8KntZdg0p0ZbOO/9iRm+jSjTdSHviKclQLyD7n/TEvNdjnVGOfgekT61VGhocwHbdQQqgFKqgzsV
-/FSEQHFyxgTDCXok/NnaXXd9sPisCa5l40eAGL8ExmQOsVVzQLqRrh0Mq29n9vGrm8RZG+zXxzag
-fExUc3vhbm3T/oLHP96fULVvMAhbYfPXmQogjMlULAqVERTzeHI0E2YbXVSbABjDtcdxX9c29TMT
-fCX1I8DFnA9gdy6zw7eNcSapEQ46JA0xwPGW4Vs+oo0FGK8Z+N7y75NEcjMcA2I+KaJstNQ8VaKD
-4T/tfoCwu69PjVBrwFc3KBZ+MqYnYuSDtgsAeNQIE1trsm+yKeVytcym9tN2sHrAL2Kpa2XiQkA3
-FlYI5iptGASzSYS0a2FFj3wGvVOayAiCi6aYeiGSzLM7/aeujaEWk5Qy19ymJuKYER+C/uIG5n0r
-YxRvLc+RZjXfUU7bHUuAar0ikEs+15Yp0sHtBSYRJgyG8Unfqa9vZhIZHojiW3fK1UjgkyE+Aebs
-/22WzbPUjkb1XnW5HcJahnanK4WHseO6b047BbUCpW2IoyWBfv+uljjv3+9VMyn5bn3B/NcHqqxz
-v2lrACou9WGA+aaJxFt6kwRbsQHJ40ILDK1gGcpOnTr6z7xKc+9oTckvL5rhVcOcL7Sf4mBQAILp
-HH1z+Wcu3+Lqm+Me3SAYCt9mCqGjWcT2G+UP2nLAnBvinKDBI4JXNOYMoh+IaosOB30tA9ZV1wjH
-1+X8a2wLgFgkNKzhF7QZYdCJpSiTs0wC3Z0W9GZuJhFh0Ob2PygTadwuy/5KBb1imacCYRnBMX9w
-ZRU/m0dUkHwzi/gBwv9D9/2pEQ58/YtA1xMjEA+GIPUagJjq3gvN40em3thja3M/uIFBjrZKgg84
-U9vaYI28YWBPWCSPSGKm0liML+SQZrj2zynDERg+wMojm3GuBobL43EDwJ6WO4w9T7CKc3zYr80P
-zP/xKg5UGoKsU3QXZRgnBgmXokO9vZ7ZFXpo1Wrzf8xnZxlzwgIjFpH+vJS5GfnEzYAYdl2xWY6O
-9NsmERBw2UpKjfiDno6wfUi/jbO+ZjrKfJtHuF+Tw2BqDqHM81bCSmEtVZR0blWfnmuLysMclbcz
-D9fJNFS6NsXJOV5Fg8zxZwtpXJTynwDYKO1cuaCtM02ptjgtovvRNlVKt2ClyS6zbuTPfn3j3+GI
-b2DXAAwPILwynbXEMI9gTbCKg2EAY/BN1wALyz6rYNgHJcwhnptmeJZ6DJ5j/tBm8IUCgoX8jaOF
-5hhRzNHq3WPY0N8GgPcD+dZw7pMvIH6s+EMkyQvYpXwiVYMcoBdxoDsP8+Y4dA1hHLuG+4tJBbs+
-Vl37OwJhqUAapqL1lAhpoLgC4EkT/AOE/8fu26HPc4EJ2EoDu8gD6tzUw9yucnJuxBDf12qAqI6V
-DmSiY8cimw4ACLwhAO/QBmnlaBFlu6i/nDa5RySeHKZdi88TTFrV35MR6D01w9LCNS9bRasgTjIs
-JEqsXYvoaVdHff1lJl91iIMfbSlpFzIp7jq09kWeWu8X2TzztGKsVVDpj6mJycH8YSxQgSIPbcAi
-AjT/qwdFumBo3S5AqS2Jnj70aIA9bc18LHNGXssXkP/Sqr1Io47oMq9pzXuUs4iV0pXCpXED75ho
-8YVoso86xFHYhri2zmqIME5vz9jcYZzQKm2UBFoUpSGmVa5Z/nZnhlzW/LtdScFCeVCa57IC4woO
-B+5R/ZrtToCLpmrW0eLQptSmbsebwkPuSQW72wziScAIBMbOTAAWt4Ikz9nAQZ5iK2WKreDh2LzF
-8dG3ilSzT4ef8vUyJmL2iNiz5c6hZKlyJDdn17HhGWHzi7TS0JAXCcg5bH7Bgt58IU94opUUvMWK
-wQmoAXNYy2ajOr1K+HNj5yYH38dhIrnpEwozTbtxv6YJCV9vfskHmk/s719wP0DYnVlfrbd84k9Y
-Rv52NYn0eP3NV3D/Ks+ENwuNAkMlQafMwMUkF96skfAmYNHjJAjjBUuv8LwvgXFKfwj4GJ5MnSl4
-OZwfCjeBitJj2Am3nwWv84NiCPDG6xmEr+neD287f4K5XrPteZ3ysS0WmKGlcCFQm/OYcTkKlmlt
-UiegYS+hcZytXK4l7SXtqD356+FKPS/5dl7Q4ZytrwNOCE1nu0e/5T2cM3dbdzXubCEAZk/hMXjj
-6+/jfLqpXbXvdSBh/pACP8cNQsfeGsg36wD9fbAgm+u3DcCbkMcHP94iRTm9vN2aOUgZx+kBgfIn
-UNx/Ip8yKNh5wmY9hkBbbUMDylPelH6W62/zUH8FJ/ddIpwWfSSAl/UMk201EA8GAfyU/CZxvhf9
-NigeZOiZNXny42/7lm0zB/888Y57ZH9WdPsNcfv7vNuva3/Qomi2s88iQxzmj8KKdIzh84j89exz
-c/969scl/npE/rLw76uH40dh87AYi4EI01AcebBApjwhxzE/wnUjjeQy5Y10vhfmQiTkZNcE48aQ
-q91EAu2o0q5KujmOfkrlf5cp/gPuBwi7s+dLICxygN0RFF/SdyUVmfAr2i5bIqy5OJDehS0vkuIH
-IC4g2IY8Lhh5YcRNLcrkVaQ0irSnJgmGxSr7TX8Xa95IZ6vq6Nc0md3W2NSFqLRQWftrSEObKW+E
-pbYV+DUAbWE21trKVyrT0lO5/lam1l3mf7YvGMbzbF31aI06WvNdOOrKKt+alyTyIW+n/byVz4yd
-WOxWzr4OY0zezSTycYXupVryc+qBDb8Am73OyU1jZ0O52EkflWuC4gAD4nHtnhOCFK5O5wHlTDTn
-/KACPlRAsqRO9hjjEqdnnNaU7+L7l8kqcN6nPCR9WBaLik1aa8ryHQBySbcznfwV8FJnoo3U3t7h
-CHUt8Oah43zj3gforX59SAssG/gq3zl3W9MPoNf8XLUGfh87ga4SGPaNe4+ubXaAjXyxoW/tzwAL
-ZIRU0DsBZMVHRlzeUbyJONA1kb+2VphB7wGQGfxOYZqjGOLABSlCM91KmUojnc68rlbPTquSyPqN
-JOVnke9NPjIoFjlBcME61ITpevxYHphGHf8Dy4gfIAz3WxphYYJNQkiCkkr9lMaAeQt2K/Q9Mfo8
-pSHzFIGhdMsAuxKCpixEejKrmzhkAw7U4SXiGhRt7cr9m9PRJ+X4cXBFgrNqtgM2oQDEAKr89Iow
-Fm3sPGewXMLo1wl+FfnIn0KyaY05PgfEy9ZR3JGWY4LxFqNJBXC+j1PnEzb4pnytJR7TANS17jNc
-mO8LQC0RBSPomObTPTrMkPZCMRvnGnoLM8TIMM/koAfW2vYOQfheiPp8EsMwXl87BrZa4xnsHiAY
-cVTHVHubtzbscZ/SCQK7wU/A7yR51c6jtRrwL5QRCmOOWYjS/WMZNQJSmrMpT67jBMEHpXkd50hp
-nTzmhQdDp86UcQymcNR9PgdTuzXHoOYD8NV5MZHZBDsXAylQ/AzZ/VU4xJkfcUafYjLxvRxPVLR5
-7ZI45tDD4sDYlm6w+zDo9evTwvrIs5asApYdELOS5AJ8U/ubea/aYtb0/mV3MAxNcdEON60wicMy
-rDTgASIbDXF+TphAdJHB5T4sp8/KIX+K5jf8FhWxUqxuqrN+6yR/p68uGffbilqOr3+fD/599wOE
-3b0Bj56vP9WBUk9w3OKQl4gXt534pTpCuwl05AliJqZ9EBjyHUDZzSBKOuk2Lcn41Gjn4sA1QaUE
-gNzxviyccx97uVkoRNiSybnX1KqmOZ5U942PMPoAAEzh8MsMeNG1CpBnwMyjjVmoy3/7GbZJSx+B
-cyaG7euY3obPu3Z1NvreQfGrA+0MrSkj0Im5uLPMkY3vYyTPiX5GUrqE57U1aHSRbwArfZx0CPzO
-WH6GzLd7Aw0CAE9At6fpcEMdfHTDNuZa0rjDFnwGTWDNvrU6jjDdq5Tnrko1oYAZgIjzOMq/76Gl
-zljHkpuGp/hzMNrIEA8MbfKkGZ6kf8+Lex/H/njQDu4ZHSxg2F/77z57+sl5qV0uBCBUzHki83do
-hdf2x7nvCgC8J9aWiQL0Up37fF3d2uFH02/q2mENswcZQO82hfDj3Mg/g+H6Kw8GBIRLedEEt6T5
-laYFlmYaIQUAW4Ln4Nn+o8EvgJZII+KueU0q7aSng+w6v60M5gZ0S3GT+UMo+0ieGmhENp2doLdd
-wR6iP6kJnpbFn3I/QNjd75pG7IDF5QS8/qdcOV8Fg4fg17xPF9oiJG94xYB/OSfv4Be2v3cQ7DtX
-NftncT3jslG9n840S/sYvYiIEUsmgRd+NcFmKVNzAK3RBzaP0JyEXTwW+Hs6+pGCj7W9uzGRxoAZ
-Dc1hl2reci7pBLkcz/P7DpxFcl7Z3QAb3/NT7ESFU8kbgyqM90sUxyAnvAMdBMlc6j3WzaUp/204
-2/oCEmMma4ZvtME7Yyv/Ww5ltYQZHKdmWCsIxvV64z5RUrXEPAm5vH2Ny7T0Sx2cNs05jyeOkMu4
-uumNh1qVNoN5AgA4g9wI0zDklU7SkSkd/MHrozEsPD6WvLZx4zdS2XfpfQl2o5HEShR+QIi8qlRP
-90upv649C1mgJFCwIXxvJNtAVR8/9izeNGDS1BXGW7iYblCsMIaGxvfRwSRCxfQRXWvbDKueJhOI
-g5/NGw67Yfq1I+JUVeyhEzJE0jTiqdpeIS1wgOFiCjFohWlIjckAJ0Zc0jmuzBEnSpapANguZbaU
-QX6A4BMAp2xF3JsdMSvr8pYDwMXDUGsaX+mAoD/qKszfOwAAIABJREFUfoAw3G+YRuxrBb7mRCZH
-uhMfOBxR/iTEjZhRlzORV5nWKwm+aoVRv8lnkEwdDuZ8gOBk8smYm8aWKgMTHLZ1ZAPVe9F2y0Y8
-LabD3KG0t5lDIH8DwEUjLAx8MZ4s9GjOrObnOeBBiXoOyWU0l8aVhv9mGtzlF+c607qr1DTlyV5/
-qutSvc75a5wmnVF+7YVa2U/pnKf34m+FI+K8c++P1uAl3z/jku60xqoksA0tWfqn9PAP9zhJofXT
-8rac9w3k8ql4R14Pp4IUe9clKwFrIDMPEWg/qS+6W5ubA/t4ZdvrOsc1zWVumzl5DLbfanx0OtOZ
-x9iRTmPI1Wm251gMRmmtb6kJlTJ+U/uD73I/yLZOH922vTjWMjTBfpqP0QePoFXW5QoNc9Br+0MY
-YR+sEb/9G+yqqth6RJduEF0AsRaziBH4kslEjAHuhbgFDfGWMfr4OJVNcFa0w/IMaR0oPyZ7s9wT
-w2s0dztMgLDweQLIKV7LXCHdbpmsZS7586ZFrhcAjEalZlgMMh3AWMifNN1/cQvCFT2N8+znrutK
-+9fcDxB2961GOCb8SwDM1D7Gyx2ajMI9JOAJfgqBgfDAx5KfxcKEjWo8qUWZ7ang/QTBPcyN1tZP
-tUO0ZP5JEqEStFPx6lIvADfvF/bC3i5t7T20yd673gyRLrRa3JCPpZfmqJX5KvmH9NAeaysmvPEu
-nbbhzHqyHMecFNbjEZriz/sccVTtV2VpzkGr8UGKBhCM/AdQgyarFZOzmunWNYz2NNRwgYu1X3qm
-/9OsvdwrTCH4Tko/5FEpwLeB5G/amGO+J6ObpvwOyEVdHM75xxqntin4FvJojRMRbJgy9Nnj+KSM
-c/3OLGgCv51eMqF1HJlF6tukkn4u2v7wW8wgGjEXza9Rf1QSZGPgoB0uroWxuFQkji402+e5LxER
-1wCrOAZ2cGoEVKHSWyv9DnxN1b8sp66Z9U1yqq4VVpECem9hPcJhErFA16uES36AYAfFqpbtExm0
-vZamDr6J7tT+Dlc/Kxnyljl9itYen5McvI7KcFylHat5jcpMGmKv6B0Ap5xM7fEQX7S8k9TTxEWU
-LzTGxNc5z590P0DY3dc2wpJ2OGwGweEdB0beiDEIcAeDFWmNL2lC/JUyFGEPgKR5z5qm2+TBpJ4a
-MYDgHAtr7UUf5ms/TzhecThAiQ8h2PFSsoxwMFBvs3oYuiEfWdrwJgFusVi1zME9nIKtmz5ILOlZ
-YNoRJyKFrWWveM7sSGXpp8o0cJnra5xlvJ1553cQ9/js/d1Nq+Zovn7I95L2dveggyFXzusZ7nXX
-sI4AaG6znr5LwY/c5b/Wgmj1H9re/btuEkKW21lqvnR5ICNngCbUQWNLoA3hQp9K68tyGA5bYm9j
-gF/W+oYJRE5cgl/NPEPXjrWtFQyLnLRU47wj2fDMEeyRKJTlQCmT8UXz2xqhVMUBeK31RzHfNLWR
-tfXoWCCNgTzqYNh5jB9zuUkLWmGiJzyUOiDdwNr5uGqAY3UNcGiJ9WmA9w6KTVV0JeCNc+ancgS+
-FfcLrTBkjg/AkxreBMRdE/wFEGaNsGn6ffK2P7kYy2xII+P57vNF04R8RnR05JVWGYHaEwAPJhOS
-8d1sgunuuAJbTOnKS6JKqT/pfoCwu987NcIKgR5HngyAOAi2aQFADKG55HvBQ5KiC/eajrb5oiMC
-PIiSCTDiYDPsBFkYIl1LP+r1/LgG4rPV11MtiRnvZuyGhklEsAfkIe0u6qYHkB2WKDdrh6lsbQIJ
-xWxrX6Rz+smF1AchxpSlf9zzBM/amMic9wR9tzgOzRSnLT+PyLeu1cHzerTs3U19ueURkXpUl5xj
-cA+nLejn+56xM37Ulzr+eZebopTuSnGIP14j53zVturg8zAyJ7twYKZ1DXvNDIjB8wKQIZ/SuFuO
-6U4j7bUQsI2w5AbAA/xSGPXzvWgtHTzg8PeVpIW3FUabjIoKMIGlEFDRUGT0G8dYGLWdGtw3z0XW
-Ps/Cv8F1orfKESr4df4MTazpeT8yO4BJgjn4FFUykdjgVB4H7gUQqxRQvHo9g4b40B6zPbL6B5pW
-aoWXudmHb5ZrZg7STSSsgd0hbwXCfRNZakNBUTdAnFOjJMtJGXdmrJN3SY9yJgX8il92WgPGgjQb
-0lgS1n7WplA+xS2rPfGf4pfsfoCwu9/aLCcSQLAS5B0QM6isACEjKovdLoTMALF2BooEg9RsV48T
-ETKXSO0vg+BJI4zru2bYm9GKx65jCEpIPkbqLAAgXcmfZgwOjGl8Rap2GLn2/6YNprx5a4xBG86j
-aWRK0fOGv7Ev5XbW9GoGQ+XoiLVejocN4X7XAB8cF1PEbGZiO7+hBdYh7pafB0r6KEkBsAc9VFLY
-nr7pKOqtGuLewzNcTwWY8qdT+lv7c7rahlf3X2uDh7YAJE7gdwLDH8VPpt9BcMZztxgsdnyoXnVf
-cwmUJSoLsNvD3lf1eD4urmuLd5jWEvUueAnaR71OPqEHjeSbJeoIv7kDUE1mGJ3cILcJgMb+MLbW
-G9YXYmUY5xhy9ut0W87L4MBbFJWgLep9G8DwFjVpvsDa2AS/jwBUG9sBq0oFxdoA8lNAsa3HNcR0
-2kRoklfaBdOpFfF1OqjMGeBa1QAfoNfsVSuM0cK0VcC4tdGcnrMgc1yJsJZ4qURE7MAi7inaX6Fw
-TUv80tPyjWw2gfqoKFvBbtgD9+Zr7dafcj9A2N3XGmHJidr+Zh5BcTzRQvnykoBndEoEOKSJCGkj
-8v4iUjBmpDmKMr9pAuQhLm5r87WB31tebl/9ylzb3tGliz9lAkh6beX5mesrAFckFuf2B9uhMSHw
-DOFsg/BjIc2tJwF2CpvpBAkpubSkGFUylFWRLivP+56bK2dAdwy0fAK+XMbm6OE+L1VR4yb+/daa
-b/LhgW4CNDWsH9Lnu9ysB3r+T6P65iaau9/KAYTQhwKATkbwWzcSfQTCGBBLcCvlcUPYnD/nNsn3
-tAOWtrawLht4CwBIQHbzpxZmQDyBYc3KS/20Dvu6q/TDf3lgqHPJlAqR8DGSx+Bc46SMeyeEGFNn
-UodtsfC4aR2X0VltS+k3Z7MY8z22e4LSLAKE4CZGhfYkwTBpidleF+YSqRmm32H/e5pJCEBwHMGm
-Uk6beJbfw/bmOdPYtKfP2kCMgWzR/joYvsY1IExyK68Ag+63jBMFWKy89lj/k0iJe0kq53A9yqSM
-jAwN4GY4ah0AcN6ot5WbxbcREcIm1HeO/28Y5990P0DY3W+dIyyV0Dg+eCP9QV4pZSo0ORxLDIrK
-NC5LC04rPwVvY20wiDBthtG2zcisVxJMklbYLe3Iu5kpA4/tG3rdoxRPm1VEgU3gISFSGfBGM63l
-r+lps4u0HLMitEWqMPdCLMyjbs17985FW6KimidYwysCOk+r6OAh78gPI+XuIjSub1Cw96TE9ao+
-5ceIH+U0bhvpPgelOeSvK6hbClfd8NnjSpHdLviwPP7InM8MX3GUD9rgz3U0oQnAx4A4wO4JgBWg
-+NKHW+zGO1qAr1KaUdciy1u8DlOMeAd0J7gFIBu0w2/5ZQLAyb/nVVLNsmZCZGaaCUUjfMTlDNd8
-OY6vYSx8pXTcg7WwRXhcFmtZABlZ+AjGz+KPhCZYcvy9AeGPr/dRm2DiIKud8buobAPE56a4DoBV
-AgCXcDub2EGxLt1nHj/7KirVBtisaYgJ7JpVUwhr9sQjEIacZnOCnPdCPu0aP7v7U/4OFRxxM/gt
-J0VwHrmnq4l/4a+C4ld9GfdJsy//C/cDhN39HdOIToxS/FYntvg7U23uAEh57xMg2eErQtGynDX/
-zqMC7av42b1BykTFvBktb2KXqxD4qisw2HDpwwX1xa1m2+iwBwbj9zafLDzzFoFmXdhJCmqaL35N
-qSIVELNwKL04+y0i8fAxpqscD2RstpFC+pRd3Odbmgg19gifaZ81xVT/haDfGNtbuan8ka01v59G
-QNh6LDJU4eUhvF8af2/VV6P2jzJ8AjnKxDiB4QaKAT7woZscj3PwMK7KAwa69PJxW0n2EetCyW+X
-eJFTOxzgVj6D3XVLZ9MKo3blTBwPxbQW62qwyA0+sqOx0ZIZLQ1cicsyUadJGdMAtz2cU3LwnM3O
-QQMaY5M0wCUl2hIPfkVdT3fC/amMuOIEm5kzQ173eJxhzFMxlcD80RFnExgu2mU/Xm0DWhy7tjY4
-XjiK7fG4R8xWaoP9Qx4Ih4nEpA0+AHCCXXvx+6z4VGuG4y0sODbsaV1BJRyP8aXJruLjQ9ylbNH8
-Ev2ZzABZMl8ByO2tuGQ12U+E9bhVxGFsRiXZv+x+gLC73zGNYJ61y25GacITOvkn84jmWIKMaaUl
-R1onNgnGaAR6pRKfUr86KuDXKH4DLR2wcq2CIVeGi+OLuw2IDT4GLlYAtwnfA9rkTNtlaVEaAVSU
-pXEJzZWR3wUZa4LrkGVLlTo/fYFu38OIgbQ8VcodjwssyPu0qZztaz2N8GwV+fvO5huWds0FRb5p
-wkQDn6qbw1Xbm9pIHfOf7j31azb+QRv8RQXVD5pk7ZxofT0tKsU0YiUgvrdGxznSTI01IaKfNcJR
-ZTuRhXhWpV8lMNzA7ZLUJAJY3cDwahVL6uiY03QesttQ16ZxXL5Sq+M1xlEnPX2zfG+/y5IDUVC7
-A6N63MwXJMakTFbxN+f8P85I17o3Q7hd6v3X2jDFfeN2sBHOPpuq6JPzFtpgAsH50NN+rAmOeWXN
-bwJggN+qCbbtPwDxCkZvXRtMmt8AxwF6SRMM8Mt2xd5fwRz7JAfl+SuPRlH1quTpDyd8fYkzSXEM
-vBK0ZR530w5HpjNfOTWCxHgq1lqzDCus4hC6y/f88x90P0DY3e/aCFvzi3RCEKedjDx4Y3f6ktgY
-+JTGRDRpgxFfgLGYnEC5MnbeXAbhEJCB0rQPgrS4mytru+khh6IVutG9EZZsH/HtGgbAV6nKGdQV
-Y6ZVOaJNeA9NnIRr6ZDmmB55nAa0lSTWcQL2lq+Mg/HdG5Ip957Sbv539w0gnnj3UeSlKdCaIH6y
-3uS4tx6P4cAL3/X5W/fvMPlso/JraYGQdZDA2l/lODaPeL+DtlHB2gEeKrjLclyN4o3qAi+Z0yUo
-XkUIOO0b2iLQFBpDITDsFRII5tNnRCR5XFmJSaXoB1oCwBK0ZaAV9yBrZypBYMyMaeB4YZQz3Py1
-Mw9kGVQfN6ucU6MabdcLDaLNgYSjAVki+kB91dIIqjAW0B5d5RQVW1LNIVT3EW0qMwDGPI5a4QqA
-D782EEx+teWv9iEHfX54oxxA7wF4m/a3g+TQlKZWN7TBofUVCQAeFJY8q8t28+zlKnbGWc2fFaa/
-A9+qHc4Koo4JJB8aYdIAW70tH82KviV+on1KE33+y+4HCLvDa4yP+ei3Jz4nlonv4G2dKCz5xCYK
-u1NA4V5W4ykK5yHG0yUvIua32tqoUsBwAZdE5Eqdgl1Q7Sh30tN7n3o/bEg8ypwDc2pCu/lEC9s9
-nVj5DrsU2XLLQggVGbB51yF8om0xZ0bp6WcIXM4RJnpCWtGU2dBe/9tBBNIqPERqTfldd18tdLfe
-mOF+hRwaE/yd1sVQk/y1Hm511jDB5sAzf398Prrf1Aa/sYb0OPV43XWjEvzLgYZvOGLtcK3xDPGA
-BUHmuggMbiW5FuOlIVhD5xyVhzwHQcXOl+1KGVTxK3cA5aI13ncH1Cir7QDFtF5vaV9ofEuaoEP3
-ssV6yjCXVrLzGAfv0MuaY6Jmf6fvGHwRBsOFs0T9O/NoAlfuIS1+jz40wmkzLAMAlg9h0AK0v0pA
-uIFh1+rKL//UM2mD9dcGrrp8sxwDWwK3wmYR1swk7ATKAYTFaZPMISoYPqUYr/eUdNYjz+uYdil3
-A77+E6sRHfzeNMJxm6xSjNI5XDYQavKHP+1+gLC7bzXCIjyJ8yTLEC54sYW/BcEVPMlRBoyx3JvD
-hf/SQjQ70kRI84unOKtgmDuh5abVZEG9Hiz5Eg4/3SeKDitLeKFUcwiUfg+fYBN8O4ATj5eF7CQb
-VGZWFnnqdGSobqCrabVP+boohXJtJ/ehy7w+FlXYc04eh/fSn9yNbEueRo9fVcrYwZHW52+fQbDU
-25w00sPTprp/hx3vMfjn6uZNUPn6eYMEbJ7dr6wZCMIcooHhw+lJCpZZFR3CK3HwC800FI9x1SFO
-cq41q6xlymt0ib7kcVjSNMEEgAGaYRohQuurcGWJB/+KGPLYR2nXbuPbAXHR+Pbj0pLpbhCrsfaL
-UhYoAXICPLfwlDaWxU/9dk/hV2WBJDOzWHPDKid+/NtOYS5RQfAtDBo+0kDzBH73Z5nXBryHFnht
-ze+yDYBNA9jpcpAHrSXA7wUUB9BlbTHHMxBWFZhAQN5mHIY9F9YpHSqZjde/k+a/AnyLlvcEvgx+
-2bZ4k4MxeVZtL5kAxjIhuVCUdnfK+dfcDxB297ePT2thmcIUlxUBZWXGgwBYQgzxE8FMRIbwRJTc
-lHx9Q7eBwPB2FHA8dphAsFV/NnLwcz9ZkEjzE2jvQC95hbdZozR63sI9ncIAxASQxWAmkfMG8FHn
-wqi+i4kEnRUsIrSJruWbypI7BZ+N8VlHCnIrcTeX6Z9y3VtZMwZNll58dsZNdf+tZO9V70WEVYbw
-CaZv9/ht9wUI/r16GcTngFTgC0AB4Jvgd4NhAsIH7tHTZzV7gDzEYRmrELVl3xzrnXGtCdEUFeHX
-6OqANjW+2gCvSJpD+HWdm+WS3xROnfyN8wRqqHGhHBCtvDERjhRip7T8+FBj6L5pQ8Hr/NbqA2QE
-iJWKo3yfQs1eZUSnbgZidrELbiNyjbFbCvM/lSQY0OTgl0s8/EKb435pguJnVe3wL3NA/KuZUNhO
-M6PzhAnwNtBbgTGdJHEDx6EJbmuxgGGPLyPl88FkISl2i//legz9UVn+Jo3vpvEpTbxM0ou1tuYb
-BYl+5DLINE4fqOmPuB8g7O73To3QYwLLb4g7CCGY8OVGwYNsjK9tskhkXtp56xEWLBZoh5NA0TQw
-dpMEw9z0qybYkvWq8WkRTfiUOvu15jeqL4qiXhHhc31RNNi9Vu1x6R+FRbbQDcDtmfIzz3zPCq07
-xKlzl/6riQTnUxKU8gIo6N49bwhK8nPJqbYr/FM5WvrmTqZ2A4BT/LdxZ5s+ze8Z1tfwP6q9/cdq
-EuGeGLURADiOq2JAHFo0hJfAVCI0bBMJlDjNOA8q5dvYRAvtTbRZNL5DPmlp1TSCANEEhpdrjVkT
-TPbEtQMQ+Ihr/CniAF6opXxaQjJIwStwHDFX+ST18s1ezTy9L2SjcrjYlkmVd9zG8mUdHgXAMxMg
-ox4eo1KM0JcNcSXrof1F4+UAuxUEC4FKCRthe5boL3MtsKU2+NcSsQS+upYYgV8xizA0wqwFBjDu
-2tA7QCbtcYB5Ar0GMNyHfUeQtAzSGEXJp6sPvV2uHQhfNb4i+6xlrsNBcJShabZ278PP7fD+RXhi
-An/A/QBhd9+O/Z7EfJKJn30f9i9VvrTh3ppUKJx5wJZwsoGRVJvXCh9t4rAqJY8kkwZjTXvVqLVJ
-sZQNLBySkQJkxns9zT51GdPxWeyoJpfrZj77AF2Z0o5J0AqEMprivOms7tJS4SmBtIVHpoY+GCVg
-7I8yjeOM/jPPm83r55R+Heo7QCM2OdW6Egw1TbpmnlI1lx9veStztsdaFdCiFnoRPbvypft3ePhL
-Y8BUtIVDqDv9hVDTEGDKcZJgLcuQZtwQn6st4mUAu3XJHP5PcTWNgRB+GmBoIM2kRa1tq4HLbPV0
-a3nHdK86/Nij4Zy1PKwQXzIAQIsxD54Y460x/mC5EChxP5p3NLfyvE9vOeYU7lNZFODhwhkk+frF
-BZgmkBu1Mi+NMa/IKESHOgUSkIyiPlBbuq2d5uPD4xrgF7+FNbPD5foQMJx+T/qV1ha/VcTLiJSR
-bnwSwJiIdbrGuPz+9QaE7YvfN/kWwKw/kD44m1lFnqXut50utr8o6H7DGc5Cb1X+sPsBwu74abcw
-dMqDMMuah+Mp/Mgm9tdwbYH0oB0UURlvfZrcCTMITmHX79Y1xTtuRwb4lWFt2hQPZMgJWNzgYPVa
-wHEA8zxL8M5SeX7uJzkcIJWDxX8TBNrCNW8ycQdU0yB3fwufZezI8x4vFchQnZWxXDjsQUcdWRxn
-BRCwYLpilMngNEunpnLPWgGcHX0ec9TvMeU7V5W2tBMQYJPZKW/+N2z5S9cFpEkCfcv0LcxI2+vh
-AMRERKEVxtoP8EDWooaVynOZc8rNe/PzGL8tFYBffr1cSVSFgW8t+MFZ8xzrjFpj1PIiL2Yu1emd
-+XKMZcxXbm4W8bXs84G55Hk1bxtvSE6Ql81nkNm73bnYsS6sJAff3wHN8RjAb9RFgDl4fVRb13K0
-wlTwxbo6/iKJLH0sTUXlac3VmLqk3+3RAHdrp8FeeJl/UMNntGmE8yctzKC35tFQ9OAouk2nAXwF
-NuNSbMJJHGY8j0FfQNys8Wpj3m+B8GdQ/Mjj2v1H8HZp0775ujU3WbIGgAV+f+jeSkYmODn9SSz/
-mPsBwuGM/srhRziIq4UDEA/pAX5tAsE2+M52jenJbTxIDIKYW2T3Bh7CqvGxFGpSNMB7XVtN5/YA
-+EoriESgMqtBbkfnf1XQTCNk9batXbWtPibadZnTqjp1w6zpVr6R2w5HJ8BtsonFn8KJB93G/GGO
-MtSz52cul2NrVDVOwmgdo/QbgyFDl5KXmVbaqNJxV9IBgdJc0evQqV1v4TfQfLizX8xfOd+/yGv/
-PdcJPh42qx/HRMXiAyCWrV3D+gbQ4uPNxIVVkHnwAg2a5rXGTTv9WoT7PT/RVjGJEKk2pqg2w8wT
-IvzNOIb/tjYv/rzVQUN1eelBe8H7PLDHN8c1lj+BoATAFvGRd+ADM8/W90EZ2S01hjXCBIZNtfKl
-477V6ZHRhcBlE7k5Dat3uDyQmQge+vLBwMRsxdgkGPb7MEiGaUTJIwkCJeMS+Na4vQk8j2RTvJmB
-aFBqq8oJhj/NBYnDwLgRl2M2AuNI8/78V7/cUCiq+whogF6RDY6XbhMS/4y2LatgGPbT9Mlr4ye5
-qf9T+L90P0DY3WOnhlNkZiIMbguhtTRogU3sAxjuzipxHxlJB9EaeoLgHbb0zmsqJMep30Cfbwy1
-xDEgLBlA3HvFG576BVphtJ+zzozwtgqO9mnmrm2eR167jwryK/iCx4bXWQl0iZJui9p6nF38vZwd
-8QxaUFRbOStPGgMABXM+gC/79QC+CUw5Lq8MehVlRVJDNE1Jl9M9jzbPZUG9CWGuv+ZpZhu/yXjv
-a/sb9+FmE3OS9Mdyw1yT39yvAACPhVYmj+1ywGuIAyhLMCxSeUI054Jf2DTgIGmni6vc63bBeAuh
-LQ15o87iKXbJx52s+1tDXgAwj8EOV6B/pNObCW5hhI0Clk1TqxkPANzz9z6wG+JGdtt4Dj6yMYLg
-i1lEX3/B7/ddaxuiQG3MycuMdC57Ys1ka3ZhDiGs/R3A72PxiWXYFCMJqNG8vQ1FSkGgeMBEOYGm
-PpU0Xb6FuXATTm9pZT6oOdmubBY/NIUpZ0sHmP17gPgp4SXi4FY3ABZxQNy0wtLAMECybN5kj6XZ
-zMDbyvUfdD9AGM5mvWOPM/5Z9Z9aYQvga3KaR5TbH3euifGUdG0+mFPdivUmYE5hJeiBxME5nqnQ
-oDX6PCVBJGaVmpyMpGWWz5etIeRw78JfZyH0dj0KvDo9srFumO2Y62s+uQ/uR8KyErfrsqPMm3YY
-Hp6bGF/k6SqHzmS4XBmnOhoJNvJaQTHlozhoC3YRLivzvOgQDfl7a97FjbRgr8F622+QrY3e33Zd
-a5hrsD6klo1tvDjpNbtSXEmnSU7bYA3a2+TCtsOpMcYnfCe51FnBDQCLC8eSFnNbB5uP0Aqi6CYS
-7C9vIkpVAgjKS6nezE4/854OPmrF4SnjoHqES57DfIIyW8aX27b4aBrFx5Kne30T5p6Uhx1rjRjA
-78BKkp+xKVwfuqkgO9AkNxDN0CX7pfymnbQH9iP0AgyT/3FTCIDkx/bGOrzWD+Ccgwnb4xD4xS+h
-BUZ/oWFWUcEGOe1XXXUYb8CP+0zNkli7lTYY/HIzKyieTB4qwP0WCG9Mo2Ea8YgINuBa1wo7GIad
-8G4f+JJWBdgk5/4b5npxP0DY3bdje2yUs9nPGmYGw8iTYHi+s3UfSZoj7Vqug+C6qgwVui/z6pAK
-pqnHWhWRapuI2MLvwHHrjupgMnoy3ipUa1+7wJ3SjquefOUlcMQd4NgrO3baj4bXXq4I2nYtgraX
-te/ijfoIJlnaJdlgI4AXk0xIsxrwSh8B5A/bTQbAqKv5Nzip8RqD2LpGtymuAWQb87XxlxbE3A3V
-T4T1iT9o/PmuvrcMEzns4AgxZmLHFGOOQypqu5KWtwHivQ/AuZRV8wkjEH09bbZpPndc7UffSFnn
-ctb8xkNX0Fvzf+Fqts5leB32dTekqR7PmPsepBlW4pvFhMP7jfZbA7h0myMNTQnecnTnv/OXe1mi
-tUkD/JZGVXXpc+Vno7Nepd/6CZpS27aoIibx1bi1nNQ97rE8U9jfiOzPLGsKiQCMpBXGOJCf102J
-93US69BZXLmKiNkT9sN5css69831ccJYoTlxayv0AsDLTY9+fQlyx9/TNMLq9sGSphG2VJ7H4xgM
-G8wkvB3QDrdh/JPuBwi7sy9H30VHA71JrKkVtqIhZtMIkQ5Rp+V/F47JEW85VbpWRcQkjmy53jPj
-GAoXMByiYjpfQRp44iZBaKPtrh0QCZsyEwgPOp1Cs0jRGLdbMgaY4t7lo15CdUdzYD86eoDTlZn/
-m1D6mEbC1v3a84EJog2lLpvjd+NTYivo/g1AbJShLZyb5ABQOvilc1tVKZ+PE4XtuD9RL2G9sWkX
-mih9ehGwNnm0x7VbvtUz5dOXjLdaP/IjGhj/Ydx5AAAgAElEQVSdrphngNYdVsn4vEIDjHWNNwvq
-aRUY88Oq0O36Wis9wDyXMPXjGhaiH9CTZ2B/H0bQGtpxDPHACDH1DCREzjVd1ic6T2A42ifB37If
-oH/uS8bVpe/5G59gcs40PdOE5qXwkLnvUVNjP6UM0NQbCKaiTAs6qD5He9Cjs70xtQ+pdRWxBftg
-3R9ZMdjnmmw7YXMtrIk8GgA4vjSn/oq+D2bhy/UKaQvwy8eMlqFRaSBY5kWjjhZsjelBk0bDYM7L
-yzSljABYBrBH5Dvonc9SHjXC6rJbYBrhNsDQCjMYhp8A8W4LHgTepfW/4X6AsLvTMvaSD4QlBIYj
-Thz02gCMZ5A8tST/nvHh4wyKMsGFz745Y2AJllVUKAHBAWJGDpGE70qr0yh9CldOrNkBF44BghUM
-88IXSws7QB/4CcfpkM6g/Rg2vYSazysNDTHipmuJs3a95ynC+RDCaMIcH1qL3v7XAZtoKMFIVqUJ
-UpAWmrsd178OVUHx/vXbHStAazMb8VfAM43RRiOZg/sdYaIkJqraqIMubyx7qiLbMuWcMt7TX8/7
-Pa4pkY2BiANb0JkZVryGMM/1maZSihTrj/MMPJMXFLAqcmyQDCqn/lhZmxq8IumopiUR6TEuxzF9
-o+s5mO/9H3vftmW5iisbIv//i0+XtR9QSCGBZ2X16VXrJZ1jpjFgDEKXsCxjJJIo4EGN3693TG8+
-8UAHxNQZM9/1mtxZa7R7fq26wyNl86GDUg37qHjbaz0Gz5J/Il0vYH5zIw+GjHYtXv1x7bB2xs96
-9KSmzeLeXb4mxzjhBcbIu9ErHB/WeLY+ciABI9Pa/zOt+obj2jr56ukNckLSTUw5RHuAiHdu8+Fy
-7TieIHgDS5k+HgfNPPNevMDPS/5LfcpniwUGejgEyvNLQAwCYrfs73edkv/L7QcIx/ZHHuGYsPfw
-B+/1QHC3AXBTdtHq/eqf4LmUqdWN3xY0F8CBQuKqV18sOb1I9X+3p77BbuxND1rDWU6pNCppmtTS
-r6oWd97dQHVtPS3fifOOuucp95SLjfUCbQpEeAdr2vlv7l3/XfbdKA7DBaRCbEZP8lvyAEnUyL2s
-VscwHChVmSwB8O0H7A82YHiA7z/ldOUw7xk0+xe5kNVDWjz67Hs/05NwIhS0SvPU3pWj+RsP+S2n
-dUNbvMzvLJcq1uZSmrjNb/KdTPIlVjib8k3RAsY+jselZrfbNuPATchtbYgaNqBE7zdTslcy2Thv
-zMhHDd9kT497Hm84C6wMGQJQMd6Snzd8we85Bq7kErIwWdZROtc129rx9Azr+bubnbc6IEbx+1VP
-ucTXygtztH+8yeJNNwGyNHGkk3bnePN62lkhug89mTG3C6GjN+CtF/sWiDLT80tv8GM7NvhZddPe
-CMVd2fSDk4auaB+Qsrw03B70pdMCjLMJ1cHGvCcOVr+eC1lk7ryVCQ6JMgWbz7dA75n3XPKWAY/v
-MRlfmkPIwrJddgPE7NcTFAtg/Le3HyAc25+FRpAX30IkAvAiZR2PePfciUnv13T5/538XjaBytiG
-nuwG+Wb85dUdGk4+4lMVJ7q6rTiQXVAtLsqG5zmvFHSiIrj3rHXRx1VdyvRyL8Ecc8Cv+aXApfs2
-yiAdvxqVvrfv1JvWQpWxkpVGZOR3LTvGdRnySevBU9bTNkDtf/MzuXJd3485Jfqr/D7b2zB7zQXP
-zuHPV87kAlm9hxHZqKZkaWXeio7mWZJY7TCqr1eKrMscajBhtkvL+5bn0tbkV0rUBfwiPFx5DMUz
-qSnaqgiiCpjQuNkauoJl1SkKGoVOmmfHRa4q8KSuX8gseTf5o4wRcGTDykAlIq58zoKUmVEeBG16
-Knm2d/FWR7pX4/U63jr2Mj4atJnHk9qLli6K2fqx5rX+es6vVUfE2VJavA3CpeORN19Y2/zm4d19
-6rPbjgiTYKjdClpzPCtBMT/2gBEaIaRrxD/ENvLJbjUOroAgL8QZ6r7bsPtMhwHFku0L2fNOZ86V
-AJAGguENKDfQCUk/E9TewfDzqc4ToRGrgG7GC8MrTZCbHuA4znjhYI1vYrH/5fYDhGP749CI2D+o
-eNYNcCsswkFw7FKOfreb1z9Tf57/BuSajfuwqSL3VFRq8UuRnRaGxrAZs9mv1M5lRFmg6/t2ENwO
-rr0WX9Zh8GauhPhysEdPzxypniQxEGPlsFN3R2dvRud172OPblRuhlnPnUVybsaGypRmxZn3zka1
-JcjICLmY+/lb/Tg/ddvrKT+XLfB2OQQAQ6urAGrXp/GrMdVgG/uQAYMu6e0hYLx4rCZpioSdqTpv
-z3Nc2lLLp9vId80f4S4+qre5FevqUlFvjmgwJ+glRPE7i1R4RM1DE1HRAzniFLDIH2BYz2uA2XSv
-9JllU7jv29QRR2LKZyGMpBdk/G1seQVrgNWvcgA0+XChlVy638SXwp11txjINd2LD3OeK28DD9TB
-GLf5CAeZvzZkO/KS48RmpJiF8kxdn3Zx9yOP86ajrqsvr+U7bukZtjh56x5b4aE2hkOEfjALj/AO
-l2ihEUKEFzHuQ0eXa/72sLkaxeoPEDJNT/Ea+ZOSsbuCX88pwgS+sy6YT2/wOxh+PoBjepIff7Ds
-BoIlROLTb3kDxj8e4X9x++5NSPMCh3A23gxvsBWvljfYx/Fo+Uz9N/kfO/8HW5g2Pu/XW9mXppp9
-umaUUWWF8taWkOrpfZTvA7gatsy3VvYJ5F7PLB2euQWGI18b9jqvaXoxMi7pq+Gd+UBnUpdEqyOF
-t+ocxOjva56qdaNh1x/SiOdLKxMAp9Ff7bjq9BliXwmPlQeKJ2ZnUcdtreQXGlzzygJNcHN7p+fG
-cy1vnDvPJFhvPHooohNcAPXoeXqQqjOewOpIZ+c0jTsIxgUeewe9J6X7OJOPOET1jh7lF4/xDehq
-+irH3zWm51ocmTqAL4guKl/CxfQ+o3eF/F/y0D3BIRNR966fTNhg1pB6CoIRvBcgKHWS6hbVNxeg
-v2ODd9nmNysavADjGxl2X4RLePPr5LvITnWWHUc3uOoVdgG4T4Y21NJkEQbhS+ZgP4a3xTqWa9dy
-TpIkNsiD+3Y8fNG5MZkHe4J+K73DOWYDNiBePVotifmUzJLUAogJfhUEVx4SEKtHGAJmb2D41Qv8
-zPI9LgLgBoKtA9y3X43jou7+wvYDhGP7r1eNEMZqx5lXddM7nMLOFm9C9pLvH+ovBSjrqPEqybpd
-wREF0BLxXc2MSWIYt9kNwo2J2a5GSRXGKxSJM2z0zfqwTft4a+ItMzstRijy0m67qHTVoHPvcY4Y
-HpcyaFmeO9q91tHGLnUUOU2N3ayX8lHfqjUx7kAHww0Er6q3rMDvGvXUGGbapXvVQcIy9qiCd0ij
-GBCNtgH+BoyTz70Amsu5kCbZvzYVduXZlkce8Xut33uIR9+dVECfN1rN2US3qHIcB6lTfgeClVWm
-d/7CRtrrFBIb+xhL0xejXrKjjf2H9J9sMs/HDQ+RBDNVPgdNj5ljn0JW9Keg+HwyYtJANSaap+VX
-90b9YBD3GFfYpldwfAXCUnbxAs/5/l1ZidsZQ5wc6UJ26eu+/pP9YJ4jAO2ql3M7KO5Ad3uCVUd5
-nx/pC9BlX8neRmed/6lqLcIw2k1xLPdmsSpEB8Txgpw2xDJwHnROSBuM9DswTkxyeIM/eIIjhOK5
-1M1PLF8AcC6fNn6UiQ6II4TkX0DCP0A4tj8LjRAgDG/Lp7HsYWUUE2fscCiZm3z5BDIfezfy+TLc
-0gPx4j3rio93J7PJwkv5iiu28lbVRrBwa6QZtbuaTEw9qlzHyT5J9cPAftjOKvZScBr22p9eYO1w
-xgo3Y1n7BC2XMo5PjU/TBf+foLjRVOJnTea8BmUv1s36jwAGBMM0JDcQvMrgrxWgeIRMRGu7O2d4
-hLWj4Rl2dGBGF8sEtMI4B+myzPvKCoMCANq9RJ4/as+zQ4SS9r0XNboOdnTy+rElFY5m+vynMEvB
-6IO7HlWkfoJgo+R/Br+NjE14uiQdL8QZiXPbR9pGug0Y0dsaw1H/t3pChNPRATFBh9JP5XcMPuVB
-9IxzTNMLrLKRN/F9XHOsfsmr8feBKgiuPg8wpWPSMspWAqzz177Y9jy4bVd1Ev248g5u14ovoD37
-OEGwbz9k9wR/Z1/geMcIM5/Dr3SR1TpvAyIb6JFnZhV15U/qxnaPLqBXPcTwB2areE/5V4Cu7r2l
-Jwh2uX+ovHvYw/D0SvjDHTDv9P4uxkKGR9jFOwz1EK/dJ5Sq3u3+hEb8q9ufrRpxD4dIg+y93qNK
-Jc/z1ubNQvvMyPxbnsjnWHUl7eTbumQUzqzPsAiIdBMgABoHkGAq99PIvTC1A29rSN7OmN22zP2d
-0NhI3urfAc8t5/AC87+P+t7HNgGyS3rvu6Ey5kHqHgfjOC855nmCOs4ndK6AA+Exz04a5s2VCRhu
-vwmCV3mB49jSQyz9Zvcyrd5ha08FEpi15RM4DlqX09Q65yenLS1WlaHijEsuSALrx+y/D9JNUrKu
-V0uzZc//l/K3WGf0asdFkwkt+9kqPxTzUY+awIvWvWQY/9avyTOjX6kf6noFci3mRsoVDDSgi+Cz
-avNVI3wK7XJNlOxeATFBMSf8eOlAx2woD3AdNxDMl7Rs9QaavhIddOQdhEXy0vAWph5qALjqML3Z
-dObH7wXwYq2P5ckjXmEW7RjSl0zrdXsfyiMsoFY9v0L/1x/0GEivvCnolXCdS1mfBsoFV9BYA+Du
-eSc/7fjhDnq3mfW7eXs854OA9tvHBxgmoL14gj94i5l+vDzFvgg76utyJwBW8LtBL/iyHMvVNv7F
-7QcIx/ZnHmEMMKzhD2GkR/4bKObVtf2ZV0ef8+q91XV9UWffPQ+XMDFDthTAwkeB0SPapdNHUzth
-lfFqlYCuXfyS+s520xazQ5cadpacrbyUES+EEmx60Hu9MqBybgLdyDrK53UGeFEekeu1m7mjDjNk
-bki7UVTbzboXSLE0BDQq9PJe9gmCT0BMV+n+Ty4c3l+O7VgzTPbtGeOFXDPtnrzayGtS2arMRhuj
-2mvd9sJUy7m1BOSNaHbWRt0+7wdzXjwqXkw0Tuj6o7zBux5vlzjf2v+tDiSk5Nad5DcFJjGrdubj
-Lb+VoepYjcK0/NKT1qsLbzTZGwyU4QS3uk0nqtKRvNuN4urp8iSPc4+xVB2/1rH6L6DoEyj2HKMM
-snkdDQlyCXgDGNnzdGlc6x0sh8wl78h51ItaVsA35DUAmoJhAwT87v32sBYgK6CMfJfhHTgXf9Iz
-3HhW50ds3b7WFv7tnQYs7zKX6Ift8dXjXGoSI4/0uc5JzR1ppADYfR4TDGveCYZ1JYnnCpYjLWCZ
-YZ8PVtK2geBnwfGUR/jZN34JfB8AFrT68Qj/e9u3PcLJf3cgPAHvGyDuXhk1xCNvHF3zmJnOtScW
-Cb/YSAww3Bq0AhFbu+xcvj1PY4TRLpsSPdHvuPU6csHWCGMeP81Dsz7XGi18Y/StQ5L3NjT/Yn4a
-BWhQJ50TxKo91e5n2sXIestXmplPEgqdeG7LrhO6sb/Q520ys8wwKVHgyHD3CjMWeAEm4RACiG0A
-4d0z8jn91WGkbYZFBARwyWcdR4d0AxhfyZSAajP6MNHgwxC2zTZM2jvwl1/KoGOlbHgr4yjt2mHO
-zUvZ6MNN7O5ahCJvwmvRi+xm9NrQqeM485QOeazAUCp8BMEKPqTuEMz2cZysO4lyjt1uuY1XvP2O
-+FUb1SYTqCf4BogXvcLrnT645VMLDUCmdcPoNBCMDYYYPyxG7EPa91JjQHyu2DvQvQBfX+sEyHHT
-ZNht1hdFqU3YRVGY2Q/+ngLDzz72GH+B29JNBYbx7i3OugBBuvOYgPjDDVuaSo7NIvTCC0TzRThO
-neMB5NjCn5rqeROp1EPymdDkzTt8yVfwmzcVL57e7g0escDMGyETK2iUtHteQHDmrw24CYwlRvjH
-I/wvbt8l/h3UXkDxxQPcLkSv4OjE2Y0PeVN7+xKj9oRBr7vKMsyestys1gidoOx7WMLsr+PESdLM
-B/TYC2/jDVB0grhZs7RDgl/rtW52kKDn/gDVLqnZjr2WbxoX0GmAxQf9vNKsoi+0HPySeZMw3nnX
-B8V8UOQKDnCS9zZfMrnN2zINfB7LKhEDENtS77A+fve4UnmE6IksysqyVVavcxXfOBr49dbifdhp
-kHkNyoNlPfJ6pv3EQpUeMdh6xUCNdUUVxO+kf7d5Uzm1mdZ4PbdJlqE8d/SeBy+7rpjw1sMg2slO
-BSSO48MbLMpG9cv0CDfdY203xzgJ9DZVx2+b8eTDsPz4+BKxgtUEwPoT73AhIx3o5XgqVq1T8wT3
-5unlTaUYrJHWvSFj/lacHCD4AMQ3L7DenLjnMVd6SF2npoe8dQPBAb4UDPvzIF/tvIDezaeqk/Ys
-KWCePDdBb/+sd+W3NgP8cjDbKbBtMqdkxgTrl+M22C0vMBBy13gaGSN9zFV6gwV/HN7f4IHHg5wd
-7M6wiPli3DNCJjTG+AkarKB3eYMHCKYXmCD4WSNGOPjqL28/QDi274dGvIPe7h0egPmSd1x9aOGb
-2Z71Wq1QUKyy5SzuNAEJl/C224rB5bgVp/wzfrLpNwVCvK4ol8NTE+PeClBMZ9+1Tnyam5vxVcAy
-m7sdvVjLax07ci4tuNDac8hiTAVIcUK1bObzGnrjcDIClIjeE1LJZP82gBs91PAOg33zBHPubcVj
-wPAANxAc6TSECkXD+zYWQS0ADAEkRktSFjVvQm5SdG6kVwPDciOjqUxTLmROlbKZDl40niSlr2CY
-4DPHVvmVvgzkwxh/p+Xm+PJJEAI0cJwWlKLHK8Z2PnWx3FX4BHkGDXzcQLC1OlIvj6XJvKjUGd24
-jngY3Zs+madQvnlDcG46rikjml49vayfewxOyy7pAYYrFCLmcYBifZQ+6+aeciAguMUCz7SA4QSi
-QAHUAMS1asSuUVwdN/bsA+dIwXB4gvHEMTmXN+ZGni2vbstTuhkOYExvcHqFpW4C5cucmsx5rlNs
-AdQTEEtMMFBMlIwUY3EUcNaipEXM3TPyxBvc84oH/OoRpjdYQK8A4RYPPEDy4+URfmIeFkHw8whN
-pxd4oV6QC2+4c5m4v7v9AOHY/uuX5TJ98Q7f8iCPKKBK9wJieokWHnWyxgTDwyuZqr5Z82o3FUVs
-p1HnHTNfmOpl7dGlIR6rCmMvKyWsV1iQfK2PrURuMWcNZKsB7GkbdSc4v5ju76WbIUy/RFVUgKSY
-rBkaOW5l2o7Mdv07jm8AuGoeM/lyrNsEL3OPNBL9ZZX4XOmyvl5ngGIXcJzeMR0H0YZXOkMdJC+Z
-eDN61UsjIAacdH3o0bOzHTlOkG1W3ZpbWe+e7XeK7pb1pMoxAo5Rzt7onEpjr136b/Ibh1jtAcqx
-VqTHzVBAYe7ReQbzeNQZx+Wtq1MbEJZ9qjbcnrRByY0+1x04vK2OMH8JJCjcrtowLpRy/SZfn8r+
-pNqtgl+OlAieNO/yMHShsL8B8XniyNDwh7VBjWEF4Kb3U3RZgGA8j+gPF1vAHtSKR3ioN7jywv7x
-Jdn0pQR/9XsGL8bNYVOmfVzT4eLZ3wN34deiTQ+/8xvHDQ1r+6YSOGVcl05y5FwUFqxrET7nV2mz
-PquK/lSdN3icda+8Ps+dZeCNypAXHZvvMZcDkOPTeuyb6P4S4r++/QDh2N4MxVFvgtqW916mL9E9
-yRrDuDWmHsdaZ5zD/ymmvNtubhwUozJtqgCoJ7yUitBFIxZTQabNK7Az07kqgCMU3gLw5Off89uK
-T1RYkhc/8wc+lSowAK01e9uPrdfqFn2kxFbe8n+X7l1Mu7hJ4Hmcha0ulYscH0k/j1sz4zhBtCjv
-HOAHpWPjdxRXWITeGKVHxgQcrwLFOH70ipWBzjV/9cU4KstjhQgvPtY89/3uBQh8o84KAxJKnF/O
-qmOkck8v2SQb0zSsSqISn4tS6QZUx1ExwcNQunrLKu/W8m275b+dnfGbCSYG2D2A7up5R10UkGhp
-gotZ5y0fZS+lKLcGGrp4HaybRrd+JyDAYfxFo18outv04+ZLVn+53JClfuaP+I/91HHpnUjKgqGx
-CutpWV4T7di1rFMNze4k0uT/ESO+rLzjz4IvlRu/jPXZ4VAPPasKioEWx0uesqdCBPh7uqSkdJim
-h32wWa+dqsMvGh6TIekAcoYP9Z30pGzd2zKg3hNrere86ntJsngl3g1L3vh0lBqlCBk8j8+XTDwt
-enrUmTLkWr5VJqyWT4pO0s3u7368EFtuXnbdi6H5S9sPEI7tzzzCoRa5j/OPMueyajtSl8fL9XrB
-cBeF5L0g8pShm8qqs7JpsdqiIPORM6UH4zGnXP9Ih7D2F6MgXC+/jHuLjV5fl8dprWyFEqVRebK+
-qfGSfilo196fADgEsk48MJ5xoOjd1gW+vyOqhmPaTvuZNnUYKhbm3XM3Q/P424A4zxEr9tb5Rrva
-e5vjsG2yVz7onuDzJukAyukpMXQAfMnTgY/QiQ1+QioXkmcIcPOTsQJMbqAYWg/CG9b5oal59xfb
-mSZkFHrJJYBaLaKb+F2oYRTVzNtTxJs2O3mB/ylDLnGVyJvcCXZ3HOOQ9VteNp2W7r/PY29Nem+k
-GtAI41IvSS0GnQB3gl4Fq8FPPvOlrGQ3JiKBs8n5Nq5hxZNtAfqS79Z3HVvKgk6hoxweQre85uzy
-oRSq3o1pgK4MAdHjwH5a5zA8++l/krZ0eQLiGH+uNHIAXtvhAKJLDt0iJDm7q7pfjscYmsW4CdQL
-WG36k3RPCaI8j96ZHt/avI9l55mAY3nF3eNlPOyumm1MsYsK/O5f8X1F7uo152o9kKOzHntPPZj1
-bJDytpHkpv0bhf/S9gOEY3vTAUc9BxoI5h5AeX83023Q++I1fgE2FDKfF9U+tmNpZwXjk1NTuQY7
-U2GGpctD4xWtCWUT8xDIMplsRkEOUF8Q0180NN+l4AsX+SPwpe5c4Sn29rKGiszWEV2c5isrHQDv
-49kGaXgFwPSUDSah0si097K0i9L+YW1cK3o/RxRNTfM731TL/alB8wZn5YvmOhAFCphomeHiteng
-tuftMIgMjRhxwk5XBsp3u8fOfCWuxg73Bb/y5ZILyFUwoqAYku4gWOIpeflQ/uqJM2i5GMMUnk77
-8g1ppZJVWawMguLqfF7XYw4GEk47PPPG0e1xbsk0AYjI/bx5gcaAc77lK4IhSPrm/e74ACBvoHfW
-y7wpULyG9+oChlUYWnjNAXKpmTu/DKHs5+S1ZR4H4D0BN5LH6gtLHuGgF9k8xDeZcc+jKmyer3yp
-IhRjF5UztqHE2GQeT90K+MWJQceFylH7+MZFdzSd0mxRh00HOD70clP293xcq6ATW+QvCcCbXZVd
-0bFW59loU/zCnX7o5zhqfJRDvrdovsMsCvgiwbCBafUEb51V1OO16kmZaoMExIZ01ik1SkOx7ZOW
-5eEd+4PWehaT77X+ye0HCMd26IPXehcQzD2oc/e6eivzLnuv9iba2U0M89WQzR0ol4IH+CiES9Qk
-MktL52MPWd6GglvCVCrBkle798ga5/fwCEOFPXz6SfhDxHISABdgrueHU6V0PX3z+E4AXCKuTgDR
-ZTotScNrGzoHojQ1L3Oa5wnjvEgcnhuv4joqHc3ceY7yVgKJeij2UenMImo1zboCXxMeWFXOEAkt
-b2/LixGXvgI0+AUAugUUngfE6Pa0guJbuoVFRFhFznl2x3MuJ5NsvrGscxrpaRC1Uq/cZsYHbcSE
-zdY10THyNHnjHPYgp0Ot2OWnS98dezai7CJxj2mhx8UbEJa+2q3n91EUXThPU8iARIHBHz3OUWRP
-0aJLmVAzUYsAYJP0HRhfrnPoA/K6HCYtxrApD3LjlB8/Yj0f5yiznKxUzeU1aQRGRyiODniM8QDB
-qc9kvCHyHz2+yX/lJaYNapUv/f8tlLrI5VkocmdlEWvuSQOVZ3Zc3Q93nabhhkCnBWt2alvSn/dK
-i97hIOmKeVMwvP0MvvspoJhhY8jejpV5OPyoY0d/hAT4Bs1PgW/yTmtqv2/of779AGFufpGmWzVs
-xriCYZfQB8nbst9B8AOXuymXLrhkqUdQlP0LSOYb9enx0vOu4JchESNCUXSwqoh6sxaYX0pKr5EA
-neYZHnG//PVQiPg93pfr8Xj5wgH41wXgqsKZQqkllWeXUvXuUQnQBpRR1TOaGhN4EgZh2hjHmSEG
-0LWOKKBuqyYgrgqf64AaEr/bPAeoWk6tlHh+QQVulb+YXgV2JVQCS7zCfFue4zXtMQcQ+4t3eGd7
-egU3/25i/BbsXtLpJYbXEwHOEQ0K+5Iv4kxDyLqkF8fQDeF177Uv7/BtgvTQ7sV5/SOo4sQOVsMB
-IF7gkPUFJNidIDiPZb1oaTeTJob/sInFa9c6KV0FEZp+mzyTOvWSnzIn6QGOJzAu3TzqH3NY8/YG
-eu2SF0YhaZ/bvPmbYBfBh9oHs6ZXWqMy9EaWt00V4OTEzKa+E95V+yO0zKX4fhncft2fLP3SlRli
-+Cbq0/Rn0r527pZXRaXOOkG8Bo2krZH7SMM9ThMBzzW2gZqr1hcreyC2FfCUs8ZF+TRGQtLYmscC
-I2G7F7vpnTRQz7Dmi/xkH7KnMwSicMpRNkZ5R8JlI+y2J/jtQ/zr2w8Qju13+qAqVmjDAYa1POtJ
-OvYP+GijhFVBTynxb9SpiqJ8ukEtxRhgLR6vtLdkEySVUc8Xn2Lopfes9hbif/UYye83m8CIF8/x
-fFnO5L+2Y+O41zQIQGktRTq1EelWSoXhJL0PckPjRbncUnvjzHPJ0BsBNbhZzTWrrjLq3PLqv2hb
-UfLH1ggiCljnMoFSqDMDpmeY6faEIEIhGELji4BZzQBXiBDD3gxxGdbdl27Vb95gTR8A+SF15FHh
-kvNySba4Rq0VBoF2ZY0SsMiQdHP6hDK3YYIAACAASURBVPuYr3snLWraqp0yrJLVL/Xb47KUDCFq
-L80FCG7ee/UI83PZ7dPZsjzU4C/VJ5mh/TkFOgpGz9vYPecmDX0DwFWe6QF0Kw1UDDHr9fLWTluT
-16o9lglAZj2f9Zpeq8tNLHXQVPUHrGQowbLWYxlGPs78zFbl32XQ9DK5L56tEL1znzeuv9BsxHGj
-FPzXPKel0PPQdf5ybNQdGGP2QT+rOWwb9aPejoYcWtnHfYnSUXva66L1khwvOHXZGEvIoHabnmK+
-8rDMwjx5ixFuYRJOUnn7lYywd+wnIKPsZWHzWp/aeW26rvtzpGi2pGqNCf5L2w8Qju1PFnHWON8J
-hlv5qJcvzrFuKIbUU6lwq60EuZJm/nleXb8eSTE/NJd6EUJxDueWGEYR2eTTycQvnD8BMe3J608f
-KTroVcDtB4pLF5g8ch6/lMeA6m6ZysEkjQaKNYyi1xua1vthlo58zPxhoPIm6KagMVjhm3lTv7im
-GiCxUV+0Wcxtjt4goDfKFACnd9hOEJwxw6H8pmVN4zH2DSDvvDJDMWjy/UjrShKkk4WF0XjGY83V
-fOHH5GU57W/JbpLOpK+HAQxAlKZy1pkT3z2igsXr0tf5LeHuLKYmrJ9T81jplONjv/JjKQWO14E1
-ju2Sd9adgjTpIvKRb7JTeIoXCABS4ATQ8man60q0Oi2/hLPKTHnAkk+K/4S3D56M/Y6lOwjSaSJt
-kB9Ubg/5fyOyElXHVnI/IyD6OIMHidDUgCidczfzvOzDL944C//KDXfOOPlQupVeVA83lMt4aLQi
-XU6lUafRbNKuyysdIS7zsI8pk2FNGCpBvdFsrzZ7hkcoCC5P8b7uA4uHZ54rTi5p2l728BpF2bvU
-DsfxCXKntujHr7zWNpud6GWX5N/cfoBwbNPsvG18tPMGhskWt1CIDJmI/Lx2Kt86H5J39xaf5/RH
-uaKgthsMTUkzLEKFVQTzNM1WYDn35QmsfYVH+KoXaQYVBVjoD+1nxwsn+3e2Zs3Le9yFpiLoinSm
-1YGTOL/lhTIUwWd6riyhtrhtbXyjygXwC5egTTnzIuNTXu8GO3DXOIdHjgOCzKPxt/PSGAThrx7h
-AxCvWk7N4hGAelqyr0F8RX3WlTNHmHkBYj1etCxl7fuz4/7ZY8zzD0DcKKrSgZZuuKX18WJcsR+r
-1stxehmOfbTnwJSCVA12sp2WIWlRc5n1RW407l9vXhIE8+MoclyhLvJ1LF5fiDLwx9nbrCsgIfSU
-TdI3IRoKRBwDdUw9o7oSlf+b8qv3kfxJuRVnQ93cS18bELYKBXvGfOSEetFiyqeC4kSwU35uBJ+8
-2+vPm6xil9GBNAhS0dvMJe1an6kfeC3qGO7E1kxlfvCP0jh1hbfLqlVLKRUwnC/ZtjIhggJ96nuT
-qOEMMdyddjDG9sOcjCE3+xp8wHsNnRd6f2MlyHcwXKRsrKEhmTdA3Kfcsx+b1KXjtJ6FTm6hDnrt
-PB7lpvufGOF/fXtXzGe9Q59hgN0BlhsIlnK4GPOmtNmh4fX1EuN7ngDiMOoU0puSLkBhBd6smDz3
-FEpUKER6ixIA2eBqMZrNI8y+DMKa9PEAx6OMp6DppsxTa9IES5RDFrL90A7He1jGO30pMAG+v2Mc
-l9/ISO9VU+RatZvE1qiQkErMpWKVSRuc/6GLr5sNg2iab8kDnH8FTQV49XjHlCYglrjS+qLWGYeX
-Y5gT3Uav51TZDm/YhJrLpt1jhbvsEBxDzju9fZsGdSN0mGnZDubKPDU/9lrv3lyZpka2RqWZvh0D
-SOAR07xXolGrlZ8EXiPNrwd2IFy917GcPbj25Tv1tsIL+stPwW8DwpB5G/upe1ue8FYmWSYoYwDf
-4rnRFnnokScSc4Bk6zbe4o3+MiH7rLoKI60Ne+nvTwpBQFi21h5HIGzGnFdLfds30kbC7tRzYQBD
-JHxeX2xTv9xT9Iw5SFvmdcnsrvsJ9FsXvRuNGSIh5dsxxvIKWQDiBle85fu4BR6WpDe7y9DDfb10
-vGOAYJrZ6M4RKxzOpnt4BK0G8rhLqGeOaT3hyYodHtuwGXYtm4bl391+gHBsf7SOcAIRF70nedGe
-Y39Io4FgVP627X5e/w0Uq6aUayabilJNZSuPdAsEMJ5MLGkIaQnG8AwLfkpvkQDiY83YI0aYIJJG
-ATgArl3ydCxp2Ib4UPCFPKlbQ2vSjqvU5jllUnY+lSjPF53I+he1JWCrhomsSdoj62hfXBMD8O98
-b/VqT77TtqSuJwmOrWq9GUFFReigOOcbRWzhgfIWWwHiiAemF9jzk8tW/Mw+qSE6wO9ON4AVmtmA
-BLU3wLuNZPBQ7OdSalVW7eQyfm0G4kfevQJWkbHrePQcBTg0QuT7KFedILa/UYfiIteqXqnH/eSB
-HIbItH4A5fwwSl8KTz+b7dKh/gh4xF32zox+ScYkrY43yTfmJYBAv5EugWk6doLeJkCit6bAkeB5
-o6nH6McTLFOv80tsMh8+r0Ea2sg7bsLKQ3z1rpFfbi/ZpU4TYT/a6Hmz+DUkTdL7hTiA4RGpP5gP
-iBKnjbnoMsP+uAb1x7xR5YhiHjKkizce2SGV65LPspVJtLCTDE9iPDRpGNyevIDtKR729U3nNk9w
-7jmPVuZaXpI7Y4M3AO+rRhS56jbjuwA4Oa9rKiON+nRcR/aBDyFj+9DCP7r9AOHY3nTsUc8LZiiw
-JUh1rzr1GeUePvG4x5KRnm1CjhMUUOm2OnWdQ0mnZKjipxR0YU6t4oDJW+XysEfUqigTK0bVx6ev
-3mB6/hJUBCHTYICSXUbK6rjFDYN5NR8ZB0XaUJ5EYo9jvynnEa3V2pI4znRjSJ7ELjbTnmP7wF1a
-ZwDg+RnuavetvbewiPAU6YTqIHnUFNNICyj2JEzMPzDALzK989fgk5UvyZXXmBfrhug1PV06Msnd
-+8tjTzorOK5ylu28rH9ZxxqP0l/la/CPHpTl+Ma4OIE3kLPTLTREycFaFBftBiYIvghFnmMl6inP
-6F8F/FpoYRH8fX2BBrJz8BjjHNek2Q3hH0nqDJUdb7/6OI/UOQTlQ/6nOsmyXnVMzmvOCDnPHbLE
-UKUnGUQfV9YtfChZBtqxlPtjC30n4PcgufXat3Y0/6Y2YPaSj3pZDlSrCvR7u6fGG/o2aavhTDEd
-TAMD+Kl8VZsnb/KJqYJgPh3dNKYNzXdzaN/mChIEyMcVSoYPT3Da3WKnZRGKEeyW4RFRdYJg9Qbr
-PLjkvwNgPyhSZUhvdQ2rrwSRaebbOO6Mdr95+4e3HyAc2599WY5KvoNhSLqFP0jd+FgwGLdWursU
-qV+P8wJoIDmTChrZRx5bGYEQRKueIgVdNaGUaK4DzQMM09jhyyoBJpxNKUrQG5liKDIuOD3H0nca
-Ua/mVHc1LzAvOSR4mJQ+saLrLeegRm6h3KwNRq8z8vTIbz89qGpVNhvoKZpNYYVR7q2ecGcN+GVr
-scJ27ismeP/yoyY3j3B6hekN1qcGDI1gHyXtNw9Kn6hbrHAHuzwuudh85imbeXPF48eLTwYAdnjE
-cwodlVY3i/GxMNIH/1wakAn2mR1kOfL/IM3NmvWytLTdQzxCI74EDH+t0cmdyMfI2dn7+PQ0bz2c
-NBFYX4ITxzJvCoJTj6Dq6UXH8XRAdIK5KMTQTTb2EwBD+C/7Gcl2gzVoAQDisCANu3xcZMbiGgOQ
-Athy6lWvafom/1KiKMWqIssECyXAMfln81yxHcd1yS1dKfcy5ueNq55N++V5ukv1G/dn+BiQdGvz
-TBuUT586CM6lSJ32ghcdOldVGl9alx4lGI6OO2LVCCu2MuxrtxjhqyfYs02CYnZsapxdW8FxlZ31
-+nE7sONAKk4eopo5efRvbT9A+A+39mW4UNROAwsBvSjPbwPBAowhe+QxmvJ0KXs/5jn75271gk8D
-w2jH/TF9xMKWjKQAWs9qMpwibG+/Wic2tdYBcpEg8+5Jseoo+4rekQS8ov/bi25Z1UZeqUvRqrvm
-lF8BxjQ3TX35+H3cbud4p4E0osPLm6OZj1JdjS10WDJx7y/GXXqqOk28vrusz7neAPm65Ms6tLnu
-dLv47uh8vN9HWmWW4xYD5jjigo+8p4wCqWZAeulua1ubeoiZtqemqw/hhQ9mRYzKYnwBdA9qP615
-gpUEcpniiB6ekOBDN9NfzIHGdY+wiPIMbwBsa8G+Vuo1voNQnXMZz8zvdWo5MOR8HZTMgYoktBvM
-B3Uj9PAEdN0rBJvsdasju+zEAEl7eKLLdNzkLZ6bURGnvM+u9PKa8OJ/4WTDeESPqkOQJI/T9GnB
-gVmaMoy2qQsjr6pIufEUq7JswsIsmVynLpekaxTxrJbkeFByyvlXuVWdqCI2m25Zg1+piwT4wiEg
-2GPqSd/N2xsYRxsin6WzhuRbjZkOJ9L2ieusqKP3Wc0L3I5dbF/RonMKdaCDOnCSqWuse73bD6Od
-960ZmN/W/l9vP0A4tv9m+TTgxSMsiq4D50rzM8yqbGeIRDs+FLd4jrMNPhaiIqCkMBZI2XmfU0Jw
-Mh8FjKtCTE/vqwd4xhSKt7Ahs3wLPPINrc9pKAnSaVzFmBnQAa/oLM2r9qeoVUjE0MWiOTVi7qYi
-eDyUjNdv3nSIThrt8Ihzfi/XnORGn/laZvikX67cT40aFHMTyuUjTUuCzsXxOyjuH17I+GAzeblK
-jHgeT4t1t2AN4iV9g78evT1g6x6fhhWeasC58giCPd/uf4o+AGBrX98cjqfxXrfY3Zzs3YWPmi4q
-vq/6QieKiVziCJM40rcQibCceVmdX6sPakww/CVgWAEx54EGM0GKEObCsI1UgNDCgvZCUEf1WUEQ
-JJ0OCnqFnztRlOYqQBh91PzsGvWUzpWOj7qndFArG9eq/8mp1/TsVp/X3Z95s2PUsdkWYuq7fvCo
-bFGuAPi2p30wvO9TVwCwX9GNX9H2L3awzMBtS/IBY84op3t+PT5G5wuwx/pUYesLZa3im6JdD2tg
-nPvOo/cXCYLD+STAuDzL0kxmdCXBfrE7W59Wmm25hbcZQ9UGay0rEVEQ3DzFqQeV37qqOrVSB8rH
-sG7zZa3r1Wcpk8RH+/RPbz9AOLYrEHipuPUYmUg8vJKmoLJueoe9Pr9M5ZfGV87pADfal3p+KbcG
-gmkELOsfZaJtirlLSPQmlmKT9axhpPpx8X2V0ryFpSGwGt9hRDzLLenQx0oaNC/wPKbgp66xccz6
-OvMvoFhdbcIIJmfxeL4A1I8uW9y8pG6c421XLBL0/3NfARC9ZvHoR1DcJrYnqaB3WjRdznkd8wap
-vTjJmFJ+mSy+Mndaiz89rpGm15ePmtcIfcibq5mHDKFo84In3u73XO/VH0c+7rQCxrYtb3NuftfU
-9GMSux+3m2GeIaSoovJaJVDuIi98IZlNljlvaHNXnmDrIFjBMHvbAJ9XeETmU+6lU44NMvJte0S4
-ipLJL/R1aUD2DQQP2VJaTmGatJZ0m5boS33KPpRL02+jPTbA9YMfbL75Jsjt6UaYKiMgEvlVXkkH
-xeALxc656gv4YrRVJTpI0NPUA/aaDj3ZEFFo0XzKJEO3IutN65lRHh/gWWSYXfxgO2Pyq30V0kAb
-cWpZMRIO8QBHjDDtS+RtEOxRvUBw2tz2DkHx9bQNipuTBDF3bhoasZ+WbLaKdNQhC8YzmZgmHWSB
-Y8/jsAsj3cvuGkvT+bM+fTWYSujTAtNc6/z3t7YfIByb/75K1iy9JsCXPB7pBnxRaZc2qKgrHW1G
-25BrsPzdS0ylL7+Wl1rkHLhXgr7PzfjDVIpQknHvnmF0w0nJcCANn0Higa36yCVfjrIao3n0MsaT
-L78luEZ5OFhnzLK1PQFwN45ZJy7Ga16i7Zqeg+cwjvzso6bZSreuozunup59UPNw2F3VYjZabMMp
-I3pcYwJczjMZw3qd5hFun+W1AlTM+y9B771OGDu+iCTg9hMgTvnSuvmhA3qEt7E122l/sMdAF9S+
-MOLLHUW3g5jN3IkcK/H1JCudcJ6OBmY0zbaHNN/T1V297LsneB0g2OKn+i3RV9NJNaY2VNd0lEXo
-gD+WvNsEmDydQxhCl8BIgfCg4zXtZ/7tGECGcigIfgX81vjq1t45P/PS+1p6PMGxxZOYwUVHWzXf
-ovunuCnCobwqGBbAvIssfwVwxtPBX8iynHZV7HLp1G5NBkLOHyAQ7z73efYN9vPsVZSeCrPiy26J
-rIVvGm0b7BNbRN0iefQIA5BQFO9E9EvT4yrMIHld1KIhQiPMhZbevME9DIKE85q+5BCPEXTJt5H+
-TtlkE90ay6jV1ITV3uqsD63+M9sPEI7tu6ERFEo+nnjzCLPNfMQdSn0vpyYe4zQYEKNA4VRv8cVz
-rOeq0TlAcNyZgl4uqZPGohtrk1Jl/FKwlnehVIRHeETzCM+GqYREaQQY5vqHGlun37FXpcLHUdTe
-lnlFK/UKUwdqh5oXeOivw0hFu2qHq00Ozy4nftjavCLBmEsbUiynlSJTPXsrh9OTcR/YtbepkE/l
-lMYvATBqvgcPuB7nmrOVZyM04jTXUzG+5THpwRve6HkDxLPcRl2H5xv9Fmk3x3YxBSC2Bz7AsBGk
-kMfn+JKNlfKHWYxD4Sfvdd9Cmmb6d3k1h5Hf5sfucys/Yz0C4qAdV+zIR9HtyU+XZXraxqOo8JiG
-vkjvKUqHJXmmThsvyblXWMuNWG+E+lhGC+6YwKr0Evuq/ZS0rpymTfu8ZPF98c4LGCbb2bP1YshY
-8/wemnCkBihrgFd5ATH/NxAs6Q6Ea3d9MRc1rX4lPMP9tGiHJhlWgGGLG1UTQFzzRJNTPES+kTlN
-jc6nMTHGsFl8uS7DHDVP+U554bKdkk/oqHQG3Ptr7k9NifwmQIaA5Nq3p78v/cj5ibZSP6CrtmSN
-yzzqINWS2Cz8F7cfIBzbFQjc6nmpoBke0dMMi9C0t3S2FBV/G/7QAHIv68aF7cgvqmgYYCsbhFCZ
-pf3Kl+JYTslQT4HVsRuN6MIEvdY8wDs/X5hrgFdAC9CMZ+Vbjc2qjhlfGoz6DTZMMxD0MWkbl+NJ
-qD/a9Fo6RzLvmn4592YWKn2GRfhIoeXJZrdRlaZLD4UC42PeBSiFR8qu5auDYr0WIIw6FeSbNbnR
-NvhAjv2g94dyhkMAFQphgK2NXPJxrC3YAYZtWPnd75PuSnTvRQeAvoy42B8Jfkb6xi8NVEks5BZp
-sVgJguz0CnPd4Bka8fWF2814HePQS3qcN4IEJPuFim5B9Xfc1Mv18KC8wfKy3CTIx2P7UEfmrIFg
-q2u1G5m3dG+qLqN8s48Z8uKtlkl577OB/Atg1Uz3mtZAjfbh7WXom1wn4F0fgLDZjhHmNX9Noqor
-YVAkPZwSdpPFIZfbdxo3T3sunMo9EKK5eIatXyavq/aJ51hRZffTsq4HL6Ydax4Vntf18Nym/JXO
-lfvBoWZVPGKK416xg98ucH3Y9Y6DxxgEKDvL9gl5nqHjCeZJX95GWfdDlf7NSf/o9gOE/3BTM3yk
-RcfTI5IGCrikO2M2uX7zFosxmcC5Gxc1Bpe0S+WLXN7GyIQjDCO5HhoSgYvS1BbHRfySlvK5/Bkz
-K99wGDchQXYRDn1hrC91Q8zi1/4w2fehAr2UdoVDSKjM8DpmedLXx16OpF6VodW75890PbqbRE4e
-0kEctTDOQc3z77bJD0DegmR7Vxe8yX8tswsr+ah5zpiP42/tm5XxHRYBA54Ftw54W6iI05TXCLJ8
-8le+7IWiJ9mweRStn8e5F9kiHV3mppfv9Fs5vffp+RNPcL70qmuD58c1JEyC8cOw0jPuyJfU2H/9
-YeS3eUUDAx8ZM88Zem724yOw1bwhM6/X5ry5zCGVx9BPb+nsloY4cKrJw5Xrcl11toi2Q4ZGrH2B
-7SlFHCt0Vp1aANG0rYv+TpadMj5WijGTT3FbxBvnsoRr32x6HW9dHWWpSFfVgcNbmQExNsqoD0ho
-Aoo3n6fSRxqa4K8Q4Zp+yshQPae5mnroVueSMX+zfuQ/MCzbuvwxB8MjuKbwYxUuwZfmbj4IUcV9
-PF7Xy7SM4wiVlPGd3IHkoYIBSsCeZnnGkv/l7QcIx/Zd4q/wZlqck2mgPY4A0Pk7H6EgFVvTkQnK
-ytgbwVtIZ63DGfWjzLUhZbRzlJdfbBcENYXhkxGyl5Rlys8a45pCiiOvQCljsEPdedA8wKehjjkX
-j2u9Om/5HqRFg/qyoT3+UvZUnVDEJultNm5p/CYNqT9rzBK0dOX5tTxzm8dTfnL3P8ucAIbe/Gc/
-fmwalfXTQ3rTusUNj/2K7FB8PplB2MLeyzoFNMtl4JU+QiRaXTlHb0KL8SKPvKxeppBBvo25FGYQ
-rBAgzYkp+rU4YdJc52R0qUJOAILYtrqD9eXP9lrAcixpWwb7+oJ9fQHrS0Dt+Olnsc22Fxw1x3yc
-22/sih7HjaJMA4e6y132wOTL9x9+/3uDJwcreZV/YkGFBk0Pq4KPvAZ+Q1YYtpBF4wY4+Q6/zW84
-2xb4kSWzFeDP4oVO8iyBaMyl0aMp9W0h19dV7+9a4VwQ3jdDxlHYihsr3/zlda7/+gV/Hviz93ge
-+LP1zd6XTOxpVZrEmG0D3uR7EP2F23uFZxgRJx1Pb6iKNjh/4qEBZSRumRMpihxb8HGCSKYZSpIF
-2cd2o9CepiBkFRd9iSqXOhb9uoLfZfGCXNAEwFd4xz32X7wxsP0OAx9yPU+aATwpg6XSEWqdvGmZ
-EssuXc8b6UYLQwFjK/qrfZgo/S9uP0A4tpVxip83cw9bY1gJgg1tqRL+glMa8NMDAblU0AaJY4rq
-fOSCuA7MBEAj42f5V5I6ezS2D8o9u+q/q2uSsmv+e877tRN/iOGncG6qdeDCwwTAAn6BDpiZ/sV8
-Ab3mTwLfyqs0PFSNlqOXtzjseZyQtVlmjqgR+zOw/V55GUiTCY0+XECGW+979f/ZxmJJ3g34MpZT
-88vyqKbMX43iI0d8r4ya+Qp2o75OxeSj0fS8ch7HOAogeBnkJ2TUgPxYyGP5Ls9uyKoTXt1MqzLB
-L/uj4JjtD0CcIHXZpU7PMwHJ7ctw87PJpiBY0mOOnfOcw+o3cHtvlT8MbwPJ3D+eEQ6cq/kiftcF
-H37XydT8m578TXkbJedP2lXkMAOCgQLEZu00l5M1DKDXscycdN7Okliv2xxcrnDz7Ir5395V8rCt
-VY4V258/9wwLWI2/4PHyWYDeyre8ZobGKQg22+D3V4DeAMHwBx66pW6EQo9xvEHa9gTGRuhDgGJ7
-yhsed6epbWDIJ4AbkD3gajBtXXPr82BWaX2aYlFYsyRyRuCbYjJA31CRd4B8yVLw+2yaeNyEUxwW
-08Z3evZLhI7tUX98h0E8T6xs5Q9Xi9xh1qRehF07kLe5LezZZMzZV0tS9M4jcYoFwD+dJn93+wHC
-sf2xR3iCYNr/UPIiSnUNOOACFy9eYEDjlyyPAVQsn8ejKqOCyIvnNefvW1saYtnf6nwoL5pUzh5G
-GPBEKpd2DtRxxlEzX8GOh4LbGE4AsHvbGyQd506g20HxPX0tv1vlk7DiuUqA/ApjJ8k/eYXP2GCM
-ckBBOZLfJnAHng24FAzLGp1wg4Xxdjeka4HgN5/JRV6CYJUKUXoToHQmqFPeyuYRkcJBkEueFyDT
-vE9XKqAAdDAMMciolQ4WACxs73rwfwPqsbuAY+Thpax5kAgCzr3dymfdZRsAJwj+kp+EPNxenJtG
-vZEzdBdKVu/74LPjePDms39HvvD1K0jGnNsbU3Ey+moNvfx3GtVfD3fyAoazb3JdFy3gvVUdRj5t
-YPvCH7wZY9yqCwCG0aO7AXDxMkMZFCRvz6NzKUy9CYq6hyc46zoIgPlS3Qa/4gl+tgOivMGh71xA
-MIeWPDbAE/n8Qd2AMmzCng2M6RF9ADPPl1/TRq3OH22FJKWxlY1zKU+fVhrjU0YO/1R0MT9h/lLP
-2NzamGM92B7htblmBSB+lmWTXwTzDyEtwHd2Hn/4vZnd+IM93/5w4Y30dQBORzIS5ZjGcnPIAkIA
-tPhwrdNuArTOT2jEv7r9iUeYTLYnTT2PFwAaXFY3R6JIqbWMxrGYyIACyFGH3k3GdBWo9vPKTYo0
-9QfbDVWBYvC5zu+vKSLUNPz78R6+gBnnyCMsQsCt7icgBgZADgP7Cfy+Ad8bSO6g8rTGNgfZvFXN
-al/BrWfJzOt0ankW12EMsPbR/Dje3hkr8LGkDj1Yj2O/JBYG7zGcYDg0bDNY+xz9m1sf3dv2Xu6U
-rRvxjn2v18DEvFqKatDGUDHOcaNK24uHwIENrPLsnZPzWuaJGLVMLOPNu5sg2Orrfq0eer21YOsr
-fuv45Seypyd46BlNcw63GNzCJYKWPvd7Ejy+GKZilCB3eIjr1+vzSnxq00EpD25a6rtlfuT+lm0B
-VFDwlp0Mc3GhUdN7v0vbmR8A1XxJGI3DBACn1zcAcAe8XBXF9lrfVuENDJvIeHH1+C6GV+wyriZE
-YOxmAYAFDMuNT0untht0LtIlD/rCXkc4ljjcN+4bAJrHyroW+t42n+1+PrHWsKJClKVOc122tHNB
-v2naIDFSao6Pl00pexMYop/HdOCNBaSHdy1v3uANkOkZBlbcgH8BAHa4iz/PnppYBvKhqxcx/Lh5
-Jwhey/E8DM0IG+ZALkXnDdOivT+k4iOAN8d23MxkwV/dfoBwbN+9C0n74fQMK99WiITWNwAhg7E5
-L7p3qZ9dlCHvoAL4ioBagGCnqFq1P3mv9+ICO24G329t3Ov2a8w6ahybBXrdkg6zjhixZk7T8BGI
-Mi3njONez3N/B7/AjgvGvTwUdUVm9d/5gZPykQ3Eg/xi3kEP1rwACS3zmce0g64NJy0DUSS4tTI+
-hUgCDKP6376uFsbOQmN6AmBHB78oY4V6MYI8qR6tc+Qvucfryi+nqoL/UH7Nm71Ij9kuUADsUQ4g
-PMEAlw10OgBJVlQaKdvSR6F5ce/zNwAAIABJREFUCwGSsjTiAm758RLNn6AYqzxzPT9CIrjqg3qC
-7QsZEpFL3wUgnoYsNSDHFP3dhCo+jZsscvRnECz8N+XpmTLW6zOvwLFfDK3O8lvZob0jZZ+qghN7
-5VaebqOe6rlbWvlT+DezI20BUHdYhPXjAMD7JrgAcN7weABivhTpXmB6hEFYhgVFmYBiRJus67Z2
-kh5gfz6AYpTqiYEVH9U12gufCwnk4Pt5/o5l3jrKnhWyE/S2sg375rxkUaexZtraNM8pP8ryhdIO
-cm9e4aunWDEi9hNph5cIPtsjvIDtTV9WIhAd8fCEu6HWPX8cj0V8dCosvntj2zEey0Y+D3V2hMIA
-qX8t5j8HLeq+ADF/RZxaX9ou9a7o4x/dfoBwbGt9j/gJgKGe4a7PTkDqE34GOKZ4VVzS9AJvgLvz
-KjRC2pygOBjq7mdrV69NDG0K/A0gSP0pr2fbduSMBylSKl6cS7/8yKNx2/vb53HVCNolD+PcKxB+
-8RJXHq7n8Trq+c10ltUA+9u4XgNHqf45HT3+90OZ5PH6LQ5YvcEsbxboKSPMUDsNm3jUAxyVxPOb
-ANj2iyst/i454/Y05gU8NEa4s+eocjnusZjv9e3kP1aLrm9xjdmUp4+enwNG0Axdzi4hEp6Wqya0
-+mnZSIIfK3Bb+9VXeBieYT4Cn15k4zJoLRRixAqbgmBr6QQmqXeKq70BYKGjsBnlWQEr5dkfbMB7
-9QAP2jrKa4xL/SI0iXjOrgAr1/wPGnUXC8+2S9i4JvlGKhM8tjm/0CsSv6+DjAumJ5cAePPDW2xw
-ABt+Bt3Ko1xPFxgzLLwwwiAQN1fevMgRk7zRWcUFTwAcnlldblTlkAs9pOzZmJsVwM6BFD4Pr7Bt
-Hb1xMfXftukZ+qWT533mj/TQDw0cZ//qN5ece1uCrh/vhg3o4PfZesbD8b1gzbxp/3dssG+dzAgt
-hktoOnTN8wC2PK9liHSEQ3ChgBbxqERogJhZCnTPMR9rTf/F7QcIx2b2zdAI+AC/9AIXOEYrP9Vn
-OrO0IDTj1oXBWdSWgUxzlQjJIycWE/XevkHi6+Nol74dhbe8Gu0cr9Kn6tQdvWrv1jSNm2aIoaw6
-JfHnS18Qz5HkP71OgtIDzOICek8g3PMRHt0nwewNFLcyUHFWfvdEuJIEaRR47D2/lTGXdSxoQhB8
-A8ACkPNxPMMi0hO8x+gEum0v2i95NLzAOdriB+aT5z5B3xduecm5bUSuesYlDrR5py9XtQ6itbrD
-6+0SyMtxQuZrGATQgTG4sz4/WT/yG5ClV3jhDJFYWSe9wQMQ15rAOzTi9sJcgmF6/gLcIG9w9py3
-uOAEwXWzXjGgbcg11EdkOGWWxyJTKdeVV/pg1FG57xMufKqT7SgenlxgufNbviOZ2rXodm3EExGr
-ygpi5rE+IfDXOmw6AJ7Vi277aSJDJcIBI6tCmFUZxmoTBaj1RsgFBPNX8cb9i6MG9xVDdcDVKxx7
-mb+2bn4OT20gvZOGQTLwEYytJ9RdeIKjXd4YWPvoCmWzpr2mslxNpLVdysfUQleIsIwDRj5MIZiY
-3mBr6Z1BdWJWDm97rHD/ilBt6WMOi5E4z44P3mFt4VnmR2bMUnftk2P/hLc8Xn60x7uTl0BX89hM
-qnsBvNAQiRMU/3iE/8Xtjz3C/D1+BYD9HJr9YpzSoqpwQ/lgp7ewezFk1LWR1z2tBL69F7e8e2+l
-qRvC+Ig6bOzvNbrBebmE168rmCNaLD1HJwg+DeisewfCPTTitRz9B6DXiwFYDGKGUiDbSJPYBx5j
-nNPhgw632gNnlQeq9D0OcKwAOMHayzFXkbCnvMHpHS5g7HjqDn+AJdO+XZjhyCG4OGpqfQ40T5LS
-TzJ+1pvX0eMOgKsNBzIMosAv0LzoUOBC49snrICxZLeQCdTN76qXk/iWvl+8xPl4vAHlFecJ2CUY
-TuAr3mBJu34sBQKKcxiXuGAx0ArszvW2T9mdwLcD3qJ10x2xt9kBkzlW/mtGOOhvkx9c6nW+ytCZ
-vFnhiQRw6OelXEZ4QeikJhIuXRSa6XByrKjzCTxPr68jQa94ffec+iWMwqAeY3p900MsL9TR64us
-W7zpCszdmxe4eYhTTgoM32KEU3MyDl9+lk3I4xjR3wWKw7MJguJBw6S/JwhO++w5dTm/YJ5WHl5e
-/fpqrc09we8lHbsNfE3SsfeeTv7QtKHra6Jxe8orzBEyJOIAwXxqsHmpMA2ywwS8BXKlTo7tBRRH
-xY5quv79pMn/m+0HCMf2Jx5hnS6+Sdny9Pdmf5tCdjSwa2QE5t+9wMkw+eJcXahM0jmC32/enSf+
-qe6njeMogziPGoprSa2jj0zLSGZaFaYYR3e0N8zVczwN7J6rz97fuqERIHsDzdG5bC/PQSpKtsHP
-z2pwS1Fugl1vNKq95Oe8XQC0gN70/oYyewPAmo/nycf/28gOMIxIb21bYCEVHBe7V9406Jvab6x2
-8xbf6jYqUrZ4nQFamvK+1bPzOi0tFuC1jrTpcpk0nhMYQ+ejLlTzUOdu59u6gGH9gMGSF3IKBPta
-PS9fiqufXcMjCG4KBDN21ISzdz/Z3xi/eNxyHH7bhyw/Jef5djuBzgiT8OkpFuCjfJzRZUlwayyR
-MqHzN/KqROd+8I+jAJ+ynld6A5PQ55TDIbTlFZXxC22FLUQfRtoIsEz6Urzh4R02AcCwJWEUwRfi
-3fUGoA0tHEJihOkBPj3GK0Fi19UODI8wAeikMIY9OV4xUDqT/I49LvIIda+AbkS55cVK3gwKfEOD
-Rf+yfc6Rsse6gWAkPqy9HfHEBxjeVw3c4RnJsSJcLd8NFJ7YQ4v5pIwwniPXVN76m7qKTgouvWaw
-WJGig2DTF6QQY2jHOWzkk+kGeDm+0h/8AItO5S39v95+gHBs3/cIIxeXTj59A8Nv+jNOVqHaEkvL
-qwY7mC4EtRphzDCgFnZ6fvU4c3Mxxt+P9wTEEx3YtR2lA4/PaooMuqFzqezH/0239rW2lueoeMOZ
-5wGOObju7V0Ero47GH4rwwUgM0/BcR4LfcJQ3zzCV/Drb/k377GXUYy+FQj24j+mCZRlQnLpNHDV
-AxevL0GwS3p6hsXLkBDYYhzxuPXgjckv0xhG7iG2NE1xDYs9apyNh9v5Wk55G9dNdreahyYC1utO
-nk4RrrhZgDxcIKrs8Es9Dw+NWQPDGIAXR14HxukNJuh5jQ3Wn/VfA8AMFnPJE770D/uUWVxvbE3A
-7hn+MOgcrDxFimEanJ8ituXcJJic3uE6SbhCeCvyckWGwazeUtbzxQ50wKtEEr04QLOXYpAhMY7T
-ct5subwMF3LfVoTwCqNYFvpC5nxxbJz/78UIV4yxF38cQHjoHtXvQv6Uv0ZTmZ6sKwKYABjg0xnq
-apd00hZAxhIHT9Tekt5c6tRSl8U5Rj6wIzTiBowP0LuqrICwpUTt0Ij9Ups/2OsJL2D57tvKoVjx
-CsnxZKIuGh/dACpUx62+ZFehlw4FwdTlXB4tuToPOIAah3qBc4wKkg/Q9M9vP0A4tj/yCK8Ki9js
-/xxgWHWdIfQDZI69ynBZGWJr8tTM6MZ8l5uPuulJxoWhXoy/Aw0Yq9LRaleNcx5bS80azReclz+u
-JQnVY81QsqsKciFgN4yo/ugd1mMqRwXC6cX1AXozr3t/e/4JgFv5rWxSSwjgLUu8aRh1xO5ePcQ0
-iJku4tanpcUb3NIPnK6Gx/PzqObPXic3gK89QIJQE89vKLq3Yx83oZMnfsczfqTI+157gtwGgqrI
-W7njpowrWiHqJo1NDPQ+t4EZdiHnyNLYZp2Uf+4IinTCu2cYZlgCgrlfw9OLK0heEivcwyOOsIjx
-c35gAwuOndZPrO/xaliE9WP1DKuMx+Bpo0tGe34C3EfPF+/eAFcJcjwvEuQtXsibjQP8TsA1FahM
-jwu/0QvHeRWA1toieNQbHsdIo/o+jt+AMceS3vq5bnA8FSBY3N5egto41rAK23LaXpxLYB2Tfvng
-hoIbX/Q2W80Hap6S+ScYRpeVpF5DXSJnDQDvcpM26VTqx/sqpjwSfFX9Asi320MsNpvlIusFcLtX
-OIFxgsOYI9NzIKC52NLyx49pbCdegl9nv6rbaD+GVgad+NU9vvcRQNhBj3Bcx9HA8MSv2rnuAWa+
-DGzkERjDBBz/5e0HCMf2Jx5hvmhZC0wHy9iTKlKfEpBhqg2XY0u9XBcowELAUMbZRQN0pbx5i8xV
-Hcm0m+j5HjihLWW6KR7rlSddssakI2MGxRJkvUtDrU85ctm72DpNz5/n4uxHWo5pQDvQxRX4rnZ8
-8Q7jHQCvtzJJr66xBhXOHLGBZ71rnjfjvL2ZNDxRi/nwCoXAqpfl8HClnd2OxSvIzwiJ0GMAnrHv
-VsfZudXGM8d9ln2u2xTuAYqB8miNRiH1fNc7+7CBgach4dW7MfZsTgGOFW1d2ksjvY8V7Dq0DeYJ
-eFkBhgMIr7XwLMPK2N4Im0jwOwCxvgAn4Jf1bIJhATsNENk3PMJe83YFvxxigtg4zhUjXMpKdis/
-pm+Ikbe8rukI2mv6hU8caCv1pHIXvaxPEDiXpkygvKObJ23akwcR2BYCBu9jkmMNn2jHMU993WAJ
-bXA5JuCNmN/jZbpFz64LoNt1C+xSdvYxw2fq4xrlXZw6ZzJFu+ErhVZ0HDcsegPOG4/sb9CYT9yo
-LwmAWx+YDjpulnDxDpPXCK7FC0yyCx/Pzyo3YJyhECNfQfFCO17UI74dYSv6ucSZleCXbBXnlHE0
-0SWysk+g7z38DYyX1/yZe8OzBeL9BDtZTo3Q8UkHxtbG/uMR/he3P/MIPw0MZ9oWLAx/3jEBaVtT
-OdLQsjCMayrcpnW97pBcuVuMOkzKqqd5pGD4tP73zV/Sl3pni4Q8Jilvsvl2uaaXRKmroXT++b6P
-nZ5f/p52XJ/vfAiKh0f4BoYV/D5abwLhqLcGgCYA5uO3BMTgcW1cbnZSpWjjoyToM+v4oGeCklDi
-yZDq9bXuJU7DXNbX+bkhnucSCpEgGLVsGHTPdcXEM8wrqziMcerY4vQX6mhKFSrHWsq5idfkSAHB
-lEeXaup1YvynS6PZJ7MmruPdt0hXprMvXqPXlSTqvPKsZjhEeHTXWnhse4SftQIM0/MXH06gN/fm
-DR5e4lpZQsIicsUIAUBi9er5hnqEZX4Yp5nHBRo6GEaL++0hEiijH2kj8Ii6PfaTIIbTFF4zk07k
-vAfPCxiuOzYN4ek6WnkgQYYJb/VdVZ1tic7LNKsIjZInctxFR8SOH7XIdYPzBsbB2OANWF1igQvQ
-6st0GgpRL8lV3eKT3UaLD848vpCndOmaqgAahcKrjHs9/QaaZCp2sguhzXmiPFH3OYR/SHcPgFu8
-1UGw9RuSNOnq6bUW8qD5uiR3AczLcdjSFfJl2G441T+LhxoW4ZVnBMNJU4JfwPFsgI3wDK/w2DrB
-MMMg6LlNzRV70bOpF1A8kmOwAYxJNJPW/t72A4Rj+65HeNv6tR81PU/OMYAAxRsMq/OJGthE+Xb5
-bRZ2nojpxSgpQztv1+ig9/OoLqX+B8c+WnD2oOcPqBH9mgJav6H2DlVIHZV2Eh30Hunnkp4eYXRQ
-yxCJBn6lTvMOS50HBYb3IyUBwDG2FXRbwP7WO8ob7I0YRWjahK66tdbF6xYHaWxNPZJlvMszjCz3
-WjQ48iJGOL+5SaPCL8vRM7zrOgy8KXThw0xHLJsV27Rx9bF9SvuZf3gbMIy5ghygLKYY2kIpr/1K
-b6IVLQEr0KvYqBneGH9a8zLWXoMoEAQkKFbPscYFL1sb/ArwdfkinGlIQwPBGiohYRQKgGW5NAiY
-qZAICwf7pnnjTWce90GDYsPiUQG8uVegm6EQJbcd/KJ0iMt5Xm3nNFvRkZk1n1EpjbnIkGo2ZZEs
-S1gl860a0ItGoteVF3KMkHQqPo5N0iTgqKuxvRXCEHMcsZ8EwLt+8EEDzAPAjhUhunfYBfDstgsU
-7/ZyaT+Rrb7v1G4KTQk/30KfRiZBGj3BdATMuVCOJV0F+EKArzvaC3cXEGw5B9G38OiW1xMRL1zH
-6Q2Wl+TKQ4yGD2lbdzOWbGbxW+R3DtOB8gaLYnLSJD62AXHPeNy4LMt7Gf1Q6O139rH6XjpYKyhN
-ClgXwP672w8Qju27xFcPMNbay4mIh5h3PJzvvS8QXE/SKHy8rjfhTVRzlEc6QWfUMQo9Ys+6tT9T
-YyNWUOU6yl+o8lZw1DpDJO7Np6pKpS8eI2xls73CnoD4CSVEIFz75wqCn1i/0oDDG5xAFxeAjACw
-Uq6eYNc9PNd2pDcYVK4sRwFo9QoLVJqmIWjTj+FlUH0cV30Bw1a1C3SJNzgt7YP4sCcKID+lUEcY
-RM02XvMVHPOm7jr/l7w3emT6VWOjRIo/7dl0TdtsO4AO5VTjQKE3FnWt5sF2aecFFPM4Z85FZlIk
-t+FdbTWI8ARnegkAVk/wBRDzWLx66R0e+T7zCYYJdg2VTvABAXBCh6THpTz33lZ+qSXUql4+/b4C
-5GSvqtMAFOeRvNHLHaM+KEOV7nUh/MDByTgxj6sNJ1HaeNB0XwHge72i7x54glgn6PB8iQ0WHk25
-wTkBMz3GAYgZ40swncB2vlBHhFY3TVjiLb5ShKS42YeZZ1LX5NzZWJWPW7SRViYhLYOH5OuFdcPF
-8ARHB8EkvaNelrN8UY40BUkugHjndfDbPcOUrE37bZd2DG/SW9/34fDZJ+z6FoB+j3b3MwFwAmGS
-YR+vmLv6uqDO14XsOQbeHoe+1/OnJ7gh67+7/QDh2Nb6XmgEHm/e4Dvw7cd5kMzpMtkiyF7cS9H9
-rWdY0hbXKh612ldn7tuMmbxr7Xb8JgOZst6b1tBN1+m11GBm9fL0po3ENgQPNvDN4wGIe/pJQEzD
-mYAXBUh7yMMue6TuLWxCvcDuBR9XAONclSJGFfdVuVh6qCaFq+O/GtwPef7mNaYhJ4HrSnUVgcKP
-b9wLz54m0OdC7A3sktFPcKxeYu3rji+dff1srvqIR/nwNpQnE59FAFpuPT+yGngLmSqaalsl48Ql
-rvWznQBNeuw6thlKIKDZNuDVFSJWAF0Pj7B6h31xfeEe95vHSS9pc3iC58c00gvMc5MOv4EdLnu/
-7Qt4wD1WjIB4iAWoJPitNg5PcNYHKpiT4Sve9R/B4UVfn2pLPMCc51are/7rjAuAzvHrWBKRHMfv
-4FjGl6ELERoTgKl5eQMA18t0AZiNTxfO+OC+rvAExYa2xnR4l/ULc4ccKq4Vch7p45h2b1aggaI+
-0hM9kzZm9PAA59fnev7myeLXBoJzOWJPL2/KVnp9CxCq93dPVeWbob9Yh8j3+MBG0HURG5BfeQee
-RsSKL1LHGL5cnkV6hUM8LmEQ4RmeAH3i2AL0w+YL8M1z5e/qtPjL2w8Qju2PPcJrbU/YAuzZb80V
-1lywkIbtPBKGdz89TxD/WPNIkKFHXtMaAXZVED5Z+wOYWm9erlJ9+F1belT/6wph7Hk8mnRJ+KVM
-jWTq+VD89AgT7OZPvL9P2z91HJ/yNHSPbj9GguPPYBkJgund9QA4GzoWCEacQ4S04hzA801ggT4F
-pHRWIrPRjvUls87lrAwwXGfJGeoTjoXW7YlHrQ7n+oF8HMqF6DNeuDiisdYAKnxhjnF+N57oef6h
-TPYCgLchB/JzzxMUt/tf6y1ajSFzreoWjUp2mXY5KW1PkwMaI86Zzs3vjxGPLiEeYQ13cPldX3hb
-5zn9BqIfX5dNk9CITY+9t3ZzoGXFy+SDvheg8SA+rSx16Jmjh/jxDJlo5R/BcenJjAV2IJ9KtNCI
-4ocOXjk23bzNOWDlmc3/ldB5bGUJcP1bx6TVFQyb3Nwseihl3WAFtS022CqsIuyVy5zbsqId5cwM
-Nl6cq5fkWEdvtpJMsQ9dMU2X3pDMcuoNlml7F3ta5zpwXMcry33bdve93Nv4gEseWx176kCUV4Os
-topOG9gWHthzU+kEm80zLGlYqCzHY9gvycHxWIQxCI/tPgR+CCVUTzcpj44vGDy+vLeD3eIlufg9
-BMQHKLaCHOj7xLMCgtsENV1z/v729gOEY/sTjzDWE2GSGxXvCZc00IQ9ga9pmuawK9eyFCM/hJeg
-Mq1rcr1Ve7yOtH8NhxBhPfL0+DjvbMsk7/QCf2MTo9EOvY7TjgEBOBUAv6UVHAsI9ge/qNhwhkPQ
-A7y9vDg8wRXbizznibICwfRc9xUhHMAXkOAX8KY7XQFPniF0mYDQP5S7GnECNQFYY7/BWXl+zzqy
-nnB4hHeLhlzhPT/TKd5gD4MwPMK0fjeWq/0c3/seQK5oUHetBHIISyEBKA/i7RJphUIsslHtjzAG
-0tNYFiUypg6ApT2vNugl/ngMNEC8BNCaeIEf8fT6AMWwc43gXC4NZZimoUogDPH+CgjKeg38jrHG
-Y9uZDwFwHvxDsFeAl2AkyCvnqbfXtQzIECSELDLsq8JXPMHWVqmO8g4TPLLDMq8CsIbqimO/lx3p
-UnBFD09g/+bt/QiAeRyhEYfn1qxig61AbXqJ4wW5bauWgOAInZAX52CWL4BNcDxjhBlwWp/lJgmj
-zCC0j3x4FFuZx7Rxld4yaP1UIGXZTGjd8np9Pslj3DT8Ca+r1xcNbes+5TP4Cl57kofzIjZDIyDg
-GODLcgSORcIaJ2m1bHfDLEIcjGB49+UJ1lyiuxjCZsFSJORebWLr8i8PMAzHevYcPfFy3HKEhzim
-6dLH+lnbt6Kccxv1ZICZ/rvbDxCO7c88wttDpt7gjZbkMQ0ZIZWDpreyLTWqhljSCWYj3z/UNxyg
-91Oc8HWTy+SjnlsdafF3bc6y6Q1mmy2PyhzyYwJoALP0/wmAfw0gXMcPnsfxK75k1L2+yBCHlmYd
-7OtMrzDra+yyH8flIa4wCo5R/zjUoooe583ArVxp6VWjA13NGXujZ6sAccUI1x4PjaLnC3I546bH
-zDYQ+if+cOSngKufc+/X/HtdJixA3tPT2x2zOyBfs6ORyH5STjvlam+GPiM2XvgTgGTjXNbPubK6
-xjwGwyLIE1HGxgKE1DJM7wDYAwD7AYK/Gii6GqXLsWMAZgLfsG81TinLMZzyDK+88gwjFcAMiVCP
-b4HfT2W8KJK/2Vmu/qHKrziidOypDuvGspWXqFVNYQCt53qijJ399Tew2z4KNOtGnpXnN2N0Cczc
-e2yw2V4fPMBveYHj/HjyUCA4ytLj+ylGeCVYrhdWgVx7OhFThFCoTQumcrnxqPtUS7bMuWS5mD4W
-86COIedL3vOIJ9gAe+rGwp9Amys/CQ2ELuNNdahNe4I+EhvM5dBAki0BhzfPsFWekGWrsMjYnmGI
-YrXGe9t2WYBg2jLAsfAFB59D0gu8IjRiucVLchLSYNLfQbZzq3lV4Hu01yZifWzxn9p+gHBsf7Jq
-xOZCS2dTMsUF/G6lg1Qom0ODmVQjiuFNReAl/Kq4+wPnSPMuNsGzXJ85qSAiT5nw2I2yI33POWun
-DxttyOz6MBYJIAkexw9+9/T+CoD765Hjh4A38sIjnHU0NAId7M70eznKi4wdUrG9wgglU6BY0E4O
-fxyAXyoKLhEYVkCoqhvQatyB4iT565YAiwfRfnohbeRHrDDqpRGLtYcz9OGJ8rXXH84WTPpiK9Pf
-21/Aqe4zbIle30v6ecIT/FKX5Xx0bUJnTqHJbBx5nG4feZJyHU2H/Dz3Or6QYRop5y/KefwQ4BAk
-aN3Lb4Le5uFVdDHVD/tEVuZdtMhsATzPZdF4E9XLnl7/eeD+FAC+hj9EGp6Ic+MWTzpnd+dNzpvH
-l7rXZr0ARzkPJc9Tl9XBWV7yrtney13zoiLHO/IK3AtAbtPkZa+WTts2aI4l9uzpXv+1NsiT8CIb
-vOIKePQlMXH8JPCmrRUbWSAJya9s10Y9h4AxXi7PyWnMOcoXwD+a+EapIu0xbSZKLLQ05TucZHx5
-3uMGc4YZNdoFHQ5b22TMk4aNFyKZoudJgjw9w+FrlrfecLo4uLdYHcLi3nrP16f9F1+g81rD/CuW
-cVzx8m7tK02amPWPAGm9v739AOHYvruOsHp/yz6cgt3AbzKxi2KA8L2F0gaSja3SLXwilXAIIfMJ
-kqfARb9415wKQ8Awr1TtiWe75edltPOSb+cZsvsWCAMK+OIOhnPlh0c8vc8GuL+cYLe8vgV8I1/y
-aCTTG4wBfoEMj+jAVz27CRfwFUD4y7dKXUCm99giTUMoYzev+yyygyeK8jpuYMlGmRJzUt3GftDf
-WkKaYV4YNDhMwA5Q6QTFz442czpm52WFjfmi8wEi3/Yv9XKkHur9eeKrdTfgaxX7TC9OGO9Mo0Ak
-dA6FtHX9mtAD9wiIPlP+zXydSQveKvDLLnuAhzLiCnZJ6wK3BDAJhFOp3baOJjbgaEgOqaMOMHtL
-O9wF+A6AnHXpeZN1hfnFuQ6GycICYVKOXHTsHocLmOnAAyULPhhV0nPYmlYZnuWTZFU3bn1bw0al
-cU6Fc2TsK9s5eUaCdsq86FATEGsUapz7aHy9XfmlgKn83OoFuvxt2hHcutioCYppp8ijHfx225YY
-MWygS7Mpk+xW2sqgQfBB0kRurvRF1amD+g1RXc+5vKo4p2w8dckwgasudqTTLC62l61spkA6H9d2
-nW+kPJCVaQ1M8uevwPC+0W4AeDF2eOHLPfZxnWUJgr8OELzyZ7bu4DjCu4xPt/7y9gOEY/t2gDa9
-RRYvxFE44SXosTe/5NPTECxrki43sWUs25bYYu8WJqGPi/J81B00bKRZU4Exz7t7gE1ytFavaeOs
-32+U8R42UIqnsNVZ3laAoJc3Yn4n6CUQTmAcnmCCZn8qrCH3TpOgeeMYwysMxmB5xv9+eXmFCYa/
-OK1loZNmT8zv0yhaj8kLHKkZQ1y9m1ZPzVc8pZNzGPHpMpl5rrwYfMy3qfWtapR3jm9d+w4yg9kj
-4DCuse8sxMiUEWuGh3s64zsBAAAgAElEQVSK0JEvj98f7Hjl8HL5ituLZ0W+wTOkaed7pi0tgnPx
-TC/Ka/97/7z1rfXPfOTVmWlUjytITRv1s93qLsEtDfYb+CV9uzcYBVQ4PNJzCnOqL46Xum7WI6B9
-XtIOuABdHj+XsvQE1/ntc8qlLMQzzC6yfHbPOxKKdM8HMkyC8nADxn1yr+mUXpF35RUt1ydBb6Fk
-jT9CmbqMlXr01BQytKQFChgatiyI57LFCF/ALkzCKtpvoUBv/xXPyR7jeOT7yKfN9pZXeuIExLyu
-1/QRYOt05k1cZy+lY94gy2ZAPA2z1H1IDyjpJ4A47XCc3dQt45T3vLZLvfFZHKvGdz12seVeTzEZ
-0eEWL8oFTQmCNwCOWOI4dl+5LOgXdiz6+qrfVwLi1TzEX18THNMjvLBsr4e+vuuU/B9uP0A4tm/f
-hYTrRe9O68545HGfFiW8uwmKgQZyFSSbnDO8wNlOas7IS0UD9NCIUigqeHpHmv+t0lrSpd7mWWcV
-zUotv7cGC7wnZxjEzFMwrCtEcBWIR0CuguD/KDiWNF9kM6C8vmFAmxf4WoYDRDMM4gue3t8Nfges
-a0qJ7ZMGAmwzLCEI2dxFCoCLyD6OrxPztjWeYr/JFI4GrJuVCLASoNfjhVKX99V8rQ2I4WFs2b0e
-Q0pxaRQTdr/WITADcAPAbtggWNMExFmP8YxPpC3pQMqKKA/ICnRALyS1ytG6VcFbjHFucZ6/TKPz
-FwaeoPixur3u4DdorQDZ+jgh53bPcINOyPm3PUCF6JQDBHDdQLbS6gkuL7B6fBUQa516aekMi0Bd
-K2SUtKsX5CgxMo58CavSmn+IV/MgS/pl+hrYFZ5RwMJMC94+0Y2cNK+V6sTzuD1dYNt95qqrKurQ
-uptOXPLs8ASPn+GW7x/PmeDXgh9nfttLfgPTQAHfaJ5tbRbd/Smwb8kzFatMwlSoye2JjE/GkHN5
-E2OArA4RNNIPnAgYbg20CfLrfDXuGKxCnhMt3W2N7PkjGDaUR9hQYRAVErHgazuFfNl+wQ4VGvGl
-3uAL4F2rQig6CN7trx+P8L+/Lfsm8adw8vbJ7SzT0AhVDukp5k7YliEPTUt9yAOa8DCuL4UNSIFL
-4GsCZFO/ZE61K2LTSofg43Kmnn3fSqIJGKm1eawvzVy9wfkLb3D+TuB7A8L/uQFhfwe5Exjf8rPv
-2GC4WbqLYSIb5M9VlU4rTCWMD2WqGefxZVb8zKr5IXNwHOTPMJME6fTCZToAZ3qCNzBOQAyU4XOU
-UQOB275UUs3YBZZ5NwoBpNNrowA4VonwOMZi+gZ6V/Rzy4ivlcYI2Se/4hFkH73yeI6ClTkt5tXI
-ZIqGTHzUsXxykOE0sIoVfgG8TCtAaS8tRTvvwqv6SwbXaABoKIPGABPo0subx1dA7OFFHnkKgC+e
-4KpTVOfIdpagP8qacy4k7eRLkQVOV6Zl/iaguaQJVOrYpMqIwp9t6KWaXkEb6x5i2Aer45vm6Nfe
-WqhWesB4+h90WKGtBCT7AMwNJH8CwmIzNTYY5FNA6spe8ltfE1DTFkpa800BsOfoQbrnTT5pSVqR
-93M2sb3eVWbRt/TqCg3SPl82Ff8xsV3fze2iLjYYLt5i21Q3eyWkmgLCmPIOl95gqIRHTPDXWilm
-fMKpAFc9wT1MYh3AeIPgAsAs/9vbDxCO7duhEQZZbsm4lokYhRCyZDHHhU2lOTEqAi5S0Fy8yASb
-XvV3C7xuVwwxsJJZUYwFfEsw9b8mlTIT+E7SMDHPaQ91m2IpDb+Fi4Bql+lqCx0M11Jov5o3mIC3
-e4L/c4DiB//5tdspYAu8Ad3PIFg8wjEml/EMaARzxy+hmWEvgdPOA9TaYvINj/0oG/UmWPm0KUA4
-gEFkKuoTMLQ5foMaLgZn8Tlm50sk9kgsXzeEbPYOhCvCsYFidlW8wRvkogDws1eJqGMDrAAyQbCv
-1Y53aASvX+Rphkn72YzUu+fYLBvq6kG3nEL/WKcvuWcf0vb79GGgP+lD8oXotsmeA7CqN7iX3WJ/
-o+wpr3GFRdSLcw3wTk8wmI758AIBOQYFupBJjrs1T6cFkA6MnL+b/uONbpXdQqgFLmVh5bnkDwZS
-8ueubq4dpAMz5CYggJn2rsQ9qMVjCwpKWEP7ohjvvIIW70B3nqu/6EnIf9sDRxjEe11rABiGBLjp
-ETbbc9nShlxKztDSTaCpvw8VyvCzmkfTvqfdrqfHBMEm/WoOqzHJBLNeV7uxgfCK9k6sg/CHOqrU
-E5x2yLZe4Iu2Zt5AsK9d/pXptfu5rIHer3WLEZ4xwQWCK/+NHv/s9gOEY7Nv34XQY4Qm1E1R+Gag
-5gW+pRvYUMAbbaR0ebUpClSVGIC62zSkEEYBNDyiwDHLKSDRzpHuqTqnHY62Rj67OuFC6mxPpXMD
-v+oNnvHBujwaPcIdBAv4fR78+vVknjcP8FY4suTjBQCPOiOvAMh4qNa1VikfH4+mQJDCE9Q4Wmuj
-lb2mdSY+KJhkIzH4e2KKb7ROGmkBG/C9vvYiBXac7n7ZBtsDGx/j2OubVoy95yUGEA4DnnkegIHL
-vPGcTDv4Bnz3Bm+wu3v7ZPwvlTmepzykyzKGWO9FBucWCTDLCaJGHkrkcopkSo7ZGXVmeY/3xTfT
-9poW1DQ6cduEB/aA0SjRXnzTMIiRHt5ffVHuBpyvnuCjDEhgLJ5h7bfrGKiHBej6oZ+rjkedmkxp
-ngcSP8zqStMUtUHpvMz1vJFBZcOE8iIBqgBlro2rbMejGiaPLY9neMQBdEmL3/x0VaPfAd3yBkud
-1BN25J/A9yV/0fdLL3DxQ84YaerncV5fbpj6+xVit53Dsuwr7fDpGa60Jxao+X4PmbHki5t14H/X
-lFd5I2P03QwtLMLjJoEg2H07Er4AcN0JM8P6sh4X/PXiAX7ZW8QHf3sFr//h9gOEY/u+R9gAGnQK
-Np8rNAHdxrruii8gxfZ5HfBSWKiQtEzBMI/ZMe/KJhWBVb+hj0BVTErpaBDECWj5v4PiK/C1M3/a
-ikymsCMNGI3aBMMFgmVNYO9xv/+JJdMUBP/nefCfXwWGEwg/LoB3e3Yzbcy7eIdfzvH8nXfwpxKK
-F+yyHYuv0gH1qK6brZOa301ftm6v6xJyM1aX9jGJp9eNeQhPsBnBJOVlE26zvSM/cgHx7loofWOL
-ks66BMGWXlo3wNN4b/Cr3t/dryeVO6jUE/CqJ7gM6Xxxr0aO7NdZHv9v5VbinGKndEaSpBvSMVeh
-OtqHrI8widxPwHwBw+h9kJ70kTWGEbmdm4DbOwiWvT9S78ENLOvLmL8Dw/3mrHg0b7ynd1cAzfFl
-OYIg4xyMc5VGN0Koqr/UU/ECuzbOyJQ24JmS80MGqU+jfzZuym6aIeEWZcgKAKfn8wZugdcy2hzK
-fV+ij3k8H1n3nlft9HMhdg8Fgrk+eZbvNrbcRN8zPGKPj8s+9rm7cndRbopI9Llj4+54MhmXzQbE
-vEPB+pXVZCYbT5nUq5qzs815k4CXOiOe8R26YoeP8VU7an3GCO8X5bpXuC+p1r3E1vaW3uK/vf0A
-4dj+DAhzb5fjUhq68H7dFXoa9DDzyc8lLAP0yt39VsJ1V4sUlOrH9AZ3QZzCWONOAVJSDGGdoqRk
-+358MEDNnIEDBL/YeQmA0b3Bj+/YQqa5dFrGCktIxH+8g97j90uWTwvyTSCsXt8GjF3rFEiOkfVx
-Ck1+gcDFK+3MQyyJpQEjnHdJM98lnRb3kv86GR9mqQFitquXIsRQXuR190j0gxwmq1b6kyvOp7zQ
-A3QA4QQgAFcocGd+Ke7dI4/Hd9Vtv3h/HYh1UQGP5dMYyuSPKP5lJ+BIspWnTcu1To8tLJJn6JJT
-JCnHOjNSp5TDAC4p/QJ8MQwXxLhhgOIT8CdPvII678XHUwp2IkBsgtPp4e0vz2GC2eYxvoRFQOqq
-9xdhmOkJZtkxIEUW5OV7/k6+5E+Eks2f+RaMcZDWOY91ToVZnOStefDc5SUz7eJbCZsRclzxwhHC
-5wiwbGFGmKYdCp1yAbps+w6AL7ay5Ud/gFa3H0O8w1bHIQ+M+XXD/vQzAXE8urfk7wC+BKAEwas0
-GB7Pa7WZayBzJoDukOrl3aaOMEVJn1vZevVBHHbhdtFmc1pHWz27/BYMD8Mhmie465BjFWKTmOCv
-24oRlzAJs5Y2AcR/e/sBwrF9l/iOtRWzKgObcVL5gGnkWeSG8vDa1+MUb+WavwEtz0t7XOmhYMob
-HGXc6Z269jM3NtIFaaqIDoIvabn8zQwxUeYrwIMjH4d3rzDKI0xg/EhIRHqCn+M3QfD/i/CIx89Q
-iAS9rh7g7amd9cw7YFY4OKlpeVwR5A9KnZSio8JRxdXT9pL/Of2yBUtdbTrzVBtTebvOHFBQXurb
-XmrQ8wkKinEOg1qgDUbQxjCEuEFIA8h4YXqIeY5vMKugl0N50IAvfxknTI+HYXs9HksSkJSCBWXc
-cg2xRVqas5E3y0j5LL6oCxEucRkzy3Y6GPa4ZguBAJfiw8WjU/y16SieOh3IaVP7WB29wgRqDfT2
-n2lIhKwm4dN7TID8EPTJDz0MIh/5i/7QpxVkx2MgZGPl7Ze941LGhvyUyE6w2hppvXL4MZ0rPW/k
-jn8OoUmma1xcjYgWSJvU4+QxOlsSiFnZpivgxTfzej7lmnLP8ybQ5Tm9ftkvrlVMbzYBsfGm2Mo7
-TFuXIDi+BusLNZ3/x94bblmO4kqjIff7v/Cd09b9gUIKCdiZ2Wem5jtrJVU7DQJjECCFZRk3fvQ1
-ynXT5tJQeHOenYxsk2bj4g6g9uiOeaHzYKtS9cLe4r13XS+VZRgJgs3WxzXmEyXE+xT8Ip0D7UW3
-Bnr/urhCyH7CbW9hW+k/HX6BcIRvf2LZYuFtflOoBU4QGgovAXCmWRmwgeC8jVeLMPLuPvGrAuNc
-BCp4qj11V8ppzyKVribZMX4vtz3cGeVOYQAoAbmkU6BfP6jRLMH8mtybX5XjC3P/4+tFuQTACoZj
-b+Fm+bWD9deGBTjGI0EzquyNB3qP8n7jR1/hQlQFjlIwZu0qnm9gOFuC07i3kRFg3ACyHiHIYpiu
-lt5lL7B6tEw2qE8dkylUipZrg0ouBXCUpVXYxXpV8bAGs/wF9NJlYge+cW1xq6h2FHN2TCJw2Dor
-BlfW2qWyDvlgMZHoAqWsyTF3KvAUGeAX45Z7BEfekn8Kip15Jn0a4NjMxIg5OnqYHx2RbZ1Ft/j2
-l97q5bmDVXj6DqdbxBtzSyzHmlagzJFpYFjSctNUaIoyGDi5TnjO+VGmddp68lPI5WNb8dsqnudr
-fJ+jNT60t+j8VJi0W4AjDdFHXKfTMjxB7wS8qpMaDVBw+xHwAvnkIvWpyXlhtfaH89jKOoygWdBe
-rP6FC1SC4NeXBbmNQnEoVahSc43WOalWD3rxqiftnFs3b3MGmGZCdUNBXA40Z9kOiBccKfcIM1tb
-o6HkAg14S1ZwVx3E+xaxL3xsC8eX5e67RtQWaXrkS3L14tyVU/+x8AuEI/zky3Jr0Rk1EmjxTRAc
-i59vYtsGiGkTjAmqIDi0kaVQAijNzEqqNdeIFE7I+ru51mRBoIRR5nPZ7mKA56vQ1jzrfy55DLuC
-LRhRyutkBXZfy27fQq3A76sWYe8vyJ2swf/6+++yCJt1a6+J9deXnTOtxKQFyys+eSo/72k+WNLf
-EkADW7gKua4iu3CTpxBfq9ERDmWo11WByrGhPz2/CWgFwEA9SZHNhRPQUclhB8KxLtKPjZYqoIAx
-wmJMMMhfNCF/D3o+XryY4HiAxgOXWlxcSK5lglW8uV29XrwzfqUStWy5e4zBGvZKloNzpfor3/8Q
-UFxKLenaP2jcc84VCBzD2xM15tr/NTCH3R3iN3yEbfgCg59RVlBMoHsAvwVw1Srs2bQs2/5atT1l
-JSd5MNsJtAQMKWBWYCzn2+k65GsLXd7m0vo4ieroI98lv/sH88a5AyZRLdKatb44Twl8S8dY8eAr
-IJzxPc31oL6/jYYPeTHv67pYIDbcIdL4Y8tX15/SudTdRteoZ60/0qjSa2DCacW5Dru/LyReN6uR
-Zpk2P3qYM2IO6pTk5xqGYICXoSuNS2x3zUnqIUfpOe648SDczh7DQxkau//geZdcfeLswC71stz5
-q3J8Ee62r3AB5F+L8H8tfHsT580SrL8SEDdAnFO2ST4kCG5uEfn6cFl+y6JRLzYAqMVpcgET9wih
-ZTIbomX4pxZ3kwvyd+Nh+7uXySUbipIrOwGvSnjemeq/Iwgu63CB4L592v/EC3TTGvwvdY24AmGL
-tBfgdWtguO8esf4Q+Co3ycsHwN/WwbDz6NYEXj3QdOGrx/8VL767lNpF5jmo8q/kqUgBBAwwkE1C
-2rN5jgEJgE9KEsNKCZQ1AuORnEs62lCWYBcwt76QtFmCsQDvewC8/L2hKLP8AfcBHdOktXCWmXGL
-dR6WYMs32GXFOUFyjGLc9HIeJEvjmC/HkXdQANxdJGhBm486024aY8odL7qSXb1QsJWDzrWbeS6/
-w8tyQa+PY6gbhO4xTJ9g+vLHuZhpuSbEDYJp7FO6AeBpDYbygfNV4hMYn2TdZd7cQ3kJF+UjhgoZ
-mUI0+u5SS9w0EhjJ0tXhMilLw0udZymLO9h1ic+8qPUCghF1cQ3p+l9pzbeR3stDQTCtwWIVNnjq
-43ZcX4+oz8A/xfPUje1GaYpI7VuMIUFx6lWv7Hy8pmP6xUSJMje5Um3Sa9UTDKuLr5Rc85G1QUDM
-9/7d6RaxCnh4hOIB3AsA59HqZTnuGHHeTu1Jf2C1EBMQW7hH/OnwC4QjfJf5udhstwpz0SkCSjma
-d6SctCFMxOK3g+AQTOOuXFtaABkieErY6C/Pm+Xy6qmOqz2a4qIHFbeNvOx2C6q8Bzd73JGCnMB3
-txDvbhH5Jbl3viznR2vwv8Ia/K+/AwjHGK1PPCoQXuOQabNwiyAotg0Mz6BcfaxeM3jc8DcEDMe8
-SMtw8qxE74r1HIj6TJDcmH3mfBuC2fAJfBtNAIAVDulBdqf0ALq0DDuQ5hgBwlS0+ki/pS36SfBr
-y1rlASw9+JDuERY8Hb/3AcpyvAPmzTosLEn2CEuLfldWOhT5dvtjsHg0a0+Nb3tyEPy2ACQm9Rm5
-HPzhrhHdJULTFhbjExjmzXr0I5+l985qX/NAkMQbW8YD4Np8OW7bNm3RG+DVMrqvMIYlWNJrOnZA
-aNnUbjXeALBw1QcYbi4QLa4M0UW0L6izZmEbKu5xzbTaHicfsv9JOpRLizC4Tufj87rqDoAxzsMA
-hSYT9BDn2p56SOIOFMANek9zbkZrrGgAxDKM1McbCOYRahGOLRS5k6OCYBeRV8oymu3gjfj2oQ6I
-wclMaOxIcJ3yE+NCAk67YJE1Bbqc1RhD16m0tsY3bqTJ0PFIKfVSnPME7x+Z5jkWWDxCLM0FiKOC
-19KaW24Rh/2D6S/819lqXO4Rvxbh/1r40a4RRx/h+tFHuFmF4QmCyzJMQUWQC3wJgmMB8UUGiBCj
-QNpcI9juAz1hGleFUFsp0xS08EXQS96lQKisBH4d9M70wU/43S3D+lll/YLc/xyswWoRXsNqHfQi
-QHICXh/gd91xT1/h+pV1L0G0lH1MrMFuAYb5U/uQWousvfxSo9UtX8659eUIjQHJCkRwNx1fwtlv
-51OQSno1I/b0NeFSKChVfHl0rieHWi8hFuAEtbE+VhlPgLsBXdkVgkB5HXmNYTEO9jU15b3LneQX
-OrtLSxQSBNtr6T2ygHAA5BgDg9XDJtTq3NxAgj8LIKu1twOK6pt161oCnbIIst1DJxfAZEYCYS93
-BO8+vTZcH/hynIlV2AaA9vcd4FfSA+Amttjy+vTsMQEjEwBnfAwm5XICvznQ/yzo2j3eYZ4mFfvm
-HA2Xc8vVZUqTLi9484OUGWo9XGuggF5Zz1V3SDyBsMTRy0wXB29xS9Yf4wJE66W4OG7g9wn3iBi2
-512uSLAQwHH0Zz24evpIVNSqe9GW0p1Wejf6aUynzKz11NbRNqBMyroCUDvQ1NhVWDQLmUlMsW4U
-YyznTRy6ruJuRbQIl4V4WcyfV1r5xPzkzxyw204RB9cI664Qa/eIXx/h/yfCd10jjBau7/xE0dcd
-qcgQRuTRXIFgj3QWFPVncgzhlcAcJZjkV72z/mO05aEd9dwNXyvIatdBtq6HgzDIx5oknS3Bx08s
-JwD2ZhH+2/VDGt7dI/4WIPwS7B6AsC3L7QLACJcIr3IJiOsFuuLsEkjpduHcnWIB3he+wC9QAJiy
-jxaTHOf5V3nvMjv4+FxArIrM797sNXCAGpusE1BAvI8u2rkJfmnWIfjN+GpbB8JqARZaukCURbhA
-8brwG+q8uZwEdyZAVqD8OuDmCYRfKt4D8P067ht9dXfNDAJi82EJ1mPe+J7GqIBuAl/2U+ZPWsOl
-fH9hTuQZONYCm2QK9R4FqG0TV4Hx4WU5tQwPcJzAN9wh8gU5jXMUCXAT9FWcyj/HQrBMWa+DwTnH
-L/Hsv4JeylqJJ1/+iQK/nROML3bPiOR5JXIoPGoRGSFDbKMK6pDqhcs9sOooF92mfBH9A5lPG0BG
-s+r2GzSkPOhxyyGZblQwxJPZJ3T4WlO0DlucQ5NEuUcsy7BxEyi8azcEPsxq3NuHid2Zurat73U3
-228qQbbFOlODwxhv/UJgAWK9+BTAQYtxUreIsh7XGBrKFSLjICjmq84hv2kBXhaKFofTRziA7l/d
-Erx/Zrl2jlifVn66q8S39dS/L/wC4QjfZv4TM+JiFS4fpNL97YgCxVCw6/TjGhZhqPVXy1JEFQxS
-GNaT2sZZLITHUSCfgG3FZl7Jj3NtDKlkh0wnAKbCokBvIPitTyy7WoP5wpwXCE4A7H3HiATDf6+P
-cawtW+qFuAWCvVmFk4YAtXnO/lNLMN/EzbrhC1wD+MvCLcIR4DiAihO8kLOl1PSRZ418twaj0U4C
-E11vtzgV3SEtGpSPUDkv5xU8QYKJTqfiXPQCw0CBshHfrMI9TkGd8wcFgl/OHzMBt2oJnnkEx54g
-eYEDl37hh3EFlFYbZ8Sj2BaH3FA5ygrMI8rNxoqtOVfS/QEFMgh8+w2G5gdQiLLag26Z3LlQf71K
-BChWcNtAsFiDFfB2S3E/4n1rZBX4KviVvGNa+rFNfAEmDQDfQG/bMaKvvI12FIQ7sdY2IIzeA9ea
-C6FFCzStlIyqTsXRkoBlEbfqclJQ6/YEgAnAshxK50waau7pjS6AmqdRtc7XWI5oT5DipNwP2J/Y
-/cGqHwSnCAvxgzX/NO1Y8zC2f8yH80ddtinRDQdwJ4WlqynQOR9rrGg1bjZeAk0OmujEPVjWwy/S
-0RyyQHjMLX2KDIu1t5L5why6ZZjjdHRU4E0R+2UH0PvXBQBf9hQ2eXnuT4dfIBzh+eauEd40lyUg
-pjuE3hVvDvopPBjq8fURDAM5eXPbJRGy011i3pkOeJrX3BbxFig2OqXOx8gTyuzi7SqipFIwhOD1
-+HP6oEbzE3b5qIYHGH7vluEJhv/1xq4RvDs269ZgHlHgtwFkWAFbE4DijK/z/gdxnpfF+S8Af7vj
-L7MEw25qveRIU8CrI0RZfjvYJTBECsJdel7GfIIBS+IBKLhoUFWrMYZixvQc61KAZXUsJXIFwrxp
-jN6WmwNdIgIY5zosgFvglnxdFuFmDfZVXsHwa1KPQIUb2G1pw7kcFR+s/IMRX1LyJwGxO8GwdTAM
-0IbV6l7At6xoCXzBOBJIlIWYskj5HrNJOjJVb1unDeCOdAPA80Mas5y8KHfaX5hpyJGjP9MCAhun
-HDvnFOC2OX/IVwDc1sBNfp4mQIU8a5y/3bT6HJOSldlrkZk9PW9rrM1kzevlYuS9tzVdZ1LA61HA
-4HZUfTjP0zm7yuc8Nku2N7eocc5Sjw/c6SLhIZVD5zrKVQL8ducCXQl+Y/05EP768cpv9rm3u41f
-gv2yAps98cTnqbkuq6mNsh8mCEejra8aodsZ2UxOWZ8gGM2uIaaI3K7TjLcC6zrPqNwV1AsYpkvD
-t0BvukfoRzX6y3N/OvwC4Qg/2TViOeILGJbFUGhI6UiLMKcfb6ITyKLuDnOLpQAgDQSrpTghqAio
-/Nnlh4GF7PAbvElaXM/2HLQSFfnE1VKsBMRecQW+I617B6s1eLpHtC/L/V0/ukX8f/my3AUAm+3g
-V90jDPVDvQRn8qY/wfL/gNZlw18egMyAv32B4teWn/ACw2UVLqBpMeYc+R0MpyVARW6zXv0gNKXX
-JCtSouo4ZryuV3bqaiWVXdFXfAfBtt5UHiC5gze6RFhsk1ZAubalC+uud4DbgC/sUs6S40dwe+q/
-XehxfGJm2POk353hXWmP4fJ1A5XL1Zd/+RviRnZnztHOF+as0hsw1nTwulnk2lTxpqM954Qo48Sn
-HdzSL3h7WW77iMYrrhG1vzDj9nrzEVbbPzBcJKCgruRIKy8AMDurS+M45yNOxqqsFYBaBKnwHN0n
-BbQdreK9ea39muHVPrkZANB8faVHFZ+gF7UynecruPVVege2oie2PLlCm5s1LyHxnT5AcsT5tAi+
-bijX0QscP5SX1JJ0ixCLMF74s3yJixm00NfYKH/SUqyGJzPAnrWLRYBhmMWXNPvI5hTLtMgY7z9d
-X00dZOhaAdJbBcENvJqJJTg4IXXzdqJ13uXa6coROKVZgYc1eLMM64c2uI/ws3j2/FqE/08EkQOg
-X17SFS/qRB1yM+vIfO8rZGpaozBDPYUKIoWUUVhvF791BEdgrLJ5RL4VTiDBZr73o8pvyiKV8U3P
-AqDvMIHOsqj6/nvrl0BZwDKtybvKDGEMH3ETsKWquWh/w/GXG/42Htdnlf/C7gZRvqzz2nXNJnQ2
-UdppRSEojrT1sSEcqPQ8dlVYYJBgaVOVIsa7JVHng3NAt7qVlwJbYmsjR2/j/beu/Q76Gyxcc4ZW
-YeZFWqzJZTVmuzyemMYAACAASURBVHrY+iSxDTyO8Oaj1xdvKN8nJv17jK91Xt/sY57JWllz2Zxr
-g+n10zgtr+a2QKq9YVYmEI1K+WLbALjtUW2Lo+/wQADrBYxrkRMoo2hAukpkvM08jkaBCB6mfFHM
-VQPRYIyAts/jtVeshbnaQLQoBb2t23b6rSx5L/WrhbddVwXkiS55em0FvSpJ1o3DxqVSTQNEZZtt
-HIEAkRjKUo7en1QwT8Ev42d6B8Kl99gbjfPI/XlMRo20ymP7AcAt3hJ7lz+Tc4uJ54kPcGC588RO
-NEtcLz44yl2BgqitF10r8tSkr6m6eWz+wqyX/M0dLS5jIiDY3MW6D3knoazDkPj2s3VT3l5eCCxk
-aTiycAWsJ6orn8MVxgBLVjc73X8j/ALhCLu6+1QyJmeexYlKZahq2OP/oMV5DGony7QA4P6Ye7ZV
-6qsZ+62+6GTeDIfG/vbraLp61KkFZj3z07LbIAZppQe2uNBEl3Z2Fqvlp/aicWfS+rHn7fFP+Yvp
-eU0R2szf8rSNQktwbAS0gFlZf0txzjZNCzFHt1Regs4NGP/k2B+sDhtbXrXpY5lnfZacQXDOS6uy
-bH+fOTPNPvZ0zi8qX4x5Fnmv93bQSqxhyomT1LitzrXICFjxdfwF0CHwij+A+xvbvL14X4PFh0HM
-Epr1lhMURf3+11p3f/lf8L/Wkwq82MBuMUuVNDZXB5dyBXQJwOWlNxkxlXnk68ROruugjYA+6t+g
-Zg8B3Cr3vK4VHH5WyS5loj3bk5teWxZpZfXqZJ23Xwk7lNBTQbi1CVK2J3UFOkq3tN5454BrgSFZ
-2lH8T5NO0KX5cXRHArIEt5HtCfDC8MCy1IFETKSHq1HJu4rXUV0mYja4Y91p8xi02ObRc7uJRfPM
-c7zmeF4DHsf7Gp7HgdfwPuuJxmMP3qdeDKUP/LyBrLx3y8s4z2ty0dpNRNFu6UKczqOL7BR5CBcY
-oaC40UvrWfB4PRXlMznPMkDdiJ8+ssP3AHLBL8H3R8MvEM5wUmmnUiXIN0AnqpxCq9EYF9Bs23W/
-kbY6pFX41J9v3WINBWI4nnNq1U5zkdvsP1J2Ny4ILUtysVMRsD6W387Zf7cOn9rbwxko18++jFsI
-oAJ445F0o4W4UBBsPe/Nmkq5JNw1dZCIMn5SVCNlxYecgTdg3Cy/83zNq7YVH8ue96ncyQ71NeCd
-1lodu24/pBKtuRRxC/0yxrk+cOKor9Z1vqBd7Ryucy3mMR/DUhSkhTTeYHc8+RaLtXLLpzE/If0a
-1tfx1vzjsTXEkQBnKde/1vp6FiB+/Al/SIyF9jnewC96mWV1jmc2TsXINU4Q1lcum21cQ2FFqvkr
-AD8NA33svxZ49/x503MocCB0OI5cMyzcgWG/Ql+jnc9exMH7JSO1GZ4HXRc280njcHH1ZJrX05bJ
-icdJfZA5CooTgXcrcQPBiJtVgjbSw3KMDTBHnWnZtNqLEuLuIKA3/YXd4gaNL9V5lPe1rtxiI10v
-y6kRvEe/CIDN8LwOPIb39fVY/13O/u/zwl7xEZ7glunthtKhO6tsvvhqQsXXaQW+laaeqqV7HNYR
-NjCMcNMiiyK/thEVAEyp7PVEavIG+YTq1zXivxauiutYTgCbUEoEFXUT9lfavTUFq3r6u609+wbP
-cpdMkZz6t8700ZvZ8+rjEciqDj3mDQ53nfAtcNytlmcr8ek8G2VmXOvf4gS1JQLkkZ4+mh8gGPpR
-Dct+7z7A/Ugd00MHxx3wUkDus5D0E22fsXer8HfL1coZrhFtrOYc1WsMC3DOlQKy+VIdZM4EGG6f
-t/ZVH90neh/28NUqnPkWDfW4WKbfGJXcpmg1clmJl3IoLLIez/q7WlrzpnwRhXEtQUvjXwTD7rmV
-0Q5qIW1UuizArWzR0iIM1huj6q1xg0FxCHnlpjOeMyFoFjTvFuN9zX4JcXNs7uPZ6/UtZtkWpc0a
-P84Xr/FZN2I0LHTDAGKOFp1DkZnV4oF0als1NqYkNFxWVDuN/VI+KD92edQE0gDGfPehvEFCP3GN
-Gmm6dlF1JkiOBr8Bfp91Y9jAL0fm4C8sPhcoAGygFdhzO4mVfn29HwILwfEYXnvxvOVC8ZrBnrAW
-xxt4+1OWHRB/i0Zu/xAMX8Hxl0iC9vU+uoblmgXop5O8rMG67pMW8yv78wEMP78W4f9a8A/ToRe8
-W4LpErFbiPWHQxotXoB3tMlQCyJmJm+i+ZGFXtOs+ytA/HWoninHSgDPfDaLACMFuB4hHPFxBETY
-+16O12ATvKfvoXhVnNtBryzlLa67hLQPMGjZlLMT6Bo6WI48Y5lhDfMCwWoLbu4Sjeo5NyBt0ll1
-tARbZ1uoonzE6TjPMbVBeaN2penSXnWxaErVIKieLfhkFT7kHaxNCoBnnL7CfbWGFahYuIVvSo3q
-c1MIyKeA9qzPW9XcXRalrP8JSzDz3wWA31b/jNWi8Nfhf2Edn/Xrm93rXQ6fMsliRLX5cxnmvZtL
-RD4aHVzeHmmJ9p0W4Ql21zrU+fP1GBwHbVbhM5Nz91RG57M32i1+au3RJWLegKSQaydW/Yd2Z9z1
-lEooWC5AzYM3nrvEs87p/sB+ukseck17xN2RLg+MpxoTVwnqrd0ajLQY5/yw6QYRlt58E6xcINZu
-Ek88cVkv29EKbPYsoB3W3wV+bblEPAFy37VF2suP4thyicDzLkBs3SLsbfxk3VxA8BkIf3J/OKcR
-cnCmKf90lCv0OasPl8W5JLdZS4CsR0e+AFxuESgZ4O8mClwb9QfDLxCO8G0gnCW76lVajaqeoT/0
-eBw+35+5lJnKLgjccu0jIDYpf8n7cP1J6z2bwJYpARhDXi+aWDsgMkLj4zhHTDm70nY+/9i3OwC2
-Q7n2woaUY7y97TxBtAqhoBH8Mu+N+NjbPfuVUNYBBb9NQTVBNnmDBnjzaN8tewK4esZ0f7jnsa7F
-Uxe+ngDvLX0Dx1SwqCOQ1qQTMK6X6daxcYPMEF59Lwj4SYHv+Z6O+gbDl44FAA9wDGcZeWz4oIBT
-swwHOBb9mW4Qvup0f9rvfZ/6xLzHaqVYkUWrwHf1RfoElxerSvEpIFZJMVeihSXQbPLWmo/8mjk6
-5/fwSZIZsLvo+uqct7TE88rjnO2KJEr8Is6P5AGA+s45wsKaRDk2xzaJrDTv9LxeVqn1ePYCPg0e
-XNOTSQdgnGAYFQfAl73K8osGcnN9xmBxrZY1GN3K+WD5mMKWu8Mru0L4yk+rsPvaIYJ57vFtAMYN
-8ADBZqhNhiNuyyd4fQ446OEOQQuxvbF9Wi5kv/xwAMc9H6EbM/1N8HtOc2ywGWHKSszz1MwSNwpR
-V5pUoi46MvQvq3bTEY8JoqOPJ59h/HmD8C8QrnCRWFspF+HgNVGLEqWGwN8UQZW+i/OSKXsZT9kz
-9EbcgvWXSXq9Nspf8ki1T9w55UwloDcMJdT3o3fuXMpQLjT5gf23NdEpxE83CjuwJZ1lCYDt0zkN
-6NZd904rwEvaK+Vf6PgNKy+vZwd1NIajAeds32DLoB9Ytp3T4ykuofPHJQ+t9dWvJaQ/WXkrjYwD
-3nvdWzRfzEmwPKzDAoC3H/Mxg1wjwuf3UgsUOf84ygonc9lCCfgzyrXPm76Al7J3rPJvKo+QSc96
-VBnoN/rjAX7XTirPw9+LN3cSRckUR82dAy13somO9XP7F+Raus2m4nCbQRaMtT77aybNZyKfoHEf
-hx0MrxoM0bzhI7HNAcqhzLyA4C0tYHC0BwA2izC88xBlMHAf7dLJNVdplvUqx3EatO0cb1dJXmnc
-J53Aca5H4YG6P6z1dgC5dH9wNPCsPsNBAPfqJ+gzwi4CP9Ba3K3E6TP81F7E6RaRL+hZxfkU8H3w
-2rIqL9BLdwhDbgrePkVMXqqVF31NTNqg5+r4R2BY+Cp6J8sCJcjybtRzZyxdVwTEjy8/acBhbmn5
-fbyn+441dJOYIPgVy89HgfofCb9AOIJvC/5SrqnnRWlgWEGyqG20v+s3IYOGI/CNDHMpM+Wulg+h
-sReZUxsDEKOsZ0Ke8F450dwWEEKdZ/i0zpZA2NQizxMryPE4f74arXpWuawyvqWvHBEgZiVQEXUW
-b0zK1n0wbKe1r3pJ+xzlI/xqebnGCQbU4Mvx0w4TohSUB9+3BK9YvgneIXaV87r2DfzW+ZPzfcZF
-w8e5lbq7SHwBfj+C4X7+OdToe2+mhL7yCBQRypfVND/hBoKx9iANK1VMkFUrXSne9arcE2P1pgUs
-6nzWWnrc0+q29s5+8fqD533is6ZviIzqscqZBnJHesW95ZU1uFZA/rwUZh9nraB+3CJynxuTdgg2
-2hxoq0amzs/H7umzSrDVq+wgWCNjvbXxHxWdogIMpq/wBo44U7ebi73+JHg/ut/z8piHE+9Z1nJC
-OPtKXjaelPLaQbAXCOZ6FfeH6Ufc4mGhXf7B3OosfIETqMkX58IfmLRsb34tdn4wS0BxusNFXMDv
-yw9pmMGeF/ZSiXrxoAHcPuZzDpzSZQlffzbwC5Sc/6qshfxsPsS54MAbjq77UuoBZulWzas/ohGt
-RqGBYcT67yD4WX4Ub3zq+g+HXyAcYXv09bFkFz4dHHdBdfvdXhgZsO2QN2kueTsw4YRVF8Ckn2rk
-Ar+Gc7tXjh850HMOeV+AXp5zkh3aGj3qfU2jK0G4bZIuWk8TaBWvBRAbBQv6Y6cTLfNONO57a+Ia
-QWlzs4HtcLIFsc7oyOVnTQefVh5prDlA4ZhHE9jquVsZlzItX1fODdh+J0/iG/jlI9Z61Mqxe8EP
-mtRLcrp7xJwbveV2Tc4zPJhDKy9Br7MRAW4LGFcZvO9S8vky3aKtvUwfuMXOETGm5i7Kfq2x/Azr
-Gx8QeB3vs172MXnSsYCsxKUvExR3usn5b4DdYRmW1d9npNeFfdViIYtYrvw/99vAFr7yGT6AW7Be
-rzZ8DYIJJhUgjnTmbxXtIQEwppCs5CWv1euS8C+OYKWQm7PLsXVFebz3m3O9WYLzuiJ7lvDcQXBa
-ZiUeZcGyOWfK+ktZTPmccYOA4LAEB4pLQDwtv3SZaAB4lGm/F+lCQYA8Aa3GcywOeady5K4C0wZi
-T+ng/QUMl/OvCRCO/pEfOcYm96M6p9P+LiC4LMJw+hh3i/AEwe7vGov3z/tG/ALhCD8Bwqp+u2Dv
-EqoDZpVQM/1RbMucK+3UHoHnybR0nFSwHfzvPimMOz+qt73H1QwtUSet+U8rRwn2oneBn/U79vJS
-DpMmzdg53G8dgjPSt/JkAgBuZWYtv/pbX/AqcZDlrJdvtDjn3WirDv1qmKH7STYQ7C59OJXp/U/e
-DPyWRzvQxjkTdveZUte+A2Qp472MCte62hegN/OoUEe5uIw7Aa+CXzk3x2mn1arq62UDyrVEtxuC
-BSDFEheVW0u/cPEDjkkSAHi5ReA10OxLa/AbQDa/TvkE6LX1NUS3BSRfjz1O3xf5JSd7QlEJkM3f
-iXYvy9F8Qsm9KMtwumoEk2KTOGFYRKNCzkdeqW8ZqPMmKBsA7mXaS11Qq7AUY75VOS3CpFPooBF7
-R0b21q5ZTATd/cW5g+7RstvF+kr2kd6sk6jjBowPazPTZI71/MLQ1ofGTay+tLxbrlOCY9ZHlwi1
-2ufwrAWA3GEkJtACu0vp2eMBgp9BjzoTBPP31Kccx289IXzRPqfcfm/Gky8N+HaabfmXshBeCD83
-8KvxD8A4jTH6w+qjJUAW3uhQOs/vs2KB3XWzv76wSjAcfeGNLkEw/cCcLwurU+CfC79AOMK3gfBm
-1fCNXoIKB5pe53JNyu1LlpbZaEaoN63Di5YK+jtzTdG26PkJBYBNNJf6CcG1i+8dSFNwNu5NgAzs
-ZTCGRfLgGNdgvXLOsW/W0ps12GhlYFk+lqqyCYpDwOy+wrQAL1p+8SzKqUX4CnBT8UTr1HoDzUO2
-pfGntVNpYw7ZgVajDNc2SC5G2Q0gpw8v58QOevu1DnFHPvanTm5WYY1zPlzjS2Hyq4WzPzZoE/Yc
-IELj87QGI3RAB71iGVbLMQ0lfDnosfBVXIr3AQBbb7abh4IOAGABhI2b/pvhsfX4Nh/pRnsJitn2
-tPQIvX7nPP2a3RML7fU1uk8uPJUcca0EQmtgeAMIdIswJLYxepK6Dm9zGgQa4PwhSO0Ctj1hUmDY
-Kus0P9BamDSxCM8PK+wfYRD5daxYrsnoydLrLnlZUI5eLGr120gHDxw4WWl88oovJx7Br6xl8RN2
-BXNqGX4dZvG05H3FChwzUgGeu4DgJy3Ea64FQKZFFCcATJDIX/gDYy9jOZcg7wTsfM4t7U5jMMcs
-5P0GhjNuJcc3MDzL1a+BfSx+FO2wvMbiCo6DrhAAXSJC9qBcohZ7K50vRtA6TCFyEvl6/DeGXyCc
-4fucTeE90NeNnvUfrMMH+X2mHQb/VC4z5K3Pa+CE+xgIS87ij2s14QstvknXcp4Ckflfpw/HuLje
-aM72rLSlsC3ldOultTW35HG3/lYvdwBscj0Hyu1B6sj8A63vI6yWSLZtSgAFA44EyBRyKMAw9dTk
-g4+8lv/pnI2HGiuptVuHu62a5/zI9YF5aRly1GN0OY+uEIzjDojLJaJblzHmxgTDt5BGRSk9rcEK
-dK0B5LUn6no828v1r8LW49gXryjhAL7h85hpM1iAhbXn6fIPXk8dBNw2YNvznlOej/P8Datw+CSL
-ktdb2zaPGggm1pH5bScQ7GkJ3AfgFL1r0TWNKMvrpALB+oRLrZ3IvrWrzZvSrNQHjTIt6jwBYOFa
-nTGNLrd1qULS87gZcVL+Cj35EQMjvGh93TpD9KQtElCMtR7V5UHBL48NBDfLcdSbIDUsiQR2oKsE
-AfACvC3PfQfBCQDpaN8BbgOLeu0GfrUceV2876C4jmubwZ0OH08vUsZLfAJjsQTXefOcaONjqJcD
-te3PhkPbmFtJd8rI9BgJK7uZWohRwpZzMvtMF4kw/3xSOufl+78Kv0A4wvd5u6la+UmZFF6HvOM5
-PwDFElFfvg1cbGC4lHqj2bxKn/ap/K3SEwpMmss/S+XXxTfAdSGW4O+kVa6j0sVR6xYc0qei2nrL
-e9rJrZ5WIJsAOORff1wk1l9Dxb9B46Pu5H2gBB1BHV3twzab5S6q+FbC7CprcryrFVWuen8GvXvY
-11id85Xl9wiOFeRmPSjFyrKhSDXfA1jRQFsfManfm01QvmNrW3G0eKSjoFzqE7fHXekJfN/Yy9SA
-+KJcAuIWXvjfwxo1QDHskP+KEscAuuP4RNxH3uO0ClsownijnBZhdDD8Yr0XM2WogUCHILgHWtgU
-BJfsaivlMAVLWOp2dbqCuGazvlwE55mbdJUrXdCIHPoEjqWwowwGOYMZXQ3vuw70vN5UBes14WrL
-LiAB8c1K3MzO5xvYCmN9tLZwYIUNwwLcQG6s2XUk/ZTvBebemNfiKpGg1wLk0j+Y5Y4g2ArRKahV
-cDstxAMwr/VUc0m3HuSNibVFDxkD5V+AYIJl1dUbsI2E6J+MH6zCBL9545HWcFpl46kT6mM+pqDD
-S+MgAC8xB9dnWoR9lbfBi3wc5gAtw/tq+8+HXyAc4bu7RkCFxlSdIjR2kDzLqIDZg0yxoolMVjBc
-tFIGdZIqC7b5AFTO2EUe/7NXHY6wF2zGKQ1QRiug7ee3+FZ2pEnzXv/US1MvpAuAdFq5UfFJL4B8
-cocoy1UBWgqmtEqzfNBy7+AbDbqPMK29YgXj1VISVq9SYOVc7P3iuGr5Gb/n13zymTnLHYAy2z7T
-NQfO8W2UHLi7VQDlFhE8dZlfpOt8goMb+i/eF8Cu931qfV2WyyombMl3TmIMi8E4W3lv8fj0MpX8
-ml9D+UIVMRL4EgwoGEYC2aIZgIeW4ogrEHYTUEzgHFa3xxBKb33K4H19gV1ahLE2e3sSaC1GvKhv
-HSTPrGZHLjDUeNfss+DtmB/zxt7a4XNoemBfV9uEp8yZiyGjOzhuckrXb1qZGS8LcXeP0LYMqzoz
-XdMuWZ5lKh61KfB1CGA+SYiSp6e87Pux0zhYeHmMa2/5Skc7z2D5cpqCXwT4JcBTlwnmTRDMl55P
-ll9Nr+KaFvBrUia7vdq/5rQnrYFCHb8gpBEpF0fw3YSPSk+2ExhDALPI7QD7/siOGGAc64YBD/zF
-+tgPvfmNhq0FcHUOGKp/FuOTINggVmFg7VIjAPh96sb/eyv13xp+gXCEKU4+lWySxidNpZAKkiad
-JJ+g5Xat27SQCdgK7C4RBHB1ylTlnKlCPlz0AEcgMCJ9nBZoVRud9v5sDV7prmoaF0U+NG6OtN5U
-bzLmUDc7u/VNhEbSCTaEri/TTWGzuUKY0ND9gYumgJjtaVfoI2wBkqMfoiWyb2xt6lyceYJozyde
-eaEThERDA71uLX+C3jmxvMX67JKZlTNpxpUn/cZqNXbxfdyWepXtn1euXSOqvHDf97WGzvkWjj0/
-Tm6J3wBxXH+zCDvAm10qyAS3mk5w0BW4hdJcPsOWO08QENMK/HCem4UhjXlRr8d5sPUpWrUIC6Bb
-L8jVMVlh0qVcw9HelBlsr8x4eUFurdvDIGwjgjZz1sEO5b1jwGzfkum6BZ6cUpU423oqs6ddAKmL
-FaAAqrQ5eSrXqFYfLqOW5UhdrL8uwlavfeCExE5a6nbeujGFAfkpZcfXFmAIskxEhQS2CX4V7Ao4
-TauwGWw99thoKeOzLnS3CNQNYQPBCYCR64Sudfn4HzXGBIwucXKsfRb7xPsbGNb4LHMq/6wtaBIM
-k6+xEw2eN8EwHnFt4DgE0FW5TSyzRJJTRYDW4BiyOHKeAcsabPD3kTp3ndCP/77wC4QzfJexlCR+
-jndVfKFL/pCSJhHzRllkq3NGjt6SjfMEOom8v/ZY5Pichqe4Ts0ZVwTQrcHKoUOeD3rSkCxXOk7l
-sB/lNlloaAC2L+sCsbUMy5JbfbWs00NYrvOEhp2W23UNsMz3ogLCZA/mA/gJgqUrI7/Psq4462Ib
-v+TKaVk4BK1jJ8651oXaDexWhV9bjbXO3YXivPpym0/UThKsU90l2m1SKoIzF0xK5zGEvxvOQPYr
-mvoE8wggLaRU6IynLCgwXCCyFHbPK5C7ALGAYTnmRwkCACxjWwAJsarBlyWIgBjOHSJqlL2lhV/G
-NcZbnVCvfMFK0t8GwSr3yNMatgJYTOt5Kk98FBkLxrXQrEvJ7bzoh3vKvQK6EYfE5xNJFYJ6se16
-sioUWKuVflxj36f4OPFR4zmasYVYo7lALJdUfs6cN9cylz3ocGxAOW8EBayqP2/uQZwAmDT5pVU4
-2tgswEVLf3QBwLzBTABM3WAFClN65zzxVNUlY3JA2jC2dLTFNZ4qTeSzaRmcwbJ7guF8F+GRhYIC
-w0350io8dmmxqDMxNRelR4sT0/C4vsKXW8fwReGGTk4L6OME+0fhFwhH+GcWYRUQlU6b4fZIyQ/l
-TmFHEzrdTOaJwjp9NDIfW5SaicO21dBMn4OWUoVfcd7lKrxREew97gfONDBccrjRGJd045gL55wc
-Uo7ufereTR34Khyq9S3WYBNaAizLNnz0Ef4AltlCXseoNZIK4XwHzaex2+MiSLcycS27nbtzqlmH
-reip7W7lxO3gBHb1Wh5C+Gwp3i3D+8r8AiC385XXnAOO+uTofd1I73upKTbi6BPwusRfL7cIKu18
-2agsM/2lGVVqCoT3I90dCHg1TouYS/ohyJB4Wp5jwjwe/sDB8dcLGD8omaszIpZqW5Ml7HR1Au3m
-dY5FHkT76qjY20kyNvnU4yaes2C0/yRYJP5VPuIJhTwaS6tsSkwHmvve5iJBOTku0gw2PTst25lP
-ADxBsTb4A1OGnP1QKPi85IICWic/lJYCt8rmF+cStMb6gJV/cM5LZDwB8DPyXc7JOqV+VB197YRM
-aDeJQLoSBTfhUw543iBvfPWdNtxyB9C9gF4r+ZVxLUMFyg9YGJACKF/gjQ9dTCsU16KzcaWJ2MkV
-92RdW7bZtdgSMnQs4suXfzr8AuEI3wbCw/pbD8pXLU2wbPlo57aJLkuiHnvjYBWuvIZgQGUi9Wxg
-eATOvy9C9xNmKzr0QMYtgKyloKafE+V0B8B+oNW6a76dAyAnN1zO8QMdO/1jf1GWYMq7FRfgixJs
-tMY1kBt86zQrmgkt8l/ji1u6dRrbU1KEo5pzJh+xqYQ5W4tLDNcFrvp5K+Oo2VnC78zDEQ5TuOo7
-WXaB0+yaluIdPCstrqJzLejq/vApzpkukKv3Q5JzXhTNWu+y8gF6U4lIGX9cwG/P071uEwDn4UM8
-22w1tw8A+LEHznQ8Nnahb4A4Xqx5sNbq+lDeOsKXz3CCX89YjRXbwvHKpqoW7f2pNero/WxdbPTl
-nmhoj11Wo+RS1tpXQyPAdI5XHNv01qPmN5rnkXIODpSPbpUptwyp7AuhVrKP9e2At+1O4Z3mrVO3
-i2yRQ0oIBmxfiyPfGZ/WYpbNxWJilbTYRq0AboJfw0YzsRJv24cJkF1tvYNfAl+AN4yx3q3rCUg8
-d4cYbLHB55r1ykW1AK9rAWg3wJ5tVhmuBo+IP09vRz5tekqCPg5w/3JHuDvIj7vqdFQfZUe6d6qK
-a/fed5z4Z8IvEI7wE4uwWnxVuJiUUdVrkp75fZlo2JFDB8sgSl6LOukD/LboxUp8m3eNPi293UKq
-PTsBAgpul9qmRQ6NJnEtJ4qkWYNHmVJkvbfFiTo20DsY4KOfSxYTKO+guATQbt3dgfGJViCZYLh6
-soNbZHukhwcfYc84SlhufJH4tczp5uo06vuxLMzUbr2GsvLWbNP6PwHjaRFu8+H2G2VugNja39Hn
-88SBSdtqNMpPrjE1dE0HxlQwVmW5/3xzpfAOEHjBrHoHhycaBAg/D4HuC4+PbrgTCK8v1S0AHL6F
-8enatQfom6A9LcJyM6vWYAVoa8p6rhda3d1qvvvWZpmJqjwHEAbipZ2NFsNnMd+S4HdBpkHurjkU
-wCj7ozxao+qSywAAIABJREFURAvo0sqbYFRvIFSmakUni5rv0e7rfADFrrRD/w91Hxnlp2JLDpS1
-FwXmHGWxJAiOeZ5WzSyDBmB3UBvA9hvgOJHaBQDni3kzHyWDy194tVWnEFDSc06vzq/Owzb9lAek
-U+fIBD9ZjlMa5TTn/IrqHEuuqFJ3wLjHr/8FLhNtW7W7CFlfFqxJsPdXT78I1P9w+AXCDJx8O4ao
-kJOvZomCXE8agEGvTypPReij+ib2AzfUMjIpXfP+k+eoh3DIjrYr6QK7anVM6CslRfYu5VV8EFsP
-GtWFKgI/OTTirP9kJYaU7S2uSHHNzmUyr+7wOycIevVxV8hvAcUUOiqokmYnYFy06SPMOKWYSyu0
-9X3koyy/6LNN5tlv62nrPPH8O24qWM47lz6HKfopUYdqiEl1cnuo9mr+sCTrE4T58wt95M2t1LT1
-pzVyExk6Um0utccT6IoIa34vKzA2y7G9HtZMUYZy47OPX8Rtp/cxLRD8vG/F7cXjZQH2+ECHP8+6
-cTGD4wlgDNnVYl0gLcKSJq/5ZTnKBbaTO36IBCt+KutPGrnlf4qbACpH+Zqi4s7CAlCjH82H9sBU
-n0ye9JbnLU/dIs7XYtvkmActO2SxWnlZJ/SawwqMvfwWLuRbXplCLHi/1iz5ntbedI9YZW+uEqsq
-k5/v4NZKPivtlM4X3FIWyTFBpFh+gQ0Er+apNu/HKQk/B2FitCOnZVwzSyZAR+uDJ01khlwhJa1s
-nVx5lNUOC8twfSmuu3eWDJttBrpxZgR+we+JcfJa7ye5Opr4bwm/QHiGmybJtCf9NBgDWhzLKXgu
-r1TNq0WkC2jGt7yvZscP8k3ayTber19206S5HBURMHLgs+sikvx6jKrKYhbrIif/0loaR91H9aGw
-eupFoOepPDP09K3cUy8N6X6sZtImEar1OKs4pyB6uUiI7zF2AFwzZYpTX/tkjrINTtjOtwmK9uFR
-gZsNG9c+if17GVruUxFl/aqc0IIqAaWenJC2oPPbR+t4SZSxlXPIPsZlzqdl/lIudamdulatz8fA
-BAVdv9hYA4B/8POuSKdbK2O6zSKtvViPKd2Rm+47XuQ2c6T5G/FVL2/0CIBpES5A7NKPIU+bldWT
-BTkl/BZfPdJ3fRKbSxwA8L5xs8Gjw993Ab489vh2Dssqk9s4eaMr+X4OcLUK86j5X5bzDm4/gdzZ
-3MA6VxCcffIj/RiUD2Ih1I1m1rUNDfCqNGO5XKAHiOQjDS8RjM/nsFq9F8rm0R8fF7uuyxo6+RN+
-YMuhtu1IBpBfC9gKb4IfTRZ8sBZv9VdNKnpgOnCO7H075eHew9Z/GGnZ0zx9ulnb25NHOXZJ/2/D
-LxD+B+E0v9tEaoJ9ljsN6QTDEzoUEBpTZ0ufF59M9QOI2BECHyVaq79fd62KCdz3SyxJZxkfl05R
-qlKSWSJmd71yWAyiyAVsJDgdIFj3Tj3mEewm6EXSTmCYbwyb1YgqMK62UYgJJ61z1M3w4gMA9nPe
-OowxlTHyVk55qPlobVP+zvx23W0W78dUdEApDeYZH1miBwNd1Pbqtc1bP8+t0qaf1tU+378Gw1UH
-x32vO+O63ExbfBhPKp5Ufr1zp/Ux+z/Tvv0N/h5B7fryXPpCulW+29oKlDd0SbPwD14Klb7WD+PR
-CZWP7p4vj7PPT/TpAQT0et7EpAFQ+OqHPIKYrP91uL9x9A5y3wCHCoy3cpU/rbkn4dTzuiDzcY5L
-/mYZHsdmoZ5laBE+geDNMnygg3Q2rcvnLbRsP+a1pMcgYVh4h0zyRGRrQEt+8bxIC/jNTxtLPdqX
-FRlP0hrgXlbP0azeDXfwk9+JTMVK7WZXD5tPx6+CszsoHZI0HzSy5iDv25NN731s7fDqeMsTOvmR
-4FaBry9a28XjiQpeoTdQ/B1O/HvDLxD+YTgNUSkyv5Y72atqyNcUu6VtlL/Rvtv+ue3JPv3PIJj9
-2Ky/oz1Zh7NcCfeKD8E6F5U0zVvcxzm959luAhF55FVAVdPYgC1B8ATIdgK+Axg/hzYkHDJtLwWW
-zJ4DGC5esFwA4EQR53FU95gZ5ixVa0ErYwfaKLePfM9LxaVjNfkACCLc01nHkND8WlxpASrLvd/7
-ejzf5NXPenPafL/HIedv8XjKkPSmVazHnX2soRbs0JfL6Jta6T4sqxZfCokvqjzLP1iBrj/w5405
-WfGct1H2DeWXm144PTtWo7kBBtDdJnI6R+sVBHNECWxjC9gOiDPtR4DcNo44WITXJ63l2MDyAQwz
-rsxkwnsSl6OfzpG0B2EeP+bxcAK9M30CyxjlMq3z5TKRJOjTimNezOOSgWSAyctyowYXmUjZwKcL
-03Lc6lRt5VKvtdLt8b3UL9QuV9MCzI4QPKvc/lkYrGlH7dYSp1aXsSrT3CUyz/Zy7RrRdld6t3sj
-5tXWYvewCK+133fkWKtQP+dsL/MRoBj1efn/UvgFwhHOkOEb56i0c6lHVm97tMBzNQ6qe2vprmT7
-70ZrGV82/nO2KusJgI+wJwQnmrWSYWhv0oBaXBcL8KpSrR+zKhvRaKFpPxQEIwEtUPGPLhAnF4l5
-VHCtHOB1WjvLHtFdJQJAJHg6zQfREiKii5e8HhWDCnhrZwnSE3rn50ZraRW6x1kRzR20cdQ3ofX0
-zUIsL9TNUPNi8CSiponRGh0zg27rW4D4O6C3r4/DzWLogJoT0qKmWIPqpaa6fu0c8JHwWx7T3jkF
-s/IDRihzdYUIZapvxk8Xidff2F1ibZhWIBhlDeYsUbzhNXPci/f0J+b7O1wLZfUNdwgv/m4gOM8T
-Lgi4LQA8XCEuILiVE4uwoottiEaZz9Zjjzpcyg/5eMmvD2GQ9g3Qq9dj23w0XfKroXrwjT6igzEi
-k0JcidjaLcT0U2A1BkBpeS1r10mbp+mlLa+eKBHRP6tz3LU0W2x7nA1v+1B3Kf3T4zX07nUdojIz
-hN2e1/2Lz0EGNgXqLm23nS8OYDg/bR1Cb4Jik+0gHQGQn+P34//j4RcI/8NwmkuddlbXtUT4U/W8
-+wzznK+A8XFufzXh0+rGNnWoUNe9+wlDypT6lya0aru2OFqokt4FtJw2Tpgt1sPJEowt3UCwPf8M
-BPPI64Ls7cComlszgQLKgQaGu/BmadUWhzwb5ZAwqvPK6qxJq7PGJWD7ObNfSssmflEuD9aOfih+
-FuSb7QJVuvfraAm2SVvp8hWu0dtfgvkOONb0vkZaW0WxborSd4U514O3Gv1DXic4PD420N0jbHOF
-WNZiSNzd1oeo1DLsYfGFbIMM3Ra5oAtQOCLt/7HW1V97AV4fFuDKN5MyQlN/8NXZt/sFn0DuN8Ew
-F2oCQhFx89h4fhB+LnkuhJOVWONVr++y8wqC0UGu47NFWNuDPVxv0LbCcm2udfcN3FZ1sRYc6O4L
-8UdvHD3WV6tj5VurNOyfTTRY8m+TIEPkRkl2YNXmvZ4WPurhPWzrnlV46wJcAPeU5970C5o4JlDO
-uj367fdrn2QPSHfETjEe1uAHBrECh67lS75mBjz8fDPSN9gfwH63T/u/EU4K7DRsV5pIhjVFTtbT
-oajld8471VCFD0vz0lhd3NU2xSjVDgJkT8RC5cSqEtI5dBX2INYOLaBJPT1k9IduFMjNRQgCYHQ3
-CK/PxG6g93Yk6H2eMzgGunuEMth6S6tL3RJcn16G/FHoY8cxu8cv3BrZfqDNchug/hj/Hi0Vlkw0
-3Rczr33ozuY7PPu9uQGNbBmnGyBmud6LkWcf8vQ8uYCFdtvBMddUqaOTgsrSA4TM9dGAUi+a6aXs
-Foi1sOrQLcKaiwSWQpP4a7ZcGaz7B3NXiNeVN3F09h0FeH0AXyAtT9xGmTKG+RMY67k1ruM26eQK
-4Z12Bb4Exknr/GwMdwjvb0c/lBFg5T1fd6k4W42VfnGLYPmNfvARTjpamO4SkwV6gh8KfXJ/WHPL
-dUFUlQY0f2Gp3FIW1ArhPvwEvszNdeWD1r5eONcT55HKWA21VrUV/8RX+NN6B0oW1tNEyRtlANto
-q13zlb/Yi8ejze7hKy1Sn09B5CZwTbqnPvTzhPwKC3GCYhAQP8D7rm0ZExhbfNYZ/5XwC4QjfIIM
-9zDvNLtqT9rUPtv11OK6x2c7b79ZzjVxBEZCG8BqKZiD9Uzb5afrC0Bu1xNv2CnBWGIKVi2q6w49
-vvO/4gU8xDoci3N9ShadPt0kbH1A4NtuExcwVTyaIw8oEGY6HyPbeV48rS7vcVc6oEJdH6cx3YO1
-JvqtzFfxj7tKRNzH9WjF2FDTYbYYVU7ngUv8FMrVJLgiysIyf7+JMS3Tzvheeq9LujzWXu+P61Ph
-VFgdQVwA7gQjUmguwYU/Ar5avCR3jGNZop5D3AyvG56nW34NZQ1uQ+sY/Fwt48txDtnWNMquHSC6
-AjcfwDf6l7tFeM/DcIVoYDh9gE9gOOjTIqygcIq3PPpIH8alWYBn2qVuGddbnsco39wgPoLg0SVp
-5Gne9M7MtbrPtaxTBnG6PzTampzRRj2Pz7pqVfD8pKV6q3VV4LeumSuKMknrA+RsZc5Jn/aUfeP4
-01C7RlQbFOC2lpjShnxlG2Lqj3uTrWs3qzCAAL8KhldJs2ddNwHxkiVr7Az2PNkG4IU7P+bxZ8Mv
-EP6HwQ5myQR6R5oP2g+u9c3fl5V8VcS4SE4vy7EPu5vECuVCIaTW/saaS3zJ4q5V8uHcydR1XDPV
-wtM/WoHnrhFztwgLq+/RDYIW4QGGTa5+btep6VXa22+KnYovK5vjOeR9+fnsCWwnOL6UmX25xvNy
-di+bym3Ub3u6WaqHwOfFQpVnfGnD3uayGCEEM2q8or0W5dY8Ye13gPvjtNW1K1x2ACEQivYkSMm6
-h6UulWOhGO9/tvLFNVtuDrClvMLlwS6AVy3GjOcnmeMa+v7LiwsgFuWqe/lr3FDWXyrNDnxXLzZf
-YhxcJYCyCAvIVUvvfInuZhGGbJ/WwN4F9CpS81ueT+A4y35IQ2RnyE0kuPVGa+CXx+zHyT1itAkS
-dN7tXd3LZ74gOk53dXWA0NJCbFUbz2NdcdOltGnV3cGv9TZanHNQNafO2TXv+4Fz/KujNFUuVX6/
-2zQDPgBkG/XL7bWT3d3UkDW4liEtPsWMpRcdBMSrcYsmO9DQAoz1AR5/n3CTeGsPxD8YfoHwPwgT
-NnQYIzQ/0LLc9L39EDfbXwA5/E4t2shb1j7ViTvqdwO/kn96bg0fn14cGmP6P0wBqE2MA5/KdGHA
-Rq+fBbNobZsW32b9BQ4vve1+wgscP+ft0wQwH8fmyJvevd0qrCzRMaJEX2lusfbgUu46F/Yxb2W/
-Sm91i8S1A/2rMiqwGwiWKkz4IsG31A7sFQQrUGrb2+W8L2Csf6UZjXJPn8+biVQmjQmybuRDA2U0
-874GhoYkoEmuKDKZyw9hV7Py8eWLLr6+4hEuErJzhLxIR79ixGeY+S0QoEDvTCtP9vQOeMkd7hjB
-UVZA7JCt1uDHa5RFeLfyzpfo7lZg77tHCOv34xiTrYxv56gF9uNYzroPluIJdBlPkAxPebpZiqMN
-rHtbd8MwUf3zns64zL0N4Xke1mDWZG98i/NcAXOKxALJCejiBIoU5pq20bhKvY/T7ESE9l7cLSIH
-+3DsFe/XamGfKsgX4w5tyqOd6blF3LhIoznW2PterkUD8MI9Xngj+F28ze3aYtcIxwN73gWAAeB5
-yzXixR8Pv0A4wvfN8b2cTbofaLPcFIRfrQBiPJy3NTvi2y/q62FvQz1Cvl1Tods41+u8rP60Ugd5
-DoErqxzb2tMOmXkjNouwefMPVheIF8Mi/DwHF4iD9VfBr5R/DrwSnLf3UY6LTYur6hrxtJIKdCve
-/NYa8BbR+1Owe0wP2k/ipXUOZajcKmsVP8xsIZU6k2uY9HmonQTECqgYN84YxNw4gdjvAOJV2c6B
-XFQba7U31Y0oIHP7qKRVqfOPt6wDrddp8NgRotwjLACuBcDlC03+UPmGfzCtwW547cHzvB8BMOmT
-Rn06aSZ09/US3vkc3+pY6WkRLkCr7hHpMiHx5jcsVuK0Jrdx6HxtoOqQdzrXT7RNRn6PtgNbYLcG
-Y7MOd/cISWO/jsy83t/qzZi7TNjmJ5wj2JbtAlf5zgAzEziH7M+aLZaLi6ix7HtKyXwqQx7EOrbe
-5i1+5P0pLcz4oJxbV2fXx3G/ZN0gf5xWPNpeD9eUxU8B7y49y384FRZ40+pI1whofF3Y4sbZsHyE
-7V1vENgTT57edZNt/mws/RPhFwj/wzBU+Nc0v5Wb4HIAKevld0Day342PorUkYPPvGxLtMfmtS7g
-2AUcH99sKh5crcE+030x70aYLiaqVQV+FyghIO5uEMuP8GAJVlCcIPg57CDxhKuEukaI0hXB0sDP
-7Bf7ZvVImdaMV3r16Dh5xUOsxIX2sdR9JBtEm4hM09aIY+LuMO+fxU/XZ7xf1zO++tenGF/vMaFU
-NS5HuiZ4zG0elyIM7qSi1L96VP7GUV4a7SyzjZXXINrHx82dhsrqE6m9xHSzGOZSk3QIkOkGgbAS
-g7tFZBr5VjgfXy+A7HBuDooAu5v8WJecoLeUrjXZp3sJ86U6y/PLgtVdKwr8Qq9BATKtwOomcQDD
-vZycn2BSxu/A609509Whlz2McxvT/RylfwV6d8sxWrkGoNvlK9WfLMw2uiZbbHN7iIFsDgdtPUi6
-XYYFomgswLQPUwZbk4TR7nCFoIzYeDg6uY3nnfdg03v3/vFx8rEd7UL/cOz3Ib7xRj+ljA9l14Kr
-TQ+bSwQoT8gNtQyvsTM8yyLsz1pXXwrJf3/4BcI/DX4eJpuFvkErQb2v7gSb3hXJPNew533VgVj6
-hxNziRzrvVmA69xNQkmdM17JWaynYwVOTTOqYgspFgmAgQK/cGuW4fayHC28Cnqnu8SwCB/9haGP
-Z2XspoaQmI84f9x/VcdliYkAvQIKV0zHYMWPglSRWkY76ATPbhNgjvuYkXmhQ7ljXsUbe0yez5jk
-y6lz+JWj563U+lWpg4Fwg0gAXP3maxsmDVFgxVhu/TVZipNVeCTYv6ZVDp0bmtHZ15w8QyG3tJ/p
-kueGZQ1ON4juImGO2i0i7igqvTq/XpR78Nq7yabd7c+6Rdi0vOeaYbd3n+GD9dfLVQJCT5kQdRaI
-HVbgAYbnjhITAPdPLBePj2Bpiq/LuPV4AZBb/tE6GSd41L9ZhDWNr0FxK9ua1uXYqU2uf7WtNkGz
-JTIrwGuomm21owHnOmetY9fSC4DBu1sULNdAuUuERnTk3N70jFjJhQGN30rSMfgsjb4TVE4KR601
-rzf3iyPjVCFq6R3PF3eI0H4ux2cdn0c0MZqLBDdVnOB3uUiECei/sHPELxCO8E8naapsWQF7XWea
-TrOuBC6gGCrcPwDhnyHjQ9m5Z3HtAnG2Dkv7vNMJPlu3dVmKwJjW3lxjl3SXpAnvQStDukG4L6AT
-8b57xOFlOfEF3i3DT6dPcOzR9wmAVaZDBXj0NxQA+5b7sAYgK67cQW+KSNk1wvXiO1JDgjVJd/Ap
-kO8Gco9xSadSO9DHNU9V+TFdjG3ToC54qM9SKXI/4AUAS2AD61PaL6zAcWuS/HU03m35Uo7x097I
-2ouc1yYJmTsb9JhKePMnioOU2/Pi1iG2WVjgt3aKSLCbL88FYHYD7MH7vOvjNLQOH152eTfZwRvW
-WieL3q3BwNxSzbc9hjfrbyjz08t3J4tw+gAfLb7DTeJiEd5A0aC55h3RyTbA2xCf6tWypYJklkyL
-cKAezpXvguLe/DkLvV8fVUbb3tapo4PaDAS8kHVgGJYE9B0lOM7xl+RYnI6x8wrXvtCrEhZ0PbRx
-/vjU5ZTXu92AcT9a73LSpzU+Wn+ZDt89av2bhvE1dwocq+Y54RmiV62Jn40LuftSNlLaKfglKPZf
-H+H/a+G+c8SnMgSIVZ4TTJXoCeCe6Kdy18ZIG45A4YCT1lZFBzeI6Mf+IhgFZwcMzaK7s6RHpklh
-SgEVXrMygt1YbsslYgBgIAHvAx/W3w50P4JefVkuLcehzEO+myH3a+yAxsDXUbZfKAKKm9MLccta
-rOOo7g8l6lzOqQHp4LftykAeKos5Q7OKA2r1XrbGa9RdF0UHxz1r0rK6QZtK2edJRhUjCsCqnrQK
-iUV49d+2/mzg1jovGgtlju73H54sO/QYQ/OUFTc70S1zWX6A22JKj/vhvDXDwvXB4u3u/ELU7haR
-luHQ2rQOvwY8NVFSwbebw2IzzEu2AJQ3iPVTNy4PwTogVt8Oii04k/lcgzIrNovwCfi6d7/gm0uE
-vzLpBiA6ACV8ord4l5FHq+83zvU4OUHpd0AxJgiGnNvnrE6hRpNrH8/RpirQbWrJqi2J2AwTONOa
-y7YYlu+xLJWgDWkYAkDL6qsF2v51k5SMEDZL5734VmqsP5tq3WO3O+UYnUFGrxX9aoq1mxN0Vq62
-OQ4aozSL7z+1JMddcJwlMndZFtZNtAPwB5ZrJ95DIAj+dY34vxNOw1Rgtg6braqvYTmnA+DK//xy
-3PwdWzcr1cZ8KJ+K5nj9vqSVbl3DZ/9KuVFJDY0xF34urEh6nTIXvIqZbJu5CFiCYaRf8AK36+zr
-55UjbVZuEw0c011CfIWzn2rxpYVam3rqLhAgmC/MeYEK5AOl7Cu3uqvR6mPaPeIkx1B0BbeqYGgl
-7X8EwJme3mJfpgc43thxyxtV3jBBp0oLrQ9J8s0kHWjNqlBcWuJS7bae6Sdsg35tWQcHzfpDkMJx
-zik1AIoq4BHXz/M2DSjllkJ0Ab5ISzB3ikBahsOPTyzD6RZhy7LzmsgMebzNYbfgpxllBpLnCYwJ
-XPz8kQ3xSIwl5fmpZVxptHCFwlVAewS5vrlKpGU4rcQTvCifv6BzLLaymi+k44Q/yE9tEsFvXOsG
-ds+W4aJXtd1FYAdXMjdbCfaFyvELoJsLRFZElqVcX+npApEWR1faknm5W0SKIAoFj2rlhT25Scj2
-b+C4BqjvzCHnbXLLClhid5+4HRufB2+3o39dzqSsvgC3A+RqeU+jTsrVGBX6AzhiZwqCY1qAV3zV
-Rq224utJ1J8Pv0D4h0EnRafd0zea5lV+WTL3/AlI++9juw2XF+n2qb2u44DtILjaKPR8fFbgiF+O
-6sivBEW7vOZ5X1quwrMJnX4cjQfibWLjEev4YH35ioCY20a1l+WaZVhfhnvKUvycfYUfh1xXlG9I
-5HZzZOwffwu4OuSTtNb3ClYwvAHjMawueYjxbDziLKMuOIBfj3jmtKkyb/X6PLKbNfiaPuhzeSS6
-5TF+AMhNZGu3ZbHQt5UuETXJYz9cm7d8GreRxhUgazc8chXwFkCf6iribQ0c1oN7jbcLLZlxUOyq
-MSOpX5QrF4lglNEtgg2Ot/7j61HLGux4nGlevrtE1M4crsXqSc1aJJtF2LCswlS3jtjDH+IC4bvv
-sK65lFUHYNtejPsmGJ43FH0IfaQvdAB+oG2q4rt5x7q9xTkHNkvxpAcqyrkpMqV3a/oFy2y+zd0Y
-1POLcR3oxuRs9ZBG1MYVxXkArDmltFU1IbHSKAn7+jgC4kmX8T+BZMehK+1oF/pZp8tofjG1zuCX
-zYWsK777Q14VQHZZLxDA7NnHvoMEK1cn30IytQpRsbixTksxFd8fDr9AOMLP7kLmkNYCtZ3UQTAX
-acweXQY3wItj/OQ3PIBIbwYORQ7tt5QxvR3V3g6K97aTbnR6nQyZtNbIEjKN5LLetk5FS2n9BcIi
-KyDYRdmmMo4zZW/gDn5P1t91nC4UuWuEQcA3waAXSNKXQgY72D++EKeuEUcw7GrxpeAj6EXLS1FP
-lGsyPwYAdslnhG1US2qNjw1Sm/GQmkqBfTuwLlWmt+DbNfdkTeytyQmI+VMxHsW8VaGXHt1Sq49e
-r0CCtewOOBROeDtPQUYp3lwzUzHrovFRVs8Fck9gkz2ByZe1x3D48iXwffB4pJ917mvLX3gNc8AT
-VgMC2wDBHlwyhOtS8M3Jfq7hAYK9A16myW/yuadDZiYPJqB1Abwd+FoDxeNcHeMD+O2Pz3W+7GUr
-Dz2kzvh+2RX1agMpVwswqUHZwHFVXt09zUyMc9ByU6w3iy5AC3EPkdkQYtyA9VJxzZJ9eUpYePWG
-Cs75OYEqz5J+b+vpRBMeuriUHK00x5Zfg/oKoy5bbcwW6yUvlmKZC6vf7ANy+7SUBZD59jHdW7Fv
-fzblrWwUbFxC8VqyhbvE++eR8C8Q/odhqvKm9CJSgst7uZn/RfwEjvffD8BFR60jsG0mRUtMKAhu
-oNhJ263ZUm3V74PmdRhN6af6rKrDCdP4AMYFiMvnNvcRDstuA7g2/YHrpTl7+HvQPrYBlI9y8sTi
-OIPCHw8hv4SrmxUA9h0ME6wqXURcXLHiykcCDnHlBGy3/ma+gN/2SHH0pYVUbkJyk/rjHO+nXBWD
-4Zh7UtCb1N6aaAnAxqQetL5Qrj2O623A+HDSzTq0zjmqr+qfghNRvHmuH44jz1te0Vdba9cIxJZp
-sBfcOYJApIAv0oUiJ8Zj6RrBOVXxsgLTJ5suRAmIo0zu8ILxcpz70SK8wDB5PC3HJW9PFmH7wk3C
-T1bir16WuyGR75TFnjb0KpL64ZycIwp/vEkc7BZg5uwgWOdpNbmvwKtVsoHm0d4Ew8Anf+G+cOIk
-K74ssBs3Xy7VUYNFu1MsCXBuTcq1Umur0cijtp6E11J2rnNqAsrnRrdZbmdFTZnOT+Xzeerd3SP0
-uV7Sjpbg6isyzQoLvJZM86I3UMOX4hzrvYQo+j5xoT//ttwvEP5h0BfdiqbBB81HuV1ymSyPAp07
-vecXIEmd/fPOXAFx4YICticQDCkHsQBXX6W/7Q7ZN5o+csqzW/4uYPc2K+i04IuC4MWxBMFA9wdO
-y+6Tx5NFuO0koRZkj5fynEp9CWweT8GFdXxA9Xq4ccDjJXwboJd1KZi2rLBbiinwFfkWfzxoHNEC
-wDXB1+8tAAAgAElEQVSKCYbbfPmINq9l9JZgz6WK0PJFx0a/KNhby2IS5yP+ICwgdvjpuU0577QJ
-jDcaEABwWqJO8zn6tYHfYTlWkIue/nhs5/PGqqy91nyBLUFy8iWsw974BSRQtmGJs3oisXjNXSNi
-pRIQ04Uo3Is45c6gt976NxQPuH1hzjSnSu4WYVp68wtxJ5CrYHlzn/jaIlwD/0WZXS2wV4tvpwne
-iFFWxW3Uuz1b8CFJs8zJQizHGwDeQNYNACuYBjYrcMjBzJvW4IMMqC0yIWtKnjxAquHac7S5uM7r
-T9SSH1xbCQpjvXAe4Z7Htdq6kLIYe/iGENNpdATDYyzO46Pt0bQfaB0on849td/kb4saCucalhx5
-PQbOyyfwD4dfIBzBvjEJoyR0gLtlc+SLMMskLK/lIfjXxFKIG4uZ9aXQ6PWtVEzLWN0Ff9qya2dS
-YGRcqqxJ3q27+Uaph/9rLPp609RaGZajsM7dM9od5YGmwrrdkUu+12GTHpawJhd2puOG8/EH7/uG
-Dy6Wb6+C3ucAek9uEY8A4PQR9u4n3ECwIV/iO0g9skRBcYkhxqm4alw8RyzGwar2LoSBtNLlFLWK
-CxguwTWFmZVV2A9lvkmzjab8sDYX/jdhCvZJU2B8FO5ZrC0u4ecHGrSfTAsYiMHR8VHlzTXoyQud
-+MO6ewS4XxxR6VornAMiICLd1hLdfSaHczK5gFpUJyl8guEe5dyJfSpda0WUvvR93ch6+dP7SMMD
-QKPAgAPbdmkNGHujlbXPYYNWPBe+J8+Ux0q/0HSqS535jOu0FGQiWeOPFvHqO+ToI63zSstu5x7G
-YtKzSyqhHa2riVCp37RTlnltHsi8yXOzPFkhDMg7+jGX9e4oWahImmsLOfaLjX7kWQHfnQ9z6m/j
-fw07iNz5zW5UzX3Mepk5PfH62jXDffnmwtEd81xqfaMfJvQ3+2bBwrxXdsDeB48CXPMd9J7iX/Lm
-3x9+gfBPQ84mQ20dFppUFmR3O698G3kei5O+mx7ltwUkAoR1bCCnNbFmUylWSQ+8wzMS4PhS2gl4
-Y7HX1+NCeWZ+L1PK5cnFaceXTuSH76V5t26jj+0ngFitv1zqz/OE0kX3B/7r2f2BD4C3f0hjAGEg
-AHEHw8Y0vN+hjBHInSNS8HXQW9aO/jJXF7yqIOq8VdEuZE+At02OzZostKlQLjQ7lcuy6s5BvVSr
-xbOM2sKVK/2yt/SN9ils5Wz0Rbvj45xTQ7xFMjuLOud3KT9zUWXMj/VGhZ15k96O0hjJ6yBX1pHn
-bmmVn2Xjy4zgUxRLuj5BqGjNqfQHFhHZ5txIuyyZdnQ9ejuSl/wwDYHxJnd+9Jvnoxqog5lHH+kL
-Tc63pkcO9WpJ30qhn+LJh15SgW7Pu1mSWY/O2ntXveiH/K07c2AveT4nAaMJblWO8YIiu5LPa27l
-fAEKV3vwawJKXiLHpkkswBzebtxs69Zo+pdCqJWPjm7AF5020xuN7accE56z1fkCneh2ypuihVyK
-43MAu+/rcdMcZW7rCnr8zJP/RPgFwhH2/XCvJftiIwAGara6lMl8i0lsrY6cpDk5Bz2OuYhinuiN
-9El2SCWrBtPkeAQiAkUj9YJc2RzLKowBgteG2EWrvQGXjHqz7CmOrOOzIqrHmKPNkVAFrp88fmIo
-Xo/HrI7s3WbVTRB8t/xW2QGKQ9nWwo89UBsY5rzQEanfkteefpQKepdwmjdKnnOkzwbvgFMZ9kMw
-nIqCNBVWP7b+9vO7BdtSaQsM7hbsVOqQcr0rp/R3V3i263KeaebMFyzX8kPLpp+0T/Di27USELNs
-oglRGFcg3PNqF5c9D851I3MUFm7ACn65lkgr0PtkCuLuUAxJnh3SCoaLVxhWetkBgOx3xTbySFr4
-UKBu0V6RW1MR+wcZRIV/BMZtbHQAB63leadF2XardwIEoWMUbp3LKUjzKk2eSB5509IJmk5USTsu
-+V+woIRXyZhJc5Ffk8Z+2Dg512VP19zp/rkrdCnaQDDnkdyo7MMZL/C51Gx6zV1k4kL7KnAXlxyV
-7F/1gdJzH5NOa7IGaC/MuVOHIvt/Ar/2Fmh+LdJynMD3jXPSUnxaZz+W1P/78AuEfxpyVinARca9
-AeMOevt5rM760WMx5hpW0CN3mK7KpDctI9aoBQoG6LdRju1UoAsvcJeKVdLML0D7yMTuyucWP94h
-elmAm+JBlbPoQ+5TnABYLF2R/9jqv0e9DhTo3YCw9Ty7gGUFx8GbsgiLVdjLtjv9rkr3idVXhIKC
-XbcSsZwfnCH8++mJQRI2H2DktWMKZHvrnH6utr3RXYp/5ToBxA1eiHArQU+fvxLvAL9IpdTpZ3sC
-wP4h75SedW1pssVPZa2xLWXE57flwBdsVNuWQo75zjXhpMeR58n6LM3oI0/yY7YYSvk9El/0HRhv
-lmILX/sxX2YaKLCs82nKM87nle3ZheZbzfaHsl6sKnlE3rnwoG8LNX+40He5VDcYg8dtzMfY4HP5
-uwU4VmKc17K8l6nKuIoOx3atr0Fx76ZXvJU6d+12bO2fi8zRrb8kTKHGfpiVDOFEYkMCPC4WsWGy
-c4RTspI3coOhQ3eLt36tNe5i6NIlflr2PwHEJ8tv8dWkRKRdwTHg3sssWswrEd3N+mtlAFNA7K/D
-ng6Qp9W3AV+1FPvZOszr/enwC4QjfNci7AlsTWbvovXhK8C7Fo41WstTgEvh76GerC+cTQ7oOs+V
-pS3ZQchxorX+rwmbjhy+hNAdGIv1d1iG4e+qR8rO+EnBdDpykZSAP2kcQF+yeLgLQwgmJwgO4Ude
-n10gPgPi7wDhLiBijoVg0JHZxzcUt+VDu/y7LKJgDAmcoTQddamVYAPo1rZPbg9K35FKLzumXV7N
-D2VR09Sl/GYNNgLDgro6378Dan+Sdwp2iWc62Wd9Kh4qoeHIlScjtBs/WReqjHRdiJY7AuAE1kKv
-L17GWo65s6w4JgBYgHH8ChDTPaK7RTwbyI0/AwxD5uPG1VG2wEpZzlXcsTtG0NvAb9HIk89g2IVX
-d2uw3rzpGMhAynGWk0IbTYLrjW6vqvFLx7pd9mLp5fEClk9dmiW2tPRpdunYRW9NP4Jc0rzRhHCL
-H/MAIIxM2e/dQqw3TcojdDIv0mYkLbaKLC9dAserzWN8DpSJtezPbhGu5T+Nh4tIdtS6CJ3ZLMQQ
-a3CA4LW23qXzXgBm6Q7xfAF8fay1XMefhON/KPwC4R+GvlgCFKd8taSVnikAnEtNgLQD6Q+pkzRd
-0rkY8zH5SYnHgjeMElJQUUMDKN5ITDTLr1Xr6oU4XKzDSg/LMBl2fCHloJCwl9no1YFU0GkVTkUt
-li4p8/jip2OBw3pB7uQCoSD5Ao5tAuFlBc54PD7KGfBhl5gmKF3hrj4Om3NBLcNV004Lbk1AAnwN
-fDc0eAa/CX8PdJbfAHj0dZEE8LqoCgOsgeFSCKx9XtIvcTbvu2D4BoRtxG7sKjATS/8rQe/SYIn7
-tg5QRyDXH+llrSxFo9fW8tv6yXWkIFh8goFhIbbcgeVkAd7AsIYrUN7ZQk679C9BhSrSUOoUss2l
-6gZ8b3LnIrPaDYk2Eq1hku8HWh+ToouN8jRdhuGCa25rSqvjZukVKJV13K3C1c0TwPKWbnnKFo75
-WHzJvjNyHFbiPV+EJwC7/AXSYJLndEDb1lxrWO9b72M1SmD1VRZtE3xbG3E97909Wn7xyS0idJ1r
-eWx8dBq/YppbyKoEx7nuwvxC+hsvqVq3CL++pxUMc/lwTU63xz8ZfoFwhG9bhPOlNwG7CrXcZFEE
-9GnAV2FZyWXKBC5VXd+8lgPN+HYExCepIhp6CUwRAG0lCD9Csl5BLvMx6Ify7kD63r1iDW6KJ/KV
-JnGfdLZTFTsEBLuDG/qXH2NUEb68DovtUucLcl/4At9cI8yGJRjrLdoAvivNW5w3vsG++pMjp8Ng
-K6cecZX1YgLk3SlCQ9T+LeCLQ9kLXefQFF4XsGwH+tJDpYDS0SEAWRaNtF7qBHKlxx/jp3DK/wyE
-7UOZyJsNY4nZmZbUuS4KwjHS5Bkyba0cmpX4Vq6tGQe468kjoLiBYJRPMN0hlo+81RjLvNJu30Dv
-ViY5oTO/clfTq4+qSMuKi66E85xvWIVxpjcL8ZCbyvdOQ117o1XExPTZlpQKBRK8pSQy6xhOEH7K
-2x0lMvYB8Hba4ejnsjOehJigroadQ9ypNGMiGN3FRvxmMVY52W6u2Sr2+dBgJ49Tj4Zu317gWW2c
-8qePG6994EfKCV6XIwVMsPtzcNzYvbOY6yPeGVguW2t+ulqD3xf2PAV63/jEurhDvHYGw7Umo65c
-b9/DYv/O8AuEfxjiJqlSww+4pnX5C7ucnTQflj6vibmMzNNCHHEPtWXISb6B4Lba5NFaapEuPo0L
-W1wmUnla9OACchMU+3SLCHeJd23IXwB45cHDRUBB8OHX3CFysVTbdVyK82ggmLtFOAyPlejx4PVH
-y68Z7K+n7xUcAPhoHX4XH8onmHfN0Q/u5/RGq+RDO+4iIXPIYpbQgt3EdzlC9LkAoQ5BrFj2BIZb
-/ABYMj7Ab8rsHfz2eSh5AorzJQnjnCwwjABpHm3Wx5owz/mrCpeK5RMAPtFmaKzwA/0Y38FxU3U6
-GEM52bjRW1Nf1oSshRx0AVfTyqtAEZFWH2EqP74ot7b+W+Xjmxm5hopGMMy4+A8HGAb9vDkvdFo0
-8FtzrEklnXfedwZRfq0iMvLRLwXB/FFZ25AvJnz9CI5xox0a5ZJodA6qzI9W1Hv5VqbmS4KWVigR
-U69PjqfYvqPEft5+9ui2+97Nw7HKow2405Vgo48KFKvmIpeCfKSfJxEshyw8yIt5k7V1YozxaXiy
-cXEtukjM5gM+Xlw/yEt4Y0NmhxXbs+8lI136VE2d4LiO7n1NLn/7cInyeOoYsmfdJK+aKHv5pBMP
-2gtyMIe9b9h4LC3Cr7wgtwAwZJ26xNHXzh8Kv0A4Ajdvzxk4F14c1yKzOiogOLhG5ARNUNwBs5/i
-nA/WZEI0w7JewRGiSPNPdU7vnqO0ZXtLIlutKAHAlj5DbHkHwgF+0Wm6Y8Rq0prspj7CB8WSSuiq
-dDYRgrTBi0KlAjdJ88MU7U7aPr38dvMP/lDGHHhLUNhDgREAOT6kYzociUZqbNmf2kqtbMBlBdZ5
-sepI4HyCeAPUuvCszw8c4nahR2TKrjE57ZJXrnuWwpgnqCsEAbAqhJOOhNAmGJ553wpScL9v2AGv
-8t026urPe5DzrT053X30k2sBckQ9FeFkEICr+TbLKy3iGwj2Ar/NTxgKgCPfykeYN+sAuoGngWBc
-AfI61gtNE4AoiFRepfpXmv54Q3oDuzfZk3z1vW5pSwe3Mi6NHqN5pM90l+MbsJrvdpyuM2Id3J5p
-Ps5VoDuP/Rp7E655uhAjrmxTPVyA2GXSCGATsJvwT4Cj7uWefzV/kxafxo5RGwXYbuv9kdL7u0Qh
-Fdow2qhZYxPcVppXOVl+FSjrWkp2psAUnoJPKGgRxjh6lQugSyvv6+Uj3GixlurjI7WOnIN/YOl/
-OvwCYQ3fWc0N+CrNMq8mpG3HNdYdELcJC1mKlL/6ktMow+vVZG65AWLr7AmAy4IXrc5H1AMA+6BR
-MfBoulNE0N4XeB45f7f8NmD8USlBaK2L0e4iF9C03DaNH6txIF/m8TjxS5eHzQI88tTHOEHHavuy
-Cq9vqNv7rEdKr683C/QLO7AyatgSLrxD9+iDOkR0wNvBMXs6y3wCu8cdIiafdzTbknn+EGQNbAfj
-bZxHQW18nBh3eimIWXbTT5Zvfs/W+eWo4VPeDCfA+xkE72WA5Zaj7uF1bZe0KDO5SZwvmIiWE6C7
-zizXB/T12spSWoVLhId1J9IEus36i05L1wioZTj6Rn/2CxjO/nLafByEkmPUmY2J0ffjNmdvxZvV
-F4eyCqo3nvdz9Kai6XA/EQ9tPgGuTK+Z2TBSlhONcJj7DRC5zKX8e0p9ju3d+KEVGIdu6sKTuLe0
-p4Bq407gxpNUB/qg6Uo3gE9kSUsL7umNtZlutHkdkn0r0+e3iyxrI5z9tHmN5PdXbhFx9FOepMX4
-x/f7kn3wvHlw0Aghx9fhj8Nik24Fv3SFSOAbH+6AOzzA8Q6EUety7Cj0J8IvEM7wPebXhFD/3xU4
-SRP8ZhnVV/LaFOmy+PXHteVSbiyZJityAWUBnVS8u0OA2wsARoCPWBVp/TUqBtYreWwghntE8gvQ
-F+Wmi0Qqli+UEu8YdRFP/Zp7mIJgOLZMQ/kJ8/RndfwKfLt/8GeQTGtxWm3ijnntmrE+I2kBfo2f
-tBNAkKBXBHm5QhTQrfF1UCCqi0TRuzxp8+JLl4hWeF8aG5rcQXFFvweMJ8I1KzCMPMgcpQC/NQmf
-wbAfzvkqDPw24gWD1Q96QmNeefq293YXzDBREiUMSnkMrSdp1NrEHSRTdizXiALA+rKcphP8Jiju
-lmHuIAGInj0wrD2As3HUc2JtEPRkNTqnJO4HHigIPv081+pZ7tzOS+swL3hpU7tLO5bxTudcagBW
-GOg7NceyzZ5+1L/Fontslt3zetNPXbu6UoxJf3SBAOVhz2tuFCzLggTBg+YJchVUKk36lU3uEkIw
-7Dk0MHyQm9QNke9a4bimZT97nfRDppGjsjX9Ka+PQ+tT8jUWuxeP0pXTq7bcGvV9lzp7F6/LFeLF
-Gy+Nl1UYlUa5Q/CGs31R9g+GXyAcwb7JewcfG0Bm1AK/Z39hTswAyAGOdT+/1PdWE5W/tpa8l9kV
-uQoIbyC4tnQhOIl4A8DBC0EKdHlIACsAeLMMoyZ1AkBfn2z81m4RU/kcjzJgvhobXC+rMBU48wxt
-SyeHpB87vCjX059A8kzjFQs6Xtj7LL9gxu2NNq54YqZs37AEo4AuBdwOivsM2OlC2UDJTu9+xLP8
-IXEBxqc6TsB4zbMQvPI2d4FhKyWS2QWN2UPVRP9uEKzd2Fli497CWp6eQ0vwY7Y+UJqWKfELdIC7
-IvCh59Lr00Ipa7atQyTQ/ZRnmofwD/ZhBZ7p6zEsw+EjPEHwBLfRzQ0Et/wPyCMpX91A6w3CtArf
-zjvR0XnPN+ePiHA2+Ysyeze9ndJL+VZ/AmAfGVKTt1S7wrWM5py68zXNr+WoklqGzgGue6D6JX40
-KQLk3NINcr5XZpl/Ko8317trhF4IUvEXQZ7eboNIv/kkSYOzL3rB0gnVFAG14tqB6IWC4T7edUk1
-KHAckmVAvSyXIFjBb/nrr5fmgmsBfN0t/YGnhXhagRfwRfSBtMmDPxN+gXCG76nGfKnnBn45YeWl
-uAK+5R5Rk3pOUgxA7K1MA+yiUHIJZyFmrjgfOSdIMypgBcCWjbBYESYCpVt+Vz18UQ4+QXN1KK+Z
-dfRt1HZA/MXLcymzduHVQHAALDNNBwgm+EyL8DeArj1fgmMgeJKOoL5eGHzkQ8+vL0D8rJcJ2RaP
-PtE3ODiNafUlZOKHNU6uEp8g32egewG4t3hopBtGVu12A8ZrqsSc3MAwQliXUkubl8jzk078akV/
-XbZyeveFJkBvz9vPfTwAcDtHjrSyIFS3A7s7hMalqVx7kbZcex0wq5U4QTGXBH88fwPB5Qecu0Rg
-HTWPcoxsSLE15pFnvu9l9RwlphgqZd6PB9nxyhGH/En7ThlV2C0+2t/aLlbB0fy946RNBqgeaE40
-Wx3Mc6H4iB3L2Cy9s/lO+945nOgznWWYYXsacOSHodIK7CIUxpGgkXptlPGWPq16iHydoZeuoZE2
-tHHhwpwdQy6AfushSBVLJ7DEJ9eHnrdbiRkv0EuZI5rGKXEpM/rZFiotXwg3Dyv7DoLf4UfsLy4g
-+cDi/3D4BcIR/tEHNdDmp9AL/NISTIG1dNXuI6zLMeW7reut/A6IWY6KZK0eK+WCKNAWY1RMEEyB
-kOlan/XSHdISDHjRHah9guPlOKNbBF+gW0fe5dn79SeUT8pn+5BGdnr1Kx/pktNW4PfJDin41eNn
-a/DZP/jkJ1xAuAu34OHLfdRe+mQsXNzas8bEKbhz3HewexfKH8IBzOZc+Q7ozbTd80NxfwTGUU4B
-YOmvuvnKLZK4WgoLx/kJF5F81j5JfB5PofKaxm0xEwIlgJbK6daUo/Irxs2rTwlSsw2hgJyKCX0N
-NEBcjbct7o3egC9m3Av8+m4FLjeIYQm2dWM5gXH3be888zH4HLmzVdhrctQwF0elzw7lTfBH3SLe
-sART6WLchDf+XmhbWtvhrU1bG2tItrJbvDHBNRVFDyAqM2v2u5wRS3MAol4u86TuLslONR/yDR/z
-My0LsnVH5wJZNV0iwJtmlEwYMkLnTG895cqpoLxQx8Zgj5Kgsqat+BRsJGRH6owUbNonpWnjqRuA
-/oW4u+sDUNO0WYw9VUxxhUvMxOziehXrVmDmyD7CcAG+Xr7CSifgLStwAeB1/KFe+zeEXyD806BC
-bgDd/I2BLL1V/sEdCNcdnIqwVGwNfJwEn+e6yTtd97TwWktHGfpMccp7AcpVVhouLhAAChg7ytfR
-D+4RpA+F8zFORTMUTrOKCRrJBzUGAcAVRyjoAsYsABb8whJ8shZ/sAibx/PvuFXOI+BpEX5xAuau
-NK/RvP6Mc8e+PDYQqNPzFv8q71bwVM53yD6FsHn5v+1KrfjRreL9klNtTX3YmnTt0jmnAWE70KRP
-jW3DKhw7DuE9AWCxFtajSKTC0LgC4tSxh/gEvuoj3AAyDLXlX5doz0gvmlqChWZlEe6MUMF5YpSA
-q43ZO6JpUlD6VMo0hW6CYadcutxsX1+gQ68vfYNTHqKHoCdZ8y9le37NYBO6o+ft9dksDNgAuY7G
-Rd9izP9sFdb4sXt+77bLHHDN3MBvAN1Zp84b99jlYAkMdaX6+phiRaaYPHECXZ5EJvAEmcIqicp7
-Y8xxlzpY5wTE2TmXqFzBcATAGq9rn8qMMfPieXvVOuRMsYVjRZcIBBiuGZlWXl9+wZjW31h/Pc11
-6XnjlfLtD4dfIBzhuxZhqgK6OrQ33eSoLhEKfjW9rBJY22OB+qtAcaYpgw1S7qAMJgI45HUQXEpw
-WapW2XSLIDBRYBzHsj7d3CMW6DN/s68T+J6VzcFqjHk01HK3lEsiT1Mxv7RUBcB8FQw/61ig9ruW
-4M+uEcV2TgYC37AIm8ElvqZRtMm84uhgVqT15fgp+AY8WrjklTL4/9l7ty3LdRRYNHCu///inWY/
-iIAAyc5ZvbtrnTNGqmqmdb8iCGMsP5QbTawsPdMGeoXhVuR6JEcbaWrH8+isMdwymaj4+W7JnJnT
-bJ1m7hRnx2tpgHXUljRpLe+ai7UXbNSlfsoBtUNd8bWHtq8wCUNQkMuy02Z4mk00E4iIo82w+X5S
-BP39pTmkiUTyKkPSr2qHm/1v+k38J2Goj2grC+coeZXMFePUNOJoH/wYZp3jZhy8cT/IbR/9P6af
-8+v7G1tWBUVb/Mo92T5BTa9vAGA7p2lZbTX9up4Pwzv6SbY20mTj+Sik9OIqq8kHoO4nPnniEHVV
-AOx6Ez8m19TmF3yiIbs9H3GQYQGU+SW0ZNBWE5Ozbhy8rsMBABsaSD6aRXjEE3cA4yauP2fUGdlm
-616FzVb31rGhi2FcNrTDcWqEmkGsbbTy0J8f3/r0ha3/ovsFwv+Jm7v/AHoxf2kjXLw5w8FwzqYQ
-ov0i9xBCqXysbWzuABUm4QLBHRCvzVx5l8LYUiAkMKZpBDeyR9sOIMwheKxKzdGu/f3RNngIp/ZJ
-U0FXOmKj3W9MO/HubRZ2vlZA8zKcTSPetMNPL9N1ILym6V6ANx8l3bFKcYMQ/VikQfooZlyLXnFN
-09tuoHreHfLJZGmKCiMbuZ/Q4BZtr+kq9Wb9OWMb+O12e2UG8dwlH3F+8J/ipv88xrjaD2EBc5Wn
-ar68tMG5z1rf6glKylAMEJZMo/aXAkONm+YSBMUNBIPaYIYPHMz1xIjDV+biBnPdW+oExMw2OhPA
-pndJor3s8cinBbVexRON7CLj5hwF+D2dHoEH/0/h9GN3PgIzD9fnkL/zepmAp7pQZm29WH9qQv7A
-rdjSvLVcOX8Cuv4Qbw/xc1hrm/c0nQulH23LhD/mJgk5NUwlNvth00af+CU5j1XdKUtxcLWTs2ar
-m+RmxiKAmPlqcGSIE3qW/7DN2/VP0tQV/3Px1xTO2Vo9KprhF+W4PxLw3rvJBH9TO1xg+I0T/2/c
-LxAO97FGmOYQx5fl1D/4MfhTDXG0jQA3vsiQgHiSXNahe2N2L/eabHwsAqXmN8dL0wgXMwoRNiu7
-F/U7tcYihHIT0y5Y03lSApp98EkgnTTFu21wxB0wnqGYjwMCiNf1Cu2vX1dqhe2ydWrEhxrfNzBM
-O2GeEMt1Wq9GLfjgXBK74ZeFOUAwPwHobpZH55zNHdqKc/SZtjM6YcA6YU/hnwDwaascBHT+fdha
-qTF1bGYRQJc/TDMJ+CigzBu9qleQ2wdxztmAbgtLrI18tue/YwzrpbJ1nJACTtfw1ERiaoMFHTgE
-6ApIyDi5cn5d2jEDAfAldTWziNQMUwscn1e20gRTQ5yaYLM6Mzn4kt4sAEhaB9RO2LMM50RiJcy1
-j8erG9PVq5dmGHfyk+3jPk/+w3psdH8MV6RJ0Fuep3psz+BFVzUD5zJNoxtr0tv3Vqzl95aadcwR
-ae90uL3ew9CyjVO9L2nOflsfbm6gIJo2Ry0TBUaPY3cjzbH2RclUyS5+VhUSNPd9U+ATvHOCRFvs
-WUkxPr4sDcpsR47BJY9LX4Ge56gVlvg2HCHlyTd3E33WcCO5g18Cass8AnbjctsAMG7HdZU2eGqJ
-/7b7BcL/gcsnGCKE0pEJzBfnUOYU/WU5BTAExEFupgC4b1by9tUhidQXCjKeOzmEUOSjJij5iSMF
-otbHKimRGjhWTY0CYKpBuVleAO+rluYUn2O2bdPCkNpgBOC9xRb4DlDsAYqfgfCu8f0RCF9XrIGj
-FWIAACAASURBVFVNVzHvOz5tt9bfVDMtHV9rzgFM+hDhlbRxBsZMm+5V66tSeqadwppgP2Rpwnvv
-A58+5AtyosFpH9VgYe917BZtu8zCCD/2dR/dutpL2KrlAsGaL9LCBtFkDPPX5IBLvtwGui+YT9BC
-3LBO8KvLW5rgc74ykTAxgai0BMFA2glPG+E7unSlP9YnJ96DHk2GoABAV53qAqEF1zlBPlpdfEaE
-KgGwvCz3RwD44C+N/RDac/tsyb7FzXJZWoTM3Dk+2z2USZ95i04eIhXXo3PpUt6MS5wEMtW4//ah
-jO4cX6Krern3Z1rtVu0zyONSDimgLB7iGjfhXoLmiltUxrRRt3Szysg16DnpmjScL82JxlO0067t
-BX9v/bKgbQLgiP8ZAKNd1d80vboujpQq2QPpjkm7HMZtvt59Wd9WxmUOt10DDN+1wPP3C4T/Tffp
-3B80wAlcTnbBI+4OZnPDwGNLbit7VwKYG8HMsbSDJGJv+VLu9ZdTczgHi58EvvSHgBOwnCBDXipo
-NsPCMa12RwrgLU/kewLBx5dU8ByftUr/i6EjtcCr4QV8Cwwb/Fo2un7Z+vJd0wDb4UMan5tHEJs0
-AeBYaIAM1ELINLtglA2lCAK9GVJGXUC3VrprSIXBEkQM5FdajIOgbXHs04TWo8JHZ3vWk+DniOyQ
-bngExLMnKtYYtlOao80szPZxp2z22gOyq07X8tvWN5OftpVglD/vz46aFhLcN8zL/skAJyFFnGlb
-7tLexs22n5pDNLMIrD1HzTD347LR57jJX4T9bH5vT3VINuknf2KPcg8ptysB3kAygn4dqDOaURp2
-FhI/bbQbqG75utAu3idLMOL2xKc4oUuYNDMKtieZmiZ7xTXVOxiNDJ1cXNJq/k/dbvXbiH8YjW/x
-I821L4cyuYn3MiEAqnKKtBP/CZmV9dA72ehkCrboP/knadYMCoJ99CMBcRCKi39hB4BaJ9c2XbZz
-dNKDwF2A9FkDXEo2+lOGGHex8MU292hgmUqxRpkeN7NhH0w74ZOcV3MJN7nGDezdsMDfdb9A+A+d
-h1Di9Za7Hce4ajrzA7XoKJ7qAL6CXDtD8VL8GISoNc9yKoQH7tp+vXSy29yotW0YoOQgZ6FXd0pK
-nMqTQdkUKBthg7c0hlNbowOE8qXJtdblCpvbpfGlmYEB1wK+Pq4giKWt8NdX/2Tyq1Z4tyFOIOy+
-2nHpgwD0eiFuXxUZudz9k9n19ff8+cgTcUa2N/iLyZICZZrAZEPdA21zXwLzlA70269zvgJFa86E
-zsuzX1H9PkrUIIsx1BT42o81B1P3IVU60l65DejBP280qnFplQBXtIqqXylq6P/yBU8XOkLHgnnv
-J1vjKW29H1CCruyYaxBJntkxgfZW11Y2wnljPq5aNA5YDH/xPdoQ3yb0YHeZO32vl0xXu3f2yfix
-mliQajKeUIV/fQr+jrHHh204D8rvwp+aaNlEmaXt2/+FAD8RlcQ3gp75FfgwSrWImk/jrJU6bbPK
-05vU8gAGAJ1Pr1DEsLUj49sLHMsrTWZ7SQRSTh7lVDuW/SWIXVtXwG08rbModw73fIg6iGybdtpQ
-2m5bo1ZtcdMcQ8Evsh6XvBmXV0u+d9IYAyWX+NQqTfVQ455zn1FyE3FlnhSAqw+O0vYS+JrDfX18
-w/2G3wa3O19oF2Oqv+Z+gXC4T29CXNc5rgVuxQ+U9tfPcfAd/LJORJyCoiZ4NV74wPqtjLYVevCr
-ZrXFoyRle+yELuDzomAqzhZuE1cA4AkYJxiWDZWaMaZP5rf9rK7rGW5pggP8EgzjQ4Dbrnbh+ro2
-wFxA2OFxfvJ9ceyW/co7cYZlODlV6E5BMGliAuMNBAMJpAtoeb0zcl7GHQSLJoD5lYoAg+LoCexW
-vndwHIinE/zTFUWWJ+zRqJzkKjZ6JRKgg6zKWIF59ftpG/2h0y20xkENrAsYQwFY8GmjhdbVcJGW
-mDfrEtJnvPe6No0XULggC6kwlAxMsvJndOtIVVOAuBrR8lfQf5pRWPG9LO8OfIc2PvfOXc3ZCmuX
-CLkqHADYwm8ChnkWupZQbR0ny0nHASIm8Z20sye58mncI5HZHnx4x4WzwJviFUdO75XH2q7o5Vp9
-e9szrurb+7HLL5My5/Gd6mv5ihAkyfZyRwA8+/UB6D1oftcS7GCYI8jzjmNDllZbtfZe8sCHHxhA
-N/i9xKm2uMmEYOjTHBPg3tG5kn2aE7TPfZlLxggJdlP+rd9FYES7qxMotnu9Jy9z+7fdLxAOd+RD
-p3zEbyhQqyBXP2CUYYywyweOAHxZCUcHyjaL4GLw1xQUw6kQZDH9XacyUrFu3JKXspmdvEQ3NaqT
-goaWDIlzcxMEywTCJZ7op+JW1QqKJU82qvCqIwDa4KZmWIBvaoS/rtIIX/woxk9Hph1MI+SLc+sJ
-wAW/eGZimWUgNMSmAPgoHFAMA2RcNXq0uBJm9fORp1bX+zLlVOaDNEsevC3rqbyK1KIUJOUUmTyD
-40UW3q6QK07Xlx1rkm3JHy+gkJ3vvU0XA/EWDkAsGpCZ/9mtERII8gVQAmDdRrXn+Asb9zDJMEfa
-vKu4UADMFi1vGEc6/ToUGYNhDNEkrmEKAcnBE0wLR3wDzRGXNsOSPwFwCMOkxRDwBL8FhGOGmlaY
-Ta04Nps8KfmR+DOfdjTCQifFaXY6zjlsbhJR+D4VNLp/tM5Dvrz4jPccwgS/y28tpgG3kaenswmV
-HtFQ42XSN/EU7xnhyHPSQp7q2fphleb0a5kTz9U+8ObSHkAuwzGfDfQKOCbhpyI4+6R8qPOl+bEQ
-fUKXL5M6Qpu6AqnxpZZY+b6A33zuZOQ/Pa7mI/as0HzjKrkpK7rtDpXvKcpdfkv7eyUovtc7q9zz
-9wLFv0D4X3Tnlw8O+dDWOrXAet3iD78U9sFneHf3BZH53Bdem4HtT1px9De8IX5DsXyD40qiVvC7
-0mbFHgI7/6jkZsMsO6fQgbCGHlpglHZ3aIdL+4sWD1SaTgD3ZgljNYcA7CowDALirytBMb5oFvHz
-i3A/5rML5h43O75AtzvgV7woQBBMhioMFyqsitcctbwZJzSBAzPM5SmBmOsX89ZA7+b0hqhfcfRb
-+idgKH89Hs0XjjiKlL4dDDcbzu3qLWxA3rCxjtTeUCtis+3T0L1Anm6L2DdNLX50OiO9Xn3p7bRf
-l19OlRAt8O3xiXCZ6yrjAnCtgd3d7zlfBaJrTLx3y0Yongbi7uW0cEQLUC6GZAJ8aco0/Ch/rsU3
-zR/YOQLhAB7ZQNcOJ48KfrRMI4D8MuatoLgNOteR4KHMIepx889uUoHL31MVdojWvfVQb+PRSFr/
-Efwa9rjWx2nSoLQvfVUaAISAZlvP5bKdDSRbu/R2Dn06+F37Z4f+ZL4H0EtenTTHfiqNF0BuT/wI
-QJOP7OGaCxQ2yODarE4sEDJGTSQSJCswNjSFCul4moToEY9p29xuMA5OeCFt7UtkhzZYgXAAXXeE
-Bjj2FLXCQM7x33a/QDjcJ+xsZXSo6r/5I3y749vX9YZeUeEQ7l8hx0srXBvAXf1QHvc4BoJhlTvr
-UC9LjbC3MqviYvcvlZv3MKQhASQqgJKlKtgdYJhxFWYD/YWhbMMYlsas/+yiYLUFhlMbXCB40wg3
-zfCLeYQ9mFEYAW99Qee2C2Zemjwroe19tmRa+yNK955SAJezpJB45hH29478NmB8Dlev6VOxqdCA
-olvDjlk+Ws9l94qoIdUc6MBbOxWd/XV0wWMmANhGbgm2aQkQJvtPREYv8LqBRp2cK+83G7l3k7wt
-Pl9KMBxCLIB4kr+X1pnKJUu/5xYtzbBlW1xkxay5eioMM10Fv4zZRHYSBCQotkofcZVmAxDbslpw
-B75XQ9QAbzhoOI4Xci1zmjtU0gF+xT6ja4ZJQELhpeZTzLK71zirCJ9ZH0fTqtjBwj4hNXzdeStD
-8gbreapcN4vQsuc2tY9CmAm49vyzTj+W6eV81tPakno1n3F8FdW+WDfKdPA7zR64705A+ZC30Q0O
-4VgZAn/maWGAJhS8gXbaBQd/WvjDUutMsDvBr4Z17p5uCh7Xe7DO2lsDD8UJEpTz/WW5G27AxYOl
-Yn2v47Pr/637BcJ0ryChZ2vaYH/Q/s6wLxD8LeHcCLzDAxZBiNBtwvUHQWuHq37kt66hFc4q+eh3
-9UUFcx/8iKG0eZg7xc0dBLvscAG5/DnOtsROFq5/vfgd8YFqXKkJNkuNMEGwXxfs6wRuBeQe48cL
-dhJXfb0SDOO2BMRdW2DZ5ynskuFFzJihipO7/ZlHNcclQGNtQ2tQYRzCwsqV6TX6OGl+d3CMVubw
-7MFLCHcNsNBX0pOIUK9suwttcIDE/vlVLaMj8s7k8+pILfAxvdfVd4rMTNwsUzYQoqh9cIjZdQ1U
-u26q1jm9qRVOWeY556yjA+Cyy+UpL6odngDlhEfYr11G7iYRJeMlTUHvVXGL/hfg1ack+RTHSA/d
-JKIeA1nr504r5TcSyn3F23kDEIumfTVsxcQGIE4wcaK7U/SRPnXPPzH3GVd8+6c8WjOpRPcrrMBu
-5XEpaT1/m2iZpw1AdXpq/TLtCw7hzgcPBPcQPqSF/wh6te1RpmmAbbcNZh3TJKLy6pM+GU+7M6d3
-xWmflK4qOxUZHiYRpQVe2T1NH3KdM7z4nscTsQLL+5y/zmf2pPrNTUc+68CSeVgMbr4od4W9cD85
-YtkIXzn+v+9+gXC4d21Zz+d4sA9+uH6jwO+3xJOWvpTouVcO2uAIaW9a33hSBO+nTOIJfktO1Gsf
-q3rGSNU2vWOSDnPWthSRd2zsI7AdceulHvZq/UrkaZz3p9Mpw0JYhyBdTOnKj2csLXBoiF81wrvZ
-g74UV3m7CcX6ml78GgBeYKx+CpmsTxwZngQXrU1grI+8UHGelUieEnOi1Kq50wiV95xSL1GteHAl
-W8OP2vfK96w5liFXDvpzEnxkRrd1Ztvkz7mPpBM5zpnw5EJgjOX52G2g25LGuR4cB4cl8AIAT1ZY
-gsyiP5fJq11RiGO3FEzAGQAXU5HtEgK+GreWaDmcGppolHkZgILggY+LFQDjKiCBSwBw+kMbnMQa
-5b5NGyrnI75tniCGywC/lgqKAPhCmEYYP0GJIqKosxnGW4KKvlLe29Wu7VGHhGl+0Cb0IfxJngPY
-tR7uvKaHhWsc2iDNfJDW/D3/J5rgM6Dey/wJ6G16HdEKl7b3DHjL/KED5JOGGMmPR5t5419jKb6t
-+YpnLvZRZg9l8wvkMWRcLwW++Y9rqzc8NX8b/Y21le6hRwZ/WR0KTTWaeUQHxXFCxM01u2I8Cxj/
-nhrxLzrfyeAhX2K47Urwq1pfguATGIZLuyRq4fsFgL21D2had8UHvI4nQoHkW/wTaBEMd8VZn5dT
-s6/sORQt1iZLTSF6nIaXsJ697A94lpyq++4SvigwHCCYL8rhKjD8qhEeIPdoJ2zWAHIC4dvje7q+
-+nArCA4BSzS8jTDmmQwPdYWGfZY951GNsGK/vJnwWtcK7y/XsahlahejUjXqhQukT1lvsyd26XN2
-VMbXBqr0WOCjty3zZw6LR4Y8Ks3isWLd/Mn8RJkOgIPJW7RvtUc+cTlSTmTQOrX+1vKFX+5WCIav
-EJB3xK0yri1EfUVXdc63STqi86WJTsyrPW54g3ESKVpgAghWok9lEGAXBA2Xxlna8a8r+hOcCYQP
-M9uc+04i5CsZpj8A8WXwOztek7E6TKbSzCJ2p5xy5PFj7N7117H9EdcddcYuJeBqu7bCmn+C572N
-w1WAk9qc1rr1PAt8nfJFfBHfihtl8VCWabumt/K6FJ9ttaeKGg762F6mG4C57Qupuyko8oZU+1V8
-NeMz6IkXUtNL/EAzCfLQAMVcx9IgKwg+3Xihz9NwyiPJuEkfKasa+LVmJnHFl+bcr3aG8HWXffCq
-+kPG+l90v0A43OcaYbmzQdn71k/NILyAb1wTGMem+Aqicve0E/4KvqQarU2RNV0Q0bSuqfeiV3vU
-ChMoWQurYFVYcZyJj6ITNKnG96AVbucGQ1+Mg5R10UxuDUEft0IFsYDftA2On8XLcj/aANsLGBYT
-igTCcfUrNrqAYTLTZn95nMYJgkum10wRQJ61xeSoyVSpGcQEuQdQnFcxl4mlrAetNYQ/Acdd/Or+
-GwD/BIJd6j35Hfl4vmmGM4ONgqj9xg7mHlwAmiS8YbGn/XhwHX6c55BunaxQHeK+XSBYBGaMseGD
-7Jc843EvLS/kpS+TF/YGBmSFimOAku/plwE2EG4QHLmAbntSc3UQvJkw0c+J/9aODNCk8yFgAh40
-pISm4De0wLw59ex0bYSTbPD8MyPR1mYW9s1zSPtjEPwc1k+1r2tRm7eYTo17fq13Lr7V2ke8H9JX
-DhOwZdLVaE+J7yMt8JO/l9d6j1phE/6VsmPFKcBd7OOgEU6gLOWblPLqQ7ExAbpIWtniw8OX4da7
-Q+WHKwCmLKg4qGbYKl2qbu4k2WfuTtbBW6gJhoBfR/9wxp3wHPmCHG/1k2d/yFD/i+4XCP+hU6Ul
-j0Jz6GkRXrbAwALDWKD4G/US3bcwZ8cEv97BL87ECQhveHF8F6QAkoDfJiBzlFs4Y7IjihokpkVT
-kqwxtZfhVAvMAiegDBQoHiy79dT0Z/HpZKTWqYPhMo2wV23v/jLcCTBvQDg+NbmOillC/boLmNM0
-AkBphwUkcupqjWoVuIYAGVrFM0XBMn8GJ1kVo328+/NaUCljwUStcjRw+yfguMqIQNbuTHV3kx7l
-6X0petC9Qy2part3+t01w71CFOKc20Pzi9ORG+dSaJv3fs2WfivffRdCwzMzO2DWx0UAnzPtMlNi
-P8xFLiCjgJbgIBOqDYIbywwdhJjhrBnm/oynMhFXT2+uBMSLeXXgr3slex1zmffTbRMIz7mWqsDj
-KOH8AnyaCTvqVABDInn6gxvVnvVGnmNJ2tqdQYSkPm3HVs0kvOew7pDisj1MWKL5d22w7Jh5V8T1
-RwFBpZNWhmCx1VP+bE/HmnXjsVwvw3YqSV+U89me1h/ZOtB9MYc45YGA6Kb1lXWp6Pyz9SvLyThc
-tcEiCzYATNCrmmLVBgP1rLXL0vT7U1rjkplm0f4FR5lIrPZ5lrCHNnhpjG/4vV7jdwCXXevlOVAm
-nnm7TMd/1f0C4XAfa4TR+eonR6Z9y49mEt9k3AZ8sXETAZ6cx9Pf5BzdK0XsBVj1/nPZHFV+Ebky
-nwlQJomWmKJgNfg67/5JCxyTmaYR0SOD92PUoi4vabeEv/Jm+Z2EL+TYNF6fAe5uA9zTOlA2W/aH
-HsemXZfDrxt2XzB+GQsCCjb1Yk0rH4Wl8ETX/CooFkVpS8PIy1yl1RWcZ961wUFvWXJf5g3cFtNS
-hr4DZz+UT0HtPaxzMNvXcLNhZrtpCqHzSWDn257f2Pt8OQ7ql/TNHSaL8V5JDbK0JyLLpQlTFLu1
-Dt2SOoDom2qClXnk7Lv08SdsMbFL7C2baRRgtgo3++D4lfb3SjBMkyWNX8D4SiBcYGgCve6fes5F
-B3WMIeJicOBWTZ+1/voGgPdp89H4tuJbvPb9THzzNcuB8F7S9rwKempXzLQe1vzewqd2BuMVgrCI
-Sy2oRdyBwN7AcdWhZSyD6vcWP+vp+d+0wifTB9JzM4eQPDjE1YvRMU4hmJxb7dNJtNLvKI0vmYit
-Qo8AeMYPf44/eaNQic6nd3pNeqpidRPuaAC4jlRzXDdl0TL0umgm4XxZTvjHX3a/QDjcxpie8hVu
-a9f9hIgyhdAX5BQUN9mc8knQCEqOsZfTTZLxQyKrpzCluYJL/GpTBU4I04yXSllAGdRPdxI5WQPw
-qhY4J6HnSR1M5HnCHounWAFf8audsIJgu76a+YOC2mkasacVUCYwXkLd823Yy651J2wFhpeIqNsE
-WIEd/lFxTrA7BX974pt5pna46jB0QZ41GQYIHqA4r33ui5UitR8qQi3zmfhd8sniuQpeNOY7SWuC
-3lNf2ZbBsjzH1jovuduAMi7WB6phHQ1p3/aoMVpu/PjpXKn0OxRtT3a29imIZLZNZkXtrNKmvtJK
-HlvHKgJkgCGkRNMnxbNw24/td21Xe4j3q6+qy7UtRU7rWke1wUZ+3TGutwF2raW9gqrv/ghcNcAc
-Y5pN6Goe2J5rBo1rnmeb4rYvHt1Ms83fAe3J9OFPNMV9zmvNRctLrkYQZUIDQAJJlk/KVzmiRNQ0
-uzbynPLrFLBNGY9xHJIu5ajR3l5+GzdLUyOMGZd5pb1ox8XT+F1Lj3gJ1AkTK6G0wgJyDRJ/AMD5
-Qh0yjZW7yHhVrFA+uA/6GLLH2gty7EecIHEML43wFaDY5foLhP9F96lGGBBCcw+Ay7OCH06PcAHA
-qKuja4OTuJLD+1nwHoTwzgbp9/bxUIeCYRUsZOBezCUlDHe09DWEbmtU5zDCGS0Cqnbaz6B4sx32
-0oXOp3T5Ik/8bGiCTUwjECdC4CtOhzgBXTkerdkJN3vhnu4EwtQMX7eYQ6h2DHWVFaOsTo2ty9qg
-g90Cij28aYIJMg2p5U14kctYa9uB5a5B5o1RM4sYmtyu+VX9A2P6qDt4aJy4u3c1bjs7uMOAoHUd
-x1MjwuQTob4dHXHYj9liTRq4kqZjGENt1WhHTfpOgXkspIPgJlQwKfGtrRMYtmy62f5Km7UHhZZz
-D0qm1Jpdh/14LdswPq2ZcfMmidcQruYAviiQUQCY1wTCDver6BoGv9epLg0Atx/nXqlVQNQGINHp
-R6OfSHfYRFa96p44/HMa63nW/p7shHvebWyzLVn/An8EjkgzE9JTmp1kVRshlbjZ4t7KzfjeRz/k
-edIKs59qKwxTM4gnc4iKg44TnR7IDlrczhIOeZSvU/OLAr8TGKMA8E3NrKRThk9TjTqtSqXAyAPN
-QzbpIcbDHCJflkNohyEAuORTB8O01/+77hcIh/sUCLt3gMuFTkCMenHu29U2eIHf/+MuGuGSyg7A
-zfEFbAJu1/5U8iY40SPJsg2W301y+VEkTEGzNgR1AERG4odUtnVht09soFcBb4uTXjhf6JF0YdfM
-15RS8dNHsdaArwjg8bLc/Fxy1/oujdUOgodWuAHhBYLdLtzXBbvufFmoH6H2vOmbhneA2zIheA6r
-kDPJ0xbLX0CxxJnWg84eiwzeNb9qD3wgHa78AUg8IYvRH9/jex/2Sp76UW14CUN2JY7Cyyo/5Ntt
-+wTt5xZ57Fhom6Ifx6332Ifae22mbIRZ0xFvmKQVMKx8qkEuwJM3pfPpzEUwrIC3wiZPazLdeev2
-3TmAk5JiJA74V9CqI+0Wl4qKZ3wz9zKXyOMVzQf4WaCGwO3AebJdqF/2WFvWAwn7lumJFu3Bv6d5
-i/fRv777+lh+SpN5bpJHGK+EFRTPD1HsoLcRXDMXOObNIZ/KI9vqUxT9V9A3tMIVJ2YO8pTgdDrE
-fLHuGAdpc9BB+u0c37XCJ+BLnr+wR4ZN8vgBHKekKM7eTq1C3Bzm3tNr71vWElrhAr1ep4neXqZe
-1zKJsPjkcr4sh7rZ+NvuFwj/oQtM1p+0Sdw8Nq1MIuQUCdTLcrkJp3ZVd0yEU2gepOeJPRrqo6Fx
-r3UEw51B6kiD8J0CziHGo0ih6mRwZ8Bi6KB3e2mO6ai4smXkqyneuEKzGDSk4FUBbHEOKU+OoAbK
-QvNk+rP9pbiuJX43n6Df7wLACwTf64U5qy/LtRfkkmcvJtrXZIJgkeUjPNOV6eoac87U/CEWINZR
-Vn6s56mcCd3sml/g3SyiP95G88v+mHlc6wi/9z3Q+ydjcKn/B1fiXyO22Be3tR40zJu8CVVmmwYe
-9cZBLvtV7Apqr9I+Xpobqwr4/sLLEZsMnNGxDjVl7KeUt8qj+w8mdsFmYp4kpkpfHSDbdQl/6sIX
-spbuWCA4hL99BU9NIIxk0u4L+OLi+RuWcU0zrJMgGm/df3rlBFR4zvJOdScq7Hz4jdZ6vtrLJnFK
-7QpknjXFJ5viU186wIVof/nEh5y7a4Tr6QIBLG1pT0Qo8U37+xIXRXNc23TayMO+Vz1HjbBofylr
-NJ9qwesc4cHDopkWR1mKA01pH48aYQJQBb44gN+1rgWaY264zsEe6kt12hevvIx3SNhT/nObXaoN
-vrGUQ9yKN+C4ceGK+9N7vUyOAMVPbw//D90vEA63acse83UMRxD8k0lE2gzLzx1LA4w4NSIbievg
-gd9BrMKi84dRxA7+uhtEA8TVZP0tTTCZEv16xTMANgS4ZT1rotrLb7GjNC4/qMH8uvNAdq/s2ftY
-U0grAFaBfMG+QvP0iUZYP66xxcnJEhJHbfB9rVMj7qYJZv+ys52Bcz1qmDnipuV1rmdnSMwHrrfE
-pxiUhTd4v7c5xCW7bHGdbpSWmlmETXCsZhEDKBzISNMenff26QI7ZgUtT5uGUVIyuVlh3wmCRzDr
-bXELzGqd+hJi8vxtfAJe8yzkEjpNkI6O8EmK6ypRuxNqaZM2aq0KIJQtJ/f4yJPlpWnLAiAgYrie
-0lztRpTmEKkJVj+BMYW7gIU1xBK8BL7+JbzlK0ypeJShX7A439txLf5k17q5uwztBbk12AacXMbm
-2Ciqa+9kYuZTFqXBE1k/k/qJ01d/GsjLuvoaf6L9LU6r8kDa3fhVSSIL4NbsZ7nnqV3fQKzMcwOq
-f5ivDd+26Ur6MY53lc3xixA5gtyog7TcXprTOFQZur5fR5zNOOvpFIeGAXjPWt5T3J0+tHgAAoKR
-VlM8vWq1udNF62/I7GUSwbpEGwzREt/5kGd9WAMBgM0WMMZSHP1t9wuEw73J2ZaPxOdBXL5MIhyH
-UyMk7slGWP6Um3dEHUk8ugYI5Xe3sG4GESoHf9vGYVBvBzB86sfmmuq8ru2luFT31JbTNMs0PkTx
-5MuFKW34TbS/BnxZ1wQfNMIb4B1gdwfMlnm/LmqCV56bdsR2AMM6WRszpL+DWczwAfSu3JALjAAA
-IABJREFU6B0kA4mHGrjNOab3EGfB3FqydN/Nyi/snNqOXfxW/BwzI973pG/kl33dyvsGivdGH+K0
-Qgp0xjVCt1m4pVneONaa5ENJ36Ds6rPF48oAw4DV1sNhDfKvxlDqE9RXKddVsF4XMPGH5BHmUuD5
-YC6heazMlNoNqgBfvTnF9ZVhfSISQ0kWsswhFj9O4PtFNuFLBdU+cmMAos5rPaxdNxnFSDqQ6aCr
-aWuVZA7k0+OVm/oxf61L+fGBP/fslkfBC0Z4341PaZz7ztdrgWkGYVkmFz7nsoFIMm3gHeTqmGb+
-rdz04xjPuWqmLcJ7SQPzpbiPNcJRJhUvqLoXPRzihr+45+hf8v393wK6JzAcYndLU3kv4NYBnjqB
-zOPirz7yBo+0USYRZZZfD2NC63tdoiVGflp5+QMY/wLhf899qBBOAuCVdjm0D3Z31FnC/qgV/j/J
-XXxvYLqXRwWBqTa5bOPn2083Q9cwZb/ewK+ijtNV+rH2i2h+gTg+rYcJPDIt2Wptv8QkUXmOMf5Y
-MCgbgpc2vs0sghphtQM+guBuB3wCyWkaEdrjKz4KcJmdAXEKg77pleHFtNV0uqyfMKET6GU8c9WH
-JUTM2srTQFXYob3HeYKjzJPaao7orPktYakit7tHaj8kDHLDGCIs5yQqcC3nvXBjAkLn9DD8H/Hp
-3tMCwSeQocXWLOeX8AJwxhNM9jRbAMcFqzFKirausYUZDoA2wi2+d7F7ZFPqC6IdEMvNaQPBX2nD
-v+K+chR1n+yp/fXgFQS//hUDukQbLB+5Wfm91FJmcLsA2n3bBMH1+F/BSeOZk4x+im/8dt8DO5kf
-0V3VY+c8n4LcHs/yM+2B8BvYXHnyfF0IeAQqft5gbMD3JU3HuoHiSuKaleQY8yRFaJPMPCeNL3Q8
-B7CsWuTmx6CBtmd/AsXjyU9sgk0r3H5lJnHfOyh2CDCOO0kqUThfKVuM8meA4imHPJ5S2dpqTL/c
-UwNccWEXzLP1ed5+zO2vjfD/T1xTaMbvdHRa2QqPF+bcUytcj7rF/QR6x+9bE1/ynX5JzCJo2osK
-yQC9eUutaB1Yjb5ywgw2XpSjhFjX+RJdi+ekQ8KgRjjlbV3JjxsAPvy+BAg/gNoJhjc74Ydyl2qD
-04Y4tMYhGLTPGBs/GZOXeFItcALcyZDEX/GiTWsa3U5nNIeAV7oBElficJ4xrBCvwPFuLlEUFTls
-lHkm/Vens9dAne4vpdOH+KOfdG7Y7XKb85bhmC/2S26jMeDW98jFv8vE5KBBT7Cl0HbWVqvQc1rr
-hwLaYgN2js8nGxGRmCQzr2zyouoEwNNWH9cCwfbPV4FiNY1Q3ovOhwFP+2C/vGmJS03lCSRW2GDO
-r0EubVWqmQdTcZjwxprnRjI6+36I29as1gBbvncw4I3Qjpx3gKkd9Kr/JzOJ3haZ7P7rH5noYHjV
-Ed8+/QkM57Xo6TFN5mAcwHFM84jqc8j8sc4PwLbOBn4AvkOT3NdA16Wvs29pcx2RphFwX8dzPv4D
-bljY5ZZybi9T+wiQfWUj3OhC5I3sQ4NzG5XSxG19AMiRH7BJ04jQCNuNpYEetPO33S8QDud+/5xp
-ZUxmugBL999CePQXMd4Rf2eaQT+BnI0cWIwX6JV8myspG1fLcPL3p2uwq2ZKkfJdhHDkb2opNu+e
-b5vWo6Gub9h2vXS9/J0lNJZi0j8bV1DjShkmWikxYWBcgVjr5g9p8sB4AtnQDmd5a21MrW/2ieNj
-RwU87KNX10EwJazaCxNFEvhC4j29ns0myHV2pbSTrCnJRuquONU5TsjlOU6FXx00l0Tq4ldH/TIn
-B7LfBM0RgWAgF28FLKO80oW+batYXa1nO1JIulC7QZo4DmiE2w3M7GvrQdv6P9arYevxrb8p2Egn
-pUXiQ9WKU/HqyS/rd5e0fEq779gnNxnNowZM13C0vDvBVARieRTdplW0YxyBwmrDYj9Vgwpa2ixr
-uvX+NbLKNO2s9AmSnm3seTdtsEvfxo58AsOA0FjUqQC3tA7lnx+daGlSftUp/baHq+bbyqHxmVxL
-AE3L+1Ma+nqwXdtiDs1vefR2lfXr/FabPa33SW/iWz+JJcjvx+9+imv7Q+h4tDP3z1tY6Yo44YqA
-MbNZmpJdvp6gw5c5xB1XNwu/bzztb7lfIEz34Qo4brjfcT0D4nv4z787gTABLgGfxs3WtbvreB+s
-O6rwP7xOHvXvwCIBY5wMYR7nzTsKUKKI3aR6bvki+qrT2UlyDdVutn49XKOSEzOy+TNebQO/PSzm
-CWkqoRpgBb2V/xX4Yl4FjJvMoU7cZPQybr1T35wwM0YQJHOO16WDZNUVqimEWdXB7jyC5OxiLcqu
-WZSReE+TJc2/K7zT5JyTHvOWv4rVjYPUs6GPU3rP28CvjT3UEIy4pPn1Z5sb/St58JRndto13Kjh
-0J9nvnbKOXVXjCu6qBuyBL7hb9rWVBjcsNuA64Z7PBm67wWUrnvN7231wtG1vjLF/ZLaqfvG+ixr
-8NZb2nbhxQLE215qWFHBJRJMJT8VgMwXeQo8CJc2IG0pdcl1fr23/0iCdAliJWLwijLVGAMzSY+4
-5CqilFjLp7vRJOUBGAftK8DdPyBxAL9PaTHvep0mKG+guJ/yIHMpc+AzTcJ7/opTbrbLnyV4dzDM
-2bMW39e4gN4u9WSu/ZTOLUU6R+6/flqEHutacbUrCizrPJX06fl1brTvXrnBYwgt75gNl4WCz335
-Y0ovWPhLg3wCxH/b/QJhuk9vRaYGg8A4fwfNxSBYgmG4jxfZCgg/ivsgtu0RUGUQMuqb8lvetCGo
-/QaOcRdMNMYmsj2AXaQ1KV9daIziT8na5jX6BvPUWnKSjiCYZU5a2qbdpYZYtMM2AbFqjzsAfgTF
-CoiF8TfN8MHtoGcA3QRCuw7VR5ZTHDW9msGh9yoiggP5nkByawMqbkV4pABSW9QuXE5Q74XyDwP8
-Ib/OneefgVCiVQ269nZoeDSTSWqUaduB6T5HegC+3nMcGtx9KqFU0h3qOdWifbGR13LtuxbYKPiG
-VlZB6UKR1O46YHfcXd/BuwzmN3CH3+4489dSCew393P06b7Xz+8FpIPXYvBYCmbXdWqjJsNgcICu
-CTIbEiIIA6jh1C8VHrbgUfs4V6KtiOmO2QHvEQQnkJxxnIcDuGW/fMRj5temPgC4h7R83N3S9v73
-sQgAHuvhh3IONBCMFi5W4cf8eq3d0JY+99wEwSWQtDeNxx1ow6OuCkfrj3m51RXgKraYaQGIMTXH
-1uIw0jDy93T2w7c6aBVeU0BZsyZ9fSl94Z3lNwHBng+JwOtfdr9AOJwfDBTOGXdgm9oPUDtBjXEB
-Y9UGd+3FDoTVJWl5ZXJ4HrumyVqKwDHDktNg+A4Qm6DRgW/KCALjrW/IxxynGSxttGby9teboN6v
-i39LWNJLfvWbhQTBzLMB1hGX2l85DYJaYpOwncPTPIIA+Jpg2HQeRbg9TeE+o/VXNHFMTZDsIyz5
-9EU6Lk0CHVkuBckFfM4geT50UGaYq+OTAmrdFJTs83DigvaSww95vGfaEIe/+rPfJGOtS+/ElJZt
-j1OR6tvYB9DIWMnhPWVzqf1kuot3jOvNHYgxacQHGCatRfu1rxcPvN3jq1IOv+NzqdQMx4s8CYIv
-g90CjhMor/2KwNIAlkb49tAMUyssfgXBMpNtDynTAA4gGA1M7VphDO3w+uNR/I3kioMd0oS3dwlg
-rW9IAClxEwTrWLAYO9OOoPcRMKPlJfBXW9kCv3gFv4i883zdHdjqWLHlqzHJRNvsK7oZRNY14kZY
-40b1K9Wtda8/5+ora9XZMZe1p7s8lD74S37y8gl8If7g5QlUvVjB0w8tXPRwTl8JM70UY57rePm6
-/81BmcVpEr7ivTTBCZJd1/3vuV8gTPfhbYhjPaJb2uCD9nfT/N5hBnEfATHwMwBWBu6os4f1rmx9
-kY7MWxkeSxWzUVne/AC+5Y53AV9LoKymEgTFl/XqDxN28m6ieRafzEjkhGiBB0hPAIoApmj2v02r
-qy+wXbs2eAO7m2b4aqA3QgWMk0EKII6+UTDYNtJiO5Mpag7vWXeBP0Gz0DYB7wI2EWd/CpKFAR56
-P1+GU8Cr49HXdvba5qg/z1P3CjIrfkiPxIQFLpEqAUz2JwViFqyRBa9f+xACfK3PWfXWR/hpzC9p
-Ke1O5ZQ4GoVUlo7Oc/3Vr2AYKG0w+Z9qhAl60wzC71XebYHheATmJiA4TCH0GN/UL3F6QyPcrqmB
-livFtczLNpOGArnRFv0xwp3ZAAWcI77ZCI/Zn9es95SnkXS9DfEzMGR9Cg7JvGPFvFYzzSM8V1bK
-TzAcVwP4QZemAT4A401L3PKgAeRTn/1xvOiAOcZXc2iydmN+XwDvMb+4aqHfsHdDsA6K6113bzJ7
-bkVlUa0vfvaTD5cmGBsQ7nmq7oxrY7VKG3Mx+1bpvqdTPiA+q2wyuWH+cHOtOSCzOFatNME3DJd5
-m4+/6X6BMN2n+vgkPjLf0gBr2v6iXNgF424vywFrIwmLk7ZmxAPTdaStMGspFllEmNArTCQMqgVe
-JhGhiIE7QsPZweY1+nUzbvTZuCky2ofvSWTIVpXNU+ewMlxgWEFmA8FNq2tlF0ytrtgIZ752zNk1
-APThTGCcXphbHeONg67IZKonV/MTvpM2eM4ZGUiPagST4Iw82iKODLcBPsYNkGy90mnuoKkqFJRu
-BUoeR64595Af07aa/CHw6K9ak18noTtS0uZkMCyTFxWtUzU6wtSt3Ec5bwY+z7MB4CAZ74N/rFVB
-8OqfP/oJLI+AWIFw8D6LF95oRoHQAmfc3bW/S3uMikMBYwBlI5zgWq/Vv7wOfpKhlNPWSShBmIJg
-TqlohFGaYo6l6p8W1vN6BsLpf9PuPoLgKld9rjBlQvsoQgPJuZoChnU8sjGQDHbT/D6aQRzzaJ/Z
-dm+XFw3vQLnPg7f4ESdpJ4mjZXrPJg+qHAqUkTyVPZb1ya1ZfHoC3uyLv5ehHFhh0QD7OV7YwouW
-9xyHVsYP6Z5t2/Y1S3Aj18kR3EOh/S3Q7GUugY+fzf9X3S8QDvfp9HfNh4JiakKGnTD2l+fc62U5
-oECmOtLVadPy+sU8rfBieKVvjEyLzy3wi0WD31gCm2GD5VnHVxCpaoP5qKOBYiAfaQj/amI0uzfk
-8JOb8skO/gorWCdoFRCMw8tuPM6paYgHAI58m1kEOujdzCHyX4zchnZ4DPII54LTzOlKBhR/lNmx
-QDLFEVYNJcFsB8QoW1bjGk6QjGS2Kizqb4f5/jDOsx74NBPecvhL2p7kMldofsQ4WNof/dKCxV7y
-DKwK82UpzlXMcjD21K8JaDqN4ux7ct79nTDa+J4qfYDWmXGtsbcYBcQKOgt4zhMh7lX4XoBsnUct
-WmE32O1wWyDYeViEITTGwttctcFeV2mrXtx7GKNuvpM/eGkDu4e8CowV/G42qmPqi7Ie0hIId7AL
-aasuE1AOEGwWc1H0CQzziPxwgstYXK6sz7PunzTAvJvpnyg+mUoMKm7AWMdTc6/8ZJtLk20wrpre
-4h7KSjOZqRQAk8OZmAse0rk9hZ/vgHZPIw/f0rz4mvL2aucUN8fe1/dVK5ztzjp6mZLxLjQZhfJu
-lgxVjlXjfuJcWKGHv+l+gTDdxxphviAnAmADxfXy3DxO7ck0Qt0EwY/djTw8Qz5BsBsw7tAMhm9+
-HENSqFn9jnYXKK4eEdBdqGPVllbYcFsB4F18snwXTF20ThFRYWM4EIlxYrwAsfLV/IGAkyAYO+hN
-s4c6Km2z/T0B4zctMP+1OILhmFNZaOPkz3VtM4QCT3L1lkEZZ8WrZqGvTtWh/Ilz7TnNnPOKA1xA
-8hjL6H/Gsz+DynsZHdBMrbQzYGa5KUjRxr77Z2Z0HtAm1EYznFxLmqR/zWWYRhjntguLp33tM2Sn
-+NHfpzE272HcLaX27WldToC4dnP4RUGgigILBYExK7XCt8PsXvQUgNcMiaWpOcybtjCH4Ity2kYH
-4xKv49CBDT8FsG/pBcYKHHdgzDNQdXqVWvcXsp6AsMl6vwBhiX+2tdV03hjT1GFosQ1DW4yeP/Od
-QC/bXr8/Asg6fmN75djmeauqrTTXZ8znG/DVONvTKnQAvTNczFFSI+yW7eRcDn5dpg21fSvsI4zk
-3RtQbvVoud5uxre5mGD4ARzX9hrlB+ZgQyJgDNxj7HPsJ/abe+mR2f1v3S8Qpvt4Bch8hzlE0wwX
-+J1geF6D9wPYRXy1OK5ejJsg+Is5hCAp2ggcgR343vTDwkzCsj8O4KLNcNA1tcKX5MlrMIW0KWN3
-TtP7MN3bTYH2nf232mMN+IYApXmCaob3I9DknOCmBT4D4Hmk2uNP+oPoR2mEkQKlhNxpXvqKzxsI
-stV5I5HhA2j2bKrALIJpdkCMZOwzzntS77Ks0Sm+wwM0QTZLzhdReg2+xdsW79l4m06NcGx5GTTJ
-lgqKZPJxTaLewXAfeN02HEneHuIPXRuD6JlcBpxA51TBy8YzzmKXdqr5LXOIiuuANLTATTMcee7Q
-CocZhLuA4LQdljiQTryD4Lve0/D0h/CntJbdUWFrpJPgtwHKaQIRE0OwFnGpFVY6mwAPO+B7As1M
-L/+fgeACh1Iu6HMzj2ja4sUPykxiB0Gt7R9Ab77oaJb5T9ph5NqyndO8cM4Oc7nN6172Ne6lzC6H
-p4lEhIUvNICcL9YpaOU2OMRBAa1svS1v5+ceFVTcSJ8guI3zA03wY9hlW8vqzJflPajJLI9SK1ry
-SOfN5KJP0uDfdr9AONynH9TYNcEvR6eB/jtNIaZW+G3Ju8iyInIroncEGHYEk+LG5Ob1gGWhFc6w
-2AeDYDi+igeszTywGrXCeXWkVpjgOHHDuJ5ZzsN1IucY1isoDsBZcRP49hflCIK7TfAb2D2YSBTk
-LRAcdxp7HkJk/LjPk9nMeGGWzKiMsjFDZqL2V2IfX5KLQGoxEzdUXIGlt76XO+L8Q0YFv77nxiZs
-pPZTfgqJilD/3qF87M9IjjcmbAk+Rz4tcUeiORJoSFh+lnxqhWUo0kc8upekkUmk6lZ47q+fW9BZ
-LJ3WnlZSe9cIUwtMcwfNvs4ORm1ogmC+NIcAygCMxBovx8UhqXlV2+S8Acghq+iGgDkUCNOFEdA1
-8xIYV1wB1g4WOsjbAJ6NcLSn/WO9aPWPPqHSq95RxoYtsOEZDG/9cbki29xMI+xg9tDS8FBG25N5
-3+J0Lp+ue7nNbzOuxjfHTA5XtdroXYHgkxZ4niyzeLQ8Pwk+ndtH05OMBWzOMmyR+aL5HKtn7Lg/
-Hmvt3VTmGRi3Zz9tDtle3pcFP7TAKDeUAVryWWqAE2uwjeSpf9f9AuF0H06+16O5fgyaguKpFaa/
-TpDIs4QP3dDHOisqbGiCKV8oTTA3ybUhE93QtZmZMrXFlEfUDlO26zUJ3uvA7Etmbm4cgyVe0HRl
-VCdn7bezOTvku4IHT9DbrgmA+2eT25nCdop70QAzDyZILoAOyofDOJ+cMjQyPKaIaBfhLxwPylAl
-jm3OL8mxbwTNxeezva413kH6p8D4XGaAX7lbUPC7m1eIwHlr2GXGfgDHZNQlMoR4DbUwiXALDJed
-cOVtxTADZ3dMPk2wSsBBD1rLII29hQEG57DT54ffOgC4HZt2+lEbvE7T9zVXd1zhy04YpMUrwU92
-LWyCqRnmxzWO7UVfn+bR6RE6U5tfgr6pEe5xALWur+D3BJQbKNOyCmp7X/4TEJxAV22Bn8DwyJNk
-xd1uwc8noLUCwY9AeQDm/wTY1nye45/87+kuceU/bbUJilcvFQSbmEl0cNzAbvqBpuF9SCsw7LIm
-c0/7CO97fgvrfDi2+fkpPNOuNj/yvoSYS8QtbgoeKgscoXyzMX1/0f0CYboP70Im6EXT/AbgjX/t
-yDTJU8eqFQB9bE/u1ObvcuArwLEPwEsNsLBIXOb4PoBfBcDan0uud9RK4KsgODfzwAbUONZG4258
-YoHPjsruBMcqG8JfWln+OmBNkwiCYrMOhmFbmZ8AMMx6WxsgllWwHlOD+2kWuP6ek53MUUoeT5eQ
-/FPjS6EyX5LLBWU61xdlRrH38GXtHkc1fUG7XnEKfifw1fJq137SlGyd/AEQp5929WnnE1cIoW+a
-4dAGJ5yOvck7jNe5EGc/ZZD409hO+8yfq9orjJ+C3hkX8Y54wiW/tBG++8tw7mgmEet6LTEZGmFi
-v6S/m8A3zCDUPwDw6WZgm0bZc6UdFpAqWLTbBoeO3Oq66hHAO7W+LWytzVcgLHV30Is9DILRkfYT
-0DUxoUBdp6a4gC4Au8hw8/d4QoQA5fxlWzv5+kPcn6b95+ldhnZQG3xbtTvc58ErWtm0EVbt7tT0
-upCvgl/f87DFA7vaeB2e8pOmir+WsqVjDZ2XPewSrvLdVKR4pVkco4Z6WrZeFyh6S/n1L7hfIEz3
-oWkEIFpgj+V8+KIcbYQ7AO4vy90DJhyJzsmMZ9zaaBf4WcIgNBDUit/WaRCGMoOYvzuItWaikFAe
-ji3hPAPQOiiunihb+Wxu80oEDQFA1LhZB8NktAWGCXxRWmI9G1hAsB6LhqkFxpN2eNcSQ9ol8K2+
-1ByrgH2bASRL6glKH2R87R5OmGfW42TMsR4UwoxXPg40kwn2NfVese5b91842CfMTaGC2nI+mUyM
-W4kU5i3W88/Bv3cuPyIS9eUcJO7l4MtUgkDCBHSkGUrJhbz5+HEuPgG/gIBv7NdtDs51aVLBngY/
-9w1MgiMofgKiaSsMwK+Mz5fkfIBhAGbXAs0oQJpz1jTCrF+v0oc5ukmsQ+MKoTeX9AK37E83h1DN
-pcazLMfQtcBlBtHBM3KiU8NruhckLHE+0zIcAFeBrtykFUjWcQspZV8dmPVuGl/s4Bcd/J60w9zt
-2uaUfdji/BD3Q5k/qP/Jndh1AuVkngMAGkqzG31PkAuU3/EezznyR9a1yYQebw9xNZc5T29bOtB4
-AXQkDfH2hnd4dZza4ohL6bf4472YKQyeykDuk/YU7i+6XyAc7uN7EX1jGQp8eZ7wDoZv7KdH0E92
-T5+eyXuJ6PTtt2uKCwAHUKTflpBXDfENAt8Cv7fH55WpuRr0qGD3foif/am+v+kAyilOtEOYPDl/
-Nvz5O2mDrX06OTXCeAG5EKB7+sm/BoZTc8NOy2Qaxuh0FkhXEfLDvLmEI6MC35ak0ZRnwVw7IEYB
-XpbP/J79Js8/75bP9tCJzXlLqUdrOi/W5qs63WMPAn6TAt78C/i+DIMaoOLtaIFUc2o0oaUA49Nk
-0OlmaRGHPOzTJ9cPxb1jv7mo/LF7EwBbSUfz4ScAvtbxZkat8ALAi3ksjrHMI+41ZbaKrqlbjKlB
-8gTAbEOv62cqoVNSy9z1LdfshBXkrlF3sFma21IvEGQ2ja8VLy+gGvWzvmxfgLSA8x3ckpccNL6j
-n9kv5n8BwwmGOA4UzapE9OzOu2mEaCYK/D6UKcri/Avp5vyp3Ci5l35nDow80u9Y52O6H/LmKkjE
-1PR2ykTnTWS0nrkIatsnjycA5hWHOO991X72sD2k+TFNgeeTJlj9bU28KITsUSgYNzXBWMC3aYRR
-9LZUiQaz3v7fdr9AmO6kTTlmC9CLp5fjztphPT1iaYLVRvgshHJTWD+lYX2nuwhnvbhmcZbZcgWC
-PUwKCgRbaLT0BTQFlLfucTZq/XpZ9e2n60l+H2Y2t2pu2YZQvPoa/SVzbuAXPNsXHQCHX0+MuKaN
-MBT0nk0mGiCW+JaWfUyIvIVjkT52U64rI1Iw7MwtQiLv2pn9h5fkKJi2I9My/WERP+Riz9m49hTk
-ZO+kiBJT7D8Ft4Lh8j74peyTv5GeoT4Q00BxEHgTnx38YqQeXVYxN905akPNGwie+U/u50wEUQCx
-wOI4rnMwwXD0g2YRqhn2+0KeJRyMy+2C+V0gGFj7CJ3t4C6TCGua4fXTr92p2D5CA0Mzh9BpXSUP
-4BLomloCXgHOzdzBCDSQ/lWH+n8Cwh34giDcMNI6eEaATxCk61XBzwkU8zrAMfu7mTko6D1ecdQW
-H0EXZUbEci1aPvK6NpcDxPmo98l/AODlSgg23iICzdhYrt1K149LFMDsIPeO6+dAmMqRorNy+622
-mpQxZsb1nXL2fxb2/NYAkhuvOeCNA83vEhjDcBs5pYMnRii9/U33C4TpPjWNILNNJn86MaKfJFHm
-EPVVufsuIOwI0wan9oFxneAIePm25YWFfS8LoroDFF5C8rJrChgrOAsbYLfUEn9nWTxKb5LrT79G
-1DnejyT1aseQgH216+IXAJ/g9wSA1TxivTCXoDg0wlBNsJhEYIsrELxpifmvAWUUapfZK1Av8QOs
-pTZEbgY6AJa5lilNYeKVh0Am/RCeTqBMQe7BWhsg5hD8uGyfsa63XMU+k4nmMKdQKi1ciZsKc+zZ
-mnvzZ4tzKF5wgH72LctaMHUDymYYS8BnfGw7CkUKsNP4Wc9pfo4AGbKAUqZtqbG/3m7yFXG2UnV7
-4Ql2F1JZYNZrApMnBldKFdYN3FdqhZGAl/bAWGBYRadhfYAjuxa0IEC4QLbyYeXLcquQApjjFe5h
-HGsBV27MqcElCN60w45WT0+f5bXeU9zoBwFt9Nuz/w9a41HmEQw/gmLS7UZBxcMML6D3k6uM2yF9
-CFIyrlmBZdU81hf+pIx30JkAV8bwBI65TskzZB9Y5ur770krPEEwvPj16sMLAJbrG0iendw/QFH9
-aZ+oHq7NTQuXdrjPlx/y1u+K8TbwC8dtcWU4engDeXxrhnH4Uu1fcr9AOFw9Wv7J7V+OW0xlPy2C
-ZhF7/N00wh0iRX+w6OpSYCyaYMNiAG7ADc+jy1ZhAVpO3r4+Y5ggOPjm5WhflrNosz3qiYtJX6g1
-PgFi3Zi5aVxju//AmrLPnBQFwyKv2lgKlBIMaxzBr5wXfHxRLuyFFdAawfKD9hjqhRq6AAAgAElE
-QVQm5QEFwakRpv/Am7aoYJ7kzmSiklisaoBkSLxLfqZTfjpwfElugZESPDM/Bc3R+eZ5dDNHiUPr
-YSt9sIogMt0SOxToyPFuDTW/N7+KkxNAFmRVG2E7KHvsGa1zyisO1Q8JOulaS1ZP4Og9z1j/zX3A
-4/ZZFUDM+AaGo16T9gl6/Vo0dseHM/yK+OVf9sAACIw5HdCbotAg+tIA2wTDh5MjDKUd1hXhGhQN
-E2T2uOaPzevSt+XXsgU8XeJIv5r/bDv8bA7RgXAyOnSwzLLDH7Sq1wS68wqOrbRy7Rr86wRq38Dv
-SXuc45e261cUyLBHAX0KqiZQZfpgjVeWPJI8PuKHX6gF6o7g17lkMy0Z6eq/o7S9WGBw1xSfQDKB
-sed9XmMLm4u2VaMxaTzz6VzrWs858WMY2v/eTM4c+TSfAdwP10X/dWP2MRT7L7pfIJzus9n31EAE
-OSsYPnxeWc0iup3wOjUC6OLzGlfdYn270b6Gpzoo+C1gXEB4fSKZoOzyOC3CqBGWl+VC0KZkCp57
-5wavftzEBcFUyWwZrmmd7Kbrx56w1ep7CdwcY/FUJOjMOAXDQ2sr2mB9UW6B3/ExjRAoR+CbZaYG
-uINgpit7sNcR18xMivQRSAYlc0wNxAp2ZlX80XN9HfjBZAJNQ3zs2Ij8cCe1jKXhLUEIIMD3SEum
-O/Jm4y7z0v1bB5t/JFQn2j7YwXD0JvzGffM2J0aNt2/xmPG6WJAxbX09je+0Gm9rVVLNsZ4eeZg+
-mICq0sBS8JOZLX5n8YJc+m8P0be41aKvC/XVuaVdtThHqXoRfl8M6xEARx8KBG+wpfwJSiWxxRGE
-VvupiQ3/avEZIJ+0vQnGpPxa1h+AcKtz5LFeT4+bYBios2QfTCLy2gER2zcz+FVPxPJ3cXP0uM1O
-+CIsEhDrvS3PX/Qz9lnOdwLg6Oss/xSfa1VttnFyS9eIW4jOWlpchcGWUnj18+ZYBeTe4l+k/QyA
-3T0tjLYuFVEjmQ+RqUmGw1BOc17+Mx045662nHCjtbK8jaUS6MYzGHaEqYQrTTzLxv+V+wXCdH9i
-GpEgdz9POO2H0bXAaiahGuMCRmhXxwDFIZBy63md1nDDcLnXqTYHjfBldWqEaoEvrw9r8GSI00xQ
-KHF78FFG95/uKM9XmdDtau1a7ZcSpgNiEPyig2BqaBX4XtZtg/lhjQTBWPF48Bf4rfwNHMsVMOkz
-UC/QWV/sw2wwdNLyTm3wdhctTL4vRoi6BnARfNIPJhMRf8if/Xvu/HATvM1ktawV6GsMS1o+Cq20
-HAMF59YvzzHUfHa/SVSDsQ6oicNKZCauaaW7VJjnab5QPp2lEGuRvZ+MYw1toeeYas7nzVVrxSou
-aybwFcG24lBAc2qFMa5+A65gl/7QDIuZxHo1+I5+TY1wTPcEwDfKnz8OKfoLoXmv5cplSRoLAaz+
-zFcaSvoR4K4BBIJlG+UeQfEZyLJs9m34N9MJ9bPdoKWudY11zLTKk8uW4yFHF9odmt0/sgse1wZE
-jUsnYHf4CdCXgkVA01jTGkfcwDGPrlOs/TH+4PK2LMGuC5NkHjruot7nm5ghxlPa3wPodUmPm79b
-O5cb1Xp7DQTXxCi70D6fwO5pPnKeI7LyCk9x1mg5XwWKkf4jCI7id5Z6Won/nfsFwuE+vQcxrMeC
-CcSCnBKcWY8nSxFWCL5RakQb2QNPElp7TQS7UbNlcf6e46I/6rjd1qkPLtpe+j1As+2AWPObv9no
-CDilkI/rHS/lqTaY2mKO66TpVGb4RP+KGdf+F0DJeCP4FRAMBcRdc1saYYNBbYGnphflZz9Yd7RO
-8K19re4RFO9j2Wf32b/jnQE8cADNLnMegEUxS66N15QGqQmwxMb3WzsPbl/p53zFp5P6a1fIeHkj
-UeBs9BklFLPU26Qeeqz+RpLZUZmM2XCmszBBjUhcvatA0RAEYKYTYGyHuBhhNpmrLesuUgqSseIy
-vYQn59p5c5I37J5PBzzHsOyAccWzqfsGrgC89/wWpY6k7ZToiohND+DrcTBk0HM/n5h5KJ2X3+6Z
-F6VJdofunTZ/IuR1qrgCDeCa0t8zwN3CLA807bGGG9gVf0qSVickDklf2U4Mxm1qh/X6cx72K3nj
-w2oqT1TeN/Pp8W1r7EC+MCUgVXlf5etpM530P2XLlEGtHOlBAWbY/NIMo4Ng3T8oeSjFV5WhOBKg
-e3PcDQRr+kMaym384ACCG6uxraSM/Q8AcfhmPlWM9V81vtfVOvavul8gHO6fDy2012cJDfdFwl2m
-BOsacVfF8TOGa5NbXuGOb7HXvSAAq3iggLwet0wf4i4rBa+De3Z1VgjS46i0kFs3gO8ls/B9Y2mT
-48B7u1aZr9jzvEaV8kln3ZriEiQg3iZdQX68w+XaNgeFSW6QWT/vMnWuBPTOnykjFuhEZmfB0q3K
-9L9oDCXjtXsJHqTHJWcXcxUtWTLcsUbl/PHqLbzPH9t0zRvhjuG8+kdMp/4odwbHVf7kesppDfeY
-yQ41nH6zc/xDeMX19lu7HU9C4B1kFh77vDccC7lNmhQ+aHz5ssu+lR76PtYlaQpoQLoAnqw1XvLC
-10ct7IZdYb9rF/y6M341vBiFGV94izK+8nKzmxv84jji6kAhGZpOOIzfab/61W+DXfz+spO43/0v
-INgdCyTfN/y+cYvf7/hA0k3gIeBDrroWqhFWENxMJcYvp0HjUN0vcFsAjC9e5gttSR4jDvPqxYPw
-H14HvdZqBt1KvG35eGszgXO/Ycu5UFpG3cjnPPmetwG4uT6tPsbp/I71ZR1yXrh7aDhPT2vURZGs
-hEA/6G8DufcI61glf46NHt37o33ybwWeFcxE4ac+5q1rYz1ze40t8ulcJNuTHzGLApeprLoynz65
-jeNb/7L7BcLhvj6c/PtaxPuPL63AfTn+cfLgAr63kdcTFMcd3hUE5wa7PejEdvCrAE3ilN6a4yYh
-UVN2hP8OP88Lzi/KeQBqAcG2VM4Bgtcm+Io2/DBNbcNFwAJd2eIn+UTz9t6XEggn8JFos4Ov0yTk
-3QJ3YOU1TmDMYM23SXWdYTP+OL8Ev0aA6D1dBfM+Sy28s+pdqM1SW05XcdAvKlBQXa/xkeeznuKX
-DRzrFJz69JP7KQ9XuYfDv/XBzmVGvl6/QTWz59X4tL99JprGJW5Gs0MYaXQUTt5WrijyeFhz7JG2
-10/g1kW46ZhduqFa3UiP02Y8+JKC4AWKsc79TSahd8/sdzKQnIMFRi2Od7yQqlUyKeNJE7YD42UP
-0MGsXPMOfwPH57gFQm74NwFwgN7by08ASD/CjzLK6s/50EBwGncxDno9/AI4rZYCQJlegTzqLMDZ
-zjm6Vk/X/ngj/nBVOmpXKx5MvqGPvTNeZuWsDS4zpwKzRbPkWTmvH+XBPvbGA2WMI8+RC5Oxxz5j
-XTUaqXwKifa0iCSo2l0EnVV8XXW8BYr7Gvb64V3cPfEt7eb80mE+beYQZIxaX/PbQ0JrrT8X2K6m
-L7T36992v0A43B9phC/Lu7kExGb45wrNr2iJXeIaKL7WayMJdtOvj+Bx9kP8Xo+MIXtFGcbt9XAy
-X4xDaYi/L3nUnycgOb4i0ln3yS99Cu7YHhGxXtUGbzJKfizoc3dZ2Ry1DcXNg/EzO+TVPLopTwxb
-Z7mY/2RC23UIEWWaKmw06SVixA+B1rKcGahWoeasun7mfkybvK7Okn3vbe+5CI4f8/YZdxST17Qn
-f4nkvd7nPp1o7bnLJTC8Nq10xDXT4fFpy39oRAH7qzjIqpW+dvpomt+mxUmCrO7dcSN0rXNo1pff
-JigGzG74dSUoNsMCyyBoNjiWHTAu2VUEwNSu+SWgw8CPcOQh5bfVi1kPQLeAsWp/57XGCgJe1QTH
-XXoDwNEMpNk29Y09mFx2EHyb8rgXQNyXtvpA8BtXBcVNKyygzbnKHDfpQ66VLrTxAIp595VgVofM
-q48wOg2X7SgS8OU4SYcufsa7jEfnx0dY+59hbO3MuC2cINhqn5N/HvhCnTGOomWujc/TIMp/H+I2
-gDzH1+bzHN/TB+gdOWqugw+SPSWRu5SdiqrRkVn/JpSRQKae4i4cpNrg3+PT/kX3dU0iObtF2AS+
-AxAn8KUGtOJaOGyh0mrOOhBLfwO/9hAPQMGw7GoH2osJBKN2L812fmr5DvtgQyp5dEMQELNOdcn7
-vcLJJL3CCbwxQbE8opHfVrlsTFaaj2NgKJQrnd02qPV0WJ/HjLFi7Ap01SWDtCbLV1ItgIc068KF
-ySpksojMQGeEs30VIK7FRGxQW0IevcCa19gOw3NJowCwrPFn95/kOQLbBOS2pW3+tj7CtMcjzdlu
-o7WhaXvrb8V7zxT02NajSawXsUWik/2r3Xts36ffpamTX6hM5yY1wli2kQ30ojTDBqxPvw17KtDP
-/bdA7NoDVgD3olaYxBXhBMECiG/u1xjbAyDeruhhAmb4BMLT7wWMD4K/wvoWCMrON4a6XgRSHoft
-N83DVlfX5lun7wQ9biAYG+gtjkEyHuvOgA+amQA482odHiPmyC1GBYnxRsI7X5WdfADB6RfaLC5W
-W2cDydXVLIdRD1r5GT7na0Kt+ffgvqNJ189A936Ib36ZH5I1n7TSVAydY3Y+vvWr59xAMEirnrw3
-63od72hAxTGsXwmC5zXTf4Hwv+r++VAd75fhnyRYE3vg97j76mHA6sMV2AVdwt6J6R7yl3AL0CBA
-djFd71rh22C0FdaSNMmL31eCUWnmMFWn2Ut75tzcJ8avTE+ALhTABNJEYV1ltNumM/rV5ETMSqzy
-UlhLTL0EqGPjuHX8HpklrYFgZbKNe3jnLDP54JQpnlODYbb5zI5USTJT1Fom/hq0VrN/YrlPvTi5
-khxPefT1N0cHtn3Kbffb3rfzLHUJdtLwVHD02Q55pNEWh9Kwa2LOpyJ9zfRCBLPvGTqCWgEzw690
-2fxA1/BeoeEF1mOjPJJmaXnLZMKizA13g10rnNpgmkSE9rcfwSYa4dsSBPvtMILgK54DCZhd96Ak
-9IfrS7q741YQ7KIJFm1c5Z9LQ7BQhJf70wbolfA90voTMm950eLW5vQAVgmU0W2Ea/U7PZ40wAp8
-f0rTvZXPXGg729Liqmw86ulmFCb97Hyha4aROTQ9yypfA0Y5nYOf80wgvWmFf3RCCMJQkyf7/FAG
-ks66XOzj0/ytf9IkSbGn99uULKKq/FGn1t3jAtRrQV/+nFkK17xIoAFekdUJhsVuGP2dnL/lfoFw
-uD83jRC7XxtxOLwsF+iywp1A6TLmk7331Mf40zWtPGEC+IanTPvu6HAxVtpsrL28GHADxodek+4d
-QuyxbawYP80yfPzY73xzOeuN7ReMt87StNpEualksyk4NoW6w1yiWmrXNpkytoZHBBg7GX8OqARp
-hoFNU7Ex6ceF9+aj8Dzm86qXzDW1CdGlIwB25HxrfuZpNwTHnr31/IA6D/l4czKb0fDZL2YRo6nz
-LO39aSHl9w95XCNsF1Lan1bOJRMONPeBU/pZYZXu/p6ODnoy1pAa4Hx0pKYPBL3o4dIKB7glWA5e
-2cAwwwQYNH8wAt9FeBnHJ3VtL8UYvQPjHl77o5lJIK7NNjiA8ADF+sIcF7PxK3ls3m2Cyeus8b00
-k4DmOfuhoDi1fwGIhD/O90CK5+sTidrPSpQKjita0hN59R3QwG4U2Xno2pWEu+3WVRhJ4/uD++U8
-aPgpn9BzLz/idOxbXLksqyD4yL6EEzmW7BxguIHeGc5faXwT9PqB5kaTEK//lNaYTIHUfDcnMp21
-xLt7shE2ChYC4GztAHxR/jre9PdluX/VffqyHE0f1otyC/CmhljAsL4s5+3OkOGlbxkysbyHjXfC
-PeebVYKg+Uiu4r8lb3e2Dq4P8Pu11W2HkB6rpnd968fj2Trj3zfdxoxiDgimAQubaAIm7URtuTR/
-aGtqveu0VZKe9m17Gi06s6GfHU/7WRGgNZofFAs+QlMEnPNuL1eIX1UNKfgeATDrwzZVg79uw/5s
-RNhK+UgB8Phi3OxH+m3vn7a19cFO6yD2caImntk2+lR//FEBoXv7ba4OO/AQOm3+aBPog/KXOI13
-39bAAdi95iKBsdk6SQKA4YbfBtUQ1xmxdbqEweJFLxPw60gQTHMIM4kjuLQCyLcAYUUG6ZfJVe0v
-yxw0xDeBb7suEEwksoGX7IMRZafZWd3oBL9V7I7gwzZPzOnAeL3vdwLA3OMBrLlmqRVmXVNzyiF7
-j8jpUaJ8uDFSMGyhARZN8AI3Hjza0OQAJg+dz3w6J5jczj+JH/sq03zEnfL5aL+VqTXOAXjzVN6m
-YUDJALeM2jS/Pn6QdI3LCjhmznExvd6bLrve0nKcEHpqtHLIs9XZqha/tEU5S/nNqFRiIbTAHSj/
-bfcLhMP9iUZ4AeA4LSLBL1+Cizynl+XEPAJu+L6E2C3I7bi5Sz7QL8VaPnLmYsKrXh5WbZna4V82
-PFFsUi74QfFWloCaVwNtjwVW2jgpwjH6V3yGd6b6gQJ9ecibxle0vXbucrM/yvzNEvjAvJdwVq3H
-EzJ09wJkoaJpgqYNUARLXF3rnEwW050o41B/RHLtG51I4EcArHHCeN9A8FN8pZ9LO4Y8we4n5XKN
-dtD80BfR7GwtH4GxUnkvdxK8Ld5H3pi847ycOrNFu6Ts5ZtmLy/jRkz8HQR1wZe49OIHLmS9m3mE
-AOTUCgPUDK+X5RTcBhhW0Ht5+W9JH37ud2IM7rfS8qJpgY/AWPaiw/vpEOOkCIYbIsGJX3ET7eud
-fA5hJ7yB4Odr+ykAhgCpoOfMZ2eeOtf4BIo7vSht1BxyjsmLLTYNaZKmEDF8iVPwi3ziVFD6oPzI
-63vaHnfs+p53sM2xBTIl8+XYOt965HO5l3S/CTlxnZ7CkLVv4eqP8nBu0CmeqvM7/yyw+nCjoaBY
-Ku2geGiMU9hHSxLen9iWzK6z/LtW+G+7XyAc7k9eljtpf28FxPpyHMQ2GB00x5dLk7jzERvbAlLQ
-1aMv7Uxd8nOtZlJ+tcvPLysgBpDa1UKORdiN2bPGOFbNrrIrXm7XAn+bJ8HDOwA+M//5KGZo6dJE
-gn2V3Z8aYPErd5Ds9VMwPBnGgYV4SyjOpOiNlyZAha3HYJnnyFZVYOXc7Nre/isNSUd1QgnKMElz
-4n8EwKMmLY9D+pPr6bNmE5BtGrv5M2ya1suc+tKFQ43mBFF9ZHXbsuiqbuVa2lHQPnXwOddp32fA
-9RptH+JmPm/pMjO3At14OW4Dv4aTVriBX5o2RDjzqHlE/NaNvoBhk/Jtcr37mcYbHQ9gDHRzCA8Y
-4OThXnx5gmH+uNcc2JbGpAtG/lV7MrtlFVd8zxoPfNQKo3j4qms1estNHU0ldp7A/jXG1OfykRY6
-XeRdhivLJRiuBPLInY9WvpJEUv3R/0Oe7civke7ncJU51K9lbPPkgLYb57wLs9YnXo5g16vf74BY
-15McrhpR3jcd8W7jZ1JD0ekLwOX1jLRbT7aGmzck7gTEYhpBbfAvEP4X3T8fTn57ES7A7y2mD7cb
-blicIuHYbIrFXIIM2oNru6Pd3QOR18ggsXiObMa1gZnbJH5tGn2cRkB8xw7UL3VVWzw2LTibYRyM
-73mEaMFID2BsBTgdeSoFN2PTButmz5pl03m/WswD4B3Uml4P9sECeXeTCdFXyC1sxvmKb6RBjilI
-LGY6+iusPAa5CZnmHJrsM+3VdVZezFMfs1FbFu0IF9QnUF5IdBuvzzg2bX/Ww2Oa9Tn35rdjvHT1
-WObTWezxIQxmcdvzU0zMNZsC5JT3/2nCTukuvfAe12zIVTV1jLPEphCcmqYOdkkcGjAuQHu1dDWF
-WC/RSQW5UQf4vUbaLU8BXPs8xzDn4DmtgeCjf+wf3VsAGuAZeyDzHQAwNcQT/G6KAQJb6+BaAW++
-ePcAgpMSW+d07WUSVWa85Dd+sYyZJvidYHlcp//ACfvfw97buz72+sMQ93K9wsdtpSknEKz5t6/i
-2C7vHFsctjg752N3rPZDw+l2mt+KIafVue1nUj8oo2QWWLbPXwfBue6xh7mdkTJaNMSRjy/K8Vzh
-v+1+gXC4f/5EI5wA2HFfiA9qdNMHNwkjCK6ZSyCOJOLmMAHBg7mxPmF60qPoVwQD+JLgHUvDEGZ/
-CwRv94Sr4cW8d3td3GELdjG3xfET/dB0M/laXoTvSFfg+3R0WoIRDRNckumetL/cOLGJcqNBQfH8
-dcOIyS4y5FtUCWUJrDfbc2ADhJQnGd+WdohooGwTBX/kTy3vGA/7Y4OJu6Bf08w2uvSwbR7kRU8b
-/TgD287AzargUxnW7Q3Vln8Du1ufJa/QZObxEZYcPX7mG40+TdJHkxeBbEAlvG9xBXJGPok7zxbx
-bNf6qklEgV95aS5/62hHbxpgjDwr7BY2wTOPDHGxA5pHFMcoM4k5L3u4NL5AB8EyT1M1l07AgdVm
-UOB7+u3HRsaLw3Y4Yx2iHWZc8maWfQDApt1entq/Z560hRvr0EDIgZAXFgxFWUcDvw0Yi/Z4MI0N
-zA7xdNoO8ynNBKiutE7fHP5P4RxIRR77snnCK8enrT7x5gXtuupV0OwtD5muo7YD+fns5uzfDow1
-dq3fy1aR6wMvbdXtHUqgC7UPDvkrAHieHPG33S8QDvf5y3K7ZrcAsOVb0vlSA9Uj1A5meJ2XR4aY
-J1BAGZ6LliDyDiHORywrEH+atqDXlX2x8i+Mbql1dtUGm8UXoaLxKyFJMLkllL6twt+8C4zxsGtT
-C3xm5NK/3Hc97qj9tYK2OTYFyuIvoBwlRBu8Sk4uK/5JJsKRTmYQqnWrtXIpCy11cL75NT/X9id/
-WeVZySIX3uUogGiVf6VZA8BtCgZDPo/hkHbYbk8a4PTb1tyxzM99GLlsF6RvFeyCYge8Gq4nLk8d
-OiS8SlyGXW7KOk11qaadlp61OE+zB4j2N1hVAs8JfHdwrPsNR8B7DiPBcD6F0bTZ/7YtehyfgDjQ
-ALIC5g42Ogieccq70gW/czsD0qPNr6Tlz5/BcKtH+vJU920934leelgnTYamtDHKN6D74i89fr3c
-pSD6jcU+xiufedoyL0N8LMu1P7Xrh7gPw3B0mRfyuGiM1zVPJ60wshzQuXJxQj3DXpNq23SAalmX
-Z3bmrhc9DdnRvN18GGdrQvcvlCCGOYSCX8jHNOxXI/xvuo9flgOgtsAbGAaANJcIoDnNI6IOw7qz
-X+f7Lo3w5XXY9jJlcHl0Zitd+pMggNw+EUsHwXwsVxtINNfiv+H5Hssy1qEphCWDTHsvB74JREJD
-8I0CwGYChIUhQPypFW5ghCgNtesItPVZnGiCmeVnTbC0EZtzn80Xf7sVH/64mVAu6lGmMzDkYP0Q
-JyVzDlTgEZRY4+iN4yYThmi/2vzlcKzmD8Mlg12tPrKnyYj35O4OwHmfUeuza+dZP5U5weEnLbAK
-hNY/WcaTAKg4b36gaJ0x3lIf+jETX/MwrICHe1M7Lj2fcQcNck6RPwNcQ2l3N+A7bzjjhtp1Y0L8
-c8Oi4px7M+KP2t85J0eQLB5vUyW8mPE97G1+pRz3ueFI+FzzO3j3jz+vvI6DdthGeKbT7z2s80Ae
-vXVUPNuTfaUbxFLQNCKUE3nDkn4cNMQ/8A91Y+9JJT3S9igNzPEfhvOaf3CPU1UPYa+/2d7Q9qJo
-mu8QtXDkW3H1FKA1YedpWa3t8foujJKs7vwi82KW/jLPD+w0rmKYOLZ7JOMCChALQP49Pu1fdJ+/
-LMeX4bqZgzsK7F6e5hIY5hH6At0CwmuT3ATAAn4NC/wqkMoPYiCEAnahwO/RL/5EEFyAuxiTbLZ4
-jLOEW9TIbzOHfx1yD9gV5UPwfoc/wa+HVtjLFENkSdvYx7DF3AGo0yPIb2XjoN9lHmGvESKNclDW
-/ASAu3tOifnSwYgGeGOST9z4UOv6ieZvqqYE9E5t8KQN1fgWM1XuvziWYv0as4zxAGSf+r+5E2M9
-lGugl2AItWajG1u44h8QMMe5dU4E2bG+HfhmvAgI1baopvFF2u7rujc+/N79XfpWHQ9aYhv5mhY4
-klLbFGnpl/iWhrjZSpAMpNTeNMUvcYh3AmwAqDHuJGcXsvZB/5JPp2xqT73Fo7WxmYsRsLRtP58E
-2JbGp3791wEzyyjAfTpz2KP8KW0n4jWYfT4B7pQeFyNdgmGVlc+Gm2yt5ke/dj95WSE510yzzzPu
-Cf31oWwJryx3ph/qetQY09+UGD2tmSqO7elSNF+sDz+SPq31kZr1XOPGq22fLtM+Wf5VWq3+Th7Y
-FQXOzS/1NaWNXrUD4waZ8njZBVu+LPd7asS/7D59WQ7XBMBWJ0Zg1xCvu8FRBktqLCAcWuHbkiEa
-wXBSft+YBMlJstxNVhnJfItQLcx6HTfv4G2B8Es+38mPPC2NMJbNXtgCUwPJj1ukPTBtg20dmH7H
-XuEjP+l6BwvCANjbnSmuylLYTayLIVOP17G4VjD4DIZxjtu46SyjYk/ihAMqvjnw8oh/4/IV0Nbs
-kNHz38iTz7rRwXF4jlvBgWVP9rBRZDqexvWWxnobAI4IP8VHmScwfG7nBeiKZPAZwT10WLtNuMnq
-dZv+0+PqIYGP0tjf01SqRpu2pddVwe8U4NxjKrfaC7oGpM0vM500xqEJ3gCuAuKMP4HgUWa6iVIe
-5+cc1ilRnrOvpaR7jyte20GC7kktv06K8NTu8sNC/H3jDHZvqasdQYnp3zXK7FrjDVvcit74h888
-LmBXX5iz9mSqYyDlJ5O3nHbo5NOSbW7sE22MKk/3kie6OLBWTC7sMqHblj2C4Ho6nPTgNRP9phnQ
-l9bKbxs9GoQHc69ubdshjinrr8f86XsU832I41gf6j25V7l8+F2Z9veR8C8QDvcnL8vdtA8OsNvO
-ExaAvOyFveyJSXgLfQJYYPe+Dfe1QKTdi9jXHZMnWPsOAZcyBvFzuSdzBOZ6IrEAACAASURBVJhd
-mfiN+tv5/W5PcwvH+qCH4qDbDRfLX54gGDxClCYSoR02X/V9O8vHV+ucQH6BYrBv5T1cQxOt+Wx4
-QsLXphJNcOLZmDFTzS9EG6wPiSYIHtcPdrvn7MXVkZpgeeZ6LHVgmy9NVjoZqz2mV6/Yj+xGoSQ0
-wZTDiMWMlzerDEuMiK0HL73/YIuluHwFwCd/CRnp7C4c3xI1JpIn89/CQ0BqnK7V+u+9cPqf4sXj
-Pbi6KPSVm8bHdf2xSQdRRk0pUiPM4obS7gYqNrPSBDsBshdy3swg5PoEjI9xVWYjTfTrkfDmZIlX
-j6msNe2PoF3yYeTLqqyXSxOzKMu4fmrEbi7xja4NJvidwLeuXbvcymhfx7xkz33finMeNS+ANIuA
-ITTEK6z6RxPP6QW6RYe2N575ii9tINhbRtQdWlbd3R+EH1h026+6b/rWHXlWpNDRotLtBisIiBrf
-+uIob7orDdsVufe2rnNPjkne89b8bSyn7RMf5R78Ijp1qzO+mT88Xa20w3/b/QLhcOdPB+/Or7Pt
-L5J4yxZ4Faj8CGFCIjRYgsfbDfft+A4tbNphkRkJMwh820ndAT6v9M16fjFPapp5ddHiGjzMGmIj
-Ue0MhEmEx/mfpR2222AB4BMAm7wA4sD3EEinTaSb0ynxxFH5YpQ+CXZr09Wmkk2oefGQV3ewXI/a
-hDHfnQUGo0pOIuONoHvFi8SERj+znMYGW7oK4gZ+RSOpjzcLRXiQ7Yn4p771dSr6NDyO4Dlei3V7
-YHvwn4Fxr3EIqaNAXZ4SXWeAPPuuGmKd/w6CFRhJ5uaH0JL3pnViD/HkMTZAb78CDRyHl2WyDhbJ
-jeJxZCE3GfGHJ9gt7e9iSC0MHK6oilD1boD4VGabkx7VALKfAbNeCwwH/IqnHbUkBB66Pzrs022b
-gMc6LRDYFNC1BLJPNsPuXbub+b3imgY4413Sq3MKRGOoMorh38pofOc/JpM6Pz/fwK+EpinWGfPU
-jlaTi9rwpDdJ60N8DbeoaeMt+3eFfwC+CWCf8h4AbbH+LOvRlyP4ddJoFZKHv+mUHzKmxdlTvqJ3
-ZjiDYNvHPzpg7ByKR1DDe3pSOwHw75fl/mX3qUY4CfNk+0ttcIJj5rEicl/5aQhzh3b1O21wfbub
-ir0TOLQ+zSwyi10bgJmSLYRX9MXCLMJA8LoKZnyAJuNbcwGCJ/jN8lFu2TlbguBbZZ3O4IMwS2di
-/6QpspGbiYSmpea3bzwFxVUdGUW3q4IrC+l4ZJ9o29OCsy2AS2YiYEVlQft7mBgUmDLJW1UN2z4F
-vy0XBCFY635qeHQsiSgGCHnHJP9hWmj9Hpo6++0xz97OlKYPzNzOET6jpWzZfpZOX8OVLq1RGB81
-wSKpT3Eaf9L+HuIMcoOm4fBn9YEsLFTDyjrypTeCF4uRNlC88pUglDnfgG7+eU7LBX17DnFwJwk9
-kgluPdqhaRt5j7Ndy2nrb9RL/7vWj+u+FBJ87yPNG7ZfgeRv4AyCvYPfI/D1/YU7eB+55ahqInr6
-8M/05BlWx9Q9lW1+tmvHeK51+2iR14rnVJNIrV6YPKI6YCQ+pYtnS+8mThh+3b/+UiZpo7Js25v0
-ktzc501Z1CQ3DtrdnALZJDUtJSg5j/tUFON7nCbsU3pgmG0Pty0ege3ECPDo8F+N8P8n3OemEQV4
-p73wYobCOVuY0qMQiCFeNAsQfN8LUH5fjjy7lxyCdkNrn+TRawMH5m6giYQ+uueLeCEKY2t4N+cj
-X+AbeUCaQTQQnJph1A/0F6AmYH6d06cE2UmN3/EOk//E7KHKKgMYZhItr/W/fuhPY5rBwre4Ahdw
-MsJgLgP5D8j0NPpTB7JcAwaumkey3QJgiXO3+qzFmPtBOxy0p9ETID/29MO0hr9Hnz7yd7H6iStI
-8FzKR7Lrz3u45ck0rssZDK/raOCHOE1Sre588e0JHBuKHs3RaDMPnZkAmDYTcXGuf6ilTEBy2m41
-iTykMwY92Tk9qVRo7V3baw/xcm0AOAAq45JVF6/lEzYfNNbDseP4ElPeBHUwXEB1N5H4RrwwHXXe
-6KYRCohVQ9y0y96BsVK5zGy/eqV0oGx7/hRHtQhNY4vO6/d2PXOhpTVu1shga0PFIQC+a9OcD8/c
-4nPDau8/Ab0EvKoBfgTOKQWEZpRL924UPXnz9xnUPrNPmeXI1ULSY9t7kltXritl4qn2iBuTh20N
-o6l2tn/GrfkuLXAB4g8P8Pqvul8gHO7Tm5DrAi43XNc6yuy6DF98YQ6ex6atL8yhNMOrlcUceX6D
-XTDzdRd0x0kL5rjuBXS/DWEzXCcy2B0mFRCtLCw+u9nvuRMuKj5sV29CpjM9ATs0A+FLdQyTOfMH
-0Qrfju8A0BOG/YnbSpoyiyfgwUhHmgiMH+470u8l/NrGVoYYQhFoyMh1trzi3O/4eXy69d76gLyy
-ugmnUOEZfUifbJU+c84KwQsTjBlyDAUWTuv1adxP0PKU+7GqV6dF6qbuKe+D5BsppzZ43X6mdNdX
-sNIKRPn/Ze9rtyTXUWUD1/u/8Wlzf4iAAMmZWT1716y5q9SdZQnJ+kQQxtj2Ako597oWKJofaM2t
-BVqOD6+h4ryjE2vOu0CIPIu6PNwZEihTQxEkhmJKDcagt2PGOYhz+DAO5LAp4aPQnTSBVm8Zayrq
-U5EAJPGWHOR8haw25Px5rNNGH63VLlPg0+VRWYrR7hbceU4HwTzvbnU81Dn50M+cvV0YSF+7yeS7
-29Fqabnkpln2TN/OsVFupFWfDUDXLMqADu45PfLyDTWylyw6kB9yidM89phzPN7nF+A+fGZdlbs+
-OkbNU5cXvp07x7Gtm6tMr5+FHHFXD/biXIXiPW+fOgDr7Q9A//maz3WMh+EYj2Ma0YzT9RfK4B8I
-v0A4wqeeEZctMPzlwH0ZvrCu3L8IcqkA6ddDpRLxAsG8CjL8MV/13qv+CYgXDQmKJ0g2LPAJtjf+
-rtgZtOy0wYhpJXRAwPDt6+0Td1h973s9RPjHo19hUVbhqNXaIZbTJcmu2iw29gGYuN7+pQuKCyD1
-QOs3cBOo3qXE84pexViBhAaU09Je/XfSbwLh+LHtBMB3pdl71WANUmkoUUgstPrcxafDl3CWauf1
-vQra0ojWNeQLYTT1yt+FUX9N7WPLvVczNcEw+aFX4CPNcystKketPNG3HfyWVbE+YFNp7ijum2Jg
-giugbsPMNeg032hU1qs8LVoFdLmUMTsC8Pi53ATDQOzzcn0Als+vgpL86AaiTILgOocnlPvl1NJj
-kZuQ6OVe8QPDBHDuxfM28usc7tvQwpxfR16IeM4Rcj6R9dTt6+QRR8IHz/SkebPqLiuvAOM4J98W
-4XgGyKc2pA+cyjYPvs/Xy7l8UbZa6NFyRetLW7fHR3nhh+3cIjY+nKylHbSRnvlddNvI82qTxqCQ
-A0USUGx4CXRXXYt51jxOYNtn9QSI97honTluHR9o6GqMK+cGB/v5xwdsDwzdgmOB3S8bQBgLHBte
-gGAeNf5qLv+l8AuEI1wf2uO/sMDfFSC4QK8owdAKZRUpC3BZgtdry/7YsgQvMOzL2hzA+LaI3/zy
-SoHiBZI9me2PMqezXYaH+IYKxqYjCJ4WYQXDoWDX2yOW+8Sf9saJ5ToCULcPoZbbvhR+6+3BarTm
-OeZd9m7f0x0Mb5bh++5AOIatj4P4A53ghvPsQr/9xh11d0B8p3V464uqrm0d5xqV1C0lFVzHsQPJ
-iVSmJTRNwFGtB8+qZXFdJGmtz4IP2guN2c58DN6L9OpmqoPfUjJ9Er2d3//22J72h9/a38Nax1vt
-lAdxAZwAuVmEOZxOS8t9K9dpfKC0gWCv+qnokha+hTW1tHKyTMeoHrcyaeF1BPjNNCpNsDqswskX
-baltpDF4DK0jp3V5BGfbnuhTp2xVgBf8I2ta65KW4KjZc/CHPOUDcM0rvQBuAd4CuB75Vb77BVf5
-lSf1uORnuurRefkkfAxAyItW6QnRtBhQ4HcDs8kmVYleaJmcdKpvZ4SVzvwhT1rZY56VLKXFNy2/
-JV0SFGeVJQ0VHCfvTVGKEyCuzpXzoshp6xK7Nq7zIOPh3R5p1yB3aA1wvSu6DCilQLVC1XBCEkb7
-wkI5X76+0rtwzIIA80jAyzdUNTCM/074BcIRPrUIu62Pb6TCMw/LMK0DYhUGkLtZdq6F4qBFmMCX
-LhK8ovpDvWIL+BrqausPkp3TZSI7KCBiPhXb+Fv3SWZY+0sFoO4QNx/YC2FtcvwDwG7gD32M1deY
-lmVQ/4unrjh+lZy1hsfKq/kETKiA0EAwiX4T/IYlOD/fR1XClgkc7YGOnJ2MK53WZrFEbz/2OtG7
-DAIjPkOs05T98FypLEYg4NEW5z/HFErdRKAqsD27SbwPCjwaPZTLR2EqKSFPndLTXhmVglp3G/3Q
-15n38mfIfYEDbaVLXuSYQosWuCJNNq/cvldaKexFJODlOuZtXaw9ZULjxa1FHttdVYawSXAbnU1L
-cKUL2SDPIQhOi7CuzFz2Ex9YL+iHIjN0MCJEoXHaSIPuklgrFvRJg6xBiagHHim+ajSvvJRRkbf5
-9mZ5BO0ZFBeI7mm28RR0vnTeJm3b/dbL9UpND20tCwTPMgMMC088guFDvY8CQeJHtwmNM8919ChQ
-O48CfSc4PoXSpDb4Wu7kfASMtcP78JPWxlTttEG7w8Q4kxZi/qZlOPvbW+d97mWUO7hIQLBK9K0s
-wnxGiQaarl1/KvwC4QifPqnoCVrDXxhlGe67ax4vWFqDyz/4Nj8A4gMojpoSAKc+WtbXC7WHF9/H
-lqHw8tWXKcB5pauKwHiiPKRHHyne8l3AN3yUPV6fFg/T/YHV69ZuT4tRfpAjr/BXh3WmbDRd+EAU
-JCyVAQei1l9PbbOD0KKHRVhaLzCTk5GKk/MVWlIUas1oXjCINTjbalfbXoLF+Ye0uUJjvVrKOzZS
-aoy33yKuSc3XqVUtaSnW8U4viQ3PbL18Fd7tsUO+z+yphqddeD/1OKO5tvHXJZ71VguiHvafKcAR
-Vwjut7QIR22pNTkuLqDcadANwDpycxSNgJeW3bIUl/+/T1rbW1JOgYmtuy6W4DYyGgie6aCFPDmq
-66PWPhC383tI3hTKBorHvkDmMxLzbTK/qPlhRIGL7qFcf93KgFiDJ6g95REEd8B79A2Ottrr03zP
-F8kwp7RYTubpRNvmuuVNgTDOED5i+W4R7u42WsUzGLaqb9YxO/ouDmC5Eu150wocwhLpRpRHbCpS
-p6TJ3MOMCnyWeex5ExjbtlKyHmR+iMzWsc6LcFizACcAVstw1s+5Upp025cVmFZh9RE+WoVxco/w
-OL562uPfC79AOMKnrhEOS5eEL1AAqQ8wBREViIn+iK9SJBguf+A/AYgJim2kr5ZeX6Ir/5sFPlUg
-Z9zR+kY5rrqWGyP3j2TWwz4FgpeiL5/IO65m/8DLCgxkvPwT13ZWABzqNntIRc0rxHxgJ0NZ3uEY
-yqJW6eQaAYLUe1lrEa4R+lUfFV/IdrzikpeKFDLHt7pCOFxuPx2twwqKsyLV8DIyOaSw9eLAvBwT
-QMCelwIfAtblwYmc6U80Cl7Q/tmQLYzxZ5ho3WuV1nRKXNevqtyOk5Y/3QM56xE3BSMnizCQ2px7
-DGgWycX8QAGwYHACZ65V7JF8MC7GqYCX+7gpVgemfzCBNKKv+pGa2n9qFZZyExR3tPJs1X1leLCu
-5veV6cnV/yLrhWFau1mWa4DBCVbyMy3xheRKJgocOXVnyiMHig/cC8S60OO8CXDPaSQv7fVPWchZ
-PEGoCW7f7/Q6SzMVuLI1Tp3pFA6LsALbMxjerb/P57wfiA+6AsMaSr1vvcBwSUxxf9hPHbTiEmoN
-XYdzno+aVj0tT0y9J060cxVdvjChINjvBMDcHVs6TmcXZOXPABhlDd6ALyQeR4hs+unwC4QjfMc1
-4jLDl3l3kwBQQnIx26oyLMErGwa6RPCNEcMKbL6sqzi7QVR6bRCTdArC4HU+sNeFY5Vpz+5kz2si
-FDwlGMZSggS/hmUJvi+PPlpZg+HwOz5WYgDfWazHNSee88JN4blJCBJUKfULjhzP9iug2R6a83hg
-7uaujtGPW9x9ZgKwvMwrSzPbme4RKYDkuNpmWhapMd5B/Y7iFmMuUByAiVUT+MhFCcUwhZDGyc9/
-I5ZUSRTls/C+zWEFds6iofMyoGuUNN/z+jk13XP/LP6vOjYwDAggJp+qRTgqN+Rcr04faLn/av3W
-+UqL9T0AXvUdLl9gBz+kg5kXYNbBB+a4cam5IOmGcKQsz5P5ToTzclGrflkHxnVlu373XIuGd7zW
-lsCY0q0Ar2VDZSVefXBtBN1ajDpN1v5wTB5BtwxHnO8Erteq7Q/L3VoH6560li5an8Hov8kwscdr
-Xvf4HiynUevf4nIBxTsMNvrTge0on0Ufzn3Z+b5nXpdZ/VA3ogLFKHf+2D/lZ4/tobld/iEbPINe
-5fIqeypDPk5a9Ftfa1cViaSUu0x0d1AQzLcMWTDU2tFLeSj4zWWV4xd2AMwH5K6YeuafwDDbA9M/
-HH6BcISPgfCFsAQvi/C69W9lBQWS8UqPhAX4Bi5cMCcAXpbg/7NKJxNZB8f6sFwxWoFhCvgUlMHz
-WxwH4dx2nwKHUD5iMVUQfOeGRFh/qxzuVe+XhbK1AYJDHGj/20/kkG4c4AEEr67vllYCYHlzRIHT
-G/wGNBV2x6JdKWfaqx96LoD+kNx9AMbZP3Y4Kxy1+STpymG+ygcSzyOVeM5WtyBW3LPFM/QtAXoK
-z0L/EB416+sNuJ82wHDSqjxQU+tbvhddFNhe7uFnuocUDAtfWs08a/VQWqUxhZYDPVuPO0hehSfQ
-LUVyAMG0cuncydImzdAemEuQDFqFE73g6Bphfd88hgeN93yeKP5ZmHggIjFl4Fp2H+Gi1+apMXgW
-9BonkBceVYWu+ZCvGSdQLavwE/3s8tBp8zPLG1CW9D5znLsOuJT12pSM4bZMjOWznkFZ3clJLRA7
-rL/JVoJyp4V4sw4zbMIwCCchWQzTywj4LVAM1EXjQ1PIqe0ehTN/TOWe5zEPaifuJcfqoSSM1KGA
-N5VB2+UN+M64AmDqZDzEDXQRrd90g2CcwPji+dTvEv+GNvnHwi8QjvCxawQBXpRPQKwLSEVxS1Lc
-IZIZ4kccXSDYBBh7Y7j8GeOejLie//IEvV1Iyq21oN+6k9E3bykCilAqgxUnCL51c91xsgJi1BYu
-QGtoX7cLN4gLc2Owg3q0nBEdC3SsHA/ng6BYgSkfmBM4P63B7YXq2K3F2i7Tt9cDed6Ab8URQmbG
-c8ZeyAFV6qL+UpCpJZgzVuvQhWyOihZCWOkBoPkR//dCqZxdMZ/AsOS29fOxXn1edlrP6z91exg0
-n+W0DhPtF736wA94t0Z6bM3gSbq3+HCNSFWK5vfo6XYUsyfMoq4R+cDcg2W4v0rN6se0zON3wrtz
-jlgmcmgUW3leYMS5S1FzrCHmzzXNmlU+7r1olP4bVuIXILjTKa9PYHcv90SboWQB5UD0m8svw5mj
-62fI3FiPl/VxtwJr/CUY1rKoso02yrYO5/6yBzogV39RMZnJYs+VhbXuoHAP+wC7pRu0yn0231mD
-d9DbzuN+bfmirzULIr9FqHcWXhLqBII3cAyhs90YuwG7fzDkzRHA+V3CEm/44G+Exn8YfoFwhM8f
-lgP8wgLEly8QDARPxiaVhTRDve+X7hB3WYCXdXQB3gS/NsCvyRXVw+8P4jaaWwpOyvuFAQlcKWTj
-7WbS/ZIXAj48odUGAm7Zec0LLR6Q87AGF+AP4BtKvFu0VzsJhmMer5xWCsXq7waCBfTWmxsg6XCH
-SMswH5ZTIFTuLUpTJenbOYgRYADgiuPQtwLDOqiqESM6wwQEnB9agnOlnOB4SRmOhx9iqFV8ldpa
-bT1pHmtbkXd7a88/tXIOOxj27W8/+iGv4p7zOfP7b1qBg2YCTFpZLm0oV1XS5C0rWlrvCCipxCln
-DN26zzWYYNgBurxsILj5CRNkWDVLAAwBxZQ4Am6eQLDO8XdCqvfc7E+1eGbTEnxOu+yTIA7EkvN/
-6rjtRH19GmURa2+vThNeUlp+Wc7lvcL+yi/49AaJknsnWpvDMRSV8To8dZ19DIe9nX9tFJhg1io+
-wXCdWlbfDRSfgHBXXjiDYC+FyHaybNApPBMMI3znC+Y2QMy/UddgqS09Z63nlbSd9mAO8giCt7FH
-X0401Niq2dIPNo9Rfzu2eEn9C0/uEfIsUzR/BMDwXMvTK1P/7fALhCN8xzXC87Vgtj0YtgqtQ4Jg
-2+PpL0OmMbEQIxjHzgC4vZJEGLQw3gLFl9MHbTHtHZ3z0eWVXwCgBtF3Uwn54fsIxx9McNDjNzy/
-NLPGXABGLcGQMcIW2E5jvVs8eGQPIDjSzvRKuKN92CK/+HbfyM+rBsDYQdKB1nyFR5n5MQ1aoQcY
-znVwr1+rtUJjTd/lOx7SnB/Kd/UR7m8bIB2KxUY4U1+FR0XQqvpenU99W3RR8z5zCUpqLTn/GR/H
-szVYfzsY5rnxVMBeDycYwMktYn40I3kkNG73M34AtRME06cRh/LpOxxzayghhbNVuIFiExr03D6f
-3wnnHXCGDSSrJbjW3/kIAJp1WKt9avRoltK1q9N0fbW6R6tv5ndLsH4uuQPok7tEnbfxpXM/OPwB
-2apkP9E17HvOOiH5YyXy+il2pPoF7yC48goM7y4S78EzOxq9nUJyCrZkGKEPSzCfGbGIq8tSAmKC
-N1ScXVKplE2MWVU3Jc+xaOlpKdZpP9QcQ2186tTeWn4VNifgVRAcuOJAezLG5XuEsb85wmDNTzhF
-TMZ5Z+q72uCfC79AOMJ3XCOIHhsoFiHZrqIMyzfY+itE/jjfBHE3IEzG0ngyzimOxeQGSwBsHgrv
-LsvyLWOgNZhxk23GflMwAEPh2wK1tWtXmc3NP4QEf2ZU1DyLV4qr3osCNOuqjbPAsOUUE4Rweyco
-zjhBcbe+QnyDCYztvnMEHGMODx0Ev6RRDhH8bq9QO1uEgTZreBlcIzy/1KuNdFqCc8ZegOAmjq2U
-x0ENcm3e9PYY+nmvxV613Ptw7hFyXfoh/iqP4Cm+g+L3P5P4AM62l0Eqa0DdIvgAI5Guy/5DgFoq
-SYLnl6DW0UFwuE+0ddbCbCfcHaIJvAPFuaEzbdvi5Lg/DKeitebs++QPDsPLz9B32qNxeWMoO3fk
-gWVzzb2kQ9GKP/hwnNLcT9ZesfK61N/OGXTv/Le6u2bJi9B6vTkWcfvPIVvLHpXFypieY+3Esgh/
-F+R+Ao5Hx1rct/2TzJ1zUjS9a9L3qde1kcmWCX6c87WzjmdO3oEBt/spT8HvkMPNRUKz4uw5/pwk
-nuJS1mtOsAPjBK5vwHB7h7DvD82l4Y/lnaJiya2sywB73KT/XvgFwhE+tQg3SzA3gwheM8MfWn4d
-CYL/3OH368v14ct9fSHOLvwf1CWCvsHLprS9hgSa9mRCw/rQxe1hEb7XJ6BXH9TVYm32P8KUalVu
-YMJkUzoVeggHW8B7Fa0PbNAlwuJBodvnl/JCQV+1HXkNYuO4T3V6Ku2AxKkwFASHchCrbPoG0084
-6y1hRvBCayGkHUjbOND8vpefsADeBYY7MOavv19YKs7Kt5GurBTEVbaJDxeBzCWlUkAHvynz7Sh2
-p/Z7wgIPCuDTjfUu9E4c9XpkuCT6lA6gewAOcxlOs7/9rHjmXVnYsNIFz1Ixp2J05N0KLtBaS5N1
-UoVmqUTmA3FpEEtLsYebktCAfkuSoDj22wTFBLz9K3QFdlSMnBdqX7cJKE5FZjXJu0yVTh+AeHbo
-A878gHUpezIutLowoiws+USgq8C4PrXc3yyx1zFAsLSl4Fk3MGHW88b59tAPhWT9Ebom+SbSwBnc
-PgLfD4FzDTX3hwozy31lIuC4cKTlRkk/8tU2+SraE+GrFxhaHapIk6nZSTEylN6dklfcI6SGbnRK
-IdIuRMgA1f9+TkoqLxxRFuBFuzLPB94ozECL7xdoDQ5A7BO7WILoBMQuoJrrOMfxQ+EXCEf4GAjD
-1ieDHWENLuFijvWZYYLhWNT7XsDwdiQwLoC4HAd2a/BV1mKIjsmf+teybYJgw58LBYDjwxYAUhZc
-EQc8X/3CTVChBACtSVTid2yOOzcVty+tjUgLEo3mBLQXLHysuzcJ4wsAr5HxnFvObwogfgQUGwgm
-4FTXCLoq3Hf216UfVBtsp9F90oeFWB7GKwB8tgbnw3KzFdfaJesQXPJKuInq9KIpQEqZjwJPCYyD
-h5GUczgD3w/Di7dQzPHZTPlTvtIFVDVQEqQNBA9r7sYTh58956nvfSt3sgKDFqvSpOoCoQ/LTaBc
-wBcFcMWqtVn8ZeH7e4RjLsUqXMAGAwRzbzM9yg100gDAE8PoPIyVRICS7XyXgwsgdsotT0FhQt/7
-FV145Ed7TDV+cYn3rmz81j6uIfx5fF1alHmyAO/0LhfY4RNgfN5B7+YipL2NdDZmuabMIX/QrUaP
-n1iFdz9h6fcYS1p0c79JPPeaVdzRwCknMF+bBkMBYiRPWk7yE2v3+VVD9MzfrcCVn3C4+T0cho86
-teuO3g4nghbkhSE889UyTBBMYEwrsVqG55fk0l8YuzEvQbQJhmEfvs2P/0z4BcIRro+dhEMiYa2u
-RdpoffUFSA3xEgUPq6Yv3EUQfLunj7DhaoC4rL/1DuLGQCgGKiZefVkg2OMzx1afXuYGqj0uAHiy
-XilkNuKx+z0f2Flg1VLJQG65c8NHfihOWoEdAXpvS1rzTIn5WkJ+TdIFtQbXliFUpAKAY4Bgiafb
-gtd7hEHXiBQLqcbYVzzS6xzS7+kj7P3XQGoq6qq78dlT8JmQ3vm5nGWrBZAOduEXDU4B/Vk4i+3v
-hcSL2ZfewpD/rYyCjMrdQS9a+oOfvcq3x/y2n0KRFwg+u0D0MigwEI/dRAAAIABJREFUncAXG+A9
-uU00vX/Ia2A3+lquEpZ4VD/FXMIpIxuLFCvveZPP6wJAztH1xKjCK+JRX0oECgXuL9kgp8u7zmd7
-I9m1bnob8iCbTN5qcdfjkk3rYTmxGIOyr+pqD8355N9zu9lj4ak2wqft7DhcFIgf+J7VlEjqpuSP
-ArukvwbBdWyg+QEQb+OR/fQyrzZDzU97dRKw/IRZLsbHjUCeUxadp2u29Kk7o2lfd6nMBSlNh1bP
-Fl7wcbms1H7gnZPCGAF+EwSjgV+9O73u6Hq+IaJbhgubvALEJUJKdv10+AXCET63CEdhDwaKe/fr
-dSOWgPSP0S0hbn95uAa443bffIQXM1zpEmEvadb9kIOZzeO1Zl5feUtrcD0Vl5vXKQM8mFA2RBOm
-cbAQ2lS+3DBAPYhnTos2LYurX/mmDYMAYk9f5VWNCO7o44Xq5xFwMO78EXSujG6BFZ9dPjAXk6GK
-CtIGRdam7J7y0hrswzp8AMSp+LwGUEOvcBQMHOBTuT5THkLegQaa2qvTgI+twU8hhf6TktXRvSrj
-e/IVSNGYv8jfAYPkeZut51/jRXugn89ZS2Y5UU6tlQp3dYRlnCcZoC4V84G404czTu8R3vzDm6tF
-+QY3qzD7dQDKJ3eJuY8T3CZBlqeVlQsWG+V0RZuil8WU/FxQBGdLWluaPJWfmR6hdSes6koreSk8
-lM3qw25e+ah3BzercJZFpNVyfPg98G3bNRnlSgOzSJuHw9jtNGFQIte/qLblvQfBlW9yWgC/4DU9
-t41Dx5M0h9z+QjH8kolZBmHoUTCWd2lqGHyvfpucMTWbDEp+OXWQmmTQjTxadx313L4U1ZG2NyRl
-pIWgSSMWUBbgGYenZbfHIfFwjXDb3CGegO+8y736RcZ52Pj/YvgFwhG+BYQzGHB1AFzxAr98iK1+
-AYgNWEBX3SPifcMQhoE1xqwra4DfQ08ADgtTtMEuxx++ISy667GR10cukFdrDmXKkhepdRzyxHsx
-6nSNSGUa59E6/MWHC4Fwi7BZVQojyH7o/peW219v/+kdz9R3A3TSV5ivTfPr3lwjulKjqJnKRfrj
-3sqvfpUvsr4tAvQNhvRndj7n4DmUO8WYu9HzXidCFnv1HR0EhRqAiKXQm15g5RBe4JW/Cy8qm6rg
-XTXzdrSCBS15BBdvf8Mf2A5l7OAznHrM0+Xh2QrsiT5YJi2bpv77JmCYe08Br9ASDItKDcXXrL1c
-cwLblzTRaKTpmHLx5PyYgwzRgaKU3DlxmaaSHwp5SkFPecCOKDSQ6a7yWqeCLW1z8Bak6TO/iKU3
-zlWf4fn6tCw76py+wbN8G+rqaI1GtvI+m88XvX0+UAgmQZyebVJmlaP/r4nmevIJ1vcMv3ShUGC8
-ddCx+f3qSBzYQDDvmD5Yeg3cR1Bim0gf6ZqvPenYTsfizbqQ3YBxxmeV7PuJVZ/W1mUYclcXhTOu
-Q/rpeaX5urSnX8Mx+pDc+P10+AXCET59a0QgScBNgG8HwPnaMvl5WGvvO45uuMMdIt0j+IBcuEQU
-4yjDqDVYmCnjBMqOP3fs3MuWBdQWAJYhxM/7BlezaPoTBp0ak9nQq8rVHsHvErzhs3gBX+IKUQ+v
-qCWM9YTQj7Zo+ZqgOJVPdE2Nqpt/cLxPrgPipVpUGFX9KzUVGjLfJS2/fDDv7g/peY93jSUd3/it
-Iioat0Jq7fJJpyqu22vuvH1fN+iw0Z7Cpnn+mfAwBU/FXvbgOJ8l/Cc4+Z41uAPc27SMSbn9PPaL
-F5WL9z3iBbrqwUak4l7x7j/Md3PzrRD64YwCvPF3WJHBC+IBXlJJ0y3CbCvzEVhmhPJjrEGhsgIW
-tWwnVCE0mdwj9miIVPeZNu/ZfEE6DMaKi4mR3WWG8kzV2a3CPZ3uDX5+WG6V6TxUD8I98Ogon/NU
-bBUXVLV/TcromD7a3Vx3TW5nK9+ggdgGghXkwqBAV8tsgDgHF0w2UaYOXK3CNAIMEGzjwoFDWBeV
-MkdPoHdOEZts0rt1DGlKsue83SYsc5x3Ph6Cv1hZyUuM4QJ+gQaApzW4fILljREnMGwdPHfgK77C
-sd9+OvwC4QjfsQivjbrArILhyy3f26tg2DVuy4p60yJsF64/N/4PAYDTVUKAsYmvcLuKss5IdzGW
-SgK/HV9xkusvbtlNpmyBlivVBPDWAgRgscSSJ0uzGdZG8cvLMhwuJfnid9nlZY1eQq8+e0Gr8K4A
-VlfV/SAUw8kqfMnX5XJECnAeQG7S7ECL33SNGAA4+wIv624NoB9b76SxjEdvG11GMZcyFTGVSolY
-AuNmKxRryitw/KwTTuWnIH5ZwcvwKN6HpT7X1CftUAYDdLz72eAd0/zdV1jBrb4z2HPKo+9iuuPt
-2rIcY4BaTiF97AiGF9hLizEOILi5RsSR4He4QXRgzIm3AwhWJa3jjIoSuEgdHlKl8QIH9oY5DqA3
-rcC6Q8VUGqJJJFcBY6gizn2ClEciqtr+53ECY13/e+Q9fzL5wIuOnSb0ybM6Pz7W5GQZ1j3kI/0y
-pPIIcBpg1zTeCk6Ae7AKs8wJEI8yzx0OYmNu8v6al3z4TEF0MolUMydpsmToK8044TnRymjA2DBA
-72xoSt+uE+yQdaZ70jvoHb9Bb69Bw/lX/sG7hZh3nrW+U7t7p38m/ALhb4YlCD0ZhTRxwV0g2PSN
-CAHjwk0CFMAX8OVLwXwFKPzyAojuiw735UoQZtHFz7F5LyB9B0UapNK0EIK2FNKXWoVt+Sm7Lev0
-pTSODT1yZNYAy1S0AOp2bEzQ7WvO7hBMdSGB9Gu+oy7e7uKbNhDgn3n5EGIc3T2/nJfgFwqIBwiV
-OIZ1RcOJPq3Bs1y1G5QD2M31AxL4vAu7nK9bzkl36VfSV1/Wrdyy+DrH4uMBCgHB+bGRAFjVoBWD
-mTQo6i7HaN2XEtCHQOTU/zBQx7Ey33JPZ0RMAEZb73do4HFzROKQLqDLP2tfIvpQQFlvz9Ji5LK/
-LPZD7P+0cmmndT2xr3GbC/bXEpx4A7lAPjRHy7ECYwIhIqGc29EHba9toqdyMU+5X5E/15ejY44b
-de5h/ctAKPdJnF0X6SfVLsv7iikg9uC7XJutNQ3aH3Uro45gndbOyKFEA3XPSvp3aH8tS1/v7SEz
-GyCrXehoD7TUJ3HpixV7aN6QMIOOvOCjPDqV6Q1+tAg1rFFO+9sncvLm6MAmjGs9XivPQ5ogHxzn
-Zp6qpg4sb4dxnVqy8btwtgBvb3uYP+8PwvXfWtlTfScwTNpPh18g/N3QzW8bjbcwioljm4csT1lg
-xVi06FwBSM1W/GqAFQvEBnjGtUDnlwPOV7hZCEvDesVbbKCvEDRf0ZevFD9oNAK5y+tGTN0SPU9H
-l6miQCRFDZNAEQD9lBeeD6Acc5Sfg6bSCcs13NZbMC6PW588YpUJa6gC5HYbMQFy7632vwEhGXTR
-fdBOoKKGrccn6eR6PBQpkFeEqQhqvaoOj4LKix2EpOZPrl3TWUpnXfSpph3S35U/DuppA8xV/I2u
-eht21usr51v+IbzsxEndvksHrWkcATW04mLcaTF02ZIXG5DBhNDwKuNeYLgsvRCfYEAfhmzXMFy+
-uNBMK7DZ7i/89BNgXMJMxqu8r/gUY7wNQ4yyBMG8vUZAbJMWe3sA5G1fRRennN5B2hluqSuKnxhI
-l5EAl6mwwlusUfnPSksBLOZQ8k06sg/5MDFfZETadqdvtLO1u6WrQpZvyorxbeh9zlLmCIp345yE
-ngne9bkgyrCyH7SFjDdeUgE/4g/82EYiwiONBH4o3NRdzFUMNXVO1JFD0XRWUUB3bfORnkDYx9TX
-JJzFsEsXpaILwJcdgO+Jhg5ijWkvwHsFtyv4tXFefk334cj4T4dfIJzhM7Vc+6mzqsbXnvV9L0ua
-zJnW5GSCBRBvA64Au/e1HNX9sgC+ZS12X24H3Ndf0pN36a+w5NJK/GUI67Q3i8S3XXbGU59pRXWU
-b2QIPr6CzbEeIlynC/CFJyCGc6N7uKQE+HWC30rXOzrVQkt5FnCcytNKVelKdiWXIn0ELTWlEzqX
-vGCxo2H4gSZyL4ulNUgEfWKmzAurMGL+GXfxRWs+chGf0rVMZ0VPpfAAHlQXkvYwxFN4ZEHfInv9
-bxpZ5UYhXbhP+N8kcogTUtRFMcFnrVN+7CKbFc/AxAHWLMhpb1+Vlb+/jdusAbzEIxwNDLP90EbT
-6jvjTz+9nc0LsT6nVO0YeX4qBvIxbo9xO+B3+RBEWd544Rc/iSCzLrIudH7laD3dOi0uEVXGj9ir
-VnvwU8g5utUlGEa/g+gY4Jdp2fTLUU4my6Rc0BKwmOBWWG5bTTOf/ax8JoN/oC4OQCt4nLuiawle
-SLS7GI9HJO8/bcMl/x4WQ3muCUMp4L3v1U7tNR2vtYv/7MB2NlO552ykR3526w0QFs3cOp537Vpl
-IdNH9+iq8AnwfQTE7s0FIn2KYQ0c8yNalxEoYz9a9MOKR38y/ALhCB/eoUbfSJuU3+JqIUYoQZ6r
-OiQtALYUUlmDAxzTXeJCuU9ggVkCEd08X5hpCh+Ui8S1W4RZjn5974Xbe5plnTGGAALNTzlAgFtY
-gk0twfGHwvAGbFiFCwyv8SQgBsRgJBZhlSFRP3VpEzWJ8bo4fwLOtKjuszKtzefgW6THOZ+63lQC
-pSxKSednlx/B61/ST/VlX+tBLc3/BAx/LAM/3K/tFvI8+aM6nnYANnq/wTz5g1owRryhUZ5mJYyi
-/D4CmbXYS7Qwl89rOmS1OU6F7IfmzdbrFo+A+DqD4KuXU4twswaPISw2mQT2cYAP9wD4vnz8CTxi
-w3tYhf2+ExESDHvMtYpr5zSzqy+YzmIua7L2VZhGEQVSIOiNPdHAMOjHXSAYvluDr9g30zp898YG
-FpMHkEyA7QaGcbAW6wiljShwAio+6HpflA9Rny5BXoPi0cg4XfUqWaI6MNbCz25jYxT7wNi0RW2O
-1I35HAzrNB337Pa8mBrgF2U1Bkq/uNSi462adYyoSWjimXzrNXYb4PddOn/ewXD8EvRK2giCcQC/
-XXy0Cze6lP5k+AXCGT7UrLk/d6SSQjPjSEUD1P7WeBNUwRh0h3BDAN8Q5kJzlItE79fD0eW86OcX
-wr3CIP7DwNflaZ3d69sFVFOySpexJgC1uP1OtwhYgF/ZwLAEyFkBXD4vR/Cr8bIE0zpcvsMTAJeX
-rKMsozWeLjQphLieVWpXfZ07zjxFa/Uzy/W5TycFr5wUit7nv5fbIfkrq/Ck51lPrhFPFmPmHTSm
-pSJpcOF7YZu3F3vXqw0/0LUP/pD3FDpeoB+v5Ix46tLNGhyZCYCtNu0UJLmn6k7KKm5p8VJ//VK+
-taarbakugc6VQkitwAl0r8h/AMTLGnypBn7AGnIL95DX0wj3D4f7DT78t159yDs9d0zdlWDYbyT4
-cqt50emde2mHMDO4LMnrkpY/AXeCTgxIEJxgwuNNOREu0ChQaRdasyC7AuiSGu/AcPbXpOxTeeHn
-dJU4zMOcx00/OB/05Hx4Z8gHnj/VrZnTasz9keksLnD1UQ6stgtXxqono1Dv+j4PIhZz7GaD56Z+
-Vflxzp+hUaQfpoNjX3I+kXLmc/C7A+Berj6oUeX0ncIFkpfVd6ajrJV1+KfDLxCOMIXh3Lw8+tyV
-A2r0c2WTyqYiY6YeQVgIqFOgrhJIAPxVT9+V5Kydk60RoNG6+2UI628pQ4LRr2v1usAxoC8M3+ZF
-ry5JP+q7nk/uLoswQkkREMc4PQR5VmgFEKJCgmAeaf1tgJhjFNeIBMDOPCR4WGu0C6AaRdDcWtd0
-rH0C1E50yD/RvR0eQxeoLjJPFbXEY4Id9M+TWjj+A50XCQ3onso/AOVuAa483SPfDtuJL2p6VHKf
-1P+6ZGLVxwLrj0mcvJQGddStVwPyAbjqLMETu9Nv1fKT2Sqs0gLs7ONnluFp0fXLALUCqxOfAOYT
-rT3JrxO7Cwm0XXBa29jn9WGQmw8PrM7fcXV833Ghb8sSbMB6bSRCxqzBEi+oTONFwjv9O2U75YPK
-3fN5wwIMWoUrvkDt+kqX4wRuq5HLDtZh2We8dW94B3CR1uA2SI2YZllV8kH5PleWfeMbG2Qj1Aly
-gfcWEI/TW6ONNvVykje4qaGaN/C9+PQNX3cpTERjCLsgbH7ANvef1Tg4S23uZs+6fG7tSr4pLfrD
-vlfjVm4NVqDWsI5fEj+C4ElzLNcHX/zUrb/iO2xCt4e0LWD80+EXCDM04CLkAy3BgNIFrCUQEeZT
-eUEZoBbhi9ZeINwhxDUiLLkee62+zGZJo6sE+5QeE/LQnIN+weFfPGnZXggsoHxrocpVBoLa/7Xf
-ulBhPQSaOQ50q3C5SdSDcpsCvS3dIuqHDnjFEpxuhfoTFaaKTBVzNi9zp5zgOnDJH1xR9Gdt/6xB
-Ja8EptAoyL3np1yOQlPkrnnfrcKrIiq0vXyjn6zB3s9rim+M6S2QfDEXp6BNvK13K1Cq5nhum3yW
-0xYLBEw45ZYZqwgvpJpvApJB0lKWDUWvpibPjpr4Hqq/calMl6o6nrCuSFMbBQBuT7AMa/A1QfIA
-x6d5nntgykbUPLRz3AHjJ9FRgjNAsONeavm+Abvgdq/nKOI96vn+dLKpDINNzBvPT6r4BKgQc9/H
-27nJQPcIxgMkeMkXBbjTX3hZhz07PX2HV3rtX9Zj/BnjJ+swRp8O+SiLsOox5fuahxq5RX8Wf07X
-CKRCoTtb5/3SL8+8/xBebBfVU5T1T2utQ82HSnkx1VC47eVHGg8WYU1bm8chR6Q2a1RLedr6E7I4
-jVCiFNif6wCGGzA2VJn2O9H2MgYcQLB3sEwREuNX8fGT4RcIfzs8SvdGa2vJh1tSp1Hp1RU/N8m0
-AnsIoA6ApWlxqKFf8LLqSlxpaRlGukYo7QoLcr3vskTG1nbt4gKOosi0PPXZAl7sX7hFiBJZAp8W
-krCOiNJf4Or0w/ZTK3D+c2+gOK/0s68iZhxcsFxVXe10t4jF9KRqIW/lZ3gEXuiCU7FDF4SldFJv
-8JY5p5XnOueUGq0EaaiivI3clFWU39VGMPWsa3OhGKdFEPY50vc5+YgobHjIn0DrfXV73Y95wz3C
-SK0SZQF+ZQ2eICDsj1t8Ag8FxQ9Hq/mJ3VSKnpbd1E4Ffm0Du1dzh7CMG4xfJ5prcBSdfqBpmgDW
-kC9Kv7UQ/aXu3J8Og9+WY9LXRfJhxKyeirfxfe9Ermv0hxCmS7pTCPDb0gSXahWu3xngrnSzBHvd
-OSTBci+WWsi6WzuvAHH1tU0U4wdrsA9y3/piHU0rqhe/NvA24mzvFMfO26eg3hafnqNN2eCZNdag
-Gd2PKAd7rclJQw54S0l6iMxd0k+KCa3LbMvNXhNg40r4HZith9+mG4S6SJQrBMtbpJvLBes0sT6b
-HK2D4Z8Ov0A4wtPt662cE9Sd/DxPdRTNNOJDABkBcAlnpiGg2PMkNBDcWnMkaObY3A3XJcDYFyhd
-tLWhL1v+wauOenjNczcXGkuLXtMd8zZnn4+0BgNpEb69rB+bv3CE9dogy2/AbSAY63gNq/BmCXb2
-sH5rLaWPppxgPT8fIOw7dQ3VD4LsBMR4fM1vJQjPQCGBzDjyzoaBSihGGetdYrNbhVVVlPuHi4Zb
-ZeriQOiigKVoJvg0c2tHom/l3mdbs5U/zFy2dazugzYaIEpiuT/MnZ4zxAKijLin8rVRUdcrMKzq
-cFnQzv2cyj5Bh6btAAoIfA+g1wX8gmB3Wou1zKMo9JF+UwZYr0xsiG3uYoLgC+53GBEGACb8Sssc
-/2lD3DNzYj33mPI9585byVZd/TVKEw6jP6B25bnLUqcPx6nrw8lv+I6L1QmUq92ha1p6jHXm55Dl
-ITuma3jZZMkd2/grZcpwrysQieLz3FS1B+riLfrwds/uG6Tx+6ch9l8C3lSAKgcjZOXksui7tKh8
-0fpik/a+l+kKIVe49dnpojf3iJhue7IKy89a2o9xy7Rt59XRAxAPC7EeDb9vjfjfCY/SG8Bk3c58
-xZwogZMMgAGAVwGn4L+eW9l6QrAZv3zlWpxbVmCklZa+wgTEBsdtluBx1WUSB9ZtLx/jk06g5NjS
-+6LAfH1cI0GwlWV43hbEbfDLy8px+/oFEL5uh1/qDnECxQ6i8GYRFsjqgmbKMsr+mwAWtY6X8Mri
-vnL1bFED24K9k+dnYDMVzyjr3o7MebIKlwW16FkrrcIx9lJQUse0iByUxEe66zT472W8rWvrx19U
-xVB1WRKa8hsgQm+xrrUXBQa8tAy7LvwD0Eg+sHPe9BVuA6HwodvDCeA+HrX82qn1FL+09ig+n8v4
-5cAfK2FJyTDFoN/AFaDXrQAxys0sb2sb1+gE+s593Dw+PkFUVkWO4NdK3h3dId7Etzzn2yQ824Pt
-Y2QcG20HuIDMi1Y4M4XW+a6s+imvlPdTCFKGoLODkGcL5yWw+OsH/pc9InvhtIyVtzrAd3U76DMc
-JUT2qb85hwTW0XpvkOyaVxndM3Op9b96n7ORoLfkTA0m+o7uCkF+PIHilXcAwaE/aRFuwNmqnAHt
-1WlpIZaj6fFh1P9m+AXCDG8sdL3sFmlAZEnNlt3KMK8JJyCviGBIa8YVHOLN6csf7x8QZF4ooEv/
-4SPoFUB8ZR5w32tT30DeUry18xP7NECnQLFANPdk5gmgVMsw2ykB7yXgY/gL7K5+8y0RSQPCa1Cd
-ItBSSWGnrfq6mqS1VGc2BJjiPVQF1ca+HtsaHVdvBP/escAvtuNaHy/rJPsqFpjmKxzjpBUNStcO
-0uLAdo5W36rP2ry9QRHf2JKn8m0LPtb1iMxeAJ1SYvPsshpbpqvGdcbCtzppeOEfKXEVHNqwsGQq
-bsa3PFHCmpcuEAvMuoBaewWAFfzG+fbSItwixySADqJj/JuvOTh/An6X8IwvcRIML4sq9/OUuW/D
-4INyqy7f+dk3bjPTNNY6b3I/+sGtclnd/XqyBmc8NpXmmXBBtbso2iZezMXWL9Q+3wZ1nKoBCLH6
-tVnVPfiRg891p3JhBVKTlWzbg+yRjTrdE5rwnzWAoDFLabtxgWXTADDqKVG4t9GZqtJ+yt/OnSV3
-hbymNGgKimNupwXYTAGwWnz9mQ4BxDatybbTbD/mtXYeP9mU/2z4BcIRPte5fow+1aJLqj6BzCMj
-LCZcFtZmFQ4Jlz58vFy64xbINdpwlPVY4uo37AAuL9C7ALEJIAZwFUhawHI90ayWiGYcTAXf58Ml
-prgt+wd6+ZnEUW4QMrU5b+7xwHhYhsNa7Fi3Ca9wi4iP0AUQ7K4SbJtWg3oYjP2jEN4BsQdd8d6R
-g/xlbi/qrfhzOZS7AwX2fvRqNSstC+8aL7Vm+AZLR7LmEM5Vl6iRzTWCzIC+aNNtIgL1Hk85DPTl
-LHxa3lqW73R/Vf6ZLqoGBB287U5yug8FCAnEGbSQBbFpPE9wOdlba2uvdUU/VGEDt4g+TjC8qp+u
-Ekvo+AH8ulqIk94BsU0r8pixXO9T8C3SounzvlWxBFyuY8isco0gCJb0CUF9rHe9T67uxDMWQvIG
-ClTmT0DxFdX1h+MWLQ0CIZuPluHckvHBDRf9Ir09p+2xTBJ5EN4GRmXVlQaGa6ZYtO5O8oxyXWWd
-LotdZ5d1eN/ZtNwaZBs1uBt76AiIH5jAUGAdniCtPk0v54aVuPUs5+5Uv43oqScjdejqyTpt7SHb
-6daBcI1AvPasgC558ewecQDBSQ+LL+sd5xrKAtzA8APtp8MvEM7wDqpEqa3Y+TzdaPn0ZmSQb21l
-dbcIlrHFoOscWy+Ih8d71SCAeOkitvXlBTTdyyeY1t+LZa7UHfGFurBAX4uW4kosVTdooSgYIHdh
-NmTBGMebyowyzuoUhwsI3o+4S7/yrRF8bdoFiQd9c42Qf+DRIXf91V/Yqm8cha81qbG5nLv7jqpN
-PGlD4X/GcXWGTUJU6kAqEn1ALkGO1zkJ8HnLWBYhwZgosOq44cjE0p8EfiffOa3r4DKxDfYb4Xwz
-R0CYf7vK750wFVPCAMt0+Ql7rA3nGfjMAlxxd2srlK9xQoFa5Re3AQis+tZRGkFtWYQtAK/blfEF
-diU+wLFRIB3n9DCxB2zaMLHfxcuapVePbnC/6gLeLT5CZOlv6yKDNlCKh1uyuoHk7gfztodLJTSA
-me1VHQsc1FoRLBzdIfxkGS7QewLMkLECfazaB5zS0mdIHW0C+vbPevuMiKxzG94+XtfWysGNRWQP
-VHWjjV60EUzdxiyqin3U4GmvraqSOsQwZASeqsTU0s39tnXwIZ1r9Fym5jPaJsBt8nhYqZ0PzvWB
-kd953EFvgdxG80nzcTx8WtkewDB5btIeVMe/GX6BMMPHis+T7xmmoi2+U2V1wBEIYeMhEAMw88ty
-S7KtAnn7iO8Ojq+r1UYsELJuE/q6PYgOfGkx1rdITBcJTyv06iitwcC6ZSf4vIGtAftytgpgIoVD
-6i/kcDbwSx0z5e7+eWX1C0b73bFxmyWYR4TXs6Pmlyu8pF2Hs95dC8BxyUN07nXGzlLeqa94TubL
-tKwXrYEd0KrC9fH0gS6ssM7wHGOI1vEQHRdoguYskzK3hG8JXav9ocrr4fbh94NvyVmrPyZ6eATI
-PuKjgdzfNtItb2XWF/ao4mI/B3+d/YHjluyxYu2a+B6DyjlZN6VPXqDAjnEDFpgOYOt8FzDj1wK5
-bhVXQKwA2QiQD2H385Q57pFMEoBsp4agWnx5rX3n3R3CzcQaXGBYDQ7VuZI9DemiCMnOSY49FBu0
-3rGOps1pdWUl820R+t5U+WZQfna5gd7oVgLmFqeVsg2rdUll6XcAMmZ6zN0pTJ0oEkUK2V5aG2/K
-7bmdBvaw5rjeuc19UOtJ2fgKdWlOVRMQV+p1Oz1geZinJ5D7QZlTLzea02JNvuSDyrHLm6U4HpRL
-/BEg9ykNtQYXIC4/YQLg+RBdgGM7gGGNG0Gw/VqE/5vhsEXcxFR/AAAgAElEQVRfFX4oXbergdJt
-udUNDQwDBL7FfLmjUypR0vl6RRE/LwwrQOrcngvl0fdTQWgDvqRdOy0twpfl08jAAsNkciCAqpfQ
-7NiBwqsmykc5+gSn8sEOghsglvMVCF/uuO+yBCfNxUfYcbYMt3Xs/mtrPjwbrgfHIG+WqLKQ2MRR
-ah32UWDy0a4oelTVhlqBNZ9tGKi0PXPXkkTcJA4Wllv32lpzi6ix70CxP4LykS/wywn4ftnJj0fa
-XIi/6kCqRRDq8K5BWyfyVHbCZPGoqFY8P5edDfT8dgvGZPppWYy1afHUtxI3623Y8qulm4PHK9EW
-GD5Zg9UivH7lTnE9r7j3iG30Q7j9IGc8j05Q7A7PI0Fx+AcTHLNKAj7rluCSR51v886y9IDvyHbd
-HsdAIKavLRMwPFwk6hkJvUu2Rt8swxJn82oVRrbFMr6B370MHsuM4Wxx175omSE+CKbJ6fvepFzd
-HwKuuP49BNG11TY7KMC13aF6qm2+L4YT1pT5XsdW1YTLdizTY1KXz1LeAa80nz7DAXoLvCPnhvy2
-W4KHFbjlM89bevkR7z7BVeYAhm3ELcD0f8Ek/AuEM3ymALfbsA8aN6GA6C5ggOHITzCMEsxuYYUl
-MA4wnJf/giIt3CMI+vh1OIJbdYNorhGOdJcoFwmkRfgEKQiIKST7j7eQCnoVEOxzV5apcrlgXBVC
-E8bxK0sw0heu/IH5ev3pGoGcDzgSEJqLtR3IspTmiZcU/DoPJWmnIpxuEC+Dj2ML+xock756S+C7
-zooec8xAswpTkDKuX53jbcQEsiZxDnBYHzoyA2VwdfJF/BGXPoWXZbuf4AcnfLsYlVrqFuHnui3a
-nx5Pn8Ik+AaG860Rn7hLsMt0jZhgmDo0gbi4UKTFes2WheU0j/QNTsBrDfSmBZhpdY3ge4S3GXsz
-v31YcmK91byEGn9LaCUIjjtZahV2W5bUtAjjAfzaSPcZB3ld2X7z+xrnFfg9geB+TJHuO9hdoHgR
-V7nVIJu83MNljVtRPNgTS+2uGjls6p9Bk4NEfKR7/LzE/qKAtdi3jFKncLy4nDLqsGDZlYPMbQKr
-06qWvc63qQ302TlmWqK3Y208cUdQ7tSZypHYQ4bd+st9UcDXj+DXJt0dV+CVlV+fT646o6ztb5fg
-HlA3ip8Ov0CY4T/ce0Btn1fHWTCd1oEEFDeWa8SN5QN0pfKL42XxkNhiclqHnX5xCGALAr6mMxCu
-dPi6FoDkJ5jLIrzcKtb9ueowx7B+ssFeTqhnVMEiLcIsc3NTMl/amqD4vssirBZgTadbRI7fS2Gq
-Uo32y6oToCGFCxIUTvCbAE7+CuSWOeigrOe8D8k7LnHW4RSMqAsulouFr5VYZxMs69Pa9SBdTkQK
-1xqdSaMC2BIMzw6P+Ivwqsg2V59M3qFMqjc/0N7Vy/36shitwlJ3WojLcgwCKK2tgV0caLEHrfb0
-KhLrlmAYua68TT5Bb8tjRrg9EPx6+gpP+rAOpzW4H9njbQ5l9s75c0az40tWfi2O5N2v3Lj3VVrd
-LT6oUVbgdJVA2RLURaKD3+4jr/yv6z8eN4Tug84nZIgJii3BgfahPRuhNBcLcDSpNgvu/dQdY56f
-wC/TFhXvtLkoSni3Gc/5g6vf1LGfWJJpTHaLn2SUyDHjmu5C6lkezRxvefZUbOYn6WAlPsVs0pju
-42mWbtCHOM50eXTRwoKLsv6albFsWomP8dA9GbdpNT74C7MdK35UQPzrI/xfDt/ZjAkstlP2FaRi
-pO5LsRmR9B9zb4yhoNfzaYglCQ1L7sORD8slEEYHvdfVLcDNVxieD9ddUvam9RlIS7QDCcg5jrRi
-z3lIYVSPpm2TZ1mk5s3KwptuGI22frcDduMAfssVJGnoVmGuG8e/HhK07JsPPzJql9XXAg8uA8lx
-jOWfoMu3zAP9RPA9aVJ5u+NAuCXjtRh/9tU68PW2GLQkjhuTAxxvGCA7MoCBHg9z9DwJFRokEUX4
-LnQw8r6db9XT8vcHY9T1IOPNUoUSBJvrw05zITULMkpx8ELOYnOyTYuj5ifP02pt9gh+sf0shMUp
-73q2CL+Z+8d8u4Of187jBvZL9jbfGEEAfFX85i/miGAYWA/+TtcsziuDx5rUg/f1Crxcl8bXDW42
-f2BesKyv8IXsF2txiPy3wLf7CIfxZJSP5e7ANkWtVw9bmerzHE0ZBnSoh92R4nQIOdvO7uENf7Rp
-9gGGZxj7q+7GWE0mq/KH8R6CbQXsENsrsaeyNnNOpYF0bziBXl6kUYZzn6e8XJNBOcDJSwDKeJSc
-FuJ1fLAG529Yg1nWrEBulrMBgE3cI2pv/mT4BcLfDL5Feu5U/5B4lxlL+rRn4rDA5h2CNoFnAlOD
-XQvkwa1cIhw7EB5HXrXRwX25QRjuA1D+CslAsecXcN31pHqzLHgOZUN+eb4SFFvlpj1MFiyswMMN
-w4E73hpBJViAGB0YDxCcH9agQs1+eVmDU3YQZNYtZs+xGE8T5dDg2jF+JpwCR9tPUz0iTbdq1V+Y
-FoDOjwKCVYPbAL4DHFfjowxB8rzVeNRPD0rr0yDjnbX4Y6IHnYe9gk8Wx3I6Use0utUdQrylBXiq
-NdgbTZspmh9omWabgmjKAhxp9nkhZPBVjFSMHnl0IygwvLSSxvFlZ+D7NUCzzMlpusfUbWUmCGg+
-TV/jGB/SWG+KUGswH5ZD/tbdtt0Fi8q3+cwnhfOcsy3XfCorq+8lzvSCpOac8Sv6SACyWYHHEQLa
-TkCZvqEKJszYb5lbsURaK5fS7RH0GTxl4ikc9xdC3mi/HI/z9lBFz/CSKOnaQlmUrwyLopJnUkbv
-enWpO2JjvIdp2fnZJq1bf7kODy32sjZrAlSeymV3YGHniai1L/kRIiAwQdx5FvC7A2Ch+Q6K66ty
-4Q+c5SsdN5420Jtvj+A5/4GK+NvwC4S/Gz7UlRPjZdyEiUWoLQajUBYliXjgKKQkBbWBgDBen3MD
-X5cA4QFu19HLfcK7tfhybxZhPkymktZzDCv/8hqPPvRnMfizhUdVjA0A7BJHjpSbhKC4u0Mg3xW8
-+QSTDqAelJu0wkBuU+nN1Tus8yn+VOZVucycldYE9Ru2feoaaHKBtK4jWevPeLvY4bhfWI6nP/H+
-/koAG03y2MFpmflkT+23X1oowNXv7ehOemrnrUKedGEJk+Q851XeKnB4OO5Vu0+ZbEc1NgEutbFZ
-B8RWADldI+Lop3i+T/gCjA/TCfAN+qNrxBPI3YdyGOy93lPsstmvO5xnDbgvuK1PK7PPBL4wyisF
-wwWcfDTZjl5vQSEf8QG5xerdr77xOfr6c2nU4soLE0MAYqil14sWgLf5C0dbjK/z1u3uWybSsi/W
-gFkWsQJNmWd5hqR1X5WjT575xty/3XF13UZ9Dxwv8OUcKA2IPV+7hNZfnw+Std1orZ4TT9qINMls
-h7Jt7ftJtucIva/NYzm521ZzpGMeaaAB39lfw3Rl+A6NLhH1sFwB4O4a8fT55Uv7YWodfpIQ/174
-BcLfDS+10sdFHjVtYgns27YEVGd28/UaFFoWlpANq/JVbg+XW6a/GjhGfIkJYREGcAHWpDDSXOFC
-8zRThNgNmisN5fahtwFFB9fEvZqodE1QT1zvIFfpkp41btOvkqxrgDWCtLIBKVpHOQDxZg+ro4CM
-VHwCNJg+ayntnD/GecHwEr7LKd3q9QIce9UPWHMb6conFCHR2OlWZOuy5MFr/t6A3WPwLbKFE9DN
-M7zTSDKJuAKiAfTXkNdc8QKQ8QmCs12OUzpgg0YgRlpaeA7nVu1D6SkTyDTPPIv9nleOd+2gNSaH
-3w7L7zzWdtG9lHHH4MYx/o1mjb6xP10htH/ef7levvps0ql8iBeleOtXQImQd25Diz6Y8qnI4pUk
-QI51d02bbI9qY4nRei1a8wWOtlfcjxZi32hsXTnBz+OB8kPJM67ELg4t8/RZipIcIgOF2dgbhXUO
-77wYjCTdqZyNWXzD3AbLO6ipV0D/1LK+n2hX5kHK9TZttmYtu0Wez939g5WiovXsNdzXcs93VX5L
-nvgDTWQO5U4e4S1e+0doKPCb58ReyHk0BAax1PFqEc74ZfKsraXn1U+HXyD8N+Ek5b8T5FxCjaYw
-bYJgggue0zcFP8mptxrom3M3mufdTbcOjue7hk+S2du9O5cyJm+aQDygshTBxc1CQSEbXUGwzXcx
-KhLJuYm/HlBhWwMfUW9nei9RaZPZ7BpBOqjKTqxCKhjN1kOMdifYVdDbALD18adiTVAs6vr0poHB
-hIp5iqsepkWmp8a/avAEYtEWax2381MXTlDL+g1Ci5niLe6N2T/YTKfBPISsXsAi5ya5QRXvqPXR
-uBUWQAParfHFA3XhkHwhO7vhfram1voBcE3zZRxq4Vc+eKU7tjxZ1lzifOdWak3Q7cDvPn8mJe1A
-e+qDS2pbD8w1klJ3gOAGgIEdDHurIEGNpJ+ONsonYPU1SUsG93heAJnIcFdQqXVUuua9ZN5nQPd1
-3qQRQFk2uIu1pDWRZ1veYVVQrm0FktdfOV9kSOVKflW702STFs02mkUPeExwKy4nCc5Cxl6Sv9KV
-X31HCzUX+546p+0hb8yv5vmL9lWJyTatjac7jKFrvlzKEwiWPWTN0tvzi77Wt8oKAM71QJUzGyAY
-glmsQPL5EYN/NfwC4b8NXbLP5H9cpSoYAO2uG7eXfvLYUMB3MZQnAHbr4DjfGhHxtATDMw5gPZjX
-pLAn8AVQ1l9KpAvhn7eEpKcVObYJhYgcN6WUOzhG78RaC3kQqK3/ww0CaO4O55+4Q1i1VJ2wdjQg
-bg9DQK+NsiiLodm6jRuffk5gHIvTwDHWYm20jSkoZgZ3JMMYGkBtgypCuT8EJf3jiNKAQkbhRkP1
-ZUUD4pawgFrj4hEhDjeXtXYdOqkP3z8aXGekh9Nt19XNDqJcy8uwAJSF2Avce67PspxvRvDWKBup
-xnr7le+Dpg9JPoWpXLsgQbGt0I39Wbdz6oI3IVZZiPkiL9W/NDQ5N+MAU6qii6t6f+edluTYJysw
-Nz3H4qqs1bJVv+C8RgMgYHXKJPqUapz9smQoy7sDxXdFYz8s+0uAWPYD8hJKrnv1Re0O7rtI9hGf
-c7sDPIGrKoeFecQGULNhfSU5+S4ncN5GKxuw2/mvD1pBofJv9Xutv759QwHvs0W4eOAa+cd5GN3e
-gOxD2Vfn7WUPeX7Oe6q/FCH3B3Jf7Hnk0QCxBMEhi5oF+EDLvUGaoQNgK1oDufHTh+QSDIdV+Pc9
-wv9j4VNjVgsHgKKKYsZ5Cq3AFLYsQYZMK0Aw1W31JOYtjPYVmOcKl4jLxE3CLOPLiY6KpgQ4LcTU
-l77uSYX/8hKUnu4RNna0CpqSgFP49CeBazYWjiKQHe4RTbmOSZ6LZFXKheaZR43IDln2uT15H0ct
-Z9cNi6uRaRVWy3DmIySBaoSgL7ApHGHAtLxOmHFSgkr3AVIKFCd3FWA1TXM9eauXa1Tgr5DAZ64R
-XibS7O1RBH53j43xd+zoW762c957Eg+CgmHYmg/PsR+xZ++DdMq1gy0ut/xP+a+CdoBjOnZK42pb
-rJ1l+dZaFr1wAsV1LFXJu1vRAygI5oqXTCvgrzZGT4Dum1WYt3eXwpY5yi1DaxXqTQrox52mgJfC
-gq4RwdMxoQS7S2SUWwSEliMVRqh6azuK95lYeledtTL7p+j9Ia5j2uO1Pknb4t1FQs/zHJBSk6NL
-ZuaZ5WK1tVeLvuo6bELTiAhtGlMU8Ja7wzOtQDJFbtH6eB/mBz1s82yVel3WHvO2/KVg0R7NfCkK
-CFTnL+oe4JfHDSTjGQQzn301hDtE8LjcAB0gWI8WX2y3tBT/dPgFwhmOKvgh/KVmhu5jf6APBd72
-pSXD6nkF1Ah8fQPABL1lDbb0B3YLMByNukpZBb6Gco/Y3CZCV11WZUNwTUvPLlzxdvqblSUtZQoO
-VrqpcN9FwCYOonGXecxdGzSCXsYByyftc+JzDa51ZRyAeAHgC+ouoWCYIJiAOkX3JhHtLQhOhmkA
-S0oK+EkrsFiAQQuwlZW4p2WqQYtu1HF0jWDVwbfDilwde9oVn+zKpxLFG7PEtnvnfprZjhyr51Pn
-eLAII+NtiId+Md6svO/iDvg+guf++4GotBlvVtc7kJmC4wmKSy1z9PWcuXbsNCfx18ir3caY++sm
-CL5H//rPoMo7fmOqbPyKtta3gZ2cG7pDOOpiPkCwqSHD5PnPAgfcNzaBY5TlZUV+Dh5y8y3KnUEy
-V0TjlnXI1P9VfLPMZac5dzETWcwbvfLIKTN4zoFeGKSeIM8nU5SeqwEW4JruDgbb3CKulg+kWwSq
-XB/BSV+JRp56bJ53TM9ZOpW1kebfodSAnn7xKwtx7Jmo6xUI5ppyj0zazOc+t9APPHLedxAM8Kvs
-CwT/Piz3PxFKgAO5SZ/1kpzDMIFLL5dkYqFRvAsn77fKAiutV/J0twgCYAW99Av+QqVxSZPT9ACk
-hG6gN10kVp882qOjWorBKVEkKaJTpItYH82SsvayWoaHO8SQFU/Lk9e5TSINjRB95pP2CxT2NMsa
-LcKX4bov3AGI1wdQBAzzX1qKo402GVa07dbDicZzBk2Fpeia0PsrTayV6VXYm0WYVt9o49B8+QAz
-s/ozDdtrDI4d3PfxKHDahtoGKBmPt2lUieupbzbwqGIDwzGWhfHXeAoGeU6FZQVVmWsfPozbU39l
-jc95Md/T3yP3l7pBLEi1LoLV6rs2dq1urGbqVqsxyV7KsqbnxV/r85YfCUmLuB9dI7q/MISRhQ7u
-pJqaTIuo6Yqd8H7JHvaH5wBiHQ4Bl9Ip67NaNxsA0elh1rlbXd0ewa4/A99pz9ewga4parCLvlN8
-yQFNN+cH6LCFO1oHQppw4ZPY1IMXoU2TV2EL4V2Sclp/8UjbLMJC66Mdc/WGfizXZ+ixLPyh/Zdh
-arpX+6T/Fj9TUj2AYNmDxnJ5FD9hoCzBxgsQ5PEJ/C7gG26bSXs76H88/AJhhvcct0KTMK+U+OHE
-D4varNZExzXZWZ2+4OIfXCDYbQDi+NE/OC3C1IOAuEbYAxgmzasLPP8OP2T2+0J8EASHOT5MepJK
-1XpKVwVZKyf1IK3EbeK8xeavt1t94WuYco7H/Z0Ep9zhYdVd5AV4YRZvjqj09sOkoegazc7ynBMv
-veBFKc5pzIsWuilwrvPNHDjMueUr5tpnS9PV4TClhzdE5PtEp/Lb+MG3WLMcPQ32Ff3T7Yrs7oo6
-YGXuKzAMpFuIwSQ++y2PxHo10B4M+iSeh9of29H7ujxOh57GQfJdjArBeEvpvqMH6z0HGfe7niuA
-tbWiHGBjeXdFQbD4oqfvNesyDBA8rcLY0tw2u1tEhyUmP0i5DeDo2if49RxhAUOx9AI59+II0QT5
-FfNzC12eOV4yPeO5Gm/jWaeOAU3E1UxYL9PKaV47V/blwXpnY2NPlakgOOdLeRAA/aWz7eD5lClC
-L2vvOloeXz8U92QlbsMd82ODuOft58xymRa5vs/i4Zxg8aMye1Rsz8GiMss4ziBYwG+Pjz0Gr7nO
-uKexR98isfkLXwWG+bCcarSp3U7z9Z+EXyD83bDhjTMw6cW+w53YV9z7JmtVSkZ/WG7Ji2YZlrdC
-NDAs3aTFON0egJSyy+pr4gYR9TWrsC9QjZR1cquD0uPRppVhzy8FtLYjQbC3MpkHtPhTG30puTtr
-TmnpDUmJHcDqebQIX8sS/MJXOH8IoTzVsmmavwKQNUeS9yKILhFLb0wpAN7eV99dvv0D9H8lg3BO
-QynZALq0MNK3uKaz8hRYg0/aH8cwtff3xGGW+MYW1LJyMyJ91Ot2t8f2j95bAVsFe63KdrWgcuMc
-t0nPwysHicN47IMjHLi4mWkJLgswjC/6qpGZY7lLkeqXuOAkbKi3bQBrT7BZ4yWCxfYOxxIrwOTu
-62L6ybrVAG+8YWJMs+4S2WXZP5VQZSkusEv+pnLX2jpbdmCcf1Neb4I779SpG8QJ6ALDRcKfylm+
-rWf0Qpel98JG3hCD/QTDlpWgf2+gUr4ThfeWWLIHOrABYO4/qBxdx2vQTg/FnWh5X06aepyrp+k5
-xEmZ8ugkwSbtuU6d80Fuvxf7hvMIPIPgLDPKQ8pYja+D37NVWA12RqvwBcz3CL+ez38u/ALhfySU
-0vuOvn27sFrh1KibICnmIgDWtJvla9MSwCLcIYSeoBgoAAzA7wDI8GYlpoXZIfEAxcsybENI7kNj
-8BbrmtolTRCcpQmOucePdQ/3iZeTvyavADBq9w6L7gTJV/oDd7/g00Nz0xpcwkNV82nS9Dr8E44r
-n2pacAoEB9ho/JSZ69wArgWUo49h2dVb4enGIhZjWp0JSPp4uL4QBaOldKH7Y5RHxar5iYdezZGo
-7odiCoYJgOudskxre2u87fqYdXTG7f3wPe4VAVq8FNK78KlkMt53J6S6LgHDDlw3/F4b3+LK2Hnn
-CFiWXb2t2RCFJdGMj2XSx9ZqDikv5C0M9A329elIsRDH6BzIdwxL0+al3Mk3ahXWXdZ33LL2W2er
-4JHia0Nd7ClKmmCnM77U2GT5sBDD+/uEo+mT5ffJKqxtTWnysDSSrsFr2dWzuZGrwG4glovmeVLu
-q7Xeyqb1RhmhG9JyWbKD4ndaf9dKdCvxuVyzEvehb3NX49slVZvTh/Nz3JN2KPdKJqmi2yzFGxKu
-U1RRLp6nXJYH4j61BIc868D4ZBUWAKzxCwKIw1J8/bpG/JfDiTVPwfuGxTOvZvlP6QJ04h5iba4m
-NFGSXeKKq9QNYv28XCDip6D4awpoukkkAEb5/IpPMF0q6mG6ii8gHGBJVNBpRjw2o3rTsVyCGd5C
-JUCDPhA3LGQ+gO9hxjk3J+Nr0frEnkCstbiC4PmQ3AXQbUJAb31cA9mBNX7RTBadhc2RjkGdk41P
-HWhgV3xGCygvLeWc6wTDAK29YtcDUV/zBaayEotxvlkhwTKEj/VxMwbVLHMTyIBEKfrMG8EO2f6U
-z2IuSwR0a3fOE891+SvDm74jB1Cc57fFQk8flv+VDMqpk+Xykc4HkxRxRbo+phH8S29Wm6DYciz6
-OsHVBK28cgTL8YG5AMnz4cqjS0Qo57QAh0J3XoDUfFof6vHHWUwwlJM6rcBB0zsgGDzDuUzi4KiN
-jWUvYn8wziHguKVfuUbst9ttJHq6W0JV9MwKtpq57Do4GftsN4vl7RPLC8lkVgHGq1/B92Zt/sxr
-zV5ZhD+mWe9vi9sDXSJP52baD7RZzvcS61wvMHuou/Qktp8nYK2Tm2tEnLc9EDfLoMo0P2GISoQj
-3SCSXq9RW/ik3mpF1Vi006z8u+EXCH87yO7cGPYzgJIP0Aw6axGJgCFvNau16sAAvvG7iv3pEqF+
-wsXFXnHxCXag3s5O0DzSfh8AscVDefEUH8er+ld1POVbWdGkJG89o17aVLrPpa7Dg3MopQhJizjI
-rdzdE+S3gd2Kb3mXrXch6i8A8P6+4FLDBq0T/TgB1JtwKq5uEH6gJQgeYBeG5irBi5GICpBSTeZ1
-fo6wspBDoluFZkbfUiGVspTRRH2dtqt+XeXT/PiW1886n2eSwbnT7TnPnctBpdYbVRjhchIVVE/P
-vm6qQ+e5vTcNJU+4tXLZbiAsvu0F4najXZQh1juswe4eflfBTBf3Dc7HqGfxkB69H8mTzQocvxtB
-AxIE576OW7je58cknmvhstWUDgHFctVgjS/pNnFQBZMbG2P0tSfMdnRrcKXLDYK0t2nWPQdtOk4b
-6TE/1st4o++Ky/rZOCW9lUVXCFGvCQheR8+TrNGQfNStvwVqy/pbVt/HcrJmnReKoHO3lXkTf0UD
-NrY4lt1kiig6cx9iJbRdXBw+uhSlnH7tH0y3CKZbXh7VIkyV1n2Dyz3C6qE5Q75H+Nci/D8SaM0Y
-xF3jfZ7dyxSW6GBRCzXhEeW52eN3hTWnuSwAycIKik6hWYOZNmwAWC3BM32FcGObOaSwAjgHxzas
-KzAOrm7AqG8wJ2e+NSKhXps/R8dTpKlwOVqHM1KgtwPjev/Lbg3eQfCTm8Quhk9icKrsom8yUAeZ
-9MVYBEh8ENFDuRwtwvkgXD0MVgDdwNXoiqoIdI3YwTI6INYxe90+z4GoRuJYNL29KLdOm3yuM+gH
-WuX5RifPHuuQ5hv20bIb01faqkA7wbWwjMnG6LYpAXJT+Zwe7jWtIr/coKA4Nr0A5HxIig8fZDm2
-R+RQRwMa8OW6+zwGCKaAePq0sqGswWlcSIvwVNp9V7Vd5ON27pikRi8/IJlEtPrbWrDtlmmtBq3y
-inFMi6/jyUoM8NPKpDF/b7FSbSuNpdLOKpCedbSteG5oo2U0H+bktuV+NZla8Rt2PW/R8g0euUaW
-8ffvEsZOs8pr/T2s76nMU/7TtDS6H2iDnmlHuUPMgmSouDj0lFOhPSW/7w1P2bNZheEy1zwn4uLy
-1n4GXBaW4ZzbVHX9C7ilPn8twv/18Onctx3/AkZSyTxXsDVf8rBY1yRT33y0gLI15XaFslO/Xb7K
-ZFqKmwXYjHfdy5TA/og7BF+zloDYAL980cUSfG3uEWtMickOCrnABS2Ti9BsbQ4BxNzz/QE57bv+
-dO47bcyzbucEqpC4HtcOnhZhu579hI9WZFqZDKjX453U9oRWRfeNthajqW2d61yLmu/+Jgmg3u5g
-UsGaC75Sbb5L2HM2y282+TfOPfsRk++lx4aeFmDRECfnSvN13CONMWNIWi+juuUkm7fyMkUsYaOs
-9tNGuqJVOiXBqCBdNbKP1uucbaKm08fUtYuXO94SoWD4Cnq6RhjcDuCXm10twq+Ary0erU8V13G1
-EYpWAHC5QdSvfCTFH5gXUl5D6zuq3//RAmMXjRm1KCO+zfsyDnnyEE4MF7QGdqPipI0yLmWfXp8G
-lF6ZXVrd7Hmmg1fmPw2HW/URzR1obn3csb9LZjgmKOkZUS0AACAASURBVIbLGIi+vKy63doLSQNn
-669Yk5Mm3T0Mu+VLwRN9m4J5h0yH/wl9iIEUyQF6j4veQLFqz6qw+QdjB8Elrw4AGQiLb6QT/MoF
-iiEs808/G3dSD+P4l8MvEP5PwtSyW5iZfiQ39Z8AxPobkFIgWJfqWgeZyksneWiCAsGUDEgLcZPI
-dHuIfOZ+3d3HmOde8UlltQRfAYL5YY+2/Xz1kft3Yp/ttn4CXQLp+Bsbu/VfaAWM66/+lBb6uCsK
-KrEEqyvdwPDVAe4CwAF+JY8f1YC8R7gBYLZDsRxrZHqB8qydz/LvIU2g1cBvswQDZT1kmRCkTFMg
-cr1SITkZGA0sK+LKNgUn+Iq0B6ZCAKd9Tqy9BDqltXe3CFGXjaY8MbdvvfGh15HzcqCTI895M7fa
-aDmCoGyk869r3aURj33Vtkyqq+kSMEz0gdr7afkF3AiASTcA8UrA5FvEelvco7+Kf2FbnOcRFCP2
-nltdWC25V/y03hpxb1+W4y3eBMHuBYJlKre1Pv1yjlXAegKdUv6VZ96hRV4XHNak1rbktfaHQ7rl
-vOnyAJytxE/0asJahzI60F5uP5WFvaJzetb1VG7qLbH85yRwcow87NhBsdw/C/4J6YngUEkfaBMY
-W+XpVGl3T/Gna4TzsPf5UXkAiW4llW+YPvC1guJ6DSH3Bc/zBMXkaQBoQNfR8p9AcKW5DhK37gpx
-feP30+EXCGf4cPabmXdjwwPlVVDtVFXbyC0wbE1vFb6g9SSswrbOubDASxprLEBN+v4q5wL5urSo
-mr7Ablh+viOvALAvAAzU2yJs3eJYlml1XbCumKms+27Gth4s4CauEVV60lrew8zvK4cCxNzVOFmC
-CWxJuwr4XpVv1z1ooy70uIlUJXx8UNmHEfWxzbSCH8fpSObrSin9glsa4/a159wRCxPcvnKNqE8S
-k8GBfFAKdX6rHNg3QKKd53mps3kL8AVYeUgrnX+7X9/5McYcj4qO7Xyp1aWfmT0At4dlda8xaTKL
-Lc12p2W4vyd8vSHCaA0OALwqo2vE4n1uevJxXryNfcQ49xf9gWEd+LZ4jP35t/L50JzKUBdlrvPw
-avfUDhOLlj/NZPHnnEp2e2vLZqSs2KTtgNYf6EpbF5JK10bP/VjUtm2mZXiUfUB4r2lPZQ4g2Ea6
-FCDze5r8ZMAOaqeVGHVUC7ACZVh110Z3G81e5evfXSKc+S/OKQFQwWu4cIw7zV5M0ZRo7Y2ir4Lu
-SB/gV5bgl1bil2n52Td+V/kJ/3T4BcLfDqKQN8qeV2GeQ6sFFZrsf8pXrW5agglaJDQAmvopwEvQ
-AMjDb+MGsRp/Zs/1IbgAvvlFOljzDyY45nuK/UZuq3tWbCUPOyjuykfBbgfWLnShZflqav6AAgNN
-4KdUs9zNHQRbWYOvAsRpEb7KJQJB2x6W0zjQwXCuqz1zVJSbeU/pPD4oGbrU7OAXYhFm2hKIcdH0
-XcJ5m74xtbDs4O3k9yCWdX7VyX1S5jbZCMqsXqQG+jLtLW/GWcWs0jQxZrjB0wQ+hzJC2oGx7EJv
-Zy36Q7tPFuz0OGEbnCqZb6ZdB3fH5udzcmr1Nb4pYm1yC2HjvOepV9v5BNqCGJQ/vBME4X+my2Ls
-uQeWXIhJusUafCvwnQ8eSx6VPvo6bsoaVPYnyXdgsMEt86G8PMv2GmZd47Kvy0hHfjWOv6NbhNd5
-u2tEyTPrf3rHKIeUbtYnDYf4kWZ7GXuZxATFZ0uxtf0h99OaRXexcHd9MGB8OMPSkjzdKdoUHPr8
-zgrcaafJqo1rBzKpc99vssurDpkWUXwhXTzm0zHcI8iDO+h9DZDnUd0gvP+YH2KkWX6v8TNLn+Gf
-Dr9A+G9CyPoU6uk7iQfUMsNHhQqnKIjg6VabxUN6kdHcwp8sFNCy6Pp6g4MtIFPKLpTNFVu2dF19
-ihk8lptCtwgHOLYdjNNNoglqX8ZoAA0EnwAKoE/CijU5lN3ROjzmudOQ0qdAgJXSEmlnEIKt+VUl
-vrtFCF3dJE4AmAADpFe/Cnywsy2zB6u8yVWPafKMA/3ht52e6dbG4HcDtlde2dk1YrMOR/m0bpLp
-5ZxsbCI8VFs1wj5PqyaX+Nb17cwj/BHFknnTAtNIkjd8A5NfHfus5tQOLpbbnBjnWBtlHweAAXiZ
-FhCXkxG3c+5ryR2REX7dML6cPEHr+qocLkt/3/bGfOXjBoy9pREyy1jHSK8Life/fGAOgp1yjmT8
-3/rpw1hCc5l3ioji9BcAWPjQBy1OoIxUCy8X9OQXzKAPzT1rGOsxE9qQh5mynTaq+iA92pjAF6hJ
-O4FgEFKKjIg8Bbzv3ghBePxoEZYuTp6ZU/BY7jAVPX+TLtv5HRSXcix/+MFHA/x2v2HKJrpHvHaN
-yLS9c4cYAFrujsiWB+GGioYOjC1fncbXqP10+AXCfxVUpX7zrONpqs5q05XRzkpQAKX4pXC6UQVj
-OfjMCgW13LLkk+EJklGfS77EChlfikPop/ZRDpvgd13JLdDsm5uEfINjDWEIwASh/jyztKz1B+MK
-EKgKKF/hoqsCaRCGMlpAbx37zt3BroXVN9IHALyAcp1PcNBew0ZxzLUGZIIqPeGQ5vW5qkjeoiV2
-1DIJhLrbQwfBQHvrQJbRexlV3kw6QD5uWybAjVn5+g4XiLxt3syZ2XgbctWxz8g7EHyiYcQnQ/Y7
-FkkoRf2QrzzbrblyftK1zlWH9pVlTv1l82pAm2BYTzA9mQ/JwfKrPG5XySRuCQCwOwGwCRhevu26
-Z4ACz9wD9EtXGgSgW7UXQNfDGtwAMMQqPMABh9RkJxQAnXbNYX9trhF7efJa4y3pz8ky2LDerDYo
-E/g6nv2CYzZQAHq22vdNJjTN9WI5O5wzhzKBy5Y/oslMkImKQk65IHnsYwPI3ENlyRUpugPdKDM/
-p3wCyq3LMj3z+B1aDG6L5gr5npd1lGBuPLYpMqEVZ0ZCbpuoa0S59nRTBy/2AHkgDr1sB8GWtHp4
-rliIoHqCYL5WLd0iIv7T4RcIj3BSjqe8v6lvhdNmINhAv81mAmKAumWsBeR8+jjRIrzicashHnZb
-Fp84KXyC7bZ0KrPbl1KLDpL9840RcIkvIZLgN98UYWWZpjJbXcjmFXunsDmMPbevAAf2o7lDpHW4
-ztN532SGFa3rBasjN+kAwB3QdlDcfIK3B+Z4S84SdO8uEXKMjuZDc9p5HY/tY53xNo8mR80nWBJQ
-nM05FwXlw4mw7rnh7BpBBhbrcHPirGpXPPK1g3yDgIfgTURddbfBkpHGbG1D0eIPtD6Z3uM5mTWx
-3vIeysHbHl998lZMVzf77Z2mfTyNAyilmXQuEcd6mhSxAiP2Lj8X7sL3CmpP1uAGhoGdlgA55q7t
-KyRvpn/u6V3CIiD0wxr8CECfu2fZbe1HJW/9xTrHGhYPBrfv/DMYqa9by8x+qkzb3MginNwievn+
-ieVTc2aDuIFZzWPRQcPTWG0f3lPa1S9YhBIZlHKyuUbITJrelj+/N3ixtV78TNoA03NsEv8bWuXN
-XavR2rUtqJBX2eKjEJlhpCk3CX4zHeX0obgls5n27I+B89/LtnyICwTTCohr28sDdPVp5XygLj63
-/NPhFwhn2EXdFHt+pL4Oa99+co6qsF1xVzwARZzjck4yZ+AHG/GLgINM51Ug92IAjWX5XUyplt+v
-AGVKJ/j9yng/vyyu5eOmM6IAOXWxKCHKVRfaFD7ccBj0uV7pr/lySR4UhILXCXDf/AAF0RBQjcyv
-Juq86madSwv/rpjsyL8BJYcIk+OG/vKkAZTHw3KFisvlIcBBgl/rVt+Ta0R+pc7KpxNAWZgzXXWp
-RTn3B/t2GP8+K98I0wWigdzeUnv47aFcTbdnsZp+AUxa19anOLKgPvnWJkIkCC9MIHQVMED2X7lL
-XxzRaEEwuYhWjdfveLBrvUz+/IHOPL8xvy5nQXdahsE8iF9kbXYCyDaOx18BYjyW9fG3L49K5j30
-8rZR1EXi4efALWdNgLzhLjX9b2lutHM6L8Ta5pQ2Tvyo4unUnzlyufq2V+UkzjsHF+LCxXhEHSG/
-4EFLn0PLNPXIXIinFXwfBk+0febHYj4Lyn7khR5dhTIda+Ox8JnGTHuU82qDDylv2oHLzwkxlOWX
-Fx9xDDemBWy7EWgB3YiH2wPzl/WXbhEWINh+H5b7/yq8Bb9NcnRSJihKO+D1F3nz5nkKdBEMl2qD
-kFkKigEI2MUDIN7pLnR9aA7XEk63V5+atWMMXQExIF4baMaqDRATMD87EEygQi1D8HAOddsHBW5h
-Pf0dUKxAGgTGaECZA6IVrUAx0YcMeqy4zmceQ/EJZGv4KeGC0oJPmvWYvOO+geG8dafAxoM/Tee+
-MNp8KYQNkDvL5q4h7+Y8+Ul/fHh8t1c5GVK7C/jJiZmKrPI3oORPPPqmOz5kAKc5slNhwqqc1YIu
-2v6X58qb0xpY5LMDFhuR8M9gwD0u8gRxUkkWqLUzGOYCb7ToxH3Dboffdz04Fz/TdADf9WjfWLMc
-j1h9xzjbz1C3kEf5TfbUkA/U96TswKdqI4J+PIPvHOZeLl90mccEQLanhY9XX04dyopaf9OtLBmR
-m9PaKYyblj0t06DZUzlgAN4AZ2Fp5ieYiyGXnFIw927idV173NuZj7UMvd7uCLUxjbNVnsmdkQLA
-HxyhwLnaaHdTqS+bGinpUHNbd+w2+viaKgEt75QWGFbAey1gnOdcee5Ph18g/JfBT2z/Roh542yN
-nLYZCoFo3lSEQLOKae/aVfHT0ZBuFJCjPgj3OSBm+XpjxHqjRClaA3Cf1IX39oFl7Qi9G2lRUAkk
-nxWTzrUBDQS7xOzhzFan9VZaHxLQnq3ApwfllgW0yiU4lvgmnRIgBL3pHnWfOI+ns9yBd42cc2Bl
-YcWM8pY2xMKCaQlGaoitrJ9B7wnkZtmqro00y7a1/d7xHJo2kjYVeHse2hPZk98cT7MbS3LqSUia
-Q1bOQ/4R0NEWTEHv6mM+YOsucxftybIdj3eA2+uhzA3wi1IEYYt1Q+i49XTjexFMpMET/CboPViI
-ax1Wmg8GzTnjcT4IV3T5ka2zfCn/RXxxQZOr0DZq39JQNtn7yiHwZvWrH99+6a1OhN818AiIlTm9
-+GTjU17ggsvCORj1yanlMjUHO5oYPJf9/UDHUrYn+2CwUubT8usyFmnj1D9ptmbl2Td/zpy6NI3D
-WKRDgxkvvtYPymzAl7C20eO87Lmmo+7JwHNTp1wJWY3a3zm/A+zaAL7rgxl8lkZpCpoLPP90+AXC
-/0B4rUjPBY9AIyMme1LTtdVO6S6oH+LmZRF+cVxWXR8A9wR8pUw+LBd+xfJmCcPweRszQL/hTrbM
-O49LBN2RVmVVyaTOVPClGkXxhPYmFXoo7xcg+PjDfh5GGWh9sHBjocqO7plYhw9Xz/5wZLzRRU/l
-bHQMBdh+TlN4FspBwHCBZIh/38rjGykshlBNrvM9FCDzFU/X+QJ6ow7VY++OLdiLPFFkFoRXAJiK
-pqpW7df9RJPvXkmQiUUa6SAPvMsOmUmYyaXEoV3WpA/JFL3GT2DXvqlhSGDb3X4UEIs1mP5ZgmIs
-aa6Vrn7eLq9QGxbg409ePeYJEXLGrI2HaZWlUoZdOpxHYfEEiBahMXAPugRW7RPDzIJcWxVhc087
-e7ftY/KiCDj3uoDmRYvKAPZju7Cdmy7OPQHiMTE2xz3nYgosOda5IhjQ2EjiBXzTZQJdFjXL9WHG
-T0tGGaDDOtWgF+XbeHX/HfMO+Qlu7xeAGNXqo5UYue7KS60jyvjgvBL40hWi3gbRQLCCXcaFdilN
-4vr76fALhP/T8EKHrfxXO77K8CZN5p8e8XaJgwJzbtcSmIauV2gxuJO2hNs1mB4Wr+Gx8vG95MG3
-Do6tXrN2V1l9p/BZmuzE+Hjrpoj672AJfrtvZL7ltnWCFncUEtbZzRZhD+kOgscr1Ao5v6SlRQyS
-jyrj1B4mk6mDVivxGHHTKaoYT+UGm/mc16ZYwyrIgqZATJUNsH04IxSPAtpVDqlb+3nSR2HTqaBT
-nz3MwUm/7onDlj5ps2kdzmxvq3DCQIpJHgWIv8gT+mfqWwAQqo8cgUkZBSmTq14fXeoKOi+ADGUZ
-DAVa/pncDwQmg9cnEHZPMGwutPlLBNfBhPbZtvTZDQLoAAvj3JfhZHGT/kzG2NezaOtnrVwHMpru
-DTR2kw3kZsWL5jVt2biNizXZjQbwwuv8JGasNfmgdWR0WrrwXO4wO951wAaGR3+W/FC3iCxwiI+m
-cu1F3vQZge7/xy2umGDLG8QhwCewfeUasbiAvaqJTVqKirP8XNMl+xVUXWvN89lY4Ogasd6i9Ax8
-N2AsVuGfDr9A+G/Ck35qCvF753dbEQv1tB22oEGYnEIhz+6gkiCCTMwmbojfsCCNejVafxguj/FB
-jv1jGtbKeTiy2T0V0GmidiVO2a3npv6M8nb47U2IlPWZdzyhBKlVmz1+tvx2X2KrcpKn1t8ECKj6
-az0Qa2/SIYhlWHqfA695fJK10xdY40P+Fq2V9RiDKsG4cege47GsoCzBhgLEFMAEtHSXUH29mJW+
-x9m3Vs1KNP87VN5Jr2r8ectm7aG/qiSfxtZ0lnAFxIuLpzfK3PWKUV4KkY2naw+oMmbPX9NTu69D
-7Iu07g+eb7yZcfoBz7SlZS75mzxD8JtgGFXhBMZwsQATADtcXCNMgAFi/juq03Wrox3SK+6HvNqj
-CbK4htwTco7nLI8wRTv7OYqu6ZpwrXw7uZb1jH+JtRqt114XS3AZRD0rIhDKV9dtyqw2nOkoTYQD
-gXOfhKzGlH46VsPnLTDK5/o4ZG1c1kofhONDuuxrjHXMsDbQ9+SizKWqs72lt316FMSa9pf5r8Dv
-Oa7nYKfZrgbJA03VGBL85haFbNXIv9QfOB+Ws+b/uwHjBMNXA8s/HX6B8L8Y/EVqBt2CtcnFGykE
-TbMc86oblDeeRxrkyLCXO18dXG9lsHJHUOsfdY9aftU9Io//j71vXXdjB5WEzrz/I5v5IQFVgLrb
-SXbOnPmWEi9LCN0lKGNaFj3yxK/LXZK/nnFtYfDxIdUNz8LHlY//OMhkLfYDi1ZinNNxCXCiqyQ4
-lHeoEf8C+EoHwocXgWFh62/N97ZM832N1X2BlXt5tArzLLQpGPKQgHojPiCZpLCy7WVNX7vuXeh7
-NizHqz/xIx6+mzHbh1J8glE/irfXyuSeYuFOsOA83tsA3FafEGA/3vZxFSzWBJ6OHTn0DIAJq+/6
-MO2pLoVkbUNjHP5gHObkuPSB5q4QIuwTbLG/CQw7kNrxtBDvGhEIm60H5vbXw/4Lc/4AUQfD+w+s
-l/cWT0uNn/Ny7r2L5ycMvLGy9iOK2mfbcsfnOk7rrTSWtPGt/sSwoR3akZ5nayDoJSAi+YFVK6/z
-7MguHx/6YG1DX3k8XKbK+KFbLgpOeS2UWybisxOmZe1D35O+x1U5HX2vjVvtMp9tiTxrxek73qHq
-2s4531n2vh+BsMxAOSoz6rmVJtq3fz4wP4KSxzT1Xp6D5hoBgBiBb9IY+KKv8I9F+H9zeNKqowIq
-0mqQEgSGN4/G0cu/LhxTZiUkCJ1iefOC77V6t6/awq2m299XJ4C7f3WuWYLzXa7d8kdCIjnEi0ed
-P3XSdo8HBbIAu4bsTbjICkta/CRhXPhYtNktdhLg0/+mYKiA2F9XebElOK1hxTrsI2o8Er62IeXb
-e9tVFCddglsO4t0qDN9u6FTWNeXKsLA2WVpZdh/DLw0AbKjwNcBcJ9BHuv8mcM5hG9SBIAHDaU6e
-8s6hgJLitsEWYU6HspTDPjv1BEjkc4j9cMaYk57vgIn733mkxO/P1Trs1Qq8trGJA2CtYNiFUZiY
-/cOSpHbFeHV7+CTotY+JAjhgMGzjGPUQf8q/Kyd7frsKV2TgxUdx74ItEll6jUQhDnveEhR7H7B0
-nGs/u75sQbdGr73Q+LuZyCrsNAmZQD9wA8BYsRoeZqdFHhSYyklZl5ATuafQ5x0/wM8nBftEpyvq
-ZR4RK7QqB3qVZQ+U+pi/8iL4nX2FN9fu3PzwHHDkrp2Uqa++H1V4Dz9hle4aUazDCIpr/BpulvjX
-4QcIfx2qOppCObgyHTM81SgsS1pkWwpY5LogwqPqyi7EE+IRT3vtlrdFRElT+aj/PPOqe/ILvgXB
-/sDcp7hGfLKfObAypj18tzar7Ade9hj802fE3QpczvGKszKJhzhcEHnSOm+8a5k7tNYSmHWhUCy7
-D5bhBA04ngTEYQkWb3uvVZMTXS3Tfjvsw65XwLIE23ByjbCdodEf17QST6nT147qe0z2/PkC+K4V
-VqJiBNgVaaHsvArX7LNKm8bszDNv35fThGZb7JKxZ4FxjnWQBBiFFeyDeKExWqXxBwPHIsmjrY70
-AUUOHAvEHeOA0mxW4bD47j2hQsB4rbNKAOAQVot2AsL+oxnx4xkfpiX4hReqe+OH5aS8n+J3+Sd5
-ljNihaC8OC6Lyr6uq5Hf9iXN4UwWZRcJPM++LKKxPHFkdc9/YFZfYz/SljXmnABD+PrDQGO9aTJo
-2HX62pSOPN5xzAMjBbWRt4KkmKoAWMRdqno/Jq3N8mAytJTpyo7WI34nqEJWlv7Y/qjTLML+INxE
-98qt9d9gMawukiqf82oJvpB2JZB1C3CxEhMALvTK+6/DDxD+w3Cvs4ZcPA/HwkVyiAvCrkpD/Jin
-BThNwFAXpclbQbZVWEJnyUeWFc4kgTC5RsgNCP50uojmzzrTYfMDuEdoXr+ly4Zo0MJdQpUFnr/r
-QMvJyQkHwcDpKXhbaBHW7IdbcyvgfaRtYZJShl8huVdaRdgyTPl7/iIbFWUG3HdsCfan64X0dC0D
-qA7aACVI4HbnhVUGtL8W39oACNX/N1W83dByb/OH1Pp1X9crJ4HLnH6mfIhW+ADDZA8MAU32t+MC
-6x170acT6Zl+yISJR6/UCtBRR2mlq0iC2/Qbjv2CwCn2PAAn38tHi7BAvAPjeJ/GVoCLd1cO729p
-eNZoTfssybyGug2mNS8OG+wZjf2E6awZJb/kOS/nGvPCY8HYnQ7HlOcMP9xk5WEZjg81We+YrtNR
-3+UbHs2/0Pc0juS5TVkuMUta+9TablKDSFYHZIV37POJFSTLWIYfhGuAVyAeAt3pEufHbcGrSW0y
-X2TPFKmmBMXL/1e2NXjHCezy/cAEhlXjIToCyw6IN6D+1+EHCP9JeKXA3jAOCCQOUW4Kxc1b+Mrn
-cSizqSr7kvmVDO8ESXAb6d2EGVh1FazCO3/9ypzJpTqD4H2bxLpHWKFjW2yDEhFJC6A/Ge0/FW2y
-AJDuMbiupHd4yfCOU+px+ppqELRR1oG3rnlWoD36AwO4JReJCpQl8xILa4BEtAxn57rAOOoS2GI5
-1AO48nXAeIF/cSMA5lZAvMHtzpJQ3aFZFy+BYteapChcATMNLTkG5DoZVIayePzvjnMCqTUPWRfo
-/U3ptKzivjUuYzJFsR9CfQG6eRz6bb1srbPF6QivhPoE+IcdQFdu6U0/4fQV9wfl/KGsVLoqDJQB
-PW0lnmB3f1PUXCIkwEDw13m6eb/LO/GYjz3Sml33fvp59TmDOY3zoPFYqFQLsK8hpnFV65luvvJ7
-aeJdZIGofUQRBIdnE9JivJmi22FwjZ/S2LGn9ykAD54T2rc20Hy2UIQ+Co7SEUKo9VwOeVZqqNU1
-hqEM1E83QpRvPgL8BtiVOA/pCCFhOd6nSKzs5ADA3jYo22UFltRTGxTjA3INAFc3iQC93VL884Ma
-/x+Eo36zGjnt9GkDMD0FoDJLEYlV6HtAa7CnDV4qun6Raeskk/WpDx+Uc99hB8G/4JaJyX0iPnZv
-F4kQ0mARNkvL77WF9SUaFp4rhLKmIIszq3lw4QCfv+6q68DSF2ebBSmA4BcvKWAXrb2ez5bgbEk2
-ODYaWA48rEF10II+g7z6LKZBccMWa3rI2EWBP4NBQVf6IsEUD8I5AEA4Fu4TXjj3kA+s+w07DMgi
-5Aiwq+VlZwV30q+d3hWjNj4j7gQuFkrWKM9K+vwe9dtUfwF2MObJIt15Up97f2lu6yz0Ze7KMtwh
-sutoPSQrMYBigXcNACz87vWLK/6VVnO3qa38TfoDc7BOuG8qePrd95yzvl+YMzcDAWUywSqVnyzA
-K9flZn7wZOkPXHBm0TXCv/2bQHBziVCR/BEcyeMLIJfuClelzzDJn+kxVOET73HYYU5hTXHblFmf
-aLj+Sbf2d1ToExBufE0IQVG7ZzN6KzTc2+wfLHJjLRbhPK9zWpQyMfztZ3nfrhGX+nsBtMXaO4Le
-ASTrlSeKZBZ37a+GHyD8bThp0t9jowLlOXmoxBqNL9UpB5jye08usQC502sJVw3rr/+S3C+V9RBc
-8Q9eND2AYNluEbuf7oshAhZhtgaHJdj2AROJr+6iylALSZP2bhRHQZifxNMH+bRoLghkz4sDYgK7
-AvckEvhdPT0C5lPekkLiwNm1uCqMTHHkPeCaMs1CQcYuaQoTdlQo8KzMYDKVCVA2C0WuA58q8HUr
-elA0McbydTf6BB+qjUqf/Pkrf74dNdkBlNpIn1p57kyv602Z1lTVppB2EBRxXCMEGjGQFaGrsfYm
-CquviAQQQp9gB8MiyedW5PqpVvJbEEKuvvlM0kdYZAPf3X+0CHtfLN3FcHTRBSu0L985MGDN+cAS
-xkDTreRw/jYF/nqdMyD2B+ac03YleLbx/Pq3feJy16cXjhXqAqxbC1WhhyLllojqI2w85/k+wZyy
-n2FJK70eV16bw4e7kTfLtHNFjZ957MhzqPKRVuSwmMRDcmKxxxMkS9CZx7N2WtYzQab+nmsuIrzR
-0SKsDILDTQIfkhvuBk5gfA0Py7HfMFqEn9fq74QfIPw3wqSpXuk+VpciQuJurrDQQznrwDtUc+if
-fxkXB0JlW2WFLL0maSF2v+AFem20BHs+OuWLamBkPAAAIABJREFUbqslACbPX4DMH9RL39UFiB20
-auhJjjMoXuNCgTpIm0kSYR0ANhO4IgiWB8vwU36CYUHwq27tgT6EctMEFNHZ5DmFZvlFBRmzkPOF
-2DbL4nxin8AyhnMa7QFQyhnd/QdBven5MB2WN8E3HL+RMk3QMc6DINtwbl6f3QqG6/sAjm069a1n
-NzwZyC3DePQOdDIuFK9jzfXAVRi6h1dR4WEzEXSDWPs3kY/6Pq5WYbIo7lbru8h2cchBxq0RsgYV
-WzOUPwwlPkhtyerbDpuR/s40O/B4BWu1k1724gCIk1WFFjCk8Y6rYCrHAaNSqDc+gGqZBi8HZ7tb
-h8s3CtC16P6Wx2hGpp9S9v7sShMce1nJ817lcJPLHJ7OxOLphadvB332xjACWjyfuF6Tjqkybai2
-0Sz/DqpqReD6NDFpQPhEk/zwSGPgA7yisckznT7CWzepgCsEu0aQdXcD39lKfLElGV7/OvwA4S/D
-Kx15KmMTtSAOcfkxqEI6IXWzsARZQms4rJICLo8IyClbYswc2DqvavgS/9pPraFfsKk2S7BdJr/i
-8mIT+TAA9oOWADzdIxwQ+y/ffUw2YATLsB/Iw4w0hdYkjIuCQWJJ6vnVBggJtAS/BLls+ZUQKGn1
-leQTtyYD0oi4loEhGumjR0UoMduYwumwqCqmSoHTct8o0CKVk7x7oKfNv78NSAWSliXZwNbrTRpW
-H7u8+EJgPYArSqiDPIUOWrx2lV7yzvVhCsXB6XVoD+y1BT0B4Il+Hj9Zf5vOhBHyU1cDwFUuQ+8C
-CteR0u4dAke08uI4fQ1D/E18+V5lRZMRwkDqmd/3mlI6c8s9HTFtBWHiHMbu1gBcbAV2q3lIL8EF
-il1oEg8949n16THN/WAi8RkmegsbLY8rjtPllu8BofUWYRBcH5gLcPrwPlmBQUhkMF6fKdzfzsK6
-gehWJGftFOYXudLUDgsx7ob1bhn+mUCvVLcI7OKmI5/T3CA1Sak4khoqJvTXJetsO4DdefGAHILh
-C/k4r7tEJDDOTtgQhw7+pfADhP8g3KnQzuTiatjpw0achWopY5m3YnD4QM9QvYoHK2WwQS88zhZg
-owflFkgGy/AWuPlwnZIvsVt9TSwUn5fJr2Zs93sB3rzGzQKEx68EDS8Z0oMUSoEhklrgKagkSJXE
-r7fuDeWVQLYDY/IhjsEALQY3jRqGVl6cbzdWYRDoWK1ZSbdmYS6FMqPO6D4UbNYJ59tgCjO3hnZ4
-gJu3+RhzF6h72Zm7Ba+DPQWLszSJa3yXiJ+/6zlN6VOgtpsy3pEKJIBGZUq9j217x3GwO66OsML6
-KymUzK3FqyV9AsS7zwmcGJDwEbcsWsHMMLaUGz1/4pvyc/14xVdq4GrzIbGXoxduSY/e+e5XyfXb
-dwnp1JM8DzhttifHIp06wZ/HcBnv/Wk+5ND3OH8q84eeuhe8hyB+BdLTvs9SPdPXXpnSGcZQz0ql
-ZafM5rxmGabzZMcmxp058ForeAOEPQ3xMoyoUwX7Cbt/VKZb66HOgx/O0EvY/YHyEAxfj37D/nBd
-htMp/LvhBwj/bpjO1nSgnjRaPQUupSqTFSAxNIrKGMWXSwqXRyIg/CR0k6A33RKG64C420NaiBO4
-xgN0+ym89Bu2tA67Sdef1LtE7GNRPwPitA6HX/AGxv7NK+JFeh3OyfzQ3AwAoNRMckFQrLe3AHi/
-0oKNwFi80gDZwb8bNJdA2KeStEiXjBzuMX2yBPtXqZmOAqGWo/vHlrLgGXBVFLD3YrsSArGRQj+o
-ssORu81s+a7rMddq3O7y27PYFCbaU+hQAOZsd4jOOMxPAF5Xgk2xZ6+qHyh1FrN8+QHw7gMrZCGM
-DYIPyYlUQNz8iClufenQzaaAEZq3g9U7ZCMM76R2OW6Uynqyr2QhNoH58drKbto8+LAj5iuVSyuw
-7nOAc4wji8/5CnI9p1/8w7HX7N/qFE8NIUt/tJBKJdbNRNBNQrPLdJnMNK8UALRhmt91XttRD3v0
-qAyA24jXaidQd5iXsFZ/c0WoXRnolNe2cgG88H6iH4GzbN9g2LZVloYlONKoxxzkCvkEs6tD/qBU
-c484XJ/GFuF/F36A8F8KJ/X6ezWdNgKepJlnCThj8FfkrivCxbutCiG7wG7lwlPd0pvAOB+a08A6
-bjWWSwEEy/oJ1Ap6VcU2WPY+BABW8BXeFskPguEAoMIvmV85dyY8hzi1LiSSrod/XnMHvddLWhEo
-8Ik7AXEOyOVCAOJiFTbto62jpvReXheJMXwojuBYokxDIvHGcEIgNZwMrbmhrYOiOhXlX5mbKp78
-AycIWXspUsYrUs7YGUzF0QKFMoHgu5P9bSDAK6iYvbE9j660DfgMy9vxg2LrrNPCurc3DdKsgF0H
-uJZx73R8sA8+GF0g07oJ+zycgM84rhjWtGPfgV8qQ+iuxE3KRvb5ETGyBgO/SM4f1Jk+8Hs+266C
-A7ytsdP5bjRJ2a1Ey3PI0+4f1nMfBQCOjaU0bHKdkKTXd8abe64O/OPSNqIVXjwjhXlwhYhZgI7x
-B0j4IH4AxMReu0p06OWNilp9i1WL+BEI3+W5vm0tXVI/reQ3l/jKbzPxZ5Prw3JxhVpxj6CH5eD2
-iB8f4f8vgk3yehbWRz4UqAe+yiMEYfM9wOOWRZYPjJhIe3DCAZJsfrQA/wJgbKLpErF9hUX94Tij
-2yTSYuwKVNhdorhOfHSDXtmHzNxPeAGdwILfBMOXC4MS33wNUgIgRSHAlt7TSxpNSl6tBy3CCH5V
-h61wmAcfaqaLb7AvvvQ6CVRFm6lUHPvMrZ5SsE8nRUSsCtYU1KgcjRhYjnutDIK5XOmHVa4+gugV
-rEVw924GxjPhWp/ib0K04+fb26evRGFS6IMeWP0OFtPHTqEAIaFjCY6ikQR+inPvgkmK1dNlkg7f
-g1nv0tTFM83G/J6e9s2pboMSA1AFa3B8WzCBYeelhoqV3kGxljbwwCqf+SX28Nu+1BchEqPvkqC5
-7I0EuAh2wdWjDMXvlsauEriG9wn0nk5g8q7K2g5mEVb2OOpoPhORwPQJ5IIOsVLOqFzpVhkbdqXx
-NrpJBcI20JhvmO/M2X8Z+G7lC/oo09Ul4qo/kDH8qAb9tDKCZi2gecf/dfgBwju8f2zlRmmISFW8
-d9zPiq+q2qlSPlWkj2BUo8wtBVKHLeHyURE1Xbc36KoD/XZN8+eQHdCy64RbjzVdIQIog4UYLcLR
-nm4rxop/zN0w1nVq+FJ6Gb1COIzTa03Qp/qAcoPi9anrCjIcHIDSObAGt25HesseFzwGo1PZFuCp
-8RgB51XhOgrbnTDlvTsCZhDM70XW3ZnQVJoEBADkFnr6mhsM2KDG3bNiFiSHhbauMwCO5ufhNL7W
-G+vg+BT3qt/ERaRZfF0p+4eNhb8spgl/aty/CYr6vGAVFndAucx7mYYm5LQSo7MKcaihtd2B8ddp
-vct/e93WIEAlgV+cmekhOHyAo2756F8BvVNnDIlwHnaFnqq++iLo+jDIPzx2d2sLo67zphDLNU/+
-+pCj4R8AbRXEMqBLXv+BldbnwdJL4/ZzAvlBwbyDVfgRDFObRfZiNwd6k9Pxx2QEw1ioVZgvxwVI
-JSXqYaerMUeqH7Dubz4R2KqSBbjxXyJ43egFbfy4RvyvCc/3k04B1TQ+zJDU20JvKoY0/dQACAwQ
-4VsAk6JFYLxla/rm6v6xi3RdiFseAuTytWpIE7hKbVmRV8VGdJNrW5GX68S+VxiA87UBuFtVL50A
-cY5XbQEAumzfPhG3z2eBy+tKGvJKSZswD0mfansdwiBsHAxG3NO+Tk1Q1dGCuhlBcN+zuENyFCrS
-OM/7vcOU3wlL4eetE0r0NSWFjuihfbKDEewJ6QDGU4wsWAbDnB6U1gSQx+M4rsm7+D3flOp/a8la
-boVvofiX8H7LjAA3yvmrRgDAOx9bTT7ufVnF+3yQee7+dV8f+3tjr9lNRCRuQxnAblpyc+u2Dxol
-CWSe1jHkmqSTUN24OPjDLos9zP2KvMD2S+4vYK9kqLb4QKUpwFwYx1eQ0A6KUOyrwHygMrMaxXHi
-7jYYFvNAC9SmlUbPZwrytuwiFzLvh85dD3qRGaYDr9OnI4Zj3a8jb2zVvVeqTtlGlvUG6+d8DdBe
-/EAcWIGnbz/pwfFN21af1cTvKZA/Dj9A+I9CBw2h9NCn6156ZdnCxso7hQcX4nhXfr2nBzXV4u6W
-sMAwuCXI3tjCADUsw5fJr/1QnP9ms4kUv2HpgHgLhl9bYOY1bv7jHrbBd36KvOAAjdbh8mlYANBa
-IPVTmgHwSvtCWc6o04yymGdYj4oaOwbTR2QZ+023ElbeNaf4Ke9t+tStyvterrnirC1sGBJKfNix
-26dnTVnd2V4H92qeJVkWvONoYL6D/8xdq3o4uo9xSiNgcqUOWpWV+SilhpxJCnwZdyCAa2lS1k24
-HOYHsJSsK8IoEYf0G3ALW43yjdM6lcc5U+iz8fAIGSYoOoKZI7hRxNBD6Cdy1gM52MlKPPJ6yoeo
-K7E+0GgcW2dS1W0F30Bn8zoYXuXQt7Y2iXu55vNhs4kue2ylgXoeaA1vzgnPYz0rsvRUWUP8QguG
-RLLDbmhIZxopF2qz7ymTfrn4rkeF7QoiCXadFwHx0ao7uAJONN8rgXmB5s0J/fnn4QcI/5Uwg10+
-UsnTOefA59vWJ+/nUsfkCQCLHwxJkVpfl/qFD/kAWwBVB8Dibg6SPmbFIiwiYf39VS3EYvvmCRO7
-lAGyg2tdYPzXrs6twftqw2Uplu0asRUHWoN1W4NtA16y9H5M7IJ0e0kpk2kCzS6sJmF+ClsI0I0U
-e47DRUIxDoszVObuFCg2Te6twtnVRWORm7RvQuV/3MGxQVFSQ9wgXniW/K+AyznugXH+HUaohWo+
-b73bCJywUNP3Na33fAe8IAGwEESUEkZad1DmrTcnoCupRU0C4AXogw8haUXt5d0/NerY45C939M3
-twLf+923eN6C5V7nlO8uBjCCvvJh1U2EQQ+bDmAELcFW8nIfaivnJTSI9UQZF6AODOm6UWt+fRjQ
-Qe6OxnV4WzaFRZSswZsGYLgjxKErhVC9HCJixAUxK0OD+T6BYsizoWPmc4I0zX5kWnoamp2svp1m
-ndaALmyacZ/NPK6fSYeETlF453gA4wvcIfClm34Aw/jQnUCeEDD+92D4Bwj/RsAN2RXqvbB+x3Nq
-F8spG0tqfmvCxtZJ8KccTxCpIpf5Xb5+pZkmwNT9i28OgMElQj67YrAIm5r8ujao+uj+cQ4Azyr9
-vuJiHQ5rsCZIR1yYLxNByOIgFYCvXAmKR7eIwTIsApZhco0Y5n5UNHuOff5RENHCaOiRsYKDwJj4
-k7bnXns371Rnpc2g4Dmc9l7mFiQ5WYBJw0BPFPKgBROmT8C4lqT88VihEoT8m61QATXFn7bQUH4C
-AGgVXrFB4VfF3lYT18EA6Ja8p7hJAqliIc6H4HrbFg9XGVRbXSSkpXEeTjzP6ZSTM4+1Hq/xlOYr
-MJEEy+3mkyKUa7HMy570sRvMca3y7mQ/pNFFYIOYXFeNNgMAgxtEtQZvpZGTZaXFwyEYRSi5NvTI
-dKLbzx/XElDHae7yLA5n7ASIs7sjsMUzGbS6HxQqaaB3S+8jGF48BnTcria5b0LeAgCOB+N8La8N
-XOF+4Le3JC2Y6+9d9f17CLzCDxD+OpyBLKra81F6rueh8p2un2Jnvg17Gi2LVYCw4wUMuwxzFwTb
-ysxd7y8Hq1tgith6SM6k/NqchBX48rSge0Rag00T/AbYVr81gsHw6pc/JLc6uPq8hYTth+dGS+8C
-w90NIsuuN0yDL7AJ1yfwHjwlXdYjPgUjOo7Jd0EiaQkupaeHPV2l1/3XVd+kNoEWuqvv2VrXe0F2
-2pVFxR8twJUXNEvUOPduUpIncNxSWtKkzJX5cRilyhM0acAa42N5y6E/fEX8JJHafIrIvC6HeLEO
-m8j2H53ra/kQ9/nMorlqWua1rnjdg3e7Budl5uHd0mWnZt9lC8iSJgBWj9CkBnCPWR8P93Eecbdj
-y5C+y+snVGTLQAK2Iu43oQXshuWXAPIgaw7dqGeM1qJ3bfXYOM2sd+ej8vd5avUAOLVKd04fyxEA
-G+VPbhVdEo2Df8gbRgDoc6UZBLvBSwDIHt0jriv8hf1hugC/B1BMAJneb4bwH4UfIPzb4Q2QdZ4v
-Qa98yX7LW2EOh2nPrR6r6L6vUs0i7a4RYQmWvJQ7jqH7AHuFV+ata9Zku0JI+gqrhLXYb5j45bdH
-bLeIsAiLhH+wg2H2D3Y1kGB4AsFmH5HPtX7+GZ/ss/uH5tDqloA55w68Jg7zjooRkK3mCNZ/LbuH
-89sLauZ3JRr19dyzkeZl7pRzDO029y4coAtpwDPAylIdIDClANfSP/ySPvgKgZLD7Rq9VlZCTd0O
-ZUhpUZ9A8eMerJ0kgEClpQeYa4W0TPHdsXADmEB00ezFQrwkyFSnJJiu6zisgcJemPYn19N9iJMH
-5zFHgeUqDcykCRTrQk5TI8L7aY+husDNs5C91UJjnlqmDO6BN/6GG0zZG+QqAWkAyM0a7C4Utb3D
-eRnp4zm0QqsCuHIN/Ic+HfMH0Et1V0CrkvpDJD4vRVHNeqz+peNkvEHpyFn2rR3FfCCY5MpeplQv
-vmYJij3dAPHJL/hKNwhXbS0N3cP3fx1+gPCXoSmovcPeqXiGpKiWZ6jyohMvGSehP3ZNJV0Ahc5C
-uEggCF7XqXk8LRF27fo2IA6LsIPg7SbBgFfEPttSjHmyQPCvsAgXa3DpZ4BhQhZ7UNWqW8Bu8/Ud
-LMPNoiwylPtyrbSPIxKnAo2kdRIicDdmX+EnGu73N2He6WOHJAEFMjykSZIXK+LYSAW2OSLkydyT
-ypRUcFibMUNJsuwYFfmOT+sWW2ro0wkE16+PKWZcuXfymIa4CQAim8vtPBONGwaObSCQRl4CyzAs
-6WsWVQgVpTDVE3uTwMcT+MW1cOSi0O/sV7oQeOsFoADruQ9Jn6UB5ES09qVrrndpkJ8Igj2t0q3E
-kQfrGTdLGG/wqatP9E2cznLGeqkRyDbOed5ObVF6kAlVTLkLidU8VBnodkF5Ze8U0FvduWd+mId9
-PklXkP5AEDxYcQ83RTzeob8bUIG6QG7/+Aj/bwhHHKBx4N+4RTTQUZQ61Ty2d9NA/7g99TYjAzsB
-TAe5mnFX9Ca2fgnmg6VBMG9AvI8rWYHRXSLplr9WF6A53SJ+IdbbYBitws0ybBIPy8WjY+6v4cj7
-c+2H5NIaPPkJs2UY5rmA4pyDO0kOq6CVpAEKVST97FBI0SpxM2kPr3TqVe3lI21KP4usLcKhgmMZ
-dIPwsprjmVuttC8fhBOo35KnnUSYc6OM09yYDNU2fjr2hZf4tK8zas9m/TKYh+JTUf2GOX6CnH2e
-78GxCLlIIBIooNdvBdE7sFwC99Q6zSCdf0ByCc3zVFdtufkP41ijkOaY6ppM4n0W+dFetwP3H+to
-/W8PVc/rbTd5KwkcrieObg8rHjdGhGU8hbUNrhGt1ZtzVboldfJOtU6xU6k39U28x/IIgEUC5OKc
-etzP8JhnWHe2ies+X5lmDQzT5zXUJaGOUteQu0K4QUhxiTgB4ivKVPeIAMreunrL/x4Ei/wA4f8w
-HBHz1+EkKEDXTpz3zZfuacljIOxb1Dauhfg+fabLVQGbxXeyCLsLhOeRdRjjbg2W/JGNfTiXVXoG
-v5cYpVPj2eG1ALAN6WYtFkgLWoGF24gJwPaHNWhpnPyMhx6S7fhBi6OnytpaYH63kLynTcpiFGGj
-gu9lurKqKPIAfvVQjsrWbmiJ9U42G7b1mqiUDrVYr8cm/oKXWEH5MErrhLOsVTb3stv8WxjdHGAt
-qgXYxLUY1DekT1Zf2kvuE9x56w7InvX5bTvFAPwCqCAQIZVW6HjEo7c+BQrzUhZzcpPwM3VQEbX/
-eozJyNnjMsRL+slVwtehWn1tW3mLRZiAMt4RDpbik3p6pENk5rX5PI6y4HCej72AvBcCz/cf3ixB
-/sDeTz/fDQAzXcS3VKe3Pgfo5b3WDPKhPlivEAiu1mBlwDu7R0iniQNsb88twtnu/0T4AcI7fPfL
-cm+svh53oJgS+O6ILYb3IBoV4dsRoAJoGXDGHfxOYNhVy+XXBUX5LSxN6OXRX07yH94YAHC4RsRD
-cxJuEXgrD/6wxukX5kRsuD7Ndj/hxoiPiVwmtt+blRfcItJVYqcj/wvFUtekOE35Z+OY/ebyUFax
-47aoj/ahyPHGiKnHJGNhWae9RvrfKmUOJwjSW5n43Pd14nPu+55mN8/guM0LVdktxFPp2s1ipGXc
-vyO5btIUO/oZnoGBcZ6VPIhjWw1ujeD4Jn2w+uqRN+P+rVOAKBj4G3eH0vNoK2hlrqb6pPHWGeJR
-S6AbQiqZFSXtVMFt/3s/TydwqumUtpJ90mKQLiDYyhqRj3CAYZXJUnySDPX0Nlrd8sxK+rDXPRD1
-mDP26DztNzW4Xtzl4xwOQNcKHfnhe4ZcB99TsfVYD+W2tL4FK/DcgJdNtRJg9fiwXL1KrfAxiE5d
-52B59RtcJvQ4yf9Z+AHCfxTeA9Ze5mXZk3waFGHIpZuWp8pO2y7Ar64DmNZgGQCxiH/EnbqAFuEa
-DzB8rcP+yyHsh4Hxr7AIOxBehyfuEJY827rnIudjC43BGny8MaKmo7xkeZEQcovM4ONxiV3x45yj
-74fiTgHJVcDwovUbIpzXZOrK2QLcFdJMG0MZ9ry/JmU7QZqswQZar6vu8nkNOkC2HjNJkFbpWP9U
-CynmTBC3nrdKgu30J+x5Xo46lMygQFsPoWEeOd7iO0Ey6Hh1ZbgFzLYx0AyAsVz/+h/HCnAd5vge
-DNstn0gB03agV/4YnnKO8bjP4Dfzp5luaZvyc814vabz5dGbvGF/RGwCwW7txXjw7DV2q3E8KDfJ
-nB7mb6L6GeMh9ZoseIc8LPcV/jrIvqEO9PgKK/A+px0AZ93eQqQCIFu2tfm11INrTF1SP27721Zn
-KC/y3wVwzC4SZ4A8ukKILLq4ldnxhc59/YfhBwj/jRDW3gS5byzGfxbmmiZh3884qWeW14BvAo9Z
-WoYvKrRYrwGcRK5/Dbbl4K+4QWLfQCGzldhBb43HTysDANbdBwfsZA2W2RocVmC1uDXizX3C/inc
-HIAQQHaRheAFFcu98PSvoIK4BYWhNZgKRCGoa1ppLJdCdlY+c5hVp7IKs85RydoZS5kBeMYc3oNf
-VB88C128Gq0LchZe6+q4FdMOmSbrqxG/SOsCgmeDMWnhs2luevoIkIF4gA2S4CRXOpL1GrsJAJsU
-oLvHY/7UOgiaoZx/gJmsQy5hsfgk97qHbR8d0e1AR/5p2yDwHVw+qvNmPqzEjQyzcQbHXymS8z7g
-PTKf8tqBBoLd+kv+w0aA2azkU7XTLrw5syKpVw79xXMzjntCXN1nhsMXdFpazW9jAvg24Fp4VaAM
-73UcS7ZjTXd39wmLvFHm1AE4WI0XAFwEw/U+4dEqLBR3QJyqDKzDh2n+L8MPEP6NYC/iKyQwnnIr
-5fUGaN+r3scrtKjtTOm4BlN8325L8FYWCUAPPffyux8BgAPs5nv8aIa6P7CMANhdKPxw4h3C3s9L
-0ZVDQgDE9Wn73TYwvn0obkwL1COhsCcXinG9TnOPQCIswQoCW3NdCHT0NCuaVO0GL863Ql+Lx/wP
-e9jGXTDwlz3YdG2FA1NrFU5jJRPUTkXyCiAfl0ubAjX+M7TtCg7Wpx5f7fR4spy7nyB67sTYn6lX
-HXzcwi5aF57hUsakAGDnSX7zv+VHNnKOsu3J9SHr4KFRFdDr007K8n3taIx2oJc+VWRD5+ZuO9/0
-rVrH0cXEDNeizx01YiXd8g77xfPC5IiAViRdICzlTtxG4O/uE53vvTeJgqzk3INigpx0npps6Y7e
-VAXJsrfKuIrhHUGZ60vCYLhagwEsY3+JBkLMtuQmsTxZhncRiPsOCV9lsvrmSw+081Vps0sEg2jH
-EjsuCIjdOvx28v9e+AHCXwfcXdIO27uybhXgCmYFd8o8b5dZefQyFC+HBn9M4/L260m/a8Q/1vrB
-tNklAm+IcDCcANl2HH2IJYDwCQyrSNwSEXcK2+4TgdqPqF1idHfwwTLsg5ADWBYfszeWwvq0RZRW
-QSO6xpDCwcdrKCQUy0oHaCfLMH2N67G+D8c+bz3Ylr4I8DdiDEHffIx6bfNJqa3ZofMDhCkfUCbA
-1dqtuKL1qCp0bouONCjsXB9XntA2dKx+694A7WTVI/1f56cKmdJY2WfeIvvxetGKQHCAvp9tJ/e3
-Ca1crVcH32BD7jb81eW+b2odDlJOdWfesPM2EPG/yXxQChWZ1PpyVsUleJmVoU7MmeJ1s5a1Hule
-L+fZbsvuQHC8Ix/yp7WYzov6mLEr+KETZ6mfd+p9mWI6P1XoeP6wQXBqj/JMOVplbpxRfJc9l5pt
-ND/h0J9IM+gs/jaAATDu8kDbLRX8uwL9wWt+BXA9gd/rkosswcUqXF4OduNbIUVA/D8XfoDwbwff
-Tn37vYn3eirTQaCiUEBBhgK9n/c5DgdoyndALBBH0HmN48kasIxd2bVf9L4sG2klxofjZlocJuH+
-5ItvjehXp5nEz9vB9Wn2+Yhcp+vTBNIuvFI4+Y0S4SPcLMPz6rdZw8EgGJ4AsA/eW6jgN8AYgwJ/
-TYEUVFHT40+zDA92YqqBjNZwUYpDj8Z8m/JPdcj+hcFT2PBj6NuZkmtQR88K3am4TlwZKms80w0o
-V0VNDUzgK1Rl6/2cRpoyzaSAS99ZhU9EZktvrWN4gM6tjUMdWbTtqOmj0FpvHejDUKcH4aYPLgr0
-zEPZXc9CP0Mt1D5Ct7G/GYd5sxMPDW5onc8huwkNGmtqxN1dDmDYXSHoXXaeCO2D7I7mOcBNr9k0
-z0QJMN10XmJLD7qzbt9BfCQZMks0boXbEBmZAAAgAElEQVSIPJcP1dXBCgjGPJH8gCEwL5bjMW8P
-19DSO3O3yVekWZRzeRXeTZO4bJbgTbv4RT+njD+zXKzCzTK82wi/Yck2ikr7Z+EHCH8ZBjGxg5/C
-ASw8hi/K3Omul/xa3iMOeshpi65xXaRKAuD9q8gMiP2w6sr3UN0dyD3iWsLdf4PjMueV5i5x7U7l
-J1UeD70cjIqJWN4pfOf6UPPqQ3PkFkFgGOf6TvmBBIbO8uFXeNNtDa50ZG/qj+oxeCWd+zj/oMZp
-DDmtTzKLYEGrqBJybmxkq/M6tW430QEZ9daG8pNLRAUadX6HHitjjaqsM803UZwsx4s2rZnJYQLH
-uZzDCSnsDjk42chNib7br5beVod/aHAL0cCHc2l7ZoZlTBKAJFD+bVTDBwfzRqQ2YW1KtaScz0qa
-eYb0sAwBVJjSGaO/0zxjF4rWGvaGPfFFp/IhODuCYM9nl4jKG1VXUKy854z2ks9yGSdXOU433sTA
-4+Kp4m2rwFYXa5DHLLo74PVh+vK7xTa2Q5zgHGuMrZgiCPDaEShTX/Y46OYgUppKtNur0C6V9kMa
-p1+Zqy8EvyqSFuFAHf80/ADh3wmvcCv4Q5lJ+TLjuzAprwM4ON0aUcHvcHb5XWU9JAfAVm0B0gqA
-PxvEYn3++xpeziRfv/bBvxAMI0h2zwT1+4Ol+Ahr1g1A0pXpIrlK2sICQaxYpgPgzncHE69I5rvQ
-ngB0rIZLKV6Uqpw9gdZg9KMKayIJqqoIkg4tFx5OHvmCPtwqgVY7KP8ouuDr0DteBCMTt51Sln2e
-S9Z6a5vHUlj5MF8DjNY2a7t5UOYjAPZ0ajDsG4LxeSgzyOFkLXhQ7i1/AMRAZ6Awg96MZwnn87VL
-S64WgQbtDF0y4ij0ATf1WrkofQ8xydrjPE/KYVYY0xJWSHuyEJ8rKVx1owxuD21fTO41IrCOeCPE
-CQQvnXfKW4P1D3wPaRjXmB5AbzszseBwRhwA1ykTCVg2rqUmZ5PPsqfF16GCYe+TSQfBlD5ZhVMX
-1fH4h0SYPipPX8z4CKoecv0j/GCbXB0MixYAfAOC0zLsdVTwm33+9zD4Bwj/lTCIlpvgp/Tm9L5p
-yAnfNT72xt8TAK+Ig1inOQi+Nuj6SIJhBCHxoJ2mVfgCwGuXLreIfegZDO/0tZ44/lXy42Ex5b6v
-d4t3V3D1vVl+PyZy3d8YYVM5BMMiLJzMIjvWpKyNtniOIoQwLQ4KiwMYToZosnYhX7zfKs8UsHz9
-wFV38jEzkoO339iwwV+o3SpHT1X9Xns3f2g0emMq9Fh7z7hNZZrHKrh1VjzKAQhKW1W5Y72TbKDk
-NNg72gMgDm3qmlUBdEJHB9Ab9Qygl39lDvvA66qlqpWc/Qx97rR2QQyjHUhb44J269zNwOmtOxE2
-qTVmdTXYPYLnt1RG4xmUReM5l4luVwDpcnynEwS7a4Qs6/B+F5ECiNcfw/0gOO+62+BxPlqGcZpx
-eRyo1m0J6NAU5dO0DxGxZb9VYfZgbqL/cFT5Pd0fRh9ilBEqIoZ3ChvgYQt9bRCPdiIvh4U6VUQS
-AK/EKhNgVtjyW35JroNgofdAui0P9Nr/QPgBwh7eLgKe/iOovSn+tj+vGZ/r6IAx38Pii3n7NKsg
-+BX52LrT9yPLfQE/sechU/lIgmC0CC++dcLD8gvl3VIcoHjnL9CcfsR4kER56XIcfvqn69M+Ih8V
-k886fHaJfGz/zPLOLw/N1buEm9uECNBwAUI8FUV0CDA+ESmDG/boy307XhdEPTwobRnuFX70hVzx
-yZKG6WP2iQxK+jSTJmsPnitkQHVbmYhMSnDEDgNUac2DlkRFRApOhAACtjX6FvdWSnLimkvWEcSb
-77E1uYcSZW5UhSzEo6VXuU6wHONPFCuAozoCpcT9tw7TN+OYYYU28THPMSG0uA8Buh9pwpwb1PiM
-JkzUkK3tDuF25JBQZVS2weS+l8KyuxGVmQPeNeb1lXuC4OoagS4SPk0WlhMRiTIi/kHKytj4vM3f
-vlQQfATHZdpCIUbWlBauJJSOcRoU69IP0BfyHTboY47HW2WJtUfv5XwY6AcsaU2OmyVEwh3IP5+S
-DELrb7wPLhHhH8wA+LpULnSVmNwm9vxNluCOTv5t+AHC3wY/xPCu8fVQYMjFKrn54z5xkP8RmuQD
-okFFVSHFji4SvmqDaEwbDfXcjG209223m91iMeybHNP+qe9Sk8+1gPRHN8DWZWX+XMvC7MAb3SfQ
-RSI/mULnI419AMHhcTOwWlWA++kAl0DvR8wfrKP8jxj8MEemsXxNwwv+kTVaYG0hjoLSHITTq9Nk
-b5dGH8JbHg9ddKlUBXUUb6FwZ6BTCXewIsY5tPzb4hW6NrXd8MaXPL8fV7mfjTu+uSyBIEXeQwCR
-1JZw77cOOlCuFRnnhbZgcT9CBwjjyBT6eJoO5WhWVwr8Nm2aoyKEaf9WAe15/PigmrvU5YmKjxcO
-cgpUqx8CTRbgItlgEN91kcxo6d2PDb4C0Hqv3NoLI8v684YkTuf4O9DlTVXnzCAPcx4POeSPn5V9
-DxLc3WktaeptVma+BthW2y+nE50zYS3tegy6G2B357gV2/Yu2WcDryrU6L/Jpbqfe/OH1DR/qVXz
-V1sZIyvEk34Mfs6P3w75xPqmtIcK/5vwA4QjvJv89QlrSWf1T8fb+uGCQeO6mAEYexx0QAt8Gnhz
-1I1lpREBbFjl7SS7QUB7Ne4awftVh64w2EWALCJyybYOSwJe3QesguG8KzgubiDf4Autw/sE0g0L
-sXxGfcjDZ3vNJOLp3uBA9ZLqK2zbOiwEdPfr8xHbbhX2sR3fgPdj8qkgWDowFqBFt+ifZLkpHqOr
-1hwOIYuVae1VAc2Bt2AeCnewgPKtc4wX/9nhnNRqjofJlRHX/XjiAyUc7g/+Kl0Byp/E/52iaC3R
-4i8Q1ORL03Mor4AnEF3KL3XZJiIG8WOnGsgY+NA3WxJK9WLTRjvQqHwdOPN2CHMuZ7u9tP7muYiv
-uyXzYzeYwyHItyIfHGtArzro5bQYlBd+/sBl9AR0wyWC2uM8kTvQiyAZ56iUURz1EL4Ax0g7bmUt
-aRhb6vR6H8r9qUagm11gEKzEDztKU8+HLgbl5yBYNffF3k0BgvFdVfKHqrbOxx+ucv0dYLio4HxZ
-gObscwW9mnGVtS/M4ssjnuMCieTvhh8g7OG1bkELsIU1NOT5XqngkfyEWBdWJAExAdoDEFl5qFSw
-zD58E+KY5PR00sdyrOhivNF3LV1mPz0/cGjlDUvw5TdEICDe7xv4XiJiliB5veAEYhf9FIqJW3zz
-BQO3nY+uEpbXpqEFWALkIoAtFmAHxJ+V/jS+G0swWKQD+rqcCB7IDR2MCjrjTfmVWWDlNYdWZhA9
-YJsay4eOOuVLz+9KQ4WGeajnfeCWDUmHPk3t3KV/l/d9HGd9Ugn3NL3l6yHlTiE+pgMVnIGxbKUd
-QlPSehbguGh5kaXYT71XGBmej9tBDvlmbVjjhpHGIDMwK7ue9nXmMdzr1l+voQLmPKUK/CAHXKZA
-WZIRkPYeTSB5rZGFVdjHGlIW5EtYjCsPlKsbhIGxSAXKIiJ2a+ERArLBw0qph7LQ8W1E9BQdfWo6
-+44jwTPWZdscx7VdWRaq3vNB7a13GiKA3xiHcd7uZwXBEzBGq/ClGxRLU787zeDXEW06Quxx4ddI
-JusbhS0zQjXfzNL3cv85/ADhHebHLHqwLaTDWLGFjjv/u8N3l/lgKZbcRPOi7kIAduPrfKAbgODb
-it/QGk/u9LUxIS0i0saC486My7ZPsRaLMABef+jOv2JD0Os87j/s1mCLE1lefSZ3xE/Y8F6sww6O
-rViGGfyiBdgB8GeBYrAKH0HwARBTWiRAcg4GwTIC5aK0JJVbm49QVvOLdFMpa5DN22kCy5jfK+vK
-obA0gAIA9ljfVGJgvsmZK//OKvw270nQc1xHeg16w/SdEoEB1yl7mdaIb0LImw0muqBMuQcyhzve
-gQ9nHWQ51XWanLKHbdh1N5PIe7r3o1s4ay6A3b3/EeyKCLk/oBlCof/+HeXZCgzyAs6iTfnQOxOF
-tMQHa9dHBmPgduG6NaBlTd0FYubDcwB5R3Bb94qW/KkMb+fQ36TjkgNjhmuBD8dZzr0TaY23PA96
-mRF8ENzfGyDGeAGqviMQlF6XFuCbrhGhUh0E7xrSBpUuEn5M69SytFKYKN9ggHFgo02fS//r8AOE
-PbzDwQvMrsi29O7NbwJGDGPLqZTjXWT+KYSSOLxrpYsLQ6h4QiN3tAMgjjFAx/uHh3WN2gcr2v3U
-7f9rwtZhAr7XEnuXpICNX5m7llvE6sLqED3t6nNPJ9DaawmbnXaAK9W3d7D2ovvDpn32Q3ZyfUQ+
-H7kuXdZgcouo9e6XzKBYJN8F0ulL7HKjxg0ECKovISuxCCrjDKTcxlcHulN5EdiDQ/6km+7qYvqc
-06kFMFZd2Hv0WGnUoz37FM90B/BTvNFAf3jEhvbrCN7rkZs1fcAYyTNMSsiQLRtTeKS2xDTuWxWQ
-MQZ5Qy9GkIxwZOaLbrfB9Lpi11ntgRFvc1eBjT67shzy3AKIgBgWPtI7VcEv1uM5KCukpFEyCtFA
-ZjhNpVmFfbarfCFrsEnc+IBAOTa08piylq6cclRKcxxhfKCYJhhoZYNjUrN97hGe5eTIU2CUNuCW
-L+KMGdD2TioveNEqnMNB8IvWWgM/YD1YhHdcBl9hf2ntCwJ0nruwDvtG0kVDjLP2iY53fP/X4QcI
-R7iDpMzmXz344VY46GSc3dZc1Bd83PcWx1MD79ij+GTslZ18hN0XaNLzXdbw8FEXoKAQEf86stH9
-aFnmacm7JK3BqhL+vnEf8Qa11QrswrVetWb7FKKLhClOwxblWr6qsu3BFg/KfdaHGbQK0wt/enml
-yUqMLhGXW4AZ8H4m1wjhuATIrRbhau1lQByAvuwTVnLFSuwvn0+o4Y34YV5U2Z2H94Ed8uaytTOn
-vtmYed8Kt3G6dGtuuzb1vYrrcewPWp2TV2/XqNN0oCbtnaRLLpqvabGfaJhull4HJgqNFZmGvVAY
-CeWX3TbJvalLJ6U70P3Mzfu97DnDZjmvnlgqB/XpBpESaThtlgAJtQkI783GVmHXJCgfvM0qM6Sm
-AcjifkyYlkahuDUiADDud9oQZVda6EyaG3XeqrxO0kcO+cyrTZ/1mkG1ZZ5iXv4ViPUrKu8lgRae
-/lihg06L/hAwDZ4NgNW1cYJg72t3g9i3OWA61Wv4D9NLhpdjH0lArPs4w6HLuHnSlVH7GPtPwg8Q
-3uHtg4rmgkZBUA1xVGpa4nGgrLfLSmcXVAHDyxIwDQTXywGxvRik8PkrMoUe7KxMg8BAycB3AGr8
-NdFwjXAQGy4SFwhlyKvAOB6gE39pgF/vQpwzoPvg/JN8QEJyN9hpB72CVtzqGnHtK9jqw3MOfDXd
-Ier7ZP09uEmIwbz4P9Qku985H8ibCg3XFgpWAlGpPhVuA0T1hDNOKkkH4qRspu4dYMor/urFdwp1
-RLcYCs/vwPNVHOTCmbeDJ+5TPdSd6R4qHMo741S40TZx3BRlwkIRspyCT9Ip2zxxuyCQccv3UMnj
-hjIi0d2sQwGr/RbfZ7UvWcLzEisYUrbOSBr2KNzXsC7Ds7tbuaVlD4+0vV2W/WDpH6YnkOF2Bmtw
-GcMtOI46B8kR0wnzCvI/5lU7W8w96K8EuezqKNBj9gcGyp4L9QnbDGHwQnpmRjy3Ps5A8qNqm0Bo
-5jMYdmDqPsPL0ovuEO4DnAAYLcMqy6AVLhLensp6EA/dKaiP01nb+ttFwz5QrorvpfV/E36AcIR3
-079wZwpy9UvDd1xGcNwV/ggAVMKKjGQGvHDINNtaT4ZCR0tDaSHuErwB4EmweNxSMMeEVB5hQc2H
-BoGuhttD3C4hKXDx9QvTu90AwvsVa9ME6ASGwzNZqqVX6eG5yVXiSiDr6Xigzq3ANw/JVcuwWAi/
-UECOfG1PiFSrcfJ1P+KM3r58nqUVfQw4y0iTO/od/hg6MPXn1Mcjb/x5Y/fdamdsBBQ8DL6otCE+
-W3OneKfpI98UzsB3Br19XiagNhC0ECd/L59/LRM2XZuD9wkjDXhyBKe2DqFuvtPGOw12LD6dAsiz
-7KCNHNMYqpUXQdQ9LfuK+iM/HDtHt/gCbfM2mrdZAG3G0VLscec/03gCMC/TNGfK6XkDdFArwvOK
-0Zw7jS2pO8P7WzWKj0eBCJTS88PJPwDimB2b6T6uDoIR/NY0WITB5SGvS6suEiL8oxj+SuBcgW+C
-X3j3iaB347Tw+zd66G+FHyDs4QuTcD5RuoR/B8dV/udXRgyWBSy9u3r/o1hetp7ZbQAtgLjigRHe
-ZEGUrqxGHhmIAuNOuhYeFxrIc6mM4JbcJTCPrL8pyH8h8BUJIK0OqHd+KArxZ603JawpFg9ABPgV
-E7cMm12rXfuIOiCOh+MSEEuktxvER4sVGN0j7oHx6ssaKF2r5uPfc0BfaYIgjfyYMy47LGyWgxx7
-fOXz67nG4BcGirxaAybVNXXtJAgb/SuhidaoVXaGMLMcqPL6TfzPaPqKD0McX/PUPDt1hLmaw9jP
-UzLkBSJoDdloEXCBpUDbkUQihwFYKTMwTRiJSDZ+ONsdPtBrhAdbS/UbDvp5xJ/e5pqUuqEjP98W
-wbSUCYvKsqTS/DyFfIHyvqXCigfnKeUHguEzjY0VOVaRbLcro+mWCc5PMtqdi76ja8Q2D+XxY7/R
-K9WxR7Mfce1v7eu7+PQxmIFn+gcHrcUHcKzSLMIJfrdVWBQswAisZwDsxzWmEnreaCZSvyaP/TjK
-5f8+/ADhHd5Ofnwi3YB4dHkQf5AODk3kI3AogtNQJ0CFtQ0jxuJ/I/UUzzSXfKWMCQB5qCBhjdJf
-KxRuBMpf/eD41Wn+08nzSwdwrACGhazBK897tZWJ7IbCSg9iuVl/t/sDWH4XIAbQu/OizOeSz/WR
-yy75fMA/uLlHPPgLB/A958UskNJKBYNWHNxcpTQpP9HG/hA6yHrccnbD+4BpJnrHKah2TtbfM2i5
-O//H9su8Ters92h6ziM5s0J80DsNIPjW35nzZgYq9qh5VBwIUx5aK0P2DGAmNid28d1GXaLXXm1o
-bGb+GveQqKzxq3bKLHAYKZ/WkFfTrNL3Ck/0GGbZzXCmThbgyJ94Nz3kBJVLQ84ycKx1pdsjyG3C
-810e78odPUF/TkqMwTOXbfykfrieqKFYoGpN7APsPSh15GiEXCVMRNsnrOnUV7g886RUuwHBijc7
-VICcD7pdkPdLJX4RLkExXJW29Wq9Z7jmd+swjg/ecSm2L41FPM/LsyT7++EHCHt4aRGOa042f0sL
-CIVX6QwkyuIEusU3mVRdIYI02ugV5QM+Udywi+7De5I/yIhxTVAMIy4iAgXMyscfzuiW33yYwgVz
-B8RQTiQBsiQtTiJ1YVe2B2ougOPUrRdZfwEQo1uEFr/gBMsfkc+1LcYIXicrMP+4hltQZ5oLBVBf
-FTBDGQyoxGhO5YvXoQxuk3pqJvqZNy1yk+g7icN+rVrl3UoLtn7n1kbZlQsC6Uk1fUv7jv/eHeKb
-ecrTd+Z7knpWI0+AOHhgjptg20xDmbRm9/Y0vm0YAgi+4k3R+SizcI4F7yrcHGWiqiWyrkK1AnM+
-S1P8lgXZ6sNyWRHX9gR0BeghFoE/61FatgDFJS5UtrhGoBEH6DyAns5e4HhnaaPxN7PrA23iKfcB
-Bn3RbnxC3U36Dnu7xtHoOKEivBhA55sSznEGuPni+33d6msElNe7xg9kLAB8Ar0aLhPkHiGHl+Ls
-puW4TBLPgfLUBJSRfx9+gPC3YW+0tYCWaXEF7WARZBLjWK8m08hjcCiJbuLX1Sx3jPUeINxEGqpw
-5XCSHyJda9AuTIEMz4DGvs6vi2YwnC4iGj+R3H2OhofjLoZ/a/h5Cj9bUC2QloD4g11nURb9YP8k
-20rG9kNwtgGvxws43r8Akr9CZ2Kfj3yuSy7/+eVqDf5df2HL0Vt2leYFWBq9zl8wTVLmRD+EqghE
-evGpyhPt1Eal2JxxqCN7VvOPX4ff1InKvPLU99/P6/7EkwX4tvugY068+OXvi6kY658wbuMBoDHn
-nSoykIEog0qoeOk0GoUI8hV2GtrJNYLKFXBFQ8k22p7RyjWfivahYKMGlLlIp04AOfduDj/lA8uY
-iGMZ0Em2O+Z32PvtEO8Asp8fT91qxQSpp3xUmnsv9Z0NNHd9aOukZYucfH0dPIuMoDd6irdH9ZOr
-MtNXFPcmrit/XEBwS7/0Rmm4Axh4AgirHl6pl8ONQqDu+trzQncLYz9xHOOZd8vwNNJ/E36A8A5a
-BfYhmG9QTVDof+gJU/NDUz6FFsWmWK8OPIU+gWTvUxxNkA/ZSN2Mnp9CbLbOYZ0OeGcwzJULnQj8
-dZrJ2lt/OQ7zRUV+XatnCBRsz+1nNxv3FENXQDSt8tFxn1CT/FllcIGQBYLD+mv+gFxahSUA8XqP
-q9LiGrWTJXiwCsN7tfYiLdSUuZrpvKjKpJf86lVD1jpYG41WP7ebDTRMHNpoMTvx3QVUZViuKMCb
-Msd5qACh8N+6OPwR7xQUapl57vK+DiTUzlks2KY6SkWtkwxr+gY7yO1pIz5uONwZpyKVAFZq6x+6
-EAzh1Wc8nmLZ5QncLHU/2mH8RuVJEkDW2TKcvTHMM2HXBskPhdFGAci3vJGqC9XTXR7cAGgHWrHl
-+MMrpTXZjYtJgGHlXmW8W4CF8uT4gXv+OD3HY9aMwW68lN/xQTYEyBQHWrUGo3FKBX9uuQLffFWV
-T3l1TQ1WNMh7z/i8zdP2n4YfIPxlWOdchdYSrozxDYs+SC4LUW4FregIlMn5YJ2JOwIrWYG3Mi/t
-c0egF+OptXqKIa0lQ+JrIwLFSiOFQW74ubuK/rwXujps4NtBsp9+5lNR+ex24e6HDYBZ7KFg856l
-f9tn9REswfiOYNfdJBT8hKW4RayfY/7Aj2l8YQm2VE2ZXxWHzJZh4ZD0tOhQ5heSZmrHq8htk6m6
-lTptABuFTwrfkNnG3Ggka0+DLkryoLhq/af3NzxzGf2NMhzqUcac6bGdCpxP4WFKzkyk+zaDDa0R
-CQegM19ryzpPkL8cnc05obiHcdY9a4AMDBhiBZRnnQ3Pw+kxKaunpVHsdB9vBb9eAuVCnu3Zaoyt
-pOzErqxzhPGsrVuDuysEjrlKC1dgz2V4p6fuWfPOUoqupGtp1uFa6RFnC/ARNOOEHk9xxmfnqGxf
-SzxeAEIDyAZ4VbLwukWYb4xAP+HqFqHsfqHcnqd7vxX6Wrys23z4/vj34QcIR3iPDvxc4lc2fPMP
-flZMoYEOBONRNrcqb2ER5UBgOug0kHs2fMq6RdiY1pIW4assFFh8GycgRZ4UG9JA9HpYzgK0+gyp
-CNCWX5MBj0Cez/eHhPB6d0AcP+e8c6IXOF84H/SwnDXA26zCAYAtfITF3SbUQTC6QyAYTkuwFDAs
-whbdEOjWednGC2zCLxxnzWuvPT9jHQ9hUN+kEEaadaVRa2xy8tAnazEd8xQ3zdArFsmoxAdAcNOP
-795LG8Pebu9lL5N8EKZPfZ2OwbfhsdzYaLF83lV0vO7sAJA3D4sym/nu2o38sZasCs5V1+swzrLl
-LJk6aMYWp/GOwAFJkOcPI0FOxKF6qzw4riiHZ6EA3XjeZQDAJmQQsuB5lhjWeN5ImUxX/+mwBLsu
-835FGmonQOuj2SnlHjGPt1R7XT5w2p4tXJAYnTEjtEEAWOEGBwe94gC3AF/P15rPt0ZUd4iwDov7
-FSsDYmibX+AmgWOir7NxxKzzx4dW/+PwA4Q9vHSNwEX033xHYUgK1LjqQYQBSN6lzSBd8zMtUqzD
-0D2Qr3nIvH8jQLY8/DSYAmbpIQINQa63vJ42udyiq2z9FbAOS3OL4Dw/fSrpE4wgGHth8b57YkjL
-+Ym7g2WBWtk/p6zXzkPAa9cGwAyMP7ZujSDg+3Ff4UXrP7tcrcMClugOfI36vRXawAMs46vpj5tw
-rINWO3lFigXSOs8ZiBXwccJCjVZz5gHWL97upuCtKK4+vN+9PwPtp3cGfpn6hn5ej3fhVRnjBJfR
-vhjHSlme9T1S3F0OfL2dA8NAbnuuuETgg1eJcZT0hMGfpOvQnpGC4e4OJ7GMB8987p034Df7Zlsx
-hC/w/oYyeVJ5jNZgd5kAOrd4ehiu0Lbei7mq6bEOhFYMs/jht/13BLp7lIqlJh4eSf2mrJ92jist
-xOw+cQSbCGBHEFx+OGO/funsKxx8UHcCZGUAPIDh7DrMkPLM+z5cBooi+/9x+AHCO7yFwbidGfz6
-/tXMIrl2shKvgmRJjvRWVVvIOMgwyFz5udk0KtwsFYWX/NZRz0dICYKGwHB1nVAuU32Jq/tDukdo
-0EUlbojwU2Ylz2cKV83BsJgDYvwEffN4ELk/5ENwKuz/K2AtRn/hj11yARi2zwLA9jkB3wd3CVc+
-oYQs3CHIOhx8dURGZXq+ULmn130Yn1un3TOmjdO1b22JTnxt5FzJyQO48vtNLk9837w/8+hI/6ad
-iAPhhPsmuksqQ3lQwrdKqfE/CNZYx9uGlN7mhiDjd5H9Le9dHx3MsgFk0YhDAjQXzGu0kDAAa5F5
-r9rw4cKy3XFPGtOcc8zfOuhkDb4DvVnvBIazfB/0HW1Ibz213BdN0jXPDvRdg6+F61OoOXWdlNHM
-N0QwkMZ7qs9STKBeirscs6TjEzmqFpbhdFuYfymOrb7508rpGlEejgO3CPoBjRsAjLOhsoB9YgRY
-YxtmOFw9RbC2fxV+gLCH1xZhXyYDoXcCv8mbgoKbi6Uvujh/Mdnvvs0K42ueai4OYU2nMSvE/lK+
-0R4d3RsKGEa6HeghfCwP5hEAX2tGTdbhZQHNM6qGN0TkRH7ED+f+tByC3IIzrOhR/74lQtwybILA
-WMrVaQFGN/hFK7GoDoD3AHxjZD3/N0AAACAASURBVGDLHflwFhj8oo3YJLo2zJmUchKuEI2p0CaX
-iYn1d9JCNDxPnY9pnXrmg7MZDWjhy3AL0KHOBiqGus48Mwg+vu8uT21gQqNuI5oU+gksvwnflJnW
-86t6teQe60p5pgL69FTnmz7dVgLdQnmPYBRAcdJy7xrI4dqS1ULYHkdI/3DXjTjj7FqlFT4r/PUd
-5AZ2h2lgPYbzVi3JPL4uEd7RfE+zLVZKHB+Sm+Kp0weAqxnP1q3UfgLDO3fPe1p9J0nR46lRU2oo
-9DEAaXnlncL1F+TgirSLf0b5Uv7FuQDEkvQEwApxiSPYgXHq49hACjMJLjxx//T/QPgBwr8dNDZA
-XTo+oiklAEuOx7n9KIcD4DhYaQ1W5DM4zlChX/VCwjLSu7RKukuIJAJX73O6QNAANN9jdCc+9R/P
-sIUd98FyYXuJiX0kXCfEwXFYhpVoMPg1BssDqYbXuKxP0ypuSYdFAikeYBjcEpTifnXaAsaK4Ncf
-mAM/4/Vrcl9Ygd2CCyonhXoFwDu2EW+4R4CVmEeGcLkHe3idyrBS4CmNtA35NgHksjalPh7RE8/c
-W+TjLzfv+abHyUaAUN7PYPdLEDzWLW3gE757T2PqW1X0n/M9FUSgbPPYFt8EJmWeyFcd47xYG2wm
-Nj98aLLKZ0Cr6LI3QHvRCg/RhjZJojC7T8Mxj973OdkKC+8pqdeoZbm/bQ2eaNbiCbGSzq4RIMuK
-esTlOLeQ48O63FLeRzwJOb8VJOtZ/Uhejp9fl+s9GUCvA+RG18Z7+mllBL4zjT9GKM7K3i8xazHh
-Hn8rJf6b8AOEd3h9fRr/WWVlXkYHiKeHpfPeYThiBodVC58IoOXi+0Tg0LUCnO5dd6aT3xQPI/b3
-BtxO7wf+IO0+6h4HHrJqLV7pTve2+GG7nGCfqbhwQhMcMweIeIOXqwOgOZQksCz7OWkAzGjR5bQQ
-jfkk2q1WYM9CtwjJN6nAV7AcvXabWuhbBqEC7LXNLw8xkyY0w7n6b9L7DNjcj7u+nWjjaXYZXEin
-cAIDd3n3YOIPfIJBd5zGXcfSaYOkMs57q45GvknP/0l9GKaFmoaiA+v4letNi990WnmNvJlK43t4
-J9qqjJpu/XDgrJVcornJH/el3eR5Obc0wB7EtggMj/H5DHFNz7tXBOU48+FfbF+Bh6mFFsh1j8qm
-s5NRHXJwtOnOIFs25prw0zOgHzcgdK2pWybq/lp1BrIz6D3T9/sl8ktVfl06+AmnNbhbguFdip+y
-+dzsjeLAeE9m+gKnvBeRMMaZ2L5+7+0B/HvhBwj/TihAsoe3C+kbYlssB7C7uDT9nMzVlTYAnTJA
-o3rS/Ns6kT7rngfAOyR1BbU4dmFazZtoyi8UFNHMPmVKIFmKFXkJrUvXOC3qWHNzKdIkDMhiDorz
-MDt4C2GJ0/YETWz6ktzEV4dBavKg0uH6kOTaKbk5xuUnlwnL7FR2Wz4Rb6W94ZHvQ0AwF+qtHtgU
-kn2utbxyixhkqWKkAYekV+XXV78od03aCVDc0vyoY95Ac94JDCvx2UDbfMOc8nR0oHi71kPmN3vj
-q30UMuuZL9c0mXuxE5h86FU/fm1N1vr120Byv5S+FlUSIrg2RX3kRqdeP+3BkAtTnmw5Mp0VAV1V
-9mrnPffv90IZu8qSJ+HEuxdEZRkuCl25oIiI1G9EnCLZUkqCoruQ+6QmXQ0HDQBvGocswHDYl3Y3
-nU9lcnMo1tyrA2L2CU7aAsEqv/SK+HqAThk4e7tbB18+H27JBQPbGquDeFC8aITDs/z/gDVY5AcI
-/1lQjqbQeJLWm8ufxPWDSXewwSnATVdoeC0MbrD8BApAdks/i6Ywz7JpPOKIILS8r0H0IVcpUrM2
-ElW4J9gBa3zlEnEXGBKfiNco/CEBFm0qUFd7d4G5uOPilpBWCH1iwoa0r5OFtTaWxCqfA4oZGCdX
-VUNTu8lbwW9tQaSC4wRZ0zVpjzyFduhehBT1HfSyCsKxwUaFvcOgNkvfis/SaK2i3xmsqdBL3gQU
-Gvi5if8WDeab8k3IKod5CgmiTXyF1sPAdZiXb8I3ZSZeWrcmXw4TI33OtOTfVDp0hgu6u1q1+rqI
-pTU05qu0w1QP9Cw08U77qrU55sHZIh6XsIf+vaE/qMWevdv0puv7VIcJgWECyWIBjN3aiiAtAa2n
-oV+6863QB36vK40t5eqz4LCSBnVq+SAclmsg99LQi7euEFe3FAcAvtIa/CveAfiq8JVpApZgHJ8D
-fB93jB/nWXN9YFclJvmfCT9A+C+F8aA/Liyc6vKzcWEFRgm5d5bGdT2w2+CrHQK5qNl9x8UbPNVa
-XSsAYDbwC91PciBJfsdp0ByqE9D668Oh16Win3UYRSWvXzOfJclDGe8MihFoRxsG/QmBVFWDx+++
-5NsrQW4VImHV9TzLurxcpk7tyk2aaaG4whVDyG+YfIXxaQ4vrNm76P4N7dyn+xBb0BpWJS4C9biX
-4qD1ko2iwv7vWH+pQjm7VRYrVIGN1FX9Lj/WTQ/03R8qBwCFlzE7DVLjFW0M0c7Xgu03ON/xU74l
-UHgCWaf1vC167AyXwHWZ1uoEhhttav7U753Z1qfUM+5HqzT+GF77E/3ADaS9v0+h8p92lSJhXBzI
-CLnuEaeZIBiWAKYueBx+7lbHr+RTTvo3pVW9VWuwineD3SPA5kNlnd+1bX5jqYUX7gm+ujW4WYiv
-k28wuEaERdhfA59mP+mnmiUBcU6hgXDXXJPJGgxTjAtvxzX/b8MPEP5LYVTNinkTMtyrTu4RwuBW
-8hCnr/D6tLjOZoLmEQDrpqGUAWc2K7zrDEPHKwguNBqzDnsYTzL0Qf00ubBSadZf8knyA72fsjPZ
-/sOWQsJnx90k8CumBoL9ELdFqe87itrjjhdUz/pqEXhAy6TluNeRtt3Dg3JQMvJtoN2lQRlHeVXm
-qzylx9jtsrxDHPa3PMtHpNY9hm0/KeO3gLfJZz+CUKiusr+/iSNt5PHjPvHsjX2q02PTbobuP9Ie
-J7OV5vDew7iW+5M2M6L3rI/t6/vOjOXrWt3RQhxorwMrrt2xwpQnRKVkHffZolnPazyc97fxybQf
-o33FERXd6TpMuUdawLCowb35ezJV0/3P9Y9UTeYPwhtZKRXzJMGgiDebVtLMnwDw6aUFaHK5SxIE
-n8DtyWVi5XEZB8BHS7CwVXgEwQJWbXeJEAW3FHgHrENWYV38+dTT395pz+EHCP9x+I1FCy3EG2Ll
-5abRvUlII5P/cB7ycPbHQ04AGKQqCosiEMx5C/BViNOQld8N8nWamjJUcl1Qhfi2AvswVNav030Y
-+F5xdOCrGj+wloJFXEaSdUCyM1LjmSZLMVl3y7tbY6WCAo4HCA5Ng0rJhlIcM+xHaNoaSmlYUju9
-6IE6PfCk8n4CDYhBOb4hsUq7zo57X1BszSs+xbcd2TyVTQ9jwX1cV3IEGH8hXgEv5e9EdU9h15I+
-5EaHryQn3hoelrgwv5OFX9WJ4aF6FHVvy2BnvlLBWK6cKzE5WnxH3r2Xg7/tRYgftzw/SNelk7HI
-Ke+17SkPZ+jtPDU+kOl1j1Zeoo2LAxbJOMdwd60J6MvNE5++NRcgxAz4EIN4QYMJukH4LU2ud/ZK
-EmCO1wHg1m8xu9UV6zj4/R78gvUaALGK6AbT/2e7RcQLwbDyD2tQ/+K1cYePXXM6fU6rm4RA3ije
-Z5H/n4cfIBzhtQj8e+0RCFY4qLB7APBWn2L162vwkPsuRK3gAhKEhXdhfrAOaVs0KNdBV7Uh4oVo
-2894AEo8hMUhjqB49Ue3e0SC4QWOla3DBvVY1rcO7J5HEGBxwINW1QbMEzzUxpZfyBd0TSAPPK5L
-RFgD1tnr0qFai7vvsIECXHvn/YNxYGkeLMW/G5o8jD15LnPOUvH5PSI/zBqEbKg1yJuqWmPXRs+8
-N/H+FXRLg94+5hf6NNY39BdTfx9eFPwTndbGMHX+qVxb03s5byeuu/05rZmLAJdXj/Tz0wkGhCOP
-JbXuDWp3yKviZ8rrYbrB4QBmtXNUOSBDutQydSGALtpuJmNHxrO1vidgs2xeBV7veQLTNakKnKhD
-RDZ4FdA94vro9FPFQFesr15xxu4PCXJfWor91gi90jUCwHC2xZbh6I+5brUcf30Gp1p/i9Ev9gZm
-91X+J+EHCP/noR98zMuH3dZuCNW+N1Raz3QfOrQSg9UYn5AVSa2B/qDt03DyhTVBJeoiAEGCiK9Y
-Y74SR8AL79UaHHi/5u/uLmvw+orHTAIEn/yCUYi4sPLh+bxymKBFVxuzmpluhwWtMrpVZHyx3LXr
-SfD/bbzYMoBhw3R1idBXD8alpVhhZLPImvb5HS1OB+3Dp9IPArMoPeKNr0YP5YY+1JU5AYVTnn2R
-3np7xfWBj7vd+nqmh5T5LvzHWuqpehrDtKFaSKLdsf1uh5DNDuvlul9K3kCfJIDvhUp/yqf2rPRp
-qsvOeTR3eWhpdvtFZXOYQG9dG6xj8IAIhuzPJhiqqj0g+vZzg1cRQQOQ4oFXgecKDPrlfw2+ZUTd
-47z80Nu1dY0qWFOtpLX64FYwrOkfrOgewQ/NtQflqi9xPFgHt0YQ+C0v0f3LdGeXjVgDARC8XR1o
-gds7Kn3bM4hX2/3b8AOE/3r4StRK3yHCm6S6QpDkKHlBEzroEwC2Jz5vBqSQEprM7luQm4Qch1iB
-bx54iUMnRFtCQtTip5RXce2+wR43rkMk/cUU584/queIhQ/iF7SNUlOxFeCMD9DJ9LCcSEdhlg/A
-tTaxHVaO7KZRXqG03eqrHQyPZZao6mKqU57AL3zE49wBvU2XrZ1aiqNxU2SyQtOWHcpOQMVV3zmP
-57CmT7Sg4/pp58f2pnHc030VbsJ/rI/+pHoem/bKDgOzU/ZXnUk5gudPTM5geBegNbZ5bQXyateI
-ZgOt8g31tH1kA+1Qrw5xUkmSZ/wu4BGc4j3syR2Y46YIJxioLYM9DvqPf2xqUbJttgA7v+snrAP1
-zmrCEuyK6y+Ty/L6M6dfUPbaddNPJYvrPig7PCjXbo6gB+XwF+QYEFdL8C+VeHWLsMct+ohz7RZh
-hCxxi0QDAJys7/+x2BnDDxD+K+Hp2N/xWQG+Eo7j7Y5hTIOP03pwzqUigNGDBdge+bIv2XX+kY/g
-55GU/DL0MtRM+7i3IPGu7bqu/AVj8AnmB+SQjsKpfyUV0yDkGoFSPUdTwqBqwESbisWEAW2Jk5/x
-VGfGrebteL0sTYQBsxW++xdaijWVtx54X+/3Dn6neU1qySf01sveCUzfi6m0bjo31aV5NOoKndL2
-RfqJ3vKs5MPxbeOWnjfTi4fxSw30fvV7278fbiFS55gaBRAFsOlY5/TV/8RHP5Ti6+TfcAivYQXD
-ftRjTWEZJgmC/Lc83l6hTzyNNvB5SJnZ553OuvI+o3xUR8prYaUeDklteSZp7RXljvtZKZZfunVE
-rAFjH2+0uAVC0zEuvfa3TRrlNkA1112rjAPJy/PjHQFwAcL7vd0ScT0D4qBVn+Fya0SCZZVf3pYk
-GNbjKw+UFleITMPCwgpXK7Dvj38dfoCwh9+afG0pu8lvIaRC7KLcKPUqEuF0gmWJMyziLK79UeJU
-AGxNGlkIg5Ri6vnUbR0AbxKMk32CYFi2nzTFZhkIa8QXIDb6eeZuFT6/3CViXhX/yuukWiBOPsBO
-tS1oobxbiPfcNiswbRZUnMY4mlrBYjbSSMk6DR6EE7lzifDyA0+kvZauKJ8Cb4FqFZ5rDdlJhPsy
-NWvKjb091sftVkt4Weljuu6gN/RXvJa03vOedzd9B2hxDIet+e/Coaun8Y4MkvvwseKhDt9xJjJa
-6237/DZAbFmG6DKvKdaL+/jp3Xmn/IjbQIv38vAeuMqlPGUg2+ezBzypRR1wWSCQewQmkI6dB7+Z
-1U9g0uz7jVQovVwge4Hc7g7hc6PGFmK8bzdArW0gaxpXk+WPVPgNDW4BhndFEDv4/t4C4gKgL781
-IsGw+wzzr8tp9IFcOfxl7nu9dXgDwbi4mmvnzzLt/IQ/v6NR/k74AcK/Fd4c95lK1rQNWlXKncHD
-fXzBI7mhVODhuQ2WV1GQss0FwqWpAkjwjanZL+FiIi50JsCrPGRsXovMoWH5A3/C153tiF4i12e7
-Q6is92sBNHfWv0wyHfWksPIm0wNizWHmd7Vz+9iKVXqxkcZg78pBHoDnBmkLsLbyN5RntUhLsQSD
-lkMge+8Ssd0gogwoclxfOYcKdifFN4u9U45vHqbOHxoG3/Vyq4G1fE46qe6GKV2BxonnxPs7r9p9
-KfRhaK/zToH3+d8J31Y1+njXPXHOopzjHra7/a3IVj5klnWKM1YelPTzqrye7d0O9Bf8E8/MZ40P
-+4mDpW/VpMdtiAcDhJMsmN57gJwdJfXTDkGVz9il/TdES7X4GoijKpe27ywYWNYLrcGWgNbYuhoA
-WBMABzh2IOv5t64QB79gtAjXh+gKGL5UhlsjukX4KmNce2IrBf+woHsNBtCba9NlPBb51+EHCH8d
-nlT/75SBo7+RIz0EJ77J8MG5Rc+bI1IiLKzrGxDAbfMzto23mcf3YX7aXmUsozCshI1ahwrKAff9
-BILTCK3xFZfXp7JO4vUR+QhbhFHwXq50LGllCHGIcV3wl3+Sd1Al5n+S7v13QBtuBhaprKO5S8DN
-DFKDQYwdIUyMCkFNBSRtSzD22gSUtu003AoB+QGGy3w3PSOzEpvDoExG7vlatbH2A6JrpUkQH6qW
-WRDXcU9zUWl3czbN6bev0zj1N/NqfuvwocxT2b+t1/j0PjfSP98DiJI7wFX4D32JD4wiR5cIEwbE
-dB5FGBCDPLh7J9qhDEuq7M9tXbVvm4Jgb80Kf8N2N494ar8DvUPlJnCQUcmgzoL6bKAF4bSxs8ds
-4YX01sm3eVvmXGIJbIVvYiArsPSfN2YgDNemVYALIJiuVCNA3O8XRreIuEeYwHB9mK/o0rAIC4Bf
-BMEV42AyAEtb5n8ZfoBwhD+Z/m8BcFFH6OrQT7yghVhVxazTyfQ6AmBpPBY8loIUvusKUZOSL3qF
-X5UFvYHkw7BhiAmCF8H86xbZNF1C5LPLd19gB8HZ/vgqwtAPcVjPBW9+sOBjVcIXaC15DKrCsDxo
-JpSylmUNiXbgp8DQsFmZgCcVsPOdXR5kW7QC/lO+dcuxb6fWoxKKUjopv9j3N1U9B0C5N/WcrmvT
-mjhUZTe0pOtAw/X4vdcn4jzrWkZAeeUc1r7XY3q3BZ8k5B8t32+EPgcyU8pavpP0Zy4EdW2dTi4R
-AuuvU7k8S/1M39DsBQ/S7Pf4fNz+qumkp9dnnUGct0kOyIE2BvO9XYwYd/t2qtAquQgIMFESIFb3
-Bx4AsGBeuXHBAGhKB8MMgtlv9wRw+SE6sBA36zCC5ORn/2AJ8Hv+QQ3/AJTva8H2XA0gOK3ESWur
-hN9W/+PwA4T/ONwhvk5vWwC/C8BNorIswIObxBEcbzCTUiQ3qAMe51nVuIaA69CsSAZF4RUIVXbt
-o6INFwqciqKdl1vHHgo2DVJLQUpdovK5RAR8hPEGiSMI3lWgMML+sMC+uSmWEILPW8bDdlr56AG2
-tBiHtqntlDRbgrMvNvC4QsU2sdbsQ7fyek1vwfDbMIm8An+ZTIOdS5/ppZ6m5GoV02r3fTvdlLHm
-4XxjRAUZ374+I33y/4Z9ESOAcZWOH4HvDc/v8P5rZZYy6h3vNA/6ugbeN+MDqfvbuw56Nb+lKXny
-EA+aDbSJr9Ls9/nEpFkDTTjNgHgISsfq+KBchC2DSGZXGQHporZ6sId8YkK3h6K5w13xDI7Rikqv
-CQCLwkvWg2qiHRijNfjJ4ot+wY22y0sBvxUM7zq7FbicAJM9doN32dby9CPm55IGmtf473HwDxD+
-s/ACBOsNHz3FioA40Wccv34OWeFjdftrinyYrtZrBATqA3YGmxVwafSj0miIEw3jZciLtAULViwL
-8H7AYnuJiF0q9rEQMiGM62G1QreSH2fQhHzIpILhQWWYLMt15OSDcgs09tsb+vftVQAU7VZ4fdnE
-+wzKNzjMqE3KNynAVg4uEWtGZxDWgV8N1R+Y86TlacutGeWcvAo6FNXOMezJU1UiQms6rOaY1+fx
-/vUGAONrkixWcip2uBniax6nIlA6883hN1f1lqHvq9+svXVurtEEz1FdJxX+tUagC8uI8dq1HTlI
-oSNtjNuc/w2fy85L8mv/23CTj7LA24DrgG/Xj9TKwybqKsge8lesus+tVWOf4BM9gLEkEMSH4ioA
-/iUViNa7fR3w1ruCy00QF4LkAn7Dr7gAYgTBIqX+E3Df4zOJsZP+CmxhA+CFeMhnzp9d5f7b8AOE
-fzvUo7rSgGEHnkVLcMuINvfPpvl1aVoeirPZRWJlOc0bMe5U1CV7I7oGcwBsrPwlq3O6iZAQjOoA
-vPD1Ocbjw2Bs+a0w6bpM7CPyAcHjD8x9PgXYit/hWIBxlawwF+zhVhkm4VlUBPrq0vndeVg20hOt
-Ap/y1LYlj1CJTjOKu5/w5OLgSrpagU8Pylko/T5n5zDfDTH5AE/wWQ5IbobaTYgewK7d8NTmYj5L
-htX8Q7m3rzsQHHnG7dF0UNRk2r0D+2P+4qlC4QTC/7sw7ZYTg93xCc6ODjTkO90yk2XS5WECxGkB
-/ogMoPi8rn8tbhm/5bU534ODO5+n+yu1Vj4HbfvUIO7t3canRSqk867f+ccsy/IA5tZ4FPLdLcCB
-IPsKuzXY3SQug4fNlMFwPJgmSpbg+iMXv5r/bwfBBIxHK/EAlEUIDEcfPU+U8lCnNp275yKAr4ic
-QLB/H9iW8qAC/uvwA4R/K1StMwPe5/Q+1eQbgzR4F0vgHF/NgOtEfMpytGrZN5OyGSWl3gbJ+HAd
-CSevDmWYggAr+vEOMIsMQkhrwnr8Urk+4OO6D9ClCSD8U3cKIAbBeVglHfvLOBFEHb/0Nkz7mMFe
-2sCvMC3e0G/3pg3D3NqnTQM2K5z8IN7JEgzWXpNXYPhNmKEqrnCu5etSDwjuKEN12HuNR3n6S4OG
-L5XywecAcIb+Ta8TCP6IEEhCfuph2XIZ04o/Cs80UoIE4qKhBqzj3Y74u4Hk1Nd8NzdGPNZa94TR
-WVvnCz/A6Aue7OcU/908389IH3ntPn8CulLSFfh+BloAWy3pmi+wr6fNOy4PH4IOjplPB94E0Ql2
-Xf6468Na0dQ1bsyZ3CTwF+IuWTKoguHlDuH+umApbtbhyZrL4Lf9gMYBIF9Xgt54R2BcaTKA4bZG
-ey5Cv84g+DYut0+d/GfhBwh/HapWeQN4e3ofLcldZHtPFL9gOISGjuh7AxG/yn7oTkKpxynf+Q6c
-mgU43CQSCVRLb+xbGEqcAc0yNT/KTtNB88KCB+nLD2KN/foogF+LrI8IXJ9mHQTv+fFq17QxXBm9
-QY3TAd4GgBtxsP6SApRloWX++vgTV1VhS7hGmHcv28Ey1cJjVEN1f+iuDyd/4qlPE3w95Z/ib4O2
-yLtK+EG5LDwe5zJ3p47w2s78J8By+zIbwXEFTSJz/2hMdQtvhhkc56G9m947YP2vQXHIoNd870Du
-m7y1FsUSbIv2iTu5nQeAL+4ZnfeAfJEe8+zMRzR7yN8hQJ2lTkAQ/IE4zo+Heu5lSDut7t869Xhp
-RMri0+rlRDzlLyZNPeGAuPgFi9QH5pJvzRGCZaEf1chr1MASrGkR9l92q0D41xH8SnebGK5Rq0C5
-Al//FvVyveq6E8Gw+Jq7DnS96ovhc+nWYVjghzh6U/zr8AOEI7yc/r8AgpkEwLaqm8kC7FKB7u6D
-r5knACwi/AlMBJ/qtCkf+hJyCLt/in8VylfZKsJfmficeGoDM3X/YZFmBQ7gu+dGQHDD1Gk2GXXn
-OCaoUdbGwBfWtpozcEMQVES7vJU6K5C2wmvInTXt5oHqpdwFwouz1Tletiblzgr88W0is0uF4yVU
-aG0bjMQaJijMNC00m/iiI1OD5evt0mRtnevLN37BBwQ/whA6/xm09PWZfYInYHya3np6p7Xps66V
-BfLm+qf2v3bpfgpKb2Po/TlCnhvQ1a3EKI0yzeV5TyDY7Q/MjR9uzPdRyvGn/fLEI0N8or3Nx/E7
-0P1QfLkESCkzxSdAjGd72mvNvW1qoPTzCI5DztY+eH72bumF1L+LisC4WoGTdoFrhIPhsMDKwWdY
-wRos/pPH04NzDmYL7XTH8PQQXQO6BqA49Wp3gWEQ7IuWxqdNtC1TFOZ0G/R0x8PlDz58/G3x8Sb8
-AOFvg9WEwkmqX/TW4+5iDngC8KQFLyWR0TtZDSvvXw97Mw/C5xW+edGxFIgO7HGm8oQ5WA6QvM0R
-Wg7fArxoCWba+uSqWS5QsIsuAZpEu0EKEyysy+PrM9JtfH0aTQ7vCXDBnxi7Jv2dlC/Mv8fxJZTu
-K07W9uC3VlZ8/od6vQzuh7dhhCbjlhtuahlb0iHVr0Gr8/gEUOilcNyP5WYQ3MrsD892Gg6FtycW
-9/sMim8B8X+swd6M4t1IM5AMovQdr6eZUtcKqt30XkNvrXwCe9WPQzrF6rG8iswP9JeeaK0g3lxG
-vwlQ07BQJkKW3sZ2KN6qsq6JqZHT3KK7YCkUVmDQ9z7+0EcjzVIXeTn1OrOWKBdt0t0vOUh4D/AI
-BrN1/agDTPdPB5BpLot061vLafF9AMC17SXYKNmMF3K+Pfs+TrqVSshWN5X55vz+rfADhP9GID08
-ieITQJY4tKnprLyXTfO3lU2TijNLCyiUDGSHWbpHNCl23weyeMOM+R23CMjw5x39IDnQDYDWaP4E
-r8BTzztPuTsRPyLLrfIAnOoEbD+F9ln0AMWfrKOnOzD2GylW0xZdcAv1E5gyQWvvBIjR8oFzwfs6
-Qe4BAEfcgO/Ecw7aUjbku0SeS/c6Ol8/sck+uT7gdliKJflk4J34busrfSGeaGevSQEPc5jk0h0n
-9uC0C5L4qgt/IbwfxZ+HgjcrNwAAIABJREFUgzikvGm9PLy5bhD3WW+hcd326a6jUznCVY5jDGil
-J/XcTj1l2ekE3qekHySzaAq8H2VejlgVy5SRUjdOs2c8x3UsNbguYl5wjXCaZl7j197X5AG9o5yf
-82nSQHDIwvXNqQJrWl3TKotX+q6oUf82e7pcei8iz8DFMNcYjL0SIFpE4sH+HTdY/PWt6r863Rx+
-gPBfCoejB3RWLHkmUXsavVvNv2nt+/BGpM4881Y1jmohl2pqzZPgtf0Vi/8GuTuHqMj+1Ctg7dU4
-vHG1C3y9g+A3rMR1XCClOI89ZdEy7AAYLbby+Ygcwe5H5ACOOV2twkJt5BVtDIDrlJ9AlkmC4A+M
-F4U2LqegsAO+yV8w4wCQjRVDgGSpcy1H2pgHSOIE3jI11/q0y9H1Ye+AeW4LyBXpc25jfX39jmu3
-F4PlimZ0Hj7UrCeWgd/5Bnlj84x+K4t+R+1VOTHl/069d/ugyqfz+qY7xFxrwo3Y/TdPcW4JCK3d
-93nsZwM7/T3y9vk0E2q1GQrgzJ/DtBKdFm3pwAWdo5LGfJk3nAwrhWp3qhyighW8sqEgwaySHBQp
-oNj5SNbueko57gP0zCfKpBmb0Bhlnm97PwJAjofNLW3BPo4EvTyLdR91EIxAN+cwLb85P4FpwCJt
-wbj08sGW/5+GHyD8F8IoxuITVIVUpUSgjFLOCh/xFBJWV4pkmMX8+3y50TDW804y8KHluAlD4NOr
-+FdNcEjA2ptW3koDQGwJfleepj8TmCurpaGBrlivfC3BsuNHKy7nNXBceStYHsCvCAJkKcDYcJvk
-SzkdYBjeRUC4t9XiXb3mXQoYNogLxdOlgq3I43yPtCVNT5D2VP6uxAQvKvSogKfSIq2+DnO5N69v
-2jsGzGybeBFr+TtgKVLxGkuyt1+On+qewr9Th72lE/DtZUru0OnTGid/1nEvhc+A+K7sBHCntTd8
-DxDTz3DtTU9bNhgkEBKgG0hN7E7VolFcuFztg2uHqPOwX7m9acYK2JUuDxsoVuA7WYURBKu308fR
-Ro+DL1bgpQPy2rLQlRv0Bn1fm+rA069NrXshvNQb6E3ZXUGwVwCuwYKWX/PFgLunDRaCXClgTe7O
-4N+WDT9A+I+D74ZGHWJaSRJgRkRGFOsbwwp9lbzp1wtg+6dhHwhO13yP7PfSJVQB65BKCF/bp0/F
-D25ezZLgFuqtgDcswEbgF32GqSeqclbpoMZIo5mkpXb2B04r8Q04/lh3i9g08hGWahVO4CuSW2gE
-VACCq0uEr4VQvC5oV0AEcCXnnvIobcB7UqzGaav52nmGMSTtPQieoIbP3Vr6erOG9LTyOoyvxnPv
-zpJiwKi9rOFGBo2DfF7bWq7lU14y3yr1L8KT4ptHfR/+VCpOyrmerbZuoxr/vhd118Mj0scWWkuw
-wRv4Hco4IEYQWgGxPKXbQtmwmUDilDwqnqK+VF8EHwVrdZyY51P0DIrj5ghFAGwk4zq47m36LLR+
-4CIMYPgWBEv5VhXyDRY81l1zDXh/8Rlv++pg+Y26CDSDG4TuvQxujVLqryv1J2d4Cj9A+E+CzQti
-R54hFhoUd16Jlx2BLH89VEn/R2GeID5EAXmh2S3iVeJGDN2n08C9wU/YHeB1qbnAGuRrCrRRstMY
-Vj0aEw+vI/A9WIILOG7AGMrJ8V0CAItIfLoPkGRFEY9Kel1Bh8oN76al8Tv4M2n3fybY9bWQIvxx
-eo3TW/jSBxJ7q2RTXM5gunMifQIOtTbkWWDHrxxEkFNBTwIjqesg0zrMdLmhV56k3oDhkRhaifKe
-jv/bfExNa/BNmEd3pr8JE6jluD7yVADcOR868G2ZsQJr1FpTA7sOcK3QpY/xeL600qbVKDRIgggX
-MqgAr/f1/7J3rUuO66wW8r3/I084PyRgcZPtdM/sOlVRddoyQuiOlglWSnY77lNBVNNGIgpftIci
-LvoaLNjYvs4CzIkPj1FTeniXAvUjtK2CX466Eeu8G5gt5STkFnXov7UnsAHQ/BC1izP9u/aNve8p
-sNX05l7L1TqEF+K0r5Llt3OLwN8x+JfhC4Q/DcOiMufvjm0TykaBO5zeYNxue2UxF3Yv7RMVbEKz
-7nNNdVk+E4WnRGJ9Wo1xZniqZbLVXy28W/mAdiguEuSA+CVM76BFbrZXdhmGOMU+2bf3ChyjFdjB
-8OBeQRTibiVWutZSrMvtA5tP9+lBcB68CBKzC4Qpd+nA8MBLcQ+cQe2Ull4usTRueJ1fAiXCtQwv
-5MN4BsMhLXya8WrzxO+A6pLKi3HiywyZI/boHcB7tYK6uuZx+c3Q9kRbxL1yI0CcN+tpfZUTIzjP
-QqzLtTbu6yMjz1FQU2QHKrOqZOp7z2cPx4FA94gcdCtpQe++Zyxv7w3HBkq4FHpHboGpA2CnNzR2
-q7DS9peMAfjOfVbLjfVj0hfM1kvITOqvoH7AwmD95Q14RY+B860R13arCxvQG10j+nuxTN4nk+V3
-JS9+0eaO5+T93fAFwp+EaR1JTJQUmdajgppIl5ihS/9ZdT18joSHAhXADrU4Kt34X7++8UVeAS36
-BNNFevcSnWmsrbTc06WDHUIKgKuFt95nn2Hl6azAy7XiDXkXrViDKfkLJ+uwkFUx13y7RERr8OVw
-2kfMKhxBbnO/C52OTss0v0qg3Y/PsCpvYT3A9v95KWiNhCiAW007xjmOBaadTpCQLK/hI6p1jVvc
-vWUdc9Rcfjd7AufN9E7oSulHKPLn1OPW+YM99Uotarq9IHfBb/PnsgTthbPzW5evy9OB2nJluJea
-bsAFPl2tDbgW3R8RbnEhxrw0pMMEQIltmzqjkXF3JN+MGTauoEegcRHYCqFf8MoXX5bz/JDelFH6
-rrvfmVY7AbFqx5LYCREKmtUvWGdINh+k7KFvtY7Wc4kXE/NpESor+xJHni3E+J/N+t8IXyD8NEgT
-7cZNhlvJ94PAsHN2PPfreT+kVUC65C8aGBbrRD/VKZahv6K3Fsz2OSI/jzAc3I0a8gLw9oDYNW7Y
-aIPeVvkC5e0PAl+q/r+9tbjxJW5ejssuEfH4tL316lQRss0Y21LBE9tB+Eg/WYSzDBzWCdiGQ9eJ
-7DnjlCeCIRrjPwHJeTav+x4E+z3H/rwAw4Wm80u6vqwAqutv/N/x1+CjdFIFnZwZO0aQ1cOFvrxn
-4BjnwQ+R7AdZ8izEeZHnSB4LpyVIakrlGjCnTKQz9GmeTJW71wEUMwobSh2DkOluQ3FDRlTpGfQS
-kX/hB3WNTfZZdJyZwN/Xf0koeoU17iVY/xSrsIT3sRlzMBXZrQ7DBiMKZWinjhOv1mQQrPfqLS0w
-sPmsaZtrCgcUuAZ44GeY4zHCi3+XYd3je7e2h4MVeEtDnn8cvkBYw+0RmBZTpAVlKTHRJkCRIVVm
-+2Tb8fW1fRruK9wUgvZK9GarKAp4Lza7kj65ujpaa99WDrwYN7wkR8OpEZi+Wy1bWYVqY11NGSma
-kV1RsfgCpvoByy688GbHp3XAtz1WbQLGCqzUIoyuERUw6Utelr43GAXACowREGv+V7rXcarAVtI9
-8NzIg9fforUbGcTPIDgCydx/uM5DWkOjnWcaozpec5rJTGCirj7dKOdt/op+0op5jQQiIoYHMqt8
-HCGnZxmBdnsz7cf+TOvzdGNkdO772mf+lcaNPHjXxln1J1E+/7WTerwGsBZBX1zDgIRsQgoo1U3P
-+4SsPh09JlTtwhwPEiXT+1Vwfpmu5qjfMHkJDnp3XIGcGV6UDrRUQn1ROFYslx77DcAwaR9r/0rK
-5qcv0d5R3U84dkm2CkNJFnFQvJSfzRHhwKfy3PIriUff+xl4/nH4AuHHAUY6rudKziBWmnXYZhyY
-y07403BHCTd1mDaatDEH4mUxXpewuHgrc95rr3vxTagCYtlpPPEu+WgRVs2Wla3XTkwhWRwswURS
-zwQOFuH00lxwk+h9hmkDYrsnB74KiLWe9jGaRLq2abcRLcPzyRGeF63Ifi5wfAEOQWmhiW8ci6e6
-TeSyn6RFnrx953gPkPN2LnBfrluJH3nSFceBSEb3CAp5mnFE9ZD1UFqfnR337qpvl/RULvJqJRt0
-WmtzXYcItg7hjsAHodOQPQh2qhDZjwDF0b4rcaqJSxmTC90RpEOhuQan6+Tfehlg6Pr55JO5A97I
-As1p6iKlzossxzxTfRlvQt5F69KN1gBg60N4EGCq0scVmhGqRtD6q0DXADDqlP5lOdK9FQQfXSUy
-KFYxmodIN2vby1Uf6Pn/I8/XR/j/U0gzgCjNW1jYkOYblxT2Lq+Vlne8vEaGtfwoPMLEHWOzKzYW
-YltwuIDIVUv+OtHA716U67fcyRbUWlwOcm1x8QZaQtVnOAFiUXlWDwpxtQYv0QLoRyhYgkmoO0JN
-X4Dr3SQ8T/UZHlwjoFyzCCeQnN0kTBkmEJwBMLb9lbbusjFuxvq13wCE9SPx7ekrYNtfz0C2B7+e
-C6d7jisf9t94BVVwhz9/Jjqmq0qY8mLIa6vcwteptzecpJKucgVeW1C9hNzvJ5kIhtvt8sH+eYaj
-VdCJ38aCI480eeb7uwrYVyi8cN/O53ANwIlM094Fxb8S7Cs/F1rk4zYCRzHiNDJ7bZAhgacVWCt0
-UV8q77lw1juqw0AZZt1n4HdnVBmxX/PM45LmdACznPMg0LXvBXzdQxqQvYRtRAol6mk5iMNV7yGA
-hePPzGpMvndbQSce0XX+b8MXCH8YgsLDxQu7Bm5Khd5FuvG3bM4zKaYq9y+FW9pRhnocVD8TWIEX
-r1uCdc3EjYAV2e2O8Z9OlvklOQTExC4TUFTZSHRb0+/qtluEAlCS965G59LwLnR1k5D3O7lOOHCu
-lmVyvI1x7RGMw1WgTTmNCEFxfYFD2/+i2ifGK6DwIX8EwtVybC/UKU/KH6880PtrpfX5M61+EU+7
-9oerbgpAn3jdAlytvP2neaCRvpxb9yGv1PSh/Sf5J37UgT6+eWPvKL2sDA8SvpvTi/C5hR041JSc
-VtZZijtEwzxTCXPJxmHruOe9ksxEZq12DCOhr6ruq+vZvoHjmhbDsFk0A17qoGuKswRfbJ10p8lQ
-+tDHgdlvum8jrP3Ai6DYXpbTOPXg1/sNAHZyb4i9EmpBiEZj/4FlmBY49pmH/1FaWou69+JsDhbd
-xezWY9HJVV6IM3Dd8iwhjHL/cfgC4U/CtAvBBPZo9AkWSLQ5MJYhDS3sK1d6cw6jzvWE43wsSiMn
-NkoqW9GH6piyhq9N7KgWom1RvHZ7UE3K9qTaAGItUGXbhposJkHLCJlvMK14/rGLzvobae9IC77E
-biH20yOoWoUJAXfdiPPmnEdLf1QDQfCL4o9tvCCu6bgxQvcl14d7LhNIx7UQ0jiCs9MV55vmwnRp
-8vg4c6Fhf536cvIZzvm6D12kZ74s9xS/uq9ypdDP+1LaPE/8vgcCTx6RjpLr9+FO+TBbBoNdmtZI
-NWZ+cKEmXiWc6aYPW17XVac2TLQlW0GxhHR8Ua4stkNYepaD66pfRRWEyz4PeGgAguK8RjPIvBRG
-tkVAvYnc2IJp+qklxYeBnR72Ri4y1hXldCtIUnVThzKWoCPo32WZzcZo/t4NUdR3xER6FCfx3jOJ
-wGWBHBTD3rzyYXqsXgbHSwfsDOL7rc4LnSL/OnyB8C8G6SK4iFugDFsPAIFecCJP/GOYlO+95FoB
-yHfFOMitSplgEXlcF9yK70UoQme3B3eXWPzxJTk89FwrEPW2A871VC37KVvIQDABCFZ6sP6e3R7K
-5x39iKeTJCr4FaitD88EuDAo8O3AqvZHBsRR8Sea6lPOvFV+puerbpQhja/z4TbTXeXA24GfE7gx
-mm4KBz6YKeP40AXfnTGd0k58U+i26JMEp+RXjsg6t6qOOxRfl4F6pX8O6VXlzcyfzg21vZ3Goa+w
-xNumLt1dpnT17tP9PGLVv/2aP4R2aBKxmVFbTa84xx4I8Ts8vqM6/XKvjL1Z673jQadVq3BIZ7AK
-q2JUBVbKTWM+0jxNj0PTuVXBML5wrv+jicetsj7Ya+xxz86+vkT5l+EKXZOEAvAlJgDUnPb6vZdM
-b07+xfAFwk/DuMPkHVD0r6RLjPTirxYuFMcN/d+F3I5Ugexw1JM8zSSkJ06i8JLVWkebMALiCo5V
-MqYR87aKOLSzxRnqlJqHWlahS2v9Tffb+lstxsmFIlmDnZcSbVdr0/TeNmPoQ6IIfF+pUZiGH98M
-71t6nwBiauI2Vjkd1kZ0x+CU9+oKY324ariK635CFPs7giIC94j5QzmPfvLYPqjfKX435DxX4CKs
-5YM8DpT7G+Ezbg01xzTmd/JQuLp9tpMTaQ5i+iLn2nT1u3PuMErs4hE0zf2b127lPNyHQY81FiK3
-RpLrfJS2eAb6sQcE+Co917W2MbUFEoKOA7cJ/wCNNw+T71umV/M9EY4UWnzzqMW+6KzAvI02mEaQ
-I8e2vAR6OzcHHIT40pyOlaRfkdv7197kESv/FybhLxD+YRD7hzSJNHH3CFNV0izIImeFE8/9Sk4h
-Kdt9+3xz6Qu+Y7U2Z3y7J8IuBLy7qwhuETu/dygC4s4nOKflyqxPtjh4zD/mH0w6rkIR8Eb3BwS8
-2TpcXCimF+asNLQAi80xSxes8bxFTsC3gtTGx7fwDGmiG8Q5Dz2IG21r2+yffL7yTT4Pj0EmLCsE
-S3esvMiHIfPkch/XcYg/WfvdrOqAg0BK92J41HWRoYdUFYy04UNFFiHGfKXm2tE6v2Dvk9qL0Rd4
-kpBzc+DDtCdxA01BFpsezn3KlRRD7jiN6voowygpyjEfTfGoqUeZt+hR0qj7EPRyBrv9fC73nFtC
-4d61FXSYaXoK6cE1wuaD63/dK4XYNtRFykebrQTbl/dLdER4HjARA4LF/KqTBZq2Tn9iyEs7n9iE
-Z23ePw5fIPxpkHgjSNP5KjlNIE2QlYgGZeJZRmVjc+1qTd8M9ZUQTOvpWRGNQVROVPRZDZjuE+fD
-b0wCmKX4YtxaVw3oJfQR9rhqte7XjPwmqpgKVzYw3e4Q0p4jvH45LoBfS08uFDdcI0goWYBT7YTK
-2bX9uNUPWbwecTbzXqQJbiB9XnoU97mj+jjz+PXsKpHBQ7cCJuA4prEve720nz1GbRrQc8hg64p2
-J727h+bcCrNOgw1aN8Ihb7R6dcAm5unA0e0KD7W9G3KfZleIiR7LBcU41m3Wr1GfSkP/JF7thnmt
-5jrEkEdNqpJVtiQkjqlEgwV3Jxc7QyxVSi36gi+qjkRODMjHmRf3H9Wn0epbR4DivaTRCfWKFt9l
-EIlWY6X43FtxtvRYXnyJjSgecebt7o5P8+Q11vWluV1bnPJM2zoMg/+PwxcI/zQkbRcBLiIQsNJl
-038CKncU/lSNT0NQn2edezNExXQpL61xW7CoNwX6AKzA/vYyvKRBDeglpvqjGkS4IxedrFZf1qdq
-GIWdhv/x+LQAXt8TmF150F1iAr+rSADB5NDXRCEN2qO1zgAoA4gIDiXdx/gEYG+l77HGAzs63jmN
-a9rWpZqC/PmqfYHXLk37BsP5nksf+3QBazDreHWfu1bjWod8fUK7Ch3viNtSHm6o0wkdysEpVtJP
-hY9pVzW+zpPnDh2uvayhJ5vjJrt6XL8cF8vIMOtpvOjhkIbBDjbT2yZfHM+ig9KE6XQUoi9hBH2d
-9Clc9XUtedZvydjCvrfYB1whsvzqa6wdIDbRJ2CrCsbAMFp8Cay4MGO9VC8PjyMVlaP7cXaNIKFs
-QTb+UK3Fx0S9fzF0hYFhwj76d+ELhHf4dbcUQbndFqSJvzPovYfYnn04CYHP536jnIN/2tq1fduq
-nXXck6TLoXU5tacWEo/cWsCWAg2AUgC9ECeC+7kB/pQqNkHM6stvEn6R0JtEXvueNqB90/utR6Pt
-OIBd5emOSJO3ECF/C4wVENMGyatmq8677tgOov2VVNwiPT75F+a3jOumlEHqlIbqOXxsevpGo5tn
-BqURrKIXHKSp8gaFn+VIkkVUy8I0bVMOmSaJKikSQKzcA7rZxeJUjzt1nGifhk7jTHy+UQ9pJoRb
-+i0ZrbXQ/+U5gPCgp9drV3a85pkwp13Ru9Kux28+oC0HcNscdENUkLnnu/GOtN1zYVBAYTcShChZ
-gJt0uzm3st9hYx7ON00mv21n1+3SZbvphd4WJmGFqgFJGl1ovdcSW+B7HBFTNrDxzo9NDpreqrBp
-hliJfA4pAJYNJRQAi/EGf9/2aDSi+BPLZPu2GDPikd/UUPfCFwh/EE5fNQNXm6dmahZLoVwF9ktT
-KZufEigxbS+0rk2++fQN5xBLi/FiTluOqd7n7KkODAsWAM1eiOtpPQIwfPp0xb9X8V7NSxHhVvUm
-odcCq3vjlW0xfv+JILj7vOHsYHkLvREcZ//hAQyrwjD3G4HNFIcpdGD8qjNu7vl9YgLqMEApaN48
-d+/QA+AVTEuAl3Oag5gOyGKbKFx74OP5PgCWcs03fUJIhQeePTVPgOnfbyODvmKPjPqM51QOkYHr
-lLbT5ZQeSyKie2AYh0hC3rha7qTNdLzeAcBV1tOAADhOQ18hGRRySN4AKWBF1akUuxoVVBzsGFQf
-XGyKUfzvrIJOb12XXhuq72kw8Ajxdl8D1wVZPW/AWODsIuwjtZ5St603ho29l60z46OG91mTRrz4
-NRDpy3aLJFtXD+4UDEOMP9QBSpyJAogOWPwfhi8Q/klQwPFgzeGmmZfLZ4HrreCUhskdHu4aDmZf
-FkLEaWF4GRt+SJKfKpJ9y6Zuup2WdWasjoMqa6fXIZwkQRRcJrKOFgTBtjK3MpJtCVYQLMsq/OYF
-yxTo4kfkHejqQyz5PrlQ5J9kRpeIDIK1f6bNNPeqwAy4AsN5JLp5e6LlvY8TXcgBb5blaVvhwtx2
-GfEt7QxYKF11NPs0rptu6sRprt6hS/pM9PDJg9EsmEsV9Jc2l1nskJIAT+HStXuqMN+XfwqTFTjy
-+GrpQDDyZbCagWyl5RxdHSPX+XpvIzpPp37VF50b+PNo3dUQKZmILk3BQJ4lnvrhTpq7OfTg9kkc
-rvaAsOeTAsA9w2R/c7k4eH/zqGAx7tnWB0E5sPWfY5KUR7AHWMGAy7ZLthJvjnQkhOz2WB5rsri4
-A/AlIgfReO7/Pw5fIPyLofj+3s9JP9mpOMQkzH2lhydEnZDEtmLicvIFRXmhGYDwRXSuU6S17F2V
-7+TTugKo0bbqXhnAMEUAjG9CK7+pLcFKpQ1O3kQIgmkdv/YmXsD2T7UAv9EVIoBicJcA94jWCpxd
-I8hBsFuJ97+mw6JqnsGwc98biTvbXgSuPU3hRga6HndAUnn8+CFsxTNwrJufT//T3HPuPnRpQrqp
-RRDTfUI+9nxasefa5vc2mNuSdO9vckQ8+wwYc3szg+MIUSrMbmDLyiqVlkubwKnHJ4Bc4esnwPaT
-mXDK3wHldY2r+jQHOL/8ZICJzxkD2B1AsVRSTXyW9jehV+w1BMH720QS+wEoBcCKExUAM6N+XzVm
-kvjTxdCWaM2FPCyVTKlnGN0sVr2Y9AQJR7T24pvl21bf5G/DwaBUgW94qa6M+78JXyD8MNwZotkN
-AuX8xjZWNwfJSXnhZH5w3o+4FkC1gs0RG2UYfVY4HwHehO0QQBnopQhgFvDdgFctwaQAWAq/wiwH
-w1qab0v2EXIQTOxXBbl/AOwGYAyuEJJ8hTUv+hOjm0QAxKsSCwwLVBPA8W5ZN9ccDDtHBAKz33Du
-kzO4ne8zmNXadi/CRTC8xzDlD0A25UMoNYHknEaQ3gc53HXcbHwnIKybTwbMWpnAl8q9E8fQtu+A
-Cp4Bhsauy3hJ+qtHtzSQ59pwnZcTbwTIdT74j0rorAM+W2cS5OEVU+N4XIPgnK+T/VnoV3UHOyuU
-6sDw7pvS6UDIYJgy71BFjgQfhciDa/+zEPXGPf48c6b4bjNnELw1gvkIsx0xpuDYfIStrb4Xh70a
-CFlf4Ogu14hIK8CX4jehpnXTXh+sw2j1VaOM8lv1xIqNP9Usm7b74lb//274AuGnIU+2O9tkjQQe
-TrGfBZ+oNm+t6LrtBW4G2LT5w7RsQC/h5H5Uw6TMurSxZZGGV601SnFAjB+2o9U0h294EFDh2BPw
-doOgBIKJNuiV1iL8fkvvP4y+wgZ800t0QslHWMEwmaIUmjZPfEmovvyGoDf2sfdjv3VSSMkzONxL
-vNd4v33EN5srGI48KGcGt1LSIk+EZsiL4QlorPniQwqOUbzfcwsqYOmpUqP2uQCzTzebWdxFB5Rk
-AMdXoJYHwHyrCpUYXFdBXw3QxaSgjkYXCVJgp/8l9usMkKWhxTxdWmrNDZ7Ij2HSpRONE62Xn8ZJ
-7F9KwU2Uxylk2UuhiShAG6dj1GpduG7XaY/u+XDGoJnF6ELLR1j1uIFGLnRsYgSqFBKk0GIrG3OY
-7RM+N7UiKT28NLfbyeQLqrP6KpYwmvObpdkm2lPN9DvhC4R3eNb9rvEe5fvVMb63A0elDROvwB2/
-YyLCry3C9tAsQG6kxLzPQsmV1vWUQwGvtsJdIBDogGWYOb2YtT9CSVnQBp+q3Fb6+hoLQDBxALzB
-JSKB43AqRPAbTu4R4Ui1Vbgdu0YS4gSX3G/YVxMY7udEBbwofvIjR52G/Yv3Oa3Go4Wekhy21uh4
-U8sr6UrttbqJXMAvb0CNjgHByylu97p5YP7rqjwIZz0ypt5QP9zFBvDLRWZvTZ7X/8M68gBdpKc7
-9BKIJxCruAz0a0jf/yuNQko3jpJ4uiCHuy48AcN4JQN3Z3qsS+pwTLMOPQwYdrpJFIj3fJF4Jfxp
-aBvUprVzCn2AFVpuK7BiRWF9id1PSLLaBtXs+3PrHwzxbltGV4oIcrVM14rLmCsmKPgGA0ao5wqv
-PTcAX5LNx9iEfx6+QPin4RIQN6rrFwFxnjQdqCl5WHm8MtXdN83KtNjaX2R70C5d6Mf0Q1p3xXRL
-40gzgExEr72WQ9tiLT8/AAAgAElEQVRtE2OIx4uowkrx6CMsySIs5v4gIgEsRz9heEHueHya91+I
-Qzty/7kyno5Fi64StVfnESnyoD73QO8JDPsWa+mSed1HuAOz5cqR58TrtZ/CfdV9Ar9jvAPDqUbd
-WB/rd1Hl39mMJuTK3SXGGsA81enJsaOX0IUrn/Z9DyuacUj+nAKp05hdu0pMsakln4c7ujfoSOxQ
-9A0GtpEf2bCjp1E6DWDiW+P5SV9czpKbaVm7iL8Qhy/Oifru6sty20+Y/YU50+faN8mHl6Drin9w
-GtDuzRDf3ut3g6xn6DfWYda6qlwTLRv4bpBr8hPw3YUbyP8PzhAm+gLhj0K7tACU/Ezyb00EmMhU
-6xxTifQ8wggIgdt+YlFJ+Lg3S5/rdo/rqj8dtDjAnWgFDG8dYldyKFiRJbn7gR5powBYlkX4LRyA
-rx+lJpFeXCLe9Eaw+85uEQvpuksE3Ks1mGDzTSA4xxEM53EwhdbOnastUi5B8BQvAFgyQK1AdwLO
-BPIzn6gMOYDkqaUjnryGDRoy0D3RMI9iiAqumvIeqpA8/s9y3q2CftPUUpu8kfH5/pi8WTM2S/3o
-8zGbCCI4JqLGaDCA3aBGehAr5X/k6dKntd3JuQrTii59kBMo9jAOT/Yj9lvVrVVWCDrRuZUW8p93
-TLmxpd7prZOQmKbfFHYpUUv5C2gIgvVlOZs7Bhq33O6pjAj8a3P6juz0OBMtt2puyg9xOS2/NLf8
-frmCWcGixV05lIaWZlDQ0xGufzt8gbCGiARqXO+bLJ2wyzczfzscd+7eRhyVXVJodq6YxAzFWemy
-En2YRDR1PCnqKe6A2I/CYSLwFV70F5EZMfzFOfXf8t0SX4A0ECzrXMYVX/V8/wFLrwFgdItIcelo
-aB2mCoYby3B4ea52c9f1BkI6QKyQs1OamqcCW+8z3ATiFtDEZU6nQosWYkwngjHkXg6Rbq0dveYJ
-6+K0ptt9sl9xGdDg9ZRGROmMzR6Gf6JnfvPXnDjfcdc93IDbG2D5XqEWJgjT/Zy6/wrXomQwqMVI
-zmhRqWTedCnsVg7Sr9Kn8BP4cNLBXd8dQS8RdCaMnLElFDsVYqxyCxAPmUe+fuXcDbkmqpmidrS4
-nanrb2PEU3uAqu4S6iIhFL+1RICLR5SFtHITgr8wF1vgcZWtwBm1r4+HnxrhYJgUzIZ6qrjdCDhV
-wk6xQKD8H4QvEM7hhCKkn1pHVdWSk1Z8PPhlRYd4XQK6meenvYkfqNnyOwLumjbsF1NJNcg5PTfd
-XpKD9RQBMfoM14/JxE7agJcAANMGpwsELyD8lqVgDMz+QeuvuHtEsAYnMCwYR5qWvcEwKWhy6IQg
-qpuRqJhT91q/lQ2/hQKRA4NufR1Ixq3DaILAVvNWQNrRSPDltwqKryy+gRevnHmm1V03/wajUmEi
-79dL0DteXfidrdw2yblivxJi+wcIxQ2vbpaNtCt8/pPWFDgT6oZv0se5izR/Gblbc/l/lnHtKjHn
-vdh3UjiBXU2/ktn7BZMDoEJLPRxue0BcwK6xyjyHwz76Kbi9Cj34vXe/49onBnJ5g1xxX+ACgtf+
-G4GlAxF3PWg2S8yTdXXa1wvY3eWwrQPcI3acPY9bMzhWEcCv29Z0LDlBDNn3f09HTeELhD8JeWf6
-YZ7sVvUbYQK3QXkDR8tfFlTm3ovlRj+4CG774I6iPoJh28R0VXVgd1uHWUoaiYIedn0qRGbq3TQ/
-wYHMEvzetPXryBEAS7q+BV6m68Bw6xdM4Bax6pTTrJ4Sq4xBeyUfUIPKrYe+c+8j6M0sGcgaTWpa
-VMfprGDJfFluPRECt+0pH9bLrlLzhG6A1mXgJjG53bcdaDh/pxqmtCsQbPteX+ln4Zh1AEUd56Tc
-uJPRgeU+f0ttiBVciV064GVqD8cNOjuvEbOSSaQHOMtRRrbgxrGXgZ7jvwH66ko/Se76vLUKq5AO
-DAdBQChpnTzXExUQ+6JraxSI09lELfNl6LlRQ8UzI1B3dpZgEger7lPrij24QgRFlZAw+vHY5r9/
-YQ4G+zjPYD066E76zor1wrpTIsJLdBRpq80UF8o/Cl8g/GEIiqgowMzXxDKzrZkHC/DDPS6A0iIO
-lfdeiLagds4r1Hoo7ZQtQLGGMYMXrKZaehdf8hfmBIgluUuET19DESJZR0MAEHZQ/Nbr+03yx10i
-KjCO7hDvbAUO4LgHwgKgFy1RwS0ibLqx57sTIjQ2b7AnVwmzIexrAshQpQ4E6xVV7NKb3PJR4Q97
-oI1jmy+BrCqvnj6BDUBadFM4AGPNx3EUsmUY4z2tAcEFY/xUd9xXKvdwcuLiztGBKxulgXpSdhMi
-xlLZMNulysQxgOPWRwgbj7trNL9u9u2uEOk1bUo58fWUOyHMc7uPqyL215Cmiz7nKAsMFdZh7EGU
-OyAEZXf+hj3U56cha7mqNdBJAgGk/eDIrs+6OAjWF+XQ4qs6pAXA+ILc8GMadd7ofp6tvkETR7q5
-vOi3y25Y6V+Wa45H27qdDCjrpsAOM/5x+ALhHe4vjYNK+q31dSecFruzjFXCpZlTwvY8CskJHWRp
-pJeN5H6dcyldBge02kGDOwQ70FKXiRBkKysDnAQgWAwYv422LcN/ZLAI97TJIqxWXzXv6i/HlTOF
-N83qiA049rXPgG4O9DmnWTOPWqMDBzVL5QU53GYKKAbgUkAxxbRwLWXkPL7h4PpSAJW7GOdhXA3N
-XdyvoRe8ghOoOVuCp8c3CrJr+GDHuaF3UDbHfzEv97QWKFvsZ0q2wjXXcwJMnNIFsxhPXTto0WpU
-SpVnaZL4cr6O87TqarjSrVOIa2g4Im0v4GZEodNj7zuIq8KWuMNks7x1P64/7NFlHIo+hk6wpB7p
-eTIARv1nVlJC8BhBMKW4tx8BMMU+1cU/WIXDPGKPFPBL7sVcNLO9xEfg78tBUZv1OihabKvzrur+
-eyT8BcIfhCtlYg9j8FR2SwHBGprX8u2dSLn7sjlcejCkMzR47OfFmBbaZW2uFfJdcQXcpsQFegDs
-svO9AJQxrTd2DTALft2PrhAOgO1+g+G3CL3flPyC0TcYrcQSfj3u/U4WYdH0pXLUHcLQlJZNUCeC
-ewJcvDtyPlLvPuj1OemK9AQqw1UO6RLvcWydR0IakY9tyAd7Ai6VLI+uruL3Li9voo3VdzPHPb7u
-+CcwczethYUNzugSw6jf1CfcxG4wxxuO95xoISXpqNtlh1B7IY5ZM/858rSlw9v1BagmkXKZNu8T
-OeVaL545rnTvveDQL6+ZFKnVmqzDiWQJtseMVenTCl3GtLLOj8Lv0Orqz04RRAvY8u4v2aBw0cW3
-3y0Gt14TXeKXOyvVGYXAVtLcd/CL2h6/G7S4nRzh9cdTItSF0stUcR1Q/vkMfRq+QPhpGMZoVmVz
-nn8V8vE/LQ/EfYPE1QiM99daDHKPDevztOuYKB2HRvAzy0gDt4gAelBhil8lXg0MvyVd088o/8F7
-tAR3wFdSegK8ehWK9cq78dhpT8FwTXsbNefJShZsCA9AcL4qwOx5JADfa/DdbN4JLGdZoWUS8/lm
-sP6HvR7WXJxbnYd2BbTxvktvUWIT+Hh7J0+b5RYWjnXMlkROoBiIVQYRfaZIJ5REsOm7nivcSREV
-OLonRwG0BfDWUR8kNvfdtzb3eLpw6JHLPHkut4IGMGxkotuA2FaHAr0D6C0lhrqcQwbD5+ndtf2C
-BgAvA2D9FkQB5ErawJAkgmAEvfUoiSG9b3H9FiKCYjjcwYFyAMUUXCoU/Lq/L9Zvl7qVfj5Ozd41
-UgvxPw5fIPxpOM0xZ+kJp4yDfnkcGo1+BT6NUxEKCmtWYzm4+1mF+vrIDZ6pBNi0AghGsMQKfnkd
-nUZiCzcowGWGDdZYMQAsIW6WYKF9drC0FuEMhsu9+QTjOcKUgHCKU8NDPizVElx7NEKArrc9zzQW
-BcAe0pbeczVcQKw0/MB7Asd0vHpLZ7CcZHHsHwTFDogXwXuQSzoCiO41nVO/Ftrlm7UAnH8AlD9X
-QxEA97RcUILJFy4SNTzYPbuf1tpKYFRnl8ujWR+mHhPggEl0BYCnIju+n4RbY12Y4sy2NbPJvt4G
-wGzMSXijRBwQ92BYXW5Lrmk/beiZjSfGUcSs/aoOWCXwrry5RQMABmwZ997i6gDXFgD3oFkgHR/q
-JdAUHBPE2TnCW24oFfBGPjZN9+R9fnKr/P9x+ALhh6EHbnniPZJg1M+UUZc+M2HKcbNUkHuwCnMm
-hEWTwtA/cUvq691BOQS5GRShTtCM8F5CBMTE5grhIGvDSUeeBogNAON5v3AfQa8UC/FEz77D9qty
-WgXCOJE40t1XvI+Krm625129H8FubPNLcc6DQFNLmsBmAJ7S802gVUvF+Zjng115yHu6WoMlgGLA
-MgkQOwBYdWZLi1vQfTBs6ce1zcBzFfZGxtMMuJf/lCHMCU735H0W7rVSTX4iqnroqhInpeMotRcz
-FdVY2YQEBzdhkB5czxbiiaPj7WJ/L/Rr5OAzbEyH3U2QB6SnW2f3Y8WK/iiyMsNvhkloR9dVn89V
-Btuq6QmpOqX1DQa6Eo4AOJJXMUz+X6uFWsq1JbZAmnR3n/S2kdBy9xCtqni9tUnNj3D8B54RXyD8
-GwHH7cpA+lfH+MkZbByXa1uvgiKEWmsK8lKACbfai6DixHMlLwAhTqA3rcHuBTqDiIIVElNO5Uct
-3ssa7NZfKaD3nS3Asiy/vfW3+RA5+CVQagCItbroKdH1cO275/Q6qmd41wFetPhSQ+vAaAtsqQfH
-I9CVga5X9rkQgavXkYj2L0Ap3V+Ew83L26zuG3wAqbHnYr+nhVrCSe61nNzGmFKID0IuL8vjlJQK
-6nRZq98OGmH65QuQF9ZUVGKz+JAGKyKpxit3iF78HQB8JeP3w3kaDKgVk3CBHLMnWSPe3P0evjmQ
-mf9H4QnodXpJRcsvKAqbQUGHMHSF+AUAsG7JxBl3DKA4ajPcShItxu2X4Si5Rey4DdvwstyqDns9
-Wwtxcpf4x+ELhH87DDjxiiGPfXvfnru57/nG+9SHCcbIsCdweOLDDcImsVBrMQap8fWAc/m3QHNe
-3zuvVYcoPEAzcwDEL1l+rm4R3i/RsXavkB6rRrJBmKHM/uMAmQqwpRzf1l7aABnzU5eHYscg8A0d
-oYRCO/dx1+eoDHu+5qs0aeQnGoLJy0qkNJyFHXCOVxnoFyGXxabmI7gVX4rxi0LYmEp5mXJ3x+5d
-KXaR98IJn2Ta3vB+vBd1An5COxbUT6DybW3lSCKa1bHThIb09m5aN4syL4GhHQfK1LS7s+tOwOcP
-vnn91UpBvlKepd3ZPW6G1N6qAZBtapQErkBmgJnG5tJUkTDmIXJXxK2czDVB/IWzo1VYf5wDxTJ+
-A9i8BEe0QXm0H+N/0xvmHyxuL8OvYoWOFmJ3l2i68y+HLxB+HABl7Ukdzv/rJhNMUv1ap3U3OG54
-U1oFx2UetVkbIpL2L93YGYOIMDuAbAsuX72a7V4DBV+6HOc1nmqg9/rRKigIdt+1TUdgTEQvYnox
-0ZuJmAXim5ect/905xJHZWpuF0oTUIbWQUKmZrDTBORBX6BrRxoRq1OnljPEijzS0GpfZ5kx7iXP
-PORKMKUZoZsTlyCnDxNALlfWe0ktqX3s2wW3PF44IOipYvWmZfRpIcB6kI2g74RGNu9hmZb8LQxw
-87n3494x7agoor2h09KfW9qa29LUdRjs2tE9X0jNyiTnuUjvfII7muXu6zTlmFswl9FxHkb6mNgN
-dbfeJyGBl3UFVRn1Wvnms7/Z9Tmdzob/7BPbwwPd9XGeqvjDayHsNE600NeXg7cZguLSfRfnR9qL
-cV8nPckhngXshoSoATOu0ZEyswN8PWdH3wF2QPCrL8YhSNZ3H47N/kvhC4SfhrDDpS0xPXnZjkrw
-BIVgGKZbLmJFLibGlSsEjzeX5MqiYF/b2bS3QApM2wvoQwDT8WewAd1twFerp8AXldCiMViEwTpM
-zYfTB9Ko44fKm5W5ybO0wAa/GwebFVr5dDqR/9M2GFcAyvCcj1iJfFNOeruFHHm4xrn6OF5/4a6T
-ezk98ySgB2C3u6a9ZRWBav8EiHH76HmIsLqSqENrTwAaBTJKnmRBhjv6AVOkRPyukcVEZBYo+wpb
-LT7w5jyRAWI7lmzv5qyTupkq9yFh4msArjetAcThtgOtB5Db8vd355Y8A8CX4c52cFx88Zcby5Wd
-48hn/L28HgQzlJE/ERD/JEQdfo/+qcx2/RelLFVBub8EtFg2EM37NcpQxEqW19Yn9RD4BIzBb2yD
-WsQGq54R/K664Iu/djrIExfPXwpfIPwwMFH704f6ZGX/TzyMT2JJvq7wUx0uJsqaaxeTqU1OUCi5
-P/S+v9zy7k6gvJpNwrC5nQJ+hZMqEQBNiEP1zCK871/k6+7F0dqbrcFV4VIFxcC36jQDYAXHZMok
-D0kEwdbrAHZN92weP6A91jPCrZMV2MvhIb2lBaAdAS7ms6ktDf0ULuZKeCa7EHUH/GJxOOMni2+M
-96DZK1BBrbRpqTWntCyIg1Qk9vx6M3TeLUgRsu8ewwd5dbVirY0D4vXngJiI1ks0WjaC9+tqtOBz
-4ITbHgBH7H8AyZJTJSfnSna1aOv5RE3+XRA8zwNd2zriqPPmK6d88VppMJ/4qorKK73+vkFDSVU/
-H2gFwDb17ZawWkvDum/4krYyTKvAuAXBDjJl6xL8BU8EuDV+DYxX1DXeGiMOWKB1h0Dwu9sht9bv
-74YvENZw9ynEBgl29NY6SmUihCNM9AlJRVFaADEJUERT9YYHt8yeeZYXJzW209M4LbSgrkN7dYV6
-ZWy5dEgh0IYFIbHjrGra9YMeQFD8kg10BVwemCq43Z9LYKxl0Fa+VL+io5RGKY02MFbloL94Z62F
-YfC+jD5VKk+HSgEyAQ1Li/cVJOd7SrRuI5jjTCOsgmnShm6udAXDVON8TQW3PBTbiX3wxE0i98xc
-/Qx4120rJ9x2o5GSQnJXetMhUx151BYmp2RXQwD0oKkCA8myMaMWoC5BqhSbSl3ph7YFGfhOMjq+
-ytNnlyZ2UY+x5OdgoMvR4qgmzNtC3E/K9c5+kiQFGdzL9rSL8i8/1WXibuj4W5rkfmjq1wLfiXZY
-90lZLUx7Ar/rGg93XAKGn8age8A45kN8YydAJHxU3CFI0yi4X/4HBuEvEH4cOof1lUD4E4P+1TS8
-dSnAYzt/XOi5KEqpPYi42qIINtfDLMvIJYP3vAphofkkTy/ZKY+JOCv4CCZ2aLL48qTd3wD+UnNM
-qUq+Mr1YCsAtwJji50WV7nncvYJCPrUAx3wGfkXgxbxNR4Ar3i8OuH0q1nsJFuMTCDa5qbtP9yZL
-YlqOn9OSe8SuaLsWsCFdPZTG836Tr1MaDbRFv3aToL05jZbhIzrpR8rWRGngBcA9JktPbu7SdKSS
-mLJpy9XHXb/p8j6Cb86gAHef2mvlrAbOdRkzFQ3jdwOwnWUP0DaI6xvxU+D7Ua5m3oXd5Zi+79MY
-t/q2XLnSuOflXcgojys/I0NbeV+FWZ9PH8+Z/IMF6xB6LyzfUp0LMGxlIFDURKFUgAPSXrvFfdit
-xaErQIbHMzCOcc2e/hcfZIJ939NkSGMEyN+fWP7/EbqX4CJYJGoBs8YRDNt/jKB6QVom3JgwoCHK
-hlyE5g0YygluD/vaQQFwxlceKbKA2lbqvnrXJa89ZrViQpxuRasS0ydPJgoAOFh+B5rSkaZ1CR/W
-IYrWYW8jgF+gOShVcAC9jICkpMVzPvRTQS+Z/B52dbz1PsfDvQHauAG1vE3aGHJl4PawxxTaCQxn
-WimWzy/G+f2EeE+9Tr7G2gAbxdhp0/rhOZmfAbPxXAkEgAwv1GzQSOx5F2nPDYYZaet18iKPaPJa
-W5w4JItr+Dv0WmXWpDs9+oT6w9AMWdiBxnTa4zYL4vZawSwRJSCNV0/o5VV9m+tw/qxVO32jV+o5
-lBHu01IN6TeUEjcyIh/OPY6K6nDFo9UcYK6GumvEkh/AsH3b24NkogkqUznVIn4DTrS+IWKvT2iT
-YgaCF2f/XfgC4U9C2vmWFUPsyUZAuZcTF3ac28EOamku+6iSer7MLwce47Sns2alhduBVy9M0RCM
-/fEo4KYV+8DfXPWisYk6DKtZi+9Fe+GT9GBXaRQVZlGkLP6hmOagFukZAJP1hSpGxcTYS9VNwoFs
-SCMqlmLtOoQWpmu5B3xhrnRpYQ3EMN9z6JPMy9Dgy3UwTJ+r/ecu8J3ii3/lyNWo98PxZxNGbtMH
-4JwLvgyH9eYK4UKnKPsNoGz7oCSaL1DbjuHri0K7HaSJdSxd6oEmtyQHxpYrrfd/v91j4Pu3XKnc
-8aXUzFMBY2P15SbfmOaW2k739s+Ss+IIejrVNeurk77r0gOvxLYQ0S3QXJPqvuzvIzFlFwn10WX7
-5tY1mL2vxCCDVFPn/5oH8u99dN1qmUSdq6idF6ydkcDwf4CDv0DYwkMf4QhyVQaVwUc+lmRJ7qox
-EEYLzCdt4SotzD1rEyyy8rVKXIDOA7yQtrokIqfYH0NdbWPJDFFLpOXsD6KwBjVBX4xTkP5iBsAr
-9eU5pgb8bl46HdeTLcGKcLEbQIYQ6ekRrnYWLUyzBvRq3PSJ8UsYuq737CEhp/EZEOf4nftKawb/
-NB86QQA+kMQDLafTDd6uWhnk1nQfg7qCfSurJe774AbRjWC+Tb19vm2CwP8uuUqYZObj0DLN1WCc
-VZyAco8ODuECgdbkJsODw679GLg71foPoS/Xm7GLA2vkndfzXetvXC/hyg2tpPXl1NXq1Kq3+ai3
-Y14usnKJdp+Uhr03wo38u8A30CKwDUrLSJmQwTEZ4Cxglojw/Z98RBrGiZI9OL3/VCy95HFz07AN
-BnWdYAtGff7bK+kLhJ+GYN7cQ7I0OpFPi3RWsPON4A/3PCRSR6eefgMAj0mQvuZzBr7kN6OrRCor
-pa2HAqLS8Mtp3W82IxDZa9Ebhve+CROns4E5Ka3m3gFyf9Qa2X1WtN09NKj0y75PU+Vk+dVzIVFh
-XILgFCe8l4YPdJeHVWLZGH5yf1PbCaVp3eEamtvbKdpc/Ng/kLNZzkfQXCrQEOQSDMdQHAnG21bZ
-DDIPArPMlLDmSzwOjYjIT4QgA9dqTUJezd8VeAvQjqEKmYHqJHeScc13JXkKN4fsx1Js1xlYOfPl
-tJR1euEt0LihWdpwtBrkdb06uz/UVvQjkPm7+ztpFqZl+wQMn4RPR6QRuD2Ul9nigWlEbtE19wia
-PYajbK0fU3xbHWkQt43EsZMAMLZ37SiOUEf7rfAFwjvcVTIAdW0QieIxaZ60J5ymdWA4lN0v1+uK
-X3AdAXJVeK629MijtNi65zLti9ZFAhcDUXloOFa+AYe4qVodd1Gavu9xjaJCMQC8q6cgl0l/VAOA
-K1MFyDTf+1fB2pfxPh8vtnx7Jd6LUNYCMCKu77BZAHpxBDiKsfs7gPgEjiml2X3j9nLOk16a0/Rh
-cpzgIOJG45P77Y5x/4/1yvc+KsADwjxPrnkGum2LDCxaY4YM9zcI11HnAG2/UjEtNIV1CoBWoD3o
-g4gT2fpMUn9dhMJx6V4xA90R2B5FSr27rPbEwEeuu3vWMUfR/WVTsmi3T1lay9/wwTXnZciDaVd5
-s46bFAeXzxk0Z7lTvKTdBbhTuOCNJ0VgFgTDuh07KFb3iPCDGlb7DWwF1iMNbhA7j++9uifnvV5p
-lNJ3jcFfmEv6vw1fIPxJYKL8AhxOnWi2YwfD5BNjgeHz2jAw0KTcWlMPAXAseU1KDi/FadvcJ7o+
-BTZXAMOuorCfYp8N+AeoseewJ0M+bWJGc5v2otWOF7ul1/2CmewEiJ316C5h9/mYnnqvIGTVDUCx
-SNq4o1uDXVM/MVHLl63BP43nMnOYaXFEs/00bn3Jbpor0IQGXsY2cKI3/RJrKSHudY64LLYCt4qJ
-Z6j9pdVXNxpowO1d1evRlHwRJESrDO44IQlOIklg3schLtKJfq7eZQ9fUgpdenoDc58UDEnnvFWb
-1d4/p+ZEviDBnpLmeHfX7U1c/keWQkuW3px+BwTn8v1Tj01rXRSGFaoyujZY+QJ1EGofBkKYlnaX
-YVQDSXNla7BB4nSS0wbDSFM+srYC36YWNwhxo4VXnb0DhneiSjppfOs1SP8PDo34AmELN32E7Vi0
-PcABDCdg57fsIBAmw6nESQHVuyH3yHQzrZu8EjghLS8sZYZFivRrdFADAkRdOI0mPryDmFDMir+I
-6a3KEhUlr48elTYeqZaUa6Rnuegekf2LU8WlguDc/wiMVVakBZX56/HcvcbQ8AQan/jq5GBkfDpv
-6AyMEeDpDMZiPM2ldFVh47raYu+A2AswTDRM9FyfmPse/1y3+4CS9rcCuORkpov3bE+/Ku1mjVuc
-e5J6AzBPlAFI3w05B5cZWfnnkbuab316NydmFwZOnDld42xy+vRzOTnPrHuxRn3/t3kPbWAi39Yu
-2pzlW9o0UIcl3zIfrMGiL8QRGQhmAKB6asQCvOtqcBj2cew/c4NghckpL05PPLJCwXvAEwRxeIL4
-viz3/ygcvuYnqucEH8FwFf6hykr1m3KOmbkKL/qDqbX0Ek5wfyOVYrfAaWvVyT4cCRZCR72AaNrM
-EyAGUUtxbusvUQC6L2J6s5Sj0oq/Lyje+dOAXoGKCPDZ3Ig7d+rx1o27tSBvOlHQn4+Ab9BzTVdO
-XTzRzrzz5hUyJWCTk6b7AClQL5PEtFSLyVUi8i0eJpjzl0HXE/Jf7IypezrOKUz78DEne+ROk/Al
-MgbdE18u4/hQQq4I/Di1W6Ucq97luynxufAbJ13cfyipeU7fI7YpPNMxwoUeb3prZxfr4tHa3PEy
-xrmh5Ty8pJZZkhp6qaPZVutcn2PbUrwoz0YuYL8QpiVvAriXjy+a7WNBFVQuFgeggm4IpIDX2y/c
-H5OmshUGe83wdlwAACAASURBVLoCZJcZgS8lYCzQFgfk2aL9r8MXCH8Q8Ig0oj0VwBocwTCBFZkT
-GAaZd8q9rtics03Kmy/SK8gN9BYUY/acluDUjH6bkBBPWixVd7imCQqqaSYqwxe4QoSj01CJIi3Q
-t4XXaB6PhbtiLC/MIRAAcFyA7UC3EzKgXwIIzvHN9AT4dmpq6mMud7gpcMkw8Y8hVWQ8/eLO/X6p
-C/snF5PdIjKf0+uLcfHuAHJDUgXeyJDbMoVpb73DWzPd+1KfPUp4NjB+Hb42V8zmaXic2q2Kl+o+
-YD7y3vgtuCdFPajNhJXIwEuffpp5dErzxRBT2v2hJtX4/g/bDI+8dODlSOOafj6951qX1LrFNXar
-7g1txHUS9487/MXKSvBo3gDJahkmO1YNtUgAtKJQt7EWNy/QrWPY2BuUX5azOPJ4B3ABz/Q9R/j/
-V9CBpTTgOU424BUMn9VULm26O+ZqWTlcerndBMbJnrIJ0QkwMxzjErqtCDmFmt69jd+CnaTZtTQh
-/NEMphdJ9QXG6614doGAuIK14BusygxqLahynK+AYr2KqbJAL8A3xwE85hN5pnyTrvbevUGH4Z5l
-1ZfoNLR14ETHJQj56r2EPiCJsiMo7sCpy3R6BKz3VPsMhju+CpBjXfqUnmfmPY1OF8T/my4EfoE5
-DQ/uvDOIMe7I2HEXPTokyynxjtw0wp+HPjfCLyl0zD2D4ctQFWJIzOuUG/oc98G+4mfgV/155nW5
-mb7e6SBq+5V3egbLnPKDHshldPUxWrNEsyvvuNigzccl34YIgLM1eLHIXkZ+bNk681fnkJbuL74p
-zQDy4QU6rz+nzYQoWntVtykPE/ouI8/XNeK/DDd9hP0oMEonQZBNJFJQcgTD3XZa6/ChqqOiibK0
-gkpStHuaQ56rEyI6UHy8xuh1MK8lqJbAf+Sc+nEB3+D2gFdVoI3bRFSis4IlpHc0OzdY5eiciUAZ
-AVzovQCOez/hXidzpUvi5Zpv6suqvHww5zk8A13P92hSEFHz8CO13t6fFRq1z3q6r6TcqZhSE9aD
-7Oda9vRQxASGa5jrEtON56JrbXyaqsIMPeffjJJ17DYIeHNDJ1P/o0N36n1/vlwD42fyfgceV5B7
-mgEdffXrpPU43VNaMFkwV1KJu7I48XBLp5K305VaRCcjljFZhyPYbcNQxkTL7cw0u78BhkP+acCD
-VXgT0RqMfsJERMx+ygQRHI22SsvW3UKj/AKd6kal6c6jpK2vR1cJduwzAuXjCP2V8AXCT8PkI6zj
-7ZG17BSR7LzhZbvD7vKjqdBOpEZLtYpP+QB9odxbLhJpMZj/8IyNQ3HaZ7rj5WvY7n1BZrAIujls
-LZZOWzki0MXrXRqmgRImitZgb57Gffs3v+CtJFiI9KxH7CrI1nVjC2h7/YtP/lGG9azEfpx0ebf3
-9/M3KtTYqFP+nnGsT07bkfpte/UJ1rw6JwOt1E83jhq8H3GjutsCoFsFJ9iTy6uiSttSrErkynUB
-PNtaceobMCJoORHUSlyzrihDWX0NjtW75uaYfg1nfwZ4uxDHCcFILDFSHu4U49bAMZlz6Z0IDlsN
-tzxbxzZpqjstfpLDPb3oZCtPsFltG6ZP7ou23L2mQ5ruZVNZvwCGHdQyYEawBpNs+tZQe98lPaVq
-K3V7gW7r1rXU4m6ja9P3Cd+sAw32fS6gFzZ4YsMDoo3RNDuGNrqd/qvwBcKfhOTecAbDBOZ/IvWR
-6wb7tHBDOM2TCQQXcrfy0pK/A3IRIgX+tnJRxuOAABLVMVD4anvg0NGoSNcJEXC2JHtaVbb66UE0
-AbgOgNfAcQTMOws54M9tpgBwM+hfc6paXPrHBqTFL7xaPp+6I6a4M217njoX7shKxY/7S0jbkZP7
-aUdTYpvWzMWYXnlqLW/sjgEQD+Ul1NrXJebr0wfdNNxNZXTAF8iEKN03f7YHkYPkQ+G1528JuGLk
-Z17HT+Yw1KLkxRNcke90/7xmPY0nDp5nA85MHtPiXtfxMPBwR4f4yYBY9TXo95ZbShm5jl1dQ5C5
-Th+B4amQPAvCV3j7tAhL0iML/Sxht/RuHazYBWl8ZTWGf+LuFQEMA1jmAI5rU/4jg/AXCOfQgYwu
-7WQZNk2ub4wmcLyyQ/5PKpnDfiKMdXdN43WPrw6sPHFjdRnxKRBB8To2brWhbYqlwXEt6bt6+2GR
-y9BtzM0vdYFiLWCu6TMlvRhcJHbVX+y+w0pb1xvW4/EjseyNXsxv2FokRj8CX6UZn7R8Kjl3Qewj
-CXwtgNZ65oVxCJc6zZfJDUkz42NALAdrMNZtSks1u+KxE1X6RJprzpWUYPhUn7IkD/W8Gqcn8LJ8
-rd8AX+PbafmYtfCA9ctWoo90ztXTxDn3ZejGhoGS1+8VGG5nVDP/2jk50JCaOXAHmdNcT1qcDnG+
-wZPu/QPf9NXGED4s5ny53tOViHwvO6SV0iewd/d52eIJ/FK0Blf/4D2ThHw/hj2+ukwsnuj+sOL5
-JAlsfPGPtrR4dFsAx9aWZ491vxW+QHgHVAinBR8QX3Z1sIFVtrjgMhhuV8NDDcopEhQSZ15I5Y5K
-e1FbA2zOlkIllxWBMsIs/LrDwTXKqoVE8gyC/ACYw5Xn9BXiaREv3mcLM21QzJsu9D9mktfqHnkt
-GQJD+pZ1/wa60pBXhOmdaMb32l97vZLPL5VniZVvd/sbaTv+bsB0vkYawuKoZ0O+tOt+orry3P3N
-cALXCq6W8h3qdJJ9M0e2hN6VeCvkQbmQdCrh6ozaJ9VZYUIAmfe86bH9k6CHfgaJezeOKnPWOT8Z
-rqe8nCh57mW57J2W5HSz9szn0zdad7t4OdkB0xjvD3yhDQ098Ti98Qne+5jO7Uj3tErvePOH6zeA
-1Fyt36a9K7ZpSl9BoPdiOTlPpfk6C/IGzGPrsgXsDpCJuBao2z9RxbTluLS913TuE/84fIHww1B9
-WADaMdksyEeomSvFtIO5uBoeb9bNBC27ULdRcYxnH56ji0QqKqMvlGn95EDPiilCaloWr+Ja+nD1
-bkrKcMtB8Ku/PPe/11IpLyF6vYj+t+zIoflv2TR6Ecl7g9f1cxxCLxKjqXrUNCL99S3Zct9vpjct
-cPqm1XVvjj8PLSoB+sgA8R6eNwzDvWu0tp8A9OrHRJFJ+f8rBbc6gw9F2raATJLTL8rIsWnN3ZRz
-Ky2TdBl+UILONg9nYHpH5inDmIePtyWhe8j56cy6bA8w5LIe98WNvHl2VQAEHKi2k0Sc51GGE7Cs
-6vPr/xHoMZTVpan4jq/wEhVAOca5owOo5ViOgeFC32k8geiufhzKP19Lb4e9JoeJ3qVN+TWCde7y
-hPhhXfZvj+QAPOmlN3MJJab8U8/15TjAFv+Bb8QXCH8SJreIyETF+lvy5HAGxyHHpPnLJOoydztP
-U5+hWS3ItfYmhuwSQUT26FD6juEBoi++DWnBh+XLeI9f88zK084QZgTEq6oLEC+ZQkT/kxfJa8FP
-Iaa3IOB1oOyWX6U1aRsc6wPUH2yH6g/y7nzxsvjiL+NhV7+xT2Aqnq4eV0jMtU9D5zcjdHrLrIS/
-C45tORyKsU2rO0TYeCB2qae5ic08OdQHjYuShoVyqua59Jr6fGtyOcd9DcxGhe2EDLCYgfXHM6v9
-OuxZXzzttwqfujQuKpvLf+AsW8AAeHkuA8FYPv3B0rjna3mbPIGO/G0dUpxBn6e8qsGmtNYyrGVa
-/TiUO32wb3O7bKE2bUaOSpedVtdmb+2N8jTCifhcPyh4vcjcZDO4o5sZ6WbFzsB7V/5ahP9/BF9W
-roXZXvIQIGc3gOxK0Unuy7tHHJi4oYXoacfZk/bWUWlNVrtF8JtYm+ygM4Znifq8WkVkdwiE3DFO
-TG4R3h8Evxg3QLzdJP5HREQvEhIAs3p9JXBMROAqQQqO5e2WY3EA+iahNzG9BKzAvNu2u17nnvIz
-LYCsSvy989wBwnq1uJ0hqT2KoVNYzTxQ+g399nsqMM6IO4DYtrvWr2JecHyD564sIq3rk50G2A/A
-sNKnzvjBGbW5rBu77ef6LfFNU68J4zQomVpFfZ3tB/w8xNc9t/321PVhynvKx8AT4pzuIX/h3ZGW
-nuNFbrzP5Z/cHQjTOMrr+P3DNV37v1zry8oE99zQc5szfQqnOZLXb8fLwBt30TtzuTGLXADcSMes
-kSf8CMg/DF8grOFu5z+0BBdXCuadLP2MG8jPAzeCuIlyw8KpnY2YlqZtx0WSgfKiLRIK8n4LDxAp
-b3m9juOSDKC4Tdv/99NnqxBpuRsI8XJFeOn9Ar16pdQNC2wOFl+6D46zRfgl7vqwfuzDz6ddL16I
-WYKZeLtGLHcK7X6Nt2C3uVqcMU087RPEqlPtkDdP2Z8B4zpR7wLiri5X/JdcfDo1+bm8vgwa2xal
-yiFN0z+rh6uWg4ygnhLfp83HfBcdzePN74YnoiNgaYAM13nJ5X/KH/q5WnkrDbg55kMQpXkQMIY0
-wrTIq3q8yNtltvQc50gPH640GuiLf7IKcymrKxP7bdpPPMS2x5Q6XzLvVXxOv16LfMVHZPtJpHVE
-ugmOoTg9Wu1rEf7/ENZgMine24O7x1YtZ9UtgihYqGh4ReVXHOC4mctpNWcGTrTHPsJKhyiQAj2X
-K33SKZtVseX1J2K5pEUFqhbfNy83jRcRWIIX+Pwf03phjhwQq+Dg4oAuEo2rBBGC4/xC3Up/0bbu
-0gbEGwSvbtf6O/jVtrxB4aIlWdvfgl+5SMexwOkjJZJCfXjxDhuyAOvPwTBVKRyr8ansdsuY1l6X
-1GT+yTNGuJEm7ZSvhHtg+F4fQNK1yCsRRHTrWWZmvlkHZz6PyhNx1+PBka9R5zijKvjZ/1GdH2iY
-b5QFZaquPPoIBz4vr48TgM0BIGuce7rr8MZFwmSfXpBDP97ZQIIfKnmx32LgQF/ziQ+8J9oUv8ub
-af187ExLN2Y5At6OP4Hj+Mtyi+E3vo16Gr5A+GlgomAJtnsiYolgmIgqaCbCM4b7Aq5IEi6BcdqE
-250Sb4dyB+A6pyUGsOhm3+BwGmH49QYJzxFEQUwoOpYmpOrZupoatwjOtH1ChLgyfbGYO4FsP2Fh
-WSc6ENH/3rRQKlbwLW7ptZfoojXYfk+dIuCl9JId0YvUqqtuEW9xEPwSWWBdiNiOsIuK3n4mWpZF
-megC8HKlncCxtx0oBdvG1VACX+NQX0kPQrc2Jil5iVwW1K2zge924CYWQ/sweCGyb9PFmJRSI+ek
-Yq7yT2om8N3t2otix+H/WG5SeDcG6U6RRyDDcVZUUMORX7mKynerb5uPXbrT6tf8yhFdDoa48Zxd
-Kiq94U/HoHXxCmqrG4TJ716QM16O+ZorAW/un1Iv7FOQgyHznGi6TmJapeUyc0In/zIIEdu3PA5m
-qYBZTd6mpg4cbxpbPpVHdPM8zV8NXyD8SWBKQA4SEMRtvgiGnY+IHu7uWAG6MZM7Pm6iSRDW0zYo
-oRHkFusxylCZygNyE0/NMn9NMluDyZRwBcv1vym53YwS34rzxezm2dfuLwTDOpySXCOKNdivlNwj
-1GfYa6cAGOok0VViHQ7P+/g0bdNW9ML0DmdZPwe8wYrcTtneVWV3BnDl4BQeeWLI86MLpmpHoZy4
-h4KGhHubx/0tpv+S9KdSG17rjyclalYHqZ+Eu/l/0zXwF0WB0HmmTqo08FxMXoRVZ8ttpuqc58RT
-wRrGGf4FWgPwjEcBYUq3OEf+lo8P+S9k6YrJPOWTLMMmW3U6VatxF6dONtaJPU/bv027NHfn9hCn
-T12r3fTqxlZfPIvhc2trrdspwA5rChkAb/lJ6Aiq/wPPiC8Q/jgwORgOO/Qe5OaEiMjLnh5CMal9
-XsFpN+SGZrfKMwBaukPbN83LdPnFOKblrxseEA74pDtsA4v2uL4m53EiMt9gIl+uHK7bDUJouULQ
-+vpGNvrc+NVcI+yIPDucfL2u1p0GoSbmlk5U3CPUR5jFP+ukiG0ZNhDsLhKrBky2Yezuz0eoZeA7
-Ad4Mik25kUPgTkEGH3ipPN0Q8yHtTjCdm4QyMrQlnmrV8U3hySbD8P9eLX6jBgFsTuusFdL27v26
-HLKy/Xtexl8PWT3a/+tRmtRvuE1iKqCq86QAY45U7jjZJUValN9ZlLNbRQSEEGdu6XbPTZ7HPOi3
-e7Lsuj7UPop8qj+b/FmO1ok1X20bQZ7Ydw0PxcApMqYf6AyEjr/wHhi6vmhDWa57DwzPvQpykRdp
-yWq8v9381+ELhHe4+6ZitVBupZh0Y3zhS9O0jJMS5X7mHR6TqsI9LKW86sJtXi46o9PTXAtyMz9U
-PYjE/kqdRqUbgW2lYHob59jLucdjHvv9HLe26vpk3udALKCuP4Yi5KBY36BjKGyB2GXlJT1LeLIS
-lxMmOABlJrcIv0XoLbxAMO2v90RBcGPhID8+zdCr3AO+OQ1HF3F/nmUCsZC2+9QZzqdp11nxS2Gu
-sG2yvyN4SubUD/ek3emLq5rf2fyuwz2gWvXRBd9BpKml+fn4V0NRg2fOC8qNtAvwMwEY7LueF2Y0
-N7RAgflf5GW3igyQ+BK8VheI5mW5wtPEWznkuo9j3Xhz6C+mBTrvNMyPMoos6Cto71hn1tpB2Yf1
-9uQ+xC/W8Clf7avpsMzmGn4FTu+JgtNhOO/TaRJo5GcMfy3C/5+CT26NmuUx/eqDHakGQJmJ9k8T
-Ez1S7TDjLdaB8mkX4oYWbptlBE29RQtp2mD2I+M2LXtJ8AZ/6DstwJSLxPscp3DvPwzRuUUYCNbP
-HjMmMUD8EjLLML14ue8S+TESb4+zULTq0rpf1thqJbafxjCgHI9V+0MUXCLKhxAEZwux28NpyzGf
-591JJ+BLkFb4KSpg/PIjwuE4ZqooMwD9DTB8D6I1hXSCngi4VWhauynPlSrIRZR+vVVyX8jjPmt6
-+ijDlZXdnFSR8nFO77q66bMnw3eux8P8D9OntAhcan9ZzzB2Td4b4D/7XQRCUXbnftFZgpWuOlPl
-wzB5PKRlazEnYCs1/50yTNbphzH2vlPoF/mxn7o6cJXnfej9G+mRJ+joA2+8745Hi+uylVPWUAa4
-h3CXJfDhzf455uCyEcGyvjj3wVL8cfgC4YdBX0paIQFc8kFdzOQmNCKKQA/oQXs/mAiXVuxu9+Am
-mpiKzy9ccfGM1mCmbBUuPhHeOVPNves61DvxErpE4DKksAjtyXWPFxP5CRGb/0XLFeL1hl9ae2VE
-uZr6R7afrhB1vsEk+ntwaiVO1mBiMr+LzbPfv6O3EP0JIFitwR0tzx91l9j9oICWAfB2oDgBX+JI
-wyC73zUh932hadkS+bLcbrjPs+YXwq3Fd3eF+hZ1yQUinwLjT3g+3Wy6TfduISdV1JI+aIROxVvh
-eRM+5p3SOjo3aQqrrlwnAkDiSGNIiGW41bfjRx6kHa24rEBvSst0hrgMPE2cPf98IsSWv08BQhlt
-fq17kdHJ9eByYr/lPb3LR8P9k7SaLjZITv+tUxlg3xc/jtTSCsDd9aG9kagxkBUg73R84e4fhi8Q
-/iA4ptsToezOToj4zyfHrPD3TQLHH9Ry3lW4oRkJtNtUbJfW0nzSxx8Syb7V64ncrcSh01Y6xZey
-YMkRkR/CXQHwtjClbmVMbZSbg+HFIJY79hkr4wakZukNLhGdz7Bag7Vi1ZJMQsRvr5P+uMbyHc4u
-ETgArnztJAytJgOYTaA4WIMnGt5baWLp1rctKJZ4z+lZieL9RLsMv4iUo6i7GpoPd5PsxK/T4mFb
-5hrKRfon8oZUnm4hz8iz+X7iKPgPs078Jzl38rTW4NJ1nV8wJdo9S7B3d5LJsS6WxrEclDf6Eo95
-Mo8Dp5mnlnG27Cr3lWV4ypva09KaftIeTSdfUBM/pV2FO3lLeXxP9goR+LYZw3nCi9/+JzcJQdcI
-A8hEhDjgH4YvEP402A7tAx9IYDWuuE4RwF2zj+6Ken+aKd0k7TafbifKS2VP0I98hK03kkjNT9hZ
-FGN06z6IDDy+4a7/vYuEptfPUsUv5XrtJiKK3MAX48sIDuBXS5R8njCeHQz+w7IPPNtlsYB7RPr8
-ESJ+45mXWtoGx9q38Kq6VVdBKvu9No/wXnkE1GC4j7Jx9qSTAm3kPhnjq1AgWaukw+V2wFl0l/tu
-OaG/Jh7YVy5W/aPyfoPP1/tDOXzBc78CvxIe98upvc0gTfI7egdcFMTFtDozL0HtdM8p/1CuAz4E
-bsnqC9OBxzx0mUclq/5maFcnh03ObBk2mcfj05r4gYb1RBo9iMcxeJ6/xPmCJ028ru4B+J5WyAav
-gUdxrWLdkox+xESKIfRdoO/Lcv9luNv76Leqg6z5JYNhjWhyB+NKAYc6NvnCEQwH1coNbZKJpDsm
-OpzsJY2hz6Jdt/ZV5Cem7Wfd5DlUSVsiQIi06mOlig6PBtZcQpkOhaKLApGD4A7gEu1JAy/LyaqN
-W4rBh3jLDK4Rb687v2i5Qry9rgZOu2EHwJsBcAHIIxj285URXOY+t3sWw+IlLXVl7trp/m4Iebr+
-KIzXOqCvR91Zekk8Alrkb8tgak5cvOdGdcXz0b5jmeIudylrb5ojX5J7kvfJnCjF3OUZMhRynmfS
-Jh/lBEDKSOkBjnJHtwlOPBzmZedXnMvoXCRa6zDUtdA58gRrb0qb5eaH/coTQSqbfM78DOltXhoB
-MQUZW07o86Y8SO0t3Eu71XGVlD/Hfd05vfMd9jp39Bw4yehDBMlRA+TdAOJCJMxbBQjZC3b2rfGi
-i1yV//vhC4SfBrDmlg16pxl0Swxsb0TKYefLk+BC3R8BfLcj5xXR5P81H2HogARwtYOyS4S/WNdU
-27KDLTJtPBEk4PLMG6u/wGcKT3rFGPzKlG8/uYajzYgiCKboCpFPinC/YXbECfFsEf7zJgO/9FaF
-Tna+MXvTSM/RtHt6CIADrxS3CJ06qtO07+M4wPnZm28CuGUtfRi6B6IYuvk+yziz9iXU/HHN4Tg9
-AsW67A716+XMLXq05YxdJ31ik/fIdXOzfsLzNPB4c11mm5bG+8RbQA83NHItFkBW6N/G8hv6FvIz
-JV6l99bjEbx2dE55Q5kovwGI3JSxF0BXRvg0NCq0HiyPVuFQH/+P6dDBQz/Efv438QvAjJMTGsEp
-ad0c1nnj97uswU5fsMDLiXsygurf2AmehS8Q/jjoROp2d9Q6MT2+bEc07mytlerBBCkAudtlOk3P
-fs0WbKb4MNiltfwuq/hXd1WnhKEH0cQDnWr35cWGtLVAIyB+NX0UvwGgCIZptesdrLyNKwR1fsM9
-YNap8UeE+M3EL3WJIKJgDablq7x/Po5NaZGPFwGw5c8AcOcjjOOA5wtz5tn5tWoTAPxtYOzS+nAF
-RCd598BylFTyXPQHSgjz+aJjrut/j6djPPfkkMpdT/SCL/n+QuDxZiRdpre0MpiVFwEKR0oBYUtm
-um/kZt/iSr+S6XoFZaCcSGerf6Bzk3fzqy4e/Yuh5as8KXymv0FmZ3l2PuAJead49CGmTi7UmUrc
-F+/M83n8ach1nXjKJhKUe12jCGmVUsxSCSjLlsWKF/5x+ALhTwJsRkxwLh6kO/5l4M9gUIVhkJ5M
-FAE28ubKlbzdLtNtRINKf/SrcgM/YVRlwCUB75XkDKE/myZJ2jRXc7rnS/cWzj2BIPgta0N4Qdpb
-327ddX4reBY1yqrF309+WJz6KxyqTJiiNRh5dzq9iN9vswj/oX0qxJvoz4uChZje3t684WA/ZRA8
-xonW2ckNANY4+ai64uMMiMPoh+E3HUhRVst/iM9hVqZyi6vjr3OmK/cK1OY62JKVJi3lxeUzyT6F
-W3zcRm+EuinOexrwdirrHwQukSbtlO8GvaUNE4FTegd4wpU7nvyy2+wX3PJf3Jc4L45A54afh/yQ
-pmWNLhBBru8QDOnhnojyL8y1PsyZJ3x4oMf65RD7Z2h34r+KV+K8hphCktOLQH/PpIbTg2iFu4Uf
-QC7bD3Th/aq0yH5YQVeJfxy+QPjTAJvRGkMOYJeYojFYB5oWT2dwdUYyvjFJCaHMgyrmhmbRYbIX
-14cYDTImWkhTdVd/jS94AltfRaGhC5viNFTgWzPh1zKrCxbQfLHCWAZZbMcFM/EyvG6grMDP3f/B
-qkvRwnu0BlP0D9ZymbYVmLZleFeKN25mWhUL9z7NQv9P1t0YPwNgIf/gWRpYnE5L0319dQ5r4LPg
-Zc1qfU6tfBpO2wRy1VgvL/PEeR/7a8onifcU7vAo423eMex1dVPQNd9pM/4sHFXeKc+DtNu0MNg3
-XBxAUOsHjPcHy+6Ynwd5BuicdnaHADp7HPmQp7bz+gU540vyNZ/fVz4qfNmCzFXOgzhBGad4HM+e
-964sItXJVQuWeFOAk+Kai6w7LSh2IXSLwFMlGlsw3MvOuvLKzvsfGIS/QFjD/V+Ws5htXC6EIlLb
-pv680dlENVbpd70yWxumUas3qwtvuKEZib2hWmem7dKhAFLAigtMozW4kak32b1kM1h3ToB5CHlY
-WkvVpuNrCqHWu1qqxNePyS3e1+YWctCs5b2Y6cVCrxfT/2SdrygvALwYFybRX+4gJv/9ZlVkr20J
-RsswuVWY3FXC7reVGK8KlCOglQBq/cMeF6E3axzympLzXjSwDT2u+tJGoOEx/dmMVAHO0M91jHva
-k/TIeQ6nrQJ5KPOJp7TAHFg6qSHPcTM7h5/sNWPeURc1+QZ9W9XVb++KV6N2Thv7fVLNF7Sshpmm
-qzNwpNR7PqSf8nPDn+6RVuLc0Lnh5ws5u7T8i3BtHMrkgQaNILrMi8aRlKGpx/GzzJylXbqKvc9c
-s+HsNBrUI1apmcvMtdqp7k13XNL7B9JKcwOJG4kUE/ieimcHr1xC+LLcWIm/Fr5A+GmweQyzK7lF
-RJBHYfOL6YmPKCZ2fHPiIC/no7g6Ap1j/PAinGSLbdcuy8qhj+KSj6AAeavF7QSCve74rHJY2VBy
-ZcLmAAM47wAAGqBJREFUMAEwJm+SgZWd/bUfjF+8XSUYgDEzvV6ywTGTvMROiVg4Ek6L2GcMq7J+
-vxfUXj+vLPRmSW4TcCW0IG+F/CZ6vxDQrp9rLq4RGSAzrwcAArrxQT/qV1rWrzB+wOsAmkJ6odE9
-Gh9omV9Dn84jzzTf8BWPKaQtD4RzbPw5c6jLYTrnki7DE94p8/Ub5g/5+YHMH4ZTKScA1fKmSTPy
-XdAiYIL7BohRx8tNXr3nhl/v+VS232N9sC5T/Cp9jN8CwfeePs6/OjfQunRo+6k8JI79wZmzGVtu
-ADI38U5WVjk5b6KvABrGoj3NHAwNIgicCEHb3QF1pPv/oquEfpPOHF9e/9fhC4Qt3O1+0HKGlBg2
-LambGBNlS/G8Bya+pvha32RbCk0ZVkRgGdpufsDUI4oAco1Ao1WYlAw0zR6E/gIIRsLF0GKp2eoY
-ROhXN5CR0zg7AF4gWBQEs9D/9F6txa8NIvdV9KftiIjoRczvBWJpdfOf7Qu8AO6LmIXe7wWY3wp+
-dzUzONZfqJP9EKOGaAXBb9EX47iAYt49IxSBMpEPfQSt7CCY43jeAsdp2AJNahoP/DiGma4pXZ7K
-helc0u/MyzZJLkq/qtztUj+u5YGp9sM5H1fSE56/EXgu5wm9a0+gycDX0HRWBlA0+flC/g74RBkD
-HfLeyTfFr9If8SYQXPg4W1kPn09A8Cntki+6VLj2xD5taMiX5k/bTzDgvnzuWYPx5s46c90Kyp5j
-av1PGxwTcecqsfcdPUXCzxy+/sb3b4QvENZwV/NOgHbNYwozD60+TLAXcU2/VanB1CB3Z3vKzA3N
-6pbgxJ1j1EIboYzywDDZ6e7SO55DUAQFvC1gDhLFn1aRbXeBBvUpVhnM21WCZYFNBcGv9Ws6/3sp
-SPTrlkTLCgzW4O108d6A+EVEf5jo9Xag+zZALMQUQbFdaYHnlwD43df3/irqtZVRB4pfG9Cun492
-1wl8OMGrg+DVm8Yr3ocBSKf8eUjCleN9niHdc4/WJ9sa7gHmlXqaiXXa8yGtYUvfrpQ8usxOstrS
-z7U85muFXK+1+kxd81S10/Dc1cc/CFMRT+i3aAhsip7J9/CffeY5GEr3kx/wD9NXWqQYEOMEyh7Q
-zj6/ZMB15DmBYM73H4JgvuBv+RoQarxr7U0v+xHI6AEyBet3GDNGXk3HelQGTpF+HnaalCBF8QC5
-9XfvE2b5JaquEGAcsW/VRN+Z4rhW/mH4AuGnYc1Vv7F7QEwBHAMx5KU4ky9BMfCGQhL5NOO7cgOd
-YzyfCqGLA81yeceezhGmxrtXJ396WBC8Ka2FeqRw7D0bo26ZRUswlhTrlLpuE/X8B2K3CKuLhITr
-Apb/237D/xMienm5AqCYwTXiz3aN+IMuEUxuLRa1/C7VYgBY/YQ3j8hSSO89pMJCrw123VqspS+Y
-q8BWe8jvV4NbUGwgGEAx7dNVlE/BOPJRHEPk63gy+O2AsalbzvSOd9PF25ZDmfJQyh2Q2gbTE9qT
-zexm8o3lgdjH9WjzHiQNSe0XnEV+p5cuZPximKTP9O4R6V7+ooql8l25OGCPHP2ATcbTdErynTby
-fUhr478Ggm/8ctxE63ggLXRKc4N8sW1du9aar8BVSr68GnyOOGOX3tfyQXA0i4SYvGPu5wvg2Hhk
-A+XIo/Uu7hL/OHyBsIUHna8jF+5xNifkpKasLm8oPtXh6DV+Ud9OHhH1QJAHcbvyrcVXbxJPZDJe
-Kectw4+LQAgn0BZRWGYMt/HB9mXqMi/pjfx9okQJQFJrq7pFyHaDCAD4tYGOMP2PdyaC7VWPY1sS
-zTWCt9wFfJcF+M92ibAf3CCiP5ROmaCVh18LFCsAXj/8AX7CQvRSv2FSgLxeDowuEsk9QhUa+bjJ
-nicGkDcoRrAv5DwGiONQ+MziMtPiVSqdKG4bV+DXaRwIHa/Sw4OZdPSJfwi6Xq/eFLklLGWBzfRZ
-Zob/hXzMY7FRr1ynXxb1aUjjO5eeaTXFKNLQTvKQCPPHVfGHL8NBOpbdnQAR04FrTKcA6PR6cq24
-ot2OJ+supjl4PfxaXPe54OkG7qkc77P4qDz1s3ISZ8swyor04CbR1TkKLzJvBQOvZO/HhBMiADDb
-DppcIzA1uELwbrOK+x6f9h+GuzPDdloG5deARLtvAN3oIpDrlCsl1/sYl8ggC+rZChiAaLb4dv7O
-TIGngOAq2PM0DYw/z1DDk2Wzun2CRgcbVKlCAsw7qtbfBf54A+PtamCAuOZnIaKX2ZaJiOjPdo1g
-fi/rLiHwZT2F2F+Koxe96b1dJpBf6A9Rsv5u8C7qHrGdMlhf4ltKawFj3lZmtQgzobVYW7OAsYPl
-CoL1eXHnByBtMoCXkvz2yjEvg0AHxWnoAo0bWgx1VnKk8YkXQjflM489UB7C410Msx1rOBfSqYqJ
-v4+2CVfpTU1+NQxa8TkN29OM8+28p6vxcUqHe75OP8oIdTlYjJtroPEhraGdXo67enHueYfHey8n
-pnNKa+MUT9Moy6YZ29g/6YW4MD7ZdYIq3+QmAfMgl4/tq+sQ96VzvP7faQH4Lpr/gAYt9wkiwpMk
-0F3i38PgLxCGcFPdHq29UndbuZFm4QbKDVm6Hb9px+VjIuTDlYKuESeLb+GZWuN9Uff66WC0pjwI
-j0Hwo5DAKlE/TZI1CBWmW4f3cKv5NpsRXpo/ukUsS/A+Ro3ei/ZefH+ULwBfDi/JMRH92b9KJ5vv
-pS4SROYecYyT/rTJGqP3rqkCXn2mK5ZfJhvT4C4hWxZ7mnZjB3DDVVzpdlfMyymv3cOazTxKOwLf
-kW8KuUJXQFd3jFvCPwhXZ16U3bFLvcWLpc3yrmXcSP4odDJ/TEuT6qm861MdmmsAVgNfAFknGV0d
-cnq9HusXyp/zXaWVeOcHnD98vsdK3spPkAkGsssX+2HDvNKPkR7HQNqtOo5OLAcn4T1rsFCLEQ4h
-wOLgI0ykbhDU0NQ1Qt3kdgujS8WjmvxO+ALhx2HPQt39g9JLaZt0TMPwCTDu5FjyoG453RcZWtF0
-DbdM1Q/Y80u2HGt7OOMA7ZceBE/vkP4eRohgF2o1llnSoAvNGswKfPfn5cwI+IIM3kqXl/sDv2VZ
-eGkB6j+8fm1OQS/zy45SMxq96C37pTmjLYuuiFuD3wKAmFdcXSUsTv4Sw2sDYPSmFlJwvO60Xdll
-IrhLBEC8LcN6D/2s/tKBRqZbY5r4KGa7RRwvNhnU8YRn3Fg+yjmDZqXVTcr4Lqy+qDL+rnkk218u
-9EnOe8nHFyK5qqMh/+1qPQhBXlDJ+THoIu9Ew+rLgS/Q0stpj192y9c8Bh1QruN0+3rId0fGaqO0
-eZiITn7B2V2Cd30e3V/xcC7T+2zKR4Evt6ceiXZ2ndBRk8Dr8VyveMWQ6zbxTQFPfAhnA2Mq6H6+
-Q5NNYyLaVuO/qvKG8AXCn4Y9cH4vcVedXpLLaVlmJgSWm1PkqRsENzRib5PVXyNwNR6d1Rv4hEZD
-cwRuoE1l62Gi/qcW7/ykRg13c+CijjSUVPvXeoSJmDm4SLzwdzL084pS3M83KtY3L19hPTrN3B92
-ngV2OVqAt6+x09iOT1OQqy/1idJo/6qeqLrzcyFFlde25DOMgYFfagAxKSh2V4hgFWYAyrANCFEF
-x7C8DBRDnxtN6ijhtpNnZbjXFzdgqJtZPMh4sqXsSiPyrgxeAW/EXwjcXQ7s/dy/4quq55B+wzr1
-sLevZYT2X9SdqPVjvAU+5MT305fdrl+Gw/jMMx/PVuitzHt83NyH+MEv+CMQ3H1u5tHCJznYwV2d
-Mb9qUKLcrwCGSx/yDHw5lmE5Gv7ARte0oEkDoI1p9rIbuf+v08hcI1oayFvpv7G6n4UvENZwt/PH
-XXLPxvLraTobM1CGtCoc6tXcjOCYm9k80CbZSrO62uzVx7cEjomQWEHqTuMMgr2fioV43PVV/o1+
-u52apeN24PlR0eQaYI15W0/xtAgiAMGvneON97R+Ill/HY62QtZj0ra7hLk/sL9Ih+kGfN9Mb5RF
-TH/e+6g32b+SJ+gGQXaqhILcF/PioXWYG8Dj3V4AwwZqabtETPTVm/EHPFZjFchaPuj7AnbJ50wG
-xdUtgssv2pUpDmo9jCenewzdy5MQTrM4cB3PFH8s8J6cT9i63TSzXPD8NH3M9zjHnPcuQCibdQNw
-+3zO3/EZ+HlsCYZ7bvgDV6xL5GloRV497/by2pS1ZPQWX4xf5cmV5+nDz+97mfXbkE4OUQS8ZSzA
-Em5XsBiHNOw/kBXGgLXM1DmFJwnPDWmhroaEhu2tt5Vk/r+ENFq7hYLchmawiWkwgP3d8AXCT4Mp
-Mp2ZaSPDF+HC5pU0YEjLPBR5j2w+4Vr6XVqIMs0/igG0ZDF2di6gGf0yexAMaYHe5CthTr+3pOpy
-Dy1mMgDe6Q4MdlTaFmIvhzH5OWssxhAswaIgmPe5wMsveAHgF/3hNwBk9yVWqzDG/QU6sdMk3gI/
-+CHZQqxW4e0WIRX4BjgstHyfSfvGQan9Sh43p0sgCIalEoAwQ5yogF7kUV2cZymq49ZnWHCbasYc
-4jru7TLP/I91+F5HkNdnYyr1ahkksb8RboFT4Gk5T+kcI3P6dfikyfk4uhEbXNCuAG6hRXUX0zvQ
-FMrpLcGet/Zlf2pE5Gtph/J8laWtI9G662V8Asin0yNSPTA8thaDzsj8WMSU5mPCVufQB9avAIZh
-XLR/UWg/N/oG9zx9uORBsKv1InhXY/sI4y5qZwOT+wRrm4SIGNwqlHflu+Ny9fvhC4QtPOl9BLk2
-mlFUB5SR3pkQPgXGhZWb5nDPF2hpmWfXiPaluJUozkRU4AS0GcS3FuLSSk+7ZWEb5dwPodZsnlEh
-eHrKJwsM64PBS78CRw2ogPglxPtFNiY9/WFZYs0qzAiAwS2ClqWWKZ4hbPE30Z/XUjj8WsW9FfSq
-KwQJvZkNDPP2Gc72X/XhwpfyJHyi6wOCUdnH4zgIrjRil4Wg2GYTz6DXpiXhs5ePVzf7lky2OGk9
-CGdQA34v7oNADHcnYivwBt/f2DiSTpjUyVQBPqTHdXM/7W54kivWMzm4wJy4kt9VuTvzuetHxrIM
-IEV+BEExjUNan9f1fKE1fNiW3rJM8IDUgLy71+bcYNW5uZ2ZHtI45j9ZdrHNpzw0pFHDi+KZXRf7
-uKR+8uEgoukFOqL8AxthbpnsLYMjnbo8HEnnZRaNQ9E/eKcLVJpckTvAJbN620kSCh9INkDmJK57
-kf7vhy8Q1nBXeyr4JaIIchk2p7RL6azQrCgDBU91CIDxxMAN24Fm0STX6og3gbhvmcoxasaus57q
-xC59AOB1SAsijgtlCbi3lnaFGVuZfGSnIobwAi0jxNv0SvYTdPzyM4ntAImtHPjN9GZZp0KYVfhN
-Lzgh4h0swRdx2f7CGxSrFVhfmjM3CXJsbnF2FwoGC7FajmX3cgTDVAAxgmSdC5J8hhXcivUbgGlJ
-95DOkMbEATBrXpxFumm04NiWL5dn18AH8Xzfp3GcL2kx+Fbn+oErWw139dXTMOkJvTukI4m79KB2
-Tnk/b9yTnJWXS/MQCugl5+vKDH01AOpAS213NXID6DZ5vSwuAKiTEWh5e8hXzvkk1aOvX07L8Y/S
-7pwakT95WjKm+36QP4uXIw3abPmhsTiORNL3EbhD5P61MU1uFNiIyW2ijjE2OhOkI5YQtniHxwZ6
-l16GSiSAq24Q5hssAjZDjtj6H4YvELZws/uDK0TatXSGZDqRj7xq0iempENSYcia9VPadF6wWYO9
-ERIExIblTX4RueT07LCC2tB2HpR3NwhcGa5ziVo9zmkceXgv+BdBn5HA7zGvuP7YBQvFOPM6MYKX
-Iqx+wBg/W4X5xeaNUQCwgK/wBrzBOkyq9BsLMW2Qi9ZdpWm6AV12Nwn9KC2A5vUP+dA3GL1t9Jfq
-dJl1YBSfTZm4sfpiHCzAMJ7dhOpnIB/SkI29kSepWJkHs/rjUKZ+Rgs5OqcroVMxP0u7H+5mvXKN
-4BzLG3UDjIsc2+iv6xhA1y6sgCK9h281WvCTHuSVu9KUP9NK64e8a46eeeq1jT+xEv/GqRF5Xl+k
-Yx0yL/ZfOyalf13G1lBpvDFffVDBMinH0/34kEKnkPfEfV+sDZzYAfQSxd/dUJcJdIEwsW4l/tfh
-C4QfBxtRIHEPcpG+s46+xVNZJRzAcZdQVstdmjaEoa6bFnyAmSawLMhHZG1v7bXmMF/T7FnY+p1b
-vlr3KUi9TQovPdQWmjF2t6EbvR+ZhFgBMJEDX9lWWACqb/bzgpnf+2ebMb4UCfoKqy9wjLP9why+
-n/cmtfAiXba7hWypYjIUAivtTQn4Guglt/gaTwLNgdeBrE4xBL6EMlCxAh1dI4KOtng8Ng1l6AbT
-AmlypRxmVANonkFVXFNntq68p6HF3MciMzoYuA5pGuXfTnsQ+lyxN7qXnvLzyFEmX+cxWhrPSbYB
-I073kP70ZbnCM5RV65FoEx+f+XPa1Y9kMN6fjlDDwCmdI+/VPTXpnbxcB+xzvK/9cLL8LqZ1f3gZ
-EeV26R3f0E+5z44BQC5ZLVcs+Ah3oJeSnzAoYNn9hlZiVfxMvV6+Vd+H4QuENdzt2acg1+jZHIWa
-A4b4I3CsGbOWSrxPaAVvMtXNO09Pp0mhUUPXZD1Cq020vH0l+9Q5zJzTwtMh5MzcxJci3NZg7bOt
-AN8q9bUsrrwtr6830xtoapVdoHN589qPZ1C0EDOtn0KOaRh3K7G6OiyLr1uFWa3CbyKmaPtdstwS
-zEQGlgPIJYrgN1l89fziApiTRZkQ5KoSpQiWsyW4LEny56/w0lwZT068OPZDvh0Js+9ToFqmdree
-ZvIUykp5tGvwIU/WLzna76p8TJt34p+4SNS69RS/iwDF0puHnvYexrKrddEdDZ+DGU4gx3tw1S+7
-NkRr4cn/N9CgsVWdHSzHLQ1XzYkvXkO8sQZP8QykOfFR5n/64Vy3NA4cy8plhzqHKa6uLqmv8niP
-L9h1/Ok+L6luMNqQ3oZRwJoyLctt6gAzeHFjjQCLRgbMsuWxinC5uPSyTvuhbaCELxC2cFPpMrmp
-isnBL+Fuyme+U9kWfQKOOxncJGda02ZbRThTbcZSfmFOtG3A5ydERIRRm7H66QSCz96+n50pPAf8
-uQhYy3nN4z2nYRd4F46I1CKMZy8w4Y9dEAlahsVdF/5s5bv49EW5zi3CeeY0cIl4qz8wWoW3tVoo
-uEcs67WfHsFw11l93wUgC0yXBY5bUAx9aMAanrvCC3cEUzHTQhq8iEE4FaMVWAdzLVnf7MaHohzP
-ywrqHehYJt7FhDYEa8rEo5JvqrK+hhTaw4c05OrUi2XgIWlM22PwcTs6aUNamgR5yw91OIBXJHTj
-dIdvAjIc0jil+f30oh3SovwyukVmSOeGluhd+kTL8b69vUtEzjdZe3PBV/xtXfjOPY/p5Wr8g+U3
-yLsx6Y4T/KIut8LSnvHwiKW8s1tg3OkcB1crMaWTI8heovt1lHsjfIHwJ6F1hdAZItd8OvLnQtro
-AVV6mUXMJ7Q4vSMtZ2xoyZ1CSvpqRN8Nqx4nkNsCig+C6H9At3ho2FJJsMwZ7hn6Z/e7ugGra8FL
-JfJ6ilYL7HoBjcx3N4Bg2j67ekzaPjWiAl61Ci+LMe0naqbkGiFE/BJ6v7e/8Estz2Ah3tbgJXHF
-mXi5b7z98DiGnkFL8JtUmUUXiHdxk9hAWvQHPdx/WJWt8bIDXW5oCPyQFkeMfMnRVr5p5vgM4vJF
-z90QZiEu3aNeh1ymPvJp1g1fEoht/FEYBfABlMaCI/iorfg8z8/CJOcMdJNNmjcw2bydXM6RBscc
-+ZK1t7o8xGwKvCjz2jU+pOAYZG2fqX1604YpvRnjtv7N/Zj2AAR393ST/1S33PfdWFgb85iFe6aT
-ZdgqC/qgPCSd7ossiiHPiQeLbUPjsG+aMiUiNZJp7SMYJgO9Ts9A+9+GLxDWcHcEOitvOT0iPear
-a8S+tHz3KxouiZqIDfUmDTCst2GwCnsNepAcQSvRjA58RfU9w0NqV3YX7vd3Vg4FbKRdQG//r727
-XU4UBsAwSvb+r1n3BwaSANaW1bq+58zsdlCQjh/haQb1UvoZ4bpd/Trh+bZus8JlG8TLpzg0p0aM
-wTsPxkPw3j4mbTw3eKqX149pq9F9qecDzwNanQ2+XvqZ33lwb+P4NmhN7cxumeo7htdzh9fZgPoN
-dt0s8O1OXd5MNzVPrWnaDeKuBYf12sehDefuJTf1MxDLbU075wkv65VmvR9onifzJ2/0rv0qj+1o
-eU6WdvGcezcyHtyXheONjr4d6ruXf3Xdd43fCDfe98vyYej24++XH5FWHlhn2HcbSn3g7ETy7nZ1
-m+3zo2z+7y/fXtbuf2c/3d1RX00Ht7HcVh+z88Lxm+HGdcu47bCPu/+2D+ju9Y8u16W9328af46P
-ValbrjPD3a+27KsMy+P1w852HG27tRmR1uidys7Vx2+MW9ddR9ZlTF5iuG5zbS7/d6/3R/15+R75
-PO3B/qk7eu6tv71+Mgrg1xiHMrw+S19PCHOeEREAPk7C4V0Ic17Cn4wAECbh8C6EOW84Jfp5El6S
-d4znhAH8EuNQBjPC8E3OEX4i5wgDb8I4xKcQwpxnagAAPk7C4V0Ic55TI17DqRHAmzAOZUiY+RfC
-nOfj017DqRHAmzAOZUj4g0cIc54REQA+TsLhvVx/9O1mAADwfzMjDABAJCEMAEAkIQwAQCQhDABA
-JCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQh
-DABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwA
-QCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAk
-IQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEM
-AEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABApL+Uk2g8s+79bwAAAABJ
-RU5ErkJggg==
-"
-width=353
-height=351
->
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;<a class="anchor-link" href="#&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#182;</a></h2><h3 id="&#26631;&#20934;&#21270;">&#26631;&#20934;&#21270;<a class="anchor-link" href="#&#26631;&#20934;&#21270;">&#182;</a></h3><p>在下面的单元中，实现 <code>normalize</code> 函数，传入图片数据 <code>x</code>，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 <code>x</code> 的形状一样。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[11]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Normalize a list of sample image data in the range of 0 to 1</span>
-<span class="sd">    : x: List of image data.  The image shape is (32, 32, 3)</span>
-<span class="sd">    : return: Numpy array of normalize data</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="mi">255</span><span class="p">)</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_normalize</span><span class="p">(</span><span class="n">normalize</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="One-hot-&#32534;&#30721;">One-hot &#32534;&#30721;<a class="anchor-link" href="#One-hot-&#32534;&#30721;">&#182;</a></h3><p>和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 <code>one_hot_encode</code> 函数。输入，也就是 <code>x</code>，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 <code>one_hot_encode</code> 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。</p>
-<p>提示：不要重复发明轮子。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[28]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">one_hot_encode</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.</span>
-<span class="sd">    : x: List of sample Labels</span>
-<span class="sd">    : return: Numpy array of one-hot encoded labels</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="kn">from</span> <span class="nn">tflearn.data_utils</span> <span class="k">import</span> <span class="n">to_categorical</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_one_hot_encode</span><span class="p">(</span><span class="n">one_hot_encode</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#38543;&#26426;&#21270;&#25968;&#25454;">&#38543;&#26426;&#21270;&#25968;&#25454;<a class="anchor-link" href="#&#38543;&#26426;&#21270;&#25968;&#25454;">&#182;</a></h3><p>之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;<a class="anchor-link" href="#&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#182;</a></h2><p>运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[29]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="c1"># Preprocess Training, Validation, and Testing Data</span>
-<span class="n">helper</span><span class="o">.</span><span class="n">preprocess_and_save_data</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">normalize</span><span class="p">,</span> <span class="n">one_hot_encode</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[30]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">import</span> <span class="nn">pickle</span>
-<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
-<span class="kn">import</span> <span class="nn">helper</span>
-
-<span class="c1"># Load the Preprocessed Validation data</span>
-<span class="n">valid_features</span><span class="p">,</span> <span class="n">valid_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_validation.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#26500;&#24314;&#32593;&#32476;">&#26500;&#24314;&#32593;&#32476;<a class="anchor-link" href="#&#26500;&#24314;&#32593;&#32476;">&#182;</a></h2><p>对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。</p>
-<blockquote><p><strong>注意</strong>：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。</p>
-<p>但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 <code>conv2d</code> 类 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/conv2d">tf.nn.conv2d</a>，而不是 TF Layers 版本的 <code>conv2d</code> 类 <a href="https://www.tensorflow.org/api_docs/python/tf/layers/conv2d">tf.layers.conv2d</a>。</p>
-</blockquote>
-<p>我们开始吧！</p>
-<h3 id="&#36755;&#20837;">&#36755;&#20837;<a class="anchor-link" href="#&#36755;&#20837;">&#182;</a></h3><p>神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：</p>
-<ul>
-<li>实现 <code>neural_net_image_input</code><ul>
-<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
-<li>使用 <code>image_shape</code> 设置形状，部分大小设为 <code>None</code></li>
-<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "x" 命名</li>
-</ul>
-</li>
-<li>实现 <code>neural_net_label_input</code><ul>
-<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
-<li>使用 <code>n_classes</code> 设置形状，部分大小设为 <code>None</code></li>
-<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "y" 命名</li>
-</ul>
-</li>
-<li>实现 <code>neural_net_keep_prob_input</code><ul>
-<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a>，用于丢弃保留概率</li>
-<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "keep_prob" 命名</li>
-</ul>
-</li>
-</ul>
-<p>这些名称将在项目结束时，用于加载保存的模型。</p>
-<p>注意：TensorFlow 中的 <code>None</code> 表示形状可以是动态大小。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[39]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-<span class="k">def</span> <span class="nf">neural_net_image_input</span><span class="p">(</span><span class="n">image_shape</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Return a Tensor for a batch of image input</span>
-<span class="sd">    : image_shape: Shape of the images</span>
-<span class="sd">    : return: Tensor for image input.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">neural_net_label_input</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Return a Tensor for a batch of label input</span>
-<span class="sd">    : n_classes: Number of classes</span>
-<span class="sd">    : return: Tensor for label input.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">neural_net_keep_prob_input</span><span class="p">():</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Return a Tensor for keep probability</span>
-<span class="sd">    : return: Tensor for keep probability.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;keep_prob&#39;</span><span class="p">)</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_image_inputs</span><span class="p">(</span><span class="n">neural_net_image_input</span><span class="p">)</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_label_inputs</span><span class="p">(</span><span class="n">neural_net_label_input</span><span class="p">)</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_keep_prob_inputs</span><span class="p">(</span><span class="n">neural_net_keep_prob_input</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Image Input Tests Passed.
-Label Input Tests Passed.
-Keep Prob Tests Passed.
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;">&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;<a class="anchor-link" href="#&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;">&#182;</a></h3><p>卷积层级适合处理图片。对于此代码单元，你应该实现函数 <code>conv2d_maxpool</code> 以便应用卷积然后进行最大池化：</p>
-<ul>
-<li>使用 <code>conv_ksize</code>、<code>conv_num_outputs</code> 和 <code>x_tensor</code> 的形状创建权重（weight）和偏置（bias）。</li>
-<li>使用权重和 <code>conv_strides</code> 对 <code>x_tensor</code> 应用卷积。<ul>
-<li>建议使用我们建议的间距（padding），当然也可以使用任何其他间距。</li>
-</ul>
-</li>
-<li>添加偏置</li>
-<li>向卷积中添加非线性激活（nonlinear activation）</li>
-<li>使用 <code>pool_ksize</code> 和 <code>pool_strides</code> 应用最大池化<ul>
-<li>建议使用我们建议的间距（padding），当然也可以使用任何其他间距。</li>
-</ul>
-</li>
-</ul>
-<p><strong>注意</strong>：对于<strong>此层</strong>，<strong>请勿使用</strong> <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a>，但是仍然可以使用 TensorFlow 的 <a href="https://www.tensorflow.org/api_docs/python/tf/nn">Neural Network</a> 包。对于所有<strong>其他层</strong>，你依然可以使用快捷方法。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[64]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv2d_maxpool</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">conv_num_outputs</span><span class="p">,</span> <span class="n">conv_ksize</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Apply convolution then max pooling to x_tensor</span>
-<span class="sd">    :param x_tensor: TensorFlow Tensor</span>
-<span class="sd">    :param conv_num_outputs: Number of outputs for the convolutional layer</span>
-<span class="sd">    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer</span>
-<span class="sd">    :param conv_strides: Stride 2-D Tuple for convolution</span>
-<span class="sd">    :param pool_ksize: kernal size 2-D Tuple for pool</span>
-<span class="sd">    :param pool_strides: Stride 2-D Tuple for pool</span>
-<span class="sd">    : return: A tensor that represents convolution and max pooling of x_tensor</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_ksize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conv_ksize</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">3</span><span class="p">],</span> <span class="n">conv_num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
-    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_num_outputs</span><span class="p">]))</span>
-    <span class="n">conv</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x_tensor</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conv_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span>
-    <span class="n">activate</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv</span><span class="p">)</span>
-    <span class="n">pool</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="n">activate</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pool_ksize</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pool_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span>
-    
-    <span class="k">return</span> <span class="n">pool</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_con_pool</span><span class="p">(</span><span class="n">conv2d_maxpool</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#25153;&#24179;&#21270;&#23618;">&#25153;&#24179;&#21270;&#23618;<a class="anchor-link" href="#&#25153;&#24179;&#21270;&#23618;">&#182;</a></h3><p>实现 <code>flatten</code> 函数，将 <code>x_tensor</code> 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（<em>部分大小（Batch Size）</em>，<em>扁平化图片大小（Flattened Image Size）</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[68]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">flatten</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Flatten x_tensor to (Batch Size, Flattened Image Size)</span>
-<span class="sd">    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.</span>
-<span class="sd">    : return: A tensor of size (Batch Size, Flattened Image Size).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="n">layer_shape</span> <span class="o">=</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span>
-    <span class="n">num_features</span> <span class="o">=</span> <span class="n">layer_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">num_elements</span><span class="p">()</span>
-    <span class="n">layer_flat</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_features</span><span class="p">])</span>
-    
-    <span class="k">return</span> <span class="n">layer_flat</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_flatten</span><span class="p">(</span><span class="n">flatten</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;">&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;<a class="anchor-link" href="#&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;">&#182;</a></h3><p>实现 <code>fully_conn</code> 函数，以向 <code>x_tensor</code> 应用完全连接的层级，形状为（<em>部分大小（Batch Size）</em>，<em>num_outputs</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[72]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fully_conn</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Apply a fully connected layer to x_tensor using weight and bias</span>
-<span class="sd">    : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
-<span class="sd">    : num_outputs: The number of output that the new tensor should be.</span>
-<span class="sd">    : return: A 2-D tensor where the second dimension is num_outputs.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
-    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
-    <span class="n">fc</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span><span class="p">)</span>
-    
-    <span class="k">return</span> <span class="n">fc</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_fully_conn</span><span class="p">(</span><span class="n">fully_conn</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#36755;&#20986;&#23618;">&#36755;&#20986;&#23618;<a class="anchor-link" href="#&#36755;&#20986;&#23618;">&#182;</a></h3><p>实现 <code>output</code> 函数，向 x_tensor 应用完全连接的层级，形状为（<em>部分大小（Batch Size）</em>，<em>num_outputs</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
-<p><strong>注意</strong>：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[74]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">output</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Apply a output layer to x_tensor using weight and bias</span>
-<span class="sd">    : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
-<span class="sd">    : num_outputs: The number of output that the new tensor should be.</span>
-<span class="sd">    : return: A 2-D tensor where the second dimension is num_outputs.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
-    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span>
-    
-    <span class="k">return</span> <span class="n">output</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_output</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;">&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;<a class="anchor-link" href="#&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;">&#182;</a></h3><p>实现函数 <code>conv_net</code>， 创建卷积神经网络模型。该函数传入一批图片 <code>x</code>，并输出对数（logits）。使用你在上方创建的层创建此模型：</p>
-<ul>
-<li>应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）</li>
-<li>应用一个扁平层（Flatten Layer）</li>
-<li>应用 1、2 或 3 个完全连接层（Fully Connected Layers）</li>
-<li>应用一个输出层（Output Layer）</li>
-<li>返回输出</li>
-<li>使用 <code>keep_prob</code> 向模型中的一个或多个层应用 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/dropout">TensorFlow 的 Dropout</a></li>
-</ul>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[123]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Create a convolutional neural network model</span>
-<span class="sd">    : x: Placeholder tensor that holds image data.</span>
-<span class="sd">    : keep_prob: Placeholder tensor that hold dropout keep probability.</span>
-<span class="sd">    : return: Tensor that represents logits</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Apply 1, 2, or 3 Convolution and Max Pool layers</span>
-    <span class="c1">#    Play around with different number of outputs, kernel size and stride</span>
-    <span class="c1"># Function Definition from Above:</span>
-    <span class="c1">#    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)</span>
-    <span class="n">conv_pool_1</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-    <span class="n">norm_layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">lrn</span><span class="p">(</span><span class="n">conv_pool_1</span><span class="p">,</span> <span class="mi">4</span> <span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.001</span> <span class="o">/</span> <span class="mf">9.0</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
-    <span class="n">conv_pool_2</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">norm_layer</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-
-    <span class="c1"># TODO: Apply a Flatten Layer</span>
-    <span class="c1"># Function Definition from Above:</span>
-    <span class="c1">#   flatten(x_tensor)</span>
-    <span class="n">flat_layer</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">conv_pool_2</span><span class="p">)</span>
-
-    <span class="c1"># TODO: Apply 1, 2, or 3 Fully Connected Layers</span>
-    <span class="c1">#    Play around with different number of outputs</span>
-    <span class="c1"># Function Definition from Above:</span>
-    <span class="c1">#   fully_conn(x_tensor, num_outputs)</span>
-    <span class="n">fc_layer1</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">flat_layer</span><span class="p">,</span> <span class="mi">384</span><span class="p">)</span>
-    <span class="n">dropout_layer_1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc_layer1</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
-    <span class="n">fc_layer2</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">dropout_layer_1</span><span class="p">,</span> <span class="mi">192</span><span class="p">)</span>
-    <span class="n">dropout_layer_2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc_layer2</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
-    
-    <span class="c1"># TODO: Apply an Output Layer</span>
-    <span class="c1">#    Set this to the number of classes</span>
-    <span class="c1"># Function Definition from Above:</span>
-    <span class="c1">#   output(x_tensor, num_outputs)</span>
-    <span class="n">logits</span> <span class="o">=</span> <span class="n">output</span><span class="p">(</span><span class="n">dropout_layer_2</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-    
-    <span class="c1"># TODO: return output</span>
-    <span class="k">return</span> <span class="n">logits</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1">##############################</span>
-<span class="c1">## Build the Neural Network ##</span>
-<span class="c1">##############################</span>
-
-<span class="c1"># Remove previous weights, bias, inputs, etc..</span>
-<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
-
-<span class="c1"># Inputs</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">neural_net_image_input</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">neural_net_label_input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">keep_prob</span> <span class="o">=</span> <span class="n">neural_net_keep_prob_input</span><span class="p">()</span>
-
-<span class="c1"># Model</span>
-<span class="n">logits</span> <span class="o">=</span> <span class="n">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
-
-<span class="c1"># Name logits Tensor, so that is can be loaded from disk after training</span>
-<span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;logits&#39;</span><span class="p">)</span>
-
-<span class="c1"># Loss and Optimizer</span>
-<span class="n">cost</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">))</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">()</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
-
-<span class="c1"># Accuracy</span>
-<span class="n">correct_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-<span class="n">accuracy</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">correct_pred</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">)</span>
-
-<span class="n">tests</span><span class="o">.</span><span class="n">test_conv_net</span><span class="p">(</span><span class="n">conv_net</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Neural Network Built!
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;<a class="anchor-link" href="#&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#182;</a></h2><h3 id="&#21333;&#27425;&#20248;&#21270;">&#21333;&#27425;&#20248;&#21270;<a class="anchor-link" href="#&#21333;&#27425;&#20248;&#21270;">&#182;</a></h3><p>实现函数 <code>train_neural_network</code> 以进行单次优化（single optimization）。该优化应该使用 <code>optimizer</code> 优化 <code>session</code>，其中 <code>feed_dict</code> 具有以下参数：</p>
-<ul>
-<li><code>x</code> 表示图片输入</li>
-<li><code>y</code> 表示标签</li>
-<li><code>keep_prob</code> 表示丢弃的保留率</li>
-</ul>
-<p>每个部分都会调用该函数，所以 <code>tf.global_variables_initializer()</code> 已经被调用。</p>
-<p>注意：不需要返回任何内容。该函数只是用来优化神经网络。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[124]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train_neural_network</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Optimize the session on a batch of images and labels</span>
-<span class="sd">    : session: Current TensorFlow session</span>
-<span class="sd">    : optimizer: TensorFlow optimizer function</span>
-<span class="sd">    : keep_probability: keep probability</span>
-<span class="sd">    : feature_batch: Batch of Numpy image data</span>
-<span class="sd">    : label_batch: Batch of Numpy label data</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">keep_prob</span><span class="p">:</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">label_batch</span><span class="p">})</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_train_nn</span><span class="p">(</span><span class="n">train_neural_network</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#26174;&#31034;&#25968;&#25454;">&#26174;&#31034;&#25968;&#25454;<a class="anchor-link" href="#&#26174;&#31034;&#25968;&#25454;">&#182;</a></h3><p>实现函数 <code>print_stats</code> 以输出损失和验证准确率。使用全局变量 <code>valid_features</code> 和 <code>valid_labels</code> 计算验证准确率。使用保留率 <code>1.0</code> 计算损失和验证准确率（loss and validation accuracy）。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[125]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">print_stats</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Print information about loss and validation accuracy</span>
-<span class="sd">    : session: Current TensorFlow session</span>
-<span class="sd">    : feature_batch: Batch of Numpy image data</span>
-<span class="sd">    : label_batch: Batch of Numpy label data</span>
-<span class="sd">    : cost: TensorFlow cost function</span>
-<span class="sd">    : accuracy: TensorFlow accuracy function</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Loss: &#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">label_batch</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;, Accuracy: &#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">label_batch</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#36229;&#21442;&#25968;">&#36229;&#21442;&#25968;<a class="anchor-link" href="#&#36229;&#21442;&#25968;">&#182;</a></h3><p>调试以下超参数：</p>
-<ul>
-<li>设置 <code>epochs</code> 表示神经网络停止学习或开始过拟合的迭代次数</li>
-<li><p>设置 <code>batch_size</code>，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：</p>
-<ul>
-<li>64</li>
-<li>128</li>
-<li>256</li>
-<li>...</li>
-</ul>
-</li>
-<li>设置 <code>keep_probability</code> 表示使用丢弃时保留节点的概率</li>
-</ul>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[130]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Tune Parameters</span>
-<span class="n">epochs</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">128</span>
-<span class="n">keep_probability</span> <span class="o">=</span> <span class="mf">0.75</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#22312;&#21333;&#20010;-CIFAR-10-&#37096;&#20998;&#19978;&#35757;&#32451;">&#22312;&#21333;&#20010; CIFAR-10 &#37096;&#20998;&#19978;&#35757;&#32451;<a class="anchor-link" href="#&#22312;&#21333;&#20010;-CIFAR-10-&#37096;&#20998;&#19978;&#35757;&#32451;">&#182;</a></h3><p>我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[131]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Checking the Training on a Single Batch...&#39;</span><span class="p">)</span>
-<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
-    <span class="c1"># Initializing the variables</span>
-    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
-    
-    <span class="c1"># Training cycle</span>
-    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
-        <span class="n">batch_i</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
-            <span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">:  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-        <span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Checking the Training on a Single Batch...
-Epoch  1, CIFAR-10 Batch 1:  Loss: 1.95307, Accuracy: 0.35
-Epoch  2, CIFAR-10 Batch 1:  Loss: 1.71162, Accuracy: 0.5
-Epoch  3, CIFAR-10 Batch 1:  Loss: 1.59222, Accuracy: 0.525
-Epoch  4, CIFAR-10 Batch 1:  Loss: 1.33961, Accuracy: 0.65
-Epoch  5, CIFAR-10 Batch 1:  Loss: 1.22308, Accuracy: 0.625
-Epoch  6, CIFAR-10 Batch 1:  Loss: 1.02561, Accuracy: 0.65
-Epoch  7, CIFAR-10 Batch 1:  Loss: 0.918526, Accuracy: 0.725
-Epoch  8, CIFAR-10 Batch 1:  Loss: 0.763063, Accuracy: 0.775
-Epoch  9, CIFAR-10 Batch 1:  Loss: 0.656814, Accuracy: 0.8
-Epoch 10, CIFAR-10 Batch 1:  Loss: 0.574128, Accuracy: 0.825
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;">&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;<a class="anchor-link" href="#&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;">&#182;</a></h3><p>现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[134]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">epochs</span> <span class="o">=</span> <span class="mi">8</span>
-<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./model/image_classification&#39;</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training...&#39;</span><span class="p">)</span>
-<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
-    <span class="c1"># Initializing the variables</span>
-    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
-    
-    <span class="c1"># Training cycle</span>
-    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
-        <span class="c1"># Loop over all batches</span>
-        <span class="n">n_batches</span> <span class="o">=</span> <span class="mi">5</span>
-        <span class="k">for</span> <span class="n">batch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_batches</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
-                <span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">:  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-            <span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
-            
-    <span class="c1"># Save Model</span>
-    <span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
-    <span class="n">save_path</span> <span class="o">=</span> <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Training...
-Epoch  1, CIFAR-10 Batch 1:  Loss: 1.95822, Accuracy: 0.35
-Epoch  1, CIFAR-10 Batch 2:  Loss: 1.64322, Accuracy: 0.4
-Epoch  1, CIFAR-10 Batch 3:  Loss: 1.36831, Accuracy: 0.55
-Epoch  1, CIFAR-10 Batch 4:  Loss: 1.41689, Accuracy: 0.45
-Epoch  1, CIFAR-10 Batch 5:  Loss: 1.59784, Accuracy: 0.425
-Epoch  2, CIFAR-10 Batch 1:  Loss: 1.36398, Accuracy: 0.475
-Epoch  2, CIFAR-10 Batch 2:  Loss: 1.1802, Accuracy: 0.475
-Epoch  2, CIFAR-10 Batch 3:  Loss: 1.07384, Accuracy: 0.6
-Epoch  2, CIFAR-10 Batch 4:  Loss: 0.988241, Accuracy: 0.675
-Epoch  2, CIFAR-10 Batch 5:  Loss: 1.24307, Accuracy: 0.55
-Epoch  3, CIFAR-10 Batch 1:  Loss: 1.05733, Accuracy: 0.625
-Epoch  3, CIFAR-10 Batch 2:  Loss: 0.952706, Accuracy: 0.675
-Epoch  3, CIFAR-10 Batch 3:  Loss: 0.922446, Accuracy: 0.65
-Epoch  3, CIFAR-10 Batch 4:  Loss: 0.753417, Accuracy: 0.8
-Epoch  3, CIFAR-10 Batch 5:  Loss: 0.917541, Accuracy: 0.7
-Epoch  4, CIFAR-10 Batch 1:  Loss: 0.868109, Accuracy: 0.725
-Epoch  4, CIFAR-10 Batch 2:  Loss: 0.818949, Accuracy: 0.7
-Epoch  4, CIFAR-10 Batch 3:  Loss: 0.680601, Accuracy: 0.725
-Epoch  4, CIFAR-10 Batch 4:  Loss: 0.577342, Accuracy: 0.825
-Epoch  4, CIFAR-10 Batch 5:  Loss: 0.650067, Accuracy: 0.8
-Epoch  5, CIFAR-10 Batch 1:  Loss: 0.748057, Accuracy: 0.8
-Epoch  5, CIFAR-10 Batch 2:  Loss: 0.633852, Accuracy: 0.8
-Epoch  5, CIFAR-10 Batch 3:  Loss: 0.480863, Accuracy: 0.95
-Epoch  5, CIFAR-10 Batch 4:  Loss: 0.522334, Accuracy: 0.85
-Epoch  5, CIFAR-10 Batch 5:  Loss: 0.571857, Accuracy: 0.85
-Epoch  6, CIFAR-10 Batch 1:  Loss: 0.642935, Accuracy: 0.8
-Epoch  6, CIFAR-10 Batch 2:  Loss: 0.585723, Accuracy: 0.825
-Epoch  6, CIFAR-10 Batch 3:  Loss: 0.395464, Accuracy: 0.9
-Epoch  6, CIFAR-10 Batch 4:  Loss: 0.397977, Accuracy: 0.875
-Epoch  6, CIFAR-10 Batch 5:  Loss: 0.392235, Accuracy: 0.925
-Epoch  7, CIFAR-10 Batch 1:  Loss: 0.489782, Accuracy: 0.85
-Epoch  7, CIFAR-10 Batch 2:  Loss: 0.459161, Accuracy: 0.825
-Epoch  7, CIFAR-10 Batch 3:  Loss: 0.273993, Accuracy: 0.95
-Epoch  7, CIFAR-10 Batch 4:  Loss: 0.319732, Accuracy: 0.925
-Epoch  7, CIFAR-10 Batch 5:  Loss: 0.30099, Accuracy: 0.95
-Epoch  8, CIFAR-10 Batch 1:  Loss: 0.327477, Accuracy: 0.9
-Epoch  8, CIFAR-10 Batch 2:  Loss: 0.365161, Accuracy: 0.925
-Epoch  8, CIFAR-10 Batch 3:  Loss: 0.260866, Accuracy: 0.9
-Epoch  8, CIFAR-10 Batch 4:  Loss: 0.2765, Accuracy: 0.95
-Epoch  8, CIFAR-10 Batch 5:  Loss: 0.264591, Accuracy: 1.0
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>模型已保存到本地。</p>
-<h2 id="&#27979;&#35797;&#27169;&#22411;">&#27979;&#35797;&#27169;&#22411;<a class="anchor-link" href="#&#27979;&#35797;&#27169;&#22411;">&#182;</a></h2><p>利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[136]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-<span class="kn">import</span> <span class="nn">pickle</span>
-<span class="kn">import</span> <span class="nn">helper</span>
-<span class="kn">import</span> <span class="nn">random</span>
-
-<span class="c1"># Set batch size if not already set</span>
-<span class="k">try</span><span class="p">:</span>
-    <span class="k">if</span> <span class="n">batch_size</span><span class="p">:</span>
-        <span class="k">pass</span>
-<span class="k">except</span> <span class="ne">NameError</span><span class="p">:</span>
-    <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
-
-<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./model/image_classification&#39;</span>
-<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">4</span>
-<span class="n">top_n_predictions</span> <span class="o">=</span> <span class="mi">3</span>
-
-<span class="k">def</span> <span class="nf">test_model</span><span class="p">():</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Test the saved model against the test dataset</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_test.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
-    <span class="n">loaded_graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
-
-    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">loaded_graph</span><span class="p">)</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
-        <span class="c1"># Load model</span>
-        <span class="n">loader</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">import_meta_graph</span><span class="p">(</span><span class="n">save_model_path</span> <span class="o">+</span> <span class="s1">&#39;.meta&#39;</span><span class="p">)</span>
-        <span class="n">loader</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
-
-        <span class="c1"># Get Tensors from loaded model</span>
-        <span class="n">loaded_x</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;x:0&#39;</span><span class="p">)</span>
-        <span class="n">loaded_y</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;y:0&#39;</span><span class="p">)</span>
-        <span class="n">loaded_keep_prob</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;keep_prob:0&#39;</span><span class="p">)</span>
-        <span class="n">loaded_logits</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;logits:0&#39;</span><span class="p">)</span>
-        <span class="n">loaded_acc</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;accuracy:0&#39;</span><span class="p">)</span>
-        
-        <span class="c1"># Get accuracy in batches for memory limitations</span>
-        <span class="n">test_batch_acc_total</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">test_batch_count</span> <span class="o">=</span> <span class="mi">0</span>
-        
-        <span class="k">for</span> <span class="n">test_feature_batch</span><span class="p">,</span> <span class="n">test_label_batch</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">batch_features_labels</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
-            <span class="n">test_batch_acc_total</span> <span class="o">+=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
-                <span class="n">loaded_acc</span><span class="p">,</span>
-                <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">test_feature_batch</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">test_label_batch</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
-            <span class="n">test_batch_count</span> <span class="o">+=</span> <span class="mi">1</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Testing Accuracy: </span><span class="si">{}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_batch_acc_total</span><span class="o">/</span><span class="n">test_batch_count</span><span class="p">))</span>
-
-        <span class="c1"># Print Random Samples</span>
-        <span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)),</span> <span class="n">n_samples</span><span class="p">)))</span>
-        <span class="n">random_test_predictions</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
-            <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">top_k</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">loaded_logits</span><span class="p">),</span> <span class="n">top_n_predictions</span><span class="p">),</span>
-            <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">random_test_features</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
-        <span class="n">helper</span><span class="o">.</span><span class="n">display_image_predictions</span><span class="p">(</span><span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">random_test_predictions</span><span class="p">)</span>
-
-
-<span class="n">test_model</span><span class="p">()</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Testing Accuracy: 0.684434335443038
-
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt"></div>
-
-
-<div class="output_png output_subarea ">
-<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA9oAAAN6CAYAAABxCzGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
-AAAewgAAHsIBbtB1PgAAIABJREFUeJzs3XeALFWZ9/Hvr3vuvWSQLKDoKoiCKEFRMee8hlURWTNi
-WNesr6uromtYVBZd16ywLmZd4wqKORNEXRHFNaBIkozEO9P1vH+cU901NdXTPT090zN3fh9tpm9P
-1anT1TXV9dQ55zmKCMzMzMzMzMxsPFqTroCZmZmZmZnZpsSBtpmZmZmZmdkYOdA2MzMzMzMzGyMH
-2mZmZmZmZmZj5EDbzMzMzMzMbIwcaJuZmZmZmZmNkQNtMzMzMzMzszFyoG1mZmZmZmY2Rg60zczM
-zMzMzMbIgbaZmZmZmZnZGDnQNjMzMzMzMxsjB9pmZmZmZmZmY+RA28zMzMzMzGyMHGibmZmZmZmZ
-jZEDbTMzMzMzM7MxcqBtZmZmZmZmNkYOtM3MzMzMzMzGyIG2mZmZmZmZ2Rg50DYzMzMzMzMbIwfa
-Zg0kbSbp7yV9SNLPJV0o6XpJRe3x35Ouq5mtfpKe13B+eeeQ6+7bsO7/LnWdVypJD2vYH1+cdL3M
-zGxtmZp0BWxlkbQtcCCwK7BdfhTANflxPvAH4E8RMT2pei4lSc8BXg/skF+Kyq9j7hpmZmOzmHOM
-z0+zeX+YmdnEONA2JO0LHAk8BLg1oCFW60g6GzgDOB34ekT8dulquTwkvRt4NukCrbxIG2Z/mK0I
-kt4AvGqBq10HXAVcSbqRdgZwGunveuN4a2hDEKMHiT5fzbaYfWlmZjYyB9prmKTbAu8G7pVfitrP
-+bSA/YDbA0/L5f0BODYi/mPMVV0Wkp5KL8juvswCLtIkbQ/cqfbypRHxk0VX0GxhFhJcbJ4fuwK3
-BR6aX79M0gnAcRFx/nirZ0skWCXBtqQdgYNqL/8lIn46xs04yDYzs4lwoL1GSXop8AZgA3MDy6pB
-rbrVdW9BCtpXXaAtqQW8lrn74irgeOD7wF+Aeuve5bV/HwCcVHvt68ADx1ZZs+GNEnBV/wa2B14C
-PFPS8yPixPFUy5bIqgiwKw4BvlR77cvAI8dU/mrbH2ZmtglxoL0GSToa+Geau0eX/74B+B0p0Lwe
-2JZ00b07KTintl513dXogcCezN4fVwB3iojfj1Deat4XtulZyPFYPxcEsA3wEUl7RcRrx1ozG5f6
-Z7yazkFLUdfVvD/MzGwT4EB7jZH0DHpBdvfl/O8/AR8APgf8OiLmXJhIapO6jB9MGtP9YFKX07Kc
-1eoelefl/viPEYPsejlmk1J2I+4Ah9L/b3QrYEfS3/XDgH3oHb/V4/jVks6LiA8uZaVtYSLil0B7
-0vVYpLGdLyPif1j9+8PMzFY5B9prSB4//K80B9lvBl4/KPFRRHSAn+fHhyRtThrP+RzgPktR72Vy
-QMNr31n2WpgtkYg4fYjFPgW8XNJjgfeQgu9qy7aAt0v6XERctjQ1NTMzM1v9PI/22vJiUvfvUhlk
-vywiXj1KduGIuD4iPhsR9yclRvvceKq67HZseO3Py14LsxUgIj5LGj/7l4Zfb8XCs5qbmZmZrSkO
-tNeWxzB7DHIAp0bEseMoPCLOjoiPj6OsCdiWud0Wr59ERcxWgoj4A/Bk5uZhEPDEiVTKzMzMbJVw
-oL1GSLo5adxl3YeWuy4r1PpJV8BspYmIrwE/Yu7Y7p0l7T+BKpmZmZmtCh6jvXbcvM/rP1vWWiyA
-pF1IY6dvQWpxngKuBc4HfgX8silh24RNPCGcpHWkuWlvC+wArCNlUD8bOD0iFtRSn8f2HwTcmvQ5
-XA9cBPw8In49xqpXt9kCbkm6OXRTUtbrzUhZ8C/P2z89Iq5Ziu3bLJ8H7trw+iHA/y62cEkC9icd
-r7sCW5Km0bsU+FxEXDlCmTcjDWXZk3TsQOoG/xfSeePcxdZ7wPa3AO4C3AbYjpSM7nLgHOCMhf4N
-rgSSbgHsC9yMtE/XAdcAV5JmqPjVCOP2J36+HIWkfYDbAXuQhlIUwNXAH4GfTWLOeUl7kM7Te+Y6
-XQ1cApwdEb9Yom1uTvrb3Yv03bANqdfLdaRj4zzgXOAPETGzFHUwM1vRIsKPNfAAHku6GOjkR/n8
-VpOuW62eOwCvBs7KdZzvcRnwYeDgBW7j+CHKHubx5Fzen8dUXgHs1qfOb2hY9p9qy+yT39tV85R/
-NfBuYOch9tPDSXOAT89T3jnAUYDG8NnfmZQR/xukC7VB+2oaOAN4JbDNiNt8U5+yH7+I9/GoPmW+
-Y5n+hspjpfq3vnER5d2nobwO8Kp51nlzw/t/eW2ZfUmzHFzWZ391gDsvoJ57AW8H/jDEsfPLXMed
-xrzvDwY+TZoesd+2ryMlnbtTbd3nVd53+fOdQ25334bt/O8Y3s9dgPcBFwyxTwvSzbx3AIfOU+al
-Q5Y1zGP7Ptt4WMOyX1zkvrg98F7SzZpB9foN8Hpgl0Vsb+BnSsqs/nTgJwPqcz4pEep2YzgmtgKe
-DXwfmBnyc7oO+F7+m7vTYuvghx9++LFaHu46vnb0+6x3XtZa9CGpJellpLvfrye1bsWAx3bAU4HT
-JH1W0m4L2GRTecMs07T8YsupZnVeSL27JL2OlAn+KaQLoX7b2ZJ0kXSOpAc0bUDSLpK+BnwRuC/p
-2OlX3q1J2al/kFu+F0zSayT9DvgxcDQpsNswzzbLRws4EHgj8CdJrxhh868GvttQ9vsl3WqE93JL
-0s2fenmnAS8ZoX4rwSV9Xm9KIFg353iVNCXp30i9aZ5B+jse9DfUl6SdJP0nqZfLi0i9dwYdO/sA
-rwB+K+mVedrCkUnaTNJ7gFNJNzXXzbPtDcDfAT+W9K7cA2VcRtqHdZIOkPQd4IfAkcAuDN6nQWrB
-fz7wPUln5Zb9+eo4kfPlQkjaUdJHScfrs0g3gwfV71akc8sf8vltMZ9xv3P+fqQA+4PAHQfUZ1fg
-ZcDvJD1k1IpIejzwa9LN2rvRy/Uy6LGBNL3gK4BTJR0zah3MzFYTB9prx6V9Xp/4lFyStgW+Srrj
-vgW9CwrVHvXXoPdF/mjgTEl3G3azfcoftMwwyy6knH7rDKp7epJ8HHgNvaEg9TKb9tm2wJcl3XdW
-wdJepFbi+zP3c+j3PoPU8vV1SduwcM8hDQ+oXkz22z/118t1tgbenG+4NF3cN4qIAjic9PdRfY9b
-A5+WNPTY/Xwx/SnSvq3W90pSC/lq7Tq52O691eN1M+BrwAvoff80fcbDFSzdG/gF8PfMnod50LFD
-XnYr0o2aL+RusAsmaTvgm6SeHWW5g+pQei5wkqQNo2y7X5VYxGeWb3ieCtyd0f8mId0s3WxAHZf1
-fLlQkg4kBdhl8r+FHl8bgNcB35Q0zI2pvlWpbkvS/YAfkFrZo2HbTfsrgJsAn5f06AVXQHoV8AnS
-UJ7yuJjvc+l3rg76HxdmZpsUj9FeO35T+3f5Jfl8Se+NiMsnUKdyLOPXgDsx+4599YLtIlL37OuA
-3UhB2RRzg+2dgZMlPSgifjRg000tHPULhYW0gtTrPko5o7S6HAc8gd7nGaTxrb8jdXHcjDTWuey5
-UL3gWQd8UtI+EXGZpJ1JAUO1Z0CQxpb+kTTOe3vSBfQGZn9GAHcgdd09coT3Uf8sIXVL/CMpUL2K
-9JlvR2pF36LPeo8mdZ9/wrAbjogLJB0BnETvPSm/n3eSegAM41jSGMnqxXgAT4uIPw1bnxWoX4DQ
-7+bdfD4K3JvZxyukniwXAzcCu5PGvs4bfEr6W+CT9FqPYe7x8HtSi/z1pL+BW9M7dqvLPZR0o+g+
-sYBpDvONmC+RbjT1O3/9OT9m8nu7BbMDk/uQekH8cNjtLhVJ7yIF/03ns/K1C0jnlstJPWR2IJ1j
-2rXl5zOp8+XQJB1AGsZSjjuG2fuiIB23F5COwT1In2+5XLXl/VDgG5LuGRFXLbJeB5HyJmzB7PP5
-JaTj7ApSQL0X6UZSPdBdB3xY0mkx5FhySYeRhqT0O8avI/2tXUkaNrEVab/tSTpGmtYxM9vkOdBe
-IyLifEnnAHvXfrUz6QLzsIioB+PL4Z00B9kd0ni4D0bEz6sr5C7KjwdeS6p/9SJiK1LwuH/0T6L0
-elJ356rPk7pHltsvg7aL5qn77/LPh9MLCu6c31M1kDgd+Md5yqlqmre4n4eSuu+V++6XpNaTkyLi
-uuqCku4KHJOXr9qetD+eRwpaygvFAjgReFdEnFEra3NSC+Kb8vrV1o2nSfpARJy2gPdBXv9GUqD/
-JVI38rMjYrppYUm3B55EakXchtkXcH8n6VkR8f6hNx5xiqQ3kbp7Vt/PkZK+HRGfmG99SY8l7cN6
-kH1cRHxx2HqsUHfq83q/LuX9/D1p3Gm5jy4E/oWU8GzW31k+xp5An78HSfsCHyMFDd2Xc9lnkG74
-nBIRVzSU+2DS38l+zD5u7gK8ldTaPqzXkoKo+vlrGvg34EMR8X+1OuxO2hf/RApCBBxG729vIvLw
-k6Ygu7xh8XbgK003jXJPhTuSxkY/gdR1up8H0vvc7g68jdnnyx8ALx2y2gtOlDeMfAP4U/QS6UGv
-fleQgs5PRMTFtfX2AZ5J6j5fvQEE6Xh7Pwu4CVgTuT6fphe83kjqxn1C1BKeSZoCHgK8hbkzjmxD
-Gkv/d4M2mv9m/o25x8UNpPH7J0bET+ZZ/xak78VH5PrcZNA2zcw2GZMeJO7H8j1I46OqSY2qyY1u
-JAVW9wemlqk+j2qoT0G6uD5kiPVvApxcK6N8/skF1uUPDWXcfIT3dL/K+uXPr41hX72hVm49adSx
-MH9CMlKL0xcb9tc1wP+rlHc5cK8h6rQfKZFVfb8dv8D39iPghcBWI+yXm5KC8/p7ughYt8CyWn3K
-ugrYa571bkW64K8fxz9crr+leY6VcSRD+0Gfv7E7zLPOm2k+XssyPg1sMWJ9NifdVKrv7+uAZw5Z
-hkiBY9P7esCQZdyB1HukXo/zgf2HWH9PUtb2+n5aTDK0ajlDJ0Mj5WJo+l6YyeeGBR3HwAOAbwE3
-GbDcwxre86KSlo2jXNK456Zj41sMl0jyAHpJMutlPHkRn2n1378Gbj1EOVuQ8lDU6zJNn+SbtfWb
-EqleBRw4wueynpS87bmL/Yz98MMPP1bDw2O015Z3kQKQpq56U6Sxql8DLpP0VUmvl/QISTcdd0Xy
-lD5vZ+5d8r8CD4qIUweVEam16tGkQKDapU+kVs27j7XSK0u1W+K7IuLFETFvl7yI6JAucupdFzcn
-tU5DChweEBHfGViBiLNIrXJN+37o8a4RcdeIOC5GmKorIi4kXVSfzuzjeifS8byQsgrSWMyLa2Vt
-RRqvPacrc+46/CnSmO7uy6SbFU+I1TsuGwBJDyJN7VU/ti6NWk+TAarrf4k0Zv26fgsP8BrS8IWS
-SMftwyLig0NVJnkJ8O/MPR8ePWQ93sbsXmEi/W09MCIGTnsWEX8EHgT8iQl2qc2J4N5bf5kUZB8e
-EW9Z6HEcEadExH2i1qNgNchJxp7O3O7iPwEeEREDex1FxE9Jn231/Zfnx2NGTI5W7WnzR+BuEfHb
-IepyHanHxF9rv2oBTx5iuw+qPC+P06Mj4sxhKl2ry8aI+HBEvHuh65qZrUYOtNeQiLiWdHf6RpqT
-l1S7Xz+A1I32C8D5ks6X9N+SXijpwBwoL8YjSOP6SuUX+KvzRcpQIs1HewRpDGbd8xdVw5WrvNiC
-lGl52G6WRMSlpJ4LTZ9fAK+LeboBNvgwc7tvbkHKBr4s8jFwVMOvnjRCWRfn9YryJdK+uj3pRlXd
-caTWq1J5HD81Is5b6PZXkpxB/T9p7kr88QUUVV3/CuDIQTeF5qlTObVQvYv+yyLiWyMU+VLSlFRl
-PQUcIqlfd/myHrcitQLX6/GqiPjlsBvPN4qes9BKj9lTSGPXS+V7eVNEfGoyVZqopmE+G4EjFnIz
-MB8HL2XuuXYnUuA7ivKzeVIsIK9KRFxAOlfX63LoEKvfrOG1/xl222Zma5kD7TUmUpKwh5HGV5YX
-lv2CrupjV1JX72NJYyD/KOnNOUv1KJ7b8NpvgP9YaEG5ZehtzG1ZfdRStMavIAH8a/QZxzyP6pjh
-asBzFWl8+fAVSNs+mbnH0LIF2rkePyNln64mM7vziGV9kzRuvX48PV1SN3jPU900BX1vj4gvj7Lt
-lSKPOT+NFBTUXUuvB8TQRZL2zX9ExELHdlcdRS+re/n5nMMI5w3oHr//ytzj9+kDVj2yYZ3/Y27u
-h2HqcBKpJ9GkWrWryf6qN/DeMIG6TFSeNeFw5v5NvztGyGESEceTWsLrx8o/LLSoSl1OiYhREudV
-b5qU5R00xHpNM0lcPcL2zczWHAfaa1AOJA4gJb8qmB1w9wu8YXbgvTtpzPfZkj6wkIA2d7e9N3Mv
-Zj6Uu++O4v30WiFLU6Qx05uq60nTrSxUvVtruf8/O2J33qZusvWke8vh9Nq/t85Js0bxBlLG4Xqw
-/R5Jt8k3mD7A3O6lPwJeOeI2l5SkQ+Z53FfS4yUdI+ls0hjq+rzo5XHyimG6z/bx4cW8B+YmkiqD
-91HPG5ACkDLTePk532PAOtW5iMv9csKoLfXAh0Zcb1Ek7QkczOwAP4B35KEma8096M1oUPWBRZRZ
-TcpYHl8HSxo1Kdiof0M/Ze535C6StmxauKIp4dxINzHNzNYaB9prVERcEBGHA/uTLgQuY3YgPWzg
-3QKeAZwl6f5Dbv5AUlKUuk8O/QbqFUnTlHyXuXW966hlrmDdbOYjtGaX3aObpjD6/oj1ObfhtW0b
-XltqTcHffNmP+8oB0+GkzNjVY2pL4DOkY3Xr2mqXkcZlr6QApaz7FOkmQL/H10k3bV4K3Ia53cXL
-Y+6YiFhwq232x9z7ZCQ5IDigVjeAz41aJkBE3MjcMf77SGo8hnM9qtnTS59ZRDW+SMrivNwe0PDa
-RtI0bGtR9fuiPB7OiohfLaLMT5LGu9fdZcTyvjfKShFxA2kqsrpB5+r6UAgB/yppl6aFzcysx4H2
-GhcRZ0fEc0jZmx9Kmt7mVNLF1rCBd5AygJ+Uu9MO0nSBcfEYxrT+uPZvsWkG2qXFXPzVE+Msprym
-skYOtCWtl/RwSUfnvABnSTpP0lWSOpKKpgcpO3LddqPWI3dxPpyUaRd6gdW+pKmMqq3ZBSmb8FDz
-0k5AfSjIfI/633qQPuMjI2KU1vqyjIWM/W9yKL25mksX5fGni3Vu7d/luPwmBzH3u/OqYRJT9ZOD
-/bPof1NzqVS7Dpef008Xkahutat/NwXp+3BkEXE1KUP4OG4CX53H9Y+qqcv3oHN1dTx2eY7YG/iF
-pJdI2mER9TEz26R5Hm0DIGeV/Wp+kLOi7k9qfT6Y1KXuNnnx6gV4twjSRfCHJf0mj5ntZ8/K8+7F
-3RjeRrWMMmC4+RjKXamGTobToKn1bNTymsrabKGF5G6s/0xK2Fe9+Ku3HM7XPbd+MTtyoA0QEd+R
-9FrSfM/VcZLV7QXwtjzWdqVaSABXfX9XAh8Bjh3DjbA/L3L9/SrPy/1+saRDFlkuwJyM8sztOl+6
-RUM9BmYZH8LPSefa5dSUY2NRgeUqtydzzy/j+m7ar/baKN9NiznnQxpuVD+HzXuuzufAHwJ3q6wX
-wA6kG/NvkfR94BRSa/vpufXczGzNc6BtjXKX5J/kxwcAclexxwHPIrXqVRNPlV/AmwPvJn0p99M0
-Nm0xd+lLo3SLW83G3eo0zvIW1DIn6ZWkLPeb02s1GamsmkHjDweKiDdJugdpmpvq8V7+/AFpmrOV
-bNDY4etJyfCuIrXunkFKhnZKbm0dh/q0cgtVbzkTqWfBjxZZbj/9Au2m89eoY9bHXcZC7c7cY2Ox
-N0RWs+X8blrIGO3yHHjtGOrSr+z5PIl0A2anyvLleboF3Cs/AGYk/RT4NvBN4FsR0TRUycxsk+eu
-4za0iLg4It4VEfuTpoS5irlBRzk9znzjtZsuMMaRxbTpQr6VM8naCiXp3cAbSS0r1e7Y9Qu6YR5z
-ih9TNU+Y53dHLjIZ11Iq90knItrzPLaKiN0j4nYR8dCIeE1EfHmMQTYs/kZOU+C7kGNjocdPv5t0
-Tb0klur8tdS2anitKfnVWtH0mS/VZztqMrRll3MrHEK68V4/R5ff/eWjDdwJeBlwEnCRpPfn+cnN
-zNYUt2jbSCLiREk/IyXQ2pq5F6mPJSVYarJ5w2tN82AvVL8ytsTTkaxIkl7E7CmyoHfhNg2cSWpJ
-+SOppe2vpK7qNzD3mHs28NQlqOMewL9Xtlff7quAJ497uzZHU4C7lGOa6+PBS03dzMfRYjfOmxrD
-anqP4zgXrzp5Now2c/++l+K7SYyht81yysH2nSUdAbyENLSs1JS3pbQt8EzgGZI+AfxDRFyxpJU1
-M1shHGjbyCLiLEkvB97L7CBEwH3nWbUp6K1ncB5FvzIm0VJkA+ShCK9jbpB9LXA08IGIGPqzk/To
-sVYwldkGPg7syOyWnGrisCdJ+k5ETGSKpjWkKXPzqNNpLUbTMTmO89cket5cCdSzR6/JHkARsVHS
-RmBd7VdL8d0UrNLvpYg4EThR0v7Aw4H7kBK7VW+gVwPvam+RJwJ3l3TviPjDctTXzGySHGjbYh0P
-vI25d+dvOc86TXezx3Fx19Ttb3oNZ9Bd6Z7C7N4QAq4BDo2IX4xQ3lJ0xXwjKdt1PciuB9vvkPTj
-iKhPhWPjU79BF8A3I6Jpiqql1NS1ehy5ICaRT+Jy5ibHWlQCwVXuCmDn2mtL9d202MRmExUR/0tK
-AvimfEPyIFLS1HuQgu9yWEL1+ArgZsBXJN1xzENTzMxWHI/RtkXJSdNOY27XsbakfoFPU6D9N2Oo
-TtOcye6itnI9svK8vBh77YhBNqRW57GR9FDSOMN6kH0ic+fX3hz4tKQtxlkHm6We9VzMTZC2HC6t
-PC9vtDRl716ovcdQxkJd2vDaON7LauXvphFERCciTouIt0fEo0jn4ocBnydNfVi/Ptgb+IdlrqaZ
-2bJzoG3jcHGf1/uNcTy78ry8UL3DGOpxQOV5GRSd3WdZm7yDmNv198RFlHdgQ3kjyeOy/7PhV2eS
-xhs+iXQBCb1j+DakYRS2NOrnDUitY8vt5w2v3VpSU+6JhajOzb5c6tMwirlzSa8lZzM3KDygacEF
-OoC5n+0m+90UEdMRcXJEPBa4N3N7gQj4+2WvmJnZMnOgbePQ1LWuExFNrSXQPB3PVpIOXGQ97s3c
-i5mlmvqnn0mMGV11cib4elKpKyLikhHL253Z8xuPLHeD/ASzW0tFGlP5+HwR+W3SOPJqZvRyvPYz
-xlEPm+NU5v59bZ/Hii6biPgtc7v9tpg/L8W8JO0F7LGYeo3oO5Xn5b69Tb7RtBxW2vmy+n1R/k3f
-fTEFSroFaX7u+ba1yYqI7wMvZPa5EuD2ksYx/t3MbMVyoG3jcBvmXjD1C7KJiHNoHp92xKgVyEH6
-vg2/Wu6LmaYxZ/XkOtY8ZvGaRZT3zEWsW/dm0jzw9S7jz6wl8PkXUmb9erD9DklNx6ItQkRcTJrf
-u97i+JgJVOdHDfV40iLKm1TW+h8xN2O6gKOWafsr7Xz5w4bXdpM08k0UUi6Kuo2kqbLWik/T6wFU
-VU/EZ2a2SXGgbYsi6QDg1tWXSAFH0wVL1WeZG6D8vaRRE/G8pOG1K4BvjFjeqP5a+/eqm8ZlmTRl
-3N1J0oLPSZK2IgUGi24dk/Qw0rFUD7LfHRGfrS4bEUG6OXQRHq+9XP6r8rw8b/xj7iEx6Xo8WtKt
-+yzfl6RtgWcxgdbdiLgB+CRzz8VHSRprzoM+6udLmOz58lTm5gIAeNEohUnaEjiSueeTL+b8JmtC
-RFwPNCUl7Te8zMxsk+BAe42QdJCk40e5EJynTAHv6PPrLwxY/d8bXtseeNMI9bgXadqQ+sXMB/KF
-5HKqJrgp63OLZa7DihcRVzP3wmszUrbahXonsGt+PvK8ypJuBpzQ8KszgRc3rRMRfwEOp3m89ntG
-rYv1dQJwWe21bYEPLnM9PgfUhzmsJx2LC/VmYKf8fCnnBe/nGOYG+TuwPPu0fr4UEzxfRkRB+rut
-33h4aE6OuFCvA3ZreL3p+2+TJWlnelnIq85f7rqYmS0nB9prxxSpC9uvJH1kseOhJa0DPkoav1a/
-SLuMAYF2RJxFam1uakkZuhtwHtv4iYY6bATePWw54xIR5zG3lWYHSU1ZZ9e67zE3sDh6Ia3aeR73
-p9I7fkaSx2V/krnjsq8mj8vut24er/065h7LR0h6+qh1srki4hrgNczd14+V9G5Ji5qyUlJL0mMk
-7TegHtOkoLpejwdJOnYB2/tH4Nks8vhdjDwl3eeZ+14eIel9o/QyAZC0Q/67ms/vgfrN0N2XcYx4
-kw8A11b+Xe6P/5R022ELkfQk0g26+nfTT/K45VVB0gck3W6Rxbyw4bX/y3/PZmabLAfaa0+L1N31
-DElnSXq5pPnmvJ4lX4j+LSnz7mHMvogoW5JfkVssB3kOzRc075X0qkEXaZLuRwrWq/OelnV4dQ56
-J+GnzL1o/udJVGSF+3zlefnZ3xX42KBu15K2lfQe4C2VdWcWUZe3kLItDxqX3c8bgVOYG6y80+O1
-xysi3sPcfQ0pYP2hpAVnzZa0j6RXkQK/TwM3H2K1Y4BfMvczf6GkT87X9VrSFpLeCfxbZb1r+y2/
-DI4CLmDuPj0S+OpCjmFJt8o3G/7IgLnBI6JDmot5xZwvI+IyUoBcrVOQbsJ9Q9JD5ls/f0e+FDi+
-/ivSmPTVlizxqcBZkr4i6bA8VGcoSl4CvJzmaRLNzDZpi7r7b6tW+YV3W1KA8RZJF5HGVZ9OGnN6
-Galb3wZSVvFbkabgehCpm2PQHGT/d0TULzCaKxHxW0kvBt5XqVd50fkG4HBJJwAnAX8Grid1wzuY
-dLPgEX3q8K2IePswdVginwHumZ+X7+fJ+WL1U6RpXa4CmlpJfxIRiwkYV5PjgVeRsi1Xj6fHA3eR
-9G7gq8DvSK1eO5Hm+P1b0udfHociHbNfYIQkTpIezuyWp/I4ek9EfGaYMiIiJB1Bmi7pppWyNgc+
-JengPE4GDJbzAAAgAElEQVTRxuMwUo+IsqWtPH4OJgXbpwNfBH4A/JY0vdD1wNak4G93YH/SOe2+
-zJ47eqix0hExnTPMf4+UwKt6DD+O1N34C6SEeecDHdL56x7AY0lDZcrj91JSD5zXLGAfjE1EXCrp
-MOCbpOuC8n0EcD/g55K+DPwP6XviYtL3w5akAHQ/0r5/GGmaPRhyP5LOl3eurCPgSEl3IN30+BXp
-fNl0Xjw9d/ceq4j4QM7X8Ehm74tdgf+RdDIpUPwxcCHp898DeADwNNKx1fTd9OqI+MW467sMAnhw
-ftwg6RvAaaRhNb8hHQtXksZcb0f6e7onaQqvpmSp59F/2JmZ2SbDgfbaU79LX34B7kLK3jsog291
-nXpZn2KBmXfzBc0+zJ3+I4B9gH/Nj351KZUXMr8EnrCQOiyBE0ldicsL6bKeB+VHP0GaF/iCpazc
-Ehs6oVNEbJR0FPAlUk+L6gXtzcg3gebZTnlRfgMpOH/QQis7z7jsn7LABEgRcYmkw0mBVfl+RDqO
-30tz9mEbQURcIek+wFdIgV29JfZO+TFUcTSf04apx2k52D6eXmKnsrwtSOP3D59nmyINc3k8zbMm
-jGKkbugR8f0cbH+UNN68PKeW5T0yP8a9/RNIN9y2ZfZncWd6AXi/7exE8wwW9eVG8WRSz4mDmb0v
-oBd0Dtpm9bj80CJuAI9zaMGo+6NcbwPphsrDhlynvi+uBY6IiKZEeGZmmxR3HV87LiZlVC2YffFU
-fcQQj+ry5NcuBY6KiCeO0roQES8BXkFqsahuoyx/mLoE8DXgnrnr38RExJWkVo1OfmmY/TuscY7j
-rO7ncZY3dJkRcRLwD6R91XRsDfrsrwIeWRvzONT2K+Oyt69t+2rgcaNkBY6I79CbX7usRzlee7m7
-jI778x21Dksiz7l+D9JNjPK8tpBzWb/zCDT3NulXjxNJLezX9Cmv6W+9/P01wKPzOH8qv6v+XIhF
-feYR8d/AvUndvod5L/3e20K2eSmpi3r53THu8+VI+yQPf7ovqafMQvdFdZsd4PUR8ayF1qH2HsZl
-1PIWeiw07bOLgPutpjHqZmaL4UB7jYiIcyPirqQuk88mXTxcQv8LwKYHteXPAv4J2DsiFpWhNiLe
-RmqB+i7NX9JNj3K584FnRcRDcpA7cRHxJVIG7f9juP07VLG1shZdzTGWOfLFcES8l9Q6dB7Dffbl
-9k4BDo6Ir/epxyBvAQ6prVMw/Ljsft5IuulTr8tyzq+9mOBkKeqwNBuIuCEinkf6HE+mF3DD4PNH
-/Zx2AXAscMeIOGWB9fgsqWX9JGa/7/m2d1Le1knVohh9v43lM4+IU0mt6/9C6hI87Pm4WofzSL16
-mqbxa9rmZ0jdrn9fq/u4zpcj7Y+IuDYiHkNq3T6X4fdFudwPgUMj4uiFbntc72GIMgd5MWlIwUaG
-+2ya9sP1wHHAbSLitDG8BzOzVUFpKlhbqyTdhpQEah9gb9JY7O1JYxm3JHXL/Suple8i4Bek5DU/
-ytlql6JOB5K6oN+PNAazKSnahcD3SfNxf36xc5JKOp7Ufb4UwFNzq9miSLon8FDSeNC9SV0ktyKN
-66sK4OYRsZq7jo8sZ4x+Mml866HMnU83SONtvw58pH7BJqmpu/CPI+LMpanxyiRpN9J40aqIiNMn
-UZ/lJOkWwN+Rzh0HMTuLfNWNpGPpbFIw9I1xnc9yxvInksao7k0as9ohBa3nkM5bn14NY3VzUsLH
-kLqM35XmqaogvbdfkW6UfhX4Xox4cZGHBDyEdL7ci5QjpN/5cueIGNR1fCxy9vVHkI6ve5CGt9QV
-pM/4m8DHI+JHy1G35ZCToB1KOg4OIY29vhnNDTZBujHxU9Lwjs+4q7iZrUUOtG1Fy917b04KTluk
-uZfP95f2pi1f1O5CCpSmSF1sz4uIGydaMVtVJG1DSk63Beni/6/5ccmogeBalgPv3UmBb7k/r1iu
-YHclkbQl6YbWlqQA+2rSOWpRN31Xk3xzdFfSjfktSDfmrwIuc/JHMzMH2mZmZmZmZmZj5THaZmZm
-ZmZmZmPkQNvMzMzMzMxsjBxom5mZmZmZmY2RA20zMzMzMzOzMXKgbWZmZmZmZjZGDrTNzMzMzMzM
-xsiBtpmZmZmZmdkYOdA2MzMzMzMzGyMH2mZmZmZmZmZj5EDbzMzMzMzMbIwcaJuZmZmZmZmNkQNt
-MzMzMzMzszFyoG1mZmZmZmY2Rg60zczMzMzMzMbIgbaZmZmZmZnZGDnQNjMzMzMzMxsjB9pmZmZm
-ZmZmY+RA28zMzMzMzGyMHGibmZmZmZmZjZEDbTMzMzMzM7MxcqBtZmZmZmZmNkYOtM3MzMzMzMzG
-yIG2mZmZmZmZ2Rg50DYzMzMzMzMbIwfaZmZmZmZmZmPkQNvMzMzMzMxsjBxom5mZmZmZmY2RA20z
-MzMzMzOzMXKgbWZmZmZmZjZGDrTNzMzMzMzMxsiBtpmZmZmZmdkYOdA2MzMzMzMzGyMH2mZmZmZm
-ZmZj5EDbzMzMzMzMbIwcaJuZmZmZmZmNkQNtMzMzMzMzszFyoG1mZmZmZmY2Rg60zczMzMzMzMbI
-gbaZmZmZmZnZGDnQtomQdK6kIj9uPqYyX1sp8zV9lrlXZZlvjmO7ZmZmZmZmVVOTroCtWZEfS1X2
-OJYxMzMzMzNbMLdoz0PSUyqtnx+edH02QZp0BczMzMzMzMbNgfZw3Pq5evizMjMzMzOziXLXcdtk
-RMTRwNGTroeZmZmZma1tbtE2MzMzMzMzGyMH2vPzGGIzMzMzMzNbkIkG2pJuLunZkj4m6ReSrpS0
-UdKlkv5X0rslHTJkWd+uJC675xDL950KStIJkgqgTIAm4KmV5YthpoiStKWkf5R0sqTzJF0v6fL8
-Xv9d0p2HfG/ltjqV1+6Q98+vJf01P34s6TmS2g1lHCTpeElnS7om7+NvSjp8mDpUypmS9DRJn8tT
-dF0n6apcjw9Kuv9CyquVfRtJ/ybpl7nMqyT9XNIbJO0yxPoDp/casV57SHq1pO9KOl/SDZIuk3Sm
-pLdK2mtc2zIzMzMzs9VvYmO0Jb0VeDG9VuNqEqubANsD+wHPlvQJ4BkRcf08RUbt57Calq9OPdVU
-v4EkPRx4P7Brbf31wHbAvsDzJH0MOHLAe5u1fUkvB94ItGv1unN+PELS30bEtKQW8C7g2bVyNgfu
-Ddxb0sOAIyJi3veYb3p8FPibWlkbgL3z4+mSTgEOj4jLBrynatlHAu/MZVXrcfv8eK6kp0bEl4Yo
-biwJ0SSJNOb7pcBmtbK3Ix2ndwReIOmYiHj1OLZrZmZmZmar2ySToe2RfxbAOflxGTAN7AAcANwq
-L3MYsDXwiGWq2ynAX4F9gPuTgqtfA99oWPb/6i9IegJwIqnHQAAd4PvAb4GtgHsAu+XFDwduIem+
-EbFxUMUkPQt4Sy7358DPcvmHALfLiz2IFLQ+B3gPcGRe5nTgV7le9wBumZc/LJd1zDzbvSfwFVKA
-Xt6IOA04m3Tz4C70Pq8HAN+XdPchg+1HAcflMv9M2lfXkAL3Q3N9bwJ8WtIjIuKUIcpclHyD4lPA
-Y+i93/NJ7/kS0ud4COk9TwH/JGnHiHh2c4lmZmZmZrZWTDLQPgM4CfhyRFzetICkQ0ndt/cCHirp
-8Ij42FJXLG/jY5KeQgq0AU6NiH8ctK6kvwE+QK9b/qnAkyLiD7XlXgi8NS93V1KQ+8IhqvcO4ELg
-sIj4Xq3MFwNvIwWFT5d0DinI/iXwxIg4q7Ks8rIvyi+9StK/N7WsS9qO1JK9OamF/5xc3s9qyz0x
-v/fNSUHyh0hBdD9l6/AxpBsBL42Id9TK3IcU8O4HrANOkHS7iLhqnnLH4XX0guyLgOdGxBfqC0l6
-LOk9bwccKenrEfGZJa6bmZmZmZmtYBMbox0Rb4+Ij/QLsvMyPwAeCNyQX3r+slRucV5Lau0UqQX7
-QfUgGyAijgNelpcTqRv5ngPKFqkHwP3qQXYu81jg63m5KeBY4GLg3tUgOy8befvn5Je2Ah7WZ7sv
-AnbP5V6et/+z+kIR8XHgiMp7eoSkuw/xntYBr6wH2bnMX5NayC/Ny+5K7+bAksifwytJQfblwKFN
-QXau32dJAXnpdUtZNzMzMzMzW/lWfNbxiPgj8C1SkHUnSVtNuEp9SdoWeHz+ZwAvi4i/zrPKO0it
-zZA+i2cN2EQA783BZz8fL6uTl39jv+7bEVGQWotL/ZKzHVnZ/usj4oK+FYz4PKmnQuk589S19AfS
-TYF+ZV4MvD7/U8AzhihzMV5IGv8OcHREnDvfwhHxbeCrpLrdVtIdl7R2ZmZmZma2ok2y63iXpJuR
-gry9SV1wyy7KpXIssYA7AD9Y1goO726kZF6QWmC/PN/CERGSPgy8Pb90nyG28dkBv//FApevtnTf
-sv5LSbell9CtA/zXgPIAPgg8hPR53Xue5cqbAR/LQf98TgT+jRQA7yZp74j4zRB1GcVDKs8/3nep
-2b5JGhsPcHfS2HkzMzMzM1uDJhpoS7orKbHX3Rl+zuodl65Gi3ZA/hnAaUMEj9C7aaDK+k3KoPSs
-eZYBuKLy/KqIuHDA8tWu+9s0/L76ns6JiCsalqmr3gjZVdKuEXHRPMv/aFCBEXFlHnNeJnw7ABh7
-oC1pe9INnwA2Aq9Lw9kHul3l+c3GXS8zMzMzM1s9Jjm919NJSaTKAHLQlExltLP1UtZrkXaqPP/j
-kOucW3m+XtJWEXFNv4Uj4uoB5c2UiwLDJAybqTxf1/D7Bb+niPiLpBvoTYm1IymhWD9/GqbcvFwZ
-0O4034KLcNPK8w3A8xa4vkgZ0s3MzMzMbI2ayBjt3B35vfmfQRqn/AJS9/FdgM0jol0+gI9UVl/J
-48qr48evHXKd+nLjvJEwjvmkR3lP9WUHvafrlqDMUW1beR4jPtqYmZmZmdmaNakW7RflbQdwMvC3
-ETEzz/JLEVQtRcBebYnecsh16svNlzxtEkZ5T/VlB72nLZagzFGVwbxIXe/dOm1mZmZmZgsyqdbh
-+1aev3pAkA0waNorgOnK82FuIGw7eJEFu6Ty/OZDrnOLyvON83Ubn5AFvydJO9HrNg4pMdx8ht1X
-1bHPg8oc1cWV59tI2qzvkmZmZmZmZg0mFWjvVnk+b3IvSdsA+zO4G3R17PIOQ9Th9kMss9Cu1z/N
-PwXcWcNl0bpbZVs/nW/BCam+p30kbTfEOodWnl80IBEawF0GFZinTtun8tKZQ9RjwXJdz6u8dLd+
-y5qZmZmZmTWZVKBdzcY9qNvwkaQkXYOC1nMrz+edx1jSTUmZzgcF0jdUnjclCqv7IXBjfr4T8LAB
-9RDwtMpL3xxiG8sqIn5FL5FZGzhiiNXKea6DNAd63+JJn+sTh7gpcQS9sc8XLuHUXjB7WrbnLuF2
-zMzMzMxsEzSpQPv3leeP7LeQpL2A1zBcy/KpledPlDRfYHwcwwXvl1We7z6oAhFxFfDJyktvlTTf
-uObn02tZL4D3D9rGhJT1EvCafKOikaRHMvsGw3v7LVtxK9K4/X5l7gL8M71kYx8coszFeDtpznAB
-j5b0lGFXzHU1MzMzM7M1bFKB9pcqz4+V9MD6ApLuR2oN3Yrhsl1/mZQgS6Qx3R+sj6+VdBNJHwEe
-x+zW6n6q3doPkbTHEOu8npRATKT5mL8m6Za1ekjSC0gBHaTg8V0RMew0V8vtOOD8/HwH4JuS7lBf
-SNJhwMfoBcRfjIjvDyi7nK/6GEnPr7ds5wz1p5B6CIg0hvq4RbyXgSLi98C/lFUAPizprZIahyRI
-akt6gKT/YmV2/zczMzMzs2U0qazjxwHPJAVPOwAnSzoTOJsUeB0I7JuffxX4C/Dk+QqMiOslvQE4
-hhQc/T3wIEnfIo3fvhlwT1JX9V/kcl86oMyLJf2QNE53c+B/JZ0MXEiv+/vvIuK9lXV+L+mZwImk
-rs53Bc6R9D3gd6QbB/eg10IewI+AV8xXl0mKiCslHQ58hbT/bgOcKelU0me2njTO+tblKsBvSJ/x
-fMo51F8OvCM/Xibp+6SbFXuTuviXN4SmgadFxJVjemt9RcTRkvYEytbslwDPl3QG6XO8DtiGlMxu
-f3oZ0ZcqSZuZmZmZma0SEwm0I+ISSX8LfAHYMb98YH5Ar0X0c6QxzO8csui3A3vRC/B2Bp5Q3TRp
-HPXjgGcNWeYLgG+QphjbFjis9vtvU+seHRGfknQNqYvzLqSA+z75Udaj7A7/MeDIiNg4ZH2GNUwi
-tqGXj4jv5V4GHwX+Jr98F2YnMivf0ynAkyKi2vV+Pl8gtWofR7oBUd3H5b66khRkf3XIMgcZuH8i
-4umSfgIcDdyENNzgbsxNkFadQ3tQC76ZmZmZmW3iJtWiTUT8WNK+wAuBR9AL3i4EfgKcGBH/A5B7
-E1eD035lBnCUpM+RAulDSC3mlwG/Av4rl9uplDmonj+RtD9pPPV9cj23opeYq7GMiPiKpFsDTwce
-Tmqh3xG4HriA1C3+IxFx+qA6VLYxbBb0JVk+Ik7LXbmPAB5FSjq3M6ml+SJSkPnxiPj6ArYbuez3
-Sfou8Gzg/kDZTf9c4IukrvUXNxXS570Ms8wwn/9/SDqB1EPiAcAdSD0xNiMNVfgz8EvSDZevRMT5
-zSWZmZmZmdlaoRSbmpmZmZmZmdk4TCoZmpmZmZmZmdkmyYG2mZmZmZmZ2Rg50DYzMzMzMzMbIwfa
-ZmZmZmZmZmPkQNvMzMzMzMxsjBxom5mZmZmZmY2RA20zMzMzMzOzMXKgbWZmZmZmZjZGDrTNzMzM
-zMzMxsiBtpmZmZmZmdkYOdA2MzMzMzMzGyMH2mZmZmZmZmZjNDXpCpiZma12kq4FNgAF8JcJV8fM
-zGxTtjOpwfjGiNhy0pXpRxEx6TqsCL8447QAENBCtBACOlHQKQqKCDpFQSc6dIoCCNa1WqxriXUS
-SGllpaus6aJgpghmImiplR6tFm21mWq1abfatNUiCAqCgoKIICL/LCIXl+qh6BaPgHYrl9duESKX
-k+o50ymYmUk/AyFaqSS1mBK0JaYEREEUHYgORVEwDcwQTAOtSvmtVos26WhuARQdotOBoiCKAoDy
-OCrUIlr5oUqHCc3e3+XyEeXzSP/IP1ON089yX7VbbaS8B9RK+xz4mwMPqpVuZra8JM0A7UnXw8zM
-bA3pRMSKbThesRVbbur+TIEtAmL277q/zwFeGVsjpZgvB9wCWi1oEbQiUtCubskEkYJpBZE3IrqF
-pQ218vbLR72yYlbFymA1ohKwdheJvP3IW4rub/JbQS3RikpYLpByWUWR65pLjaK3rtQtHyCkHPjn
-OqlcR71tKe2PiMiri4gyYE9FBunmQkgE6v5MQXb+cHyPyMxWjnSzVmK33XabdF3MbAgbN27kkksu
-YaeddmL9+vWTro6ZDemCCy4oG+1WdDTgQDtTK7W+qiG47i4z68Ve0JeCUqUycqAdoVRYUQmkS5GD
-7SIqxaQlCloo/TYF4dELxsnBuMpgU5WgkxzYFgVEICIHynTr1A2ecyV6z3NLvkQbCPVeg4Iydi9y
-cJtDXyBIu03dusWs30Zat1t10erWOb3nFFAHRCu15hfdt0OUNyfU6rZgx6wPYUX/bZnZ2nI5sPOO
-O+7In//850nXxcyGcOaZZ3LQQQdx8sknc+CBB066OmY2pJ133plLLrkE0nfviuVAO2tVAjhB2Qzb
-fUWznqcgumyZTUF272dItIscJJeNr93W6aD6v17gnMpuVbZKkFqS87rdKs4J+INuNFxp0a42kEvR
-C/YrwXaOnGkJCkRL0M6hfllW5BsDRFBApcW73G/q3iwoKu+zEjMTuQt95BsSs29kBFEURFEG6+Wr
-OWCvtGrn2xgOsc3MzMzMbMVyoF1TaSCuqL1QRrC1gFdKLbYhES0RRdAmBZwpwK72Bk9jqkUrd0Gv
-lwd0g+zeuirLV6XVuKxjJcBOLdT5JZVdxcsx0GWrdeQAPK3VotL1u6h0/46gKFvJI3cT795koBtk
-d29IlC32Ue35ru7P3o4mh/RlK3f5r/Tv8t0ELQJRVMp3sG1mZmZmZiuVA+2smhRO1Qguer8vhwLM
-SuSVW4Lb3Tbq7gDlyoNuy7C6Aa9ya3dQRC8oTtupJEUjtYrnoczdOhRlfaOotGLnod2CVp65LZR6
-r5dllcuFcsIxpTC227ecyvD0XBeKotKOHJUAuNtpPb+3Sv3L5myV4XegXnN3bScDRe8GQY9ygJ3r
-UhQ50C7fy1AfrZnZsrn00kvZY489Jl0NMxvCxo0bAXjwgx/sMdrLaNddd+WMM86YdDXMlpwD7awM
-nlUL9aAaz0Ut4C4DziBCKXYsu5zXAu1ZLdPqtdyWgWrRDeiLXrBaCSbLOhSUXdtz0FuIbpqwsvt5
-NypPQbYiKKLo1rtsKQ9F6iZeecu95Ol5+0V5YyF6wXbZ7N/NBle2YEcaIl6k8efdmw5li3f3BkT3
-XfS2291HZYHleGxV9k2kPTArS7mZrVSS9gT+kP/51Ij4yIjlPAU4nnQWuWVE/Kn2++OBpwDnRsTf
-LKLKixYRnH/++ZOsgpktUB7raWY2Vg60a7pdl+e8Xm2oju6SZeBdZhCPsrW615M7NylTCdCj2/W7
-249bZSt5zHpQtjZX6gFQ5KBdEbQUtMoAWakLOBLKzdlFrlcRvam4ylbs7tjy+hj1tGA38O92AFfZ
-XV3d/ZUXrdS/u6MqreSVseT1OFu95Xs998twv7yRkdvQuy341RHgZrbCrbE/1t0nXQEzG8pG4BJg
-J8At2kvvQtJFoNna4EC7QeRprma1nFa6ZwPdjGndbtTVwLrbKlx/9ILzokjhryRaLUFRHaPdmwqr
-rA/Uumkrz2lNCrp73alT63inCDoBReVn5ORsre4Ya7rjvXtjoqkE0XnsdJTjqFMNClKA34noJW+r
-7ZpySrPuIG5Si7+K3KJeJlArd2UarZ6ToeW9Va5b3qDohfW9z8VswiR9G7gn8O2IuO+Eq7Mp693x
-W9EEOOu42epwJnAQcDLgrONLbw/APX5s7XCg3cfsluXZV3fdtGLd+DvNid1t680LS6RW5VRiJWDM
-wXPkIJtWniarOlS6DHarq3dDbVqRxi6X04JFkVt5i4KZTsFMUTDTidyYnlvPJdqtMkN4K/Uuz4F2
-mtc6bbcbbPcync2qS0S6L0AU3aC8FxBrdjbyvN1ynHa3H3i1pb6cIi0H55Gzk8+aJgxyr/FqgL0K
-rrltLVglAeDqFRH/CfznpOthZmZmNiwH2g164ezsqa26TbfqjeXuLlN29c4t2lBpye42BveWRbnb
-eSVQbbVa3fHMkiiKolKnVHaRyylyHVqU2y2g0yGKDhunO0zPpEeBUKsNrVae57uV1hPQaqVm8VZv
-fupqpvAyOKcy6VhELYt6pQW/91/RVqu3I/JrKSlbUdtP6nVhbwnl+bKLHGgXAgrVMpiDW7NthZk7
-3sTMzMzM1iwH2ll3sqhulFkJrOsxXQ6+yyzgZVfqVlRHL9NttFWlvIhei3aQW5aj16V81ljpbjCe
-E43lR0Ce8CqnQSs6FDMdYmaGYmaGjTMdNk7PsHF6Blpt1A5aU1O5a3buuq2CAIpIQaxaZZXL1Gqt
-sg94pdt8bl9WdN9zKFCod0OBFCyrDLTL1n3odmsvewnMHqadt5pb2Fv0etNXW/JnFehY28xWjs6k
-K2BmC3VT4LX5p5mtFu12u3y6or97W4MXWRuKoug+ImfoLgM8lVmzqxm2CYoiPTpFkbN6V1q+q0Fg
-twv67Izi1ZbjXpfx6mplJu80HroTRXoUndQ9vNNhptNh48wMN27cyPU33sh1N9zAddffwHU33Mj1
-N27kho3TbJyZYaZT0AmY7hTcMD3D9TdOc/2NG7l+Y3rcOD3NxpmCmSIHw1IK0ltT6dGegtwyHmpR
-IDqITuSfSvNcF600hzhlC3U7/aQcB14mgctjsaPbw7zcv5WdF3ne7vK1WQPelVvkfQivZZL2lfQq
-SSdLOk/SDZL+Kuk3kk6QdMg86x4vqZD0+wHbeEperiPp5pXXT5BUAPfKL907L1d9/KFPmftJel+u
-57WSrpZ0lqRjc6bufnXZs1L2k/Nrj5H0NUkXS7pG0s8k/YOkqdq6h0v6dl7uWkk/kXTUfO99sfXt
-U9bjJH091+M6Sb+S9CZJ286zTuNnsFCStpH0Sknfl/QXSTdKukDSFyU9dtRys9wFyZ0bzFaPmwKv
-w4G22epSCbRXdHY9t2hnRVGkWE45kVd9gTJpNmX36RQApm7UUEgU3WmzmNX/utu1vJuQLIeOqrSk
-dzfSkzKFl48iBfZRdOfQLluQOzMzTG+cZvrGG9PPmV7X8dZUsE4taKftd6fHioJ2W6xrt5hqial2
-m/YUtBHttrrjqtP/000CQkQo3TAoXyNo5URmIdGWUvd0tXrj07st+amFO3KCNXUTn9GdK7z67iuj
-wunmHy9verg1e82TdC/gW/mf1SNiHXAr4NbAkyW9OSJetQRVqB6k/fpYzPkCkPRK4A1Ux2QktwVu
-BzxH0rMi4r8GbBtJ7waeXStnf+CdwL0kPZ60Pz4KPLa23AHAeyQdEBHP7rehMdW3LOtDwNNq5ewN
-/D/SZ3W/iDhnUDmjkHQ/4JPA9rXt7wI8HHi4pK8Aj4+I65aiDmZmZrZ2ONDOyvHQitydm0rgV47J
-FpTZvSOKWWOVWy0R0eqN587X3dVprXqt2ZXW3D4BYy8JW95WUdDJ2crLQLs0MzPDxumN3HjjxhRs
-zxTp0ekwFYKpKVrr8hjvoqDodCiKGdotMdNuMdVusW4qWIdYp3ZqhVYrjxVP19ZRFBCdFBEXEBSp
-2zm9aCMN985BeivfrijHpJdTeqnMLF7uV2Y35ZeBPWWAn/dnzjDXvQWipknYbI2ZAq4BvkwKuH8N
-XA3sDOwL/COwJ/D/JP0mJ9Qap38C3gqcABwMnEEKIqs2Vv8h6bnAG0l/An8B3gL8EGgD9wdeBmwF
-HM6gXSMAACAASURBVC/pkog4eZ7tPwc4hPT+PwT8EbgZ8ErgLsBjgKcDd8jPTwQ+TppfZS9SM85t
-gSMl/XdEfK2+gTHX93nAnYAfA8cB/0f6rJ4KPJ7UpHSypP0i4tp5ylkwSYcCXyEdMxcB/w78HLgA
-2A14AnAE8BBS0rXHjXP7ZmZmtvY40J5D3Ti5bL2GXjKycpleA1Y30xl5wfwzj8kuZo/xVqULdbl4
-2XJNMXuYQacMsHNwXQbp1YRpaUz3OqKTmtLbrTZTBawvoBNBe9161m3YwLoNm9GemmJmZpqZmWli
-RqgFkROhFYiCVv6ZWqgL1Ev6JhG0UrfyFrkJukVlNnAKoCN1E5u1uvspLaBCqF2gooVyQrhuoF12
-Ky+D7jxftgikyDdAZn9Otub9FNgjIq5u+N0pkt4F/A/wAOC1kj4SUbtLtQgRcSFwoaQyKLw2Is7u
-t7ykHYFjSH8uFwCHRMQFlUV+JOlLwPeALYD3S7plRPQbf3Rn4NiIeGnltZ9J+jpwNnBzUmB8E+AF
-EfGu2nLfBX5DCpSfA8wKtJegvnci3RR4VKTuMKWvSjqL1Gp+c+CfSS3cY5G70J9I+r47Cfi7iLih
-ssjPgK9I+h7wfuAxuWX9G+Oqg5mZma09HuDalYLCMnwrM2N3g+3uPNW9pSvpttM69MZv98ZrRx5r
-nANjcotv2QU6l18UBZ1OQSePu56ZmaEz06HTKceN5+3nVmG1WrTabdpTU0ytW8/6DRvYsNlmbNh8
-C7bYciu22nprtt52O7baelu22GprNttiS9ZvtjnrNmygvW49ranamOscbAet/FDlZ+V1tUDtnGSt
-jVptopXW76TG7tyNXhTtFtEWTLVgXRutn0Lrp2itn0Ib1nX/rXVTMNWGvHy0qIzdLoNtaFVvMLR6
-D1ubIuLyPkF2+fsZUosrpJbtOy5Lxfp7GikgBXhRLWgFICJ+BryZdPTvDjxqnvLOA17RUMb1pFZZ
-kbpJ/7gWZJfLXQx8Li93jyWur4AbgGfVguzSm4Cz8nLPqI8vX6TDSJ//DcCTa0F2V0R8EDgt//Op
-Y9y+mZmZrUEOtOfoBW5lYD0r6XZ1uaYYr7tsJdjurqFuK27Zsg3l+O3cep2D7OmZGWY6HTpFJ3cX
-L3qDQZWmAusF2utYt34D6zdszmY50N5y623YZtvt2Hqbbdlyq63ZfIst2bDZ5kyt34z2uvUpuVm7
-TajdC7K7LduzW7eDVjd4TnNctyAnSaM9BWpTqEWH1KLdERStnBhtqkVMtWFdG9blwHr9Olr5wfp1
-sH4K1rXTsuVUY2WQrWqQnX62WmWwnacsMwMkrZd0M0m3zUnS9mX2Oe4Ok6pbdv/880pSgNvPBxvW
-afLf87Qe/7zy/FPzlFEudxNJ29R+N876BvC1iLio8Zepp0HZtX974MB5trdQj8w/vxMRlw9Y9ruk
-s89dx7h9MzMzW4PcdbymOzo4Kv+uDtamnL26KDtVp//mbuKhXuKwXuu3yqHJqcN5pVt6mU2cSqt1
-OZa71U6BZCs/6GbzbtFut5lqT6WsewHFVEGsD6ITOUN4mSm81W25Lsqe7rkiUXRSF+3yvSi3YAcU
-xew7C2lMeqqjWsrBbhq/XUTq3p5a32foFCkjervdTo+pNlOtqbzhNu2phjsUEeS5v6AooKM8L3jv
-k1G3o3rqGRBqutNha4mkLYAXkMbY7ksaO9zPjstSqf72I/1RnTlPgExE/EXSuaRW2P3mKe838/zu
-yhGW25o0xr007vqePs/voNeaDHD72r8X42DS+3hwzhI/jF1H31xBGno+SJv5D1czs03NhZOugK1w
-F154IRdeOPg42bhx48BlVgIH2lk3kze91Lqi958c2uVgOefKztOAld3KUyiaE4nl0lJSsdx1vJyq
-qpy2K4JOZ4bOTHqU04qlAc1i/fr1rN/Qpr1uHe2pqRR4t1u0Wu2cJXyKdnsKBUQnypg5B9a5W7da
-3fHPnUitwe12m6l1U3Rmpik6HTqdGYgCtdqgHJAXKct5OSVZynaeHlNTbaamppiaagOiUwTT0x2m
-p6e7ycwkmGq3Wb9hPevXr6dYn/5dBt/KE3eX3ednBdqdDtHJ84J3Wt2p01T2MOgG3LaW5WmlvgXc
-gkp2hKZF88/Nl6Fa89k+//zLEMteRHpf28+zzHyZsasB5bDL1aO+cdd3UDkXN2x7HMqodyGnjc0W
-t8lLFre6mZnZGvS+972Po48+etLVGBsH2lk1R1JOrE03XC7HRQcoWqgoA+1e63dRpAm/CorcxblN
-K48nTlNfBRFKAX10Umt2UTA9PcPGjTcyPb2RotPpdieXhNpt1km5a/h62lPtXnfxdpt2Owfa5AqX
-TeXlOGq1ulOJFYhOFLTbLabWtVk3PcX09DQz0xu72y6Tl0UR3W7snU6nkpCtQxEF69evB1q02lNA
-MNMJpmc63LhxmiI6RJ6LfGqqzWZFzs6ex3O3Wy3aU+totdt5zHrewWWQHUF0OhTT0xRl9/ooUplF
-MXsSJUfba92JpOCuAD5MmrrpV8AlETENoHQnp2yNXSldIFbbkTuu+k7qfZc3EE4CXr7UG5PEjjsO
-7jxR3nQ0M1trdt11EZ2GbJN21FFH8chHPnLgcv+fvXcPtm3L77o+vzHGfKzH3vuc07c7fW86UqBB
-q4wikEgwFokiIRCJPAxIWZiAmIhKqQVWKiolKbAqliQBJVARo6KUYEUqFBLLUIFKipdAeKQqoiaW
-PNJNQ9/HOWevteZjPP3jN9ba+17uvae7vd3ndN/xuTVqrn3nXHPNue8+5+7v/P1+3+/XfM3X8Oqr
-L/5D7Sa0K+eKthG5cxo/V7PPkVXng+Wy815FW0V0yYVitIX87A6uB57fp83puYrZECM+BJZ1Jad0
-MfsyxjLkjIjB9Wp2pvPYDlcr2ef2caFmVpdz3d1c+sPPRe4EpKxC20Zbz7HiRR8URAKisdi1qJyJ
-sZqypaSt4SXVGDQV2S7rPaaUCUGFdkqRmHTrnCXV6rwapxlc6aDOlxujedvGvFlo55SquZpGjJET
-JWWKpJoFXu61HDTej4jIPwp8BfqT8J+WUv6Tdzj03Sqj52ruswb9d5/i5b0Tb6AtyV/wSRz7YfTe
-njVT/Jnkvb7eZ53n/v738r5fR6PD+ndzhX+veOWVV/joRz/6mf6YRqPRaDQ+73j55Zd5+eWXn3mc
-Fv1efJrQrpT7s9YXgXz+d7q9COmz0dmFKo7lPFN9bo2u1eSciXWFnAk51ddJBX6t8p7Fs7EGZx2b
-7Y5xs2UYxiq071qvjVGhytnJvM6On9vcVWhr/Ja2rFedn7VinGJd56p1yph6DhFtLz/foohAipRU
-W8hzJoaIr0ZkwUdCUIf0GJIauUVP8EJOGb8Gpmliu92y3ezY7raMw0jf9wxDj7EdmDvdbMSQatt9
-shZiBJMgJSQnfUiRtGreeN/yj997/W5mX1/6LvsOdfvgGZ/1M5+x/5Ot1P44Kvh+joiYd3DfRkQ+
-iM47n9/zvHivr/fLnvF59/e/l/f919Cs7C8VEVfd6BuNRqPRaDQ+ozTL5spZPJ//gVI7sXX6upAv
-c9U5l5prDWeRLWeRXSu3crbJRvOwY0r4EPDeE0KoYjSRC4io0O6GgX4c2Wx2bHf7KrQ3DONGxXY/
-0HU9ruux1t0JetHKMObetVTBfPmnVt45Z3MnjQ+LNUIsJZ0PF6jmayroz9Vz/Sy9n5wLMSa8D/jV
-433Q80St0Acf8GtgnhcOt0cev/GY1z7xGm+89gZPHj/m9slTTscTfl1V4IvFGouprfDWdXqP/YCr
-y/Y9tuswtkOMrbFe93K6G+837j8kfLeK8296l31/s26vROSL3+4AEemAX/WMaznHRQ3POO6H6vYB
-8Cvf5bjfyN0P9g+9y3Gfad7L6xXgq0XkbavatcX/G+qXj4G/+qld6rvyx+v2Bo0sazQajUaj0fiM
-04R25SK0S+2fPq+z2C5nY7B8Mee6K2qf271VoJqz2ZfUee+ciUlbxHVFQtD4LnXxNtga0TWMG8bt
-lu1ux2a7ZdxsGccN/Vlo9wPOnc3RbDVbO2dzn0X3XWb3eQ76bOJWsrZm369mp3iOEKtt3kbvxdY5
-cOfcm6r0Z6Ed1sC6Br2X+xVtH1lXzzLNHA5HnrzxhFdffY3XX3+Dx2884emTW07HE+viq9Cuwt5Y
-rO2wrsd2vYrsYcD2A7YbsE7zv++upfE+5ifvvf7GtztARH4TGu30ThXnH7n3+re8wzHfheZDvxtn
-e8yf8Yzj/lvUmEyA7xCRV956gIj8LOBb65cfBf7YM875meS9vN6CPoj4Hnn7P7zfijqNF+B7zzP2
-7xF/EM0cF+B3icjbZYZfEJGvEJFf8B5+fqPRaDQajfchrXW8Ymq19lyI1nXXIl6LwfcEeRWu3GVi
-n92wc9HW5lwjr3yMhCpscyk6m2wMrkZ1nb+2ztH3HX2nTt3jONL1w5152Lkt/Mw5guw8m335ulRD
-Mog5EbN+dkiR1Qe814p6SgmKVrDP5JyJQE5Jc7zvrUvkmNyLG9NvQH0gUY3hSiGnQoxaOY8xkXPC
-iCUnCCGSUgYE5zq6bqgu5g7n5G6uvX5PjS2YXCg2Y3ImyzsmDTXeJ5RS/pqI/DgaJ/Vvisgj4H9A
-Re9HgF+HVqL/LPDP8jZiu5Ty10XkL6CZyd8kIgMqyp4CXwx8M/BVwJ9D58HfiT+PVko/JCLfiZq0
-Pa37Qinl79TPe01E/gPgu4EvAv6KiHx7fb8DfhHwW4E9Oj/+Te8Wq/WZ5jNwvT+KPvj4cyLyXejD
-kg+hD0p+TT3mp4Df+R7fhxeRX4061O+BPy0ifwR9KPA30QfOLwM/F63cfwnw76CZ2o1Go9FoNBqf
-Fk1oV6zVUC/DJa6as+nWWViXOqNNvj/LfXeOc+U7laLt4UHbqOM5LzsXxGr12vUdtuvVFKyuu9is
-Duccfd/Tdf2lFRzO5mtvcwOXInypmdY6dx1ixMdaRY+BWAV0TFGNzUTUzTwbFdklUUIk1up7CEFF
-8jnP29m718ZUR3KLpLvKOvceOKRUSElN1aZpJsbEPC+EkNTozXU41zGOI+Nmow8VRNQ4PYOYcjFM
-y+fqPXcPPBrva34d8KeAh8CvrutMAX4M+HrePbjzNwA/jAq+b+Cuffl8jv8cdTJ/N6H9R9CK7E8H
-/r26zvwt7lW6Sym/X0RugN9RP/O73nKugrai/xullB98l8/8rPAeX+93A1+JCus/8jbn+bvALy6l
-HHiPKaX8RRH5KnSe/4uAf7Wuf+DQum7fZl+j0Wg0Go3GJ00T2hVnz4ZiBSlgqppNdTb7YoJWq9pn
-13Eu1WxqjBaUlNVJfFlZlqXGbGkFWOeeO8Zxw7Dd1Jnkuu4ZnVn7ZuMzLhXrdxCXdZ86hldDshBZ
-vWf1K4v3+BAuDwNyvXRt23YUWyghEEMmhIBfV5Z1ZV1XSs4Mw0A/DAzOqci2tRKfMzalu9n08/ei
-dgCknIlRXcljXJjnFRGtauvDhIFhGDlXtxnQ8wBIubTGn8X2/Yq+OqA33q+UUn5MRP4pVOT+EtTw
-6gD8P2jU1++r1cx3O8f/LSI/B/iPgF+KVjafotXX/7KU8oMi8g3cCbC3O8dJRH5+vY6vRo3Btufd
-b3P8t4vIn0Crpv98ve4M/B3gB4Hfc66Cv9Nlv9O1fCaOey+vt5Tyr4vInwS+CW0V3wN/G/h+4D8r
-pTx9x7M8+36edR9/qc7ifyPwy4CfDbxU7+VV9IHKjwB/tJTyk+90nkaj0Wg0Go1PBmlVQeXH/tKf
-rdZmBSlZa7KX6nDdFkilELP+Nmeq+DPVcdwawYohxcQ8zczzzDTNKqK7Dus6+s2G7X7PZr9js9/j
-3iK0z27i2s59NjfTa7ybC3/Lf7OajEVW8bl6j69r8Z51XfXfxajnq/3x9wUzpdTjVFwvy8wyLyzL
-QilFHcN3W7bb7b2HAYaUM+uysi4Ly7owTxPzPLFME8uy4FcV+n71d3ncKXFzc8PLr7zMKy+/wodf
-/jAPbm64eXDDzc0NzjlyPud3J1IIpBhJMRLWFb/MrOtC8B6AL/uyn98c0RqNxnNFRD4KfOEXfuEX
-tnivRqPRaDQ+g3zkIx/hYx/7GMDHSikfed7X8060ivaZfB4tvGeEVmOxVPNKLVfrPHK+l+OcKeQU
-iVEr3ynEi7gNMaqjeNczbDYa1zWO9N2gGdjmzmDs/Lkide76fnD3xeG8UIpc8rgp59Zsbc8OIbIs
-C/MysywLMd25pcP9QrzoDHbwdR47Vjf0QAj+TnSHVefRvQVrKCJ3Le7WkUvGB4+POvcdgs6Arz6w
-rP4i1tflbMyshJjqDHd1Pc+5urmfM8b1vnI+G7ap0I4xEGu1PoaW0tNoNBqNRqPRaDRePJrQrtxF
-xJ4tzc4t5Hct1iI6N0wBKefoL600xxRJoYpBXyOuvM5pjxvBdR3b7Y5xu6UbBlyvEV3nlvCSzyK7
-qLDnPIv81ggrqUI8X1rZY0ysq6+V6IXTNDFNE6fpBKKf7boO62zN2NZTxhRZ1pVlXfDea+RXFd0+
-aEU8eG03xwoYveeu6+g6h+s6KKWKbF06D+7VdXxZmWat7M/zjL3MdlsVyjERa7RYSiquSy5kU9QF
-Pec35X3fF9gxBGJ4L42JG41Go9FoNBqNRuO9oQntM2+qaKNKGmo8llG1fXbYLmgbea5xXxTNya5t
-2sEHFYdBRWJB6LqezWbLdrfXiKrOYaxTcVmzuUvJGGPvuYdr67imbqnYPs8oazVbXcxjzKyrr+J6
-4nA8cjgeOByPuM6x2W7Zyhbj7Js0e0iJeZk5Ho/My0K+RJflKpzjnZg16rGeSqbPva6SEYQQ/EVo
-31W0Pcu6Ms8Lp5MK/+5s9NY5dWG/l+GtbeJaeTe5XBzbz47nMcZ761zVbkK70Wg0Go1Go9FovHg0
-oV3JSYX2uV3bSLkXM3WvT/ySqcUl0gpQ1/C+BzF0XU/JQNaZ6pubB+yurthst3R9r3PeZyFeW6ZT
-zljrqqg2OHdXydborjtDNs2xVsEZYrxUspfaqp5LwVhL1/fYmrWdsrZ456xiOefCsixM88S0LPjg
-1enbCGINBoutNXt1Uk+sfiXEwCaNQMFaU1vQa9t58EzzzOF44PbpLcfDUavZ08y8zAz9wDBo50DK
-SUV9/abrAwPN3w6GWrH22sY+zyzzzLrMhHUlBk/0gRSb0G40Go1Go9FoNBovHk1oV1KtaJ+7tkWg
-iNQB7dpOfje6/SZ72yKC7dTQbBhBipqjGTEYDNvdTg3QtlvEWGLw1Q08VLM1nVHWKC/BOW0pP8ts
-oLqJ31V41+oIvvq15mLrijlhrKEfBsRqJJgYQy53JmlrCGqWFrTNOwS9DtdZnLHY3qnYtgbjTK08
-R9ag7eWFe0JejArtqNX8aTpxe3vLG2+8wfF4VKO0dcV7X+PEwFijQpuiXxshZ62iL+uClIL3K8Gv
-rOvC6XDgeDhwOhyIwUPOlJyguY43Go1Go9FoNBqNF5AmtCvnijbV5TtXxa1ZznJpJS/lbnEW4wLW
-qDmYO2+to7PnPOyRvsZjpZQp3uN94Hg6XVqnU8oMQ6Sr0V+Xa9EXlCrIY4x47+9mn6eJkLSKfW5j
-F2Poh55+HC4z1zElfDi3l89M80RMqUZ96XtMZxBrcH1HySqyTbIE74lzwIeVeZ4xxtD3PSklxKpp
-WajifZomFdqP3+B4ONZKt7Z6AxhjcJ0jZf1sRL+/uWis2LIslJxYlwW/6v09ffyYJ3XlGKu7u9Ss
-80aj0Wg0Go1Go9F4sWhCu3KpTlO0HfzSGo46fJdCztQ2b4ilkICEbvtOcE4wnaPrBm2T7nqGbsC6
-DusczjkKUXO3a3RYvqzaSl3uO49Xt/BSCGcn87rmZWGZ58tstdqjS31QIGB0m1PUTO91YX6TUdpU
-q8tW296timzTWbrekbNBoj50SMlc3MXneaZzjr7r6LsOayzTdOJ0OjGdTszziWWZ8etKjGqkZq3B
-2J5+7Bk2A+NmZBgHXOe0mo2aya1+JZdMCoFlnlnmiel05I3XX+f1117jjddeo+RE7xy9czhnn88P
-S6PRaDQajUaj0Wi8C01oV4xV0WYAU6vaUmewcwFyIaaMT5mQCj4lfZ0TPifGYWQzJjYZQDBiscbR
-uYKUguRCyupSbq1jGEZ2uagBWTX/6vuecRxq6zjqAl4NwJZ1ZZ5mpmlScV0FughYYzQmzGqJN8RI
-WCMhBuZ55jSruJ7muRqWRXwMOOewxtD1Hf3QMwwDfd/rHHkMWkUXqjFZjQ/znuk0IQVSCAgwnSam
-UxXbVcBvd1uGjd6Lc47OOba7Hbvtlu12x4MHD7i63mOdJQRPzpEQPMZYgl+ZTkfm44nj4ZbXX3uN
-1199lddfew0pmbHvGYee3nXP4Sel0Wg0Go1Go9FoNN6dJrQrxui3QovC5b45N6VmO8eUCSGxxsQa
-IksMLCGwxIDfRFIG0Pgqazq6LpMLSC6IZCRlKGg7+TCAMbXlW5dzTiu9zmr7etZ273VZmaqb+PF4
-ZJqmi3t35xzGWW39tpYshTUElnVlmu87kB+Y55nLEwQRjLMYq0J7GAdtNx96uq4DyqXdu5RMSury
-7b1XAR4D6zyTU2aeTiq2p6mauRW2uw1d17HZbNhsN7qta7vZqAP7dot1hhA9IUId2WaZF06HW06H
-A4enT3nt1Vd57dVXef3VVzHAdjOyHUeGvv+s/ow0Go1Go9FoNBqNxidDE9qVS0VbwFQvcaG8KX4r
-pUyIEe8ji/ec1pVpXTj5lZASoGLXuo7O9cSk8VtSFzmDCNZahnFUl3KoadxamXZd96aKtvcr8zJx
-PB24vX3K06e3nE6nS3W46xzW2iqaLZlSjc9WDscjT54+4enTpzx5+pRpnuiHga7vtXI99JeZ7GEc
-6Meevu/o+o5SEqEaweWiFW2thnuiVydwg5BiZDpNzLUlfbvdstlu2e027K+uuL6+5ubmhuuba4Zh
-YBxGhmGobuj6OMOHVSv0Sdd0OnF48pTD06c8ffxYhfYnPsFrr76KFeFqt8Vvt2zG8bP9Y9JoNBqN
-RqPRaDQaz6QJ7UqRu3zqjIrtUvT1eYWYWFbPcZo5zDO308RhOnGYJ8Zh5PH2CdvNjqvdnpv9NTdX
-19xc3TAM453A7Lr6WUKpRmBGDKZuRYDaTh6CZ1kXpunEPE+E4CklYYy2tiNqgKbiNxNzxsfA7e2B
-p7e33B4OHI8n5nUhpHiJ0jLWXGbGnetwrsNYdTrXmK3qSu5rbFiN11qq+ZpBsMZgxSDA0Hf07pqr
-/Z7dfsf+as9uv2e32+oDgV2tXlt9KGCtAPfzs3N1Ntds7XnW+11mnfWmFIa+52q/x4poNXsY6Frr
-eKPRaDQajUaj0XgBaUK7cm4Wz6VcgrWgto3XOK+QMvO6cjhNPDkceHw48OR4y+Pjgc52DLVSvN/u
-eXTzkEcPHvLo5sjV/or9/or9fs8wjhjrLhVoQTBGLpnUaoKmpmgheNZl5jSdmBedr1ZzMdHUMQoF
-deueqkHaaVk4Ho8cTieOxyPzsrCuqzqMV8M0Y63GkXXdJZbM1ApzzokYssaArQvLsqi7eRXZ8zRh
-xdBZjQLru46xHxjqjPfV9RVX19dc3Vyx2W7oz9XzvrvkgJeiTuUxx5oHnkihvg6ReZpYlpllmVlX
-jyAMw8jNNVgR+k7N0LpmhtZoNBqNRqPRaDReQJrQrrxlKpusGzKXFG1CSsyL53A68fjpLa8/fczr
-T5/w+pMniAjWWKwx7DY7PvjoJY6PjswfmHn48CE+aEU5F3B9T0ePM0bN14yprdRUIaot1FrRnpkm
-dfJOKQEZY82bKto+eE6nI08OB24PR05zjfCaJs3WTpmUk45mm3N7u3vLskCtLudM8Dobvi61mj1N
-LJNunbVk21Gco7OWcei5ubnR9eCGm4c3XD+4YdwMiBikOqCHoLFmIQRyTKSkr33wRK8mbdGHGl02
-scwLfl0RYBwGxr7HClgxl4ivRqPRaDQajUaj0XjRaEK7krPOSctlPhsoEJPGcMWUmZeV0zRxezjW
-1uwTx9PEaV4oOdeAbZiGmZIKOZ1bojMgWOvICH2M9CnRl0SXuyrrNerqXPHNKVFK4hz1JefYLtFM
-75QT3ntSzkzLwnE6cTydOE6nSxXbh0DKOicOVWSLVLFtQDQ/PASPSCGXRMmJXBJTdRCfp5l1XohB
-XciNMXSuYzMMbPqRq/2eBw8e8PDhAx49fMj++or99Z791a7mcefqrJ71AUKOxOjVAC3cLe8DfvWE
-1bMuC8F7UooIqCu66+idU7O6UqBk3TYajUaj0Wg0Go3GC0YT2pUYE6DxXjWGWtu3Y8KHgI+R4zTx
-9Hjk8dNbntwemNeVlAuu64gx1iptYvWe0zTR2Q7JqAu5c7iu18ztcaD3uoahq6vXLGsR1IKsIEbo
-+57dfouxwrqu5LVmToeV2S/kUtSYbV7w0auoFlGTs84hSWo+d0Yus+AAhRQD8wIxBawx5KziPud4
-mcdepgm/LlAKfd9xtdux3+243l1xtd9zfXXF1dUVV1d7rq+v6MYeaw0pBdIade46J2LSBwN+9Xjv
-CUGr2TEEYogEv2oFfV7wqydnnUXv+55NdRnfjiOmPhwoKVLU5r3RaDQajUaj0Wg0Xiia0K6kKrSL
-6Mw0F6EdWbzGZR1OWs1+8vSWp4cDIWdSKVjXqSs5KipXXzhNE2SIPmBsh+t6un7QirYf6IeBfhwY
-Q88m9qQ0VAdxoy3lVRT3Q8eOLWKEQsHHSCazeq8rBHwIrCHhYySRQcCchbaRmrmdVMRXIzUBYgzE
-GFhmoBRyVpFdcsKvK+uy4BetZkspDF3HYB0Pb7R6/YGHj7i+vtK4ru3IZrOhSKEYfRiQYlJBHWN1
-LA+E2joeo+5PUfO5vdc29XmeST5SkrbUu8Gy22643u+52u8x9bpT8KQaP9ZoNBqNRqPRaDQa1yeg
-OAAAIABJREFULxJNaFdS1OpoMdo4rgK1XJzGT/PC8TTx9HDkye2Bp4cj4izitFodY6IAMWVIkVMR
-Ukis80rXjwzjhnGzBWPpg6cLnj6shDiQ0kjOkWHo6FxH6RzO2lrR7bDOUgR88MgipJxY/MppnjlN
-EyElchF1Ry9cDM9cbRPPKZISaBu6ADrbnUImViGcYtSKdk6UlFSEB08KAUqhc45uGOhdx6NHD/mC
-D36QD33wQ1rF7jr6ztH1HWtc8WFljZ41eM0B97qNIRKq4VlKiZwyJWl02Nl8bZ4nSio4cWq25nq2
-2w1X11c8vLlBKIR1xfuVFPzz+nFpNBqNRqPRaDQajXekCe2KqdvzbHbJ6oy9rCvH01Edxg8HjtPE
-tC6sPmBywdSM7BgTKaqJGbmQS6lu5RrjVURFfBYIJVNSIAXIkgk5sPiZ7Tiw224wovnY5LvKekqJ
-NQSmZeEwTyzBs6ZIFMjWAHX+WgSTCzZnSi4qpNdMikHnsdeFkhMp+FpJViGcYqyRXRrd1RnDOG6x
-W0PnnJqRjQPjMHB9dcXN9TWbzYi1hkLGR0/IAR/93QoeH7XirqZsiZgiMWuLfY6JnNRxPKwev6z4
-eUWK4Pqa7913tdJfndZLQaQgojZ1jUaj0Wg0Go1Go/Gi0YR2xVTXcanW4MWg4nZdOJxOvPHkCU9u
-bzmcJuZlZfUBWwqGgi064x2T5l+TS42yUimoYlvIIiSBUhIpaVZ3zJ7VC86A9xtEoO97trW/W98P
-MWfWGJjWhcM8E1Ii5EQUKMZgjEWMxYqppmz6AMALpKhV6RQCJSdi8Hhjak62ZmXnlBi6nrHvGboe
-t9mwG7Zstxt2W91uax62RnnpssaQco3oyrGK7EA4b1OsreN1Xrs6oOvrSAqJWI3QzjPazlhwHdZY
-FdrO4pzBGDWKwxT9DyVtRrvRaDQajUaj0Wi8eDShXTlXtC9F0qzO3zqbfeKNp095crhVV+91ZQ0B
-B2golgrtlDIpZaRmb59rrll0FaPbVDIlFcgJ8QkpCVMSMXqGoWe/3yGmOoxTK9o5a0V7XTjOE0n0
-/MnoLDPWYqzDGquGbqgeLTnjVwNFq9oxaPY2pbAsizqLzzM5ZXabLbLZ0W0N3dayGzc8unnIzYMb
-rq72XF3v2V9dcZb/hUJMgeC1or36RWexYyAkFdkxpcs2JXUfv4jtmNQMrQptX4U2XU8ZC84a+r6j
-6yzWCmJB8rmiXcV2o9FoNBqNRqPRaLxgNKFdsaJSO1NqHJW6jWuk14nbwy3Hk0ZneR/UPE3kUnFW
-J+wMhToHrWiBvJBKJqSEj+EivDMFUkR7yDXK6vpqJaYEyEVgh1Sdz0Ng9YE1BLIxFCMUY0AEZw3W
-WZxzmKIPDkyBYOQisv26anxXjdoKIUBOWBG6zrHbqOnY9dU1Dx884NGDhzx6+JDrm2utZu82bMaR
-mKPOcCc1OVMzNs+6rlVUn0W2CuxYXcdVYFehHRKpiuzoA8FrtFdYPVb75VVod53Oq4sgRVvyqS7q
-lFbRbjQaLxYf//jH+chHPvK8L+OF5cMf/jA/+qM/+rwvo9FoNBqNzzhNaFectQB1fjhf4qiWZeE0
-TRxOJ6Z5Zl19NQ7LSHUqB61+UzJGwIioMKx7c87EGhNmvVcxTyFRIAZKCpQY6K1VEZ8yBUjVjO3O
-XTxqy3iNtSpiKKVoBdsYrHN0rrvMjZuile0ck+ZTzzOlJErOlJwRIwxdp/PX/cDDBw95ePOQRzeP
-uL655ub6Wl3Fd9vqYG6IKd7NX/tqfOZ1+Sq+Y430Olf4Y8rErJniMWklO4VE8oHkqxN5dSMPIdA7
-h1CwxtBXJ3aNW9Prvr9tNBovFiLyRcB/DPxC4AuBoe765aWUP/7cLuyzRM6Zj33sY8/7MhqNRqPR
-aDxnmtCunIV2KplcCjFG1tUzV6F9PB6ZppnVa8U5p1yFtLaA68uiItuoKdlZaeecVaCGgFk9iUwq
-mUShBE+OnhI8Q+fw3pNyBuROoPtQnbsjvs6CI2hruRRKdRe31tJ1Tturc0GyXmNOkbCurPOkIrWo
-YdswDgzDwGa74Wp/xQcevcRLj17ipQ98kP1ux263Zbvb0vc9uT4eCCnoAwi/sK6LCuygudg+BHKp
-IjvnywOLlFRgx6iz3DEmko8kH8hvqmprZTt1PRQV2p1zOGMQpM6+58vKuQntRuNFoorsvwp8gLtB
-nHLv9fuEL3zeF/AC8nF04KnRaDQajfcHTWhXjNHWcUGFcYgqbpdlYZ4XTtPMsi4Er/nPJav0pGSo
-wlpqFVvdv/Vchdr+HSOr92AtsSRSycSSKSFQoqdEz7puCDGpoZoIuRRCdRv352ispCKWLGAKUg3X
-EMFai7UWSEhWQ7SSMjlGYvBE7+s9FgTorGW72XBzfcPDhw956QMv8cGXPsgHP/Ahxs3IMAwM44Ax
-Uh3EV0IIrGFlXVfmZWENKzFqRFiMQWewS53FzmeBXd3GYyKGapwWIilEcog6p31vpRi14g7Y85x6
-zjUSLGqbftE580aj8ULx21CRHYD/EPgzwLHu+9vP66I+uxjgo8/7Il5APgK0Sn+j0Wg03j80oV3J
-VbSlnO5Etl/xQWOp8qVd+c7R2yAYVAyKxm8jCEbQeeKsztqhzi8vy0KiEHO+zC5bMpaCLYIRgxWD
-qS7iWtUutQocSTlTsrqYX/4RQYpWe3NMJImkdSWtnrR65uOR6D2mwND3DH1HX7f76ytubm64vrnR
-7fUNu/2ObugQK6SSWP1CoWgedlhrLvaq1exaydYHACqgzwI7V2EcaiU7pEhONf4sZci6LtXplOoD
-gUDwK+uysEwz0+lE11mis8TOQcnkFCHfzdU3Go0Xhl+IPvv7Y6WU73jeF9NoNBqNRqPxvGhCu1Jq
-Z2NKd9XnddUKbkw6k52LxnbxFrFt4dIqftcxrgLyYhhWq+OxZEJUJ+6QEoMzDNbgnMWKxZjzVr3D
-81mUR23Fzpcq7llqm7tIspTIgJ8X/DSzThPT8URcPQIMXcfVbs9+v2N/tef6+prrm2uub27YX12x
-2WzZbLa4oQN0Xj3kUDO8V7z3+Cq2dUbbX/Kx08VZXCvuOeW7dvEUibVKXfLZkv1unYV2ipEUtH3c
-LyvLPFWh7eg7S3SutuVXV3VjP7s/JI1G41mce6Z/4rleRaPRaDQajcZzpgntylnAxktFe9WKdlSH
-8XwWibVlWarRmAGtYFOlr6CxU6WQS4JUiEHP1y0LNiV8jDVrOsIw4MZe47nMXTXbyFsq2imSajY3
-Re7LbBX2WdvEUy74ZWE6HpkOB/w014p2Yeh6rvY7Hj16xKNHj7h5cM3VzQ3X19dsdzuscxjnsK7T
-KnQ1J7szPtNq9rlVPMRQK+3nXGydH1cTtPp1PS7FqI7s53Dxs9Cu1e1yrmjHoMZttaI9jxOx02p2
-1zk6Z3Vu21lsE9qNxotGj/4JD8/7QhqNRqPRaDSeJ633tnKOzzpXalevFdxSCq5ztdqrc8t919FZ
-ixXBcBaLiZLTPaOupFXaexXtdV20Su69zidXQzVnLEM/MPQDneuwxiIi5JQJIdY5cW2nXhedj16X
-81pY5ypKTxPLaSKsnhxTbRfXKvYHHj3iwx/6EB966YN88KUP8IEPPOLm+obddkPfdxgjNRc71ocM
-i65V18VZvH6fQgh13joSQyScZ8jD3dcxqMO4ZoZrBjbVpI2UqhHaSlgXUvDkFJGcKSkS/coya+v4
-PM2sy4JfV2KIpJT0YQcXv7lGo/GcEJFvEJEsIhkV2QL89vO/q+u/qcf+cP36T9evv1hEfq+I/ISI
-nOq+f+htPuOXicj3ichPicgiIq+JyJ8XkW8Rkd0ncY0bEfltIvJjInKs7/8zIvLr6/6vvHetv+A9
-/QY1Go1Go9F4X9Iq2hXvVwDW1bOsK8uqohKBvu+5utrjjGORFVsWAgGKRnrlHJFSncaLaL41WeO3
-EGLw6vrdddga3ZWLVsJVZPdsx5HNMNJ3PdYaKOiMs/fM88I8TUzTzDxPLPOMWIupKzpLcpZkLZ0x
-5BCQUuicox8GjfDqOzbjwPX1FVdXV1xd7+n6Htvpj0CMgYRW9jNcKtY6gx0ulemY7lrBQ4x3LePx
-XlZ2SqSYa153QTJaea8t91rJTuTgieuCnyeiXykxIEUfWsTgWeeJqXPkNEKp5mjG4Kyt3QXP66el
-0Wi8hbuZljd//db9FwdyEfk64H8ENm9zHPWYAfjDwC9/y/6HwM8Dvhz4zSLytaWUH3u7CxORjwB/
-GvhH7r1/A/wzwFeIyK8A/ot3uO5Go9FoNBqNT4smtCtrdeQ+V27XVVulQeiHgX0Bg8UUgySQBCmp
-AVmOiWJAjDZzU4SMkCVTEEIIWL9ilg6XC2IMUlvEnbUMXc9m3LAZR/quu7REp5rlPc+ziuxpYp5m
-5mnGWItYgxhLZ00V2obOGJzo3HjvHPvNhqv9jpurPVe7HZvtlu1uw2a7ATnHmanLesyadx1yuojp
-WJ3OY4y1HTy+ae46nd3E64x1TJlU58lLrhnforPs+oQBrWjHRPaecBHaCyWp0C45Eb1nWWaNXSsZ
-EbB1lj05p6Z0jUbjReD7gb9cX/84+if99wO/794xj9/ynp8G/CHUkfy3A38WSMCXcedSDvDfoyK7
-AH8d+E7g/wQeAf8K8I3AK8APicg/WUr5+P0PEREH/AB3IvtPAP81agv+EeCbgK8FPvhp3Hej0Wg0
-Go3GO9KEdiUEHSn04ew47vEhUIDOObYbg2SQJEgEkwvBQ8iZTKh1mprzLEIGFdsIqc47m1UdvJ3r
-sM5hraW3lqHr2I4j22FkcB3WGCiFFCPrsjKdJk6nY22jnlim6VLNNtZC57DJ4TqdX7Zdx1jXzf6K
-BzfXPHxww/V+Tz+o63g/dISU8MHXFvCATxGf1KjtEsmV7uav1UU913bxcBHil2p2TKRU1LQt1Xix
-8/y6QEmJEiMpBuK6ENdVt36FFDEUemdwVjBSIGc1SKufnXO5COymsxuNF4NSyi3wN6CaQiqfKKX8
-jXd4iwA/Hc16+vJSyv3Mp798OUjka4GvR/92/SHga0sp8d6xPyQifwH4A6jw/k7g177ls/5t4J+o
-5/iuUspvvbfvrwH/i4j8HuA3f3J322g0Go1Go/HJ0YR2JeWs25QIMVZnbW2nRgTrLH0/wAZcMQzW
-scwTi9E57VQyCa0Ol1Iod/bjKhJjIAaLEdHZbmtwAr01bPue/TiyHQd6Z7FAiRG/LszziePhlsPt
-LcfDgdPxwHyacH1H1/V0fYftHGPfsd9u2G9GdsNYhfvAfrtlv9uy324ZxwGps9ghBEKKdw7iMRDS
-2Q29VrFrBTvdbwlP6d5sdqji+rw/U4p6wZn6C7fkXNvrM2FdVFgvC2Gd8etC9is2J8QKbuwpnaNz
-HcO4Zaxrs9kwDCP90ONcp9V8Y1S9NxqNz0UK8C1vEdlv5d+q2wD8+reIbD1JKd8rIr8G+BeAXyki
-X1BK+fv3Dvnmuv0o8K3v8DnfAvwqtDLeaDQajUaj8Z7QhHblLLTjRWirMVoxDqzFWsvQC65YRtPj
-XY8zRt3ksrZek6O2k5d8bxARSq3MivFYEYqzmOLoBAZn2fRdFcgDg3MYIKdIWFeW6Sy0n3I8HJiO
-B6ZpZkwjUgrOCBZRR/HdjgdXV1zttlxtt1xtd2yGnqHvGYceZy25pJoVnvUe4z2hneNFbKcqtGO8
-V9FOWavXMVSzs7Mje9F9OasPuhgV2ucktJQpMRLmmeV0YjkdSX6h5EhJAVMSzhps5zDG0HcDfb+h
-H7YM44ZhHOnHga7vcV2HbUK70fhcxwP/8zvtFBELfCX61+ifLKX83Xc51x9AhbYDvgr4n+o5XgH+
-sXqO7yulvK0TeillEZHvA/7dT/02Go1Go9FoNN6eJrQrby+0A7YTjNNZahkcYoEeYjdgAWq1GiCF
-AiWScwFzdtXRLOyUInhwItB1mJJxCIO1l4r2bhzfVNEOfmWeJg6HpxwOTzkdj5yOJ+Z5QShYY8iu
-wwqMfcfVdsvDBzfcXF3x4OqKm6sreuewRqvoUFj9SsqRUCvZanimQltzs++q2fdF9qVyHWtFO6jY
-TjFRStG27lwwBowRjSeTmj1eHcbDvLAcjxyfPiGHFWfAmoKz+n3o+55h6On6ka7f3Fs93TDQV6Ft
-ap52o9H4nOUnSyn+Xfb/DGCL/jX6F59xrvv7v4QqtOvrM3/lGef40Wfs/xTIwIc+ieNsXe8XPv7s
-QxqNRqPxvubjH/84H//4s/9/4f27/Qrx4tCEdsUYTTozVrOszxXTmBNlXSkhIkkwCUwUckiUAp3r
-2W53dOPImKMaiZVEzKmaiyWt7MJFlJcUIEYkRxyF3ho2fc/YOTpjMGRyiirGjTB0jt1mxIgw9D0+
-Jvb7Pfvdnt1+z4Obax49uOHhgxuurvZsN+fILgNyzgjX2LGY1C383CLuY6iZ3oFYRXbIb567jrEK
-7VhbyeM5titBzjVPXECEkgoxBshBc7H9SvIraV1Zp9OlVdwZGDpL3xndViHdDwOuG7HdiO0GrOtw
-tT1eLgZwpr5+P/2S2mh8XvFWc7S38uje608849i/9w7ve3jv9avPOMez9n+KvMenazQajUbjfcD3
-fM/38G3f9m3P+zLeM5rQrhh7FtoW42xtTxZiSvgUCCmrCVoSbBIkoxnbTluZixGKhWKEWFLNolYH
-c82c1qisHKHEAEmXvQhtjeDqrKjzdjUH65xhM/Rc73bsthtS1vnvq6trjem6uuZqt2O301ns7XZk
-6HqscyCFlBNCIZVCyUmFdQoXgX0W2SF6fThwie+q89jxroqtojtSkraKa8RWQTAa32VEI79CIPhI
-XD1hnYmLzmaXsFLCiiPTWcu279iMurq+p+8HuqHHuAFxA9gecT3WdRhnL+L6Irhti4FvND5HSZ/C
-sZ9T1ociwksvvfTM42wdSXq/8eEPf/h5X0Kj0Wg0XlC++Zu/ma/7uq975nFf8zVfw6uvvvgPtZvQ
-rpj6C4+xBmsNxmnlNPrAsq5Mi0ci2CS4ZLBY+r6j6zo1JOsddtBtJHGaTrpmdQovORHWRC6ZHHuN
-skoBS74T2p1WoU3J5JQxAr2zbIaeXLZY5zBOP/P6+oab6xuub27YjCNd3+n1OKtma9X0rFQndIq2
-r4dUhfW9rU8en8K9qK46o312FA+RGGp8V0iag12H0AVBi//aCRCyJ66BZZ7x80yYJ/w8E5dZBTYZ
-R2F0lt3Qsd8O7Lajzl/3PV3fg+3JtqeYnmI6cA6xWtHG3MWaNaHdaHze8sa911/wjGPvK7f777tf
-NX9WfNd7Fu/1yiuv8NGPfvS9Ol2j0Wg0Gu8bXn75ZV5++eVnHtf3/Wfhav7/04T2WxBjsM5phXXo
-WUOkFIgxgs/EACGAFUspmontSo+xDtf19ONAL0WNuoyom7Yx1bSsYChsh45N5xitYbBCJwVLwZKR
-6totRUX2brPh4YMbNmGrs8pdTz+MWs3eX3F1dUXXdZrhbUTjdUrWeLCsmdQ5JXLSCK95XVjWhcUv
-l+q2Cu54z2H83B4e39w2HhMl5YvAlqJz3zkmhExG8MvCMk3Mpyqw15m0LCS/0DlD1xlG17EfO/bj
-wNVmZL8dsV13WcV2JHEk40hiwZ6XubSOc942Go3PR/5fYAI2wM97xrH/9L3XP37v9f9x7/XPBf7w
-u5zjSz+lq2s0Go1Go9F4Bk1oV2K662R0rmOz2eBTImbwKeNDJMRAyoHoIyRIKRNSYg2RIQWGHBlK
-xnYWEMZhpOsdm2Fgvx0JVztMKWz7nm3XsRsGtp3DlUxeZ+LcY/se02ur9HYcePTwhiyFkBK25m87
-1zGOo0ZeOYeIUEompXPOdEGKVp1T1Lb1GDzer5zmqa4TqSRSyWTUKT1XUZ6yiuyzuM4pQy4Y9EGE
-FKAIAuSUNSrMqznaPM0s08w8zyTvMbUF3jnHdujYDR27wbHf9Oy2A7tNz2bo1eDMaZU6G533zlIf
-HNSHCBiDMaLXIPp1o9H4/KOUkkTkR4BfAvwiEXnlXZzHf2PdRuCH753jYyLyE8DPBL5eRL717ZzH
-RWRA87objUaj0Wg03jOa0K7cCW3BdR3jZkukEFJmDZFlDaRFDc5W78lezcTWEOhWr0ZoJZMo9JsB
-11mGccB1W8p2Q447cgzYkhnEMJjqOH4W2stC7HuEgrGCdYbtZuCRPKDfjKRSEGOrUZvF1tfGWHLJ
-hHPOdU71LrQqHkLArwvrsrIsM4fTQdfxQJZSZ51VyOacKLkK7ksVO1JywYrFYLAXoQ3UOfWwrkyn
-mek0sS4Ly7KyLgukxGCE3ghj59iOA1ebgesqsLejY7Pp2AyOYrRijTEkETQoDLIAUqv11aTu/LrF
-ezUan9d8Nyq0e+B7ReSXvTVLW0R+A/DV6N9If/QtGdoA3wN8B/AR4NuB3/I2n/O7aBnajUaj0Wg0
-3mOa0K7EqL+/FcB1jo3ZUIywhsi8evp5xduVnAvBe/y0YkLEdh6zdviSiBSyUUM0120ZhpHdfoMp
-GVMSpiR13M5ZV8lsO4srSSvai8Nagd5h6NiOI/1m5IobsghVPgPUFm/NtfaxtopXR/Gz/BTU/n5Z
-VuZ5YjqdeHq45entE54cniJGcEN19XYWcqaUOs8dz2I7aiu7BWsNrgrtkgEp6qy+eubjkdunt6zr
-il+1eu7Q89u+Z3COzdCz3264udqy23SMvWUcLH1vKQIZUWGNZoMntGouwqWyfXGEP7fJNxqNzzU+
-KXOzUsr/WvOtvx74xcD/LiLfCfxfqKP4rwV+fT38dd5eRP/eesyXAP++iHwxmrv9UVR8fzPwS9GI
-sHOL+ueU+Vqj0Wg0Go0Xkya0K/OyABBzJuRIKCpkne3YbfcgFofDZI2wEmNIpZAo+LiS50IokWWd
-mecN67onhB0pB3oLvRV6K9icwK+wevCeEiNp9YRpIUwz9uEDLJnkDMkYotFtAkqh5lXnmnUdiTES
-zq9rRbvUY8iF0/HE7e1Tbp/e6vZ44HC45fZ4wHaWfhwYNgNdjQOztT2bok7ixloM0InBieCA6D1x
-8YR1ZZlmlsMRfziRpiOSC46C6Xo6a9mM2hq+GXvGzYZ+HFR8dx2mM0htF6cauBkR9MfSIej3nPMx
-VrToLVzm3RuNxuccn8oTsn8NDZv+FcDPBv7QW/YXVDR/bSnlHwjeLKUEEfla4E8B/zDwL9Z1//3/
-G/C76xZg+RSur9FoNBqNRuNtaUK7Ms36u1UqmVgyqS5nO3Y7yzBusMVAKtpSjbaQBx9YgyfkwLLO
-WGsZp4EQVlIOZBK7sUMGSzc4JCdknZHThJwm8upJ00o8ToRlVVO0zmI3A8k5knNEcaRSq9hRW8TP
-BmYhhYu4zqWQcyaHfGn9Ph6OPH79Ma+99jqPHz/meDpwPB05no50fc9mt2Gz2zBsRnVQ73q6rsMZ
-wYgKb5W94ARsKSq0T0fmw4HpdGKZZsI0k6YZbIdzHeLUBX2zGdiMA5vNwLAd6MYB1/fY3mE6QZwa
-m5lzldoYBIf6ujsSNS/bqNAWIxg51/ab0G40Pgc5D588+8BSVuBfrmL5G4EvB14CTsBPAN8PfHcp
-ZXqXc/yUiPwstOL99ajgXtHK+B8spfxXIvIv3XvL00/5jhqNRqPRaDTeQhPalXle6m9/WqVOqHO4
-7Ry967CuQ4oQY2L1njUFwjGTwsoSV0qqjtypMAw9ManIxhQkj3QysOlBcoR1huMBnt5SpoXUT4R+
-Q1gDrrPE7Yi93hFLTwKSMYRCjdnS5ZPHR8+avLqfIxgEqkN69GradjwceOONx/z9v/cJXnv1VU7T
-xDSfmKaJYTOwW/YEH9iExDhuyKMKXes0T7y3jk4EWxI2Z2zJLN4TTgfmx28wHQ4sq8evnrSuuHGH
-c07n3MeRzaau7cgwdvSbvraTW4wTxKkzu1idNxdrMLgqsy0ZpyK7CvFL97yUJrMbjReMUsq7OhSW
-Uv65T/O8PwD8wKd1Ufr+Gfiddb0dX1K3Efhbn+7nNBqNRqPRaJxpQruScr5sQ07EHCkIfRkwYjGd
-4DrHuBnYXW0JKZClEIn47ImrvjfnSApCDF5nub3He0PohRCELkXEr5Rlhmmi+ER2geRWkrXEmz3h
-dMIsMyFnQikEUKHtgwptH/BZs69D9pQCRgSDgQLrurLOC+u0crg9cjycOB1PTKeZZVnwiyesQXOv
-e4+vlexse6QHK07vGYNkKGRSjOToiTHgp4k4z2S/QPTYkhgM2M7heocberpNzzAOjEPPMHR0ncN1
-DlN7v4voyiIkEawYirEqto1FsBgcRSwi5k5si6Egum0z2o1G473h19TtXy+l+Od6JY1Go9FoND4v
-aEK74px+K5JficEzrysxJ/oYGGIkpkiKAWst2/2WIgXjBNMJtjP4aSXMK2FasMbSO4e1WtzJOeFj
-YFnBpEBZV4z3dMFTEuRQyCaTxokwTZhpgmnGp0zIhZALsVa0k4+ab10iqURKyVqJL0KmUGJmmWdO
-tyeOt2pQtswLOWZEDM44OtuR+6yVerHqIp4KRgyd7Rj6AScgpbbJx0BeZ9Iykxa9xug9hsLYdQxd
-B+gMuRm32GGDGTd0w8DYd3TOYq0goi7lKWdiFshGbcWNACqipbqqUwW2MbrVSC89hrPQ/pRGPRuN
-xvsREflpwEdLKekd9v8OtKJdgP/us3hpjUaj0Wg0Po9pQrtirQX0N60QAvM0sQbPEINmSqdIAY3d
-2m1xvcU4g+kMtjOs3cRqLUsRpEDnHLbmPKesWdOLzZjgMetK5z3FB4pAkUwmEscRM03INJGnCf//
-sXfvcZZmdX3vP7+1nufZVd3DgDrADI4YFY+ioBFE8YqKiRNRYzTJwRMiAvFgJEaPYrwl4ng0ejRe
-OF6OIygC6vHCQY9RxEskokRg8IZ38S5DCwM4TF/23s/zrPXLH2s9u3bXVFVXdVd3VXeomHjSAAAg
-AElEQVR93/N6Zu+q/dSz1q7pPbu/e631WznTT0EbIw8jeah7XFsiWyKTASe7YRhpTCwWC86dPct9
-f/8uzt5/lsWFBWnMdXuuSNu04NA0kcbKqDXZiQTa2LDRzrCcyONQ1novlvTnz7M8f5b+/FkYB2wc
-iEDbNcQYibHcWrcB7QzrNghtS9tG2iaWAms4GS/r4LNBttWe3NOccLOtbcssREIMhPXAXYN22dVb
-+2iLyCV9HvAMM/tR4DXAW4AWeHR97En1vD8AXngE/RMREZEbkIJ2NQVtA9I4slgsmC/mdRutUtW7
-7Uq17I3Njm6jgeCEBmJrzENkYYEmgyenm3U0TUMIAXdnTIlln4hDCdlpKBXHszvJE4mRcT6HC3Py
-/AJpfoHenT5D72WrKx8TPiZySuSQ8eAQysrynB2yMw4ji/mcc+fOcd9993Hh3Jx+2YND27QYECzQ
-xBJimxAJGJad4EZjgTY05JxJY9m6q5/PmZ8/z/zsWRb330cXoKv7Y8+ahm42Y9Z1dF2HNx3etHjs
-sKaG5hgI0WrYhuROcC/bhq2OusrcQhnNjoEQSyG0qShbsFBXok8hW0FbRPblvYCv3OUxB/4I+DR3
-H65dl0RERORGpqBddW35VbRNpImRGAzDScPAwjNDv6TtOtpZR9t1WDA8JZoQOLUxo82wGRpuajfI
-2YkbHXFjRtzsaEIihoyTSA4jRk9gaYHgRsCxnEk50YwDTd/TLJeMFkmhITdtmTKdneCUwmcW8eA0
-MZQq5GmsIXvBYj5nPp+zmM/JOdHNOjY2NsFhGHrGoWcce7KX0XDIRMDHgWE+Z97cz9gvWF64wHJ+
-gX5xgbQo08W9bvnVdi2bbcPGrGXWdcxmHbOug9isDovTlO9SLdzqPthhtR92mSKeiSQCuOEZcigf
-HKSQiW6rtdhmde9sXw/cIiJ7eiFwH/CPgUcBDwVOAe8Efhd4OfAidx+PrIciIiJyw1HQrraCdkMT
-Qwna7oxDT+7L1lntrBT46jZmNG2D47Qx0MQZOTR4u0HeTLg73kZoYrn1AfKAe2Z0SnEzsxK0jTp9
-umzb1YwDbd/TLBblmk1LbkcsNFguo8ABA7OyXDlamV6eMsOyL/ta15A9X8yJ1nDq1CanNk/TtTOG
-fsnQL+mHshZ9qKEbHB8H+vkF5u4sF3Pm588xP3+OYbnAciLksYyIx6asv97c4NRU8Kxr2Zh1WKgj
-0aviZVZnhk+Budyur8XOFoGAT0E7QwoQ3IleqotbXco9he1Q/xER2Yu73wM8vx4iIiIi14SCdnXx
-iHYgWsCAcRjo+57l0NPNOsZhk5QGZhuzUkm7KxW7Q2fYJoQMnmEMMFo9xgVpKNO6kxtDHdEubTi4
-4w5jHmnHkbGOaFvTQtth40iIhrnVEfBAqGE1xEAeE0xBez5fOxZsbp5i1nU85MEP5vTpm+iXi3L0
-CxbzC8zn51nMM+MwlBHthcPQM79wnnNnz3L+3FnGfknXRLoY6ZpAjA1dN2Pj1GlOndpgY9axOStB
-OwSjFBYvHwaUMfNym4Dk5X62ACHiFnAC7qW4Wa5r3KeQndwxnAhEs/K862i2graIiIiIiBxHCtrV
-VCG8aSJdW0LjouvIKTEAnkbGwejrlHLPiTzrcM8YmdYamtCUtc8hkkIgRyPFwDg0jEPD0Jc127Ns
-dDnQEIgpE5JjybHTp2BjBl0LMUIdGS6HYblW2fa6JnvMJId+sWB+Yc75c+c4d/Yci/mCcRxxd5oY
-mW1scPqm0zz4QTezXLQs28hyEbA8Mi7nLFIi9Ut8HMgWGENguZgzLhfkcYCcMSIxBpqmJTYNoWmw
-aQ11XU8dY6CJRhugiWXkOVO28MoYo28d2QLZQgnaFiFM90MZsSfUgme1UFothBZCYP0fERERERGR
-40ZBuwp1HXATI13XsrmxwdD35JwYxp7lMkDOpGFg6U5KZZutNA6ksSW3M6yZEVsjxIbYtVg90tgx
-DjPGcQObzWibrhxtR5syXXK6lGlPnyLefBPx9Cni5gybddisxdqm7C2dHFKZxp5SLkXRSJw/f4Fz
-Z89x9v6zpcr4fEEaEzFG2rZjYzbj1KlTnL7pFE2AaE4g0y8i5k4eevr5hTpaXEaNx3HA00hjBm1L
-V/fa7rqW2LRYCGWU2kt/smfcE4FAEwIbEULYCtSZwEBg8EAkMBJIBBJWR7fLhwmY1eriddzaysh/
-XBVEK8XRgtZoi4iIiIjIMaWgXU0VsZsmMus6Njc3GMeBYRzolwtisFKJe8hlH+shkFMJ2WlssQ2n
-2TC6piHEQNu1tJubtKc2GceBlHrSOMBsg9h05WhntCnR5kyTMs3mJvFBD6I5vUnY2CDMZljXEtqm
-7hudy77ZOTOmkSENjKnn/PnznDs3Be376ZcjOeWyn3fXsrGxwalTm9x0+vQqZJsn5rEheCYNPcNi
-Dl52pra6LhogBiPGMsrftS1t1xGbqT+shexyBDPa4MyiEaORQyDXddiRSPBIIGIeMKwUQCOUAuJ1
-IbYFq2ux2Rq7tnBR9fHpeyIiIiIiIseNgnZl4YEj2mkcWS6XLNqOtmkYhoGUy3ZfGGX6eBpJaSBi
-tCEyNh05J8zKdPS2bWliIOdI9g6ahhAaYmwJ7ayEbM+0ORNmM8KDbyKcPoVtdFjXQIx4MLwG2pwT
-KY30Q08/LFkOyzKife48586e4/zZ86WomFMDcqkIvrGxwcbGBp4GfBjIw7JM78bxYWBcLjG2gnaI
-kdA0xNjQtC1d3b6rbWdl27IY6xpsp8TycgQyjRldhKahTBsPgRQieIMTcW/quuxpTXYZyaYWTTMz
-gkEEGjOixTqqHVeB2+rou4iIiIiIyHGjoL1St5AKkaZpS7GvjcSpzZ5hGBjHkcWiVOvuvSflsp/1
-OIB7ZmGRQMAzpJzpU2I2jszGkRBKhXCz8gtv2o7upkC3sUmD0+I0gLUNvjkjb26Qm4YRL6Pmi0xK
-ThpGUj+ShpFh6BnGUjX8/LSd12JB3/erIN80DU3bEGMs08KNWngt4TmVtdc5YziNQYxNWWcdAqFp
-yhT42BCbpnxg0La0TUvTNjRNuW4TIk0oVdobKwXLYg3JAUq1caZQXKuO16JnXjY2A2xVTdyCEc1o
-gM6MFqMl0hLLmvYpXju4+Q7/HUVERERERI6Wgnblq6AdiE1LNytrj/thYBhH0pjqVGUnjWVUO6eM
-u1MGuA0ypDEx1IA9G0e6YaBtG9quKVXKY0PsOmYbG2xaoA1GE0oBMY+BIQaGJpYiatnpx4E+9wxD
-IvUDYz8yDkM5xnJcuHCB+WJRPghY9nRdoG2MtinBOMZYp8bXkeec8VTCtnkuFb4DpbJ4V0avYyxB
-eytwx/K9JpYAP+03Hst07iZYCdsBYt2KK9YB52ysRp/dS9Aulchr2F5fl21GY0ZrRmswM6O1SEug
-mQJ7DdmunC0iIiIiIseQgvZKCYIhRJq2xaxUvB6HErJzynh20jjSL5aYg6dcvm+OZyePmWEY6YeR
-ZQ3Zs75nY3ODzc0NjA28Bu1utsHpjQ2aGGibciQcPJFyCcHjcmAxDsz7nn45MPTlGPuhFmIrx4X5
-gvl8wbIG7SZ2mBlt29LWkWeb1jO713XeCXICzwQy0YyuiWzOOjY2N8vU9hqyLTQXVT/fCtllK7Sy
-Hdo0or01qh0oITvUUG1ro9m5jmb7VFGcWlXcyoh2a0YHdFgN2eWgbhXmGtEWEREREZFjSkG7SvU2
-Y6tRVywQQkPTdHTdBl030LY9bdszjomUR5KDp0S2zGgJGLb2jc5ezhsGxr5nWC7Jmz30A35qxHMq
-+3Y3gbaNJJxFGlfHfDmw6EvQHoaRcUikYWQcR/JYpq7ncVxNbR/HUgTNYFUIre3aGorL1HH3XKe8
-D3hKRIO2adjoWmazjtlsxmzWEWILIZaQXYN6qGG7bSNdG5l1kY02MGuNWRuYNYE2GjGW0IwFqIE6
-Y6RShq1OG69F0Oo5tlbsLNa9uGOogX215VcZzZ7mjmtEW0REREREjiMF7Srnclu2qypfu5ewHWOz
-tR1XO6NtS/AlGT5Sqm5nSCkDCWcguzOmXEJw39MvF/TzjmExZ1ycYlgu6JeLst65jTRtJOMsx5E+
-lRHx5TCyHAaWw8g4ltHzlMttHlMJ+CmVML8adc+YGU1smLUdXdvSNLGE5FXQHhmHHs+JYMasbbDZ
-jNlU8KzrsNDU/a3LHtcxlGuEKWh3DRs1aG+0xkZrzFqjbco08hCsViYvW3ulWvgsew3bW6XX6vrs
-GrKtjI6HOv082Nb67lINvc5HVx00ERERERE5phS0q1RHR3Mu97OX6ckWIiG2tG1ejWaXY8QdsmXw
-kZwcyLiXkeyQEmEYCXFJ3za084ZF27CczxhOLVn2Cxb9kqaLJWx3DRkvU8/HcgzjSD8mhjGRc63t
-7eDueMr4mPGUGesa8jLKXUqPlerpHV3blSrhdY2252l7sr6OaBuztiV6ppvN6GYlbLtFcg3K1JAd
-63Txro3M2oaNNrLR1aDdGBsttKFs62XB8LWgneue2cnLkTHcDNamjD9gRLtOO1/VF59Gs73cVdgW
-EREREZHjSEG7Srkk7Zy9VPhOmTF5GdnGIIQSukMkhLJFV7ARCGUvaPeyxjonzDKWDLMRMyMNpbjZ
-ECOp70l1D+x+HGi6Whm8K/8pyhTwxJhGxpRLP3Kmdq9MuXbIKeFjGdnup6nldTo57pgZsa6pngJ2
-TolUC6iNfU/OZep41zQ05jRdS9OUtdlep3TnOr07xIZY12Y3TaRrG2ZdU6aON9A10EWIoe5JboYT
-t0K2B7KHWmk8lOvXUuy2VnU81MOAus12ed4Oud7x6UEREREREZFjSEG7GmuSTSnXddWJsRY2G1bH
-FL4dn6aWu4EHnIwnXxXoCqGGxmBkd1IOWM4M5lgoU7jHPBLbpgTctsHM8Dpv3ach9eyEMoxNXk1r
-z6VIWz8yDiPL83P6+YKh7+todS2UlspzGPqexXKB58SyTlkf6oi21TXaHqwWPytVwKfRaKcWQYuB
-EGPZ6quJtE1TqpQ3gTY4jU2bdZVR6rIuexrJLke2WNdll82/zLb2z542/nLy2pruMqLtQJzWZNtW
-wNY+2iIiIiIichwpaFdjmoK2M4yJcVwP2KkcY2JMTsqUgl5ew3YtoJY9kz0DjtdFxpaNHDKWjZQD
-eNkSbMwjy9QT27aOaLdl2rQbpQZ3YZQ9qclOrgE8pcSw7BkWpRr5cj5nuShB28e8CtkpJcZxoO97
-losFnsZy3nJZRrRTIgCxjZgHvG3wGGvQrgXLzGq18UiIkSaWrb3aJjJrIl20ErRDJloZSWc1XTzW
-I5Rp6B7Kuu/pGVpde82Un8t/gzIJvxSos1q93LES+53VVmDroVtEREREROS4UNCupqnjQ8oMY6Yf
-Uq30vRa4x0RKeduIdigj2rl8P3nGPdOEsk7ZgmEZcjBI4Ckw5hHGAH0kti2xa2naliY2dBZoQ6Sz
-WKZTB9sa6Z6Knw2JYb5kuViymC/o50v6xZJh2UP2VQXyErRH+r5nsViQxshysaDvlwz9EksjrVHW
-cBukGBlDIJuRV2G7rNG2uo/2NKLdrQ5oLdOYE9naI/vitdmRxNqab+IqJJeQbXXcGsAvqlIOdTQb
-iA5uRqwj54rZItcvM6slKPk6d//6y7zGk4BX1S8/wd1ffSiduwJnzpzh9ttvP+pucOutt/KGN7zh
-qLshIiJyYilor0yLoL0UG/NcpminzDhV9q6jxDmXMI2vNqkiuJOy4znhnkm5zCrPBtlsVeDLc6Bs
-IhYxcwgBUoAYMcu12JfVUd1a9cudIWWW41D26B4Glsue5aKE7WFZ1lyncQSnTBW/cIHzZ88SzCEN
-kHq6tqFfXMDTQBNL8bHGnIayXNprVfFci585ESwSYiDErX20CYYHW418l+foZAMPEbeITz9PIBBW
-IXyaJx63/e7LmWXEmlyun6f7tShaMiN6JnkguBM0oi1yvTusTfqOzWZ/OWfuueeeo+6GiIiIHDEF
-7Wo9sjllHXbKZTutlKbiZCM5l/2v8QSeMc8Ed8wdyxlSxnMiT6uNa9AuA99GaMJUDqwG7TJabDmz
-GuCxulo51a28UqIfRhbDwHLoWfQD/bJn2Zep4+MwkOsWX8GMfrlgceE859oGfMTHHh+XbMxavC9T
-yNtopZK4eRkhNiOHUIu9lSnebnFVAK6s0y7TyAmlkFkOgRwgWyAbJKtbeq1+vhRUCxixFlWrUbt8
-fsDWv2xap12/djdynQkQoBaXM1KoIdu9fIggIifd6n8nx8d7HmHbZ6ilI0VEROQIKWg/QBnRzl7D
-9hSyx5GUxjqiXUatzXMd0a5B29eCdh3xds943BoFjh4Bh1oUjZggZyzX7+GUvbiNXIudjcPIsh9Y
-LJcs+p5539P3A/0w0PdDqUBeC6e5GcNyyfzCBWIwyCOMS3xckDY6Gs805BK0KUE71OnaMURyCNi0
-f7aVgB1C3HVEu0wzz6QQiMbW3tsWVkHb6oi21ZHpYKuB+tLvVeL21V+X3a38VTEbyepKbjOCl721
-g9cPKkTkxHL3X2X7BJkjF4A3H2H7twMaURcRETlqCtrVFNrsosERXzsyZmUUNYayxdRUjyvVqdBp
-+vm8NfU857SaCu3B8Jwpow1lJXI2I4caWGMmrRVXG5YDQz8w9mMZve771e1Q11+ncQr90/Owulf2
-UIqeNYGxMVIbSNFpYqCJxizGMlLMFO4NQsStqVO/Yw3Noe6hHQjRyocDdUp8MmM0K8XJ6jVsOsFC
-3Se71iGvI9bBnGilgrhPe2JPM+TrWHe26Tc+7R1eQrd7LY5m9YMNBW0RERERETmGFLSrqRhXCXL1
-CEaMZX11Ew2PAZqApUBIRnYjuRGz4RlygnFal53L3tWpbMRdiqcZeK51xHPCckNysOxYyniI5Xpl
-cJuxH+v667LNWD8OjONITiOec6lIHg33UIqDOaVyeSyjy2Htg4E2QBcDsyYwayMbbailxkrSdayM
-YFvEaOr071CnjwdiCGV/7FBDNjC4Y3V/62RGW0uUhdqX8gstTyaQpz28yhZoVvYfn4qirfYHp4Tq
-bGVNdqk+blsj3F4+2FAlNBE5ZupmEfoAUOR6cebMGe666y6e/exnc9tttx11d0Rkn1JK092w13lH
-7Vh37lq6qF62lWXIIWyF7CYG2mi00eiaEli7xsr2VhGaYOUwq1PJKftn1325y3ZhZYR6WA6My560
-WDLOF4zzOcOFC/QXLtCfP8/i/HkW584zP3dudX954QL9YlG25RrGMt0crx8GlHDdNJGmibXwWqnO
-Hc1pDJoAXTRmbWTWNWzMOmYbHbNZSzdraWctXdfStS1d19A10xFpm0ATS9C2unV4Agagd1hiLDEW
-BHoCPcZQ98HO01i1ZYJloiVaMp0lupCZRWcWjS4YXQx0sVRdb0IgWinYNtUkz26MDkOGPkGfnD7p
-L7UiR83MbjOzbzaz3zSz+8ysN7O/M7M3mtmPmtnTzeymS1zjCWb2/5rZ35rZwszebGYvMbMP3ONn
-nmRmuR4fv8PjL6qP/UX9+hFm9u1m9idmdt7M3mZmP2tmn3Llv4XjNoVdRC7lzJkz3HnnnZw5c+ao
-uyIiB7AWtI/1e69GtKs6XlqmJa9GtCEGI0fDo2GNEVIg1T2xUw6M0QjJSKHs2DWNhoNDzqsK5V7X
-cmNgORNzIKeE1WJrnkaCBchgGTw7Yz9uHTkzupOmqdWxrJcOMWAW6kpoI4YSimMwQqCGbKMN5UOB
-rpmCdltGl2uV9eTUkmWRULflKlXFy1TwstVYnZpep8sPPm3FVUJwskAEGowIq8Onyuzk0kfz1V7Y
-VjfRzl6qjGeM4LZVMM3r4LWzmkGwNfleRI6amX0c8F+Bm7n4ZfnQejwGeCpwL/CKXa7xhcB3cvEb
-5m3A04DPMrM73P3X9+jGJf93YGaPr+3fsvbtDeBTgU81s29z9y+/1HVERERE9kNBe2VrjXY0pwng
-0QiNEXMkeSTRkHwkERk9MJSS2/hIDaGsgqOFQIiR6F43uCqVshujjJBbKKPfboTsxCFhVkbCQ10W
-XiprU0arA4zAaM5Yg7bX6exmgWixbNcVIhuzjs1ZW0atNzeYzTrariO2LaFpCDHW7cTA3KcV2rVo
-WSROddGnfbRXJcFLMI41yFsoP1X2vN5aW15XtJfwbJQ12QYxTBXHbeuSrC5dgreXkzJWt02rs88p
-H0DY1vzza/znQ0S2M7MO+DHgQcD9wPcC/x14G9AB7wN8NPDP9rjMHcBHAL8LPB/4fWCz/swX1/sv
-NbP3d/fxMrt6CvjJ2s9vAn4eWAIfCXwV8AjgS83sb9z9uy6zDREREZEVBe2JT9uh1HAbDKIRm0D2
-QLaGxFgDd2TIEUuGj0aKRrAy6julxhCsBG1KYI7rQfuiaeZgyQmeViO+RqmuHdkaUR+Jq2ngY4Ac
-Qz2MECIxRBpraGLD5qxjc6NjYzZjY2ODbmNWgnbTEpsGiw0WYvlwYO1XECjrsnMdfy5Bu3x64NOe
-1VYCtoUyIj0VMcs1ZMe1ad7ZKKPU07psynT8UIer19fD5/q8rbYZ10bKQ67F6gyYzlutqheRI/Qx
-lJFnBz7H3X9+2+OvB37czP4PStjdyROBnwU+a1uQfo2ZvRP4BuCRwFOA//8y+/kwoAee7O6vWfv+
-G8zs5cDrKOW6v9HMftTd33GZ7YiIiIgACtprpmJopYBYU9doexOAiIdMIq6CdpMDPgZStDJSWw9s
-mmZdio1N06ensN0AjRlNndZt0x7cKROsBMzpNlPOyTGsAva4Np09xUAKRowlYDehpW3KSPbGxozN
-2YyNzRndbEbbtTRtS2gioZlGtG0VdjHKXtd1BNtr5XCwGrgp23lh60PQZdp3Xd1uNWwHL6PTJWSX
-qelet+iK1MHotdAcanVytylAhzqiXa/FdJQR/9UFlLRFjtqta/d/bbeT3D0D53Z4yIA58MxdRqv/
-b+BrgRb4OC4/aDvwfdtC9tS3M2b2ZcCPA6eBpwPffpntiIiIiAAK2ivuNWjX6dqEskbbnLpqsIxc
-p8ZIybBoDNFYhvXMOY32ltrbJWhPo9Nebq2um7Yyqh2m/bitrms2Wx3U9dBg5ABjMMZA6Ucwxnob
-YkMMDU1saWJbCpx1LW3XEJuGEMuTmfa9Tm6M7qUy+TRpvAbhUJ8Hq0Bdfz9ToDZbjXTn1Uh3mMrI
-raaNmxtet+cqv9/y3JNDnLb08vXt03xtOrjXf9vaz9ctwVYbcF+tPwkicgDrFYSeARx02rUDv+Tu
-b9/xQfdzZvYm4IOA9728Lq780B6P/RRwH/Bg4JNR0BYREZErpKA9qVPHp7gYzPGw2p0KcycFVkXP
-coAYfKvwWQCCYTFgORIs13CaV2uNy5rrWhHcjDZMZccywfMqhMd6joVQpqSHstd2CdglaI+hhu1g
-ECIhRII1xBjp2kjTlP2vLZQwnIDRS4E2spETRN/qS0MghrKTdSTXafBT4La61VfdC5xQ9v22ULfj
-Kmum10e2Vxum+VS2zBndiFPRs/p7zT6tE1//p045p+wrnmoFd18F9K3gLSJH6teBv6CE4Oeb2dMo
-ofXVwN3uPuzjGn98icffSfnI70FX0M+esgZ8R+4+mtlvA58IPPYK2hEREREBFLS35JravARts3qE
-rQJlKThjcEKEHHy1VzXTiPa0djl6Gd2ua65XIdunEF0qgzchEMlbh21VCW8MQt22K8QAsUxTz9No
-9jTCbeAWt/bADpGmbWhjJMayB7bXadxjdjw7njKJWpTNAx4oe13XddNxej5Ma6PLOVPQXoVsCyRC
-2ds6l+Jn6xO9p32va3Qm1tCccpmubl6qq6/+McfJZX03YRXIs3vZp7zez9O+5BrWFjlSNaB+GvAy
-4NHAhwNPqA/PzezVwEuAH6/Tx3dy4RLNTD93JVt4vNP9kh/NvbXevvsVtEP5YPFh+zhvWlh02LRN
-kYiIXJ/OnDmzr+32hmE/n+MfPQXtla2q42UidN1L2+uWX8FX67BttYZ7GvSdKnuthW0y5mG1p/YU
-1s2MEMLWNlwEmhq0p/2u21DCdmwCTYzEJkI0cmOrAmiDwViPbIFabg2sWe2rHWIt0EbZz3r0GrQt
-1wJlgbLHNYTsq5nbkbVl2NPvpo7wE8o2XslKtXMnkHM9GXCfppHbqlr4VEM8OVvbc02j0kzj3b71
-tYN7LkXoarDO69PNV9POReSoufsfm9ljgU+vx8cDj6JsnfUp9fhSM/snu00RvxbdvLbN3XttmxMR
-EbkB3HXXXdx5551H3Y1Do6C9H9fNTlJH3dGjbl9EjkIdLf6ZemBmD6ds2/Uc4PHA44C7gM8+oi6+
-h5nZJUa1H15v33mZbaxGwqcPOC9lv+ddjre97W3cfvvtV+36IjeCvu8BuOOOO+i67oh7IyIpJR76
-0Ide8rx77119oH2Fs9CuLgXt6gu+8EuUEkVEDoG7vxV4sZn9CPBaStD+NDObufvyCLrUAR8K/M5O
-D5pZBP4hZeT79y+zjdV7yH5n3FzNmTk5Z+65556rdn2RG8naX9pF5PpyrPObgraIiFwVdQ33r1KC
-dgM8hK210Nfa09klaAOfBbwbJWj/8mVefwnMKGvK33aZ1xAREZFLexilFPVRfHi/bwraIiJyWczs
-Y4Ez7v7nuzzeAk+qX57j6BYvG/Bvzewn3f1/XPSA2a3At9YvLwAvvpwG3P30lXVRREREbiQK2iIi
-crmeDPwnM/s14OeAN1LC9CbwvwBfQBnNduCFe1Qev5QrnWP9NkqI/mUz+w7gFZRPwT8S+CrgEbWN
-/3iEBdtERETkBqKgLSIiV8IolcaftMNj08YCPw189RW2cSUuAP8c+HlKsP6qtcemPj7f3Z9/he2I
-iIiIAAraIiJy+b4V+F3gk4EPo4wMT5tI/x3weuDF7v7KK2xnCsMHfWzrJPffMsqxLmEAACAASURB
-VLPHAc8FngK8J3AeuJsSsn/xCvsoIiIismLaj1hERG5EZvYiShG0v3L39z3q/oiIiMjJEY66AyIi
-IiIiIiI3EgVtERERERERkUOkoC0iIiIiIiJyiBS0RURERERERA6RgraIiNzI9lWVXEREROQwqeq4
-iIiIiIiIyCHSiLaIiJx4ZvZIM/s2M/sjMztnZu8ws9eb2XPNbPMQ2/kcM/sFMztjZnMz+ysze6mZ
-PfGw2hA5Sa7ma9fMnmdmeZ/Hxx/WcxK5UZnZQ83sKWZ2p5m9wszuXXsN/eBVavPI3nc1oi0iIiea
-mX068FLgZh44zdyAPwWe4u5/fgVtbAD/H/BPdmkjA1/v7l9/uW2InDRX+7VrZs8DnrfDtbdz4JPc
-/dWX047ISWFmedu31l9bL3b3Zx5iW0f+vqsRbRERObHM7MOAHwMeBJwFvhr4aODJwAsob87vD/ys
-mZ2+gqZexNab/a8Anwl8BPAs4M8o78fPM7N/cwVtiJwY1/C1O3kM8Nhdjg8B7j6ENkROgql2yl8D
-v0gJvVfDkb/vakRbREROLDN7NfCxwAB8nLu/ftvjXwZ8K+WN+s7L+eTbzD4J+OV6jZ8BPsvX3nzN
-7D2A3wQeCfw98L7u/q7Le0YiJ8M1eu2uRrTdPV55r0VOtvqauhu4293vNbP3Bv6S8jo9tBHt4/K+
-qxFtERE5kczsCZS/qDvwwu1/Ua++HfgjyifuX2xml/OX7S+rtyPwHN/2Cbe7vwP4ivrlQwCNaovs
-4Rq+dkXkELn7ne7+Cne/9yo3dSzedxW0RUTkpPrMtfs/tNMJ9c35JfXLhwCfeJAGzOwmylRWB37Z
-3d+yy6kvB+6v9//ZQdoQOYGu+mtXRK5Px+l9V0FbREROqo+tt+cpU8h286tr9z/mgG08Aeh2uM5F
-3H0AXksZfXuCRt9E9nQtXrsicn06Nu+7CtoiInJSPZryifefufv2Sqjr/njbzxzEB+1ynb3aaShF
-nERkZ9fitXuRuj3QW81sWW9fZWZfYWYPuZLrisihOzbvuwraIiJy4pjZDLilfvnmvc519/soI2cA
-73XApm5fu79nO8Dfrt0/aDsiJ8I1fO1u98m13abefjzwTcBfmNlnXOG1ReTwHJv33eawLygiInId
-eNDa/XP7OP88cAq46Sq2c37t/kHbETkprtVrd/JG4KeB1wNvAVrgA4B/Bfxjyvrvl5nZp7v7L1xm
-GyJyeI7N+66CtoiInEQba/f7fZy/pKzj2ryK7SzX7h+0HZGT4lq9dgG+w93v3OH7dwM/bGb/O/B9
-QAReaGbv5+776ZOIXD3H5n1XU8dFROQkWqzd73Y9a8uMsiZ0fhXbma3dP2g7IifFtXrt4u73X+Lx
-7wd+gBLkHwF89kHbEJFDd2zedxW0RUTkJDq7dn8/08VO19v9TFW93HZOr90/aDsiJ8W1eu3u111r
-9590ldoQkf07Nu+7CtoiInLiuPsSeEf98va9zq1Vhac347/d69wdrBdi2bMdLi7EctB2RE6Ea/ja
-3a8/XLv/nlepDRHZv2PzvqugLSIiJ9UfUqZ8PsrM9no//MC1+390GW3sdJ292hmBNx2wHZGT5Fq8
-dvfLr9J1ReTyHJv3XQVtERE5qX693p4GHr/HeevTQV9zwDbuZqsYy67TSs2sBZ5I+Uv73e6eDtiO
-yElyLV67+7W+Z+9brlIbIrJ/x+Z9V0FbREROqp9eu/+MnU4wMwM+t355H/CqgzTg7ueA/0YZfftk
-M3vELqd+NnBzvf/yg7QhcgJd9dfuAXzB2v1fvUptiMg+Haf3XQVtERE5kdz9buDXKG/GzzKzj9zh
-tOcCj6Z84v2d2z/xNrOnm1mux9fu0tR/qbcN8D3bp7qa2S3AN9cv76NUMRaRXVyL166ZPcbM3m+v
-ftTtvZ5Vv/w74KcO/mxE5CCup/dd7aMtIiIn2RdTppRuAr9kZv+ZMvK1CXwO8Pn1vD8Bvn2P6+y6
-TtPdX2VmPwY8FfintZ3vpEwz/RDgq4FH1mv8B3d/1xU9I5GT4Wq/dh9P2Rv7VcDPA79HKcLWUNZ1
-Pg34R/XcEfh8d9e2fCJ7MLOPAR619q1b1u4/ysyevn6+u794j8sd+/ddBW0RETmx3P13zOxfAj9M
-mUL2n7efQvmL+lPc/fwVNPVM4EHApwKfAHzitjYS8PXurtFskX24Rq/dADwZ+OTdukEJ389091dc
-ZhsiJ8m/AZ6+w/cN+Nh6TBzYK2hfypG/7ypoi4jIiebuP2dmH0IZIXsKZTuQHvgz4CeA73H3xV6X
-2EcbC+DTzeypwOcBHwo8BHgr8Oraxuuu5HmInDRX+bX7c5Rp4R8FfBjwcOA9KIHgncDvAq8Efqiu
-CRWR/dlvpf69zrsu3nfNXbsSiIiIiIiIiBwWFUMTEREREREROUQK2iIiIiIiIiKHSEFbRERERERE
-5BApaIuIiIiIiIgcIgVtERERERERkUOkoC0iIiIiIiJyiBS0RURERERERA6RgraIiIiIiIjIIVLQ
-vobMLNcjHeI1X7R23c89rOteZl+et9aXrz3KvoiIiIiIiBwVBe1rz6+z616O49QXERERERGRa0pB
-+9qzo+6AiIiIiIiIXD0K2jcGR6PIIiIiIiIix0Jz1B2QK+PuzwCecdT9EBERERERkUIj2iIiIiIi
-IiKHSEFbRERERERE5BApaB8xM/twM3uBmf2JmZ0zs3eY2evM7CvN7EH7+PlLbu+107ZbZrZhZs8y
-s18ws782s2V9/EN2ucYnmtmPmNlfmdnczN5iZq82s39rZptX9lsQERERERG5cWiN9hEys68D/iPl
-A4+pmNkm8IR6PMfM/oW7v3Yfl9tPMTSv7X4g8DLgg7b97AOuYWYR+H4uXgfuwMOBW4GPrf38rH20
-LyIiIiIicsNT0L72prD7RcDX1q/fBLwO6IHHAh9ez31P4OfN7Enu/sZDav8W4JXAewFz4NeBvwZu
-Ap64w/kvBZ7KVgi/D3gV8A7gkcAnAI8GXgH8zCH1UURERERE5LqloH10vpUSdJ/l7j+2/oCZfRTw
-48DtwM3AS8zs8e6eDqHdLwAi8JPAc9z9Hdvajmv3/zUXh+zvAr7C3Zdr5zwc+GHgycAXHkL/RERE
-RERErmtao300DGiBp28P2QDu/hvAHcCynvtY4F8fUtsR+AV3f+r2kF3bTgBmZsA3sBWyX+TuX7Ie
-suv5bwU+HXhjfU4iIiIiIiInmoL20XDg19z9Zbue4P6HwPesfevzD6Fdq7dfso9zP4UyvdwoI+9f
-vtuJ7r4AnlvP3c9acRERERERkRuWgvbReck+znlxvTXgCYdQ3duBN7r7n+7j3E9c+5lXuPvf73lh
-918G7mErzIuIiIiIiJxICtrX3hREf+NSJ7r77wHn6pcR2HHrrQP6zX2e92Fr9y/Z1+p1B+yLiIiI
-iIjIDUdB++j8zT7Pe/Pa/YceQrv37vO89bb229f9niciIiIiInLDUtA+Ohf2ed75tfsPOoR25/s8
-76a1+5fTVxERERERkRNJQfvonNrneafX7p+9Gh3Zxbm1+5fTVxERERERkRNJQfvoPHKf573n2v23
-X42O7GJ9ivl++/peV6MjIiIiIiIi1xMF7Wtv2v7qiZc60cwew9Z08QT87tXq1A5+e+3+JftafeTV
-6IiIiIiIiMj1REH76DxtH+c8vd46cLe773d99WF4Vb014FPN7CF7nWxmTwZuR/toi4iIiIjICaeg
-fTQM+AQz+6xdTzB7NPActoLrC65Fx9b8IvC39f4p4Ft2O9HMZsC3TV9e5X6JiIiIiIgcawraR8OB
-HnipmT11+4Nm9lHAK4EZJbj+PvDD17SD7hn4T1OXgGeZ2XfUUL3e11uBn6Xs8b28ln0UERERERE5
-jhS0j85/ADaBHzWzPzGzl5jZD5jZ64DXUAqLGaXS+NPdfbzWHXT3lwA/QflgwIAvBt5iZi8zs7vM
-7BXAXwJPBv4C+N5r3UcREREREZHjpjnqDpxABri7f5eZ3QJ8DfAo4P3Xzpmmi98D/Et3/51r3Md1
-/4qyj/a0XvzdgPUp7w78Yf3e51zbromIiIiIiBw/GtG+tnztwN2fB3w08CLgTcB54D7gN4GvBj7Y
-3V97gOte6pyDd9g9ufszKaPWP05Zt70E/g74deDfAx/h7n96Je2IiIiIiIjcKMxduUhERERERETk
-sGhEW0REREREROQQKWiLiIiIiIiIHCIFbREREREREZFDpKAtIiIiIiIicogUtEVEREREREQOkYK2
-iIiIiIiIyCFS0BYRERERERE5RAraIiIiIiIiIodIQVtERERERETkECloi4iIiIiIiBwiBW0RERER
-ERGRQ6SgLSIiIiIiInKImqPugIiIyPXOzM4DMyADbzvi7oiIiNzIHkYZMF66++mj7sxuzN2Pug/H
-wjO+5bUOUH4fO/9O7BDaecCV137/e/2XWG97z/P2eHB7/y86ddufg/3/qfCLbn2/P7nH817/M7m6
-v7qtz8PB6jN66dd90mH8pxERuWxmNgLxqPshIiJygiR3P7YDx8e2Y9feDgHxssPnHnZNzH6JBL32
-g3t8ODI9snX21r3D+1Dl6n44Y1z8PB4QxJmelT4kEpFjwwHMjEc84hFH3RcR2Ye+77n33nt56EMf
-Std1R90dEdmnt7zlLVOuOdZhQEF7m1Ww00j/ARz+72qngC0icoy9E3jYLbfcwpvf/Oaj7ouI7MNv
-/dZv8fjHP55XvvKVPO5xjzvq7ojIPj3sYQ/j3nvvhfLee2ypGNoDHEW8U6TciW27FRERERERuR5o
-RLuawtw0LflqRd/9rrXe8xpm+54Gfrnt7Tfcbl+Tvf+f2/1nLtVPBW8RERERETnONKJ9LJToaHbx
-UuzyPcO2f7N+f++rXXzv6tnexuG2OT3PrVsuuhURERERETluNKL9ANN49rVeJbwWj/cZIncN29d8
-Jvr239Xh/u7W/4tc9F2FbTkhzOzpwIsoL4X3cfe/OeIuYWbPA54HuLur2nb19re/ndtvv/2ouyEi
-+9D3PQB33HGHiqGJXCO33norb3jDG466G9eEgvYuzA6/HtrFudhqG1chFe+Sc8uUc3Z+8DIb2One
-np3Yz9W3T403w9xxs7J92fo8fxGRY8Tdueeee466GyJyALWokojIoVLQXnlgets+YrzvddEHmNd8
-kHP35RJ9LM0drM2dn/fW78t2+vYlunKpdebT72XPczSkLSeLo4+XrhPvedQdEJF96YF7gYcCGtEW
-ubrOAPmoO3FNKWjLIfEDT+XeGmGvP7+/Vkozihtygrj7i4EXH3U/ZD8M0PZeIteH3wIeD7wS0PZe
-IlfX7cDJmvGlYmhyeC4j/G4N6Bt7JXXlahERERERuV5oRPsADn2a97Hiu3+1ttzat08VX509fX9r
-xTZra9B3Whs+fct2Wc+9vQCawraIHGPpqDsgIgd1G6Wm421H3REROYAYVzVYj/V7r0a0t7uRs/QV
-cHPc1qeHT+E6E8hEMpFEQ6Il0ZFoGWl9IOYlMc8JaYGlBTbOYZxjwwLGBYzzervE0hJLPZYHgo8E
-TwTPBJwABDOMnbc8EznOzOyDzexrzOyVZva3ZrYws7Nm9qdm9kNm9pF7/OzTzSybWTKzR+7w+H+v
-j/9K/fr9zey767XP18ceWR97Uv06m9nHW/H5ZvYaM3uHmZ0zs98xs680s9kVPN/WzD7NzL7LzF5v
-Zu80s97M3m5mrzWz55nZe1ziGn9V+/mD9esPMLMXmNlf1t/f35nZy/f63W273vuZ2XeY2RvN7D4z
-u2Bmf25mLzKzx1/uc63qwjP9v0nk+nEb8HUoaItcX9aC9rFe9K0R7RvVFe2w5atr7HwJB/c62XsK
-wdNtCcRmDp5JJHJOpJxwd9wh1591t61bYzWP3CxACJhFCAEIOBG30iGfKsLr77NynTCzJwGvql+u
-v6xa4P2ARwGfa2bf5O5fcxlNrAqlmdlnAD8KbG57fKefmQGvAD5l2zmPBT4EeJqZfZK7v+0y+vQC
-4HN3aPvdgCcAHwH8OzP7p+7+P3a5xvrz+kzgR4CNtccfCnwm8Olm9r+5+0/u1hkzey7wjZTf+Xqf
-/gHwPpTf/ze4+/P29/REREREdqegfUOqKfuywvalQjZgjuGY+ypkx9XIthPNCTVojz6Q8ghpIE8h
-O3uZFu6GY5iXPbHNrGzlZREjYiGDN7UvofyNu4bt9WnpIteBBjgH/CwlcP8xcD/wMOCDgX8PvDfw
-lWb2p7X42eV4b+CHa1tfB/w6ZVrVE+r3tvsG4MMplYC+D/hb4L2ALwT+EfBo4L+a2RP94C+4CPw5
-8HLgbuBvgLH28ZOBZwLvAbzczB7j7m/f41ofAjwVeAvwX4DfpPwf7lOAr6SE7+83s19x93ds/2Ez
-+3Lg/6L83+N36nN9E3Af8AHAvwM+CviPZnavu3/3AZ+riIiIyEUUtCd1dNR8CnMP/Dvl1Z6ufDjB
-0bbdPvCRrW9stbfHCu2t3830/ZwgJzyP5HrrecR8LFPMg9MYQIacsDwSc8JTxlMip8yYEjk5KWVS
-zmWd9tROaLDY1NsWix2EGRY7LDQQ4tYhcn34beB2d79/h8d+ycy+G/g5Srh9npm95DKCrVFGZu8B
-nuju66U9797lZz4cuMvdv3BbX3/GzF4APKue82xKOD2Ir3X3v9zh+78F/JSZfS/wG5RR6S+iLJTc
-zeMoz+HJ7r7+gcHrzezPKR8u3Aw8DXj++g+a2aMpHyg48HXu/n9uu/ZvAz9mZi+pP/+NZvZSd3/X
-Pp+niIiIyAMoaD+AYbgKb21Txp7L4T7iqS+j1GOPpSVp7LHU48HxAITVGDQBxzyThgEfRsZxYOgH
-+qHcjuO49fv2GrSbErBD7AjtJqHdJDabhHaD0M6I7QxrtOelXB/c/Z2XeHyso66/Qxnx/YeUAHjg
-poCv2Bay9/JW4Et3eexLgM8AbqGMcB8oaO8Sstcf/wMze2Ft5zPZPWhPc3OeuS1kT9f5UTP7Fsoi
-y49jW9AGnkuZLv76HUL2ui8C/gVwE/DPgR/Yq/8iIiIie1HQ3kXZ4/mkxe3dnq9fFLTxEc89eVzg
-wwLv5zAs8GFeQnZ0CBADBIMQjGBgwxJf9qTlkn65ZLlYsFgsWC6X9Xdd1nBbaAhNGcEOzQbN7Caa
-2U3E2U00s9O0m6chGDGqlp9cn8ysAx5OCXXTH+T1P9AfyuUF7R542QHO/wl3X+z0gLufN7OfAJ4D
-fLCZPewy12oDYGYPAd6dMs17mmBzX739IDOL7r5T9VAHfs/d/2CPy/828AjgfXd47NPqNV6+V//c
-/V1m9nuUTXU/CgVtERERuQIK2pWtNqfaCpvXurL1enuHFfL3ego7TI5n/Tcwjeybb1UXNzI59TDO
-yf15cn+BtDxPXl4gLy+QIuRo5AhtNGKMWBMIIcC4hHGBD0vycs64uMAwn9PP57hncnbcM1gDscPC
-jNDMaDbmtJtL2mGgywmCYU1LaNpD+R2JXAtmdgr4YuB/pazL3mvtwy2X2cyb3L0/wPm7TSmfvJ4S
-tKEUSPtvB+mMmT2GMmJ+B3DrHqcGSpG03dZp//ElmppmDDxoW/uPpExNd+CbzeybL9Xnaq++ioiI
-iFySgrZU6yF7q4pa2dHLMU8ERoKP5PECuT9LWtzPuDjHMD/HuDjPuDhHFwNDE+iaSNdGuq7Fcwtt
-g+cl+EiwkRgSMSSaMNLEkTQm3BM5JbJbCeO2AOtoUy5rucdUiqc1LXG2Scwbuz8dkWPEzN6bUgTt
-H7D1GddOn6ZNH41t7vDYfvz9Ac+/1Aj1W9fuv/tBLmxmzwL+H8r7zKp6+E6n1tu9nvOFSzQ3be+x
-/cOLh63dP8inl6cOcO4OXXnYJc8qXVWdCREROSnOXPqMM2c4c+bS5/X9QcYUjo6Ctqz4alS//M3X
-vGzjFTyX/awZCD5gwxxfniMt3sVw4V0sL5ylv3CW5fwcsybStQ2pbchdh+cZ5jOCdXgawEeMRLBE
-MwXtMIINZB/xNJASpBwYcyDTMKbEmBIpJYgNcbZJN/Z4PtZ71Ius+2FKyM7ADwI/DvwRcK+7DwBW
-prRMf6gvdzrNQV8UV2V9jJl9ACVkR0pY/xbKBw1/BZydpoib2TPYmqJ9NaYQrSfZrwd23f5rm/NX
-1uy9V/bjIiIiJ9Bdd93FnXfeedTdODQK2gKsD7Gt7QnmELwE7uCJkAeCL2G8gPdnSYv7GOZ/z/L8
-/SzO38/83P2MXcvYdaS2I2/MgFNl9Do6OY/gqY5oj0QbaUKijSN5HDBfQurJQ2YYoB9hzIFxLCE7
-pURoO7pTDyIpaMt1oobOj6G8sL5xj32aDzRifEgefoDH9yzots3nUd5fRuDj3f1Nu5x3tZ/z+lZf
-g7v/4VVuDzPjllsuPfM/xkiMGtEWEZGT5dZbd1+d9exnP5vP+IzPuOQ17rjjDu699/h/qK2gfaNZ
-y8mXGh6yrQ27tq3OruG6rssOjEQf6tETc4/lJZaXkJaQ+7LFl6cyEp0SFkYYAr5Ykhz6MZPdyV5u
-xxGSNdBsED3Q0JNtRrYebxzrjDBC8obZ5s10p26m27yZjZsezGzzNE03I0T98ZXrwgev3f+JPc77
-8KvdkR08AfiRSzw++f0DXHd6zr+7R8iGq/+c/wJ4F2Xrr4+5ym0B8IhHPII3v/nN16IpERGRG8pt
-t93Gbbfddsnzuu762HlISeVGZBdtkX2pUy+aO1oKoJXq4iVkJyKJyEikBO3gS0Iu23qth2zHydkZ
-U8It4IwkX9KnzLwfwawUfLOplQaaQAwzmjDgYcTDgCWIOdBkw2mYnXows1M3Mzt1M91N78bs1E10
-3QZN8z/Zu/N42bKyvv+fZ+2hqs5whx6gu2lRUREVpxciDqighqAEojH6E4OBQBDUKFEwRpMIbV4Z
-XqKI0fijDSqiPyUGExOHxIA4J4EmKEpQInEItNee7nSmGvbez++PtatqV52qOnXOPdPt833z2l27
-qlattXZd6tZ96lmD/u8rN4Xm/1FXF5T7+qPuyAxfaWbf7u696Sfqxdu+ivhXxPvd/YFdr55veM1z
-r9fM7iRuH3Zk3L0ys18Gng88y8w+3t0/cJRtioiIiMDkdjLyKGGj/+yj/NRLrF5hPKGKQbbHrHbq
-/RhsVz2o4lBvqgF4iXtF6RVFWTEoSrqDgq1un42tLtc2t9nY7rK502enV9AvoCTF0k69bdc50vZ5
-ss4FWisXaa9epLN+KyvnbmP1/G2snb+NtQu3s7p+kfbKOlmrTZJmJFp5XE6/Zkb3RbMKmNnXE4PO
-495T8A7g++Y89/2MV/X64X3WO7zmjzOzz5p+0sw6wE8Tt/o6av+COHc9AG81s8fNK2hmwcy+xszu
-OoZ+iYiIyKOYUoIzTed5T6AHB91abDI9vTDenijqPloEbbhvdrAYZKce51OPDgrMC6jKOpNdv8JS
-CCme5HiS4UmKW6CygHkg8ZSkLmNJhqUZlmUkSQZpBVlFKCsqNyDBSbCQ01pZj0dnjaS1SshXCGmO
-aX6j3ATc/XfN7H3Ak4GXm9ktwE8Sl9+8G/ha4CuA3waezsH/8jnI694NfIOZPQF4A/Ah4COAbwCe
-VZd5D3DvPuv9SeCbiIuR/bKZvZZ4fV3icPFvAT4G+B3iNR8Zd3+fmb0KeB1xSPv7zOxHgHcQF2pr
-Exeq+2zgK4k/PjwZ+Iuj7JeIiIg8uinQlpGJIJsqDhm3uDJ4HDpeklISrAKDyuPq4E6KhxakCZa1
-SfI2Wd4hJBmEBCxgFhf+CUlCCAlpmo4y0kmaYRWEyonrmwWwFLMES1LS1gppq0PS6hDSNpbmEFI0
-IENuIl9L3IP6InE49lc1nnPgvcQgb+89LeY7yK9z/wh4JfBXiXtdNzlxZfTnuns1/cJF3P3dZvZq
-4DXAeeCfzaj7e4H3s3egfcOrkbv7D5jZJvB64nztb6uPXUWJPwZ0b7RNEREROdsUaC/LDPxks9xH
-q7E0mhMDbatIKUiJq4SnFCRWkVgMxt2h8kBlGR4CJBkhXyVtr5K110iyFoQEswRCSgiBYCHeJinJ
-MNhOMhIM9xD7YQkWEsxSQpJiaU5IM0IWA2wLKYQEP2jWX+SYuft7zezTgO8AvgS4C9gAPkjc6uuH
-3b2/x0iWRXtRL/P8LH3gS4GvA/428CQgB/4P8Bbg+2fN316mTXf/p2Z2H/AK4qJqq8R9u98JvMHd
-32FmL1yi38te18Jy7v6jZvafgJcRs/UfD1wAesD9wB8AbwN+zt33s8K6iIiIyC7mj+rgcXkvee07
-x1HmcAz11FvjBx7R2axjt2FTtrBks9ScDo5eNi5jc/tsM6opMa+AipweLevSsi4ZXRLvkvoOiXe5
-cuUKj1y+zCOXL7OxtUNvAN3C6BXQWjlHe/Uc7ZVzpHlnFBhbSDEzjLggWkgSkiQjpBlJErPXYRhc
-h4QQ6iA7JDGIt4CHgNvwimy07/dPfuczFXGLLMnMvoC4p7UDz3T33zzhLj0qmNmHgcc97nGP06rj
-IiIiR+juu+/m/vvvB7jf3e8+6f7Mo4z2NJ+6HTrCaduzq51ucLpjczpjNDLveyWJALfR/eGQcaMi
-sbI+4rDx4AVGDMJjoJyRZB3SVgp5SvCUrErrudTr5J110rwFltbDx5uBNjGoDsOh5AGzgFHfWoKF
-AJbE1cvNGgF2vEj9PCQiIiIiIqeVAu1lHXtkN6/BJTqy548C1igyzqUHvD4aW3pZvDUrMS+BCkLA
-0pwk65B5m8TaZKFFFdrk7TWyzip5e42QtcCS8VG3a/U2X2aGhVAv/GZ1L+I5Fupbw42pQFtERERE
-ROT0UqB9as3LaM+7P3zNrJc3c8HNYeXjYNsmFkGLGe3UyjgvmxIowUrwCgsxox2yFVILWLaKZWtY
-tkraWiVrr5C2VglpjlNnpgnjULq5GrrV/RvNroxzv5uTLb0etO80fxY4vMgtDwAAIABJREFUhBWS
-REREREREjoAC7X2YDuwOkmGdiH/nVBhjTx9No54s7FMbZfvk81PbezXD1fHd4cJuzbDbxwug1UH2
-cOh48BKnwt2pHLCUkAbSVkaWZoR8jZCvY/kaad4myTskeRsLWZxbXefJxwHysNXx4e7gMcgeDX33
-4az4uszuixMRERERETl1FGjvsvxk7OlQd69yTT4nTjTARsEwM+LnOQH2TI7XC6LtHng9vk5z4rzs
-UJFaQW4FWb3aeKAkUFK6425UVYCQELJA1jbc8zqjHY+QZFjIYiYbq2PmySB5nJ8eXsdkkD1/+TZr
-nIvIDTjICuUiIiIisiQF2rUbyVYvG/RNry6+qI1RnR7nKO9+zZxaxi9stOijYdnNQqPMeZ1zTojZ
-7CzUgbaXJF7Pza4D4dIDWEaS5WSWAW1IO6PDQhIXPyOMhoDHlr0O+5sazzYy2bN+Etj9A4hCbZGD
-cPffAJKT7oeIiIjIo5kC7dowWzrMp84O4w4Y3NXDwHEw86lwd3g2HsptTLXkNtozehwcz+6T25y5
-3Oaj4eLNp6wOxIPFedlZKMlDHWQTh43jVR1oG5UHPOSE0CFNV8A6kLTwpI0nLcY1NsNinxFEN87m
-bTE32rs8vj82OZY+Pi8iIiIiInLKKNA+JjbMKDcTzY3s83Ce9GTgPA67bWIY+fycuNVj0mNg7sS1
-xOu6hpV43GYLqxpzs8dzslMrCBSjPbWHueiqPtxSnAxChlmGh3S0SvhBxqOa2fxgu2miiIJsERER
-ERE5nRRoH4NZq2RPbHfdeGyczd49H3lYfs+ZynWM7aPgenpe9LCFcZCd1gF2aiVJKLGqxChxr6jc
-h7trU1nAyXDLwHKwjNF+1xjzZ1gvtmewrdmkIiIiIiJyk1CgfUysOT95zjpmuwNoq5cym1l8Vwuj
-6uvF1OLIa4/ZbavGc6Tr7Hrc09rHK42HccANJRUVTkXl1IF2oCKlCilYVq8qnhGnezYy57Oi4iXW
-mIvBNnsXFBEREREROcUUaB+b4cDw3Qt+NdffbqwFPvXs/toy4pzq0XBzr7PbPgz6nWBOFpw8OK36
-NgtOFmIW2ysfDRevSChJKS2lsozKUtxShntk+ygXv3u5tlkP7Dkte4/rG16liIiIiIjIaaNA+xg0
-19qePN+9NJhjmDW39preCmtX5TQz4OPbegExB6jA4l7VVs+2DjhpqMhTp5NAOzEyg9wgMyjcqEKg
-sECFUZJQWUpJFrPallIxDLIDk2H18lukLWvpedwiIiIiIiInTIH2sbCZWepZWe1RWTOgis/Ui5ZN
-lByNJ58OrutMrw+HnY/3qa6XMiMMB4EHp5U4nQzaqZFiZGakxAx4URpYoDKoSGNGmzxmtUmpLKlD
-9omB8UdMWWwRERERETndFGjvgzW2k9qdXJ21cRXszjIPz2dvdzURkBsMZ0c7zZx3o6rRlle7Nwaz
-Ufm4bjjmJF7VW3lVcch46nRyo5MaASNxSNwoS8NCqMNyqwPrjIqsHjpeZ7MtTF6nH3Y2uzGYXtt5
-iYiIiIjITUCB9kxHESw2gmDzRsgdM85OVd8fh8fm1WjV8HFNs7b/Gp+PWrJ6UHcCSWL1EQhekmAE
-N1YyZ60Fay1oJQZlAWUKVUoIQDA8BCozKnIqckpyKlK8HjY+ztYfjflzthV0i4iIiIjI6aRA+zjY
-5MloILk51HtVj3egHi6aNtqjK5YbmsheT4TWo3uhXugs4GSJkedGKzeyzEjcCF4S3OhkzmrLWMkh
-T4xqkFIOUsoixRIwC7gleDCqKqfyfJTR9tEiaNMOP+zW/GwREREREbmZKNCe6yiy2uPTUahsHjPX
-9XzscZDdXKK7+frJrb+mg2yAYE5iTrCKVhpYacFKO6GdhzrIhsShnRGHjedGFgL9UDCgoF8NCMEg
-VBAqKgtUIY/BNnlccZyksdL4qJNT16vgWEREREREzh4F2lMWTwNuLFk2VW5xwnWYuTbcK9wHeFVA
-Vd96gVeDRiWNBdGsGUQ35kFbwCxgBDCj+T+SCoJjwQlZRitpsd5OWesk9bDxQEJCnkArgzw1jAKv
-EopBPS/boSJQulN6qDPZKRUZXu+bvfvN8omHDpqEnpwLP78STdkWEREREZHTSIH2XPvJ0M7bP3qc
-7XUqcMfLgmrQpSx2KAddqqJPVfYoi94oGI8vrbPFdbA9DrTrM0sIlsRgu3EEC2QJZCl4CtZaoWWB
-tVaLi6spqcVcdGpOEuIRAlQlDPoBCJSVUVaBojIKh9LjHtqjudkWy01nsxX3ioiIiIiIKNDeh0Xp
-2VnP2WiIuFeOeYlXFRQ9iv4Wg+4m/e4m5WCHYrBD2d/BqxKwOlNrEGwqqz0eNh5CSggx2A4hGQXe
-IQSqLOBZgDzAqpOHFustuLiakoW4T3YaYr/dK5yK/sDZSRIgbutVVEZZGUUV4h7aw0DbUqZXN2++
-C8cXbB9vayIiIiIiIstSoD0ye1fr5V41K+DzcSzoBVUxwMsBRX+b/vY1etvX6G5fp+hvUfS2GfS3
-8bKIcfVoEncYB9vNQNuMZBhoh5Rgw/OEJKRUrYQqT/BBSrUGSdmhFdZZzaGVBvIk0EoClTtFVVJU
-cXXzNEnAEioPlG4UHig9UAwz2pbUw8ZnrTV+fDtpN9sUERERERE5bRRoLzSeM737sanHJ4o47mXM
-UHvJoLdFv7sdb3c26G4NA+1rVINuPZS8i1dlI5hmnM0229VUsAQbZrQbWe0kpLRaKa08o9VKWcv6
-XFuBa6sJaxlUnTah06aVtglJICUh4FQhkLUK0rzAsgEUxNXGzag84PU+W0Y5432IvXKWnzetlcRF
-Hn3M7NXAqwF39+SAdfw68PnAr7v7Fx5i9xa1ecP9Hrp06RJ33333UmXvuOMO3v3ud99IcyIiInJK
-KdAeGgWIdSZ6QYZ7uAHXOJs9GV06FV6VVGUfL/v0dq6zvXmFnc2rdLeu0tu6Ngq2vexDOcCrfj1H
-u1FPPYTcR0009t+u52MHCxhJfT9muFutjHYrp5VnrGUF11YC11cz1lsJwc/RSlNsxQhJShIMEsOT
-hGynIMkHhKwPfcdD3HisBCo3HMesGC1yNrz+YQbe9rk62X6CbQXmImfGTf1Br6qK+++//6S7ISIi
-IidMgfYs83as2hV8zwgsh9tfe0FV9imLHXo719jeeJiNKw+yvfEI3WGgvXWNQIl5gXk5CrS90QIG
-bs1H4hHMMEIMc0fBdsxut1steq2cdqvFWl5xfTXl2lrOuU5OK01Y63QAI0kTQppgaYInGVlrQJL3
-sawPaYUHpzSo6m3Hhqun28RvEYbXq5/7aH75Pt5qBdsistvwL7ub1OP2eP4ScUtHERERebRSoF0b
-BojezGYvChp9vA641y90KqhKyqJPv7vBoLtBv7vB5tUH2bz6IFvXHmRn8zKD7ibFziZVfxOnwqgw
-H/+7cpi39omts70Od+MR1ycf/i+uAm6EOIScEvcBVTVgY6PNlSsdVto5aRKovIx7dyfGyuoKeadN
-3m7VQbKRJAlZlpImAwIllAOqfkFVVVReUlUl1MPULSQQ0nhrVme0m1ugzX8DmwHzogB6+jlt6SXy
-6ObuzzzpPtyYAHx4jzJ3A8p6i4iIPJop0N6XOgCG8fZb9axq9xIv6wXPelt0Ny+zs3GZ7c3LbF9/
-mO3rD7Oz8Qj9netUgy4UXRIKwKeC7JildhsPyaYOYI2qLleNovDpUe5V5RRFSZxS7WxsbXL56hWS
-xKi8YFD2GZQDinLAufPnWTt3jvVz5whZBlVFGqCVJ2ShT/AeDHYouz2KYkBZDCiKASFrkWQtkrRN
-yHIgjwu3jd6j/SeilK0WEREREZFHCwXa+zC5wriNZmebQekllH2qQZdBd5Pu5hU2rj7IxpUH6G5e
-prd1me7WFcr+FuYl5iUJZWNKuOOERmAdYka4uRiaV1Dnsoevia+3+jYG7WVZ4lSUVcnm9hbJVaOs
-BvQGPQbFgKIsKMqC7qBP6U6SpbRYxb0kCdDOErLESbyPD7aoulsU/R79fo9Bv0faWiFrr2HtOIQ9
-ro5+4/9XmhdsKwgXEREREZGbiQLtBeaHdsMQu85EO3g5oBx0KXpb9Lev0d24zPa1h9i4/JcMdq7R
-37nGoHsNL3qkwQgBEgO3OLcZC7jFodhY3GbLQsAsTAXaJe4eA0/3uDd3nMg97lswCFCZ0S9KtrZ3
-cK8oyiImns2pqoLKK0ISyFstIFAOSoyKLDXSUGJVDwablN3rFN0ug94OvW4XLwcEM9I0gzSHKqtX
-JF8+l90cVj49jHziz0ABtshNy8zOA98KfAXwkUAfeC/wI+7+ljmv+XXmrDpuZh8J/Gl990Xu/mYz
-+xvA3wU+FXgM8FszXvc44DuBZwN3AZeBdwP/yt1/9RAuVURERGSCAu1pC6NFaxyAV3i9iFnR26K3
-dZXe9lW2rz9Cd+MRBttXKXvX8cEOwQckBiQJSQiEYIQQsJBiIYOQEpKckNZHksdAO8R5114vROZe
-4l7h7lRVFQPt0app8TbWbVhiZKmRp0aSGR4Sev0B165fj/PCQ5yTnaYpRVFSYpSEOHi96uGDbcre
-dYqdqwx6PQbdHoNejyTN8XZRb/Ud52ab7fnmicgZYmYfBbwdeALjvxhWgGcAzzCzLwO+xt2nVwVb
-ZiG0uJug2ZuBFywqb2afB/wCcK5R7g7grwHPNbPXLHVBIiIiIvugQHvf6qHcw2xyVeDVgKIbA+3t
-6w+xdf1huhuP0N+6QtndwMseVhWkBoSEkNT7XoeEJG2RZG1C2iLN26RZhyTvkObtUbmQxG1dKy/x
-elEyryqqMgbao6yvx+HsIQRCYlgIJFYQKAgMcCvp9gfYxnX6vS3MIEtT8izDK0iynCTPCVmOVX28
-2BoF2kVvQNHrM+gNyForeFlgOKHOuA/3/x7OJlfALXLm/VtiFvuHgZ8DrgGfAnw78ETgK4krgr1y
-xmuXWfbwW+r6fgN4A/C/gQvAR40qMfsIYpC9DpTAvVN9+YfAa4jZbREREZFDo0B7X5oZ7TrDXA6g
-7FH0NultXWXr2sNsXXuQ7tZl+ttXKbt19hgnGHWWOsWSFAspab5Cmq/W855Xydtr5O1V8vZq3Oc6
-SQhpCsQMdlXFlb+rcnhbDZdKH8W2IQSSJBBCwKseVbGFF1t4sUO336ff22KjGmBAnma0shyzQGd1
-hTardLIwymhX3esU3asUvYJBfZSddaqqiO9Icx756D1SkC1yxhnwGcDz3f1nG4+/x8z+HfDbxKHe
-32xmP+ru7z9AG58MvMndX7ygzOsYZ7L/1oK+fMYB2hcRERGZS4H20MTe2bOCxeFj9T7SZUHR36bo
-blL0Ntm6/ghb1x+J+2RvXqHf3aQcdHEvsDp7HUJCkmQkeZs0a5NkbbL2Gllrjay9StZarW9XyFor
-o2z2KKNdDTPaMZM9vN8MtIdrqRlGMCj7Wwx6MKgKKu9TlEBRQtlje3ub6xsbrF69SpJm9IsBg6qk
-wtne2aLX3aHfjyuOx6ngAUvzeCTZ6CAk+AH33dIcbJFHJQd+YSqwjU+4b5nZ1wHvJO6F9XLgm/dZ
-vwFXgG+aW8DsscCX7aMvIiIiIodGgfaUZohtE48Sn6m34vKyP1pdvLd5mc2rccj4zsZlutvXKAdd
-yqJfvzzB6vnXaWuFvLNGq7MWs9eddbJ2PE9bK3HoeBYD8eEcbULcOss9BtU+HLbu1eixcT7ZoXKs
-ihn3fkjqLcd2qDzBS8NLx4uSbq/H1tYW165dIyQJ/bJgUJUUOJtbW+x0d+j1+xRlRWUppHWGvbVC
-yNpY2oIkqxdwC4iINLxp3hPufp+Z/S/gk4AvPkDdw+B5a0GZZwJJXXbZvoiIiIgcCgXauywY+uyO
-eR3gFj0G3U12Nq6wdfVBtq49yNb1h9neuEx/5zruRVwoDY8Z7bRFyFdI2+u01i6ysnaBlfUL5O11
-8k48krxTL4jWIiRZY0h2c7g6jFc7r/fVdh8tTG4eg2gvCyhKzJ2it4PZBl4lVJVRlU41qOh1+2xt
-bZNn1yAYA68ocUqDza0ttrtd+oMBRVnhScCSnCS0Yz/zzjjQtmRGoK0h5CJn3H17PP8uYnD7RDNL
-3b3YZ/2/v8fzn3yAvoiIiIgcCgXateFiXo4P1zqbej5mlKtqAPWw8d72dXY2HmHjyoPsbDzMzuYV
-ulvXKAbbWBiu/p0QshZpa5WsvU5r9SKd9VtYOXcrq+duJe+sjTLaSdaeWIU89qfZg2Z/vN5Nu3Fb
-Z7irXp+y36Mq+qPAuigqBoOCalDGoyjp9fts72yTpjEjXdazyUuMzc0tut0e/UFB5cRV0bM2Sb5G
-0lrBsnbc2itkTKzEPjyz3X1uOviQ8UY7BxutLiLH48E9nn+gvjXgIvDQPuu/ssfztxygL4egIu4y
-tsjDh9eciIjIo8SlS5e4dOnSnuX6/f4x9ObGKdBeQgxkjaocUPS2KXrbdDevsHXtYTavPcTmtQfp
-bV+jv7NJUfSoqpIkyQlpRpLl5Cvn6azdQnv1FjprF+msXWRl/QLttQskWYd0mB0OGT7cN7vBR1ls
-aw5ir/sGlVdUZRGPok9/e7M+NtjZvFz/CPAwve2rUO7EoxrQ76f0egO6eZ8062JpSmWBgXsdaPcp
-KyBkJGmb0F4j6VwgbfwogBnuzf42FmZTICxylh31kJZyH2WPeXjNfn8zEBERkXvvvZd77rnnpLtx
-aBRozzXeqCrec6piwKC7Pdore+vaw2xefYjNqw8y6G1TDnYoiy4AqYW4ZVdrhdbqeTrrt7J2/jF1
-wH2e1so52ivnGouK5fVc7BBbnf5n4eTC4hMDs6uyoigGFIMeg94O2xtX2Ll+mZ3rl+luXaG/fYXe
-9hUG3eujrb6CDxikKf3+gG6vT5L0YpBdQb8o2N7u0+3Vi6eFjJB14sJtq+fJOuskeRuSNPZ11BkN
-FReRkccSt+9a9DzEvzj2yk4fRLPOZftyw8yM2267bc9ySZJwxx13HFazIiIiN72XvexlPO95z9uz
-3LOf/Wweeuj0/6itQHsOa0Syw2C7KgcMelvsbF5l6/rDbF17iK1rD7F59SGqsod7AV6QpBmMAu1V
-WivnWTl3G2sX72Bl/dZ6EbQ4ZNzrwNppznGeiqpH5+M0sdfZbTcoK2cwKOj3evR2ttjcuMLG1QfY
-vPwAve2rFN3rDLobVINt0uCjo99P6WUD0l6fEFIG7vQGJTu9Hv2B0+tWlJWDxWHjaXuNfPUCab4a
-F0MbLoJWLxAnItLwVBYHt0+tb//4APOzl/EHB+jLDbvrrrv48Ic/fFjViYiInBl33nknd955557l
-8jw/ht7cOAXatXlzob0q8arAq5L+zgY7m1fq4PpBtjev0NvZpBjsAB634gopebtDe+0CK+dupXPu
-FlbP387K+kXaK+fi6uL1omdYEtvz5hznYZA9a5L4MMs+vPX6qfE87bgqeYVXJZUXVFVBWZ+XVQnu
-VJVTmhOSgjQrSNICCwU5CW4lBKeqEkKak1uAdCUu2NZeJc07JFlOSNK4h/bEu+ZT4bbGjoucYS8E
-fn7WE2b2VODJxL/w3n5E7f8acXh5WLIvIiIiIodGezLVvA5X6x2osWE4WxaUgz6D3ja9retsX7/M
-5pUH4wJow/2yiwEAIcnI2qu0VmKQvXbxMZy/9S7WLz6GlfVbaK2cI2t1SNIcC2mc2+zjHgwPmzrC
-8LBx/tsYrjQ+7HXsuZlhoT4MLDgEr3cAdwqvGJQl/UFBb1DS6xf0egW9fkl/UDEYQFEYWE6SrdBa
-vUBn7SKtlfNkrbW4/VjSIoQktueT4bRCaxEh/lXwPDP7m7ueMFsF3lDfrYB7j6ID7v6XwH9coi/3
-oiE5IiIicsiU0R6ZXtXbRpnhctCj6A1XGb/MxtUH2bjyAP2dDfrdLcqiT5KmJGlG1lqltXqelXO3
-sn7hsZy/7U5anfO0OufJO+ukWQcswUlGSWsfNd+YE96Y9zzRs+GS6DZcpG24C5gR6t3AgsUR3Ran
-fI+S5hUOVbwmyhILMZudJCUhKSA4lkCSGVmakearpK01kvY6Sfscob1KyNuYxSHjw8z6ruz7ft51
-sxtYgVxETikH3g38jJk9A3grcB34FODbgY+vy/yQu79vzusPwyuBvwKsz+jLpwL/EPjYuq+HNnxc
-RERERIH2yPR63nGf6mrQZ9Ddor9znZ3NK+xsXmF74zI7W1cpBz3KqsBCIMlb5J01OmsXWD13K6vn
-bmP1/G2snr+dLF8lzVfJ8hUsyepMtlGN2vbG9OvGkHBr/FuzmfmenkBuhoVASGKwn2Yt8laHVmcV
-90FdX4UFqIq4OrkXRb3SeQu3DLccLIfQAmtj6Qppa428c460cw7L1yDvxEXb6oEQ1li0bXrQ+L7e
-eQXbIo9GXwX8KvD1wDdMPefEgPeVc157KINj3P3Pzex5xMz2et2PZl8cuKduT4G2iIiIHBoF2rN4
-BVR42WfQ26S7dYXt6w+zff0RdrauxuHigz6YkWUtsrzF6votrF24nbULt7F24TGsnb+d9upFstY6
-SdbCkjxu3TUc+G1g3sxmN5qP+fTJXPbodwCr71p96pglhCQjzWJ9K+t9gkGWZfR2ztHf2aDX3WTQ
-26Ic9KmKAeVgQJ5ltPMOrbxNu7VCq92h1Vkhb6+QddZJ2+dIWuuEfBXq7cdGKfLYNJMdVLAtInEO
-TB3kPgV4FfDlwEcCA+C9wL3u/pa96jjAc7sLu/+GmX0S8B3AlwJ3Elckvw/4QXd/u5m9er/1ioiI
-iCyiQHtktLZ4DLS9oKoD7Z2tK2xee4CtjYfpbl2j392iKPqkeYs0a5HmOSvnbmH94u2cu+UO1i48
-hvbaRTprF8jbaxBSzFLcwkTw7AtyNj4vobPrYYMQCGmGhQRLEoI5WZrR7qzQ727S726OAu1i0ItD
-4fs9siQlT1vkWU6etclbHfJ2h7zVIW2vxf2yW6uEbAVPWnjIcML0cnE0g+wb+Veqgm2Rm5u730PM
-EA/vXwP+SX0sW8czFzz350BygH7dD/y9Bc9P9FtERETkRinQnuaAl7gXVGUvZrQ3L7N59QG2rz/M
-ztZVesNAu9Umzdu0V9ZZPXcL6xcew4Xb7orDxTvr5O31uIWXGxVGtSiyPkA3R0IghDhnOvWcPMuo
-Oqt4cZFBfzsG2r0YaA/6XYr6CBbIQkqaZKRJTp63yVptsrxN2lohaa2StFawtE1lKW4JcZ9vsLlb
-etVBty1xrcP55hMPKdgWEREREZGbmwLt2nD59cpLqnJAVXTpd7fodbfodrfo7mxRlAUhzWivnqe1
-ss7K+gVW1y+wsnaB9YuPYfX8bbRXz5O3V0myNhYy3OssdnOM+OTNlGWC8dkBrhMDVSyNi6GlgQTI
-MEhSkqxNNuhTFj3Kok8gkISExFKSJCVNc9I0I81yQtbC0pjFxpJ62HsYTyWfGCnuoz6Mn1xstDXY
-VGCtIFtERERERG52CrRrowSsl5RFP64yXgfYw2C7rCqSvE0nzUmzjLVzt7J2/hbWzt1CZ/0WVupt
-sNJ8tV5obBhoH/HEPx/+p97sywKEFAiE1EgtwdKcNO/EhdDKgqoq40ZmlhAIhJAQQiBJEkKSYEkG
-SQYhpbLhpmLNBptH43086msVERERERE55RRo10ZxdlVSFj0G/Z16bnMMtrvdbcwCWd4iy9t0VtZZ
-v3Ab5y7ezrkLt9XDxFfJ22uErA2W4qRTw8V9uYT1Pk0GwAaEep+vJO53nWQk3sZH88+9XvBtvGc4
-2HhrMIjzyS3gIaG5e/euN813nR78OpTNFhERERGRRwEF2rVRvtYdryqqsqAsyrhltSWEJCfLW7RX
-1uOc7LXzrF+4jfXzt7F24VaSrEOStgjDIeME3BsrdNdriR9OvnePeixuHxaHkScEUrBGTtqGIXO9
-wfZoaHu9Ddhosy5rbjY2rn9U1uPS6RO9OoJfEkRERERERG4iCrSn2CjLmxKSFu32Omvrt0MFWatN
-ezUG2p3Vc3RWz9FaPU+SrxGSPA61tnQqyJ7KaO/ar/vgPR3WE7fVHg/tHm8A1mhyah+xUfBcB9rD
-LcXic16H0TZRI1Ov3n0No/D9QFe3r4XQFM+LiIiIiMgppUB7pBHgWYJZShJatFrnYB2yJCdrd2Kg
-vbZOq7NG1loha62Q5B3MkrhoGMNFw5qB9mEG2NPGK6uZTzwyPq+Hr8dY2xv7ihnu9VDzUc8CwyHu
-k/OyhyWmg+zpazuaCHgiCFeQLSIiIiIip5gC7SmjjLalJEmLVvscWZLT6ayTd1Zor67TWlsnb69i
-SYqFDEuyGIJ6vVvVcDj2VDbbplrCZs9LthlbYy0sNxz1PifYnnjEmyHyMMhuZq7HWe2pHsw4nw62
-x60eRVZ71/ti7NoeTERERERE5KQp0K5ZHbBZCCRpRpa341TnLKMqW3g5IG21ydodkrQdV/W2BCyM
-Y71d210NQ8/5mdhZQfXM/i1RbmLbrVHmenoO9XSOvaoD7smM++S94UDyydtpfgQZ7T2Hky/5/omI
-iIiIiBwXBdpDw1jYAmmaYThJkuBe4lUJVUlIM0KWEdIcC2kMspnMKk/Pgh6fHfkmXxOGm31NPjJ7
-IPvEjwETV+CN5+Yd7GrpsO1r7raIiIiIiMgJU6A9JYSApSkhBDyr6jnOwxW2A4TGQT38em7CupEd
-tt2DsY+G705dT2kOEB8H0s1w26aei7e+K8BmooSIiIiIiIgo0G5oBMUhYMGAZDx72awRZs5YJKy5
-ltrMmmcsh3bEEepwofH5g6tnzPue+bxPhuI2fmzRqw96fc1h8osy2Ro0LiIiIiIip5EC7WnDPbE8
-ZnDdhrtfN3PA47Bz1wrfi6qsJ1Ef1m7aCzUaWa6tWSujN5dNiz8xzJ7xLSIiIiIiIkMKtEeawaXj
-Nh4qPdoNqxFc2pzodVbYOb3eeLOmozLMZo9aW5BGb87Knjf8fXh2j1JeAAAgAElEQVS2e3m14a0C
-bhEREREREVCgPTZK5taZ7DrYnuR1PnrWy+dvbWX1smITW1gzIzTdvR7Z7udm9Xmqh3M6ONm7Rn3D
-RcpHqfeZjU8OmN8dbM/qkGZvi4iIiIjI2aNAe0qdr2YUjNr04/Oz2c065pkOtme+YNn4dDKaH93M
-GgQ+r86J2HpGfc07w8HzPje4PthQcq0qLiIiIiIijyYKtHdpDA+H0dzq4YxsmxyTPXx0fy0cRUw5
-o87ZYa8vcXfewHZrPD/LwYePK9gWEREREZFHCwXaM00uWDYaVj0jyIZhjvf0zFFeuic+83TXvfjI
-dK2n53pFREREREROEwXaC0wEz7tiT1t4d4IfX1g6d/T38NmJ4HrOvHKbulybXatRbzE+R3ObrkWU
-yRYRERERkUeTcNIdkGN0yuPZZQNzERERERGR00yB9lxnL+iz0X9ERERERETkoBRoL2QLjtNpPz1e
-eDUTTx7f+6CstsjNz8xeaGaVmZVm9viT7s9xunTpEnfffTd33303n/EZn3HS3REREZETojna0+Yt
-uC3HQvO1ReRmVlUV999//0l3Q0RERE6YMtoypmSyiMgh0FeriIjIWad/DRyLmyCCPQVdVDZbRG5+
-AbjzpDshIiIiJ0xDx4eGgaYDZjP3rZqcPjwdmc7dlLpR9Gj2ol42QJ2e/zz3Vbbw2cV1KlgWERER
-EZEzThntKXYaUrsnwObcHmsftBCaiNy86u9T/dgocrO4dOkSr3nNa7h06dJJd0VE9qEsy+HpqY5l
-T3XnTkpcaHsy6LvxGPAmCSJt5qmIyIiZXTCzf2lmf2hm22b2gJm9zcz+5j7qaJnZ3zOzt5vZJTPr
-Nep5sZklR1mHmf1ZvTL6j9X3n2JmbzKzPzGzrplVy15Lbc/+isjpcunSJe655x4F2iI3mUagfaq/
-ezV0vNYcOT68741I09kr270gQj3iBEfzR4AbGbk9XHC9ufD6fhdhX74vs580s6WHnytvJHL8zOwT
-gLcTJyIPP4Yt4AuBLzKzHwd+c486PhX4j8Djmfwo3zasB3iZmT3X3R88ojp8+Dozexnwg0x+Ye83
-0BYREREZUaA9x+EHccMQVhaZDLIVSoucJma2DvwKcAfxA/oW4M3Ag8ATgW8FXgQ8eUEdHwv8OnAO
-uAb8EHAf8CHgVuB5wMuApwI/b2af5+7lYdfR8JnA1wJ/Dnwv8D+J342ft8x7IiIiIjKLAu1dfOr2
-7PDhf07s94B5QfbZ+7MQOaW+C7ib+KH8Dnf/nsZzv2tmbwV+CXjWgjp+AjhPDGif5e5Xpp5/u5n9
-Ul3P04iB+48eQR1Dnwi8F/gCd7/eePy/L7gGERERkYU0R/tYnd6A8eR75o2h5iffGxGZZGYZ8GLi
-B/T3p4JsAOqs8UuAwZw6ng58dl3HC2cEyMN6fgV4K/Fnvxcddh3N6up6vnEqyBYRERG5IQq0a+PJ
-evNy2jYqc5AwML5uso79HEszJjLSy9Q5Uf+MHcgOuhCcsas7c8r57LY5jEXoROSQPAW4WJ//xLxC
-7n4/8F/nPP28+vYD7v7+PdobzvN+qpk1v6sOo46mD7n7f9ujHhEREZF90dDxkcZSYOZ4vRLaoYyk
-ttF/ji9Zu0ynm33ZY4tvG/4AMaf/B32Pprvgc1Zf27W1uQJwkeP2yY3z+/Yo+y7gOTMe/4z69kn7
-WNU7A24BHj7EOoYc+P0l6xARERFZmgLtoToYHs9T9lHAd+PB9uSK5LNi1WONG4fXNavRBR2ZXol9
-OhG915rs09c9WuHcJt7uXUvA71qJXEG2yEm4pXE+cyXwhgfmPP4Y9v9zowMrh1xH08yh5wfnwF8A
-0O/3ec973jOz1J133smdd955uE2LiIjcxC5durTUdnuDwcwZaqeOAu2h4Rhl9/pfcLZrCPaSm3vt
-MjlMe3bJ/QTzu3cPW7L15p5de7XRGLPtjTpsup5dwXbzR4Xx66avz30q+LZm/eM7zQB71zByjSsX
-OSkHHZsz3D7rvcAL9vG6+w+5jqZ5q5HfgPj2PPTQQzzlKU+ZWeLVr341r3nNaw6/aRERkZvUvffe
-yz333HPS3Tg0CrTl0CjsFXlUa2Z+Hwt8cEHZx855/BHiXxVrS8yvnucw6jgKo4z/8IfKJEm4ePHi
-zML33nsvb3zjG4+nZyIyU7/fB+DZz342eZ6fcG9EpCxLbr/99j3LPfTQQ8PTWxaVO2kKtGv/+hVP
-VZwoIjLfHzTOnwr8zoKyT53z+O8CnwM8wcwe4+57DUE/qjqOwug7ZDgSpyiK5j8GROSU0udU5KZ1
-quM3BdoiIrKM/0nMal8AvhZ4/axCZvY45u+j/Z+AbyR+Mb4C+EcH6Mdh1HEUekALqNh7DruIiIgc
-3GOIu2f1TrojiyjQFhGRPbl738x+HPhW4NPM7FXu/r3NMmaWAP+GuMr3rDreZmbvAj4T+DYz+113
-f+u8Ns3sycBHufsvHmYdR8HdV4+yfhEREbm52MRqziIiInOY2TngfcDd9UM/A7yZmMF9IvBK4n7b
-7yYOH3fgo939/zbqeALwTuK8KgN+Afi3wB8TFyZ7DPDpxP2ynwZ8r7v/g6l+HEYdfwo8HvgJd3/x
-jb0zIiIiIpOU0RYRkaW4+3UzezbwNuAO4Pn1MSoC/DjwW/XtrDr+xMw+G/g54MnAXwOeO6tofVw7
-ijpqp3pul4iIiNy8FGiLiMjS3P39ZvZJwLcDX07MCm8QF0v7EXf/WTN7IeMgd1YdHzSzTwO+CvgK
-Yvb7duLWXY8AHwB+G/gP7v57R1XHoj6KiIiI3AgNHRcRERERERE5ROGkOyAiIiIiIiLyaKJAW0RE
-REREROQQKdAWEREREREROUQKtEVEREREREQOkQJtERERERERkUOkQFtERM48M3u8mX2fmf2hmW2a
-2SNm9i4ze5WZdQ6xneeb2a+Y2SUz2zGzPzOznzSzzzqsNkTOkqP87JrZq82sWvL4/MO6JpFHKzO7
-3cyeY2b3mNkvm9lDjc/Qjx1Rmyf2vavtvURE5Ewzs+cCPwmcY/e+2gb8b+A57v5/bqCNNvBzwJfM
-aaMCvtvdv/ugbYicNUf92TWzVwOvnlH3NAe+0N1/8yDtiJwVZlZNPdT8bP2Eu7/4ENs68e9dZbRF
-ROTMMrNPB94CrAMbwHcCnwN8EfBviF/OHwf8opmt3kBTP874y/4dwJcBnwm8BPgg8fv41Wb2d2+g
-DZEz4xg/u0NPBj55zvEpwH2H0IbIWeD18efAfyUGvUfhxL93ldEWEZEzy8x+E3g6MAA+z93fNfX8
-K4HXEr+o7znIL99m9oXA2+s6/hPwN7zx5WtmtwL/E3g8cAV4grtfO9gViZwNx/TZHWW03T258V6L
-nG31Z+o+4D53f8jMPhL4U+Ln9NAy2qfle1cZbREROZPM7KnEf6g78Mbpf6jXXgf8IfEX91eY2UH+
-sf3K+rYAvtGnfuF290eAb6/vXgCU1RZZ4Bg/uyJyiNz9Hnf/ZXd/6IibOhXfuwq0RUTkrPqyxvmb
-ZhWov5zfXN+9ADxzPw2Y2RpxKKsDb3f3v5hT9N8D1+vzL99PGyJn0JF/dkXk5nSavncVaIuIyFn1
-9Pp2iziEbJ7faJx/7j7beCqQz6hngrsPgP9BzL49Vdk3kYWO47MrIjenU/O9q0BbRETOqk8g/uL9
-QXefXgm16Y+mXrMfnzinnkXtpMRFnERktuP47E6otwd6wMx69e2vmdm3m9mFG6lXRA7dqfneVaAt
-IiJnjpm1gNvqux9eVNbdrxIzZwAfsc+m7m6cL2wH+FDjfL/tiJwJx/jZnfbFdbtpffv5wL8A/sTM
-nneDdYvI4Tk137vpYVcoIiJyE1hvnG8uUX4LWAHWjrCdrcb5ftsROSuO67M79PvAzwPvAv4CyICP
-B/4W8Czi/O+3mtlz3f1XDtiGiByeU/O9q0BbRETOonbjvL9E+R5xHlfnCNvpNc73247IWXFcn12A
-73f3e2Y8fh/wU2b2dcAbgAR4o5l9jLsv0ycROTqn5ntXQ8dFROQs6jbO87mlxlrEOaE7R9hOq3G+
-33ZEzorj+uzi7tf3eP5HgB8lBvJ3AV+x3zZE5NCdmu9dBdoiInIWbTTOlxkutlrfLjNU9aDtrDbO
-99uOyFlxXJ/dZd3bOP+CI2pDRJZ3ar53FWiLiMiZ4+494JH67t2LytarCg+/jD+0qOwMzYVYFrbD
-5EIs+21H5Ew4xs/ust7fOH/cEbUhIss7Nd+7CrRFROSsej9xyOfHmtmi78MnNc7/8ABtzKpnUTsF
-8Mf7bEfkLDmOz+6y/IjqFZGDOTXfuwq0RUTkrPrt+nYVeMqCcs3hoL+zzzbuY7wYy9xhpWaWAZ9F
-/Ef7fe5e7rMdkbPkOD67y2ru2fsXR9SGiCzv1HzvKtAWEZGz6ucb539nVgEzM+Bv13evAr+2nwbc
-fRP4VWL27YvN7K45Rb8COFef//v9tCFyBh35Z3cfXt44/40jakNElnSavncVaIuIyJnk7vcBv0X8
-Mn6JmT1tRrFXAZ9A/MX79dO/eJvZC82sqo/vmtPU99a3KfCvp4e6mtltwL+s714lrmIsInMcx2fX
-zJ5sZh+zqB/19l4vqe/+JfAf9n81IrIfN9P3rvbRFhGRs+wVxCGlHeBtZvbPiZmvDvB84KV1uQ8A
-r1tQz9x5mu7+a2b2FuCrgb9et/N64jDTTwG+E3h8Xcc/cPdrN3RFImfDUX92n0LcG/vXgP8M/AFx
-EbaUOK/zBcBfqcsWwEvdXdvyiSxgZp8LfGzjodsa5x9rZi9slnf3n1hQ3an/3lWgLSIiZ5a7/56Z
-fRXwU8QhZP98ugjxH+rPcfetG2jqxcA68KXAM4BnTrVRAt/t7spmiyzhmD67Afgi4IvndYMYfL/Y
-3X/5gG2InCV/F3jhjMcNeHp9DDmwKNDey4l/7yrQFhGRM83df8nMPoWYIXsOcTuQPvBB4GeBf+3u
-3UVVLNFGF3iumX018CLgU4ELwAPAb9ZtvPNGrkPkrDniz+4vEYeFfzbw6cBjgVuJAcFl4L3AfwHe
-VM8JFZHlLLtS/6JyN8X3rrlrVwIRERERERGRw6LF0EREREREREQOkQJtERERERERkUOkQFtERERE
-RETkECnQFhERERERETlECrRFREREREREDpECbREREREREZFDpEBbRERERERE5BAp0BYRERERERE5
-RAq0RURERERERA6RAm05EWb2Z2ZW1cfjD6nOVzfq/K45Zb6gUeYdh9GuiIiIiIhIU3rSHZAzy+vj
-qOo+jDIiIiIiIiL7poz2Amb2wkb288dOuj+PQnbSHRARERERETlsCrSXo+znzUN/ViIiIiIicqI0
-dFweNdz9HuCek+6HiIiIiIicbcpoi4iIiIiIiBwiBdqLaQ6xiIiIiIiI7MuJBtpm9ngze7mZ/bSZ
-/YGZXTWzvpk9bGa/b2Y/bGZPW7KuX28sXPb5S5SfuxWUmb3JzCpguACaAS9qlK+W2SLKzFbN7JvN
-7L+Y2YfMbMfMLtfX+oNm9plLXtuwrbLx2KfW788fmdlGffwPM/t6M0tm1PEUM/txM3u/mW3W7/E7
-zOxrlulDo57UzP6Omf2HeouubTO7VvfjjWb2xfupb6rujzez7zez/1XXec3M3mtm/9TMHrvE6/fc
-3uuA/brbzP6xmf2mmd1vZl0ze8TM3mNmrzWzjzustkRERERE5OZ3YnO0zey1wLcyzho3F7G6CNwC
-PBl4uZm9BXiJu+8sqNKnbpc1q3xz66lZ/duTmf014EeAO6ZenwMXgE8CvtHMfhp46R7XNtG+mf0D
-4J8ByVS/PrM+nmtmf93dB2YWgB8CXj5VTwd4BvAMM3sO8AJ3X3iN9Y8e/x/whKm6WsAT6+PFZvY2
-4Gvc/ZE9rqlZ90uBf1XX1ezHJ9fHN5jZi9z9F5ao7lAWRDMzI875fhXQnqr7AvH/p58GvMLMvsfd
-//FhtCsiIiIiIje3k1wM7e76tgI+UB+PAAPgVuDTgY+py3w1sA4895j69jZgA3gS8MXE4OqPgF+d
-UfaPpx8ws/8H+CniiAEHSuC3gQ8Ca8DnAXfVxb8G+Cgz+0J37+/VMTP7OuBf1vW+F/i9uv6nAZ9Y
-F/urxKD164H/F3hpXeY+4A/rfn0e8NF1+a+u6/qeBe1+PvDLxAB9+EPEu4D3E388+CzGf15/Bfht
-M3v6ksH2lwGvr+v8MPG92iQG7p9b9/ci8O/M7Lnu/rYl6rwh9Q8UPwv8DcbXez/xmh8i/jk+jXjN
-KfCdZnabu798do0iIiIiInJWnGSg/W7gPwO/6O6XZxUws88lDt/+OOBLzexr3P2nj7pjdRs/bWYv
-JAbaAO9092/e67Vm9gTg3zAelv9O4G+5+59Olfv7wGvrcp9NDHL//hLd+wHgEvDV7v5bU3V+K/C9
-xKDwxWb2AWKQ/b+A57v7+xplrS77LfVD/8jMfnBWZt3MLhAz2R1ihv8DdX2/N1Xu+fW1d4hB8o8S
-g+h5htnh7yH+EPAqd/+BqTqfRAx4nwxkwJvM7BPd/dqCeg/DaxgH2X8JfIO7/8fpQmb2FcRrvgC8
-1Mze7u5vPeK+iYiIiIjIKXZic7Td/fvc/c3zguy6zO8AzwK69UPfdCyduzGvJmY7jZjB/qvTQTaA
-u78e+La6nBGHkX/kHnUbcQTAF00H2XWdrwPeXpdLgdcBDwDPaAbZdVmv2/9A/dAa8Jw57X4L8Li6
-3st1+783XcjdfwZ4QeOanmtmT1/imjLgO6aD7LrOPyJmyB+uy97B+MeBI1H/OXwHMci+DHzurCC7
-7t/PEQPyodccZd9EREREROT0O/Wrjrv7nwO/RgyynmpmayfcpbnM7DzwVfVdB77N3TcWvOQHiNlm
-iH8WX7dHEw68oQ4+5/mZYXfq8v9s3vBtd6+I2eKheYuzvbTR/ne7+1/M7aD7zxNHKgx9/YK+Dv0p
-8UeBeXU+AHx3fdeAlyxR5434+8T57wD3uPufLSrs7r8O/Aqxb59gZp92pL0TEREREZFT7SSHjo+Y
-2UcQg7wnEofgDocoDw3nEhvwqcDvHGsHl/c5xMW8IGZgf3FRYXd3M/sx4Pvqh565RBs/t8fzf7DP
-8s1M90dPP2lmn8B4QbcS+Mk96gN4I/AlxD+vZywoN/wx4KfroH+RnwK+nxgA32VmT3T3/71EXw7i
-SxrnPzO31KR3EOfGAzydOHdeRERERETOoBMNtM3ss4kLez2d5fesvu3oenTDPr2+deBdSwSPMP7R
-wBqvn2UYlL5vQRmAK43za+5+aY/yzaH752Y837ymD7j7lRllpjV/CLnDzO5w979cUP6/71Whu1+t
-55wPF3z7dODQA20zu4X4g48DfeA1cTr7nj6xcf4Rh90vERERERG5eZzk9l4vJi4iNQwg99qSaRjt
-rB9lv27Q7Y3zP1/yNX/WOM/NbM3dN+cVdvfre9RXDIsCyywYVjTOsxnP7/ua3P1BM+sy3hLrNuKC
-YvP832XqrcsNA9rbFxW8AXc2zlvAN+7z9UZcIV1ERERERM6oE5mjXQ9HfkN914nzlF9BHD7+WKDj
-7snwAN7cePlpnlfenD++teRrpssd5g8Jh7Gf9EGuabrsXte0fQR1HtT5xrkf8EgQEREREZEz66Qy
-2t9St+3AfwH+ursXC8ofRVB1FAF7MxO9uuRrpsstWjztJBzkmqbL7nVNK0dQ50ENg3kjDr1XdlpE
-RERERPblpLLDX9g4/8d7BNkAe217BTBonC/zA8L5vYvs20ON88cv+ZqPapz3Fw0bPyH7viYzu53x
-sHGIC8Mtsux71Zz7vFedB/VA4/ycmbXnlhQREREREZnhpALtuxrnCxf3MrNzwKew9zDo5tzlW5fo
-wycvUWa/Q69/t7414DNtuVW0PqfR1u8uKnhCmtf0JDO7sMRrPrdx/pd7LIQG8Fl7VVhvnfakxkPv
-WaIf+1b39UONhz5nXlkREREREZFZTirQbq7Gvdew4ZcSF+naK2j9s8b5wn2MzexO4krnewXS3cb5
-rIXCpv03oFef3w48Z49+GPB3Gg+9Y4k2jpW7/yHjhcwS4AVLvGy4z7UT90CfWz3xz/X5S/wo8QLG
-c58vHeHWXjC5Lds3HGE7IiIiIiLyKHRSgfafNM6fN6+QmX0c8F0sl1l+Z+P8+Wa2KDB+PcsF7480
-zh+3Vwfc/RrwbxsPvdbMFs1r/ibGmfUK+JG92jghw34Z8F31DxUzmdnzmPyB4Q3zyjZ8DHHe/rw6
-Hwv8E8aLjb1xiTpvxPcR9ww34MvN7IXLvrDuq4iIiIiInGEnFWj/QuP8dWb2rOkCZvZFxGzoGsut
-dv2LxAWyjDin+43T82vN7KKZvRn4Siaz1fM0h7U/zczuXuI1301cQMyI+zH/VzP76Kl+mJm9ghjQ
-QQwef8jdl93m6ri9Hri/Pr8VeMf/z967R1u25XV9n9+cc639OK+qU6fq1u2+gBDAQZqANE2QEAUV
-FBNIGBAeQ4UGhAYSDWYIKiEO4vCFEAEfaDAiTxUdiUSibTeP8BQQmwYSBMEICP2g+166763HOXvv
-tebvlz9+c+19bnW9urtuV13q97lj3rXP2WvPNdc6596q7/z+HiLyobeeJCKfCfwDdoL4u83sR+8x
-99Sv+qtF5I/f6my3CvXfi0cICJ5D/fXvxr3cEzP7ZeAvTEsA/p6IfI2I3DYlQUSyiHy8iHw7j2b4
-fxAEQRAEQRAE70EeVtXxrwc+HxdPl4DXiMjrgZ/HhdfLgZe1168F3gp89t0mNLMzEfnzwFfj4uiz
-gD8gIj+A52+/F/C78VD1/7fN+6X3mPMtIvJjeJ7uAvh/ROQ1wJvZhb//ezP7X8995pdF5POB78BD
-nT8K+EUR+RHg3+MbB7+LnUNuwI8Df/pua3mYmNmzIvKHgFfjz++3A68XkX+F/8x6PM/6/aePAL+E
-/4zvxtRD/U8Bf62NLxORH8U3Kz4QD/GfNoQG4HPN7NkHdGt3xMz+nIi8DzC52X8S+OMi8jr853gK
-HOLF7D6EXUX0F6pIWxAEQRAEQRAELxIeitA2s6dF5L8G/ilw0r798jZg54h+F57D/Nfvc+q/CnwA
-O4F3BfiM85fG86g/DXjVfc75JcD34y3GjoDPvOX9H+SW8Ggz+8cicgMPcX4CF9y/p41pHVM4/D8A
-vsDMNve5nvvlfgqx3ff5ZvYjLcrg7wPv1779O3l+IbPpnr4X+MNmdj70/m78U9zV/np8A+L8M56e
-1bO4yH7tfc55L+75fMzs80Tkp4A/B1zE0w3+M96xQNr5Htr3cvCDIAiCIAiCIPgtzsNytDGznxCR
-lwF/AvgkduLtzcBPAd9hZv8coEUTnxend5rTgC8Uke/ChfRH4o75bwK/AHx7m7eem/Ne6/wpEfkQ
-PJ/697R17rMrzHXbOczs1SLy/sDnAZ+IO/QnwBnwJjws/tvM7F/faw3nrnG/VdBfkPPN7CdbKPcf
-AT4ZLzp3BXeafwMXmf/QzL7vnbiutbm/UUR+GPgi4OOAKUz/V4HvxkPr33K7Se5wL/dzzv38/L9B
-RL4Fj5D4eOBD8UiMOZ6q8Abg3+AbLq82szfefqYgCIIgCIIgCB4XxLVpEARBEARBEARBEAQPgodV
-DC0IgiAIgiAIgiAIfksSQjsIgiAIgiAIgiAIHiAhtIMgCIIgCIIgCILgARJCOwiCIAiCIAiCIAge
-ICG0gyAIgiAIgiAIguABEkI7CIIgCIIgCIIgCB4gIbSDIAiCIAiCIAiC4AESQjsIgiAIgiAIgiAI
-HiAhtIMgCIIgCIIgCILgARJCOwiCIAiCIAiCIAgeICG0gyAIgiAIgiAIguABEkI7CIIgCIIgCIIg
-CB4g5WEvIAiCIAhe7IjITWAGKPDWh7ycIAiCIPitzBXcMF6b2d7DXsydEDN72Gt4JPiaV3yEAaxM
-WWGcoWwAKQnJPlbjyNlm4HQYuDkMPDtUnh0rzw2V3HV0fU/X9cy6wl6BvSIsCxx1cFTgsMA8GQkj
-mSGmPHNjzdM3VjxzY821jXKqmVNLrCyTSiZ3mVwS8z6zP8vszxJ7vdDZpo01SUByIuWMJeFsGFmN
-ldU4crqpnG5GzjaV1aCMClWFUcE0YSYwHU38iGAGt/5uyPYoIH4eAoq1AWKGmJHa59UUNaO291WE
-2uYQhWSQ1CgYxXwkjASIGEkgdSAFUoHcQ5kZZWbkztfzutcPQhAEwUNEREYgP+x1BEEQBMFjRDWz
-R9Y4fmQX9p5mKymlaTYTRIwmJxHMh+DCdhrsxjTJVvUJiIh/Jgk5Q0nWxKgiBin5fLs5rX3eEDHA
-2rQG0q6fIJv/8DpLZDEQQQBTKCYU82OH0ImwSZCTYQiKISaYGBiYGK6pZfscnnfc/QsxMPEjggvy
-9o82gYyB+hsogrZPm4A1cT496mTt/s3vKRtbkS3mQlsMEtKepW1/REEQBI8Q/n9REV7ykpc87LUE
-QXAfbDYbnn76aS5fvkzf9w97OUEQ3CdvetObJkPwkXaMQ2g3VDxd3ZosFGQnpieBK0ZKhiQXzufF
-NpwT2JiLQvwvXTkJOQulJEoysIqogYqL5tQEd2oiU82FrMtTtiK3iW8RyAKdCDPELRTbudBVQU0w
-ElWUIUGXYTSwalTExT5gJpjtxPzuSu1OblHeZtPadmeZ2E5s+35AmxtUPI5S21YFApLExbMYSWy7
-aVCADne0t5sW5j+LaSMipd0mByG4gyB4dHgbcOXk5IQ3vOEND3stQRDcB69//ev58A//cF7zmtfw
-8pe//GEvJwiC++TKlSs8/fTT4H/2PrKE0G6YnJPLtnNOJzc7bUWuudhrYjvtPtWOk1SVW9zsvBPa
-aiAKTAJbts72TkTa1iZ3ITw52n79ItCLMJNENsOqu9laoVoT2iaMCENztMfsojebMU76valmDxl3
-x3t3PGdm7/Q+mLX7lO0b079tu3Q/YwoXN8Qd7XM7EyK+sX0T6VoAACAASURBVJAwMh5zWabbni6o
-TWjjz3xytUNkB0EQBEEQBEHwqBJCu6Ht6ILQ5fWtYeJTiPfkPj8vfNyayH6eUNy52ikJKSd3xMWV
-uuFu960ieyu2wUPCZXLPxQW+CCUJXUrMUiKrO8laXcNXFdR8dAhFhC7BhhaanTxUW3VauMdnb8X2
-5HJbc7SbSz252TIJ6q3zbC0sfHK73V33B9FC2pvDPz2dKQ97K7JbfnbG12ZT2PwUUZD83iWde+aT
-tR4EQRAEQRAEQfAIEUK7UVuMdJXJkW5u6zmxnRMujK0J563QPh/e3TBgK1bdlcZsUoiklICMpOzq
-8Xy2t/nVRaaRSZJIksmSyCmRE3RZ6LOQq6HVUIHRjM6EqjCqkc1Dy1MLMd9uFJg76VMRNExQnULF
-dyJbleZgN/N7F9X9vKSIxBRw3/Kpweds3zea4y4u0rMZWZWsRlElmxeIS60Y2rQsyUAWJE+v28XC
-0Q6C4BHkmWee4amnnnrYywiC4D7YbDYAfMInfELkaAfBi4SrV68+7CXcNyG0G5PQVqy5qecc7JYT
-PTZnOjeRKrd0Id+a0FNicxOrU+j3JEw9RzkhUkjJxfQkVWmB0v5vF9cpZVJK5HZMIpTsQnuWEwlF
-E1S8yFo13B1W9SJj50R2bqHsySBltiLbTFqBNDC1rchW3bn0U6Ezk13QOG3F240J8I0IfG4PH2/5
-42ItZ9sFtYtsP2bbDZEmtBNYE9dSgCLItlra+Vj2IAgeRUTkfYBfaV9+jpl927s4zyuBb8b/o39f
-M/u1W97/ZuCVwK+a2fu9G0t+tzEz3vjGNz7MJQRB8E7Scj2DIAgeKCG0G7UddRKSzVyexGlOtBBn
-d2yTCqmFdO9ylqcj59xstm6251lLK+7lYlpSbkJ75yxvxXZztNNWYDdXO7XQ8Zzoi7vBVYwRRUzc
-yVZ1N7s52hkhizw//N385qwJbdPt0r011zmhPTnWu0rj28hwpkPz6P35THna0zNoRdHq1P4LI1df
-Z2kCe3K0RbyImk8o7cGL/7aGox0EL0Yes12xlz7sBQRBcF9sgKeBy0A42kHwaPNmdsm+Lw5CaDfq
-pDKbW5tabnROu5DxvG03ZR4Hfa7l1y7UXHbFzdI5Mb4tbLYrEratzI2cq8qdtipfzoeLS24h44ks
-qc2r21xlSUZOguXk1btFqCJsDIop2SrZEjkZBRhh62JLaeXGzDA1VKZNA1+zq+opw3oqlubtvHzv
-QLZV2l1se8i62M7FN/N5sxi1Cep3ENgtGdySu/4UgQ7oXGTbrSI7esAHjwAi8oPA7wZ+0Mx+70Ne
-zm9lXiRhLAJE1fEgeHHweuDDgdcAUXU8CB5tngJeXBFjIbQb2oS2SHNkJXl177QT24MJqQIYpgqm
-W3fWneJdYTMX27sxCdWpBRZ49HOd3GQEk9QKkqWto+052ZmS8lZk5yk/vM1Iq8SdMuRCK36W0Jzo
-K3RWKTq6e4wx4mKYJvC1ueumhqbz2eJe3GxbebwJXJvuxHY9radibQlr4eot1JypR7ehk9gGxHQb
-Kj6JbJmKnyVary9BOrCp91dpk07nvwj+yh08FrxIBOCLFzP7VuBbH/Y6giAIgiAI7pcQ2o1JaKet
-wPWK4EWEMrnamlrucXO0twJxW+Ns52jLTmRPPbW3H53EtnevagXDUsviTs8LL09bce3HsnW0pyJs
-Lag7JS8Wpi601RKmmS4ZpY4UMsUqoxg5udil5YFr8utpFurova11e18713qHi+bJ0U+kbRTAJLIz
-5mLeppv2OVW8U7mcK3yWWl72tuJaauHiTWxPjnZT6O/YbiwIHj6RzBAEQRAEQRBsCaHd0KbaJnfV
-tZ60FGHZtp0SA/RcFXGeVy+c55Xlnt68RRlqa5Nl6mHUav69aZgaaoppxTRhljGvIuYR660tl4l/
-Lk3XyR4KnsxzybMKJWWKZArZQ8dRskJuwlaTeUvvNBVK27Uys+fdi52/od19CUiybX/r57VBY+dU
-00LNtd2/T2G7c2R6wExKHSktfDxPYeO7kPZkk2MeBEHwSFDvfUoQBI8WTwJf2Y5BELxYyDlPLx/p
-P3tDq2yx541kTfdpK789KlT1183NbinFW4FtqmhVVOt2mOq26vjk2BpGNaVapZoyWmW0StVK1YGx
-DozjhmHcMA5rxs2Zj2HFuFn798aBWkefB0OTYdmgmwZIZ6QOchFKTnS5hZ236uRi1Y9UDyIX3YbB
-nw8fP1fVDdMKTRhvHfvkYptkSFJICqmCKJIUyUoqRspGStbE/FRd3HOyrRU+s8nB7gQpXm1csvn6
-rK1v2ghpI3h8EZGXichXiMhrROTXRWQlItdF5JdE5FtE5CPv8tlvFhEVkV++xzVe2c6rIvLe577/
-LSKiwMe0b31sO+/8+JU7zPnBIvKNbZ03ReSaiPyciHxtq9R9p7W8z7m5P7t971NE5HtE5C0ickNE
-fkZE/piIlFs++4dE5AfbeTdF5KdE5Avvdu/v7nrvMNenicj3tXWcisgviMhfEpGju3zmtj+DdxYR
-ORSRLxeRHxWRt4rIWkTeJCLfLSKf+q7O29gV+giC4EXCk8D/TAjtIHhxcU5oP9LV0cLR3rJLnBbb
-hTaLTj2krYnu3ZgqjE/mrlfqVmqtaBI0J1SnHOWpiJi0QmKKTmJbK7WJ7bHCWIVRBRkUsREsM0hh
-yJlxGghjEupU/Tx5ODmZtgHg95EGyCYUSz60kqQJbXYh7ZOoTmKtqNsUPj7laU/l2NvmQmqVy9OU
-n00L//bhBdr8e9v6ZWaYnuvM1YZhWBasNBe7E6RrRdpya/PVNiikOfheiDz+Qvs4IyIfA/xA+/J8
-HEkH/EfA+wOfLSJ/2cy+4gVYwvkEhvMd787zDn8AiMiXA3+eXU+/iQ8C/mPgi0XkVWb27fe4NiLy
-t4AvumWeDwH+OvAxIvLp+PP4+8Cn3nLehwF/W0Q+zMy+6E4XekDrneb6JuBzb5nnA4E/g/+sfp+Z
-/eK95nlXEJHfB/wj4PiW6z8BfCLwiSLyauDTzez0hVhDEARBEASPDyG0J2QXzrwdZqRJUBvN0W5C
-u/0V2x1taSJS0VrRRBPZoCqYJQ81B7YVu7eOdt0621VHqtLENlOjLMQyo2TGXHZDEjUnas6knEii
-SE5bQTvdUmIS2kLRRK5Cxrz3tk1Ce9cH23Otz4XCn+sJvi1sNuWgZy/+tq18LlN5ta2CdmHeqpGj
-bRpt/crZdRe3BFbkeTnZk6M9iXdMd726W7uy4LGmADeAf4YL7n8LXAOuAC8D/nvgfYA/IyK/1Apq
-PUj+R+BrgG8BXgG8DheR59mc/0JE/lvgL+L/KbwV+Crgx/C9o48DvgzYB75ZRJ42s9fc5fpfDHwk
-fv/fBPwH4L2ALwd+J/ApwOcBH9pefwfwD/H+GB+A2zgfBHyBiPwTM/ueWy/wgNf73wEfAfwE8PXA
-v8N/Vp8DfDpuKb1GRD7YzG7eZZ53GhH5aODV+O/MbwB/A/hZ4E3AS4DPAP4I8Afxomuf9iCvHwRB
-EATB40cI7UZKTXJOvaZpoeOyE6yTqJ76XpsZVSvjWKE5rcncFR5TRjNYyRjeLzvlQu4SyZRkRjZl
-rsKeJQ4ko7ORMhh5NLrRyK3qeUkw6zL9LNH1iVKEXAQpCYpgyftOmygIjC33e2wh5VKETKanY8QY
-xagCg0JVoarfjxbophxwm3LChXpLG60kshu7bmTNxW4tw87lum/dbhWoLfJevcq5Wct3bw62dcmd
-7Va2/FxnNGBqpy2tX3fwmPPTwFNmdu02732viPxN4J8DHw98pYh8m9ktv8zvBmb2ZuDNIjKJwptm
-9vN3Ol9EToCvxn+b3wR8pJm96dwpPy4i/xfwI8AS+Dsi8r5mdqf8o/8U+Foz+9Jz3/sZEfk+4OeB
-98aF8UXgS8zsb95y3g8Dv4QL5S8Gnie0X4D1fgS+KfDJZnbe6X+tiPwc7pq/N/BncYf7gdBC6L8D
-//PuXwD/jZmtzp3yM8CrReRHgL8DfEpz1r//Qa0hCIIgCILHj9AqjW0rrklANiHnqceT4ytN+Hkl
-8V2o+Mg4jgzDwNCOtY5UVS/8JbSq4IXcdZRZT7+YMVvOWewtWB4s2T/a5+Boz8fhkv2DBft7c5bL
-jvm8MJ8n+j7RdUJuI7UkZcug2RizMqTKmKofZUSzQoHSZfq+0M86Zn0bXaErmVISOQtlGmV6PbU3
-k9ZS7NxzEQ/d3rY0S4JkIZVE6oTUC2mWkLnANGZt9Ak6z8fW4sMmkd35a7JsW4pPoeLFfLRaaa1I
-Xbjajytm9rY7iOzp/RF3XMGd7d/xHlnYnflcXJAC/A+3iFYAzOxngL+M7y+9FPjku8z368Cfvs0c
-Z7grK3iY9E/cIrKn894CfFc773e9wOsVYAW86haRPfGXgJ9r5/3RW/PL300+E//5r4DPvkVkbzGz
-vwv8ZPvycx7g9YMgCIIgeAwJod1IKe1GE5NTxHIy21YcT+zadZmBVmUcK+O4E9vjODLWVgzNPC8b
-SaScyaWj9DO6+Yx+uWC+v2R5sMf+0R6HF/Y5OFpycLjk4HDB3v6M5bJnscjMZplZE9qlCKm4A+xC
-W6jZqEnPiezKINUd7SxNaE8iu2feBHffFUpOzxPZXRG69rW76rvCY5PITs35z9MGRfYhRZBJZM8E
-OS+2J5HdC9YnrEtoJz5K2gruqdI4MlUm9yrqZTsmR1taY7EgABHpReS9ROSDWpG0l/H8/8d96MNa
-W+Pj2vFZXODeib97m8/cjn9yF/f4Z8+9/sd3mWM676KIHN7y3oNcrwHfY2a/cds3PdJgCu0/Bl5+
-l+u9s/xX7fhDZva2e5z7w7jY/6gHeP0gCIIgCB5DInS8MfWJnvKUtznL53p3eTS1Fwmr28rhRlWF
-1kNaEFLKdN2M2dwd672DPfaOXEzvL3t3fYs7wHm1oVttmK823FiPnG5GzjaVs03F6tjGQJdg2QmL
-Tlh2wqxk5l1i1iVE8OJqKNV0m1ueVD2d3AQzoTejDAPdMDIbBjajshmNoRrDYIxDO26M9Wr0UUY2
-m4pWo1avqu4bElOP8OnZ7FpzSYu9l2m3Ykr6nkquyZQEri3mHf9NnJztDJbEC6AJYLr9eSSbnPUU
-IjtARJbAl+A5ti8D8l1OP3mPLOrOfDD+2/76uwhkzOytIvKruAv7wXeZ75fu8t6z78J5B3iO+8SD
-Xu+/vst7sHOTAf6TW75+d3gFfh+f0KrE3w9X3/XLKZ56fi+m2JwgCIIgCJzKnTt2PQPAZrO5w/uP
-HiG0G+fLgm3bWk25xcn7OKsZoxqDVTY6MpoLWwWSJFIulNIz6xcs9w44PDrk4sUDjo+PuHR8xKWL
-Rxzszcld8lGEg/WaC+s1N9drTtcDp5uR03XlbDNSx4E6jtRxJKPMMtvRZ6Evib64aFWbenK3Y8vT
-dkcdMD9nM45sxspQK2NVxgpDNcZRGTaVYePHs9M1pzdWnN5cszrbMGxGhmFk3FT/u+G0CyHWcsOn
-sma+ITFZ39ZamiHi4jlP5cxadXL1QnLS8rKlJXWbgIn4s/Xu214pXVoOeAjtx57WVuoHgN/Gror0
-7XKwp1+UxXtgWXfjuB3feh/n/gZ+X8d3OedulbHPC8r7Pe9W1feg13uved5ym2s/CCbV+87k58/f
-vUs+/e59PAiCIAiC2/L00y+eP2NDaDcmR3trYJttTVhrScKqRkXZWGVjlcGq97DGWq5ypisd/WzB
-cu+QgwvHXDw55uLJJY5Pjrl0+RKH+0tKnyldonSJ1WZ1bmw4XY+crgfONmMTtwObzYhYpROliPkx
-e3h3l73Gd23tvmsT1NPwiMxW39uUWttQpaq11uDGWJXNemSzGdisB25eP+X67Caz7iY3yxnr1caH
-Dd6zW8yrq297dYk/ialseW7dwNpDdHe6iW2SO+CqNI2ObOPS2+clodJy4Wm9y9FWkG7ys0NoP+Z8
-By7uFPh7eOumXwCeNrMBQPw/7Glr9FH5hXlgBdneQzyo9T6s+542EP4F8Kde6IuJCCcn9w6eyDmf
-7wMaBEEQBI89tVZqvWMQHQAnJyc888wzLwrBHUJ7Yvor+GT/tq+3wlGgCowo4ySym3ts23Dxntls
-wXK5z8HBERcunnDp8hUuXbnMpStXOL58mcPDfbo+0fWZrktshjOGzRmb4YzV5GqvNpyuB9brkfVm
-YL0ZsVpJVLJVEvq8/GlDWvVwQVXOiWxcpJ4b20rfOr0PakKtyma9Yb3esFlvuDa7Tl+uUVJPSR1n
-eUWRFaJnjCgVZcQwqedCwdnF3E+j9Q0XE3+OLTjfQ86nonLNzc7bEuWYJERyy9BWkEpqvb8T4Wg/
-7ojIbwc+Gv8V/otm9pV3OPVuzujk5t6rVsXeO7m8O/E2PCT5ifs49yp+b/fKKX4hedDrvdc8599/
-kPf9m3jrsP5uVeEfFC95yUt4wxve8EJfJgiCIAgeW5566qmHvYT7IoT2ROuj7cLTnWEwTGX6gqGq
-t85Sw9Sdi5IzXUnsLfe4cOGYo6OLnFy6xJNPXuHJJ69w9eplLl065ujiMftHF1jsLd3NbtW+lcRY
-gU2l1pHNesPp6ciNmyvPoR58oF7lO5PJklAVck5odvGqmlCbjnhF9O2mwU5oT622JJmXfBO361WF
-lEZyrpQyovU6XtD5Grlco3TXSeU6pOts6sBGB0wHzCA1h9tEKQVyYtsiDVpYvkz5660FeeuxjXi/
-ckm7VmCpiewkhSSZYkrRTE5KoT0HSVHJ7/HmZede363Y1yvu8t71drxwj2t94D3ev1+n9udwwfdy
-EUl3qL6NiFzG852nzzwsHvR6P+Ie1zv//oO875/Ge2W/QkRKq0YfBEEQBEHwghJCeyI1oa0utMfm
-9pq2kGdgrMZYm9BuIcw5JfoC+3v7HF885srlqzxx9QmuXr3M1Scvc/XqZQ6Pjjg4OGTv8JD5Yk7J
-qYUNCqMaaVMxNtRxxXptnN0cuHF97UXKRlyIA0XERWaCapliGSUhZNQKZhmzhKk0sQ3nhba0Mupp
-ErWpkFKHSIeRycUondF1BlxH5DlyukYpz5LK2yH3Hs49rLBhRR1c0Hv8t0IScrImtM1F/dZ1difa
-wMW2bVO3/dhaeU3t05JkUurIUsimFJRium3rNVU+Dx5bzv+/626O8xff5b1faccDEfkAM/t3t54g
-Ih3wqfdYy9QuanaP874P7+l9AfgU4H+/w3mfj/+nYO0zD4sHuV4Bfr+IPNHaij3/TQ/xf2X78u3A
-69+Ndd/KdwP/JXCEtyz73x7g3EEQBEEQBLclhPbE5GjLLqFTtYlt3B0etrnNTSiK95/uu8T+co/j
-Cxd58upVXvKSl/LEkyc8cfUyV568zHK5x2K5x2K5pO97UkpkSUhKlE0l5Q1wRq2Zzdo4Ox25cW3F
-aImqwmgeLF2SUJK34lIrGBmTgkjBrIOt2PYq46rSHGNPhBaUnHD3OBs5z3ykOUjHWLx92NgLkq6T
-0nOUco3c7UPuMRGqVWydqSu8fVgdEcmQvFCZt/wyF9r+YF1st4Rta+425k52MkNwUc4ktsWFdpZC
-Tj1F1d1sdaGd5Fwh8+Bx5bwo/hxuU6FaRL4Yb+10J8f5h869/pPAF93mnK/D+0PfjTe34/vd47xv
-Br4S7039V0Xkx27tTS0iHwp8efvyDcD/eY85X0ge5HoN34j4RhH5lNu441+OVxo34JumHPsHxLcC
-/xPwXsD/IiL/1sx+5E4ni8hHA9nMfvgBriEIgiAIgseMENoTrSbNFDpuurNorH1dTTDz3OAihXnf
-UfqOOR2XLl7k8skJT1y5wpUrlzk+vsjh0RF7e3vM5gv62YzSdaRSWmh0cke25SGrJWqFYais1yNn
-qw1qGSVTzcOpXcWat8RqydCGz9XKdLujbaAqzdH2awiGpOz52dbKo1lBrUNzh0hPVb+eSSGVRDfP
-mMzQ1FElYTlDKZSza5Sza6TTns3QY7ZqQxA8l3xbuZ1zCpppTUxFx1tLtMn5nnKv/Z/c/ikiFBJZ
-zO+49dcOQ/vxxcx+WkR+Dm8n9UUicgx8Oy56nwI+C3eifxT4z7mN2DaznxGRH8d7Jr9KRGa4KHsO
-+ADgC4GPBf4lng9+J34Md0qviMjX4kXanmvvDWb2a+16z4jIlwHfgIu+nxKRr2qfL7h7/KXAPp7r
-8aq7tdV6oXkB1vs6fOPjX4rI1+GbJVfwjZLPaOf8OvAXHvB9bETk0/EK9fvA/y0i34lvCvwKnuXy
-JPDhuHP/wcAfw3tqB0EQBEEQvEuE0G5IEab62WatCBrshCFedEzwPOm+JPpugfRL6JZcPrnE5ZMT
-Ll++zKVLlzg8OmCxXFC6jlwyKSdvXQW7vtxMgre151L16t/D6AXQpvUgaJIW155cbGO7NYm/whSs
-okobzVNuRceSgGahFiOr50u7u+1h5ao0oZ5RmZGK0EnPIvVYKkg/pyz26G++ne7m2ynzOavVc4zD
-DeqYGQcQG5roH9satw21n/88zZCt2G4iWyahnckkF9ckMkLGtkKbrch+sRVvDh4wnwV8P3AR+PQ2
-Jgz4WeDT2DnOt+PzgB/EBd8r2YUvT3N8DV7J/G5C+ztxR/Z9gT/RxsSvcs7pNrO/LSJHwJ9v1/y6
-W+YyPBT9C8zstXe55nuEB7zebwA+BhfW33mbed4E/AEzu84Dxsz+lYh8LJ7P/17AH27jHU5t49pt
-3guCIAiCILhvQmhPlNQiml1wqti5BrOCmItQwcO++5ToFwu65SHd3iGXT064crkJ7ZNLLPbmLBYz
-cldIJbf+0OyqmU9CeWrH1YqsDWNlGL3NlrXK3IiSVJCUvJCZnmvZ1U5xBas+X22tvuokshNJsott
-9d7VKbcc6ia2RfxurRVHE+ZI19P1S9JsifQzynKP+f4h/Y19ynxBnnX0Nzs2q8JmBRsbsclJV235
-2638uEyJ2VPcfUvtft4/7flu3exEIVMwihiZySXX7f0Hjy9m9rMi8jtwkfsH8YJX14H/D2/19bea
-m3m3OX5RRF4OfAXwX+DO5nO4+/o3zOy1IvJKdgLsdnPcFJGPauv4/XhhsOX09m3O/yoR+We4a/p7
-27oV+DXgtcBfm1zwOy37Tmt5Ic57kOs1sz8qIt8DvAoPFd8H/gPwXcBfMbPn7jjLve/nXvfxkyLy
-AbjQ/yTgw4CTdi9P4xsqPwT8H7fL1w+CIAiCIHhnELMQKwB/9pNeYQB1MOraqBuoY/Owm0Ze6chp
-HTjTgVoKywuXWF44YXl0zEvf+7fxXu/9fjz1Pu/L8ckJ3bzQzQrdvFC6zkfpyLmQZBKWcO3Zt3Pt
-2bfx3LNv45mnn+FNv/E0b37L07zlrb+JpA6kR1JHlkxJaXcsPbl05NJ5jvTWJhcX7dUFN7jITpJd
-qKfWSitZm7+D5KHjIjOQGZJmXqyteNE2kcpYTxnHM8Z6yvXrT3PjxtNcv/5Wbt54mtWNZ1jdfIbV
-jWfQuvIxrlDTbZUzk0Q1RTGq6dZ9F1MfdCQKYh1Zekqa0aW5txczo+C7QoI2ie1iG+Af/fi/iSDy
-IAgeKiLyBuClL33pS6O9VxAEQRC8gDz11FO88Y1vBHijmT2yvb7C0W7ouWMVGMRQOd9ySpCc6LvO
-c55ncw6PL3B4conDk6tcunyJ/aMD+tmMVAqS20jFhbCr21YQzFtsGWCimCgkRZNSpTJaZaMVIZNS
-RSy702wtbNrAVNGqKNVdboFteLa2PtotdFvJmGVE0849VoFkSKpgIyIDyAZkBdqh9Bg9xgyRhFqH
-iJBzz2xmmGVS6unLnNPU05HJKoybG4zDzVbIzV15Q7ZH0K3tJCIkphDygtAhVkjSkZLnsvumRHOy
-bXePnlYZm0RBEARBEARBEDx6hNBuaBNwFWMERnZCO7ew75QzXc7kIvR7exxfusDxE5e5ePUqR8cn
-7B8e0M1nSCmkXJCUYRoeo91yqlvIN14N3FIbolSUwSobG0mW21C8SK94EbAWap7wKugpneuPLeYF
-z7ZyNoG1ommWoAluUkKsgiXPq95a3RmTjLLAWKJAzjOggMzIKTGfFXKe0/d7zLsFHZmiQqrKJnWs
-SUhVhDWGojI50I5hmOQWLN7Cx61sh/fPbkJ7ykE3/FltZwkTOwiCIAiCIAiCR5MQ2o1JBlaEKsoo
-RhWjtBRjEqQu0/WF1GeWh/tcvHSBkydOuPLSqyz2L7LYO6Cfz1xk54zkAqk0of18R9t7W7u49h7U
-LrYrymiVQStJKskqCc93lqllloGoIaqI1FbUTL2lVtJtvraIbSuRY1OudEHIoGWXPp1p4d3TAOOg
-HQtGT04dKc/JyUc/2wM7ZN0vyAppVNhsOLME1bBhAAWVilBR6lb661RWTry6uEhGLDeh3cqgtXD3
-tBXjTWI/z9UOsR0EQRAEQRAEwaNHCO3GOLrUrmpUM1QUS6AZUhIsC2VRmC0X9MsFBxcvcnR8kQvH
-Fzg6vkg32yf3cyRnL2KWBElCzkISaX2l3Y0W8eMu9LmV+DJBDUYVhoqLTHMxasnd6Ml5FhJTkack
-RkqGZCWpIpPIRlt78NbH2hIiilFa8bPmFqu56G6Vvw1BbECmHGr1omaiBeh3ofSpQK/UxRnsD2RV
-utyREUQrSRKjrhl1jVlr+WVG2trS0lR+wfur+dGLok0i2jBTtG0eYIaF0A6CIAiCIAiC4BEmhHZj
-rJPQVi/aNYV054Q1DZjnHfODJftHhxxdusTRpYscXLzA/tEhUhaY9CDJPysgSUhZWp63IVN/aaMJ
-YdtpbfOw8mqpCW05J7Qzph6CPuV7T5nLIE1oQ9ImuMVIoi6mbeeCe/X06fsFRFz472Y696qSTP19
-azndJMCdcN88SEiuLObHZFVmOZNTQkzROgCGDIKNlTqut32zzay1TxMg+waCTSI7t+ucy83GMCpV
-W2X08+XbgyAIgiAIgiAIHjFCaDfGsQK4m22KiaHJ0VIWaQAAIABJREFUo76tgHVCWfQsDvY4PL7A
-hZNjjo4vcnjxAvsXjlArDDUxjF4Xm9Y6K+fkkedinpPtydm0U1qRL3eRzRKqwliFoSYX2TmRLGOp
-hVdPhdWgudS4yE4usnMyUlJU1MV2q849FRMTUZJ5A2tRti2+krFtxeW9rCsZP9eLlQmivuMgMm0A
-FHIW8txFts7miCk2DoybFVoHzCq1rpuT7fnjqbnSPhI2hbNbhtbEq5V1Y3K0fdS2YTDF8wdBEARB
-EARBEDx6hNBu1OpC25rl7CJZKH2m6zu6Wcf8YI+9C4ccHh9z4dIlDi5cYHlwyHyxZKiCboxR1d1s
-8SJlSTzHOuEh2IJ5b+vJkdUKVj0k2kBVUBVvz2UtiNo89Fu2vbTleR1jk7rYzi263L9u9jHaRLa2
-a/tnBPXSask7bSECmv1cmTKpp7VVF7qpCXfBi6khpDSj9HtIJ6R5zzicMmxOGTY3UF2BbFA7pdbc
-8tN9KIlqhWqJartO2mxfm/va0oqnTRsgJluXOwiCIAiCIAiC4FEkhPaEN51GilBKQrpE7guzxZJ+
-uWC2XHLh5ISjkyscnTzBwcXLLPYv0PULLzC2FdcuAl1gG9kGxMZzw5ojnb1I2XgKdYPUEdHqBc7M
-ne+pTReiXuwsKaMokmRXFA08xN3ATMjWCpqlhMkk6ltbMAyRimEtV9tFb27524jnaCNgqWJpwNKa
-lM/I2dcsKftsBmr4feZETj1FYLF/yGY4YqzHICtytyHnM0o6JWclZyNnZVThbONjtTFQBRVQmXQ8
-yZ8SWGtX1sLOkRDaQRAEQRAEQRA8uoTQbpi50M7i7btKn+kWPYvDJfODQxYHhxydXObCyWWOLl3h
-4OJl5ntHlH6BSAG86ncSQUTJMuJNtQyxDaJr0I0XFqMAHZBhPIO6RnTciWw1r/uFt8WaKpOnpKRU
-ScltaWk9plPLeTYTTJOHkKthTTgLLUR8K7S9WrnZLlR8m8MtqbUcUzQNpLQm6RngYetmGTNBm8tu
-CJKF0vXMup75cMA4XkD1JimdkfMZmRtkrjProe9g1sNmVK7dbBXJR0XVtwJUtysiJ8jij+N5Ke2t
-1VcQBEEQBEEQBMGjSAjtiSa0RRJdEfpZZrbs2TtYsnfxiOWFYxfZJ1e4cPIEB0eXyN2C3C3wvOXq
-RciSh2gnGclUko2InkE9g7ryOG3rgd6P4xppQhut7uw2u9hwJ9dzxpvQzoJMfbNb1XATIaUmtEUw
-9bZhXrBMzolsbb22AYxs7hmLFe/+NfXpbo62pBFNa7J5T/CUM8kSatkrmGvGciblQul7+nlhMR6i
-dhPkjJTOSNwg2XNkm7NcJJZzYbkQ1ptKkjXjsGa9qlQxqlVMp9ZfQjIhGaTmapt5grtJU9xTHHwQ
-BEEQBEEQBMEjRAjtxnzmxbVmy57l0ZLl4R7LowOWFy+zd/Eyy+MTji5c4eDoEou9I/rZPpI6d7PV
-86cTQvF+WUhdUzenDOMZjDdgvAnjDaxW1DpUe1Q7Ts8q6zNlWFV0fRMbriPjTZKdUceRWjcM9Qyh
-MKbEIImSpjBv15opCUm84neWRM5GyVASvgHAADYCI0bFGMGqu92TGGcS3VOI+JyUF0iek8uSbrZH
-3+/RzZaU0m1H33eozXwwY6g3QUa6WWK+7NGhh3FGqnMO9oT9pXCwFFabkVqVcVRqVTYr2CBsqqeW
-l5ajnVqeepaEpqnfuW8ohNAOgiAIgiAIguBRJIR2Yz73St57+z0HR3scHh+xf3yRvePLLI+fYHn8
-BHv7l9jfP2axPKLv9zBLXim8CmLenEqSuCNdV+h4nY09C8M1GJ6D4Ro6DoxjR62FsRbONonVkBg2
-ibo+w4ZrUG+S9BQbM+MmsR4yqJBFWIsfvVKYF21LKZOlo6RCToW+K/QloyWTqJgNYBvMBqptUB2o
-uoHmFPvAW4xZAhNSniFlRso9pVvQz5a70c/o+zldP2NW51RbUG3BaAvQm0gaKH1irh029Mg4I+uM
-w33hcA+O9oX1OlHHJrTHypkYZ2owGFQ71+irCW4RcusdboiHyofQDoIgCIIgCILgESSEdmO+aEJ7
-r+foaMnxyRGHJ5dYXrrM8vgqy+MnmS8uMJ8dMp8dktOCWpU6KlTF+1m3xlSmWF1Th2uM42/C5m2w
-fjts3k4d1myGwjAUNmNmU2estWeoPXU9YJtryHijCW1hXMNmJVR10en51q2ieDJMjJw6ujyjpBld
-nlHHHutnYD1CxXSD6RrVDWNdMeiKsa5QrWitmFZMteV44w59LuTSk0pH6WbM5gv6+ZLZfMF8vmQ+
-WzCbLal1j6p7VNtjtD36PNLnkW4mZOlg7Em1p7MZh/twcV+4cACbTWIcK8NQGYdKqRU2lTEZpkah
-3SKToy1o09WWmtCOXtpBEARBEARBEDyChNBuLJYdAHsHCw4vHnDh0kUuXD5hfvES8wsXWRxdpOsP
-6PIepcxJdJhVTCracqAhIckwElXVQ79XK2xzBuub2PoG4+aM9Sax3girTaKyYJQlygJRpUuVRSfs
-z7ttrraqMY627YkN6iI7GyQlJyg50+VCX5RZD7M+seiLZ12rYlpRrQw1UaowVKhV0TpS64DWsbX5
-cvGeUvEhhSwdiVOS3kDqHMYFlpeQl9iwRNMeo+yRbI/cA72RE0g6w/IGupE8U/quhbSLMTKQGciM
-ZKvef1sVHSt19D7bVVxojwJVhBEX256LHo52EARBEARBEASPJiG0G4uDGQDLoyX7F444vHSJw0sn
-dPsX6Jb7lNmcnDtSyp4abLbNj8454QI4A4JqBgqqmXHM2DCNxLARVuvK2Uo522grrV0g93Q5sZz3
-XDhIjDpnOVduLIzlmTJUbQ2vFUMhu9gmKzl39GW+HfNuzrz3Y0Ix24D1mM4YtaNqodbOXe26po4J
-rQlTr3huqogkb7GVjJSVXAZvzVVGum6gKxu6dEbhJqXeIG3mYHNkatGlgtgpSd9G4TqkM0xHhk3l
-JpXVauTmjYHTGwNnNwdObxqnp8rpyhgHawXcWntvAU2CiudmaxPZIbSDIAiCIAiCIHgUCaHd2Art
-wz32L05C+zJpfoE0PyD18+bsJlr3ahfaWUCm9lj4e1qAgtZCHQs6ZmzM2JgYBlitK6erDaerkdIX
-ct9T0khJM/bmM8aDOSkLN86U5Uq5uVKGUTEbUepOaGeFbHSlo+9m9GXOrGtCu9sJbRgQGzBbo1pQ
-K1T1PPE6JuoIdaT1slbQuq387WJXQTaIDICQs/fWTqkjWU+qfpSxB83I1DbM1qT6LJlrkE9BN2w2
-AwwbTk9Hbl5XTm9UTm8op6dwegY3z2AYz/XzEoMkWBI/yjRShI4HQRAEQRAEQfBIEkK7Md+fhPaS
-vQuHHFw85uD4EpR9rOxhpUesIJqQVkBMBEhCFmj9sbxzdnWhbebOsdYOa8dhzGyGDetN5Wy9ppcZ
-szJQqHRFWM5npDxjPp+xt6rcWCk3V5WhVtRG1CrKiGSD4mK76zpm3ezcmDMvM2ZlTp6ENgNY532w
-8X7YdUyMI9RBqdW8vVit7khbax9mYKaez62KqvrGghQQr1COZqgZrJDICIVEIaWBojcQuUHK3t5s
-HFdUXXN6c+TsFFY3YX0KqzNhdSacrYXN6P3DFQ9jlwySvF+3SWpOdgpHOwiCIAiCIAiCR5IQ2o35
-cg7AbNbTlY6cMgnBTMEG0A1ihpgipkxh4rtWU5OpLVA68nyPTo89l3iYYZs9bHNIWj3HuLhOnV9H
-z27QdUtKv6D0BUk9HYfscUS1A1aDsRpgNRhjncRu9TWlaRglC10pdF2my5kiUJJQBF+rjWAb0A1G
-bVXGU3Pbe+q4QKuAVkxH0NHvFbabClUNrUZVWqa4eqstM28VZgmxDX1JzEpilhJJRiyvUDaYjL7e
-iseCd4nVzFgvlM3aGFUYqrCqYGsYqqLVGKuBCZKA6sLbvW7B7B1/jkEQBEEQBEEQBA+bENqN+cKF
-dj+f0XUdOWeSCIahNoKuvRiZ1TYKkFrYeDu23s5CR5rt0wmkrseGPWw8wsZj0uo56vzt6Pzt2Onb
-yblsRyk9uT+kdJfJ5TKDio8qqJo7ytWLhm2LoomRknn+dIacFLG1r1c3rRDa6CJb14jVFpGd0epC
-W6uhNYGOoIMLbXxDQTDMKrWaV1mvUM2oplQ1zBQxI/lS6DJ0yUdCIQ0gQxPx6hsPmpBO2cyMzQKG
-wRjUWI9wNsBoUDcu7IeK5423Z2umqBmGhdAOgiAIgiAIguCRJIR2Y7FYADCbzei6Qk7ZW3WhJBtR
-Yyu0pRUlc1e7uJstuxxipCOnPVLXUeb7WD2Eegr1jLx+ljrfR+czbFYQUQQlidH3Pcu9Q5Z7V1gs
-XopKRsmoZExpgtiwap5LbVPhskpKA5JHhA11vEYdrqHDgFZFxxGtLrTTlPZsHuLu8wlWM9iAqR/F
-ahPbLszHqoyjUCuMagzVGFtKd7JKUiWpksUoKBkjoYh4tXTJvnZNuHvep63QHquxHo2zAW5uYF2N
-TfWyb0Nt7jXWXGxzZ78J7iAIgiAIgiAIgkeNENqNXFy05TQitsbqDWwzw1LeDujAOrAeKCgFa4NU
-sFwgd+5qiyE5Qe5dk2sBnWE5MxOjJsOKNHE7gA10/ZLZcsl8ucdibx+TDCljkr2lVVWsGla9Ojiq
-/pph56hbZRxgTJVRNtS6wdIGrQOmI8kyyTtT46q3g+rtvzzEfAAbESrC2Nz7gbEO1NHnc5HdRHCt
-iA6kOiCqJK1kqy6+Tb10nEFiqpcuKAktsJlDNSB5Ky/NhhXIMyPfALJ/cByNsfrRnX22Dn8QBMGj
-xJvf/Gaeeuoprl69yute97qHvZwgCIIgCB4SIbQbKQ3+ws6w+hy27tHV2ER2QlNGrHeR3YadF9ql
-99H1SC7YVJ1cpBVNy5C8CninG+ZUJAuqa6yuUV1Ryj6pm0PpMO+t5Z9PgrRwaQ8Xd2FstYJUzAbM
-Nl5V3FaYnWGcYnIT5AxLa8wGoLogJyN07tibIOZFz5hC46mIjcDon7EBqRtSHch1Q6pKrkpRRccB
-xjNkXEFLw05VSWokteaeg5iQRFzbW2ZWhP15QnIm94r0Ql5Atw/z68b8OaObGaU3VmfeDm2liqls
-c7SJquNBENwHIvIxwA+0Lz/WzH74hbqWqvLGN77xhZo+CIIgCIIXCSG0G966CsROYXwOXQu1rLCU
-XGznBDZDdAY2d6FtBaNDKUg3R2ZzBH9PkrvcUCAlhAIIWYSOihQh9x11PKWOZ9TxlJyX5G6BZBfa
-kppQb226BfNWW1qxOmLixcumsHCzNaYrdCu020gbd86lgnTN/e48l1wKSQoiGcFar27DRXYbNpDq
-hqwDWjfkWil1pNaKjmsYshvhm0qqiowJr33WcrdVSGqoQJLkKe1dQopR5jBToyyFfiXM18J8z+hn
-Su6UlJXreQQbGTdGFRfZiiCkh/XrEgTBi5PINwmCIAiC4D1CCO2G2QYArWfUTWJcKaOcQk4uslMG
-m4HOEJ1j1mPWuaNtHWm2AJvGHEkzDxvPM0SKC9tUSKmj6xakDKUrjMOMcej/f/beP2iy9Lrv+pzz
-PPd29/vu7ErKytaqQookhCIJghDFGPwbMGhtKyb+ATipVBRRUKqQAioBVzlFBUmOCaGMU45TMd7C
-5STGP2LywxXbYCCpAis2FRJbsY2LcmJinFjJ2Po5uzPv233vfZ5z+OM8t7tntLO72hntzEbPZ/Xo
-dr99+/bt3t6Z93u/53wPpWREtuiwRfJwdLNRQFvaORWRAlJwCs6CU8An3CbwA+4HoC3ZIzpHCTgW
-v2KKxgUAGRDdoLoh6QaVIZx3mgPfRLYTZe1mUeLudabWgtUFqwUre3wSbHY8VWRxSIZoQWo43OtW
-TcORNkERVGFMwlaEtAjjImyLMO6MlA1px1EUKzAdDDMHB3OnF453Og8XEXkX8GeJPy1+vbv/w0d8
-Sp1Op9PpdDqvS7rQbty+vY8b1UjuZDdkmZAsaBY0t35mG6GOrVc7g0cgms9bfG6u9rCBtEXSJoS2
-jqhuQDchnmmiGSH5AHqB5hjvldMFKgNRFh2jtUJRFtynGDNmc4heW/C6QF0Qm1FrY8ioiDiqikvG
-RHEdcBdEt4hsQOKcREdoS0QRSWjM0uIktgvJlihRt3a7hvi2ZYPnhI0DPo+w7KEcoOzbeXlbhleN
-4DVrbrQqqglVpbYqda2CDwapkjaFcVsYhgOqB9yV21cz+0PBJqdafRRflU6n0+l0Op1Op9N5SbrQ
-bpyEdiW7MVpBl4E8SFu0QLMMZQDLIRY9xXbYwLzBp82Z0I5F2uJpB2kbglYzognVhMiASsLzNgR5
-2pF0dZchnGjDfUbsAHbA7QA2t7W0sVwVsYp6ONgqjifBPB1FtnvCNUS2S4hs0fNtlJEnHYh+7lVs
-R1Cae5Rwh8ifcZuxssGGAV9GbNhA2eNtYQti9XhuXgWrghfBSYgOuGayZtwcdcgOuq2ksTDuCpvd
-gmjGTChLG+sFzNWYS/e0O51Op9PpdDqdzuNHF9qNVWhLKYxWGOtMKplxBNsAI4gplIRXRWqM/1JX
-cMGHEaYRxgHyBsm7ENp5h6ZLPF8g6RLyBeQLJO8gh5NNSkhKkFbBuzra1nqma0sDn3C/Brs6Cl2v
-M9QarrETyeFScfU2dzr6yGObMRmOKwT2CGdiW1NzuiVeVyTC0Y7LK3jrCbcZrwdsGPFlg407vOyx
-2sT2XRcCFrwKUlahHQ4+ssF1QHEGcTY4w0WI7IsnFnaXM95E9nSoLNVYqnE96dnFiE6n0+l0Op1O
-p9N5fOhCu/GJT0wAlEOF2WCq2CGx28HFBegukrOpAlXBQmQ70W8cpeQDYi2BPG1x3bTy8Qu8LdIF
-NoTwtuECHTbIMKLDBvImAs4iuhuhhNCVivoB9T1iB9SmKBkvc6x13Fd1sIIxYT61baTgmlesDdgy
-qZgURCuq0fedUiGnSs4FckU05nOLVuRcaNNEs89NSM9NfBOBb7pBHTwlXOfmgkfJOTUq7T0LERLX
-LizIgLijOOpOtsIwLIzjwnaTuLgoPHFZefLJymF2DrMx7hfSoSvtTudhcE8qN8SVvl+ST76a9SXu
-/gER+XPA7wN+yd1/g4i8BfhDwFcAvw544mzfV5z4LXGFD+B97v6NL7Hf5wHvBr4QeAbYAh8Gfhr4
-UeB73f35l3/ndx3zDe25nwsswO939+//VI7R6XQ6nU6ns9KFduOjH4swtHlbsb1h+0q5zDx1A8Rg
-aLlkNINZHEIaCr7OpSYjnpGacR0RGaNsW7eI7jDZQtpRhx2WL6jDjrS5QDc7dBNOt3GByQ6THdqE
-sGolMZPYk3yKcVtlgVLwUqEYmMU87LpQ6kSxPaVeUw2q5eMyyTgxm1skHG2VkZRGxmHDOEYJeEpG
-SgVNq6tdWvBajeC4NlIsEtA9xH6lKekRJEaIrcFtpIIrePIw6UlHJx0doc0H9+q4FJAMRL/4OFQu
-LoynnnT2B+fOdfRtp/Roviudzj+hrInccs/9ex+/67aIfC7wI8Cb7rPvS/3spc7jkxCRLfBdwNe9
-yL5vbesrgKeB+wr1FznuW4H/FfitwDXwte7+v7zS53c6nU6n0+ncSxfajY99LBzteZuxg+F7ww7l
-KLJ3AyRZw8nafGgREoqLIKRWRp7wmhAyLrltNyAbhBFLW2reUvOOknek3RPkeoPkT+D5CSoThQWj
-klJtgrfiuiBMKBMQIptSkcXwWqBWvBpWZupyYF4OzMs1S3VKzZQaQttFMVJcHJABlRGVgZxG6maD
-bUfYbPBc8RwOt+gqsqNPe00id1/APVx8T0gLh1vHbrm0yPE1KV0dd8fMY5yYrqnsAywxI9zN47VI
-EcymyjhWLnbGkzfg6rpw6/aBcUhdaHc6D4+/BbwN+F3ANxEC9t8Cbt6z3/93z/0ngL8MjO15f50Q
-qm97kec+EBL2+g8BX9rO7xeAbwd+sr3mM8DnAf/up3jc3wj8NeCfBp4H3unuP/HQTrzT6XQ6nc5n
-JF1oN56/XQCwxUnV0eIkV7YjXG6jQtvVQmi7NR87RLaKIK5oWyIJVpfbEzAcl+lIzRtK3lDyFrMb
-VH+SZE9S9YqpXHGoV8x2yTA4w2AMo7PJxjZXdrmStOJW8dpWqVhZsLJQl5l5PnCY9hymPXNx5iUx
-l0SpCSREdgjZAZUZZSDngWV7oGwH6nZkGCvDUMhDlJW7ny2W6AVnARfER4QNygbQ9hopRL0ILmAi
-VDfMjepRcK8o6oIgLAZzVebFWZbEUjKlgJmT1NhuhRs3MrevCxe3Dmw2dxhyn6Pd6TwM3H0P/D8i
-8jlnP/6FVzDe62ngNvD57v5zZz//qYd9jsB/zElk/xXg97j7cvb4Wjb+R0XkmVdyQBF5G+FkvwX4
-VeBZd/+Zh3rWnU6n0+l0PiPpQruxuLQtTGYcrLCvylSduThLcTwZ4Ig7IudDugQ1DfHoioq2zm1t
-yeQLEC63aaYyUH3AfMDkCq+38el59mXHrastn7iz5YX9lu1Oj+vJy8wbb4y86cbIcDHgHv3Wrk6l
-sNSJMh+Ypz376Yr9Yc9hmthPxmFWDpMwF0V1QCTmeQuGYiiVnAqb7cRmo2y2yjgWxrEwjCG08YIf
-w9AWaA63AOIhsoUx3GnJiA6tyr5SvVIomDvVjYqBKJpmtCWdTzPME8zrdnLmyZmmRLEtKQ/sLi+4
-vHQuLiYutnfYjdeP7gvT6XQgRO9/c4/Ifug0N/s/b6/3IeBd94jsu0/K/WXd9Nbn/cPAG4B/APyb
-7v7/Ppwz7nQ6nU6n85lOF9qN0oT27M7sxmTOwSpTNeaWdO14CEs5NTGCgAsqq0PbhLaHzD6K7bZ1
-U6onzBQzpdQdddpR9IJb1yP/+KOZmx/LfPgTmcsbA5c3Ri5vDLz5TZfYZz3FNj/FE7sUQ67EQIzi
-hbkeOMxXHA532O+vuT7s2R9mrveVq4NztYd5EZKOpLQhqaFiqIfQTrqw2TjjxtlsjHFT2GwWxs1C
-ToVIQF+FdomgNi/tHYbIVjbHJHPRDYawWKVYZbFKxZrQjkR0TQlJUQEwz8I8KfMkLLOGqz0nSkmo
-DqRBGTaJyyvn8uKKi+0tLjbjI/mudDqdu/i+1+A1fhvwawmh/d+7+wNdZRORdxAl7zvg5wmR/Y8f
-+Cw7nU6n0+l0Gl1oN1ZrZDGayDYO1ZlqZa6VpVTAo0y8VSw7grtgAqmVjSfRkNR+ktjS/oFQ6NW9
-pYHDMm+YPNbHPpH4Rx+CX/xH8Mu/Ijz5xh1PvmHLk2/cMe3fxCZX3nBjpNpFG/vlIOFoT2ViP19x
-vb/N9f7A1f7A9X7iznXh9pVx+8o4zDCkLUN2coLU3Gz1SlJhHBeGsTBuFjabhc12Zrtdok/bY9SY
-uJ9Etpd4jxIiWxmRtEPTFk0L1ZW5VuZSmatRsKPYdgVNiiYBEeYpMc8aDvYyUMuGUhS3xHa3ZTtu
-2O62XD5Rubi4xcV2y64L7U7nUXPH3X/pNXidf+ns9t94wGP9O8B/SPTz/BTwZe7+sQc85icxzzMf
-/OAH7/v4M888wzPPvKIK906n0+l0PiO4efMmN2++fMTLPM+vwdk8OF1oN6qFei7u0S9swmzObMZi
-QjFibJVElNgplldgTSD3KMZu+eSA4H5ywAXw1rOMhPSuizIvwvUCd55Xnr9l3Pq48bGPOXOdmcrE
-oUxsxsSvecMld+5csz9cNpFsJDewVfguCIWkMOaEjRuWOXFIC0kWpAWRlVpwX6jEexIgq0CqiFWS
-G8UMLYbMRjWP0WamiK/vRRCP96CktjU0V1IuJCuYKKUYpTq1OrZeYPCwpVwdk/iAShHqQszYtiiL
-RwySI0nRPJDyhnHccrHd8eTlJdMTl6/dF6TT6bwYt16j13n67PaDhqz9wbY9AF/16RDZAB/5yEd4
-+9vfft/H3/ve9/K+973v0/HSnU6n0+m8Lnnuued4//vf/6hP46HRhXbDWul4daE4LHcJbaOYkDz6
-sx1DxMDPZsy6NLGtx2Nx8rFBmjhXwBRvfdy2FKa9cr1X7jwPt29Vnr9VuHWrRkm4LUx15nI38vzT
-VyG09wcGdUZxVP0otJVKVmPMMddbJVPKzGEShuwkLYC3udrhRotHUbujJHcqjklMMSsmSBHMBTGN
-9+uKeEWOHeptkrgL4GQ3Mkam4uIxecy9ZchJjEYzxd1xwOI6BWYOxnGetovHKDBxdBA0ZyRvGIYt
-l7sLnrq8xG7ceI2+HZ1O5z7UR30Cr4K/DHw1MXv7fxSRd7j7nYd1cBHh6aef5umnn+Z7vud77rtf
-d7M7nU6n07mb97znPXzlV37ly+737LPP8pGPfOQ1OKMHowvtxlFom9zlaC+WQmi7kM1RNRKVUIXr
-PG1wj+Rx9+jZjlzy1dt2RDy2SMyY9kgkr7My7wvXt5U7zzu3n595/tbCrVuF2QpTWTgsM09cbrj1
-/B3uXF2z3+/xLGiCnAWxgnpBKSSpjCmhZHJKLIuyH5wxFbJUqjvVKtbed4twwyVE8oDjArW1gFPj
-M8ESYqmN7zJO77AlsbftgDNgmFREY8R3teZi1xDa6wp3uznd4tEDLzGZXLR9ZupIFiRlNI0MzdF+
-6vISufHEa/sl6XQ6rwY7u33fUQEicvESx/jo2e1ngL/3AOfzp4G/CXwz8K8C/5OIfLm7Xz3AMY+8
-9a1v5UMf+tDDOFSn0+l0Op9RvNK2qnF8fbSPdqHdMPO2lQgpa+XixYxqSjGlupJ8DUWLmdoOrZxa
-4r7rXU63ISDR24xECbY0MS6u1OosszEdKoe9M03GshhlqcxTQQcgOVd3Dlzf2XN155rrqyt0TAyb
-CBJTX0iEkDa1GCwmSjaYMmxH2I3OPBq1vaditZ1drKxKTs6QnTE7KVVUKkoNF98q1vLQZH3/bblF
-SFokoYPjuFREEmZCdYnnFvDiIbSPDnaslBIvMEy6AAAgAElEQVSaHDRjalQpVFVIiTEv5DSTZGZI
-ld0INy4yeuP18R9Zp/M6wj8Nx7x9dvuNL7HfP/sSj503O38R8GMPckLu/i0iMgB/HPhC4Eea2N4/
-yHE7nU6n0+l0VrrQbniJCkhPzXm1hJtgFj3KtXorb7Y2M9sxQJvYhigZjC2tlPz4QAhyiR5tAdQj
-SC2Er1BqyF5NiXGE3UViHJVBU5R2F2M5TBzuXHH1/AsMlwM7xuhdpmBNZJMi1bt4RZjZpImL4cCy
-nRCb4+JBjdfFM0IGZnJKXGzhYge7naCyICwgC25GdaFalJGHgw3gUQJuhlXDzOIDtIzXhKi2Cxet
-YqA60txsIQaexYehaBqQPKBDlIybGqYV10rKQlYj+cIgV2yGKy62C3r56dAEnc5nNIez25uHdMxf
-Orv9O4AfvM9+v+cljvEzwC8D/xTwH4jItzxo8ri7/wkRUeCbgC8GflhE3unuh5d5aqfT6XQ6nc7L
-0oV24yi0s+CWcFPMFXfDVqFdDU/1FALWysabGR4C+yxdHJombT+Pm/G4tRC16lFavVj0h2vKDJvE
-buekAXKKPmorleUwsb9zxdULIxdssVxJO0eb0HaN8LDiFbFwqked2Q0Ttp1IzFSDWp1SvV1VCLGd
-UuJiq+y2ysVOiTnZc4Sm1cpizlKdxaLEu/2vTf0yanFqtbhAURUrCip4E9newuTEQM0REVS1rURO
-G/Jg5FEgO6YVSwXXpY0xW8AOZA5s8hW+Xci1C+1O5yFzHjT2G4FfeNADuvstEflZ4F8A3i0i3+zu
-d4WoicgXAP8J93HU3d1F5JuBbyPGfH23iPzuF5ul3WZuv+WVzNJ29z8uIhl4H/CvAz/UxPbrI860
-0+l0Op3OY0sX2g2v0UZoxXGLnmt3xSyHODXCsbXaHG05imzFz9zrs2Me70u7J83dDvEM4RBHiXo4
-2pKUcRR2FxL9yhqC2RdjOcwhtJ9PPJUrtnOSKVkLLhVSqHbVBZEFpDDqzMUwo7uFMS3UapRVFLs2
-BzqRcma3TVxsYwuFagtmM2UpaKk4leo1gs3WtHWLz8wWp5YoLbcqWImLDe6nlRySx3sXFTQnsiaS
-ZjbJGAdhHBOanWgHF0yFYgvVD1S7wqSwHfbobmaULrQ7nYfM3yFc7Q3wx0SkAP+AU5/1h9x9ehXH
-/TPAc8BnAz8uIn8M+LvAm4B3An8A+NvA53P/8vU/A/xO4EuJMLP/W0S+HfhJ4Bp4C9Fz/XXA9wLf
-+EpOzN2/UUQS8Efbsf+qiHzli4n4TqfT6XQ6nVdKF9qNnIe2zahmVBMqSvx+KadqaWnjunStoG7l
-0xK3j/u33Y+GNkQzMspxRhbNHZZIMo9QNQ+Xu4KqoNJSwd0py8J82HO4Ug7bwrydmLfXkCtW9lAO
-aD2gVskUkBIO/BCJ5IMa1ayVwdvxV2dxSOpstpXtWNgkASouC6aFSmXjxk6MOa192IG5UCsxnquC
-JlAl+q1lvWBxEtranO0ksY8mI6VCHhaGYSInkKRxDUDBFKoMFB+oOqCjIbWAL0h+PQYedzqPL+5+
-R0S+Dfh64LcD/9s9u3wJ8IFXcejvBJ4Ffhfwm4HvP39Z4GeBrwF+5SXOzUXk3wb+PPC1wG8CvvXF
-dv1UT87d39uc7T8CvAP4QRH5qi62O51Op9PpvFq60G7knI/blBKqioge+6797Fe3VWjLGra9Otpi
-bcJ2e849ryHCMVnb19gwuVtsRxu4UyuIhNoUFCyE9nQ4sL9yDruJaXfNcjGguWI24xZ92MkNcUOk
-Islj3rY6JVskfFsI7TWIDCCJMY4wDDAmWupZwb3iUini1OSUcXXq4+KCe4THrUs0HostpxJzl1Y2
-frrOIBop7qJO0gVNQkqGqODquBguUCRTyBSGdn2ipZGP3dHudB427v4NIvL3gN8H/FbgKSDxyX+k
-+Yv87H7HdBH5WuA9wO8Hfkt76O8DfwH4Vnef1j9vX+I4B+DfE5EvBt4NfAHhZCfgV4GfBn64HfOT
-nv5S5+vu/0Vztr8e+DLgL4nI17h7eSXvsdPpdDqdTuecLrQbw3BytFMKR1tUmtA+ZWyHyBZUJDxo
-jfLpo9g+iu7zo6892015Ht3vNaHbcIkC9BDC4TqrZpI20W5OXQrzfs/+amHaKfNOWa4SaaxAARbU
-S0zFllgpQVZhzEL1cMzNHPcYzyXuCEaSELk5GVkdqAiRJo63Uu4xUtld2vtuDv063uzYjy6cFDyt
-zBwiCM1Aavtw2siz+HiWNtqrtIC06Dl3cYqkENuS0GaZe1ao950U1Ol0HgB3/y7gu17i8XcTQvdT
-OaYD39HW/fZ5Rf9Ru/uP8Skkj7f90yvY7xuAb3ilx+10Op1Op9O5H11oN1KOgF1NehTaKtLUYeEu
-5Sxnd1f1vd59Mb9EQoGuY8HcPdztVlK9utjmcpTrIpFiLiohLiUS0JfFmKfCMgllgjoLTkUoIVKp
-Ld38LP3cE07M+Db3SEx3RxxUrM3SBnVDagU3hIoSW3BSS0lHJJzs1dEWO3vf0pz5M6u8DQOLOnI5
-/iycbj9doPDof3dPzfKusdRwifM3SVTP1JopNlD8ZX9v7nQ6nU6n0+l0Op3XnC60G9qEdsqK5oSm
-hCQQbSXUTSy6yJnQFnzt416Tx4FjvfS5cXt6MPZYRXboS2qJvmxcEYGUYrZ0SjnORQT3cLqXxSmL
-Ywv4EqJUWJrYrnGuQihtU8wTxRKLKeZOtSa0IZxsEVQ5zuE2PYns2HoTyS0xvWlmE4/Pg3DKz3vO
-4UxXr5+dASZg0XO+OviRYh6hbHirUG1C28VYUGLYmHKwgb0NHMyYvH99O51Op9PpdDqdzuNHVyqN
-NDRHO2sT2Yqqt/TuqGb0c6Hc3OKoBlfWAmk/G+KF+1GQx5Pu2hyFdq3EHO0aJdiqQs5KyiGyNaUI
-XyPCzEqp1GLY0uaCSQFKSFGvuAiGgihuiVqNpTpzVWoT2sU8RLZCUiErmFSSVnIT2utqOentfSkm
-vurlGFPW3re73RPytn5ackwpD5EtLfSt9Yy7R+qZNbHdhLZIxTFml1gIByvsvbJ3Z/I1CLnT6XQ6
-nU6n0+l0Hh+60G6sjrZmbWJbkWSIZkS09VCHox2u9iqyo1xaCFd37eY+FkkfxbWcbUN4mslJaBen
-VsU9QthS0ghlS60vWSIorVZjWQp1qVipeDHQBfHwfMUrSELQKBevmVrCBZ9rojSRXQySxpzunISq
-TpZKkkpdhbavYjvmXguKiGFAlQgtNzm5005ccFj7rteLDuH800R2fGbWhHasJrRrim0Uip8JbZgM
-ZpOjyL5GmF60Tr/T6XQ6nU6n0+l0Hi1daDdEh5CFKohqpI6rgWZcM07CJUdPsthRaIvEuCpfS6xD
-QscxTxFq0G65K0bCPUVJdxGWBabJmRfHTJrQzq1c3Cm1UIqxlMqyxBzsUqHWEOtu2kRuJLOZxXzu
-6sqywGFyrmZnvzSx3kaIpQQ5OUMSkkZ42hqkFo3jbR3L4uPGWgG+DjM7utlwV8BZ9GufjTo7G/Vl
-DoZRWR1tmnqXCHFTJymoCLPB7MJiwt6EaxOuzZmsC+1Op9PpdDqdTqfz+NGF9kqK0VGSQmhH6bgh
-MoAMR5EdIWCOS4R2ncztVWhbCxNrirLN1Q6ipNs8hLabUishtGdnnp1qAkRv9rGHuRpLMcpSKYOx
-FD/2dK+iWo+BY4qbUE2pJsyTcNjD1d65nuwokJ0mtDOUFMJWmmyGKGO3Er3jdia28XZJ4ew44Wi3
-PvU2Myyq7dsQMz/fN8S20crPaRPETaAaYkISGDLkFGXtpYns4sK+KlcFriscahfanU6n0+l0Op1O
-5/GjC+2GaG7bENly7mhLxiQ3SeitV7seM9FUAK9NqLZtE9nSptUcA8pcEU+4JcwSpYaTPc/GvIRL
-HY52olqNUV9WKVpZyiq4nVL9WHoejnYraXfBTbAq1Coscwjt6zvG7YOcTlggZRiyh6BNTTC74x7i
-f1mgLFBKe2x9W5yFoNGC0JxwtdURjQsQiJ9EtnvIeKddklj7vP1YVi5VwIxBhXFwNlkYslOayK6m
-7ItwXYSrxTmULrQ7nU6n0+l0Op3O40cX2g3VGBUVs7NDaLuAkSieWCyzuIfgc8UkRCUQs6jFUK+I
-W/RJu7WRVacy85bzjVpCSEDGqVSrLMUpJXZ3BFWN0nQVEsowKDlXNAmikcZtOKUtAFoF9uIwm7NU
-ZyrGVGBahHkBzYK2VHONvDQkAeqtJD22h0Os/cFZ5jZ6zKLkW1jni0OkiXtUmHs42XHcKB+3Jtwj
-XXytRA9xbdpGhCmIGeKCOGyycLFtJeoqrdw90s4LUMxZijOXHobW6XQ6nU6n0+l0Hj+60G6oattG
-6TgSQru20VhTycwVFk8sZAYsepq19TW7oVTSKrStgldcahPaCiKIJ6pkVJrQloXqwlLDubUYTo0I
-5JQQTYgKu6GyGQvjUEh5QVLFtFKApXnt1jqiF48AscVgclqPMxQXBgmxnUcYhigdzwOAs3iEsh0K
-3Jmc21fOnTvOYWpjyIwoU5fTYh1RZo4ZaGpLaUJ7fd6p3Tvy0BxP4OqgtOi2CF672CgkIY1CTqc+
-8Lha4Vgxqhjl0XxVOp1Op9PpdDqdTucl6UK7oSmEtqiAaqSL0wLFLDHXzFyVxZziThWDJrZDIFYS
-RiKcbazgVs5EdrN6PZEsI5YRMiYSzrRVShXqOoJbhJwSeUiknNgNle24MAxCyuFCmxhFnBknCtvD
-XZ8dZjNmcyaLxO7FoDhkFTQLw0YYBic3se0OFCg4hxpC+9aV84kXnOvrUy94NSWJkDXmbwuRhF4r
-WHU0QcqCpnDxaxPh1Y5Dv45CmwSeY5vESOIkUYpD2ggblI2ezS2X+HBscaraycnvdDqdTqfT6XQ6
-nceILrQb2kqhRdd0s4g3W0w4zFHSnLMwDMIwgGhiUCOLkdVAFKGiEuFn5zO3Q2Sn6Pv2THzsLc1c
-HKNiXokQ7YSIogg5J4YhM4yZzZAYMqRsaDLQiielilBVgLg4IK4UE5YKi0KRmJjl6khqfdmjM26E
-YfRwtHOkkMsc511MOBS4muD2tXD76lxoC1mUrEIWQXBKEWp1anFSjgsBKcfYs9rGl9VWEh9C26NP
-vIlsMmSN8LOszjA6swtVgWFNgo/54klAC8jiSBE6nU6n0+l0Op1O53GjC+0jNTZrKrYrSzWuysLk
-Ey+wZ3/tXF8LV9fwxE7YZGczONvBGdQYxRjVSNTWc+yIeSSZJ0FTokpittx6vjOGgRqaIQ8CjMCI
-yEBKioogHvXX3mqwPeaQISmRxoG0UZJmkmYEIy1OHhybnVFgJ2DJGWfY7ZzdNlYeQJOTcowLG0cY
-xriYMGYYh8SQnSEL5opbJKarQBJIrZQ7aUsW1yjB1yaKoy/dTqnka193XBc4utmSnZyUIStDEjY7
-ZbsTdjvh4kLRJGiKrYyJmmLJqI/mq9LpdDqdTqfT6XQ6L0EX2g1ZhTaKewRweTWmuVCniTrvubN1
-7lwJt+8oNy7gYitcbmO7ScZGYw1iKCG01Vsp9aAkoj979ia2PVMlepRT1tYrPSIMqAxo8kg0xyLR
-2+2YDC4qaE7oKKRtIiUjpQERJ81GGow8GKM6pkB2touz3RibjbHdGClVJDmqTqrOZlLGQRmH5txn
-YcgwrkLbE+baJnZHtBst7Exd8Caw1+Vr1bcbSutR1yiLJ9Hc7BDb49CE9qBstsJ2J2wvYHchpKxt
-CbpJ1JQpKSNjeu2/KJ1Op9PpdDqdTqfzMnShfWR1tB0nyqTNjf2hsL89s7+z5/bGubNTXrhQnrwU
-blwqN9p2l51tMrZpdbUt+o5ZtaSSSVhKLJ4jVM0zBpAEzUoaBBgQMioZkco6l/tcZHN0tBUdHd2A
-DuFMizppqOS5YrkyJofspAGsGJtjqJq34DdAHC3OODZXexDGQRizMmZhyIr7Ka5M3NsCM2vC2jDl
-WOKtqhF4xjpH285EuB6FtufYDoMyjolxULZbYbtzdhdwcQlpUHJbOmdKGig5QxfanU6n0+l0Op1O
-5zGkC+2GhuRtY6sMF8HMmZbK1WHh9p2Z/d7Y74Xb18rtK+WpJxLXh8xhgt3g7LKxS+EiJ8LVTlgT
-ipU8RF/1IsICzMDV5MxFcMkRyCYZkQEjx+xpq1gtFF8oXlisMEtlWZRiieoJEyWpwBAl6kkqrhVS
-RZOTk1MG8MUYUmFIlUELogWXAhQ8OZshcbFJ1ItMXRSvShLlxqW2xPCWih5DsyMB3JzaRoLVaq3M
-u/Vo41S32Mctkspbabmo4ImoQc8SZepDYhwSTz4Bb7jhPLFztqOTh3Rc7plpzMxLwmoX2p1Op9Pp
-dDqdTufxowvthmqbybymhOOYO3OpXB8qL1wtJKlc7aN/+WKjXB9GDjNMs3AxOLvsXGRnVItxXx4J
-5GlQ8lhIQzi5VZ2iRhHj6lqZSkhySSlKxnXAJVFKwapRa0HKwpwKSQsTlXkZWZZEqSPVM6klnemg
-JDUkGZorOTueHRscL0aikKgkCs7SpnAvuBibIVO3CfGEemJMictN4jBFKb21z8RpzjonoV0r1Oox
-QztF7/f6GVrr01Zp/duirI3ergJJGXJmyJmcM5c7uPFE5YmdsRuMPGRyHsg54zUxpVgl9R7tTqfT
-6XQ6nU6n8/jRhXZD5CS0XQxQqjtzMa6nwgvXC15LCwFztqNymEJkT0vmcnAuhlVoO2IhssUqaRDy
-oKShIBksO56Mmow7+4F5UUwSogOSMqIZNGM24e7UUnBbSLWgWhgw5nlkKYmljlQfMRnxNCJDJiVD
-s5Gr4YPD4DDGjC+xitQCteC+YCyYz6DGJidkkxg0h8jeZp66zCxFwpl2i3T0NrPbsRDgNZLFzUDE
-QaOEHbE20iv+UdFYKKKKq+Apxp7lNJDTSE4D29HZjoXdprAdKjmP5GEk5wGriV1WpiTULrQ7nU6n
-0+l0Op3OY0gX2g1tQtsQ3CqGUGphXgrTXNhPhbrU6E3GOcwtgCzH2K86QMlQsjCoICasLdZpgJSd
-lA3JFR9Wse1cz8p+TizVqYAS5dUSKWghtGvFamWmglSyOdd74/a188KVoKOyIbGVzGgD4gZrH3Vp
-qxpijpfSTrRilqlkzBPVjFoTxaLoXXMme2armVylCWyjeqV51NgqtA3cBKuAWohtMZAmsVuvtiJI
-+/91rri3bdKRlAayRgicI5QiTK6UOpBKJqWB/awc9so0CcvUx3t1Op1Op9PpdDqdx48utI80oW2w
-mFG8cJgK8zyzlEq1EI2IIAgiCSQDAzCGIDZjKRaOeI0B1l4LWnOUhacEWbEkWI6RW4elcpgLhyXm
-Sw8jDBLzos2iv9kqeCF6oj1KwD/+QmXz0YWUJz5xBZsLZ7Mzhm1B28UAcVBztHrMnq4eA61rjAkz
-J0LfSG1GtlItZmWXVgpezWKG9rFsHEwij81ordoGHrXl0MaQI+unKm1/j33ccItHPJLYQBRVR9VQ
-qSR1shSSLiQxRAsiGZEDU0lcHRJXk3KYu6Pd6XQ6nU6n0+l0Hj+60D7iABE8VmEqzmG/ME9z9Eqv
-Y6ykKclzoe0DZlAwZqtUDGqIbDeFoqApHFwNoV2TUBPMxZjKwlwMUcdFEU2ophDZ5phFebZVWMxZ
-xPj4CwXNC5XExW0Yt8a4NYaxoBLjt1QgxZju2FbC6W6i2L0JYRKGUF3btvVkewjouwrAm8h2iNve
-BHZbIt6W4ED435HpbsWxGstbuBqE2BYxRGqkrcdZIBQEwz32cxfmmphK5rAk5tqFdqfT6XQ6nU6n
-03n86EK7IbRRVFYpizFNlcNhZpoXlhqOduy3lj2vQjsDI+aRur2QqF6xGqndVgvepk67KFVCYBeF
-mmCpRjFjqbWN50qknMk5haNdDWup3lYcLx5zqV+oFF+4noXt1hg2lbypDEOOPnKBJBJTtFxIFltx
-EG9l6dBGdoHJupUmpgWX8K0daE+kPTW2LYAcAzGPanHxuA5BON/VQ2RXoCxOWYyyGGbtIC64Sxtl
-psDSZnNX3CpuRm2ufjWnWKJ4pthA9Z463um8XpFjMAbvc/dvfJXH+GLgf293v8TdP/BQTq7T6XQ6
-nU7nAelCu1Gm+J1vmSvzVJgOhel6ps4FipMI9zRF4Ti4U2plKoWraSKLk1dv2CtWDa9t2zxaRzAR
-qkJVMIVip2ruYTCGlCnDgA0DANIcbtEM0nqkq3J1MKov7KdKHmfSkEhDJmUNgS0t2Nub0I6MsqPI
-FpfWO+2r3sXbk1z1mL4uRwefVhbu7XYrTwfEHbV2/HV3iUsXixvFnOLGPBfmqTLPhVq9iWxaMUFz
-ttG45OGGm+EeotzMqQYuGXTE1dqFjk6n8zrGH7PjPDB37tx51KfQ6XQ6nU7nMaArlUadQ2iXqbLs
-C/N+Zt7P2FSRCrkJbZGI8wIotXKYZ1IWUvOG1Q3x5kSb4dVOItsVl9a+vS5vvdLmbDbOOIxsN+Hk
-hpOsqGZEK+t07mJCOVSu54LeMSQJmhRJEaK2imyVk8DWo9BurjxAE9gouEo8PyuSosxdNIR2bFft
-LcfbUaJ+Onby9vmIIBql54tVFjMWq+wPC4e2SrHTOG5rfd2rmgdWBe5rX3rrKdc0kIZKGgzN42v2
-/eh0Oo8tx8t1jwO3b99+1KfQ6XQ6nU7nMaAL7UaZa2ynynJYmK9nlv1MnStUyJ5w8ZMUbGng07Lg
-B1BCYK+J396CzLx6CG2X1g/NXYFifiYocZg3W0qpWLsvomgKR9ulUkksVlhmo5SFpS64+6mU+1wA
-y0lcn0Q2qyd/2kmApOiQ0CHHVlfhrqhqE8/SbkdZuko4/Il1GyJbRVCVGI9W63Fd7Seuryeu9zPL
-UprIjn5t1s9V1iRxP25ibnd8VnncsNk6my0MXWd3Op/RuPuPAb2HpNPpdDqdzmNHF9oNr37aVj+K
-0yzKqIoMuaWOh6hVDWEYSeMR2tXmXIGdyp2tCUlDMJejuD5ZMHZ0b5MqyzzHWhZqrbh7lI9L88xd
-WAymxZnmSCyvVtvxWh+5nPTzUWD76SLBJwntttKQ0aGE0E5NbK9CW0M8qyhJpZWnx8pNZGeJx7UJ
-cnNnMWOuzlSNq71xdTCu9oVlruH4t8/oqK85d7fv+XcEbLxCMnSM99XpdDqdTqfT6XQ6jxtdaDdW
-WZdEGHJiM2TcnKzCmIQlK+an/G3EkRRl1BHe5c3J9qN4tOoR5HUco9VKpR2OLvZpUBbLsjDPM4dp
-ZjxMMQbLo/HZW0hZ9Thmcad4CxtrAv6sInx92jpx67wo++x22OrehG6pdnTDpfpxnvcqnqVt7xLZ
-ImRRsihVFFVIqiR1TKBYXGCABJIQaf3mCrhgYlgbrXZ+fudCe+0TFwHNmTQM5DwwdEu70+k8Piis
-VUqdTuf1wM2bN3nuued4z3vewzPPPPOoT6fT6bxCaq3rzcd6BNFjfXKvJav4TCoMSdkOid1m4HIb
-68Zu5IntwOVm4GLMbIfEkJQk4Ra7+9HBrubU6hQzSj1bxVhKpZR63JalUJZCXQplXpinhekwsd9P
-4WqbR+SYtNJzh9KCwaq1MLU2Yat6DMWqHoOxStsuCIvE9ngfYXGYndgazNWYinGYC9NcOEwLhym2
-+8Ny2q5rfXwuTHNlWirzUtvIMmMpTjFab3oIbTS1cLeEqx6D4uxYWt/eA6f73lLeRXP0aOeBPAyM
-Y6xOp/NoEZFnRORPiMhPicgtEZlF5FdE5GdF5PtE5F0i8sTLHONzROT7ReSXReQgIh8Ske8WkX/u
-JZ7zxSJibX3Rizz+Z9tjv9juv1VE/qSI/F0RuRKRD4vIj4jIOx78U+gl7J3O642bN2/y/ve/n5s3
-bz7qU+l0Op8CZ0L7sf67tzvajfWKQ1JhzAlo5c9ZqCXGdFVLFKtUM6oZxZ1KuMur2Ma8VY/7KS3b
-z0LPnFZivjrara8bp6TCMi9M00zOB4ZhIGcYsh5nVq+Odj1ztM1PVvY6ceveaKAoF19HlK34aV9r
-PeFem3tszcG2CERr/deyOtm6bpVBE1WdQRMpQTIhKYhq9KKrHEeJwTpTPK4QhJ99t4e9loSHSy+t
-dD5C2iRlUsrkHK52p9N5tIjIFwI/DDzJ3X/yvLmtfx74OuAjwP98n2P8R8C3cvdfmM8Avxf4ahF5
-1t1//CVO42VtZBF5e3v9p89+vAW+HPhyEfkWd//6lztOp9PpdDqdziuhC+1GGuOjcHPIYRenmqhV
-sRJzsc2MWqO3uJq1su2YNG3VY+61GbWVjNf1tocDbb72bMe+vs6LdsOtkjThbizLwuFwYFkKOS/k
-lFlKZX+YmOeFUkr0b9vdJdfxBtr2k1uc2zCuu38fdY993dsscROk9aGLWLsfQt2b4HaJedsqQhGh
-aCWLsmj0dSddA9xOJe8uMM2FeSnUUo+fw9qsLnK2vff9+Kni4K6LBL1Es9N5pIjICPwF4AbwAvDt
-wP8BfBgYgV8PfB7wVS9xmGeBfxn4GeBPAT8H7Npz/tN2+38Qkd/k7uVVnuoF8Bfbef7XwI8CE/C5
-wB8B3gr8YRH5h+7+p1/la3Q6nU6n0+kc6UK7kTbtozBHzNEmlK1qm4etMbKrCW2rFuFmzaVeS8aj
-L9ua0PYzob32a7fjrqLcKmYFs+iHNotebXcnpURKCdVErcZhmpnmmWUV2h6Dw5pWZr3xYo726e76
-jLP97xXb7c46J3sdCGYiR7GrTXyrQBGNkLRjGJoe+7u99Ve7xDi0pdTjRYK1R309d/nk6wBHe1uO
-/9DGgnWh3ek8Bnw+4Tw78Lvd/UfvefxvAT8gIn+IELsvxr8C/Ajw1fcI6Z8QkY8D3wT8OuArgL/6
-Ks/zs4AZ+Dfc/SfOfv6TIvJXgP8L+LXAfyUi3+fuH3uVr9PpdDqdTqcDdKF9RJujHWnjTnLaHGzF
-reJVsVrxmrBqLfQsxnbhUOtJbIfAPu3UAmcAACAASURBVBPbq7vtTql2Kj03o9ZCrUqpBSfE57KE
-a31K/Q6hvSyFeS6UWqjtHFa395wX06sr9/58Fdnn2nudtyVNsR/F9vG1zmV7zOtW2tK11DvGgZ0i
-0OMigrWLDmvi+Hqo8wsF957w3SFp8cMutDudx4K3nN3+G/fbyd0NuPMiDwmwB/79+7jV3wb8l8AA
-fCGvXmg78B33iOz13G6KyH8G/ABwCbwL+JOv8nU6nU6n0+l0gC60j+gYZou3UVi6uqZW8Fqa2F6X
-RS92G4jtHm3XR9f6LoHtR2G9Ot2llaBXsyaaQzybWySbu2O+9kaD+zp921uqtyAoJoaKnjztpkQ9
-hk8f5fC5HPUzx9rX499HlgvnQt7vGqe1PmcV5muomXiUnosIYnJmf8txFnYY2eGYK/eUix/P6Xi3
-ze0OQb/OMPf2GXY6nUfKeYLQu4FPtezagb/m7h990Qfd74jILwC/BfgNr+4Uj/y5l3jsB4FbwFPA
-l9KFdqfT6XQ6nQekC+2G5hsApznX3gSuVTyF0KaJbaz1R59N56oe5eapCe7kZ6XiTVTX5mYPbq18
-3ENkW2m937GqR1k5tHFgCFbt5B6L4GaYR9849zi7x/NvcvisFfp0/0yU+7lX3KxlcY6iF+52lc+P
-deJstpjcI92P48xOT5Km4I8iW84C0c6Ut0hcWIgFiuPeLlSUV9uu2el0HhI/DvwiIYL/lIj8XkK0
-fgD42+6+vIJj/PzLPP5x4o+GGw9wnjPRA/6iuHsRkb8D/GvA2x7gdTqdTqfT6XSALrSPyBC/w8mZ
-QKWFlGEVvMT2bK2zs/EmsJu4TmvC+LFUOvqS1yA1czuGga2PmZXTflaptURqeZvNXbU2dzdEp7ce
-bzc5KWn85BrjuEs83+8KFsckHrtLfB8/Cb9L9N6VBH4U50TI2T0l5GdHaNvTed1jq5/Czdo8bWjz
-stvBhDajvL1fVWnGeLjZViulC+1O55HSBOo7gb8E/GbgdwCf0x7ei8gHgO8GfqCVj78Y1y/zMuvz
-HmSEx8f95XtNfrVt3/QAr4O788EPfvBl93vmmWf63N5Op9PpdM64efPmKxq3tyyv5Dr+o6cL7Ybm
-S4B7Arr8JLB9FdgFvCJWwS0WIbDFHVnTxVsPsUPrw17F85o2vs7dtpOTba3/uhRK1QhSqyGo1yAw
-JUqp3aQtTkJ7fU2nBbWdRor5mo6OoBBjtzzuxzs+FmsfN9I097nIhlVo08Zy3b8b3M+fcPfRoQnp
-KB2/uzFbpCWdt3Fi0fe9BrDF+1zD5DqdzqPF3X9eRN4G/M62vgj4Z4jRWe9o6w+LyJfdr0T8tTjN
-1/LF3v72t7/sPu9973t53/ve9+k/mU6n0+l0Xic899xzvP/973/Up/HQ6EK70+l0Og9Ec4t/qC1E
-5LOJsV1/EHg78NuB54CveUSn+GtERF7G1f7stv34q3yNoxP+5je/+WV3fu655/jO7/zOV/lSnU7n
-YTDPMwDPPvss4zg+4rPpdDq11lf0d+hHPvKR9eYDVaF9uulCu/Hffcd/+yKTpzudTqfzqeLuvwr8
-eRH5XuBvEkL7nSKycffpEZzSCPyLwE+/2IMikoDfRjjfP/cqX+P4d8jZLwCdTud1QP9vttN53fJY
-67cutDudTqfzaaH1cP8YIbQz8AZOvdCvNe/iPkIb+GrgjYTQ/uuv8vgTsCF6yj/8Ko/R6XQ6nU7n
-5fksogP1UVy8f8V0od3pdDqdV4WIfAFw093//n0eH4AvbnfvAI/KNhLgD4jIX3T3//OuB0TeAnzz
-/8/enQdalpX13f8+a5/hDjX0UA09AYoagyBEGgQEIQoir4QhaohTQCEKaozG4OtAXhuM0xtHXk2U
-CDKIvsYgGiMIxoBBEaURBA2IiCFC00LT0F1Vdzhn772e/LHW3mefc8+5U92qe6vu7wOnz7TP3muf
-6tu3fvtZQ366DrxyPwdw99ULa6KIiIhcSRS0RURkvx4P/D9m9gfA64D3kML0MvD3gOeRqtkOvHSb
-mcd3cqGTmX2cFKJ/z8x+Cng96Sr4I4DvBW7Mx/g3hzhhm4iIiFxBFLRFRORCGGmm8cfNea9ZPfA3
-ge+7wGNciHXgK4HfIQXr7+2817Txxe7+4gs8joiIiAigoC0iIvv3Y8C7gScAn0eqDN8rv/d3wNuB
-V7r7Gy7wOE0Y3ut7k43c32lmDwWeDzwZuAlYA24jhezfvcA2ioiIiLRs+9VORERELk9m9nLSJGgf
-cvf7H3Z7RERE5PgIh90AERERERERkSuJgraIiIiIiIjIAVLQFhERERERETlACtoiIiIiIiIiB0hB
-W0RErmS7mpVcRERE5CBp1nERERERERGRA6SKtoiIHHtmdl8z+wkze5+ZnTezu8zs7Wb2fDNbPsDj
-fLWZvdHM7jCzDTP7kJn9kpk98qCOIXKcXMyfXTO71cziLm+PPahzErlSmdl1ZvZkM3uRmb3ezO7s
-/Az94kU65qH93lVFW0REjjUzewrwS8AptnYzN+CvgCe7+wcv4BhLwK8D/9eCY0TgB9z9B/Z7DJHj
-5mL/7JrZrcCtc/Y9y4Evdve37Oc4IseFmcWZl7o/W69092cf4LEO/feuKtoiInJsmdnnAb8KnATO
-Ad8HfAHweOAXSL+cPwv4bTNbvYBDvZzJL/s3AU8HPh94DvDXpN/Ht5rZP7+AY4gcG5fwZ7fxIOBz
-F9weDNx2AMcQOQ6auVP+N/C7pNB7MRz6711VtEVE5Ngys7cAjwFK4Avd/e0z7/9r4MdIv6hftJ8r
-32b2xcDv5X38FvDl3vnla2bXAn8K3Bf4FHB/d79nf2ckcjxcop/dtqLt7sWFt1rkeMs/U7cBt7n7
-nWZ2P+B/kX5OD6yifVR+76qiLSIix5KZPZz0F3UHXjr7F/XsJ4H3ka64f7uZ7ecv2/8631fAt/rM
-FW53vwv47vz0KkBVbZFtXMKfXRE5QO7+Ind/vbvfeZEPdSR+7ypoi4jIcfX0zuNXzNsg/3J+VX56
-FfBFezmAmZ0gdWV14Pfc/aMLNn0tcDY//sd7OYbIMXTRf3ZF5PJ0lH7vKmiLiMhx9Zh8v0bqQrbI
-/+g8fvQej/FwYDBnP1PcvQT+mFR9e7iqbyLbuhQ/uyJyeToyv3cVtEVE5Lh6AOmK91+7++xMqF1/
-OfOZvficBfvZ7jg90iROIjLfpfjZnZKXB/qYmY3y/ZvN7LvN7KoL2a+IHLgj83tXQVtERI4dMxsC
-Z/LTj2y3rbvfTaqcAdxnj4e6ufN42+MAH+483utxRI6FS/izO+sJ+bi9fP9Y4EeAvzGzp17gvkXk
-4ByZ37u9g96hiIjIZeBk5/H5XWy/BqwAJy7icdY6j/d6HJHj4lL97DbeA/wm8Hbgo0Af+Gzga4En
-ksZ/v8bMnuLub9znMUTk4ByZ37sK2iIichwtdR6Pd7H9iDSOa/kiHmfUebzX44gcF5fqZxfgp9z9
-RXNevw14tZl9E/DzQAG81Mw+w9130yYRuXiOzO9ddR0XEZHjaLPzeLBwq4khaUzoxkU8zrDzeK/H
-ETkuLtXPLu5+dof3/yPwMlKQvxH4ir0eQ0QO3JH5vaugLSIix9G5zuPddBdbzfe76aq63+Osdh7v
-9Tgix8Wl+tndrZd0Hj/uIh1DRHbvyPzeVdAWEZFjx91HwF356c3bbZtnFW5+GX94u23n6E7Esu1x
-mJ6IZa/HETkWLuHP7m69t/P4pot0DBHZvSPze1dBW0REjqv3krp8fqaZbff78O93Hr9vH8eYt5/t
-jlMBH9jjcUSOk0vxs7tbfpH2KyL7c2R+7ypoi4jIcfWH+X4VuGWb7brdQd+6x2PcxmQyloXdSs2s
-DzyS9Jf229y93uNxRI6TS/Gzu1vdNXs/epGOISK7d2R+7ypoi4jIcfWbncffMG8DMzPgmfnp3cCb
-93IAdz8P/HdS9e0JZnbjgk2/AjiVH792L8cQOYYu+s/uHjyv8/h/XKRjiMguHaXfuwraIiJyLLn7
-bcAfkH4ZP8fMHjFns+cDDyBd8f7p2SveZvYsM4v59v0LDvXj+b4H/PvZrq5mdgb40fz0btIsxiKy
-wKX42TWzB5nZZ2zXjry813Py078DfmPvZyMie3E5/d7VOtoiInKcfTupS+ky8N/M7IdJla9l4KuB
-b8zbvR/4yW32s3Ccpru/2cx+Ffgq4Gn5OD9N6mb6YOD7gPvmffzf7n7PBZ2RyPFwsX92byGtjf1m
-4HeAPydNwtYjjev8OuBL8rYV8I3urmX5RLZhZo8GPrPz0pnO4880s2d1t3f3V26zuyP/e1dBW0RE
-ji13/zMzewbwalIXsh+e3YT0F/Unu/vaBRzq2cBJ4MuAfwh80cwxauAH3F3VbJFduEQ/uwF4PPCE
-Rc0ghe9nu/vr93kMkePknwPPmvO6AY/Jt4YD2wXtnRz6710FbREROdbc/XVm9mBShezJpOVAxsBf
-A78G/Ht339xuF7s4xibwFDP7KuDrgYcAVwEfA96Sj/EnF3IeIsfNRf7ZfR2pW/ijgM8D7g1cSwoE
-nwTeDbwBeEUeEyoiu7Pbmfq32+6y+L1r7lqVQEREREREROSgaDI0ERERERERkQOkoC0iIiIiIiJy
-gBS0RURERERERA6QgraIiIiIiIjIAVLQFhERERERETlACtoiIiIiIiIiB0hBW0REREREROQAKWiL
-iIiIiIiIHCAF7T0ys1vNLObb9x92e3ZiZo/rtPdNh90eERERERGRK52C9v75YTdgjy639oqIiIiI
-iFyWFLRFREREREREDpCC9v6oOiwiIiIiIiJz9Q67AZcbd38R8KLDboeIiIiIiIgcTapoi4iIiIiI
-iBwgBW0RERERERGRA3QsgraZ3dfMnmdmv2Jmf25md5vZ2Mw+YWbvMbP/YGaP2OW+dlzey8ye1dnm
-F/Nrwcz+qZn9ppl90MzW8/tP7Xzu5Z3PPTO/do2ZfbeZ/YmZfTx/7q/N7CVm9g8O4vvpHP/vm9l3
-mNmvm9lfmtnZ/D193MxuM7OfNLMH7HJfv985l8fm167O5/J2M7szn8sHzeylZvbAfbT36Wb2CjN7
-f/4z3TCzvzWz3zCzZ5pZsdd9ioiIiIiIXKgrfoy2mf0Y8J2A5Ze6E5ldDVwDPAh4npn9KvAcd9/Y
-xa53MyGa5zbcAPwa8OiZzy7aR/O5RwK/Dtwws+398+3ZZvaDedz4BTGzXwO+crYN2bXAGeAW4NvN
-7MXA8909brPLqXM0s0cD/wm4cWbfn55vzzKzb3b3l+6irQ8GXgk8ZE5bbwJuBp4GfK+Zfbm7v2+n
-fYqIiIiIiByUKz5ok0IXQATen293ASUpQH4e8Bl5m68CTgJPOcDjLwG/RQqpJfBHwAeBIfDQbT73
-acBPAVcB54A3AR8jBdUvAlZIPRJuNTNz9xdeYDvvQwqsFfBe4APA3UAN3At4OCnEAnwHMAD+xS73
-/bnAjwCr+Rz+gPRncBPwxcAyUAA/Z2bvcfe3L9pRro7/FnAqt7cEbsvtLUnf22NI3/tnA281s0e5
-+/t32VYREREREZELchyC9juA3wF+290/OW+DXG39ReCzgC8zs69x9185oON/JSlEvhn4enf/8Myx
-+ws+931AH3g18K3ufr7zmdPAS4GvyC+9wMze4O5/fAHtfBPw48Abu8eaaeuTgZeRgvc3m9mvuPsf
-7WLfP076Dr4T+JluJdzMbiL9+TyIdOHgh4EnLDj+vUk9A06SQvYrge9194/NbHcd8HPAlwOngf9k
-Zp/n7lqWTURERERELrorfoy2u/+Eu79qUcjO27wVeCKwmV/6tgNsQgG8B/iy2ZCdj10u+FwfeJ27
-P2s2+Lr7PaTq++/nlwLwoxfSSHd/gbv/+qKQnbd5HdPV/t18T0aqfn+zu794tru5u98OfDUpOBvw
-D3OgnueHSSEf4MXu/uzZkJ33eSfwDNLFAyNV1L9ydjsREREREZGL4YoP2rvl7v+bVHU24OFmduIA
-dtuMC/9udx/t8XMO/MtFG7h73XnfgC80s8/aVyv3wN1vA96Xj/n43XwE+HN3f9k2+/yfpO7f5P0+
-bHYbMzsDfG3e398B37NDOyPwgs5LX7uLtoqIiIiIiFyw49B1vGVm9wE+H/h7pLHPy0zCMKRJuciv
-PQR46wEc9lPAf9vjZxz4I3f/0LYbuf+Fmb2LNM4c0tjtD+y5hTNyYH8Yaez6adJ48u73dDrfX2tm
-N+Wq9Hb+8y4O+y7Snw2kcdaznkCqjDvwWncf77RDd/8TM1sjjQ1/zC7aICIiIiIicsGORdA2s0eR
-ulY/hunAuJ0zB3BoB/5sn2OD37aH7Zqg/XnbbbiTPAb7B/a4nzPATkH7z3exn7s6j0/Nef9RnccP
-MbOf2cU+u642s+VdzigvIiIiIiKyb1d80DazZwO/wKQ79k6htwniJw+oCXfu83N/u4/trtvnsTCz
-FwLNuuC7uTCwl+/pnl1s0x2rPm+CuBs7jx/D/irUVwMK2iIiIiIiclFd0WO0zewBwM/npw78T+Db
-SV2U7w0su3vR3IBXdT5+UN/NfoPd+i63W+s83tfFATP7ElLIbi5EvA34JlJl+wywNPM9vaXz8d18
-Twcx2/fpzmPf5+2Kv7AkIiIiIiKH70oPHv+KdI4OvAF4mrtX22x/UFXsg7Cyy+1WO4/P7fNY39V5
-/DJ3/6Ydtj+M76l7QeE73f3Fh9AGERERERGRHV3RFW3gizuP/80OIRvgfhezMXt0311ud5/O40/s
-9SBmFoDH5qeRtH73TnbbtoPUXcbr+kM4voiIiIiIyK5c6UG7O673L7bb0MxOAQ/mYLo5H4RH7nK7
-7iRh79zHcc4wmc374+6+bVjP3fEPYqK4vfqTzuNHH8LxRUREREREduVKD9qx83inrtjfSJqEa7ez
-kl9MBjzazLatsJvZA4GHdl76/X0cq/mOjLTc2U6+ZR/HOAhvBCpSO7/AzD73kNohIiIiIiKyrSs9
-aP9N5/FTF22U141uJgM7CpwUKBeOQ85dvv+/zkt/4O5/tY9j3cVkVvDTZvaF2xzz0cDzOITvyd0/
-Cry6aQrwKjPb1VhxSw6jCi8iIiIiIsfQlR60/2vn8U+a2RNnNzCzxwNvBk4wPeHWYRsDTzWzV5jZ
-ie4bZnYV8KvAF+WXIvC9+zlIXuP79Z2XXmFmD5/dzsyeAbyO9O/MYX1PLwDuIAXthwBvzzOmz2Vm
-N5nZvwLeDzzj0jRRRERERESOuyt91vGfBv45aX3pa4E3mNk7gfeSqrIPBR6YH78R+DjwzMNp6hY/
-QlqK7JnAPzazN5Hadz1pkrdmtnEHftjd33YBx/pB4OmkruOfDvyxmb0N+CvS+O1H5dedtCb5ZwOP
-u4Dj7Yu732FmTyMF/jO5HW80s9uBt5PWLO/n9x6U2wxHp6eCiIiIiIgcA1d00Hb3O3Mw+y9MJvB6
-KJNxzc36yr8BfAPTXbEP24eAJwOvAW4AntZ5r2l3DfyIu9+6i/0tHHvu7u8zs68GfpnJWPYvyLfu
-8V5CCv+/u+uzOGDu/g4zexjwMuDx+eUbgX88uymTgP13wAcuTQtFREREROS4u6KDNoC7/3GeNOw7
-gKcA989v3QH8KfBqd38dgJnBdEBbuNvdHHoP287fQWr7Q4BvIgXJTyN1cf8o8N+Bn3P3PzuItrj7
-b5nZg4DvBJ5IWsKrysd6K/AKd/9D2PP3tJfz39W27v5h4Ilm9gjgn5CWJ7sPcHVu812kYP0O0kWB
-33f3uGB3IiIiIiIiB8rSEF05Cszs5cCzSIHzG9z9VYfcJBEREREREdmjK30yNBEREREREZFLSkFb
-RERERERE5AApaIuIiIiIiIgcIAVtERERERERkQOkoC0iIiIiIiJygBS0j569LoklIiIiIiIiR4iW
-9xIRERERERE5QKpoi4iIiIiIiBwgBW0RERERERGRA6SgLSIiIiIiInKAFLRFREREREREDpCCtoiI
-iIiIiMgB6h12A0RERC53ZrYGDIEIfPyQmyMiInIluxepYDxy99XDbswiWt4re8Ev/tGWL8K2eW42
-/a5ZvuX3mhtAaJ53Ptd9v7uPRvPH4u5pYe32fvIYbxbcnvNnaFvb2baBSTtDp63BIIR03ysCRTCK
-EOgFI4T0PJjlbdK9mVEEw0J63gtGUaTPhTC97aQN09/H7PfQlf799PY8o+fvIE7O+gs+59rFOxAR
-uQTMrAKKw26HiIjIMVK7+5EtHB/Zhh2WeYnNuo9sZps2XG95kt5uEnjzGMuhvLvNnCNZJz573rXT
-/COFbCMn8m6LHHKIZe4xOu3E8PzZ7j/T65Pn3t1/Pod2h5078/ZRakMO1d3zb0O2kd/rNmnrt2+W
-TtHy1xBI5SICc68viIgckvRfQDNuvPHGw26LiOzCeDzmzjvv5LrrrmMwGBx2c0Rklz760Y/mYtzR
-TgMK2lmwZrj64j8vY1FwbV5rAuR0Vds6wddy0Ka9nx+026bY9NPpLWdD9mzDbG47m5BN57iew3Vz
-Pxu2u6F8av9TNfLuq53APaeC3X4fnYsOUxc0pk7JcE9Hj0wytquOLSJHxyeBe505c4aPfOQjh90W
-EdmFd77zndxyyy284Q1v4KEPfehhN0dEdule97oXd955J6TfvUeWgnY2qTDbpN82k5dma75bnhjT
-AXYqZE8C5bygPX38rhSkp+L0pKC99Rym2rQghU4F90lI3q6iPf28qVAbPlPVJgfq2XOfVMHT9zKp
-cE8uTkw+392lNWV73FJ7gzvuEJ0jfg1LRERERESOKwXtebZUWJvXt/3Q5K4TJpvXFoXs7cYnN/3H
-zRzPgbPpS21GCqEG82rd8xprne3mdDifqmi7WxoLzZxb7rZuTgrbbTtmmtKG+Ob77NS622o27Xcy
-Oe1J34F0upM/D8cmVW1VtEVERERE5AhS0J6n2+V/uyA886HZkQJNxdc8v5vDsdkk5rr74rDdzog2
-s+OtjV3w+nT6bYZ4O5Oxz+0LGI7ncJ26akeHGGmjbbo3PBjEACF9PALmNgnhueJsnivRnrt/063P
-N+22qfbO7RJvnWbS+SNRRVtERERERI4gBe2GL3jivkPY9slmTKqs3WCYCr42Sbo5yDYBuxl/PHv0
-Nri2s26zy/A9s233pVx8Tu313H28CeKGR8dDE7Chbj+ZQzYGBCxEzEMK1G07c1uj48Hb0M3MuU+H
-7EmotpmwbZ3Xmwp685F23jWRY8DMngW8nPSv/qe7+98ecpMws1uBWwF3d822nX3iE5/g5ptvPuxm
-iMgujMdjAJ70pCdpMjSRQ3T99dfzjne847CbceAUtLeYF17nTDo2Z7tJgdgngTuH6qaKPBuytzvq
-VFDeEpono6ZnY+viHdKGVGuz7yTEt13Hm3NwiLEJvClgp+pyCtnm3l5Q8LxTd0+3mL8DN3xqpvTp
-ec7nNnBOP/S2d3mn/apoi8hR4+7cfvvth90MEdmDPKmSiMiBUtDeohkJPHk6M4S4sx20XcYXmKpc
-z9vNlr1taUp+aO2RunXgqX3msc27WRp9+nPd1yfdx6OnkN1MPGae+ombGcHJodryRGWd582ePAVv
-fHJxYTJae+v5W+cc0sPOt9IJ2O2s4zufpsiVRP/aXzZuOuwGiMiujIE7gesAVbRFLr07yAv3XpEU
-tBtTQ4InCXcq/M1JyT4bGtt5wWzmA+l5t3P0nENPArelcc9tFTfNPJYnRuuOee62ZZfmHRByR3Jr
-/zbvDjEnd/NJ4A4O0Z3QTJjWfKBTpe/2cJ9tV/NddqdKa9fVbnfT+c5zmN8SztV1XI4Jd38l8MrD
-bofshgFa3kvk8vBO4BbgDYCW9xK59G4GrtxeYAraMzpzX6fIaZMgOL8e3YmRtmjs8NZQvPV40y+0
-473zB61Z5mr2IkCnFVOV7t10rZ4TuCf78XZSNLdU3U5VbWtDdpoizdvzaz/tk3HouE321Z5MDtjW
-BOxJ4G7em1ywmP/Fqee4iIiIiIgcVQramc2dyroTsm1rHbqpAHc/tXOR1eY8atow86Q7RDuH/nZp
-L3bXRXxxKxa31HOXcbfcmaMN+5NKdnTymtbdID2pYM/O2ea+uON8+903oXv2e+h8sjs3nQraInKE
-1DtvIiJHyw2kOR1vOOyGiMgeFEU7B+uR/t0bDrsBR9FUp++5oW4y0ti6n+lWaNvu5rNx0Kertc3n
-F3Tntk74nO2Mvq1tQvh2Ibv7cc9V7KZ7eDdET8Zye+467pNx2sy+PkndTTdznznWLpsuctkyswea
-2QvM7A1m9mEz2zSzc2b2V2b2CjN7xDaffZaZRTOrzey+c97//fz+m/LzzzKzn837Xsvv3Te/97j8
-PJrZYy35RjN7q5ndZWbnzezPzOx7zGx4AefbN7N/ZGY/Y2ZvN7NPmtnYzD5hZn9sZrea2bU77OND
-uZ2/mJ9/tpn9gpn9r/z9/Z2ZvXa7725mf59hZj9lZu8xs7vNbN3MPmhmLzezW/Z7rlkeZKZLgCKX
-jxuAF6KgLXJ56QTtIz3AWxXtbF4envdmd7bwpgP37CtT+926sx1DdjfktxXcdvburYfxBffzLFyz
-e2pvk07oTUhOmTmNyY7k8dnMVLSbqrVP9uG5P/0kgOc9O/iWXgJbW7G1/Ts0X+QIMrPHAW/OT7s/
-on3gM4DPBJ5pZj/i7i/YxyHa6RDM7KnArwDLM+/P+8wQeD3wpTPbfC7wYODrzOyL3f3j+2jTLwDP
-nHPsq4GHA58P/Asze5q7/9GCfXTP6+nALwNLnfevA54OPMXMvsbd//OixpjZ84EfIn3n3TZ9GvDp
-pO//B9391t2dnoiIiMhiCtqLzKkgz4bUrZ3Nu5N5bWfS/7m77eznZoeFL8jZ03verpK9oP1bW9eJ
-uc0x84Rsqbo9E7J9ErJToLa2At7sjfxap7idw/bW77H7XLlarhA94Dzw26TA/ZfAWeBewAOBfwnc
-D/geM/urPPnZftwPeHU+1guBKJ5k2QAAIABJREFUPyR1q3p4fm3WDwIPI80E9PPAh4H7AN8CfAnw
-AOC/mtkj3fc8WKUAPgi8FrgN+Fugym18AvBs4FrgtWb2IHf/xDb7ejDwVcBHgR8H/pT0n4cvBb6H
-FL7/o5m9yd3vmv2wmX0X8P+S/rPyZ/lcPwDcDXw28C+ARwH/xszudPef3eO5ioiIiExR0M6mMuhM
-NXu2C/hsVXteRXuqVrugctuONZ4N2J0x2N1Jv6YmO2uP6DNdsRdE021C9pYK++zMaM2xcjBul/Hy
-Jng7sRm/3YZtbyvgza6a+6ZK73mnnhvRzLLeHfu+yM6VeZEj5V3Aze5+ds57/83MfhZ4HSnc3mpm
-r9pHsDVSZfZ24JHu3p3G87YFn3kY8BJ3/5aZtv6Wmf0C8Jy8zXNJ4XQvvt/d/9ec198J/IaZ/Qfg
-baSq9LeRBkou8lDSOTze3bsXDN5uZh8kXVw4BXwd8OLuB83sAaQLCg680N3/7cy+3wX8qpm9Kn/+
-h8zsl9z9nl2ep4iIiMgWGqM9j3VuMJNKtwbWyW3yv+bNyWzaNtUt3ObsrhnjPXuo3Y7NXvi38pmg
-b1v+OXtGs697O9bau2Ox3fGYAnbzPE51Efc2WHfbN/V5bz4/qX3vRCFbLjfu/skFIbt5vwK+Kz+9
-H/AP9nso4LtnQvZ2PgZ854L3voO0wCykCvfeGjI/ZHff/5/AS0n/uXn6Nps21xafPROym/38CqnS
-DfCFcz7/fFJ38dvmhOyubwNGwAngK7dru4iIiMhOFLS3YW0ynl96ti23zv9seqq0NiwvSM2LsqN1
-k/mWSdS8889FJ7Goku3bVrXb17qTl/l0SI5NsJ4KzJNbt43poU8F7KYa3tynrbcP2wrZciUws4GZ
-3cfMHpAnSXsg0/89fsg+dz0GXrOH7X/N3TfnveHua8Cvkf5T8EAzu9c+2wSAmV1lZvc3s8/pnPPd
-+e3PMbNiwUcd+PMczBd5V27n/ee894/yPl67XftyBfvP89NHbbetiIiIyE7UdTyb5Lf5wXR2Salu
-4Nt25LNtDdHtOtEz+X1LiMzB1pplvZiMad6xU6ltfbhTRp09j6me47nbeNN/vBuUo1ueII02gIfm
-Pu9ndnWv2fHYzWvbdRnfcmFAoVsuI2a2Anw78E9J47IXBUuAM/s8zAfcfbyH7Rd1KW+8HfjW/Phz
-gf++l8aY2YNIFfMnAddvs2kgTZK2aJz2X+5wqE/m+5Mzx78vqWu6Az9qZj+6U5uz7doqIiIisiMF
-7V3Y2t3b5r6+KPgZTbdwm7Ov6X1232tn5c4h1TpjttugmQdyuy8+9jZNWzhYe3Ys+FS72lnGuxVu
-y+O06ay37cTohHbIt+U6+rzvb1L1b76lRRczbPYFkSPOzO5HmgTt09h+gYDm3+zlOe/txqf2uP1O
-s4l/rPP4mr3s2MyeA/wc6fdMZyrErZvm++3OeX2HwzXLe8xevOhW4fcy5n1lD9vOacpuiv8F219r
-ERERudLdMf3sjju44447Fmw7MR7vpaZweBS0s21zqC0OrGZzarBbtpm8aGZTz9Ojzj5mQnyTeN1T
-0m7CNtZUtW3yMZ9fKd4Sshec7E6zfLeTmDVdvg2iGyEHa3PL906MRjQINukePv1X7W68zhcipkL4
-nJA950KFyGXi1aSQHYFfBP4T8D7gTncvASxdWarz9vv9V7zeeZMpe51wbVfM7LNJIbsghfV/R7rQ
-8CHgnLvXebtvAF7WfOwiNKWbZH8AWLj814y1CzvsnTtvIiIiIlNe8pKX8KIXveiwm3FgFLS3Mb/6
-3AnMW97bqlnaymzmb5HW2d+CruPdPG6ANWtp+8z7zUu56j21jwUV6y12W83uvNntOm4OgfQ4RCda
-qmZ78LmToXUHo85WrueG7Obg6i4ul5kcOh9N+lf/h7ZZp3lPFeMDcu89vP/JhVtt9fWk3y8V8Fh3
-/8CC7S72OXeX+ird/b0X+XiYGWfO7NzzvygKikIVbRERkeuvTyO2nvvc5/LUpz51x+2f9KQnceed
-R/+itoJ2FmbX92pLw5NZw7cE387jJkzPmsrS3c82n9mxZc2WjhmEnNAdT30lu2XoeSXp2YDfbdSi
-YlbnA7MXCJoWtwXqPFu4uxEjBJuEbe+E7RgjZkb0kLqW53Np9tPtOL61EfMq9PO/b5Ej6IGdx7+2
-zXYPu9gNmePhwC/v8H7jL/aw3+ac371NyIaLf85/A9xDWvrr0Rf5WADceOONfOQjH7kUhxIREbmi
-3HDDDdxwww07bjcYDC5Bay6cZh3PmqW1morq1PPcBbqpYgfrvN+5Te3H2LKPbsCeOvbuWpi7nRsh
-5HvrvEbTxq3Hpn3eNHTREacT7fQEcXPqzN4J253lvrozj6fXIu4xz0oeJ7OSt/3Jp49iTPcWmGqh
-QrZcfroXNFe32e6bL3ZD5vgnZjac90aevO0ZpB/S97r7x+Ztt0BzzgvP18xuAHa+bH0B3D0Cryf9
-5+SJuXeBiIiIyEWnoJ219dQ2oKb/dQP2omDNTLBdtO2WAzXPd9O+pk02G7Kb3TWvzw/czYZb2jP7
-Jcw87l506H5wsk72nOW6YrrF2FkGLDaB24lN2Ga2pm7Tt5kJ3hSy5TLVreh+/bwNzOybSaHzooyZ
-3sb1wE8seO+nmMzq9R/2uN/mnD/LzB45+6aZLQO/Aiztcb/78SOksesBeI2Z3bRoQzMLZvY1Znbj
-JWiXiIiIXMHUdTybHh/tW163JqjO9APvfmxuFXaHmci7psZEz36snXm8W1f23B6f2mzSOqafmbXr
-Vc9OyNZ9NpmcbbobdxPopz7RTNZGs5Y2hDwDebPEV3ft7GbStBS0u6PBm/3OPp5u3dT3rcQtlwF3
-f5eZ/QXwIOB5ZnYN8EukqTZvBv4Z8BXAHwKPYf9hez+fewfwLWZ2f+DngQ8D9wG+BXhi3uadwEv2
-uN9fAr6NNBnZ683sx0jnt0nqLv6vgM8A3ko654vG3f/CzJ4P/CSpS/tfmNl/BN5EmqhtCfg00trZ
-/4R08eFBwEcvZrtERETkyqagnYXZ0NbpNt2+1RmvvWUzZmLpohDY9s6eBMrtZ/ve7l2jWe3aF0Ts
-pt2Th0237/krC20pvreTvk2fc3f+b8/NdOtWuifrZ0+W/UozlE8vbjQJ296exfQ5z/uWFLLlMvPP
-SGtQX03qjv2MznsOvJsU8nZe02Kx/fxQvAD418CXkta67nLSzOhPyV2wd83d32FmtwIvBE4DPzRn
-3z8OvJedg/YF/7C7+4vN7Dzw06Tx2t+Vb1s2JV0M2LzQY4qIiMjxpq7jWdP1evbWjIduumUv6kbe
-jIFub3P3OVUMv9AW77yv3LAmGE+Nf55q6/S46EnX+e7zrZXubvjudiWP+XETsGP7/pzFdL376Tjp
-jj71qa3nLnI5cfd3A/+AVDX+EDAmzYj9J6Sg+4hdjIHebi3q3bw/zxj4MlIF+22kdbjXgPeQQvgt
-7v53+zmmu/9b4MnA75JmLB+RKuavAb7E3b97l+3e7Xltu527vwy4P3Arqbp+J1AC54H353Y9D7jZ
-3f9mF8cTERERWch8bmXz+PmJ//yuzhcxb4Gr6SrqvIpqt+t1Z8Mt+1m0j+n9T2/rTZfxZiw0dMZG
-T8Ktz+xgdums6Z3ObRaQrsBMXzCY7GtqfLgZIUzGhReFUQSjCIFeCPSK5lbQKyzfAkV+XASjKAJm
-TE3u1nyP8y5cNKfXPP7M65eUvEV2ycweR1rT2oEvcve3HHKTrghm9hHgpptuukmzjouIiFxEN998
-M7fffjvA7e5+82G3ZxF1Hd/CZ+6nu4IvqiJPdxvf+q51U+3cjbbrHj4zpnqm97djKWhPTTBm0zm6
-Hds92w7a2cOn27hgbHlT5p7T6qnJ4Npjxc7U5JbW9cqV9tS9POAYhIBbwEJYcPzZixDK1iIiIiIi
-cjQpaC9gM0+asdCdecXmbm9TL3Srydv28V74ejdkN7ucHS+dwrVNOlvnLttN8J6Jw/k+j4aeGQLe
-PY/pCwzTK11PZiLvfNy7W3h63QHqHLLzW3HyYScQCQQCHop88SBgUzue5r7wOoCIiIiIiMihU9Ce
-sW2deWaM9Zb42g3BC4J4ox2avE0Ab7tLdx5DCpmpa3fqct0EbWcy8ViMTh1tm97hzXttWXy6dZ0K
-/mwVe/7erP1+uhVta8rlTX/3TthuqtlOIP2raGCBNIv64kngFLJFREREROQoU9DOto6v9vaNbnXV
-Jol66nPdT06vgDVdfZ500576cLNQ11SYD3m97JDHQwcma1o3rzXtiZ6r2g51dKocVpt82zmj7kHB
-pqvaNjfgTk52dh3uycN0II/eToRm0akt4sFxi0RzYnBqc0LI5xaMEAK9/pBef0gfJ9CHsHWG9/ZI
-jrqPi4iIiIjIkaWgnYWwfWWZTjV54TZMB++tj5jqat1067b2n5NJxcxoJxUrmkDaBO6Z+1TJNmqH
-GKGscyfydh1r2kDdLsXVtqDbmsnj6S7wW89j0nW82TYHbY/UscaJRGpqrzHSLXUSrwkWMSAE8mRq
-gaXlVZaWVoET9LE0HX4Rpir6InJg9jNDuYiIiIjskoJ21l1He/Zvn93q6XaF1HkjobfTHHKyaNZk
-Bu8QmMzaHQJFsE4VuNOl3FINuo5Q52p22nEkEtNcZPmv1M3frGNzksYkebdP5iTrOSfTXdasnQod
-J3qJxYroFcQSYoV5mR57nW8VhufJ05yiCNRVCQ5Fr08RCswChLbOP/96gIjsmbv/D6A47HaIiIiI
-XMkUtLPJMlhTI5e3Zs15oXMm/c1OVjb/gNCUo5uYHWwSsguDXmEMikC/V9BrgnZo1vaehO3UXRyq
-CFXtuEeiO4Wn9z1XupsTa07Vc8j2qZDdTPrmU22fjNO2yQWCHJTTjiLuEa8rqEd4Pd56iyUxVnhd
-ApFm3vGiCIDR6/UZLq3Q7w0IRW9y3MZMyNbSdCIiIiIichQpaGf1eCPP2RVxr4kewWOecCzkbtp5
-+anm8dTU27n6arYlcm+Jg806WJCOVUc81hhQFFAEsGCEYki/CCwPCnq9Incrz93FPa+bnScaS5Og
-OXXuLj7pGJpaMDWu2qcz65ZKfKd7u9n0JYemqzoeU3j3iNclHqsUpKsRXm3i1YhYjfEcumM1JtZj
-vC6J9RiI7RjtXq/H0tIJqrJM3//0l9X5BjutVmVbRERERESOKAXtrCo3AIixItYVdZ1CXwjF9K0o
-sNAjhABtF+e0HFXbAbyp/ObnjTZA2iTQxlhT1yWxHGPENAlYAV4YYRgYFAOWhwX9Xq/9nNPMKu7E
-GKlJoTvGfMvjsidjmzvJOdevZ3tiN9tPNk0XD6BzLcEmG6eQ73isqKsxsRwRq01iuUldbhLLjRS2
-6zFejYn1iLps7keYOUVRUBQF/f6A1dUNqqokxpj23Xx1M1cq3NMFDTfHXElbRERERESOHgXtrBqv
-AxDrkqoaU1djPNYURY9Q9PJ9n6LoE3p9rOilinfo5WSaBld3Z+XeMo3XljKy4zESqzHleAPzGooU
-tIteIMQB/WKF5WHBoN9rJzNzd6o6Qh1xT0nUm4p2dDx2J1rzNjPHzgUAc88znU/mRJqqes/NsJ2L
-Bp67qNc1VTmmGq9TjdeJ4w3q8Qb1eD2H71zZrkZU1SZ1OaIuNzFzer0+/V6ferjEaLROWY7TRYLd
-/IGpoi0iIiIiIkeUgnZWjtYAqMsxVblBVW4S65IipKAdQkHRG1D0+ilw9/qE0MOKHqFI1W4LBSEE
-LBRp3mwLbXdy75aEO2G8Gq8z3jjPeGMNYkndC9SF4f2CemWAxVX6BfR7oV0fOzpNaiaSxmh7s362
-O9FjWmYrxlzdnizz5Uza0sxjBrTV61mTwJ27qdNU/etU+a9GVKO1dBufzyF7gzjeSBXuKle0q1TJ
-rnLFOxSBAHivh4XQ9hZovsfZdcSa8diWH5uZxmiLiIiIiMiRpKCdjTfPA1CPNylHa5SjNWI5SiHQ
-Unhuqtohh+2Qq90piOfAnR9bmIRGsyJ1L2/vJ5OvjTfOsrl2D5vn78FjyaAo6PcC9aDHaHWJujqF
-kdad9pjKuDFPblbHyWzjTdj2JozXFXVVp67Y7rlLtuUQW0DosWh1n9Qyb7vAt0t3kfZVlWUK2OWY
-arxBPT5PNTpPPVojlhvEMnUhj1WeFK1Kk6HVubod6zEhDCiKgsFwmeWVUwyXVhkMlun1h+l7DmlS
-ZG9OKj3JXcqt09ddRERERETkaFHQzsoctMvRGuXGOcYb56jHG3ncchqBHZqgXfRy2O5NvVb0ctfy
-tpt5et9Ces2KXg7elidVy0H7/KdYP/tJvB5T9nr0ewXVYMD49ClilcYzh5DWyXbzTvU6Be0mdLdh
-O0bquqauylR5jjEHbQi9AaEwQpgXsre+NpmhPHUVx526KhmPNilHG1TjNerR+XQbn8erTWI5wqtR
-ngitbGcdj1WZgnZVQq/IQXuJpdWTDJdX6Q+X6fUH6TsMaSZy97ilaeaOz51lTkRERERE5PApaGfV
-OE2GVo3WKTfPU66fpRqtpSpqXiM6Val7WFO5LnrYTMhuupen57mreW+QA24a2526l6dZy8uNs4zW
-7mbj/F3Ecky/36Pf6xGHS4w2rqUqN9OM3l7j0Yl1Wiu7jpGqjtR1um8mQkvb1MSqpCpH1HXq5l3X
-KbAWDj0LBO9PZhCfNadSnCY+i8RYU1VjyvEm4801ytE5Yg7Z9eh8WtqrGkMO19QlHsc5cFdp+a9Y
-Eczo9dNyXsurpxgun2AwXKLXT0t7NRX/2e7hbb5WyBYRERERkSNKQTtbWj0NDr1en17Ro98fUI1W
-UxW2Lol1RYw17k5dl9R1mSvdKTBPdSMvem1luyj6OYz3sWZ8d1FQ9HoURWC0djfV5j14uQZVSaRH
-HXuU1Gys3c3ZT93JXasrLC2vUsVAHY0qGlWEsnbKOq2f3XQjL6vIaGONzY01RhtrqZt3XVPVNRBY
-Wj3F0uppQjFIVXXIw8anZ0dPy3rlsd3NzOhVOu9ytE49XqMu14jlOrHawKsNqDdhS8iu8FiBp4nl
-iv4As8DyiatYOXktq6euZeXkNQyXT1D0l4CQurpjhEUznpl6jYuIiIiIyNGloJ0tr5wGoB4MqfoD
-quEy1WidarxBWW5SjTeoylGqElcpdDbjh9PkXGFqMrRQ9CjyvYUUti13Oe/3063X6zHeOEe1eQ4v
-1/C6InqPuu5ResX6+bs5e/cKw6UBg6VVnILoPSKB2gM1gejpcXQjulHVkdHGOTbO38PG+bOMxyOq
-qqaqKswKojuhN2CwfCJP1AbmNllOC/JM5LTjsmOcVMircpNqvJZnGF8jjtfwch2vNqEZk103YTtV
-4okVeCT0B/T6S/QGy6yeuobVU9ewcupaVk9dw3D5JL3+ECxMJl2DHLaZWtoMV9IWEREREZGjS0E7
-W17NQbtaph4sUy9tUI03GG+cp9g8xzikSmtVjYh1yXi8kYJxlbtDt7Nk22QW7XxP6KdlwEKPXn/A
-YDBgMBzSHwyox+vU43V8vIF7xOuCOvQglmycv4ezwzSmerC0CtYH6+PWh9DDQw+3Hm4FTsC9oKrr
-HLTv5vw9dzEejSjLkrKsUmW9N2C4fAKPNd7O7k0buhs+tVZ2qmhX5SblaJ2qqWg3IbvcgKai3VSy
-67Q8GjGmsI1TFD0GSysMV06zcuoaVnPIXj11DYPhMkVvSKpoQ9M3PNJtVtPWvE74/DXIRERERERE
-DpWCdra0fAJI62j7YEislqnKTcbDJcabS4w3l9NkXRtL9PpL9Efr1ONRXhd6nKq+MU88lpe+almB
-W5rtu+j1qYZDqnLIcDhIM3Pn5a88RqIVYCV1VbK+dg+hMGKs6A2WwXoQ+pP70GtDvOXn7rB57lOM
-1j5FuXEP5WhEWdVUZYUVgzR52XiNerwOXoPlZcgsgDntROOxxj2dU12NqcsN6nKDqtygLteJZeou
-7tUmXm+2lex28rNYgnuetX1ACAXD5ZMsn7ia5ZPXsnrqDMsnrma4fJL+YCUvm9bDyZXs7kBsI1ex
-HXPwZmkyDdQWEREREZEjSEE7W1paBsDjAI9DiBWxXqEcL1OOT1CONxhvrjPaXGO0ucZ4cz3Nuj1a
-pxxvUI03qcoR5XiTqkpjuuu6oq4qIhXuRiRQFL085rvE60Eav1yXuXu149Sksm1JOB+IsaYcbxKK
-flqWi1S9JlfIsR6hN0jLYvWGEApG6+cYr5+j3jyXZviu0/TkZg5l6vJdbZ6FuJSW+wpFOzkbAGb5
-okFFjFUO2us5YK+n8djVJlaPsDjCY+oqTizxWOKexmWbBULRo9dfougvsXTiGlZOXcfq6evSuOzV
-0/SHq4Rervhb6iju7kSD0NSyPY0Z9zR4PHdtVzVbRERERESOJgXtrAnaaWByTN2T8wzbdVVSV2PG
-o03Gow1Go03Go3XGG+cZb55jvHGe0cZ5RhvnUnDdXGccI3V0yqqkjp6X4HJCKIj1IIftPuZOIGLE
-PN47LdMFEGPNeLTB+tpZzAKOgVu6tyJVtq2gN1iiP1xmMFyh6A+oRhuUow3q0QZe1xDBYg6r1QZx
-fJ56dA7zcTt2vFl2LJhhZnhMVfk6ltTlOHVxz0E7lht43XQVH6VbHKfAHasUtmPqql70+vQGKwyW
-T02C9lXXs3LiKpaWV+gPV2aW86LtOt7Wq3NF24z8ZqpuK2yLiIiIiMhRpKCdLQ2XgMlQazNLa0bH
-Oq9FXTMejynHI8bliPHmBqONs4w2zjHaOEv//N0UvT4WUlW2dseqktojVV1TV5GqqtN+Y1riyus+
-hUEwCIGU8T0v0+VOWY7YtICF3FE6v+4OTpG7fRf0h8ssLa8yXDnBYLBErHIX7jxhm3vA3LDoeLlO
-NTpHuT4kVkuT2dBzVTvkNb7rXJGvYkVVjqnG69TjDerxBl6uQ5m6jaegnUJ2qmbX4BFwQggU/SH9
-pRMMV69m+cS1LJ+8lpVTZ1heOcVgMKDXzzOyW/rym4p2MyVb/mNIwbqtaIuIiIiIiBxdCtrZoB/a
-x9aZFyzUgRqnNuj3e4BDSFVfC0bR79MfLtMfrjBYPslw9TTDtXtYP3c3xblPgRVpQjIb4T5u16Iu
-y/Q4BAhmhGYuNZ+MPLZomMXcpXsSwFPQrnEM90B0n8zFFkuIdRp/HWNbIXY3YuWMN88SzoLX4xRw
-Z7qOhxzsozvRY7rFiliNqesxsRpDPcKqzbai3Qb7ukozrvd7mBX0hydYOnEtSyevY/nEGYar19Ab
-niQUw9ztPbSzjM+f16wz6VmngO04itsiR5eZPQt4Oekn99Pd/W8PuUmXzB133MHNN9+88P3rr7+e
-d7zjHZewRSIiInIYFLSzQX9qbmua9a4COXjXk/HLFAVWFClkV8vUVZlD9hrLm2sMz99N0V8GC9R1
-jdka7qQqsdepS3oZqcuKEFJ37ZDDO7mabrlymx43YdnbqnZ08pJeED2NvzaLuI8J5Co5jru1Qds9
-Um6cw+uScrSWJkCjmVxscvFgktrTHGRArlSnCdJCLLE4JsRmXHZFrCs81mmd8N4SRX/IYOUqhieu
-ZfnUdaycujfDlVP0lk4QesMU8K2AqcDs+Tvw5lmzntfUDOMK2SJyVMUYuf322w+7GSIiInLIFLSz
-QW9S0ca7S0gFrLYUSkPAigKLNUV/kGYYj050Z1iO8oRomwxWTqU1q+uacrSJu6du2OUmsSqJdd0G
-ZmtCdghT96ETtlObmmp2qmzX0akj+XGNWbrhY4oQ6IUCipDGdLdBu2JcjylH51M1udlnjLlXdhP2
-LZ1nsyZ4SAE8hLRNoKLwGqfC8oWDdIvYIFD0hvSXTjJcuZqlk9eyfOpeLJ++N/3BEv3BMlYM8/jy
-NC47n17O9lOl66kqdhqn7fnlzp+XiMiRctOc1+4grakgIiIix4GCduadB03Idme6K3dT9bWCEDzd
-FynEBgsUIc0q7rGmGm1Ql2kt6f5whcFwhf5gmdHmGuV4RJXHeze9oiFNlmYeidHa7uvWrHGdt2q7
-jnsKnE6zznVFXY6pgqcB36HA6zTBWRu0m1N0crfzmEJ2jGlcdGdNbSsKQlEQih6hCGm8dWF50rKI
-UQM17V4tYEWgGKwwWD7N8MQ1LJ28juHqNfSXT9Efrqbx2L1erprTKWZPV6y3/sk0Xceb5J221Tra
-InL0BOAjc16/GVClW0RE5LhQ0M7qunnkbXdld9qKdfRJyM0JNwVSUtfrJtxS9BgMllhZPQ0x0uv1
-WVo9zcbaWZZPnGVj7Swba+fYWD8L6+dwj3ksdXNLXQ9px12n8dtAW21usmaAtqs57sS6pi6BEHCr
-8e6SXVhqJ7lLNuk4acx4Dtp5f2AEL3AvcpfxFKLdQ6qSE/F8w8jreBdY6NEfnmS4eg3Lp69n+cS1
-DFeuzkt4pbW0m+7q5p5ubdl63rhrzSwuIiIiIiKXHwXtrK6988zbJaZS0O5Wkkkh2yfjmFM375AC
-Z88xX8JWT1P0+gyXT7C0fi6F67VzrJ37FL3BJyAE6jy2ubml+ctqYh3xOqa5wpqJ0kJzjM5Ybsjd
-zIEY03JcxDR7dzCipVnEabqhW7oo4PnDMfpU2E5nnP4ZvKDwHniFeQ/zQCgCeeQ37pFIzLOVGxb6
-hN6Q3tJJhieuYeXUvVk6cS39pdW0hFevP2k7U1crpkwVujt/HgrcIiIiIiJyuVDQzurYREyahZxz
-oKSdfKytZmfNZGWQu1R7wOgR+hCsoD9Yol45xXDpBMPlkyytrNEfrmBFgRvUXlOXo9TluxxRlyUV
-eXIxj2k4X3AieR3s4HndbWsb0MRPbyraHvG8HnZaNmwyk3gz0VkTtNPFgzSzeOqK7pPKNk4wx/Ot
-mSG8Hdud1iLL1XHDQi8F7cEK/eXTabbxE9dQ9AYUvQEh9PLM6k09fXKceT3ArRuuuzm7+7Jv/ZyI
-XHxmdhXwPcDTgPsB54D0cbdgAAAgAElEQVT3AC9x99fsch9D4BuBpwMPBK4B7s77+f+BV7p7vXgP
-F7YPM/sQcF/gFe7+bDO7Bfg24LHAjcDA3TUZhIiIiOyLgnZW1bOT1HjTgzyHym7Gtk44bLo/O8GM
-GIxAAT3SxGZFL1WLzdrqbygC/eESS6unKEcbjEfrlKN1xpsblBsbjDc3GI82wCPudepentvjMVWS
-m27rmEGcjF12oDBop0vPun9btOYcgGABgqUwDW16LYqCoijo9QqKIlCYUTQzoeftvGkTlpYIK9Ka
-2OnWIxQ9LITuNO5b5QsZ3S7u08G6mSWts7b2wj9FEbnYzOwBwO8BNzD5wR4CXww83sxeDrxlh308
-BPgvpKDb/Y/DmWY/wHPN7Cnu/vGLtI/20qmZPRf4GaDovK+Zy0RERGTfFLSzpqI96Tw9YVMZsQnZ
-np9NV2YDlsdGB0KRu6CbEYqCoten1x+0IfvkVdexuXGO0fp5NtfPpdv5s2yunSWEQKzLNMlZXRJj
-Whc7esSjT5YA6yzFRVN1L0jrcnlqu89k1+Y8gqVpzYL51AZGCtq9IuTA3amQm09Ctqfx64UFzHpY
-0Z8O26EgTI0Tz/u3mbY0f93Nedq69/MsXnhbRC4iMzsJvBG4nvRT+6vAq4CPA38P+E7g64EHbbOP
-zwR+HzgF3AP8LHAb8GHgWuCpwHOBhwO/aWZfOFuVPoh9dHw+8M+A/w38OPCnpN+NX7ib70RERERk
-HgXtbKqibdMPmy7i3nktTd01GWtsTRo3wzwwWYs6EIqCXq/PYLDEcHmV5eo0ZVVSVSUb5+9mvbmd
-+xT93iB3Q49U5YiyHEHpeJXGU8c6jadOy4LlEBsMj0Y0S93KLYD5pOrdtnm6mk3uYt6eV2ft7iIY
-vV4O2yGda+50TmzCtjdl/jCpaPf6eS3tVNVuLwa0Fybamd22/iHM5ufukl9ueGd28ukquIhcIt9P
-mj7bge9193/Xee9dZvYa4HXAE7fZxyuB06RA+0R3/9TM+79nZq/L+3kEKbi/7CLso/E5wLuBx7n7
-2c7rb9vmHERERES2paCdxe6A35xI2xiXZ/Vu3prekJxiJ0tjpf93wrYXWC+Pl65zZbuu0/rXkKvf
-PYqiT783YDBcZmn5ROpOPtpgPN6gHG1SjkdpabBynENuGmPtbVOcGFMQDqGpOFtaMswNYp5QzTx3
-Jbd2ZnOaSdcsxfOiMIqU4WcmKEvhOnpejsycsqrxsiSOxozH6QJCXaUqvIVA6Ewc59BOKjd7a3qN
-0x4vjQE37zTA26+6M55cRC42M+sDzyb9mL5nJmQD4O61mT0H+BugP2cfjwEelffxrDkBudnPG3No
-fwYzIfkg9tFtUt7Pt86EbBEREZELoqCdNUHbuoGOPOv3vEDX7Y7dhuzOm1hbcQ0hVbbNeilUx5iC
-dR2JS3XepqDXGzAcrrC8eprxqTVGm2uMN88z2kiPRxvrjDbWGG+uU1dNoK3SOO7cldvc8WCTdbY9
-hW8H3DzPYA4Q0mzlueIc8ozgTdgucrW7qWJ3u9RHn9y8drwsqW1EGddZ2dxgPNqkLMf0qzGh6ANG
-EQJNAbxpV3cm9+6tuW7RfJPTcnV7UiQXkUvjFuBq0k/eKxdt5O63m9nvAk+e8/ZT8/373f29Oxzv
-LaSQ/HAzC54mqziofXR92N3/aIf9iIiIiOyJgnbWjrSelIenJrre0qc5d2vudsduK9r5+eR1oygC
-7qm7d3QIDrFIxwmhoOgNGAxXqFZOUVdjqvGI0frZNG574xyba2kN7s3BgI1ej/FoEzY3iNHx2sHr
-VOG1VMV2n4TtNGu5p7HalidGC2BeTJYIC6EN2Wksdqp2p4q2T41cdzxXtCFapC4rzEdYbWy2QXtE
-VZVpArVQdBf0ymuFgwfa2dw977eZXG02YHeGcE/mVlPPcZFL6XM7j2/bYdu3Mz9oPyzf/30z2+1k
-Y33SbOKfOMB9NJw0Q7mIiIjIgVLQzrpdx7sVXADvdB1vt7HOnFzdGbOtE7Cn/jmZqdyaoJ2nArdQ
-UBR96sEyMdZ4rIl1xeb6SZaasL20ymC4zKA/pNcfsrF2nhB6gFGVI2JekzvN92OTZcny8bA0KzrB
-J5O7NV3Fu8uBmdFMFB7aIJsDdtM9PXcbryPUHiFWeJ3Gko+aoD3epCrHmAVi0SN4PwX9ttJOp42p
-Te6TUe/d7vtT4Rra2C8il9Q1ncdzZwLv+NiC1+/F3vuiOLBywPvomtv1fP8iqYmzUsYfj8e8853v
-5IYbbuCGG2442EOLiIhcxu644w7uuOOOHbcbj8eXoDUXTkE7m+Tozt/f0kDgFOxsusrq3gTnqenF
-ultAp7o9FVa7h8iTmhW9PhZCDtqRWPQYuqcu5f0lBsMVBkurLK+cYmX9LGvn72H9/FkG5+9hvLlG
-Od6kHG9QV2PMUvW6rtMY6nYcdkizojfHTLc08VkI0+OxuzOrt1Xo3NW7rp26dso6UrtBLPNa25Fy
-tMl4tM54c51yeSON0e71879pnSXB2qp7rrw3IduZ+r6bixne/TpF5LDt92pXs3zWu4Gv28Pnbj/g
-fXRtu1b3/ty5+J077+SWW27h1ltv5YUvfOHBH1pEROQy9ZKXvIQXvehFh92MA6OgnTVrZTfprrN0
-83Qx27ZG6pkB2vN2nkc6N12jaWfsNtL62mAplMaC6E7w2Ibs4dIJyuWTLK2cpBytM9o8z/CeTzJc
-+hT9wRKba/ekZcI2jPEmuNfEmCdb63QHB4MiHycUFCFQhEBo1sjO3cStE7jb9uex3tGdKjpV7VR1
-pIrNtxdxq9Lkbe3a4BuEok+vvzQZQ95E7SZkE9pw3VyEcEsToDmTRkwNyZ53UURELrZu5ffewF9v
-s+29F7x+F+nH+cQuxlcvchD7uGjMjDNnzix8/8yZM7z61a9WNVtERGTGc5/7XJ76f9q79yjJ1rO+
-79/n3beq6u65nIukIxQSA3aMIzCyIsDcZIwSywgRA1lZkJAogB2c8AcrgMOCXIS0EtvLYAzJcpaP
-rUQIHAcwBoJB2IZEQUCWkcTVXlwCJk4QDHDQ5ZyZ7qrae7/vkz/evXft7um598z0Uf8+Z9XUbfd+
-d1WfmppnP+/7PJ//+Xfc7vWvfz3PPXfrk9rnhQLtwRRMjzfm0fUs6LYhaN5ls4+XQLvd2mHHdi2x
-ht3nILjIRdKGYDONWfRqkYNxd2LsiN2G2OeMcb3Yo2oWFFVFWZWEIrcE89TTdx0pJWLKyxdDCHiY
-H/ssoz208gpDYfB5Dn533D4UPxumiw9BdhcTMfpQjC1nqbt2naulb3LF9LJakGKcZgY4KQfbU5Cd
-dsH2kNUee39Pb/18gfbdvNEi8jD8s9nt1wA/fZttX3OLx38e+DTgY8zsJe5+pynoD2sfD83LX/5y
-3v/+9z/uwxAREXnRudtlVXVdP4KjeXAKtAdjq6hdV+mh/PU4NXy8PWa0bbp7Uz/nk0XKpyrbzMuJ
-7cZNKZGSTwH2WJHbQiBY7lFdjOOFAFaw2MvB9NinOxQFFgwLOdhtt2vYGpCGdddGGcLQG3voj12E
-qYVXmE0Vt+kogbRr5TUG2DmTPfb09t2aaiD1HX27oRsqppdlTVHVFGWdp5inHmJPWRY0iwV1sxje
-25DfSw/DNHxnKsM2zB3fvc85UFdGW+SR+llyVvsK8B8C33baRmb2Udy6j/YPAV9F/hv0q4H/8j6O
-4yz2ISIiIvJQKdAejdHxvOoW40PzYJsh2LZT1mXftNNcVZvZdPFjz0JKToyJvu9JKQ0BeQ60i6KE
-MmerzQIWSkJpFBZolpdwjFBUhLIghCFQDcb26AYWhmWMHqdAughGVRZDsB2OZ7JPK3zmY4Xw4Rij
-0/WJPkZiTMTkQ7C/yy2n2NF3G9rtIdv1DYoiB9lFURG77XQpq5K9/gBwijIA5XBCIOQ+4NNU+3nV
-ufHAbN7aXEQeAXdvzeztwNcAn2RmX+fu3zLfxswK4O9wSg/tYR8/ZmbvAT4Z+Etm9vPu/n23GtPM
-Xgn8a+7+w2e5DxEREZGHTYH2xHdXNo/g5pOp87RrG9dkH6s4fsrefBdgz6/nAXryHLj2XU+MMU+l
-HoPtCrCAhZzdtmA5ux1K6iWEoqSql4SyxGzM/uap4uCk1EPsCEP/7CJAVRZURaAqiymLbcNl9y74
-Lsh2mzLafZ/o+kgfEzGmnIl3Z3b+IQfa7ZZ2c0i7zhntsqopinKYTp6nldd1jeEUZUHV1JhBYQEP
-jo+vBcsVycdZBEPA7cPtkzMJROSheyu5L/UrgL9mZq8CvpNchfyPAF9L7rf9Pm49ffzfB36GXMX8
-e83sHwLfA/w6uTDZS4BXkftlfwrwLcDJIPks9iEiIiLy0CjQvsnJbPbNt473y77FT94iyJ6qag/6
-PtJut2zXG/q+H6aNJxxoFvnaxn5bWM5sF4GiavJxFAUxtnTbI7p2Td9vSLEj9i2x2+DRMJxgkcI4
-ls0eC77ZcMDuuzXRY/Gz6EPhsz7RxThNHY8pkYaTAsC0rxxor2nXN9hUDSEM2XZgs77B9uiQzfqQ
-ZrGkqiqa5ZLY9xQhEEOi8NzqK0+K9/nbOZ3UsDHgVkpb5JFy9xfM7PXAjwEvA75kuEybAG8HfnK4
-Pm0fv2lmfxL4B8Argc8D3njapsPl+Yexj4HO1omIiMhDoUD7JrsqXDbLWs9beY3FuWxanGzHfjQ/
-ekq352H7sSBacme72XDjhevcuH6Dtm2H4DHvc7m/YtX3OE6V6qFaeMDMiH1H33X03XYofpaDzyJU
-lGVFXdWkpsF7MI+5LzaJMkCYTRnfHVqeiz32t46JIaj2Ycp4pB+mjafkeEpMG8/ek77raLdrNkfX
-8/T1oUBb7NZsN2u26zWbzRGx32e5WtG1l4ixJ8aCskxTkA9jsG27t27+K3JuNZlARB4id/9lM/s3
-gK8HvgD4aOA6uVja33b37zWzN3Gs7ONN+/gNM/skcnb8i8jZ76fJrbs+APwa8FPAD7j7Lzysfdzu
-GEVEREQehALtk45V356vxD5ej/t4JWyfBd7Hu0+dLIQ2BpKenJQim82GG9dv8KEPfojtZrsLz824
-FHsAiqoAnKIooCixEIixp+9bunZL388Lo5WUZUVV13hX4yFBMkhOwCmCUYTZa5yfQGCWyR5aeLUn
-povHGPO686Fg25SfHwP0PgfaoajyY7HD45bYHrLdbthsNvl1pp7t5jJduyX2PamodmvUZ//szTXJ
-d1XI84GO5d8VaYs8Du7+YeAbhstpz78DeMcd9uHk6d7f8wDHcd/7cPc/dL/jioiIiNyJAu0TptDN
-8nrsXVr72LPZGFhPbb+OP3VyKnm+3gXZMcac0b5+nQ998EOsj44Ya36HEHCcUBbUi4oQwD3XFyo8
-EGNH380CbU9DT+4caHtVYU2NFwnvgRgxD0M2e+yrPXtNw9r08WRAHAqftV2k6yMxDYF2Ssdf2Tyj
-DfR9h23W4EaKPSluSf0R/XbBdtuy2XZsty3gbNY36NpN3i7FqfK6eQ6u529dcpsem/c4FxERERER
-OW8UaA+mwlpTnbN5FDd/5MTj4/anBH02rHvePemzaehZiomu72m3LZv1NlfyHitrFwVYILmz2tvQ
-1DVNU1GVJV17RLddD32rD4nbI2K3xvsNHltIPUYCEmbj4uvduGMhb98dWc5kDwF210farqdtc6Cd
-3G9ekz3tayye5oQ+EkJHF7aEYPSFEQuIRX7/qqoihIrl3j7NYkVVNYSiytPMhwPyU05a4LvMeRpn
-HWjCp4iIiIiInEMKtAdjoD3OpB5Da/N5YHwisz0Ezcd+9lg0u9vXmClOQyG1sZJ28kTsI23bsdm0
-9DHR9bmat1sgOvR9z/poyWpZs1w2LOqSvl8Tuw2xW9O3a2K3JnZHpH6N91tIHZZ68BxsjxXJ58fo
-nh/wISSPyemGQHvbRdo2sm07+n738wxVwI933MpZencnxoj1PVibC5yVgVgFvA+EYkFTN1i5YG//
-CsvlPlWzpCgqQiiwqfzZfH27kdwZZ8CPAfbxlmQiIiIiIiLnhwLtwbxX9u3jt2Eq+RhgzzLUu3Db
-p213WW0fxnB8NoYnp+8jXdez2ba0bc4kd30aWmrlbPfe+oh2b0G/1xCXFanf4HFLihtSv5mC7lxp
-fAupxTyCR6bS4j5bXz4d6hBsOzmoj07bR9ousu162ranjzGfGDCwwHDbjgXayXOBtBgjZj1gFMGI
-VSD1+VJVS6pmQbW8zP7BZRar/aE9WY2Fcjph4dMC+J1ErjY+Bthh/jpERERERETOEQXaA5tP72Y+
-WXwX8O2KjDvmhpPyVObjWwyrrI9H7Hn7Wabc5pcAFnCMmJy2i7RtTygCRl7Pnfotqd3QtzXtuiR4
-i9Fi3uJpi/dbPI7XLcQOPE8f97HXtVnuxz2MZ6HYXZLTe4vFLd5BIpG8Jw6tvcIYYLsRghOCkdIQ
-GA/Z7FzoLZFijwGxCKQYc4VyoCwrmuUei4MrLPev0iz3qeoFoSgJIU+TnxUyn715nKw+RxpOYoiI
-iIiIiJw3CrRvYZzCbMOfs7rcMATZKeaMsY8FwoZK3Dnzm/tHBwu7gmrDlOsxGx5CQVFV1IuGxWpJ
-s2mJyWj7BF2cMsR917JZ96R+Q7s2Diuoi0RVRqoiEuhz4O0dltpc6Tv1eIqQxmA0YAFCWROKilBU
-FFVDKBuKqsmbHR6RikOiHdL7IV3vWOiAMYgeTjA4eDJs6MU9dSSbWoQlUurB8/9eFkqKsqFs9mlW
-l1kdPMVy/wnqxQFFtSBYkfuDn1i/Pv0eTia4jyfoRUREREREzhUF2ndyLJjz3RrilEixw2NH6jvc
-03DJa5iLoqQoK4qi3GWQLTBmZXOgHXIrrqahWa5YLFu63im2eY1zXvPc07UJ750tPYdECousFsay
-geXCqItIQU9hPYEeUi6G5ilO6WEzg1AQipqiXlLWS8p6RbnYo2z2cAKpfJ5oz9N5Qdk5YdthYT0V
-OptOPji47davG+MU+mFqvCc8pTy+GVaUhGpB1ezRrK6wPHiS5cFV6sUeZdkMhdDG92a2inxcFj6b
-4j5NdUftvURERERE5HxSoH0HY9HwqTzX2DvaI6lv6bstsdviKQ5VuRPBAmVV457APRf6CgUEzwH3
-EJiGIlBWFXWzyBntdct62xPK7VBwzIl9T+/Qe0ds81psTy2X9gr6/RLrS6idqkiEImIhQZpltGEK
-8C0UhLIZAux96uUl6tVl6tVlnIJoDX0qaPvEdttSrNfDz45Tw32cOT8E2kwnDMI0Vd5hOOHgKWJA
-CBWhbKgWezSryywPnmSxf4WqqijKOk9dP7Y43jgWR89njR8v4C4iIiIiInLuKNAejOuCpwwqx+M6
-M8vBdOxJqadvt2yOXmB7dJ3t+gVi3w19oyMYlGVNUVaUZT2sQS4JRYGFklCUQ+BdcPT8dbr1C1h/
-SMmGOrQsyp6+SkDEPNH3idRth1ZeR6R+CzEQ20C7DiwbY1EbiwqaCiwYwUqKOgeyRZWnh5f1kqrZ
-p1rs764Xe1TNPm4BQkWollTLA+rFPlW9oqwajq5/mLbd0g0XH/9LYOa54FtwwlBJ3QzCULQseaLv
-O9ptS9d1xNhP72M3nqxoc7BehEBR5Eso8vtVFMXu9zNfq32iXZmIiIiIiMh5oUB74CfvjH2ax/XV
-GO6JGDti19JubnB0/YMcvvAH3Hj+A0NmuyX2ecr3bup4NV2HYgx863xd1hwdrenXGyxupkC7KTv6
-OuZsduyJfUfXbmjXR7SbQ/p2TWqNdg3rQ2O5COwtClbLguWipG4amqahbBZUi70cNI/Bc7NPvdin
-bvbzFPJqQVEvcALFEGQv969SNznILsqKqqo5OrzO0eELxNiTUhrWYaehCrgPjbl8CrJDMCzkrHbX
-dUOg3tH3PSnFfGLCEz15entZFBRlDqzLqqaqaiproCiGX8fYXHuW2lZGW0REREREziEF2rcyBdm7
-KuGenBR7+m7Ddn2Dw+sf5PkPXOP5D1yj3R7Rt1u6doOnSChKiiGLnbPbDWVVU1aL3OKqXlI2S7ou
-0rUR+khJlzPaVU+qIxvPQX3ftbTbIzabG2yPbtBuj2g3ThWcsnBWy5J2r6bramJs2As11aKiqFfU
-e1dY7D/BYv8qzSqvi66bHGjPC6NhRr08YNlv6buWetbfuijy1PcYe7abo9zOK0EaMtrBHA/D+2YQ
-AhQhP5dSpO87ttstXdcOmf9I6jtibEl9i6eOsqyGGQAl9dBOrCir6QzIdCIkn/1QjC0iIiIiIueW
-Au2Bz3tKzZOlN7d0nm3j00ZjcbTYbYl9l4uADRvmrHY5XDdDoL2grBcktylo9S5isaXwjtJaQtqS
-4jYH8Nsjuu2art3QtRuSOTE4RXDMaqqqoO6h9xKKJaG5RL3/BM3+kywOnsjB9t5VqnpF1ayo6yUh
-lFN7L4CirEhpiaecdU4xr/EOIeAWiJ7oupZ2u6Vtt6S0xT3mXPZUevx4b/GUctV0225oN0ds14ds
-j67jsaPvNvTdhti3VFVDVS+o6gZPKZ+gqJpZR/Jdf+1dxXHNHRcRERERkfNHgfZpThTfcveh+Fag
-KCqoF3jap7/0JLhTlRWbo+ts1jfYrm/Qbdd5innfkfo+7zJG+phyJrfPgXNRlDhG8txCrI+Jro3E
-LuJ9JHVbYrul327p2w2p3+CpA09TkW4LIWelqyWhPqBYXKbae4rFpadZXX4Jq4OrNKtLNKvLVM0+
-ZVXn12DFrpqY5z7XBgQzPBTUzYrVpScwC4SyglBORdWObrzA0Y3rpJjo+1138OS53pu74RjuRkwR
-+hbfHrE5ep6j63/A9eWSqmrySYPtmth3NIs96uUezXIPB4qqpl70u+JrQ6u1sSydzWYciIiIiIiI
-nCcKtAfzjLYN/a6HblU5b5p2gXYY+keTEmVRslisWB++wPrwedZHL7A5uk63PaLd5EuKcbj0jDu2
-sR3W7JISdNGJEbxPpK7dBdrb3TRrIwK5XZiFgBUVNgXaV3KgffAyVleeYbV3mWqxol7sUdULwtDf
-OwfNMPb/njLwQxuyullhZjnz3iymINuBUFTE5Gw3a/rY5ceH3fhw0gALuJNft2+J7qyPnqe5vszV
-xouKdrum227ou5bF/mUW7WX6vseKknqxIsY4FKkbfwlDRlvxtYiIiIiInGMKtO9gV+zap+yuUVIU
-JWVR0ixWxP0rHB1+mMPre9Q3llT1gs3h8xiW13T7hthHYp/XKHuKecp1SliwHCwHy72s3Uge8GSk
-fpfRjl1P6iOe+pzRtjD9bChrimpJ0RxQLK9Sr56kOXgpqysfxXK1T1k1+VKW5DB4nObtu3LrQ2/v
-MaCumxVlvWCxd5lmuc8umHZiTGw3Gw6r57F2M71PY2Y+B+R5lBTz6yT2bNcvcHS9oghGsILtdkO7
-WdO3XV6/HSMJo2wWrPYuEYcTE2MxtKko2vSniIiIiIjI+aNA+64MQegURg69qYsKq5wiBHzol13V
-CxbLA7b7V9heusF2fZ12s6bdHtFtjujaDX2XC4713RYfem87aUgsBwoChRtdUxJjfr7aBvo+0vcl
-yWGxWrFY7rFY7XFw+SqXrz7FpSee4vLVp7j8xNOs9i9TVrlHdZ7+nkgpMlYGn9aX23hlu+zx+JqH
-WyEU1M2S5f5l+i4XNeuHS1mWpNiRYov33TAVPmfnfeo+7mDQtS3bzRFVdZ2irPAYKQqjWDYslkuW
-q3329i6xXO5T1Q0hlLOA2o6F18pqi4iIiIjIeaVA+1amiuMj3wWllqdGB0qCBbysCKGkahYsVgfD
-2uOjqYDZZn2DzVFev73d3GC7PmK7OaTdHA5ruVti7KasuYUCx1gOQTY4dRXp+kTXJaIHDi5fZf/K
-ExxcfoJLV57k0tWnuHTlKfYvP8Fq/4DV3j5F1WBFXovtJFJialtmw+sIJ1/rLMM9vtZQlNTNktX+
-ZQyn7zv6vqXvW0JhtMNraWOc1pzHNK6jHoPtXBSt3azZhIKqaXILtKKkquoh0N5jtXfAYrWfp7kX
-xclm5gqwRURERETk3FOgfTtj+vqUuzkArbByyLXWS/Cxv3SuPp57a29ZH77A0biGe7g+OnyeUBR0
-7ZquDaStY54IRSAURQ7ghyC7CLCtCtreaTsnecHlq1e5+vTLuPL0M1y++jSXrjzFwZUn2Tu4Slnl
-Ht5lVQ4nBYaMtqdhLbZNQbaPa57dZgXH0vQaHaMYMto4lGVF3w8nB/oWM+fwhTxFvt2sGaeYJwfz
-WUabnNEuwlGeBu49i8WKsq5oFg3LIaO92r/EYrlPVTWEUByPs4c/zU78YkRERERERM4RBdp3NE61
-hrHvVw7zhmB1KCw2bmNjUFsvhinVXV4fXS+oF0vqxYqqWVIO7b22Q6XyUOQp2CHYVGxtV+3cKYpE
-2UNVOskq9g9WHFw64PKVq1y68gT7l6+yd3CF1f6lHDhPxdbyWmx3cPOxDfVQX8xwG5LYNj68mzI+
-FYiz3KKsahwLgdX+Fbp2Q4z9NI3e3en7duhTli9pOFEwZslTjMS+o+8Cqa6xoqBqliz3LrHYO2Cx
-t89iuU/TLCmrmhDCeKBD1n2X0R4z8iIiIiIiIueNAu1bOq3clu8C7jFX646Rhkrbuy3NAqHIGeV6
-scIsDL2il7mP9WKfZnWZ9eGHWR8+T1k19N1mCFIjeBz6azvuccigQ1kaWMlqUbFY1DRNQ13XlGWZ
-p1pbGILrXaC+i5yHCuc+ZLmHADzBkN2ebTr/c6xKHgqKsqJe7LE6eCKvKQ9FbhXmTor9LpPftsP4
-u6y2u+eCafkHKasFzeoSy4MnWe5dYbE6oFnuUTULyrIiWJhVaR8PbhZpi4iIiIiInEMKtG9rXK98
-+4B7VxXbpmDWQsDcIBSYFZRVQxP3aJb7NMt9FqvLLNaH1Nf3KOsFoShpN4d4zG28UsyBqqccdIcQ
-qZIRk2GhYrWsWV9f5GwAABcpSURBVC5qFosh0K4qiqLEgk2tsPJJgNmrsKkE2vTqphXZvluDPgXZ
-Q1A8vsIQSrCw63Vd1hRlnbdLuar6dn2D7ZHhfU+Ku65cPhzPGGxbKCjrJc3qMqtLT7LYv8JidSln
-/OsmzxQIYwG0/F7bsVuKtEVERERE5HxSoD2wWeh8PJBmyKrOq1+PfHreh8dzsth27beAsqynac4x
-9tTbA5rVmmazHqZI54JlZVUR2zV9uyG2IWe3UwTvKcKw9nkIeBdNyaKuqKuSqiopigILIWe0SXia
-Z5NtN/Xa8zGOM7KnbLPZTSntnJFOeZthenywgmqxRyhqmkVuHRZjT9+3dN06TyOPib7dYrGHBE7a
-vWtDsI0FiqqhXh6w3L86tRFrFiuKsjrWgsym0wV2PLyenzUQkRcVM0vDzW9y97fe5z5eC7xruPun
-3P3dZ3JwD+DatWu84hWv4GUvexnve9/7HvfhiIiIyGOiQPsm43TpeQusE9nTYQqzjYHfuB56eGq3
-DjpPe56vdfYhCA9FSVnV1M0ey70rJPc8fXy7pm9ztXJbv0CiIMRESi3JEykmPLX07Zp2c4Pt0fMU
-ZUMoqrzuOy2G8U5kgv3m/Pyweptdl+rpkKdp3+OGftO0+FyorKwXLFaX2L/0JCl2VNWCul5Q1Q3t
-5jBPI++3xL6lKEtCkaeDe0qzi0+V0EMwimC7swFTa7UTU8bHAxWRF7OzOl12bk67pZT47d/+7cd9
-GCIiIvKYKdC+DT+RTd0ZMsSzomMnm4HtAttZ9bHxp8MQaJdOvVjlomGhoKqXdNsj2u0R5fYIt0Af
-E227xfu464Xtkb5d021u0B4+T1nlwmrVcp+Uem6Zn59ls7HddHcb1mpPh368w9fx1w1Dtr6ksEBV
-L1nuHZBSj5lRVTnIruqGzdELdJtD2s0Nuu1RzvAPUX/uHz5e56JpwXKQHcKwNnt2nsNmJzPmvx8R
-ufDmZxZFREREzgUF2rfk09ri49nUYX3wyaz2yWnN417GXtK+W29sFiiKEjBq38OsoCgbqmZFu8lB
-9nZzSN9HttstVhyCbXHv8BRJMR7LaJfNknq5T+xbUooYYYxOb5robvOAevhzKuQ2TjUfA9zpD6bX
-bAAhDO3HnKp2mtWlaep7WddUdU1VNdRNzeawIrfyjjl7PQTWYzabtJvmnqem2zRNfixYPrYoO/Hb
-0SptkQvO3X8CKB73cYiIiIicpEB7cjyl64zrsu1YsbNxkrX5zcHftBs7niHObalysJ3XOw/Tx0Ne
-vw2GFSWhqPKlrAllTd919H1P33V5m1AMa5w3pBTp2w3t+jrVYo9ue0RsN6S+y2u+Q4FZOLHM3HdT
-wGfB9LGCaLPn3I4H5tMPTJltG6bAN7inPB5GEfK0+Kquc/XwIq/v7me9xT1FUt/lx9o1sW/x1LPL
-v4Pb6ScxxhZkrhyWiIiIiIicQwq0B8enIftsXbMPrbBsmg4+rxvmwwJms1nu+2QA6FNoeKwCeAiB
-oiiG+0M/7hAIRUVR1qQYh+DXKKqG4kY1BJh5pNS3tJsbVJvrdNtDYrcm9VsoKoLlYxqGZzysaf34
-OK18rFA+33A6bpte79Rse9rT7lYI5a76uHsOvpsFVb2Y1mUHM7ZH19kCsWtJsafvtrSbI7ZHN+hW
-B/RdO2S958dhx4539pZqibaInCfhcR+AiNyba9eu8eyzz/KVX/mVPPPMM4/7cETkLsUYx5vn+rv3
-XB/cozS2nsqX2X04VkhsbFPFbNv88+Oz43YnLr77uZzhHoui5d7UZVVTNQuaxR6L1SWW+1dZHTzJ
-/uWXsH/1GfYvv4Tl3lXqodI3QIwt7eY67foG/faQvt2Qum3ODHvctcOaFxAbD25st4WTxuJkU/ut
-4T0Y//Pda0hpWD49vRTDioKirIfCaAesDp7g4OpLOXjyZRxcfSn7V55m79KTNKuDXIEdjgfa6xt0
-2zWx70gp4rP38eT09+O/M2W1Rc4DM3vGzP6qmf2smX3YzFoz+10z+yUz+3tm9iYz27/DPl5jZv+r
-mf2WmW3M7P1m9p1m9kdv8zOvNbM0XD7rlOffPjz3m8P9l5vZt5rZr5nZoZn9vpn9sJn9mQd/FzSF
-XeTF5tq1a7zlLW/h2rVrj/tQROQezALtc/3dq4z2wE+5Nd712frlsRf1vIK3u88KdY3Z61P3Nq7w
-Jk8FtyEbDqGA4E5RJoqUKMdstgWsqDAzUuzou00OqFMkpUjsO7r2KD/Wb0ixI8QSL6s8+ph+n58R
-sHl18dmBHXvZs1Ljx2u5nXhlhllBKIY+21UzjOtUTUOus54wT6S+pdscEkKRg/a+y9PfN4d03YbY
-d3nd9lSlPRxbIi4i54+ZfSbwD4FLHP+b4unh8krgi4HngHfeYh//GfBtHP/CfAb4UuALzez17v5T
-tzmMO55yM7NXD+M/NXt4AXwu8Llm9tfd/S/daT8iIiIid0OB9p0ci/RuEe3ZWN37+Ba3/pffbn13
-DtBnoblZ7mkdAlVVkxarvK/Uk2KLp4iFQN9u6Iee20VZEYpimH6eDydgBAu79mJDsGzTuDcd/qmv
-6aZq6ydf59Rj3KYTEeOPl2VDs9jH9zvMndi1dG3OYhtOUTUQAslzQbQxw76bFn78HVWsLXK+mFkN
-fDdwALwA/I/A/wn8PlADfwj4NOALbrOb1wOfDPwi8O3APweWw8989XD7u8zsD7t7f5+HugL+/nCc
-fwX4UWALfArwDcDLga8xs//P3f+H+xxDREREZKJA+3ZOiex8qDM+b9p1WgOw04Ps472gd0F2LrEW
-xgA85ExuVdUAhKLMIw7TwYuyyOudj15ga0ZR1nmb4efGVlhj/2kfk9pT1fPxIPx4kG12m7D2tNZl
-x5/fteHK12VZ0yz2CDhlUdK3W9rtmnZ9g5R6iqrBLJBSysH2fCo+8/Xkpx2Nwm6Rc+DTyZlnB77E
-3X/0xPPvAb7HzP5zcrB7mk8Ffhj4whOB9E+b2QeB/xb4aOANwP92n8f5EqAFPsfdf3r2+PvM7PuB
-nwFeAfx3Zvb33P0D9zmOiIiICKA12jfxYwHcbbLYs+ec3Wa3buh6vEXYlPk9EaCGkFtchSGj3SxW
-LPcusTp4gr3LT3Fw9SUcPPEMq0tP0uxdoqwXhCmjvWvBNbbK2gXcu8enIzp2ewhdx5T49AOzYz/l
-3Tr+duTp8BYKQiimQHu5f5X9y3md9mr/Kov9K7u15kOgPWa0x4Jqt5oOMG+oJiKP3ctmt3/yVhu5
-e3L3G6c8ZcAa+PJbZKv/e3KADPCZ932U+W+Uv3UiyB6P7RrwtcPdPeBNDzCOiIiICKCM9mRe6XoK
-nH3sNG1D5XGmtdrTdlM+ehdIG34iTtz10PaxQvnYH3ra1mdbD2dALEDIRdNS1dAs9vAUmVZ5W8Cs
-oGpWNKtLVM2SsshBd5ivbx4qp594xXf5xjA7xvnjx08p7LLku4DdQiCUVe4bHgKL1SVWB1dpt3lN
-eVlVlFVFXS8omwWhKPNU92H/jmNu07r4kzG/Am6Rx25eQejLgHuddu3Aj7n7H5z6pPsNM/t14I8B
-H3N/hzj5jts89wPAh4HLwOuAb33AsUREROSCU6A9SGk3rToxC3bJ90OCFPLa5zEwHq/TscnkJ82D
-6cF8vvmxZ3z2mE8/nWeTB8qyoW72GDPiRVFT1UvKqmGxf5VmuU9ZNxRFRQjzIz2+z+PHMWtJdmrR
-s9kxTld+7NjN7KatzcfnAlaAU1Mv91geXCXGjti3FEVBUeSe24vlAWW9AAtTxfO8pnwMtsl9td13
-AfZNTb5F5BH7KeA3yUHwt5vZl5KD1ncD73X37i728at3eP6D5L+dDh7gOFvyGvBTuXtvZj8PfDbw
-CQ8wjoiIiAigQHvinoYbu0JliZuDbQ+7x+fPz+Ps3c1bRq7Ddn5Kotmn+HEeoodQUFY1mOU+20VN
-Va9olgcURUW1WFEv9iirmhB267XzGCdrqs9Kjc8bUg8Lo+1EiD4e1+7KT/S6zvsKs4UIeVaAYYSp
-R3i12GO1fxUzI8aWIoShl3iZW39VzZDR3gXyY+bfyEG2mw2VzPPsABF5fIYA9fOA7wM+Hvg3gdcM
-T6/N7N3AdwLf49Nfsjc5usMw4889SAuPD/rNf2md9HvD9RMPMM6kbVt+7ud+7pbPP/PMM+rbKyIi
-MnPt2rW7arfXdXdzHv/xU6A98Fm2dooXzXbB9DBlfLfdLAyeB6swTXWeTy6fjTQlg+1YCLzbflio
-fGyZdBiKi1koKcp66ltdL/axEPL9qqYoK3JbrF01c5+y2X4i9B+yw/PDHw5unqT2KcjeXc//zTpm
-tI+/Dfl0Qw6yDaOgqpf4KmKFkWJPGNamFyFQNyuKsp4y2vNg3sgZ8mkd+e7XIyKPmbv/qpl9AvDG
-4fJZwMeRW2f9meHyNWb2Z281RfxRHOajHvC5557j1a9+9S2ff/Ob38w3fdM3PboDEhEROeeeffZZ
-3vKWtzzuwzgzCrTvxYs0sLt9Xv0sB3qAN+hF+t6KCAzZ4h8aLpjZS8ltu74KeDXwJ4BngS96TIf4
-pJnZHbLaLx2uP3ifY0yZ8Ly0p+Dq1au33PjZZ5/lbW97230OJSJnoW1zrcXXv/711HX9mI9GRGKM
-PP3003fc7rnnnhtvnskstIdFgfbgKz//kxTqiYicAXf/PeAdZva/AP+UHGh/npk17r59DIdUA38c
-+IXTnjSzAvgk8jnJf36fY+zm87jT9/38HwIico7psyryonWu4zcF2iIi8lAMa7h/ghxol8AVdmuh
-H7U3cYtAG/hC4Co50P7x+9z/FmjIa8p//z73ISIiInf2EvLq3sdx8v6uKdAWEZH7YmafAVxz939x
-i+cr4LXD3RvA40obGfCfmtnfd/f/69gTZi8Dvnm4ewS8434GcPe9BztEERER+UiiQFtERO7X5wD/
-tZn9JPAjwC+Rg+kl8EeAv0jOZjvwtttUHr+TBy0z8fvkIPrHzexvAO8knwX/FOAbgJcPY/xXj7Fg
-m4iIiHwEUaAtIiIPwsiVxl97ynNjy4MfBL7xAcd4EEfAvwv8KDmw/obZc+Mxfru7f/sDjiMiIiIC
-KNAWEZH7983ALwKvA15Fzgy/ZHjud4H3AO9w93/0gOOMwfC9PrfbyP3nzOxPAF8HvAH4KOAQeC85
-yP4nD3iMIiIiIhO7fbcTERGRFyczezu5CNq/dPePedzHIyIiIhdHeNwHICIiIiIiIvKRRIG2iIiI
-iIiIyBlSoC0iIiIiIiJyhhRoi4iIiIiIiJwhBdoiIvKR7K6qkouIiIicJQXaIiLyEcndv8zdC3f/
-2Dtta2YfbWZ/3cx+xcxumNkHzOw9ZvZ1ZrY8q2Mysy8xs39sZtfMbG1m/9LMvsvMPvWsxhC5SB7m
-Z9fM3mxm6S4vn3VWr0nkI5WZPW1mbzCzt5jZO83sudln6H9+SGM+tu9dtfcSEZELzczeCHwXcImb
-s98G/N/AG9z9XzzAGAvgHwB/9hZjJOCt7v7W+x1D5KJ52J9dM3sz8OZT9n2SA3/a3d99P+OIXBRm
-lk48NP9svcPdv/wMx3rs37vKaIuIyIVlZq8Cvhs4AK4D3wh8GvA5wN8hfzn/YeCHzWzvAYZ6O7sv
-+/8D+HPAJwNfAfwG+fv4zWb25x9gDJEL4xF+dkevBD7hFpdPBN57BmOIXATjkq7/F/gn5KD3YXjs
-37vKaIuIyIVlZu8GPgPogM909/eceP5rgW8mf1G/5X7OfJvZnwZ+fNjHDwFf6LMvXzN7EvhZ4KOB
-DwEf4+7P398rErkYHtFnd8pou3vx4EctcrENn6n3Au919+fM7F8F/h/y5/TMMtrn5XtXGW0REbmQ
-zOw15H+oO/C2k/9QH3wr8CvkM+5fbWb384/trx2ue+Cr/MQZbnf/APD1w90rgLLaIrfxCD+7InKG
-3P0t7v5Od3/uIQ91Lr53FWiLiMhF9edmt7/jtA2GL+fvHO5eAT77XgYws33yVFYHftzdf+cWm34/
-8MJw+wvuZQyRC+ihf3ZF5MXpPH3vKtAWEZGL6jOG60PyFLJb+YnZ7U+/xzFeA9Sn7OcYd++Af0rO
-vr1G2TeR23oUn10ReXE6N9+7CrRFROSi+njyGe/fcPeTlVDnfvXEz9yLP3aL/dxunJJcxElETvco
-PrvHDO2Bfs/MtsP1u8zs683syoPsV0TO3Ln53lWgLSIiF46ZNcBTw933325bd/8wOXMG8K/c41Cv
-mN2+7TjAb81u3+s4IhfCI/zsnvS6YdxyuP4s4K8Av2lmn/+A+xaRs3NuvnfLs96hiIjIi8DB7PaN
-u9j+EFgB+w9xnMPZ7XsdR+SieFSf3dEvAT8IvAf4HaAC/nXgPwD+bfL67+8zsze6+z++zzFE5Oyc
-m+9dBdoiInIRLWa327vYfktex7V8iONsZ7fvdRyRi+JRfXYB/oa7v+WUx98L/F0z+0+AvwUUwNvM
-7GPd/W6OSUQennPzvaup4yIichFtZrfrW26105DXhK4f4jjN7Pa9jiNyUTyqzy7u/sIdnv/bwP9E
-DuRfDnzRvY4hImfu3HzvKtAWEZGL6Prs9t1MF9sbru9mqur9jrM3u32v44hcFI/qs3u3np3dfu1D
-GkNE7t65+d5VoC0iIheOu2+BDwx3X3G7bYeqwuOX8W/dbttTzAux3HYcjhdiuddxRC6ER/jZvVu/
-PLv9UQ9pDBG5e+fme1eBtoiIXFS/TJ7y+XFmdrvvwz86u/0r9zHGafu53Tg98Ov3OI7IRfIoPrt3
-yx/SfkXk/pyb710F2iIiclH91HC9B7z6NtvNp4P+9D2O8V52xVhuOa3UzCrgU8n/aH+vu8d7HEfk
-InkUn927Ne/Z+zsPaQwRuXvn5ntXgbaIiFxUPzi7/WWnbWBmBvxHw90PA++6lwHc/Qbwv5Ozb68z
-s5ffYtMvAi4Nt7//XsYQuYAe+mf3HvzF2e2feEhjiMhdOk/fuwq0RUTkQnL39wI/Sf4y/goz+5RT
-Nvs64OPJZ7y/7eQZbzN7k5ml4fLf3GKobxmuS+BvnpzqamZPAX91uPthchVjEbmFR/HZNbNXmtnH
-3u44hvZeXzHc/V3gB+791YjIvXgxfe+qj7aIiFxkX02eUroEfszM/jI587UEvgT4C8N2vwZ86232
-c8t1mu7+LjP7buCLgX9nGOfbyNNMPxH4RuCjh338F+7+/AO9IpGL4WF/dl9N7o39LuBHgX9GLsJW
-ktd1finwbw3b9sBfcHe15RO5DTP7dODjZg89Nbv9cWb2pvn27v6O2+zu3H/vKtAWEZELy91/wcz+
-PeDvkqeQ/eWTm5D/of4Gdz98gKG+HDgAPhf4U8BnnxgjAm91d2WzRe7CI/rsBuBzgNfd6jDIwfeX
-u/s773MMkYvkzwNvOuVxAz5juIwcuF2gfSeP/XtXgbaIiFxo7v4jZvaJ5AzZG8jtQFrgN4DvBf6m
-u29ut4u7GGMDvNHMvhj4j4E/DlwBfg949zDGzzzI6xC5aB7yZ/dHyNPC/yTwKuClwJPkgOCDwC8C
-/wj4jmFNqIjcnbut1H+77V4U37vmrq4EIiIiIiIiImdFxdBEREREREREzpACbREREREREZEzpEBb
-RERERERE5Awp0BYRERERERE5Qwq0RURERERERM6QAm0RERERERGRM6RAW0REREREROQMKdAWERER
-EREROUMKtEVERERERETOkAJtERERERERkTOkQFtERERERETkDCnQFhERERERETlDCrRFRERERERE
-zpACbREREREREZEzpEBbRERERERE5Awp0BYRERERERE5Qwq0RURERERERM6QAm0RERERERGRM6RA
-W0REREREROQMKdAWEREREREROUMKtEVERERERETOkAJtERERERERkTOkQFtERERERETkDCnQFhER
-ERERETlDCrRFREREREREztD/D+iauRi3tIFfAAAAAElFTkSuQmCC
-"
-width=493
-height=445
->
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;">&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;<a class="anchor-link" href="#&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;">&#182;</a></h2><p>你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率<a href="http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130">远远超过 80%</a>。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。</p>
-<h2 id="&#25552;&#20132;&#39033;&#30446;">&#25552;&#20132;&#39033;&#30446;<a class="anchor-link" href="#&#25552;&#20132;&#39033;&#30446;">&#182;</a></h2><p>提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 "File" -&gt; "Download as" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。</p>
-
-</div>
-</div>
-</div>
-    </div>
-  </div>
-</body>
-</html>

From 2081a53f2b53a42be00ea3a1a0f99c14c842a460 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 9 Jul 2017 17:09:33 +0800
Subject: [PATCH 06/16] Delete dlnd_image_classification.ipynb

---
 .../dlnd_image_classification.ipynb           | 1107 -----------------
 1 file changed, 1107 deletions(-)
 delete mode 100644 image-classification/dlnd_image_classification.ipynb

diff --git a/image-classification/dlnd_image_classification.ipynb b/image-classification/dlnd_image_classification.ipynb
deleted file mode 100644
index 314c0bc..0000000
--- a/image-classification/dlnd_image_classification.ipynb
+++ /dev/null
@@ -1,1107 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# 图像分类\n",
-    "\n",
-    "在此项目中，你将对 [CIFAR-10 数据集](https://www.cs.toronto.edu/~kriz/cifar.html) 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。\n",
-    "\n",
-    "\n",
-    "## 获取数据\n",
-    "\n",
-    "请运行以下单元，以下载 [CIFAR-10 数据集（Python版）](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All files found!\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "from urllib.request import urlretrieve\n",
-    "from os.path import isfile, isdir\n",
-    "from tqdm import tqdm\n",
-    "import problem_unittests as tests\n",
-    "import tarfile\n",
-    "\n",
-    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
-    "\n",
-    "# Use Floyd's cifar-10 dataset if present\n",
-    "floyd_cifar10_location = '/input/cifar-10/python.tar.gz'\n",
-    "if isfile(floyd_cifar10_location):\n",
-    "    tar_gz_path = floyd_cifar10_location\n",
-    "else:\n",
-    "    tar_gz_path = 'cifar-10-python.tar.gz'\n",
-    "\n",
-    "class DLProgress(tqdm):\n",
-    "    last_block = 0\n",
-    "\n",
-    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
-    "        self.total = total_size\n",
-    "        self.update((block_num - self.last_block) * block_size)\n",
-    "        self.last_block = block_num\n",
-    "\n",
-    "if not isfile(tar_gz_path):\n",
-    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
-    "        urlretrieve(\n",
-    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
-    "            tar_gz_path,\n",
-    "            pbar.hook)\n",
-    "\n",
-    "if not isdir(cifar10_dataset_folder_path):\n",
-    "    with tarfile.open(tar_gz_path) as tar:\n",
-    "        tar.extractall()\n",
-    "        tar.close()\n",
-    "\n",
-    "\n",
-    "tests.test_folder_path(cifar10_dataset_folder_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 探索数据\n",
-    "\n",
-    "该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 `data_batch_1`、`data_batch_2`，以此类推。每个部分都包含以下某个类别的标签和图片：\n",
-    "\n",
-    "* 飞机\n",
-    "* 汽车\n",
-    "* 鸟类\n",
-    "* 猫\n",
-    "* 鹿\n",
-    "* 狗\n",
-    "* 青蛙\n",
-    "* 马\n",
-    "* 船只\n",
-    "* 卡车\n",
-    "\n",
-    "了解数据集也是对数据进行预测的必经步骤。你可以通过更改 `batch_id` 和 `sample_id` 探索下面的代码单元。`batch_id` 是数据集一个部分的 ID（1 到 5）。`sample_id` 是该部分中图片和标签对（label pair）的 ID。\n",
-    "\n",
-    "问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Stats of batch 1:\n",
-      "Samples: 10000\n",
-      "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n",
-      "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n",
-      "\n",
-      "Example of Image 5:\n",
-      "Image - Min Value: 0 Max Value: 252\n",
-      "Image - Shape: (32, 32, 3)\n",
-      "Label - Label Id: 1 Name: automobile\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAK/CAYAAABjrqvGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x263597db048>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 351,
-       "width": 353
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "\n",
-    "import helper\n",
-    "import numpy as np\n",
-    "\n",
-    "# Explore the dataset\n",
-    "batch_id = 1\n",
-    "sample_id = 5\n",
-    "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 实现预处理函数\n",
-    "\n",
-    "### 标准化\n",
-    "\n",
-    "在下面的单元中，实现 `normalize` 函数，传入图片数据 `x`，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 `x` 的形状一样。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def normalize(x):\n",
-    "    \"\"\"\n",
-    "    Normalize a list of sample image data in the range of 0 to 1\n",
-    "    : x: List of image data.  The image shape is (32, 32, 3)\n",
-    "    : return: Numpy array of normalize data\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return np.array(x/255)\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_normalize(normalize)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### One-hot 编码\n",
-    "\n",
-    "和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 `one_hot_encode` 函数。输入，也就是 `x`，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 `one_hot_encode` 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。\n",
-    "\n",
-    "提示：不要重复发明轮子。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def one_hot_encode(x):\n",
-    "    \"\"\"\n",
-    "    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n",
-    "    : x: List of sample Labels\n",
-    "    : return: Numpy array of one-hot encoded labels\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    from tflearn.data_utils import to_categorical\n",
-    "    return np.array(to_categorical(x, 10))\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_one_hot_encode(one_hot_encode)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 随机化数据\n",
-    "\n",
-    "之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 预处理所有数据并保存\n",
-    "\n",
-    "运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "# Preprocess Training, Validation, and Testing Data\n",
-    "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 检查点\n",
-    "\n",
-    "这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import pickle\n",
-    "import problem_unittests as tests\n",
-    "import helper\n",
-    "\n",
-    "# Load the Preprocessed Validation data\n",
-    "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 构建网络\n",
-    "\n",
-    "对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。\n",
-    "\n",
-    ">**注意**：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。\n",
-    "\n",
-    ">但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 `conv2d` 类 [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d)，而不是 TF Layers 版本的 `conv2d` 类 [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d)。\n",
-    "\n",
-    "我们开始吧！\n",
-    "\n",
-    "\n",
-    "### 输入\n",
-    "\n",
-    "神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：\n",
-    "\n",
-    "* 实现 `neural_net_image_input`\n",
-    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
-    " * 使用 `image_shape` 设置形状，部分大小设为 `None`\n",
-    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"x\" 命名\n",
-    "* 实现 `neural_net_label_input`\n",
-    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
-    " * 使用 `n_classes` 设置形状，部分大小设为 `None`\n",
-    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"y\" 命名\n",
-    "* 实现 `neural_net_keep_prob_input`\n",
-    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)，用于丢弃保留概率\n",
-    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"keep_prob\" 命名\n",
-    "\n",
-    "这些名称将在项目结束时，用于加载保存的模型。\n",
-    "\n",
-    "注意：TensorFlow 中的 `None` 表示形状可以是动态大小。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Image Input Tests Passed.\n",
-      "Label Input Tests Passed.\n",
-      "Keep Prob Tests Passed.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "def neural_net_image_input(image_shape):\n",
-    "    \"\"\"\n",
-    "    Return a Tensor for a batch of image input\n",
-    "    : image_shape: Shape of the images\n",
-    "    : return: Tensor for image input.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], name='x')\n",
-    "\n",
-    "\n",
-    "def neural_net_label_input(n_classes):\n",
-    "    \"\"\"\n",
-    "    Return a Tensor for a batch of label input\n",
-    "    : n_classes: Number of classes\n",
-    "    : return: Tensor for label input.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return tf.placeholder(tf.int32, [None, n_classes], name='y')\n",
-    "\n",
-    "\n",
-    "def neural_net_keep_prob_input():\n",
-    "    \"\"\"\n",
-    "    Return a Tensor for keep probability\n",
-    "    : return: Tensor for keep probability.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return tf.placeholder(tf.float32, name='keep_prob')\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tf.reset_default_graph()\n",
-    "tests.test_nn_image_inputs(neural_net_image_input)\n",
-    "tests.test_nn_label_inputs(neural_net_label_input)\n",
-    "tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 卷积和最大池化层\n",
-    "\n",
-    "卷积层级适合处理图片。对于此代码单元，你应该实现函数 `conv2d_maxpool` 以便应用卷积然后进行最大池化：\n",
-    "\n",
-    "* 使用 `conv_ksize`、`conv_num_outputs` 和 `x_tensor` 的形状创建权重（weight）和偏置（bias）。\n",
-    "* 使用权重和 `conv_strides` 对 `x_tensor` 应用卷积。\n",
-    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
-    "* 添加偏置\n",
-    "* 向卷积中添加非线性激活（nonlinear activation）\n",
-    "* 使用 `pool_ksize` 和 `pool_strides` 应用最大池化\n",
-    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
-    "\n",
-    "**注意**：对于**此层**，**请勿使用** [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers)，但是仍然可以使用 TensorFlow 的 [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) 包。对于所有**其他层**，你依然可以使用快捷方法。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n",
-    "    \"\"\"\n",
-    "    Apply convolution then max pooling to x_tensor\n",
-    "    :param x_tensor: TensorFlow Tensor\n",
-    "    :param conv_num_outputs: Number of outputs for the convolutional layer\n",
-    "    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n",
-    "    :param conv_strides: Stride 2-D Tuple for convolution\n",
-    "    :param pool_ksize: kernal size 2-D Tuple for pool\n",
-    "    :param pool_strides: Stride 2-D Tuple for pool\n",
-    "    : return: A tensor that represents convolution and max pooling of x_tensor\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    weights = tf.Variable(tf.truncated_normal(shape=[conv_ksize[0], conv_ksize[1], x_tensor.get_shape().as_list()[3], conv_num_outputs], stddev=0.1))\n",
-    "    bias = tf.Variable(tf.constant(0.1, shape=[conv_num_outputs]))\n",
-    "    conv = tf.nn.conv2d(input=x_tensor, filter=weights, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME') + bias\n",
-    "    activate = tf.nn.relu(conv)\n",
-    "    pool = tf.nn.max_pool(value=activate, ksize=[1, pool_ksize[0], pool_ksize[1], 1], strides=[1, pool_strides[0], pool_strides[1], 1], padding='SAME')\n",
-    "    \n",
-    "    return pool\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_con_pool(conv2d_maxpool)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 扁平化层\n",
-    "\n",
-    "实现 `flatten` 函数，将 `x_tensor` 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（*部分大小（Batch Size）*，*扁平化图片大小（Flattened Image Size）*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def flatten(x_tensor):\n",
-    "    \"\"\"\n",
-    "    Flatten x_tensor to (Batch Size, Flattened Image Size)\n",
-    "    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n",
-    "    : return: A tensor of size (Batch Size, Flattened Image Size).\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    layer_shape = x_tensor.get_shape()\n",
-    "    num_features = layer_shape[1:4].num_elements()\n",
-    "    layer_flat = tf.reshape(x_tensor, [-1, num_features])\n",
-    "    \n",
-    "    return layer_flat\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_flatten(flatten)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 完全连接的层\n",
-    "\n",
-    "实现 `fully_conn` 函数，以向 `x_tensor` 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def fully_conn(x_tensor, num_outputs):\n",
-    "    \"\"\"\n",
-    "    Apply a fully connected layer to x_tensor using weight and bias\n",
-    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
-    "    : num_outputs: The number of output that the new tensor should be.\n",
-    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n",
-    "    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n",
-    "    fc = tf.nn.relu(tf.matmul(x_tensor, weights) + bias)\n",
-    "    \n",
-    "    return fc\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_fully_conn(fully_conn)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 输出层\n",
-    "\n",
-    "实现 `output` 函数，向 x_tensor 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n",
-    "\n",
-    "**注意**：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def output(x_tensor, num_outputs):\n",
-    "    \"\"\"\n",
-    "    Apply a output layer to x_tensor using weight and bias\n",
-    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
-    "    : num_outputs: The number of output that the new tensor should be.\n",
-    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n",
-    "    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n",
-    "    output = tf.matmul(x_tensor, weights) + bias\n",
-    "    \n",
-    "    return output\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_output(output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 创建卷积模型\n",
-    "\n",
-    "实现函数 `conv_net`， 创建卷积神经网络模型。该函数传入一批图片 `x`，并输出对数（logits）。使用你在上方创建的层创建此模型：\n",
-    "\n",
-    "* 应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）\n",
-    "* 应用一个扁平层（Flatten Layer）\n",
-    "* 应用 1、2 或 3 个完全连接层（Fully Connected Layers）\n",
-    "* 应用一个输出层（Output Layer）\n",
-    "* 返回输出\n",
-    "* 使用 `keep_prob` 向模型中的一个或多个层应用 [TensorFlow 的 Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Neural Network Built!\n"
-     ]
-    }
-   ],
-   "source": [
-    "def conv_net(x, keep_prob):\n",
-    "    \"\"\"\n",
-    "    Create a convolutional neural network model\n",
-    "    : x: Placeholder tensor that holds image data.\n",
-    "    : keep_prob: Placeholder tensor that hold dropout keep probability.\n",
-    "    : return: Tensor that represents logits\n",
-    "    \"\"\"\n",
-    "    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers\n",
-    "    #    Play around with different number of outputs, kernel size and stride\n",
-    "    # Function Definition from Above:\n",
-    "    #    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
-    "    conv_pool_1 = conv2d_maxpool(x, 64, [5, 5], [1, 1], [3, 3], [2, 2])\n",
-    "    norm_layer = tf.nn.lrn(conv_pool_1, 4 , bias=1.0, alpha=0.001 / 9.0, beta=0.75)\n",
-    "    conv_pool_2 = conv2d_maxpool(norm_layer, 64, [5, 5], [1, 1], [3, 3], [2, 2])\n",
-    "\n",
-    "    # TODO: Apply a Flatten Layer\n",
-    "    # Function Definition from Above:\n",
-    "    #   flatten(x_tensor)\n",
-    "    flat_layer = flatten(conv_pool_2)\n",
-    "\n",
-    "    # TODO: Apply 1, 2, or 3 Fully Connected Layers\n",
-    "    #    Play around with different number of outputs\n",
-    "    # Function Definition from Above:\n",
-    "    #   fully_conn(x_tensor, num_outputs)\n",
-    "    fc_layer1 = fully_conn(flat_layer, 384)\n",
-    "    dropout_layer_1 = tf.nn.dropout(fc_layer1, keep_prob)\n",
-    "    fc_layer2 = fully_conn(dropout_layer_1, 192)\n",
-    "    dropout_layer_2 = tf.nn.dropout(fc_layer2, keep_prob)\n",
-    "    \n",
-    "    # TODO: Apply an Output Layer\n",
-    "    #    Set this to the number of classes\n",
-    "    # Function Definition from Above:\n",
-    "    #   output(x_tensor, num_outputs)\n",
-    "    logits = output(dropout_layer_2, 10)\n",
-    "    \n",
-    "    # TODO: return output\n",
-    "    return logits\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "\n",
-    "##############################\n",
-    "## Build the Neural Network ##\n",
-    "##############################\n",
-    "\n",
-    "# Remove previous weights, bias, inputs, etc..\n",
-    "tf.reset_default_graph()\n",
-    "\n",
-    "# Inputs\n",
-    "x = neural_net_image_input((32, 32, 3))\n",
-    "y = neural_net_label_input(10)\n",
-    "keep_prob = neural_net_keep_prob_input()\n",
-    "\n",
-    "# Model\n",
-    "logits = conv_net(x, keep_prob)\n",
-    "\n",
-    "# Name logits Tensor, so that is can be loaded from disk after training\n",
-    "logits = tf.identity(logits, name='logits')\n",
-    "\n",
-    "# Loss and Optimizer\n",
-    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
-    "optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
-    "\n",
-    "# Accuracy\n",
-    "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
-    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n",
-    "\n",
-    "tests.test_conv_net(conv_net)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 训练神经网络\n",
-    "\n",
-    "### 单次优化\n",
-    "\n",
-    "实现函数 `train_neural_network` 以进行单次优化（single optimization）。该优化应该使用 `optimizer` 优化 `session`，其中 `feed_dict` 具有以下参数：\n",
-    "\n",
-    "* `x` 表示图片输入\n",
-    "* `y` 表示标签\n",
-    "* `keep_prob` 表示丢弃的保留率\n",
-    "\n",
-    "每个部分都会调用该函数，所以 `tf.global_variables_initializer()` 已经被调用。\n",
-    "\n",
-    "注意：不需要返回任何内容。该函数只是用来优化神经网络。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n",
-    "    \"\"\"\n",
-    "    Optimize the session on a batch of images and labels\n",
-    "    : session: Current TensorFlow session\n",
-    "    : optimizer: TensorFlow optimizer function\n",
-    "    : keep_probability: keep probability\n",
-    "    : feature_batch: Batch of Numpy image data\n",
-    "    : label_batch: Batch of Numpy label data\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    session.run(optimizer, feed_dict = {keep_prob: keep_probability, x: feature_batch, y: label_batch})\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_train_nn(train_neural_network)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 显示数据\n",
-    "\n",
-    "实现函数 `print_stats` 以输出损失和验证准确率。使用全局变量 `valid_features` 和 `valid_labels` 计算验证准确率。使用保留率 `1.0` 计算损失和验证准确率（loss and validation accuracy）。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def print_stats(session, feature_batch, label_batch, cost, accuracy):\n",
-    "    \"\"\"\n",
-    "    Print information about loss and validation accuracy\n",
-    "    : session: Current TensorFlow session\n",
-    "    : feature_batch: Batch of Numpy image data\n",
-    "    : label_batch: Batch of Numpy label data\n",
-    "    : cost: TensorFlow cost function\n",
-    "    : accuracy: TensorFlow accuracy function\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    print('Loss: ', end='')\n",
-    "    print(session.run(cost, feed_dict = {x: feature_batch, y: label_batch, keep_prob: 1.0}), end='')\n",
-    "    print(', Accuracy: ', end='')\n",
-    "    print(session.run(accuracy, feed_dict = {x: feature_batch, y: label_batch, keep_prob: 1.0}))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 超参数\n",
-    "\n",
-    "调试以下超参数：\n",
-    "* 设置 `epochs` 表示神经网络停止学习或开始过拟合的迭代次数\n",
-    "* 设置 `batch_size`，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：\n",
-    "\n",
-    " * 64\n",
-    " * 128\n",
-    " * 256\n",
-    " * ...\n",
-    "* 设置 `keep_probability` 表示使用丢弃时保留节点的概率"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# TODO: Tune Parameters\n",
-    "epochs = 10\n",
-    "batch_size = 128\n",
-    "keep_probability = 0.75"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 在单个 CIFAR-10 部分上训练\n",
-    "\n",
-    "我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Checking the Training on a Single Batch...\n",
-      "Epoch  1, CIFAR-10 Batch 1:  Loss: 1.95307, Accuracy: 0.35\n",
-      "Epoch  2, CIFAR-10 Batch 1:  Loss: 1.71162, Accuracy: 0.5\n",
-      "Epoch  3, CIFAR-10 Batch 1:  Loss: 1.59222, Accuracy: 0.525\n",
-      "Epoch  4, CIFAR-10 Batch 1:  Loss: 1.33961, Accuracy: 0.65\n",
-      "Epoch  5, CIFAR-10 Batch 1:  Loss: 1.22308, Accuracy: 0.625\n",
-      "Epoch  6, CIFAR-10 Batch 1:  Loss: 1.02561, Accuracy: 0.65\n",
-      "Epoch  7, CIFAR-10 Batch 1:  Loss: 0.918526, Accuracy: 0.725\n",
-      "Epoch  8, CIFAR-10 Batch 1:  Loss: 0.763063, Accuracy: 0.775\n",
-      "Epoch  9, CIFAR-10 Batch 1:  Loss: 0.656814, Accuracy: 0.8\n",
-      "Epoch 10, CIFAR-10 Batch 1:  Loss: 0.574128, Accuracy: 0.825\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "print('Checking the Training on a Single Batch...')\n",
-    "with tf.Session() as sess:\n",
-    "    # Initializing the variables\n",
-    "    sess.run(tf.global_variables_initializer())\n",
-    "    \n",
-    "    # Training cycle\n",
-    "    for epoch in range(epochs):\n",
-    "        batch_i = 1\n",
-    "        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
-    "            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
-    "        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
-    "        print_stats(sess, batch_features, batch_labels, cost, accuracy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 完全训练模型\n",
-    "\n",
-    "现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training...\n",
-      "Epoch  1, CIFAR-10 Batch 1:  Loss: 1.95822, Accuracy: 0.35\n",
-      "Epoch  1, CIFAR-10 Batch 2:  Loss: 1.64322, Accuracy: 0.4\n",
-      "Epoch  1, CIFAR-10 Batch 3:  Loss: 1.36831, Accuracy: 0.55\n",
-      "Epoch  1, CIFAR-10 Batch 4:  Loss: 1.41689, Accuracy: 0.45\n",
-      "Epoch  1, CIFAR-10 Batch 5:  Loss: 1.59784, Accuracy: 0.425\n",
-      "Epoch  2, CIFAR-10 Batch 1:  Loss: 1.36398, Accuracy: 0.475\n",
-      "Epoch  2, CIFAR-10 Batch 2:  Loss: 1.1802, Accuracy: 0.475\n",
-      "Epoch  2, CIFAR-10 Batch 3:  Loss: 1.07384, Accuracy: 0.6\n",
-      "Epoch  2, CIFAR-10 Batch 4:  Loss: 0.988241, Accuracy: 0.675\n",
-      "Epoch  2, CIFAR-10 Batch 5:  Loss: 1.24307, Accuracy: 0.55\n",
-      "Epoch  3, CIFAR-10 Batch 1:  Loss: 1.05733, Accuracy: 0.625\n",
-      "Epoch  3, CIFAR-10 Batch 2:  Loss: 0.952706, Accuracy: 0.675\n",
-      "Epoch  3, CIFAR-10 Batch 3:  Loss: 0.922446, Accuracy: 0.65\n",
-      "Epoch  3, CIFAR-10 Batch 4:  Loss: 0.753417, Accuracy: 0.8\n",
-      "Epoch  3, CIFAR-10 Batch 5:  Loss: 0.917541, Accuracy: 0.7\n",
-      "Epoch  4, CIFAR-10 Batch 1:  Loss: 0.868109, Accuracy: 0.725\n",
-      "Epoch  4, CIFAR-10 Batch 2:  Loss: 0.818949, Accuracy: 0.7\n",
-      "Epoch  4, CIFAR-10 Batch 3:  Loss: 0.680601, Accuracy: 0.725\n",
-      "Epoch  4, CIFAR-10 Batch 4:  Loss: 0.577342, Accuracy: 0.825\n",
-      "Epoch  4, CIFAR-10 Batch 5:  Loss: 0.650067, Accuracy: 0.8\n",
-      "Epoch  5, CIFAR-10 Batch 1:  Loss: 0.748057, Accuracy: 0.8\n",
-      "Epoch  5, CIFAR-10 Batch 2:  Loss: 0.633852, Accuracy: 0.8\n",
-      "Epoch  5, CIFAR-10 Batch 3:  Loss: 0.480863, Accuracy: 0.95\n",
-      "Epoch  5, CIFAR-10 Batch 4:  Loss: 0.522334, Accuracy: 0.85\n",
-      "Epoch  5, CIFAR-10 Batch 5:  Loss: 0.571857, Accuracy: 0.85\n",
-      "Epoch  6, CIFAR-10 Batch 1:  Loss: 0.642935, Accuracy: 0.8\n",
-      "Epoch  6, CIFAR-10 Batch 2:  Loss: 0.585723, Accuracy: 0.825\n",
-      "Epoch  6, CIFAR-10 Batch 3:  Loss: 0.395464, Accuracy: 0.9\n",
-      "Epoch  6, CIFAR-10 Batch 4:  Loss: 0.397977, Accuracy: 0.875\n",
-      "Epoch  6, CIFAR-10 Batch 5:  Loss: 0.392235, Accuracy: 0.925\n",
-      "Epoch  7, CIFAR-10 Batch 1:  Loss: 0.489782, Accuracy: 0.85\n",
-      "Epoch  7, CIFAR-10 Batch 2:  Loss: 0.459161, Accuracy: 0.825\n",
-      "Epoch  7, CIFAR-10 Batch 3:  Loss: 0.273993, Accuracy: 0.95\n",
-      "Epoch  7, CIFAR-10 Batch 4:  Loss: 0.319732, Accuracy: 0.925\n",
-      "Epoch  7, CIFAR-10 Batch 5:  Loss: 0.30099, Accuracy: 0.95\n",
-      "Epoch  8, CIFAR-10 Batch 1:  Loss: 0.327477, Accuracy: 0.9\n",
-      "Epoch  8, CIFAR-10 Batch 2:  Loss: 0.365161, Accuracy: 0.925\n",
-      "Epoch  8, CIFAR-10 Batch 3:  Loss: 0.260866, Accuracy: 0.9\n",
-      "Epoch  8, CIFAR-10 Batch 4:  Loss: 0.2765, Accuracy: 0.95\n",
-      "Epoch  8, CIFAR-10 Batch 5:  Loss: 0.264591, Accuracy: 1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "epochs = 8\n",
-    "save_model_path = './model/image_classification'\n",
-    "\n",
-    "print('Training...')\n",
-    "with tf.Session() as sess:\n",
-    "    # Initializing the variables\n",
-    "    sess.run(tf.global_variables_initializer())\n",
-    "    \n",
-    "    # Training cycle\n",
-    "    for epoch in range(epochs):\n",
-    "        # Loop over all batches\n",
-    "        n_batches = 5\n",
-    "        for batch_i in range(1, n_batches + 1):\n",
-    "            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
-    "                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
-    "            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
-    "            print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
-    "            \n",
-    "    # Save Model\n",
-    "    saver = tf.train.Saver()\n",
-    "    save_path = saver.save(sess, save_model_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 检查点\n",
-    "\n",
-    "模型已保存到本地。\n",
-    "\n",
-    "## 测试模型\n",
-    "\n",
-    "利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Testing Accuracy: 0.684434335443038\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9oAAAN6CAYAAABxCzGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERETlDCrRFREREREREztD/D+iauRi3tIFfAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2635fd7a2b0>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 445,
-       "width": 493
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "import pickle\n",
-    "import helper\n",
-    "import random\n",
-    "\n",
-    "# Set batch size if not already set\n",
-    "try:\n",
-    "    if batch_size:\n",
-    "        pass\n",
-    "except NameError:\n",
-    "    batch_size = 64\n",
-    "\n",
-    "save_model_path = './model/image_classification'\n",
-    "n_samples = 4\n",
-    "top_n_predictions = 3\n",
-    "\n",
-    "def test_model():\n",
-    "    \"\"\"\n",
-    "    Test the saved model against the test dataset\n",
-    "    \"\"\"\n",
-    "\n",
-    "    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))\n",
-    "    loaded_graph = tf.Graph()\n",
-    "\n",
-    "    with tf.Session(graph=loaded_graph) as sess:\n",
-    "        # Load model\n",
-    "        loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
-    "        loader.restore(sess, save_model_path)\n",
-    "\n",
-    "        # Get Tensors from loaded model\n",
-    "        loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
-    "        loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
-    "        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
-    "        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
-    "        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
-    "        \n",
-    "        # Get accuracy in batches for memory limitations\n",
-    "        test_batch_acc_total = 0\n",
-    "        test_batch_count = 0\n",
-    "        \n",
-    "        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n",
-    "            test_batch_acc_total += sess.run(\n",
-    "                loaded_acc,\n",
-    "                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})\n",
-    "            test_batch_count += 1\n",
-    "\n",
-    "        print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
-    "\n",
-    "        # Print Random Samples\n",
-    "        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
-    "        random_test_predictions = sess.run(\n",
-    "            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n",
-    "            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n",
-    "        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
-    "\n",
-    "\n",
-    "test_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 为何准确率只有50-80%？\n",
-    "\n",
-    "你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率[远远超过 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130)。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。\n",
-    "\n",
-    "## 提交项目\n",
-    "\n",
-    "提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 \"File\" -> \"Download as\" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。\n"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python [default]",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

From b8a40ed0d485299858229ca678530250e8a0da4a Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 9 Jul 2017 17:14:29 +0800
Subject: [PATCH 07/16] Add files via upload

---
 .../dlnd_image_classification.html            | 19507 ++++++++++++++++
 .../dlnd_image_classification.ipynb           |  1107 +
 2 files changed, 20614 insertions(+)
 create mode 100644 image-classification/dlnd_image_classification.html
 create mode 100644 image-classification/dlnd_image_classification.ipynb

diff --git a/image-classification/dlnd_image_classification.html b/image-classification/dlnd_image_classification.html
new file mode 100644
index 0000000..1f8d9dd
--- /dev/null
+++ b/image-classification/dlnd_image_classification.html
@@ -0,0 +1,19507 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>dlnd_image_classification</title>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.6 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: 1px dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    background: transparent !important;
+    color: #000 !important;
+    box-shadow: none !important;
+    text-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: 'Glyphicons Halflings';
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Glyphicons Halflings';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+  content: "\e156";
+}
+.glyphicon-unchecked:before {
+  content: "\e157";
+}
+.glyphicon-expand:before {
+  content: "\e158";
+}
+.glyphicon-collapse-down:before {
+  content: "\e159";
+}
+.glyphicon-collapse-up:before {
+  content: "\e160";
+}
+.glyphicon-log-in:before {
+  content: "\e161";
+}
+.glyphicon-flash:before {
+  content: "\e162";
+}
+.glyphicon-log-out:before {
+  content: "\e163";
+}
+.glyphicon-new-window:before {
+  content: "\e164";
+}
+.glyphicon-record:before {
+  content: "\e165";
+}
+.glyphicon-save:before {
+  content: "\e166";
+}
+.glyphicon-open:before {
+  content: "\e167";
+}
+.glyphicon-saved:before {
+  content: "\e168";
+}
+.glyphicon-import:before {
+  content: "\e169";
+}
+.glyphicon-export:before {
+  content: "\e170";
+}
+.glyphicon-send:before {
+  content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+  content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+  content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+  content: "\e174";
+}
+.glyphicon-floppy-save:before {
+  content: "\e175";
+}
+.glyphicon-floppy-open:before {
+  content: "\e176";
+}
+.glyphicon-credit-card:before {
+  content: "\e177";
+}
+.glyphicon-transfer:before {
+  content: "\e178";
+}
+.glyphicon-cutlery:before {
+  content: "\e179";
+}
+.glyphicon-header:before {
+  content: "\e180";
+}
+.glyphicon-compressed:before {
+  content: "\e181";
+}
+.glyphicon-earphone:before {
+  content: "\e182";
+}
+.glyphicon-phone-alt:before {
+  content: "\e183";
+}
+.glyphicon-tower:before {
+  content: "\e184";
+}
+.glyphicon-stats:before {
+  content: "\e185";
+}
+.glyphicon-sd-video:before {
+  content: "\e186";
+}
+.glyphicon-hd-video:before {
+  content: "\e187";
+}
+.glyphicon-subtitles:before {
+  content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+  content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+  content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+  content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+  content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+  content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
+.glyphicon-pawn:before {
+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
+.glyphicon-bitcoin:before {
+  content: "\e227";
+}
+.glyphicon-btc:before {
+  content: "\e227";
+}
+.glyphicon-xbt:before {
+  content: "\e227";
+}
+.glyphicon-yen:before {
+  content: "\00a5";
+}
+.glyphicon-jpy:before {
+  content: "\00a5";
+}
+.glyphicon-ruble:before {
+  content: "\20bd";
+}
+.glyphicon-rub:before {
+  content: "\20bd";
+}
+.glyphicon-scale:before {
+  content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+  content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+  content: "\e232";
+}
+.glyphicon-education:before {
+  content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+  content: "\e234";
+}
+.glyphicon-option-vertical:before {
+  content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+  content: "\e236";
+}
+.glyphicon-modal-window:before {
+  content: "\e237";
+}
+.glyphicon-oil:before {
+  content: "\e238";
+}
+.glyphicon-grain:before {
+  content: "\e239";
+}
+.glyphicon-sunglasses:before {
+  content: "\e240";
+}
+.glyphicon-text-size:before {
+  content: "\e241";
+}
+.glyphicon-text-color:before {
+  content: "\e242";
+}
+.glyphicon-text-background:before {
+  content: "\e243";
+}
+.glyphicon-object-align-top:before {
+  content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+  content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+  content: "\e246";
+}
+.glyphicon-object-align-left:before {
+  content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+  content: "\e248";
+}
+.glyphicon-object-align-right:before {
+  content: "\e249";
+}
+.glyphicon-triangle-right:before {
+  content: "\e250";
+}
+.glyphicon-triangle-left:before {
+  content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+  content: "\e252";
+}
+.glyphicon-triangle-top:before {
+  content: "\e253";
+}
+.glyphicon-console:before {
+  content: "\e254";
+}
+.glyphicon-superscript:before {
+  content: "\e255";
+}
+.glyphicon-subscript:before {
+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
+h5 small,
+h6 small,
+.h1 small,
+.h2 small,
+.h3 small,
+.h4 small,
+.h5 small,
+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 18px;
+  margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
+h2,
+.h2 {
+  font-size: 27px;
+}
+h3,
+.h3 {
+  font-size: 23px;
+}
+h4,
+.h4 {
+  font-size: 17px;
+}
+h5,
+.h5 {
+  font-size: 13px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
+address {
+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
+.col-xs-9 {
+  width: 75%;
+}
+.col-xs-8 {
+  width: 66.66666667%;
+}
+.col-xs-7 {
+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+  content: "\f044";
+}
+.fa-share-square-o:before {
+  content: "\f045";
+}
+.fa-check-square-o:before {
+  content: "\f046";
+}
+.fa-arrows:before {
+  content: "\f047";
+}
+.fa-step-backward:before {
+  content: "\f048";
+}
+.fa-fast-backward:before {
+  content: "\f049";
+}
+.fa-backward:before {
+  content: "\f04a";
+}
+.fa-play:before {
+  content: "\f04b";
+}
+.fa-pause:before {
+  content: "\f04c";
+}
+.fa-stop:before {
+  content: "\f04d";
+}
+.fa-forward:before {
+  content: "\f04e";
+}
+.fa-fast-forward:before {
+  content: "\f050";
+}
+.fa-step-forward:before {
+  content: "\f051";
+}
+.fa-eject:before {
+  content: "\f052";
+}
+.fa-chevron-left:before {
+  content: "\f053";
+}
+.fa-chevron-right:before {
+  content: "\f054";
+}
+.fa-plus-circle:before {
+  content: "\f055";
+}
+.fa-minus-circle:before {
+  content: "\f056";
+}
+.fa-times-circle:before {
+  content: "\f057";
+}
+.fa-check-circle:before {
+  content: "\f058";
+}
+.fa-question-circle:before {
+  content: "\f059";
+}
+.fa-info-circle:before {
+  content: "\f05a";
+}
+.fa-crosshairs:before {
+  content: "\f05b";
+}
+.fa-times-circle-o:before {
+  content: "\f05c";
+}
+.fa-check-circle-o:before {
+  content: "\f05d";
+}
+.fa-ban:before {
+  content: "\f05e";
+}
+.fa-arrow-left:before {
+  content: "\f060";
+}
+.fa-arrow-right:before {
+  content: "\f061";
+}
+.fa-arrow-up:before {
+  content: "\f062";
+}
+.fa-arrow-down:before {
+  content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+  content: "\f064";
+}
+.fa-expand:before {
+  content: "\f065";
+}
+.fa-compress:before {
+  content: "\f066";
+}
+.fa-plus:before {
+  content: "\f067";
+}
+.fa-minus:before {
+  content: "\f068";
+}
+.fa-asterisk:before {
+  content: "\f069";
+}
+.fa-exclamation-circle:before {
+  content: "\f06a";
+}
+.fa-gift:before {
+  content: "\f06b";
+}
+.fa-leaf:before {
+  content: "\f06c";
+}
+.fa-fire:before {
+  content: "\f06d";
+}
+.fa-eye:before {
+  content: "\f06e";
+}
+.fa-eye-slash:before {
+  content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+  content: "\f071";
+}
+.fa-plane:before {
+  content: "\f072";
+}
+.fa-calendar:before {
+  content: "\f073";
+}
+.fa-random:before {
+  content: "\f074";
+}
+.fa-comment:before {
+  content: "\f075";
+}
+.fa-magnet:before {
+  content: "\f076";
+}
+.fa-chevron-up:before {
+  content: "\f077";
+}
+.fa-chevron-down:before {
+  content: "\f078";
+}
+.fa-retweet:before {
+  content: "\f079";
+}
+.fa-shopping-cart:before {
+  content: "\f07a";
+}
+.fa-folder:before {
+  content: "\f07b";
+}
+.fa-folder-open:before {
+  content: "\f07c";
+}
+.fa-arrows-v:before {
+  content: "\f07d";
+}
+.fa-arrows-h:before {
+  content: "\f07e";
+}
+.fa-bar-chart-o:before,
+.fa-bar-chart:before {
+  content: "\f080";
+}
+.fa-twitter-square:before {
+  content: "\f081";
+}
+.fa-facebook-square:before {
+  content: "\f082";
+}
+.fa-camera-retro:before {
+  content: "\f083";
+}
+.fa-key:before {
+  content: "\f084";
+}
+.fa-gears:before,
+.fa-cogs:before {
+  content: "\f085";
+}
+.fa-comments:before {
+  content: "\f086";
+}
+.fa-thumbs-o-up:before {
+  content: "\f087";
+}
+.fa-thumbs-o-down:before {
+  content: "\f088";
+}
+.fa-star-half:before {
+  content: "\f089";
+}
+.fa-heart-o:before {
+  content: "\f08a";
+}
+.fa-sign-out:before {
+  content: "\f08b";
+}
+.fa-linkedin-square:before {
+  content: "\f08c";
+}
+.fa-thumb-tack:before {
+  content: "\f08d";
+}
+.fa-external-link:before {
+  content: "\f08e";
+}
+.fa-sign-in:before {
+  content: "\f090";
+}
+.fa-trophy:before {
+  content: "\f091";
+}
+.fa-github-square:before {
+  content: "\f092";
+}
+.fa-upload:before {
+  content: "\f093";
+}
+.fa-lemon-o:before {
+  content: "\f094";
+}
+.fa-phone:before {
+  content: "\f095";
+}
+.fa-square-o:before {
+  content: "\f096";
+}
+.fa-bookmark-o:before {
+  content: "\f097";
+}
+.fa-phone-square:before {
+  content: "\f098";
+}
+.fa-twitter:before {
+  content: "\f099";
+}
+.fa-facebook:before {
+  content: "\f09a";
+}
+.fa-github:before {
+  content: "\f09b";
+}
+.fa-unlock:before {
+  content: "\f09c";
+}
+.fa-credit-card:before {
+  content: "\f09d";
+}
+.fa-rss:before {
+  content: "\f09e";
+}
+.fa-hdd-o:before {
+  content: "\f0a0";
+}
+.fa-bullhorn:before {
+  content: "\f0a1";
+}
+.fa-bell:before {
+  content: "\f0f3";
+}
+.fa-certificate:before {
+  content: "\f0a3";
+}
+.fa-hand-o-right:before {
+  content: "\f0a4";
+}
+.fa-hand-o-left:before {
+  content: "\f0a5";
+}
+.fa-hand-o-up:before {
+  content: "\f0a6";
+}
+.fa-hand-o-down:before {
+  content: "\f0a7";
+}
+.fa-arrow-circle-left:before {
+  content: "\f0a8";
+}
+.fa-arrow-circle-right:before {
+  content: "\f0a9";
+}
+.fa-arrow-circle-up:before {
+  content: "\f0aa";
+}
+.fa-arrow-circle-down:before {
+  content: "\f0ab";
+}
+.fa-globe:before {
+  content: "\f0ac";
+}
+.fa-wrench:before {
+  content: "\f0ad";
+}
+.fa-tasks:before {
+  content: "\f0ae";
+}
+.fa-filter:before {
+  content: "\f0b0";
+}
+.fa-briefcase:before {
+  content: "\f0b1";
+}
+.fa-arrows-alt:before {
+  content: "\f0b2";
+}
+.fa-group:before,
+.fa-users:before {
+  content: "\f0c0";
+}
+.fa-chain:before,
+.fa-link:before {
+  content: "\f0c1";
+}
+.fa-cloud:before {
+  content: "\f0c2";
+}
+.fa-flask:before {
+  content: "\f0c3";
+}
+.fa-cut:before,
+.fa-scissors:before {
+  content: "\f0c4";
+}
+.fa-copy:before,
+.fa-files-o:before {
+  content: "\f0c5";
+}
+.fa-paperclip:before {
+  content: "\f0c6";
+}
+.fa-save:before,
+.fa-floppy-o:before {
+  content: "\f0c7";
+}
+.fa-square:before {
+  content: "\f0c8";
+}
+.fa-navicon:before,
+.fa-reorder:before,
+.fa-bars:before {
+  content: "\f0c9";
+}
+.fa-list-ul:before {
+  content: "\f0ca";
+}
+.fa-list-ol:before {
+  content: "\f0cb";
+}
+.fa-strikethrough:before {
+  content: "\f0cc";
+}
+.fa-underline:before {
+  content: "\f0cd";
+}
+.fa-table:before {
+  content: "\f0ce";
+}
+.fa-magic:before {
+  content: "\f0d0";
+}
+.fa-truck:before {
+  content: "\f0d1";
+}
+.fa-pinterest:before {
+  content: "\f0d2";
+}
+.fa-pinterest-square:before {
+  content: "\f0d3";
+}
+.fa-google-plus-square:before {
+  content: "\f0d4";
+}
+.fa-google-plus:before {
+  content: "\f0d5";
+}
+.fa-money:before {
+  content: "\f0d6";
+}
+.fa-caret-down:before {
+  content: "\f0d7";
+}
+.fa-caret-up:before {
+  content: "\f0d8";
+}
+.fa-caret-left:before {
+  content: "\f0d9";
+}
+.fa-caret-right:before {
+  content: "\f0da";
+}
+.fa-columns:before {
+  content: "\f0db";
+}
+.fa-unsorted:before,
+.fa-sort:before {
+  content: "\f0dc";
+}
+.fa-sort-down:before,
+.fa-sort-desc:before {
+  content: "\f0dd";
+}
+.fa-sort-up:before,
+.fa-sort-asc:before {
+  content: "\f0de";
+}
+.fa-envelope:before {
+  content: "\f0e0";
+}
+.fa-linkedin:before {
+  content: "\f0e1";
+}
+.fa-rotate-left:before,
+.fa-undo:before {
+  content: "\f0e2";
+}
+.fa-legal:before,
+.fa-gavel:before {
+  content: "\f0e3";
+}
+.fa-dashboard:before,
+.fa-tachometer:before {
+  content: "\f0e4";
+}
+.fa-comment-o:before {
+  content: "\f0e5";
+}
+.fa-comments-o:before {
+  content: "\f0e6";
+}
+.fa-flash:before,
+.fa-bolt:before {
+  content: "\f0e7";
+}
+.fa-sitemap:before {
+  content: "\f0e8";
+}
+.fa-umbrella:before {
+  content: "\f0e9";
+}
+.fa-paste:before,
+.fa-clipboard:before {
+  content: "\f0ea";
+}
+.fa-lightbulb-o:before {
+  content: "\f0eb";
+}
+.fa-exchange:before {
+  content: "\f0ec";
+}
+.fa-cloud-download:before {
+  content: "\f0ed";
+}
+.fa-cloud-upload:before {
+  content: "\f0ee";
+}
+.fa-user-md:before {
+  content: "\f0f0";
+}
+.fa-stethoscope:before {
+  content: "\f0f1";
+}
+.fa-suitcase:before {
+  content: "\f0f2";
+}
+.fa-bell-o:before {
+  content: "\f0a2";
+}
+.fa-coffee:before {
+  content: "\f0f4";
+}
+.fa-cutlery:before {
+  content: "\f0f5";
+}
+.fa-file-text-o:before {
+  content: "\f0f6";
+}
+.fa-building-o:before {
+  content: "\f0f7";
+}
+.fa-hospital-o:before {
+  content: "\f0f8";
+}
+.fa-ambulance:before {
+  content: "\f0f9";
+}
+.fa-medkit:before {
+  content: "\f0fa";
+}
+.fa-fighter-jet:before {
+  content: "\f0fb";
+}
+.fa-beer:before {
+  content: "\f0fc";
+}
+.fa-h-square:before {
+  content: "\f0fd";
+}
+.fa-plus-square:before {
+  content: "\f0fe";
+}
+.fa-angle-double-left:before {
+  content: "\f100";
+}
+.fa-angle-double-right:before {
+  content: "\f101";
+}
+.fa-angle-double-up:before {
+  content: "\f102";
+}
+.fa-angle-double-down:before {
+  content: "\f103";
+}
+.fa-angle-left:before {
+  content: "\f104";
+}
+.fa-angle-right:before {
+  content: "\f105";
+}
+.fa-angle-up:before {
+  content: "\f106";
+}
+.fa-angle-down:before {
+  content: "\f107";
+}
+.fa-desktop:before {
+  content: "\f108";
+}
+.fa-laptop:before {
+  content: "\f109";
+}
+.fa-tablet:before {
+  content: "\f10a";
+}
+.fa-mobile-phone:before,
+.fa-mobile:before {
+  content: "\f10b";
+}
+.fa-circle-o:before {
+  content: "\f10c";
+}
+.fa-quote-left:before {
+  content: "\f10d";
+}
+.fa-quote-right:before {
+  content: "\f10e";
+}
+.fa-spinner:before {
+  content: "\f110";
+}
+.fa-circle:before {
+  content: "\f111";
+}
+.fa-mail-reply:before,
+.fa-reply:before {
+  content: "\f112";
+}
+.fa-github-alt:before {
+  content: "\f113";
+}
+.fa-folder-o:before {
+  content: "\f114";
+}
+.fa-folder-open-o:before {
+  content: "\f115";
+}
+.fa-smile-o:before {
+  content: "\f118";
+}
+.fa-frown-o:before {
+  content: "\f119";
+}
+.fa-meh-o:before {
+  content: "\f11a";
+}
+.fa-gamepad:before {
+  content: "\f11b";
+}
+.fa-keyboard-o:before {
+  content: "\f11c";
+}
+.fa-flag-o:before {
+  content: "\f11d";
+}
+.fa-flag-checkered:before {
+  content: "\f11e";
+}
+.fa-terminal:before {
+  content: "\f120";
+}
+.fa-code:before {
+  content: "\f121";
+}
+.fa-mail-reply-all:before,
+.fa-reply-all:before {
+  content: "\f122";
+}
+.fa-star-half-empty:before,
+.fa-star-half-full:before,
+.fa-star-half-o:before {
+  content: "\f123";
+}
+.fa-location-arrow:before {
+  content: "\f124";
+}
+.fa-crop:before {
+  content: "\f125";
+}
+.fa-code-fork:before {
+  content: "\f126";
+}
+.fa-unlink:before,
+.fa-chain-broken:before {
+  content: "\f127";
+}
+.fa-question:before {
+  content: "\f128";
+}
+.fa-info:before {
+  content: "\f129";
+}
+.fa-exclamation:before {
+  content: "\f12a";
+}
+.fa-superscript:before {
+  content: "\f12b";
+}
+.fa-subscript:before {
+  content: "\f12c";
+}
+.fa-eraser:before {
+  content: "\f12d";
+}
+.fa-puzzle-piece:before {
+  content: "\f12e";
+}
+.fa-microphone:before {
+  content: "\f130";
+}
+.fa-microphone-slash:before {
+  content: "\f131";
+}
+.fa-shield:before {
+  content: "\f132";
+}
+.fa-calendar-o:before {
+  content: "\f133";
+}
+.fa-fire-extinguisher:before {
+  content: "\f134";
+}
+.fa-rocket:before {
+  content: "\f135";
+}
+.fa-maxcdn:before {
+  content: "\f136";
+}
+.fa-chevron-circle-left:before {
+  content: "\f137";
+}
+.fa-chevron-circle-right:before {
+  content: "\f138";
+}
+.fa-chevron-circle-up:before {
+  content: "\f139";
+}
+.fa-chevron-circle-down:before {
+  content: "\f13a";
+}
+.fa-html5:before {
+  content: "\f13b";
+}
+.fa-css3:before {
+  content: "\f13c";
+}
+.fa-anchor:before {
+  content: "\f13d";
+}
+.fa-unlock-alt:before {
+  content: "\f13e";
+}
+.fa-bullseye:before {
+  content: "\f140";
+}
+.fa-ellipsis-h:before {
+  content: "\f141";
+}
+.fa-ellipsis-v:before {
+  content: "\f142";
+}
+.fa-rss-square:before {
+  content: "\f143";
+}
+.fa-play-circle:before {
+  content: "\f144";
+}
+.fa-ticket:before {
+  content: "\f145";
+}
+.fa-minus-square:before {
+  content: "\f146";
+}
+.fa-minus-square-o:before {
+  content: "\f147";
+}
+.fa-level-up:before {
+  content: "\f148";
+}
+.fa-level-down:before {
+  content: "\f149";
+}
+.fa-check-square:before {
+  content: "\f14a";
+}
+.fa-pencil-square:before {
+  content: "\f14b";
+}
+.fa-external-link-square:before {
+  content: "\f14c";
+}
+.fa-share-square:before {
+  content: "\f14d";
+}
+.fa-compass:before {
+  content: "\f14e";
+}
+.fa-toggle-down:before,
+.fa-caret-square-o-down:before {
+  content: "\f150";
+}
+.fa-toggle-up:before,
+.fa-caret-square-o-up:before {
+  content: "\f151";
+}
+.fa-toggle-right:before,
+.fa-caret-square-o-right:before {
+  content: "\f152";
+}
+.fa-euro:before,
+.fa-eur:before {
+  content: "\f153";
+}
+.fa-gbp:before {
+  content: "\f154";
+}
+.fa-dollar:before,
+.fa-usd:before {
+  content: "\f155";
+}
+.fa-rupee:before,
+.fa-inr:before {
+  content: "\f156";
+}
+.fa-cny:before,
+.fa-rmb:before,
+.fa-yen:before,
+.fa-jpy:before {
+  content: "\f157";
+}
+.fa-ruble:before,
+.fa-rouble:before,
+.fa-rub:before {
+  content: "\f158";
+}
+.fa-won:before,
+.fa-krw:before {
+  content: "\f159";
+}
+.fa-bitcoin:before,
+.fa-btc:before {
+  content: "\f15a";
+}
+.fa-file:before {
+  content: "\f15b";
+}
+.fa-file-text:before {
+  content: "\f15c";
+}
+.fa-sort-alpha-asc:before {
+  content: "\f15d";
+}
+.fa-sort-alpha-desc:before {
+  content: "\f15e";
+}
+.fa-sort-amount-asc:before {
+  content: "\f160";
+}
+.fa-sort-amount-desc:before {
+  content: "\f161";
+}
+.fa-sort-numeric-asc:before {
+  content: "\f162";
+}
+.fa-sort-numeric-desc:before {
+  content: "\f163";
+}
+.fa-thumbs-up:before {
+  content: "\f164";
+}
+.fa-thumbs-down:before {
+  content: "\f165";
+}
+.fa-youtube-square:before {
+  content: "\f166";
+}
+.fa-youtube:before {
+  content: "\f167";
+}
+.fa-xing:before {
+  content: "\f168";
+}
+.fa-xing-square:before {
+  content: "\f169";
+}
+.fa-youtube-play:before {
+  content: "\f16a";
+}
+.fa-dropbox:before {
+  content: "\f16b";
+}
+.fa-stack-overflow:before {
+  content: "\f16c";
+}
+.fa-instagram:before {
+  content: "\f16d";
+}
+.fa-flickr:before {
+  content: "\f16e";
+}
+.fa-adn:before {
+  content: "\f170";
+}
+.fa-bitbucket:before {
+  content: "\f171";
+}
+.fa-bitbucket-square:before {
+  content: "\f172";
+}
+.fa-tumblr:before {
+  content: "\f173";
+}
+.fa-tumblr-square:before {
+  content: "\f174";
+}
+.fa-long-arrow-down:before {
+  content: "\f175";
+}
+.fa-long-arrow-up:before {
+  content: "\f176";
+}
+.fa-long-arrow-left:before {
+  content: "\f177";
+}
+.fa-long-arrow-right:before {
+  content: "\f178";
+}
+.fa-apple:before {
+  content: "\f179";
+}
+.fa-windows:before {
+  content: "\f17a";
+}
+.fa-android:before {
+  content: "\f17b";
+}
+.fa-linux:before {
+  content: "\f17c";
+}
+.fa-dribbble:before {
+  content: "\f17d";
+}
+.fa-skype:before {
+  content: "\f17e";
+}
+.fa-foursquare:before {
+  content: "\f180";
+}
+.fa-trello:before {
+  content: "\f181";
+}
+.fa-female:before {
+  content: "\f182";
+}
+.fa-male:before {
+  content: "\f183";
+}
+.fa-gittip:before {
+  content: "\f184";
+}
+.fa-sun-o:before {
+  content: "\f185";
+}
+.fa-moon-o:before {
+  content: "\f186";
+}
+.fa-archive:before {
+  content: "\f187";
+}
+.fa-bug:before {
+  content: "\f188";
+}
+.fa-vk:before {
+  content: "\f189";
+}
+.fa-weibo:before {
+  content: "\f18a";
+}
+.fa-renren:before {
+  content: "\f18b";
+}
+.fa-pagelines:before {
+  content: "\f18c";
+}
+.fa-stack-exchange:before {
+  content: "\f18d";
+}
+.fa-arrow-circle-o-right:before {
+  content: "\f18e";
+}
+.fa-arrow-circle-o-left:before {
+  content: "\f190";
+}
+.fa-toggle-left:before,
+.fa-caret-square-o-left:before {
+  content: "\f191";
+}
+.fa-dot-circle-o:before {
+  content: "\f192";
+}
+.fa-wheelchair:before {
+  content: "\f193";
+}
+.fa-vimeo-square:before {
+  content: "\f194";
+}
+.fa-turkish-lira:before,
+.fa-try:before {
+  content: "\f195";
+}
+.fa-plus-square-o:before {
+  content: "\f196";
+}
+.fa-space-shuttle:before {
+  content: "\f197";
+}
+.fa-slack:before {
+  content: "\f198";
+}
+.fa-envelope-square:before {
+  content: "\f199";
+}
+.fa-wordpress:before {
+  content: "\f19a";
+}
+.fa-openid:before {
+  content: "\f19b";
+}
+.fa-institution:before,
+.fa-bank:before,
+.fa-university:before {
+  content: "\f19c";
+}
+.fa-mortar-board:before,
+.fa-graduation-cap:before {
+  content: "\f19d";
+}
+.fa-yahoo:before {
+  content: "\f19e";
+}
+.fa-google:before {
+  content: "\f1a0";
+}
+.fa-reddit:before {
+  content: "\f1a1";
+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+ul#tabs {
+  margin-bottom: 4px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+@-moz-document url-prefix() {
+  div.inner_cell {
+    overflow-x: hidden;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 20ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
+    display: none;
+  }
+  span.autosave_status {
+    font-size: x-small;
+  }
+}
+.toolbar {
+  padding: 0px;
+  margin-left: -5px;
+  margin-top: 2px;
+  margin-bottom: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+  width: auto;
+  vertical-align: middle;
+  margin-right: 2px;
+  margin-bottom: 0px;
+  display: inline;
+  font-size: 92%;
+  margin-left: 0.3em;
+  margin-right: 0.3em;
+  padding: 0px;
+  padding-top: 3px;
+}
+.toolbar .btn {
+  padding: 2px 8px;
+}
+.toolbar .btn-group {
+  margin-top: 0px;
+  margin-left: 5px;
+}
+#maintoolbar {
+  margin-bottom: -3px;
+  margin-top: -8px;
+  border: 0px;
+  min-height: 27px;
+  margin-left: 0px;
+  padding-top: 11px;
+  padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+  float: none;
+  vertical-align: middle;
+  text-align: right;
+  margin-left: 5px;
+  margin-right: 0px;
+  margin-top: 0px;
+}
+.select-xs {
+  height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+  background-color: #F37626;
+  color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-webkit-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-moz-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+@-webkit-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+  overflow: auto;
+  height: 200px;
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+  text-overflow: ellipsis;
+  overflow: hidden;
+  height: 80px;
+}
+.tooltipbuttons {
+  position: absolute;
+  padding-right: 15px;
+  top: 0px;
+  right: 0px;
+}
+.tooltiptext {
+  /*avoid the button to overlap on some docstring*/
+  padding-right: 30px;
+}
+.ipython_tooltip {
+  max-width: 700px;
+  /*fade-in animation when inserted*/
+  -webkit-animation: fadeOut 400ms;
+  -moz-animation: fadeOut 400ms;
+  animation: fadeOut 400ms;
+  -webkit-animation: fadeIn 400ms;
+  -moz-animation: fadeIn 400ms;
+  animation: fadeIn 400ms;
+  vertical-align: middle;
+  background-color: #f7f7f7;
+  overflow: visible;
+  border: #ababab 1px solid;
+  outline: none;
+  padding: 3px;
+  margin: 0px;
+  padding-left: 7px;
+  font-family: monospace;
+  min-height: 50px;
+  -moz-box-shadow: 0px 6px 10px -1px #adadad;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  border-radius: 2px;
+  position: absolute;
+  z-index: 1000;
+}
+.ipython_tooltip a {
+  float: right;
+}
+.ipython_tooltip .tooltiptext pre {
+  border: 0;
+  border-radius: 0;
+  font-size: 100%;
+  background-color: #f7f7f7;
+}
+.pretooltiparrow {
+  left: 0px;
+  margin: 0px;
+  top: -16px;
+  width: 40px;
+  height: 16px;
+  overflow: hidden;
+  position: absolute;
+}
+.pretooltiparrow:before {
+  background-color: #f7f7f7;
+  border: 1px #ababab solid;
+  z-index: 11;
+  content: "";
+  position: absolute;
+  left: 15px;
+  top: 10px;
+  width: 25px;
+  height: 25px;
+  -webkit-transform: rotate(45deg);
+  -moz-transform: rotate(45deg);
+  -ms-transform: rotate(45deg);
+  -o-transform: rotate(45deg);
+}
+ul.typeahead-list i {
+  margin-left: -10px;
+  width: 18px;
+}
+ul.typeahead-list {
+  max-height: 80vh;
+  overflow: auto;
+}
+ul.typeahead-list > li > a {
+  /** Firefox bug **/
+  /* see https://github.com/jupyter/notebook/issues/559 */
+  white-space: normal;
+}
+.cmd-palette .modal-body {
+  padding: 7px;
+}
+.cmd-palette form {
+  background: white;
+}
+.cmd-palette input {
+  outline: none;
+}
+.no-shortcut {
+  display: none;
+}
+.command-shortcut:before {
+  content: "(command)";
+  padding-right: 3px;
+  color: #777777;
+}
+.edit-shortcut:before {
+  content: "(edit)";
+  padding-right: 3px;
+  color: #777777;
+}
+#find-and-replace #replace-preview .match,
+#find-and-replace #replace-preview .insert {
+  background-color: #BBDEFB;
+  border-color: #90CAF9;
+  border-style: solid;
+  border-width: 1px;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .match {
+  background-color: #FFCDD2;
+  border-color: #EF9A9A;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .insert {
+  background-color: #C8E6C9;
+  border-color: #A5D6A7;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview {
+  max-height: 60vh;
+  overflow: auto;
+}
+#find-and-replace #replace-preview pre {
+  padding: 5px 10px;
+}
+.terminal-app {
+  background: #EEE;
+}
+.terminal-app #header {
+  background: #fff;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.terminal-app .terminal {
+  float: left;
+  font-family: monospace;
+  color: white;
+  background: black;
+  padding: 0.4em;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+}
+.terminal-app .terminal,
+.terminal-app .terminal dummy-screen {
+  line-height: 1em;
+  font-size: 14px;
+}
+.terminal-app .terminal-cursor {
+  color: black;
+  background: white;
+}
+.terminal-app #terminado-container {
+  margin-top: 20px;
+}
+/*# sourceMappingURL=style.min.css.map */
+    </style>
+<style type="text/css">
+    .highlight .hll { background-color: #ffffcc }
+.highlight  { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #7D9029 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #A0A000 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+    </style>
+<style type="text/css">
+    
+/* Temporary definitions which will become obsolete with Notebook release 5.0 */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-bold { font-weight: bold; }
+
+    </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+  overflow: visible;
+  padding: 8px;
+}
+
+div#notebook {
+  overflow: visible;
+  border-top: none;
+}
+
+@media print {
+  div.cell {
+    display: block;
+    page-break-inside: avoid;
+  } 
+  div.output_wrapper { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+  div.output { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+    <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    MathJax.Hub.Config({
+        tex2jax: {
+            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+            processEscapes: true,
+            processEnvironments: true
+        },
+        // Center justify equations in code and markdown cells. Elsewhere
+        // we use CSS to left justify single line equations in code cells.
+        displayAlign: 'center',
+        "HTML-CSS": {
+            styles: {'.MathJax_Display': {"margin": 0}},
+            linebreaks: { automatic: true }
+        }
+    });
+    </script>
+    <!-- End of mathjax configuration --></head>
+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#22270;&#20687;&#20998;&#31867;">&#22270;&#20687;&#20998;&#31867;<a class="anchor-link" href="#&#22270;&#20687;&#20998;&#31867;">&#182;</a></h1><p>在此项目中，你将对 <a href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10 数据集</a> 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。</p>
+<h2 id="&#33719;&#21462;&#25968;&#25454;">&#33719;&#21462;&#25968;&#25454;<a class="anchor-link" href="#&#33719;&#21462;&#25968;&#25454;">&#182;</a></h2><p>请运行以下单元，以下载 <a href="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz">CIFAR-10 数据集（Python版）</a>。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">urllib.request</span> <span class="k">import</span> <span class="n">urlretrieve</span>
+<span class="kn">from</span> <span class="nn">os.path</span> <span class="k">import</span> <span class="n">isfile</span><span class="p">,</span> <span class="n">isdir</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+<span class="kn">import</span> <span class="nn">tarfile</span>
+
+<span class="n">cifar10_dataset_folder_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-batches-py&#39;</span>
+
+<span class="c1"># Use Floyd&#39;s cifar-10 dataset if present</span>
+<span class="n">floyd_cifar10_location</span> <span class="o">=</span> <span class="s1">&#39;/input/cifar-10/python.tar.gz&#39;</span>
+<span class="k">if</span> <span class="n">isfile</span><span class="p">(</span><span class="n">floyd_cifar10_location</span><span class="p">):</span>
+    <span class="n">tar_gz_path</span> <span class="o">=</span> <span class="n">floyd_cifar10_location</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">tar_gz_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-python.tar.gz&#39;</span>
+
+<span class="k">class</span> <span class="nc">DLProgress</span><span class="p">(</span><span class="n">tqdm</span><span class="p">):</span>
+    <span class="n">last_block</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="k">def</span> <span class="nf">hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block_num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">block_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">total_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">total</span> <span class="o">=</span> <span class="n">total_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">block_num</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span><span class="p">)</span> <span class="o">*</span> <span class="n">block_size</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span> <span class="o">=</span> <span class="n">block_num</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">isfile</span><span class="p">(</span><span class="n">tar_gz_path</span><span class="p">):</span>
+    <span class="k">with</span> <span class="n">DLProgress</span><span class="p">(</span><span class="n">unit</span><span class="o">=</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">unit_scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">miniters</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;CIFAR-10 Dataset&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pbar</span><span class="p">:</span>
+        <span class="n">urlretrieve</span><span class="p">(</span>
+            <span class="s1">&#39;https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz&#39;</span><span class="p">,</span>
+            <span class="n">tar_gz_path</span><span class="p">,</span>
+            <span class="n">pbar</span><span class="o">.</span><span class="n">hook</span><span class="p">)</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">isdir</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">):</span>
+    <span class="k">with</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">tar_gz_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">tar</span><span class="p">:</span>
+        <span class="n">tar</span><span class="o">.</span><span class="n">extractall</span><span class="p">()</span>
+        <span class="n">tar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+
+<span class="n">tests</span><span class="o">.</span><span class="n">test_folder_path</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>All files found!
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#25506;&#32034;&#25968;&#25454;">&#25506;&#32034;&#25968;&#25454;<a class="anchor-link" href="#&#25506;&#32034;&#25968;&#25454;">&#182;</a></h2><p>该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 <code>data_batch_1</code>、<code>data_batch_2</code>，以此类推。每个部分都包含以下某个类别的标签和图片：</p>
+<ul>
+<li>飞机</li>
+<li>汽车</li>
+<li>鸟类</li>
+<li>猫</li>
+<li>鹿</li>
+<li>狗</li>
+<li>青蛙</li>
+<li>马</li>
+<li>船只</li>
+<li>卡车</li>
+</ul>
+<p>了解数据集也是对数据进行预测的必经步骤。你可以通过更改 <code>batch_id</code> 和 <code>sample_id</code> 探索下面的代码单元。<code>batch_id</code> 是数据集一个部分的 ID（1 到 5）。<code>sample_id</code> 是该部分中图片和标签对（label pair）的 ID。</p>
+<p>问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
+
+<span class="kn">import</span> <span class="nn">helper</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># Explore the dataset</span>
+<span class="n">batch_id</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">sample_id</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">display_stats</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">batch_id</span><span class="p">,</span> <span class="n">sample_id</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Stats of batch 1:
+Samples: 10000
+Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}
+First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]
+
+Example of Image 5:
+Image - Min Value: 0 Max Value: 252
+Image - Shape: (32, 32, 3)
+Label - Label Id: 1 Name: automobile
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsIAAAK/CAYAAABjrqvGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAAewgAAHsIBbtB1PgAAIABJREFUeJzsvWu23LjOJbhBf2PtnlbPocdQa9Wg7iHqB/HYAKmIOE6n
+M2+ZsOOIL1F8gMAWBFGiqrh06dKlS5cuXbp06U+j8U834NKlS5cuXbp06dKlf4IuEL506dKlS5cu
+Xbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLhS5cuXbp0
+6dKlS38kXSB86dKlS5cuXbp06Y+kC4QvXbp06dKlS5cu/ZF0gfClS5cuXbp06dKlP5IuEL506dKl
+S5cuXbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLhS5cu
+Xbp06dKlS38kXSB86dKlS5cuXbp06Y+kC4QvXbp06dKlS5cu/ZF0gfClS5cuXbp06dKlP5IuEL50
+6dKlS5cuXbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLh
+S5cuXbp06dKlS38kXSB86dKlS5cuXbp06Y+k//mnG/Bvof/v//1/9JNyqgqs/4CqHQG1RLUE9do4
+z895dSUpB4tIz24FABFZ5YYfB8UHpff4qOdRngJQu7ZSvKbveTjEf2BVO0Twwy43APwQsXTgB1b4
+h5VbaRS2c1Qn9Gtizq84zjmhX+04vzC/7EhldU5MP+/rC//5mvb7st/Ef+bM8IfHrzmhxhNq8578
+YfEIJ/9oy4fnP7GIbCkv8og/fhEJX8iOAiHe9bBQkSyfUc+X7QcPjz3v1S/Oa03c4ytRKP9Udohg
+WBvGEIwxIu3pmGU9Lc/5nx8/8OPHD/z4MfDjxw+K/8D/WNqPHz/wP/+TZWq5EeGNXB5ZGMAWj7Qm
+hFQ1edbDwZ89jcIP5+TE57xkPPN8vjy8+IMnYcmQOaf9tBy/5sRUbXkW1nX8aud+fX3hP//5Wsev
+L4pP/OfrP/j6z4z0rex/6jlH6kvttPZOy7HL+aYLMluO+bQErbytA+T6fxWXrOYQPzRdfHbOXRXo
+WZ9h572Sc+BdrSeU+KZPtV2BZaq25MO5n9HHBQHEjGVYntO3tOOVcwwUdbxqXFPf8Dk0xqF/SDeF
+HmppfATlvR1HJR54U/ZTTfX//6///cuU2rUIX/rj6Hsi7FKnO37PdMfmQ7oDdenSTv9F6+K/qKlv
+6QLhS38c/Trb6J9Jd/ye6Y7Nh3QH6tKlnf6L1sV/UVPf0gXC/0L6exnsH2Lff82q6Q/zLn2Xfsf4
+/WvY5Zv0r+EtevT9r6R/zUBduvQvor9pXexy4K9Lhv+blvAFwv9C+nsZ7B9i33/NqvnXNOTSC7qz
+9Bep+1leunTpj6VdDlzJwHSB8KU/jv61VrL/Errj90x3bD6kO1CXLu30X7Qu/oua+pYuEP4X0nWN
++Dvpukb8VbquEc/0r+Gt6xpx6dJ/H13XiH+ELhD+F9J1jfg7Sf+94OC/hH7H+P1r2OWb9K/hrX+7
+a8S/ZqAuXfoX0d+0Lv4O14j/m5bwBcKX/jj614KD/xK64/dMd2w+pDtQly7t9F+0Lv6LmvqW7gc1
+jHTOz8rRn9x02qwvuVt1buJd8vYPbZQNswWVuzwrTqId03s5VQCDdtXmXcQnoKN2IMrNdd7007Ps
+2ix7tXJtxS0ZV10f01CBiuWJ5ZUjbNN8ACoQUUxZG7ar+Gb8FvbzuJnbbac87OpNO4CD0mk39dyg
+vQ9giz/c6tpW//bxhn6U+uEIqlZ943mVOqdlo/1Dflz4xYMtqann9IdzOeWbt/dS2iVUhQf2D2fU
+y7x+dP+4+TplPmYZb8KGuAxluMbottxqglgpxVT/kAvaB1PyOO28wem2lOacwBCIDkxViP2GKqYd
+Ve06EdZVl9J146MVyT4sj2LIDqx94v0iy+Br0fvEH9PoaZSO8wc4Yn5dBmiygirxi7VNhGSmf1TA
+q9GtA3Xm9JDGcuGYX4vWgVK8cqAKrm+8dWRm7+RHC2yVa0smruUp0vJ47fVz+aMa/rEGsfo8jFau
+fEyDz6f+7OtYSxebWKJ4HQtVpY/9BDN4Zu2861KxVdyKF/5h9nBZYG3p/FjWx2manlnhI+Iqt7kD
+tSMOxAcmxLiLsh03SdZq1/i7jmvMGVawrFTAdDu1SzNNmwr2vGig1Zlz8GIA+9L4i2P9s3SBsJFu
+2uOx5AaAEbKWgW79qlgFpvwFMS0LMunzFZnLZAIqUIylhSHGoQMwZYw5gDGBKSWu8DBWWQeqauBX
+TD3wUWtaYFT/spzlAbCvS9WjCDBNqMUX6kTXV+v8yL0uinH9lJFB+dHg98/kgOIP4yrl51/DQggl
+8X/9y2bH+dmvEGlNEJxFWmqS4xVKnhzzHs99KvzmhLjWQUFX4MuN4/SfIVOErh+Puee2agttJ7J2
+Qa7LCUlwOwHFxBwCu5tbJ4wRS6o0zNbDiIbOwitTdN0YDgbGS4EMRQPGBsbpF2x8GIAqzvhrliSz
+KJCY8QB2t6/JPYBgBsIx1ActZ0rSs9eSlsIXC+hrPbd1nL+ZVe5MCCxnt7OcRhlt5Ttp+QULSy/z
+wNCflqOynYUABk91jDKcALjm0Y271dPTPi7H7erzki0Aj6VUNFT65TpRhG45WBYWQ4EjLsm86Kzm
+JW29SamHLsxNoXgHzhttc6bH4KvztzH040GuP6WX+EsB6lzh5iuxo68b09W87mlog+NpaBcOaOq0
+laHCRT7FxMQ84jAvJoceRPTfTRcIG6l+ZhGGKYCKpVKZdCYoYLmU8z8SvFEv8+TLekpNrlIdcECc
+lt5Z8+ZS3p6nHqe8sNQ6SD0CYg6vdp3ySbpuQHgBAlusqmcwLEsoxl3n5BXcjj4pYQlqeS1+ugES
+Dokf2LKyQFAojBdA+LSuX6WdZUBTgKcSJbOVfHPuQ40fXKdXfrACR5GTKshzntuXI6LqAr7naqQ/
+6b1TCuvaWINKitHz5rpH9ILDlte0JQOsG0dxgExH4ThGgF8ZGtbhOWvcwW9aiStLB2s3nMAd2wBg
+DZ7L0Zr4KRBM6Z1E6vzFww9rQ7fqBQ/VjuWhXIPWNgnYBMosrPPH7aRb9yhfAIvVz2mdzamyMxWE
+VstnTlpIKyCqMsCfPnmZTK/nDuSTqpRX7QgQCH440hhkozTb8GLlHYeEq4HJYbtWCduCXE8/1twu
+8GxzpKQXDuCqgFwfZ44rjXNfR5/QG8Eq21/U+eKyVWRSeuWbjYu4Ay3k4+YtKGJDqvoEhddT37ze
+uZw01apRccE4flF/ehzxKJQ3wmWCfh9dIGz0qWvEKpyLEvD517qQCiCuLhGtYPyta+pphb1iErYE
+u5YWU0BpFS4W4zmsqFmMrZzKAwhugHhGGA8A2foiKC4Ec1S3Age/7IoR4Lr3+pUVuFt/1RUipRXF
++XpIt8eH4pZg7o9giGDKPoPaYic1saUp4rF3EaEPLCFboCY8JH+DDl+83yVxCOTannZm383gQfjn
+GjkVSAAnyvDkPL5PyrlfrgKSxS78EEVt2TgoHpPAMD1o4bZKiU98yYAMhUwtFuFh8WGAOF0mKmuz
+xSbajcbWPHylzF5YOQwQuMUzCI70szXYAeY26gJsejDATwPInibRMuqMy1ZlLVw6r2Wt918fwBNv
+ZB8YuAS3OTqMa7xeo8y7J55OGUPxuK4c0qWWbwB4NS/l7REAy7nM6Hle8YHJUkIzGuWZ5zBLNDL1
+lDtSCkOJIQ5hIEHTqQllQesR9AqFA4g90s8BNBaDLEl33mp8JrKnx3rYT9rcvto1o78gS7ENZcyQ
+ydNYFSRv/EY246v2LJeFlQfV5ASPb0ocBufc+t9LFwgbfcc1gnGVxzUTyh1SlR0dEL++ThOZB5JY
+yIvYEjyhAWyR7g8GkLsVWF3jyzov/HYLCKZFInXBTHnOC+spAWHRdU0HwJPyAxQL1h3kks40NNp+
+hzQfwbpqc7AO+s91W8RJ+qTCQSgJbMDYH16fodeT3nwqdxCDL4Ajtgx5XeptRXvyoaAc8kzCl9JH
+t5GHC4fUpCQXj8aDLjiLbvSRi0Mqhlo/XZr1K/Y0f6Cidh9ZAO9oYFgAIWCsExjhP79OEJmY010j
+BGMMzGlgmAGw1TtUMUGgGMnKJ5bmrvLNepFTLd3lWD5EYWCLFxbiloe0ZMf0auVnH9dWpNz4FcDi
+faKOb3PaQG/mnNa/h7sAyDTZ0lZbTha7E/vz2Y/L792yJJ48gmAheUXuCy+tvyIJclluwW/mT8A4
+3SUqg8VsoUyUZpqW8eVByf7xUj9ZgnNx97AzvQ+Olrr3cGW8XgVbiffGfkin4kUWU7ilBxA8uaIA
+gKjJMy3n7wUtIjwr2uJmKVaEa0pClnfxXCFa4mteQn5AYl2qCMTD3tYyVjkRKvpPGIQvEA760DVi
+B7/xZ3tEsMXpDyuCqBs7f28A48RAQf6yXLUKL6ZciljJ8qtsIWawPMcCow0ET7ilFq+B8CFfBglY
+B5AqkMEAGA340ohYl+itpfyB46hhb0GuWouelWAf9VACcNcHcoUIpYJQJN7MPjN7mos1aoKcz+1c
+cRSEpzO2Mu+07+f1bwWeTnqsLG8uXtS6SJMP8sVDE+ggQa8+jA8A+A11YxSzHiaWtXahUwwIprk9
+jGlPA9wVgvjEmWLVOyEGfN0SPOesYNgA8GYRNp4tYNi1GQ8TqP2gJRBlOiB00IFi2fX89xbiHQSX
+dpwZuvC645tyTp0ZbyXSvzfTIxzXPpXR6M85ny9bbVL84txa61k+X+Zt0lteLKEXLhJ+qMC2guCa
+RkAV1VXiFfgdh6db4wCCHRwniuJxs2OMeb44DX2261UTz2ks1Bf6kpFb2K/hYwmUu1m7k4psiaQt
+HN2Qdd5fwmDHKc3ExxspqWmZ7tLMSavMPISjj5QulFlG3grHClDQ+z2WpzzTbP3t5VKGZHwdy/sA
+muPfx1qh5uryl2bhp+gCYaOf2TUCxCRppvHsrqCyMNfRWf3hihl8B1B0iVEtVmExFwhzlWgvyKn4
+i3QCFctTcnsI/CiY1gx2gYi0SK9pEMGXC7NBQFLsRaQhZgG231Do9DhiwwsAC5V00Ft8hjXuSitq
+IKXflGcf5qOQYuXiR1cuZOketapY8G/TRCoIqTDhY0z7vtx+4juWOhd4cdbR+tvOk2PqzuOq9pa4
+C8pVWkVdd6VQDWTyIEif9G8Ls9VSRDGGLJ6egjGWRXfxMjZLsBl+Cfyu9g/LmHOuF0QJ/IaP8Ew3
+CR2LrR9dJLzdfIQrIupLCagPaZ7kPFcszgcA/AoER94BCPPkauXqrpS3vCb6igCNm+BIoE5zf1te
+4Y1T3qb2rQu1UyIaip3r01e8X7L0lHgARgx065HdI9hKnOB48V0Jn8BwB8ElLvSyJ2LMi7uZ5Kzp
+tkWD2uhlmVihingSAKUX5yKdFmOETWMqAnSLajUMZ6UHRkOmgaqkFv4a+kkAfCjb008AuKdl/Ssj
+X1rPYjunM/B9AsTpTQ/4UvO5R8qR0MVSjz74vA0FTZKD4d9NFwgbfe9luTjpgzRljtvTPE6hRzYI
+Rf9E5BpRrMKAhgtEukYE6IUc8tIRXrFbgwN7diCMtRNEVysOgKdKhN2vVqAYAYLFro8EwBPQQSNz
+sgizYD5ZhkuYOvZqrLEUjP+D1GPfMaLrwBMke0yj7tUyb4TCGVe+LJvRvyBwZAt8dmE5DXvRtCjz
+LBQ2CR+WAyxAotqs6zgDrg6Bnsq4jA7dye+XHjZfwQBkrrb6UwMOexIwMefAlyhkTszmFrHu6c4+
+wivNdpGgoelWYNf/uR5YNvmh8r5SegfAcJCLTNtAcAHCqGCc54P5u7XfIZK0k0Jelplrjed+FWFL
+6z16wXkkGwqdV5+gW4qt5AN4eZW4SXzit2AXS0vwyxbfFj6UGV4Xg+AAx2bt5bQNFHv6uob6uFm7
+3ZXBx3M9clcalBxxxqEAGpAltVbq9HHplmCtQJtlRAG+UtOAZLpSjZbL/rxYfD5R/G+b58iPSF0F
+0q3C/eQ4HpiQjrKlC60GvwGp7/XwUW0xRpjPg+QNcMgGKWGJfIRVOMl457DafxddIGyk89Pht8kq
+svmkdJSLb8onldV51whfvHveK4hUXSO6VVi7O4SD3mFg2bdNGyOskwvkLiE4gWIhzjwUMJzhBZIh
+MDcIA74UHmLWNgMaalh+eXWYhSXewqchWOggw48Wohj9La8DhD78MQkMNkPxpHUlAP0HmLVfqqd9
+CwRziW8I7/2cvwCITzVTdadV9XS1ks6MT8pO1USm0CO3OPvpZbkHKtdoDaC4ufwuEEwbsUyzFs9p
+PEC7D0JAVmK3FA58zXSPCIuw7tZgVywBgNFY3doYYoTaXfma3QV6vsaxLiMCuzik9XINBNfdGAiD
+cFikYhik6t2mghtXJqiBskiqeTXf6+H8p/p7XdmPCEvPpda/WFZ16eU5QvEOuhPHvAbB8eLcCeSe
+jtjTwy1ChlmEGfBaez1sIDV0nD3d8jY4f0HbdmnbmDEcorxiCSarewHNfl2A96Rm4EvF+JJVDPxi
+EMzz3OfTm/e+rNbEDeAmv8mhzNO5rrvdqAAh0NuOSi1ZY9bTHAxLygBFxmeu/6qLcy6T6z+W4L+M
+LhA2+tgiDBTt8wSIs5i28yzA4Tb3XRF8RoLy4psDXSxUuRgwXSPCTcLBsVtjadeIBLQa4bQEvwfC
+fgRk7RKhdjSrg8qKj7BAp1U4+4H45ocGIlFrCKEC/TTe56EuTboXh9jj96Vc1h+x9A0QNyD8GuC+
+KPPB5L/Mfsj8mJ/eofkPqPfpVX4pV+S9K1s6mOKrYNiU8L6Mfq7hecnQs2EBhluEdfExpLhDiANg
+0fAXVkmrGjAh09wjhh3nqIA4fu4W4eByZ2VW5DywJzynXEgBbRkbyIVfy8DPCQA/gODzi8cSf+2K
+G0j2VNfdx6Wg3OHVj2LpBXUgpFfL74NT1n9ruxAIFSxAZoCDMVY9p7b8yJN7x8BjFFi6gZ2MJwj2
+fAfBkS8o8onB7hEQPx6HzVXKS7cGr6PzkAFWNVcFzec0ZW2uxbzWMK35lH2KtWXayRJ8AsFUgV2X
+tyeIl7WoEXEpAsXHufyIXpwV8uwwt/3sE1iOsloLCOUd0hgcP4HiuOmQCmpPQDhXxkMZBXSyLFgD
+qtNupkemoUAtmj8sHvtLTyp/ki4QNvqOa0QNuxAolR3Ka1mEHhYq9lemP98nbS/M9RfkAISbhCxA
+rFEOUN41Aq8Bbmyfdsjr7hKiY1mB3RpsoHigWoZVRi4c+iBe1WUNEVTNXeOb8rO0plDrWNaIizCB
+pnJxoHwAwh+DYJr0d6D427whx+DPVXJow8PQuY47pp/ac6ynLIamBDsYtnxWmvI8tftCo3i0XWpr
+NwswBOFWHwDZtjsSsf2EEyxB1NIO4NeVx7RfWINNoaAD0Mba6LLnnKYlkazEmnGDOARutS4xBsAB
+gs95TN1xha2orvQyzkISyJXRO0RW3jhhL7PLgexlvVCeHy4Qzgc+hx5Eo+MCS8bq8uRUsmeVa5kM
+cheFtAB7uoNkKWXSqotwg3gCwWOMF2A5hXC8bEjWYAfIvkfsWoapkbh/SscyTgRs84WpPOPkPlFc
+JhQkH4B4V6AD3dYIbqOE0H2YqL+goB8B8EG2CryhLd3HpAHeHQhnGbEyFUBnrSeQ6yvglJbn1HNV
+sV6wn1V2WEG4P6WAdLtl9ZcB/gEX4QuEnb7rGlFlrm7Znq5b+ist/VepbYdGvsLpd+vMO6DkK+wv
+zCnMYgx9BLcdHFcrsWxlAZjFSzDHgBj4nViAeIzVhglg2Ac+FAKYANZiDQaKdi6W4fZzha/1nCqE
+F20vw4DUmBDoDUDs4FdDceQLhFlLgwARXwJgSecn4MyC8jv0maL+tB45JSZQL6SlDKcyru19PVRf
+TyzWJymWYZAePLVftVbaoitNXclYPulVKGwHQt8pIoHFHBq7RMSNkPkLKwEJFVlePUIg2AHx0AeL
+cFqGdzcF6heLI80OqXfMcotS2sqDwG9fRnr+4Tl/qjbe0epCoAyAtZRM6xSixA5my+yh3tRqKRer
+7QUorvXmkSFCp8h7sbbCheDN+su+a4JYykgQTGmbBbiBYP93ALfDbtaGjEP62EDw8IvTDRD4GE9l
+NI/QwKJxJIalZV3mu6zXkm8Z6u4VVrHwEZnnNTIwLkDdL0hHb3Ncr8zOu9k759KkvQXAFGGHyZJe
+QDBcPVGcgXHynkiNe6EdCH8jXShdsUCwgeFZbpbpJt8BQXyMKPujFpZdsfztdIGw0c+6RhQ52SeQ
+hbOe09u6/1nMYme6FViw3CHS2rtotwQHIIZknkzbDi1B7iMopvxjGUMUbgGeUIgOTH8TXwQTA3PM
+5SIB81GeWG1USbMbKfrTb/MTLPk8/o4kymQcx17iZ9LSrX7FGoxiEeZz9UW8CvpWpjPDp8r0E/rp
+unoHK7/HQ622NArkcVncpuR83aVkw1JjNw7xqNPOylX0oY+w61Rq22nx+SWHvQg3BZBBTkdTwn1d
+hqmHSaBYnF0cELlFmAExgV6PT2ZdzbVF7M0iKMcyB/XA3kcrcJxJ13v+0fVflivoIoI5Q3ndXDPu
+a0gvpAmXrWubbidpEOpa1zpaVI/XdRgkjxMjMwyI/vjvxGwF5OyM9ama79ilp/ERHQQLyr7AI6zD
+DoIJ7I4EvGLW4cz3dNDcuqw14GPjmYAXKIba6LM2HrA+2Zrm0sUqTGu/ukPQkSfjdPHDsVfrmLnS
+QTC8mrBD4gnsPgPgDBQw7H/sZol5kIFvAGW3KBdGQT03OLsD3gMAFinxXnaxgEJ1pvqljxCVjxF1
+AOzbEZJc/t10gbDRt74sB5aTrGm4wjadSoEPZvoby68JXAO9/uU4ArpKrhFsCXbf4MjDco3ogJbj
+4RKhnLaXjxvAMcgCvIThxBLAcU2hNjgYBuwbs1bZkKrwYhXSryjNHFGNfBrkXMX7nOXgRjBAL8gi
+8wCE39EOlKv9+NWJ37zUL67T1RnZy0pH9jXBnl9qQOP0UahIOW2mz2AYiLAIX4dbuIdLuyQjiiqI
+hQpOa/sA7F7SngIMuvWcai9NOo+sCsV8if3GbuhoFt9ZXpxjq+rZQosNeMSQ9TFXznArMZenvKdr
+gX+vwS/XEReKNrpCz3R/E90bxNYz6kCbNH7KAw6UcttaLz3ZBumQv1NiinSTOC2avYbKUdspxHPH
+epv1twPezG8yisHvZhUeGwh2ANzB8BiuU6qVL+KiJU+EwvBni7mEHcCKuGNCDlM3DDD4Dfl4Mula
+OTV5kKcdQPGRXsldmr9vCd48R/bUFwCY0zXmlwsn4HV+EUqnMDxMYDjurt4D4bT6nsoQKFZgyoyn
+ySA+Ucz6MaJ4UG010t3B+Wbk76cLhJ0COPYVc0o7yeiW0EBYiZOOWNF+j/whbQubtk1zK2r5EgW9
+IAckALW9hHMvX/+gRgO/muF11B0I6w6KAeALyxK8fCyXC8WE0Ic4zEo9qa0LMdtNiluF32hrX4QB
+iKlMG/+T8Av54gpE3fJrRzUR4GnKrhLPU/Oz8bd8QWDuM9prPF7j0wvzMglwQbYMMf7WLO7W9bLl
+qBN3WvN8sGXYpb+tubADu+IrDfuEdAsyDvd7y1w96xrTdoTIrQCdb8wnGMuX2MusXSPsc8pmGdY5
+DPQiALDHi1tEWGTJFtoxXUvb4y2R0gxi7v98LX0AfpXauw9tF3p0G5WMgsoQ+1xq/PX1zTnZizyB
+GtcHbDsyHcp8KKRPxeqzv71Q73IBthyWGvbVVFwlGiAeQHGJCFDsIPgAhoubxLCX5ZgXHOyatUKB
+3BaWRbIAosNmhQAxD7Ov5aO1d605PVmAC+h1EJwuVH7D3DV4ls3L8vHlRH5M3vZWHc1vucTLsqtR
+FcxSWU5rgLim5UXEAK5v8ahxJSlHCPY0L88gWXW9CAy7MZrLMrzwwMDoYNheKI6XjXXpg+1G6DfR
+BcJGehSU2NNOIKrN3Objoi1SQMFe9JP1dy5TQW9+lKJaftmFQh8sxtNedlA0H2B9AYQ5HPEF84eu
+7aUWAHZfYnPDQL6JurZum1BdL/mpAeBt+6PTjyWwD6ZSgExJqawPy66jUkpMa0uC3wDIcj79bdzk
+e89/zwcvpPabk+WTQueT6qUbAPaDEMDyx3EOiF3ovrZ/u7LLa2ixEtvFCRDHHEWL3ohUqrtE6RIi
+MDBrn0vugLdYhs3H0wAwmvsMMHfXCCWXCCWX9+mWYWJxVNBZFDeLJ80OKOXVOOdb/18srde/k2sE
+iNEtcIpvAJg0Id896ckdwvsRA0RARtvPi/Z6clCE02I+vS15EAocHn5UKsCnLJqtfKm7nZ/hQDJx
+2EGw5FMrqb+08p6ODQzTcTWY53ga8B01HTPWEW+16R2I5Uogz58WBW/G3rlmIvK00w4SkVeTy1Af
+fIlZyLoVcq33J8n7QiJvybLHTgBY9tJHKzGlO+ewi4Mcj2cQzMcAseIz08CuHNKiMRU087pSe6FH
+4V/ObGBYYNtOenvXpK0x0YeF9PfSBcJG33WNWCetP9LS9BDpZVrg7WWOS3DLEJRt0ygOPFl+5xae
+tkjCBUI7uPVw3VZtUrpSechaCBOKLx0Yqpg61jkGzqe9jqR09I2FQ9hOH9Nv/HwOWMd1HbjNRQKq
+fDnOhEgobQfBpoDsTrer6m5lkOOVavhzOdBre13yzEQPwv2jCrGuz19KiEGVIyDmb8kvi4S2eQDx
+tWuqve4aRxmK85yeae0+kXVUqFIb5S4SKf6l7BiRN0ZmmRL31UyL8JyaVmH1XSNmhtlnuFiG24+7
+v2M66l8dpwCplp5A+hO3h/c/d+nIa9haOoJiBsQgoMPx0twW6KvF1zut/Z4X8qAKggQo+0tyxRXC
+2utp/Qa205kDfTWcKXmsuTxsYalhAzQsl4aA3CHkEegWyzCl/7AwgDbXawzZMryGfeTNkM4i0/hW
+I6283G9FfiL+AAAgAElEQVQCxb7mA9nm2FTLL8BA97zlWgx5iKuCq4MfWUk8zM53xWUDu3JIAx4A
+sPTzqt45gd5yHA/pJquULlLB7qr4lPYIllXNZWzd5AP+BMC3Z4V9E8CeEk9vi8tKLF9x7vRvpAuE
+jb7/shzJ76wkikgrq/S3WxF4nT5SKJKKqur7SgyC8wU5t/Y+ukaQVTgstfBHsS9A7xEIa1qNlVwj
+FMUq7PU64D6DYaVx0xeuEe2HF2ldifYxpeGOgAFgBsRuAS4uEyHlQsSUml/Fi/A/lN2pQraflh3y
+GHlZVDngWNU7UEAqCzlX//zI0v9KrTvGwhcQaw/lpUYtO82jlnHdSbdLRX9MWbvi8a+V5+Snv3CC
+D44zIF7XGW4RVn45LgHk7hvsljYtT2YSKDQWtnGOP5GvyIPmsIIrfbWsvgeIfc5c3ar6PBF4oXAo
+ei8XPFShVEwzRYF05Sg84ANlP93qWWEplaLW4c32dKnpbLQ+0YntlP42JFjrBv2OoLeFAwSvbSmr
+f3CGz6A4QfAPBsTuNwzmgVkAb5XXk0bZDBovRwP5slxZpxyhl+cUCOuCZP6j1ZeqilOK8KI8zuIK
+viNcpRaWQ9YpraQ3LCgtjUHw0Ro8ki86+IVguW2Jy2KTvl6BY4UOejswLuXNNSJmW+H77AAzrcEY
+kDntBePavrAKn/be/A10gbDR59unAam+IwqgAwU9pgO56MrJx6t8F+Cc/IEzzoBYKW895nKrMMxd
+oVqAl07RSDsDYaU8B7rmGgE1q3C6RygSFLPgVPHHbf66qSszvAgffjaIDBS0p9M8ORWBpK5w1h8G
+wFBSRPI0Vwz3euoe5vjz3H+fMz4m2QIvMCQpIwsrEHf2S1+55UzCZcjD3ou+b2SMR8EKDKioVZan
+bQ5LPY9r7Kj24uKucz23uj+0sJ5BMIcB2iXCwbD/GAwXYLz6vH1EkZpfLbCVx2ucx1Db+br/2yyA
+BVe+BsILOa1LHIFvhiEO3iXHm9xg6uy1RU5teyoTIDnKNQFRZIDWekgxF2ssmL9eAd6dpJVSS+11
+b5WW658A8AoPb6sI5dWdInjnCLYIdxDscQCFF86gmMLuI1rkO4VNdOxjRHOvbvkF6kdKQnIgF6mf
+R2l2txhPoWjCdqtwDafMqVp9m5QPxPAJ7H4HAHOY9cwZCMshLAl+KW0HuyPjByBc80bJ0+m3QADc
+Eozc83Ri6c58l0IK+F0GA5cJ78f0V9MFwkblBY/3pV9Gc7W9vOA6niaedQb24+nCK29YykRafQEt
+cRNWshb+lBm7Ncx+nsmUbuVla7Fqy4cJychb7UyLr7dTlysGWxbCN5htwRplYWWhCB9KkFAGK+KP
+w9nmUJxkVSu68wVJmSR/ROdzw96wCRAX0BbDCBR/KwleSd9v8LF4675PDG6X3nGrADajzJl3W5NJ
+A52WU6QRWj3NS1GXRYc53Dgr3+f29dYftGkglzNFm9gSrJpuEBMNAHvcjs6zJZ8gIjGrRl89TkDv
+cA6DaO0/xIp4+IftF/31+gkEh2sRAV/YC6d5Q2MKG45SkA16OcL6WE56OYp3UNrDCTwpj+b7DOb6
+dV+vSokSFQktvFIBb4AhcQBcgS4D3wS/o7woF7tGlJ0hGBD7L0ExkHI2gXAHvv40D9AxMN2QEW6H
+uXdWGHWz5w/yglY0o2dp/BFgmCvf0zz4nPZiojpSPd2pxEH2Uu+A7osyDITDimoJPt+P4QFKR9wI
+hfNfgFyrmwEurQBIBb8MjFd6NcRNFXuKZuXtaXC+gG43KWLuh/bvpxTSX6QLhI2+4yEskLSqlHRP
+qmpVStE9vTC+cJrmAlA65yHNldOEtPDKc1eEc96hnKayC/1JIDcEFWlAzwti7Ui/JUglTljAVEKZ
+FT80VdtKzS07cwnhubaf0rnik+JnwHGKT3xZ/GsqvjTzvuicL/Pj5DT++XlqYwQeIxozHsMso23s
+zhbOZ/pW4UUEULQlvzzHA6UPqbLO6QTSqG/1vL0vpy8MaawvPJbjqjqsLh4WVNpLfSKD9dPCfX06
+e1sd6QaxW4OdN9l94mtOjDi6xzKBWQeD1Mi8wfM0nwuNU3xOEuDs1uDAmGUNn3+OYb3T8dDAhy2Q
+R6Y/vqP0AQUu9cADGD79SgEKO6A85h/qBl6vwn76U1nGDY4JEvAetkM7gWEGweMVyJUFhsMFwsDx
+SFcJzweA4gc8mzsEA19dMnrYVlrb1xIt7P68vt3awqVpRMjdHVociVlfSr4Aw+ew14VWb5mxDdQe
+kOvbc1qWp1NE/O8BawuXL2A3eSHD1koGxN5uLh/tYqC79y1BcmubXSeAcD4qzcUskq5jILDrVuBI
+VTrvn6ELhI0+ngLti6UkJKOcKtRugeBz8oUS4fQtTR+B8VKycgS9HSA/lWNAzEo7zgtwB4DStj5z
+2tZpSwygKwR4JV/AIPCrArMUm8MFfZqWXzrK+DQgMQkczy19dhA8Z4LhBnwDMHN+B9kOBH3sGARq
+jitoXDOMAJKvgfDPCwzmqdPkbBivMFgt9R4A1/QM0xj44HD1flE99FTirK3RSmVO4ERKWd2VDvb+
+b9cnfXrIquMqFAbMSr6Dy3Jz1sDw15wYOjcQ/PU1zcJ6AMExvgyAvYynl4V8BL3xw/n3RB3YRpDm
++ZN7COFxfAC4QSYTX9bb0a/sWXLgiVKF1LLcJD7v3Qo98V2FW27dZf/zBDIMeE8AOIFwBbrs9+th
+4ZflhlB8xKeX3UXBZfDa3WfJ3GngdyhCJisAHcxTezj4VDX3Iu7gOMYm9pBIt3HSjVpGkLjHQW+E
+l747h3G4ITuBWwle6GkdBDso3eb9wGdbuagy695B8DpxSyfg68xK8BduTfak6HbcgdXGZPkdNCvs
+qSb7ETZgrNFe2lnnBJJxeCL4G+gCYaP5rQ9cs+aVEj9ivhqIYLy3Seu3g2Deii3TKI4E12p3tjsI
+9pfTNMIfAWKlcq4QCUiwEkhdyyCI+ku/tCwxCBZg6hKe00TbkBSg0wUrCdVJgLgBiKfjyeqWIHg/
+duDbQfHU6uvJNwrF0mtjk3kEGA9jmmP7a8VCB3pHYsH4Yl9en+lsc5b5PkjmeveFVMtl5nHZautn
+ADIFRbPcAyA+rmUCdzWPtjQ6thn8wAP5AlxahIM/2RKsucvE15wYXxNfY0LmAQhbO7hN57xD3NuF
+w0/f/Fp57rsD4LZBgEmiZLTjeP8kSftF4mEpud7Oxp7DHUO/a+yJnz4pH9ch3PEEfl8BYDmCYN4u
+rYNishrLWG4UVHaRb42TVl/YLkDFQjwe/IY1P7hQgS/iZTjhNCDdr9q2Wg/TmYNd7sQ0B9Xy1sH0
+TABqs2wWsNhmszGLdOZp4Hg7fYsTyG3d6Dfc3f83AG6AzxPwBSTaUQFzXsSBM/ch80rjiDn5nLVR
+hMc1XhAWA9GDLMVK6fECus8o4ZvfSRcIG31r87TySaeMl9Q+mbvmXPOe8x/pZ8DraVWZx8Jy0CqA
+6m759fAs6XbOAzguSi4AnkZaBXkJNEpfWTNu2rWD4EQKYSmgY1gaGiitYHc+gOBmCY5fAt1nMDyP
+1uDTr48THsdNKS/Hvpf/VeAgKHhlRbb7b2Ji7fkdnHqDI9z6hYPl91Tm1Miq98r1yxmnIMltbyKv
+oQLSLH93mWjgjMr0cwEcAbm0PAaOk+K8PdqcOHx2mS3CCvmaGMNdI7jnPGgNkiraXBPzWTyt1f0H
+sJvUcSlr8kC5RJ8LHzuVAzj+SX5/hYpMN7MrN+GGl1XWch0MWFAOlz605RNgzMa4bCtbhp8B70sg
+HCB4FHcHBsXVCiwEiLtrRPcJ3i3EGwi2dz4USCux8VoCX6WX10z/ODCmMcxxazpXgf0xBJ2pWPUB
+sSAzvsqo+6vHZOS8M6zcwC7HN6Dc+aAy3g6M+RpN91uF5QaJ2lCtwxyWOJ/O2IFvNDjdJzZQXNLT
+dWKlKSBrRwnfcz2B78EKLL5b0Fqg1yL8L6DvbBoRzNok/0kRtEBTPigKqqwn9WtFYAPG+eZ1Cond
+EvzdsCk9fxRmjeygeO8Kg5vaxW2QzDdYS4UJit1fmN0jAFSLcLhCmL9whPXBDeIMnL8srwPdfjz+
+OtgOIExj0eMxftX6u5fxkf91dFL8oSo6WmG185DH8/8xAAYe8lqrpMQoULXGcXw0i24grOnKqNFZ
+EPs4lXKcToULYEY9QUBrCgge7j7CG2/1j29Q/Otr+U5yK7MN5/R6y6Gb5YUBbVnvLh+ovU+MGYC4
+jf0T6Fx5XOJVac//kJ4AsuzhAj5Pl9/Swoa188U7NK/vecyBSjxdBjZrsBjAfQLFK1xBcLpD9C3S
+2ApMgNi/RAfgY+DrfOKubSPzpipE55LzbhUuYNi4zfRdrONT+GlwaYHHXuYOhgEDXZYfQFejfJnu
+BnIrUOX0HQhLraRmE0lL3MtkXY9gN0tWHuJrOObk9PibIDcszJEm1OUdEOeYrDkdkmHnRUVzgwiL
+cryyh3/EFGx0gbDR2Xv3XDL1jIlCpTAoKU+pEdLs5YYLzgsatUeetvL2h8sHqBUrGVacBnbFAdsL
+QNwtQA38BmBrAK7p2hp2GeXK0oAvDAhqPDtGCFLEixYgi3C+LFf8hAmUxpe5DmB1c3342WMHy275
+oH4/AmMeU23xjYF+DfXvFwTZsFfxqAkMN2SXnan8wH3X5/5S/7o9ODi/918O5TuQI/SVig47INZc
+P572BIaj/zRoUcch7XQtYJWNm7iwstYX5vg3zfcyX5ZbVmHhX1faTW48xnkto1rVCgjW9itjTf1A
+/lDKHOBtgJNfRK/A7gdryMHvY2YvS4D56RpPIPexACoGKBY8cUAsBQz3XSB4B4iwBsvpRbn6Ipwc
+yokBZZE8b3VE4e4QW9i/nxuc4B9EquWG8Yw47ziwdT6K+NqVdq0bf6GKgDLzLQ90LGQb6OC3Kjd8
+e0cXEnQGGOimnDiBYQa9JyC8M1ACz54Wk99LkEyTuOwOiBvY7fl2fuAGArTRPe9zEyxctnbRz3d9
+YfsLm0/wMICiYDCMDHubDOBIpPx+ukDY6GPXCHaLOEl+Tn4ChQAIMQTQ5cUaDGurlJlj5VWLzqri
+DGqxpVe3CFB4CScuQ0ox6kERHvqq32181JFF2THCEYkDhLUv4ZKdQi9f8KO5CnTzk7RPPsLTXByq
+tfgd2N39gHH0Dfa8R8uutniMIZUn0Mh1dPqrgqLgusbO7KtVGyAdQZX2Kv3pfXkFjo/9FKqTGx0N
+xhErc2UFbFlyBa7y7Re6QllSGTyl2XpWqRco68n4PXlKCfjab7hrzlhfo2NALPVBYpURz+na0n20
+4wUmJFBnoItDWtzUEGt4WQf/zjrdTeIEkoPvXjA5g1f5zs9BBMUPAxTHct6pER/SEx/1QtymFRZq
+w9k/uIPgsAjTC3H1Zbj9pTkhqzAD5UFAGTaX4eNbPqKhbWeIvi3mnheW4LAAG8i1+HpZbgRP+pg8
+ycQyuLzwARz9npA37UJ/fPcKQoFAgGYHhAlKPa0D4eJ6EHPcQB4JYdnSQNfNvHCHkGhapHsaA+Nu
+wOVrMaCV0jfudzJ/Bc+5QNIarvbSHAPi5SahMAAsaRFGyC+yFAPvn6j8DXSBsNHnL8u5RiN1QqA2
+AYEnlMim+MXKbLL4AHQd1jJWiTS4ElqZHnbwChAIVmNY7BZhUDj2BUYqP1C8dJXauo2Bx12S6epf
+2T7N6yQQDAUBYtijtplWs8POEeH7e9gd4rQ12lfbOWKz+L4Dyn3XiBibOteepjSI7yzDT/RWGRxL
+VyHcwRCn5xMIT1v+XKcLr25p4YPvAeBuE8YuCQvo7IxHUdIa5VGql3HFJ7VNrsROAPEpXMpRpryI
+hxXMxoEtwlNBwJf4dE7IHBCZGbZdI0beQRdgiBZmcJUrnPpkkVjnsSYrKI4yXM5GssuIPmantHf5
+dbS5Dxr9Oq6VE2LqaTuLVeB5KncYZL7P+XRhvtY0bvnNC1TwiwCvvh9s+TBGWHLTR/hH9/097Bxx
+BsoJtH3c2c3hM+Db4u7qJtrK9LQZY8Uvmfa0Pq7nKVBXOfvEgedOadwZ4Nm8ENitrhAdCDtYLZU1
+jLtzwcmC7Gd18Ltbh/dwORfn/NhKTfIi/EQir3VOKy8LGtBVUQyYrzDUXpxLn+DcNxgZpg7el+X+
+Qfp47EPYH1YifwyhawROauC4yldTbLHos2zwirY0Us0F+BrYTMCRrhPe1gKSFWbBerIIE7CjbkT6
+aRADAGsAHKUTHPx6WZ0aT9hip4hp1xiWpu5fmdui5fHsw5sgeBr4nRUE6+H40kq8+xB/qYPfBvaO
+aQfwqH0cf6VEWHNahfGai55WAQOhJC8QZbIPSg0vYaD2n/JPPKNySqwNdIC7d5Gu4UJbs3wHrhmv
+ViBV0lPqde15apkRD2UqpW/Sn7BoguDhR9sxpViFxwK+c05MGQmMvwQiM6zNqZyQcRAAJgDB08fH
+tRa1AI4A7bZgOwCOMqApUq7Tt61CtQZrslWA8A4UStuy/W9XBPHmqyJ+fG3tlZbWgAVdRVrxaME3
+lnC0JQCJt7G6RbA1eIgD4hHgeNseTU4+wdKswg6uz6B5dSiEclpvN7/gZil+YTleT0y4Hh7XEXlg
+ULwNaRFWj0lb8oO1ocvItILu8bT6HoBwA8HZlBLZ4tLO4Y506+4jCAbnS+a3Icl6DIASuM4+0HUD
+JEucly/LLUWiqNbgsAhbnggBYLYOgyzCx5n5e+kCYaPPX5YzoFiwbE5fw7gUbCinScnYH5jyOuPu
+VmLNfHGFZcxoamS3CD+FQx2CLckMcjOeq3S7F2j9tmZv2ldLmC2pLm8lhGamIQWpvRjnbhHl2EBw
+cYVgYOwW4S1NN4DM7g+bNZh+BSAS6K1jo6WvPnIRbsDxUzphw05FcVPa4iGBA3SO5wV8wuhcBp8N
+HH8CgLd+EsqNHNkKtLXlbaOmGrgKblXYYzlqG4MznMeE0ysIRnWHsLwY37g+NVkIOAbPJyieqhC2
+Ck+FDONJ3zJtCr5kkGuEXa8rOrH2IYG6ULuibzzFsRY11mhZ/3Tz1vP8EGWljo+08evj/G06acyW
+JkiAwLtGPOCM4zV4TL2+KP5okuYyLVH3Zp/OEwIeDmjybftmJW6uEU+W3gDEDHIPZd0/WMgqbE0H
+vzC3yP2CDayOV5biHl9CXbxeqek+UNKvWeZZKOkwsqfBluc5EFsUQpNfrJXdKvwSCHeQW+M9vzCL
+UPnIqmA4MEMwJ/F34tVSfU1znqr9lAZ+IVTOeNHbwgYEmDXYAbDYi3OxHwRZih30ooV/Uhr8ZbpA
+2Gh+PAGu4Qw8FvMILbACFnAoo6E8PeXZCsz53Y2iLul0jfAXDAAtXxDbgW+m1fwEvqTg7JxIA+BW
+t024KLG1IwdGGGQJ5rFRB1AmX8MSHBbhGWB3B8O0NVr7aMbRLUKrm8MGgj+xBkdeTvZbgKs5phs4
+fKFcX9HLswTlJkpbfAMWW/ys9Fe7NcMW15K//vDa2MbBr3nqSRTtkp3LVAQUPsAAWYc1XIJ82dHX
+fW07paycQdoW7iCPusAW5x5mq2u4ROh6iWgq1tEtwqIQswbLVExZvsLLTSIBygJOGfa5dtkhunrl
+ffX20hllLtT++FOiXPfJm8TqJcxjdQK5r/J+FXEf3xbsURqjChBen/udxj2xOTciwPATIJaTW8Ru
+6a3uDs314QEwc74D4sW/nwHcs+VYif/38/ycUA38ueYYG1cAPFwPuucVvSgcW6oRCASBQCCB4A6G
+saXxiQlIO+NVjV55K+M7yO3A2MPp4rHlozYtwGizCLuTUL1WdZvIFzlhX1dO1wi3AMeewXETl9uk
+5TsOPpaabfrNdIGw0afYowNdLYlPoPe5PLTYV5NZgQaAa36Ei5IXAiEV2OYZSkfOP7hQwIVSbvvU
+x8KB8Tr5BQuztCLAl8A3wS58t4gTCA6LsJZjguAKjBkE84tyCYLZIowKcg9W4SdrsJfJvmVHtfT7
+GfTGOcwrr0f1W/QKfGiolFVItcZrYyTaWVmZ+rISMmzxCoZLLvEXtbI0uA1MBJvyUONqVwa2Z+0K
+rz8MeWNcGjD2AidA3IFvWIot7FPJ4ahPl20rvsTlINiswUI8JVPxJdUS/CUC+XKry2qFsALXBFKr
+Da58ECDe3TfCGoNcx8HGSsBXCfhqfaIR7N3Cfu8kAtRbjDM9geRPgC1bfQsbaS3DrBKAl0EEn8vH
+hzTJwePDZ7TxbLbHY26dYxBc3CKG1PTtQxnnfYTZ6isnwNwANSFUvHSNeADGou1FZ3kG09UXjuMA
+iI9dN31G31DwR344WIA/AsKN7x9Bb5d5JyYjECymsRnkUl6cVgDvkgVee/aB+E4qzz3Hqb/eAFFM
+49PhgBg7OOYPbTA4ppb+drpA2OjzD2rYPrcOJt0t4gB0tSUoaQ+Gpuhh2udQsqY9bBZWrmMHt6my
+exq/MrNeVsi0ahFmwPrqDtyQxHnYdhTRjwSuOjjmY2w5RW4PSi4O1UeYP6LRrb+zAN6jdZjDMy14
+uzV4haM7ZC7TGijW4kgufLKN6k/RPhM7AAQSMCmFSyWaW9/Uj8l0N4fWp4hmheUGoXdO6KRTfkj4
+vV+1nvRNXTy9rKIQULqNRZRBGZcM79bjV8A3mug3ppzuINMA5mYJNuArsnzZRWRZg4d9SEMmvr4k
+lA9bWeCKxRSkz1UARMnxWGe5vGHpsToULhDKaahpwDEt+Ajt6OPXj2289xl4opRCnWU5kdUr13q0
+8n5AXJ8D/cjgVr9YtMeshoMS/KLNdf3VvYH3D2VUcPvKRzhdJzKc5Va7+Z/77Y4tXQ9pKH7Czk+n
+c3oYqNbhEdfeBu40yprl9nHfU07W3nQFaED4lLadR+3soLcDXqAxZjsnAG++0yAMbgP4skxAMGzs
+0LDxGlmEOe4g2XjQrb/lnBiulR8AOPLpBTmOuww6yaN/4G25C4SNPnaNUAKOnBTA2BN35VDCdD5b
+3oQKVxeJFPou4buleDWjAfU4S7kU9EUa0HeTSLUTVtyeTrWUxc3aEHSMcPbXt28DkLtFABUMtzeL
+T9umVfeI7tqw3r5PEOxfl6Mvep1AsGoD2OQzTHnOENr6WEAdg0NCFfWcv069mgB3b2UNoQjefFhr
+dl6H59ATtU71Zi2OMw8NfgF4ge1GYUM1qmmFDKCr4f/nwOwMhvlvvXIHUwyCC7ALwLn6LSKRluto
+/QoItp+7R4i4b/BSIl+uTL4oHMqwKRYfQgdTmkfvgD8FYKDIVt8Yawctyd45dyTPUgacx+uXEfHf
+EQTvxXb+RQsT5khlX0GveOanbXTS56yeLls4YYNb2tgCvG2l1sDx0TLcQPCrcvESHkCWWxxdIaSl
+d0vv2U0iP7Sx7VE8/fEgsHyRJ43Oi9F0Hd2ydygs7RzEOQ4geR09p78Dwg/A9wH06kP5vKaDYiUr
+bQLfU1tXvkNdTbmAg0W48H0C3npDlnEE+PU9gquvsMq6dUlArHkuSwnP+wf2T7tA2Ojzl+VA2E1S
+4TMQUBzTgAoIUiMb02oDuz0sFNZTmbT6PoFbxBna8vY61Eol0CVR0k0h1KVtKAt6sINZsx38Rtvc
+PQJin11mMGyWatUCcsNNIkBwBaxfZhnmXSJOPsGbL/DE0fL7ZA2eNPnbOJ3SOLilvVLvPwcu+jS4
+oFR9H04NobVpEd1BqrYE7n+pYkNL2k5wknPHOzI24Cnia1MCsMKBKXLdFCu5psLpYO40ftvRztet
+0BLwCQDyN6diDIoTKF6uEWrKY4UhCnwphoCUivXC05SsL8qgzsCv5fu4qtC8KIHdDn5LvAJfzpOY
+A6tPXo/XX6KCeLGzqeWHcn/4gY5b/Z+kvWofWhufygkCXCQA4R+BYH/xzb8IN8b+6+D25CMsJxBc
+64gmygnoJgCWLa+/HLfcJGYAXgCqGLHbBFl+Y994u8aUsEMfJ8IZUPqNbNNVTb6qnQPky+gBOj2y
+AVwCviYkz0D4GQRnC6TkxzW3/MQADmLj6XHIgFrdBoLLexAJaKPdWB/C6BbfwQB5403BFGCK7xCx
+wPAk/+AhFkfKIHiY274t5t9DFwgbfTr0So+Gu1LY0xgUsbWYnBMSHZSFewTADkwOILn0gkAm4Jq5
+Wof9qIXxahqDX42Ubgm2utoAlqjUfDXzWQWGBLxdPhIYBgwMC+Avy6V7BLtAdICabhEOfL9Uwyrc
+rb9nQOwAGHjyDa4WYQKHjBR8vFpa5RMfjydi2OlztpP0CAGXtzr8CHxbuLeF+sJx7lPv1XZT2Fsv
+PUuxVXJCUWoW4djaTCnsHE5ADWeQeyQHdQzucokFq59dATT5u/3cAnz+Lb/gjopUUpGFdacoLPpZ
+28bWMWqkOn+ytZd4czs2qzDqsY9nP7Yp+zYgPqlM6ekuAg9lj+i3p3GjDuWFO/lBg3nJn4sQGEIH
+xWJzKMU3OL4AJ6+svQ3gnsq1MsJWYSDBLBT+DHV3g7A8wcE6nNbgYUaLAL/DAe86znkY8DEMMItd
+hfIaA1XdWAfeX5j1uBCzKAEy9qFdc4Ed6L6LVwRL18m5rvks+w754WpgbXcA7/X1F+WsGqFzEYAU
+9S/Lkgi7FbjJF0mAy2A5LMCC2CHCAfACw9UiXAGwA+RvCoJfQBcIG33ngxpL+AuBmnSVUMiuIBgU
+tDTkWZHoTBth9TDDn3N4B7isArrF+DkvW8QAOuMVtmuJb6pub17N4P2XCfy6yIUDYE/T3QrM1rUN
+GHP6Bozna7B7BMo4u0wUINxAW2MEPaRl2QSY77myD+6euufGsNedFQLkHs6O5CW8atMroCV4vGH6
+oxvFRqcCch6M002DkEX4BIY3ELsStr1suRwejgx+vcmPhUE3eWss2E84rMDl564RCYpdibgf3nDF
+gq5gZAPAE6jWYuRxjZEPK8/f4Sb+ERzTFLZxkTYeR0B8SDvREwjWlnEExk91tjyCMREuPyH26+Lv
+RdmtFWEAACAASURBVLtLQPe8AkriWrL9fFeH8oU5fvlNHkBwA8T+5bnqH5yfYXaXi7T+JshFsQJX
+6zAMstZ/5AMcewpjGTQA+JvSw63Bc2KyW8SQcJlYf09A03UiAWBJ/czuhzBgWIo5uIu5qKA4ALEh
+QL5ZcSC8wuPQNskJjqQOglmO1/Jp9WVQm093OgiWVi6PT6DX+Y54Dwl4e9kR/UbsFzwlfYUnBAMG
+gLG2T5shdxBXYR/hkzf3300XCBt9/rIcwiqsDQTz+urW4q5QHBDkpCvxf897Clc4+qyyEeEjQOZN
+P+GgQONKLkL0Ic4teKQQ/FXVObB1IO7iE3MtqgKAwyLsoLeC2unC+ASACRh/mcX3iz6xvFt/093h
+1S4R7kIRQNjnmQfkA8Bb0cSHY7oN7wka1Lp49tHCfooqyV5TDPnY8YA9tYLhmt/GIoJ66i614wCR
+ToVPZkRNv1wHw0v2kxuEauwrvI2HooDio8UXfKQTelk6x+tS4ykx8OuuEJE+G+iFAjKRmsg87sxi
+t6aIlZOSUstHoeEiYU1darquRQa6PHeFhduRsuxI49rHslzxOe1joheGN+5vvCom5hzMlKOXobTA
+JWgF3rbJjp8s3mi4Ax0YrzroSotc+AS7pVbcAiy55+8Y+LF9RpnB7w6I64c4zmUWjQS25V0NrbtC
+0A8Ha/AWnsAcE0NHTcNcVmCd9mnnuYDvADAlWjRj4JyM550BbaBXVH2Akfyd4Ks4DgTAjdmpaU9A
+WASCkYuNJ7szk1Ae9SNgYQf4difPx3R90HKJ4Gs5HXOlJuhdoDXWgDAAloe486yD33SPcFeJKd0a
+zPIq49GHn5cGP00XCBt9CjgqCH4CvHS0P3kk60oHtErhYFQOV9gZebGuSUULNeIDgLyr9lDxdCVt
+rUuwXMNETSkQXMprhkWYr5ml4iU5EXuCSxZhsgzP0/ZpBwDsL9Jl2C2/OADoZi1+yHNLcYDbA+Ct
+A9CA3QMQ/pTOp/FjJy7ZxI0hhk/TTqCjgl0t3czDG/BbKmylToD3RTl+Sa1YfAkAO58zaOOhegRt
+Du7ouG4Q3P847E71RDtUQMDAWG2/4GoJhkxgSgDj1aqJoWwVNtAbyijjQ2DbGlUAvGxzpXkxdp6Y
+87i7S8R5D8CYJUofz9N4Pw56oxP/9YxepoDiU/2vrrnldQDTZSldNMbqsdVRLm5YPCmw0W4Nzpfi
+2CpMFt4nEMwfzmg7Q7yyHAMJYOHA13q0fsNuOvefuIFCQWCZLL5jrK0EzVViAWDBOIDfHfgmEFy0
+GEgag6mmL23q7wSPgNj6tekNGbFKFfArBB0bAF7HEedwO3OuT3Frg8QVD2WRfPJkCY48TmOgbNbX
+aG+CXinhBLl7WFp4XTOB8HKHWHLH4mD/4G4BJv73NfOb6QJho/po/3VJhq8ei7TEdqk0OB+vpnnP
+YacDWta1vF+H76heKpSW+YQxtsxDWEKcWDRBVoj2ECB5LCaZV42oPcUSYhrKOIFFAtDMr2Vg6RpZ
+HKa6Telznmbl23lK162NPQGGXgY7mNsKOH3Ko1k62g1syqKaLfFx2uYGo3trlfuK2vUVPIyDbIFe
+Yevgc7kFgq0PhlYXcOX+AA5bgVp2DVHvu+S2aC09rdAMAbkdqZCV+GwaqBCV2DJtfUTDgO+UWGNu
+efEhShBsIJ8Uk0ZYSzkR2NvdDprNbULq+CXYVUquc1BjPm40lgYUvPIIRx88T/L6BQH2+fbxTOAh
+muMiDEzG2nN5yOrgnAYY/R0tUfNLRW5U4I/qQWub5QT4x+4ifTyY0alv3KGNnRvQLXHEmAn2YWEq
+653AYJFTQrKZ2xXCugw4hYlNzDpsW0WEewQsjV+aWzJTioV4+ktwtqbmNP9SXTd9qrL2Gp5rDNWA
+91RZFmIA03aZGPYi9YydJhRj2At5UwFMjGE7U8zleiFj2mPg5WCxlrvrLiHwS3oLUsYl54V1mK3z
+AMuI8a/lHtJdRq1KLFl4KuvcblvVNNkFynf5RfziejrD7RIWSn7pJVDj20I5KAdkcf45+3xXz/0K
+ukD4m8Syz+fM+a3jIG3hPOojb9TST1fOkLS8tEUxW/fjokd2OwKSFITa0+0YOAIpUJTKycvQfs3o
+FYELNWEBdWVN4+HKmtej/cmwp2vUEaBWs54ALMi8mE8/j8+lvGg9T+MBSABPnPBKcjyTtBk9Xc2F
+7dlhk8cZtvduMveKelpeoHDklsZZNXXDttoCpTsHbj3eQDD4XO3VphwE5JMeIBYGsLwPCWbz+poK
+5sWxgOXW+v3xsD/NWO4QDobhR8wGUGh9mDWPQXAFxx62R6mjPwil2vrYx0RqzS6RHJNYo4GpHESl
+pcznJtKiW9YSEeqi1BYa6FVoAODYq9TzkZbSiUmA2MD+mJhz7cssul4AE7WPBJNc0BJv+rwMD7nF
+8ZDtkb0MD3ywogPfESAsxsv62IFpETF0SVXNDxoYez6gnKyPwyVqc6cIkBay3tcKA2KTix528Atv
+l7orxXpc7k9SpgrUnnx4eIqsF+R0+QMvN4mFd5cVWQ37KsYE5koEpgH1kduwCfcFw/KANNgsEF1B
+sFSepTjzJutBfsqUN0KmqU9xatfiqZqmzmsknnw+VdTWBSBxI7omWGEW402eZXhPCenfEAOHtIyl
+K1wpi4WU4iHM+Hjj4ddq7m+hC4SNPh5702/tOxrl+DZNQAL0/Kh5ryFD8pD3YfO32hPk7rC0K18P
+iy9MpCApR8dWXl6AvIt2hXhq1aGBwtE1mkoryYEFHJj66R3MWhoD47D4+LllVVIdqmV956Tadb08
+SuDQGQ6e5u75JulhlD44k2to2rBsKiyUdshnf4DGk/3Sp3HQvUBrdevh+YTPALJI7NQA/8Sw9939
+Sgkw1z6tMtWCvDchLckS1+RjsQYToHSe8hfk3C0ijv4xjblAsEyBv4nPIGWoQscJ+LJF2MK2D7e7
+THB+AqzG/CX4iiu1KUUGtR4mEOdAN4RBSpUsW6fX5yJcT6xc+HkHEB6ArNdxxhiYZhX2x++APZJn
+KzAMVMXcsLzg7q/2hk6HT2sdq2RtLewZ9/MOJKxOpSEUGcUqXABZGfMadu6Km33+mb5RsyxGGvWj
+16vI15kir091JJCcUFsVJI9DXtsgLFm6gK/IwHTL8HRgLMCcJFP9KYCBYJ8vXX7EQ9c8uz/xNJQ8
+/EW7YZ97tjKY/oqd84q9jEf92kBvrBEGqckPiy9pjpT4HWz5FeODlmcAVind+x46OMTTGu8UzS5j
+DuGYOy3R0J0NTaSGaG5jQNlRw+cyaFeMdMx8N0yVBRTp21m/jS4Q/iadVEWdet3TjAk19eFWj7Qa
+zm9OdgbrZU3dxx6hFSQUVSflUGoNJS+dKWlRlkqknBdHyXiAX9ZxIq2JrDXQrm+LHzSGWgUuC93q
+KoEUwH6+rtErCoPO5XWd67bmFWWoNPN9jg9Wo/Oqfy0C/rKAYNNQCGOz3jCIe8qPcK3y1DI9NlYf
+0jP/mRpvvDingldX1iu8/Bi9vib+uTyn9yPVz/ql1pXjyV+Z63uwTrVHw6bhxUH4nNTtXKzOYwvE
+LctoAcEjQXABxugAeXV1GBAUmpic331sZUupYxkgNoCE5ROgSKDbgEZpAJWFyxFz8wgQYOcHfw6I
+pH/p9EfisI+WGA7qIDfWeaTxS8FJZc0HqEk5w2u6r/01xhFKUKwmv208+CMZ4SIBtuL7+O6zEbzh
+WoiFm49vqAUSsFL7pVx9zJ/Pbmoc8blSRexGYHJU7PrRDm+bjcGcE6ITU8wtyHgTYRVeaZjsImF9
+FCxAHLhW13EhY4yhOzB2XrD0WF9D1runEOjwOUqLfPBhswoXPiV9aJ67xDu20MR5Bjn/4HSXD1m3
+xvnO/yCrMDkriK9fobDPDc1HmT3mmaOUQwHDJOt2bOIMxMoyj2mc4r82n7SolOO/mS4QNvrO2PNc
+lbBjCJMNEBQmOJ0DNIC6xTvYDbtWaY+0UM9NX8i8hudGnGRlFuVFzxkV6DpwTlHpd8pZNq8jpZnC
+Fz+0PQUgjUqiWZKzCbQYGKdA9vlgQLtbe9Xqd+AcTYn8h3OKBgQ1jHv0xGmn9F8pEXjGn8Pxwogz
+71P4qXU+/j1Xj0FLIEX9SK/Gop2r5OqAB7eHzZfO67F+VslPx6YumvW3VifZtwBM/QmDrhc+7UWj
+BMR2vvk49m4uHl3bXBXAqxUMh0vErAB5mJsEzHeW16cSW+yzog957NZgMbL4BohwYBHpiHLFn7jM
+rWYZhYFhpB+mxNUDEGNMiAMgIVA8ATHA4zcB66aiuUjA51pqWh8NWuMpb1jZu5tR/KH7TY2Xo5yl
+GATzE7QcThsHGqEOJgKAuAXY+2W66SXFJFrfaf54YoQZvdw8N6BVZKCPTbpCrCcfCXzDKjyz0+ki
+IWENXp6+9mVGLH/gYS/eYbpPsLlIsHuE2NHKxc2TvYbnumt/kkHHCOd8uOFn9TJvXGI0GhgO8Kso
+Y6w+yJphtesQC+XEh/UX5iZh5wgIECPllosvapsnhRoVFnnEv8QA3AxSrMRsp2NLy7NL6LWx5O+h
+C4S/ScFDiqITA4CBGOptmFP4+HFL2vEEcIj3WwnWRXFGASS82F/HDXJYtINhAa31PPMgYE/ddOGd
+IDcXjFqgv+Dmi7OuxT2tIN03gNirReRXl4oq8w9z2dOiwtPM/2ppENIypdqb8BKA5/C75uoxonuZ
+YN9X/X2nveu5TyBYQulUt4elJywsvSm0yFmRlEUlpJTW0V9KSYB8ACXq4FcN8CrY1xJutWo9TYWl
+a+eI8BU+W389vtwGFhDEJP0+Zrx0dpITvkyjy97VKCv7FBGI4PwdDDcA3UGHZa7x9LkTe0xrylwA
+f7ztoNf3ol2PyfPx+Rij6GMGv6BwiJAY8woOw3Ugbr7paUe5gc6bcC+z2MT5xOSSsVH9UEb9+SCV
+oXb903mEZdJavNGTJbMkgPG28rblJiTTqbxb4z1na1gDS3yQCdEveyl0gWG4VXiK34OYD2/OBeDg
+18Cw+wubr7Afl/sD3RCNCooF645IzZfc9ZXPcedDvgmpR0pHzlGTSNaHBLWRxkC5gV4Gq/niHJpN
+wn2a6aLFL7iNfyOe/5hfrSKuyoIHOV2YDsfjo/W3hf8JukDY6O2dchRsyuhb4QRUr+db3x4rU5KV
+uFjXmI0rRYkjGD4URGYG6JUEuxUMV2uxbMquhV+MAEnOFPi+cCygpajGoitlT2norhGe9gEg9uul
+lltJLV7a9S7tufc/R3yyUOLmA/ydMHLSjo1Lhfey7d/q2KeFTQn52DpaO1mCWXNs6JfKeVCAcucb
+9Wuew2ndYqb0uPjxtx4BK97sa25859bg5StMYBjN+oux3CBkQseyrK385Ss7fIti7y/1siJgiqqD
+1ihI+QYSDqC3lH+TLyE0FH5zgQC+zoNCMiRl0iICw7RzwFCEbzA4jLrmWdZ3WZ7ld6CbR59vAOIu
+a4gbI7cIM7AvPsI8hjw+D1TklLiDRAO+3njZzy0Ul01h7X93W4n06jbqAGqK2NwtMAyyBkOwXCEc
+CAqAmVZgOPiFfSDGzMTrPt5uesDuMHlDJHEUcxEWcpUIxox+7zdmNB8EfHMMqzU4e07H4g9sYT52
+azDcF954yJqYgFgz3MFvW7sJqN2Xu0i0whqfhPNSrDDVdGyNP4NjL0/8+ZvpAuFvkrZwF5ArrHue
+cZq6j2LXvb+sdZVFxdqTAPawNiKXw5KP7YAqhNkibIpHLb0D4yxjuvFUZden3Egat8RYJuA9SV0h
+ndMYLG+PpkHnKc9JLuwCiMu59CPL0DYlOFlRD5P/kwyRTX5xvgEXjTNIrClQH2+ew7H/7pvrHIKU
++J0+Es99dBoVcqusCf91UNMNgr6ThJ+DMoeVCSvGNcVE1+FdDXIf42pxLjtFBNMZ+o03uOhnVmHd
+fro+QOCuEQyGwRbhZfXybcRkjtTnAQgGJMAZAhSH8oWD2xyOdIFCzYfn2xw4mNssxBJDngHKJ5AR
+rfBz7K/a/CWXVJcIT/OvlM25dqnVMda3zeyluUHjGi/NEUd1mZ885oq7A+C0fmk8ieCjt92fTKy0
+NQwNDMNBsctQBmONgjmz4cFKbSnteIOs3G38fVeSUlryvPjb5XiPBw/psnxP/8jCF9IneBWZ0WEQ
+KCYwrOtLdAJ3kZjLL9ytwe4CsRgd4f7gH6OJ/FouGZzGIHgTkfYMkE+iioGtlxGc3R8on8/TlSa2
+DhIEtwsyKDbZV0lLyE/v4aoh6j5RZ3LlTAr4MBonMd7Tfk4L/jW6QNjoO4NfhIiQoLGjaird/oKc
+K7G8ZmeY3drrqfKmHH9OtrIy6XhKDyUmvXSLNOC7QgSU2RpsShKUz0KSQfFG0fY6Ihwvj1BMyrtv
+biiflraKVksvIs3L7IA45qrdsXr6bjnW2tgTV7miPGe8oBf5/bKP5UhCFmnpZxqHafoJbz7DfMqb
+i+qb/NcnffdEKr6BYI31uRhQI+xAryiUUCB6iNuxKKBD2oNl2HnRP60cHxbQtYVatwW7n+fIFOO9
+AR0JhoeBbh3D3CVyf2G3ECvGsgqL+RHPYZ9epi/YxerNcYst8/jxEQ1ZgmDKD+suAd+jBViibJU/
+DDj4RSzkVQVIu2C2ar0Qx2O6zhnDb2DXmI9BN8g+dVq5rrzgRMCIX5Zb51UQzNuFQV2OWFw83W/I
+nH8M9PrHLtgy3MBWzoC1s+geknelLw8y9SXMYUDIIFfalNd8Bs4uP1jcrJ1RCADLl/Uf8ZJcjnvt
+7pT1FMMpXSQ0XSXMb1hscBabT4h9olmKq0S6fsSFnP/KuJcBqOkPv0dge0pXvxk5lcn++821K+6Q
+3F2sK+GGIq+sHkrqiCF0XM5cpNcYn0C1WTz19Sktf8UQ9U2x/yvoAuFv0ulFuM5Mn8VdyXxn3nU7
+rqZ0cSeP8arOyUosRc1naelhVlzm0wWkYKS04tcmKR5MOpVq9xZa60lPrOxUMLH2fAwdAPt5dplS
+1vMorYLfF4A4FnhN57Vex72RPqRzfx/SH+mhyLkeFnM+4T4okuEXeQrhdxY/atte/CCRC71SzMcK
+69kEghfA8DBQdo0oVmEgAXM/jy5Y8knz9DSA8jPNt7FK6zDCR7h/BAAAvdTj/Cb0aN8eqTMYBsg9
+woAv/DO5DoKH+Qj7a/eJU305uywoz5F4Z4BY9glY1v/0p6x7BXOaYLMQb2nMBlxPl29ocbfqrTFj
+ULx26RgxfqBv64VcJnm8QMm0tERewQ2uA+JG2SRChGuev8DETwvyRi1l5g5+LT26WvufOkDimn4D
+lXy2y6597Lh/BPxYcNtReE7L0Xim5Uv0A9EfBsCrq7Lwr9Trrk/0+pRquEk4VAx/YV3bpZnx1z6o
+Yi9KytpCT8yXIttoT1QkvllHfZdzuOhFbOEYv+YC4enPYNffI9jL+8S4uILPbYlTe1hGHuP5zoJn
+86/w05Z+6DgB3Xq0dra0YpSKxed5+O10gbDRd8d+MXMFMFryEyRxfo8vLuTU5+NJBcTiAFBfKd2p
+g93tKO5/V86iA2emsGMwbKowyufnbL2OXK0hTB10PJHCfN5ACierKu4PsZDcIkKPKYGS5uU88wiI
+cUqvbStmZpqrUuaRw07p+rbEp3VtNanPm4+X5LyEVPWBFRPs+TbySxT8nUX0sqy+jL47TePxR909
+whUAAJSX5/zEyNca9n4/5fU0t0gDAXjiZTkHJQKzDK8XfNaXsfxln7Xvaex3OmTtAbw6Z4/z3Sq8
+Pn+qcH9gGAiydAPFsDI7IHZXAVLz6so1+xO8Y8lHGRHAKcHDtj0aBfm0DDfAERlK+U/XZ2KHB3KT
+gAIjc4atA39pzvPWCiHwm1iktM3dW1w2pJI3OWB58aU1543gz/Ukz62W3RLcQXFIU0mQwt/GYdec
+bJe3nWRXAxwZJBAmNS1feqabHYBArgTfrJsqaWE+fkV5P87CEJXcJUKx3FeSUbE+Se43dGNiIHcH
+AdwvGBD/SE0DxLz3NPeXLf/JuO0XIPfEsyZfXPccAC6HvfwzKG7i+U08unKUnxrXZ9SgcD7peam3
+88AMFAqSjggerFZDblDlyX+KLhD+JsVctrge4u/SggkembVfuR2JqRbrdgGy2FiolDNyU+N0Jmm5
+opw83sJkEQAkFAZYCCKcJcKyUe4s365ejUZG763v+ejRi6UCyHJIxVCGrrpGkMQi8EsWX1AaKZn8
+kUQqpIdQi31DGsRcflNwlPPYjMBxt3CmZt3iZcsnYOO6Nw34NWVf5dukL0BghQXhw5vsJaiPZJLv
+0i+VriXABobjgx3pRrDOk2Q+SaCjAVKcV9cxfRxn7IGbL/G4byMS7KrYo/7qIqEKjPUZNajMFZ4W
+Nh9anetTw+A9VQN0GTCBW9tIQmwTTVbKNBEiQJMDBWlp2NOK3zDoclKuFHPBUyabe0Rp4rKYk2VU
+kaAKOqG2m4YOrvvgNhH9mckSBQRrkQ8AhVXy+gSGY3cTl8sj/arrRzVyMGLp5mgV0KKFbbXIM+4X
+kCwKa0VUKSUQ85tuLkigDlCbbTYdwAc/VUCc1uDWoR1LBuWWaQkP7ZYvtk5bng7LBWLMgSkg94dh
+O6bYThHmMy/2EqnMBKMbH+MZFOshLdMBLeGsv4e5R/t5jf99fdB6EJ98UJkuJ6Wew4C34gAmbUzn
+MtPXqytCfvKgnbmqims/NjZ9V6/9CrpAmIlkflV+e5o6IKA8PaT7IzFOZ7/h7dpB2o4ZkzdlksUZ
+bq4zw1gWl0z4nHVLY3ygLJFQcnu8+v5xXdxyVqx9oI8dpW66kmlgly29aILfwWwsNk7rbhDN5ksL
+/AR8y7nbAn5a0Q8C4q0E2HnhO+UXSctzAas5XzYO8iJ+avVRiH5XqL0q/2me8aFGn9BAsN0csq+w
+rdVVmBeoaZMGgPklMn2yFjcAvNqZlvgJtT1R/chvtedLPAqE28M6KkSGgeHVRvcFXtPzBIgBfmEo
+Pik7oqdYZyTZq0XmS+wDzTJDKO4gqYIIlgkBmDpw4LRiReYbeZ/bOu+5fRqtTtG0wpZVux6Xr100
+ENZ1UIkF/hPsaozOrHFJcNtBcMgkqfECnA2GxM0RCFwykOQxzj+VXBYKgjfrTb7layn+Iix5YBN+
+ad/e3nThOKR7eYhZhD3+dbiWtabwnR2VZ2aFl5/wAsMCmHuEWXz9BVF/OVTEXtYzSzDxXnL3+jHI
+FTQwzD9qdh1PL+Oz7fXiISw2ZxSn+XAAnABXGyimeet44qBi91+61UQ82sRcyz1dN3aN6ewiNF+x
+PjJMU3gcv99FFwgbpSWzHVsazzGwM9I5/dlFopwhOKGpVquBkmMxZ9tdUPIa6GDYa97D0sbDlVOL
+R1pVYF4mrcC+7PuVuDVtpfrYCY0jry0GxbzAUMErXCForYPnp4LaCnyjQduE14nQQwi9xMPcnVMe
+6unD9EnZkJZCk+xXEdr0H1SGLyR4dI3QykN0wc/oxXB995zYm9VA6To4w6T1NhirfH5Ukrk2ZcLn
+8nkeXiesKrws1efNpnW+9kTVeJQ7sLZ6KlYsd2UA4F9WG0Ohsh7s54tfYwfEWODZPyerQFiJMRQ6
+zVIs6/pCPR0+Br6+IicVMo8NA6UiC0paFu8ipdTGYobL85B7hu7t6VPn06C6ADPLY48PioPOX6Bk
+Un0rDqC6RrDV2QSKgtPSZWFtF51xJ9/veQPDfANR2sFS1Hak8Owi66jPvhTqiNNBKJ9ke/s97Xcc
+P9oTmb+YF9UKykS7NZkvu9MaS4aJ/nGU5e2wPpbhDzxEbPs0LIvvGkcDy0JPQsBufFk7PcsEwGC4
+t7GWA5VbquIQdx/i7EmJu7bkPAbAgMuDNkRlOzVPI6VP/K3S1Bl64AkheJ9DEaPqtqJgSbFSMdav
+mqe8lPl/E10g/FMkCZ5Q563jpMzXQxrwNOsKXwa9jNp/F1NazjjTyiuQU2rNR2jaNFUaBljJHdJc
+S3XLsF2giplyxWPLAaS1znvulhiQYPdx0Zq2WXTjyBbdtAXnulU8WYzLXLLya/14N8+fpL+WC/pc
+pku2x+dp5zIKF7wOrPgG7MxrlQtft/zlS3c/P1zGu2K81kCvM2D0SwnPEmh1tOc9Ou0/vIFoOqeE
+XQmtdFWqYwJzrM3//Y13wbQvwa2tv1yBq+p65GvWYAnXiKUs10tgDRArlssEsHyBzdVCAxQPcpEw
+dqA9Wof1mdgg+KKAT17qYGsmGsaix83NIvxkKQZQ7zlsKHcwbteJaRRIWGQ1w9CwerNszlaS5VcE
+a2AQbeR4WJ1PIPiQHmH/gMpqZciUvocwg0cGac/rT+t4acoqEKsWMEzprFE2Awe6tVq2HS7GBoDH
++SMhXx7+KjziE7z3TuHSd7BcUwXs/VJjXWDOtS3bkPQXXvjX6s01BfvC4nKR8OukZXgHwK5Hn4Bv
+gl1YOaXeqJ5Bb/cXznKgummW3bjE8pA+fFRBsMYcMyednkzr4Veleuprb1tUHsykrRLtFVYGJP3q
+4d9NFwgbfTr2vGsE8cSRgbjOXMYJpJ5bcco9p+Wy7SBkpdiD1EgpsCd0jgOdJ/EKArWk1aRUUuUz
+lS+iQtrABRjeu3gcI1cyFnbwWbZMizwb9ZLG5YEN6CrlxY/AbynX1/gTF+3p+pD+oueU+sytqSMe
+eGhpW0qycgyI/dSeJoujqqj/gNpeloUHj+X/SpoGEkos6jxraRTPhYA2ZgxovYwew/7CU7EW27kC
+A7A+dnrgEwXMMSIU8lLcEzqrNU1UoGO5RoyFduFW4fWYP9enwl6s4311fV/hQbtTGBgOS5o1Lz9h
+K8Wty5vs4GC5bxCIDfzUwG2zEoPOCSkRoKuKGrWEAsA5XMigSuwQgriRANbuHO4Q4XtGpORNkAwR
+2rEjAXIKuon5AHQLEJ4s9w30jgqSobCXFBtg9H/Sxrb1mO/fANdTKbPgfEfL++lYBz8vKCTv5YkD
+cQAAIABJREFUl8V3lHDwqd2s9fAogBjAFzW4HF06Vi0a8+UyHMvVRADIXK4u5gURaeH6MNgibOB9
+ABo7pvhewi7jHsCwr//tR3NRgHGmVXDr2jnTllxqaXR977VANgAMO5+vGOPZd76x9MSi1U+4o4k9
+jZECpR0twF23b0ozlfM/SBcIf5vybqj8ngCype9HrcxRq6c0ZpCjyDqwrJbUp17UWqUYYo4St4Pc
+lI4WNYHRlF4VFQalGlCTAi4aueWXIUQI9gZ0PQ0VqGaabmuUl3pdyMh160qOi8S/UsM2smd6Kv3+
+nOcSLhBfVOEFeI5LGqWrEtCQVEAERJKeeY0LHkRyTe2ZR354kxYgONuc7FUB7nokynwnDRRX0Ps6
+nHU7AF5VSLbp0Gz/iMNaJzPBj1uCOzhSA9/2ueAExKbUFAaK/VvKgC4HjAqMB1bBqZa+QPHyV17t
+HiarlmyTvM/AQcI8rH8Gw5vf8EG+CNXFiWtbPNmt0b0NPHdIS3CA4YnlN718UVAtxJqnTr5lYX4g
+i/DcLb8OjqFqdxJFiOTgzWzj4hsDOQSC864ox0KOHadWkpqIG3+P4xRvE3A80lyaeTVdIEY5ju24
+gOcwUFym9WVfflB4cbDfYAjWPtzLPLw4sd9IhrsEEE87hiBeoDutr1WT39jBwCX7CtN4+C/kSnVp
+WO2uANh1bYBd3QEvA+PuNlFsvS6ElR0OSeYAbS1YvmMTnABwlCJt45ingRs+h/h78/1VFN1b9KpH
+Ke2lCvyb6AJho0/H/hn07Pnvfj+l5bUHUrS1ByJBO+h9ymeAS3Hpi4+KAkXB7ZYfL+wAeLUiXqoo
+dw9ZtneTF5zGqiHB3yzCcSMai5PT7KSTNdirs1ZoH/vT3Wxb+Nvclml5xWl73jOffJJ3ypdMrmaF
+lHGlz66x6jjs3PZuBUmZ5ZqzaeRzlY9l2hyZQmMzWfhOFl9hHEDwyVXCrL1UZukTBtESQCb4yxBj
++Ap7AzWHWpC+jJt+FUBEdythKG1TLoOsmwaKfSgUQknmBes+GM60I7eYggHFMSTWwzTUOQyIOhjm
+GawWRLK8FRlCSDeA7JuyPH9vwTBxmN98CBaYYTCMtARjAprP2nO9qyD9gj1sIHhSmrtbECAWAsMJ
+AA4W4uHAGVHnAsJIa3BYhV2cHmRyrFsPk2yEBj9uqoPGTbcskt0xHWxRrSB4jJFWYTty2gqTpVgO
+/WjqK9dwynxVA8BIQCyqSHw97amIYM62ZtZ+gsU6HGspbj68Ic7n3YnPfXu53bRgYwyr9RdcLxgA
+2xqldPS0SG8Lz2RT/kXmnwCw54dhqeORGt7jBzAsgaqLPswntP7L/FIn6c8Chn8zXSD8F2jDQ9/4
+gY6n2EqpnkFP5d6DkKQTVD6Dk1Z4swJXhVeBLxdJQQ511wgEt0ux4JwbxqAmxk4RC82tv7zuUnjm
+Olz5ZC0GNgvx0S9Yta3l3RKckPnQl+iHnifg5fzpnqvx53t5lhpwoSAJDR1KKOV9+lb/sYMEJDmN
+FUdvZkvthV7FGQAbwI+xV7+yNt9hoDyR6HzZLY0bAAb6fsWbq0Q2CqERWhdS7ZrSFilguICQYgk2
+HpQR8QC/Y+ZT/UhTaDz2Xy/bQeG+ELRoxL6AJ/WpFtiSZePcwHC1ZnpyA7xFrlAVdkK+XKUJhkG3
+/YojIOaxD9cVA61jVPuvW8IzBSRvFuCNNhAIhgjmnEs+SAXDcMDLLhAGgKdbiM1PeEChYwbwWsOU
+QLiOae2n800yUA5IvrcAOuY6rMcKfLc0mq/kRbIEjxH+wGOM9dviaSV+EBO1XaoAfhAvI8cW+WVG
+Mf/2Yc2czHpkgA4f/PIBmVxb7ne7XJh8HMgiHOvd0kJ2VABMsBRoVl0FSJd0y3CzDh8swqxL8gaJ
+JHEBwKkHYsh1z2M+qGGNsnF9KbUiDFn9iYcVCX18ulAvjxL8rXSBsNOLhVlI1YTNqyNAPBR6k+Nh
+CAVi9qMJYbUqF66FCPB4kjz83pHpp/ormRmWfjGPRrwqv3Idwgm0FiMsW7iDhU+Wia2ybQjJuts1
+wcf1fp8+O4sGow2bBrPU4sf0yHt94ciSgDAtT0N6yikdmceXcXVQ05qyBqjOE59/L/6Ov5nFnqrk
+B4uRp5WFa4nzdXaMkspD2noQBxn26+e78nrqB1s3l4IjsArXMw7CMn2Swhoi9uSebv4MNOZ5hzjM
+xaAjUGr0Ph5NKrnl2IEFyY7uWyzW81THXcC2NHZTaWligHWBJHeJEED9IbqhpDnjvAWUBkTnOo6J
+r7n2Xx5jQKdZgO3mwq3Cbi2eDpBlrpfkmrV4mhU+bwJoPCycK7EBL+PTfJnYgQviRb45l3vIVA0V
+Vd9x6MaBnM7wpy+U87Q+RmG/kaB4jdNIEDzMNcKAcK1WofhRwa6lOeAF3No+IGo3D2r7Z5fZ91sk
+u5Egy68edFLXlmzOUBpnB72B5xQtTFZ1bedGmpfrFmEu8wSanTOsj9EX53XvDvfRtmCMsII/LpP9
+Rwuf0ixM47DnE32k+A4Y4ZPT/ga6QDjoU6CTrPs8aRuLtx9CNifqOzSl6JkGfAg1Bvt2MJv65jGP
+gW/8SAkVE80uiylcx0+8QQCqpW1J32y+LXnxoeDwKp9fulrCfVWtcXMR+RSP9N4mYBvuz4iV7qs4
+CSi+lpQYCmA85NUstjDoOZ1A6npkv6cfGnQmas8TkATIKtda/TNp2/XfDTVelGnjKX18CXBsDZHa
+p7CCcViA8HV1EOfAABS2hRWPuD0eZRlQYLf4otXNIFFgFre0yjnI6GWLcqSww42idB0Mw10vZI+b
+7o29FdxU7ItQ7at2pKMJkiZ+dtkE2fqYL1pJyglhv2vNa2qp/cAMDto1TvFt6dYLgr5GLHM6+LXH
+7iKQocv/dKht0eUAd2JOs07aVwHV4lPMYhnuE7K+gjbXB1QcCLuPtttQ1jRJ5VGaO+U/0f/VT79B
+WnJ19QEyl1/5XG0SA+pQD8MM1AbgYbswOFin+StgDnkjFjcLkiD4+ecfhbHr40cAW3GwixUe9jGU
+NQ9+I6KA2sttMtCtus5YBVCWttf+TKybhBlP/siSjnR1Cq3vZVxGkojVF/EKenVvi9I5vi4LECY+
+CD0gYBbJ/V58bdlYio+GLL6PMUPKGGQYUsMEDrbwWv4SI7QUL424ZFrMivh84vD7OQ39V+gC4SB9
+XwRAhR6HJfbCKpxHZ5h+aQKIh6Yd+UNdWOrOr6wHxZXKQeH0n50fYKoD4hwMasQhvyujEB3eVwZ8
+OWYnMOxhEQQYPjVHvpHOeb/qxzVrjA12KoCLeOrEAEruDIUJlO4xXCBabEs/k2yBw/VLC1HazTa6
+XvbztA8E37ZWDmmnyS1AIuPS+9vZV1o5AqwJjh+AKtDAKAjY+hrsZd4D4P3cfAEptqgiMAxqDyRt
+Zc6buSpNMYtUBewYF2jxD6zG5Erh+HhhiHiga+O+yyQYCBbvF5C+2HEjTYo3hIU1EP5Ql8OVDSRe
+GlyuCjLF3EPmqmcyEDYrpIHfORaQdUtvAcRzYpplWIZC/ctlqmtLLwejBoqXe0paZztrP62OVCV+
+Q5o31mLAWAwAR1987HSBcQfoDoALIGbtFmCMHtE/KRr6CVuI3X3ixwLKy5rrbg8K1R8B0MXDouu8
+AO3D8hMQi1ofQ0cNhGWkCDeJceN+TaX+BRj2sjamWrWVjznA47PG/DMQTPnHcjsI7jwMkxFnubzG
+IPBIAcILAA9oAZ5+/7CmNOM+xyJo48nXlow9KF5nlaqMfa40+DP49DfTBcJG3a39idjDqgJfLXf0
+aHkbGLYi6+LNutx5quhy0y4h/P8Pe+e6JSmuc9slR7//C58K6/zQbUk2kVm9d1fvb4wkB4kxhjC+
+SBMhm1TtA2b7H1A+VB9kVwPngoDYegdojZluSLyEjoxHV+ZyiUtSeMCw1C2WMvXLshVYJLKhxeRa
+6UDpOP67dX4unJnbPt39AFcAON82Si/CQmiPlHasFKZDVqTXSnXGz4zdlscDbenc6QrgcoW4i4n8
+81euwvxepPf4oz9JPySUiPXjUe7toF+mAy24fzHk3kAVl7gb+H4Bv2f6uFYNTgoL6gHB1aHz3sIi
+WzB1A95ygZguEY/7BwwD/StVLFNmviqvOS9tzlGLlKPi127W4JsrRCKFy0wKp51wudXRZ3ZIdw9l
+VwILrwDGhQwbBO8GwntvLLcMyzZ3CBFx94SwCNtUeVDb2kdUesPOZjjaevYf5UMd9sOCGVZtBNBv
+s8Kp+KeHEzzLVaO7TzgsUolG+0mzS6tHqr9l7hJsHbYBcy8DMXW3B4LgpS/EYDhN6HWL79oOwhvQ
+gGMD4gApzPYeeaL2l1utFhLwvzVchyp9lGdzmdAJxVZjHXI7zFYeCqS1xXUIrjz0tab2Kwwof2bq
+SqgdYRgOII6iwiw2kpMcz4JS6tqctybYWa4KHeOmAr4+/9739NB/c/kB4Vy+D0UBwOHLVMo7Xk3p
+SR/DUiwwZTMFHre5+r2evd5OJDHkS8Cl61UjfFK8npKUJ6jBatwTpyG1k/vcKbm8qIwmDBf0nmGh
+n+1wq80Vove9/qAhHPii2oUSsgyoVfL6s/sewNuAFoiG0LJADaCVI8GvSMY2CWT1kpKY4rX9hlxC
+f2d5ht8z7qs08xhAdTuXdjG5x8f56MezdbZo6ecKoXs2OuorJMk/W2zZSvwQ/9D/no/zNc65Wtkq
+DDq/+rNUOWsVXypoYReIh3192D9guJR8F1rUS497XGgfaYgPHoS8zcFgIT+ikXCrmmGXTJ7H6qtm
+Gdsr4MsyG7M+hJuArHptb+PcGIQNfnVvbDEA3ttm4VjiH3YQswpvt8xuEdjsE4KamcJgmtu7csch
+fdJUy2M8AAlQ3FW+KgS/aNbgkJ9RmjluEgxqBIgDgAOCsTy83G84YfiVQGwQ/KJyDRcIAuC5FbfO
+52t1vzf/VnaH4Wpzym2e7mV7OF0icguqkwh3n91oU2U9pj5FEMzHznLkcz6ni2ULfeQmuoF0Y4Nl
+13WWl0kwi5Vd6dkqKjmLrivROgiKo99LRxpyi5hhyTxxfn4swv9DSzT3u0iNfUsZ4sC7hD9xVxeJ
+dqgJaMklqgmSXN/zIUgqS8fxlvQCfycEPyhZyKOVOH9QpO4HpTgLhiNeEygKgOMFW8B6iqIOg0CD
+4ScATlh2ARBpuL/Cjx2gfCs7LrOHVUf4bBV8LbIxTyu/jLMk/3GwWlA1NipXKoSc/ovjkAJRuXHp
+ISb/s2VcLF7O9jhO9n0XinmNZNQq2Oe85AVvnUVaOceFex3N+nDlMFvFE+w+Auv3wfc5DRDW0QOE
+AxhnnMQ9l4aremE4OMH16hLB4YTjOPYAw/6DDaKEy1ogWJ6/Vffgr9XDnassv0FrpGgbGFPnp37R
+BYHErMqASEJwzTJToKgIC6nDsh/fGltzb9h7Y799Kw7Fsh3Ydt7XjkF4EJSXtYV3M71y/7V86ohX
+Ji3Qm6IIbMF7bev/YRFeZQVmdwieAzmPgSzE0RbiJ6LnNBiOD2ywr3DA7wvysu1aK38vrMDTKpzu
+EVcwDquwWYnDgh/9JvODMoE0V4ZYtUA3LcJbqeWwRVezDOJ6B+hGPYQe0Pl73m9IT3Tg/WxFhgh9
+BtzjCfJLX5Hya+Br4ZVxTTR0/Q+UcGwKlsMkM1I5X0R1VAnF8e+cvII/vvyAsC9P4yCPOhEk9Gqe
+ly80ukDOllktW4QEmGv4/A0dv33T+XyqU180/Nao44+f9GZj47SHMq5SiVcV6lBQvsPntjzW4t5K
+KeXDQRzLcnGxc4Hhds+8H309yzz7fT9GBdYePCjb4HRfLB2JJEVtQH9cs2HwrGcZv8hJSVpoBr18
+lBWQ1qnxqo5uUEhQtxv/Yvl+yl5+820AX+vvxPU8Se8L10wKjiKnk0TOdHLbZ2mdx2/9qKA11ju8
+XuLX96H4a1D+MEDu0CrWUvVS0vZaXBwqygq8vUPF/L0bScIQiVfJXoYZ7jKgFP8QGJfyazBMvs88
+E4NZas26mnOKZztx6y0KCiIHIYqjjle6b5QlUBwot1PMThB2Ke/bnG3OIVjeDrybV6FPCQvaNHgk
+RdxRImetgz9M0CgD+ud3EhDmhKW+U/sOhjssqL66e0T43p4rsiwK0uqNkkb7Eas/xVl/EGkD45qf
+sPsIi75ythJJCL5ZhTsA2/2YhR2+rTZP/sEzj7hAqZZVOCzDW7Wn/RJ8CQQ1ymceu6WfEFz1qQ/n
+5bn+UKfZtuv+JBVd6KZQhiGPdGyzmKprUlwTh6Q0A4DzuFK6eQ6fmyJJqO7KSvxjEf4/ssQLAAAk
+dI26ehgZLgms1GB0gK82nSUV3dtUNCKigVDopxJu7e+qxGNU9kwTF1baCkjntTSh+Hzr1tDosLVE
+eVDeLzAsUVyZBs2nt/YtIVuHozzaMSrPhOXLcumzx/qpi+ZxqWvVnUslorzU8XnT3EziXALtrADN
+tJoV5r+Yuz3+afld8fNdqP1P4mKvydkz0DIvR0c6O5aMSmrnoIT1QwfK9RPofgbiE2Bv5z/+xidA
+xhl/CgMvNlLO0UwMCgCIYrl1d4sJtUVW3602BVtzhSBLMK9VRQzFvRzrXv0jDQFUXCnNEhx1pBXf
+KnrGISVVpgl5JHXJtAIj4jTLquDELYR74x1WYLIMv9ka7JAMeeNxUZ/jVkFwL2U0cZgBgRo0yjvy
+Z+UQFt5yIZAGvjHDhYrSPMfI8+qv12GX5B2IbzB8+gi/yiIMc46oGSMWlKzA4SesCfIxRdsmQDbL
+MMjSzXnp7x2l8oxejzVjhI46RksLbwt9P8LDX5av086Vdv2q3W4NplpHDYjkYzKup2ADVipFQUHv
+qvDiqgPp3aH/gXM/FVPIkJYf75tjK3Mriprx4xStf3r5AWFffm+wXFgcyNKQzRFZ2dkD2NIZ1kv7
+Ufp9T0r5YF0cMe24Ujp81NelIFGQjKc09EQdHc2sRGg/WKBVcTUQ56aUomxwwLBEWSELAvEdee50
+HZD1sABPIOaf5/4V5zIX0KWueY+weJitwdUO8g56WXEZtcyB6pis7e1YWHyoJUg+jtH9ah+oqHRu
+KwxavNy/1/rrFBn78ftxhEvsTPd8rR73JBEv8TfpKXQVuv+WVLoVu+0LMKHy43oA8N9MEzD4zbTg
+fhvgfYFMLrfeos0yZkBiVuAVSllIaYsr7DlY7gbD+TedoqJgS85IlDvfD89H67LAgM+V/AHEfDf1
+i9mnfQ7hkNpNTsQ9gtwAIr86885xPkfv3tjyxt4b73fB71veKVOrLZLE4c7ulVA9sUsi9R/mGL7T
+Nj9xWFoZgLfPtCA+S8awApc7BMJ9s1xBLmstQ5EsISuwYM4akStZhFXdhWJagXekJfjd4fNcluH4
+YlwnKdsqPwR6QR4Wbw/HYLm8x2tbON0euE3MY7f9p7i7FbneM3K55/zXQ4+0naxMaUVja3ePAKKY
+tE6la1oaTXUThgMN/ST1s19t5bKdHPKnlx8QzuW7KDAgOIQ05qt/YAJwWhkCYjTVgm2p/o/2M+Eu
+0xccc58/Ffg8DpcVlzR+LBRGdl4h8ZwZsY7Gz6xhKShngakGNbRTu+Hst6MQmlWXOy/pxey0kZbd
+UWa54bJzqX55WJW2PX1i5xUAMx3rv5ExucWnP7CecegFxhasKBQr6of2zdJO89+xzFhW11/FyWPo
+03kDUB/icInhAha+v6Y3pO8f0FznVKBrk28D7kegtSnQns/9YDkmCI6+DES813yDTfvH1qVWvooP
+gEuWXgl4oDQHDHeIBP/e7IwE8mlBZGtinB1uETFDRLMEx379Vt2lrWEw5MZx9OeHPJPkavHb3SC2
+LLzfb4hs3wrwFohsAO+qA5ZGSr/pX0CLr81RLz4qqiy4+nGVrTlNmuSAwOEHzFZi8DX94egGbKMf
+FHiutt7mFO4+wivdIsyiu9w9wv2E13oA4wUMNwk58lIS+8x/h097uCPLcNQ1tYXDUvuNY32/Hgpn
+mjSfjbKesCzeTkL/Zv4lqISWFF/CRVFVFl/cC0swqm+wa0L2l/Ip8s1U1L2RMiPI2Jrs9P58cYv4
+cY34V5fvFf4Bv+1cBryAEeoxUcGk+eN8qajWxrit9TgCYG+I8TSVUItq4P2Yr+iADEpjcIvyC/Zs
+K/WFCXH9BU7cVZRJL6fMF7/6ozI6rcEVH79v51P5RFKyurPlB5m+13VeC3ENRtrnJRBD6k5T3TVB
+QGEdF4ginE/UGc9l4tLviAMKdKm+utWd3yXoLIKR0Q4U51JCkYr2chQf07SfbOfJ2J9n3/Ytrsnm
+WZhjX7gCEIK60ohHHg+S2V8scAXdEbda3AVu13P8enCjiLxZNiUz3SyQTQsJvbGhnhqv1oVcGoSV
+7IRhpbQFzuE+YYOzShRq/oT0NSGeAep278jX/kggrmuaTKbjHSce2xGXQ7XBOj7dmfoxK1PzD377
+wDjfH1BseXx7Hdh+5J2Bx4A4vjS364fi/innVidoLg02cC9A1l0itllMZS/L41pt0Nza8ZtAzYqB
+vP4Gx3kZCIWjrc26Oz6q8coBc7JeEGizCOtrHe4RDYQ3gbEDMHwgYPiUiGzEA1UK9KzAcI2g1qE0
+SC73vdjZMkztmMO1T289jmOzNV7Ov17T6wBdbudvRXqSX6kZe5evKsqqMnm2GgQPvZr6mBqh0C9J
+vP9TkhWC+kRi9dXcDtCdzy3MKX96+QFhX37HNUJoiwsY63zVfwXg6lmDgeoJKuNDZFO8zvNOsGV5
+8HysK1VunCHs5rzA4sqxehyDkdYNNBWk962XncxjdOmmPiV+jsov4lDxqdzG+XwnUkkeFy7jWu+g
+bHXfzwPFRV4jQZ4vlV+Oq1uIA5ppWxxAoFv3r1wPyvcaP6gPN28FejcisytQBwOhEDX1luarOL0c
+l+N3pqAc+wGB/V+VfRPqdmA0caSVdUppsLKPKO4/51oQfJ/lYX0FxCMurhcaRNo9BdjJuLc6zvMI
+81KK94MV+ALDWyzBYstwgBpCaQ8IEc7/LDOyCq+A5bAGO5Xx1ehmZvupe/M+q/7mLtsEta1LWY2G
+gwCASBPgu90N4v02C7CF336Nd123bQtklyp0Adhq8wqLfSHMjN/m5qAatYQCrrTyMgz7Z57dEhxW
+4R3hZXMnB3SnRVjrYxorQKyBXPRwyftv9VikZe0z3VteA4hXwq/whzTSZWM166+F3T1ie38gIDb/
+a/tdCH2r2vPVLLGoNrk5rOQnnG222o5S+zqhWGn/AxTTuRVXcDvz1/a91Ujri3aPSo0+HfeyCIax
+yyE4LcKkU9KDWvhKQLfQdv1ufSYyoPCZobvCvSjehHRIwrHgxyL8P7B8r/DTF9SB6xP8Gq91uIu5
+Kf1iILGC1vjqlNpHQVgq6dZgq/2dCjniY+3xXd+7kDuUV+/EXeP0DpIJohzihriMqFyA02dXuADa
+ZSktKcEA4tnvMqDe2W8UQOny97UEUHfx6Nd3PMq2MO8w46THRLkrJYoi7Odr1ocdjDZHJ0ZcKnGl
+ZwWKQwlZjr+Wx7jlUWvt3v4bcXIJn1jToeT5GO0LlbjwYelnHQBNl6C+FJ3lyULL1uD1AMDriOv7
+5/HLdQ5Qazd33Bff/2yfqaRlbqerhMXFDBMQMWhiQIYcgHCvF9KKWX48hdrK/RN+tSjyagkO6KX9
+Ue/1+15eIVNllOtIxwLTQPiNd+Z/HxA8bt66m7JFVx2Ggb024F+jA2yrEkKCgQMFWgHD8WEPh2GI
+pG/wWhvbPxGdHwdJCA7Lb0Egu0XkfMINDqksmLQCgh8+prHWgrwIfl/kF8wW4AbBEQ6LdvkGyxYD
+7j3cI3wt2VoPMAWjBf/Nqu5CuaBU855tX2p/lEmlZdeKsux2wFXMeYirXk9XCkh8+hrt7U09nFEb
+87oR4TBBsAOstfnBIMKl5RkKPUHtTyTCgua7yWKZulCkj/5b4Durbcr1f375AeFcnsjoXMoaOKzD
+bkVrz3cNNLhB0S56+8mtfDiWYalGSw3pkE8Z361Z+aSY8ZL7DL67KdlzqflsGUa4M4XMpLKos+na
+UTZ1bnbUkLuKKgAtWcyXDCjOi7R6oXSXlTlxZq+v3S7M53IZzssh9NrQt+we0X9aMv9KJwjIanvE
+ufAjpZktRnRUARVWbqhwFZeSs1RpgRixYBDjchlx574cxw6AOa408iCz0HvbFS7gkV4ovXjDEuqI
+V/DleHaJmB+GELfmHgPhlsPvmb6+HneCcO9rT2UTGcf9uHq704BfsupKKO8etxU+9dgJyDWQrvg0
+FXz7bdaQVX5o9+kQFb/tmr8Gzm18tcw2RhXr9ZcVO+ra9/OYpUvY8jR71xzBAsEbPE/wnCWCXms3
+CC4QWxvuZrGgatcSWJkiyiHvLUCuw3CsEB/Mt2orykBc8LcUZB32eKAswxiwJoRPWSZRPjFQLsLT
+RYJdIRZ0LewbCB9W4bGVlRDclRltU4NQ+ccadQD+spyXLLdbnhEijud+yasTiil82/cd7h/X63hc
+PozEKg7cKTNDQYY8DDnGMsq2iyzCcxt6uEtidyKi0eklr708DpnquY/Rl7hBL692/R+L8L+4fN81
+wv6bXOzgq0BVIluC7QdyN4FJ+EkMI6xnXLRRyP04BuTG8dYJbvEBTdQgPY/bwzGRd1M93im6svGy
+YADhcvAykryf/jSaZ8k4j8gtIFc4rfb7ymKntPn7rdzu9S6XtXKnI13Zg6ma6148QCwKaftV/oev
+sCuiVMRAWh1M8fjvjbhUl3OgXf4oFcqI01Eks4SOe/sQx/sYZXQ7r58jtJk1SGE54w+gPdLagX5I
+RjKPaVDkxw8YvlmFLxDcBr/xQLkOx2uka6Ac8xAfpTvL5gaglxavcFnWH37LRaJguKZMw6MVeIeb
+BOpa9jPU+EvYEFyuA4bDMn6H3gW0X8lcI9FFFRqP8eLqXADI6nU7t5Avj4kI3jkbw9tMipLPAAAg
+AElEQVTl75vuh1YVQN+WxVeA6wAbh09j3voanFmESe6Mc3igl60b22da2Ko2mC9nj6gv6bFbRMCw
+WYOnXzBDHLkaeB0qCoCrP8yBcgbAK3yElVwhXoqlGxouE3tZfi8QjLX882oCbK6b+oJelL0i8lfN
+nO+n2jjIMhwQzNbYast3KL5Yey8wy9A79/t5pVc5XbSN/FCNCqVjeRBtF1UeXlcNikHwG7UrZ9zz
+9mYGonWKqKZ4QzH7Gr8d1feHlx8QzuUORHMJeEoFPlwk8lri2wCyOGdYSau9dLeIa1ge4lEW4ExH
+ylowLcDP8QVXQ4BITLFUnTKOMkAw2JwvRrWHuRBavyEn/liFjtOPNNDNETpnH0xMIH3Cy/w9nQfj
+mD6n4/347XmcyzUhluWXbxUcF4Nq4h66P/AtLs/JwYd9v35UKeNy7Gvb11mDaPXvcXKk+V7clH8K
+tLYofJZgnEFhuaTJjVwuISMpdzT/1QG9wBP83iy5w0WCXR8Y+hh+F/sOLzSXigHCvc9VBJdpU2PK
+cdUeroPhwg1CACjNGzxhWNTnHuYvtRHw5e9PCSYAFuohnnyD08dUkhKan/Bt3XdgVti5ZnEadRmw
+fXugIbgD1WfA1/IZIkKeMgC39jwg6fVSKh+FbsUOEFgwQBSu5y7RGMbyOrtbhMXnNpa1cpq3tVYC
+s2w1rgwYRvkK27W11V8Xm5QnKps28HFOmfZ6GRC//GtyL3PlUF3Y61VgHKAefsEBxCt8hG+WYfuA
+SfMTlmpr+XZjlF1MmRbuEbald76KBN3Q8wmeWS4XFwe13vfkA4x4eOT4lLE3P2Oa0jDzRECss41U
+3TT4zentIgVbhJuUQP0KKEcytn6frpu0N1PPSijpeox6cokQUax1UdD/8PIDwrn8TuF36A09waY0
+dpeoyBMjnuF27ifakJgdwBhtH6DGX78hrVNw46t4eBxwgu9CATA/tTWLOKokJyjyfR/7cjl2Ka8s
+M+lFO+cFjms2cKUMTfVyq/0o85YmySLKv9Ic4BF1G5AaoCX9jhy1jjSZJw2f4Lq6BKhSuYkLSH7Y
+qqmm+n5en463QrgUylMPucPvCci4putlVuVoGe7X5ji+UhZ0XU84jDojyjfPiaR5oF8zISj6F/cV
+GX1qAHHbDph12D0hmQA4jlOYrcjRZqxeuTPA31RxG8JxHDgfbNKemhZgcoMQEASfsLziODp0nFno
+SjpBqq2LVsBg+faSfoDvsk/u6lbIOKZweUDwy+UccMtuKQy/wsDs5+y18losmetHbS9g8qVwAHxh
+pWW4rLTwcq37DnCvsuM6NdDSBDpzj9g1iG+HFdgtxNtgM10m1GTJVqR7RFqIR0nX6mDpa4P/ReuH
+6dP2K+A2rMPbZpQ4wHdsjzgpX+E2WM7yZXkktPObmNbugOBwM0Hcp1YvaQPhCHAn2B7WYOX+Vfk4
+4fhzmrAINxeJQsusgxRWXg6nno8Htw7Bt3AWVkqTZy3PeWX4bca/I5uej5xVAv/K8gPCvnzXNSKW
+/lKgw1wooakGCm6GStC7neTr/Q5q0eAhMuKiwT3EkSyLuGiR1gE1IXhu87bH9tsAfNnPDin9fvO+
+GwAD/dPMcR9ooNyv/XmJNHc47ivnuqz+VT8H7FEhCaTvR/uQeY5fjQR0FETCryVwKMoz/Zpx4WiY
+SUKHTEtwxmy9Z1go5u4rPNM9x82zVc64BqkZHK1PPoU9wD/en+gKY6jtWX+Svh0wfIJcrTzwrSy6
+A4JXB+TpF7wIhMNNAgiLk/XbqOKo54ThIEA/FG0sFa3SftsqQLDbP5gh/irf2lZ8jEMRabW9Uj7a
+D2lFhmEQAK+Ap/jNIhaU9Jm4trP+3IaG/v4KwHgQEa6T8VByT0cgvMuPt7bVJ1WBl9cRHIJf+oLG
+vL7rhb3NClZ+vIDYCMQE7JLeyOvWa31yj9i1FQkXCfcLDreI4R7B8wunfI+ipnDWYxOgXHdWf7G2
+j6Ksqk9Z/jGNtROI13pBdXeXiBkOa7CHzT2Cp967+ArTOm2f2ZygCcF7EwRHvWX60PoFyNZ/Ahk1
+ZW22yN+CYK3for4a8XFuPLRYH+z3p6B7F2o/rR27bIFb4A8I1sooCmS5n2nqENIjIi0NtRCqDrP6
+xiwRoaNatX2pof/7yw8I5/J7IBzijs9PJT8JTLlRUDo/bwIf0Pc/xzEMl9UrG1Z0goyTM46UUZMh
+COgVgt8O7crb7OB6HONy4jI8n2Vv9XB2qswnl23Ej0vka5gPVcx50CM+yphqj/gqPTKSsZ4BuBSl
+HYz6CgEbXNYG2YUAlrq60gUyV37S/OqcQMtnOwH4KUwF4GGd4bincY+/G3fb17rNvkhYP6nguRdM
+AXoD3HaqUBh1Za6nENzjGh/B97AGD3eI1a26FZ7HBgBfQDnbTAKtehVLtsDqj1JArNXeUm4NC699
+YQ40JRrBMGjKtMNaDOzhR2nbm6Km8k4A7uUX1vEGugvIT+vCfIBrcTCWTffPQEzlzsCbdbDIAl8u
+Gu0BhtJ3EEbvIww8Dl0vVejLynUpDIiXuQos/9zxEoXKMj0SyoHkRr9mWTEtvN01gl0iOvg2q++I
+qwcYLV9h8Dp8UpvCIR9hfoigWSPkZX7CWwcQx9fmMjwtwOID49z6TmFe0fJVi17X4frBYZKx+R4i
+3R+GuwOVUbMMQwmSKR96B95Wv3xN35YlmNwiuC549cGbCcNSMiVlE8gnWHvJSMsh5cTdRgTalFs+
+8vrPN08N6r+s35k1wi2iZnn5s8sPCPtyVb6X5fxCV4e0sxL16JQywqQPbk2a4oUaKKfpll0LR+MH
+HZORhuKiDFKgnHcY46EXhTl/SoP/GgTrratOWq1fmulmWVSRarvv2yUPa3E7BnYrPo9f4u3YAGP/
+cSHhmOcOsCJ+tTsIIAsBksc9vVvlMr0L1wTmwEOHHK1fc7ip/fjsatYJhQOGJfJ0g+SHZULtd+Nu
+gNxjvJZZr82A9LTcdhsc83kcxeXvsYcsSGVS1+f+1ZXNd+D4AgtyA2WyFF8gOdVL/Iu6qk2VdUKw
+3//FWjvVXiQ/fIK9obZZItR6o021pWS1+tR8UgtWhbV1ZdmU7y9ZgNeGbotrll8FIPzOKuRQyDqu
+m1VQ3KA3AI7SkM9yhPe7/0YDmVgXsJbitRT6Mvhbaxn4+nbHlGAOwAYpbrToDdbAowFqHyyXX4/b
+OwfEpV/wAwyHpbFcJC4zRjTwCtnVlAnC7QQBq+2hwiy/NnBOsV/hA/wy14jp/sAPHTlnsF83Bsqt
+1dpMvl3wdmW+wXLWkdfN5nCUnxd2WX39fhlyGWaV04w+9QFy/05c9j1XFGWBZi1JEIzV6iXfTLkL
+Efvs8n4IgO4eoehjTLz10cN1pyHqEb1aPD/hK1w+w0tohqo/uPyA8G8uZZ3IGN/e1Hqp/qeqLbgt
+6DvjOxDieiwU5GoNvvyBbnH0JC3SfjMmlxdXaqDfY5cI26/ySLBC9zG+eDH1ONfj0sqyl3LzXQp/
+olkdoA77AMEQrde3KGHPYkfzf4/HQ/rcplInKMub5H0pJqN9wBXM0WD8F4/7ISedEejFQnej+nnL
+6WnLac5FjrI+2/yIUebTUPeSgMo9JwE00kjb87L8DLUnJF/CI7e3B2SuxtxKqEW5xgmdK54XAdLi
+Ga4QtT2tkUsIzmRZEYay5u2Iw4yLdk9x4Lw8fewjOxwvZUXb8Om/FuyjDkugW2x6rL2xt2CLYO+F
+Le7Lunx/2b5ugcqyL6SJnSPiPsk+Ty67ScRX1OqLahQXadoxYJmvhUGgE1/MmgCtQWNQxd4G9pXG
+ZRtbU3d3Uejh4SaiVEfa6yPLE1w/E976cVzWagOVRi9rwq4Ch4tE3hvK3WKAdm0/tbnLfaBAjzYN
+pFiUsR2RQWte4xN09nvX3lcIgLPM4NA7r3ndZwguM1VYbg/tF8LBzaYCcX1baVIHa2p3kg/1Rill
+Az8oHzPWnK49IV9LWk3PfhLm9sWwbJuiesxCpOwPnANz3NUHp/vFch1cyl8LjJ9UzD+4/IBwLL/1
+EBKKTVvcdy4l7eiEw2f4ZSy4QjAwLFDje+8DfAuGWaFXvCm2iLN83IGYRJPqtMGQFdn8z+6rHTvv
+rcpgFOKkPFt5ZJxyd3bBF52PhSf5P1VXR4EfxfN99dDYP2Re0g/CgnghowOIMR+67sHHmBZLIBv3
+92n7CZa5fBr4PQmwbwm2gtu6fW6ZuMAxpx9w3OC2ypSSjL44j3FYL6m1ElDo1vfnsQbDgrSQpJIL
+GI1Xyq7gymfWtkDVxyfQLSgpaK3XwAXC7IpgYeATBDMU2DiCjQ2BJPAKtrsPPO77p38Dkt9hGXUA
+lm15mqALAuK27g3c4hOcga0by10reJDaAb3+JYkE4O2Q7O+oN107Zmpgf90GxFzmDMyYxyasUZ1R
+y+P/fT1xpgCP89IhsID4HDRXsM8wjITHHufXyd8cUMqyFyfgXmdYmGG9xCvfJ63cR8B9hKzBozzO
+PCjatGkA7Qv9PkGwHzsg2I/f4HeuM008pK58K1Hygt2ojmkaVwFwHwxqN10P8J9akcUGBIu3zQRk
+7TBsz+nRoAiAoSiYKEtwQvNGPmz+yeUHhH9zaapApnKM4wUJHxWjCKJd3C2mT91E72lWB1+QEmtW
+4Phja3AoZUSXEAi0W3/F2vmEXQt2q0ZuXdCsOP96n0/3RivlLadLg3c6qbxRhvK41k35kcLb2u/5
+bpODn3ebEv0GyAxrafnNTKJtC4wvcb+56Ie9yrZeofYpLsMz7vgJPX8youSW/lymZbfglnD3gON7
+er4eZwHynWOPGcSEYrnF+c1WTCka4XwzAJNlOIE31rQC1VogzJAzFP8lDsrHCwyibBugQ0aY2nHe
+WVVy+JRCFNv9UxNyY3/FvkPwJ0j2WQ8gQFqCnbTukLsvcdosyZZpA+ugv0foXWQlVlfiWvMk47BC
+M+iOcj6OeelxHEmlqh8q4zhvgh6tbcm0BMDZFuyvimG6SwQUszW4W4cThlt+uO2RdCTxwPmc94CR
+JsrhiOfrEbhC7+eecBxpOf/8cChe8nJcr+3nbzL8jjxx3vOB0kH3C03PadaqgbITettMMzllI78F
+HjAsAoZdGnFAd2Z1aGNOwhLskiysxF6QzU0qZokguC3LcICuFhAHCKvp8ue3jv/c8gPCvvwud8gM
+NyUq9zQeYIVZgPcd6P0ibXONGINPLmtc6fYX7TxS7aNb06JuMeY0WuIiMGCpW4AfgPjRKpywEQWn
+BcA36T8swxkP6oCCGhlP+ew4fwHkzMNDWYyMM+i28GLwvYerrXyjcX6n/WpXUvhG+OlYFEuWDzvy
+Fm94/fRKempJca/NsouzHMpNojpdg2EqjysAPxwPgAX9Zl1VMWuD49rdCefGt9JTzt63JhAvXhmA
+XxkGWHkTiOgIH+kGoHGeEoarrLI+uGDpdvMti9ogOlEpoH0EXoJk2g/XCD4OkRNuVc39YsZ9sR/k
+spekJRhboeTQrESbAYOn9VjMz7UB7rASJwzegLfHAf06JJZyc1tnxA2O81IEg82HG/EVue5HnNsG
+vwrdoPur+2llwb+vl7xkuAOjpY+eWG1NpwX2dm5VL22rLBsURz+YfUUH/OqZ5/PYEwSTliMA7pp8
+tWP0fjfjY99kxIDh1S3Ax5crecaTpv8Xlab2cIo1yWNQPSzBMYYAkIyzfHsJ+dOUTehh2yX+5iXm
+7BMkAIuq+Qj/gPC/uXyHJEAK89wCoTS+c0yo6T8AILgb9LVDY/gIB3D1Bn8D4jMd2qpwpahP+esv
+fjaLiwY/JmCS+2D9ZMkNfC+/RQWYClpnbXknPEa+ddEV846HcrLzQpBX3kM5ZP7bfj/GdzqXAN8q
+b/hXs6THr54u4+gu+4PaaKtyxNwPMCAAHRL+xn7dPCnuGScIaZnAJJ6mzYFMPaZ4K1GMbiWOM5QG
+sFF5tWISDp4lOMtv7M8+fbuO+a1LP2/GZfVe+pz79iX8utJbwpbgV4PiKL0TgAlGjmN3OPas9nsa
+Zd7L1NuDiLvie19QQEQfgHe4QqzTNSL2F7lXPIHw9wGY3SVQMKu4+wj7dGZWOGID8g7rcc3W0L5I
+dt0GGI+6YGBMuZOlS/XU64r7mt7W5Bmh8880nK+V27qntassVGNqtj7fbpy/SW4ycE+QtDYiLf/G
+Wd9wiaC+xeVwS9/K7rqt/nFataM9S7923ssA3whTmX+p2ScYD+g1udE1ZMqHOSf5YQEevv0Uj1Xx
+nk0gZ5kZq6rrx5IDBcHq8tzTiliZKcDTrdmYVYfgaGPwrcO0TfUHu+b6cY34d5c7STwmC6Uw1Pcl
+3rek2PnY90H33qUWX6NZg29AHB2M/zgfknnUIEp7V9ELYEokFkMu7AWg+ZH1hF+/9LNvcFfMvB+R
+8hX80sVap20pK+8KNDjLY23D/1shZF4TelGgA4edfAChcFiIz3C76gF3cf2+yCWOcjlg4WYBrtfo
+Z1qO9yCq3lGC0KfUSmGq1bKitMy6wPfB2OVlxj2M7l0qQT9rwvDYmXGzqEq1xV6pgbp8d3voA/lC
+Td7jZvvuUFyW4bXWYQ1+LZt6KqA4cvpo7f10TAla8pZ15PkoPgi1f/FrxlJxZhk+3CAWD5AT+5Su
+PLtGrOYaoa09fg96L+eA3R9wWnmhfWDccJHolmGk7/JnSzDGPj9cIvMVFutb/WSphyzK63QI5LqI
+a1U76ddm8I38xQc2pnV4j/tMV4lDRnB+O1pBz3hc0uat6ojPvPd7svKQ9hv92Gz/sx/0MoFrsNIC
+JRWOvLbp0aZvcC2t5ycEu04e8dNKHGH2DV45MI7GFIyBc3e/4BpEV/qwy7sDih16xWU5FDWzUINi
+9xNO2ecl6HNjQ93lRuwBNNqwCLLdRdyfXn5A2JcneLimzS0BD6r6jzhS6D1uAu19XXqH5eO8dI14
+sATztCqkfJsy9lxquy4g/tRY9wl00Ru7BUsgUbJgQjfgdwGfYXiUYdtPqJ1gQzNJBMN08Wr5yRGr
+rngIzjL//J+ecPlmub9maiLgJ+s7hqBi+M245Q8med+d5PjY3H8CwlQCD2uDi6/SAp4+gkpbcflp
+5WEbL2vJYm8L57n9b/dxKQ/qbxzby2b81qWvyyVhvGmpQzp++fyN3F5+n/MmUvIj4Tfqv0GwGAAT
+BL9eL7cIS4ctDCUf+0PZt31Kn6o7+41vtd8/93O2BgPVDgpuY7vH/sJ7b4hcLMXSB82FRfgj4DrI
+3eJbHGCuENCcmHVaeQuMzccxLFlCluLyox0AnH7DGIDM/Wn0xSg3kkWzvhgC55rQeDmOdq0zD+wP
+HABs1l/Qvd9njIjrxAC5WMtaTG1MZ55PgMw3RyAca9ZjP96sx6cbQysLjkPPZ+sPWhZ75G9Td7iV
+6czXJRz6NiVHWkWk7VvYzVo3UBaCXyEAZotvm2ucwHjAcE45d4PeXGTEt5ZZaRXZP9mIZJdw2dEq
+IuYm721DqL38G8sPCP/mEgqbgVAe9iP9tAbP/RsIdsB9TtfjA6yGk3xYgS9QVgPoPE8OxKbkCn4f
+y+IiGlS5o1gHCtcIg2H5eD9z7YAHUsxATLnS6ap3VhssZ2n6rBG8r3wGq/YmHpT+Y3b8yNIgn1bW
+E4Ln0zsJOsgF/nL3d8OsBP+DFbCR+VHPWXz1AGQsrKbEQkB6uhCvfUsWVuo7CZ0XQM6yiXQ3GK4o
+3rQ6uwPw3E81RsvXcfIQlzJEcPTFsO6E0lsEwWYRfiUMA13Rf4LchKA8dqZHEhXaln2AW/kFOIxz
+VTWtvuEeoevuD7yFLMUJxGpz4Ep3jeiQW3LmI/hqB2QoDleIbuWVBsZ53ka3FKtZkAsMCaZucQRc
+mnmmepn11oqc4fgEhoYneZK0uFb/o330qdMMSmKdPsK3VdVm04iVkanlHcD1AxMP4Xl/t3juA8B5
+rWyarfxH+x/9os7XLMczHzLiJpwLpTMNmzNAXLYMvTKB2D+O0eRCwDD5C8+vUXbXiYuPcHtLelt9
+Q9bfYxttWSTLy4sD0OblbbcSsif1RW/zUZ9/evkB4VieaO+SsCkzMKyWqm5w/CkdTiD8e/vRcdg5
+PiD4MosE/0nkMfLpqp/h0huxLdVSQ9ixkkx0DEUFpGuEwbAm3H+5ZrqCunpIrt+9QQ4E9Xq+H6k8
+Av6RgPMIv6LR454fYovnuqV9CY4HEHqSv4YlAK/D7ae4gnCCL4/8BLh7P0xHdVsTTAqEEjzymFsr
+RfzLeAIJOE5BWzh84K9QOAqWAR+ocric14/0ZaYrVfZhGTAtLS7u4au4vI0uI7yt5FygAcPhDhFW
+YYLh12uZwmWFjq5kWlwq/QFF0YVCERFclkaKevJ0uRvKS8EQCsDnASb3iL1s7tDV/YFXAnHEmevE
+ojQQnKBLeawt8tgBw3ne3UcYyu4Qw2VC7WPN0JEuIOCYPeLW13qZh8h8AuJKUIDGQAbc4gnO+NzZ
+Flp7sG0A8Bp5nh/hOPxpP61NPva8W/5PqX31FVYK8/3NsuBttFWc8Ud/yLWXP5dny09uJ9Rbz47b
+ivOHmQyuvHLbPnoxgTiNWOt8SB6WXv7YS3OXmEax/C1knvrjiheI1H7UQ4lsqUYmJPsvV3Xjs53q
+BZxq2esiEYLFzh9cfkDYl4sd6SlhOyPANgHtGs8gzBDc3eF/B4Cvx7LjTJeICwQTqEVOMw4YDbrH
+3EuqpLvSfjTy5SCdMBz5tv4zfJ3771ixakGFhgji6gjLMBVK9DSMPHk6/qgGkN29wtLEQArXut7D
+0mVMwTBZhRdbgGP0b/ssKdVNg9yC3WxL39ya0t4Xvz770pUOGD7SHbDsJaUh4DwsEXYohuTXxxiD
+qeoaWeb/A4brXlpakRnji/JlPlTVbPHxW2dbBNgnODNObfUbcRLyodoEf8yi5gvus0YkDL8WXutF
+rxcvCh4c/xAHPj/q7L4Fl1DbvwNnzBixchaI7vuroqc/cFiG3VIsnqYervz6IOiN+JZ/XOPjwTYt
+vujA++gygYufMNRnjcC9r1Af+QTI4LIHQUHUJXDEN8DziiiouKyz7rONnPkLX820CruLhG5t/tBt
+vUElcI/jfF/CGGE7LhlR8XLCMDXPIw8U7mmiH/DDimavBZCv/OdUapEPjHjViCvgZDeJ1P5S+/km
+F2SxRcErwMYScoUgF4nFg+TYsHKdRUJAd88SFr11BeTe4uqmzWcYQ9jGdGgFwymPs93TNWYj+IPL
+DwjH8hscnNAIhlyKy3BYhDoI8/YJbnlQGbs+fEqPeOqjDlTW4L6fgBx5ofyK+HRp8Slflx69iKoR
+64gvwe4WBs6vMhRTuSBEB+1HnqLzJNQoJfJOqvX7XbxyOtBr+i529QjNNB4jFa5i0IwpMC/4bQ8h
+0p/k87OtA4TXWnUNv/HcEuBW+zrTcNsDXMivRdMgFdhu1bT0JhzrsBT78b133ncpdD63hGycc7xd
+9ypp8zUH6HNE1vkNhim9tLOOEP/yRzB+PKgzZ/1a8nUcz9VZCq+3C/7SHA+UYxgOII4r/h4AX9JG
+3I66pDl7/YOnpqQ07yD6f4GO93mfs7d9QW6NuYSlBsxx3BquE0vKNQLKD17+Wwm7GPuRN0oX5wHH
+dGlPX5g7pkxTmgP1Ar/TLaIGzRVgdfg66wLzeN53l2yR9hqPiq+dCXx1/ZoUo2AwLMH8tTmbRxjt
+nhtM81/83jVv3T+Y88rpuoTmttpuq9J+49i1X8x96b8/yz+PCe+fQMzx5fcbT8Fh+hmD2EM3XGA4
+B9BKAXCmv4DuszXY0qs62odF95DQVV9xjKG4AbL28rDTLD5lqpJs5HY6K/pfWH5A+HeXpu06kJxQ
+fAJmbDvECoUH6EqPv53HoDyfKiPcO5qc8TOvEPCLjjsflFLgbbPaWOhu+XUx8VQWHR4KkeLp8rAK
+cy9qkKyZj1IzHiK3iFSgdK12bPyOCcPRc30EoDjJx0M/P8GnRTjnhq0w71ddgervFr7HVf16rhl6
+94aulUC8HZIDdBOGRQ5wlgSTsiR1K5fBcCnLUnrT3mACVKsWpeo5IgpOpf/PUyg9Sd3eZkkaj+UT
+GNt1JgBTfkecPMZ5uLUJn0klALhNoTbg92V+wbGu1wsADZYDLtvfBGExiI15YhUbqv6mgMu2QQ61
+g/hwhQPk48cyHuLeW7B8wNw72n76CKPLF29ztV+g/AmSAXTgbRZfS3MfGKfmMoEaQBfg3wfJPfWL
+D+Goq+gbcZxa6zUOZ5ylpRkU4vrUBrbLzpx0h8C3VmQ9PvsI18C4/Eob/+b4/cxzy2vEDR9bBa4D
+6uK+RtlUedD9znLLssexv1u6uh4Qst9cHvg+SldguEOUSac9xMtlnuA0lAwD1txioc8m08NXGP5g
+FTa9kD04JEnWhEY/ItDNewxrEgP0RYgSIrUCPZJSg/gsi/+55QeEfXm2BI10F8gN5V2wW8fWRxDu
+IHtae+8W4Gcojk5VT5oJxwzCmHDc8x3AksAZD4SXRce2x7mSURssFwPlltbguVo7HIcAiQcBK2PK
+0+w9zS2ClCBKYWfeBOUeoaCjoNdgdTddMOpTUXiKCUj08HGxANaT/iIwXmUd9roB1ROD7nGM61Pi
+wcFRbEIvhVeE9y743bt8fD2sagOZnhT+CcjoULxneVVJNdAVLkehw9+A4XZNHTFPjfmMr3YYP63t
+l25xeIhLnUfXrXpCTZIf6wWI28C5F0+fNrdfwHHsN2CJAWymCrd9bxg+fQBEzUWh+pWXF9V9wPBW
+8gO+zRUst7hlwOVxS+aX5TQJZVp+K6x3YE4itLzPj2LEsQJeuQyMU6yYcorSTuvvnuXR9gnMWl1F
+vql+snKf4tDj6lCDNK5rzPbAbQF1H3JZ44tydY+V9z6bxGhvoN+m/jBne8A1Tb+P47xxjRbWfu61
+P7S4un8QFGoqwHEfT1A846eW845/6IMPELx8vM+cQ7ilYVeqKxTzdUs3hKSaML0JsKAAACAASURB
+VDzXCcZ5z0PsTu3XQiWu29EmJ/HvLD8gzEvoQtaJM66orAHv4fLwCMTiltGCvAJbaa4Q9+0dntNH
+uMEuTZfGHS3jCIAR/V1c312A4RKlc8+Vkqp9kjS24QphQ3w8z8ruEewWEWXDooSgh6C3vpdeeZCZ
+pwHJ8Yq0hB1oH8d+XZDuVsc+FxPJvgavCcAdeI99h50b6Fb90T4B72FFZqHHILw3tgOwQZA9oGyH
+3RZ2q12cK3GtQ9GTZfD4c5G6jK2ORkTSsoIP4jLulyvnAGLuwLPu7tB7Lp4xOdOcAj3aq2RT47jj
+x1h+SK/fUmqu/HLatPIPfoVrBAMVHhR+bqdFDGTJU8gWbAjUP6Ku25gQYtOc2WdVxcOjftUAWHWT
+lffiCiFzPmF3n2gf0fCZI+jLcgm5qP4bD11BIffweZ66FXhOl8a+v1FAe+uYWzgLLOfWvQFvWRjH
+sdhH7GeWqQ79HrjfTLBDf0Dnes04rv9xfW430y9YN6CrW4mnL/STb/DGKI/IjwL8gQ/OY/SSORND
+Ha/0fL8cb+V4+SzELCP0vB0PhpQ+oTbjrC2az7L0fVC8dI1Wciq1W9uvAe5dRxToUrjNKPMMvc1f
+mAGY3jQqbFyNhkwH2tfiov+kK0T0J9ep0YarFv0aq981330eEY470//p5QeEYwmle9dwtY0KZPgI
+MEmAK2BZFL8yvkD5ANoGx3Rc71DM+wW8dqQgSlqeejzdA3XRLUB9WBxIaUAhzf/RQWzPPt5YnSZe
+sefruCgLXAC4xXGn0SxHKDJdr0OletIC4FCE7R48vxJ7Hq9jn9K3fRn7Sr8NKueEHNSTeX4J6LT8
+Cc0XawJsAC7V2WFZQKXFiEeAbMDvstfSsV2xFemW4bAKj62ShcjWsCYCupIrgAm9sDbeFZZGJSNK
+jyqVDkmL7ilH/82vJfHxXkdNzY6mxMttABy7c1TcRWxc4rKnkaxgS3C9JYiZI2ou4XSTWC9T1MVs
+D1vtwDu2Evv+4AOpotgItxCxqRSlA0joSQRYBBxu7b7B0oH3+MqcyJhuzeJE2Ee4ZMpj2O/3Mewk
+2QbIKQ2eG3MEo/kNxzGY2wTs2GkRfYDjLGfQMYazSgOuJwZ5vkZUAYWzTkZzPmCYfjPmD+5uAqd7
+RHxUY84e0e49IBMkX0d+5j4uxyyulG7Fy8N9EUCT4FaKS16j8jvrgx4upK7hP51qUD/tR1xaQxCa
+CwUQtZ7yeo3PIgfQrmb15Q9jtPmFb9A7rMHxhhgJwe2Ri9Yq51YGqAfhAGIqJv5ncSHn6FDq9wbE
+tf+nlx8QjuWbpd8qMmGY4Rf1yhsFmYuPoQD5E9h2a+8FmhGALBcQnsA7rcHS8ox2Dw6tl/uPTp9S
+ANVl0vbHUOyWg8jrCghWnkKNAJg7jdAztdQvSYKu55sHzIVm5/wOAAaUrqct/yzQJhSPUuj7N9rJ
+vIaQIqhNAbaGBbC+HHZMuYZxjasgvacDcMKvQ2/bSrcMiwM0bw2OzXewXo+SQvc1BiWFJVrhXHzt
+awWyB/QmNI60XsD9ckL1kVRcxxrJTTjm5L2OGYBl5pDiMr65R5QqLNkBahNVV80ton1euU+d9nqZ
+n993wVfG/tyGpV8AvDXkiyXY/qGaaFtH6w+QyFkFNpbSBzUW+QKzFbj5DK8DnvnLciFTmBSbBZgJ
+B9Nq7PVJcez3C+iA4++7TcyPS0wgPl0lNMsMqPPi1gogS4bWMZJGBHoN+oBxPQo/QmBBLVuAI4zL
+V+UaBB/X49/S9pvVaKxXlOzl+Iqb9x+9qclonWkvDxUN9fSS1yp3CNUJkGIiwlnmJDPibckcNBfa
+TF0mIwGZdLEDbDdsPEFwuEKccwe3eYTjgfrBGmxvj6tk2F8YXgboJZYF2sbVUL2EnilRTqAr9CgQ
+YRlpKPynlx8Q9uXbhZ8wSZV6VOwNfAmARdI1YkGu1t45EK7AeMCyRPgJcs840LFmDWYlrYI2T69M
+iAiBHmGlw2HhKPHUwbffZ/gMn1biyJNS2K4f9XVRyz1MU6XxzBGqmtNPkXo5oLhdOZRYL4GeqDJ2
+Ausi0KGJzvmrYbY12EmBFvX0AMHfWoFyiXDLcISVQGVahkWk/IRFINtnEVAXiLterSoIAmySVrIS
+L3/NvO/uEV5o0spRzmM9QR2VnjZriDWZJazwbDp8ulYwEh+wy4BLce0+jruAt42SHzx1mpCrTFiD
+wy0ipk6z9vEaINwthgv+ej7ivtiKKhnv7ea32sAwUQFCcbLyj98lQNp7k0X3CXh5EN0YNPeuuDd9
+Wa6DsG0ZdjMu61xxA+RMr+H3S/sJu5puEzFzQqQXmkaN23oNmOtl0lwlvLzaXLz585X3Bo63fdA+
+0Oo960Z7fMOarC8TiRtA+4CGwvy11+kjfLpIVN7jHjf93gm2X31Qo3rRPA4d9+w96iiLdv4Ac165
+7ICsl/b7LC4mEEciocGJUifGBzRCctwGyk0YNqBdJRdCHqTV95xDOAGZwxN+hxVZ4rVclmFrIbhZ
+idnQlfsSIB3l4sAfgAR64A/+iTiE0RDESi6i//DyA8KxfLPwS5FNeBQC3AijxS0G4AgjrKQDcrX7
+Dj8BcXONYMC9QS/HHRDc4ySdhUq6NqGE6h4cNp/gCitAoNvBt8TELTwBmMIadZEB9HmENaE3lWHm
+fYjeeLrVOj7vMeNkHuDr9kaS0Jadu4Mpj/qVw+pHM0h8AbiL9z+lBzr03sBXumVYwhLsYaX7SF9j
+kA+hC0mbkQLAXmYR3tumaFqwDyRgY0kY47RJPqH/Jww7YkqPaUcFqaHMh08rnVIY2nj5WK4w/Cms
+kbsjLtIWMPPDTa/H7h5xmT2CfIUZArpCt62M+K9AuIqjYM/eCpi/bsgOIEb1T4hwOHLXiEW+wTq/
+ICdkBWZ3CZpOTaS7RgBs3UVmwIJBK173TI3jvBUy4KOPcBVOvN0o1wmpWSMYiBlyox4mFGc2OK7L
+IKX89v3I0t0i1/cLKLOu+bbiupmP+sxyWIax40GIwHdrTqF2fEoZ2rc6fh+Vt4jjfILScpVm+nGc
+z8t7p4Sch1YW4Dzqmd+ojLAMC+WnmqKHLaKsw/UuVUkC5FflAoLhD5c8jqetiyBYas5gMqJMH+Gn
+cHOLWDVdG5fGxB++774EIlPbIUFp+qLktDgglQ4sIF5j3/I9peifWX5A+HeXaEOIh7qh1DI+Klhy
+WyAc4WfQTYsyCHb1MxDfoRfP0NvSRJ7zNjPEnyW2pcNixJSg6jC8vaPlJ5u/guD4eEajkQhHr9Ki
+C+WO3EQj7Yf48xhhe7Uec0eGsrpdEUD6CLcDjd8ky3NahTsQl3Br0PN65fHfAeB1SStrJQgn9BIQ
+J/hKuUXk9RxiheEYyLB9GEEh/jUuSWuzpkU4/JI34ABsBbPgfqla9TqRmAqUdmUkGYIz23Roq9mG
+uFIJlqeClapYadnh/FbFRzbLBsRZL4WT8iHXs+6OD2nkIMqaQm2C8G8B8DzH6MbB5uVhgezKE+tw
+uyPvUw2CabAcf1Bj0WA4ublLnNOpidSX5ZiOFLQf9Ii7hdg2fZ99fqFi7Rd9ujSzBCPhWKbrhArW
+7gPm7h/VKDl4xuMAtsNVgkFOSWYxWFK47Ue5UJtu9a8Fv3wPPJ1aWMGPTyrTfbQvzjEMRwsZ6uMm
+odndIR+0xrntvMsX6Kakb+VB99geCtqxKuNQPe03Oc7o9rAAFxQzDJ8QnA+VYkD8lXznmSPaYLiA
+YQ7nOJSnGSNi/FCVnLZS1Co0Op5/cqS0WiuFZ/rPIlPGMewm/wQL/YDw/8ry3cK/jgMfarr+buDZ
+AEmL56i7tDWahkjnP1D6yEMMRosem6+MvbXKAbVPS0gIG0ceEqPNPxmKXWOAHAsJV/sOtQvRAbzB
+gyzE9OCQ952dguLlLBu+f1zC19XzZ/va6rPYitwvqLxCvJldk+ICyP1e8iEl9qPz51bw8v2Xg89r
+cTz5gE0heZkKZ35n/nbe9jwrUO4RHpfHPD7TxeoKotIp9oqteYFnGlUoNrYuE5wEx3tt6F60JYDJ
+8v0CdCNO5hHe81pVzQo3pRiVf4HiS1z015jTu7sj1XY5ALNLU9hslw80e8HgfzmIvTKd5vrytjXj
+F/x8P3chII+hp+9/2kbb5X7Lai+7PJVmrK2vz7BUmupzPBVXyKmKSxpbNk2FikOFCLDfUFn9Bv1X
+03J32Sb+HecBiHmRY/HXEy1OFVuXj4BH+gNzvlUMovd727rf2G9b3++37e+3H3tYdZe7Un71cdNc
+3vsyMHVAHSqu6oMglM5Fu06HeHmIC9jPbaQ58sUPBudvT8u4znqhJayWh6EppwsTkx/S35hsH1+x
+1sZruc/5S/Ha263ZNaAz8s95FpHsbdETVVaFYVZczd4tvk/mHJGsh6a3hpLp800MmTfPQZFHXF3z
+hJAF4nVc4QP0t/v8e9zzFxHvbeTpmOlAk6tLYC4TKXfLoi+cZ+kGsIUaVP+nlx8Q9uW7Ra+A+8y5
+mCcn+Qd1PQKMygXDkeS4hn56PgrhL7mfej4Qz3V+4l48wvpgHlXPiWiGBTHYxkWquuqMJ0QGYph4
+SAUp9i9/xv/S/SNAkMMBFnwMBJK071nt0HwJryOeVq1ZOCJtwAEDboTDnWpzWoJ/r4ksg7q3vhr4
+CF4c5/f78pUBuSD6Zi0AbsAbDxo3y0JY2lTKIqdSFroE3YhnID7iCnRzoFwD3hsk2wcadm5d0bc2
+PBd52H3qF6EYCoL7o472NLdjBM+hxMrnH/ct5rYeAEO1Lij+cth9AXipHutSxUt3hmvdEN25LzmI
+rBohzfrVoTdh8Dnt3jZNnm4DrwCm0KTR9aEGsXEh1u3vqJdlbfgvSLbrgP4X6p4CKDMccxaLz1ks
+An0LJDLLkBv7AMGu/WuAdQPh9MuJajfhpKD7XXJAb+RRVozstz70dvDldb8HFPP+3gXIA5KVAJnB
+WONrf7l/gm3dpmbR5C36/3IF+AzGBwQxmDc4n3OKa0/31bUjzykDCiTbW7RtsKtrQdfLzl2K18vl
+zsvBlvy0QQ8H2fZT1xJgI97cLrzXG/XouRzi5iMpx3WcnT7QBcEWa287PVVaZAIPdWTU26OYZFYs
+2FcfV6nivW0OboWXt6T7mUD8u5AhK0PjKfqXQL8PvPd09lsaio/If7m+0QUqK84LiqW8L6bx7g8u
+PyD8u0voSvRVgITiiV7XP3uEujWL2t7o/BIXT2SKeppiNV8wHNc36M1UeTA6dNxXCFgtJRGdmMIJ
+k2IKNts3AEMBJXAoC1uCnpcHO9I3AJYOtHXtJxi+rXWcoSXgDyDwpfgzrM9wjAG/GGseM1B4eZq0
+AHvcS+KBgOGWwLfFE/SCwgOGQyhtkTY4LuBWx35ahyN93KMLXhvwplC27OJiDVYk8G74l+tgkKxr
+Y+91upk8QjGn+JRGvKYeQJe3KtTR6ljAcz2IoLdhINtxbjEBOFQmIEoPPDog2OF4EQy38PaVgXgb
+LJ7QS7f6JSAjAXG5Qostg3DZ71jFe4klu1gbiweuaMfZzgVXC7g9UG8zLqjYhzvUygvpH5ySrIPN
+AcJauVMqCO1xZtXbddwHAmpYgFUhuoD4KqX71icEx1YW3logvH8F+O4rFL/9mO3vtBi/93tYih3u
+wlqsZb3kz6IX1AIBufkg4PeWMtwfZpiTGYgPS/BWqDhkSXxym6yqBME5ZR7l7Tal3ASsG7CDdMB0
+IdMdILyguoDXAnRBXy/Lg8NwWKsPEK6fuILwkoVfbwdhlWqhWsBrD/V07EibGtIkSIxdcVmSOkwm
+NnuaAcDq/QP+VkQdeA1kDYhtRh672ibNuPPXpPhEzfilH0CYH2C+hmL+2igKhEuZu5U93vIAuurh
+AZGvrA8fnPuHlx8Q9kVnh3xKF/UNdB372wtZhekyE4Ifofi27wPcJgJoKHxPqN7g2tMhqhtWd6y/
+UjwdgHldIcUuWcvp4hJ+ub/INa5Z21KIRKl9D3onAK9L+mkFfobgT2F+pc3g2y28rwYEDL4MDkKu
+JNPaS0CcgBbK4ozLeHwDgp8AeLhRfAt0BxDX9GqeTnzQDX3g5LFtH8tXnW4Cb+CBXI61R8Yzjh/K
+olxBch4Bv9SGOQ4RV3BbVmFfA3wpzcth97U31tpYvp/rMmW4ubz0GXpVByQj0nodOnAjLcJm0Uul
+7GuTU9Qn3942t9jnkV8i+EvCIiwDgtGs3WV53e6T7IXtv6ER/gC3FqSGc0sbgV3X13h6Z/AVSeUu
+ewMEv9jbXs8vexDZWw1w9wTd2u9AvOlYWIcLim/W4QOKJ6AgILc/vES1Rfs/IQZ3uIlZP1Ssv6qU
+dXq6bGzv0w2eyGK8P4FV9MuqN2tXBagxi4LKbjAM92PHi8G3rst8eZqhanaGmqHBwq+3W1UhbauQ
+AmCNsDykrYfMdIVMGaKI6RbvQIzKuKo/sJlcTYNBdsedULvzQXJZfeyp5UKmrehUVVdfwPAdiPtx
+iX4q4jNSmDDMsSUIy7BAdwFx5EXg5zbDxJ9bfkA4l+8RbeLftP7+jjW44RzqDUnsP0LveILWHpQr
+BPPWIRjqHZYtyX2rKaLCwtA7qvj1GCa5LEOXxW+n9RLoYJHpuhWY07SyaiVLv0VxN9CdABxxNwDG
+h2Nh+WYIZvE13SECiF+QBsjN8psWNLYSD+h6gt8b+PLW/Y4BA+K90SzDDXrJ/UGBfO1m1t/tg568
+LMIaHC4OPlhOc2YKtddhzS9Y66tVlG6S77cNAo9Pr9zikeGAnI8wrAFe8UjID2dnuNqwuoodgKzV
+D5prhD6smC4RekBjuUZoB1+G3rhthmT0NBHOmRO2wS8PCjsg2Mu8+mNZugyCra2+AoIFzT0i/Jwl
+tgOCbdSlbwE0IRI3cglrBT6mS0G7BLoVIqtgOKxu7hah3o8CaBoEb4O0994OwcNFYu8GvtMKnND7
+ntbg+DpfWF43AeWHNdouFwFJcKX7T4tsAjNDsObD6d5mzVTZ7lM7LMNXYNrNKptW44d8tyrLFuXb
+JtMIhuOLPQ7Dr6VmDb5AcFuU2yzDdg1WfgcIKwx2HXCNsw04t7/diePi6UVj5h1J/ZE6RzRlRLlG
+lMblDDcrsSl2Dzv4Clt1V3XRDez41SXAAcNhBXYgPdrV/hJ4qy5PEA4IFrXfF7WBsGkRXjAIDouw
+EjshIJi1/Z9bfkDYl+8+g+SIbLn3tUyHC1pLP1ZVPiwtHE5AJmHOAEwA3cG228Duyt9vJC8o7Wjc
+Y6kZ76hS145Xv+EWAP/tTVCqIgS8BAZj36BCxn4/3svvEwDfoTggmMPfAWCA3CEUDYIZig+XCIZh
+sgyfFuIAZSmrcAAwLvCLCwyTgF9jG+J3C/LJ3CyyZRW+huHuCwstnJ9XJT/gb1uDdfoN43nR1tz7
+gbQUXo9S6Al6BekrP/uFL9mWojxRAJxtKdonlNpXtb+l1ZanO8T0D2aXCNvuvg6LcIJvygYLjBnA
+LulquzSmzNpmFdYdc3/VSic1T7/sm2H5sjYW7T3bPIAlfdBfWMkLiDfMPVguI1Ep0w3q8HgMT8fE
+v5knPpZBpNrCEkDtjYUddwBey5R0grqB8NYC3gDgtATvYRWmYwXFTzBMUBk+w+l3uxvAgmE44Jeh
+WKOsyAr7CNUOW+EaoWwZ7jNHdKuwP9xegL2lz3wi7yHyV3akLsd0LfePD/cAtwZDAZi/8CvuLRv8
+idf5JzSLE1uDZeG9dkJw355xVi72zBYwHE/xqf+kHpJT13unCeBl81PJpKIL1QBnb9aezG4z6ivg
+1sMwCG0w7IBa7hF30G0PL1sf0vUHnCVoEJwPMigrsFI48pH58j4oWiX1J5cfEPblozLmdLOOiiHP
+hGQh7nZgyRPiaF5ClcLjNyicD4qDFK4QrBQ+Ms2Y7F3VT86J0T0zSqcI1OcLtCXGn5jFlGDY4fEG
+whOCr/Db1mkFvlmKn6F3Wuwky/DZ9SHuLQB4poWYpS/E1tNAuRowJ7muWAXkU9mtxQnA4ClmQpjP
+/edjBwjDPmxgggnDQuzgmy4RYdEFhbe/prwMllMrj2419kEeFzCGKriJk/Zu/Yqj2zL4tbf+z9v2
+/kQDrmu/z/LS2+VzOw3XiN7OYqaIF4C/CIjXBGKwS8TqfsIrgNEHlHnDu1l7c55mhl46nnAMuFsE
+Q/AmoAhQLQhu/S7LpQAmZ0TxcqoBczTsKOBX4av4LBKbq53qBCTuLuD7nf3I9Q6fxQBeA11V8dfX
+cdzWLG8R1CdqDU4DetP6y9bgWzwPltu0Tz7C7BIxX1+nRS7+EoaZAauCwyLM4FkgjXbdOXNEfi47
+Z1rYbXvOcsGg9AxZBcB9aXqB5Jo7b+cTnugr+5UuBZiNs9mK66rxJ4IFdon4ZdZgtwhvBd4bBcAe
+fqtkmOMhqOl4FNa28j5ChmiGE3pdKndF/xR2+SnFyVaHy8dbGGBuSFqDF8xCfUDwEix/uLk+qFzA
+N9vgExgL6jfgQLuqzM2thWDYXTkyb2QN/gHh/yOLq4asvNNbtlck73XFGVinlMY7x8kB5TIxAFj5
+GGMAXaMpj7hqo/9ylYhDKUsU5wNAdEjy4wi3ATvPn3MFJBQ+gPB3jnPcES9jezvWIWYCb9xDswgL
+wQIB75YBH6ixPYevMK8yrMFSljNew7f4hFoM4D2312N4gF/YK0f+/LLBLlmHD8twh9kE47AK8zkp
+qPvgOnOPQH6COVtyMGgj4Gpj7QXGsQRQR6Ln7bdhWXp7axbhW9scbbY/eJEVGGgW4ZpWbeMV8Bvr
+1vIT1o21V855e4Jvlc9hFY4HlKFrrR2TZmcIJosdF7x4v7f+4q+PTf/655FP3/jWD5QeGNRcPdzj
+mWrerYdybwt5D7NBzLi53yqwgDgqW8dxOeIKim3WiF3W3rYltwmeRm1C8p6zR9R8zB08J4yUJbjk
+O/WViA9C9HKYADyhNwbK6RZ3k+AZI6Y18A67py/zJV3kpSom6wD0UJXTpvnHPdYi4oXipS8LvrQV
+Qcp9AqyyCJsV+Jf8cmvwG78ChDfw3tqA+L0VawPvRWDs05Bhe5v1TqgLWJv0HrVp0/UmX/j9a8S1
+ekop6NLKfdjDNULgMAtpD7RKALzE4dj3VRyC3W3tBsDPdTbqnc5f4vDr61quW/yONxQrrMB7ZUno
+6ufZ1BOPAv4fW35A2JemeL9K51T45MuY49ByM1AtOmbIX4S9rtInHDf4RXaSua/aoTmSNW44AwOi
+pXSGFAQ2PvB8g+4RqO4c8BzlE2Gh8pige433grzBRZapPENuL/Eef/gLM8yC3B7ktAKnfzCdw3ET
+dh8twwEHl7g8hgLgGkBIMIwBxgsfgdj8c8VnathpAW4Q7L7ABa5IkC0Avll3w10i4OzuJhGWqa0F
+zpqKWqhPURsXB9fcP5PEQNE48MlTnreZjtwk2n4q4i/gN9vp/bHY2p2UW4Te1gsEp1tEWIbdUuwW
+yj7zwwTfCm+SGze/4u2/dUIw+/HWGiW1qVzy64QBwCiXnxwsF/clYRE2qcPW4aL1VfV91Y16j5+w
+jDjfcZErE7Nie1ymBQ6ABoQswgTAHi7AddeJPYF5WoIHDKc7ArklHJbXuQZCdYjq4Dm2DXTsnsoC
+Yq4gBegKtljHfLxsOcxZJEbebgP9QHmMug5Y1QBgCfhdNq+xp47pO/Gi9kGv1gt8FxZsTvZf4Qss
+b7zXG69fC7+Wh98vrxsD3/dWvDy8GIb9mIiFMzPbZcpWn0GJrcHnFmklrrYpdSMobeRbh950zwyZ
+CfsgjX0oyZSWUDsOdwVVuKuJwfAeMPsZhsfxeZ4A0JVlvpNhTE8ZDC8LL4UG9AYk+L5ofMHyzy4/
+IPy7C+tbTE71Co0n2o5tfdsgjqmSkDwvzlBcwl85fuiEGmCagfx90gl1SLkT0n1KAW5bghscxtTv
+qSBY8vy4oYKFvo+ECx37QmXVy5CvVdOr9e30BX4CFF7SSuwF1XyBZ5yUzg7r8W2wXANc1HzBS6S7
+Sgi5SyAswmwB/iKMOwQnCHtdbkFahefWXCTgVtvdtuUOEXAcVmI9BsGV9ZEV8QMQeztqsJJ+N9Gw
+zTrVG3kwT38qsyhFe2Ib+zqPP22F2tgE4slR5AvYV4djpfmDcfEVPgB547UXXkvTN9im71IbsKVy
+gVpFG3+o1GYpTcGxH3fIWnubyYsgOOYh5nuKiRdqLvGA4u6ec/jM5xqgH1ZhM6XJ9vcx4Ws16pZq
++AGOuc1cj1RbSCsZWmVqCiHfB0aFO0xPEGYAzn23Ck+rL/sFZ/y7IFP1tAQ/WuoCfgOO4l7HMe6L
+X61sFQbDUHd5CFDP6dOug/ru1mB4XriuxOsj+hskpn1cWKL2wQzvTzn5h8KE6ljYAsyW5bAGr/iE
+sYPw+5dvHXRtRbklvdWswL6+y6enINibl31QAh1+Xa7E+7myAltHktaoz21KJnUZ5jJT87jYQ4sI
+1lLs9GU3xSwLZREWUB1S/R5g/GQhDteYAuF0h1TBVntoWQTAlsdVoJBKtLT9j2vEv7zcjAjXdAF6
+GUPq4SazM+r28h50hCzCAQaF1/f+QaA8MkX3ow65lITvtZ0zKMMVhNbn6vo9EUgwQqQyaVaWE3Tj
+OjEQoJ7kGZD1iLOn/lGSUmnmegPieD0bxag4XSRavFyswF6Y7EJxdYUguK1PbdfxBskyrMXo06jZ
+Oc+uEJ/2zXIt7vMcYOzWYUwrccHwblBc0Ntgdg6CIwsz+w2bpTgsyr51ZahhkVUAPiCzLLXwNKPN
+tlap1Fp6PMOtjv3btoadzrZ6gi4QbXse89x4ew0raHOPIPA9LcGrhUXZIly/MMGXp0ULzpjgG/Bb
+x0zZ1+eHzToc1vr87C6VSZRWfqJbxWAl263k8YBfsxCpDyAMi/CGBATHVGUpJD4oxavMfmwcfQlr
+b4T992zrSCIPcZR26ybrLgHwAbg9TaZ9zynTHE6mVXhPEBlQGZWNqvs2dZo9cAAAIABJREFUhiR1
+hPe1bB83AL6F+8C46RpxTsN1c6O4zSV8qRruaSKAw6/6G4Jo7ikKXnXe8cdyEB2CX+EX/Mu3KyzC
+ivdb8WvvBOC1HYD3thlb3gTB2FWeghw3kQ/IIRvIEhy5j15cmq/dmUkiUtohu0JKwQfJWS9SB06z
+EIeve8h++8JlWNpxracE332rw5sPsbUL+ADqt1oPj7xsibozP3xrS66FzcHb9LkpHLu3p1ft/+Dy
+A8K+fNc1opptPZG19puputrsypSPaB3RaOT0evUGrSTYVLULvJkWWofa8ZvCGNeIef4o5+lJLNGR
+66azMycEU5ivJOj7vM1jgTR0bnvtfCvTv7fm7SKErHYoFgIKDMuwVHVcLcIP6wG8cvoK908yg4T6
+Le60FK9Fg+VCGIIAeG2H2tXAVddKi29ahNOSS9A7fX0TkGtO4YBf/uqcDf4m14lU4jKAWDv4Slhy
+K06bS0Spi1unvB9Tgu1wwaj9aJNXK3C2oUrLfVog7l0R7VZ8ejQ0K3B8MrmA+ITg5iKxfRopWMNr
+1l5vpwXH1XCfXCLymG6fQs2oOeE3IdiuG/JJrUoMurzd2YNWtEPkw5tIPIQq4pPTS+M7XbBXoggY
+FjcvR13IvVb1VtPnY44XU7WEbEdVU2EFPraRHkhwDhiO8nsnAL/TOsxA/N4dkk+f4A7C4QKRQOJ1
+coVhh+B4KO3uBhRPfc3gKs77YA1WsX4Lodki+mC+6RpxwFTA85GGZSopHRLMEo1oC+Jb1z6ph59H
+DzPVW0s/uBx8S8wb/DaZSBD8Wi9zk3jVzB+/3opf7n70623++fJWiGzIXpC3jYwTsa0q8PLZb8ww
+UA+EpQq7JVhkwi/Orfdfa4s0D5NyK4eBqAun7bC9XEDtlP/WbvmrpDUv9ZMPOPukzzcB8xyY2xQM
+yG3cgFnxE4K3PczYw5VPW6jIexIH4mFz+yPLDwj/5qLcRgHi1ye8KkAssz8rzQG++UP8oxQgCOan
+++pDI4MH8F7S3o4JhgNgRachOKA38i0U8PNiqphIVdbf+uEO00pp/fg4X3KvgNiEy9hv2+kjLNfB
+crEcFuHKQgGFEFB4mUyLcJs/OOPQ4LVNMyXnHMNhBW6Qm8dC4J/H7q4RBMBeEQqQiwRZhNlqOwa6
+MfSaFZh8fg83CRpIl9cjFwr3dw2wA8h1QdWUHbXVaHrcXK0XDMClLSuOJDdKc55LVe5lH+02FS51
+j2qPBMUabdfbbYDvgOA2aE4vQOzgG4PmwnrqvcmhQttguY8uE6Rvl5+rgLlGOISttNjZVmAWn7Nk
+7Y3RUlPCC2FxKmmX0ySiYFiU3tQ484rPgSosd6TDTkDT7Tn+JjLvYpSBNvpyShvSzQOEub/A37A4
+FE7ofQ/A7TCszQ3iCsLpbtAHnHWL6i6YdSCL+uKHgGgIHYjhRpQ4ly194gPnwv0moHdDLzDevx7H
+A+VGfi+D/FpFVs+hjhWyaxUAK9+W0Dib9/HfZi7aCcRmEbaZcV7rZb7Bbgnea+O93vbGZW388v72
+670NgmXb6DkIIBt4u9uI5+XlZZcQHA/A4ncVbhKhw0qRUo96aLUs6/jN2SG7TFZuAl6e2lAjP4Kq
+781t6wGK90N8vDUSEADPrcMvFnGF90OlbcrnP0/CPyDsy+iPz4vMZnqCbyTsuDuPUOfQUGlDzbA5
+jBVY259wXHEFFiMt9yoXmke8d5ywCks4/IoVQIJvbuh+pV5rNRCWKIm6kV5yOkqqoIIz1kv2u6sr
+5dhqV87MCDxTBB7A95pGpuVXcLMCx7RSNaVUuEVMi/AFfL0M+/VnmvFKUOwpW33cRXOLiI9geBhs
+EcawDvuI3uYC4ZDFFqkaJEdKFzr8hUkQo6ybbK1rEOyb7BqjlfTwHYZLaTynyd/m9ifVCkk/c7P2
+U6r9yiVs1lAcU6YZ/Br0vlDW4BOGzSViybYR2BCy/LqrQ4Kvt0+SEzmY7jhuF1hhDSZFF9Np5dRa
+UMdVq8MYypNvG6AFwsEzR1/U7HcC0CQM8eq4VqWy/+8sXf5oQm7UFOlnjGMZX8fCejsh+O1uDXc4
+1tNK3AaZlQU4oTIgMuMIRsEPLXWnHoMAYFGggWg87Iz97XErZID3i7QK04C5mNu4uXZcLcAnRHXd
+g+zzEOTHFRDw5A+ApaYkZUPI9w3BG2/YXNYLIm/zKX4H+G63Am/s18u+8Lde2Gtjv2p+59f7jfXe
+WO+NX2tD1huyCoZF3h2Cs1zN/3apWJtGtH/N/sDK0+JDN/E7aS2xFNNQpEiasky8hsQfvL3fKYEw
+AIZh8bKe7g0dhm8g/AzDpvPNGzgBOK3BalNyircLd5HAXg2IReMN2n+zv39v+QFhX77LwSUwe9Ot
+JcR97UWIAbgfDeALnzlaPwFw7hfgpiBMAHY0oDTPsEzH+Il8rT6dAuANn5NeQMF3ZHXlk3f+WOhR
+ynzeDaCfrb8MuU3peplyGvrJtjRAFs3i2ZSVdJvw7dOUaeUCIQ2QY/o0GzjHYBw+xeRjeQHemz/w
+PY1nOQE4rMKwT5buneGwCEM7BJsvGFl8yT3iOxbiT8es7QblhotC7iLcFUIPNChO94gH0FUpd4vZ
+nuK6BxTHbxYX10T4sR/oFMoljvHAGD+qpvTqy3HIadNuH9SwTyzbNGkFw5r7sq11HpZfNctMlN15
+/MlyzGB0vn4XB2C7W23AGABsD0CSejutUa2v1YN/xqcxIOL9wTtIIpe/qyD5PCWYjXuQHoeKA2qK
+WOBM+2YQ1g65AcO7wfCMI6twA92AzttUZBeYZRjOjkN9yNNJ6AP6myCMsArn7/JUbgW/aeW9Qu8T
+BFebsoxkiHpgPQBZ17f3dwr/sIl6a1QBdJfsdwPMll1A/N7YbgHee2OJDfrd7429Xg7Fb+z3C/tl
+ftzr/Yb8MsuwyBviUG3zpL29DbytJyigL8XOWV3izZ3bkDDkhFCbb3eNlB/V8qJADIb1SK6VOvqL
+b1UKKqMvIsrUt/3T2FGf+xmI8yHtBGKI+MA4ezzY4rMKYfnc9Q7AAnflWHUvGixhV0ijW8pjUHjK
+8f/O8gPCuXxPyF5nUMjmO69ReCYjPhukzifC0PLjKp8AWEHgQAJmgO+E4es5vk1rsPrEiUvcod07
+l19rKjoWShDYpNq3JzyNm5oN/UhE993hgkH2+P2RhiGY4fhUgLUy8HJllIVYGwSHEeM2c8QxlRQB
+cIFytwaXe8R/ui0QblbhGJ8AFAADQLMIA9juH+wwC4LYTYDLfsMNlh+OsctEKWTvFIq0+HTo7V2A
+txY+25RS27kJ1rtbhIedcJsVeMAwwEBMYBy/pgq4Ykr4Rc0d3OK0+wXXdGrbAdgs/Gu5XVbLzzdu
+q6ZRUz9OjRqn5Titwr4ur48CFyRElYX/BEf2mY+yYZYtK3lZsKKMEgDIPztOvkkGKt0vlrjeWB6i
+7lDcv5eQqxIIExB3v2F1QO5W4jYYrllXvV8N+N032ATXT6viaHII2M2HFwDTCszhGDRpTkxWfgvj
+d78BvreBVfvym2d1hRakWl8mqERXNeZlIId8gCoQjgew/d5Q8bKO7VoOwQ73r50QvN8b79fG+vU2
+AF5vICBYAn5DA1kZb/+0817uG7xgMLykfSXctkr9IcCUXf88fjZOBVQKEA8ZlzLTHvjNGuypGIRp
+Hw6kDLsJuU/xCcRnPMScUezrkZpWYQNggmBRQFa2WQusErXpI3yX7mfcf2f5AWFfbn3yW+ddzfiM
+aBXHFuF6JQKw8k1LSdDVuGpKYJLGyZQeH1Axw3DF6P9aGK7sojDUhcoCaISC1u9n/jXdDfLVS25r
+bR08gOZaoBcwyZ4bkMGA+wF6EcBrx6Y7xK2WeGnTpE3lT/DL2wa9ggNoyzXCobhB8PmBja8svd9a
+lzTohwAhruAwrEADYISP8DYANisRbMTvgNjbYLkDfjXmGaZjNAAvrbYNhjWtweGHllZjifYaD6bR
+bqKTSMY2CNZqT985Fk0v34CQIsmN9HYUv8xvNKrdkTVYyVVCu0W4PqaxHXwrzj6FXIN1bbBcAVGf
+HeK0GrM7RIHzBX5pH7P9z1X6fki2ggDfutKUkCEaZWadSGKKvHyFEe21Srj32UsPFg5oXWcsd6D3
+MhpxO9NqO7a33iE4Ybgsqg18H+Ki7Pnztpvq5LAKeyXWg2QBJktS9bJny3Cr2wjveOCN61m/b/MH
+e95qVotyiTi/hFdzIE+3iNQ9VEGtlrmPhQRbdJ4L3XLFcf/ftAK7BXtve2CPAYqL4rfWfliE1y8D
+4LXcGuxTfuXj2jvrZTsEr6V47Y29AoYNfDP/oS/znvQiM1DtlcUSHVc1I1XIvjzHZZbxsORbTx1y
+i8OboPYrID4sxGk59vpcMGv72yA43CHSGryi/lev9vDx9lVGafyp5QeEf3OpOitFVFaQQqtbKPek
+YM7iopF2iV3giykv+k4KloCDEGKWLi3BWgITruiezgt4Ne4wCI65Pi0sOUq0Q2d0+D5jwSXXtO+F
+iADwApMGNy4ASphQmY71ad7gGT+nULutOViOhNPT9tESLNM9Ij6aEfMIk4uEECCDrLsJ1zKu8w0Y
+5iaU8Os+v2u7lRjNJSKOqaJZhaEn9N5dIQKmOhBbHExxLn/tF1Cr1QYZfJslmPnG+dVbeiWQ0XZa
+ZUXbDxKjiwcE07Rt2XxDNw+4A10V3H7BPu/lF9sswrjA77EfbhLhIyympKMc9YMvsJc3W4W7O0SB
+c8IRahtyosXNPhL1RDBMxVQPD7l4zahUOOKz6OicdnKXkofi5Kq8KlRvT6h0eR8OlubiQTCsBb8B
+xD5fQAfhsT2g+LLdtC3oxQHAXx1r7gaeN/H+lPUqYRlmAAYSoMP1ya8fAyb3tjbIoNRgaQJxnJcA
+xXEF2Vz4VwCu7gogHpIy03ZegnAMgrM3Jvxlvu0zbxjw6hn/qvj93li/FiC/IP+vm0vMVxpU5qj7
+3PYxC+ufyLchAD8MEiWkDutscGhIEk/q56nr3arvEoLqD5mW1uIrH9xbwkeY623jAOIDlAmKyW9Y
+FOQWYWtYg7csuK0ji1Pj1Wz6B1ed/0yf9i8uE9A+L6OiciDZTDMtl9K15wWAp5V4Ngl+egoBFnEJ
+EdFrQuighE8Ivq/irCevfHUuDMSegbAYM2CGEKiZDsT9ukqBMrwgfloIUwhyBH5fYIzpDxO/byGu
+7XfqvfsKuyB82ObcwQ18OxTHfMEBvekrjCeLcIffO/iOY+ucXcK9Qwt4wxIcA+XUFAPiuALNNzjb
+yxfQG8cyjo7ponmEq+3ltR2Cap8UdlhCqH00q3C0K/IZrt5FGoVheMTVOdQnx5OXwXDtMBxXMBq1
+P7hpvJWY8wUz/M5p06Tvh1uEmqUYkHSL6FbfDrh2vKA50s1zQ3ZEfYHDrlytK8ZRVH3FvM+Yj/N9
+if5dZX0JXy/Q6yB2tB29yWDeDaLwFpFlUfcTAzoTeJUhuI7FgNCtA4T1BsPuEqEdfPncCE+43RSu
+fqStnlmOt4cVF+PZyuPWs3twn9aqZ2885h4hWEv7ILiv/IL32I41puIrXRJ5FEDiYUiqTqEOwQXD
+cT4AwOfM1YBgsbcoNge60jRvu6agSyDW8sPeZhGWtWAuEb9SD1pbk2ozCIvwwmsvsyb7w2rM4nO8
+DcFlX3S2UlCBgMWZPfjGeyZu+SXJzH+angfHQyjv3aG3u0Gcg+Tu4Aw1F5qXaFqFGxDvAmPTNWEZ
+Xtnfg2P8PfQfXX5AOJfvPoUcaJr/yzl9Nr0JZWfchOKK7+GZA4bmXBguEm5DCN7gmLYwYbpQadsr
+tbhQStQOxgaY5a8Vk+qnr58LEol5W9Vf9cCszApFWNXqNbiEprZjWq4Y6ZaB+D2kewb75dbHLApG
+bbS7JMgGiKn6CPHcp3S4xJGv8Iuun6tcVszVrbsj/jr4DoqXSgG2FmzXp2wJwFXNu4Wax+PW76ni
+Nd1YKp7Lx21MUWZxLOOU4mJUPo/Ol+/ly9sl/JzMj5RSUKX801zEQaoaZBAkneFodzjOycX3W/RQ
+dLw0GM5Sc9cIUF0eQMwwTEC8xV/T7uZulIAL9gW2iKvltwpzzCKhftuJwF5MJROyPdCxiov2U+2k
+liOix8dGL8daocpw9z3rJ5UqH88qoHYDf40cbYEz4MeV4qtYq7y7ZVbLejanGNuXMMf5fly4rPL5
+o5Q3jtfMq92m8q4HSk6TxaT2tVbzL3U57zJNlO9lj/tSiv8CiscKoCyX6PdRrjP1diXkfcKwn29R
+AtkbippH12Sy+ayq1FcxV4Cxw/3m+vDp0sDAm33L3tL8teth5/Va2LvmJF5rpxFCBDlYjp/PssVK
+1/XzGe721vOAXmoWCb7taLXdMq8pHRqA+xGEKW5Yg1VtKsctNpxoCzKsghxmlN6VHgbFdQFym9D0
+n11+QNiX38HgCbM1H+Z0EyiLaT+vFCEyTtv1QWktrA/xpXTb039kOF8zaO468/V0uaQarDkmC2FS
+SOcrtnGtOJvnp2z9kjti3EBECZCvfNLhn18128KzJYh/N33Oy2ufeF7kqO8QJMufWCxu8X3UXeY+
+l0imUYyBcgK4RewlC39JfTb5LwheCvylgpcK/lL4fn1VLMJ/bXuifsk2izB8qizYaz+zBvqcsrIN
+dpcDsQ/UsCl0lg3MUN/KytdkJYk8vLsy5DWUYqRJJflJwYdgZQV4KMNS9qX4vU14g0q00oNPUpHm
+PvpxbmDVq2ZLJ6tvHmZ1UuH2YEsk3GDrSNklSkiGfDBTh2Bo7kvEhSuEqK/bH+R2tfv4DSq4ANsd
+BdkUzN0qXJZGLlcCXI0YVH3VUf/pEaNV1nf5clv0G0m0lX8/p2QogHrIyWS9DYjW7BkCJfeQmnVD
+4FZzTpfhsKjf+5FewkoF39oy/UaVaZVrydnzwWOuXhRZVMcbu7HtcSYBYxt6qbZxjuYajUe4LEAy
+JsNhOfQwlamCewt16Oh/VHWSD6TldiRxYMEGqIUVZLuzLoAVX8m0PQRsrRivgJjrFvnRDV5fa+G1
+BO+X4LXFp2ELABa8fN0ZXvYBIVTfRxrJKOcUV5qd41H3SIEnXjkfwKMkeZYKLt7S8TN9GMyOOCJX
+lulQ2of2/f/P3rUtSo6jSFDN//9xm30QAQFCzqye7pp9OKrKY92tC4IwRjLkwGp+vz6Pa4+f/Rbi
+T7sfIOzueyB80fAOILjm2UAZAJjLQuyeQNfI1kgpj7XyCW7PThFoEAmN7G0QjNKxnEJoctjr4oWk
+kouAwXKUIgZn7b5sBApZN4Fg8XFUfw221DazMpNfuuTRDSb3AewmZhUMwy/QFOCcTGpZ3Tlu0aoi
+lKJMBcj/EQfBDsgjLHuxQdP7H9mAeP9M/mN5jNYvB8RLGPySiYQ1AExj0MdkPxxsLUkHssf1qeED
+EPefa7HyjUEDwC18MMagFYk6GQgkMLBCPsjP8rKkXRyD20Z8UoGxL4QmdT4C4i6tJAU3/qo4CBZr
+4Bcb4fau/aVkD+yaMdVHlOqJDhcURJpeEXrwoHVM4Bcb55J/EOiVPtY9jeelw+Mo3Kchk95m62Bm
+PoLJeCIuhT/nG5lh+B5v2wa+Hm8D0I20DpA9Y1sTloRLcekPWmaQQWkBKKRUUcLo7fRTSdvmK/BV
+ilMGv12GNfnlXE8JCAUgZq4ZIDg/093X/151OZ5CbZ7oQgkNq0gcrxec2z8IpOggjvskMCxuurDH
+0VUgujXFj7d7mcr6tfaD6AMgrCM4XgGCl/yl/LU6B82kEj4QQ/CF4d1wYTN3ZHIDvTmaMqRZ8dsQ
+ro9ZHtsBsCS/OPIJ5vqp4gKksVJ8PCu1yKZ+QNVLn/8t9wOE3b0RXM1Xf8uJ+hMIvmuHWdObCz6W
+BcwASr58RZvx+HsCBO4ZL48URhY79ptUFYaFAXAJ7HYArJzi9eEV2MngSNBoCUVfpkU+aYHXUjGD
+NniDwV+HRti1wa45FTX5S1YR4CPAPcI5xj0c9r2StsAMgAF+oRlm8PsfhUZ4h5dYaAFTK+yv3WAa
+AVtgg022a4DNYid1fKhgArPtpy64P4JmCDX6CEMBxGYBgoXj4idFMAbYCiDgo07EzBgDiZ3WiUoa
+k69pcdwQ8sSCcE+AYS6T/vZoVvw6xleAEQDFJAGwg2BVgGETfTD/+bC9hB5/J3TkYzOGI98OLxQn
+8MUj1+FthOxM72WKi8m9uVZqzJr9zFHuGeeHHbZFrTRjXG38Qf826GogONrgoDC0wTKvK8m0NEvL
+vJXuvS0xHwAd9BBiFD9cUR0Uo9r9ktpfbXIp/Tb8aESdR3ziJ/kAjc11rhmWUy51Tn+GSesPmata
+97Es2ajKX72XsIjk5oenhJfvWxATUQK565cSIE6Am9fVwip/uZLm13J0N4LeHke9Vpbo00i8uGPd
+GPn0msIUBHpNesp0Y9qVys+txPk+EMgB2oQZJ3WYbnrQPQ2POSj+AcL/O/ft0Cfp6gCC9QDBsE/l
+fGy7WupUEWhnsBSCASkvGc9juWBwbuBpel8FTBUfxtkKyaMkA1sVIX8uikPWsgw22SD0cGcctONH
+axX995KPyrM22FsMepftr3Ox1hcb9cgkQhwsSjGNyOdrE8ABHgsNgQQ71NjIRXapAYLJPCJ+VsHw
+/rTuCYh/qe0vjAnMQLZWOD6csRL0Lluh9V0OfJfa/rpRAGPNsW2gtQPfM42vThPO3Kr9YAPIUd/T
+wh0MS4Iqy0BltklMTF/ywZ/EY1KOnTioD2n9ChLsgoootiw3LWVOQaYFAO9nMgutMMCwOghez+M8
+BVeJY5Gi7mPxCQEy7wmmHes1cBmtX6sgivnGN/E1xznCiLqmcaZPWUawm/HDo3OpPjXA+dloaIYx
+vttvJW/Gb+7Qz1+O3wCMrYWTSSY4FpFYZ/F1Rsyb5BUmGjb9NPtynJAjCXAnOaWX9EhzGp21wPg9
+w5i0I9MepxyaFj08HDwfQgHuVaSA3v3tCfpMPMIGedU0wmZi8mxlidvHbOCrxSzir5VxrPmdrzCT
+wEawE/xu72zkOIWZm5w8zrLMBGRu686VX/wQi1wVFBNIDj7MdEvxHQSXH2mIn72p8VnLT90g2+Kx
+E/+u+wHC7r7XCJNdcIDbtP2rzOU0o1hUB/9Ek9nmYrcEwE5ouN/+T+FC4xy4Qd9JkNiRwyBMZZM/
+nskzvgPjSUN8G8vuOQLRkvKKmTTB1q6/VDdQdXvgfUyLH8/l2lG+PvHBWBbspAX2TWBi5ubWrAE+
+yyUIzq/D/UdyI1uCYUsA7MAnQPDzeFlzc4gEwQpTiQjvh4GwlwYgJoAcQJjmhoVyB7y7M3Pa5p2c
+F8A4hV0ImKeGAwSTWA8/AbkKDEQygWBYZhfKMQR68B30Gmeu7ygbj5jBsU5p5MMaD1BiAMES2uBt
+HsEgWMj0SqOeaDsv23hgwRTmeEFYxR4pYbCFljLQpbC2cE8fwuls9N5y17wk5G+VEH883QCaLUEt
+01EHvu9+871n9uFH9MzhWAeSD32W8CP9BIZ5qhEnMv46WDwB7wmOCwA2CbOJBMCZF7JIIg1x2Vd+
+EJYAQvt3PCSymGq7T3c79OyP0oosGmCRY3dwMZUw0QDEInHKjStswuzhF5lH/FL59Sw/GaJrhmv4
+8fCjGjJjBMLBUW7pgyv8qaf11TSM7bW65MmpeQC/zizW8gSvV/CSCQQDAK8z7hF/Y+ua4R8g/L91
+3w59LEYAVJlMHpp5hPLr0ATSoRHGLtlIt5CtSmdt5sK3ODkBgLgy/Mb8rVwGR8BDUjhVbXAHt2f8
+BG42E8wFeYgzPcdej8momrewEV66v+bjgJdNIX755jgjTTDAr6jFkW6Pz0LqWjxkssF0GQ3krOYS
+OD5KTPYrMQLEGwBDG+xhswDD+LhCAcWyN8ytB5pfdbBbQXGmmQPkHqc5VqJXkCsU3zXFInftsdIc
+c12T9reA4CEe0r0CN4xvUm8BxpiBj/Q9u1No2HBFWs8LOp3TDj/vIbDUCKvBVtgBxcOblR7iJfA3
+UVmRUe4BoHGEJp+w2Z5rASlUMBuAWGmcLFdC5NUMcfkuXqt7n6wiyq2mdJfnjd4qG0CwNGArecwc
+k5ZNec1NJDieQC/ouNK1BdBNvig1H48XzwWDYcGSqHNlJR/9GgDe8uM0hZjMIxIYT/FJt6Ah70wd
+C8HGOIzBszXBRmscZd0ubi8nfxpN4ZYTFRPIAO8Ghj3A2mILtiKW50MK8zgxSXOIZ7IP1kEz7GBY
+lzzFZIKPAcNi6qAX0h3h3mEC9H0cMPSUFiui8ckoy+yVKMYKHdIDGcJEv6DZYjcc9CwDCF50fNoG
+vI/L6v3hE/GPb2y/6s/xaf8zN34G+JJv0vTm76YFriCY82MphF+TySvCosF4tka4mUf4CQsWzDqF
+BJ5KlVZBF1XGPhOccx1rZ/Q3ps3mEgyWY6mbECaeEDBHtdfOlLZ0P20DDG+t7f6lXRY2xKWfzSL2
+w4x/Bcd7BICArwUe4Bgz4oyaT4tAj/dJD2kagaPUFsCwuXYYfgfB69kfWfilfqYsbETVN7fQiQGL
+wG2G7Ux3TXEcYefMsW5+63GYqBoH0FvAsMwAOGzCJ8ArPX+2qwB1oisi0wJWOmjpgSYG5A526aog
+UlpDSuW11zf5O30rxU48gLRutk+NiA/WmIk+BIKNgIjzBHQ2x93v5uObcac2OMCwV8JCtQ59AmOq
+LsBwgzbfhSZh3XIzZioa+VtZ88bz3azGWE3addsJjlU+aYUlgF791fgDIANMBK6gvJIgBnMU1RWA
+MoDf9pvlE0Ax7IS7HLOBRvPRn7XB4ScweQBiMwEgxqkRqRGuawaKh0yzI098uRR+8c5gQhgMu+Gp
+0kkFsvbh6crHefn4bqC8QbCuyTxCyUwiT4sAGH4WnybhskhcTqBPAPgIW/Z4AsF2DoEPFsaMkmMd
+S+656UtEz7i+QkFfINARMFMaNMKlrBAIbqAYH+TYLwzxlVGRx7aBrmQ9AAAgAElEQVSZxHrkj7sf
+IOzuOxh8Yyz8b9qhexOA+Yu6VQRHAWGxA4gUkwjaNVtMI/ypP9glHSvEQg4CMF1lsQkFLUBP9wP8
+xV/DOrNY/PXOKcoM7T4mQKN9gv5TmxG3XNtrDnbLUWnLHBwv+U9oRvkoMQk/m0b0nksZOQLA0UuJ
+MdhZHARLmsqkiUSaR2wtMF/zS2K/nsfBs4X2dwK/FfBqmD/UuDSL0CCJKqzZVKKC2503/JQ3zCMi
+bASqWABajM0EAlBvATwEhqe4g6ZKUufyHr5oBiN5jJ/qSn/Fwzr7j3K05i1/C+ubwDDAL3iCDmWz
+/9SJmDcShAXwNtMJzoviMS4+0oQikR4jY5kecPgmhOWMPHnQW3a7Ziso3Rt68HQvl4BWw0/ss8bZ
+CyD28AH8JNdMsY0NPFEBBnfHbJtnLDsgCBUHb3/JI9LoZwK9/ddth62VOwFwAcG4c+lzntMrAKPP
+E8oGjKMwjSlkVuX9SsIy33b5bQGApfoV59SGVjjthQW8Sy029i4R0vyyiYQeG+aqltjTFEeobe2w
+GRouMXKFVrXHhboqx6bF8dDEmNE4YU0FGxOKRJwTd6eeMRw0fU8Dj7e40oMcmUY8YRKR5hDQBm/T
+CMjkP+t+gLC7b5XxVbv7ov1Vf61N6X137hLdwNUgWq3UJW4yEX4XWunHE/FmVtAELzF5YFIRQiqF
+JwusDgTqMx1gXjOHMIqzlk5MvtiVeptNKhxmhFs+ARlP+y0ssj8va5aA2LXCVfPr40J+xCcgTtMI
+hwQ0BgR2Yfowpft8YNwqENY0h8CcW4Lf/indX+anBcgjv0R845sEcJ9AMIPfDEvNh3IhqOXiT4Sk
+JMwnP4NmAWgKwd/CIiMQriAYTFUCNMQDBkjF04zDzRkT9qtDnsuVgySQq794mv9Mi3VtBE4AfEVD
+06uuxVL/AkY5Ns1kpzGCRHNjPnPsDgBs4AUQVLuCMsxaux63YoCr59SISHt+CIZzmQ2bLtf0Gq0t
+Ld86TVVMZeNR1nwTnHTAq0Nc8uewMW4PfxX41rTUDs/lYin4uggwgaraXL39RO6KlxPgfhMWwUNV
+0l/vs+2ROcZiA6KwGfZ6RVOBsiPLwvN51Wj4lq1gipAZnr+DYQwEm0Z43F4Xmg/ykrxqOiJtMotg
+M4iHNMJLAYhXsjQRiQ+6sFAWTZI0hv8VRBN2TdK3KBa0jyKFd5YK0tnxF3WQEiKGMSNy6okuiblY
+zDV9YTQA8TaTePQJDbCZyPOQacTBR/999wOE3X07+B38nkfPOBhpedbhZwCdoDVsgh24iEj4QyMc
+/mRWIhso7bMvtzZQBHU4CD0WA0lKSWIHJLTw0aJDFUb5AHBQAowl4jQqUAeV8cwf6NYvLn1DEUyv
+v4IJPk8Bv6biKhSyCV4qYrAL1gKen7C3NX94yH5y74NJhT8RwdYO54CiDL5g90sTDAco9maFdthw
+fJY4ADb5tSz8rBFWn9NVwtASS8sHcCyZD30qYDP9DHDF51QlGdvoFwg4HykAXBIqExgOPxgug3C6
+WgsXBEBX6/Hsjrg3kNzB8Duw/f00AsOSD7Kx2chM9JEEwevZYdC+iW/0WdS0Oi7x0CIJdnOZN82w
+8NBbgtuQ05p+Gja2HGHhfJ0GG2OL8L3NCQvkCnSboL/5hzTwXAa/qelN8KtCH9po+bMeybU0flSD
+6H4AxPnLNYV5iZMjIjn1deDP4+kRYLeJH/OnCYJFOiD+EiCbHf7gLQ34JgCqH9xxVc5ug5v1JUmD
++de3hlvmAQAnz8tJ1gKAFZ+QXy53AHqNFTUYq9xDsX6prL9IE/wsqadHrLAjTkCc2mKAYmyWM/SJ
+/dTNMJ3Q9FtQYlsnevHHLcCPZZD3EjKs/k3q6Y9ZrCCxEgZvST4e9DmBX9vA154NdvfRabI1wLql
+8KP6A4T/1+7boWemwtrhsBvOtdryZF4wltXqBGDFma8AfupcbdIIb43yvu/jQA9CVsTCVghaZ1MS
+gMXxooDQbNpe6XGp402gnAukgGMR+ptg+JwBgGAGcugzAT4/JgynRYhrhqVohjVMIf4is4htkG/7
+lUwwAfR+BsGZJpQOBGDBxAoA1jSJwDm/4bdto4ezY38tDf9aOE9WyqkPcRxa+J2OGjAu40TjKILX
+4Bb0xXbBbNubaeQXiYccIdDFILiDZImyBAqYPgCC2nUGwcyk7y5mrGSbAPBeP1ViNDAc+biYXvzx
+p/pbKwDo9jzt+wNUiIhrhPdp8/uTylvVFQ/Ky8vxUFTVUzF3wLxi7nlDHUAWmmvU7AJ4kQC/p1nr
+4jQzdiScuewt3Qb/FPelH59uD1AbIQa/WijiEbx1k5i71Ah7BxjoBl9p2nbiifUqcQWoANmXnFbD
+Z435Y3ALDvBuIlFBr5R8AL00mPwASwBp+x2il+PU8HW57K/GXbPNKQpSkCpdeRNwAGPckp2D4gTB
+2TRdDRh7uohvkmvmEF0T/PiX4/6KeKX4BMablzEIltyDUpZwaoNz5JmXadX0BogWkvGW69dyCIPn
+DmzuBMHpP9IC9GY4wbEEAIYsSEAMTbAfU2dPmEbsj2xsAGwPHhx+gPD/zP2+Rjj/5evrCn77pjho
+iZdUEI2pTygIIQlAHPpTF4ZWwHAIU02zCJwrrFa1wbEWgAE8MoCHuLA8FiazbHqilmYWYQ0c+4JA
+e3nBi9+NzSACR4DheaADPJhELMPAu2aYAPDWBPjDAW+Q8/RHRSxOEu29leQyPAImGVdMJvbY7XON
+00Y4NcJOE0bAWDoglgDBa/mGqdDsMiBmv9PkoRkWYQAdY23eE59vBsYiMoNhjxc5wa56PRwvVH8F
+w1KE4Jl3ujbH0p/z9LwRvgBgjlOROxjOywls38IXgEycJgGxr+3HZG+YNv9UrINgfQgAO0+IndU8
+BixZMW9VSHYwzBrkENbeXt7jw/FZuzT/mVZyXKaUE8b0aY57n1s+HeJw4bdcmA9wgdzGZa4BTvAL
+vgYQrFGhrwfWiAIoFA0p8iELAecA0Fnlrh7aNskrxWWfJDTBCBeZkpiSfvSWUbrmlwGxtTKSWmFD
+eu13/T3Ux/1RDVks1aihcgSqO0Ax5WdtsN8aIDgG5dHYPLfzaJqGOUo+bIQH0wj4z010FRifIFgk
+tL4pRpy+lGTxua4iUie/xRnSWbdl8bJMrFzrvRAPekwtcAHHBIajJtC3pBZ4X0WKlviRbQYBMKzQ
+FC95Hhgk/Vn3A4TdHW9Bb/n818FvxIe/geUjvf4E5g/QAAs9KysDXok8+RW6ZCkAwZtZpQYY5avm
+Bc6Gv6f2dwvTfHpPrUUulEmLnLdJpnswO2WmrTkh8fSfgI8/sQzge/tpA8CPLvnLkx81seCeWvqf
+XMavZrGrOSkh8+AEiaoNzrlPrTAAsX/+2LzMY/scYHxeV1Y89BQALHUsMF4V+NJYYTxjDuy4MjAW
+uYPhCPu8F+Bb6sw8iUBaHiFapLiUGLc0moLs1Og9Xc6f9jgI1HmBZAmAxFLJl4DYJWLiy6Ql+AGA
+8eAmbh6z+YMQXQOW5XxB9pXXo2InGJY9pwByIRj5gVRcmIa6yYeG4uopErQ2QrAPjsb3Po02Bcf5
+11talezl+kT9Qc3eNI3YtA8GL652xLhu1pcgN/wAEAR0++kqgSNaE832fZcEtiAOK+WHNvb4sBtt
+8inkjbDsSuAr5D9NKOqPH57BRyYwnDaj+cn1fBCToC2A8jp5SZMafUptsPJ5wYdcqevdBCBYmqlE
+hnOz3LkRrsQpnRzhx6b9Io0wrvHA4uvFfA33BxmMgbVWx5rW2hPuKjZx15db/ja4LDIrxSeqSr7d
+lhA/3EW+phEWi2mXAL5VG/z4RsnQCOMq6seoqejT+em/736AsLtvh34Cv6OWmNLnc4Yn0wjKA/ns
+BA5CL6YShnwJoAMEARBbLjSuT0WqEqwtDRENcHTXAru22QCOaYEYLzIIU9wMzK0xLge8wbwZBJN2
+c8UkqEh8OQ5xfCoEA+flFhM7HqA4mQB6r+O1aI2BMqJIpvHpDvkGgPzm9KASIFhd6C3XCG7/UwAw
+xqyDXsF8tzQ58krM5x76qpk9wbAEU4xHBBZ8XjaVDlZoiesS8htz5tHPEuItLqtXikpXiPtDOoM4
+SArNJC5ycAq9pk2v+ObSGGd2gGIqeNuTD30rH27sHGP2TxpgFoSFlAsT0jvwbVqupH8W1rUdXR5z
+wMZ4ztIk83979RbyZrit+Y0RklzzO4aBcWiJvc4KeDk+0w/NcAEep0YYcxS5OFz8zLfr75ApHhtv
+mMCHKb0CXwbEnsdy/e8w3dH67zn6nfbCkJJgp5u+wcpDRsiOiLVBD2vVRlhyGbdf1fxKMZWQ0Bhv
+OfaIjCD4V5g7nCYSfzkwZvOIv3RriUMTGiB4t4W1wR0Ad384SuANchy3h4vWOBj/IOurDyuR6BK0
+SXylaoHZ73Qu6I81+hepGmKyHX50m0wAJH+rlfwH3Q8QdvetOl4lNX3pT3A7Ad7ur1rj1LGGZrdr
+hwvoJTAbJhGI91YBECOOjmMri+NwSeAB/2gna5C85UKJq6/2zFsZfHmFFUsrgS+P8I4HAE6Gl4wP
+QN9BMT5l6ZvkdtoCZpBlW/sL8PtsbCHPkv2aLnr3d6/ebrNCH10jXGjAEDaK3+YRqvTGIYQX04fT
+jibdBugVvaQnKNpTQPQQgg0M1MNGc2VpH5ikYwRCrVyyXIk86e43gLHe8l6cFp/9nv9A140/NGZd
+QO8Bii+8xSTGtMeLz39IO80IDWYgx5gFZRJACjDsEd3PtxUAX1TPT/0AwOgfBPvR1wnc1j7O7MeG
+nBRRrlaRwm9cVaSYPSQIPuMybHKcIiGYnsrrgj8yOAYooD6l9izHI0HRHsOHummUzno8zHnfOIdZ
+YXl0ht8AsYU/P/IC3gC/2w478FXumMeFbfDzZNyzxNbWEu7n/A2MNOiMe+BtozXBChISTpxb0iQQ
+IBjXLdcwT+H3W64nN8v9etY+0eBZ8usv1/T+WvLEJjo/NeKXykNfnfu1lph/8ClIFUPi7d23tQaS
+a1fGrhENnSIp+9F5b6xKl/8mJrAhsSNfpbJUcqEvWS6VG3sONwlU0Ls3yolrgVUe8VMiRENTvLXC
+7eHmD7kfIOzu26HXy+8d8N7juM4tf2/gWFIhhE1ySIur+AJLoMiguMh6ZPYIfjJFSjdzSC2w+JP1
+kB6LKrXEYOo5hlpaELghmBz8FQAD+D667bPE3B+vi7EZrmqDjdKWSgJhtX3KxMxRRC/xwcUwqKRW
+y6PMEszGhxCEAK+cD1ALcxVlsx2sJYH2JP3S/KQ1KaNNMMWZJOaSZz43WlkAh2CjxrDHymWKqzMf
+N7vGXcEu35erIN4+S4t3ByrNWi5+GsRD06t9hKlplzQILc+UkSz/Qz2WbwXKK5Nyoxz3WG/kV6/v
+5hf1ftHaA2nnKzCl26ENOU7FPIKbN83LJ9ArRDvMlEzOONQHWpjyWM9GexkUMQx+8yrJXQt1xEa7
+pgktr5EJIAdooPjy5gwaNfyo+wgzAO4/aWGQiNJUafQm+Q9G45A5AgWMBf/KvOZrMvu6r48k+KXx
+iP766RHyiNra+WRtkFqEVLYlBpzfdKkECGZOHcXj5xHOrrfcUm+PRj8AkkX8Yxr4utzLmcLFFELX
+VrSUTywvARgPuoNfoeXXY87qvBIRDCIoZM+QXuxPsMaJhuBO4MvrhOiZ6DTKWbbS/E/Q6qEB1hp+
+Kgg2wYlOE8P4d90PEHb3Oxrht39dG9j9NY5ADRalkByylhaaYX+Kla41drDoT5kJnCUYgQ6Lhgkd
+xM5saK+hCnolctGCQTxrRaooLmt1Ht86zq9g2FGjOXI03zina+0vQ2kCXxw9hqd0c+3wFoK1J4Ut
+YWzBeQZmzaw4TnLg2TY6z9eFCzTBKgJFdgisJRZzmW3TYBAd6Ma40SvETUOUFm31/gWvmbWD/RW6
+CABGh7ZWLnm/CzOzSxqD4RaGP8FuvecnEHz2vwO2M41HrrWMiLeBXmkAtaR3EMzXYUSh1ikAGH6u
+O+emzEx7uMje5byWh2fnJcl8xNcW3csyLV9f8hXU0le39SkbAzfSSLIlujWp/hJnr+mPxkjsqyFs
+43WfN77XeL3y/SroBR+8haEwMCrLXWSwYiWefi1OROqrdsaQkv6MS95V+Egpk8A4fgaeaUA8MQZz
+GKdF5Mc1bOVmKlXkl+C3TOvcvvJQtpLfwnynL+s4KYJkKd5eYi9OmkmY5Ga5YaPcAIKPUyUKINYc
+ApXT3+YL815dXefhHcGvjSC4mKO1xVA0wU7LAYyJb6Q8T1pGXphEgI9UEkgwvE+I2I+YpltGPxHe
+cvnxEz3+tPsBwu6+HXql36cNcD3tLf+uWxvIlQMM49URXpWLWTADA9giUwoGwVgkwWcGl8ukgV7s
+dPW/AYydiScUrNphPG2jP9mM9ugBWY9XIwQMOxgW/9DEs0yWg1oAYtj/QhNsqnsTmi3XAvsRw+73
+3UlSWsbbb8WotY3z+NjvFe/zJNAG82MPzbXlhrmtZbGWbvlJZGUB5WJbQSlJjx0Ax98QhA00SWeO
+mNNkhjktKaA53+lqfUeeg974HhydbZTeFg5bCur7fV8IPSh2ytvKMRj0CLumTSOUiCTSIPAtHztx
+X2MQ7Gun2s1p2SFehSSNnwul0mLD2s78uHMADLpaC3dIZS3MY1dGvgXsiMt8xgHOZ91vl3iqqKf5
+dWt7B3B7gN76uWUVf7AW2iwHMMBaYXkLU/MtmxratJduvP2khc+Z6nyiXuvbxXo9+JV3ILXDUuyF
+44QI+rgG4iSuK9ICxB2tj8bF27JuNhfyDdmVxraEU5YWuRRodN/zvlHuBMH4yhx/VKN8dtmB9xUM
+Y35ZQ6wS4DLyyq7n2EdGIin2/OQrPcEKLadIYH1GHCu3cg6qplhiDhMTZPkoZRZR+Rzktr/m4Bdx
+wgB4uWnPQye3ZDc6J59l0N93P0DY3bcDqy+/bwDxbBqRzCbv4YtUJTa+iaRGePsTBIvZBnhGzAIa
+Urcjrq+35KAukLNJAtudrWqDO+hNxvcCmEUkz0JMplsHP8OxK9j7lx+JSMG8/Mg07+a2+fUxyc8u
+QytsceYwAPLjoBot6qYQ3ToaDx54ktA2gGGTLantL1fMjaXgyTxW071qBmtgD/zqSMl3zG2JrbMM
+mdNfkmnJlQw1gBXlflszX6VxJuumD6cpBIO5UryEO8t8SzvDTPW9jSYi1dQhM0wzIlp6ceY1GufW
+kgRBmC9oZ9s9yhylwMqcXjcq5FZ4/Vltri2+4kHzSFuoze/MmjzuJPWnO2v52HskseoM8TlQ36fF
+SGnMKbgavsiJ1f2oHfnVHATjQaZJfj5nNW5viRlCi9a0ar2LvSvxsyGO84JNeUzV9lYTiNt1BsMW
+D6Mhr7xBxUSiaYXzIQGnRkBLaOWXdN17VtdcgmE9tYcFPflA0IlLcRvSCLPcWko2wpdNc0/zAwTz
+BjqkwTY5wLC4aR7PoXoep4YnOiLUGe9StD+zmBMlW+pF5shvJS5BLtNOj/P5i5bNcQzag+bHH8wj
+8IDgdsMigmNMn5CIteeddQys5L9yP0DY3ToE3D0ffpAFE8DtcQF0KE6KfxMQ8ha/J3c/l8t4obJc
+P2uL+eoaUnFTA3FzAepj+TjDUWftS10Fe5HksU17kwSeAEIIB1UbXRqps3BnDQPdL+xLS307jb+4
+abJ3jMdntZ1hBrB1MBqdMilpMc7BdF1gBGg/x5/nWYUeYswK+GWQfIDb3Xl/Y9Digx4U2YIYOmUb
+jSeX5xHvoDMB2d2NafqSbi2tADgpdMCgt7BJo/xdaLy09XfyTHk7I57HWCpudu0v4hJuubxywRx/
+g8g8h9OaHXOac1dsrEmoVe23xzOdU85eQoe03R6ARlofBYlks7VHUO5JqsXxbtw0o7xm5G91mJ3l
+KCJ5l7fcJwufgec3KNusyYJf5HFqFvwDtwDP59N46svnc3h4JYc9KdW5rIbN6z/irG2WU5+TUB78
+/lUANHV/KGj/ZH80SPPko/0zOiZyn4uu5fdAFbg3zbXx0M2U3TVurV4O7PypfWfeYEIDRrQm0FRy
+XPe3K5ZffQ5UV8qQ34GyNg3yr7XkAQAmIJzg2I++MzKhEAtzDnN+8UR5X2Vm+dVBw1uLBKXY4B73
+djrOMnt1P0+OH8wH7fE3H4q9NCrqH7xQFX+TurGDOT6AsulXOWv580/H35I4Iv0Puh8g7O5bs5TK
+ABJIAuhUEMzgFwKQ4kgQ5dO259mX9ItI1ZoxQMgn9Z3fyE8Ai6+SABgfY5ClYm4DJbHQV9kA1gEx
+2pAgWAOolvc/0TPb1ghLJD4CP4Yjcrvn2adDoO8AvtOPJW0Xsh7mg9P61OvF3wFyARE0tjEmtKFx
+ugrCBRR7ephatLbgdd7URm1tHwBG5k+QdNBZTS1lbKjz9Rky6Pt3nJ3zJhJ0NM3ZVOAE7ycxHCDt
+qPNDy3uWD+EtyNyveUfGR+F3PmHK8dWoqPaR11m9J4rw/JV4NEjzqrzIh3pLPdHRve7r/XM+S3yr
+jM11Krjhgo02it/m+BbZITs/xHJ/eKSDzwaMzr4BFIOfLL7Ge/m8YY67pumabG00AzwTGUHvQ2PJ
++Y5TIwBmpV0Z6N6usWlhBdgLMCwivwSgeP9wTjq+PqciFQg/7ergWJ+XFYYJekQOcEyfkStjDGIx
+XIheOE1ExNhEZ8eHVjPmKGmm8vYBEOv50Q3YJG8t6G5PAF8BKJYBFPs7WEvg3MEvA2L1vAUQtzjs
+IwCwTmArYYUVZoWuLDNVsaX7c9UeXkv9yDP4n00HS+U/bhryC/SyPv9WB9C/Jyz+EfcDhN2tL0X1
+/lgCA2I/HktZAwyw6+DRFxkAcbHhAWPEgiOuv9dhE+ZFU8JCAcBQIj/aUsIq2642APBmeOZtW5B+
+zgzLJjXR7JNmndEu3vnLWuH4Ylb2LcGvL1IKR7oS+BXZEmCJ1M92guNZuSfi85Wd0Filq4ASyMPD
+N3+vo6X1X4lnsMtgWMgOD4B2uB+DlyP+1r4WNvp7QiopNFmH7NQuU1Wf3csSA6MuYUJPAC5nfqP8
+c6POtOHe3yYGcLl1GyuJWlHqoIDtPsL+F0BXgjJ2JrwBiIMaJHXCvWlw2vof8VSgzPcb6KW0eZzS
+3lKcvx1AFlXZUfKk27FMW8PdT7QyOyvJHQzvuAH86gyIl+JkCVco4K2TUvtr5eTXaDN4x+arbJVJ
+rCx+e1bxDYn+A/uMoXFmcWh6h7hIW35GNc6qdk1PaoRTM1y/ntm0w2YJjO2hK2uEh0/Bsbq3fzIZ
+TkUkvlwmsacmSIDpJSbdMqqnZyGphdhsSGKc6DmiaYWhOFryS7dpBGuDVUhLa5v2GfgmcJVibgC/
+uv+RLN/jVIxANfgk4nAfL/NImBXur71JgmNLv4V/g2J7HCRDceYPBP9x85Ar4NVZM/yrx/0A4f+d
+W4XiJ+61w1v7O4HfBJ55IgQDYguAUX4hBFILDGaLFnTt73seZ5fUbF/DpS0LuXHSBHLuXWhbnhEI
+3nUA2Hs95eoOqxdP6WA6TeuLhU0JnzXBMK0IhmnxK6/yKd6MAdNVSp6g8prmA4k5CEEmAZCKmQTG
+ngBOaIYN+WAfnH6RClpKG2yOF8n79PIlT9HQp09vcQVj3MdwbJA02nhx2XIPWwuLyCdwLJynpPEa
+OhvT63ppVbuDCSHUo8hkyHBwFSwTRVrSinAYXdejBRJzRZUzF+s9ynh1sEeRmm2pDUff0UG/WqO1
+6f6WiUbhj+AXf3n5TgBmoi07PDQ8sAoWGufkqxP4FZSgBxf8lpcKcwmehwMEZ+fVtXt/4UQD7uYF
+8AaL815wvCgPFUDu9k+a4PCvNcSlf6lr+xwIL3GNsAzmEZZyiE0kYqcUgLBSww+zCNL44geR8uRs
+YCa3h4iA6e2gGyK28JKsasR0yGyFQijHJj6kVDTCsINWAq5pBhFg1dPgxxdcWTP8kD81u1yvj1qA
+ZvNw1l00wmIEgDWOXLXuH4Cv6ZK1nn3iw9qfzN6mEbMmOACvnnEJfn+A8P8L96tw8jssgkY4AKCe
+T8R8xBlAEH/pjTWAEsKeBHUwErQgFy7nLRKlxYeGUfoCRvwGYqEBjtqXg1ER1RWMMEEwA2OvF7IB
+yAUcq7XRWl+laYFNxDXD7ZU1NMEoVLS/boNM2uFuD1nqonEGy1Ma3NH04Us/5rJqy6HxTZB6guGc
+jy2zKiAW4faf4Q54GVxMPIX1vKC3I67lyy5OiKNUTvlvSPxTWStRn8HxWY0eMff8SLmnfbzBa54Y
+Y5oYgLASBhkq2pOAq4QZYMk5HsjANM+zxsCvxA1ERivzKF+TSCtMQJHNWeDp85n1Jp878ncwwwWl
+p/UZP7oVsWmR3ey1RQTAvzxcUr3LGQBocmnee9GdogUOgmONg087IH6KRjhfnzM0i/AIkrXkCVAr
+ALgSfnyYaAbF3b/N46qdsINgbR8MMgBgaWYRrBVeDoxvKl/ZNaqJ2bOnwWTQEC8x8U+QE8EU0ih+
+m7xVjlpuBGtCd9QEY8NegGDljXVr0AALgdcEuw/me8gXIHbSBstpDlHAr1zKmREAfvIhQ2Eq4UA4
+bIHh9w+hPLvfBhMJBsIEeteatcK/biB44a30n3U/QNjd16YRmmAYAPgAnOXHINiREzP/WGsMfCVW
+aoIpoXVZ04pwsAEAHz/S8FJ4cQvAGJt5hOhZn2SpeAiINrGWF+cPraYFPkwgHolvDSMTbbJjzUKC
+YAmbYSO/c4j0C8Se+wnAcj8++kmadj/PI2gAsDAALj2U5HF4KfTDTzcdcMp21tPPF+fZxMkWOGlK
+JIXE9HpdS6i71sKbkfKtuFwwpk15rIXnIt9ph6f7vzRSKpg6G+GBiPP5Bwlq1pF3IiAWtFR7qq3n
+vKdgAsXcizrnKpUONJrbF/VNJm2TBudnXjjy9jcObX0ly2VZnv8AACAASURBVJreOLzk79rffiV3
+zHsrWseew9UGm9NE0iQCsZtv1tssJg7wF80HXSVAnEddpkYY9VSwm1RZflaHAXn3XHaQu8ivH/1h
+HrFwRm41j1gKe+E9BqEdNpFDG+wgWJ40jdh6ZZMD4WL8wjb4BMzoq4q6jXDPYG/BMf+u01qa84oi
+GrdSKABcXLFR7skNdADCwpvXJMwlKjgFgNWS7yG/crz3C+YQuAfAL+jjkVpu55MDAAfoXepnPTvQ
+XSrr2UecAQTvuDVqhP+jJyAuoFhV1pIEw1qBsWpdf3/C/QBhd9+q49kkIu2E+dWQuo1Ugl/+Mk/8
+IEME1yqcu3lDEVyXVQ2glTCP4gN4JSBbwXF2zY8uWX6+o6EMMUel0ng9FPWz0B8EGXfGHhFdZCIh
+0kwgRA7wi7BKAFs1E3vI3zTEJ6DpY4qxfvEzUH4BzUjvIDj9YNsJtDRiiD60gpSOJa9geAy7UAdI
+6to5akuP6znr81uDDx2RX4LdAXj14kmRc/jbPOhXH5OzjB05DvptEWa94XprRHEgX0aOdbzJJMLz
+QTAU8NhmjtffOb/ZyjLeUnt+vBxCnqFPdTjQly1RI39bK9HvbGppQ7cRZz4XaS/XUqanl3bfbay1
+hT4BY6xt8PPFN2RAHHEabaynxuzTKYqpA3WFf8J5tHY1f2S0FvxbCOR+AMCrpilrg6UC4PM3nBrh
+yovtr6dGhIYYNg9Pn48EwyZ1nkwacGrT3+OP6CZj8Vax8D5cNVdn2TxOmuF69rAl4BXSAAubNQjl
+mfyawJY3zEkzkfB4vFk6bIRFTk2x2wiHeYSf96sPtMFP0QqvZXuT3FKxAor3uf4JgKWaR7AWuGl/
+AYrrA8UPEP6fua+PT8OPXgktbSBXHGh2cwgCxHAQCHosvNl0IvNUwLmfChOtsGkEFDfcvq3ZkGCX
+aYW7ROWJRc6MUHzxC/W3tNdEau+83Y+IrWYp6SYQxRUTCM/UwbAsOeyDAXy9/xBz8cWgC0C7gkhN
+wXvMVUsPvwu9CnQ1NAmgie1XsgPmNwFbSPLYxn3jT7c4/dAXBye7pJW0FAKnfXCGT4FQoaQcwzu6
+U1Zl84726xE3YcwpT97qaOU4bjfsOoGiv+UIoKKOHH+P0z7ec74q7+tc4g63uHqPdKVObRm1XHqH
+ZrBRjvf7wh648UOcHMF543IFwV7mhQ4PPEp9scEEIlN7CMAYZRrLGsGvhPkYP/B2EPwcGuFmM0w/
+8ZaUsNU8BdxKB7tDeE3hrUU+TSOgFd7y75ckj1OjfhZA/ITCI4BvUfae9sGcZi6XgFf3m78VNDM6
+G73nBHWmhBHencicLhcLCA7w+2xlkj4RZvvgTxrgcmqEDKDYm5J2vgC/8j345bLqbVCYRwD8iuC8
+3zjjWFXMLE+MWBYguGiED22wVPvgGyhW+TGN+P/gvj267jCJcLmR9sFGJgfdTliCiR5A0hIoRRwB
+FAC6Uyixd7PA+klVvo+/0vHFoJrmEI8SLvUjHtQ7mBrhqGVop5Zwb55Y2wQn8gXwneKeA/zm1cdw
+c4gcGArnVp9ZczYCX4xFEdpaTEB6ecy50GjlXPC8QlOT89+B8M4nh7uD3x5XwUUHTLPNMMJWyvT7
+HM268bAPGU/QMQPdUsoZeWi9PYPJ/LAwx79rjV/dWXAEvmM+IcxEE4L5aoYLOxOAks5zMJqy6JBv
+iIs6D0KWuGeJN3H6x9g7Q3FpjX5VkEt9D/9vgGUbEm2YrQ9rpSefU2gC06WS6l5+Wb/hmfN3ITYF
+fuFAN80p7AqCMS8Bbguw1ZJG3c80rWkMgifg280lRoC80jzuAMAiTTO8bYP3m1E/OUKaZtismEYw
+8M2R9hHGArEEvfI8ZVZMkjfzRH25gsPlePYR5qFUGia3C1ZXKmkCOwvziDWYO9zBLptRxCY78hdt
+sPxN8EvAeWuDVUwdsOsTwDg+foG0pf4xlG0CYvYECLZFD0qkDT4BsBQzCIDiBMQSGw//tPsBwu5+
+XyMMf7UT7mB4MwKkqwhsQScAZS9AVwgolbJsMlHwwAzOnHOobOD7KGuDu30bNMD1tZqSxKztR3sq
+ZzKReRMcgdyMfwHDGGA3h+g/aMU3Q2jg2Md3cgFE7bs4FvCY0/h6URsT1gKfaRivXme95p3O9k9U
+e4uzlv4N2EVKhFumA2BcJVAK63r3Wn5ehTrnwbSKHQ8KN3B7v8fpeLzi7f+1DoqdMnjcCcIs/vIN
+GYTxSlKqpNoF1znMJdrs4ammDcoobia8177WFZ2dLHQR/MpozrIIz2chLxqUQndWrzrEHWVe+zPT
+fk3VcmUep5pKhP64n6BUQisMJQTM5vbr73oWsYiOZg+gjB4/pUVPJoArE/BdsVHuMJ1YWu2DpR+h
+Jg6CSQY2bbA+G8jqwls9n+P41DJ9NMPWNqeQDnz3G8syK9r6OzKhykOuLuxRcmTrssAbUgwNADG+
+OGdiDoJ/LSugdNIMHwBZOggG+JWLNtitrB0gB/htoDjAL5d/JD/t/BjZCW+bCTMLTXDRDkMbbBZm
+EtAIh3lEB8UdEDdTiLx+j8X+SfcDhN39lo2wAJMp+RPcBCMQIe2wiBAoFpmu1gBRBVbidRyOtZ0B
+CBvrJkYslqYR0G6sXnsgQYA9CUla7E65WQpB7XdtGwO3BMibBQQ4lMUWG+tsNTgQ21/zh12woSkO
+YASuWscttcJCcaef+ethKoE4SpvKHoD3kud+rcz9RqZTfI8rNRmH7UynMl1jzCDmWv/BzDoCurhE
+cJG9PwDsuHNVaPEwKJzK18Z0ehhaXkEmgb4rsjqGIAVXTat2qAHg9Owpv+tRSMejbUN7tc2rRxoR
+Aq/rXtlt2hQNJm1wjDibObTpL/bAZf1Yzcvrjhsx5BnbRjc4qTivpzEO30iPpCUS561CK7yc38Xb
+tcYKsRs/LY/xdytOAL1oCCQ2vgkD3u/iu6Z3KzQqCB41wWQSAYAMTd0GwRIgOD6mET9zW2mXgSYB
+hsVPjJAnv9NX5mncGFftg8ETy+wFz6iMuKqVhhVShVf4D/Mc3APDISJsG7xB8N5Qto8cAyCGPFIC
+tF+aQbC/lVeRUxtM/gMUy4tGOEAuzDgeAr9b2yvPBIBdK+y2wgtvDCYzCI8PGvK4slFOsVHuBwj/
+T933H9SoP32JU5EExPST63XSDtqYr7qUCsnaHRBqrW/J/lyimO0jg4klLd1PhDPqI4bD8ZxNRAoC
+P1z/khzitGh8zb84HnlVZJ80gQzWTowAo/UHgOL3gQlJ4eNJUQfDs3N+Shr1Tz+Um3DF6zXqOJkx
+u9+JTxbfwzand9AiM9gqU0w0scnrBp1uDfVeW94PDrarVaxx2+uqqFVPpg8ik5kEQbjBueQA3jsL
+DkVALDXzpNWtYp6ixOKDGyXtMicRp9QXbrP2PJVwrVdW6up3odYwGOaxfQXANIctH68lXr4dDB/z
+AMoYpvE7Dl8B8j1+X1mR0BUKKsm+Nkez+HztHi6VZfsYq/0pZadK7eB2t77HsbnEGKd+sw8A+GYS
+wT9sllt0ZfOIqhXe/LhsmHtMdDnfDoXKBHzfOBj6qXUmDrmESGbSjWs5rZ6SFKNsR1EazgKAFSB4
++c8SCJcPYwCsOo9LsCs136UM/PiCIEwhAHDzFAkqJ3SSBNX52B6jbQJh1Rxi2TZ5cCPiEQCbiT1L
+ZD27v3KC4NAIt7g4Qk21mEyEVvi7hfqPuh8g7O53NcLl58R3/7F5RP1Jub6A3rJiT+0is0OAvAOE
+OVqDeYSIJCP2Wh/JQ+EPLlBuSIxnr0qW6tEebsf2+2kRfkJaz3+C5NW8D63otBHeXM31IZaM4pCc
+ZUx6uHb7o8aX8ojJO4j2+5c0q/mmcgVUDK4DjiN+iPsW4JIoiXbwbUp+66U/NMZ6hgG0FXQ2CTXk
+tSGu3uTMca6RW9xXDmugl/5QIW+6nCE76Fg/Vt3nUUSq+YRSHiK2Yo/cb0/hY2apAZuerVRQNL0D
+SOU1YCXfVL7dvYOY414v7f7kzqGmmtpiFwfBuuVAfORMvf2uXVNNkwjzM4M3ywQY3vG4CyQBg1yE
+p7i53JYy5Y3eb/3yIxt5Tq7EsWmnmSDkoYNgkWoeATBsvnHORAQa4kBqaTaRvw2+dAiHsiMewjCB
+WONgKNNqabJNpBKdNyDktfpoappGwD5Yl4o+S3TtfsJWeDSLEBk0wGQWIacGOdMkTCRMNDW9JPfD
+HMO7gzegp1Y4zSIEZg/QBDvdisvXtBE2MR9/We5/tlJtBr9SP88NDTEDX7INhlb4T7sfIOzut2yE
+zarmV9M+aselHXAsIknGP5k8jEAp/DV/F/KISy0c2XR6otITcj8Lk7csrCYYmbccN2QXzCYLGad1
+m1/SDO+W9LOFJYFvjw/OAqYaKzxAcRGcAZTl6m5gVClDB8bjXIG3smCe5psGZ6xnaOtXgPiWRt0H
+/XDYWv4OqjJsJbxFA9VGsqXM/6W15o3TktehrdZWAUSpWCI7YTCcd+5i78w3uS8gU6m43WW6KVCR
+trgB1e4unWkx4rGYd3zMa5fnauf8TYAYcRPYHfjhGVX7tueHKSMrzLmj0rxGynKl9TLxkqF+vkdv
+Yoc/UbbEarmGDTUv/Ml5MZhHiGy/0b3ghxlEfrDAgYj6UVhsVhLlezhXyxQucZOm9wvwO2qL6dU2
+wHD8hMwiGDgSCMZZXYrv+Lo2WEVE1tooDeGLOUQPx2xBxS4igaiN/DxRWD8T84vFwiPZ0hRVVNMI
+/gqb4Yty6wZqb7bBu4dPiKsKluOYNEF9KQ4f6cAXJhhnXgOtwuRBLbS8YhyHN66kKTbzI0tJM7ys
+aoQHQBw0Q1rfxTTFYPhNmP1L7gcIu1ufs0Q+fJIYT73VPjhBcMZbMOTNvL8DvOmftYKzRhNCsLHR
+IhH3lgOTrcngvrmF8SwwSMB2V4QQgcFoDWmAC7tvoBenG0daB8MiBxCOn0iYRWxOkvFnw0lgOzPs
+c3LOA11Rpl0jT9RpRx3X+e8g4uLejol6KxskQHR2yIB2j0leACAYZYz0t7Zpv0t6t9yq6ZupA2hl
+Swy3JYBSU6nO0oATIJ81jDD1e9e0olxpAaG3u2AQqY4DtBGqrbW0davD/HbwS3kIY1/cxaBkE8Jo
+Ft7pSYQwCwWYdrSV5bU51R3NtyPL4ZJHMvC997tRndTFXumVN7uZSOzLeBynYTMSNHQmGnaaMEuz
+qLFSJcxjbulM+Tmf34Lft98Gxura4QS/DIBzs1+YR7hWeLn2V02DN2s5NYJNIzh8ppkD5T3yBIxD
++6BgGntuaC0az18IqDLBITJ4lDlr/boqhdcG+EsJBNsSbA4/Nr/Juz1waHybDXEFspQmJvx57vjS
+nKQMzi/LIb8E2A3wy8C3AOFMMzPXBEMzbHeNsDZwrNVWuIPfH9OI/wfue42w5e5Y1QTDmsAYuK6a
+RFTzCAZDgjiZ4qqgvgJibeGbC5S26wQgnn4SufB3Og+ii4RTQoX4vGl6S3CIVxH+Gl1+NW763Tpg
+2dPj9VcOS9H6BpilofvimvXlWNzmkDcHFdE2Cf/mxvRh6n8LAA/hOS0Zswx5AaoKRVttHOcV0WD2
+O44gi1GFdHfeLMajx+Ni0sb1Nc8n5zk/FWjpxXSD5PRbRfF2Z+RLDH9qzBj2JscQoimEH+baz+a9
+biqzzBH5LdMrAKZ7Ma0T+yjg+WjYXKak9/I9Xeb0Sklvk022lyLtK10Zr8LaXyGNG8pppHFbzjdz
+n7TFndI1f661/C0Q3GyEFzSgIn64BINhZ9NmqQwSMotwABymFmQaocUUYsXxaqLbTMIe9IzHg06P
+UDAbvrb5K9PY0to0G/0Vkf1mCmtFhEAwaYQdAC/TMBeQtT+vctr/ztrg0TZYehlJUCynGYT5uGda
+8tAYYnONMINb8mOf0Jw2hF0jnCYQUuyCyxuEASifbxrkj7sfIOzuaxthwyYBB8R62gjjE5NariJC
+fmj/DlAU/nrd6/VMD9dARgi9HilygIdg5orFQi/ASchkKQg1vIRBE7BAuS3wz+D20AB7/KGjj08x
+o5E2H6OG5U5xRTQPEjHmplxpDnyAWBHUAfNkT8x1hKgiYJCAgGihzWOZw0maD/0oznp6lQk2pCH8
+W2na0qznPBpKbcx8W5ZpjoMmLbJtuxDlVeByQpdJj9kaMJS5w5+a4ZLTZMawXF6EBHZ1KbNJWF/a
+8KntZdg0p0ZbOO/9iRm+jSjTdSHviKclQLyD7n/TEvNdjnVGOfgekT61VGhocwHbdQQqgFKqgzsV
+/FSEQHFyxgTDCXok/NnaXXd9sPisCa5l40eAGL8ExmQOsVVzQLqRrh0Mq29n9vGrm8RZG+zXxzag
+fExUc3vhbm3T/oLHP96fULVvMAhbYfPXmQogjMlULAqVERTzeHI0E2YbXVSbABjDtcdxX9c29TMT
+fCX1I8DFnA9gdy6zw7eNcSapEQ46JA0xwPGW4Vs+oo0FGK8Z+N7y75NEcjMcA2I+KaJstNQ8VaKD
+4T/tfoCwu69PjVBrwFc3KBZ+MqYnYuSDtgsAeNQIE1trsm+yKeVytcym9tN2sHrAL2Kpa2XiQkA3
+FlYI5iptGASzSYS0a2FFj3wGvVOayAiCi6aYeiGSzLM7/aeujaEWk5Qy19ymJuKYER+C/uIG5n0r
+YxRvLc+RZjXfUU7bHUuAar0ikEs+15Yp0sHtBSYRJgyG8Unfqa9vZhIZHojiW3fK1UjgkyE+Aebs
+/22WzbPUjkb1XnW5HcJahnanK4WHseO6b047BbUCpW2IoyWBfv+uljjv3+9VMyn5bn3B/NcHqqxz
+v2lrACou9WGA+aaJxFt6kwRbsQHJ40ILDK1gGcpOnTr6z7xKc+9oTckvL5rhVcOcL7Sf4mBQAILp
+HH1z+Wcu3+Lqm+Me3SAYCt9mCqGjWcT2G+UP2nLAnBvinKDBI4JXNOYMoh+IaosOB30tA9ZV1wjH
+1+X8a2wLgFgkNKzhF7QZYdCJpSiTs0wC3Z0W9GZuJhFh0Ob2PygTadwuy/5KBb1imacCYRnBMX9w
+ZRU/m0dUkHwzi/gBwv9D9/2pEQ58/YtA1xMjEA+GIPUagJjq3gvN40em3thja3M/uIFBjrZKgg84
+U9vaYI28YWBPWCSPSGKm0liML+SQZrj2zynDERg+wMojm3GuBobL43EDwJ6WO4w9T7CKc3zYr80P
+zP/xKg5UGoKsU3QXZRgnBgmXokO9vZ7ZFXpo1Wrzf8xnZxlzwgIjFpH+vJS5GfnEzYAYdl2xWY6O
+9NsmERBw2UpKjfiDno6wfUi/jbO+ZjrKfJtHuF+Tw2BqDqHM81bCSmEtVZR0blWfnmuLysMclbcz
+D9fJNFS6NsXJOV5Fg8zxZwtpXJTynwDYKO1cuaCtM02ptjgtovvRNlVKt2ClyS6zbuTPfn3j3+GI
+b2DXAAwPILwynbXEMI9gTbCKg2EAY/BN1wALyz6rYNgHJcwhnptmeJZ6DJ5j/tBm8IUCgoX8jaOF
+5hhRzNHq3WPY0N8GgPcD+dZw7pMvIH6s+EMkyQvYpXwiVYMcoBdxoDsP8+Y4dA1hHLuG+4tJBbs+
+Vl37OwJhqUAapqL1lAhpoLgC4EkT/AOE/8fu26HPc4EJ2EoDu8gD6tzUw9yucnJuxBDf12qAqI6V
+DmSiY8cimw4ACLwhAO/QBmnlaBFlu6i/nDa5RySeHKZdi88TTFrV35MR6D01w9LCNS9bRasgTjIs
+JEqsXYvoaVdHff1lJl91iIMfbSlpFzIp7jq09kWeWu8X2TzztGKsVVDpj6mJycH8YSxQgSIPbcAi
+AjT/qwdFumBo3S5AqS2Jnj70aIA9bc18LHNGXssXkP/Sqr1Io47oMq9pzXuUs4iV0pXCpXED75ho
+8YVoso86xFHYhri2zmqIME5vz9jcYZzQKm2UBFoUpSGmVa5Z/nZnhlzW/LtdScFCeVCa57IC4woO
+B+5R/ZrtToCLpmrW0eLQptSmbsebwkPuSQW72wziScAIBMbOTAAWt4Ikz9nAQZ5iK2WKreDh2LzF
+8dG3ilSzT4ef8vUyJmL2iNiz5c6hZKlyJDdn17HhGWHzi7TS0JAXCcg5bH7Bgt58IU94opUUvMWK
+wQmoAXNYy2ajOr1K+HNj5yYH38dhIrnpEwozTbtxv6YJCV9vfskHmk/s719wP0DYnVlfrbd84k9Y
+Rv52NYn0eP3NV3D/Ks+ENwuNAkMlQafMwMUkF96skfAmYNHjJAjjBUuv8LwvgXFKfwj4GJ5MnSl4
+OZwfCjeBitJj2Am3nwWv84NiCPDG6xmEr+neD287f4K5XrPteZ3ysS0WmKGlcCFQm/OYcTkKlmlt
+UiegYS+hcZytXK4l7SXtqD356+FKPS/5dl7Q4ZytrwNOCE1nu0e/5T2cM3dbdzXubCEAZk/hMXjj
+6+/jfLqpXbXvdSBh/pACP8cNQsfeGsg36wD9fbAgm+u3DcCbkMcHP94iRTm9vN2aOUgZx+kBgfIn
+UNx/Ip8yKNh5wmY9hkBbbUMDylPelH6W62/zUH8FJ/ddIpwWfSSAl/UMk201EA8GAfyU/CZxvhf9
+NigeZOiZNXny42/7lm0zB/888Y57ZH9WdPsNcfv7vNuva3/Qomi2s88iQxzmj8KKdIzh84j89exz
+c/969scl/npE/rLw76uH40dh87AYi4EI01AcebBApjwhxzE/wnUjjeQy5Y10vhfmQiTkZNcE48aQ
+q91EAu2o0q5KujmOfkrlf5cp/gPuBwi7s+dLICxygN0RFF/SdyUVmfAr2i5bIqy5OJDehS0vkuIH
+IC4g2IY8Lhh5YcRNLcrkVaQ0irSnJgmGxSr7TX8Xa95IZ6vq6Nc0md3W2NSFqLRQWftrSEObKW+E
+pbYV+DUAbWE21trKVyrT0lO5/lam1l3mf7YvGMbzbF31aI06WvNdOOrKKt+alyTyIW+n/byVz4yd
+WOxWzr4OY0zezSTycYXupVryc+qBDb8Am73OyU1jZ0O52EkflWuC4gAD4nHtnhOCFK5O5wHlTDTn
+/KACPlRAsqRO9hjjEqdnnNaU7+L7l8kqcN6nPCR9WBaLik1aa8ryHQBySbcznfwV8FJnoo3U3t7h
+CHUt8Oah43zj3gforX59SAssG/gq3zl3W9MPoNf8XLUGfh87ga4SGPaNe4+ubXaAjXyxoW/tzwAL
+ZIRU0DsBZMVHRlzeUbyJONA1kb+2VphB7wGQGfxOYZqjGOLABSlCM91KmUojnc68rlbPTquSyPqN
+JOVnke9NPjIoFjlBcME61ITpevxYHphGHf8Dy4gfIAz3WxphYYJNQkiCkkr9lMaAeQt2K/Q9Mfo8
+pSHzFIGhdMsAuxKCpixEejKrmzhkAw7U4SXiGhRt7cr9m9PRJ+X4cXBFgrNqtgM2oQDEAKr89Iow
+Fm3sPGewXMLo1wl+FfnIn0KyaY05PgfEy9ZR3JGWY4LxFqNJBXC+j1PnEzb4pnytJR7TANS17jNc
+mO8LQC0RBSPomObTPTrMkPZCMRvnGnoLM8TIMM/koAfW2vYOQfheiPp8EsMwXl87BrZa4xnsHiAY
+cVTHVHubtzbscZ/SCQK7wU/A7yR51c6jtRrwL5QRCmOOWYjS/WMZNQJSmrMpT67jBMEHpXkd50hp
+nTzmhQdDp86UcQymcNR9PgdTuzXHoOYD8NV5MZHZBDsXAylQ/AzZ/VU4xJkfcUafYjLxvRxPVLR5
+7ZI45tDD4sDYlm6w+zDo9evTwvrIs5asApYdELOS5AJ8U/ubea/aYtb0/mV3MAxNcdEON60wicMy
+rDTgASIbDXF+TphAdJHB5T4sp8/KIX+K5jf8FhWxUqxuqrN+6yR/p68uGffbilqOr3+fD/599wOE
+3b0Bj56vP9WBUk9w3OKQl4gXt534pTpCuwl05AliJqZ9EBjyHUDZzSBKOuk2Lcn41Gjn4sA1QaUE
+gNzxviyccx97uVkoRNiSybnX1KqmOZ5U942PMPoAAEzh8MsMeNG1CpBnwMyjjVmoy3/7GbZJSx+B
+cyaG7euY3obPu3Z1NvreQfGrA+0MrSkj0Im5uLPMkY3vYyTPiX5GUrqE57U1aHSRbwArfZx0CPzO
+WH6GzLd7Aw0CAE9At6fpcEMdfHTDNuZa0rjDFnwGTWDNvrU6jjDdq5Tnrko1oYAZgIjzOMq/76Gl
+zljHkpuGp/hzMNrIEA8MbfKkGZ6kf8+Lex/H/njQDu4ZHSxg2F/77z57+sl5qV0uBCBUzHki83do
+hdf2x7nvCgC8J9aWiQL0Up37fF3d2uFH02/q2mENswcZQO82hfDj3Mg/g+H6Kw8GBIRLedEEt6T5
+laYFlmYaIQUAW4Ln4Nn+o8EvgJZII+KueU0q7aSng+w6v60M5gZ0S3GT+UMo+0ieGmhENp2doLdd
+wR6iP6kJnpbFn3I/QNjd75pG7IDF5QS8/qdcOV8Fg4fg17xPF9oiJG94xYB/OSfv4Be2v3cQ7DtX
+NftncT3jslG9n840S/sYvYiIEUsmgRd+NcFmKVNzAK3RBzaP0JyEXTwW+Hs6+pGCj7W9uzGRxoAZ
+Dc1hl2reci7pBLkcz/P7DpxFcl7Z3QAb3/NT7ESFU8kbgyqM90sUxyAnvAMdBMlc6j3WzaUp/204
+2/oCEmMma4ZvtME7Yyv/Ww5ltYQZHKdmWCsIxvV64z5RUrXEPAm5vH2Ny7T0Sx2cNs05jyeOkMu4
+uumNh1qVNoN5AgA4g9wI0zDklU7SkSkd/MHrozEsPD6WvLZx4zdS2XfpfQl2o5HEShR+QIi8qlRP
+90upv649C1mgJFCwIXxvJNtAVR8/9izeNGDS1BXGW7iYblCsMIaGxvfRwSRCxfQRXWvbDKueJhOI
+g5/NGw67Yfq1I+JUVeyhEzJE0jTiqdpeIS1wgOFiCjFohWlIjckAJ0Zc0jmuzBEnSpapANguZbaU
+QX6A4BMAp2xF3JsdMSvr8pYDwMXDUGsaX+mAoD/qKszfOwAAIABJREFUfoAw3G+YRuxrBb7mRCZH
+uhMfOBxR/iTEjZhRlzORV5nWKwm+aoVRv8lnkEwdDuZ8gOBk8smYm8aWKgMTHLZ1ZAPVe9F2y0Y8
+LabD3KG0t5lDIH8DwEUjLAx8MZ4s9GjOrObnOeBBiXoOyWU0l8aVhv9mGtzlF+c607qr1DTlyV5/
+qutSvc75a5wmnVF+7YVa2U/pnKf34m+FI+K8c++P1uAl3z/jku60xqoksA0tWfqn9PAP9zhJofXT
+8rac9w3k8ql4R14Pp4IUe9clKwFrIDMPEWg/qS+6W5ubA/t4ZdvrOsc1zWVumzl5DLbfanx0OtOZ
+x9iRTmPI1Wm251gMRmmtb6kJlTJ+U/uD73I/yLZOH922vTjWMjTBfpqP0QePoFXW5QoNc9Br+0MY
+YR+sEb/9G+yqqth6RJduEF0AsRaziBH4kslEjAHuhbgFDfGWMfr4OJVNcFa0w/IMaR0oPyZ7s9wT
+w2s0dztMgLDweQLIKV7LXCHdbpmsZS7586ZFrhcAjEalZlgMMh3AWMifNN1/cQvCFT2N8+znrutK
++9fcDxB2961GOCb8SwDM1D7Gyx2ajMI9JOAJfgqBgfDAx5KfxcKEjWo8qUWZ7ang/QTBPcyN1tZP
+tUO0ZP5JEqEStFPx6lIvADfvF/bC3i5t7T20yd673gyRLrRa3JCPpZfmqJX5KvmH9NAeaysmvPEu
+nbbhzHqyHMecFNbjEZriz/sccVTtV2VpzkGr8UGKBhCM/AdQgyarFZOzmunWNYz2NNRwgYu1X3qm
+/9OsvdwrTCH4Tko/5FEpwLeB5G/amGO+J6ObpvwOyEVdHM75xxqntin4FvJojRMRbJgy9Nnj+KSM
+c/3OLGgCv51eMqF1HJlF6tukkn4u2v7wW8wgGjEXza9Rf1QSZGPgoB0uroWxuFQkji402+e5LxER
+1wCrOAZ2cGoEVKHSWyv9DnxN1b8sp66Z9U1yqq4VVpECem9hPcJhErFA16uES36AYAfFqpbtExm0
+vZamDr6J7tT+Dlc/Kxnyljl9itYen5McvI7KcFylHat5jcpMGmKv6B0Ap5xM7fEQX7S8k9TTxEWU
+LzTGxNc5z590P0DY3dc2wpJ2OGwGweEdB0beiDEIcAeDFWmNL2lC/JUyFGEPgKR5z5qm2+TBpJ4a
+MYDgHAtr7UUf5ms/TzhecThAiQ8h2PFSsoxwMFBvs3oYuiEfWdrwJgFusVi1zME9nIKtmz5ILOlZ
+YNoRJyKFrWWveM7sSGXpp8o0cJnra5xlvJ1553cQ9/js/d1Nq+Zovn7I95L2dveggyFXzusZ7nXX
+sI4AaG6znr5LwY/c5b/Wgmj1H9re/btuEkKW21lqvnR5ICNngCbUQWNLoA3hQp9K68tyGA5bYm9j
+gF/W+oYJRE5cgl/NPEPXjrWtFQyLnLRU47wj2fDMEeyRKJTlQCmT8UXz2xqhVMUBeK31RzHfNLWR
+tfXoWCCNgTzqYNh5jB9zuUkLWmGiJzyUOiDdwNr5uGqAY3UNcGiJ9WmA9w6KTVV0JeCNc+ancgS+
+FfcLrTBkjg/AkxreBMRdE/wFEGaNsGn6ffK2P7kYy2xII+P57vNF04R8RnR05JVWGYHaEwAPJhOS
+8d1sgunuuAJbTOnKS6JKqT/pfoCwu987NcIKgR5HngyAOAi2aQFADKG55HvBQ5KiC/eajrb5oiMC
+PIiSCTDiYDPsBFkYIl1LP+r1/LgG4rPV11MtiRnvZuyGhklEsAfkIe0u6qYHkB2WKDdrh6lsbQIJ
+xWxrX6Rz+smF1AchxpSlf9zzBM/amMic9wR9tzgOzRSnLT+PyLeu1cHzerTs3U19ueURkXpUl5xj
+cA+nLejn+56xM37Ulzr+eZebopTuSnGIP14j53zVturg8zAyJ7twYKZ1DXvNDIjB8wKQIZ/SuFuO
+6U4j7bUQsI2w5AbAA/xSGPXzvWgtHTzg8PeVpIW3FUabjIoKMIGlEFDRUGT0G8dYGLWdGtw3z0XW
+Ps/Cv8F1orfKESr4df4MTazpeT8yO4BJgjn4FFUykdjgVB4H7gUQqxRQvHo9g4b40B6zPbL6B5pW
+aoWXudmHb5ZrZg7STSSsgd0hbwXCfRNZakNBUTdAnFOjJMtJGXdmrJN3SY9yJgX8il92WgPGgjQb
+0lgS1n7WplA+xS2rPfGf4pfsfoCwu9/aLCcSQLAS5B0QM6isACEjKovdLoTMALF2BooEg9RsV48T
+ETKXSO0vg+BJI4zru2bYm9GKx65jCEpIPkbqLAAgXcmfZgwOjGl8Rap2GLn2/6YNprx5a4xBG86j
+aWRK0fOGv7Ev5XbW9GoGQ+XoiLVejocN4X7XAB8cF1PEbGZiO7+hBdYh7pafB0r6KEkBsAc9VFLY
+nr7pKOqtGuLewzNcTwWY8qdT+lv7c7rahlf3X2uDh7YAJE7gdwLDH8VPpt9BcMZztxgsdnyoXnVf
+cwmUJSoLsNvD3lf1eD4urmuLd5jWEvUueAnaR71OPqEHjeSbJeoIv7kDUE1mGJ3cILcJgMb+MLbW
+G9YXYmUY5xhy9ut0W87L4MBbFJWgLep9G8DwFjVpvsDa2AS/jwBUG9sBq0oFxdoA8lNAsa3HNcR0
+2kRoklfaBdOpFfF1OqjMGeBa1QAfoNfsVSuM0cK0VcC4tdGcnrMgc1yJsJZ4qURE7MAi7inaX6Fw
+TUv80tPyjWw2gfqoKFvBbtgD9+Zr7dafcj9A2N3XGmHJidr+Zh5BcTzRQvnykoBndEoEOKSJCGkj
+8v4iUjBmpDmKMr9pAuQhLm5r87WB31tebl/9ylzb3tGliz9lAkh6beX5mesrAFckFuf2B9uhMSHw
+DOFsg/BjIc2tJwF2CpvpBAkpubSkGFUylFWRLivP+56bK2dAdwy0fAK+XMbm6OE+L1VR4yb+/daa
+b/LhgW4CNDWsH9Lnu9ysB3r+T6P65iaau9/KAYTQhwKATkbwWzcSfQTCGBBLcCvlcUPYnD/nNsn3
+tAOWtrawLht4CwBIQHbzpxZmQDyBYc3KS/20Dvu6q/TDf3lgqHPJlAqR8DGSx+Bc46SMeyeEGFNn
+UodtsfC4aR2X0VltS+k3Z7MY8z22e4LSLAKE4CZGhfYkwTBpidleF+YSqRmm32H/e5pJCEBwHMGm
+Uk6beJbfw/bmOdPYtKfP2kCMgWzR/joYvsY1IExyK68Ag+63jBMFWKy89lj/k0iJe0kq53A9yqSM
+jAwN4GY4ah0AcN6ot5WbxbcREcIm1HeO/28Y5990P0DY3W+dIyyV0Dg+eCP9QV4pZSo0ORxLDIrK
+NC5LC04rPwVvY20wiDBthtG2zcisVxJMklbYLe3Iu5kpA4/tG3rdoxRPm1VEgU3gISFSGfBGM63l
+r+lps4u0HLMitEWqMPdCLMyjbs17985FW6KimidYwysCOk+r6OAh78gPI+XuIjSub1Cw96TE9ao+
+5ceIH+U0bhvpPgelOeSvK6hbClfd8NnjSpHdLviwPP7InM8MX3GUD9rgz3U0oQnAx4A4wO4JgBWg
++NKHW+zGO1qAr1KaUdciy1u8DlOMeAd0J7gFIBu0w2/5ZQLAyb/nVVLNsmZCZGaaCUUjfMTlDNd8
+OY6vYSx8pXTcg7WwRXhcFmtZABlZ+AjGz+KPhCZYcvy9AeGPr/dRm2DiIKud8buobAPE56a4DoBV
+AgCXcDub2EGxLt1nHj/7KirVBtisaYgJ7JpVUwhr9sQjEIacZnOCnPdCPu0aP7v7U/4OFRxxM/gt
+J0VwHrmnq4l/4a+C4ld9GfdJsy//C/cDhN39HdOIToxS/FYntvg7U23uAEh57xMg2eErQtGynDX/
+zqMC7av42b1BykTFvBktb2KXqxD4qisw2HDpwwX1xa1m2+iwBwbj9zafLDzzFoFmXdhJCmqaL35N
+qSIVELNwKL04+y0i8fAxpqscD2RstpFC+pRd3Odbmgg19gifaZ81xVT/haDfGNtbuan8ka01v59G
+QNh6LDJU4eUhvF8af2/VV6P2jzJ8AjnKxDiB4QaKAT7woZscj3PwMK7KAwa69PJxW0n2EetCyW+X
+eJFTOxzgVj6D3XVLZ9MKo3blTBwPxbQW62qwyA0+sqOx0ZIZLQ1cicsyUadJGdMAtz2cU3LwnM3O
+QQMaY5M0wCUl2hIPfkVdT3fC/amMuOIEm5kzQ173eJxhzFMxlcD80RFnExgu2mU/Xm0DWhy7tjY4
+XjiK7fG4R8xWaoP9Qx4Ih4nEpA0+AHCCXXvx+6z4VGuG4y0sODbsaV1BJRyP8aXJruLjQ9ylbNH8
+Ev2ZzABZMl8ByO2tuGQ12U+E9bhVxGFsRiXZv+x+gLC73zGNYJ61y25GacITOvkn84jmWIKMaaUl
+R1onNgnGaAR6pRKfUr86KuDXKH4DLR2wcq2CIVeGi+OLuw2IDT4GLlYAtwnfA9rkTNtlaVEaAVSU
+pXEJzZWR3wUZa4LrkGVLlTo/fYFu38OIgbQ8VcodjwssyPu0qZztaz2N8GwV+fvO5huWds0FRb5p
+wkQDn6qbw1Xbm9pIHfOf7j31azb+QRv8RQXVD5pk7ZxofT0tKsU0YiUgvrdGxznSTI01IaKfNcJR
+ZTuRhXhWpV8lMNzA7ZLUJAJY3cDwahVL6uiY03QesttQ16ZxXL5Sq+M1xlEnPX2zfG+/y5IDUVC7
+A6N63MwXJMakTFbxN+f8P85I17o3Q7hd6v3X2jDFfeN2sBHOPpuq6JPzFtpgAsH50NN+rAmOeWXN
+bwJggN+qCbbtPwDxCkZvXRtMmt8AxwF6SRMM8Mt2xd5fwRz7JAfl+SuPRlH1quTpDyd8fYkzSXEM
+vBK0ZR530w5HpjNfOTWCxHgq1lqzDCus4hC6y/f88x90P0DY3e/aCFvzi3RCEKedjDx4Y3f6ktgY
++JTGRDRpgxFfgLGYnEC5MnbeXAbhEJCB0rQPgrS4mytru+khh6IVutG9EZZsH/HtGgbAV6nKGdQV
+Y6ZVOaJNeA9NnIRr6ZDmmB55nAa0lSTWcQL2lq+Mg/HdG5Ip957Sbv539w0gnnj3UeSlKdCaIH6y
+3uS4tx6P4cAL3/X5W/fvMPlso/JraYGQdZDA2l/lODaPeL+DtlHB2gEeKrjLclyN4o3qAi+Z0yUo
+XkUIOO0b2iLQFBpDITDsFRII5tNnRCR5XFmJSaXoB1oCwBK0ZaAV9yBrZypBYMyMaeB4YZQz3Py1
+Mw9kGVQfN6ucU6MabdcLDaLNgYSjAVki+kB91dIIqjAW0B5d5RQVW1LNIVT3EW0qMwDGPI5a4QqA
+D782EEx+teWv9iEHfX54oxxA7wF4m/a3g+TQlKZWN7TBofUVCQAeFJY8q8t28+zlKnbGWc2fFaa/
+A9+qHc4Koo4JJB8aYdIAW70tH82KviV+on1KE33+y+4HCLvDa4yP+ei3Jz4nlonv4G2dKCz5xCYK
+u1NA4V5W4ykK5yHG0yUvIua32tqoUsBwAZdE5Eqdgl1Q7Sh30tN7n3o/bEg8ypwDc2pCu/lEC9s9
+nVj5DrsU2XLLQggVGbB51yF8om0xZ0bp6WcIXM4RJnpCWtGU2dBe/9tBBNIqPERqTfldd18tdLfe
+mOF+hRwaE/yd1sVQk/y1Hm511jDB5sAzf398Prrf1Aa/sYb0OPV43XWjEvzLgYZvOGLtcK3xDPGA
+BUHmuggMbiW5FuOlIVhD5xyVhzwHQcXOl+1KGVTxK3cA5aI13ncH1Cir7QDFtF5vaV9ofEuaoEP3
+ssV6yjCXVrLzGAfv0MuaY6Jmf6fvGHwRBsOFs0T9O/NoAlfuIS1+jz40wmkzLAMAlg9h0AK0v0pA
+uIFh1+rKL//UM2mD9dcGrrp8sxwDWwK3wmYR1swk7ATKAYTFaZPMISoYPqUYr/eUdNYjz+uYdil3
+A77+E6sRHfzeNMJxm6xSjNI5XDYQavKHP+1+gLC7bzXCIjyJ8yTLEC54sYW/BcEVPMlRBoyx3JvD
+hf/SQjQ70kRI84unOKtgmDuh5abVZEG9Hiz5Eg4/3SeKDitLeKFUcwiUfg+fYBN8O4ATj5eF7CQb
+VGZWFnnqdGSobqCrabVP+boohXJtJ/ehy7w+FlXYc04eh/fSn9yNbEueRo9fVcrYwZHW52+fQbDU
+25w00sPTprp/hx3vMfjn6uZNUPn6eYMEbJ7dr6wZCMIcooHhw+lJCpZZFR3CK3HwC800FI9x1SFO
+cq41q6xlymt0ib7kcVjSNMEEgAGaYRohQuurcGWJB/+KGPLYR2nXbuPbAXHR+Pbj0pLpbhCrsfaL
+UhYoAXICPLfwlDaWxU/9dk/hV2WBJDOzWHPDKid+/NtOYS5RQfAtDBo+0kDzBH73Z5nXBryHFnht
+ze+yDYBNA9jpcpAHrSXA7wUUB9BlbTHHMxBWFZhAQN5mHIY9F9YpHSqZjde/k+a/AnyLlvcEvgx+
+2bZ4k4MxeVZtL5kAxjIhuVCUdnfK+dfcDxB297ePT2thmcIUlxUBZWXGgwBYQgzxE8FMRIbwRJTc
+lHx9Q7eBwPB2FHA8dphAsFV/NnLwcz9ZkEjzE2jvQC95hbdZozR63sI9ncIAxASQxWAmkfMG8FHn
+wqi+i4kEnRUsIrSJruWbypI7BZ+N8VlHCnIrcTeX6Z9y3VtZMwZNll58dsZNdf+tZO9V70WEVYbw
+CaZv9/ht9wUI/r16GcTngFTgC0AB4Jvgd4NhAsIH7tHTZzV7gDzEYRmrELVl3xzrnXGtCdEUFeHX
+6OqANjW+2gCvSJpD+HWdm+WS3xROnfyN8wRqqHGhHBCtvDERjhRip7T8+FBj6L5pQ8Hr/NbqA2QE
+iJWKo3yfQs1eZUSnbgZidrELbiNyjbFbCvM/lSQY0OTgl0s8/EKb435pguJnVe3wL3NA/KuZUNhO
+M6PzhAnwNtBbgTGdJHEDx6EJbmuxgGGPLyPl88FkISl2i//legz9UVn+Jo3vpvEpTbxM0ou1tuYb
+BYl+5DLINE4fqOmPuB8g7O73To3QYwLLb4g7CCGY8OVGwYNsjK9tskhkXtp56xEWLBZoh5NA0TQw
+dpMEw9z0qybYkvWq8WkRTfiUOvu15jeqL4qiXhHhc31RNNi9Vu1x6R+FRbbQDcDtmfIzz3zPCq07
+xKlzl/6riQTnUxKU8gIo6N49bwhK8nPJqbYr/FM5WvrmTqZ2A4BT/LdxZ5s+ze8Z1tfwP6q9/cdq
+EuGeGLURADiOq2JAHFo0hJfAVCI0bBMJlDjNOA8q5dvYRAvtTbRZNL5DPmlp1TSCANEEhpdrjVkT
+TPbEtQMQ+Ihr/CniAF6opXxaQjJIwStwHDFX+ST18s1ezTy9L2SjcrjYlkmVd9zG8mUdHgXAMxMg
+ox4eo1KM0JcNcSXrof1F4+UAuxUEC4FKCRthe5boL3MtsKU2+NcSsQS+upYYgV8xizA0wqwFBjDu
+2tA7QCbtcYB5Ar0GMNyHfUeQtAzSGEXJp6sPvV2uHQhfNb4i+6xlrsNBcJShabZ278PP7fD+RXhi
+An/A/QBhd9+O/Z7EfJKJn30f9i9VvrTh3ppUKJx5wJZwsoGRVJvXCh9t4rAqJY8kkwZjTXvVqLVJ
+sZQNLBySkQJkxns9zT51GdPxWeyoJpfrZj77AF2Z0o5J0AqEMprivOms7tJS4SmBtIVHpoY+GCVg
+7I8yjeOM/jPPm83r55R+Heo7QCM2OdW6Egw1TbpmnlI1lx9veStztsdaFdCiFnoRPbvypft3ePhL
+Y8BUtIVDqDv9hVDTEGDKcZJgLcuQZtwQn6st4mUAu3XJHP5PcTWNgRB+GmBoIM2kRa1tq4HLbPV0
+a3nHdK86/Nij4Zy1PKwQXzIAQIsxD54Y460x/mC5EChxP5p3NLfyvE9vOeYU7lNZFODhwhkk+frF
+BZgmkBu1Mi+NMa/IKESHOgUSkIyiPlBbuq2d5uPD4xrgF7+FNbPD5foQMJx+T/qV1ha/VcTLiJSR
+bnwSwJiIdbrGuPz+9QaE7YvfN/kWwKw/kD44m1lFnqXut50utr8o6H7DGc5Cb1X+sPsBwu74abcw
+dMqDMMuah+Mp/Mgm9tdwbYH0oB0UURlvfZrcCTMITmHX79Y1xTtuRwb4lWFt2hQPZMgJWNzgYPVa
+wHEA8zxL8M5SeX7uJzkcIJWDxX8TBNrCNW8ycQdU0yB3fwufZezI8x4vFchQnZWxXDjsQUcdWRxn
+BRCwYLpilMngNEunpnLPWgGcHX0ec9TvMeU7V5W2tBMQYJPZKW/+N2z5S9cFpEkCfcv0LcxI2+vh
+AMRERKEVxtoP8EDWooaVynOZc8rNe/PzGL8tFYBffr1cSVSFgW8t+MFZ8xzrjFpj1PIiL2Yu1emd
++XKMZcxXbm4W8bXs84G55Hk1bxtvSE6Ql81nkNm73bnYsS6sJAff3wHN8RjAb9RFgDl4fVRb13K0
+wlTwxbo6/iKJLH0sTUXlac3VmLqk3+3RAHdrp8FeeJl/UMNntGmE8yctzKC35tFQ9OAouk2nAXwF
+NuNSbMJJHGY8j0FfQNys8Wpj3m+B8GdQ/Mjj2v1H8HZp0775ujU3WbIGgAV+f+jeSkYmODn9SSz/
+mPsBwuGM/srhRziIq4UDEA/pAX5tAsE2+M52jenJbTxIDIKYW2T3Bh7CqvGxFGpSNMB7XVtN5/YA
++EoriESgMqtBbkfnf1XQTCNk9batXbWtPibadZnTqjp1w6zpVr6R2w5HJ8BtsonFn8KJB93G/GGO
+MtSz52cul2NrVDVOwmgdo/QbgyFDl5KXmVbaqNJxV9IBgdJc0evQqV1v4TfQfLizX8xfOd+/yGv/
+PdcJPh42qx/HRMXiAyCWrV3D+gbQ4uPNxIVVkHnwAg2a5rXGTTv9WoT7PT/RVjGJEKk2pqg2w8wT
+IvzNOIb/tjYv/rzVQUN1eelBe8H7PLDHN8c1lj+BoATAFvGRd+ADM8/W90EZ2S01hjXCBIZNtfKl
+477V6ZHRhcBlE7k5Dat3uDyQmQge+vLBwMRsxdgkGPb7MEiGaUTJIwkCJeMS+Na4vQk8j2RTvJmB
+aFBqq8oJhj/NBYnDwLgRl2M2AuNI8/78V7/cUCiq+whogF6RDY6XbhMS/4y2LatgGPbT9Mlr4ye5
+qf9T+L90P0DY3WOnhlNkZiIMbguhtTRogU3sAxjuzipxHxlJB9EaeoLgHbb0zmsqJMep30Cfbwy1
+xDEgLBlA3HvFG576BVphtJ+zzozwtgqO9mnmrm2eR167jwryK/iCx4bXWQl0iZJui9p6nF38vZwd
+8QxaUFRbOStPGgMABXM+gC/79QC+CUw5Lq8MehVlRVJDNE1Jl9M9jzbPZUG9CWGuv+ZpZhu/yXjv
+a/sb9+FmE3OS9Mdyw1yT39yvAACPhVYmj+1ywGuIAyhLMCxSeUI054Jf2DTgIGmni6vc63bBeAuh
+LQ15o87iKXbJx52s+1tDXgAwj8EOV6B/pNObCW5hhI0Clk1TqxkPANzz9z6wG+JGdtt4Dj6yMYLg
+i1lEX3/B7/ddaxuiQG3MycuMdC57Ys1ka3ZhDiGs/R3A72PxiWXYFCMJqNG8vQ1FSkGgeMBEOYGm
+PpU0Xb6FuXATTm9pZT6oOdmubBY/NIUpZ0sHmP17gPgp4SXi4FY3ABZxQNy0wtLAMECybN5kj6XZ
+zMDbyvUfdD9AGM5mvWOPM/5Z9Z9aYQvga3KaR5TbH3euifGUdG0+mFPdivUmYE5hJeiBxME5nqnQ
+oDX6PCVBJGaVmpyMpGWWz5etIeRw78JfZyH0dj0KvDo9srFumO2Y62s+uQ/uR8KyErfrsqPMm3YY
+Hp6bGF/k6SqHzmS4XBmnOhoJNvJaQTHlozhoC3YRLivzvOgQDfl7a97FjbRgr8F622+QrY3e33Zd
+a5hrsD6klo1tvDjpNbtSXEmnSU7bYA3a2+TCtsOpMcYnfCe51FnBDQCLC8eSFnNbB5uP0Aqi6CYS
+7C9vIkpVAgjKS6nezE4/854OPmrF4SnjoHqES57DfIIyW8aX27b4aBrFx5Kne30T5p6Uhx1rjRjA
+78BKkp+xKVwfuqkgO9AkNxDN0CX7pfymnbQH9iP0AgyT/3FTCIDkx/bGOrzWD+Ccgwnb4xD4xS+h
+BUZ/oWFWUcEGOe1XXXUYb8CP+0zNkli7lTYY/HIzKyieTB4qwP0WCG9Mo2Ea8YgINuBa1wo7GIad
+8G4f+JJWBdgk5/4b5npxP0DY3bdje2yUs9nPGmYGw8iTYHi+s3UfSZoj7Vqug+C6qgwVui/z6pAK
+pqnHWhWRapuI2MLvwHHrjupgMnoy3ipUa1+7wJ3SjquefOUlcMQd4NgrO3baj4bXXq4I2nYtgraX
+te/ijfoIJlnaJdlgI4AXk0xIsxrwSh8B5A/bTQbAqKv5Nzip8RqD2LpGtymuAWQb87XxlxbE3A3V
+T4T1iT9o/PmuvrcMEzns4AgxZmLHFGOOQypqu5KWtwHivQ/AuZRV8wkjEH09bbZpPndc7UffSFnn
+ctb8xkNX0Fvzf+Fqts5leB32dTekqR7PmPsepBlW4pvFhMP7jfZbA7h0myMNTQnecnTnv/OXe1mi
+tUkD/JZGVXXpc+Vno7Nepd/6CZpS27aoIibx1bi1nNQ97rE8U9jfiOzPLGsKiQCMpBXGOJCf102J
+93US69BZXLmKiNkT9sN5css69831ccJYoTlxayv0AsDLTY9+fQlyx9/TNMLq9sGSphG2VJ7H4xgM
+G8wkvB3QDrdh/JPuBwi7sy9H30VHA71JrKkVtqIhZtMIkQ5Rp+V/F47JEW85VbpWRcQkjmy53jPj
+GAoXMByiYjpfQRp44iZBaKPtrh0QCZsyEwgPOp1Cs0jRGLdbMgaY4t7lo15CdUdzYD86eoDTlZn/
+m1D6mEbC1v3a84EJog2lLpvjd+NTYivo/g1AbJShLZyb5ABQOvilc1tVKZ+PE4XtuD9RL2G9sWkX
+mih9ehGwNnm0x7VbvtUz5dOXjLdaP/IjGhj/Ydx5AAAgAElEQVSdrphngNYdVsn4vEIDjHWNNwvq
+aRUY88Oq0O36Wis9wDyXMPXjGhaiH9CTZ2B/H0bQGtpxDPHACDH1DCREzjVd1ic6T2A42ifB37If
+oH/uS8bVpe/5G59gcs40PdOE5qXwkLnvUVNjP6UM0NQbCKaiTAs6qD5He9Cjs70xtQ+pdRWxBftg
+3R9ZMdjnmmw7YXMtrIk8GgA4vjSn/oq+D2bhy/UKaQvwy8eMlqFRaSBY5kWjjhZsjelBk0bDYM7L
+yzSljABYBrBH5Dvonc9SHjXC6rJbYBrhNsDQCjMYhp8A8W4LHgTepfW/4X6AsLvTMvaSD4QlBIYj
+Thz02gCMZ5A8tST/nvHh4wyKMsGFz745Y2AJllVUKAHBAWJGDpGE70qr0yh9CldOrNkBF44BghUM
+88IXSws7QB/4CcfpkM6g/Rg2vYSazysNDTHipmuJs3a95ynC+RDCaMIcH1qL3v7XAZtoKMFIVqUJ
+UpAWmrsd178OVUHx/vXbHStAazMb8VfAM43RRiOZg/sdYaIkJqraqIMubyx7qiLbMuWcMt7TX8/7
+Pa4pkY2BiANb0JkZVryGMM/1maZSihTrj/MMPJMXFLAqcmyQDCqn/lhZmxq8IumopiUR6TEuxzF9
+o+s5mO/9H3vftmW5iisbIv//i0+XtR9QSCGBZ2X16VXrJZ1jpjFgDEKXsCxjJJIo4EGN3693TG8+
+8UAHxNQZM9/1mtxZa7R7fq26wyNl86GDUg37qHjbaz0Gz5J/Il0vYH5zIw+GjHYtXv1x7bB2xs96
+9KSmzeLeXb4mxzjhBcbIu9ErHB/WeLY+ciABI9Pa/zOt+obj2jr56ukNckLSTUw5RHuAiHdu8+Fy
+7TieIHgDS5k+HgfNPPNevMDPS/5LfcpniwUGejgEyvNLQAwCYrfs73edkv/L7QcIx/ZHHuGYsPfw
+B+/1QHC3AXBTdtHq/eqf4LmUqdWN3xY0F8CBQuKqV18sOb1I9X+3p77BbuxND1rDWU6pNCppmtTS
+r6oWd97dQHVtPS3fifOOuucp95SLjfUCbQpEeAdr2vlv7l3/XfbdKA7DBaRCbEZP8lvyAEnUyL2s
+VscwHChVmSwB8O0H7A82YHiA7z/ldOUw7xk0+xe5kNVDWjz67Hs/05NwIhS0SvPU3pWj+RsP+S2n
+dUNbvMzvLJcq1uZSmrjNb/KdTPIlVjib8k3RAsY+jselZrfbNuPATchtbYgaNqBE7zdTslcy2Thv
+zMhHDd9kT497Hm84C6wMGQJQMd6Snzd8we85Bq7kErIwWdZROtc129rx9Azr+bubnbc6IEbx+1VP
+ucTXygtztH+8yeJNNwGyNHGkk3bnePN62lkhug89mTG3C6GjN+CtF/sWiDLT80tv8GM7NvhZddPe
+CMVd2fSDk4auaB+Qsrw03B70pdMCjLMJ1cHGvCcOVr+eC1lk7ryVCQ6JMgWbz7dA75n3XPKWAY/v
+MRlfmkPIwrJddgPE7NcTFAtg/Le3HyAc25+FRpAX30IkAvAiZR2PePfciUnv13T5/538XjaBytiG
+nuwG+Wb85dUdGk4+4lMVJ7q6rTiQXVAtLsqG5zmvFHSiIrj3rHXRx1VdyvRyL8Ecc8Cv+aXApfs2
+yiAdvxqVvrfv1JvWQpWxkpVGZOR3LTvGdRnySevBU9bTNkDtf/MzuXJd3485Jfqr/D7b2zB7zQXP
+zuHPV87kAlm9hxHZqKZkaWXeio7mWZJY7TCqr1eKrMscajBhtkvL+5bn0tbkV0rUBfwiPFx5DMUz
+qSnaqgiiCpjQuNkauoJl1SkKGoVOmmfHRa4q8KSuX8gseTf5o4wRcGTDykAlIq58zoKUmVEeBG16
+Knm2d/FWR7pX4/U63jr2Mj4atJnHk9qLli6K2fqx5rX+es6vVUfE2VJavA3CpeORN19Y2/zm4d19
+6rPbjgiTYKjdClpzPCtBMT/2gBEaIaRrxD/ENvLJbjUOroAgL8QZ6r7bsPtMhwHFku0L2fNOZ86V
+AJAGguENKDfQCUk/E9TewfDzqc4ToRGrgG7GC8MrTZCbHuA4znjhYI1vYrH/5fYDhGP749CI2D+o
+eNYNcCsswkFw7FKOfreb1z9Tf57/BuSajfuwqSL3VFRq8UuRnRaGxrAZs9mv1M5lRFmg6/t2ENwO
+rr0WX9Zh8GauhPhysEdPzxypniQxEGPlsFN3R2dvRud172OPblRuhlnPnUVybsaGypRmxZn3zka1
+JcjICLmY+/lb/Tg/ddvrKT+XLfB2OQQAQ6urAGrXp/GrMdVgG/uQAYMu6e0hYLx4rCZpioSdqTpv
+z3Nc2lLLp9vId80f4S4+qre5FevqUlFvjmgwJ+glRPE7i1R4RM1DE1HRAzniFLDIH2BYz2uA2XSv
+9JllU7jv29QRR2LKZyGMpBdk/G1seQVrgNWvcgA0+XChlVy638SXwp11txjINd2LD3OeK28DD9TB
+GLf5CAeZvzZkO/KS48RmpJiF8kxdn3Zx9yOP86ajrqsvr+U7bukZtjh56x5b4aE2hkOEfjALj/AO
+l2ihEUKEFzHuQ0eXa/72sLkaxeoPEDJNT/Ea+ZOSsbuCX88pwgS+sy6YT2/wOxh+PoBjepIff7Ds
+BoIlROLTb3kDxj8e4X9x++5NSPMCh3A23gxvsBWvljfYx/Fo+Uz9N/kfO/8HW5g2Pu/XW9mXppp9
+umaUUWWF8taWkOrpfZTvA7gatsy3VvYJ5F7PLB2euQWGI18b9jqvaXoxMi7pq+Gd+UBnUpdEqyOF
+t+ocxOjva56qdaNh1x/SiOdLKxMAp9Ff7bjq9BliXwmPlQeKJ2ZnUcdtreQXGlzzygJNcHN7p+fG
+cy1vnDvPJFhvPHooohNcAPXoeXqQqjOewOpIZ+c0jTsIxgUeewe9J6X7OJOPOET1jh7lF4/xDehq
++irH3zWm51ocmTqAL4guKl/CxfQ+o3eF/F/y0D3BIRNR966fTNhg1pB6CoIRvBcgKHWS6hbVNxeg
+v2ODd9nmNysavADjGxl2X4RLePPr5LvITnWWHUc3uOoVdgG4T4Y21NJkEQbhS+ZgP4a3xTqWa9dy
+TpIkNsiD+3Y8fNG5MZkHe4J+K73DOWYDNiBePVotifmUzJLUAogJfhUEVx4SEKtHGAJmb2D41Qv8
+zPI9LgLgBoKtA9y3X43jou7+wvYDhGP7r1eNEMZqx5lXddM7nMLOFm9C9pLvH+ovBSjrqPEqybpd
+wREF0BLxXc2MSWIYt9kNwo2J2a5GSRXGKxSJM2z0zfqwTft4a+ItMzstRijy0m67qHTVoHPvcY4Y
+HpcyaFmeO9q91tHGLnUUOU2N3ayX8lHfqjUx7kAHww0Er6q3rMDvGvXUGGbapXvVQcIy9qiCd0ij
+GBCNtgH+BoyTz70Amsu5kCbZvzYVduXZlkce8Xut33uIR9+dVECfN1rN2US3qHIcB6lTfgeClVWm
+d/7CRtrrFBIb+xhL0xejXrKjjf2H9J9sMs/HDQ+RBDNVPgdNj5ljn0JW9Keg+HwyYtJANSaap+VX
+90b9YBD3GFfYpldwfAXCUnbxAs/5/l1ZidsZQ5wc6UJ26eu+/pP9YJ4jAO2ql3M7KO5Ad3uCVUd5
+nx/pC9BlX8neRmed/6lqLcIw2k1xLPdmsSpEB8Txgpw2xDJwHnROSBuM9DswTkxyeIM/eIIjhOK5
+1M1PLF8AcC6fNn6UiQ6II4TkX0DCP0A4tj8LjRAgDG/Lp7HsYWUUE2fscCiZm3z5BDIfezfy+TLc
+0gPx4j3rio93J7PJwkv5iiu28lbVRrBwa6QZtbuaTEw9qlzHyT5J9cPAftjOKvZScBr22p9eYO1w
+xgo3Y1n7BC2XMo5PjU/TBf+foLjRVOJnTea8BmUv1s36jwAGBMM0JDcQvMrgrxWgeIRMRGu7O2d4
+hLWj4Rl2dGBGF8sEtMI4B+myzPvKCoMCANq9RJ4/as+zQ4SS9r0XNboOdnTy+rElFY5m+vynMEvB
+6IO7HlWkfoJgo+R/Br+NjE14uiQdL8QZiXPbR9pGug0Y0dsaw1H/t3pChNPRATFBh9JP5XcMPuVB
+9IxzTNMLrLKRN/F9XHOsfsmr8feBKgiuPg8wpWPSMspWAqzz177Y9jy4bVd1Ev248g5u14ovoD37
+OEGwbz9k9wR/Z1/geMcIM5/Dr3SR1TpvAyIb6JFnZhV15U/qxnaPLqBXPcTwB2areE/5V4Cu7r2l
+Jwh2uX+ovHvYw/D0SvjDHTDv9P4uxkKGR9jFOwz1EK/dJ5Sq3u3+hEb8q9ufrRpxD4dIg+y93qNK
+Jc/z1ubNQvvMyPxbnsjnWHUl7eTbumQUzqzPsAiIdBMgABoHkGAq99PIvTC1A29rSN7OmN22zP2d
+0NhI3urfAc8t5/AC87+P+t7HNgGyS3rvu6Ey5kHqHgfjOC855nmCOs4ndK6AA+Exz04a5s2VCRhu
+vwmCV3mB49jSQyz9Zvcyrd5ha08FEpi15RM4DlqX09Q65yenLS1WlaHijEsuSALrx+y/D9JNUrKu
+V0uzZc//l/K3WGf0asdFkwkt+9kqPxTzUY+awIvWvWQY/9avyTOjX6kf6noFci3mRsoVDDSgi+Cz
+avNVI3wK7XJNlOxeATFBMSf8eOlAx2woD3AdNxDMl7Rs9QaavhIddOQdhEXy0vAWph5qALjqML3Z
+dObH7wXwYq2P5ckjXmEW7RjSl0zrdXsfyiMsoFY9v0L/1x/0GEivvCnolXCdS1mfBsoFV9BYA+Du
+eSc/7fjhDnq3mfW7eXs854OA9tvHBxgmoL14gj94i5l+vDzFvgg76utyJwBW8LtBL/iyHMvVNv7F
+7QcIx/ZnHmEMMKzhD2GkR/4bKObVtf2ZV0ef8+q91XV9UWffPQ+XMDFDthTAwkeB0SPapdNHUzth
+lfFqlYCuXfyS+s520xazQ5cadpacrbyUES+EEmx60Hu9MqBybgLdyDrK53UGeFEekeu1m7mjDjNk
+bki7UVTbzboXSLE0BDQq9PJe9gmCT0BMV+n+Ty4c3l+O7VgzTPbtGeOFXDPtnrzayGtS2arMRhuj
+2mvd9sJUy7m1BOSNaHbWRt0+7wdzXjwqXkw0Tuj6o7zBux5vlzjf2v+tDiSk5Nad5DcFJjGrdubj
+Lb+VoepYjcK0/NKT1qsLbzTZGwyU4QS3uk0nqtKRvNuN4urp8iSPc4+xVB2/1rH6L6DoEyj2HKMM
+snkdDQlyCXgDGNnzdGlc6x0sh8wl78h51ItaVsA35DUAmoJhAwT87v32sBYgK6CMfJfhHTgXf9Iz
+3HhW50ds3b7WFv7tnQYs7zKX6Ift8dXjXGoSI4/0uc5JzR1ppADYfR4TDGveCYZ1JYnnCpYjLWCZ
+YZ8PVtK2geBnwfGUR/jZN34JfB8AFrT68Qj/e9u3PcLJf3cgPAHvGyDuXhk1xCNvHF3zmJnOtScW
+Cb/YSAww3Bq0AhFbu+xcvj1PY4TRLpsSPdHvuPU6csHWCGMeP81Dsz7XGi18Y/StQ5L3NjT/Yn4a
+BWhQJ50TxKo91e5n2sXIestXmplPEgqdeG7LrhO6sb/Q520ys8wwKVHgyHD3CjMWeAEm4RACiG0A
+4d0z8jn91WGkbYZFBARwyWcdR4d0AxhfyZSAajP6MNHgwxC2zTZM2jvwl1/KoGOlbHgr4yjt2mHO
+zUvZ6MNN7O5ahCJvwmvRi+xm9NrQqeM485QOeazAUCp8BMEKPqTuEMz2cZysO4lyjt1uuY1XvP2O
++FUb1SYTqCf4BogXvcLrnT645VMLDUCmdcPoNBCMDYYYPyxG7EPa91JjQHyu2DvQvQBfX+sEyHHT
+ZNht1hdFqU3YRVGY2Q/+ngLDzz72GH+B29JNBYbx7i3OugBBuvOYgPjDDVuaSo7NIvTCC0TzRThO
+neMB5NjCn5rqeROp1EPymdDkzTt8yVfwmzcVL57e7g0escDMGyETK2iUtHteQHDmrw24CYwlRvjH
+I/wvbt8l/h3UXkDxxQPcLkSv4OjE2Y0PeVN7+xKj9oRBr7vKMsyestys1gidoOx7WMLsr+PESdLM
+B/TYC2/jDVB0grhZs7RDgl/rtW52kKDn/gDVLqnZjr2WbxoX0GmAxQf9vNKsoi+0HPySeZMw3nnX
+B8V8UOQKDnCS9zZfMrnN2zINfB7LKhEDENtS77A+fve4UnmE6IksysqyVVavcxXfOBr49dbifdhp
+kHkNyoNlPfJ6pv3EQpUeMdh6xUCNdUUVxO+kf7d5Uzm1mdZ4PbdJlqE8d/SeBy+7rpjw1sMg2slO
+BSSO48MbLMpG9cv0CDfdY203xzgJ9DZVx2+b8eTDsPz4+BKxgtUEwPoT73AhIx3o5XgqVq1T8wT3
+5unlTaUYrJHWvSFj/lacHCD4AMQ3L7DenLjnMVd6SF2npoe8dQPBAb4UDPvzIF/tvIDezaeqk/Ys
+KWCePDdBb/+sd+W3NgP8cjDbKbBtMqdkxgTrl+M22C0vMBBy13gaGSN9zFV6gwV/HN7f4IHHg5wd
+7M6wiPli3DNCJjTG+AkarKB3eYMHCKYXmCD4WSNGOPjqL28/QDi274dGvIPe7h0egPmSd1x9aOGb
+2Z71Wq1QUKyy5SzuNAEJl/C224rB5bgVp/wzfrLpNwVCvK4ol8NTE+PeClBMZ9+1Tnyam5vxVcAy
+m7sdvVjLax07ci4tuNDac8hiTAVIcUK1bObzGnrjcDIClIjeE1LJZP82gBs91PAOg33zBHPubcVj
+wPAANxAc6TSECkXD+zYWQS0ADAEkRktSFjVvQm5SdG6kVwPDciOjqUxTLmROlbKZDl40niSlr2CY
+4DPHVvmVvgzkwxh/p+Xm+PJJEAI0cJwWlKLHK8Z2PnWx3FX4BHkGDXzcQLC1OlIvj6XJvKjUGd24
+jngY3Zs+madQvnlDcG46rikjml49vayfewxOyy7pAYYrFCLmcYBifZQ+6+aeciAguMUCz7SA4QSi
+QAHUAMS1asSuUVwdN/bsA+dIwXB4gvHEMTmXN+ZGni2vbstTuhkOYExvcHqFpW4C5cucmsx5rlNs
+AdQTEEtMMFBMlIwUY3EUcNaipEXM3TPyxBvc84oH/OoRpjdYQK8A4RYPPEDy4+URfmIeFkHw8whN
+pxd4oV6QC2+4c5m4v7v9AOHY/uuX5TJ98Q7f8iCPKKBK9wJieokWHnWyxgTDwyuZqr5Z82o3FUVs
+p1HnHTNfmOpl7dGlIR6rCmMvKyWsV1iQfK2PrURuMWcNZKsB7GkbdSc4v5ju76WbIUy/RFVUgKSY
+rBkaOW5l2o7Mdv07jm8AuGoeM/lyrNsEL3OPNBL9ZZX4XOmyvl5ngGIXcJzeMR0H0YZXOkMdJC+Z
+eDN61UsjIAacdH3o0bOzHTlOkG1W3ZpbWe+e7XeK7pb1pMoxAo5Rzt7onEpjr136b/Ibh1jtAcqx
+VqTHzVBAYe7ReQbzeNQZx+Wtq1MbEJZ9qjbcnrRByY0+1x04vK2OMH8JJCjcrtowLpRy/SZfn8r+
+pNqtgl+OlAieNO/yMHShsL8B8XniyNDwh7VBjWEF4Kb3U3RZgGA8j+gPF1vAHtSKR3ioN7jywv7x
+Jdn0pQR/9XsGL8bNYVOmfVzT4eLZ3wN34deiTQ+/8xvHDQ1r+6YSOGVcl05y5FwUFqxrET7nV2mz
+PquK/lSdN3icda+8Ps+dZeCNypAXHZvvMZcDkOPTeuyb6P4S4r++/QDh2N4MxVFvgtqW916mL9E9
+yRrDuDWmHsdaZ5zD/ymmvNtubhwUozJtqgCoJ7yUitBFIxZTQabNK7Az07kqgCMU3gLw5Off89uK
+T1RYkhc/8wc+lSowAK01e9uPrdfqFn2kxFbe8n+X7l1Mu7hJ4Hmcha0ulYscH0k/j1sz4zhBtCjv
+HOAHpWPjdxRXWITeGKVHxgQcrwLFOH70ipWBzjV/9cU4KstjhQgvPtY89/3uBQh8o84KAxJKnF/O
+qmOkck8v2SQb0zSsSqISn4tS6QZUx1ExwcNQunrLKu/W8m275b+dnfGbCSYG2D2A7up5R10UkGhp
+gotZ5y0fZS+lKLcGGrp4HaybRrd+JyDAYfxFo18outv04+ZLVn+53JClfuaP+I/91HHpnUjKgqGx
+CutpWV4T7di1rFMNze4k0uT/ESO+rLzjz4IvlRu/jPXZ4VAPPasKioEWx0uesqdCBPh7uqSkdJim
+h32wWa+dqsMvGh6TIekAcoYP9Z30pGzd2zKg3hNrere86ntJsngl3g1L3vh0lBqlCBk8j8+XTDwt
+enrUmTLkWr5VJqyWT4pO0s3u7368EFtuXnbdi6H5S9sPEI7tzzzCoRa5j/OPMueyajtSl8fL9XrB
+cBeF5L0g8pShm8qqs7JpsdqiIPORM6UH4zGnXP9Ih7D2F6MgXC+/jHuLjV5fl8dprWyFEqVRebK+
+qfGSfilo196fADgEsk48MJ5xoOjd1gW+vyOqhmPaTvuZNnUYKhbm3XM3Q/P424A4zxEr9tb5Rrva
+e5vjsG2yVz7onuDzJukAyukpMXQAfMnTgY/QiQ1+QioXkmcIcPOTsQJMbqAYWg/CG9b5oal59xfb
+mSZkFHrJJYBaLaKb+F2oYRTVzNtTxJs2O3mB/ylDLnGVyJvcCXZ3HOOQ9VteNp2W7r/PY29Nem+k
+GtAI41IvSS0GnQB3gl4Fq8FPPvOlrGQ3JiKBs8n5Nq5hxZNtAfqS79Z3HVvKgk6hoxweQre85uzy
+oRSq3o1pgK4MAdHjwH5a5zA8++l/krZ0eQLiGH+uNHIAXtvhAKJLDt0iJDm7q7pfjscYmsW4CdQL
+WG36k3RPCaI8j96ZHt/avI9l55mAY3nF3eNlPOyumm1MsYsK/O5f8X1F7uo152o9kKOzHntPPZj1
+bJDytpHkpv0bhf/S9gOEY3vTAUc9BxoI5h5AeX83023Q++I1fgE2FDKfF9U+tmNpZwXjk1NTuQY7
+U2GGpctD4xWtCWUT8xDIMplsRkEOUF8Q0180NN+l4AsX+SPwpe5c4Sn29rKGiszWEV2c5isrHQDv
+49kGaXgFwPSUDSah0si097K0i9L+YW1cK3o/RxRNTfM731TL/alB8wZn5YvmOhAFCphomeHiteng
+tuftMIgMjRhxwk5XBsp3u8fOfCWuxg73Bb/y5ZILyFUwoqAYku4gWOIpeflQ/uqJM2i5GMMUnk77
+8g1ppZJVWawMguLqfF7XYw4GEk47PPPG0e1xbsk0AYjI/bx5gcaAc77lK4IhSPrm/e74ACBvoHfW
+y7wpULyG9+oChlUYWnjNAXKpmTu/DKHs5+S1ZR4H4D0BN5LH6gtLHuGgF9k8xDeZcc+jKmyer3yp
+IhRjF5UztqHE2GQeT90K+MWJQceFylH7+MZFdzSd0mxRh00HOD70clP293xcq6ATW+QvCcCbXZVd
+0bFW59loU/zCnX7o5zhqfJRDvrdovsMsCvgiwbCBafUEb51V1OO16kmZaoMExIZ01ik1SkOx7ZOW
+5eEd+4PWehaT77X+ye0HCMd26IPXehcQzD2oc/e6eivzLnuv9iba2U0M89WQzR0ol4IH+CiES9Qk
+MktL52MPWd6GglvCVCrBkle798ga5/fwCEOFPXz6SfhDxHISABdgrueHU6V0PX3z+E4AXCKuTgDR
+ZTotScNrGzoHojQ1L3Oa5wnjvEgcnhuv4joqHc3ceY7yVgKJeij2UenMImo1zboCXxMeWFXOEAkt
+b2/LixGXvgI0+AUAugUUngfE6Pa0guJbuoVFRFhFznl2x3MuJ5NsvrGscxrpaRC1Uq/cZsYHbcSE
+zdY10THyNHnjHPYgp0Ot2OWnS98dezai7CJxj2mhx8UbEJa+2q3n91EUXThPU8iARIHBHz3OUWRP
+0aJLmVAzUYsAYJP0HRhfrnPoA/K6HCYtxrApD3LjlB8/Yj0f5yiznKxUzeU1aQRGRyiODniM8QDB
+qc9kvCHyHz2+yX/lJaYNapUv/f8tlLrI5VkocmdlEWvuSQOVZ3Zc3Q93nabhhkCnBWt2alvSn/dK
+i97hIOmKeVMwvP0MvvspoJhhY8jejpV5OPyoY0d/hAT4Bs1PgW/yTmtqv2/of779AGFufpGmWzVs
+xriCYZfQB8nbst9B8AOXuymXLrhkqUdQlP0LSOYb9enx0vOu4JchESNCUXSwqoh6sxaYX0pKr5EA
+neYZHnG//PVQiPg93pfr8Xj5wgH41wXgqsKZQqkllWeXUvXuUQnQBpRR1TOaGhN4EgZh2hjHmSEG
+0LWOKKBuqyYgrgqf64AaEr/bPAeoWk6tlHh+QQVulb+YXgV2JVQCS7zCfFue4zXtMQcQ+4t3eGd7
+egU3/25i/BbsXtLpJYbXEwHOEQ0K+5Iv4kxDyLqkF8fQDeF177Uv7/BtgvTQ7sV5/SOo4sQOVsMB
+IF7gkPUFJNidIDiPZb1oaTeTJob/sInFa9c6KV0FEZp+mzyTOvWSnzIn6QGOJzAu3TzqH3NY8/YG
+eu2SF0YhaZ/bvPmbYBfBh9oHs6ZXWqMy9EaWt00V4OTEzKa+E95V+yO0zKX4fhncft2fLP3SlRli
++Cbq0/Rn0r527pZXRaXOOkG8Bo2krZH7SMM9ThMBzzW2gZqr1hcreyC2FfCUs8ZF+TRGQtLYmscC
+I2G7F7vpnTRQz7Dmi/xkH7KnMwSicMpRNkZ5R8JlI+y2J/jtQ/zr2w8Qju13+qAqVmjDAYa1POtJ
+OvYP+GijhFVBTynxb9SpiqJ8ukEtxRhgLR6vtLdkEySVUc8Xn2Lopfes9hbif/UYye83m8CIF8/x
+fFnO5L+2Y+O41zQIQGktRTq1EelWSoXhJL0PckPjRbncUnvjzHPJ0BsBNbhZzTWrrjLq3PLqv2hb
+UfLH1ggiCljnMoFSqDMDpmeY6faEIEIhGELji4BZzQBXiBDD3gxxGdbdl27Vb95gTR8A+SF15FHh
+kvNySba4Rq0VBoF2ZY0SsMiQdHP6hDK3YYIAACAASURBVPuYr3snLWraqp0yrJLVL/Xb47KUDCFq
+L80FCG7ee/UI83PZ7dPZsjzU4C/VJ5mh/TkFOgpGz9vYPecmDX0DwFWe6QF0Kw1UDDHr9fLWTluT
+16o9lglAZj2f9Zpeq8tNLHXQVPUHrGQowbLWYxlGPs78zFbl32XQ9DK5L56tEL1znzeuv9BsxHGj
+FPzXPKel0PPQdf5ybNQdGGP2QT+rOWwb9aPejoYcWtnHfYnSUXva66L1khwvOHXZGEvIoHabnmK+
+8rDMwjx5ixFuYRJOUnn7lYywd+wnIKPsZWHzWp/aeW26rvtzpGi2pGqNCf5L2w8Qju1PFnHWON8J
+hlv5qJcvzrFuKIbUU6lwq60EuZJm/nleXb8eSTE/NJd6EUJxDueWGEYR2eTTycQvnD8BMe3J608f
+KTroVcDtB4pLF5g8ch6/lMeA6m6ZysEkjQaKNYyi1xua1vthlo58zPxhoPIm6KagMVjhm3lTv7im
+GiCxUV+0Wcxtjt4goDfKFACnd9hOEJwxw6H8pmVN4zH2DSDvvDJDMWjy/UjrShKkk4WF0XjGY83V
+fOHH5GU57W/JbpLOpK+HAQxAlKZy1pkT3z2igsXr0tf5LeHuLKYmrJ9T81jplONjv/JjKQWO14E1
+ju2Sd9adgjTpIvKRb7JTeIoXCABS4ATQ8man60q0Oi2/hLPKTHnAkk+K/4S3D56M/Y6lOwjSaSJt
+kB9Ubg/5fyOyElXHVnI/IyD6OIMHidDUgCidczfzvOzDL944C//KDXfOOPlQupVeVA83lMt4aLQi
+XU6lUafRbNKuyysdIS7zsI8pk2FNGCpBvdFsrzZ7hkcoCC5P8b7uA4uHZ54rTi5p2l728BpF2bvU
+DsfxCXKntujHr7zWNpud6GWX5N/cfoBwbNPsvG18tPMGhskWt1CIDJmI/Lx2Kt86H5J39xaf5/RH
+uaKgthsMTUkzLEKFVQTzNM1WYDn35QmsfYVH+KoXaQYVBVjoD+1nxwsn+3e2Zs3Le9yFpiLoinSm
+1YGTOL/lhTIUwWd6riyhtrhtbXyjygXwC5egTTnzIuNTXu8GO3DXOIdHjgOCzKPxt/PSGAThrx7h
+AxCvWk7N4hGAelqyr0F8RX3WlTNHmHkBYj1etCxl7fuz4/7ZY8zzD0DcKKrSgZZuuKX18WJcsR+r
+1stxehmOfbTnwJSCVA12sp2WIWlRc5n1RW407l9vXhIE8+MoclyhLvJ1LF5fiDLwx9nbrCsgIfSU
+TdI3IRoKRBwDdUw9o7oSlf+b8qv3kfxJuRVnQ93cS18bELYKBXvGfOSEetFiyqeC4kSwU35uBJ+8
+2+vPm6xil9GBNAhS0dvMJe1an6kfeC3qGO7E1kxlfvCP0jh1hbfLqlVLKRUwnC/ZtjIhggJ96nuT
+qOEMMdyddjDG9sOcjCE3+xp8wHsNnRd6f2MlyHcwXKRsrKEhmTdA3Kfcsx+b1KXjtJ6FTm6hDnrt
+PB7lpvufGOF/fXtXzGe9Q59hgN0BlhsIlnK4GPOmtNmh4fX1EuN7ngDiMOoU0puSLkBhBd6smDz3
+FEpUKER6ixIA2eBqMZrNI8y+DMKa9PEAx6OMp6DppsxTa9IES5RDFrL90A7He1jGO30pMAG+v2Mc
+l9/ISO9VU+RatZvE1qiQkErMpWKVSRuc/6GLr5sNg2iab8kDnH8FTQV49XjHlCYglrjS+qLWGYeX
+Y5gT3Uav51TZDm/YhJrLpt1jhbvsEBxDzju9fZsGdSN0mGnZDubKPDU/9lrv3lyZpka2RqWZvh0D
+SOAR07xXolGrlZ8EXiPNrwd2IFy917GcPbj25Tv1tsIL+stPwW8DwpB5G/upe1ue8FYmWSYoYwDf
+4rnRFnnokScSc4Bk6zbe4o3+MiH7rLoKI60Ne+nvTwpBQFi21h5HIGzGnFdLfds30kbC7tRzYQBD
+JHxeX2xTv9xT9Iw5SFvmdcnsrvsJ9FsXvRuNGSIh5dsxxvIKWQDiBle85fu4BR6WpDe7y9DDfb10
+vGOAYJrZ6M4RKxzOpnt4BK0G8rhLqGeOaT3hyYodHtuwGXYtm4bl391+gHBsf7SOcAIRF70nedGe
+Y39Io4FgVP627X5e/w0Uq6aUayabilJNZSuPdAsEMJ5MLGkIaQnG8AwLfkpvkQDiY83YI0aYIJJG
+ATgArl3ydCxp2Ib4UPCFPKlbQ2vSjqvU5jllUnY+lSjPF53I+he1JWCrhomsSdoj62hfXBMD8O98
+b/VqT77TtqSuJwmOrWq9GUFFReigOOcbRWzhgfIWWwHiiAemF9jzk8tW/Mw+qSE6wO9ON4AVmtmA
+BLU3wLuNZPBQ7OdSalVW7eQyfm0G4kfevQJWkbHrePQcBTg0QuT7KFedILa/UYfiIteqXqnH/eSB
+HIbItH4A5fwwSl8KTz+b7dKh/gh4xF32zox+ScYkrY43yTfmJYBAv5EugWk6doLeJkCit6bAkeB5
+o6nH6McTLFOv80tsMh8+r0Ea2sg7bsLKQ3z1rpFfbi/ZpU4TYT/a6Hmz+DUkTdL7hTiA4RGpP5gP
+iBKnjbnoMsP+uAb1x7xR5YhiHjKkizce2SGV65LPspVJtLCTDE9iPDRpGNyevIDtKR729U3nNk9w
+7jmPVuZaXpI7Y4M3AO+rRhS56jbjuwA4Oa9rKiON+nRcR/aBDyFj+9DCP7r9AOHY3nTsUc8LZiiw
+JUh1rzr1GeUePvG4x5KRnm1CjhMUUOm2OnWdQ0mnZKjipxR0YU6t4oDJW+XysEfUqigTK0bVx6ev
+3mB6/hJUBCHTYICSXUbK6rjFDYN5NR8ZB0XaUJ5EYo9jvynnEa3V2pI4znRjSJ7ELjbTnmP7wF1a
+ZwDg+RnuavetvbewiPAU6YTqIHnUFNNICyj2JEzMPzDALzK989fgk5UvyZXXmBfrhug1PV06Msnd
++8tjTzorOK5ylu28rH9ZxxqP0l/la/CPHpTl+Ma4OIE3kLPTLTREycFaFBftBiYIvghFnmMl6inP
+6F8F/FpoYRH8fX2BBrJz8BjjHNek2Q3hH0nqDJUdb7/6OI/UOQTlQ/6nOsmyXnVMzmvOCDnPHbLE
+UKUnGUQfV9YtfChZBtqxlPtjC30n4PcgufXat3Y0/6Y2YPaSj3pZDlSrCvR7u6fGG/o2aavhTDEd
+TAMD+Kl8VZsnb/KJqYJgPh3dNKYNzXdzaN/mChIEyMcVSoYPT3Da3WKnZRGKEeyW4RFRdYJg9Qbr
+PLjkvwNgPyhSZUhvdQ2rrwSRaebbOO6Mdr95+4e3HyAc2599WY5KvoNhSLqFP0jd+FgwGLdWursU
+qV+P8wJoIDmTChrZRx5bGYEQRKueIgVdNaGUaK4DzQMM09jhyyoBJpxNKUrQG5liKDIuOD3H0nca
+Ua/mVHc1LzAvOSR4mJQ+saLrLeegRm6h3KwNRq8z8vTIbz89qGpVNhvoKZpNYYVR7q2ecGcN+GVr
+scJ27ismeP/yoyY3j3B6hekN1qcGDI1gHyXtNw9Kn6hbrHAHuzwuudh85imbeXPF48eLTwYAdnjE
+cwodlVY3i/GxMNIH/1wakAn2mR1kOfL/IM3NmvWytLTdQzxCI74EDH+t0cmdyMfI2dn7+PQ0bz2c
+NBFYX4ITxzJvCoJTj6Dq6UXH8XRAdIK5KMTQTTb2EwBD+C/7Gcl2gzVoAQDisCANu3xcZMbiGgOQ
+Athy6lWvafom/1KiKMWqIssECyXAMfln81yxHcd1yS1dKfcy5ueNq55N++V5ukv1G/dn+BiQdGvz
+TBuUT586CM6lSJ32ghcdOldVGl9alx4lGI6OO2LVCCu2MuxrtxjhqyfYs02CYnZsapxdW8FxlZ31
++nE7sONAKk4eopo5efRvbT9A+A+39mW4UNROAwsBvSjPbwPBAowhe+QxmvJ0KXs/5jn75271gk8D
+w2jH/TF9xMKWjKQAWs9qMpwibG+/Wic2tdYBcpEg8+5Jseoo+4rekQS8ov/bi25Z1UZeqUvRqrvm
+lF8BxjQ3TX35+H3cbud4p4E0osPLm6OZj1JdjS10WDJx7y/GXXqqOk28vrusz7neAPm65Ms6tLnu
+dLv47uh8vN9HWmWW4xYD5jjigo+8p4wCqWZAeulua1ubeoiZtqemqw/hhQ9mRYzKYnwBdA9qP615
+gpUEcpniiB6ekOBDN9NfzIHGdY+wiPIMbwBsa8G+Vuo1voNQnXMZz8zvdWo5MOR8HZTMgYoktBvM
+B3Uj9PAEdN0rBJvsdasju+zEAEl7eKLLdNzkLZ6bURGnvM+u9PKa8OJ/4WTDeESPqkOQJI/T9GnB
+gVmaMoy2qQsjr6pIufEUq7JswsIsmVynLpekaxTxrJbkeFByyvlXuVWdqCI2m25Zg1+piwT4wiEg
+2GPqSd/N2xsYRxsin6WzhuRbjZkOJ9L2ieusqKP3Wc0L3I5dbF/RonMKdaCDOnCSqWuse73bD6Od
+960ZmN/W/l9vP0A4tv9m+TTgxSMsiq4D50rzM8yqbGeIRDs+FLd4jrMNPhaiIqCkMBZI2XmfU0Jw
+Mh8FjKtCTE/vqwd4xhSKt7Ahs3wLPPINrc9pKAnSaVzFmBnQAa/oLM2r9qeoVUjE0MWiOTVi7qYi
+eDyUjNdv3nSIThrt8Ihzfi/XnORGn/laZvikX67cT40aFHMTyuUjTUuCzsXxOyjuH17I+GAzeblK
+jHgeT4t1t2AN4iV9g78evT1g6x6fhhWeasC58giCPd/uf4o+AGBrX98cjqfxXrfY3Zzs3YWPmi4q
+vq/6QieKiVziCJM40rcQibCceVmdX6sPakww/CVgWAEx54EGM0GKEObCsI1UgNDCgvZCUEf1WUEQ
+JJ0OCnqFnztRlOYqQBh91PzsGvWUzpWOj7qndFArG9eq/8mp1/TsVp/X3Z95s2PUsdkWYuq7fvCo
+bFGuAPi2p30wvO9TVwCwX9GNX9H2L3awzMBtS/IBY84op3t+PT5G5wuwx/pUYesLZa3im6JdD2tg
+nPvOo/cXCYLD+STAuDzL0kxmdCXBfrE7W59Wmm25hbcZQ9UGay0rEVEQ3DzFqQeV37qqOrVSB8rH
+sG7zZa3r1Wcpk8RH+/RPbz9AOLYrEHipuPUYmUg8vJKmoLJueoe9Pr9M5ZfGV87pADfal3p+KbcG
+gmkELOsfZaJtirlLSPQmlmKT9axhpPpx8X2V0ryFpSGwGt9hRDzLLenQx0oaNC/wPKbgp66xccz6
+OvMvoFhdbcIIJmfxeL4A1I8uW9y8pG6c421XLBL0/3NfARC9ZvHoR1DcJrYnqaB3WjRdznkd8wap
+vTjJmFJ+mSy+Mndaiz89rpGm15ePmtcIfcibq5mHDKFo84In3u73XO/VH0c+7rQCxrYtb3NuftfU
+9GMSux+3m2GeIaSoovJaJVDuIi98IZlNljlvaHNXnmDrIFjBMHvbAJ9XeETmU+6lU44NMvJte0S4
+ipLJL/R1aUD2DQQP2VJaTmGatJZ0m5boS33KPpRL02+jPTbA9YMfbL75Jsjt6UaYKiMgEvlVXkkH
+xeALxc656gv4YrRVJTpI0NPUA/aaDj3ZEFFo0XzKJEO3IutN65lRHh/gWWSYXfxgO2Pyq30V0kAb
+cWpZMRIO8QBHjDDtS+RtEOxRvUBw2tz2DkHx9bQNipuTBDF3bhoasZ+WbLaKdNQhC8YzmZgmHWSB
+Y8/jsAsj3cvuGkvT+bM+fTWYSujTAtNc6/z3t7YfIByb/75K1iy9JsCXPB7pBnxRaZc2qKgrHW1G
+25BrsPzdS0ylL7+Wl1rkHLhXgr7PzfjDVIpQknHvnmF0w0nJcCANn0Higa36yCVfjrIao3n0MsaT
+L78luEZ5OFhnzLK1PQFwN45ZJy7Ga16i7Zqeg+cwjvzso6bZSreuozunup59UPNw2F3VYjZabMMp
+I3pcYwJczjMZw3qd5hFun+W1AlTM+y9B771OGDu+iCTg9hMgTvnSuvmhA3qEt7E122l/sMdAF9S+
+MOLLHUW3g5jN3IkcK/H1JCudcJ6OBmY0zbaHNN/T1V297LsneB0g2OKn+i3RV9NJNaY2VNd0lEXo
+gD+WvNsEmDydQxhCl8BIgfCg4zXtZ/7tGECGcigIfgX81vjq1t45P/PS+1p6PMGxxZOYwUVHWzXf
+ovunuCnCobwqGBbAvIssfwVwxtPBX8iynHZV7HLp1G5NBkLOHyAQ7z73efYN9vPsVZSeCrPiy26J
+rIVvGm0b7BNbRN0iefQIA5BQFO9E9EvT4yrMIHld1KIhQiPMhZbevME9DIKE85q+5BCPEXTJt5H+
+TtlkE90ay6jV1ITV3uqsD63+M9sPEI7tu6ERFEo+nnjzCLPNfMQdSn0vpyYe4zQYEKNA4VRv8cVz
+rOeq0TlAcNyZgl4uqZPGohtrk1Jl/FKwlnehVIRHeETzCM+GqYREaQQY5vqHGlun37FXpcLHUdTe
+lnlFK/UKUwdqh5oXeOivw0hFu2qHq00Ozy4nftjavCLBmEsbUiynlSJTPXsrh9OTcR/YtbepkE/l
+lMYvATBqvgcPuB7nmrOVZyM04jTXUzG+5THpwRve6HkDxLPcRl2H5xv9Fmk3x3YxBSC2Bz7AsBGk
+kMfn+JKNlfKHWYxD4Sfvdd9Cmmb6d3k1h5Hf5sfucys/Yz0C4qAdV+zIR9HtyU+XZXraxqOo8JiG
+vkjvKUqHJXmmThsvyblXWMuNWG+E+lhGC+6YwKr0Evuq/ZS0rpymTfu8ZPF98c4LGCbb2bP1YshY
+8/wemnCkBihrgFd5ATH/NxAs6Q6Ea3d9MRc1rX4lPMP9tGiHJhlWgGGLG1UTQFzzRJNTPES+kTlN
+jc6nMTHGsFl8uS7DHDVP+U554bKdkk/oqHQG3Ptr7k9NifwmQIaA5Nq3p78v/cj5ibZSP6CrtmSN
+yzzqINWS2Cz8F7cfIBzbFQjc6nmpoBke0dMMi9C0t3S2FBV/G/7QAHIv68aF7cgvqmgYYCsbhFCZ
+pf3Kl+JYTslQT4HVsRuN6MIEvdY8wDs/X5hrgFdAC9CMZ+Vbjc2qjhlfGoz6DTZMMxD0MWkbl+NJ
+qD/a9Fo6RzLvmn4592YWKn2GRfhIoeXJZrdRlaZLD4UC42PeBSiFR8qu5auDYr0WIIw6FeSbNbnR
+NvhAjv2g94dyhkMAFQphgK2NXPJxrC3YAYZtWPnd75PuSnTvRQeAvoy42B8Jfkb6xi8NVEks5BZp
+sVgJguz0CnPd4Bka8fWF2814HePQS3qcN4IEJPuFim5B9Xfc1Mv18KC8wfKy3CTIx2P7UEfmrIFg
+q2u1G5m3dG+qLqN8s48Z8uKtlkl577OB/Atg1Uz3mtZAjfbh7WXom1wn4F0fgLDZjhHmNX9Noqor
+YVAkPZwSdpPFIZfbdxo3T3sunMo9EKK5eIatXyavq/aJ51hRZffTsq4HL6Ydax4Vntf18Nym/JXO
+lfvBoWZVPGKK416xg98ucH3Y9Y6DxxgEKDvL9gl5nqHjCeZJX95GWfdDlf7NSf/o9gOE/3BTM3yk
+RcfTI5IGCrikO2M2uX7zFosxmcC5Gxc1Bpe0S+WLXN7GyIQjDCO5HhoSgYvS1BbHRfySlvK5/Bkz
+K99wGDchQXYRDn1hrC91Q8zi1/4w2fehAr2UdoVDSKjM8DpmedLXx16OpF6VodW75890PbqbRE4e
+0kEctTDOQc3z77bJD0DegmR7Vxe8yX8tswsr+ah5zpiP42/tm5XxHRYBA54Ftw54W6iI05TXCLJ8
+8le+7IWiJ9mweRStn8e5F9kiHV3mppfv9Fs5vffp+RNPcL70qmuD58c1JEyC8cOw0jPuyJfU2H/9
+YeS3eUUDAx8ZM88Zem724yOw1bwhM6/X5ry5zCGVx9BPb+nsloY4cKrJw5Xrcl11toi2Q4ZGrH2B
+7SlFHCt0Vp1aANG0rYv+TpadMj5WijGTT3FbxBvnsoRr32x6HW9dHWWpSFfVgcNbmQExNsqoD0ho
+Aoo3n6fSRxqa4K8Q4Zp+yshQPae5mnroVueSMX+zfuQ/MCzbuvwxB8MjuKbwYxUuwZfmbj4IUcV9
+PF7Xy7SM4wiVlPGd3IHkoYIBSsCeZnnGkv/l7QcIx/Zd4q/wZlqck2mgPY4A0Pk7H6EgFVvTkQnK
+ytgbwVtIZ63DGfWjzLUhZbRzlJdfbBcENYXhkxGyl5Rlys8a45pCiiOvQCljsEPdedA8wKehjjkX
+j2u9Om/5HqRFg/qyoT3+UvZUnVDEJultNm5p/CYNqT9rzBK0dOX5tTxzm8dTfnL3P8ucAIbe/Gc/
+fmwalfXTQ3rTusUNj/2K7FB8PplB2MLeyzoFNMtl4JU+QiRaXTlHb0KL8SKPvKxeppBBvo25FGYQ
+rBAgzYkp+rU4YdJc52R0qUJOAILYtrqD9eXP9lrAcixpWwb7+oJ9fQHrS0Dt+Olnsc22Fxw1x3yc
+22/sih7HjaJMA4e6y132wOTL9x9+/3uDJwcreZV/YkGFBk0Pq4KPvAZ+Q1YYtpBF4wY4+Q6/zW84
+2xb4kSWzFeDP4oVO8iyBaMyl0aMp9W0h19dV7+9a4VwQ3jdDxlHYihsr3/zlda7/+gV/Hviz93ge
++LP1zd6XTOxpVZrEmG0D3uR7EP2F23uFZxgRJx1Pb6iKNjh/4qEBZSRumRMpihxb8HGCSKYZSpIF
+2cd2o9CepiBkFRd9iSqXOhb9uoLfZfGCXNAEwFd4xz32X7wxsP0OAx9yPU+aATwpg6XSEWqdvGmZ
+EssuXc8b6UYLQwFjK/qrfZgo/S9uP0A4tpVxip83cw9bY1gJgg1tqRL+glMa8NMDAblU0AaJY4rq
+fOSCuA7MBEAj42f5V5I6ezS2D8o9u+q/q2uSsmv+e877tRN/iOGncG6qdeDCwwTAAn6BDpiZ/sV8
+Ab3mTwLfyqs0PFSNlqOXtzjseZyQtVlmjqgR+zOw/V55GUiTCY0+XECGW+979f/ZxmJJ3g34MpZT
+88vyqKbMX43iI0d8r4ya+Qp2o75OxeSj0fS8ch7HOAogeBnkJ2TUgPxYyGP5Ls9uyKoTXt1MqzLB
+L/uj4JjtD0CcIHXZpU7PMwHJ7ctw87PJpiBY0mOOnfOcw+o3cHtvlT8MbwPJ3D+eEQ6cq/kiftcF
+H37XydT8m578TXkbJedP2lXkMAOCgQLEZu00l5M1DKDXscycdN7Okliv2xxcrnDz7Ir5395V8rCt
+VY4V258/9wwLWI2/4PHyWYDeyre8ZobGKQg22+D3V4DeAMHwBx66pW6EQo9xvEHa9gTGRuhDgGJ7
+yhsed6epbWDIJ4AbkD3gajBtXXPr82BWaX2aYlFYsyRyRuCbYjJA31CRd4B8yVLw+2yaeNyEUxwW
+08Z3evZLhI7tUX98h0E8T6xs5Q9Xi9xh1qRehF07kLe5LezZZMzZV0tS9M4jcYoFwD+dJn93+wHC
+sf2xR3iCYNr/UPIiSnUNOOACFy9eYEDjlyyPAVQsn8ejKqOCyIvnNefvW1saYtnf6nwoL5pUzh5G
+GPBEKpd2DtRxxlEzX8GOh4LbGE4AsHvbGyQd506g20HxPX0tv1vlk7DiuUqA/ApjJ8k/eYXP2GCM
+ckBBOZLfJnAHng24FAzLGp1wg4Xxdjeka4HgN5/JRV6CYJUKUXoToHQmqFPeyuYRkcJBkEueFyDT
+vE9XKqAAdDAMMciolQ4WACxs73rwfwPqsbuAY+Thpax5kAgCzr3dymfdZRsAJwj+kp+EPNxenJtG
+vZEzdBdKVu/74LPjePDms39HvvD1K0jGnNsbU3Ey+moNvfx3GtVfD3fyAoazb3JdFy3gvVUdRj5t
+YPvCH7wZY9yqCwCG0aO7AXDxMkMZFCRvz6NzKUy9CYq6hyc46zoIgPlS3Qa/4gl+tgOivMGh71xA
+MIeWPDbAE/n8Qd2AMmzCng2M6RF9ADPPl1/TRq3OH22FJKWxlY1zKU+fVhrjU0YO/1R0MT9h/lLP
+2NzamGM92B7htblmBSB+lmWTXwTzDyEtwHd2Hn/4vZnd+IM93/5w4Y30dQBORzIS5ZjGcnPIAkIA
+tPhwrdNuArTOT2jEv7r9iUeYTLYnTT2PFwAaXFY3R6JIqbWMxrGYyIACyFGH3k3GdBWo9vPKTYo0
+9QfbDVWBYvC5zu+vKSLUNPz78R6+gBnnyCMsQsCt7icgBgZADgP7Cfy+Ad8bSO6g8rTGNgfZvFXN
+al/BrWfJzOt0ankW12EMsPbR/Dje3hkr8LGkDj1Yj2O/JBYG7zGcYDg0bDNY+xz9m1sf3dv2Xu6U
+rRvxjn2v18DEvFqKatDGUDHOcaNK24uHwIENrPLsnZPzWuaJGLVMLOPNu5sg2Orrfq0eer21YOsr
+fuv45Seypyd46BlNcw63GNzCJYKWPvd7Ejy+GKZilCB3eIjr1+vzSnxq00EpD25a6rtlfuT+lm0B
+VFDwlp0Mc3GhUdN7v0vbmR8A1XxJGI3DBACn1zcAcAe8XBXF9lrfVuENDJvIeHH1+C6GV+wyriZE
+YOxmAYAFDMuNT0untht0LtIlD/rCXkc4ljjcN+4bAJrHyroW+t42n+1+PrHWsKJClKVOc122tHNB
+v2naIDFSao6Pl00pexMYop/HdOCNBaSHdy1v3uANkOkZBlbcgH8BAHa4iz/PnppYBvKhqxcx/Lh5
+Jwhey/E8DM0IG+ZALkXnDdOivT+k4iOAN8d23MxkwV/dfoBwbN+9C0n74fQMK99WiITWNwAhg7E5
+L7p3qZ9dlCHvoAL4ioBagGCnqFq1P3mv9+ICO24G329t3Ov2a8w6ahybBXrdkg6zjhixZk7T8BGI
+Mi3njONez3N/B7/AjgvGvTwUdUVm9d/5gZPykQ3Eg/xi3kEP1rwACS3zmce0g64NJy0DUSS4tTI+
+hUgCDKP6376uFsbOQmN6AmBHB78oY4V6MYI8qR6tc+Qvucfryi+nqoL/UH7Nm71Ij9kuUADsUQ4g
+PMEAlw10OgBJVlQaKdvSR6F5ce/zNwAAIABJREFUCwGSsjTiAm758RLNn6AYqzxzPT9CIrjqg3qC
+7QsZEpFL3wUgnoYsNSDHFP3dhCo+jZsscvRnECz8N+XpmTLW6zOvwLFfDK3O8lvZob0jZZ+qghN7
+5VaebqOe6rlbWvlT+DezI20BUHdYhPXjAMD7JrgAcN7weABivhTpXmB6hEFYhgVFmYBiRJus67Z2
+kh5gfz6AYpTqiYEVH9U12gufCwnk4Pt5/o5l3jrKnhWyE/S2sg375rxkUaexZtraNM8pP8ryhdIO
+cm9e4aunWDEi9hNph5cIPtsjvIDtTV9WIhAd8fCEu6HWPX8cj0V8dCosvntj2zEey0Y+D3V2hMIA
+qX8t5j8HLeq+ADF/RZxaX9ou9a7o4x/dfoBwbGt9j/gJgKGe4a7PTkDqE34GOKZ4VVzS9AJvgLvz
+KjRC2pygOBjq7mdrV69NDG0K/A0gSP0pr2fbduSMBylSKl6cS7/8yKNx2/vb53HVCNolD+PcKxB+
+8RJXHq7n8Trq+c10ltUA+9u4XgNHqf45HT3+90OZ5PH6LQ5YvcEsbxboKSPMUDsNm3jUAxyVxPOb
+ANj2iyst/i454/Y05gU8NEa4s+eocjnusZjv9e3kP1aLrm9xjdmUp4+enwNG0Axdzi4hEp6Wqya0
++mnZSIIfK3Bb+9VXeBieYT4Cn15k4zJoLRRixAqbgmBr6QQmqXeKq70BYKGjsBnlWQEr5dkfbMB7
+9QAP2jrKa4xL/SI0iXjOrgAr1/wPGnUXC8+2S9i4JvlGKhM8tjm/0CsSv6+DjAumJ5cAePPDW2xw
+ABt+Bt3Ko1xPFxgzLLwwwiAQN1fevMgRk7zRWcUFTwAcnlldblTlkAs9pOzZmJsVwM6BFD4Pr7Bt
+Hb1xMfXftukZ+qWT533mj/TQDw0cZ//qN5ece1uCrh/vhg3o4PfZesbD8b1gzbxp/3dssG+dzAgt
+hktoOnTN8wC2PK9liHSEQ3ChgBbxqERogJhZCnTPMR9rTf/F7QcIx2b2zdAI+AC/9AIXOEYrP9Vn
+OrO0IDTj1oXBWdSWgUxzlQjJIycWE/XevkHi6+Nol74dhbe8Gu0cr9Kn6tQdvWrv1jSNm2aIoaw6
+JfHnS18Qz5HkP71OgtIDzOICek8g3PMRHt0nwewNFLcyUHFWfvdEuJIEaRR47D2/lTGXdSxoQhB8
+A8ACkPNxPMMi0hO8x+gEum0v2i95NLzAOdriB+aT5z5B3xduecm5bUSuesYlDrR5py9XtQ6itbrD
+6+0SyMtxQuZrGATQgTG4sz4/WT/yG5ClV3jhDJFYWSe9wQMQ15rAOzTi9sJcgmF6/gLcIG9w9py3
+uOAEwXWzXjGgbcg11EdkOGWWxyJTKdeVV/pg1FG57xMufKqT7SgenlxgufNbviOZ2rXodm3EExGr
+ygpi5rE+IfDXOmw6AJ7Vi277aSJDJcIBI6tCmFUZxmoTBaj1RsgFBPNX8cb9i6MG9xVDdcDVKxx7
+mb+2bn4OT20gvZOGQTLwEYytJ9RdeIKjXd4YWPvoCmWzpr2mslxNpLVdysfUQleIsIwDRj5MIZiY
+3mBr6Z1BdWJWDm97rHD/ilBt6WMOi5E4z44P3mFt4VnmR2bMUnftk2P/hLc8Xn60x7uTl0BX89hM
+qnsBvNAQiRMU/3iE/8Xtjz3C/D1+BYD9HJr9YpzSoqpwQ/lgp7ewezFk1LWR1z2tBL69F7e8e2+l
+qRvC+Ig6bOzvNbrBebmE168rmCNaLD1HJwg+DeisewfCPTTitRz9B6DXiwFYDGKGUiDbSJPYBx5j
+nNPhgw632gNnlQeq9D0OcKwAOMHayzFXkbCnvMHpHS5g7HjqDn+AJdO+XZjhyCG4OGpqfQ40T5LS
+TzJ+1pvX0eMOgKsNBzIMosAv0LzoUOBC49snrICxZLeQCdTN76qXk/iWvl+8xPl4vAHlFecJ2CUY
+TuAr3mBJu34sBQKKcxiXuGAx0ArszvW2T9mdwLcD3qJ10x2xt9kBkzlW/mtGOOhvkx9c6nW+ytCZ
+vFnhiQRw6OelXEZ4QeikJhIuXRSa6XByrKjzCTxPr68jQa94ffec+iWMwqAeY3p900MsL9TR64us
+W7zpCszdmxe4eYhTTgoM32KEU3MyDl9+lk3I4xjR3wWKw7MJguJBw6S/JwhO++w5dTm/YJ5WHl5e
+/fpqrc09we8lHbsNfE3SsfeeTv7QtKHra6Jxe8orzBEyJOIAwXxqsHmpMA2ywwS8BXKlTo7tBRRH
+xY5quv79pMn/m+0HCMf2Jx5hnS6+Sdny9Pdmf5tCdjSwa2QE5t+9wMkw+eJcXahM0jmC32/enSf+
+qe6njeMogziPGoprSa2jj0zLSGZaFaYYR3e0N8zVczwN7J6rz97fuqERIHsDzdG5bC/PQSpKtsHP
+z2pwS1Fugl1vNKq95Oe8XQC0gN70/oYyewPAmo/nycf/28gOMIxIb21bYCEVHBe7V9406Jvab6x2
+8xbf6jYqUrZ4nQFamvK+1bPzOi0tFuC1jrTpcpk0nhMYQ+ejLlTzUOdu59u6gGH9gMGSF3IKBPta
+PS9fiqufXcMjCG4KBDN21ISzdz/Z3xi/eNxyHH7bhyw/Jef5djuBzgiT8OkpFuCjfJzRZUlwayyR
+MqHzN/KqROd+8I+jAJ+ynld6A5PQ55TDIbTlFZXxC22FLUQfRtoIsEz6Urzh4R02AcCwJWEUwRfi
+3fUGoA0tHEJihOkBPj3GK0Fi19UODI8wAeikMIY9OV4xUDqT/I49LvIIda+AbkS55cVK3gwKfEOD
+Rf+yfc6Rsse6gWAkPqy9HfHEBxjeVw3c4RnJsSJcLd8NFJ7YQ4v5pIwwniPXVN76m7qKTgouvWaw
+WJGig2DTF6QQY2jHOWzkk+kGeDm+0h/8AItO5S39v95+gHBs3/cIIxeXTj59A8Nv+jNOVqHaEkvL
+qwY7mC4EtRphzDCgFnZ6fvU4c3Mxxt+P9wTEEx3YtR2lA4/PaooMuqFzqezH/0239rW2lueoeMOZ
+5wGOObju7V0Ero47GH4rwwUgM0/BcR4LfcJQ3zzCV/Drb/k377GXUYy+FQj24j+mCZRlQnLpNHDV
+AxevL0GwS3p6hsXLkBDYYhzxuPXgjckv0xhG7iG2NE1xDYs9apyNh9v5Wk55G9dNdreahyYC1utO
+nk4RrrhZgDxcIKrs8Es9Dw+NWQPDGIAXR14HxukNJuh5jQ3Wn/VfA8AMFnPJE770D/uUWVxvbE3A
+7hn+MOgcrDxFimEanJ8ituXcJJic3uE6SbhCeCvyckWGwazeUtbzxQ50wKtEEr04QLOXYpAhMY7T
+ct5subwMF3LfVoTwCqNYFvpC5nxxbJz/78UIV4yxF38cQHjoHtXvQv6Uv0ZTmZ6sKwKYABjg0xnq
+apd00hZAxhIHT9Tekt5c6tRSl8U5Rj6wIzTiBowP0LuqrICwpUTt0Ij9Ups/2OsJL2D57tvKoVjx
+CsnxZKIuGh/dACpUx62+ZFehlw4FwdTlXB4tuToPOIAah3qBc4wKkg/Q9M9vP0A4tj/yCK8Ki9js
+/xxgWHWdIfQDZI69ynBZGWJr8tTM6MZ8l5uPuulJxoWhXoy/Aw0Yq9LRaleNcx5bS80azReclz+u
+JQnVY81QsqsKciFgN4yo/ugd1mMqRwXC6cX1AXozr3t/e/4JgFv5rWxSSwjgLUu8aRh1xO5ePcQ0
+iJku4tanpcUb3NIPnK6Gx/PzqObPXic3gK89QIJQE89vKLq3Yx83oZMnfsczfqTI+157gtwGgqrI
+W7njpowrWiHqJo1NDPQ+t4EZdiHnyNLYZp2Uf+4IinTCu2cYZlgCgrlfw9OLK0heEivcwyOOsIjx
+c35gAwuOndZPrO/xaliE9WP1DKuMx+Bpo0tGe34C3EfPF+/eAFcJcjwvEuQtXsibjQP8TsA1FahM
+jwu/0QvHeRWA1toieNQbHsdIo/o+jt+AMceS3vq5bnA8FSBY3N5egto41rAK23LaXpxLYB2Tfvng
+hoIbX/Q2W80Hap6S+ScYRpeVpF5DXSJnDQDvcpM26VTqx/sqpjwSfFX9Asi320MsNpvlIusFcLtX
+OIFxgsOYI9NzIKC52NLyx49pbCdegl9nv6rbaD+GVgad+NU9vvcRQNhBj3Bcx9HA8MSv2rnuAWa+
+DGzkERjDBBz/5e0HCMf2Jx5hvmhZC0wHy9iTKlKfEpBhqg2XY0u9XBcowELAUMbZRQN0pbx5i8xV
+Hcm0m+j5HjihLWW6KR7rlSddssakI2MGxRJkvUtDrU85ctm72DpNz5/n4uxHWo5pQDvQxRX4rnZ8
+8Q7jHQCvtzJJr66xBhXOHLGBZ71rnjfjvL2ZNDxRi/nwCoXAqpfl8HClnd2OxSvIzwiJ0GMAnrHv
+VsfZudXGM8d9ln2u2xTuAYqB8miNRiH1fNc7+7CBgach4dW7MfZsTgGOFW1d2ksjvY8V7Dq0DeYJ
+eFkBhgMIr7XwLMPK2N4Im0jwOwCxvgAn4Jf1bIJhATsNENk3PMJe83YFvxxigtg4zhUjXMpKdis/
+pm+Ikbe8rukI2mv6hU8caCv1pHIXvaxPEDiXpkygvKObJ23akwcR2BYCBu9jkmMNn2jHMU993WAJ
+bXA5JuCNmN/jZbpFz64LoNt1C+xSdvYxw2fq4xrlXZw6ZzJFu+ErhVZ0HDcsegPOG4/sb9CYT9yo
+LwmAWx+YDjpulnDxDpPXCK7FC0yyCx/Pzyo3YJyhECNfQfFCO17UI74dYSv6ucSZleCXbBXnlHE0
+0SWysk+g7z38DYyX1/yZe8OzBeL9BDtZTo3Q8UkHxtbG/uMR/he3P/MIPw0MZ9oWLAx/3jEBaVtT
+OdLQsjCMayrcpnW97pBcuVuMOkzKqqd5pGD4tP73zV/Sl3pni4Q8Jilvsvl2uaaXRKmroXT++b6P
+nZ5f/p52XJ/vfAiKh0f4BoYV/D5abwLhqLcGgCYA5uO3BMTgcW1cbnZSpWjjoyToM+v4oGeCklDi
+yZDq9bXuJU7DXNbX+bkhnucSCpEgGLVsGHTPdcXEM8wrqziMcerY4vQX6mhKFSrHWsq5idfkSAHB
+lEeXaup1YvynS6PZJ7MmruPdt0hXprMvXqPXlSTqvPKsZjhEeHTXWnhse4SftQIM0/MXH06gN/fm
+DR5e4lpZQsIicsUIAUBi9er5hnqEZX4Yp5nHBRo6GEaL++0hEiijH2kj8Ii6PfaTIIbTFF4zk07k
+vAfPCxiuOzYN4ek6WnkgQYYJb/VdVZ1tic7LNKsIjZInctxFR8SOH7XIdYPzBsbB2OANWF1igQvQ
+6st0GgpRL8lV3eKT3UaLD848vpCndOmaqgAahcKrjHs9/QaaZCp2sguhzXmiPFH3OYR/SHcPgFu8
+1UGw9RuSNOnq6bUW8qD5uiR3AczLcdjSFfJl2G441T+LhxoW4ZVnBMNJU4JfwPFsgI3wDK/w2DrB
+MMMg6LlNzRV70bOpF1A8kmOwAYxJNJPW/t72A4Rj+65HeNv6tR81PU/OMYAAxRsMq/OJGthE+Xb5
+bRZ2nojpxSgpQztv1+ig9/OoLqX+B8c+WnD2oOcPqBH9mgJav6H2DlVIHZV2Eh30Hunnkp4eYXRQ
+yxCJBn6lTvMOS50HBYb3IyUBwDG2FXRbwP7WO8ob7I0YRWjahK66tdbF6xYHaWxNPZJlvMszjCz3
+WjQ48iJGOL+5SaPCL8vRM7zrOgy8KXThw0xHLJsV27Rx9bF9SvuZf3gbMIy5ghygLKYY2kIpr/1K
+b6IVLQEr0KvYqBneGH9a8zLWXoMoEAQkKFbPscYFL1sb/ArwdfkinGlIQwPBGiohYRQKgGW5NAiY
+qZAICwf7pnnjTWce90GDYsPiUQG8uVegm6EQJbcd/KJ0iMt5Xm3nNFvRkZk1n1EpjbnIkGo2ZZEs
+S1gl860a0ItGoteVF3KMkHQqPo5N0iTgqKuxvRXCEHMcsZ8EwLt+8EEDzAPAjhUhunfYBfDstgsU
+7/ZyaT+Rrb7v1G4KTQk/30KfRiZBGj3BdATMuVCOJV0F+EKArzvaC3cXEGw5B9G38OiW1xMRL1zH
+6Q2Wl+TKQ4yGD2lbdzOWbGbxW+R3DtOB8gaLYnLSJD62AXHPeNy4LMt7Gf1Q6O139rH6XjpYKyhN
+ClgXwP672w8Qju27xFcPMNbay4mIh5h3PJzvvS8QXE/SKHy8rjfhTVRzlEc6QWfUMQo9Ys+6tT9T
+YyNWUOU6yl+o8lZw1DpDJO7Np6pKpS8eI2xls73CnoD4CSVEIFz75wqCn1i/0oDDG5xAFxeAjACw
+Uq6eYNc9PNd2pDcYVK4sRwFo9QoLVJqmIWjTj+FlUH0cV30Bw1a1C3SJNzgt7YP4sCcKID+lUEcY
+RM02XvMVHPOm7jr/l7w3emT6VWOjRIo/7dl0TdtsO4AO5VTjQKE3FnWt5sF2aecFFPM4Z85FZlIk
+t+FdbTWI8ARnegkAVk/wBRDzWLx66R0e+T7zCYYJdg2VTvABAXBCh6THpTz33lZ+qSXUql4+/b4C
+5GSvqtMAFOeRvNHLHaM+KEOV7nUh/MDByTgxj6sNJ1HaeNB0XwHge72i7x54glgn6PB8iQ0WHk25
+wTkBMz3GAYgZ40swncB2vlBHhFY3TVjiLb5ShKS42YeZZ1LX5NzZWJWPW7SRViYhLYOH5OuFdcPF
+8ARHB8EkvaNelrN8UY40BUkugHjndfDbPcOUrE37bZd2DG/SW9/34fDZJ+z6FoB+j3b3MwFwAmGS
+YR+vmLv6uqDO14XsOQbeHoe+1/OnJ7gh67+7/QDh2Nb6XmgEHm/e4Dvw7cd5kMzpMtkiyF7cS9H9
+rWdY0hbXKh612ldn7tuMmbxr7Xb8JgOZst6b1tBN1+m11GBm9fL0po3ENgQPNvDN4wGIe/pJQEzD
+mYAXBUh7yMMue6TuLWxCvcDuBR9XAONclSJGFfdVuVh6qCaFq+O/GtwPef7mNaYhJ4HrSnUVgcKP
+b9wLz54m0OdC7A3sktFPcKxeYu3rji+dff1srvqIR/nwNpQnE59FAFpuPT+yGngLmSqaalsl48Ql
+rvWznQBNeuw6thlKIKDZNuDVFSJWAF0Pj7B6h31xfeEe95vHSS9pc3iC58c00gvMc5MOv4EdLnu/
+7Qt4wD1WjIB4iAWoJPitNg5PcNYHKpiT4Sve9R/B4UVfn2pLPMCc51are/7rjAuAzvHrWBKRHMfv
+4FjGl6ELERoTgKl5eQMA18t0AZiNTxfO+OC+rvAExYa2xnR4l/ULc4ccKq4Vch7p45h2b1aggaI+
+0hM9kzZm9PAA59fnev7myeLXBoJzOWJPL2/KVnp9CxCq93dPVeWbob9Yh8j3+MBG0HURG5BfeQee
+RsSKL1LHGL5cnkV6hUM8LmEQ4RmeAH3i2AL0w+YL8M1z5e/qtPjL2w8Qju2PPcJrbU/YAuzZb80V
+1lywkIbtPBKGdz89TxD/WPNIkKFHXtMaAXZVED5Z+wOYWm9erlJ9+F1belT/6wph7Hk8mnRJ+KVM
+jWTq+VD89AgT7OZPvL9P2z91HJ/yNHSPbj9GguPPYBkJgund9QA4GzoWCEacQ4S04hzA801ggT4F
+pHRWIrPRjvUls87lrAwwXGfJGeoTjoXW7YlHrQ7n+oF8HMqF6DNeuDiisdYAKnxhjnF+N57oef6h
+TPYCgLchB/JzzxMUt/tf6y1ajSFzreoWjUp2mXY5KW1PkwMaI86Zzs3vjxGPLiEeYQ13cPldX3hb
+5zn9BqIfX5dNk9CITY+9t3ZzoGXFy+SDvheg8SA+rSx16Jmjh/jxDJlo5R/BcenJjAV2IJ9KtNCI
+4ocOXjk23bzNOWDlmc3/ldB5bGUJcP1bx6TVFQyb3Nwseihl3WAFtS022CqsIuyVy5zbsqId5cwM
+Nl6cq5fkWEdvtpJMsQ9dMU2X3pDMcuoNlml7F3ta5zpwXMcry33bdve93Nv4gEseWx176kCUV4Os
+topOG9gWHthzU+kEm80zLGlYqCzHY9gvycHxWIQxCI/tPgR+CCVUTzcpj44vGDy+vLeD3eIlufg9
+BMQHKLaCHOj7xLMCgtsENV1z/v729gOEY/sTjzDWE2GSGxXvCZc00IQ9ga9pmuawK9eyFCM/hJeg
+Mq1rcr1Ve7yOtH8NhxBhPfL0+DjvbMsk7/QCf2MTo9EOvY7TjgEBOBUAv6UVHAsI9ge/qNhwhkPQ
+A7y9vDg8wRXbizznibICwfRc9xUhHMAXkOAX8KY7XQFPniF0mYDQP5S7GnECNQFYY7/BWXl+zzqy
+nnB4hHeLhlzhPT/TKd5gD4MwPMK0fjeWq/0c3/seQK5oUHetBHIISyEBKA/i7RJphUIsslHtjzAG
+0tNYFiUypg6ApT2vNugl/ngMNEC8BNCaeIEf8fT6AMWwc43gXC4NZZimoUogDPH+CgjKeg38jrHG
+Y9uZDwFwHvxDsFeAl2AkyCvnqbfXtQzIECSELDLsq8JXPMHWVqmO8g4TPLLDMq8CsIbqimO/lx3p
+UnBFD09g/+bt/QiAeRyhEYfn1qxig61AbXqJ4wW5bauWgOAInZAX52CWL4BNcDxjhBlwWp/lJgmj
+zCC0j3x4FFuZx7Rxld4yaP1UIGXZTGjd8np9Pslj3DT8Ca+r1xcNbes+5TP4Cl57kofzIjZDIyDg
+GODLcgSORcIaJ2m1bHfDLEIcjGB49+UJ1lyiuxjCZsFSJORebWLr8i8PMAzHevYcPfFy3HKEhzim
+6dLH+lnbt6Kccxv1ZICZ/rvbDxCO7c88wttDpt7gjZbkMQ0ZIZWDpreyLTWqhljSCWYj3z/UNxyg
+91Oc8HWTy+SjnlsdafF3bc6y6Q1mmy2PyhzyYwJoALP0/wmAfw0gXMcPnsfxK75k1L2+yBCHlmYd
+7OtMrzDra+yyH8flIa4wCo5R/zjUoooe583ArVxp6VWjA13NGXujZ6sAccUI1x4PjaLnC3I546bH
+zDYQ+if+cOSngKufc+/X/HtdJixA3tPT2x2zOyBfs6ORyH5STjvlam+GPiM2XvgTgGTjXNbPubK6
+xjwGwyLIE1HGxgKE1DJM7wDYAwD7AYK/Gii6GqXLsWMAZgLfsG81TinLMZzyDK+88gwjFcAMiVCP
+b4HfT2W8KJK/2Vmu/qHKrziidOypDuvGspWXqFVNYQCt53qijJ399Tew2z4KNOtGnpXnN2N0Cczc
+e2yw2V4fPMBveYHj/HjyUCA4ytLj+ylGeCVYrhdWgVx7OhFThFCoTQumcrnxqPtUS7bMuWS5mD4W
+86COIedL3vOIJ9gAe+rGwp9Amys/CQ2ELuNNdahNe4I+EhvM5dBAki0BhzfPsFWekGWrsMjYnmGI
+YrXGe9t2WYBg2jLAsfAFB59D0gu8IjRiucVLchLSYNLfQbZzq3lV4Hu01yZifWzxn9p+gHBsf7Jq
+xOZCS2dTMsUF/G6lg1Qom0ODmVQjiuFNReAl/Kq4+wPnSPMuNsGzXJ85qSAiT5nw2I2yI33POWun
+DxttyOz6MBYJIAkexw9+9/T+CoD765Hjh4A38sIjnHU0NAId7M70eznKi4wdUrG9wgglU6BY0E4O
+fxyAXyoKLhEYVkCoqhvQatyB4iT565YAiwfRfnohbeRHrDDqpRGLtYcz9OGJ8rXXH84WTPpiK9Pf
+21/Aqe4zbIle30v6ecIT/FKX5Xx0bUJnTqHJbBx5nG4feZJyHU2H/Dz3Or6QYRop5y/KefwQ4BAk
+aN3Lb4Le5uFVdDHVD/tEVuZdtMhsATzPZdF4E9XLnl7/eeD+FAC+hj9EGp6Ic+MWTzpnd+dNzpvH
+l7rXZr0ARzkPJc9Tl9XBWV7yrtney13zoiLHO/IK3AtAbtPkZa+WTts2aI4l9uzpXv+1NsiT8CIb
+vOIKePQlMXH8JPCmrRUbWSAJya9s10Y9h4AxXi7PyWnMOcoXwD+a+EapIu0xbSZKLLQ05TucZHx5
+3uMGc4YZNdoFHQ5b22TMk4aNFyKZoudJgjw9w+FrlrfecLo4uLdYHcLi3nrP16f9F1+g81rD/CuW
+cVzx8m7tK02amPWPAGm9v739AOHYvruOsHp/yz6cgt3AbzKxi2KA8L2F0gaSja3SLXwilXAIIfMJ
+kqfARb9415wKQ8Awr1TtiWe75edltPOSb+cZsvsWCAMK+OIOhnPlh0c8vc8GuL+cYLe8vgV8I1/y
+aCTTG4wBfoEMj+jAVz27CRfwFUD4y7dKXUCm99giTUMoYzev+yyygyeK8jpuYMlGmRJzUt3GftDf
+WkKaYV4YNDhMwA5Q6QTFz442czpm52WFjfmi8wEi3/Yv9XKkHur9eeKrdTfgaxX7TC9OGO9Mo0Ak
+dA6FtHX9mtAD9wiIPlP+zXydSQveKvDLLnuAhzLiCnZJ6wK3BDAJhFOp3baOJjbgaEgOqaMOMHtL
+O9wF+A6AnHXpeZN1hfnFuQ6GycICYVKOXHTsHocLmOnAAyULPhhV0nPYmlYZnuWTZFU3bn1bw0al
+cU6Fc2TsK9s5eUaCdsq86FATEGsUapz7aHy9XfmlgKn83OoFuvxt2hHcutioCYppp8ijHfx225YY
+MWygS7Mpk+xW2sqgQfBB0kRurvRF1amD+g1RXc+5vKo4p2w8dckwgasudqTTLC62l61spkA6H9d2
+nW+kPJCVaQ1M8uevwPC+0W4AeDF2eOHLPfZxnWUJgr8OELzyZ7bu4DjCu4xPt/7y9gOEY/t2gDa9
+RRYvxFE44SXosTe/5NPTECxrki43sWUs25bYYu8WJqGPi/J81B00bKRZU4Exz7t7gE1ytFavaeOs
+32+U8R42UIqnsNVZ3laAoJc3Yn4n6CUQTmAcnmCCZn8qrCH3TpOgeeMYwysMxmB5xv9+eXmFCYa/
+OK1loZNmT8zv0yhaj8kLHKkZQ1y9m1ZPzVc8pZNzGPHpMpl5rrwYfMy3qfWtapR3jm9d+w4yg9kj
+4DCuse8sxMiUEWuGh3s64zsBAAAgAElEQVSK0JEvj98f7Hjl8HL5ituLZ0W+wTOkaed7pi0tgnPx
+TC/Ka/97/7z1rfXPfOTVmWlUjytITRv1s93qLsEtDfYb+CV9uzcYBVQ4PNJzCnOqL46Xum7WI6B9
+XtIOuABdHj+XsvQE1/ntc8qlLMQzzC6yfHbPOxKKdM8HMkyC8nADxn1yr+mUXpF35RUt1ydBb6Fk
+jT9CmbqMlXr01BQytKQFChgatiyI57LFCF/ALkzCKtpvoUBv/xXPyR7jeOT7yKfN9pZXeuIExLyu
+1/QRYOt05k1cZy+lY94gy2ZAPA2z1H1IDyjpJ4A47XCc3dQt45T3vLZLvfFZHKvGdz12seVeTzEZ
+0eEWL8oFTQmCNwCOWOI4dl+5LOgXdiz6+qrfVwLi1TzEX18THNMjvLBsr4e+vuuU/B9uP0A4tm/f
+hYTrRe9O68545HGfFiW8uwmKgQZyFSSbnDO8wNlOas7IS0UD9NCIUigqeHpHmv+t0lrSpd7mWWcV
+zUotv7cGC7wnZxjEzFMwrCtEcBWIR0CuguD/KDiWNF9kM6C8vmFAmxf4WoYDRDMM4gue3t8Nfges
+a0qJ7ZMGAmwzLCEI2dxFCoCLyD6OrxPztjWeYr/JFI4GrJuVCLASoNfjhVKX99V8rQ2I4WFs2b0e
+Q0pxaRQTdr/WITADcAPAbtggWNMExFmP8YxPpC3pQMqKKA/ICnRALyS1ytG6VcFbjHFucZ6/TKPz
+FwaeoPixur3u4DdorQDZ+jgh53bPcINOyPm3PUCF6JQDBHDdQLbS6gkuL7B6fBUQa516aekMi0Bd
+K2SUtKsX5CgxMo58CavSmn+IV/MgS/pl+hrYFZ5RwMJMC94+0Y2cNK+V6sTzuD1dYNt95qqrKurQ
+uptOXPLs8ASPn+GW7x/PmeDXgh9nfttLfgPTQAHfaJ5tbRbd/Smwb8kzFatMwlSoye2JjE/GkHN5
+E2OArA4RNNIPnAgYbg20CfLrfDXuGKxCnhMt3W2N7PkjGDaUR9hQYRAVErHgazuFfNl+wQ4VGvGl
+3uAL4F2rQig6CN7trx+P8L+/Lfsm8adw8vbJ7SzT0AhVDukp5k7YliEPTUt9yAOa8DCuL4UNSIFL
+4GsCZFO/ZE61K2LTSofg43Kmnn3fSqIJGKm1eawvzVy9wfkLb3D+TuB7A8L/uQFhfwe5Exjf8rPv
+2GC4WbqLYSIb5M9VlU4rTCWMD2WqGefxZVb8zKr5IXNwHOTPMJME6fTCZToAZ3qCNzBOQAyU4XOU
+UQOB275UUs3YBZZ5NwoBpNNrowA4VonwOMZi+gZ6V/Rzy4ivlcYI2Se/4hFkH73yeI6ClTkt5tXI
+ZIqGTHzUsXxykOE0sIoVfgG8TCtAaS8tRTvvwqv6SwbXaABoKIPGABPo0subx1dA7OFFHnkKgC+e
+4KpTVOfIdpagP8qacy4k7eRLkQVOV6Zl/iaguaQJVOrYpMqIwp9t6KWaXkEb6x5i2Aer45vm6Nfe
+WqhWesB4+h90WKGtBCT7AMwNJH8CwmIzNTYY5FNA6spe8ltfE1DTFkpa800BsOfoQbrnTT5pSVqR
+93M2sb3eVWbRt/TqCg3SPl82Ff8xsV3fze2iLjYYLt5i21Q3eyWkmgLCmPIOl95gqIRHTPDXWilm
+fMKpAFc9wT1MYh3AeIPgAsAs/9vbDxCO7duhEQZZbsm4lokYhRCyZDHHhU2lOTEqAi5S0Fy8yASb
+XvV3C7xuVwwxsJJZUYwFfEsw9b8mlTIT+E7SMDHPaQ91m2IpDb+Fi4Bql+lqCx0M11Jov5o3mIC3
+e4L/c4DiB//5tdspYAu8Ad3PIFg8wjEml/EMaARzxy+hmWEvgdPOA9TaYvINj/0oG/UmWPm0KUA4
+gEFkKuoTMLQ5foMaLgZn8Tlm50sk9kgsXzeEbPYOhCvCsYFidlW8wRvkogDws1eJqGMDrAAyQbCv
+1Y53aASvX+Rphkn72YzUu+fYLBvq6kG3nEL/WKcvuWcf0vb79GGgP+lD8oXotsmeA7CqN7iX3WJ/
+o+wpr3GFRdSLcw3wTk8wmI758AIBOQYFupBJjrs1T6cFkA6MnL+b/uONbpXdQqgFLmVh5bnkDwZS
+8ueubq4dpAMz5CYggJn2rsQ9qMVjCwpKWEP7ohjvvIIW70B3nqu/6EnIf9sDRxjEe11rABiGBLjp
+ETbbc9nShlxKztDSTaCpvw8VyvCzmkfTvqfdrqfHBMEm/WoOqzHJBLNeV7uxgfCK9k6sg/CHOqrU
+E5x2yLZe4Iu2Zt5AsK9d/pXptfu5rIHer3WLEZ4xwQWCK/+NHv/s9gOEY7Nv34XQY4Qm1E1R+Gag
+5gW+pRvYUMAbbaR0ebUpClSVGIC62zSkEEYBNDyiwDHLKSDRzpHuqTqnHY62Rj67OuFC6mxPpXMD
+v+oNnvHBujwaPcIdBAv4fR78+vVknjcP8FY4suTjBQCPOiOvAMh4qNa1VikfH4+mQJDCE9Q4Wmuj
+lb2mdSY+KJhkIzH4e2KKb7ROGmkBG/C9vvYiBXac7n7ZBtsDGx/j2OubVoy95yUGEA4DnnkegIHL
+vPGcTDv4Bnz3Bm+wu3v7ZPwvlTmepzykyzKGWO9FBucWCTDLCaJGHkrkcopkSo7ZGXVmeY/3xTfT
+9poW1DQ6cduEB/aA0SjRXnzTMIiRHt5ffVHuBpyvnuCjDEhgLJ5h7bfrGKiHBej6oZ+rjkedmkxp
+ngcSP8zqStMUtUHpvMz1vJFBZcOE8iIBqgBlro2rbMejGiaPLY9neMQBdEmL3/x0VaPfAd3yBkud
+1BN25J/A9yV/0fdLL3DxQ84YaerncV5fbpj6+xVit53Dsuwr7fDpGa60Jxao+X4PmbHki5t14H/X
+lFd5I2P03QwtLMLjJoEg2H07Er4AcN0JM8P6sh4X/PXiAX7ZW8QHf3sFr//h9gOEY/u+R9gAGnQK
+Np8rNAHdxrruii8gxfZ5HfBSWKiQtEzBMI/ZMe/KJhWBVb+hj0BVTErpaBDECWj5v4PiK/C1M3/a
+ikymsCMNGI3aBMMFgmVNYO9xv/+JJdMUBP/nefCfXwWGEwg/LoB3e3Yzbcy7eIdfzvH8nXfwpxKK
+F+yyHYuv0gH1qK6brZOa301ftm6v6xJyM1aX9jGJp9eNeQhPsBnBJOVlE26zvSM/cgHx7loofWOL
+ks66BMGWXlo3wNN4b/Cr3t/dryeVO6jUE/CqJ7gM6Xxxr0aO7NdZHv9v5VbinGKndEaSpBvSMVeh
+OtqHrI8widxPwHwBw+h9kJ70kTWGEbmdm4DbOwiWvT9S78ENLOvLmL8Dw/3mrHg0b7ynd1cAzfFl
+OYIg4xyMc5VGN0Koqr/UU/ECuzbOyJQ24JmS80MGqU+jfzZuym6aIeEWZcgKAKfn8wZugdcy2hzK
+fV+ij3k8H1n3nlft9HMhdg8Fgrk+eZbvNrbcRN8zPGKPj8s+9rm7cndRbopI9Llj4+54MhmXzQbE
+vEPB+pXVZCYbT5nUq5qzs815k4CXOiOe8R26YoeP8VU7an3GCO8X5bpXuC+p1r3E1vaW3uK/vf0A
+4dj+DAhzb5fjUhq68H7dFXoa9DDzyc8lLAP0yt39VsJ1V4sUlOrH9AZ3QZzCWONOAVJSDGGdoqRk
++358MEDNnIEDBL/YeQmA0b3Bj+/YQqa5dFrGCktIxH+8g97j90uWTwvyTSCsXt8GjF3rFEiOkfVx
+Ck1+gcDFK+3MQyyJpQEjnHdJM98lnRb3kv86GR9mqQFitquXIsRQXuR190j0gxwmq1b6kyvOp7zQ
+A3QA4QQgAFcocGd+Ke7dI4/Hd9Vtv3h/HYh1UQGP5dMYyuSPKP5lJ+BIspWnTcu1To8tLJJn6JJT
+JCnHOjNSp5TDAC4p/QJ8MQwXxLhhgOIT8CdPvII678XHUwp2IkBsgtPp4e0vz2GC2eYxvoRFQOqq
+9xdhmOkJZtkxIEUW5OV7/k6+5E+Eks2f+RaMcZDWOY91ToVZnOStefDc5SUz7eJbCZsRclzxwhHC
+5wiwbGFGmKYdCp1yAbps+w6AL7ay5Ud/gFa3H0O8w1bHIQ+M+XXD/vQzAXE8urfk7wC+BKAEwas0
+GB7Pa7WZayBzJoDukOrl3aaOMEVJn1vZevVBHHbhdtFmc1pHWz27/BYMD8Mhmie465BjFWKTmOCv
+24oRlzAJs5Y2AcR/e/sBwrF9l/iOtRWzKgObcVL5gGnkWeSG8vDa1+MUb+WavwEtz0t7XOmhYMob
+HGXc6Z269jM3NtIFaaqIDoIvabn8zQwxUeYrwIMjH4d3rzDKI0xg/EhIRHqCn+M3QfD/i/CIx89Q
+iAS9rh7g7amd9cw7YFY4OKlpeVwR5A9KnZSio8JRxdXT9pL/Of2yBUtdbTrzVBtTebvOHFBQXurb
+XmrQ8wkKinEOg1qgDUbQxjCEuEFIA8h4YXqIeY5vMKugl0N50IAvfxknTI+HYXs9HksSkJSCBWXc
+cg2xRVqas5E3y0j5LL6oCxEucRkzy3Y6GPa4ZguBAJfiw8WjU/y16SieOh3IaVP7WB29wgRqDfT2
+n2lIhKwm4dN7TID8EPTJDz0MIh/5i/7QpxVkx2MgZGPl7Ze941LGhvyUyE6w2hppvXL4MZ0rPW/k
+jn8OoUmma1xcjYgWSJvU4+QxOlsSiFnZpivgxTfzej7lmnLP8ybQ5Tm9ftkvrlVMbzYBsfGm2Mo7
+TFuXIDi+BusLNZ3/x94bblmO4kqjIff7v/Cd09b9gUIKCdiZ2Wem5jtrJVU7DQJjECCFZRk3fvQ1
+ynXT5tJQeHOenYxsk2bj4g6g9uiOeaHzYKtS9cLe4r13XS+VZRgJgs3WxzXmEyXE+xT8Ip0D7UW3
+Bnr/urhCyH7CbW9hW+k/HX6BcIRvf2LZYuFtflOoBU4QGgovAXCmWRmwgeC8jVeLMPLuPvGrAuNc
+BCp4qj11V8ppzyKVribZMX4vtz3cGeVOYQAoAbmkU6BfP6jRLMH8mtybX5XjC3P/4+tFuQTACoZj
+b+Fm+bWD9deGBTjGI0EzquyNB3qP8n7jR1/hQlQFjlIwZu0qnm9gOFuC07i3kRFg3ACyHiHIYpiu
+lt5lL7B6tEw2qE8dkylUipZrg0ouBXCUpVXYxXpV8bAGs/wF9NJlYge+cW1xq6h2FHN2TCJw2Dor
+BlfW2qWyDvlgMZHoAqWsyTF3KvAUGeAX45Z7BEfekn8Kip15Jn0a4NjMxIg5OnqYHx2RbZ1Ft/j2
+l97q5bmDVXj6DqdbxBtzSyzHmlagzJFpYFjSctNUaIoyGDi5TnjO+VGmddp68lPI5WNb8dsqnudr
+fJ+jNT60t+j8VJi0W4AjDdFHXKfTMjxB7wS8qpMaDVBw+xHwAvnkIvWpyXlhtfaH89jKOoygWdBe
+rP6FC1SC4NeXBbmNQnEoVahSc43WOalWD3rxqiftnFs3b3MGmGZCdUNBXA40Z9kOiBccKfcIM1tb
+o6HkAg14S1ZwVx3E+xaxL3xsC8eX5e67RtQWaXrkS3L14tyVU/+x8AuEI/zky3Jr0Rk1EmjxTRAc
+i59vYtsGiGkTjAmqIDi0kaVQAijNzEqqNdeIFE7I+ru51mRBoIRR5nPZ7mKA56vQ1jzrfy55DLuC
+LRhRyutkBXZfy27fQq3A76sWYe8vyJ2swf/6+++yCJt1a6+J9deXnTOtxKQFyys+eSo/72k+WNLf
+EkADW7gKua4iu3CTpxBfq9ERDmWo11WByrGhPz2/CWgFwEA9SZHNhRPQUclhB8KxLtKPjZYqoIAx
+wmJMMMhfNCF/D3o+XryY4HiAxgOXWlxcSK5lglW8uV29XrwzfqUStWy5e4zBGvZKloNzpfor3/8Q
+UFxKLenaP2jcc84VCBzD2xM15tr/NTCH3R3iN3yEbfgCg59RVlBMoHsAvwVw1Srs2bQs2/5atT1l
+JSd5MNsJtAQMKWBWYCzn2+k65GsLXd7m0vo4ieroI98lv/sH88a5AyZRLdKatb44Twl8S8dY8eAr
+IJzxPc31oL6/jYYPeTHv67pYIDbcIdL4Y8tX15/SudTdRteoZ60/0qjSa2DCacW5Dru/LyReN6uR
+Zpk2P3qYM2IO6pTk5xqGYICXoSuNS2x3zUnqIUfpOe648SDczh7DQxkau//geZdcfeLswC71stz5
+q3J8Ee62r3AB5F+L8H8tfHsT580SrL8SEDdAnFO2ST4kCG5uEfn6cFl+y6JRLzYAqMVpcgET9wih
+ZTIbomX4pxZ3kwvyd+Nh+7uXySUbipIrOwGvSnjemeq/Iwgu63CB4L592v/EC3TTGvwvdY24AmGL
+tBfgdWtguO8esf4Q+Co3ycsHwN/WwbDz6NYEXj3QdOGrx/8VL767lNpF5jmo8q/kqUgBBAwwkE1C
+2rN5jgEJgE9KEsNKCZQ1AuORnEs62lCWYBcwt76QtFmCsQDvewC8/L2hKLP8AfcBHdOktXCWmXGL
+dR6WYMs32GXFOUFyjGLc9HIeJEvjmC/HkXdQANxdJGhBm486024aY8odL7qSXb1QsJWDzrWbeS6/
+w8tyQa+PY6gbhO4xTJ9g+vLHuZhpuSbEDYJp7FO6AeBpDYbygfNV4hMYn2TdZd7cQ3kJF+UjhgoZ
+mUI0+u5SS9w0EhjJ0tXhMilLw0udZymLO9h1ic+8qPUCghF1cQ3p+l9pzbeR3stDQTCtwWIVNnjq
+43ZcX4+oz8A/xfPUje1GaYpI7VuMIUFx6lWv7Hy8pmP6xUSJMje5Um3Sa9UTDKuLr5Rc85G1QUDM
+9/7d6RaxCnh4hOIB3AsA59HqZTnuGHHeTu1Jf2C1EBMQW7hH/OnwC4QjfJf5udhstwpz0SkCSjma
+d6SctCFMxOK3g+AQTOOuXFtaABkieErY6C/Pm+Xy6qmOqz2a4qIHFbeNvOx2C6q8Bzd73JGCnMB3
+txDvbhH5Jbl3viznR2vwv8Ia/K+/AwjHGK1PPCoQXuOQabNwiyAotg0Mz6BcfaxeM3jc8DcEDMe8
+SMtw8qxE74r1HIj6TJDcmH3mfBuC2fAJfBtNAIAVDulBdqf0ALq0DDuQ5hgBwlS0+ki/pS36SfBr
+y1rlASw9+JDuERY8Hb/3AcpyvAPmzTosLEn2CEuLfldWOhT5dvtjsHg0a0+Nb3tyEPy2ACQm9Rm5
+HPzhrhHdJULTFhbjExjmzXr0I5+l985qX/NAkMQbW8YD4Np8OW7bNm3RG+DVMrqvMIYlWNJrOnZA
+aNnUbjXeALBw1QcYbi4QLa4M0UW0L6izZmEbKu5xzbTaHicfsv9JOpRLizC4Tufj87rqDoAxzsMA
+hSYT9BDn2p56SOIOFMANek9zbkZrrGgAxDKM1McbCOYRahGOLRS5k6OCYBeRV8oymu3gjfj2oQ6I
+wclMaOxIcJ3yE+NCAk67YJE1Bbqc1RhD16m0tsY3bqTJ0PFIKfVSnPME7x+Z5jkWWDxCLM0FiKOC
+19KaW24Rh/2D6S/819lqXO4Rvxbh/1r40a4RRx/h+tFHuFmF4QmCyzJMQUWQC3wJgmMB8UUGiBCj
+QNpcI9juAz1hGleFUFsp0xS08EXQS96lQKisBH4d9M70wU/43S3D+lll/YLc/xyswWoRXsNqHfQi
+QHICXh/gd91xT1/h+pV1L0G0lH1MrMFuAYb5U/uQWousvfxSo9UtX8659eUIjQHJCkRwNx1fwtlv
+51OQSno1I/b0NeFSKChVfHl0rieHWi8hFuAEtbE+VhlPgLsBXdkVgkB5HXmNYTEO9jU15b3LneQX
+OrtLSxQSBNtr6T2ygHAA5BgDg9XDJtTq3NxAgj8LIKu1twOK6pt161oCnbIIst1DJxfAZEYCYS93
+BO8+vTZcH/hynIlV2AaA9vcd4FfSA+Amttjy+vTsMQEjEwBnfAwm5XICvznQ/yzo2j3eYZ4mFfvm
+HA2Xc8vVZUqTLi9484OUGWo9XGuggF5Zz1V3SDyBsMTRy0wXB29xS9Yf4wJE66W4OG7g9wn3iBi2
+512uSLAQwHH0Zz24evpIVNSqe9GW0p1Wejf6aUynzKz11NbRNqBMyroCUDvQ1NhVWDQLmUlMsW4U
+YyznTRy6ruJuRbQIl4V4WcyfV1r5xPzkzxyw204RB9cI664Qa/eIXx/h/yfCd10jjBau7/xE0dcd
+qcgQRuTRXIFgj3QWFPVncgzhlcAcJZjkV72z/mO05aEd9dwNXyvIatdBtq6HgzDIx5oknS3Bx08s
+JwD2ZhH+2/VDGt7dI/4WIPwS7B6AsC3L7QLACJcIr3IJiOsFuuLsEkjpduHcnWIB3he+wC9QAJiy
+jxaTHOf5V3nvMjv4+FxArIrM797sNXCAGpusE1BAvI8u2rkJfmnWIfjN+GpbB8JqARZaukCURbhA
+8brwG+q8uZwEdyZAVqD8OuDmCYRfKt4D8P067ht9dXfNDAJi82EJ1mPe+J7GqIBuAl/2U+ZPWsOl
+fH9hTuQZONYCm2QK9R4FqG0TV4Hx4WU5tQwPcJzAN9wh8gU5jXMUCXAT9FWcyj/HQrBMWa+DwTnH
+L/Hsv4JeylqJJ1/+iQK/nROML3bPiOR5JXIoPGoRGSFDbKMK6pDqhcs9sOooF92mfBH9A5lPG0BG
+s+r2GzSkPOhxyyGZblQwxJPZJ3T4WlO0DlucQ5NEuUcsy7BxEyi8azcEPsxq3NuHid2Zurat73U3
+228qQbbFOlODwxhv/UJgAWK9+BTAQYtxUreIsh7XGBrKFSLjICjmq84hv2kBXhaKFofTRziA7l/d
+Erx/Zrl2jlifVn66q8S39dS/L/wC4QjfZv4TM+JiFS4fpNL97YgCxVCw6/TjGhZhqPVXy1JEFQxS
+GNaT2sZZLITHUSCfgG3FZl7Jj3NtDKlkh0wnAKbCokBvIPitTyy7WoP5wpwXCE4A7H3HiATDf6+P
+cawtW+qFuAWCvVmFk4YAtXnO/lNLMN/EzbrhC1wD+MvCLcIR4DiAihO8kLOl1PSRZ418twaj0U4C
+E11vtzgV3SEtGpSPUDkv5xU8QYKJTqfiXPQCw0CBshHfrMI9TkGd8wcFgl/OHzMBt2oJnnkEx54g
+eYEDl37hh3EFlFYbZ8Sj2BaH3FA5ygrMI8rNxoqtOVfS/QEFMgh8+w2G5gdQiLLag26Z3LlQf71K
+BChWcNtAsFiDFfB2S3E/4n1rZBX4KviVvGNa+rFNfAEmDQDfQG/bMaKvvI12FIQ7sdY2IIzeA9ea
+C6FFCzStlIyqTsXRkoBlEbfqclJQ6/YEgAnAshxK50waau7pjS6AmqdRtc7XWI5oT5DipNwP2J/Y
+/cGqHwSnCAvxgzX/NO1Y8zC2f8yH80ddtinRDQdwJ4WlqynQOR9rrGg1bjZeAk0OmujEPVjWwy/S
+0RyyQHjMLX2KDIu1t5L5why6ZZjjdHRU4E0R+2UH0PvXBQBf9hQ2eXnuT4dfIBzh+eauEd40lyUg
+pjuE3hVvDvopPBjq8fURDAM5eXPbJRGy011i3pkOeJrX3BbxFig2OqXOx8gTyuzi7SqipFIwhOD1
++HP6oEbzE3b5qIYHGH7vluEJhv/1xq4RvDs269ZgHlHgtwFkWAFbE4DijK/z/gdxnpfF+S8Af7vj
+L7MEw25qveRIU8CrI0RZfjvYJTBECsJdel7GfIIBS+IBKLhoUFWrMYZixvQc61KAZXUsJXIFwrxp
+jN6WmwNdIgIY5zosgFvglnxdFuFmDfZVXsHwa1KPQIUb2G1pw7kcFR+s/IMRX1LyJwGxO8GwdTAM
+0IbV6l7At6xoCXzBOBJIlIWYskj5HrNJOjJVb1unDeCOdAPA80Mas5y8KHfaX5hpyJGjP9MCAhun
+HDvnFOC2OX/IVwDc1sBNfp4mQIU8a5y/3bT6HJOSldlrkZk9PW9rrM1kzevlYuS9tzVdZ1LA61HA
+4HZUfTjP0zm7yuc8Nku2N7eocc5Sjw/c6SLhIZVD5zrKVQL8ducCXQl+Y/05EP768cpv9rm3u41f
+gv2yAps98cTnqbkuq6mNsh8mCEejra8aodsZ2UxOWZ8gGM2uIaaI3K7TjLcC6zrPqNwV1AsYpkvD
+t0BvukfoRzX6y3N/OvwC4Qg/2TViOeILGJbFUGhI6UiLMKcfb6ITyKLuDnOLpQAgDQSrpTghqAio
+/Nnlh4GF7PAbvElaXM/2HLQSFfnE1VKsBMRecQW+I617B6s1eLpHtC/L/V0/ukX8f/my3AUAm+3g
+V90jDPVDvQRn8qY/wfL/gNZlw18egMyAv32B4teWn/ACw2UVLqBpMeYc+R0MpyVARW6zXv0gNKXX
+JCtSouo4ZryuV3bqaiWVXdFXfAfBtt5UHiC5gze6RFhsk1ZAubalC+uud4DbgC/sUs6S40dwe+q/
+XehxfGJm2POk353hXWmP4fJ1A5XL1Zd/+RviRnZnztHOF+as0hsw1nTwulnk2lTxpqM954Qo48Sn
+HdzSL3h7WW77iMYrrhG1vzDj9nrzEVbbPzBcJKCgruRIKy8AMDurS+M45yNOxqqsFYBaBKnwHN0n
+BbQdreK9ea39muHVPrkZANB8faVHFZ+gF7UynecruPVVege2oie2PLlCm5s1LyHxnT5AcsT5tAi+
+bijX0QscP5SX1JJ0ixCLMF74s3yJixm00NfYKH/SUqyGJzPAnrWLRYBhmMWXNPvI5hTLtMgY7z9d
+X00dZOhaAdJbBcENvJqJJTg4IXXzdqJ13uXa6coROKVZgYc1eLMM64c2uI/ws3j2/FqE/08EkQOg
+X17SFS/qRB1yM+vIfO8rZGpaozBDPYUKIoWUUVhvF791BEdgrLJ5RL4VTiDBZr73o8pvyiKV8U3P
+AqDvMIHOsqj6/nvrl0BZwDKtybvKDGEMH3ETsKWquWh/w/GXG/42Htdnlf/C7gZRvqzz2nXNJnQ2
+UdppRSEojrT1sSEcqPQ8dlVYYJBgaVOVIsa7JVHng3NAt7qVlwJbYmsjR2/j/beu/Q76Gyxcc4ZW
+YeZFWqzJZTVmuzyemMYAACAASURBVHrY+iSxDTyO8Oaj1xdvKN8nJv17jK91Xt/sY57JWllz2Zxr
+g+n10zgtr+a2QKq9YVYmEI1K+WLbALjtUW2Lo+/wQADrBYxrkRMoo2hAukpkvM08jkaBCB6mfFHM
+VQPRYIyAts/jtVeshbnaQLQoBb2t23b6rSx5L/WrhbddVwXkiS55em0FvSpJ1o3DxqVSTQNEZZtt
+HIEAkRjKUo7en1QwT8Ev42d6B8Kl99gbjfPI/XlMRo20ymP7AcAt3hJ7lz+Tc4uJ54kPcGC588RO
+NEtcLz44yl2BgqitF10r8tSkr6m6eWz+wqyX/M0dLS5jIiDY3MW6D3knoazDkPj2s3VT3l5eCCxk
+aTiycAWsJ6orn8MVxgBLVjc73X8j/ALhCLu6+1QyJmeexYlKZahq2OP/oMV5DGony7QA4P6Ye7ZV
+6qsZ+62+6GTeDIfG/vbraLp61KkFZj3z07LbIAZppQe2uNBEl3Z2Fqvlp/aicWfS+rHn7fFP+Yvp
+eU0R2szf8rSNQktwbAS0gFlZf0txzjZNCzFHt1Regs4NGP/k2B+sDhtbXrXpY5lnfZacQXDOS6uy
+bH+fOTPNPvZ0zi8qX4x5Fnmv93bQSqxhyomT1LitzrXICFjxdfwF0CHwij+A+xvbvL14X4PFh0HM
+Epr1lhMURf3+11p3f/lf8L/Wkwq82MBuMUuVNDZXB5dyBXQJwOWlNxkxlXnk68ROruugjYA+6t+g
+Zg8B3Cr3vK4VHH5WyS5loj3bk5teWxZpZfXqZJ23Xwk7lNBTQbi1CVK2J3UFOkq3tN5454BrgSFZ
+2lH8T5NO0KX5cXRHArIEt5HtCfDC8MCy1IFETKSHq1HJu4rXUV0mYja4Y91p8xi02ObRc7uJRfPM
+c7zmeF4DHsf7Gp7HgdfwPuuJxmMP3qdeDKUP/LyBrLx3y8s4z2ty0dpNRNFu6UKczqOL7BR5CBcY
+oaC40UvrWfB4PRXlMznPMkDdiJ8+ssP3AHLBL8H3R8MvEM5wUmmnUiXIN0AnqpxCq9EYF9Bs23W/
+kbY6pFX41J9v3WINBWI4nnNq1U5zkdvsP1J2Ny4ILUtysVMRsD6W387Zf7cOn9rbwxko18++jFsI
+oAJ445F0o4W4UBBsPe/Nmkq5JNw1dZCIMn5SVCNlxYecgTdg3Cy/83zNq7YVH8ue96ncyQ71NeCd
+1lodu24/pBKtuRRxC/0yxrk+cOKor9Z1vqBd7Ryucy3mMR/DUhSkhTTeYHc8+RaLtXLLpzE/If0a
+1tfx1vzjsTXEkQBnKde/1vp6FiB+/Al/SIyF9jnewC96mWV1jmc2TsXINU4Q1lcum21cQ2FFqvkr
+AD8NA33svxZ49/x503MocCB0OI5cMyzcgWG/Ql+jnc9exMH7JSO1GZ4HXRc280njcHH1ZJrX05bJ
+icdJfZA5CooTgXcrcQPBiJtVgjbSw3KMDTBHnWnZtNqLEuLuIKA3/YXd4gaNL9V5lPe1rtxiI10v
+y6kRvEe/CIDN8LwOPIb39fVY/13O/u/zwl7xEZ7glunthtKhO6tsvvhqQsXXaQW+laaeqqV7HNYR
+NjCMcNMiiyK/thEVAEyp7PVEavIG+YTq1zXivxauiutYTgCbUEoEFXUT9lfavTUFq3r6u609+wbP
+cpdMkZz6t8700ZvZ8+rjEciqDj3mDQ53nfAtcNytlmcr8ek8G2VmXOvf4gS1JQLkkZ4+mh8gGPpR
+Dct+7z7A/Ugd00MHxx3wUkDus5D0E22fsXer8HfL1coZrhFtrOYc1WsMC3DOlQKy+VIdZM4EGG6f
+t/ZVH90neh/28NUqnPkWDfW4WKbfGJXcpmg1clmJl3IoLLIez/q7WlrzpnwRhXEtQUvjXwTD7rmV
+0Q5qIW1UuizArWzR0iIM1huj6q1xg0FxCHnlpjOeMyFoFjTvFuN9zX4JcXNs7uPZ6/UtZtkWpc0a
+P84Xr/FZN2I0LHTDAGKOFp1DkZnV4oF0als1NqYkNFxWVDuN/VI+KD92edQE0gDGfPehvEFCP3GN
+Gmm6dlF1JkiOBr8Bfp91Y9jAL0fm4C8sPhcoAGygFdhzO4mVfn29HwILwfEYXnvxvOVC8ZrBnrAW
+xxt4+1OWHRB/i0Zu/xAMX8Hxl0iC9vU+uoblmgXop5O8rMG67pMW8yv78wEMP78W4f9a8A/ToRe8
+W4LpErFbiPWHQxotXoB3tMlQCyJmJm+i+ZGFXtOs+ytA/HWoninHSgDPfDaLACMFuB4hHPFxBETY
++16O12ATvKfvoXhVnNtBryzlLa67hLQPMGjZlLMT6Bo6WI48Y5lhDfMCwWoLbu4Sjeo5NyBt0ll1
+tARbZ1uoonzE6TjPMbVBeaN2penSXnWxaErVIKieLfhkFT7kHaxNCoBnnL7CfbWGFahYuIVvSo3q
+c1MIyKeA9qzPW9XcXRalrP8JSzDz3wWA31b/jNWi8Nfhf2Edn/Xrm93rXQ6fMsliRLX5cxnmvZtL
+RD4aHVzeHmmJ9p0W4Ql21zrU+fP1GBwHbVbhM5Nz91RG57M32i1+au3RJWLegKSQaydW/Yd2Z9z1
+lEooWC5AzYM3nrvEs87p/sB+ukseck17xN2RLg+MpxoTVwnqrd0ajLQY5/yw6QYRlt58E6xcINZu
+Ek88cVkv29EKbPYsoB3W3wV+bblEPAFy37VF2suP4thyicDzLkBs3SLsbfxk3VxA8BkIf3J/OKcR
+cnCmKf90lCv0OasPl8W5JLdZS4CsR0e+AFxuESgZ4O8mClwb9QfDLxCO8G0gnCW76lVajaqeoT/0
+eBw+35+5lJnKLgjccu0jIDYpf8n7cP1J6z2bwJYpARhDXi+aWDsgMkLj4zhHTDm70nY+/9i3OwC2
+Q7n2woaUY7y97TxBtAqhoBH8Mu+N+NjbPfuVUNYBBb9NQTVBNnmDBnjzaN8tewK4esZ0f7jnsa7F
+Uxe+ngDvLX0Dx1SwqCOQ1qQTMK6X6daxcYPMEF59Lwj4SYHv+Z6O+gbDl44FAA9wDGcZeWz4oIBT
+swwHOBb9mW4Qvup0f9rvfZ/6xLzHaqVYkUWrwHf1RfoElxerSvEpIFZJMVeihSXQbPLWmo/8mjk6
+5/fwSZIZsLvo+uqct7TE88rjnO2KJEr8Is6P5AGA+s45wsKaRDk2xzaJrDTv9LxeVqn1ePYCPg0e
+XNOTSQdgnGAYFQfAl73K8osGcnN9xmBxrZY1GN3K+WD5mMKWu8Mru0L4yk+rsPvaIYJ57vFtAMYN
+8ADBZqhNhiNuyyd4fQ446OEOQQuxvbF9Wi5kv/xwAMc9H6EbM/1N8HtOc2ywGWHKSszz1MwSNwpR
+V5pUoi46MvQvq3bTEY8JoqOPJ59h/HmD8C8QrnCRWFspF+HgNVGLEqWGwN8UQZW+i/OSKXsZT9kz
+9EbcgvWXSXq9Nspf8ki1T9w55UwloDcMJdT3o3fuXMpQLjT5gf23NdEpxE83CjuwJZ1lCYDt0zkN
+6NZd904rwEvaK+Vf6PgNKy+vZwd1NIajAeds32DLoB9Ytp3T4ykuofPHJQ+t9dWvJaQ/WXkrjYwD
+3nvdWzRfzEmwPKzDAoC3H/Mxg1wjwuf3UgsUOf84ygonc9lCCfgzyrXPm76Al7J3rPJvKo+QSc96
+VBnoN/rjAX7XTirPw9+LN3cSRckUR82dAy13somO9XP7F+Raus2m4nCbQRaMtT77aybNZyKfoHEf
+hx0MrxoM0bzhI7HNAcqhzLyA4C0tYHC0BwA2izC88xBlMHAf7dLJNVdplvUqx3EatO0cb1dJXmnc
+J53Aca5H4YG6P6z1dgC5dH9wNPCsPsNBAPfqJ+gzwi4CP9Ba3K3E6TP81F7E6RaRL+hZxfkU8H3w
+2rIqL9BLdwhDbgrePkVMXqqVF31NTNqg5+r4R2BY+Cp6J8sCJcjybtRzZyxdVwTEjy8/acBhbmn5
+fbyn+441dJOYIPgVy89HgfofCb9AOIJvC/5SrqnnRWlgWEGyqG20v+s3IYOGI/CNDHMpM+Wulg+h
+sReZUxsDEKOsZ0Ke8F450dwWEEKdZ/i0zpZA2NQizxMryPE4f74arXpWuawyvqWvHBEgZiVQEXUW
+b0zK1n0wbKe1r3pJ+xzlI/xqebnGCQbU4Mvx0w4TohSUB9+3BK9YvgneIXaV87r2DfzW+ZPzfcZF
+w8e5lbq7SHwBfj+C4X7+OdToe2+mhL7yCBQRypfVND/hBoKx9iANK1VMkFUrXSne9arcE2P1pgUs
+6nzWWnrc0+q29s5+8fqD533is6ZviIzqscqZBnJHesW95ZU1uFZA/rwUZh9nraB+3CJynxuTdgg2
+2hxoq0amzs/H7umzSrDVq+wgWCNjvbXxHxWdogIMpq/wBo44U7ebi73+JHg/ut/z8piHE+9Z1nJC
+OPtKXjaelPLaQbAXCOZ6FfeH6Ufc4mGhXf7B3OosfIETqMkX58IfmLRsb34tdn4wS0BxusNFXMDv
+yw9pmMGeF/ZSiXrxoAHcPuZzDpzSZQlffzbwC5Sc/6qshfxsPsS54MAbjq77UuoBZulWzas/ohGt
+RqGBYcT67yD4WX4Ub3zq+g+HXyAcYXv09bFkFz4dHHdBdfvdXhgZsO2QN2kueTsw4YRVF8Ckn2rk
+Ar+Gc7tXjh850HMOeV+AXp5zkh3aGj3qfU2jK0G4bZIuWk8TaBWvBRAbBQv6Y6cTLfNONO57a+Ia
+QWlzs4HtcLIFsc7oyOVnTQefVh5prDlA4ZhHE9jquVsZlzItX1fODdh+J0/iG/jlI9Z61Mqxe8EP
+mtRLcrp7xJwbveV2Tc4zPJhDKy9Br7MRAW4LGFcZvO9S8vky3aKtvUwfuMXOETGm5i7Kfq2x/Azr
+Gx8QeB3vs172MXnSsYCsxKUvExR3usn5b4DdYRmW1d9npNeFfdViIYtYrvw/99vAFr7yGT6AW7Be
+rzZ8DYIJJhUgjnTmbxXtIQEwppCs5CWv1euS8C+OYKWQm7PLsXVFebz3m3O9WYLzuiJ7lvDcQXBa
+ZiUeZcGyOWfK+ktZTPmccYOA4LAEB4pLQDwtv3SZaAB4lGm/F+lCQYA8Aa3GcywOeady5K4C0wZi
+T+ng/QUMl/OvCRCO/pEfOcYm96M6p9P+LiC4LMJw+hh3i/AEwe7vGov3z/tG/ALhCD8Bwqp+u2Dv
+EqoDZpVQM/1RbMucK+3UHoHnybR0nFSwHfzvPimMOz+qt73H1QwtUSet+U8rRwn2oneBn/U79vJS
+DpMmzdg53G8dgjPSt/JkAgBuZWYtv/pbX/AqcZDlrJdvtDjn3WirDv1qmKH7STYQ7C59OJXp/U/e
+DPyWRzvQxjkTdveZUte+A2Qp472MCte62hegN/OoUEe5uIw7Aa+CXzk3x2mn1arq62UDyrVEtxuC
+BSDFEheVW0u/cPEDjkkSAHi5ReA10OxLa/AbQDa/TvkE6LX1NUS3BSRfjz1O3xf5JSd7QlEJkM3f
+iXYvy9F8Qsm9KMtwumoEk2KTOGFYRKNCzkdeqW8ZqPMmKBsA7mXaS11Qq7AUY75VOS3CpFPooBF7
+R0b21q5ZTATd/cW5g+7RstvF+kr2kd6sk6jjBowPazPTZI71/MLQ1ofGTay+tLxbrlOCY9ZHlwi1
+2ufwrAWA3GEkJtACu0vp2eMBgp9BjzoTBPP31Kccx289IXzRPqfcfm/Gky8N+HaabfmXshBeCD83
+8KvxD8A4jTH6w+qjJUAW3uhQOs/vs2KB3XWzv76wSjAcfeGNLkEw/cCcLwurU+CfC79AOMK3gfBm
+1fCNXoIKB5pe53JNyu1LlpbZaEaoN63Di5YK+jtzTdG26PkJBYBNNJf6CcG1i+8dSFNwNu5NgAzs
+ZTCGRfLgGNdgvXLOsW/W0ps12GhlYFk+lqqyCYpDwOy+wrQAL1p+8SzKqUX4CnBT8UTr1HoDzUO2
+pfGntVNpYw7ZgVajDNc2SC5G2Q0gpw8v58QOevu1DnFHPvanTm5WYY1zPlzjS2Hyq4WzPzZoE/Yc
+IELj87QGI3RAB71iGVbLMQ0lfDnosfBVXIr3AQBbb7abh4IOAGABhI2b/pvhsfX4Nh/pRnsJitn2
+tPQIvX7nPP2a3RML7fU1uk8uPJUcca0EQmtgeAMIdIswJLYxepK6Dm9zGgQa4PwhSO0Ctj1hUmDY
+Kus0P9BamDSxCM8PK+wfYRD5daxYrsnoydLrLnlZUI5eLGr120gHDxw4WWl88oovJx7Br6xl8RN2
+BXNqGX4dZvG05H3FChwzUgGeu4DgJy3Ea64FQKZFFCcATJDIX/gDYy9jOZcg7wTsfM4t7U5jMMcs
+5P0GhjNuJcc3MDzL1a+BfSx+FO2wvMbiCo6DrhAAXSJC9qBcohZ7K50vRtA6TCFyEvl6/DeGXyCc
+4fucTeE90NeNnvUfrMMH+X2mHQb/VC4z5K3Pa+CE+xgIS87ij2s14QstvknXcp4Ckflfpw/HuLje
+aM72rLSlsC3ldOultTW35HG3/lYvdwBscj0Hyu1B6sj8A63vI6yWSLZtSgAFA44EyBRyKMAw9dTk
+g4+8lv/pnI2HGiuptVuHu62a5/zI9YF5aRly1GN0OY+uEIzjDojLJaJblzHmxgTDt5BGRSk9rcEK
+dK0B5LUn6no828v1r8LW49gXryjhAL7h85hpM1iAhbXn6fIPXk8dBNw2YNvznlOej/P8Datw+CSL
+ktdb2zaPGggm1pH5bScQ7GkJ3AfgFL1r0TWNKMvrpALB+oRLrZ3IvrWrzZvSrNQHjTIt6jwBYOFa
+nTGNLrd1qULS87gZcVL+Cj35EQMjvGh93TpD9KQtElCMtR7V5UHBL48NBDfLcdSbIDUsiQR2oKsE
+AfACvC3PfQfBCQDpaN8BbgOLeu0GfrUceV2876C4jmubwZ0OH08vUsZLfAJjsQTXefOcaONjqJcD
+te3PhkPbmFtJd8rI9BgJK7uZWohRwpZzMvtMF4kw/3xSOufl+78Kv0A4wvd5u6la+UmZFF6HvOM5
+PwDFElFfvg1cbGC4lHqj2bxKn/ap/K3SEwpMmss/S+XXxTfAdSGW4O+kVa6j0sVR6xYc0qei2nrL
+e9rJrZ5WIJsAOORff1wk1l9Dxb9B46Pu5H2gBB1BHV3twzab5S6q+FbC7CprcryrFVWuen8GvXvY
+11id85Xl9wiOFeRmPSjFyrKhSDXfA1jRQFsfManfm01QvmNrW3G0eKSjoFzqE7fHXekJfN/Yy9SA
++KJcAuIWXvjfwxo1QDHskP+KEscAuuP4RNxH3uO0ClsownijnBZhdDD8Yr0XM2WogUCHILgHWtgU
+BJfsaivlMAVLWOp2dbqCuGazvlwE55mbdJUrXdCIHPoEjqWwowwGOYMZXQ3vuw70vN5UBes14WrL
+LiAB8c1K3MzO5xvYCmN9tLZwYIUNwwLcQG6s2XUk/ZTvBebemNfiKpGg1wLk0j+Y5Y4g2ArRKahV
+cDstxAMwr/VUc0m3HuSNibVFDxkD5V+AYIJl1dUbsI2E6J+MH6zCBL9545HWcFpl46kT6mM+pqDD
+S+MgAC8xB9dnWoR9lbfBi3wc5gAtw/tq+8+HXyAc4bu7RkCFxlSdIjR2kDzLqIDZg0yxoolMVjBc
+tFIGdZIqC7b5AFTO2EUe/7NXHY6wF2zGKQ1QRiug7ee3+FZ2pEnzXv/US1MvpAuAdFq5UfFJL4B8
+cocoy1UBWgqmtEqzfNBy7+AbDbqPMK29YgXj1VISVq9SYOVc7P3iuGr5Gb/n13zymTnLHYAy2z7T
+NQfO8W2UHLi7VQDlFhE8dZlfpOt8goMb+i/eF8Cu931qfV2WyyombMl3TmIMi8E4W3lv8fj0MpX8
+ml9D+UIVMRL4EgwoGEYC2aIZgIeW4ogrEHYTUEzgHFa3xxBKb33K4H19gV1ahLE2e3sSaC1GvKhv
+HSTPrGZHLjDUeNfss+DtmB/zxt7a4XNoemBfV9uEp8yZiyGjOzhuckrXb1qZGS8LcXeP0LYMqzoz
+XdMuWZ5lKh61KfB1CGA+SYiSp6e87Pux0zhYeHmMa2/5Skc7z2D5cpqCXwT4JcBTlwnmTRDMl55P
+ll9Nr+KaFvBrUia7vdq/5rQnrYFCHb8gpBEpF0fw3YSPSk+2ExhDALPI7QD7/siOGGAc64YBD/zF
++tgPvfmNhq0FcHUOGKp/FuOTINggVmFg7VIjAPh96sb/eyv13xp+gXCEKU4+lWySxidNpZAKkiad
+JJ+g5Xat27SQCdgK7C4RBHB1ylTlnKlCPlz0AEcgMCJ9nBZoVRud9v5sDV7prmoaF0U+NG6OtN5U
+bzLmUDc7u/VNhEbSCTaEri/TTWGzuUKY0ND9gYumgJjtaVfoI2wBkqMfoiWyb2xt6lyceYJozyde
+eaEThERDA71uLX+C3jmxvMX67JKZlTNpxpUn/cZqNXbxfdyWepXtn1euXSOqvHDf97WGzvkWjj0/
+Tm6J3wBxXH+zCDvAm10qyAS3mk5w0BW4hdJcPsOWO08QENMK/HCem4UhjXlRr8d5sPUpWrUIC6Bb
+L8jVMVlh0qVcw9HelBlsr8x4eUFurdvDIGwjgjZz1sEO5b1jwGzfkum6BZ6cUpU423oqs6ddAKmL
+FaAAqrQ5eSrXqFYfLqOW5UhdrL8uwlavfeCExE5a6nbeujGFAfkpZcfXFmAIskxEhQS2CX4V7Ao4
+TauwGWw99thoKeOzLnS3CNQNYQPBCYCR64Sudfn4HzXGBIwucXKsfRb7xPsbGNb4LHMq/6wtaBIM
+k6+xEw2eN8EwHnFt4DgE0FW5TSyzRJJTRYDW4BiyOHKeAcsabPD3kTp3ndCP/77wC4QzfJexlCR+
+jndVfKFL/pCSJhHzRllkq3NGjt6SjfMEOom8v/ZY5Pichqe4Ts0ZVwTQrcHKoUOeD3rSkCxXOk7l
+sB/lNlloaAC2L+sCsbUMy5JbfbWs00NYrvOEhp2W23UNsMz3ogLCZA/mA/gJgqUrI7/Psq4462Ib
+v+TKaVk4BK1jJ8651oXaDexWhV9bjbXO3YXivPpym0/UThKsU90l2m1SKoIzF0xK5zGEvxvOQPYr
+mvoE8wggLaRU6IynLCgwXCCyFHbPK5C7ALGAYTnmRwkCACxjWwAJsarBlyWIgBjOHSJqlL2lhV/G
+NcZbnVCvfMFK0t8GwSr3yNMatgJYTOt5Kk98FBkLxrXQrEvJ7bzoh3vKvQK6EYfE5xNJFYJ6se16
+sioUWKuVflxj36f4OPFR4zmasYVYo7lALJdUfs6cN9cylz3ocGxAOW8EBayqP2/uQZwAmDT5pVU4
+2tgswEVLf3QBwLzBTABM3WAFClN65zzxVNUlY3JA2jC2dLTFNZ4qTeSzaRmcwbJ7guF8F+GRhYIC
+w0350io8dmmxqDMxNRelR4sT0/C4vsKXW8fwReGGTk4L6OME+0fhFwhH+GcWYRUQlU6b4fZIyQ/l
+TmFHEzrdTOaJwjp9NDIfW5SaicO21dBMn4OWUoVfcd7lKrxREew97gfONDBccrjRGJd045gL55wc
+Uo7ufereTR34Khyq9S3WYBNaAizLNnz0Ef4AltlCXseoNZIK4XwHzaex2+MiSLcycS27nbtzqlmH
+reip7W7lxO3gBHb1Wh5C+Gwp3i3D+8r8AiC385XXnAOO+uTofd1I73upKTbi6BPwusRfL7cIKu18
+2agsM/2lGVVqCoT3I90dCHg1TouYS/ohyJB4Wp5jwjwe/sDB8dcLGD8omaszIpZqW5Ml7HR1Au3m
+dY5FHkT76qjY20kyNvnU4yaes2C0/yRYJP5VPuIJhTwaS6tsSkwHmvve5iJBOTku0gw2PTst25lP
+ADxBsTb4A1OGnP1QKPi85IICWic/lJYCt8rmF+cStMb6gJV/cM5LZDwB8DPyXc7JOqV+VB197YRM
+aDeJQLoSBTfhUw543iBvfPWdNtxyB9C9gF4r+ZVxLUMFyg9YGJACKF/gjQ9dTCsU16KzcaWJ2MkV
+92RdW7bZtdgSMnQs4suXfzr8AuEI3wbCw/pbD8pXLU2wbPlo57aJLkuiHnvjYBWuvIZgQGUi9Wxg
+eATOvy9C9xNmKzr0QMYtgKyloKafE+V0B8B+oNW6a76dAyAnN1zO8QMdO/1jf1GWYMq7FRfgixJs
+tMY1kBt86zQrmgkt8l/ji1u6dRrbU1KEo5pzJh+xqYQ5W4tLDNcFrvp5K+Oo2VnC78zDEQ5TuOo7
+WXaB0+yaluIdPCstrqJzLejq/vApzpkukKv3Q5JzXhTNWu+y8gF6U4lIGX9cwG/P071uEwDn4UM8
+22w1tw8A+LEHznQ8Nnahb4A4Xqx5sNbq+lDeOsKXz3CCX89YjRXbwvHKpqoW7f2pNero/WxdbPTl
+nmhoj11Wo+RS1tpXQyPAdI5XHNv01qPmN5rnkXIODpSPbpUptwyp7AuhVrKP9e2At+1O4Z3mrVO3
+i2yRQ0oIBmxfiyPfGZ/WYpbNxWJilbTYRq0AboJfw0YzsRJv24cJkF1tvYNfAl+AN4yx3q3rCUg8
+d4cYbLHB55r1ykW1AK9rAWg3wJ5tVhmuBo+IP09vRz5tekqCPg5w/3JHuDvIj7vqdFQfZUe6d6qK
+a/fed5z4Z8IvEI7wE4uwWnxVuJiUUdVrkp75fZlo2JFDB8sgSl6LOukD/LboxUp8m3eNPi293UKq
+PTsBAgpul9qmRQ6NJnEtJ4qkWYNHmVJkvbfFiTo20DsY4KOfSxYTKO+guATQbt3dgfGJViCZYLh6
+soNbZHukhwcfYc84SlhufJH4tczp5uo06vuxLMzUbr2GsvLWbNP6PwHjaRFu8+H2G2VugNja39Hn
+88SBSdtqNMpPrjE1dE0HxlQwVmW5/3xzpfAOEHjBrHoHhycaBAg/D4HuC4+PbrgTCK8v1S0AHL6F
+8enatQfom6A9LcJyM6vWYAVoa8p6rhda3d1qvvvWZpmJqjwHEAbipZ2NFsNnMd+S4HdBpkHurjkU
+wCj7ozxao+qSywAAIABJREFURAvo0sqbYFRvIFSmakUni5rv0e7rfADFrrRD/w91Hxnlp2JLDpS1
+FwXmHGWxJAiOeZ5WzSyDBmB3UBvA9hvgOJHaBQDni3kzHyWDy194tVWnEFDSc06vzq/Owzb9lAek
+U+fIBD9ZjlMa5TTn/IrqHEuuqFJ3wLjHr/8FLhNtW7W7CFlfFqxJsPdXT78I1P9w+AXCDJx8O4ao
+kJOvZomCXE8agEGvTypPReij+ib2AzfUMjIpXfP+k+eoh3DIjrYr6QK7anVM6CslRfYu5VV8EFsP
+GtWFKgI/OTTirP9kJYaU7S2uSHHNzmUyr+7wOycIevVxV8hvAcUUOiqokmYnYFy06SPMOKWYSyu0
+9X3koyy/6LNN5tlv62nrPPH8O24qWM47lz6HKfopUYdqiEl1cnuo9mr+sCTrE4T58wt95M2t1LT1
+pzVyExk6Um0utccT6IoIa34vKzA2y7G9HtZMUYZy47OPX8Rtp/cxLRD8vG/F7cXjZQH2+ECHP8+6
+cTGD4wlgDNnVYl0gLcKSJq/5ZTnKBbaTO36IBCt+KutPGrnlf4qbACpH+Zqi4s7CAlCjH82H9sBU
+n0ye9JbnLU/dIs7XYtvkmActO2SxWnlZJ/SawwqMvfwWLuRbXplCLHi/1iz5ntbedI9YZW+uEqsq
+k5/v4NZKPivtlM4X3FIWyTFBpFh+gQ0Er+apNu/HKQk/B2FitCOnZVwzSyZAR+uDJ01khlwhJa1s
+nVx5lNUOC8twfSmuu3eWDJttBrpxZgR+we+JcfJa7ye5Opr4bwm/QHiGmybJtCf9NBgDWhzLKXgu
+r1TNq0WkC2jGt7yvZscP8k3ayTber19206S5HBURMHLgs+sikvx6jKrKYhbrIif/0loaR91H9aGw
+eupFoOepPDP09K3cUy8N6X6sZtImEar1OKs4pyB6uUiI7zF2AFwzZYpTX/tkjrINTtjOtwmK9uFR
+gZsNG9c+if17GVruUxFl/aqc0IIqAaWenJC2oPPbR+t4SZSxlXPIPsZlzqdl/lIudamdulatz8fA
+BAVdv9hYA4B/8POuSKdbK2O6zSKtvViPKd2Rm+47XuQ2c6T5G/FVL2/0CIBpES5A7NKPIU+bldWT
+BTkl/BZfPdJ3fRKbSxwA8L5xs8Gjw993Ab489vh2Dssqk9s4eaMr+X4OcLUK86j5X5bzDm4/gdzZ
+3MA6VxCcffIj/RiUD2Ih1I1m1rUNDfCqNGO5XKAHiOQjDS8RjM/nsFq9F8rm0R8fF7uuyxo6+RN+
+YMuhtu1IBpBfC9gKb4IfTRZ8sBZv9VdNKnpgOnCO7H075eHew9Z/GGnZ0zx9ulnb25NHOXZJ/2/D
+LxD+B+E0v9tEaoJ9ljsN6QTDEzoUEBpTZ0ufF59M9QOI2BECHyVaq79fd62KCdz3SyxJZxkfl05R
+qlKSWSJmd71yWAyiyAVsJDgdIFj3Tj3mEewm6EXSTmCYbwyb1YgqMK62UYgJJ61z1M3w4gMA9nPe
+OowxlTHyVk55qPlobVP+zvx23W0W78dUdEApDeYZH1miBwNd1Pbqtc1bP8+t0qaf1tU+378Gw1UH
+x32vO+O63ExbfBhPKp5Ufr1zp/Ux+z/Tvv0N/h5B7fryXPpCulW+29oKlDd0SbPwD14Klb7WD+PR
+CZWP7p4vj7PPT/TpAQT0et7EpAFQ+OqHPIKYrP91uL9x9A5y3wCHCoy3cpU/rbkn4dTzuiDzcY5L
+/mYZHsdmoZ5laBE+geDNMnygg3Q2rcvnLbRsP+a1pMcgYVh4h0zyRGRrQEt+8bxIC/jNTxtLPdqX
+FRlP0hrgXlbP0azeDXfwk9+JTMVK7WZXD5tPx6+CszsoHZI0HzSy5iDv25NN731s7fDqeMsTOvmR
+4FaBry9a28XjiQpeoTdQ/B1O/HvDLxD+YTgNUSkyv5Y72atqyNcUu6VtlL/Rvtv+ue3JPv3PIJj9
+2Ky/oz1Zh7NcCfeKD8E6F5U0zVvcxzm959luAhF55FVAVdPYgC1B8ATIdgK+Axg/hzYkHDJtLwWW
+zJ4DGC5esFwA4EQR53FU95gZ5ixVa0ErYwfaKLePfM9LxaVjNfkACCLc01nHkND8WlxpASrLvd/7
+ejzf5NXPenPafL/HIedv8XjKkPSmVazHnX2soRbs0JfL6Jta6T4sqxZfCokvqjzLP1iBrj/w5405
+WfGct1H2DeWXm144PTtWo7kBBtDdJnI6R+sVBHNECWxjC9gOiDPtR4DcNo44WITXJ63l2MDyAQwz
+rsxkwnsSl6OfzpG0B2EeP+bxcAK9M30CyxjlMq3z5TKRJOjTimNezOOSgWSAyctyowYXmUjZwKcL
+03Lc6lRt5VKvtdLt8b3UL9QuV9MCzI4QPKvc/lkYrGlH7dYSp1aXsSrT3CUyz/Zy7RrRdld6t3sj
+5tXWYvewCK+133fkWKtQP+dsL/MRoBj1efn/UvgFwhHOkOEb56i0c6lHVm97tMBzNQ6qe2vprmT7
+70ZrGV82/nO2KusJgI+wJwQnmrWSYWhv0oBaXBcL8KpSrR+zKhvRaKFpPxQEIwEtUPGPLhAnF4l5
+VHCtHOB1WjvLHtFdJQJAJHg6zQfREiKii5e8HhWDCnhrZwnSE3rn50ZraRW6x1kRzR20cdQ3ofX0
+zUIsL9TNUPNi8CSiponRGh0zg27rW4D4O6C3r4/DzWLogJoT0qKmWIPqpaa6fu0c8JHwWx7T3jkF
+s/IDRihzdYUIZapvxk8Xidff2F1ibZhWIBhlDeYsUbzhNXPci/f0J+b7O1wLZfUNdwgv/m4gOM8T
+Lgi4LQA8XCEuILiVE4uwoottiEaZz9Zjjzpcyg/5eMmvD2GQ9g3Qq9dj23w0XfKroXrwjT6igzEi
+k0JcidjaLcT0U2A1BkBpeS1r10mbp+mlLa+eKBHRP6tz3LU0W2x7nA1v+1B3Kf3T4zX07nUdojIz
+hN2e1/2Lz0EGNgXqLm23nS8OYDg/bR1Cb4Jik+0gHQGQn+P34//j4RcI/8NwmkuddlbXtUT4U/W8
++wzznK+A8XFufzXh0+rGNnWoUNe9+wlDypT6lya0aru2OFqokt4FtJw2Tpgt1sPJEowt3UCwPf8M
+BPPI64Ls7cComlszgQLKgQaGu/BmadUWhzwb5ZAwqvPK6qxJq7PGJWD7ObNfSssmflEuD9aOfih+
+FuSb7QJVuvfraAm2SVvp8hWu0dtfgvkOONb0vkZaW0WxborSd4U514O3Gv1DXic4PD420N0jbHOF
+WNZiSNzd1oeo1DLsYfGFbIMM3Ra5oAtQOCLt/7HW1V97AV4fFuDKN5MyQlN/8NXZt/sFn0DuN8Ew
+F2oCQhFx89h4fhB+LnkuhJOVWONVr++y8wqC0UGu47NFWNuDPVxv0LbCcm2udfcN3FZ1sRYc6O4L
+8UdvHD3WV6tj5VurNOyfTTRY8m+TIEPkRkl2YNXmvZ4WPurhPWzrnlV46wJcAPeU5970C5o4JlDO
+uj367fdrn2QPSHfETjEe1uAHBrECh67lS75mBjz8fDPSN9gfwH63T/u/EU4K7DRsV5pIhjVFTtbT
+oajld8471VCFD0vz0lhd3NU2xSjVDgJkT8RC5cSqEtI5dBX2INYOLaBJPT1k9IduFMjNRQgCYHQ3
+CK/PxG6g93Yk6H2eMzgGunuEMth6S6tL3RJcn16G/FHoY8cxu8cv3BrZfqDNchug/hj/Hi0Vlkw0
+3Rczr33ozuY7PPu9uQGNbBmnGyBmud6LkWcf8vQ8uYCFdtvBMddUqaOTgsrSA4TM9dGAUi+a6aXs
+Foi1sOrQLcKaiwSWQpP4a7ZcGaz7B3NXiNeVN3F09h0FeH0AXyAtT9xGmTKG+RMY67k1ruM26eQK
+4Z12Bb4Exknr/GwMdwjvb0c/lBFg5T1fd6k4W42VfnGLYPmNfvARTjpamO4SkwV6gh8KfXJ/WHPL
+dUFUlQY0f2Gp3FIW1ArhPvwEvszNdeWD1r5eONcT55HKWA21VrUV/8RX+NN6B0oW1tNEyRtlANto
+q13zlb/Yi8ejze7hKy1Sn09B5CZwTbqnPvTzhPwKC3GCYhAQP8D7rm0ZExhbfNYZ/5XwC4QjfIIM
+9zDvNLtqT9rUPtv11OK6x2c7b79ZzjVxBEZCG8BqKZiD9Uzb5afrC0Bu1xNv2CnBWGIKVi2q6w49
+vvO/4gU8xDoci3N9ShadPt0kbH1A4NtuExcwVTyaIw8oEGY6HyPbeV48rS7vcVc6oEJdH6cx3YO1
+JvqtzFfxj7tKRNzH9WjF2FDTYbYYVU7ngUv8FMrVJLgiysIyf7+JMS3Tzvheeq9LujzWXu+P61Ph
+VFgdQVwA7gQjUmguwYU/Ar5avCR3jGNZop5D3AyvG56nW34NZQ1uQ+sY/Fwt48txDtnWNMquHSC6
+AjcfwDf6l7tFeM/DcIVoYDh9gE9gOOjTIqygcIq3PPpIH8alWYBn2qVuGddbnsco39wgPoLg0SVp
+5Gne9M7MtbrPtaxTBnG6PzTampzRRj2Pz7pqVfD8pKV6q3VV4LeumSuKMknrA+RsZc5Jn/aUfeP4
+01C7RlQbFOC2lpjShnxlG2Lqj3uTrWs3qzCAAL8KhldJs2ddNwHxkiVr7Az2PNkG4IU7P+bxZ8Mv
+EP6HwQ5myQR6R5oP2g+u9c3fl5V8VcS4SE4vy7EPu5vECuVCIaTW/saaS3zJ4q5V8uHcydR1XDPV
+wtM/WoHnrhFztwgLq+/RDYIW4QGGTa5+btep6VXa22+KnYovK5vjOeR9+fnsCWwnOL6UmX25xvNy
+di+bym3Ub3u6WaqHwOfFQpVnfGnD3uayGCEEM2q8or0W5dY8Ye13gPvjtNW1K1x2ACEQivYkSMm6
+h6UulWOhGO9/tvLFNVtuDrClvMLlwS6AVy3GjOcnmeMa+v7LiwsgFuWqe/lr3FDWXyrNDnxXLzZf
+YhxcJYCyCAvIVUvvfInuZhGGbJ/WwN4F9CpS81ueT+A4y35IQ2RnyE0kuPVGa+CXx+zHyT1itAkS
+dN7tXd3LZ74gOk53dXWA0NJCbFUbz2NdcdOltGnV3cGv9TZanHNQNafO2TXv+4Fz/KujNFUuVX6/
+2zQDPgBkG/XL7bWT3d3UkDW4liEtPsWMpRcdBMSrcYsmO9DQAoz1AR5/n3CTeGsPxD8YfoHwPwgT
+NnQYIzQ/0LLc9L39EDfbXwA5/E4t2shb1j7ViTvqdwO/kn96bg0fn14cGmP6P0wBqE2MA5/KdGHA
+Rq+fBbNobZsW32b9BQ4vve1+wgscP+ft0wQwH8fmyJvevd0qrCzRMaJEX2lusfbgUu46F/Yxb2W/
+Sm91i8S1A/2rMiqwGwiWKkz4IsG31A7sFQQrUGrb2+W8L2Csf6UZjXJPn8+biVQmjQmybuRDA2U0
+874GhoYkoEmuKDKZyw9hV7Py8eWLLr6+4hEuErJzhLxIR79ixGeY+S0QoEDvTCtP9vQOeMkd7hjB
+UVZA7JCt1uDHa5RFeLfyzpfo7lZg77tHCOv34xiTrYxv56gF9uNYzroPluIJdBlPkAxPebpZiqMN
+rHtbd8MwUf3zns64zL0N4Xke1mDWZG98i/NcAXOKxALJCejiBIoU5pq20bhKvY/T7ESE9l7cLSIH
++3DsFe/XamGfKsgX4w5tyqOd6blF3LhIoznW2PterkUD8MI9Xngj+F28ze3aYtcIxwN73gWAAeB5
+yzXixR8Pv0A4wvfN8b2cTbofaLPcFIRfrQBiPJy3NTvi2y/q62FvQz1Cvl1Tods41+u8rP60Ugd5
+DoErqxzb2tMOmXkjNouwefMPVheIF8Mi/DwHF4iD9VfBr5R/DrwSnLf3UY6LTYur6hrxtJIKdCve
+/NYa8BbR+1Owe0wP2k/ipXUOZajcKmsVP8xsIZU6k2uY9HmonQTECqgYN84YxNw4gdjvAOJV2c6B
+XFQba7U31Y0oIHP7qKRVqfOPt6wDrddp8NgRotwjLACuBcDlC03+UPmGfzCtwW547cHzvB8BMOmT
+Rn06aSZ09/US3vkc3+pY6WkRLkCr7hHpMiHx5jcsVuK0Jrdx6HxtoOqQdzrXT7RNRn6PtgNbYLcG
+Y7MOd/cISWO/jsy83t/qzZi7TNjmJ5wj2JbtAlf5zgAzEziH7M+aLZaLi6ix7HtKyXwqQx7EOrbe
+5i1+5P0pLcz4oJxbV2fXx3G/ZN0gf5xWPNpeD9eUxU8B7y49y384FRZ40+pI1whofF3Y4sbZsHyE
+7V1vENgTT57edZNt/mws/RPhFwj/wzBU+Nc0v5Wb4HIAKevld0Day342PorUkYPPvGxLtMfmtS7g
+2AUcH99sKh5crcE+030x70aYLiaqVQV+FyghIO5uEMuP8GAJVlCcIPg57CDxhKuEukaI0hXB0sDP
+7Bf7ZvVImdaMV3r16Dh5xUOsxIX2sdR9JBtEm4hM09aIY+LuMO+fxU/XZ7xf1zO++tenGF/vMaFU
+NS5HuiZ4zG0elyIM7qSi1L96VP7GUV4a7SyzjZXXINrHx82dhsrqE6m9xHSzGOZSk3QIkOkGgbAS
+g7tFZBr5VjgfXy+A7HBuDooAu5v8WJecoLeUrjXZp3sJ86U6y/PLgtVdKwr8Qq9BATKtwOomcQDD
+vZycn2BSxu/A609509Whlz2McxvT/RylfwV6d8sxWrkGoNvlK9WfLMw2uiZbbHN7iIFsDgdtPUi6
+XYYFomgswLQPUwZbk4TR7nCFoIzYeDg6uY3nnfdg03v3/vFx8rEd7UL/cOz3Ib7xRj+ljA9l14Kr
+TQ+bSwQoT8gNtQyvsTM8yyLsz1pXXwrJf3/4BcI/DX4eJpuFvkErQb2v7gSb3hXJPNew533VgVj6
+hxNziRzrvVmA69xNQkmdM17JWaynYwVOTTOqYgspFgmAgQK/cGuW4fayHC28Cnqnu8SwCB/9haGP
+Z2XspoaQmI84f9x/VcdliYkAvQIKV0zHYMWPglSRWkY76ATPbhNgjvuYkXmhQ7ljXsUbe0yez5jk
+y6lz+JWj563U+lWpg4Fwg0gAXP3maxsmDVFgxVhu/TVZipNVeCTYv6ZVDp0bmtHZ15w8QyG3tJ/p
+kueGZQ1ON4juImGO2i0i7igqvTq/XpR78Nq7yabd7c+6Rdi0vOeaYbd3n+GD9dfLVQJCT5kQdRaI
+HVbgAYbnjhITAPdPLBePj2Bpiq/LuPV4AZBb/tE6GSd41L9ZhDWNr0FxK9ua1uXYqU2uf7WtNkGz
+JTIrwGuomm21owHnOmetY9fSC4DBu1sULNdAuUuERnTk3N70jFjJhQGN30rSMfgsjb4TVE4KR601
+rzf3iyPjVCFq6R3PF3eI0H4ux2cdn0c0MZqLBDdVnOB3uUiECei/sHPELxCO8E8naapsWQF7XWea
+TrOuBC6gGCrcPwDhnyHjQ9m5Z3HtAnG2Dkv7vNMJPlu3dVmKwJjW3lxjl3SXpAnvQStDukG4L6AT
+8b57xOFlOfEF3i3DT6dPcOzR9wmAVaZDBXj0NxQA+5b7sAYgK67cQW+KSNk1wvXiO1JDgjVJd/Ap
+kO8Gco9xSadSO9DHNU9V+TFdjG3ToC54qM9SKXI/4AUAS2AD61PaL6zAcWuS/HU03m35Uo7x097I
+2ouc1yYJmTsb9JhKePMnioOU2/Pi1iG2WVjgt3aKSLCbL88FYHYD7MH7vOvjNLQOH152eTfZwRvW
+WieL3q3BwNxSzbc9hjfrbyjz08t3J4tw+gAfLb7DTeJiEd5A0aC55h3RyTbA2xCf6tWypYJklkyL
+cKAezpXvguLe/DkLvV8fVUbb3tapo4PaDAS8kHVgGJYE9B0lOM7xl+RYnI6x8wrXvtCrEhZ0PbRx
+/vjU5ZTXu92AcT9a73LSpzU+Wn+ZDt89av2bhvE1dwocq+Y54RmiV62Jn40LuftSNlLaKfglKPZf
+H+H/a+G+c8SnMgSIVZ4TTJXoCeCe6Kdy18ZIG45A4YCT1lZFBzeI6Mf+IhgFZwcMzaK7s6RHpklh
+SgEVXrMygt1YbsslYgBgIAHvAx/W3w50P4JefVkuLcehzEO+myH3a+yAxsDXUbZfKAKKm9MLccta
+rOOo7g8l6lzOqQHp4LftykAeKos5Q7OKA2r1XrbGa9RdF0UHxz1r0rK6QZtK2edJRhUjCsCqnrQK
+iUV49d+2/mzg1jovGgtlju73H54sO/QYQ/OUFTc70S1zWX6A22JKj/vhvDXDwvXB4u3u/ELU7haR
+luHQ2rQOvwY8NVFSwbebw2IzzEu2AJQ3iPVTNy4PwTogVt8Oii04k/lcgzIrNovwCfi6d7/gm0uE
+vzLpBiA6ACV8ord4l5FHq+83zvU4OUHpd0AxJgiGnNvnrE6hRpNrH8/RpirQbWrJqi2J2AwTONOa
+y7YYlu+xLJWgDWkYAkDL6qsF2v51k5SMEDZL5734VmqsP5tq3WO3O+UYnUFGrxX9aoq1mxN0Vq62
+OQ4aozSL7z+1JMddcJwlMndZFtZNtAPwB5ZrJ95DIAj+dY34vxNOw1Rgtg6braqvYTmnA+DK//xy
+3PwdWzcr1cZ8KJ+K5nj9vqSVbl3DZ/9KuVFJDY0xF34urEh6nTIXvIqZbJu5CFiCYaRf8AK36+zr
+55UjbVZuEw0c011CfIWzn2rxpYVam3rqLhAgmC/MeYEK5AOl7Cu3uqvR6mPaPeIkx1B0BbeqYGgl
+7X8EwJme3mJfpgc43thxyxtV3jBBp0oLrQ9J8s0kHWjNqlBcWuJS7bae6Sdsg35tWQcHzfpDkMJx
+zik1AIoq4BHXz/M2DSjllkJ0Ab5ISzB3ikBahsOPTyzD6RZhy7LzmsgMebzNYbfgpxllBpLnCYwJ
+XPz8kQ3xSIwl5fmpZVxptHCFwlVAewS5vrlKpGU4rcQTvCifv6BzLLaymi+k44Q/yE9tEsFvXOsG
+ds+W4aJXtd1FYAdXMjdbCfaFyvELoJsLRFZElqVcX+npApEWR1faknm5W0SKIAoFj2rlhT25Scj2
+b+C4BqjvzCHnbXLLClhid5+4HRufB2+3o39dzqSsvgC3A+RqeU+jTsrVGBX6AzhiZwqCY1qAV3zV
+Rq224utJ1J8Pv0D4h0EnRafd0zea5lV+WTL3/AlI++9juw2XF+n2qb2u44DtILjaKPR8fFbgiF+O
+6sivBEW7vOZ5X1quwrMJnX4cjQfibWLjEev4YH35ioCY20a1l+WaZVhfhnvKUvycfYUfh1xXlG9I
+5HZzZOwffwu4OuSTtNb3ClYwvAHjMawueYjxbDziLKMuOIBfj3jmtKkyb/X6PLKbNfiaPuhzeSS6
+5TF+AMhNZGu3ZbHQt5UuETXJYz9cm7d8GreRxhUgazc8chXwFkCf6iribQ0c1oN7jbcLLZlxUOyq
+MSOpX5QrF4lglNEtgg2Ot/7j61HLGux4nGlevrtE1M4crsXqSc1aJJtF2LCswlS3jtjDH+IC4bvv
+sK65lFUHYNtejPsmGJ43FH0IfaQvdAB+oG2q4rt5x7q9xTkHNkvxpAcqyrkpMqV3a/oFy2y+zd0Y
+1POLcR3oxuRs9ZBG1MYVxXkArDmltFU1IbHSKAn7+jgC4kmX8T+BZMehK+1oF/pZp8tofjG1zuCX
+zYWsK777Q14VQHZZLxDA7NnHvoMEK1cn30IytQpRsbixTksxFd8fDr9AOMLP7kLmkNYCtZ3UQTAX
+acweXQY3wItj/OQ3PIBIbwYORQ7tt5QxvR3V3g6K97aTbnR6nQyZtNbIEjKN5LLetk5FS2n9BcIi
+KyDYRdmmMo4zZW/gDn5P1t91nC4UuWuEQcA3waAXSNKXQgY72D++EKeuEUcw7GrxpeAj6EXLS1FP
+lGsyPwYAdslnhG1US2qNjw1Sm/GQmkqBfTuwLlWmt+DbNfdkTeytyQmI+VMxHsW8VaGXHt1Sq49e
+r0CCtewOOBROeDtPQUYp3lwzUzHrovFRVs8Fck9gkz2ByZe1x3D48iXwffB4pJ917mvLX3gNc8AT
+VgMC2wDBHlwyhOtS8M3Jfq7hAYK9A16myW/yuadDZiYPJqB1Abwd+FoDxeNcHeMD+O2Pz3W+7GUr
+Dz2kzvh+2RX1agMpVwswqUHZwHFVXt09zUyMc9ByU6w3iy5AC3EPkdkQYtyA9VJxzZJ9eUpYePWG
+Cs75OYEqz5J+b+vpRBMeuriUHK00x5Zfg/oKoy5bbcwW6yUvlmKZC6vf7ANy+7SUBZD59jHdW7Fv
+fzblrWwUbFxC8VqyhbvE++eR8C8Q/odhqvKm9CJSgst7uZn/RfwEjvffD8BFR60jsG0mRUtMKAhu
+oNhJ263ZUm3V74PmdRhN6af6rKrDCdP4AMYFiMvnNvcRDstuA7g2/YHrpTl7+HvQPrYBlI9y8sTi
+OIPCHw8hv4SrmxUA9h0ME6wqXURcXLHiykcCDnHlBGy3/ma+gN/2SHH0pYVUbkJyk/rjHO+nXBWD
+4Zh7UtCb1N6aaAnAxqQetL5Qrj2O623A+HDSzTq0zjmqr+qfghNRvHmuH44jz1te0Vdba9cIxJZp
+sBfcOYJApIAv0oUiJ8Zj6RrBOVXxsgLTJ5suRAmIo0zu8ILxcpz70SK8wDB5PC3HJW9PFmH7wk3C
+T1bir16WuyGR75TFnjb0KpL64ZycIwp/vEkc7BZg5uwgWOdpNbmvwKtVsoHm0d4Ew8Anf+G+cOIk
+K74ssBs3Xy7VUYNFu1MsCXBuTcq1Umur0cijtp6E11J2rnNqAsrnRrdZbmdFTZnOT+Xzeerd3SP0
+uV7Sjpbg6isyzQoLvJZM86I3UMOX4hzrvYQo+j5xoT//ttwvEP5h0BfdiqbBB81HuV1ymSyPAp07
+vecXIEmd/fPOXAFx4YICticQDCkHsQBXX6W/7Q7ZN5o+csqzW/4uYPc2K+i04IuC4MWxBMFA9wdO
+y+6Tx5NFuO0koRZkj5fynEp9CWweT8GFdXxA9Xq4ccDjJXwboJd1KZi2rLBbiinwFfkWfzxoHNEC
+wDXB1+8tAAAgAElEQVSKCYbbfPmINq9l9JZgz6WK0PJFx0a/KNhby2IS5yP+ICwgdvjpuU0577QJ
+jDcaEABwWqJO8zn6tYHfYTlWkIue/nhs5/PGqqy91nyBLUFy8iWsw974BSRQtmGJs3oisXjNXSNi
+pRIQ04Uo3Is45c6gt976NxQPuH1hzjSnSu4WYVp68wtxJ5CrYHlzn/jaIlwD/0WZXS2wV4tvpwne
+iFFWxW3Uuz1b8CFJs8zJQizHGwDeQNYNACuYBjYrcMjBzJvW4IMMqC0yIWtKnjxAquHac7S5uM7r
+T9SSH1xbCQpjvXAe4Z7Htdq6kLIYe/iGENNpdATDYyzO46Pt0bQfaB0on849td/kb4saCucalhx5
+PQbOyyfwD4dfIBzBvjEJoyR0gLtlc+SLMMskLK/lIfjXxFKIG4uZ9aXQ6PWtVEzLWN0Ff9qya2dS
+YGRcqqxJ3q27+Uaph/9rLPp609RaGZajsM7dM9od5YGmwrrdkUu+12GTHpawJhd2puOG8/EH7/uG
+Dy6Wb6+C3ucAek9uEY8A4PQR9u4n3ECwIV/iO0g9skRBcYkhxqm4alw8RyzGwar2LoSBtNLlFLWK
+CxguwTWFmZVV2A9lvkmzjab8sDYX/jdhCvZJU2B8FO5ZrC0u4ecHGrSfTAsYiMHR8VHlzTXoyQud
++MO6ewS4XxxR6VornAMiICLd1hLdfSaHczK5gFpUJyl8guEe5dyJfSpda0WUvvR93ch6+dP7SMMD
+QKPAgAPbdmkNGHujlbXPYYNWPBe+J8+Ux0q/0HSqS535jOu0FGQiWeOPFvHqO+ToI63zSstu5x7G
+YtKzSyqhHa2riVCp37RTlnltHsi8yXOzPFkhDMg7+jGX9e4oWahImmsLOfaLjX7kWQHfnQ9z6m/j
+fw07iNz5zW5UzX3Mepk5PfH62jXDffnmwtEd81xqfaMfJvQ3+2bBwrxXdsDeB48CXPMd9J7iX/Lm
+3x9+gfBPQ84mQ20dFppUFmR3O698G3kei5O+mx7ltwUkAoR1bCCnNbFmUylWSQ+8wzMS4PhS2gl4
+Y7HX1+NCeWZ+L1PK5cnFaceXTuSH76V5t26jj+0ngFitv1zqz/OE0kX3B/7r2f2BD4C3f0hjAGEg
+AHEHw8Y0vN+hjBHInSNS8HXQW9aO/jJXF7yqIOq8VdEuZE+At02OzZostKlQLjQ7lcuy6s5BvVSr
+xbOM2sKVK/2yt/SN9ils5Wz0Rbvj45xTQ7xFMjuLOud3KT9zUWXMj/VGhZ15k96O0hjJ6yBX1pHn
+bmmVn2Xjy4zgUxRLuj5BqGjNqfQHFhHZ5txIuyyZdnQ9ejuSl/wwDYHxJnd+9Jvnoxqog5lHH+kL
+Tc63pkcO9WpJ30qhn+LJh15SgW7Pu1mSWY/O2ntXveiH/K07c2AveT4nAaMJblWO8YIiu5LPa27l
+fAEKV3vwawJKXiLHpkkswBzebtxs69Zo+pdCqJWPjm7AF5020xuN7accE56z1fkCneh2ypuihVyK
+43MAu+/rcdMcZW7rCnr8zJP/RPgFwhH2/XCvJftiIwAGara6lMl8i0lsrY6cpDk5Bz2OuYhinuiN
+9El2SCWrBtPkeAQiAkUj9YJc2RzLKowBgteG2EWrvQGXjHqz7CmOrOOzIqrHmKPNkVAFrp88fmIo
+Xo/HrI7s3WbVTRB8t/xW2QGKQ9nWwo89UBsY5rzQEanfkteefpQKepdwmjdKnnOkzwbvgFMZ9kMw
+nIqCNBVWP7b+9vO7BdtSaQsM7hbsVOqQcr0rp/R3V3i263KeaebMFyzX8kPLpp+0T/Di27USELNs
+oglRGFcg3PNqF5c9D851I3MUFm7ACn65lkgr0PtkCuLuUAxJnh3SCoaLVxhWetkBgOx3xTbySFr4
+UKBu0V6RW1MR+wcZRIV/BMZtbHQAB63leadF2XardwIEoWMUbp3LKUjzKk2eSB5509IJmk5USTsu
++V+woIRXyZhJc5Ffk8Z+2Dg512VP19zp/rkrdCnaQDDnkdyo7MMZL/C51Gx6zV1k4kL7KnAXlxyV
+7F/1gdJzH5NOa7IGaC/MuVOHIvt/Ar/2Fmh+LdJynMD3jXPSUnxaZz+W1P/78AuEfxpyVinARca9
+AeMOevt5rM760WMx5hpW0CN3mK7KpDctI9aoBQoG6LdRju1UoAsvcJeKVdLML0D7yMTuyucWP94h
+elmAm+JBlbPoQ+5TnABYLF2R/9jqv0e9DhTo3YCw9Ty7gGUFx8GbsgiLVdjLtjv9rkr3idVXhIKC
+XbcSsZwfnCH8++mJQRI2H2DktWMKZHvrnH6utr3RXYp/5ToBxA1eiHArQU+fvxLvAL9IpdTpZ3sC
+wP4h75SedW1pssVPZa2xLWXE57flwBdsVNuWQo75zjXhpMeR58n6LM3oI0/yY7YYSvk9El/0HRhv
+lmILX/sxX2YaKLCs82nKM87nle3ZheZbzfaHsl6sKnlE3rnwoG8LNX+40He5VDcYg8dtzMfY4HP5
+uwU4VmKc17K8l6nKuIoOx3atr0Fx76ZXvJU6d+12bO2fi8zRrb8kTKHGfpiVDOFEYkMCPC4WsWGy
+c4RTspI3coOhQ3eLt36tNe5i6NIlflr2PwHEJ8tv8dWkRKRdwTHg3sssWswrEd3N+mtlAFNA7K/D
+ng6Qp9W3AV+1FPvZOszr/enwC4QjfNci7AlsTWbvovXhK8C7Fo41WstTgEvh76GerC+cTQ7oOs+V
+pS3ZQchxorX+rwmbjhy+hNAdGIv1d1iG4e+qR8rO+EnBdDpykZSAP2kcQF+yeLgLQwgmJwgO4Ude
+n10gPgPi7wDhLiBijoVg0JHZxzcUt+VDu/y7LKJgDAmcoTQddamVYAPo1rZPbg9K35FKLzumXV7N
+D2VR09Sl/GYNNgLDgro6378Dan+Sdwp2iWc62Wd9Kh4qoeHIlScjtBs/WReqjHRdiJY7AuAE1kKv
+L17GWo65s6w4JgBYgHH8ChDTPaK7RTwbyI0/AwxD5uPG1VG2wEpZzlXcsTtG0NvAb9HIk89g2IVX
+d2uw3rzpGMhAynGWk0IbTYLrjW6vqvFLx7pd9mLp5fEClk9dmiW2tPRpdunYRW9NP4Jc0rzRhHCL
+H/MAIIxM2e/dQqw3TcojdDIv0mYkLbaKLC9dAserzWN8DpSJtezPbhGu5T+Nh4tIdtS6CJ3ZLMQQ
+a3CA4LW23qXzXgBm6Q7xfAF8fay1XMefhON/KPwC4R+GvlgCFKd8taSVnikAnEtNgLQD6Q+pkzRd
+0rkY8zH5SYnHgjeMElJQUUMDKN5ITDTLr1Xr6oU4XKzDSg/LMBl2fCHloJCwl9no1YFU0GkVTkUt
+li4p8/jip2OBw3pB7uQCoSD5Ao5tAuFlBc54PD7KGfBhl5gmKF3hrj4Om3NBLcNV004Lbk1AAnwN
+fDc0eAa/CX8PdJbfAHj0dZEE8LqoCgOsgeFSCKx9XtIvcTbvu2D4BoRtxG7sKjATS/8rQe/SYIn7
+tg5QRyDXH+llrSxFo9fW8tv6yXWkIFh8goFhIbbcgeVkAd7AsIYrUN7ZQk679C9BhSrSUOoUss2l
+6gZ8b3LnIrPaDYk2Eq1hku8HWh+ToouN8jRdhuGCa25rSqvjZukVKJV13K3C1c0TwPKWbnnKFo75
+WHzJvjNyHFbiPV+EJwC7/AXSYJLndEDb1lxrWO9b72M1SmD1VRZtE3xbG3E97909Wn7xyS0idJ1r
+eWx8dBq/YppbyKoEx7nuwvxC+hsvqVq3CL++pxUMc/lwTU63xz8ZfoFwhG9bhPOlNwG7CrXcZFEE
+9GnAV2FZyWXKBC5VXd+8lgPN+HYExCepIhp6CUwRAG0lCD9Csl5BLvMx6Ify7kD63r1iDW6KJ/KV
+JnGfdLZTFTsEBLuDG/qXH2NUEb68DovtUucLcl/4At9cI8yGJRjrLdoAvivNW5w3vsG++pMjp8Ng
+K6cecZX1YgLk3SlCQ9T+LeCLQ9kLXefQFF4XsGwH+tJDpYDS0SEAWRaNtF7qBHKlxx/jp3DK/wyE
+7UOZyJsNY4nZmZbUuS4KwjHS5Bkyba0cmpX4Vq6tGQe468kjoLiBYJRPMN0hlo+81RjLvNJu30Dv
+ViY5oTO/clfTq4+qSMuKi66E85xvWIVxpjcL8ZCbyvdOQ117o1XExPTZlpQKBRK8pSQy6xhOEH7K
+2x0lMvYB8Hba4ejnsjOehJigroadQ9ypNGMiGN3FRvxmMVY52W6u2Sr2+dBgJ49Tj4Zu317gWW2c
+8qePG6994EfKCV6XIwVMsPtzcNzYvbOY6yPeGVguW2t+ulqD3xf2PAV63/jEurhDvHYGw7Umo65c
+b9/DYv/O8AuEfxjiJqlSww+4pnX5C7ucnTQflj6vibmMzNNCHHEPtWXISb6B4Lba5NFaapEuPo0L
+W1wmUnla9OACchMU+3SLCHeJd23IXwB45cHDRUBB8OHX3CFysVTbdVyK82ggmLtFOAyPlejx4PVH
+y68Z7K+n7xUcAPhoHX4XH8onmHfN0Q/u5/RGq+RDO+4iIXPIYpbQgt3EdzlC9LkAoQ5BrFj2BIZb
+/ABYMj7Ab8rsHfz2eSh5AorzJQnjnCwwjABpHm3Wx5owz/mrCpeK5RMAPtFmaKzwA/0Y38FxU3U6
+GEM52bjRW1Nf1oSshRx0AVfTyqtAEZFWH2EqP74ot7b+W+Xjmxm5hopGMMy4+A8HGAb9vDkvdFo0
+8FtzrEklnXfedwZRfq0iMvLRLwXB/FFZ25AvJnz9CI5xox0a5ZJodA6qzI9W1Hv5VqbmS4KWVigR
+U69PjqfYvqPEft5+9ui2+97Nw7HKow2405Vgo48KFKvmIpeCfKSfJxEshyw8yIt5k7V1YozxaXiy
+cXEtukjM5gM+Xlw/yEt4Y0NmhxXbs+8lI136VE2d4LiO7n1NLn/7cInyeOoYsmfdJK+aKHv5pBMP
+2gtyMIe9b9h4LC3Cr7wgtwAwZJ26xNHXzh8Kv0A4Ajdvzxk4F14c1yKzOiogOLhG5ARNUNwBs5/i
+nA/WZEI0w7JewRGiSPNPdU7vnqO0ZXtLIlutKAHAlj5DbHkHwgF+0Wm6Y8Rq0prspj7CB8WSSuiq
+dDYRgrTBi0KlAjdJ88MU7U7aPr38dvMP/lDGHHhLUNhDgREAOT6kYzociUZqbNmf2kqtbMBlBdZ5
+sepI4HyCeAPUuvCszw8c4nahR2TKrjE57ZJXrnuWwpgnqCsEAbAqhJOOhNAmGJ553wpScL9v2AGv
+8t026urPe5DzrT053X30k2sBckQ9FeFkEICr+TbLKy3iGwj2Ar/NTxgKgCPfykeYN+sAuoGngWBc
+AfI61gtNE4AoiFRepfpXmv54Q3oDuzfZk3z1vW5pSwe3Mi6NHqN5pM90l+MbsJrvdpyuM2Id3J5p
+Ps5VoDuP/Rp7E655uhAjrmxTPVyA2GXSCGATsJvwT4Cj7uWefzV/kxafxo5RGwXYbuv9kdL7u0Qh
+Fdow2qhZYxPcVppXOVl+FSjrWkp2psAUnoJPKGgRxjh6lQugSyvv6+Uj3GixlurjI7WOnIN/YOl/
+OvwCYQ3fWc0N+CrNMq8mpG3HNdYdELcJC1mKlL/6ktMow+vVZG65AWLr7AmAy4IXrc5H1AMA+6BR
+MfBoulNE0N4XeB45f7f8NmD8USlBaK2L0e4iF9C03DaNH6txIF/m8TjxS5eHzQI88tTHOEHHavuy
+Cq9vqNv7rEdKr683C/QLO7AyatgSLrxD9+iDOkR0wNvBMXs6y3wCu8cdIiafdzTbknn+EGQNbAfj
+bZxHQW18nBh3eimIWXbTT5Zvfs/W+eWo4VPeDCfA+xkE72WA5Zaj7uF1bZe0KDO5SZwvmIiWE6C7
+zizXB/T12spSWoVLhId1J9IEus36i05L1wioZTj6Rn/2CxjO/nLafByEkmPUmY2J0ffjNmdvxZvV
+F4eyCqo3nvdz9Kai6XA/EQ9tPgGuTK+Z2TBSlhONcJj7DRC5zKX8e0p9ju3d+KEVGIdu6sKTuLe0
+p4Bq407gxpNUB/qg6Uo3gE9kSUsL7umNtZlutHkdkn0r0+e3iyxrI5z9tHmN5PdXbhFx9FOepMX4
+x/f7kn3wvHlw0Aghx9fhj8Nik24Fv3SFSOAbH+6AOzzA8Q6EUety7Cj0J8IvEM7wPebXhFD/3xU4
+SRP8ZhnVV/LaFOmy+PXHteVSbiyZJityAWUBnVS8u0OA2wsARoCPWBVp/TUqBtYreWwghntE8gvQ
+F+Wmi0Qqli+UEu8YdRFP/Zp7mIJgOLZMQ/kJ8/RndfwKfLt/8GeQTGtxWm3ijnntmrE+I2kBfo2f
+tBNAkKBXBHm5QhTQrfF1UCCqi0TRuzxp8+JLl4hWeF8aG5rcQXFFvweMJ8I1KzCMPMgcpQC/NQmf
+wbAfzvkqDPw24gWD1Q96QmNeefq293YXzDBREiUMSnkMrSdp1NrEHSRTdizXiALA+rKcphP8Jiju
+lmHuIAGInj0wrD2As3HUc2JtEPRkNTqnJO4HHigIPv081+pZ7tzOS+swL3hpU7tLO5bxTudcagBW
+GOg7NceyzZ5+1L/Fontslt3zetNPXbu6UoxJf3SBAOVhz2tuFCzLggTBg+YJchVUKk36lU3uEkIw
+7Dk0MHyQm9QNke9a4bimZT97nfRDppGjsjX9Ka+PQ+tT8jUWuxeP0pXTq7bcGvV9lzp7F6/LFeLF
+Gy+Nl1UYlUa5Q/CGs31R9g+GXyAcwb7JewcfG0Bm1AK/Z39hTswAyAGOdT+/1PdWE5W/tpa8l9kV
+uQoIbyC4tnQhOIl4A8DBC0EKdHlIACsAeLMMoyZ1AkBfn2z81m4RU/kcjzJgvhobXC+rMBU48wxt
+SyeHpB87vCjX059A8kzjFQs6Xtj7LL9gxu2NNq54YqZs37AEo4AuBdwOivsM2OlC2UDJTu9+xLP8
+IXEBxqc6TsB4zbMQvPI2d4FhKyWS2QWN2UPVRP9uEKzd2Fli497CWp6eQ0vwY7Y+UJqWKfELdIC7
+IvCh59Lr00Ipa7atQyTQ/ZRnmofwD/ZhBZ7p6zEsw+EjPEHwBLfRzQ0Et/wPyCMpX91A6w3CtArf
+zjvR0XnPN+ePiHA2+Ysyeze9ndJL+VZ/AmAfGVKTt1S7wrWM5py68zXNr+WoklqGzgGue6D6JX40
+KQLk3NINcr5XZpl/Ko8317trhF4IUvEXQZ7eboNIv/kkSYOzL3rB0gnVFAG14tqB6IWC4T7edUk1
+KHAckmVAvSyXIFjBb/nrr5fmgmsBfN0t/YGnhXhagRfwRfSBtMmDPxN+gXCG76nGfKnnBn45YeWl
+uAK+5R5Rk3pOUgxA7K1MA+yiUHIJZyFmrjgfOSdIMypgBcCWjbBYESYCpVt+Vz18UQ4+QXN1KK+Z
+dfRt1HZA/MXLcymzduHVQHAALDNNBwgm+EyL8DeArj1fgmMgeJKOoL5eGHzkQ8+vL0D8rJcJ2RaP
+PtE3ODiNafUlZOKHNU6uEp8g32egewG4t3hopBtGVu12A8ZrqsSc3MAwQliXUkubl8jzk078akV/
+XbZyeveFJkBvz9vPfTwAcDtHjrSyIFS3A7s7hMalqVx7kbZcex0wq5U4QTGXBH88fwPB5Qecu0Rg
+HTWPcoxsSLE15pFnvu9l9RwlphgqZd6PB9nxyhGH/En7ThlV2C0+2t/aLlbB0fy946RNBqgeaE40
+Wx3Mc6H4iB3L2Cy9s/lO+945nOgznWWYYXsacOSHodIK7CIUxpGgkXptlPGWPq16iHydoZeuoZE2
+tHHhwpwdQy6AfushSBVLJ7DEJ9eHnrdbiRkv0EuZI5rGKXEpM/rZFiotXwg3Dyv7DoLf4UfsLy4g
++cDi/3D4BcIR/tEHNdDmp9AL/NISTIG1dNXuI6zLMeW7reut/A6IWY6KZK0eK+WCKNAWY1RMEEyB
+kOlan/XSHdISDHjRHah9guPlOKNbBF+gW0fe5dn79SeUT8pn+5BGdnr1Kx/pktNW4PfJDin41eNn
+a/DZP/jkJ1xAuAu34OHLfdRe+mQsXNzas8bEKbhz3HewexfKH8IBzOZc+Q7ozbTd80NxfwTGUU4B
+YOmvuvnKLZK4WgoLx/kJF5F81j5JfB5PofKaxm0xEwIlgJbK6daUo/Irxs2rTwlSsw2hgJyKCX0N
+NEBcjbct7o3egC9m3Av8+m4FLjeIYQm2dWM5gXH3be888zH4HLmzVdhrctQwF0elzw7lTfBH3SLe
+sART6WLchDf+XmhbWtvhrU1bG2tItrJbvDHBNRVFDyAqM2v2u5wRS3MAol4u86TuLslONR/yDR/z
+My0LsnVH5wJZNV0iwJtmlEwYMkLnTG895cqpoLxQx8Zgj5Kgsqat+BRsJGRH6owUbNonpWnjqRuA
+/oW4u+sDUNO0WYw9VUxxhUvMxOziehXrVmDmyD7CcAG+Xr7CSifgLStwAeB1/KFe+zeEXyD806BC
+bgDd/I2BLL1V/sEdCNcdnIqwVGwNfJwEn+e6yTtd97TwWktHGfpMccp7AcpVVhouLhAAChg7ytfR
+D+4RpA+F8zFORTMUTrOKCRrJBzUGAcAVRyjoAsYsABb8whJ8shZ/sAibx/PvuFXOI+BpEX5xAuau
+NK/RvP6Mc8e+PDYQqNPzFv8q71bwVM53yD6FsHn5v+1KrfjRreL9klNtTX3YmnTt0jmnAWE70KRP
+jW3DKhw7DuE9AWCxFtajSKTC0LgC4tSxh/gEvuoj3AAyDLXlX5doz0gvmlqChWZlEe6MUMF5YpSA
+q43ZO6JpUlD6VMo0hW6CYadcutxsX1+gQ68vfYNTHqKHoCdZ8y9le37NYBO6o+ft9dksDNgAuY7G
+Rd9izP9sFdb4sXt+77bLHHDN3MBvAN1Zp84b99jlYAkMdaX6+phiRaaYPHECXZ5EJvAEmcIqicp7
+Y8xxlzpY5wTE2TmXqFzBcATAGq9rn8qMMfPieXvVOuRMsYVjRZcIBBiuGZlWXl9+wZjW31h/Pc11
+6XnjlfLtD4dfIBzhuxZhqgK6OrQ33eSoLhEKfjW9rBJY22OB+qtAcaYpgw1S7qAMJgI45HUQXEpw
+WapW2XSLIDBRYBzHsj7d3CMW6DN/s68T+J6VzcFqjHk01HK3lEsiT1Mxv7RUBcB8FQw/61ig9ruW
+4M+uEcV2TgYC37AIm8ElvqZRtMm84uhgVqT15fgp+AY8WrjklTL4/9l7ty3LdRRYNHCu///inWY/
+iIAAyc5ZvbtrnTNGqmqmdb8iCGMsP5QbTawsPdMGeoXhVuR6JEcbaWrH8+isMdwymaj4+W7JnJnT
+bJ1m7hRnx2tpgHXUljRpLe+ai7UXbNSlfsoBtUNd8bWHtq8wCUNQkMuy02Z4mk00E4iIo82w+X5S
+BP39pTmkiUTyKkPSr2qHm/1v+k38J2Goj2grC+coeZXMFePUNOJoH/wYZp3jZhy8cT/IbR/9P6af
+8+v7G1tWBUVb/Mo92T5BTa9vAGA7p2lZbTX9up4Pwzv6SbY20mTj+Sik9OIqq8kHoO4nPnniEHVV
+AOx6Ez8m19TmF3yiIbs9H3GQYQGU+SW0ZNBWE5Ozbhy8rsMBABsaSD6aRXjEE3cA4yauP2fUGdlm
+616FzVb31rGhi2FcNrTDcWqEmkGsbbTy0J8f3/r0ha3/ovsFwv+Jm7v/AHoxf2kjXLw5w8FwzqYQ
+ov0i9xBCqXysbWzuABUm4QLBHRCvzVx5l8LYUiAkMKZpBDeyR9sOIMwheKxKzdGu/f3RNngIp/ZJ
+U0FXOmKj3W9MO/HubRZ2vlZA8zKcTSPetMNPL9N1ILym6V6ANx8l3bFKcYMQ/VikQfooZlyLXnFN
+09tuoHreHfLJZGmKCiMbuZ/Q4BZtr+kq9Wb9OWMb+O12e2UG8dwlH3F+8J/ipv88xrjaD2EBc5Wn
+ar68tMG5z1rf6glKylAMEJZMo/aXAkONm+YSBMUNBIPaYIYPHMz1xIjDV+biBnPdW+oExMw2OhPA
+pndJor3s8cinBbVexRON7CLj5hwF+D2dHoEH/0/h9GN3PgIzD9fnkL/zepmAp7pQZm29WH9qQv7A
+rdjSvLVcOX8Cuv4Qbw/xc1hrm/c0nQulH23LhD/mJgk5NUwlNvth00af+CU5j1XdKUtxcLWTs2ar
+m+RmxiKAmPlqcGSIE3qW/7DN2/VP0tQV/3Px1xTO2Vo9KprhF+W4PxLw3rvJBH9TO1xg+I0T/2/c
+LxAO97FGmOYQx5fl1D/4MfhTDXG0jQA3vsiQgHiSXNahe2N2L/eabHwsAqXmN8dL0wgXMwoRNiu7
+F/U7tcYihHIT0y5Y03lSApp98EkgnTTFu21wxB0wnqGYjwMCiNf1Cu2vX1dqhe2ydWrEhxrfNzBM
+O2GeEMt1Wq9GLfjgXBK74ZeFOUAwPwHobpZH55zNHdqKc/SZtjM6YcA6YU/hnwDwaascBHT+fdha
+qTF1bGYRQJc/TDMJ+CigzBu9qleQ2wdxztmAbgtLrI18tue/YwzrpbJ1nJACTtfw1ERiaoMFHTgE
+6ApIyDi5cn5d2jEDAfAldTWziNQMUwscn1e20gRTQ5yaYLM6Mzn4kt4sAEhaB9RO2LMM50RiJcy1
+j8erG9PVq5dmGHfyk+3jPk/+w3psdH8MV6RJ0Fuep3psz+BFVzUD5zJNoxtr0tv3Vqzl95aadcwR
+ae90uL3ew9CyjVO9L2nOflsfbm6gIJo2Ry0TBUaPY3cjzbH2RclUyS5+VhUSNPd9U+ATvHOCRFvs
+WUkxPr4sDcpsR47BJY9LX4Ge56gVlvg2HCHlyTd3E33WcCO5g18Cass8AnbjctsAMG7HdZU2eGqJ
+/7b7BcL/gcsnGCKE0pEJzBfnUOYU/WU5BTAExEFupgC4b1by9tUhidQXCjKeOzmEUOSjJij5iSMF
+otbHKimRGjhWTY0CYKpBuVleAO+rluYUn2O2bdPCkNpgBOC9xRb4DlDsAYqfgfCu8f0RCF9XrIGj
+FWIAACAASURBVFVNVzHvOz5tt9bfVDMtHV9rzgFM+hDhlbRxBsZMm+5V66tSeqadwppgP2Rpwnvv
+A58+5AtyosFpH9VgYe917BZtu8zCCD/2dR/dutpL2KrlAsGaL9LCBtFkDPPX5IBLvtwGui+YT9BC
+3LBO8KvLW5rgc74ykTAxgai0BMFA2glPG+E7unSlP9YnJ96DHk2GoABAV53qAqEF1zlBPlpdfEaE
+KgGwvCz3RwD44C+N/RDac/tsyb7FzXJZWoTM3Dk+2z2USZ95i04eIhXXo3PpUt6MS5wEMtW4//ah
+jO4cX6Krern3Z1rtVu0zyONSDimgLB7iGjfhXoLmiltUxrRRt3Szysg16DnpmjScL82JxlO0067t
+BX9v/bKgbQLgiP8ZAKNd1d80vboujpQq2QPpjkm7HMZtvt59Wd9WxmUOt10DDN+1wPP3C4T/Tffp
+3B80wAlcTnbBI+4OZnPDwGNLbit7VwKYG8HMsbSDJGJv+VLu9ZdTczgHi58EvvSHgBOwnCBDXipo
+NsPCMa12RwrgLU/kewLBx5dU8ByftUr/i6EjtcCr4QV8Cwwb/Fo2un7Z+vJd0wDb4UMan5tHEJs0
+AeBYaIAM1ELINLtglA2lCAK9GVJGXUC3VrprSIXBEkQM5FdajIOgbXHs04TWo8JHZ3vWk+DniOyQ
+bngExLMnKtYYtlOao80szPZxp2z22gOyq07X8tvWN5OftpVglD/vz46aFhLcN8zL/skAJyFFnGlb
+7tLexs22n5pDNLMIrD1HzTD347LR57jJX4T9bH5vT3VINuknf2KPcg8ptysB3kAygn4dqDOaURp2
+FhI/bbQbqG75utAu3idLMOL2xKc4oUuYNDMKtieZmiZ7xTXVOxiNDJ1cXNJq/k/dbvXbiH8YjW/x
+I821L4cyuYn3MiEAqnKKtBP/CZmV9dA72ehkCrboP/knadYMCoJ99CMBcRCKi39hB4BaJ9c2XbZz
+dNKDwF2A9FkDXEo2+lOGGHex8MU292hgmUqxRpkeN7NhH0w74ZOcV3MJN7nGDezdsMDfdb9A+A+d
+h1Di9Za7Hce4ajrzA7XoKJ7qAL6CXDtD8VL8GISoNc9yKoQH7tp+vXSy29yotW0YoOQgZ6FXd0pK
+nMqTQdkUKBthg7c0hlNbowOE8qXJtdblCpvbpfGlmYEB1wK+Pq4giKWt8NdX/2Tyq1Z4tyFOIOy+
+2nHpgwD0eiFuXxUZudz9k9n19ff8+cgTcUa2N/iLyZICZZrAZEPdA21zXwLzlA70269zvgJFa86E
+zsuzX1H9PkrUIIsx1BT42o81B1P3IVU60l65DejBP280qnFplQBXtIqqXylq6P/yBU8XOkLHgnnv
+J1vjKW29H1CCruyYaxBJntkxgfZW11Y2wnljPq5aNA5YDH/xPdoQ3yb0YHeZO32vl0xXu3f2yfix
+mliQajKeUIV/fQr+jrHHh204D8rvwp+aaNlEmaXt2/+FAD8RlcQ3gp75FfgwSrWImk/jrJU6bbPK
+05vU8gAGAJ1Pr1DEsLUj49sLHMsrTWZ7SQRSTh7lVDuW/SWIXVtXwG08rbModw73fIg6iGybdtpQ
+2m5bo1ZtcdMcQ8Evsh6XvBmXV0u+d9IYAyWX+NQqTfVQ455zn1FyE3FlnhSAqw+O0vYS+JrDfX18
+w/2G3wa3O19oF2Oqv+Z+gXC4T29CXNc5rgVuxQ+U9tfPcfAd/LJORJyCoiZ4NV74wPqtjLYVevCr
+ZrXFoyRle+yELuDzomAqzhZuE1cA4AkYJxiWDZWaMaZP5rf9rK7rGW5pggP8EgzjQ4Dbrnbh+ro2
+wFxA2OFxfvJ9ceyW/co7cYZlODlV6E5BMGliAuMNBAMJpAtoeb0zcl7GHQSLJoD5lYoAg+LoCexW
+vndwHIinE/zTFUWWJ+zRqJzkKjZ6JRKgg6zKWIF59ftpG/2h0y20xkENrAsYQwFY8GmjhdbVcJGW
+mDfrEtJnvPe6No0XULggC6kwlAxMsvJndOtIVVOAuBrR8lfQf5pRWPG9LO8OfIc2PvfOXc3ZCmuX
+CLkqHADYwm8ChnkWupZQbR0ny0nHASIm8Z20sye58mncI5HZHnx4x4WzwJviFUdO75XH2q7o5Vp9
+e9szrurb+7HLL5My5/Gd6mv5ihAkyfZyRwA8+/UB6D1oftcS7GCYI8jzjmNDllZbtfZe8sCHHxhA
+N/i9xKm2uMmEYOjTHBPg3tG5kn2aE7TPfZlLxggJdlP+rd9FYES7qxMotnu9Jy9z+7fdLxAOd+RD
+p3zEbyhQqyBXP2CUYYywyweOAHxZCUcHyjaL4GLw1xQUw6kQZDH9XacyUrFu3JKXspmdvEQ3NaqT
+goaWDIlzcxMEywTCJZ7op+JW1QqKJU82qvCqIwDa4KZmWIBvaoS/rtIIX/woxk9Hph1MI+SLc+sJ
+wAW/eGZimWUgNMSmAPgoHFAMA2RcNXq0uBJm9fORp1bX+zLlVOaDNEsevC3rqbyK1KIUJOUUmTyD
+40UW3q6QK07Xlx1rkm3JHy+gkJ3vvU0XA/EWDkAsGpCZ/9mtERII8gVQAmDdRrXn+Asb9zDJMEfa
+vKu4UADMFi1vGEc6/ToUGYNhDNEkrmEKAcnBE0wLR3wDzRGXNsOSPwFwCMOkxRDwBL8FhGOGmlaY
+Ta04Nps8KfmR+DOfdjTCQifFaXY6zjlsbhJR+D4VNLp/tM5Dvrz4jPccwgS/y28tpgG3kaenswmV
+HtFQ42XSN/EU7xnhyHPSQp7q2fphleb0a5kTz9U+8ObSHkAuwzGfDfQKOCbhpyI4+6R8qPOl+bEQ
+fUKXL5M6Qpu6AqnxpZZY+b6A33zuZOQ/Pa7mI/as0HzjKrkpK7rtDpXvKcpdfkv7eyUovtc7q9zz
+9wLFv0D4X3Tnlw8O+dDWOrXAet3iD78U9sFneHf3BZH53Bdem4HtT1px9De8IX5DsXyD40qiVvC7
+0mbFHgI7/6jkZsMsO6fQgbCGHlpglHZ3aIdL+4sWD1SaTgD3ZgljNYcA7CowDALirytBMb5oFvHz
+i3A/5rML5h43O75AtzvgV7woQBBMhioMFyqsitcctbwZJzSBAzPM5SmBmOsX89ZA7+b0hqhfcfRb
++idgKH89Hs0XjjiKlL4dDDcbzu3qLWxA3rCxjtTeUCtis+3T0L1Anm6L2DdNLX50OiO9Xn3p7bRf
+l19OlRAt8O3xiXCZ6yrjAnCtgd3d7zlfBaJrTLx3y0Yongbi7uW0cEQLUC6GZAJ8aco0/Ch/rsU3
+zR/YOQLhAB7ZQNcOJ48KfrRMI4D8MuatoLgNOteR4KHMIepx889uUoHL31MVdojWvfVQb+PRSFr/
+Efwa9rjWx2nSoLQvfVUaAISAZlvP5bKdDSRbu/R2Dn06+F37Z4f+ZL4H0EtenTTHfiqNF0BuT/wI
+QJOP7OGaCxQ2yODarE4sEDJGTSQSJCswNjSFCul4moToEY9p29xuMA5OeCFt7UtkhzZYgXAAXXeE
+Bjj2FLXCQM7x33a/QDjcJ+xsZXSo6r/5I3y749vX9YZeUeEQ7l8hx0srXBvAXf1QHvc4BoJhlTvr
+UC9LjbC3MqviYvcvlZv3MKQhASQqgJKlKtgdYJhxFWYD/YWhbMMYlsas/+yiYLUFhlMbXCB40wg3
+zfCLeYQ9mFEYAW99Qee2C2Zemjwroe19tmRa+yNK955SAJezpJB45hH29478NmB8Dlev6VOxqdCA
+olvDjlk+Ws9l94qoIdUc6MBbOxWd/XV0wWMmANhGbgm2aQkQJvtPREYv8LqBRp2cK+83G7l3k7wt
+Pl9KMBxCLIB4kr+X1pnKJUu/5xYtzbBlW1xkxay5eioMM10Fv4zZRHYSBCQotkofcZVmAxDbslpw
+B75XQ9QAbzhoOI4Xci1zmjtU0gF+xT6ja4ZJQELhpeZTzLK71zirCJ9ZH0fTqtjBwj4hNXzdeStD
+8gbreapcN4vQsuc2tY9CmAm49vyzTj+W6eV81tPakno1n3F8FdW+WDfKdPA7zR64705A+ZC30Q0O
+4VgZAn/maWGAJhS8gXbaBQd/WvjDUutMsDvBr4Z17p5uCh7Xe7DO2lsDD8UJEpTz/WW5G27AxYOl
+Yn2v47Pr/637BcJ0ryChZ2vaYH/Q/s6wLxD8LeHcCLzDAxZBiNBtwvUHQWuHq37kt66hFc4q+eh3
+9UUFcx/8iKG0eZg7xc0dBLvscAG5/DnOtsROFq5/vfgd8YFqXKkJNkuNMEGwXxfs6wRuBeQe48cL
+dhJXfb0SDOO2BMRdW2DZ5ynskuFFzJihipO7/ZlHNcclQGNtQ2tQYRzCwsqV6TX6OGl+d3CMVubw
+7MFLCHcNsNBX0pOIUK9suwttcIDE/vlVLaMj8s7k8+pILfAxvdfVd4rMTNwsUzYQoqh9cIjZdQ1U
+u26q1jm9qRVOWeY556yjA+Cyy+UpL6odngDlhEfYr11G7iYRJeMlTUHvVXGL/hfg1ack+RTHSA/d
+JKIeA1nr504r5TcSyn3F23kDEIumfTVsxcQGIE4wcaK7U/SRPnXPPzH3GVd8+6c8WjOpRPcrrMBu
+5XEpaT1/m2iZpw1AdXpq/TLtCw7hzgcPBPcQPqSF/wh6te1RpmmAbbcNZh3TJKLy6pM+GU+7M6d3
+xWmflK4qOxUZHiYRpQVe2T1NH3KdM7z4nscTsQLL+5y/zmf2pPrNTUc+68CSeVgMbr4od4W9cD85
+YtkIXzn+v+9+gXC4d21Zz+d4sA9+uH6jwO+3xJOWvpTouVcO2uAIaW9a33hSBO+nTOIJfktO1Gsf
+q3rGSNU2vWOSDnPWthSRd2zsI7AdceulHvZq/UrkaZz3p9Mpw0JYhyBdTOnKj2csLXBoiF81wrvZ
+g74UV3m7CcX6ml78GgBeYKx+CpmsTxwZngQXrU1grI+8UHGelUieEnOi1Kq50wiV95xSL1GteHAl
+W8OP2vfK96w5liFXDvpzEnxkRrd1Ztvkz7mPpBM5zpnw5EJgjOX52G2g25LGuR4cB4cl8AIAT1ZY
+gsyiP5fJq11RiGO3FEzAGQAXU5HtEgK+GreWaDmcGppolHkZgILggY+LFQDjKiCBSwBw+kMbnMQa
+5b5NGyrnI75tniCGywC/lgqKAPhCmEYYP0GJIqKosxnGW4KKvlLe29Wu7VGHhGl+0Cb0IfxJngPY
+tR7uvKaHhWsc2iDNfJDW/D3/J5rgM6Dey/wJ6G16HdEKl7b3DHjL/KED5JOGGMmPR5t5419jKb6t
++YpnLvZRZg9l8wvkMWRcLwW++Y9rqzc8NX8b/Y21le6hRwZ/WR0KTTWaeUQHxXFCxM01u2I8Cxj/
+nhrxLzrfyeAhX2K47Urwq1pfguATGIZLuyRq4fsFgL21D2had8UHvI4nQoHkW/wTaBEMd8VZn5dT
+s6/sORQt1iZLTSF6nIaXsJ697A94lpyq++4SvigwHCCYL8rhKjD8qhEeIPdoJ2zWAHIC4dvje7q+
++nArCA4BSzS8jTDmmQwPdYWGfZY951GNsGK/vJnwWtcK7y/XsahlahejUjXqhQukT1lvsyd26XN2
+VMbXBqr0WOCjty3zZw6LR4Y8Ks3isWLd/Mn8RJkOgIPJW7RvtUc+cTlSTmTQOrX+1vKFX+5WCIav
+EJB3xK0yri1EfUVXdc63STqi86WJTsyrPW54g3ESKVpgAghWok9lEGAXBA2Xxlna8a8r+hOcCYQP
+M9uc+04i5CsZpj8A8WXwOztek7E6TKbSzCJ2p5xy5PFj7N7117H9EdcddcYuJeBqu7bCmn+C572N
+w1WAk9qc1rr1PAt8nfJFfBHfihtl8VCWabumt/K6FJ9ttaeKGg762F6mG4C57Qupuyko8oZU+1V8
+NeMz6IkXUtNL/EAzCfLQAMVcx9IgKwg+3Xihz9NwyiPJuEkfKasa+LVmJnHFl+bcr3aG8HWXffCq
++kPG+l90v0A43OcaYbmzQdn71k/NILyAb1wTGMem+Aqicve0E/4KvqQarU2RNV0Q0bSuqfeiV3vU
+ChMoWQurYFVYcZyJj6ITNKnG96AVbucGQ1+Mg5R10UxuDUEft0IFsYDftA2On8XLcj/aANsLGBYT
+igTCcfUrNrqAYTLTZn95nMYJgkum10wRQJ61xeSoyVSpGcQEuQdQnFcxl4mlrAetNYQ/Acdd/Or+
+GwD/BIJd6j35Hfl4vmmGM4ONgqj9xg7mHlwAmiS8YbGn/XhwHX6c55BunaxQHeK+XSBYBGaMseGD
+7Jc843EvLS/kpS+TF/YGBmSFimOAku/plwE2EG4QHLmAbntSc3UQvJkw0c+J/9aODNCk8yFgAh40
+pISm4De0wLw59ex0bYSTbPD8MyPR1mYW9s1zSPtjEPwc1k+1r2tRm7eYTo17fq13Lr7V2ke8H9JX
+DhOwZdLVaE+J7yMt8JO/l9d6j1phE/6VsmPFKcBd7OOgEU6gLOWblPLqQ7ExAbpIWtniw8OX4da7
+Q+WHKwCmLKg4qGbYKl2qbu4k2WfuTtbBW6gJhoBfR/9wxp3wHPmCHG/1k2d/yFD/i+4XCP+hU6Ul
+j0Jz6GkRXrbAwALDWKD4G/US3bcwZ8cEv97BL87ECQhveHF8F6QAkoDfJiBzlFs4Y7IjihokpkVT
+kqwxtZfhVAvMAiegDBQoHiy79dT0Z/HpZKTWqYPhMo2wV23v/jLcCTBvQDg+NbmOillC/boLmNM0
+AkBphwUkcupqjWoVuIYAGVrFM0XBMn8GJ1kVo328+/NaUCljwUStcjRw+yfguMqIQNbuTHV3kx7l
+6X0petC9Qy2part3+t01w71CFOKc20Pzi9ORG+dSaJv3fs2WfivffRdCwzMzO2DWx0UAnzPtMlNi
+P8xFLiCjgJbgIBOqDYIbywwdhJjhrBnm/oynMhFXT2+uBMSLeXXgr3slex1zmffTbRMIz7mWqsDj
+KOH8AnyaCTvqVABDInn6gxvVnvVGnmNJ2tqdQYSkPm3HVs0kvOew7pDisj1MWKL5d22w7Jh5V8T1
+RwFBpZNWhmCx1VP+bE/HmnXjsVwvw3YqSV+U89me1h/ZOtB9MYc45YGA6Kb1lXWp6Pyz9SvLyThc
+tcEiCzYATNCrmmLVBgP1rLXL0vT7U1rjkplm0f4FR5lIrPZ5lrCHNnhpjG/4vV7jdwCXXevlOVAm
+nnm7TMd/1f0C4XAfa4TR+eonR6Z9y49mEt9k3AZ8sXETAZ6cx9Pf5BzdK0XsBVj1/nPZHFV+Ebky
+nwlQJomWmKJgNfg67/5JCxyTmaYR0SOD92PUoi4vabeEv/Jm+Z2EL+TYNF6fAe5uA9zTOlA2W/aH
+HsemXZfDrxt2XzB+GQsCCjb1Yk0rH4Wl8ETX/CooFkVpS8PIy1yl1RWcZ961wUFvWXJf5g3cFtNS
+hr4DZz+UT0HtPaxzMNvXcLNhZrtpCqHzSWDn257f2Pt8OQ7ql/TNHSaL8V5JDbK0JyLLpQlTFLu1
+Dt2SOoDom2qClXnk7Lv08SdsMbFL7C2baRRgtgo3++D4lfb3SjBMkyWNX8D4SiBcYGgCve6fes5F
+B3WMIeJicOBWTZ+1/voGgPdp89H4tuJbvPb9THzzNcuB8F7S9rwKempXzLQe1vzewqd2BuMVgrCI
+Sy2oRdyBwN7AcdWhZSyD6vcWP+vp+d+0wifTB9JzM4eQPDjE1YvRMU4hmJxb7dNJtNLvKI0vmYit
+Qo8AeMYPf44/eaNQic6nd3pNeqpidRPuaAC4jlRzXDdl0TL0umgm4XxZTvjHX3a/QDjcxpie8hVu
+a9f9hIgyhdAX5BQUN9mc8knQCEqOsZfTTZLxQyKrpzCluYJL/GpTBU4I04yXSllAGdRPdxI5WQPw
+qhY4J6HnSR1M5HnCHounWAFf8audsIJgu76a+YOC2mkasacVUCYwXkLd823Yy651J2wFhpeIqNsE
+WIEd/lFxTrA7BX974pt5pna46jB0QZ41GQYIHqA4r33ui5UitR8qQi3zmfhd8sniuQpeNOY7SWuC
+3lNf2ZbBsjzH1jovuduAMi7WB6phHQ1p3/aoMVpu/PjpXKn0OxRtT3a29imIZLZNZkXtrNKmvtJK
+HlvHKgJkgCGkRNMnxbNw24/td21Xe4j3q6+qy7UtRU7rWke1wUZ+3TGutwF2raW9gqrv/ghcNcAc
+Y5pN6Goe2J5rBo1rnmeb4rYvHt1Ms83fAe3J9OFPNMV9zmvNRctLrkYQZUIDQAJJlk/KVzmiRNQ0
+uzbynPLrFLBNGY9xHJIu5ajR3l5+GzdLUyOMGZd5pb1ox8XT+F1Lj3gJ1AkTK6G0wgJyDRJ/AMD5
+Qh0yjZW7yHhVrFA+uA/6GLLH2gty7EecIHEML43wFaDY5foLhP9F96lGGBBCcw+Ay7OCH06PcAHA
+qKuja4OTuJLD+1nwHoTwzgbp9/bxUIeCYRUsZOBezCUlDHe09DWEbmtU5zDCGS0Cqnbaz6B4sx32
+0oXOp3T5Ik/8bGiCTUwjECdC4CtOhzgBXTkerdkJN3vhnu4EwtQMX7eYQ6h2DHWVFaOsTo2ty9qg
+g90Cij28aYIJMg2p5U14kctYa9uB5a5B5o1RM4sYmtyu+VX9A2P6qDt4aJy4u3c1bjs7uMOAoHUd
+x1MjwuQTob4dHXHYj9liTRq4kqZjGENt1WhHTfpOgXkspIPgJlQwKfGtrRMYtmy62f5Km7UHhZZz
+D0qm1Jpdh/14LdswPq2ZcfMmidcQruYAviiQUQCY1wTCDver6BoGv9epLg0Atx/nXqlVQNQGINHp
+R6OfSHfYRFa96p44/HMa63nW/p7shHvebWyzLVn/An8EjkgzE9JTmp1kVRshlbjZ4t7KzfjeRz/k
+edIKs59qKwxTM4gnc4iKg44TnR7IDlrczhIOeZSvU/OLAr8TGKMA8E3NrKRThk9TjTqtSqXAyAPN
+QzbpIcbDHCJflkNohyEAuORTB8O01/+77hcIh/sUCLt3gMuFTkCMenHu29U2eIHf/+MuGuGSyg7A
+zfEFbAJu1/5U8iY40SPJsg2W301y+VEkTEGzNgR1AERG4odUtnVht09soFcBb4uTXjhf6JF0YdfM
+15RS8dNHsdaArwjg8bLc/Fxy1/oujdUOgodWuAHhBYLdLtzXBbvufFmoH6H2vOmbhneA2zIheA6r
+kDPJ0xbLX0CxxJnWg84eiwzeNb9qD3wgHa78AUg8IYvRH9/jex/2Sp76UW14CUN2JY7Cyyo/5Ntt
++wTt5xZ57Fhom6Ifx6332Ifae22mbIRZ0xFvmKQVMKx8qkEuwJM3pfPpzEUwrIC3wiZPazLdeev2
+3TmAk5JiJA74V9CqI+0Wl4qKZ3wz9zKXyOMVzQf4WaCGwO3AebJdqF/2WFvWAwn7lumJFu3Bv6d5
+i/fRv777+lh+SpN5bpJHGK+EFRTPD1HsoLcRXDMXOObNIZ/KI9vqUxT9V9A3tMIVJ2YO8pTgdDrE
+fLHuGAdpc9BB+u0c37XCJ+BLnr+wR4ZN8vgBHKekKM7eTq1C3Bzm3tNr71vWElrhAr1ep4neXqZe
+1zKJsPjkcr4sh7rZ+NvuFwj/oQtM1p+0Sdw8Nq1MIuQUCdTLcrkJp3ZVd0yEU2gepOeJPRrqo6Fx
+r3UEw51B6kiD8J0CziHGo0ih6mRwZ8Bi6KB3e2mO6ai4smXkqyneuEKzGDSk4FUBbHEOKU+OoAbK
+QvNk+rP9pbiuJX43n6Df7wLACwTf64U5qy/LtRfkkmcvJtrXZIJgkeUjPNOV6eoac87U/CEWINZR
+Vn6s56mcCd3sml/g3SyiP95G88v+mHlc6wi/9z3Q+ydjcKn/B1fiXyO22Be3tR40zJu8CVVmmwYe
+9cZBLvtV7Apqr9I+Xpobqwr4/sLLEZsMnNGxDjVl7KeUt8qj+w8mdsFmYp4kpkpfHSDbdQl/6sIX
+spbuWCA4hL99BU9NIIxk0u4L+OLi+RuWcU0zrJMgGm/df3rlBFR4zvJOdScq7Hz4jdZ6vtrLJnFK
+7QpknjXFJ5viU186wIVof/nEh5y7a4Tr6QIBLG1pT0Qo8U37+xIXRXNc23TayMO+Vz1HjbBofylr
+NJ9qwesc4cHDopkWR1mKA01pH48aYQJQBb44gN+1rgWaY264zsEe6kt12hevvIx3SNhT/nObXaoN
+vrGUQ9yKN+C4ceGK+9N7vUyOAMVPbw//D90vEA63acse83UMRxD8k0lE2gzLzx1LA4w4NSIbievg
+gd9BrMKi84dRxA7+uhtEA8TVZP0tTTCZEv16xTMANgS4ZT1rotrLb7GjNC4/qMH8uvNAdq/s2ftY
+U0grAFaBfMG+QvP0iUZYP66xxcnJEhJHbfB9rVMj7qYJZv+ys52Bcz1qmDnipuV1rmdnSMwHrrfE
+pxiUhTd4v7c5xCW7bHGdbpSWmlmETXCsZhEDKBzISNMenff26QI7ZgUtT5uGUVIyuVlh3wmCRzDr
+bXELzGqd+hJi8vxtfAJe8yzkEjpNkI6O8EmK6ypRuxNqaZM2aq0KIJQtJ/f4yJPlpWnLAiAgYrie
+0lztRpTmEKkJVj+BMYW7gIU1xBK8BL7+JbzlK0ypeJShX7A439txLf5k17q5uwztBbk12AacXMbm
+2Ciqa+9kYuZTFqXBE1k/k/qJ01d/GsjLuvoaf6L9LU6r8kDa3fhVSSIL4NbsZ7nnqV3fQKzMcwOq
+f5ivDd+26Ur6MY53lc3xixA5gtyog7TcXprTOFQZur5fR5zNOOvpFIeGAXjPWt5T3J0+tHgAAoKR
+VlM8vWq1udNF62/I7GUSwbpEGwzREt/5kGd9WAMBgM0WMMZSHP1t9wuEw73J2ZaPxOdBXL5MIhyH
+UyMk7slGWP6Um3dEHUk8ugYI5Xe3sG4GESoHf9vGYVBvBzB86sfmmuq8ru2luFT31JbTNMs0PkTx
+5MuFKW34TbS/BnxZ1wQfNMIb4B1gdwfMlnm/LmqCV56bdsR2AMM6WRszpL+DWczwAfSu3JALjAAA
+IABJREFU6B0kA4mHGrjNOab3EGfB3FqydN/Nyi/snNqOXfxW/BwzI973pG/kl33dyvsGivdGH+K0
+Qgp0xjVCt1m4pVneONaa5ENJ36Ds6rPF48oAw4DV1sNhDfKvxlDqE9RXKddVsF4XMPGH5BHmUuD5
+YC6heazMlNoNqgBfvTnF9ZVhfSISQ0kWsswhFj9O4PtFNuFLBdU+cmMAos5rPaxdNxnFSDqQ6aCr
+aWuVZA7k0+OVm/oxf61L+fGBP/fslkfBC0Z4341PaZz7ztdrgWkGYVkmFz7nsoFIMm3gHeTqmGb+
+rdz04xjPuWqmLcJ7SQPzpbiPNcJRJhUvqLoXPRzihr+45+hf8v393wK6JzAcYndLU3kv4NYBnjqB
+zOPirz7yBo+0USYRZZZfD2NC63tdoiVGflp5+QMY/wLhf899qBBOAuCVdjm0D3Z31FnC/qgV/j/J
+XXxvYLqXRwWBqTa5bOPn2083Q9cwZb/ewK+ijtNV+rH2i2h+gTg+rYcJPDIt2Wptv8QkUXmOMf5Y
+MCgbgpc2vs0sghphtQM+guBuB3wCyWkaEdrjKz4KcJmdAXEKg77pleHFtNV0uqyfMKET6GU8c9WH
+JUTM2srTQFXYob3HeYKjzJPaao7orPktYakit7tHaj8kDHLDGCIs5yQqcC3nvXBjAkLn9DD8H/Hp
+3tMCwSeQocXWLOeX8AJwxhNM9jRbAMcFqzFKirausYUZDoA2wi2+d7F7ZFPqC6IdEMvNaQPBX2nD
+v+K+chR1n+yp/fXgFQS//hUDukQbLB+5Wfm91FJmcLsA2n3bBMH1+F/BSeOZk4x+im/8dt8DO5kf
+0V3VY+c8n4LcHs/yM+2B8BvYXHnyfF0IeAQqft5gbMD3JU3HuoHiSuKaleQY8yRFaJPMPCeNL3Q8
+B7CsWuTmx6CBtmd/AsXjyU9sgk0r3H5lJnHfOyh2CDCOO0kqUThfKVuM8meA4imHPJ5S2dpqTL/c
+UwNccWEXzLP1ed5+zO2vjfD/T1xTaMbvdHRa2QqPF+bcUytcj7rF/QR6x+9bE1/ynX5JzCJo2osK
+yQC9eUutaB1Yjb5ywgw2XpSjhFjX+RJdi+ekQ8KgRjjlbV3JjxsAPvy+BAg/gNoJhjc74Ydyl2qD
+04Y4tMYhGLTPGBs/GZOXeFItcALcyZDEX/GiTWsa3U5nNIeAV7oBElficJ4xrBCvwPFuLlEUFTls
+lHkm/Vens9dAne4vpdOH+KOfdG7Y7XKb85bhmC/2S26jMeDW98jFv8vE5KBBT7Cl0HbWVqvQc1rr
+hwLaYgN2js8nGxGRmCQzr2zyouoEwNNWH9cCwfbPV4FiNY1Q3ovOhwFP+2C/vGmJS03lCSRW2GDO
+r0EubVWqmQdTcZjwxprnRjI6+36I29as1gBbvncw4I3Qjpx3gKkd9Kr/JzOJ3haZ7P7rH5noYHjV
+Ed8+/QkM57Xo6TFN5mAcwHFM84jqc8j8sc4PwLbOBn4AvkOT3NdA16Wvs29pcx2RphFwX8dzPv4D
+bljY5ZZybi9T+wiQfWUj3OhC5I3sQ4NzG5XSxG19AMiRH7BJ04jQCNuNpYEetPO33S8QDud+/5xp
+ZUxmugBL999CePQXMd4Rf2eaQT+BnI0cWIwX6JV8myspG1fLcPL3p2uwq2ZKkfJdhHDkb2opNu+e
+b5vWo6Gub9h2vXS9/J0lNJZi0j8bV1DjShkmWikxYWBcgVjr5g9p8sB4AtnQDmd5a21MrW/2ieNj
+RwU87KNX10EwJazaCxNFEvhC4j29ns0myHV2pbSTrCnJRuquONU5TsjlOU6FXx00l0Tq4ldH/TIn
+B7LfBM0RgWAgF28FLKO80oW+batYXa1nO1JIulC7QZo4DmiE2w3M7GvrQdv6P9arYevxrb8p2Egn
+pUXiQ9WKU/HqyS/rd5e0fEq779gnNxnNowZM13C0vDvBVARieRTdplW0YxyBwmrDYj9Vgwpa2ixr
+uvX+NbLKNO2s9AmSnm3seTdtsEvfxo58AsOA0FjUqQC3tA7lnx+daGlSftUp/baHq+bbyqHxmVxL
+AE3L+1Ma+nqwXdtiDs1vefR2lfXr/FabPa33SW/iWz+JJcjvx+9+imv7Q+h4tDP3z1tY6Yo44YqA
+MbNZmpJdvp6gw5c5xB1XNwu/bzztb7lfIEz34Qo4brjfcT0D4nv4z787gTABLgGfxs3WtbvreB+s
+O6rwP7xOHvXvwCIBY5wMYR7nzTsKUKKI3aR6bvki+qrT2UlyDdVutn49XKOSEzOy+TNebQO/PSzm
+CWkqoRpgBb2V/xX4Yl4FjJvMoU7cZPQybr1T35wwM0YQJHOO16WDZNUVqimEWdXB7jyC5OxiLcqu
+WZSReE+TJc2/K7zT5JyTHvOWv4rVjYPUs6GPU3rP28CvjT3UEIy4pPn1Z5sb/St58JRndto13Kjh
+0J9nvnbKOXVXjCu6qBuyBL7hb9rWVBjcsNuA64Z7PBm67wWUrnvN7231wtG1vjLF/ZLaqfvG+ixr
+8NZb2nbhxQLE215qWFHBJRJMJT8VgMwXeQo8CJc2IG0pdcl1fr23/0iCdAliJWLwijLVGAMzSY+4
+5CqilFjLp7vRJOUBGAftK8DdPyBxAL9PaTHvep0mKG+guJ/yIHMpc+AzTcJ7/opTbrbLnyV4dzDM
+2bMW39e4gN4u9WSu/ZTOLUU6R+6/flqEHutacbUrCizrPJX06fl1brTvXrnBYwgt75gNl4WCz335
+Y0ovWPhLg3wCxH/b/QJhuk9vRaYGg8A4fwfNxSBYgmG4jxfZCgg/ivsgtu0RUGUQMuqb8lvetCGo
+/QaOcRdMNMYmsj2AXaQ1KV9daIziT8na5jX6BvPUWnKSjiCYZU5a2qbdpYZYtMM2AbFqjzsAfgTF
+CoiF8TfN8MHtoGcA3QRCuw7VR5ZTHDW9msGh9yoiggP5nkByawMqbkV4pABSW9QuXE5Q74XyDwP8
+Ib/OneefgVCiVQ269nZoeDSTSWqUaduB6T5HegC+3nMcGtx9KqFU0h3qOdWifbGR13LtuxbYKPiG
+VlZB6UKR1O46YHfcXd/BuwzmN3CH3+4489dSCew393P06b7Xz+8FpIPXYvBYCmbXdWqjJsNgcICu
+CTIbEiIIA6jh1C8VHrbgUfs4V6KtiOmO2QHvEQQnkJxxnIcDuGW/fMRj5temPgC4h7R83N3S9v73
+sQgAHuvhh3IONBCMFi5W4cf8eq3d0JY+99wEwSWQtDeNxx1ow6OuCkfrj3m51RXgKraYaQGIMTXH
+1uIw0jDy93T2w7c6aBVeU0BZsyZ9fSl94Z3lNwHBng+JwOtfdr9AOJwfDBTOGXdgm9oPUDtBjXEB
+Y9UGd+3FDoTVJWl5ZXJ4HrumyVqKwDHDktNg+A4Qm6DRgW/KCALjrW/IxxynGSxttGby9teboN6v
+i39LWNJLfvWbhQTBzLMB1hGX2l85DYJaYpOwncPTPIIA+Jpg2HQeRbg9TeE+o/VXNHFMTZDsIyz5
+9EU6Lk0CHVkuBckFfM4geT50UGaYq+OTAmrdFJTs83DigvaSww95vGfaEIe/+rPfJGOtS+/ElJZt
+j1OR6tvYB9DIWMnhPWVzqf1kuot3jOvNHYgxacQHGCatRfu1rxcPvN3jq1IOv+NzqdQMx4s8CYIv
+g90CjhMor/2KwNIAlkb49tAMUyssfgXBMpNtDynTAA4gGA1M7VphDO3w+uNR/I3kioMd0oS3dwlg
+rW9IAClxEwTrWLAYO9OOoPcRMKPlJfBXW9kCv3gFv4i883zdHdjqWLHlqzHJRNvsK7oZRNY14kZY
+40b1K9Wtda8/5+ora9XZMZe1p7s8lD74S37y8gl8If7g5QlUvVjB0w8tXPRwTl8JM70UY57rePm6
+/81BmcVpEr7ivTTBCZJd1/3vuV8gTPfhbYhjPaJb2uCD9nfT/N5hBnEfATHwMwBWBu6os4f1rmx9
+kY7MWxkeSxWzUVne/AC+5Y53AV9LoKymEgTFl/XqDxN28m6ieRafzEjkhGiBB0hPAIoApmj2v02r
+qy+wXbs2eAO7m2b4aqA3QgWMk0EKII6+UTDYNtJiO5Mpag7vWXeBP0Gz0DYB7wI2EWd/CpKFAR56
+P1+GU8Cr49HXdvba5qg/z1P3CjIrfkiPxIQFLpEqAUz2JwViFqyRBa9f+xACfK3PWfXWR/hpzC9p
+Ke1O5ZQ4GoVUlo7Oc/3Vr2AYKG0w+Z9qhAl60wzC71XebYHheATmJiA4TCH0GN/UL3F6QyPcrqmB
+livFtczLNpOGArnRFv0xwp3ZAAWcI77ZCI/Zn9es95SnkXS9DfEzMGR9Cg7JvGPFvFYzzSM8V1bK
+TzAcVwP4QZemAT4A401L3PKgAeRTn/1xvOiAOcZXc2iydmN+XwDvMb+4aqHfsHdDsA6K6113bzJ7
+bkVlUa0vfvaTD5cmGBsQ7nmq7oxrY7VKG3Mx+1bpvqdTPiA+q2wyuWH+cHOtOSCzOFatNME3DJd5
+m4+/6X6BMN2n+vgkPjLf0gBr2v6iXNgF424vywFrIwmLk7ZmxAPTdaStMGspFllEmNArTCQMqgVe
+JhGhiIE7QsPZweY1+nUzbvTZuCky2ofvSWTIVpXNU+ewMlxgWEFmA8FNq2tlF0ytrtgIZ752zNk1
+APThTGCcXphbHeONg67IZKonV/MTvpM2eM4ZGUiPagST4Iw82iKODLcBPsYNkGy90mnuoKkqFJRu
+BUoeR64595Af07aa/CHw6K9ak18noTtS0uZkMCyTFxWtUzU6wtSt3Ec5bwY+z7MB4CAZ74N/rFVB
+8OqfP/oJLI+AWIFw8D6LF95oRoHQAmfc3bW/S3uMikMBYwBlI5zgWq/Vv7wOfpKhlNPWSShBmIJg
+TqlohFGaYo6l6p8W1vN6BsLpf9PuPoLgKld9rjBlQvsoQgPJuZoChnU8sjGQDHbT/D6aQRzzaJ/Z
+dm+XFw3vQLnPg7f4ESdpJ4mjZXrPJg+qHAqUkTyVPZb1ya1ZfHoC3uyLv5ehHFhh0QD7OV7YwouW
+9xyHVsYP6Z5t2/Y1S3Aj18kR3EOh/S3Q7GUugY+fzf9X3S8QDvfp9HfNh4JiakKGnTD2l+fc62U5
+oECmOtLVadPy+sU8rfBieKVvjEyLzy3wi0WD31gCm2GD5VnHVxCpaoP5qKOBYiAfaQj/amI0uzfk
+8JOb8skO/gorWCdoFRCMw8tuPM6paYgHAI58m1kEOujdzCHyX4zchnZ4DPII54LTzOlKBhR/lNmx
+QDLFEVYNJcFsB8QoW1bjGk6QjGS2Kizqb4f5/jDOsx74NBPecvhL2p7kMldofsQ4WNof/dKCxV7y
+DKwK82UpzlXMcjD21K8JaDqN4ux7ct79nTDa+J4qfYDWmXGtsbcYBcQKOgt4zhMh7lX4XoBsnUct
+WmE32O1wWyDYeViEITTGwttctcFeV2mrXtx7GKNuvpM/eGkDu4e8CowV/G42qmPqi7Ie0hIId7AL
+aasuE1AOEGwWc1H0CQzziPxwgstYXK6sz7PunzTAvJvpnyg+mUoMKm7AWMdTc6/8ZJtLk20wrpre
+4h7KSjOZqRQAk8OZmAse0rk9hZ/vgHZPIw/f0rz4mvL2aucUN8fe1/dVK5ztzjp6mZLxLjQZhfJu
+lgxVjlXjfuJcWKGHv+l+gTDdxxphviAnAmADxfXy3DxO7ck0Qt0EwY/djTw8Qz5BsBsw7tAMhm9+
+HENSqFn9jnYXKK4eEdBdqGPVllbYcFsB4F18snwXTF20ThFRYWM4EIlxYrwAsfLV/IGAkyAYO+hN
+s4c6Km2z/T0B4zctMP+1OILhmFNZaOPkz3VtM4QCT3L1lkEZZ8WrZqGvTtWh/Ilz7TnNnPOKA1xA
+8hjL6H/Gsz+DynsZHdBMrbQzYGa5KUjRxr77Z2Z0HtAm1EYznFxLmqR/zWWYRhjntguLp33tM2Sn
++NHfpzE272HcLaX27WldToC4dnP4RUGgigILBYExK7XCt8PsXvQUgNcMiaWpOcybtjCH4Ity2kYH
+4xKv49CBDT8FsG/pBcYKHHdgzDNQdXqVWvcXsp6AsMl6vwBhiX+2tdV03hjT1GFosQ1DW4yeP/Od
+QC/bXr8/Asg6fmN75djmeauqrTTXZ8znG/DVONvTKnQAvTNczFFSI+yW7eRcDn5dpg21fSvsI4zk
+3RtQbvVoud5uxre5mGD4ARzX9hrlB+ZgQyJgDNxj7HPsJ/abe+mR2f1v3S8Qpvt4Bch8hzlE0wwX
++J1geF6D9wPYRXy1OK5ejJsg+Is5hCAp2ggcgR343vTDwkzCsj8O4KLNcNA1tcKX5MlrMIW0KWN3
+TtP7MN3bTYH2nf232mMN+IYApXmCaob3I9DknOCmBT4D4Hmk2uNP+oPoR2mEkQKlhNxpXvqKzxsI
+stV5I5HhA2j2bKrALIJpdkCMZOwzzntS77Ks0Sm+wwM0QTZLzhdReg2+xdsW79l4m06NcGx5GTTJ
+lgqKZPJxTaLewXAfeN02HEneHuIPXRuD6JlcBpxA51TBy8YzzmKXdqr5LXOIiuuANLTATTMcee7Q
+CocZhLuA4LQdljiQTryD4Lve0/D0h/CntJbdUWFrpJPgtwHKaQIRE0OwFnGpFVY6mwAPO+B7As1M
+L/+fgeACh1Iu6HMzj2ja4sUPykxiB0Gt7R9Ab77oaJb5T9ph5NqyndO8cM4Oc7nN6172Ne6lzC6H
+p4lEhIUvNICcL9YpaOU2OMRBAa1svS1v5+ceFVTcSJ8guI3zA03wY9hlW8vqzJflPajJLI9SK1ry
+SOfN5KJP0uDfdr9AONynH9TYNcEvR6eB/jtNIaZW+G3Ju8iyInIroncEGHYEk+LG5Ob1gGWhFc6w
+2AeDYDi+igeszTywGrXCeXWkVpjgOHHDuJ5ZzsN1IucY1isoDsBZcRP49hflCIK7TfAb2D2YSBTk
+LRAcdxp7HkJk/LjPk9nMeGGWzKiMsjFDZqL2V2IfX5KLQGoxEzdUXIGlt76XO+L8Q0YFv77nxiZs
+pPZTfgqJilD/3qF87M9IjjcmbAk+Rz4tcUeiORJoSFh+lnxqhWUo0kc8upekkUmk6lZ47q+fW9BZ
+LJ3WnlZSe9cIUwtMcwfNvs4ORm1ogmC+NIcAygCMxBovx8UhqXlV2+S8Acghq+iGgDkUCNOFEdA1
+8xIYV1wB1g4WOsjbAJ6NcLSn/WO9aPWPPqHSq95RxoYtsOEZDG/9cbki29xMI+xg9tDS8FBG25N5
+3+J0Lp+ue7nNbzOuxjfHTA5XtdroXYHgkxZ4niyzeLQ8Pwk+ndtH05OMBWzOMmyR+aL5HKtn7Lg/
+Hmvt3VTmGRi3Zz9tDtle3pcFP7TAKDeUAVryWWqAE2uwjeSpf9f9AuF0H06+16O5fgyaguKpFaa/
+TpDIs4QP3dDHOisqbGiCKV8oTTA3ybUhE93QtZmZMrXFlEfUDlO26zUJ3uvA7Etmbm4cgyVe0HRl
+VCdn7bezOTvku4IHT9DbrgmA+2eT25nCdop70QAzDyZILoAOyofDOJ+cMjQyPKaIaBfhLxwPylAl
+jm3OL8mxbwTNxeezva413kH6p8D4XGaAX7lbUPC7m1eIwHlr2GXGfgDHZNQlMoR4DbUwiXALDJed
+cOVtxTADZ3dMPk2wSsBBD1rLII29hQEG57DT54ffOgC4HZt2+lEbvE7T9zVXd1zhy04YpMUrwU92
+LWyCqRnmxzWO7UVfn+bR6RE6U5tfgr6pEe5xALWur+D3BJQbKNOyCmp7X/4TEJxAV22Bn8DwyJNk
+xd1uwc8noLUCwY9AeQDm/wTY1nye45/87+kuceU/bbUJilcvFQSbmEl0cNzAbvqBpuF9SCsw7LIm
+c0/7CO97fgvrfDi2+fkpPNOuNj/yvoSYS8QtbgoeKgscoXyzMX1/0f0CYboP70Im6EXT/AbgjX/t
+yDTJU8eqFQB9bE/u1ObvcuArwLEPwEsNsLBIXOb4PoBfBcDan0uud9RK4KsgODfzwAbUONZG4258
+YoHPjsruBMcqG8JfWln+OmBNkwiCYrMOhmFbmZ8AMMx6WxsgllWwHlOD+2kWuP6ek53MUUoeT5eQ
+/FPjS6EyX5LLBWU61xdlRrH38GXtHkc1fUG7XnEKfifw1fJq137SlGyd/AEQp5929WnnE1cIoW+a
+4dAGJ5yOvck7jNe5EGc/ZZD409hO+8yfq9orjJ+C3hkX8Y54wiW/tBG++8tw7mgmEet6LTEZGmFi
+v6S/m8A3zCDUPwDw6WZgm0bZc6UdFpAqWLTbBoeO3Oq66hHAO7W+LWytzVcgLHV30Is9DILRkfYT
+0DUxoUBdp6a4gC4Au8hw8/d4QoQA5fxlWzv5+kPcn6b95+ldhnZQG3xbtTvc58ErWtm0EVbt7tT0
+upCvgl/f87DFA7vaeB2e8pOmir+WsqVjDZ2XPewSrvLdVKR4pVkco4Z6WrZeFyh6S/n1L7hfIEz3
+oWkEIFpgj+V8+KIcbYQ7AO4vy90DJhyJzsmMZ9zaaBf4WcIgNBDUit/WaRCGMoOYvzuItWaikFAe
+ji3hPAPQOiiunihb+Wxu80oEDQFA1LhZB8NktAWGCXxRWmI9G1hAsB6LhqkFxpN2eNcSQ9ol8K2+
+1ByrgH2bASRL6glKH2R87R5OmGfW42TMsR4UwoxXPg40kwn2NfVese5b91842CfMTaGC2nI+mUyM
+W4kU5i3W88/Bv3cuPyIS9eUcJO7l4MtUgkDCBHSkGUrJhbz5+HEuPgG/gIBv7NdtDs51aVLBngY/
+9w1MgiMofgKiaSsMwK+Mz5fkfIBhAGbXAs0oQJpz1jTCrF+v0oc5ukmsQ+MKoTeX9AK37E83h1DN
+pcazLMfQtcBlBtHBM3KiU8NruhckLHE+0zIcAFeBrtykFUjWcQspZV8dmPVuGl/s4Bcd/J60w9zt
+2uaUfdji/BD3Q5k/qP/Jndh1AuVkngMAGkqzG31PkAuU3/EezznyR9a1yYQebw9xNZc5T29bOtB4
+AXQkDfH2hnd4dZza4ohL6bf4472YKQyeykDuk/YU7i+6XyAc7uN7EX1jGQp8eZ7wDoZv7KdH0E92
+T5+eyXuJ6PTtt2uKCwAHUKTflpBXDfENAt8Cv7fH55WpuRr0qGD3foif/am+v+kAyilOtEOYPDl/
+Nvz5O2mDrX06OTXCeAG5EKB7+sm/BoZTc8NOy2Qaxuh0FkhXEfLDvLmEI6MC35ak0ZRnwVw7IEYB
+XpbP/J79Js8/75bP9tCJzXlLqUdrOi/W5qs63WMPAn6TAt78C/i+DIMaoOLtaIFUc2o0oaUA49Nk
+0OlmaRGHPOzTJ9cPxb1jv7mo/LF7EwBbSUfz4ScAvtbxZkat8ALAi3ksjrHMI+41ZbaKrqlbjKlB
+8gTAbEOv62cqoVNSy9z1LdfshBXkrlF3sFma21IvEGQ2ja8VLy+gGvWzvmxfgLSA8x3ckpccNL6j
+n9kv5n8BwwmGOA4UzapE9OzOu2mEaCYK/D6UKcri/Avp5vyp3Ci5l35nDow80u9Y52O6H/LmKkjE
+1PR2ykTnTWS0nrkIatsnjycA5hWHOO991X72sD2k+TFNgeeTJlj9bU28KITsUSgYNzXBWMC3aYRR
+9LZUiQaz3v7fdr9AmO6kTTlmC9CLp5fjztphPT1iaYLVRvgshHJTWD+lYX2nuwhnvbhmcZbZcgWC
+PUwKCgRbaLT0BTQFlLfucTZq/XpZ9e2n60l+H2Y2t2pu2YZQvPoa/SVzbuAXPNsXHQCHX0+MuKaN
+MBT0nk0mGiCW+JaWfUyIvIVjkT52U64rI1Iw7MwtQiLv2pn9h5fkKJi2I9My/WERP+Riz9m49hTk
+ZO+kiBJT7D8Ft4Lh8j74peyTv5GeoT4Q00BxEHgTnx38YqQeXVYxN905akPNGwie+U/u50wEUQCx
+wOI4rnMwwXD0g2YRqhn2+0KeJRyMy+2C+V0gGFj7CJ3t4C6TCGua4fXTr92p2D5CA0Mzh9BpXSUP
+4BLomloCXgHOzdzBCDSQ/lWH+n8Cwh34giDcMNI6eEaATxCk61XBzwkU8zrAMfu7mTko6D1ecdQW
+H0EXZUbEci1aPvK6NpcDxPmo98l/AODlSgg23iICzdhYrt1K149LFMDsIPeO6+dAmMqRorNy+622
+mpQxZsb1nXL2fxb2/NYAkhuvOeCNA83vEhjDcBs5pYMnRii9/U33C4TpPjWNILNNJn86MaKfJFHm
+EPVVufsuIOwI0wan9oFxneAIePm25YWFfS8LoroDFF5C8rJrChgrOAsbYLfUEn9nWTxKb5LrT79G
+1DnejyT1aseQgH216+IXAJ/g9wSA1TxivTCXoDg0wlBNsJhEYIsrELxpifmvAWUUapfZK1Av8QOs
+pTZEbgY6AJa5lilNYeKVh0Am/RCeTqBMQe7BWhsg5hD8uGyfsa63XMU+k4nmMKdQKi1ciZsKc+zZ
+mnvzZ4tzKF5wgH72LctaMHUDymYYS8BnfGw7CkUKsNP4Wc9pfo4AGbKAUqZtqbG/3m7yFXG2UnV7
+4Ql2F1JZYNZrApMnBldKFdYN3FdqhZGAl/bAWGBYRadhfYAjuxa0IEC4QLbyYeXLcquQApjjFe5h
+HGsBV27MqcElCN60w45WT0+f5bXeU9zoBwFt9Nuz/w9a41HmEQw/gmLS7UZBxcMML6D3k6uM2yF9
+CFIyrlmBZdU81hf+pIx30JkAV8bwBI65TskzZB9Y5ur770krPEEwvPj16sMLAJbrG0iendw/QFH9
+aZ+oHq7NTQuXdrjPlx/y1u+K8TbwC8dtcWU4engDeXxrhnH4Uu1fcr9AOFw9Wv7J7V+OW0xlPy2C
+ZhF7/N00wh0iRX+w6OpSYCyaYMNiAG7ADc+jy1ZhAVpO3r4+Y5ggOPjm5WhflrNosz3qiYtJX6g1
+PgFi3Zi5aVxju//AmrLPnBQFwyKv2lgKlBIMaxzBr5wXfHxRLuyFFdAawfKD9hjqhRq6AAAgAElE
+QVQm5QEFwakRpv/Am7aoYJ7kzmSiklisaoBkSLxLfqZTfjpwfElugZESPDM/Bc3R+eZ5dDNHiUPr
+YSt9sIogMt0SOxToyPFuDTW/N7+KkxNAFmRVG2E7KHvsGa1zyisO1Q8JOulaS1ZP4Og9z1j/zX3A
+4/ZZFUDM+AaGo16T9gl6/Vo0dseHM/yK+OVf9sAACIw5HdCbotAg+tIA2wTDh5MjDKUd1hXhGhQN
+E2T2uOaPzevSt+XXsgU8XeJIv5r/bDv8bA7RgXAyOnSwzLLDH7Sq1wS68wqOrbRy7Rr86wRq38Dv
+SXuc45e261cUyLBHAX0KqiZQZfpgjVeWPJI8PuKHX6gF6o7g17lkMy0Z6eq/o7S9WGBw1xSfQDKB
+sed9XmMLm4u2VaMxaTzz6VzrWs858WMY2v/eTM4c+TSfAdwP10X/dWP2MRT7L7pfIJzus9n31EAE
+OSsYPnxeWc0iup3wOjUC6OLzGlfdYn270b6Gpzoo+C1gXEB4fSKZoOzyOC3CqBGWl+VC0KZkCp57
+5wavftzEBcFUyWwZrmmd7Kbrx56w1ep7CdwcY/FUJOjMOAXDQ2sr2mB9UW6B3/ExjRAoR+CbZaYG
+uINgpit7sNcR18xMivQRSAYlc0wNxAp2ZlX80XN9HfjBZAJNQ3zs2Ij8cCe1jKXhLUEIIMD3SEum
+O/Jm4y7z0v1bB5t/JFQn2j7YwXD0JvzGffM2J0aNt2/xmPG6WJAxbX09je+0Gm9rVVLNsZ4eeZg+
+mICq0sBS8JOZLX5n8YJc+m8P0be41aKvC/XVuaVdtThHqXoRfl8M6xEARx8KBG+wpfwJSiWxxRGE
+VvupiQ3/avEZIJ+0vQnGpPxa1h+AcKtz5LFeT4+bYBios2QfTCLy2gER2zcz+FVPxPJ3cXP0uM1O
++CIsEhDrvS3PX/Qz9lnOdwLg6Oss/xSfa1VttnFyS9eIW4jOWlpchcGWUnj18+ZYBeTe4l+k/QyA
+3T0tjLYuFVEjmQ+RqUmGw1BOc17+Mx045662nHCjtbK8jaUS6MYzGHaEqYQrTTzLxv+V+wXCdH9i
+GpEgdz9POO2H0bXAaiahGuMCRmhXxwDFIZBy63md1nDDcLnXqTYHjfBldWqEaoEvrw9r8GSI00xQ
+KHF78FFG95/uKM9XmdDtau1a7ZcSpgNiEPyig2BqaBX4XtZtg/lhjQTBWPF48Bf4rfwNHMsVMOkz
+UC/QWV/sw2wwdNLyTm3wdhctTL4vRoi6BnARfNIPJhMRf8if/Xvu/HATvM1ktawV6GsMS1o+Cq20
+HAMF59YvzzHUfHa/SVSDsQ6oicNKZCauaaW7VJjnab5QPp2lEGuRvZ+MYw1toeeYas7nzVVrxSou
+aybwFcG24lBAc2qFMa5+A65gl/7QDIuZxHo1+I5+TY1wTPcEwDfKnz8OKfoLoXmv5cplSRoLAaz+
+zFcaSvoR4K4BBIJlG+UeQfEZyLJs9m34N9MJ9bPdoKWudY11zLTKk8uW4yFHF9odmt0/sgse1wZE
+jUsnYHf4CdCXgkVA01jTGkfcwDGPrlOs/TH+4PK2LMGuC5NkHjruot7nm5ghxlPa3wPodUmPm79b
+O5cb1Xp7DQTXxCi70D6fwO5pPnKeI7LyCk9x1mg5XwWKkf4jCI7id5Z6Won/nfsFwuE+vQcxrMeC
+CcSCnBKcWY8nSxFWCL5RakQb2QNPElp7TQS7UbNlcf6e46I/6rjd1qkPLtpe+j1As+2AWPObv9no
+CDilkI/rHS/lqTaY2mKO66TpVGb4RP+KGdf+F0DJeCP4FRAMBcRdc1saYYNBbYGnphflZz9Yd7RO
+8K19re4RFO9j2Wf32b/jnQE8cADNLnMegEUxS66N15QGqQmwxMb3WzsPbl/p53zFp5P6a1fIeHkj
+UeBs9BklFLPU26Qeeqz+RpLZUZmM2XCmszBBjUhcvatA0RAEYKYTYGyHuBhhNpmrLesuUgqSseIy
+vYQn59p5c5I37J5PBzzHsOyAccWzqfsGrgC89/wWpY6k7ZToiohND+DrcTBk0HM/n5h5KJ2X3+6Z
+F6VJdofunTZ/IuR1qrgCDeCa0t8zwN3CLA807bGGG9gVf0qSVickDklf2U4Mxm1qh/X6cx72K3nj
+w2oqT1TeN/Pp8W1r7EC+MCUgVXlf5etpM530P2XLlEGtHOlBAWbY/NIMo4Ng3T8oeSjFV5WhOBKg
+e3PcDQRr+kMaym384ACCG6uxraSM/Q8AcfhmPlWM9V81vtfVOvavul8gHO6fDy2012cJDfdFwl2m
+BOsacVfF8TOGa5NbXuGOb7HXvSAAq3iggLwet0wf4i4rBa+De3Z1VgjS46i0kFs3gO8ls/B9Y2mT
+48B7u1aZr9jzvEaV8kln3ZriEiQg3iZdQX68w+XaNgeFSW6QWT/vMnWuBPTOnykjFuhEZmfB0q3K
+9L9oDCXjtXsJHqTHJWcXcxUtWTLcsUbl/PHqLbzPH9t0zRvhjuG8+kdMp/4odwbHVf7kesppDfeY
+yQ41nH6zc/xDeMX19lu7HU9C4B1kFh77vDccC7lNmhQ+aHz5ssu+lR76PtYlaQpoQLoAnqw1XvLC
+10ct7IZdYb9rF/y6M341vBiFGV94izK+8nKzmxv84jji6kAhGZpOOIzfab/61W+DXfz+spO43/0v
+INgdCyTfN/y+cYvf7/hA0k3gIeBDrroWqhFWENxMJcYvp0HjUN0vcFsAjC9e5gttSR4jDvPqxYPw
+H14HvdZqBt1KvG35eGszgXO/Ycu5UFpG3cjnPPmetwG4uT6tPsbp/I71ZR1yXrh7aDhPT2vURZGs
+hEA/6G8DufcI61glf46NHt37o33ybwWeFcxE4ac+5q1rYz1ze40t8ulcJNuTHzGLApeprLoynz65
+jeNb/7L7BcLhvj6c/PtaxPuPL63AfTn+cfLgAr63kdcTFMcd3hUE5wa7PejEdvCrAE3ilN6a4yYh
+UVN2hP8OP88Lzi/KeQBqAcG2VM4Bgtcm+Io2/DBNbcNFwAJd2eIn+UTz9t6XEggn8JFos4Ov0yTk
+3QJ3YOU1TmDMYM23SXWdYTP+OL8Ev0aA6D1dBfM+Sy28s+pdqM1SW05XcdAvKlBQXa/xkeeznuKX
+DRzrFJz69JP7KQ9XuYfDv/XBzmVGvl6/QTWz59X4tL99JprGJW5Gs0MYaXQUTt5WrijyeFhz7JG2
+10/g1kW46ZhduqFa3UiP02Y8+JKC4AWKsc79TSahd8/sdzKQnIMFRi2Od7yQqlUyKeNJE7YD42UP
+0MGsXPMOfwPH57gFQm74NwFwgN7by08ASD/CjzLK6s/50EBwGncxDno9/AI4rZYCQJlegTzqLMDZ
+zjm6Vk/X/ngj/nBVOmpXKx5MvqGPvTNeZuWsDS4zpwKzRbPkWTmvH+XBPvbGA2WMI8+RC5Oxxz5j
+XTUaqXwKifa0iCSo2l0EnVV8XXW8BYr7Gvb64V3cPfEt7eb80mE+beYQZIxaX/PbQ0JrrT8X2K6m
+L7T36992v0A43B9phC/Lu7kExGb45wrNr2iJXeIaKL7WayMJdtOvj+Bx9kP8Xo+MIXtFGcbt9XAy
+X4xDaYi/L3nUnycgOb4i0ln3yS99Cu7YHhGxXtUGbzJKfizoc3dZ2Ry1DcXNg/EzO+TVPLopTwxb
+Z7mY/2RC23UIEWWaKmw06SVixA+B1rKcGahWoeasun7mfkybvK7Okn3vbe+5CI4f8/YZdxST17Qn
+f4nkvd7nPp1o7bnLJTC8Nq10xDXT4fFpy39oRAH7qzjIqpW+dvpomt+mxUmCrO7dcSN0rXNo1pff
+JigGzG74dSUoNsMCyyBoNjiWHTAu2VUEwNSu+SWgw8CPcOQh5bfVi1kPQLeAsWp/57XGCgJe1QTH
+XXoDwNEMpNk29Y09mFx2EHyb8rgXQNyXtvpA8BtXBcVNKyygzbnKHDfpQ66VLrTxAIp595VgVofM
+q48wOg2X7SgS8OU4SYcufsa7jEfnx0dY+59hbO3MuC2cINhqn5N/HvhCnTGOomWujc/TIMp/H+I2
+gDzH1+bzHN/TB+gdOWqugw+SPSWRu5SdiqrRkVn/JpSRQKae4i4cpNrg3+PT/kX3dU0iObtF2AS+
+AxAn8KUGtOJaOGyh0mrOOhBLfwO/9hAPQMGw7GoH2osJBKN2L812fmr5DvtgQyp5dEMQELNOdcn7
+vcLJJL3CCbwxQbE8opHfVrlsTFaaj2NgKJQrnd02qPV0WJ/HjLFi7Ap01SWDtCbLV1ItgIc068KF
+ySpksojMQGeEs30VIK7FRGxQW0IevcCa19gOw3NJowCwrPFn95/kOQLbBOS2pW3+tj7CtMcjzdlu
+o7WhaXvrb8V7zxT02NajSawXsUWik/2r3Xts36ffpamTX6hM5yY1wli2kQ30ojTDBqxPvw17KtDP
+/bdA7NoDVgD3olaYxBXhBMECiG/u1xjbAyDeruhhAmb4BMLT7wWMD4K/wvoWCMrON4a6XgRSHoft
+N83DVlfX5lun7wQ9biAYG+gtjkEyHuvOgA+amQA482odHiPmyC1GBYnxRsI7X5WdfADB6RfaLC5W
+W2cDydXVLIdRD1r5GT7na0Kt+ffgvqNJ189A936Ib36ZH5I1n7TSVAydY3Y+vvWr59xAMEirnrw3
+63od72hAxTGsXwmC5zXTf4Hwv+r++VAd75fhnyRYE3vg97j76mHA6sMV2AVdwt6J6R7yl3AL0CBA
+djFd71rh22C0FdaSNMmL31eCUWnmMFWn2Ut75tzcJ8avTE+ALhTABNJEYV1ltNumM/rV5ETMSqzy
+UlhLTL0EqGPjuHX8HpklrYFgZbKNe3jnLDP54JQpnlODYbb5zI5USTJT1Fom/hq0VrN/YrlPvTi5
+khxPefT1N0cHtn3Kbffb3rfzLHUJdtLwVHD02Q55pNEWh9Kwa2LOpyJ9zfRCBLPvGTqCWgEzw690
+2fxA1/BeoeEF1mOjPJJmaXnLZMKizA13g10rnNpgmkSE9rcfwSYa4dsSBPvtMILgK54DCZhd96Ak
+9IfrS7q741YQ7KIJFm1c5Z9LQ7BQhJf70wbolfA90voTMm950eLW5vQAVgmU0W2Ea/U7PZ40wAp8
+f0rTvZXPXGg729Liqmw86ulmFCb97Hyha4aROTQ9yypfA0Y5nYOf80wgvWmFf3RCCMJQkyf7/FAG
+ks66XOzj0/ytf9IkSbGn99uULKKq/FGn1t3jAtRrQV/+nFkK17xIoAFekdUJhsVuGP2dnL/lfoFw
+uD83jRC7XxtxOLwsF+iywp1A6TLmk7331Mf40zWtPGEC+IanTPvu6HAxVtpsrL28GHADxodek+4d
+QuyxbawYP80yfPzY73xzOeuN7ReMt87StNpEualksyk4NoW6w1yiWmrXNpkytoZHBBg7GX8OqARp
+hoFNU7Ex6ceF9+aj8Dzm86qXzDW1CdGlIwB25HxrfuZpNwTHnr31/IA6D/l4czKb0fDZL2YRo6nz
+LO39aSHl9w95XCNsF1Lan1bOJRMONPeBU/pZYZXu/p6ODnoy1pAa4Hx0pKYPBL3o4dIKB7glWA5e
+2cAwwwQYNH8wAt9FeBnHJ3VtL8UYvQPjHl77o5lJIK7NNjiA8ADF+sIcF7PxK3ls3m2Cyeus8b00
+k4DmOfuhoDi1fwGIhD/O90CK5+sTidrPSpQKjita0hN59R3QwG4U2Xno2pWEu+3WVRhJ4/uD++U8
+aPgpn9BzLz/idOxbXLksqyD4yL6EEzmW7BxguIHeGc5faXwT9PqB5kaTEK//lNaYTIHUfDcnMp21
+xLt7shE2ChYC4GztAHxR/jre9PdluX/VffqyHE0f1otyC/CmhljAsL4s5+3OkOGlbxkysbyHjXfC
+PeebVYKg+Uiu4r8lb3e2Dq4P8Pu11W2HkB6rpnd968fj2Trj3zfdxoxiDgimAQubaAIm7URtuTR/
+aGtqveu0VZKe9m17Gi06s6GfHU/7WRGgNZofFAs+QlMEnPNuL1eIX1UNKfgeATDrwzZVg79uw/5s
+RNhK+UgB8Phi3OxH+m3vn7a19cFO6yD2caImntk2+lR//FEBoXv7ba4OO/AQOm3+aBPog/KXOI13
+39bAAdi95iKBsdk6SQKA4YbfBtUQ1xmxdbqEweJFLxPw60gQTHMIM4kjuLQCyLcAYUUG6ZfJVe0v
+yxw0xDeBb7suEEwksoGX7IMRZafZWd3oBL9V7I7gwzZPzOnAeL3vdwLA3OMBrLlmqRVmXVNzyiF7
+j8jpUaJ8uDFSMGyhARZN8AI3Hjza0OQAJg+dz3w6J5jczj+JH/sq03zEnfL5aL+VqTXOAXjzVN6m
+YUDJALeM2jS/Pn6QdI3LCjhmznExvd6bLrve0nKcEHpqtHLIs9XZqha/tEU5S/nNqFRiIbTAHSj/
+bfcLhMP9iUZ4AeA4LSLBL1+Cizynl+XEPAJu+L6E2C3I7bi5Sz7QL8VaPnLmYsKrXh5WbZna4V82
+PFFsUi74QfFWloCaVwNtjwVW2jgpwjH6V3yGd6b6gQJ9ecibxle0vXbucrM/yvzNEvjAvJdwVq3H
+EzJ09wJkoaJpgqYNUARLXF3rnEwW050o41B/RHLtG51I4EcArHHCeN9A8FN8pZ9LO4Y8we4n5XKN
+dtD80BfR7GwtH4GxUnkvdxK8Ld5H3pi847ycOrNFu6Ts5ZtmLy/jRkz8HQR1wZe49OIHLmS9m3mE
+AOTUCgPUDK+X5RTcBhhW0Ht5+W9JH37ud2IM7rfS8qJpgY/AWPaiw/vpEOOkCIYbIsGJX3ET7eud
+fA5hJ7yB4Odr+ykAhgCpoOfMZ2eeOtf4BIo7vSht1BxyjsmLLTYNaZKmEDF8iVPwi3ziVFD6oPzI
+63vaHnfs+p53sM2xBTIl8+XYOt965HO5l3S/CTlxnZ7CkLVv4eqP8nBu0CmeqvM7/yyw+nCjoaBY
+Ku2geGiMU9hHSxLen9iWzK6z/LtW+G+7XyAc7k9eljtpf28FxPpyHMQ2GB00x5dLk7jzERvbAlLQ
+1aMv7Uxd8nOtZlJ+tcvPLysgBpDa1UKORdiN2bPGOFbNrrIrXm7XAn+bJ8HDOwA+M//5KGZo6dJE
+gn2V3Z8aYPErd5Ds9VMwPBnGgYV4SyjOpOiNlyZAha3HYJnnyFZVYOXc7Nre/isNSUd1QgnKMElz
+4n8EwKMmLY9D+pPr6bNmE5BtGrv5M2ya1suc+tKFQ43mBFF9ZHXbsuiqbuVa2lHQPnXwOddp32fA
+9RptH+JmPm/pMjO3At14OW4Dv4aTVriBX5o2RDjzqHlE/NaNvoBhk/Jtcr37mcYbHQ9gDHRzCA8Y
+4OThXnx5gmH+uNcc2JbGpAtG/lV7MrtlFVd8zxoPfNQKo3j4qms1estNHU0ldp7A/jXG1OfykRY6
+XeRdhivLJRiuBPLInY9WvpJEUv3R/0Oe7civke7ncJU51K9lbPPkgLYb57wLs9YnXo5g16vf74BY
+15McrhpR3jcd8W7jZ1JD0ekLwOX1jLRbT7aGmzck7gTEYhpBbfAvEP4X3T8fTn57ES7A7y2mD7cb
+blicIuHYbIrFXIIM2oNru6Pd3QOR18ggsXiObMa1gZnbJH5tGn2cRkB8xw7UL3VVWzw2LTibYRyM
+73mEaMFID2BsBTgdeSoFN2PTButmz5pl03m/WswD4B3Uml4P9sECeXeTCdFXyC1sxvmKb6RBjilI
+LGY6+iusPAa5CZnmHJrsM+3VdVZezFMfs1FbFu0IF9QnUF5IdBuvzzg2bX/Ww2Oa9Tn35rdjvHT1
+WObTWezxIQxmcdvzU0zMNZsC5JT3/2nCTukuvfAe12zIVTV1jLPEphCcmqYOdkkcGjAuQHu1dDWF
+WC/RSQW5UQf4vUbaLU8BXPs8xzDn4DmtgeCjf+wf3VsAGuAZeyDzHQAwNcQT/G6KAQJb6+BaAW++
+ePcAgpMSW+d07WUSVWa85Dd+sYyZJvidYHlcp//ACfvfw97buz72+sMQ93K9wsdtpSknEKz5t6/i
+2C7vHFsctjg752N3rPZDw+l2mt+KIafVue1nUj8oo2QWWLbPXwfBue6xh7mdkTJaNMSRjy/K8Vzh
+v+1+gXC4f/5EI5wA2HFfiA9qdNMHNwkjCK6ZSyCOJOLmMAHBg7mxPmF60qPoVwQD+JLgHUvDEGZ/
+CwRv94Sr4cW8d3td3GELdjG3xfET/dB0M/laXoTvSFfg+3R0WoIRDRNckumetL/cOLGJcqNBQfH8
+dcOIyS4y5FtUCWUJrDfbc2ADhJQnGd+WdohooGwTBX/kTy3vGA/7Y4OJu6Bf08w2uvSwbR7kRU8b
+/TgD287AzargUxnW7Q3Vln8Du1ufJa/QZObxEZYcPX7mG40+TdJHkxeBbEAlvG9xBXJGPok7zxbx
+bNf6qklEgV95aS5/62hHbxpgjDwr7BY2wTOPDHGxA5pHFMcoM4k5L3u4NL5AB8EyT1M1l07AgdVm
+UOB7+u3HRsaLw3Y4Yx2iHWZc8maWfQDApt1entq/Z560hRvr0EDIgZAXFgxFWUcDvw0Yi/Z4MI0N
+zA7xdNoO8ynNBKiutE7fHP5P4RxIRR77snnCK8enrT7x5gXtuupV0OwtD5muo7YD+fns5uzfDow1
+dq3fy1aR6wMvbdXtHUqgC7UPDvkrAHieHPG33S8QDvf5y3K7ZrcAsOVb0vlSA9Uj1A5meJ2XR4aY
+J1BAGZ6LliDyDiHORywrEH+atqDXlX2x8i+Mbql1dtUGm8UXoaLxKyFJMLkllL6twt+8C4zxsGtT
+C3xm5NK/3Hc97qj9tYK2OTYFyuIvoBwlRBu8Sk4uK/5JJsKRTmYQqnWrtXIpCy11cL75NT/X9id/
+WeVZySIX3uUogGiVf6VZA8BtCgZDPo/hkHbYbk8a4PTb1tyxzM99GLlsF6RvFeyCYge8Gq4nLk8d
+OiS8SlyGXW7KOk11qaadlp61OE+zB4j2N1hVAs8JfHdwrPsNR8B7DiPBcD6F0bTZ/7YtehyfgDjQ
+ALIC5g42Ogieccq70gW/czsD0qPNr6Tlz5/BcKtH+vJU920934leelgnTYamtDHKN6D74i89fr3c
+pSD6jcU+xiufedoyL0N8LMu1P7Xrh7gPw3B0mRfyuGiM1zVPJ60wshzQuXJxQj3DXpNq23SAalmX
+Z3bmrhc9DdnRvN18GGdrQvcvlCCGOYSCX8jHNOxXI/xvuo9flgOgtsAbGAaANJcIoDnNI6IOw7qz
+X+f7Lo3w5XXY9jJlcHl0Zitd+pMggNw+EUsHwXwsVxtINNfiv+H5Hssy1qEphCWDTHsvB74JREJD
+8I0CwGYChIUhQPypFW5ghCgNtesItPVZnGiCmeVnTbC0EZtzn80Xf7sVH/64mVAu6lGmMzDkYP0Q
+JyVzDlTgEZRY4+iN4yYThmi/2vzlcKzmD8Mlg12tPrKnyYj35O4OwHmfUeuza+dZP5U5weEnLbAK
+hNY/WcaTAKg4b36gaJ0x3lIf+jETX/MwrICHe1M7Lj2fcQcNck6RPwNcQ2l3N+A7bzjjhtp1Y0L8
+c8Oi4px7M+KP2t85J0eQLB5vUyW8mPE97G1+pRz3ueFI+FzzO3j3jz+vvI6DdthGeKbT7z2s80Ae
+vXVUPNuTfaUbxFLQNCKUE3nDkn4cNMQ/8A91Y+9JJT3S9igNzPEfhvOaf3CPU1UPYa+/2d7Q9qJo
+mu8QtXDkW3H1FKA1YedpWa3t8foujJKs7vwi82KW/jLPD+w0rmKYOLZ7JOMCChALQP49Pu1fdJ+/
+LMeX4bqZgzsK7F6e5hIY5hH6At0CwmuT3ATAAn4NC/wqkMoPYiCEAnahwO/RL/5EEFyAuxiTbLZ4
+jLOEW9TIbzOHfx1yD9gV5UPwfoc/wa+HVtjLFENkSdvYx7DF3AGo0yPIb2XjoN9lHmGvESKNclDW
+/ASAu3tOifnSwYgGeGOST9z4UOv6ieZvqqYE9E5t8KQN1fgWM1XuvziWYv0as4zxAGSf+r+5E2M9
+lGugl2AItWajG1u44h8QMMe5dU4E2bG+HfhmvAgI1baopvFF2u7rujc+/N79XfpWHQ9aYhv5mhY4
+klLbFGnpl/iWhrjZSpAMpNTeNMUvcYh3AmwAqDHuJGcXsvZB/5JPp2xqT73Fo7WxmYsRsLRtP58E
+2JbGp3791wEzyyjAfTpz2KP8KW0n4jWYfT4B7pQeFyNdgmGVlc+Gm2yt5ke/dj95WSE510yzzzPu
+Cf31oWwJryx3ph/qetQY09+UGD2tmSqO7elSNF+sDz+SPq31kZr1XOPGq22fLtM+Wf5VWq3+Th7Y
+FQXOzS/1NaWNXrUD4waZ8njZBVu+LPd7asS/7D59WQ7XBMBWJ0Zg1xCvu8FRBktqLCAcWuHbkiEa
+wXBSft+YBMlJstxNVhnJfItQLcx6HTfv4G2B8Es+38mPPC2NMJbNXtgCUwPJj1ukPTBtg20dmH7H
+XuEjP+l6BwvCANjbnSmuylLYTayLIVOP17G4VjD4DIZxjtu46SyjYk/ihAMqvjnw8oh/4/IV0Nbs
+kNHz38iTz7rRwXF4jlvBgWVP9rBRZDqexvWWxnobAI4IP8VHmScwfG7nBeiKZPAZwT10WLtNuMnq
+dZv+0+PqIYGP0tjf01SqRpu2pddVwe8U4NxjKrfaC7oGpM0vM500xqEJ3gCuAuKMP4HgUWa6iVIe
+5+cc1ilRnrOvpaR7jyte20GC7kktv06K8NTu8sNC/H3jDHZvqasdQYnp3zXK7FrjDVvcit74h888
+LmBXX5iz9mSqYyDlJ5O3nHbo5NOSbW7sE22MKk/3kie6OLBWTC7sMqHblj2C4Ho6nPTgNRP9phnQ
+l9bKbxs9GoQHc69ubdshjinrr8f86XsU832I41gf6j25V7l8+F2Z9veR8C8QDvcnL8vdtA8OsNvO
+ExaAvOyFveyJSXgLfQJYYPe+Dfe1QKTdi9jXHZMnWPsOAZcyBvFzuSdzBOZ6IrEAACAASURBVJhd
+mfiN+tv5/W5PcwvH+qCH4qDbDRfLX54gGDxClCYSoR02X/V9O8vHV+ucQH6BYrBv5T1cQxOt+Wx4
+QsLXphJNcOLZmDFTzS9EG6wPiSYIHtcPdrvn7MXVkZpgeeZ6LHVgmy9NVjoZqz2mV6/Yj+xGoSQ0
+wZTDiMWMlzerDEuMiK0HL73/YIuluHwFwCd/CRnp7C4c3xI1JpIn89/CQ0BqnK7V+u+9cPqf4sXj
+Pbi6KPSVm8bHdf2xSQdRRk0pUiPM4obS7gYqNrPSBDsBshdy3swg5PoEjI9xVWYjTfTrkfDmZIlX
+j6msNe2PoF3yYeTLqqyXSxOzKMu4fmrEbi7xja4NJvidwLeuXbvcymhfx7xkz33finMeNS+ANIuA
+ITTEK6z6RxPP6QW6RYe2N575ii9tINhbRtQdWlbd3R+EH1h026+6b/rWHXlWpNDRotLtBisIiBrf
++uIob7orDdsVufe2rnNPjkne89b8bSyn7RMf5R78Ijp1qzO+mT88Xa20w3/b/QLhcOdPB+/Or7Pt
+L5J4yxZ4Faj8CGFCIjRYgsfbDfft+A4tbNphkRkJMwh820ndAT6v9M16fjFPapp5ddHiGjzMGmIj
+Ue0MhEmEx/mfpR2222AB4BMAm7wA4sD3EEinTaSb0ynxxFH5YpQ+CXZr09Wmkk2oefGQV3ewXI/a
+hDHfnQUGo0pOIuONoHvFi8SERj+znMYGW7oK4gZ+RSOpjzcLRXiQ7Yn4p771dSr6NDyO4Dlei3V7
+YHvwn4Fxr3EIqaNAXZ4SXWeAPPuuGmKd/w6CFRhJ5uaH0JL3pnViD/HkMTZAb78CDRyHl2WyDhbJ
+jeJxZCE3GfGHJ9gt7e9iSC0MHK6oilD1boD4VGabkx7VALKfAbNeCwwH/IqnHbUkBB66Pzrs022b
+gMc6LRDYFNC1BLJPNsPuXbub+b3imgY4413Sq3MKRGOoMorh38pofOc/JpM6Pz/fwK+EpinWGfPU
+jlaTi9rwpDdJ60N8DbeoaeMt+3eFfwC+CWCf8h4AbbH+LOvRlyP4ddJoFZKHv+mUHzKmxdlTvqJ3
+ZjiDYNvHPzpg7ByKR1DDe3pSOwHw75fl/mX3qUY4CfNk+0ttcIJj5rEicl/5aQhzh3b1O21wfbub
+ir0TOLQ+zSwyi10bgJmSLYRX9MXCLMJA8LoKZnyAJuNbcwGCJ/jN8lFu2TlbguBbZZ3O4IMwS2di
+/6QpspGbiYSmpea3bzwFxVUdGUW3q4IrC+l4ZJ9o29OCsy2AS2YiYEVlQft7mBgUmDLJW1UN2z4F
+vy0XBCFY635qeHQsiSgGCHnHJP9hWmj9Hpo6++0xz97OlKYPzNzOET6jpWzZfpZOX8OVLq1RGB81
+wSKpT3Eaf9L+HuIMcoOm4fBn9YEsLFTDyjrypTeCF4uRNlC88pUglDnfgG7+eU7LBX17DnFwJwk9
+kgluPdqhaRt5j7Ndy2nrb9RL/7vWj+u+FBJ87yPNG7ZfgeRv4AyCvYPfI/D1/YU7eB+55ahqInr6
+8M/05BlWx9Q9lW1+tmvHeK51+2iR14rnVJNIrV6YPKI6YCQ+pYtnS+8mThh+3b/+UiZpo7Js25v0
+ktzc501Z1CQ3DtrdnALZJDUtJSg5j/tUFON7nCbsU3pgmG0Pty0ege3ECPDo8F+N8P8n3OemEQV4
+p73wYobCOVuY0qMQiCFeNAsQfN8LUH5fjjy7lxyCdkNrn+TRawMH5m6giYQ+uueLeCEKY2t4N+cj
+X+AbeUCaQTQQnJph1A/0F6AmYH6d06cE2UmN3/EOk//E7KHKKgMYZhItr/W/fuhPY5rBwre4Ahdw
+MsJgLgP5D8j0NPpTB7JcAwaumkey3QJgiXO3+qzFmPtBOxy0p9ETID/29MO0hr9Hnz7yd7H6iStI
+8FzKR7Lrz3u45ck0rssZDK/raOCHOE1Sre588e0JHBuKHs3RaDMPnZkAmDYTcXGuf6ilTEBy2m41
+iTykMwY92Tk9qVRo7V3baw/xcm0AOAAq45JVF6/lEzYfNNbDseP4ElPeBHUwXEB1N5H4RrwwHXXe
+6KYRCohVQ9y0y96BsVK5zGy/eqV0oGx7/hRHtQhNY4vO6/d2PXOhpTVu1shga0PFIQC+a9OcD8/c
+4nPDau8/Ab0EvKoBfgTOKQWEZpRL924UPXnz9xnUPrNPmeXI1ULSY9t7kltXritl4qn2iBuTh20N
+o6l2tn/GrfkuLXAB4g8P8Pqvul8gHO7Tm5DrAi43XNc6yuy6DF98YQ6ex6atL8yhNMOrlcUceX6D
+XTDzdRd0x0kL5rjuBXS/DWEzXCcy2B0mFRCtLCw+u9nvuRMuKj5sV29CpjM9ATs0A+FLdQyTOfMH
+0Qrfju8A0BOG/YnbSpoyiyfgwUhHmgiMH+470u8l/NrGVoYYQhFoyMh1trzi3O/4eXy69d76gLyy
+ugmnUOEZfUifbJU+c84KwQsTjBlyDAUWTuv1adxP0PKU+7GqV6dF6qbuKe+D5BsppzZ43X6mdNdX
+sNIKRPn/Ze9rtyTXUWUD1/u/8Wlzf4iAAMmZWT1716y5q9SdZQnJ+kQQxtj2Ako597oWKJofaM2t
+BVqOD6+h4ryjE2vOu0CIPIu6PNwZEihTQxEkhmJKDcagt2PGOYhz+DAO5LAp4aPQnTSBVm8Zayrq
+U5EAJPGWHOR8haw25Px5rNNGH63VLlPg0+VRWYrR7hbceU4HwTzvbnU81Dn50M+cvV0YSF+7yeS7
+29Fqabnkpln2TN/OsVFupFWfDUDXLMqADu45PfLyDTWylyw6kB9yidM89phzPN7nF+A+fGZdlbs+
+OkbNU5cXvp07x7Gtm6tMr5+FHHFXD/biXIXiPW+fOgDr7Q9A//maz3WMh+EYj2Ma0YzT9RfK4B8I
+v0A4wqeeEZctMPzlwH0ZvrCu3L8IcqkA6ddDpRLxAsG8CjL8MV/13qv+CYgXDQmKJ0g2LPAJtjf+
+rtgZtOy0wYhpJXRAwPDt6+0Td1h973s9RPjHo19hUVbhqNXaIZbTJcmu2iw29gGYuN7+pQuKCyD1
+QOs3cBOo3qXE84pexViBhAaU09Je/XfSbwLh+LHtBMB3pdl71WANUmkoUUgstPrcxafDl3CWauf1
+vQra0ojWNeQLYTT1yt+FUX9N7WPLvVczNcEw+aFX4CPNcystKketPNG3HfyWVbE+YFNp7ijum2Jg
+giugbsPMNeg032hU1qs8LVoFdLmUMTsC8Pi53ATDQOzzcn0Als+vgpL86AaiTILgOocnlPvl1NJj
+kZuQ6OVe8QPDBHDuxfM28usc7tvQwpxfR16IeM4Rcj6R9dTt6+QRR8IHz/SkebPqLiuvAOM4J98W
+4XgGyKc2pA+cyjYPvs/Xy7l8UbZa6NFyRetLW7fHR3nhh+3cIjY+nKylHbSRnvlddNvI82qTxqCQ
+A0USUGx4CXRXXYt51jxOYNtn9QSI97honTluHR9o6GqMK+cGB/v5xwdsDwzdgmOB3S8bQBgLHBte
+gGAeNf5qLv+l8AuEI1wf2uO/sMDfFSC4QK8owdAKZRUpC3BZgtdry/7YsgQvMOzL2hzA+LaI3/zy
+SoHiBZI9me2PMqezXYaH+IYKxqYjCJ4WYQXDoWDX2yOW+8Sf9saJ5ToCULcPoZbbvhR+6+3BarTm
+OeZd9m7f0x0Mb5bh++5AOIatj4P4A53ghvPsQr/9xh11d0B8p3V464uqrm0d5xqV1C0lFVzHsQPJ
+iVSmJTRNwFGtB8+qZXFdJGmtz4IP2guN2c58DN6L9OpmqoPfUjJ9Er2d3//22J72h9/a38Nax1vt
+lAdxAZwAuVmEOZxOS8t9K9dpfKC0gWCv+qnokha+hTW1tHKyTMeoHrcyaeF1BPjNNCpNsDqswskX
+baltpDF4DK0jp3V5BGfbnuhTp2xVgBf8I2ta65KW4KjZc/CHPOUDcM0rvQBuAd4CuB75Vb77BVf5
+lSf1uORnuurRefkkfAxAyItW6QnRtBhQ4HcDs8kmVYleaJmcdKpvZ4SVzvwhT1rZY56VLKXFNy2/
+JV0SFGeVJQ0VHCfvTVGKEyCuzpXzoshp6xK7Nq7zIOPh3R5p1yB3aA1wvSu6DCilQLVC1XBCEkb7
+wkI5X76+0rtwzIIA80jAyzdUNTCM/074BcIRPrUIu62Pb6TCMw/LMK0DYhUGkLtZdq6F4qBFmMCX
+LhK8ovpDvWIL+BrqausPkp3TZSI7KCBiPhXb+Fv3SWZY+0sFoO4QNx/YC2FtcvwDwG7gD32M1deY
+lmVQ/4unrjh+lZy1hsfKq/kETKiA0EAwiX4T/IYlOD/fR1XClgkc7YGOnJ2MK53WZrFEbz/2OtG7
+DAIjPkOs05T98FypLEYg4NEW5z/HFErdRKAqsD27SbwPCjwaPZTLR2EqKSFPndLTXhmVglp3G/3Q
+15n38mfIfYEDbaVLXuSYQosWuCJNNq/cvldaKexFJODlOuZtXaw9ZULjxa1FHttdVYawSXAbnU1L
+cKUL2SDPIQhOi7CuzFz2Ex9YL+iHIjN0MCJEoXHaSIPuklgrFvRJg6xBiagHHim+ajSvvJRRkbf5
+9mZ5BO0ZFBeI7mm28RR0vnTeJm3b/dbL9UpND20tCwTPMgMMC088guFDvY8CQeJHtwmNM8919ChQ
+O48CfSc4PoXSpDb4Wu7kfASMtcP78JPWxlTttEG7w8Q4kxZi/qZlOPvbW+d97mWUO7hIQLBK9K0s
+wnxGiQaarl1/KvwC4QifPqnoCVrDXxhlGe67ax4vWFqDyz/4Nj8A4gMojpoSAKc+WtbXC7WHF9/H
+lqHw8tWXKcB5pauKwHiiPKRHHyne8l3AN3yUPV6fFg/T/YHV69ZuT4tRfpAjr/BXh3WmbDRd+EAU
+JCyVAQei1l9PbbOD0KKHRVhaLzCTk5GKk/MVWlIUas1oXjCINTjbalfbXoLF+Ye0uUJjvVrKOzZS
+aoy33yKuSc3XqVUtaSnW8U4viQ3PbL18Fd7tsUO+z+yphqddeD/1OKO5tvHXJZ71VguiHvafKcAR
+Vwjut7QIR22pNTkuLqDcadANwDpycxSNgJeW3bIUl/+/T1rbW1JOgYmtuy6W4DYyGgie6aCFPDmq
+66PWPhC383tI3hTKBorHvkDmMxLzbTK/qPlhRIGL7qFcf93KgFiDJ6g95REEd8B79A2Ottrr03zP
+F8kwp7RYTubpRNvmuuVNgTDOED5i+W4R7u42WsUzGLaqb9YxO/ouDmC5Eu150wocwhLpRpRHbCpS
+p6TJ3MOMCnyWeex5ExjbtlKyHmR+iMzWsc6LcFizACcAVstw1s+5Upp025cVmFZh9RE+WoVxco/w
+OL562uPfC79AOMKnrhEOS5eEL1AAqQ8wBREViIn+iK9SJBguf+A/AYgJim2kr5ZeX6Ir/5sFPlUg
+Z9zR+kY5rrqWGyP3j2TWwz4FgpeiL5/IO65m/8DLCgxkvPwT13ZWABzqNntIRc0rxHxgJ0NZ3uEY
+yqJW6eQaAYLUe1lrEa4R+lUfFV/IdrzikpeKFDLHt7pCOFxuPx2twwqKsyLV8DIyOaSw9eLAvBwT
+QMCelwIfAtblwYmc6U80Cl7Q/tmQLYzxZ5ho3WuV1nRKXNevqtyOk5Y/3QM56xE3BSMnizCQ2px7
+DGgWycX8QAGwYHACZ65V7JF8MC7GqYCX+7gpVgemfzCBNKKv+pGa2n9qFZZyExR3tPJs1X1leLCu
+5veV6cnV/yLrhWFau1mWa4DBCVbyMy3xheRKJgocOXVnyiMHig/cC8S60OO8CXDPaSQv7fVPWchZ
+PEGoCW7f7/Q6SzMVuLI1Tp3pFA6LsALbMxjerb/P57wfiA+6AsMaSr1vvcBwSUxxf9hPHbTiEmoN
+XYdzno+aVj0tT0y9J060cxVdvjChINjvBMDcHVs6TmcXZOXPABhlDd6ALyQeR4hs+unwC4QjfMc1
+4jLDl3l3kwBQQnIx26oyLMErGwa6RPCNEcMKbL6sqzi7QVR6bRCTdArC4HU+sNeFY5Vpz+5kz2si
+FDwlGMZSggS/hmUJvi+PPlpZg+HwOz5WYgDfWazHNSee88JN4blJCBJUKfULjhzP9iug2R6a83hg
+7uaujtGPW9x9ZgKwvMwrSzPbme4RKYDkuNpmWhapMd5B/Y7iFmMuUByAiVUT+MhFCcUwhZDGyc9/
+I5ZUSRTls/C+zWEFds6iofMyoGuUNN/z+jk13XP/LP6vOjYwDAggJp+qRTgqN+Rcr04faLn/av3W
++UqL9T0AXvUdLl9gBz+kg5kXYNbBB+a4cam5IOmGcKQsz5P5ToTzclGrflkHxnVlu373XIuGd7zW
+lsCY0q0Ar2VDZSVefXBtBN1ajDpN1v5wTB5BtwxHnO8Erteq7Q/L3VoH6560li5an8Hov8kwscdr
+Xvf4HiynUevf4nIBxTsMNvrTge0on0Ufzn3Z+b5nXpdZ/VA3ogLFKHf+2D/lZ4/tobld/iEbPINe
+5fIqeypDPk5a9Ftfa1cViaSUu0x0d1AQzLcMWTDU2tFLeSj4zWWV4xd2AMwH5K6YeuafwDDbA9M/
+HH6BcISPgfCFsAQvi/C69W9lBQWS8UqPhAX4Bi5cMCcAXpbg/7NKJxNZB8f6sFwxWoFhCvgUlMHz
+WxwH4dx2nwKHUD5iMVUQfOeGRFh/qxzuVe+XhbK1AYJDHGj/20/kkG4c4AEEr67vllYCYHlzRIHT
+G/wGNBV2x6JdKWfaqx96LoD+kNx9AMbZP3Y4Kxy1+STpymG+ygcSzyOVeM5WtyBW3LPFM/QtAXoK
+z0L/EB416+sNuJ82wHDSqjxQU+tbvhddFNhe7uFnuocUDAtfWs08a/VQWqUxhZYDPVuPO0hehSfQ
+LUVyAMG0cuncydImzdAemEuQDFqFE73g6Bphfd88hgeN93yeKP5ZmHggIjFl4Fp2H+Gi1+apMXgW
+9BonkBceVYWu+ZCvGSdQLavwE/3s8tBp8zPLG1CW9D5znLsOuJT12pSM4bZMjOWznkFZ3clJLRA7
+rL/JVoJyp4V4sw4zbMIwCCchWQzTywj4LVAM1EXjQ1PIqe0ehTN/TOWe5zEPaifuJcfqoSSM1KGA
+N5VB2+UN+M64AmDqZDzEDXQRrd90g2CcwPji+dTvEv+GNvnHwi8QjvCxawQBXpRPQKwLSEVxS1Lc
+IZIZ4kccXSDYBBh7Y7j8GeOejLie//IEvV1Iyq21oN+6k9E3bykCilAqgxUnCL51c91xsgJi1BYu
+QGtoX7cLN4gLc2Owg3q0nBEdC3SsHA/ng6BYgSkfmBM4P63B7YXq2K3F2i7Tt9cDed6Ab8URQmbG
+c8ZeyAFV6qL+UpCpJZgzVuvQhWyOihZCWOkBoPkR//dCqZxdMZ/AsOS29fOxXn1edlrP6z91exg0
+n+W0DhPtF736wA94t0Z6bM3gSbq3+HCNSFWK5vfo6XYUsyfMoq4R+cDcg2W4v0rN6se0zON3wrtz
+jlgmcmgUW3leYMS5S1FzrCHmzzXNmlU+7r1olP4bVuIXILjTKa9PYHcv90SboWQB5UD0m8svw5mj
+62fI3FiPl/VxtwJr/CUY1rKoso02yrYO5/6yBzogV39RMZnJYs+VhbXuoHAP+wC7pRu0yn0231mD
+d9DbzuN+bfmirzULIr9FqHcWXhLqBII3cAyhs90YuwG7fzDkzRHA+V3CEm/44G+Exn8YfoFwhM8f
+lgP8wgLEly8QDARPxiaVhTRDve+X7hB3WYCXdXQB3gS/NsCvyRXVw+8P4jaaWwpOyvuFAQlcKWTj
+7WbS/ZIXAj48odUGAm7Zec0LLR6Q87AGF+AP4BtKvFu0VzsJhmMer5xWCsXq7waCBfTWmxsg6XCH
+SMswH5ZTIFTuLUpTJenbOYgRYADgiuPQtwLDOqiqESM6wwQEnB9agnOlnOB4SRmOhx9iqFV8ldpa
+bT1pHmtbkXd7a88/tXIOOxj27W8/+iGv4p7zOfP7b1qBg2YCTFpZLm0oV1XS5C0rWlrvCCipxCln
+DN26zzWYYNgBurxsILj5CRNkWDVLAAwBxZQ4Am6eQLDO8XdCqvfc7E+1eGbTEnxOu+yTIA7EkvN/
+6rjtRH19GmURa2+vThNeUlp+Wc7lvcL+yi/49AaJknsnWpvDMRSV8To8dZ19DIe9nX9tFJhg1io+
+wXCdWlbfDRSfgHBXXjiDYC+FyHaybNApPBMMI3znC+Y2QMy/UddgqS09Z63nlbSd9mAO8giCt7FH
+X0401Niq2dIPNo9Rfzu2eEn9C0/uEfIsUzR/BMDwXMvTK1P/7fALhCN8xzXC87Vgtj0YtgqtQ4Jg
+2+PpL0OmMbEQIxjHzgC4vZJEGLQw3gLFl9MHbTHtHZ3z0eWVXwCgBtF3Uwn54fsIxx9McNDjNzy/
+NLPGXABGLcGQMcIW2E5jvVs8eGQPIDjSzvRKuKN92CK/+HbfyM+rBsDYQdKB1nyFR5n5MQ1aoQcY
+znVwr1+rtUJjTd/lOx7SnB/Kd/UR7m8bIB2KxUY4U1+FR0XQqvpenU99W3RR8z5zCUpqLTn/GR/H
+szVYfzsY5rnxVMBeDycYwMktYn40I3kkNG73M34AtRME06cRh/LpOxxzayghhbNVuIFiExr03D6f
+3wnnHXCGDSSrJbjW3/kIAJp1WKt9avRoltK1q9N0fbW6R6tv5ndLsH4uuQPok7tEnbfxpXM/OPwB
+2apkP9E17HvOOiH5YyXy+il2pPoF7yC48goM7y4S78EzOxq9nUJyCrZkGKEPSzCfGbGIq8tSAmKC
+N1ScXVKplE2MWVU3Jc+xaOlpKdZpP9QcQ2186tTeWn4VNifgVRAcuOJAezLG5XuEsb85wmDNTzhF
+TMZ5Z+q72uCfC79AOMJ3XCOIHhsoFiHZrqIMyzfY+itE/jjfBHE3IEzG0ngyzimOxeQGSwBsHgrv
+LsvyLWOgNZhxk23GflMwAEPh2wK1tWtXmc3NP4QEf2ZU1DyLV4qr3osCNOuqjbPAsOUUE4Rweyco
+zjhBcbe+QnyDCYztvnMEHGMODx0Ev6RRDhH8bq9QO1uEgTZreBlcIzy/1KuNdFqCc8ZegOAmjq2U
+x0ENcm3e9PYY+nmvxV613Ptw7hFyXfoh/iqP4Cm+g+L3P5P4AM62l0Eqa0DdIvgAI5Guy/5DgFoq
+SYLnl6DW0UFwuE+0ddbCbCfcHaIJvAPFuaEzbdvi5Lg/DKeitebs++QPDsPLz9B32qNxeWMoO3fk
+gWVzzb2kQ9GKP/hwnNLcT9ZesfK61N/OGXTv/Le6u2bJi9B6vTkWcfvPIVvLHpXFypieY+3Esgh/
+F+R+Ao5Hx1rct/2TzJ1zUjS9a9L3qde1kcmWCX6c87WzjmdO3oEBt/spT8HvkMPNRUKz4uw5/pwk
+nuJS1mtOsAPjBK5vwHB7h7DvD82l4Y/lnaJiya2sywB73KT/XvgFwhE+tQg3SzA3gwheM8MfWn4d
+CYL/3OH368v14ct9fSHOLvwf1CWCvsHLprS9hgSa9mRCw/rQxe1hEb7XJ6BXH9TVYm32P8KUalVu
+YMJkUzoVeggHW8B7Fa0PbNAlwuJBodvnl/JCQV+1HXkNYuO4T3V6Ku2AxKkwFASHchCrbPoG0084
+6y1hRvBCayGkHUjbOND8vpefsADeBYY7MOavv19YKs7Kt5GurBTEVbaJDxeBzCWlUkAHvynz7Sh2
+p/Z7wgIPCuDTjfUu9E4c9XpkuCT6lA6gewAOcxlOs7/9rHjmXVnYsNIFz1Ixp2J05N0KLtBaS5N1
+UoVmqUTmA3FpEEtLsYebktCAfkuSoDj22wTFBLz9K3QFdlSMnBdqX7cJKE5FZjXJu0yVTh+AeHbo
+A878gHUpezIutLowoiws+USgq8C4PrXc3yyx1zFAsLSl4Fk3MGHW88b59tAPhWT9Ebom+SbSwBnc
+PgLfD4FzDTX3hwozy31lIuC4cKTlRkk/8tU2+SraE+GrFxhaHapIk6nZSTEylN6dklfcI6SGbnRK
+IdIuRMgA1f9+TkoqLxxRFuBFuzLPB94ozECL7xdoDQ5A7BO7WILoBMQuoJrrOMfxQ+EXCEf4GAjD
+1ieDHWENLuFijvWZYYLhWNT7XsDwdiQwLoC4HAd2a/BV1mKIjsmf+teybYJgw58LBYDjwxYAUhZc
+EQc8X/3CTVChBACtSVTid2yOOzcVty+tjUgLEo3mBLQXLHysuzcJ4wsAr5HxnFvObwogfgQUGwgm
+4FTXCLoq3Hf216UfVBtsp9F90oeFWB7GKwB8tgbnw3KzFdfaJesQXPJKuInq9KIpQEqZjwJPCYyD
+h5GUczgD3w/Di7dQzPHZTPlTvtIFVDVQEqQNBA9r7sYTh58956nvfSt3sgKDFqvSpOoCoQ/LTaBc
+wBcFcMWqtVn8ZeH7e4RjLsUqXMAGAwRzbzM9yg100gDAE8PoPIyVRICS7XyXgwsgdsotT0FhQt/7
+FV145Ed7TDV+cYn3rmz81j6uIfx5fF1alHmyAO/0LhfY4RNgfN5B7+YipL2NdDZmuabMIX/QrUaP
+n1iFdz9h6fcYS1p0c79JPPeaVdzRwCknMF+bBkMBYiRPWk7yE2v3+VVD9MzfrcCVn3C4+T0cho86
+teuO3g4nghbkhSE889UyTBBMYEwrsVqG55fk0l8YuzEvQbQJhmEfvs2P/0z4BcIRro+dhEMiYa2u
+RdpoffUFSA3xEgUPq6Yv3EUQfLunj7DhaoC4rL/1DuLGQCgGKiZefVkg2OMzx1afXuYGqj0uAHiy
+XilkNuKx+z0f2Flg1VLJQG65c8NHfihOWoEdAXpvS1rzTIn5WkJ+TdIFtQbXliFUpAKAY4Bgiafb
+gtd7hEHXiBQLqcbYVzzS6xzS7+kj7P3XQGoq6qq78dlT8JmQ3vm5nGWrBZAOduEXDU4B/Vk4i+3v
+hcSL2ZfewpD/rYyCjMrdQS9a+oOfvcq3x/y2n0KRFwg+u0D0MigwEI/dRAAAIABJREFUncAXG+A9
+uU00vX/Ia2A3+lquEpZ4VD/FXMIpIxuLFCvveZPP6wJAztH1xKjCK+JRX0oECgXuL9kgp8u7zmd7
+I9m1bnob8iCbTN5qcdfjkk3rYTmxGIOyr+pqD8355N9zu9lj4ak2wqft7DhcFIgf+J7VlEjqpuSP
+ArukvwbBdWyg+QEQb+OR/fQyrzZDzU97dRKw/IRZLsbHjUCeUxadp2u29Kk7o2lfd6nMBSlNh1bP
+Fl7wcbms1H7gnZPCGAF+EwSjgV+9O73u6Hq+IaJbhgubvALEJUJKdv10+AXCET63CEdhDwaKe/fr
+dSOWgPSP0S0hbn95uAa443bffIQXM1zpEmEvadb9kIOZzeO1Zl5feUtrcD0Vl5vXKQM8mFA2RBOm
+cbAQ2lS+3DBAPYhnTos2LYurX/mmDYMAYk9f5VWNCO7o44Xq5xFwMO78EXSujG6BFZ9dPjAXk6GK
+CtIGRdam7J7y0hrswzp8AMSp+LwGUEOvcBQMHOBTuT5THkLegQaa2qvTgI+twU8hhf6TktXRvSrj
+e/IVSNGYv8jfAYPkeZut51/jRXugn89ZS2Y5UU6tlQp3dYRlnCcZoC4V84G404czTu8R3vzDm6tF
++QY3qzD7dQDKJ3eJuY8T3CZBlqeVlQsWG+V0RZuil8WU/FxQBGdLWluaPJWfmR6hdSes6koreSk8
+lM3qw25e+ah3BzercJZFpNVyfPg98G3bNRnlSgOzSJuHw9jtNGFQIte/qLblvQfBlW9yWgC/4DU9
+t41Dx5M0h9z+QjH8kolZBmHoUTCWd2lqGHyvfpucMTWbDEp+OXWQmmTQjTxadx313L4U1ZG2NyRl
+pIWgSSMWUBbgGYenZbfHIfFwjXDb3CGegO+8y736RcZ52Pj/YvgFwhG+BYQzGHB1AFzxAr98iK1+
+AYgNWEBX3SPifcMQhoE1xqwra4DfQ08ADgtTtMEuxx++ISy667GR10cukFdrDmXKkhepdRzyxHsx
+6nSNSGUa59E6/MWHC4Fwi7BZVQojyH7o/peW219v/+kdz9R3A3TSV5ivTfPr3lwjulKjqJnKRfrj
+3sqvfpUvsr4tAvQNhvRndj7n4DmUO8WYu9HzXidCFnv1HR0EhRqAiKXQm15g5RBe4JW/Cy8qm6rg
+XTXzdrSCBS15BBdvf8Mf2A5l7OAznHrM0+Xh2QrsiT5YJi2bpv77JmCYe08Br9ASDItKDcXXrL1c
+cwLblzTRaKTpmHLx5PyYgwzRgaKU3DlxmaaSHwp5SkFPecCOKDSQ6a7yWqeCLW1z8Bak6TO/iKU3
+zlWf4fn6tCw76py+wbN8G+rqaI1GtvI+m88XvX0+UAgmQZyebVJmlaP/r4nmevIJ1vcMv3ShUGC8
+ddCx+f3qSBzYQDDvmD5Yeg3cR1Bim0gf6ZqvPenYTsfizbqQ3YBxxmeV7PuJVZ/W1mUYclcXhTOu
+Q/rpeaX5urSnX8Mx+pDc+P10+AXCET59a0QgScBNgG8HwPnaMvl5WGvvO45uuMMdIt0j+IBcuEQU
+4yjDqDVYmCnjBMqOP3fs3MuWBdQWAJYhxM/7BlezaPoTBp0ak9nQq8rVHsHvErzhs3gBX+IKUQ+v
+qCWM9YTQj7Zo+ZqgOJVPdE2Nqpt/cLxPrgPipVpUGFX9KzUVGjLfJS2/fDDv7g/peY93jSUd3/it
+Iioat0Jq7fJJpyqu22vuvH1fN+iw0Z7Cpnn+mfAwBU/FXvbgOJ8l/Cc4+Z41uAPc27SMSbn9PPaL
+F5WL9z3iBbrqwUak4l7x7j/Md3PzrRD64YwCvPF3WJHBC+IBXlJJ0y3CbCvzEVhmhPJjrEGhsgIW
+tWwnVCE0mdwj9miIVPeZNu/ZfEE6DMaKi4mR3WWG8kzV2a3CPZ3uDX5+WG6V6TxUD8I98Ogon/NU
+bBUXVLV/TcromD7a3Vx3TW5nK9+ggdgGghXkwqBAV8tsgDgHF0w2UaYOXK3CNAIMEGzjwoFDWBeV
+MkdPoHdOEZts0rt1DGlKsue83SYsc5x3Ph6Cv1hZyUuM4QJ+gQaApzW4fILljREnMGwdPHfgK77C
+sd9+OvwC4QjfsQivjbrArILhyy3f26tg2DVuy4p60yJsF64/N/4PAYDTVUKAsYmvcLuKss5IdzGW
+SgK/HV9xkusvbtlNpmyBlivVBPDWAgRgscSSJ0uzGdZG8cvLMhwuJfnid9nlZY1eQq8+e0Gr8K4A
+VlfV/SAUw8kqfMnX5XJECnAeQG7S7ECL33SNGAA4+wIv624NoB9b76SxjEdvG11GMZcyFTGVSolY
+AuNmKxRryitw/KwTTuWnIH5ZwcvwKN6HpT7X1CftUAYDdLz72eAd0/zdV1jBrb4z2HPKo+9iuuPt
+2rIcY4BaTiF97AiGF9hLizEOILi5RsSR4He4QXRgzIm3AwhWJa3jjIoSuEgdHlKl8QIH9oY5DqA3
+rcC6Q8VUGqJJJFcBY6gizn2ClEciqtr+53ECY13/e+Q9fzL5wIuOnSb0ybM6Pz7W5GQZ1j3kI/0y
+pPIIcBpg1zTeCk6Ae7AKs8wJEI8yzx0OYmNu8v6al3z4TEF0MolUMydpsmToK8044TnRymjA2DBA
+72xoSt+uE+yQdaZ70jvoHb9Bb69Bw/lX/sG7hZh3nrW+U7t7p38m/ALhb4YlCD0ZhTRxwV0g2PSN
+CAHjwk0CFMAX8OVLwXwFKPzyAojuiw735UoQZtHFz7F5LyB9B0UapNK0EIK2FNKXWoVt+Sm7Lev0
+pTSODT1yZNYAy1S0AOp2bEzQ7WvO7hBMdSGB9Gu+oy7e7uKbNhDgn3n5EGIc3T2/nJfgFwqIBwiV
+OIZ1RcOJPq3Bs1y1G5QD2M31AxL4vAu7nK9bzkl36VfSV1/Wrdyy+DrH4uMBCgHB+bGRAFjVoBWD
+mTQo6i7HaN2XEtCHQOTU/zBQx7Ey33JPZ0RMAEZb73do4HFzROKQLqDLP2tfIvpQQFlvz9Ji5LK/
+LPZD7P+0cmmndT2xr3GbC/bXEpx4A7lAPjRHy7ECYwIhIqGc29EHba9toqdyMU+5X5E/15ejY44b
+de5h/ctAKPdJnF0X6SfVLsv7iikg9uC7XJutNQ3aH3Uro45gndbOyKFEA3XPSvp3aH8tS1/v7SEz
+GyCrXehoD7TUJ3HpixV7aN6QMIOOvOCjPDqV6Q1+tAg1rFFO+9sncvLm6MAmjGs9XivPQ5ogHxzn
+Zp6qpg4sb4dxnVqy8btwtgBvb3uYP+8PwvXfWtlTfScwTNpPh18g/N3QzW8bjbcwioljm4csT1lg
+xVi06FwBSM1W/GqAFQvEBnjGtUDnlwPOV7hZCEvDesVbbKCvEDRf0ZevFD9oNAK5y+tGTN0SPU9H
+l6miQCRFDZNAEQD9lBeeD6Acc5Sfg6bSCcs13NZbMC6PW588YpUJa6gC5HYbMQFy7632vwEhGXTR
+fdBOoKKGrccn6eR6PBQpkFeEqQhqvaoOj4LKix2EpOZPrl3TWUpnXfSpph3S35U/DuppA8xV/I2u
+eht21usr51v+IbzsxEndvksHrWkcATW04mLcaTF02ZIXG5DBhNDwKuNeYLgsvRCfYEAfhmzXMFy+
+uNBMK7DZ7i/89BNgXMJMxqu8r/gUY7wNQ4yyBMG8vUZAbJMWe3sA5G1fRRennN5B2hluqSuKnxhI
+l5EAl6mwwlusUfnPSksBLOZQ8k06sg/5MDFfZETadqdvtLO1u6WrQpZvyorxbeh9zlLmCIp345yE
+ngne9bkgyrCyH7SFjDdeUgE/4g/82EYiwiONBH4o3NRdzFUMNXVO1JFD0XRWUUB3bfORnkDYx9TX
+JJzFsEsXpaILwJcdgO+Jhg5ijWkvwHsFtyv4tXFefk334cj4T4dfIJzhM7Vc+6mzqsbXnvV9L0ua
+zJnW5GSCBRBvA64Au/e1HNX9sgC+ZS12X24H3Ndf0pN36a+w5NJK/GUI67Q3i8S3XXbGU59pRXWU
+b2QIPr6CzbEeIlynC/CFJyCGc6N7uKQE+HWC30rXOzrVQkt5FnCcytNKVelKdiWXIn0ELTWlEzqX
+vGCxo2H4gSZyL4ulNUgEfWKmzAurMGL+GXfxRWs+chGf0rVMZ0VPpfAAHlQXkvYwxFN4ZEHfInv9
+bxpZ5UYhXbhP+N8kcogTUtRFMcFnrVN+7CKbFc/AxAHWLMhpb1+Vlb+/jdusAbzEIxwNDLP90EbT
+6jvjTz+9nc0LsT6nVO0YeX4qBvIxbo9xO+B3+RBEWd544Rc/iSCzLrIudH7laD3dOi0uEVXGj9ir
+VnvwU8g5utUlGEa/g+gY4Jdp2fTLUU4my6Rc0BKwmOBWWG5bTTOf/ax8JoN/oC4OQCt4nLuiawle
+SLS7GI9HJO8/bcMl/x4WQ3muCUMp4L3v1U7tNR2vtYv/7MB2NlO552ykR3526w0QFs3cOp537Vpl
+IdNH9+iq8AnwfQTE7s0FIn2KYQ0c8yNalxEoYz9a9MOKR38y/ALhCB/eoUbfSJuU3+JqIUYoQZ6r
+OiQtALYUUlmDAxzTXeJCuU9ggVkCEd08X5hpCh+Ui8S1W4RZjn5974Xbe5plnTGGAALNTzlAgFtY
+gk0twfGHwvAGbFiFCwyv8SQgBsRgJBZhlSFRP3VpEzWJ8bo4fwLOtKjuszKtzefgW6THOZ+63lQC
+pSxKSednlx/B61/ST/VlX+tBLc3/BAx/LAM/3K/tFvI8+aM6nnYANnq/wTz5g1owRryhUZ5mJYyi
+/D4CmbXYS7Qwl89rOmS1OU6F7IfmzdbrFo+A+DqD4KuXU4twswaPISw2mQT2cYAP9wD4vnz8CTxi
+w3tYhf2+ExESDHvMtYpr5zSzqy+YzmIua7L2VZhGEQVSIOiNPdHAMOjHXSAYvluDr9g30zp898YG
+FpMHkEyA7QaGcbAW6wiljShwAio+6HpflA9Rny5BXoPi0cg4XfUqWaI6MNbCz25jYxT7wNi0RW2O
+1I35HAzrNB337Pa8mBrgF2U1Bkq/uNSi462adYyoSWjimXzrNXYb4PddOn/ewXD8EvRK2giCcQC/
+XXy0Cze6lP5k+AXCGT7UrLk/d6SSQjPjSEUD1P7WeBNUwRh0h3BDAN8Q5kJzlItE79fD0eW86OcX
+wr3CIP7DwNflaZ3d69sFVFOySpexJgC1uP1OtwhYgF/ZwLAEyFkBXD4vR/Cr8bIE0zpcvsMTAJeX
+rKMsozWeLjQphLieVWpXfZ07zjxFa/Uzy/W5TycFr5wUit7nv5fbIfkrq/Ck51lPrhFPFmPmHTSm
+pSJpcOF7YZu3F3vXqw0/0LUP/pD3FDpeoB+v5Ix46tLNGhyZCYCtNu0UJLmn6k7KKm5p8VJ//VK+
+taarbakugc6VQkitwAl0r8h/AMTLGnypBn7AGnIL95DX0wj3D4f7DT78t159yDs9d0zdlWDYbyT4
+cqt50emde2mHMDO4LMnrkpY/AXeCTgxIEJxgwuNNOREu0ChQaRdasyC7AuiSGu/AcPbXpOxTeeHn
+dJU4zMOcx00/OB/05Hx4Z8gHnj/VrZnTasz9keksLnD1UQ6stgtXxqono1Dv+j4PIhZz7GaD56Z+
+Vflxzp+hUaQfpoNjX3I+kXLmc/C7A+Berj6oUeX0ncIFkpfVd6ajrJV1+KfDLxCOMIXh3Lw8+tyV
+A2r0c2WTyqYiY6YeQVgIqFOgrhJIAPxVT9+V5Kydk60RoNG6+2UI628pQ4LRr2v1usAxoC8M3+ZF
+ry5JP+q7nk/uLoswQkkREMc4PQR5VmgFEKJCgmAeaf1tgJhjFNeIBMDOPCR4WGu0C6AaRdDcWtd0
+rH0C1E50yD/RvR0eQxeoLjJPFbXEY4Id9M+TWjj+A50XCQ3onso/AOVuAa483SPfDtuJL2p6VHKf
+1P+6ZGLVxwLrj0mcvJQGddStVwPyAbjqLMETu9Nv1fKT2Sqs0gLs7ONnluFp0fXLALUCqxOfAOYT
+rT3JrxO7Cwm0XXBa29jn9WGQmw8PrM7fcXV833Ghb8sSbMB6bSRCxqzBEi+oTONFwjv9O2U75YPK
+3fN5wwIMWoUrvkDt+kqX4wRuq5HLDtZh2We8dW94B3CR1uA2SI2YZllV8kH5PleWfeMbG2Qj1Aly
+gfcWEI/TW6ONNvVykje4qaGaN/C9+PQNX3cpTERjCLsgbH7ANvef1Tg4S23uZs+6fG7tSr4pLfrD
+vlfjVm4NVqDWsI5fEj+C4ElzLNcHX/zUrb/iO2xCt4e0LWD80+EXCDM04CLkAy3BgNIFrCUQEeZT
+eUEZoBbhi9ZeINwhxDUiLLkee62+zGZJo6sE+5QeE/LQnIN+weFfPGnZXggsoHxrocpVBoLa/7Xf
+ulBhPQSaOQ50q3C5SdSDcpsCvS3dIuqHDnjFEpxuhfoTFaaKTBVzNi9zp5zgOnDJH1xR9Gdt/6xB
+Ja8EptAoyL3np1yOQlPkrnnfrcKrIiq0vXyjn6zB3s9rim+M6S2QfDEXp6BNvK13K1Cq5nhum3yW
+0xYLBEw45ZYZqwgvpJpvApJB0lKWDUWvpibPjpr4Hqq/calMl6o6nrCuSFMbBQBuT7AMa/A1QfIA
+x6d5nntgykbUPLRz3AHjJ9FRgjNAsONeavm+Abvgdq/nKOI96vn+dLKpDINNzBvPT6r4BKgQc9/H
+27nJQPcIxgMkeMkXBbjTX3hZhz07PX2HV3rtX9Zj/BnjJ+swRp8O+SiLsOox5fuahxq5RX8Wf07X
+CKRCoTtb5/3SL8+8/xBebBfVU5T1T2utQ82HSnkx1VC47eVHGg8WYU1bm8chR6Q2a1RLedr6E7I4
+jVCiFNif6wCGGzA2VJn2O9H2MgYcQLB3sEwREuNX8fGT4RcIfzs8SvdGa2vJh1tSp1Hp1RU/N8m0
+AnsIoA6ApWlxqKFf8LLqSlxpaRlGukYo7QoLcr3vskTG1nbt4gKOosi0PPXZAl7sX7hFiBJZAp8W
+krCOiNJf4Or0w/ZTK3D+c2+gOK/0s68iZhxcsFxVXe10t4jF9KRqIW/lZ3gEXuiCU7FDF4SldFJv
+8JY5p5XnOueUGq0EaaiivI3clFWU39VGMPWsa3OhGKdFEPY50vc5+YgobHjIn0DrfXV73Y95wz3C
+SK0SZQF+ZQ2eICDsj1t8Ag8FxQ9Hq/mJ3VSKnpbd1E4Ffm0Du1dzh7CMG4xfJ5prcBSdfqBpmgDW
+kC9Kv7UQ/aXu3J8Og9+WY9LXRfJhxKyeirfxfe9Ermv0hxCmS7pTCPDb0gSXahWu3xngrnSzBHvd
+OSTBci+WWsi6WzuvAHH1tU0U4wdrsA9y3/piHU0rqhe/NvA24mzvFMfO26eg3hafnqNN2eCZNdag
+Gd2PKAd7rclJQw54S0l6iMxd0k+KCa3LbMvNXhNg40r4HZith9+mG4S6SJQrBMtbpJvLBes0sT6b
+HK2D4Z8Ov0A4wtPt662cE9Sd/DxPdRTNNOJDABkBcAlnpiGg2PMkNBDcWnMkaObY3A3XJcDYFyhd
+tLWhL1v+wauOenjNczcXGkuLXtMd8zZnn4+0BgNpEb69rB+bv3CE9dogy2/AbSAY63gNq/BmCXb2
+sH5rLaWPppxgPT8fIOw7dQ3VD4LsBMR4fM1vJQjPQCGBzDjyzoaBSihGGetdYrNbhVVVlPuHi4Zb
+ZeriQOiigKVoJvg0c2tHom/l3mdbs5U/zFy2dazugzYaIEpiuT/MnZ4zxAKijLin8rVRUdcrMKzq
+cFnQzv2cyj5Bh6btAAoIfA+g1wX8gmB3Wou1zKMo9JF+UwZYr0xsiG3uYoLgC+53GBEGACb8Sssc
+/2lD3DNzYj33mPI9585byVZd/TVKEw6jP6B25bnLUqcPx6nrw8lv+I6L1QmUq92ha1p6jHXm55Dl
+ITuma3jZZMkd2/grZcpwrysQieLz3FS1B+riLfrwds/uG6Tx+6ch9l8C3lSAKgcjZOXksui7tKh8
+0fpik/a+l+kKIVe49dnpojf3iJhue7IKy89a2o9xy7Rt59XRAxAPC7EeDb9vjfjfCY/SG8Bk3c58
+xZwogZMMgAGAVwGn4L+eW9l6QrAZv3zlWpxbVmCklZa+wgTEBsdtluBx1WUSB9ZtLx/jk06g5NjS
++6LAfH1cI0GwlWV43hbEbfDLy8px+/oFEL5uh1/qDnECxQ6i8GYRFsjqgmbKMsr+mwAWtY6X8Mri
+vnL1bFED24K9k+dnYDMVzyjr3o7MebIKlwW16FkrrcIx9lJQUse0iByUxEe66zT472W8rWvrx19U
+xVB1WRKa8hsgQm+xrrUXBQa8tAy7LvwD0Eg+sHPe9BVuA6HwodvDCeA+HrX82qn1FL+09ig+n8v4
+5cAfK2FJyTDFoN/AFaDXrQAxys0sb2sb1+gE+s593Dw+PkFUVkWO4NdK3h3dId7Etzzn2yQ824Pt
+Y2QcG20HuIDMi1Y4M4XW+a6s+imvlPdTCFKGoLODkGcL5yWw+OsH/pc9InvhtIyVtzrAd3U76DMc
+JUT2qb85hwTW0XpvkOyaVxndM3Op9b96n7ORoLfkTA0m+o7uCkF+PIHilXcAwaE/aRFuwNmqnAHt
+1WlpIZaj6fFh1P9m+AXCDG8sdL3sFmlAZEnNlt3KMK8JJyCviGBIa8YVHOLN6csf7x8QZF4ooEv/
+4SPoFUB8ZR5w32tT30DeUry18xP7NECnQLFANPdk5gmgVMsw2ykB7yXgY/gL7K5+8y0RSQPCa1Cd
+ItBSSWGnrfq6mqS1VGc2BJjiPVQF1ca+HtsaHVdvBP/escAvtuNaHy/rJPsqFpjmKxzjpBUNStcO
+0uLAdo5W36rP2ry9QRHf2JKn8m0LPtb1iMxeAJ1SYvPsshpbpqvGdcbCtzppeOEfKXEVHNqwsGQq
+bsa3PFHCmpcuEAvMuoBaewWAFfzG+fbSItwixySADqJj/JuvOTh/An6X8IwvcRIML4sq9/OUuW/D
+4INyqy7f+dk3bjPTNNY6b3I/+sGtclnd/XqyBmc8NpXmmXBBtbso2iZezMXWL9Q+3wZ1nKoBCLH6
+tVnVPfiRg891p3JhBVKTlWzbg+yRjTrdE5rwnzWAoDFLabtxgWXTADDqKVG4t9GZqtJ+yt/OnSV3
+hbymNGgKimNupwXYTAGwWnz9mQ4BxDatybbTbD/mtXYeP9mU/2z4BcIRPte5fow+1aJLqj6BzCMj
+LCZcFtZmFQ4Jlz58vFy64xbINdpwlPVY4uo37AAuL9C7ALEJIAZwFUhawHI90ayWiGYcTAXf58Ml
+prgt+wd6+ZnEUW4QMrU5b+7xwHhYhsNa7Fi3Ca9wi4iP0AUQ7K4SbJtWg3oYjP2jEN4BsQdd8d6R
+g/xlbi/qrfhzOZS7AwX2fvRqNSstC+8aL7Vm+AZLR7LmEM5Vl6iRzTWCzIC+aNNtIgL1Hk85DPTl
+LHxa3lqW73R/Vf6ZLqoGBB287U5yug8FCAnEGbSQBbFpPE9wOdlba2uvdUU/VGEDt4g+TjC8qp+u
+Ekvo+AH8ulqIk94BsU0r8pixXO9T8C3SounzvlWxBFyuY8isco0gCJb0CUF9rHe9T67uxDMWQvIG
+ClTmT0DxFdX1h+MWLQ0CIZuPluHckvHBDRf9Ir09p+2xTBJ5EN4GRmXVlQaGa6ZYtO5O8oxyXWWd
+LotdZ5d1eN/ZtNwaZBs1uBt76AiIH5jAUGAdniCtPk0v54aVuPUs5+5Uv43oqScjdejqyTpt7SHb
+6daBcI1AvPasgC558ewecQDBSQ+LL+sd5xrKAtzA8APtp8MvEM7wDqpEqa3Y+TzdaPn0ZmSQb21l
+dbcIlrHFoOscWy+Ih8d71SCAeOkitvXlBTTdyyeY1t+LZa7UHfGFurBAX4uW4kosVTdooSgYIHdh
+NmTBGMebyowyzuoUhwsI3o+4S7/yrRF8bdoFiQd9c42Qf+DRIXf91V/Yqm8cha81qbG5nLv7jqpN
+PGlD4X/GcXWGTUJU6kAqEn1ALkGO1zkJ8HnLWBYhwZgosOq44cjE0p8EfiffOa3r4DKxDfYb4Xwz
+R0CYf7vK750wFVPCAMt0+Ql7rA3nGfjMAlxxd2srlK9xQoFa5Re3AQis+tZRGkFtWYQtAK/blfEF
+diU+wLFRIB3n9DCxB2zaMLHfxcuapVePbnC/6gLeLT5CZOlv6yKDNlCKh1uyuoHk7gfztodLJTSA
+me1VHQsc1FoRLBzdIfxkGS7QewLMkLECfazaB5zS0mdIHW0C+vbPevuMiKxzG94+XtfWysGNRWQP
+VHWjjV60EUzdxiyqin3U4GmvraqSOsQwZASeqsTU0s39tnXwIZ1r9Fym5jPaJsBt8nhYqZ0PzvWB
+kd953EFvgdxG80nzcTx8WtkewDB5btIeVMe/GX6BMMPHis+T7xmmoi2+U2V1wBEIYeMhEAMw88ty
+S7KtAnn7iO8Ojq+r1UYsELJuE/q6PYgOfGkx1rdITBcJTyv06iitwcC6ZSf4vIGtAftytgpgIoVD
+6i/kcDbwSx0z5e7+eWX1C0b73bFxmyWYR4TXs6Pmlyu8pF2Hs95dC8BxyUN07nXGzlLeqa94TubL
+tKwXrYEd0KrC9fH0gS6ssM7wHGOI1vEQHRdoguYskzK3hG8JXav9ocrr4fbh94NvyVmrPyZ6eATI
+PuKjgdzfNtItb2XWF/ao4mI/B3+d/YHjluyxYu2a+B6DyjlZN6VPXqDAjnEDFpgOYOt8FzDj1wK5
+bhVXQKwA2QiQD2H385Q57pFMEoBsp4agWnx5rX3n3R3CzcQaXGBYDQ7VuZI9DemiCMnOSY49FBu0
+3rGOps1pdWUl820R+t5U+WZQfna5gd7oVgLmFqeVsg2rdUll6XcAMmZ6zN0pTJ0oEkUK2V5aG2/K
+7bmdBvaw5rjeuc19UOtJ2fgKdWlOVRMQV+p1Oz1geZinJ5D7QZlTLzea02JNvuSDyrHLm6U4HpRL
+/BEg9ykNtQYXIC4/YQLg+RBdgGM7gGGNG0Gw/VqE/5vhsEXcxFR/AAAgAElEQVRfFX4oXbergdJt
+udUNDQwDBL7FfLmjUypR0vl6RRE/LwwrQOrcngvl0fdTQWgDvqRdOy0twpfl08jAAsNkciCAqpfQ
+7NiBwqsmykc5+gSn8sEOghsglvMVCF/uuO+yBCfNxUfYcbYMt3Xs/mtrPjwbrgfHIG+WqLKQ2MRR
+ah32UWDy0a4oelTVhlqBNZ9tGKi0PXPXkkTcJA4Wllv32lpzi6ix70CxP4LykS/wywn4ftnJj0fa
+XIi/6kCqRRDq8K5BWyfyVHbCZPGoqFY8P5edDfT8dgvGZPppWYy1afHUtxI3623Y8qulm4PHK9EW
+GD5Zg9UivH7lTnE9r7j3iG30Q7j9IGc8j05Q7A7PI0Fx+AcTHLNKAj7rluCSR51v886y9IDvyHbd
+HsdAIKavLRMwPFwk6hkJvUu2Rt8swxJn82oVRrbFMr6B370MHsuM4Wxx175omSE+CKbJ6fvepFzd
+HwKuuP49BNG11TY7KMC13aF6qm2+L4YT1pT5XsdW1YTLdizTY1KXz1LeAa80nz7DAXoLvCPnhvy2
+W4KHFbjlM89bevkR7z7BVeYAhm3ELcD0f8Ek/AuEM3ymALfbsA8aN6GA6C5ggOHITzCMEsxuYYUl
+MA4wnJf/giIt3CMI+vh1OIJbdYNorhGOdJcoFwmkRfgEKQiIKST7j7eQCnoVEOxzV5apcrlgXBVC
+E8bxK0sw0heu/IH5ev3pGoGcDzgSEJqLtR3IspTmiZcU/DoPJWmnIpxuEC+Dj2ML+xock756S+C7
+zooec8xAswpTkDKuX53jbcQEsiZxDnBYHzoyA2VwdfJF/BGXPoWXZbuf4AcnfLsYlVrqFuHnui3a
+nx5Pn8Ik+AaG860Rn7hLsMt0jZhgmDo0gbi4UKTFes2WheU0j/QNTsBrDfSmBZhpdY3ge4S3GXsz
+v31YcmK91byEGn9LaCUIjjtZahV2W5bUtAjjAfzaSPcZB3ld2X7z+xrnFfg9geB+TJHuO9hdoHgR
+V7nVIJu83MNljVtRPNgTS+2uGjls6p9Bk4NEfKR7/LzE/qKAtdi3jFKncLy4nDLqsGDZlYPMbQKr
+06qWvc63qQ302TlmWqK3Y208cUdQ7tSZypHYQ4bd+st9UcDXj+DXJt0dV+CVlV+fT646o6ztb5fg
+HlA3ip8Ov0CY4T/ce0Btn1fHWTCd1oEEFDeWa8SN5QN0pfKL42XxkNhiclqHnX5xCGALAr6mMxCu
+dPi6FoDkJ5jLIrzcKtb9ueowx7B+ssFeTqhnVMEiLcIsc3NTMl/amqD4vssirBZgTadbRI7fS2Gq
+Uo32y6oToCGFCxIUTvCbAE7+CuSWOeigrOe8D8k7LnHW4RSMqAsulouFr5VYZxMs69Pa9SBdTkQK
+1xqdSaMC2BIMzw6P+Ivwqsg2V59M3qFMqjc/0N7Vy/36shitwlJ3WojLcgwCKK2tgV0caLEHrfb0
+KhLrlmAYua68TT5Bb8tjRrg9EPx6+gpP+rAOpzW4H9njbQ5l9s75c0az40tWfi2O5N2v3Lj3VVrd
+LT6oUVbgdJVA2RLURaKD3+4jr/yv6z8eN4Tug84nZIgJii3BgfahPRuhNBcLcDSpNgvu/dQdY56f
+wC/TFhXvtLkoSni3Gc/5g6vf1LGfWJJpTHaLn2SUyDHjmu5C6lkezRxvefZUbOYn6WAlPsVs0pju
+42mWbtCHOM50eXTRwoKLsv6albFsWomP8dA9GbdpNT74C7MdK35UQPzrI/xfDt/ZjAkstlP2FaRi
+pO5LsRmR9B9zb4yhoNfzaYglCQ1L7sORD8slEEYHvdfVLcDNVxieD9ddUvam9RlIS7QDCcg5jrRi
+z3lIYVSPpm2TZ1mk5s3KwptuGI22frcDduMAfssVJGnoVmGuG8e/HhK07JsPPzJql9XXAg8uA8lx
+jOWfoMu3zAP9RPA9aVJ5u+NAuCXjtRh/9tU68PW2GLQkjhuTAxxvGCA7MoCBHg9z9DwJFRokEUX4
+LnQw8r6db9XT8vcHY9T1IOPNUoUSBJvrw05zITULMkpx8ELOYnOyTYuj5ifP02pt9gh+sf0shMUp
+73q2CL+Z+8d8u4Of187jBvZL9jbfGEEAfFX85i/miGAYWA/+TtcsziuDx5rUg/f1Crxcl8bXDW42
+f2BesKyv8IXsF2txiPy3wLf7CIfxZJSP5e7ANkWtVw9bmerzHE0ZBnSoh92R4nQIOdvO7uENf7Rp
+9gGGZxj7q+7GWE0mq/KH8R6CbQXsENsrsaeyNnNOpYF0bziBXl6kUYZzn6e8XJNBOcDJSwDKeJSc
+FuJ1fLAG529Yg1nWrEBulrMBgE3cI2pv/mT4BcLfDL5Feu5U/5B4lxlL+rRn4rDA5h2CNoFnAlOD
+XQvkwa1cIhw7EB5HXrXRwX25QRjuA1D+CslAsecXcN31pHqzLHgOZUN+eb4SFFvlpj1MFiyswMMN
+w4E73hpBJViAGB0YDxCcH9agQs1+eVmDU3YQZNYtZs+xGE8T5dDg2jF+JpwCR9tPUz0iTbdq1V+Y
+FoDOjwKCVYPbAL4DHFfjowxB8rzVeNRPD0rr0yDjnbX4Y6IHnYe9gk8Wx3I6Use0utUdQrylBXiq
+NdgbTZspmh9omWabgmjKAhxp9nkhZPBVjFSMHnl0IygwvLSSxvFlZ+D7NUCzzMlpusfUbWUmCGg+
+TV/jGB/SWG+KUGswH5ZD/tbdtt0Fi8q3+cwnhfOcsy3XfCorq+8lzvSCpOac8Sv6SACyWYHHEQLa
+TkCZvqEKJszYb5lbsURaK5fS7RH0GTxl4ikc9xdC3mi/HI/z9lBFz/CSKOnaQlmUrwyLopJnUkbv
+enWpO2JjvIdp2fnZJq1bf7kODy32sjZrAlSeymV3YGHniai1L/kRIiAwQdx5FvC7A2Ch+Q6K66ty
+4Q+c5SsdN5420Jtvj+A5/4GK+NvwC4S/Gz7UlRPjZdyEiUWoLQajUBYliXjgKKQkBbWBgDBen3MD
+X5cA4QFu19HLfcK7tfhybxZhPkymktZzDCv/8hqPPvRnMfizhUdVjA0A7BJHjpSbhKC4u0Mg3xW8
++QSTDqAelJu0wkBuU+nN1Tus8yn+VOZVucycldYE9Ru2feoaaHKBtK4jWevPeLvY4bhfWI6nP/H+
+/koAG03y2MFpmflkT+23X1oowNXv7ehOemrnrUKedGEJk+Q851XeKnB4OO5Vu0+ZbEc1NgEutbFZ
+B8RWADldI+Lop3i+T/gCjA/TCfAN+qNrxBPI3YdyGOy93lPsstmvO5xnDbgvuK1PK7PPBL4wyisF
+wwWcfDTZjl5vQSEf8QG5xerdr77xOfr6c2nU4soLE0MAYqil14sWgLf5C0dbjK/z1u3uWybSsi/W
+gFkWsQJNmWd5hqR1X5WjT575xty/3XF13UZ9Dxwv8OUcKA2IPV+7hNZfnw+Std1orZ4TT9qINMls
+h7Jt7ftJtucIva/NYzm521ZzpGMeaaAB39lfw3Rl+A6NLhH1sFwB4O4a8fT55Uv7YWodfpIQ/174
+BcLfDS+10sdFHjVtYgns27YEVGd28/UaFFoWlpANq/JVbg+XW6a/GjhGfIkJYREGcAHWpDDSXOFC
+8zRThNgNmisN5fahtwFFB9fEvZqodE1QT1zvIFfpkp41btOvkqxrgDWCtLIBKVpHOQDxZg+ro4CM
+VHwCNJg+ayntnD/GecHwEr7LKd3q9QIce9UPWHMb6conFCHR2OlWZOuy5MFr/t6A3WPwLbKFE9DN
+M7zTSDKJuAKiAfTXkNdc8QKQ8QmCs12OUzpgg0YgRlpaeA7nVu1D6SkTyDTPPIv9nleOd+2gNSaH
+3w7L7zzWdtG9lHHH4MYx/o1mjb6xP10htH/ef7levvps0ql8iBeleOtXQImQd25Diz6Y8qnI4pUk
+QI51d02bbI9qY4nRei1a8wWOtlfcjxZi32hsXTnBz+OB8kPJM67ELg4t8/RZipIcIgOF2dgbhXUO
+77wYjCTdqZyNWXzD3AbLO6ipV0D/1LK+n2hX5kHK9TZttmYtu0Wez939g5WiovXsNdzXcs93VX5L
+nvgDTWQO5U4e4S1e+0doKPCb58ReyHk0BAax1PFqEc74ZfKsraXn1U+HXyD8N+Ek5b8T5FxCjaYw
+bYJgggue0zcFP8mptxrom3M3mufdTbcOjue7hk+S2du9O5cyJm+aQDygshTBxc1CQSEbXUGwzXcx
+KhLJuYm/HlBhWwMfUW9nei9RaZPZ7BpBOqjKTqxCKhjN1kOMdifYVdDbALD18adiTVAs6vr0poHB
+hIp5iqsepkWmp8a/avAEYtEWax2381MXTlDL+g1Ci5niLe6N2T/YTKfBPISsXsAi5ya5QRXvqPXR
+uBUWQAParfHFA3XhkHwhO7vhfram1voBcE3zZRxq4Vc+eKU7tjxZ1lzifOdWak3Q7cDvPn8mJe1A
+e+qDS2pbD8w1klJ3gOAGgIEdDHurIEGNpJ+ONsonYPU1SUsG93heAJnIcFdQqXVUuua9ZN5nQPd1
+3qQRQFk2uIu1pDWRZ1veYVVQrm0FktdfOV9kSOVKflW702STFs02mkUPeExwKy4nCc5Cxl6Sv9KV
+X31HCzUX+546p+0hb8yv5vmL9lWJyTatjac7jKFrvlzKEwiWPWTN0tvzi77Wt8oKAM71QJUzGyAY
+glmsQPL5EYN/NfwC4b8NXbLP5H9cpSoYAO2uG7eXfvLYUMB3MZQnAHbr4DjfGhHxtATDMw5gPZjX
+pLAn8AVQ1l9KpAvhn7eEpKcVObYJhYgcN6WUOzhG78RaC3kQqK3/ww0CaO4O55+4Q1i1VJ2wdjQg
+bg9DQK+NsiiLodm6jRuffk5gHIvTwDHWYm20jSkoZgZ3JMMYGkBtgypCuT8EJf3jiNKAQkbhRkP1
+ZUUD4pawgFrj4hEhDjeXtXYdOqkP3z8aXGekh9Nt19XNDqJcy8uwAJSF2Avce67PspxvRvDWKBup
+xnr7le+Dpg9JPoWpXLsgQbGt0I39Wbdz6oI3IVZZiPkiL9W/NDQ5N+MAU6qii6t6f+edluTYJysw
+Nz3H4qqs1bJVv+C8RgMgYHXKJPqUapz9smQoy7sDxXdFYz8s+0uAWPYD8hJKrnv1Re0O7rtI9hGf
+c7sDPIGrKoeFecQGULNhfSU5+S4ncN5GKxuw2/mvD1pBofJv9Xutv759QwHvs0W4eOAa+cd5GN3e
+gOxD2Vfn7WUPeX7Oe6q/FCH3B3Jf7Hnk0QCxBMEhi5oF+EDLvUGaoQNgK1oDufHTh+QSDIdV+Pc9
+wv9j4VNjVgsHgKKKYsZ5Cq3AFLYsQYZMK0Aw1W31JOYtjPYVmOcKl4jLxE3CLOPLiY6KpgQ4LcTU
+l77uSYX/8hKUnu4RNna0CpqSgFP49CeBazYWjiKQHe4RTbmOSZ6LZFXKheaZR43IDln2uT15H0ct
+Z9cNi6uRaRVWy3DmIySBaoSgL7ApHGHAtLxOmHFSgkr3AVIKFCd3FWA1TXM9eauXa1Tgr5DAZ64R
+XibS7O1RBH53j43xd+zoW762c957Eg+CgmHYmg/PsR+xZ++DdMq1gy0ut/xP+a+CdoBjOnZK42pb
+rJ1l+dZaFr1wAsV1LFXJu1vRAygI5oqXTCvgrzZGT4Dum1WYt3eXwpY5yi1DaxXqTQrox52mgJfC
+gq4RwdMxoQS7S2SUWwSEliMVRqh6azuK95lYeledtTL7p+j9Ia5j2uO1Pknb4t1FQs/zHJBSk6NL
+ZuaZ5WK1tVeLvuo6bELTiAhtGlMU8Ja7wzOtQDJFbtH6eB/mBz1s82yVel3WHvO2/KVg0R7NfCkK
+CFTnL+oe4JfHDSTjGQQzn301hDtE8LjcAB0gWI8WX2y3tBT/dPgFwhmOKvgh/KVmhu5jf6APBd72
+pSXD6nkF1Ah8fQPABL1lDbb0B3YLMByNukpZBb6Gco/Y3CZCV11WZUNwTUvPLlzxdvqblSUtZQoO
+VrqpcN9FwCYOonGXecxdGzSCXsYByyftc+JzDa51ZRyAeAHgC+ouoWCYIJiAOkX3JhHtLQhOhmkA
+S0oK+EkrsFiAQQuwlZW4p2WqQYtu1HF0jWDVwbfDilwde9oVn+zKpxLFG7PEtnvnfprZjhyr51Pn
+eLAII+NtiId+Md6svO/iDvg+guf++4GotBlvVtc7kJmC4wmKSy1z9PWcuXbsNCfx18ir3caY++sm
+CL5H//rPoMo7fmOqbPyKtta3gZ2cG7pDOOpiPkCwqSHD5PnPAgfcNzaBY5TlZUV+Dh5y8y3KnUEy
+V0TjlnXI1P9VfLPMZac5dzETWcwbvfLIKTN4zoFeGKSeIM8nU5SeqwEW4JruDgbb3CKulg+kWwSq
+XB/BSV+JRp56bJ53TM9ZOpW1kebfodSAnn7xKwtx7Jmo6xUI5ppyj0zazOc+t9APPHLedxAM8Kvs
+CwT/Piz3PxFKgAO5SZ/1kpzDMIFLL5dkYqFRvAsn77fKAiutV/J0twgCYAW99Av+QqVxSZPT9ACk
+hG6gN10kVp882qOjWorBKVEkKaJTpItYH82SsvayWoaHO8SQFU/Lk9e5TSINjRB95pP2CxT2NMsa
+LcKX4bov3AGI1wdQBAzzX1qKo402GVa07dbDicZzBk2Fpeia0PsrTayV6VXYm0WYVt9o49B8+QAz
+s/ozDdtrDI4d3PfxKHDahtoGKBmPt2lUieupbzbwqGIDwzGWhfHXeAoGeU6FZQVVmWsfPozbU39l
+jc95Md/T3yP3l7pBLEi1LoLV6rs2dq1urGbqVqsxyV7KsqbnxV/r85YfCUmLuB9dI7q/MISRhQ7u
+pJqaTIuo6Yqd8H7JHvaH5wBiHQ4Bl9Ip67NaNxsA0elh1rlbXd0ewa4/A99pz9ewga4parCLvlN8
+yQFNN+cH6LCFO1oHQppw4ZPY1IMXoU2TV2EL4V2Sclp/8UjbLMJC66Mdc/WGfizXZ+ixLPyh/Zdh
+arpX+6T/Fj9TUj2AYNmDxnJ5FD9hoCzBxgsQ5PEJ/C7gG26bSXs76H88/AJhhvcct0KTMK+U+OHE
+D4varNZExzXZWZ2+4OIfXCDYbQDi+NE/OC3C1IOAuEbYAxgmzasLPP8OP2T2+0J8EASHOT5MepJK
+1XpKVwVZKyf1IK3EbeK8xeavt1t94WuYco7H/Z0Ep9zhYdVd5AV4YRZvjqj09sOkoegazc7ynBMv
+veBFKc5pzIsWuilwrvPNHDjMueUr5tpnS9PV4TClhzdE5PtEp/Lb+MG3WLMcPQ32Ff3T7Yrs7oo6
+YGXuKzAMpFuIwSQ++y2PxHo10B4M+iSeh9of29H7ujxOh57GQfJdjArBeEvpvqMH6z0HGfe7niuA
+tbWiHGBjeXdFQbD4oqfvNesyDBA8rcLY0tw2u1tEhyUmP0i5DeDo2if49RxhAUOx9AI59+II0QT5
+FfNzC12eOV4yPeO5Gm/jWaeOAU3E1UxYL9PKaV47V/blwXpnY2NPlakgOOdLeRAA/aWz7eD5lClC
+L2vvOloeXz8U92QlbsMd82ODuOft58xymRa5vs/i4Zxg8aMye1Rsz8GiMss4ziBYwG+Pjz0Gr7nO
+uKexR98isfkLXwWG+bCcarSp3U7z9Z+EXyD83bDhjTMw6cW+w53YV9z7JmtVSkZ/WG7Ji2YZlrdC
+NDAs3aTFON0egJSyy+pr4gYR9TWrsC9QjZR1cquD0uPRppVhzy8FtLYjQbC3MpkHtPhTG30puTtr
+TmnpDUmJHcDqebQIX8sS/MJXOH8IoTzVsmmavwKQNUeS9yKILhFLb0wpAN7eV99dvv0D9H8lg3BO
+QynZALq0MNK3uKaz8hRYg0/aH8cwtff3xGGW+MYW1LJyMyJ91Ot2t8f2j95bAVsFe63KdrWgcuMc
+t0nPwysHicN47IMjHLi4mWkJLgswjC/6qpGZY7lLkeqXuOAkbKi3bQBrT7BZ4yWCxfYOxxIrwOTu
+62L6ybrVAG+8YWJMs+4S2WXZP5VQZSkusEv+pnLX2jpbdmCcf1Neb4I779SpG8QJ6ALDRcKfylm+
+rWf0Qpel98JG3hCD/QTDlpWgf2+gUr4ThfeWWLIHOrABYO4/qBxdx2vQTg/FnWh5X06aepyrp+k5
+xEmZ8ugkwSbtuU6d80Fuvxf7hvMIPIPgLDPKQ8pYja+D37NVWA12RqvwBcz3CL+ez38u/ALhfySU
+0vuOvn27sFrh1KibICnmIgDWtJvla9MSwCLcIYSeoBgoAAzA7wDI8GYlpoXZIfEAxcsybENI7kNj
+8BbrmtolTRCcpQmOucePdQ/3iZeTvyavADBq9w6L7gTJV/oDd7/g00Nz0xpcwkNV82nS9Dr8E44r
+n2pacAoEB9ho/JSZ69wArgWUo49h2dVb4enGIhZjWp0JSPp4uL4QBaOldKH7Y5RHxar5iYdezZGo
+7odiCoYJgOudskxre2u87fqYdXTG7f3wPe4VAVq8FNK78KlkMt53J6S6LgHDDlw3/F4b3+LK2Hnn
+CFiWXb2t2RCFJdGMj2XSx9ZqDikv5C0M9A329elIsRDH6BzIdwxL0+al3Mk3ahXWXdZ33LL2W2er
+4JHia0Nd7ClKmmCnM77U2GT5sBDD+/uEo+mT5ffJKqxtTWnysDSSrsFr2dWzuZGrwG4glovmeVLu
+q7Xeyqb1RhmhG9JyWbKD4ndaf9dKdCvxuVyzEvehb3NX49slVZvTh/Nz3JN2KPdKJqmi2yzFGxKu
+U1RRLp6nXJYH4j61BIc868D4ZBUWAKzxCwKIw1J8/bpG/JfDiTVPwfuGxTOvZvlP6QJ04h5iba4m
+NFGSXeKKq9QNYv28XCDip6D4awpoukkkAEb5/IpPMF0q6mG6ii8gHGBJVNBpRjw2o3rTsVyCGd5C
+JUCDPhA3LGQ+gO9hxjk3J+Nr0frEnkCstbiC4PmQ3AXQbUJAb31cA9mBNX7RTBadhc2RjkGdk41P
+HWhgV3xGCygvLeWc6wTDAK29YtcDUV/zBaayEotxvlkhwTKEj/VxMwbVLHMTyIBEKfrMG8EO2f6U
+z2IuSwR0a3fOE891+SvDm74jB1Cc57fFQk8flv+VDMqpk+Xykc4HkxRxRbo+phH8S29Wm6DYciz6
+OsHVBK28cgTL8YG5AMnz4cqjS0Qo57QAh0J3XoDUfFof6vHHWUwwlJM6rcBB0zsgGDzDuUzi4KiN
+jWUvYn8wziHguKVfuUbst9ttJHq6W0JV9MwKtpq57Do4GftsN4vl7RPLC8lkVgHGq1/B92Zt/sxr
+zV5ZhD+mWe9vi9sDXSJP52baD7RZzvcS61wvMHuou/Qktp8nYK2Tm2tEnLc9EDfLoMo0P2GISoQj
+3SCSXq9RW/ik3mpF1Vi006z8u+EXCH87yO7cGPYzgJIP0Aw6axGJgCFvNau16sAAvvG7iv3pEqF+
+wsXFXnHxCXag3s5O0DzSfh8AscVDefEUH8er+ld1POVbWdGkJG89o17aVLrPpa7Dg3MopQhJizjI
+rdzdE+S3gd2Kb3mXrXch6i8A8P6+4FLDBq0T/TgB1JtwKq5uEH6gJQgeYBeG5irBi5GICpBSTeZ1
+fo6wspBDoluFZkbfUiGVspTRRH2dtqt+XeXT/PiW1886n2eSwbnT7TnPnctBpdYbVRjhchIVVE/P
+vm6qQ+e5vTcNJU+4tXLZbiAsvu0F4najXZQh1juswe4eflfBTBf3Dc7HqGfxkB69H8mTzQocvxtB
+AxIE576OW7je58cknmvhstWUDgHFctVgjS/pNnFQBZMbG2P0tSfMdnRrcKXLDYK0t2nWPQdtOk4b
+6TE/1st4o++Ky/rZOCW9lUVXCFGvCQheR8+TrNGQfNStvwVqy/pbVt/HcrJmnReKoHO3lXkTf0UD
+NrY4lt1kiig6cx9iJbRdXBw+uhSlnH7tH0y3CKZbXh7VIkyV1n2Dyz3C6qE5Q75H+Nci/D8SaM0Y
+xF3jfZ7dyxSW6GBRCzXhEeW52eN3hTWnuSwAycIKik6hWYOZNmwAWC3BM32FcGObOaSwAjgHxzas
+KzAOrm7AqG8wJ2e+NSKhXps/R8dTpKlwOVqHM1KgtwPjev/Lbg3eQfCTm8Quhk9icKrsom8yUAeZ
+9MVYBEh8ENFDuRwtwvkgXD0MVgDdwNXoiqoIdI3YwTI6INYxe90+z4GoRuJYNL29KLdOm3yuM+gH
+WuX5RifPHuuQ5hv20bIb01faqkA7wbWwjMnG6LYpAXJT+Zwe7jWtIr/coKA4Nr0A5HxIig8fZDm2
+R+RQRwMa8OW6+zwGCKaAePq0sqGswWlcSIvwVNp9V7Vd5ON27pikRi8/IJlEtPrbWrDtlmmtBq3y
+inFMi6/jyUoM8NPKpDF/b7FSbSuNpdLOKpCedbSteG5oo2U0H+bktuV+NZla8Rt2PW/R8g0euUaW
+8ffvEsZOs8pr/T2s76nMU/7TtDS6H2iDnmlHuUPMgmSouDj0lFOhPSW/7w1P2bNZheEy1zwn4uLy
+1n4GXBaW4ZzbVHX9C7ilPn8twv/18Onctx3/AkZSyTxXsDVf8rBY1yRT33y0gLI15XaFslO/Xb7K
+ZFqKmwXYjHfdy5TA/og7BF+zloDYAL980cUSfG3uEWtMickOCrnABS2Ti9BsbQ4BxNzz/QE57bv+
+dO47bcyzbucEqpC4HtcOnhZhu579hI9WZFqZDKjX453U9oRWRfeNthajqW2d61yLmu/+Jgmg3u5g
+UsGaC75Sbb5L2HM2y282+TfOPfsRk++lx4aeFmDRECfnSvN13CONMWNIWi+juuUkm7fyMkUsYaOs
+9tNGuqJVOiXBqCBdNbKP1uucbaKm08fUtYuXO94SoWD4Cnq6RhjcDuCXm10twq+Ary0erU8V13G1
+EYpWAHC5QdSvfCTFH5gXUl5D6zuq3//RAmMXjRm1KCO+zfsyDnnyEE4MF7QGdqPipI0yLmWfXp8G
+lF6ZXVrd7Hmmg1fmPw2HW/URzR1obn3csb9LZjgmKOkZUS0AACAASURBVIbLGIi+vKy63doLSQNn
+669Yk5Mm3T0Mu+VLwRN9m4J5h0yH/wl9iIEUyQF6j4veQLFqz6qw+QdjB8Elrw4AGQiLb6QT/MoF
+iiEs808/G3dSD+P4l8MvEP5PwtSyW5iZfiQ39Z8AxPobkFIgWJfqWgeZyksneWiCAsGUDEgLcZPI
+dHuIfOZ+3d3HmOde8UlltQRfAYL5YY+2/Xz1kft3Yp/ttn4CXQLp+Bsbu/VfaAWM66/+lBb6uCsK
+KrEEqyvdwPDVAe4CwAF+JY8f1YC8R7gBYLZDsRxrZHqB8qydz/LvIU2g1cBvswQDZT1kmRCkTFMg
+cr1SITkZGA0sK+LKNgUn+Iq0B6ZCAKd9Tqy9BDqltXe3CFGXjaY8MbdvvfGh15HzcqCTI895M7fa
+aDmCoGyk869r3aURj33Vtkyqq+kSMEz0gdr7afkF3AiASTcA8UrA5FvEelvco7+Kf2FbnOcRFCP2
+nltdWC25V/y03hpxb1+W4y3eBMHuBYJlKre1Pv1yjlXAegKdUv6VZ96hRV4XHNak1rbktfaHQ7rl
+vOnyAJytxE/0asJahzI60F5uP5WFvaJzetb1VG7qLbH85yRwcow87NhBsdw/C/4J6YngUEkfaBMY
+W+XpVGl3T/Gna4TzsPf5UXkAiW4llW+YPvC1guJ6DSH3Bc/zBMXkaQBoQNfR8p9AcKW5DhK37gpx
+feP30+EXCGf4cPabmXdjwwPlVVDtVFXbyC0wbE1vFb6g9SSswrbOubDASxprLEBN+v4q5wL5urSo
+mr7Ablh+viOvALAvAAzU2yJs3eJYlml1XbCumKms+27Gth4s4CauEVV60lrew8zvK4cCxNzVOFmC
+CWxJuwr4XpVv1z1ooy70uIlUJXx8UNmHEfWxzbSCH8fpSObrSin9glsa4/a159wRCxPcvnKNqE8S
+k8GBfFAKdX6rHNg3QKKd53mps3kL8AVYeUgrnX+7X9/5McYcj4qO7Xyp1aWfmT0At4dlda8xaTKL
+Lc12p2W4vyd8vSHCaA0OALwqo2vE4n1uevJxXryNfcQ49xf9gWEd+LZ4jP35t/L50JzKUBdlrvPw
+avfUDhOLlj/NZPHnnEp2e2vLZqSs2KTtgNYf6EpbF5JK10bP/VjUtm2mZXiUfUB4r2lPZQ4g2Ea6
+FCDze5r8ZMAOaqeVGHVUC7ACZVh110Z3G81e5evfXSKc+S/OKQFQwWu4cIw7zV5M0ZRo7Y2ir4Lu
+SB/gV5bgl1bil2n52Td+V/kJ/3T4BcLfDqKQN8qeV2GeQ6sFFZrsf8pXrW5agglaJDQAmvopwEvQ
+AMjDb+MGsRp/Zs/1IbgAvvlFOljzDyY45nuK/UZuq3tWbCUPOyjuykfBbgfWLnShZflqav6AAgNN
+4KdUs9zNHQRbWYOvAsRpEb7KJQJB2x6W0zjQwXCuqz1zVJSbeU/pPD4oGbrU7OAXYhFm2hKIcdH0
+XcJ5m74xtbDs4O3k9yCWdX7VyX1S5jbZCMqsXqQG+jLtLW/GWcWs0jQxZrjB0wQ+hzJC2oGx7EJv
+Zy36Q7tPFuz0OGEbnCqZb6ZdB3fH5udzcmr1Nb4pYm1yC2HjvOepV9v5BNqCGJQ/vBME4X+my2Ls
+uQeWXIhJusUafCvwnQ8eSx6VPvo6bsoaVPYnyXdgsMEt86G8PMv2GmZd47Kvy0hHfjWOv6NbhNd5
+u2tEyTPrf3rHKIeUbtYnDYf4kWZ7GXuZxATFZ0uxtf0h99OaRXexcHd9MGB8OMPSkjzdKdoUHPr8
+zgrcaafJqo1rBzKpc99vssurDpkWUXwhXTzm0zHcI8iDO+h9DZDnUd0gvP+YH2KkWX6v8TNLn+Gf
+Dr9A+G9CyPoU6uk7iQfUMsNHhQqnKIjg6VabxUN6kdHcwp8sFNCy6Pp6g4MtIFPKLpTNFVu2dF19
+ihk8lptCtwgHOLYdjNNNoglqX8ZoAA0EnwAKoE/CijU5lN3ROjzmudOQ0qdAgJXSEmlnEIKt+VUl
+vrtFCF3dJE4AmAADpFe/Cnywsy2zB6u8yVWPafKMA/3ht52e6dbG4HcDtlde2dk1YrMOR/m0bpLp
+5ZxsbCI8VFs1wj5PqyaX+Nb17cwj/BHFknnTAtNIkjd8A5NfHfus5tQOLpbbnBjnWBtlHweAAXiZ
+FhCXkxG3c+5ryR2REX7dML6cPEHr+qocLkt/3/bGfOXjBoy9pREyy1jHSK8Life/fGAOgp1yjmT8
+3/rpw1hCc5l3ioji9BcAWPjQBy1OoIxUCy8X9OQXzKAPzT1rGOsxE9qQh5mynTaq+iA92pjAF6hJ
+O4FgEFKKjIg8Bbzv3ghBePxoEZYuTp6ZU/BY7jAVPX+TLtv5HRSXcix/+MFHA/x2v2HKJrpHvHaN
+yLS9c4cYAFrujsiWB+GGioYOjC1fncbXqP10+AXCfxVUpX7zrONpqs5q05XRzkpQAKX4pXC6UQVj
+OfjMCgW13LLkk+EJklGfS77EChlfikPop/ZRDpvgd13JLdDsm5uEfINjDWEIwASh/jyztKz1B+MK
+EKgKKF/hoqsCaRCGMlpAbx37zt3BroXVN9IHALyAcp1PcNBew0ZxzLUGZIIqPeGQ5vW5qkjeoiV2
+1DIJhLrbQwfBQHvrQJbRexlV3kw6QD5uWybAjVn5+g4XiLxt3syZ2XgbctWxz8g7EHyiYcQnQ/Y7
+FkkoRf2QrzzbrblyftK1zlWH9pVlTv1l82pAm2BYTzA9mQ/JwfKrPG5XySRuCQCwOwGwCRhevu26
+Z4ACz9wD9EtXGgSgW7UXQNfDGtwAMMQqPMABh9RkJxQAnXbNYX9trhF7efJa4y3pz8ky2LDerDYo
+E/g6nv2CYzZQAHq22vdNJjTN9WI5O5wzhzKBy5Y/oslMkImKQk65IHnsYwPI3ENlyRUpugPdKDM/
+p3wCyq3LMj3z+B1aDG6L5gr5npd1lGBuPLYpMqEVZ0ZCbpuoa0S59nRTBy/2AHkgDr1sB8GWtHp4
+rliIoHqCYL5WLd0iIv7T4RcIj3BSjqe8v6lvhdNmINhAv81mAmKAumWsBeR8+jjRIrzicashHnZb
+Fp84KXyC7bZ0KrPbl1KLDpL9840RcIkvIZLgN98UYWWZpjJbXcjmFXunsDmMPbevAAf2o7lDpHW4
+ztN532SGFa3rBasjN+kAwB3QdlDcfIK3B+Z4S84SdO8uEXKMjuZDc9p5HY/tY53xNo8mR80nWBJQ
+nM05FwXlw4mw7rnh7BpBBhbrcHPirGpXPPK1g3yDgIfgTURddbfBkpHGbG1D0eIPtD6Z3uM5mTWx
+3vIeysHbHl998lZMVzf77Z2mfTyNAyilmXQuEcd6mhSxAiP2Lj8X7sL3CmpP1uAGhoGdlgA55q7t
+KyRvpn/u6V3CIiD0wxr8CECfu2fZbe1HJW/9xTrHGhYPBrfv/DMYqa9by8x+qkzb3MginNwievn+
+ieVTc2aDuIFZzWPRQcPTWG0f3lPa1S9YhBIZlHKyuUbITJrelj+/N3ixtV78TNoA03NsEv8bWuXN
+XavR2rUtqJBX2eKjEJlhpCk3CX4zHeX0obgls5n27I+B89/LtnyICwTTCohr28sDdPVp5XygLj63
+/NPhFwhn2EXdFHt+pL4Oa99+co6qsF1xVzwARZzjck4yZ+AHG/GLgINM51Ug92IAjWX5XUyplt+v
+AGVKJ/j9yng/vyyu5eOmM6IAOXWxKCHKVRfaFD7ccBj0uV7pr/lySR4UhILXCXDf/AAF0RBQjcyv
+Juq86madSwv/rpjsyL8BJYcIk+OG/vKkAZTHw3KFisvlIcBBgl/rVt+Ta0R+pc7KpxNAWZgzXXWp
+RTn3B/t2GP8+K98I0wWigdzeUnv47aFcTbdnsZp+AUxa19anOLKgPvnWJkIkCC9MIHQVMED2X7lL
+XxzRaEEwuYhWjdfveLBrvUz+/IHOPL8xvy5nQXdahsE8iF9kbXYCyDaOx18BYjyW9fG3L49K5j30
+8rZR1EXi4efALWdNgLzhLjX9b2lutHM6L8Ta5pQ2Tvyo4unUnzlyufq2V+UkzjsHF+LCxXhEHSG/
+4EFLn0PLNPXIXIinFXwfBk+0febHYj4Lyn7khR5dhTIda+Ox8JnGTHuU82qDDylv2oHLzwkxlOWX
+Fx9xDDemBWy7EWgB3YiH2wPzl/WXbhEWINh+H5b7/yq8Bb9NcnRSJihKO+D1F3nz5nkKdBEMl2qD
+kFkKigEI2MUDIN7pLnR9aA7XEk63V5+atWMMXQExIF4baMaqDRATMD87EEygQi1D8HAOddsHBW5h
+Pf0dUKxAGgTGaECZA6IVrUAx0YcMeqy4zmceQ/EJZGv4KeGC0oJPmvWYvOO+geG8dafAxoM/Tee+
+MNp8KYQNkDvL5q4h7+Y8+Ul/fHh8t1c5GVK7C/jJiZmKrPI3oORPPPqmOz5kAKc5slNhwqqc1YIu
+2v6X58qb0xpY5LMDFhuR8M9gwD0u8gRxUkkWqLUzGOYCb7ToxH3Dboffdz04Fz/TdADf9WjfWLMc
+j1h9xzjbz1C3kEf5TfbUkA/U96TswKdqI4J+PIPvHOZeLl90mccEQLanhY9XX04dyopaf9OtLBmR
+m9PaKYyblj0t06DZUzlgAN4AZ2Fp5ieYiyGXnFIw927idV173NuZj7UMvd7uCLUxjbNVnsmdkQLA
+HxyhwLnaaHdTqS+bGinpUHNbd+w2+viaKgEt75QWGFbAey1gnOdcee5Ph18g/JfBT2z/Roh542yN
+nLYZCoFo3lSEQLOKae/aVfHT0ZBuFJCjPgj3OSBm+XpjxHqjRClaA3Cf1IX39oFl7Qi9G2lRUAkk
+nxWTzrUBDQS7xOzhzFan9VZaHxLQnq3ApwfllgW0yiU4lvgmnRIgBL3pHnWfOI+ns9yBd42cc2Bl
+YcWM8pY2xMKCaQlGaoitrJ9B7wnkZtmqro00y7a1/d7xHJo2kjYVeHse2hPZk98cT7MbS3LqSUia
+Q1bOQ/4R0NEWTEHv6mM+YOsucxftybIdj3eA2+uhzA3wi1IEYYt1Q+i49XTjexFMpMET/CboPViI
+ax1Wmg8GzTnjcT4IV3T5ka2zfCn/RXxxQZOr0DZq39JQNtn7yiHwZvWrH99+6a1OhN818AiIlTm9
++GTjU17ggsvCORj1yanlMjUHO5oYPJf9/UDHUrYn+2CwUubT8usyFmnj1D9ptmbl2Td/zpy6NI3D
+WKRDgxkvvtYPymzAl7C20eO87Lmmo+7JwHNTp1wJWY3a3zm/A+zaAL7rgxl8lkZpCpoLPP90+AXC
+/0B4rUjPBY9AIyMme1LTtdVO6S6oH+LmZRF+cVxWXR8A9wR8pUw+LBd+xfJmCcPweRszQL/hTrbM
+O49LBN2RVmVVyaTOVPClGkXxhPYmFXoo7xcg+PjDfh5GGWh9sHBjocqO7plYhw9Xz/5wZLzRRU/l
+bHQMBdh+TlN4FspBwHCBZIh/38rjGykshlBNrvM9FCDzFU/X+QJ6ow7VY++OLdiLPFFkFoRXAJiK
+pqpW7df9RJPvXkmQiUUa6SAPvMsOmUmYyaXEoV3WpA/JFL3GT2DXvqlhSGDb3X4UEIs1mP5ZgmIs
+aa6Vrn7eLq9QGxbg409ePeYJEXLGrI2HaZWlUoZdOpxHYfEEiBahMXAPugRW7RPDzIJcWxVhc087
+e7ftY/KiCDj3uoDmRYvKAPZju7Cdmy7OPQHiMTE2xz3nYgosOda5IhjQ2EjiBXzTZQJdFjXL9WHG
+T0tGGaDDOtWgF+XbeHX/HfMO+Qlu7xeAGNXqo5UYue7KS60jyvjgvBL40hWi3gbRQLCCXcaFdilN
+4vr76fALhP/T8EKHrfxXO77K8CZN5p8e8XaJgwJzbtcSmIauV2gxuJO2hNs1mB4Wr+Gx8vG95MG3
+Do6tXrN2V1l9p/BZmuzE+Hjrpoj672AJfrtvZL7ltnWCFncUEtbZzRZhD+kOgscr1Ao5v6SlRQyS
+jyrj1B4mk6mDVivxGHHTKaoYT+UGm/mc16ZYwyrIgqZATJUNsH04IxSPAtpVDqlb+3nSR2HTqaBT
+nz3MwUm/7onDlj5ps2kdzmxvq3DCQIpJHgWIv8gT+mfqWwAQqo8cgUkZBSmTq14fXeoKOi+ADGUZ
+DAVa/pncDwQmg9cnEHZPMGwutPlLBNfBhPbZtvTZDQLoAAvj3JfhZHGT/kzG2NezaOtnrVwHMpru
+DTR2kw3kZsWL5jVt2biNizXZjQbwwuv8JGasNfmgdWR0WrrwXO4wO951wAaGR3+W/FC3iCxwiI+m
+cu1F3vQZge7/xy2umGDLG8QhwCewfeUasbiAvaqJTVqKirP8XNMl+xVUXWvN89lY4Ogasd6i9Ax8
+N2AsVuGfDr9A+G/Ck35qCvF753dbEQv1tB22oEGYnEIhz+6gkiCCTMwmbojfsCCNejVafxguj/FB
+jv1jGtbKeTiy2T0V0GmidiVO2a3npv6M8nb47U2IlPWZdzyhBKlVmz1+tvx2X2KrcpKn1t8ECKj6
+az0Qa2/SIYhlWHqfA695fJK10xdY40P+Fq2V9RiDKsG4cege47GsoCzBhgLEFMAEtHSXUH29mJW+
+x9m3Vs1KNP87VN5Jr2r8ectm7aG/qiSfxtZ0lnAFxIuLpzfK3PWKUV4KkY2naw+oMmbPX9NTu69D
+7Iu07g+eb7yZcfoBz7SlZS75mzxD8JtgGFXhBMZwsQATADtcXCNMgAFi/juq03Wrox3SK+6HvNqj
+CbK4htwTco7nLI8wRTv7OYqu6ZpwrXw7uZb1jH+JtRqt114XS3AZRD0rIhDKV9dtyqw2nOkoTYQD
+gXOfhKzGlH46VsPnLTDK5/o4ZG1c1kofhONDuuxrjHXMsDbQ9+SizKWqs72lt316FMSa9pf5r8Dv
+Oa7nYKfZrgbJA03VGBL85haFbNXIv9QfOB+Ws+b/uwHjBMNXA8s/HX6B8L8Y/EVqBt2CtcnFGykE
+TbMc86oblDeeRxrkyLCXO18dXG9lsHJHUOsfdY9aftU9Io//j71vXXdjB5WEzrz/I5v5IQFVgLrb
+SXbOnPmWEi9LCN0lKGNaFj3yxK/LXZK/nnFtYfDxIdUNz8LHlY//OMhkLfYDi1ZinNNxCXCiqyQ4
+lHeoEf8C+EoHwocXgWFh62/N97ZM832N1X2BlXt5tArzLLQpGPKQgHojPiCZpLCy7WVNX7vuXeh7
+NizHqz/xIx6+mzHbh1J8glE/irfXyuSeYuFOsOA83tsA3FafEGA/3vZxFSzWBJ6OHTn0DIAJq+/6
+MO2pLoVkbUNjHP5gHObkuPSB5q4QIuwTbLG/CQw7kNrxtBDvGhEIm60H5vbXw/4Lc/4AUQfD+w+s
+l/cWT0uNn/Ny7r2L5ycMvLGy9iOK2mfbcsfnOk7rrTSWtPGt/sSwoR3akZ5nayDoJSAi+YFVK6/z
+7MguHx/6YG1DX3k8XKbK+KFbLgpOeS2UWybisxOmZe1D35O+x1U5HX2vjVvtMp9tiTxrxek73qHq
+2s4531n2vh+BsMxAOSoz6rmVJtq3fz4wP4KSxzT1Xp6D5hoBgBiBb9IY+KKv8I9F+H9zeNKqowIq
+0mqQEgSGN4/G0cu/LhxTZiUkCJ1iefOC77V6t6/awq2m299XJ4C7f3WuWYLzXa7d8kdCIjnEi0ed
+P3XSdo8HBbIAu4bsTbjICkta/CRhXPhYtNktdhLg0/+mYKiA2F9XebElOK1hxTrsI2o8Er62IeXb
+e9tVFCddglsO4t0qDN9u6FTWNeXKsLA2WVpZdh/DLw0AbKjwNcBcJ9BHuv8mcM5hG9SBIAHDaU6e
+8s6hgJLitsEWYU6HspTDPjv1BEjkc4j9cMaYk57vgIn733mkxO/P1Trs1Qq8trGJA2CtYNiFUZiY
+/cOSpHbFeHV7+CTotY+JAjhgMGzjGPUQf8q/Kyd7frsKV2TgxUdx74ItEll6jUQhDnveEhR7H7B0
+nGs/u75sQbdGr73Q+LuZyCrsNAmZQD9wA8BYsRoeZqdFHhSYyklZl5ATuafQ5x0/wM8nBftEpyvq
+ZR4RK7QqB3qVZQ+U+pi/8iL4nX2FN9fu3PzwHHDkrp2Uqa++H1V4Dz9hle4aUazDCIpr/BpulvjX
+4QcIfx2qOppCObgyHTM81SgsS1pkWwpY5LogwqPqyi7EE+IRT3vtlrdFRElT+aj/PPOqe/ILvgXB
+/sDcp7hGfLKfObAypj18tzar7Ade9hj802fE3QpczvGKszKJhzhcEHnSOm+8a5k7tNYSmHWhUCy7
+D5bhBA04ngTEYQkWb3uvVZMTXS3Tfjvsw65XwLIE23ByjbCdodEf17QST6nT147qe0z2/PkC+K4V
+VqJiBNgVaaHsvArX7LNKm8bszDNv35fThGZb7JKxZ4FxjnWQBBiFFeyDeKExWqXxBwPHIsmjrY70
+AUUOHAvEHeOA0mxW4bD47j2hQsB4rbNKAOAQVot2AsL+oxnx4xkfpiX4hReqe+OH5aS8n+J3+Sd5
+ljNihaC8OC6Lyr6uq5Hf9iXN4UwWZRcJPM++LKKxPHFkdc9/YFZfYz/SljXmnABD+PrDQGO9aTJo
+2HX62pSOPN5xzAMjBbWRt4KkmKoAWMRdqno/Jq3N8mAytJTpyo7WI34nqEJWlv7Y/qjTLML+INxE
+98qt9d9gMawukiqf82oJvpB2JZB1C3CxEhMALvTK+6/DDxD+w3Cvs4ZcPA/HwkVyiAvCrkpD/Jin
+BThNwFAXpclbQbZVWEJnyUeWFc4kgTC5RsgNCP50uojmzzrTYfMDuEdoXr+ly4Zo0MJdQpUFnr/r
+QMvJyQkHwcDpKXhbaBHW7IdbcyvgfaRtYZJShl8huVdaRdgyTPl7/iIbFWUG3HdsCfan64X0dC0D
+qA7aACVI4HbnhVUGtL8W39oACNX/N1W83dByb/OH1Pp1X9crJ4HLnH6mfIhW+ADDZA8MAU32t+MC
+6x170acT6Zl+yISJR6/UCtBRR2mlq0iC2/Qbjv2CwCn2PAAn38tHi7BAvAPjeJ/GVoCLd1cO729p
+eNZoTfssybyGug2mNS8OG+wZjf2E6awZJb/kOS/nGvPCY8HYnQ7HlOcMP9xk5WEZjg81We+YrtNR
+3+UbHs2/0Pc0juS5TVkuMUta+9TablKDSFYHZIV37POJFSTLWIYfhGuAVyAeAt3pEufHbcGrSW0y
+X2TPFKmmBMXL/1e2NXjHCezy/cAEhlXjIToCyw6IN6D+1+EHCP9JeKXA3jAOCCQOUW4Kxc1b+Mrn
+cSizqSr7kvmVDO8ESXAb6d2EGVh1FazCO3/9ypzJpTqD4H2bxLpHWKFjW2yDEhFJC6A/Ge0/FW2y
+AJDuMbiupHd4yfCOU+px+ppqELRR1oG3rnlWoD36AwO4JReJCpQl8xILa4BEtAxn57rAOOoS2GI5
+1AO48nXAeIF/cSMA5lZAvMHtzpJQ3aFZFy+BYteapChcATMNLTkG5DoZVIayePzvjnMCqTUPWRfo
+/U3ptKzivjUuYzJFsR9CfQG6eRz6bb1srbPF6QivhPoE+IcdQFdu6U0/4fQV9wfl/KGsVLoqDJQB
+PW0lnmB3f1PUXCIkwEDw13m6eb/LO/GYjz3Sml33fvp59TmDOY3zoPFYqFQLsK8hpnFV65luvvJ7
+aeJdZIGofUQRBIdnE9JivJmi22FwjZ/S2LGn9ykAD54T2rc20Hy2UIQ+Co7SEUKo9VwOeVZqqNU1
+hqEM1E83QpRvPgL8BtiVOA/pCCFhOd6nSKzs5ADA3jYo22UFltRTGxTjA3INAFc3iQC93VL884Ma
+/x+Eo36zGjnt9GkDMD0FoDJLEYlV6HtAa7CnDV4qun6Raeskk/WpDx+Uc99hB8G/4JaJyX0iPnZv
+F4kQ0mARNkvL77WF9SUaFp4rhLKmIIszq3lw4QCfv+6q68DSF2ebBSmA4BcvKWAXrb2ez5bgbEk2
+ODYaWA48rEF10II+g7z6LKZBccMWa3rI2EWBP4NBQVf6IsEUD8I5AEA4Fu4TXjj3kA+s+w07DMgi
+5Aiwq+VlZwV30q+d3hWjNj4j7gQuFkrWKM9K+vwe9dtUfwF2MObJIt15Up97f2lu6yz0Ze7KMtwh
+sutoPSQrMYBigXcNACz87vWLK/6VVnO3qa38TfoDc7BOuG8qePrd95yzvl+YMzcDAWUywSqVnyzA
+K9flZn7wZOkPXHBm0TXCv/2bQHBziVCR/BEcyeMLIJfuClelzzDJn+kxVOET73HYYU5hTXHblFmf
+aLj+Sbf2d1ToExBufE0IQVG7ZzN6KzTc2+wfLHJjLRbhPK9zWpQyMfztZ3nfrhGX+nsBtMXaO4Le
+ASTrlSeKZBZ37a+GHyD8bThp0t9jowLlOXmoxBqNL9UpB5jye08usQC502sJVw3rr/+S3C+V9RBc
+8Q9eND2AYNluEbuf7oshAhZhtgaHJdj2AROJr+6iylALSZP2bhRHQZifxNMH+bRoLghkz4sDYgK7
+AvckEvhdPT0C5lPekkLiwNm1uCqMTHHkPeCaMs1CQcYuaQoTdlQo8KzMYDKVCVA2C0WuA58q8HUr
+elA0McbydTf6BB+qjUqf/Pkrf74dNdkBlNpIn1p57kyv602Z1lTVppB2EBRxXCMEGjGQFaGrsfYm
+CquviAQQQp9gB8MiyedW5PqpVvJbEEKuvvlM0kdYZAPf3X+0CHtfLN3FcHTRBSu0L985MGDN+cAS
+xkDTreRw/jYF/nqdMyD2B+ac03YleLbx/Pq3feJy16cXjhXqAqxbC1WhhyLllojqI2w85/k+wZyy
+n2FJK70eV16bw4e7kTfLtHNFjZ957MhzqPKRVuSwmMRDcmKxxxMkS9CZx7N2WtYzQab+nmsuIrzR
+0SKsDILDTQIfkhvuBk5gfA0Py7HfMFqEn9fq74QfIPw3wqSpXuk+VpciQuJurrDQQznrwDtUc+if
+fxkXB0JlW2WFLL0maSF2v+AFem20BHs+OuWLamBkPAAAIABJREFUbqslACbPX4DMH9RL39UFiB20
+auhJjjMoXuNCgTpIm0kSYR0ANhO4IgiWB8vwU36CYUHwq27tgT6EctMEFNHZ5DmFZvlFBRmzkPOF
+2DbL4nxin8AyhnMa7QFQyhnd/QdBven5MB2WN8E3HL+RMk3QMc6DINtwbl6f3QqG6/sAjm069a1n
+NzwZyC3DePQOdDIuFK9jzfXAVRi6h1dR4WEzEXSDWPs3kY/6Pq5WYbIo7lbru8h2cchBxq0RsgYV
+WzOUPwwlPkhtyerbDpuR/s40O/B4BWu1k1724gCIk1WFFjCk8Y6rYCrHAaNSqDc+gGqZBi8HZ7tb
+h8s3CtC16P6Wx2hGpp9S9v7sShMce1nJ817lcJPLHJ7OxOLphadvB332xjACWjyfuF6Tjqkybai2
+0Sz/DqpqReD6NDFpQPhEk/zwSGPgA7yisckznT7CWzepgCsEu0aQdXcD39lKfLElGV7/OvwA4S/D
+Kx15KmMTtSAOcfkxqEI6IXWzsARZQms4rJICLo8IyClbYswc2DqvavgS/9pPraFfsKk2S7BdJr/i
+8mIT+TAA9oOWADzdIxwQ+y/ffUw2YATLsB/Iw4w0hdYkjIuCQWJJ6vnVBggJtAS/BLls+ZUQKGn1
+leQTtyYD0oi4loEhGumjR0UoMduYwumwqCqmSoHTct8o0CKVk7x7oKfNv78NSAWSliXZwNbrTRpW
+H7u8+EJgPYArSqiDPIUOWrx2lV7yzvVhCsXB6XVoD+y1BT0B4Il+Hj9Zf5vOhBHyU1cDwFUuQ+8C
+CteR0u4dAke08uI4fQ1D/E18+V5lRZMRwkDqmd/3mlI6c8s9HTFtBWHiHMbu1gBcbAV2q3lIL8EF
+il1oEg8949n16THN/WAi8RkmegsbLY8rjtPllu8BofUWYRBcH5gLcPrwPlmBQUhkMF6fKdzfzsK6
+gehWJGftFOYXudLUDgsx7ob1bhn+mUCvVLcI7OKmI5/T3CA1Sak4khoqJvTXJetsO4DdefGAHILh
+C/k4r7tEJDDOTtgQhw7+pfADhP8g3KnQzuTiatjpw0achWopY5m3YnD4QM9QvYoHK2WwQS88zhZg
+owflFkgGy/AWuPlwnZIvsVt9TSwUn5fJr2Zs93sB3rzGzQKEx68EDS8Z0oMUSoEhklrgKagkSJXE
+r7fuDeWVQLYDY/IhjsEALQY3jRqGVl6cbzdWYRDoWK1ZSbdmYS6FMqPO6D4UbNYJ59tgCjO3hnZ4
+gJu3+RhzF6h72Zm7Ba+DPQWLszSJa3yXiJ+/6zlN6VOgtpsy3pEKJIBGZUq9j217x3GwO66OsML6
+KymUzK3FqyV9AsS7zwmcGJDwEbcsWsHMMLaUGz1/4pvyc/14xVdq4GrzIbGXoxduSY/e+e5XyfXb
+dwnp1JM8DzhttifHIp06wZ/HcBnv/Wk+5ND3OH8q84eeuhe8hyB+BdLTvs9SPdPXXpnSGcZQz0ql
+ZafM5rxmGabzZMcmxp058ForeAOEPQ3xMoyoUwX7Cbt/VKZb66HOgx/O0EvY/YHyEAxfj37D/nBd
+htMp/LvhBwj/bpjO1nSgnjRaPQUupSqTFSAxNIrKGMWXSwqXRyIg/CR0k6A33RKG64C420NaiBO4
+xgN0+ym89Bu2tA67Sdef1LtE7GNRPwPitA6HX/AGxv7NK+JFeh3OyfzQ3AwAoNRMckFQrLe3AHi/
+0oKNwFi80gDZwb8bNJdA2KeStEiXjBzuMX2yBPtXqZmOAqGWo/vHlrLgGXBVFLD3YrsSArGRQj+o
+ssORu81s+a7rMddq3O7y27PYFCbaU+hQAOZsd4jOOMxPAF5Xgk2xZ6+qHyh1FrN8+QHw7gMrZCGM
+DYIPyYlUQNz8iClufenQzaaAEZq3g9U7ZCMM76R2OW6Uynqyr2QhNoH58drKbto8+LAj5iuVSyuw
+7nOAc4wji8/5CnI9p1/8w7HX7N/qFE8NIUt/tJBKJdbNRNBNQrPLdJnMNK8UALRhmt91XttRD3v0
+qAyA24jXaidQd5iXsFZ/c0WoXRnolNe2cgG88H6iH4GzbN9g2LZVloYlONKoxxzkCvkEs6tD/qBU
+c484XJ/GFuF/F36A8F8KJ/X6ezWdNgKepJlnCThj8FfkrivCxbutCiG7wG7lwlPd0pvAOB+a08A6
+bjWWSwEEy/oJ1Ap6VcU2WPY+BABW8BXeFskPguEAoMIvmV85dyY8hzi1LiSSrod/XnMHvddLWhEo
+8Ik7AXEOyOVCAOJiFTbto62jpvReXheJMXwojuBYokxDIvHGcEIgNZwMrbmhrYOiOhXlX5mbKp78
+AycIWXspUsYrUs7YGUzF0QKFMoHgu5P9bSDAK6iYvbE9j660DfgMy9vxg2LrrNPCurc3DdKsgF0H
+uJZx73R8sA8+GF0g07oJ+zycgM84rhjWtGPfgV8qQ+iuxE3KRvb5ETGyBgO/SM4f1Jk+8Hs+266C
+A7ytsdP5bjRJ2a1Ey3PI0+4f1nMfBQCOjaU0bHKdkKTXd8abe64O/OPSNqIVXjwjhXlwhYhZgI7x
+B0j4IH4AxMReu0p06OWNilp9i1WL+BEI3+W5vm0tXVI/reQ3l/jKbzPxZ5Prw3JxhVpxj6CH5eD2
+iB8f4f8vgk3yehbWRz4UqAe+yiMEYfM9wOOWRZYPjJhIe3DCAZJsfrQA/wJgbKLpErF9hUX94Tij
+2yTSYuwKVNhdorhOfHSDXtmHzNxPeAGdwILfBMOXC4MS33wNUgIgRSHAlt7TSxpNSl6tBy3CCH5V
+h61wmAcfaqaLb7AvvvQ6CVRFm6lUHPvMrZ5SsE8nRUSsCtYU1KgcjRhYjnutDIK5XOmHVa4+gugV
+rEVw924GxjPhWp/ib0K04+fb26evRGFS6IMeWP0OFtPHTqEAIaFjCY6ikQR+inPvgkmK1dNlkg7f
+g1nv0tTFM83G/J6e9s2pboMSA1AFa3B8WzCBYeelhoqV3kGxljbwwCqf+SX28Nu+1BchEqPvkqC5
+7I0EuAh2wdWjDMXvlsauEriG9wn0nk5g8q7K2g5mEVb2OOpoPhORwPQJ5IIOsVLOqFzpVhkbdqXx
+NrpJBcI20JhvmO/M2X8Z+G7lC/oo09Ul4qo/kDH8qAb9tDKCZi2gecf/dfgBwju8f2zlRmmISFW8
+d9zPiq+q2qlSPlWkj2BUo8wtBVKHLeHyURE1Xbc36KoD/XZN8+eQHdCy64RbjzVdIQIog4UYLcLR
+nm4rxop/zN0w1nVq+FJ6Gb1COIzTa03Qp/qAcoPi9anrCjIcHIDSObAGt25HesseFzwGo1PZFuCp
+8RgB51XhOgrbnTDlvTsCZhDM70XW3ZnQVJoEBADkFnr6mhsM2KDG3bNiFiSHhbauMwCO5ufhNL7W
+G+vg+BT3qt/ERaRZfF0p+4eNhb8spgl/aty/CYr6vGAVFndAucx7mYYm5LQSo7MKcaihtd2B8ddp
+vct/e93WIEAlgV+cmekhOHyAo2756F8BvVNnDIlwHnaFnqq++iLo+jDIPzx2d2sLo67zphDLNU/+
++pCj4R8AbRXEMqBLXv+BldbnwdJL4/ZzAvlBwbyDVfgRDFObRfZiNwd6k9Pxx2QEw1ioVZgvxwVI
+JSXqYaerMUeqH7Dubz4R2KqSBbjxXyJ43egFbfy4RvyvCc/3k04B1TQ+zJDU20JvKoY0/dQACAwQ
+4VsAk6JFYLxla/rm6v6xi3RdiFseAuTytWpIE7hKbVmRV8VGdJNrW5GX68S+VxiA87UBuFtVL50A
+cY5XbQEAumzfPhG3z2eBy+tKGvJKSZswD0mfansdwiBsHAxG3NO+Tk1Q1dGCuhlBcN+zuENyFCrS
+OM/7vcOU3wlL4eetE0r0NSWFjuihfbKDEewJ6QDGU4wsWAbDnB6U1gSQx+M4rsm7+D3flOp/a8la
+boVvofiX8H7LjAA3yvmrRgDAOx9bTT7ufVnF+3yQee7+dV8f+3tjr9lNRCRuQxnAblpyc+u2Dxol
+CWSe1jHkmqSTUN24OPjDLos9zP2KvMD2S+4vYK9kqLb4QKUpwFwYx1eQ0A6KUOyrwHygMrMaxXHi
+7jYYFvNAC9SmlUbPZwrytuwiFzLvh85dD3qRGaYDr9OnI4Zj3a8jb2zVvVeqTtlGlvUG6+d8DdBe
+/EAcWIGnbz/pwfFN21af1cTvKZA/Dj9A+I9CBw2h9NCn6156ZdnCxso7hQcX4nhXfr2nBzXV4u6W
+sMAwuCXI3tjCADUsw5fJr/1QnP9ms4kUv2HpgHgLhl9bYOY1bv7jHrbBd36KvOAAjdbh8mlYANBa
+IPVTmgHwSvtCWc6o04yymGdYj4oaOwbTR2QZ+023ElbeNaf4Ke9t+tStyvterrnirC1sGBJKfNix
+26dnTVnd2V4H92qeJVkWvONoYL6D/8xdq3o4uo9xSiNgcqUOWpWV+SilhpxJCnwZdyCAa2lS1k24
+HOYHsJSsK8IoEYf0G3ALW43yjdM6lcc5U+iz8fAIGSYoOoKZI7hRxNBD6Cdy1gM52MlKPPJ6yoeo
+K7E+0GgcW2dS1W0F30Bn8zoYXuXQt7Y2iXu55vNhs4kue2ylgXoeaA1vzgnPYz0rsvRUWUP8QguG
+RLLDbmhIZxopF2qz7ymTfrn4rkeF7QoiCXadFwHx0ao7uAJONN8rgXmB5s0J/fnn4QcI/5Uwg10+
+UsnTOefA59vWJ+/nUsfkCQCLHwxJkVpfl/qFD/kAWwBVB8Dibg6SPmbFIiwiYf39VS3EYvvmCRO7
+lAGyg2tdYPzXrs6twftqw2Uplu0asRUHWoN1W4NtA16y9H5M7IJ0e0kpk2kCzS6sJmF+ClsI0I0U
+e47DRUIxDoszVObuFCg2Te6twtnVRWORm7RvQuV/3MGxQVFSQ9wgXniW/K+AyznugXH+HUaohWo+
+b73bCJywUNP3Na33fAe8IAGwEESUEkZad1DmrTcnoCupRU0C4AXogw8haUXt5d0/NerY45C939M3
+twLf+923eN6C5V7nlO8uBjCCvvJh1U2EQQ+bDmAELcFW8nIfaivnJTSI9UQZF6AODOm6UWt+fRjQ
+Qe6OxnV4WzaFRZSswZsGYLgjxKErhVC9HCJixAUxK0OD+T6BYsizoWPmc4I0zX5kWnoamp2svp1m
+ndaALmyacZ/NPK6fSYeETlF453gA4wvcIfClm34Aw/jQnUCeEDD+92D4Bwj/RsAN2RXqvbB+x3Nq
+F8spG0tqfmvCxtZJ8KccTxCpIpf5Xb5+pZkmwNT9i28OgMElQj67YrAIm5r8ujao+uj+cQ4Azyr9
+vuJiHQ5rsCZIR1yYLxNByOIgFYCvXAmKR7eIwTIsApZhco0Y5n5UNHuOff5RENHCaOiRsYKDwJj4
+k7bnXns371Rnpc2g4Dmc9l7mFiQ5WYBJw0BPFPKgBROmT8C4lqT88VihEoT8m61QATXFn7bQUH4C
+AGgVXrFB4VfF3lYT18EA6Ja8p7hJAqliIc6H4HrbFg9XGVRbXSSkpXEeTjzP6ZSTM4+1Hq/xlOYr
+MJEEy+3mkyKUa7HMy570sRvMca3y7mQ/pNFFYIOYXFeNNgMAgxtEtQZvpZGTZaXFwyEYRSi5NvTI
+dKLbzx/XElDHae7yLA5n7ASIs7sjsMUzGbS6HxQqaaB3S+8jGF48BnTcria5b0LeAgCOB+N8La8N
+XOF+4Le3JC2Y6+9d9f17CLzCDxD+OpyBLKra81F6rueh8p2un2Jnvg17Gi2LVYCw4wUMuwxzFwTb
+ysxd7y8Hq1tgith6SM6k/NqchBX48rSge0Rag00T/AbYVr81gsHw6pc/JLc6uPq8hYTth+dGS+8C
+w90NIsuuN0yDL7AJ1yfwHjwlXdYjPgUjOo7Jd0EiaQkupaeHPV2l1/3XVd+kNoEWuqvv2VrXe0F2
+2pVFxR8twJUXNEvUOPduUpIncNxSWtKkzJX5cRilyhM0acAa42N5y6E/fEX8JJHafIrIvC6HeLEO
+m8j2H53ra/kQ9/nMorlqWua1rnjdg3e7Budl5uHd0mWnZt9lC8iSJgBWj9CkBnCPWR8P93Eecbdj
+y5C+y+snVGTLQAK2Iu43oQXshuWXAPIgaw7dqGeM1qJ3bfXYOM2sd+ej8vd5avUAOLVKd04fyxEA
+G+VPbhVdEo2Df8gbRgDoc6UZBLvBSwDIHt0jriv8hf1hugC/B1BMAJneb4bwH4UfIPzb4Q2QdZ4v
+Qa98yX7LW2EOh2nPrR6r6L6vUs0i7a4RYQmWvJQ7jqH7AHuFV+ata9Zku0JI+gqrhLXYb5j45bdH
+bLeIsAiLhH+wg2H2D3Y1kGB4AsFmH5HPtX7+GZ/ss/uH5tDqloA55w68Jg7zjooRkK3mCNZ/LbuH
+89sLauZ3JRr19dyzkeZl7pRzDO029y4coAtpwDPAylIdIDClANfSP/ySPvgKgZLD7Rq9VlZCTd0O
+ZUhpUZ9A8eMerJ0kgEClpQeYa4W0TPHdsXADmEB00ezFQrwkyFSnJJiu6zisgcJemPYn19N9iJMH
+5zFHgeUqDcykCRTrQk5TI8L7aY+husDNs5C91UJjnlqmDO6BN/6GG0zZG+QqAWkAyM0a7C4Utb3D
+eRnp4zm0QqsCuHIN/Ic+HfMH0Et1V0CrkvpDJD4vRVHNeqz+peNkvEHpyFn2rR3FfCCY5MpeplQv
+vmYJij3dAPHJL/hKNwhXbS0N3cP3fx1+gPCXoSmovcPeqXiGpKiWZ6jyohMvGSehP3ZNJV0Ahc5C
+uEggCF7XqXk8LRF27fo2IA6LsIPg7SbBgFfEPttSjHmyQPCvsAgXa3DpZ4BhQhZ7UNWqW8Bu8/Ud
+LMPNoiwylPtyrbSPIxKnAo2kdRIicDdmX+EnGu73N2He6WOHJAEFMjykSZIXK+LYSAW2OSLkydyT
+ypRUcFibMUNJsuwYFfmOT+sWW2ro0wkE16+PKWZcuXfymIa4CQAim8vtPBONGwaObSCQRl4CyzAs
+6WsWVQgVpTDVE3uTwMcT+MW1cOSi0O/sV7oQeOsFoADruQ9Jn6UB5ES09qVrrndpkJ8Igj2t0q3E
+kQfrGTdLGG/wqatP9E2cznLGeqkRyDbOed5ObVF6kAlVTLkLidU8VBnodkF5Ze8U0FvduWd+mId9
+PklXkP5AEDxYcQ83RTzeob8bUIG6QG7/+Aj/bwhHHKBx4N+4RTTQUZQ61Ty2d9NA/7g99TYjAzsB
+TAe5mnFX9Ca2fgnmg6VBMG9AvI8rWYHRXSLplr9WF6A53SJ+IdbbYBitws0ybBIPy8WjY+6v4cj7
+c+2H5NIaPPkJs2UY5rmA4pyDO0kOq6CVpAEKVST97FBI0SpxM2kPr3TqVe3lI21KP4usLcKhgmMZ
+dIPwsprjmVuttC8fhBOo35KnnUSYc6OM09yYDNU2fjr2hZf4tK8zas9m/TKYh+JTUf2GOX6CnH2e
+78GxCLlIIBIooNdvBdE7sFwC99Q6zSCdf0ByCc3zVFdtufkP41ijkOaY6ppM4n0W+dFetwP3H+to
+/W8PVc/rbTd5KwkcrieObg8rHjdGhGU8hbUNrhGt1ZtzVboldfJOtU6xU6k39U28x/IIgEUC5OKc
+etzP8JhnWHe2ies+X5lmDQzT5zXUJaGOUteQu0K4QUhxiTgB4ivKVPeIAMreunrL/x4Ei/wA4f8w
+HBHz1+EkKEDXTpz3zZfuacljIOxb1Dauhfg+fabLVQGbxXeyCLsLhOeRdRjjbg2W/JGNfTiXVXoG
+v5cYpVPj2eG1ALAN6WYtFkgLWoGF24gJwPaHNWhpnPyMhx6S7fhBi6OnytpaYH63kLynTcpiFGGj
+gu9lurKqKPIAfvVQjsrWbmiJ9U42G7b1mqiUDrVYr8cm/oKXWEH5MErrhLOsVTb3stv8WxjdHGAt
+qgXYxLUY1DekT1Zf2kvuE9x56w7InvX5bTvFAPwCqCAQIZVW6HjEo7c+BQrzUhZzcpPwM3VQEbX/
+eozJyNnjMsRL+slVwtehWn1tW3mLRZiAMt4RDpbik3p6pENk5rX5PI6y4HCej72AvBcCz/cf3ixB
+/sDeTz/fDQAzXcS3VKe3Pgfo5b3WDPKhPlivEAiu1mBlwDu7R0iniQNsb88twtnu/0T4AcI7fPfL
+cm+svh53oJgS+O6ILYb3IBoV4dsRoAJoGXDGHfxOYNhVy+XXBUX5LSxN6OXRX07yH94YAHC4RsRD
+cxJuEXgrD/6wxukX5kRsuD7Ndj/hxoiPiVwmtt+blRfcItJVYqcj/wvFUtekOE35Z+OY/ebyUFax
+47aoj/ahyPHGiKnHJGNhWae9RvrfKmUOJwjSW5n43Pd14nPu+55mN8/guM0LVdktxFPp2s1ipGXc
+vyO5btIUO/oZnoGBcZ6VPIhjWw1ujeD4Jn2w+uqRN+P+rVOAKBj4G3eH0vNoK2hlrqb6pPHWGeJR
+S6AbQiqZFSXtVMFt/3s/TydwqumUtpJ90mKQLiDYyhqRj3CAYZXJUnySDPX0Nlrd8sxK+rDXPRD1
+mDP26DztNzW4Xtzl4xwOQNcKHfnhe4ZcB99TsfVYD+W2tL4FK/DcgJdNtRJg9fiwXL1KrfAxiE5d
+52B59RtcJvQ4yf9Z+AHCfxTeA9Ze5mXZk3waFGHIpZuWp8pO2y7Ar64DmNZgGQCxiH/EnbqAFuEa
+DzB8rcP+yyHsh4Hxr7AIOxBehyfuEJY827rnIudjC43BGny8MaKmo7xkeZEQcovM4ONxiV3x45yj
+74fiTgHJVcDwovUbIpzXZOrK2QLcFdJMG0MZ9ry/JmU7QZqswQZar6vu8nkNOkC2HjNJkFbpWP9U
+CynmTBC3nrdKgu30J+x5Xo46lMygQFsPoWEeOd7iO0Ey6Hh1ZbgFzLYx0AyAsVz/+h/HCnAd5vge
+DNstn0gB03agV/4YnnKO8bjP4Dfzp5luaZvyc814vabz5dGbvGF/RGwCwW7txXjw7DV2q3E8KDfJ
+nB7mb6L6GeMh9ZoseIc8LPcV/jrIvqEO9PgKK/A+px0AZ93eQqQCIFu2tfm11INrTF1SP27721Zn
+KC/y3wVwzC4SZ4A8ukKILLq4ldnxhc59/YfhBwj/jRDW3gS5byzGfxbmmiZh3884qWeW14BvAo9Z
+WoYvKrRYrwGcRK5/Dbbl4K+4QWLfQCGzldhBb43HTysDANbdBwfsZA2W2RocVmC1uDXizX3C/inc
+HIAQQHaRheAFFcu98PSvoIK4BYWhNZgKRCGoa1ppLJdCdlY+c5hVp7IKs85RydoZS5kBeMYc3oNf
+VB88C128Gq0LchZe6+q4FdMOmSbrqxG/SOsCgmeDMWnhs2luevoIkIF4gA2S4CRXOpL1GrsJAJsU
+oLvHY/7UOgiaoZx/gJmsQy5hsfgk97qHbR8d0e1AR/5p2yDwHVw+qvNmPqzEjQyzcQbHXymS8z7g
+PTKf8tqBBoLd+kv+w0aA2azkU7XTLrw5syKpVw79xXMzjntCXN1nhsMXdFpazW9jAvg24Fp4VaAM
+73UcS7ZjTXd39wmLvFHm1AE4WI0XAFwEw/U+4dEqLBR3QJyqDKzDh2n+L8MPEP6NYC/iKyQwnnIr
+5fUGaN+r3scrtKjtTOm4BlN8325L8FYWCUAPPffyux8BgAPs5nv8aIa6P7CMANhdKPxw4h3C3s9L
+0ZVDQgDE9Wn73TYwvn0obkwL1COhsCcXinG9TnOPQCIswQoCW3NdCHT0NCuaVO0GL863Ql+Lx/wP
+e9jGXTDwlz3YdG2FA1NrFU5jJRPUTkXyCiAfl0ubAjX+M7TtCg7Wpx5f7fR4spy7nyB67sTYn6lX
+HXzcwi5aF57hUsakAGDnSX7zv+VHNnKOsu3J9SHr4KFRFdDr007K8n3taIx2oJc+VWRD5+ZuO9/0
+rVrH0cXEDNeizx01YiXd8g77xfPC5IiAViRdICzlTtxG4O/uE53vvTeJgqzk3INigpx0npps6Y7e
+VAXJsrfKuIrhHUGZ60vCYLhagwEsY3+JBkLMtuQmsTxZhncRiPsOCV9lsvrmSw+081Vps0sEg2jH
+EjsuCIjdOvx28v9e+AHCXwfcXdIO27uybhXgCmYFd8o8b5dZefQyFC+HBn9M4/L260m/a8Q/1vrB
+tNklAm+IcDCcANl2HH2IJYDwCQyrSNwSEXcK2+4TgdqPqF1idHfwwTLsg5ADWBYfszeWwvq0RZRW
+QSO6xpDCwcdrKCQUy0oHaCfLMH2N67G+D8c+bz3Ylr4I8DdiDEHffIx6bfNJqa3ZofMDhCkfUCbA
+1dqtuKL1qCp0bouONCjsXB9XntA2dKx+694A7WTVI/1f56cKmdJY2WfeIvvxetGKQHCAvp9tJ/e3
+Ca1crVcH32BD7jb81eW+b2odDlJOdWfesPM2EPG/yXxQChWZ1PpyVsUleJmVoU7MmeJ1s5a1Hule
+L+fZbsvuQHC8Ix/yp7WYzov6mLEr+KETZ6mfd+p9mWI6P1XoeP6wQXBqj/JMOVplbpxRfJc9l5pt
+ND/h0J9IM+gs/jaAATDu8kDbLRX8uwL9wWt+BXA9gd/rkosswcUqXF4OduNbIUVA/D8XfoDwbwff
+Tn37vYn3eirTQaCiUEBBhgK9n/c5DgdoyndALBBH0HmN48kasIxd2bVf9L4sG2klxofjZlocJuH+
+5ItvjehXp5nEz9vB9Wn2+Yhcp+vTBNIuvFI4+Y0S4SPcLMPz6rdZw8EgGJ4AsA/eW6jgN8AYgwJ/
+TYEUVFHT40+zDA92YqqBjNZwUYpDj8Z8m/JPdcj+hcFT2PBj6NuZkmtQR88K3am4TlwZKms80w0o
+V0VNDUzgK1Rl6/2cRpoyzaSAS99ZhU9EZktvrWN4gM6tjUMdWbTtqOmj0FpvHejDUKcH4aYPLgr0
+zEPZXc9CP0Mt1D5Ct7G/GYd5sxMPDW5onc8huwkNGmtqxN1dDmDYXSHoXXaeCO2D7I7mOcBNr9k0
+z0QJMN10XmJLD7qzbt9BfCQZMks0boXbEBmZAAAgAElEQVSIPJcP1dXBCgjGPJH8gCEwL5bjMW8P
+19DSO3O3yVekWZRzeRXeTZO4bJbgTbv4RT+njD+zXKzCzTK82wi/Yck2ikr7Z+EHCH8ZBjGxg5/C
+ASw8hi/K3Omul/xa3iMOeshpi65xXaRKAuD9q8gMiP2w6sr3UN0dyD3iWsLdf4PjMueV5i5x7U7l
+J1UeD70cjIqJWN4pfOf6UPPqQ3PkFkFgGOf6TvmBBIbO8uFXeNNtDa50ZG/qj+oxeCWd+zj/oMZp
+DDmtTzKLYEGrqBJybmxkq/M6tW430QEZ9daG8pNLRAUadX6HHitjjaqsM803UZwsx4s2rZnJYQLH
+uZzDCSnsDjk42chNib7br5beVod/aHAL0cCHc2l7ZoZlTBKAJFD+bVTDBwfzRqQ2YW1KtaScz0qa
+eYb0sAwBVJjSGaO/0zxjF4rWGvaGPfFFp/IhODuCYM9nl4jKG1VXUKy854z2ks9yGSdXOU433sTA
+4+Kp4m2rwFYXa5DHLLo74PVh+vK7xTa2Q5zgHGuMrZgiCPDaEShTX/Y46OYgUppKtNur0C6V9kMa
+p1+Zqy8EvyqSFuFAHf80/ADh3wmvcCv4Q5lJ+TLjuzAprwM4ON0aUcHvcHb5XWU9JAfAVm0B0gqA
+PxvEYn3++xpeziRfv/bBvxAMI0h2zwT1+4Ol+Ahr1g1A0pXpIrlK2sICQaxYpgPgzncHE69I5rvQ
+ngB0rIZLKV6Uqpw9gdZg9KMKayIJqqoIkg4tFx5OHvmCPtwqgVY7KP8ouuDr0DteBCMTt51Sln2e
+S9Z6a5vHUlj5MF8DjNY2a7t5UOYjAPZ0ajDsG4LxeSgzyOFkLXhQ7i1/AMRAZ6Awg96MZwnn87VL
+S64WgQbtDF0y4ij0ATf1WrkofQ8xydrjPE/KYVYY0xJWSHuyEJ8rKVx1owxuD21fTO41IrCOeCPE
+CQQvnXfKW4P1D3wPaRjXmB5AbzszseBwRhwA1ykTCVg2rqUmZ5PPsqfF16GCYe+TSQfBlD5ZhVMX
+1fH4h0SYPipPX8z4CKoecv0j/GCbXB0MixYAfAOC0zLsdVTwm33+9zD4Bwj/lTCIlpvgp/Tm9L5p
+yAnfNT72xt8TAK+Ig1inOQi+Nuj6SIJhBCHxoJ2mVfgCwGuXLreIfegZDO/0tZ44/lXy42Ex5b6v
+d4t3V3D1vVl+PyZy3d8YYVM5BMMiLJzMIjvWpKyNtniOIoQwLQ4KiwMYToZosnYhX7zfKs8UsHz9
+wFV38jEzkoO339iwwV+o3SpHT1X9Xns3f2g0emMq9Fh7z7hNZZrHKrh1VjzKAQhKW1W5Y72TbKDk
+NNg72gMgDm3qmlUBdEJHB9Ab9Qygl39lDvvA66qlqpWc/Qx97rR2QQyjHUhb44J269zNwOmtOxE2
+qTVmdTXYPYLnt1RG4xmUReM5l4luVwDpcnynEwS7a4Qs6/B+F5ECiNcfw/0gOO+62+BxPlqGcZpx
+eRyo1m0J6NAU5dO0DxGxZb9VYfZgbqL/cFT5Pd0fRh9ilBEqIoZ3ChvgYQt9bRCPdiIvh4U6VUQS
+AK/EKhNgVtjyW35JroNgofdAui0P9Nr/QPgBwh7eLgKe/iOovSn+tj+vGZ/r6IAx38Pii3n7NKsg
++BX52LrT9yPLfQE/sechU/lIgmC0CC++dcLD8gvl3VIcoHjnL9CcfsR4kER56XIcfvqn69M+Ih8V
+k886fHaJfGz/zPLOLw/N1buEm9uECNBwAUI8FUV0CDA+ESmDG/boy307XhdEPTwobRnuFX70hVzx
+yZKG6WP2iQxK+jSTJmsPnitkQHVbmYhMSnDEDgNUac2DlkRFRApOhAACtjX6FvdWSnLimkvWEcSb
+77E1uYcSZW5UhSzEo6VXuU6wHONPFCuAozoCpcT9tw7TN+OYYYU28THPMSG0uA8Buh9pwpwb1PiM
+JkzUkK3tDuF25JBQZVS2weS+l8KyuxGVmQPeNeb1lXuC4OoagS4SPk0WlhMRiTIi/kHKytj4vM3f
+vlQQfATHZdpCIUbWlBauJJSOcRoU69IP0BfyHTboY47HW2WJtUfv5XwY6AcsaU2OmyVEwh3IP5+S
+DELrb7wPLhHhH8wA+LpULnSVmNwm9vxNluCOTv5t+AHC3wY/xPCu8fVQYMjFKrn54z5xkP8RmuQD
+okFFVSHFji4SvmqDaEwbDfXcjG209223m91iMeybHNP+qe9Sk8+1gPRHN8DWZWX+XMvC7MAb3SfQ
+RSI/mULnI419AMHhcTOwWlWA++kAl0DvR8wfrKP8jxj8MEemsXxNwwv+kTVaYG0hjoLSHITTq9Nk
+b5dGH8JbHg9ddKlUBXUUb6FwZ6BTCXewIsY5tPzb4hW6NrXd8MaXPL8fV7mfjTu+uSyBIEXeQwCR
+1JZw77cOOlCuFRnnhbZgcT9CBwjjyBT6eJoO5WhWVwr8Nm2aoyKEaf9WAe15/PigmrvU5YmKjxcO
+cgpUqx8CTRbgItlgEN91kcxo6d2PDb4C0Hqv3NoLI8v684YkTuf4O9DlTVXnzCAPcx4POeSPn5V9
+DxLc3WktaeptVma+BthW2y+nE50zYS3tegy6G2B357gV2/Yu2WcDryrU6L/Jpbqfe/OH1DR/qVXz
+V1sZIyvEk34Mfs6P3w75xPqmtIcK/5vwA4QjvJv89QlrSWf1T8fb+uGCQeO6mAEYexx0QAt8Gnhz
+1I1lpREBbFjl7SS7QUB7Ne4awftVh64w2EWALCJyybYOSwJe3QesguG8KzgubiDf4Autw/sE0g0L
+sXxGfcjDZ3vNJOLp3uBA9ZLqK2zbOiwEdPfr8xHbbhX2sR3fgPdj8qkgWDowFqBFt+ifZLkpHqOr
+1hwOIYuVae1VAc2Bt2AeCnewgPKtc4wX/9nhnNRqjofJlRHX/XjiAyUc7g/+Kl0Byp/E/52iaC3R
+4i8Q1ORL03Mor4AnEF3KL3XZJiIG8WOnGsgY+NA3WxJK9WLTRjvQqHwdOPN2CHMuZ7u9tP7muYiv
+uyXzYzeYwyHItyIfHGtArzro5bQYlBd+/sBl9AR0wyWC2uM8kTvQiyAZ56iUURz1EL4Ax0g7bmUt
+aRhb6vR6H8r9qUagm11gEKzEDztKU8+HLgbl5yBYNffF3k0BgvFdVfKHqrbOxx+ucv0dYLio4HxZ
+gObscwW9mnGVtS/M4ssjnuMCieTvhh8g7OG1bkELsIU1NOT5XqngkfyEWBdWJAExAdoDEFl5qFSw
+zD58E+KY5PR00sdyrOhivNF3LV1mPz0/cGjlDUvw5TdEICDe7xv4XiJiliB5veAEYhf9FIqJW3zz
+BQO3nY+uEpbXpqEFWALkIoAtFmAHxJ+V/jS+G0swWKQD+rqcCB7IDR2MCjrjTfmVWWDlNYdWZhA9
+YJsay4eOOuVLz+9KQ4WGeajnfeCWDUmHPk3t3KV/l/d9HGd9Ugn3NL3l6yHlTiE+pgMVnIGxbKUd
+QlPSehbguGh5kaXYT71XGBmej9tBDvlmbVjjhpHGIDMwK7ue9nXmMdzr1l+voQLmPKUK/CAHXKZA
+WZIRkPYeTSB5rZGFVdjHGlIW5EtYjCsPlKsbhIGxSAXKIiJ2a+ERArLBw0qph7LQ8W1E9BQdfWo6
++44jwTPWZdscx7VdWRaq3vNB7a13GiKA3xiHcd7uZwXBEzBGq/ClGxRLU787zeDXEW06Quxx4ddI
+JusbhS0zQjXfzNL3cv85/ADhHebHLHqwLaTDWLGFjjv/u8N3l/lgKZbcRPOi7kIAduPrfKAbgODb
+it/QGk/u9LUxIS0i0saC486My7ZPsRaLMABef+jOv2JD0Os87j/s1mCLE1lefSZ3xE/Y8F6sww6O
+rViGGfyiBdgB8GeBYrAKH0HwARBTWiRAcg4GwTIC5aK0JJVbm49QVvOLdFMpa5DN22kCy5jfK+vK
+obA0gAIA9ljfVGJgvsmZK//OKvw270nQc1xHeg16w/SdEoEB1yl7mdaIb0LImw0muqBMuQcyhzve
+gQ9nHWQ51XWanLKHbdh1N5PIe7r3o1s4ay6A3b3/EeyKCLk/oBlCof/+HeXZCgzyAs6iTfnQOxOF
+tMQHa9dHBmPgduG6NaBlTd0FYubDcwB5R3Bb94qW/KkMb+fQ36TjkgNjhmuBD8dZzr0TaY23PA96
+mRF8ENzfGyDGeAGqviMQlF6XFuCbrhGhUh0E7xrSBpUuEn5M69SytFKYKN9ggHFgo02fS//r8AOE
+PbzDwQvMrsi29O7NbwJGDGPLqZTjXWT+KYSSOLxrpYsLQ6h4QiN3tAMgjjFAx/uHh3WN2gcr2v3U
+7f9rwtZhAr7XEnuXpICNX5m7llvE6sLqED3t6nNPJ9DaawmbnXaAK9W3d7D2ovvDpn32Q3ZyfUQ+
+H7kuXdZgcouo9e6XzKBYJN8F0ulL7HKjxg0ECKovISuxCCrjDKTcxlcHulN5EdiDQ/6km+7qYvqc
+06kFMFZd2Hv0WGnUoz37FM90B/BTvNFAf3jEhvbrCN7rkZs1fcAYyTNMSsiQLRtTeKS2xDTuWxWQ
+MQZ5Qy9GkIxwZOaLbrfB9Lpi11ntgRFvc1eBjT67shzy3AKIgBgWPtI7VcEv1uM5KCukpFEyCtFA
+ZjhNpVmFfbarfCFrsEnc+IBAOTa08piylq6cclRKcxxhfKCYJhhoZYNjUrN97hGe5eTIU2CUNuCW
+L+KMGdD2TioveNEqnMNB8IvWWgM/YD1YhHdcBl9hf2ntCwJ0nruwDvtG0kVDjLP2iY53fP/X4QcI
+R7iDpMzmXz344VY46GSc3dZc1Bd83PcWx1MD79ij+GTslZ18hN0XaNLzXdbw8FEXoKAQEf86stH9
+aFnmacm7JK3BqhL+vnEf8Qa11QrswrVetWb7FKKLhClOwxblWr6qsu3BFg/KfdaHGbQK0wt/enml
+yUqMLhGXW4AZ8H4m1wjhuATIrRbhau1lQByAvuwTVnLFSuwvn0+o4Y34YV5U2Z2H94Ed8uaytTOn
+vtmYed8Kt3G6dGtuuzb1vYrrcewPWp2TV2/XqNN0oCbtnaRLLpqvabGfaJhull4HJgqNFZmGvVAY
+CeWX3TbJvalLJ6U70P3Mzfu97DnDZjmvnlgqB/XpBpESaThtlgAJtQkI783GVmHXJCgfvM0qM6Sm
+AcjifkyYlkahuDUiADDud9oQZVda6EyaG3XeqrxO0kcO+cyrTZ/1mkG1ZZ5iXv4ViPUrKu8lgRae
+/lihg06L/hAwDZ4NgNW1cYJg72t3g9i3OWA61Wv4D9NLhpdjH0lArPs4w6HLuHnSlVH7GPtPwg8Q
+3uHtg4rmgkZBUA1xVGpa4nGgrLfLSmcXVAHDyxIwDQTXywGxvRik8PkrMoUe7KxMg8BAycB3AGr8
+NdFwjXAQGy4SFwhlyKvAOB6gE39pgF/vQpwzoPvg/JN8QEJyN9hpB72CVtzqGnHtK9jqw3MOfDXd
+Ier7ZP09uEmIwbz4P9Qku985H8ibCg3XFgpWAlGpPhVuA0T1hDNOKkkH4qRspu4dYMor/urFdwp1
+RLcYCs/vwPNVHOTCmbeDJ+5TPdSd6R4qHMo741S40TZx3BRlwkIRspyCT9Ip2zxxuyCQccv3UMnj
+hjIi0d2sQwGr/RbfZ7UvWcLzEisYUrbOSBr2KNzXsC7Ds7tbuaVlD4+0vV2W/WDpH6YnkOF2Bmtw
+GcMtOI46B8kR0wnzCvI/5lU7W8w96K8EuezqKNBj9gcGyp4L9QnbDGHwQnpmRjy3Ps5A8qNqm0Bo
+5jMYdmDqPsPL0ovuEO4DnAAYLcMqy6AVLhLensp6EA/dKaiP01nb+ttFwz5QrorvpfV/E36AcIR3
+079wZwpy9UvDd1xGcNwV/ggAVMKKjGQGvHDINNtaT4ZCR0tDaSHuErwB4EmweNxSMMeEVB5hQc2H
+BoGuhttD3C4hKXDx9QvTu90AwvsVa9ME6ASGwzNZqqVX6eG5yVXiSiDr6Xigzq3ANw/JVcuwWAi/
+UECOfG1PiFSrcfJ1P+KM3r58nqUVfQw4y0iTO/od/hg6MPXn1Mcjb/x5Y/fdamdsBBQ8DL6otCE+
+W3OneKfpI98UzsB3Br19XiagNhC0ECd/L59/LRM2XZuD9wkjDXhyBKe2DqFuvtPGOw12LD6dAsiz
+7KCNHNMYqpUXQdQ9LfuK+iM/HDtHt/gCbfM2mrdZAG3G0VLscec/03gCMC/TNGfK6XkDdFArwvOK
+0Zw7jS2pO8P7WzWKj0eBCJTS88PJPwDimB2b6T6uDoIR/NY0WITB5SGvS6suEiL8oxj+SuBcgW+C
+X3j3iaB347Tw+zd66G+FHyDs4QuTcD5RuoR/B8dV/udXRgyWBSy9u3r/o1hetp7ZbQAtgLjigRHe
+ZEGUrqxGHhmIAuNOuhYeFxrIc6mM4JbcJTCPrL8pyH8h8BUJIK0OqHd+KArxZ603JawpFg9ABPgV
+E7cMm12rXfuIOiCOh+MSEEuktxvER4sVGN0j7oHx6ssaKF2r5uPfc0BfaYIgjfyYMy47LGyWgxx7
+fOXz67nG4BcGirxaAybVNXXtJAgb/SuhidaoVXaGMLMcqPL6TfzPaPqKD0McX/PUPDt1hLmaw9jP
+UzLkBSJoDdloEXCBpUDbkUQihwFYKTMwTRiJSDZ+ONsdPtBrhAdbS/UbDvp5xJ/e5pqUuqEjP98W
+wbSUCYvKsqTS/DyFfIHyvqXCigfnKeUHguEzjY0VOVaRbLcro+mWCc5PMtqdi76ja8Q2D+XxY7/R
+K9WxR7Mfce1v7eu7+PQxmIFn+gcHrcUHcKzSLMIJfrdVWBQswAisZwDsxzWmEnreaCZSvyaP/TjK
+5f8+/ADhHd5Ofnwi3YB4dHkQf5AODk3kI3AogtNQJ0CFtQ0jxuJ/I/UUzzSXfKWMCQB5qCBhjdJf
+KxRuBMpf/eD41Wn+08nzSwdwrACGhazBK897tZWJ7IbCSg9iuVl/t/sDWH4XIAbQu/OizOeSz/WR
+yy75fMA/uLlHPPgLB/A958UskNJKBYNWHNxcpTQpP9HG/hA6yHrccnbD+4BpJnrHKah2TtbfM2i5
+O//H9su8Ters92h6ziM5s0J80DsNIPjW35nzZgYq9qh5VBwIUx5aK0P2DGAmNid28d1GXaLXXm1o
+bGb+GveQqKzxq3bKLHAYKZ/WkFfTrNL3Ck/0GGbZzXCmThbgyJ94Nz3kBJVLQ84ycKx1pdsjyG3C
+810e78odPUF/TkqMwTOXbfykfrieqKFYoGpN7APsPSh15GiEXCVMRNsnrOnUV7g886RUuwHBijc7
+VICcD7pdkPdLJX4RLkExXJW29Wq9Z7jmd+swjg/ecSm2L41FPM/LsyT7++EHCHt4aRGOa042f0sL
+CIVX6QwkyuIEusU3mVRdIYI02ugV5QM+Udywi+7De5I/yIhxTVAMIy4iAgXMyscfzuiW33yYwgVz
+B8RQTiQBsiQtTiJ1YVe2B2ougOPUrRdZfwEQo1uEFr/gBMsfkc+1LcYIXicrMP+4hltQZ5oLBVBf
+FTBDGQyoxGhO5YvXoQxuk3pqJvqZNy1yk+g7icN+rVrl3UoLtn7n1kbZlQsC6Uk1fUv7jv/eHeKb
+ecrTd+Z7knpWI0+AOHhgjptg20xDmbRm9/Y0vm0YAgi+4k3R+SizcI4F7yrcHGWiqiWyrkK1AnM+
+S1P8lgXZ6sNyWRHX9gR0BeghFoE/61FatgDFJS5UtrhGoBEH6DyAns5e4HhnaaPxN7PrA23iKfcB
+Bn3RbnxC3U36Dnu7xtHoOKEivBhA55sSznEGuPni+33d6msElNe7xg9kLAB8Ar0aLhPkHiGHl+Ls
+puW4TBLPgfLUBJSRfx9+gPC3YW+0tYCWaXEF7WARZBLjWK8m08hjcCiJbuLX1Sx3jPUeINxEGqpw
+5XCSHyJda9AuTIEMz4DGvs6vi2YwnC4iGj+R3H2OhofjLoZ/a/h5Cj9bUC2QloD4g11nURb9YP8k
+20rG9kNwtgGvxws43r8Akr9CZ2Kfj3yuSy7/+eVqDf5df2HL0Vt2leYFWBq9zl8wTVLmRD+EqghE
+evGpyhPt1Eal2JxxqCN7VvOPX4ff1InKvPLU99/P6/7EkwX4tvugY068+OXvi6kY658wbuMBoDHn
+nSoykIEog0qoeOk0GoUI8hV2GtrJNYLKFXBFQ8k22p7RyjWfivahYKMGlLlIp04AOfduDj/lA8uY
+iGMZ0Em2O+Z32PvtEO8Asp8fT91qxQSpp3xUmnsv9Z0NNHd9aOukZYucfH0dPIuMoDd6irdH9ZOr
+MtNXFPcmrit/XEBwS7/0Rmm4Axh4AgirHl6pl8ONQqDu+trzQncLYz9xHOOZd8vwNNJ/E36A8A5a
+BfYhmG9QTVDof+gJU/NDUz6FFsWmWK8OPIU+gWTvUxxNkA/ZSN2Mnp9CbLbOYZ0OeGcwzJULnQj8
+dZrJ2lt/OQ7zRUV+XatnCBRsz+1nNxv3FENXQDSt8tFxn1CT/FllcIGQBYLD+mv+gFxahSUA8XqP
+q9LiGrWTJXiwCsN7tfYiLdSUuZrpvKjKpJf86lVD1jpYG41WP7ebDTRMHNpoMTvx3QVUZViuKMCb
+Msd5qACh8N+6OPwR7xQUapl57vK+DiTUzlks2KY6SkWtkwxr+gY7yO1pIz5uONwZpyKVAFZq6x+6
+EAzh1Wc8nmLZ5QncLHU/2mH8RuVJEkDW2TKcvTHMM2HXBskPhdFGAci3vJGqC9XTXR7cAGgHWrHl
++MMrpTXZjYtJgGHlXmW8W4CF8uT4gXv+OD3HY9aMwW68lN/xQTYEyBQHWrUGo3FKBX9uuQLffFWV
+T3l1TQ1WNMh7z/i8zdP2n4YfIPxlWOdchdYSrozxDYs+SC4LUW4FregIlMn5YJ2JOwIrWYG3Mi/t
+c0egF+OptXqKIa0lQ+JrIwLFSiOFQW74ubuK/rwXujps4NtBsp9+5lNR+ex24e6HDYBZ7KFg856l
+f9tn9REswfiOYNfdJBT8hKW4RayfY/7Aj2l8YQm2VE2ZXxWHzJZh4ZD0tOhQ5heSZmrHq8htk6m6
+lTptABuFTwrfkNnG3Ggka0+DLkryoLhq/af3NzxzGf2NMhzqUcac6bGdCpxP4WFKzkyk+zaDDa0R
+CQegM19ryzpPkL8cnc05obiHcdY9a4AMDBhiBZRnnQ3Pw+kxKaunpVHsdB9vBb9eAuVCnu3Zaoyt
+pOzErqxzhPGsrVuDuysEjrlKC1dgz2V4p6fuWfPOUoqupGtp1uFa6RFnC/ARNOOEHk9xxmfnqGxf
+SzxeAEIDyAZ4VbLwukWYb4xAP+HqFqHsfqHcnqd7vxX6Wrys23z4/vj34QcIR3iPDvxc4lc2fPMP
+flZMoYEOBONRNrcqb2ER5UBgOug0kHs2fMq6RdiY1pIW4assFFh8GycgRZ4UG9JA9HpYzgK0+gyp
+CNCWX5MBj0Cez/eHhPB6d0AcP+e8c6IXOF84H/SwnDXA26zCAYAtfITF3SbUQTC6QyAYTkuwFDAs
+whbdEOjWednGC2zCLxxnzWuvPT9jHQ9hUN+kEEaadaVRa2xy8tAnazEd8xQ3zdArFsmoxAdAcNOP
+795LG8Pebu9lL5N8EKZPfZ2OwbfhsdzYaLF83lV0vO7sAJA3D4sym/nu2o38sZasCs5V1+swzrLl
+LJk6aMYWp/GOwAFJkOcPI0FOxKF6qzw4riiHZ6EA3XjeZQDAJmQQsuB5lhjWeN5ImUxX/+mwBLsu
+835FGmonQOuj2SnlHjGPt1R7XT5w2p4tXJAYnTEjtEEAWOEGBwe94gC3AF/P15rPt0ZUd4iwDov7
+FSsDYmibX+AmgWOir7NxxKzzx4dW/+PwA4Q9vHSNwEX033xHYUgK1LjqQYQBSN6lzSBd8zMtUqzD
+0D2Qr3nIvH8jQLY8/DSYAmbpIQINQa63vJ42udyiq2z9FbAOS3OL4Dw/fSrpE4wgGHth8b57YkjL
++Ym7g2WBWtk/p6zXzkPAa9cGwAyMP7ZujSDg+3Ff4UXrP7tcrcMClugOfI36vRXawAMs46vpj5tw
+rINWO3lFigXSOs8ZiBXwccJCjVZz5gHWL97upuCtKK4+vN+9PwPtp3cGfpn6hn5ej3fhVRnjBJfR
+vhjHSlme9T1S3F0OfL2dA8NAbnuuuETgg1eJcZT0hMGfpOvQnpGC4e4OJ7GMB8987p034Df7Zlsx
+hC/w/oYyeVJ5jNZgd5kAOrd4ehiu0Lbei7mq6bEOhFYMs/jht/13BLp7lIqlJh4eSf2mrJ92jist
+xOw+cQSbCGBHEFx+OGO/funsKxx8UHcCZGUAPIDh7DrMkPLM+z5cBooi+/9x+AHCO7yFwbidGfz6
+/tXMIrl2shKvgmRJjvRWVVvIOMgwyFz5udk0KtwsFYWX/NZRz0dICYKGwHB1nVAuU32Jq/tDukdo
+0EUlbojwU2Ylz2cKV83BsJgDYvwEffN4ELk/5ENwKuz/K2AtRn/hj11yARi2zwLA9jkB3wd3CVc+
+oYQs3CHIOhx8dURGZXq+ULmn130Yn1un3TOmjdO1b22JTnxt5FzJyQO48vtNLk9837w/8+hI/6ad
+iAPhhPsmuksqQ3lQwrdKqfE/CNZYx9uGlN7mhiDjd5H9Le9dHx3MsgFk0YhDAjQXzGu0kDAAa5F5
+r9rw4cKy3XFPGtOcc8zfOuhkDb4DvVnvBIazfB/0HW1Ibz213BdN0jXPDvRdg6+F61OoOXWdlNHM
+N0QwkMZ7qs9STKBeirscs6TjEzmqFpbhdFuYfymOrb7508rpGlEejgO3CPoBjRsAjLOhsoB9YgRY
+YxtmOFw9RbC2fxV+gLCH1xZhXyYDoXcCv8mbgoKbi6Uvujh/Mdnvvs0K42ueai4OYU2nMSvE/lK+
+0R4d3RsKGEa6HeghfCwP5hEAX2tGTdbhZQHNM6qGN0TkRH7ED+f+tByC3IIzrOhR/74lQtwybILA
+WMrVaQFGN/hFK7GoDoD3AHxjZD3/N0AAACAASURBVGDLHflwFhj8oo3YJLo2zJmUchKuEI2p0CaX
+iYn1d9JCNDxPnY9pnXrmg7MZDWjhy3AL0KHOBiqGus48Mwg+vu8uT21gQqNuI5oU+gksvwnflJnW
+86t6teQe60p5pgL69FTnmz7dVgLdQnmPYBRAcdJy7xrI4dqS1ULYHkdI/3DXjTjj7FqlFT4r/PUd
+5AZ2h2lgPYbzVi3JPL4uEd7RfE+zLVZKHB+Sm+Kp0weAqxnP1q3UfgLDO3fPe1p9J0nR46lRU2oo
+9DEAaXnlncL1F+TgirSLf0b5Uv7FuQDEkvQEwApxiSPYgXHq49hACjMJLjxx//T/QPgBwr8dNDZA
+XTo+oiklAEuOx7n9KIcD4DhYaQ1W5DM4zlChX/VCwjLSu7RKukuIJAJX73O6QNAANN9jdCc+9R/P
+sIUd98FyYXuJiX0kXCfEwXFYhpVoMPg1BssDqYbXuKxP0ypuSYdFAikeYBjcEpTifnXaAsaK4Ncf
+mAM/4/Vrcl9Ygd2CCyonhXoFwDu2EW+4R4CVmEeGcLkHe3idyrBS4CmNtA35NgHksjalPh7RE8/c
+W+TjLzfv+abHyUaAUN7PYPdLEDzWLW3gE757T2PqW1X0n/M9FUSgbPPYFt8EJmWeyFcd47xYG2wm
+Nj98aLLKZ0Cr6LI3QHvRCg/RhjZJojC7T8Mxj973OdkKC+8pqdeoZbm/bQ2eaNbiCbGSzq4RIMuK
+esTlOLeQ48O63FLeRzwJOb8VJOtZ/Uhejp9fl+s9GUCvA+RG18Z7+mllBL4zjT9GKM7K3i8xazHh
+Hn8rJf6b8AOEd3h9fRr/WWVlXkYHiKeHpfPeYThiBodVC58IoOXi+0Tg0LUCnO5dd6aT3xQPI/b3
+BtxO7wf+IO0+6h4HHrJqLV7pTve2+GG7nGCfqbhwQhMcMweIeIOXqwOgOZQksCz7OWkAzGjR5bQQ
+jfkk2q1WYM9CtwjJN6nAV7AcvXabWuhbBqEC7LXNLw8xkyY0w7n6b9L7DNjcj7u+nWjjaXYZXEin
+cAIDd3n3YOIPfIJBd5zGXcfSaYOkMs57q45GvknP/0l9GKaFmoaiA+v4letNi990WnmNvJlK43t4
+J9qqjJpu/XDgrJVcornJH/el3eR5Obc0wB7EtggMj/H5DHFNz7tXBOU48+FfbF+Bh6mFFsh1j8qm
+s5NRHXJwtOnOIFs25prw0zOgHzcgdK2pWybq/lp1BrIz6D3T9/sl8ktVfl06+AmnNbhbguFdip+y
++dzsjeLAeE9m+gKnvBeRMMaZ2L5+7+0B/HvhBwj/TihAsoe3C+kbYlssB7C7uDT9nMzVlTYAnTJA
+o3rS/Ns6kT7rngfAOyR1BbU4dmFazZtoyi8UFNHMPmVKIFmKFXkJrUvXOC3qWHNzKdIkDMhiDorz
+MDt4C2GJ0/YETWz6ktzEV4dBavKg0uH6kOTaKbk5xuUnlwnL7FR2Wz4Rb6W94ZHvQ0AwF+qtHtgU
+kn2utbxyixhkqWKkAYekV+XXV78od03aCVDc0vyoY95Ac94JDCvx2UDbfMOc8nR0oHi71kPmN3vj
+q30UMuuZL9c0mXuxE5h86FU/fm1N1vr120Byv5S+FlUSIrg2RX3kRqdeP+3BkAtTnmw5Mp0VAV1V
+9mrnPffv90IZu8qSJ+HEuxdEZRkuCl25oIiI1G9EnCLZUkqCoruQ+6QmXQ0HDQBvGocswHDYl3Y3
+nU9lcnMo1tyrA2L2CU7aAsEqv/SK+HqAThk4e7tbB18+H27JBQPbGquDeFC8aITDs/z/gDVY5AcI
+/1lQjqbQeJLWm8ufxPWDSXewwSnATVdoeC0MbrD8BApAdks/i6Ywz7JpPOKIILS8r0H0IVcpUrM2
+ElW4J9gBa3zlEnEXGBKfiNco/CEBFm0qUFd7d4G5uOPilpBWCH1iwoa0r5OFtTaWxCqfA4oZGCdX
+VUNTu8lbwW9tQaSC4wRZ0zVpjzyFduhehBT1HfSyCsKxwUaFvcOgNkvfis/SaK2i3xmsqdBL3gQU
+Gvi5if8WDeab8k3IKod5CgmiTXyF1sPAdZiXb8I3ZSZeWrcmXw4TI33OtOTfVDp0hgu6u1q1+rqI
+pTU05qu0w1QP9Cw08U77qrU55sHZIh6XsIf+vaE/qMWevdv0puv7VIcJgWECyWIBjN3aiiAtAa2n
+oV+6863QB36vK40t5eqz4LCSBnVq+SAclmsg99LQi7euEFe3FAcAvtIa/CveAfiq8JVpApZgHJ8D
+fB93jB/nWXN9YFclJvmfCT9A+C+F8aA/Liyc6vKzcWEFRgm5d5bGdT2w2+CrHQK5qNl9x8UbPNVa
+XSsAYDbwC91PciBJfsdp0ByqE9D668Oh16Win3UYRSWvXzOfJclDGe8MihFoRxsG/QmBVFWDx+++
+5NsrQW4VImHV9TzLurxcpk7tyk2aaaG4whVDyG+YfIXxaQ4vrNm76P4N7dyn+xBb0BpWJS4C9biX
+4qD1ko2iwv7vWH+pQjm7VRYrVIGN1FX9Lj/WTQ/03R8qBwCFlzE7DVLjFW0M0c7Xgu03ON/xU74l
+UHgCWaf1vC167AyXwHWZ1uoEhhttav7U753Z1qfUM+5HqzT+GF77E/3ADaS9v0+h8p92lSJhXBzI
+CLnuEaeZIBiWAKYueBx+7lbHr+RTTvo3pVW9VWuwineD3SPA5kNlnd+1bX5jqYUX7gm+ujW4WYiv
+k28wuEaERdhfA59mP+mnmiUBcU6hgXDXXJPJGgxTjAtvxzX/b8MPEP5LYVTNinkTMtyrTu4RwuBW
+8hCnr/D6tLjOZoLmEQDrpqGUAWc2K7zrDEPHKwguNBqzDnsYTzL0Qf00ubBSadZf8knyA72fsjPZ
+/sOWQsJnx90k8CumBoL9ELdFqe87itrjjhdUz/pqEXhAy6TluNeRtt3Dg3JQMvJtoN2lQRlHeVXm
+qzylx9jtsrxDHPa3PMtHpNY9hm0/KeO3gLfJZz+CUKiusr+/iSNt5PHjPvHsjX2q02PTbobuP9Ie
+J7OV5vDew7iW+5M2M6L3rI/t6/vOjOXrWt3RQhxorwMrrt2xwpQnRKVkHffZolnPazyc97fxybQf
+o33FERXd6TpMuUdawLCowb35ezJV0/3P9Y9UTeYPwhtZKRXzJMGgiDebVtLMnwDw6aUFaHK5SxIE
+n8DtyWVi5XEZB8BHS7CwVXgEwQJWbXeJEAW3FHgHrENWYV38+dTT395pz+EHCP9x+I1FCy3EG2Ll
+5abRvUlII5P/cB7ycPbHQ04AGKQqCosiEMx5C/BViNOQld8N8nWamjJUcl1Qhfi2AvswVNav030Y
++F5xdOCrGj+wloJFXEaSdUCyM1LjmSZLMVl3y7tbY6WCAo4HCA5Ng0rJhlIcM+xHaNoaSmlYUju9
+6IE6PfCk8n4CDYhBOb4hsUq7zo57X1BszSs+xbcd2TyVTQ9jwX1cV3IEGH8hXgEv5e9EdU9h15I+
+5EaHryQn3hoelrgwv5OFX9WJ4aF6FHVvy2BnvlLBWK6cKzE5WnxH3r2Xg7/tRYgftzw/SNelk7HI
+Ke+17SkPZ+jtPDU+kOl1j1Zeoo2LAxbJOMdwd60J6MvNE5++NRcgxAz4EIN4QYMJukH4LU2ud/ZK
+EmCO1wHg1m8xu9UV6zj4/R78gvUaALGK6AbT/2e7RcQLwbDyD2tQ/+K1cYePXXM6fU6rm4RA3ije
+Z5H/n4cfIBzhtQj8e+0RCFY4qLB7APBWn2L162vwkPsuRK3gAhKEhXdhfrAOaVs0KNdBV7Uh4oVo
+2894AEo8hMUhjqB49Ue3e0SC4QWOla3DBvVY1rcO7J5HEGBxwINW1QbMEzzUxpZfyBd0TSAPPK5L
+RFgD1tnr0qFai7vvsIECXHvn/YNxYGkeLMW/G5o8jD15LnPOUvH5PSI/zBqEbKg1yJuqWmPXRs+8
+N/H+FXRLg94+5hf6NNY39BdTfx9eFPwTndbGMHX+qVxb03s5byeuu/05rZmLAJdXj/Tz0wkGhCOP
+JbXuDWp3yKviZ8rrYbrB4QBmtXNUOSBDutQydSGALtpuJmNHxrO1vidgs2xeBV7veQLTNakKnKhD
+RDZ4FdA94vro9FPFQFesr15xxu4PCXJfWor91gi90jUCwHC2xZbh6I+5brUcf30Gp1p/i9Ev9gZm
+91X+J+EHCP/noR98zMuH3dZuCNW+N1Raz3QfOrQSg9UYn5AVSa2B/qDt03DyhTVBJeoiAEGCiK9Y
+Y74SR8AL79UaHHi/5u/uLmvw+orHTAIEn/yCUYi4sPLh+bxymKBFVxuzmpluhwWtMrpVZHyx3LXr
+SfD/bbzYMoBhw3R1idBXD8alpVhhZLPImvb5HS1OB+3Dp9IPArMoPeKNr0YP5YY+1JU5AYVTnn2R
+3np7xfWBj7vd+nqmh5T5LvzHWuqpehrDtKFaSKLdsf1uh5DNDuvlul9K3kCfJIDvhUp/yqf2rPRp
+qsvOeTR3eWhpdvtFZXOYQG9dG6xj8IAIhuzPJhiqqj0g+vZzg1cRQQOQ4oFXgecKDPrlfw2+ZUTd
+47z80Nu1dY0qWFOtpLX64FYwrOkfrOgewQ/NtQflqi9xPFgHt0YQ+C0v0f3LdGeXjVgDARC8XR1o
+gds7Kn3bM4hX2/3b8AOE/3r4StRK3yHCm6S6QpDkKHlBEzroEwC2Jz5vBqSQEprM7luQm4Qch1iB
+bx54iUMnRFtCQtTip5RXce2+wR43rkMk/cUU584/queIhQ/iF7SNUlOxFeCMD9DJ9LCcSEdhlg/A
+tTaxHVaO7KZRXqG03eqrHQyPZZao6mKqU57AL3zE49wBvU2XrZ1aiqNxU2SyQtOWHcpOQMVV3zmP
+57CmT7Sg4/pp58f2pnHc030VbsJ/rI/+pHoem/bKDgOzU/ZXnUk5gudPTM5geBegNbZ5bQXyateI
+ZgOt8g31tH1kA+1Qrw5xUkmSZ/wu4BGc4j3syR2Y46YIJxioLYM9DvqPf2xqUbJttgA7v+snrAP1
+zmrCEuyK6y+Ty/L6M6dfUPbaddNPJYvrPig7PCjXbo6gB+XwF+QYEFdL8C+VeHWLsMct+ohz7RZh
+hCxxi0QDAJys7/+x2BnDDxD+K+Hp2N/xWQG+Eo7j7Y5hTIOP03pwzqUigNGDBdge+bIv2XX+kY/g
+55GU/DL0MtRM+7i3IPGu7bqu/AVj8AnmB+SQjsKpfyUV0yDkGoFSPUdTwqBqwESbisWEAW2Jk5/x
+VGfGrebteL0sTYQBsxW++xdaijWVtx54X+/3Dn6neU1qySf01sveCUzfi6m0bjo31aV5NOoKndL2
+RfqJ3vKs5MPxbeOWnjfTi4fxSw30fvV7278fbiFS55gaBRAFsOlY5/TV/8RHP5Ti6+TfcAivYQXD
+ftRjTWEZJgmC/Lc83l6hTzyNNvB5SJnZ553OuvI+o3xUR8prYaUeDklteSZp7RXljvtZKZZfunVE
+rAFjH2+0uAVC0zEuvfa3TRrlNkA1112rjAPJy/PjHQFwAcL7vd0ScT0D4qBVn+Fya0SCZZVf3pYk
+GNbjKw+UFleITMPCwgpXK7Dvj38dfoCwh9+afG0pu8lvIaRC7KLcKPUqEuF0gmWJMyziLK79UeJU
+AGxNGlkIg5Ri6vnUbR0AbxKMk32CYFi2nzTFZhkIa8QXIDb6eeZuFT6/3CViXhX/yuukWiBOPsBO
+tS1oobxbiPfcNiswbRZUnMY4mlrBYjbSSMk6DR6EE7lzifDyA0+kvZauKJ8Cb4FqFZ5rDdlJhPsy
+NWvKjb091sftVkt4Weljuu6gN/RXvJa03vOedzd9B2hxDIet+e/Coaun8Y4MkvvwseKhDt9xJjJa
+6237/DZAbFmG6DKvKdaL+/jp3Xmn/IjbQIv38vAeuMqlPGUg2+ezBzypRR1wWSCQewQmkI6dB7+Z
+1U9g0uz7jVQovVwge4Hc7g7hc6PGFmK8bzdArW0gaxpXk+WPVPgNDW4BhndFEDv4/t4C4gKgL781
+IsGw+wzzr8tp9IFcOfxl7nu9dXgDwbi4mmvnzzLt/IQ/v6NR/k74AcK/Fd4c95lK1rQNWlXKncHD
+fXzBI7mhVODhuQ2WV1GQss0FwqWpAkjwjanZL+FiIi50JsCrPGRsXovMoWH5A3/C153tiF4i12e7
+Q6is92sBNHfWv0wyHfWksPIm0wNizWHmd7Vz+9iKVXqxkcZg78pBHoDnBmkLsLbyN5RntUhLsQSD
+lkMge+8Ssd0gogwoclxfOYcKdifFN4u9U45vHqbOHxoG3/Vyq4G1fE46qe6GKV2BxonnxPs7r9p9
+KfRhaK/zToH3+d8J31Y1+njXPXHOopzjHra7/a3IVj5klnWKM1YelPTzqrye7d0O9Bf8E8/MZ40P
++4mDpW/VpMdtiAcDhJMsmN57gJwdJfXTDkGVz9il/TdES7X4GoijKpe27ywYWNYLrcGWgNbYuhoA
+WBMABzh2IOv5t64QB79gtAjXh+gKGL5UhlsjukX4KmNce2IrBf+woHsNBtCba9NlPBb51+EHCH8d
+nlT/75SBo7+RIz0EJ77J8MG5Rc+bI1IiLKzrGxDAbfMzto23mcf3YX7aXmUsozCshI1ahwrKAff9
+BILTCK3xFZfXp7JO4vUR+QhbhFHwXq50LGllCHGIcV3wl3+Sd1Al5n+S7v13QBtuBhaprKO5S8DN
+DFKDQYwdIUyMCkFNBSRtSzD22gSUtu003AoB+QGGy3w3PSOzEpvDoExG7vlatbH2A6JrpUkQH6qW
+WRDXcU9zUWl3czbN6bev0zj1N/NqfuvwocxT2b+t1/j0PjfSP98DiJI7wFX4D32JD4wiR5cIEwbE
+dB5FGBCDPLh7J9qhDEuq7M9tXbVvm4Jgb80Kf8N2N494ar8DvUPlJnCQUcmgzoL6bKAF4bSxs8ds
+4YX01sm3eVvmXGIJbIVvYiArsPSfN2YgDNemVYALIJiuVCNA3O8XRreIuEeYwHB9mK/o0rAIC4Bf
+BMEV42AyAEtb5n8ZfoBwhD+Z/m8BcFFH6OrQT7yghVhVxazTyfQ6AmBpPBY8loIUvusKUZOSL3qF
+X5UFvYHkw7BhiAmCF8H86xbZNF1C5LPLd19gB8HZ/vgqwtAPcVjPBW9+sOBjVcIXaC15DKrCsDxo
+JpSylmUNiXbgp8DQsFmZgCcVsPOdXR5kW7QC/lO+dcuxb6fWoxKKUjopv9j3N1U9B0C5N/WcrmvT
+mjhUZTe0pOtAw/X4vdcn4jzrWkZAeeUc1r7XY3q3BZ8k5B8t32+EPgcyU8pavpP0Zy4EdW2dTi4R
+AuuvU7k8S/1M39DsBQ/S7Pf4fNz+qumkp9dnnUGct0kOyIE2BvO9XYwYd/t2qtAquQgIMFESIFb3
+Bx4AsGBeuXHBAGhKB8MMgtlv9wRw+SE6sBA36zCC5ORn/2AJ8Hv+QQ3/AJTva8H2XA0gOK3ESWur
+hN9W/+PwA4T/ONwhvk5vWwC/C8BNorIswIObxBEcbzCTUiQ3qAMe51nVuIaA69CsSAZF4RUIVXbt
+o6INFwqciqKdl1vHHgo2DVJLQUpdovK5RAR8hPEGiSMI3lWgMML+sMC+uSmWEILPW8bDdlr56AG2
+tBiHtqntlDRbgrMvNvC4QsU2sdbsQ7fyek1vwfDbMIm8An+ZTIOdS5/ppZ6m5GoV02r3fTvdlLHm
+4XxjRAUZ374+I33y/4Z9ESOAcZWOH4HvDc/v8P5rZZYy6h3vNA/6ugbeN+MDqfvbuw56Nb+lKXny
+EA+aDbSJr9Ls9/nEpFkDTTjNgHgISsfq+KBchC2DSGZXGQHporZ6sId8YkK3h6K5w13xDI7Rikqv
+CQCLwkvWg2qiHRijNfjJ4ot+wY22y0sBvxUM7zq7FbicAJM9doN32dby9CPm55IGmtf473HwDxD+
+s/ACBOsNHz3FioA40Wccv34OWeFjdftrinyYrtZrBATqA3YGmxVwafSj0miIEw3jZciLtAULViwL
+8H7AYnuJiF0q9rEQMiGM62G1QreSH2fQhHzIpILhQWWYLMt15OSDcgs09tsb+vftVQAU7VZ4fdnE
++wzKNzjMqE3KNynAVg4uEWtGZxDWgV8N1R+Y86TlacutGeWcvAo6FNXOMezJU1UiQms6rOaY1+fx
+/vUGAONrkixWcip2uBniax6nIlA6883hN1f1lqHvq9+svXVurtEEz1FdJxX+tUagC8uI8dq1HTlI
+oSNtjNuc/w2fy85L8mv/23CTj7LA24DrgG/Xj9TKwybqKsge8lesus+tVWOf4BM9gLEkEMSH4ioA
+/iUViNa7fR3w1ruCy00QF4LkAn7Dr7gAYgTBIqX+E3Df4zOJsZP+CmxhA+CFeMhnzp9d5f7b8AOE
+fzvUo7rSgGEHnkVLcMuINvfPpvl1aVoeirPZRWJlOc0bMe5U1CV7I7oGcwBsrPwlq3O6iZAQjOoA
+vPD1Ocbjw2Bs+a0w6bpM7CPyAcHjD8x9PgXYit/hWIBxlawwF+zhVhkm4VlUBPrq0vndeVg20hOt
+Ap/y1LYlj1CJTjOKu5/w5OLgSrpagU8Pylko/T5n5zDfDTH5AE/wWQ5IbobaTYgewK7d8NTmYj5L
+htX8Q7m3rzsQHHnG7dF0UNRk2r0D+2P+4qlC4QTC/7sw7ZYTg93xCc6ODjTkO90yk2XS5WECxGkB
+/ogMoPi8rn8tbhm/5bU534ODO5+n+yu1Vj4HbfvUIO7t3canRSqk867f+ccsy/IA5tZ4FPLdLcCB
+IPsKuzXY3SQug4fNlMFwPJgmSpbg+iMXv5r/bwfBBIxHK/EAlEUIDEcfPU+U8lCnNp275yKAr4ic
+QLB/H9iW8qAC/uvwA4R/K1StMwPe5/Q+1eQbgzR4F0vgHF/NgOtEfMpytGrZN5OyGSWl3gbJ+HAd
+CSevDmWYggAr+vEOMIsMQkhrwnr8Urk+4OO6D9ClCSD8U3cKIAbBeVglHfvLOBFEHb/0Nkz7mMFe
+2sCvMC3e0G/3pg3D3NqnTQM2K5z8IN7JEgzWXpNXYPhNmKEqrnCu5etSDwjuKEN12HuNR3n6S4OG
+L5XywecAcIb+Ta8TCP6IEEhCfuph2XIZ04o/Cs80UoIE4qKhBqzj3Y74u4Hk1Nd8NzdGPNZa94TR
+WVvnCz/A6Aue7OcU/908389IH3ntPn8CulLSFfh+BloAWy3pmi+wr6fNOy4PH4IOjplPB94E0Ql2
+Xf6468Na0dQ1bsyZ3CTwF+IuWTKoguHlDuH+umApbtbhyZrL4Lf9gMYBIF9Xgt54R2BcaTKA4bZG
+ey5Cv84g+DYut0+d/GfhBwh/HapWeQN4e3ofLcldZHtPFL9gOISGjuh7AxG/yn7oTkKpxynf+Q6c
+mgU43CQSCVRLb+xbGEqcAc0yNT/KTtNB88KCB+nLD2KN/foogF+LrI8IXJ9mHQTv+fFq17QxXBm9
+QY3TAd4GgBtxsP6SApRloWX++vgTV1VhS7hGmHcv28Ey1cJjVEN1f+iuDyd/4qlPE3w95Z/ib4O2
+yLtK+EG5LDwe5zJ3p47w2s78J8By+zIbwXEFTSJz/2hMdQtvhhkc56G9m947YP2vQXHIoNd870Du
+m7y1FsUSbIv2iTu5nQeAL+4ZnfeAfJEe8+zMRzR7yN8hQJ2lTkAQ/IE4zo+Heu5lSDut7t869Xhp
+RMri0+rlRDzlLyZNPeGAuPgFi9QH5pJvzRGCZaEf1chr1MASrGkR9l92q0D41xH8SnebGK5Rq0C5
+Al//FvVyveq6E8Gw+Jq7DnS96ovhc+nWYVjghzh6U/zr8AOEI7yc/r8AgpkEwLaqm8kC7FKB7u6D
+r5knACwi/AlMBJ/qtCkf+hJyCLt/in8VylfZKsJfmficeGoDM3X/YZFmBQ7gu+dGQHDD1Gk2GXXn
+OCaoUdbGwBfWtpozcEMQVES7vJU6K5C2wmvInTXt5oHqpdwFwouz1Tletiblzgr88W0is0uF4yVU
+aG0bjMQaJijMNC00m/iiI1OD5evt0mRtnevLN37BBwQ/whA6/xm09PWZfYInYHya3np6p7Xps66V
+BfLm+qf2v3bpfgpKb2Po/TlCnhvQ1a3EKI0yzeV5TyDY7Q/MjR9uzPdRyvGn/fLEI0N8or3Nx/E7
+0P1QfLkESCkzxSdAjGd72mvNvW1qoPTzCI5DztY+eH72bumF1L+LisC4WoGTdoFrhIPhsMDKwWdY
+wRos/pPH04NzDmYL7XTH8PQQXQO6BqA49Wp3gWEQ7IuWxqdNtC1TFOZ0G/R0x8PlDz58/G3x8Sb8
+AOFvg9WEwkmqX/TW4+5iDngC8KQFLyWR0TtZDSvvXw97Mw/C5xW+edGxFIgO7HGm8oQ5WA6QvM0R
+Wg7fArxoCWba+uSqWS5QsIsuAZpEu0EKEyysy+PrM9JtfH0aTQ7vCXDBnxi7Jv2dlC/Mv8fxJZTu
+K07W9uC3VlZ8/od6vQzuh7dhhCbjlhtuahlb0iHVr0Gr8/gEUOilcNyP5WYQ3MrsD892Gg6FtycW
+9/sMim8B8X+swd6M4t1IM5AMovQdr6eZUtcKqt30XkNvrXwCe9WPQzrF6rG8iswP9JeeaK0g3lxG
+vwlQ07BQJkKW3sZ2KN6qsq6JqZHT3KK7YCkUVmDQ9z7+0EcjzVIXeTn1OrOWKBdt0t0vOUh4D/AI
+BrN1/agDTPdPB5BpLot061vLafF9AMC17SXYKNmMF3K+Pfs+TrqVSshWN5X55vz+rfADhP9GID08
+ieITQJY4tKnprLyXTfO3lU2TijNLCyiUDGSHWbpHNCl23weyeMOM+R23CMjw5x39IDnQDYDWaP4E
+r8BTzztPuTsRPyLLrfIAnOoEbD+F9ln0AMWfrKOnOzD2GylW0xZdcAv1E5gyQWvvBIjR8oFzwfs6
+Qe4BAEfcgO/Ecw7aUjbku0SeS/c6Ol8/sck+uT7gdliKJflk4J34busrfSGeaGevSQEPc5jk0h0n
+9uC0C5L4qgt/IbwfxZ+HgjcrNwAAIABJREFUgzikvGm9PLy5bhD3WW+hcd326a6jUznCVY5jDGil
+J/XcTj1l2ekE3qekHySzaAq8H2VejlgVy5SRUjdOs2c8x3UsNbguYl5wjXCaZl7j197X5AG9o5yf
+82nSQHDIwvXNqQJrWl3TKotX+q6oUf82e7pcei8iz8DFMNcYjL0SIFpE4sH+HTdY/PWt6r863Rx+
+gPBfCoejB3RWLHkmUXsavVvNv2nt+/BGpM4881Y1jmohl2pqzZPgtf0Vi/8GuTuHqMj+1Ctg7dU4
+vHG1C3y9g+A3rMR1XCClOI89ZdEy7AAYLbby+Ygcwe5H5ACOOV2twkJt5BVtDIDrlJ9AlkmC4A+M
+F4U2LqegsAO+yV8w4wCQjRVDgGSpcy1H2pgHSOIE3jI11/q0y9H1Ye+AeW4LyBXpc25jfX39jmu3
+F4PlimZ0Hj7UrCeWgd/5Bnlj84x+K4t+R+1VOTHl/069d/ugyqfz+qY7xFxrwo3Y/TdPcW4JCK3d
+93nsZwM7/T3y9vk0E2q1GQrgzJ/DtBKdFm3pwAWdo5LGfJk3nAwrhWp3qhyighW8sqEgwaySHBQp
+oNj5SNbueko57gP0zCfKpBmb0Bhlnm97PwJAjofNLW3BPo4EvTyLdR91EIxAN+cwLb85P4FpwCJt
+wbj08sGW/5+GHyD8F8IoxuITVIVUpUSgjFLOCh/xFBJWV4pkmMX8+3y50TDW804y8KHluAlD4NOr
++FdNcEjA2ptW3koDQGwJfleepj8TmCurpaGBrlivfC3BsuNHKy7nNXBceStYHsCvCAJkKcDYcJvk
+SzkdYBjeRUC4t9XiXb3mXQoYNogLxdOlgq3I43yPtCVNT5D2VP6uxAQvKvSogKfSIq2+DnO5N69v
+2jsGzGybeBFr+TtgKVLxGkuyt1+On+qewr9Th72lE/DtZUru0OnTGid/1nEvhc+A+K7sBHCntTd8
+DxDTz3DtTU9bNhgkEBKgG0hN7E7VolFcuFztg2uHqPOwX7m9acYK2JUuDxsoVuA7WYURBKu308fR
+Ro+DL1bgpQPy2rLQlRv0Bn1fm+rA069NrXshvNQb6E3ZXUGwVwCuwYKWX/PFgLunDRaCXClgTe7O
+4N+WDT9A+I+D74ZGHWJaSRJgRkRGFOsbwwp9lbzp1wtg+6dhHwhO13yP7PfSJVQB65BKCF/bp0/F
+D25ezZLgFuqtgDcswEbgF32GqSeqclbpoMZIo5mkpXb2B04r8Q04/lh3i9g08hGWahVO4CuSW2gE
+VACCq0uEr4VQvC5oV0AEcCXnnvIobcB7UqzGaav52nmGMSTtPQieoIbP3Vr6erOG9LTyOoyvxnPv
+zpJiwKi9rOFGBo2DfF7bWq7lU14y3yr1L8KT4ptHfR/+VCpOyrmerbZuoxr/vhd118Mj0scWWkuw
+wRv4Hco4IEYQWgGxPKXbQtmwmUDilDwqnqK+VF8EHwVrdZyY51P0DIrj5ghFAGwk4zq47m36LLR+
+4CIMYPgWBEv5VhXyDRY81l1zDXh/8Rlv++pg+Y26CDSDG4TuvQxujVLqryv1J2d4Cj9A+E+CzQti
+R54hFhoUd16Jlx2BLH89VEn/R2GeID5EAXmh2S3iVeJGDN2n08C9wU/YHeB1qbnAGuRrCrRRstMY
+Vj0aEw+vI/A9WIILOG7AGMrJ8V0CAItIfLoPkGRFEY9Kel1Bh8oN76al8Tv4M2n3fybY9bWQIvxx
+eo3TW/jSBxJ7q2RTXM5gunMifQIOtTbkWWDHrxxEkFNBTwIjqesg0zrMdLmhV56k3oDhkRhaifKe
+jv/bfExNa/BNmEd3pr8JE6jluD7yVADcOR868G2ZsQJr1FpTA7sOcK3QpY/xeL600qbVKDRIgggX
+MqgAr/f1/7J3rUuO66wW8r3/I084PyRgcZPtdM/sOlVRddoyQuiOlglWSnY77lNBVNNGIgpftIci
+LvoaLNjYvs4CzIkPj1FTeniXAvUjtK2CX466Eeu8G5gt5STkFnXov7UnsAHQ/BC1izP9u/aNve8p
+sNX05l7L1TqEF+K0r5Llt3OLwN8x+JfhC4Q/DcOiMufvjm0TykaBO5zeYNxue2UxF3Yv7RMVbEKz
+7nNNdVk+E4WnRGJ9Wo1xZniqZbLVXy28W/mAdiguEuSA+CVM76BFbrZXdhmGOMU+2bf3ChyjFdjB
+8OBeQRTibiVWutZSrMvtA5tP9+lBcB68CBKzC4Qpd+nA8MBLcQ+cQe2Ull4usTRueJ1fAiXCtQwv
+5MN4BsMhLXya8WrzxO+A6pLKi3HiywyZI/boHcB7tYK6uuZx+c3Q9kRbxL1yI0CcN+tpfZUTIzjP
+QqzLtTbu6yMjz1FQU2QHKrOqZOp7z2cPx4FA94gcdCtpQe++Zyxv7w3HBkq4FHpHboGpA2CnNzR2
+q7DS9peMAfjOfVbLjfVj0hfM1kvITOqvoH7AwmD95Q14RY+B860R13arCxvQG10j+nuxTN4nk+V3
+JS9+0eaO5+T93fAFwp+EaR1JTJQUmdajgppIl5ihS/9ZdT18joSHAhXADrU4Kt34X7++8UVeAS36
+BNNFevcSnWmsrbTc06WDHUIKgKuFt95nn2Hl6azAy7XiDXkXrViDKfkLJ+uwkFUx13y7RERr8OVw
+2kfMKhxBbnO/C52OTss0v0qg3Y/PsCpvYT3A9v95KWiNhCiAW007xjmOBaadTpCQLK/hI6p1jVvc
+vWUdc9Rcfjd7AufN9E7oSulHKPLn1OPW+YM99Uotarq9IHfBb/PnsgTthbPzW5evy9OB2nJluJea
+bsAFPl2tDbgW3R8RbnEhxrw0pMMEQIltmzqjkXF3JN+MGTauoEegcRHYCqFf8MoXX5bz/JDelFH6
+rrvfmVY7AbFqx5LYCREKmtUvWGdINh+k7KFvtY7Wc4kXE/NpESor+xJHni3E+J/N+t8IXyD8NEgT
+7cZNhlvJ94PAsHN2PPfreT+kVUC65C8aGBbrRD/VKZahv6K3Fsz2OSI/jzAc3I0a8gLw9oDYNW7Y
+aIPeVvkC5e0PAl+q/r+9tbjxJW5ejssuEfH4tL316lQRss0Y21LBE9tB+Eg/WYSzDBzWCdiGQ9eJ
+7DnjlCeCIRrjPwHJeTav+x4E+z3H/rwAw4Wm80u6vqwAqutv/N/x1+CjdFIFnZwZO0aQ1cOFvrxn
+4BjnwQ+R7AdZ8izEeZHnSB4LpyVIakrlGjCnTKQz9GmeTJW71wEUMwobSh2DkOluQ3FDRlTpGfQS
+kX/hB3WNTfZZdJyZwN/Xf0koeoU17iVY/xSrsIT3sRlzMBXZrQ7DBiMKZWinjhOv1mQQrPfqLS0w
+sPmsaZtrCgcUuAZ44GeY4zHCi3+XYd3je7e2h4MVeEtDnn8cvkBYw+0RmBZTpAVlKTHRJkCRIVVm
++2Tb8fW1fRruK9wUgvZK9GarKAp4Lza7kj65ujpaa99WDrwYN7wkR8OpEZi+Wy1bWYVqY11NGSma
+kV1RsfgCpvoByy688GbHp3XAtz1WbQLGCqzUIoyuERUw6Utelr43GAXACowREGv+V7rXcarAVtI9
+8NzIg9fforUbGcTPIDgCydx/uM5DWkOjnWcaozpec5rJTGCirj7dKOdt/op+0op5jQQiIoYHMqt8
+HCGnZxmBdnsz7cf+TOvzdGNkdO772mf+lcaNPHjXxln1J1E+/7WTerwGsBZBX1zDgIRsQgoo1U3P
++4SsPh09JlTtwhwPEiXT+1Vwfpmu5qjfMHkJDnp3XIGcGV6UDrRUQn1ROFYslx77DcAwaR9r/0rK
+5qcv0d5R3U84dkm2CkNJFnFQvJSfzRHhwKfy3PIriUff+xl4/nH4AuHHAUY6rudKziBWmnXYZhyY
+y07403BHCTd1mDaatDEH4mUxXpewuHgrc95rr3vxTagCYtlpPPEu+WgRVs2Wla3XTkwhWRwswURS
+zwQOFuH00lxwk+h9hmkDYrsnB74KiLWe9jGaRLq2abcRLcPzyRGeF63Ifi5wfAEOQWmhiW8ci6e6
+TeSyn6RFnrx953gPkPN2LnBfrluJH3nSFceBSEb3CAp5mnFE9ZD1UFqfnR337qpvl/RULvJqJRt0
+WmtzXYcItg7hjsAHodOQPQh2qhDZjwDF0b4rcaqJSxmTC90RpEOhuQan6+Tfehlg6Pr55JO5A97I
+As1p6iKlzossxzxTfRlvQt5F69KN1gBg60N4EGCq0scVmhGqRtD6q0DXADDqlP5lOdK9FQQfXSUy
+KFYxmodIN2vby1Uf6Pn/I8/XR/j/U0gzgCjNW1jYkOYblxT2Lq+Vlne8vEaGtfwoPMLEHWOzKzYW
+YltwuIDIVUv+OtHA716U67fcyRbUWlwOcm1x8QZaQtVnOAFiUXlWDwpxtQYv0QLoRyhYgkmoO0JN
+X4Dr3SQ8T/UZHlwjoFyzCCeQnN0kTBkmEJwBMLb9lbbusjFuxvq13wCE9SPx7ekrYNtfz0C2B7+e
+C6d7jisf9t94BVVwhz9/Jjqmq0qY8mLIa6vcwteptzecpJKucgVeW1C9hNzvJ5kIhtvt8sH+eYaj
+VdCJ38aCI480eeb7uwrYVyi8cN/O53ANwIlM094Fxb8S7Cs/F1rk4zYCRzHiNDJ7bZAhgacVWCt0
+UV8q77lw1juqw0AZZt1n4HdnVBmxX/PM45LmdACznPMg0LXvBXzdQxqQvYRtRAol6mk5iMNV7yGA
+hePPzGpMvndbQSce0XX+b8MXCH8YgsLDxQu7Bm5Khd5FuvG3bM4zKaYq9y+FW9pRhnocVD8TWIEX
+r1uCdc3EjYAV2e2O8Z9OlvklOQTExC4TUFTZSHRb0+/qtluEAlCS965G59LwLnR1k5D3O7lOOHCu
+lmVyvI1x7RGMw1WgTTmNCEFxfYFD2/+i2ifGK6DwIX8EwtVybC/UKU/KH6880PtrpfX5M61+EU+7
+9oerbgpAn3jdAlytvP2neaCRvpxb9yGv1PSh/Sf5J37UgT6+eWPvKL2sDA8SvpvTi/C5hR041JSc
+VtZZijtEwzxTCXPJxmHruOe9ksxEZq12DCOhr6ruq+vZvoHjmhbDsFk0A17qoGuKswRfbJ10p8lQ
++tDHgdlvum8jrP3Ai6DYXpbTOPXg1/sNAHZyb4i9EmpBiEZj/4FlmBY49pmH/1FaWou69+JsDhbd
+xezWY9HJVV6IM3Dd8iwhjHL/cfgC4U/CtAvBBPZo9AkWSLQ5MJYhDS3sK1d6cw6jzvWE43wsSiMn
+NkoqW9GH6piyhq9N7KgWom1RvHZ7UE3K9qTaAGItUGXbhposJkHLCJlvMK14/rGLzvobae9IC77E
+biH20yOoWoUJAXfdiPPmnEdLf1QDQfCL4o9tvCCu6bgxQvcl14d7LhNIx7UQ0jiCs9MV55vmwnRp
+8vg4c6Fhf536cvIZzvm6D12kZ74s9xS/uq9ypdDP+1LaPE/8vgcCTx6RjpLr9+FO+TBbBoNdmtZI
+NWZ+cKEmXiWc6aYPW17XVac2TLQlW0GxhHR8Ua4stkNYepaD66pfRRWEyz4PeGgAguK8RjPIvBRG
+tkVAvYnc2IJp+qklxYeBnR72Ri4y1hXldCtIUnVThzKWoCPo32WZzcZo/t4NUdR3xER6FCfx3jOJ
+wGWBHBTD3rzyYXqsXgbHSwfsDOL7rc4LnSL/OnyB8C8G6SK4iFugDFsPAIFecCJP/GOYlO+95FoB
+yHfFOMitSplgEXlcF9yK70UoQme3B3eXWPzxJTk89FwrEPW2A871VC37KVvIQDABCFZ6sP6e3R7K
+5x39iKeTJCr4FaitD88EuDAo8O3AqvZHBsRR8Sea6lPOvFV+puerbpQhja/z4TbTXeXA24GfE7gx
+mm4KBz6YKeP40AXfnTGd0k58U+i26JMEp+RXjsg6t6qOOxRfl4F6pX8O6VXlzcyfzg21vZ3Goa+w
+xNumLt1dpnT17tP9PGLVv/2aP4R2aBKxmVFbTa84xx4I8Ts8vqM6/XKvjL1Z673jQadVq3BIZ7AK
+q2JUBVbKTWM+0jxNj0PTuVXBML5wrv+jicetsj7Ya+xxz86+vkT5l+EKXZOEAvAlJgDUnPb6vZdM
+b07+xfAFwk/DuMPkHVD0r6RLjPTirxYuFMcN/d+F3I5Ugexw1JM8zSSkJ06i8JLVWkebMALiCo5V
+MqYR87aKOLSzxRnqlJqHWlahS2v9Tffb+lstxsmFIlmDnZcSbVdr0/TeNmPoQ6IIfF+pUZiGH98M
+71t6nwBiauI2Vjkd1kZ0x+CU9+oKY324ariK635CFPs7giIC94j5QzmPfvLYPqjfKX435DxX4CKs
+5YM8DpT7G+Ezbg01xzTmd/JQuLp9tpMTaQ5i+iLn2nT1u3PuMErs4hE0zf2b127lPNyHQY81FiK3
+RpLrfJS2eAb6sQcE+Co917W2MbUFEoKOA7cJ/wCNNw+T71umV/M9EY4UWnzzqMW+6KzAvI02mEaQ
+I8e2vAR6OzcHHIT40pyOlaRfkdv7197kESv/FybhLxD+YRD7hzSJNHH3CFNV0izIImeFE8/9Sk4h
+Kdt9+3xz6Qu+Y7U2Z3y7J8IuBLy7qwhuETu/dygC4s4nOKflyqxPtjh4zD/mH0w6rkIR8Eb3BwS8
+2TpcXCimF+asNLQAi80xSxes8bxFTsC3gtTGx7fwDGmiG8Q5Dz2IG21r2+yffL7yTT4Pj0EmLCsE
+S3esvMiHIfPkch/XcYg/WfvdrOqAg0BK92J41HWRoYdUFYy04UNFFiHGfKXm2tE6v2Dvk9qL0Rd4
+kpBzc+DDtCdxA01BFpsezn3KlRRD7jiN6voowygpyjEfTfGoqUeZt+hR0qj7EPRyBrv9fC73nFtC
+4d61FXSYaXoK6cE1wuaD63/dK4XYNtRFykebrQTbl/dLdER4HjARA4LF/KqTBZq2Tn9iyEs7n9iE
+Z23ePw5fIPxpkHgjSNP5KjlNIE2QlYgGZeJZRmVjc+1qTd8M9ZUQTOvpWRGNQVROVPRZDZjuE+fD
+b0wCmKX4YtxaVw3oJfQR9rhqte7XjPwmqpgKVzYw3e4Q0p4jvH45LoBfS08uFDdcI0goWYBT7YTK
+2bX9uNUPWbwecTbzXqQJbiB9XnoU97mj+jjz+PXsKpHBQ7cCJuA4prEve720nz1GbRrQc8hg64p2
+J727h+bcCrNOgw1aN8Ihb7R6dcAm5unA0e0KD7W9G3KfZleIiR7LBcU41m3Wr1GfSkP/JF7thnmt
+5jrEkEdNqpJVtiQkjqlEgwV3Jxc7QyxVSi36gi+qjkRODMjHmRf3H9Wn0epbR4DivaTRCfWKFt9l
+EIlWY6X43FtxtvRYXnyJjSgecebt7o5P8+Q11vWluV1bnPJM2zoMg/+PwxcI/zQkbRcBLiIQsNJl
+038CKncU/lSNT0NQn2edezNExXQpL61xW7CoNwX6AKzA/vYyvKRBDeglpvqjGkS4IxedrFZf1qdq
+GIWdhv/x+LQAXt8TmF150F1iAr+rSADB5NDXRCEN2qO1zgAoA4gIDiXdx/gEYG+l77HGAzs63jmN
+a9rWpZqC/PmqfYHXLk37BsP5nksf+3QBazDreHWfu1bjWod8fUK7Ch3viNtSHm6o0wkdysEpVtJP
+hY9pVzW+zpPnDh2uvayhJ5vjJrt6XL8cF8vIMOtpvOjhkIbBDjbT2yZfHM+ig9KE6XQUoi9hBH2d
+9Clc9XUtedZvydjCvrfYB1whsvzqa6wdIDbRJ2CrCsbAMFp8Cay4MGO9VC8PjyMVlaP7cXaNIKFs
+QTb+UK3Fx0S9fzF0hYFhwj76d+ELhHf4dbcUQbndFqSJvzPovYfYnn04CYHP536jnIN/2tq1fduq
+nXXck6TLoXU5tacWEo/cWsCWAg2AUgC9ECeC+7kB/pQqNkHM6stvEn6R0JtEXvueNqB90/utR6Pt
+OIBd5emOSJO3ECF/C4wVENMGyatmq8677tgOov2VVNwiPT75F+a3jOumlEHqlIbqOXxsevpGo5tn
+BqURrKIXHKSp8gaFn+VIkkVUy8I0bVMOmSaJKikSQKzcA7rZxeJUjzt1nGifhk7jTHy+UQ9pJoRb
++i0ZrbXQ/+U5gPCgp9drV3a85pkwp13Ru9Kux28+oC0HcNscdENUkLnnu/GOtN1zYVBAYTcShChZ
+gJt0uzm3st9hYx7ON00mv21n1+3SZbvphd4WJmGFqgFJGl1ovdcSW+B7HBFTNrDxzo9NDpreqrBp
+hliJfA4pAJYNJRQAi/EGf9/2aDSi+BPLZPu2GDPikd/UUPfCFwh/EE5fNQNXm6dmahZLoVwF9ktT
+KZufEigxbS+0rk2++fQN5xBLi/FiTluOqd7n7KkODAsWAM1eiOtpPQIwfPp0xb9X8V7NSxHhVvUm
+odcCq3vjlW0xfv+JILj7vOHsYHkLvREcZ//hAQyrwjD3G4HNFIcpdGD8qjNu7vl9YgLqMEApaN48
+d+/QA+AVTEuAl3Oag5gOyGKbKFx74OP5PgCWcs03fUJIhQeePTVPgOnfbyODvmKPjPqM51QOkYHr
+lLbT5ZQeSyKie2AYh0hC3rha7qTNdLzeAcBV1tOAADhOQ18hGRRySN4AKWBF1akUuxoVVBzsGFQf
+XGyKUfzvrIJOb12XXhuq72kw8Ajxdl8D1wVZPW/AWODsIuwjtZ5St603ho29l60z46OG91mTRrz4
+NRDpy3aLJFtXD+4UDEOMP9QBSpyJAogOWPwfhi8Q/klQwPFgzeGmmZfLZ4HrreCUhskdHu4aDmZf
+FkLEaWF4GRt+SJKfKpJ9y6Zuup2WdWasjoMqa6fXIZwkQRRcJrKOFgTBtjK3MpJtCVYQLMsq/OYF
+yxTo4kfkHejqQyz5PrlQ5J9kRpeIDIK1f6bNNPeqwAy4AsN5JLp5e6LlvY8TXcgBb5blaVvhwtx2
+GfEt7QxYKF11NPs0rptu6sRprt6hS/pM9PDJg9EsmEsV9Jc2l1nskJIAT+HStXuqMN+XfwqTFTjy
++GrpQDDyZbCagWyl5RxdHSPX+XpvIzpPp37VF50b+PNo3dUQKZmILk3BQJ4lnvrhTpq7OfTg9kkc
+rvaAsOeTAsA9w2R/c7k4eH/zqGAx7tnWB0E5sPWfY5KUR7AHWMGAy7ZLthJvjnQkhOz2WB5rsri4
+A/AlIgfReO7/Pw5fIPyLofj+3s9JP9mpOMQkzH2lhydEnZDEtmLicvIFRXmhGYDwRXSuU6S17F2V
+7+TTugKo0bbqXhnAMEUAjG9CK7+pLcFKpQ1O3kQIgmkdv/YmXsD2T7UAv9EVIoBicJcA94jWCpxd
+I8hBsFuJ97+mw6JqnsGwc98biTvbXgSuPU3hRga6HndAUnn8+CFsxTNwrJufT//T3HPuPnRpQrqp
+RRDTfUI+9nxasefa5vc2mNuSdO9vckQ8+wwYc3szg+MIUSrMbmDLyiqVlkubwKnHJ4Bc4esnwPaT
+mXDK3wHldY2r+jQHOL/8ZICJzxkD2B1AsVRSTXyW9jehV+w1BMH720QS+wEoBcCKExUAM6N+XzVm
+kvjTxdCWaM2FPCyVTKlnGN0sVr2Y9AQJR7T24pvl21bf5G/DwaBUgW94qa6M+78JXyD8MNwZotkN
+AuX8xjZWNwfJSXnhZH5w3o+4FkC1gs0RG2UYfVY4HwHehO0QQBnopQhgFvDdgFctwaQAWAq/wiwH
+w1qab0v2EXIQTOxXBbl/AOwGYAyuEJJ8hTUv+hOjm0QAxKsSCwwLVBPA8W5ZN9ccDDtHBAKz33Du
+kzO4ne8zmNXadi/CRTC8xzDlD0A25UMoNYHknEaQ3gc53HXcbHwnIKybTwbMWpnAl8q9E8fQtu+A
+Cp4Bhsauy3hJ+qtHtzSQ59pwnZcTbwTIdT74j0rorAM+W2cS5OEVU+N4XIPgnK+T/VnoV3UHOyuU
+6sDw7pvS6UDIYJgy71BFjgQfhciDa/+zEPXGPf48c6b4bjNnELw1gvkIsx0xpuDYfIStrb4Xh70a
+CFlf4Ogu14hIK8CX4jehpnXTXh+sw2j1VaOM8lv1xIqNP9Usm7b74lb//274AuGnIU+2O9tkjQQe
+TrGfBZ+oNm+t6LrtBW4G2LT5w7RsQC/h5H5Uw6TMurSxZZGGV601SnFAjB+2o9U0h294EFDh2BPw
+doOgBIKJNuiV1iL8fkvvP4y+wgZ800t0QslHWMEwmaIUmjZPfEmovvyGoDf2sfdjv3VSSMkzONxL
+vNd4v33EN5srGI48KGcGt1LSIk+EZsiL4QlorPniQwqOUbzfcwsqYOmpUqP2uQCzTzebWdxFB5Rk
+AMdXoJYHwHyrCpUYXFdBXw3QxaSgjkYXCVJgp/8l9usMkKWhxTxdWmrNDZ7Ij2HSpRONE62Xn8ZJ
+7F9KwU2Uxylk2UuhiShAG6dj1GpduG7XaY/u+XDGoJnF6ELLR1j1uIFGLnRsYgSqFBKk0GIrG3OY
+7RM+N7UiKT28NLfbyeQLqrP6KpYwmvObpdkm2lPN9DvhC4R3eNb9rvEe5fvVMb63A0elDROvwB2/
+YyLCry3C9tAsQG6kxLzPQsmV1vWUQwGvtsJdIBDogGWYOb2YtT9CSVnQBp+q3Fb6+hoLQDBxALzB
+JSKB43AqRPAbTu4R4Ui1Vbgdu0YS4gSX3G/YVxMY7udEBbwofvIjR52G/Yv3Oa3Go4Wekhy21uh4
+U8sr6UrttbqJXMAvb0CNjgHByylu97p5YP7rqjwIZz0ypt5QP9zFBvDLRWZvTZ7X/8M68gBdpKc7
+9BKIJxCruAz0a0jf/yuNQko3jpJ4uiCHuy48AcN4JQN3Z3qsS+pwTLMOPQwYdrpJFIj3fJF4Jfxp
+aBvUprVzCn2AFVpuK7BiRWF9id1PSLLaBtXs+3PrHwzxbltGV4oIcrVM14rLmCsmKPgGA0ao5wqv
+PTcAX5LNx9iEfx6+QPin4RIQN6rrFwFxnjQdqCl5WHm8MtXdN83KtNjaX2R70C5d6Mf0Q1p3xXRL
+40gzgExEr72WQ9tiLT8/AAAgAElEQVRtE2OIx4uowkrx6CMsySIs5v4gIgEsRz9heEHueHya91+I
+Qzty/7kyno5Fi64StVfnESnyoD73QO8JDPsWa+mSed1HuAOz5cqR58TrtZ/CfdV9Ar9jvAPDqUbd
+WB/rd1Hl39mMJuTK3SXGGsA81enJsaOX0IUrn/Z9DyuacUj+nAKp05hdu0pMsakln4c7ujfoSOxQ
+9A0GtpEf2bCjp1E6DWDiW+P5SV9czpKbaVm7iL8Qhy/Oifru6sty20+Y/YU50+faN8mHl6Drin9w
+GtDuzRDf3ut3g6xn6DfWYda6qlwTLRv4bpBr8hPw3YUbyP8PzhAm+gLhj0K7tACU/Ezyb00EmMhU
+6xxTifQ8wggIgdt+YlFJ+Lg3S5/rdo/rqj8dtDjAnWgFDG8dYldyKFiRJbn7gR5powBYlkX4LRyA
+rx+lJpFeXCLe9Eaw+85uEQvpuksE3Ks1mGDzTSA4xxEM53EwhdbOnastUi5B8BQvAFgyQK1AdwLO
+BPIzn6gMOYDkqaUjnryGDRoy0D3RMI9iiAqumvIeqpA8/s9y3q2CftPUUpu8kfH5/pi8WTM2S/3o
+8zGbCCI4JqLGaDCA3aBGehAr5X/k6dKntd3JuQrTii59kBMo9jAOT/Yj9lvVrVVWCDrRuZUW8p93
+TLmxpd7prZOQmKbfFHYpUUv5C2gIgvVlOZs7Bhq33O6pjAj8a3P6juz0OBMtt2puyg9xOS2/NLf8
+frmCWcGixV05lIaWZlDQ0xGufzt8gbCGiARqXO+bLJ2wyzczfzscd+7eRhyVXVJodq6YxAzFWemy
+En2YRDR1PCnqKe6A2I/CYSLwFV70F5EZMfzFOfXf8t0SX4A0ECzrXMYVX/V8/wFLrwFgdItIcelo
+aB2mCoYby3B4ea52c9f1BkI6QKyQs1OamqcCW+8z3ATiFtDEZU6nQosWYkwngjHkXg6Rbq0dveYJ
+6+K0ptt9sl9xGdDg9ZRGROmMzR6Gf6JnfvPXnDjfcdc93IDbG2D5XqEWJgjT/Zy6/wrXomQwqMVI
+zmhRqWTedCnsVg7Sr9Kn8BP4cNLBXd8dQS8RdCaMnLElFDsVYqxyCxAPmUe+fuXcDbkmqpmidrS4
+nanrb2PEU3uAqu4S6iIhFL+1RICLR5SFtHITgr8wF1vgcZWtwBm1r4+HnxrhYJgUzIZ6qrjdCDhV
+wk6xQKD8H4QvEM7hhCKkn1pHVdWSk1Z8PPhlRYd4XQK6meenvYkfqNnyOwLumjbsF1NJNcg5PTfd
+XpKD9RQBMfoM14/JxE7agJcAANMGpwsELyD8lqVgDMz+QeuvuHtEsAYnMCwYR5qWvcEwKWhy6IQg
+qpuRqJhT91q/lQ2/hQKRA4NufR1Ixq3DaILAVvNWQNrRSPDltwqKryy+gRevnHmm1V03/wajUmEi
+79dL0DteXfidrdw2yblivxJi+wcIxQ2vbpaNtCt8/pPWFDgT6oZv0se5izR/Gblbc/l/lnHtKjHn
+vdh3UjiBXU2/ktn7BZMDoEJLPRxue0BcwK6xyjyHwz76Kbi9Cj34vXe/49onBnJ5g1xxX+ACgtf+
+G4GlAxF3PWg2S8yTdXXa1wvY3eWwrQPcI3acPY9bMzhWEcCv29Z0LDlBDNn3f09HTeELhD8JeWf6
+YZ7sVvUbYQK3QXkDR8tfFlTm3ovlRj+4CG774I6iPoJh28R0VXVgd1uHWUoaiYIedn0qRGbq3TQ/
+wYHMEvzetPXryBEAS7q+BV6m68Bw6xdM4Bax6pTTrJ4Sq4xBeyUfUIPKrYe+c+8j6M0sGcgaTWpa
+VMfprGDJfFluPRECt+0pH9bLrlLzhG6A1mXgJjG53bcdaDh/pxqmtCsQbPteX+ln4Zh1AEUd56Tc
+uJPRgeU+f0ttiBVciV064GVqD8cNOjuvEbOSSaQHOMtRRrbgxrGXgZ7jvwH66ko/Se76vLUKq5AO
+DAdBQChpnTzXExUQ+6JraxSI09lELfNl6LlRQ8UzI1B3dpZgEger7lPrij24QgRFlZAw+vHY5r9/
+YQ4G+zjPYD066E76zor1wrpTIsJLdBRpq80UF8o/Cl8g/GEIiqgowMzXxDKzrZkHC/DDPS6A0iIO
+lfdeiLagds4r1Hoo7ZQtQLGGMYMXrKZaehdf8hfmBIgluUuET19DESJZR0MAEHZQ/Nbr+03yx10i
+KjCO7hDvbAUO4LgHwgKgFy1RwS0ibLqx57sTIjQ2b7AnVwmzIexrAshQpQ4E6xVV7NKb3PJR4Q97
+oI1jmy+BrCqvnj6BDUBadFM4AGPNx3EUsmUY4z2tAcEFY/xUd9xXKvdwcuLiztGBKxulgXpSdhMi
+xlLZMNulysQxgOPWRwgbj7trNL9u9u2uEOk1bUo58fWUOyHMc7uPqyL215Cmiz7nKAsMFdZh7EGU
+OyAEZXf+hj3U56cha7mqNdBJAgGk/eDIrs+6OAjWF+XQ4qs6pAXA+ILc8GMadd7ofp6tvkETR7q5
+vOi3y25Y6V+Wa45H27qdDCjrpsAOM/5x+ALhHe4vjYNK+q31dSecFruzjFXCpZlTwvY8CskJHWRp
+pJeN5H6dcyldBge02kGDOwQ70FKXiRBkKysDnAQgWAwYv422LcN/ZLAI97TJIqxWXzXv6i/HlTOF
+N83qiA049rXPgG4O9DmnWTOPWqMDBzVL5QU53GYKKAbgUkAxxbRwLWXkPL7h4PpSAJW7GOdhXA3N
+XdyvoRe8ghOoOVuCp8c3CrJr+GDHuaF3UDbHfzEv97QWKFvsZ0q2wjXXcwJMnNIFsxhPXTto0WpU
+SpVnaZL4cr6O87TqarjSrVOIa2g4Im0v4GZEodNj7zuIq8KWuMNks7x1P64/7NFlHIo+hk6wpB7p
+eTIARv1nVlJC8BhBMKW4tx8BMMU+1cU/WIXDPGKPFPBL7sVcNLO9xEfg78tBUZv1OihabKvzrur+
+eyT8BcIfhCtlYg9j8FR2SwHBGprX8u2dSLn7sjlcejCkMzR47OfFmBbaZW2uFfJdcQXcpsQFegDs
+svO9AJQxrTd2DTALft2PrhAOgO1+g+G3CL3flPyC0TcYrcQSfj3u/U4WYdH0pXLUHcLQlJZNUCeC
+ewJcvDtyPlLvPuj1OemK9AQqw1UO6RLvcWydR0IakY9tyAd7Ai6VLI+uruL3Li9voo3VdzPHPb7u
++CcwczethYUNzugSw6jf1CfcxG4wxxuO95xoISXpqNtlh1B7IY5ZM/858rSlw9v1BagmkXKZNu8T
+OeVaL545rnTvveDQL6+ZFKnVmqzDiWQJtseMVenTCl3GtLLOj8Lv0Orqz04RRAvY8u4v2aBw0cW3
+3y0Gt14TXeKXOyvVGYXAVtLcd/CL2h6/G7S4nRzh9cdTItSF0stUcR1Q/vkMfRq+QPhpGMZoVmVz
+nn8V8vE/LQ/EfYPE1QiM99daDHKPDevztOuYKB2HRvAzy0gDt4gAelBhil8lXg0MvyVd088o/8F7
+tAR3wFdSegK8ehWK9cq78dhpT8FwTXsbNefJShZsCA9AcL4qwOx5JADfa/DdbN4JLGdZoWUS8/lm
+sP6HvR7WXJxbnYd2BbTxvktvUWIT+Hh7J0+b5RYWjnXMlkROoBiIVQYRfaZIJ5REsOm7nivcSREV
+OLonRwG0BfDWUR8kNvfdtzb3eLpw6JHLPHkut4IGMGxkotuA2FaHAr0D6C0lhrqcQwbD5+ndtf2C
+BgAvA2D9FkQB5ErawJAkgmAEvfUoiSG9b3H9FiKCYjjcwYFyAMUUXCoU/Lq/L9Zvl7qVfj5Ozd41
+UgvxPw5fIPxpOM0xZ+kJp4yDfnkcGo1+BT6NUxEKCmtWYzm4+1mF+vrIDZ6pBNi0AghGsMQKfnkd
+nUZiCzcowGWGDdZYMQAsIW6WYKF9drC0FuEMhsu9+QTjOcKUgHCKU8NDPizVElx7NEKArrc9zzQW
+BcAe0pbeczVcQKw0/MB7Asd0vHpLZ7CcZHHsHwTFDogXwXuQSzoCiO41nVO/Ftrlm7UAnH8AlD9X
+QxEA97RcUILJFy4SNTzYPbuf1tpKYFRnl8ujWR+mHhPggEl0BYCnIju+n4RbY12Y4sy2NbPJvt4G
+wGzMSXijRBwQ92BYXW5Lrmk/beiZjSfGUcSs/aoOWCXwrry5RQMABmwZ997i6gDXFgD3oFkgHR/q
+JdAUHBPE2TnCW24oFfBGPjZN9+R9fnKr/P9x+ALhh6EHbnniPZJg1M+UUZc+M2HKcbNUkHuwCnMm
+hEWTwtA/cUvq691BOQS5GRShTtCM8F5CBMTE5grhIGvDSUeeBogNAON5v3AfQa8UC/FEz77D9qty
+WgXCOJE40t1XvI+Krm625129H8FubPNLcc6DQFNLmsBmAJ7S802gVUvF+Zjng115yHu6WoMlgGLA
+MgkQOwBYdWZLi1vQfTBs6ce1zcBzFfZGxtMMuJf/lCHMCU735H0W7rVSTX4iqnroqhInpeMotRcz
+FdVY2YQEBzdhkB5czxbiiaPj7WJ/L/Rr5OAzbEyH3U2QB6SnW2f3Y8WK/iiyMsNvhkloR9dVn89V
+Btuq6QmpOqX1DQa6Eo4AOJJXMUz+X6uFWsq1JbZAmnR3n/S2kdBy9xCtqni9tUnNj3D8B54RXyD8
+GwHH7cpA+lfH+MkZbByXa1uvgiKEWmsK8lKACbfai6DixHMlLwAhTqA3rcHuBTqDiIIVElNO5Uct
+3ssa7NZfKaD3nS3Asiy/vfW3+RA5+CVQagCItbroKdH1cO275/Q6qmd41wFetPhSQ+vAaAtsqQfH
+I9CVga5X9rkQgavXkYj2L0Ap3V+Ew83L26zuG3wAqbHnYr+nhVrCSe61nNzGmFKID0IuL8vjlJQK
+6nRZq98OGmH65QuQF9ZUVGKz+JAGKyKpxit3iF78HQB8JeP3w3kaDKgVk3CBHLMnWSPe3P0evjmQ
+mf9H4QnodXpJRcsvKAqbQUGHMHSF+AUAsG7JxBl3DKA4ajPcShItxu2X4Si5Rey4DdvwstyqDns9
+Wwtxcpf4x+ELhH87DDjxiiGPfXvfnru57/nG+9SHCcbIsCdweOLDDcImsVBrMQap8fWAc/m3QHNe
+3zuvVYcoPEAzcwDEL1l+rm4R3i/RsXavkB6rRrJBmKHM/uMAmQqwpRzf1l7aABnzU5eHYscg8A0d
+oYRCO/dx1+eoDHu+5qs0aeQnGoLJy0qkNJyFHXCOVxnoFyGXxabmI7gVX4rxi0LYmEp5mXJ3x+5d
+KXaR98IJn2Ta3vB+vBd1An5COxbUT6DybW3lSCKa1bHThIb09m5aN4syL4GhHQfK1LS7s+tOwOcP
+vnn91UpBvlKepd3ZPW6G1N6qAZBtapQErkBmgJnG5tJUkTDmIXJXxK2czDVB/IWzo1VYf5wDxTJ+
+A9i8BEe0QXm0H+N/0xvmHyxuL8OvYoWOFmJ3l2i68y+HLxB+HABl7Ukdzv/rJhNMUv1ap3U3OG54
+U1oFx2UetVkbIpL2L93YGYOIMDuAbAsuX72a7V4DBV+6HOc1nmqg9/rRKigIdt+1TUdgTEQvYnox
+0ZuJmAXim5ect/905xJHZWpuF0oTUIbWQUKmZrDTBORBX6BrRxoRq1OnljPEijzS0GpfZ5kx7iXP
+PORKMKUZoZsTlyCnDxNALlfWe0ktqX3s2wW3PF44IOipYvWmZfRpIcB6kI2g74RGNu9hmZb8LQxw
+87n3494x7agoor2h09KfW9qa29LUdRjs2tE9X0jNyiTnuUjvfII7muXu6zTlmFswl9FxHkb6mNgN
+dbfeJyGBl3UFVRn1Wvnms7/Z9Tmdzob/7BPbwwPd9XGeqvjDayHsNE600NeXg7cZguLSfRfnR9qL
+cV8nPckhngXshoSoATOu0ZEyswN8PWdH3wF2QPCrL8YhSNZ3H47N/kvhC4SfhrDDpS0xPXnZjkrw
+BIVgGKZbLmJFLibGlSsEjzeX5MqiYF/b2bS3QApM2wvoQwDT8WewAd1twFerp8AXldCiMViEwTpM
+zYfTB9Ko44fKm5W5ybO0wAa/GwebFVr5dDqR/9M2GFcAyvCcj1iJfFNOeruFHHm4xrn6OF5/4a6T
+ezk98ySgB2C3u6a9ZRWBav8EiHH76HmIsLqSqENrTwAaBTJKnmRBhjv6AVOkRPyukcVEZBYo+wpb
+LT7w5jyRAWI7lmzv5qyTupkq9yFh4msArjetAcThtgOtB5Db8vd355Y8A8CX4c52cFx88Zcby5Wd
+48hn/L28HgQzlJE/ERD/JEQdfo/+qcx2/RelLFVBub8EtFg2EM37NcpQxEqW19Yn9RD4BIzBb2yD
+WsQGq54R/K664Iu/djrIExfPXwpfIPwwMFH704f6ZGX/TzyMT2JJvq7wUx0uJsqaaxeTqU1OUCi5
+P/S+v9zy7k6gvJpNwrC5nQJ+hZMqEQBNiEP1zCK871/k6+7F0dqbrcFV4VIFxcC36jQDYAXHZMok
+D0kEwdbrAHZN92weP6A91jPCrZMV2MvhIb2lBaAdAS7ms6ktDf0ULuZKeCa7EHUH/GJxOOMni2+M
+96DZK1BBrbRpqTWntCyIg1Qk9vx6M3TeLUgRsu8ewwd5dbVirY0D4vXngJiI1ks0WjaC9+tqtOBz
+4ITbHgBH7H8AyZJTJSfnSna1aOv5RE3+XRA8zwNd2zriqPPmK6d88VppMJ/4qorKK73+vkFDSVU/
+H2gFwDb17ZawWkvDum/4krYyTKvAuAXBDjJl6xL8BU8EuDV+DYxX1DXeGiMOWKB1h0Dwu9sht9bv
+74YvENZw9ynEBgl29NY6SmUihCNM9AlJRVFaADEJUERT9YYHt8yeeZYXJzW209M4LbSgrkN7dYV6
+ZWy5dEgh0IYFIbHjrGra9YMeQFD8kg10BVwemCq43Z9LYKxl0Fa+VL+io5RGKY02MFbloL94Z62F
+YfC+jD5VKk+HSgEyAQ1Li/cVJOd7SrRuI5jjTCOsgmnShm6udAXDVON8TQW3PBTbiX3wxE0i98xc
+/Qx4120rJ9x2o5GSQnJXetMhUx151BYmp2RXQwD0oKkCA8myMaMWoC5BqhSbSl3ph7YFGfhOMjq+
+ytNnlyZ2UY+x5OdgoMvR4qgmzNtC3E/K9c5+kiQFGdzL9rSL8i8/1WXibuj4W5rkfmjq1wLfiXZY
+90lZLUx7Ar/rGg93XAKGn8age8A45kN8YydAJHxU3CFI0yi4X/4HBuEvEH4cOof1lUD4E4P+1TS8
+dSnAYzt/XOi5KEqpPYi42qIINtfDLMvIJYP3vAphofkkTy/ZKY+JOCv4CCZ2aLL48qTd3wD+UnNM
+qUq+Mr1YCsAtwJji50WV7nncvYJCPrUAx3wGfkXgxbxNR4Ar3i8OuH0q1nsJFuMTCDa5qbtP9yZL
+YlqOn9OSe8SuaLsWsCFdPZTG836Tr1MaDbRFv3aToL05jZbhIzrpR8rWRGngBcA9JktPbu7SdKSS
+mLJpy9XHXb/p8j6Cb86gAHef2mvlrAbOdRkzFQ3jdwOwnWUP0DaI6xvxU+D7Ua5m3oXd5Zi+79MY
+t/q2XLnSuOflXcgojys/I0NbeV+FWZ9PH8+Z/IMF6xB6LyzfUp0LMGxlIFDURKFUgAPSXrvFfdit
+xaErQIbHMzCOcc2e/hcfZIJ939NkSGMEyN+fWP7/EbqX4CJYJGoBs8YRDNt/jKB6QVom3JgwoCHK
+hlyE5g0YygluD/vaQQFwxlceKbKA2lbqvnrXJa89ZrViQpxuRasS0ydPJgoAOFh+B5rSkaZ1CR/W
+IYrWYW8jgF+gOShVcAC9jICkpMVzPvRTQS+Z/B52dbz1PsfDvQHauAG1vE3aGHJl4PawxxTaCQxn
+WimWzy/G+f2EeE+9Tr7G2gAbxdhp0/rhOZmfAbPxXAkEgAwv1GzQSOx5F2nPDYYZaet18iKPaPJa
+W5w4JItr+Dv0WmXWpDs9+oT6w9AMWdiBxnTa4zYL4vZawSwRJSCNV0/o5VV9m+tw/qxVO32jV+o5
+lBHu01IN6TeUEjcyIh/OPY6K6nDFo9UcYK6GumvEkh/AsH3b24NkogkqUznVIn4DTrS+IWKvT2iT
+YgaCF2f/XfgC4U9C2vmWFUPsyUZAuZcTF3ac28EOamku+6iSer7MLwce47Sns2alhduBVy9M0RCM
+/fEo4KYV+8DfXPWisYk6DKtZi+9Fe+GT9GBXaRQVZlGkLP6hmOagFukZAJP1hSpGxcTYS9VNwoFs
+SCMqlmLtOoQWpmu5B3xhrnRpYQ3EMN9z6JPMy9Dgy3UwTJ+r/ecu8J3ii3/lyNWo98PxZxNGbtMH
+4JwLvgyH9eYK4UKnKPsNoGz7oCSaL1DbjuHri0K7HaSJdSxd6oEmtyQHxpYrrfd/v91j4Pu3XKnc
+8aXUzFMBY2P15SbfmOaW2k739s+Ss+IIejrVNeurk77r0gOvxLYQ0S3QXJPqvuzvIzFlFwn10WX7
+5tY1mL2vxCCDVFPn/5oH8u99dN1qmUSdq6idF6ydkcDwf4CDv0DYwkMf4QhyVQaVwUc+lmRJ7qox
+EEYLzCdt4SotzD1rEyyy8rVKXIDOA7yQtrokIqfYH0NdbWPJDFFLpOXsD6KwBjVBX4xTkP5iBsAr
+9eU5pgb8bl46HdeTLcGKcLEbQIYQ6ekRrnYWLUyzBvRq3PSJ8UsYuq737CEhp/EZEOf4nftKawb/
+NB86QQA+kMQDLafTDd6uWhnk1nQfg7qCfSurJe774AbRjWC+Tb19vm2CwP8uuUqYZObj0DLN1WCc
+VZyAco8ODuECgdbkJsODw679GLg71foPoS/Xm7GLA2vkndfzXetvXC/hyg2tpPXl1NXq1Kq3+ai3
+Y14usnKJdp+Uhr03wo38u8A30CKwDUrLSJmQwTEZ4Cxglojw/Z98RBrGiZI9OL3/VCy95HFz07AN
+BnWdYAtGff7bK+kLhJ+GYN7cQ7I0OpFPi3RWsPON4A/3PCRSR6eefgMAj0mQvuZzBr7kN6OrRCor
+pa2HAqLS8Mtp3W82IxDZa9Ebhve+CROns4E5Ka3m3gFyf9Qa2X1WtN09NKj0y75PU+Vk+dVzIVFh
+XILgFCe8l4YPdJeHVWLZGH5yf1PbCaVp3eEamtvbKdpc/Ng/kLNZzkfQXCrQEOQSDMdQHAnG21bZ
+DDIPArPMlLDmSzwOjYjIT4QgA9dqTUJezd8VeAvQjqEKmYHqJHeScc13JXkKN4fsx1Js1xlYOfPl
+tJR1euEt0LihWdpwtBrkdb06uz/UVvQjkPm7+ztpFqZl+wQMn4RPR6QRuD2Ul9nigWlEbtE19wia
+PYajbK0fU3xbHWkQt43EsZMAMLZ37SiOUEf7rfAFwjvcVTIAdW0QieIxaZ60J5ymdWA4lN0v1+uK
+X3AdAXJVeK629MijtNi65zLti9ZFAhcDUXloOFa+AYe4qVodd1Gavu9xjaJCMQC8q6cgl0l/VAOA
+K1MFyDTf+1fB2pfxPh8vtnx7Jd6LUNYCMCKu77BZAHpxBDiKsfs7gPgEjiml2X3j9nLOk16a0/Rh
+cpzgIOJG45P77Y5x/4/1yvc+KsADwjxPrnkGum2LDCxaY4YM9zcI11HnAG2/UjEtNIV1CoBWoD3o
+g4gT2fpMUn9dhMJx6V4xA90R2B5FSr27rPbEwEeuu3vWMUfR/WVTsmi3T1lay9/wwTXnZciDaVd5
+s46bFAeXzxk0Z7lTvKTdBbhTuOCNJ0VgFgTDuh07KFb3iPCDGlb7DWwF1iMNbhA7j++9uifnvV5p
+lNJ3jcFfmEv6vw1fIPxJYKL8AhxOnWi2YwfD5BNjgeHz2jAw0KTcWlMPAXAseU1KDi/FadvcJ7o+
+BTZXAMOuorCfYp8N+AeoseewJ0M+bWJGc5v2otWOF7ul1/2CmewEiJ316C5h9/mYnnqvIGTVDUCx
+SNq4o1uDXVM/MVHLl63BP43nMnOYaXFEs/00bn3Jbpor0IQGXsY2cKI3/RJrKSHudY64LLYCt4qJ
+Z6j9pdVXNxpowO1d1evRlHwRJESrDO44IQlOIklg3schLtKJfq7eZQ9fUgpdenoDc58UDEnnvFWb
+1d4/p+ZEviDBnpLmeHfX7U1c/keWQkuW3px+BwTn8v1Tj01rXRSGFaoyujZY+QJ1EGofBkKYlnaX
+YVQDSXNla7BB4nSS0wbDSFM+srYC36YWNwhxo4VXnb0DhneiSjppfOs1SP8PDo34AmELN32E7Vi0
+PcABDCdg57fsIBAmw6nESQHVuyH3yHQzrZu8EjghLS8sZYZFivRrdFADAkRdOI0mPryDmFDMir+I
+6a3KEhUlr48elTYeqZaUa6Rnuegekf2LU8WlguDc/wiMVVakBZX56/HcvcbQ8AQan/jq5GBkfDpv
+6AyMEeDpDMZiPM2ldFVh47raYu+A2AswTDRM9FyfmPse/1y3+4CS9rcCuORkpov3bE+/Ku1mjVuc
+e5J6AzBPlAFI3w05B5cZWfnnkbuab316NydmFwZOnDld42xy+vRzOTnPrHuxRn3/t3kPbWAi39Yu
+2pzlW9o0UIcl3zIfrMGiL8QRGQhmAKB6asQCvOtqcBj2cew/c4NghckpL05PPLJCwXvAEwRxeIL4
+viz3/ygcvuYnqucEH8FwFf6hykr1m3KOmbkKL/qDqbX0Ek5wfyOVYrfAaWvVyT4cCRZCR72AaNrM
+EyAGUUtxbusvUQC6L2J6s5Sj0oq/Lyje+dOAXoGKCPDZ3Ig7d+rx1o27tSBvOlHQn4+Ab9BzTVdO
+XTzRzrzz5hUyJWCTk6b7AClQL5PEtFSLyVUi8i0eJpjzl0HXE/Jf7IypezrOKUz78DEne+ROk/Al
+MgbdE18u4/hQQq4I/Di1W6Ucq97luynxufAbJ13cfyipeU7fI7YpPNMxwoUeb3prZxfr4tHa3PEy
+xrmh5Ty8pJZZkhp6qaPZVutcn2PbUrwoz0YuYL8QpiVvAriXjy+a7WNBFVQuFgeggm4IpIDX2y/c
+H5OmshUGe83wdlwAACAASURBVLoCZJcZgS8lYCzQFgfk2aL9r8MXCH8Q8Ig0oj0VwBocwTCBFZkT
+GAaZd8q9rtics03Kmy/SK8gN9BYUY/acluDUjH6bkBBPWixVd7imCQqqaSYqwxe4QoSj01CJIi3Q
+t4XXaB6PhbtiLC/MIRAAcFyA7UC3EzKgXwIIzvHN9AT4dmpq6mMud7gpcMkw8Y8hVWQ8/eLO/X6p
+C/snF5PdIjKf0+uLcfHuAHJDUgXeyJDbMoVpb73DWzPd+1KfPUp4NjB+Hb42V8zmaXic2q2Kl+o+
+YD7y3vgtuCdFPajNhJXIwEuffpp5dErzxRBT2v2hJtX4/g/bDI+8dODlSOOafj6951qX1LrFNXar
+7g1txHUS9487/MXKSvBo3gDJahkmO1YNtUgAtKJQt7EWNy/QrWPY2BuUX5azOPJ4B3ABz/Q9R/j/
+V9CBpTTgOU424BUMn9VULm26O+ZqWTlcerndBMbJnrIJ0QkwMxzjErqtCDmFmt69jd+CnaTZtTQh
+/NEMphdJ9QXG6614doGAuIK14BusygxqLahynK+AYr2KqbJAL8A3xwE85hN5pnyTrvbevUGH4Z5l
+1ZfoNLR14ETHJQj56r2EPiCJsiMo7sCpy3R6BKz3VPsMhju+CpBjXfqUnmfmPY1OF8T/my4EfoE5
+DQ/uvDOIMe7I2HEXPTokyynxjtw0wp+HPjfCLyl0zD2D4ctQFWJIzOuUG/oc98G+4mfgV/155nW5
+mb7e6SBq+5V3egbLnPKDHshldPUxWrNEsyvvuNigzccl34YIgLM1eLHIXkZ+bNk681fnkJbuL74p
+zQDy4QU6rz+nzYQoWntVtykPE/ouI8/XNeK/DDd9hP0oMEonQZBNJFJQcgTD3XZa6/ChqqOiibK0
+gkpStHuaQ56rEyI6UHy8xuh1MK8lqJbAf+Sc+nEB3+D2gFdVoI3bRFSis4IlpHc0OzdY5eiciUAZ
+AVzovQCOez/hXidzpUvi5Zpv6suqvHww5zk8A13P92hSEFHz8CO13t6fFRq1z3q6r6TcqZhSE9aD
+7Oda9vRQxASGa5jrEtON56JrbXyaqsIMPeffjJJ17DYIeHNDJ1P/o0N36n1/vlwD42fyfgceV5B7
+mgEdffXrpPU43VNaMFkwV1KJu7I48XBLp5K305VaRCcjljFZhyPYbcNQxkTL7cw0u78BhkP+acCD
+VXgT0RqMfsJERMx+ygQRHI22SsvW3UKj/AKd6kal6c6jpK2vR1cJduwzAuXjCP2V8AXCT8PkI6zj
+7ZG17BSR7LzhZbvD7vKjqdBOpEZLtYpP+QB9odxbLhJpMZj/8IyNQ3HaZ7rj5WvY7n1BZrAIujls
+LZZOWzki0MXrXRqmgRImitZgb57Gffs3v+CtJFiI9KxH7CrI1nVjC2h7/YtP/lGG9azEfpx0ebf3
+9/M3KtTYqFP+nnGsT07bkfpte/UJ1rw6JwOt1E83jhq8H3GjutsCoFsFJ9iTy6uiSttSrErkynUB
+PNtaceobMCJoORHUSlyzrihDWX0NjtW75uaYfg1nfwZ4uxDHCcFILDFSHu4U49bAMZlz6Z0IDlsN
+tzxbxzZpqjstfpLDPb3oZCtPsFltG6ZP7ou23L2mQ5ruZVNZvwCGHdQyYEawBpNs+tZQe98lPaVq
+K3V7gW7r1rXU4m6ja9P3Cd+sAw32fS6gFzZ4YsMDoo3RNDuGNrqd/qvwBcKfhOTecAbDBOZ/IvWR
+6wb7tHBDOM2TCQQXcrfy0pK/A3IRIgX+tnJRxuOAABLVMVD4anvg0NGoSNcJEXC2JHtaVbb66UE0
+AbgOgNfAcQTMOws54M9tpgBwM+hfc6paXPrHBqTFL7xaPp+6I6a4M217njoX7shKxY/7S0jbkZP7
+aUdTYpvWzMWYXnlqLW/sjgEQD+Ul1NrXJebr0wfdNNxNZXTAF8iEKN03f7YHkYPkQ+G1528JuGLk
+Z17HT+Yw1KLkxRNcke90/7xmPY0nDp5nA85MHtPiXtfxMPBwR4f4yYBY9TXo95ZbShm5jl1dQ5C5
+Th+B4amQPAvCV3j7tAhL0iML/Sxht/RuHazYBWl8ZTWGf+LuFQEMA1jmAI5rU/4jg/AXCOfQgYwu
+7WQZNk2ub4wmcLyyQ/5PKpnDfiKMdXdN43WPrw6sPHFjdRnxKRBB8To2brWhbYqlwXEt6bt6+2GR
+y9BtzM0vdYFiLWCu6TMlvRhcJHbVX+y+w0pb1xvW4/EjseyNXsxv2FokRj8CX6UZn7R8Kjl3Qewj
+CXwtgNZ65oVxCJc6zZfJDUkz42NALAdrMNZtSks1u+KxE1X6RJprzpWUYPhUn7IkD/W8Gqcn8LJ8
+rd8AX+PbafmYtfCA9ctWoo90ztXTxDn3ZejGhoGS1+8VGG5nVDP/2jk50JCaOXAHmdNcT1qcDnG+
+wZPu/QPf9NXGED4s5ny53tOViHwvO6SV0iewd/d52eIJ/FK0Blf/4D2ThHw/hj2+ukwsnuj+sOL5
+JAlsfPGPtrR4dFsAx9aWZ491vxW+QHgHVAinBR8QX3Z1sIFVtrjgMhhuV8NDDcopEhQSZ15I5Y5K
+e1FbA2zOlkIllxWBMsIs/LrDwTXKqoVE8gyC/ACYw5Xn9BXiaREv3mcLM21QzJsu9D9mktfqHnkt
+GQJD+pZ1/wa60pBXhOmdaMb32l97vZLPL5VniZVvd/sbaTv+bsB0vkYawuKoZ0O+tOt+orry3P3N
+cALXCq6W8h3qdJJ9M0e2hN6VeCvkQbmQdCrh6ozaJ9VZYUIAmfe86bH9k6CHfgaJezeOKnPWOT8Z
+rqe8nCh57mW57J2W5HSz9szn0zdad7t4OdkB0xjvD3yhDQ098Ti98Qne+5jO7Uj3tErvePOH6zeA
+1Fyt36a9K7ZpSl9BoPdiOTlPpfk6C/IGzGPrsgXsDpCJuBao2z9RxbTluLS913TuE/84fIHww1B9
+WADaMdksyEeomSvFtIO5uBoeb9bNBC27ULdRcYxnH56ji0QqKqMvlGn95EDPiilCaloWr+Ja+nD1
+bkrKcMtB8Ku/PPe/11IpLyF6vYj+t+zIoflv2TR6Ecl7g9f1cxxCLxKjqXrUNCL99S3Zct9vpjct
+cPqm1XVvjj8PLSoB+sgA8R6eNwzDvWu0tp8A9OrHRJFJ+f8rBbc6gw9F2raATJLTL8rIsWnN3ZRz
+Ky2TdBl+UILONg9nYHpH5inDmIePtyWhe8j56cy6bA8w5LIe98WNvHl2VQAEHKi2k0Sc51GGE7Cs
+6vPr/xHoMZTVpan4jq/wEhVAOca5owOo5ViOgeFC32k8geiufhzKP19Lb4e9JoeJ3qVN+TWCde7y
+hPhhXfZvj+QAPOmlN3MJJab8U8/15TjAFv+Bb8QXCH8SJreIyETF+lvy5HAGxyHHpPnLJOoydztP
+U5+hWS3ItfYmhuwSQUT26FD6juEBoi++DWnBh+XLeI9f88zK084QZgTEq6oLEC+ZQkT/kxfJa8FP
+Iaa3IOB1oOyWX6U1aRsc6wPUH2yH6g/y7nzxsvjiL+NhV7+xT2Aqnq4eV0jMtU9D5zcjdHrLrIS/
+C45tORyKsU2rO0TYeCB2qae5ic08OdQHjYuShoVyqua59Jr6fGtyOcd9DcxGhe2EDLCYgfXHM6v9
+OuxZXzzttwqfujQuKpvLf+AsW8AAeHkuA8FYPv3B0rjna3mbPIGO/G0dUpxBn6e8qsGmtNYyrGVa
+/TiUO32wb3O7bKE2bUaOSpedVtdmb+2N8jTCifhcPyh4vcjcZDO4o5sZ6WbFzsB7V/5ahP9/BF9W
+roXZXvIQIGc3gOxK0Unuy7tHHJi4oYXoacfZk/bWUWlNVrtF8JtYm+ygM4Znifq8WkVkdwiE3DFO
+TG4R3h8Evxg3QLzdJP5HREQvEhIAs3p9JXBMROAqQQqO5e2WY3EA+iahNzG9BKzAvNu2u17nnvIz
+LYCsSvy989wBwnq1uJ0hqT2KoVNYzTxQ+g399nsqMM6IO4DYtrvWr2JecHyD564sIq3rk50G2A/A
+sNKnzvjBGbW5rBu77ef6LfFNU68J4zQomVpFfZ3tB/w8xNc9t/321PVhynvKx8AT4pzuIX/h3ZGW
+nuNFbrzP5Z/cHQjTOMrr+P3DNV37v1zry8oE99zQc5szfQqnOZLXb8fLwBt30TtzuTGLXADcSMes
+kSf8CMg/DF8grOFu5z+0BBdXCuadLP2MG8jPAzeCuIlyw8KpnY2YlqZtx0WSgfKiLRIK8n4LDxAp
+b3m9juOSDKC4Tdv/99NnqxBpuRsI8XJFeOn9Ar16pdQNC2wOFl+6D46zRfgl7vqwfuzDz6ddL16I
+WYKZeLtGLHcK7X6Nt2C3uVqcMU087RPEqlPtkDdP2Z8B4zpR7wLiri5X/JdcfDo1+bm8vgwa2xal
+yiFN0z+rh6uWg4ygnhLfp83HfBcdzePN74YnoiNgaYAM13nJ5X/KH/q5WnkrDbg55kMQpXkQMIY0
+wrTIq3q8yNtltvQc50gPH640GuiLf7IKcymrKxP7bdpPPMS2x5Q6XzLvVXxOv16LfMVHZPtJpHVE
+ugmOoTg9Wu1rEf7/ENZgMine24O7x1YtZ9UtgihYqGh4ReVXHOC4mctpNWcGTrTHPsJKhyiQAj2X
+K33SKZtVseX1J2K5pEUFqhbfNy83jRcRWIIX+Pwf03phjhwQq+Dg4oAuEo2rBBGC4/xC3Up/0bbu
+0gbEGwSvbtf6O/jVtrxB4aIlWdvfgl+5SMexwOkjJZJCfXjxDhuyAOvPwTBVKRyr8ansdsuY1l6X
+1GT+yTNGuJEm7ZSvhHtg+F4fQNK1yCsRRHTrWWZmvlkHZz6PyhNx1+PBka9R5zijKvjZ/1GdH2iY
+b5QFZaquPPoIBz4vr48TgM0BIGuce7rr8MZFwmSfXpBDP97ZQIIfKnmx32LgQF/ziQ+8J9oUv8ub
+af187ExLN2Y5At6OP4Hj+Mtyi+E3vo16Gr5A+GlgomAJtnsiYolgmIgqaCbCM4b7Aq5IEi6BcdqE
+250Sb4dyB+A6pyUGsOhm3+BwGmH49QYJzxFEQUwoOpYmpOrZupoatwjOtH1ChLgyfbGYO4FsP2Fh
+WSc6ENH/3rRQKlbwLW7ptZfoojXYfk+dIuCl9JId0YvUqqtuEW9xEPwSWWBdiNiOsIuK3n4mWpZF
+megC8HKlncCxtx0oBdvG1VACX+NQX0kPQrc2Jil5iVwW1K2zge924CYWQ/sweCGyb9PFmJRSI+ek
+Yq7yT2om8N3t2otix+H/WG5SeDcG6U6RRyDDcVZUUMORX7mKynerb5uPXbrT6tf8yhFdDoa48Zxd
+Kiq94U/HoHXxCmqrG4TJ716QM16O+ZorAW/un1Iv7FOQgyHznGi6TmJapeUyc0In/zIIEdu3PA5m
+qYBZTd6mpg4cbxpbPpVHdPM8zV8NXyD8SWBKQA4SEMRtvgiGnY+IHu7uWAG6MZM7Pm6iSRDW0zYo
+oRHkFusxylCZygNyE0/NMn9NMluDyZRwBcv1vym53YwS34rzxezm2dfuLwTDOpySXCOKNdivlNwj
+1GfYa6cAGOok0VViHQ7P+/g0bdNW9ML0DmdZPwe8wYrcTtneVWV3BnDl4BQeeWLI86MLpmpHoZy4
+h4KGhHubx/0tpv+S9KdSG17rjyclalYHqZ+Eu/l/0zXwF0WB0HmmTqo08FxMXoRVZ8ttpuqc58RT
+wRrGGf4FWgPwjEcBYUq3OEf+lo8P+S9k6YrJPOWTLMMmW3U6VatxF6dONtaJPU/bv027NHfn9hCn
+T12r3fTqxlZfPIvhc2trrdspwA5rChkAb/lJ6Aiq/wPPiC8Q/jgwORgOO/Qe5OaEiMjLnh5CMal9
+XsFpN+SGZrfKMwBaukPbN83LdPnFOKblrxseEA74pDtsA4v2uL4m53EiMt9gIl+uHK7bDUJouULQ
++vpGNvrc+NVcI+yIPDucfL2u1p0GoSbmlk5U3CPUR5jFP+ukiG0ZNhDsLhKrBky2Yezuz0eoZeA7
+Ad4Mik25kUPgTkEGH3ipPN0Q8yHtTjCdm4QyMrQlnmrV8U3hySbD8P9eLX6jBgFsTuusFdL27v26
+HLKy/Xtexl8PWT3a/+tRmtRvuE1iKqCq86QAY45U7jjZJUValN9ZlLNbRQSEEGdu6XbPTZ7HPOi3
+e7Lsuj7UPop8qj+b/FmO1ok1X20bQZ7Ydw0PxcApMqYf6AyEjr/wHhi6vmhDWa57DwzPvQpykRdp
+yWq8v9381+ELhHe4+6ZitVBupZh0Y3zhS9O0jJMS5X7mHR6TqsI9LKW86sJtXi46o9PTXAtyMz9U
+PYjE/kqdRqUbgW2lYHob59jLucdjHvv9HLe26vpk3udALKCuP4Yi5KBY36BjKGyB2GXlJT1LeLIS
+lxMmOABlJrcIv0XoLbxAMO2v90RBcGPhID8+zdCr3AO+OQ1HF3F/nmUCsZC2+9QZzqdp11nxS2Gu
+sG2yvyN4SubUD/ek3emLq5rf2fyuwz2gWvXRBd9BpKml+fn4V0NRg2fOC8qNtAvwMwEY7LueF2Y0
+N7RAgflf5GW3igyQ+BK8VheI5mW5wtPEWznkuo9j3Xhz6C+mBTrvNMyPMoos6Cto71hn1tpB2Yf1
+9uQ+xC/W8Clf7avpsMzmGn4FTu+JgtNhOO/TaRJo5GcMfy3C/5+CT26NmuUx/eqDHakGQJmJ9k8T
+Ez1S7TDjLdaB8mkX4oYWbptlBE29RQtp2mD2I+M2LXtJ8AZ/6DstwJSLxPscp3DvPwzRuUUYCNbP
+HjMmMUD8EjLLML14ue8S+TESb4+zULTq0rpf1thqJbafxjCgHI9V+0MUXCLKhxAEZwux28NpyzGf
+591JJ+BLkFb4KSpg/PIjwuE4ZqooMwD9DTB8D6I1hXSCngi4VWhauynPlSrIRZR+vVVyX8jjPmt6
++ijDlZXdnFSR8nFO77q66bMnw3eux8P8D9OntAhcan9ZzzB2Td4b4D/7XQRCUXbnftFZgpWuOlPl
+wzB5PKRlazEnYCs1/50yTNbphzH2vlPoF/mxn7o6cJXnfej9G+mRJ+joA2+8745Hi+uylVPWUAa4
+h3CXJfDhzf455uCyEcGyvjj3wVL8cfgC4YdBX0paIQFc8kFdzOQmNCKKQA/oQXs/mAiXVuxu9+Am
+mpiKzy9ccfGM1mCmbBUuPhHeOVPNves61DvxErpE4DKksAjtyXWPFxP5CRGb/0XLFeL1hl9ae2VE
+uZr6R7afrhB1vsEk+ntwaiVO1mBiMr+LzbPfv6O3EP0JIFitwR0tzx91l9j9oICWAfB2oDgBX+JI
+wyC73zUh932hadkS+bLcbrjPs+YXwq3Fd3eF+hZ1yQUinwLjT3g+3Wy6TfduISdV1JI+aIROxVvh
+eRM+5p3SOjo3aQqrrlwnAkDiSGNIiGW41bfjRx6kHa24rEBvSst0hrgMPE2cPf98IsSWv08BQhlt
+fq17kdHJ9eByYr/lPb3LR8P9k7SaLjZITv+tUxlg3xc/jtTSCsDd9aG9kagxkBUg73R84e4fhi8Q
+/iA4ptsToezOToj4zyfHrPD3TQLHH9Ry3lW4oRkJtNtUbJfW0nzSxx8Syb7V64ncrcSh01Y6xZey
+YMkRkR/CXQHwtjClbmVMbZSbg+HFIJY79hkr4wakZukNLhGdz7Bag7Vi1ZJMQsRvr5P+uMbyHc4u
+ETgArnztJAytJgOYTaA4WIMnGt5baWLp1rctKJZ4z+lZieL9RLsMv4iUo6i7GpoPd5PsxK/T4mFb
+5hrKRfon8oZUnm4hz8iz+X7iKPgPs078Jzl38rTW4NJ1nV8wJdo9S7B3d5LJsS6WxrEclDf6Eo95
+Mo8Dp5mnlnG27Cr3lWV4ypva09KaftIeTSdfUBM/pV2FO3lLeXxP9goR+LYZw3nCi9/+JzcJQdcI
+A8hEhDjgH4YvEP402A7tAx9IYDWuuE4RwF2zj+6Ken+aKd0k7TafbifKS2VP0I98hK03kkjNT9hZ
+FGN06z6IDDy+4a7/vYuEptfPUsUv5XrtJiKK3MAX48sIDuBXS5R8njCeHQz+w7IPPNtlsYB7RPr8
+ESJ+45mXWtoGx9q38Kq6VVdBKvu9No/wXnkE1GC4j7Jx9qSTAm3kPhnjq1AgWaukw+V2wFl0l/tu
+OaG/Jh7YVy5W/aPyfoPP1/tDOXzBc78CvxIe98upvc0gTfI7egdcFMTFtDozL0HtdM8p/1CuAz4E
+bsnqC9OBxzx0mUclq/5maFcnh03ObBk2mcfj05r4gYb1RBo9iMcxeJ6/xPmCJ028ru4B+J5WyAav
+gUdxrWLdkox+xESKIfRdoO/Lcv9luNv76Leqg6z5JYNhjWhyB+NKAYc6NvnCEQwH1coNbZKJpDsm
+OpzsJY2hz6Jdt/ZV5Cem7Wfd5DlUSVsiQIi06mOlig6PBtZcQpkOhaKLApGD4A7gEu1JAy/LyaqN
+W4rBh3jLDK4Rb687v2i5Qry9rgZOu2EHwJsBcAHIIxj285URXOY+t3sWw+IlLXVl7trp/m4Iebr+
+KIzXOqCvR91Zekk8Alrkb8tgak5cvOdGdcXz0b5jmeIudylrb5ojX5J7kvfJnCjF3OUZMhRynmfS
+Jh/lBEDKSOkBjnJHtwlOPBzmZedXnMvoXCRa6zDUtdA58gRrb0qb5eaH/coTQSqbfM78DOltXhoB
+MQUZW07o86Y8SO0t3Eu71XGVlD/Hfd05vfMd9jp39Bw4yehDBMlRA+TdAOJCJMxbBQjZC3b2rfGi
+i1yV//vhC4SfBrDmlg16pxl0Swxsb0TKYefLk+BC3R8BfLcj5xXR5P81H2HogARwtYOyS4S/WNdU
+27KDLTJtPBEk4PLMG6u/wGcKT3rFGPzKlG8/uYajzYgiCKboCpFPinC/YXbECfFsEf7zJgO/9FaF
+Tna+MXvTSM/RtHt6CIADrxS3CJ06qtO07+M4wPnZm28CuGUtfRi6B6IYuvk+yziz9iXU/HHN4Tg9
+AsW67A716+XMLXq05YxdJ31ik/fIdXOzfsLzNPB4c11mm5bG+8RbQA83NHItFkBW6N/G8hv6FvIz
+JV6l99bjEbx2dE55Q5kovwGI3JSxF0BXRvg0NCq0HiyPVuFQH/+P6dDBQz/Efv438QvAjJMTGsEp
+ad0c1nnj97uswU5fsMDLiXsygurf2AmehS8Q/jjoROp2d9Q6MT2+bEc07mytlerBBCkAudtlOk3P
+fs0WbKb4MNiltfwuq/hXd1WnhKEH0cQDnWr35cWGtLVAIyB+NX0UvwGgCIZptesdrLyNKwR1fsM9
+YNap8UeE+M3EL3WJIKJgDablq7x/Po5NaZGPFwGw5c8AcOcjjOOA5wtz5tn5tWoTAPxtYOzS+nAF
+RCd598BylFTyXPQHSgjz+aJjrut/j6djPPfkkMpdT/SCL/n+QuDxZiRdpre0MpiVFwEKR0oBYUtm
+um/kZt/iSr+S6XoFZaCcSGerf6Bzk3fzqy4e/Yuh5as8KXymv0FmZ3l2PuAJead49CGmTi7UmUrc
+F+/M83n8ach1nXjKJhKUe12jCGmVUsxSCSjLlsWKF/5x+ALhTwJsRkxwLh6kO/5l4M9gUIVhkJ5M
+FAE28ubKlbzdLtNtRINKf/SrcgM/YVRlwCUB75XkDKE/myZJ2jRXc7rnS/cWzj2BIPgta0N4Qdpb
+327ddX4reBY1yqrF309+WJz6KxyqTJiiNRh5dzq9iN9vswj/oX0qxJvoz4uChZje3t684WA/ZRA8
+xonW2ckNANY4+ai64uMMiMPoh+E3HUhRVst/iM9hVqZyi6vjr3OmK/cK1OY62JKVJi3lxeUzyT6F
+W3zcRm+EuinOexrwdirrHwQukSbtlO8GvaUNE4FTegd4wpU7nvyy2+wX3PJf3Jc4L45A54afh/yQ
+pmWNLhBBru8QDOnhnojyL8y1PsyZJ3x4oMf65RD7Z2h34r+KV+K8hphCktOLQH/PpIbTg2iFu4Uf
+QC7bD3Th/aq0yH5YQVeJfxy+QPjTAJvRGkMOYJeYojFYB5oWT2dwdUYyvjFJCaHMgyrmhmbRYbIX
+14cYDTImWkhTdVd/jS94AltfRaGhC5viNFTgWzPh1zKrCxbQfLHCWAZZbMcFM/EyvG6grMDP3f/B
+qkvRwnu0BlP0D9ZymbYVmLZleFeKN25mWhUL9z7NQv9P1t0YPwNgIf/gWRpYnE5L0319dQ5r4LPg
+Zc1qfU6tfBpO2wRy1VgvL/PEeR/7a8onifcU7vAo423eMex1dVPQNd9pM/4sHFXeKc+DtNu0MNg3
+XBxAUOsHjPcHy+6Ynwd5BuicdnaHADp7HPmQp7bz+gU540vyNZ/fVz4qfNmCzFXOgzhBGad4HM+e
+964sItXJVQuWeFOAk+Kai6w7LSh2IXSLwFMlGlsw3MvOuvLKzvsfGIS/QFjD/V+Ws5htXC6EIlLb
+pv680dlENVbpd70yWxumUas3qwtvuKEZib2hWmem7dKhAFLAigtMozW4kak32b1kM1h3ToB5CHlY
+WkvVpuNrCqHWu1qqxNePyS3e1+YWctCs5b2Y6cVCrxfT/2SdrygvALwYFybRX+4gJv/9ZlVkr20J
+RsswuVWY3FXC7reVGK8KlCOglQBq/cMeF6E3axzympLzXjSwDT2u+tJGoOEx/dmMVAHO0M91jHva
+k/TIeQ6nrQJ5KPOJp7TAHFg6qSHPcTM7h5/sNWPeURc1+QZ9W9XVb++KV6N2Thv7fVLNF7Sshpmm
+qzNwpNR7PqSf8nPDn+6RVuLc0Lnh5ws5u7T8i3BtHMrkgQaNILrMi8aRlKGpx/GzzJylXbqKvc9c
+s+HsNBrUI1apmcvMtdqp7k13XNL7B9JKcwOJG4kUE/ieimcHr1xC+LLcWIm/Fr5A+GmweQyzK7lF
+RJBHYfOL6YmPKCZ2fHPiIC/no7g6Ap1j/PAinGSLbdcuy8qhj+KSj6AAeavF7QSCve74rHJY2VBy
+ZcLmAAM47wAAGqBJREFUMAEwJm+SgZWd/bUfjF+8XSUYgDEzvV6ywTGTvMROiVg4Ek6L2GcMq7J+
+vxfUXj+vLPRmSW4TcCW0IG+F/CZ6vxDQrp9rLq4RGSAzrwcAArrxQT/qV1rWrzB+wOsAmkJ6odE9
+Gh9omV9Dn84jzzTf8BWPKaQtD4RzbPw5c6jLYTrnki7DE94p8/Ub5g/5+YHMH4ZTKScA1fKmSTPy
+XdAiYIL7BohRx8tNXr3nhl/v+VS232N9sC5T/Cp9jN8CwfeePs6/OjfQunRo+6k8JI79wZmzGVtu
+ADI38U5WVjk5b6KvABrGoj3NHAwNIgicCEHb3QF1pPv/oquEfpPOHF9e/9fhC4Qt3O1+0HKGlBg2
+LambGBNlS/G8Bya+pvha32RbCk0ZVkRgGdpufsDUI4oAco1Ao1WYlAw0zR6E/gIIRsLF0GKp2eoY
+ROhXN5CR0zg7AF4gWBQEs9D/9F6txa8NIvdV9KftiIjoRczvBWJpdfOf7Qu8AO6LmIXe7wWY3wp+
+dzUzONZfqJP9EKOGaAXBb9EX47iAYt49IxSBMpEPfQSt7CCY43jeAsdp2AJNahoP/DiGma4pXZ7K
+helc0u/MyzZJLkq/qtztUj+u5YGp9sM5H1fSE56/EXgu5wm9a0+gycDX0HRWBlA0+flC/g74RBkD
+HfLeyTfFr9If8SYQXPg4W1kPn09A8Cntki+6VLj2xD5taMiX5k/bTzDgvnzuWYPx5s46c90Kyp5j
+av1PGxwTcecqsfcdPUXCzxy+/sb3b4QvENZwV/NOgHbNYwozD60+TLAXcU2/VanB1CB3Z3vKzA3N
+6pbgxJ1j1EIboYzywDDZ6e7SO55DUAQFvC1gDhLFn1aRbXeBBvUpVhnM21WCZYFNBcGv9Ws6/3sp
+SPTrlkTLCgzW4O108d6A+EVEf5jo9Xag+zZALMQUQbFdaYHnlwD43df3/irqtZVRB4pfG9Cun492
+1wl8OMGrg+DVm8Yr3ocBSKf8eUjCleN9niHdc4/WJ9sa7gHmlXqaiXXa8yGtYUvfrpQ8usxOstrS
+z7U85muFXK+1+kxd81S10/Dc1cc/CFMRT+i3aAhsip7J9/CffeY5GEr3kx/wD9NXWqQYEOMEyh7Q
+zj6/ZMB15DmBYM73H4JgvuBv+RoQarxr7U0v+xHI6AEyBet3GDNGXk3HelQGTpF+HnaalCBF8QC5
+9XfvE2b5JaquEGAcsW/VRN+Z4rhW/mH4AuGnYc1Vv7F7QEwBHAMx5KU4ky9BMfCGQhL5NOO7cgOd
+YzyfCqGLA81yeceezhGmxrtXJ396WBC8Ka2FeqRw7D0bo26ZRUswlhTrlLpuE/X8B2K3CKuLhITr
+Apb/237D/xMienm5AqCYwTXiz3aN+IMuEUxuLRa1/C7VYgBY/YQ3j8hSSO89pMJCrw123VqspS+Y
+q8BWe8jvV4NbUGwgGEAx7dNVlE/BOPJRHEPk63gy+O2AsalbzvSOd9PF25ZDmfJQyh2Q2gbTE9qT
+zexm8o3lgdjH9WjzHiQNSe0XnEV+p5cuZPximKTP9O4R6V7+ooql8l25OGCPHP2ATcbTdErynTby
+fUhr478Ggm/8ctxE63ggLXRKc4N8sW1du9aar8BVSr68GnyOOGOX3tfyQXA0i4SYvGPu5wvg2Hhk
+A+XIo/Uu7hL/OHyBsIUHna8jF+5xNifkpKasLm8oPtXh6DV+Ud9OHhH1QJAHcbvyrcVXbxJPZDJe
+Kectw4+LQAgn0BZRWGYMt/HB9mXqMi/pjfx9okQJQFJrq7pFyHaDCAD4tYGOMP2PdyaC7VWPY1sS
+zTWCt9wFfJcF+M92ibAf3CCiP5ROmaCVh18LFCsAXj/8AX7CQvRSv2FSgLxeDowuEsk9QhUa+bjJ
+nicGkDcoRrAv5DwGiONQ+MziMtPiVSqdKG4bV+DXaRwIHa/Sw4OZdPSJfwi6Xq/eFLklLGWBzfRZ
+Zob/hXzMY7FRr1ynXxb1aUjjO5eeaTXFKNLQTvKQCPPHVfGHL8NBOpbdnQAR04FrTKcA6PR6cq24
+ot2OJ+supjl4PfxaXPe54OkG7qkc77P4qDz1s3ISZ8swyor04CbR1TkKLzJvBQOvZO/HhBMiADDb
+DppcIzA1uELwbrOK+x6f9h+GuzPDdloG5deARLtvAN3oIpDrlCsl1/sYl8ggC+rZChiAaLb4dv7O
+TIGngOAq2PM0DYw/z1DDk2Wzun2CRgcbVKlCAsw7qtbfBf54A+PtamCAuOZnIaKX2ZaJiOjPdo1g
+fi/rLiHwZT2F2F+Koxe96b1dJpBf6A9Rsv5u8C7qHrGdMlhf4ltKawFj3lZmtQgzobVYW7OAsYPl
+CoL1eXHnByBtMoCXkvz2yjEvg0AHxWnoAo0bWgx1VnKk8YkXQjflM489UB7C410Msx1rOBfSqYqJ
+v4+2CVfpTU1+NQxa8TkN29OM8+28p6vxcUqHe75OP8oIdTlYjJtroPEhraGdXo67enHueYfHey8n
+pnNKa+MUT9Moy6YZ29g/6YW4MD7ZdYIq3+QmAfMgl4/tq+sQ96VzvP7faQH4Lpr/gAYt9wkiwpMk
+0F3i38PgLxCGcFPdHq29UndbuZFm4QbKDVm6Hb9px+VjIuTDlYKuESeLb+GZWuN9Uff66WC0pjwI
+j0Hwo5DAKlE/TZI1CBWmW4f3cKv5NpsRXpo/ukUsS/A+Ro3ei/ZefH+ULwBfDi/JMRH92b9KJ5vv
+pS4SROYecYyT/rTJGqP3rqkCXn2mK5ZfJhvT4C4hWxZ7mnZjB3DDVVzpdlfMyymv3cOazTxKOwLf
+kW8KuUJXQFd3jFvCPwhXZ16U3bFLvcWLpc3yrmXcSP4odDJ/TEuT6qm861MdmmsAVgNfAFknGV0d
+cnq9HusXyp/zXaWVeOcHnD98vsdK3spPkAkGsssX+2HDvNKPkR7HQNqtOo5OLAcn4T1rsFCLEQ4h
+wOLgI0ykbhDU0NQ1Qt3kdgujS8WjmvxO+ALhx2HPQt39g9JLaZt0TMPwCTDu5FjyoG453RcZWtF0
+DbdM1Q/Y80u2HGt7OOMA7ZceBE/vkP4eRohgF2o1llnSoAvNGswKfPfn5cwI+IIM3kqXl/sDv2VZ
+eGkB6j+8fm1OQS/zy45SMxq96C37pTmjLYuuiFuD3wKAmFdcXSUsTv4Sw2sDYPSmFlJwvO60Xdll
+IrhLBEC8LcN6D/2s/tKBRqZbY5r4KGa7RRwvNhnU8YRn3Fg+yjmDZqXVTcr4Lqy+qDL+rnkk218u
+9EnOe8nHFyK5qqMh/+1qPQhBXlDJ+THoIu9Ew+rLgS/Q0stpj192y9c8Bh1QruN0+3rId0fGaqO0
+eZiITn7B2V2Cd30e3V/xcC7T+2zKR4Evt6ceiXZ2ndBRk8Dr8VyveMWQ6zbxTQFPfAhnA2Mq6H6+
+Q5NNYyLaVuO/qvKG8AXCn4Y9cH4vcVedXpLLaVlmJgSWm1PkqRsENzRib5PVXyNwNR6d1Rv4hEZD
+cwRuoE1l62Gi/qcW7/ykRg13c+CijjSUVPvXeoSJmDm4SLzwdzL084pS3M83KtY3L19hPTrN3B92
+ngV2OVqAt6+x09iOT1OQqy/1idJo/6qeqLrzcyFFlde25DOMgYFfagAxKSh2V4hgFWYAyrANCFEF
+x7C8DBRDnxtN6ijhtpNnZbjXFzdgqJtZPMh4sqXsSiPyrgxeAW/EXwjcXQ7s/dy/4quq55B+wzr1
+sLevZYT2X9SdqPVjvAU+5MT305fdrl+Gw/jMMx/PVuitzHt83NyH+MEv+CMQ3H1u5tHCJznYwV2d
+Mb9qUKLcrwCGSx/yDHw5lmE5Gv7ARte0oEkDoI1p9rIbuf+v08hcI1oayFvpv7G6n4UvENZwt/PH
+XXLPxvLraTobM1CGtCoc6tXcjOCYm9k80CbZSrO62uzVx7cEjomQWEHqTuMMgr2fioV43PVV/o1+
+u52apeN24PlR0eQaYI15W0/xtAgiAMGvneON97R+Ill/HY62QtZj0ra7hLk/sL9Ih+kGfN9Mb5RF
+TH/e+6g32b+SJ+gGQXaqhILcF/PioXWYG8Dj3V4AwwZqabtETPTVm/EHPFZjFchaPuj7AnbJ50wG
+xdUtgssv2pUpDmo9jCenewzdy5MQTrM4cB3PFH8s8J6cT9i63TSzXPD8NH3M9zjHnPcuQCibdQNw
++3zO3/EZ+HlsCYZ7bvgDV6xL5GloRV497/by2pS1ZPQWX4xf5cmV5+nDz+97mfXbkE4OUQS8ZSzA
+Em5XsBiHNOw/kBXGgLXM1DmFJwnPDWmhroaEhu2tt5Vk/r+ENFq7hYLchmawiWkwgP3d8AXCT4Mp
+Mp2ZaSPDF+HC5pU0YEjLPBR5j2w+4Vr6XVqIMs0/igG0ZDF2di6gGf0yexAMaYHe5CthTr+3pOpy
+Dy1mMgDe6Q4MdlTaFmIvhzH5OWssxhAswaIgmPe5wMsveAHgF/3hNwBk9yVWqzDG/QU6sdMk3gI/
++CHZQqxW4e0WIRX4BjgstHyfSfvGQan9Sh43p0sgCIalEoAwQ5yogF7kUV2cZymq49ZnWHCbasYc
+4jru7TLP/I91+F5HkNdnYyr1ahkksb8RboFT4Gk5T+kcI3P6dfikyfk4uhEbXNCuAG6hRXUX0zvQ
+FMrpLcGet/Zlf2pE5Gtph/J8laWtI9G662V8Asin0yNSPTA8thaDzsj8WMSU5mPCVufQB9avAIZh
+XLR/UWg/N/oG9zx9uORBsKv1InhXY/sI4y5qZwOT+wRrm4SIGNwqlHflu+Ny9fvhC4QtPOl9BLk2
+mlFUB5SR3pkQPgXGhZWb5nDPF2hpmWfXiPaluJUozkRU4AS0GcS3FuLSSk+7ZWEb5dwPodZsnlEh
+eHrKJwsM64PBS78CRw2ogPglxPtFNiY9/WFZYs0qzAiAwS2ClqWWKZ4hbPE30Z/XUjj8WsW9FfSq
+KwQJvZkNDPP2Gc72X/XhwpfyJHyi6wOCUdnH4zgIrjRil4Wg2GYTz6DXpiXhs5ePVzf7lky2OGk9
+CGdQA34v7oNADHcnYivwBt/f2DiSTpjUyVQBPqTHdXM/7W54kivWMzm4wJy4kt9VuTvzuetHxrIM
+IEV+BEExjUNan9f1fKE1fNiW3rJM8IDUgLy71+bcYNW5uZ2ZHtI45j9ZdrHNpzw0pFHDi+KZXRf7
+uKR+8uEgoukFOqL8AxthbpnsLYMjnbo8HEnnZRaNQ9E/eKcLVJpckTvAJbN620kSCh9INkDmJK57
+kf7vhy8Q1nBXeyr4JaIIchk2p7RL6azQrCgDBU91CIDxxMAN24Fm0STX6og3gbhvmcoxasaus57q
+xC59AOB1SAsijgtlCbi3lnaFGVuZfGSnIobwAi0jxNv0SvYTdPzyM4ntAImtHPjN9GZZp0KYVfhN
+Lzgh4h0swRdx2f7CGxSrFVhfmjM3CXJsbnF2FwoGC7FajmX3cgTDVAAxgmSdC5J8hhXcivUbgGlJ
+95DOkMbEATBrXpxFumm04NiWL5dn18AH8Xzfp3GcL2kx+Fbn+oErWw139dXTMOkJvTukI4m79KB2
+Tnk/b9yTnJWXS/MQCugl5+vKDH01AOpAS213NXID6DZ5vSwuAKiTEWh5e8hXzvkk1aOvX07L8Y/S
+7pwakT95WjKm+36QP4uXIw3abPmhsTiORNL3EbhD5P61MU1uFNiIyW2ijjE2OhOkI5YQtniHxwZ6
+l16GSiSAq24Q5hssAjZDjtj6H4YvELZws/uDK0TatXSGZDqRj7xq0iempENSYcia9VPadF6wWYO9
+ERIExIblTX4RueT07LCC2tB2HpR3NwhcGa5ziVo9zmkceXgv+BdBn5HA7zGvuP7YBQvFOPM6MYKX
+Iqx+wBg/W4X5xeaNUQCwgK/wBrzBOkyq9BsLMW2Qi9ZdpWm6AV12Nwn9KC2A5vUP+dA3GL1t9Jfq
+dJl1YBSfTZm4sfpiHCzAMJ7dhOpnIB/SkI29kSepWJkHs/rjUKZ+Rgs5OqcroVMxP0u7H+5mvXKN
+4BzLG3UDjIsc2+iv6xhA1y6sgCK9h281WvCTHuSVu9KUP9NK64e8a46eeeq1jT+xEv/GqRF5Xl+k
+Yx0yL/ZfOyalf13G1lBpvDFffVDBMinH0/34kEKnkPfEfV+sDZzYAfQSxd/dUJcJdIEwsW4l/tfh
+C4QfBxtRIHEPcpG+s46+xVNZJRzAcZdQVstdmjaEoa6bFnyAmSawLMhHZG1v7bXmMF/T7FnY+p1b
+vlr3KUi9TQovPdQWmjF2t6EbvR+ZhFgBMJEDX9lWWACqb/bzgpnf+2ebMb4UCfoKqy9wjLP9why+
+n/cmtfAiXba7hWypYjIUAivtTQn4Guglt/gaTwLNgdeBrE4xBL6EMlCxAh1dI4KOtng8Ng1l6AbT
+AmlypRxmVANonkFVXFNntq68p6HF3MciMzoYuA5pGuXfTnsQ+lyxN7qXnvLzyFEmX+cxWhrPSbYB
+I073kP70ZbnCM5RV65FoEx+f+XPa1Y9kMN6fjlDDwCmdI+/VPTXpnbxcB+xzvK/9cLL8LqZ1f3gZ
+EeV26R3f0E+5z44BQC5ZLVcs+Ah3oJeSnzAoYNn9hlZiVfxMvV6+Vd+H4QuENdzt2acg1+jZHIWa
+A4b4I3CsGbOWSrxPaAVvMtXNO09Pp0mhUUPXZD1Cq020vH0l+9Q5zJzTwtMh5MzcxJci3NZg7bOt
+AN8q9bUsrrwtr6830xtoapVdoHN589qPZ1C0EDOtn0KOaRh3K7G6OiyLr1uFWa3CbyKmaPtdstwS
+zEQGlgPIJYrgN1l89fziApiTRZkQ5KoSpQiWsyW4LEny56/w0lwZT068OPZDvh0Js+9ToFqmdree
+ZvIUykp5tGvwIU/WLzna76p8TJt34p+4SNS69RS/iwDF0puHnvYexrKrddEdDZ+DGU4gx3tw1S+7
+NkRr4cn/N9CgsVWdHSzHLQ1XzYkvXkO8sQZP8QykOfFR5n/64Vy3NA4cy8plhzqHKa6uLqmv8niP
+L9h1/Ok+L6luMNqQ3oZRwJoyLctt6gAzeHFjjQCLRgbMsuWxinC5uPSyTvuhbaCELxC2cFPpMrmp
+isnBL+Fuyme+U9kWfQKOOxncJGda02ZbRThTbcZSfmFOtG3A5ydERIRRm7H66QSCz96+n50pPAf8
+uQhYy3nN4z2nYRd4F46I1CKMZy8w4Y9dEAlahsVdF/5s5bv49EW5zi3CeeY0cIl4qz8wWoW3tVoo
+uEcs67WfHsFw11l93wUgC0yXBY5bUAx9aMAanrvCC3cEUzHTQhq8iEE4FaMVWAdzLVnf7MaHohzP
+ywrqHehYJt7FhDYEa8rEo5JvqrK+hhTaw4c05OrUi2XgIWlM22PwcTs6aUNamgR5yw91OIBXJHTj
+dIdvAjIc0jil+f30oh3SovwyukVmSOeGluhd+kTL8b69vUtEzjdZe3PBV/xtXfjOPY/p5Wr8g+U3
+yLsx6Y4T/KIut8LSnvHwiKW8s1tg3OkcB1crMaWTI8heovt1lHsjfIHwJ6F1hdAZItd8OvLnQtro
+AVV6mUXMJ7Q4vSMtZ2xoyZ1CSvpqRN8Nqx4nkNsCig+C6H9At3ho2FJJsMwZ7hn6Z/e7ugGra8FL
+JfJ6ilYL7HoBjcx3N4Bg2j67ekzaPjWiAl61Ci+LMe0naqbkGiFE/BJ6v7e/8Estz2Ah3tbgJXHF
+mXi5b7z98DiGnkFL8JtUmUUXiHdxk9hAWvQHPdx/WJWt8bIDXW5oCPyQFkeMfMnRVr5p5vgM4vJF
+z90QZiEu3aNeh1ymPvJp1g1fEoht/FEYBfABlMaCI/iorfg8z8/CJOcMdJNNmjcw2bydXM6RBscc
++ZK1t7o8xGwKvCjz2jU+pOAYZG2fqX1604YpvRnjtv7N/Zj2AAR393ST/1S33PfdWFgb85iFe6aT
+ZdgqC/qgPCSd7ossiiHPiQeLbUPjsG+aMiUiNZJp7SMYJgO9Ts9A+9+GLxDWcHcEOitvOT0iPear
+a8S+tHz3KxouiZqIDfUmDTCst2GwCnsNepAcQSvRjA58RfU9w0NqV3YX7vd3Vg4FbKRdQG//r727
+XU4UBsAwSvb+r1n3BwaSANaW1bq+58zsdlCQjh/haQb1UvoZ4bpd/Trh+bZus8JlG8TLpzg0p0aM
+wTsPxkPw3j4mbTw3eKqX149pq9F9qecDzwNanQ2+XvqZ33lwb+P4NmhN7cxumeo7htdzh9fZgPoN
+dt0s8O1OXd5MNzVPrWnaDeKuBYf12sehDefuJTf1MxDLbU075wkv65VmvR9onifzJ2/0rv0qj+1o
+eU6WdvGcezcyHtyXheONjr4d6ruXf3Xdd43fCDfe98vyYej24++XH5FWHlhn2HcbSn3g7ETy7nZ1
+m+3zo2z+7y/fXtbuf2c/3d1RX00Ht7HcVh+z88Lxm+HGdcu47bCPu/+2D+ju9Y8u16W9328af46P
+ValbrjPD3a+27KsMy+P1w852HG27tRmR1uidys7Vx2+MW9ddR9ZlTF5iuG5zbS7/d6/3R/15+R75
+PO3B/qk7eu6tv71+Mgrg1xiHMrw+S19PCHOeEREAPk7C4V0Ic17Cn4wAECbh8C6EOW84Jfp5El6S
+d4znhAH8EuNQBjPC8E3OEX4i5wgDb8I4xKcQwpxnagAAPk7C4V0Ic55TI17DqRHAmzAOZUiY+RfC
+nOfj017DqRHAmzAOZUj4g0cIc54REQA+TsLhvVx/9O1mAADwfzMjDABAJCEMAEAkIQwAQCQhDABA
+JCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQh
+DABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwA
+QCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAk
+IQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEM
+AEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABApL+Uk2g8s+79bwAAAABJ
+RU5ErkJggg==
+"
+width=353
+height=351
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;<a class="anchor-link" href="#&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#182;</a></h2><h3 id="&#26631;&#20934;&#21270;">&#26631;&#20934;&#21270;<a class="anchor-link" href="#&#26631;&#20934;&#21270;">&#182;</a></h3><p>在下面的单元中，实现 <code>normalize</code> 函数，传入图片数据 <code>x</code>，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 <code>x</code> 的形状一样。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Normalize a list of sample image data in the range of 0 to 1</span>
+<span class="sd">    : x: List of image data.  The image shape is (32, 32, 3)</span>
+<span class="sd">    : return: Numpy array of normalize data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="mi">255</span><span class="p">)</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_normalize</span><span class="p">(</span><span class="n">normalize</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="One-hot-&#32534;&#30721;">One-hot &#32534;&#30721;<a class="anchor-link" href="#One-hot-&#32534;&#30721;">&#182;</a></h3><p>和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 <code>one_hot_encode</code> 函数。输入，也就是 <code>x</code>，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 <code>one_hot_encode</code> 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。</p>
+<p>提示：不要重复发明轮子。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">one_hot_encode</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.</span>
+<span class="sd">    : x: List of sample Labels</span>
+<span class="sd">    : return: Numpy array of one-hot encoded labels</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="kn">from</span> <span class="nn">tflearn.data_utils</span> <span class="k">import</span> <span class="n">to_categorical</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_one_hot_encode</span><span class="p">(</span><span class="n">one_hot_encode</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#38543;&#26426;&#21270;&#25968;&#25454;">&#38543;&#26426;&#21270;&#25968;&#25454;<a class="anchor-link" href="#&#38543;&#26426;&#21270;&#25968;&#25454;">&#182;</a></h3><p>之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;<a class="anchor-link" href="#&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#182;</a></h2><p>运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="c1"># Preprocess Training, Validation, and Testing Data</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">preprocess_and_save_data</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">normalize</span><span class="p">,</span> <span class="n">one_hot_encode</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+
+<span class="c1"># Load the Preprocessed Validation data</span>
+<span class="n">valid_features</span><span class="p">,</span> <span class="n">valid_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_validation.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#26500;&#24314;&#32593;&#32476;">&#26500;&#24314;&#32593;&#32476;<a class="anchor-link" href="#&#26500;&#24314;&#32593;&#32476;">&#182;</a></h2><p>对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。</p>
+<blockquote><p><strong>注意</strong>：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。</p>
+<p>但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 <code>conv2d</code> 类 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/conv2d">tf.nn.conv2d</a>，而不是 TF Layers 版本的 <code>conv2d</code> 类 <a href="https://www.tensorflow.org/api_docs/python/tf/layers/conv2d">tf.layers.conv2d</a>。</p>
+</blockquote>
+<p>我们开始吧！</p>
+<h3 id="&#36755;&#20837;">&#36755;&#20837;<a class="anchor-link" href="#&#36755;&#20837;">&#182;</a></h3><p>神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：</p>
+<ul>
+<li>实现 <code>neural_net_image_input</code><ul>
+<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
+<li>使用 <code>image_shape</code> 设置形状，部分大小设为 <code>None</code></li>
+<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "x" 命名</li>
+</ul>
+</li>
+<li>实现 <code>neural_net_label_input</code><ul>
+<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
+<li>使用 <code>n_classes</code> 设置形状，部分大小设为 <code>None</code></li>
+<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "y" 命名</li>
+</ul>
+</li>
+<li>实现 <code>neural_net_keep_prob_input</code><ul>
+<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a>，用于丢弃保留概率</li>
+<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "keep_prob" 命名</li>
+</ul>
+</li>
+</ul>
+<p>这些名称将在项目结束时，用于加载保存的模型。</p>
+<p>注意：TensorFlow 中的 <code>None</code> 表示形状可以是动态大小。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+
+<span class="k">def</span> <span class="nf">neural_net_image_input</span><span class="p">(</span><span class="n">image_shape</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return a Tensor for a batch of image input</span>
+<span class="sd">    : image_shape: Shape of the images</span>
+<span class="sd">    : return: Tensor for image input.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">neural_net_label_input</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return a Tensor for a batch of label input</span>
+<span class="sd">    : n_classes: Number of classes</span>
+<span class="sd">    : return: Tensor for label input.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">neural_net_keep_prob_input</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return a Tensor for keep probability</span>
+<span class="sd">    : return: Tensor for keep probability.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;keep_prob&#39;</span><span class="p">)</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_image_inputs</span><span class="p">(</span><span class="n">neural_net_image_input</span><span class="p">)</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_label_inputs</span><span class="p">(</span><span class="n">neural_net_label_input</span><span class="p">)</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_keep_prob_inputs</span><span class="p">(</span><span class="n">neural_net_keep_prob_input</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Image Input Tests Passed.
+Label Input Tests Passed.
+Keep Prob Tests Passed.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;">&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;<a class="anchor-link" href="#&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;">&#182;</a></h3><p>卷积层级适合处理图片。对于此代码单元，你应该实现函数 <code>conv2d_maxpool</code> 以便应用卷积然后进行最大池化：</p>
+<ul>
+<li>使用 <code>conv_ksize</code>、<code>conv_num_outputs</code> 和 <code>x_tensor</code> 的形状创建权重（weight）和偏置（bias）。</li>
+<li>使用权重和 <code>conv_strides</code> 对 <code>x_tensor</code> 应用卷积。<ul>
+<li>建议使用我们建议的间距（padding），当然也可以使用任何其他间距。</li>
+</ul>
+</li>
+<li>添加偏置</li>
+<li>向卷积中添加非线性激活（nonlinear activation）</li>
+<li>使用 <code>pool_ksize</code> 和 <code>pool_strides</code> 应用最大池化<ul>
+<li>建议使用我们建议的间距（padding），当然也可以使用任何其他间距。</li>
+</ul>
+</li>
+</ul>
+<p><strong>注意</strong>：对于<strong>此层</strong>，<strong>请勿使用</strong> <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a>，但是仍然可以使用 TensorFlow 的 <a href="https://www.tensorflow.org/api_docs/python/tf/nn">Neural Network</a> 包。对于所有<strong>其他层</strong>，你依然可以使用快捷方法。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv2d_maxpool</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">conv_num_outputs</span><span class="p">,</span> <span class="n">conv_ksize</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Apply convolution then max pooling to x_tensor</span>
+<span class="sd">    :param x_tensor: TensorFlow Tensor</span>
+<span class="sd">    :param conv_num_outputs: Number of outputs for the convolutional layer</span>
+<span class="sd">    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer</span>
+<span class="sd">    :param conv_strides: Stride 2-D Tuple for convolution</span>
+<span class="sd">    :param pool_ksize: kernal size 2-D Tuple for pool</span>
+<span class="sd">    :param pool_strides: Stride 2-D Tuple for pool</span>
+<span class="sd">    : return: A tensor that represents convolution and max pooling of x_tensor</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_ksize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conv_ksize</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">3</span><span class="p">],</span> <span class="n">conv_num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
+    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_num_outputs</span><span class="p">]))</span>
+    <span class="n">conv</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x_tensor</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conv_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span>
+    <span class="n">activate</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv</span><span class="p">)</span>
+    <span class="n">pool</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="n">activate</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pool_ksize</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pool_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">pool</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_con_pool</span><span class="p">(</span><span class="n">conv2d_maxpool</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#25153;&#24179;&#21270;&#23618;">&#25153;&#24179;&#21270;&#23618;<a class="anchor-link" href="#&#25153;&#24179;&#21270;&#23618;">&#182;</a></h3><p>实现 <code>flatten</code> 函数，将 <code>x_tensor</code> 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（<em>部分大小（Batch Size）</em>，<em>扁平化图片大小（Flattened Image Size）</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">flatten</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Flatten x_tensor to (Batch Size, Flattened Image Size)</span>
+<span class="sd">    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.</span>
+<span class="sd">    : return: A tensor of size (Batch Size, Flattened Image Size).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">layer_shape</span> <span class="o">=</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span>
+    <span class="n">num_features</span> <span class="o">=</span> <span class="n">layer_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">num_elements</span><span class="p">()</span>
+    <span class="n">layer_flat</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_features</span><span class="p">])</span>
+    
+    <span class="k">return</span> <span class="n">layer_flat</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_flatten</span><span class="p">(</span><span class="n">flatten</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;">&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;<a class="anchor-link" href="#&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;">&#182;</a></h3><p>实现 <code>fully_conn</code> 函数，以向 <code>x_tensor</code> 应用完全连接的层级，形状为（<em>部分大小（Batch Size）</em>，<em>num_outputs</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fully_conn</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Apply a fully connected layer to x_tensor using weight and bias</span>
+<span class="sd">    : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
+<span class="sd">    : num_outputs: The number of output that the new tensor should be.</span>
+<span class="sd">    : return: A 2-D tensor where the second dimension is num_outputs.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
+    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
+    <span class="n">fc</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">fc</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_fully_conn</span><span class="p">(</span><span class="n">fully_conn</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#36755;&#20986;&#23618;">&#36755;&#20986;&#23618;<a class="anchor-link" href="#&#36755;&#20986;&#23618;">&#182;</a></h3><p>实现 <code>output</code> 函数，向 x_tensor 应用完全连接的层级，形状为（<em>部分大小（Batch Size）</em>，<em>num_outputs</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
+<p><strong>注意</strong>：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">output</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Apply a output layer to x_tensor using weight and bias</span>
+<span class="sd">    : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
+<span class="sd">    : num_outputs: The number of output that the new tensor should be.</span>
+<span class="sd">    : return: A 2-D tensor where the second dimension is num_outputs.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
+    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
+    <span class="n">output</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span>
+    
+    <span class="k">return</span> <span class="n">output</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_output</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;">&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;<a class="anchor-link" href="#&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;">&#182;</a></h3><p>实现函数 <code>conv_net</code>， 创建卷积神经网络模型。该函数传入一批图片 <code>x</code>，并输出对数（logits）。使用你在上方创建的层创建此模型：</p>
+<ul>
+<li>应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）</li>
+<li>应用一个扁平层（Flatten Layer）</li>
+<li>应用 1、2 或 3 个完全连接层（Fully Connected Layers）</li>
+<li>应用一个输出层（Output Layer）</li>
+<li>返回输出</li>
+<li>使用 <code>keep_prob</code> 向模型中的一个或多个层应用 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/dropout">TensorFlow 的 Dropout</a></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a convolutional neural network model</span>
+<span class="sd">    : x: Placeholder tensor that holds image data.</span>
+<span class="sd">    : keep_prob: Placeholder tensor that hold dropout keep probability.</span>
+<span class="sd">    : return: Tensor that represents logits</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Apply 1, 2, or 3 Convolution and Max Pool layers</span>
+    <span class="c1">#    Play around with different number of outputs, kernel size and stride</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)</span>
+    <span class="n">conv_pool_1</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+    <span class="n">norm_layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">lrn</span><span class="p">(</span><span class="n">conv_pool_1</span><span class="p">,</span> <span class="mi">4</span> <span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.001</span> <span class="o">/</span> <span class="mf">9.0</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
+    <span class="n">conv_pool_2</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">norm_layer</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+
+    <span class="c1"># TODO: Apply a Flatten Layer</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#   flatten(x_tensor)</span>
+    <span class="n">flat_layer</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">conv_pool_2</span><span class="p">)</span>
+
+    <span class="c1"># TODO: Apply 1, 2, or 3 Fully Connected Layers</span>
+    <span class="c1">#    Play around with different number of outputs</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#   fully_conn(x_tensor, num_outputs)</span>
+    <span class="n">fc_layer1</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">flat_layer</span><span class="p">,</span> <span class="mi">384</span><span class="p">)</span>
+    <span class="n">dropout_layer_1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc_layer1</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+    <span class="n">fc_layer2</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">dropout_layer_1</span><span class="p">,</span> <span class="mi">192</span><span class="p">)</span>
+    <span class="n">dropout_layer_2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc_layer2</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+    
+    <span class="c1"># TODO: Apply an Output Layer</span>
+    <span class="c1">#    Set this to the number of classes</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#   output(x_tensor, num_outputs)</span>
+    <span class="n">logits</span> <span class="o">=</span> <span class="n">output</span><span class="p">(</span><span class="n">dropout_layer_2</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+    
+    <span class="c1"># TODO: return output</span>
+    <span class="k">return</span> <span class="n">logits</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="c1">##############################</span>
+<span class="c1">## Build the Neural Network ##</span>
+<span class="c1">##############################</span>
+
+<span class="c1"># Remove previous weights, bias, inputs, etc..</span>
+<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
+
+<span class="c1"># Inputs</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">neural_net_image_input</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">neural_net_label_input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">keep_prob</span> <span class="o">=</span> <span class="n">neural_net_keep_prob_input</span><span class="p">()</span>
+
+<span class="c1"># Model</span>
+<span class="n">logits</span> <span class="o">=</span> <span class="n">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+
+<span class="c1"># Name logits Tensor, so that is can be loaded from disk after training</span>
+<span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;logits&#39;</span><span class="p">)</span>
+
+<span class="c1"># Loss and Optimizer</span>
+<span class="n">cost</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">))</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">()</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
+
+<span class="c1"># Accuracy</span>
+<span class="n">correct_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">accuracy</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">correct_pred</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">)</span>
+
+<span class="n">tests</span><span class="o">.</span><span class="n">test_conv_net</span><span class="p">(</span><span class="n">conv_net</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Neural Network Built!
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;<a class="anchor-link" href="#&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#182;</a></h2><h3 id="&#21333;&#27425;&#20248;&#21270;">&#21333;&#27425;&#20248;&#21270;<a class="anchor-link" href="#&#21333;&#27425;&#20248;&#21270;">&#182;</a></h3><p>实现函数 <code>train_neural_network</code> 以进行单次优化（single optimization）。该优化应该使用 <code>optimizer</code> 优化 <code>session</code>，其中 <code>feed_dict</code> 具有以下参数：</p>
+<ul>
+<li><code>x</code> 表示图片输入</li>
+<li><code>y</code> 表示标签</li>
+<li><code>keep_prob</code> 表示丢弃的保留率</li>
+</ul>
+<p>每个部分都会调用该函数，所以 <code>tf.global_variables_initializer()</code> 已经被调用。</p>
+<p>注意：不需要返回任何内容。该函数只是用来优化神经网络。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[13]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train_neural_network</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Optimize the session on a batch of images and labels</span>
+<span class="sd">    : session: Current TensorFlow session</span>
+<span class="sd">    : optimizer: TensorFlow optimizer function</span>
+<span class="sd">    : keep_probability: keep probability</span>
+<span class="sd">    : feature_batch: Batch of Numpy image data</span>
+<span class="sd">    : label_batch: Batch of Numpy label data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">keep_prob</span><span class="p">:</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">label_batch</span><span class="p">})</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_train_nn</span><span class="p">(</span><span class="n">train_neural_network</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#26174;&#31034;&#25968;&#25454;">&#26174;&#31034;&#25968;&#25454;<a class="anchor-link" href="#&#26174;&#31034;&#25968;&#25454;">&#182;</a></h3><p>实现函数 <code>print_stats</code> 以输出损失和验证准确率。使用全局变量 <code>valid_features</code> 和 <code>valid_labels</code> 计算验证准确率。使用保留率 <code>1.0</code> 计算损失和验证准确率（loss and validation accuracy）。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[14]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">print_stats</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Print information about loss and validation accuracy</span>
+<span class="sd">    : session: Current TensorFlow session</span>
+<span class="sd">    : feature_batch: Batch of Numpy image data</span>
+<span class="sd">    : label_batch: Batch of Numpy label data</span>
+<span class="sd">    : cost: TensorFlow cost function</span>
+<span class="sd">    : accuracy: TensorFlow accuracy function</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Valid Loss: &#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">valid_features</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">valid_labels</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;, Valid Accuracy: &#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">valid_features</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">valid_labels</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#36229;&#21442;&#25968;">&#36229;&#21442;&#25968;<a class="anchor-link" href="#&#36229;&#21442;&#25968;">&#182;</a></h3><p>调试以下超参数：</p>
+<ul>
+<li>设置 <code>epochs</code> 表示神经网络停止学习或开始过拟合的迭代次数</li>
+<li><p>设置 <code>batch_size</code>，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：</p>
+<ul>
+<li>64</li>
+<li>128</li>
+<li>256</li>
+<li>...</li>
+</ul>
+</li>
+<li>设置 <code>keep_probability</code> 表示使用丢弃时保留节点的概率</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[21]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Tune Parameters</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">128</span>
+<span class="n">keep_probability</span> <span class="o">=</span> <span class="mf">0.75</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#22312;&#21333;&#20010;-CIFAR-10-&#37096;&#20998;&#19978;&#35757;&#32451;">&#22312;&#21333;&#20010; CIFAR-10 &#37096;&#20998;&#19978;&#35757;&#32451;<a class="anchor-link" href="#&#22312;&#21333;&#20010;-CIFAR-10-&#37096;&#20998;&#19978;&#35757;&#32451;">&#182;</a></h3><p>我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[22]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Checking the Training on a Single Batch...&#39;</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+    <span class="c1"># Initializing the variables</span>
+    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
+    
+    <span class="c1"># Training cycle</span>
+    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
+        <span class="n">batch_i</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+            <span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">:  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+        <span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Checking the Training on a Single Batch...
+Epoch  1, CIFAR-10 Batch 1:  Valid Loss: 1.82877, Valid Accuracy: 0.3608
+Epoch  2, CIFAR-10 Batch 1:  Valid Loss: 1.60118, Valid Accuracy: 0.429
+Epoch  3, CIFAR-10 Batch 1:  Valid Loss: 1.5015, Valid Accuracy: 0.4502
+Epoch  4, CIFAR-10 Batch 1:  Valid Loss: 1.38333, Valid Accuracy: 0.4996
+Epoch  5, CIFAR-10 Batch 1:  Valid Loss: 1.32223, Valid Accuracy: 0.5298
+Epoch  6, CIFAR-10 Batch 1:  Valid Loss: 1.34756, Valid Accuracy: 0.5206
+Epoch  7, CIFAR-10 Batch 1:  Valid Loss: 1.28294, Valid Accuracy: 0.5466
+Epoch  8, CIFAR-10 Batch 1:  Valid Loss: 1.31494, Valid Accuracy: 0.5374
+Epoch  9, CIFAR-10 Batch 1:  Valid Loss: 1.30606, Valid Accuracy: 0.5576
+Epoch 10, CIFAR-10 Batch 1:  Valid Loss: 1.32294, Valid Accuracy: 0.555
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;">&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;<a class="anchor-link" href="#&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;">&#182;</a></h3><p>现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[17]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">8</span>
+<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./model/image_classification&#39;</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training...&#39;</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+    <span class="c1"># Initializing the variables</span>
+    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
+    
+    <span class="c1"># Training cycle</span>
+    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
+        <span class="c1"># Loop over all batches</span>
+        <span class="n">n_batches</span> <span class="o">=</span> <span class="mi">5</span>
+        <span class="k">for</span> <span class="n">batch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_batches</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+                <span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">:  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+            <span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
+            
+    <span class="c1"># Save Model</span>
+    <span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
+    <span class="n">save_path</span> <span class="o">=</span> <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Training...
+Epoch  1, CIFAR-10 Batch 1:  Valid Loss: 1.88313, Valid Accuracy: 0.3366
+Epoch  1, CIFAR-10 Batch 2:  Valid Loss: 1.60399, Valid Accuracy: 0.4198
+Epoch  1, CIFAR-10 Batch 3:  Valid Loss: 1.50717, Valid Accuracy: 0.4498
+Epoch  1, CIFAR-10 Batch 4:  Valid Loss: 1.41716, Valid Accuracy: 0.4816
+Epoch  1, CIFAR-10 Batch 5:  Valid Loss: 1.33043, Valid Accuracy: 0.5132
+Epoch  2, CIFAR-10 Batch 1:  Valid Loss: 1.28486, Valid Accuracy: 0.537
+Epoch  2, CIFAR-10 Batch 2:  Valid Loss: 1.30711, Valid Accuracy: 0.53
+Epoch  2, CIFAR-10 Batch 3:  Valid Loss: 1.22172, Valid Accuracy: 0.558
+Epoch  2, CIFAR-10 Batch 4:  Valid Loss: 1.18755, Valid Accuracy: 0.5712
+Epoch  2, CIFAR-10 Batch 5:  Valid Loss: 1.18258, Valid Accuracy: 0.576
+Epoch  3, CIFAR-10 Batch 1:  Valid Loss: 1.12699, Valid Accuracy: 0.5932
+Epoch  3, CIFAR-10 Batch 2:  Valid Loss: 1.13514, Valid Accuracy: 0.5916
+Epoch  3, CIFAR-10 Batch 3:  Valid Loss: 1.10883, Valid Accuracy: 0.5996
+Epoch  3, CIFAR-10 Batch 4:  Valid Loss: 1.06464, Valid Accuracy: 0.6178
+Epoch  3, CIFAR-10 Batch 5:  Valid Loss: 1.07656, Valid Accuracy: 0.6128
+Epoch  4, CIFAR-10 Batch 1:  Valid Loss: 1.10849, Valid Accuracy: 0.605
+Epoch  4, CIFAR-10 Batch 2:  Valid Loss: 1.08009, Valid Accuracy: 0.6166
+Epoch  4, CIFAR-10 Batch 3:  Valid Loss: 1.01519, Valid Accuracy: 0.637
+Epoch  4, CIFAR-10 Batch 4:  Valid Loss: 1.00247, Valid Accuracy: 0.643
+Epoch  4, CIFAR-10 Batch 5:  Valid Loss: 1.02331, Valid Accuracy: 0.6448
+Epoch  5, CIFAR-10 Batch 1:  Valid Loss: 0.995853, Valid Accuracy: 0.6502
+Epoch  5, CIFAR-10 Batch 2:  Valid Loss: 1.00064, Valid Accuracy: 0.65
+Epoch  5, CIFAR-10 Batch 3:  Valid Loss: 0.93919, Valid Accuracy: 0.6738
+Epoch  5, CIFAR-10 Batch 4:  Valid Loss: 0.991811, Valid Accuracy: 0.6504
+Epoch  5, CIFAR-10 Batch 5:  Valid Loss: 0.927782, Valid Accuracy: 0.6826
+Epoch  6, CIFAR-10 Batch 1:  Valid Loss: 0.969924, Valid Accuracy: 0.666
+Epoch  6, CIFAR-10 Batch 2:  Valid Loss: 1.01257, Valid Accuracy: 0.6408
+Epoch  6, CIFAR-10 Batch 3:  Valid Loss: 0.961456, Valid Accuracy: 0.6636
+Epoch  6, CIFAR-10 Batch 4:  Valid Loss: 0.935574, Valid Accuracy: 0.674
+Epoch  6, CIFAR-10 Batch 5:  Valid Loss: 0.904234, Valid Accuracy: 0.6824
+Epoch  7, CIFAR-10 Batch 1:  Valid Loss: 0.925582, Valid Accuracy: 0.6806
+Epoch  7, CIFAR-10 Batch 2:  Valid Loss: 0.962076, Valid Accuracy: 0.674
+Epoch  7, CIFAR-10 Batch 3:  Valid Loss: 0.935451, Valid Accuracy: 0.6754
+Epoch  7, CIFAR-10 Batch 4:  Valid Loss: 0.88064, Valid Accuracy: 0.6912
+Epoch  7, CIFAR-10 Batch 5:  Valid Loss: 0.912521, Valid Accuracy: 0.694
+Epoch  8, CIFAR-10 Batch 1:  Valid Loss: 0.932409, Valid Accuracy: 0.6876
+Epoch  8, CIFAR-10 Batch 2:  Valid Loss: 0.959626, Valid Accuracy: 0.6722
+Epoch  8, CIFAR-10 Batch 3:  Valid Loss: 0.958519, Valid Accuracy: 0.6904
+Epoch  8, CIFAR-10 Batch 4:  Valid Loss: 0.886022, Valid Accuracy: 0.7024
+Epoch  8, CIFAR-10 Batch 5:  Valid Loss: 0.947139, Valid Accuracy: 0.6868
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>模型已保存到本地。</p>
+<h2 id="&#27979;&#35797;&#27169;&#22411;">&#27979;&#35797;&#27169;&#22411;<a class="anchor-link" href="#&#27979;&#35797;&#27169;&#22411;">&#182;</a></h2><p>利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[18]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
+
+<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+<span class="kn">import</span> <span class="nn">random</span>
+
+<span class="c1"># Set batch size if not already set</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">batch_size</span><span class="p">:</span>
+        <span class="k">pass</span>
+<span class="k">except</span> <span class="ne">NameError</span><span class="p">:</span>
+    <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
+
+<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./model/image_classification&#39;</span>
+<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">top_n_predictions</span> <span class="o">=</span> <span class="mi">3</span>
+
+<span class="k">def</span> <span class="nf">test_model</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Test the saved model against the test dataset</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_test.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
+    <span class="n">loaded_graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
+
+    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">loaded_graph</span><span class="p">)</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+        <span class="c1"># Load model</span>
+        <span class="n">loader</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">import_meta_graph</span><span class="p">(</span><span class="n">save_model_path</span> <span class="o">+</span> <span class="s1">&#39;.meta&#39;</span><span class="p">)</span>
+        <span class="n">loader</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
+
+        <span class="c1"># Get Tensors from loaded model</span>
+        <span class="n">loaded_x</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;x:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_y</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;y:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_keep_prob</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;keep_prob:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_logits</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;logits:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_acc</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;accuracy:0&#39;</span><span class="p">)</span>
+        
+        <span class="c1"># Get accuracy in batches for memory limitations</span>
+        <span class="n">test_batch_acc_total</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">test_batch_count</span> <span class="o">=</span> <span class="mi">0</span>
+        
+        <span class="k">for</span> <span class="n">test_feature_batch</span><span class="p">,</span> <span class="n">test_label_batch</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">batch_features_labels</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+            <span class="n">test_batch_acc_total</span> <span class="o">+=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+                <span class="n">loaded_acc</span><span class="p">,</span>
+                <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">test_feature_batch</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">test_label_batch</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
+            <span class="n">test_batch_count</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Testing Accuracy: </span><span class="si">{}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_batch_acc_total</span><span class="o">/</span><span class="n">test_batch_count</span><span class="p">))</span>
+
+        <span class="c1"># Print Random Samples</span>
+        <span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)),</span> <span class="n">n_samples</span><span class="p">)))</span>
+        <span class="n">random_test_predictions</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+            <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">top_k</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">loaded_logits</span><span class="p">),</span> <span class="n">top_n_predictions</span><span class="p">),</span>
+            <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">random_test_features</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
+        <span class="n">helper</span><span class="o">.</span><span class="n">display_image_predictions</span><span class="p">(</span><span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">random_test_predictions</span><span class="p">)</span>
+
+
+<span class="n">test_model</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Testing Accuracy: 0.6769185126582279
+
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA9oAAAN6CAYAAABxCzGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAAewgAAHsIBbtB1PgAAIABJREFUeJzs3XecLFWZ//HPU9UzNxAFQQTTrgHMSzKhu2ZU1F3DGjGL
+cd11jetP17jGnzlgQEX9sWvAiAEUlTUioJgQcxYkZ7h3urvq+f3xnJqu6ame6e7pSXe+b17FzO2p
+OnW6uqamnjrnPMfcHRERERERERGZjGy1KyAiIiIiIiKyI1GgLSIiIiIiIjJBCrRFREREREREJkiB
+toiIiIiIiMgEKdAWERERERERmSAF2iIiIiIiIiITpEBbREREREREZIIUaIuIiIiIiIhMkAJtERER
+ERERkQlSoC0iIiIiIiIyQQq0RURERERERCZIgbaIiIiIiIjIBCnQFhEREREREZkgBdoiIiIiIiIi
+E6RAW0RERERERGSCFGiLiIiIiIiITJACbREREREREZEJUqAtIiIiIiIiMkEKtEVEREREREQmSIG2
+SAMz22xmjzGzD5jZj83sr2a2zczKvuXTq11XEVn/zOyZDdeXtw+57S0btv3Jctd5rTKzIxqOxwmr
+XS8REdlYWqtdAVlbzGw34CBgH2D3tJTAVWk5B/g98Cd376xWPZeTmT0deCWwZ3rJaz/2+VuIiEzM
+Uq4xuj7NpeMhIiKrRoG2YGa3BI4C7gvcBLAhNivM7Gzg+8AZwFfd/TfLV8uVYWZHA08jbtCqm7Rh
+jofImmBmrwJePOJm1wCXA5cRD9K+D5xO/F63J1tDGYIxfpCo69VcSzmWIiIiY1OgvYGZ2c2Bo4F/
+SC9539eFZMCtgFsDT0jl/R54s7u/a8JVXRFm9nh6Qfbsy4xwk2ZmewCH9r18kbv/YMkVFBnNKMHF
+lrTsA9wcuF96/WIz+xDwVnc/Z7LVk2XirJNg28yuDRzc9/IF7v7DCe5GQbaIiKwKBdoblJk9D3gV
+sIn5gWXdYq269W1vRATt6y7QNrMMeBnzj8XlwLHAt4ELgP7WvUv6/n0gcGLfa18F7j2xyooMb5yA
+q/47sAfwXODJZvYsdz9uMtWSZbIuAuya2wOf73vtC8ADJ1T+ejseIiKyA1GgvQGZ2SuA/6S5e3T1
+7+3Ab4lAcxuwG3HTvR8RnNO3XX3b9ejewA2ZezwuBQ5199+NUd56Phay4xnlfOy/FjiwK/ARM7up
+u79sojWTSen/jNfTNWg56rqej4eIiOwAFGhvMGb2JHpB9uzL6d9/Ao4BPgP8wt3n3ZiYWU50GT+E
+GNN9H6LLaVXOenWX2vfV8XjXmEF2fzkiq6XqRlwAhzH4d3Rn4NrE7/URwAH0zt/6efwSM/uzu79/
+OSsto3H3nwH5atdjiSZ2vXT3L7L+j4eIiKxzCrQ3kDR++PU0B9mvBV65WOIjdy+AH6flA2a2hRjP
++XTgbstR7xVyYMNr31jxWogsE3c/Y4jVPgG8wMweArybCL7rLdsGvMnMPuPuFy9PTUVERETWP82j
+vbE8h+j+XamC7Oe7+0vGyS7s7tvc/VPufk8iMdpnJlPVFXfthtf+suK1EFkD3P1TxPjZCxp+vDOj
+ZzUXERER2VAUaG8sD2buGGQHTnP3N0+icHc/290/OomyVsFuzO+2uG01KiKyFrj774HHMj8PgwGP
+XJVKiYiIiKwTCrQ3CDO7ATHust8HVroua9T0aldAZK1x968ApzJ/bPfeZnabVaiSiIiIyLqgMdob
+xw0GvP6jFa3FCMzsOsTY6RsRLc4t4GrgHODnwM+aEratslVPCGdmU8TctDcH9gSmiAzqZwNnuPtI
+LfVpbP/BwE2Iz2EbcB7wY3f/xQSrXt9nBvwN8XDoukTW681EFvxL0v7PcPerlmP/MsdngTs2vH57
+4CdLLdzMDLgNcb7uA+xETKN3EfAZd79sjDKvTwxluSFx7kB0g7+AuG78Yan1XmT/W4E7APsDuxPJ
+6C4Bfgl8f9TfwbXAzG4E3BK4PnFMp4CrgMuIGSp+Psa4/VW/Xo7DzA4AbgFcjxhKUQJXAH8EfrQa
+c86b2fWI6/QNU52uAC4Eznb3ny7TPrcQv7s3Jf427Er0ermGODf+DPwB+L27d5ejDiIia5q7a9kA
+C/AQ4magSEv1/Y1Xu2599dwTeAlwVqrjQsvFwAeBQ0bcx7FDlD3M8thU3l8mVF4J7Dugzq9qWPf/
+9K1zQHpvly9Q/hXA0cDeQxyn+xNzgHcWKO+XwFMBm8BnfzsiI/7XiBu1xY5VB/g+8CJg1zH3+ZoB
+ZT9sCe/jnwaU+bYV+h2qzpX673p7CeXdraG8AnjxAtu8tuH9v6BvnVsSsxxcPOB4FcDtRqjnTYE3
+Ab8f4tz5WarjXhM+9ocAxxPTIw7a9zVE0rlD+7Z9Zu19V1/fPuR+b9mwn59M4P3cAXgvcO4Qx7Qk
+Hua9DThsgTIvGrKsYZY9BuzjiIZ1T1jisbg18B7iYc1i9foV8ErgOkvY36KfKZFZ/YnADxapzzlE
+ItTdJ3BO7Aw8Dfg20B3yc7oG+Fb6nTt0qXXQokWLlvWyqOv4xjHos957RWsxgJllZvZ84un3K4nW
+LV9k2R14PHC6mX3KzPYdYZdN5Q2zTtP6Sy2nntV5lHrPMrOXE5ngH0fcCA3az07ETdIvzexeTTsw
+s+uY2VeAE4C7E+fOoPJuQmSn/k5q+R6Zmb3UzH4LfA94BRHYbVpgn9WSAQcBrwb+ZGYvHGP3LwG+
+2VD2+8zsxmO8l78hHv70l3c68Nwx6rcWXDjg9aYEgv3mna9m1jKztxC9aZ5E/B4v9js0kJntZWYf
+Jnq5/DvRe2exc+cA4IXAb8zsRWnawrGZ2WYzezdwGvFQc2qBfW8CHgp8z8zemXqgTMpYx7CfmR1o
+Zt8AvgscBVyHxY+pEy34zwK+ZWZnpZb9heq4KtfLUZjZtc3sv4nz9SnEw+DF6ndj4try+3R9W8pn
+POiafysiwH4/8HeL1Gcf4PnAb83svuNWxMweBvyCeFh7J3q5XhZbNhHTC74QOM3M3jBuHURE1hMF
+2hvHRQNeX/UpucxsN+DLxBP3rfRuKKxv6X8Nen/IHwScaWZ3Gna3A8pfbJ1h1h2lnEHbLFb3+CZ8
+FHgpvaEg/WU2HbPdgC+Y2d3nFGx2U6KV+J7M/xwGvU8nWr6+ama7MrqnE8MD6jeTg45P/+vVNrsA
+r00PXJpu7hu5ewk8ivj9qL/HXYDjzWzosfvpZvoTxLGt1/cyooV8vXadXGr33vr5uhn4CvBv9P7+
+NH3GwxVsdlfgp8BjmDsP82LnDmndnYkHNZ9L3WBHZma7A18nenZU5S5Wh8ozgBPNbNM4+x5UJZbw
+maUHnqcBd2b830mIh6WbF6njil4vR2VmBxEBdpX8b9TzaxPwcuDrZjbMg6mBVanvy8zuAXyHaGX3
+hn03HS8HrgV81sweNHIFzF4MfIwYylOdFwt9LoOu1c7g80JEZIeiMdobx6/6/l39kXyWmb3H3S9Z
+hTpVYxm/AhzK3Cf29Ru284ju2dcA+xJBWYv5wfbewElmdri7n7rIrptaOPpvFEZpBemv+zjljNPq
+8lbg4fQ+TyfGt/6W6OK4mRjrXPVcqN/wTAEfN7MD3P1iM9ubCBjqPQOcGFv6R2Kc9x7EDfQm5n5G
+ALcluu4eNcb76P8sIbol/pEIVC8nPvPdiVb0rQO2exDRff7hw+7Y3c81syOBE+m9J0vv5+1ED4Bh
+vJkYI1m/GXfgCe7+p2HrswYNChAGPbxbyH8Dd2Xu+QrRk+V8YAbYjxj7umDwaWb/CHycXusxzD8f
+fke0yG8jfgduQu/cra93P+JB0d18hGkO04OYzxMPmgZdv/6Slm56bzdibmByN6IXxHeH3e9yMbN3
+EsF/0/Wseu1c4tpyCdFDZk/iGpP3rb+Q1bpeDs3MDiSGsVTjjmHusSiJ8/Zc4hy8HvH5VuvVW94P
+A75mZn/v7pcvsV4HE3kTtjL3en4hcZ5dSgTUNyUeJPUHulPAB83sdB9yLLmZPYIYkjLoHL+G+F27
+jBg2sTNx3G5InCNN24iI7PAUaG8Q7n6Omf0SuFnfj/YmbjAf4e79wfhKeDvNQXZBjId7v7v/uL5B
+6qL8MOBlRP3rNxE7E8HjbXxwEqVXEt2d6z5LdI+s9l8FbectUPffpq/3pxcU3C69p3ogcQbwrwuU
+U9c0b/Eg9yO671XH7mdE68mJ7n5NfUUzuyPwhrR+3R7E8XgmEbRUN4olcBzwTnf/fl9ZW4gWxNek
+7eutG08ws2Pc/fQR3gdp+xki0P880Y38bHfvNK1sZrcGHk20Iu7K3Bu4h5rZU9z9fUPv3P1kM3sN
+0d2z/n6OMrP/dfePLbS9mT2EOIb9QfZb3f2EYeuxRh064PVBXcoHeQwx7rQ6Rn8F/otIeDbn9yyd
+Yw9nwO+Dmd0S+B8iaJh9OZX9feKBz8nufmlDufchfk9uxdzz5g7A/yVa24f1MiKI6r9+dYC3AB9w
+91/31WE/4lj8HyIIMeAR9H73VkUaftIUZFcPLN4EfKnpoVHqqfB3xNjohxNdpwe5N73P7c7AG5l7
+vfwO8Lwhqz1yorxhpAfAn6CXSA969buUCDo/5u7n9213APBkovt8/QEQxPn2PkZ4CNjHU32Opxe8
+zhDduD/kfQnPzKwF3Bd4HfNnHNmVGEv/0MV2mn5n3sL882I7MX7/OHf/wQLb34j4u/iAVJ9rLbZP
+EZEdxmoPEteycgsxPqqe1Kie3GiGCKzuCbRWqD7/1FCfkri5vv0Q218LOKmvjOr7j49Yl983lHGD
+Md7TPWrbV1+/MoFj9aq+cvuTRr0ZFk5IRrQ4ndBwvK4C/qNW3iXAPwxRp1sRiaz6j9uxI763U4Fn
+AzuPcVyuSwTn/e/pPGBqxLKyAWVdDtx0ge1uTNzw95/H312p36UFzpVJJEP7zoDfsdsusM1raT5f
+qzKOB7aOWZ8txEOl/uN9DfDkIcswInBsel/3GrKM2xK9R/rrcQ5wmyG2vyGRtb3/OC0lGVq9nKGT
+oRG5GJr+LnTTtWGk8xi4F3AKcK1F1jui4T0vKWnZJMolxj03nRunMFwiyQPpJcnsL+OxS/hM6//+
+BXCTIcrZSuSh6K9LhwHJN/u2b0qkejlw0BifyzSRvO0ZS/2MtWjRomU9LBqjvbG8kwhAmrrqtYix
+ql8BLjazL5vZK83sAWZ23UlXJE3p8ybmPyW/Ejjc3U9brAyP1qoHEYFAvUufEa2ad55opdeWerfE
+d7r7c9x9wS557l4QNzn9XRe3EK3TEIHDvdz9G4tWwP0solWu6dgPPd7V3e/o7m/1Mabqcve/EjfV
+ZzD3vN6LOJ9HKaskxmKe31fWzsR47XldmVPX4U8QY7pnXyYeVjzc1++4bADM7HBiaq/+c+si7+tp
+soj69p8nxqxfM2jlRbyUGL5QMeK8PcLd3z9UZcJzgXcw/3r4iiHr8Ubm9goz4nfr3u6+6LRn7v5H
+4HDgT6xil9qUCO49/S8TQfaj3P11o57H7n6yu9/N+3oUrAcpydgTmd9d/AfAA9x90V5H7v5D4rOt
+v//q+viGMZOj1Xva/BG4k7v/Zoi6XEP0mLiy70cZ8Ngh9nt47fvqPH2Fu585TKX76tJ29w+6+9Gj
+bisish4p0N5A3P1q4un0DM3JS+rdr+9FdKP9HHCOmZ1jZp82s2eb2UEpUF6KBxDj+irVH/CXpJuU
+oXjMR3skMQaz37OWVMO1q7rZgsi0PGw3S9z9IqLnQtPn58DLfYFugA0+yPzum1uJbOArIp0DT234
+0aPHKOv8tF1ZvUQcq1sTD6r6vZVovapU5/Hj3f3Po+5/LUkZ1D9Mc1fij45QVH37S4GjFnsotECd
+qqmF+rvoP9/dTxmjyOcRU1JV9TTg9mY2qLt8VY8bE63A/fV4sbv/bNidpwdFTx+10hP2OGLseqV6
+L69x90+sTpVWVdMwnzZw5CgPA9N58DzmX2v3IgLfcVSfzaN9hLwq7n4uca3ur8thQ2x+/YbXvjjs
+vkVENjIF2huMR5KwI4jxldWN5aCgq77sQ3T1fjMxBvKPZvbalKV6HM9oeO1XwLtGLSi1DL2R+S2r
+/7QcrfFriAOv9wHjmBdQHzNcD3guJ8aXD1+B2PdJzD+HVizQTvX4EZF9up7M7HZjlvV1Ytx6//n0
+RDObDd7TVDdNQd+b3P0L4+x7rUhjzk8ngoJ+V9PrATF0kcSxeZe7jzq2u+6p9LK6V5/PLxnjugGz
+5+/rmX/+PnGRTY9q2ObXzM/9MEwdTiR6Eq1Wq3Y92V/9Ad6rVqEuqyrNmvAo5v9OH+1j5DBx92OJ
+lvD+c+VfRi2qVpeT3X2cxHn1hyZVeQcPsV3TTBJXjLF/EZENR4H2BpQCiQOJ5FclcwPuQYE3zA28
+9yPGfJ9tZseMEtCm7rZ3Zf7NzAdS991xvI9eK2SlRYyZ3lFtI6ZbGVV/t9bq+H9qzO68Td1k+5Pu
+rYQz+v69S0qaNY5XERmH+4Ptd5vZ/ukB0zHM7156KvCiMfe5rMzs9gssdzezh5nZG8zsbGIMdf+8
+6NV58sJhus8O8MGlvAfmJ5KqgvdxrxsQAUiVabz6nO+yyDb1uYir4/KhcVvqgQ+Mud2SmNkNgUOY
+G+A78LY01GSjuQu9GQ3qjllCmfWkjNX5dYiZjZsUbNzfoR8y/2/kdcxsp6aVa5oSzo31EFNEZKNR
+oL1Bufu57v4o4DbEjcDFzA2khw28M+BJwFlmds8hd38QkRSl38eHfgP9FYlpSr7J/Lrecdwy17DZ
+bOZjtGZX3aObpjD69pj1+UPDa7s1vLbcmoK/hbIfD5QCpkcRmbHr59ROwCeJc3WXvs0uJsZlr6UA
+pap7i3gIMGj5KvHQ5nnA/szvLl6dc29w95FbbZM/pt4nY0kBwYF9dQP4zLhlArj7DPPH+B9gZo3n
+cKpHPXt65ZNLqMYJRBbnlXavhtfaxDRsG1H970V1Ppzl7j9fQpkfJ8a797vDmOV9a5yN3H07MRVZ
+v8Wu1f1DIQx4vZldp2llERHpUaC9wbn72e7+dCJ78/2I6W1OI262hg28ncgAfmLqTruYphuM8ycw
+pvV7ff82dsxAu7KUm7/+xDhLKa+prLEDbTObNrP7m9krUl6As8zsz2Z2uZkVZlY2LUR25H67j1uP
+1MX5UUSmXegFVrckpjKqt2aXRDbhoealXQX9Q0EWWvp/1534jI9y93Fa66syRhn73+QwenM1V85L
+40+X6g99/67G5Tc5mPl/Oy8fJjHVICnYP4vBDzWXS73rcPU5/XAJierWu/6/TU78PRybu19BZAif
+xEPgK9K4/nE1dfle7FpdH49dXSNuBvzUzJ5rZnsuoT4iIjs0zaMtAKSssl9OCykr6m2I1udDiC51
++6fV6zfgs0UQN8EfNLNfpTGzg9yw9v3szd0E3ka9jCpguMEEyl2rhk6G06Cp9Wzc8prK2jxqIakb
+638SCfvqN3/9LYcLdc/tv5kdO9AGcPdvmNnLiPme6+Mk6/tz4I1prO1aNUoAV39/lwEfAd48gQdh
+f1ni9reqfV8d9/PN7PZLLBdgXkZ55nedr9yooR6LZhkfwo+Ja+1KasqxsaTAcp27IfOvL5P623Sr
+vtfG+du0lGs+xHCj/mvYgtfqdA38LnCn2nYO7Ek8mH+dmX0bOJlobT8jtZ6LiGx4CrSlUeqS/IO0
+HAOQuor9M/AUolWvnniq+gO8BTia+KM8SNPYtKU8pa+M0y1uPZt0q9MkyxupZc7MXkRkud9Cr9Vk
+rLL6LDb+cFHu/hozuwsxzU39fK++foeY5mwtW2zs8DYiGd7lROvu94lkaCen1tZJ6J9WblT9LWdG
+9Cw4dYnlDjIo0G66fo07Zn3SZYxqP+afG0t9ILKereTfplHGaFfXwKsnUJdBZS/k0cQDmL1q61fX
+6Qz4h7QAdM3sh8D/Al8HTnH3pqFKIiI7PHUdl6G5+/nu/k53vw0xJczlzA86qulxFhqv3XSDMYks
+pk038lnKJCtrlJkdDbyaaFmpd8fuv6EbZplX/ISq+aEFfnbUEpNxLafqmBTuni+w7Ozu+7n7Ldz9
+fu7+Unf/wgSDbFj6g5ymwHeUc2PU82fQQ7qmXhLLdf1abjs3vNaU/GqjaPrMl+uzHTcZ2opLuRVu
+Tzx4779GV3/7qyUHDgWeD5wInGdm70vzk4uIbChq0ZaxuPtxZvYjIoHWLsy/SX0IkWCpyZaG15rm
+wR7VoDJ2QtORrElm9u/MnSILejduHeBMoiXlj0RL25VEV/XtzD/nngY8fhnqeD3gHbX99e/3xcBj
+J71fmacpwF3OMc3948ErTd3MJ9FiN8mHGsNqeo+TuBavO2k2jJz5v9/L8bfJmEBvm5WUgu3bmdmR
+wHOJoWWVprwtld2AJwNPMrOPAf/i7pcua2VFRNYIBdoyNnc/y8xeALyHuUGIAXdfYNOmoLc/g/M4
+BpWxGi1Fsog0FOHlzA+yrwZeARzj7kN/dmb2oIlWMMrMgY8C12ZuS049cdijzewb7r4qUzRtIE2Z
+m8edTmspms7JSVy/VqPnzWVAf/boDdkDyN3bZtYGpvp+tBx/m5x1+nfJ3Y8DjjOz2wD3B+5GJHar
+P0CvB9713iKPBO5sZnd199+vRH1FRFaTAm1ZqmOBNzL/6fzfLLBN09PsSdzcNXX762zgDLpr3eOY
+2xvCgKuAw9z9p2OUtxxdMV9NZLvuD7L7g+23mdn33L1/KhyZnP4HdA583d2bpqhaTk1dqyeRC2I1
+8klcwvzkWEtKILjOXQrs3ffacv1tWmpis1Xl7j8hkgC+Jj2QPJhImnoXIviuhiXUzy8Hrg98ycz+
+bsJDU0RE1hyN0ZYlSUnTTmd+17HczAYFPk2B9t9OoDpNcyari9ra9cDa99XN2MvGDLIhWp0nxszu
+R4wz7A+yj2P+/NpbgOPNbOsk6yBz9Gc9N+YnSFsJF9W+rx60NGXvHtXNJlDGqC5qeG0S72W90t+m
+Mbh74e6nu/ub3P2fiGvxEcBniakP++8Pbgb8ywpXU0RkxSnQlkk4f8Drg8Y4nl37vrpRve0E6nFg
+7fsqKDp7wLqy+g5mftff45ZQ3kEN5Y0ljcv+cMOPziTGGz6auIGE3jm8PzGMQpZH/3UDonVspf24
+4bWbmFlT7olR1OdmXyn90zAa8+eS3kjOZn5QeGDTiiM6kPmf7Q77t8ndO+5+krs/BLgr83uBGPCY
+Fa+YiMgKU6Atk9DUta5w96bWEmiejmdnMztoifW4K/NvZpZr6p9BVmPM6LqTMsH3J5W61N0vHLO8
+/Zg7v/HYUjfIjzG3tdSIMZUPSzeR/0uMI69nRq/Gaz9pEvWQeU5j/u/XHmms6Ipx998wv9tvxsJ5
+KRZkZjcFrreUeo3pG7Xvq2O7f3rQtBLW2vWy/vei+p2+81IKNLMbEfNzL7SvHZa7fxt4NnOvlQC3
+NrNJjH8XEVmzFGjLJOzP/BumQUE27v5LmsenHTluBVKQfsuGH630zUzTmLP+5DrSPGbxqiWU9+Ql
+bNvvtcQ88P1dxp/cl8Dnv4jM+v3B9tvMrOlclCVw9/OJ+b37WxwfvArVObWhHo9eQnmrlbX+VOZn
+TDfgqSu0/7V2vfxuw2v7mtnYD1GIXBT92sRUWRvF8fR6ANX1J+ITEdmhKNCWJTGzA4Gb1F8iAo6m
+G5a6TzE/QHmMmY2biOe5Da9dCnxtzPLGdWXfv9fdNC4rpCnj7l5mNvI1ycx2JgKDJbeOmdkRxLnU
+H2Qf7e6fqq/r7k48HDoPjddeKf+v9n113fjX1ENitevxIDO7yYD1BzKz3YCnsAqtu+6+Hfg486/F
+TzWzieY8GKD/egmre708jfm5AAD+fZzCzGwn4CjmX09OSPlNNgR33wY0JSUdNLxMRGSHoEB7gzCz
+g83s2HFuBBco04C3Dfjx5xbZ/B0Nr+0BvGaMevwDMW1I/83MMelGciXVE9xU9bnRCtdhzXP3K5h/
+47WZyFY7qrcD+6Tvx55X2cyuD3yo4UdnAs9p2sbdLwAeRfN47XePWxcZ6EPAxX2v7Qa8f4Xr8Rmg
+f5jDNHEujuq1wF7p++WcF3yQNzA/yN+TlTmm/ddLYxWvl+5eEr+3/Q8e7peSI47q5cC+Da83/f3b
+YZnZ3vSykNeds9J1ERFZSQq0N44W0YXt52b2kaWOhzazKeC/ifFr/TdpF7NIoO3uZxGtzU0tKUN3
+A05jGz/WUIc2cPSw5UyKu/+Z+a00e5pZU9bZje5bzA8sXjFKq3aax/3x9M6fsaRx2R9n/rjsK0jj
+sgdtm8Zrv5z55/KRZvbEcesk87n7VcBLmX+sH2JmR5vZkqasNLPMzB5sZrdapB4dIqjur8fhZvbm
+Efb3r8DTWOL5uxRpSrrPMv+9PMDM3jtOLxMAM9sz/V4t5HdA/8PQ/VZwjHiTY4Cra/+ujseHzezm
+wxZiZo8mHtD1/236QRq3vC6Y2TFmdoslFvPshtd+nX6fRUR2WAq0N56M6O76fTM7y8xeYGYLzXk9
+R7oR/Uci8+4jmHsTUbUkvzC1WC7m6TTf0LzHzF682E2amd2DCNbr855WdXhJCnpXww+Zf9P8n6tR
+kTXus7Xvq8/+jsD/LNbt2sx2M7N3A6+rbdtdQl1eR2RbXmxc9iCvBk5mfrDydo3Xnix3fzfzjzVE
+wPpdMxs5a7aZHWBmLyYCv+OBGwyx2RuAnzH/M3+2mX18oa7XZrbVzN4OvKW23dWD1l8BTwXOZf4x
+PQr48ijnsJndOD1s+COLzA3u7gUxF/OauV66+8VEgFyvkxMP4b5mZvddaPv0N/J5wLH9PyLGpK+3
+ZImPB84ysy+Z2SPSUJ2hWHgu8AKap0kUEdmhLenpv6xb1R+8mxMBxuvM7DxiXPUZxJjTi4lufZuI
+rOI3JqbgOpzo5ug0B9mfdvf+G4zmSrj/xsyeA7y3Vq/qpvNVwKPM7EPAicBfgG1EN7xDiIcFDxhQ
+h1Pc/U3D1GGZfBL4+/R99X4em25WP0FM63I50NRK+gN3X0rAuJ4cC7yYyLZcP58eBtzBzI4Gvgz8
+lmj12ovNNWLkAAAgAElEQVSY4/cfic+/Og+NOGc/xxhJnMzs/sxtearOo3e7+yeHKcPd3cyOJKZL
+um6trC3AJ8zskDROUSbjEUSPiKqlrTp/DiGC7TOAE4DvAL8hphfaBuxCBH/7Abchrml3Z+7c0UON
+lXb3Tsow/y0igVf9HP5norvx54iEeecABXH9ugvwEGKoTHX+XkT0wHnpCMdgYtz9IjN7BPB14r6g
+eh8O3AP4sZl9Afgi8XfifOLvw05EAHor4tgfQUyzB0MeR+J6ebvaNgYcZWa3JR56/Jy4XjZdF89I
+3b0nyt2PSfkaHsjcY7EP8EUzO4kIFL8H/JX4/K8H3At4AnFuNf1teom7/3TS9V0BDtwnLdvN7GvA
+6cSwml8R58JlxJjr3Ynfp78npvBqSpb6ZwYPOxMR2WEo0N54+p/SV38Ar0Nk710sg299m/6yPsGI
+mXfTDc0BzJ/+w4EDgNenZVBdKtWNzM+Ah49Sh2VwHNGVuLqRrup5cFoGcWJe4HOXs3LLbOiETu7e
+NrOnAp8nelrUb2ivT3oItMB+qpvy7URwfviolV1gXPYPGTEBkrtfaGaPIgKr6v0YcR6/h+bswzIG
+d7/UzO4GfIkI7PpbYg9Ny1DF0XxNG6Yep6dg+1h6iZ2q8rYS4/cftcA+jRjm8jCaZ00Yx1jd0N39
+2ynY/m9ivHl1Ta3Ke2BaJr3/DxEP3HZj7mdxO3oB+KD97EXzDBb9643jsUTPiUOYeyygF3Quts/6
+efmBJTwAnuTQgnGPR7XdJuKByhFDbtN/LK4GjnT3pkR4IiI7FHUd3zjOJzKqlsy9eaovPsRSX5/0
+2kXAU939keO0Lrj7c4EXEi0W9X1U5Q9TFwe+Avx96vq3atz9MqJVo0gvDXN8hzXJcZz14zzJ8oYu
+091PBP6FOFZN59Zin/3lwAP7xjwOtf/auOw9+vZ9BfDP42QFdvdv0Jtfu6pHNV57pbuMTvrzHbcO
+yyLNuX4X4iFGdV0b5Vo26DoCzb1NBtXjOKKF/aoB5TX9rlc/vwp4UBrnT+1n9a+jWNJn7u6fBu5K
+dPse5r0Mem+j7PMioot69bdj0tfLsY5JGv50d6KnzKjHor7PAniluz9l1Dr0vYdJGbe8Uc+FpmN2
+HnCP9TRGXURkKRRobxDu/gd3vyPRZfJpxM3DhQy+AWxa6Fv/LOD/ADdz9yVlqHX3NxItUN+k+Y90
+01Ktdw7wFHe/bwpyV527f57IoP1rhju+QxXbV9aSqznBMse+GXb39xCtQ39muM++2t/JwCHu/tUB
+9VjM64Db921TMvy47EFeTTz06a/LSs6vvZTgZDnqsDw7cN/u7s8kPseT6AXcsPj1o/+adi7wZuDv
+3P3kEevxKaJl/UTmvu+F9ndi2teJ9aIY/7hN5DN399OI1vX/IroED3s9rtfhz0SvnqZp/Jr2+Umi
+2/Xv+uo+qevlWMfD3a929wcTrdt/YPhjUa33XeAwd3/FqPue1HsYoszFPIcYUtBmuM+m6ThsA94K
+7O/up0/gPYiIrAsWU8HKRmVm+xNJoA4AbkaMxd6DGMu4E9Et90qile884KdE8ppTU7ba5ajTQUQX
+9HsQYzCbkqL9Ffg2MR/3Z5c6J6mZHUt0n6848PjUarYkZvb3wP2I8aA3I7pI7kyM66tz4Abuvp67
+jo8tZYx+LDG+9TDmz6frxHjbrwIf6b9hM7Om7sLfc/czl6fGa5OZ7UuMF61zdz9jNeqzkszsRsBD
+iWvHwczNIl83Q5xLZxPB0NcmdT1LGcsfSYxRvRkxZrUggtZfEtet49fDWN2UlPDBRJfxO9I8VRXE
+e/s58aD0y8C3fMybizQk4L7E9fKmRI6QQdfLvd19sa7jE5Gyrz+AOL/uQgxv6VcSn/HXgY+6+6kr
+UbeVkJKgHUacB7cnxl5fn+YGGyceTPyQGN7xSXUVF5GNSIG2rGmpe+8NiOA0I+ZePkd/tHds6ab2
+OkSg1CK62P7Z3WdWtWKyrpjZrkRyuq3Ezf+Vablw3EBwI0uB935E4Fsdz0tXKthdS8xsJ+KB1k5E
+gH0FcY1a0kPf9SQ9HN2HeDC/lXgwfzlwsZI/iogo0BYRERERERGZKI3RFhEREREREZkgBdoiIiIi
+IiIiE6RAW0RERERERGSCFGiLiIiIiIiITJACbREREREREZEJUqAtIiIiIiIiMkEKtEVEREREREQm
+SIG2iIiIiIiIyAQp0BYRERERERGZIAXaIiIiIiIiIhOkQFtERERERERkghRoi4iIiIiIiEyQAm0R
+ERERERGRCVKgLSIiIiIiIjJBCrRFREREREREJkiBtoiIiIiIiMgEKdAWERERERERmSAF2iIiIiIi
+IiITpEBbREREREREZIIUaIuIiIiIiIhMkAJtERERERERkQlSoC0iIiIiIiIyQQq0RURERERERCZI
+gbaIiIiIiIjIBCnQFhEREREREZkgBdoiIiIiIiIiE6RAW0RERERERGSCFGiLiIiIiIiITJACbRER
+EREREZEJUqAtIiIiIiIiMkEKtEVEREREREQmSIG2iIiIiIiIyAQp0BYRERERERGZIAXaIiIiIiIi
+IhOkQFtERERERERkghRoi4iIiIiIiEyQAm0RERERERGRCVKgLSIiIiIiIjJBCrRFREREREREJqi1
+2hVYr8ysTN+6u+erWhkRERERERFZM9SivTS+2hUQERERERGRtUWB9tLYaldARERERERE1hYF2iIi
+IiIiIiITpEBbREREREREZIIUaIuIiIiIiIhMkAJtERERERERkQlSoD1BZnYzM3urmZ1tZlea2eVm
+9iMze42Z7TliWbcwszeY2ZlmdqGZbTezc8zsFDN7gZntMUQZjzOzMi0fTK9lZvZwM/usmf3WzK5J
+P39g37YtMzvSzD6V1rvSzDpmdoWZ/drMTjKzV5jZoUO+nwPM7NVmdpqZnWdmM2Z2gZl9L5Vz3VGO
+j4iIiIiIyFpl7pqhahz982ib2dOAtwCbmDvtV5WZ/GLgcHc/c5Fy81TO04Fqfu6m8i4Dnu3uH1mg
+rMcBx6btPwy8GPgEcFhDuQ9y9xPSdjcDPgsc0LBefz0cuKm7/25AHaaBtwNPWuT9bANe4O7vGvR+
+RERERERE1oPWaldgR5AC2qOJAPIXwPeJwPEAIqg1YE/gBDO7ubtfOaAcAz4NPCCV5cAlwP+mr9cH
+7gZMA7sDHzKz3dz9HUNUczNwAnAw0AG+C/yWeDBwUK0OOwNfBfZL+y+BHwI/B64Ctqaf3Ra49iLH
+ZSvwFeBOtffzW+AHwKXAHun47AtsAd5hZru4++uGeD8iIiIiIiJrkgLtpalaZt8DXAA8xt1Prq9g
+ZncGvgDsClwX+DfgvwaU93x6QTbAa4GXu3u3Vt7eROv04Wm9N5rZ99z9jEXq+lCiRfkU4PHu/ue+
+ek6lb58IXC+VfTbR0v2bpgLN7GDgCcDMgH2+m16Q/Uvgqe7+rb4yDHgK0Yq/GXilmZ3i7qct8n5E
+RERERETWJHUdH1PqOu5Ea/V24FB3/9mAdZ8BvDOt/wt3v2XDOrsA5wA7pZf+r7v/x4DypoFvAYem
+Mk9x93s2rFfvOg7wE+AO7j4oMMbMjgcekra5p7ufMmjdhZjZXYBv0GvFvoO7X7LA+vW6nuTuR4yz
+XxERERERkdWmZGhL58B7BwXZyUeALhGU75+6Z/d7FLBzWud84GUDd+jeBv4l/dOAu5nZTRfYfzUO
++oULBdnJrrXvL1pk3YU8p/79QkE2gLt/mOh2b8DhZnatJexbRERERERk1SjQnoxPLvRDd7+KaNWF
+CCRv2LDa3avVgY8uFhCnruI/rb10t0XqeClw8iLrANS7lD9tiPXnSQndqhb2K4AvDrlp1Xpu9BK2
+iYiIiIiIrCsao700RgTGP11sRSLreGXXhp8fWPv+u0Pu/zvArdP3By2wngM/8uHGCXyCGKdtwNPN
+7BBiTPiX3f23C27ZcxuiC7wTidfeHkOxF1WfKuz6Q+5LRERERERkTVGgPQHufsUQq3Vq3081/Hyv
+2vd/HHLXf6h9v2AGcODCYQp096+Y2duBZ6WXDk0LZnY+8G0iC/pn3f2cAcXs21evZw6z7z7qOi4i
+IiIiIuuSuo6vHfVx21cPuU19vV0WWXfbsBVx92cDDwZOpzctlwN7E4nS3gH8ycyON7Omlufd6sWN
+sYAeAomIiIiIyDqlYGbtuIpegLrTQivW1NdrnJt7XO7+OeBzZnY94K7ENF13AW5RW+0hwF3N7I59
+U4DVHwD8xN3r3eJFRERERER2aGrRXjvqXbtvMOQ2N6p9v5QM4QO5+1/c/Th3f4a735qo28uAa4jW
+5z2AN/dtdn76asA+y1EvERERERGRtUqB9trxw9r3dxpym/p6Z06wLgO5+znu/l/AU4lA2oB7m1l9
+3PmPgCpr+t5m9rcrUTcREREREZG1QIH22vH19NWAR5jZ9EIrp2zgt6m9dMqgdZfJCbXvp4iWbQDc
+fTu99wPwjJWqlIiIiIiIyGpToL12/A8xThvgukT37Eap9fgdtZe+7u6/nkQlzGzPIVetd28vmTt9
+GcDrqyKBZ5nZPUaow3WGXVdERERERGStUaC9Rrj7lcCr0j8N+A8ze2Vfl+wqCD0BuH16qQO8aIJV
+OdXM/tvM7tO/71odbgZ8qPbSV929W1/H3b9JzL8N0eL9RTP7DzNrTPRmZpvM7B/N7LPA55b8LkRE
+RERERFaJso6vLW8EDgMeQATbLwGebmanAJcC1wfuBmxK6zvwPHf//gTrMAU8Mi3bzOwnwO+AK4i5
+rf8WOKS2/jXA8waU9VQiGdq9gWngNcBLzOw04E/EOO7dgRsDt6L3vib5fkRERERERFaUAu01xN3d
+zB4MvAV4OpATY58fWl8tLZcD/+bu/2/C1biS3lzWm4mW89v3rVP9/HfAke7+s6aC3L1tZvcjusE/
+F9gKbCEeFsxbPS0d4NSlvAEREREREZHVpEB7fN73ddhtFlzf3Uvg38zsPcATgXsQLdm7AJcAvwK+
+CLzf3S9dhnreFrgDEQzfDtgf2JcIkq8BziOyip8AfMLdO4u8HwdebmbvAB4L3JOYi/vaROv5FcAf
+gZ8SCd2+5O79471FRERERETWDYs4SEREREREREQmQcnQRERERERERCZIgbaIiIiIiIjIBCnQFhER
+EREREZkgBdoiIiIiIiIiE6RAW0RERERERGSCFGiLiIiIiIiITJACbREREREREZEJUqAtIiIiIiIi
+MkEKtEVEREREREQmSIG2iIiIiIiIyAQp0BYRERERERGZIAXaIiIiIiIiIhPUWu0KiIiIrHdmdjWw
+CSiBC1a5OiIiIjuyvYkG4xl332m1KzOIuftq12FNuPLyOBDuzsBj4pA5GEBZUhRdim5BWXS5Zttl
+XL3tMq7edinbt13MTPsC2jPn0565APzK2cWsTZ4beW5kuVEUBd1ukb52Z5eiKMiyjCzPyPMcBwp3
+yrKkcKc906XdLmi3q+2doltSFCWll7jH102bpti6ZRNbtmxiy+ZppqdaTE21mJqeIrMMswww3KHT
+6dLtdOh0uhRFSVGWdEun2y3Y3u4w0+4y0+6SkZFZTkaeto8ywOgUJd0ylrIsgBKnBMq037T/qSla
+rSla+RR5PoWXRllmeJnRbhdsn+mwfXuHmXYHrAPWBeuS5cTxa2XkeXTIeOVLfm3LfX6IiCzEzLpA
+vtr1EBER2UAKd1+zDcdrtmKrycwag22r/jfnR45TUnqXopih291Gu7uNbnc73aJDUXSBAsMxN7AM
+T0VUuzCLfZpFvOgpoO7feXoUADiWgWWQpaUs078dDKN0wzyC0TzPaLVy8jzHsiz2445bVMLMUn2c
+0qEsnaIsI2juFnSLkm7hlCWUbnhaYh/gXuIe9SvNcXMyK7E8Vdxin61WRpaBWTpmZUEx+55zvIxC
+orx44OFllOfpOOeAWU6m50MisrY4xLV83333Xe26iMgQ2u02F154IXvttRfT09OrXR0RGdK5555b
+xWprOiJQoN1nYJBdtZm6zb4QIW+0HBdlh06xnZnONbQ7V9HpbKOTgm2zggwnwzC3FJSmQDJuzWqB
+dgSaZVGkn5eUnmGZxa7NMHPMnCxzLIugO8uZLcuBjAxwpqZypqZatNKSZxlksQ7us2+sqk9ZlhSl
+0+2WdLpdOt0iLU63APcMLzLKbgaFpZbokrIs8bIkm2J2yXOwzCK4zyDLjCwzLHPwkrLsxhEsS/Ac
+owXkKcAuI8h2j8DfS0ovcDeyLKtuaZfrNBARGdUlwN7Xvva1+ctf/rLadRGRIZx55pkcfPDBnHTS
+SRx00EGrXR0RGdLee+/NhRdeCPG3d81SoD1AFXBbLZYzLFqfiRZd6LW0FmWXTjFDu3M1M+2r6Xav
+oejO0C3b5BRgqeXYs9Rq2+uibhZBaLUvL52iKDB3SjeyssTyFLDmVVBebdcLsj2P+DMzwzAwmJrK
+aU3lTLVatFqt2e1iz15b6LVmFyXdoqTTKZjpRLBdlEaZFu9meMfwjlF2iW7mRUFZdpnaYkxnRms6
+o5UbWZ6RteIr+Gxs7KQHCUVJtPinugFlvVW7hCK1sHfLEsfIW9UDChERERERkbVHgXZS77Y9eCV6
+LdqJ47iVlHQpyzbdcjvdYjvdok1RtOkWXbASMsjSeOYIInt90Kt9Wy2qd6LVl9IoLLbNzMFT1+/U
+qm1ZKj4jlclsIJ5l0Grl0W08LZ76eJfumDPbOl5vzS6rFu1OQbvdpd0pcM8pPYuu4x0oZ8DbRtFx
+isIpU7Cd5Rk+nWE4WWbkOeR5RtbKesEzvQcNpRt4GSO8zcgsusFXreRzAv+iBIxiKl5f251FRERE
+RERko1KgvYha7+r5gZ1F1+jMI7GZ5dVXh25J4THG2fIyxegGqUu3VU27Vbyduo5nWUaWZeRZXuUX
+m1OX2dbwskwtv71gvWoVr1rII9DNybIcy6KwKsitxoCbOUaU1S1SoO3QLZx215mZiYRrsa8YR+3t
+FGjPGN5NY7PdwKM7ublFN/nZevfqGvVNLdK14+mUeFlE0rfCKKru6F5SFhH4dzsFRhbB/XQE+CIi
+a8lFF13E9a53vdWuhogMod1uA3Cf+9xHY7RF1ol99tlntaswNAXaA8xv2a5avOkFiFUX7swi4M6j
+G7dl4OaUXtItumSU5EYEorMFlLOlulkkS5stp8o0noLS2a9RAS9JmcUjcLVUlyyLEqsgO8t62bkz
+yyKhWhmt2UUZCcyMaCUuy16gXZRQFNDplMzMlMzMFBFgl2ksdwqymTG86HV5xzKsjEB79hnB7IOB
+avw5ta/pWAN4BNq4UxRGWUQ9Z1u0uyWdTgkG3W5J0XXKlgJtkXGY2cuAlwHu7sqUPUHuzjnnnLPa
+1RCREaSxniIiE6VAewi9pHZWb2COYDKL1tsIsuOr5Q7mlGVBtyjIU3doz6oW7ZJeU7Wl9GWpRduy
+2Wm9opt1CV51JffUMlxl+vZevQwsZfeOVvGYQqyV52RZhmVpGi+gKKObt1e9r1Mv9W7hqUXb6Bae
+Au2C7dsj0KZ0vMxhxmCmxLdn0W09T2PMc4tAu7TZ8ey91usFpk0jtXqnLuPRoj23C3mRAm3LoNtN
+3dXLgcWJiKyi/Va7AiIylDZwIbAXoBZtkbXtr1QNleuFAu0FeJr6qu9VfLbbdwoosyymnMpaZNkU
+WTaFWQusyqIdXacjVo2x0f0hZ2qvjm7jeWQKn00KVjolvWmuylQDs9QyboYRdTDrBdkRYOeRIT3t
+uyyihbhTlL35xdI82mWZpvAqPcZ7e0ZGHlNqkQM5Rh7vLWtBq4UVFl3jswKykpYZuVkkZKslNSuL
+crblO77GQwEj5ikrsRj/Xo1dr7rHl+VsgB5d46sA3pQQTUTWIAOUdVxkfTgTOBg4CVDWcZG17XrA
++uoxpkB7EXOCba/aj0nRYtVCnWFZC5sNtKcxS8E2eZqjupoGLKbkyrzK++2zAW8E7TGuutp3dBFP
+gTolZWp+LjOwojc+OwLgjMxy8sxSwG5klhMJ2IixzmUkFut2u7VtSfNi94Lt6Faek1mLVpaR0SIn
+J6OF5S2s1cLKVkw7lhVgBZ4VTGVGK6vGaMeUXGVZUNrc+cKj9T5PrfB5HNPSUjf6EqdIXccL3Ms0
+nVl6eGBpBLj3PwQRERERERFZfQq0B6h3c66CbU9JzGYThtdakrMstWjn02TZdK1VO0+t0BFkZ1l9
+/mxmm7ZnW20tg1a0bJdlOTsWu/RIrmYpE3lkHi/iq2epJTsnszwlU6u6kEd7dJm6b1dTcXWLAqgy
+fRtOdNeOJaYvMzJya+HmtLIWLVrktMjyKTJvkXkrBcddSisoraCVES3aAFW3dy8pSS3SWcwtVgXY
+eZZHq7tH13ov0zMML1KLdhHH3VLX/Kyab7wvU5yIiIiIiMgaoUC7QVOX5Eg6Zr0WbYjvZoPtFlk2
+TZ5topVvmm3VjkMc03lF9+yy10pdlsxNENZr2Z7NaF5mlJTR2u21ADN97+lrL/1YlV08vpalp1zj
+kfys241M6N1uBNpZZpRZlBdjoyPgxqOVPM8ik3qrbDFFLNFFPifLW5GcDCiALk7mQGmU8QIlRmk2
+d0SFGTZ7jLM4frX3TbTbz8lSbpmRW0YrJXersqmLiKwRxWpXQERGdV0iL+R1V7siIjKCqvcva/xv
+b7baFVi7BreWRpKyqjE6uo5nWYs8n2aqtYVWayt5CrYza8X4ZrfUMt2bczqWcjb4LlPwXRQF3W6X
+ohvrxPpl6tbdW7wkLdXYamazdBdpHuxYunTaXTqdDt1Ol063iAzjqWW7Cry7abuiiDHaedZiOt/E
+ptZmNueb2JRNs8mm2JS10pIzbRktN6yMKc3KjtOdKenMOO0Zp9Nxul0oi2pxyq5TFFAUVQt7qn89
+o7v3Am0zI88zploxdn1qaiot00xNKXmJyFKZ2SYze76Z/cDMrkjLaWb2TDNb8ImWmd3QzN5iZmel
+7a42s1+Z2XvM7FaLbFum5aXp33c3s+PN7E9m1jaz3/Wtf10ze12q52VpnfPM7Cdm9j9m9jgz23mB
+/e1qZi8ys2+b2QVmNmNm55rZCWb2kFGOWYP0PFE9bUTWj+sCL0eBtsj6Ugu013R2NLVoj8Bn/2+9
+r2lubMumojW7tYVWvoVWvpncpjFaQIdofY4EX4WVWFliRUHViGsWPy/KXvDdm3M6BejVkgLsCE4d
+3MgskqRh1TzZNjuM3NLk2g50iy7dbkHRTfsume2KHdm8M7oF4K3oht7KyciZspwpMqY8IyurrupZ
+ZDAHshK8W+Idw9vAdsPLElqO5xDzm5EypqeI2h3yavqvGCfuqZXe04973cYzLCMF2r1gW0SWxsz2
+Br4M3Ja5eRoPTcu9gH8asO1jgfcCm/q2vTFwE+BJZvaf7v66BargqaxXAy/qK6e+r7sAnwd27Vtn
+r7TcCngEkUL4Sw3b3wP4OLBH3/bXAe4P3N/MvgQ8zN2vWaC+IiIiIotSoF037/auoWWi6uZNFW4b
+pKRheR6B9lRrC3m2OXUfTy3aVWZvjyC7KAqMopcgLKsC7V5rd71Sc4Jsr1q0I0DFndKii3ivT3at
+cSUieRynKLrRil2N0c7iWYF7llqYoSiMzI2Wtchb07SsxZRntEpjKk1BFt3aja5B16FdOBRO2XHK
+tlGmAJoSbCqCZYfoWp7Gi5P3HiS4W1Q5JTibnUHcncxizHneyuYE2dMKtEUm4dPAAcBbgS8AlwD7
+A/8J3AJ4gJkd5e7H1DcysyOAY9M/rwTeCHyNGE1yJyJo3gt4tZld6u7vXaAODwFuDfwYeAvwM2AL
+8HdpX9PAx4BdgCuAo4H/BS4g5uT5m7TPBzUVbmaHEcF3CzgPeEfa17nAvsDDgSOB+wIfBv55gbqK
+iIiILEqBdqUvOVmv1bqPkcZFVzGhgTmW5eT5FFOtTUxPbWHT1GY2TW1i09Qm8A6k7uMRVAOZ4Vls
+71btLQWd6b9qDmmvuounKcKKWsCdzSbfdqCgSOO/e+ObqzqmNYqCouymJGPxs2q6LGOKVraZ1vQm
+pnwzU2xi2jeRd3Mo29Du4O02ZZHGjpcZeEkry9jUmoLMKFtQZFA4lGVB0TWKlITNMk/HyslyyFsZ
+rVZJnpe1w2vpMUaMX49s45DnGa1WTivLI6u5NT4GEZHRGHAIcC93/1bt9R+Z2VeAs4G9gWcAs4G2
+xZQKVeB8FXBnd/9pbfvTzezTwKlEn8w3mtnx7n7JgHrcGjgZuL+7d2qvfzt9PSyV48Aj3f3Evu1P
+Bz5uZv8ObJ3zBqOuxxF/704EHuru2+vvFfiSmX0LeB/wYDO7h7t/bUBdRURERBalMdqNrPa1KckY
+s0FqtcQ0WNNMtzazaWoz01ObZpep1hStPE9Zt4HMIIss22WWpo9O82PPLql1u9staLc7zLTbtNsd
+2u02nXaHolvE+OUMsgzMqimxOhRlh063Tbszw0xnhnZnJm3XptNp0+1E9/FuJ5ZOu6TbAcop8mwL
+m6d2Zeum3dh587XYZese7Lx5dzbnW8jLjHKmi3e6lN0uXhTgTm4Z060ptk5vYktrE5uzaTYzzXQ5
+HUF6J6M7Y3S2Q3u7M7OtZGZ7SXumpNN2up1qnHaazqwaAW8lVIF2ltHKc1p5Rp5lpEnLyJp7mYrI
+cBx4e1+QHT9wv5RosTbg1ma2S+3HDyJaggFe1RdkV9v/CXh++udW4AkD6mBEMpMn9wXZdfvUvp9X
+19o+S3e/qu/lRwA3BLYDj+0Lsuvbvp8I2AEeP2gfIiIiIsNQi/ZA81u0Zxu9Z6fV7rUGZ1Zr0W7N
+bdEuihZFmTMbwM+2aNucruhe1lq13SmKgk63S7fTSQnKUks2kOV5ZN7OY/owL312vu1uLclZGv04
+m5V8drqwskzjobOU+IyYwivfwqbpXdmS78RW28qWbCuWlWy7uqD0bbTbHUqP+bpj/HROnht5lkOW
+0QQNspAAACAASURBVM08Fpx22aXrBU6Xgsg8XppTWkneigzs8aYzsryM6buq7uSWWr/T/Nl5njGV
+57TyPKYPMxRki0zG/yzwsx+kr0Z0z/5J+vc901en1328yfHAu4hx1fcE3tSwjgPfcfc/L1DOX2vf
+P4Ho+j2sB6av31igRb3yTeB2wB1HKF9ERERkHgXaC5rfObnew9yIoca9YDsjt5xW3qKVTzHVmmZ6
+apquTWFFjnuOk0GaJsyr/8Xg7TRvdDXdl1VN3bXM4j7785jPOwOv2thnU5DjXuBlkaYPozb1V7U/
+aq/nxBjzzUy3dmbLpmux89a92WJb2Gyb2cxmKNt07EqsgG67k5Kv5bNzdxtTZJalsdSxq+jSbkx5
+QdsLzAvcofCSrhNdyXEyS3OLw2y3ck/HpOpGT3q/GZGYrfov2rVFZIl+scDP6oFpvUW7yib+e3e/
+eNDG7t4xsx8Cd61t0+QnC/wMogv574C/Bd5mZkcCnyEC4zMWaAmH6BrvwH3MbNjspPssvsogJdHb
+fjF5WkRERCQUDJ6x6yIA2u32itVmqRRojyEaob3Xnlpl0q7mwTbSdFQtNk1PYTaF06L0nKLMcIyy
+tCijLGtLSmjmNhu4Vv9F8WkOrJLIKlaWUMQY7ypg782gXVuyCHwtjS8vyypn2hT4NM4mplu7sHXz
+nuy603XYfZd9mfZppoqcqSKnO3MNmU9RFtBpd2YfBLhlZHmLlpe0zMkzw7I86p1luBkdK5nyklYZ
+ida8LOh2Y77tPPfUsh1d6mczjafEb54SvlEalBmUOeYtjBYZU7R0kyqyZIO6Uif1wLT+C1dl7r5g
+iF2cV9tmkEsXKsDdu2Z2f+CTwM2J4PnQ9ONtZvZN4CPAx929P5iuot5RusBsHmHdBhcubXMRERFp
+dOGF6+dvrALtpaoC7irIxsnMYkzxVIvp6WmcKUqP7uOFpxbtEkocL0rKoqAsopt3nlktwC5nlxIw
+d6y06PZdgJvjVkZX9CrIrgXbkWstuoVnGWRmlJ4yjZcGtDA2gW9lempXtm7eg1122ptr7bovrW6O
+tZ2sXTJjjnmLsuu02+1oWcdwM7K8xTROmWVM5y1yz6Jrd94CyyLIToF4pygoiw7dDlgZQXariIcO
+aTg2VgXZ1VK18KdAOytzMm+Re4tcp6/IapvU+I1Bj697O3L/hZndGnhAWv6emEJsM3B4Wp5jZvf9
+/+y9a6xsW3bf9RtjzrVW1d7nnHvO7dO3uy8dg3lYQIyIcZwoJI4RQZYlEvOIiMQjshUEjWQQyAFC
+CGA7H2N/QCJfnIBMJ0YkliOMg4EQ83BCgDiOjUMwihLxiNtpku52uvveu3etNeccgw9jrqo6t++j
+H+c+jOevNW/tXbVq1aq16+ze//Uf4z/c/dNXT90vEPxXwL/5nI73TRERnj59+rbbpd7+MxgMBoPB
+IHh28tIb8/TpUz796U//khDcQ6l8BTxT4tzTvqNz29Ee3jVNE80mastRZm2KWzjUZo7VSOS22sJ9
+ThlNgqheXG1JaA9I643c1/ZvjNDqAWJCuMXhZkcPeRIhRQs1bRe1IggZ4YDILcscQvvR7Us8fvgy
+ujmmG24bTTfUc3e0a8z6Jv4y1pyxpHjOiDUgoQhJFFcJQexGwoANt5jXLe59bje0tov/OHa/EtoR
+QC7ging42uoTiZkk4+M7GLxH/CLxa+ZDX8S2exn22/VHvy0eoxR+tC9E5EPAtwDfAXw98A8Qaei/
+9eppnyESy2d3/7mv9BjejpdffplPfOIT7/TLDAaDwWDwy5aPfvSj7/UhfFEMpfIG7J3Zr7dq9tzx
+fcYztAgXs4a1jdZWrK7UtmFeI9ArIrORlENEW8a9XPqvu1B222d8RTeykBAxRBoqgqqiaqhGUHzS
+FOnbSUHoad0RLCZiXaDb1WgsidTzJlTAm5B0YUoPmPITbg9PuT2+yO3hMbeHF3AKdVOq2rnHe3/f
+EW8GNYrgwSvuBfNEbVAalOY0h5MXileax7my7vzvFxpqa5QaZeYetjtm3kehpXDiNdzrWBNZZrIu
+TDrmaA8G7xF/kQgM+2oR+cCb9Wn30VpfR/w6/YvP+yDc/a8DHxeR/wT4Xwih/ZtFZHH3tW/2M0RC
++q8Wkezu9Xkfx2AwGAwGg8HrGeO93oI3G+6lAirdXbaCt43WNlrdKPVEbRvNa4jxpIgmJCW0i21U
+EZfot74ypqPZOoq+VTQEt+xrF9t9pVhJE6ox8kr37SSkenwdYjwnZcqJlMIhh0TShXl6wM3hCQ9u
+nvLg+AFuj0+4ObzAYX7AlA8kycge4ObE2DF3KkbFKN7YvLJ65WSFO9u4ayuv1pW7unJqG5sVqjWa
+2WVGeE8+b9UopVFrjDJrzWgWveRKImkmSUYlozKRZCbpQtYDUzoypeN79fEYDH458+P9VnjzsV0A
+/xTwwuue89zp4vkn+rcZeHz18I/22xd462MdDAaDwWAweG4Mof0mvOEkbeE8VkpxxBtYxaxgbaW1
+lVpXalsxa7g4ooKcxfaEakYk+vKi97ivXiItrrF2kf2M2L6I7HCz03mpXoWn7YIbIamQVZhyIud9
+24R0ob08I7Rf5PbwhNsutOe8kDQjcnG0zQlH253ixkZj9cbJC/deuLfCXdvO69QKxa4dbdunZNO6
+o11ro9RdbPfkdXZHO8eSjBJie3e0sx7I+hVmFg0Ggy+HHwH+GvGr8feIyBckiovIrwC+t397x1uP
+AXtLROQ3iMjf8RaPT8A39W9f5dk0so8DP9+P9ftE5Bvf5rV+vYj8xi/3WAeDwWAwGAxglI6/MXtv
+sDsuu+jex2J1mdhFtlsFK1E6Xk/Uck+p95S6UmuhttaFY/RFIxql2NLndHeLXCyBRMCYC9h57nQI
+WxeHJCG2XdApkfryfTxY8/P4b9G+JJLARRIqmSSJSROkxGF6xHF+gdvlCbfLCxymG+Y0k0RRN7CG
+d8ferGJuNMBEcAUXxbLSMpDA1WjaS927Q99oNDeatIh0ExCVPsvbKdUwKi4JFCT1s+1xjlRjBrif
+HfBKKZUtlagKGAwG7zp9bNe/CPxxwin+MyLyvcB/S8Q3/HrgdxGJ3w78zi9ihvVb8ZuAf1dE/jTw
+Y8Q4sE8BR+BrgH+JKBt34D+8Th53901Efhvw3wMPgP9ORP4IcbHg/yIuOH+E6PH+J4kxZP8yMTps
+MBgMBoPB4MtiCO3X08X0eVyX7/3Ye3922M9u7SyysRKhYXUNoV3uqa2L7bbRWo0xVYRQpotp7za5
+qCLZo0RbI0TMJHqhGyFSm9hZlKsoOqWz2DY3rCox6BpIe4J3bA8KnhGfyLIgaSExc5yecDuHyL6Z
+H7LkAwlBWohrbydavaO1e5pt8Tp9PrYniU9PVnwKse05ZmKLGLqXmmOY9F72fmFBVXAXmjmtNEpr
+vZ+dKImXfT55hLk5hnmj1o1NomPcrVHzL505eoPB/99w9/9SRL6dCB97APzevs6bEL+R/h13/wPP
+4SWFSBr/pjd4bC+U+RHg336DY/2zIvIPAT8E/Argn+3rzfbz+edwvIPBYDAYDH4ZM4T2F3DlZtsl
+yTvGeJ1rvHFr0CpYLL92tLc7ajtRWhfa3Q323cmW7sTuDd89HVwQSIrFNzSx3gvdMPFwd7sw13l3
+tDPiDZMeWuYeAjuBeBfxokBCfCLJkaQ3iNxyMz/hZn7M7fyYm/kRS1rIInHxoK1YPWH1nlbvMdsw
+bzHWSzU+OZPAJPis+CRYpge4yVWgnOEebraLd0dbwbpD3QwjSuw1KSkbKSmyJ6drWPRmjUpBHLyF
+s73pGI0zGHwF7KLyy97O3f+wiPwE8K8B3wx8FeEQ/zXC3f797v6/P4dj/V7gZ4F/hAhXe5nLfOz/
+F/hJ4OPu/l+/6Ztw/0kR+buAbyfGg30d8JRo3vkU8H8Qfd5/zN3/8nM45sFgMBgMBr+MGUJ7p/8Z
+uZcoW2shps8jtXrCNvG9e6PZirUt+rPrSishtGu5p7ZTXz2B3FuMALN9fBV9XNezUWvWk8itQTWn
+uVG9/50r0XMdKeZXyx3a3kAuoDFvW9R7f3UCy8BMkiM5PSTJI26mxxznRxznhxynI7Nm1AwvJ1q5
+o5VXKdsrlO1Vaj1RrUY5uxKO9qQwKzaFu+3Z+3naz9t+cSJGn5lfSsfFBXOnNqNaI2Ult0xt+yzy
+yw/G3GheI51dPMrHtw2VETEwGHw5uPv3AN/zRWz3E1zmUL/ZNn8V+M4v8zi+qH/E7n5HuNU/8uW8
+ztV+CvAH+xoMBoPBYDB4xxhC+3WYGbVs1FJopeAW7rV7C0Er4XC7G2YxZ9p8o6z31HLC6gmvuxu8
+0toJs1N3hDdq3ailUkujFcNdMBPi780Q07vhXZtRG1i7imbrpey1Wg9IsxDuRozE8kg0F7fwaSSB
+Z9AJ0YWcb1jSQ+b8mGN+yDEdWdLEJIK0DfNCaXesr/0id69+htde+TSvvfqL3N+/wlZOVG80BFPB
+U4hrT32p42Z9XJn18ns7i21zO/dpn9vUz0Q5OS6YS+yHRnMnnav4hebW3x+jR3swGAwGg8FgMBi8
+LxlC+3VYM2oprKcTZV0xq3irWKuXadICguFecN9wL2zdyW4lZmlbXbFywsqJZuvZ/a6tUEql1kYt
+3h3sLkyRKLUmxHS4wIQIFz+PATP33f+Ovm53zARMEU+I21mIRshahjZDWsjphlkfcjM95mZ6yCEf
+WXQiA9IKVgubF06vfYb7LrRfffUz3J9eYasnqlsEtamEyM4huC2Bpz62qwv/ENsWFQGvE9ii+xdx
+3vfa1LjwQAh0a716gPO7xaJ03Jv10v7BYDAYDAaDwWAweH8xhPbrMDNKF9rr6R6rhVZDgAp+DuiK
+YVflvGq7p9ZwtK2FwLbtRCsnWjvR9vFfbaPWLrSrnd1ssxDQdhbYjqiFg94FqUhoVtE+Xkx2+el9
+DreigLlE6bgZeEI8AxPYQppuWLrQPk4POeQbFp2YBLwWrL5Gq6+xvvaL3L/66Wcc7bWsVDNcFFeP
+pPFele7JMRWaGA3HmmHWRbaHYJakaJI+fozLDDVgT4dzNIR6i3241XiIHiBXjVoqrVSs2hf+AAeD
+wWAwGAwGg8HgPWYI7Y6799sQiGaNVutZZLe6hUssPZAMQyggIbRbjRFfVjesbLRtpW4n2hoCvJaV
+Wk+UeqKUlVIqpTasCc1CcDejl1eHK6zJ0eykxFmYingI7my0BC15nxRm537yVj0y2hp9xFaEk1mO
+KnLmCCinNrysNL2P0LbtFer6Cm17lbtXPsv9K5/n/tVXWO/uKNtKbbWnjsexuHqM+VLHe294OO+9
+TNxad7VDaCuOoyBxUcHdu3MPzYxSGqIFVe9OuCOuGNrfg/ZZ3oq50IahPRgMBoPBYDAYDN6HDKHd
+uQjtvf+6XVarWK0AqMRcZxEnxsU2hH2b0oX2Gm72ek893VG2E2VbL6ts1K1QSqM2oTWhVjCD1vuY
+zZ08QZ6ENEEEbPs5tNzUUHWqWne2odvaYSA3j95uIUaFidMmw3LD5wql4GmjiVI8RnaV0+fP6+7z
+n+X+1Vc4vfYa6/09Zdtozc5jyZ4R23sJuNJd6t6bbZfz6NZIpEgetyuh3cPfamtQSvRkq6Aa51pV
+ME+oJIzUnW0/jwMbDAaDwWAwGAwGg/cbQ2h3zkLbvAeg9RC01mit0nqPtqPd0e7p2jREGlZDjHsp
+WFlp10L7dGI7rZR1ZVu7yF4LZWvUGiK7FGKudC8bN4xpUaZFmWcJoX3laFeJZO49ofuSXU6EoLlE
+a7SAKEhybDZsNnxreKmYrlR3kjUMZ737HNvd59juP8fdK5/j/pVdaJ8oW6VZC0dZAPUrsc1ZZPve
+YX4ltFurIbbFSZIRjXT1i6tNjPmyitQY75VzX5KwLrLF4yTEPG5/tvR8MBgMBoPBYDAYDN4nDKHd
+aa2eb/dlfT0bhqbhaOMIFaiIVKwUrGzUstLWlbqu1PsT5f6eetr6KrS10lajbY5tQitO3aCWnjLu
+RvMQ3H4ADqCLkrKcE8/DTfdIQY9ibGSf7MVuOROCOzmSDEmN1hq2FOwQrnsDStugZLDGevc51tc+
+x3rXRfbdHdv9StkKpVqUuEceGRCjtvYYs+hZ74npsjvvl8ftnBZuNJOzkx0IZjEKzJuRLPVpa3Gu
+97p5Dys/etp7GflgMBgMBoPBYDAYvN8YQruzrfcAlO0+xHLZaHWjtQ1rBbfSZ21rr5s2nIJQcApt
+W6NEfD2xnk5s9yfWu5XtbqNuDduAqmjLqAnZIhF8T8/25lgVaH3EmMUP55xKnvxKuEb/ssVML5xe
+RS0h/89C2wVJDZsqlgvOiq3htLf1Nba2UUXYRPBWWe9eZb37POvdK9zf3bGuG7UZzaC5U90pez64
+Rwm4GNC8H0UcV1LQSdCw3nv5ejjrzi60w9VGFE2OXc0Xb+ZI9Z6m7iSM4kaixUUFJ8aYDUt7MBgM
+BoPBYDAYvA8ZQrtTtl1on6jb2sd0RRDa7mrv+lIQxB1hwynghbZt1NPKdrpnu79nvT+x3a2sdxte
+watDTUgVUlPEErKHhTXDmqEVqE6rTm3hl08ekl5TjBTbhfY+OsvNzq6vi3RHm3MjdcsNnSo+K8aG
+rSdsvaOtC15TiHx3Wi1sd6+x3b/Gevcq93f3bFvpyejQPDrSK7szHY66m3fH2WMMuDuqguRIF3fZ
+i+xDFFsoafYBZTE3XM9jvMwNaSHqzZzUDHVDrKEuJBGyQlZFR4/2YDAYDAaDwWAweB8yhHbn4mif
+qOUUbnbdsFrw1ldvDQ4Ra11kb0h3tOu6did7F9nhaIt1Ye2KNkWaI81QS7g1rMWq1WFzrBi1Rqq2
+9R5mUgjiENtdnPce6L2QXWNAdc9EC8Etk2CzYA1cFDt1R3ubQzi31md6F7b7O7a7e7b7cLPXrVCr
+03rCd3WndomsDro72l1k0/PJkkJSwVJks5sL6nKer23Weq97QkQR0XC3reF+mcUtrb+eGWKCWiMn
+xSdFsqI6HO3BYDAYDAaDwWDw/mMI7c5rr34OgLKe2O7v2e7uKesaKeItBLf0Ou4oXQ5/Fw9Hezu9
+ynp/x3Z/om4VDLLMyARKRj2jZLw5batYKjQptLZFj7f13nBrVGsxrxpFVJmSMmdQN5QQrW2PYuvl
+1tLvj+MLh9ldovbcLERsrVhZqes9232OSeC1UVuLPuzTiW09UbaNrVZqs56rpqAatd8WvdHNDKsg
+EvOxRRVJChr92WcR3A+hmdMa1Ba37oSbDSDhXscsccVMevVABNOFnW7QGodlIutEXhYOy/zuf1AG
+g8FgMBgMBoPB4G0YQrvz2qufBaBuG+X+RDmFQ21nZ3tD3MAccYuvu8jGK9vahfZppZaKmJJ1YZpn
+kkwkmUk64dWp6USRU3R4lxpzsa3SrFAtQseqGS7h2uacmLMQ2dtRSl2lRc90L/0GOKeDeYhsOfdS
+h9Cm9cC29Z5yn2g4pTa22mKu97pR1zVuzWnmGIKLhtj2ENsuUTLupU+3tihtTz0MTSV0uV+Vi9fm
+1Aq1eh9l1lPedyv8jGAWDrw1C1FewWrDK4glbpfElI8cDzfvzodjMBgMBoPBYDAYDL4EhtDu3J2F
+dqGu23lZ3bAagjvUXxfZFgpQroT2tt6xrSesVqaUmXQi54msMzkt5LTgzdg0hyh1Y81rOLpeqa3Q
+mlMtepSd6HeecmKehIyQPNbmjlgCi1A030PTehW3uOP9NcQEaUCTcLS3xHYSqjtbNbba2Ert771Q
+txIuNBpCWxUn0r5BMell69UQhzTl3ieuoOFSp7T3YHdHuzm1OluBUmLGdxxrHLRqXFRQDaFd+3at
+NFpx2ua04ky6YJbI6cDx8OA9/MQMBoPBYDAYDAaDwRszhHbndHoNAK8t3FOrqPQmZHGkB2+dXWJi
+zjYe2+4zt62G661JyTqzTAdymkk6k3TBqFRdwxkmnN1mRt1Hiu27J0RnSolpykyTkt3INHQX1hau
+L7b3cu9Plr6PcLZ9n1fdw9O894Sbe+8Pj15v633fMc9bMAlH2wiLWjWTVRCpNAXEEBWyKjllpjwx
+Zcip92gb4Ak3ibLxCrV4zAyv/Zi6I5+S9/cbbnYpfeTZRgjt4rRitKqozCzzA26OL7z7H5TBYDAY
+DAaDwWAweBuG0O7UsgIRdqYKeU7opHhTsARtir5sN8QMrNLqSqtKK4LbitVE0Uj7ThrCc55mRDKK
+9nFVRu2l2utaWEthK5W1NEozzCKFOyclp0TOiZwzOSvJjYRGAncixGly3BuOgRnGpZTcHdQvxdne
+XWdRRTWRHHJSjBYRZy2cZ1Hv4h2qQROBfixLnmhaYglIFuZ5Zl4W5nmO0V7qqEJFwRvW4hy1Aq3G
+7PBWw7mOcDRoupecezjaVagVWgWrirW4X5iY8g2H5SEPbp68dx+YwWAwGAwGg8FgMHgThtDulC60
+J02klJlTJosgnhDPiLdzb7a44a1QNqVuQkmOtYm6JVLvYU4pkfPEPM+4aV/QWgjtrVTWrbBulbVU
+ttp6+FhChO4SJ6b0eqFtiCnJQhSnFEFimF/GZ3OZSd0f2rPCQ2hLQjUBYKJkidLwVh3tUeJO9GhX
+i1HYSCLlxLIoTZWmQhPQCZZlZlkOLIfDpX4do4ggXvCWQmhXaAWsdsFdBWvhYEsX2SLhhLcGrT/m
+TfAWs8ZV5t6f/Yjbm8fv1cdlMBgMBoPBYDAYDN6UIbQ7u6OdpxmdEvOUWKaMYmgPIRO3GKTVhfZ6
+gk0NpVG3zJYSqhrznzX1cuo5BGYv37ZdaG+V01pCbJfK1h1tV0VkL8dWUs5noa1uJDdEDW2OJkNT
+zJ1GHMeihHzXuntyN1diWyQSwjWhIiRxXCLhvCZDNfbvLfZVzWgCkMhpIs8TLQkm0NTQSTgcFw6H
+A4fDTZSfN6PVhgL4GkK7Snend1c73Op9iUTJu0RwOq073WZCTDSLZm9hIqcjh1E6PhgMBoPBYDAY
+DN6nDKG94xUAIaHipBRl1UkiTVslxmeF0BZMo1+4ZSWlKHuWfVwV4H3Gda2VVv28tm1l2zbWbeO0
+baylsRVja+Eeq3IOBUt9aS/3lksNOKIKmkBbf5KELSxyFteOd0fbz73f7jHXOt6sICLn19OU0eRI
+cmgNo8VULRESCdFMShMpg2fwLORZORxvOR5vORweUEuLpY2yKVMqJF1Jmuhd6bh5lIK3XWg7oDEj
+XPoxmtBMdnP8/N5ba5RaWbeNdd3e5Q/JYDAYDAaDwWAwGLw9Q2jvWNu/CKGtwpSEpEoSJ4mgrj0a
+TGjVaCVR00WoisBuybo5rTVarbRq1GI9CCxEdgjtwqmXjZfmtBhaTRbISc5J3KLRty0C4hKiU7v4
+VkWuRTYSJeRdXBuX8vFLGXm424JGULhDSor2nm9NDhr7aeY0EZSESianGZkUZkHmxLQkjsdbjseH
+HI8PKWtl08pGZcswpZWcJlQySkN6Pbsb54sPtcSZFzSceY+QuH0EuBDvWYDaGrUUtq1wOq3vwQdl
+MBgMBoPBYDAYDN6aIbR3dkfbGypOVshZyeIkhSx7l7OE0MaouZd3a3eGrxxtc8O60A6X1yilUbaV
+rXSRvfYe7RqOtjso9NLxi5ut0sW2a5RYO4i27mpH33XMt6aL7BDYUTLuXyiyPQS5iKCEQFcu4Wqa
+CGcZp3mjIWQU0YmcZ3RS0qLokpiPEzfHW25uHnA8PmRNlSQF9cI6OTndk3UmSerH3tPPW7jarSeR
+7+dNIJLO7XLM+zlRLo72NhztwWAwGAwGg8Fg8D5lCO0zBtDFtJ9vVSBJL+fuPrC44BrCT2BXr+dx
+VdYM00aVSmGjlejLbrVRa+mrUpr15ZQWO5t6mXpOu9Duh9dFsp2/jn7rfXY1Et9fVZdj/fj28vFm
+4Zqbh6NsfWTZ2YV3OY/zak4cV4UqTqowNXDrTjipn5sJlQlhRglBnTVhKTOlStYZldyfo6gYiqLa
+Z20nkEmgPx6jyeTsxLv3cy9CEmWeMypCa41tG472YDB4f/HJT36Sj370o3z4wx/mp37qp97rwxkM
+BoPBYPAeMYR25+Kohro7z8vee6I9HtvX/pgbVzOtQ2S3ZlSpqCvqRJl5jfublSgpN6Puqd4Gpfdn
+QwjLqfeHC3sZuCP9uNydtrvWwqVcnPjaYtgX1o+7uaMG1XaxHUtb1GO7eBfiRm3ehb9TqkdZO0Kq
+xtTfh1SgglTp482UloWaBKsJ6ePNshZUJ4QELr0WQFF1clIUZdKE2UVk75cz9rJ3EFLSKOFX5fbB
+kWlOgFHKcLQHg/cjIvJtwA8Qv5a+2t3/6nt8SO8aZsYv/MIvvNeHMRgMBoPB4D1mCO1OBHVxFrOc
+R3lx7g+OILTLdnu8t9tlWXOsNpoL1WI7q3ZO47ZWaFYjzduc6k5xp5qTuoxPV6Xj53Jri5Fbu8a3
+c+DZ7mTHre3C20Nsi+/zsJ3UBba16IHe35hLjAjbBXg9i2xjLUZFmIpRayzpItuaYFVpValFqSq4
+KeKZrEJOhSQzugtt197/7pAFSVOkqpGhF+VzrieIiweIxDzxvm5vj0xTwr2dR7INBoPBYDAYDAaD
+wfuJIbR3dgHdx3ddhHQX2X7e8Cy2z462hVC15pgZZkYjxltJF8khsh2zill3tP3K0Q5VGf3SqkxZ
+SbqXflsEpfXj3HuwDS4haFcztPfHdkHezEG7kG7hXLdmOPtz4/5ajVIbW2ls+20xKjDXRilGK4Zk
+IAFZ0CRUhSJxV4SmpRhvphtJLo62aiInxacMUyLrgZQWks68Xmjv7wlVppyYcianzO3xwIPbW+Zl
+RtNehzAYDAaDwWAwGAwG7x+G0O64+fl2F88htK8ULNBTwsCuRHbbt+eqRBoi1gvc940b7hXzFmK7
+dYHu0RGOKCmFqFxyJongZtRScOul1RLH0Cwc7V1onxPHo7j84nr3/nG6U11bi1C2UpFmZ2FepXBE
+/AAAIABJREFUqnF3X3jtrvDqXdzer5W1VEyEbWvMW2Xb4vvqkAxqabStUddCnQvzrCxTJs8TSefe
+vz0hnjnMMzeHGU0T83zL7c1Dbo6POB5u4dzxfuk3j7s03OwcjvZhXrhZFo7LwmGe3p0Px2AwGAwG
+g8FgMBh8CQyhvdMd47PI7to4aq+jWfrsn+4a3DzSsVvcunOW2CF59x03oOF0oW2192hb77UWTARU
+0ZSZpsw8ZZKG0C7FaS2c7n3U11lEX4ns/QjPjrd7VIebg1i8ZrUIYisVRMLx9ujFvusC+9XXNu5O
+jdPaWEvDVZlLiOx5LTRXtAlShVIadauUuVKmwu3NRL5VZM4Xoe0ZmFjmW47HBxxuHvLo4WOePH7K
+k8dPefToyfmsnYX2PrJMo3Q8aSKlxNwvRMwpMaX0bnwyBoPBYDAYDAaDweBLYgjtzrWjTRfN1xHe
+l9Lx/phxFtmtOd66MPeLn31W7zSc+oyj3Tx6tPdQM0NBEro72lMGDPcYDSYqaIrHRfeD6Jxdbfao
+tt6j7cgeUy4eIW2t9fLwGGfWLAT4qdRwtF/bePW1jfvNuC/OVhzXxFa6o71WxBTp9eJpbdSpUqbK
+NhWSGIdZECaSTigXR3uZb3n08EUeP/kAT59+mA9/6CN86KWX+eDTl9gFtvT3cy20IwgtoUlJPTZN
+/XKWB4PBYDAYDAaDweD9xBDaHSstyqhzw2rDa8OqxczpRHeMrwLQKt2ovgyoFpEYRaWKqEeQmcTc
+aDOjWaO2vmrMgxZR5nlBsnB7mHn48MCDB0cePFgodaXUjVo3WrNujvt5HFeIf6eUyrZFP3WpRm3W
+y8S7z+2GmlNdyKUxlcr0jND23o/dWEvlVCqlRTm7JkGmiTxNpGki5ykuG1SnVUNTxVrDo36eVsFd
+UcnktDDPRw6HW26Pj3jh0Yu8+OJLPH36IT740od5qa+nH/jg1U+ilw7oXj7e55Pv88T3DLfz+x8M
+Bu82IvIY+LeAfwz4W4FXgL8AfL+7//AXuY8F+BeAfxz4lcCLwGf7fv5T4OPu3t6pfYjI/w18FfAf
+u/vvEJGvB/4V4DcCLwOzu+sbPXcwGAwGg8Hg7RhCu9O68LTUaFOjlYalhpliprh1/7Q73F4cr+Fk
+Y7s2FDSFONWemO2Es2zWBXCtlC6yS23olDksB26nAw9vFp48OvDCCwsPH8zcnxKcoLZKq5VG6254
+d9i7q117Wfe21Sjlbr0svQeeiTkgTA651LPYRqT3iIco36qxVWcrRiNBSuRpIs8z8+HIfDgwHw6s
+tVJKYS0VUQFvqBg5hciGhOpEzs6y3HB7fMijhyeePH7K0w98iA+99DJPP/ghPvCBD/L48RMePnoU
+p3avIDj3Z3dXG6JXnv19D6E9GLxXiMjfA/w48BEu6RUL8A8Dv0lEfgD4U2+zj78f+M8JoXv9L/np
+vh/gYyLyW9z9b7xD+zinb4jIx4D/gMh03LE3eM5gMBgMBoPBF8UQ2p22henRUsVKw7ZdaBOu9nmm
+9u5ox6L1de1oJ0Wu/kbbk7/Dxd5FdqW0ynE5cjweON4+5PGDGx4/Wnjh4cLDBxMolFbhdE+1fb51
+CPY9DR13Wo1ws1Ij6Ow8Osxi5jaEI18M8hxu9lxq7/WOMvNSnVItRnpVg5RJKZOmmeV4iLUcmJcD
+xU/YWti2hrmj0sjJaRO4CUJCJJOzcJhvuL15yLpVnrzwAZ5+4CVeeullnn7wJZ48ecILT57w8NEL
+5/N7rojfxTbRR34JqNtHq3FJfx8MBu8KIvIQ+BPAh4nfiH8E+EPA3wC+BvhO4NuBr32LffydwP8A
+PAI+B/x+4M8BPw98APhW4GPANwA/IiLf+HpX+nns44pfA/x24P8Bvg/488T/N37jF3NOBoPBYDAY
+DN6IIbQ7rZS4zRNWClZrlJB7Lxs/i+0oFffiUB3vpeP7rO0Q2nJ2Xt0jWby2KOsufRZ1beE455y5
+uTny+PELPHl0y+MHMw9vZ25vElsp5NMJ0YxTadbCca6G9B5lAVqF0qKcuzRoSCyRcNN7cFozY2tO
+sRgrJrpfN5CY+91nf4fOVjRn5sPCcjwwHxbmw8K0zKRaQTTc8L52QS+9zDulDJJYlgM3x1tKrTx6
+9JgXHr3Ik8cf4PELL/Lw4SNubx5wOB7PInrfz7WbHbO/jdYupfsyhPZg8F7w7wEfJUT273b333f1
+2M+IyA8DPwZ881vs4+PAC4Sg/WZ3/5uve/zHReTH+n5+LSHc/6N3YB87fy/ws8A3ufvnr+7/n9/i
+PQwGg8FgMBi8JaP/rNPKPa3cY+WE1Q1vJRSstavbrkKr4dXwZtCsi0SLGLI+pWqfCmYeTyu9JHst
+Tm1gLqCJeV54cHvLi49f4MUnj3n48BHH21vyckNejuT5SJoOaF6QtIDOuM5IWtB8IE03pHxEdMFl
+xslIWkjLken2AfnmFl2O+DTT8oRPMzLNpHkhTTOaZ0gT6ISkDDmWzhPTMrMcF5abhfkwMy0TecpM
+c2ZeJpbDxOGQWZbMsiTmWZgmZZqUlJScE/M8cTgsHI8HDsvMMsc+UkpIL61vZjEr3Nt5DvlFxNNF
+ttFaizL6fVmswWDwziMiE/A7iF9tf+F1IhuA7hr/80B5k338BuDX9X182xsI5H0/fwL4YeJa4rc/
+731c767v5zteJ7IHg8FgMBgMviKGo92p5R6ANiWsLdA2xOYIPBOgaR/5ZdA8hHa1UNLn2V49AE2B
+dmkArBal2evmbFu4zubh/M7LzIMHt7z44mOePHrAzaQcZ2FSmJYTeb7vQtuRViAp3hpoQjWRNeEU
+pF36wTVP6Dyhc6a0Rts2vJRwi/OETDM6zwiCN5DmkGoX2w1Si+CzQwjtw83CcgyhnebEVDPLIVPa
+hLtwOCTmJTHPyjSHwM5ZEdUutGeqHTgcFuZ5YsoZVY0e9u607051n1kGgLjGxQrbE9Nrd7MvE8MH
+g8G7xtcDT4hfax9/s43c/RdE5L8B/tE3ePhb++1fcvefe5vX+1PAbwO+QUTU3fd+nOexj2t+3t3/
+p7fZz2AwGAwGg8GXxBDanbYL7ZrxesSt4BaBYTQB9Jz67V1ox9dXIhH6lCrHxDHi7tq60C7GVoxa
+oXmM85rnJYT24xd48sLDENjqJCxE9tnRNqQpVMW1ISkjaSKlTPMNUcel4uKQFtKykI8LXitFE4aA
+NbwL7TQtIbTFMBxUIBmkhuSGTt3Rvlk4HBfmw8S05HC0l8zcJg5daIejrcxLONp5UlJOpCuhbX7g
+cJiZ54mcrx3t6GGPJuyL0BaPCwcg4Whbo7V2rhwYQnsweNf5+66+/nNvs+1P8sZC+1f3279bRL7Y
+sLGJSBP/9HPcx44TCeXPnW3b+Omf/uk3ffwjH/kIH/nIR96Jlx4MBoPB4Jckn/zkJ/nkJz/5tttt
+2/YuHM1XzhDaHZ0ibDZNik6KZkWzRAatgifvc6r7amDquIaodiVEbk8bV1Wk9z+rJByJMmgXJGWm
+ObEoLDe3HG9uOd4+4HD7IAQ2hnhF54U0LaR5YVqMWTJNZ2Q2lmk+r7JuTPNEmhLrtjLdHplvj0y3
+R9a6Md29RrrLtFo43C4sh4lpTliLQLfaKrXFfG/R6M/O3Z2eZmGahZwd1QYCKRnzDO4x1fpwSBwO
+ymER5tmZspG0IWqIVKASlaT7TDTj3NQu0dcdZQDn2HEg5pF7T30TolrAsfPXMkKBB4N3kxevvn7D
+JPAr/vqb3P8SfMlXyBy4ec77uOYNS8+/Uj71qU/x9V//9W/6+Hd913fx3d/93e/ESw8Gg8Fg8EuS
+7//+7+d7vud73uvDeG4Mod3JS5yKNGd0TuisyKyIaAjtvfeavjJ4AtMQ3PY64SiikBTEUQ1Bah4+
+rKbEJIrPmcPN7XktxxvUG+INrJLmw3nl5szZ8NnIDY7L4bzKupKXiTRlTuuJw8Mbloc3HB7ecr+d
+mA6ZNCtlW7k5ziyHiTwnylpxGrUVSi2YN1Anz4k8KbmL7Gl2Ug7hDI2UGssMKWVUEsuSWBZlmYVl
+dnJuJC24CMIGbJivuBeceM2LYN6FdkekJ6rLuXtSxGMuuV0EtogNR3sweO/4cv/x7eOzfhb4576E
+5/3Cc97HNW85q/tLRUR4+vQpT58+5Qd/8AffdLvhZg8Gg8Fg8Cwf+9jH+NZv/da33e5bvuVb+NSn
+PvUuHNFXxhDanTTH3246J9KcQmxPGiJQBBfHuyB06ZXOCSyFq+3qMep5F9oqqEsf+ZUAieegaJqY
+pglNSwjsmwcst7csN7c9eK3irZDmBe0rGzA5aiFRbw833B5jbetKmhIpK/Np5vjCA46PHnDz6AF3
+2z1pViTBekoc58wyZ6ZJiZZn60J7wzySyNP0Ojd7AlWL2eA4KTlJhZlESpl5ziyTMs8XR1tTjZ5w
+KbhvuG+YF9xDaJ+Hj4sgeo4/j7R2efZnIwpiINq3EUPFoh9+MBi8W1w7vx8C/spbbPuhN7n/M8S/
+8AdfRH/1m/E89vGO8fLLL/OJT3zivT6MwWAwGAx+yfHFtlXN8/wuHM1XzhDancPtQwCW4y35cETn
+BZlmQHFRHO1C2rsmbPikkBVy9HFLEqR1cd1Ln7X/1/d50ECaMjItzMsNh9vjOdU7LxPeIqDMqqHL
+zHRcWG6PeFYMoTkgyu3xyM3hhtvjkbJmJDUkG/OaOT56wPHRLcdHD5jWFEFnqXK6hxsVliQkFWQj
+erSt0ayBKCkrE8p8SCyHzPGYON6kcJK7uFXV88zwlDLTlJhzYpqEZXHyVFEtmDmqBU2VlIyUDE2O
+ahfP2qd4ARd1/XrxHDUE8fqxmYrQs9QGg8G7x/929fU3AH/mLbb9hje5/2eAfxD420XkJXd/uxL0
+d2ofg8FgMBgMBu8oQ2h3Hj4OA+YwH1gON+TDDdIDw+hCGXfcjAiudaQmtCa0JZIn1BSpvdy815jH
+iKp9fJXh4uEY38zk2wOHBxPTMaELyNxD1tRwMdJRWOrMDUemknGJ0nNEOMwLyzyxzIk0ZywtyHTk
+sCnLg4XldmK+VXTOmCxIuuF0gMmMyRraWjjE4rAL6ARZFVLmeJy4fTDz4NHC7e0SIlu9O8mCSJ8Z
+romcpkgaT8q8ODlXRFZEouR8XqB5Yunp5JFMrqiG8x/SOtzqmFW+/1TiwkT8R84l+SqgCqrD0R4M
+3kX+POFqPwZ+O/Dvv9FGIvK38OZztH8U+A7iytq/CvyeL+M4nsc+BoPBYDAYDN5RhtDuPHz8YQDm
+PHGYZvI0o2mKOnEi1AwaMSa24WrQMtIy2jLaUghvTZjU/hwHc8z7TGg3XIU0J+bbmeWFI8vtHEJ7
+FphC9LqGkM8HZfEJS0damyMZvJdaTykxpcyUlFoSMs3kw5FaMvk4Mx1iv6nEXO08G6ejIusK6wpr
+O/c+Iw7qpCTIpKQpcbiZuHkw8eDRwoMHB1QMFUdl76veHWklpUzShKowT7vQPiEKOdcITpMutOc+
+ZzsLmuTSn+1xvtzsLLS9N2nv6eMi2gU+pCRDaA8G7yLuvonIDwDfCfwqEfnX3f37rrcRkQT8QSLl
++4328SdF5CeBXwP8GyLyM+7+w2/2miLytcDf5u7/xfPcx2AwGAwGg8E7zRDanYePXwIgS8ymzhrO
+tHRnOqZ3NXxPz9aG1Iy0cLS1JnTTXlatMdHawdtFZDe3KM+eleVm4vbRgcODmXxM6OLIZGeH2TGS
+K0uakOWIu8W+UzjBiqBEVnduKWZeF2htikC3KQLQcs2RWn6Aw0mpr0KlUep65Wh7d58hzQpL5niT
+ub2defBw4dGj5SyyVfwcYrb3o6skRFJ3uI2U6rkkPKVwtCUnloMyLZFonrOGWD7rbO9C26+Kx3uZ
+fv9Oepr75XXepQ/HYDDY+b3EXOqPAr9PRL4O+ENECvnXAL+TmLf9U7x5+fg/A/xZIsX8h0TkjwN/
+FPjLRDDZS8DXEfOyfy3wfcDrRfLz2MdgMBgMBoPBO8YQ2p3KCQjnuplSXVGRSLruehQqLg2n0trK
+yU4U26heqVZp1mJUVqm00mhbrG3bqN4ggSYlzUJeYDo4kjcad5zq5/FtwlrBWsVaoXqhUWhaoqxa
+w9V1icFWu9A2bViq4BXRBtnwVDFJkBqaK9kNM8cnsEyEtWUh5USecoStzRPMCeYuhhdlWoQ8C0nC
+UFd6b3QPIhPZ+6ctiuy1ISpdwAuanUkFMWXqaeYp7W42vTXbL0vsXCreX6AnkPdud3VUPQLURhja
+YPCu4u6fF5FvAf4k8GHgn+7rvAnwA8Cf7rdvtI//U0R+HfDHgK8FfjPwW95o074+907sozOSHgaD
+wWAwGLwjDKHd2YW27ZXUvTtb9iRsd8LRbrgYrW1sds/mG5VyEdutUWulbJVyKtS1spYSYWMJdBbS
+AtMBpqMjuYTQLhmThLWKWwuxbRWjYRql6HsAWYzNir8ejQg08xTHhluEn6niGnOoNRkJYzKwCVoG
+TSApws/ylJlMYMr4lGFKz6SOT4vEOPEutC9/mnZnm+uxW62PQYtLASkLuJKk73N3ss9haPtVjN3J
+vxbPMUn7IrYJR30X20NoDwbvOu7+cyLyK4HfBfwTwFcBrxBhaX/A3X9IRL6Ni8h9o338FRH5VYQ7
+/lsJ9/uDxOiuzwB/Cfgfgf/M3f/Xd2ofb3WMg8FgMBgMBl8JQ2h3KvfAbrA60sdNYVdfEyFlLoa1
+QrET1VaqV1oX2bvQ3raNbd1Y7zfWUqhukASdhLQI+QDzwZFpo3LHWp2GYhZ94G4xaxqJfm3EUeki
+u5dlG12janeC1bswjbnfLoAKkiFrzKeuE2gOkatpd7QnsivM+RlH+1psPyO0gXPvtNDnWRsXBe7g
+0svJc1QGaGI+O9q9/Ft4RrRfLh3sP4mIFg8xvyedX0S2jB7tweA9wd0/C/zuvt7o8Y8DH3+bfThR
+7v1Hv4Lj+LL34e5f/eW+7mAwGAwGg8HbMYR2p9jnu9bbk6/9WcHdk8ZdQmC6NWrbqLbSWqFYpbZC
+rVE6Xkpl3QrrVijWMAxPIJOQJsgz5AUkNVxWioE1uYhs7/3axG0Ia+n/65XZ7Dp1F9ix3fnW90fC
+PhaN5HDZBbgqmhKaEimBJwUVXOmOs4Ma0sPZdgc73OrLfY7vR9UT2eUcZCbE60ji/NrnknHZR2lH
+GJt7F89n/RyueDzHr0T3YDAYDAaDwWAwGLx/GUK7s9a/GV+chbY9K7R39ddHbLkZ1QqtVVor1LpR
+aqGUQqm1r8ZWG1WMtovbScPVnmCaIWXDpdIc3AQ8nOwoA+9l1d77off/+WXy9F7efhbb1/cDuOCu
+uAnN6Knp3kdl9SRvTV38xqxwJ8LbzCvmlWal39+T1Pde6rMLfXlF7/3VkdLuiAtiEZi2j0Y7X8jo
+BytdfWvqLryfB36xX15ALhcTzq/rZ0U+GAwGg8FgMBgMBu8bhtDurG0X2ldCcJ+XfRbae2Nxwg2a
+tXO5eKmFUgu1xColxPbWGlUdS+BJkNxDyCYhT6DJQCpm1r3hcIt9d4x9d7UlyrG9l1NzqbqWPmxa
+zquHlgF0ke2mmIHt7+ksshWVhOqVR+2Oe8O8dbGdzunffg4ve73QhmeFdve1XeOYvXXx3s/v/oxd
+aPdss/0Rv7pwAHsd/P69XQT/1asPBoPBYDAYDAaDwfuBIbQ7pUUo7e5mu1/EpJxFXwIyeMZNaWY0
+M6xZLxvvpeO1UlujNKO2cLNNpPdLKzrpRWhr9GEbcukD773Zfi1o/SKfpXdK7762+H5850Jx3HsP
+tBtuirthTjjKe2/1ldjeQ8n213TsLLSbp9jnLqQlzpHIXjp+JXW9y+OuiXUX2paj/9wMN4sLC97X
+9cUF6Y573/Nln3Let/TX3oX4pW98MBgMBoPBYDAYDN57htDuqGi4sOLdo73cPuPcdjd57y/W3rcd
+I67Ym477g4KrELOxYJ+RpRqBYDmlnvd16V32qzLsEJtdRvZAsKs89Mvd54bsLwzQPXvcLrj3ed5m
+4cS7sct5V8F747fvQWdX7/ziJYNbF9jeQmbL1Xk5e+n9+Pcqb5xmlVJX1u0OSQvNjNoqqWzgMZ7M
+vcLlqM5vy/fSevwssneh/fjh06/shz8YDAaDwWAwGAwGz5EhtDvSQ7c4SzzrzvEu+CzKxrvYRq4E
+8jOu96WXGxVI2vVxL49WjbFamkgpRe/xPuKqq2ZBr2TufruniO1iO7aF6x7t6+dwcZbPYtVxC7Fd
+rYUb7x5meRfLjkXgm3C+RfrYs95zbV2wRzL6pQxdRUCsn8ves70fizvWKrVubNsdyESuIbJTXvFd
+ZHu99Kd3we1GzAC3eEPSw9728V6Pn89HYDAYDAaDwWAwGAyeC0Nod1TCOd6dXBGuysdhHzV1icp+
+VmRzLsfmPJkqxLWcRfY56bunfaeUY//XZeKi7Gnbz5ZQX3de78XS+/H4OThNzup6Zy+/DnfY3C8l
+77ujLXIlri+Otj+7h91gprljLfbhbvF+1DFRVBS9MuH3Cwbu3dEuK9t2j3um5oKmFdUZp5wd7Ugu
+NyDS160LbYva92cucAwGg8FgMBgMBoPB+40htDuyz43y6xRt6LXUl6+vvnLfe6KdJJCSkKZEmhPJ
+QQ3UHMmCTOBZmKZEzomclKz7s8NBdxTZb+UideXcoy1feGz9VoQIS5N91JZfhK5fie1d+orjSoSz
+TZBEITmeBE/Sx3vBZZ6WnPPI3KGZU1v0sic33ENs70UASfZYtnDcHaO2ja28hp4+S60FTROqE6I5
+ytC9gvc54ti5Vz7cbMf8Wmj7ENqDwWAwGAwGg8HgfckQ2p1dlO6y8uLiPhv0tbdfu0d/9p5Orgo5
+K7ZkWnOKCBkn4ZAFsuBZmKfM1IW2qobA9itf3LX3hz8rJK9yur8gZftcRt5FtnzBY74b7r06vbvv
+iRg15kLOgO5XB0CzILqXyeszB7H3TFvbR6HJWcvLdYm77K8e56jWEyKv4AYp3SGaUc2I7EK7XQLS
++ng1N+8p6CG0pTv/u9geDAaDwWAwGAwGg/cbQ2hfsfvAFwf52bLp0LJXUWTee7ndSAL/H3v3HmZZ
+VtZ5/vuutU9kVSGCWBRVJWKr6HjBC5algKAiNKIIXrunVbQUVBydfmwvra3TCqVP2w6D3Y6Xebps
+bCy151Hb9o4ioCi2jVKC19GmUURuKRSgUJfMiLPXeuePd629d0RGREZmRmRGVfw+yeaciLPP3vuc
+qqjI33nXepcPCd8YqBXWMAVty9Ft3AZjYyOzscoMQyLnWG+6tg1fNkTbPnDb2PbltoZs21/BufXu
+6ZX5/OVUzSaRzaiVFqgTbkbKRko2zbVensPd2nDuNnd6udSW9Rnk8/Pi8UIZN3Enqtm2wiy3LU3h
+urrjdRmymZZb67PnzWJouYK2iIiIiIgcRwrajfkcbJfheuqcjWHemqAthmZnWuE2G6wyVo2KsQJW
+OEMbOp6ykYbExmpoITuah9VWgzZfxuN5+Pe5NfYeaG3b7Xb9+dAX32ojy0nJSDmRhkTyGDLuObVG
+Y726nkg5KtrnBO3FCPu9tj5ZfXkN7hWvm5Q6ApvRaM5SLC9GWoTsVsGu0d28N0CbO7vFnPZYBqwi
+IiIiIiJy3ChoT3bOY14M1o7OaNOwcWgtyZJBzqQBkjvJoCaHVFgDW+4MpUZozRG2+5DxXfPx4ti+
+uBfV6x3Vamvzsqe55XscK7XytQMJhmFgY+X4KSMlJxXDRrDqWFvz2twYkpFtuZDY8v2I+ejVE6la
+a4YWwTyq4L0p3DIgW+vH1oZ+EwEbN9zSNAe7B+1aiLDtfdh7b6vW53D3pmkiIiIiIiLHi4L2ZFEx
+9mVAbLfnFHUjYFoGW0WjsJqgDgapsOXOZq2sSsFSryRbVLOnSvG5oX77OZZna1vv6N0qxr2i7S3U
+zh3K+9NTDLW2hCVYDQO+YVAzKVXSCJYcKxVqiaBdE0OKintaHqtda0qQcmIgR0M4M5LFkl49aM/v
+W78uorkZNoVr81gWDLcWtKOK3Zfy6lXtfiwzqLVQ60jxNV7Lwf7RioiIiIiIXEYK2pOdi1k5O4vO
+EXb7kG0iiOZMGhKk6NZNTVga2ayVjVIYxjH2bdXenNuwbNsZr70tKbb9fNP9Vk23XmU2mzZgapa+
+fP60xnVKrXubscoGq0TygZQq5BIN0EagZqwUsOiInnt12ufj9euMx+ag3Tuf98p2X4LL+wcWLWxP
+w8udGDbewnZt3cXrYsh4v2+L96DUkVLWjGVNreMF/RMWERERERG5HBS0m1rnYch+ToG5Rb3FUld9
+6HhOkNpEbTfDq5FqIvV52bkHY5iq11ParFOjL/C5c/eOhG/n3NnO2xN9x4UbfRh3LNfFQEuvfVGx
+EsuL1RLVZMbWnI02sNtIPs+0jv9vQ+hTPDZV3PvQ9kRrUrbjg4t5sjvzUPJ+2+ZnLwL99Epsnmce
+36tMa5jvN/5eRERERETkClHQbnaG1L1SrS3/tApuzuBm1JY7rc3JtmxtqPcchJfLV3lbX9p7+N7l
+3Lt3Fz/3mreF7cXOqQ1xz70CPjjmTnbHPVF9pFQoteI1UTGKx3LYqR0qVvemlbbb2uGtu5qzPfDO
+89gXcXn6kGFx20J2bc3Tenfx6SVt68HmU8wnebumNA2jFxEREREROU4UtJte0V522T63sjwvWxXz
+kttwcOaQXZNjJQJ2bDF+2qcK9vaNHr7Pmadt55x/qQ/Z3rWavS2s9iW8EskSyZ1M1LLdE7VCKVBz
+odYI5LHI1xyyl5nX23z1ZLS9dlx1D9XW54r74vvzxTltHvaiU/k5M9andunLkO4x590MPO/9BomI
+iIiIiFwhCto7uC9D4RwvZ72enZjaa5thqUXECqS5qp0Gw4vRxmbTK72x5NW8NvQ8xLp1Gt82QXv7
+JfRr3FmEX4ZtW04Cn8au93nd3uZS1+nDgmRGai8nYVjvY1YdLxW3SqVQKfPxL/i9ndddHsRUAAAg
+AElEQVTFXobs+H6fa26L/Zfv/vbJ67ZXmV9EREREROQKU9Bu5uBo+4TIOWRPTciIZamqxyDwiuNW
+ITlpMPKQcAMvMdO4L8s1zXveZTmxOVnbPGzc+u6+vci7sNtQ8v5UiCXI6ujUEerolDJSaqHWglPo
+DeAixDulFMb1SEpOoVAZKYw7jsp07XvNFV9+XjGH7X6dwGKu97zq944nW69595Z0O/cTERERERE5
+HhS0m+1BO267bRG4VVPNE7SqdK0tZHulmlOtxrDxbORVBO0+OHxa/sp2Cdi9vfd8KYvFveYr6U3E
+dvYtXzZU88Vw69LuVndqmbexlAjaXtqa1JWIulEur6VS1iNb5lTWlBa0twf6ft7lPPT56/mDgj6E
+fvm8xQudAnTa1nSO1vSsj+iPufF9QLsmaYuIiIiIyPGjoN0sg/Z59mxBL9G7hkc1O/64Lyra2RhW
+mQpY22+5NrUvq9k2B+N2QUxV7W2B2qdwu5tzA+98Lqs1KtljVLYjaI+4l6kp27KiXUtlHEeqVYqv
+GX1NYb0I1/NrWAbs+PChz3mP/+svZ/ul99EDbTa4tXW1+/dtMQKgjWtPFsuKxbrd5/lHJSLHnpnd
+AryI+C/DB7v7m67wJYmIiIhcMgXtc/RwuxebhkBTW6AsLai2arabRzV7SAw1gnbfck6xzjRMTdB8
+URGOkLuoqm/L2DEH/NyK8vaAXWudbhOJ5IlExtxibnSJLt/F67RVHEtGHgZIxjBE8K3V8bEwet/G
+7fPFFxfii/Wyp5EBPSi38fJpR0O5mGu9+PCih+3pOfPz49tt36mqLSJyfNx9991X+hJERETkGFDQ
+buagu6wkB8PawG+bHnIHrxUvJTareHLcYiNBXiVWDFQsNo/maCnNVWbvVXBvZ7B+xmWIjf/z2u63
+fVmE7GUlebmZG4kcQZsUQ97dMDfGHrI9ZpdbMnLKZHJ8GGBt2W0qYy2MdWRdxwi/TBfb3qOleZ57
+WgblHqhtewU7AneaH1tUs+egzTyMvIdzBW0ROWbuuuuuK30JIiIicgwoaO/Ql83afx+iwXiraNcy
+QvLo6UXcWo4h4oOlFrSj8XhKsa411tfU9hZ0fT42MHc/ax8CTH3TfO6fxty1O6rYEa5LKdNmnmKo
+dQvbyXJUuS1TaoTsXtHOKZNSiutrTd3cK2UsjLWwLoV1LS34pu1BuF3zVMVmGa6tzU2P194r0snm
++1PgtrQtZNO6pGPbP4TQuHERERERETmuFLSbZUV7+xJfy53mKrPXc+cl90qzG5AiaGcMGxyrGatM
+S2hFOC7Uto52ndLzoglbP+YiXC9D9nIY+86gXUuJijZgbmARnrFYOsx7LzFvy5B5IudMzpkhD7GU
+lxeqG16dRCYzRKM3s1bx7uG4D+leDveOr6eAbWCpheu0M2gv52Yvl02jhXZv9/2cyrmIiIiIiMhx
+o6DdbF+DeroHU60ZeuOyc5avwqJC3Sva/altXnFKhuVEGpiDrlfGUqeQXZdrXrXz9zBfY/z2tpXA
++hzlaZmxVh0HnyrH0aU7k2xoW1S0I+TGnG1LEbSBCNlpIOeMk6ieqV7JnsmeKazY8I1tleopWLc3
+bnslm6kSPQ8fXwwZn64/TcPBpxdvxrSkti0+XbDFhxHK2SJyfCTYZXlDETm2Tp8+zW233cZznvMc
+brjhhit9OSJyQKWUfvdYL0GkoN3s9pcjm3qR9f+fq9fmUfmeV6lahO3FNO++zJfXNLfcXgzJ7iE7
+GqNtt5xrzSJk4xGkk/Vh3sxV73bdliK89nCd0tC6dedpqDaesFxJNardOQ8MOZPz0I43z9+urZrt
+raI9Va3p89rb4PFtS3H1N9IXQdy27c+0JnZ/I21e9MyYm8XRJ6jbYhi5iBx3ZvZg4F8Bnwt8EHAX
+8KfAbe7+cwc8xgcB/wL4x8AjgAy8Ffgt4Ifd/c8PcIynA18H3AQ8AHgL8EvA97v7283sje3YP+7u
+z7qQ19jki3iOiFxBp0+f5tZbb+UZz3iGgrbIfcgiaB/r370K2pM5Utu27/T7LSxOw8Rb7doMN4sm
+aEw170WVlxhGngEyeKEmj2HZZZyXB/N6zhWVNvy7dxGPJmaAGzln2kEjuC4+KEhm0Dqbp5QxG0ip
+h+1ePU7gTnJrXc5bI7ScyWlYfKjQ5o+nvjGdc6po017oYgj59vfV5/dvW6hue8ypevosYjFrnejX
+bu0IfRj5tkOIyDFkZh8JvBy4gfk/qaeAzwCeZGYvAl55nmN8OXBbe97yP8sfCjwSeLaZfae7f98+
+x/gR4H9rX/ZjPBL4FuCZZvbZzB9lioiIiFwyBe1uHrE8VaWngeNt7nWE4UTqEdDasl1tmLi3tbV7
+ydV6GkyOtc9b3InAanMsN3NSq57bohxsyaKiXH0eMk50DM8ptfA8L3M1fUiwbc50hqmKvUymy0nf
+vQodYdpTaYG3zuPVE4t50rYIujsntM+Rev6WL/4GO6fpbX+jnceJTyF/sfhZdGenTvPBReR4M7MH
+Ar8BXE/81P808BPAO4APB74J+ArgUfsc42nEGtsQlfAXAL8JjMDjgG8HHgr8GzP7e3e/bZdjfCsR
+sh14M/BvgdcQwf0z23X8HHDNpbxeERERkSUF7a6P8F9kOLe+7NbcqKyvlx11Vqca1G1zhlO7G3OM
++7ciAM/zsKdq8fTUHoxjne02kRv3NFV5k9sUtKeh49uqyu1Y07Bumz8AsPk4O9ftngrEieg03uea
++7wfvXnatlHfO+v/NoXpaUm0/npZNHSbwva2q2bx0UYMW58C9lwVX+6uwC1yrH0X8HDip/3b3f35
+i8f+yMx+Dngx8JTdnmxmA1HJBrgbeLy7/9lil1eb2c8DryIq5i8ws//i7u9eHONhwPPaNfwV8Bh3
+//vFMX7PzH4deAWwgSraIiIickgUtLsdU+nngctzRTVq2W2+cAuuU0V7ql/Pk5NtcWzrQbUS4bN1
+CZ+qybTGZMlifnXafkFGC9pubXmu1s2bNBeUt61dHZtj7ZQ2n9rnoGu2HPLdK/UlArEtxnIvuoAv
+51z3d6sP/d72/s2F+Phem0e+LcBPl97ePbNpybNKzBGfK+dzsDc3lLNFjiczWwHPIn78/3RHyAbA
+3YuZPRt4A7Da5TCfD9zYjvE9O0J2P8abzOxfAj9FVKS/Evj+xS63AFe1Y3zDjpDdj/GqNrT8Gy/s
+VYqIiIjs7Vh3aruczPI0zDrelsVmO+73LWUsZSytsDzEbRrO6fKdpv0yluPrlAdSWpHTipRWpDxv
+eec2rMh5gzRskIcNct83rUh5wFJsqW22Y4vv9WtI7XUmSPE6bPmaWgXcW0ifbpcToz21YfJx6+R2
+2zbPsZGhbz4Qn+v093fx2PT9He+9t/2m29jPGMD68fRZkcgxdBPwfu3+7Xvt5O5vBV66x8NP7rsx
+Dx/fzX8B3rPjOTuP8U53f8k+x/iJfR4TERERuWBKKY0tCipTrbUPr27VVJtCYWsqliDleZ415pHJ
+p+prP06dh427RcXZEykNi0nVrYFZr2jbXBZ3ooKbfK6b9+d5H0C9raDcA3EPytCbs7l7e7ovjm+9
+zdjUsGwart0rz71hGUQ43zaOfHnync3OphZmzPVtZ/HyFq+nf6eCRZdxmyra/dE+bmDZrVxEjpmP
+Wdy/4zz7vhp42i7f73O3/8bd37XXk919bWZ/BHw65873fhTxH50/Ps81/Bmwxe6VdREREZELpqDd
+xHTAyMI9ZoZl2I5qqjFgKZG8NzCL/XrY3jFgun2vtqZgCWsV30R0GvfFsOw+P9uS9bP2HmTMh7dt
+wbeP7l6GVfc2HLuF7eV4dts537k/3eNa+lH7n+nY/dypBW1rYdtbnF6E7Z0heJqjvnhvpn1s+VzA
+KuZ90Ht7jxYBPv4kkoK2yHH1kMX9d5xn37fvcww/wPMB/m6X88JcVb9zvye7ezWzdwMPO8C59uXu
+vPa1rz3vfjfccIOWExIREVk4ffo0p0+fPu9+6/X6MlzNpVPQbnrQnqq4Lb3atn1yq2rHZmk5J7sH
+yHpOoPTWrRyr4AmzCuR5HvhyjnVvhJamWm+/rJ45t/Ug27VzT0//06cAfZJ4z8s+V7d3Pq1fue9o
+hrbsQ+YJt9yq2mmXC+mV9uW7199L36VCzbbvuVf67HI33/awkaagbaaZDyL3AZfaYOw+16Dspptu
+Ou8+z33uc3ne85539BcjIiJyH3Hbbbdx6623XunLODQK2rKrHUtzi4hciGXTsYcRHb/3slcV+d3E
+Z2wHqTJfv3jOzut4GLEE2J4sPrV7v/32OYCpmv7Qh+57OiD+MvHCF77wEk8pIpdia2sLgKc+9als
+bGxc4asRkVLKgX6H3nnnNFBt50i2Y0VBu/mCz32hxiGLiByOZYfwm4Hf22ffm/f4/p8DjwU+2Mze
+f6952m0ZsEcTle8/3/Hw/0eE8I8/z/V+DLGu9qV8vDj9Dln8BUBE7gP0Mytyn3Ws85uCtoiIHLbX
+ENXkBwNfBvzAbjuZ2QewxzrawMuBryZ+iX4l8II99vsnwIOIkPzyHY/9JvAk4Foz+yx3//U9jnHL
+Ht+/EJtEWK8cbF65iIiIXJzriPmrm1f6QvZjO+fpioiIXCozewHwTUQA/jZ3f8GOxzPwK8BnMreH
++GB3f1N7fAX8DbGW9nuAJ7j7n+84xgcCr2r73AN8kLu/e/H4DcQ63RvE8PXH7ayMm9ljgVcwdxy/
+3d2fdclvgIiIiJxo6iYlIiJH4buBtxAh+vlm9p/N7DPN7NFm9r8SAfkzgT/c7cnuvga+hgjgDwJ+
+z8z+tZk91sw+ycy+kVg67Ma2zzcvQ3Y7xmng1nYNHwa8xsy+1sw+0cw+xcy+h6iCvxV4Z3/aYb4J
+IiIicjKpoi0iIkfCzD4KeBkxT3rnPCoHXgT8brvdVtFeHOPLgNuIYdm7HaMA/9rdn7/Pdfw/wHP6
+lzsefgfw2cAvAA8H/oO7f/1BXp+IiIjIXlTRFhGRI+HufwF8NPB84H8CZ4k1rX8L+GJ3/6q+K3ut
+Vuj+k8BHAP838BfA3cC9xFDwHwUevV/Ibsf4OuBzgZcC7wLOAK8n5o4/2t1fC7xv2/09F/NaRURE
+RJZU0RYRkROtNWV7MxH2v8rdX3SFL0lERETu41TRFhGRk+5LFvd//4pdhYiIiNxvqKItIiL3W2Z2
+DfC+7v53ezz+aOC3gQcCd7j7J1/GyxMREZH7Ka2jLSIi92cPBf7SzH4ReAnwOmLdzRuBzwKeBVxN
+rH/9TVfqIkVEROT+RRVtERG53zKzDyLW43bO7ThO+/4WMTf7P1/OaxMREZH7LwVtERG53zKzAfg8
+4KnAzUSF+yFE5/I3EsuP/bC7v/lKXaOIiIjc/yhoi4iIiIiIiBwidR0XEZETz8weYWbfb2Z/aWZ3
+m9m7zOzVZvYtZnb1IZ7ni83sN8zstJmdMbM3mtlPmtljDuscIifJUf7smtlzzawecPvUw3pNIvdX
+ZvZQM3uamd1qZr9mZncufob+0xGd84r93lVFW0RETjQzezrwk8D7EnO2tz0M/E/gae7+15dwjquA
+/0o0YNvtHBX4bnf/7os9h8hJc9Q/u2b2XOC5uxx7Jwc+w91feTHnETkpzKzu+NbyZ+t2d3/WIZ7r
+iv/eVUVbREROrLa8108Ty3vdBXwH8DjgScB/JH45fxjwq2b2gEs41YuYf9n/FjFv/JOAZwN/Rfw+
+fq6ZfdUlnEPkxLiMP7vdo4CP2WP7WOCOQziHyEngbftb4KXs3qj0MFzx37uqaIuIyIllZq8EHg+s
+gSe4+6t3PP7NwP9F/KK+9WI++TazzwBe3o7xy8AX+OKXr5m9P/Aa4BHA3wMf4u7vubhXJHIyXKaf
+3ami7e750q9a5GRrP1N3AHe4+507VgY5tIr2cfm9q4q2iIicSGZ2M/EXdQdeuPMv6s2/A/6S+MT9
+G8zsYv6y/c3tdgS+3nd8wu3u7wK+rX35YEBVbZF9XMafXRE5RO5+q7v/mrvfecSnOha/dxW0RUTk
+pPq8xf0f322H9sv5J9qXDwaeeCEnMLP3IYayOvByd3/bHrv+PPDedv/zL+QcIifQkf/sish903H6
+vaugLSIiJ9Xj2+09xBCyvfzO4v6nXOA5bgY2djnONu6+Bn6fqL7drOqbyL4ux8+uiNw3HZvfuwra
+IiJyUn0k8Yn3X7n7zk6oS/9jx3MuxEftcZz9zjMQTZxEZHeX42d3m7Y80NvNbLPdvsLMvs3MHnwp
+xxWRQ3dsfu8qaIuIyIljZqeAa9uXb9lvX3f/B6JyBvCBF3iqhy/u73se4M2L+xd6HpET4TL+7O70
+5Hbeod1+KvBvgTeY2TMu8dgicniOze/d4bAPKCIich/wwMX9uw+w/z3ANcD7HOF57lncv9DziJwU
+l+tnt/tT4BeBVwNvA1bA/wJ8KfAUYv73z5nZ0939Ny7yHCJyeI7N710FbREROYmuWtzfOsD+m8Q8
+rquP8Dybi/sXeh6Rk+Jy/ewC/Ht3v3WX798B/JSZfQ3wH4AMvNDMPtTdD3JNInJ0js3vXQ0dFxGR
+k+js4v7GnnvNThFzQs8c4XlOLe5f6HlETorL9bOLu7/3PI//KPBjRJC/EfjCCz2HiBy6Y/N7V0Fb
+REROorsW9w8yXOwB7fYgQ1Uv9jwPWNy/0POInBSX62f3oG5b3P+0IzqHiBzcsfm9q6AtIiInjrtv
+Au9qXz58v31bV+H+y/jN++27i2Ujln3Pw/ZGLBd6HpET4TL+7B7UXyzuf8ARnUNEDu7Y/N5V0BYR
+kZPqL4ghn480s/1+H37E4v5fXsQ5djvOfucZgddf4HlETpLL8bN7UH5ExxWRi3Nsfu8qaIuIyEn1
+39rtA4Cb9tlvORz09y7wHHcwN2PZc1ipma2AxxB/ab/D3csFnkfkJLkcP7sHtVyz921HdA4RObhj
+83tXQVtERE6qX1zc/8rddjAzA768ffkPwCsu5ATufjfwm0T17clmduMeu34h8L7t/s9fyDlETqAj
+/9m9AF+7uP87R3QOETmg4/R7V0FbREROJHe/A/hd4pfxs83sk3fZ7VuAjyQ+8f6BnZ94m9ktZlbb
+9l17nOoF7XYAfmTnUFczuxb4vvblPxBdjEVkD5fjZ9fMHmVmH7rfdbTlvZ7dvvw74Bcu/NWIyIW4
+L/3e1TraIiJykn0DMaT0auBlZva9ROXrauCLga9u+70O+Hf7HGfPeZru/goz+2ngnwGf287zA8Qw
+048FvgN4RDvGt7r7ey7pFYmcDEf9s3sTsTb2K4BfB/6MaMI2EPM6nwn847bvCHy1u2tZPpF9mNmn
+AI9cfOvaxf1Hmtkty/3d/fZ9Dnfsf+8qaIuIyInl7n9sZv8U+CliCNn37tyF+Iv609z9nks41bOA
+BwKfDXw68MQd5yjAd7u7qtkiB3CZfnYT8CTgyXtdBhG+n+Xuv3aR5xA5Sb4KuGWX7xvw+LZ1DuwX
+tM/niv/eVdAWEZETzd1fbGYfS1TInkYsB7IF/BXws8CPuPvZ/Q5xgHOcBZ5uZv8M+Arg44AHA28H
+XtnO8QeX8jpETpoj/tl9MTEs/LHAo4GHAe9PBIJ3A38CvAT48TYnVEQO5qCd+vfb7z7xe9fctSqB
+iIiIiIiIyGFRMzQRERERERGRQ6SgLSIiIiIiInKIFLRFREREREREDpGCtoiIiIiIiMghUtAWERER
+EREROUQK2iIiIiIiIiKHSEFbRERERERE5BApaIuIiIiIiIgcIgXtY8TMftvMats+9Upfj4iIiIiI
+iFw4Be3jxXfcioiIiIiIyH2MgraIiIiIiIjIIVLQFhERERERETlECtoiIiIiIiIih0hBW0RERERE
+ROQQKWiLiIiIiIiIHCIF7cvAwi1m9lIzO21mZ8zsb8zsF83scy/ymIOZPdPMfsbM/trM3mtmd5vZ
+G8zs/zWzz7uIY36Emf0bM/sDM/s7M9s0s3eY2e+b2a1mdsMBjnHOEmVmdr2ZfUc77mkzG83s3Rfz
+ukVERERERI47c9dKUkfJzB4G/BLwSYtv9zfd2u3PA18B/Arwae3xJ7r7K/c45qcDLwQ+ZMfxpl3a
+7e8DX+TubzvPNW4APwg8G8i7HLMf7wzwre7+I/sc6xXL1wC8H/Ai4ME7jvked3/IftclIiIiIiJy
+XzRc6Qu4PzOzBwGvAD6COWT+DfAqYBP4aCKAfz4HXDvbzP4J8FPEPzsnwu/vA28EKvDhwGPb448B
+/ruZ3ezud+5xvGuAlwKPa8dz4K+B1wB/DzwE+BTgRuBq4IfM7IHu/n0HuNxPAZ7XruWdwCvb7XXA
+xx/k9YqIiIiIiNzXqKJ9hMzsx4CvbF9uAl/r7rfv2OcTgZ8F/hGwBWywR0XbzD4aeDVwVdvnBcD3
+uvt7d+z3j4DbgSe0/X7d3T9nj2u8Hfiytt/rgOe4++/u2MeArwH+fTv3CDzB3f9gl+MtK9qFmJ7w
+ncDz3b0s9lu5+3q3axIREREREbkvU9A+Imb2YcD/WHzrFnf/qX32/SOiYmzsHbR/kxiO7cA3uvsP
+7nP+a4hQ/lFt/8e4+x079nkC8DvMVezHuPuec6fN7BZiGLgDL3H3p+2yTw/atP3+jwNWv0VERERE
+RO4X1Azt6DybeW7zq/cK2QDu/nrgBxb7n8PMPpY5ZL92v5Ddjnkv8D2Lb33pLrt90/L+fiG7HfN2
+4sMDAz7TzN5vv/2BtwHPP88+IiIiIiIi9ysK2kfniYv7P3mA/W8/z+Ofvbj/0we8ht9a3H/88gEz
+y8CT25fvBV58wGO+oh+CmIO9Fwd+zt3rAY8rIiIiIiJyv6BmaEfn4xb3X3W+nd399W3Jq706cT92
+cf8z2jzs87HF7QfueOxjgQcQgXgN/GBMxT6vmxf3dx5zp9cc5IAiIiIiIiL3JwraR6B1G99YfOtN
+B3zqm9g7aN+4uP9ZF3FZO4d5L493LfD1h3DMnXbtdC4iIiIiInJ/pqB9NN5nx9f3HvB59+zz2IOY
+lwC7mA52O6cJPGhx/2I74p3v358zF3lcERERERGR+ywF7aNx946vr+FgofMB+zx2D3NH8s9391++
+yGtbHq/7U3d/9CUeT0RERERERFAztCPh7u8h1sTuHnHAp+435/nti/s3XPBF7X08A64/hOOJiIiI
+iIgICtpH6U8W9x9zvp3N7JHA+++zyx8s7u/X7fug/hjYbPevM7MPOYRjioiIiIiInHgK2kfnFYv7
+zzzA/rec5/FfbbcGfIGZPfSirqpx97NsX/7r6y7leCIiIiIiIhIUtI/Ojy3uP8bMvmSvHVs1+1+w
+T1Myd78D+O325dXAT5rZ6iAXYmYrM3vwLg/9n30X4J+b2ZMOcrx2zIcddF8REREREZGTREH7iLj7
+64EfJ0KsAS80sy/fuZ+ZfSLwMqJh2tbOx3f450SjNQOeArzSzD5pr53N7MPM7DuBNwKP2+UaXwnc
+3r5cAS82s39lZrs2ZTOzU2b2uWb2i8AvnedaRURERERETiRzv9iVneR8WhX5VcCHE+EY4A3te5vA
+RwM9KP88sZ71pxGV7Se2ILzzmE8DfpoI5v2Yfw28Fng3cBVwHfCxwAe0xx14urv/2i7H2yBC81MW
+x7uXmBP+pnadDwY+FHgUcKrt84fufk7IN7NXnO81iIiIiIiI3J9pea8j5O7/YGZPJILsJ7Zvf0jb
+YB4q/kvAVzDPw97vmC82s8cRQ9Nvat/+0LZt23Vx/L8B3rLH8bbM7LOB5wLfTAT4q4En7rZ729bE
+hwUiIiIiIiKyg4L2EXP3vzOzxwBfDnwpUWl+ELG81p8AP+7uvwBgZrA9IO91zD8DPsnMngx8HtGF
+/Eai8rwJ3Am8jqhK/4a7//55jufA88zsh9p1Phn4KKLCvgLeC/wt8GdEk7dfc/d37XfI870GERER
+ERGR+ysNHRcRERERERE5RGqGJiIiIiIiInKIFLRFREREREREDpGCtoiIiIiIiMghUtAWERERERER
+OUQK2iIiIiIiIiKHSEFbRERERERE5BApaIuIiIiIiIgcIgVtERERERERkUOkoC0iIiIiIiJyiBS0
+RURERERERA6RgraIiIiIiIjIIVLQFhERERERETlEw5W+ABERkfs6M7sHOAVU4B1X+HJERETuz64j
+Csab7v6AK30xezF3v9LXcCz8xMs+zQG8OF7BC+CQLMdGIqdMypmcBiwZUHCrQKUC7rHVCuPojKNT
+Snt/rd1Mtw44Y12zLluMZQs8MdgpBjvFyq7igfkhPDC/Pw/MD8HKwObZNVuba7a21qzzmWnz1RZ2
+KjY21kCdrovkcS4DLF4bHrfmA9lXJFZYzXh1vFQojrOmMk63I2tGHymMcawUm+VETu29SYlEvFeJ
+BDXh1aatVqjVqQVKKazXhXFdGMfCarViWG2wWq1I2UjmWIrNGcEKzoglw3LCcpwP4JlP+F27PP+W
+iIjszsxGIF/p6xARETlBirsf28Lxsb2wy63WuvjKMDOMRLIWG6fAnbH2xzFwww3M43mYkQxScnJy
+5g8yFh9omLfAbZhBSkZyA4yEkczivCm1++0xi/MCeO2BPg5tyUg5YSlBctzAraV+83aBHv+r7Tne
+wrgXzMFrxd3x9n23irc/nfcX2w5HjUfd2gcNONXjOeYV97R49XH9yQw3Y0gGOcL5MAzkPLTAbph5
+2ypOjtdCbh9YpHjf9RmRiBwf8VvAjBtvvPFKX4uIHMDW1hZ33nknD33oQ9nY2LjSlyMiB/S2t72t
+Z6xjnQYUtBuvBZiiLmYJI5MXATtZwizH496CrNf4R9xCsJnhycjurbYRYdvpodtjX4vHLMUZsyXw
+RLaoCWeLSnEP2/PxI2t6gVJhXR0q5EjspJTx5HgqkCpuJYJyC82RiOMykht4wt2wVor3Gq/JKUCJ
+55m3UBuBnxaoaZ9NOFDNMaDimDtGndK3ewRj+vtqGUuQciaZ49nJOZOHTM7xGEqypS8AACAASURB
+VNYr8dYu2eN05hgJsAj9IiLHw7uB66699lre8pa3XOlrEZEDeO1rX8tNN93ES17yEj7hEz7hSl+O
+iBzQddddx5133gnxu/fYUtBuoooLtJqxWYqQTSbbQLLcwrdhFlVa84q3wGdYDGu2FJXuBNDCp0el
+uE6hPAIkFmHXLeEelfOBxECOoG1GTlHdnirlvZpeoKyjou0FPCUYcoT7IcK1W8VbaK69jN2uFTec
+gnvC3OK11FYm9wp96Dm9qh0V63heu2/xemp/nYC1knk/Rw/m8RFGjDdPluJDAYOhxvFSSjFkPKdW
+tY4PB7DU8rxHKG+HnMbii4iIiIiIHDMK2k0f4h0hO6rMZm3OsUXYNjNoYTuqvDYFTwBz66PHY0i4
+WQTfatQalV7vw7inMNn2hV5Ljwo3bfj4Yuh4Hzg+VbTXznrToTo2OLYyLM/X4xZhu7aQ7+4Rqvuf
+FraTG+apfdhQiMjsEZqnUNtfe7tu2rBwow3jbpXuXu32FrpJUS1nfg3JMkbub9L8IUWKIfDYPFzd
+pwp2wkn08eLHf7CIiIiIiIicVArajbWVzvqw5Kl2bG2etmUspRa2W9CuhWkotTuVAjWiaPUeR6NV
+WqVSrbQwOw8jr1ai6ZiVNqK7V5Pjq2nIeds/CuFteHep1LHEMO9VwTcrngpGifnNGZwUo9udaHbG
+FJvjlbYmaZiTHPC+4ptP34/H2nvi/braUHSLarSl+Ihgns/trdK/7aODmOPusTF9gDCdcRIV+X6c
+SvV4D6fqOqioLSIiIiIix5KCdmc9YNoctr03JsvkFEPHSS1oU6kpQbVpDvacFD2C9hS2K5XSgmJt
+IbvNfabgVqhWWvG4h+w4Tg/xNsXLNnfZI+iXdaH6iG8VfCjUVMhWSdnJbm1oe8Zriqp6C8nmHl3D
+abeLhm7x+uegG8O2ezhvVfl51nSbc95K80SF3X2uwU9N5Twq2dZu+9z2VhZn+mChhfz+JyryTmlV
+eWd7KBeR48XMbgFeRPyofrC7v+kKX9Jl8853vpOHP/zhV/oyROQAtra2AHjqU596v26Gdv311/OH
+f/iHV/oyRE4cBe1mWdG2bX+2V7T7cOeKYXXuqD2H7e1V1ylot2p2pfT42OZP19ZwrMZcbWpr+NWb
+jbVQ7d6Gtcete6WWSl0XSi14HvEcQdtzZdiYq9Depl1HRTtCtreh4d4atsWo+D403VrluzVya3PD
+515o24N2H3Qft8sPE1oTtz4Ef1HNtp7up+O2Dx6sB/k5dldi6HutMQy+f6ahruMicty4O29961uv
+9GWIyAVoTZVERA6VgnZjNg9grrXQO8ZPs6LdohqbYhj0NHjaphnLVC+4F2qfX9yP2AJ1LJfVm5O1
+BmWtYRktSFfbiMesUBgZ68hWLTA6m1tbbG1ucfbsJpubW2xtrlmv15Q6koeCrwt5VbGxwFhhLHF9
+1ajeVvqaXpXFnOdYDBtSnuaVm9k0XLuPO4+50ouh3g6t4xs9es8vui+PZvOyaDZEwI6J2HPH8J6W
+fbmI2KK23avY1adlyaYO6AraInIsfcCVvgAROZAt4E7gocD9saJ9mmmJGBG57BS0mz5yPKq+BWqh
+eqJPj46R1tETPMWqWK12C24WQ5upLUDHUmFTDrQ2W9uik3atMS+7Ms7DyGsM4c6sKTZSKIy1sK4j
+eVxT18bZrS3OnjnLmTNnuffMJmc3t9jaWuPDCKsKY8XGShkLrCN4xyjwCLY+DdGGqblYC9mkoXVN
+j07ntY54dWrtlWubXwrEBw+L0fJ9znafA86yoVwP2pbjfIvh4n0O+jQ0vj3WQ3b1Oofsam3qeK+4
+a5K2iBw3Bmh5L5H7htcCNwEvAe6Py3s9HNAIG5ErRUG7mzJbW4qr1tZNuzU6c8g42SClCJl9ASy3
+WEe61EKpI9XHbcft3bstteq2jVRfU3xkW2OxapQ0klsIH31kXUdSGSkjnNnc5N6zZzlz71nuPbvJ
+2c1NtrbW0YJ87RG0S9z6WEjrNeTe9iy1pcdaQDVorwhswCyCdspGSjHcnLEu5ogv3qRpTPz2Nma9
+OVocftFILsWwcVJuzdbSPCPb++ufm7T1Q87d0mtr5tbebPoSZYf6b4CIiIiIiMihUNBu+vJeEapL
+q6QCKUFNOBlPhnvUtXuH7Rg6Prcqi6p2G+PcmpbF3Opew+2N0Xrlu85V3TTGuSmtoj2yVdbYuMV6
+C+7d3OSee89yzz1nOLt5lrNno6JtVKwYqSX/WoHiUCLkTwVkLO5PkbZXjw232LDoIN4mZsfI8alR
+WjtGC7mGtW7rTK+R+VTRjbx3He/ri08hv8//jiAdb1Uct7b3sNbYtlW0+/m3x3IRkSutXOkLEJEL
+dQPw3HYrIvcVOed+91j/7k3n3+VkKKXGVp1aW+jbFkJpWxvubBGyY/3nhOUEud9aDC0375GZUkfG
+smasMad6DvatkdoiWJZaqG3/9XqLs1ubnNk8y71nznL3vWe56+4z3HPPJvee3eLs5sh6XSkF8ITZ
+imQDyVYkW5Ftg5xWDGnFkAayrchpINlAryxXrxRv11jjGmOeem9zthwCHsPAc4pjD6lteYNV2mCV
+4vg5ZVJfc7yNEJir1/EhhPUt9e7ihVJGShkZx74V6hjzzI28GL6/itdiqyv1r4zIiWVmDzaz7zOz
+vzSze83s7Wb2MjP7ogs4xikz+9/N7OVmdtrMNhfHeZaZ5aM8hpm90cyqmf2n9vVNZvbjZvYGMztr
+Zhc6sbEvw3CBTxORK+cG4HkoaIvctyyC9rFuQqCKdjOO7Z9Tm04dQ5lTWzM7gnMP2tUc63OuAbNW
+tfU0D432iteoXk9dvGoLmd6HTLfT1dYF3Fo1PRWKFRjX+HpN2drk7NkaQfueM9x191nW4xbjuGY9
+rtnIiY2SgR6EIVtfmixCsvVKNfOIa2vLcVUvrQzel+iyaOpW61S57mthZ8tTlTq149XW5K3aooN6
+H6Teht9Xr1hrgDY1nrO5N7sTYbxOgbu0DxwqvWe50cL7YhORy8vMPhJ4OfE30/6fk1PAZwBPMrMX
+Aa88zzE+Dvgl4BGLYwBc248DPMfMnu7u7ziiY0wrBZrZc4AfApbB/Fj/8hYREZHjTUG7KaV3v+4j
+m+eKdjQdX1S1qdE5G2L5qh5EPQIoWAzhtracVy1tnnGZgmsfxt0brVV3zOr0nGojXtbUcYtxvcmZ
+zco9Z85y9z1nueueM1H5rVH9tdWKUgHPrZJtJGvh1GjzpPvw7Xlxrh5w8UqtbUGvNhR+MSm6dRDv
+cXdoleoIvWCkaXmyGmuCt87q7u19q47XOsX1Pqze6GuGt2vwQmkV/ahsR9AeksW8eMuLsJ0VtEUu
+MzN7IPAbwPXED+9PAz8BvAP4cOCbgK8AHrXPMR4J/DbwvsB7gB8G7gDeDLw/8AzgOcDNwC+a2RPc
+vRz2MRY+Cfgy4G+BFwCvIX43PuEg74mIiIjIbhS0m1Li72DzElZtNWmveC0kW0cILoVCiurstOaz
+M9YtShsW3udZw3Ku9nx/mqvcu2v3iq5Htby6U9zJtc3d9mjCtpETV58aqFdtUGqi1EwtA6dODVw9
+nOKqtMEGG2Rfkb2SvZJqhG7zCN2OtzDv0fm8rqm+xr2QKqRq1EQLxdDXFad/gEBbHsx71Z/2YUOa
+Rkym+JwhXp21ofQW71n/w6JjOa3DeKy/3T6UYCo1tXPnmD9uUbm36b6IXEbfRbSxdeDb3f35i8f+
+yMx+Dngx8JR9jnE78CAi0D7F3f9+x+MvN7MXt+N8MhHcf+wIjtF9FPAnwKe5+3sX33/VPq9BRERE
+ZF8K2k2pc9CehoJjbfj3SLTyGimeSG5YWgRt86gu+0ipa6qPERgXDdGWw6S7WOO6h15ap20oOMkd
+c+IWGJJx1UbGr14xVKeUIeZzl8LGVQPXbGxwdT7FVazIwFAh19aQrEb372hsHhXnaoU1ZxmnKvJI
+tej9Ftk4A+15lplDNuAx07ovGIYzVb37N1Kr/pu1tdDSPLS+DyePQy1Ddm3V8HlIfhy7V69jebAp
+bKvFgMhlY2Yr4FnEfwb+dEfIBsDdi5k9G3gDcE4DBTN7PPDYdoxbdgnI/Ti/0UL7P2VHSD6MYywv
+qR3n63eEbBEREZFLoqDdlBpLchmJZD04RiXaaqyRbdDCL7Tp2/RvFgq1FoqPrZv49op270veQ2RU
+wo3am6t5W5fbW0W7hW3iVAw5cdVqIF/lnCLF/OVSKbWyWmVObWxwKm1wihWDJwZP5JpILWRbG/de
+bKTaSLERvFLqFrU6xUcsQUlg5niKRm8pzcuDOXNAhrbsl9GGg/du4P096dXumNxutPnrHvO2a/V2
+rOU87b6U1xS1I6gvQnavbhv9vohcJjcB70f8wN6+107u/lYzeynwtF0efka7fZ27/8V5zvdKIiTf
+bGbJ3fuc6cM4xtKb3f2/n+c4IiIiIhdEQbsppQVty1GtNZ8qqlgbC50iWFqa18WO5M1UjY2QvZjf
+THQwr3URUqe1sFq112nLgLXw6Mv2Y8YQE60ZNjKnHEpKrZodXcrzkFmtVqzyihUDg2eGOjCUeR61
+uUGG0ROlVeRHz1g1KN6athEl9eSYG5ncpqrPQ717JDaPweERoHvkXXbb7UuJRRM2a6+LGq/ZcKzS
+GsPVab3s6pXal/qa36z+FrWPO+L7Sd19RS6nj1ncv+M8+76a3YP2J7bbj7iArt4r4CHAOw/xGJ0D
+f3rAY4iIiIgcmIJ2M5Z1u1faMOU2J7kvgD3dtnWpk2+/32OotUptpQXLTG2Nx9vodFKOJcFSjuHn
+iagiJzJDRGUGEisSG5Y4lRIpZ3wwnISnPHXkrqWSspGHxJCNwaLeO1TIBcxjPW03x5OTUqGmiue5
++Vsw3Aqk1i3dUptr3juKJxK1DR23qeJMH0Tu0S6tV/GnruPWu7K34ecpwraZt1zv7QODGMJeawRt
+PMX76I7VWCDcrUS/cyO6qW9rNCwiR+whi/u7dgJfePse378OLvgH14FrDvkYS7sOPb94lbjE88ls
+b3IuInLYTl/pCxC5IKdPn+b06fP/e7u1tXUZrubSKWg3Y42g3dfPjjDZg/YicBOhNQrSta0DXWOo
+ubUqricohhfDS6KOUEejjnHoPCSGIWGrRM/0KTvZImivGFiR2SBzyhKnLJFzm5OcHHKl1kytMVzd
+zEgtuCeDHLuQ2+h1bwG5tGslewTqRWV+apRWC9UKKSUqA9O619QI+dbbnNEq9DbN1nZv9eY+cXsx
+9Nsst8VyetiuUaGuHnPEy7yOeJ2mske1vLT1t6tXkpUYyG608C4iV8DFfsrVf2j/BHjmBTzvrYd8
+jKW9upFfgjsP/5AiIiL3c7fddhu33nrrlb6MQ6Og3fSKdl9uy1vDLu8ptC/nhcdwamI5rgjZlZQS
+ObVltDzj6wEfDV8nypppM4zVRsI2MqkkbIC0ioA8pLSoaOeoaJO4KiWGnKJRWY6sHSE7UfvQ897P
+29os5gp5tG3zvqFSUsVyjYMMDtmxHMPKI84Wiq/JnqeKdrVYy9p72G7BvdJ7vLUh8O2TCHNbfC4x
+r+Pdq9kQc+AdpoZuUzW7BfCpR1Ffc9wr5iVG8MP0OkXksllWfh8G/NU++z5sj++/i/jhfp8DzK/e
+y2Ec48iYGddee+1598s5k7M+LBSRo3f99ddf6UsQOZDnPOc5POMZzzjvfk996lO5887j/6G2gnbT
+19GOKiutE7a1im+KRGnzfOEpCFbDrZe6wWu0Dq+blbpplE3DR/Ax4WMiYdQxUdZG2ozAbacSeSMz
+DJmVDWyk2Fa+weAD2fsQcMcrEUrbutxeS2s8lpjW9G7zxL1Orb0xKoaT2rJfK5ziUN3a1pfxssjC
+vRu4F6q3Unyb772tf7rF0l1ujrd1u+f3aRGU8TaqPG6r+/QBQbRTi+jeB4R7O0FUyW1q3N6XE0u+
+eN9F5HL4s8X9m4Hf22ffm/f4/h8BjwM+xMyuc/fzDUE/qmMcmRtvvJG3vOUtV/oyRERE7nNuuOEG
+brjhhvPut7GxcRmu5tKpKNiU0eetVMZSGUtf+iqW36re2pt578IdFeXe7KyW9vytyvpsZfPeytm7
+Klt3w3hPop4Z8DMD9Z5MvTtT7kr4PQN27yny5lUMW9ewKtewUa9mw69hwzcYPJOqYdXxUinjyLge
+KeNIGWPouNcCXrCp0/mIe6yR7XULfI35SPKRTGFFZeXOhsPKjZUnVp4YPAJscsOmJmURtIuPFF9T
+iK0yEr3RS8zttkrrbsY8R7t1VK8Vry2410qdqtQwDS/vjdX63HjvS395VLnr3DDNPV6nzwujicjR
+ew1zVfvL9trJzD6AvdfR/uW+G/ANF3kdh3EMERERkSOloN2U0rcI2tNW+9DrPpw84d6Xqmoh241a
+4/m1OON6EbTvrmzd44z3GvVMpp4ZqPdmyt2J8a5EvWeAMxuks1czrK9mNV7NRr2GU341Kz81V7Rb
+0K5jiaC9HqllpJYWtj06rhkVvOA+4r6F+xrrQZtC9sLQKtorYIWxMQXtRHZrvd2ic3oP2ZVxW8iu
+xDJhbiNQYBG2fdEQrYdlr7HV2oN3X8arV7TTdBtLkdH2n0N2DGUvi4DdNxE5au6+BbyICLgfb2bf
+snMfi0Xu/yO7rKHdjvEyoiO5Af/SzL5ov3Oa2aPM7HMO+xgiIiIiR01Dx5t56Hjrs92WWzWrkFKs
+aW3eGpD3KmwsA9ZHkU89wNzAM5QMY8bqgNWB5JnkiR5Cq3kM6LaEp4GIvStSWpHzitSWw6oVvFXY
+x1bJtlY9NqtRgcYXw6+ZWhUtF8mal7m2thp1ZeVQs1E942nAU8GtYjVFJb3Quo7HC6zGNMR8Dset
+8Xq7CrO0vaJNH4buLb87xcu0T282Z4vqdp+fPU0EtxohPLWl19qtiFxW302sS/1w4Plm9mjgJ4gu
+5B8OfDOx3vYfsvfw8S8B/oDoYv6zZvYrwM8Arycak10HPJpYL/uTgRcAv3oExxARERE5MgraTS0R
+rBfRsM017iGxdxb3WF4qMbXrNqvRAIxoBFZrwoaBtBoYTg3k0UijkUoitTncvapbU2G0qBUP7qws
+UfOADxvRibs441jxsTCOEbTrWNo637GlPtc61W3LXm1fiTp4IZp5J7BcSNkZcsLzgCWPpcLygBda
+0zeL4dtWcavRkZxEshwNziyTUswATy10e/8AYupWXnBsasrmtbZ55i1s9wvsQ8jN2vW2fuYGlozU
+tpyMnCBrPIbIZeXu7zWzpwIvA64Hvrht0y5E1ft32+1ux3iDmT0W+K/Ao4DPAZ6+265te89RHKNR
+owcRERE5EgrazRy025rTvbP41Myr0BuCpeTRXdysZe3cmpDFxpDIqxXDxoqy3oiqczsWLTzXsVDH
+QkkjI5VEZTCnZKNuDLhvUOqIlxFGp65Lm5cdW2q9yVJb8SvVqDanqdt3C6i+CNxToG19zVrQXuWE
+5YE0GJmBgUqxEkPnqVF9tsLISLGRlDLZaoTtDLVGAHbvS3r5FLbd57Adw78rxds87b50WAvZUdie
+K9qtro0Z5AwpR7jOGXKy+LBDRC4rd/8LM/to4NuAzwceAdxFNEv7UXf/WTO7hUXPxF2O8Vdm9vFE
+dfwLier3Q4mlu94FvA74b8AvuPsfH9Ux9rtGERERkUuhoN3UPnTcYoi4Tz25omN3NO6KOdApZXKC
+lAxrgTu1ucXmCWomDxv4xgY+bgAF6gjjSPXKWJy6rvhWoVAYvUAtDAbjKlGuHvC6EYG8FMoYc7PL
+ulDHkTqOLWDHfOoI163S3oJrWwX83M0Wt9nJOarYachkzwzAymBd1mz5GnxNSSOVSrGRNVvkHMPM
+h+RUM2pK0xJcc1WaFqIruLXZ1JVS56W84v2O998WF7eswBvtw4QEORt5sBa042sRufzc/R+Ab2/b
+bo/fDtx+nmM4Mdz7Zy7hOi76GO7+wRd7XhEREZHzUdBuciuPxpLQPgVu6xOe2/BxvODV4vE2VDqn
+3CrH7U9KpJzxPMCwivW0U1ufihK5e6synh1JVqi5xj+J6tMa3tVhdCjVGYtTirfgHVtMHY9wbS1g
+J/MpcFv/2ha9vBdD3w3w0oawT8c1KIaNFtXtIeOrGGZezSlWwUawTKwrnmJwd7tOM2/N2BzzGutn
+R292jEylUK22be48PjVMa1X4lFuDNEu4Qx4SeZXIQybneG/7muUiIiIiIiLHjYJ2k3MG+rBxx61O
+1VZa9+zo5k2770AmmZFSInJ0Gzoe5VdsGGC1wkeoKeY4u1ush70ulLMjJReGVZ3WyI41pmPOdamw
+Ls66tCp4cbx1NjcDa7epDdPuYTu1MJ37fHKzbbf9A4QYvF3nZmdDrPXNkLANI9cMGD7AaIWURqyF
+7Bi3ndrc6/gModT4QIIEeMxdz+a4JZLFMPR+PsenZb5qjSHntLnYANlSjIV3ayE7kYZEzpmUEym1
+OeIiIiIiIiLHjIJ2sy1oU1vGW6wH7eBeYpkqS3jyqGJbIttiPnRNWMpYC9q2GqhbTs2tktuCdt2q
+jJsjw6riG1FVbsXgqdn26LCusFm9NUarU9i2dl2923iiRsDGScnJLWhH07BoHuYpqs/e5nK7V2ot
+lFpiLvSQYQDLRqqxXrilCNajFdYeQdtSW9qsnb1iUY2nJW4nwnyKyn9KiUyKmN2q2dVa5K6VWj06
+ipPa3PcUQ/AtY6RW0bYI2znFcP22n4iIiIiIyHGjoN3ktKxoR+drp63f3DuP1xSdt/ua2n0dZ2uV
+XCyapnmhz0rePi86Zh97jeZrZaswbo2M68IwFspYGEuNtbxrbKM7Y4Wx0p7n0bitz4n2iLpzNRuG
+BEOOId8TIxJ87zAGEXJLdAB3976KVgvIOTp950yyREojyUdSLlhtlfu+jnhtQ+7bEmjTsHsHb8uP
+eUrzOtre1ybv763Pjdra/O4+JL43QsvZyNmwaV58au+niIiIiIjI8aKg3aTU3oo2bDyl6DQ+1kLk
+2doCopF8Dow+LV/lPQlj1aklk+qA1zXuYwvlTMtcjcVZj4U0FtJ6xLa2GLbWbKzXjGWk1Epxp0xD
+yaHWWO+7jh6V6BJNxWxZxU6wMUSN29ra1EuLxcvaRwFOaUtute5vEXozWJ4rxylvkIfKKrVAXYHq
+bV1tosN4WwIM4r4lcIvu4DVZn+o+LZE9D8GH+FCiLZXWV+pObch7G63ev+7LqMUYdRERERERkeNF
+QbvpFe3oOJ7APBbdcijUCNv0+dvRMK16m29sFWqNJmkVqDVCdhmodRUJuQVKJ0JzKZVxrKT1SFqv
+sa01w3qLU+uR9VgYS+vO7R69zt1a8HbKWBlLjXW1S2lBO4aIDzmGc5sZmTQF7b62tk9bfERQ3dty
+WxUKbQh4bc3GokmcpUQi5lsPKVG9xFZLvF/T6jj/P3tvF3PdmqVlXWM8z1zv++2iEAjSVU2ZRu20
+MTRwACTGqOkGVALSAYLGH0IrJCJEMdoo9AF2VYyREKIHiqEOFOREtFsjIYo/zZ/REOlSFFAjahTs
+ygfdQqeqq/Z+15zPM4YHYzxzrW/3/q/dtd+qGtfOrPV+3/qba31779r3vMe477DEXQzEEIvvSjTO
+D08J7XGE0F5/AkKo9xTby9FWz5VwQTUD1jJ47lXLviiKoiiKoiiK4nlQQjtRya9C/TyE2F+WdHqX
+C2spEs9AL2YK6Pj1EtpqG2pHCkM/R6M9Hephjk5DjgHpaB/jYIzBmDlGbs504picCeT7YRxjsh8D
+Eaep0pqwWQjk3oXujWwpyzNcYttPwW/Ea1sK7NWF7Wr0BtoUtKPiIeS9MezA7QhHm7So12uL4TKJ
+kfqYDBCzTDvXcxebFNxZBpab3nm7ktRXT3gLV3ylqUM62l6j40VRFEVRFEVRPD9KaC9uE8zptFqO
+g4cQVNVwVUXRmGM+d5RnOsErRZvcP3Yb2DxQbzRptNZoF9geH3l47eDFPpHLhnRlYuxj5+n6RH/9
+y7AJx3VwXA/GfjD3gR8HPmMUHZmIOq2BqtJ7o/fG1ht9U7RHINtpIK907xS84Wfnx16L5Ol+r61v
+M2UeggsMF6YJY8LcBNvifm/hOqM5Pu7E52eGu+2OSAS1udwqyITsHD8997UgnpcFxHLYXhDviLWs
+DxMwRbzl84uiKIqiKIqiKJ4XJbQTt/xh1Xt5urXurwhtyVopifgxzASb3Bzv3H42n2AD5sHmQpOO
+9k6/SAjtjw3GNEwEU2H65Eihra93rBFBaUtkj4nMiUyLjupMGW9daE3Ztsa2bSG0u9KaIk1TO/t5
+bre/9Bwhj5H4lV+eWebemFNO03gYIbKHYFMwE9wV34CWjdotcuHO8DhmiGO1ENncxLai2eilkP3e
+K8M8zjic8rg2YCm2QVwRn7jFaHpRFEVRFEVRFMVzo4T2IoW2C1hWfEW9VyZfS+4qazsrr9L4Dkcb
+cg97jWDPdLR31DubCkpHLp3L42QOwwz2OdhtMufAx4Fen/DXhcFkHiOOfSJmdKDhNMjOaWhA60rf
+OtvDxmXrdJW8KJBesYdgxXIsPnu3zxwyIdxkOMW20cAUH3J+xjFgNHATzONCQzw9U8Zbdod5Vodh
+iERcupyOtoFo1ITlOL1k0viKPXdxYtg99s/FHbGbCy7WEbMMcCuKoiiKoiiKonhelNBOJEWjp4N9
+alCRHBUnK6WUSLtejw+xLZKp3dlN5WJMRoSYsTF5iIRuVfSysb14jPc4DvzYmQfQBHNjjAO5CnYM
+5hjYMbO+S2kpoJsoLZeZ+7bx8HDh4fGBy2Wjq9JU6aohVC1P1MINhxkXAiSC3qZr7JbfOdq4xKi5
+Raf4MYxdjF0NHwarz3sKbQIP8V25y9kDnnFrEYyWlV+RbHY3qb4OE0QzDO0uEx2ihkwkRgbUc+fb
+IoCuKIqiKIqiKIriuVFCO9GzKirkYeRs5z6zRLVUqMPYKz6bqdLBDZc2d2yqvwAAIABJREFUxKYp
+OYI+wpf1C8MPDh80OtKV7fGCtgbHFfaG7xoD3S2cXhsDmwOf4YwjEhq1NXqOhYe7rlwuIbIfHx+5
+PDyw9U5vna03JHem3RyfkzF25rHHrRvDhWERsJadWvE51745k+nG7oPdBrsf+IPDcDgcPaC/yGAz
+iZKuV+LR0ql2LJLNVuf4GT7uuMYIvXp8d5qfVfJPxl2zKTyEfIyQe140KIqiKIqiKIqieF6U0E6U
+W70XMfR8jjS7Kq7L8b13fcFX+rVHDZW0cF2HOiaDIYPGheYHzQ5Aka701ugPAnuDrtCFMUeoSw+h
+7XPgNsEmqKAitAZtkwhW653WOg8PFx5fhNB+fHzBtl24bBvb5ZLd1dGTPcfg+vQG1ydhuGFzMBAO
+iwR0WQJXVjK6hSNvznUMruNgnwMOhx1khzZIkS1ok9j1FjlFNLmXTTrSaIyOe46Imxjq2bUtMVfg
+oiiKaIsLHq7xHhk+p55CmxLaRVEURVEURVE8P0poJyrL0Y6xcEdvkloc13Rpzw7o+Nlt9VbHcyUd
+WtQwjT3lKQeTg+ETFaf3ztY2mm7INYX2phzHgc3BnAObM5zfKOaOaqysuuo9U8a3zpZj4y9ePPDi
+xWO42pdwti+XRwDcDDPnOA5c4LAR4+oow4XdojZMNf16Ecyirmxa1Ixd9xHHMeAAOQQZQrebyJ5d
+cFVMJb4vkdO5Bs/ucaI6LfvIJe+3FNuO4NogBTUobim2LVxvCKGttxLuoiiKoiiKoiiKZ0MJ7UQk
+HO1wSsMtFbdzB5sMRluuNoQYXQnlStZYYbg500NYygYyY/Rcm9CksfULD9sjl/7A9rDxMB547XjB
+MY4c6z4Yx4GNwRwH8xg0hcvW2bZO652+dfq20beNtvUYQ083GRVEM3WcEL9Y1o61zpDG7sLuwtXg
+Op0xHFULsW1grphHuvg0Z1qIYSdC4GQCu8S+dYuxc3OQLtA70h+jXqxNROPA7kLYxM9AN8+J/HXR
+Ig4FGkjLpPE88q8mchaDFUVRFEVRFEVRPCdKaCeaQpuspoqOZj0Dt1xWBdZ6VOSLrzqrbJ5GCEFp
+xNPZMv/LQE1oqlwuG48PjzxePhYOtg2mHczj4Niv5zGOg2PfGceOAFtb1V2dtm0hti8bvYfQFk1X
+XgSW0BaNMDSNye2pjYGyu3I12Cdcx5uEtmTtl0uOjsdo+fT4lJGn5rfQs+zpnhPaRWkPG+2i6NYQ
+n0gbiESKuPuETHSXnBJAHbF0wc8vLg9VyM5sQW9/ldAuiqIoiqIoiuKZUkI7WUI7xLOFQEz3Fp/p
+anO6xrGj7ZinMGeGC563lk6ydEFSSIrHbvX2eOHh8QWvPXyMW7r2ZI6D69Mb7Ncn9qcn9v3Kce3s
+1xaJ2xp93k013OzLcrQ3tGmmnZOOdrjaaFvKGZzT0T5c2O3e0bboChdD1cFv4WNLbJ+OtsUIuAtM
+y+bwCeNwLo9rxLsDG8JAZSA+cD8wjw34/PpIqY0rYIJrjOOTjrbQ8qJHOx1tFUHztiiKoiiKoiiK
+4rlRQjtRTUfbPIO6Gp7J2zHSHDvFEYpGCsW8PwPHwMAn4DBzjxs9R7k7jd43tssWSeEPD8BEJLK6
+5xAUo6U7ruK5Mx171iq5Dy1yBqG11lBdSejONGPfdxxhmDFdOKZzTOdpH3zxC1/kC1/4El/88de5
+Pl05rgfH4ZgJHbm54BIOsosiLtDClW9GlnZ5fifRc80wpjuDUOA+oR2gW6NdQC/pkkvUfaFAX21f
+crrV4opINIbLLZocSGEugoii2mlSf/sWRVEURVEURfH8KKWStBTakqPjSMfdw331GcnduaftGlVc
+97il8LaQoEw5NberoE3pumUa+Ma2Rf1WvGc8TlSZTbHesNnw2c/dajfL+rAIK2u90ZqGyM5RbzPD
+x2BO4+m6gyjXw3jaB2/sgzeeDr785dfPY44BNvE5Q+pqA2201s/qMJEQ3pbOtiNYOvmGZzL5wHxi
+YzBw5jD2J6Nt0B6EdlH6pUE3pDWkGbrlLjmCaornU+CHkx2uOqtHLW6z17xpp+vlq/c3SFEURVEU
+RVEUxXukhHbS1n4zjZZp3y4hIidZkaWE253Oag49x/9mHdZ0C6GeKdlujrfsv87Krcu90F7j0+6I
+C9YUbw3vDayfItPMQpBLOMCtt3Mv+6zjMsdtMIZxzMkYk9ffOPjSGztfev3Kl9/YeXraebruXK+x
+991V6CpsvUXSumz0vqEtXj8EdwMEl9ibPoW2OcMmx7FzHDs2YY6ZjneEpG2P0B+U/iDoxWhbQy8T
+EYGmqCtNboJeJD5PLJVLfjdkp7fFBQlpNN1orYR2URRFURRFURTPjxLaySm03bPnuaFimXYdv78S
+yF0MbI0zyykELcWu5Uh5xJKvwC9F++q+VlqLOqusmg5EaCr0plhreM+EbiSF9u3BTWNkXHJP2dOJ
+N3P2fefpenC97nzxy0988Utv8IUff+JLr185jskxJscx2Hrn4bLBZaNLQyTG0Xvf4jx7uNvS2imE
+kXaK7KgMG2CCDeeYlreTYzg0Z+zQH4TtQeiPSn9sdO+oel5MUNQjMV0yAC0y3W945pCH2I7vQ0TP
+KYSiKIqiKIqiKIrnRAntkyzvEkdUaB5d2k2UaZncTbjZc2QHNAKZhn2nqOPWsmPb4vFTJ0MGBzu7
+dbbZ2A7JSrAMULMZqWLuqMRYeGst3OqZPvHZN5b92M6dGwwqQu+dR4TWOi7xORxFW+M4BnseW+88
+Plx4uGw8Xi48XjqPDxsPl3662bqC1bI2jOzYnhIlZ9agNTKoLXuzDWw6Ng0zGAP23dkOuIzONrMi
+bNV1iURmWwfNqrD4w8gu7jU6jmEMph8Mu0bFWFEUzwYR+T7g+wB39w90JUxE/hTw9wF/yt1/yYd4
+eu/0nl/xeS9evnzJpz71qQ/nxL6O+MQnPsHnPve5j/o0iqIoiuKrRgntk1vImaaobKKoZcI1wvQY
+4Z4W49EqsUus4ufOdm4d4x5Ce84Q2kONIYPhB8e8cgxhbx6BZ+o0BXeLXWw89rC1RQp3C0HuNnMH
+PM/YHGMi4uGOt4aIsGVQ2uUBRGMkHAnRvu8H12Ow7wfb1nl8eDjF9mVrXHrj0jXd8nTMNfbCU8lj
+M349iX7tJbJVQzjHxQhjmsFwuMYY+WUoNhW3DTzKuSIdHfoWIWmiN9feRRDx7DCPiwvGwPxg2BoH
+KIri64yv6X+wzYzPf/7zH/VpFEVRFEXxEVNC+2Q52jkhjYIrLbKwI3nbHbfJnEfsQ2skg4v0EH0S
+z5MsmF4G9RRnyGQwOWwPka1OU6M3aE3OPwnPMXWV2IluLfaVp0xcBGOEwLYIInPzqA+TcNdVBbnb
+r1btZ1J3a8p137nsB9ersm0bLx4feHx84HLZYle7Cb2tjuo8JFztlcY2xYnF9Rh1V72JbSEvAEzn
+OOzc53acORWfDXwDNJ7XnGhWG0iD1gfgdyJbcLntZ5tPpu85YVCWdlF8nXK/VPM1yM/+qE/gGfES
+8kJpURRFUXwjUUI7GTbihxxTjp1ruznVKjnqvCabMwUbw07Bt8ae4xGyBLqBT2cyGPPgEOfKQGVn
+dqV3wXo4wmiLSi1pZ8q4KrgrJlHxZRLiNlK5HdVGa42eTra2ftZ/hdUdr71tneu+s+87132jt87l
+4cLlcmHrjaaSB7f/zHWiM/wU2kR9lwiu4aS37PbWu2OliEcvedagTcVGY+6d0eGqhhGBao9miBpt
+c9T9/HpNYmdeIgUu3G1xXOZaky+K4usId//Oj/ocvjIU+OGP+iSeEZ8CyuEviqIovvEooZ0c48if
+0kTxGFe2jOI666dUUdPojz4fN2FFlaXQFqKGKkK+BKbhPpnsHD5Qj87ovinbptimtN7RtiH9Eoni
+mTDuxDi7r+RvcSSqraFFMFqEl3V667QzzKwjLc65bY2Hxwv7NYT2vh+xA95bPKc1VMKZbpJp6e55
+S77v7TM2idqydo6Nyym2W2u0HqFpksFp7tCkId7woYyrMzF2O2hjYOLo5lxeZACc5/6557eZI/0i
+EuPlaiH8i6IoiqIoiqIonhkltJN97MCradeIYxI70SI3l1ZVo8KL2KvGJNuoUgyvcWtSnOfYudlg
++s4xgem4wbY17KL4aPTLhX4J4Sx9S4HpKdslx8lv4WRLeKr2cLP7ul1Ce6N1o2+dh4eNYwzGdefY
+D459jwsIKhmklq+HoLLGvw2TFNvEx1xC21XQHHFvKbLbEtktbs1BzTEN8azSwRo2FGPi07B9wHVH
+u3B5BDM5x+fdHXHyggX5WSWdfqfS0IqiKIqiKIqieI7ouz/kG4MxB2MOjjkYNpl5mK/d7RyLlnaK
+7dsY+RoxX+PkSxQqTXuEq3mmhI/BPHbG9cr+9DrH9Q2O6xPH/sQ8dszGGYZ2E77p5J5HVFtFFdeF
+bdvoeSyxvY5t23h4uPDixSMfe+2Rn/KxF3z8p7zgp/6U1/j4xx557fGBF5k0ftk6ly1G0HtreSi9
+xX73bR87jpa/fz8yHq52u93X9BThIjFGb8MZh7NfJ9enwdMbB9c9+r/NbmP70+JwzwH+/MJjdNzO
+oyiK54mI/E0i8hkR+Ysi8uMi8tdF5E+IyD/6Ds/5UyJiIvIn3uK+b8n7TER+Q/7erxWR/1xEPi8i
+x9s872eLyO8Tkf9LRN7Ix/4REfmlH+4nLoqiKIqiCMrRTsYMd/TeKYboyXaJQLJwqBtNewhnjbCv
+lQRuEGPdREWYiiKt02ejzUgwP3WhLFEeI+iagrqliO7amL4WlS1G0kVxDYe7tRa72Bl81jRupTVc
+hOngc+JEUnocK7tbYgQ+X9/OajJPJz4uFqiAmMQIdzwr1qQNxByZEXS2BPXq9ZYcn/fVL57vLe6Y
+GsIEnaAD3QwujnbDNcbJh8M5N+6A6/n95x8SkgnnRVE8T0Tk5wA/CPxt3ILNXgO+A/gOEfnVwD/u
+azzoxnsJQotBI5E/BPz6d3q8iPy9wB8Ffurd4z4B/EPArxKRT7+nD1QURVEURfE+KKGdTFtCW3HN
+pPGzwlkz5yyqvJoYaCRghwh2LLPCHEfEw/2l0eh0bVEVNpYjfZc0RopYCWG+3N/eWhZSW4ykewaS
+uWJAO3ext0wYj11sEY3R7hS4N6Eb1WHufoajmVvsQPutu3qJbGmOaFxokAxEUwFDkOkhtBWmQ+sz
+ne0cRV9xcbnjbWbMaYhHcri4IM0QHWg39AG0O6gzPUQ86zsSzq7yOPK7wrHa0S6K58x/CHwL8O8A
+/zHwBeDnA78D+DbgHyZSsr7nLZ77Xv7h/hfy9f408PuBvwT8NODnnC8i8rcQIvvjRE3BZ990Lr8T
++DRQBc9FURRFUXyolNBOlqOtmoaHCnq62WTvc+5pa3xtknlcUxwRi33qrPlSFbo2unb60WmuiK39
+4jM6LcfE478qNcV21wgowyauRE93uuqkY9z6Rts2+nZBtZ0VXginy25mWQEWP5M7zwKRcO6xYy4u
+cXv3n7brxwggT/G++sRb1nup091pe0N1ptC/TQMAtxHwOU8RD5Es3nWim9EfHOmko50p5XYT2sLM
+d25x/qoh+LWEdlE8UwT4RcA/5u7/0d3v/48i8v3Afwv8AuC3ici/6+7/6wd4j58H/EF3/43v8Jh/
+g5uT/U+8w7n8og/w/kVRFEVRFG9LCe3Es6LLM2Wc7LHmFMQQLdmN1S1NhpNFIvgEn7mrbZhPzAfm
+A2ciCq0r3RsNobnSmPS+5dFpbTt/vbUeNVo5Ri0a72WhyOmXB7btgX55QFs7zxnCnbc85ozbMN8n
+bkb2jTEs9tKHHcwltFcfeIaPndcZcnjcRZhEMNoU5xBjtIPZBt4PvA/YBmwTcaMpSFPaBtrXIbQH
+o72mtNcm7TXh8mi0Hufmd7Vi4hm+tgLaMgt+Il/TLbtF8XWOA3/0TcI27nD/soj808B/T1zL+2eA
+3/Y+X1+AHwP+ubd9gMg3Ab/6fZxLURRFURTFh0YJ7cRXgrWQadZyV4m9FF0miZ8xaIIT+8OZQY77
+xDGmDfA9ksV9C/dXla4bXYwmna7OtnX6luFl24XeLyG2ty3eI0Wn4qAx1i6t0S+PbA9xSDraGdbN
+tMGcA7PBGNHd7QNsxE65MTA72MeVfezs48qwgSjQQDQPkfN2iWxcMGKF2hwOnF0Go01mn/i24+NA
+LoMmRvPoABdv9IvQL0q/CPpg6ONAXwz08aA/TPoGIjnejsb0ODEuj0mK7Yieux93L4riWfIH3+4O
+d/8hEflfgJ8L/LIP8NpLPH/5HR7znUQLor+PcymKoiiKovhQKKG9SKF9uqdrnnvdncJaQ/IS/+2m
+KbQFSZc4DOgZSpUjwsj0BdKcpo2tbXScLtAl6r22rdO2niL7wtYvbH07HW33HB1vDWkdaZ3t8QWX
+xxdsjy8QbbdBdHfGPJjjYMwD2o4fYGIIAzNnMBhz52k88bS/wdPxBsfckU4I7c65b63OWQEW6Wix
+L56+OFNgV2M0Y26GjQPmgcyBqkdKOw2VxuVRuTw0Lo+N9mjwsCMXgQfQBtLuurF9xa+l0HbBTWIf
+PkfKvSztonjO/NC73P9nCXH7bSLS3X28z9f/8+9y/8/7AOdSFEVRFEXxoVBCeyGrliuDuOQcIicT
+uTKUK3alNWVexn6RRdopeKOeitg4huZoU/q2ceGBrsLWoDcJod3zuDyybZd0trcQ2edfRLd229C+
+nW725eEFnEI7RLmMCEZjCIYzfSKuMCXd9smRjvZ1PPHG/jq7XV/JH5MG6qvBW05XW0QzCD0uApjD
+EJjN8Q5cBmqT5o4YdFFa7p0/vOhcHhsPLxrtMvHN8MvE+8w98LhoEeegyApBc72JbcLVdomO76Io
+ni0/8i73/7W8FeCnAz/6Pl//x97l/p/xAc7lQ8CAn/UeHtfy+Hrn5Ud9AkVRFMXXCC9fvuTly3f/
+/419378KZ/OVU0I7aS0DzjLd2l0w91fqquC2rZ3pYOAphD2qrqLzeQWTNaCj+sDWX/Dw8DEe5WNc
+NmUbyjaVrWsKbWW7XLg8vGC7XNBto5E7yW7xmlnfJRpd3nGe3F0MeHWj/Jb+HfvNPh2bjk1jjhm3
+GZgWoWkZQpZBZOc1h3OMPB1ty2MStyiugmwSf0OpoJcGturQGk0a2wW2i9MuhvQJbeAM3I/ccfdM
+N1eU1Vnesptb4/clAutE9C5crSiKZ8hP9j+g83089qv8L4v3e82gKIqiKIrPfvazfOYzn/moT+ND
+o4R20toWPzi3ELL8tcutgou8XWPLS2RHlVXuLpPj1nREN1p7oG8htF+0j3OZjYspF2tsTek9BHff
+Ov1yoW0XWu+YG80N8x7nkyJbtIWQJ2u/XM5U8DzRqARb9WQx1Y5Px6dh07ARlVs2c287xbZZ/F6q
+89iFtrtgtNzTDrFNus5xAUC10VVom7KZnL3jKlFv1jq07vQ+8TaZMnAZxMTo6dsDikqnZWf5SmNX
+Wantfkt4L4riufJNRH3XO90P8W/dd3OnPwj3r/lez+UrRkT4mT/zZ77r41prtPaN4GgHn/jEJz7q
+UyiKoiieOb/5N/9mvuu7vutdH/fLf/kv50d/9Plf1C6hnbSs7Drd3xTNZyt0/nCK7XxwiGzDbP2c
+gteXo72h7YEthfbj9nEerPHgnQdv9KZsXehNaV2R3tHWkKan+A2xLYj2FNp6Cu1TbEP2bN+d7Jv6
+rE9H+15knzVgzrDJlDjc7ezxRvyVFHJ3idjx7BbvstF0i0ozaVmD1l5xpkUUFUfUUHVMBsYEwtU+
+JwSQ/HyNJhtdeuyKy62D/FajVkK7KJ4xv5h3Fre/OG//jw+wn/1e+Asf4Fy+Yr75m7+ZH/7hH/6w
+Xq4oiqIovmH45Cc/ySc/+cl3fdzlcvkqnM1XTgntZGuPAJgZ0w3Dcgw89Z9HV7ZqjJcjHk736bY2
+1BtKBwSVC00f6O0BlY67MKYxNfaREVDRcDV6o3dFmyJNiTdZHVsZAZ7nEf3Sho+BoUyXfHwEuBnG
+OHaOsTOOK8d+5dh3xjGYc+bzBdWWR6e1jtpEFezOtZc8B9HbCL1kGNpytgWlaY/xcF0iO/LYBQ9x
+zQxx3OJUWyPH3hXzhnu7NWwvsS1rN5ys+vJzFT5GBm7VY0VRPEu+G/hP3+oOEfnFwLcT/1T/4E/S
++/9JYrxc3+O5FEVRFEVRfGiU0E62HkJ7zgk2XunF9hTdqjFSrin+4Ca0mzSm9EjpRunyQNcHentE
+6LjBOA52djYVXBvSYsRcVWm9o03PtHO/c6Vd9OaWu4HD9AMxhzlzVDz0p7uzH1eOsXMcV8ZxMI9I
+IZ9jhpAVRVtHfaMxUC6oWIxoq2S4eFxYEPWb0F5VX2tc3WMXvWsPsS3RMb4a0QS7c53jsbqce2m4
+Ncw6bnlRw+yViLlb6vv67Lk3LoIq5wWIoiieHQJ8l4j8Onf/gVfuEPkY8PvzlwZ89ifjBNz9r4rI
+HwF+7bucy2fh/l88RVEURVEUXzkltJPlaIsPXBVjx1YvtpMOd9Rd4auFylP0KeqNJk5DcGk0vdD1
+ga09otIxc45jcPjO3BousROuKmhr4So3zdqsW6jacrajNmyNtM8IOZ8GMm4iO4PT9uPKcVzZ92u4
+2HPmmPjEzFPwdppvNB9xiOEoJmtM26OeSx1pftuP1hTZOR4vohl2pqi2nL2/OxIXEO1oV9omqDR8
+dmwaTo6viyBuvDL2DueIPktkS3Rzl6VdFM8WBz4H/Aci8h3ADwBfBH4+8DuAvyMf82+7+198m+d/
+GHwP8PcDH3+Lc/kFwO8EvjXP9UMbHy+KoiiKoiihnahGKI1ICD1HWAHckfztWX8VQWFyVn7Fz7GP
+3GgSQlul525yw82Zc7L7ld07Qzes2ekQR2d1VnKZpbC02P12x8xjnH06ZsQ+OANb57l8d4/H7Smy
+j+OK2Qo24+51/Qx3k5XonWnrgrLms2N0O5vCU+DqClrzcNpjpFzz/AUsQ818udO3VDnPUXjRFvvu
+Lqgr6h2ICxprYh7I79zOJHTPiwSo3tz1oiieK/8I8MeB3wL81jfd54Tg/Z63ee6H8g+3u/9lEfku
+4I8QYvu3vulcHPhMvl8J7aIoiqIoPjRKaCfDDyBGskce0weefdoxL04mc78aN7bcbVWhSYx/a7rh
+c+4c1thniPAHa4x+wXlx1mUFOTptk7mOcTDGwRiDMSdzOnMa0zxd9hhlnx5d2WbGsMlx7Bzjyn7s
+iKeDLXlpwCfGjFs7cBu3dPH1+z6iOkvAxTGyOVxTYJ81ZnnBoUXHeEjy20UCUtAvUR3Pm7FXHt8m
+IuHqO4ouB3w1jGVg2wpyczOaNmgb0gRtNTpeFM+QuKwXIvcXAr8d+DXAtwAH8D8Dn3X3P/xur/EB
+7vuJD3b/0yLyc4HvBX4F8EkikfyHgH/L3X9QRL7v/b5uURRFURTFO1FCO5kexefDBzNF9vRxSxtX
+Od1gt3CyVcL7XWPVKtBFccmwtBTaA2WP+C+uNMZ8gbnddXRz9nGbTWwO5hwpsg+OcTDmZGRa+BiW
+QjsE6TRjzMGYg2mD4zhiR3sccU4aNTIqgjOjMzsFtduENbDuMV4+fWTWmWPLxXYiST13xaMSLL4e
+F8Wl0e+c+NinDiEuWTtmmZ5uNnO92s/vVl1AWyaoh5CfFt3adtaSOV1BtkYTIlmtKIpng7t/hnCI
+16+/APyuPN7ra3znO9z3l4H33Ynl7p8H/tl3uP+V8y6KoiiKovhKKaGdjDuhPU5Xe2Y9lcZOtGUq
+ecxv085U7lDjmuPTLpJCdGBmHLnbLe7sbIxx4FiMiishynMM/RTa4ziPENuDMeZ52OrsdmfMybEe
+M/PxeduacumdrW+0piGmmTmWfmS1lkVw2RLgNl9JVffcEU9ZnZ8tRtqj9nqN0OvtYoQb4iGyYyA9
+39vWSHh0kUsK+XDEHXXB5jz7vO97v30YdKGJ441Y/C6KoiiKoiiKonhmlNBODrsCMNzC1WbG2Pi9
+4ASmw5gWY94pqrOrC4jEccNinNtiTFosHFtxYZ87h+0cM8QwYhEAlkc40yGwj2NnP3b242A/Do5j
+sB+D45ghsi3E7k1oH4w5MBtMC8FsTRGL1zZTpuX9c0RnNoMhk8HBsJ3hO8OuuRcNMgVtQmud1oze
+Ou6e7rSn9BbMhWl3iePOzc322Pke5viYGPGaa687erPzK1Rwk3P/etV6RaXaTbaHu15CuyiKoiiK
+oiiK50cJ7WS3cLSnGRPDxDCJ/WbPRWN3YZqn0J64x97y2fucG35LkE+HYY6m0MaF3fYQ23lAA2+4
+TYQQ2jPF9jh2jj2Cza77wXU/2I/Bvo8ISDuF9uAYI4T2OMj88bjtLZO8J9OUMfY8Dkwm1gxXY8iM
+CwB+5bAdV8dn6FltSu/G5n0tX57ONURI3LRo24pO8dgJj4C1FMwu+AwnfJjTmtK2TpeeYWv31eES
+7bf5vLMzW0CkRfgaUkK7KIqiKIqiKIpnSQntZDna02HiTBxXyT5pYhycNaqdY81tVa9m2JjmcHUG
+lA0zDnPEUhRaCO1jhvt8zAN8giloA7dwopfY3q8c+xPX6xNP153rfvB0PbgeI+u6nDlTaB/xmnOO
+dInDIXZr8R5M1ORMI9/3K64G3aHBVGP4NRxt35niuIIJSFO2TBF3ydauO6GNRdc24uc+eNcewjhn
+7gXBpkHuhLeuXARa0xTP8qrYXsoaOC9kSAj5SEa/v78oiqIoiqIoiuL5UEI72UekjsceNpiHgy0W
+QWF4Jn7PELc2/S55PASomiNioalzl3vOyWEhsnHYjyvX44mn44mn6xuYNqwppg3Fcz85UsfHOBhH
+HMexs+871+vO034wMxxszhw3H3FMm7SW494aI9uIYRgyYb8+se9x0ByZIF3wZgwOJhEEZ+pMT6GN
+Zg3YiL3yMx18lYrn6DuO9BDJppZutmFoXoDIkXsfOI3Woj87Eszm4LtaAAAgAElEQVSBvKgh66KF
+hrst6YwLGqFuGv3dJbSLoiiKoiiKoniOlNBOjmMAWTltEocrzkSAgTOODCIb0Wft2WN91lQTDrfj
+4TjPSAmXOcBidPrJnnhje50v9y/xpfbAQ+s8tA1rnUYkc7tl+vcYzDnz18bMILTjOJjDovJrhJiP
+nexMDs9uaxdjGthwxCYuxrFfYxz9uCKTDGmL/uuhAxM7k9DPrm0k07+NKfPOZwYRv/1K7iu7Y789
+7orxcl/N2q650+2MYajmnPhyrTWmCVoLgS7Wom/blKadpre6sqIoiqIoiqIoiudGCe3kJrQzAdvD
+gZ5OjEZbiOY5DJshxo0Ulu2+gDUD1MYS25MxwGb8+mpv8EZ/g9fbl/mSPjD7hvcL9C2qwULpR73V
+mK8kcEfF1+A4BuMYdwnkK+nb0xUOke2q2Jy4OSaO+eQ4rnnsaIuU77Y+q8bONrrcejl7sN39FPsi
+gp7j3Ys7+e3rskM0cAsWlWee35dLTA0sR35MVOUceZecpI+R8YZ6o3lDraHSaNJKaBdFURRFURRF
+8WwpoZ0soR2CsRHx1+AjOpx9CmYREGa2HNpVsXV7HU3fNiqpZt46MkJQtqm80V7ny/rAg2zY9gDb
+QLYHTBtnmpp7JIOvequ5OrRzH/sIwX3sIzqr73abwxUO0T7dGD7jsBFBaMeVY+zohOZCJ4LcrEd3
+NllPLfdiOoU2PmJPWjR32Mm48FWqfXP3PT3/+Hosf1/i+zJhGug0RCat56h7inhyVFyFFNqdRkO8
+nYPkN1FfFEVRFEVRFEXxfCihnZjd/cLjF+Eup7t95+62FLNNlKaao8ycFVWGp/PqNAGyxooW4vTw
+yRvjypf21/HcbQa4tB7lVTmuPS1SusOpBlWh9c52uaCt0beN7cHO+5fYlRTLJs40OKZx+OCwEZVf
+EMnduoT5CiJLwY6nDx3J4XjuSXvWmYlkKJpjKjFA39YoOXffxZLD8XwXv2WY5fNnTguIKepOXKpY
+n0UiZM0cZ0admMRj1kuVrV0URVEURVEUxXOjhHbid0L7Vl8lSIq/lXytoURRlshuNG2IegpXEAwV
+R7XRnOjIahneJcrhxtPc0esbmdwdYnS60aXRRWmiTF9hYenvaqNvnQuO+YZ5hret7WcxDI/9bp+Y
+x573MONqk2MOJLuvSaGtuhxwiVCzOJlTZDur3Pp2m01lpyBe4jzEtb8ituWs+VLcc/9bwuU2HHFn
+mqG+dt1v6eOo4yZ5O5luOTHQ4vW/Kn9nFEVRFEVRFEVRvD9KaCdLaDuOO+kkg7imzF5iO0SpInRt
+IYy1pRbPHWlCaDYBV0lHXPF8jeHGG2NnLnGfo9jmcGmOaWfTNznaRBVW95aussbes4RTbFgIazeO
+uWPjiL1uh2MYezraLdq8aKqvpntLutzicaFgBb0tse3Lhk5Hm7saa4158dPJPhPDOce8BU2RbTmO
+nmLbDTHBXHME/y6JjRW2Ztgp0ONb9MwiL4qiKIqiKIqieG6U0E7WnrV5jkS7g0tUbomHU4vnGPWr
+bqrzZnc1x7FVQ4OqnsJYvOEIU5zDJ1c/6HOnj0Z44avqKh6DCtKURqOr4E0Rc6Q1RBtowzCGDaZP
+sIOxD9xhTFuFXRwex+nMsyznNT6+AtBi/3z1fpPnElcSUpHftXuJeK5Jr11pPw9Zo+zrr5Vgnq/h
+eZg7Zp694Bbu+t3utecDHcvvWVBRTEpoF0VRFEVRFEXx/CihnQw7N4NzdJxUkuEA3zqv/BwtN5vM
+u9cQiDHnHI1eYltEI5pcI0FbpSF5mAqHG2/MI4LC1ntpOsC90boi1hE3xJyWQhttSOsMGzAFm4YP
+Z4ix+8GT7exz50gRnm3a2NqDzqXqc1eb3IdOF90ldtRRyZ3ullb1TSX73WHmcWEh58qdnAaQFMaW
+2j6rvlYXmK8EcjVERux2i5zf6Zmovna081CtILSiKIqiKIqiKJ4fJbSTsRzU1fd85nb5OTV9S9S2
+GK6OBekU1alBo9HqFLKigrgCDaEhdFRuYtuAwyc+9gw1E7RpJG2rxri4NhxQM9Sc6YSj3TrSOvs8
+sMMZx4EbTInws+vcudrBboPhk4nRwqOPkfB7RzvmsW9R6h6Bap7utjRFaKi23F+3eJ2ll+221+65
+V85ZNxYp6CrLk5ZzDGB97WYwR7yurp7s3Il/K6EtGvVlRVEURVEURVEUz40S2sk8x6Fvc9FCVE3d
+3F8AT8fWWHnhvpxaE0L7rTSvHDNXzY3iENiqd0J7wj4mY2aqtirNos7qokrvjbZtITrNae6Yc4ps
+eoehHHIgrvhwpsyb0J47wweDmRcIwNC7ADW57Wjfi+x1LCNfw5VW7ZjNTB23HAHPUXvzU2SHWR1L
+6Geyebr08ZKWS9556cJyF9tB1VCJoDlf33k64BHmFuP8Z3hbURRFURRFURTFM6KEdtKkxQ8pOCV3
+i5toJIx73ras80JjB1sU1QZquDq0JWFzpDr3uj27qkNb+hnkbZPYuZ6ARXUYDjYnr/ECbcImG9qU
+1sITdwS0ReqYNg4DMKYNxjgY82B6HM5MB/j2WUMYR8CYCUxZ0tdiX3rGfW6Cy0wnf8XBgduMmrA4
+eZoaqCMZ3raC5PD1PdgpylWEpg0zid+39Neb59eSVrdIFIyJ3kb13VDlfJx6Ce2iKIqiKIqiKJ4f
+JbSTJvFVRGJ4ONmKIJ6Z2SY0aXTtdOk0TWe6KaqKycSYmIxM/47l4xixXnvJ6RpngJiLg5EiO0U5
+EQg250C6sl02XEG60qRlerfiqpjE+LdMwXGGDY65M+bOtAPzEUJZM+hM5dzBNpdTaFv2aNsS4Cl+
+XcIBJybkb6P0uZ9uZojaLSdNPUfIb3vVIbLDrZas+dJ0+6d7jLqnC649969RRGKfXCQqzsyW0I5z
+azja5pv/GIuiKIqiKIqiKD5ySmgny9FWCde4rWTumLUGhy6dTTcu/UJvHW0tD2X6wfCD6c4ww1Kh
+OvNM2CbTuV08e6gdZlaLTY/e7DkZEs709rDxwh8x9Uge10bTHuPbhHQ1AT0ET0f7GDtjHplCPjBm
+GMSN2150JnwvkW1E5dbpaFsK7nyKK5m47kg6y3FMNHQx0gQzwWzta9/2qm9i2xGJCwaIxGvMmWPj
+t31uX3VeWQs2zbBpTDNEod225L+qf48URVEURVEURVG8F0poJ5e2ARlZJkrLUemzQtqgt42tbXTd
+UvAqIi0StTHwkZ50utl3QnuJbfEU2SJRT5WuLhkoBhPzgY3BdX/kOHbGGHgP57ip0ltjEgFuywUP
+l3ncnGwfQM6j66rWArXYJRfz+NNvEYhu4sx0tWfugbsbtk7eYjQcs9tYvK975ZW17leP2/mRKeTr
++kVGvGMzney8dRdcI71cJO6feXFAAOZ8c59aURRFURRFURTFs6GEdvKiPwC5lw2s+DJxgRZ70007
+KnGIK256isnplsdk2mT6TLE7ziCvm+jMne2MJ1+RYBCj13MCTMa+c1yvHE9PHNpoKLRLCHu3rOOy
+EL828xi4p8AWR9TPZHEkwtrU8zM2hS54j1qxbAljrNFvOCu4liUveaaiHhPjGsebK61PB//OzZfz
++Y5mmZlbHDbjooHhqMZ5No0Jg9MlNzm/I5851l4URVEURVEURfHMKKGdPG4ptD2E6Ar+ip3i1YUd
+y8ji8XPme+HmDPcQ2cwY2fbY1XYm96nZIVNzqXm9QKaTk0FkTMMNjv3Kcb2yPz3x0DZcL8jFaSKR
+PL4Cx1Jkm41IBPeYR48wMQ9HW/2sy2ooXQRVQVpcSDB3psKA2J3OMfclsuXW9XX2b6vE85eIX6xK
+MM/j3uIWoqLL7ru47/bTcUOb0NZ9KvE13b2Wm2ES2elFURRFURRFURTPjRLayUNbjrYjEaKdAWLt
+TBY3E9wkbzO9O8eoJ+loM3O/OUR2jI7fUrjDk9WM87JwzP1cng7BPCY2/RVHe/QLtj0i5hHWBtnj
+Pe/c7BDbMYseIltIVztdYlWJfm7RqPVa6ee2EshDbK+C6xWAdn+OoppHvKbcO9p3LrYbt310ubnZ
+IsvRzmA2i9l5zxF18zhf2qpSE9zXiRLj5TJzHL0oiq9lROS7gT9A/Nvjb3X3v/IRn9JXxMuXL/nU
+pz71UZ/GV4VPfOITfO5zn/uoT6MoiqIoniUltJOzPhsQCdEn6zfcEJu4LSc7RJ/LuSEdo9YWNdSW
+zrVnuvYS5UsA+ynCNYUkZJ9W7j2H8FzhZtfjietx4XE8MuaOzQfmHHGki20eKeDLdVaF1tIpT3te
+MmwshKrH+eUHNzcGsZ89WfVmZAx7iOlwxuUU6KJZG7a6xhHmDPHrBtbBt+wTF1nmfbxfNnkDiEvu
+qcd54WBtCfw4gWWaSyp6cbm9WFEUxTPBzPj85z//UZ9GURRFURQfMSW0E/PbzyFIU0KbpVAGvCF5
+gKbJG9a3r7RuyyCxO2F5ikh38IlLIzxwzVTz1bPl59g3nkJ77uz7E/t+4TiuzLFjdjBtxC54Cu4Y
+GY90b8ne7FNosxxlTlfZFZaL7BmAtkS25ZM0xbYqeArs6D2Tm/g+r0bEdxRa35jT6RZfgEgI9LZO
+RiJ8zbGcRJf4bizOCI2LFbjgLXa2w5m/9ZtTQrsoimfLz/6oT+AnmZdQqztFURRF8Y6U0E5OoZ2C
+NBegz17pOaPaSnHUJZ1hIJ3vVWdlM19L7hzttUud49fOxDx2v2MtOUai3XIc/M7RHmPnejT245rV
+XTtzHthcIjvC11av9bkFLim0fcWshTYVWTPx4ZtHd3Z8zuHnsPv5+VRiNd1Pkc3paKN+E765z23m
++aNkmrjEmHmL55Kvfaazkye2AtgsXtchE89DZNPWGPt6vyW2i6IonhMK/PBHfRI/yXwKKNe+KIqi
+KN6JEtrJHCN+EJDmiNpZQ3Vndp8p48s5RhzXFUhmeV88yXP22leoGDeBKXdSM56TQV9uGWQW4WqH
+Dfaxs4/97Mi2eYTAPo/BnHY66n4GrEVgmctytmP+WuTciM6c79yLFs9O7Lux8TU63iL4THs7nWXV
+5Smn+4yf7r9lf9ccxuyDMfPLW056Pj5eR+PqxPqiM+lcfP2RhCuumufutz+HoiiKoiiKoiiK54Z+
+1CfwXDiug+M6GMdgjpmCNZO6tdG106RHE7QvB9rCSZ7HK2nfK/wsdrblrPPyXJZewvHcgybGrc0t
+3OWUv9ONYZPDDo55MObBHK8eYyxX225j6x4p3Uslayant6anWBX87lxzL1wcbU7bQDtoy4sODbQJ
+2pXWW7xO03Nfezn7qxLsvNBgsa84pjHmYMwRn8MOpo8Ig1OhtUbTHodsNOk0OioNpcWtaPaWp7S/
+G3cviqJ4BuT/n9a/k4ria4WXL1/y6U9/mpcvX37Up1IUxftgRhcyPHMt+6xP7qvJsR8c+8E4DuaY
+p0MMkiJ1Q7VlzZfgFmnZNkccNvBMz14p3Wfytq1qqrvhZ5FTNJ4i1WNvecWlDbdwtFOgnmI7x8fP
+QLQ5sTzfWxVW3C5R37ShqlHJdU5cZ3P1ctHV0AatQ+uC9nS3myA93Oy2NbQ3tGUwmpye9u0z5Hub
+wzRnzskYI8V2fgYbsTe+hHZrIbClo7L9BJHdNIV2Lo5bppVHYnlRFM8VEflpIvK7ReR/E5HXReSv
+ich/LSK/7n28xreIyL8pIn9RRL4oIl8Wkb8kIr9fRL79Pb7GrxKRPyYiP5LP/99F5PeIyDfl/f+P
+iJiI/Hsf8KO2D/i8oig+Il6+fMlnPvOZEtpF8TXGndB+1v/fW6PjyTxydJysvWoaO8kxgI1KCyM7
+RSzZ/exieOR1p2C9H4G+/SiQI9y3zulX47zuxXZYwtON4ZNhwjGPFNcHc+So+DrGTWibrYiafHVZ
+Y9eR3L3c8pVsftrPOcYdDnWGk+XdouloN0VbI1PLAMPNz53r9ZnPT+XEBYnpxD8PTsyhG43sKF/f
+tUt0qpnG48QQsRgtzx5zVckJ8wxwK5FdFM8aEfk7gR8EPsntXxMPwC8BfqmI/AHgv3mX1/gNwGfz
+efd28d8OfCvwm0Tkd7n7736H1/h9wG/JX67X+FbgtwO/XkR+BetfhEVRFEVRFB8CJbSTLnFBREVo
+OW4tHqng5rmDPQUbgo9wtF1jkVh0DWmv2iyPELLMCIMVRPYWb3yX2h3aOJ+08r58ubeOTcPHxMfM
+4msHy/c6M9RWtniOqrtnmpmc6ehrJ3tdGJAU2Zk9Fm+f4vm2Gy2RQn5rBLuFmYkgoZwRVTDFc8y7
+qaPiyNq59pV8bvm+2fOdVWnm+Z457q4a4/vC2mHPaYGfsD1fFMVzQkQ+DvyXwCeIf1j/MPCHgB8B
+vg34F4F/EnhbR1pEfiXRsQ3w48DvBf44MIC/G/he4G8G/jUR+TF3/+xbvMa/TIhsB/5f4F8H/gdC
+uP+DeR4/ALz2lXzeoiiKoiiKe0poJ11DaAsptF2z31mw4fgMoe1T8RGiT1qGhwmoeJiyLKc7BfBS
+r3fVVm/JErErhUxu9opZpHn7NHwYjIw2d0dSYN+L7SWyz0IvlRwj55bu7Suh3Ffe2Sns1zmendWr
+VuvuYxh+PlbysSIS1WfRB5a1XpZC23M7PW3+U3kb8SE4R+3XJIFmJZjIq8nur4rtoiieKf8KEU/t
+wPe6+++5u+/PicgPAP8Z8A+81ZNFpBNONsCXgL/H3f/C3UP+rIj8J8CfIRzz3ysi3+/uf+PuNb4J
++HSew/8J/F3u/mN3r/HficgfA/4kcKGu3hVFURRF8SFRO9pJk0aTRs/bRkNNYYINZ+zG2I25GzY8
+xTdnOrYQYls0j6zQWlVa8BZziZ7/s9xuuQlasofbs35r7YT7TEd7GkxHLI7T4M1ObjfBTV/dDV9B
+aRavd4p17jqzT+GcbV4KqvJqwvj9bHza36IRlhaBaUrvjd5a7IajIbJPVzuq0uTO1V7fkZ275TGu
+3zRC0c6rAOnI+3ke9d/FRfHcEJEN+I3EP6B//k0iGwB3n8BvAo63eZlfA3xz/vyvvklkr9f4K8C/
+lL98Dfin3vSQ7wYe8+d//k0ie73GnwF+3zt+oKIoiqIoivdJCe3ksm1cto3ee+wEZ7dzpIsvF/Xm
+3q4E7BW6LW/+S6JaS1RSqAqaZu9yciPyzFib06cSPQW33MamiXNZO9k+DXGPvmyROJCsupYlbXPk
+WjIB/JZIflvNlhTnmRI+Y6fa5wpwi1cjw83sfJ1bwjm5271SyM+LDGSqueVFiRxtF2LnWkSyl/u2
+H67rNVTyMZp1YcYYkzliLzx4dcu9KIpnwy8Efnr+/O+/3YPc/fPAf/U2d/+y9TBu4+NvxfcDX3jT
+c978Gv+fu/8X7/Aaf+gd7iuKoiiKonjf1Oh48nC5AEvyxl+nwCaF7xJ/riHx0vK9ieElIh3FcFEa
+dpscz1otcTt3r0Pv3tni67U0hbrErrIQ++I+HRsz3lcjjbtpCm0VmmgEtHn4xcBtXHx1bDuIx5D7
+Wrh2lsiOMXXVEOstx8At3X3u/GTgdqkmLypgxGcxw9K9Xm/qoaTzMkDLCxL5nSznvHFemJDThY9g
+uKgEm7iEuld91kGDRfGNzM+7+/mH3uWxfxb4lW/x+2t3+/9297/+dk9290NE/hzwHfzEfe9vJ/71
+9j+9yzn8BWAHtnd5XFEURVEUxXuihHZyuTwAMG0wbMSYtt1cbEjHFUE9a61u9m3+pa/8rGK3xeb7
+MLS0ht3tzC87PdpbMXW64ktoZxjYcrR7jnOnm931drgLJkJzPUPD3Dj3vm+j5CsBLWvBJszhzOm0
+Fp/ERQGNXXVC9JKBbwio3yWbt6wVy91pt/isvsxtBXqKbGl5UQLImi9p2d/Nq/VnZs60yTEHiEW3
+912ndlEUz46fcffzj7zLY//aO7yGv4fnA/zVt3hfuLnqP/pOT3Z3E5G/AXzTe3ivd8GBn/UeHtd4
+5q0k70BVIRVFURQfPi9fvnxPdXvH8XZbZ8+LEtrJZQsj4xhgbsgZGnZL3xbkTCUXWcnea3z5JrBj
+79hOoYzmHnI2V616qrVW7XC62et3JN9D0tU+3d0MRaP5+ZSW7vc6Wm4/n0HkfsrpFPTrooCfhvNt
+bBzmiNMxFVqOj0PufXt+Dvn/2bvzcMu2u6z339+Yc661dlN91TnJyYGIBC9oAEMMPdIKgYCAiIBc
+IYRGH3kUFb1cfbgXgvfx+iAg3AteaZSgMSI2xAaMGAitgpEoCokijZpzTpLTVLObtWYzxvjdP8bc
+u1btU7tqV51dtXfVfj/PM8+aa60x5xxr11l77XeNDgg+/gx2fwS7U4vv/OxwdruN+07Xca9ufH3s
+zp+Gh7H7e9hZ77uE95RK13FCHlvvy7raInLsvdCJFB7AiRhumetFRETkJr73e7+X17/+9UddjUOj
+oH0AO/FXREQOZHnSsUcpM37vZ79W5MuUX78HaWV+0dIxe+vxKGUJsH1Z6Up07lZlDmC3Nf2gvW0e
+9F45Tz/9NI8//vhRV0PkrvV9D8CrX/1qJuMQQhE5OiklLl265Uc2AM88s/uF9t6ebMeKgvboG//C
+zzzYf/GIiBwfyzOEvwr4hVuUfdU+j/8q8DHAB5jZhf3GaY/LgL2C8n3or+55+tcoIfz33qa+H0pZ
+V/uFfKe6+xmyM7fH7Ry03HGVc+bJJ5886mqIvGBLf7SLyIPlWOc3BW0RETlsv0xpTT4L/DHgO29W
+yMxewj7raANvBb6a8iH6FcC37VPuC4EzlJD81j3P/STwqcBFM/tMd/+X+5zjy/d5/E50lLCeOdi4
+chEREbk7j1AGs3ZHXZFbsQf9G3URETl+zOzbgD9HCcDf4O7ftuf5CvjnwGdwfYTOB4xrY++sxf3b
+lLW0rwGf4O6/uucc7wf827HMNvBSd7+89PyLgd8CJpTu6x+7t2XczD4GeBvXZxz/IXd/3Qv+AYiI
+iMiJptmkRETkXvgW4AlKiP5WM/t7ZvYZZvYKM/siSkD+DODf3+xgdx+Ar6EE8DPAL5jZN5rZx5jZ
+R5rZn6UsHfbYWObrl0P2eI73AK8f6/BBwC+b2Z8ws99nZh9nZn+Z0gr+JPDszmGH+UMQERGRk0kt
+2iIick+Y2e8G/jVlnPTecVQO/CDwc+PtDS3aS+f4Y8D3Urpl3+wcCfhGd//WW9TjbwB/fOfunqef
+Bj4L+FHgceBvuvvXHuT1iYiIiOxHLdoiInJPuPs7gd8DfCvw60BLWfvqp4Avcfev2inKPi3J7v53
+gQ8Gvgt4J7AFzCldwb8PeMWtQvZ4jj8JfC7wE8BzwAL4b5Sx469w93cAp8fi1+7mtYqIiIgsU4u2
+iIicaOOkbO+mhP2vcvcfPOIqiYiIyANOLdoiInLS/dGl/V88slqIiIjIQ0Mt2iIi8tAys1XgtLu/
+d5/nXwH8NHAKeLu7f9R9rJ6IiIg8pLSOtoiIPMwuAe8yszcDbwH+K2XdzceAzwReB6xQ1r/+c0dV
+SREREXm4qEVbREQeWmb2Usp63M7zZxxnfLynjM3+e/ezbiIiIvLwUtAWEZGHlpnVwOcBrwZeRWnh
+Pk+Zufy/U5Yf+253f/dR1VFEREQePgraIiJy4pnZ+wNfR1lT+/0o3ct/E/gR4HvcfXFI1/kS4LXA
+hwFngfdR1hL/HnfXRGwid+hevnfN7JuAbzpg8U9y95+922uJnARmdgn4yHF71bhdGJ9+g7u/7h5c
+88g+dxW0RUTkRDOzzwH+LmUt7b0fikZZA/w17v6bL+AaM+AfU8aF3+waGfgWd/+Wu72GyElzr9+7
+S0H7dn8sO/ApCtoit2Zmec9Dy++tHzrMoH0cPne1vJeIiJxY46zjP0yZdXwT+EvAxwKfCnw/5cP5
+g4B/YWZrL+BSP8j1D/ufonRn/0jgK4HfoHwef5OZfdULuIbIiXEf37s7Xg586D7bhwFvP4RriJwE
+Pm7/A/gJbj5/ymE48s9dtWiLiMiJZWY/C3w8MACf4O7/bs/zXw/8NcoH9evv5ptvM/sU4K3jOf4Z
+8Id86cPXzC4Avwy8P3AF+J3ufu3uXpHIyXCf3ru7LdruXr3wWoucbON76u2U5TSf2TNh6aG1aB+X
+z121aIuIyIlkZq+i/KHuwA/s/UN99B3AuyjfuH+dmd3NH9tfP95G4Gt9zzfc7v4c8A3j3bOAWrVF
+buE+vndF5BC5++vd/cfd/Zl7fKlj8bmroC0iIifV5y3tv+FmBcYP578z3j0LfPKdXMDM1ildWR14
+q7s/tU/RfwJsjPuffyfXEDmB7vl7V0QeTMfpc1dBW0RETqqPH2+3KV3I9vMzS/sfd4fXeBUwucl5
+buDuA/CLlNa3V6n1TeSW7sd7V0QeTMfmc1dBW0RETqoPoXzj/Rvuvncm1GX/Zc8xd+J373OeW12n
+pkziJCI3dz/euzcws39lZu8zs268fZuZfYOZnX0h5xWRQ3dsPncVtEVE5MQxsylwcbz7xK3KuvtV
+SssZlHV678TjS/u3vA7w7qX9O72OyIlwH9+7e33aeN16vP39wP8N/JaZ/cEXeG4ROTzH5nO3PuwT
+ioiIPABOLe1vHaD8NrAKrN/D62wv7d/pdUROivv13t3xn4A3A/8OeApogP8F+FLg0ynjv/+RmX2O
+u/+ru7yGiByeY/O5q6AtIiIn0Wxpvz9A+Y4yjmvlHl6nW9q/0+uInBT3670L8Nfd/fU3efztwBvN
+7GuAvwlUwA+Y2Qe6+0HqJCL3zrH53FXXcREROYnapf3JvqWum1LGhC7u4XWmS/t3eh2Rk+J+vXdx
+943bPP99wN+iBPnHgC+402uIyKE7Np+7CtoiInISbS7tH6S72Np4e5Cuqnd7nbWl/Tu9jshJcb/e
+uwf1vUv7n3iPriEiB3dsPncVtEVE5MRx9w54brz7+K3KjrMK73wYv/tWZW9ieSKWW16HGydiudPr
+iJwI9/G9e1DvXNp/yT26hogc3LH53FXQFhGRk+qdlC6fL+B1ivAAACAASURBVDOzW30efvDS/rvu
+4ho3O8+trhOB/3aH1xE5Se7He/eg/B6dV0TuzrH53FXQFhGRk+rnx9s14JW3KLfcHfQX7vAab+f6
+ZCz7dis1swb4aMof7W9393SH1xE5Se7He/egltfsfeoeXUNEDu7YfO4qaIuIyEn15qX9r7hZATMz
+4MvGu1eBt93JBdx9C/hJSuvbp5nZY/sU/QLg9Lj/T+7kGiIn0D1/796BP7G0/zP36BoickDH6XNX
+QVtERE4kd3878HOUD+OvNLOPukmxPw98COUb7+/c+423mX25meVx+z/3udS3jbc18D17u7qa2UXg
+r453r1JmMRaRfdyP966ZvdzMPvBW9RiX9/rK8e57gR+981cjInfiQfrc1TraIiJykn0dpUvpCvCv
+zeyvUFq+VoAvAb56LPdfge+4xXn2Hafp7m8zsx8Gvhj43PE630npZvphwF8C3n88x//m7tde0CsS
+ORnu9Xv3lZS1sd8G/EvgP1MmYasp4zr/V+APjGUj8NXurmX5RG7BzD4OeNnSQxeX9l9mZl++XN7d
+f+gWpzv2n7sK2iIicmK5+380sz8CvJHSheyv7C1C+UP9Ne6+/QIu9TrgFPBZwCcBn7znGgn4FndX
+a7bIAdyn924APhX4tP2qQQnfr3P3H7/La4icJF8FfPlNHjfg48dthwO3Ctq3c+SfuwraIiJyorn7
+j5nZh1FayF5DWQ6kB34D+BHge9y9vdUpDnCNFvgcM/ti4LXAhwNngfcBPzte45deyOsQOWnu8Xv3
+xyjdwj8GeAXwKHCBEgguA78CvAV4wzgmVEQO5qAz9d+q3APxuWvuWpVARERERERE5LBoMjQRERER
+ERGRQ6SgLSIiIiIiInKIFLRFREREREREDpGCtoiIiIiIiMghUtAWEREREREROUQK2iIiIiIiIiKH
+SEFbRERERERE5BApaIuIiIiIiIgcIgVtERERERERkUOkoH0EzOy8mX2Tmf2imV02s2hmedy+7Kjr
+JyIiIiIiInevPuoKnDRm9gHAzwGPjQ/5nlsRERERERF5gClo33/fRwnZDiyAtwJPAml8/l1HVC8R
+ERERERE5BOauhtT7xcxeBDxFCdkd8HJ3/62jrZWIiIiIiIgcJo3Rvr9esbT/cwrZIiIiIiIiDx8F
+7fvr3NL+e46sFiIiIiIiInLPKGjfX5Ol/XxktRAREREREZF7RkH7HjOzT9xZugv42zsPA69dWtJr
+Z/vbe48xs59aOtdnmtmbzOzXzWxzfP5P73PdNTP702b2FjN7t5ktxqXE/rOZ/b9m9pF3+DqCmX2l
+mf1rM3vveL7fNrM3m9nnLZX76aW6//47/oGJiIiIiIg84DTr+P2zM+uc7bl/22PM7DTwBuDzlh/f
+7xxm9tmU2c1ftKfcBDgL/B7ga83sTcBXu/viVpUws5cA/4zrY8x3zvf+wEuBP2hmbwa+7HZ1ExER
+ERERedgpaN97TwLfPe5/MPBplBD6X4Cf3FP2F29yvAFvBD6b0t383wPvHB9/OXsCrZl90Vg+jM8l
+4OeB3wDWgU/g+hrefxT4HWb2Ke7e36zyZnYeeBvwgUvX+k3glygzp38I8FHA53K9xV5EREREROTE
+UtC+x9z9N4A/DWBmX04J2gC/5O437fa9x8dR/p1+BfhSd3/n8pNm1izt/07g+7k+JOCXxmN+e88x
+fwb4a2O5jwG+Ffgz+1z/u4CXjfsL4Cvd/Yf3nO/DgR8B/jAlfIuIiIiIiJxYGqN9/NWUGco/ZW/I
+BnD3YenuN1FarY3Sgv0Ze0P2eMx3An9hLGeUbuQv3VvOzD4Y+FJKS7YDr90bssfz/QrwB4ANbpzw
+TURERERE5MRR0D7+HHi9u1+5VSEzOwP8kaVj/oK7b97ikO8Cfm3cD8DX3KTM65b2/427/8N9K+n+
+P4Fv5/oYdBERERERkRNJQft42wmtP3KAsh8LTMf9Z4F/cavC7u7cOKb6k29S7JOW9t94gDocpIyI
+iIiIiMhDTUH7eHPgt9396gHKLs8I/u/c/SDrdP/CeGtLxy/7sKX9X7rdycZu6s8e4LoiIiIiIiIP
+LQXt4++ZA5a7tLT/Pw54zH9f2p+Y2frOnbEr+vJ463cf8JxPHLCciIiIiIjIQ0lB+/i75RrXS9aX
+9rcPeMzecqf2OR/A/IDn3DpgORERERERkYeSgvbDYzngrh3wmL3llidP2xuYV+/ynCIiIiIiIieK
+gvbDY7mL+fsf8JjfsbTfu/tuuHb3a8Dy0mGPH/CcBy0nIiIiIiLyUFLQfnj8h/HWgI80s4Mss/Wx
+460vHb/sPy3tf9TtTjauxX3pduVEREREREQeZgraD49/A3Tj/iXgNbcqPAbxr1h66KduUuynl/a/
+9AB1+GMHKCMiIiIiIvJQU9B+SIxdvf/B0kN/zcxuNV76TwEfOu5n4PtuUmZ5ne2PN7Mv2O9kZvZ+
+wNdTWsdFREREREROLAXth8u3UCYxM+B3AT9hZh+wXMCKrwO+fXzIge929/+592Tu/i7gTeP5DPgh
+M/viveXM7MOBtwKnud6qLiIiIiIiciLVR10BOTzu/ltm9lXAG4EK+Bjgv5rZzwG/SVmy6xOAl+wc
+Avxb4BtucdqvAz4a+ADKzONvMrNvAX4R6IEPHq8D8I+AR4BPHO/nw3llIiIiIiIiDw4F7ePtIBOa
+3cDdf8TMtoAfAB6lBO5PHjco4Xqne/ebgK929/4W53vOzD4J+KfA7x0fftm47RYD3gy8DvhXS49v
+3Gn9RUREREREHnQK2vef77l9oeWef6D7j5vZyyjB97OB3wNcBBbAU8DbgL/j7m8/4PmeMLNXUSZP
++xLg5cAZ4L3ArwBvcPc3A5jZ+aVDr95p3UVERERERB505q65q+RwmNkKcI3yBc6Wu58+4iqJiIiI
+iIjcd5oMTQ7TF1BCtgPvOOK6iIiIiIiIHAkFbTkUZnYO+MtLD/29o6qLiIiIiIjIUVLQltsysx82
+sy8ws+k+z38c8PPAS8eHnqBMtCYiIiIiInLiaIy23JaZ/TYlRG8B/wH4bcrEaueAj+DGGch74LPc
+/afudz1FRERERESOAwVtua0xaL//zt2bFNn5n+gp4Mvc/W33pWIiIiIiIiLHkIK23JaZvRT4fOAT
+gA+kLBV2ARiAZymt3G+hLBnWHVU9RUREREREjgMFbREREREREZFDpMnQRERERERERA6RgraIiIiI
+iIjIIVLQFhERERERETlECtoiIiIiIiIih0hBW0REREREROQQ1UddARERkQedmW0DUyADTx9xdURE
+RB5mj1AajDt3XzvqyuxHy3tdd7x/ELv/Tn6TbamYXS9Vbm3pSbDxGcOxGw61pW3nuLK57zy2VIU9
+zJZud/Z36ueOu9O1G3TdJl23QRw63CM5J9ydyXStbJM16npKqBqqqiFUzfUTYtcvhGHXX9vSixQR
+uf/MLALVUddDRETkBEnufmwbjo9txY7K8f7iwfeE3DHILmdph+tx+nqpZebluJ3IXXaWSy3nVr/h
+Ud/d8aWnffy5+Rj0d47JeM64J3JOtItrzOdXWGxfoR8W5JzIKeFkJpN1JtOyTadrTGarTKerNBbA
+AtgYrN1hjNi7r8CUs0XkyJVfj2Y89thjR10XETmAvu955plnuHTpEpPJ5KirIyIH9NRTT+1ktuMc
+3BS079bRBfK9Ldl7WrRvjKC7wZQ9pR3GFu2dR/aG1aXH7cawjY+BeidcewbP5RYfyzueEylHcoqk
+1LO99Syb155hY+Np+nablBM5xbFF+xTN7BST6WlW186wfuocZkbVTMavDQK+E7bxG6qooC0ix8Bl
+4JGLFy/yxBNPHHVdROQA3vGOd/DKV76St7zlLXzER3zEUVdHRA7okUce4ZlnnoHy2XtsKWjvY2+Q
+3glzR9vifWPA9htj83jPd/uP71vTpRbtUvJ6eLWl/9742GinxXr3HKXF2nPCPY2PZ5xMzpEUe2Ls
+GWLH9tZzXLv6Xq5cfop2vjmG8ETOmcnsDJPpaSazM5w602EWmExXmK2s4VZjYayjgbnh+HLXcRER
+ERERkWNDQfsOHH3I3rs33veblbpJwd2HbSmW3ziou8TvXFqtbwjUO63XO2OuM55Lq3XeDcwRPOGU
+0J3TwBA74tAxDC0b155m49r72LjyXhaLTXJKpJTI2WmmLc1kQTNdkLMxna6wtn6alM6MPccNs7D7
+JYIitoiIiIiIHFcK2gew05ptZkcYtndanXduAzebEM3c9hyzfLvs+spuu+3iuyHaS2t0iqQ8jPsD
+OQ2kFMu465zxlPGxXB67iOex/M4xMXbEoSfGlq2NZ9naeJa+3SD2C3Jycs54ht5b4lDRdVA3a6yf
+2qZdLOi7jqqBmoqwE7iP9WgMETnJnn32WR5//PGjroaIHEDf9wC8+tWv1hhtkQfIs88+e9RVOBAF
+7dvYO/736MP2Po/f2IucGzuB7z3Odrud39AR3fMYfBND7BhiS4wtw7BgGFqGvmUYWjylErJTIscS
+qFOKYxDviWkgxZ6UelIciLEnxY6u3aRfbNG3m8Shx93IGTwbOS5KR3RL1M0a89Nbu0F7QiCEmsqX
+Jh0XkWPJzN4P+EbgU4GXUJa8Avg8d/9nR1ax+8TdefLJJ4+6GiJyB8axniIih0pBex+3mmDraMM2
+3NhS7TeG7HGM9m6J570MY2fe7hsXDMtkh5QzOUWGoaPrt+n6Lbpum67bomu36dotcox4TOSYyHEo
+QXu8jUO32128BO1+93lP/fUtO+6lK3j2iphgyIkh9Uwma8y3S4v20Hdlqa8638Ofp4gchjFkvwO4
+AHt+xZwoLznqCojIgfTAM8AlQC3aIg+G9wAPRi5Q0L5LdzvT9d0E9BLsl+5fP1sZb708AziUGcB3
+1q/ec4tdH2+dcialNM7+nUhj63SOw/WQ3W/Rtpss5pss5hss5ht4ypATpJ3lu3IZm+2ZYRyPPfQd
+OQ1lojRPpbwnzCPBMlQ7ryngHrBQYbkiVBXBMrFfsL15hSvPrbC63rG6PgBO3UwIoSZYhQUtWSty
+zPwflJA9AH8J+Dlga3zufxxVpe4vAzTruMiD4R3AK4G3AJp1XOTB8DjwYPQcU9B+ALj784L9znzg
+O8ttjXdhXGorj8F3Z9ktz2Um8J0ZwSETh4FhGOiHnjgMJWjH0vrc9WMrdr9Fu9hge/sq21tX2d66
+BjljXtbjNjNCgMqMEIy+LyG771s8R8wgGARzzByzjNnOFxUBrAJqKq+pc0PyhrqCOMzZ2riMWWAY
+etwTIRjT6Sp1M6VppoCCtsgx86mUX09vdvdvP+rKiIiIiBwVBe0H1POX3GKpVTuBxzKG2mNZeisn
+sidg3DzS9z1d29K1LX3flbA9DMRhoN/tNr7FYnGNzY3LbG5eZnPjMiUiByozqhBompqmrqmbmr5r
+y9a3kDN1HbCqwupACJTNwEKZRdysAqvI1GSvyTRUwYn9gq2NKwxDJI8hu67r3ZdaVTWVcrbIcbPT
+Z/rXj7QWIiIiIkdMQftu7dcF/MbVsp7/xJ41qpdOePNT75wvZxgnLPOdQO1eWrDx3ZZs97w0Q/hA
+ThEfZwF331l+KwKJvmtp25Zu0dJ13W7IjkPP0M/p+236fk7fbtBvX6Xfvka/vYGNATuZ0VQ15g3B
+aypr8NjisSu3nnEqnBq3CieABSwEoHQdzz6uue2R5IHsRh8XLPqIbS+omi1iTqTsZM8MMbK+nglV
+Q13vjKcKiMixMKH8MhuOuiJHIB11BUTkTr0Y+KbxVkQeQMf6s1dB+7D57lLPd3bQ8/bHsdR5HFF9
+wzJa15fQKmtX56WgncoEZLEnxv7GoJ1vbNGOw8DQ9/R9z9D3u0G73Hbk2MLQEoaO2hNTM2JdE8bW
+6BACVRVoKqMOTkWiDhmvHK/GIeHmZI8MKVNbDVaatT0bQ0rEmBhiz5AqYgoMsbRuY2ULzZRFP7Dd
+dmzN51xYtFy4kAjVZOw+DlXVvNB/NRG5S2b25cAPjnd31j34ZjP75qVib3D315nZTwO/H/hpd/8U
+M/sg4OuAT6e0hq8Av8Pd/+eea3wO8GXAR1NmLdqitJr/U+C73X37NnVcAf488IeBDwRa4F3A33b3
+HzSzTwTeNhb/JHf/2Tv+QezOzKKlEUQeHC8GvvmoKyEid+9Yz4qmoH0EfPc/Zef68OvlyXl9HGt9
+vZU6Dh0xtsShJaZuDNQt7glbCtpx6Ha3naDty0HbM7CzNFckxkgaImkYSEMJ4DmWVnHSQIg9TY5M
+DbxpylrWIWDBCJVRhTFoW8KtBG1qSLm0WGcHj3kM2Y55IGZou8SijbRdohug68uWPOAE3AKhapi3
+LVvzOZtbm/RDJFQNK2unWV07BcB0iogcrZ1fXLfrsrP7S87M/iDwJkq43luOscwU+PvA5+15/hzw
+UZTg/afM7DXu/is3q5iZPQ78FPCypeNXgI8FPs7MPh/4f/apt4iIiMhdUdC+E0t/Su402/jNCjhl
+Xa3SpLtzc5Pe5vv9TVe6h2dPeC4Tm6XY0w+lO/fQzRmGOXHcco4laJPxHIl9t7vudU5DWfd6DNq2
+E7R3gnkuW86Z1A+7gdtzhnGzFEuLdjBC3Yyzm5WgXRqoM8EylWUIGSrH3LAEMWVScmJ2CDVWUYJ2
+chZdYmves7XdsegyizaxaBND2vlr3LBQsb1YsLm9zbXNDZzA6topzp1/hBj7w/l3FZEX4keBt4/7
+v0p5+/5/wN9YKnNlzzEvBd5IaZn+ZuDnKd1tXsX1WcoB/g4lZDvwH4HvoLREnwe+GHgt8BjwVjP7
+MHd/z/JFzKwGfozrIftfAD9AmRb8ceBrgNdQWslFREREDo2C9t3wG272KePjTYnkO7c3MtwzOadx
+Gazrodc9lZbmcax138/pus3dbei3GIZtYr+Ne8Twsnkmxp409Est2qlMjJZTqZf7OCN5qZFB6aKe
+IzmVidPMIVgJ0pU1NHWDz7wcbjAugY0Fp7JMFTIhODFGYhqIMdINA/PFwND29MPAkBPt0GMG3eBs
+z3vm857tRaQfnH5whmSk7KRx/LYZLNqOUG+DBTY3N9ne3mY+36ZtFwCcOXOY/7gicifcfQN4J9yw
+7OHT7v7OfQ4x4AMoa3N8tLsvr9Hx9t1CZq8BvpDyq/atwGvcPS6VfauZ/Vvg+ynB+zuAL9lzra8F
+PnQ8x1939z+/9Nx/AP65mX0X8KcO9mpFREREDkZB+x66dR/EsjRX9kxOkZwHUkrjfipduod+XJe6
+p+u2aNurLNqrtO01hn6TYdhk6DfB47iElmE4OQ5lPHccSnAeQ3a5HbN2trI0184Gu0HdPFNZhVlF
+FSqqUFNVDVXdUFU12bxMYIZDyNQhj2HbiSkyjN3Rt9uOwefQZ4Y4kHIsITolut5ZtAPzRek6HrMR
+M6RspExZ43tckiy0XZmZPMPm5iZb21ssFnPabnFf/h1F5NA58A17QvZef3K8HYCv2BOyy0nc/5aZ
+fRHwacAfMrNH3f19S0X++Hj7BPAX97nONwBfQGkZFxERETkUCtr3wI0B2/Y8Yzc86rkE7Rj7Mg47
+Xl/Puu9bum4xzg5+jfniMvPFZRaLywz9Nfp+g6G/BiSqUGYCD3C9BTslPOXrITs5ORs5GTkbIVTU
+oaKqqvF4qAyq4ISqjMWuqopJXTOdrjCdrTKdrpJIJE8kIja2ZtchUwdn2AnaKWJbC+Z9xkLPkJyu
+T3Rdouuh7TJdl8v47N5xC2VmcjNyhpidlPLYqt2Rc2kt32nRXizmuy3aIvLA6YF/tN+TZlYBn0j5
+pfkT7v7ULc71/ZSgXQOfBPyD8RyPAR88nuMfuvtNZ0J399bM/iFlUjYRERGRQ6GgfbeeN7X4nlHb
+N0xwdn3XfWxdzpmcI303p+u36bv52NW7tGqnFOnHkN11C7p2g0V7hUV7mba9WkL2sEHfb2Ik6qqi
+qgKVhdItPGc8OzklUozkmEpwTUZKRk6BYGXW8NJqHWjq61uwmqYOVPWEajIl1BMINdkqUnaGHIk5
+4yTq4KSdLRvJazIBQiZUU0I9o24GhpTAyutP2Ze2XJb8GlvlQx2oCbs/uaoqM5yTMznl8WdUNhF5
+IP03d7/VJAu/E1il/AL9pduca/n5lzMG7XF/xy/f5hz//jbP34EMPHKActW4iYiISLGzQtKtPHs/
+KnIoFLQPzU7ILt23bxyS7eOYbSfnYXeisr5fMJ9fY759lfn8GnFowR1zx3Nm6FuGvmPo2nFN6w26
+fpOh3yDGbWJsSUOPWTnGvMIqyhjsDPjYOpycGBNxSKU1O0FKhnkZU2lAFQKTScN00pAmDVVleJgQ
+JqtUkxmRijhk5kNLHzu62NLFjpwjdTAqM+oANq6TbcFo+4rsU5pJZnU9QOhxBrL3ZMZlx5KTk2OV
+jZOrBeqmYdI0NJOGuq7LlwUpk1Jm1jRMqpoqVJhp/WyRB9TeydH2Or+0//Rtyr53n+POLe0/c5tz
+3O75O3TIpxMREZEHjoL2oboxbF9XxjRDJueevt+mXWywWGywsfEsmxvPsnHtWYZhQWUltAYg9j1x
+6Il9R4wLYpwzxHm5TW1Z6iuVycXMfZzYzHYuCUB2SMkZYmYYIilBjpAT5Oxjy3cmhMBsNiNlx6mY
+TgMeGqrJCmGyQj8kuj7SDz2LfsGiW7Do56QUqS1QWUVtgbpuaJqGuqnJXuFMqSeBVRqcFveWnA3P
+Rk6+G6BDMKwqIX02bVhbW2N9bY3ZbErb9rSLjrbtmNYNTVWXbvJB69WKPKDupDvKA7Xklplx8eLF
+25arqjJsR0RERIqUEukAPVafffbZcaLp401B+xae/w9482B347rYhtn1buRmvjuLuHskxpau22R7
++zJbW89x9cp7uXr1vVy98l6GvqWpAnWoqEMgx0ge17ROqSfnjpw7Uu5Iqd/dQggkyqRmCcMIYIZx
+fbzzEDN9zORYWpBzLP8z51i6lpsFshtYhVUTVjJkq7F6BvWUvluw3UW2thdst9u7WxwilVVUVNRU
+TGcrzGaZ2dQIdUWmxqrAZNYQkxETxFiW/Eopk2LGY7phubDZpObU6grnzpxibW2N7a0F29WC2ipW
+pjOmTUNTl1ZtEXkoXV7af/Q2ZV+0z3HLrea3W77r0Jb3euyxx3jiiScO63QiIiKyx+OPP86TT95q
+PtXjQUF7Hzf/luT5S3TdbB1tdx/DdrmfUs/QLxiGlsXiGtc2nmHj2tNsbDzN1tazbG09Q9teJsUO
+D4EcKnKoCDjBoG7Aax+XtTZSCthgZAeiX18HO5VWc8NLF24zkjvZS8u2Y4SqjMe2xohDIlo/1rls
+MfnYel22to/0uefKxjaXr25w+co1ttsF827BdjdnGCLBA8ED5hVrqyusjlszqaHKEMrs5H3XM8QB
+t0xVG5NJjbnRhJohZWJ0hmGgbzu66YLFfEIVaoIF1tbWWF1Z5+LFi5w9e4619XVmKyuH+48uIsfF
+bwFzYAX4qNuU/cil/V9d2v+1pf1XAn//Fuf4fXdUOxEREZHbUNC+Y9fD9s1C9o33S3qNsaPrtlks
+NtncfK60YF99D9euvpd2cYVFe4W2vYKnfjdk5xCY1A1N1TCpa4yy9FXOgZhK6/MQy5JcnstM4ykZ
+uI3jpEvYztnx7JSO5YGqqqlCTR1qhjCUJcbGGb4dIyXoh0w3ZNo+segjDHBlY5v3PXuF9z79XAna
+fce8axligmRl/p9srK+vcWp9lfVTq8xmDaFxqsYJtUN0SGXJrroybNrQhIZJ7czbjpx60jDQW0u7
+mDCpJ9ShYWVlldXVVWazVS5cusTZc2dZXz/FioK2yEPJ3ZOZ/QzwmcAfMLPHbjHz+FeNtxH46aVz
+PGlmvw78LuALzewv3mzmcTObUtbrFhERETk0Ctp3ZSe43vy5na2M086k2NO222xvXWVj41muXn0f
+Vy4/xeXLTxLjBnHYZBg2MI8lZFsgh5rGVgj1CpNmhcrK+tI5GzEFYjSqYGOWL7OJQwZP4+RogTJR
+9/UWbSwQqpqmmTCtpwSr8FwmSnOPuBsxOXnIY4t2pO3LDOElaF/lifc8zXbXMe975n1PPyRyAo+Q
+k3PmdMuZMy1n2p7VtYZmCs0UJlOoCTSEMqa7Ll3kranwaOSUaRc9cRggQ1e3tNWESTVhdXWdtdU1
+zp+/yIWLlzh79hzr6+sK2iIPt++hBO0J8LfM7HP2rqVtZq8DPp3yS/cf71lDG+B7gW8HHgf+KvD1
+N7nOt6E1tEVEROSQKWjfxH6D6/drwfad//pO1/GE54GcB7IPLOYbLObXmM+v0S02SUMLnkqrLjXB
+GqpqCrkiYFQ+LnMVKqq6oa4nBAuQAjDOFh4iIQyEUIE7IVQEC7tdxo0Swo0yOU+wAObUdc2kaZhO
+JmXKtrHbuVkFVoGFsQt5ousHFouWmI2u64kxkTNAwKyiqhqqHMAzKXhZsxtIDtEzfUxES/Q50Q6Z
+WTVhVjdY1VCFirqeUFuDNTXzNhKqDncrS4NZTaga6smMldVTnD57nvOXHuHs+QusnzrFdDajqvW/
+r8gD6ECzl7j7j4/rW38h8BnAL5rZdwD/hTKj+JcAXzEWf46bh+jvHsu8HPizZvZBlHW3n6CE7z8O
+fBZlibCdLurHf3YVEREROfaUVF6g5bjtY9jOKTIMLX0/ZxgWbG9fZb59jXaxwTAsMMtMpxNOnTpN
+SjU51aQ0wVOP51ySanbqZoWqWiFUq5gBXuF5Z9KyiIUJoY7gThUqQgjY0gRhhmFGCe9mWGXUVUVT
+N0x2graXpchCGEheuqanbKSU6fue+WJRuqynslb36soKVdPQpMQ0pRKmh0SMmTgkVtdmzGYTmkmN
+VeU8QxxwBnwCYRJoJg3eVITQ0DQr1NbQTDqqqsWthlBTT2ZMVtZZWT/D+plznD53gXMXH+HMufOs
+rq3TNBPMNOu4yAPoTt64X0ZZbPrzgVcAb9zzvFNCg9lodAAAIABJREFU82vc/T17D3b3wcxeA/wk
+8IHAZ4/b8vFvAb5zvAVo76B+IiIiIjeloH1H9h+V7eNsYu6ZlCJ9t2Cx2GKx2GAxv8J8fo3FYoNh
+mGM408mESX2alBtyakh5Soo9qR9IQyTHRFWv7G5laHi12yHdLUKIhKrB3KmqMLZq2zixmZdJ2cbZ
+yEMIBIemqmia0qodxtZrMEKoGWKZnTy7k8egDXOSB9IYtNdWZjQ5M81OnzNDSgxDZOgjwxBZWZ0x
+XZnQTBosZIY+0/U9w9BiK4HaG6YG1IEQJkya2bi1hGobp4LQUDU3Bu0z5y9y7uIjrJ86TTNboZlM
+CAraIg+i6zNF3q6gewf84TEsvxb4aOAisA38OvCjwPe4+/wW53i3mX04pcX7CymBu6O0jP+Qu3+f
+mX3u0iHX7vgViYiIiOyhoH0LN+1CvrNMtfmePxWd7AnPmZR6un7BYrHJ9tZVFosN2naDrt0kpZYQ
+MnUdqMKsTESWIadAjDWD9UTriURCNcGqBqzZGe1N8oqYQwmkVhNCjeOEKlBVpft46Q5eZiN3c0Iw
+6mC4G1UIu2tQhxCo63ocx23l/DkBZQ27rusZIrgHcoo0dWB9bZXoTnQnAUNMtH1P1/V0/cB0NmE2
+bZhManKOeHaGLjFveyZM6S0xVE5qDM8Bs5oqNFT1lKZZYTJbZTpbY3X9DKfOnOf0+YucPneR02cv
+cPrseVbW1rCqJlQ1WLjn/w+IyMG5+y3flO7+yXd53h8DfuyuKlWOXwD/17jdzMvH2wj897u9joiI
+iMgOBe07VtK1MYbt8bHskZQGUox03YK+m9N1c9p2m67fph8WDMNiN2gHy8SQ8dyRvSX7uDZ2Hkge
+yUSSDwypp4olDPexp4s9fRwYUiK74xZK9/BQjTOKB3Iq589krBpnIsfK7OOe6PuOnDM5eenyHcvS
+YDmX1mx3J8aIDxn3HqiwUDGb1KzMpiX0m+NAPwxszY0ty4Q8MK2MSRWYhoroTpUrLAXoAik4g2V6
+BhbeYd5Aruin5XWsnzrDi5tV1k6f5dEXvZhHXvRiHn30xVy4eJHV02cJkymEuownRyFbRA7NF423
+/9HLLz0RERGRF0RB+44sNWFb6cRdxmWDj0F7GDr6fkE7huy226Lv5mW8dmzJqcUsYZYxS7j3uHdk
+enIaQ7ZH3BLJB2Ku6GJZpqsfBvqhBO2YE8nL7OdmAavqMjlZqAiWStgmkcpIbYJBThn3TN+3dH2H
+ZyO74bks65Wyk3N5jTFGhiEThzJR2traGrPZlLW1VQhW1gkP0HU9k5AJaSD3xqQOTKqKaVXRZ6i9
+IsQK7wMpwGCJjkjwHs8L8mD0Myc0M9ZPr3Hq/IyzFy7y6Ise45EXP8alRx5lbW2dtfU1qmYGoR6/
+XFC3cRG5PTN7KfCEu6d9nv/LlBZtB95wH6smIiIiDzEF7bthNy7h5Va6jcfU0w8tXT+n7xd03TZt
+u80wbDMMC2IsLdpG6Z5tlsokYfTAQM6RnBOZRCaPLdqhzMSdnS4O9OOWPZGdEjpDRQilO3Vd1eQU
+CCTyGLIzEAwSZRx1P5Rbp8KstBC7j2tuj5OjxRjp2p52MVBVFauzKbNJxdnTa9QVhApCgLarsTyQ
+upZubkxqo6kCk6qGBJVXWAx4H8gGkUyXB0iBHI3YQz84p8+vsXbqDGfOXeLioy/mkRc/xqMvfgkX
+Lj1CVVW7G2Zj931NDCwiB/Ja4CvM7E3ALwBPAQ3wIeNznziW+zXgB46gfiIiIvIQUtC+rdJFukx0
+VkKweyKlSIwdw9AzDD1dt6BbLOjaBe1im3ZRJj9rF5sMw9a4zcneUwUnBKjCTlwPuAfcK3AjEAjm
+NGFCU09oqoaUvHT9zhnPpdt4ypC9rOidU6bPkWTlvgHBy9JfOZex1MMw0PcD/TDQ9QPBaqoKqiqM
+M5Czu+UEMWaGIZJS6UqeU4KcyDvj1Mcu5ilG4jj7uPtASoEhQooZc2c6aThzapXZdMJsUraV2QrT
+lTVmszVWVtc5d/HRpe0Rzp27wNr6KSbTWVmurKxTtvSvYsraInJQ7wf87/s858C7gM929+H+VUlE
+REQeZgra+9hp2YWdqJ1JOZJSX1qu+wWL+Rbz+Sbz+VYJ2G0J2kO3IMaWFEsrdmnRLhtEmrrM/F1Z
+hXuF42R38IB5aSUPwZnUUyb1lGkzLV3BnfK857IEV47Esbt3HNIYwocys3hVYVVNzokhOn0f6YaB
+ru/p+56+H6gqZ0KFWUMIY8B2I2dImTFgZ4JRlvAaBoZhICTAMpjTth1tO9B1kbZLhGHAuoxVA2Dg
+zspKw8qsZjqZlqA9nbCyss7q2hnW1s+wtn6Wcxcf5fzFF3Hu4qOcOnOO1fVTzKYrBKvYXal8N1iP
+wfuOVgkSkRPqB4CrwKcDLwMuAavAZeBXgH8C/KC7xyOroYiIiDx0FLRvyXdDtuOkHBliyzAsWMw3
+uXbtMlevPsfG1cu07Zy2benaBSn2mCXCuA3DNv0wZxjmBMswmVJZgKqGsSXbHXAnGFQGtcGkmjJr
+ZqxMpqSUxubmDDkRM8TkmHlZYism+i6TojObTLBJRW0Bz0ZMTjckFl0J2V3f0fc9TQ1mDVXlhJ1l
+w8oS3iVs7wRtvCzhNUTiMIA5TsbJLNqORduPYTuNjxuOUdcVK7MJK7MJs9mE6WTKdFJuV9dOc+r0
+BU6dvsip0xe4cOlFnL/4Is5ffJTZ6inqyYSqmRBChXv5+btnMKNM+G4apy0it+XuTwLfNW4iIiIi
+94WC9k3srEHtnsex0GmcrXtO227RtmXZrqtXnuHyc89w5fLTdF1L33X0fYfnRFNDXUNTG30/L7OQ
+99sEczw55oZ5IHsmeyZ5xnBqs9I124wcSuI2D9QBmqohNwnHqVJFlSpCqsipB8/EGOm7RB0qcu2Y
+Bcrs3EbGyA7Jx9bqDJWDuwFh7L7uZebxfP1x25l4zJ2cSvfz7GUZsJgT80UJ2f2QSal0Zd+ZuXxn
++bDVtRlnTq8xm64wm5Vtbf0sp05f5PTpS5w6c4mz5y9x7vwlzp67QDOZ4RZgXBO8LIEWSSlhtrNE
+2ThIHKA6sv9VREREREREnkdBe4+ydvY4HjsnhtQTY0dMPfP5BvPtq2xvXWVz8wrXrjzHtSvPsXn1
+MjEO5JTIqcwo7mMLccYYuo52vqBdzHFPDItIPx1oJ11pK7edlnOncifgVAbDaiStZUhGXQewQDOZ
+UTUThhSJeaBOEbeWfnAs9PjYpTuM48DrumKSG5LPyjWsxr0ie0VVTwnVFKwB6vKlQi6t2BBomglr
+KxCCMWkaAIZxjHfb97TdwKIbmLeJFI26nuJjK7MbrKxMWT+1xukzpzh7dp319TOsr59mff00a+vn
+WFs/z+pa2dbWzzCbrVGFGgjjTO6lfby0wrf0fUcIgUlTxq7Xdfnfd9JMj+D/FBERERERkZtT0H4e
+H7sqZ7JHYmzp+gV9P2dr6zIb155jY+M5Nq9dZvPqFTauXmHz2tUydtoYW1xL1+ZMCdqx6+kWC+Zb
+26QY6ZuBtumZNFO8NDjjgdLhOmfwTOVOHhyyUVnNbDahmtQ0zYRQB+oUiTlSp0jKxqLtscpwStC2
+cbI1LNDQlHWvzXBKyE5eUYcJoZpiYYJT4+6knEmptIZPmoZJVVEFo2lqcCcOA/N5x9Z2y9a8pe0T
+KVckD9T1tCz9NW6rqzPW1tdL0D5/mrNnL3L27AXOnb3I6vo5Zitnmc7OMp2doZnMmDQzQmjGidnG
+Tvs503c9i8WC+XybqqpYma3gM9dkaCIiIiIiciwpaD/POP7YEzmXmcW7bptFu1lasa+VruLXrjzL
+9sZG2a5tUFeBpqlpmppQV6VF23eCdkc3XzDf3GboB+q6p6476mqCVQEqw6rSPdtzghQx3wnZFU3d
+EKqKlcmkBNKVKU2KDCnSpIEhZupmgQUbJynLmEGoDKPCbVK6WVd1Cdm5IqZAsOZ6i7ZXpRt7TqTk
+VFZeTx2MujKqYBilRXsxb9nYmHPl2jbd4DSTFerJCvVkSlUHQhUItbG6tsL6qTFonzvLpUuXuHjx
+RVy89GLW1s9TN6epJ6eom/XSBO47s6+Pk5/lTE6Zoe9ZzOdsbm5Q1zWeyxcBZaI0ERERERGR40VB
+e1cJdyknhqFjGFq6bs72/BrzxTW259fY2rrMYrFB32+Tc5nwrKphMiutvckTuU8McQynVbmNKVFV
+FbPZCpNmQl01VFW5HXIJzP0Qy9hrg8qMqgpkoI+RRddBFehzZhEjTduVycGsfC3QDZkhpdIaPY75
+vj623IgpE6Mz9E7bJbYXkc3tHjxRhUwVIhDou4Ghj/TdwGxSE+qGyaSmaSrqML6eYMyGyKyPzLqI
+VZl6MqFuGurJhOlsOm4TTp1Z59LFc1y4eJbzF85x6swlVtcvMJ2doW7WqOoZwRp2xpEzrvvtO/8e
+e+c6c663Yo/jwEVERERERI4bBe1dJbTlFOn7jrbdZrHYZHP7CltbV9javsx8u6yNvbMetoVM0wR8
+1oxLX0XiEHHP1FUZV11XRsqZqqpZWV3BHKqqoa5qqlCzvViUda27Dndn2tRUk4aqbnCgj4l52zF4
+php66rYdn68IdU1VV3RDYoiZ6JlM3p1gLedMdiPFzDA4fZdZLBJb2wPXNnuyG0bErMM9lDHmMZf1
+skP5AiE0Fc2soRlfT1MZXUzMhsSsT1jv1M2Uuilhe219jfVT66ytr3Pm7BkuXDzH+QvnOH/hPOun
+zrK6do5mepqqXiOEKWb12Jo9rv5tN/xz7N73Gx72PYVERERERESODwXtPVJODH3LYrHN1vY1tjav
+sLH5HBubz9J1WwzdnCHO8TFo141hNLgnuj7R9S0pxjFkl62qyuzbk0lDFQJ1qKmqimAVfRzwed4N
+2lUITMyomho3Y0iJedcS4gBthVUVVtdMp1MmsynT6ZRuSPQpEfMYsMkkT6WFPY0t2kOm751Fm9ia
+D2xsdQzJx67aoSyX5V7ald2pJxXJwJpAM2uY1IGmDkxqo/v/2bv7WNvyPK/r7+/vaa39cM65t6pr
+emqCEAQkJhCRdqIM+ASoHSc0GTUIBgVmJBMkBiVoJILdPagYBTHEIek4EZgIIRmCAR8YiYojISSA
+zcgQ/hAFA4zFTHdXd9U9Z++91u/h6x+/tffZ59xzH7qrputW1feVrD5Pa6+z9rld597P/v5+32+u
+rOba52Z7xYdEiAkfEtvtmkePHvHo8Ws8eu0xr732Go9fe51Hrz1mtbpgHLekdNH3cxNA+oiz24S9
+vBV5Zo5WPc42N8YYY4wxxphXjwXte7RVSsnM84HDfsd+d83u5l12N+8wzztK3lPygVpmtAHLXuiG
+kmvhsMyoDt73oB0cQ4ogER8CLgSc6yHbi8O54+EBJcRASok0jID2kNwKilDpe77VOVarFav1mnHV
+yHk6jdma5sIQCqvYm5rVCrlor2jPjcNc2U+Vm0NhLg1VoWnfT+5kOYCxROZaKDQKbdkzXqEKlYo6
+xUchIoTo8METgmO1Gri42PDo0RWPH7/G1aPXubh6ne3Fa6S0IsYVPoyIi4B/OmSfLRcX6bOyvfek
+GBmGAR/6+8EH3HG8lzHGGGOMMca8Qixo39OaUmvtS8HnPhd7nifm6cA07Sl5T857as0IDlGH4Jhr
+Ycozu2limqbboO09tSlNl07kCtUpQXr1WkWIKbHerHFOWK/XrNcrVuPInOe+X3yaybWSm5KbUoH1
+lFnPhdVcyPnAzfWO6+sDeZ5IbmCMjfVAD9oZ5gJzUebc93T3fd19aXnf+iz0unbvfn7ImX2e2c0T
+PiiOtowda0yHypQLjYrzDh+UEJUYYRg942pgs1mz2WxZrS8YxwuG4RIfIs4nIKJ4bvdlc3c/tiy7
+tZ3g1JFSYq395+OcY0gDKQ3EZeSYMcYYY4wxxrxKLGjfo9qDds7zErAn8nRgPhyYlzFfOe+pLeMk
+4l3Audir2Tmzmw4c9j1o+yVsV6XPl3Y9ylYHVSA4BRwxJTa+V25Xq5H1asUwJOpN4zAd2E8Th2nm
+UGrfj92Uw1Q4TIXNXCll5uZmx263p+SZMRTWqZFXSqtCLvSK9uloTKUH7eO8b4Vl6Xg/9jmzn3vQ
+FtegZagFbQWtoFVoCM4HXFBiVFJSxsGzWiXW2x601+st4+qCNF7gxCPiQPzZnuynq9migooi9GCd
+UjrN8hbn+v5235ffG2OMMcYYY8yrxoL2SU95qkqrlVIKec7keWaeekV7ng7M+cCU97RWeiXXgxdP
+bo15CduHecYfg7bz4Ja91T6gOKKDJkpzDhUIKeIlEqJnHEbGcSClxP5woDblMGVu9gd2U2Y/ZebS
+yLkfpSi1Zna7A/v9RC0zm6Ewz41SoLa+dHzOyjz3kD0X7deorb8AwBK0UdA+x3sqhUPJHPKMc42a
+p9MRXMC7SJBACA7v26mqnQbHuIqsV2Nf2j6uGcYNKW373uq+wfpOrzOVe38Kcqxzaw/YKfY53oyA
+LEvKHSLnZXBjjDHGGGOMeTVY0L7HicP7QAqJFBNePNqg5EotjVYVaYKow6ngcDiEISUuNhdIc0zr
+eRlUBSBE7/ESaBlya6gHdUpzDXXaD2EZzaXkkvHOs9vtmHMGpO/pdq2HWgXnlqB5LEcraBO0OVp1
+lOrIxVGKcJgau0Nhd8jMuaJNCeL6Zmx60O3VY8E5cCJcrNdcbrZcbjesBk+ePHlyZOG0XB7tS+1V
+G1ARqTSdqeXAnG+Y5mvivCLOG2Le0b+h6yF5Ccz9J3RLZXk+6J0g3cd+9Sr3va3cxhhjjDHGGPNK
+saB9jyxBO4aBGAacC6egXZaQ2kc8u+UQnAhjGnBbxxBHSq602k6HqqKt0YqSa0VdQ72jegdOwSnq
+G6UJOedesQXyPJNzb4QmzuNcW+Ze9zB8DPO6hOzWHK05anOU0oN2zsphbuwOmd1hJi/PwYsgp2Zi
+PdSGEAjBE4LnYrPmcrPhartlHDyTcxwA1/qLDdqW6r82VCvHoK2aKXXPPC9BO6+J+YaQ1zgJiIs4
+F3DHsA24pXqNnO3X1uOfx+3YL1nq7sYYY4wxxhjzKrOgfc9xD3CMiRQH3LGiPfeKtiqgsnTnvo3b
+Q0wMYeRi7ahVKbmQ50LJmXmamaaZucxoqzQvNK/40HrQ9n3JtqKnWdZaW78hBZa9ys45vPN9ubrr
+lfRTNVvlFLbrUtEuxTHnymFq7JeKds4VFIJzvRK9BFrnHCn2EWQpJS7Way42Gy63F4zJswNEG5RK
+oVC0UbSiraH0ijZSaW2m1KWiPV0T5w1p3hHzHu/T0lBN0LOf3/IUT/m6h+3bAdq3lW/H+WAvVRvx
+ZYwxxhhjjHn1WNA+6WHuuHQ8hESMA8EnvATA9VFU7Th3ulG00Uomz40UUg/nKSA4avCUECglMnlP
+8p7JO1qrON+XaXsvFCpVC/XYhG3uHc9rrr1zeQhLYzUhBKhNUOlLyYFeMW/9vmOIS6XYUXJjt8/s
+D4Wbm5nrm4ndbqK0Qq2th3RxyBJwYwishxXrzYr1Zs3VxZrtes0QE9715eKtwJyVWlgOAS+0djsi
+rJTCNE3sdjf4+C64ASVS1BHjmhhXxFQIIZ0axon3dxqgHReKnzdLk1Ml+/hWbY+2McYYY4wx5pVk
+QfseEcG5QDwG7ZDwPhFcpBJp2qB5am3kmqltprXGejWy3awZvSPFiIqjeUGbJ0dHXgXmnGhNESfL
+AYd54jBP7PNEKYWSlXlq5FwZksd5h/iId0pQoWnf4+ycW4JtRbXhvWMcE9oU5xw5V66f7NkdMk+e
+7HlyfeBmPyPSEGl9/rc4vCwV+ZC4WPf5148eXTGuIuPg8eKpJTNPjf2+cHM9L/uyBVUhiqdWT619
+b3jOrXddDzdApFTPlGE/lT7ma9wyjBcMw0iKA0NKOJHTUngsPBtjPsSur68/6FswxhhjzCvAgvY9
+xz3aIQ7EOBLDErZdwslMo6K1UDPMU6/ezvMMl5XRO9yYGHxAvCCxh+LaAqWmZZ5234usSyn53Zsb
+Ksp+ztQq5KxMcyNPFe+UlBzORRAICLo0EwOgQW0VEQjOE4MDBKon50I+7Lnezbx7feDJ9YH9NBMC
+xCjECF4EL54gjjEMXKy3vH71mDfeeAPvFShAWUadVfb7wvX1DOL6qC7Xj1KPYduT5x60RW6oTZgy
+DIfCbjex3hxYb2bWpdFaBW141/eG67HCfqcf+dNssbgx5lX25MmTD/oWjDHGGPMKsKB9j9xZOj4S
+40AKAzEkqk/QGk0qtEItjXnK7Hd71inQyojTQpTWx3s5h/eepp6q0BQqoAgNaNBHgs2Z4GecFJAC
+6mhL9bqp6+EaEDzOKc5pb7TWlFob3gsxemIMOCfMeyXnynwo3NxM3NwcuLmZ2M+Zceidv51zeEBc
+X4YeXGQ1rLjcXvL649doOi9zw/e0quS5MU2V3b7gfSRGT3AJkeMxIJJozVGKMs8Z5EBtgVxhzg1V
+AfG4kPAhEEOgtoJq689RleWJ3v553P8D0tP/GGPMN0xEfgbwt5YPf72q/uAHeT/GGGOM+WixoH1P
+3zvdQ+swJFarFZvNhouLC2KAnAPz7EnJ9cpwcgyDZ3MxMAyCc4WmB6QFRALSArk2clVybZTaqErf
+k61KKY0hRR4/umK1WrHfTxz2fSY29I7i19d7miqt9eZjrdVeHa89cPvg+mgw6UvIS+vXbkDTRm3a
+Z2nPimql1MY8C0NUhiCk4EiR09xtlaW5Gr31WENR6dV0xBPTivVmy2p9wXq1Yr2K/RgTwzAwjAPD
+MBKHkZjWhLQiDmvWqy3r1ZrVMDKkgRgC3vmzn74tGzfGfNPZK3fGGGOMed9Z0L5HxOGDByLDMCxB
+e83FxZYYlDl78uxIgyMlRxo94yqwHhzD4BBX+jgvbUtHbu0NwubKlAtzaZTWw25pSkwDKY1styO1
+KrslZO93B252e3a7PTe7PaX0WnhvMd7nVx/DdohCEw9OCThqg9pPo6lS6jFoN2qFnHtVPCco0dFS
+YByUXPvjoHcFV1Ga9Bnfvdm6Q8UT0orV5opHj15ju9myGiLjEFgN/Wc2DD1whzQQ4khIIyGOrFcb
+VuOacVgxxETwxzFfnEZ9PTtq22gvY4wxxhhjzIeDBe17nBMg4ATaOLBajWw2Gy4vtsSozLNnnh1p
+7sF6nD3T7Em+knzDuYJq6ZVnGgKUMjPNmd0hc8iZXCq5VEppXD1+zMV2y6NHl4jz7HcTu/3Ebren
+femrXN8cuL4+MM0z3i1Nw0RoTZewDbE51DXwjSaeVulLz0WoNGrrFfUpK5KXZmg0yiC0wYM2Vhly
+WbqaLz+LY0VbUZpID9oEQhpZb664evwGV5dXDNEzpsCwrAIY0kAaehd2HxIuREJMrJagvRpHUhoI
+IeKdO+so/iJWeDLGvG/slTtjjDHG/JSxoH1Pn9LcZ0OrVpAG0hDXCB5IgnO3Y6lwvbGZB0QaTStN
+Hd47QojEOJAVYlVCbQStNG00hUbDu4ZzDSe1z+Z2De8b3mn/2hKKhdanTkuf4V3py7xzbbSsqFea
+U3JraBUoDq3Sl60v1e/aTkO3UW0Ep5TQr9OaLpX2zFwmms7kMlHqTGsZ55QUPavVwHa74erqkseP
+X+PR1WNS8KTgidEzpF7RPgZp8QHnAuIDw7BhSCtSHIkx4nzAOQcid5uNn+dp+6ewMR8LIvIdwG8A
+/nHgTWAEfhL4UeBPA39EVd85O/9bge8CfinwDwHfRv877cvAXwb+KPBDqvrUK3TSX208fQj8IRH5
+Q/dO+5yqft/78uSMMcYY87FjQfs+1R6EW6G24/iufiAZHyrOK84LDV2qxRXRPmarasOrIN4ThoFx
+2FDFU3FUAAcuCD6Dr0qMirhCawdac7Q696NNoBknjehBw9LAzHtEhKoNldr3YxelzUqm4Ysgtc/7
+pjmm0iitUbW/hKAK2pTWoDROn2+iVC3MdWLKO1Rn5rwn5z2tzXhRhuTZbkYuL7Y8urri9dde4+rq
+NYJ3BO+J3pGGsS8fTwPOBxC37O12DMOGlFbEYwh3ri8dl2Wsl9gCcWM+bkRkBP5r4FcvnzoPxt+2
+HN8JfAL4vuUxDvhx+q+L+0H6TeAzy/E9IvJdqrq7d8798Qa2XMYYY4wx7ysL2vcoirbag3adqS3T
+dKbpDFLwriEefICivYLsa4VSl8dVGh7nPTENrNYbmvSQXWngFFcUHxq+NEJUnGRaPaAItWZqXYI9
+BSeV4Htzsh60+3ivXBXN9H3aTSmAVHAeRD0Oh6hnLrVXvZemZq0prYJWpVZdKuu9hl9aJZeJQ96D
+zpSyp+QDtc4414M2G8/VxYZHx4r2o9fxbnkRwDnSMDCkHradC73beutL2dMwElPv5H58wQDoFe3T
+Dm1Fn7mY/KF/UxtjPqyk/xL4U8Avp//H/TeAP0CvSO/oofk7gF91/6H0X11/Fvhh4MeALwEXwN8P
+/EbgFy3X/X56pfzcz6cH+D+zfN/fAfzJe+f85Ht9fsYYY4z5+LKgfY/eqWgXapnJeWKe9ygT3lc8
+laqZ1vLpPG1Ly+7a90CXuizXXpqSifQQ7IOQnO/jrRqk6HCuoTrTGtQy00qm1Qxa+izt2EO288uS
+dXFkVUJT/NJ5vLRGq735Wh/gpQjKlCul9ZgtfpmepT24ixNwgO+Nz0rLTOXA/nADOlPznloO1FxA
+Pd75vh97SKzXK7abNdvtdgnaHuc8aej7r9Mw4sRTa2/G1hqklIixV7Od67PA9V5wXsaMn7H6tjEf
+Yf8mtyH7TwD/iqrms68fl43/ThF58/hJVa0i8nNV9W8+cM0/B/xhEfks8FngXxWR/1BV/5+zx/91
+Ebk5e8yPq+pff/+eljHGGGM+7ixoP0VB21L5LczzxG5/w5PrJ9SyB8lAprWZwzxxmA5M8wStIaqI
+NkqGpjdMGW52M7kuR5lQWeZeDx7vAyJ+6bw5kpFBAAAgAElEQVSttNYDfqmZkmegEaIwriKtKT70
+oO2cxyVPGiLDqrI/lH5MmXmqoA3RBg1yadRacb6RBkUby/wuzzgIwwrioEgoFJ3YTzveuRZEM61M
+aJlotaI10kpAa6CWQqsF1YpIw/k+EzuERExLEzQfceIR119gaE0IMeC85xieH6pNv3xjNGPMh9lS
+zf5t9F8Ffxf4dfdC9h2q+ta9jx8K2ed+F/Cbgdfpy8h/33u6YWOMMcaYr4MF7fuU03iuWivTPLHf
+7Xjy5F3m+ea0f7q2TC6FXHsHcYfiRPFAdsqUb7jZzXh3Da6CFFQqMTlCGhjHgXGVaBVqgVobqscq
+eqaUjFCJQViN/Y/JR08Iy7L0HBizsirKu9eZ1ib2e2U+LGPFajt1Pm+03sxs0F7RXkZqDYNjGIQw
+NCRUSpvYT/DudUW0QJ3RmqEq0iq0BKpnQbsg9BcOQooMacCHgRASPkREAk5lmckteO/w7u4y8XMW
+sY35WPkFwE+j/yL4rx7YR/3SltD+rfSl4/H4aXqAf53eLM0YY4wx5pvGgvY9ip6Wj9dal4r2jusn
+77LfPyHnA6VMlJppClWFpuCdEBwELzjqshRcUYWQIEYlJFj7xMYHhpXj8nJkOlSmqdIOvULcaqHW
+TCkzoIQgPbA6R4ieED0uOIYq5Aq5CsjMfqdoK8wT1NJopVBr6Y3bIvjU95W7Jex674jRkSKEqIgv
+VIX9XOFmxrUKrSC1IA08rS9JV6GU3IN26xVt74UYA8M44lzE+YRzPWjL0ggNHCLa35Wnl4yfkwfe
+M8Z85PzDZ+//uW/kAiLya4HvBv5RYPWM05TeSO2bQlX54he/+MLz3nzzTd58880XnmeMMcZ8XLz1
+1lu89dZbLzxvnudvwt28dxa07xEEkd4N2zlPCIkhjYzjhjzPzFPmcKgcDjOlQalQmjIOnnEIrEaP
+eKGe9no3qNr3QheYZ+FwmIlxwrvAdKgcDoVpX5imSs6K4glhoFHRVqitUlqlaqVUwTl6eBVPFEdy
+hSiNKEp0wpACMngcER8hJMUP4CN9r/dyDMPAalyxWq2IKQAVpFJrpdQKRdHScCokD8kLPjjQyjzv
+2N18lesnA60VoOGcEMJIYKmai+txWpeRYqLQFET7Puxjs/FlNvjyB/D8Px+x8G3MR8R5+H3x36pn
+RGQA/lvg09wujXlep8RnhfD3naryqU996oXnffazn+Vzn/vcT/0NGWOMMR8SX/jCF/j85z//Qd/G
++8aC9n0iS0j0eB9JcWQc1qzXF0yHid3NxDzB7qYy58aUG3NpbLcJcKQh4p1bgmWfld10mWFdwWVh
+v58Q8bQqS8juYbsWllZmkZgSpU00bczLUm2RZU+0KCF4QgyEEIgUoiskpwxeGKInJUeKjpggDNrD
+dgRxvZka4lhvtmy3l2y3l4QY2e137PY37PY7amnUXGgZvPagTnDE6FEtzNMNN0/eJkVHa7n3CndC
+GhqI9NnZ4kBbf07altUCDVAQ8N7hnCBuCeRiO7SNMS/ld3Absv83eqfyLwJ/T1X3x5NE5Efoc7m/
+ab9a3njjDX74h3/4hedZNdsYY4y563u/93v5zGc+88LzPv3pT/OlL33pm3BH740F7XtuK9o9aMc4
+MI4bNusLbp7cAE/IE+xuCvupsJ8qh6mgCilFqgo4hzZBRVH6DGuaQlGywF5mWhPy3Hp1fAna6LFr
+dyDGxJSVpplcGjkXaDNo37u9GiOeiPeJII0kleSU0QubMbLZRNbrSBp6s7Mw9KANvRKOOLYXV1w9
+ep2rR5/Ah8SX336b9hXh+iaTS6bMQp0VL5BCrzqH4EELeb7h+slXCGGpUDuHDx5wOB8IccCpR1V6
+yFbpe8a1H+IE8IBHTvu2bYq2MR8jXz57/03g//o6Hvs99F8Yf05Vf9lzznuNb/JMwJQSv/AX/sJv
+5rc0xhhjPhJedltVSumbcDfvnQXt+5aKtnMB7yIhDMQ4ktIa38u6lCLMkzJNynRoHKbG4dA4zJVp
+rjgPtRZq6R3EfQNfIfg+T1q1Lt3FHftdYb/L7HcFJ4FVC71q7YRSlJwb01TIOSNacFS8VKR5gsAY
+oCXPxVqYLjxelO02cbFJXGwTaXSEdKxoC0gEF0ACl1ev8/i1T/L4tW9BfELcwJwd1zeVmmFulcN8
+wLXK4Ct5aMve9UzOe+YpMh0iaejjvNI44n3Ah0gMaWngLv1QWfa+ax9B5hwichrz9bDjv48tfBvz
+EXS+kfmfAH7kZR4kIq/RG58p8EPPOW8D/NznXOqbGsCNMcYY8/FiQfseQXB94DXeR7yLiMQeUCWi
+GlA8iu9dxr0SQt+GPE+Z6+s9c5be1Kxlas3E4AjBEYOjvwBzuw98npX9vnJzPYMUavO05igVdrsD
+u/2B3X5CWyZ5cN4zxMBqHNmuBi7XI2P0eAdjgt0BNuvIeh3YrAMxOXwUQuwVZ/EjLow4P3Jx9Qke
+v/ZJHr32LeAStY3kHJmngLTAvC/M0xPKPBNdIPnAEAKr1UCtBShARVum1gN53jH72MO28xSfaU3Q
+KrTWl4gfDyT07u4v9adilW5jPoL+T+DvAH8f8K+LyO99yc7j539vbZ5z3m9czn3Wr5nD2fvDS3xf
+Y4wxxpiXZkH7HhHp8589+GP37GPYJvRD+xJpEY9bOo2jMM0FuW74SWmtLEclxUBKgSH2QN7DvOCd
+Y5qU/b5wfTOh6mjNo+qoDW52e3a7A/v9ASeNMAR8DAwpsB5GNqsVl5sVTQPj4LjYeubiGEfHauxv
+Q/D40OdX+5AIaYuP/bh89AaPXvsWHj3+JOoipSbmOXA4eA77wjtvP2GeYH8zk1wghcAQPfN2oNYM
+VISC6kwtPWh7H3DO9z3uLtOqUOsyRztEfIz4EHHO9f3aeu/fwM/M1Ba2jfkoUVUVkf8M+P30MV8/
+KCK/5qFZ2sfxXcss7S8BXwOugF8jIr/v/mNE5NuB7+P5VeuvADN9HNjPej+ekzHGGGPMkQXte0Rc
+H0GlQvAR5xLuWNEmwFLNRj0i4B2EICiVeS6UVhFXaa3RtKLaGIbGWIXW/NIoTAjeUfxtRfv6eu7N
+uftEbmqD3W5iv5/YHyaiF1YxEJxniCOrYcV2XHO5XuN84mIbyDVQCaQEQ4KUZAm+/cUCH0bi8Ig0
+PiIOj7h49AZXr32SR48/SZPYQ/besbuBr739BOd+gnmGm5uZIQTG6NklxzyvKMegLZXW8m3QdqG/
+UCEO5/IyI1xoVYjDSFIFEXw4r2jL8s9heTBLLw9B74Vy60BuzIfe9wO/AvjlwL8A/JiI/AHgLwM7
++hLxXwT8auCPAN+3BPQ/Avxm+nzsPy8i/znwN+jh+zuB3wQ8AX6cZywfV9UqIn8J+MXAd4vIjwI/
+ChxD+9uq+tX3/ykbY4wx5uPAgvaDlsAnrnfp9hHxCRcTPiXCkEirREKXcKw0LTQyTTNVC0pFm6e0
+SmwD0PdAi0uIRFQCTftRm6NW18Omepx4og9sN2uGIXF5uSXFwOVmxcVmxeV6xdV24HIzst6OeB+p
+GihEFE+MEKIQI/iQ8H7A+5EQ18ThkjRcEodL1ttHjMMFzg+AYxzWXF5c8YnXD7z7tbd596tf5slX
+v0yksl55vPfUohymym6XeXI94fyeMXvmDOOslFkpudGyEuJqWQ2QiDESgydGT4yR4EN/EeA4WPs4
+7+t0dMdsfb/wbYz58FtC868E/jDwLwE/B/gvHjr13sf/PvAdwC8A/hHgj977+peBfxH4XTx/n/bv
+Bv4U8PoD1/gcvSpujDHGGPN1s6D9kNNMZ4Flv7YL8RS0Y4oMqwHvhOAF74Q5z8x5Ys7CnIWKR7VS
+q0c1oTL0oO0TuIQuobi1ftTqeudt9TgJBB9JQ+ojsLwwDgMXmzXbdT/WY2Q9RNZjxPke3FV6kzMf
+wAfBBQhhJMY1IayJaU1MF6RhSxwuSMOGYdzgfUJUGMcetPNcefK1t3nn7U/w7tufQFrGS8W7SimV
+aWrs9pl3rydwfU96npUyV2pWWlEoMIyVkNbE5Jemcn0kWQyREPo+bnGOPmT8ftg2xnwcqOoB+JdF
+5J8EfgPwS+iVbA/8BL3K/N8Bf+zsMe+KyC8Gfivwq+gBvdD3fP/3wO9X1f9vWfWiPGMJuar+jyLy
+y4DfAnw78AZ9KbkxxhhjzHtiQfuZ+pgujhXtkHDhrKI9JsbkGaJnTJ79fuJm5xCFWgSnvaJdW6Vp
+4ryi3RurBZp6mnpq7YeDHrTxxBBZrQfW63F5u2a73rBZb1mv1r2xmvfE4PE+IC4gfjkciBfECTGt
+iGlLWo6YNqS0IaYNPiScDzgfoSnjsObqsiI4nrzzVd59+0s8+eobaD6Q856S9+RcmObKbp+J1xMq
+njwrea6UOdOyokWh9pFmTjySRmJ0hOiJISzzvyPO973cdyvZFrKN+ThS1R/hJTuPL+cfgP94OZ51
+zj/9fn9fY4wxxpiXYUH7nvOyh4gQY2Rcrdlsr1hv32V892ukcUWYdvggeC84pwzJ42RkiIH1urKb
++7GfG6t1ZL1OrMbEakgMQ2JIiSFG5o0wHxxl9jgRrq42XF1tubrccnG56cfFhvVmw2rcsF5tGIcV
+3jn8Mh7LO4/4gPMB8X0uNQ5wQogjKfZgHeOGEEdiXBHi2EOuuL4vXRopRdarFQK89viKd994nZsn
+nwDds7t5l90N3NxkQJnmzJObHbU18qpQcqHlAs0h9OXvzgV8HIjjitYq2ipNW9+brQ1Vt7yvyxxt
+lpxtYdsYY4wxxhjz4WVB+0G6NOAShmHk4uISbYXD/oYn736NYVjj/RNUMzlnWs6kFLnYjgxDQvHc
+TJXdVLmZKzE4YvSk6BmGxGocWa1WjONA8BtSumG1usKL49HVZjm2XF5ecHl1wcXlBevVhjSsGNKK
+FEfc0lTNifR95M73qrZzp6AtDpxP+ND3aPe3CXGR3tRtWbKtvZLsfWAYE+Lg0aML3viWx8zTtxDC
+xDvveL72tYrzEwiUmnlyXZimealkF7RWRHrg977PIA/pQJgOhLQH8SBhGZMmeAXfe7DTXMPhrMGZ
+McYYY4wx5kPPgvYDeuOthggMw8DFxSUxeK6vn/CVt7+yBO2IlkIumbnsGIdLLi5WvP7aFTGN3EyV
+m33h5lBP1xWUlBLrzYbNZsN6vSbFA6vxwGazxzvP1cWGq4sNl5cbrq6u+vHoinHc9OAaxh6WRc6O
+XpVm6faNWzKto4dvWUK4LO/L0jl9CdnHzt8heJxLxOi4erRlnh6h7Q1inBjGivMHmj5hf5g5TJnD
+LuOco9WCtoagPWD7SPCJEAfCNBKnkTiM4OLtIQ5d5ok38Tgc6qzjmTHGGGOMMebDz4L2iZ697cuZ
+ESENAz44VquRr73zVbbbK1brLTGtyHUm10qeJpDGZp34xOuXrLcbbvaV630P27U2aulHjJHt9oLN
+xQWb7ZYUZ8ZhYr2e8c5zebHhYrvhcrvh6upRPx49IqUVziXELbO9RZZl38tbbpeB92q2IP74jOR0
+9Kd3Nk7rjPeeEATEc3m1odVHOLcnxhnnJ5rumPO7qMBhLkw5o40+XztE5pSYc2EuhXJ+1H64knG+
+4HxBXB8N5lzDuWaVbGOMMcYYY8xHhgXtB52mO/fxUy4gOFbjlqvLx7z++ieZp4mbdxzXFOq8o9XG
+NO25uXmHqhP7qbE/VA5T7fuVxROTJ0Yh+obXgpYZrRO0CdqhV5vV4XA4cdQi7PcFZEeMA0ifh+1c
+WpZmp2V8V8K7vizc+YQce+w20LMAK+iyTPz00RlZPicIgveJYdyw3T6ilJk5Z2pTEMd6s2O73XF5
+tUcbXG7WXGzWXG43bLcXXGwvudhesNlcst5esd5estpcEtOmjxhLK0JMhBDx3uOcO1XnjTHGGGOM
+MebDzoL2id57CyCI+D5iyymr1ZbLy8d84vVPUuYZT6HMO/bXkdqUaTpwffMOue44zO10DHEgphVj
+HEkJkms4CtSpH+3Qg7ZzyBKynQilZPa7HTn3EV4s47ucS6S0Jg1rhrTpY7uCAh6RXok/Va6f81Tl
+9KEsn7zt/B1CYhjW6PYRqo1aG+DwPrLd7rm+OXB104P2dr3qx2rFZrPtx3rLar1lXG8ZVlvG1RYf
+Rnwc+1sf8cdZ2s7hnHu//0CNMcYYY4wx5gNhQfsBqvSl47AEwF5tXY0bLi8fc/jERC2ZMu/YPfkq
+4iK1Nqb5wM1NIWdhKsqcG1NuBLnADY4hJsYEzjecZrQI1CVk6x7Oqtk9aO/JWdE9vXGZeJSAc5HV
++orV6oq6yoyqCB7vh37zDY4V69No6uP7d8rYcorXt188NkaLfV+4F7zvndW8T6Q0stsduNlP7HYH
+VJX1ON4eqzWrVe+OPowr0rAmjitiWuGWirvzCecCIm6pZn/9FW2rfhtjjDHGGGNeVRa0HyDLiKke
+tm8bjsU0sNlc8vhRoZVCzXu0HhCdiWFmNVZiWvYfu7o0I1OcV7xXfADv+8fOKU4aXiqegiPjcNBm
+WgnU7Gh9FDVNBRXXx3G5ivpGyQdKiOQQ8G4ZpyUBUcEFj/N9Hne/CVmq3OfLyAWVpZYt50m8h3px
+AR9GRARVoVZBJBLjmsM0cTjMHKYZFIaUGFNiSJFhWDEOI8MwEtPQG6LFAR+H3pHc9ZnfvXGbPHW8
++M/GArYxxhhjjDHm1WZB++S8oqvcy56oQgiR9WqDqtInaGVSgM0qoe0Jwg0iN7R2QOeMupnmMiF6
+XBDEK7KE7OAV7yB7JbhGcBVHRdtMzZ48OVQ8xzFc4hzij/OpHU4aqplW95TSPy/q0NbwNRFiJGjq
+c7VPXckd4JbndFwwvjxvOfsxIEso7oE8JUdrAedGUrokl0LOhVwKKMTgCT4QgyeG/r1jiH15eAg4
+H/uM79Pcbg9nwdrCszHGGGOMMeajxIL2HceQ3avZ9wNgDJH1ekOMiSEmUoD1KnF1sWE6fIVpept5
++grT/AR1B5oTqij+GLSdIq716raH4CA4JbiKl9KbmNVMzZ55cohLy1zsHk4dHr8EbaH1UF6hZEHU
+9eZnrRHTCAw4URwRccv8aie3LdDkdqP2aUX52TpzkT6TGwk4GXBuxTBcUGultkbTRmu965oT6cvd
+nSwvAnicuw34vaHccu1jhf34E7eMbYwxxhhjjPmIsaB9ctsaDJaV1nf2M+tSnQ2kNDKkRAywGiOX
+2zU31xuubwZurj27fcTPB/y8J8x7xpQYh5E0DMQ4EF0iuEh0kZICpQRK8b1S7vo3blXx4nBEvFst
+1WGPj8sRepjto8gqrWVqnU6VaO+F2vzpaTnnkLakahyn6r0s1eyzPdp6XC5/rH478Of/T3kwHOvZ
+/m85feqBs85OefHcbKt2G2OMMcYYYz5sLGh/I0QQ54lpZFxvcQ5CFIZVYrPZMs1PmPK0HDMpeKL3
+pOAJzuOPlWlxSEi4OBCGRGuN4FM/QiKmC2K6IKULfOzzvHt13CNO+tJuJ33clxvwbsT7ARci6oSm
+DdEGWkGPy8b1NlffybC3y+ZPnzn78KlTX+RlzjHGGGOMMcaYjyAL2s91TItyenMMnM735mjOXZBi
+ZBgTm7yl5MfkciDXzFwyuWZEFbfshnYoov1AFR8TYRxI00CrFZHYq9KSGFePGcdHjONjQhpxwS2N
+zhynYdmiyxLtgJN42sONczTOQrY2oOFU+giwO9FZn3rvPHafx29RufuIpwrO8lTIvrtWwBK4McYY
+Y4wx5qPNgvaD7ofBZTn5sqy6bzXuFe0YI4xr0C3aZlRnaivUVimtUmql1kwrmVYLrWaoBW0ZrYU4
+DMQ8MOSBWiraPKoB1chq8zrrzSfYbD5BTCvEO5z3iBeaVlQbTSu9pZksdygoDaTRqL0srR5aA9cQ
+HPdj9rOed9+3ffvp47ZuffAhcr8gfueUe/HcGGOMMcYYYz6yLGg/6G7n8aM+7Ut7VXcZ+XVKowp4
+DxpxWnGt4VTxrdFaodWC1kyrFW1lCduFWNaksqWUHbUWWnW0JrTmGVeXpGGNjwkXwhKye0XbqaI0
+VPVecFag9bBN7U3UxCPil87jy6ive0vH71Sv5Xa/tdz5opzmi995pNz98HQbd396d7/+Ml5yH/fX
+d1FjjDHGGGOM+allQfuZzuddHTtx03t+CX3p9+mTAvjlC4IQcG5Z2u3At4r6irYG2tBWobXexKwe
+qKej0CrUqrQmxLQmxAG89NXfDpyT3g0cEMKplt0dQ2lDpQK9gi0cg/bZXO3Tcng9a/2tp0899dMQ
++isNwoPf8fSj0mOTtfMv6NPnGWOMMcYYY8xHlAXtpzxj/fPxq2eBU2HpGHYMrw7wp4KxY9nT7Hvo
+Fvq+7PND20zTmdYmWi19fNYyQquP2IqIW7qDu+WiTnDiYTnuxt5e0b49ABzS76ZXtZHjawLLUvPb
+pfG3ofhZafv4NTn9tI6f01PAPq+x23JxY4wxxhhjzMeLBe0HnZeuH/iqHLO29GXWyFPnyjG2ityu
+1D4rIsPyLTSDFlQzrS1Be3mrgGqffH07T/t4+NN8bLnz4oCC3Avael6ddzy9Y1qe+uhFLzg87P0P
+2A8sVH/6i1YhN8YYY4wxxrxCLGg/i56FbbiTH19u1/BS4V0eoPdz650mYg6IiHM4Wg/Prt0Wvumd
+xZ3rs7P7MvBj87MHLqpn1z37Ps/Mo8v+67tB+eFndX7r8lRg/6mny/9YtjbGGGOMMca8qixoP+QY
+ppcqtRzf5+sNlXobCPWBSdV6DKtLozI8zjXEaW92djr6A2+r2e6sy/hDd7V8V30ojvbH6f2zTy3G
+X+QZ+7Pvfe2n2lP3b4wxxhhjjDGvCAvaD7jde61nFezzpmEv56FF1OdNu5fe5sv+6aVP2Z1zFdV2
+t9P3shT9Nkw/dF/Pus+Hg7eeWqo9d6H2C7xMRdwYY+4SkV8H/EH6L5Cfqap/+wO+JWOMMcaY98yC
+9gNOe6/PK9qLp6PovaXl96jq3S/dO+/4+Gdfx3FqcibH7udfX+X57qflgTPkqc98YyxkG2M+3q6v
+rz/oWzDGGGPMK8CC9kNk6Rb+QP582Sh5rEK/TCh+avf0eeYWoY+1ltPH79m9a1g8NsaY98eTJ08+
+6FswxhhjzCvAgvazvMf0+XJV5xde5DZsv/erGWOMMcYYY4z5JrCgvTgc9qf39evreHbydYXheyc/
+1NxLH/riN/K9n7f2/QW+mRO0vtHu5avV6n29D2OMMcYYY4x5LyxoL9555x2gj9K6nZK1LP9+3gMf
+SIfy7HeeOvFZ4fLUmuxF6fNed7XbGdgv6c43kXspX++9APDwXPHnXPiB+79/Db3935dN2vfuwYK2
+MeYV4IC7zSuNMa+0t956iy984Qt87/d+L2+++eYHfTvGmJdUaz2+6z7I+3gRC9qLr73zteW9ZZyW
+6t1q7r2m3Odju57pqf5md0Pmgw+9X+l+IIHK+aceKIWf2qs9EIqPl9MHgvB5M/M73daP13vg/u4/
+9vzfmA9W5J83zPslfiB3ppAtb7/1W7/tWRc1xnzAROQR8O8BvxL4GcAT4K8CX1DVP/6S1/gZwL8F
+/DPATwc88OPA/wr8l6r6117iGr8C+DeATwEb4O8CfxL4var6EyLy/y7X/kOq+t1fz3Nc+G/gMcaY
+D9Bbb73F5z//eT7zmc9Y0DbmQ+QsaL/Sf/da0F68++5S0VZdQraeQuN5YJX7YfCh8P3gJ+6nbjmr
+aD9VN15C6/LVBwP+wxVjOX2be2PATtPA7o0se94LBXqvoi3y3L3ncn7P92/8/Cae06H9wfOf+kbP
+GBFujHmliMg/CPzPwJvc/rYZgF8K/DIR+YPA//6Ca/xrwBeWx53/kvhZwM8GvkdEfqeq/ifPucb3
+A79p+fB4jZ8N/Dbg14rIP89pvIMxxhhjzHtnQXuRp75Huwfs26r2g9nwOf8UOxWEl5B6G9zvdSJf
+QqveD7vHovcx0D6QPQXQdrzPp6vdp+s/UNW+W2W+u2z7OLP77jVvA/sxaN9+fHqyp2B9/jzvLMNX
+EHd+jeU4PVd9Opc/jwVtY15pInIB/E/At9J/E/wx4AeBnwT+AeC3Ar8e+HnPucZ30mdsQ6+E/x7g
+fwEK8B3AbwfeAP4jEfmqqn7hgWv8u/SQrcDfAX438H/Qg/s/t9zHHwfW7+X5GmOMMcacs6C9yPMS
+tPU2aL9wj/ZTIbgHx+M1miraGk0brfX3FXDOISI4586q1no3wMrthO3l0sdvAija9HTduy8IPB2I
+H9xarXJnB7aq0lqj1kpr7bbb+Z3jeN+3n+uPbaf7OS5LP/389DZ4O+dOhziHEzmF71Mmf5m0bSHb
+mA+D/wD4afT/tH+7qv6nZ1/7KyLyx4H/AfhnH3qwiAR6JRvgGvglqvpjZ6f8RRH5E8BfoFfMf4+I
+/JCqvn12jU8Cn1vu4f8G/jFV/erZNf68iPxp4M8CCatoG2OMMeZ9YkF7MS8VbVRpercae3Q/3z30
+LzJZ9im31pajUmsPsMf9BM45vPdnQftYRe7h+BRil7Ddm5DdrU7X1mi1f487FWihB9glyN/e1/PT
+aWtKLYVSC7VUOAVqd/bCwPHj27f9sctzrW35ud1Wtk+rA6A/Z+/wzp+efz/kTjB/FsvXxnw4iEgE
+vpv+X/RfvReyAVDVKiLfA/xNID5wme8Cvm25xu+6F7KP1/jbIvLvAP8NvSL9G4Dfe3bKrwPG5Rq/
+5V7IPl7jLyxLy//tr+9ZGmOMMcY8mwXtRQp9L/2pGnsWEJ8V8B7aJ3ysaNcGrSpVHKo9bKsWtCmo
+A63oErSPy8thCdnufOn3wxVqbW1Z6r1Uy09LvhW3hOPmbqvOd5ajc9oJfgr00GhaabVQSkZElsff
+VqFVexUaPN4rvf+Aoq3SaqWUsiyZ13sh+/g24iQg3uEceC94704vCLyoW+/LLt83xnzgPgU8pv+X
++oefdZKq/riI/BngOx/48i8/nsbt8uECLKMAACAASURBVPGH/BDw/cDl8pjzoH28xpdV9Yefc40f
+xIK2McYYY95HFrQX203fnne+7PtlnHfBPkZa1UZtt1XseXJLxu2f6zlagQbHPdyqy6JwgXZW0RZB
+cKfKdl9Wvux3xuEEqvRqcm2Vpoq4XlF2y+Pu3u0xYutp+bZzDkWWwCz03KvLee30aKG/COBdD8gh
++OX+G60dr9+W/ePttE/9yAn44EgpEGMihEAMAR/C6YUGfU6CfqlO78aYV8HPP3v/L73g3L/Iw0H7
+uHf7b6nqV571YFXNIvJXgH+Kp/d7/zz6b4wffcE9/Bgw83Bl3RhjjDHm62ZBe7G5F7TvNAU77ZeG
+p/qE3+8sLr1RWQ/Z5RSsoVeL0YYcm9vediE7Lbc+Lhbvh+vVb1rfH91vZWmiJoinn1P6nm2loa2i
+4pdMLRzD9e0d6uk9J4r3vbIMQqsO74VSzp9rv88e8hUn4LwQvCMGv+ztrpSyhOxjlV3bWZO0fivO
+KSE4UooMKRFTJMZIDPHs+72EF6w0MMZ84F47e/8nX3DuTzznGvoSjwf4ew98X+hVdYAvPe/BqtpE
+5G3gky/xvZ5LVfniF7/4wvPefPNNGydkjDHGnHnrrbd46623XnhezvmbcDfvnQXtxXo1ArdNwR5c
+xnyW7J766vnXtAftUvzSXKxSayZn1yu/S0fz2yXjt0vH74Rs2hJSHccK87GqfTu6S1DfqJWlslyX
+vdMOQZel4w8FWD3ts/a+L932vlez+7XPH9P69Zbve17RVlVKkdNjjkFbT0/0+DM5PrYH9JQCKaXl
++DqKSHe3oxtjXn3vdQ3Kh24Ny6c+9akXnvPZz36Wz33ucz/1N2OMMcZ8SHzhC1/g85///Ad9G+8b
+C9rGGGPeb+dNxz5J7/j9LM+qIr9Nfz3tZarM33r2mPv38Un6CLBnkv5q5uPnnfMSTtX0N9547rcD
++j8mfuAHfuA9fktjzHsxzzMAn/70p0kpfcB3Y4yptb7U36Ff+tJpodr9lWyvFAvai5/+cz5lBVJj
+jHl/nHcI/3bgzz/n3G9/xuf/GvCLgJ8p/z979x4t21rWd/77vO+cVbUue+8DHOBwPGC8pWO8RAQM
+oDEBTUAZXugYo4mKgkZbewyN2rGT0K3g6MRB7Gj3EEcwUUBjhsPYiokXohgS0jSJXEzihagkGAWO
+cgDPvqxL1Zzv+/Qf7zur5qq91tprn7PPXmvv9fucUdRt1pyz1mbt2r963vd5zZ5w1DztugzY0ymV
+799Ye/o3KSH8025wvp9CWVf70VTPl58ho38AiMgdQL+zInesM53fFLRFRORWeyelmnwP8JXA9x+2
+kZl9FEesow28Cfg6yofo1wDfe8R2fwW4RAnJb1p77leAzwHuNbPPc/dfPGIfLzni8Zsxp4T1zMnm
+lYuIiMgj8yTKPNv5aZ/IcexGSyqJiIjcLDP7XuBbKQH4O9z9e9eej8C/BF5AXRkR+Bh3//36fAu8
+l7KW9mXgz7n7b6zt46nA2+o2O8BHu/tHRs8/hbJO94QyfP2565VxM3sO8GZWHcdf7+4vfdQ/ABER
+ETnXwmmfgIiI3JVeCbyPEqJfZWY/bmYvMLOnm9lfpQTkFwDvOOzF7t4Bf4MSwC8BbzWzl5vZc8zs
+M8zsb1KWDru/bvNt45Bd9/Eg8Ip6Dp8AvNPMvsHMnmlmn2lm302pgr8f+NDwslv5QxAREZHzSRVt
+ERF5TJjZnwZ+mTJPen0elQOvBf5dvT5Q0R7t4yuB11CGZR+2jwS83N1fdcx5/CDw9cPdtac/CHw+
+8DPAA8A/cvdvOsn7ExERETmKKtoiIvKYcPffAj4JeBXwO8A+ZU3rfw18ubt/7bApR1SS3f3HgD8F
+/F/AbwHXgF3KUPAfAp5+XMiu+/hG4IuAXwI+DOwBv0uZO/50d38XcLFufvmRvFcRERGRMVW0RUTk
+XKtN2f6AEva/1t1fe8qnJCIiInc4VbRFROS8+2uj2//+1M5CRERE7hqqaIuIyF3LzDaBi+7+h0c8
+/3Tg3wAXgLe7+5+9jacnIiIidymtoy0iInezJwLvNrM3AG8Efpuy7ub9wOcBLwU2KOtff+tpnaSI
+iIjcXVTRFhGRu5aZfTRlPW7n+o7j1McXlLnZP347z01ERETuXgraIiJy1zKzBvhi4IXAsygV7sdT
+Opf/HmX5sR9w9z84rXMUERGRu4+CtoiIiIiIiMgtpK7jIiJy7pnZ08zs/zSzd5vZNTP7sJn9qpl9
+u5lt3MLjfLmZ/Ssze9DM9szs98zsx8zs2bfqGCLnyWP5u2tm32lm+YSXz75V70nkbmVmTzSzF5nZ
+K8zsF8zsodHv0I88Rsc8tc9dVbRFRORcM7MvAH4MuEiZs33gaeB3gBe5+399FMeYAf8PpQHbYcfI
+wCvd/ZWP9Bgi581j/btrZt8JfOch+17nwPPd/S2P5Dgi54WZ5bWHxr9br3f3l97CY536564q2iIi
+cm7V5b1+grK811Xg7wDPBT4H+MeUD+dPAH7OzLYexaFey+rD/l9T5o1/BvAy4D2Uz+PvNLOvfRTH
+EDk3buPv7uCTgU854vKpwNtvwTFEzgOvl/8O/BKHNyq9FU79c1cVbRERObfM7C3AZwEd8Ofc/VfX
+nv824B9QPqhf8Ui++Taz5wNvqvv4F8D/6KMPXzN7AvBO4GnAHwMf6+6XH9k7EjkfbtPv7rKi7e7x
+0Z+1yPlWf6feDrzd3R9aWxnkllW0z8rnriraIiJyLpnZsyj/UHfgn6z/Q736h8C7Kd+4f7OZPZJ/
+bH9bve6Bb/K1b7jd/cPAd9S79wCqaosc4zb+7orILeTur3D3X3D3hx7jQ52Jz10FbREROa++eHT7
+dYdtUD+cf7TevQd43s0cwMy2KUNZHXiTu3/giE1/GrhSb7/4Zo4hcg495r+7InJnOkufuwraIiJy
+Xn1Wvd6hDCE7yr8d3f7MmzzGs4DJIfs5wN074N9Tqm/PUvVN5Fi343dXRO5MZ+ZzV0FbRETOq0+k
+fOP9Hndf74Q69l/WXnMz/vQR+znuOA2liZOIHO52/O4eUJcH+iMzm9frN5vZd5jZPY9mvyJyy52Z
+z10FbREROXfMbArcW+++77ht3f1hSuUM4Kk3eagHRrePPQ7wB6PbN3sckXPhNv7urvvcetymXn82
+8PeB/2ZmX/go9y0it86Z+dxtbvUORURE7gAXRrevnWD7HWAT2H4Mj7Mzun2zxxE5L27X7+7gPwNv
+AH4V+ADQAv8D8NeBv0SZ//1TZvYF7v6vHuExROTWOTOfuwraIiJyHs1Gtxcn2H5Omce18RgeZz66
+fbPHETkvbtfvLsD3ufsrDnn87cA/NbO/AfwjIAL/xMw+zt1Pck4i8tg5M5+7GjouIiLn0f7o9uTI
+rVamlDmhe4/hcaaj2zd7HJHz4nb97uLuV27w/A8BP0wJ8vcDf/lmjyEit9yZ+dxV0BYRkfPo6uj2
+SYaLbdXrkwxVfaTH2RrdvtnjiJwXt+t396ReM7r95x+jY4jIyZ2Zz10FbREROXfcfQ58uN594Lht
+a1fh4cP4D47b9hDjRizHHoeDjVhu9jgi58Jt/N09qd8a3f6ox+gYInJyZ+ZzV0FbRETOq9+iDPn8
+eDM77vPwT41uv/sRHOOw/Rx3nB743Zs8jsh5cjt+d0/KH6P9isgjc2Y+dxW0RUTkvPp/6/UW8Ixj
+thsPB33rTR7j7ayasRw5rNTMWuDZlH+0v93d000eR+Q8uR2/uyc1XrP3A4/RMUTk5M7M566CtoiI
+nFdvGN3+msM2MDMDvqrefRh4880cwN2vAb9Cqb59rpndf8Smfxm4WG//9M0cQ+Qcesx/d2/CN4xu
+/9vH6BgickJn6XNXQVtERM4ld3878O8oH8YvM7M/e8hm3w58IuUb7+9f/8bbzF5iZrle/vcjDvW9
+9boBXr0+1NXM7gW+p959mNLFWESOcDt+d83sk83s4447j7q818vq3T8Efubm342I3Iw76XNX62iL
+iMh59s2UIaUbwC+b2d+jVL42gC8Hvq5u99vAPzxmP0fO03T3N5vZTwBfBnxRPc73U4aZfirwd4Cn
+1X38LXe//Kjekcj58Fj/7j6Dsjb2m4FfBH6d0oStoczr/ArgL9Zte+Dr3F3L8okcw8w+E/j40UP3
+jm5/vJm9ZLy9u7/+mN2d+c9dBW0RETm33P0/mtmXAv+UMoTs761vQvmH+ovcfedRHOqlwAXg84G/
+ADxv7RgJeKW7q5otcgK36Xc3AJ8DfO5Rp0EJ3y919194hMcQOU++FnjJIY8b8Fn1MnDguKB9I6f+
+uaugLSIi55q7/7yZfSqlQvYiynIgC+A9wE8Cr3b3/eN2cYJj7ANfYGZfBnw18GeAe4A/At5Sj/Ef
+Hs37EDlvHuPf3Z+nDAt/DvB04MnAEyiB4CPAfwLeCLyuzgkVkZM5aaf+47a7Iz53zV2rEoiIiIiI
+iIjcKmqGJiIiIiIiInILKWiLiIiIiIiI3EIK2iIiIiIiIiK3kIK2iIiIiIiIyC2koC0iIiIiIiJy
+Cyloi4iIiIiIiNxCCtoiIiIiIiIit5CCtoiIiIiIiMgtpKB9hzCzf2NmuV4++7TPR0RERERERA6n
+oH3n8LVrEREREREROYMUtEVERERERERuIQVtERERERERkVtIQVtERERERETkFlLQFhEREREREbmF
+FLRFREREREREbiEF7VNmxUvM7JfM7EEz2zOz95rZG8zsix7hPp9mZq8ws7eZ2R+a2bxev83MvsvM
+HrjJ/d1jZi83s7eb2UfM7KqZ/Rcz+8dm9szRdsPyY+mRnLeIiIiIiMjdwNy1WtRpMbMnAz8LfMbo
+4eEPxOr1TwNfDfxL4M/X55/n7m85Yp9/F/i7wGxtf+N97gPf5e6vOsE5Pg/4Z8CTj9hfBl7h7t9t
+Znl43t3jjfYtIiIiIiJyN2pO+wTOKzO7BLwZ+FOswut7gbcBc+CTKAH8xZxw7Wwz+wHgG+v2Dlyr
+x/hD4D7gecA2MAW+x8ye7O7fdsz+nk0J+Bujfb4d+E1gUs/vE4DvMrMPDy876fmKiIiIiIjcjVTR
+PiVm9sPA19S7c+Ab3P31a9s8E/hJ4E8AC0q4PbSibWZfCvwEq5D7WuBb3P3aaJtt4NXAV462+8vu
+/oZDzm8K/DrwcZTw/N+AL3X3d61t9yX1WE09PwNcFW0RERERETmvFLRPgZl9AvBfRg+9xN3/6THb
+/hqlqjxUiw8EbTMz4D2UQA7wk+7+5ccc/2eAL6r7+q/u/icP2eYbgB+sd3eAT3H33ztif19MGeLu
+KGiLiIiIiMg5p2Zop+NlrOZL/+pRIRvA3X8X+P7R9of5S8DH1G0WwDff4PjfBHR1+48zs794yDYv
+HU4B+L6jQnY9xzdQhqgfd44iIiIiIiLngoL26Xje6PaPnWD719/g+efXawd+wd0/eNzG7v4B4I1H
+nM8wxPzTRw/9+AnO8cgvC0RERERERM4TBe3T8WdGt992o41rVfsjx2zy9NHt/++E5/DW0e1PX3vu
+U1n9f+OKu//2Cfb3H054XBERERERkbuagvZtVruNT0YP/f4JX3rcdk8c3f7vJ9zf741u33vE/hx4
+3wn3d9LtRERERERE7moK2rff9tr93RO+bueE+zxuu6P2d+GY/Z30/K7deBMREREREZG7n4L27bce
+SDdP+LqtE+7zuO2O2t/VY/Z3K85PRERERETk3FDQvs3c/TKlM/jgaSd86VOPee6hR7C/PzG6/aG1
+54b7BnzUCff3wAm3ExERERERuaspaJ+O/zS6/ewbbWxmHw884ZhNfm10+7knPIfxdu9ae+4/A7ne
+vmRm162zfYjPOOFxRURERERE7moK2qfjzaPbX3GC7V9yg+f/db024PPNbL252QFm9hTg8w55PQDu
+fpWD4f2vn+AcT/I+RERERERE7noK2qfjh0e3n21mf+2oDWs1+1soHcCP8kvAe+vtKfD9Nzj+DwBt
+vf0ed3/TIdv8yHAKwLeY2Ucfc45fCHzODc5RRERERETkXFDQPgV1XezXUUKsAf/EzL5qfTszeybw
+y5SGZIv150f7c+B/HV4GfLmZ/ZCZHWhQZmbbZvY64MXDS4G/dcRuXwu8p97eBn7FzNbX28bMvgT4
+cWD/qPMTERERERE5T6xkNLndzOwe4G3An6SEY4D/Vh+bA5/Eat7zT1PWuv7zlHD8PHd/yyH7/L+B
+bxrt7yplmPofAU+iVJ2Hpbsc+D53//ZjzvG5lKC/MXrNfwB+i7IW+GfU83fgfwZeXbfL7t6c4Mcg
+IiIiIiJy11HQPkVmdh/ws8Azh4dGTw9/MD8LfCXwc9wgaNd9/h3g5ZQh5Eftcx94hbu/6gTn+Hzg
+nwFPPGJ/GXgF8D2squ4Pu/vjb7RvERERERGRu5GC9ikzMwO+itJw7FOBS5QK9H8CXufuP1O3ezPw
+2ZRw+/yjgnbd9qnA1wIvAD4GuAd4mFIxfyPww+7+vps4x3soFesvBj6WMr/7/cBbgNe4+zvN7EnA
+H9bz++/u/rEn3b+IiIiIiMjdREFbbgkz+1xKUzYH3ujuLzrlUxIRERERETkVaoYmt8qXjW6//dTO
+QkRERERE5JSpoi2Pmpn9Wcow8pZS0f5Ed/+d0z0rERERERGR06GKthzJzJ5qZj9pZp95xPPBzL6C
+Mu+7oYTsn1XIFhERERGR80wVbTmSmX008N5694PAO4EHgQQ8GXgOq27kUBqkPdPd/+h2nqeIiIiI
+iMhZoqAtRxoF7eH/JLa2yfj/PG8HvuRmupmLiIiIiIjcjRS05Vhm9kzgC4BnAw8A91KWC7tGWYbs
+bcBPu/vPn9pJioiIiIiInCEK2iIiIiIiIiK3kJqhiYiIiIiIiNxCCtoiIiIiIiIit5CCtoiIiIiI
+iMgtpKAtIiIiIiIicgspaIuIiIiIiIjcQs1pn4CIiMidzsx2gCmQgQ+e8umIiIjczZ5EKRjP3X3r
+tE/mKFreq/q0L/o6Bzj052Hjay+X+pjV62DloQAYhtXNDWhDpImRJgamkwmbGzM2N2ZszKZkd7Jn
+cs4sFgt29/bY3d9nd28Ph/o8LE/LyznmnJfXZkYIgRAjIdQzsHL0nJ0+OSk7KZXj5FxeF2OkaRra
+tiHGWN+PLY+D+/J4y+v6czCz5YX6njEj50TK5TgpJ/rUk/pEyokQArGeY4wRC6Gcd1gbWDF6j8s/
+Artuk6V3/Mxr1p4VEbm9zKwH4mmfh4iIyDmS3P3MFo7P7IndbkOIdHyU4pwhURvDw162YRX+hrAZ
+DAJGNKNtIm2MtLFhOmmZtm25nrTMJi3TyYTppKXrOrpuwaJLZHpC7qCbkxf7ZKcEZHfcV8dZZmB3
+3B2zUM7PDD+QSG11zge+QPDRZRTiD/u5LK+Hbb1+jbB6tJzXwZ/b8F95bP34RzPswM9X3wOJyB3C
+ofx9eP/995/2uYjICSwWCx566CGe+MQnMplMTvt0ROSEPvCBDwzZ4kwnBQXtKvuqesqBPzcHN9wA
+d9xWoTNQythGCYVmRjSYxMDGbMLmdMrmbMrmxoytjQ22ZjOmk5Y2BpoYaKKxt7vL7m5iL+2Tckfs
+57DYI+/v0mdfXpaB1sa1cnCMEGM5fjDcw+ot4MuKODWUew3t7uM3OwT2wwrDPro1bDs8Yqsob0NE
+9rWX+pG/AetHWw4cWAbs1dcbIiJn3EeAJ9177728733vO+1zEZETeNe73sUznvEM3vjGN/Lpn/7p
+p306InJCT3rSk3jooYegfPaeWQra1YGK61Dd9qFq7UPeLiHVVkOol2HbjGAQDdomsDWbcHF7k0vb
+m1zc3ubihW0ubW8xm7SYZwKOeeZKyDRpH+aZuY+C9t4OfXa65HQp4xgWAhYCBMMsgIV6gu1y+Lib
+jxKs47mG6wPvcRWuT/yzqZcSyFd7GcLwqu6/2v9xIXtwYFQ+tgzZqmaLiIiIiMidSkF7XQ2l47AN
+JUj7UNF2DjxXqs0QA0Qzpk1kc9ZycWvG4y9uc8/FC8vLtG3wviP3C3LfkXZhETJ7eUHs97FurwTt
++R65z/Qps+jLcalB22IkhIiFiA1zq0Mg54iFIZ2u6stDJZu128P9g++fZfodF/aX0Xm0vY1u+TBv
+vVbMDybsY47HwUr2gcdtNKR/9Odw8NgiIiIiIiJni4J2ZasJ15gfXom15XDtet8p/WXdCWY0FpjG
+wKyNbLQNW5OW7WnLRhuYBCd6D32in+/R7++zmO+xd/Vh9q8+zPzaZbrdK6T9HXy+V+Zp95nUJfou
+kyjDs8tE8EDTtIS2JTalmu2pwZvMsvpOKQsfVbUehpCvbo8anR0VY48J6MuB44cezg69edg5HTw/
+lbRFREREROTOo6BdDZ2vV4Hz8LHLy4Lv8Fwuldzg0JgxiYFZ07A5BO3ZhM0mMjWIOUFKpP1d5rvX
+2Nu5yt6VP2Z+7WEW1y7T7Vwl7e3ii32sW+BdIi16ukWidydbDdtmNNMpbZ6WkB8isUl4bU/uZquw
+zXLU96Hzs4/NssuS9sES9TCf++iwfeMh48dRwBY5P8zsJcBrKX/JfIy7//4pn9Kj8qEPfYgHHnjg
+tE9DRE5gsVgA8MIXvlDN0ERO2X333cc73vGO0z6NW0pBu1oOSa4Bcr2qPYS/VXuug2PIDwbtoaLd
+cGHaMm1KRbvxntR3pPku850r7F15eK2ifY20P8fnc+jn+KInzRP9omeRMhkn4XgwpjmBOxYCsWnJ
+OYHn2unby1xtt7WmZ6vrVeA+JNRe95rrNzsqbB9M7ierZI/3OVyroi0idyJ35/3vf/9pn4aI3ITa
+VElE5JZS0K6Wbby8Dgmvjy77aNcq8YqXYJsz2TM9PQuP7PeBPRK7beBaE9iIxqRtmLSRSdvQd3Ou
+Xn6Ya1ce5uqVh7l2+TJXr1zm2uWr7OzssrvfsbffsVj09H1PTglPCXIdFj50Qe97ct+TFwtSbEix
+oY9NbZYWsdCURb3rudbG4wfq046Vi6+K1zaaa73ezGx43Xhxr/Ew8/WvIg78tHytI/kR1sP1ejd0
+hW8ROfs+6rRPQEROZAE8BDwRUEVb5HQ8SJmLe/dR0B4MFetRo7NVP+0aVMdZ2x33DKnHc0+XnP3O
+8eCEbpfWe2LusLRg0ja0Tbn03ZwrVy5z9XIN2levsnP1GjtXr7K3P2fRJRZdYt4luj6Tc1p2KXd3
+Al7+r5hzCdthQQqRFCJ9DKX7eNNiDYRQg/Ro3vYqbNvB0O1g5qsqtw/dxuvrRsPHh+Zv1J/JsNrY
+oauDsRbXj8jJxzVsExG5cxig5b1E7gzvAp4BvBHQ8l4ip+MB4O4cCaagPVgr3Y7rss7aCtH1SfeM
+5x76BV3uce/pc4/vR2LuCamDbkFTg3bTNHSLBVeuPMyVy5e5cvlhdnd32d3ZYWdnl27RkZKTs5Oy
+0+VMTqXjmrkToIZtICXoOzJGskgfIxZLV/KIE4JhBCAcCNPl2lZh2225OJc7YI4NIXv4GaxnXhuF
+7fUL1PS9Fq4PTvM++o9hLXAP14ev8S0iIiIiInL2KGhXo6WnR4/4aJj0cD2k1Yynntwt8G4fTx0p
+LehTB4tASybkhPcdTdMQYyxBu1tw9coVrl69wtUrV9nd22Nvb4/d3T36PtUDlSSb3MmeIfsyx8Z6
+LsEdUgZ6PHZ415BjJIVQqssx4DmW883DXO1hmLgfyL7jWrWNhniPly9bXqz+rIawfeCHOCyCzbIS
+fhKqXovIXSCd9gmIyM16CvCd9VpE7hSxLm/MGf/sDTfe5Jw5pH/XsubrucyVzj3eL0iLOf18j8Xu
+Dov9XfrFnNR39F3H/v6cnd0dLl+9yuXLl7l8+WEe/uOPcOXhh9nducZ8f5++70h9IqVMypCy02en
+z5kuZ1LO5OzL5mwRaEKgDZHWAo0ZEYjuZQh735EXC3JXL/2iPJZ6PCc85/IelpOyr7esY4/Wr14+
+Nl5fG1ZjxW1t3Pghy4ApTIucT2Z2j5l9j5m928x2zeyPzOyXzexLbmIfH21m32dmv2FmV8xsx8x+
+x8z+kZl98gn38QVm9otm9sH6+t82s1eZ2ZPr879nZtnMfuQRvtU6wUyjb0TuHE8BvgsFbZE7yyho
+n+nJ3apoH8bKXO2hYrtaPdvBE+RETh1psU+3v0e3t0NLLsOuzek9MJ8beCZ1PdGMGCCY4TmxWCxY
+LBakriOlnj7lGrJZLsO1bEZWb5sZweoc7FCGiJsZAcOyYynhXUc2sGDkEAgx4NbgHnFCnVc+zNvm
+4OhuH7/PYYj5KDCPLjauZq8N6fbRaw8bAi4i54eZfSLwJsq/Yoe/AKbA84HPMbPXAm+5wT6+CnhN
+fd34L5GPAz4eeJmZ/W/u/j3H7OPVwP9U7w77+Hjg24GvMLPP58QTXERERERuTEH7KDY0Rhv10vYM
+OeFDRbub0813WeztlCHV0QgBUg7s50zfdcz394lWZkoHAM/knMkpkXOmrxXtPlOD9mppK/M89AUn
+WCAYxBCINWQz9Pz2jKeEG2TKkl85lguhDgW3WMP1eBDDqs3ZcG/Z+m08bNztwL9AS9g+WdVG4Vrk
+fDKzC8C/Au6j/LXxE8CPAh8E/iTwrcBXA0dWpM3sRZQ1tgGuAt8L/ArQA88F/jalXfD/YWZ/7O6v
+OWQff4sSsh34A+DvA++kBPcX1PP4KWDz0bxfERERkTEF7cqHUdAw1HuBYWy91yHXCU8due+wbkFI
+Pa07BGMSjEkTmDaRJhjlPyfnRAwlgLfBiBYJoSUGwyyws79gZ3/BZH/B/ryj63q6rlyv9xkLlKp4
+DLacyr0c5u0ZErhZqWyHQMIgZjxkWC735ZQ6eGCYkz1Uz+veDq4VXq+W3djtYN+z201N0UTuGP87
+pZWoA3/b3V81eu7XzOyngJ8H/tJhLzazhlLJBrgGfJa7//pok181s58G3kapmH+vmf1zd//IaB9P
+powLdeA9wLPd/Y9H+3irmf0idA4RHwAAIABJREFU8GbK2j76ZlBERERuCc3RHqyl2jLtuAwFD3jp
+5J0Tlnqsm2PdnCb3tObMYmDWNszaltlkwnQyoW0iIZTh48GcNhrTJrA1a7lne4Mn3HORpzzxcTz5
+CffwxMdd4gmXLnLpwhabs/LaZSWbGrCxErKthO1gwxcCdZmxXNb09pTIXU+aL+j390nzfbybQ9+X
+c88JPGHk0RcKvsraXirY4yht9QdiowscDL3jSvyBH+tom8Ned+gfhcK0yB3NzFrgpZS/Vf7zWsgG
+wN0T8DKgO2I3Lwbur7e/ey1kD/v4feB/qXc3ga9Z2+QlwKze/ua1kD3s423Aq499QyIiIiI3SUH7
+KEPYxgmUdaxL0O6gX2D9gpgTE4NZrWTPJi3TyYTJZELTNITahTsYNMGYtpHN2YRL25vc+7gLPPne
+x/OkJ9zDvY+/xOPvucClC1tsbUyZNGE51HyZ/ev5rEI2lEHeGcjLsE1KtSnanLS/T5rPyYsFnjrI
+PeZlXW4bDU0/MEx8/YdQj2SjR+DwMLw+F3scrE8anhWyRe4KzwAeV2+//qiN3P39wC8d8fTnDpux
+Gj5+mH8OXF57zfo+PuTubzxmHz96zHMiIiIiN01DxysfQuF40LQNQ6tLiA25Dh3vFoR+QajV6hAj
+TROZxEgbI2aGWypt8DxjBGIwmhiYTho2N2dc3N7m4oULNJMpcbJPM5kRYoOnzGLRMd+fD53RalV8
+NIx9rWGZjwdy1y7l5IylRKhjzC0EsLK8l0UDG0L2sow96io+Gkc/unFYBL7ZYHxY+DazI+dyaw1t
+kTvSp4xuv/0G2/4q8KJDHh/mbr/X3T981IvdvTOzXwP+AtfP9/5kyl+V//EG5/DrwAJob7CdiIiI
+yIkoaK9ZzlEehWzPCU8J+g76jpAWkDoagzg0KLM66znnusx2rTKPA6QZFiKxmTCZbTLbukCKM2gX
+NBsdoZ2S3ehTpu96UurJfU/q+1pcLhXs5JC9LByXVpO1D8bh5FjOuAUI5RKw8iduAbNhRe7VUl7j
+sD066UN/TseF33FwXg/Wxw0/P84QuNf3KyJn0uNHtz94g23/6Jh9+AleD/CHhxwXVlX1h457sbtn
+M/sI8OQTHOsGMvCkE2wX60VEROQ8e3B168EHefDBB4/ZtlgsFo/lCd0yCtqHWk1Ydk94TpA6SB2W
+OkLfEVJHEyNtDLSxVovxGrC9vCaXYd0lyJZZ1xYiTTuhnW0y27yATRLNRmLWZWIzoe8zi8WC+Xyf
+bj5nsTA6MtlzXVYr4w7JvQRtALcynxwAgzx0D6+B1kJpkkZpwBZCA7EOH18u9jUaP76WYVeF7SPC
+rdlym8Mq0+uh+qiQfVRl+7CqtrqZi9wxHu0v6x34y35srhcREZFDvOY1r+EVr3jFaZ/GLaOgfZ1x
+VXcYNt7jfV+Cdt9haUHMPW2EiQUmMZAxeofkmZydnA6paNegHdspk+kGs60LNBmmDn12QjthvujY
+29tjb3eX/VA6l3vu6FNdisudjB8M2maYl4ZpRqhV+NFbMcOtDI+PocFjItSQzaHBdsjUwxDy46vX
+6/dvFLYPm7N92OsOu69KtsgdYdx07MmUjt9HOaqK/BHKX0InqTLfN3rN+nk8mbIE2JHMLLCqfj8q
+Zsa99957w+1ijMSoiraIiAjAfffdx9d//dfzhV/4hTfc9oUvfCEPPXT2v9RW0K58VDQpt3NZ8zol
+ct/j3QK6DlKPpVLl9hxwr4HaQm2eFggRzGKZXh0DTdNgIeCUQL3oe/a7jr35HEJTqtxNy2w6ZXNz
+gwvb2+xdvEisXctT3+GUKnaZk13nlC+D6Ko1WlmGjGECN9Q1uy2k0nk8dFhs8djjMdbth6q2jcvX
+ox/OweeWlfPlfVvezScM2euX8ZDw9dvr3c1F5Mwbdwh/FvDWY7Z91hGP/wbwHOBjzOwJR83TrsuA
+PZ3yt95vrD39m5QQ/mk3ON9Poayr/aj/grn//vt53/ve92h3IyIici495SlPueE2k8nkNpzJo6eu
+41Wp+Ja52cMyVdkzOffkviN1C1LfkfuenBM55/p8Xm5vBqE2PWvbhsmkZTqd0LQNFgMZ6FJm3vfs
+zxfs7O2z6DoyEJvIZDplc2OT7QvbXLp0ka2tLWazGU3bEmJcNTQbTnoZVGGYa+11ua8yxLwOZc/l
+y4Lc9+TUk+ta4J7K8Har7c+WedaM9eHjq/W6V13KS09yq5sPVWo7sM72uEv5SS6HbXvkn9khy4mJ
+yJnwTlZV7a88aiMz+yiOWEcbeNOwGdcv2zX2V4BLa68Z/Eq9vtfMPu+YfbzkmOdEREREbpqC9iG8
+LpuVPZFzIvV9CdpdV4JqbXQ2NDzLnks12UrfsRgDTRNp25bpbErTtliIZXh5ziy6nr3Fgt29fRZ9
+jzs0sWE6nbBRK9qXLl5ke3sI2g0xRkIIeK2M+7De14Eg6ssK9dCt3D2XYeypfGEwBG5P5YKnMlfb
+hmXEbC0qr3Y9nsp9WMge7h/VQG3sqOHjJwnYoMq2yFnm7gvKklwGfJqZffv6NlY6Mv5jju70/Qbg
+A3Uff9fM1juKY2ZPBf5BvbvL9cuAvR6Y19vfb2ZPOGQfzwG+kTtyLriIiIicVQrag5otfYjZNUin
+1JP6jr7rSF1XQndKpJzpc6bPiS4lUq1yew3dZkaMgdg0hBiwUOZJp5xZdB27e3tc3bnGzs4Ou3u7
+7O3vsZgvyCkB1GAdsTCqZJduaOVfg8vAO/xXHxyeHHcSH74USBlPqV56GDVss/G/MZcBvu53/M9P
+H+17tcVqve/lIxwM5UeE6MMapN2oU7mI3BFeCbyP8lfAq8zsx83sBWb2dDP7q8DbgBcA7zjsxe7e
+AX+D8jfJJeCtZvZyM3uOmX2Gmf1NytJh99dtvs3dP7K2jweBV9Rz+ATgnWb2DWb2TDP7TDP7bkoV
+/P3Ah4aX3cofgoiIiJxPmqN9nTL0mpzLEPG+VLT7boH1PSGlsmyWZwgJ7yEbZU1t9+U1TQnJ5ZuM
+YQa0k3JmvpgTdq+Rc+kwvr+/z+7ODjlndnd22N3ZZXd3l/35nK7vScnJufZlcy+Nzqzs8UDPsmXc
+tvr86rnlkO46b7vMMU9lne86HNyG8ePjQLtc78zXAvfq5vH5d4jgoznwNagft1TXcU3V1pf6EpGz
+x92vmNkLgV+mzJP+8npZbkKpQP87rq9ED/v4BTP7auA1wDYlvL9ybR898HJ3/6Ej9vE9ZvY04OuB
+pwI/uLbJBynDz3+m3t8/4VsUEREROZKC9jpnObc5p3Sgol06jveEnMiU6nA2yDjBnehO9EwELED0
+QInXvsy7OScWi3kN2XP29/fY3d1hOp2Bw3x/znw+Z79eL7qelJ3sNWT7EFBHIdsZ15GXS3stU/JQ
+IYZlhZs6P3tI8IaPwvn6sPFRq7hl/zU/eEyvr7tB9h2H7BtVqG80P1sVbpGzzd1/y8w+CfgO4MXA
+04CrlGZpP+TuP2lmL+HAxJTr9vFjZvZvgW+hzOd+GmU01gcoc7B/wN1/8wbn8Y1m9gvANwHPBDYp
+1fafB/6Buz9oZhfr5pcfzXsWERERAQXtJatV21wr2tlTCdp9T9/19IsSss0TIWeCZXKCZE6P0bgT
+cya601LmaecaUK2GXjcje6loz+dzzKBpWtp2QttMACP1iT5l+q4vFe2uI+VMdq+5uITtcbOxQwPn
+kMLt4EPjija1oo17eXz4QawF5mVR+5AH7cAGB5cyO6n1yvTwfo5aP1sBW+TO4e4PA3+7Xg57/vWU
+udTH7eP3gW99lOfxc8DPHfZcbcp2ifK32e8+muOIiIiIgIL20hA0zR3PjqUMdXh1zj0p91jusZzI
+ngiUYeLZjZSM1DhNzsQhCNf51SEmghkhGKEm0xIsy3H6vidnp+tKU7TUZ1LK9H2qQ8c7Uiprc/sN
+ysXLgDq8jwPPDQPYy1JeVudy23CxYftxYan+TDii1HT9CRy8u/b0uJo9DtdHBefDhodr6LiIPAb+
+2uj2vz+1sxAREZG7hoJ2NTQDM3dsrXlYTomcekgJ84TVTuPmYLnkyyY7KWaappR3Q4z10tDE0szM
+Qg2V7nUYeCb1GfcevFSrU3JSyqSUmM8XLLquNFrzslzXUYl3FVathuZVPDZbtQwfgjU1bK+amMEq
+ZK8i98Gf0Wjk+GHh+BYF36PmZq9vo8q2iNyImW0CF939D494/unAy+vdd7j7u2/byYmIiMhdS0G7
+WgZtMjbMXc6pBu2elHpIPea5hG0y5FXlN+VM0zTkYf50DdmhSWWIdTSihdrZvAzDzrlUr1Of6VO5
+neslJafrO7qup8+pBvPxUPRD3sNyyPVqlvaqUZqvwrSPKtk4gYPV5+Uo8uW7O2FV22wtuF9vqGbf
+TFBe70w+3peIyA08EXi3mb0BeCPw25Qlv+4HPg94KbABZB7l8HQRERGRgYJ2tZyjnL1WsxO568u6
+2bWq7TnVoJ1L0GY0DBzDPZXeYsugHbEYMYMQAw21pmy2rBtnd5JnUi5LhqWcSwDPJbynZcD2A4F3
+uazXCcLqaotRRdvH1etVtduXFe9lVD+wpzKF+4hj3mTwXW1+/euOmrd9o8dERA4xA/4q8GWHPOeU
+4P217v7W23pWIiIictdS0B7kEpxJCe/7smZ2tyDVsJ3qmtNDyLZxRzKoncHTsnFZGSdeLmZDc7Rm
+NY3ZDIJhMRCAxqyOQy+XTIIcsJCHdbdKj7LgWL7R2tJD+B+anA3zstcC9uj+MmQ7+LIKXkO9raL3
+DbuKjy6HObgu9hH7GM3hPslcbhGRY7wf+FLghcCzKBXuxwO7wO9Rlh/7AXf/g9M6QREREbn7KGgP
+hmCXM7nvyd2iBO2+I6WeXLt0D9Xg1VDsEhpzDerelznWJWCXudkxBtq2DCuPFvChs3cwAjWMh2HC
+d8JJlLsZkg2dzIajQfDxQ9cH0HHaNT/wgK0F7PFjJWQPFe36v3bLpl4vHRWY3YeTH+4fXApMQ8VF
+5Ga5ew/8VL2IiIiI3BYK2oOhop3rnOyuIy06ct+V4F0r2qvh16tluwzDc5lzPcy7JkQsBCwEJm1b
+5l8PncCHaraXOdvBSvEby7iViYLmjqXy+rqGVzm/5dzmEsAPm7fs46Q9dBe31ZDw8fzs5ZzqsvbX
+aj/L/72JKvIhef+op8fnvT6EfDyPe9h2PKf7sGq3iIiIiIjIWaGgPahBO6dErkPH+25B35WKtg9r
+TgOrQLgKukOALkt6RZrYlDWymwmxaQmhoSzwFYaMDIQSMkMJm2a5jug23ClN0UIiWCDbEDxL9Tws
+S9qj+Fqfx70WrYegWrYKBk0MxLYhTlpmsykbGzM2tjZophv02eky9DkxdG3zOo7cRtXmIZhb3fGB
+06j3zVchfjlM/cDZHtFibT08+yjsjzdXyBYRERERkTNKQbvyYeh3SqS+p+86+kUZOp6HoePu12Vb
+YBmAoczHDqEhxpammdC0E5rYEEJcDiUPFuvQcl9l0ANBGzw7KQRiCIRgWDLMrXYfL/OobRz0vS7p
+NQrZnus08Vq9jkATAm3T0E4mbM6mbG7O2NzcoJnOmHc9tujJuV82davfIFD+11ZfEpiBef3CwK77
+maxedXAZsfL4USF7+QOtl7UnvMwkXxXqFbZFREREROTsUdAe5FSvErkv1eyu6+j7npRSDeLXD4Yu
+Fd/a3CsYZoEQI7EZVbRjuwzatgzjdbj4MlwaZulA0O5jJIRAsECwTKY8l2t/NLfVeZR9+Cp7DhXt
+IcQDwYwmBiZtZDZt2ZxN2NqYsb1Vgrbt7Zf1uxd5NWd7CNsWlkPWy7UvK9qMz2NUfDYfhexlh7Tx
+z3BV5V5l5tXiZc4qbDvj17pCtoiIiIiInFkK2oMhuHmZZ13Wsy5LepXy7hC01+ZE1+sylDoQ6pDx
+2ExomwmTyZS2bctjoSHGSIhGjEaoQbtUtJ0wzPVOTg6JGAJNCPQhkEPAw6r6HSwQhpS7nl3Hbb8d
+AmWt7GDQBJjEwKSJbExbLmxOubi9STudYZ5J3YL5fsY90dX1vYcu6mZlzngIZdmyQJmHDmHVGZ1x
+o7hDCt2jKvWBOduHbGa1F/rw/DKAj96biIiIiIjIWaOgvW6tG7cNHcZrqDOcMEyFHl4CZUh4CMTY
+lJDdTmgn03JpG9qmoVleArEJxBjKmt117W7DSsC3RMKIFmhCpI0NZMqyYhjB8qruO1SJ/eDFvZx7
+WM7PdqI5TYAmGpNobEwaLmxMedzFTSbTDTx3dPM99oPT5wV50bGYL+j6tJx7biES25bYtMS2XT5W
+mrZFxkn/0DZqdvDrilWAHs/eHt7dELdX//mo27sraYuIiIiIyBmkoL3kB28ul8CCYTmvUnUeVVaX
+17XxV4jEpi3zsmvQnkymtJNI0zY0baRtyu1yHZeV85zK/OwcEskCwUrQjiHSxFiHf2cCoXYwz6Pu
+3Izz7XKoeDlvqxVtJ9aKdhtgEmFjEtnenPK4C5tMNzbpFnvs7USuxsx+7siLXeY7e8y7jhCbcmla
+mlTem1nGbEIZRt6U4fPk+rNa/TiX89qXWXoVtW0I1cZoLHyp7tdZ86vO6PUVDnV9cBERERERkbNH
+Qfs6a0Ofh+7Zy7nG9Tmv86TLVoARQiTWoeOrsD2hbUuobmIgNg1t2zBpW9q2IfV9CdchQXZyTPSh
+L9XsGPAYoWlKv3LLJMv0lsk5kXIN6XBdRXv4kmAYMj6E7CYYbTTaJjCbNGzPJlza2mC2ucHutQnX
+JpFZY+ySoF/Q7+8w318QmhKyQ2zwPCuV/VA6j8emHCRYWEZos/Uu4wf7jZdHbGghN/r5HwzY60uY
+HfiSQ1lbRERERETOIAXtQQ1zZmUOcgiRGCIWGrC+DIv2Wq1dJrxS3zazZcU3ti1xMiG2DaGJWAwQ
+A1YvIZZmaSFGYowlrNfu4qUpmC+7mzexoW16uq4tndBTJvWJvk90fUfXdXSeIGW8Lv/llmvnsRJh
+J01g0jZM24bZbMJs2jKbNEzbyCQGYgDzRPDMxqTl4vYW3eJxeHK6Rc/e7h7doivD27uOvksEjGhG
+snIdY2ASWiaThs4yPUaHgadS33ar52Z4netdFw6vUXsYT+7Larzb0Je8BvYauLV2toiIiIiInHUK
+2tVQNR26fMcatAkRt1jbicEwNns5e9hCCY+hdBqP7aTMXW4arIlQw7athe3YBGITCTlAyHiMZW3s
+PMwLN/qmJ/WJSVuu+1Sv+579fcc8kZOXSjg1bOPLdbPNjEkTmU4aZpOWjWkJ2tNJy7RtaGOgMcc8
+E3BmbcOlrU0Mp1907O3scbWdsG/79CnTZ6dPmUCpkMfaPj1MJkyCsdm2zEnMPZXu6Msh90Z2Vs3b
+hu7rjMP26AuMek5lCTMOVLRFRERERETOOgXtypYV7drULETiqJrtxNESU8Pg5tqJ2wIhNITY1mHj
+LaFtS9COAWuGkB2X1eyhok1gOdQ7WKgV3TIMO/WprOGdEimVgJ36nq5bgCc8dfQLcCvhPFvGcUJ9
+DyEYbVuD9rQ9WNGeNLRNIBoEzwTPzCYNtr3JdNIy35tz5fI1NtqWaxbInmpFuyMapFrR9qYh5sQ0
+GJttUyJzdnJ2es+YG54hm+EGbqXSXhqn1RZnQ6J2x2tVG7PyUB2zv1r0S0RERERE5GxT0K4s1KAd
+S1ftZjIh91NC7TyOe1nuyzNel/oKIdZqdiBOJrTTKe1sWq4nJXDHJo6Gi9eh42F1WTb68rLO9dDA
+LIZQm6SlGrR7+q6j7yMxGDmnOk870fSxNEfL5auAGMPyMptO2dyYsrExZXNjg82tLba2ttjc3OLi
+xYtsbm4ynUyY1A7ik3bKxkZmZ2efK/fscPXqLt0isbO7h7FL3/dl3jclnEecWRPZnk153IUtZoue
+2SSx2/VM5h078wXMF6S8KF8AmNWl0CgBGxtPdq/Kz8DNrwvYGj4uIiIiIiJnnYL2wMrQ8BAjsZ3Q
+zmbgTh9Wc4pz7pcB193LsPIQIQTa6Yx2NmNSL+1kUoN2s1zKq6ydHZbD00Oddxwo4TPUKm60QB/j
+KmjnUs2OMRK6BRaMVLuOY5BSWi3pZUbTxLqMWGRjNmNrc8bm5gZbW5tsbW2ztb3N1tYWW9sX2Nq+
+wMbmFtPZjIlD9jILfefSnGs7c/b3etyN9vIVMOi6nraJddg5TKKxNZtwaXuTe++5yF6X2O8Se4vE
+tf05zbUd3DNd1xEDRBuWHHNsCNLj9b4OydCqZouIiIiIyJ1EQXtQK9qhaYiTFnxWqq6hzCnGAil1
+pJSw1JPdIQao87ib2Yx2OmMy2yhhe9LSTuq62bE0DIuhVKqHYd0WhmW8asduK8PHc4i0TUOqITun
+TN93dHF4ndVQDRZDWRqMWhkPRtu2tG3LpG1GAXuT7a0tti9ss7V9ge3tbWazDabTGdPpjKad1GHw
+5YuDvf2evf2exSKTvXwh0Pc9e3t7NE2kjUYTStDenLbcs73JvY+7yH6X2e8z+11itrOHu7NYLNjb
+2yMY9bKaR46v2rerTi0iIiIiIncDBe3KQqloD0PHh2ZiQ8gmBKzvsFTmSZtnLEYsRIhxVc2eDmtn
+N8vKcoxGE4y4DNxDVbsGbyuPEUsHco8RT5mc8yhox+Wwc4tluHpoyrl6zsuu3MECk0nLZDJhMpmw
+tbXB1vYW28vLheWlbVtibJaXEGOda95wad4znyf6BO5Gyon5fM7u7g4hDFXzyGzSsr0x5eL2Jo+/
+dIF578z7zLx3Jm1L33fM9/fZ29st1X8LDCnbagX+4ELgB/5U1hb+GqZv27J6LyIiIiIictYoaA+G
+ZmghYE1TCtzB8GCloVlbK8ypJ6UydNxC6TZuITLd2KCdzojtZNnorImRNkTaYLSNMYmBNpaAGoem
+aFaq1CGEurpVCfduuc67DuSYCbWZWmwamn7CZNLT9R1935cGasGWTdCGkN1O2lLR3iwV7c2tzTIn
+e2OLdjqjGYfr+j4sRMwi7XTG1oVtHpfLOWW8HKOJdQ1tIwS4ePEily5dZHtrk9l0QjsxZtlIDpOm
+wXPC3Gmisegzi5SY95mUvfSAy7muXA5DN3ev2XsI1sByTe1hfXDN1RaRs+jBBx/kgQceuKnX3Hff
+fbzjHe94jM5IREREToOC9mAI2jEQrMGDYTEuQ3aYTup86VybojkWYgnIFplOJrTTCbGpVeIQaUKk
+CYE21ktTg3YN4gfma9fGaKUrGmXOdc5kd9wzIWdC09DkSQ38mVSr3cByXzGWoN22pao92yjzszc3
+N9jYqEPFZ1Payays/T0c2+r5WMRCYDKZsb0NIZblyghGbAJN0+CeGLqvb29tceniBba3NtiYTcAa
+3AIQmU0mGJkYyjJj1/b2ubq3z7W9OfOuJ2UnWVkGDNZq2qOUPYRs1a9F5KzLOfP+97//tE9DRERE
+TpmC9mAYhmwBC4YRSsW0bbCUCDnjOZFzCcBlOa5Y51kH2tjQNk3tMt4QYygV7Vgah01iZBJDrWaH
+6yvaFspSV4Ha5Ry8Njhzd6I7uV78wKU0UQsxEkPZbztpl0F7OpuysbHBxmYN2M2EdliCzGIZcl6X
+KQvDe7fAZDrFmpbpxiaTjRkhBpq27DulHq+N4TZmMy5dusDW1iaz6ZQY2+WXDVuzKdGgjYFZE/nw
+lauEAH0qr4WyDFipZNeq9rhSXVf9GodsVbJF5FYws48G3lvvfrW7/+it2/tHnXC7BxmWixQREZG7
+i4J2tZrvO5RSy9jlUNeiMnc8N4RhGS2nBFOLyznWIQ7ralvtKl4eb8IqXDfDUl8h1HnhZYmrVU23
+nIONjj085sPQ6nHO9DLcvRy/VMqHZmjtpGU6nTCdTmnbErKbWEJwCE0J9mbLmdA++h8zK+caAtmd
+ixe28ZxoYiDVtb1z7plM2rJM2MZGbfxWh803EdzZ3pyRPRGHTmh19Hcb99mdd+zRkxdpuTr5dQ3I
+xyG7/gxsbTsRkUfhFn9zF4D3nXDbBwBVv0VERO5GCtoDG08MttHQ5VCbopXwbR5xc/BahbZVuB6H
+daMG7Rq2Yx3WHcJ4yPgQcJ2US4AMNU4Gq13JQ6lUD8uPDaF8PJB6ub8a5tu2pWmaErabtoRrixix
+VK8JWO0kvur27eAZr8F7PDe6jYHN2QzzzLRtlkuc5Zxpmsjm5gaz2QZxOZQecCcGYzppuOAbNDEs
+G5kFC+W8ru3jeZ9usb+s6eRhnnr939W7rO+4nq/52hcOIiIiIiIiZ4SC9thy+PiqXmql/XipaNcl
+wIaOXTYO2vV1JRyWEDss51UucRm2LQyXMgw655IaS34vURhKRbtUwVtCKN26S5gPo9O11dDxWi1v
+mjKMvRkusSVYswrZQ4T1Emi9plevncCXleQautsYsI0p07Zhe2uTnDPupSt66UDe0Nbh4mVtcIBM
+CDCbtLRNZDYry4ctK/yhwXNgPk/s2YIEZV76qLhka7Xr5dcYXtumKWmLyCOnQTEiIiLymFHQXlN7
+Wo/u1bg3hFyvQ8nrf9gQXodZxsNrVlVtC7bqLG5W50GznI88hG1zXw1VH4agh0BsSjC1WuW2Wt0e
+qtrjanYIYRWw6+tiKMPbzW35JQFDWIXVnGdfxdzVsSAEI8YJNhmfc16e+xD4y+uGW2X4edtGJtYw
+gzLkvp6vu7FYJHZ3F+w0c7pUBo7nlFcjC0bv8Yg/KBG5S5jZc4GvAf4c8BRgBnwQ+I/ALwI/7u6X
+R9vfB7wYeD7wZ4D7KZ9pHwLeAfwz4J/7IU0dzGw8MdqA15nZ69Y2+y53f+UteXMiIiJy7ihoH2bt
+32U+fsxLkB6W4TJ3MGf1X5lrnGsmL5nWyF6GRYfaTbyE80yu62V7yiVSjhqxeQ2+y27kdvALgFXa
+9FG4XQXdIUyX6nMNwQY522pXcF1zsVI1txL+hy8YaqXavM4Vr43YcKfk51xfO4T0ISSvLk0MzKYT
+cnbmXWZ3v2N3v2feJfYaTWREAAAgAElEQVQWHfuLBSlnUq3uq94kcvczsxnwI8CX1YfGfyHdXy8v
+Au4FXllfEyiTm43rv3Z7CvCF9fIyM3uxu++ubXNg5cBD9iEiIiLyqChorxuHTveD//oawuW4Fdd4
+NPnQrKzWtvNQ567zit2dXBeJtpzrtOhV2C5xNODBDlSZh8OsWpatrzA9HHW8Tb32XIJwrd+YQUq2
+rEKX4xxswubOMmCPL6uq/urn4MswXy4hlIp98IMp2cyJTWQ2nRBCpEvO3rxjb16Ctu3ukT0zX/Tq
+wStyTlj5Vu9fAJ9L+Yvod4EfpFSkdymh+bnAl66/lPK32puBNwK/DjwEXAA+Fvg64Dl1v6+mVMrH
+PoUS4H+pHvflwM+ubfPBR/v+RERE5PxS0D7KELJHwXst+8I4VtuwsvRqPeghbJdLec7c///27j1M
+kuys7/z3PRGZWdWXGd3RaEE8RmCMH4TRCi0GydwkvMK6GJCNxZoFdLPs1e7iNazZxbDDCBv7eSQu
+axs/HtAuFhc/whjBw0WALZAlwbO2ZMtgsMSa2+4i1JIHSTPdXVWZEXHOu3+ccyIia6qru2eqe2qm
+fp+HoLKqIyMis1WT/Yv3nPdAysETgxTLsmExYWU/K/Oup0rzvHJ9uMx7uE93DeazYeEpkUoztxyy
+IzFOS2XV0Gy1am2G+/QY8tD3WtmeupNPS5ClMpS8NoJjnGteO6dPXcyXiyUxwcFm4GAzsO4jyRNd
+3xPs8GsSkcew/4EpZL8V+G/cvZ/9eR02/m1mdlf9obtHM/t0d/+9I475buDNZnY3cDfw35rZ33b3
+3509//1mtjd7zh+6+/tP7mWJiIjIWaegXfhRlewjm235oceOk7ZmaCfPXcSHlOhjbgrWxEAMdT5z
+CdxGGdbtuaJtRkpGMMfLz1KKxBSBWll2tqcXgnvIjdisyeE4lOOEVOaQ16tNuMdxGPn4amrVvDZ+
+w2aP58HbxuA9vQPb63onDwQS7gEfXyvj8UOZe75cNOzuLrl4YZduiAxxYN117O23JbRDwrf+CrSG
+tshjR6lmfxP5P0MfBL7uUMje4u6XDn1/VMie+w7gdcATycPIv+dhXbCIiIjITVDQLh4U3w4PG4dx
+lPYYls1zZbocYDtoJ4aY6OJQlvSCJjGrGpexj2lWDXYnlbAdMVJKxBiJQ8Sb2XDusUpcLylAnFWz
+zUjBSG407rjl9bqNgFOHks+vu1bB7eiwfWg7/IbMh48HcvCvw+i3+4YzVv/bxthdLbhwfpeYYNP1
+7B9sWC338zztMm/9iL8FEXls+GzyQtIO/MAR86hvWAntTyUPHV/UH5MD/BPJzdJEREREbhsF7cOO
+Cthz4/Tow3Oly9OB5InoRh8jzQBNyNvQWKnq1qZhpXt3CduGEZKTyI9jjMQQGMJAkydOz8I2Y8XZ
+CdPlGYSUw7p7oE7Bzr2DZuebDy2vVeJZxXpsajarao9N0TBmRe2xKl56o5V56Y674ebzXalzyZsm
+sLNccPEcYIG9gzVX9vZYLRf0Q4SBHLZLQ7bp9WlIuchjxLNmj9/9UA5gZl8DvBL4XGD3Grs5uZGa
+iIiIyG2joF08aOi4T9XhB5l39q5PmdVt3XNIHBL0g9OGXMGNKREs4AGa+qw67LpUmGvIDpaHjg8x
+5iHTjU9zpseKc2lSVruNmxNjWe7LQw7VWKkiTxVvBzzF8XU/6LXOKtrb1expznYN49vvQgnaXhc7
+S+N7kt+z6cZE28By2eIhYE3D5as7nNvdYXd3RR8jDgwpQVRrNJHHqHn4vXTNvY5gZivgJ4EXstX9
+8ZquFcJvgQQ85Qb2a1C/NRERkcmlS5e4dOn6/yTouu42XM3Dp6BdTF2+S2g+csmrQ93Qxg7d5ZsS
+th3yHO2YCDhtNBbRGKKN1Wyv1ejZ/OYcvKd1qmOKWLQyxDyV9bdzo7QQyvrc5GHhyROWDAuJ5BFP
+gZQCoZwwmNE0Yes1pUPrYU93EGoVmyl0z849hWyb3XSY3o/8GmYV9VqBL8PGDaMJxqIFp8Edzu+u
+uHB+lzsuXsij2m3NkBJdPxz+m3pof8Ei8ljyrUwh+1+RO5W/D/iwux/UnczsneR1uW/zUJj7bu/p
+REREHgPuvfde7rnnnkf6Mk6MgnY1BmxmoddzoN1edJopcDOGS2D8p1ydpz3E3Iq8bYw+BhYp0Xpt
+eMY4z7rOb86Hr+G3zNFmwD3lKrWVpbNCoPFAQ4OHebDNHcxzyM6N1NwD0JTn2bjMWH0dTpkjnqZq
+9/hSZkPUg09LfM0r6tSXMd5nmO8H5Fnb2/uSlwFbEMZq+bmdFRfOn+OOdc8QnSE6602P2fyO1XZz
+NBF5VPuj2eO7gP90E899Ffm/OO929+cfs98TuM1358yMJz3p+iPVm6ahafLYpqc+9am3+rJERERO
+vde+9rW89KUvve5+L3zhC7nvvtN/U1tBu9gOujnszn8O8+Hi82HmbJd0KX3IHQZPeMpzs4c2EGMg
+NnXprjAdwA8F7RK8U8rV5lA6koeQK9S5Mj11GB+DuYN5rm4njyQPJM/DykMwQhNIqQZs8OSl63kq
+xyhXP07ZtvFr7mKe1/geQ7Ydqnwbuau5G1bCPz41jJs3gTMzLASC5+s6t7vk4vldDrpIN0TWXU+7
+PxammO51KGmLPEa8b/b4C4B33siTzOwJ5MZnDvz4MfudBz79mEPdkv+YPO1pT+ODH/zgrTi0iIjI
+Y9pdd93FXXfddd39lsvlbbiah09Bu4hxFqxnQRsYq9peGnnVDuPzb8aR5Qa4jV23E3kIeT9ENgGM
+RGoC3oZchY456CbPxzZPWMpNxG1WHQ4hn8AskBI0oRmHhFupdtels/LryB3NpyA9DU+fWoDnanZd
+QsxLN7NpFPkUtEMIJE+EVCrr9Zpq2C7hO98CcIJBY+TrKuuCTx3TA+OzLN9wWC5adndWXDwfWXcd
++wdrdlYLVsu2vD+lMRpeur3fqv8liMht8uvAHwCfBLzazL7rBjuPzz+3zh+z32vKvtcK1OvZ49UN
+nFdERETkhoXr73I2xJiIMZFiWVIr5iWmUkwlsNZh5bN/tY3V7sNzusuSV+6kup52P7Dpetabjk2X
+t67r6YfIEHPQrsuC5UZqOfxOW9knzSrOJagGC2UJsfx1Cttelg8rgdpTuQGQw3YdNh5TZBiGvMWB
+IfYMw0A/DAwx5m0YiGW96xiHPCw9RkgxV61xgjsBpzFozWjKFmwWtMv1TjcHcmBfLVrO7eSq9sXz
+u5w/t2J3Z8lq2bJYBNpgmKVxSbUQSpU+KHGLPBp5vvP3hvLtJwI/ZGaLo/a1rN7ivg+4vzz+6qOe
+Y2bPAV7P8VXrjwJ1bsozbvLyRURERI6linYRh/rvsTqMOo2NvvLw7Knr19ba0FM3NMBzc+9xfemc
+ymNMdDikiEfDUwMpgDeMPbzHhmG5I3ggh8hgVtbBZmxG5nUJsnE4ebP11QjgjCF7XtWeLWidz+VO
+jDlA15sD7j6u1W1lHnUIRkh5rjihDAkPpSJdhqwHCzSlkp03G6vaRj1WKMPMQ57HnRN/qWhDsoZ1
+13Hl6g7ndpbsLFu63ulSJMV8kyAASfla5LHg+4CXAC8AvhL4DTP7R8C/BfbJQ8Q/D3g58KPA693d
+zexHgdeR18f+VTP7buC3gTuBFwF/DbgC/CHXGD7u7tHM3gs8F3ilmf0a8GtAX3b5mLt//ORfsoiI
+iJwFCtrFNEea6as7Hg434JoW8prC9vQDnw0n97KqVowOnnDzsr61l47cuSlYEwLBan71Ovq8rENt
+eMhDvs0glSHiNcTDrFIcwjR8fFx7q1a1SxdzmzqGz4eNpxTLet5TB/I8rDvlY6cpbNOEXMEm1JW+
+MJr8GspbEYBAHQ4/e2PHDu1T5g9mtE1gtWiIbuyuFuwsW1aLhmUbSBEGc/AIaVoyzDRdW+RRrYTm
+Pw+8GfgLwKcB33vUroe+/1vA5wOfDXwO8E8P/fkfAS8DvoPj52n/XeCngScecYxvJ1fFRURERG6a
+gnYx74qdq9c5TFoIR84HLlF09nUKj7UJWA3bQx0WTiKmvG9ySG60IdA2TttYCap1BWqnKfOzPSVo
+avU7B+tU53Zvzb+uTcbKfO1QQrU7KUbiuHpX/nkaBlKMpVu5j1+Tp+1XZylfRwn+RkPASfW9mt7E
+8ubUKvo0XzyUarrV5mgW8ns8lvHzzYRAIpBoSDSWaC0SfMBih/cbUkylq7vaook8Frj7GvhLZvaF
+wCuA55Er2Q3wEXKV+WeAt8yec9nMngv8DeCryAF9IM/5/lng77v7h6wuc3CN/1y4+9vM7PnANwDP
+AZ4MHDl8XURERORmKGgXtSnXuNazp3Gpqtkq0A9+3tbyrDmM2nxfh+hO9IR5IpZh6MmN5M6idZb5
+qTSWm6cFTxj5/G6Wq+S1WRo5RNeQnTcnrxJT50DbOH/ZzMATKQKkaUi4Mc21rl3HU93K0HTS2Lgs
+zIJ2sDx0OyUjhTA2hrPSUjx3NE/5hgK58uzJIYAlrxk739QItQbuOYSTCF5CNonWEo0PEHt82JCG
+SKI0ezuhv3sReeS5+zu5wc7jZf818J1lu9Y+X3zS5xURERG5EQraxXytbJsPiR7Xg95ax4txB6gD
+pKc/d7ZqKCnloJlSyiHVIboRUw7clC7dhFzxTSVsegnfblbyv41BO8Y4q2inaWT2vOlYqKHfy5ra
+20t2xRhzRTvFPLTdZ1Xy8bXm6rZbWbKrBO1gRpPqEPbapnxe0c6v18r4ebM8f93KuPjglIp7qXDP
+QnataLclbAePWOpJfZer8FCGuJ/QX76IiIiIiMgJUtAuQpgq2k4izzL2rQB+ONmZ1fA7j9zbNW4Y
+67VQBmMnN2Jyhug0IdGY0Rt4gOCRQMqDx2tuNSD4tIRWCdrzLYRI0+S1urdnkNfEn8C8dCjPw8o9
+pbLOdQ3AZS55CeWO5XCO4cEIKU8kT2akYGWd7kRMTiib4zSW38N83FC6hU/D3q1Wui0f063MQa/L
+goV8g6EN+XEgYR4h9aXL+YP/LkRERERERE4LBe1iK1Afisrbf7a9hx2xHXXsWmWueTYlGEizBmK5
+8VqD01iiDoz2sdrtUwfwErSHYaBpasfxHLLN8vrcIZQO4dTgHIFEaBqa0NA0DXUSeSBXzROU5ml1
+znZtEleW0QoBUiBZIDWBmNIYsENKWHQaT3iAxo1QhoePYZs8f9w8byGlPJg9MA6ND/OQbdCa05Sq
+NilCGqalwJW1RURERETkFFLQLmpF20mYh1LVnq3cNTOG6jIUOmz93I8M2/Nat7uRSjevgZQboHmC
+AG6eK7yW17yuQ79pvATW7Yp2Ddv1+xq085ZDdkwDKfbgibZt8bYFb8fu5EZdgov8nBjzmttp6sZO
+CpTFq0lNIMWG1ORh5tFTnnudEk6g8QQh4J6wshpYCAFPeXi4MY0DyN/Xbuu1om1j2G4DBCvvTxrG
+oF3eVBERERERkVNHQbuYul/nocyHWpqND6fKdQ6GYyewmbo02Nh5POUSdl2SK+XT5OHTybAEBMMb
+8FLiDoEStNM4D3oMp2Zblez5ZrVpmefAneJAjD0p9mUu9zR5PJR1sPNzEikN0/7Jc1U75fKxh4bc
+BJgyh9sZB3yXynfMJfFStc7D7q2MYM/zw2sndh87k+M2m+ddlgUrxfM8hNzGij+eIKVZf3cRERER
+EZHTR0H7WmrGnq9eNa4LnTtk1yWpUl3T2nPDL5+F7Gkd6xJc6zJc9QRNnv/sDXgToG0IbSC0ZXmx
+0hwtH67MnfY0Dkev4bp+tbHjeChV5NzwLMYIHknGuMxXmlW0U3L6vqPv8lav1WvAb32sphuM58zn
+zatmjw3RxqHyZaj4uNzYbB8YA7qV4J7fmPr8fFwr88ln3eke/BcjIiIiIiJyiihoX8eDGpt5HsZs
+7lCWxqIsizVWr33avATs5FNwnRp5eW4uVjZvG8JyQcOCJlgeLl26cbvlGnJdisvmYXYWeoMZoQk0
+IeAh4J6DdipBO9rUrM1mRfuUEl3XlbDdb13rOGS9lpmZlhmzGrRtvA1RlhCr62SHsQHbGLDL/3fq
+ezQfO7AdssPs+Pkc9W/FjhzWLyIiIiIi8khT0H6Qmvqm5bymGqqP1WxLEY8DxPzVy3rWebh12q5o
+ew3IU3gdQ2bp4h2DwWKRm6EFY9GEci0JvCwLllIO5Sluh+tDQ8cbD3hoaBrHPeY51zE3RDODOFt7
+rF5TSom+6+j6Ple0680AZzx20zS5Al0KyznYN7OKdnm3SmfxwzcB8uFmYbvefJitimZlSbVcCZ+C
+vG1VsvNxNUVbREREREROIwXtwuexzWZhexzR7BBjDrkxQhzyms5xwIdhDNKkedV6mpPsJVxP85PL
+DGcv3cI8NxkbAy5l7nJpSOZ4HvadnBC3g2hoQu4m3jSEJuBlLjVliDgWsNASStdyx/La3mWetHsi
+xkg/DAxDTz/0Y+o1pkqyl2PVKeiprA3uuXcbyfJNiOCQ2vKyzEgpN5arxf5Ulw4bj12Pn0N01/V0
+3ZC3fmCIkbjVBV1EREREROT0UtAeHW5lPVV967xsUsKHgdQP+NDnoF2+4lPTsnFouE+DpKEOkZ5X
+tMlB2wMWnJSasWFa7iDGbG53XfLLCeaEJmChyQF7aGiagb5tCEMO2bWjeO6tZoSmwWjGBm6pDENP
+KeW1sEsH874f6Pp+mo9t+Txeg7eVVnBlmHyKaRzCbSQCDYE8wtzNSJbGEQEx+bjV483DdvL8tet6
+ur6n6/P19EMkxjR2aq/vnaZpi4iIiIjIaaSgfUiNxrXpeA2mRq7epiGS+o7U9Tlk9z2xBO0xnNfn
+zY7qZeHncch0DYu51ThGk6viNYSSlxgbK8ee8jDwFMGc0MxC9tDQtEMJ3E25M1CGbjd5rnQIOXTX
+Od6ecpO2mBIpRYYh0seBvlS0zQJNaAgNBK+3CijzrHPCTe5YdPINgNxBPJA7hacQxpCdyrsQY2SI
+iSEmUr2ZYIbnBc5I5I7pm34oVe0ctochkuI09N6nt1VEREREROTUUdAupvh2uKI9q06nlOdkD7FU
+s2PZhnFfGyvWs2PZdPyxxl2Gptd+ZMksh+lx/e5SBc9xO4f8FIlxAPJ62O0w5PWzU9k8r2kd6rxr
+IDh5DLfVRbI8v4yUSDHl58WBYRgY+kg/5NBtIS8u1pgRUmJIiTY5MTpDjIQhYhbxBvISXZSg7TRj
+Z/OGFBIh5Qp7PySGGBmGlF9lnXttoYTtgJvlaxnyUPa+BO1Yqu++9d6qpC0iIiIiIqePgvYRapSz
+ecg+1EWcusGD19wejzF11Z46bvvW6PJcfC5LXI3rZadxne4yqLqsc51DcZ5TPeTwOXYzP/QixvHc
+5ShueJnjnYeJ52PVbYg9XQm4Q0wEp0R8g5AIMdIMAyH04/GSQxParXsSxhIrw9vd41i5d5whJmKp
+aDt1jnkzboQGswZPToyJOOTh7LEsT+ZlTvj0zqmkLSIiIiIip4+CdrFd0fbxJz5fM9ungO3JZ+ES
+Dg8Wz1XrWXXbZ0eeNfUyPAdtK43J8sTt3F3MpnnZzhS0Y4oMcVkqvHWN7en2QJnePVtzOkueg3Yf
+U6kaD8TYM8TcBK2G7JhyEHZLuBmUCnYIA1hPKiE7JWhCnF5YYgzZTYCYAinFMg88MsQ8VH2IDmY0
+TUto2jIMfoE1EJowDmmPQ2ToI7EMHU+lqn3UjQ0REREREZHTQkG7mLpZT19tFrrLTlPH8Fp9LnON
+3X0cyFyDNV47hs+OOsvsudDteah37VZe5jublfPXsO2xBO3IEKch48mniE2+FKZ241N373pNqVSL
++yHS96WaPfQldJcO3zHPCQ8YAQNLWBywocnDvOvrinmJr/kdhBByyG4aIyQbA/0Qh61maBYCTbug
+bSNNu6BxIxDASqCOsQwznyraqc5hZ3qvRUREREREThsF7cPG4DobKl6DZa1Ez3/GtHRXXdIrNzGb
+L+lVD17XgGbs3r1sGxaLluWiYXd3xc5qxWq5YLlo6Il5PjW1cp1KU7R8wPn61rU5WqjLfdV1tcN8
+yLrnedyphNgYyxzogb6vle0cvMc1rEOgaQdWQyIunRSduMhDwGPrtKHJHcoxghkx5aZqTZfDftd1
+dH1H13V5EHy5wRBCS7vIncRbh+gQEjTR83O6Ll/TWHmfwnbtqK6wLSIiIiIip5GCdjFWtA+H5sOh
+mllncmCrE/YspM+D9qy8nEN2Wc/aLLBoF+wsF+zsLNjdXbG7s2RnmYO3JyMG6D2BT2F7HHZuNobs
+pmloauiehWybTk0iV4tjaW7Wx9z8rK/rVQ99WUd7oJwAQh7iHYdETE7yMqy7LLcVm5Y25A7lhJBD
+/NDTlaHw6/Wa9WbDZrMZG7JhuZqdq/G54h4SZeh4YrPZ0HU9fdePYbuG7JRSrqLX6xMRERERETll
+FLSLKWhzZNCeh+ep0r0dwreDdhqryFmZye2GhbxGdQgNy7ZlZ7Xi3E4J2aslq9WCxSIwDEaw2gwt
+jstyzYN2CCEH7KMq2yEw1rPLdSZPJSiXdbOHgU0/5FA75GDb9/24JJcDTduUJcbyD1J00qIsEbaI
+eNvm4dyhJaaBYcixPsaBg4MD9g8OODg4IIQGaxostLSLmGefl+o0ybGYsBCvWdEeu46nBKF0LBcR
+ERERETllFLSLrTnaNVDPgvL050f0F58H8dqSzEJZVashWEMIeWtCKI9zFXh3d8W53WUZNr5guWxZ
+tA2N5ZDsJRTHoQ6djqS0XdFuDofruoa2laZhPg5oz5tPlemuH9h0HZtNR991dH1P1/f59Zamam3b
+jHPKoSwN5mms7JvlYeyOE1Osi4gx9D3rzYb1ugTtsfnZUOZfR7qup2k3eFniC4zLV/d44IH7uXr1
+Cvv7+2w2mzF0A3gINGZbfyciIiIiIiKnhYL2YceG7LGvN/Ph4FPILl3KDZqmoW3ztlguWC6WLBZL
+2rYtYTsH7dWyZbVasFoucsBuoAmWryM6w5CmIdT9kIdsz+ZoN7Nh43WoeJ7+PVV768zxrXnj5MDc
+D5HNpufgYM2mVJI3my7PgQ55a2NugoZZWU68nN+MEIwmBlIbgTbPA095aa9cHe9KkN8QmkjTRELT
+Qj8A63H5sSE5McKQnP39Ax64fIUHLl/h6tW9sco+DMNYxVY1W0RERERETisF7WoM1fOu4duLfh1u
+7j1+W3unlTAbQq40L5dLVqslu7s77O7usrOzw3K5GId6hxBYtA2LtqVtG4KBpwieSHHIjdCGSNcN
+dF1f1qGOY4jeqmiHQChzoK0sgIXV11Oq2KRS1aYEbacfBtZdx/56w2a9Yb3JodiCEZpA0wba2FI7
+q/u8mh4CTROIbZMr3JbK6me5G3vXT43Quq4jNGnckjvDUOaIDwNdH+mGSNcPHKw7Dg7W7O8fcLDe
+5CHvh+dni4iIiIiInFIK2oXPU/M4F3trca8Hr9zs84fzIedT0N7Z2eH8+fNcuJC31WqZA2oJqiHU
+edZGSpGh6xn6npggDYmhj/Sbnr4b8txq9zFshhBom7YMHZ81P6vb+Nq8zM/OW308xBxs1yXYHqw3
+rMsWmkC7KBX5RRqPk1Kp2AfLNwkWDTHFrTnpnhKkRF+6mfd9vxW0myY3YsvnK+fd9BxsOtbrLs8Z
+L9sQ87nrUPimaQ4NjReRm2VmdwN3A+7uzSN9PSIiIiKPNQraxazpeO2Dlr+fbVPlulSvZzOfD+1J
+Dew5806rWQcS5l6GYUdihDg4vcMwDHRlrvRmveHKlavs7e2zf7BhiENeaqtpaZcLlssdVssdVqsd
+VssVbbOgsZZAwNzy0tvUudh5PvYwJLpu4OBgw/7+AXt7B+zt7bO3f8De/joPG+86+n4gJMsdymMk
+prT18hZty3KR51inmEgxN0arL91qZ/FyI2DRLlguV6XlesiBPSbiUAJ1l4N4Xxug1SHypfnZONd8
+9r6OjelERE6Rq1evPtKXICIiIqeAgnaRZm22y6jxcXh4nZntbrMFvnJn7cNh28qf2XgwH38eSBgp
+h++SEVOMeU3qmHLQPZiqylevXmVv74CDgzUOLJdLwqJhuVixWqzGoL1c7NA0tct4yEHXyV3Bvax9
+nZx+SHk+9rrL4XrvgL39A/b3c2fwYVZJtmg0TSLEQIwpX2/ZlosFw3IoDdpKJ/Q8ZnwspJvlzupN
+07BYLFgtI7FUxKMzhvihzOPuy3JeXdflIfIp7+vusznx827vCU+qaIvI6XLlypVH+hJERETkFFDQ
+LrwE7Vq5rnO1A5SGXds/n1qKTdVsn4fsMk+5fjUcMydYymG8BPBa1e26ns2mY39/zUHZ9vdzEF4f
+bAhNQ9suCaFluViNFe2d5Q7LxWpsgmZmUzXeS9fysu71UOZ7H6w37O2tD4Xtda5el83MiDF3MI9t
+MwZtA7rlslSdS0U71WXH6qj1PEd8XtFOy9x4bfBITLFUtPMSY11XwnYZZh5jXkpstuJaUZrEeQIP
++auIiIiIiMgpo6B9mM+D3fgjwOr/Ub+dyrf5STnj1srrtFZ1121YrwNNgKFvMZsq3+PQ6X6g2/S5
+mr3ZsO57BnesaVisViyWSy5cvMjFixe54847uPPOO7lw4QLnds+xWCxyM7JUh1unUj1OeR52WS/7
+YJMr2ft7uYp9cLBms+lyo7VhyGtlp2nNbUglcBvDEAlhIJjRbTrWiw3LxYJF09KGhjY0LEr387rl
+oFzmUpeO5TGlUjXv6YeBoVTFU6m+j29vmYPtZnmZtPJ9XsasztNWYzQRERERETl9FLQPGZeeHkO1
+YYd2KCtd5R0CkPLqV5RgCTVQ9uVJToyRTdfRtk0O2Zar3HU96aFUd/s+MgyJAQiLBTvtgtW5c5zb
+3eVxd97J4x53J3fOvp6/cJ5gRj/kJmop1WMOOeT3PeuuZ9P3HJR531f39tnf32e9zutTp5TmLy+H
+WOpryVXkWoHuMdJeJy0AABUJSURBVDbrLodqC7laX7aAsWhbFosFy0Wbh4knJ8a89cNUua9zwety
+ZU4OziE0jC3Ty5ucm7xZWU4sbK0ZLiJySgRAvSNEHkUuXbrEvffey2tf+1ruuuuuR/pyROQGxRjr
+w1MdBhS0q0PTfc1nResSPN0s/3WaQVlj2tL8uaXrN0aKiZ6elCIxDmy6jsVBm8NhPThlrnIqFd3S
+/CsPw7YSWBcsFgvuuHCBJzzh8Tzx8Y/n8Y9/HBfOn+f8+XOcP3+OlBK2yfO93Z0Yh3FJrYPNJncV
+33Tsr9fsXd3L294B6/WGrsthd/q3YVmLe1a+r2tnD32E5GxK1/Q6mb3OOQ8YO6sV7pSlxsjzz8tr
+zEPX+7KEWDd2Fa8jwK3M6TbPIdtK87Rgs7BdAnawgCloi5wIM1sB/yPwcuDTyo8/APwQ8I/dPR7z
+3E8G/jrwpcDTgQb4Q+CXgX/o7r95zHPrXb5vd/fXm9mXAH8N+FzgqcAH3f1TZvvfBXxDOdczgHPA
+x4D/DPwm8IvAT7j7kR3JzOwO4HXAi4A/DtwJfBT4t8Cb3f0nrnWtN0Dd20UeZS5dusQ999zDS1/6
+UgVtkUeRWdA+1Z+9CtqF2bSOdg3LWytlzeZAj3OhjRII6w6M486Tp7w8F5abi23CGBYxww8twYV7
+Xru6Bsm2Ybm7w/nd3bGa/eQnPoEnPeEJPOHxj2O1WrJcrlitVrlT99BjBilF+r5ns9lwcHDA/nqT
+u4yvN+wfrHMX8zJsvOu6MmR8ewmtrYpMqdKn5BiJwZ2u6wlmuflZcqYWbKE0lbM8vBtjGBL9UEJ2
+P7Dp8rVtun5W0S7vdwi1lVseFm6hhOtc7T78WEFb5OEzs6eQA+qfYnvmzHPK9qXAl1/juV8L3Aus
+Dj33GcCnAq8ys29z9793zCV4OdbfAf5XHjx7p57rzwA/A9xxaJ8nl+0zyTcK7gPedsTznw/8GPCE
+Q8//BODFwIvN7G3AV7n7/jHXKyIiInJdCtqj+WrZs20WhseGYyGMWw7Nedy45SW0x+pwzd3JwdL0
+55iXUdGW16lu2/x12bJcLlkuF6yWC3Z3dtjd3eXczg4XL1zgzjsvcvHCLrs7C5q2oWkAEjH2dN2G
+g/U+V/evjmF6b2+fg/VmXJ96vcnLhs2Hi4cQxrWpj3pL6j2EYCVMl9efHIaU2AwDzabDQkN06GOk
+G/JmZuOSYV2XA//BpmPdDXR9pC8dyN0CBCMAXt+kWUW73twIU8e3MlZfRE7AW4E/AXwv8LPkCvGn
+A98G/EngJWb2Gnf/gfmTzOxFwA+Wb68AbwR+CRiAzyeH5icDf8fMPu7u9x5zDS8Dngn8OvA9wH8E
+doHPLudaAm8BLgKXgX8E/CtyJXsJ/LFyzq846uBm9lxy+G6BDwP/oJzrQ8DTgL8EfA3wZcCbgb94
+zLWKiIiIXJeC9qiOXy7trsf1rMqPLYfAXHU2aAIpTWGbsvcYpktTNYNZczGm+dmlKt4splB97tx8
+22V3tWJnZ8Xuzopzu7ucO3ee8+d32dlZzqrPkSH2bPpcwd7b2+PK1T2uXr3Klat7rNddriJ3PV03
+5CW8ahXbyzWUOc9zh2ZpT1V+y9VmB4aYsH7AbEMqIbsfcvO1rs9V7650Eu/7acj4etMzxJiX8KLc
+qCjvSeOU+drzsF2r3DZeQ3knReThMeBzgC9193fPfv5rZvYvgPcDTwH+O2AM2mbWkivZAFeB57n7
+b8ye/x4zeyvwfwF3AW80sx93949d4zqeCfxL4MXu3s9+/ivl63PLcRz4anf/+UPPfw/wY2b2P5GH
+k08vMF/rj5A/734e+Avuvp6/VuBtZvZu4PuBrzSz57v7L13jWkVERESuS0F7NKto15DtXuYr58g8
+NUArFe0yvBkL48LbXirbPg4j97qS17iG2DQE3WktsLNYcuHcLnfccYE77zjPHXdc4I6L59lZLVmt
+VuzsLEule8VquaRtFyT3stb0VNHeLxXtK1evcvnyFS5fucp6nedCd31uuFbX1vY8XjvPdW5KqJ5V
+tesyXfXx9PP8OpM7HiMJL5XsxLrr6fo6RHwgBBubsg19Dt+bspRZTCmPBqBWp600YatN0GbD8+uV
+HFV1F5GHw4G/fyhk5z9w/7iZ/SDwvwDPNLOL7l4Xif4KciXYge84FLLr8/8/M/ufySH3HPAK4LuO
+uAYDIvDqQyF77qmzxw+61tk5Ezn4z70c+GTgAPjaQyF7/tw3mdmrycPlv55cnRcRERF5SDT+ttiq
+X8++cR9XxobSGdvKPOpczc4hcd6G3Ld+ViredQw2U5U4GDRmtMFYNIFlYyxDYBmM5exnixBYBKPB
+ISVSjHmJrf0Drl65wpXLZbtylcuXS1fxg3VpdpaX0Yox5mZruV48zQVvAm3b0rYtTdPQNs30fdvQ
+HtqatqVpWkLTYKEBa0gYQ3L6mIeSH3S5w/n+OlevN92QlxiLqVSxwQl5s4CHfCxr8haahtC2hGa2
+le+bpqVpF1ubiDws//SYP/t35auRh2dXLyhfnWn4+FF+HHjg0HMOc+BX3f0PjjnOpdnjVxyz31Fe
+Wr6+85iKevUu8mv9vJs8h4iIiMgWVbRHtcV2+dbrXGHKMPBaba2V7GasaI9ztJk9t3blHiviDsHH
+gF1GnxNCiZyeIEbS0BO7jn7d0OAM7rTk+dC+SKTohBDZX69zmD3Y8MDly3z84w9w//2XuXz5ap4L
+ve7phkRM4F6XztquUW81ceOoijalqn+4kjwbtm258Rkhvw/RjT6XuAkWZ+t758p3wsCaqXt7rWiX
+YeI2e1yv6fDjut+R88pF5Gb91jF/Ng+mF2ePP7N8/X13/+i1nuzuvZn9e+CLZs85yn+4zjX+CvB7
+wKcA/7uZfQ3wk+Rg/N5jKuGQh8Y78MJZl/Preer1dxERERG5NgXtymtxvy51lbuaeY3JdVgzAbMG
+gm+F7bwvpXv59pBrYzZkHGgMgjlNsPFx8ISliA89cbNhaIwBZwAGy83UUnRS6xAC+1f3ubK3x5W9
+fe5/4Aoff+Ay999/mQcu79GVYdp9n5cLw63MxWa8rjGk2oOHZU/XXRq4Hdov3zaoIXm6+eDBiG4Q
+E8kHgtW56Y67k9xyFTvUmxAPPkbtzD7Nx97ewqwzu6khmsjDdq2h1MU8mM6X0Kidu//zDZziw7Pn
+XMvHjzuAuw9m9mLgnwOfQQ7Pzyl/fGBm7yIvRfZjZfj43FPqYW7gWqudm9j38LXyvve977r73XXX
+XVpOSEREZObSpUtcunTpuvv1/XH3108PBe1RaWjmNlazyxTrsTrtBIJ5DpVjZbuGRcbn2/Yhy8NE
+jpkQQh4y3gRoLFe0jQQp4sNA6juGjRGDEUMglmWvxjW2LbA+yF3FL1++wuXLV7hy+SpXruyzt7fP
+kJwYE0PyMVTXrt01LI9hez4fev5ueL3FMLttMIbzUG5AWF5bfDZ0Pjl5HniK0+7lmPWGxfZQ+xq0
+bTb3/cEBewrauUv6UQ3cROS2O6mehNdcp3s8kftvmdkzgZeU7QvIS4jtAP912f6GmX2Zu//R7Kn1
+BsHPA3/zhK73WM9+9rOvu8/dd9/Nt3/7t9/6ixEREXmUuPfee7nnnnse6cs4MQraIiJysz5GvlP2
+CTewbx2Gfb350dfleYjMT5cNM/sE4IXA64BnA/8luRv6y2ZP+yi5Y/nS3d//cK/hGGPF/slPfvJ1
+d7733nt505vedAsvR0Sup+s6AF74wheyXC4f4asRkRjjDX2G3nffffXhcaPlHnEK2sVvv/3HNeFX
+ROTG/Ca5YdgfM7MnXmuedlla61nkyvdvnvRFuPtHgDeb2Y8C/5octF9sZit335Td/j25Q/rnmFnr
+7sNJX0cxfobM/gEgIo8C+p0VedQ61flNQVtERG7W24HXkD/gXgG88Rr7/UXgTnLQfvutupgyh/ud
+5KDdAo8DPlL++KeBF5XreAWz9cBP2AZYkee138jcdREREXlonkJurby53o6PJAVtERG5WT8FfIhc
+Kf5bZvYL7r5VsTazTwLeUL7d5/hlwI5lZs8DLrn7717jzxfAF5ZvrwLz8tSbgW8FPgl4o5n91lHr
+hs+O9Vygcfd33cw1uvv5m9lfREREHtsUtEVE5KaUZbv+CvAz5Erxr5rZG4BfIjc2ey7wzeQ7zg58
+4w2sYX2c5wPfZmbvBn6OvBzYfcAu8MeBv0quZjvwpnnncXfvzOyrgHcAF4BfNrO3kG8W/D75jvhd
+5DneX0lehuy/Jy8dJiIiIvKQKGiLiMhNc/e3mdnXk5uPXQBeX7ZxF2AAvtXdv/8ETmnkTuNfeMSf
+edl+CviWI67135jZFwH/jFzZ/stlu9ZxLp/A9YqIiMgZpqAtInI21VD5kPdz9x8uc6P/OvBngaeT
+K8QfIle3/6G7/8cTuNY3AL8OvIDcXO1pTOtjfxh4D/Bmd/+Fa74I9/eY2acBX09eHuxZwJPIc6rv
+Az4AvBP4CXf/7RO4ZhERETnDLK+WIiIiIiIiIiInITzSFyAiIiIiIiLyWKKgLSIiIiIiInKCFLRF
+RERERERETpCCtoiIiIiIiMgJUtAWEREREREROUEK2iIicuaZ2dPN7LvM7ANmdtXMPmpm7zGzbzKz
+3RM8z1eb2S+a2SUzOzCz/8fMftjM/vRJnUPkLLmVv7tmdreZpRvcvuCkXpPIY5WZPdnMXmRm95jZ
+28zsvtnv0P95i875iH3uankvERE508zsJcAPA3fw4DXDDfhPwIvc/Xcfxjl2gJ8Avuwa50jA6939
+9Q/1HCJnza3+3TWzu4G7jzj2YQ58ibu/66GcR+SsMLN06Efz3603u/srT/Bcj/jnriraIiJyZpnZ
+s4C3ABeBK8C3AJ8PPB/4AfKH86cBP2tm5x/GqX6Q6cP+l4EvB/4r4FXA75A/j+82s1c/jHOInBm3
+8Xe3+kzgmdfYPgt47wmcQ+Qs8LL9v8C/IIfeW+ER/9xVRVtERM4sM3sX8DygB/6Mu7/n0J9/I/AG
+8gf1PQ/lzreZfQnw9nKMnwa+0mcfvmb2RODfAU8HPg58irs/8NBekcjZcJt+d8eKtrs3D/+qRc62
+8jv1XuC97n6fmX0y8Pvk39MTq2ifls9dVbRFRORMMrPnkP+h7sCbDv9Dvfhu4APkO+7fYGYP5R/b
+31i+DsDr/NAdbnf/KPDN5dvHAapqixzjNv7uisgJcvd73P1t7n7fLT7VqfjcVdAWEZGz6stnj//J
+UTuUD+cfKt8+DvjimzmBmV0gD2V14O3u/qFr7PpW4HJ5/BU3cw6RM+iW/+6KyKPTafrcVdAWEZGz
+6nnl6x55CNm1vHP2+Lk3eY7nAMsjjrPF3XvgX5Orb89R9U3kWLfjd1dEHp1OzeeugraIiJxVn0G+
+4/077n64E+rcbx16zs34k9c4znHnaclNnETkaLfjd3dLWR7oI2a2KV/fYWbfbGaPezjHFZETd2o+
+dxW0RUTkzDGzFfCk8u0Hj9vX3e8nV84APukmT/WJs8fHngf4g9njmz2PyJlwG393D3tBOW9bvn4B
+8HeB3zOzlz7MY4vIyTk1n7vtSR9QRETkUeDi7PHVG9h/DzgHXLiF59mbPb7Z84icFbfrd7f6D8BP
+Ae8BPgQsgE8H/jLwZ8nzv/+5mb3E3X/xIZ5DRE7OqfncVdAWEZGzaGf2uLuB/TfkeVy7t/A8m9nj
+mz2PyFlxu353Ab7H3e854ufvBX7EzP4K8I+BBniTmT3D3W/kmkTk1jk1n7saOi4iImfRevZ4ec29
+JivynNCDW3ie1ezxzZ5H5Ky4Xb+7uPvl6/z59wP/BznIPw142c2eQ0RO3Kn53FXQFhGRs+jK7PGN
+DBc7X77eyFDVh3qe87PHN3sekbPidv3u3qh7Z4+/8BadQ0Ru3Kn53FXQFhGRM8fdN8BHy7efeNy+
+patw/TD+g+P2PcK8Ecux52G7EcvNnkfkTLiNv7s36v2zx//FLTqHiNy4U/O5q6AtIiJn1fvJQz4/
+1cyO+zz8E7PHH3gI5zjqOMedZwB++ybPI3KW3I7f3Rvlt+i4IvLQnJrPXQVtERE5q36lfD0PPPuY
+/ebDQX/1Js/xXqZmLNccVmpmC+BPk//R/l53jzd5HpGz5Hb87t6o+Zq9H7pF5xCRG3dqPncVtEVE
+5Kz6qdnjVxy1g5kZ8LXl2/uBd9zMCdz9KvBL5OrbC8zsadfY9WXAHeXxW2/mHCJn0C3/3b0Jf3X2
++J236BwicoNO0+eugraIiJxJ7v5e4N3kD+NXmdnnHrHbNwGfQb7j/b2H73ib2deZWSrb/3aNU72x
+fG2B7zs81NXMngT8vfLt/eQuxiJyDbfjd9fMPtPMnnHcdZTlvV5Vvv0w8JM3/2pE5GY8mj53tY62
+iIicZd9AHlK6C/xLM/tOcuVrF/hq4DVlv/8b+O5jjnPNeZru/g4zewvwcuDPl/N8L3mY6WcB3wI8
+vRzjb7r7Aw/rFYmcDbf6d/fZ5LWx3wH8PPAb5CZsLXle59cAX1r2HYDXuLuW5RM5hpk9F/jU2Y+e
+NHv8qWb2dfP93f3Nxxzu1H/uKmiLiMiZ5e6/ZmZfBfwIeQjZdx7ehfwP9Re5+97DONUrgYvAnwO+
+CPjiQ+eIwOvdXdVskRtwm353A/B84AXXugxy+H6lu7/tIZ5D5Cx5NfB1R/zcgOeVrXLguKB9PY/4
+566CtoiInGnu/nNm9lnkCtmLyMuBdMDvAP8M+D53Xx93iBs4xxp4iZm9HPh64E8BjwM+AryrnOPf
+PJzXIXLW3OLf3Z8jDwv/POBZwCcATyQHgo8Bvw78AvBPypxQEbkxN9qp/7j9HhWfu+auVQlERERE
+REREToqaoYmIiIiIiIicIAVtERERERERkROkoC0iIiIiIiJyghS0RURERERERE6QgraIiIiIiIjI
+CVLQFhERERERETlBCtoiIiIiIiIiJ0hBW0REREREROQEKWiLiIiIiIiInCAFbREREREREZETpKAt
+IiIiIiIicoIUtEVEREREREROkIK2iIiIiIiIyAlS0BYRERERERE5QQraIiIiIiIiIidIQVtERERE
+RETkBCloi4iIiIiIiJwgBW0RERERERGRE6SgLSIiIiIiInKCFLRFRERERERETpCCtoiIiIiIiMgJ
+UtAWEREREREROUEK2iIiIiIiIiInSEFbRERERERE5AQpaIuIiIiIiIicoP8fEX4BoxSB8CgAAAAA
+SUVORK5CYII=
+"
+width=493
+height=445
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;">&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;<a class="anchor-link" href="#&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;">&#182;</a></h2><p>你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率<a href="http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130">远远超过 80%</a>。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。</p>
+<h2 id="&#25552;&#20132;&#39033;&#30446;">&#25552;&#20132;&#39033;&#30446;<a class="anchor-link" href="#&#25552;&#20132;&#39033;&#30446;">&#182;</a></h2><p>提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 "File" -&gt; "Download as" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。</p>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+</html>
diff --git a/image-classification/dlnd_image_classification.ipynb b/image-classification/dlnd_image_classification.ipynb
new file mode 100644
index 0000000..26b0065
--- /dev/null
+++ b/image-classification/dlnd_image_classification.ipynb
@@ -0,0 +1,1107 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# 图像分类\n",
+    "\n",
+    "在此项目中，你将对 [CIFAR-10 数据集](https://www.cs.toronto.edu/~kriz/cifar.html) 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。\n",
+    "\n",
+    "\n",
+    "## 获取数据\n",
+    "\n",
+    "请运行以下单元，以下载 [CIFAR-10 数据集（Python版）](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All files found!\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "from urllib.request import urlretrieve\n",
+    "from os.path import isfile, isdir\n",
+    "from tqdm import tqdm\n",
+    "import problem_unittests as tests\n",
+    "import tarfile\n",
+    "\n",
+    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
+    "\n",
+    "# Use Floyd's cifar-10 dataset if present\n",
+    "floyd_cifar10_location = '/input/cifar-10/python.tar.gz'\n",
+    "if isfile(floyd_cifar10_location):\n",
+    "    tar_gz_path = floyd_cifar10_location\n",
+    "else:\n",
+    "    tar_gz_path = 'cifar-10-python.tar.gz'\n",
+    "\n",
+    "class DLProgress(tqdm):\n",
+    "    last_block = 0\n",
+    "\n",
+    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
+    "        self.total = total_size\n",
+    "        self.update((block_num - self.last_block) * block_size)\n",
+    "        self.last_block = block_num\n",
+    "\n",
+    "if not isfile(tar_gz_path):\n",
+    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
+    "        urlretrieve(\n",
+    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
+    "            tar_gz_path,\n",
+    "            pbar.hook)\n",
+    "\n",
+    "if not isdir(cifar10_dataset_folder_path):\n",
+    "    with tarfile.open(tar_gz_path) as tar:\n",
+    "        tar.extractall()\n",
+    "        tar.close()\n",
+    "\n",
+    "\n",
+    "tests.test_folder_path(cifar10_dataset_folder_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 探索数据\n",
+    "\n",
+    "该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 `data_batch_1`、`data_batch_2`，以此类推。每个部分都包含以下某个类别的标签和图片：\n",
+    "\n",
+    "* 飞机\n",
+    "* 汽车\n",
+    "* 鸟类\n",
+    "* 猫\n",
+    "* 鹿\n",
+    "* 狗\n",
+    "* 青蛙\n",
+    "* 马\n",
+    "* 船只\n",
+    "* 卡车\n",
+    "\n",
+    "了解数据集也是对数据进行预测的必经步骤。你可以通过更改 `batch_id` 和 `sample_id` 探索下面的代码单元。`batch_id` 是数据集一个部分的 ID（1 到 5）。`sample_id` 是该部分中图片和标签对（label pair）的 ID。\n",
+    "\n",
+    "问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Stats of batch 1:\n",
+      "Samples: 10000\n",
+      "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n",
+      "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n",
+      "\n",
+      "Example of Image 5:\n",
+      "Image - Min Value: 0 Max Value: 252\n",
+      "Image - Shape: (32, 32, 3)\n",
+      "Label - Label Id: 1 Name: automobile\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAK/CAYAAABjrqvGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21411d80e80>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 351,
+       "width": 353
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "import helper\n",
+    "import numpy as np\n",
+    "\n",
+    "# Explore the dataset\n",
+    "batch_id = 1\n",
+    "sample_id = 5\n",
+    "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 实现预处理函数\n",
+    "\n",
+    "### 标准化\n",
+    "\n",
+    "在下面的单元中，实现 `normalize` 函数，传入图片数据 `x`，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 `x` 的形状一样。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def normalize(x):\n",
+    "    \"\"\"\n",
+    "    Normalize a list of sample image data in the range of 0 to 1\n",
+    "    : x: List of image data.  The image shape is (32, 32, 3)\n",
+    "    : return: Numpy array of normalize data\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return np.array(x/255)\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_normalize(normalize)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### One-hot 编码\n",
+    "\n",
+    "和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 `one_hot_encode` 函数。输入，也就是 `x`，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 `one_hot_encode` 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。\n",
+    "\n",
+    "提示：不要重复发明轮子。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def one_hot_encode(x):\n",
+    "    \"\"\"\n",
+    "    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n",
+    "    : x: List of sample Labels\n",
+    "    : return: Numpy array of one-hot encoded labels\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    from tflearn.data_utils import to_categorical\n",
+    "    return np.array(to_categorical(x, 10))\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_one_hot_encode(one_hot_encode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 随机化数据\n",
+    "\n",
+    "之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 预处理所有数据并保存\n",
+    "\n",
+    "运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "# Preprocess Training, Validation, and Testing Data\n",
+    "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 检查点\n",
+    "\n",
+    "这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import pickle\n",
+    "import problem_unittests as tests\n",
+    "import helper\n",
+    "\n",
+    "# Load the Preprocessed Validation data\n",
+    "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 构建网络\n",
+    "\n",
+    "对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。\n",
+    "\n",
+    ">**注意**：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。\n",
+    "\n",
+    ">但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 `conv2d` 类 [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d)，而不是 TF Layers 版本的 `conv2d` 类 [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d)。\n",
+    "\n",
+    "我们开始吧！\n",
+    "\n",
+    "\n",
+    "### 输入\n",
+    "\n",
+    "神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：\n",
+    "\n",
+    "* 实现 `neural_net_image_input`\n",
+    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
+    " * 使用 `image_shape` 设置形状，部分大小设为 `None`\n",
+    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"x\" 命名\n",
+    "* 实现 `neural_net_label_input`\n",
+    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
+    " * 使用 `n_classes` 设置形状，部分大小设为 `None`\n",
+    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"y\" 命名\n",
+    "* 实现 `neural_net_keep_prob_input`\n",
+    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)，用于丢弃保留概率\n",
+    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"keep_prob\" 命名\n",
+    "\n",
+    "这些名称将在项目结束时，用于加载保存的模型。\n",
+    "\n",
+    "注意：TensorFlow 中的 `None` 表示形状可以是动态大小。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image Input Tests Passed.\n",
+      "Label Input Tests Passed.\n",
+      "Keep Prob Tests Passed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "def neural_net_image_input(image_shape):\n",
+    "    \"\"\"\n",
+    "    Return a Tensor for a batch of image input\n",
+    "    : image_shape: Shape of the images\n",
+    "    : return: Tensor for image input.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], name='x')\n",
+    "\n",
+    "\n",
+    "def neural_net_label_input(n_classes):\n",
+    "    \"\"\"\n",
+    "    Return a Tensor for a batch of label input\n",
+    "    : n_classes: Number of classes\n",
+    "    : return: Tensor for label input.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return tf.placeholder(tf.int32, [None, n_classes], name='y')\n",
+    "\n",
+    "\n",
+    "def neural_net_keep_prob_input():\n",
+    "    \"\"\"\n",
+    "    Return a Tensor for keep probability\n",
+    "    : return: Tensor for keep probability.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return tf.placeholder(tf.float32, name='keep_prob')\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tf.reset_default_graph()\n",
+    "tests.test_nn_image_inputs(neural_net_image_input)\n",
+    "tests.test_nn_label_inputs(neural_net_label_input)\n",
+    "tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 卷积和最大池化层\n",
+    "\n",
+    "卷积层级适合处理图片。对于此代码单元，你应该实现函数 `conv2d_maxpool` 以便应用卷积然后进行最大池化：\n",
+    "\n",
+    "* 使用 `conv_ksize`、`conv_num_outputs` 和 `x_tensor` 的形状创建权重（weight）和偏置（bias）。\n",
+    "* 使用权重和 `conv_strides` 对 `x_tensor` 应用卷积。\n",
+    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
+    "* 添加偏置\n",
+    "* 向卷积中添加非线性激活（nonlinear activation）\n",
+    "* 使用 `pool_ksize` 和 `pool_strides` 应用最大池化\n",
+    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
+    "\n",
+    "**注意**：对于**此层**，**请勿使用** [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers)，但是仍然可以使用 TensorFlow 的 [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) 包。对于所有**其他层**，你依然可以使用快捷方法。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n",
+    "    \"\"\"\n",
+    "    Apply convolution then max pooling to x_tensor\n",
+    "    :param x_tensor: TensorFlow Tensor\n",
+    "    :param conv_num_outputs: Number of outputs for the convolutional layer\n",
+    "    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n",
+    "    :param conv_strides: Stride 2-D Tuple for convolution\n",
+    "    :param pool_ksize: kernal size 2-D Tuple for pool\n",
+    "    :param pool_strides: Stride 2-D Tuple for pool\n",
+    "    : return: A tensor that represents convolution and max pooling of x_tensor\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    weights = tf.Variable(tf.truncated_normal(shape=[conv_ksize[0], conv_ksize[1], x_tensor.get_shape().as_list()[3], conv_num_outputs], stddev=0.1))\n",
+    "    bias = tf.Variable(tf.constant(0.1, shape=[conv_num_outputs]))\n",
+    "    conv = tf.nn.conv2d(input=x_tensor, filter=weights, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME') + bias\n",
+    "    activate = tf.nn.relu(conv)\n",
+    "    pool = tf.nn.max_pool(value=activate, ksize=[1, pool_ksize[0], pool_ksize[1], 1], strides=[1, pool_strides[0], pool_strides[1], 1], padding='SAME')\n",
+    "    \n",
+    "    return pool\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_con_pool(conv2d_maxpool)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 扁平化层\n",
+    "\n",
+    "实现 `flatten` 函数，将 `x_tensor` 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（*部分大小（Batch Size）*，*扁平化图片大小（Flattened Image Size）*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def flatten(x_tensor):\n",
+    "    \"\"\"\n",
+    "    Flatten x_tensor to (Batch Size, Flattened Image Size)\n",
+    "    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n",
+    "    : return: A tensor of size (Batch Size, Flattened Image Size).\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    layer_shape = x_tensor.get_shape()\n",
+    "    num_features = layer_shape[1:4].num_elements()\n",
+    "    layer_flat = tf.reshape(x_tensor, [-1, num_features])\n",
+    "    \n",
+    "    return layer_flat\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_flatten(flatten)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 完全连接的层\n",
+    "\n",
+    "实现 `fully_conn` 函数，以向 `x_tensor` 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def fully_conn(x_tensor, num_outputs):\n",
+    "    \"\"\"\n",
+    "    Apply a fully connected layer to x_tensor using weight and bias\n",
+    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
+    "    : num_outputs: The number of output that the new tensor should be.\n",
+    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n",
+    "    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n",
+    "    fc = tf.nn.relu(tf.matmul(x_tensor, weights) + bias)\n",
+    "    \n",
+    "    return fc\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_fully_conn(fully_conn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 输出层\n",
+    "\n",
+    "实现 `output` 函数，向 x_tensor 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n",
+    "\n",
+    "**注意**：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def output(x_tensor, num_outputs):\n",
+    "    \"\"\"\n",
+    "    Apply a output layer to x_tensor using weight and bias\n",
+    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
+    "    : num_outputs: The number of output that the new tensor should be.\n",
+    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n",
+    "    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n",
+    "    output = tf.matmul(x_tensor, weights) + bias\n",
+    "    \n",
+    "    return output\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_output(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 创建卷积模型\n",
+    "\n",
+    "实现函数 `conv_net`， 创建卷积神经网络模型。该函数传入一批图片 `x`，并输出对数（logits）。使用你在上方创建的层创建此模型：\n",
+    "\n",
+    "* 应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）\n",
+    "* 应用一个扁平层（Flatten Layer）\n",
+    "* 应用 1、2 或 3 个完全连接层（Fully Connected Layers）\n",
+    "* 应用一个输出层（Output Layer）\n",
+    "* 返回输出\n",
+    "* 使用 `keep_prob` 向模型中的一个或多个层应用 [TensorFlow 的 Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Network Built!\n"
+     ]
+    }
+   ],
+   "source": [
+    "def conv_net(x, keep_prob):\n",
+    "    \"\"\"\n",
+    "    Create a convolutional neural network model\n",
+    "    : x: Placeholder tensor that holds image data.\n",
+    "    : keep_prob: Placeholder tensor that hold dropout keep probability.\n",
+    "    : return: Tensor that represents logits\n",
+    "    \"\"\"\n",
+    "    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers\n",
+    "    #    Play around with different number of outputs, kernel size and stride\n",
+    "    # Function Definition from Above:\n",
+    "    #    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
+    "    conv_pool_1 = conv2d_maxpool(x, 64, [5, 5], [1, 1], [3, 3], [2, 2])\n",
+    "    norm_layer = tf.nn.lrn(conv_pool_1, 4 , bias=1.0, alpha=0.001 / 9.0, beta=0.75)\n",
+    "    conv_pool_2 = conv2d_maxpool(norm_layer, 128, [5, 5], [1, 1], [3, 3], [2, 2])\n",
+    "\n",
+    "    # TODO: Apply a Flatten Layer\n",
+    "    # Function Definition from Above:\n",
+    "    #   flatten(x_tensor)\n",
+    "    flat_layer = flatten(conv_pool_2)\n",
+    "\n",
+    "    # TODO: Apply 1, 2, or 3 Fully Connected Layers\n",
+    "    #    Play around with different number of outputs\n",
+    "    # Function Definition from Above:\n",
+    "    #   fully_conn(x_tensor, num_outputs)\n",
+    "    fc_layer1 = fully_conn(flat_layer, 384)\n",
+    "    dropout_layer_1 = tf.nn.dropout(fc_layer1, keep_prob)\n",
+    "    fc_layer2 = fully_conn(dropout_layer_1, 192)\n",
+    "    dropout_layer_2 = tf.nn.dropout(fc_layer2, keep_prob)\n",
+    "    \n",
+    "    # TODO: Apply an Output Layer\n",
+    "    #    Set this to the number of classes\n",
+    "    # Function Definition from Above:\n",
+    "    #   output(x_tensor, num_outputs)\n",
+    "    logits = output(dropout_layer_2, 10)\n",
+    "    \n",
+    "    # TODO: return output\n",
+    "    return logits\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "\n",
+    "##############################\n",
+    "## Build the Neural Network ##\n",
+    "##############################\n",
+    "\n",
+    "# Remove previous weights, bias, inputs, etc..\n",
+    "tf.reset_default_graph()\n",
+    "\n",
+    "# Inputs\n",
+    "x = neural_net_image_input((32, 32, 3))\n",
+    "y = neural_net_label_input(10)\n",
+    "keep_prob = neural_net_keep_prob_input()\n",
+    "\n",
+    "# Model\n",
+    "logits = conv_net(x, keep_prob)\n",
+    "\n",
+    "# Name logits Tensor, so that is can be loaded from disk after training\n",
+    "logits = tf.identity(logits, name='logits')\n",
+    "\n",
+    "# Loss and Optimizer\n",
+    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
+    "optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
+    "\n",
+    "# Accuracy\n",
+    "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n",
+    "\n",
+    "tests.test_conv_net(conv_net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 训练神经网络\n",
+    "\n",
+    "### 单次优化\n",
+    "\n",
+    "实现函数 `train_neural_network` 以进行单次优化（single optimization）。该优化应该使用 `optimizer` 优化 `session`，其中 `feed_dict` 具有以下参数：\n",
+    "\n",
+    "* `x` 表示图片输入\n",
+    "* `y` 表示标签\n",
+    "* `keep_prob` 表示丢弃的保留率\n",
+    "\n",
+    "每个部分都会调用该函数，所以 `tf.global_variables_initializer()` 已经被调用。\n",
+    "\n",
+    "注意：不需要返回任何内容。该函数只是用来优化神经网络。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n",
+    "    \"\"\"\n",
+    "    Optimize the session on a batch of images and labels\n",
+    "    : session: Current TensorFlow session\n",
+    "    : optimizer: TensorFlow optimizer function\n",
+    "    : keep_probability: keep probability\n",
+    "    : feature_batch: Batch of Numpy image data\n",
+    "    : label_batch: Batch of Numpy label data\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    session.run(optimizer, feed_dict = {keep_prob: keep_probability, x: feature_batch, y: label_batch})\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_train_nn(train_neural_network)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 显示数据\n",
+    "\n",
+    "实现函数 `print_stats` 以输出损失和验证准确率。使用全局变量 `valid_features` 和 `valid_labels` 计算验证准确率。使用保留率 `1.0` 计算损失和验证准确率（loss and validation accuracy）。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def print_stats(session, feature_batch, label_batch, cost, accuracy):\n",
+    "    \"\"\"\n",
+    "    Print information about loss and validation accuracy\n",
+    "    : session: Current TensorFlow session\n",
+    "    : feature_batch: Batch of Numpy image data\n",
+    "    : label_batch: Batch of Numpy label data\n",
+    "    : cost: TensorFlow cost function\n",
+    "    : accuracy: TensorFlow accuracy function\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    print('Valid Loss: ', end='')\n",
+    "    print(session.run(cost, feed_dict = {x: valid_features, y: valid_labels, keep_prob: 1.0}), end='')\n",
+    "    print(', Valid Accuracy: ', end='')\n",
+    "    print(session.run(accuracy, feed_dict = {x: valid_features, y: valid_labels, keep_prob: 1.0}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 超参数\n",
+    "\n",
+    "调试以下超参数：\n",
+    "* 设置 `epochs` 表示神经网络停止学习或开始过拟合的迭代次数\n",
+    "* 设置 `batch_size`，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：\n",
+    "\n",
+    " * 64\n",
+    " * 128\n",
+    " * 256\n",
+    " * ...\n",
+    "* 设置 `keep_probability` 表示使用丢弃时保留节点的概率"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# TODO: Tune Parameters\n",
+    "epochs = 10\n",
+    "batch_size = 128\n",
+    "keep_probability = 0.75"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 在单个 CIFAR-10 部分上训练\n",
+    "\n",
+    "我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking the Training on a Single Batch...\n",
+      "Epoch  1, CIFAR-10 Batch 1:  Valid Loss: 1.82877, Valid Accuracy: 0.3608\n",
+      "Epoch  2, CIFAR-10 Batch 1:  Valid Loss: 1.60118, Valid Accuracy: 0.429\n",
+      "Epoch  3, CIFAR-10 Batch 1:  Valid Loss: 1.5015, Valid Accuracy: 0.4502\n",
+      "Epoch  4, CIFAR-10 Batch 1:  Valid Loss: 1.38333, Valid Accuracy: 0.4996\n",
+      "Epoch  5, CIFAR-10 Batch 1:  Valid Loss: 1.32223, Valid Accuracy: 0.5298\n",
+      "Epoch  6, CIFAR-10 Batch 1:  Valid Loss: 1.34756, Valid Accuracy: 0.5206\n",
+      "Epoch  7, CIFAR-10 Batch 1:  Valid Loss: 1.28294, Valid Accuracy: 0.5466\n",
+      "Epoch  8, CIFAR-10 Batch 1:  Valid Loss: 1.31494, Valid Accuracy: 0.5374\n",
+      "Epoch  9, CIFAR-10 Batch 1:  Valid Loss: 1.30606, Valid Accuracy: 0.5576\n",
+      "Epoch 10, CIFAR-10 Batch 1:  Valid Loss: 1.32294, Valid Accuracy: 0.555\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "print('Checking the Training on a Single Batch...')\n",
+    "with tf.Session() as sess:\n",
+    "    # Initializing the variables\n",
+    "    sess.run(tf.global_variables_initializer())\n",
+    "    \n",
+    "    # Training cycle\n",
+    "    for epoch in range(epochs):\n",
+    "        batch_i = 1\n",
+    "        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
+    "            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
+    "        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
+    "        print_stats(sess, batch_features, batch_labels, cost, accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 完全训练模型\n",
+    "\n",
+    "现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training...\n",
+      "Epoch  1, CIFAR-10 Batch 1:  Valid Loss: 1.88313, Valid Accuracy: 0.3366\n",
+      "Epoch  1, CIFAR-10 Batch 2:  Valid Loss: 1.60399, Valid Accuracy: 0.4198\n",
+      "Epoch  1, CIFAR-10 Batch 3:  Valid Loss: 1.50717, Valid Accuracy: 0.4498\n",
+      "Epoch  1, CIFAR-10 Batch 4:  Valid Loss: 1.41716, Valid Accuracy: 0.4816\n",
+      "Epoch  1, CIFAR-10 Batch 5:  Valid Loss: 1.33043, Valid Accuracy: 0.5132\n",
+      "Epoch  2, CIFAR-10 Batch 1:  Valid Loss: 1.28486, Valid Accuracy: 0.537\n",
+      "Epoch  2, CIFAR-10 Batch 2:  Valid Loss: 1.30711, Valid Accuracy: 0.53\n",
+      "Epoch  2, CIFAR-10 Batch 3:  Valid Loss: 1.22172, Valid Accuracy: 0.558\n",
+      "Epoch  2, CIFAR-10 Batch 4:  Valid Loss: 1.18755, Valid Accuracy: 0.5712\n",
+      "Epoch  2, CIFAR-10 Batch 5:  Valid Loss: 1.18258, Valid Accuracy: 0.576\n",
+      "Epoch  3, CIFAR-10 Batch 1:  Valid Loss: 1.12699, Valid Accuracy: 0.5932\n",
+      "Epoch  3, CIFAR-10 Batch 2:  Valid Loss: 1.13514, Valid Accuracy: 0.5916\n",
+      "Epoch  3, CIFAR-10 Batch 3:  Valid Loss: 1.10883, Valid Accuracy: 0.5996\n",
+      "Epoch  3, CIFAR-10 Batch 4:  Valid Loss: 1.06464, Valid Accuracy: 0.6178\n",
+      "Epoch  3, CIFAR-10 Batch 5:  Valid Loss: 1.07656, Valid Accuracy: 0.6128\n",
+      "Epoch  4, CIFAR-10 Batch 1:  Valid Loss: 1.10849, Valid Accuracy: 0.605\n",
+      "Epoch  4, CIFAR-10 Batch 2:  Valid Loss: 1.08009, Valid Accuracy: 0.6166\n",
+      "Epoch  4, CIFAR-10 Batch 3:  Valid Loss: 1.01519, Valid Accuracy: 0.637\n",
+      "Epoch  4, CIFAR-10 Batch 4:  Valid Loss: 1.00247, Valid Accuracy: 0.643\n",
+      "Epoch  4, CIFAR-10 Batch 5:  Valid Loss: 1.02331, Valid Accuracy: 0.6448\n",
+      "Epoch  5, CIFAR-10 Batch 1:  Valid Loss: 0.995853, Valid Accuracy: 0.6502\n",
+      "Epoch  5, CIFAR-10 Batch 2:  Valid Loss: 1.00064, Valid Accuracy: 0.65\n",
+      "Epoch  5, CIFAR-10 Batch 3:  Valid Loss: 0.93919, Valid Accuracy: 0.6738\n",
+      "Epoch  5, CIFAR-10 Batch 4:  Valid Loss: 0.991811, Valid Accuracy: 0.6504\n",
+      "Epoch  5, CIFAR-10 Batch 5:  Valid Loss: 0.927782, Valid Accuracy: 0.6826\n",
+      "Epoch  6, CIFAR-10 Batch 1:  Valid Loss: 0.969924, Valid Accuracy: 0.666\n",
+      "Epoch  6, CIFAR-10 Batch 2:  Valid Loss: 1.01257, Valid Accuracy: 0.6408\n",
+      "Epoch  6, CIFAR-10 Batch 3:  Valid Loss: 0.961456, Valid Accuracy: 0.6636\n",
+      "Epoch  6, CIFAR-10 Batch 4:  Valid Loss: 0.935574, Valid Accuracy: 0.674\n",
+      "Epoch  6, CIFAR-10 Batch 5:  Valid Loss: 0.904234, Valid Accuracy: 0.6824\n",
+      "Epoch  7, CIFAR-10 Batch 1:  Valid Loss: 0.925582, Valid Accuracy: 0.6806\n",
+      "Epoch  7, CIFAR-10 Batch 2:  Valid Loss: 0.962076, Valid Accuracy: 0.674\n",
+      "Epoch  7, CIFAR-10 Batch 3:  Valid Loss: 0.935451, Valid Accuracy: 0.6754\n",
+      "Epoch  7, CIFAR-10 Batch 4:  Valid Loss: 0.88064, Valid Accuracy: 0.6912\n",
+      "Epoch  7, CIFAR-10 Batch 5:  Valid Loss: 0.912521, Valid Accuracy: 0.694\n",
+      "Epoch  8, CIFAR-10 Batch 1:  Valid Loss: 0.932409, Valid Accuracy: 0.6876\n",
+      "Epoch  8, CIFAR-10 Batch 2:  Valid Loss: 0.959626, Valid Accuracy: 0.6722\n",
+      "Epoch  8, CIFAR-10 Batch 3:  Valid Loss: 0.958519, Valid Accuracy: 0.6904\n",
+      "Epoch  8, CIFAR-10 Batch 4:  Valid Loss: 0.886022, Valid Accuracy: 0.7024\n",
+      "Epoch  8, CIFAR-10 Batch 5:  Valid Loss: 0.947139, Valid Accuracy: 0.6868\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "epochs = 8\n",
+    "save_model_path = './model/image_classification'\n",
+    "\n",
+    "print('Training...')\n",
+    "with tf.Session() as sess:\n",
+    "    # Initializing the variables\n",
+    "    sess.run(tf.global_variables_initializer())\n",
+    "    \n",
+    "    # Training cycle\n",
+    "    for epoch in range(epochs):\n",
+    "        # Loop over all batches\n",
+    "        n_batches = 5\n",
+    "        for batch_i in range(1, n_batches + 1):\n",
+    "            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
+    "                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
+    "            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
+    "            print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
+    "            \n",
+    "    # Save Model\n",
+    "    saver = tf.train.Saver()\n",
+    "    save_path = saver.save(sess, save_model_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 检查点\n",
+    "\n",
+    "模型已保存到本地。\n",
+    "\n",
+    "## 测试模型\n",
+    "\n",
+    "利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing Accuracy: 0.6769185126582279\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9oAAAN6CAYAAABxCzGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2140dfa1dd8>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 445,
+       "width": 493
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "import pickle\n",
+    "import helper\n",
+    "import random\n",
+    "\n",
+    "# Set batch size if not already set\n",
+    "try:\n",
+    "    if batch_size:\n",
+    "        pass\n",
+    "except NameError:\n",
+    "    batch_size = 64\n",
+    "\n",
+    "save_model_path = './model/image_classification'\n",
+    "n_samples = 4\n",
+    "top_n_predictions = 3\n",
+    "\n",
+    "def test_model():\n",
+    "    \"\"\"\n",
+    "    Test the saved model against the test dataset\n",
+    "    \"\"\"\n",
+    "\n",
+    "    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))\n",
+    "    loaded_graph = tf.Graph()\n",
+    "\n",
+    "    with tf.Session(graph=loaded_graph) as sess:\n",
+    "        # Load model\n",
+    "        loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
+    "        loader.restore(sess, save_model_path)\n",
+    "\n",
+    "        # Get Tensors from loaded model\n",
+    "        loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
+    "        loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
+    "        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
+    "        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
+    "        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
+    "        \n",
+    "        # Get accuracy in batches for memory limitations\n",
+    "        test_batch_acc_total = 0\n",
+    "        test_batch_count = 0\n",
+    "        \n",
+    "        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n",
+    "            test_batch_acc_total += sess.run(\n",
+    "                loaded_acc,\n",
+    "                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})\n",
+    "            test_batch_count += 1\n",
+    "\n",
+    "        print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
+    "\n",
+    "        # Print Random Samples\n",
+    "        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
+    "        random_test_predictions = sess.run(\n",
+    "            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n",
+    "            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n",
+    "        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
+    "\n",
+    "\n",
+    "test_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 为何准确率只有50-80%？\n",
+    "\n",
+    "你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率[远远超过 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130)。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。\n",
+    "\n",
+    "## 提交项目\n",
+    "\n",
+    "提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 \"File\" -> \"Download as\" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From 94dbbadac91cca55e05ebbc7bc13e1c26d8caacf Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 9 Jul 2017 20:28:39 +0800
Subject: [PATCH 08/16] Delete dlnd_image_classification.html

---
 .../dlnd_image_classification.html            | 19507 ----------------
 1 file changed, 19507 deletions(-)
 delete mode 100644 image-classification/dlnd_image_classification.html

diff --git a/image-classification/dlnd_image_classification.html b/image-classification/dlnd_image_classification.html
deleted file mode 100644
index 1f8d9dd..0000000
--- a/image-classification/dlnd_image_classification.html
+++ /dev/null
@@ -1,19507 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head><meta charset="utf-8" />
-<title>dlnd_image_classification</title>
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
-
-<style type="text/css">
-    /*!
-*
-* Twitter Bootstrap
-*
-*/
-/*!
- * Bootstrap v3.3.6 (http://getbootstrap.com)
- * Copyright 2011-2015 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- */
-/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
-html {
-  font-family: sans-serif;
-  -ms-text-size-adjust: 100%;
-  -webkit-text-size-adjust: 100%;
-}
-body {
-  margin: 0;
-}
-article,
-aside,
-details,
-figcaption,
-figure,
-footer,
-header,
-hgroup,
-main,
-menu,
-nav,
-section,
-summary {
-  display: block;
-}
-audio,
-canvas,
-progress,
-video {
-  display: inline-block;
-  vertical-align: baseline;
-}
-audio:not([controls]) {
-  display: none;
-  height: 0;
-}
-[hidden],
-template {
-  display: none;
-}
-a {
-  background-color: transparent;
-}
-a:active,
-a:hover {
-  outline: 0;
-}
-abbr[title] {
-  border-bottom: 1px dotted;
-}
-b,
-strong {
-  font-weight: bold;
-}
-dfn {
-  font-style: italic;
-}
-h1 {
-  font-size: 2em;
-  margin: 0.67em 0;
-}
-mark {
-  background: #ff0;
-  color: #000;
-}
-small {
-  font-size: 80%;
-}
-sub,
-sup {
-  font-size: 75%;
-  line-height: 0;
-  position: relative;
-  vertical-align: baseline;
-}
-sup {
-  top: -0.5em;
-}
-sub {
-  bottom: -0.25em;
-}
-img {
-  border: 0;
-}
-svg:not(:root) {
-  overflow: hidden;
-}
-figure {
-  margin: 1em 40px;
-}
-hr {
-  box-sizing: content-box;
-  height: 0;
-}
-pre {
-  overflow: auto;
-}
-code,
-kbd,
-pre,
-samp {
-  font-family: monospace, monospace;
-  font-size: 1em;
-}
-button,
-input,
-optgroup,
-select,
-textarea {
-  color: inherit;
-  font: inherit;
-  margin: 0;
-}
-button {
-  overflow: visible;
-}
-button,
-select {
-  text-transform: none;
-}
-button,
-html input[type="button"],
-input[type="reset"],
-input[type="submit"] {
-  -webkit-appearance: button;
-  cursor: pointer;
-}
-button[disabled],
-html input[disabled] {
-  cursor: default;
-}
-button::-moz-focus-inner,
-input::-moz-focus-inner {
-  border: 0;
-  padding: 0;
-}
-input {
-  line-height: normal;
-}
-input[type="checkbox"],
-input[type="radio"] {
-  box-sizing: border-box;
-  padding: 0;
-}
-input[type="number"]::-webkit-inner-spin-button,
-input[type="number"]::-webkit-outer-spin-button {
-  height: auto;
-}
-input[type="search"] {
-  -webkit-appearance: textfield;
-  box-sizing: content-box;
-}
-input[type="search"]::-webkit-search-cancel-button,
-input[type="search"]::-webkit-search-decoration {
-  -webkit-appearance: none;
-}
-fieldset {
-  border: 1px solid #c0c0c0;
-  margin: 0 2px;
-  padding: 0.35em 0.625em 0.75em;
-}
-legend {
-  border: 0;
-  padding: 0;
-}
-textarea {
-  overflow: auto;
-}
-optgroup {
-  font-weight: bold;
-}
-table {
-  border-collapse: collapse;
-  border-spacing: 0;
-}
-td,
-th {
-  padding: 0;
-}
-/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
-@media print {
-  *,
-  *:before,
-  *:after {
-    background: transparent !important;
-    color: #000 !important;
-    box-shadow: none !important;
-    text-shadow: none !important;
-  }
-  a,
-  a:visited {
-    text-decoration: underline;
-  }
-  a[href]:after {
-    content: " (" attr(href) ")";
-  }
-  abbr[title]:after {
-    content: " (" attr(title) ")";
-  }
-  a[href^="#"]:after,
-  a[href^="javascript:"]:after {
-    content: "";
-  }
-  pre,
-  blockquote {
-    border: 1px solid #999;
-    page-break-inside: avoid;
-  }
-  thead {
-    display: table-header-group;
-  }
-  tr,
-  img {
-    page-break-inside: avoid;
-  }
-  img {
-    max-width: 100% !important;
-  }
-  p,
-  h2,
-  h3 {
-    orphans: 3;
-    widows: 3;
-  }
-  h2,
-  h3 {
-    page-break-after: avoid;
-  }
-  .navbar {
-    display: none;
-  }
-  .btn > .caret,
-  .dropup > .btn > .caret {
-    border-top-color: #000 !important;
-  }
-  .label {
-    border: 1px solid #000;
-  }
-  .table {
-    border-collapse: collapse !important;
-  }
-  .table td,
-  .table th {
-    background-color: #fff !important;
-  }
-  .table-bordered th,
-  .table-bordered td {
-    border: 1px solid #ddd !important;
-  }
-}
-@font-face {
-  font-family: 'Glyphicons Halflings';
-  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
-  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
-}
-.glyphicon {
-  position: relative;
-  top: 1px;
-  display: inline-block;
-  font-family: 'Glyphicons Halflings';
-  font-style: normal;
-  font-weight: normal;
-  line-height: 1;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-.glyphicon-asterisk:before {
-  content: "\002a";
-}
-.glyphicon-plus:before {
-  content: "\002b";
-}
-.glyphicon-euro:before,
-.glyphicon-eur:before {
-  content: "\20ac";
-}
-.glyphicon-minus:before {
-  content: "\2212";
-}
-.glyphicon-cloud:before {
-  content: "\2601";
-}
-.glyphicon-envelope:before {
-  content: "\2709";
-}
-.glyphicon-pencil:before {
-  content: "\270f";
-}
-.glyphicon-glass:before {
-  content: "\e001";
-}
-.glyphicon-music:before {
-  content: "\e002";
-}
-.glyphicon-search:before {
-  content: "\e003";
-}
-.glyphicon-heart:before {
-  content: "\e005";
-}
-.glyphicon-star:before {
-  content: "\e006";
-}
-.glyphicon-star-empty:before {
-  content: "\e007";
-}
-.glyphicon-user:before {
-  content: "\e008";
-}
-.glyphicon-film:before {
-  content: "\e009";
-}
-.glyphicon-th-large:before {
-  content: "\e010";
-}
-.glyphicon-th:before {
-  content: "\e011";
-}
-.glyphicon-th-list:before {
-  content: "\e012";
-}
-.glyphicon-ok:before {
-  content: "\e013";
-}
-.glyphicon-remove:before {
-  content: "\e014";
-}
-.glyphicon-zoom-in:before {
-  content: "\e015";
-}
-.glyphicon-zoom-out:before {
-  content: "\e016";
-}
-.glyphicon-off:before {
-  content: "\e017";
-}
-.glyphicon-signal:before {
-  content: "\e018";
-}
-.glyphicon-cog:before {
-  content: "\e019";
-}
-.glyphicon-trash:before {
-  content: "\e020";
-}
-.glyphicon-home:before {
-  content: "\e021";
-}
-.glyphicon-file:before {
-  content: "\e022";
-}
-.glyphicon-time:before {
-  content: "\e023";
-}
-.glyphicon-road:before {
-  content: "\e024";
-}
-.glyphicon-download-alt:before {
-  content: "\e025";
-}
-.glyphicon-download:before {
-  content: "\e026";
-}
-.glyphicon-upload:before {
-  content: "\e027";
-}
-.glyphicon-inbox:before {
-  content: "\e028";
-}
-.glyphicon-play-circle:before {
-  content: "\e029";
-}
-.glyphicon-repeat:before {
-  content: "\e030";
-}
-.glyphicon-refresh:before {
-  content: "\e031";
-}
-.glyphicon-list-alt:before {
-  content: "\e032";
-}
-.glyphicon-lock:before {
-  content: "\e033";
-}
-.glyphicon-flag:before {
-  content: "\e034";
-}
-.glyphicon-headphones:before {
-  content: "\e035";
-}
-.glyphicon-volume-off:before {
-  content: "\e036";
-}
-.glyphicon-volume-down:before {
-  content: "\e037";
-}
-.glyphicon-volume-up:before {
-  content: "\e038";
-}
-.glyphicon-qrcode:before {
-  content: "\e039";
-}
-.glyphicon-barcode:before {
-  content: "\e040";
-}
-.glyphicon-tag:before {
-  content: "\e041";
-}
-.glyphicon-tags:before {
-  content: "\e042";
-}
-.glyphicon-book:before {
-  content: "\e043";
-}
-.glyphicon-bookmark:before {
-  content: "\e044";
-}
-.glyphicon-print:before {
-  content: "\e045";
-}
-.glyphicon-camera:before {
-  content: "\e046";
-}
-.glyphicon-font:before {
-  content: "\e047";
-}
-.glyphicon-bold:before {
-  content: "\e048";
-}
-.glyphicon-italic:before {
-  content: "\e049";
-}
-.glyphicon-text-height:before {
-  content: "\e050";
-}
-.glyphicon-text-width:before {
-  content: "\e051";
-}
-.glyphicon-align-left:before {
-  content: "\e052";
-}
-.glyphicon-align-center:before {
-  content: "\e053";
-}
-.glyphicon-align-right:before {
-  content: "\e054";
-}
-.glyphicon-align-justify:before {
-  content: "\e055";
-}
-.glyphicon-list:before {
-  content: "\e056";
-}
-.glyphicon-indent-left:before {
-  content: "\e057";
-}
-.glyphicon-indent-right:before {
-  content: "\e058";
-}
-.glyphicon-facetime-video:before {
-  content: "\e059";
-}
-.glyphicon-picture:before {
-  content: "\e060";
-}
-.glyphicon-map-marker:before {
-  content: "\e062";
-}
-.glyphicon-adjust:before {
-  content: "\e063";
-}
-.glyphicon-tint:before {
-  content: "\e064";
-}
-.glyphicon-edit:before {
-  content: "\e065";
-}
-.glyphicon-share:before {
-  content: "\e066";
-}
-.glyphicon-check:before {
-  content: "\e067";
-}
-.glyphicon-move:before {
-  content: "\e068";
-}
-.glyphicon-step-backward:before {
-  content: "\e069";
-}
-.glyphicon-fast-backward:before {
-  content: "\e070";
-}
-.glyphicon-backward:before {
-  content: "\e071";
-}
-.glyphicon-play:before {
-  content: "\e072";
-}
-.glyphicon-pause:before {
-  content: "\e073";
-}
-.glyphicon-stop:before {
-  content: "\e074";
-}
-.glyphicon-forward:before {
-  content: "\e075";
-}
-.glyphicon-fast-forward:before {
-  content: "\e076";
-}
-.glyphicon-step-forward:before {
-  content: "\e077";
-}
-.glyphicon-eject:before {
-  content: "\e078";
-}
-.glyphicon-chevron-left:before {
-  content: "\e079";
-}
-.glyphicon-chevron-right:before {
-  content: "\e080";
-}
-.glyphicon-plus-sign:before {
-  content: "\e081";
-}
-.glyphicon-minus-sign:before {
-  content: "\e082";
-}
-.glyphicon-remove-sign:before {
-  content: "\e083";
-}
-.glyphicon-ok-sign:before {
-  content: "\e084";
-}
-.glyphicon-question-sign:before {
-  content: "\e085";
-}
-.glyphicon-info-sign:before {
-  content: "\e086";
-}
-.glyphicon-screenshot:before {
-  content: "\e087";
-}
-.glyphicon-remove-circle:before {
-  content: "\e088";
-}
-.glyphicon-ok-circle:before {
-  content: "\e089";
-}
-.glyphicon-ban-circle:before {
-  content: "\e090";
-}
-.glyphicon-arrow-left:before {
-  content: "\e091";
-}
-.glyphicon-arrow-right:before {
-  content: "\e092";
-}
-.glyphicon-arrow-up:before {
-  content: "\e093";
-}
-.glyphicon-arrow-down:before {
-  content: "\e094";
-}
-.glyphicon-share-alt:before {
-  content: "\e095";
-}
-.glyphicon-resize-full:before {
-  content: "\e096";
-}
-.glyphicon-resize-small:before {
-  content: "\e097";
-}
-.glyphicon-exclamation-sign:before {
-  content: "\e101";
-}
-.glyphicon-gift:before {
-  content: "\e102";
-}
-.glyphicon-leaf:before {
-  content: "\e103";
-}
-.glyphicon-fire:before {
-  content: "\e104";
-}
-.glyphicon-eye-open:before {
-  content: "\e105";
-}
-.glyphicon-eye-close:before {
-  content: "\e106";
-}
-.glyphicon-warning-sign:before {
-  content: "\e107";
-}
-.glyphicon-plane:before {
-  content: "\e108";
-}
-.glyphicon-calendar:before {
-  content: "\e109";
-}
-.glyphicon-random:before {
-  content: "\e110";
-}
-.glyphicon-comment:before {
-  content: "\e111";
-}
-.glyphicon-magnet:before {
-  content: "\e112";
-}
-.glyphicon-chevron-up:before {
-  content: "\e113";
-}
-.glyphicon-chevron-down:before {
-  content: "\e114";
-}
-.glyphicon-retweet:before {
-  content: "\e115";
-}
-.glyphicon-shopping-cart:before {
-  content: "\e116";
-}
-.glyphicon-folder-close:before {
-  content: "\e117";
-}
-.glyphicon-folder-open:before {
-  content: "\e118";
-}
-.glyphicon-resize-vertical:before {
-  content: "\e119";
-}
-.glyphicon-resize-horizontal:before {
-  content: "\e120";
-}
-.glyphicon-hdd:before {
-  content: "\e121";
-}
-.glyphicon-bullhorn:before {
-  content: "\e122";
-}
-.glyphicon-bell:before {
-  content: "\e123";
-}
-.glyphicon-certificate:before {
-  content: "\e124";
-}
-.glyphicon-thumbs-up:before {
-  content: "\e125";
-}
-.glyphicon-thumbs-down:before {
-  content: "\e126";
-}
-.glyphicon-hand-right:before {
-  content: "\e127";
-}
-.glyphicon-hand-left:before {
-  content: "\e128";
-}
-.glyphicon-hand-up:before {
-  content: "\e129";
-}
-.glyphicon-hand-down:before {
-  content: "\e130";
-}
-.glyphicon-circle-arrow-right:before {
-  content: "\e131";
-}
-.glyphicon-circle-arrow-left:before {
-  content: "\e132";
-}
-.glyphicon-circle-arrow-up:before {
-  content: "\e133";
-}
-.glyphicon-circle-arrow-down:before {
-  content: "\e134";
-}
-.glyphicon-globe:before {
-  content: "\e135";
-}
-.glyphicon-wrench:before {
-  content: "\e136";
-}
-.glyphicon-tasks:before {
-  content: "\e137";
-}
-.glyphicon-filter:before {
-  content: "\e138";
-}
-.glyphicon-briefcase:before {
-  content: "\e139";
-}
-.glyphicon-fullscreen:before {
-  content: "\e140";
-}
-.glyphicon-dashboard:before {
-  content: "\e141";
-}
-.glyphicon-paperclip:before {
-  content: "\e142";
-}
-.glyphicon-heart-empty:before {
-  content: "\e143";
-}
-.glyphicon-link:before {
-  content: "\e144";
-}
-.glyphicon-phone:before {
-  content: "\e145";
-}
-.glyphicon-pushpin:before {
-  content: "\e146";
-}
-.glyphicon-usd:before {
-  content: "\e148";
-}
-.glyphicon-gbp:before {
-  content: "\e149";
-}
-.glyphicon-sort:before {
-  content: "\e150";
-}
-.glyphicon-sort-by-alphabet:before {
-  content: "\e151";
-}
-.glyphicon-sort-by-alphabet-alt:before {
-  content: "\e152";
-}
-.glyphicon-sort-by-order:before {
-  content: "\e153";
-}
-.glyphicon-sort-by-order-alt:before {
-  content: "\e154";
-}
-.glyphicon-sort-by-attributes:before {
-  content: "\e155";
-}
-.glyphicon-sort-by-attributes-alt:before {
-  content: "\e156";
-}
-.glyphicon-unchecked:before {
-  content: "\e157";
-}
-.glyphicon-expand:before {
-  content: "\e158";
-}
-.glyphicon-collapse-down:before {
-  content: "\e159";
-}
-.glyphicon-collapse-up:before {
-  content: "\e160";
-}
-.glyphicon-log-in:before {
-  content: "\e161";
-}
-.glyphicon-flash:before {
-  content: "\e162";
-}
-.glyphicon-log-out:before {
-  content: "\e163";
-}
-.glyphicon-new-window:before {
-  content: "\e164";
-}
-.glyphicon-record:before {
-  content: "\e165";
-}
-.glyphicon-save:before {
-  content: "\e166";
-}
-.glyphicon-open:before {
-  content: "\e167";
-}
-.glyphicon-saved:before {
-  content: "\e168";
-}
-.glyphicon-import:before {
-  content: "\e169";
-}
-.glyphicon-export:before {
-  content: "\e170";
-}
-.glyphicon-send:before {
-  content: "\e171";
-}
-.glyphicon-floppy-disk:before {
-  content: "\e172";
-}
-.glyphicon-floppy-saved:before {
-  content: "\e173";
-}
-.glyphicon-floppy-remove:before {
-  content: "\e174";
-}
-.glyphicon-floppy-save:before {
-  content: "\e175";
-}
-.glyphicon-floppy-open:before {
-  content: "\e176";
-}
-.glyphicon-credit-card:before {
-  content: "\e177";
-}
-.glyphicon-transfer:before {
-  content: "\e178";
-}
-.glyphicon-cutlery:before {
-  content: "\e179";
-}
-.glyphicon-header:before {
-  content: "\e180";
-}
-.glyphicon-compressed:before {
-  content: "\e181";
-}
-.glyphicon-earphone:before {
-  content: "\e182";
-}
-.glyphicon-phone-alt:before {
-  content: "\e183";
-}
-.glyphicon-tower:before {
-  content: "\e184";
-}
-.glyphicon-stats:before {
-  content: "\e185";
-}
-.glyphicon-sd-video:before {
-  content: "\e186";
-}
-.glyphicon-hd-video:before {
-  content: "\e187";
-}
-.glyphicon-subtitles:before {
-  content: "\e188";
-}
-.glyphicon-sound-stereo:before {
-  content: "\e189";
-}
-.glyphicon-sound-dolby:before {
-  content: "\e190";
-}
-.glyphicon-sound-5-1:before {
-  content: "\e191";
-}
-.glyphicon-sound-6-1:before {
-  content: "\e192";
-}
-.glyphicon-sound-7-1:before {
-  content: "\e193";
-}
-.glyphicon-copyright-mark:before {
-  content: "\e194";
-}
-.glyphicon-registration-mark:before {
-  content: "\e195";
-}
-.glyphicon-cloud-download:before {
-  content: "\e197";
-}
-.glyphicon-cloud-upload:before {
-  content: "\e198";
-}
-.glyphicon-tree-conifer:before {
-  content: "\e199";
-}
-.glyphicon-tree-deciduous:before {
-  content: "\e200";
-}
-.glyphicon-cd:before {
-  content: "\e201";
-}
-.glyphicon-save-file:before {
-  content: "\e202";
-}
-.glyphicon-open-file:before {
-  content: "\e203";
-}
-.glyphicon-level-up:before {
-  content: "\e204";
-}
-.glyphicon-copy:before {
-  content: "\e205";
-}
-.glyphicon-paste:before {
-  content: "\e206";
-}
-.glyphicon-alert:before {
-  content: "\e209";
-}
-.glyphicon-equalizer:before {
-  content: "\e210";
-}
-.glyphicon-king:before {
-  content: "\e211";
-}
-.glyphicon-queen:before {
-  content: "\e212";
-}
-.glyphicon-pawn:before {
-  content: "\e213";
-}
-.glyphicon-bishop:before {
-  content: "\e214";
-}
-.glyphicon-knight:before {
-  content: "\e215";
-}
-.glyphicon-baby-formula:before {
-  content: "\e216";
-}
-.glyphicon-tent:before {
-  content: "\26fa";
-}
-.glyphicon-blackboard:before {
-  content: "\e218";
-}
-.glyphicon-bed:before {
-  content: "\e219";
-}
-.glyphicon-apple:before {
-  content: "\f8ff";
-}
-.glyphicon-erase:before {
-  content: "\e221";
-}
-.glyphicon-hourglass:before {
-  content: "\231b";
-}
-.glyphicon-lamp:before {
-  content: "\e223";
-}
-.glyphicon-duplicate:before {
-  content: "\e224";
-}
-.glyphicon-piggy-bank:before {
-  content: "\e225";
-}
-.glyphicon-scissors:before {
-  content: "\e226";
-}
-.glyphicon-bitcoin:before {
-  content: "\e227";
-}
-.glyphicon-btc:before {
-  content: "\e227";
-}
-.glyphicon-xbt:before {
-  content: "\e227";
-}
-.glyphicon-yen:before {
-  content: "\00a5";
-}
-.glyphicon-jpy:before {
-  content: "\00a5";
-}
-.glyphicon-ruble:before {
-  content: "\20bd";
-}
-.glyphicon-rub:before {
-  content: "\20bd";
-}
-.glyphicon-scale:before {
-  content: "\e230";
-}
-.glyphicon-ice-lolly:before {
-  content: "\e231";
-}
-.glyphicon-ice-lolly-tasted:before {
-  content: "\e232";
-}
-.glyphicon-education:before {
-  content: "\e233";
-}
-.glyphicon-option-horizontal:before {
-  content: "\e234";
-}
-.glyphicon-option-vertical:before {
-  content: "\e235";
-}
-.glyphicon-menu-hamburger:before {
-  content: "\e236";
-}
-.glyphicon-modal-window:before {
-  content: "\e237";
-}
-.glyphicon-oil:before {
-  content: "\e238";
-}
-.glyphicon-grain:before {
-  content: "\e239";
-}
-.glyphicon-sunglasses:before {
-  content: "\e240";
-}
-.glyphicon-text-size:before {
-  content: "\e241";
-}
-.glyphicon-text-color:before {
-  content: "\e242";
-}
-.glyphicon-text-background:before {
-  content: "\e243";
-}
-.glyphicon-object-align-top:before {
-  content: "\e244";
-}
-.glyphicon-object-align-bottom:before {
-  content: "\e245";
-}
-.glyphicon-object-align-horizontal:before {
-  content: "\e246";
-}
-.glyphicon-object-align-left:before {
-  content: "\e247";
-}
-.glyphicon-object-align-vertical:before {
-  content: "\e248";
-}
-.glyphicon-object-align-right:before {
-  content: "\e249";
-}
-.glyphicon-triangle-right:before {
-  content: "\e250";
-}
-.glyphicon-triangle-left:before {
-  content: "\e251";
-}
-.glyphicon-triangle-bottom:before {
-  content: "\e252";
-}
-.glyphicon-triangle-top:before {
-  content: "\e253";
-}
-.glyphicon-console:before {
-  content: "\e254";
-}
-.glyphicon-superscript:before {
-  content: "\e255";
-}
-.glyphicon-subscript:before {
-  content: "\e256";
-}
-.glyphicon-menu-left:before {
-  content: "\e257";
-}
-.glyphicon-menu-right:before {
-  content: "\e258";
-}
-.glyphicon-menu-down:before {
-  content: "\e259";
-}
-.glyphicon-menu-up:before {
-  content: "\e260";
-}
-* {
-  -webkit-box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  box-sizing: border-box;
-}
-*:before,
-*:after {
-  -webkit-box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  box-sizing: border-box;
-}
-html {
-  font-size: 10px;
-  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
-}
-body {
-  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
-  font-size: 13px;
-  line-height: 1.42857143;
-  color: #000;
-  background-color: #fff;
-}
-input,
-button,
-select,
-textarea {
-  font-family: inherit;
-  font-size: inherit;
-  line-height: inherit;
-}
-a {
-  color: #337ab7;
-  text-decoration: none;
-}
-a:hover,
-a:focus {
-  color: #23527c;
-  text-decoration: underline;
-}
-a:focus {
-  outline: thin dotted;
-  outline: 5px auto -webkit-focus-ring-color;
-  outline-offset: -2px;
-}
-figure {
-  margin: 0;
-}
-img {
-  vertical-align: middle;
-}
-.img-responsive,
-.thumbnail > img,
-.thumbnail a > img,
-.carousel-inner > .item > img,
-.carousel-inner > .item > a > img {
-  display: block;
-  max-width: 100%;
-  height: auto;
-}
-.img-rounded {
-  border-radius: 3px;
-}
-.img-thumbnail {
-  padding: 4px;
-  line-height: 1.42857143;
-  background-color: #fff;
-  border: 1px solid #ddd;
-  border-radius: 2px;
-  -webkit-transition: all 0.2s ease-in-out;
-  -o-transition: all 0.2s ease-in-out;
-  transition: all 0.2s ease-in-out;
-  display: inline-block;
-  max-width: 100%;
-  height: auto;
-}
-.img-circle {
-  border-radius: 50%;
-}
-hr {
-  margin-top: 18px;
-  margin-bottom: 18px;
-  border: 0;
-  border-top: 1px solid #eeeeee;
-}
-.sr-only {
-  position: absolute;
-  width: 1px;
-  height: 1px;
-  margin: -1px;
-  padding: 0;
-  overflow: hidden;
-  clip: rect(0, 0, 0, 0);
-  border: 0;
-}
-.sr-only-focusable:active,
-.sr-only-focusable:focus {
-  position: static;
-  width: auto;
-  height: auto;
-  margin: 0;
-  overflow: visible;
-  clip: auto;
-}
-[role="button"] {
-  cursor: pointer;
-}
-h1,
-h2,
-h3,
-h4,
-h5,
-h6,
-.h1,
-.h2,
-.h3,
-.h4,
-.h5,
-.h6 {
-  font-family: inherit;
-  font-weight: 500;
-  line-height: 1.1;
-  color: inherit;
-}
-h1 small,
-h2 small,
-h3 small,
-h4 small,
-h5 small,
-h6 small,
-.h1 small,
-.h2 small,
-.h3 small,
-.h4 small,
-.h5 small,
-.h6 small,
-h1 .small,
-h2 .small,
-h3 .small,
-h4 .small,
-h5 .small,
-h6 .small,
-.h1 .small,
-.h2 .small,
-.h3 .small,
-.h4 .small,
-.h5 .small,
-.h6 .small {
-  font-weight: normal;
-  line-height: 1;
-  color: #777777;
-}
-h1,
-.h1,
-h2,
-.h2,
-h3,
-.h3 {
-  margin-top: 18px;
-  margin-bottom: 9px;
-}
-h1 small,
-.h1 small,
-h2 small,
-.h2 small,
-h3 small,
-.h3 small,
-h1 .small,
-.h1 .small,
-h2 .small,
-.h2 .small,
-h3 .small,
-.h3 .small {
-  font-size: 65%;
-}
-h4,
-.h4,
-h5,
-.h5,
-h6,
-.h6 {
-  margin-top: 9px;
-  margin-bottom: 9px;
-}
-h4 small,
-.h4 small,
-h5 small,
-.h5 small,
-h6 small,
-.h6 small,
-h4 .small,
-.h4 .small,
-h5 .small,
-.h5 .small,
-h6 .small,
-.h6 .small {
-  font-size: 75%;
-}
-h1,
-.h1 {
-  font-size: 33px;
-}
-h2,
-.h2 {
-  font-size: 27px;
-}
-h3,
-.h3 {
-  font-size: 23px;
-}
-h4,
-.h4 {
-  font-size: 17px;
-}
-h5,
-.h5 {
-  font-size: 13px;
-}
-h6,
-.h6 {
-  font-size: 12px;
-}
-p {
-  margin: 0 0 9px;
-}
-.lead {
-  margin-bottom: 18px;
-  font-size: 14px;
-  font-weight: 300;
-  line-height: 1.4;
-}
-@media (min-width: 768px) {
-  .lead {
-    font-size: 19.5px;
-  }
-}
-small,
-.small {
-  font-size: 92%;
-}
-mark,
-.mark {
-  background-color: #fcf8e3;
-  padding: .2em;
-}
-.text-left {
-  text-align: left;
-}
-.text-right {
-  text-align: right;
-}
-.text-center {
-  text-align: center;
-}
-.text-justify {
-  text-align: justify;
-}
-.text-nowrap {
-  white-space: nowrap;
-}
-.text-lowercase {
-  text-transform: lowercase;
-}
-.text-uppercase {
-  text-transform: uppercase;
-}
-.text-capitalize {
-  text-transform: capitalize;
-}
-.text-muted {
-  color: #777777;
-}
-.text-primary {
-  color: #337ab7;
-}
-a.text-primary:hover,
-a.text-primary:focus {
-  color: #286090;
-}
-.text-success {
-  color: #3c763d;
-}
-a.text-success:hover,
-a.text-success:focus {
-  color: #2b542c;
-}
-.text-info {
-  color: #31708f;
-}
-a.text-info:hover,
-a.text-info:focus {
-  color: #245269;
-}
-.text-warning {
-  color: #8a6d3b;
-}
-a.text-warning:hover,
-a.text-warning:focus {
-  color: #66512c;
-}
-.text-danger {
-  color: #a94442;
-}
-a.text-danger:hover,
-a.text-danger:focus {
-  color: #843534;
-}
-.bg-primary {
-  color: #fff;
-  background-color: #337ab7;
-}
-a.bg-primary:hover,
-a.bg-primary:focus {
-  background-color: #286090;
-}
-.bg-success {
-  background-color: #dff0d8;
-}
-a.bg-success:hover,
-a.bg-success:focus {
-  background-color: #c1e2b3;
-}
-.bg-info {
-  background-color: #d9edf7;
-}
-a.bg-info:hover,
-a.bg-info:focus {
-  background-color: #afd9ee;
-}
-.bg-warning {
-  background-color: #fcf8e3;
-}
-a.bg-warning:hover,
-a.bg-warning:focus {
-  background-color: #f7ecb5;
-}
-.bg-danger {
-  background-color: #f2dede;
-}
-a.bg-danger:hover,
-a.bg-danger:focus {
-  background-color: #e4b9b9;
-}
-.page-header {
-  padding-bottom: 8px;
-  margin: 36px 0 18px;
-  border-bottom: 1px solid #eeeeee;
-}
-ul,
-ol {
-  margin-top: 0;
-  margin-bottom: 9px;
-}
-ul ul,
-ol ul,
-ul ol,
-ol ol {
-  margin-bottom: 0;
-}
-.list-unstyled {
-  padding-left: 0;
-  list-style: none;
-}
-.list-inline {
-  padding-left: 0;
-  list-style: none;
-  margin-left: -5px;
-}
-.list-inline > li {
-  display: inline-block;
-  padding-left: 5px;
-  padding-right: 5px;
-}
-dl {
-  margin-top: 0;
-  margin-bottom: 18px;
-}
-dt,
-dd {
-  line-height: 1.42857143;
-}
-dt {
-  font-weight: bold;
-}
-dd {
-  margin-left: 0;
-}
-@media (min-width: 541px) {
-  .dl-horizontal dt {
-    float: left;
-    width: 160px;
-    clear: left;
-    text-align: right;
-    overflow: hidden;
-    text-overflow: ellipsis;
-    white-space: nowrap;
-  }
-  .dl-horizontal dd {
-    margin-left: 180px;
-  }
-}
-abbr[title],
-abbr[data-original-title] {
-  cursor: help;
-  border-bottom: 1px dotted #777777;
-}
-.initialism {
-  font-size: 90%;
-  text-transform: uppercase;
-}
-blockquote {
-  padding: 9px 18px;
-  margin: 0 0 18px;
-  font-size: inherit;
-  border-left: 5px solid #eeeeee;
-}
-blockquote p:last-child,
-blockquote ul:last-child,
-blockquote ol:last-child {
-  margin-bottom: 0;
-}
-blockquote footer,
-blockquote small,
-blockquote .small {
-  display: block;
-  font-size: 80%;
-  line-height: 1.42857143;
-  color: #777777;
-}
-blockquote footer:before,
-blockquote small:before,
-blockquote .small:before {
-  content: '\2014 \00A0';
-}
-.blockquote-reverse,
-blockquote.pull-right {
-  padding-right: 15px;
-  padding-left: 0;
-  border-right: 5px solid #eeeeee;
-  border-left: 0;
-  text-align: right;
-}
-.blockquote-reverse footer:before,
-blockquote.pull-right footer:before,
-.blockquote-reverse small:before,
-blockquote.pull-right small:before,
-.blockquote-reverse .small:before,
-blockquote.pull-right .small:before {
-  content: '';
-}
-.blockquote-reverse footer:after,
-blockquote.pull-right footer:after,
-.blockquote-reverse small:after,
-blockquote.pull-right small:after,
-.blockquote-reverse .small:after,
-blockquote.pull-right .small:after {
-  content: '\00A0 \2014';
-}
-address {
-  margin-bottom: 18px;
-  font-style: normal;
-  line-height: 1.42857143;
-}
-code,
-kbd,
-pre,
-samp {
-  font-family: monospace;
-}
-code {
-  padding: 2px 4px;
-  font-size: 90%;
-  color: #c7254e;
-  background-color: #f9f2f4;
-  border-radius: 2px;
-}
-kbd {
-  padding: 2px 4px;
-  font-size: 90%;
-  color: #888;
-  background-color: transparent;
-  border-radius: 1px;
-  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
-}
-kbd kbd {
-  padding: 0;
-  font-size: 100%;
-  font-weight: bold;
-  box-shadow: none;
-}
-pre {
-  display: block;
-  padding: 8.5px;
-  margin: 0 0 9px;
-  font-size: 12px;
-  line-height: 1.42857143;
-  word-break: break-all;
-  word-wrap: break-word;
-  color: #333333;
-  background-color: #f5f5f5;
-  border: 1px solid #ccc;
-  border-radius: 2px;
-}
-pre code {
-  padding: 0;
-  font-size: inherit;
-  color: inherit;
-  white-space: pre-wrap;
-  background-color: transparent;
-  border-radius: 0;
-}
-.pre-scrollable {
-  max-height: 340px;
-  overflow-y: scroll;
-}
-.container {
-  margin-right: auto;
-  margin-left: auto;
-  padding-left: 0px;
-  padding-right: 0px;
-}
-@media (min-width: 768px) {
-  .container {
-    width: 768px;
-  }
-}
-@media (min-width: 992px) {
-  .container {
-    width: 940px;
-  }
-}
-@media (min-width: 1200px) {
-  .container {
-    width: 1140px;
-  }
-}
-.container-fluid {
-  margin-right: auto;
-  margin-left: auto;
-  padding-left: 0px;
-  padding-right: 0px;
-}
-.row {
-  margin-left: 0px;
-  margin-right: 0px;
-}
-.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
-  position: relative;
-  min-height: 1px;
-  padding-left: 0px;
-  padding-right: 0px;
-}
-.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
-  float: left;
-}
-.col-xs-12 {
-  width: 100%;
-}
-.col-xs-11 {
-  width: 91.66666667%;
-}
-.col-xs-10 {
-  width: 83.33333333%;
-}
-.col-xs-9 {
-  width: 75%;
-}
-.col-xs-8 {
-  width: 66.66666667%;
-}
-.col-xs-7 {
-  width: 58.33333333%;
-}
-.col-xs-6 {
-  width: 50%;
-}
-.col-xs-5 {
-  width: 41.66666667%;
-}
-.col-xs-4 {
-  width: 33.33333333%;
-}
-.col-xs-3 {
-  width: 25%;
-}
-.col-xs-2 {
-  width: 16.66666667%;
-}
-.col-xs-1 {
-  width: 8.33333333%;
-}
-.col-xs-pull-12 {
-  right: 100%;
-}
-.col-xs-pull-11 {
-  right: 91.66666667%;
-}
-.col-xs-pull-10 {
-  right: 83.33333333%;
-}
-.col-xs-pull-9 {
-  right: 75%;
-}
-.col-xs-pull-8 {
-  right: 66.66666667%;
-}
-.col-xs-pull-7 {
-  right: 58.33333333%;
-}
-.col-xs-pull-6 {
-  right: 50%;
-}
-.col-xs-pull-5 {
-  right: 41.66666667%;
-}
-.col-xs-pull-4 {
-  right: 33.33333333%;
-}
-.col-xs-pull-3 {
-  right: 25%;
-}
-.col-xs-pull-2 {
-  right: 16.66666667%;
-}
-.col-xs-pull-1 {
-  right: 8.33333333%;
-}
-.col-xs-pull-0 {
-  right: auto;
-}
-.col-xs-push-12 {
-  left: 100%;
-}
-.col-xs-push-11 {
-  left: 91.66666667%;
-}
-.col-xs-push-10 {
-  left: 83.33333333%;
-}
-.col-xs-push-9 {
-  left: 75%;
-}
-.col-xs-push-8 {
-  left: 66.66666667%;
-}
-.col-xs-push-7 {
-  left: 58.33333333%;
-}
-.col-xs-push-6 {
-  left: 50%;
-}
-.col-xs-push-5 {
-  left: 41.66666667%;
-}
-.col-xs-push-4 {
-  left: 33.33333333%;
-}
-.col-xs-push-3 {
-  left: 25%;
-}
-.col-xs-push-2 {
-  left: 16.66666667%;
-}
-.col-xs-push-1 {
-  left: 8.33333333%;
-}
-.col-xs-push-0 {
-  left: auto;
-}
-.col-xs-offset-12 {
-  margin-left: 100%;
-}
-.col-xs-offset-11 {
-  margin-left: 91.66666667%;
-}
-.col-xs-offset-10 {
-  margin-left: 83.33333333%;
-}
-.col-xs-offset-9 {
-  margin-left: 75%;
-}
-.col-xs-offset-8 {
-  margin-left: 66.66666667%;
-}
-.col-xs-offset-7 {
-  margin-left: 58.33333333%;
-}
-.col-xs-offset-6 {
-  margin-left: 50%;
-}
-.col-xs-offset-5 {
-  margin-left: 41.66666667%;
-}
-.col-xs-offset-4 {
-  margin-left: 33.33333333%;
-}
-.col-xs-offset-3 {
-  margin-left: 25%;
-}
-.col-xs-offset-2 {
-  margin-left: 16.66666667%;
-}
-.col-xs-offset-1 {
-  margin-left: 8.33333333%;
-}
-.col-xs-offset-0 {
-  margin-left: 0%;
-}
-@media (min-width: 768px) {
-  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
-    float: left;
-  }
-  .col-sm-12 {
-    width: 100%;
-  }
-  .col-sm-11 {
-    width: 91.66666667%;
-  }
-  .col-sm-10 {
-    width: 83.33333333%;
-  }
-  .col-sm-9 {
-    width: 75%;
-  }
-  .col-sm-8 {
-    width: 66.66666667%;
-  }
-  .col-sm-7 {
-    width: 58.33333333%;
-  }
-  .col-sm-6 {
-    width: 50%;
-  }
-  .col-sm-5 {
-    width: 41.66666667%;
-  }
-  .col-sm-4 {
-    width: 33.33333333%;
-  }
-  .col-sm-3 {
-    width: 25%;
-  }
-  .col-sm-2 {
-    width: 16.66666667%;
-  }
-  .col-sm-1 {
-    width: 8.33333333%;
-  }
-  .col-sm-pull-12 {
-    right: 100%;
-  }
-  .col-sm-pull-11 {
-    right: 91.66666667%;
-  }
-  .col-sm-pull-10 {
-    right: 83.33333333%;
-  }
-  .col-sm-pull-9 {
-    right: 75%;
-  }
-  .col-sm-pull-8 {
-    right: 66.66666667%;
-  }
-  .col-sm-pull-7 {
-    right: 58.33333333%;
-  }
-  .col-sm-pull-6 {
-    right: 50%;
-  }
-  .col-sm-pull-5 {
-    right: 41.66666667%;
-  }
-  .col-sm-pull-4 {
-    right: 33.33333333%;
-  }
-  .col-sm-pull-3 {
-    right: 25%;
-  }
-  .col-sm-pull-2 {
-    right: 16.66666667%;
-  }
-  .col-sm-pull-1 {
-    right: 8.33333333%;
-  }
-  .col-sm-pull-0 {
-    right: auto;
-  }
-  .col-sm-push-12 {
-    left: 100%;
-  }
-  .col-sm-push-11 {
-    left: 91.66666667%;
-  }
-  .col-sm-push-10 {
-    left: 83.33333333%;
-  }
-  .col-sm-push-9 {
-    left: 75%;
-  }
-  .col-sm-push-8 {
-    left: 66.66666667%;
-  }
-  .col-sm-push-7 {
-    left: 58.33333333%;
-  }
-  .col-sm-push-6 {
-    left: 50%;
-  }
-  .col-sm-push-5 {
-    left: 41.66666667%;
-  }
-  .col-sm-push-4 {
-    left: 33.33333333%;
-  }
-  .col-sm-push-3 {
-    left: 25%;
-  }
-  .col-sm-push-2 {
-    left: 16.66666667%;
-  }
-  .col-sm-push-1 {
-    left: 8.33333333%;
-  }
-  .col-sm-push-0 {
-    left: auto;
-  }
-  .col-sm-offset-12 {
-    margin-left: 100%;
-  }
-  .col-sm-offset-11 {
-    margin-left: 91.66666667%;
-  }
-  .col-sm-offset-10 {
-    margin-left: 83.33333333%;
-  }
-  .col-sm-offset-9 {
-    margin-left: 75%;
-  }
-  .col-sm-offset-8 {
-    margin-left: 66.66666667%;
-  }
-  .col-sm-offset-7 {
-    margin-left: 58.33333333%;
-  }
-  .col-sm-offset-6 {
-    margin-left: 50%;
-  }
-  .col-sm-offset-5 {
-    margin-left: 41.66666667%;
-  }
-  .col-sm-offset-4 {
-    margin-left: 33.33333333%;
-  }
-  .col-sm-offset-3 {
-    margin-left: 25%;
-  }
-  .col-sm-offset-2 {
-    margin-left: 16.66666667%;
-  }
-  .col-sm-offset-1 {
-    margin-left: 8.33333333%;
-  }
-  .col-sm-offset-0 {
-    margin-left: 0%;
-  }
-}
-@media (min-width: 992px) {
-  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
-    float: left;
-  }
-  .col-md-12 {
-    width: 100%;
-  }
-  .col-md-11 {
-    width: 91.66666667%;
-  }
-  .col-md-10 {
-    width: 83.33333333%;
-  }
-  .col-md-9 {
-    width: 75%;
-  }
-  .col-md-8 {
-    width: 66.66666667%;
-  }
-  .col-md-7 {
-    width: 58.33333333%;
-  }
-  .col-md-6 {
-    width: 50%;
-  }
-  .col-md-5 {
-    width: 41.66666667%;
-  }
-  .col-md-4 {
-    width: 33.33333333%;
-  }
-  .col-md-3 {
-    width: 25%;
-  }
-  .col-md-2 {
-    width: 16.66666667%;
-  }
-  .col-md-1 {
-    width: 8.33333333%;
-  }
-  .col-md-pull-12 {
-    right: 100%;
-  }
-  .col-md-pull-11 {
-    right: 91.66666667%;
-  }
-  .col-md-pull-10 {
-    right: 83.33333333%;
-  }
-  .col-md-pull-9 {
-    right: 75%;
-  }
-  .col-md-pull-8 {
-    right: 66.66666667%;
-  }
-  .col-md-pull-7 {
-    right: 58.33333333%;
-  }
-  .col-md-pull-6 {
-    right: 50%;
-  }
-  .col-md-pull-5 {
-    right: 41.66666667%;
-  }
-  .col-md-pull-4 {
-    right: 33.33333333%;
-  }
-  .col-md-pull-3 {
-    right: 25%;
-  }
-  .col-md-pull-2 {
-    right: 16.66666667%;
-  }
-  .col-md-pull-1 {
-    right: 8.33333333%;
-  }
-  .col-md-pull-0 {
-    right: auto;
-  }
-  .col-md-push-12 {
-    left: 100%;
-  }
-  .col-md-push-11 {
-    left: 91.66666667%;
-  }
-  .col-md-push-10 {
-    left: 83.33333333%;
-  }
-  .col-md-push-9 {
-    left: 75%;
-  }
-  .col-md-push-8 {
-    left: 66.66666667%;
-  }
-  .col-md-push-7 {
-    left: 58.33333333%;
-  }
-  .col-md-push-6 {
-    left: 50%;
-  }
-  .col-md-push-5 {
-    left: 41.66666667%;
-  }
-  .col-md-push-4 {
-    left: 33.33333333%;
-  }
-  .col-md-push-3 {
-    left: 25%;
-  }
-  .col-md-push-2 {
-    left: 16.66666667%;
-  }
-  .col-md-push-1 {
-    left: 8.33333333%;
-  }
-  .col-md-push-0 {
-    left: auto;
-  }
-  .col-md-offset-12 {
-    margin-left: 100%;
-  }
-  .col-md-offset-11 {
-    margin-left: 91.66666667%;
-  }
-  .col-md-offset-10 {
-    margin-left: 83.33333333%;
-  }
-  .col-md-offset-9 {
-    margin-left: 75%;
-  }
-  .col-md-offset-8 {
-    margin-left: 66.66666667%;
-  }
-  .col-md-offset-7 {
-    margin-left: 58.33333333%;
-  }
-  .col-md-offset-6 {
-    margin-left: 50%;
-  }
-  .col-md-offset-5 {
-    margin-left: 41.66666667%;
-  }
-  .col-md-offset-4 {
-    margin-left: 33.33333333%;
-  }
-  .col-md-offset-3 {
-    margin-left: 25%;
-  }
-  .col-md-offset-2 {
-    margin-left: 16.66666667%;
-  }
-  .col-md-offset-1 {
-    margin-left: 8.33333333%;
-  }
-  .col-md-offset-0 {
-    margin-left: 0%;
-  }
-}
-@media (min-width: 1200px) {
-  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
-    float: left;
-  }
-  .col-lg-12 {
-    width: 100%;
-  }
-  .col-lg-11 {
-    width: 91.66666667%;
-  }
-  .col-lg-10 {
-    width: 83.33333333%;
-  }
-  .col-lg-9 {
-    width: 75%;
-  }
-  .col-lg-8 {
-    width: 66.66666667%;
-  }
-  .col-lg-7 {
-    width: 58.33333333%;
-  }
-  .col-lg-6 {
-    width: 50%;
-  }
-  .col-lg-5 {
-    width: 41.66666667%;
-  }
-  .col-lg-4 {
-    width: 33.33333333%;
-  }
-  .col-lg-3 {
-    width: 25%;
-  }
-  .col-lg-2 {
-    width: 16.66666667%;
-  }
-  .col-lg-1 {
-    width: 8.33333333%;
-  }
-  .col-lg-pull-12 {
-    right: 100%;
-  }
-  .col-lg-pull-11 {
-    right: 91.66666667%;
-  }
-  .col-lg-pull-10 {
-    right: 83.33333333%;
-  }
-  .col-lg-pull-9 {
-    right: 75%;
-  }
-  .col-lg-pull-8 {
-    right: 66.66666667%;
-  }
-  .col-lg-pull-7 {
-    right: 58.33333333%;
-  }
-  .col-lg-pull-6 {
-    right: 50%;
-  }
-  .col-lg-pull-5 {
-    right: 41.66666667%;
-  }
-  .col-lg-pull-4 {
-    right: 33.33333333%;
-  }
-  .col-lg-pull-3 {
-    right: 25%;
-  }
-  .col-lg-pull-2 {
-    right: 16.66666667%;
-  }
-  .col-lg-pull-1 {
-    right: 8.33333333%;
-  }
-  .col-lg-pull-0 {
-    right: auto;
-  }
-  .col-lg-push-12 {
-    left: 100%;
-  }
-  .col-lg-push-11 {
-    left: 91.66666667%;
-  }
-  .col-lg-push-10 {
-    left: 83.33333333%;
-  }
-  .col-lg-push-9 {
-    left: 75%;
-  }
-  .col-lg-push-8 {
-    left: 66.66666667%;
-  }
-  .col-lg-push-7 {
-    left: 58.33333333%;
-  }
-  .col-lg-push-6 {
-    left: 50%;
-  }
-  .col-lg-push-5 {
-    left: 41.66666667%;
-  }
-  .col-lg-push-4 {
-    left: 33.33333333%;
-  }
-  .col-lg-push-3 {
-    left: 25%;
-  }
-  .col-lg-push-2 {
-    left: 16.66666667%;
-  }
-  .col-lg-push-1 {
-    left: 8.33333333%;
-  }
-  .col-lg-push-0 {
-    left: auto;
-  }
-  .col-lg-offset-12 {
-    margin-left: 100%;
-  }
-  .col-lg-offset-11 {
-    margin-left: 91.66666667%;
-  }
-  .col-lg-offset-10 {
-    margin-left: 83.33333333%;
-  }
-  .col-lg-offset-9 {
-    margin-left: 75%;
-  }
-  .col-lg-offset-8 {
-    margin-left: 66.66666667%;
-  }
-  .col-lg-offset-7 {
-    margin-left: 58.33333333%;
-  }
-  .col-lg-offset-6 {
-    margin-left: 50%;
-  }
-  .col-lg-offset-5 {
-    margin-left: 41.66666667%;
-  }
-  .col-lg-offset-4 {
-    margin-left: 33.33333333%;
-  }
-  .col-lg-offset-3 {
-    margin-left: 25%;
-  }
-  .col-lg-offset-2 {
-    margin-left: 16.66666667%;
-  }
-  .col-lg-offset-1 {
-    margin-left: 8.33333333%;
-  }
-  .col-lg-offset-0 {
-    margin-left: 0%;
-  }
-}
-table {
-  background-color: transparent;
-}
-caption {
-  padding-top: 8px;
-  padding-bottom: 8px;
-  color: #777777;
-  text-align: left;
-}
-th {
-  text-align: left;
-}
-.table {
-  width: 100%;
-  max-width: 100%;
-  margin-bottom: 18px;
-}
-.table > thead > tr > th,
-.table > tbody > tr > th,
-.table > tfoot > tr > th,
-.table > thead > tr > td,
-.table > tbody > tr > td,
-.table > tfoot > tr > td {
-  padding: 8px;
-  line-height: 1.42857143;
-  vertical-align: top;
-  border-top: 1px solid #ddd;
-}
-.table > thead > tr > th {
-  vertical-align: bottom;
-  border-bottom: 2px solid #ddd;
-}
-.table > caption + thead > tr:first-child > th,
-.table > colgroup + thead > tr:first-child > th,
-.table > thead:first-child > tr:first-child > th,
-.table > caption + thead > tr:first-child > td,
-.table > colgroup + thead > tr:first-child > td,
-.table > thead:first-child > tr:first-child > td {
-  border-top: 0;
-}
-.table > tbody + tbody {
-  border-top: 2px solid #ddd;
-}
-.table .table {
-  background-color: #fff;
-}
-.table-condensed > thead > tr > th,
-.table-condensed > tbody > tr > th,
-.table-condensed > tfoot > tr > th,
-.table-condensed > thead > tr > td,
-.table-condensed > tbody > tr > td,
-.table-condensed > tfoot > tr > td {
-  padding: 5px;
-}
-.table-bordered {
-  border: 1px solid #ddd;
-}
-.table-bordered > thead > tr > th,
-.table-bordered > tbody > tr > th,
-.table-bordered > tfoot > tr > th,
-.table-bordered > thead > tr > td,
-.table-bordered > tbody > tr > td,
-.table-bordered > tfoot > tr > td {
-  border: 1px solid #ddd;
-}
-.table-bordered > thead > tr > th,
-.table-bordered > thead > tr > td {
-  border-bottom-width: 2px;
-}
-.table-striped > tbody > tr:nth-of-type(odd) {
-  background-color: #f9f9f9;
-}
-.table-hover > tbody > tr:hover {
-  background-color: #f5f5f5;
-}
-table col[class*="col-"] {
-  position: static;
-  float: none;
-  display: table-column;
-}
-table td[class*="col-"],
-table th[class*="col-"] {
-  position: static;
-  float: none;
-  display: table-cell;
-}
-.table > thead > tr > td.active,
-.table > tbody > tr > td.active,
-.table > tfoot > tr > td.active,
-.table > thead > tr > th.active,
-.table > tbody > tr > th.active,
-.table > tfoot > tr > th.active,
-.table > thead > tr.active > td,
-.table > tbody > tr.active > td,
-.table > tfoot > tr.active > td,
-.table > thead > tr.active > th,
-.table > tbody > tr.active > th,
-.table > tfoot > tr.active > th {
-  background-color: #f5f5f5;
-}
-.table-hover > tbody > tr > td.active:hover,
-.table-hover > tbody > tr > th.active:hover,
-.table-hover > tbody > tr.active:hover > td,
-.table-hover > tbody > tr:hover > .active,
-.table-hover > tbody > tr.active:hover > th {
-  background-color: #e8e8e8;
-}
-.table > thead > tr > td.success,
-.table > tbody > tr > td.success,
-.table > tfoot > tr > td.success,
-.table > thead > tr > th.success,
-.table > tbody > tr > th.success,
-.table > tfoot > tr > th.success,
-.table > thead > tr.success > td,
-.table > tbody > tr.success > td,
-.table > tfoot > tr.success > td,
-.table > thead > tr.success > th,
-.table > tbody > tr.success > th,
-.table > tfoot > tr.success > th {
-  background-color: #dff0d8;
-}
-.table-hover > tbody > tr > td.success:hover,
-.table-hover > tbody > tr > th.success:hover,
-.table-hover > tbody > tr.success:hover > td,
-.table-hover > tbody > tr:hover > .success,
-.table-hover > tbody > tr.success:hover > th {
-  background-color: #d0e9c6;
-}
-.table > thead > tr > td.info,
-.table > tbody > tr > td.info,
-.table > tfoot > tr > td.info,
-.table > thead > tr > th.info,
-.table > tbody > tr > th.info,
-.table > tfoot > tr > th.info,
-.table > thead > tr.info > td,
-.table > tbody > tr.info > td,
-.table > tfoot > tr.info > td,
-.table > thead > tr.info > th,
-.table > tbody > tr.info > th,
-.table > tfoot > tr.info > th {
-  background-color: #d9edf7;
-}
-.table-hover > tbody > tr > td.info:hover,
-.table-hover > tbody > tr > th.info:hover,
-.table-hover > tbody > tr.info:hover > td,
-.table-hover > tbody > tr:hover > .info,
-.table-hover > tbody > tr.info:hover > th {
-  background-color: #c4e3f3;
-}
-.table > thead > tr > td.warning,
-.table > tbody > tr > td.warning,
-.table > tfoot > tr > td.warning,
-.table > thead > tr > th.warning,
-.table > tbody > tr > th.warning,
-.table > tfoot > tr > th.warning,
-.table > thead > tr.warning > td,
-.table > tbody > tr.warning > td,
-.table > tfoot > tr.warning > td,
-.table > thead > tr.warning > th,
-.table > tbody > tr.warning > th,
-.table > tfoot > tr.warning > th {
-  background-color: #fcf8e3;
-}
-.table-hover > tbody > tr > td.warning:hover,
-.table-hover > tbody > tr > th.warning:hover,
-.table-hover > tbody > tr.warning:hover > td,
-.table-hover > tbody > tr:hover > .warning,
-.table-hover > tbody > tr.warning:hover > th {
-  background-color: #faf2cc;
-}
-.table > thead > tr > td.danger,
-.table > tbody > tr > td.danger,
-.table > tfoot > tr > td.danger,
-.table > thead > tr > th.danger,
-.table > tbody > tr > th.danger,
-.table > tfoot > tr > th.danger,
-.table > thead > tr.danger > td,
-.table > tbody > tr.danger > td,
-.table > tfoot > tr.danger > td,
-.table > thead > tr.danger > th,
-.table > tbody > tr.danger > th,
-.table > tfoot > tr.danger > th {
-  background-color: #f2dede;
-}
-.table-hover > tbody > tr > td.danger:hover,
-.table-hover > tbody > tr > th.danger:hover,
-.table-hover > tbody > tr.danger:hover > td,
-.table-hover > tbody > tr:hover > .danger,
-.table-hover > tbody > tr.danger:hover > th {
-  background-color: #ebcccc;
-}
-.table-responsive {
-  overflow-x: auto;
-  min-height: 0.01%;
-}
-@media screen and (max-width: 767px) {
-  .table-responsive {
-    width: 100%;
-    margin-bottom: 13.5px;
-    overflow-y: hidden;
-    -ms-overflow-style: -ms-autohiding-scrollbar;
-    border: 1px solid #ddd;
-  }
-  .table-responsive > .table {
-    margin-bottom: 0;
-  }
-  .table-responsive > .table > thead > tr > th,
-  .table-responsive > .table > tbody > tr > th,
-  .table-responsive > .table > tfoot > tr > th,
-  .table-responsive > .table > thead > tr > td,
-  .table-responsive > .table > tbody > tr > td,
-  .table-responsive > .table > tfoot > tr > td {
-    white-space: nowrap;
-  }
-  .table-responsive > .table-bordered {
-    border: 0;
-  }
-  .table-responsive > .table-bordered > thead > tr > th:first-child,
-  .table-responsive > .table-bordered > tbody > tr > th:first-child,
-  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
-  .table-responsive > .table-bordered > thead > tr > td:first-child,
-  .table-responsive > .table-bordered > tbody > tr > td:first-child,
-  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
-    border-left: 0;
-  }
-  .table-responsive > .table-bordered > thead > tr > th:last-child,
-  .table-responsive > .table-bordered > tbody > tr > th:last-child,
-  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
-  .table-responsive > .table-bordered > thead > tr > td:last-child,
-  .table-responsive > .table-bordered > tbody > tr > td:last-child,
-  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
-    border-right: 0;
-  }
-  .table-responsive > .table-bordered > tbody > tr:last-child > th,
-  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
-  .table-responsive > .table-bordered > tbody > tr:last-child > td,
-  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
-    border-bottom: 0;
-  }
-}
-fieldset {
-  padding: 0;
-  margin: 0;
-  border: 0;
-  min-width: 0;
-}
-legend {
-  display: block;
-  width: 100%;
-  padding: 0;
-  margin-bottom: 18px;
-  font-size: 19.5px;
-  line-height: inherit;
-  color: #333333;
-  border: 0;
-  border-bottom: 1px solid #e5e5e5;
-}
-label {
-  display: inline-block;
-  max-width: 100%;
-  margin-bottom: 5px;
-  font-weight: bold;
-}
-input[type="search"] {
-  -webkit-box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  box-sizing: border-box;
-}
-input[type="radio"],
-input[type="checkbox"] {
-  margin: 4px 0 0;
-  margin-top: 1px \9;
-  line-height: normal;
-}
-input[type="file"] {
-  display: block;
-}
-input[type="range"] {
-  display: block;
-  width: 100%;
-}
-select[multiple],
-select[size] {
-  height: auto;
-}
-input[type="file"]:focus,
-input[type="radio"]:focus,
-input[type="checkbox"]:focus {
-  outline: thin dotted;
-  outline: 5px auto -webkit-focus-ring-color;
-  outline-offset: -2px;
-}
-output {
-  display: block;
-  padding-top: 7px;
-  font-size: 13px;
-  line-height: 1.42857143;
-  color: #555555;
-}
-.form-control {
-  display: block;
-  width: 100%;
-  height: 32px;
-  padding: 6px 12px;
-  font-size: 13px;
-  line-height: 1.42857143;
-  color: #555555;
-  background-color: #fff;
-  background-image: none;
-  border: 1px solid #ccc;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-}
-.form-control:focus {
-  border-color: #66afe9;
-  outline: 0;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
-  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
-}
-.form-control::-moz-placeholder {
-  color: #999;
-  opacity: 1;
-}
-.form-control:-ms-input-placeholder {
-  color: #999;
-}
-.form-control::-webkit-input-placeholder {
-  color: #999;
-}
-.form-control::-ms-expand {
-  border: 0;
-  background-color: transparent;
-}
-.form-control[disabled],
-.form-control[readonly],
-fieldset[disabled] .form-control {
-  background-color: #eeeeee;
-  opacity: 1;
-}
-.form-control[disabled],
-fieldset[disabled] .form-control {
-  cursor: not-allowed;
-}
-textarea.form-control {
-  height: auto;
-}
-input[type="search"] {
-  -webkit-appearance: none;
-}
-@media screen and (-webkit-min-device-pixel-ratio: 0) {
-  input[type="date"].form-control,
-  input[type="time"].form-control,
-  input[type="datetime-local"].form-control,
-  input[type="month"].form-control {
-    line-height: 32px;
-  }
-  input[type="date"].input-sm,
-  input[type="time"].input-sm,
-  input[type="datetime-local"].input-sm,
-  input[type="month"].input-sm,
-  .input-group-sm input[type="date"],
-  .input-group-sm input[type="time"],
-  .input-group-sm input[type="datetime-local"],
-  .input-group-sm input[type="month"] {
-    line-height: 30px;
-  }
-  input[type="date"].input-lg,
-  input[type="time"].input-lg,
-  input[type="datetime-local"].input-lg,
-  input[type="month"].input-lg,
-  .input-group-lg input[type="date"],
-  .input-group-lg input[type="time"],
-  .input-group-lg input[type="datetime-local"],
-  .input-group-lg input[type="month"] {
-    line-height: 45px;
-  }
-}
-.form-group {
-  margin-bottom: 15px;
-}
-.radio,
-.checkbox {
-  position: relative;
-  display: block;
-  margin-top: 10px;
-  margin-bottom: 10px;
-}
-.radio label,
-.checkbox label {
-  min-height: 18px;
-  padding-left: 20px;
-  margin-bottom: 0;
-  font-weight: normal;
-  cursor: pointer;
-}
-.radio input[type="radio"],
-.radio-inline input[type="radio"],
-.checkbox input[type="checkbox"],
-.checkbox-inline input[type="checkbox"] {
-  position: absolute;
-  margin-left: -20px;
-  margin-top: 4px \9;
-}
-.radio + .radio,
-.checkbox + .checkbox {
-  margin-top: -5px;
-}
-.radio-inline,
-.checkbox-inline {
-  position: relative;
-  display: inline-block;
-  padding-left: 20px;
-  margin-bottom: 0;
-  vertical-align: middle;
-  font-weight: normal;
-  cursor: pointer;
-}
-.radio-inline + .radio-inline,
-.checkbox-inline + .checkbox-inline {
-  margin-top: 0;
-  margin-left: 10px;
-}
-input[type="radio"][disabled],
-input[type="checkbox"][disabled],
-input[type="radio"].disabled,
-input[type="checkbox"].disabled,
-fieldset[disabled] input[type="radio"],
-fieldset[disabled] input[type="checkbox"] {
-  cursor: not-allowed;
-}
-.radio-inline.disabled,
-.checkbox-inline.disabled,
-fieldset[disabled] .radio-inline,
-fieldset[disabled] .checkbox-inline {
-  cursor: not-allowed;
-}
-.radio.disabled label,
-.checkbox.disabled label,
-fieldset[disabled] .radio label,
-fieldset[disabled] .checkbox label {
-  cursor: not-allowed;
-}
-.form-control-static {
-  padding-top: 7px;
-  padding-bottom: 7px;
-  margin-bottom: 0;
-  min-height: 31px;
-}
-.form-control-static.input-lg,
-.form-control-static.input-sm {
-  padding-left: 0;
-  padding-right: 0;
-}
-.input-sm {
-  height: 30px;
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-}
-select.input-sm {
-  height: 30px;
-  line-height: 30px;
-}
-textarea.input-sm,
-select[multiple].input-sm {
-  height: auto;
-}
-.form-group-sm .form-control {
-  height: 30px;
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-}
-.form-group-sm select.form-control {
-  height: 30px;
-  line-height: 30px;
-}
-.form-group-sm textarea.form-control,
-.form-group-sm select[multiple].form-control {
-  height: auto;
-}
-.form-group-sm .form-control-static {
-  height: 30px;
-  min-height: 30px;
-  padding: 6px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-}
-.input-lg {
-  height: 45px;
-  padding: 10px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-  border-radius: 3px;
-}
-select.input-lg {
-  height: 45px;
-  line-height: 45px;
-}
-textarea.input-lg,
-select[multiple].input-lg {
-  height: auto;
-}
-.form-group-lg .form-control {
-  height: 45px;
-  padding: 10px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-  border-radius: 3px;
-}
-.form-group-lg select.form-control {
-  height: 45px;
-  line-height: 45px;
-}
-.form-group-lg textarea.form-control,
-.form-group-lg select[multiple].form-control {
-  height: auto;
-}
-.form-group-lg .form-control-static {
-  height: 45px;
-  min-height: 35px;
-  padding: 11px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-}
-.has-feedback {
-  position: relative;
-}
-.has-feedback .form-control {
-  padding-right: 40px;
-}
-.form-control-feedback {
-  position: absolute;
-  top: 0;
-  right: 0;
-  z-index: 2;
-  display: block;
-  width: 32px;
-  height: 32px;
-  line-height: 32px;
-  text-align: center;
-  pointer-events: none;
-}
-.input-lg + .form-control-feedback,
-.input-group-lg + .form-control-feedback,
-.form-group-lg .form-control + .form-control-feedback {
-  width: 45px;
-  height: 45px;
-  line-height: 45px;
-}
-.input-sm + .form-control-feedback,
-.input-group-sm + .form-control-feedback,
-.form-group-sm .form-control + .form-control-feedback {
-  width: 30px;
-  height: 30px;
-  line-height: 30px;
-}
-.has-success .help-block,
-.has-success .control-label,
-.has-success .radio,
-.has-success .checkbox,
-.has-success .radio-inline,
-.has-success .checkbox-inline,
-.has-success.radio label,
-.has-success.checkbox label,
-.has-success.radio-inline label,
-.has-success.checkbox-inline label {
-  color: #3c763d;
-}
-.has-success .form-control {
-  border-color: #3c763d;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-}
-.has-success .form-control:focus {
-  border-color: #2b542c;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
-}
-.has-success .input-group-addon {
-  color: #3c763d;
-  border-color: #3c763d;
-  background-color: #dff0d8;
-}
-.has-success .form-control-feedback {
-  color: #3c763d;
-}
-.has-warning .help-block,
-.has-warning .control-label,
-.has-warning .radio,
-.has-warning .checkbox,
-.has-warning .radio-inline,
-.has-warning .checkbox-inline,
-.has-warning.radio label,
-.has-warning.checkbox label,
-.has-warning.radio-inline label,
-.has-warning.checkbox-inline label {
-  color: #8a6d3b;
-}
-.has-warning .form-control {
-  border-color: #8a6d3b;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-}
-.has-warning .form-control:focus {
-  border-color: #66512c;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
-}
-.has-warning .input-group-addon {
-  color: #8a6d3b;
-  border-color: #8a6d3b;
-  background-color: #fcf8e3;
-}
-.has-warning .form-control-feedback {
-  color: #8a6d3b;
-}
-.has-error .help-block,
-.has-error .control-label,
-.has-error .radio,
-.has-error .checkbox,
-.has-error .radio-inline,
-.has-error .checkbox-inline,
-.has-error.radio label,
-.has-error.checkbox label,
-.has-error.radio-inline label,
-.has-error.checkbox-inline label {
-  color: #a94442;
-}
-.has-error .form-control {
-  border-color: #a94442;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-}
-.has-error .form-control:focus {
-  border-color: #843534;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
-}
-.has-error .input-group-addon {
-  color: #a94442;
-  border-color: #a94442;
-  background-color: #f2dede;
-}
-.has-error .form-control-feedback {
-  color: #a94442;
-}
-.has-feedback label ~ .form-control-feedback {
-  top: 23px;
-}
-.has-feedback label.sr-only ~ .form-control-feedback {
-  top: 0;
-}
-.help-block {
-  display: block;
-  margin-top: 5px;
-  margin-bottom: 10px;
-  color: #404040;
-}
-@media (min-width: 768px) {
-  .form-inline .form-group {
-    display: inline-block;
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .form-inline .form-control {
-    display: inline-block;
-    width: auto;
-    vertical-align: middle;
-  }
-  .form-inline .form-control-static {
-    display: inline-block;
-  }
-  .form-inline .input-group {
-    display: inline-table;
-    vertical-align: middle;
-  }
-  .form-inline .input-group .input-group-addon,
-  .form-inline .input-group .input-group-btn,
-  .form-inline .input-group .form-control {
-    width: auto;
-  }
-  .form-inline .input-group > .form-control {
-    width: 100%;
-  }
-  .form-inline .control-label {
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .form-inline .radio,
-  .form-inline .checkbox {
-    display: inline-block;
-    margin-top: 0;
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .form-inline .radio label,
-  .form-inline .checkbox label {
-    padding-left: 0;
-  }
-  .form-inline .radio input[type="radio"],
-  .form-inline .checkbox input[type="checkbox"] {
-    position: relative;
-    margin-left: 0;
-  }
-  .form-inline .has-feedback .form-control-feedback {
-    top: 0;
-  }
-}
-.form-horizontal .radio,
-.form-horizontal .checkbox,
-.form-horizontal .radio-inline,
-.form-horizontal .checkbox-inline {
-  margin-top: 0;
-  margin-bottom: 0;
-  padding-top: 7px;
-}
-.form-horizontal .radio,
-.form-horizontal .checkbox {
-  min-height: 25px;
-}
-.form-horizontal .form-group {
-  margin-left: 0px;
-  margin-right: 0px;
-}
-@media (min-width: 768px) {
-  .form-horizontal .control-label {
-    text-align: right;
-    margin-bottom: 0;
-    padding-top: 7px;
-  }
-}
-.form-horizontal .has-feedback .form-control-feedback {
-  right: 0px;
-}
-@media (min-width: 768px) {
-  .form-horizontal .form-group-lg .control-label {
-    padding-top: 11px;
-    font-size: 17px;
-  }
-}
-@media (min-width: 768px) {
-  .form-horizontal .form-group-sm .control-label {
-    padding-top: 6px;
-    font-size: 12px;
-  }
-}
-.btn {
-  display: inline-block;
-  margin-bottom: 0;
-  font-weight: normal;
-  text-align: center;
-  vertical-align: middle;
-  touch-action: manipulation;
-  cursor: pointer;
-  background-image: none;
-  border: 1px solid transparent;
-  white-space: nowrap;
-  padding: 6px 12px;
-  font-size: 13px;
-  line-height: 1.42857143;
-  border-radius: 2px;
-  -webkit-user-select: none;
-  -moz-user-select: none;
-  -ms-user-select: none;
-  user-select: none;
-}
-.btn:focus,
-.btn:active:focus,
-.btn.active:focus,
-.btn.focus,
-.btn:active.focus,
-.btn.active.focus {
-  outline: thin dotted;
-  outline: 5px auto -webkit-focus-ring-color;
-  outline-offset: -2px;
-}
-.btn:hover,
-.btn:focus,
-.btn.focus {
-  color: #333;
-  text-decoration: none;
-}
-.btn:active,
-.btn.active {
-  outline: 0;
-  background-image: none;
-  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
-  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
-}
-.btn.disabled,
-.btn[disabled],
-fieldset[disabled] .btn {
-  cursor: not-allowed;
-  opacity: 0.65;
-  filter: alpha(opacity=65);
-  -webkit-box-shadow: none;
-  box-shadow: none;
-}
-a.btn.disabled,
-fieldset[disabled] a.btn {
-  pointer-events: none;
-}
-.btn-default {
-  color: #333;
-  background-color: #fff;
-  border-color: #ccc;
-}
-.btn-default:focus,
-.btn-default.focus {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #8c8c8c;
-}
-.btn-default:hover {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-.btn-default:active,
-.btn-default.active,
-.open > .dropdown-toggle.btn-default {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-.btn-default:active:hover,
-.btn-default.active:hover,
-.open > .dropdown-toggle.btn-default:hover,
-.btn-default:active:focus,
-.btn-default.active:focus,
-.open > .dropdown-toggle.btn-default:focus,
-.btn-default:active.focus,
-.btn-default.active.focus,
-.open > .dropdown-toggle.btn-default.focus {
-  color: #333;
-  background-color: #d4d4d4;
-  border-color: #8c8c8c;
-}
-.btn-default:active,
-.btn-default.active,
-.open > .dropdown-toggle.btn-default {
-  background-image: none;
-}
-.btn-default.disabled:hover,
-.btn-default[disabled]:hover,
-fieldset[disabled] .btn-default:hover,
-.btn-default.disabled:focus,
-.btn-default[disabled]:focus,
-fieldset[disabled] .btn-default:focus,
-.btn-default.disabled.focus,
-.btn-default[disabled].focus,
-fieldset[disabled] .btn-default.focus {
-  background-color: #fff;
-  border-color: #ccc;
-}
-.btn-default .badge {
-  color: #fff;
-  background-color: #333;
-}
-.btn-primary {
-  color: #fff;
-  background-color: #337ab7;
-  border-color: #2e6da4;
-}
-.btn-primary:focus,
-.btn-primary.focus {
-  color: #fff;
-  background-color: #286090;
-  border-color: #122b40;
-}
-.btn-primary:hover {
-  color: #fff;
-  background-color: #286090;
-  border-color: #204d74;
-}
-.btn-primary:active,
-.btn-primary.active,
-.open > .dropdown-toggle.btn-primary {
-  color: #fff;
-  background-color: #286090;
-  border-color: #204d74;
-}
-.btn-primary:active:hover,
-.btn-primary.active:hover,
-.open > .dropdown-toggle.btn-primary:hover,
-.btn-primary:active:focus,
-.btn-primary.active:focus,
-.open > .dropdown-toggle.btn-primary:focus,
-.btn-primary:active.focus,
-.btn-primary.active.focus,
-.open > .dropdown-toggle.btn-primary.focus {
-  color: #fff;
-  background-color: #204d74;
-  border-color: #122b40;
-}
-.btn-primary:active,
-.btn-primary.active,
-.open > .dropdown-toggle.btn-primary {
-  background-image: none;
-}
-.btn-primary.disabled:hover,
-.btn-primary[disabled]:hover,
-fieldset[disabled] .btn-primary:hover,
-.btn-primary.disabled:focus,
-.btn-primary[disabled]:focus,
-fieldset[disabled] .btn-primary:focus,
-.btn-primary.disabled.focus,
-.btn-primary[disabled].focus,
-fieldset[disabled] .btn-primary.focus {
-  background-color: #337ab7;
-  border-color: #2e6da4;
-}
-.btn-primary .badge {
-  color: #337ab7;
-  background-color: #fff;
-}
-.btn-success {
-  color: #fff;
-  background-color: #5cb85c;
-  border-color: #4cae4c;
-}
-.btn-success:focus,
-.btn-success.focus {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #255625;
-}
-.btn-success:hover {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #398439;
-}
-.btn-success:active,
-.btn-success.active,
-.open > .dropdown-toggle.btn-success {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #398439;
-}
-.btn-success:active:hover,
-.btn-success.active:hover,
-.open > .dropdown-toggle.btn-success:hover,
-.btn-success:active:focus,
-.btn-success.active:focus,
-.open > .dropdown-toggle.btn-success:focus,
-.btn-success:active.focus,
-.btn-success.active.focus,
-.open > .dropdown-toggle.btn-success.focus {
-  color: #fff;
-  background-color: #398439;
-  border-color: #255625;
-}
-.btn-success:active,
-.btn-success.active,
-.open > .dropdown-toggle.btn-success {
-  background-image: none;
-}
-.btn-success.disabled:hover,
-.btn-success[disabled]:hover,
-fieldset[disabled] .btn-success:hover,
-.btn-success.disabled:focus,
-.btn-success[disabled]:focus,
-fieldset[disabled] .btn-success:focus,
-.btn-success.disabled.focus,
-.btn-success[disabled].focus,
-fieldset[disabled] .btn-success.focus {
-  background-color: #5cb85c;
-  border-color: #4cae4c;
-}
-.btn-success .badge {
-  color: #5cb85c;
-  background-color: #fff;
-}
-.btn-info {
-  color: #fff;
-  background-color: #5bc0de;
-  border-color: #46b8da;
-}
-.btn-info:focus,
-.btn-info.focus {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #1b6d85;
-}
-.btn-info:hover {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #269abc;
-}
-.btn-info:active,
-.btn-info.active,
-.open > .dropdown-toggle.btn-info {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #269abc;
-}
-.btn-info:active:hover,
-.btn-info.active:hover,
-.open > .dropdown-toggle.btn-info:hover,
-.btn-info:active:focus,
-.btn-info.active:focus,
-.open > .dropdown-toggle.btn-info:focus,
-.btn-info:active.focus,
-.btn-info.active.focus,
-.open > .dropdown-toggle.btn-info.focus {
-  color: #fff;
-  background-color: #269abc;
-  border-color: #1b6d85;
-}
-.btn-info:active,
-.btn-info.active,
-.open > .dropdown-toggle.btn-info {
-  background-image: none;
-}
-.btn-info.disabled:hover,
-.btn-info[disabled]:hover,
-fieldset[disabled] .btn-info:hover,
-.btn-info.disabled:focus,
-.btn-info[disabled]:focus,
-fieldset[disabled] .btn-info:focus,
-.btn-info.disabled.focus,
-.btn-info[disabled].focus,
-fieldset[disabled] .btn-info.focus {
-  background-color: #5bc0de;
-  border-color: #46b8da;
-}
-.btn-info .badge {
-  color: #5bc0de;
-  background-color: #fff;
-}
-.btn-warning {
-  color: #fff;
-  background-color: #f0ad4e;
-  border-color: #eea236;
-}
-.btn-warning:focus,
-.btn-warning.focus {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #985f0d;
-}
-.btn-warning:hover {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #d58512;
-}
-.btn-warning:active,
-.btn-warning.active,
-.open > .dropdown-toggle.btn-warning {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #d58512;
-}
-.btn-warning:active:hover,
-.btn-warning.active:hover,
-.open > .dropdown-toggle.btn-warning:hover,
-.btn-warning:active:focus,
-.btn-warning.active:focus,
-.open > .dropdown-toggle.btn-warning:focus,
-.btn-warning:active.focus,
-.btn-warning.active.focus,
-.open > .dropdown-toggle.btn-warning.focus {
-  color: #fff;
-  background-color: #d58512;
-  border-color: #985f0d;
-}
-.btn-warning:active,
-.btn-warning.active,
-.open > .dropdown-toggle.btn-warning {
-  background-image: none;
-}
-.btn-warning.disabled:hover,
-.btn-warning[disabled]:hover,
-fieldset[disabled] .btn-warning:hover,
-.btn-warning.disabled:focus,
-.btn-warning[disabled]:focus,
-fieldset[disabled] .btn-warning:focus,
-.btn-warning.disabled.focus,
-.btn-warning[disabled].focus,
-fieldset[disabled] .btn-warning.focus {
-  background-color: #f0ad4e;
-  border-color: #eea236;
-}
-.btn-warning .badge {
-  color: #f0ad4e;
-  background-color: #fff;
-}
-.btn-danger {
-  color: #fff;
-  background-color: #d9534f;
-  border-color: #d43f3a;
-}
-.btn-danger:focus,
-.btn-danger.focus {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #761c19;
-}
-.btn-danger:hover {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #ac2925;
-}
-.btn-danger:active,
-.btn-danger.active,
-.open > .dropdown-toggle.btn-danger {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #ac2925;
-}
-.btn-danger:active:hover,
-.btn-danger.active:hover,
-.open > .dropdown-toggle.btn-danger:hover,
-.btn-danger:active:focus,
-.btn-danger.active:focus,
-.open > .dropdown-toggle.btn-danger:focus,
-.btn-danger:active.focus,
-.btn-danger.active.focus,
-.open > .dropdown-toggle.btn-danger.focus {
-  color: #fff;
-  background-color: #ac2925;
-  border-color: #761c19;
-}
-.btn-danger:active,
-.btn-danger.active,
-.open > .dropdown-toggle.btn-danger {
-  background-image: none;
-}
-.btn-danger.disabled:hover,
-.btn-danger[disabled]:hover,
-fieldset[disabled] .btn-danger:hover,
-.btn-danger.disabled:focus,
-.btn-danger[disabled]:focus,
-fieldset[disabled] .btn-danger:focus,
-.btn-danger.disabled.focus,
-.btn-danger[disabled].focus,
-fieldset[disabled] .btn-danger.focus {
-  background-color: #d9534f;
-  border-color: #d43f3a;
-}
-.btn-danger .badge {
-  color: #d9534f;
-  background-color: #fff;
-}
-.btn-link {
-  color: #337ab7;
-  font-weight: normal;
-  border-radius: 0;
-}
-.btn-link,
-.btn-link:active,
-.btn-link.active,
-.btn-link[disabled],
-fieldset[disabled] .btn-link {
-  background-color: transparent;
-  -webkit-box-shadow: none;
-  box-shadow: none;
-}
-.btn-link,
-.btn-link:hover,
-.btn-link:focus,
-.btn-link:active {
-  border-color: transparent;
-}
-.btn-link:hover,
-.btn-link:focus {
-  color: #23527c;
-  text-decoration: underline;
-  background-color: transparent;
-}
-.btn-link[disabled]:hover,
-fieldset[disabled] .btn-link:hover,
-.btn-link[disabled]:focus,
-fieldset[disabled] .btn-link:focus {
-  color: #777777;
-  text-decoration: none;
-}
-.btn-lg,
-.btn-group-lg > .btn {
-  padding: 10px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-  border-radius: 3px;
-}
-.btn-sm,
-.btn-group-sm > .btn {
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-}
-.btn-xs,
-.btn-group-xs > .btn {
-  padding: 1px 5px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-}
-.btn-block {
-  display: block;
-  width: 100%;
-}
-.btn-block + .btn-block {
-  margin-top: 5px;
-}
-input[type="submit"].btn-block,
-input[type="reset"].btn-block,
-input[type="button"].btn-block {
-  width: 100%;
-}
-.fade {
-  opacity: 0;
-  -webkit-transition: opacity 0.15s linear;
-  -o-transition: opacity 0.15s linear;
-  transition: opacity 0.15s linear;
-}
-.fade.in {
-  opacity: 1;
-}
-.collapse {
-  display: none;
-}
-.collapse.in {
-  display: block;
-}
-tr.collapse.in {
-  display: table-row;
-}
-tbody.collapse.in {
-  display: table-row-group;
-}
-.collapsing {
-  position: relative;
-  height: 0;
-  overflow: hidden;
-  -webkit-transition-property: height, visibility;
-  transition-property: height, visibility;
-  -webkit-transition-duration: 0.35s;
-  transition-duration: 0.35s;
-  -webkit-transition-timing-function: ease;
-  transition-timing-function: ease;
-}
-.caret {
-  display: inline-block;
-  width: 0;
-  height: 0;
-  margin-left: 2px;
-  vertical-align: middle;
-  border-top: 4px dashed;
-  border-top: 4px solid \9;
-  border-right: 4px solid transparent;
-  border-left: 4px solid transparent;
-}
-.dropup,
-.dropdown {
-  position: relative;
-}
-.dropdown-toggle:focus {
-  outline: 0;
-}
-.dropdown-menu {
-  position: absolute;
-  top: 100%;
-  left: 0;
-  z-index: 1000;
-  display: none;
-  float: left;
-  min-width: 160px;
-  padding: 5px 0;
-  margin: 2px 0 0;
-  list-style: none;
-  font-size: 13px;
-  text-align: left;
-  background-color: #fff;
-  border: 1px solid #ccc;
-  border: 1px solid rgba(0, 0, 0, 0.15);
-  border-radius: 2px;
-  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
-  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
-  background-clip: padding-box;
-}
-.dropdown-menu.pull-right {
-  right: 0;
-  left: auto;
-}
-.dropdown-menu .divider {
-  height: 1px;
-  margin: 8px 0;
-  overflow: hidden;
-  background-color: #e5e5e5;
-}
-.dropdown-menu > li > a {
-  display: block;
-  padding: 3px 20px;
-  clear: both;
-  font-weight: normal;
-  line-height: 1.42857143;
-  color: #333333;
-  white-space: nowrap;
-}
-.dropdown-menu > li > a:hover,
-.dropdown-menu > li > a:focus {
-  text-decoration: none;
-  color: #262626;
-  background-color: #f5f5f5;
-}
-.dropdown-menu > .active > a,
-.dropdown-menu > .active > a:hover,
-.dropdown-menu > .active > a:focus {
-  color: #fff;
-  text-decoration: none;
-  outline: 0;
-  background-color: #337ab7;
-}
-.dropdown-menu > .disabled > a,
-.dropdown-menu > .disabled > a:hover,
-.dropdown-menu > .disabled > a:focus {
-  color: #777777;
-}
-.dropdown-menu > .disabled > a:hover,
-.dropdown-menu > .disabled > a:focus {
-  text-decoration: none;
-  background-color: transparent;
-  background-image: none;
-  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
-  cursor: not-allowed;
-}
-.open > .dropdown-menu {
-  display: block;
-}
-.open > a {
-  outline: 0;
-}
-.dropdown-menu-right {
-  left: auto;
-  right: 0;
-}
-.dropdown-menu-left {
-  left: 0;
-  right: auto;
-}
-.dropdown-header {
-  display: block;
-  padding: 3px 20px;
-  font-size: 12px;
-  line-height: 1.42857143;
-  color: #777777;
-  white-space: nowrap;
-}
-.dropdown-backdrop {
-  position: fixed;
-  left: 0;
-  right: 0;
-  bottom: 0;
-  top: 0;
-  z-index: 990;
-}
-.pull-right > .dropdown-menu {
-  right: 0;
-  left: auto;
-}
-.dropup .caret,
-.navbar-fixed-bottom .dropdown .caret {
-  border-top: 0;
-  border-bottom: 4px dashed;
-  border-bottom: 4px solid \9;
-  content: "";
-}
-.dropup .dropdown-menu,
-.navbar-fixed-bottom .dropdown .dropdown-menu {
-  top: auto;
-  bottom: 100%;
-  margin-bottom: 2px;
-}
-@media (min-width: 541px) {
-  .navbar-right .dropdown-menu {
-    left: auto;
-    right: 0;
-  }
-  .navbar-right .dropdown-menu-left {
-    left: 0;
-    right: auto;
-  }
-}
-.btn-group,
-.btn-group-vertical {
-  position: relative;
-  display: inline-block;
-  vertical-align: middle;
-}
-.btn-group > .btn,
-.btn-group-vertical > .btn {
-  position: relative;
-  float: left;
-}
-.btn-group > .btn:hover,
-.btn-group-vertical > .btn:hover,
-.btn-group > .btn:focus,
-.btn-group-vertical > .btn:focus,
-.btn-group > .btn:active,
-.btn-group-vertical > .btn:active,
-.btn-group > .btn.active,
-.btn-group-vertical > .btn.active {
-  z-index: 2;
-}
-.btn-group .btn + .btn,
-.btn-group .btn + .btn-group,
-.btn-group .btn-group + .btn,
-.btn-group .btn-group + .btn-group {
-  margin-left: -1px;
-}
-.btn-toolbar {
-  margin-left: -5px;
-}
-.btn-toolbar .btn,
-.btn-toolbar .btn-group,
-.btn-toolbar .input-group {
-  float: left;
-}
-.btn-toolbar > .btn,
-.btn-toolbar > .btn-group,
-.btn-toolbar > .input-group {
-  margin-left: 5px;
-}
-.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
-  border-radius: 0;
-}
-.btn-group > .btn:first-child {
-  margin-left: 0;
-}
-.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
-  border-bottom-right-radius: 0;
-  border-top-right-radius: 0;
-}
-.btn-group > .btn:last-child:not(:first-child),
-.btn-group > .dropdown-toggle:not(:first-child) {
-  border-bottom-left-radius: 0;
-  border-top-left-radius: 0;
-}
-.btn-group > .btn-group {
-  float: left;
-}
-.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
-  border-radius: 0;
-}
-.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
-.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
-  border-bottom-right-radius: 0;
-  border-top-right-radius: 0;
-}
-.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
-  border-bottom-left-radius: 0;
-  border-top-left-radius: 0;
-}
-.btn-group .dropdown-toggle:active,
-.btn-group.open .dropdown-toggle {
-  outline: 0;
-}
-.btn-group > .btn + .dropdown-toggle {
-  padding-left: 8px;
-  padding-right: 8px;
-}
-.btn-group > .btn-lg + .dropdown-toggle {
-  padding-left: 12px;
-  padding-right: 12px;
-}
-.btn-group.open .dropdown-toggle {
-  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
-  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
-}
-.btn-group.open .dropdown-toggle.btn-link {
-  -webkit-box-shadow: none;
-  box-shadow: none;
-}
-.btn .caret {
-  margin-left: 0;
-}
-.btn-lg .caret {
-  border-width: 5px 5px 0;
-  border-bottom-width: 0;
-}
-.dropup .btn-lg .caret {
-  border-width: 0 5px 5px;
-}
-.btn-group-vertical > .btn,
-.btn-group-vertical > .btn-group,
-.btn-group-vertical > .btn-group > .btn {
-  display: block;
-  float: none;
-  width: 100%;
-  max-width: 100%;
-}
-.btn-group-vertical > .btn-group > .btn {
-  float: none;
-}
-.btn-group-vertical > .btn + .btn,
-.btn-group-vertical > .btn + .btn-group,
-.btn-group-vertical > .btn-group + .btn,
-.btn-group-vertical > .btn-group + .btn-group {
-  margin-top: -1px;
-  margin-left: 0;
-}
-.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
-  border-radius: 0;
-}
-.btn-group-vertical > .btn:first-child:not(:last-child) {
-  border-top-right-radius: 2px;
-  border-top-left-radius: 2px;
-  border-bottom-right-radius: 0;
-  border-bottom-left-radius: 0;
-}
-.btn-group-vertical > .btn:last-child:not(:first-child) {
-  border-top-right-radius: 0;
-  border-top-left-radius: 0;
-  border-bottom-right-radius: 2px;
-  border-bottom-left-radius: 2px;
-}
-.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
-  border-radius: 0;
-}
-.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
-.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
-  border-bottom-right-radius: 0;
-  border-bottom-left-radius: 0;
-}
-.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
-  border-top-right-radius: 0;
-  border-top-left-radius: 0;
-}
-.btn-group-justified {
-  display: table;
-  width: 100%;
-  table-layout: fixed;
-  border-collapse: separate;
-}
-.btn-group-justified > .btn,
-.btn-group-justified > .btn-group {
-  float: none;
-  display: table-cell;
-  width: 1%;
-}
-.btn-group-justified > .btn-group .btn {
-  width: 100%;
-}
-.btn-group-justified > .btn-group .dropdown-menu {
-  left: auto;
-}
-[data-toggle="buttons"] > .btn input[type="radio"],
-[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
-[data-toggle="buttons"] > .btn input[type="checkbox"],
-[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
-  position: absolute;
-  clip: rect(0, 0, 0, 0);
-  pointer-events: none;
-}
-.input-group {
-  position: relative;
-  display: table;
-  border-collapse: separate;
-}
-.input-group[class*="col-"] {
-  float: none;
-  padding-left: 0;
-  padding-right: 0;
-}
-.input-group .form-control {
-  position: relative;
-  z-index: 2;
-  float: left;
-  width: 100%;
-  margin-bottom: 0;
-}
-.input-group .form-control:focus {
-  z-index: 3;
-}
-.input-group-lg > .form-control,
-.input-group-lg > .input-group-addon,
-.input-group-lg > .input-group-btn > .btn {
-  height: 45px;
-  padding: 10px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-  border-radius: 3px;
-}
-select.input-group-lg > .form-control,
-select.input-group-lg > .input-group-addon,
-select.input-group-lg > .input-group-btn > .btn {
-  height: 45px;
-  line-height: 45px;
-}
-textarea.input-group-lg > .form-control,
-textarea.input-group-lg > .input-group-addon,
-textarea.input-group-lg > .input-group-btn > .btn,
-select[multiple].input-group-lg > .form-control,
-select[multiple].input-group-lg > .input-group-addon,
-select[multiple].input-group-lg > .input-group-btn > .btn {
-  height: auto;
-}
-.input-group-sm > .form-control,
-.input-group-sm > .input-group-addon,
-.input-group-sm > .input-group-btn > .btn {
-  height: 30px;
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-}
-select.input-group-sm > .form-control,
-select.input-group-sm > .input-group-addon,
-select.input-group-sm > .input-group-btn > .btn {
-  height: 30px;
-  line-height: 30px;
-}
-textarea.input-group-sm > .form-control,
-textarea.input-group-sm > .input-group-addon,
-textarea.input-group-sm > .input-group-btn > .btn,
-select[multiple].input-group-sm > .form-control,
-select[multiple].input-group-sm > .input-group-addon,
-select[multiple].input-group-sm > .input-group-btn > .btn {
-  height: auto;
-}
-.input-group-addon,
-.input-group-btn,
-.input-group .form-control {
-  display: table-cell;
-}
-.input-group-addon:not(:first-child):not(:last-child),
-.input-group-btn:not(:first-child):not(:last-child),
-.input-group .form-control:not(:first-child):not(:last-child) {
-  border-radius: 0;
-}
-.input-group-addon,
-.input-group-btn {
-  width: 1%;
-  white-space: nowrap;
-  vertical-align: middle;
-}
-.input-group-addon {
-  padding: 6px 12px;
-  font-size: 13px;
-  font-weight: normal;
-  line-height: 1;
-  color: #555555;
-  text-align: center;
-  background-color: #eeeeee;
-  border: 1px solid #ccc;
-  border-radius: 2px;
-}
-.input-group-addon.input-sm {
-  padding: 5px 10px;
-  font-size: 12px;
-  border-radius: 1px;
-}
-.input-group-addon.input-lg {
-  padding: 10px 16px;
-  font-size: 17px;
-  border-radius: 3px;
-}
-.input-group-addon input[type="radio"],
-.input-group-addon input[type="checkbox"] {
-  margin-top: 0;
-}
-.input-group .form-control:first-child,
-.input-group-addon:first-child,
-.input-group-btn:first-child > .btn,
-.input-group-btn:first-child > .btn-group > .btn,
-.input-group-btn:first-child > .dropdown-toggle,
-.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
-.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
-  border-bottom-right-radius: 0;
-  border-top-right-radius: 0;
-}
-.input-group-addon:first-child {
-  border-right: 0;
-}
-.input-group .form-control:last-child,
-.input-group-addon:last-child,
-.input-group-btn:last-child > .btn,
-.input-group-btn:last-child > .btn-group > .btn,
-.input-group-btn:last-child > .dropdown-toggle,
-.input-group-btn:first-child > .btn:not(:first-child),
-.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
-  border-bottom-left-radius: 0;
-  border-top-left-radius: 0;
-}
-.input-group-addon:last-child {
-  border-left: 0;
-}
-.input-group-btn {
-  position: relative;
-  font-size: 0;
-  white-space: nowrap;
-}
-.input-group-btn > .btn {
-  position: relative;
-}
-.input-group-btn > .btn + .btn {
-  margin-left: -1px;
-}
-.input-group-btn > .btn:hover,
-.input-group-btn > .btn:focus,
-.input-group-btn > .btn:active {
-  z-index: 2;
-}
-.input-group-btn:first-child > .btn,
-.input-group-btn:first-child > .btn-group {
-  margin-right: -1px;
-}
-.input-group-btn:last-child > .btn,
-.input-group-btn:last-child > .btn-group {
-  z-index: 2;
-  margin-left: -1px;
-}
-.nav {
-  margin-bottom: 0;
-  padding-left: 0;
-  list-style: none;
-}
-.nav > li {
-  position: relative;
-  display: block;
-}
-.nav > li > a {
-  position: relative;
-  display: block;
-  padding: 10px 15px;
-}
-.nav > li > a:hover,
-.nav > li > a:focus {
-  text-decoration: none;
-  background-color: #eeeeee;
-}
-.nav > li.disabled > a {
-  color: #777777;
-}
-.nav > li.disabled > a:hover,
-.nav > li.disabled > a:focus {
-  color: #777777;
-  text-decoration: none;
-  background-color: transparent;
-  cursor: not-allowed;
-}
-.nav .open > a,
-.nav .open > a:hover,
-.nav .open > a:focus {
-  background-color: #eeeeee;
-  border-color: #337ab7;
-}
-.nav .nav-divider {
-  height: 1px;
-  margin: 8px 0;
-  overflow: hidden;
-  background-color: #e5e5e5;
-}
-.nav > li > a > img {
-  max-width: none;
-}
-.nav-tabs {
-  border-bottom: 1px solid #ddd;
-}
-.nav-tabs > li {
-  float: left;
-  margin-bottom: -1px;
-}
-.nav-tabs > li > a {
-  margin-right: 2px;
-  line-height: 1.42857143;
-  border: 1px solid transparent;
-  border-radius: 2px 2px 0 0;
-}
-.nav-tabs > li > a:hover {
-  border-color: #eeeeee #eeeeee #ddd;
-}
-.nav-tabs > li.active > a,
-.nav-tabs > li.active > a:hover,
-.nav-tabs > li.active > a:focus {
-  color: #555555;
-  background-color: #fff;
-  border: 1px solid #ddd;
-  border-bottom-color: transparent;
-  cursor: default;
-}
-.nav-tabs.nav-justified {
-  width: 100%;
-  border-bottom: 0;
-}
-.nav-tabs.nav-justified > li {
-  float: none;
-}
-.nav-tabs.nav-justified > li > a {
-  text-align: center;
-  margin-bottom: 5px;
-}
-.nav-tabs.nav-justified > .dropdown .dropdown-menu {
-  top: auto;
-  left: auto;
-}
-@media (min-width: 768px) {
-  .nav-tabs.nav-justified > li {
-    display: table-cell;
-    width: 1%;
-  }
-  .nav-tabs.nav-justified > li > a {
-    margin-bottom: 0;
-  }
-}
-.nav-tabs.nav-justified > li > a {
-  margin-right: 0;
-  border-radius: 2px;
-}
-.nav-tabs.nav-justified > .active > a,
-.nav-tabs.nav-justified > .active > a:hover,
-.nav-tabs.nav-justified > .active > a:focus {
-  border: 1px solid #ddd;
-}
-@media (min-width: 768px) {
-  .nav-tabs.nav-justified > li > a {
-    border-bottom: 1px solid #ddd;
-    border-radius: 2px 2px 0 0;
-  }
-  .nav-tabs.nav-justified > .active > a,
-  .nav-tabs.nav-justified > .active > a:hover,
-  .nav-tabs.nav-justified > .active > a:focus {
-    border-bottom-color: #fff;
-  }
-}
-.nav-pills > li {
-  float: left;
-}
-.nav-pills > li > a {
-  border-radius: 2px;
-}
-.nav-pills > li + li {
-  margin-left: 2px;
-}
-.nav-pills > li.active > a,
-.nav-pills > li.active > a:hover,
-.nav-pills > li.active > a:focus {
-  color: #fff;
-  background-color: #337ab7;
-}
-.nav-stacked > li {
-  float: none;
-}
-.nav-stacked > li + li {
-  margin-top: 2px;
-  margin-left: 0;
-}
-.nav-justified {
-  width: 100%;
-}
-.nav-justified > li {
-  float: none;
-}
-.nav-justified > li > a {
-  text-align: center;
-  margin-bottom: 5px;
-}
-.nav-justified > .dropdown .dropdown-menu {
-  top: auto;
-  left: auto;
-}
-@media (min-width: 768px) {
-  .nav-justified > li {
-    display: table-cell;
-    width: 1%;
-  }
-  .nav-justified > li > a {
-    margin-bottom: 0;
-  }
-}
-.nav-tabs-justified {
-  border-bottom: 0;
-}
-.nav-tabs-justified > li > a {
-  margin-right: 0;
-  border-radius: 2px;
-}
-.nav-tabs-justified > .active > a,
-.nav-tabs-justified > .active > a:hover,
-.nav-tabs-justified > .active > a:focus {
-  border: 1px solid #ddd;
-}
-@media (min-width: 768px) {
-  .nav-tabs-justified > li > a {
-    border-bottom: 1px solid #ddd;
-    border-radius: 2px 2px 0 0;
-  }
-  .nav-tabs-justified > .active > a,
-  .nav-tabs-justified > .active > a:hover,
-  .nav-tabs-justified > .active > a:focus {
-    border-bottom-color: #fff;
-  }
-}
-.tab-content > .tab-pane {
-  display: none;
-}
-.tab-content > .active {
-  display: block;
-}
-.nav-tabs .dropdown-menu {
-  margin-top: -1px;
-  border-top-right-radius: 0;
-  border-top-left-radius: 0;
-}
-.navbar {
-  position: relative;
-  min-height: 30px;
-  margin-bottom: 18px;
-  border: 1px solid transparent;
-}
-@media (min-width: 541px) {
-  .navbar {
-    border-radius: 2px;
-  }
-}
-@media (min-width: 541px) {
-  .navbar-header {
-    float: left;
-  }
-}
-.navbar-collapse {
-  overflow-x: visible;
-  padding-right: 0px;
-  padding-left: 0px;
-  border-top: 1px solid transparent;
-  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
-  -webkit-overflow-scrolling: touch;
-}
-.navbar-collapse.in {
-  overflow-y: auto;
-}
-@media (min-width: 541px) {
-  .navbar-collapse {
-    width: auto;
-    border-top: 0;
-    box-shadow: none;
-  }
-  .navbar-collapse.collapse {
-    display: block !important;
-    height: auto !important;
-    padding-bottom: 0;
-    overflow: visible !important;
-  }
-  .navbar-collapse.in {
-    overflow-y: visible;
-  }
-  .navbar-fixed-top .navbar-collapse,
-  .navbar-static-top .navbar-collapse,
-  .navbar-fixed-bottom .navbar-collapse {
-    padding-left: 0;
-    padding-right: 0;
-  }
-}
-.navbar-fixed-top .navbar-collapse,
-.navbar-fixed-bottom .navbar-collapse {
-  max-height: 340px;
-}
-@media (max-device-width: 540px) and (orientation: landscape) {
-  .navbar-fixed-top .navbar-collapse,
-  .navbar-fixed-bottom .navbar-collapse {
-    max-height: 200px;
-  }
-}
-.container > .navbar-header,
-.container-fluid > .navbar-header,
-.container > .navbar-collapse,
-.container-fluid > .navbar-collapse {
-  margin-right: 0px;
-  margin-left: 0px;
-}
-@media (min-width: 541px) {
-  .container > .navbar-header,
-  .container-fluid > .navbar-header,
-  .container > .navbar-collapse,
-  .container-fluid > .navbar-collapse {
-    margin-right: 0;
-    margin-left: 0;
-  }
-}
-.navbar-static-top {
-  z-index: 1000;
-  border-width: 0 0 1px;
-}
-@media (min-width: 541px) {
-  .navbar-static-top {
-    border-radius: 0;
-  }
-}
-.navbar-fixed-top,
-.navbar-fixed-bottom {
-  position: fixed;
-  right: 0;
-  left: 0;
-  z-index: 1030;
-}
-@media (min-width: 541px) {
-  .navbar-fixed-top,
-  .navbar-fixed-bottom {
-    border-radius: 0;
-  }
-}
-.navbar-fixed-top {
-  top: 0;
-  border-width: 0 0 1px;
-}
-.navbar-fixed-bottom {
-  bottom: 0;
-  margin-bottom: 0;
-  border-width: 1px 0 0;
-}
-.navbar-brand {
-  float: left;
-  padding: 6px 0px;
-  font-size: 17px;
-  line-height: 18px;
-  height: 30px;
-}
-.navbar-brand:hover,
-.navbar-brand:focus {
-  text-decoration: none;
-}
-.navbar-brand > img {
-  display: block;
-}
-@media (min-width: 541px) {
-  .navbar > .container .navbar-brand,
-  .navbar > .container-fluid .navbar-brand {
-    margin-left: 0px;
-  }
-}
-.navbar-toggle {
-  position: relative;
-  float: right;
-  margin-right: 0px;
-  padding: 9px 10px;
-  margin-top: -2px;
-  margin-bottom: -2px;
-  background-color: transparent;
-  background-image: none;
-  border: 1px solid transparent;
-  border-radius: 2px;
-}
-.navbar-toggle:focus {
-  outline: 0;
-}
-.navbar-toggle .icon-bar {
-  display: block;
-  width: 22px;
-  height: 2px;
-  border-radius: 1px;
-}
-.navbar-toggle .icon-bar + .icon-bar {
-  margin-top: 4px;
-}
-@media (min-width: 541px) {
-  .navbar-toggle {
-    display: none;
-  }
-}
-.navbar-nav {
-  margin: 3px 0px;
-}
-.navbar-nav > li > a {
-  padding-top: 10px;
-  padding-bottom: 10px;
-  line-height: 18px;
-}
-@media (max-width: 540px) {
-  .navbar-nav .open .dropdown-menu {
-    position: static;
-    float: none;
-    width: auto;
-    margin-top: 0;
-    background-color: transparent;
-    border: 0;
-    box-shadow: none;
-  }
-  .navbar-nav .open .dropdown-menu > li > a,
-  .navbar-nav .open .dropdown-menu .dropdown-header {
-    padding: 5px 15px 5px 25px;
-  }
-  .navbar-nav .open .dropdown-menu > li > a {
-    line-height: 18px;
-  }
-  .navbar-nav .open .dropdown-menu > li > a:hover,
-  .navbar-nav .open .dropdown-menu > li > a:focus {
-    background-image: none;
-  }
-}
-@media (min-width: 541px) {
-  .navbar-nav {
-    float: left;
-    margin: 0;
-  }
-  .navbar-nav > li {
-    float: left;
-  }
-  .navbar-nav > li > a {
-    padding-top: 6px;
-    padding-bottom: 6px;
-  }
-}
-.navbar-form {
-  margin-left: 0px;
-  margin-right: 0px;
-  padding: 10px 0px;
-  border-top: 1px solid transparent;
-  border-bottom: 1px solid transparent;
-  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
-  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
-  margin-top: -1px;
-  margin-bottom: -1px;
-}
-@media (min-width: 768px) {
-  .navbar-form .form-group {
-    display: inline-block;
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .navbar-form .form-control {
-    display: inline-block;
-    width: auto;
-    vertical-align: middle;
-  }
-  .navbar-form .form-control-static {
-    display: inline-block;
-  }
-  .navbar-form .input-group {
-    display: inline-table;
-    vertical-align: middle;
-  }
-  .navbar-form .input-group .input-group-addon,
-  .navbar-form .input-group .input-group-btn,
-  .navbar-form .input-group .form-control {
-    width: auto;
-  }
-  .navbar-form .input-group > .form-control {
-    width: 100%;
-  }
-  .navbar-form .control-label {
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .navbar-form .radio,
-  .navbar-form .checkbox {
-    display: inline-block;
-    margin-top: 0;
-    margin-bottom: 0;
-    vertical-align: middle;
-  }
-  .navbar-form .radio label,
-  .navbar-form .checkbox label {
-    padding-left: 0;
-  }
-  .navbar-form .radio input[type="radio"],
-  .navbar-form .checkbox input[type="checkbox"] {
-    position: relative;
-    margin-left: 0;
-  }
-  .navbar-form .has-feedback .form-control-feedback {
-    top: 0;
-  }
-}
-@media (max-width: 540px) {
-  .navbar-form .form-group {
-    margin-bottom: 5px;
-  }
-  .navbar-form .form-group:last-child {
-    margin-bottom: 0;
-  }
-}
-@media (min-width: 541px) {
-  .navbar-form {
-    width: auto;
-    border: 0;
-    margin-left: 0;
-    margin-right: 0;
-    padding-top: 0;
-    padding-bottom: 0;
-    -webkit-box-shadow: none;
-    box-shadow: none;
-  }
-}
-.navbar-nav > li > .dropdown-menu {
-  margin-top: 0;
-  border-top-right-radius: 0;
-  border-top-left-radius: 0;
-}
-.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
-  margin-bottom: 0;
-  border-top-right-radius: 2px;
-  border-top-left-radius: 2px;
-  border-bottom-right-radius: 0;
-  border-bottom-left-radius: 0;
-}
-.navbar-btn {
-  margin-top: -1px;
-  margin-bottom: -1px;
-}
-.navbar-btn.btn-sm {
-  margin-top: 0px;
-  margin-bottom: 0px;
-}
-.navbar-btn.btn-xs {
-  margin-top: 4px;
-  margin-bottom: 4px;
-}
-.navbar-text {
-  margin-top: 6px;
-  margin-bottom: 6px;
-}
-@media (min-width: 541px) {
-  .navbar-text {
-    float: left;
-    margin-left: 0px;
-    margin-right: 0px;
-  }
-}
-@media (min-width: 541px) {
-  .navbar-left {
-    float: left !important;
-    float: left;
-  }
-  .navbar-right {
-    float: right !important;
-    float: right;
-    margin-right: 0px;
-  }
-  .navbar-right ~ .navbar-right {
-    margin-right: 0;
-  }
-}
-.navbar-default {
-  background-color: #f8f8f8;
-  border-color: #e7e7e7;
-}
-.navbar-default .navbar-brand {
-  color: #777;
-}
-.navbar-default .navbar-brand:hover,
-.navbar-default .navbar-brand:focus {
-  color: #5e5e5e;
-  background-color: transparent;
-}
-.navbar-default .navbar-text {
-  color: #777;
-}
-.navbar-default .navbar-nav > li > a {
-  color: #777;
-}
-.navbar-default .navbar-nav > li > a:hover,
-.navbar-default .navbar-nav > li > a:focus {
-  color: #333;
-  background-color: transparent;
-}
-.navbar-default .navbar-nav > .active > a,
-.navbar-default .navbar-nav > .active > a:hover,
-.navbar-default .navbar-nav > .active > a:focus {
-  color: #555;
-  background-color: #e7e7e7;
-}
-.navbar-default .navbar-nav > .disabled > a,
-.navbar-default .navbar-nav > .disabled > a:hover,
-.navbar-default .navbar-nav > .disabled > a:focus {
-  color: #ccc;
-  background-color: transparent;
-}
-.navbar-default .navbar-toggle {
-  border-color: #ddd;
-}
-.navbar-default .navbar-toggle:hover,
-.navbar-default .navbar-toggle:focus {
-  background-color: #ddd;
-}
-.navbar-default .navbar-toggle .icon-bar {
-  background-color: #888;
-}
-.navbar-default .navbar-collapse,
-.navbar-default .navbar-form {
-  border-color: #e7e7e7;
-}
-.navbar-default .navbar-nav > .open > a,
-.navbar-default .navbar-nav > .open > a:hover,
-.navbar-default .navbar-nav > .open > a:focus {
-  background-color: #e7e7e7;
-  color: #555;
-}
-@media (max-width: 540px) {
-  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
-    color: #777;
-  }
-  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
-  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
-    color: #333;
-    background-color: transparent;
-  }
-  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
-  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
-  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
-    color: #555;
-    background-color: #e7e7e7;
-  }
-  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
-  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
-  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
-    color: #ccc;
-    background-color: transparent;
-  }
-}
-.navbar-default .navbar-link {
-  color: #777;
-}
-.navbar-default .navbar-link:hover {
-  color: #333;
-}
-.navbar-default .btn-link {
-  color: #777;
-}
-.navbar-default .btn-link:hover,
-.navbar-default .btn-link:focus {
-  color: #333;
-}
-.navbar-default .btn-link[disabled]:hover,
-fieldset[disabled] .navbar-default .btn-link:hover,
-.navbar-default .btn-link[disabled]:focus,
-fieldset[disabled] .navbar-default .btn-link:focus {
-  color: #ccc;
-}
-.navbar-inverse {
-  background-color: #222;
-  border-color: #080808;
-}
-.navbar-inverse .navbar-brand {
-  color: #9d9d9d;
-}
-.navbar-inverse .navbar-brand:hover,
-.navbar-inverse .navbar-brand:focus {
-  color: #fff;
-  background-color: transparent;
-}
-.navbar-inverse .navbar-text {
-  color: #9d9d9d;
-}
-.navbar-inverse .navbar-nav > li > a {
-  color: #9d9d9d;
-}
-.navbar-inverse .navbar-nav > li > a:hover,
-.navbar-inverse .navbar-nav > li > a:focus {
-  color: #fff;
-  background-color: transparent;
-}
-.navbar-inverse .navbar-nav > .active > a,
-.navbar-inverse .navbar-nav > .active > a:hover,
-.navbar-inverse .navbar-nav > .active > a:focus {
-  color: #fff;
-  background-color: #080808;
-}
-.navbar-inverse .navbar-nav > .disabled > a,
-.navbar-inverse .navbar-nav > .disabled > a:hover,
-.navbar-inverse .navbar-nav > .disabled > a:focus {
-  color: #444;
-  background-color: transparent;
-}
-.navbar-inverse .navbar-toggle {
-  border-color: #333;
-}
-.navbar-inverse .navbar-toggle:hover,
-.navbar-inverse .navbar-toggle:focus {
-  background-color: #333;
-}
-.navbar-inverse .navbar-toggle .icon-bar {
-  background-color: #fff;
-}
-.navbar-inverse .navbar-collapse,
-.navbar-inverse .navbar-form {
-  border-color: #101010;
-}
-.navbar-inverse .navbar-nav > .open > a,
-.navbar-inverse .navbar-nav > .open > a:hover,
-.navbar-inverse .navbar-nav > .open > a:focus {
-  background-color: #080808;
-  color: #fff;
-}
-@media (max-width: 540px) {
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
-    border-color: #080808;
-  }
-  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
-    background-color: #080808;
-  }
-  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
-    color: #9d9d9d;
-  }
-  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
-  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
-    color: #fff;
-    background-color: transparent;
-  }
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
-    color: #fff;
-    background-color: #080808;
-  }
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
-  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
-    color: #444;
-    background-color: transparent;
-  }
-}
-.navbar-inverse .navbar-link {
-  color: #9d9d9d;
-}
-.navbar-inverse .navbar-link:hover {
-  color: #fff;
-}
-.navbar-inverse .btn-link {
-  color: #9d9d9d;
-}
-.navbar-inverse .btn-link:hover,
-.navbar-inverse .btn-link:focus {
-  color: #fff;
-}
-.navbar-inverse .btn-link[disabled]:hover,
-fieldset[disabled] .navbar-inverse .btn-link:hover,
-.navbar-inverse .btn-link[disabled]:focus,
-fieldset[disabled] .navbar-inverse .btn-link:focus {
-  color: #444;
-}
-.breadcrumb {
-  padding: 8px 15px;
-  margin-bottom: 18px;
-  list-style: none;
-  background-color: #f5f5f5;
-  border-radius: 2px;
-}
-.breadcrumb > li {
-  display: inline-block;
-}
-.breadcrumb > li + li:before {
-  content: "/\00a0";
-  padding: 0 5px;
-  color: #5e5e5e;
-}
-.breadcrumb > .active {
-  color: #777777;
-}
-.pagination {
-  display: inline-block;
-  padding-left: 0;
-  margin: 18px 0;
-  border-radius: 2px;
-}
-.pagination > li {
-  display: inline;
-}
-.pagination > li > a,
-.pagination > li > span {
-  position: relative;
-  float: left;
-  padding: 6px 12px;
-  line-height: 1.42857143;
-  text-decoration: none;
-  color: #337ab7;
-  background-color: #fff;
-  border: 1px solid #ddd;
-  margin-left: -1px;
-}
-.pagination > li:first-child > a,
-.pagination > li:first-child > span {
-  margin-left: 0;
-  border-bottom-left-radius: 2px;
-  border-top-left-radius: 2px;
-}
-.pagination > li:last-child > a,
-.pagination > li:last-child > span {
-  border-bottom-right-radius: 2px;
-  border-top-right-radius: 2px;
-}
-.pagination > li > a:hover,
-.pagination > li > span:hover,
-.pagination > li > a:focus,
-.pagination > li > span:focus {
-  z-index: 2;
-  color: #23527c;
-  background-color: #eeeeee;
-  border-color: #ddd;
-}
-.pagination > .active > a,
-.pagination > .active > span,
-.pagination > .active > a:hover,
-.pagination > .active > span:hover,
-.pagination > .active > a:focus,
-.pagination > .active > span:focus {
-  z-index: 3;
-  color: #fff;
-  background-color: #337ab7;
-  border-color: #337ab7;
-  cursor: default;
-}
-.pagination > .disabled > span,
-.pagination > .disabled > span:hover,
-.pagination > .disabled > span:focus,
-.pagination > .disabled > a,
-.pagination > .disabled > a:hover,
-.pagination > .disabled > a:focus {
-  color: #777777;
-  background-color: #fff;
-  border-color: #ddd;
-  cursor: not-allowed;
-}
-.pagination-lg > li > a,
-.pagination-lg > li > span {
-  padding: 10px 16px;
-  font-size: 17px;
-  line-height: 1.3333333;
-}
-.pagination-lg > li:first-child > a,
-.pagination-lg > li:first-child > span {
-  border-bottom-left-radius: 3px;
-  border-top-left-radius: 3px;
-}
-.pagination-lg > li:last-child > a,
-.pagination-lg > li:last-child > span {
-  border-bottom-right-radius: 3px;
-  border-top-right-radius: 3px;
-}
-.pagination-sm > li > a,
-.pagination-sm > li > span {
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-}
-.pagination-sm > li:first-child > a,
-.pagination-sm > li:first-child > span {
-  border-bottom-left-radius: 1px;
-  border-top-left-radius: 1px;
-}
-.pagination-sm > li:last-child > a,
-.pagination-sm > li:last-child > span {
-  border-bottom-right-radius: 1px;
-  border-top-right-radius: 1px;
-}
-.pager {
-  padding-left: 0;
-  margin: 18px 0;
-  list-style: none;
-  text-align: center;
-}
-.pager li {
-  display: inline;
-}
-.pager li > a,
-.pager li > span {
-  display: inline-block;
-  padding: 5px 14px;
-  background-color: #fff;
-  border: 1px solid #ddd;
-  border-radius: 15px;
-}
-.pager li > a:hover,
-.pager li > a:focus {
-  text-decoration: none;
-  background-color: #eeeeee;
-}
-.pager .next > a,
-.pager .next > span {
-  float: right;
-}
-.pager .previous > a,
-.pager .previous > span {
-  float: left;
-}
-.pager .disabled > a,
-.pager .disabled > a:hover,
-.pager .disabled > a:focus,
-.pager .disabled > span {
-  color: #777777;
-  background-color: #fff;
-  cursor: not-allowed;
-}
-.label {
-  display: inline;
-  padding: .2em .6em .3em;
-  font-size: 75%;
-  font-weight: bold;
-  line-height: 1;
-  color: #fff;
-  text-align: center;
-  white-space: nowrap;
-  vertical-align: baseline;
-  border-radius: .25em;
-}
-a.label:hover,
-a.label:focus {
-  color: #fff;
-  text-decoration: none;
-  cursor: pointer;
-}
-.label:empty {
-  display: none;
-}
-.btn .label {
-  position: relative;
-  top: -1px;
-}
-.label-default {
-  background-color: #777777;
-}
-.label-default[href]:hover,
-.label-default[href]:focus {
-  background-color: #5e5e5e;
-}
-.label-primary {
-  background-color: #337ab7;
-}
-.label-primary[href]:hover,
-.label-primary[href]:focus {
-  background-color: #286090;
-}
-.label-success {
-  background-color: #5cb85c;
-}
-.label-success[href]:hover,
-.label-success[href]:focus {
-  background-color: #449d44;
-}
-.label-info {
-  background-color: #5bc0de;
-}
-.label-info[href]:hover,
-.label-info[href]:focus {
-  background-color: #31b0d5;
-}
-.label-warning {
-  background-color: #f0ad4e;
-}
-.label-warning[href]:hover,
-.label-warning[href]:focus {
-  background-color: #ec971f;
-}
-.label-danger {
-  background-color: #d9534f;
-}
-.label-danger[href]:hover,
-.label-danger[href]:focus {
-  background-color: #c9302c;
-}
-.badge {
-  display: inline-block;
-  min-width: 10px;
-  padding: 3px 7px;
-  font-size: 12px;
-  font-weight: bold;
-  color: #fff;
-  line-height: 1;
-  vertical-align: middle;
-  white-space: nowrap;
-  text-align: center;
-  background-color: #777777;
-  border-radius: 10px;
-}
-.badge:empty {
-  display: none;
-}
-.btn .badge {
-  position: relative;
-  top: -1px;
-}
-.btn-xs .badge,
-.btn-group-xs > .btn .badge {
-  top: 0;
-  padding: 1px 5px;
-}
-a.badge:hover,
-a.badge:focus {
-  color: #fff;
-  text-decoration: none;
-  cursor: pointer;
-}
-.list-group-item.active > .badge,
-.nav-pills > .active > a > .badge {
-  color: #337ab7;
-  background-color: #fff;
-}
-.list-group-item > .badge {
-  float: right;
-}
-.list-group-item > .badge + .badge {
-  margin-right: 5px;
-}
-.nav-pills > li > a > .badge {
-  margin-left: 3px;
-}
-.jumbotron {
-  padding-top: 30px;
-  padding-bottom: 30px;
-  margin-bottom: 30px;
-  color: inherit;
-  background-color: #eeeeee;
-}
-.jumbotron h1,
-.jumbotron .h1 {
-  color: inherit;
-}
-.jumbotron p {
-  margin-bottom: 15px;
-  font-size: 20px;
-  font-weight: 200;
-}
-.jumbotron > hr {
-  border-top-color: #d5d5d5;
-}
-.container .jumbotron,
-.container-fluid .jumbotron {
-  border-radius: 3px;
-  padding-left: 0px;
-  padding-right: 0px;
-}
-.jumbotron .container {
-  max-width: 100%;
-}
-@media screen and (min-width: 768px) {
-  .jumbotron {
-    padding-top: 48px;
-    padding-bottom: 48px;
-  }
-  .container .jumbotron,
-  .container-fluid .jumbotron {
-    padding-left: 60px;
-    padding-right: 60px;
-  }
-  .jumbotron h1,
-  .jumbotron .h1 {
-    font-size: 59px;
-  }
-}
-.thumbnail {
-  display: block;
-  padding: 4px;
-  margin-bottom: 18px;
-  line-height: 1.42857143;
-  background-color: #fff;
-  border: 1px solid #ddd;
-  border-radius: 2px;
-  -webkit-transition: border 0.2s ease-in-out;
-  -o-transition: border 0.2s ease-in-out;
-  transition: border 0.2s ease-in-out;
-}
-.thumbnail > img,
-.thumbnail a > img {
-  margin-left: auto;
-  margin-right: auto;
-}
-a.thumbnail:hover,
-a.thumbnail:focus,
-a.thumbnail.active {
-  border-color: #337ab7;
-}
-.thumbnail .caption {
-  padding: 9px;
-  color: #000;
-}
-.alert {
-  padding: 15px;
-  margin-bottom: 18px;
-  border: 1px solid transparent;
-  border-radius: 2px;
-}
-.alert h4 {
-  margin-top: 0;
-  color: inherit;
-}
-.alert .alert-link {
-  font-weight: bold;
-}
-.alert > p,
-.alert > ul {
-  margin-bottom: 0;
-}
-.alert > p + p {
-  margin-top: 5px;
-}
-.alert-dismissable,
-.alert-dismissible {
-  padding-right: 35px;
-}
-.alert-dismissable .close,
-.alert-dismissible .close {
-  position: relative;
-  top: -2px;
-  right: -21px;
-  color: inherit;
-}
-.alert-success {
-  background-color: #dff0d8;
-  border-color: #d6e9c6;
-  color: #3c763d;
-}
-.alert-success hr {
-  border-top-color: #c9e2b3;
-}
-.alert-success .alert-link {
-  color: #2b542c;
-}
-.alert-info {
-  background-color: #d9edf7;
-  border-color: #bce8f1;
-  color: #31708f;
-}
-.alert-info hr {
-  border-top-color: #a6e1ec;
-}
-.alert-info .alert-link {
-  color: #245269;
-}
-.alert-warning {
-  background-color: #fcf8e3;
-  border-color: #faebcc;
-  color: #8a6d3b;
-}
-.alert-warning hr {
-  border-top-color: #f7e1b5;
-}
-.alert-warning .alert-link {
-  color: #66512c;
-}
-.alert-danger {
-  background-color: #f2dede;
-  border-color: #ebccd1;
-  color: #a94442;
-}
-.alert-danger hr {
-  border-top-color: #e4b9c0;
-}
-.alert-danger .alert-link {
-  color: #843534;
-}
-@-webkit-keyframes progress-bar-stripes {
-  from {
-    background-position: 40px 0;
-  }
-  to {
-    background-position: 0 0;
-  }
-}
-@keyframes progress-bar-stripes {
-  from {
-    background-position: 40px 0;
-  }
-  to {
-    background-position: 0 0;
-  }
-}
-.progress {
-  overflow: hidden;
-  height: 18px;
-  margin-bottom: 18px;
-  background-color: #f5f5f5;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
-  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
-}
-.progress-bar {
-  float: left;
-  width: 0%;
-  height: 100%;
-  font-size: 12px;
-  line-height: 18px;
-  color: #fff;
-  text-align: center;
-  background-color: #337ab7;
-  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
-  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
-  -webkit-transition: width 0.6s ease;
-  -o-transition: width 0.6s ease;
-  transition: width 0.6s ease;
-}
-.progress-striped .progress-bar,
-.progress-bar-striped {
-  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-size: 40px 40px;
-}
-.progress.active .progress-bar,
-.progress-bar.active {
-  -webkit-animation: progress-bar-stripes 2s linear infinite;
-  -o-animation: progress-bar-stripes 2s linear infinite;
-  animation: progress-bar-stripes 2s linear infinite;
-}
-.progress-bar-success {
-  background-color: #5cb85c;
-}
-.progress-striped .progress-bar-success {
-  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-}
-.progress-bar-info {
-  background-color: #5bc0de;
-}
-.progress-striped .progress-bar-info {
-  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-}
-.progress-bar-warning {
-  background-color: #f0ad4e;
-}
-.progress-striped .progress-bar-warning {
-  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-}
-.progress-bar-danger {
-  background-color: #d9534f;
-}
-.progress-striped .progress-bar-danger {
-  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
-}
-.media {
-  margin-top: 15px;
-}
-.media:first-child {
-  margin-top: 0;
-}
-.media,
-.media-body {
-  zoom: 1;
-  overflow: hidden;
-}
-.media-body {
-  width: 10000px;
-}
-.media-object {
-  display: block;
-}
-.media-object.img-thumbnail {
-  max-width: none;
-}
-.media-right,
-.media > .pull-right {
-  padding-left: 10px;
-}
-.media-left,
-.media > .pull-left {
-  padding-right: 10px;
-}
-.media-left,
-.media-right,
-.media-body {
-  display: table-cell;
-  vertical-align: top;
-}
-.media-middle {
-  vertical-align: middle;
-}
-.media-bottom {
-  vertical-align: bottom;
-}
-.media-heading {
-  margin-top: 0;
-  margin-bottom: 5px;
-}
-.media-list {
-  padding-left: 0;
-  list-style: none;
-}
-.list-group {
-  margin-bottom: 20px;
-  padding-left: 0;
-}
-.list-group-item {
-  position: relative;
-  display: block;
-  padding: 10px 15px;
-  margin-bottom: -1px;
-  background-color: #fff;
-  border: 1px solid #ddd;
-}
-.list-group-item:first-child {
-  border-top-right-radius: 2px;
-  border-top-left-radius: 2px;
-}
-.list-group-item:last-child {
-  margin-bottom: 0;
-  border-bottom-right-radius: 2px;
-  border-bottom-left-radius: 2px;
-}
-a.list-group-item,
-button.list-group-item {
-  color: #555;
-}
-a.list-group-item .list-group-item-heading,
-button.list-group-item .list-group-item-heading {
-  color: #333;
-}
-a.list-group-item:hover,
-button.list-group-item:hover,
-a.list-group-item:focus,
-button.list-group-item:focus {
-  text-decoration: none;
-  color: #555;
-  background-color: #f5f5f5;
-}
-button.list-group-item {
-  width: 100%;
-  text-align: left;
-}
-.list-group-item.disabled,
-.list-group-item.disabled:hover,
-.list-group-item.disabled:focus {
-  background-color: #eeeeee;
-  color: #777777;
-  cursor: not-allowed;
-}
-.list-group-item.disabled .list-group-item-heading,
-.list-group-item.disabled:hover .list-group-item-heading,
-.list-group-item.disabled:focus .list-group-item-heading {
-  color: inherit;
-}
-.list-group-item.disabled .list-group-item-text,
-.list-group-item.disabled:hover .list-group-item-text,
-.list-group-item.disabled:focus .list-group-item-text {
-  color: #777777;
-}
-.list-group-item.active,
-.list-group-item.active:hover,
-.list-group-item.active:focus {
-  z-index: 2;
-  color: #fff;
-  background-color: #337ab7;
-  border-color: #337ab7;
-}
-.list-group-item.active .list-group-item-heading,
-.list-group-item.active:hover .list-group-item-heading,
-.list-group-item.active:focus .list-group-item-heading,
-.list-group-item.active .list-group-item-heading > small,
-.list-group-item.active:hover .list-group-item-heading > small,
-.list-group-item.active:focus .list-group-item-heading > small,
-.list-group-item.active .list-group-item-heading > .small,
-.list-group-item.active:hover .list-group-item-heading > .small,
-.list-group-item.active:focus .list-group-item-heading > .small {
-  color: inherit;
-}
-.list-group-item.active .list-group-item-text,
-.list-group-item.active:hover .list-group-item-text,
-.list-group-item.active:focus .list-group-item-text {
-  color: #c7ddef;
-}
-.list-group-item-success {
-  color: #3c763d;
-  background-color: #dff0d8;
-}
-a.list-group-item-success,
-button.list-group-item-success {
-  color: #3c763d;
-}
-a.list-group-item-success .list-group-item-heading,
-button.list-group-item-success .list-group-item-heading {
-  color: inherit;
-}
-a.list-group-item-success:hover,
-button.list-group-item-success:hover,
-a.list-group-item-success:focus,
-button.list-group-item-success:focus {
-  color: #3c763d;
-  background-color: #d0e9c6;
-}
-a.list-group-item-success.active,
-button.list-group-item-success.active,
-a.list-group-item-success.active:hover,
-button.list-group-item-success.active:hover,
-a.list-group-item-success.active:focus,
-button.list-group-item-success.active:focus {
-  color: #fff;
-  background-color: #3c763d;
-  border-color: #3c763d;
-}
-.list-group-item-info {
-  color: #31708f;
-  background-color: #d9edf7;
-}
-a.list-group-item-info,
-button.list-group-item-info {
-  color: #31708f;
-}
-a.list-group-item-info .list-group-item-heading,
-button.list-group-item-info .list-group-item-heading {
-  color: inherit;
-}
-a.list-group-item-info:hover,
-button.list-group-item-info:hover,
-a.list-group-item-info:focus,
-button.list-group-item-info:focus {
-  color: #31708f;
-  background-color: #c4e3f3;
-}
-a.list-group-item-info.active,
-button.list-group-item-info.active,
-a.list-group-item-info.active:hover,
-button.list-group-item-info.active:hover,
-a.list-group-item-info.active:focus,
-button.list-group-item-info.active:focus {
-  color: #fff;
-  background-color: #31708f;
-  border-color: #31708f;
-}
-.list-group-item-warning {
-  color: #8a6d3b;
-  background-color: #fcf8e3;
-}
-a.list-group-item-warning,
-button.list-group-item-warning {
-  color: #8a6d3b;
-}
-a.list-group-item-warning .list-group-item-heading,
-button.list-group-item-warning .list-group-item-heading {
-  color: inherit;
-}
-a.list-group-item-warning:hover,
-button.list-group-item-warning:hover,
-a.list-group-item-warning:focus,
-button.list-group-item-warning:focus {
-  color: #8a6d3b;
-  background-color: #faf2cc;
-}
-a.list-group-item-warning.active,
-button.list-group-item-warning.active,
-a.list-group-item-warning.active:hover,
-button.list-group-item-warning.active:hover,
-a.list-group-item-warning.active:focus,
-button.list-group-item-warning.active:focus {
-  color: #fff;
-  background-color: #8a6d3b;
-  border-color: #8a6d3b;
-}
-.list-group-item-danger {
-  color: #a94442;
-  background-color: #f2dede;
-}
-a.list-group-item-danger,
-button.list-group-item-danger {
-  color: #a94442;
-}
-a.list-group-item-danger .list-group-item-heading,
-button.list-group-item-danger .list-group-item-heading {
-  color: inherit;
-}
-a.list-group-item-danger:hover,
-button.list-group-item-danger:hover,
-a.list-group-item-danger:focus,
-button.list-group-item-danger:focus {
-  color: #a94442;
-  background-color: #ebcccc;
-}
-a.list-group-item-danger.active,
-button.list-group-item-danger.active,
-a.list-group-item-danger.active:hover,
-button.list-group-item-danger.active:hover,
-a.list-group-item-danger.active:focus,
-button.list-group-item-danger.active:focus {
-  color: #fff;
-  background-color: #a94442;
-  border-color: #a94442;
-}
-.list-group-item-heading {
-  margin-top: 0;
-  margin-bottom: 5px;
-}
-.list-group-item-text {
-  margin-bottom: 0;
-  line-height: 1.3;
-}
-.panel {
-  margin-bottom: 18px;
-  background-color: #fff;
-  border: 1px solid transparent;
-  border-radius: 2px;
-  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
-  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
-}
-.panel-body {
-  padding: 15px;
-}
-.panel-heading {
-  padding: 10px 15px;
-  border-bottom: 1px solid transparent;
-  border-top-right-radius: 1px;
-  border-top-left-radius: 1px;
-}
-.panel-heading > .dropdown .dropdown-toggle {
-  color: inherit;
-}
-.panel-title {
-  margin-top: 0;
-  margin-bottom: 0;
-  font-size: 15px;
-  color: inherit;
-}
-.panel-title > a,
-.panel-title > small,
-.panel-title > .small,
-.panel-title > small > a,
-.panel-title > .small > a {
-  color: inherit;
-}
-.panel-footer {
-  padding: 10px 15px;
-  background-color: #f5f5f5;
-  border-top: 1px solid #ddd;
-  border-bottom-right-radius: 1px;
-  border-bottom-left-radius: 1px;
-}
-.panel > .list-group,
-.panel > .panel-collapse > .list-group {
-  margin-bottom: 0;
-}
-.panel > .list-group .list-group-item,
-.panel > .panel-collapse > .list-group .list-group-item {
-  border-width: 1px 0;
-  border-radius: 0;
-}
-.panel > .list-group:first-child .list-group-item:first-child,
-.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
-  border-top: 0;
-  border-top-right-radius: 1px;
-  border-top-left-radius: 1px;
-}
-.panel > .list-group:last-child .list-group-item:last-child,
-.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
-  border-bottom: 0;
-  border-bottom-right-radius: 1px;
-  border-bottom-left-radius: 1px;
-}
-.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
-  border-top-right-radius: 0;
-  border-top-left-radius: 0;
-}
-.panel-heading + .list-group .list-group-item:first-child {
-  border-top-width: 0;
-}
-.list-group + .panel-footer {
-  border-top-width: 0;
-}
-.panel > .table,
-.panel > .table-responsive > .table,
-.panel > .panel-collapse > .table {
-  margin-bottom: 0;
-}
-.panel > .table caption,
-.panel > .table-responsive > .table caption,
-.panel > .panel-collapse > .table caption {
-  padding-left: 15px;
-  padding-right: 15px;
-}
-.panel > .table:first-child,
-.panel > .table-responsive:first-child > .table:first-child {
-  border-top-right-radius: 1px;
-  border-top-left-radius: 1px;
-}
-.panel > .table:first-child > thead:first-child > tr:first-child,
-.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
-.panel > .table:first-child > tbody:first-child > tr:first-child,
-.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
-  border-top-left-radius: 1px;
-  border-top-right-radius: 1px;
-}
-.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
-.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
-.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
-.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
-.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
-.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
-.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
-.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
-  border-top-left-radius: 1px;
-}
-.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
-.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
-.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
-.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
-.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
-.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
-.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
-.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
-  border-top-right-radius: 1px;
-}
-.panel > .table:last-child,
-.panel > .table-responsive:last-child > .table:last-child {
-  border-bottom-right-radius: 1px;
-  border-bottom-left-radius: 1px;
-}
-.panel > .table:last-child > tbody:last-child > tr:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
-.panel > .table:last-child > tfoot:last-child > tr:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
-  border-bottom-left-radius: 1px;
-  border-bottom-right-radius: 1px;
-}
-.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
-.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
-.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
-.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
-.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
-.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
-.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
-.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
-  border-bottom-left-radius: 1px;
-}
-.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
-.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
-.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
-.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
-.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
-  border-bottom-right-radius: 1px;
-}
-.panel > .panel-body + .table,
-.panel > .panel-body + .table-responsive,
-.panel > .table + .panel-body,
-.panel > .table-responsive + .panel-body {
-  border-top: 1px solid #ddd;
-}
-.panel > .table > tbody:first-child > tr:first-child th,
-.panel > .table > tbody:first-child > tr:first-child td {
-  border-top: 0;
-}
-.panel > .table-bordered,
-.panel > .table-responsive > .table-bordered {
-  border: 0;
-}
-.panel > .table-bordered > thead > tr > th:first-child,
-.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
-.panel > .table-bordered > tbody > tr > th:first-child,
-.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
-.panel > .table-bordered > tfoot > tr > th:first-child,
-.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
-.panel > .table-bordered > thead > tr > td:first-child,
-.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
-.panel > .table-bordered > tbody > tr > td:first-child,
-.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
-.panel > .table-bordered > tfoot > tr > td:first-child,
-.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
-  border-left: 0;
-}
-.panel > .table-bordered > thead > tr > th:last-child,
-.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
-.panel > .table-bordered > tbody > tr > th:last-child,
-.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
-.panel > .table-bordered > tfoot > tr > th:last-child,
-.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
-.panel > .table-bordered > thead > tr > td:last-child,
-.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
-.panel > .table-bordered > tbody > tr > td:last-child,
-.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
-.panel > .table-bordered > tfoot > tr > td:last-child,
-.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
-  border-right: 0;
-}
-.panel > .table-bordered > thead > tr:first-child > td,
-.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
-.panel > .table-bordered > tbody > tr:first-child > td,
-.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
-.panel > .table-bordered > thead > tr:first-child > th,
-.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
-.panel > .table-bordered > tbody > tr:first-child > th,
-.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
-  border-bottom: 0;
-}
-.panel > .table-bordered > tbody > tr:last-child > td,
-.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
-.panel > .table-bordered > tfoot > tr:last-child > td,
-.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
-.panel > .table-bordered > tbody > tr:last-child > th,
-.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
-.panel > .table-bordered > tfoot > tr:last-child > th,
-.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
-  border-bottom: 0;
-}
-.panel > .table-responsive {
-  border: 0;
-  margin-bottom: 0;
-}
-.panel-group {
-  margin-bottom: 18px;
-}
-.panel-group .panel {
-  margin-bottom: 0;
-  border-radius: 2px;
-}
-.panel-group .panel + .panel {
-  margin-top: 5px;
-}
-.panel-group .panel-heading {
-  border-bottom: 0;
-}
-.panel-group .panel-heading + .panel-collapse > .panel-body,
-.panel-group .panel-heading + .panel-collapse > .list-group {
-  border-top: 1px solid #ddd;
-}
-.panel-group .panel-footer {
-  border-top: 0;
-}
-.panel-group .panel-footer + .panel-collapse .panel-body {
-  border-bottom: 1px solid #ddd;
-}
-.panel-default {
-  border-color: #ddd;
-}
-.panel-default > .panel-heading {
-  color: #333333;
-  background-color: #f5f5f5;
-  border-color: #ddd;
-}
-.panel-default > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #ddd;
-}
-.panel-default > .panel-heading .badge {
-  color: #f5f5f5;
-  background-color: #333333;
-}
-.panel-default > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #ddd;
-}
-.panel-primary {
-  border-color: #337ab7;
-}
-.panel-primary > .panel-heading {
-  color: #fff;
-  background-color: #337ab7;
-  border-color: #337ab7;
-}
-.panel-primary > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #337ab7;
-}
-.panel-primary > .panel-heading .badge {
-  color: #337ab7;
-  background-color: #fff;
-}
-.panel-primary > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #337ab7;
-}
-.panel-success {
-  border-color: #d6e9c6;
-}
-.panel-success > .panel-heading {
-  color: #3c763d;
-  background-color: #dff0d8;
-  border-color: #d6e9c6;
-}
-.panel-success > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #d6e9c6;
-}
-.panel-success > .panel-heading .badge {
-  color: #dff0d8;
-  background-color: #3c763d;
-}
-.panel-success > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #d6e9c6;
-}
-.panel-info {
-  border-color: #bce8f1;
-}
-.panel-info > .panel-heading {
-  color: #31708f;
-  background-color: #d9edf7;
-  border-color: #bce8f1;
-}
-.panel-info > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #bce8f1;
-}
-.panel-info > .panel-heading .badge {
-  color: #d9edf7;
-  background-color: #31708f;
-}
-.panel-info > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #bce8f1;
-}
-.panel-warning {
-  border-color: #faebcc;
-}
-.panel-warning > .panel-heading {
-  color: #8a6d3b;
-  background-color: #fcf8e3;
-  border-color: #faebcc;
-}
-.panel-warning > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #faebcc;
-}
-.panel-warning > .panel-heading .badge {
-  color: #fcf8e3;
-  background-color: #8a6d3b;
-}
-.panel-warning > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #faebcc;
-}
-.panel-danger {
-  border-color: #ebccd1;
-}
-.panel-danger > .panel-heading {
-  color: #a94442;
-  background-color: #f2dede;
-  border-color: #ebccd1;
-}
-.panel-danger > .panel-heading + .panel-collapse > .panel-body {
-  border-top-color: #ebccd1;
-}
-.panel-danger > .panel-heading .badge {
-  color: #f2dede;
-  background-color: #a94442;
-}
-.panel-danger > .panel-footer + .panel-collapse > .panel-body {
-  border-bottom-color: #ebccd1;
-}
-.embed-responsive {
-  position: relative;
-  display: block;
-  height: 0;
-  padding: 0;
-  overflow: hidden;
-}
-.embed-responsive .embed-responsive-item,
-.embed-responsive iframe,
-.embed-responsive embed,
-.embed-responsive object,
-.embed-responsive video {
-  position: absolute;
-  top: 0;
-  left: 0;
-  bottom: 0;
-  height: 100%;
-  width: 100%;
-  border: 0;
-}
-.embed-responsive-16by9 {
-  padding-bottom: 56.25%;
-}
-.embed-responsive-4by3 {
-  padding-bottom: 75%;
-}
-.well {
-  min-height: 20px;
-  padding: 19px;
-  margin-bottom: 20px;
-  background-color: #f5f5f5;
-  border: 1px solid #e3e3e3;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
-}
-.well blockquote {
-  border-color: #ddd;
-  border-color: rgba(0, 0, 0, 0.15);
-}
-.well-lg {
-  padding: 24px;
-  border-radius: 3px;
-}
-.well-sm {
-  padding: 9px;
-  border-radius: 1px;
-}
-.close {
-  float: right;
-  font-size: 19.5px;
-  font-weight: bold;
-  line-height: 1;
-  color: #000;
-  text-shadow: 0 1px 0 #fff;
-  opacity: 0.2;
-  filter: alpha(opacity=20);
-}
-.close:hover,
-.close:focus {
-  color: #000;
-  text-decoration: none;
-  cursor: pointer;
-  opacity: 0.5;
-  filter: alpha(opacity=50);
-}
-button.close {
-  padding: 0;
-  cursor: pointer;
-  background: transparent;
-  border: 0;
-  -webkit-appearance: none;
-}
-.modal-open {
-  overflow: hidden;
-}
-.modal {
-  display: none;
-  overflow: hidden;
-  position: fixed;
-  top: 0;
-  right: 0;
-  bottom: 0;
-  left: 0;
-  z-index: 1050;
-  -webkit-overflow-scrolling: touch;
-  outline: 0;
-}
-.modal.fade .modal-dialog {
-  -webkit-transform: translate(0, -25%);
-  -ms-transform: translate(0, -25%);
-  -o-transform: translate(0, -25%);
-  transform: translate(0, -25%);
-  -webkit-transition: -webkit-transform 0.3s ease-out;
-  -moz-transition: -moz-transform 0.3s ease-out;
-  -o-transition: -o-transform 0.3s ease-out;
-  transition: transform 0.3s ease-out;
-}
-.modal.in .modal-dialog {
-  -webkit-transform: translate(0, 0);
-  -ms-transform: translate(0, 0);
-  -o-transform: translate(0, 0);
-  transform: translate(0, 0);
-}
-.modal-open .modal {
-  overflow-x: hidden;
-  overflow-y: auto;
-}
-.modal-dialog {
-  position: relative;
-  width: auto;
-  margin: 10px;
-}
-.modal-content {
-  position: relative;
-  background-color: #fff;
-  border: 1px solid #999;
-  border: 1px solid rgba(0, 0, 0, 0.2);
-  border-radius: 3px;
-  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
-  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
-  background-clip: padding-box;
-  outline: 0;
-}
-.modal-backdrop {
-  position: fixed;
-  top: 0;
-  right: 0;
-  bottom: 0;
-  left: 0;
-  z-index: 1040;
-  background-color: #000;
-}
-.modal-backdrop.fade {
-  opacity: 0;
-  filter: alpha(opacity=0);
-}
-.modal-backdrop.in {
-  opacity: 0.5;
-  filter: alpha(opacity=50);
-}
-.modal-header {
-  padding: 15px;
-  border-bottom: 1px solid #e5e5e5;
-}
-.modal-header .close {
-  margin-top: -2px;
-}
-.modal-title {
-  margin: 0;
-  line-height: 1.42857143;
-}
-.modal-body {
-  position: relative;
-  padding: 15px;
-}
-.modal-footer {
-  padding: 15px;
-  text-align: right;
-  border-top: 1px solid #e5e5e5;
-}
-.modal-footer .btn + .btn {
-  margin-left: 5px;
-  margin-bottom: 0;
-}
-.modal-footer .btn-group .btn + .btn {
-  margin-left: -1px;
-}
-.modal-footer .btn-block + .btn-block {
-  margin-left: 0;
-}
-.modal-scrollbar-measure {
-  position: absolute;
-  top: -9999px;
-  width: 50px;
-  height: 50px;
-  overflow: scroll;
-}
-@media (min-width: 768px) {
-  .modal-dialog {
-    width: 600px;
-    margin: 30px auto;
-  }
-  .modal-content {
-    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
-    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
-  }
-  .modal-sm {
-    width: 300px;
-  }
-}
-@media (min-width: 992px) {
-  .modal-lg {
-    width: 900px;
-  }
-}
-.tooltip {
-  position: absolute;
-  z-index: 1070;
-  display: block;
-  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
-  font-style: normal;
-  font-weight: normal;
-  letter-spacing: normal;
-  line-break: auto;
-  line-height: 1.42857143;
-  text-align: left;
-  text-align: start;
-  text-decoration: none;
-  text-shadow: none;
-  text-transform: none;
-  white-space: normal;
-  word-break: normal;
-  word-spacing: normal;
-  word-wrap: normal;
-  font-size: 12px;
-  opacity: 0;
-  filter: alpha(opacity=0);
-}
-.tooltip.in {
-  opacity: 0.9;
-  filter: alpha(opacity=90);
-}
-.tooltip.top {
-  margin-top: -3px;
-  padding: 5px 0;
-}
-.tooltip.right {
-  margin-left: 3px;
-  padding: 0 5px;
-}
-.tooltip.bottom {
-  margin-top: 3px;
-  padding: 5px 0;
-}
-.tooltip.left {
-  margin-left: -3px;
-  padding: 0 5px;
-}
-.tooltip-inner {
-  max-width: 200px;
-  padding: 3px 8px;
-  color: #fff;
-  text-align: center;
-  background-color: #000;
-  border-radius: 2px;
-}
-.tooltip-arrow {
-  position: absolute;
-  width: 0;
-  height: 0;
-  border-color: transparent;
-  border-style: solid;
-}
-.tooltip.top .tooltip-arrow {
-  bottom: 0;
-  left: 50%;
-  margin-left: -5px;
-  border-width: 5px 5px 0;
-  border-top-color: #000;
-}
-.tooltip.top-left .tooltip-arrow {
-  bottom: 0;
-  right: 5px;
-  margin-bottom: -5px;
-  border-width: 5px 5px 0;
-  border-top-color: #000;
-}
-.tooltip.top-right .tooltip-arrow {
-  bottom: 0;
-  left: 5px;
-  margin-bottom: -5px;
-  border-width: 5px 5px 0;
-  border-top-color: #000;
-}
-.tooltip.right .tooltip-arrow {
-  top: 50%;
-  left: 0;
-  margin-top: -5px;
-  border-width: 5px 5px 5px 0;
-  border-right-color: #000;
-}
-.tooltip.left .tooltip-arrow {
-  top: 50%;
-  right: 0;
-  margin-top: -5px;
-  border-width: 5px 0 5px 5px;
-  border-left-color: #000;
-}
-.tooltip.bottom .tooltip-arrow {
-  top: 0;
-  left: 50%;
-  margin-left: -5px;
-  border-width: 0 5px 5px;
-  border-bottom-color: #000;
-}
-.tooltip.bottom-left .tooltip-arrow {
-  top: 0;
-  right: 5px;
-  margin-top: -5px;
-  border-width: 0 5px 5px;
-  border-bottom-color: #000;
-}
-.tooltip.bottom-right .tooltip-arrow {
-  top: 0;
-  left: 5px;
-  margin-top: -5px;
-  border-width: 0 5px 5px;
-  border-bottom-color: #000;
-}
-.popover {
-  position: absolute;
-  top: 0;
-  left: 0;
-  z-index: 1060;
-  display: none;
-  max-width: 276px;
-  padding: 1px;
-  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
-  font-style: normal;
-  font-weight: normal;
-  letter-spacing: normal;
-  line-break: auto;
-  line-height: 1.42857143;
-  text-align: left;
-  text-align: start;
-  text-decoration: none;
-  text-shadow: none;
-  text-transform: none;
-  white-space: normal;
-  word-break: normal;
-  word-spacing: normal;
-  word-wrap: normal;
-  font-size: 13px;
-  background-color: #fff;
-  background-clip: padding-box;
-  border: 1px solid #ccc;
-  border: 1px solid rgba(0, 0, 0, 0.2);
-  border-radius: 3px;
-  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
-  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
-}
-.popover.top {
-  margin-top: -10px;
-}
-.popover.right {
-  margin-left: 10px;
-}
-.popover.bottom {
-  margin-top: 10px;
-}
-.popover.left {
-  margin-left: -10px;
-}
-.popover-title {
-  margin: 0;
-  padding: 8px 14px;
-  font-size: 13px;
-  background-color: #f7f7f7;
-  border-bottom: 1px solid #ebebeb;
-  border-radius: 2px 2px 0 0;
-}
-.popover-content {
-  padding: 9px 14px;
-}
-.popover > .arrow,
-.popover > .arrow:after {
-  position: absolute;
-  display: block;
-  width: 0;
-  height: 0;
-  border-color: transparent;
-  border-style: solid;
-}
-.popover > .arrow {
-  border-width: 11px;
-}
-.popover > .arrow:after {
-  border-width: 10px;
-  content: "";
-}
-.popover.top > .arrow {
-  left: 50%;
-  margin-left: -11px;
-  border-bottom-width: 0;
-  border-top-color: #999999;
-  border-top-color: rgba(0, 0, 0, 0.25);
-  bottom: -11px;
-}
-.popover.top > .arrow:after {
-  content: " ";
-  bottom: 1px;
-  margin-left: -10px;
-  border-bottom-width: 0;
-  border-top-color: #fff;
-}
-.popover.right > .arrow {
-  top: 50%;
-  left: -11px;
-  margin-top: -11px;
-  border-left-width: 0;
-  border-right-color: #999999;
-  border-right-color: rgba(0, 0, 0, 0.25);
-}
-.popover.right > .arrow:after {
-  content: " ";
-  left: 1px;
-  bottom: -10px;
-  border-left-width: 0;
-  border-right-color: #fff;
-}
-.popover.bottom > .arrow {
-  left: 50%;
-  margin-left: -11px;
-  border-top-width: 0;
-  border-bottom-color: #999999;
-  border-bottom-color: rgba(0, 0, 0, 0.25);
-  top: -11px;
-}
-.popover.bottom > .arrow:after {
-  content: " ";
-  top: 1px;
-  margin-left: -10px;
-  border-top-width: 0;
-  border-bottom-color: #fff;
-}
-.popover.left > .arrow {
-  top: 50%;
-  right: -11px;
-  margin-top: -11px;
-  border-right-width: 0;
-  border-left-color: #999999;
-  border-left-color: rgba(0, 0, 0, 0.25);
-}
-.popover.left > .arrow:after {
-  content: " ";
-  right: 1px;
-  border-right-width: 0;
-  border-left-color: #fff;
-  bottom: -10px;
-}
-.carousel {
-  position: relative;
-}
-.carousel-inner {
-  position: relative;
-  overflow: hidden;
-  width: 100%;
-}
-.carousel-inner > .item {
-  display: none;
-  position: relative;
-  -webkit-transition: 0.6s ease-in-out left;
-  -o-transition: 0.6s ease-in-out left;
-  transition: 0.6s ease-in-out left;
-}
-.carousel-inner > .item > img,
-.carousel-inner > .item > a > img {
-  line-height: 1;
-}
-@media all and (transform-3d), (-webkit-transform-3d) {
-  .carousel-inner > .item {
-    -webkit-transition: -webkit-transform 0.6s ease-in-out;
-    -moz-transition: -moz-transform 0.6s ease-in-out;
-    -o-transition: -o-transform 0.6s ease-in-out;
-    transition: transform 0.6s ease-in-out;
-    -webkit-backface-visibility: hidden;
-    -moz-backface-visibility: hidden;
-    backface-visibility: hidden;
-    -webkit-perspective: 1000px;
-    -moz-perspective: 1000px;
-    perspective: 1000px;
-  }
-  .carousel-inner > .item.next,
-  .carousel-inner > .item.active.right {
-    -webkit-transform: translate3d(100%, 0, 0);
-    transform: translate3d(100%, 0, 0);
-    left: 0;
-  }
-  .carousel-inner > .item.prev,
-  .carousel-inner > .item.active.left {
-    -webkit-transform: translate3d(-100%, 0, 0);
-    transform: translate3d(-100%, 0, 0);
-    left: 0;
-  }
-  .carousel-inner > .item.next.left,
-  .carousel-inner > .item.prev.right,
-  .carousel-inner > .item.active {
-    -webkit-transform: translate3d(0, 0, 0);
-    transform: translate3d(0, 0, 0);
-    left: 0;
-  }
-}
-.carousel-inner > .active,
-.carousel-inner > .next,
-.carousel-inner > .prev {
-  display: block;
-}
-.carousel-inner > .active {
-  left: 0;
-}
-.carousel-inner > .next,
-.carousel-inner > .prev {
-  position: absolute;
-  top: 0;
-  width: 100%;
-}
-.carousel-inner > .next {
-  left: 100%;
-}
-.carousel-inner > .prev {
-  left: -100%;
-}
-.carousel-inner > .next.left,
-.carousel-inner > .prev.right {
-  left: 0;
-}
-.carousel-inner > .active.left {
-  left: -100%;
-}
-.carousel-inner > .active.right {
-  left: 100%;
-}
-.carousel-control {
-  position: absolute;
-  top: 0;
-  left: 0;
-  bottom: 0;
-  width: 15%;
-  opacity: 0.5;
-  filter: alpha(opacity=50);
-  font-size: 20px;
-  color: #fff;
-  text-align: center;
-  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
-  background-color: rgba(0, 0, 0, 0);
-}
-.carousel-control.left {
-  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
-  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
-  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
-  background-repeat: repeat-x;
-  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
-}
-.carousel-control.right {
-  left: auto;
-  right: 0;
-  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
-  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
-  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
-  background-repeat: repeat-x;
-  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
-}
-.carousel-control:hover,
-.carousel-control:focus {
-  outline: 0;
-  color: #fff;
-  text-decoration: none;
-  opacity: 0.9;
-  filter: alpha(opacity=90);
-}
-.carousel-control .icon-prev,
-.carousel-control .icon-next,
-.carousel-control .glyphicon-chevron-left,
-.carousel-control .glyphicon-chevron-right {
-  position: absolute;
-  top: 50%;
-  margin-top: -10px;
-  z-index: 5;
-  display: inline-block;
-}
-.carousel-control .icon-prev,
-.carousel-control .glyphicon-chevron-left {
-  left: 50%;
-  margin-left: -10px;
-}
-.carousel-control .icon-next,
-.carousel-control .glyphicon-chevron-right {
-  right: 50%;
-  margin-right: -10px;
-}
-.carousel-control .icon-prev,
-.carousel-control .icon-next {
-  width: 20px;
-  height: 20px;
-  line-height: 1;
-  font-family: serif;
-}
-.carousel-control .icon-prev:before {
-  content: '\2039';
-}
-.carousel-control .icon-next:before {
-  content: '\203a';
-}
-.carousel-indicators {
-  position: absolute;
-  bottom: 10px;
-  left: 50%;
-  z-index: 15;
-  width: 60%;
-  margin-left: -30%;
-  padding-left: 0;
-  list-style: none;
-  text-align: center;
-}
-.carousel-indicators li {
-  display: inline-block;
-  width: 10px;
-  height: 10px;
-  margin: 1px;
-  text-indent: -999px;
-  border: 1px solid #fff;
-  border-radius: 10px;
-  cursor: pointer;
-  background-color: #000 \9;
-  background-color: rgba(0, 0, 0, 0);
-}
-.carousel-indicators .active {
-  margin: 0;
-  width: 12px;
-  height: 12px;
-  background-color: #fff;
-}
-.carousel-caption {
-  position: absolute;
-  left: 15%;
-  right: 15%;
-  bottom: 20px;
-  z-index: 10;
-  padding-top: 20px;
-  padding-bottom: 20px;
-  color: #fff;
-  text-align: center;
-  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
-}
-.carousel-caption .btn {
-  text-shadow: none;
-}
-@media screen and (min-width: 768px) {
-  .carousel-control .glyphicon-chevron-left,
-  .carousel-control .glyphicon-chevron-right,
-  .carousel-control .icon-prev,
-  .carousel-control .icon-next {
-    width: 30px;
-    height: 30px;
-    margin-top: -10px;
-    font-size: 30px;
-  }
-  .carousel-control .glyphicon-chevron-left,
-  .carousel-control .icon-prev {
-    margin-left: -10px;
-  }
-  .carousel-control .glyphicon-chevron-right,
-  .carousel-control .icon-next {
-    margin-right: -10px;
-  }
-  .carousel-caption {
-    left: 20%;
-    right: 20%;
-    padding-bottom: 30px;
-  }
-  .carousel-indicators {
-    bottom: 20px;
-  }
-}
-.clearfix:before,
-.clearfix:after,
-.dl-horizontal dd:before,
-.dl-horizontal dd:after,
-.container:before,
-.container:after,
-.container-fluid:before,
-.container-fluid:after,
-.row:before,
-.row:after,
-.form-horizontal .form-group:before,
-.form-horizontal .form-group:after,
-.btn-toolbar:before,
-.btn-toolbar:after,
-.btn-group-vertical > .btn-group:before,
-.btn-group-vertical > .btn-group:after,
-.nav:before,
-.nav:after,
-.navbar:before,
-.navbar:after,
-.navbar-header:before,
-.navbar-header:after,
-.navbar-collapse:before,
-.navbar-collapse:after,
-.pager:before,
-.pager:after,
-.panel-body:before,
-.panel-body:after,
-.modal-header:before,
-.modal-header:after,
-.modal-footer:before,
-.modal-footer:after,
-.item_buttons:before,
-.item_buttons:after {
-  content: " ";
-  display: table;
-}
-.clearfix:after,
-.dl-horizontal dd:after,
-.container:after,
-.container-fluid:after,
-.row:after,
-.form-horizontal .form-group:after,
-.btn-toolbar:after,
-.btn-group-vertical > .btn-group:after,
-.nav:after,
-.navbar:after,
-.navbar-header:after,
-.navbar-collapse:after,
-.pager:after,
-.panel-body:after,
-.modal-header:after,
-.modal-footer:after,
-.item_buttons:after {
-  clear: both;
-}
-.center-block {
-  display: block;
-  margin-left: auto;
-  margin-right: auto;
-}
-.pull-right {
-  float: right !important;
-}
-.pull-left {
-  float: left !important;
-}
-.hide {
-  display: none !important;
-}
-.show {
-  display: block !important;
-}
-.invisible {
-  visibility: hidden;
-}
-.text-hide {
-  font: 0/0 a;
-  color: transparent;
-  text-shadow: none;
-  background-color: transparent;
-  border: 0;
-}
-.hidden {
-  display: none !important;
-}
-.affix {
-  position: fixed;
-}
-@-ms-viewport {
-  width: device-width;
-}
-.visible-xs,
-.visible-sm,
-.visible-md,
-.visible-lg {
-  display: none !important;
-}
-.visible-xs-block,
-.visible-xs-inline,
-.visible-xs-inline-block,
-.visible-sm-block,
-.visible-sm-inline,
-.visible-sm-inline-block,
-.visible-md-block,
-.visible-md-inline,
-.visible-md-inline-block,
-.visible-lg-block,
-.visible-lg-inline,
-.visible-lg-inline-block {
-  display: none !important;
-}
-@media (max-width: 767px) {
-  .visible-xs {
-    display: block !important;
-  }
-  table.visible-xs {
-    display: table !important;
-  }
-  tr.visible-xs {
-    display: table-row !important;
-  }
-  th.visible-xs,
-  td.visible-xs {
-    display: table-cell !important;
-  }
-}
-@media (max-width: 767px) {
-  .visible-xs-block {
-    display: block !important;
-  }
-}
-@media (max-width: 767px) {
-  .visible-xs-inline {
-    display: inline !important;
-  }
-}
-@media (max-width: 767px) {
-  .visible-xs-inline-block {
-    display: inline-block !important;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  .visible-sm {
-    display: block !important;
-  }
-  table.visible-sm {
-    display: table !important;
-  }
-  tr.visible-sm {
-    display: table-row !important;
-  }
-  th.visible-sm,
-  td.visible-sm {
-    display: table-cell !important;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  .visible-sm-block {
-    display: block !important;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  .visible-sm-inline {
-    display: inline !important;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  .visible-sm-inline-block {
-    display: inline-block !important;
-  }
-}
-@media (min-width: 992px) and (max-width: 1199px) {
-  .visible-md {
-    display: block !important;
-  }
-  table.visible-md {
-    display: table !important;
-  }
-  tr.visible-md {
-    display: table-row !important;
-  }
-  th.visible-md,
-  td.visible-md {
-    display: table-cell !important;
-  }
-}
-@media (min-width: 992px) and (max-width: 1199px) {
-  .visible-md-block {
-    display: block !important;
-  }
-}
-@media (min-width: 992px) and (max-width: 1199px) {
-  .visible-md-inline {
-    display: inline !important;
-  }
-}
-@media (min-width: 992px) and (max-width: 1199px) {
-  .visible-md-inline-block {
-    display: inline-block !important;
-  }
-}
-@media (min-width: 1200px) {
-  .visible-lg {
-    display: block !important;
-  }
-  table.visible-lg {
-    display: table !important;
-  }
-  tr.visible-lg {
-    display: table-row !important;
-  }
-  th.visible-lg,
-  td.visible-lg {
-    display: table-cell !important;
-  }
-}
-@media (min-width: 1200px) {
-  .visible-lg-block {
-    display: block !important;
-  }
-}
-@media (min-width: 1200px) {
-  .visible-lg-inline {
-    display: inline !important;
-  }
-}
-@media (min-width: 1200px) {
-  .visible-lg-inline-block {
-    display: inline-block !important;
-  }
-}
-@media (max-width: 767px) {
-  .hidden-xs {
-    display: none !important;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  .hidden-sm {
-    display: none !important;
-  }
-}
-@media (min-width: 992px) and (max-width: 1199px) {
-  .hidden-md {
-    display: none !important;
-  }
-}
-@media (min-width: 1200px) {
-  .hidden-lg {
-    display: none !important;
-  }
-}
-.visible-print {
-  display: none !important;
-}
-@media print {
-  .visible-print {
-    display: block !important;
-  }
-  table.visible-print {
-    display: table !important;
-  }
-  tr.visible-print {
-    display: table-row !important;
-  }
-  th.visible-print,
-  td.visible-print {
-    display: table-cell !important;
-  }
-}
-.visible-print-block {
-  display: none !important;
-}
-@media print {
-  .visible-print-block {
-    display: block !important;
-  }
-}
-.visible-print-inline {
-  display: none !important;
-}
-@media print {
-  .visible-print-inline {
-    display: inline !important;
-  }
-}
-.visible-print-inline-block {
-  display: none !important;
-}
-@media print {
-  .visible-print-inline-block {
-    display: inline-block !important;
-  }
-}
-@media print {
-  .hidden-print {
-    display: none !important;
-  }
-}
-/*!
-*
-* Font Awesome
-*
-*/
-/*!
- *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
- *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
- */
-/* FONT PATH
- * -------------------------- */
-@font-face {
-  font-family: 'FontAwesome';
-  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
-  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
-  font-weight: normal;
-  font-style: normal;
-}
-.fa {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-/* makes the font 33% larger relative to the icon container */
-.fa-lg {
-  font-size: 1.33333333em;
-  line-height: 0.75em;
-  vertical-align: -15%;
-}
-.fa-2x {
-  font-size: 2em;
-}
-.fa-3x {
-  font-size: 3em;
-}
-.fa-4x {
-  font-size: 4em;
-}
-.fa-5x {
-  font-size: 5em;
-}
-.fa-fw {
-  width: 1.28571429em;
-  text-align: center;
-}
-.fa-ul {
-  padding-left: 0;
-  margin-left: 2.14285714em;
-  list-style-type: none;
-}
-.fa-ul > li {
-  position: relative;
-}
-.fa-li {
-  position: absolute;
-  left: -2.14285714em;
-  width: 2.14285714em;
-  top: 0.14285714em;
-  text-align: center;
-}
-.fa-li.fa-lg {
-  left: -1.85714286em;
-}
-.fa-border {
-  padding: .2em .25em .15em;
-  border: solid 0.08em #eee;
-  border-radius: .1em;
-}
-.pull-right {
-  float: right;
-}
-.pull-left {
-  float: left;
-}
-.fa.pull-left {
-  margin-right: .3em;
-}
-.fa.pull-right {
-  margin-left: .3em;
-}
-.fa-spin {
-  -webkit-animation: fa-spin 2s infinite linear;
-  animation: fa-spin 2s infinite linear;
-}
-@-webkit-keyframes fa-spin {
-  0% {
-    -webkit-transform: rotate(0deg);
-    transform: rotate(0deg);
-  }
-  100% {
-    -webkit-transform: rotate(359deg);
-    transform: rotate(359deg);
-  }
-}
-@keyframes fa-spin {
-  0% {
-    -webkit-transform: rotate(0deg);
-    transform: rotate(0deg);
-  }
-  100% {
-    -webkit-transform: rotate(359deg);
-    transform: rotate(359deg);
-  }
-}
-.fa-rotate-90 {
-  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
-  -webkit-transform: rotate(90deg);
-  -ms-transform: rotate(90deg);
-  transform: rotate(90deg);
-}
-.fa-rotate-180 {
-  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
-  -webkit-transform: rotate(180deg);
-  -ms-transform: rotate(180deg);
-  transform: rotate(180deg);
-}
-.fa-rotate-270 {
-  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
-  -webkit-transform: rotate(270deg);
-  -ms-transform: rotate(270deg);
-  transform: rotate(270deg);
-}
-.fa-flip-horizontal {
-  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
-  -webkit-transform: scale(-1, 1);
-  -ms-transform: scale(-1, 1);
-  transform: scale(-1, 1);
-}
-.fa-flip-vertical {
-  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
-  -webkit-transform: scale(1, -1);
-  -ms-transform: scale(1, -1);
-  transform: scale(1, -1);
-}
-:root .fa-rotate-90,
-:root .fa-rotate-180,
-:root .fa-rotate-270,
-:root .fa-flip-horizontal,
-:root .fa-flip-vertical {
-  filter: none;
-}
-.fa-stack {
-  position: relative;
-  display: inline-block;
-  width: 2em;
-  height: 2em;
-  line-height: 2em;
-  vertical-align: middle;
-}
-.fa-stack-1x,
-.fa-stack-2x {
-  position: absolute;
-  left: 0;
-  width: 100%;
-  text-align: center;
-}
-.fa-stack-1x {
-  line-height: inherit;
-}
-.fa-stack-2x {
-  font-size: 2em;
-}
-.fa-inverse {
-  color: #fff;
-}
-/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
-   readers do not read off random characters that represent icons */
-.fa-glass:before {
-  content: "\f000";
-}
-.fa-music:before {
-  content: "\f001";
-}
-.fa-search:before {
-  content: "\f002";
-}
-.fa-envelope-o:before {
-  content: "\f003";
-}
-.fa-heart:before {
-  content: "\f004";
-}
-.fa-star:before {
-  content: "\f005";
-}
-.fa-star-o:before {
-  content: "\f006";
-}
-.fa-user:before {
-  content: "\f007";
-}
-.fa-film:before {
-  content: "\f008";
-}
-.fa-th-large:before {
-  content: "\f009";
-}
-.fa-th:before {
-  content: "\f00a";
-}
-.fa-th-list:before {
-  content: "\f00b";
-}
-.fa-check:before {
-  content: "\f00c";
-}
-.fa-remove:before,
-.fa-close:before,
-.fa-times:before {
-  content: "\f00d";
-}
-.fa-search-plus:before {
-  content: "\f00e";
-}
-.fa-search-minus:before {
-  content: "\f010";
-}
-.fa-power-off:before {
-  content: "\f011";
-}
-.fa-signal:before {
-  content: "\f012";
-}
-.fa-gear:before,
-.fa-cog:before {
-  content: "\f013";
-}
-.fa-trash-o:before {
-  content: "\f014";
-}
-.fa-home:before {
-  content: "\f015";
-}
-.fa-file-o:before {
-  content: "\f016";
-}
-.fa-clock-o:before {
-  content: "\f017";
-}
-.fa-road:before {
-  content: "\f018";
-}
-.fa-download:before {
-  content: "\f019";
-}
-.fa-arrow-circle-o-down:before {
-  content: "\f01a";
-}
-.fa-arrow-circle-o-up:before {
-  content: "\f01b";
-}
-.fa-inbox:before {
-  content: "\f01c";
-}
-.fa-play-circle-o:before {
-  content: "\f01d";
-}
-.fa-rotate-right:before,
-.fa-repeat:before {
-  content: "\f01e";
-}
-.fa-refresh:before {
-  content: "\f021";
-}
-.fa-list-alt:before {
-  content: "\f022";
-}
-.fa-lock:before {
-  content: "\f023";
-}
-.fa-flag:before {
-  content: "\f024";
-}
-.fa-headphones:before {
-  content: "\f025";
-}
-.fa-volume-off:before {
-  content: "\f026";
-}
-.fa-volume-down:before {
-  content: "\f027";
-}
-.fa-volume-up:before {
-  content: "\f028";
-}
-.fa-qrcode:before {
-  content: "\f029";
-}
-.fa-barcode:before {
-  content: "\f02a";
-}
-.fa-tag:before {
-  content: "\f02b";
-}
-.fa-tags:before {
-  content: "\f02c";
-}
-.fa-book:before {
-  content: "\f02d";
-}
-.fa-bookmark:before {
-  content: "\f02e";
-}
-.fa-print:before {
-  content: "\f02f";
-}
-.fa-camera:before {
-  content: "\f030";
-}
-.fa-font:before {
-  content: "\f031";
-}
-.fa-bold:before {
-  content: "\f032";
-}
-.fa-italic:before {
-  content: "\f033";
-}
-.fa-text-height:before {
-  content: "\f034";
-}
-.fa-text-width:before {
-  content: "\f035";
-}
-.fa-align-left:before {
-  content: "\f036";
-}
-.fa-align-center:before {
-  content: "\f037";
-}
-.fa-align-right:before {
-  content: "\f038";
-}
-.fa-align-justify:before {
-  content: "\f039";
-}
-.fa-list:before {
-  content: "\f03a";
-}
-.fa-dedent:before,
-.fa-outdent:before {
-  content: "\f03b";
-}
-.fa-indent:before {
-  content: "\f03c";
-}
-.fa-video-camera:before {
-  content: "\f03d";
-}
-.fa-photo:before,
-.fa-image:before,
-.fa-picture-o:before {
-  content: "\f03e";
-}
-.fa-pencil:before {
-  content: "\f040";
-}
-.fa-map-marker:before {
-  content: "\f041";
-}
-.fa-adjust:before {
-  content: "\f042";
-}
-.fa-tint:before {
-  content: "\f043";
-}
-.fa-edit:before,
-.fa-pencil-square-o:before {
-  content: "\f044";
-}
-.fa-share-square-o:before {
-  content: "\f045";
-}
-.fa-check-square-o:before {
-  content: "\f046";
-}
-.fa-arrows:before {
-  content: "\f047";
-}
-.fa-step-backward:before {
-  content: "\f048";
-}
-.fa-fast-backward:before {
-  content: "\f049";
-}
-.fa-backward:before {
-  content: "\f04a";
-}
-.fa-play:before {
-  content: "\f04b";
-}
-.fa-pause:before {
-  content: "\f04c";
-}
-.fa-stop:before {
-  content: "\f04d";
-}
-.fa-forward:before {
-  content: "\f04e";
-}
-.fa-fast-forward:before {
-  content: "\f050";
-}
-.fa-step-forward:before {
-  content: "\f051";
-}
-.fa-eject:before {
-  content: "\f052";
-}
-.fa-chevron-left:before {
-  content: "\f053";
-}
-.fa-chevron-right:before {
-  content: "\f054";
-}
-.fa-plus-circle:before {
-  content: "\f055";
-}
-.fa-minus-circle:before {
-  content: "\f056";
-}
-.fa-times-circle:before {
-  content: "\f057";
-}
-.fa-check-circle:before {
-  content: "\f058";
-}
-.fa-question-circle:before {
-  content: "\f059";
-}
-.fa-info-circle:before {
-  content: "\f05a";
-}
-.fa-crosshairs:before {
-  content: "\f05b";
-}
-.fa-times-circle-o:before {
-  content: "\f05c";
-}
-.fa-check-circle-o:before {
-  content: "\f05d";
-}
-.fa-ban:before {
-  content: "\f05e";
-}
-.fa-arrow-left:before {
-  content: "\f060";
-}
-.fa-arrow-right:before {
-  content: "\f061";
-}
-.fa-arrow-up:before {
-  content: "\f062";
-}
-.fa-arrow-down:before {
-  content: "\f063";
-}
-.fa-mail-forward:before,
-.fa-share:before {
-  content: "\f064";
-}
-.fa-expand:before {
-  content: "\f065";
-}
-.fa-compress:before {
-  content: "\f066";
-}
-.fa-plus:before {
-  content: "\f067";
-}
-.fa-minus:before {
-  content: "\f068";
-}
-.fa-asterisk:before {
-  content: "\f069";
-}
-.fa-exclamation-circle:before {
-  content: "\f06a";
-}
-.fa-gift:before {
-  content: "\f06b";
-}
-.fa-leaf:before {
-  content: "\f06c";
-}
-.fa-fire:before {
-  content: "\f06d";
-}
-.fa-eye:before {
-  content: "\f06e";
-}
-.fa-eye-slash:before {
-  content: "\f070";
-}
-.fa-warning:before,
-.fa-exclamation-triangle:before {
-  content: "\f071";
-}
-.fa-plane:before {
-  content: "\f072";
-}
-.fa-calendar:before {
-  content: "\f073";
-}
-.fa-random:before {
-  content: "\f074";
-}
-.fa-comment:before {
-  content: "\f075";
-}
-.fa-magnet:before {
-  content: "\f076";
-}
-.fa-chevron-up:before {
-  content: "\f077";
-}
-.fa-chevron-down:before {
-  content: "\f078";
-}
-.fa-retweet:before {
-  content: "\f079";
-}
-.fa-shopping-cart:before {
-  content: "\f07a";
-}
-.fa-folder:before {
-  content: "\f07b";
-}
-.fa-folder-open:before {
-  content: "\f07c";
-}
-.fa-arrows-v:before {
-  content: "\f07d";
-}
-.fa-arrows-h:before {
-  content: "\f07e";
-}
-.fa-bar-chart-o:before,
-.fa-bar-chart:before {
-  content: "\f080";
-}
-.fa-twitter-square:before {
-  content: "\f081";
-}
-.fa-facebook-square:before {
-  content: "\f082";
-}
-.fa-camera-retro:before {
-  content: "\f083";
-}
-.fa-key:before {
-  content: "\f084";
-}
-.fa-gears:before,
-.fa-cogs:before {
-  content: "\f085";
-}
-.fa-comments:before {
-  content: "\f086";
-}
-.fa-thumbs-o-up:before {
-  content: "\f087";
-}
-.fa-thumbs-o-down:before {
-  content: "\f088";
-}
-.fa-star-half:before {
-  content: "\f089";
-}
-.fa-heart-o:before {
-  content: "\f08a";
-}
-.fa-sign-out:before {
-  content: "\f08b";
-}
-.fa-linkedin-square:before {
-  content: "\f08c";
-}
-.fa-thumb-tack:before {
-  content: "\f08d";
-}
-.fa-external-link:before {
-  content: "\f08e";
-}
-.fa-sign-in:before {
-  content: "\f090";
-}
-.fa-trophy:before {
-  content: "\f091";
-}
-.fa-github-square:before {
-  content: "\f092";
-}
-.fa-upload:before {
-  content: "\f093";
-}
-.fa-lemon-o:before {
-  content: "\f094";
-}
-.fa-phone:before {
-  content: "\f095";
-}
-.fa-square-o:before {
-  content: "\f096";
-}
-.fa-bookmark-o:before {
-  content: "\f097";
-}
-.fa-phone-square:before {
-  content: "\f098";
-}
-.fa-twitter:before {
-  content: "\f099";
-}
-.fa-facebook:before {
-  content: "\f09a";
-}
-.fa-github:before {
-  content: "\f09b";
-}
-.fa-unlock:before {
-  content: "\f09c";
-}
-.fa-credit-card:before {
-  content: "\f09d";
-}
-.fa-rss:before {
-  content: "\f09e";
-}
-.fa-hdd-o:before {
-  content: "\f0a0";
-}
-.fa-bullhorn:before {
-  content: "\f0a1";
-}
-.fa-bell:before {
-  content: "\f0f3";
-}
-.fa-certificate:before {
-  content: "\f0a3";
-}
-.fa-hand-o-right:before {
-  content: "\f0a4";
-}
-.fa-hand-o-left:before {
-  content: "\f0a5";
-}
-.fa-hand-o-up:before {
-  content: "\f0a6";
-}
-.fa-hand-o-down:before {
-  content: "\f0a7";
-}
-.fa-arrow-circle-left:before {
-  content: "\f0a8";
-}
-.fa-arrow-circle-right:before {
-  content: "\f0a9";
-}
-.fa-arrow-circle-up:before {
-  content: "\f0aa";
-}
-.fa-arrow-circle-down:before {
-  content: "\f0ab";
-}
-.fa-globe:before {
-  content: "\f0ac";
-}
-.fa-wrench:before {
-  content: "\f0ad";
-}
-.fa-tasks:before {
-  content: "\f0ae";
-}
-.fa-filter:before {
-  content: "\f0b0";
-}
-.fa-briefcase:before {
-  content: "\f0b1";
-}
-.fa-arrows-alt:before {
-  content: "\f0b2";
-}
-.fa-group:before,
-.fa-users:before {
-  content: "\f0c0";
-}
-.fa-chain:before,
-.fa-link:before {
-  content: "\f0c1";
-}
-.fa-cloud:before {
-  content: "\f0c2";
-}
-.fa-flask:before {
-  content: "\f0c3";
-}
-.fa-cut:before,
-.fa-scissors:before {
-  content: "\f0c4";
-}
-.fa-copy:before,
-.fa-files-o:before {
-  content: "\f0c5";
-}
-.fa-paperclip:before {
-  content: "\f0c6";
-}
-.fa-save:before,
-.fa-floppy-o:before {
-  content: "\f0c7";
-}
-.fa-square:before {
-  content: "\f0c8";
-}
-.fa-navicon:before,
-.fa-reorder:before,
-.fa-bars:before {
-  content: "\f0c9";
-}
-.fa-list-ul:before {
-  content: "\f0ca";
-}
-.fa-list-ol:before {
-  content: "\f0cb";
-}
-.fa-strikethrough:before {
-  content: "\f0cc";
-}
-.fa-underline:before {
-  content: "\f0cd";
-}
-.fa-table:before {
-  content: "\f0ce";
-}
-.fa-magic:before {
-  content: "\f0d0";
-}
-.fa-truck:before {
-  content: "\f0d1";
-}
-.fa-pinterest:before {
-  content: "\f0d2";
-}
-.fa-pinterest-square:before {
-  content: "\f0d3";
-}
-.fa-google-plus-square:before {
-  content: "\f0d4";
-}
-.fa-google-plus:before {
-  content: "\f0d5";
-}
-.fa-money:before {
-  content: "\f0d6";
-}
-.fa-caret-down:before {
-  content: "\f0d7";
-}
-.fa-caret-up:before {
-  content: "\f0d8";
-}
-.fa-caret-left:before {
-  content: "\f0d9";
-}
-.fa-caret-right:before {
-  content: "\f0da";
-}
-.fa-columns:before {
-  content: "\f0db";
-}
-.fa-unsorted:before,
-.fa-sort:before {
-  content: "\f0dc";
-}
-.fa-sort-down:before,
-.fa-sort-desc:before {
-  content: "\f0dd";
-}
-.fa-sort-up:before,
-.fa-sort-asc:before {
-  content: "\f0de";
-}
-.fa-envelope:before {
-  content: "\f0e0";
-}
-.fa-linkedin:before {
-  content: "\f0e1";
-}
-.fa-rotate-left:before,
-.fa-undo:before {
-  content: "\f0e2";
-}
-.fa-legal:before,
-.fa-gavel:before {
-  content: "\f0e3";
-}
-.fa-dashboard:before,
-.fa-tachometer:before {
-  content: "\f0e4";
-}
-.fa-comment-o:before {
-  content: "\f0e5";
-}
-.fa-comments-o:before {
-  content: "\f0e6";
-}
-.fa-flash:before,
-.fa-bolt:before {
-  content: "\f0e7";
-}
-.fa-sitemap:before {
-  content: "\f0e8";
-}
-.fa-umbrella:before {
-  content: "\f0e9";
-}
-.fa-paste:before,
-.fa-clipboard:before {
-  content: "\f0ea";
-}
-.fa-lightbulb-o:before {
-  content: "\f0eb";
-}
-.fa-exchange:before {
-  content: "\f0ec";
-}
-.fa-cloud-download:before {
-  content: "\f0ed";
-}
-.fa-cloud-upload:before {
-  content: "\f0ee";
-}
-.fa-user-md:before {
-  content: "\f0f0";
-}
-.fa-stethoscope:before {
-  content: "\f0f1";
-}
-.fa-suitcase:before {
-  content: "\f0f2";
-}
-.fa-bell-o:before {
-  content: "\f0a2";
-}
-.fa-coffee:before {
-  content: "\f0f4";
-}
-.fa-cutlery:before {
-  content: "\f0f5";
-}
-.fa-file-text-o:before {
-  content: "\f0f6";
-}
-.fa-building-o:before {
-  content: "\f0f7";
-}
-.fa-hospital-o:before {
-  content: "\f0f8";
-}
-.fa-ambulance:before {
-  content: "\f0f9";
-}
-.fa-medkit:before {
-  content: "\f0fa";
-}
-.fa-fighter-jet:before {
-  content: "\f0fb";
-}
-.fa-beer:before {
-  content: "\f0fc";
-}
-.fa-h-square:before {
-  content: "\f0fd";
-}
-.fa-plus-square:before {
-  content: "\f0fe";
-}
-.fa-angle-double-left:before {
-  content: "\f100";
-}
-.fa-angle-double-right:before {
-  content: "\f101";
-}
-.fa-angle-double-up:before {
-  content: "\f102";
-}
-.fa-angle-double-down:before {
-  content: "\f103";
-}
-.fa-angle-left:before {
-  content: "\f104";
-}
-.fa-angle-right:before {
-  content: "\f105";
-}
-.fa-angle-up:before {
-  content: "\f106";
-}
-.fa-angle-down:before {
-  content: "\f107";
-}
-.fa-desktop:before {
-  content: "\f108";
-}
-.fa-laptop:before {
-  content: "\f109";
-}
-.fa-tablet:before {
-  content: "\f10a";
-}
-.fa-mobile-phone:before,
-.fa-mobile:before {
-  content: "\f10b";
-}
-.fa-circle-o:before {
-  content: "\f10c";
-}
-.fa-quote-left:before {
-  content: "\f10d";
-}
-.fa-quote-right:before {
-  content: "\f10e";
-}
-.fa-spinner:before {
-  content: "\f110";
-}
-.fa-circle:before {
-  content: "\f111";
-}
-.fa-mail-reply:before,
-.fa-reply:before {
-  content: "\f112";
-}
-.fa-github-alt:before {
-  content: "\f113";
-}
-.fa-folder-o:before {
-  content: "\f114";
-}
-.fa-folder-open-o:before {
-  content: "\f115";
-}
-.fa-smile-o:before {
-  content: "\f118";
-}
-.fa-frown-o:before {
-  content: "\f119";
-}
-.fa-meh-o:before {
-  content: "\f11a";
-}
-.fa-gamepad:before {
-  content: "\f11b";
-}
-.fa-keyboard-o:before {
-  content: "\f11c";
-}
-.fa-flag-o:before {
-  content: "\f11d";
-}
-.fa-flag-checkered:before {
-  content: "\f11e";
-}
-.fa-terminal:before {
-  content: "\f120";
-}
-.fa-code:before {
-  content: "\f121";
-}
-.fa-mail-reply-all:before,
-.fa-reply-all:before {
-  content: "\f122";
-}
-.fa-star-half-empty:before,
-.fa-star-half-full:before,
-.fa-star-half-o:before {
-  content: "\f123";
-}
-.fa-location-arrow:before {
-  content: "\f124";
-}
-.fa-crop:before {
-  content: "\f125";
-}
-.fa-code-fork:before {
-  content: "\f126";
-}
-.fa-unlink:before,
-.fa-chain-broken:before {
-  content: "\f127";
-}
-.fa-question:before {
-  content: "\f128";
-}
-.fa-info:before {
-  content: "\f129";
-}
-.fa-exclamation:before {
-  content: "\f12a";
-}
-.fa-superscript:before {
-  content: "\f12b";
-}
-.fa-subscript:before {
-  content: "\f12c";
-}
-.fa-eraser:before {
-  content: "\f12d";
-}
-.fa-puzzle-piece:before {
-  content: "\f12e";
-}
-.fa-microphone:before {
-  content: "\f130";
-}
-.fa-microphone-slash:before {
-  content: "\f131";
-}
-.fa-shield:before {
-  content: "\f132";
-}
-.fa-calendar-o:before {
-  content: "\f133";
-}
-.fa-fire-extinguisher:before {
-  content: "\f134";
-}
-.fa-rocket:before {
-  content: "\f135";
-}
-.fa-maxcdn:before {
-  content: "\f136";
-}
-.fa-chevron-circle-left:before {
-  content: "\f137";
-}
-.fa-chevron-circle-right:before {
-  content: "\f138";
-}
-.fa-chevron-circle-up:before {
-  content: "\f139";
-}
-.fa-chevron-circle-down:before {
-  content: "\f13a";
-}
-.fa-html5:before {
-  content: "\f13b";
-}
-.fa-css3:before {
-  content: "\f13c";
-}
-.fa-anchor:before {
-  content: "\f13d";
-}
-.fa-unlock-alt:before {
-  content: "\f13e";
-}
-.fa-bullseye:before {
-  content: "\f140";
-}
-.fa-ellipsis-h:before {
-  content: "\f141";
-}
-.fa-ellipsis-v:before {
-  content: "\f142";
-}
-.fa-rss-square:before {
-  content: "\f143";
-}
-.fa-play-circle:before {
-  content: "\f144";
-}
-.fa-ticket:before {
-  content: "\f145";
-}
-.fa-minus-square:before {
-  content: "\f146";
-}
-.fa-minus-square-o:before {
-  content: "\f147";
-}
-.fa-level-up:before {
-  content: "\f148";
-}
-.fa-level-down:before {
-  content: "\f149";
-}
-.fa-check-square:before {
-  content: "\f14a";
-}
-.fa-pencil-square:before {
-  content: "\f14b";
-}
-.fa-external-link-square:before {
-  content: "\f14c";
-}
-.fa-share-square:before {
-  content: "\f14d";
-}
-.fa-compass:before {
-  content: "\f14e";
-}
-.fa-toggle-down:before,
-.fa-caret-square-o-down:before {
-  content: "\f150";
-}
-.fa-toggle-up:before,
-.fa-caret-square-o-up:before {
-  content: "\f151";
-}
-.fa-toggle-right:before,
-.fa-caret-square-o-right:before {
-  content: "\f152";
-}
-.fa-euro:before,
-.fa-eur:before {
-  content: "\f153";
-}
-.fa-gbp:before {
-  content: "\f154";
-}
-.fa-dollar:before,
-.fa-usd:before {
-  content: "\f155";
-}
-.fa-rupee:before,
-.fa-inr:before {
-  content: "\f156";
-}
-.fa-cny:before,
-.fa-rmb:before,
-.fa-yen:before,
-.fa-jpy:before {
-  content: "\f157";
-}
-.fa-ruble:before,
-.fa-rouble:before,
-.fa-rub:before {
-  content: "\f158";
-}
-.fa-won:before,
-.fa-krw:before {
-  content: "\f159";
-}
-.fa-bitcoin:before,
-.fa-btc:before {
-  content: "\f15a";
-}
-.fa-file:before {
-  content: "\f15b";
-}
-.fa-file-text:before {
-  content: "\f15c";
-}
-.fa-sort-alpha-asc:before {
-  content: "\f15d";
-}
-.fa-sort-alpha-desc:before {
-  content: "\f15e";
-}
-.fa-sort-amount-asc:before {
-  content: "\f160";
-}
-.fa-sort-amount-desc:before {
-  content: "\f161";
-}
-.fa-sort-numeric-asc:before {
-  content: "\f162";
-}
-.fa-sort-numeric-desc:before {
-  content: "\f163";
-}
-.fa-thumbs-up:before {
-  content: "\f164";
-}
-.fa-thumbs-down:before {
-  content: "\f165";
-}
-.fa-youtube-square:before {
-  content: "\f166";
-}
-.fa-youtube:before {
-  content: "\f167";
-}
-.fa-xing:before {
-  content: "\f168";
-}
-.fa-xing-square:before {
-  content: "\f169";
-}
-.fa-youtube-play:before {
-  content: "\f16a";
-}
-.fa-dropbox:before {
-  content: "\f16b";
-}
-.fa-stack-overflow:before {
-  content: "\f16c";
-}
-.fa-instagram:before {
-  content: "\f16d";
-}
-.fa-flickr:before {
-  content: "\f16e";
-}
-.fa-adn:before {
-  content: "\f170";
-}
-.fa-bitbucket:before {
-  content: "\f171";
-}
-.fa-bitbucket-square:before {
-  content: "\f172";
-}
-.fa-tumblr:before {
-  content: "\f173";
-}
-.fa-tumblr-square:before {
-  content: "\f174";
-}
-.fa-long-arrow-down:before {
-  content: "\f175";
-}
-.fa-long-arrow-up:before {
-  content: "\f176";
-}
-.fa-long-arrow-left:before {
-  content: "\f177";
-}
-.fa-long-arrow-right:before {
-  content: "\f178";
-}
-.fa-apple:before {
-  content: "\f179";
-}
-.fa-windows:before {
-  content: "\f17a";
-}
-.fa-android:before {
-  content: "\f17b";
-}
-.fa-linux:before {
-  content: "\f17c";
-}
-.fa-dribbble:before {
-  content: "\f17d";
-}
-.fa-skype:before {
-  content: "\f17e";
-}
-.fa-foursquare:before {
-  content: "\f180";
-}
-.fa-trello:before {
-  content: "\f181";
-}
-.fa-female:before {
-  content: "\f182";
-}
-.fa-male:before {
-  content: "\f183";
-}
-.fa-gittip:before {
-  content: "\f184";
-}
-.fa-sun-o:before {
-  content: "\f185";
-}
-.fa-moon-o:before {
-  content: "\f186";
-}
-.fa-archive:before {
-  content: "\f187";
-}
-.fa-bug:before {
-  content: "\f188";
-}
-.fa-vk:before {
-  content: "\f189";
-}
-.fa-weibo:before {
-  content: "\f18a";
-}
-.fa-renren:before {
-  content: "\f18b";
-}
-.fa-pagelines:before {
-  content: "\f18c";
-}
-.fa-stack-exchange:before {
-  content: "\f18d";
-}
-.fa-arrow-circle-o-right:before {
-  content: "\f18e";
-}
-.fa-arrow-circle-o-left:before {
-  content: "\f190";
-}
-.fa-toggle-left:before,
-.fa-caret-square-o-left:before {
-  content: "\f191";
-}
-.fa-dot-circle-o:before {
-  content: "\f192";
-}
-.fa-wheelchair:before {
-  content: "\f193";
-}
-.fa-vimeo-square:before {
-  content: "\f194";
-}
-.fa-turkish-lira:before,
-.fa-try:before {
-  content: "\f195";
-}
-.fa-plus-square-o:before {
-  content: "\f196";
-}
-.fa-space-shuttle:before {
-  content: "\f197";
-}
-.fa-slack:before {
-  content: "\f198";
-}
-.fa-envelope-square:before {
-  content: "\f199";
-}
-.fa-wordpress:before {
-  content: "\f19a";
-}
-.fa-openid:before {
-  content: "\f19b";
-}
-.fa-institution:before,
-.fa-bank:before,
-.fa-university:before {
-  content: "\f19c";
-}
-.fa-mortar-board:before,
-.fa-graduation-cap:before {
-  content: "\f19d";
-}
-.fa-yahoo:before {
-  content: "\f19e";
-}
-.fa-google:before {
-  content: "\f1a0";
-}
-.fa-reddit:before {
-  content: "\f1a1";
-}
-.fa-reddit-square:before {
-  content: "\f1a2";
-}
-.fa-stumbleupon-circle:before {
-  content: "\f1a3";
-}
-.fa-stumbleupon:before {
-  content: "\f1a4";
-}
-.fa-delicious:before {
-  content: "\f1a5";
-}
-.fa-digg:before {
-  content: "\f1a6";
-}
-.fa-pied-piper:before {
-  content: "\f1a7";
-}
-.fa-pied-piper-alt:before {
-  content: "\f1a8";
-}
-.fa-drupal:before {
-  content: "\f1a9";
-}
-.fa-joomla:before {
-  content: "\f1aa";
-}
-.fa-language:before {
-  content: "\f1ab";
-}
-.fa-fax:before {
-  content: "\f1ac";
-}
-.fa-building:before {
-  content: "\f1ad";
-}
-.fa-child:before {
-  content: "\f1ae";
-}
-.fa-paw:before {
-  content: "\f1b0";
-}
-.fa-spoon:before {
-  content: "\f1b1";
-}
-.fa-cube:before {
-  content: "\f1b2";
-}
-.fa-cubes:before {
-  content: "\f1b3";
-}
-.fa-behance:before {
-  content: "\f1b4";
-}
-.fa-behance-square:before {
-  content: "\f1b5";
-}
-.fa-steam:before {
-  content: "\f1b6";
-}
-.fa-steam-square:before {
-  content: "\f1b7";
-}
-.fa-recycle:before {
-  content: "\f1b8";
-}
-.fa-automobile:before,
-.fa-car:before {
-  content: "\f1b9";
-}
-.fa-cab:before,
-.fa-taxi:before {
-  content: "\f1ba";
-}
-.fa-tree:before {
-  content: "\f1bb";
-}
-.fa-spotify:before {
-  content: "\f1bc";
-}
-.fa-deviantart:before {
-  content: "\f1bd";
-}
-.fa-soundcloud:before {
-  content: "\f1be";
-}
-.fa-database:before {
-  content: "\f1c0";
-}
-.fa-file-pdf-o:before {
-  content: "\f1c1";
-}
-.fa-file-word-o:before {
-  content: "\f1c2";
-}
-.fa-file-excel-o:before {
-  content: "\f1c3";
-}
-.fa-file-powerpoint-o:before {
-  content: "\f1c4";
-}
-.fa-file-photo-o:before,
-.fa-file-picture-o:before,
-.fa-file-image-o:before {
-  content: "\f1c5";
-}
-.fa-file-zip-o:before,
-.fa-file-archive-o:before {
-  content: "\f1c6";
-}
-.fa-file-sound-o:before,
-.fa-file-audio-o:before {
-  content: "\f1c7";
-}
-.fa-file-movie-o:before,
-.fa-file-video-o:before {
-  content: "\f1c8";
-}
-.fa-file-code-o:before {
-  content: "\f1c9";
-}
-.fa-vine:before {
-  content: "\f1ca";
-}
-.fa-codepen:before {
-  content: "\f1cb";
-}
-.fa-jsfiddle:before {
-  content: "\f1cc";
-}
-.fa-life-bouy:before,
-.fa-life-buoy:before,
-.fa-life-saver:before,
-.fa-support:before,
-.fa-life-ring:before {
-  content: "\f1cd";
-}
-.fa-circle-o-notch:before {
-  content: "\f1ce";
-}
-.fa-ra:before,
-.fa-rebel:before {
-  content: "\f1d0";
-}
-.fa-ge:before,
-.fa-empire:before {
-  content: "\f1d1";
-}
-.fa-git-square:before {
-  content: "\f1d2";
-}
-.fa-git:before {
-  content: "\f1d3";
-}
-.fa-hacker-news:before {
-  content: "\f1d4";
-}
-.fa-tencent-weibo:before {
-  content: "\f1d5";
-}
-.fa-qq:before {
-  content: "\f1d6";
-}
-.fa-wechat:before,
-.fa-weixin:before {
-  content: "\f1d7";
-}
-.fa-send:before,
-.fa-paper-plane:before {
-  content: "\f1d8";
-}
-.fa-send-o:before,
-.fa-paper-plane-o:before {
-  content: "\f1d9";
-}
-.fa-history:before {
-  content: "\f1da";
-}
-.fa-circle-thin:before {
-  content: "\f1db";
-}
-.fa-header:before {
-  content: "\f1dc";
-}
-.fa-paragraph:before {
-  content: "\f1dd";
-}
-.fa-sliders:before {
-  content: "\f1de";
-}
-.fa-share-alt:before {
-  content: "\f1e0";
-}
-.fa-share-alt-square:before {
-  content: "\f1e1";
-}
-.fa-bomb:before {
-  content: "\f1e2";
-}
-.fa-soccer-ball-o:before,
-.fa-futbol-o:before {
-  content: "\f1e3";
-}
-.fa-tty:before {
-  content: "\f1e4";
-}
-.fa-binoculars:before {
-  content: "\f1e5";
-}
-.fa-plug:before {
-  content: "\f1e6";
-}
-.fa-slideshare:before {
-  content: "\f1e7";
-}
-.fa-twitch:before {
-  content: "\f1e8";
-}
-.fa-yelp:before {
-  content: "\f1e9";
-}
-.fa-newspaper-o:before {
-  content: "\f1ea";
-}
-.fa-wifi:before {
-  content: "\f1eb";
-}
-.fa-calculator:before {
-  content: "\f1ec";
-}
-.fa-paypal:before {
-  content: "\f1ed";
-}
-.fa-google-wallet:before {
-  content: "\f1ee";
-}
-.fa-cc-visa:before {
-  content: "\f1f0";
-}
-.fa-cc-mastercard:before {
-  content: "\f1f1";
-}
-.fa-cc-discover:before {
-  content: "\f1f2";
-}
-.fa-cc-amex:before {
-  content: "\f1f3";
-}
-.fa-cc-paypal:before {
-  content: "\f1f4";
-}
-.fa-cc-stripe:before {
-  content: "\f1f5";
-}
-.fa-bell-slash:before {
-  content: "\f1f6";
-}
-.fa-bell-slash-o:before {
-  content: "\f1f7";
-}
-.fa-trash:before {
-  content: "\f1f8";
-}
-.fa-copyright:before {
-  content: "\f1f9";
-}
-.fa-at:before {
-  content: "\f1fa";
-}
-.fa-eyedropper:before {
-  content: "\f1fb";
-}
-.fa-paint-brush:before {
-  content: "\f1fc";
-}
-.fa-birthday-cake:before {
-  content: "\f1fd";
-}
-.fa-area-chart:before {
-  content: "\f1fe";
-}
-.fa-pie-chart:before {
-  content: "\f200";
-}
-.fa-line-chart:before {
-  content: "\f201";
-}
-.fa-lastfm:before {
-  content: "\f202";
-}
-.fa-lastfm-square:before {
-  content: "\f203";
-}
-.fa-toggle-off:before {
-  content: "\f204";
-}
-.fa-toggle-on:before {
-  content: "\f205";
-}
-.fa-bicycle:before {
-  content: "\f206";
-}
-.fa-bus:before {
-  content: "\f207";
-}
-.fa-ioxhost:before {
-  content: "\f208";
-}
-.fa-angellist:before {
-  content: "\f209";
-}
-.fa-cc:before {
-  content: "\f20a";
-}
-.fa-shekel:before,
-.fa-sheqel:before,
-.fa-ils:before {
-  content: "\f20b";
-}
-.fa-meanpath:before {
-  content: "\f20c";
-}
-/*!
-*
-* IPython base
-*
-*/
-.modal.fade .modal-dialog {
-  -webkit-transform: translate(0, 0);
-  -ms-transform: translate(0, 0);
-  -o-transform: translate(0, 0);
-  transform: translate(0, 0);
-}
-code {
-  color: #000;
-}
-pre {
-  font-size: inherit;
-  line-height: inherit;
-}
-label {
-  font-weight: normal;
-}
-/* Make the page background atleast 100% the height of the view port */
-/* Make the page itself atleast 70% the height of the view port */
-.border-box-sizing {
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-.corner-all {
-  border-radius: 2px;
-}
-.no-padding {
-  padding: 0px;
-}
-/* Flexible box model classes */
-/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
-/* This file is a compatability layer.  It allows the usage of flexible box 
-model layouts accross multiple browsers, including older browsers.  The newest,
-universal implementation of the flexible box model is used when available (see
-`Modern browsers` comments below).  Browsers that are known to implement this 
-new spec completely include:
-
-    Firefox 28.0+
-    Chrome 29.0+
-    Internet Explorer 11+ 
-    Opera 17.0+
-
-Browsers not listed, including Safari, are supported via the styling under the
-`Old browsers` comments below.
-*/
-.hbox {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-}
-.hbox > * {
-  /* Old browsers */
-  -webkit-box-flex: 0;
-  -moz-box-flex: 0;
-  box-flex: 0;
-  /* Modern browsers */
-  flex: none;
-}
-.vbox {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-}
-.vbox > * {
-  /* Old browsers */
-  -webkit-box-flex: 0;
-  -moz-box-flex: 0;
-  box-flex: 0;
-  /* Modern browsers */
-  flex: none;
-}
-.hbox.reverse,
-.vbox.reverse,
-.reverse {
-  /* Old browsers */
-  -webkit-box-direction: reverse;
-  -moz-box-direction: reverse;
-  box-direction: reverse;
-  /* Modern browsers */
-  flex-direction: row-reverse;
-}
-.hbox.box-flex0,
-.vbox.box-flex0,
-.box-flex0 {
-  /* Old browsers */
-  -webkit-box-flex: 0;
-  -moz-box-flex: 0;
-  box-flex: 0;
-  /* Modern browsers */
-  flex: none;
-  width: auto;
-}
-.hbox.box-flex1,
-.vbox.box-flex1,
-.box-flex1 {
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-}
-.hbox.box-flex,
-.vbox.box-flex,
-.box-flex {
-  /* Old browsers */
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-}
-.hbox.box-flex2,
-.vbox.box-flex2,
-.box-flex2 {
-  /* Old browsers */
-  -webkit-box-flex: 2;
-  -moz-box-flex: 2;
-  box-flex: 2;
-  /* Modern browsers */
-  flex: 2;
-}
-.box-group1 {
-  /*  Deprecated */
-  -webkit-box-flex-group: 1;
-  -moz-box-flex-group: 1;
-  box-flex-group: 1;
-}
-.box-group2 {
-  /* Deprecated */
-  -webkit-box-flex-group: 2;
-  -moz-box-flex-group: 2;
-  box-flex-group: 2;
-}
-.hbox.start,
-.vbox.start,
-.start {
-  /* Old browsers */
-  -webkit-box-pack: start;
-  -moz-box-pack: start;
-  box-pack: start;
-  /* Modern browsers */
-  justify-content: flex-start;
-}
-.hbox.end,
-.vbox.end,
-.end {
-  /* Old browsers */
-  -webkit-box-pack: end;
-  -moz-box-pack: end;
-  box-pack: end;
-  /* Modern browsers */
-  justify-content: flex-end;
-}
-.hbox.center,
-.vbox.center,
-.center {
-  /* Old browsers */
-  -webkit-box-pack: center;
-  -moz-box-pack: center;
-  box-pack: center;
-  /* Modern browsers */
-  justify-content: center;
-}
-.hbox.baseline,
-.vbox.baseline,
-.baseline {
-  /* Old browsers */
-  -webkit-box-pack: baseline;
-  -moz-box-pack: baseline;
-  box-pack: baseline;
-  /* Modern browsers */
-  justify-content: baseline;
-}
-.hbox.stretch,
-.vbox.stretch,
-.stretch {
-  /* Old browsers */
-  -webkit-box-pack: stretch;
-  -moz-box-pack: stretch;
-  box-pack: stretch;
-  /* Modern browsers */
-  justify-content: stretch;
-}
-.hbox.align-start,
-.vbox.align-start,
-.align-start {
-  /* Old browsers */
-  -webkit-box-align: start;
-  -moz-box-align: start;
-  box-align: start;
-  /* Modern browsers */
-  align-items: flex-start;
-}
-.hbox.align-end,
-.vbox.align-end,
-.align-end {
-  /* Old browsers */
-  -webkit-box-align: end;
-  -moz-box-align: end;
-  box-align: end;
-  /* Modern browsers */
-  align-items: flex-end;
-}
-.hbox.align-center,
-.vbox.align-center,
-.align-center {
-  /* Old browsers */
-  -webkit-box-align: center;
-  -moz-box-align: center;
-  box-align: center;
-  /* Modern browsers */
-  align-items: center;
-}
-.hbox.align-baseline,
-.vbox.align-baseline,
-.align-baseline {
-  /* Old browsers */
-  -webkit-box-align: baseline;
-  -moz-box-align: baseline;
-  box-align: baseline;
-  /* Modern browsers */
-  align-items: baseline;
-}
-.hbox.align-stretch,
-.vbox.align-stretch,
-.align-stretch {
-  /* Old browsers */
-  -webkit-box-align: stretch;
-  -moz-box-align: stretch;
-  box-align: stretch;
-  /* Modern browsers */
-  align-items: stretch;
-}
-div.error {
-  margin: 2em;
-  text-align: center;
-}
-div.error > h1 {
-  font-size: 500%;
-  line-height: normal;
-}
-div.error > p {
-  font-size: 200%;
-  line-height: normal;
-}
-div.traceback-wrapper {
-  text-align: left;
-  max-width: 800px;
-  margin: auto;
-}
-/**
- * Primary styles
- *
- * Author: Jupyter Development Team
- */
-body {
-  background-color: #fff;
-  /* This makes sure that the body covers the entire window and needs to
-       be in a different element than the display: box in wrapper below */
-  position: absolute;
-  left: 0px;
-  right: 0px;
-  top: 0px;
-  bottom: 0px;
-  overflow: visible;
-}
-body > #header {
-  /* Initially hidden to prevent FLOUC */
-  display: none;
-  background-color: #fff;
-  /* Display over codemirror */
-  position: relative;
-  z-index: 100;
-}
-body > #header #header-container {
-  padding-bottom: 5px;
-  padding-top: 5px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-body > #header .header-bar {
-  width: 100%;
-  height: 1px;
-  background: #e7e7e7;
-  margin-bottom: -1px;
-}
-@media print {
-  body > #header {
-    display: none !important;
-  }
-}
-#header-spacer {
-  width: 100%;
-  visibility: hidden;
-}
-@media print {
-  #header-spacer {
-    display: none;
-  }
-}
-#ipython_notebook {
-  padding-left: 0px;
-  padding-top: 1px;
-  padding-bottom: 1px;
-}
-@media (max-width: 991px) {
-  #ipython_notebook {
-    margin-left: 10px;
-  }
-}
-#noscript {
-  width: auto;
-  padding-top: 16px;
-  padding-bottom: 16px;
-  text-align: center;
-  font-size: 22px;
-  color: red;
-  font-weight: bold;
-}
-#ipython_notebook img {
-  height: 28px;
-}
-#site {
-  width: 100%;
-  display: none;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  overflow: auto;
-}
-@media print {
-  #site {
-    height: auto !important;
-  }
-}
-/* Smaller buttons */
-.ui-button .ui-button-text {
-  padding: 0.2em 0.8em;
-  font-size: 77%;
-}
-input.ui-button {
-  padding: 0.3em 0.9em;
-}
-span#login_widget {
-  float: right;
-}
-span#login_widget > .button,
-#logout {
-  color: #333;
-  background-color: #fff;
-  border-color: #ccc;
-}
-span#login_widget > .button:focus,
-#logout:focus,
-span#login_widget > .button.focus,
-#logout.focus {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #8c8c8c;
-}
-span#login_widget > .button:hover,
-#logout:hover {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-span#login_widget > .button:active,
-#logout:active,
-span#login_widget > .button.active,
-#logout.active,
-.open > .dropdown-togglespan#login_widget > .button,
-.open > .dropdown-toggle#logout {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-span#login_widget > .button:active:hover,
-#logout:active:hover,
-span#login_widget > .button.active:hover,
-#logout.active:hover,
-.open > .dropdown-togglespan#login_widget > .button:hover,
-.open > .dropdown-toggle#logout:hover,
-span#login_widget > .button:active:focus,
-#logout:active:focus,
-span#login_widget > .button.active:focus,
-#logout.active:focus,
-.open > .dropdown-togglespan#login_widget > .button:focus,
-.open > .dropdown-toggle#logout:focus,
-span#login_widget > .button:active.focus,
-#logout:active.focus,
-span#login_widget > .button.active.focus,
-#logout.active.focus,
-.open > .dropdown-togglespan#login_widget > .button.focus,
-.open > .dropdown-toggle#logout.focus {
-  color: #333;
-  background-color: #d4d4d4;
-  border-color: #8c8c8c;
-}
-span#login_widget > .button:active,
-#logout:active,
-span#login_widget > .button.active,
-#logout.active,
-.open > .dropdown-togglespan#login_widget > .button,
-.open > .dropdown-toggle#logout {
-  background-image: none;
-}
-span#login_widget > .button.disabled:hover,
-#logout.disabled:hover,
-span#login_widget > .button[disabled]:hover,
-#logout[disabled]:hover,
-fieldset[disabled] span#login_widget > .button:hover,
-fieldset[disabled] #logout:hover,
-span#login_widget > .button.disabled:focus,
-#logout.disabled:focus,
-span#login_widget > .button[disabled]:focus,
-#logout[disabled]:focus,
-fieldset[disabled] span#login_widget > .button:focus,
-fieldset[disabled] #logout:focus,
-span#login_widget > .button.disabled.focus,
-#logout.disabled.focus,
-span#login_widget > .button[disabled].focus,
-#logout[disabled].focus,
-fieldset[disabled] span#login_widget > .button.focus,
-fieldset[disabled] #logout.focus {
-  background-color: #fff;
-  border-color: #ccc;
-}
-span#login_widget > .button .badge,
-#logout .badge {
-  color: #fff;
-  background-color: #333;
-}
-.nav-header {
-  text-transform: none;
-}
-#header > span {
-  margin-top: 10px;
-}
-.modal_stretch .modal-dialog {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  min-height: 80vh;
-}
-.modal_stretch .modal-dialog .modal-body {
-  max-height: calc(100vh - 200px);
-  overflow: auto;
-  flex: 1;
-}
-@media (min-width: 768px) {
-  .modal .modal-dialog {
-    width: 700px;
-  }
-}
-@media (min-width: 768px) {
-  select.form-control {
-    margin-left: 12px;
-    margin-right: 12px;
-  }
-}
-/*!
-*
-* IPython auth
-*
-*/
-.center-nav {
-  display: inline-block;
-  margin-bottom: -4px;
-}
-/*!
-*
-* IPython tree view
-*
-*/
-/* We need an invisible input field on top of the sentense*/
-/* "Drag file onto the list ..." */
-.alternate_upload {
-  background-color: none;
-  display: inline;
-}
-.alternate_upload.form {
-  padding: 0;
-  margin: 0;
-}
-.alternate_upload input.fileinput {
-  text-align: center;
-  vertical-align: middle;
-  display: inline;
-  opacity: 0;
-  z-index: 2;
-  width: 12ex;
-  margin-right: -12ex;
-}
-.alternate_upload .btn-upload {
-  height: 22px;
-}
-/**
- * Primary styles
- *
- * Author: Jupyter Development Team
- */
-ul#tabs {
-  margin-bottom: 4px;
-}
-ul#tabs a {
-  padding-top: 6px;
-  padding-bottom: 4px;
-}
-ul.breadcrumb a:focus,
-ul.breadcrumb a:hover {
-  text-decoration: none;
-}
-ul.breadcrumb i.icon-home {
-  font-size: 16px;
-  margin-right: 4px;
-}
-ul.breadcrumb span {
-  color: #5e5e5e;
-}
-.list_toolbar {
-  padding: 4px 0 4px 0;
-  vertical-align: middle;
-}
-.list_toolbar .tree-buttons {
-  padding-top: 1px;
-}
-.dynamic-buttons {
-  padding-top: 3px;
-  display: inline-block;
-}
-.list_toolbar [class*="span"] {
-  min-height: 24px;
-}
-.list_header {
-  font-weight: bold;
-  background-color: #EEE;
-}
-.list_placeholder {
-  font-weight: bold;
-  padding-top: 4px;
-  padding-bottom: 4px;
-  padding-left: 7px;
-  padding-right: 7px;
-}
-.list_container {
-  margin-top: 4px;
-  margin-bottom: 20px;
-  border: 1px solid #ddd;
-  border-radius: 2px;
-}
-.list_container > div {
-  border-bottom: 1px solid #ddd;
-}
-.list_container > div:hover .list-item {
-  background-color: red;
-}
-.list_container > div:last-child {
-  border: none;
-}
-.list_item:hover .list_item {
-  background-color: #ddd;
-}
-.list_item a {
-  text-decoration: none;
-}
-.list_item:hover {
-  background-color: #fafafa;
-}
-.list_header > div,
-.list_item > div {
-  padding-top: 4px;
-  padding-bottom: 4px;
-  padding-left: 7px;
-  padding-right: 7px;
-  line-height: 22px;
-}
-.list_header > div input,
-.list_item > div input {
-  margin-right: 7px;
-  margin-left: 14px;
-  vertical-align: baseline;
-  line-height: 22px;
-  position: relative;
-  top: -1px;
-}
-.list_header > div .item_link,
-.list_item > div .item_link {
-  margin-left: -1px;
-  vertical-align: baseline;
-  line-height: 22px;
-}
-.new-file input[type=checkbox] {
-  visibility: hidden;
-}
-.item_name {
-  line-height: 22px;
-  height: 24px;
-}
-.item_icon {
-  font-size: 14px;
-  color: #5e5e5e;
-  margin-right: 7px;
-  margin-left: 7px;
-  line-height: 22px;
-  vertical-align: baseline;
-}
-.item_buttons {
-  line-height: 1em;
-  margin-left: -5px;
-}
-.item_buttons .btn,
-.item_buttons .btn-group,
-.item_buttons .input-group {
-  float: left;
-}
-.item_buttons > .btn,
-.item_buttons > .btn-group,
-.item_buttons > .input-group {
-  margin-left: 5px;
-}
-.item_buttons .btn {
-  min-width: 13ex;
-}
-.item_buttons .running-indicator {
-  padding-top: 4px;
-  color: #5cb85c;
-}
-.item_buttons .kernel-name {
-  padding-top: 4px;
-  color: #5bc0de;
-  margin-right: 7px;
-  float: left;
-}
-.toolbar_info {
-  height: 24px;
-  line-height: 24px;
-}
-.list_item input:not([type=checkbox]) {
-  padding-top: 3px;
-  padding-bottom: 3px;
-  height: 22px;
-  line-height: 14px;
-  margin: 0px;
-}
-.highlight_text {
-  color: blue;
-}
-#project_name {
-  display: inline-block;
-  padding-left: 7px;
-  margin-left: -2px;
-}
-#project_name > .breadcrumb {
-  padding: 0px;
-  margin-bottom: 0px;
-  background-color: transparent;
-  font-weight: bold;
-}
-#tree-selector {
-  padding-right: 0px;
-}
-#button-select-all {
-  min-width: 50px;
-}
-#select-all {
-  margin-left: 7px;
-  margin-right: 2px;
-}
-.menu_icon {
-  margin-right: 2px;
-}
-.tab-content .row {
-  margin-left: 0px;
-  margin-right: 0px;
-}
-.folder_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f114";
-}
-.folder_icon:before.pull-left {
-  margin-right: .3em;
-}
-.folder_icon:before.pull-right {
-  margin-left: .3em;
-}
-.notebook_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f02d";
-  position: relative;
-  top: -1px;
-}
-.notebook_icon:before.pull-left {
-  margin-right: .3em;
-}
-.notebook_icon:before.pull-right {
-  margin-left: .3em;
-}
-.running_notebook_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f02d";
-  position: relative;
-  top: -1px;
-  color: #5cb85c;
-}
-.running_notebook_icon:before.pull-left {
-  margin-right: .3em;
-}
-.running_notebook_icon:before.pull-right {
-  margin-left: .3em;
-}
-.file_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f016";
-  position: relative;
-  top: -2px;
-}
-.file_icon:before.pull-left {
-  margin-right: .3em;
-}
-.file_icon:before.pull-right {
-  margin-left: .3em;
-}
-#notebook_toolbar .pull-right {
-  padding-top: 0px;
-  margin-right: -1px;
-}
-ul#new-menu {
-  left: auto;
-  right: 0;
-}
-.kernel-menu-icon {
-  padding-right: 12px;
-  width: 24px;
-  content: "\f096";
-}
-.kernel-menu-icon:before {
-  content: "\f096";
-}
-.kernel-menu-icon-current:before {
-  content: "\f00c";
-}
-#tab_content {
-  padding-top: 20px;
-}
-#running .panel-group .panel {
-  margin-top: 3px;
-  margin-bottom: 1em;
-}
-#running .panel-group .panel .panel-heading {
-  background-color: #EEE;
-  padding-top: 4px;
-  padding-bottom: 4px;
-  padding-left: 7px;
-  padding-right: 7px;
-  line-height: 22px;
-}
-#running .panel-group .panel .panel-heading a:focus,
-#running .panel-group .panel .panel-heading a:hover {
-  text-decoration: none;
-}
-#running .panel-group .panel .panel-body {
-  padding: 0px;
-}
-#running .panel-group .panel .panel-body .list_container {
-  margin-top: 0px;
-  margin-bottom: 0px;
-  border: 0px;
-  border-radius: 0px;
-}
-#running .panel-group .panel .panel-body .list_container .list_item {
-  border-bottom: 1px solid #ddd;
-}
-#running .panel-group .panel .panel-body .list_container .list_item:last-child {
-  border-bottom: 0px;
-}
-.delete-button {
-  display: none;
-}
-.duplicate-button {
-  display: none;
-}
-.rename-button {
-  display: none;
-}
-.shutdown-button {
-  display: none;
-}
-.dynamic-instructions {
-  display: inline-block;
-  padding-top: 4px;
-}
-/*!
-*
-* IPython text editor webapp
-*
-*/
-.selected-keymap i.fa {
-  padding: 0px 5px;
-}
-.selected-keymap i.fa:before {
-  content: "\f00c";
-}
-#mode-menu {
-  overflow: auto;
-  max-height: 20em;
-}
-.edit_app #header {
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-}
-.edit_app #menubar .navbar {
-  /* Use a negative 1 bottom margin, so the border overlaps the border of the
-    header */
-  margin-bottom: -1px;
-}
-.dirty-indicator {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  width: 20px;
-}
-.dirty-indicator.pull-left {
-  margin-right: .3em;
-}
-.dirty-indicator.pull-right {
-  margin-left: .3em;
-}
-.dirty-indicator-dirty {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  width: 20px;
-}
-.dirty-indicator-dirty.pull-left {
-  margin-right: .3em;
-}
-.dirty-indicator-dirty.pull-right {
-  margin-left: .3em;
-}
-.dirty-indicator-clean {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  width: 20px;
-}
-.dirty-indicator-clean.pull-left {
-  margin-right: .3em;
-}
-.dirty-indicator-clean.pull-right {
-  margin-left: .3em;
-}
-.dirty-indicator-clean:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f00c";
-}
-.dirty-indicator-clean:before.pull-left {
-  margin-right: .3em;
-}
-.dirty-indicator-clean:before.pull-right {
-  margin-left: .3em;
-}
-#filename {
-  font-size: 16pt;
-  display: table;
-  padding: 0px 5px;
-}
-#current-mode {
-  padding-left: 5px;
-  padding-right: 5px;
-}
-#texteditor-backdrop {
-  padding-top: 20px;
-  padding-bottom: 20px;
-}
-@media not print {
-  #texteditor-backdrop {
-    background-color: #EEE;
-  }
-}
-@media print {
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
-    background-color: #fff;
-  }
-}
-@media not print {
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
-    background-color: #fff;
-  }
-}
-@media not print {
-  #texteditor-backdrop #texteditor-container {
-    padding: 0px;
-    background-color: #fff;
-    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  }
-}
-/*!
-*
-* IPython notebook
-*
-*/
-/* CSS font colors for translated ANSI colors. */
-.ansibold {
-  font-weight: bold;
-}
-/* use dark versions for foreground, to improve visibility */
-.ansiblack {
-  color: black;
-}
-.ansired {
-  color: darkred;
-}
-.ansigreen {
-  color: darkgreen;
-}
-.ansiyellow {
-  color: #c4a000;
-}
-.ansiblue {
-  color: darkblue;
-}
-.ansipurple {
-  color: darkviolet;
-}
-.ansicyan {
-  color: steelblue;
-}
-.ansigray {
-  color: gray;
-}
-/* and light for background, for the same reason */
-.ansibgblack {
-  background-color: black;
-}
-.ansibgred {
-  background-color: red;
-}
-.ansibggreen {
-  background-color: green;
-}
-.ansibgyellow {
-  background-color: yellow;
-}
-.ansibgblue {
-  background-color: blue;
-}
-.ansibgpurple {
-  background-color: magenta;
-}
-.ansibgcyan {
-  background-color: cyan;
-}
-.ansibggray {
-  background-color: gray;
-}
-div.cell {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  border-radius: 2px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  border-width: 1px;
-  border-style: solid;
-  border-color: transparent;
-  width: 100%;
-  padding: 5px;
-  /* This acts as a spacer between cells, that is outside the border */
-  margin: 0px;
-  outline: none;
-  border-left-width: 1px;
-  padding-left: 5px;
-  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
-}
-div.cell.jupyter-soft-selected {
-  border-left-color: #90CAF9;
-  border-left-color: #E3F2FD;
-  border-left-width: 1px;
-  padding-left: 5px;
-  border-right-color: #E3F2FD;
-  border-right-width: 1px;
-  background: #E3F2FD;
-}
-@media print {
-  div.cell.jupyter-soft-selected {
-    border-color: transparent;
-  }
-}
-div.cell.selected {
-  border-color: #ababab;
-  border-left-width: 0px;
-  padding-left: 6px;
-  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
-}
-@media print {
-  div.cell.selected {
-    border-color: transparent;
-  }
-}
-div.cell.selected.jupyter-soft-selected {
-  border-left-width: 0;
-  padding-left: 6px;
-  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
-}
-.edit_mode div.cell.selected {
-  border-color: #66BB6A;
-  border-left-width: 0px;
-  padding-left: 6px;
-  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
-}
-@media print {
-  .edit_mode div.cell.selected {
-    border-color: transparent;
-  }
-}
-.prompt {
-  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
-  min-width: 14ex;
-  /* This padding is tuned to match the padding on the CodeMirror editor. */
-  padding: 0.4em;
-  margin: 0px;
-  font-family: monospace;
-  text-align: right;
-  /* This has to match that of the the CodeMirror class line-height below */
-  line-height: 1.21429em;
-  /* Don't highlight prompt number selection */
-  -webkit-touch-callout: none;
-  -webkit-user-select: none;
-  -khtml-user-select: none;
-  -moz-user-select: none;
-  -ms-user-select: none;
-  user-select: none;
-  /* Use default cursor */
-  cursor: default;
-}
-@media (max-width: 540px) {
-  .prompt {
-    text-align: left;
-  }
-}
-div.inner_cell {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-}
-@-moz-document url-prefix() {
-  div.inner_cell {
-    overflow-x: hidden;
-  }
-}
-/* input_area and input_prompt must match in top border and margin for alignment */
-div.input_area {
-  border: 1px solid #cfcfcf;
-  border-radius: 2px;
-  background: #f7f7f7;
-  line-height: 1.21429em;
-}
-/* This is needed so that empty prompt areas can collapse to zero height when there
-   is no content in the output_subarea and the prompt. The main purpose of this is
-   to make sure that empty JavaScript output_subareas have no height. */
-div.prompt:empty {
-  padding-top: 0;
-  padding-bottom: 0;
-}
-div.unrecognized_cell {
-  padding: 5px 5px 5px 0px;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-}
-div.unrecognized_cell .inner_cell {
-  border-radius: 2px;
-  padding: 5px;
-  font-weight: bold;
-  color: red;
-  border: 1px solid #cfcfcf;
-  background: #eaeaea;
-}
-div.unrecognized_cell .inner_cell a {
-  color: inherit;
-  text-decoration: none;
-}
-div.unrecognized_cell .inner_cell a:hover {
-  color: inherit;
-  text-decoration: none;
-}
-@media (max-width: 540px) {
-  div.unrecognized_cell > div.prompt {
-    display: none;
-  }
-}
-div.code_cell {
-  /* avoid page breaking on code cells when printing */
-}
-@media print {
-  div.code_cell {
-    page-break-inside: avoid;
-  }
-}
-/* any special styling for code cells that are currently running goes here */
-div.input {
-  page-break-inside: avoid;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-}
-@media (max-width: 540px) {
-  div.input {
-    /* Old browsers */
-    display: -webkit-box;
-    -webkit-box-orient: vertical;
-    -webkit-box-align: stretch;
-    display: -moz-box;
-    -moz-box-orient: vertical;
-    -moz-box-align: stretch;
-    display: box;
-    box-orient: vertical;
-    box-align: stretch;
-    /* Modern browsers */
-    display: flex;
-    flex-direction: column;
-    align-items: stretch;
-  }
-}
-/* input_area and input_prompt must match in top border and margin for alignment */
-div.input_prompt {
-  color: #303F9F;
-  border-top: 1px solid transparent;
-}
-div.input_area > div.highlight {
-  margin: 0.4em;
-  border: none;
-  padding: 0px;
-  background-color: transparent;
-}
-div.input_area > div.highlight > pre {
-  margin: 0px;
-  border: none;
-  padding: 0px;
-  background-color: transparent;
-}
-/* The following gets added to the <head> if it is detected that the user has a
- * monospace font with inconsistent normal/bold/italic height.  See
- * notebookmain.js.  Such fonts will have keywords vertically offset with
- * respect to the rest of the text.  The user should select a better font.
- * See: https://github.com/ipython/ipython/issues/1503
- *
- * .CodeMirror span {
- *      vertical-align: bottom;
- * }
- */
-.CodeMirror {
-  line-height: 1.21429em;
-  /* Changed from 1em to our global default */
-  font-size: 14px;
-  height: auto;
-  /* Changed to auto to autogrow */
-  background: none;
-  /* Changed from white to allow our bg to show through */
-}
-.CodeMirror-scroll {
-  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
-  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
-  overflow-y: hidden;
-  overflow-x: auto;
-}
-.CodeMirror-lines {
-  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
-  /* we have set a different line-height and want this to scale with that. */
-  padding: 0.4em;
-}
-.CodeMirror-linenumber {
-  padding: 0 8px 0 4px;
-}
-.CodeMirror-gutters {
-  border-bottom-left-radius: 2px;
-  border-top-left-radius: 2px;
-}
-.CodeMirror pre {
-  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
-  /* .CodeMirror-lines */
-  padding: 0;
-  border: 0;
-  border-radius: 0;
-}
-/*
-
-Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
-Adapted from GitHub theme
-
-*/
-.highlight-base {
-  color: #000;
-}
-.highlight-variable {
-  color: #000;
-}
-.highlight-variable-2 {
-  color: #1a1a1a;
-}
-.highlight-variable-3 {
-  color: #333333;
-}
-.highlight-string {
-  color: #BA2121;
-}
-.highlight-comment {
-  color: #408080;
-  font-style: italic;
-}
-.highlight-number {
-  color: #080;
-}
-.highlight-atom {
-  color: #88F;
-}
-.highlight-keyword {
-  color: #008000;
-  font-weight: bold;
-}
-.highlight-builtin {
-  color: #008000;
-}
-.highlight-error {
-  color: #f00;
-}
-.highlight-operator {
-  color: #AA22FF;
-  font-weight: bold;
-}
-.highlight-meta {
-  color: #AA22FF;
-}
-/* previously not defined, copying from default codemirror */
-.highlight-def {
-  color: #00f;
-}
-.highlight-string-2 {
-  color: #f50;
-}
-.highlight-qualifier {
-  color: #555;
-}
-.highlight-bracket {
-  color: #997;
-}
-.highlight-tag {
-  color: #170;
-}
-.highlight-attribute {
-  color: #00c;
-}
-.highlight-header {
-  color: blue;
-}
-.highlight-quote {
-  color: #090;
-}
-.highlight-link {
-  color: #00c;
-}
-/* apply the same style to codemirror */
-.cm-s-ipython span.cm-keyword {
-  color: #008000;
-  font-weight: bold;
-}
-.cm-s-ipython span.cm-atom {
-  color: #88F;
-}
-.cm-s-ipython span.cm-number {
-  color: #080;
-}
-.cm-s-ipython span.cm-def {
-  color: #00f;
-}
-.cm-s-ipython span.cm-variable {
-  color: #000;
-}
-.cm-s-ipython span.cm-operator {
-  color: #AA22FF;
-  font-weight: bold;
-}
-.cm-s-ipython span.cm-variable-2 {
-  color: #1a1a1a;
-}
-.cm-s-ipython span.cm-variable-3 {
-  color: #333333;
-}
-.cm-s-ipython span.cm-comment {
-  color: #408080;
-  font-style: italic;
-}
-.cm-s-ipython span.cm-string {
-  color: #BA2121;
-}
-.cm-s-ipython span.cm-string-2 {
-  color: #f50;
-}
-.cm-s-ipython span.cm-meta {
-  color: #AA22FF;
-}
-.cm-s-ipython span.cm-qualifier {
-  color: #555;
-}
-.cm-s-ipython span.cm-builtin {
-  color: #008000;
-}
-.cm-s-ipython span.cm-bracket {
-  color: #997;
-}
-.cm-s-ipython span.cm-tag {
-  color: #170;
-}
-.cm-s-ipython span.cm-attribute {
-  color: #00c;
-}
-.cm-s-ipython span.cm-header {
-  color: blue;
-}
-.cm-s-ipython span.cm-quote {
-  color: #090;
-}
-.cm-s-ipython span.cm-link {
-  color: #00c;
-}
-.cm-s-ipython span.cm-error {
-  color: #f00;
-}
-.cm-s-ipython span.cm-tab {
-  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
-  background-position: right;
-  background-repeat: no-repeat;
-}
-div.output_wrapper {
-  /* this position must be relative to enable descendents to be absolute within it */
-  position: relative;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  z-index: 1;
-}
-/* class for the output area when it should be height-limited */
-div.output_scroll {
-  /* ideally, this would be max-height, but FF barfs all over that */
-  height: 24em;
-  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
-  width: 100%;
-  overflow: auto;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
-  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
-  display: block;
-}
-/* output div while it is collapsed */
-div.output_collapsed {
-  margin: 0px;
-  padding: 0px;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-}
-div.out_prompt_overlay {
-  height: 100%;
-  padding: 0px 0.4em;
-  position: absolute;
-  border-radius: 2px;
-}
-div.out_prompt_overlay:hover {
-  /* use inner shadow to get border that is computed the same on WebKit/FF */
-  -webkit-box-shadow: inset 0 0 1px #000;
-  box-shadow: inset 0 0 1px #000;
-  background: rgba(240, 240, 240, 0.5);
-}
-div.output_prompt {
-  color: #D84315;
-}
-/* This class is the outer container of all output sections. */
-div.output_area {
-  padding: 0px;
-  page-break-inside: avoid;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-}
-div.output_area .MathJax_Display {
-  text-align: left !important;
-}
-div.output_area .rendered_html table {
-  margin-left: 0;
-  margin-right: 0;
-}
-div.output_area .rendered_html img {
-  margin-left: 0;
-  margin-right: 0;
-}
-div.output_area img,
-div.output_area svg {
-  max-width: 100%;
-  height: auto;
-}
-div.output_area img.unconfined,
-div.output_area svg.unconfined {
-  max-width: none;
-}
-/* This is needed to protect the pre formating from global settings such
-   as that of bootstrap */
-.output {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-}
-@media (max-width: 540px) {
-  div.output_area {
-    /* Old browsers */
-    display: -webkit-box;
-    -webkit-box-orient: vertical;
-    -webkit-box-align: stretch;
-    display: -moz-box;
-    -moz-box-orient: vertical;
-    -moz-box-align: stretch;
-    display: box;
-    box-orient: vertical;
-    box-align: stretch;
-    /* Modern browsers */
-    display: flex;
-    flex-direction: column;
-    align-items: stretch;
-  }
-}
-div.output_area pre {
-  margin: 0;
-  padding: 0;
-  border: 0;
-  vertical-align: baseline;
-  color: black;
-  background-color: transparent;
-  border-radius: 0;
-}
-/* This class is for the output subarea inside the output_area and after
-   the prompt div. */
-div.output_subarea {
-  overflow-x: auto;
-  padding: 0.4em;
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-  max-width: calc(100% - 14ex);
-}
-div.output_scroll div.output_subarea {
-  overflow-x: visible;
-}
-/* The rest of the output_* classes are for special styling of the different
-   output types */
-/* all text output has this class: */
-div.output_text {
-  text-align: left;
-  color: #000;
-  /* This has to match that of the the CodeMirror class line-height below */
-  line-height: 1.21429em;
-}
-/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
-div.output_stderr {
-  background: #fdd;
-  /* very light red background for stderr */
-}
-div.output_latex {
-  text-align: left;
-}
-/* Empty output_javascript divs should have no height */
-div.output_javascript:empty {
-  padding: 0;
-}
-.js-error {
-  color: darkred;
-}
-/* raw_input styles */
-div.raw_input_container {
-  line-height: 1.21429em;
-  padding-top: 5px;
-}
-pre.raw_input_prompt {
-  /* nothing needed here. */
-}
-input.raw_input {
-  font-family: monospace;
-  font-size: inherit;
-  color: inherit;
-  width: auto;
-  /* make sure input baseline aligns with prompt */
-  vertical-align: baseline;
-  /* padding + margin = 0.5em between prompt and cursor */
-  padding: 0em 0.25em;
-  margin: 0em 0.25em;
-}
-input.raw_input:focus {
-  box-shadow: none;
-}
-p.p-space {
-  margin-bottom: 10px;
-}
-div.output_unrecognized {
-  padding: 5px;
-  font-weight: bold;
-  color: red;
-}
-div.output_unrecognized a {
-  color: inherit;
-  text-decoration: none;
-}
-div.output_unrecognized a:hover {
-  color: inherit;
-  text-decoration: none;
-}
-.rendered_html {
-  color: #000;
-  /* any extras will just be numbers: */
-}
-.rendered_html em {
-  font-style: italic;
-}
-.rendered_html strong {
-  font-weight: bold;
-}
-.rendered_html u {
-  text-decoration: underline;
-}
-.rendered_html :link {
-  text-decoration: underline;
-}
-.rendered_html :visited {
-  text-decoration: underline;
-}
-.rendered_html h1 {
-  font-size: 185.7%;
-  margin: 1.08em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-}
-.rendered_html h2 {
-  font-size: 157.1%;
-  margin: 1.27em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-}
-.rendered_html h3 {
-  font-size: 128.6%;
-  margin: 1.55em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-}
-.rendered_html h4 {
-  font-size: 100%;
-  margin: 2em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-}
-.rendered_html h5 {
-  font-size: 100%;
-  margin: 2em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-  font-style: italic;
-}
-.rendered_html h6 {
-  font-size: 100%;
-  margin: 2em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-  font-style: italic;
-}
-.rendered_html h1:first-child {
-  margin-top: 0.538em;
-}
-.rendered_html h2:first-child {
-  margin-top: 0.636em;
-}
-.rendered_html h3:first-child {
-  margin-top: 0.777em;
-}
-.rendered_html h4:first-child {
-  margin-top: 1em;
-}
-.rendered_html h5:first-child {
-  margin-top: 1em;
-}
-.rendered_html h6:first-child {
-  margin-top: 1em;
-}
-.rendered_html ul {
-  list-style: disc;
-  margin: 0em 2em;
-  padding-left: 0px;
-}
-.rendered_html ul ul {
-  list-style: square;
-  margin: 0em 2em;
-}
-.rendered_html ul ul ul {
-  list-style: circle;
-  margin: 0em 2em;
-}
-.rendered_html ol {
-  list-style: decimal;
-  margin: 0em 2em;
-  padding-left: 0px;
-}
-.rendered_html ol ol {
-  list-style: upper-alpha;
-  margin: 0em 2em;
-}
-.rendered_html ol ol ol {
-  list-style: lower-alpha;
-  margin: 0em 2em;
-}
-.rendered_html ol ol ol ol {
-  list-style: lower-roman;
-  margin: 0em 2em;
-}
-.rendered_html ol ol ol ol ol {
-  list-style: decimal;
-  margin: 0em 2em;
-}
-.rendered_html * + ul {
-  margin-top: 1em;
-}
-.rendered_html * + ol {
-  margin-top: 1em;
-}
-.rendered_html hr {
-  color: black;
-  background-color: black;
-}
-.rendered_html pre {
-  margin: 1em 2em;
-}
-.rendered_html pre,
-.rendered_html code {
-  border: 0;
-  background-color: #fff;
-  color: #000;
-  font-size: 100%;
-  padding: 0px;
-}
-.rendered_html blockquote {
-  margin: 1em 2em;
-}
-.rendered_html table {
-  margin-left: auto;
-  margin-right: auto;
-  border: 1px solid black;
-  border-collapse: collapse;
-}
-.rendered_html tr,
-.rendered_html th,
-.rendered_html td {
-  border: 1px solid black;
-  border-collapse: collapse;
-  margin: 1em 2em;
-}
-.rendered_html td,
-.rendered_html th {
-  text-align: left;
-  vertical-align: middle;
-  padding: 4px;
-}
-.rendered_html th {
-  font-weight: bold;
-}
-.rendered_html * + table {
-  margin-top: 1em;
-}
-.rendered_html p {
-  text-align: left;
-}
-.rendered_html * + p {
-  margin-top: 1em;
-}
-.rendered_html img {
-  display: block;
-  margin-left: auto;
-  margin-right: auto;
-}
-.rendered_html * + img {
-  margin-top: 1em;
-}
-.rendered_html img,
-.rendered_html svg {
-  max-width: 100%;
-  height: auto;
-}
-.rendered_html img.unconfined,
-.rendered_html svg.unconfined {
-  max-width: none;
-}
-div.text_cell {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-}
-@media (max-width: 540px) {
-  div.text_cell > div.prompt {
-    display: none;
-  }
-}
-div.text_cell_render {
-  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
-  outline: none;
-  resize: none;
-  width: inherit;
-  border-style: none;
-  padding: 0.5em 0.5em 0.5em 0.4em;
-  color: #000;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-a.anchor-link:link {
-  text-decoration: none;
-  padding: 0px 20px;
-  visibility: hidden;
-}
-h1:hover .anchor-link,
-h2:hover .anchor-link,
-h3:hover .anchor-link,
-h4:hover .anchor-link,
-h5:hover .anchor-link,
-h6:hover .anchor-link {
-  visibility: visible;
-}
-.text_cell.rendered .input_area {
-  display: none;
-}
-.text_cell.rendered .rendered_html {
-  overflow-x: auto;
-  overflow-y: hidden;
-}
-.text_cell.unrendered .text_cell_render {
-  display: none;
-}
-.cm-header-1,
-.cm-header-2,
-.cm-header-3,
-.cm-header-4,
-.cm-header-5,
-.cm-header-6 {
-  font-weight: bold;
-  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
-}
-.cm-header-1 {
-  font-size: 185.7%;
-}
-.cm-header-2 {
-  font-size: 157.1%;
-}
-.cm-header-3 {
-  font-size: 128.6%;
-}
-.cm-header-4 {
-  font-size: 110%;
-}
-.cm-header-5 {
-  font-size: 100%;
-  font-style: italic;
-}
-.cm-header-6 {
-  font-size: 100%;
-  font-style: italic;
-}
-/*!
-*
-* IPython notebook webapp
-*
-*/
-@media (max-width: 767px) {
-  .notebook_app {
-    padding-left: 0px;
-    padding-right: 0px;
-  }
-}
-#ipython-main-app {
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  height: 100%;
-}
-div#notebook_panel {
-  margin: 0px;
-  padding: 0px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  height: 100%;
-}
-div#notebook {
-  font-size: 14px;
-  line-height: 20px;
-  overflow-y: hidden;
-  overflow-x: auto;
-  width: 100%;
-  /* This spaces the page away from the edge of the notebook area */
-  padding-top: 20px;
-  margin: 0px;
-  outline: none;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  min-height: 100%;
-}
-@media not print {
-  #notebook-container {
-    padding: 15px;
-    background-color: #fff;
-    min-height: 0;
-    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  }
-}
-@media print {
-  #notebook-container {
-    width: 100%;
-  }
-}
-div.ui-widget-content {
-  border: 1px solid #ababab;
-  outline: none;
-}
-pre.dialog {
-  background-color: #f7f7f7;
-  border: 1px solid #ddd;
-  border-radius: 2px;
-  padding: 0.4em;
-  padding-left: 2em;
-}
-p.dialog {
-  padding: 0.2em;
-}
-/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
-   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
- */
-pre,
-code,
-kbd,
-samp {
-  white-space: pre-wrap;
-}
-#fonttest {
-  font-family: monospace;
-}
-p {
-  margin-bottom: 0;
-}
-.end_space {
-  min-height: 100px;
-  transition: height .2s ease;
-}
-.notebook_app > #header {
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-}
-@media not print {
-  .notebook_app {
-    background-color: #EEE;
-  }
-}
-kbd {
-  border-style: solid;
-  border-width: 1px;
-  box-shadow: none;
-  margin: 2px;
-  padding-left: 2px;
-  padding-right: 2px;
-  padding-top: 1px;
-  padding-bottom: 1px;
-}
-/* CSS for the cell toolbar */
-.celltoolbar {
-  border: thin solid #CFCFCF;
-  border-bottom: none;
-  background: #EEE;
-  border-radius: 2px 2px 0px 0px;
-  width: 100%;
-  height: 29px;
-  padding-right: 4px;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-  /* Old browsers */
-  -webkit-box-pack: end;
-  -moz-box-pack: end;
-  box-pack: end;
-  /* Modern browsers */
-  justify-content: flex-end;
-  display: -webkit-flex;
-}
-@media print {
-  .celltoolbar {
-    display: none;
-  }
-}
-.ctb_hideshow {
-  display: none;
-  vertical-align: bottom;
-}
-/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
-   Cell toolbars are only shown when the ctb_global_show class is also set.
-*/
-.ctb_global_show .ctb_show.ctb_hideshow {
-  display: block;
-}
-.ctb_global_show .ctb_show + .input_area,
-.ctb_global_show .ctb_show + div.text_cell_input,
-.ctb_global_show .ctb_show ~ div.text_cell_render {
-  border-top-right-radius: 0px;
-  border-top-left-radius: 0px;
-}
-.ctb_global_show .ctb_show ~ div.text_cell_render {
-  border: 1px solid #cfcfcf;
-}
-.celltoolbar {
-  font-size: 87%;
-  padding-top: 3px;
-}
-.celltoolbar select {
-  display: block;
-  width: 100%;
-  height: 32px;
-  padding: 6px 12px;
-  font-size: 13px;
-  line-height: 1.42857143;
-  color: #555555;
-  background-color: #fff;
-  background-image: none;
-  border: 1px solid #ccc;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-  height: 30px;
-  padding: 5px 10px;
-  font-size: 12px;
-  line-height: 1.5;
-  border-radius: 1px;
-  width: inherit;
-  font-size: inherit;
-  height: 22px;
-  padding: 0px;
-  display: inline-block;
-}
-.celltoolbar select:focus {
-  border-color: #66afe9;
-  outline: 0;
-  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
-  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
-}
-.celltoolbar select::-moz-placeholder {
-  color: #999;
-  opacity: 1;
-}
-.celltoolbar select:-ms-input-placeholder {
-  color: #999;
-}
-.celltoolbar select::-webkit-input-placeholder {
-  color: #999;
-}
-.celltoolbar select::-ms-expand {
-  border: 0;
-  background-color: transparent;
-}
-.celltoolbar select[disabled],
-.celltoolbar select[readonly],
-fieldset[disabled] .celltoolbar select {
-  background-color: #eeeeee;
-  opacity: 1;
-}
-.celltoolbar select[disabled],
-fieldset[disabled] .celltoolbar select {
-  cursor: not-allowed;
-}
-textarea.celltoolbar select {
-  height: auto;
-}
-select.celltoolbar select {
-  height: 30px;
-  line-height: 30px;
-}
-textarea.celltoolbar select,
-select[multiple].celltoolbar select {
-  height: auto;
-}
-.celltoolbar label {
-  margin-left: 5px;
-  margin-right: 5px;
-}
-.completions {
-  position: absolute;
-  z-index: 110;
-  overflow: hidden;
-  border: 1px solid #ababab;
-  border-radius: 2px;
-  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
-  box-shadow: 0px 6px 10px -1px #adadad;
-  line-height: 1;
-}
-.completions select {
-  background: white;
-  outline: none;
-  border: none;
-  padding: 0px;
-  margin: 0px;
-  overflow: auto;
-  font-family: monospace;
-  font-size: 110%;
-  color: #000;
-  width: auto;
-}
-.completions select option.context {
-  color: #286090;
-}
-#kernel_logo_widget {
-  float: right !important;
-  float: right;
-}
-#kernel_logo_widget .current_kernel_logo {
-  display: none;
-  margin-top: -1px;
-  margin-bottom: -1px;
-  width: 32px;
-  height: 32px;
-}
-#menubar {
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  margin-top: 1px;
-}
-#menubar .navbar {
-  border-top: 1px;
-  border-radius: 0px 0px 2px 2px;
-  margin-bottom: 0px;
-}
-#menubar .navbar-toggle {
-  float: left;
-  padding-top: 7px;
-  padding-bottom: 7px;
-  border: none;
-}
-#menubar .navbar-collapse {
-  clear: left;
-}
-.nav-wrapper {
-  border-bottom: 1px solid #e7e7e7;
-}
-i.menu-icon {
-  padding-top: 4px;
-}
-ul#help_menu li a {
-  overflow: hidden;
-  padding-right: 2.2em;
-}
-ul#help_menu li a i {
-  margin-right: -1.2em;
-}
-.dropdown-submenu {
-  position: relative;
-}
-.dropdown-submenu > .dropdown-menu {
-  top: 0;
-  left: 100%;
-  margin-top: -6px;
-  margin-left: -1px;
-}
-.dropdown-submenu:hover > .dropdown-menu {
-  display: block;
-}
-.dropdown-submenu > a:after {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  display: block;
-  content: "\f0da";
-  float: right;
-  color: #333333;
-  margin-top: 2px;
-  margin-right: -10px;
-}
-.dropdown-submenu > a:after.pull-left {
-  margin-right: .3em;
-}
-.dropdown-submenu > a:after.pull-right {
-  margin-left: .3em;
-}
-.dropdown-submenu:hover > a:after {
-  color: #262626;
-}
-.dropdown-submenu.pull-left {
-  float: none;
-}
-.dropdown-submenu.pull-left > .dropdown-menu {
-  left: -100%;
-  margin-left: 10px;
-}
-#notification_area {
-  float: right !important;
-  float: right;
-  z-index: 10;
-}
-.indicator_area {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
-}
-#kernel_indicator {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
-  border-left: 1px solid;
-}
-#kernel_indicator .kernel_indicator_name {
-  padding-left: 5px;
-  padding-right: 5px;
-}
-#modal_indicator {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
-}
-#readonly-indicator {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
-  margin-top: 2px;
-  margin-bottom: 0px;
-  margin-left: 0px;
-  margin-right: 0px;
-  display: none;
-}
-.modal_indicator:before {
-  width: 1.28571429em;
-  text-align: center;
-}
-.edit_mode .modal_indicator:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f040";
-}
-.edit_mode .modal_indicator:before.pull-left {
-  margin-right: .3em;
-}
-.edit_mode .modal_indicator:before.pull-right {
-  margin-left: .3em;
-}
-.command_mode .modal_indicator:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: ' ';
-}
-.command_mode .modal_indicator:before.pull-left {
-  margin-right: .3em;
-}
-.command_mode .modal_indicator:before.pull-right {
-  margin-left: .3em;
-}
-.kernel_idle_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f10c";
-}
-.kernel_idle_icon:before.pull-left {
-  margin-right: .3em;
-}
-.kernel_idle_icon:before.pull-right {
-  margin-left: .3em;
-}
-.kernel_busy_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f111";
-}
-.kernel_busy_icon:before.pull-left {
-  margin-right: .3em;
-}
-.kernel_busy_icon:before.pull-right {
-  margin-left: .3em;
-}
-.kernel_dead_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f1e2";
-}
-.kernel_dead_icon:before.pull-left {
-  margin-right: .3em;
-}
-.kernel_dead_icon:before.pull-right {
-  margin-left: .3em;
-}
-.kernel_disconnected_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f127";
-}
-.kernel_disconnected_icon:before.pull-left {
-  margin-right: .3em;
-}
-.kernel_disconnected_icon:before.pull-right {
-  margin-left: .3em;
-}
-.notification_widget {
-  color: #777;
-  z-index: 10;
-  background: rgba(240, 240, 240, 0.5);
-  margin-right: 4px;
-  color: #333;
-  background-color: #fff;
-  border-color: #ccc;
-}
-.notification_widget:focus,
-.notification_widget.focus {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #8c8c8c;
-}
-.notification_widget:hover {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-.notification_widget:active,
-.notification_widget.active,
-.open > .dropdown-toggle.notification_widget {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-.notification_widget:active:hover,
-.notification_widget.active:hover,
-.open > .dropdown-toggle.notification_widget:hover,
-.notification_widget:active:focus,
-.notification_widget.active:focus,
-.open > .dropdown-toggle.notification_widget:focus,
-.notification_widget:active.focus,
-.notification_widget.active.focus,
-.open > .dropdown-toggle.notification_widget.focus {
-  color: #333;
-  background-color: #d4d4d4;
-  border-color: #8c8c8c;
-}
-.notification_widget:active,
-.notification_widget.active,
-.open > .dropdown-toggle.notification_widget {
-  background-image: none;
-}
-.notification_widget.disabled:hover,
-.notification_widget[disabled]:hover,
-fieldset[disabled] .notification_widget:hover,
-.notification_widget.disabled:focus,
-.notification_widget[disabled]:focus,
-fieldset[disabled] .notification_widget:focus,
-.notification_widget.disabled.focus,
-.notification_widget[disabled].focus,
-fieldset[disabled] .notification_widget.focus {
-  background-color: #fff;
-  border-color: #ccc;
-}
-.notification_widget .badge {
-  color: #fff;
-  background-color: #333;
-}
-.notification_widget.warning {
-  color: #fff;
-  background-color: #f0ad4e;
-  border-color: #eea236;
-}
-.notification_widget.warning:focus,
-.notification_widget.warning.focus {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #985f0d;
-}
-.notification_widget.warning:hover {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #d58512;
-}
-.notification_widget.warning:active,
-.notification_widget.warning.active,
-.open > .dropdown-toggle.notification_widget.warning {
-  color: #fff;
-  background-color: #ec971f;
-  border-color: #d58512;
-}
-.notification_widget.warning:active:hover,
-.notification_widget.warning.active:hover,
-.open > .dropdown-toggle.notification_widget.warning:hover,
-.notification_widget.warning:active:focus,
-.notification_widget.warning.active:focus,
-.open > .dropdown-toggle.notification_widget.warning:focus,
-.notification_widget.warning:active.focus,
-.notification_widget.warning.active.focus,
-.open > .dropdown-toggle.notification_widget.warning.focus {
-  color: #fff;
-  background-color: #d58512;
-  border-color: #985f0d;
-}
-.notification_widget.warning:active,
-.notification_widget.warning.active,
-.open > .dropdown-toggle.notification_widget.warning {
-  background-image: none;
-}
-.notification_widget.warning.disabled:hover,
-.notification_widget.warning[disabled]:hover,
-fieldset[disabled] .notification_widget.warning:hover,
-.notification_widget.warning.disabled:focus,
-.notification_widget.warning[disabled]:focus,
-fieldset[disabled] .notification_widget.warning:focus,
-.notification_widget.warning.disabled.focus,
-.notification_widget.warning[disabled].focus,
-fieldset[disabled] .notification_widget.warning.focus {
-  background-color: #f0ad4e;
-  border-color: #eea236;
-}
-.notification_widget.warning .badge {
-  color: #f0ad4e;
-  background-color: #fff;
-}
-.notification_widget.success {
-  color: #fff;
-  background-color: #5cb85c;
-  border-color: #4cae4c;
-}
-.notification_widget.success:focus,
-.notification_widget.success.focus {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #255625;
-}
-.notification_widget.success:hover {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #398439;
-}
-.notification_widget.success:active,
-.notification_widget.success.active,
-.open > .dropdown-toggle.notification_widget.success {
-  color: #fff;
-  background-color: #449d44;
-  border-color: #398439;
-}
-.notification_widget.success:active:hover,
-.notification_widget.success.active:hover,
-.open > .dropdown-toggle.notification_widget.success:hover,
-.notification_widget.success:active:focus,
-.notification_widget.success.active:focus,
-.open > .dropdown-toggle.notification_widget.success:focus,
-.notification_widget.success:active.focus,
-.notification_widget.success.active.focus,
-.open > .dropdown-toggle.notification_widget.success.focus {
-  color: #fff;
-  background-color: #398439;
-  border-color: #255625;
-}
-.notification_widget.success:active,
-.notification_widget.success.active,
-.open > .dropdown-toggle.notification_widget.success {
-  background-image: none;
-}
-.notification_widget.success.disabled:hover,
-.notification_widget.success[disabled]:hover,
-fieldset[disabled] .notification_widget.success:hover,
-.notification_widget.success.disabled:focus,
-.notification_widget.success[disabled]:focus,
-fieldset[disabled] .notification_widget.success:focus,
-.notification_widget.success.disabled.focus,
-.notification_widget.success[disabled].focus,
-fieldset[disabled] .notification_widget.success.focus {
-  background-color: #5cb85c;
-  border-color: #4cae4c;
-}
-.notification_widget.success .badge {
-  color: #5cb85c;
-  background-color: #fff;
-}
-.notification_widget.info {
-  color: #fff;
-  background-color: #5bc0de;
-  border-color: #46b8da;
-}
-.notification_widget.info:focus,
-.notification_widget.info.focus {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #1b6d85;
-}
-.notification_widget.info:hover {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #269abc;
-}
-.notification_widget.info:active,
-.notification_widget.info.active,
-.open > .dropdown-toggle.notification_widget.info {
-  color: #fff;
-  background-color: #31b0d5;
-  border-color: #269abc;
-}
-.notification_widget.info:active:hover,
-.notification_widget.info.active:hover,
-.open > .dropdown-toggle.notification_widget.info:hover,
-.notification_widget.info:active:focus,
-.notification_widget.info.active:focus,
-.open > .dropdown-toggle.notification_widget.info:focus,
-.notification_widget.info:active.focus,
-.notification_widget.info.active.focus,
-.open > .dropdown-toggle.notification_widget.info.focus {
-  color: #fff;
-  background-color: #269abc;
-  border-color: #1b6d85;
-}
-.notification_widget.info:active,
-.notification_widget.info.active,
-.open > .dropdown-toggle.notification_widget.info {
-  background-image: none;
-}
-.notification_widget.info.disabled:hover,
-.notification_widget.info[disabled]:hover,
-fieldset[disabled] .notification_widget.info:hover,
-.notification_widget.info.disabled:focus,
-.notification_widget.info[disabled]:focus,
-fieldset[disabled] .notification_widget.info:focus,
-.notification_widget.info.disabled.focus,
-.notification_widget.info[disabled].focus,
-fieldset[disabled] .notification_widget.info.focus {
-  background-color: #5bc0de;
-  border-color: #46b8da;
-}
-.notification_widget.info .badge {
-  color: #5bc0de;
-  background-color: #fff;
-}
-.notification_widget.danger {
-  color: #fff;
-  background-color: #d9534f;
-  border-color: #d43f3a;
-}
-.notification_widget.danger:focus,
-.notification_widget.danger.focus {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #761c19;
-}
-.notification_widget.danger:hover {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #ac2925;
-}
-.notification_widget.danger:active,
-.notification_widget.danger.active,
-.open > .dropdown-toggle.notification_widget.danger {
-  color: #fff;
-  background-color: #c9302c;
-  border-color: #ac2925;
-}
-.notification_widget.danger:active:hover,
-.notification_widget.danger.active:hover,
-.open > .dropdown-toggle.notification_widget.danger:hover,
-.notification_widget.danger:active:focus,
-.notification_widget.danger.active:focus,
-.open > .dropdown-toggle.notification_widget.danger:focus,
-.notification_widget.danger:active.focus,
-.notification_widget.danger.active.focus,
-.open > .dropdown-toggle.notification_widget.danger.focus {
-  color: #fff;
-  background-color: #ac2925;
-  border-color: #761c19;
-}
-.notification_widget.danger:active,
-.notification_widget.danger.active,
-.open > .dropdown-toggle.notification_widget.danger {
-  background-image: none;
-}
-.notification_widget.danger.disabled:hover,
-.notification_widget.danger[disabled]:hover,
-fieldset[disabled] .notification_widget.danger:hover,
-.notification_widget.danger.disabled:focus,
-.notification_widget.danger[disabled]:focus,
-fieldset[disabled] .notification_widget.danger:focus,
-.notification_widget.danger.disabled.focus,
-.notification_widget.danger[disabled].focus,
-fieldset[disabled] .notification_widget.danger.focus {
-  background-color: #d9534f;
-  border-color: #d43f3a;
-}
-.notification_widget.danger .badge {
-  color: #d9534f;
-  background-color: #fff;
-}
-div#pager {
-  background-color: #fff;
-  font-size: 14px;
-  line-height: 20px;
-  overflow: hidden;
-  display: none;
-  position: fixed;
-  bottom: 0px;
-  width: 100%;
-  max-height: 50%;
-  padding-top: 8px;
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  /* Display over codemirror */
-  z-index: 100;
-  /* Hack which prevents jquery ui resizable from changing top. */
-  top: auto !important;
-}
-div#pager pre {
-  line-height: 1.21429em;
-  color: #000;
-  background-color: #f7f7f7;
-  padding: 0.4em;
-}
-div#pager #pager-button-area {
-  position: absolute;
-  top: 8px;
-  right: 20px;
-}
-div#pager #pager-contents {
-  position: relative;
-  overflow: auto;
-  width: 100%;
-  height: 100%;
-}
-div#pager #pager-contents #pager-container {
-  position: relative;
-  padding: 15px 0px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-div#pager .ui-resizable-handle {
-  top: 0px;
-  height: 8px;
-  background: #f7f7f7;
-  border-top: 1px solid #cfcfcf;
-  border-bottom: 1px solid #cfcfcf;
-  /* This injects handle bars (a short, wide = symbol) for 
-        the resize handle. */
-}
-div#pager .ui-resizable-handle::after {
-  content: '';
-  top: 2px;
-  left: 50%;
-  height: 3px;
-  width: 30px;
-  margin-left: -15px;
-  position: absolute;
-  border-top: 1px solid #cfcfcf;
-}
-.quickhelp {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
-  line-height: 1.8em;
-}
-.shortcut_key {
-  display: inline-block;
-  width: 20ex;
-  text-align: right;
-  font-family: monospace;
-}
-.shortcut_descr {
-  display: inline-block;
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-}
-span.save_widget {
-  margin-top: 6px;
-}
-span.save_widget span.filename {
-  height: 1em;
-  line-height: 1em;
-  padding: 3px;
-  margin-left: 16px;
-  border: none;
-  font-size: 146.5%;
-  border-radius: 2px;
-}
-span.save_widget span.filename:hover {
-  background-color: #e6e6e6;
-}
-span.checkpoint_status,
-span.autosave_status {
-  font-size: small;
-}
-@media (max-width: 767px) {
-  span.save_widget {
-    font-size: small;
-  }
-  span.checkpoint_status,
-  span.autosave_status {
-    display: none;
-  }
-}
-@media (min-width: 768px) and (max-width: 991px) {
-  span.checkpoint_status {
-    display: none;
-  }
-  span.autosave_status {
-    font-size: x-small;
-  }
-}
-.toolbar {
-  padding: 0px;
-  margin-left: -5px;
-  margin-top: 2px;
-  margin-bottom: 5px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-.toolbar select,
-.toolbar label {
-  width: auto;
-  vertical-align: middle;
-  margin-right: 2px;
-  margin-bottom: 0px;
-  display: inline;
-  font-size: 92%;
-  margin-left: 0.3em;
-  margin-right: 0.3em;
-  padding: 0px;
-  padding-top: 3px;
-}
-.toolbar .btn {
-  padding: 2px 8px;
-}
-.toolbar .btn-group {
-  margin-top: 0px;
-  margin-left: 5px;
-}
-#maintoolbar {
-  margin-bottom: -3px;
-  margin-top: -8px;
-  border: 0px;
-  min-height: 27px;
-  margin-left: 0px;
-  padding-top: 11px;
-  padding-bottom: 3px;
-}
-#maintoolbar .navbar-text {
-  float: none;
-  vertical-align: middle;
-  text-align: right;
-  margin-left: 5px;
-  margin-right: 0px;
-  margin-top: 0px;
-}
-.select-xs {
-  height: 24px;
-}
-.pulse,
-.dropdown-menu > li > a.pulse,
-li.pulse > a.dropdown-toggle,
-li.pulse.open > a.dropdown-toggle {
-  background-color: #F37626;
-  color: white;
-}
-/**
- * Primary styles
- *
- * Author: Jupyter Development Team
- */
-/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
- * of chance of beeing generated from the ../less/[samename].less file, you can
- * try to get back the less file by reverting somme commit in history
- **/
-/*
- * We'll try to get something pretty, so we
- * have some strange css to have the scroll bar on
- * the left with fix button on the top right of the tooltip
- */
-@-moz-keyframes fadeOut {
-  from {
-    opacity: 1;
-  }
-  to {
-    opacity: 0;
-  }
-}
-@-webkit-keyframes fadeOut {
-  from {
-    opacity: 1;
-  }
-  to {
-    opacity: 0;
-  }
-}
-@-moz-keyframes fadeIn {
-  from {
-    opacity: 0;
-  }
-  to {
-    opacity: 1;
-  }
-}
-@-webkit-keyframes fadeIn {
-  from {
-    opacity: 0;
-  }
-  to {
-    opacity: 1;
-  }
-}
-/*properties of tooltip after "expand"*/
-.bigtooltip {
-  overflow: auto;
-  height: 200px;
-  -webkit-transition-property: height;
-  -webkit-transition-duration: 500ms;
-  -moz-transition-property: height;
-  -moz-transition-duration: 500ms;
-  transition-property: height;
-  transition-duration: 500ms;
-}
-/*properties of tooltip before "expand"*/
-.smalltooltip {
-  -webkit-transition-property: height;
-  -webkit-transition-duration: 500ms;
-  -moz-transition-property: height;
-  -moz-transition-duration: 500ms;
-  transition-property: height;
-  transition-duration: 500ms;
-  text-overflow: ellipsis;
-  overflow: hidden;
-  height: 80px;
-}
-.tooltipbuttons {
-  position: absolute;
-  padding-right: 15px;
-  top: 0px;
-  right: 0px;
-}
-.tooltiptext {
-  /*avoid the button to overlap on some docstring*/
-  padding-right: 30px;
-}
-.ipython_tooltip {
-  max-width: 700px;
-  /*fade-in animation when inserted*/
-  -webkit-animation: fadeOut 400ms;
-  -moz-animation: fadeOut 400ms;
-  animation: fadeOut 400ms;
-  -webkit-animation: fadeIn 400ms;
-  -moz-animation: fadeIn 400ms;
-  animation: fadeIn 400ms;
-  vertical-align: middle;
-  background-color: #f7f7f7;
-  overflow: visible;
-  border: #ababab 1px solid;
-  outline: none;
-  padding: 3px;
-  margin: 0px;
-  padding-left: 7px;
-  font-family: monospace;
-  min-height: 50px;
-  -moz-box-shadow: 0px 6px 10px -1px #adadad;
-  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
-  box-shadow: 0px 6px 10px -1px #adadad;
-  border-radius: 2px;
-  position: absolute;
-  z-index: 1000;
-}
-.ipython_tooltip a {
-  float: right;
-}
-.ipython_tooltip .tooltiptext pre {
-  border: 0;
-  border-radius: 0;
-  font-size: 100%;
-  background-color: #f7f7f7;
-}
-.pretooltiparrow {
-  left: 0px;
-  margin: 0px;
-  top: -16px;
-  width: 40px;
-  height: 16px;
-  overflow: hidden;
-  position: absolute;
-}
-.pretooltiparrow:before {
-  background-color: #f7f7f7;
-  border: 1px #ababab solid;
-  z-index: 11;
-  content: "";
-  position: absolute;
-  left: 15px;
-  top: 10px;
-  width: 25px;
-  height: 25px;
-  -webkit-transform: rotate(45deg);
-  -moz-transform: rotate(45deg);
-  -ms-transform: rotate(45deg);
-  -o-transform: rotate(45deg);
-}
-ul.typeahead-list i {
-  margin-left: -10px;
-  width: 18px;
-}
-ul.typeahead-list {
-  max-height: 80vh;
-  overflow: auto;
-}
-ul.typeahead-list > li > a {
-  /** Firefox bug **/
-  /* see https://github.com/jupyter/notebook/issues/559 */
-  white-space: normal;
-}
-.cmd-palette .modal-body {
-  padding: 7px;
-}
-.cmd-palette form {
-  background: white;
-}
-.cmd-palette input {
-  outline: none;
-}
-.no-shortcut {
-  display: none;
-}
-.command-shortcut:before {
-  content: "(command)";
-  padding-right: 3px;
-  color: #777777;
-}
-.edit-shortcut:before {
-  content: "(edit)";
-  padding-right: 3px;
-  color: #777777;
-}
-#find-and-replace #replace-preview .match,
-#find-and-replace #replace-preview .insert {
-  background-color: #BBDEFB;
-  border-color: #90CAF9;
-  border-style: solid;
-  border-width: 1px;
-  border-radius: 0px;
-}
-#find-and-replace #replace-preview .replace .match {
-  background-color: #FFCDD2;
-  border-color: #EF9A9A;
-  border-radius: 0px;
-}
-#find-and-replace #replace-preview .replace .insert {
-  background-color: #C8E6C9;
-  border-color: #A5D6A7;
-  border-radius: 0px;
-}
-#find-and-replace #replace-preview {
-  max-height: 60vh;
-  overflow: auto;
-}
-#find-and-replace #replace-preview pre {
-  padding: 5px 10px;
-}
-.terminal-app {
-  background: #EEE;
-}
-.terminal-app #header {
-  background: #fff;
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-}
-.terminal-app .terminal {
-  float: left;
-  font-family: monospace;
-  color: white;
-  background: black;
-  padding: 0.4em;
-  border-radius: 2px;
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
-}
-.terminal-app .terminal,
-.terminal-app .terminal dummy-screen {
-  line-height: 1em;
-  font-size: 14px;
-}
-.terminal-app .terminal-cursor {
-  color: black;
-  background: white;
-}
-.terminal-app #terminado-container {
-  margin-top: 20px;
-}
-/*# sourceMappingURL=style.min.css.map */
-    </style>
-<style type="text/css">
-    .highlight .hll { background-color: #ffffcc }
-.highlight  { background: #f8f8f8; }
-.highlight .c { color: #408080; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
-.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #A00000 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gr { color: #FF0000 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #00A000 } /* Generic.Inserted */
-.highlight .go { color: #888888 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
-.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: #008000 } /* Keyword.Pseudo */
-.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: #B00040 } /* Keyword.Type */
-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
-.highlight .na { color: #7D9029 } /* Name.Attribute */
-.highlight .nb { color: #008000 } /* Name.Builtin */
-.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
-.highlight .no { color: #880000 } /* Name.Constant */
-.highlight .nd { color: #AA22FF } /* Name.Decorator */
-.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
-.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
-.highlight .nf { color: #0000FF } /* Name.Function */
-.highlight .nl { color: #A0A000 } /* Name.Label */
-.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
-.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #19177C } /* Name.Variable */
-.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mb { color: #666666 } /* Literal.Number.Bin */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
-    </style>
-<style type="text/css">
-    
-/* Temporary definitions which will become obsolete with Notebook release 5.0 */
-.ansi-black-fg { color: #3E424D; }
-.ansi-black-bg { background-color: #3E424D; }
-.ansi-black-intense-fg { color: #282C36; }
-.ansi-black-intense-bg { background-color: #282C36; }
-.ansi-red-fg { color: #E75C58; }
-.ansi-red-bg { background-color: #E75C58; }
-.ansi-red-intense-fg { color: #B22B31; }
-.ansi-red-intense-bg { background-color: #B22B31; }
-.ansi-green-fg { color: #00A250; }
-.ansi-green-bg { background-color: #00A250; }
-.ansi-green-intense-fg { color: #007427; }
-.ansi-green-intense-bg { background-color: #007427; }
-.ansi-yellow-fg { color: #DDB62B; }
-.ansi-yellow-bg { background-color: #DDB62B; }
-.ansi-yellow-intense-fg { color: #B27D12; }
-.ansi-yellow-intense-bg { background-color: #B27D12; }
-.ansi-blue-fg { color: #208FFB; }
-.ansi-blue-bg { background-color: #208FFB; }
-.ansi-blue-intense-fg { color: #0065CA; }
-.ansi-blue-intense-bg { background-color: #0065CA; }
-.ansi-magenta-fg { color: #D160C4; }
-.ansi-magenta-bg { background-color: #D160C4; }
-.ansi-magenta-intense-fg { color: #A03196; }
-.ansi-magenta-intense-bg { background-color: #A03196; }
-.ansi-cyan-fg { color: #60C6C8; }
-.ansi-cyan-bg { background-color: #60C6C8; }
-.ansi-cyan-intense-fg { color: #258F8F; }
-.ansi-cyan-intense-bg { background-color: #258F8F; }
-.ansi-white-fg { color: #C5C1B4; }
-.ansi-white-bg { background-color: #C5C1B4; }
-.ansi-white-intense-fg { color: #A1A6B2; }
-.ansi-white-intense-bg { background-color: #A1A6B2; }
-
-.ansi-bold { font-weight: bold; }
-
-    </style>
-
-
-<style type="text/css">
-/* Overrides of notebook CSS for static HTML export */
-body {
-  overflow: visible;
-  padding: 8px;
-}
-
-div#notebook {
-  overflow: visible;
-  border-top: none;
-}
-
-@media print {
-  div.cell {
-    display: block;
-    page-break-inside: avoid;
-  } 
-  div.output_wrapper { 
-    display: block;
-    page-break-inside: avoid; 
-  }
-  div.output { 
-    display: block;
-    page-break-inside: avoid; 
-  }
-}
-</style>
-
-<!-- Custom stylesheet, it must be in the same directory as the html file -->
-<link rel="stylesheet" href="custom.css">
-
-<!-- Loading mathjax macro -->
-<!-- Load mathjax -->
-    <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
-    <!-- MathJax configuration -->
-    <script type="text/x-mathjax-config">
-    MathJax.Hub.Config({
-        tex2jax: {
-            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
-            processEscapes: true,
-            processEnvironments: true
-        },
-        // Center justify equations in code and markdown cells. Elsewhere
-        // we use CSS to left justify single line equations in code cells.
-        displayAlign: 'center',
-        "HTML-CSS": {
-            styles: {'.MathJax_Display': {"margin": 0}},
-            linebreaks: { automatic: true }
-        }
-    });
-    </script>
-    <!-- End of mathjax configuration --></head>
-<body>
-  <div tabindex="-1" id="notebook" class="border-box-sizing">
-    <div class="container" id="notebook-container">
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="&#22270;&#20687;&#20998;&#31867;">&#22270;&#20687;&#20998;&#31867;<a class="anchor-link" href="#&#22270;&#20687;&#20998;&#31867;">&#182;</a></h1><p>在此项目中，你将对 <a href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10 数据集</a> 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。</p>
-<h2 id="&#33719;&#21462;&#25968;&#25454;">&#33719;&#21462;&#25968;&#25454;<a class="anchor-link" href="#&#33719;&#21462;&#25968;&#25454;">&#182;</a></h2><p>请运行以下单元，以下载 <a href="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz">CIFAR-10 数据集（Python版）</a>。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[1]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">from</span> <span class="nn">urllib.request</span> <span class="k">import</span> <span class="n">urlretrieve</span>
-<span class="kn">from</span> <span class="nn">os.path</span> <span class="k">import</span> <span class="n">isfile</span><span class="p">,</span> <span class="n">isdir</span>
-<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
-<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
-<span class="kn">import</span> <span class="nn">tarfile</span>
-
-<span class="n">cifar10_dataset_folder_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-batches-py&#39;</span>
-
-<span class="c1"># Use Floyd&#39;s cifar-10 dataset if present</span>
-<span class="n">floyd_cifar10_location</span> <span class="o">=</span> <span class="s1">&#39;/input/cifar-10/python.tar.gz&#39;</span>
-<span class="k">if</span> <span class="n">isfile</span><span class="p">(</span><span class="n">floyd_cifar10_location</span><span class="p">):</span>
-    <span class="n">tar_gz_path</span> <span class="o">=</span> <span class="n">floyd_cifar10_location</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="n">tar_gz_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-python.tar.gz&#39;</span>
-
-<span class="k">class</span> <span class="nc">DLProgress</span><span class="p">(</span><span class="n">tqdm</span><span class="p">):</span>
-    <span class="n">last_block</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="k">def</span> <span class="nf">hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block_num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">block_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">total_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">total</span> <span class="o">=</span> <span class="n">total_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">block_num</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span><span class="p">)</span> <span class="o">*</span> <span class="n">block_size</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span> <span class="o">=</span> <span class="n">block_num</span>
-
-<span class="k">if</span> <span class="ow">not</span> <span class="n">isfile</span><span class="p">(</span><span class="n">tar_gz_path</span><span class="p">):</span>
-    <span class="k">with</span> <span class="n">DLProgress</span><span class="p">(</span><span class="n">unit</span><span class="o">=</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">unit_scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">miniters</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;CIFAR-10 Dataset&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pbar</span><span class="p">:</span>
-        <span class="n">urlretrieve</span><span class="p">(</span>
-            <span class="s1">&#39;https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz&#39;</span><span class="p">,</span>
-            <span class="n">tar_gz_path</span><span class="p">,</span>
-            <span class="n">pbar</span><span class="o">.</span><span class="n">hook</span><span class="p">)</span>
-
-<span class="k">if</span> <span class="ow">not</span> <span class="n">isdir</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">):</span>
-    <span class="k">with</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">tar_gz_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">tar</span><span class="p">:</span>
-        <span class="n">tar</span><span class="o">.</span><span class="n">extractall</span><span class="p">()</span>
-        <span class="n">tar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-
-<span class="n">tests</span><span class="o">.</span><span class="n">test_folder_path</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>All files found!
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#25506;&#32034;&#25968;&#25454;">&#25506;&#32034;&#25968;&#25454;<a class="anchor-link" href="#&#25506;&#32034;&#25968;&#25454;">&#182;</a></h2><p>该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 <code>data_batch_1</code>、<code>data_batch_2</code>，以此类推。每个部分都包含以下某个类别的标签和图片：</p>
-<ul>
-<li>飞机</li>
-<li>汽车</li>
-<li>鸟类</li>
-<li>猫</li>
-<li>鹿</li>
-<li>狗</li>
-<li>青蛙</li>
-<li>马</li>
-<li>船只</li>
-<li>卡车</li>
-</ul>
-<p>了解数据集也是对数据进行预测的必经步骤。你可以通过更改 <code>batch_id</code> 和 <code>sample_id</code> 探索下面的代码单元。<code>batch_id</code> 是数据集一个部分的 ID（1 到 5）。<code>sample_id</code> 是该部分中图片和标签对（label pair）的 ID。</p>
-<p>问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[2]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
-
-<span class="kn">import</span> <span class="nn">helper</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1"># Explore the dataset</span>
-<span class="n">batch_id</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">sample_id</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">helper</span><span class="o">.</span><span class="n">display_stats</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">batch_id</span><span class="p">,</span> <span class="n">sample_id</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Stats of batch 1:
-Samples: 10000
-Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}
-First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]
-
-Example of Image 5:
-Image - Min Value: 0 Max Value: 252
-Image - Shape: (32, 32, 3)
-Label - Label Id: 1 Name: automobile
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt"></div>
-
-
-<div class="output_png output_subarea ">
-<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsIAAAK/CAYAAABjrqvGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
-AAAewgAAHsIBbtB1PgAAIABJREFUeJzsvWu23LjOJbhBf2PtnlbPocdQa9Wg7iHqB/HYAKmIOE6n
-M2+ZsOOIL1F8gMAWBFGiqrh06dKlS5cuXbp06U+j8U834NKlS5cuXbp06dKlf4IuEL506dKlS5cu
-Xbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLhS5cuXbp0
-6dKlS38kXSB86dKlS5cuXbp06Y+kC4QvXbp06dKlS5cu/ZF0gfClS5cuXbp06dKlP5IuEL506dKl
-S5cuXbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLhS5cu
-Xbp06dKlS38kXSB86dKlS5cuXbp06Y+kC4QvXbp06dKlS5cu/ZF0gfClS5cuXbp06dKlP5IuEL50
-6dKlS5cuXbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLh
-S5cuXbp06dKlS38kXSB86dKlS5cuXbp06Y+k//mnG/Bvof/v//1/9JNyqgqs/4CqHQG1RLUE9do4
-z895dSUpB4tIz24FABFZ5YYfB8UHpff4qOdRngJQu7ZSvKbveTjEf2BVO0Twwy43APwQsXTgB1b4
-h5VbaRS2c1Qn9Gtizq84zjmhX+04vzC/7EhldU5MP+/rC//5mvb7st/Ef+bM8IfHrzmhxhNq8578
-YfEIJ/9oy4fnP7GIbCkv8og/fhEJX8iOAiHe9bBQkSyfUc+X7QcPjz3v1S/Oa03c4ytRKP9Udohg
-WBvGEIwxIu3pmGU9Lc/5nx8/8OPHD/z4MfDjxw+K/8D/WNqPHz/wP/+TZWq5EeGNXB5ZGMAWj7Qm
-hFQ1edbDwZ89jcIP5+TE57xkPPN8vjy8+IMnYcmQOaf9tBy/5sRUbXkW1nX8aud+fX3hP//5Wsev
-L4pP/OfrP/j6z4z0rex/6jlH6kvttPZOy7HL+aYLMluO+bQErbytA+T6fxWXrOYQPzRdfHbOXRXo
-WZ9h572Sc+BdrSeU+KZPtV2BZaq25MO5n9HHBQHEjGVYntO3tOOVcwwUdbxqXFPf8Dk0xqF/SDeF
-HmppfATlvR1HJR54U/ZTTfX//6///cuU2rUIX/rj6Hsi7FKnO37PdMfmQ7oDdenSTv9F6+K/qKlv
-6QLhS38c/Trb6J9Jd/ye6Y7Nh3QH6tKlnf6L1sV/UVPf0gXC/0L6exnsH2Lff82q6Q/zLn2Xfsf4
-/WvY5Zv0r+EtevT9r6R/zUBduvQvor9pXexy4K9Lhv+blvAFwv9C+nsZ7B9i33/NqvnXNOTSC7qz
-9Bep+1leunTpj6VdDlzJwHSB8KU/jv61VrL/Errj90x3bD6kO1CXLu30X7Qu/oua+pYuEP4X0nWN
-+Dvpukb8VbquEc/0r+Gt6xpx6dJ/H13XiH+ELhD+F9J1jfg7Sf+94OC/hH7H+P1r2OWb9K/hrX+7
-a8S/ZqAuXfoX0d+0Lv4O14j/m5bwBcKX/jj614KD/xK64/dMd2w+pDtQly7t9F+0Lv6LmvqW7gc1
-jHTOz8rRn9x02qwvuVt1buJd8vYPbZQNswWVuzwrTqId03s5VQCDdtXmXcQnoKN2IMrNdd7007Ps
-2ix7tXJtxS0ZV10f01CBiuWJ5ZUjbNN8ACoQUUxZG7ar+Gb8FvbzuJnbbac87OpNO4CD0mk39dyg
-vQ9giz/c6tpW//bxhn6U+uEIqlZ943mVOqdlo/1Dflz4xYMtqann9IdzOeWbt/dS2iVUhQf2D2fU
-y7x+dP+4+TplPmYZb8KGuAxluMbottxqglgpxVT/kAvaB1PyOO28wem2lOacwBCIDkxViP2GKqYd
-Ve06EdZVl9J146MVyT4sj2LIDqx94v0iy+Br0fvEH9PoaZSO8wc4Yn5dBmiygirxi7VNhGSmf1TA
-q9GtA3Xm9JDGcuGYX4vWgVK8cqAKrm+8dWRm7+RHC2yVa0smruUp0vJ47fVz+aMa/rEGsfo8jFau
-fEyDz6f+7OtYSxebWKJ4HQtVpY/9BDN4Zu2861KxVdyKF/5h9nBZYG3p/FjWx2manlnhI+Iqt7kD
-tSMOxAcmxLiLsh03SdZq1/i7jmvMGVawrFTAdDu1SzNNmwr2vGig1Zlz8GIA+9L4i2P9s3SBsJFu
-2uOx5AaAEbKWgW79qlgFpvwFMS0LMunzFZnLZAIqUIylhSHGoQMwZYw5gDGBKSWu8DBWWQeqauBX
-TD3wUWtaYFT/spzlAbCvS9WjCDBNqMUX6kTXV+v8yL0uinH9lJFB+dHg98/kgOIP4yrl51/DQggl
-8X/9y2bH+dmvEGlNEJxFWmqS4xVKnhzzHs99KvzmhLjWQUFX4MuN4/SfIVOErh+Puee2agttJ7J2
-Qa7LCUlwOwHFxBwCu5tbJ4wRS6o0zNbDiIbOwitTdN0YDgbGS4EMRQPGBsbpF2x8GIAqzvhrliSz
-KJCY8QB2t6/JPYBgBsIx1ActZ0rSs9eSlsIXC+hrPbd1nL+ZVe5MCCxnt7OcRhlt5Ttp+QULSy/z
-wNCflqOynYUABk91jDKcALjm0Y271dPTPi7H7erzki0Aj6VUNFT65TpRhG45WBYWQ4EjLsm86Kzm
-JW29SamHLsxNoXgHzhttc6bH4KvztzH040GuP6WX+EsB6lzh5iuxo68b09W87mlog+NpaBcOaOq0
-laHCRT7FxMQ84jAvJoceRPTfTRcIG6l+ZhGGKYCKpVKZdCYoYLmU8z8SvFEv8+TLekpNrlIdcECc
-lt5Z8+ZS3p6nHqe8sNQ6SD0CYg6vdp3ySbpuQHgBAlusqmcwLEsoxl3n5BXcjj4pYQlqeS1+ugES
-Dokf2LKyQFAojBdA+LSuX6WdZUBTgKcSJbOVfHPuQ40fXKdXfrACR5GTKshzntuXI6LqAr7naqQ/
-6b1TCuvaWINKitHz5rpH9ILDlte0JQOsG0dxgExH4ThGgF8ZGtbhOWvcwW9aiStLB2s3nMAd2wBg
-DZ7L0Zr4KRBM6Z1E6vzFww9rQ7fqBQ/VjuWhXIPWNgnYBMosrPPH7aRb9yhfAIvVz2mdzamyMxWE
-VstnTlpIKyCqMsCfPnmZTK/nDuSTqpRX7QgQCH440hhkozTb8GLlHYeEq4HJYbtWCduCXE8/1twu
-8GxzpKQXDuCqgFwfZ44rjXNfR5/QG8Eq21/U+eKyVWRSeuWbjYu4Ay3k4+YtKGJDqvoEhddT37ze
-uZw01apRccE4flF/ehzxKJQ3wmWCfh9dIGz0qWvEKpyLEvD517qQCiCuLhGtYPyta+pphb1iErYE
-u5YWU0BpFS4W4zmsqFmMrZzKAwhugHhGGA8A2foiKC4Ec1S3Age/7IoR4Lr3+pUVuFt/1RUipRXF
-+XpIt8eH4pZg7o9giGDKPoPaYic1saUp4rF3EaEPLCFboCY8JH+DDl+83yVxCOTannZm383gQfjn
-GjkVSAAnyvDkPL5PyrlfrgKSxS78EEVt2TgoHpPAMD1o4bZKiU98yYAMhUwtFuFh8WGAOF0mKmuz
-xSbajcbWPHylzF5YOQwQuMUzCI70szXYAeY26gJsejDATwPInibRMuqMy1ZlLVw6r2Wt918fwBNv
-ZB8YuAS3OTqMa7xeo8y7J55OGUPxuK4c0qWWbwB4NS/l7REAy7nM6Hle8YHJUkIzGuWZ5zBLNDL1
-lDtSCkOJIQ5hIEHTqQllQesR9AqFA4g90s8BNBaDLEl33mp8JrKnx3rYT9rcvto1o78gS7ENZcyQ
-ydNYFSRv/EY246v2LJeFlQfV5ASPb0ocBufc+t9LFwgbfcc1gnGVxzUTyh1SlR0dEL++ThOZB5JY
-yIvYEjyhAWyR7g8GkLsVWF3jyzov/HYLCKZFInXBTHnOC+spAWHRdU0HwJPyAxQL1h3kks40NNp+
-hzQfwbpqc7AO+s91W8RJ+qTCQSgJbMDYH16fodeT3nwqdxCDL4Ajtgx5XeptRXvyoaAc8kzCl9JH
-t5GHC4fUpCQXj8aDLjiLbvSRi0Mqhlo/XZr1K/Y0f6Cidh9ZAO9oYFgAIWCsExjhP79OEJmY010j
-BGMMzGlgmAGw1TtUMUGgGMnKJ5bmrvLNepFTLd3lWD5EYWCLFxbiloe0ZMf0auVnH9dWpNz4FcDi
-faKOb3PaQG/mnNa/h7sAyDTZ0lZbTha7E/vz2Y/L792yJJ48gmAheUXuCy+tvyIJclluwW/mT8A4
-3SUqg8VsoUyUZpqW8eVByf7xUj9ZgnNx97AzvQ+Olrr3cGW8XgVbiffGfkin4kUWU7ilBxA8uaIA
-gKjJMy3n7wUtIjwr2uJmKVaEa0pClnfxXCFa4mteQn5AYl2qCMTD3tYyVjkRKvpPGIQvEA760DVi
-B7/xZ3tEsMXpDyuCqBs7f28A48RAQf6yXLUKL6ZciljJ8qtsIWawPMcCow0ET7ilFq+B8CFfBglY
-B5AqkMEAGA340ohYl+itpfyB46hhb0GuWouelWAf9VACcNcHcoUIpYJQJN7MPjN7mos1aoKcz+1c
-cRSEpzO2Mu+07+f1bwWeTnqsLG8uXtS6SJMP8sVDE+ggQa8+jA8A+A11YxSzHiaWtXahUwwIprk9
-jGlPA9wVgvjEmWLVOyEGfN0SPOesYNgA8GYRNp4tYNi1GQ8TqP2gJRBlOiB00IFi2fX89xbiHQSX
-dpwZuvC645tyTp0ZbyXSvzfTIxzXPpXR6M85ny9bbVL84txa61k+X+Zt0lteLKEXLhJ+qMC2guCa
-RkAV1VXiFfgdh6db4wCCHRwniuJxs2OMeb44DX2261UTz2ks1Bf6kpFb2K/hYwmUu1m7k4psiaQt
-HN2Qdd5fwmDHKc3ExxspqWmZ7tLMSavMPISjj5QulFlG3grHClDQ+z2WpzzTbP3t5VKGZHwdy/sA
-muPfx1qh5uryl2bhp+gCYaOf2TUCxCRppvHsrqCyMNfRWf3hihl8B1B0iVEtVmExFwhzlWgvyKn4
-i3QCFctTcnsI/CiY1gx2gYi0SK9pEMGXC7NBQFLsRaQhZgG231Do9DhiwwsAC5V00Ft8hjXuSitq
-IKXflGcf5qOQYuXiR1cuZOketapY8G/TRCoIqTDhY0z7vtx+4juWOhd4cdbR+tvOk2PqzuOq9pa4
-C8pVWkVdd6VQDWTyIEif9G8Ls9VSRDGGLJ6egjGWRXfxMjZLsBl+Cfyu9g/LmHOuF0QJ/IaP8Ew3
-CR2LrR9dJLzdfIQrIupLCagPaZ7kPFcszgcA/AoER94BCPPkauXqrpS3vCb6igCNm+BIoE5zf1te
-4Y1T3qb2rQu1UyIaip3r01e8X7L0lHgARgx065HdI9hKnOB48V0Jn8BwB8ElLvSyJ2LMi7uZ5Kzp
-tkWD2uhlmVihingSAKUX5yKdFmOETWMqAnSLajUMZ6UHRkOmgaqkFv4a+kkAfCjb008AuKdl/Ssj
-X1rPYjunM/B9AsTpTQ/4UvO5R8qR0MVSjz74vA0FTZKD4d9NFwgbfe9luTjpgzRljtvTPE6hRzYI
-Rf9E5BpRrMKAhgtEukYE6IUc8tIRXrFbgwN7diCMtRNEVysOgKdKhN2vVqAYAYLFro8EwBPQQSNz
-sgizYD5ZhkuYOvZqrLEUjP+D1GPfMaLrwBMke0yj7tUyb4TCGVe+LJvRvyBwZAt8dmE5DXvRtCjz
-LBQ2CR+WAyxAotqs6zgDrg6Bnsq4jA7dye+XHjZfwQBkrrb6UwMOexIwMefAlyhkTszmFrHu6c4+
-wivNdpGgoelWYNf/uR5YNvmh8r5SegfAcJCLTNtAcAHCqGCc54P5u7XfIZK0k0Jelplrjed+FWFL
-6z16wXkkGwqdV5+gW4qt5AN4eZW4SXzit2AXS0vwyxbfFj6UGV4Xg+AAx2bt5bQNFHv6uob6uFm7
-3ZXBx3M9clcalBxxxqEAGpAltVbq9HHplmCtQJtlRAG+UtOAZLpSjZbL/rxYfD5R/G+b58iPSF0F
-0q3C/eQ4HpiQjrKlC60GvwGp7/XwUW0xRpjPg+QNcMgGKWGJfIRVOMl457DafxddIGyk89Pht8kq
-svmkdJSLb8onldV51whfvHveK4hUXSO6VVi7O4SD3mFg2bdNGyOskwvkLiE4gWIhzjwUMJzhBZIh
-MDcIA74UHmLWNgMaalh+eXWYhSXewqchWOggw48Wohj9La8DhD78MQkMNkPxpHUlAP0HmLVfqqd9
-CwRziW8I7/2cvwCITzVTdadV9XS1ks6MT8pO1USm0CO3OPvpZbkHKtdoDaC4ufwuEEwbsUyzFs9p
-PEC7D0JAVmK3FA58zXSPCIuw7tZgVywBgNFY3doYYoTaXfma3QV6vsaxLiMCuzik9XINBNfdGAiD
-cFikYhik6t2mghtXJqiBskiqeTXf6+H8p/p7XdmPCEvPpda/WFZ16eU5QvEOuhPHvAbB8eLcCeSe
-jtjTwy1ChlmEGfBaez1sIDV0nD3d8jY4f0HbdmnbmDEcorxiCSarewHNfl2A96Rm4EvF+JJVDPxi
-EMzz3OfTm/e+rNbEDeAmv8mhzNO5rrvdqAAh0NuOSi1ZY9bTHAxLygBFxmeu/6qLcy6T6z+W4L+M
-LhA2+tgiDBTt8wSIs5i28yzA4Tb3XRF8RoLy4psDXSxUuRgwXSPCTcLBsVtjadeIBLQa4bQEvwfC
-fgRk7RKhdjSrg8qKj7BAp1U4+4H45ocGIlFrCKEC/TTe56EuTboXh9jj96Vc1h+x9A0QNyD8GuC+
-KPPB5L/Mfsj8mJ/eofkPqPfpVX4pV+S9K1s6mOKrYNiU8L6Mfq7hecnQs2EBhluEdfExpLhDiANg
-0fAXVkmrGjAh09wjhh3nqIA4fu4W4eByZ2VW5DywJzynXEgBbRkbyIVfy8DPCQA/gODzi8cSf+2K
-G0j2VNfdx6Wg3OHVj2LpBXUgpFfL74NT1n9ruxAIFSxAZoCDMVY9p7b8yJN7x8BjFFi6gZ2MJwj2
-fAfBkS8o8onB7hEQPx6HzVXKS7cGr6PzkAFWNVcFzec0ZW2uxbzWMK35lH2KtWXayRJ8AsFUgV2X
-tyeIl7WoEXEpAsXHufyIXpwV8uwwt/3sE1iOsloLCOUd0hgcP4HiuOmQCmpPQDhXxkMZBXSyLFgD
-qtNupkemoUAtmj8sHvtLTyp/ki4QNvqOa0QNuxAolR3Ka1mEHhYq9lemP98nbS/M9RfkAISbhCxA
-rFEOUN41Aq8Bbmyfdsjr7hKiY1mB3RpsoHigWoZVRi4c+iBe1WUNEVTNXeOb8rO0plDrWNaIizCB
-pnJxoHwAwh+DYJr0d6D427whx+DPVXJow8PQuY47pp/ac6ynLIamBDsYtnxWmvI8tftCo3i0XWpr
-NwswBOFWHwDZtjsSsf2EEyxB1NIO4NeVx7RfWINNoaAD0Mba6LLnnKYlkazEmnGDOARutS4xBsAB
-gs95TN1xha2orvQyzkISyJXRO0RW3jhhL7PLgexlvVCeHy4Qzgc+hx5Eo+MCS8bq8uRUsmeVa5kM
-cheFtAB7uoNkKWXSqotwg3gCwWOMF2A5hXC8bEjWYAfIvkfsWoapkbh/SscyTgRs84WpPOPkPlFc
-JhQkH4B4V6AD3dYIbqOE0H2YqL+goB8B8EG2CryhLd3HpAHeHQhnGbEyFUBnrSeQ6yvglJbn1HNV
-sV6wn1V2WEG4P6WAdLtl9ZcB/gEX4QuEnb7rGlFlrm7Znq5b+ist/VepbYdGvsLpd+vMO6DkK+wv
-zCnMYgx9BLcdHFcrsWxlAZjFSzDHgBj4nViAeIzVhglg2Ac+FAKYANZiDQaKdi6W4fZzha/1nCqE
-F20vw4DUmBDoDUDs4FdDceQLhFlLgwARXwJgSecn4MyC8jv0maL+tB45JSZQL6SlDKcyru19PVRf
-TyzWJymWYZAePLVftVbaoitNXclYPulVKGwHQt8pIoHFHBq7RMSNkPkLKwEJFVlePUIg2AHx0AeL
-cFqGdzcF6heLI80OqXfMcotS2sqDwG9fRnr+4Tl/qjbe0epCoAyAtZRM6xSixA5my+yh3tRqKRer
-7QUorvXmkSFCp8h7sbbCheDN+su+a4JYykgQTGmbBbiBYP93ALfDbtaGjEP62EDw8IvTDRD4GE9l
-NI/QwKJxJIalZV3mu6zXkm8Z6u4VVrHwEZnnNTIwLkDdL0hHb3Ncr8zOu9k759KkvQXAFGGHyZJe
-QDBcPVGcgXHynkiNe6EdCH8jXShdsUCwgeFZbpbpJt8BQXyMKPujFpZdsfztdIGw0c+6RhQ52SeQ
-hbOe09u6/1nMYme6FViw3CHS2rtotwQHIIZknkzbDi1B7iMopvxjGUMUbgGeUIgOTH8TXwQTA3PM
-5SIB81GeWG1USbMbKfrTb/MTLPk8/o4kymQcx17iZ9LSrX7FGoxiEeZz9UW8CvpWpjPDp8r0E/rp
-unoHK7/HQ622NArkcVncpuR83aVkw1JjNw7xqNPOylX0oY+w61Rq22nx+SWHvQg3BZBBTkdTwn1d
-hqmHSaBYnF0cELlFmAExgV6PT2ZdzbVF7M0iKMcyB/XA3kcrcJxJ13v+0fVflivoIoI5Q3ndXDPu
-a0gvpAmXrWubbidpEOpa1zpaVI/XdRgkjxMjMwyI/vjvxGwF5OyM9ama79ilp/ERHQQLyr7AI6zD
-DoIJ7I4EvGLW4cz3dNDcuqw14GPjmYAXKIba6LM2HrA+2Zrm0sUqTGu/ukPQkSfjdPHDsVfrmLnS
-QTC8mrBD4gnsPgPgDBQw7H/sZol5kIFvAGW3KBdGQT03OLsD3gMAFinxXnaxgEJ1pvqljxCVjxF1
-AOzbEZJc/t10gbDRt74sB5aTrGm4wjadSoEPZvoby68JXAO9/uU4ArpKrhFsCXbf4MjDco3ogJbj
-4RKhnLaXjxvAMcgCvIThxBLAcU2hNjgYBuwbs1bZkKrwYhXSryjNHFGNfBrkXMX7nOXgRjBAL8gi
-8wCE39EOlKv9+NWJ37zUL67T1RnZy0pH9jXBnl9qQOP0UahIOW2mz2AYiLAIX4dbuIdLuyQjiiqI
-hQpOa/sA7F7SngIMuvWcai9NOo+sCsV8if3GbuhoFt9ZXpxjq+rZQosNeMSQ9TFXznArMZenvKdr
-gX+vwS/XEReKNrpCz3R/E90bxNYz6kCbNH7KAw6UcttaLz3ZBumQv1NiinSTOC2avYbKUdspxHPH
-epv1twPezG8yisHvZhUeGwh2ANzB8BiuU6qVL+KiJU+EwvBni7mEHcCKuGNCDlM3DDD4Dfl4Mula
-OTV5kKcdQPGRXsldmr9vCd48R/bUFwCY0zXmlwsn4HV+EUqnMDxMYDjurt4D4bT6nsoQKFZgyoyn
-ySA+Ucz6MaJ4UG010t3B+Wbk76cLhJ0COPYVc0o7yeiW0EBYiZOOWNF+j/whbQubtk1zK2r5EgW9
-IAckALW9hHMvX/+gRgO/muF11B0I6w6KAeALyxK8fCyXC8WE0Ic4zEo9qa0LMdtNiluF32hrX4QB
-iKlMG/+T8Av54gpE3fJrRzUR4GnKrhLPU/Oz8bd8QWDuM9prPF7j0wvzMglwQbYMMf7WLO7W9bLl
-qBN3WvN8sGXYpb+tubADu+IrDfuEdAsyDvd7y1w96xrTdoTIrQCdb8wnGMuX2MusXSPsc8pmGdY5
-DPQiALDHi1tEWGTJFtoxXUvb4y2R0gxi7v98LX0AfpXauw9tF3p0G5WMgsoQ+1xq/PX1zTnZizyB
-GtcHbDsyHcp8KKRPxeqzv71Q73IBthyWGvbVVFwlGiAeQHGJCFDsIPgAhoubxLCX5ZgXHOyatUKB
-3BaWRbIAosNmhQAxD7Ov5aO1d605PVmAC+h1EJwuVH7D3DV4ls3L8vHlRH5M3vZWHc1vucTLsqtR
-FcxSWU5rgLim5UXEAK5v8ahxJSlHCPY0L88gWXW9CAy7MZrLMrzwwMDoYNheKI6XjXXpg+1G6DfR
-BcJGehSU2NNOIKrN3Objoi1SQMFe9JP1dy5TQW9+lKJaftmFQh8sxtNedlA0H2B9AYQ5HPEF84eu
-7aUWAHZfYnPDQL6JurZum1BdL/mpAeBt+6PTjyWwD6ZSgExJqawPy66jUkpMa0uC3wDIcj79bdzk
-e89/zwcvpPabk+WTQueT6qUbAPaDEMDyx3EOiF3ovrZ/u7LLa2ixEtvFCRDHHEWL3ohUqrtE6RIi
-MDBrn0vugLdYhs3H0wAwmvsMMHfXCCWXCCWX9+mWYWJxVNBZFDeLJ80OKOXVOOdb/18srde/k2sE
-iNEtcIpvAJg0Id896ckdwvsRA0RARtvPi/Z6clCE02I+vS15EAocHn5UKsCnLJqtfKm7nZ/hQDJx
-2EGw5FMrqb+08p6ODQzTcTWY53ga8B01HTPWEW+16R2I5Uogz58WBW/G3rlmIvK00w4SkVeTy1Af
-fIlZyLoVcq33J8n7QiJvybLHTgBY9tJHKzGlO+ewi4Mcj2cQzMcAseIz08CuHNKiMRU087pSe6FH
-4V/ObGBYYNtOenvXpK0x0YeF9PfSBcJG33WNWCetP9LS9BDpZVrg7WWOS3DLEJRt0ygOPFl+5xae
-tkjCBUI7uPVw3VZtUrpSechaCBOKLx0Yqpg61jkGzqe9jqR09I2FQ9hOH9Nv/HwOWMd1HbjNRQKq
-fDnOhEgobQfBpoDsTrer6m5lkOOVavhzOdBre13yzEQPwv2jCrGuz19KiEGVIyDmb8kvi4S2eQDx
-tWuqve4aRxmK85yeae0+kXVUqFIb5S4SKf6l7BiRN0ZmmRL31UyL8JyaVmH1XSNmhtlnuFiG24+7
-v2M66l8dpwCplp5A+hO3h/c/d+nIa9haOoJiBsQgoMPx0twW6KvF1zut/Z4X8qAKggQo+0tyxRXC
-2utp/Qa205kDfTWcKXmsuTxsYalhAzQsl4aA3CHkEegWyzCl/7AwgDbXawzZMryGfeTNkM4i0/hW
-I6283G9FfiL+AAAgAElEQVQCxb7mA9nm2FTLL8BA97zlWgx5iKuCq4MfWUk8zM53xWUDu3JIAx4A
-sPTzqt45gd5yHA/pJquULlLB7qr4lPYIllXNZWzd5AP+BMC3Z4V9E8CeEk9vi8tKLF9x7vRvpAuE
-jb7/shzJ76wkikgrq/S3WxF4nT5SKJKKqur7SgyC8wU5t/Y+ukaQVTgstfBHsS9A7xEIa1qNlVwj
-FMUq7PU64D6DYaVx0xeuEe2HF2ldifYxpeGOgAFgBsRuAS4uEyHlQsSUml/Fi/A/lN2pQraflh3y
-GHlZVDngWNU7UEAqCzlX//zI0v9KrTvGwhcQaw/lpUYtO82jlnHdSbdLRX9MWbvi8a+V5+Snv3CC
-D44zIF7XGW4RVn45LgHk7hvsljYtT2YSKDQWtnGOP5GvyIPmsIIrfbWsvgeIfc5c3ar6PBF4oXAo
-ei8XPFShVEwzRYF05Sg84ANlP93qWWEplaLW4c32dKnpbLQ+0YntlP42JFjrBv2OoLeFAwSvbSmr
-f3CGz6A4QfAPBsTuNwzmgVkAb5XXk0bZDBovRwP5slxZpxyhl+cUCOuCZP6j1ZeqilOK8KI8zuIK
-viNcpRaWQ9YpraQ3LCgtjUHw0Ro8ki86+IVguW2Jy2KTvl6BY4UOejswLuXNNSJmW+H77AAzrcEY
-kDntBePavrAKn/be/A10gbDR59unAam+IwqgAwU9pgO56MrJx6t8F+Cc/IEzzoBYKW895nKrMMxd
-oVqAl07RSDsDYaU8B7rmGgE1q3C6RygSFLPgVPHHbf66qSszvAgffjaIDBS0p9M8ORWBpK5w1h8G
-wFBSRPI0Vwz3euoe5vjz3H+fMz4m2QIvMCQpIwsrEHf2S1+55UzCZcjD3ou+b2SMR8EKDKioVZan
-bQ5LPY9r7Kj24uKucz23uj+0sJ5BMIcB2iXCwbD/GAwXYLz6vH1EkZpfLbCVx2ucx1Db+br/2yyA
-BVe+BsILOa1LHIFvhiEO3iXHm9xg6uy1RU5teyoTIDnKNQFRZIDWekgxF2ssmL9eAd6dpJVSS+11
-b5WW658A8AoPb6sI5dWdInjnCLYIdxDscQCFF86gmMLuI1rkO4VNdOxjRHOvbvkF6kdKQnIgF6mf
-R2l2txhPoWjCdqtwDafMqVp9m5QPxPAJ7H4HAHOY9cwZCMshLAl+KW0HuyPjByBc80bJ0+m3QADc
-Eozc83Ri6c58l0IK+F0GA5cJ78f0V9MFwkblBY/3pV9Gc7W9vOA6niaedQb24+nCK29YykRafQEt
-cRNWshb+lBm7Ncx+nsmUbuVla7Fqy4cJychb7UyLr7dTlysGWxbCN5htwRplYWWhCB9KkFAGK+KP
-w9nmUJxkVSu68wVJmSR/ROdzw96wCRAX0BbDCBR/KwleSd9v8LF4675PDG6X3nGrADajzJl3W5NJ
-A52WU6QRWj3NS1GXRYc53Dgr3+f29dYftGkglzNFm9gSrJpuEBMNAHvcjs6zJZ8gIjGrRl89TkDv
-cA6DaO0/xIp4+IftF/31+gkEh2sRAV/YC6d5Q2MKG45SkA16OcL6WE56OYp3UNrDCTwpj+b7DOb6
-dV+vSokSFQktvFIBb4AhcQBcgS4D3wS/o7woF7tGlJ0hGBD7L0ExkHI2gXAHvv40D9AxMN2QEW6H
-uXdWGHWz5w/yglY0o2dp/BFgmCvf0zz4nPZiojpSPd2pxEH2Uu+A7osyDITDimoJPt+P4QFKR9wI
-hfNfgFyrmwEurQBIBb8MjFd6NcRNFXuKZuXtaXC+gG43KWLuh/bvpxTSX6QLhI2+4yEskLSqlHRP
-qmpVStE9vTC+cJrmAlA65yHNldOEtPDKc1eEc96hnKayC/1JIDcEFWlAzwti7Ui/JUglTljAVEKZ
-FT80VdtKzS07cwnhubaf0rnik+JnwHGKT3xZ/GsqvjTzvuicL/Pj5DT++XlqYwQeIxozHsMso23s
-zhbOZ/pW4UUEULQlvzzHA6UPqbLO6QTSqG/1vL0vpy8MaawvPJbjqjqsLh4WVNpLfSKD9dPCfX06
-e1sd6QaxW4OdN9l94mtOjDi6xzKBWQeD1Mi8wfM0nwuNU3xOEuDs1uDAmGUNn3+OYb3T8dDAhy2Q
-R6Y/vqP0AQUu9cADGD79SgEKO6A85h/qBl6vwn76U1nGDY4JEvAetkM7gWEGweMVyJUFhsMFwsDx
-SFcJzweA4gc8mzsEA19dMnrYVlrb1xIt7P68vt3awqVpRMjdHVociVlfSr4Aw+ew14VWb5mxDdQe
-kOvbc1qWp1NE/O8BawuXL2A3eSHD1koGxN5uLh/tYqC79y1BcmubXSeAcD4qzcUskq5jILDrVuBI
-VTrvn6ELhI0+ngLti6UkJKOcKtRugeBz8oUS4fQtTR+B8VKycgS9HSA/lWNAzEo7zgtwB4DStj5z
-2tZpSwygKwR4JV/AIPCrArMUm8MFfZqWXzrK+DQgMQkczy19dhA8Z4LhBnwDMHN+B9kOBH3sGARq
-jitoXDOMAJKvgfDPCwzmqdPkbBivMFgt9R4A1/QM0xj44HD1flE99FTirK3RSmVO4ERKWd2VDvb+
-b9cnfXrIquMqFAbMSr6Dy3Jz1sDw15wYOjcQ/PU1zcJ6AMExvgyAvYynl4V8BL3xw/n3RB3YRpDm
-+ZN7COFxfAC4QSYTX9bb0a/sWXLgiVKF1LLcJD7v3Qo98V2FW27dZf/zBDIMeE8AOIFwBbrs9+th
-4ZflhlB8xKeX3UXBZfDa3WfJ3GngdyhCJisAHcxTezj4VDX3Iu7gOMYm9pBIt3HSjVpGkLjHQW+E
-l747h3G4ITuBWwle6GkdBDso3eb9wGdbuagy695B8DpxSyfg68xK8BduTfak6HbcgdXGZPkdNCvs
-qSb7ETZgrNFe2lnnBJJxeCL4G+gCYaP5rQ9cs+aVEj9ivhqIYLy3Seu3g2Deii3TKI4E12p3tjsI
-9pfTNMIfAWKlcq4QCUiwEkhdyyCI+ku/tCwxCBZg6hKe00TbkBSg0wUrCdVJgLgBiKfjyeqWIHg/
-duDbQfHU6uvJNwrF0mtjk3kEGA9jmmP7a8VCB3pHYsH4Yl9en+lsc5b5PkjmeveFVMtl5nHZautn
-ADIFRbPcAyA+rmUCdzWPtjQ6thn8wAP5AlxahIM/2RKsucvE15wYXxNfY0LmAQhbO7hN57xD3NuF
-w0/f/Fp57rsD4LZBgEmiZLTjeP8kSftF4mEpud7Oxp7DHUO/a+yJnz4pH9ch3PEEfl8BYDmCYN4u
-rYNishrLWG4UVHaRb42TVl/YLkDFQjwe/IY1P7hQgS/iZTjhNCDdr9q2Wg/TmYNd7sQ0B9Xy1sH0
-TABqs2wWsNhmszGLdOZp4Hg7fYsTyG3d6Dfc3f83AG6AzxPwBSTaUQFzXsSBM/ch80rjiDn5nLVR
-hMc1XhAWA9GDLMVK6fECus8o4ZvfSRcIG31r87TySaeMl9Q+mbvmXPOe8x/pZ8DraVWZx8Jy0CqA
-6m759fAs6XbOAzguSi4AnkZaBXkJNEpfWTNu2rWD4EQKYSmgY1gaGiitYHc+gOBmCY5fAt1nMDyP
-1uDTr48THsdNKS/Hvpf/VeAgKHhlRbb7b2Ji7fkdnHqDI9z6hYPl91Tm1Miq98r1yxmnIMltbyKv
-oQLSLH93mWjgjMr0cwEcAbm0PAaOk+K8PdqcOHx2mS3CCvmaGMNdI7jnPGgNkiraXBPzWTyt1f0H
-sJvUcSlr8kC5RJ8LHzuVAzj+SX5/hYpMN7MrN+GGl1XWch0MWFAOlz605RNgzMa4bCtbhp8B70sg
-HCB4FHcHBsXVCiwEiLtrRPcJ3i3EGwi2dz4USCux8VoCX6WX10z/ODCmMcxxazpXgf0xBJ2pWPUB
-sSAzvsqo+6vHZOS8M6zcwC7HN6Dc+aAy3g6M+RpN91uF5QaJ2lCtwxyWOJ/O2IFvNDjdJzZQXNLT
-dWKlKSBrRwnfcz2B78EKLL5b0Fqg1yL8L6DvbBoRzNok/0kRtEBTPigKqqwn9WtFYAPG+eZ1Cond
-EvzdsCk9fxRmjeygeO8Kg5vaxW2QzDdYS4UJit1fmN0jAFSLcLhCmL9whPXBDeIMnL8srwPdfjz+
-OtgOIExj0eMxftX6u5fxkf91dFL8oSo6WmG185DH8/8xAAYe8lqrpMQoULXGcXw0i24grOnKqNFZ
-EPs4lXKcToULYEY9QUBrCgge7j7CG2/1j29Q/Otr+U5yK7MN5/R6y6Gb5YUBbVnvLh+ovU+MGYC4
-jf0T6Fx5XOJVac//kJ4AsuzhAj5Pl9/Swoa188U7NK/vecyBSjxdBjZrsBjAfQLFK1xBcLpD9C3S
-2ApMgNi/RAfgY+DrfOKubSPzpipE55LzbhUuYNi4zfRdrONT+GlwaYHHXuYOhgEDXZYfQFejfJnu
-BnIrUOX0HQhLraRmE0lL3MtkXY9gN0tWHuJrOObk9PibIDcszJEm1OUdEOeYrDkdkmHnRUVzgwiL
-cryyh3/EFGx0gbDR2Xv3XDL1jIlCpTAoKU+pEdLs5YYLzgsatUeetvL2h8sHqBUrGVacBnbFAdsL
-QNwtQA38BmBrAK7p2hp2GeXK0oAvDAhqPDtGCFLEixYgi3C+LFf8hAmUxpe5DmB1c3342WMHy275
-oH4/AmMeU23xjYF+DfXvFwTZsFfxqAkMN2SXnan8wH3X5/5S/7o9ODi/918O5TuQI/SVig47INZc
-P572BIaj/zRoUcch7XQtYJWNm7iwstYX5vg3zfcyX5ZbVmHhX1faTW48xnkto1rVCgjW9itjTf1A
-/lDKHOBtgJNfRK/A7gdryMHvY2YvS4D56RpPIPexACoGKBY8cUAsBQz3XSB4B4iwBsvpRbn6Ipwc
-yokBZZE8b3VE4e4QW9i/nxuc4B9EquWG8Yw47ziwdT6K+NqVdq0bf6GKgDLzLQ90LGQb6OC3Kjd8
-e0cXEnQGGOimnDiBYQa9JyC8M1ACz54Wk99LkEyTuOwOiBvY7fl2fuAGArTRPe9zEyxctnbRz3d9
-YfsLm0/wMICiYDCMDHubDOBIpPx+ukDY6GPXCHaLOEl+Tn4ChQAIMQTQ5cUaDGurlJlj5VWLzqri
-DGqxpVe3CFB4CScuQ0ox6kERHvqq32181JFF2THCEYkDhLUv4ZKdQi9f8KO5CnTzk7RPPsLTXByq
-tfgd2N39gHH0Dfa8R8uutniMIZUn0Mh1dPqrgqLgusbO7KtVGyAdQZX2Kv3pfXkFjo/9FKqTGx0N
-xhErc2UFbFlyBa7y7Re6QllSGTyl2XpWqRco68n4PXlKCfjab7hrzlhfo2NALPVBYpURz+na0n20
-4wUmJFBnoItDWtzUEGt4WQf/zjrdTeIEkoPvXjA5g1f5zs9BBMUPAxTHct6pER/SEx/1QtymFRZq
-w9k/uIPgsAjTC3H1Zbj9pTkhqzAD5UFAGTaX4eNbPqKhbWeIvi3mnheW4LAAG8i1+HpZbgRP+pg8
-ycQyuLzwARz9npA37UJ/fPcKQoFAgGYHhAlKPa0D4eJ6EHPcQB4JYdnSQNfNvHCHkGhapHsaA+Nu
-wOVrMaCV0jfudzJ/Bc+5QNIarvbSHAPi5SahMAAsaRFGyC+yFAPvn6j8DXSBsNHnL8u5RiN1QqA2
-AYEnlMim+MXKbLL4AHQd1jJWiTS4ElqZHnbwChAIVmNY7BZhUDj2BUYqP1C8dJXauo2Bx12S6epf
-2T7N6yQQDAUBYtijtplWs8POEeH7e9gd4rQ12lfbOWKz+L4Dyn3XiBibOteepjSI7yzDT/RWGRxL
-VyHcwRCn5xMIT1v+XKcLr25p4YPvAeBuE8YuCQvo7IxHUdIa5VGql3HFJ7VNrsROAPEpXMpRpryI
-hxXMxoEtwlNBwJf4dE7IHBCZGbZdI0beQRdgiBZmcJUrnPpkkVjnsSYrKI4yXM5GssuIPmantHf5
-dbS5Dxr9Oq6VE2LqaTuLVeB5KncYZL7P+XRhvtY0bvnNC1TwiwCvvh9s+TBGWHLTR/hH9/097Bxx
-BsoJtH3c2c3hM+Db4u7qJtrK9LQZY8Uvmfa0Pq7nKVBXOfvEgedOadwZ4Nm8ENitrhAdCDtYLZU1
-jLtzwcmC7Gd18Ltbh/dwORfn/NhKTfIi/EQir3VOKy8LGtBVUQyYrzDUXpxLn+DcNxgZpg7el+X+
-Qfp47EPYH1YifwyhawROauC4yldTbLHos2zwirY0Us0F+BrYTMCRrhPe1gKSFWbBerIIE7CjbkT6
-aRADAGsAHKUTHPx6WZ0aT9hip4hp1xiWpu5fmdui5fHsw5sgeBr4nRUE6+H40kq8+xB/qYPfBvaO
-aQfwqH0cf6VEWHNahfGai55WAQOhJC8QZbIPSg0vYaD2n/JPPKNySqwNdIC7d5Gu4UJbs3wHrhmv
-ViBV0lPqde15apkRD2UqpW/Sn7BoguDhR9sxpViFxwK+c05MGQmMvwQiM6zNqZyQcRAAJgDB08fH
-tRa1AI4A7bZgOwCOMqApUq7Tt61CtQZrslWA8A4UStuy/W9XBPHmqyJ+fG3tlZbWgAVdRVrxaME3
-lnC0JQCJt7G6RbA1eIgD4hHgeNseTU4+wdKswg6uz6B5dSiEclpvN7/gZil+YTleT0y4Hh7XEXlg
-ULwNaRFWj0lb8oO1ocvItILu8bT6HoBwA8HZlBLZ4tLO4Y506+4jCAbnS+a3Icl6DIASuM4+0HUD
-JEucly/LLUWiqNbgsAhbnggBYLYOgyzCx5n5e+kCYaPPX5YzoFiwbE5fw7gUbCinScnYH5jyOuPu
-VmLNfHGFZcxoamS3CD+FQx2CLckMcjOeq3S7F2j9tmZv2ldLmC2pLm8lhGamIQWpvRjnbhHl2EBw
-cYVgYOwW4S1NN4DM7g+bNZh+BSAS6K1jo6WvPnIRbsDxUzphw05FcVPa4iGBA3SO5wV8wuhcBp8N
-HH8CgLd+EsqNHNkKtLXlbaOmGrgKblXYYzlqG4MznMeE0ysIRnWHsLwY37g+NVkIOAbPJyieqhC2
-Ck+FDONJ3zJtCr5kkGuEXa8rOrH2IYG6ULuibzzFsRY11mhZ/3Tz1vP8EGWljo+08evj/G06acyW
-JkiAwLtGPOCM4zV4TL2+KP5okuYyLVH3Zp/OEwIeDmjybftmJW6uEU+W3gDEDHIPZd0/WMgqbE0H
-vzC3yP2CDayOV5biHl9CXbxeqek+UNKvWeZZKOkwsqfBluc5EFsUQpNfrJXdKvwSCHeQW+M9vzCL
-UPnIqmA4MEMwJ/F34tVSfU1znqr9lAZ+IVTOeNHbwgYEmDXYAbDYi3OxHwRZih30ooV/Uhr8ZbpA
-2Gh+PAGu4Qw8FvMILbACFnAoo6E8PeXZCsz53Y2iLul0jfAXDAAtXxDbgW+m1fwEvqTg7JxIA+BW
-t024KLG1IwdGGGQJ5rFRB1AmX8MSHBbhGWB3B8O0NVr7aMbRLUKrm8MGgj+xBkdeTvZbgKs5phs4
-fKFcX9HLswTlJkpbfAMWW/ys9Fe7NcMW15K//vDa2MbBr3nqSRTtkp3LVAQUPsAAWYc1XIJ82dHX
-fW07paycQdoW7iCPusAW5x5mq2u4ROh6iWgq1tEtwqIQswbLVExZvsLLTSIBygJOGfa5dtkhunrl
-ffX20hllLtT++FOiXPfJm8TqJcxjdQK5r/J+FXEf3xbsURqjChBen/udxj2xOTciwPATIJaTW8Ru
-6a3uDs314QEwc74D4sW/nwHcs+VYif/38/ycUA38ueYYG1cAPFwPuucVvSgcW6oRCASBQCCB4A6G
-saXxiQlIO+NVjV55K+M7yO3A2MPp4rHlozYtwGizCLuTUL1WdZvIFzlhX1dO1wi3AMeewXETl9uk
-5TsOPpaabfrNdIGw0afYowNdLYlPoPe5PLTYV5NZgQaAa36Ei5IXAiEV2OYZSkfOP7hQwIVSbvvU
-x8KB8Tr5BQuztCLAl8A3wS58t4gTCA6LsJZjguAKjBkE84tyCYLZIowKcg9W4SdrsJfJvmVHtfT7
-GfTGOcwrr0f1W/QKfGiolFVItcZrYyTaWVmZ+rISMmzxCoZLLvEXtbI0uA1MBJvyUONqVwa2Z+0K
-rz8MeWNcGjD2AidA3IFvWIot7FPJ4ahPl20rvsTlINiswUI8JVPxJdUS/CUC+XKry2qFsALXBFKr
-Da58ECDe3TfCGoNcx8HGSsBXCfhqfaIR7N3Cfu8kAtRbjDM9geRPgC1bfQsbaS3DrBKAl0EEn8vH
-hzTJwePDZ7TxbLbHY26dYxBc3CKG1PTtQxnnfYTZ6isnwNwANSFUvHSNeADGou1FZ3kG09UXjuMA
-iI9dN31G31DwR344WIA/AsKN7x9Bb5d5JyYjECymsRnkUl6cVgDvkgVee/aB+E4qzz3Hqb/eAFFM
-49PhgBg7OOYPbTA4ppb+drpA2OjzD2rYPrcOJt0t4gB0tSUoaQ+Gpuhh2udQsqY9bBZWrmMHt6my
-exq/MrNeVsi0ahFmwPrqDtyQxHnYdhTRjwSuOjjmY2w5RW4PSi4O1UeYP6LRrb+zAN6jdZjDMy14
-uzV4haM7ZC7TGijW4kgufLKN6k/RPhM7AAQSMCmFSyWaW9/Uj8l0N4fWp4hmheUGoXdO6KRTfkj4
-vV+1nvRNXTy9rKIQULqNRZRBGZcM79bjV8A3mug3ppzuINMA5mYJNuArsnzZRWRZg4d9SEMmvr4k
-lA9bWeCKxRSkz1UARMnxWGe5vGHpsToULhDKaahpwDEt+Ajt6OPXj2289xl4opRCnWU5kdUr13q0
-8n5AXJ8D/cjgVr9YtMeshoMS/KLNdf3VvYH3D2VUcPvKRzhdJzKc5Va7+Z/77Y4tXQ9pKH7Czk+n
-c3oYqNbhEdfeBu40yprl9nHfU07W3nQFaED4lLadR+3soLcDXqAxZjsnAG++0yAMbgP4skxAMGzs
-0LDxGlmEOe4g2XjQrb/lnBiulR8AOPLpBTmOuww6yaN/4G25C4SNPnaNUAKOnBTA2BN35VDCdD5b
-3oQKVxeJFPou4buleDWjAfU4S7kU9EUa0HeTSLUTVtyeTrWUxc3aEHSMcPbXt28DkLtFABUMtzeL
-T9umVfeI7tqw3r5PEOxfl6Mvep1AsGoD2OQzTHnOENr6WEAdg0NCFfWcv069mgB3b2UNoQjefFhr
-dl6H59ATtU71Zi2OMw8NfgF4ge1GYUM1qmmFDKCr4f/nwOwMhvlvvXIHUwyCC7ALwLn6LSKRluto
-/QoItp+7R4i4b/BSIl+uTL4oHMqwKRYfQgdTmkfvgD8FYKDIVt8Yawctyd45dyTPUgacx+uXEfHf
-EQTvxXb+RQsT5khlX0GveOanbXTS56yeLls4YYNb2tgCvG2l1sDx0TLcQPCrcvESHkCWWxxdIaSl
-d0vv2U0iP7Sx7VE8/fEgsHyRJ43Oi9F0Hd2ydygs7RzEOQ4geR09p78Dwg/A9wH06kP5vKaDYiUr
-bQLfU1tXvkNdTbmAg0W48H0C3npDlnEE+PU9gquvsMq6dUlArHkuSwnP+wf2T7tA2Ojzl+VA2E1S
-4TMQUBzTgAoIUiMb02oDuz0sFNZTmbT6PoFbxBna8vY61Eol0CVR0k0h1KVtKAt6sINZsx38Rtvc
-PQJin11mMGyWatUCcsNNIkBwBaxfZhnmXSJOPsGbL/DE0fL7ZA2eNPnbOJ3SOLilvVLvPwcu+jS4
-oFR9H04NobVpEd1BqrYE7n+pYkNL2k5wknPHOzI24Cnia1MCsMKBKXLdFCu5psLpYO40ftvRztet
-0BLwCQDyN6diDIoTKF6uEWrKY4UhCnwphoCUivXC05SsL8qgzsCv5fu4qtC8KIHdDn5LvAJfzpOY
-A6tPXo/XX6KCeLGzqeWHcn/4gY5b/Z+kvWofWhufygkCXCQA4R+BYH/xzb8IN8b+6+D25CMsJxBc
-64gmygnoJgCWLa+/HLfcJGYAXgCqGLHbBFl+Y994u8aUsEMfJ8IZUPqNbNNVTb6qnQPky+gBOj2y
-AVwCviYkz0D4GQRnC6TkxzW3/MQADmLj6XHIgFrdBoLLexAJaKPdWB/C6BbfwQB5403BFGCK7xCx
-wPAk/+AhFkfKIHiY274t5t9DFwgbfTr0So+Gu1LY0xgUsbWYnBMSHZSFewTADkwOILn0gkAm4Jq5
-Wof9qIXxahqDX42Ubgm2utoAlqjUfDXzWQWGBLxdPhIYBgwMC+Avy6V7BLtAdICabhEOfL9Uwyrc
-rb9nQOwAGHjyDa4WYQKHjBR8vFpa5RMfjydi2OlztpP0CAGXtzr8CHxbuLeF+sJx7lPv1XZT2Fsv
-PUuxVXJCUWoW4djaTCnsHE5ADWeQeyQHdQzucokFq59dATT5u/3cAnz+Lb/gjopUUpGFdacoLPpZ
-28bWMWqkOn+ytZd4czs2qzDqsY9nP7Yp+zYgPqlM6ekuAg9lj+i3p3GjDuWFO/lBg3nJn4sQGEIH
-xWJzKMU3OL4AJ6+svQ3gnsq1MsJWYSDBLBT+DHV3g7A8wcE6nNbgYUaLAL/DAe86znkY8DEMMItd
-hfIaA1XdWAfeX5j1uBCzKAEy9qFdc4Ed6L6LVwRL18m5rvks+w754WpgbXcA7/X1F+WsGqFzEYAU
-9S/Lkgi7FbjJF0mAy2A5LMCC2CHCAfACw9UiXAGwA+RvCoJfQBcIG33ngxpL+AuBmnSVUMiuIBgU
-tDTkWZHoTBth9TDDn3N4B7isArrF+DkvW8QAOuMVtmuJb6pub17N4P2XCfy6yIUDYE/T3QrM1rUN
-GHP6Bozna7B7BMo4u0wUINxAW2MEPaRl2QSY77myD+6euufGsNedFQLkHs6O5CW8atMroCV4vGH6
-oxvFRqcCch6M002DkEX4BIY3ELsStr1suRwejgx+vcmPhUE3eWss2E84rMDl564RCYpdibgf3nDF
-gq5gZAPAE6jWYuRxjZEPK8/f4Sb+ERzTFLZxkTYeR0B8SDvREwjWlnEExk91tjyCMREuPyH26+Lv
-RdmtFWEAACAASURBVLtLQPe8AkriWrL9fFeH8oU5fvlNHkBwA8T+5bnqH5yfYXaXi7T+JshFsQJX
-6zAMstZ/5AMcewpjGTQA+JvSw63Bc2KyW8SQcJlYf09A03UiAWBJ/czuhzBgWIo5uIu5qKA4ALEh
-QL5ZcSC8wuPQNskJjqQOglmO1/Jp9WVQm093OgiWVi6PT6DX+Y54Dwl4e9kR/UbsFzwlfYUnBAMG
-gLG2T5shdxBXYR/hkzf3300XCBt9/rIcwiqsDQTz+urW4q5QHBDkpCvxf897Clc4+qyyEeEjQOZN
-P+GgQONKLkL0Ic4teKQQ/FXVObB1IO7iE3MtqgKAwyLsoLeC2unC+ASACRh/mcX3iz6xvFt/093h
-1S4R7kIRQNjnmQfkA8Bb0cSHY7oN7wka1Lp49tHCfooqyV5TDPnY8YA9tYLhmt/GIoJ66i614wCR
-ToVPZkRNv1wHw0v2kxuEauwrvI2HooDio8UXfKQTelk6x+tS4ykx8OuuEJE+G+iFAjKRmsg87sxi
-t6aIlZOSUstHoeEiYU1darquRQa6PHeFhduRsuxI49rHslzxOe1joheGN+5vvCom5hzMlKOXobTA
-JWgF3rbJjp8s3mi4Ax0YrzroSotc+AS7pVbcAiy55+8Y+LF9RpnB7w6I64c4zmUWjQS25V0NrbtC
-0A8Ha/AWnsAcE0NHTcNcVmCd9mnnuYDvADAlWjRj4JyM550BbaBXVH2Akfyd4Ks4DgTAjdmpaU9A
-WASCkYuNJ7szk1Ae9SNgYQf4difPx3R90HKJ4Gs5HXOlJuhdoDXWgDAAloe486yD33SPcFeJKd0a
-zPIq49GHn5cGP00XCBt9CjgqCH4CvHS0P3kk60oHtErhYFQOV9gZebGuSUULNeIDgLyr9lDxdCVt
-rUuwXMNETSkQXMprhkWYr5ml4iU5EXuCSxZhsgzP0/ZpBwDsL9Jl2C2/OADoZi1+yHNLcYDbA+Ct
-A9CA3QMQ/pTOp/FjJy7ZxI0hhk/TTqCjgl0t3czDG/BbKmylToD3RTl+Sa1YfAkAO58zaOOhegRt
-Du7ouG4Q3P847E71RDtUQMDAWG2/4GoJhkxgSgDj1aqJoWwVNtAbyijjQ2DbGlUAvGxzpXkxdp6Y
-87i7S8R5D8CYJUofz9N4Pw56oxP/9YxepoDiU/2vrrnldQDTZSldNMbqsdVRLm5YPCmw0W4Nzpfi
-2CpMFt4nEMwfzmg7Q7yyHAMJYOHA13q0fsNuOvefuIFCQWCZLL5jrK0EzVViAWDBOIDfHfgmEFy0
-GEgag6mmL23q7wSPgNj6tekNGbFKFfArBB0bAF7HEedwO3OuT3Frg8QVD2WRfPJkCY48TmOgbNbX
-aG+CXinhBLl7WFp4XTOB8HKHWHLH4mD/4G4BJv73NfOb6QJho/po/3VJhq8ei7TEdqk0OB+vpnnP
-YacDWta1vF+H76heKpSW+YQxtsxDWEKcWDRBVoj2ECB5LCaZV42oPcUSYhrKOIFFAtDMr2Vg6RpZ
-HKa6Telznmbl23lK162NPQGGXgY7mNsKOH3Ko1k62g1syqKaLfFx2uYGo3trlfuK2vUVPIyDbIFe
-Yevgc7kFgq0PhlYXcOX+AA5bgVp2DVHvu+S2aC09rdAMAbkdqZCV+GwaqBCV2DJtfUTDgO+UWGNu
-efEhShBsIJ8Uk0ZYSzkR2NvdDprNbULq+CXYVUquc1BjPm40lgYUvPIIRx88T/L6BQH2+fbxTOAh
-muMiDEzG2nN5yOrgnAYY/R0tUfNLRW5U4I/qQWub5QT4x+4ifTyY0alv3KGNnRvQLXHEmAn2YWEq
-653AYJFTQrKZ2xXCugw4hYlNzDpsW0WEewQsjV+aWzJTioV4+ktwtqbmNP9SXTd9qrL2Gp5rDNWA
-91RZFmIA03aZGPYi9YydJhRj2At5UwFMjGE7U8zleiFj2mPg5WCxlrvrLiHwS3oLUsYl54V1mK3z
-AMuI8a/lHtJdRq1KLFl4KuvcblvVNNkFynf5RfziejrD7RIWSn7pJVDj20I5KAdkcf45+3xXz/0K
-ukD4m8Syz+fM+a3jIG3hPOojb9TST1fOkLS8tEUxW/fjokd2OwKSFITa0+0YOAIpUJTKycvQfs3o
-FYELNWEBdWVN4+HKmtej/cmwp2vUEaBWs54ALMi8mE8/j8+lvGg9T+MBSABPnPBKcjyTtBk9Xc2F
-7dlhk8cZtvduMveKelpeoHDklsZZNXXDttoCpTsHbj3eQDD4XO3VphwE5JMeIBYGsLwPCWbz+poK
-5sWxgOXW+v3xsD/NWO4QDobhR8wGUGh9mDWPQXAFxx62R6mjPwil2vrYx0RqzS6RHJNYo4GpHESl
-pcznJtKiW9YSEeqi1BYa6FVoAODYq9TzkZbSiUmA2MD+mJhz7cssul4AE7WPBJNc0BJv+rwMD7nF
-8ZDtkb0MD3ywogPfESAsxsv62IFpETF0SVXNDxoYez6gnKyPwyVqc6cIkBay3tcKA2KTix528Atv
-l7orxXpc7k9SpgrUnnx4eIqsF+R0+QMvN4mFd5cVWQ37KsYE5koEpgH1kduwCfcFw/KANNgsEF1B
-sFSepTjzJutBfsqUN0KmqU9xatfiqZqmzmsknnw+VdTWBSBxI7omWGEW402eZXhPCenfEAOHtIyl
-K1wpi4WU4iHM+Hjj4ddq7m+hC4SNPh5702/tOxrl+DZNQAL0/Kh5ryFD8pD3YfO32hPk7rC0K18P
-iy9MpCApR8dWXl6AvIt2hXhq1aGBwtE1mkoryYEFHJj66R3MWhoD47D4+LllVVIdqmV956Tadb08
-SuDQGQ6e5u75JulhlD44k2to2rBsKiyUdshnf4DGk/3Sp3HQvUBrdevh+YTPALJI7NQA/8Sw9939
-Sgkw1z6tMtWCvDchLckS1+RjsQYToHSe8hfk3C0ijv4xjblAsEyBv4nPIGWoQscJ+LJF2MK2D7e7
-THB+AqzG/CX4iiu1KUUGtR4mEOdAN4RBSpUsW6fX5yJcT6xc+HkHEB6ArNdxxhiYZhX2x++APZJn
-KzAMVMXcsLzg7q/2hk6HT2sdq2RtLewZ9/MOJKxOpSEUGcUqXABZGfMadu6Km33+mb5RsyxGGvWj
-16vI15kir091JJCcUFsVJI9DXtsgLFm6gK/IwHTL8HRgLMCcJFP9KYCBYJ8vXX7EQ9c8uz/xNJQ8
-/EW7YZ97tjKY/oqd84q9jEf92kBvrBEGqckPiy9pjpT4HWz5FeODlmcAVind+x46OMTTGu8UzS5j
-DuGYOy3R0J0NTaSGaG5jQNlRw+cyaFeMdMx8N0yVBRTp21m/jS4Q/iadVEWdet3TjAk19eFWj7Qa
-zm9OdgbrZU3dxx6hFSQUVSflUGoNJS+dKWlRlkqknBdHyXiAX9ZxIq2JrDXQrm+LHzSGWgUuC93q
-KoEUwH6+rtErCoPO5XWd67bmFWWoNPN9jg9Wo/Oqfy0C/rKAYNNQCGOz3jCIe8qPcK3y1DI9NlYf
-0jP/mRpvvDingldX1iu8/Bi9vib+uTyn9yPVz/ql1pXjyV+Z63uwTrVHw6bhxUH4nNTtXKzOYwvE
-LctoAcEjQXABxugAeXV1GBAUmpic331sZUupYxkgNoCE5ROgSKDbgEZpAJWFyxFz8wgQYOcHfw6I
-pH/p9EfisI+WGA7qIDfWeaTxS8FJZc0HqEk5w2u6r/01xhFKUKwmv208+CMZ4SIBtuL7+O6zEbzh
-WoiFm49vqAUSsFL7pVx9zJ/Pbmoc8blSRexGYHJU7PrRDm+bjcGcE6ITU8wtyHgTYRVeaZjsImF9
-FCxAHLhW13EhY4yhOzB2XrD0WF9D1runEOjwOUqLfPBhswoXPiV9aJ67xDu20MR5Bjn/4HSXD1m3
-xvnO/yCrMDkriK9fobDPDc1HmT3mmaOUQwHDJOt2bOIMxMoyj2mc4r82n7SolOO/mS4QNvrO2PNc
-lbBjCJMNEBQmOJ0DNIC6xTvYDbtWaY+0UM9NX8i8hudGnGRlFuVFzxkV6DpwTlHpd8pZNq8jpZnC
-Fz+0PQUgjUqiWZKzCbQYGKdA9vlgQLtbe9Xqd+AcTYn8h3OKBgQ1jHv0xGmn9F8pEXjGn8Pxwogz
-71P4qXU+/j1Xj0FLIEX9SK/Gop2r5OqAB7eHzZfO67F+VslPx6YumvW3VifZtwBM/QmDrhc+7UWj
-BMR2vvk49m4uHl3bXBXAqxUMh0vErAB5mJsEzHeW16cSW+yzog957NZgMbL4BohwYBHpiHLFn7jM
-rWYZhYFhpB+mxNUDEGNMiAMgIVA8ATHA4zcB66aiuUjA51pqWh8NWuMpb1jZu5tR/KH7TY2Xo5yl
-GATzE7QcThsHGqEOJgKAuAXY+2W66SXFJFrfaf54YoQZvdw8N6BVZKCPTbpCrCcfCXzDKjyz0+ki
-IWENXp6+9mVGLH/gYS/eYbpPsLlIsHuE2NHKxc2TvYbnumt/kkHHCOd8uOFn9TJvXGI0GhgO8Kso
-Y6w+yJphtesQC+XEh/UX5iZh5wgIECPllosvapsnhRoVFnnEv8QA3AxSrMRsp2NLy7NL6LWx5O+h
-C4S/ScFDiqITA4CBGOptmFP4+HFL2vEEcIj3WwnWRXFGASS82F/HDXJYtINhAa31PPMgYE/ddOGd
-IDcXjFqgv+Dmi7OuxT2tIN03gNirReRXl4oq8w9z2dOiwtPM/2ppENIypdqb8BKA5/C75uoxonuZ
-YN9X/X2nveu5TyBYQulUt4elJywsvSm0yFmRlEUlpJTW0V9KSYB8ACXq4FcN8CrY1xJutWo9TYWl
-a+eI8BU+W389vtwGFhDEJP0+Zrx0dpITvkyjy97VKCv7FBGI4PwdDDcA3UGHZa7x9LkTe0xrylwA
-f7ztoNf3ol2PyfPx+Rij6GMGv6BwiJAY8woOw3Ugbr7paUe5gc6bcC+z2MT5xOSSsVH9UEb9+SCV
-oXb903mEZdJavNGTJbMkgPG28rblJiTTqbxb4z1na1gDS3yQCdEveyl0gWG4VXiK34OYD2/OBeDg
-18Cw+wubr7Afl/sD3RCNCooF645IzZfc9ZXPcedDvgmpR0pHzlGTSNaHBLWRxkC5gV4Gq/niHJpN
-wn2a6aLFL7iNfyOe/5hfrSKuyoIHOV2YDsfjo/W3hf8JukDY6O2dchRsyuhb4QRUr+db3x4rU5KV
-uFjXmI0rRYkjGD4URGYG6JUEuxUMV2uxbMquhV+MAEnOFPi+cCygpajGoitlT2norhGe9gEg9uul
-lltJLV7a9S7tufc/R3yyUOLmA/ydMHLSjo1Lhfey7d/q2KeFTQn52DpaO1mCWXNs6JfKeVCAcucb
-9Wuew2ndYqb0uPjxtx4BK97sa25859bg5StMYBjN+oux3CBkQseyrK385Ss7fIti7y/1siJgiqqD
-1ihI+QYSDqC3lH+TLyE0FH5zgQC+zoNCMiRl0iICw7RzwFCEbzA4jLrmWdZ3WZ7ld6CbR59vAOIu
-a4gbI7cIM7AvPsI8hjw+D1TklLiDRAO+3njZzy0Ul01h7X93W4n06jbqAGqK2NwtMAyyBkOwXCEc
-CAqAmVZgOPiFfSDGzMTrPt5uesDuMHlDJHEUcxEWcpUIxox+7zdmNB8EfHMMqzU4e07H4g9sYT52
-azDcF954yJqYgFgz3MFvW7sJqN2Xu0i0whqfhPNSrDDVdGyNP4NjL0/8+ZvpAuFvkrZwF5ArrHue
-cZq6j2LXvb+sdZVFxdqTAPawNiKXw5KP7YAqhNkibIpHLb0D4yxjuvFUZden3Egat8RYJuA9SV0h
-ndMYLG+PpkHnKc9JLuwCiMu59CPL0DYlOFlRD5P/kwyRTX5xvgEXjTNIrClQH2+ew7H/7pvrHIKU
-+J0+Es99dBoVcqusCf91UNMNgr6ThJ+DMoeVCSvGNcVE1+FdDXIf42pxLjtFBNMZ+o03uOhnVmHd
-fro+QOCuEQyGwRbhZfXybcRkjtTnAQgGJMAZAhSH8oWD2xyOdIFCzYfn2xw4mNssxBJDngHKJ5AR
-rfBz7K/a/CWXVJcIT/OvlM25dqnVMda3zeyluUHjGi/NEUd1mZ885oq7A+C0fmk8ieCjt92fTKy0
-NQwNDMNBsctQBmONgjmz4cFKbSnteIOs3G38fVeSUlryvPjb5XiPBw/psnxP/8jCF9IneBWZ0WEQ
-KCYwrOtLdAJ3kZjLL9ytwe4CsRgd4f7gH6OJ/FouGZzGIHgTkfYMkE+iioGtlxGc3R8on8/TlSa2
-DhIEtwsyKDbZV0lLyE/v4aoh6j5RZ3LlTAr4MBonMd7Tfk4L/jW6QNjoO4NfhIiQoLGjaird/oKc
-K7G8ZmeY3drrqfKmHH9OtrIy6XhKDyUmvXSLNOC7QgSU2RpsShKUz0KSQfFG0fY6Ihwvj1BMyrtv
-biiflraKVksvIs3L7IA45qrdsXr6bjnW2tgTV7miPGe8oBf5/bKP5UhCFmnpZxqHafoJbz7DfMqb
-i+qb/NcnffdEKr6BYI31uRhQI+xAryiUUCB6iNuxKKBD2oNl2HnRP60cHxbQtYVatwW7n+fIFOO9
-AR0JhoeBbh3D3CVyf2G3ECvGsgqL+RHPYZ9epi/YxerNcYst8/jxEQ1ZgmDKD+suAd+jBViibJU/
-DDj4RSzkVQVIu2C2ar0Qx2O6zhnDb2DXmI9BN8g+dVq5rrzgRMCIX5Zb51UQzNuFQV2OWFw83W/I
-nH8M9PrHLtgy3MBWzoC1s+geknelLw8y9SXMYUDIIFfalNd8Bs4uP1jcrJ1RCADLl/Uf8ZJcjnvt
-7pT1FMMpXSQ0XSXMb1hscBabT4h9olmKq0S6fsSFnP/KuJcBqOkPv0dge0pXvxk5lcn++821K+6Q
-3F2sK+GGIq+sHkrqiCF0XM5cpNcYn0C1WTz19Sktf8UQ9U2x/yvoAuFv0ulFuM5Mn8VdyXxn3nU7
-rqZ0cSeP8arOyUosRc1naelhVlzm0wWkYKS04tcmKR5MOpVq9xZa60lPrOxUMLH2fAwdAPt5dplS
-1vMorYLfF4A4FnhN57Vex72RPqRzfx/SH+mhyLkeFnM+4T4okuEXeQrhdxY/atte/CCRC71SzMcK
-69kEghfA8DBQdo0oVmEgAXM/jy5Y8knz9DSA8jPNt7FK6zDCR7h/BAAAvdTj/Cb0aN8eqTMYBsg9
-woAv/DO5DoKH+Qj7a/eJU305uywoz5F4Z4BY9glY1v/0p6x7BXOaYLMQb2nMBlxPl29ocbfqrTFj
-ULx26RgxfqBv64VcJnm8QMm0tERewQ2uA+JG2SRChGuev8DETwvyRi1l5g5+LT26WvufOkDimn4D
-lXy2y6597Lh/BPxYcNtReE7L0Xim5Uv0A9EfBsCrq7Lwr9Trrk/0+pRquEk4VAx/YV3bpZnx1z6o
-Yi9KytpCT8yXIttoT1QkvllHfZdzuOhFbOEYv+YC4enPYNffI9jL+8S4uILPbYlTe1hGHuP5zoJn
-86/w05Z+6DgB3Xq0dra0YpSKxed5+O10gbDRd8d+MXMFMFryEyRxfo8vLuTU5+NJBcTiAFBfKd2p
-g93tKO5/V86iA2emsGMwbKowyufnbL2OXK0hTB10PJHCfN5ACierKu4PsZDcIkKPKYGS5uU88wiI
-cUqvbStmZpqrUuaRw07p+rbEp3VtNanPm4+X5LyEVPWBFRPs+TbySxT8nUX0sqy+jL47TePxR909
-whUAAJSX5/zEyNca9n4/5fU0t0gDAXjiZTkHJQKzDK8XfNaXsfxln7Xvaex3OmTtAbw6Z4/z3Sq8
-Pn+qcH9gGAiydAPFsDI7IHZXAVLz6so1+xO8Y8lHGRHAKcHDtj0aBfm0DDfAERlK+U/XZ2KHB3KT
-gAIjc4atA39pzvPWCiHwm1iktM3dW1w2pJI3OWB58aU1543gz/Ukz62W3RLcQXFIU0mQwt/GYdec
-bJe3nWRXAxwZJBAmNS1feqabHYBArgTfrJsqaWE+fkV5P87CEJXcJUKx3FeSUbE+Se43dGNiIHcH
-AdwvGBD/SE0DxLz3NPeXLf/JuO0XIPfEsyZfXPccAC6HvfwzKG7i+U08unKUnxrXZ9SgcD7peam3
-88AMFAqSjggerFZDblDlyX+KLhD+JsVctrge4u/SggkembVfuR2JqRbrdgGy2FiolDNyU+N0Jmm5
-opw83sJkEQAkFAZYCCKcJcKyUe4s365ejUZG763v+ejRi6UCyHJIxVCGrrpGkMQi8EsWX1AaKZn8
-kUQqpIdQi31DGsRcflNwlPPYjMBxt3CmZt3iZcsnYOO6Nw34NWVf5dukL0BghQXhw5vsJaiPZJLv
-0i+VriXABobjgx3pRrDOk2Q+SaCjAVKcV9cxfRxn7IGbL/G4byMS7KrYo/7qIqEKjPUZNajMFZ4W
-Nh9anetTw+A9VQN0GTCBW9tIQmwTTVbKNBEiQJMDBWlp2NOK3zDoclKuFHPBUyabe0Rp4rKYk2VU
-kaAKOqG2m4YOrvvgNhH9mckSBQRrkQ8AhVXy+gSGY3cTl8sj/arrRzVyMGLp5mgV0KKFbbXIM+4X
-kCwKa0VUKSUQ85tuLkigDlCbbTYdwAc/VUCc1uDWoR1LBuWWaQkP7ZYvtk5bng7LBWLMgSkg94dh
-O6bYThHmMy/2EqnMBKMbH+MZFOshLdMBLeGsv4e5R/t5jf99fdB6EJ98UJkuJ6Wew4C34gAmbUzn
-MtPXqytCfvKgnbmqims/NjZ9V6/9CrpAmIlkflV+e5o6IKA8PaT7IzFOZ7/h7dpB2o4ZkzdlksUZ
-bq4zw1gWl0z4nHVLY3ygLJFQcnu8+v5xXdxyVqx9oI8dpW66kmlgly29aILfwWwsNk7rbhDN5ksL
-/AR8y7nbAn5a0Q8C4q0E2HnhO+UXSctzAas5XzYO8iJ+avVRiH5XqL0q/2me8aFGn9BAsN0csq+w
-rdVVmBeoaZMGgPklMn2yFjcAvNqZlvgJtT1R/chvtedLPAqE28M6KkSGgeHVRvcFXtPzBIgBfmEo
-Pik7oqdYZyTZq0XmS+wDzTJDKO4gqYIIlgkBmDpw4LRiReYbeZ/bOu+5fRqtTtG0wpZVux6Xr100
-ENZ1UIkF/hPsaozOrHFJcNtBcMgkqfECnA2GxM0RCFwykOQxzj+VXBYKgjfrTb7layn+Iix5YBN+
-ad/e3nThOKR7eYhZhD3+dbiWtabwnR2VZ2aFl5/wAsMCmHuEWXz9BVF/OVTEXtYzSzDxXnL3+jHI
-FTQwzD9qdh1PL+Oz7fXiISw2ZxSn+XAAnABXGyimeet44qBi91+61UQ82sRcyz1dN3aN6ewiNF+x
-PjJMU3gcv99FFwgbpSWzHVsazzGwM9I5/dlFopwhOKGpVquBkmMxZ9tdUPIa6GDYa97D0sbDlVOL
-R1pVYF4mrcC+7PuVuDVtpfrYCY0jry0GxbzAUMErXCForYPnp4LaCnyjQduE14nQQwi9xMPcnVMe
-6unD9EnZkJZCk+xXEdr0H1SGLyR4dI3QykN0wc/oxXB995zYm9VA6To4w6T1NhirfH5Ukrk2ZcLn
-8nkeXiesKrws1efNpnW+9kTVeJQ7sLZ6KlYsd2UA4F9WG0Ohsh7s54tfYwfEWODZPyerQFiJMRQ6
-zVIs6/pCPR0+Br6+IicVMo8NA6UiC0paFu8ipdTGYobL85B7hu7t6VPn06C6ADPLY48PioPOX6Bk
-Un0rDqC6RrDV2QSKgtPSZWFtF51xJ9/veQPDfANR2sFS1Hak8Owi66jPvhTqiNNBKJ9ke/s97Xcc
-P9oTmb+YF9UKykS7NZkvu9MaS4aJ/nGU5e2wPpbhDzxEbPs0LIvvGkcDy0JPQsBufFk7PcsEwGC4
-t7GWA5VbquIQdx/i7EmJu7bkPAbAgMuDNkRlOzVPI6VP/K3S1Bl64AkheJ9DEaPqtqJgSbFSMdav
-mqe8lPl/E10g/FMkCZ5Q563jpMzXQxrwNOsKXwa9jNp/F1NazjjTyiuQU2rNR2jaNFUaBljJHdJc
-S3XLsF2giplyxWPLAaS1znvulhiQYPdx0Zq2WXTjyBbdtAXnulU8WYzLXLLya/14N8+fpL+WC/pc
-pku2x+dp5zIKF7wOrPgG7MxrlQtft/zlS3c/P1zGu2K81kCvM2D0SwnPEmh1tOc9Ou0/vIFoOqeE
-XQmtdFWqYwJzrM3//Y13wbQvwa2tv1yBq+p65GvWYAnXiKUs10tgDRArlssEsHyBzdVCAxQPcpEw
-dqA9Wof1mdgg+KKAT17qYGsmGsaix83NIvxkKQZQ7zlsKHcwbteJaRRIWGQ1w9CwerNszlaS5VcE
-a2AQbeR4WJ1PIPiQHmH/gMpqZciUvocwg0cGac/rT+t4acoqEKsWMEzprFE2Awe6tVq2HS7GBoDH
-+SMhXx7+KjziE7z3TuHSd7BcUwXs/VJjXWDOtS3bkPQXXvjX6s01BfvC4nKR8OukZXgHwK5Hn4Bv
-gl1YOaXeqJ5Bb/cXznKgummW3bjE8pA+fFRBsMYcMyednkzr4Veleuprb1tUHsykrRLtFVYGJP3q
-4d9NFwgbfTr2vGsE8cSRgbjOXMYJpJ5bcco9p+Wy7SBkpdiD1EgpsCd0jgOdJ/EKArWk1aRUUuUz
-lS+iQtrABRjeu3gcI1cyFnbwWbZMizwb9ZLG5YEN6CrlxY/AbynX1/gTF+3p+pD+oueU+sytqSMe
-eGhpW0qycgyI/dSeJoujqqj/gNpeloUHj+X/SpoGEkos6jxraRTPhYA2ZgxovYwew/7CU7EW27kC
-A7A+dnrgEwXMMSIU8lLcEzqrNU1UoGO5RoyFduFW4fWYP9enwl6s4311fV/hQbtTGBgOS5o1Lz9h
-K8Wty5vs4GC5bxCIDfzUwG2zEoPOCSkRoKuKGrWEAsA5XMigSuwQgriRANbuHO4Q4XtGpORNkAwR
-2rEjAXIKuon5AHQLEJ4s9w30jgqSobCXFBtg9H/Sxrb1mO/fANdTKbPgfEfL++lYBz8vKCTv5YkD
-cQAAIABJREFUl8V3lHDwqd2s9fAogBjAFzW4HF06Vi0a8+UyHMvVRADIXK4u5gURaeH6MNgibOB9
-ABo7pvhewi7jHsCwr//tR3NRgHGmVXDr2jnTllxqaXR977VANgAMO5+vGOPZd76x9MSi1U+4o4k9
-jZECpR0twF23b0ozlfM/SBcIf5vybqj8ngCype9HrcxRq6c0ZpCjyDqwrJbUp17UWqUYYo4St4Pc
-lI4WNYHRlF4VFQalGlCTAi4aueWXIUQI9gZ0PQ0VqGaabmuUl3pdyMh160qOi8S/UsM2smd6Kv3+
-nOcSLhBfVOEFeI5LGqWrEtCQVEAERJKeeY0LHkRyTe2ZR354kxYgONuc7FUB7nokynwnDRRX0Ps6
-nHU7AF5VSLbp0Gz/iMNaJzPBj1uCOzhSA9/2ueAExKbUFAaK/VvKgC4HjAqMB1bBqZa+QPHyV17t
-HiarlmyTvM/AQcI8rH8Gw5vf8EG+CNXFiWtbPNmt0b0NPHdIS3CA4YnlN718UVAtxJqnTr5lYX4g
-i/DcLb8OjqFqdxJFiOTgzWzj4hsDOQSC864ox0KOHadWkpqIG3+P4xRvE3A80lyaeTVdIEY5ju24
-gOcwUFym9WVfflB4cbDfYAjWPtzLPLw4sd9IhrsEEE87hiBeoDutr1WT39jBwCX7CtN4+C/kSnVp
-WO2uANh1bYBd3QEvA+PuNlFsvS6ElR0OSeYAbS1YvmMTnABwlCJt45ingRs+h/h78/1VFN1b9KpH
-Ke2lCvyb6AJho0/H/hn07Pnvfj+l5bUHUrS1ByJBO+h9ymeAS3Hpi4+KAkXB7ZYfL+wAeLUiXqoo
-dw9ZtneTF5zGqiHB3yzCcSMai5PT7KSTNdirs1ZoH/vT3Wxb+Nvclml5xWl73jOffJJ3ypdMrmaF
-lHGlz66x6jjs3PZuBUmZ5ZqzaeRzlY9l2hyZQmMzWfhOFl9hHEDwyVXCrL1UZukTBtESQCb4yxBj
-+Ap7AzWHWpC+jJt+FUBEdythKG1TLoOsmwaKfSgUQknmBes+GM60I7eYggHFMSTWwzTUOQyIOhjm
-GawWRLK8FRlCSDeA7JuyPH9vwTBxmN98CBaYYTCMtARjAprP2nO9qyD9gj1sIHhSmrtbECAWAsMJ
-AA4W4uHAGVHnAsJIa3BYhV2cHmRyrFsPk2yEBj9uqoPGTbcskt0xHWxRrSB4jJFWYTty2gqTpVgO
-/WjqK9dwynxVA8BIQCyqSHw97amIYM62ZtZ+gsU6HGspbj68Ic7n3YnPfXu53bRgYwyr9RdcLxgA
-2xqldPS0SG8Lz2RT/kXmnwCw54dhqeORGt7jBzAsgaqLPswntP7L/FIn6c8Chn8zXSD8F2jDQ9/4
-gY6n2EqpnkFP5d6DkKQTVD6Dk1Z4swJXhVeBLxdJQQ511wgEt0ux4JwbxqAmxk4RC82tv7zuUnjm
-Olz5ZC0GNgvx0S9Yta3l3RKckPnQl+iHnifg5fzpnqvx53t5lhpwoSAJDR1KKOV9+lb/sYMEJDmN
-FUdvZkvthV7FGQAbwI+xV7+yNt9hoDyR6HzZLY0bAAb6fsWbq0Q2CqERWhdS7ZrSFilguICQYgk2
-HpQR8QC/Y+ZT/UhTaDz2Xy/bQeG+ELRoxL6AJ/WpFtiSZePcwHC1ZnpyA7xFrlAVdkK+XKUJhkG3
-/YojIOaxD9cVA61jVPuvW8IzBSRvFuCNNhAIhgjmnEs+SAXDcMDLLhAGgKdbiM1PeEChYwbwWsOU
-QLiOae2n800yUA5IvrcAOuY6rMcKfLc0mq/kRbIEjxH+wGOM9dviaSV+EBO1XaoAfhAvI8cW+WVG
-Mf/2Yc2czHpkgA4f/PIBmVxb7ne7XJh8HMgiHOvd0kJ2VABMsBRoVl0FSJd0y3CzDh8swqxL8gaJ
-JHEBwKkHYsh1z2M+qGGNsnF9KbUiDFn9iYcVCX18ulAvjxL8rXSBsNOLhVlI1YTNqyNAPBR6k+Nh
-CAVi9qMJYbUqF66FCPB4kjz83pHpp/ormRmWfjGPRrwqv3Idwgm0FiMsW7iDhU+Wia2ybQjJuts1
-wcf1fp8+O4sGow2bBrPU4sf0yHt94ciSgDAtT0N6yikdmceXcXVQ05qyBqjOE59/L/6Ov5nFnqrk
-B4uRp5WFa4nzdXaMkspD2noQBxn26+e78nrqB1s3l4IjsArXMw7CMn2Swhoi9uSebv4MNOZ5hzjM
-xaAjUGr0Ph5NKrnl2IEFyY7uWyzW81THXcC2NHZTaWligHWBJHeJEED9IbqhpDnjvAWUBkTnOo6J
-r7n2Xx5jQKdZgO3mwq3Cbi2eDpBlrpfkmrV4mhU+bwJoPCycK7EBL+PTfJnYgQviRb45l3vIVA0V
-Vd9x6MaBnM7wpy+U87Q+RmG/kaB4jdNIEDzMNcKAcK1WofhRwa6lOeAF3No+IGo3D2r7Z5fZ91sk
-u5Egy68edFLXlmzOUBpnB72B5xQtTFZ1bedGmpfrFmEu8wSanTOsj9EX53XvDvfRtmCMsII/LpP9
-Rwuf0ixM47DnE32k+A4Y4ZPT/ga6QDjoU6CTrPs8aRuLtx9CNifqOzSl6JkGfAg1Bvt2MJv65jGP
-gW/8SAkVE80uiylcx0+8QQCqpW1J32y+LXnxoeDwKp9fulrCfVWtcXMR+RSP9N4mYBvuz4iV7qs4
-CSi+lpQYCmA85NUstjDoOZ1A6npkv6cfGnQmas8TkATIKtda/TNp2/XfDTVelGnjKX18CXBsDZHa
-p7CCcViA8HV1EOfAABS2hRWPuD0eZRlQYLf4otXNIFFgFre0yjnI6GWLcqSww42idB0Mw10vZI+b
-7o29FdxU7ItQ7at2pKMJkiZ+dtkE2fqYL1pJyglhv2vNa2qp/cAMDto1TvFt6dYLgr5GLHM6+LXH
-7iKQocv/dKht0eUAd2JOs07aVwHV4lPMYhnuE7K+gjbXB1QcCLuPtttQ1jRJ5VGaO+U/0f/VT79B
-WnJ19QEyl1/5XG0SA+pQD8MM1AbgYbswOFin+StgDnkjFjcLkiD4+ecfhbHr40cAW3GwixUe9jGU
-NQ9+I6KA2sttMtCtus5YBVCWttf+TKybhBlP/siSjnR1Cq3vZVxGkojVF/EKenVvi9I5vi4LECY+
-CD0gYBbJ/V58bdlYio+GLL6PMUPKGGQYUsMEDrbwWv4SI7QUL424ZFrMivh84vD7OQ39V+gC4SB9
-XwRAhR6HJfbCKpxHZ5h+aQKIh6Yd+UNdWOrOr6wHxZXKQeH0n50fYKoD4hwMasQhvyujEB3eVwZ8
-OWYnMOxhEQQYPjVHvpHOeb/qxzVrjA12KoCLeOrEAEruDIUJlO4xXCBabEs/k2yBw/VLC1HazTa6
-XvbztA8E37ZWDmmnyS1AIuPS+9vZV1o5AqwJjh+AKtDAKAjY+hrsZd4D4P3cfAEptqgiMAxqDyRt
-Zc6buSpNMYtUBewYF2jxD6zG5Erh+HhhiHiga+O+yyQYCBbvF5C+2HEjTYo3hIU1EP5Ql8OVDSRe
-GlyuCjLF3EPmqmcyEDYrpIHfORaQdUtvAcRzYpplWIZC/ctlqmtLLwejBoqXe0paZztrP62OVCV+
-Q5o31mLAWAwAR1987HSBcQfoDoALIGbtFmCMHtE/KRr6CVuI3X3ixwLKy5rrbg8K1R8B0MXDouu8
-AO3D8hMQi1ofQ0cNhGWkCDeJceN+TaX+BRj2sjamWrWVjznA47PG/DMQTPnHcjsI7jwMkxFnubzG
-IPBIAcILAA9oAZ5+/7CmNOM+xyJo48nXlow9KF5nlaqMfa40+DP49DfTBcJG3a39idjDqgJfLXf0
-aHkbGLYi6+LNutx5quhy0y4h/P8Pe+e6JSmuc9slR7//C58K6/zQbUk2kVm9d1fvb4wkB4kxhjC+
-SBMhm1TtA2b7H1A+VB9kVwPngoDYegdojZluSLyEjoxHV+ZyiUtSeMCw1C2WMvXLshVYJLKhxeRa
-6UDpOP67dX4unJnbPt39AFcAON82Si/CQmiPlHasFKZDVqTXSnXGz4zdlscDbenc6QrgcoW4i4n8
-81euwvxepPf4oz9JPySUiPXjUe7toF+mAy24fzHk3kAVl7gb+H4Bv2f6uFYNTgoL6gHB1aHz3sIi
-WzB1A95ygZguEY/7BwwD/StVLFNmviqvOS9tzlGLlKPi127W4JsrRCKFy0wKp51wudXRZ3ZIdw9l
-VwILrwDGhQwbBO8GwntvLLcMyzZ3CBFx94SwCNtUeVDb2kdUesPOZjjaevYf5UMd9sOCGVZtBNBv
-s8Kp+KeHEzzLVaO7TzgsUolG+0mzS6tHqr9l7hJsHbYBcy8DMXW3B4LgpS/EYDhN6HWL79oOwhvQ
-gGMD4gApzPYeeaL2l1utFhLwvzVchyp9lGdzmdAJxVZjHXI7zFYeCqS1xXUIrjz0tab2Kwwof2bq
-SqgdYRgOII6iwiw2kpMcz4JS6tqctybYWa4KHeOmAr4+/9739NB/c/kB4Vy+D0UBwOHLVMo7Xk3p
-SR/DUiwwZTMFHre5+r2evd5OJDHkS8Cl61UjfFK8npKUJ6jBatwTpyG1k/vcKbm8qIwmDBf0nmGh
-n+1wq80Vove9/qAhHPii2oUSsgyoVfL6s/sewNuAFoiG0LJADaCVI8GvSMY2CWT1kpKY4rX9hlxC
-f2d5ht8z7qs08xhAdTuXdjG5x8f56MezdbZo6ecKoXs2OuorJMk/W2zZSvwQ/9D/no/zNc65Wtkq
-DDq/+rNUOWsVXypoYReIh3192D9guJR8F1rUS497XGgfaYgPHoS8zcFgIT+ikXCrmmGXTJ7H6qtm
-Gdsr4MsyG7M+hJuArHptb+PcGIQNfnVvbDEA3ttm4VjiH3YQswpvt8xuEdjsE4KamcJgmtu7csch
-fdJUy2M8AAlQ3FW+KgS/aNbgkJ9RmjluEgxqBIgDgAOCsTy83G84YfiVQGwQ/KJyDRcIAuC5FbfO
-52t1vzf/VnaH4Wpzym2e7mV7OF0icguqkwh3n91oU2U9pj5FEMzHznLkcz6ni2ULfeQmuoF0Y4Nl
-13WWl0kwi5Vd6dkqKjmLrivROgiKo99LRxpyi5hhyTxxfn4swv9DSzT3u0iNfUsZ4sC7hD9xVxeJ
-dqgJaMklqgmSXN/zIUgqS8fxlvQCfycEPyhZyKOVOH9QpO4HpTgLhiNeEygKgOMFW8B6iqIOg0CD
-4ScATlh2ARBpuL/Cjx2gfCs7LrOHVUf4bBV8LbIxTyu/jLMk/3GwWlA1NipXKoSc/ovjkAJRuXHp
-ISb/s2VcLF7O9jhO9n0XinmNZNQq2Oe85AVvnUVaOceFex3N+nDlMFvFE+w+Auv3wfc5DRDW0QOE
-AxhnnMQ9l4aremE4OMH16hLB4YTjOPYAw/6DDaKEy1ogWJ6/Vffgr9XDnassv0FrpGgbGFPnp37R
-BYHErMqASEJwzTJToKgIC6nDsh/fGltzb9h7Y799Kw7Fsh3Ydt7XjkF4EJSXtYV3M71y/7V86ohX
-Ji3Qm6IIbMF7bev/YRFeZQVmdwieAzmPgSzE0RbiJ6LnNBiOD2ywr3DA7wvysu1aK38vrMDTKpzu
-EVcwDquwWYnDgh/9JvODMoE0V4ZYtUA3LcJbqeWwRVezDOJ6B+hGPYQe0Pl73m9IT3Tg/WxFhgh9
-BtzjCfJLX5Hya+Br4ZVxTTR0/Q+UcGwKlsMkM1I5X0R1VAnF8e+cvII/vvyAsC9P4yCPOhEk9Gqe
-ly80ukDOllktW4QEmGv4/A0dv33T+XyqU180/Nao44+f9GZj47SHMq5SiVcV6lBQvsPntjzW4t5K
-KeXDQRzLcnGxc4Hhds+8H309yzz7fT9GBdYePCjb4HRfLB2JJEVtQH9cs2HwrGcZv8hJSVpoBr18
-lBWQ1qnxqo5uUEhQtxv/Yvl+yl5+820AX+vvxPU8Se8L10wKjiKnk0TOdHLbZ2mdx2/9qKA11ju8
-XuLX96H4a1D+MEDu0CrWUvVS0vZaXBwqygq8vUPF/L0bScIQiVfJXoYZ7jKgFP8QGJfyazBMvs88
-E4NZas26mnOKZztx6y0KCiIHIYqjjle6b5QlUBwot1PMThB2Ke/bnG3OIVjeDrybV6FPCQvaNHgk
-RdxRImetgz9M0CgD+ud3EhDmhKW+U/sOhjssqL66e0T43p4rsiwK0uqNkkb7Eas/xVl/EGkD45qf
-sPsIi75ythJJCL5ZhTsA2/2YhR2+rTZP/sEzj7hAqZZVOCzDW7Wn/RJ8CQQ1ymceu6WfEFz1qQ/n
-5bn+UKfZtuv+JBVd6KZQhiGPdGyzmKprUlwTh6Q0A4DzuFK6eQ6fmyJJqO7KSvxjEf4/ssQLAAAk
-dI26ehgZLgms1GB0gK82nSUV3dtUNCKigVDopxJu7e+qxGNU9kwTF1baCkjntTSh+Hzr1tDosLVE
-eVDeLzAsUVyZBs2nt/YtIVuHozzaMSrPhOXLcumzx/qpi+ZxqWvVnUslorzU8XnT3EziXALtrADN
-tJoV5r+Yuz3+afld8fNdqP1P4mKvydkz0DIvR0c6O5aMSmrnoIT1QwfK9RPofgbiE2Bv5z/+xidA
-xhl/CgMvNlLO0UwMCgCIYrl1d4sJtUVW3602BVtzhSBLMK9VRQzFvRzrXv0jDQFUXCnNEhx1pBXf
-KnrGISVVpgl5JHXJtAIj4jTLquDELYR74x1WYLIMv9ka7JAMeeNxUZ/jVkFwL2U0cZgBgRo0yjvy
-Z+UQFt5yIZAGvjHDhYrSPMfI8+qv12GX5B2IbzB8+gi/yiIMc46oGSMWlKzA4SesCfIxRdsmQDbL
-MMjSzXnp7x2l8oxejzVjhI46RksLbwt9P8LDX5av086Vdv2q3W4NplpHDYjkYzKup2ADVipFQUHv
-qvDiqgPp3aH/gXM/FVPIkJYf75tjK3Mriprx4xStf3r5AWFffm+wXFgcyNKQzRFZ2dkD2NIZ1kv7
-Ufp9T0r5YF0cMe24Ujp81NelIFGQjKc09EQdHc2sRGg/WKBVcTUQ56aUomxwwLBEWSELAvEdee50
-HZD1sABPIOaf5/4V5zIX0KWueY+weJitwdUO8g56WXEZtcyB6pis7e1YWHyoJUg+jtH9ah+oqHRu
-KwxavNy/1/rrFBn78ftxhEvsTPd8rR73JBEv8TfpKXQVuv+WVLoVu+0LMKHy43oA8N9MEzD4zbTg
-fhvgfYFMLrfeos0yZkBiVuAVSllIaYsr7DlY7gbD+TedoqJgS85IlDvfD89H67LAgM+V/AHEfDf1
-i9mnfQ7hkNpNTsQ9gtwAIr86885xPkfv3tjyxt4b73fB71veKVOrLZLE4c7ulVA9sUsi9R/mGL7T
-Nj9xWFoZgLfPtCA+S8awApc7BMJ9s1xBLmstQ5EsISuwYM4akStZhFXdhWJagXekJfjd4fNcluH4
-YlwnKdsqPwR6QR4Wbw/HYLm8x2tbON0euE3MY7f9p7i7FbneM3K55/zXQ4+0naxMaUVja3ePAKKY
-tE6la1oaTXUThgMN/ST1s19t5bKdHPKnlx8QzuW7KDAgOIQ05qt/YAJwWhkCYjTVgm2p/o/2M+Eu
-0xccc58/Ffg8DpcVlzR+LBRGdl4h8ZwZsY7Gz6xhKShngakGNbRTu+Hst6MQmlWXOy/pxey0kZbd
-UWa54bJzqX55WJW2PX1i5xUAMx3rv5ExucWnP7CecegFxhasKBQr6of2zdJO89+xzFhW11/FyWPo
-03kDUB/icInhAha+v6Y3pO8f0FznVKBrk28D7kegtSnQns/9YDkmCI6+DES813yDTfvH1qVWvooP
-gEuWXgl4oDQHDHeIBP/e7IwE8mlBZGtinB1uETFDRLMEx379Vt2lrWEw5MZx9OeHPJPkavHb3SC2
-LLzfb4hs3wrwFohsAO+qA5ZGSr/pX0CLr81RLz4qqiy4+nGVrTlNmuSAwOEHzFZi8DX94egGbKMf
-FHiutt7mFO4+wivdIsyiu9w9wv2E13oA4wUMNwk58lIS+8x/h097uCPLcNQ1tYXDUvuNY32/Hgpn
-mjSfjbKesCzeTkL/Zv4lqISWFF/CRVFVFl/cC0swqm+wa0L2l/Ip8s1U1L2RMiPI2Jrs9P58cYv4
-cY34V5fvFf4Bv+1cBryAEeoxUcGk+eN8qajWxrit9TgCYG+I8TSVUItq4P2Yr+iADEpjcIvyC/Zs
-K/WFCXH9BU7cVZRJL6fMF7/6ozI6rcEVH79v51P5RFKyurPlB5m+13VeC3ENRtrnJRBD6k5T3TVB
-QGEdF4ginE/UGc9l4tLviAMKdKm+utWd3yXoLIKR0Q4U51JCkYr2chQf07SfbOfJ2J9n3/Ytrsnm
-WZhjX7gCEIK60ohHHg+S2V8scAXdEbda3AVu13P8enCjiLxZNiUz3SyQTQsJvbGhnhqv1oVcGoSV
-7IRhpbQFzuE+YYOzShRq/oT0NSGeAep278jX/kggrmuaTKbjHSce2xGXQ7XBOj7dmfoxK1PzD377
-wDjfH1BseXx7Hdh+5J2Bx4A4vjS364fi/innVidoLg02cC9A1l0itllMZS/L41pt0Nza8ZtAzYqB
-vP4Gx3kZCIWjrc26Oz6q8coBc7JeEGizCOtrHe4RDYQ3gbEDMHwgYPiUiGzEA1UK9KzAcI2g1qE0
-SC73vdjZMkztmMO1T289jmOzNV7Ov17T6wBdbudvRXqSX6kZe5evKsqqMnm2GgQPvZr6mBqh0C9J
-vP9TkhWC+kRi9dXcDtCdzy3MKX96+QFhX37HNUJoiwsY63zVfwXg6lmDgeoJKuNDZFO8zvNOsGV5
-8HysK1VunCHs5rzA4sqxehyDkdYNNBWk962XncxjdOmmPiV+jsov4lDxqdzG+XwnUkkeFy7jWu+g
-bHXfzwPFRV4jQZ4vlV+Oq1uIA5ppWxxAoFv3r1wPyvcaP6gPN28FejcisytQBwOhEDX1luarOL0c
-l+N3pqAc+wGB/V+VfRPqdmA0caSVdUppsLKPKO4/51oQfJ/lYX0FxCMurhcaRNo9BdjJuLc6zvMI
-81KK94MV+ALDWyzBYstwgBpCaQ8IEc7/LDOyCq+A5bAGO5Xx1ehmZvupe/M+q/7mLtsEta1LWY2G
-gwCASBPgu90N4v02C7CF336Nd123bQtklyp0Adhq8wqLfSHMjN/m5qAatYQCrrTyMgz7Z57dEhxW
-4R3hZXMnB3SnRVjrYxorQKyBXPRwyftv9VikZe0z3VteA4hXwq/whzTSZWM166+F3T1ie38gIDb/
-a/tdCH2r2vPVLLGoNrk5rOQnnG222o5S+zqhWGn/AxTTuRVXcDvz1/a91Ujri3aPSo0+HfeyCIax
-yyE4LcKkU9KDWvhKQLfQdv1ufSYyoPCZobvCvSjehHRIwrHgxyL8P7B8r/DTF9SB6xP8Gq91uIu5
-Kf1iILGC1vjqlNpHQVgq6dZgq/2dCjniY+3xXd+7kDuUV+/EXeP0DpIJohzihriMqFyA02dXuADa
-ZSktKcEA4tnvMqDe2W8UQOny97UEUHfx6Nd3PMq2MO8w46THRLkrJYoi7Odr1ocdjDZHJ0ZcKnGl
-ZwWKQwlZjr+Wx7jlUWvt3v4bcXIJn1jToeT5GO0LlbjwYelnHQBNl6C+FJ3lyULL1uD1AMDriOv7
-5/HLdQ5Qazd33Bff/2yfqaRlbqerhMXFDBMQMWhiQIYcgHCvF9KKWX48hdrK/RN+tSjyagkO6KX9
-Ue/1+15eIVNllOtIxwLTQPiNd+Z/HxA8bt66m7JFVx2Ggb024F+jA2yrEkKCgQMFWgHD8WEPh2GI
-pG/wWhvbPxGdHwdJCA7Lb0Egu0XkfMINDqksmLQCgh8+prHWgrwIfl/kF8wW4AbBEQ6LdvkGyxYD
-7j3cI3wt2VoPMAWjBf/Nqu5CuaBU855tX2p/lEmlZdeKsux2wFXMeYirXk9XCkh8+hrt7U09nFEb
-87oR4TBBsAOstfnBIMKl5RkKPUHtTyTCgua7yWKZulCkj/5b4Durbcr1f375AeFcnsjoXMoaOKzD
-bkVrz3cNNLhB0S56+8mtfDiWYalGSw3pkE8Z361Z+aSY8ZL7DL67KdlzqflsGUa4M4XMpLKos+na
-UTZ1bnbUkLuKKgAtWcyXDCjOi7R6oXSXlTlxZq+v3S7M53IZzssh9NrQt+we0X9aMv9KJwjIanvE
-ufAjpZktRnRUARVWbqhwFZeSs1RpgRixYBDjchlx574cxw6AOa408iCz0HvbFS7gkV4ovXjDEuqI
-V/DleHaJmB+GELfmHgPhlsPvmb6+HneCcO9rT2UTGcf9uHq704BfsupKKO8etxU+9dgJyDWQrvg0
-FXz7bdaQVX5o9+kQFb/tmr8Gzm18tcw2RhXr9ZcVO+ra9/OYpUvY8jR71xzBAsEbPE/wnCWCXms3
-CC4QWxvuZrGgatcSWJkiyiHvLUCuw3CsEB/Mt2orykBc8LcUZB32eKAswxiwJoRPWSZRPjFQLsLT
-RYJdIRZ0LewbCB9W4bGVlRDclRltU4NQ+ccadQD+spyXLLdbnhEijud+yasTiil82/cd7h/X63hc
-PozEKg7cKTNDQYY8DDnGMsq2iyzCcxt6uEtidyKi0eklr708DpnquY/Rl7hBL692/R+L8L+4fN81
-wv6bXOzgq0BVIluC7QdyN4FJ+EkMI6xnXLRRyP04BuTG8dYJbvEBTdQgPY/bwzGRd1M93im6svGy
-YADhcvAykryf/jSaZ8k4j8gtIFc4rfb7ymKntPn7rdzu9S6XtXKnI13Zg6ma6148QCwKaftV/oev
-sCuiVMRAWh1M8fjvjbhUl3OgXf4oFcqI01Eks4SOe/sQx/sYZXQ7r58jtJk1SGE54w+gPdLagX5I
-RjKPaVDkxw8YvlmFLxDcBr/xQLkOx2uka6Ac8xAfpTvL5gaglxavcFnWH37LRaJguKZMw6MVeIeb
-BOpa9jPU+EvYEFyuA4bDMn6H3gW0X8lcI9FFFRqP8eLqXADI6nU7t5Avj4kI3jkbw9tMipLPAAAg
-AElEQVTl75vuh1YVQN+WxVeA6wAbh09j3voanFmESe6Mc3igl60b22da2Ko2mC9nj6gv6bFbRMCw
-WYOnXzBDHLkaeB0qCoCrP8yBcgbAK3yElVwhXoqlGxouE3tZfi8QjLX882oCbK6b+oJelL0i8lfN
-nO+n2jjIMhwQzNbYast3KL5Yey8wy9A79/t5pVc5XbSN/FCNCqVjeRBtF1UeXlcNikHwG7UrZ9zz
-9mYGonWKqKZ4QzH7Gr8d1feHlx8QzuUORHMJeEoFPlwk8lri2wCyOGdYSau9dLeIa1ge4lEW4ExH
-ylowLcDP8QVXQ4BITLFUnTKOMkAw2JwvRrWHuRBavyEn/liFjtOPNNDNETpnH0xMIH3Cy/w9nQfj
-mD6n4/347XmcyzUhluWXbxUcF4Nq4h66P/AtLs/JwYd9v35UKeNy7Gvb11mDaPXvcXKk+V7clH8K
-tLYofJZgnEFhuaTJjVwuISMpdzT/1QG9wBP83iy5w0WCXR8Y+hh+F/sOLzSXigHCvc9VBJdpU2PK
-cdUeroPhwg1CACjNGzxhWNTnHuYvtRHw5e9PCSYAFuohnnyD08dUkhKan/Bt3XdgVti5ZnEadRmw
-fXugIbgD1WfA1/IZIkKeMgC39jwg6fVSKh+FbsUOEFgwQBSu5y7RGMbyOrtbhMXnNpa1cpq3tVYC
-s2w1rgwYRvkK27W11V8Xm5QnKps28HFOmfZ6GRC//GtyL3PlUF3Y61VgHKAefsEBxCt8hG+WYfuA
-SfMTlmpr+XZjlF1MmRbuEbald76KBN3Q8wmeWS4XFwe13vfkA4x4eOT4lLE3P2Oa0jDzRECss41U
-3TT4zentIgVbhJuUQP0KKEcytn6frpu0N1PPSijpeox6cokQUax1UdD/8PIDwrn8TuF36A09waY0
-dpeoyBMjnuF27ifakJgdwBhtH6DGX78hrVNw46t4eBxwgu9CATA/tTWLOKokJyjyfR/7cjl2Ka8s
-M+lFO+cFjms2cKUMTfVyq/0o85YmySLKv9Ic4BF1G5AaoCX9jhy1jjSZJw2f4Lq6BKhSuYkLSH7Y
-qqmm+n5en463QrgUylMPucPvCci4putlVuVoGe7X5ji+UhZ0XU84jDojyjfPiaR5oF8zISj6F/cV
-GX1qAHHbDph12D0hmQA4jlOYrcjRZqxeuTPA31RxG8JxHDgfbNKemhZgcoMQEASfsLziODp0nFno
-SjpBqq2LVsBg+faSfoDvsk/u6lbIOKZweUDwy+UccMtuKQy/wsDs5+y18losmetHbS9g8qVwAHxh
-pWW4rLTwcq37DnCvsuM6NdDSBDpzj9g1iG+HFdgtxNtgM10m1GTJVqR7RFqIR0nX6mDpa4P/ReuH
-6dP2K+A2rMPbZpQ4wHdsjzgpX+E2WM7yZXkktPObmNbugOBwM0Hcp1YvaQPhCHAn2B7WYOX+Vfk4
-4fhzmrAINxeJQsusgxRWXg6nno8Htw7Bt3AWVkqTZy3PeWX4bca/I5uej5xVAv/K8gPCvnzXNSKW
-/lKgw1wooakGCm6GStC7neTr/Q5q0eAhMuKiwT3EkSyLuGiR1gE1IXhu87bH9tsAfNnPDin9fvO+
-GwAD/dPMcR9ooNyv/XmJNHc47ivnuqz+VT8H7FEhCaTvR/uQeY5fjQR0FETCryVwKMoz/Zpx4WiY
-SUKHTEtwxmy9Z1go5u4rPNM9x82zVc64BqkZHK1PPoU9wD/en+gKY6jtWX+Svh0wfIJcrTzwrSy6
-A4JXB+TpF7wIhMNNAgiLk/XbqOKo54ThIEA/FG0sFa3SftsqQLDbP5gh/irf2lZ8jEMRabW9Uj7a
-D2lFhmEQAK+Ap/jNIhaU9Jm4trP+3IaG/v4KwHgQEa6T8VByT0cgvMuPt7bVJ1WBl9cRHIJf+oLG
-vL7rhb3NClZ+vIDYCMQE7JLeyOvWa31yj9i1FQkXCfcLDreI4R7B8wunfI+ipnDWYxOgXHdWf7G2
-j6Ksqk9Z/jGNtROI13pBdXeXiBkOa7CHzT2Cp967+ArTOm2f2ZygCcF7EwRHvWX60PoFyNZ/Ahk1
-ZW22yN+CYK3for4a8XFuPLRYH+z3p6B7F2o/rR27bIFb4A8I1sooCmS5n2nqENIjIi0NtRCqDrP6
-xiwRoaNatX2pof/7yw8I5/J7IBzijs9PJT8JTLlRUDo/bwIf0Pc/xzEMl9UrG1Z0goyTM46UUZMh
-COgVgt8O7crb7OB6HONy4jI8n2Vv9XB2qswnl23Ej0vka5gPVcx50CM+yphqj/gqPTKSsZ4BuBSl
-HYz6CgEbXNYG2YUAlrq60gUyV37S/OqcQMtnOwH4KUwF4GGd4bincY+/G3fb17rNvkhYP6nguRdM
-AXoD3HaqUBh1Za6nENzjGh/B97AGD3eI1a26FZ7HBgBfQDnbTAKtehVLtsDqj1JArNXeUm4NC699
-YQ40JRrBMGjKtMNaDOzhR2nbm6Km8k4A7uUX1vEGugvIT+vCfIBrcTCWTffPQEzlzsCbdbDIAl8u
-Gu0BhtJ3EEbvIww8Dl0vVejLynUpDIiXuQos/9zxEoXKMj0SyoHkRr9mWTEtvN01gl0iOvg2q++I
-qwcYLV9h8Dp8UpvCIR9hfoigWSPkZX7CWwcQx9fmMjwtwOID49z6TmFe0fJVi17X4frBYZKx+R4i
-3R+GuwOVUbMMQwmSKR96B95Wv3xN35YlmNwiuC549cGbCcNSMiVlE8gnWHvJSMsh5cTdRgTalFs+
-8vrPN08N6r+s35k1wi2iZnn5s8sPCPtyVb6X5fxCV4e0sxL16JQywqQPbk2a4oUaKKfpll0LR+MH
-HZORhuKiDFKgnHcY46EXhTl/SoP/GgTrratOWq1fmulmWVSRarvv2yUPa3E7BnYrPo9f4u3YAGP/
-cSHhmOcOsCJ+tTsIIAsBksc9vVvlMr0L1wTmwEOHHK1fc7ip/fjsatYJhQOGJfJ0g+SHZULtd+Nu
-gNxjvJZZr82A9LTcdhsc83kcxeXvsYcsSGVS1+f+1ZXNd+D4AgtyA2WyFF8gOdVL/Iu6qk2VdUKw
-3//FWjvVXiQ/fIK9obZZItR6o021pWS1+tR8UgtWhbV1ZdmU7y9ZgNeGbotrll8FIPzOKuRQyDqu
-m1VQ3KA3AI7SkM9yhPe7/0YDmVgXsJbitRT6Mvhbaxn4+nbHlGAOwAYpbrToDdbAowFqHyyXX4/b
-OwfEpV/wAwyHpbFcJC4zRjTwCtnVlAnC7QQBq+2hwiy/NnBOsV/hA/wy14jp/sAPHTlnsF83Bsqt
-1dpMvl3wdmW+wXLWkdfN5nCUnxd2WX39fhlyGWaV04w+9QFy/05c9j1XFGWBZi1JEIzV6iXfTLkL
-Efvs8n4IgO4eoehjTLz10cN1pyHqEb1aPD/hK1w+w0tohqo/uPyA8G8uZZ3IGN/e1Hqp/qeqLbgt
-6DvjOxDieiwU5GoNvvyBbnH0JC3SfjMmlxdXaqDfY5cI26/ySLBC9zG+eDH1ONfj0sqyl3LzXQp/
-olkdoA77AMEQrde3KGHPYkfzf4/HQ/rcplInKMub5H0pJqN9wBXM0WD8F4/7ISedEejFQnej+nnL
-6WnLac5FjrI+2/yIUebTUPeSgMo9JwE00kjb87L8DLUnJF/CI7e3B2SuxtxKqEW5xgmdK54XAdLi
-Ga4QtT2tkUsIzmRZEYay5u2Iw4yLdk9x4Lw8fewjOxwvZUXb8Om/FuyjDkugW2x6rL2xt2CLYO+F
-Le7Lunx/2b5ugcqyL6SJnSPiPsk+Ty67ScRX1OqLahQXadoxYJmvhUGgE1/MmgCtQWNQxd4G9pXG
-ZRtbU3d3Uejh4SaiVEfa6yPLE1w/E976cVzWagOVRi9rwq4Ch4tE3hvK3WKAdm0/tbnLfaBAjzYN
-pFiUsR2RQWte4xN09nvX3lcIgLPM4NA7r3ndZwguM1VYbg/tF8LBzaYCcX1baVIHa2p3kg/1Rill
-Az8oHzPWnK49IV9LWk3PfhLm9sWwbJuiesxCpOwPnANz3NUHp/vFch1cyl8LjJ9UzD+4/IBwLL/1
-EBKKTVvcdy4l7eiEw2f4ZSy4QjAwLFDje+8DfAuGWaFXvCm2iLN83IGYRJPqtMGQFdn8z+6rHTvv
-rcpgFOKkPFt5ZJxyd3bBF52PhSf5P1VXR4EfxfN99dDYP2Re0g/CgnghowOIMR+67sHHmBZLIBv3
-92n7CZa5fBr4PQmwbwm2gtu6fW6ZuMAxpx9w3OC2ypSSjL44j3FYL6m1ElDo1vfnsQbDgrSQpJIL
-GI1Xyq7gymfWtkDVxyfQLSgpaK3XwAXC7IpgYeATBDMU2DiCjQ2BJPAKtrsPPO77p38Dkt9hGXUA
-lm15mqALAuK27g3c4hOcga0by10reJDaAb3+JYkE4O2Q7O+oN107Zmpgf90GxFzmDMyYxyasUZ1R
-y+P/fT1xpgCP89IhsID4HDRXsM8wjITHHufXyd8cUMqyFyfgXmdYmGG9xCvfJ63cR8B9hKzBozzO
-PCjatGkA7Qv9PkGwHzsg2I/f4HeuM008pK58K1Hygt2ojmkaVwFwHwxqN10P8J9akcUGBIu3zQRk
-7TBsz+nRoAiAoSiYKEtwQvNGPmz+yeUHhH9zaapApnKM4wUJHxWjCKJd3C2mT91E72lWB1+QEmtW
-4Phja3AoZUSXEAi0W3/F2vmEXQt2q0ZuXdCsOP96n0/3RivlLadLg3c6qbxRhvK41k35kcLb2u/5
-bpODn3ebEv0GyAxrafnNTKJtC4wvcb+56Ie9yrZeofYpLsMz7vgJPX8youSW/lymZbfglnD3gON7
-er4eZwHynWOPGcSEYrnF+c1WTCka4XwzAJNlOIE31rQC1VogzJAzFP8lDsrHCwyibBugQ0aY2nHe
-WVVy+JRCFNv9UxNyY3/FvkPwJ0j2WQ8gQFqCnbTukLsvcdosyZZpA+ugv0foXWQlVlfiWvMk47BC
-M+iOcj6OeelxHEmlqh8q4zhvgh6tbcm0BMDZFuyvimG6SwQUszW4W4cThlt+uO2RdCTxwPmc94CR
-JsrhiOfrEbhC7+eecBxpOf/8cChe8nJcr+3nbzL8jjxx3vOB0kH3C03PadaqgbITettMMzllI78F
-HjAsAoZdGnFAd2Z1aGNOwhLskiysxF6QzU0qZokguC3LcICuFhAHCKvp8ue3jv/c8gPCvvwud8gM
-NyUq9zQeYIVZgPcd6P0ibXONGINPLmtc6fYX7TxS7aNb06JuMeY0WuIiMGCpW4AfgPjRKpywEQWn
-BcA36T8swxkP6oCCGhlP+ew4fwHkzMNDWYyMM+i28GLwvYerrXyjcX6n/WpXUvhG+OlYFEuWDzvy
-Fm94/fRKempJca/NsouzHMpNojpdg2EqjysAPxwPgAX9Zl1VMWuD49rdCefGt9JTzt63JhAvXhmA
-XxkGWHkTiOgIH+kGoHGeEoarrLI+uGDpdvMti9ogOlEpoH0EXoJk2g/XCD4OkRNuVc39YsZ9sR/k
-spekJRhboeTQrESbAYOn9VjMz7UB7rASJwzegLfHAf06JJZyc1tnxA2O81IEg82HG/EVue5HnNsG
-vwrdoPur+2llwb+vl7xkuAOjpY+eWG1NpwX2dm5VL22rLBsURz+YfUUH/OqZ5/PYEwSTliMA7pp8
-tWP0fjfjY99kxIDh1S3Ax5crecaTpv8Xlab2cIo1yWNQPSzBMYYAkIyzfHsJ+dOUTehh2yX+5iXm
-7BMkAIuq+Qj/gPC/uXyHJEAK89wCoTS+c0yo6T8AILgb9LVDY/gIB3D1Bn8D4jMd2qpwpahP+esv
-fjaLiwY/JmCS+2D9ZMkNfC+/RQWYClpnbXknPEa+ddEV846HcrLzQpBX3kM5ZP7bfj/GdzqXAN8q
-b/hXs6THr54u4+gu+4PaaKtyxNwPMCAAHRL+xn7dPCnuGScIaZnAJJ6mzYFMPaZ4K1GMbiWOM5QG
-sFF5tWISDp4lOMtv7M8+fbuO+a1LP2/GZfVe+pz79iX8utJbwpbgV4PiKL0TgAlGjmN3OPas9nsa
-Zd7L1NuDiLvie19QQEQfgHe4QqzTNSL2F7lXPIHw9wGY3SVQMKu4+wj7dGZWOGID8g7rcc3W0L5I
-dt0GGI+6YGBMuZOlS/XU64r7mt7W5Bmh8880nK+V27qntassVGNqtj7fbpy/SW4ycE+QtDYiLf/G
-Wd9wiaC+xeVwS9/K7rqt/nFataM9S7923ssA3whTmX+p2ScYD+g1udE1ZMqHOSf5YQEevv0Uj1Xx
-nk0gZ5kZq6rrx5IDBcHq8tzTiliZKcDTrdmYVYfgaGPwrcO0TfUHu+b6cY34d5c7STwmC6Uw1Pcl
-3rek2PnY90H33qUWX6NZg29AHB2M/zgfknnUIEp7V9ELYEokFkMu7AWg+ZH1hF+/9LNvcFfMvB+R
-8hX80sVap20pK+8KNDjLY23D/1shZF4TelGgA4edfAChcFiIz3C76gF3cf2+yCWOcjlg4WYBrtfo
-Z1qO9yCq3lGC0KfUSmGq1bKitMy6wPfB2OVlxj2M7l0qQT9rwvDYmXGzqEq1xV6pgbp8d3voA/lC
-Td7jZvvuUFyW4bXWYQ1+LZt6KqA4cvpo7f10TAla8pZ15PkoPgi1f/FrxlJxZhk+3CAWD5AT+5Su
-PLtGrOYaoa09fg96L+eA3R9wWnmhfWDccJHolmGk7/JnSzDGPj9cIvMVFutb/WSphyzK63QI5LqI
-a1U76ddm8I38xQc2pnV4j/tMV4lDRnB+O1pBz3hc0uat6ojPvPd7svKQ9hv92Gz/sx/0MoFrsNIC
-JRWOvLbp0aZvcC2t5ycEu04e8dNKHGH2DV45MI7GFIyBc3e/4BpEV/qwy7sDih16xWU5FDWzUINi
-9xNO2ecl6HNjQ93lRuwBNNqwCLLdRdyfXn5A2JcneLimzS0BD6r6jzhS6D1uAu19XXqH5eO8dI14
-sATztCqkfJsy9lxquy4g/tRY9wl00Ru7BUsgUbJgQjfgdwGfYXiUYdtPqJ1gQzNJBMN08Wr5yRGr
-rngIzjL//J+ecPlmub9maiLgJ+s7hqBi+M245Q8med+d5PjY3H8CwlQCD2uDi6/SAp4+gkpbcflp
-5WEbL2vJYm8L57n9b/dxKQ/qbxzby2b81qWvyyVhvGmpQzp++fyN3F5+n/MmUvIj4Tfqv0GwGAAT
-BL9eL7cIS4ctDCUf+0PZt31Kn6o7+41vtd8/93O2BgPVDgpuY7vH/sJ7b4hcLMXSB82FRfgj4DrI
-3eJbHGCuENCcmHVaeQuMzccxLFlCluLyox0AnH7DGIDM/Wn0xSg3kkWzvhgC55rQeDmOdq0zD+wP
-HABs1l/Qvd9njIjrxAC5WMtaTG1MZ55PgMw3RyAca9ZjP96sx6cbQysLjkPPZ+sPWhZ75G9Td7iV
-6czXJRz6NiVHWkWk7VvYzVo3UBaCXyEAZotvm2ucwHjAcE45d4PeXGTEt5ZZaRXZP9mIZJdw2dEq
-IuYm721DqL38G8sPCP/mEgqbgVAe9iP9tAbP/RsIdsB9TtfjA6yGk3xYgS9QVgPoPE8OxKbkCn4f
-y+IiGlS5o1gHCtcIg2H5eD9z7YAHUsxATLnS6ap3VhssZ2n6rBG8r3wGq/YmHpT+Y3b8yNIgn1bW
-E4Ln0zsJOsgF/nL3d8OsBP+DFbCR+VHPWXz1AGQsrKbEQkB6uhCvfUsWVuo7CZ0XQM6yiXQ3GK4o
-3rQ6uwPw3E81RsvXcfIQlzJEcPTFsO6E0lsEwWYRfiUMA13Rf4LchKA8dqZHEhXaln2AW/kFOIxz
-VTWtvuEeoevuD7yFLMUJxGpz4Ep3jeiQW3LmI/hqB2QoDleIbuWVBsZ53ka3FKtZkAsMCaZucQRc
-mnmmepn11oqc4fgEhoYneZK0uFb/o330qdMMSmKdPsK3VdVm04iVkanlHcD1AxMP4Xl/t3juA8B5
-rWyarfxH+x/9os7XLMczHzLiJpwLpTMNmzNAXLYMvTKB2D+O0eRCwDD5C8+vUXbXiYuPcHtLelt9
-Q9bfYxttWSTLy4sD0OblbbcSsif1RW/zUZ9/evkB4VieaO+SsCkzMKyWqm5w/CkdTiD8e/vRcdg5
-PiD4MosE/0nkMfLpqp/h0huxLdVSQ9ixkkx0DEUFpGuEwbAm3H+5ZrqCunpIrt+9QQ4E9Xq+H6k8
-Av6RgPMIv6LR454fYovnuqV9CY4HEHqSv4YlAK/D7ae4gnCCL4/8BLh7P0xHdVsTTAqEEjzymFsr
-RfzLeAIJOE5BWzh84K9QOAqWAR+ocric14/0ZaYrVfZhGTAtLS7u4au4vI0uI7yt5FygAcPhDhFW
-YYLh12uZwmWFjq5kWlwq/QFF0YVCERFclkaKevJ0uRvKS8EQCsDnASb3iL1s7tDV/YFXAnHEmevE
-ojQQnKBLeawt8tgBw3ne3UcYyu4Qw2VC7WPN0JEuIOCYPeLW13qZh8h8AuJKUIDGQAbc4gnO+NzZ
-Flp7sG0A8Bp5nh/hOPxpP61NPva8W/5PqX31FVYK8/3NsuBttFWc8Ud/yLWXP5dny09uJ9Rbz47b
-ivOHmQyuvHLbPnoxgTiNWOt8SB6WXv7YS3OXmEax/C1knvrjiheI1H7UQ4lsqUYmJPsvV3Xjs53q
-BZxq2esiEYLFzh9cfkDYl4sd6SlhOyPANgHtGs8gzBDc3eF/B4Cvx7LjTJeICwQTqEVOMw4YDbrH
-3EuqpLvSfjTy5SCdMBz5tv4zfJ3771ixakGFhgji6gjLMBVK9DSMPHk6/qgGkN29wtLEQArXut7D
-0mVMwTBZhRdbgGP0b/ssKdVNg9yC3WxL39ya0t4Xvz770pUOGD7SHbDsJaUh4DwsEXYohuTXxxiD
-qeoaWeb/A4brXlpakRnji/JlPlTVbPHxW2dbBNgnODNObfUbcRLyodoEf8yi5gvus0YkDL8WXutF
-rxcvCh4c/xAHPj/q7L4Fl1DbvwNnzBixchaI7vuroqc/cFiG3VIsnqYervz6IOiN+JZ/XOPjwTYt
-vujA++gygYufMNRnjcC9r1Af+QTI4LIHQUHUJXDEN8DziiiouKyz7rONnPkLX820CruLhG5t/tBt
-vUElcI/jfF/CGGE7LhlR8XLCMDXPIw8U7mmiH/DDimavBZCv/OdUapEPjHjViCvgZDeJ1P5S+/km
-F2SxRcErwMYScoUgF4nFg+TYsHKdRUJAd88SFr11BeTe4uqmzWcYQ9jGdGgFwymPs93TNWYj+IPL
-DwjH8hscnNAIhlyKy3BYhDoI8/YJbnlQGbs+fEqPeOqjDlTW4L6fgBx5ofyK+HRp8Slflx69iKoR
-64gvwe4WBs6vMhRTuSBEB+1HnqLzJNQoJfJOqvX7XbxyOtBr+i529QjNNB4jFa5i0IwpMC/4bQ8h
-0p/k87OtA4TXWnUNv/HcEuBW+zrTcNsDXMivRdMgFdhu1bT0JhzrsBT78b133ncpdD63hGycc7xd
-9ypp8zUH6HNE1vkNhim9tLOOEP/yRzB+PKgzZ/1a8nUcz9VZCq+3C/7SHA+UYxgOII4r/h4AX9JG
-3I66pDl7/YOnpqQ07yD6f4GO93mfs7d9QW6NuYSlBsxx3BquE0vKNQLKD17+Wwm7GPuRN0oX5wHH
-dGlPX5g7pkxTmgP1Ar/TLaIGzRVgdfg66wLzeN53l2yR9hqPiq+dCXx1/ZoUo2AwLMH8tTmbRxjt
-nhtM81/83jVv3T+Y88rpuoTmttpuq9J+49i1X8x96b8/yz+PCe+fQMzx5fcbT8Fh+hmD2EM3XGA4
-B9BKAXCmv4DuszXY0qs62odF95DQVV9xjKG4AbL28rDTLD5lqpJs5HY6K/pfWH5A+HeXpu06kJxQ
-fAJmbDvECoUH6EqPv53HoDyfKiPcO5qc8TOvEPCLjjsflFLgbbPaWOhu+XUx8VQWHR4KkeLp8rAK
-cy9qkKyZj1IzHiK3iFSgdK12bPyOCcPRc30EoDjJx0M/P8GnRTjnhq0w71ddgervFr7HVf16rhl6
-94aulUC8HZIDdBOGRQ5wlgSTsiR1K5fBcCnLUnrT3mACVKsWpeo5IgpOpf/PUyg9Sd3eZkkaj+UT
-GNt1JgBTfkecPMZ5uLUJn0klALhNoTbg92V+wbGu1wsADZYDLtvfBGExiI15YhUbqv6mgMu2QQ61
-g/hwhQPk48cyHuLeW7B8wNw72n76CKPLF29ztV+g/AmSAXTgbRZfS3MfGKfmMoEaQBfg3wfJPfWL
-D+Goq+gbcZxa6zUOZ5ylpRkU4vrUBrbLzpx0h8C3VmQ9PvsI18C4/Eob/+b4/cxzy2vEDR9bBa4D
-6uK+RtlUedD9znLLssexv1u6uh4Qst9cHvg+SldguEOUSac9xMtlnuA0lAwD1txioc8m08NXGP5g
-FTa9kD04JEnWhEY/ItDNewxrEgP0RYgSIrUCPZJSg/gsi/+55QeEfXm2BI10F8gN5V2wW8fWRxDu
-IHtae+8W4Gcojk5VT5oJxwzCmHDc8x3AksAZD4SXRce2x7mSURssFwPlltbguVo7HIcAiQcBK2PK
-0+w9zS2ClCBKYWfeBOUeoaCjoNdgdTddMOpTUXiKCUj08HGxANaT/iIwXmUd9roB1ROD7nGM61Pi
-wcFRbEIvhVeE9y743bt8fD2sagOZnhT+CcjoULxneVVJNdAVLkehw9+A4XZNHTFPjfmMr3YYP63t
-l25xeIhLnUfXrXpCTZIf6wWI28C5F0+fNrdfwHHsN2CJAWymCrd9bxg+fQBEzUWh+pWXF9V9wPBW
-8gO+zRUst7hlwOVxS+aX5TQJZVp+K6x3YE4itLzPj2LEsQJeuQyMU6yYcorSTuvvnuXR9gnMWl1F
-vql+snKf4tDj6lCDNK5rzPbAbQF1H3JZ44tydY+V9z6bxGhvoN+m/jBne8A1Tb+P47xxjRbWfu61
-P7S4un8QFGoqwHEfT1A846eW845/6IMPELx8vM+cQ7ilYVeqKxTzdUs3hKSaML0JsKAAACAASURB
-VDzXCcZ5z0PsTu3XQiWu29EmJ/HvLD8gzEvoQtaJM66orAHv4fLwCMTiltGCvAJbaa4Q9+0dntNH
-uMEuTZfGHS3jCIAR/V1c312A4RKlc8+Vkqp9kjS24QphQ3w8z8ruEewWEWXDooSgh6C3vpdeeZCZ
-pwHJ8Yq0hB1oH8d+XZDuVsc+FxPJvgavCcAdeI99h50b6Fb90T4B72FFZqHHILw3tgOwQZA9oGyH
-3RZ2q12cK3GtQ9GTZfD4c5G6jK2ORkTSsoIP4jLulyvnAGLuwLPu7tB7Lp4xOdOcAj3aq2RT47jj
-x1h+SK/fUmqu/HLatPIPfoVrBAMVHhR+bqdFDGTJU8gWbAjUP6Ku25gQYtOc2WdVxcOjftUAWHWT
-lffiCiFzPmF3n2gf0fCZI+jLcgm5qP4bD11BIffweZ66FXhOl8a+v1FAe+uYWzgLLOfWvQFvWRjH
-sdhH7GeWqQ79HrjfTLBDf0Dnes04rv9xfW430y9YN6CrW4mnL/STb/DGKI/IjwL8gQ/OY/SSORND
-Ha/0fL8cb+V4+SzELCP0vB0PhpQ+oTbjrC2az7L0fVC8dI1Wciq1W9uvAe5dRxToUrjNKPMMvc1f
-mAGY3jQqbFyNhkwH2tfiov+kK0T0J9ep0YarFv0aq981330eEY470//p5QeEYwmle9dwtY0KZPgI
-MEmAK2BZFL8yvkD5ANoGx3Rc71DM+wW8dqQgSlqeejzdA3XRLUB9WBxIaUAhzf/RQWzPPt5YnSZe
-sefruCgLXAC4xXGn0SxHKDJdr0OletIC4FCE7R48vxJ7Hq9jn9K3fRn7Sr8NKueEHNSTeX4J6LT8
-Cc0XawJsAC7V2WFZQKXFiEeAbMDvstfSsV2xFemW4bAKj62ShcjWsCYCupIrgAm9sDbeFZZGJSNK
-jyqVDkmL7ilH/82vJfHxXkdNzY6mxMttABy7c1TcRWxc4rKnkaxgS3C9JYiZI2ou4XSTWC9T1MVs
-D1vtwDu2Evv+4AOpotgItxCxqRSlA0joSQRYBBxu7b7B0oH3+MqcyJhuzeJE2Ee4ZMpj2O/3Mewk
-2QbIKQ2eG3MEo/kNxzGY2wTs2GkRfYDjLGfQMYazSgOuJwZ5vkZUAYWzTkZzPmCYfjPmD+5uAqd7
-RHxUY84e0e49IBMkX0d+5j4uxyyulG7Fy8N9EUCT4FaKS16j8jvrgx4upK7hP51qUD/tR1xaQxCa
-CwUQtZ7yeo3PIgfQrmb15Q9jtPmFb9A7rMHxhhgJwe2Ri9Yq51YGqAfhAGIqJv5ncSHn6FDq9wbE
-tf+nlx8QjuWbpd8qMmGY4Rf1yhsFmYuPoQD5E9h2a+8FmhGALBcQnsA7rcHS8ox2Dw6tl/uPTp9S
-ANVl0vbHUOyWg8jrCghWnkKNAJg7jdAztdQvSYKu55sHzIVm5/wOAAaUrqct/yzQJhSPUuj7N9rJ
-vIaQIqhNAbaGBbC+HHZMuYZxjasgvacDcMKvQ2/bSrcMiwM0bw2OzXewXo+SQvc1BiWFJVrhXHzt
-awWyB/QmNI60XsD9ckL1kVRcxxrJTTjm5L2OGYBl5pDiMr65R5QqLNkBahNVV80ton1euU+d9nqZ
-n993wVfG/tyGpV8AvDXkiyXY/qGaaFtH6w+QyFkFNpbSBzUW+QKzFbj5DK8DnvnLciFTmBSbBZgJ
-B9Nq7PVJcez3C+iA4++7TcyPS0wgPl0lNMsMqPPi1gogS4bWMZJGBHoN+oBxPQo/QmBBLVuAI4zL
-V+UaBB/X49/S9pvVaKxXlOzl+Iqb9x+9qclonWkvDxUN9fSS1yp3CNUJkGIiwlnmJDPibckcNBfa
-TF0mIwGZdLEDbDdsPEFwuEKccwe3eYTjgfrBGmxvj6tk2F8YXgboJZYF2sbVUL2EnilRTqAr9CgQ
-YRlpKPynlx8Q9uXbhZ8wSZV6VOwNfAmARdI1YkGu1t45EK7AeMCyRPgJcs840LFmDWYlrYI2T69M
-iAiBHmGlw2HhKPHUwbffZ/gMn1biyJNS2K4f9XVRyz1MU6XxzBGqmtNPkXo5oLhdOZRYL4GeqDJ2
-Ausi0KGJzvmrYbY12EmBFvX0AMHfWoFyiXDLcISVQGVahkWk/IRFINtnEVAXiLterSoIAmySVrIS
-L3/NvO/uEV5o0spRzmM9QR2VnjZriDWZJazwbDp8ulYwEh+wy4BLce0+jruAt42SHzx1mpCrTFiD
-wy0ipk6z9vEaINwthgv+ej7ivtiKKhnv7ea32sAwUQFCcbLyj98lQNp7k0X3CXh5EN0YNPeuuDd9
-Wa6DsG0ZdjMu61xxA+RMr+H3S/sJu5puEzFzQqQXmkaN23oNmOtl0lwlvLzaXLz585X3Bo63fdA+
-0Oo960Z7fMOarC8TiRtA+4CGwvy11+kjfLpIVN7jHjf93gm2X31Qo3rRPA4d9+w96iiLdv4Ac165
-7ICsl/b7LC4mEEciocGJUifGBzRCctwGyk0YNqBdJRdCHqTV95xDOAGZwxN+hxVZ4rVclmFrIbhZ
-idnQlfsSIB3l4sAfgAR64A/+iTiE0RDESi6i//DyA8KxfLPwS5FNeBQC3AijxS0G4AgjrKQDcrX7
-Dj8BcXONYMC9QS/HHRDc4ySdhUq6NqGE6h4cNp/gCitAoNvBt8TELTwBmMIadZEB9HmENaE3lWHm
-fYjeeLrVOj7vMeNkHuDr9kaS0Jadu4Mpj/qVw+pHM0h8AbiL9z+lBzr03sBXumVYwhLsYaX7SF9j
-kA+hC0mbkQLAXmYR3tumaFqwDyRgY0kY47RJPqH/Jww7YkqPaUcFqaHMh08rnVIY2nj5WK4w/Cms
-kbsjLtIWMPPDTa/H7h5xmT2CfIUZArpCt62M+K9AuIqjYM/eCpi/bsgOIEb1T4hwOHLXiEW+wTq/
-ICdkBWZ3CZpOTaS7RgBs3UVmwIJBK173TI3jvBUy4KOPcBVOvN0o1wmpWSMYiBlyox4mFGc2OK7L
-IKX89v3I0t0i1/cLKLOu+bbiupmP+sxyWIax40GIwHdrTqF2fEoZ2rc6fh+Vt4jjfILScpVm+nGc
-z8t7p4Sch1YW4Dzqmd+ojLAMC+WnmqKHLaKsw/UuVUkC5FflAoLhD5c8jqetiyBYas5gMqJMH+Gn
-cHOLWDVdG5fGxB++774EIlPbIUFp+qLktDgglQ4sIF5j3/I9peifWX5A+HeXaEOIh7qh1DI+Klhy
-WyAc4WfQTYsyCHb1MxDfoRfP0NvSRJ7zNjPEnyW2pcNixJSg6jC8vaPlJ5u/guD4eEajkQhHr9Ki
-C+WO3EQj7Yf48xhhe7Uec0eGsrpdEUD6CLcDjd8ky3NahTsQl3Br0PN65fHfAeB1SStrJQgn9BIQ
-J/hKuUXk9RxiheEYyLB9GEEh/jUuSWuzpkU4/JI34ABsBbPgfqla9TqRmAqUdmUkGYIz23Roq9mG
-uFIJlqeClapYadnh/FbFRzbLBsRZL4WT8iHXs+6OD2nkIMqaQm2C8G8B8DzH6MbB5uVhgezKE+tw
-uyPvUw2CabAcf1Bj0WA4ublLnNOpidSX5ZiOFLQf9Ii7hdg2fZ99fqFi7Rd9ujSzBCPhWKbrhArW
-7gPm7h/VKDl4xuMAtsNVgkFOSWYxWFK47Ue5UJtu9a8Fv3wPPJ1aWMGPTyrTfbQvzjEMRwsZ6uMm
-odndIR+0xrntvMsX6Kakb+VB99geCtqxKuNQPe03Oc7o9rAAFxQzDJ8QnA+VYkD8lXznmSPaYLiA
-YQ7nOJSnGSNi/FCVnLZS1Co0Op5/cqS0WiuFZ/rPIlPGMewm/wQL/YDw/8ry3cK/jgMfarr+buDZ
-AEmL56i7tDWahkjnP1D6yEMMRosem6+MvbXKAbVPS0gIG0ceEqPNPxmKXWOAHAsJV/sOtQvRAbzB
-gyzE9OCQ952dguLlLBu+f1zC19XzZ/va6rPYitwvqLxCvJldk+ICyP1e8iEl9qPz51bw8v2Xg89r
-cTz5gE0heZkKZ35n/nbe9jwrUO4RHpfHPD7TxeoKotIp9oqteYFnGlUoNrYuE5wEx3tt6F60JYDJ
-8v0CdCNO5hHe81pVzQo3pRiVf4HiS1z015jTu7sj1XY5ALNLU9hslw80e8HgfzmIvTKd5vrytjXj
-F/x8P3chII+hp+9/2kbb5X7Lai+7PJVmrK2vz7BUmupzPBVXyKmKSxpbNk2FikOFCLDfUFn9Bv1X
-03J32Sb+HecBiHmRY/HXEy1OFVuXj4BH+gNzvlUMovd727rf2G9b3++37e+3H3tYdZe7Un71cdNc
-3vsyMHVAHSqu6oMglM5Fu06HeHmIC9jPbaQ58sUPBudvT8u4znqhJayWh6EppwsTkx/S35hsH1+x
-1sZruc/5S/Ha263ZNaAz8s95FpHsbdETVVaFYVZczd4tvk/mHJGsh6a3hpLp800MmTfPQZFHXF3z
-hJAF4nVc4QP0t/v8e9zzFxHvbeTpmOlAk6tLYC4TKXfLoi+cZ+kGsIUaVP+nlx8Q9uW7Ra+A+8y5
-mCcn+Qd1PQKMygXDkeS4hn56PgrhL7mfej4Qz3V+4l48wvpgHlXPiWiGBTHYxkWquuqMJ0QGYph4
-SAUp9i9/xv/S/SNAkMMBFnwMBJK071nt0HwJryOeVq1ZOCJtwAEDboTDnWpzWoJ/r4ksg7q3vhr4
-CF4c5/f78pUBuSD6Zi0AbsAbDxo3y0JY2lTKIqdSFroE3YhnID7iCnRzoFwD3hsk2wcadm5d0bc2
-PBd52H3qF6EYCoL7o472NLdjBM+hxMrnH/ct5rYeAEO1Lij+cth9AXipHutSxUt3hmvdEN25LzmI
-rBohzfrVoTdh8Dnt3jZNnm4DrwCm0KTR9aEGsXEh1u3vqJdlbfgvSLbrgP4X6p4CKDMccxaLz1ks
-An0LJDLLkBv7AMGu/WuAdQPh9MuJajfhpKD7XXJAb+RRVozstz70dvDldb8HFPP+3gXIA5KVAJnB
-WONrf7l/gm3dpmbR5C36/3IF+AzGBwQxmDc4n3OKa0/31bUjzykDCiTbW7RtsKtrQdfLzl2K18vl
-zsvBlvy0QQ8H2fZT1xJgI97cLrzXG/XouRzi5iMpx3WcnT7QBcEWa287PVVaZAIPdWTU26OYZFYs
-2FcfV6nivW0OboWXt6T7mUD8u5AhK0PjKfqXQL8PvPd09lsaio/If7m+0QUqK84LiqW8L6bx7g8u
-PyD8u0voSvRVgITiiV7XP3uEujWL2t7o/BIXT2SKeppiNV8wHNc36M1UeTA6dNxXCFgtJRGdmMIJ
-k2IKNts3AEMBJXAoC1uCnpcHO9I3AJYOtHXtJxi+rXWcoSXgDyDwpfgzrM9wjAG/GGseM1B4eZq0
-AHvcS+KBgOGWwLfFE/SCwgOGQyhtkTY4LuBWx35ahyN93KMLXhvwplC27OJiDVYk8G74l+tgkKxr
-Y+91upk8QjGn+JRGvKYeQJe3KtTR6ljAcz2IoLdhINtxbjEBOFQmIEoPPDog2OF4EQy38PaVgXgb
-LJ7QS7f6JSAjAXG5Qostg3DZ71jFe4klu1gbiweuaMfZzgVXC7g9UG8zLqjYhzvUygvpH5ySrIPN
-AcJauVMqCO1xZtXbddwHAmpYgFUhuoD4KqX71icEx1YW3logvH8F+O4rFL/9mO3vtBi/93tYih3u
-wlqsZb3kz6IX1AIBufkg4PeWMtwfZpiTGYgPS/BWqDhkSXxym6yqBME5ZR7l7Tal3ASsG7CDdMB0
-IdMdILyguoDXAnRBXy/Lg8NwWKsPEK6fuILwkoVfbwdhlWqhWsBrD/V07EibGtIkSIxdcVmSOkwm
-NnuaAcDq/QP+VkQdeA1kDYhtRh672ibNuPPXpPhEzfilH0CYH2C+hmL+2igKhEuZu5U93vIAuurh
-AZGvrA8fnPuHlx8Q9kVnh3xKF/UNdB372wtZhekyE4Ifofi27wPcJgJoKHxPqN7g2tMhqhtWd6y/
-UjwdgHldIcUuWcvp4hJ+ub/INa5Z21KIRKl9D3onAK9L+mkFfobgT2F+pc3g2y28rwYEDL4MDkKu
-JNPaS0CcgBbK4ozLeHwDgp8AeLhRfAt0BxDX9GqeTnzQDX3g5LFtH8tXnW4Cb+CBXI61R8Yzjh/K
-olxBch4Bv9SGOQ4RV3BbVmFfA3wpzcth97U31tpYvp/rMmW4ubz0GXpVByQj0nodOnAjLcJm0Uul
-7GuTU9Qn3942t9jnkV8i+EvCIiwDgtGs3WV53e6T7IXtv6ER/gC3FqSGc0sbgV3X13h6Z/AVSeUu
-ewMEv9jbXs8vexDZWw1w9wTd2u9AvOlYWIcLim/W4QOKJ6AgILc/vES1Rfs/IQZ3uIlZP1Ssv6qU
-dXq6bGzv0w2eyGK8P4FV9MuqN2tXBagxi4LKbjAM92PHi8G3rst8eZqhanaGmqHBwq+3W1UhbauQ
-AmCNsDykrYfMdIVMGaKI6RbvQIzKuKo/sJlcTYNBdsedULvzQXJZfeyp5UKmrehUVVdfwPAdiPtx
-iX4q4jNSmDDMsSUIy7BAdwFx5EXg5zbDxJ9bfkA4l+8RbeLftP7+jjW44RzqDUnsP0LveILWHpQr
-BPPWIRjqHZYtyX2rKaLCwtA7qvj1GCa5LEOXxW+n9RLoYJHpuhWY07SyaiVLv0VxN9CdABxxNwDG
-h2Nh+WYIZvE13SECiF+QBsjN8psWNLYSD+h6gt8b+PLW/Y4BA+K90SzDDXrJ/UGBfO1m1t/tg568
-LMIaHC4OPlhOc2YKtddhzS9Y66tVlG6S77cNAo9Pr9zikeGAnI8wrAFe8UjID2dnuNqwuoodgKzV
-D5prhD6smC4RekBjuUZoB1+G3rhthmT0NBHOmRO2wS8PCjsg2Mu8+mNZugyCra2+AoIFzT0i/Jwl
-tgOCbdSlbwE0IRI3cglrBT6mS0G7BLoVIqtgOKxu7hah3o8CaBoEb4O0994OwcNFYu8GvtMKnND7
-ntbg+DpfWF43AeWHNdouFwFJcKX7T4tsAjNDsObD6d5mzVTZ7lM7LMNXYNrNKptW44d8tyrLFuXb
-JtMIhuOLPQ7Dr6VmDb5AcFuU2yzDdg1WfgcIKwx2HXCNsw04t7/diePi6UVj5h1J/ZE6RzRlRLlG
-lMblDDcrsSl2Dzv4Clt1V3XRDez41SXAAcNhBXYgPdrV/hJ4qy5PEA4IFrXfF7WBsGkRXjAIDouw
-EjshIJi1/Z9bfkDYl+8+g+SIbLn3tUyHC1pLP1ZVPiwtHE5AJmHOAEwA3cG228Duyt9vJC8o7Wjc
-Y6kZ76hS145Xv+EWAP/tTVCqIgS8BAZj36BCxn4/3svvEwDfoTggmMPfAWCA3CEUDYIZig+XCIZh
-sgyfFuIAZSmrcAAwLvCLCwyTgF9jG+J3C/LJ3CyyZRW+huHuCwstnJ9XJT/gb1uDdfoN43nR1tz7
-gbQUXo9S6Al6BekrP/uFL9mWojxRAJxtKdonlNpXtb+l1ZanO8T0D2aXCNvuvg6LcIJvygYLjBnA
-LulquzSmzNpmFdYdc3/VSic1T7/sm2H5sjYW7T3bPIAlfdBfWMkLiDfMPVguI1Ep0w3q8HgMT8fE
-v5knPpZBpNrCEkDtjYUddwBey5R0grqB8NYC3gDgtATvYRWmYwXFTzBMUBk+w+l3uxvAgmE44Jeh
-WKOsyAr7CNUOW+EaoWwZ7jNHdKuwP9xegL2lz3wi7yHyV3akLsd0LfePD/cAtwZDAZi/8CvuLRv8
-idf5JzSLE1uDZeG9dkJw355xVi72zBYwHE/xqf+kHpJT13unCeBl81PJpKIL1QBnb9aezG4z6ivg
-1sMwCG0w7IBa7hF30G0PL1sf0vUHnCVoEJwPMigrsFI48pH58j4oWiX1J5cfEPblozLmdLOOiiHP
-hGQh7nZgyRPiaF5ClcLjNyicD4qDFK4QrBQ+Ms2Y7F3VT86J0T0zSqcI1OcLtCXGn5jFlGDY4fEG
-whOCr/Db1mkFvlmKn6F3Wuwky/DZ9SHuLQB4poWYpS/E1tNAuRowJ7muWAXkU9mtxQnA4ClmQpjP
-/edjBwjDPmxgggnDQuzgmy4RYdEFhbe/prwMllMrj2419kEeFzCGKriJk/Zu/Yqj2zL4tbf+z9v2
-/kQDrmu/z/LS2+VzOw3XiN7OYqaIF4C/CIjXBGKwS8TqfsIrgNEHlHnDu1l7c55mhl46nnAMuFsE
-Q/AmoAhQLQhu/S7LpQAmZ0TxcqoBczTsKOBX4av4LBKbq53qBCTuLuD7nf3I9Q6fxQBeA11V8dfX
-cdzWLG8R1CdqDU4DetP6y9bgWzwPltu0Tz7C7BIxX1+nRS7+EoaZAauCwyLM4FkgjXbdOXNEfi47
-Z1rYbXvOcsGg9AxZBcB9aXqB5Jo7b+cTnugr+5UuBZiNs9mK66rxJ4IFdon4ZdZgtwhvBd4bBcAe
-fqtkmOMhqOl4FNa28j5ChmiGE3pdKndF/xR2+SnFyVaHy8dbGGBuSFqDF8xCfUDwEix/uLk+qFzA
-N9vgExgL6jfgQLuqzM2thWDYXTkyb2QN/gHh/yOLq4asvNNbtlck73XFGVinlMY7x8kB5TIxAFj5
-GGMAXaMpj7hqo/9ylYhDKUsU5wNAdEjy4wi3ATvPn3MFJBQ+gPB3jnPcES9jezvWIWYCb9xDswgL
-wQIB75YBH6ixPYevMK8yrMFSljNew7f4hFoM4D2312N4gF/YK0f+/LLBLlmHD8twh9kE47AK8zkp
-qPvgOnOPQH6COVtyMGgj4Gpj7QXGsQRQR6Ln7bdhWXp7axbhW9scbbY/eJEVGGgW4ZpWbeMV8Bvr
-1vIT1o21V855e4Jvlc9hFY4HlKFrrR2TZmcIJosdF7x4v7f+4q+PTf/655FP3/jWD5QeGNRcPdzj
-mWrerYdybwt5D7NBzLi53yqwgDgqW8dxOeIKim3WiF3W3rYltwmeRm1C8p6zR9R8zB08J4yUJbjk
-O/WViA9C9HKYADyhNwbK6RZ3k+AZI6Y18A67py/zJV3kpSom6wD0UJXTpvnHPdYi4oXipS8LvrQV
-Qcp9AqyyCJsV+Jf8cmvwG78ChDfw3tqA+L0VawPvRWDs05Bhe5v1TqgLWJv0HrVp0/UmX/j9a8S1
-ekop6NLKfdjDNULgMAtpD7RKALzE4dj3VRyC3W3tBsDPdTbqnc5f4vDr61quW/yONxQrrMB7ZUno
-6ufZ1BOPAv4fW35A2JemeL9K51T45MuY49ByM1AtOmbIX4S9rtInHDf4RXaSua/aoTmSNW44AwOi
-pXSGFAQ2PvB8g+4RqO4c8BzlE2Gh8pige433grzBRZapPENuL/Eef/gLM8yC3B7ktAKnfzCdw3ET
-dh8twwEHl7g8hgLgGkBIMIwBxgsfgdj8c8VnathpAW4Q7L7ABa5IkC0Avll3w10i4OzuJhGWqa0F
-zpqKWqhPURsXB9fcP5PEQNE48MlTnreZjtwk2n4q4i/gN9vp/bHY2p2UW4Te1gsEp1tEWIbdUuwW
-yj7zwwTfCm+SGze/4u2/dUIw+/HWGiW1qVzy64QBwCiXnxwsF/clYRE2qcPW4aL1VfV91Y16j5+w
-jDjfcZErE7Nie1ymBQ6ABoQswgTAHi7AddeJPYF5WoIHDKc7ArklHJbXuQZCdYjq4Dm2DXTsnsoC
-Yq4gBegKtljHfLxsOcxZJEbebgP9QHmMug5Y1QBgCfhdNq+xp47pO/Gi9kGv1gt8FxZsTvZf4Qss
-b7zXG69fC7+Wh98vrxsD3/dWvDy8GIb9mIiFMzPbZcpWn0GJrcHnFmklrrYpdSMobeRbh950zwyZ
-CfsgjX0oyZSWUDsOdwVVuKuJwfAeMPsZhsfxeZ4A0JVlvpNhTE8ZDC8LL4UG9AYk+L5ofMHyzy4/
-IPy7C+tbTE71Co0n2o5tfdsgjqmSkDwvzlBcwl85fuiEGmCagfx90gl1SLkT0n1KAW5bghscxtTv
-qSBY8vy4oYKFvo+ECx37QmXVy5CvVdOr9e30BX4CFF7SSuwF1XyBZ5yUzg7r8W2wXANc1HzBS6S7
-Sgi5SyAswmwB/iKMOwQnCHtdbkFahefWXCTgVtvdtuUOEXAcVmI9BsGV9ZEV8QMQeztqsJJ+N9Gw
-zTrVG3kwT38qsyhFe2Ib+zqPP22F2tgE4slR5AvYV4djpfmDcfEVPgB547UXXkvTN9im71IbsKVy
-gVpFG3+o1GYpTcGxH3fIWnubyYsgOOYh5nuKiRdqLvGA4u6ec/jM5xqgH1ZhM6XJ9vcx4Ws16pZq
-+AGOuc1cj1RbSCsZWmVqCiHfB0aFO0xPEGYAzn23Ck+rL/sFZ/y7IFP1tAQ/WuoCfgOO4l7HMe6L
-X61sFQbDUHd5CFDP6dOug/ru1mB4XriuxOsj+hskpn1cWKL2wQzvTzn5h8KE6ljYAsyW5bAGr/iE
-sYPw+5dvHXRtRbklvdWswL6+y6enINibl31QAh1+Xa7E+7myAltHktaoz21KJnUZ5jJT87jYQ4sI
-1lLs9GU3xSwLZREWUB1S/R5g/GQhDteYAuF0h1TBVntoWQTAlsdVoJBKtLT9j2vEv7zcjAjXdAF6
-GUPq4SazM+r28h50hCzCAQaF1/f+QaA8MkX3ow65lITvtZ0zKMMVhNbn6vo9EUgwQqQyaVaWE3Tj
-OjEQoJ7kGZD1iLOn/lGSUmnmegPieD0bxag4XSRavFyswF6Y7EJxdYUguK1PbdfxBskyrMXo06jZ
-Oc+uEJ/2zXIt7vMcYOzWYUwrccHwblBc0Ntgdg6CIwsz+w2bpTgsyr51ZahhkVUAPiCzLLXwNKPN
-tlap1Fp6PMOtjv3btoadzrZ6gi4QbXse89x4ew0raHOPIPA9LcGrhUXZIly/MMGXp0ULzpjgG/Bb
-x0zZ1+eHzToc1vr87C6VSZRWfqJbxWAl263k8YBfsxCpDyAMi/CGBATHVGUpJD4oxavMfmwcfQlr
-b4T992zrSCIPcZR26ybrLgHwAbg9TaZ9zynTHE6mVXhPEBlQGZWNqvs2dZo9cAAAIABJREFUhiR1
-hPe1bB83AL6F+8C46RpxTsN1c6O4zSV8qRruaSKAw6/6G4Jo7ikKXnXe8cdyEB2CX+EX/Mu3KyzC
-ivdb8WvvBOC1HYD3thlb3gTB2FWeghw3kQ/IIRvIEhy5j15cmq/dmUkiUtohu0JKwQfJWS9SB06z
-EIeve8h++8JlWNpxracE332rw5sPsbUL+ADqt1oPj7xsibozP3xrS66FzcHb9LkpHLu3p1ft/+Dy
-A8K+fNc1opptPZG19puputrsypSPaB3RaOT0evUGrSTYVLULvJkWWofa8ZvCGNeIef4o5+lJLNGR
-66azMycEU5ivJOj7vM1jgTR0bnvtfCvTv7fm7SKErHYoFgIKDMuwVHVcLcIP6wG8cvoK908yg4T6
-Le60FK9Fg+VCGIIAeG2H2tXAVddKi29ahNOSS9A7fX0TkGtO4YBf/uqcDf4m14lU4jKAWDv4Slhy
-K06bS0Spi1unvB9Tgu1wwaj9aJNXK3C2oUrLfVog7l0R7VZ8ejQ0K3B8MrmA+ITg5iKxfRopWMNr
-1l5vpwXH1XCfXCLymG6fQs2oOeE3IdiuG/JJrUoMurzd2YNWtEPkw5tIPIQq4pPTS+M7XbBXoggY
-FjcvR13IvVb1VtPnY44XU7WEbEdVU2EFPraRHkhwDhiO8nsnAL/TOsxA/N4dkk+f4A7C4QKRQOJ1
-coVhh+B4KO3uBhRPfc3gKs77YA1WsX4Lodki+mC+6RpxwFTA85GGZSopHRLMEo1oC+Jb1z6ph59H
-DzPVW0s/uBx8S8wb/DaZSBD8Wi9zk3jVzB+/3opf7n70623++fJWiGzIXpC3jYwTsa0q8PLZb8ww
-UA+EpQq7JVhkwi/Orfdfa4s0D5NyK4eBqAun7bC9XEDtlP/WbvmrpDUv9ZMPOPukzzcB8xyY2xQM
-yG3cgFnxE4K3PczYw5VPW6jIexIH4mFz+yPLDwj/5qLcRgHi1ye8KkAssz8rzQG++UP8oxQgCOan
-++pDI4MH8F7S3o4JhgNgRachOKA38i0U8PNiqphIVdbf+uEO00pp/fg4X3KvgNiEy9hv2+kjLNfB
-crEcFuHKQgGFEFB4mUyLcJs/OOPQ4LVNMyXnHMNhBW6Qm8dC4J/H7q4RBMBeEQqQiwRZhNlqOwa6
-MfSaFZh8fg83CRpIl9cjFwr3dw2wA8h1QdWUHbXVaHrcXK0XDMClLSuOJDdKc55LVe5lH+02FS51
-j2qPBMUabdfbbYDvgOA2aE4vQOzgG4PmwnrqvcmhQttguY8uE6Rvl5+rgLlGOISttNjZVmAWn7Nk
-7Y3RUlPCC2FxKmmX0ySiYFiU3tQ484rPgSosd6TDTkDT7Tn+JjLvYpSBNvpyShvSzQOEub/A37A4
-FE7ofQ/A7TCszQ3iCsLpbtAHnHWL6i6YdSCL+uKHgGgIHYjhRpQ4ly194gPnwv0moHdDLzDevx7H
-A+VGfi+D/FpFVs+hjhWyaxUAK9+W0Dib9/HfZi7aCcRmEbaZcV7rZb7Bbgnea+O93vbGZW388v72
-670NgmXb6DkIIBt4u9uI5+XlZZcQHA/A4ncVbhKhw0qRUo96aLUs6/jN2SG7TFZuAl6e2lAjP4Kq
-781t6wGK90N8vDUSEADPrcMvFnGF90OlbcrnP0/CPyDsy+iPz4vMZnqCbyTsuDuPUOfQUGlDzbA5
-jBVY259wXHEFFiMt9yoXmke8d5ywCks4/IoVQIJvbuh+pV5rNRCWKIm6kV5yOkqqoIIz1kv2u6sr
-5dhqV87MCDxTBB7A95pGpuVXcLMCx7RSNaVUuEVMi/AFfL0M+/VnmvFKUOwpW33cRXOLiI9geBhs
-EcawDvuI3uYC4ZDFFqkaJEdKFzr8hUkQo6ybbK1rEOyb7BqjlfTwHYZLaTynyd/m9ifVCkk/c7P2
-U6r9yiVs1lAcU6YZ/Br0vlDW4BOGzSViybYR2BCy/LqrQ4Kvt0+SEzmY7jhuF1hhDSZFF9Np5dRa
-UMdVq8MYypNvG6AFwsEzR1/U7HcC0CQM8eq4VqWy/+8sXf5oQm7UFOlnjGMZX8fCejsh+O1uDXc4
-1tNK3AaZlQU4oTIgMuMIRsEPLXWnHoMAYFGggWg87Iz97XErZID3i7QK04C5mNu4uXZcLcAnRHXd
-g+zzEOTHFRDw5A+ApaYkZUPI9w3BG2/YXNYLIm/zKX4H+G63Am/s18u+8Lde2Gtjv2p+59f7jfXe
-WO+NX2tD1huyCoZF3h2Cs1zN/3apWJtGtH/N/sDK0+JDN/E7aS2xFNNQpEiasky8hsQfvL3fKYEw
-AIZh8bKe7g0dhm8g/AzDpvPNGzgBOK3BalNyircLd5HAXg2IReMN2n+zv39v+QFhX77LwSUwe9Ot
-JcR97UWIAbgfDeALnzlaPwFw7hfgpiBMAHY0oDTPsEzH+Il8rT6dAuANn5NeQMF3ZHXlk3f+WOhR
-ynzeDaCfrb8MuU3peplyGvrJtjRAFs3i2ZSVdJvw7dOUaeUCIQ2QY/o0GzjHYBw+xeRjeQHemz/w
-PY1nOQE4rMKwT5buneGwCEM7BJsvGFl8yT3iOxbiT8es7QblhotC7iLcFUIPNChO94gH0FUpd4vZ
-nuK6BxTHbxYX10T4sR/oFMoljvHAGD+qpvTqy3HIadNuH9SwTyzbNGkFw5r7sq11HpZfNctMlN15
-/MlyzGB0vn4XB2C7W23AGABsD0CSejutUa2v1YN/xqcxIOL9wTtIIpe/qyD5PCWYjXuQHoeKA2qK
-WOBM+2YQ1g65AcO7wfCMI6twA92AzttUZBeYZRjOjkN9yNNJ6AP6myCMsArn7/JUbgW/aeW9Qu8T
-BFebsoxkiHpgPQBZ17f3dwr/sIl6a1QBdJfsdwPMll1A/N7YbgHee2OJDfrd7429Xg7Fb+z3C/tl
-ftzr/Yb8MsuwyBviUG3zpL29DbytJyigL8XOWV3izZ3bkDDkhFCbb3eNlB/V8qJADIb1SK6VOvqL
-b1UKKqMvIsrUt/3T2FGf+xmI8yHtBGKI+MA4ezzY4rMKYfnc9Q7AAnflWHUvGixhV0ijW8pjUHjK
-8f/O8gPCuXxPyF5nUMjmO69ReCYjPhukzifC0PLjKp8AWEHgQAJmgO+E4es5vk1rsPrEiUvcod07
-l19rKjoWShDYpNq3JzyNm5oN/UhE993hgkH2+P2RhiGY4fhUgLUy8HJllIVYGwSHEeM2c8QxlRQB
-cIFytwaXe8R/ui0QblbhGJ8AFAADQLMIA9juH+wwC4LYTYDLfsMNlh+OsctEKWTvFIq0+HTo7V2A
-txY+25RS27kJ1rtbhIedcJsVeMAwwEBMYBy/pgq4Ykr4Rc0d3OK0+wXXdGrbAdgs/Gu5XVbLzzdu
-q6ZRUz9OjRqn5Titwr4ur48CFyRElYX/BEf2mY+yYZYtK3lZsKKMEgDIPztOvkkGKt0vlrjeWB6i
-7lDcv5eQqxIIExB3v2F1QO5W4jYYrllXvV8N+N032ATXT6viaHII2M2HFwDTCszhGDRpTkxWfgvj
-d78BvreBVfvym2d1hRakWl8mqERXNeZlIId8gCoQjgew/d5Q8bKO7VoOwQ73r50QvN8b79fG+vU2
-AF5vICBYAn5DA1kZb/+0817uG7xgMLykfSXctkr9IcCUXf88fjZOBVQKEA8ZlzLTHvjNGuypGIRp
-Hw6kDLsJuU/xCcRnPMScUezrkZpWYQNggmBRQFa2WQusErXpI3yX7mfcf2f5AWFfbn3yW+ddzfiM
-aBXHFuF6JQKw8k1LSdDVuGpKYJLGyZQeH1Axw3DF6P9aGK7sojDUhcoCaISC1u9n/jXdDfLVS25r
-bR08gOZaoBcwyZ4bkMGA+wF6EcBrx6Y7xK2WeGnTpE3lT/DL2wa9ggNoyzXCobhB8PmBja8svd9a
-lzTohwAhruAwrEADYISP8DYANisRbMTvgNjbYLkDfjXmGaZjNAAvrbYNhjWtweGHllZjifYaD6bR
-bqKTSMY2CNZqT985Fk0v34CQIsmN9HYUv8xvNKrdkTVYyVVCu0W4PqaxHXwrzj6FXIN1bbBcAVGf
-HeK0GrM7RIHzBX5pH7P9z1X6fki2ggDfutKUkCEaZWadSGKKvHyFEe21Srj32UsPFg5oXWcsd6D3
-MhpxO9NqO7a33iE4Ybgsqg18H+Ki7Pnztpvq5LAKeyXWg2QBJktS9bJny3Cr2wjveOCN61m/b/MH
-e95qVotyiTi/hFdzIE+3iNQ9VEGtlrmPhQRbdJ4L3XLFcf/ftAK7BXtve2CPAYqL4rfWfliE1y8D
-4LXcGuxTfuXj2jvrZTsEr6V47Y29AoYNfDP/oS/znvQiM1DtlcUSHVc1I1XIvjzHZZbxsORbTx1y
-i8OboPYrID4sxGk59vpcMGv72yA43CHSGryi/lev9vDx9lVGafyp5QeEf3OpOitFVFaQQqtbKPek
-YM7iopF2iV3giykv+k4KloCDEGKWLi3BWgITruiezgt4Ne4wCI65Pi0sOUq0Q2d0+D5jwSXXtO+F
-iADwApMGNy4ASphQmY71ad7gGT+nULutOViOhNPT9tESLNM9Ij6aEfMIk4uEECCDrLsJ1zKu8w0Y
-5iaU8Os+v2u7lRjNJSKOqaJZhaEn9N5dIQKmOhBbHExxLn/tF1Cr1QYZfJslmPnG+dVbeiWQ0XZa
-ZUXbDxKjiwcE07Rt2XxDNw+4A10V3H7BPu/lF9sswrjA77EfbhLhIyympKMc9YMvsJc3W4W7O0SB
-c8IRahtyosXNPhL1RDBMxVQPD7l4zahUOOKz6OicdnKXkofi5Kq8KlRvT6h0eR8OlubiQTCsBb8B
-xD5fQAfhsT2g+LLdtC3oxQHAXx1r7gaeN/H+lPUqYRlmAAYSoMP1ya8fAyb3tjbIoNRgaQJxnJcA
-xXEF2Vz4VwCu7gogHpIy03ZegnAMgrM3Jvxlvu0zbxjw6hn/qvj93li/FiC/IP+vm0vMVxpU5qj7
-3PYxC+ufyLchAD8MEiWkDutscGhIEk/q56nr3arvEoLqD5mW1uIrH9xbwkeY623jAOIDlAmKyW9Y
-FOQWYWtYg7csuK0ji1Pj1Wz6B1ed/0yf9i8uE9A+L6OiciDZTDMtl9K15wWAp5V4Ngl+egoBFnEJ
-EdFrQuighE8Ivq/irCevfHUuDMSegbAYM2CGEKiZDsT9ukqBMrwgfloIUwhyBH5fYIzpDxO/byGu
-7XfqvfsKuyB82ObcwQ18OxTHfMEBvekrjCeLcIffO/iOY+ucXcK9Qwt4wxIcA+XUFAPiuALNNzjb
-yxfQG8cyjo7ponmEq+3ltR2Cap8UdlhCqH00q3C0K/IZrt5FGoVheMTVOdQnx5OXwXDtMBxXMBq1
-P7hpvJWY8wUz/M5p06Tvh1uEmqUYkHSL6FbfDrh2vKA50s1zQ3ZEfYHDrlytK8ZRVH3FvM+Yj/N9
-if5dZX0JXy/Q6yB2tB29yWDeDaLwFpFlUfcTAzoTeJUhuI7FgNCtA4T1BsPuEqEdfPncCE+43RSu
-fqStnlmOt4cVF+PZyuPWs3twn9aqZ2885h4hWEv7ILiv/IL32I41puIrXRJ5FEDiYUiqTqEOwQXD
-cT4AwOfM1YBgsbcoNge60jRvu6agSyDW8sPeZhGWtWAuEb9SD1pbk2ozCIvwwmsvsyb7w2rM4nO8
-DcFlX3S2UlCBgMWZPfjGeyZu+SXJzH+angfHQyjv3aG3u0Gcg+Tu4Aw1F5qXaFqFGxDvAmPTNWEZ
-Xtnfg2P8PfQfXX5AOJfvPoUcaJr/yzl9Nr0JZWfchOKK7+GZA4bmXBguEm5DCN7gmLYwYbpQadsr
-tbhQStQOxgaY5a8Vk+qnr58LEol5W9Vf9cCszApFWNXqNbiEprZjWq4Y6ZaB+D2kewb75dbHLApG
-bbS7JMgGiKn6CPHcp3S4xJGv8Iuun6tcVszVrbsj/jr4DoqXSgG2FmzXp2wJwFXNu4Wax+PW76ni
-Nd1YKp7Lx21MUWZxLOOU4mJUPo/Ol+/ly9sl/JzMj5RSUKX801zEQaoaZBAkneFodzjOycX3W/RQ
-dLw0GM5Sc9cIUF0eQMwwTEC8xV/T7uZulIAL9gW2iKvltwpzzCKhftuJwF5MJROyPdCxiov2U+2k
-liOix8dGL8daocpw9z3rJ5UqH88qoHYDf40cbYEz4MeV4qtYq7y7ZVbLejanGNuXMMf5fly4rPL5
-o5Q3jtfMq92m8q4HSk6TxaT2tVbzL3U57zJNlO9lj/tSiv8CiscKoCyX6PdRrjP1diXkfcKwn29R
-AtkbippH12Sy+ayq1FcxV4Cxw/3m+vDp0sDAm33L3tL8teth5/Va2LvmJF5rpxFCBDlYjp/PssVK
-1/XzGe721vOAXmoWCb7taLXdMq8pHRqA+xGEKW5Yg1VtKsctNpxoCzKsghxmlN6VHgbFdQFym9D0
-n11+QNiX38HgCbM1H+Z0EyiLaT+vFCEyTtv1QWktrA/xpXTb039kOF8zaO468/V0uaQarDkmC2FS
-SOcrtnGtOJvnp2z9kjti3EBECZCvfNLhn18128KzJYh/N33Oy2ufeF7kqO8QJMufWCxu8X3UXeY+
-l0imUYyBcgK4RewlC39JfTb5LwheCvylgpcK/lL4fn1VLMJ/bXuifsk2izB8qizYaz+zBvqcsrIN
-dpcDsQ/UsCl0lg3MUN/KytdkJYk8vLsy5DWUYqRJJflJwYdgZQV4KMNS9qX4vU14g0q00oNPUpHm
-PvpxbmDVq2ZLJ6tvHmZ1UuH2YEsk3GDrSNklSkiGfDBTh2Bo7kvEhSuEqK/bH+R2tfv4DSq4ANsd
-BdkUzN0qXJZGLlcCXI0YVH3VUf/pEaNV1nf5clv0G0m0lX8/p2QogHrIyWS9DYjW7BkCJfeQmnVD
-4FZzTpfhsKjf+5FewkoF39oy/UaVaZVrydnzwWOuXhRZVMcbu7HtcSYBYxt6qbZxjuYajUe4LEAy
-JsNhOfQwlamCewt16Oh/VHWSD6TldiRxYMEGqIUVZLuzLoAVX8m0PQRsrRivgJjrFvnRDV5fa+G1
-BO+X4LXFp2ELABa8fN0ZXvYBIVTfRxrJKOcUV5qd41H3SIEnXjkfwKMkeZYKLt7S8TN9GMyOOCJX
-lulQ2of2/f/P3rUtSo6jSFDN//9xm30QAQFCzqye7pp9OKrKY92tC4IwRjLkwGp+vz6Pa4+f/Rbi
-T7sfIOzueyB80fAOILjm2UAZAJjLQuyeQNfI1kgpj7XyCW7PThFoEAmN7G0QjNKxnEJoctjr4oWk
-kouAwXKUIgZn7b5sBApZN4Fg8XFUfw221DazMpNfuuTRDSb3AewmZhUMwy/QFOCcTGpZ3Tlu0aoi
-lKJMBcj/EQfBDsgjLHuxQdP7H9mAeP9M/mN5jNYvB8RLGPySiYQ1AExj0MdkPxxsLUkHssf1qeED
-EPefa7HyjUEDwC18MMagFYk6GQgkMLBCPsjP8rKkXRyD20Z8UoGxL4QmdT4C4i6tJAU3/qo4CBZr
-4Bcb4fau/aVkD+yaMdVHlOqJDhcURJpeEXrwoHVM4Bcb55J/EOiVPtY9jeelw+Mo3Kchk95m62Bm
-PoLJeCIuhT/nG5lh+B5v2wa+Hm8D0I20DpA9Y1sTloRLcekPWmaQQWkBKKRUUcLo7fRTSdvmK/BV
-ilMGv12GNfnlXE8JCAUgZq4ZIDg/093X/151OZ5CbZ7oQgkNq0gcrxec2z8IpOggjvskMCxuurDH
-0VUgujXFj7d7mcr6tfaD6AMgrCM4XgGCl/yl/LU6B82kEj4QQ/CF4d1wYTN3ZHIDvTmaMqRZ8dsQ
-ro9ZHtsBsCS/OPIJ5vqp4gKksVJ8PCu1yKZ+QNVLn/8t9wOE3b0RXM1Xf8uJ+hMIvmuHWdObCz6W
-BcwASr58RZvx+HsCBO4ZL48URhY79ptUFYaFAXAJ7HYArJzi9eEV2MngSNBoCUVfpkU+aYHXUjGD
-NniDwV+HRti1wa45FTX5S1YR4CPAPcI5xj0c9r2StsAMgAF+oRlm8PsfhUZ4h5dYaAFTK+yv3WAa
-AVtgg022a4DNYid1fKhgArPtpy64P4JmCDX6CEMBxGYBgoXj4idFMAbYCiDgo07EzBgDiZ3WiUoa
-k69pcdwQ8sSCcE+AYS6T/vZoVvw6xleAEQDFJAGwg2BVgGETfTD/+bC9hB5/J3TkYzOGI98OLxQn
-8MUj1+FthOxM72WKi8m9uVZqzJr9zFHuGeeHHbZFrTRjXG38Qf826GogONrgoDC0wTKvK8m0NEvL
-vJXuvS0xHwAd9BBiFD9cUR0Uo9r9ktpfbXIp/Tb8aESdR3ziJ/kAjc11rhmWUy51Tn+GSesPmata
-97Es2ajKX72XsIjk5oenhJfvWxATUQK565cSIE6Am9fVwip/uZLm13J0N4LeHke9Vpbo00i8uGPd
-GPn0msIUBHpNesp0Y9qVys+txPk+EMgB2oQZJ3WYbnrQPQ2POSj+AcL/O/ft0Cfp6gCC9QDBsE/l
-fGy7WupUEWhnsBSCASkvGc9juWBwbuBpel8FTBUfxtkKyaMkA1sVIX8uikPWsgw22SD0cGcctONH
-axX995KPyrM22FsMepftr3Ox1hcb9cgkQhwsSjGNyOdrE8ABHgsNgQQ71NjIRXapAYLJPCJ+VsHw
-/rTuCYh/qe0vjAnMQLZWOD6csRL0Lluh9V0OfJfa/rpRAGPNsW2gtQPfM42vThPO3Kr9YAPIUd/T
-wh0MS4Iqy0BltklMTF/ywZ/EY1KOnTioD2n9ChLsgoootiw3LWVOQaYFAO9nMgutMMCwOghez+M8
-BVeJY5Gi7mPxCQEy7wmmHes1cBmtX6sgivnGN/E1xznCiLqmcaZPWUawm/HDo3OpPjXA+dloaIYx
-vttvJW/Gb+7Qz1+O3wCMrYWTSSY4FpFYZ/F1Rsyb5BUmGjb9NPtynJAjCXAnOaWX9EhzGp21wPg9
-w5i0I9MepxyaFj08HDwfQgHuVaSA3v3tCfpMPMIGedU0wmZi8mxlidvHbOCrxSzir5VxrPmdrzCT
-wEawE/xu72zkOIWZm5w8zrLMBGRu686VX/wQi1wVFBNIDj7MdEvxHQSXH2mIn72p8VnLT90g2+Kx
-E/+u+wHC7r7XCJNdcIDbtP2rzOU0o1hUB/9Ek9nmYrcEwE5ouN/+T+FC4xy4Qd9JkNiRwyBMZZM/
-nskzvgPjSUN8G8vuOQLRkvKKmTTB1q6/VDdQdXvgfUyLH8/l2lG+PvHBWBbspAX2TWBi5ubWrAE+
-yyUIzq/D/UdyI1uCYUsA7MAnQPDzeFlzc4gEwQpTiQjvh4GwlwYgJoAcQJjmhoVyB7y7M3Pa5p2c
-F8A4hV0ImKeGAwSTWA8/AbkKDEQygWBYZhfKMQR68B30Gmeu7ygbj5jBsU5p5MMaD1BiAMES2uBt
-HsEgWMj0SqOeaDsv23hgwRTmeEFYxR4pYbCFljLQpbC2cE8fwuls9N5y17wk5G+VEH883QCaLUEt
-01EHvu9+871n9uFH9MzhWAeSD32W8CP9BIZ5qhEnMv46WDwB7wmOCwA2CbOJBMCZF7JIIg1x2Vd+
-EJYAQvt3PCSymGq7T3c79OyP0oosGmCRY3dwMZUw0QDEInHKjStswuzhF5lH/FL59Sw/GaJrhmv4
-8fCjGjJjBMLBUW7pgyv8qaf11TSM7bW65MmpeQC/zizW8gSvV/CSCQQDAK8z7hF/Y+ua4R8g/L91
-3w59LEYAVJlMHpp5hPLr0ATSoRHGLtlIt5CtSmdt5sK3ODkBgLgy/Mb8rVwGR8BDUjhVbXAHt2f8
-BG42E8wFeYgzPcdej8momrewEV66v+bjgJdNIX755jgjTTDAr6jFkW6Pz0LqWjxkssF0GQ3krOYS
-OD5KTPYrMQLEGwBDG+xhswDD+LhCAcWyN8ytB5pfdbBbQXGmmQPkHqc5VqJXkCsU3zXFInftsdIc
-c12T9reA4CEe0r0CN4xvUm8BxpiBj/Q9u1No2HBFWs8LOp3TDj/vIbDUCKvBVtgBxcOblR7iJfA3
-UVmRUe4BoHGEJp+w2Z5rASlUMBuAWGmcLFdC5NUMcfkuXqt7n6wiyq2mdJfnjd4qG0CwNGArecwc
-k5ZNec1NJDieQC/ouNK1BdBNvig1H48XzwWDYcGSqHNlJR/9GgDe8uM0hZjMIxIYT/FJt6Ah70wd
-C8HGOIzBszXBRmscZd0ubi8nfxpN4ZYTFRPIAO8Ghj3A2mILtiKW50MK8zgxSXOIZ7IP1kEz7GBY
-lzzFZIKPAcNi6qAX0h3h3mEC9H0cMPSUFiui8ckoy+yVKMYKHdIDGcJEv6DZYjcc9CwDCF50fNoG
-vI/L6v3hE/GPb2y/6s/xaf8zN34G+JJv0vTm76YFriCY82MphF+TySvCosF4tka4mUf4CQsWzDqF
-BJ5KlVZBF1XGPhOccx1rZ/Q3ps3mEgyWY6mbECaeEDBHtdfOlLZ0P20DDG+t7f6lXRY2xKWfzSL2
-w4x/Bcd7BICArwUe4Bgz4oyaT4tAj/dJD2kagaPUFsCwuXYYfgfB69kfWfilfqYsbETVN7fQiQGL
-wG2G7Ux3TXEcYefMsW5+63GYqBoH0FvAsMwAOGzCJ8ArPX+2qwB1oisi0wJWOmjpgSYG5A526aog
-UlpDSuW11zf5O30rxU48gLRutk+NiA/WmIk+BIKNgIjzBHQ2x93v5uObcac2OMCwV8JCtQ59AmOq
-LsBwgzbfhSZh3XIzZioa+VtZ88bz3azGWE3addsJjlU+aYUlgF791fgDIANMBK6gvJIgBnMU1RWA
-MoDf9pvlE0Ax7IS7HLOBRvPRn7XB4ScweQBiMwEgxqkRqRGuawaKh0yzI098uRR+8c5gQhgMu+Gp
-0kkFsvbh6crHefn4bqC8QbCuyTxCyUwiT4sAGH4WnybhskhcTqBPAPgIW/Z4AsF2DoEPFsaMkmMd
-S+656UtEz7i+QkFfINARMFMaNMKlrBAIbqAYH+TYLwzxlVGRx7aBrmQ9AAAgAElEQVSZxHrkj7sf
-IOzuOxh8Yyz8b9qhexOA+Yu6VQRHAWGxA4gUkwjaNVtMI/ypP9glHSvEQg4CMF1lsQkFLUBP9wP8
-xV/DOrNY/PXOKcoM7T4mQKN9gv5TmxG3XNtrDnbLUWnLHBwv+U9oRvkoMQk/m0b0nksZOQLA0UuJ
-MdhZHARLmsqkiUSaR2wtMF/zS2K/nsfBs4X2dwK/FfBqmD/UuDSL0CCJKqzZVKKC2503/JQ3zCMi
-bASqWABajM0EAlBvATwEhqe4g6ZKUufyHr5oBiN5jJ/qSn/Fwzr7j3K05i1/C+ubwDDAL3iCDmWz
-/9SJmDcShAXwNtMJzoviMS4+0oQikR4jY5kecPgmhOWMPHnQW3a7Ziso3Rt68HQvl4BWw0/ss8bZ
-CyD28AH8JNdMsY0NPFEBBnfHbJtnLDsgCBUHb3/JI9LoZwK9/ddth62VOwFwAcG4c+lzntMrAKPP
-E8oGjKMwjSlkVuX9SsIy33b5bQGApfoV59SGVjjthQW8Sy029i4R0vyyiYQeG+aqltjTFEeobe2w
-GRouMXKFVrXHhboqx6bF8dDEmNE4YU0FGxOKRJwTd6eeMRw0fU8Dj7e40oMcmUY8YRKR5hDQBm/T
-CMjkP+t+gLC7b5XxVbv7ov1Vf61N6X137hLdwNUgWq3UJW4yEX4XWunHE/FmVtAELzF5YFIRQiqF
-JwusDgTqMx1gXjOHMIqzlk5MvtiVeptNKhxmhFs+ARlP+y0ssj8va5aA2LXCVfPr40J+xCcgTtMI
-hwQ0BgR2Yfowpft8YNwqENY0h8CcW4Lf/indX+anBcgjv0R845sEcJ9AMIPfDEvNh3IhqOXiT4Sk
-JMwnP4NmAWgKwd/CIiMQriAYTFUCNMQDBkjF04zDzRkT9qtDnsuVgySQq794mv9Mi3VtBE4AfEVD
-06uuxVL/AkY5Ns1kpzGCRHNjPnPsDgBs4AUQVLuCMsxaux63YoCr59SISHt+CIZzmQ2bLtf0Gq0t
-Ld86TVVMZeNR1nwTnHTAq0Nc8uewMW4PfxX41rTUDs/lYin4uggwgaraXL39RO6KlxPgfhMWwUNV
-0l/vs+2ROcZiA6KwGfZ6RVOBsiPLwvN51Wj4lq1gipAZnr+DYQwEm0Z43F4Xmg/ykrxqOiJtMotg
-M4iHNMJLAYhXsjQRiQ+6sFAWTZI0hv8VRBN2TdK3KBa0jyKFd5YK0tnxF3WQEiKGMSNy6okuiblY
-zDV9YTQA8TaTePQJDbCZyPOQacTBR/999wOE3X07+B38nkfPOBhpedbhZwCdoDVsgh24iEj4QyMc
-/mRWIhso7bMvtzZQBHU4CD0WA0lKSWIHJLTw0aJDFUb5AHBQAowl4jQqUAeV8cwf6NYvLn1DEUyv
-v4IJPk8Bv6biKhSyCV4qYrAL1gKen7C3NX94yH5y74NJhT8RwdYO54CiDL5g90sTDAco9maFdthw
-fJY4ADb5tSz8rBFWn9NVwtASS8sHcCyZD30qYDP9DHDF51QlGdvoFwg4HykAXBIqExgOPxgug3C6
-WgsXBEBX6/Hsjrg3kNzB8Duw/f00AsOSD7Kx2chM9JEEwevZYdC+iW/0WdS0Oi7x0CIJdnOZN82w
-8NBbgtuQ05p+Gja2HGHhfJ0GG2OL8L3NCQvkCnSboL/5hzTwXAa/qelN8KtCH9po+bMeybU0flSD
-6H4AxPnLNYV5iZMjIjn1deDP4+kRYLeJH/OnCYJFOiD+EiCbHf7gLQ34JgCqH9xxVc5ug5v1JUmD
-+de3hlvmAQAnz8tJ1gKAFZ+QXy53AHqNFTUYq9xDsX6prL9IE/wsqadHrLAjTkCc2mKAYmyWM/SJ
-/dTNMJ3Q9FtQYlsnevHHLcCPZZD3EjKs/k3q6Y9ZrCCxEgZvST4e9DmBX9vA154NdvfRabI1wLql
-8KP6A4T/1+7boWemwtrhsBvOtdryZF4wltXqBGDFma8AfupcbdIIb43yvu/jQA9CVsTCVghaZ1MS
-gMXxooDQbNpe6XGp402gnAukgGMR+ptg+JwBgGAGcugzAT4/JgynRYhrhqVohjVMIf4is4htkG/7
-lUwwAfR+BsGZJpQOBGDBxAoA1jSJwDm/4bdto4ezY38tDf9aOE9WyqkPcRxa+J2OGjAu40TjKILX
-4Bb0xXbBbNubaeQXiYccIdDFILiDZImyBAqYPgCC2nUGwcyk7y5mrGSbAPBeP1ViNDAc+biYXvzx
-p/pbKwDo9jzt+wNUiIhrhPdp8/uTylvVFQ/Ky8vxUFTVUzF3wLxi7nlDHUAWmmvU7AJ4kQC/p1nr
-4jQzdiScuewt3Qb/FPelH59uD1AbIQa/WijiEbx1k5i71Ah7BxjoBl9p2nbiifUqcQWoANmXnFbD
-Z435Y3ALDvBuIlFBr5R8AL00mPwASwBp+x2il+PU8HW57K/GXbPNKQpSkCpdeRNwAGPckp2D4gTB
-2TRdDRh7uohvkmvmEF0T/PiX4/6KeKX4BMablzEIltyDUpZwaoNz5JmXadX0BogWkvGW69dyCIPn
-DmzuBMHpP9IC9GY4wbEEAIYsSEAMTbAfU2dPmEbsj2xsAGwPHhx+gPD/zP2+Rjj/5evrCn77pjho
-iZdUEI2pTygIIQlAHPpTF4ZWwHAIU02zCJwrrFa1wbEWgAE8MoCHuLA8FiazbHqilmYWYQ0c+4JA
-e3nBi9+NzSACR4DheaADPJhELMPAu2aYAPDWBPjDAW+Q8/RHRSxOEu29leQyPAImGVdMJvbY7XON
-00Y4NcJOE0bAWDoglgDBa/mGqdDsMiBmv9PkoRkWYQAdY23eE59vBsYiMoNhjxc5wa56PRwvVH8F
-w1KE4Jl3ujbH0p/z9LwRvgBgjlOROxjOywls38IXgEycJgGxr+3HZG+YNv9UrINgfQgAO0+IndU8
-BixZMW9VSHYwzBrkENbeXt7jw/FZuzT/mVZyXKaUE8b0aY57n1s+HeJw4bdcmA9wgdzGZa4BTvAL
-vgYQrFGhrwfWiAIoFA0p8iELAecA0Fnlrh7aNskrxWWfJDTBCBeZkpiSfvSWUbrmlwGxtTKSWmFD
-eu13/T3Ux/1RDVks1aihcgSqO0Ax5WdtsN8aIDgG5dHYPLfzaJqGOUo+bIQH0wj4z010FRifIFgk
-tL4pRpy+lGTxua4iUie/xRnSWbdl8bJMrFzrvRAPekwtcAHHBIajJtC3pBZ4X0WKlviRbQYBMKzQ
-FC95Hhgk/Vn3A4TdHW9Bb/n818FvxIe/geUjvf4E5g/QAAs9KysDXok8+RW6ZCkAwZtZpQYY5avm
-Bc6Gv6f2dwvTfHpPrUUulEmLnLdJpnswO2WmrTkh8fSfgI8/sQzge/tpA8CPLvnLkx81seCeWvqf
-XMavZrGrOSkh8+AEiaoNzrlPrTAAsX/+2LzMY/scYHxeV1Y89BQALHUsMF4V+NJYYTxjDuy4MjAW
-uYPhCPu8F+Bb6sw8iUBaHiFapLiUGLc0moLs1Og9Xc6f9jgI1HmBZAmAxFLJl4DYJWLiy6Ql+AGA
-8eAmbh6z+YMQXQOW5XxB9pXXo2InGJY9pwByIRj5gVRcmIa6yYeG4uopErQ2QrAPjsb3Po02Bcf5
-11talezl+kT9Qc3eNI3YtA8GL652xLhu1pcgN/wAEAR0++kqgSNaE832fZcEtiAOK+WHNvb4sBtt
-8inkjbDsSuAr5D9NKOqPH57BRyYwnDaj+cn1fBCToC2A8jp5SZMafUptsPJ5wYdcqevdBCBYmqlE
-hnOz3LkRrsQpnRzhx6b9Io0wrvHA4uvFfA33BxmMgbVWx5rW2hPuKjZx15db/ja4LDIrxSeqSr7d
-lhA/3EW+phEWi2mXAL5VG/z4RsnQCOMq6seoqejT+em/736AsLtvh34Cv6OWmNLnc4Yn0wjKA/ns
-BA5CL6YShnwJoAMEARBbLjSuT0WqEqwtDRENcHTXAru22QCOaYEYLzIIU9wMzK0xLge8wbwZBJN2
-c8UkqEh8OQ5xfCoEA+flFhM7HqA4mQB6r+O1aI2BMqJIpvHpDvkGgPzm9KASIFhd6C3XCG7/UwAw
-xqyDXsF8tzQ58krM5x76qpk9wbAEU4xHBBZ8XjaVDlZoiesS8htz5tHPEuItLqtXikpXiPtDOoM4
-SArNJC5ycAq9pk2v+ObSGGd2gGIqeNuTD30rH27sHGP2TxpgFoSFlAsT0jvwbVqupH8W1rUdXR5z
-wMZ4ztIk83979RbyZrit+Y0RklzzO4aBcWiJvc4KeDk+0w/NcAEep0YYcxS5OFz8zLfr75ApHhtv
-mMCHKb0CXwbEnsdy/e8w3dH67zn6nfbCkJJgp5u+wcpDRsiOiLVBD2vVRlhyGbdf1fxKMZWQ0Bhv
-OfaIjCD4V5g7nCYSfzkwZvOIv3RriUMTGiB4t4W1wR0Ad384SuANchy3h4vWOBj/IOurDyuR6BK0
-SXylaoHZ73Qu6I81+hepGmKyHX50m0wAJH+rlfwH3Q8QdvetOl4lNX3pT3A7Ad7ur1rj1LGGZrdr
-hwvoJTAbJhGI91YBECOOjmMri+NwSeAB/2gna5C85UKJq6/2zFsZfHmFFUsrgS+P8I4HAE6Gl4wP
-QN9BMT5l6ZvkdtoCZpBlW/sL8PtsbCHPkv2aLnr3d6/ebrNCH10jXGjAEDaK3+YRqvTGIYQX04fT
-jibdBugVvaQnKNpTQPQQgg0M1MNGc2VpH5ikYwRCrVyyXIk86e43gLHe8l6cFp/9nv9A140/NGZd
-QO8Bii+8xSTGtMeLz39IO80IDWYgx5gFZRJACjDsEd3PtxUAX1TPT/0AwOgfBPvR1wnc1j7O7MeG
-nBRRrlaRwm9cVaSYPSQIPuMybHKcIiGYnsrrgj8yOAYooD6l9izHI0HRHsOHummUzno8zHnfOIdZ
-YXl0ht8AsYU/P/IC3gC/2w478FXumMeFbfDzZNyzxNbWEu7n/A2MNOiMe+BtozXBChISTpxb0iQQ
-IBjXLdcwT+H3W64nN8v9etY+0eBZ8usv1/T+WvLEJjo/NeKXykNfnfu1lph/8ClIFUPi7d23tQaS
-a1fGrhENnSIp+9F5b6xKl/8mJrAhsSNfpbJUcqEvWS6VG3sONwlU0Ls3yolrgVUe8VMiRENTvLXC
-7eHmD7kfIOzu26HXy+8d8N7juM4tf2/gWFIhhE1ySIur+AJLoMiguMh6ZPYIfjJFSjdzSC2w+JP1
-kB6LKrXEYOo5hlpaELghmBz8FQAD+D667bPE3B+vi7EZrmqDjdKWSgJhtX3KxMxRRC/xwcUwqKRW
-y6PMEszGhxCEAK+cD1ALcxVlsx2sJYH2JP3S/KQ1KaNNMMWZJOaSZz43WlkAh2CjxrDHymWKqzMf
-N7vGXcEu35erIN4+S4t3ByrNWi5+GsRD06t9hKlplzQILc+UkSz/Qz2WbwXKK5Nyoxz3WG/kV6/v
-5hf1ftHaA2nnKzCl26ENOU7FPIKbN83LJ9ArRDvMlEzOONQHWpjyWM9GexkUMQx+8yrJXQt1xEa7
-pgktr5EJIAdooPjy5gwaNfyo+wgzAO4/aWGQiNJUafQm+Q9G45A5AgWMBf/KvOZrMvu6r48k+KXx
-iP766RHyiNra+WRtkFqEVLYlBpzfdKkECGZOHcXj5xHOrrfcUm+PRj8AkkX8Yxr4utzLmcLFFELX
-VrSUTywvARgPuoNfoeXXY87qvBIRDCIoZM+QXuxPsMaJhuBO4MvrhOiZ6DTKWbbS/E/Q6qEB1hp+
-Kgg2wYlOE8P4d90PEHb3Oxrht39dG9j9NY5ADRalkByylhaaYX+Kla41drDoT5kJnCUYgQ6Lhgkd
-xM5saK+hCnolctGCQTxrRaooLmt1Ht86zq9g2FGjOXI03zina+0vQ2kCXxw9hqd0c+3wFoK1J4Ut
-YWzBeQZmzaw4TnLg2TY6z9eFCzTBKgJFdgisJRZzmW3TYBAd6Ma40SvETUOUFm31/gWvmbWD/RW6
-CABGh7ZWLnm/CzOzSxqD4RaGP8FuvecnEHz2vwO2M41HrrWMiLeBXmkAtaR3EMzXYUSh1ikAGH6u
-O+emzEx7uMje5byWh2fnJcl8xNcW3csyLV9f8hXU0le39SkbAzfSSLIlujWp/hJnr+mPxkjsqyFs
-43WfN77XeL3y/SroBR+8haEwMCrLXWSwYiWefi1OROqrdsaQkv6MS95V+Egpk8A4fgaeaUA8MQZz
-GKdF5Mc1bOVmKlXkl+C3TOvcvvJQtpLfwnynL+s4KYJkKd5eYi9OmkmY5Ga5YaPcAIKPUyUKINYc
-ApXT3+YL815dXefhHcGvjSC4mKO1xVA0wU7LAYyJb6Q8T1pGXphEgI9UEkgwvE+I2I+YpltGPxHe
-cvnxEz3+tPsBwu6+HXql36cNcD3tLf+uWxvIlQMM49URXpWLWTADA9giUwoGwVgkwWcGl8ukgV7s
-dPW/AYydiScUrNphPG2jP9mM9ugBWY9XIwQMOxgW/9DEs0yWg1oAYtj/QhNsqnsTmi3XAvsRw+73
-3UlSWsbbb8WotY3z+NjvFe/zJNAG82MPzbXlhrmtZbGWbvlJZGUB5WJbQSlJjx0Ax98QhA00SWeO
-mNNkhjktKaA53+lqfUeeg974HhydbZTeFg5bCur7fV8IPSh2ytvKMRj0CLumTSOUiCTSIPAtHztx
-X2MQ7Gun2s1p2SFehSSNnwul0mLD2s78uHMADLpaC3dIZS3MY1dGvgXsiMt8xgHOZ91vl3iqqKf5
-dWt7B3B7gN76uWUVf7AW2iwHMMBaYXkLU/MtmxratJduvP2khc+Z6nyiXuvbxXo9+JV3ILXDUuyF
-44QI+rgG4iSuK9ICxB2tj8bF27JuNhfyDdmVxraEU5YWuRRodN/zvlHuBMH4yhx/VKN8dtmB9xUM
-Y35ZQ6wS4DLyyq7n2EdGIin2/OQrPcEKLadIYH1GHCu3cg6qplhiDhMTZPkoZRZR+Rzktr/m4Bdx
-wgB4uWnPQye3ZDc6J59l0N93P0DY3bcDqy+/bwDxbBqRzCbv4YtUJTa+iaRGePsTBIvZBnhGzAIa
-Urcjrq+35KAukLNJAtudrWqDO+hNxvcCmEUkz0JMplsHP8OxK9j7lx+JSMG8/Mg07+a2+fUxyc8u
-QytsceYwAPLjoBot6qYQ3ToaDx54ktA2gGGTLantL1fMjaXgyTxW071qBmtgD/zqSMl3zG2JrbMM
-mdNfkmnJlQw1gBXlflszX6VxJuumD6cpBIO5UryEO8t8SzvDTPW9jSYi1dQhM0wzIlp6ceY1GufW
-kgRBmC9oZ9s9yhylwMqcXjcq5FZ4/Vltri2+4kHzSFuoze/MmjzuJPWnO2v52HskseoM8TlQ36fF
-SGnMKbgavsiJ1f2oHfnVHATjQaZJfj5nNW5viRlCi9a0ar2LvSvxsyGO84JNeUzV9lYTiNt1BsMW
-D6Mhr7xBxUSiaYXzIQGnRkBLaOWXdN17VtdcgmE9tYcFPflA0IlLcRvSCLPcWko2wpdNc0/zAwTz
-BjqkwTY5wLC4aR7PoXoep4YnOiLUGe9StD+zmBMlW+pF5shvJS5BLtNOj/P5i5bNcQzag+bHH8wj
-8IDgdsMigmNMn5CIteeddQys5L9yP0DY3ToE3D0ffpAFE8DtcQF0KE6KfxMQ8ha/J3c/l8t4obJc
-P2uL+eoaUnFTA3FzAepj+TjDUWftS10Fe5HksU17kwSeAEIIB1UbXRqps3BnDQPdL+xLS307jb+4
-abJ3jMdntZ1hBrB1MBqdMilpMc7BdF1gBGg/x5/nWYUeYswK+GWQfIDb3Xl/Y9Digx4U2YIYOmUb
-jSeX5xHvoDMB2d2NafqSbi2tADgpdMCgt7BJo/xdaLy09XfyTHk7I57HWCpudu0v4hJuubxywRx/
-g8g8h9OaHXOac1dsrEmoVe23xzOdU85eQoe03R6ARlofBYlks7VHUO5JqsXxbtw0o7xm5G91mJ3l
-KCJ5l7fcJwufgec3KNusyYJf5HFqFvwDtwDP59N46svnc3h4JYc9KdW5rIbN6z/irG2WU5+TUB78
-/lUANHV/KGj/ZH80SPPko/0zOiZyn4uu5fdAFbg3zbXx0M2U3TVurV4O7PypfWfeYEIDRrQm0FRy
-XPe3K5ZffQ5UV8qQ34GyNg3yr7XkAQAmIJzg2I++MzKhEAtzDnN+8UR5X2Vm+dVBw1uLBKXY4B73
-djrOMnt1P0+OH8wH7fE3H4q9NCrqH7xQFX+TurGDOT6AsulXOWv580/H35I4Iv0Puh8g7O5bs5TK
-ABJIAuhUEMzgFwKQ4kgQ5dO259mX9ItI1ZoxQMgn9Z3fyE8Ai6+SABgfY5ClYm4DJbHQV9kA1gEx
-2pAgWAOolvc/0TPb1ghLJD4CP4Yjcrvn2adDoO8AvtOPJW0Xsh7mg9P61OvF3wFyARE0tjEmtKFx
-ugrCBRR7ephatLbgdd7URm1tHwBG5k+QdNBZTS1lbKjz9Rky6Pt3nJ3zJhJ0NM3ZVOAE7ycxHCDt
-qPNDy3uWD+EtyNyveUfGR+F3PmHK8dWoqPaR11m9J4rw/JV4NEjzqrzIh3pLPdHRve7r/XM+S3yr
-jM11Krjhgo02it/m+BbZITs/xHJ/eKSDzwaMzr4BFIOfLL7Ge/m8YY67pumabG00AzwTGUHvQ2PJ
-+Y5TIwBmpV0Z6N6usWlhBdgLMCwivwSgeP9wTjq+PqciFQg/7ergWJ+XFYYJekQOcEyfkStjDGIx
-XIheOE1ExNhEZ8eHVjPmKGmm8vYBEOv50Q3YJG8t6G5PAF8BKJYBFPs7WEvg3MEvA2L1vAUQtzjs
-IwCwTmArYYUVZoWuLDNVsaX7c9UeXkv9yDP4n00HS+U/bhryC/SyPv9WB9C/Jyz+EfcDhN2tL0X1
-/lgCA2I/HktZAwyw6+DRFxkAcbHhAWPEgiOuv9dhE+ZFU8JCAcBQIj/aUsIq2642APBmeOZtW5B+
-zgzLJjXR7JNmndEu3vnLWuH4Ylb2LcGvL1IKR7oS+BXZEmCJ1M92guNZuSfi85Wd0Filq4ASyMPD
-N3+vo6X1X4lnsMtgWMgOD4B2uB+DlyP+1r4WNvp7QiopNFmH7NQuU1Wf3csSA6MuYUJPAC5nfqP8
-c6POtOHe3yYGcLl1GyuJWlHqoIDtPsL+F0BXgjJ2JrwBiIMaJHXCvWlw2vof8VSgzPcb6KW0eZzS
-3lKcvx1AFlXZUfKk27FMW8PdT7QyOyvJHQzvuAH86gyIl+JkCVco4K2TUvtr5eTXaDN4x+arbJVJ
-rCx+e1bxDYn+A/uMoXFmcWh6h7hIW35GNc6qdk1PaoRTM1y/ntm0w2YJjO2hK2uEh0/Bsbq3fzIZ
-TkUkvlwmsacmSIDpJSbdMqqnZyGphdhsSGKc6DmiaYWhOFryS7dpBGuDVUhLa5v2GfgmcJVibgC/
-uv+RLN/jVIxANfgk4nAfL/NImBXur71JgmNLv4V/g2J7HCRDceYPBP9x85Ar4NVZM/yrx/0A4f+d
-W4XiJ+61w1v7O4HfBJ55IgQDYguAUX4hBFILDGaLFnTt73seZ5fUbF/DpS0LuXHSBHLuXWhbnhEI
-3nUA2Hs95eoOqxdP6WA6TeuLhU0JnzXBMK0IhmnxK6/yKd6MAdNVSp6g8prmA4k5CEEmAZCKmQTG
-ngBOaIYN+WAfnH6RClpKG2yOF8n79PIlT9HQp09vcQVj3MdwbJA02nhx2XIPWwuLyCdwLJynpPEa
-OhvT63ppVbuDCSHUo8hkyHBwFSwTRVrSinAYXdejBRJzRZUzF+s9ynh1sEeRmm2pDUff0UG/WqO1
-6f6WiUbhj+AXf3n5TgBmoi07PDQ8sAoWGufkqxP4FZSgBxf8lpcKcwmehwMEZ+fVtXt/4UQD7uYF
-8AaL815wvCgPFUDu9k+a4PCvNcSlf6lr+xwIL3GNsAzmEZZyiE0kYqcUgLBSww+zCNL44geR8uRs
-YCa3h4iA6e2gGyK28JKsasR0yGyFQijHJj6kVDTCsINWAq5pBhFg1dPgxxdcWTP8kD81u1yvj1qA
-ZvNw1l00wmIEgDWOXLXuH4Cv6ZK1nn3iw9qfzN6mEbMmOACvnnEJfn+A8P8L96tw8jssgkY4AKCe
-T8R8xBlAEH/pjTWAEsKeBHUwErQgFy7nLRKlxYeGUfoCRvwGYqEBjtqXg1ER1RWMMEEwA2OvF7IB
-yAUcq7XRWl+laYFNxDXD7ZU1NMEoVLS/boNM2uFuD1nqonEGy1Ma3NH04Us/5rJqy6HxTZB6guGc
-jy2zKiAW4faf4Q54GVxMPIX1vKC3I67lyy5OiKNUTvlvSPxTWStRn8HxWY0eMff8SLmnfbzBa54Y
-Y5oYgLASBhkq2pOAq4QZYMk5HsjANM+zxsCvxA1ERivzKF+TSCtMQJHNWeDp85n1Jp878ncwwwWl
-p/UZP7oVsWmR3ey1RQTAvzxcUr3LGQBocmnee9GdogUOgmONg087IH6KRjhfnzM0i/AIkrXkCVAr
-ALgSfnyYaAbF3b/N46qdsINgbR8MMgBgaWYRrBVeDoxvKl/ZNaqJ2bOnwWTQEC8x8U+QE8EU0ih+
-m7xVjlpuBGtCd9QEY8NegGDljXVr0AALgdcEuw/me8gXIHbSBstpDlHAr1zKmREAfvIhQ2Eq4UA4
-bIHh9w+hPLvfBhMJBsIEeteatcK/biB44a30n3U/QNjd16YRmmAYAPgAnOXHINiREzP/WGsMfCVW
-aoIpoXVZ04pwsAEAHz/S8FJ4cQvAGJt5hOhZn2SpeAiINrGWF+cPraYFPkwgHolvDSMTbbJjzUKC
-YAmbYSO/c4j0C8Se+wnAcj8++kmadj/PI2gAsDAALj2U5HF4KfTDTzcdcMp21tPPF+fZxMkWOGlK
-JIXE9HpdS6i71sKbkfKtuFwwpk15rIXnIt9ph6f7vzRSKpg6G+GBiPP5Bwlq1pF3IiAWtFR7qq3n
-vKdgAsXcizrnKpUONJrbF/VNJm2TBudnXjjy9jcObX0ly2VZnv8AACAASURBVJreOLzk79rffiV3
-zHsrWseew9UGm9NE0iQCsZtv1tssJg7wF80HXSVAnEddpkYY9VSwm1RZflaHAXn3XHaQu8ivH/1h
-HrFwRm41j1gKe+E9BqEdNpFDG+wgWJ40jdh6ZZMD4WL8wjb4BMzoq4q6jXDPYG/BMf+u01qa84oi
-GrdSKABcXLFR7skNdADCwpvXJMwlKjgFgNWS7yG/crz3C+YQuAfAL+jjkVpu55MDAAfoXepnPTvQ
-XSrr2UecAQTvuDVqhP+jJyAuoFhV1pIEw1qBsWpdf3/C/QBhd9+q49kkIu2E+dWQuo1Ugl/+Mk/8
-IEME1yqcu3lDEVyXVQ2glTCP4gN4JSBbwXF2zY8uWX6+o6EMMUel0ng9FPWz0B8EGXfGHhFdZCIh
-0kwgRA7wi7BKAFs1E3vI3zTEJ6DpY4qxfvEzUH4BzUjvIDj9YNsJtDRiiD60gpSOJa9geAy7UAdI
-6to5akuP6znr81uDDx2RX4LdAXj14kmRc/jbPOhXH5OzjB05DvptEWa94XprRHEgX0aOdbzJJMLz
-QTAU8NhmjtffOb/ZyjLeUnt+vBxCnqFPdTjQly1RI39bK9HvbGppQ7cRZz4XaS/XUqanl3bfbay1
-hT4BY6xt8PPFN2RAHHEabaynxuzTKYqpA3WFf8J5tHY1f2S0FvxbCOR+AMCrpilrg6UC4PM3nBrh
-yovtr6dGhIYYNg9Pn48EwyZ1nkwacGrT3+OP6CZj8Vax8D5cNVdn2TxOmuF69rAl4BXSAAubNQjl
-mfyawJY3zEkzkfB4vFk6bIRFTk2x2wiHeYSf96sPtMFP0QqvZXuT3FKxAor3uf4JgKWaR7AWuGl/
-AYrrA8UPEP6fua+PT8OPXgktbSBXHGh2cwgCxHAQCHosvNl0IvNUwLmfChOtsGkEFDfcvq3ZkGCX
-aYW7ROWJRc6MUHzxC/W3tNdEau+83Y+IrWYp6SYQxRUTCM/UwbAsOeyDAXy9/xBz8cWgC0C7gkhN
-wXvMVUsPvwu9CnQ1NAmgie1XsgPmNwFbSPLYxn3jT7c4/dAXBye7pJW0FAKnfXCGT4FQoaQcwzu6
-U1Zl84726xE3YcwpT97qaOU4bjfsOoGiv+UIoKKOHH+P0z7ec74q7+tc4g63uHqPdKVObRm1XHqH
-ZrBRjvf7wh648UOcHMF543IFwV7mhQ4PPEp9scEEIlN7CMAYZRrLGsGvhPkYP/B2EPwcGuFmM0w/
-8ZaUsNU8BdxKB7tDeE3hrUU+TSOgFd7y75ckj1OjfhZA/ITCI4BvUfae9sGcZi6XgFf3m78VNDM6
-G73nBHWmhBHencicLhcLCA7w+2xlkj4RZvvgTxrgcmqEDKDYm5J2vgC/8j345bLqbVCYRwD8iuC8
-3zjjWFXMLE+MWBYguGiED22wVPvgGyhW+TGN+P/gvj267jCJcLmR9sFGJgfdTliCiR5A0hIoRRwB
-FAC6Uyixd7PA+klVvo+/0vHFoJrmEI8SLvUjHtQ7mBrhqGVop5Zwb55Y2wQn8gXwneKeA/zm1cdw
-c4gcGArnVp9ZczYCX4xFEdpaTEB6ecy50GjlXPC8QlOT89+B8M4nh7uD3x5XwUUHTLPNMMJWyvT7
-HM268bAPGU/QMQPdUsoZeWi9PYPJ/LAwx79rjV/dWXAEvmM+IcxEE4L5aoYLOxOAks5zMJqy6JBv
-iIs6D0KWuGeJN3H6x9g7Q3FpjX5VkEt9D/9vgGUbEm2YrQ9rpSefU2gC06WS6l5+Wb/hmfN3ITYF
-fuFAN80p7AqCMS8Bbguw1ZJG3c80rWkMgifg280lRoC80jzuAMAiTTO8bYP3m1E/OUKaZtismEYw
-8M2R9hHGArEEvfI8ZVZMkjfzRH25gsPlePYR5qFUGia3C1ZXKmkCOwvziDWYO9zBLptRxCY78hdt
-sPxN8EvAeWuDVUwdsOsTwDg+foG0pf4xlG0CYvYECLZFD0qkDT4BsBQzCIDiBMQSGw//tPsBwu5+
-XyMMf7UT7mB4MwKkqwhsQScAZS9AVwgolbJsMlHwwAzOnHOobOD7KGuDu30bNMD1tZqSxKztR3sq
-ZzKReRMcgdyMfwHDGGA3h+g/aMU3Q2jg2Md3cgFE7bs4FvCY0/h6URsT1gKfaRivXme95p3O9k9U
-e4uzlv4N2EVKhFumA2BcJVAK63r3Wn5ehTrnwbSKHQ8KN3B7v8fpeLzi7f+1DoqdMnjcCcIs/vIN
-GYTxSlKqpNoF1znMJdrs4ammDcoobia8177WFZ2dLHQR/MpozrIIz2chLxqUQndWrzrEHWVe+zPT
-fk3VcmUep5pKhP64n6BUQisMJQTM5vbr73oWsYiOZg+gjB4/pUVPJoArE/BdsVHuMJ1YWu2DpR+h
-Jg6CSQY2bbA+G8jqwls9n+P41DJ9NMPWNqeQDnz3G8syK9r6OzKhykOuLuxRcmTrssAbUgwNADG+
-OGdiDoJ/LSugdNIMHwBZOggG+JWLNtitrB0gB/htoDjAL5d/JD/t/BjZCW+bCTMLTXDRDkMbbBZm
-EtAIh3lEB8UdEDdTiLx+j8X+SfcDhN39lo2wAJMp+RPcBCMQIe2wiBAoFpmu1gBRBVbidRyOtZ0B
-CBvrJkYslqYR0G6sXnsgQYA9CUla7E65WQpB7XdtGwO3BMibBQQ4lMUWG+tsNTgQ21/zh12woSkO
-YASuWscttcJCcaef+ethKoE4SpvKHoD3kud+rcz9RqZTfI8rNRmH7UynMl1jzCDmWv/BzDoCurhE
-cJG9PwDsuHNVaPEwKJzK18Z0ehhaXkEmgb4rsjqGIAVXTat2qAHg9Owpv+tRSMejbUN7tc2rRxoR
-Aq/rXtlt2hQNJm1wjDibObTpL/bAZf1Yzcvrjhsx5BnbRjc4qTivpzEO30iPpCUS561CK7yc38Xb
-tcYKsRs/LY/xdytOAL1oCCQ2vgkD3u/iu6Z3KzQqCB41wWQSAYAMTd0GwRIgOD6mET9zW2mXgSYB
-hsVPjJAnv9NX5mncGFftg8ETy+wFz6iMuKqVhhVShVf4D/Mc3APDISJsG7xB8N5Qto8cAyCGPFIC
-tF+aQbC/lVeRUxtM/gMUy4tGOEAuzDgeAr9b2yvPBIBdK+y2wgtvDCYzCI8PGvK4slFOsVHuBwj/
-T933H9SoP32JU5EExPST63XSDtqYr7qUCsnaHRBqrW/J/lyimO0jg4klLd1PhDPqI4bD8ZxNRAoC
-P1z/khzitGh8zb84HnlVZJ80gQzWTowAo/UHgOL3gQlJ4eNJUQfDs3N+Shr1Tz+Um3DF6zXqOJkx
-u9+JTxbfwzand9AiM9gqU0w0scnrBp1uDfVeW94PDrarVaxx2+uqqFVPpg8ik5kEQbjBueQA3jsL
-DkVALDXzpNWtYp6ixOKDGyXtMicRp9QXbrP2PJVwrVdW6up3odYwGOaxfQXANIctH68lXr4dDB/z
-AMoYpvE7Dl8B8j1+X1mR0BUKKsm+Nkez+HztHi6VZfsYq/0pZadK7eB2t77HsbnEGKd+sw8A+GYS
-wT9sllt0ZfOIqhXe/LhsmHtMdDnfDoXKBHzfOBj6qXUmDrmESGbSjWs5rZ6SFKNsR1EazgKAFSB4
-+c8SCJcPYwCsOo9LsCs136UM/PiCIEwhAHDzFAkqJ3SSBNX52B6jbQJh1Rxi2TZ5cCPiEQCbiT1L
-ZD27v3KC4NAIt7g4Qk21mEyEVvi7hfqPuh8g7O53NcLl58R3/7F5RP1Jub6A3rJiT+0is0OAvAOE
-OVqDeYSIJCP2Wh/JQ+EPLlBuSIxnr0qW6tEebsf2+2kRfkJaz3+C5NW8D63otBHeXM31IZaM4pCc
-ZUx6uHb7o8aX8ojJO4j2+5c0q/mmcgVUDK4DjiN+iPsW4JIoiXbwbUp+66U/NMZ6hgG0FXQ2CTXk
-tSGu3uTMca6RW9xXDmugl/5QIW+6nCE76Fg/Vt3nUUSq+YRSHiK2Yo/cb0/hY2apAZuerVRQNL0D
-SOU1YCXfVL7dvYOY414v7f7kzqGmmtpiFwfBuuVAfORMvf2uXVNNkwjzM4M3ywQY3vG4CyQBg1yE
-p7i53JYy5Y3eb/3yIxt5Tq7EsWmnmSDkoYNgkWoeATBsvnHORAQa4kBqaTaRvw2+dAiHsiMewjCB
-WONgKNNqabJNpBKdNyDktfpoappGwD5Yl4o+S3TtfsJWeDSLEBk0wGQWIacGOdMkTCRMNDW9JPfD
-HMO7gzegp1Y4zSIEZg/QBDvdisvXtBE2MR9/We5/tlJtBr9SP88NDTEDX7INhlb4T7sfIOzut2yE
-zarmV9M+aselHXAsIknGP5k8jEAp/DV/F/KISy0c2XR6otITcj8Lk7csrCYYmbccN2QXzCYLGad1
-m1/SDO+W9LOFJYFvjw/OAqYaKzxAcRGcAZTl6m5gVClDB8bjXIG3smCe5psGZ6xnaOtXgPiWRt0H
-/XDYWv4OqjJsJbxFA9VGsqXM/6W15o3TktehrdZWAUSpWCI7YTCcd+5i78w3uS8gU6m43WW6KVCR
-trgB1e4unWkx4rGYd3zMa5fnauf8TYAYcRPYHfjhGVX7tueHKSMrzLmj0rxGynKl9TLxkqF+vkdv
-Yoc/UbbEarmGDTUv/Ml5MZhHiGy/0b3ghxlEfrDAgYj6UVhsVhLlezhXyxQucZOm9wvwO2qL6dU2
-wHD8hMwiGDgSCMZZXYrv+Lo2WEVE1tooDeGLOUQPx2xBxS4igaiN/DxRWD8T84vFwiPZ0hRVVNMI
-/gqb4Yty6wZqb7bBu4dPiKsKluOYNEF9KQ4f6cAXJhhnXgOtwuRBLbS8YhyHN66kKTbzI0tJM7ys
-aoQHQBw0Q1rfxTTFYPhNmP1L7gcIu1ufs0Q+fJIYT73VPjhBcMZbMOTNvL8DvOmftYKzRhNCsLHR
-IhH3lgOTrcngvrmF8SwwSMB2V4QQgcFoDWmAC7tvoBenG0daB8MiBxCOn0iYRWxOkvFnw0lgOzPs
-c3LOA11Rpl0jT9RpRx3X+e8g4uLejol6KxskQHR2yIB2j0leACAYZYz0t7Zpv0t6t9yq6ZupA2hl
-Swy3JYBSU6nO0oATIJ81jDD1e9e0olxpAaG3u2AQqY4DtBGqrbW0davD/HbwS3kIY1/cxaBkE8Jo
-Ft7pSYQwCwWYdrSV5bU51R3NtyPL4ZJHMvC997tRndTFXumVN7uZSOzLeBynYTMSNHQmGnaaMEuz
-qLFSJcxjbulM+Tmf34Lft98Gxura4QS/DIBzs1+YR7hWeLn2V02DN2s5NYJNIzh8ppkD5T3yBIxD
-+6BgGntuaC0az18IqDLBITJ4lDlr/boqhdcG+EsJBNsSbA4/Nr/Juz1waHybDXEFspQmJvx57vjS
-nKQMzi/LIb8E2A3wy8C3AOFMMzPXBEMzbHeNsDZwrNVWuIPfH9OI/wfue42w5e5Y1QTDmsAYuK6a
-RFTzCAZDgjiZ4qqgvgJibeGbC5S26wQgnn4SufB3Og+ii4RTQoX4vGl6S3CIVxH+Gl1+NW763Tpg
-2dPj9VcOS9H6BpilofvimvXlWNzmkDcHFdE2Cf/mxvRh6n8LAA/hOS0Zswx5AaoKRVttHOcV0WD2
-O44gi1GFdHfeLMajx+Ni0sb1Nc8n5zk/FWjpxXSD5PRbRfF2Z+RLDH9qzBj2JscQoimEH+baz+a9
-biqzzBH5LdMrAKZ7Ma0T+yjg+WjYXKak9/I9Xeb0Sklvk022lyLtK10Zr8LaXyGNG8pppHFbzjdz
-n7TFndI1f661/C0Q3GyEFzSgIn64BINhZ9NmqQwSMotwABymFmQaocUUYsXxaqLbTMIe9IzHg06P
-UDAbvrb5K9PY0to0G/0Vkf1mCmtFhEAwaYQdAC/TMBeQtT+vctr/ztrg0TZYehlJUCynGYT5uGda
-8tAYYnONMINb8mOf0Jw2hF0jnCYQUuyCyxuEASifbxrkj7sfIOzuaxthwyYBB8R62gjjE5NariJC
-fmj/DlAU/nrd6/VMD9dARgi9HilygIdg5orFQi/ASchkKQg1vIRBE7BAuS3wz+D20AB7/KGjj08x
-o5E2H6OG5U5xRTQPEjHmplxpDnyAWBHUAfNkT8x1hKgiYJCAgGihzWOZw0maD/0oznp6lQk2pCH8
-W2na0qznPBpKbcx8W5ZpjoMmLbJtuxDlVeByQpdJj9kaMJS5w5+a4ZLTZMawXF6EBHZ1KbNJWF/a
-8KntZdg0p0ZbOO/9iRm+jSjTdSHviKclQLyD7n/TEvNdjnVGOfgekT61VGhocwHbdQQqgFKqgzsV
-/FSEQHFyxgTDCXok/NnaXXd9sPisCa5l40eAGL8ExmQOsVVzQLqRrh0Mq29n9vGrm8RZG+zXxzag
-fExUc3vhbm3T/oLHP96fULVvMAhbYfPXmQogjMlULAqVERTzeHI0E2YbXVSbABjDtcdxX9c29TMT
-fCX1I8DFnA9gdy6zw7eNcSapEQ46JA0xwPGW4Vs+oo0FGK8Z+N7y75NEcjMcA2I+KaJstNQ8VaKD
-4T/tfoCwu69PjVBrwFc3KBZ+MqYnYuSDtgsAeNQIE1trsm+yKeVytcym9tN2sHrAL2Kpa2XiQkA3
-FlYI5iptGASzSYS0a2FFj3wGvVOayAiCi6aYeiGSzLM7/aeujaEWk5Qy19ymJuKYER+C/uIG5n0r
-YxRvLc+RZjXfUU7bHUuAar0ikEs+15Yp0sHtBSYRJgyG8Unfqa9vZhIZHojiW3fK1UjgkyE+Aebs
-/22WzbPUjkb1XnW5HcJahnanK4WHseO6b047BbUCpW2IoyWBfv+uljjv3+9VMyn5bn3B/NcHqqxz
-v2lrACou9WGA+aaJxFt6kwRbsQHJ40ILDK1gGcpOnTr6z7xKc+9oTckvL5rhVcOcL7Sf4mBQAILp
-HH1z+Wcu3+Lqm+Me3SAYCt9mCqGjWcT2G+UP2nLAnBvinKDBI4JXNOYMoh+IaosOB30tA9ZV1wjH
-1+X8a2wLgFgkNKzhF7QZYdCJpSiTs0wC3Z0W9GZuJhFh0Ob2PygTadwuy/5KBb1imacCYRnBMX9w
-ZRU/m0dUkHwzi/gBwv9D9/2pEQ58/YtA1xMjEA+GIPUagJjq3gvN40em3thja3M/uIFBjrZKgg84
-U9vaYI28YWBPWCSPSGKm0liML+SQZrj2zynDERg+wMojm3GuBobL43EDwJ6WO4w9T7CKc3zYr80P
-zP/xKg5UGoKsU3QXZRgnBgmXokO9vZ7ZFXpo1Wrzf8xnZxlzwgIjFpH+vJS5GfnEzYAYdl2xWY6O
-9NsmERBw2UpKjfiDno6wfUi/jbO+ZjrKfJtHuF+Tw2BqDqHM81bCSmEtVZR0blWfnmuLysMclbcz
-D9fJNFS6NsXJOV5Fg8zxZwtpXJTynwDYKO1cuaCtM02ptjgtovvRNlVKt2ClyS6zbuTPfn3j3+GI
-b2DXAAwPILwynbXEMI9gTbCKg2EAY/BN1wALyz6rYNgHJcwhnptmeJZ6DJ5j/tBm8IUCgoX8jaOF
-5hhRzNHq3WPY0N8GgPcD+dZw7pMvIH6s+EMkyQvYpXwiVYMcoBdxoDsP8+Y4dA1hHLuG+4tJBbs+
-Vl37OwJhqUAapqL1lAhpoLgC4EkT/AOE/8fu26HPc4EJ2EoDu8gD6tzUw9yucnJuxBDf12qAqI6V
-DmSiY8cimw4ACLwhAO/QBmnlaBFlu6i/nDa5RySeHKZdi88TTFrV35MR6D01w9LCNS9bRasgTjIs
-JEqsXYvoaVdHff1lJl91iIMfbSlpFzIp7jq09kWeWu8X2TzztGKsVVDpj6mJycH8YSxQgSIPbcAi
-AjT/qwdFumBo3S5AqS2Jnj70aIA9bc18LHNGXssXkP/Sqr1Io47oMq9pzXuUs4iV0pXCpXED75ho
-8YVoso86xFHYhri2zmqIME5vz9jcYZzQKm2UBFoUpSGmVa5Z/nZnhlzW/LtdScFCeVCa57IC4woO
-B+5R/ZrtToCLpmrW0eLQptSmbsebwkPuSQW72wziScAIBMbOTAAWt4Ikz9nAQZ5iK2WKreDh2LzF
-8dG3ilSzT4ef8vUyJmL2iNiz5c6hZKlyJDdn17HhGWHzi7TS0JAXCcg5bH7Bgt58IU94opUUvMWK
-wQmoAXNYy2ajOr1K+HNj5yYH38dhIrnpEwozTbtxv6YJCV9vfskHmk/s719wP0DYnVlfrbd84k9Y
-Rv52NYn0eP3NV3D/Ks+ENwuNAkMlQafMwMUkF96skfAmYNHjJAjjBUuv8LwvgXFKfwj4GJ5MnSl4
-OZwfCjeBitJj2Am3nwWv84NiCPDG6xmEr+neD287f4K5XrPteZ3ysS0WmKGlcCFQm/OYcTkKlmlt
-UiegYS+hcZytXK4l7SXtqD356+FKPS/5dl7Q4ZytrwNOCE1nu0e/5T2cM3dbdzXubCEAZk/hMXjj
-6+/jfLqpXbXvdSBh/pACP8cNQsfeGsg36wD9fbAgm+u3DcCbkMcHP94iRTm9vN2aOUgZx+kBgfIn
-UNx/Ip8yKNh5wmY9hkBbbUMDylPelH6W62/zUH8FJ/ddIpwWfSSAl/UMk201EA8GAfyU/CZxvhf9
-NigeZOiZNXny42/7lm0zB/888Y57ZH9WdPsNcfv7vNuva3/Qomi2s88iQxzmj8KKdIzh84j89exz
-c/969scl/npE/rLw76uH40dh87AYi4EI01AcebBApjwhxzE/wnUjjeQy5Y10vhfmQiTkZNcE48aQ
-q91EAu2o0q5KujmOfkrlf5cp/gPuBwi7s+dLICxygN0RFF/SdyUVmfAr2i5bIqy5OJDehS0vkuIH
-IC4g2IY8Lhh5YcRNLcrkVaQ0irSnJgmGxSr7TX8Xa95IZ6vq6Nc0md3W2NSFqLRQWftrSEObKW+E
-pbYV+DUAbWE21trKVyrT0lO5/lam1l3mf7YvGMbzbF31aI06WvNdOOrKKt+alyTyIW+n/byVz4yd
-WOxWzr4OY0zezSTycYXupVryc+qBDb8Am73OyU1jZ0O52EkflWuC4gAD4nHtnhOCFK5O5wHlTDTn
-/KACPlRAsqRO9hjjEqdnnNaU7+L7l8kqcN6nPCR9WBaLik1aa8ryHQBySbcznfwV8FJnoo3U3t7h
-CHUt8Oah43zj3gforX59SAssG/gq3zl3W9MPoNf8XLUGfh87ga4SGPaNe4+ubXaAjXyxoW/tzwAL
-ZIRU0DsBZMVHRlzeUbyJONA1kb+2VphB7wGQGfxOYZqjGOLABSlCM91KmUojnc68rlbPTquSyPqN
-JOVnke9NPjIoFjlBcME61ITpevxYHphGHf8Dy4gfIAz3WxphYYJNQkiCkkr9lMaAeQt2K/Q9Mfo8
-pSHzFIGhdMsAuxKCpixEejKrmzhkAw7U4SXiGhRt7cr9m9PRJ+X4cXBFgrNqtgM2oQDEAKr89Iow
-Fm3sPGewXMLo1wl+FfnIn0KyaY05PgfEy9ZR3JGWY4LxFqNJBXC+j1PnEzb4pnytJR7TANS17jNc
-mO8LQC0RBSPomObTPTrMkPZCMRvnGnoLM8TIMM/koAfW2vYOQfheiPp8EsMwXl87BrZa4xnsHiAY
-cVTHVHubtzbscZ/SCQK7wU/A7yR51c6jtRrwL5QRCmOOWYjS/WMZNQJSmrMpT67jBMEHpXkd50hp
-nTzmhQdDp86UcQymcNR9PgdTuzXHoOYD8NV5MZHZBDsXAylQ/AzZ/VU4xJkfcUafYjLxvRxPVLR5
-7ZI45tDD4sDYlm6w+zDo9evTwvrIs5asApYdELOS5AJ8U/ubea/aYtb0/mV3MAxNcdEON60wicMy
-rDTgASIbDXF+TphAdJHB5T4sp8/KIX+K5jf8FhWxUqxuqrN+6yR/p68uGffbilqOr3+fD/599wOE
-3b0Bj56vP9WBUk9w3OKQl4gXt534pTpCuwl05AliJqZ9EBjyHUDZzSBKOuk2Lcn41Gjn4sA1QaUE
-gNzxviyccx97uVkoRNiSybnX1KqmOZ5U942PMPoAAEzh8MsMeNG1CpBnwMyjjVmoy3/7GbZJSx+B
-cyaG7euY3obPu3Z1NvreQfGrA+0MrSkj0Im5uLPMkY3vYyTPiX5GUrqE57U1aHSRbwArfZx0CPzO
-WH6GzLd7Aw0CAE9At6fpcEMdfHTDNuZa0rjDFnwGTWDNvrU6jjDdq5Tnrko1oYAZgIjzOMq/76Gl
-zljHkpuGp/hzMNrIEA8MbfKkGZ6kf8+Lex/H/njQDu4ZHSxg2F/77z57+sl5qV0uBCBUzHki83do
-hdf2x7nvCgC8J9aWiQL0Up37fF3d2uFH02/q2mENswcZQO82hfDj3Mg/g+H6Kw8GBIRLedEEt6T5
-laYFlmYaIQUAW4Ln4Nn+o8EvgJZII+KueU0q7aSng+w6v60M5gZ0S3GT+UMo+0ieGmhENp2doLdd
-wR6iP6kJnpbFn3I/QNjd75pG7IDF5QS8/qdcOV8Fg4fg17xPF9oiJG94xYB/OSfv4Be2v3cQ7DtX
-NftncT3jslG9n840S/sYvYiIEUsmgRd+NcFmKVNzAK3RBzaP0JyEXTwW+Hs6+pGCj7W9uzGRxoAZ
-Dc1hl2reci7pBLkcz/P7DpxFcl7Z3QAb3/NT7ESFU8kbgyqM90sUxyAnvAMdBMlc6j3WzaUp/204
-2/oCEmMma4ZvtME7Yyv/Ww5ltYQZHKdmWCsIxvV64z5RUrXEPAm5vH2Ny7T0Sx2cNs05jyeOkMu4
-uumNh1qVNoN5AgA4g9wI0zDklU7SkSkd/MHrozEsPD6WvLZx4zdS2XfpfQl2o5HEShR+QIi8qlRP
-90upv649C1mgJFCwIXxvJNtAVR8/9izeNGDS1BXGW7iYblCsMIaGxvfRwSRCxfQRXWvbDKueJhOI
-g5/NGw67Yfq1I+JUVeyhEzJE0jTiqdpeIS1wgOFiCjFohWlIjckAJ0Zc0jmuzBEnSpapANguZbaU
-QX6A4BMAp2xF3JsdMSvr8pYDwMXDUGsaX+mAoD/qKszfOwAAIABJREFUfoAw3G+YRuxrBb7mRCZH
-uhMfOBxR/iTEjZhRlzORV5nWKwm+aoVRv8lnkEwdDuZ8gOBk8smYm8aWKgMTHLZ1ZAPVe9F2y0Y8
-LabD3KG0t5lDIH8DwEUjLAx8MZ4s9GjOrObnOeBBiXoOyWU0l8aVhv9mGtzlF+c607qr1DTlyV5/
-qutSvc75a5wmnVF+7YVa2U/pnKf34m+FI+K8c++P1uAl3z/jku60xqoksA0tWfqn9PAP9zhJofXT
-8rac9w3k8ql4R14Pp4IUe9clKwFrIDMPEWg/qS+6W5ubA/t4ZdvrOsc1zWVumzl5DLbfanx0OtOZ
-x9iRTmPI1Wm251gMRmmtb6kJlTJ+U/uD73I/yLZOH922vTjWMjTBfpqP0QePoFXW5QoNc9Br+0MY
-YR+sEb/9G+yqqth6RJduEF0AsRaziBH4kslEjAHuhbgFDfGWMfr4OJVNcFa0w/IMaR0oPyZ7s9wT
-w2s0dztMgLDweQLIKV7LXCHdbpmsZS7586ZFrhcAjEalZlgMMh3AWMifNN1/cQvCFT2N8+znrutK
-+9fcDxB2961GOCb8SwDM1D7Gyx2ajMI9JOAJfgqBgfDAx5KfxcKEjWo8qUWZ7ang/QTBPcyN1tZP
-tUO0ZP5JEqEStFPx6lIvADfvF/bC3i5t7T20yd673gyRLrRa3JCPpZfmqJX5KvmH9NAeaysmvPEu
-nbbhzHqyHMecFNbjEZriz/sccVTtV2VpzkGr8UGKBhCM/AdQgyarFZOzmunWNYz2NNRwgYu1X3qm
-/9OsvdwrTCH4Tko/5FEpwLeB5G/amGO+J6ObpvwOyEVdHM75xxqntin4FvJojRMRbJgy9Nnj+KSM
-c/3OLGgCv51eMqF1HJlF6tukkn4u2v7wW8wgGjEXza9Rf1QSZGPgoB0uroWxuFQkji402+e5LxER
-1wCrOAZ2cGoEVKHSWyv9DnxN1b8sp66Z9U1yqq4VVpECem9hPcJhErFA16uES36AYAfFqpbtExm0
-vZamDr6J7tT+Dlc/Kxnyljl9itYen5McvI7KcFylHat5jcpMGmKv6B0Ap5xM7fEQX7S8k9TTxEWU
-LzTGxNc5z590P0DY3dc2wpJ2OGwGweEdB0beiDEIcAeDFWmNL2lC/JUyFGEPgKR5z5qm2+TBpJ4a
-MYDgHAtr7UUf5ms/TzhecThAiQ8h2PFSsoxwMFBvs3oYuiEfWdrwJgFusVi1zME9nIKtmz5ILOlZ
-YNoRJyKFrWWveM7sSGXpp8o0cJnra5xlvJ1553cQ9/js/d1Nq+Zovn7I95L2dveggyFXzusZ7nXX
-sI4AaG6znr5LwY/c5b/Wgmj1H9re/btuEkKW21lqvnR5ICNngCbUQWNLoA3hQp9K68tyGA5bYm9j
-gF/W+oYJRE5cgl/NPEPXjrWtFQyLnLRU47wj2fDMEeyRKJTlQCmT8UXz2xqhVMUBeK31RzHfNLWR
-tfXoWCCNgTzqYNh5jB9zuUkLWmGiJzyUOiDdwNr5uGqAY3UNcGiJ9WmA9w6KTVV0JeCNc+ancgS+
-FfcLrTBkjg/AkxreBMRdE/wFEGaNsGn6ffK2P7kYy2xII+P57vNF04R8RnR05JVWGYHaEwAPJhOS
-8d1sgunuuAJbTOnKS6JKqT/pfoCwu987NcIKgR5HngyAOAi2aQFADKG55HvBQ5KiC/eajrb5oiMC
-PIiSCTDiYDPsBFkYIl1LP+r1/LgG4rPV11MtiRnvZuyGhklEsAfkIe0u6qYHkB2WKDdrh6lsbQIJ
-xWxrX6Rz+smF1AchxpSlf9zzBM/amMic9wR9tzgOzRSnLT+PyLeu1cHzerTs3U19ueURkXpUl5xj
-cA+nLejn+56xM37Ulzr+eZebopTuSnGIP14j53zVturg8zAyJ7twYKZ1DXvNDIjB8wKQIZ/SuFuO
-6U4j7bUQsI2w5AbAA/xSGPXzvWgtHTzg8PeVpIW3FUabjIoKMIGlEFDRUGT0G8dYGLWdGtw3z0XW
-Ps/Cv8F1orfKESr4df4MTazpeT8yO4BJgjn4FFUykdjgVB4H7gUQqxRQvHo9g4b40B6zPbL6B5pW
-aoWXudmHb5ZrZg7STSSsgd0hbwXCfRNZakNBUTdAnFOjJMtJGXdmrJN3SY9yJgX8il92WgPGgjQb
-0lgS1n7WplA+xS2rPfGf4pfsfoCwu9/aLCcSQLAS5B0QM6isACEjKovdLoTMALF2BooEg9RsV48T
-ETKXSO0vg+BJI4zru2bYm9GKx65jCEpIPkbqLAAgXcmfZgwOjGl8Rap2GLn2/6YNprx5a4xBG86j
-aWRK0fOGv7Ev5XbW9GoGQ+XoiLVejocN4X7XAB8cF1PEbGZiO7+hBdYh7pafB0r6KEkBsAc9VFLY
-nr7pKOqtGuLewzNcTwWY8qdT+lv7c7rahlf3X2uDh7YAJE7gdwLDH8VPpt9BcMZztxgsdnyoXnVf
-cwmUJSoLsNvD3lf1eD4urmuLd5jWEvUueAnaR71OPqEHjeSbJeoIv7kDUE1mGJ3cILcJgMb+MLbW
-G9YXYmUY5xhy9ut0W87L4MBbFJWgLep9G8DwFjVpvsDa2AS/jwBUG9sBq0oFxdoA8lNAsa3HNcR0
-2kRoklfaBdOpFfF1OqjMGeBa1QAfoNfsVSuM0cK0VcC4tdGcnrMgc1yJsJZ4qURE7MAi7inaX6Fw
-TUv80tPyjWw2gfqoKFvBbtgD9+Zr7dafcj9A2N3XGmHJidr+Zh5BcTzRQvnykoBndEoEOKSJCGkj
-8v4iUjBmpDmKMr9pAuQhLm5r87WB31tebl/9ylzb3tGliz9lAkh6beX5mesrAFckFuf2B9uhMSHw
-DOFsg/BjIc2tJwF2CpvpBAkpubSkGFUylFWRLivP+56bK2dAdwy0fAK+XMbm6OE+L1VR4yb+/daa
-b/LhgW4CNDWsH9Lnu9ysB3r+T6P65iaau9/KAYTQhwKATkbwWzcSfQTCGBBLcCvlcUPYnD/nNsn3
-tAOWtrawLht4CwBIQHbzpxZmQDyBYc3KS/20Dvu6q/TDf3lgqHPJlAqR8DGSx+Bc46SMeyeEGFNn
-UodtsfC4aR2X0VltS+k3Z7MY8z22e4LSLAKE4CZGhfYkwTBpidleF+YSqRmm32H/e5pJCEBwHMGm
-Uk6beJbfw/bmOdPYtKfP2kCMgWzR/joYvsY1IExyK68Ag+63jBMFWKy89lj/k0iJe0kq53A9yqSM
-jAwN4GY4ah0AcN6ot5WbxbcREcIm1HeO/28Y5990P0DY3W+dIyyV0Dg+eCP9QV4pZSo0ORxLDIrK
-NC5LC04rPwVvY20wiDBthtG2zcisVxJMklbYLe3Iu5kpA4/tG3rdoxRPm1VEgU3gISFSGfBGM63l
-r+lps4u0HLMitEWqMPdCLMyjbs17985FW6KimidYwysCOk+r6OAh78gPI+XuIjSub1Cw96TE9ao+
-5ceIH+U0bhvpPgelOeSvK6hbClfd8NnjSpHdLviwPP7InM8MX3GUD9rgz3U0oQnAx4A4wO4JgBWg
-+NKHW+zGO1qAr1KaUdciy1u8DlOMeAd0J7gFIBu0w2/5ZQLAyb/nVVLNsmZCZGaaCUUjfMTlDNd8
-OY6vYSx8pXTcg7WwRXhcFmtZABlZ+AjGz+KPhCZYcvy9AeGPr/dRm2DiIKud8buobAPE56a4DoBV
-AgCXcDub2EGxLt1nHj/7KirVBtisaYgJ7JpVUwhr9sQjEIacZnOCnPdCPu0aP7v7U/4OFRxxM/gt
-J0VwHrmnq4l/4a+C4ld9GfdJsy//C/cDhN39HdOIToxS/FYntvg7U23uAEh57xMg2eErQtGynDX/
-zqMC7av42b1BykTFvBktb2KXqxD4qisw2HDpwwX1xa1m2+iwBwbj9zafLDzzFoFmXdhJCmqaL35N
-qSIVELNwKL04+y0i8fAxpqscD2RstpFC+pRd3Odbmgg19gifaZ81xVT/haDfGNtbuan8ka01v59G
-QNh6LDJU4eUhvF8af2/VV6P2jzJ8AjnKxDiB4QaKAT7woZscj3PwMK7KAwa69PJxW0n2EetCyW+X
-eJFTOxzgVj6D3XVLZ9MKo3blTBwPxbQW62qwyA0+sqOx0ZIZLQ1cicsyUadJGdMAtz2cU3LwnM3O
-QQMaY5M0wCUl2hIPfkVdT3fC/amMuOIEm5kzQ173eJxhzFMxlcD80RFnExgu2mU/Xm0DWhy7tjY4
-XjiK7fG4R8xWaoP9Qx4Ih4nEpA0+AHCCXXvx+6z4VGuG4y0sODbsaV1BJRyP8aXJruLjQ9ylbNH8
-Ev2ZzABZMl8ByO2tuGQ12U+E9bhVxGFsRiXZv+x+gLC73zGNYJ61y25GacITOvkn84jmWIKMaaUl
-R1onNgnGaAR6pRKfUr86KuDXKH4DLR2wcq2CIVeGi+OLuw2IDT4GLlYAtwnfA9rkTNtlaVEaAVSU
-pXEJzZWR3wUZa4LrkGVLlTo/fYFu38OIgbQ8VcodjwssyPu0qZztaz2N8GwV+fvO5huWds0FRb5p
-wkQDn6qbw1Xbm9pIHfOf7j31azb+QRv8RQXVD5pk7ZxofT0tKsU0YiUgvrdGxznSTI01IaKfNcJR
-ZTuRhXhWpV8lMNzA7ZLUJAJY3cDwahVL6uiY03QesttQ16ZxXL5Sq+M1xlEnPX2zfG+/y5IDUVC7
-A6N63MwXJMakTFbxN+f8P85I17o3Q7hd6v3X2jDFfeN2sBHOPpuq6JPzFtpgAsH50NN+rAmOeWXN
-bwJggN+qCbbtPwDxCkZvXRtMmt8AxwF6SRMM8Mt2xd5fwRz7JAfl+SuPRlH1quTpDyd8fYkzSXEM
-vBK0ZR530w5HpjNfOTWCxHgq1lqzDCus4hC6y/f88x90P0DY3e/aCFvzi3RCEKedjDx4Y3f6ktgY
-+JTGRDRpgxFfgLGYnEC5MnbeXAbhEJCB0rQPgrS4mytru+khh6IVutG9EZZsH/HtGgbAV6nKGdQV
-Y6ZVOaJNeA9NnIRr6ZDmmB55nAa0lSTWcQL2lq+Mg/HdG5Ip957Sbv539w0gnnj3UeSlKdCaIH6y
-3uS4tx6P4cAL3/X5W/fvMPlso/JraYGQdZDA2l/lODaPeL+DtlHB2gEeKrjLclyN4o3qAi+Z0yUo
-XkUIOO0b2iLQFBpDITDsFRII5tNnRCR5XFmJSaXoB1oCwBK0ZaAV9yBrZypBYMyMaeB4YZQz3Py1
-Mw9kGVQfN6ucU6MabdcLDaLNgYSjAVki+kB91dIIqjAW0B5d5RQVW1LNIVT3EW0qMwDGPI5a4QqA
-D782EEx+teWv9iEHfX54oxxA7wF4m/a3g+TQlKZWN7TBofUVCQAeFJY8q8t28+zlKnbGWc2fFaa/
-A9+qHc4Koo4JJB8aYdIAW70tH82KviV+on1KE33+y+4HCLvDa4yP+ei3Jz4nlonv4G2dKCz5xCYK
-u1NA4V5W4ykK5yHG0yUvIua32tqoUsBwAZdE5Eqdgl1Q7Sh30tN7n3o/bEg8ypwDc2pCu/lEC9s9
-nVj5DrsU2XLLQggVGbB51yF8om0xZ0bp6WcIXM4RJnpCWtGU2dBe/9tBBNIqPERqTfldd18tdLfe
-mOF+hRwaE/yd1sVQk/y1Hm511jDB5sAzf398Prrf1Aa/sYb0OPV43XWjEvzLgYZvOGLtcK3xDPGA
-BUHmuggMbiW5FuOlIVhD5xyVhzwHQcXOl+1KGVTxK3cA5aI13ncH1Cir7QDFtF5vaV9ofEuaoEP3
-ssV6yjCXVrLzGAfv0MuaY6Jmf6fvGHwRBsOFs0T9O/NoAlfuIS1+jz40wmkzLAMAlg9h0AK0v0pA
-uIFh1+rKL//UM2mD9dcGrrp8sxwDWwK3wmYR1swk7ATKAYTFaZPMISoYPqUYr/eUdNYjz+uYdil3
-A77+E6sRHfzeNMJxm6xSjNI5XDYQavKHP+1+gLC7bzXCIjyJ8yTLEC54sYW/BcEVPMlRBoyx3JvD
-hf/SQjQ70kRI84unOKtgmDuh5abVZEG9Hiz5Eg4/3SeKDitLeKFUcwiUfg+fYBN8O4ATj5eF7CQb
-VGZWFnnqdGSobqCrabVP+boohXJtJ/ehy7w+FlXYc04eh/fSn9yNbEueRo9fVcrYwZHW52+fQbDU
-25w00sPTprp/hx3vMfjn6uZNUPn6eYMEbJ7dr6wZCMIcooHhw+lJCpZZFR3CK3HwC800FI9x1SFO
-cq41q6xlymt0ib7kcVjSNMEEgAGaYRohQuurcGWJB/+KGPLYR2nXbuPbAXHR+Pbj0pLpbhCrsfaL
-UhYoAXICPLfwlDaWxU/9dk/hV2WBJDOzWHPDKid+/NtOYS5RQfAtDBo+0kDzBH73Z5nXBryHFnht
-ze+yDYBNA9jpcpAHrSXA7wUUB9BlbTHHMxBWFZhAQN5mHIY9F9YpHSqZjde/k+a/AnyLlvcEvgx+
-2bZ4k4MxeVZtL5kAxjIhuVCUdnfK+dfcDxB297ePT2thmcIUlxUBZWXGgwBYQgzxE8FMRIbwRJTc
-lHx9Q7eBwPB2FHA8dphAsFV/NnLwcz9ZkEjzE2jvQC95hbdZozR63sI9ncIAxASQxWAmkfMG8FHn
-wqi+i4kEnRUsIrSJruWbypI7BZ+N8VlHCnIrcTeX6Z9y3VtZMwZNll58dsZNdf+tZO9V70WEVYbw
-CaZv9/ht9wUI/r16GcTngFTgC0AB4Jvgd4NhAsIH7tHTZzV7gDzEYRmrELVl3xzrnXGtCdEUFeHX
-6OqANjW+2gCvSJpD+HWdm+WS3xROnfyN8wRqqHGhHBCtvDERjhRip7T8+FBj6L5pQ8Hr/NbqA2QE
-iJWKo3yfQs1eZUSnbgZidrELbiNyjbFbCvM/lSQY0OTgl0s8/EKb435pguJnVe3wL3NA/KuZUNhO
-M6PzhAnwNtBbgTGdJHEDx6EJbmuxgGGPLyPl88FkISl2i//legz9UVn+Jo3vpvEpTbxM0ou1tuYb
-BYl+5DLINE4fqOmPuB8g7O73To3QYwLLb4g7CCGY8OVGwYNsjK9tskhkXtp56xEWLBZoh5NA0TQw
-dpMEw9z0qybYkvWq8WkRTfiUOvu15jeqL4qiXhHhc31RNNi9Vu1x6R+FRbbQDcDtmfIzz3zPCq07
-xKlzl/6riQTnUxKU8gIo6N49bwhK8nPJqbYr/FM5WvrmTqZ2A4BT/LdxZ5s+ze8Z1tfwP6q9/cdq
-EuGeGLURADiOq2JAHFo0hJfAVCI0bBMJlDjNOA8q5dvYRAvtTbRZNL5DPmlp1TSCANEEhpdrjVkT
-TPbEtQMQ+Ihr/CniAF6opXxaQjJIwStwHDFX+ST18s1ezTy9L2SjcrjYlkmVd9zG8mUdHgXAMxMg
-ox4eo1KM0JcNcSXrof1F4+UAuxUEC4FKCRthe5boL3MtsKU2+NcSsQS+upYYgV8xizA0wqwFBjDu
-2tA7QCbtcYB5Ar0GMNyHfUeQtAzSGEXJp6sPvV2uHQhfNb4i+6xlrsNBcJShabZ278PP7fD+RXhi
-An/A/QBhd9+O/Z7EfJKJn30f9i9VvrTh3ppUKJx5wJZwsoGRVJvXCh9t4rAqJY8kkwZjTXvVqLVJ
-sZQNLBySkQJkxns9zT51GdPxWeyoJpfrZj77AF2Z0o5J0AqEMprivOms7tJS4SmBtIVHpoY+GCVg
-7I8yjeOM/jPPm83r55R+Heo7QCM2OdW6Egw1TbpmnlI1lx9veStztsdaFdCiFnoRPbvypft3ePhL
-Y8BUtIVDqDv9hVDTEGDKcZJgLcuQZtwQn6st4mUAu3XJHP5PcTWNgRB+GmBoIM2kRa1tq4HLbPV0
-a3nHdK86/Nij4Zy1PKwQXzIAQIsxD54Y460x/mC5EChxP5p3NLfyvE9vOeYU7lNZFODhwhkk+frF
-BZgmkBu1Mi+NMa/IKESHOgUSkIyiPlBbuq2d5uPD4xrgF7+FNbPD5foQMJx+T/qV1ha/VcTLiJSR
-bnwSwJiIdbrGuPz+9QaE7YvfN/kWwKw/kD44m1lFnqXut50utr8o6H7DGc5Cb1X+sPsBwu74abcw
-dMqDMMuah+Mp/Mgm9tdwbYH0oB0UURlvfZrcCTMITmHX79Y1xTtuRwb4lWFt2hQPZMgJWNzgYPVa
-wHEA8zxL8M5SeX7uJzkcIJWDxX8TBNrCNW8ycQdU0yB3fwufZezI8x4vFchQnZWxXDjsQUcdWRxn
-BRCwYLpilMngNEunpnLPWgGcHX0ec9TvMeU7V5W2tBMQYJPZKW/+N2z5S9cFpEkCfcv0LcxI2+vh
-AMRERKEVxtoP8EDWooaVynOZc8rNe/PzGL8tFYBffr1cSVSFgW8t+MFZ8xzrjFpj1PIiL2Yu1emd
-+XKMZcxXbm4W8bXs84G55Hk1bxtvSE6Ql81nkNm73bnYsS6sJAff3wHN8RjAb9RFgDl4fVRb13K0
-wlTwxbo6/iKJLH0sTUXlac3VmLqk3+3RAHdrp8FeeJl/UMNntGmE8yctzKC35tFQ9OAouk2nAXwF
-NuNSbMJJHGY8j0FfQNys8Wpj3m+B8GdQ/Mjj2v1H8HZp0775ujU3WbIGgAV+f+jeSkYmODn9SSz/
-mPsBwuGM/srhRziIq4UDEA/pAX5tAsE2+M52jenJbTxIDIKYW2T3Bh7CqvGxFGpSNMB7XVtN5/YA
-+EoriESgMqtBbkfnf1XQTCNk9batXbWtPibadZnTqjp1w6zpVr6R2w5HJ8BtsonFn8KJB93G/GGO
-MtSz52cul2NrVDVOwmgdo/QbgyFDl5KXmVbaqNJxV9IBgdJc0evQqV1v4TfQfLizX8xfOd+/yGv/
-PdcJPh42qx/HRMXiAyCWrV3D+gbQ4uPNxIVVkHnwAg2a5rXGTTv9WoT7PT/RVjGJEKk2pqg2w8wT
-IvzNOIb/tjYv/rzVQUN1eelBe8H7PLDHN8c1lj+BoATAFvGRd+ADM8/W90EZ2S01hjXCBIZNtfKl
-477V6ZHRhcBlE7k5Dat3uDyQmQge+vLBwMRsxdgkGPb7MEiGaUTJIwkCJeMS+Na4vQk8j2RTvJmB
-aFBqq8oJhj/NBYnDwLgRl2M2AuNI8/78V7/cUCiq+whogF6RDY6XbhMS/4y2LatgGPbT9Mlr4ye5
-qf9T+L90P0DY3WOnhlNkZiIMbguhtTRogU3sAxjuzipxHxlJB9EaeoLgHbb0zmsqJMep30Cfbwy1
-xDEgLBlA3HvFG576BVphtJ+zzozwtgqO9mnmrm2eR167jwryK/iCx4bXWQl0iZJui9p6nF38vZwd
-8QxaUFRbOStPGgMABXM+gC/79QC+CUw5Lq8MehVlRVJDNE1Jl9M9jzbPZUG9CWGuv+ZpZhu/yXjv
-a/sb9+FmE3OS9Mdyw1yT39yvAACPhVYmj+1ywGuIAyhLMCxSeUI054Jf2DTgIGmni6vc63bBeAuh
-LQ15o87iKXbJx52s+1tDXgAwj8EOV6B/pNObCW5hhI0Clk1TqxkPANzz9z6wG+JGdtt4Dj6yMYLg
-i1lEX3/B7/ddaxuiQG3MycuMdC57Ys1ka3ZhDiGs/R3A72PxiWXYFCMJqNG8vQ1FSkGgeMBEOYGm
-PpU0Xb6FuXATTm9pZT6oOdmubBY/NIUpZ0sHmP17gPgp4SXi4FY3ABZxQNy0wtLAMECybN5kj6XZ
-zMDbyvUfdD9AGM5mvWOPM/5Z9Z9aYQvga3KaR5TbH3euifGUdG0+mFPdivUmYE5hJeiBxME5nqnQ
-oDX6PCVBJGaVmpyMpGWWz5etIeRw78JfZyH0dj0KvDo9srFumO2Y62s+uQ/uR8KyErfrsqPMm3YY
-Hp6bGF/k6SqHzmS4XBmnOhoJNvJaQTHlozhoC3YRLivzvOgQDfl7a97FjbRgr8F622+QrY3e33Zd
-a5hrsD6klo1tvDjpNbtSXEmnSU7bYA3a2+TCtsOpMcYnfCe51FnBDQCLC8eSFnNbB5uP0Aqi6CYS
-7C9vIkpVAgjKS6nezE4/854OPmrF4SnjoHqES57DfIIyW8aX27b4aBrFx5Kne30T5p6Uhx1rjRjA
-78BKkp+xKVwfuqkgO9AkNxDN0CX7pfymnbQH9iP0AgyT/3FTCIDkx/bGOrzWD+Ccgwnb4xD4xS+h
-BUZ/oWFWUcEGOe1XXXUYb8CP+0zNkli7lTYY/HIzKyieTB4qwP0WCG9Mo2Ea8YgINuBa1wo7GIad
-8G4f+JJWBdgk5/4b5npxP0DY3bdje2yUs9nPGmYGw8iTYHi+s3UfSZoj7Vqug+C6qgwVui/z6pAK
-pqnHWhWRapuI2MLvwHHrjupgMnoy3ipUa1+7wJ3SjquefOUlcMQd4NgrO3baj4bXXq4I2nYtgraX
-te/ijfoIJlnaJdlgI4AXk0xIsxrwSh8B5A/bTQbAqKv5Nzip8RqD2LpGtymuAWQb87XxlxbE3A3V
-T4T1iT9o/PmuvrcMEzns4AgxZmLHFGOOQypqu5KWtwHivQ/AuZRV8wkjEH09bbZpPndc7UffSFnn
-ctb8xkNX0Fvzf+Fqts5leB32dTekqR7PmPsepBlW4pvFhMP7jfZbA7h0myMNTQnecnTnv/OXe1mi
-tUkD/JZGVXXpc+Vno7Nepd/6CZpS27aoIibx1bi1nNQ97rE8U9jfiOzPLGsKiQCMpBXGOJCf102J
-93US69BZXLmKiNkT9sN5css69831ccJYoTlxayv0AsDLTY9+fQlyx9/TNMLq9sGSphG2VJ7H4xgM
-G8wkvB3QDrdh/JPuBwi7sy9H30VHA71JrKkVtqIhZtMIkQ5Rp+V/F47JEW85VbpWRcQkjmy53jPj
-GAoXMByiYjpfQRp44iZBaKPtrh0QCZsyEwgPOp1Cs0jRGLdbMgaY4t7lo15CdUdzYD86eoDTlZn/
-m1D6mEbC1v3a84EJog2lLpvjd+NTYivo/g1AbJShLZyb5ABQOvilc1tVKZ+PE4XtuD9RL2G9sWkX
-mih9ehGwNnm0x7VbvtUz5dOXjLdaP/IjGhj/Ydx5AAAgAElEQVSdrphngNYdVsn4vEIDjHWNNwvq
-aRUY88Oq0O36Wis9wDyXMPXjGhaiH9CTZ2B/H0bQGtpxDPHACDH1DCREzjVd1ic6T2A42ifB37If
-oH/uS8bVpe/5G59gcs40PdOE5qXwkLnvUVNjP6UM0NQbCKaiTAs6qD5He9Cjs70xtQ+pdRWxBftg
-3R9ZMdjnmmw7YXMtrIk8GgA4vjSn/oq+D2bhy/UKaQvwy8eMlqFRaSBY5kWjjhZsjelBk0bDYM7L
-yzSljABYBrBH5Dvonc9SHjXC6rJbYBrhNsDQCjMYhp8A8W4LHgTepfW/4X6AsLvTMvaSD4QlBIYj
-Thz02gCMZ5A8tST/nvHh4wyKMsGFz745Y2AJllVUKAHBAWJGDpGE70qr0yh9CldOrNkBF44BghUM
-88IXSws7QB/4CcfpkM6g/Rg2vYSazysNDTHipmuJs3a95ynC+RDCaMIcH1qL3v7XAZtoKMFIVqUJ
-UpAWmrsd178OVUHx/vXbHStAazMb8VfAM43RRiOZg/sdYaIkJqraqIMubyx7qiLbMuWcMt7TX8/7
-Pa4pkY2BiANb0JkZVryGMM/1maZSihTrj/MMPJMXFLAqcmyQDCqn/lhZmxq8IumopiUR6TEuxzF9
-o+s5mO/9H3vftmW5iisbIv//i0+XtR9QSCGBZ2X16VXrJZ1jpjFgDEKXsCxjJJIo4EGN3693TG8+
-8UAHxNQZM9/1mtxZa7R7fq26wyNl86GDUg37qHjbaz0Gz5J/Il0vYH5zIw+GjHYtXv1x7bB2xs96
-9KSmzeLeXb4mxzjhBcbIu9ErHB/WeLY+ciABI9Pa/zOt+obj2jr56ukNckLSTUw5RHuAiHdu8+Fy
-7TieIHgDS5k+HgfNPPNevMDPS/5LfcpniwUGejgEyvNLQAwCYrfs73edkv/L7QcIx/ZHHuGYsPfw
-B+/1QHC3AXBTdtHq/eqf4LmUqdWN3xY0F8CBQuKqV18sOb1I9X+3p77BbuxND1rDWU6pNCppmtTS
-r6oWd97dQHVtPS3fifOOuucp95SLjfUCbQpEeAdr2vlv7l3/XfbdKA7DBaRCbEZP8lvyAEnUyL2s
-VscwHChVmSwB8O0H7A82YHiA7z/ldOUw7xk0+xe5kNVDWjz67Hs/05NwIhS0SvPU3pWj+RsP+S2n
-dUNbvMzvLJcq1uZSmrjNb/KdTPIlVjib8k3RAsY+jselZrfbNuPATchtbYgaNqBE7zdTslcy2Thv
-zMhHDd9kT497Hm84C6wMGQJQMd6Snzd8we85Bq7kErIwWdZROtc129rx9Azr+bubnbc6IEbx+1VP
-ucTXygtztH+8yeJNNwGyNHGkk3bnePN62lkhug89mTG3C6GjN+CtF/sWiDLT80tv8GM7NvhZddPe
-CMVd2fSDk4auaB+Qsrw03B70pdMCjLMJ1cHGvCcOVr+eC1lk7ryVCQ6JMgWbz7dA75n3XPKWAY/v
-MRlfmkPIwrJddgPE7NcTFAtg/Le3HyAc25+FRpAX30IkAvAiZR2PePfciUnv13T5/538XjaBytiG
-nuwG+Wb85dUdGk4+4lMVJ7q6rTiQXVAtLsqG5zmvFHSiIrj3rHXRx1VdyvRyL8Ecc8Cv+aXApfs2
-yiAdvxqVvrfv1JvWQpWxkpVGZOR3LTvGdRnySevBU9bTNkDtf/MzuXJd3485Jfqr/D7b2zB7zQXP
-zuHPV87kAlm9hxHZqKZkaWXeio7mWZJY7TCqr1eKrMscajBhtkvL+5bn0tbkV0rUBfwiPFx5DMUz
-qSnaqgiiCpjQuNkauoJl1SkKGoVOmmfHRa4q8KSuX8gseTf5o4wRcGTDykAlIq58zoKUmVEeBG16
-Knm2d/FWR7pX4/U63jr2Mj4atJnHk9qLli6K2fqx5rX+es6vVUfE2VJavA3CpeORN19Y2/zm4d19
-6rPbjgiTYKjdClpzPCtBMT/2gBEaIaRrxD/ENvLJbjUOroAgL8QZ6r7bsPtMhwHFku0L2fNOZ86V
-AJAGguENKDfQCUk/E9TewfDzqc4ToRGrgG7GC8MrTZCbHuA4znjhYI1vYrH/5fYDhGP749CI2D+o
-eNYNcCsswkFw7FKOfreb1z9Tf57/BuSajfuwqSL3VFRq8UuRnRaGxrAZs9mv1M5lRFmg6/t2ENwO
-rr0WX9Zh8GauhPhysEdPzxypniQxEGPlsFN3R2dvRud172OPblRuhlnPnUVybsaGypRmxZn3zka1
-JcjICLmY+/lb/Tg/ddvrKT+XLfB2OQQAQ6urAGrXp/GrMdVgG/uQAYMu6e0hYLx4rCZpioSdqTpv
-z3Nc2lLLp9vId80f4S4+qre5FevqUlFvjmgwJ+glRPE7i1R4RM1DE1HRAzniFLDIH2BYz2uA2XSv
-9JllU7jv29QRR2LKZyGMpBdk/G1seQVrgNWvcgA0+XChlVy638SXwp11txjINd2LD3OeK28DD9TB
-GLf5CAeZvzZkO/KS48RmpJiF8kxdn3Zx9yOP86ajrqsvr+U7bukZtjh56x5b4aE2hkOEfjALj/AO
-l2ihEUKEFzHuQ0eXa/72sLkaxeoPEDJNT/Ea+ZOSsbuCX88pwgS+sy6YT2/wOxh+PoBjepIff7Ds
-BoIlROLTb3kDxj8e4X9x++5NSPMCh3A23gxvsBWvljfYx/Fo+Uz9N/kfO/8HW5g2Pu/XW9mXppp9
-umaUUWWF8taWkOrpfZTvA7gatsy3VvYJ5F7PLB2euQWGI18b9jqvaXoxMi7pq+Gd+UBnUpdEqyOF
-t+ocxOjva56qdaNh1x/SiOdLKxMAp9Ff7bjq9BliXwmPlQeKJ2ZnUcdtreQXGlzzygJNcHN7p+fG
-cy1vnDvPJFhvPHooohNcAPXoeXqQqjOewOpIZ+c0jTsIxgUeewe9J6X7OJOPOET1jh7lF4/xDehq
-+irH3zWm51ocmTqAL4guKl/CxfQ+o3eF/F/y0D3BIRNR966fTNhg1pB6CoIRvBcgKHWS6hbVNxeg
-v2ODd9nmNysavADjGxl2X4RLePPr5LvITnWWHUc3uOoVdgG4T4Y21NJkEQbhS+ZgP4a3xTqWa9dy
-TpIkNsiD+3Y8fNG5MZkHe4J+K73DOWYDNiBePVotifmUzJLUAogJfhUEVx4SEKtHGAJmb2D41Qv8
-zPI9LgLgBoKtA9y3X43jou7+wvYDhGP7r1eNEMZqx5lXddM7nMLOFm9C9pLvH+ovBSjrqPEqybpd
-wREF0BLxXc2MSWIYt9kNwo2J2a5GSRXGKxSJM2z0zfqwTft4a+ItMzstRijy0m67qHTVoHPvcY4Y
-HpcyaFmeO9q91tHGLnUUOU2N3ayX8lHfqjUx7kAHww0Er6q3rMDvGvXUGGbapXvVQcIy9qiCd0ij
-GBCNtgH+BoyTz70Amsu5kCbZvzYVduXZlkce8Xut33uIR9+dVECfN1rN2US3qHIcB6lTfgeClVWm
-d/7CRtrrFBIb+xhL0xejXrKjjf2H9J9sMs/HDQ+RBDNVPgdNj5ljn0JW9Keg+HwyYtJANSaap+VX
-90b9YBD3GFfYpldwfAXCUnbxAs/5/l1ZidsZQ5wc6UJ26eu+/pP9YJ4jAO2ql3M7KO5Ad3uCVUd5
-nx/pC9BlX8neRmed/6lqLcIw2k1xLPdmsSpEB8Txgpw2xDJwHnROSBuM9DswTkxyeIM/eIIjhOK5
-1M1PLF8AcC6fNn6UiQ6II4TkX0DCP0A4tj8LjRAgDG/Lp7HsYWUUE2fscCiZm3z5BDIfezfy+TLc
-0gPx4j3rio93J7PJwkv5iiu28lbVRrBwa6QZtbuaTEw9qlzHyT5J9cPAftjOKvZScBr22p9eYO1w
-xgo3Y1n7BC2XMo5PjU/TBf+foLjRVOJnTea8BmUv1s36jwAGBMM0JDcQvMrgrxWgeIRMRGu7O2d4
-hLWj4Rl2dGBGF8sEtMI4B+myzPvKCoMCANq9RJ4/as+zQ4SS9r0XNboOdnTy+rElFY5m+vynMEvB
-6IO7HlWkfoJgo+R/Br+NjE14uiQdL8QZiXPbR9pGug0Y0dsaw1H/t3pChNPRATFBh9JP5XcMPuVB
-9IxzTNMLrLKRN/F9XHOsfsmr8feBKgiuPg8wpWPSMspWAqzz177Y9jy4bVd1Ev248g5u14ovoD37
-OEGwbz9k9wR/Z1/geMcIM5/Dr3SR1TpvAyIb6JFnZhV15U/qxnaPLqBXPcTwB2areE/5V4Cu7r2l
-Jwh2uX+ovHvYw/D0SvjDHTDv9P4uxkKGR9jFOwz1EK/dJ5Sq3u3+hEb8q9ufrRpxD4dIg+y93qNK
-Jc/z1ubNQvvMyPxbnsjnWHUl7eTbumQUzqzPsAiIdBMgABoHkGAq99PIvTC1A29rSN7OmN22zP2d
-0NhI3urfAc8t5/AC87+P+t7HNgGyS3rvu6Ey5kHqHgfjOC855nmCOs4ndK6AA+Exz04a5s2VCRhu
-vwmCV3mB49jSQyz9Zvcyrd5ha08FEpi15RM4DlqX09Q65yenLS1WlaHijEsuSALrx+y/D9JNUrKu
-V0uzZc//l/K3WGf0asdFkwkt+9kqPxTzUY+awIvWvWQY/9avyTOjX6kf6noFci3mRsoVDDSgi+Cz
-avNVI3wK7XJNlOxeATFBMSf8eOlAx2woD3AdNxDMl7Rs9QaavhIddOQdhEXy0vAWph5qALjqML3Z
-dObH7wXwYq2P5ckjXmEW7RjSl0zrdXsfyiMsoFY9v0L/1x/0GEivvCnolXCdS1mfBsoFV9BYA+Du
-eSc/7fjhDnq3mfW7eXs854OA9tvHBxgmoL14gj94i5l+vDzFvgg76utyJwBW8LtBL/iyHMvVNv7F
-7QcIx/ZnHmEMMKzhD2GkR/4bKObVtf2ZV0ef8+q91XV9UWffPQ+XMDFDthTAwkeB0SPapdNHUzth
-lfFqlYCuXfyS+s520xazQ5cadpacrbyUES+EEmx60Hu9MqBybgLdyDrK53UGeFEekeu1m7mjDjNk
-bki7UVTbzboXSLE0BDQq9PJe9gmCT0BMV+n+Ty4c3l+O7VgzTPbtGeOFXDPtnrzayGtS2arMRhuj
-2mvd9sJUy7m1BOSNaHbWRt0+7wdzXjwqXkw0Tuj6o7zBux5vlzjf2v+tDiSk5Nad5DcFJjGrdubj
-Lb+VoepYjcK0/NKT1qsLbzTZGwyU4QS3uk0nqtKRvNuN4urp8iSPc4+xVB2/1rH6L6DoEyj2HKMM
-snkdDQlyCXgDGNnzdGlc6x0sh8wl78h51ItaVsA35DUAmoJhAwT87v32sBYgK6CMfJfhHTgXf9Iz
-3HhW50ds3b7WFv7tnQYs7zKX6Ift8dXjXGoSI4/0uc5JzR1ppADYfR4TDGveCYZ1JYnnCpYjLWCZ
-YZ8PVtK2geBnwfGUR/jZN34JfB8AFrT68Qj/e9u3PcLJf3cgPAHvGyDuXhk1xCNvHF3zmJnOtScW
-Cb/YSAww3Bq0AhFbu+xcvj1PY4TRLpsSPdHvuPU6csHWCGMeP81Dsz7XGi18Y/StQ5L3NjT/Yn4a
-BWhQJ50TxKo91e5n2sXIestXmplPEgqdeG7LrhO6sb/Q520ys8wwKVHgyHD3CjMWeAEm4RACiG0A
-4d0z8jn91WGkbYZFBARwyWcdR4d0AxhfyZSAajP6MNHgwxC2zTZM2jvwl1/KoGOlbHgr4yjt2mHO
-zUvZ6MNN7O5ahCJvwmvRi+xm9NrQqeM485QOeazAUCp8BMEKPqTuEMz2cZysO4lyjt1uuY1XvP2O
-+FUb1SYTqCf4BogXvcLrnT645VMLDUCmdcPoNBCMDYYYPyxG7EPa91JjQHyu2DvQvQBfX+sEyHHT
-ZNht1hdFqU3YRVGY2Q/+ngLDzz72GH+B29JNBYbx7i3OugBBuvOYgPjDDVuaSo7NIvTCC0TzRThO
-neMB5NjCn5rqeROp1EPymdDkzTt8yVfwmzcVL57e7g0escDMGyETK2iUtHteQHDmrw24CYwlRvjH
-I/wvbt8l/h3UXkDxxQPcLkSv4OjE2Y0PeVN7+xKj9oRBr7vKMsyestys1gidoOx7WMLsr+PESdLM
-B/TYC2/jDVB0grhZs7RDgl/rtW52kKDn/gDVLqnZjr2WbxoX0GmAxQf9vNKsoi+0HPySeZMw3nnX
-B8V8UOQKDnCS9zZfMrnN2zINfB7LKhEDENtS77A+fve4UnmE6IksysqyVVavcxXfOBr49dbifdhp
-kHkNyoNlPfJ6pv3EQpUeMdh6xUCNdUUVxO+kf7d5Uzm1mdZ4PbdJlqE8d/SeBy+7rpjw1sMg2slO
-BSSO48MbLMpG9cv0CDfdY203xzgJ9DZVx2+b8eTDsPz4+BKxgtUEwPoT73AhIx3o5XgqVq1T8wT3
-5unlTaUYrJHWvSFj/lacHCD4AMQ3L7DenLjnMVd6SF2npoe8dQPBAb4UDPvzIF/tvIDezaeqk/Ys
-KWCePDdBb/+sd+W3NgP8cjDbKbBtMqdkxgTrl+M22C0vMBBy13gaGSN9zFV6gwV/HN7f4IHHg5wd
-7M6wiPli3DNCJjTG+AkarKB3eYMHCKYXmCD4WSNGOPjqL28/QDi274dGvIPe7h0egPmSd1x9aOGb
-2Z71Wq1QUKyy5SzuNAEJl/C224rB5bgVp/wzfrLpNwVCvK4ol8NTE+PeClBMZ9+1Tnyam5vxVcAy
-m7sdvVjLax07ci4tuNDac8hiTAVIcUK1bObzGnrjcDIClIjeE1LJZP82gBs91PAOg33zBHPubcVj
-wPAANxAc6TSECkXD+zYWQS0ADAEkRktSFjVvQm5SdG6kVwPDciOjqUxTLmROlbKZDl40niSlr2CY
-4DPHVvmVvgzkwxh/p+Xm+PJJEAI0cJwWlKLHK8Z2PnWx3FX4BHkGDXzcQLC1OlIvj6XJvKjUGd24
-jngY3Zs+madQvnlDcG46rikjml49vayfewxOyy7pAYYrFCLmcYBifZQ+6+aeciAguMUCz7SA4QSi
-QAHUAMS1asSuUVwdN/bsA+dIwXB4gvHEMTmXN+ZGni2vbstTuhkOYExvcHqFpW4C5cucmsx5rlNs
-AdQTEEtMMFBMlIwUY3EUcNaipEXM3TPyxBvc84oH/OoRpjdYQK8A4RYPPEDy4+URfmIeFkHw8whN
-pxd4oV6QC2+4c5m4v7v9AOHY/uuX5TJ98Q7f8iCPKKBK9wJieokWHnWyxgTDwyuZqr5Z82o3FUVs
-p1HnHTNfmOpl7dGlIR6rCmMvKyWsV1iQfK2PrURuMWcNZKsB7GkbdSc4v5ju76WbIUy/RFVUgKSY
-rBkaOW5l2o7Mdv07jm8AuGoeM/lyrNsEL3OPNBL9ZZX4XOmyvl5ngGIXcJzeMR0H0YZXOkMdJC+Z
-eDN61UsjIAacdH3o0bOzHTlOkG1W3ZpbWe+e7XeK7pb1pMoxAo5Rzt7onEpjr136b/Ibh1jtAcqx
-VqTHzVBAYe7ReQbzeNQZx+Wtq1MbEJZ9qjbcnrRByY0+1x04vK2OMH8JJCjcrtowLpRy/SZfn8r+
-pNqtgl+OlAieNO/yMHShsL8B8XniyNDwh7VBjWEF4Kb3U3RZgGA8j+gPF1vAHtSKR3ioN7jywv7x
-Jdn0pQR/9XsGL8bNYVOmfVzT4eLZ3wN34deiTQ+/8xvHDQ1r+6YSOGVcl05y5FwUFqxrET7nV2mz
-PquK/lSdN3icda+8Ps+dZeCNypAXHZvvMZcDkOPTeuyb6P4S4r++/QDh2N4MxVFvgtqW916mL9E9
-yRrDuDWmHsdaZ5zD/ymmvNtubhwUozJtqgCoJ7yUitBFIxZTQabNK7Az07kqgCMU3gLw5Off89uK
-T1RYkhc/8wc+lSowAK01e9uPrdfqFn2kxFbe8n+X7l1Mu7hJ4Hmcha0ulYscH0k/j1sz4zhBtCjv
-HOAHpWPjdxRXWITeGKVHxgQcrwLFOH70ipWBzjV/9cU4KstjhQgvPtY89/3uBQh8o84KAxJKnF/O
-qmOkck8v2SQb0zSsSqISn4tS6QZUx1ExwcNQunrLKu/W8m275b+dnfGbCSYG2D2A7up5R10UkGhp
-gotZ5y0fZS+lKLcGGrp4HaybRrd+JyDAYfxFo18outv04+ZLVn+53JClfuaP+I/91HHpnUjKgqGx
-CutpWV4T7di1rFMNze4k0uT/ESO+rLzjz4IvlRu/jPXZ4VAPPasKioEWx0uesqdCBPh7uqSkdJim
-h32wWa+dqsMvGh6TIekAcoYP9Z30pGzd2zKg3hNrere86ntJsngl3g1L3vh0lBqlCBk8j8+XTDwt
-enrUmTLkWr5VJqyWT4pO0s3u7368EFtuXnbdi6H5S9sPEI7tzzzCoRa5j/OPMueyajtSl8fL9XrB
-cBeF5L0g8pShm8qqs7JpsdqiIPORM6UH4zGnXP9Ih7D2F6MgXC+/jHuLjV5fl8dprWyFEqVRebK+
-qfGSfilo196fADgEsk48MJ5xoOjd1gW+vyOqhmPaTvuZNnUYKhbm3XM3Q/P424A4zxEr9tb5Rrva
-e5vjsG2yVz7onuDzJukAyukpMXQAfMnTgY/QiQ1+QioXkmcIcPOTsQJMbqAYWg/CG9b5oal59xfb
-mSZkFHrJJYBaLaKb+F2oYRTVzNtTxJs2O3mB/ylDLnGVyJvcCXZ3HOOQ9VteNp2W7r/PY29Nem+k
-GtAI41IvSS0GnQB3gl4Fq8FPPvOlrGQ3JiKBs8n5Nq5hxZNtAfqS79Z3HVvKgk6hoxweQre85uzy
-oRSq3o1pgK4MAdHjwH5a5zA8++l/krZ0eQLiGH+uNHIAXtvhAKJLDt0iJDm7q7pfjscYmsW4CdQL
-WG36k3RPCaI8j96ZHt/avI9l55mAY3nF3eNlPOyumm1MsYsK/O5f8X1F7uo152o9kKOzHntPPZj1
-bJDytpHkpv0bhf/S9gOEY3vTAUc9BxoI5h5AeX83023Q++I1fgE2FDKfF9U+tmNpZwXjk1NTuQY7
-U2GGpctD4xWtCWUT8xDIMplsRkEOUF8Q0180NN+l4AsX+SPwpe5c4Sn29rKGiszWEV2c5isrHQDv
-49kGaXgFwPSUDSah0si097K0i9L+YW1cK3o/RxRNTfM731TL/alB8wZn5YvmOhAFCphomeHiteng
-tuftMIgMjRhxwk5XBsp3u8fOfCWuxg73Bb/y5ZILyFUwoqAYku4gWOIpeflQ/uqJM2i5GMMUnk77
-8g1ppZJVWawMguLqfF7XYw4GEk47PPPG0e1xbsk0AYjI/bx5gcaAc77lK4IhSPrm/e74ACBvoHfW
-y7wpULyG9+oChlUYWnjNAXKpmTu/DKHs5+S1ZR4H4D0BN5LH6gtLHuGgF9k8xDeZcc+jKmyer3yp
-IhRjF5UztqHE2GQeT90K+MWJQceFylH7+MZFdzSd0mxRh00HOD70clP293xcq6ATW+QvCcCbXZVd
-0bFW59loU/zCnX7o5zhqfJRDvrdovsMsCvgiwbCBafUEb51V1OO16kmZaoMExIZ01ik1SkOx7ZOW
-5eEd+4PWehaT77X+ye0HCMd26IPXehcQzD2oc/e6eivzLnuv9iba2U0M89WQzR0ol4IH+CiES9Qk
-MktL52MPWd6GglvCVCrBkle798ga5/fwCEOFPXz6SfhDxHISABdgrueHU6V0PX3z+E4AXCKuTgDR
-ZTotScNrGzoHojQ1L3Oa5wnjvEgcnhuv4joqHc3ceY7yVgKJeij2UenMImo1zboCXxMeWFXOEAkt
-b2/LixGXvgI0+AUAugUUngfE6Pa0guJbuoVFRFhFznl2x3MuJ5NsvrGscxrpaRC1Uq/cZsYHbcSE
-zdY10THyNHnjHPYgp0Ot2OWnS98dezai7CJxj2mhx8UbEJa+2q3n91EUXThPU8iARIHBHz3OUWRP
-0aJLmVAzUYsAYJP0HRhfrnPoA/K6HCYtxrApD3LjlB8/Yj0f5yiznKxUzeU1aQRGRyiODniM8QDB
-qc9kvCHyHz2+yX/lJaYNapUv/f8tlLrI5VkocmdlEWvuSQOVZ3Zc3Q93nabhhkCnBWt2alvSn/dK
-i97hIOmKeVMwvP0MvvspoJhhY8jejpV5OPyoY0d/hAT4Bs1PgW/yTmtqv2/of779AGFufpGmWzVs
-xriCYZfQB8nbst9B8AOXuymXLrhkqUdQlP0LSOYb9enx0vOu4JchESNCUXSwqoh6sxaYX0pKr5EA
-neYZHnG//PVQiPg93pfr8Xj5wgH41wXgqsKZQqkllWeXUvXuUQnQBpRR1TOaGhN4EgZh2hjHmSEG
-0LWOKKBuqyYgrgqf64AaEr/bPAeoWk6tlHh+QQVulb+YXgV2JVQCS7zCfFue4zXtMQcQ+4t3eGd7
-egU3/25i/BbsXtLpJYbXEwHOEQ0K+5Iv4kxDyLqkF8fQDeF177Uv7/BtgvTQ7sV5/SOo4sQOVsMB
-IF7gkPUFJNidIDiPZb1oaTeTJob/sInFa9c6KV0FEZp+mzyTOvWSnzIn6QGOJzAu3TzqH3NY8/YG
-eu2SF0YhaZ/bvPmbYBfBh9oHs6ZXWqMy9EaWt00V4OTEzKa+E95V+yO0zKX4fhncft2fLP3SlRli
-+Cbq0/Rn0r527pZXRaXOOkG8Bo2krZH7SMM9ThMBzzW2gZqr1hcreyC2FfCUs8ZF+TRGQtLYmscC
-I2G7F7vpnTRQz7Dmi/xkH7KnMwSicMpRNkZ5R8JlI+y2J/jtQ/zr2w8Qju13+qAqVmjDAYa1POtJ
-OvYP+GijhFVBTynxb9SpiqJ8ukEtxRhgLR6vtLdkEySVUc8Xn2Lopfes9hbif/UYye83m8CIF8/x
-fFnO5L+2Y+O41zQIQGktRTq1EelWSoXhJL0PckPjRbncUnvjzHPJ0BsBNbhZzTWrrjLq3PLqv2hb
-UfLH1ggiCljnMoFSqDMDpmeY6faEIEIhGELji4BZzQBXiBDD3gxxGdbdl27Vb95gTR8A+SF15FHh
-kvNySba4Rq0VBoF2ZY0SsMiQdHP6hDK3YYIAACAASURBVPuYr3snLWraqp0yrJLVL/Xb47KUDCFq
-L80FCG7ee/UI83PZ7dPZsjzU4C/VJ5mh/TkFOgpGz9vYPecmDX0DwFWe6QF0Kw1UDDHr9fLWTluT
-16o9lglAZj2f9Zpeq8tNLHXQVPUHrGQowbLWYxlGPs78zFbl32XQ9DK5L56tEL1znzeuv9BsxHGj
-FPzXPKel0PPQdf5ybNQdGGP2QT+rOWwb9aPejoYcWtnHfYnSUXva66L1khwvOHXZGEvIoHabnmK+
-8rDMwjx5ixFuYRJOUnn7lYywd+wnIKPsZWHzWp/aeW26rvtzpGi2pGqNCf5L2w8Qju1PFnHWON8J
-hlv5qJcvzrFuKIbUU6lwq60EuZJm/nleXb8eSTE/NJd6EUJxDueWGEYR2eTTycQvnD8BMe3J608f
-KTroVcDtB4pLF5g8ch6/lMeA6m6ZysEkjQaKNYyi1xua1vthlo58zPxhoPIm6KagMVjhm3lTv7im
-GiCxUV+0Wcxtjt4goDfKFACnd9hOEJwxw6H8pmVN4zH2DSDvvDJDMWjy/UjrShKkk4WF0XjGY83V
-fOHH5GU57W/JbpLOpK+HAQxAlKZy1pkT3z2igsXr0tf5LeHuLKYmrJ9T81jplONjv/JjKQWO14E1
-ju2Sd9adgjTpIvKRb7JTeIoXCABS4ATQ8man60q0Oi2/hLPKTHnAkk+K/4S3D56M/Y6lOwjSaSJt
-kB9Ubg/5fyOyElXHVnI/IyD6OIMHidDUgCidczfzvOzDL944C//KDXfOOPlQupVeVA83lMt4aLQi
-XU6lUafRbNKuyysdIS7zsI8pk2FNGCpBvdFsrzZ7hkcoCC5P8b7uA4uHZ54rTi5p2l728BpF2bvU
-DsfxCXKntujHr7zWNpud6GWX5N/cfoBwbNPsvG18tPMGhskWt1CIDJmI/Lx2Kt86H5J39xaf5/RH
-uaKgthsMTUkzLEKFVQTzNM1WYDn35QmsfYVH+KoXaQYVBVjoD+1nxwsn+3e2Zs3Le9yFpiLoinSm
-1YGTOL/lhTIUwWd6riyhtrhtbXyjygXwC5egTTnzIuNTXu8GO3DXOIdHjgOCzKPxt/PSGAThrx7h
-AxCvWk7N4hGAelqyr0F8RX3WlTNHmHkBYj1etCxl7fuz4/7ZY8zzD0DcKKrSgZZuuKX18WJcsR+r
-1stxehmOfbTnwJSCVA12sp2WIWlRc5n1RW407l9vXhIE8+MoclyhLvJ1LF5fiDLwx9nbrCsgIfSU
-TdI3IRoKRBwDdUw9o7oSlf+b8qv3kfxJuRVnQ93cS18bELYKBXvGfOSEetFiyqeC4kSwU35uBJ+8
-2+vPm6xil9GBNAhS0dvMJe1an6kfeC3qGO7E1kxlfvCP0jh1hbfLqlVLKRUwnC/ZtjIhggJ96nuT
-qOEMMdyddjDG9sOcjCE3+xp8wHsNnRd6f2MlyHcwXKRsrKEhmTdA3Kfcsx+b1KXjtJ6FTm6hDnrt
-PB7lpvufGOF/fXtXzGe9Q59hgN0BlhsIlnK4GPOmtNmh4fX1EuN7ngDiMOoU0puSLkBhBd6smDz3
-FEpUKER6ixIA2eBqMZrNI8y+DMKa9PEAx6OMp6DppsxTa9IES5RDFrL90A7He1jGO30pMAG+v2Mc
-l9/ISO9VU+RatZvE1qiQkErMpWKVSRuc/6GLr5sNg2iab8kDnH8FTQV49XjHlCYglrjS+qLWGYeX
-Y5gT3Uav51TZDm/YhJrLpt1jhbvsEBxDzju9fZsGdSN0mGnZDubKPDU/9lrv3lyZpka2RqWZvh0D
-SOAR07xXolGrlZ8EXiPNrwd2IFy917GcPbj25Tv1tsIL+stPwW8DwpB5G/upe1ue8FYmWSYoYwDf
-4rnRFnnokScSc4Bk6zbe4o3+MiH7rLoKI60Ne+nvTwpBQFi21h5HIGzGnFdLfds30kbC7tRzYQBD
-JHxeX2xTv9xT9Iw5SFvmdcnsrvsJ9FsXvRuNGSIh5dsxxvIKWQDiBle85fu4BR6WpDe7y9DDfb10
-vGOAYJrZ6M4RKxzOpnt4BK0G8rhLqGeOaT3hyYodHtuwGXYtm4bl391+gHBsf7SOcAIRF70nedGe
-Y39Io4FgVP627X5e/w0Uq6aUayabilJNZSuPdAsEMJ5MLGkIaQnG8AwLfkpvkQDiY83YI0aYIJJG
-ATgArl3ydCxp2Ib4UPCFPKlbQ2vSjqvU5jllUnY+lSjPF53I+he1JWCrhomsSdoj62hfXBMD8O98
-b/VqT77TtqSuJwmOrWq9GUFFReigOOcbRWzhgfIWWwHiiAemF9jzk8tW/Mw+qSE6wO9ON4AVmtmA
-BLU3wLuNZPBQ7OdSalVW7eQyfm0G4kfevQJWkbHrePQcBTg0QuT7KFedILa/UYfiIteqXqnH/eSB
-HIbItH4A5fwwSl8KTz+b7dKh/gh4xF32zox+ScYkrY43yTfmJYBAv5EugWk6doLeJkCit6bAkeB5
-o6nH6McTLFOv80tsMh8+r0Ea2sg7bsLKQ3z1rpFfbi/ZpU4TYT/a6Hmz+DUkTdL7hTiA4RGpP5gP
-iBKnjbnoMsP+uAb1x7xR5YhiHjKkizce2SGV65LPspVJtLCTDE9iPDRpGNyevIDtKR729U3nNk9w
-7jmPVuZaXpI7Y4M3AO+rRhS56jbjuwA4Oa9rKiON+nRcR/aBDyFj+9DCP7r9AOHY3nTsUc8LZiiw
-JUh1rzr1GeUePvG4x5KRnm1CjhMUUOm2OnWdQ0mnZKjipxR0YU6t4oDJW+XysEfUqigTK0bVx6ev
-3mB6/hJUBCHTYICSXUbK6rjFDYN5NR8ZB0XaUJ5EYo9jvynnEa3V2pI4znRjSJ7ELjbTnmP7wF1a
-ZwDg+RnuavetvbewiPAU6YTqIHnUFNNICyj2JEzMPzDALzK989fgk5UvyZXXmBfrhug1PV06Msnd
-+8tjTzorOK5ylu28rH9ZxxqP0l/la/CPHpTl+Ma4OIE3kLPTLTREycFaFBftBiYIvghFnmMl6inP
-6F8F/FpoYRH8fX2BBrJz8BjjHNek2Q3hH0nqDJUdb7/6OI/UOQTlQ/6nOsmyXnVMzmvOCDnPHbLE
-UKUnGUQfV9YtfChZBtqxlPtjC30n4PcgufXat3Y0/6Y2YPaSj3pZDlSrCvR7u6fGG/o2aavhTDEd
-TAMD+Kl8VZsnb/KJqYJgPh3dNKYNzXdzaN/mChIEyMcVSoYPT3Da3WKnZRGKEeyW4RFRdYJg9Qbr
-PLjkvwNgPyhSZUhvdQ2rrwSRaebbOO6Mdr95+4e3HyAc2599WY5KvoNhSLqFP0jd+FgwGLdWursU
-qV+P8wJoIDmTChrZRx5bGYEQRKueIgVdNaGUaK4DzQMM09jhyyoBJpxNKUrQG5liKDIuOD3H0nca
-Ua/mVHc1LzAvOSR4mJQ+saLrLeegRm6h3KwNRq8z8vTIbz89qGpVNhvoKZpNYYVR7q2ecGcN+GVr
-scJ27ismeP/yoyY3j3B6hekN1qcGDI1gHyXtNw9Kn6hbrHAHuzwuudh85imbeXPF48eLTwYAdnjE
-cwodlVY3i/GxMNIH/1wakAn2mR1kOfL/IM3NmvWytLTdQzxCI74EDH+t0cmdyMfI2dn7+PQ0bz2c
-NBFYX4ITxzJvCoJTj6Dq6UXH8XRAdIK5KMTQTTb2EwBD+C/7Gcl2gzVoAQDisCANu3xcZMbiGgOQ
-Athy6lWvafom/1KiKMWqIssECyXAMfln81yxHcd1yS1dKfcy5ueNq55N++V5ukv1G/dn+BiQdGvz
-TBuUT586CM6lSJ32ghcdOldVGl9alx4lGI6OO2LVCCu2MuxrtxjhqyfYs02CYnZsapxdW8FxlZ31
-+nE7sONAKk4eopo5efRvbT9A+A+39mW4UNROAwsBvSjPbwPBAowhe+QxmvJ0KXs/5jn75271gk8D
-w2jH/TF9xMKWjKQAWs9qMpwibG+/Wic2tdYBcpEg8+5Jseoo+4rekQS8ov/bi25Z1UZeqUvRqrvm
-lF8BxjQ3TX35+H3cbud4p4E0osPLm6OZj1JdjS10WDJx7y/GXXqqOk28vrusz7neAPm65Ms6tLnu
-dLv47uh8vN9HWmWW4xYD5jjigo+8p4wCqWZAeulua1ubeoiZtqemqw/hhQ9mRYzKYnwBdA9qP615
-gpUEcpniiB6ekOBDN9NfzIHGdY+wiPIMbwBsa8G+Vuo1voNQnXMZz8zvdWo5MOR8HZTMgYoktBvM
-B3Uj9PAEdN0rBJvsdasju+zEAEl7eKLLdNzkLZ6bURGnvM+u9PKa8OJ/4WTDeESPqkOQJI/T9GnB
-gVmaMoy2qQsjr6pIufEUq7JswsIsmVynLpekaxTxrJbkeFByyvlXuVWdqCI2m25Zg1+piwT4wiEg
-2GPqSd/N2xsYRxsin6WzhuRbjZkOJ9L2ieusqKP3Wc0L3I5dbF/RonMKdaCDOnCSqWuse73bD6Od
-960ZmN/W/l9vP0A4tv9m+TTgxSMsiq4D50rzM8yqbGeIRDs+FLd4jrMNPhaiIqCkMBZI2XmfU0Jw
-Mh8FjKtCTE/vqwd4xhSKt7Ahs3wLPPINrc9pKAnSaVzFmBnQAa/oLM2r9qeoVUjE0MWiOTVi7qYi
-eDyUjNdv3nSIThrt8Ihzfi/XnORGn/laZvikX67cT40aFHMTyuUjTUuCzsXxOyjuH17I+GAzeblK
-jHgeT4t1t2AN4iV9g78evT1g6x6fhhWeasC58giCPd/uf4o+AGBrX98cjqfxXrfY3Zzs3YWPmi4q
-vq/6QieKiVziCJM40rcQibCceVmdX6sPakww/CVgWAEx54EGM0GKEObCsI1UgNDCgvZCUEf1WUEQ
-JJ0OCnqFnztRlOYqQBh91PzsGvWUzpWOj7qndFArG9eq/8mp1/TsVp/X3Z95s2PUsdkWYuq7fvCo
-bFGuAPi2p30wvO9TVwCwX9GNX9H2L3awzMBtS/IBY84op3t+PT5G5wuwx/pUYesLZa3im6JdD2tg
-nPvOo/cXCYLD+STAuDzL0kxmdCXBfrE7W59Wmm25hbcZQ9UGay0rEVEQ3DzFqQeV37qqOrVSB8rH
-sG7zZa3r1Wcpk8RH+/RPbz9AOLYrEHipuPUYmUg8vJKmoLJueoe9Pr9M5ZfGV87pADfal3p+KbcG
-gmkELOsfZaJtirlLSPQmlmKT9axhpPpx8X2V0ryFpSGwGt9hRDzLLenQx0oaNC/wPKbgp66xccz6
-OvMvoFhdbcIIJmfxeL4A1I8uW9y8pG6c421XLBL0/3NfARC9ZvHoR1DcJrYnqaB3WjRdznkd8wap
-vTjJmFJ+mSy+Mndaiz89rpGm15ePmtcIfcibq5mHDKFo84In3u73XO/VH0c+7rQCxrYtb3NuftfU
-9GMSux+3m2GeIaSoovJaJVDuIi98IZlNljlvaHNXnmDrIFjBMHvbAJ9XeETmU+6lU44NMvJte0S4
-ipLJL/R1aUD2DQQP2VJaTmGatJZ0m5boS33KPpRL02+jPTbA9YMfbL75Jsjt6UaYKiMgEvlVXkkH
-xeALxc656gv4YrRVJTpI0NPUA/aaDj3ZEFFo0XzKJEO3IutN65lRHh/gWWSYXfxgO2Pyq30V0kAb
-cWpZMRIO8QBHjDDtS+RtEOxRvUBw2tz2DkHx9bQNipuTBDF3bhoasZ+WbLaKdNQhC8YzmZgmHWSB
-Y8/jsAsj3cvuGkvT+bM+fTWYSujTAtNc6/z3t7YfIByb/75K1iy9JsCXPB7pBnxRaZc2qKgrHW1G
-25BrsPzdS0ylL7+Wl1rkHLhXgr7PzfjDVIpQknHvnmF0w0nJcCANn0Higa36yCVfjrIao3n0MsaT
-L78luEZ5OFhnzLK1PQFwN45ZJy7Ga16i7Zqeg+cwjvzso6bZSreuozunup59UPNw2F3VYjZabMMp
-I3pcYwJczjMZw3qd5hFun+W1AlTM+y9B771OGDu+iCTg9hMgTvnSuvmhA3qEt7E122l/sMdAF9S+
-MOLLHUW3g5jN3IkcK/H1JCudcJ6OBmY0zbaHNN/T1V297LsneB0g2OKn+i3RV9NJNaY2VNd0lEXo
-gD+WvNsEmDydQxhCl8BIgfCg4zXtZ/7tGECGcigIfgX81vjq1t45P/PS+1p6PMGxxZOYwUVHWzXf
-ovunuCnCobwqGBbAvIssfwVwxtPBX8iynHZV7HLp1G5NBkLOHyAQ7z73efYN9vPsVZSeCrPiy26J
-rIVvGm0b7BNbRN0iefQIA5BQFO9E9EvT4yrMIHld1KIhQiPMhZbevME9DIKE85q+5BCPEXTJt5H+
-TtlkE90ay6jV1ITV3uqsD63+M9sPEI7tu6ERFEo+nnjzCLPNfMQdSn0vpyYe4zQYEKNA4VRv8cVz
-rOeq0TlAcNyZgl4uqZPGohtrk1Jl/FKwlnehVIRHeETzCM+GqYREaQQY5vqHGlun37FXpcLHUdTe
-lnlFK/UKUwdqh5oXeOivw0hFu2qHq00Ozy4nftjavCLBmEsbUiynlSJTPXsrh9OTcR/YtbepkE/l
-lMYvATBqvgcPuB7nmrOVZyM04jTXUzG+5THpwRve6HkDxLPcRl2H5xv9Fmk3x3YxBSC2Bz7AsBGk
-kMfn+JKNlfKHWYxD4Sfvdd9Cmmb6d3k1h5Hf5sfucys/Yz0C4qAdV+zIR9HtyU+XZXraxqOo8JiG
-vkjvKUqHJXmmThsvyblXWMuNWG+E+lhGC+6YwKr0Evuq/ZS0rpymTfu8ZPF98c4LGCbb2bP1YshY
-8/wemnCkBihrgFd5ATH/NxAs6Q6Ea3d9MRc1rX4lPMP9tGiHJhlWgGGLG1UTQFzzRJNTPES+kTlN
-jc6nMTHGsFl8uS7DHDVP+U554bKdkk/oqHQG3Ptr7k9NifwmQIaA5Nq3p78v/cj5ibZSP6CrtmSN
-yzzqINWS2Cz8F7cfIBzbFQjc6nmpoBke0dMMi9C0t3S2FBV/G/7QAHIv68aF7cgvqmgYYCsbhFCZ
-pf3Kl+JYTslQT4HVsRuN6MIEvdY8wDs/X5hrgFdAC9CMZ+Vbjc2qjhlfGoz6DTZMMxD0MWkbl+NJ
-qD/a9Fo6RzLvmn4592YWKn2GRfhIoeXJZrdRlaZLD4UC42PeBSiFR8qu5auDYr0WIIw6FeSbNbnR
-NvhAjv2g94dyhkMAFQphgK2NXPJxrC3YAYZtWPnd75PuSnTvRQeAvoy42B8Jfkb6xi8NVEks5BZp
-sVgJguz0CnPd4Bka8fWF2814HePQS3qcN4IEJPuFim5B9Xfc1Mv18KC8wfKy3CTIx2P7UEfmrIFg
-q2u1G5m3dG+qLqN8s48Z8uKtlkl577OB/Atg1Uz3mtZAjfbh7WXom1wn4F0fgLDZjhHmNX9Noqor
-YVAkPZwSdpPFIZfbdxo3T3sunMo9EKK5eIatXyavq/aJ51hRZffTsq4HL6Ydax4Vntf18Nym/JXO
-lfvBoWZVPGKK416xg98ucH3Y9Y6DxxgEKDvL9gl5nqHjCeZJX95GWfdDlf7NSf/o9gOE/3BTM3yk
-RcfTI5IGCrikO2M2uX7zFosxmcC5Gxc1Bpe0S+WLXN7GyIQjDCO5HhoSgYvS1BbHRfySlvK5/Bkz
-K99wGDchQXYRDn1hrC91Q8zi1/4w2fehAr2UdoVDSKjM8DpmedLXx16OpF6VodW75890PbqbRE4e
-0kEctTDOQc3z77bJD0DegmR7Vxe8yX8tswsr+ah5zpiP42/tm5XxHRYBA54Ftw54W6iI05TXCLJ8
-8le+7IWiJ9mweRStn8e5F9kiHV3mppfv9Fs5vffp+RNPcL70qmuD58c1JEyC8cOw0jPuyJfU2H/9
-YeS3eUUDAx8ZM88Zem724yOw1bwhM6/X5ry5zCGVx9BPb+nsloY4cKrJw5Xrcl11toi2Q4ZGrH2B
-7SlFHCt0Vp1aANG0rYv+TpadMj5WijGTT3FbxBvnsoRr32x6HW9dHWWpSFfVgcNbmQExNsqoD0ho
-Aoo3n6fSRxqa4K8Q4Zp+yshQPae5mnroVueSMX+zfuQ/MCzbuvwxB8MjuKbwYxUuwZfmbj4IUcV9
-PF7Xy7SM4wiVlPGd3IHkoYIBSsCeZnnGkv/l7QcIx/Zd4q/wZlqck2mgPY4A0Pk7H6EgFVvTkQnK
-ytgbwVtIZ63DGfWjzLUhZbRzlJdfbBcENYXhkxGyl5Rlys8a45pCiiOvQCljsEPdedA8wKehjjkX
-j2u9Om/5HqRFg/qyoT3+UvZUnVDEJultNm5p/CYNqT9rzBK0dOX5tTxzm8dTfnL3P8ucAIbe/Gc/
-fmwalfXTQ3rTusUNj/2K7FB8PplB2MLeyzoFNMtl4JU+QiRaXTlHb0KL8SKPvKxeppBBvo25FGYQ
-rBAgzYkp+rU4YdJc52R0qUJOAILYtrqD9eXP9lrAcixpWwb7+oJ9fQHrS0Dt+Olnsc22Fxw1x3yc
-22/sih7HjaJMA4e6y132wOTL9x9+/3uDJwcreZV/YkGFBk0Pq4KPvAZ+Q1YYtpBF4wY4+Q6/zW84
-2xb4kSWzFeDP4oVO8iyBaMyl0aMp9W0h19dV7+9a4VwQ3jdDxlHYihsr3/zlda7/+gV/Hviz93ge
-+LP1zd6XTOxpVZrEmG0D3uR7EP2F23uFZxgRJx1Pb6iKNjh/4qEBZSRumRMpihxb8HGCSKYZSpIF
-2cd2o9CepiBkFRd9iSqXOhb9uoLfZfGCXNAEwFd4xz32X7wxsP0OAx9yPU+aATwpg6XSEWqdvGmZ
-EssuXc8b6UYLQwFjK/qrfZgo/S9uP0A4tpVxip83cw9bY1gJgg1tqRL+glMa8NMDAblU0AaJY4rq
-fOSCuA7MBEAj42f5V5I6ezS2D8o9u+q/q2uSsmv+e877tRN/iOGncG6qdeDCwwTAAn6BDpiZ/sV8
-Ab3mTwLfyqs0PFSNlqOXtzjseZyQtVlmjqgR+zOw/V55GUiTCY0+XECGW+979f/ZxmJJ3g34MpZT
-88vyqKbMX43iI0d8r4ya+Qp2o75OxeSj0fS8ch7HOAogeBnkJ2TUgPxYyGP5Ls9uyKoTXt1MqzLB
-L/uj4JjtD0CcIHXZpU7PMwHJ7ctw87PJpiBY0mOOnfOcw+o3cHtvlT8MbwPJ3D+eEQ6cq/kiftcF
-H37XydT8m578TXkbJedP2lXkMAOCgQLEZu00l5M1DKDXscycdN7Okliv2xxcrnDz7Ir5395V8rCt
-VY4V258/9wwLWI2/4PHyWYDeyre8ZobGKQg22+D3V4DeAMHwBx66pW6EQo9xvEHa9gTGRuhDgGJ7
-yhsed6epbWDIJ4AbkD3gajBtXXPr82BWaX2aYlFYsyRyRuCbYjJA31CRd4B8yVLw+2yaeNyEUxwW
-08Z3evZLhI7tUX98h0E8T6xs5Q9Xi9xh1qRehF07kLe5LezZZMzZV0tS9M4jcYoFwD+dJn93+wHC
-sf2xR3iCYNr/UPIiSnUNOOACFy9eYEDjlyyPAVQsn8ejKqOCyIvnNefvW1saYtnf6nwoL5pUzh5G
-GPBEKpd2DtRxxlEzX8GOh4LbGE4AsHvbGyQd506g20HxPX0tv1vlk7DiuUqA/ApjJ8k/eYXP2GCM
-ckBBOZLfJnAHng24FAzLGp1wg4Xxdjeka4HgN5/JRV6CYJUKUXoToHQmqFPeyuYRkcJBkEueFyDT
-vE9XKqAAdDAMMciolQ4WACxs73rwfwPqsbuAY+Thpax5kAgCzr3dymfdZRsAJwj+kp+EPNxenJtG
-vZEzdBdKVu/74LPjePDms39HvvD1K0jGnNsbU3Ey+moNvfx3GtVfD3fyAoazb3JdFy3gvVUdRj5t
-YPvCH7wZY9yqCwCG0aO7AXDxMkMZFCRvz6NzKUy9CYq6hyc46zoIgPlS3Qa/4gl+tgOivMGh71xA
-MIeWPDbAE/n8Qd2AMmzCng2M6RF9ADPPl1/TRq3OH22FJKWxlY1zKU+fVhrjU0YO/1R0MT9h/lLP
-2NzamGM92B7htblmBSB+lmWTXwTzDyEtwHd2Hn/4vZnd+IM93/5w4Y30dQBORzIS5ZjGcnPIAkIA
-tPhwrdNuArTOT2jEv7r9iUeYTLYnTT2PFwAaXFY3R6JIqbWMxrGYyIACyFGH3k3GdBWo9vPKTYo0
-9QfbDVWBYvC5zu+vKSLUNPz78R6+gBnnyCMsQsCt7icgBgZADgP7Cfy+Ad8bSO6g8rTGNgfZvFXN
-al/BrWfJzOt0ankW12EMsPbR/Dje3hkr8LGkDj1Yj2O/JBYG7zGcYDg0bDNY+xz9m1sf3dv2Xu6U
-rRvxjn2v18DEvFqKatDGUDHOcaNK24uHwIENrPLsnZPzWuaJGLVMLOPNu5sg2Orrfq0eer21YOsr
-fuv45Seypyd46BlNcw63GNzCJYKWPvd7Ejy+GKZilCB3eIjr1+vzSnxq00EpD25a6rtlfuT+lm0B
-VFDwlp0Mc3GhUdN7v0vbmR8A1XxJGI3DBACn1zcAcAe8XBXF9lrfVuENDJvIeHH1+C6GV+wyriZE
-YOxmAYAFDMuNT0untht0LtIlD/rCXkc4ljjcN+4bAJrHyroW+t42n+1+PrHWsKJClKVOc122tHNB
-v2naIDFSao6Pl00pexMYop/HdOCNBaSHdy1v3uANkOkZBlbcgH8BAHa4iz/PnppYBvKhqxcx/Lh5
-Jwhey/E8DM0IG+ZALkXnDdOivT+k4iOAN8d23MxkwV/dfoBwbN+9C0n74fQMK99WiITWNwAhg7E5
-L7p3qZ9dlCHvoAL4ioBagGCnqFq1P3mv9+ICO24G329t3Ov2a8w6ahybBXrdkg6zjhixZk7T8BGI
-Mi3njONez3N/B7/AjgvGvTwUdUVm9d/5gZPykQ3Eg/xi3kEP1rwACS3zmce0g64NJy0DUSS4tTI+
-hUgCDKP6376uFsbOQmN6AmBHB78oY4V6MYI8qR6tc+Qvucfryi+nqoL/UH7Nm71Ij9kuUADsUQ4g
-PMEAlw10OgBJVlQaKdvSR6F5ce/zNwAAIABJREFUCwGSsjTiAm758RLNn6AYqzxzPT9CIrjqg3qC
-7QsZEpFL3wUgnoYsNSDHFP3dhCo+jZsscvRnECz8N+XpmTLW6zOvwLFfDK3O8lvZob0jZZ+qghN7
-5VaebqOe6rlbWvlT+DezI20BUHdYhPXjAMD7JrgAcN7weABivhTpXmB6hEFYhgVFmYBiRJus67Z2
-kh5gfz6AYpTqiYEVH9U12gufCwnk4Pt5/o5l3jrKnhWyE/S2sg375rxkUaexZtraNM8pP8ryhdIO
-cm9e4aunWDEi9hNph5cIPtsjvIDtTV9WIhAd8fCEu6HWPX8cj0V8dCosvntj2zEey0Y+D3V2hMIA
-qX8t5j8HLeq+ADF/RZxaX9ou9a7o4x/dfoBwbGt9j/gJgKGe4a7PTkDqE34GOKZ4VVzS9AJvgLvz
-KjRC2pygOBjq7mdrV69NDG0K/A0gSP0pr2fbduSMBylSKl6cS7/8yKNx2/vb53HVCNolD+PcKxB+
-8RJXHq7n8Trq+c10ltUA+9u4XgNHqf45HT3+90OZ5PH6LQ5YvcEsbxboKSPMUDsNm3jUAxyVxPOb
-ANj2iyst/i454/Y05gU8NEa4s+eocjnusZjv9e3kP1aLrm9xjdmUp4+enwNG0Axdzi4hEp6Wqya0
-+mnZSIIfK3Bb+9VXeBieYT4Cn15k4zJoLRRixAqbgmBr6QQmqXeKq70BYKGjsBnlWQEr5dkfbMB7
-9QAP2jrKa4xL/SI0iXjOrgAr1/wPGnUXC8+2S9i4JvlGKhM8tjm/0CsSv6+DjAumJ5cAePPDW2xw
-ABt+Bt3Ko1xPFxgzLLwwwiAQN1fevMgRk7zRWcUFTwAcnlldblTlkAs9pOzZmJsVwM6BFD4Pr7Bt
-Hb1xMfXftukZ+qWT533mj/TQDw0cZ//qN5ece1uCrh/vhg3o4PfZesbD8b1gzbxp/3dssG+dzAgt
-hktoOnTN8wC2PK9liHSEQ3ChgBbxqERogJhZCnTPMR9rTf/F7QcIx2b2zdAI+AC/9AIXOEYrP9Vn
-OrO0IDTj1oXBWdSWgUxzlQjJIycWE/XevkHi6+Nol74dhbe8Gu0cr9Kn6tQdvWrv1jSNm2aIoaw6
-JfHnS18Qz5HkP71OgtIDzOICek8g3PMRHt0nwewNFLcyUHFWfvdEuJIEaRR47D2/lTGXdSxoQhB8
-A8ACkPNxPMMi0hO8x+gEum0v2i95NLzAOdriB+aT5z5B3xduecm5bUSuesYlDrR5py9XtQ6itbrD
-6+0SyMtxQuZrGATQgTG4sz4/WT/yG5ClV3jhDJFYWSe9wQMQ15rAOzTi9sJcgmF6/gLcIG9w9py3
-uOAEwXWzXjGgbcg11EdkOGWWxyJTKdeVV/pg1FG57xMufKqT7SgenlxgufNbviOZ2rXodm3EExGr
-ygpi5rE+IfDXOmw6AJ7Vi277aSJDJcIBI6tCmFUZxmoTBaj1RsgFBPNX8cb9i6MG9xVDdcDVKxx7
-mb+2bn4OT20gvZOGQTLwEYytJ9RdeIKjXd4YWPvoCmWzpr2mslxNpLVdysfUQleIsIwDRj5MIZiY
-3mBr6Z1BdWJWDm97rHD/ilBt6WMOi5E4z44P3mFt4VnmR2bMUnftk2P/hLc8Xn60x7uTl0BX89hM
-qnsBvNAQiRMU/3iE/8Xtjz3C/D1+BYD9HJr9YpzSoqpwQ/lgp7ewezFk1LWR1z2tBL69F7e8e2+l
-qRvC+Ig6bOzvNbrBebmE168rmCNaLD1HJwg+DeisewfCPTTitRz9B6DXiwFYDGKGUiDbSJPYBx5j
-nNPhgw632gNnlQeq9D0OcKwAOMHayzFXkbCnvMHpHS5g7HjqDn+AJdO+XZjhyCG4OGpqfQ40T5LS
-TzJ+1pvX0eMOgKsNBzIMosAv0LzoUOBC49snrICxZLeQCdTN76qXk/iWvl+8xPl4vAHlFecJ2CUY
-TuAr3mBJu34sBQKKcxiXuGAx0ArszvW2T9mdwLcD3qJ10x2xt9kBkzlW/mtGOOhvkx9c6nW+ytCZ
-vFnhiQRw6OelXEZ4QeikJhIuXRSa6XByrKjzCTxPr68jQa94ffec+iWMwqAeY3p900MsL9TR64us
-W7zpCszdmxe4eYhTTgoM32KEU3MyDl9+lk3I4xjR3wWKw7MJguJBw6S/JwhO++w5dTm/YJ5WHl5e
-/fpqrc09we8lHbsNfE3SsfeeTv7QtKHra6Jxe8orzBEyJOIAwXxqsHmpMA2ywwS8BXKlTo7tBRRH
-xY5quv79pMn/m+0HCMf2Jx5hnS6+Sdny9Pdmf5tCdjSwa2QE5t+9wMkw+eJcXahM0jmC32/enSf+
-qe6njeMogziPGoprSa2jj0zLSGZaFaYYR3e0N8zVczwN7J6rz97fuqERIHsDzdG5bC/PQSpKtsHP
-z2pwS1Fugl1vNKq95Oe8XQC0gN70/oYyewPAmo/nycf/28gOMIxIb21bYCEVHBe7V9406Jvab6x2
-8xbf6jYqUrZ4nQFamvK+1bPzOi0tFuC1jrTpcpk0nhMYQ+ejLlTzUOdu59u6gGH9gMGSF3IKBPta
-PS9fiqufXcMjCG4KBDN21ISzdz/Z3xi/eNxyHH7bhyw/Jef5djuBzgiT8OkpFuCjfJzRZUlwayyR
-MqHzN/KqROd+8I+jAJ+ynld6A5PQ55TDIbTlFZXxC22FLUQfRtoIsEz6Urzh4R02AcCwJWEUwRfi
-3fUGoA0tHEJihOkBPj3GK0Fi19UODI8wAeikMIY9OV4xUDqT/I49LvIIda+AbkS55cVK3gwKfEOD
-Rf+yfc6Rsse6gWAkPqy9HfHEBxjeVw3c4RnJsSJcLd8NFJ7YQ4v5pIwwniPXVN76m7qKTgouvWaw
-WJGig2DTF6QQY2jHOWzkk+kGeDm+0h/8AItO5S39v95+gHBs3/cIIxeXTj59A8Nv+jNOVqHaEkvL
-qwY7mC4EtRphzDCgFnZ6fvU4c3Mxxt+P9wTEEx3YtR2lA4/PaooMuqFzqezH/0239rW2lueoeMOZ
-5wGOObju7V0Ero47GH4rwwUgM0/BcR4LfcJQ3zzCV/Drb/k377GXUYy+FQj24j+mCZRlQnLpNHDV
-AxevL0GwS3p6hsXLkBDYYhzxuPXgjckv0xhG7iG2NE1xDYs9apyNh9v5Wk55G9dNdreahyYC1utO
-nk4RrrhZgDxcIKrs8Es9Dw+NWQPDGIAXR14HxukNJuh5jQ3Wn/VfA8AMFnPJE770D/uUWVxvbE3A
-7hn+MOgcrDxFimEanJ8ituXcJJic3uE6SbhCeCvyckWGwazeUtbzxQ50wKtEEr04QLOXYpAhMY7T
-ct5subwMF3LfVoTwCqNYFvpC5nxxbJz/78UIV4yxF38cQHjoHtXvQv6Uv0ZTmZ6sKwKYABjg0xnq
-apd00hZAxhIHT9Tekt5c6tRSl8U5Rj6wIzTiBowP0LuqrICwpUTt0Ij9Ups/2OsJL2D57tvKoVjx
-CsnxZKIuGh/dACpUx62+ZFehlw4FwdTlXB4tuToPOIAah3qBc4wKkg/Q9M9vP0A4tj/yCK8Ki9js
-/xxgWHWdIfQDZI69ynBZGWJr8tTM6MZ8l5uPuulJxoWhXoy/Aw0Yq9LRaleNcx5bS80azReclz+u
-JQnVY81QsqsKciFgN4yo/ugd1mMqRwXC6cX1AXozr3t/e/4JgFv5rWxSSwjgLUu8aRh1xO5ePcQ0
-iJku4tanpcUb3NIPnK6Gx/PzqObPXic3gK89QIJQE89vKLq3Yx83oZMnfsczfqTI+157gtwGgqrI
-W7njpowrWiHqJo1NDPQ+t4EZdiHnyNLYZp2Uf+4IinTCu2cYZlgCgrlfw9OLK0heEivcwyOOsIjx
-c35gAwuOndZPrO/xaliE9WP1DKuMx+Bpo0tGe34C3EfPF+/eAFcJcjwvEuQtXsibjQP8TsA1FahM
-jwu/0QvHeRWA1toieNQbHsdIo/o+jt+AMceS3vq5bnA8FSBY3N5egto41rAK23LaXpxLYB2Tfvng
-hoIbX/Q2W80Hap6S+ScYRpeVpF5DXSJnDQDvcpM26VTqx/sqpjwSfFX9Asi320MsNpvlIusFcLtX
-OIFxgsOYI9NzIKC52NLyx49pbCdegl9nv6rbaD+GVgad+NU9vvcRQNhBj3Bcx9HA8MSv2rnuAWa+
-DGzkERjDBBz/5e0HCMf2Jx5hvmhZC0wHy9iTKlKfEpBhqg2XY0u9XBcowELAUMbZRQN0pbx5i8xV
-Hcm0m+j5HjihLWW6KR7rlSddssakI2MGxRJkvUtDrU85ctm72DpNz5/n4uxHWo5pQDvQxRX4rnZ8
-8Q7jHQCvtzJJr66xBhXOHLGBZ71rnjfjvL2ZNDxRi/nwCoXAqpfl8HClnd2OxSvIzwiJ0GMAnrHv
-VsfZudXGM8d9ln2u2xTuAYqB8miNRiH1fNc7+7CBgach4dW7MfZsTgGOFW1d2ksjvY8V7Dq0DeYJ
-eFkBhgMIr7XwLMPK2N4Im0jwOwCxvgAn4Jf1bIJhATsNENk3PMJe83YFvxxigtg4zhUjXMpKdis/
-pm+Ikbe8rukI2mv6hU8caCv1pHIXvaxPEDiXpkygvKObJ23akwcR2BYCBu9jkmMNn2jHMU993WAJ
-bXA5JuCNmN/jZbpFz64LoNt1C+xSdvYxw2fq4xrlXZw6ZzJFu+ErhVZ0HDcsegPOG4/sb9CYT9yo
-LwmAWx+YDjpulnDxDpPXCK7FC0yyCx/Pzyo3YJyhECNfQfFCO17UI74dYSv6ucSZleCXbBXnlHE0
-0SWysk+g7z38DYyX1/yZe8OzBeL9BDtZTo3Q8UkHxtbG/uMR/he3P/MIPw0MZ9oWLAx/3jEBaVtT
-OdLQsjCMayrcpnW97pBcuVuMOkzKqqd5pGD4tP73zV/Sl3pni4Q8Jilvsvl2uaaXRKmroXT++b6P
-nZ5f/p52XJ/vfAiKh0f4BoYV/D5abwLhqLcGgCYA5uO3BMTgcW1cbnZSpWjjoyToM+v4oGeCklDi
-yZDq9bXuJU7DXNbX+bkhnucSCpEgGLVsGHTPdcXEM8wrqziMcerY4vQX6mhKFSrHWsq5idfkSAHB
-lEeXaup1YvynS6PZJ7MmruPdt0hXprMvXqPXlSTqvPKsZjhEeHTXWnhse4SftQIM0/MXH06gN/fm
-DR5e4lpZQsIicsUIAUBi9er5hnqEZX4Yp5nHBRo6GEaL++0hEiijH2kj8Ii6PfaTIIbTFF4zk07k
-vAfPCxiuOzYN4ek6WnkgQYYJb/VdVZ1tic7LNKsIjZInctxFR8SOH7XIdYPzBsbB2OANWF1igQvQ
-6st0GgpRL8lV3eKT3UaLD848vpCndOmaqgAahcKrjHs9/QaaZCp2sguhzXmiPFH3OYR/SHcPgFu8
-1UGw9RuSNOnq6bUW8qD5uiR3AczLcdjSFfJl2G441T+LhxoW4ZVnBMNJU4JfwPFsgI3wDK/w2DrB
-MMMg6LlNzRV70bOpF1A8kmOwAYxJNJPW/t72A4Rj+65HeNv6tR81PU/OMYAAxRsMq/OJGthE+Xb5
-bRZ2nojpxSgpQztv1+ig9/OoLqX+B8c+WnD2oOcPqBH9mgJav6H2DlVIHZV2Eh30Hunnkp4eYXRQ
-yxCJBn6lTvMOS50HBYb3IyUBwDG2FXRbwP7WO8ob7I0YRWjahK66tdbF6xYHaWxNPZJlvMszjCz3
-WjQ48iJGOL+5SaPCL8vRM7zrOgy8KXThw0xHLJsV27Rx9bF9SvuZf3gbMIy5ghygLKYY2kIpr/1K
-b6IVLQEr0KvYqBneGH9a8zLWXoMoEAQkKFbPscYFL1sb/ArwdfkinGlIQwPBGiohYRQKgGW5NAiY
-qZAICwf7pnnjTWce90GDYsPiUQG8uVegm6EQJbcd/KJ0iMt5Xm3nNFvRkZk1n1EpjbnIkGo2ZZEs
-S1gl860a0ItGoteVF3KMkHQqPo5N0iTgqKuxvRXCEHMcsZ8EwLt+8EEDzAPAjhUhunfYBfDstgsU
-7/ZyaT+Rrb7v1G4KTQk/30KfRiZBGj3BdATMuVCOJV0F+EKArzvaC3cXEGw5B9G38OiW1xMRL1zH
-6Q2Wl+TKQ4yGD2lbdzOWbGbxW+R3DtOB8gaLYnLSJD62AXHPeNy4LMt7Gf1Q6O139rH6XjpYKyhN
-ClgXwP672w8Qju27xFcPMNbay4mIh5h3PJzvvS8QXE/SKHy8rjfhTVRzlEc6QWfUMQo9Ys+6tT9T
-YyNWUOU6yl+o8lZw1DpDJO7Np6pKpS8eI2xls73CnoD4CSVEIFz75wqCn1i/0oDDG5xAFxeAjACw
-Uq6eYNc9PNd2pDcYVK4sRwFo9QoLVJqmIWjTj+FlUH0cV30Bw1a1C3SJNzgt7YP4sCcKID+lUEcY
-RM02XvMVHPOm7jr/l7w3emT6VWOjRIo/7dl0TdtsO4AO5VTjQKE3FnWt5sF2aecFFPM4Z85FZlIk
-t+FdbTWI8ARnegkAVk/wBRDzWLx66R0e+T7zCYYJdg2VTvABAXBCh6THpTz33lZ+qSXUql4+/b4C
-5GSvqtMAFOeRvNHLHaM+KEOV7nUh/MDByTgxj6sNJ1HaeNB0XwHge72i7x54glgn6PB8iQ0WHk25
-wTkBMz3GAYgZ40swncB2vlBHhFY3TVjiLb5ShKS42YeZZ1LX5NzZWJWPW7SRViYhLYOH5OuFdcPF
-8ARHB8EkvaNelrN8UY40BUkugHjndfDbPcOUrE37bZd2DG/SW9/34fDZJ+z6FoB+j3b3MwFwAmGS
-YR+vmLv6uqDO14XsOQbeHoe+1/OnJ7gh67+7/QDh2Nb6XmgEHm/e4Dvw7cd5kMzpMtkiyF7cS9H9
-rWdY0hbXKh612ldn7tuMmbxr7Xb8JgOZst6b1tBN1+m11GBm9fL0po3ENgQPNvDN4wGIe/pJQEzD
-mYAXBUh7yMMue6TuLWxCvcDuBR9XAONclSJGFfdVuVh6qCaFq+O/GtwPef7mNaYhJ4HrSnUVgcKP
-b9wLz54m0OdC7A3sktFPcKxeYu3rji+dff1srvqIR/nwNpQnE59FAFpuPT+yGngLmSqaalsl48Ql
-rvWznQBNeuw6thlKIKDZNuDVFSJWAF0Pj7B6h31xfeEe95vHSS9pc3iC58c00gvMc5MOv4EdLnu/
-7Qt4wD1WjIB4iAWoJPitNg5PcNYHKpiT4Sve9R/B4UVfn2pLPMCc51are/7rjAuAzvHrWBKRHMfv
-4FjGl6ELERoTgKl5eQMA18t0AZiNTxfO+OC+rvAExYa2xnR4l/ULc4ccKq4Vch7p45h2b1aggaI+
-0hM9kzZm9PAA59fnev7myeLXBoJzOWJPL2/KVnp9CxCq93dPVeWbob9Yh8j3+MBG0HURG5BfeQee
-RsSKL1LHGL5cnkV6hUM8LmEQ4RmeAH3i2AL0w+YL8M1z5e/qtPjL2w8Qju2PPcJrbU/YAuzZb80V
-1lywkIbtPBKGdz89TxD/WPNIkKFHXtMaAXZVED5Z+wOYWm9erlJ9+F1belT/6wph7Hk8mnRJ+KVM
-jWTq+VD89AgT7OZPvL9P2z91HJ/yNHSPbj9GguPPYBkJgund9QA4GzoWCEacQ4S04hzA801ggT4F
-pHRWIrPRjvUls87lrAwwXGfJGeoTjoXW7YlHrQ7n+oF8HMqF6DNeuDiisdYAKnxhjnF+N57oef6h
-TPYCgLchB/JzzxMUt/tf6y1ajSFzreoWjUp2mXY5KW1PkwMaI86Zzs3vjxGPLiEeYQ13cPldX3hb
-5zn9BqIfX5dNk9CITY+9t3ZzoGXFy+SDvheg8SA+rSx16Jmjh/jxDJlo5R/BcenJjAV2IJ9KtNCI
-4ocOXjk23bzNOWDlmc3/ldB5bGUJcP1bx6TVFQyb3Nwseihl3WAFtS022CqsIuyVy5zbsqId5cwM
-Nl6cq5fkWEdvtpJMsQ9dMU2X3pDMcuoNlml7F3ta5zpwXMcry33bdve93Nv4gEseWx176kCUV4Os
-topOG9gWHthzU+kEm80zLGlYqCzHY9gvycHxWIQxCI/tPgR+CCVUTzcpj44vGDy+vLeD3eIlufg9
-BMQHKLaCHOj7xLMCgtsENV1z/v729gOEY/sTjzDWE2GSGxXvCZc00IQ9ga9pmuawK9eyFCM/hJeg
-Mq1rcr1Ve7yOtH8NhxBhPfL0+DjvbMsk7/QCf2MTo9EOvY7TjgEBOBUAv6UVHAsI9ge/qNhwhkPQ
-A7y9vDg8wRXbizznibICwfRc9xUhHMAXkOAX8KY7XQFPniF0mYDQP5S7GnECNQFYY7/BWXl+zzqy
-nnB4hHeLhlzhPT/TKd5gD4MwPMK0fjeWq/0c3/seQK5oUHetBHIISyEBKA/i7RJphUIsslHtjzAG
-0tNYFiUypg6ApT2vNugl/ngMNEC8BNCaeIEf8fT6AMWwc43gXC4NZZimoUogDPH+CgjKeg38jrHG
-Y9uZDwFwHvxDsFeAl2AkyCvnqbfXtQzIECSELDLsq8JXPMHWVqmO8g4TPLLDMq8CsIbqimO/lx3p
-UnBFD09g/+bt/QiAeRyhEYfn1qxig61AbXqJ4wW5bauWgOAInZAX52CWL4BNcDxjhBlwWp/lJgmj
-zCC0j3x4FFuZx7Rxld4yaP1UIGXZTGjd8np9Pslj3DT8Ca+r1xcNbes+5TP4Cl57kofzIjZDIyDg
-GODLcgSORcIaJ2m1bHfDLEIcjGB49+UJ1lyiuxjCZsFSJORebWLr8i8PMAzHevYcPfFy3HKEhzim
-6dLH+lnbt6Kccxv1ZICZ/rvbDxCO7c88wttDpt7gjZbkMQ0ZIZWDpreyLTWqhljSCWYj3z/UNxyg
-91Oc8HWTy+SjnlsdafF3bc6y6Q1mmy2PyhzyYwJoALP0/wmAfw0gXMcPnsfxK75k1L2+yBCHlmYd
-7OtMrzDra+yyH8flIa4wCo5R/zjUoooe583ArVxp6VWjA13NGXujZ6sAccUI1x4PjaLnC3I546bH
-zDYQ+if+cOSngKufc+/X/HtdJixA3tPT2x2zOyBfs6ORyH5STjvlam+GPiM2XvgTgGTjXNbPubK6
-xjwGwyLIE1HGxgKE1DJM7wDYAwD7AYK/Gii6GqXLsWMAZgLfsG81TinLMZzyDK+88gwjFcAMiVCP
-b4HfT2W8KJK/2Vmu/qHKrziidOypDuvGspWXqFVNYQCt53qijJ399Tew2z4KNOtGnpXnN2N0Cczc
-e2yw2V4fPMBveYHj/HjyUCA4ytLj+ylGeCVYrhdWgVx7OhFThFCoTQumcrnxqPtUS7bMuWS5mD4W
-86COIedL3vOIJ9gAe+rGwp9Amys/CQ2ELuNNdahNe4I+EhvM5dBAki0BhzfPsFWekGWrsMjYnmGI
-YrXGe9t2WYBg2jLAsfAFB59D0gu8IjRiucVLchLSYNLfQbZzq3lV4Hu01yZifWzxn9p+gHBsf7Jq
-xOZCS2dTMsUF/G6lg1Qom0ODmVQjiuFNReAl/Kq4+wPnSPMuNsGzXJ85qSAiT5nw2I2yI33POWun
-DxttyOz6MBYJIAkexw9+9/T+CoD765Hjh4A38sIjnHU0NAId7M70eznKi4wdUrG9wgglU6BY0E4O
-fxyAXyoKLhEYVkCoqhvQatyB4iT565YAiwfRfnohbeRHrDDqpRGLtYcz9OGJ8rXXH84WTPpiK9Pf
-21/Aqe4zbIle30v6ecIT/FKX5Xx0bUJnTqHJbBx5nG4feZJyHU2H/Dz3Or6QYRop5y/KefwQ4BAk
-aN3Lb4Le5uFVdDHVD/tEVuZdtMhsATzPZdF4E9XLnl7/eeD+FAC+hj9EGp6Ic+MWTzpnd+dNzpvH
-l7rXZr0ARzkPJc9Tl9XBWV7yrtney13zoiLHO/IK3AtAbtPkZa+WTts2aI4l9uzpXv+1NsiT8CIb
-vOIKePQlMXH8JPCmrRUbWSAJya9s10Y9h4AxXi7PyWnMOcoXwD+a+EapIu0xbSZKLLQ05TucZHx5
-3uMGc4YZNdoFHQ5b22TMk4aNFyKZoudJgjw9w+FrlrfecLo4uLdYHcLi3nrP16f9F1+g81rD/CuW
-cVzx8m7tK02amPWPAGm9v739AOHYvruOsHp/yz6cgt3AbzKxi2KA8L2F0gaSja3SLXwilXAIIfMJ
-kqfARb9415wKQ8Awr1TtiWe75edltPOSb+cZsvsWCAMK+OIOhnPlh0c8vc8GuL+cYLe8vgV8I1/y
-aCTTG4wBfoEMj+jAVz27CRfwFUD4y7dKXUCm99giTUMoYzev+yyygyeK8jpuYMlGmRJzUt3GftDf
-WkKaYV4YNDhMwA5Q6QTFz442czpm52WFjfmi8wEi3/Yv9XKkHur9eeKrdTfgaxX7TC9OGO9Mo0Ak
-dA6FtHX9mtAD9wiIPlP+zXydSQveKvDLLnuAhzLiCnZJ6wK3BDAJhFOp3baOJjbgaEgOqaMOMHtL
-O9wF+A6AnHXpeZN1hfnFuQ6GycICYVKOXHTsHocLmOnAAyULPhhV0nPYmlYZnuWTZFU3bn1bw0al
-cU6Fc2TsK9s5eUaCdsq86FATEGsUapz7aHy9XfmlgKn83OoFuvxt2hHcutioCYppp8ijHfx225YY
-MWygS7Mpk+xW2sqgQfBB0kRurvRF1amD+g1RXc+5vKo4p2w8dckwgasudqTTLC62l61spkA6H9d2
-nW+kPJCVaQ1M8uevwPC+0W4AeDF2eOHLPfZxnWUJgr8OELzyZ7bu4DjCu4xPt/7y9gOEY/t2gDa9
-RRYvxFE44SXosTe/5NPTECxrki43sWUs25bYYu8WJqGPi/J81B00bKRZU4Exz7t7gE1ytFavaeOs
-32+U8R42UIqnsNVZ3laAoJc3Yn4n6CUQTmAcnmCCZn8qrCH3TpOgeeMYwysMxmB5xv9+eXmFCYa/
-OK1loZNmT8zv0yhaj8kLHKkZQ1y9m1ZPzVc8pZNzGPHpMpl5rrwYfMy3qfWtapR3jm9d+w4yg9kj
-4DCuse8sxMiUEWuGh3s64zsBAAAgAElEQVSK0JEvj98f7Hjl8HL5ituLZ0W+wTOkaed7pi0tgnPx
-TC/Ka/97/7z1rfXPfOTVmWlUjytITRv1s93qLsEtDfYb+CV9uzcYBVQ4PNJzCnOqL46Xum7WI6B9
-XtIOuABdHj+XsvQE1/ntc8qlLMQzzC6yfHbPOxKKdM8HMkyC8nADxn1yr+mUXpF35RUt1ydBb6Fk
-jT9CmbqMlXr01BQytKQFChgatiyI57LFCF/ALkzCKtpvoUBv/xXPyR7jeOT7yKfN9pZXeuIExLyu
-1/QRYOt05k1cZy+lY94gy2ZAPA2z1H1IDyjpJ4A47XCc3dQt45T3vLZLvfFZHKvGdz12seVeTzEZ
-0eEWL8oFTQmCNwCOWOI4dl+5LOgXdiz6+qrfVwLi1TzEX18THNMjvLBsr4e+vuuU/B9uP0A4tm/f
-hYTrRe9O68545HGfFiW8uwmKgQZyFSSbnDO8wNlOas7IS0UD9NCIUigqeHpHmv+t0lrSpd7mWWcV
-zUotv7cGC7wnZxjEzFMwrCtEcBWIR0CuguD/KDiWNF9kM6C8vmFAmxf4WoYDRDMM4gue3t8Nfges
-a0qJ7ZMGAmwzLCEI2dxFCoCLyD6OrxPztjWeYr/JFI4GrJuVCLASoNfjhVKX99V8rQ2I4WFs2b0e
-Q0pxaRQTdr/WITADcAPAbtggWNMExFmP8YxPpC3pQMqKKA/ICnRALyS1ytG6VcFbjHFucZ6/TKPz
-FwaeoPixur3u4DdorQDZ+jgh53bPcINOyPm3PUCF6JQDBHDdQLbS6gkuL7B6fBUQa516aekMi0Bd
-K2SUtKsX5CgxMo58CavSmn+IV/MgS/pl+hrYFZ5RwMJMC94+0Y2cNK+V6sTzuD1dYNt95qqrKurQ
-uptOXPLs8ASPn+GW7x/PmeDXgh9nfttLfgPTQAHfaJ5tbRbd/Smwb8kzFatMwlSoye2JjE/GkHN5
-E2OArA4RNNIPnAgYbg20CfLrfDXuGKxCnhMt3W2N7PkjGDaUR9hQYRAVErHgazuFfNl+wQ4VGvGl
-3uAL4F2rQig6CN7trx+P8L+/Lfsm8adw8vbJ7SzT0AhVDukp5k7YliEPTUt9yAOa8DCuL4UNSIFL
-4GsCZFO/ZE61K2LTSofg43Kmnn3fSqIJGKm1eawvzVy9wfkLb3D+TuB7A8L/uQFhfwe5Exjf8rPv
-2GC4WbqLYSIb5M9VlU4rTCWMD2WqGefxZVb8zKr5IXNwHOTPMJME6fTCZToAZ3qCNzBOQAyU4XOU
-UQOB275UUs3YBZZ5NwoBpNNrowA4VonwOMZi+gZ6V/Rzy4ivlcYI2Se/4hFkH73yeI6ClTkt5tXI
-ZIqGTHzUsXxykOE0sIoVfgG8TCtAaS8tRTvvwqv6SwbXaABoKIPGABPo0subx1dA7OFFHnkKgC+e
-4KpTVOfIdpagP8qacy4k7eRLkQVOV6Zl/iaguaQJVOrYpMqIwp9t6KWaXkEb6x5i2Aer45vm6Nfe
-WqhWesB4+h90WKGtBCT7AMwNJH8CwmIzNTYY5FNA6spe8ltfE1DTFkpa800BsOfoQbrnTT5pSVqR
-93M2sb3eVWbRt/TqCg3SPl82Ff8xsV3fze2iLjYYLt5i21Q3eyWkmgLCmPIOl95gqIRHTPDXWilm
-fMKpAFc9wT1MYh3AeIPgAsAs/9vbDxCO7duhEQZZbsm4lokYhRCyZDHHhU2lOTEqAi5S0Fy8yASb
-XvV3C7xuVwwxsJJZUYwFfEsw9b8mlTIT+E7SMDHPaQ91m2IpDb+Fi4Bql+lqCx0M11Jov5o3mIC3
-e4L/c4DiB//5tdspYAu8Ad3PIFg8wjEml/EMaARzxy+hmWEvgdPOA9TaYvINj/0oG/UmWPm0KUA4
-gEFkKuoTMLQ5foMaLgZn8Tlm50sk9kgsXzeEbPYOhCvCsYFidlW8wRvkogDws1eJqGMDrAAyQbCv
-1Y53aASvX+Rphkn72YzUu+fYLBvq6kG3nEL/WKcvuWcf0vb79GGgP+lD8oXotsmeA7CqN7iX3WJ/
-o+wpr3GFRdSLcw3wTk8wmI758AIBOQYFupBJjrs1T6cFkA6MnL+b/uONbpXdQqgFLmVh5bnkDwZS
-8ueubq4dpAMz5CYggJn2rsQ9qMVjCwpKWEP7ohjvvIIW70B3nqu/6EnIf9sDRxjEe11rABiGBLjp
-ETbbc9nShlxKztDSTaCpvw8VyvCzmkfTvqfdrqfHBMEm/WoOqzHJBLNeV7uxgfCK9k6sg/CHOqrU
-E5x2yLZe4Iu2Zt5AsK9d/pXptfu5rIHer3WLEZ4xwQWCK/+NHv/s9gOEY7Nv34XQY4Qm1E1R+Gag
-5gW+pRvYUMAbbaR0ebUpClSVGIC62zSkEEYBNDyiwDHLKSDRzpHuqTqnHY62Rj67OuFC6mxPpXMD
-v+oNnvHBujwaPcIdBAv4fR78+vVknjcP8FY4suTjBQCPOiOvAMh4qNa1VikfH4+mQJDCE9Q4Wmuj
-lb2mdSY+KJhkIzH4e2KKb7ROGmkBG/C9vvYiBXac7n7ZBtsDGx/j2OubVoy95yUGEA4DnnkegIHL
-vPGcTDv4Bnz3Bm+wu3v7ZPwvlTmepzykyzKGWO9FBucWCTDLCaJGHkrkcopkSo7ZGXVmeY/3xTfT
-9poW1DQ6cduEB/aA0SjRXnzTMIiRHt5ffVHuBpyvnuCjDEhgLJ5h7bfrGKiHBej6oZ+rjkedmkxp
-ngcSP8zqStMUtUHpvMz1vJFBZcOE8iIBqgBlro2rbMejGiaPLY9neMQBdEmL3/x0VaPfAd3yBkud
-1BN25J/A9yV/0fdLL3DxQ84YaerncV5fbpj6+xVit53Dsuwr7fDpGa60Jxao+X4PmbHki5t14H/X
-lFd5I2P03QwtLMLjJoEg2H07Er4AcN0JM8P6sh4X/PXiAX7ZW8QHf3sFr//h9gOEY/u+R9gAGnQK
-Np8rNAHdxrruii8gxfZ5HfBSWKiQtEzBMI/ZMe/KJhWBVb+hj0BVTErpaBDECWj5v4PiK/C1M3/a
-ikymsCMNGI3aBMMFgmVNYO9xv/+JJdMUBP/nefCfXwWGEwg/LoB3e3Yzbcy7eIdfzvH8nXfwpxKK
-F+yyHYuv0gH1qK6brZOa301ftm6v6xJyM1aX9jGJp9eNeQhPsBnBJOVlE26zvSM/cgHx7loofWOL
-ks66BMGWXlo3wNN4b/Cr3t/dryeVO6jUE/CqJ7gM6Xxxr0aO7NdZHv9v5VbinGKndEaSpBvSMVeh
-OtqHrI8widxPwHwBw+h9kJ70kTWGEbmdm4DbOwiWvT9S78ENLOvLmL8Dw/3mrHg0b7ynd1cAzfFl
-OYIg4xyMc5VGN0Koqr/UU/ECuzbOyJQ24JmS80MGqU+jfzZuym6aIeEWZcgKAKfn8wZugdcy2hzK
-fV+ij3k8H1n3nlft9HMhdg8Fgrk+eZbvNrbcRN8zPGKPj8s+9rm7cndRbopI9Llj4+54MhmXzQbE
-vEPB+pXVZCYbT5nUq5qzs815k4CXOiOe8R26YoeP8VU7an3GCO8X5bpXuC+p1r3E1vaW3uK/vf0A
-4dj+DAhzb5fjUhq68H7dFXoa9DDzyc8lLAP0yt39VsJ1V4sUlOrH9AZ3QZzCWONOAVJSDGGdoqRk
-+358MEDNnIEDBL/YeQmA0b3Bj+/YQqa5dFrGCktIxH+8g97j90uWTwvyTSCsXt8GjF3rFEiOkfVx
-Ck1+gcDFK+3MQyyJpQEjnHdJM98lnRb3kv86GR9mqQFitquXIsRQXuR190j0gxwmq1b6kyvOp7zQ
-A3QA4QQgAFcocGd+Ke7dI4/Hd9Vtv3h/HYh1UQGP5dMYyuSPKP5lJ+BIspWnTcu1To8tLJJn6JJT
-JCnHOjNSp5TDAC4p/QJ8MQwXxLhhgOIT8CdPvII678XHUwp2IkBsgtPp4e0vz2GC2eYxvoRFQOqq
-9xdhmOkJZtkxIEUW5OV7/k6+5E+Eks2f+RaMcZDWOY91ToVZnOStefDc5SUz7eJbCZsRclzxwhHC
-5wiwbGFGmKYdCp1yAbps+w6AL7ay5Ud/gFa3H0O8w1bHIQ+M+XXD/vQzAXE8urfk7wC+BKAEwas0
-GB7Pa7WZayBzJoDukOrl3aaOMEVJn1vZevVBHHbhdtFmc1pHWz27/BYMD8Mhmie465BjFWKTmOCv
-24oRlzAJs5Y2AcR/e/sBwrF9l/iOtRWzKgObcVL5gGnkWeSG8vDa1+MUb+WavwEtz0t7XOmhYMob
-HGXc6Z269jM3NtIFaaqIDoIvabn8zQwxUeYrwIMjH4d3rzDKI0xg/EhIRHqCn+M3QfD/i/CIx89Q
-iAS9rh7g7amd9cw7YFY4OKlpeVwR5A9KnZSio8JRxdXT9pL/Of2yBUtdbTrzVBtTebvOHFBQXurb
-XmrQ8wkKinEOg1qgDUbQxjCEuEFIA8h4YXqIeY5vMKugl0N50IAvfxknTI+HYXs9HksSkJSCBWXc
-cg2xRVqas5E3y0j5LL6oCxEucRkzy3Y6GPa4ZguBAJfiw8WjU/y16SieOh3IaVP7WB29wgRqDfT2
-n2lIhKwm4dN7TID8EPTJDz0MIh/5i/7QpxVkx2MgZGPl7Ze941LGhvyUyE6w2hppvXL4MZ0rPW/k
-jn8OoUmma1xcjYgWSJvU4+QxOlsSiFnZpivgxTfzej7lmnLP8ybQ5Tm9ftkvrlVMbzYBsfGm2Mo7
-TFuXIDi+BusLNZ3/x94bblmO4kqjIff7v/Cd09b9gUIKCdiZ2Wem5jtrJVU7DQJjECCFZRk3fvQ1
-ynXT5tJQeHOenYxsk2bj4g6g9uiOeaHzYKtS9cLe4r13XS+VZRgJgs3WxzXmEyXE+xT8Ip0D7UW3
-Bnr/urhCyH7CbW9hW+k/HX6BcIRvf2LZYuFtflOoBU4QGgovAXCmWRmwgeC8jVeLMPLuPvGrAuNc
-BCp4qj11V8ppzyKVribZMX4vtz3cGeVOYQAoAbmkU6BfP6jRLMH8mtybX5XjC3P/4+tFuQTACoZj
-b+Fm+bWD9deGBTjGI0EzquyNB3qP8n7jR1/hQlQFjlIwZu0qnm9gOFuC07i3kRFg3ACyHiHIYpiu
-lt5lL7B6tEw2qE8dkylUipZrg0ouBXCUpVXYxXpV8bAGs/wF9NJlYge+cW1xq6h2FHN2TCJw2Dor
-BlfW2qWyDvlgMZHoAqWsyTF3KvAUGeAX45Z7BEfekn8Kip15Jn0a4NjMxIg5OnqYHx2RbZ1Ft/j2
-l97q5bmDVXj6DqdbxBtzSyzHmlagzJFpYFjSctNUaIoyGDi5TnjO+VGmddp68lPI5WNb8dsqnudr
-fJ+jNT60t+j8VJi0W4AjDdFHXKfTMjxB7wS8qpMaDVBw+xHwAvnkIvWpyXlhtfaH89jKOoygWdBe
-rP6FC1SC4NeXBbmNQnEoVahSc43WOalWD3rxqiftnFs3b3MGmGZCdUNBXA40Z9kOiBccKfcIM1tb
-o6HkAg14S1ZwVx3E+xaxL3xsC8eX5e67RtQWaXrkS3L14tyVU/+x8AuEI/zky3Jr0Rk1EmjxTRAc
-i59vYtsGiGkTjAmqIDi0kaVQAijNzEqqNdeIFE7I+ru51mRBoIRR5nPZ7mKA56vQ1jzrfy55DLuC
-LRhRyutkBXZfy27fQq3A76sWYe8vyJ2swf/6+++yCJt1a6+J9deXnTOtxKQFyys+eSo/72k+WNLf
-EkADW7gKua4iu3CTpxBfq9ERDmWo11WByrGhPz2/CWgFwEA9SZHNhRPQUclhB8KxLtKPjZYqoIAx
-wmJMMMhfNCF/D3o+XryY4HiAxgOXWlxcSK5lglW8uV29XrwzfqUStWy5e4zBGvZKloNzpfor3/8Q
-UFxKLenaP2jcc84VCBzD2xM15tr/NTCH3R3iN3yEbfgCg59RVlBMoHsAvwVw1Srs2bQs2/5atT1l
-JSd5MNsJtAQMKWBWYCzn2+k65GsLXd7m0vo4ieroI98lv/sH88a5AyZRLdKatb44Twl8S8dY8eAr
-IJzxPc31oL6/jYYPeTHv67pYIDbcIdL4Y8tX15/SudTdRteoZ60/0qjSa2DCacW5Dru/LyReN6uR
-Zpk2P3qYM2IO6pTk5xqGYICXoSuNS2x3zUnqIUfpOe648SDczh7DQxkau//geZdcfeLswC71stz5
-q3J8Ee62r3AB5F+L8H8tfHsT580SrL8SEDdAnFO2ST4kCG5uEfn6cFl+y6JRLzYAqMVpcgET9wih
-ZTIbomX4pxZ3kwvyd+Nh+7uXySUbipIrOwGvSnjemeq/Iwgu63CB4L592v/EC3TTGvwvdY24AmGL
-tBfgdWtguO8esf4Q+Co3ycsHwN/WwbDz6NYEXj3QdOGrx/8VL767lNpF5jmo8q/kqUgBBAwwkE1C
-2rN5jgEJgE9KEsNKCZQ1AuORnEs62lCWYBcwt76QtFmCsQDvewC8/L2hKLP8AfcBHdOktXCWmXGL
-dR6WYMs32GXFOUFyjGLc9HIeJEvjmC/HkXdQANxdJGhBm486024aY8odL7qSXb1QsJWDzrWbeS6/
-w8tyQa+PY6gbhO4xTJ9g+vLHuZhpuSbEDYJp7FO6AeBpDYbygfNV4hMYn2TdZd7cQ3kJF+UjhgoZ
-mUI0+u5SS9w0EhjJ0tXhMilLw0udZymLO9h1ic+8qPUCghF1cQ3p+l9pzbeR3stDQTCtwWIVNnjq
-43ZcX4+oz8A/xfPUje1GaYpI7VuMIUFx6lWv7Hy8pmP6xUSJMje5Um3Sa9UTDKuLr5Rc85G1QUDM
-9/7d6RaxCnh4hOIB3AsA59HqZTnuGHHeTu1Jf2C1EBMQW7hH/OnwC4QjfJf5udhstwpz0SkCSjma
-d6SctCFMxOK3g+AQTOOuXFtaABkieErY6C/Pm+Xy6qmOqz2a4qIHFbeNvOx2C6q8Bzd73JGCnMB3
-txDvbhH5Jbl3viznR2vwv8Ia/K+/AwjHGK1PPCoQXuOQabNwiyAotg0Mz6BcfaxeM3jc8DcEDMe8
-SMtw8qxE74r1HIj6TJDcmH3mfBuC2fAJfBtNAIAVDulBdqf0ALq0DDuQ5hgBwlS0+ki/pS36SfBr
-y1rlASw9+JDuERY8Hb/3AcpyvAPmzTosLEn2CEuLfldWOhT5dvtjsHg0a0+Nb3tyEPy2ACQm9Rm5
-HPzhrhHdJULTFhbjExjmzXr0I5+l985qX/NAkMQbW8YD4Np8OW7bNm3RG+DVMrqvMIYlWNJrOnZA
-aNnUbjXeALBw1QcYbi4QLa4M0UW0L6izZmEbKu5xzbTaHicfsv9JOpRLizC4Tufj87rqDoAxzsMA
-hSYT9BDn2p56SOIOFMANek9zbkZrrGgAxDKM1McbCOYRahGOLRS5k6OCYBeRV8oymu3gjfj2oQ6I
-wclMaOxIcJ3yE+NCAk67YJE1Bbqc1RhD16m0tsY3bqTJ0PFIKfVSnPME7x+Z5jkWWDxCLM0FiKOC
-19KaW24Rh/2D6S/819lqXO4Rvxbh/1r40a4RRx/h+tFHuFmF4QmCyzJMQUWQC3wJgmMB8UUGiBCj
-QNpcI9juAz1hGleFUFsp0xS08EXQS96lQKisBH4d9M70wU/43S3D+lll/YLc/xyswWoRXsNqHfQi
-QHICXh/gd91xT1/h+pV1L0G0lH1MrMFuAYb5U/uQWousvfxSo9UtX8659eUIjQHJCkRwNx1fwtlv
-51OQSno1I/b0NeFSKChVfHl0rieHWi8hFuAEtbE+VhlPgLsBXdkVgkB5HXmNYTEO9jU15b3LneQX
-OrtLSxQSBNtr6T2ygHAA5BgDg9XDJtTq3NxAgj8LIKu1twOK6pt161oCnbIIst1DJxfAZEYCYS93
-BO8+vTZcH/hynIlV2AaA9vcd4FfSA+Amttjy+vTsMQEjEwBnfAwm5XICvznQ/yzo2j3eYZ4mFfvm
-HA2Xc8vVZUqTLi9484OUGWo9XGuggF5Zz1V3SDyBsMTRy0wXB29xS9Yf4wJE66W4OG7g9wn3iBi2
-512uSLAQwHH0Zz24evpIVNSqe9GW0p1Wejf6aUynzKz11NbRNqBMyroCUDvQ1NhVWDQLmUlMsW4U
-YyznTRy6ruJuRbQIl4V4WcyfV1r5xPzkzxyw204RB9cI664Qa/eIXx/h/yfCd10jjBau7/xE0dcd
-qcgQRuTRXIFgj3QWFPVncgzhlcAcJZjkV72z/mO05aEd9dwNXyvIatdBtq6HgzDIx5oknS3Bx08s
-JwD2ZhH+2/VDGt7dI/4WIPwS7B6AsC3L7QLACJcIr3IJiOsFuuLsEkjpduHcnWIB3he+wC9QAJiy
-jxaTHOf5V3nvMjv4+FxArIrM797sNXCAGpusE1BAvI8u2rkJfmnWIfjN+GpbB8JqARZaukCURbhA
-8brwG+q8uZwEdyZAVqD8OuDmCYRfKt4D8P067ht9dXfNDAJi82EJ1mPe+J7GqIBuAl/2U+ZPWsOl
-fH9hTuQZONYCm2QK9R4FqG0TV4Hx4WU5tQwPcJzAN9wh8gU5jXMUCXAT9FWcyj/HQrBMWa+DwTnH
-L/Hsv4JeylqJJ1/+iQK/nROML3bPiOR5JXIoPGoRGSFDbKMK6pDqhcs9sOooF92mfBH9A5lPG0BG
-s+r2GzSkPOhxyyGZblQwxJPZJ3T4WlO0DlucQ5NEuUcsy7BxEyi8azcEPsxq3NuHid2Zurat73U3
-228qQbbFOlODwxhv/UJgAWK9+BTAQYtxUreIsh7XGBrKFSLjICjmq84hv2kBXhaKFofTRziA7l/d
-Erx/Zrl2jlifVn66q8S39dS/L/wC4QjfZv4TM+JiFS4fpNL97YgCxVCw6/TjGhZhqPVXy1JEFQxS
-GNaT2sZZLITHUSCfgG3FZl7Jj3NtDKlkh0wnAKbCokBvIPitTyy7WoP5wpwXCE4A7H3HiATDf6+P
-cawtW+qFuAWCvVmFk4YAtXnO/lNLMN/EzbrhC1wD+MvCLcIR4DiAihO8kLOl1PSRZ418twaj0U4C
-E11vtzgV3SEtGpSPUDkv5xU8QYKJTqfiXPQCw0CBshHfrMI9TkGd8wcFgl/OHzMBt2oJnnkEx54g
-eYEDl37hh3EFlFYbZ8Sj2BaH3FA5ygrMI8rNxoqtOVfS/QEFMgh8+w2G5gdQiLLag26Z3LlQf71K
-BChWcNtAsFiDFfB2S3E/4n1rZBX4KviVvGNa+rFNfAEmDQDfQG/bMaKvvI12FIQ7sdY2IIzeA9ea
-C6FFCzStlIyqTsXRkoBlEbfqclJQ6/YEgAnAshxK50waau7pjS6AmqdRtc7XWI5oT5DipNwP2J/Y
-/cGqHwSnCAvxgzX/NO1Y8zC2f8yH80ddtinRDQdwJ4WlqynQOR9rrGg1bjZeAk0OmujEPVjWwy/S
-0RyyQHjMLX2KDIu1t5L5why6ZZjjdHRU4E0R+2UH0PvXBQBf9hQ2eXnuT4dfIBzh+eauEd40lyUg
-pjuE3hVvDvopPBjq8fURDAM5eXPbJRGy011i3pkOeJrX3BbxFig2OqXOx8gTyuzi7SqipFIwhOD1
-+HP6oEbzE3b5qIYHGH7vluEJhv/1xq4RvDs269ZgHlHgtwFkWAFbE4DijK/z/gdxnpfF+S8Af7vj
-L7MEw25qveRIU8CrI0RZfjvYJTBECsJdel7GfIIBS+IBKLhoUFWrMYZixvQc61KAZXUsJXIFwrxp
-jN6WmwNdIgIY5zosgFvglnxdFuFmDfZVXsHwa1KPQIUb2G1pw7kcFR+s/IMRX1LyJwGxO8GwdTAM
-0IbV6l7At6xoCXzBOBJIlIWYskj5HrNJOjJVb1unDeCOdAPA80Mas5y8KHfaX5hpyJGjP9MCAhun
-HDvnFOC2OX/IVwDc1sBNfp4mQIU8a5y/3bT6HJOSldlrkZk9PW9rrM1kzevlYuS9tzVdZ1LA61HA
-4HZUfTjP0zm7yuc8Nku2N7eocc5Sjw/c6SLhIZVD5zrKVQL8ducCXQl+Y/05EP768cpv9rm3u41f
-gv2yAps98cTnqbkuq6mNsh8mCEejra8aodsZ2UxOWZ8gGM2uIaaI3K7TjLcC6zrPqNwV1AsYpkvD
-t0BvukfoRzX6y3N/OvwC4Qg/2TViOeILGJbFUGhI6UiLMKcfb6ITyKLuDnOLpQAgDQSrpTghqAio
-/Nnlh4GF7PAbvElaXM/2HLQSFfnE1VKsBMRecQW+I617B6s1eLpHtC/L/V0/ukX8f/my3AUAm+3g
-V90jDPVDvQRn8qY/wfL/gNZlw18egMyAv32B4teWn/ACw2UVLqBpMeYc+R0MpyVARW6zXv0gNKXX
-JCtSouo4ZryuV3bqaiWVXdFXfAfBtt5UHiC5gze6RFhsk1ZAubalC+uud4DbgC/sUs6S40dwe+q/
-XehxfGJm2POk353hXWmP4fJ1A5XL1Zd/+RviRnZnztHOF+as0hsw1nTwulnk2lTxpqM954Qo48Sn
-HdzSL3h7WW77iMYrrhG1vzDj9nrzEVbbPzBcJKCgruRIKy8AMDurS+M45yNOxqqsFYBaBKnwHN0n
-BbQdreK9ea39muHVPrkZANB8faVHFZ+gF7UynecruPVVege2oie2PLlCm5s1LyHxnT5AcsT5tAi+
-bijX0QscP5SX1JJ0ixCLMF74s3yJixm00NfYKH/SUqyGJzPAnrWLRYBhmMWXNPvI5hTLtMgY7z9d
-X00dZOhaAdJbBcENvJqJJTg4IXXzdqJ13uXa6coROKVZgYc1eLMM64c2uI/ws3j2/FqE/08EkQOg
-X17SFS/qRB1yM+vIfO8rZGpaozBDPYUKIoWUUVhvF791BEdgrLJ5RL4VTiDBZr73o8pvyiKV8U3P
-AqDvMIHOsqj6/nvrl0BZwDKtybvKDGEMH3ETsKWquWh/w/GXG/42Htdnlf/C7gZRvqzz2nXNJnQ2
-UdppRSEojrT1sSEcqPQ8dlVYYJBgaVOVIsa7JVHng3NAt7qVlwJbYmsjR2/j/beu/Q76Gyxcc4ZW
-YeZFWqzJZTVmuzyemMYAACAASURBVHrY+iSxDTyO8Oaj1xdvKN8nJv17jK91Xt/sY57JWllz2Zxr
-g+n10zgtr+a2QKq9YVYmEI1K+WLbALjtUW2Lo+/wQADrBYxrkRMoo2hAukpkvM08jkaBCB6mfFHM
-VQPRYIyAts/jtVeshbnaQLQoBb2t23b6rSx5L/WrhbddVwXkiS55em0FvSpJ1o3DxqVSTQNEZZtt
-HIEAkRjKUo7en1QwT8Ev42d6B8Kl99gbjfPI/XlMRo20ymP7AcAt3hJ7lz+Tc4uJ54kPcGC588RO
-NEtcLz44yl2BgqitF10r8tSkr6m6eWz+wqyX/M0dLS5jIiDY3MW6D3knoazDkPj2s3VT3l5eCCxk
-aTiycAWsJ6orn8MVxgBLVjc73X8j/ALhCLu6+1QyJmeexYlKZahq2OP/oMV5DGony7QA4P6Ye7ZV
-6qsZ+62+6GTeDIfG/vbraLp61KkFZj3z07LbIAZppQe2uNBEl3Z2Fqvlp/aicWfS+rHn7fFP+Yvp
-eU0R2szf8rSNQktwbAS0gFlZf0txzjZNCzFHt1Regs4NGP/k2B+sDhtbXrXpY5lnfZacQXDOS6uy
-bH+fOTPNPvZ0zi8qX4x5Fnmv93bQSqxhyomT1LitzrXICFjxdfwF0CHwij+A+xvbvL14X4PFh0HM
-Epr1lhMURf3+11p3f/lf8L/Wkwq82MBuMUuVNDZXB5dyBXQJwOWlNxkxlXnk68ROruugjYA+6t+g
-Zg8B3Cr3vK4VHH5WyS5loj3bk5teWxZpZfXqZJ23Xwk7lNBTQbi1CVK2J3UFOkq3tN5454BrgSFZ
-2lH8T5NO0KX5cXRHArIEt5HtCfDC8MCy1IFETKSHq1HJu4rXUV0mYja4Y91p8xi02ObRc7uJRfPM
-c7zmeF4DHsf7Gp7HgdfwPuuJxmMP3qdeDKUP/LyBrLx3y8s4z2ty0dpNRNFu6UKczqOL7BR5CBcY
-oaC40UvrWfB4PRXlMznPMkDdiJ8+ssP3AHLBL8H3R8MvEM5wUmmnUiXIN0AnqpxCq9EYF9Bs23W/
-kbY6pFX41J9v3WINBWI4nnNq1U5zkdvsP1J2Ny4ILUtysVMRsD6W387Zf7cOn9rbwxko18++jFsI
-oAJ445F0o4W4UBBsPe/Nmkq5JNw1dZCIMn5SVCNlxYecgTdg3Cy/83zNq7YVH8ue96ncyQ71NeCd
-1lodu24/pBKtuRRxC/0yxrk+cOKor9Z1vqBd7Ryucy3mMR/DUhSkhTTeYHc8+RaLtXLLpzE/If0a
-1tfx1vzjsTXEkQBnKde/1vp6FiB+/Al/SIyF9jnewC96mWV1jmc2TsXINU4Q1lcum21cQ2FFqvkr
-AD8NA33svxZ49/x503MocCB0OI5cMyzcgWG/Ql+jnc9exMH7JSO1GZ4HXRc280njcHH1ZJrX05bJ
-icdJfZA5CooTgXcrcQPBiJtVgjbSw3KMDTBHnWnZtNqLEuLuIKA3/YXd4gaNL9V5lPe1rtxiI10v
-y6kRvEe/CIDN8LwOPIb39fVY/13O/u/zwl7xEZ7glunthtKhO6tsvvhqQsXXaQW+laaeqqV7HNYR
-NjCMcNMiiyK/thEVAEyp7PVEavIG+YTq1zXivxauiutYTgCbUEoEFXUT9lfavTUFq3r6u609+wbP
-cpdMkZz6t8700ZvZ8+rjEciqDj3mDQ53nfAtcNytlmcr8ek8G2VmXOvf4gS1JQLkkZ4+mh8gGPpR
-Dct+7z7A/Ugd00MHxx3wUkDus5D0E22fsXer8HfL1coZrhFtrOYc1WsMC3DOlQKy+VIdZM4EGG6f
-t/ZVH90neh/28NUqnPkWDfW4WKbfGJXcpmg1clmJl3IoLLIez/q7WlrzpnwRhXEtQUvjXwTD7rmV
-0Q5qIW1UuizArWzR0iIM1huj6q1xg0FxCHnlpjOeMyFoFjTvFuN9zX4JcXNs7uPZ6/UtZtkWpc0a
-P84Xr/FZN2I0LHTDAGKOFp1DkZnV4oF0als1NqYkNFxWVDuN/VI+KD92edQE0gDGfPehvEFCP3GN
-Gmm6dlF1JkiOBr8Bfp91Y9jAL0fm4C8sPhcoAGygFdhzO4mVfn29HwILwfEYXnvxvOVC8ZrBnrAW
-xxt4+1OWHRB/i0Zu/xAMX8Hxl0iC9vU+uoblmgXop5O8rMG67pMW8yv78wEMP78W4f9a8A/ToRe8
-W4LpErFbiPWHQxotXoB3tMlQCyJmJm+i+ZGFXtOs+ytA/HWoninHSgDPfDaLACMFuB4hHPFxBETY
-+16O12ATvKfvoXhVnNtBryzlLa67hLQPMGjZlLMT6Bo6WI48Y5lhDfMCwWoLbu4Sjeo5NyBt0ll1
-tARbZ1uoonzE6TjPMbVBeaN2penSXnWxaErVIKieLfhkFT7kHaxNCoBnnL7CfbWGFahYuIVvSo3q
-c1MIyKeA9qzPW9XcXRalrP8JSzDz3wWA31b/jNWi8Nfhf2Edn/Xrm93rXQ6fMsliRLX5cxnmvZtL
-RD4aHVzeHmmJ9p0W4Ql21zrU+fP1GBwHbVbhM5Nz91RG57M32i1+au3RJWLegKSQaydW/Yd2Z9z1
-lEooWC5AzYM3nrvEs87p/sB+ukseck17xN2RLg+MpxoTVwnqrd0ajLQY5/yw6QYRlt58E6xcINZu
-Ek88cVkv29EKbPYsoB3W3wV+bblEPAFy37VF2suP4thyicDzLkBs3SLsbfxk3VxA8BkIf3J/OKcR
-cnCmKf90lCv0OasPl8W5JLdZS4CsR0e+AFxuESgZ4O8mClwb9QfDLxCO8G0gnCW76lVajaqeoT/0
-eBw+35+5lJnKLgjccu0jIDYpf8n7cP1J6z2bwJYpARhDXi+aWDsgMkLj4zhHTDm70nY+/9i3OwC2
-Q7n2woaUY7y97TxBtAqhoBH8Mu+N+NjbPfuVUNYBBb9NQTVBNnmDBnjzaN8tewK4esZ0f7jnsa7F
-Uxe+ngDvLX0Dx1SwqCOQ1qQTMK6X6daxcYPMEF59Lwj4SYHv+Z6O+gbDl44FAA9wDGcZeWz4oIBT
-swwHOBb9mW4Qvup0f9rvfZ/6xLzHaqVYkUWrwHf1RfoElxerSvEpIFZJMVeihSXQbPLWmo/8mjk6
-5/fwSZIZsLvo+uqct7TE88rjnO2KJEr8Is6P5AGA+s45wsKaRDk2xzaJrDTv9LxeVqn1ePYCPg0e
-XNOTSQdgnGAYFQfAl73K8osGcnN9xmBxrZY1GN3K+WD5mMKWu8Mru0L4yk+rsPvaIYJ57vFtAMYN
-8ADBZqhNhiNuyyd4fQ446OEOQQuxvbF9Wi5kv/xwAMc9H6EbM/1N8HtOc2ywGWHKSszz1MwSNwpR
-V5pUoi46MvQvq3bTEY8JoqOPJ59h/HmD8C8QrnCRWFspF+HgNVGLEqWGwN8UQZW+i/OSKXsZT9kz
-9EbcgvWXSXq9Nspf8ki1T9w55UwloDcMJdT3o3fuXMpQLjT5gf23NdEpxE83CjuwJZ1lCYDt0zkN
-6NZd904rwEvaK+Vf6PgNKy+vZwd1NIajAeds32DLoB9Ytp3T4ykuofPHJQ+t9dWvJaQ/WXkrjYwD
-3nvdWzRfzEmwPKzDAoC3H/Mxg1wjwuf3UgsUOf84ygonc9lCCfgzyrXPm76Al7J3rPJvKo+QSc96
-VBnoN/rjAX7XTirPw9+LN3cSRckUR82dAy13somO9XP7F+Raus2m4nCbQRaMtT77aybNZyKfoHEf
-hx0MrxoM0bzhI7HNAcqhzLyA4C0tYHC0BwA2izC88xBlMHAf7dLJNVdplvUqx3EatO0cb1dJXmnc
-J53Aca5H4YG6P6z1dgC5dH9wNPCsPsNBAPfqJ+gzwi4CP9Ba3K3E6TP81F7E6RaRL+hZxfkU8H3w
-2rIqL9BLdwhDbgrePkVMXqqVF31NTNqg5+r4R2BY+Cp6J8sCJcjybtRzZyxdVwTEjy8/acBhbmn5
-fbyn+441dJOYIPgVy89HgfofCb9AOIJvC/5SrqnnRWlgWEGyqG20v+s3IYOGI/CNDHMpM+Wulg+h
-sReZUxsDEKOsZ0Ke8F450dwWEEKdZ/i0zpZA2NQizxMryPE4f74arXpWuawyvqWvHBEgZiVQEXUW
-b0zK1n0wbKe1r3pJ+xzlI/xqebnGCQbU4Mvx0w4TohSUB9+3BK9YvgneIXaV87r2DfzW+ZPzfcZF
-w8e5lbq7SHwBfj+C4X7+OdToe2+mhL7yCBQRypfVND/hBoKx9iANK1VMkFUrXSne9arcE2P1pgUs
-6nzWWnrc0+q29s5+8fqD533is6ZviIzqscqZBnJHesW95ZU1uFZA/rwUZh9nraB+3CJynxuTdgg2
-2hxoq0amzs/H7umzSrDVq+wgWCNjvbXxHxWdogIMpq/wBo44U7ebi73+JHg/ut/z8piHE+9Z1nJC
-OPtKXjaelPLaQbAXCOZ6FfeH6Ufc4mGhXf7B3OosfIETqMkX58IfmLRsb34tdn4wS0BxusNFXMDv
-yw9pmMGeF/ZSiXrxoAHcPuZzDpzSZQlffzbwC5Sc/6qshfxsPsS54MAbjq77UuoBZulWzas/ohGt
-RqGBYcT67yD4WX4Ub3zq+g+HXyAcYXv09bFkFz4dHHdBdfvdXhgZsO2QN2kueTsw4YRVF8Ckn2rk
-Ar+Gc7tXjh850HMOeV+AXp5zkh3aGj3qfU2jK0G4bZIuWk8TaBWvBRAbBQv6Y6cTLfNONO57a+Ia
-QWlzs4HtcLIFsc7oyOVnTQefVh5prDlA4ZhHE9jquVsZlzItX1fODdh+J0/iG/jlI9Z61Mqxe8EP
-mtRLcrp7xJwbveV2Tc4zPJhDKy9Br7MRAW4LGFcZvO9S8vky3aKtvUwfuMXOETGm5i7Kfq2x/Azr
-Gx8QeB3vs172MXnSsYCsxKUvExR3usn5b4DdYRmW1d9npNeFfdViIYtYrvw/99vAFr7yGT6AW7Be
-rzZ8DYIJJhUgjnTmbxXtIQEwppCs5CWv1euS8C+OYKWQm7PLsXVFebz3m3O9WYLzuiJ7lvDcQXBa
-ZiUeZcGyOWfK+ktZTPmccYOA4LAEB4pLQDwtv3SZaAB4lGm/F+lCQYA8Aa3GcywOeady5K4C0wZi
-T+ng/QUMl/OvCRCO/pEfOcYm96M6p9P+LiC4LMJw+hh3i/AEwe7vGov3z/tG/ALhCD8Bwqp+u2Dv
-EqoDZpVQM/1RbMucK+3UHoHnybR0nFSwHfzvPimMOz+qt73H1QwtUSet+U8rRwn2oneBn/U79vJS
-DpMmzdg53G8dgjPSt/JkAgBuZWYtv/pbX/AqcZDlrJdvtDjn3WirDv1qmKH7STYQ7C59OJXp/U/e
-DPyWRzvQxjkTdveZUte+A2Qp472MCte62hegN/OoUEe5uIw7Aa+CXzk3x2mn1arq62UDyrVEtxuC
-BSDFEheVW0u/cPEDjkkSAHi5ReA10OxLa/AbQDa/TvkE6LX1NUS3BSRfjz1O3xf5JSd7QlEJkM3f
-iXYvy9F8Qsm9KMtwumoEk2KTOGFYRKNCzkdeqW8ZqPMmKBsA7mXaS11Qq7AUY75VOS3CpFPooBF7
-R0b21q5ZTATd/cW5g+7RstvF+kr2kd6sk6jjBowPazPTZI71/MLQ1ofGTay+tLxbrlOCY9ZHlwi1
-2ufwrAWA3GEkJtACu0vp2eMBgp9BjzoTBPP31Kccx289IXzRPqfcfm/Gky8N+HaabfmXshBeCD83
-8KvxD8A4jTH6w+qjJUAW3uhQOs/vs2KB3XWzv76wSjAcfeGNLkEw/cCcLwurU+CfC79AOMK3gfBm
-1fCNXoIKB5pe53JNyu1LlpbZaEaoN63Di5YK+jtzTdG26PkJBYBNNJf6CcG1i+8dSFNwNu5NgAzs
-ZTCGRfLgGNdgvXLOsW/W0ps12GhlYFk+lqqyCYpDwOy+wrQAL1p+8SzKqUX4CnBT8UTr1HoDzUO2
-pfGntVNpYw7ZgVajDNc2SC5G2Q0gpw8v58QOevu1DnFHPvanTm5WYY1zPlzjS2Hyq4WzPzZoE/Yc
-IELj87QGI3RAB71iGVbLMQ0lfDnosfBVXIr3AQBbb7abh4IOAGABhI2b/pvhsfX4Nh/pRnsJitn2
-tPQIvX7nPP2a3RML7fU1uk8uPJUcca0EQmtgeAMIdIswJLYxepK6Dm9zGgQa4PwhSO0Ctj1hUmDY
-Kus0P9BamDSxCM8PK+wfYRD5daxYrsnoydLrLnlZUI5eLGr120gHDxw4WWl88oovJx7Br6xl8RN2
-BXNqGX4dZvG05H3FChwzUgGeu4DgJy3Ea64FQKZFFCcATJDIX/gDYy9jOZcg7wTsfM4t7U5jMMcs
-5P0GhjNuJcc3MDzL1a+BfSx+FO2wvMbiCo6DrhAAXSJC9qBcohZ7K50vRtA6TCFyEvl6/DeGXyCc
-4fucTeE90NeNnvUfrMMH+X2mHQb/VC4z5K3Pa+CE+xgIS87ij2s14QstvknXcp4Ckflfpw/HuLje
-aM72rLSlsC3ldOultTW35HG3/lYvdwBscj0Hyu1B6sj8A63vI6yWSLZtSgAFA44EyBRyKMAw9dTk
-g4+8lv/pnI2HGiuptVuHu62a5/zI9YF5aRly1GN0OY+uEIzjDojLJaJblzHmxgTDt5BGRSk9rcEK
-dK0B5LUn6no828v1r8LW49gXryjhAL7h85hpM1iAhbXn6fIPXk8dBNw2YNvznlOej/P8Datw+CSL
-ktdb2zaPGggm1pH5bScQ7GkJ3AfgFL1r0TWNKMvrpALB+oRLrZ3IvrWrzZvSrNQHjTIt6jwBYOFa
-nTGNLrd1qULS87gZcVL+Cj35EQMjvGh93TpD9KQtElCMtR7V5UHBL48NBDfLcdSbIDUsiQR2oKsE
-AfACvC3PfQfBCQDpaN8BbgOLeu0GfrUceV2876C4jmubwZ0OH08vUsZLfAJjsQTXefOcaONjqJcD
-te3PhkPbmFtJd8rI9BgJK7uZWohRwpZzMvtMF4kw/3xSOufl+78Kv0A4wvd5u6la+UmZFF6HvOM5
-PwDFElFfvg1cbGC4lHqj2bxKn/ap/K3SEwpMmss/S+XXxTfAdSGW4O+kVa6j0sVR6xYc0qei2nrL
-e9rJrZ5WIJsAOORff1wk1l9Dxb9B46Pu5H2gBB1BHV3twzab5S6q+FbC7CprcryrFVWuen8GvXvY
-11id85Xl9wiOFeRmPSjFyrKhSDXfA1jRQFsfManfm01QvmNrW3G0eKSjoFzqE7fHXekJfN/Yy9SA
-+KJcAuIWXvjfwxo1QDHskP+KEscAuuP4RNxH3uO0ClsownijnBZhdDD8Yr0XM2WogUCHILgHWtgU
-BJfsaivlMAVLWOp2dbqCuGazvlwE55mbdJUrXdCIHPoEjqWwowwGOYMZXQ3vuw70vN5UBes14WrL
-LiAB8c1K3MzO5xvYCmN9tLZwYIUNwwLcQG6s2XUk/ZTvBebemNfiKpGg1wLk0j+Y5Y4g2ArRKahV
-cDstxAMwr/VUc0m3HuSNibVFDxkD5V+AYIJl1dUbsI2E6J+MH6zCBL9545HWcFpl46kT6mM+pqDD
-S+MgAC8xB9dnWoR9lbfBi3wc5gAtw/tq+8+HXyAc4bu7RkCFxlSdIjR2kDzLqIDZg0yxoolMVjBc
-tFIGdZIqC7b5AFTO2EUe/7NXHY6wF2zGKQ1QRiug7ee3+FZ2pEnzXv/US1MvpAuAdFq5UfFJL4B8
-cocoy1UBWgqmtEqzfNBy7+AbDbqPMK29YgXj1VISVq9SYOVc7P3iuGr5Gb/n13zymTnLHYAy2z7T
-NQfO8W2UHLi7VQDlFhE8dZlfpOt8goMb+i/eF8Cu931qfV2WyyombMl3TmIMi8E4W3lv8fj0MpX8
-ml9D+UIVMRL4EgwoGEYC2aIZgIeW4ogrEHYTUEzgHFa3xxBKb33K4H19gV1ahLE2e3sSaC1GvKhv
-HSTPrGZHLjDUeNfss+DtmB/zxt7a4XNoemBfV9uEp8yZiyGjOzhuckrXb1qZGS8LcXeP0LYMqzoz
-XdMuWZ5lKh61KfB1CGA+SYiSp6e87Pux0zhYeHmMa2/5Skc7z2D5cpqCXwT4JcBTlwnmTRDMl55P
-ll9Nr+KaFvBrUia7vdq/5rQnrYFCHb8gpBEpF0fw3YSPSk+2ExhDALPI7QD7/siOGGAc64YBD/zF
-+tgPvfmNhq0FcHUOGKp/FuOTINggVmFg7VIjAPh96sb/eyv13xp+gXCEKU4+lWySxidNpZAKkiad
-JJ+g5Xat27SQCdgK7C4RBHB1ylTlnKlCPlz0AEcgMCJ9nBZoVRud9v5sDV7prmoaF0U+NG6OtN5U
-bzLmUDc7u/VNhEbSCTaEri/TTWGzuUKY0ND9gYumgJjtaVfoI2wBkqMfoiWyb2xt6lyceYJozyde
-eaEThERDA71uLX+C3jmxvMX67JKZlTNpxpUn/cZqNXbxfdyWepXtn1euXSOqvHDf97WGzvkWjj0/
-Tm6J3wBxXH+zCDvAm10qyAS3mk5w0BW4hdJcPsOWO08QENMK/HCem4UhjXlRr8d5sPUpWrUIC6Bb
-L8jVMVlh0qVcw9HelBlsr8x4eUFurdvDIGwjgjZz1sEO5b1jwGzfkum6BZ6cUpU423oqs6ddAKmL
-FaAAqrQ5eSrXqFYfLqOW5UhdrL8uwlavfeCExE5a6nbeujGFAfkpZcfXFmAIskxEhQS2CX4V7Ao4
-TauwGWw99thoKeOzLnS3CNQNYQPBCYCR64Sudfn4HzXGBIwucXKsfRb7xPsbGNb4LHMq/6wtaBIM
-k6+xEw2eN8EwHnFt4DgE0FW5TSyzRJJTRYDW4BiyOHKeAcsabPD3kTp3ndCP/77wC4QzfJexlCR+
-jndVfKFL/pCSJhHzRllkq3NGjt6SjfMEOom8v/ZY5Pichqe4Ts0ZVwTQrcHKoUOeD3rSkCxXOk7l
-sB/lNlloaAC2L+sCsbUMy5JbfbWs00NYrvOEhp2W23UNsMz3ogLCZA/mA/gJgqUrI7/Psq4462Ib
-v+TKaVk4BK1jJ8651oXaDexWhV9bjbXO3YXivPpym0/UThKsU90l2m1SKoIzF0xK5zGEvxvOQPYr
-mvoE8wggLaRU6IynLCgwXCCyFHbPK5C7ALGAYTnmRwkCACxjWwAJsarBlyWIgBjOHSJqlL2lhV/G
-NcZbnVCvfMFK0t8GwSr3yNMatgJYTOt5Kk98FBkLxrXQrEvJ7bzoh3vKvQK6EYfE5xNJFYJ6se16
-sioUWKuVflxj36f4OPFR4zmasYVYo7lALJdUfs6cN9cylz3ocGxAOW8EBayqP2/uQZwAmDT5pVU4
-2tgswEVLf3QBwLzBTABM3WAFClN65zzxVNUlY3JA2jC2dLTFNZ4qTeSzaRmcwbJ7guF8F+GRhYIC
-w0350io8dmmxqDMxNRelR4sT0/C4vsKXW8fwReGGTk4L6OME+0fhFwhH+GcWYRUQlU6b4fZIyQ/l
-TmFHEzrdTOaJwjp9NDIfW5SaicO21dBMn4OWUoVfcd7lKrxREew97gfONDBccrjRGJd045gL55wc
-Uo7ufereTR34Khyq9S3WYBNaAizLNnz0Ef4AltlCXseoNZIK4XwHzaex2+MiSLcycS27nbtzqlmH
-reip7W7lxO3gBHb1Wh5C+Gwp3i3D+8r8AiC385XXnAOO+uTofd1I73upKTbi6BPwusRfL7cIKu18
-2agsM/2lGVVqCoT3I90dCHg1TouYS/ohyJB4Wp5jwjwe/sDB8dcLGD8omaszIpZqW5Ml7HR1Au3m
-dY5FHkT76qjY20kyNvnU4yaes2C0/yRYJP5VPuIJhTwaS6tsSkwHmvve5iJBOTku0gw2PTst25lP
-ADxBsTb4A1OGnP1QKPi85IICWic/lJYCt8rmF+cStMb6gJV/cM5LZDwB8DPyXc7JOqV+VB197YRM
-aDeJQLoSBTfhUw543iBvfPWdNtxyB9C9gF4r+ZVxLUMFyg9YGJACKF/gjQ9dTCsU16KzcaWJ2MkV
-92RdW7bZtdgSMnQs4suXfzr8AuEI3wbCw/pbD8pXLU2wbPlo57aJLkuiHnvjYBWuvIZgQGUi9Wxg
-eATOvy9C9xNmKzr0QMYtgKyloKafE+V0B8B+oNW6a76dAyAnN1zO8QMdO/1jf1GWYMq7FRfgixJs
-tMY1kBt86zQrmgkt8l/ji1u6dRrbU1KEo5pzJh+xqYQ5W4tLDNcFrvp5K+Oo2VnC78zDEQ5TuOo7
-WXaB0+yaluIdPCstrqJzLejq/vApzpkukKv3Q5JzXhTNWu+y8gF6U4lIGX9cwG/P071uEwDn4UM8
-22w1tw8A+LEHznQ8Nnahb4A4Xqx5sNbq+lDeOsKXz3CCX89YjRXbwvHKpqoW7f2pNero/WxdbPTl
-nmhoj11Wo+RS1tpXQyPAdI5XHNv01qPmN5rnkXIODpSPbpUptwyp7AuhVrKP9e2At+1O4Z3mrVO3
-i2yRQ0oIBmxfiyPfGZ/WYpbNxWJilbTYRq0AboJfw0YzsRJv24cJkF1tvYNfAl+AN4yx3q3rCUg8
-d4cYbLHB55r1ykW1AK9rAWg3wJ5tVhmuBo+IP09vRz5tekqCPg5w/3JHuDvIj7vqdFQfZUe6d6qK
-a/fed5z4Z8IvEI7wE4uwWnxVuJiUUdVrkp75fZlo2JFDB8sgSl6LOukD/LboxUp8m3eNPi293UKq
-PTsBAgpul9qmRQ6NJnEtJ4qkWYNHmVJkvbfFiTo20DsY4KOfSxYTKO+guATQbt3dgfGJViCZYLh6
-soNbZHukhwcfYc84SlhufJH4tczp5uo06vuxLMzUbr2GsvLWbNP6PwHjaRFu8+H2G2VugNja39Hn
-88SBSdtqNMpPrjE1dE0HxlQwVmW5/3xzpfAOEHjBrHoHhycaBAg/D4HuC4+PbrgTCK8v1S0AHL6F
-8enatQfom6A9LcJyM6vWYAVoa8p6rhda3d1qvvvWZpmJqjwHEAbipZ2NFsNnMd+S4HdBpkHurjkU
-wCj7ozxao+qSywAAIABJREFURAvo0sqbYFRvIFSmakUni5rv0e7rfADFrrRD/w91Hxnlp2JLDpS1
-FwXmHGWxJAiOeZ5WzSyDBmB3UBvA9hvgOJHaBQDni3kzHyWDy194tVWnEFDSc06vzq/Owzb9lAek
-U+fIBD9ZjlMa5TTn/IrqHEuuqFJ3wLjHr/8FLhNtW7W7CFlfFqxJsPdXT78I1P9w+AXCDJx8O4ao
-kJOvZomCXE8agEGvTypPReij+ib2AzfUMjIpXfP+k+eoh3DIjrYr6QK7anVM6CslRfYu5VV8EFsP
-GtWFKgI/OTTirP9kJYaU7S2uSHHNzmUyr+7wOycIevVxV8hvAcUUOiqokmYnYFy06SPMOKWYSyu0
-9X3koyy/6LNN5tlv62nrPPH8O24qWM47lz6HKfopUYdqiEl1cnuo9mr+sCTrE4T58wt95M2t1LT1
-pzVyExk6Um0utccT6IoIa34vKzA2y7G9HtZMUYZy47OPX8Rtp/cxLRD8vG/F7cXjZQH2+ECHP8+6
-cTGD4wlgDNnVYl0gLcKSJq/5ZTnKBbaTO36IBCt+KutPGrnlf4qbACpH+Zqi4s7CAlCjH82H9sBU
-n0ye9JbnLU/dIs7XYtvkmActO2SxWnlZJ/SawwqMvfwWLuRbXplCLHi/1iz5ntbedI9YZW+uEqsq
-k5/v4NZKPivtlM4X3FIWyTFBpFh+gQ0Er+apNu/HKQk/B2FitCOnZVwzSyZAR+uDJ01khlwhJa1s
-nVx5lNUOC8twfSmuu3eWDJttBrpxZgR+we+JcfJa7ye5Opr4bwm/QHiGmybJtCf9NBgDWhzLKXgu
-r1TNq0WkC2jGt7yvZscP8k3ayTber19206S5HBURMHLgs+sikvx6jKrKYhbrIif/0loaR91H9aGw
-eupFoOepPDP09K3cUy8N6X6sZtImEar1OKs4pyB6uUiI7zF2AFwzZYpTX/tkjrINTtjOtwmK9uFR
-gZsNG9c+if17GVruUxFl/aqc0IIqAaWenJC2oPPbR+t4SZSxlXPIPsZlzqdl/lIudamdulatz8fA
-BAVdv9hYA4B/8POuSKdbK2O6zSKtvViPKd2Rm+47XuQ2c6T5G/FVL2/0CIBpES5A7NKPIU+bldWT
-BTkl/BZfPdJ3fRKbSxwA8L5xs8Gjw993Ab489vh2Dssqk9s4eaMr+X4OcLUK86j5X5bzDm4/gdzZ
-3MA6VxCcffIj/RiUD2Ih1I1m1rUNDfCqNGO5XKAHiOQjDS8RjM/nsFq9F8rm0R8fF7uuyxo6+RN+
-YMuhtu1IBpBfC9gKb4IfTRZ8sBZv9VdNKnpgOnCO7H075eHew9Z/GGnZ0zx9ulnb25NHOXZJ/2/D
-LxD+B+E0v9tEaoJ9ljsN6QTDEzoUEBpTZ0ufF59M9QOI2BECHyVaq79fd62KCdz3SyxJZxkfl05R
-qlKSWSJmd71yWAyiyAVsJDgdIFj3Tj3mEewm6EXSTmCYbwyb1YgqMK62UYgJJ61z1M3w4gMA9nPe
-OowxlTHyVk55qPlobVP+zvx23W0W78dUdEApDeYZH1miBwNd1Pbqtc1bP8+t0qaf1tU+378Gw1UH
-x32vO+O63ExbfBhPKp5Ufr1zp/Ux+z/Tvv0N/h5B7fryXPpCulW+29oKlDd0SbPwD14Klb7WD+PR
-CZWP7p4vj7PPT/TpAQT0et7EpAFQ+OqHPIKYrP91uL9x9A5y3wCHCoy3cpU/rbkn4dTzuiDzcY5L
-/mYZHsdmoZ5laBE+geDNMnygg3Q2rcvnLbRsP+a1pMcgYVh4h0zyRGRrQEt+8bxIC/jNTxtLPdqX
-FRlP0hrgXlbP0azeDXfwk9+JTMVK7WZXD5tPx6+CszsoHZI0HzSy5iDv25NN731s7fDqeMsTOvmR
-4FaBry9a28XjiQpeoTdQ/B1O/HvDLxD+YTgNUSkyv5Y72atqyNcUu6VtlL/Rvtv+ue3JPv3PIJj9
-2Ky/oz1Zh7NcCfeKD8E6F5U0zVvcxzm959luAhF55FVAVdPYgC1B8ATIdgK+Axg/hzYkHDJtLwWW
-zJ4DGC5esFwA4EQR53FU95gZ5ixVa0ErYwfaKLePfM9LxaVjNfkACCLc01nHkND8WlxpASrLvd/7
-ejzf5NXPenPafL/HIedv8XjKkPSmVazHnX2soRbs0JfL6Jta6T4sqxZfCokvqjzLP1iBrj/w5405
-WfGct1H2DeWXm144PTtWo7kBBtDdJnI6R+sVBHNECWxjC9gOiDPtR4DcNo44WITXJ63l2MDyAQwz
-rsxkwnsSl6OfzpG0B2EeP+bxcAK9M30CyxjlMq3z5TKRJOjTimNezOOSgWSAyctyowYXmUjZwKcL
-03Lc6lRt5VKvtdLt8b3UL9QuV9MCzI4QPKvc/lkYrGlH7dYSp1aXsSrT3CUyz/Zy7RrRdld6t3sj
-5tXWYvewCK+133fkWKtQP+dsL/MRoBj1efn/UvgFwhHOkOEb56i0c6lHVm97tMBzNQ6qe2vprmT7
-70ZrGV82/nO2KusJgI+wJwQnmrWSYWhv0oBaXBcL8KpSrR+zKhvRaKFpPxQEIwEtUPGPLhAnF4l5
-VHCtHOB1WjvLHtFdJQJAJHg6zQfREiKii5e8HhWDCnhrZwnSE3rn50ZraRW6x1kRzR20cdQ3ofX0
-zUIsL9TNUPNi8CSiponRGh0zg27rW4D4O6C3r4/DzWLogJoT0qKmWIPqpaa6fu0c8JHwWx7T3jkF
-s/IDRihzdYUIZapvxk8Xidff2F1ibZhWIBhlDeYsUbzhNXPci/f0J+b7O1wLZfUNdwgv/m4gOM8T
-Lgi4LQA8XCEuILiVE4uwoottiEaZz9Zjjzpcyg/5eMmvD2GQ9g3Qq9dj23w0XfKroXrwjT6igzEi
-k0JcidjaLcT0U2A1BkBpeS1r10mbp+mlLa+eKBHRP6tz3LU0W2x7nA1v+1B3Kf3T4zX07nUdojIz
-hN2e1/2Lz0EGNgXqLm23nS8OYDg/bR1Cb4Jik+0gHQGQn+P34//j4RcI/8NwmkuddlbXtUT4U/W8
-+wzznK+A8XFufzXh0+rGNnWoUNe9+wlDypT6lya0aru2OFqokt4FtJw2Tpgt1sPJEowt3UCwPf8M
-BPPI64Ls7cComlszgQLKgQaGu/BmadUWhzwb5ZAwqvPK6qxJq7PGJWD7ObNfSssmflEuD9aOfih+
-FuSb7QJVuvfraAm2SVvp8hWu0dtfgvkOONb0vkZaW0WxborSd4U514O3Gv1DXic4PD420N0jbHOF
-WNZiSNzd1oeo1DLsYfGFbIMM3Ra5oAtQOCLt/7HW1V97AV4fFuDKN5MyQlN/8NXZt/sFn0DuN8Ew
-F2oCQhFx89h4fhB+LnkuhJOVWONVr++y8wqC0UGu47NFWNuDPVxv0LbCcm2udfcN3FZ1sRYc6O4L
-8UdvHD3WV6tj5VurNOyfTTRY8m+TIEPkRkl2YNXmvZ4WPurhPWzrnlV46wJcAPeU5970C5o4JlDO
-uj367fdrn2QPSHfETjEe1uAHBrECh67lS75mBjz8fDPSN9gfwH63T/u/EU4K7DRsV5pIhjVFTtbT
-oajld8471VCFD0vz0lhd3NU2xSjVDgJkT8RC5cSqEtI5dBX2INYOLaBJPT1k9IduFMjNRQgCYHQ3
-CK/PxG6g93Yk6H2eMzgGunuEMth6S6tL3RJcn16G/FHoY8cxu8cv3BrZfqDNchug/hj/Hi0Vlkw0
-3Rczr33ozuY7PPu9uQGNbBmnGyBmud6LkWcf8vQ8uYCFdtvBMddUqaOTgsrSA4TM9dGAUi+a6aXs
-Foi1sOrQLcKaiwSWQpP4a7ZcGaz7B3NXiNeVN3F09h0FeH0AXyAtT9xGmTKG+RMY67k1ruM26eQK
-4Z12Bb4Exknr/GwMdwjvb0c/lBFg5T1fd6k4W42VfnGLYPmNfvARTjpamO4SkwV6gh8KfXJ/WHPL
-dUFUlQY0f2Gp3FIW1ArhPvwEvszNdeWD1r5eONcT55HKWA21VrUV/8RX+NN6B0oW1tNEyRtlANto
-q13zlb/Yi8ejze7hKy1Sn09B5CZwTbqnPvTzhPwKC3GCYhAQP8D7rm0ZExhbfNYZ/5XwC4QjfIIM
-9zDvNLtqT9rUPtv11OK6x2c7b79ZzjVxBEZCG8BqKZiD9Uzb5afrC0Bu1xNv2CnBWGIKVi2q6w49
-vvO/4gU8xDoci3N9ShadPt0kbH1A4NtuExcwVTyaIw8oEGY6HyPbeV48rS7vcVc6oEJdH6cx3YO1
-JvqtzFfxj7tKRNzH9WjF2FDTYbYYVU7ngUv8FMrVJLgiysIyf7+JMS3Tzvheeq9LujzWXu+P61Ph
-VFgdQVwA7gQjUmguwYU/Ar5avCR3jGNZop5D3AyvG56nW34NZQ1uQ+sY/Fwt48txDtnWNMquHSC6
-AjcfwDf6l7tFeM/DcIVoYDh9gE9gOOjTIqygcIq3PPpIH8alWYBn2qVuGddbnsco39wgPoLg0SVp
-5Gne9M7MtbrPtaxTBnG6PzTampzRRj2Pz7pqVfD8pKV6q3VV4LeumSuKMknrA+RsZc5Jn/aUfeP4
-01C7RlQbFOC2lpjShnxlG2Lqj3uTrWs3qzCAAL8KhldJs2ddNwHxkiVr7Az2PNkG4IU7P+bxZ8Mv
-EP6HwQ5myQR6R5oP2g+u9c3fl5V8VcS4SE4vy7EPu5vECuVCIaTW/saaS3zJ4q5V8uHcydR1XDPV
-wtM/WoHnrhFztwgLq+/RDYIW4QGGTa5+btep6VXa22+KnYovK5vjOeR9+fnsCWwnOL6UmX25xvNy
-di+bym3Ub3u6WaqHwOfFQpVnfGnD3uayGCEEM2q8or0W5dY8Ye13gPvjtNW1K1x2ACEQivYkSMm6
-h6UulWOhGO9/tvLFNVtuDrClvMLlwS6AVy3GjOcnmeMa+v7LiwsgFuWqe/lr3FDWXyrNDnxXLzZf
-YhxcJYCyCAvIVUvvfInuZhGGbJ/WwN4F9CpS81ueT+A4y35IQ2RnyE0kuPVGa+CXx+zHyT1itAkS
-dN7tXd3LZ74gOk53dXWA0NJCbFUbz2NdcdOltGnV3cGv9TZanHNQNafO2TXv+4Fz/KujNFUuVX6/
-2zQDPgBkG/XL7bWT3d3UkDW4liEtPsWMpRcdBMSrcYsmO9DQAoz1AR5/n3CTeGsPxD8YfoHwPwgT
-NnQYIzQ/0LLc9L39EDfbXwA5/E4t2shb1j7ViTvqdwO/kn96bg0fn14cGmP6P0wBqE2MA5/KdGHA
-Rq+fBbNobZsW32b9BQ4vve1+wgscP+ft0wQwH8fmyJvevd0qrCzRMaJEX2lusfbgUu46F/Yxb2W/
-Sm91i8S1A/2rMiqwGwiWKkz4IsG31A7sFQQrUGrb2+W8L2Csf6UZjXJPn8+biVQmjQmybuRDA2U0
-874GhoYkoEmuKDKZyw9hV7Py8eWLLr6+4hEuErJzhLxIR79ixGeY+S0QoEDvTCtP9vQOeMkd7hjB
-UVZA7JCt1uDHa5RFeLfyzpfo7lZg77tHCOv34xiTrYxv56gF9uNYzroPluIJdBlPkAxPebpZiqMN
-rHtbd8MwUf3zns64zL0N4Xke1mDWZG98i/NcAXOKxALJCejiBIoU5pq20bhKvY/T7ESE9l7cLSIH
-+3DsFe/XamGfKsgX4w5tyqOd6blF3LhIoznW2PterkUD8MI9Xngj+F28ze3aYtcIxwN73gWAAeB5
-yzXixR8Pv0A4wvfN8b2cTbofaLPcFIRfrQBiPJy3NTvi2y/q62FvQz1Cvl1Tods41+u8rP60Ugd5
-DoErqxzb2tMOmXkjNouwefMPVheIF8Mi/DwHF4iD9VfBr5R/DrwSnLf3UY6LTYur6hrxtJIKdCve
-/NYa8BbR+1Owe0wP2k/ipXUOZajcKmsVP8xsIZU6k2uY9HmonQTECqgYN84YxNw4gdjvAOJV2c6B
-XFQba7U31Y0oIHP7qKRVqfOPt6wDrddp8NgRotwjLACuBcDlC03+UPmGfzCtwW547cHzvB8BMOmT
-Rn06aSZ09/US3vkc3+pY6WkRLkCr7hHpMiHx5jcsVuK0Jrdx6HxtoOqQdzrXT7RNRn6PtgNbYLcG
-Y7MOd/cISWO/jsy83t/qzZi7TNjmJ5wj2JbtAlf5zgAzEziH7M+aLZaLi6ix7HtKyXwqQx7EOrbe
-5i1+5P0pLcz4oJxbV2fXx3G/ZN0gf5xWPNpeD9eUxU8B7y49y384FRZ40+pI1whofF3Y4sbZsHyE
-7V1vENgTT57edZNt/mws/RPhFwj/wzBU+Nc0v5Wb4HIAKevld0Day342PorUkYPPvGxLtMfmtS7g
-2AUcH99sKh5crcE+030x70aYLiaqVQV+FyghIO5uEMuP8GAJVlCcIPg57CDxhKuEukaI0hXB0sDP
-7Bf7ZvVImdaMV3r16Dh5xUOsxIX2sdR9JBtEm4hM09aIY+LuMO+fxU/XZ7xf1zO++tenGF/vMaFU
-NS5HuiZ4zG0elyIM7qSi1L96VP7GUV4a7SyzjZXXINrHx82dhsrqE6m9xHSzGOZSk3QIkOkGgbAS
-g7tFZBr5VjgfXy+A7HBuDooAu5v8WJecoLeUrjXZp3sJ86U6y/PLgtVdKwr8Qq9BATKtwOomcQDD
-vZycn2BSxu/A609509Whlz2McxvT/RylfwV6d8sxWrkGoNvlK9WfLMw2uiZbbHN7iIFsDgdtPUi6
-XYYFomgswLQPUwZbk4TR7nCFoIzYeDg6uY3nnfdg03v3/vFx8rEd7UL/cOz3Ib7xRj+ljA9l14Kr
-TQ+bSwQoT8gNtQyvsTM8yyLsz1pXXwrJf3/4BcI/DX4eJpuFvkErQb2v7gSb3hXJPNew533VgVj6
-hxNziRzrvVmA69xNQkmdM17JWaynYwVOTTOqYgspFgmAgQK/cGuW4fayHC28Cnqnu8SwCB/9haGP
-Z2XspoaQmI84f9x/VcdliYkAvQIKV0zHYMWPglSRWkY76ATPbhNgjvuYkXmhQ7ljXsUbe0yez5jk
-y6lz+JWj563U+lWpg4Fwg0gAXP3maxsmDVFgxVhu/TVZipNVeCTYv6ZVDp0bmtHZ15w8QyG3tJ/p
-kueGZQ1ON4juImGO2i0i7igqvTq/XpR78Nq7yabd7c+6Rdi0vOeaYbd3n+GD9dfLVQJCT5kQdRaI
-HVbgAYbnjhITAPdPLBePj2Bpiq/LuPV4AZBb/tE6GSd41L9ZhDWNr0FxK9ua1uXYqU2uf7WtNkGz
-JTIrwGuomm21owHnOmetY9fSC4DBu1sULNdAuUuERnTk3N70jFjJhQGN30rSMfgsjb4TVE4KR601
-rzf3iyPjVCFq6R3PF3eI0H4ux2cdn0c0MZqLBDdVnOB3uUiECei/sHPELxCO8E8naapsWQF7XWea
-TrOuBC6gGCrcPwDhnyHjQ9m5Z3HtAnG2Dkv7vNMJPlu3dVmKwJjW3lxjl3SXpAnvQStDukG4L6AT
-8b57xOFlOfEF3i3DT6dPcOzR9wmAVaZDBXj0NxQA+5b7sAYgK67cQW+KSNk1wvXiO1JDgjVJd/Ap
-kO8Gco9xSadSO9DHNU9V+TFdjG3ToC54qM9SKXI/4AUAS2AD61PaL6zAcWuS/HU03m35Uo7x097I
-2ouc1yYJmTsb9JhKePMnioOU2/Pi1iG2WVjgt3aKSLCbL88FYHYD7MH7vOvjNLQOH152eTfZwRvW
-WieL3q3BwNxSzbc9hjfrbyjz08t3J4tw+gAfLb7DTeJiEd5A0aC55h3RyTbA2xCf6tWypYJklkyL
-cKAezpXvguLe/DkLvV8fVUbb3tapo4PaDAS8kHVgGJYE9B0lOM7xl+RYnI6x8wrXvtCrEhZ0PbRx
-/vjU5ZTXu92AcT9a73LSpzU+Wn+ZDt89av2bhvE1dwocq+Y54RmiV62Jn40LuftSNlLaKfglKPZf
-H+H/a+G+c8SnMgSIVZ4TTJXoCeCe6Kdy18ZIG45A4YCT1lZFBzeI6Mf+IhgFZwcMzaK7s6RHpklh
-SgEVXrMygt1YbsslYgBgIAHvAx/W3w50P4JefVkuLcehzEO+myH3a+yAxsDXUbZfKAKKm9MLccta
-rOOo7g8l6lzOqQHp4LftykAeKos5Q7OKA2r1XrbGa9RdF0UHxz1r0rK6QZtK2edJRhUjCsCqnrQK
-iUV49d+2/mzg1jovGgtlju73H54sO/QYQ/OUFTc70S1zWX6A22JKj/vhvDXDwvXB4u3u/ELU7haR
-luHQ2rQOvwY8NVFSwbebw2IzzEu2AJQ3iPVTNy4PwTogVt8Oii04k/lcgzIrNovwCfi6d7/gm0uE
-vzLpBiA6ACV8ord4l5FHq+83zvU4OUHpd0AxJgiGnNvnrE6hRpNrH8/RpirQbWrJqi2J2AwTONOa
-y7YYlu+xLJWgDWkYAkDL6qsF2v51k5SMEDZL5734VmqsP5tq3WO3O+UYnUFGrxX9aoq1mxN0Vq62
-OQ4aozSL7z+1JMddcJwlMndZFtZNtAPwB5ZrJ95DIAj+dY34vxNOw1Rgtg6braqvYTmnA+DK//xy
-3PwdWzcr1cZ8KJ+K5nj9vqSVbl3DZ/9KuVFJDY0xF34urEh6nTIXvIqZbJu5CFiCYaRf8AK36+zr
-55UjbVZuEw0c011CfIWzn2rxpYVam3rqLhAgmC/MeYEK5AOl7Cu3uqvR6mPaPeIkx1B0BbeqYGgl
-7X8EwJme3mJfpgc43thxyxtV3jBBp0oLrQ9J8s0kHWjNqlBcWuJS7bae6Sdsg35tWQcHzfpDkMJx
-zik1AIoq4BHXz/M2DSjllkJ0Ab5ISzB3ikBahsOPTyzD6RZhy7LzmsgMebzNYbfgpxllBpLnCYwJ
-XPz8kQ3xSIwl5fmpZVxptHCFwlVAewS5vrlKpGU4rcQTvCifv6BzLLaymi+k44Q/yE9tEsFvXOsG
-ds+W4aJXtd1FYAdXMjdbCfaFyvELoJsLRFZElqVcX+npApEWR1faknm5W0SKIAoFj2rlhT25Scj2
-b+C4BqjvzCHnbXLLClhid5+4HRufB2+3o39dzqSsvgC3A+RqeU+jTsrVGBX6AzhiZwqCY1qAV3zV
-Rq224utJ1J8Pv0D4h0EnRafd0zea5lV+WTL3/AlI++9juw2XF+n2qb2u44DtILjaKPR8fFbgiF+O
-6sivBEW7vOZ5X1quwrMJnX4cjQfibWLjEev4YH35ioCY20a1l+WaZVhfhnvKUvycfYUfh1xXlG9I
-5HZzZOwffwu4OuSTtNb3ClYwvAHjMawueYjxbDziLKMuOIBfj3jmtKkyb/X6PLKbNfiaPuhzeSS6
-5TF+AMhNZGu3ZbHQt5UuETXJYz9cm7d8GreRxhUgazc8chXwFkCf6iribQ0c1oN7jbcLLZlxUOyq
-MSOpX5QrF4lglNEtgg2Ot/7j61HLGux4nGlevrtE1M4crsXqSc1aJJtF2LCswlS3jtjDH+IC4bvv
-sK65lFUHYNtejPsmGJ43FH0IfaQvdAB+oG2q4rt5x7q9xTkHNkvxpAcqyrkpMqV3a/oFy2y+zd0Y
-1POLcR3oxuRs9ZBG1MYVxXkArDmltFU1IbHSKAn7+jgC4kmX8T+BZMehK+1oF/pZp8tofjG1zuCX
-zYWsK777Q14VQHZZLxDA7NnHvoMEK1cn30IytQpRsbixTksxFd8fDr9AOMLP7kLmkNYCtZ3UQTAX
-acweXQY3wItj/OQ3PIBIbwYORQ7tt5QxvR3V3g6K97aTbnR6nQyZtNbIEjKN5LLetk5FS2n9BcIi
-KyDYRdmmMo4zZW/gDn5P1t91nC4UuWuEQcA3waAXSNKXQgY72D++EKeuEUcw7GrxpeAj6EXLS1FP
-lGsyPwYAdslnhG1US2qNjw1Sm/GQmkqBfTuwLlWmt+DbNfdkTeytyQmI+VMxHsW8VaGXHt1Sq49e
-r0CCtewOOBROeDtPQUYp3lwzUzHrovFRVs8Fck9gkz2ByZe1x3D48iXwffB4pJ917mvLX3gNc8AT
-VgMC2wDBHlwyhOtS8M3Jfq7hAYK9A16myW/yuadDZiYPJqB1Abwd+FoDxeNcHeMD+O2Pz3W+7GUr
-Dz2kzvh+2RX1agMpVwswqUHZwHFVXt09zUyMc9ByU6w3iy5AC3EPkdkQYtyA9VJxzZJ9eUpYePWG
-Cs75OYEqz5J+b+vpRBMeuriUHK00x5Zfg/oKoy5bbcwW6yUvlmKZC6vf7ANy+7SUBZD59jHdW7Fv
-fzblrWwUbFxC8VqyhbvE++eR8C8Q/odhqvKm9CJSgst7uZn/RfwEjvffD8BFR60jsG0mRUtMKAhu
-oNhJ263ZUm3V74PmdRhN6af6rKrDCdP4AMYFiMvnNvcRDstuA7g2/YHrpTl7+HvQPrYBlI9y8sTi
-OIPCHw8hv4SrmxUA9h0ME6wqXURcXLHiykcCDnHlBGy3/ma+gN/2SHH0pYVUbkJyk/rjHO+nXBWD
-4Zh7UtCb1N6aaAnAxqQetL5Qrj2O623A+HDSzTq0zjmqr+qfghNRvHmuH44jz1te0Vdba9cIxJZp
-sBfcOYJApIAv0oUiJ8Zj6RrBOVXxsgLTJ5suRAmIo0zu8ILxcpz70SK8wDB5PC3HJW9PFmH7wk3C
-T1bir16WuyGR75TFnjb0KpL64ZycIwp/vEkc7BZg5uwgWOdpNbmvwKtVsoHm0d4Ew8Anf+G+cOIk
-K74ssBs3Xy7VUYNFu1MsCXBuTcq1Umur0cijtp6E11J2rnNqAsrnRrdZbmdFTZnOT+Xzeerd3SP0
-uV7Sjpbg6isyzQoLvJZM86I3UMOX4hzrvYQo+j5xoT//ttwvEP5h0BfdiqbBB81HuV1ymSyPAp07
-vecXIEmd/fPOXAFx4YICticQDCkHsQBXX6W/7Q7ZN5o+csqzW/4uYPc2K+i04IuC4MWxBMFA9wdO
-y+6Tx5NFuO0koRZkj5fynEp9CWweT8GFdXxA9Xq4ccDjJXwboJd1KZi2rLBbiinwFfkWfzxoHNEC
-wDXB1+8tAAAgAElEQVSKCYbbfPmINq9l9JZgz6WK0PJFx0a/KNhby2IS5yP+ICwgdvjpuU0577QJ
-jDcaEABwWqJO8zn6tYHfYTlWkIue/nhs5/PGqqy91nyBLUFy8iWsw974BSRQtmGJs3oisXjNXSNi
-pRIQ04Uo3Is45c6gt976NxQPuH1hzjSnSu4WYVp68wtxJ5CrYHlzn/jaIlwD/0WZXS2wV4tvpwne
-iFFWxW3Uuz1b8CFJs8zJQizHGwDeQNYNACuYBjYrcMjBzJvW4IMMqC0yIWtKnjxAquHac7S5uM7r
-T9SSH1xbCQpjvXAe4Z7Htdq6kLIYe/iGENNpdATDYyzO46Pt0bQfaB0on849td/kb4saCucalhx5
-PQbOyyfwD4dfIBzBvjEJoyR0gLtlc+SLMMskLK/lIfjXxFKIG4uZ9aXQ6PWtVEzLWN0Ff9qya2dS
-YGRcqqxJ3q27+Uaph/9rLPp609RaGZajsM7dM9od5YGmwrrdkUu+12GTHpawJhd2puOG8/EH7/uG
-Dy6Wb6+C3ucAek9uEY8A4PQR9u4n3ECwIV/iO0g9skRBcYkhxqm4alw8RyzGwar2LoSBtNLlFLWK
-CxguwTWFmZVV2A9lvkmzjab8sDYX/jdhCvZJU2B8FO5ZrC0u4ecHGrSfTAsYiMHR8VHlzTXoyQud
-+MO6ewS4XxxR6VornAMiICLd1hLdfSaHczK5gFpUJyl8guEe5dyJfSpda0WUvvR93ch6+dP7SMMD
-QKPAgAPbdmkNGHujlbXPYYNWPBe+J8+Ux0q/0HSqS535jOu0FGQiWeOPFvHqO+ToI63zSstu5x7G
-YtKzSyqhHa2riVCp37RTlnltHsi8yXOzPFkhDMg7+jGX9e4oWahImmsLOfaLjX7kWQHfnQ9z6m/j
-fw07iNz5zW5UzX3Mepk5PfH62jXDffnmwtEd81xqfaMfJvQ3+2bBwrxXdsDeB48CXPMd9J7iX/Lm
-3x9+gfBPQ84mQ20dFppUFmR3O698G3kei5O+mx7ltwUkAoR1bCCnNbFmUylWSQ+8wzMS4PhS2gl4
-Y7HX1+NCeWZ+L1PK5cnFaceXTuSH76V5t26jj+0ngFitv1zqz/OE0kX3B/7r2f2BD4C3f0hjAGEg
-AHEHw8Y0vN+hjBHInSNS8HXQW9aO/jJXF7yqIOq8VdEuZE+At02OzZostKlQLjQ7lcuy6s5BvVSr
-xbOM2sKVK/2yt/SN9ils5Wz0Rbvj45xTQ7xFMjuLOud3KT9zUWXMj/VGhZ15k96O0hjJ6yBX1pHn
-bmmVn2Xjy4zgUxRLuj5BqGjNqfQHFhHZ5txIuyyZdnQ9ejuSl/wwDYHxJnd+9Jvnoxqog5lHH+kL
-Tc63pkcO9WpJ30qhn+LJh15SgW7Pu1mSWY/O2ntXveiH/K07c2AveT4nAaMJblWO8YIiu5LPa27l
-fAEKV3vwawJKXiLHpkkswBzebtxs69Zo+pdCqJWPjm7AF5020xuN7accE56z1fkCneh2ypuihVyK
-43MAu+/rcdMcZW7rCnr8zJP/RPgFwhH2/XCvJftiIwAGara6lMl8i0lsrY6cpDk5Bz2OuYhinuiN
-9El2SCWrBtPkeAQiAkUj9YJc2RzLKowBgteG2EWrvQGXjHqz7CmOrOOzIqrHmKPNkVAFrp88fmIo
-Xo/HrI7s3WbVTRB8t/xW2QGKQ9nWwo89UBsY5rzQEanfkteefpQKepdwmjdKnnOkzwbvgFMZ9kMw
-nIqCNBVWP7b+9vO7BdtSaQsM7hbsVOqQcr0rp/R3V3i263KeaebMFyzX8kPLpp+0T/Di27USELNs
-oglRGFcg3PNqF5c9D851I3MUFm7ACn65lkgr0PtkCuLuUAxJnh3SCoaLVxhWetkBgOx3xTbySFr4
-UKBu0V6RW1MR+wcZRIV/BMZtbHQAB63leadF2XardwIEoWMUbp3LKUjzKk2eSB5509IJmk5USTsu
-+V+woIRXyZhJc5Ffk8Z+2Dg512VP19zp/rkrdCnaQDDnkdyo7MMZL/C51Gx6zV1k4kL7KnAXlxyV
-7F/1gdJzH5NOa7IGaC/MuVOHIvt/Ar/2Fmh+LdJynMD3jXPSUnxaZz+W1P/78AuEfxpyVinARca9
-AeMOevt5rM760WMx5hpW0CN3mK7KpDctI9aoBQoG6LdRju1UoAsvcJeKVdLML0D7yMTuyucWP94h
-elmAm+JBlbPoQ+5TnABYLF2R/9jqv0e9DhTo3YCw9Ty7gGUFx8GbsgiLVdjLtjv9rkr3idVXhIKC
-XbcSsZwfnCH8++mJQRI2H2DktWMKZHvrnH6utr3RXYp/5ToBxA1eiHArQU+fvxLvAL9IpdTpZ3sC
-wP4h75SedW1pssVPZa2xLWXE57flwBdsVNuWQo75zjXhpMeR58n6LM3oI0/yY7YYSvk9El/0HRhv
-lmILX/sxX2YaKLCs82nKM87nle3ZheZbzfaHsl6sKnlE3rnwoG8LNX+40He5VDcYg8dtzMfY4HP5
-uwU4VmKc17K8l6nKuIoOx3atr0Fx76ZXvJU6d+12bO2fi8zRrb8kTKHGfpiVDOFEYkMCPC4WsWGy
-c4RTspI3coOhQ3eLt36tNe5i6NIlflr2PwHEJ8tv8dWkRKRdwTHg3sssWswrEd3N+mtlAFNA7K/D
-ng6Qp9W3AV+1FPvZOszr/enwC4QjfNci7AlsTWbvovXhK8C7Fo41WstTgEvh76GerC+cTQ7oOs+V
-pS3ZQchxorX+rwmbjhy+hNAdGIv1d1iG4e+qR8rO+EnBdDpykZSAP2kcQF+yeLgLQwgmJwgO4Ude
-n10gPgPi7wDhLiBijoVg0JHZxzcUt+VDu/y7LKJgDAmcoTQddamVYAPo1rZPbg9K35FKLzumXV7N
-D2VR09Sl/GYNNgLDgro6378Dan+Sdwp2iWc62Wd9Kh4qoeHIlScjtBs/WReqjHRdiJY7AuAE1kKv
-L17GWo65s6w4JgBYgHH8ChDTPaK7RTwbyI0/AwxD5uPG1VG2wEpZzlXcsTtG0NvAb9HIk89g2IVX
-d2uw3rzpGMhAynGWk0IbTYLrjW6vqvFLx7pd9mLp5fEClk9dmiW2tPRpdunYRW9NP4Jc0rzRhHCL
-H/MAIIxM2e/dQqw3TcojdDIv0mYkLbaKLC9dAserzWN8DpSJtezPbhGu5T+Nh4tIdtS6CJ3ZLMQQ
-a3CA4LW23qXzXgBm6Q7xfAF8fay1XMefhON/KPwC4R+GvlgCFKd8taSVnikAnEtNgLQD6Q+pkzRd
-0rkY8zH5SYnHgjeMElJQUUMDKN5ITDTLr1Xr6oU4XKzDSg/LMBl2fCHloJCwl9no1YFU0GkVTkUt
-li4p8/jip2OBw3pB7uQCoSD5Ao5tAuFlBc54PD7KGfBhl5gmKF3hrj4Om3NBLcNV004Lbk1AAnwN
-fDc0eAa/CX8PdJbfAHj0dZEE8LqoCgOsgeFSCKx9XtIvcTbvu2D4BoRtxG7sKjATS/8rQe/SYIn7
-tg5QRyDXH+llrSxFo9fW8tv6yXWkIFh8goFhIbbcgeVkAd7AsIYrUN7ZQk679C9BhSrSUOoUss2l
-6gZ8b3LnIrPaDYk2Eq1hku8HWh+ToouN8jRdhuGCa25rSqvjZukVKJV13K3C1c0TwPKWbnnKFo75
-WHzJvjNyHFbiPV+EJwC7/AXSYJLndEDb1lxrWO9b72M1SmD1VRZtE3xbG3E97909Wn7xyS0idJ1r
-eWx8dBq/YppbyKoEx7nuwvxC+hsvqVq3CL++pxUMc/lwTU63xz8ZfoFwhG9bhPOlNwG7CrXcZFEE
-9GnAV2FZyWXKBC5VXd+8lgPN+HYExCepIhp6CUwRAG0lCD9Csl5BLvMx6Ify7kD63r1iDW6KJ/KV
-JnGfdLZTFTsEBLuDG/qXH2NUEb68DovtUucLcl/4At9cI8yGJRjrLdoAvivNW5w3vsG++pMjp8Ng
-K6cecZX1YgLk3SlCQ9T+LeCLQ9kLXefQFF4XsGwH+tJDpYDS0SEAWRaNtF7qBHKlxx/jp3DK/wyE
-7UOZyJsNY4nZmZbUuS4KwjHS5Bkyba0cmpX4Vq6tGQe468kjoLiBYJRPMN0hlo+81RjLvNJu30Dv
-ViY5oTO/clfTq4+qSMuKi66E85xvWIVxpjcL8ZCbyvdOQ117o1XExPTZlpQKBRK8pSQy6xhOEH7K
-2x0lMvYB8Hba4ejnsjOehJigroadQ9ypNGMiGN3FRvxmMVY52W6u2Sr2+dBgJ49Tj4Zu317gWW2c
-8qePG6994EfKCV6XIwVMsPtzcNzYvbOY6yPeGVguW2t+ulqD3xf2PAV63/jEurhDvHYGw7Umo65c
-b9/DYv/O8AuEfxjiJqlSww+4pnX5C7ucnTQflj6vibmMzNNCHHEPtWXISb6B4Lba5NFaapEuPo0L
-W1wmUnla9OACchMU+3SLCHeJd23IXwB45cHDRUBB8OHX3CFysVTbdVyK82ggmLtFOAyPlejx4PVH
-y68Z7K+n7xUcAPhoHX4XH8onmHfN0Q/u5/RGq+RDO+4iIXPIYpbQgt3EdzlC9LkAoQ5BrFj2BIZb
-/ABYMj7Ab8rsHfz2eSh5AorzJQnjnCwwjABpHm3Wx5owz/mrCpeK5RMAPtFmaKzwA/0Y38FxU3U6
-GEM52bjRW1Nf1oSshRx0AVfTyqtAEZFWH2EqP74ot7b+W+Xjmxm5hopGMMy4+A8HGAb9vDkvdFo0
-8FtzrEklnXfedwZRfq0iMvLRLwXB/FFZ25AvJnz9CI5xox0a5ZJodA6qzI9W1Hv5VqbmS4KWVigR
-U69PjqfYvqPEft5+9ui2+97Nw7HKow2405Vgo48KFKvmIpeCfKSfJxEshyw8yIt5k7V1YozxaXiy
-cXEtukjM5gM+Xlw/yEt4Y0NmhxXbs+8lI136VE2d4LiO7n1NLn/7cInyeOoYsmfdJK+aKHv5pBMP
-2gtyMIe9b9h4LC3Cr7wgtwAwZJ26xNHXzh8Kv0A4Ajdvzxk4F14c1yKzOiogOLhG5ARNUNwBs5/i
-nA/WZEI0w7JewRGiSPNPdU7vnqO0ZXtLIlutKAHAlj5DbHkHwgF+0Wm6Y8Rq0prspj7CB8WSSuiq
-dDYRgrTBi0KlAjdJ88MU7U7aPr38dvMP/lDGHHhLUNhDgREAOT6kYzociUZqbNmf2kqtbMBlBdZ5
-sepI4HyCeAPUuvCszw8c4nahR2TKrjE57ZJXrnuWwpgnqCsEAbAqhJOOhNAmGJ553wpScL9v2AGv
-8t026urPe5DzrT053X30k2sBckQ9FeFkEICr+TbLKy3iGwj2Ar/NTxgKgCPfykeYN+sAuoGngWBc
-AfI61gtNE4AoiFRepfpXmv54Q3oDuzfZk3z1vW5pSwe3Mi6NHqN5pM90l+MbsJrvdpyuM2Id3J5p
-Ps5VoDuP/Rp7E655uhAjrmxTPVyA2GXSCGATsJvwT4Cj7uWefzV/kxafxo5RGwXYbuv9kdL7u0Qh
-Fdow2qhZYxPcVppXOVl+FSjrWkp2psAUnoJPKGgRxjh6lQugSyvv6+Uj3GixlurjI7WOnIN/YOl/
-OvwCYQ3fWc0N+CrNMq8mpG3HNdYdELcJC1mKlL/6ktMow+vVZG65AWLr7AmAy4IXrc5H1AMA+6BR
-MfBoulNE0N4XeB45f7f8NmD8USlBaK2L0e4iF9C03DaNH6txIF/m8TjxS5eHzQI88tTHOEHHavuy
-Cq9vqNv7rEdKr683C/QLO7AyatgSLrxD9+iDOkR0wNvBMXs6y3wCu8cdIiafdzTbknn+EGQNbAfj
-bZxHQW18nBh3eimIWXbTT5Zvfs/W+eWo4VPeDCfA+xkE72WA5Zaj7uF1bZe0KDO5SZwvmIiWE6C7
-zizXB/T12spSWoVLhId1J9IEus36i05L1wioZTj6Rn/2CxjO/nLafByEkmPUmY2J0ffjNmdvxZvV
-F4eyCqo3nvdz9Kai6XA/EQ9tPgGuTK+Z2TBSlhONcJj7DRC5zKX8e0p9ju3d+KEVGIdu6sKTuLe0
-p4Bq407gxpNUB/qg6Uo3gE9kSUsL7umNtZlutHkdkn0r0+e3iyxrI5z9tHmN5PdXbhFx9FOepMX4
-x/f7kn3wvHlw0Aghx9fhj8Nik24Fv3SFSOAbH+6AOzzA8Q6EUety7Cj0J8IvEM7wPebXhFD/3xU4
-SRP8ZhnVV/LaFOmy+PXHteVSbiyZJityAWUBnVS8u0OA2wsARoCPWBVp/TUqBtYreWwghntE8gvQ
-F+Wmi0Qqli+UEu8YdRFP/Zp7mIJgOLZMQ/kJ8/RndfwKfLt/8GeQTGtxWm3ijnntmrE+I2kBfo2f
-tBNAkKBXBHm5QhTQrfF1UCCqi0TRuzxp8+JLl4hWeF8aG5rcQXFFvweMJ8I1KzCMPMgcpQC/NQmf
-wbAfzvkqDPw24gWD1Q96QmNeefq293YXzDBREiUMSnkMrSdp1NrEHSRTdizXiALA+rKcphP8Jiju
-lmHuIAGInj0wrD2As3HUc2JtEPRkNTqnJO4HHigIPv081+pZ7tzOS+swL3hpU7tLO5bxTudcagBW
-GOg7NceyzZ5+1L/Fontslt3zetNPXbu6UoxJf3SBAOVhz2tuFCzLggTBg+YJchVUKk36lU3uEkIw
-7Dk0MHyQm9QNke9a4bimZT97nfRDppGjsjX9Ka+PQ+tT8jUWuxeP0pXTq7bcGvV9lzp7F6/LFeLF
-Gy+Nl1UYlUa5Q/CGs31R9g+GXyAcwb7JewcfG0Bm1AK/Z39hTswAyAGOdT+/1PdWE5W/tpa8l9kV
-uQoIbyC4tnQhOIl4A8DBC0EKdHlIACsAeLMMoyZ1AkBfn2z81m4RU/kcjzJgvhobXC+rMBU48wxt
-SyeHpB87vCjX059A8kzjFQs6Xtj7LL9gxu2NNq54YqZs37AEo4AuBdwOivsM2OlC2UDJTu9+xLP8
-IXEBxqc6TsB4zbMQvPI2d4FhKyWS2QWN2UPVRP9uEKzd2Fli497CWp6eQ0vwY7Y+UJqWKfELdIC7
-IvCh59Lr00Ipa7atQyTQ/ZRnmofwD/ZhBZ7p6zEsw+EjPEHwBLfRzQ0Et/wPyCMpX91A6w3CtArf
-zjvR0XnPN+ePiHA2+Ysyeze9ndJL+VZ/AmAfGVKTt1S7wrWM5py68zXNr+WoklqGzgGue6D6JX40
-KQLk3NINcr5XZpl/Ko8317trhF4IUvEXQZ7eboNIv/kkSYOzL3rB0gnVFAG14tqB6IWC4T7edUk1
-KHAckmVAvSyXIFjBb/nrr5fmgmsBfN0t/YGnhXhagRfwRfSBtMmDPxN+gXCG76nGfKnnBn45YeWl
-uAK+5R5Rk3pOUgxA7K1MA+yiUHIJZyFmrjgfOSdIMypgBcCWjbBYESYCpVt+Vz18UQ4+QXN1KK+Z
-dfRt1HZA/MXLcymzduHVQHAALDNNBwgm+EyL8DeArj1fgmMgeJKOoL5eGHzkQ8+vL0D8rJcJ2RaP
-PtE3ODiNafUlZOKHNU6uEp8g32egewG4t3hopBtGVu12A8ZrqsSc3MAwQliXUkubl8jzk078akV/
-XbZyeveFJkBvz9vPfTwAcDtHjrSyIFS3A7s7hMalqVx7kbZcex0wq5U4QTGXBH88fwPB5Qecu0Rg
-HTWPcoxsSLE15pFnvu9l9RwlphgqZd6PB9nxyhGH/En7ThlV2C0+2t/aLlbB0fy946RNBqgeaE40
-Wx3Mc6H4iB3L2Cy9s/lO+945nOgznWWYYXsacOSHodIK7CIUxpGgkXptlPGWPq16iHydoZeuoZE2
-tHHhwpwdQy6AfushSBVLJ7DEJ9eHnrdbiRkv0EuZI5rGKXEpM/rZFiotXwg3Dyv7DoLf4UfsLy4g
-+cDi/3D4BcIR/tEHNdDmp9AL/NISTIG1dNXuI6zLMeW7reut/A6IWY6KZK0eK+WCKNAWY1RMEEyB
-kOlan/XSHdISDHjRHah9guPlOKNbBF+gW0fe5dn79SeUT8pn+5BGdnr1Kx/pktNW4PfJDin41eNn
-a/DZP/jkJ1xAuAu34OHLfdRe+mQsXNzas8bEKbhz3HewexfKH8IBzOZc+Q7ozbTd80NxfwTGUU4B
-YOmvuvnKLZK4WgoLx/kJF5F81j5JfB5PofKaxm0xEwIlgJbK6daUo/Irxs2rTwlSsw2hgJyKCX0N
-NEBcjbct7o3egC9m3Av8+m4FLjeIYQm2dWM5gXH3be888zH4HLmzVdhrctQwF0elzw7lTfBH3SLe
-sART6WLchDf+XmhbWtvhrU1bG2tItrJbvDHBNRVFDyAqM2v2u5wRS3MAol4u86TuLslONR/yDR/z
-My0LsnVH5wJZNV0iwJtmlEwYMkLnTG895cqpoLxQx8Zgj5Kgsqat+BRsJGRH6owUbNonpWnjqRuA
-/oW4u+sDUNO0WYw9VUxxhUvMxOziehXrVmDmyD7CcAG+Xr7CSifgLStwAeB1/KFe+zeEXyD806BC
-bgDd/I2BLL1V/sEdCNcdnIqwVGwNfJwEn+e6yTtd97TwWktHGfpMccp7AcpVVhouLhAAChg7ytfR
-D+4RpA+F8zFORTMUTrOKCRrJBzUGAcAVRyjoAsYsABb8whJ8shZ/sAibx/PvuFXOI+BpEX5xAuau
-NK/RvP6Mc8e+PDYQqNPzFv8q71bwVM53yD6FsHn5v+1KrfjRreL9klNtTX3YmnTt0jmnAWE70KRP
-jW3DKhw7DuE9AWCxFtajSKTC0LgC4tSxh/gEvuoj3AAyDLXlX5doz0gvmlqChWZlEe6MUMF5YpSA
-q43ZO6JpUlD6VMo0hW6CYadcutxsX1+gQ68vfYNTHqKHoCdZ8y9le37NYBO6o+ft9dksDNgAuY7G
-Rd9izP9sFdb4sXt+77bLHHDN3MBvAN1Zp84b99jlYAkMdaX6+phiRaaYPHECXZ5EJvAEmcIqicp7
-Y8xxlzpY5wTE2TmXqFzBcATAGq9rn8qMMfPieXvVOuRMsYVjRZcIBBiuGZlWXl9+wZjW31h/Pc11
-6XnjlfLtD4dfIBzhuxZhqgK6OrQ33eSoLhEKfjW9rBJY22OB+qtAcaYpgw1S7qAMJgI45HUQXEpw
-WapW2XSLIDBRYBzHsj7d3CMW6DN/s68T+J6VzcFqjHk01HK3lEsiT1Mxv7RUBcB8FQw/61ig9ruW
-4M+uEcV2TgYC37AIm8ElvqZRtMm84uhgVqT15fgp+AY8WrjklTL4/9l7ty3LdRRYNHCu///inWY/
-iIAAyc5ZvbtrnTNGqmqmdb8iCGMsP5QbTawsPdMGeoXhVuR6JEcbaWrH8+isMdwymaj4+W7JnJnT
-bJ1m7hRnx2tpgHXUljRpLe+ai7UXbNSlfsoBtUNd8bWHtq8wCUNQkMuy02Z4mk00E4iIo82w+X5S
-BP39pTmkiUTyKkPSr2qHm/1v+k38J2Goj2grC+coeZXMFePUNOJoH/wYZp3jZhy8cT/IbR/9P6af
-8+v7G1tWBUVb/Mo92T5BTa9vAGA7p2lZbTX9up4Pwzv6SbY20mTj+Sik9OIqq8kHoO4nPnniEHVV
-AOx6Ez8m19TmF3yiIbs9H3GQYQGU+SW0ZNBWE5Ozbhy8rsMBABsaSD6aRXjEE3cA4yauP2fUGdlm
-616FzVb31rGhi2FcNrTDcWqEmkGsbbTy0J8f3/r0ha3/ovsFwv+Jm7v/AHoxf2kjXLw5w8FwzqYQ
-ov0i9xBCqXysbWzuABUm4QLBHRCvzVx5l8LYUiAkMKZpBDeyR9sOIMwheKxKzdGu/f3RNngIp/ZJ
-U0FXOmKj3W9MO/HubRZ2vlZA8zKcTSPetMNPL9N1ILym6V6ANx8l3bFKcYMQ/VikQfooZlyLXnFN
-09tuoHreHfLJZGmKCiMbuZ/Q4BZtr+kq9Wb9OWMb+O12e2UG8dwlH3F+8J/ipv88xrjaD2EBc5Wn
-ar68tMG5z1rf6glKylAMEJZMo/aXAkONm+YSBMUNBIPaYIYPHMz1xIjDV+biBnPdW+oExMw2OhPA
-pndJor3s8cinBbVexRON7CLj5hwF+D2dHoEH/0/h9GN3PgIzD9fnkL/zepmAp7pQZm29WH9qQv7A
-rdjSvLVcOX8Cuv4Qbw/xc1hrm/c0nQulH23LhD/mJgk5NUwlNvth00af+CU5j1XdKUtxcLWTs2ar
-m+RmxiKAmPlqcGSIE3qW/7DN2/VP0tQV/3Px1xTO2Vo9KprhF+W4PxLw3rvJBH9TO1xg+I0T/2/c
-LxAO97FGmOYQx5fl1D/4MfhTDXG0jQA3vsiQgHiSXNahe2N2L/eabHwsAqXmN8dL0wgXMwoRNiu7
-F/U7tcYihHIT0y5Y03lSApp98EkgnTTFu21wxB0wnqGYjwMCiNf1Cu2vX1dqhe2ydWrEhxrfNzBM
-O2GeEMt1Wq9GLfjgXBK74ZeFOUAwPwHobpZH55zNHdqKc/SZtjM6YcA6YU/hnwDwaascBHT+fdha
-qTF1bGYRQJc/TDMJ+CigzBu9qleQ2wdxztmAbgtLrI18tue/YwzrpbJ1nJACTtfw1ERiaoMFHTgE
-6ApIyDi5cn5d2jEDAfAldTWziNQMUwscn1e20gRTQ5yaYLM6Mzn4kt4sAEhaB9RO2LMM50RiJcy1
-j8erG9PVq5dmGHfyk+3jPk/+w3psdH8MV6RJ0Fuep3psz+BFVzUD5zJNoxtr0tv3Vqzl95aadcwR
-ae90uL3ew9CyjVO9L2nOflsfbm6gIJo2Ry0TBUaPY3cjzbH2RclUyS5+VhUSNPd9U+ATvHOCRFvs
-WUkxPr4sDcpsR47BJY9LX4Ge56gVlvg2HCHlyTd3E33WcCO5g18Cass8AnbjctsAMG7HdZU2eGqJ
-/7b7BcL/gcsnGCKE0pEJzBfnUOYU/WU5BTAExEFupgC4b1by9tUhidQXCjKeOzmEUOSjJij5iSMF
-otbHKimRGjhWTY0CYKpBuVleAO+rluYUn2O2bdPCkNpgBOC9xRb4DlDsAYqfgfCu8f0RCF9XrIGj
-FWIAACAASURBVFVNVzHvOz5tt9bfVDMtHV9rzgFM+hDhlbRxBsZMm+5V66tSeqadwppgP2Rpwnvv
-A58+5AtyosFpH9VgYe917BZtu8zCCD/2dR/dutpL2KrlAsGaL9LCBtFkDPPX5IBLvtwGui+YT9BC
-3LBO8KvLW5rgc74ykTAxgai0BMFA2glPG+E7unSlP9YnJ96DHk2GoABAV53qAqEF1zlBPlpdfEaE
-KgGwvCz3RwD44C+N/RDac/tsyb7FzXJZWoTM3Dk+2z2USZ95i04eIhXXo3PpUt6MS5wEMtW4//ah
-jO4cX6Krern3Z1rtVu0zyONSDimgLB7iGjfhXoLmiltUxrRRt3Szysg16DnpmjScL82JxlO0067t
-BX9v/bKgbQLgiP8ZAKNd1d80vboujpQq2QPpjkm7HMZtvt59Wd9WxmUOt10DDN+1wPP3C4T/Tffp
-3B80wAlcTnbBI+4OZnPDwGNLbit7VwKYG8HMsbSDJGJv+VLu9ZdTczgHi58EvvSHgBOwnCBDXipo
-NsPCMa12RwrgLU/kewLBx5dU8ByftUr/i6EjtcCr4QV8Cwwb/Fo2un7Z+vJd0wDb4UMan5tHEJs0
-AeBYaIAM1ELINLtglA2lCAK9GVJGXUC3VrprSIXBEkQM5FdajIOgbXHs04TWo8JHZ3vWk+DniOyQ
-bngExLMnKtYYtlOao80szPZxp2z22gOyq07X8tvWN5OftpVglD/vz46aFhLcN8zL/skAJyFFnGlb
-7tLexs22n5pDNLMIrD1HzTD347LR57jJX4T9bH5vT3VINuknf2KPcg8ptysB3kAygn4dqDOaURp2
-FhI/bbQbqG75utAu3idLMOL2xKc4oUuYNDMKtieZmiZ7xTXVOxiNDJ1cXNJq/k/dbvXbiH8YjW/x
-I821L4cyuYn3MiEAqnKKtBP/CZmV9dA72ehkCrboP/knadYMCoJ99CMBcRCKi39hB4BaJ9c2XbZz
-dNKDwF2A9FkDXEo2+lOGGHex8MU292hgmUqxRpkeN7NhH0w74ZOcV3MJN7nGDezdsMDfdb9A+A+d
-h1Di9Za7Hce4ajrzA7XoKJ7qAL6CXDtD8VL8GISoNc9yKoQH7tp+vXSy29yotW0YoOQgZ6FXd0pK
-nMqTQdkUKBthg7c0hlNbowOE8qXJtdblCpvbpfGlmYEB1wK+Pq4giKWt8NdX/2Tyq1Z4tyFOIOy+
-2nHpgwD0eiFuXxUZudz9k9n19ff8+cgTcUa2N/iLyZICZZrAZEPdA21zXwLzlA70269zvgJFa86E
-zsuzX1H9PkrUIIsx1BT42o81B1P3IVU60l65DejBP280qnFplQBXtIqqXylq6P/yBU8XOkLHgnnv
-J1vjKW29H1CCruyYaxBJntkxgfZW11Y2wnljPq5aNA5YDH/xPdoQ3yb0YHeZO32vl0xXu3f2yfix
-mliQajKeUIV/fQr+jrHHh204D8rvwp+aaNlEmaXt2/+FAD8RlcQ3gp75FfgwSrWImk/jrJU6bbPK
-05vU8gAGAJ1Pr1DEsLUj49sLHMsrTWZ7SQRSTh7lVDuW/SWIXVtXwG08rbModw73fIg6iGybdtpQ
-2m5bo1ZtcdMcQ8Evsh6XvBmXV0u+d9IYAyWX+NQqTfVQ455zn1FyE3FlnhSAqw+O0vYS+JrDfX18
-w/2G3wa3O19oF2Oqv+Z+gXC4T29CXNc5rgVuxQ+U9tfPcfAd/LJORJyCoiZ4NV74wPqtjLYVevCr
-ZrXFoyRle+yELuDzomAqzhZuE1cA4AkYJxiWDZWaMaZP5rf9rK7rGW5pggP8EgzjQ4Dbrnbh+ro2
-wFxA2OFxfvJ9ceyW/co7cYZlODlV6E5BMGliAuMNBAMJpAtoeb0zcl7GHQSLJoD5lYoAg+LoCexW
-vndwHIinE/zTFUWWJ+zRqJzkKjZ6JRKgg6zKWIF59ftpG/2h0y20xkENrAsYQwFY8GmjhdbVcJGW
-mDfrEtJnvPe6No0XULggC6kwlAxMsvJndOtIVVOAuBrR8lfQf5pRWPG9LO8OfIc2PvfOXc3ZCmuX
-CLkqHADYwm8ChnkWupZQbR0ny0nHASIm8Z20sye58mncI5HZHnx4x4WzwJviFUdO75XH2q7o5Vp9
-e9szrurb+7HLL5My5/Gd6mv5ihAkyfZyRwA8+/UB6D1oftcS7GCYI8jzjmNDllZbtfZe8sCHHxhA
-N/i9xKm2uMmEYOjTHBPg3tG5kn2aE7TPfZlLxggJdlP+rd9FYES7qxMotnu9Jy9z+7fdLxAOd+RD
-p3zEbyhQqyBXP2CUYYywyweOAHxZCUcHyjaL4GLw1xQUw6kQZDH9XacyUrFu3JKXspmdvEQ3NaqT
-goaWDIlzcxMEywTCJZ7op+JW1QqKJU82qvCqIwDa4KZmWIBvaoS/rtIIX/woxk9Hph1MI+SLc+sJ
-wAW/eGZimWUgNMSmAPgoHFAMA2RcNXq0uBJm9fORp1bX+zLlVOaDNEsevC3rqbyK1KIUJOUUmTyD
-40UW3q6QK07Xlx1rkm3JHy+gkJ3vvU0XA/EWDkAsGpCZ/9mtERII8gVQAmDdRrXn+Asb9zDJMEfa
-vKu4UADMFi1vGEc6/ToUGYNhDNEkrmEKAcnBE0wLR3wDzRGXNsOSPwFwCMOkxRDwBL8FhGOGmlaY
-Ta04Nps8KfmR+DOfdjTCQifFaXY6zjlsbhJR+D4VNLp/tM5Dvrz4jPccwgS/y28tpgG3kaenswmV
-HtFQ42XSN/EU7xnhyHPSQp7q2fphleb0a5kTz9U+8ObSHkAuwzGfDfQKOCbhpyI4+6R8qPOl+bEQ
-fUKXL5M6Qpu6AqnxpZZY+b6A33zuZOQ/Pa7mI/as0HzjKrkpK7rtDpXvKcpdfkv7eyUovtc7q9zz
-9wLFv0D4X3Tnlw8O+dDWOrXAet3iD78U9sFneHf3BZH53Bdem4HtT1px9De8IX5DsXyD40qiVvC7
-0mbFHgI7/6jkZsMsO6fQgbCGHlpglHZ3aIdL+4sWD1SaTgD3ZgljNYcA7CowDALirytBMb5oFvHz
-i3A/5rML5h43O75AtzvgV7woQBBMhioMFyqsitcctbwZJzSBAzPM5SmBmOsX89ZA7+b0hqhfcfRb
-+idgKH89Hs0XjjiKlL4dDDcbzu3qLWxA3rCxjtTeUCtis+3T0L1Anm6L2DdNLX50OiO9Xn3p7bRf
-l19OlRAt8O3xiXCZ6yrjAnCtgd3d7zlfBaJrTLx3y0Yongbi7uW0cEQLUC6GZAJ8aco0/Ch/rsU3
-zR/YOQLhAB7ZQNcOJ48KfrRMI4D8MuatoLgNOteR4KHMIepx889uUoHL31MVdojWvfVQb+PRSFr/
-Efwa9rjWx2nSoLQvfVUaAISAZlvP5bKdDSRbu/R2Dn06+F37Z4f+ZL4H0EtenTTHfiqNF0BuT/wI
-QJOP7OGaCxQ2yODarE4sEDJGTSQSJCswNjSFCul4moToEY9p29xuMA5OeCFt7UtkhzZYgXAAXXeE
-Bjj2FLXCQM7x33a/QDjcJ+xsZXSo6r/5I3y749vX9YZeUeEQ7l8hx0srXBvAXf1QHvc4BoJhlTvr
-UC9LjbC3MqviYvcvlZv3MKQhASQqgJKlKtgdYJhxFWYD/YWhbMMYlsas/+yiYLUFhlMbXCB40wg3
-zfCLeYQ9mFEYAW99Qee2C2Zemjwroe19tmRa+yNK955SAJezpJB45hH29478NmB8Dlev6VOxqdCA
-olvDjlk+Ws9l94qoIdUc6MBbOxWd/XV0wWMmANhGbgm2aQkQJvtPREYv8LqBRp2cK+83G7l3k7wt
-Pl9KMBxCLIB4kr+X1pnKJUu/5xYtzbBlW1xkxay5eioMM10Fv4zZRHYSBCQotkofcZVmAxDbslpw
-B75XQ9QAbzhoOI4Xci1zmjtU0gF+xT6ja4ZJQELhpeZTzLK71zirCJ9ZH0fTqtjBwj4hNXzdeStD
-8gbreapcN4vQsuc2tY9CmAm49vyzTj+W6eV81tPakno1n3F8FdW+WDfKdPA7zR64705A+ZC30Q0O
-4VgZAn/maWGAJhS8gXbaBQd/WvjDUutMsDvBr4Z17p5uCh7Xe7DO2lsDD8UJEpTz/WW5G27AxYOl
-Yn2v47Pr/637BcJ0ryChZ2vaYH/Q/s6wLxD8LeHcCLzDAxZBiNBtwvUHQWuHq37kt66hFc4q+eh3
-9UUFcx/8iKG0eZg7xc0dBLvscAG5/DnOtsROFq5/vfgd8YFqXKkJNkuNMEGwXxfs6wRuBeQe48cL
-dhJXfb0SDOO2BMRdW2DZ5ynskuFFzJihipO7/ZlHNcclQGNtQ2tQYRzCwsqV6TX6OGl+d3CMVubw
-7MFLCHcNsNBX0pOIUK9suwttcIDE/vlVLaMj8s7k8+pILfAxvdfVd4rMTNwsUzYQoqh9cIjZdQ1U
-u26q1jm9qRVOWeY556yjA+Cyy+UpL6odngDlhEfYr11G7iYRJeMlTUHvVXGL/hfg1ack+RTHSA/d
-JKIeA1nr504r5TcSyn3F23kDEIumfTVsxcQGIE4wcaK7U/SRPnXPPzH3GVd8+6c8WjOpRPcrrMBu
-5XEpaT1/m2iZpw1AdXpq/TLtCw7hzgcPBPcQPqSF/wh6te1RpmmAbbcNZh3TJKLy6pM+GU+7M6d3
-xWmflK4qOxUZHiYRpQVe2T1NH3KdM7z4nscTsQLL+5y/zmf2pPrNTUc+68CSeVgMbr4od4W9cD85
-YtkIXzn+v+9+gXC4d21Zz+d4sA9+uH6jwO+3xJOWvpTouVcO2uAIaW9a33hSBO+nTOIJfktO1Gsf
-q3rGSNU2vWOSDnPWthSRd2zsI7AdceulHvZq/UrkaZz3p9Mpw0JYhyBdTOnKj2csLXBoiF81wrvZ
-g74UV3m7CcX6ml78GgBeYKx+CpmsTxwZngQXrU1grI+8UHGelUieEnOi1Kq50wiV95xSL1GteHAl
-W8OP2vfK96w5liFXDvpzEnxkRrd1Ztvkz7mPpBM5zpnw5EJgjOX52G2g25LGuR4cB4cl8AIAT1ZY
-gsyiP5fJq11RiGO3FEzAGQAXU5HtEgK+GreWaDmcGppolHkZgILggY+LFQDjKiCBSwBw+kMbnMQa
-5b5NGyrnI75tniCGywC/lgqKAPhCmEYYP0GJIqKosxnGW4KKvlLe29Wu7VGHhGl+0Cb0IfxJngPY
-tR7uvKaHhWsc2iDNfJDW/D3/J5rgM6Dey/wJ6G16HdEKl7b3DHjL/KED5JOGGMmPR5t5419jKb6t
-+YpnLvZRZg9l8wvkMWRcLwW++Y9rqzc8NX8b/Y21le6hRwZ/WR0KTTWaeUQHxXFCxM01u2I8Cxj/
-nhrxLzrfyeAhX2K47Urwq1pfguATGIZLuyRq4fsFgL21D2had8UHvI4nQoHkW/wTaBEMd8VZn5dT
-s6/sORQt1iZLTSF6nIaXsJ697A94lpyq++4SvigwHCCYL8rhKjD8qhEeIPdoJ2zWAHIC4dvje7q+
-+nArCA4BSzS8jTDmmQwPdYWGfZY951GNsGK/vJnwWtcK7y/XsahlahejUjXqhQukT1lvsyd26XN2
-VMbXBqr0WOCjty3zZw6LR4Y8Ks3isWLd/Mn8RJkOgIPJW7RvtUc+cTlSTmTQOrX+1vKFX+5WCIav
-EJB3xK0yri1EfUVXdc63STqi86WJTsyrPW54g3ESKVpgAghWok9lEGAXBA2Xxlna8a8r+hOcCYQP
-M9uc+04i5CsZpj8A8WXwOztek7E6TKbSzCJ2p5xy5PFj7N7117H9EdcddcYuJeBqu7bCmn+C572N
-w1WAk9qc1rr1PAt8nfJFfBHfihtl8VCWabumt/K6FJ9ttaeKGg762F6mG4C57Qupuyko8oZU+1V8
-NeMz6IkXUtNL/EAzCfLQAMVcx9IgKwg+3Xihz9NwyiPJuEkfKasa+LVmJnHFl+bcr3aG8HWXffCq
-+kPG+l90v0A43OcaYbmzQdn71k/NILyAb1wTGMem+Aqicve0E/4KvqQarU2RNV0Q0bSuqfeiV3vU
-ChMoWQurYFVYcZyJj6ITNKnG96AVbucGQ1+Mg5R10UxuDUEft0IFsYDftA2On8XLcj/aANsLGBYT
-igTCcfUrNrqAYTLTZn95nMYJgkum10wRQJ61xeSoyVSpGcQEuQdQnFcxl4mlrAetNYQ/Acdd/Or+
-GwD/BIJd6j35Hfl4vmmGM4ONgqj9xg7mHlwAmiS8YbGn/XhwHX6c55BunaxQHeK+XSBYBGaMseGD
-7Jc843EvLS/kpS+TF/YGBmSFimOAku/plwE2EG4QHLmAbntSc3UQvJkw0c+J/9aODNCk8yFgAh40
-pISm4De0wLw59ex0bYSTbPD8MyPR1mYW9s1zSPtjEPwc1k+1r2tRm7eYTo17fq13Lr7V2ke8H9JX
-DhOwZdLVaE+J7yMt8JO/l9d6j1phE/6VsmPFKcBd7OOgEU6gLOWblPLqQ7ExAbpIWtniw8OX4da7
-Q+WHKwCmLKg4qGbYKl2qbu4k2WfuTtbBW6gJhoBfR/9wxp3wHPmCHG/1k2d/yFD/i+4XCP+hU6Ul
-j0Jz6GkRXrbAwALDWKD4G/US3bcwZ8cEv97BL87ECQhveHF8F6QAkoDfJiBzlFs4Y7IjihokpkVT
-kqwxtZfhVAvMAiegDBQoHiy79dT0Z/HpZKTWqYPhMo2wV23v/jLcCTBvQDg+NbmOillC/boLmNM0
-AkBphwUkcupqjWoVuIYAGVrFM0XBMn8GJ1kVo328+/NaUCljwUStcjRw+yfguMqIQNbuTHV3kx7l
-6X0petC9Qy2part3+t01w71CFOKc20Pzi9ORG+dSaJv3fs2WfivffRdCwzMzO2DWx0UAnzPtMlNi
-P8xFLiCjgJbgIBOqDYIbywwdhJjhrBnm/oynMhFXT2+uBMSLeXXgr3slex1zmffTbRMIz7mWqsDj
-KOH8AnyaCTvqVABDInn6gxvVnvVGnmNJ2tqdQYSkPm3HVs0kvOew7pDisj1MWKL5d22w7Jh5V8T1
-RwFBpZNWhmCx1VP+bE/HmnXjsVwvw3YqSV+U89me1h/ZOtB9MYc45YGA6Kb1lXWp6Pyz9SvLyThc
-tcEiCzYATNCrmmLVBgP1rLXL0vT7U1rjkplm0f4FR5lIrPZ5lrCHNnhpjG/4vV7jdwCXXevlOVAm
-nnm7TMd/1f0C4XAfa4TR+eonR6Z9y49mEt9k3AZ8sXETAZ6cx9Pf5BzdK0XsBVj1/nPZHFV+Ebky
-nwlQJomWmKJgNfg67/5JCxyTmaYR0SOD92PUoi4vabeEv/Jm+Z2EL+TYNF6fAe5uA9zTOlA2W/aH
-HsemXZfDrxt2XzB+GQsCCjb1Yk0rH4Wl8ETX/CooFkVpS8PIy1yl1RWcZ961wUFvWXJf5g3cFtNS
-hr4DZz+UT0HtPaxzMNvXcLNhZrtpCqHzSWDn257f2Pt8OQ7ql/TNHSaL8V5JDbK0JyLLpQlTFLu1
-Dt2SOoDom2qClXnk7Lv08SdsMbFL7C2baRRgtgo3++D4lfb3SjBMkyWNX8D4SiBcYGgCve6fes5F
-B3WMIeJicOBWTZ+1/voGgPdp89H4tuJbvPb9THzzNcuB8F7S9rwKempXzLQe1vzewqd2BuMVgrCI
-Sy2oRdyBwN7AcdWhZSyD6vcWP+vp+d+0wifTB9JzM4eQPDjE1YvRMU4hmJxb7dNJtNLvKI0vmYit
-Qo8AeMYPf44/eaNQic6nd3pNeqpidRPuaAC4jlRzXDdl0TL0umgm4XxZTvjHX3a/QDjcxpie8hVu
-a9f9hIgyhdAX5BQUN9mc8knQCEqOsZfTTZLxQyKrpzCluYJL/GpTBU4I04yXSllAGdRPdxI5WQPw
-qhY4J6HnSR1M5HnCHounWAFf8audsIJgu76a+YOC2mkasacVUCYwXkLd823Yy651J2wFhpeIqNsE
-WIEd/lFxTrA7BX974pt5pna46jB0QZ41GQYIHqA4r33ui5UitR8qQi3zmfhd8sniuQpeNOY7SWuC
-3lNf2ZbBsjzH1jovuduAMi7WB6phHQ1p3/aoMVpu/PjpXKn0OxRtT3a29imIZLZNZkXtrNKmvtJK
-HlvHKgJkgCGkRNMnxbNw24/td21Xe4j3q6+qy7UtRU7rWke1wUZ+3TGutwF2raW9gqrv/ghcNcAc
-Y5pN6Goe2J5rBo1rnmeb4rYvHt1Ms83fAe3J9OFPNMV9zmvNRctLrkYQZUIDQAJJlk/KVzmiRNQ0
-uzbynPLrFLBNGY9xHJIu5ajR3l5+GzdLUyOMGZd5pb1ox8XT+F1Lj3gJ1AkTK6G0wgJyDRJ/AMD5
-Qh0yjZW7yHhVrFA+uA/6GLLH2gty7EecIHEML43wFaDY5foLhP9F96lGGBBCcw+Ay7OCH06PcAHA
-qKuja4OTuJLD+1nwHoTwzgbp9/bxUIeCYRUsZOBezCUlDHe09DWEbmtU5zDCGS0Cqnbaz6B4sx32
-0oXOp3T5Ik/8bGiCTUwjECdC4CtOhzgBXTkerdkJN3vhnu4EwtQMX7eYQ6h2DHWVFaOsTo2ty9qg
-g90Cij28aYIJMg2p5U14kctYa9uB5a5B5o1RM4sYmtyu+VX9A2P6qDt4aJy4u3c1bjs7uMOAoHUd
-x1MjwuQTob4dHXHYj9liTRq4kqZjGENt1WhHTfpOgXkspIPgJlQwKfGtrRMYtmy62f5Km7UHhZZz
-D0qm1Jpdh/14LdswPq2ZcfMmidcQruYAviiQUQCY1wTCDver6BoGv9epLg0Atx/nXqlVQNQGINHp
-R6OfSHfYRFa96p44/HMa63nW/p7shHvebWyzLVn/An8EjkgzE9JTmp1kVRshlbjZ4t7KzfjeRz/k
-edIKs59qKwxTM4gnc4iKg44TnR7IDlrczhIOeZSvU/OLAr8TGKMA8E3NrKRThk9TjTqtSqXAyAPN
-QzbpIcbDHCJflkNohyEAuORTB8O01/+77hcIh/sUCLt3gMuFTkCMenHu29U2eIHf/+MuGuGSyg7A
-zfEFbAJu1/5U8iY40SPJsg2W301y+VEkTEGzNgR1AERG4odUtnVht09soFcBb4uTXjhf6JF0YdfM
-15RS8dNHsdaArwjg8bLc/Fxy1/oujdUOgodWuAHhBYLdLtzXBbvufFmoH6H2vOmbhneA2zIheA6r
-kDPJ0xbLX0CxxJnWg84eiwzeNb9qD3wgHa78AUg8IYvRH9/jex/2Sp76UW14CUN2JY7Cyyo/5Ntt
-+wTt5xZ57Fhom6Ifx6332Ifae22mbIRZ0xFvmKQVMKx8qkEuwJM3pfPpzEUwrIC3wiZPazLdeev2
-3TmAk5JiJA74V9CqI+0Wl4qKZ3wz9zKXyOMVzQf4WaCGwO3AebJdqF/2WFvWAwn7lumJFu3Bv6d5
-i/fRv777+lh+SpN5bpJHGK+EFRTPD1HsoLcRXDMXOObNIZ/KI9vqUxT9V9A3tMIVJ2YO8pTgdDrE
-fLHuGAdpc9BB+u0c37XCJ+BLnr+wR4ZN8vgBHKekKM7eTq1C3Bzm3tNr71vWElrhAr1ep4neXqZe
-1zKJsPjkcr4sh7rZ+NvuFwj/oQtM1p+0Sdw8Nq1MIuQUCdTLcrkJp3ZVd0yEU2gepOeJPRrqo6Fx
-r3UEw51B6kiD8J0CziHGo0ih6mRwZ8Bi6KB3e2mO6ai4smXkqyneuEKzGDSk4FUBbHEOKU+OoAbK
-QvNk+rP9pbiuJX43n6Df7wLACwTf64U5qy/LtRfkkmcvJtrXZIJgkeUjPNOV6eoac87U/CEWINZR
-Vn6s56mcCd3sml/g3SyiP95G88v+mHlc6wi/9z3Q+ydjcKn/B1fiXyO22Be3tR40zJu8CVVmmwYe
-9cZBLvtV7Apqr9I+Xpobqwr4/sLLEZsMnNGxDjVl7KeUt8qj+w8mdsFmYp4kpkpfHSDbdQl/6sIX
-spbuWCA4hL99BU9NIIxk0u4L+OLi+RuWcU0zrJMgGm/df3rlBFR4zvJOdScq7Hz4jdZ6vtrLJnFK
-7QpknjXFJ5viU186wIVof/nEh5y7a4Tr6QIBLG1pT0Qo8U37+xIXRXNc23TayMO+Vz1HjbBofylr
-NJ9qwesc4cHDopkWR1mKA01pH48aYQJQBb44gN+1rgWaY264zsEe6kt12hevvIx3SNhT/nObXaoN
-vrGUQ9yKN+C4ceGK+9N7vUyOAMVPbw//D90vEA63acse83UMRxD8k0lE2gzLzx1LA4w4NSIbievg
-gd9BrMKi84dRxA7+uhtEA8TVZP0tTTCZEv16xTMANgS4ZT1rotrLb7GjNC4/qMH8uvNAdq/s2ftY
-U0grAFaBfMG+QvP0iUZYP66xxcnJEhJHbfB9rVMj7qYJZv+ys52Bcz1qmDnipuV1rmdnSMwHrrfE
-pxiUhTd4v7c5xCW7bHGdbpSWmlmETXCsZhEDKBzISNMenff26QI7ZgUtT5uGUVIyuVlh3wmCRzDr
-bXELzGqd+hJi8vxtfAJe8yzkEjpNkI6O8EmK6ypRuxNqaZM2aq0KIJQtJ/f4yJPlpWnLAiAgYrie
-0lztRpTmEKkJVj+BMYW7gIU1xBK8BL7+JbzlK0ypeJShX7A439txLf5k17q5uwztBbk12AacXMbm
-2Ciqa+9kYuZTFqXBE1k/k/qJ01d/GsjLuvoaf6L9LU6r8kDa3fhVSSIL4NbsZ7nnqV3fQKzMcwOq
-f5ivDd+26Ur6MY53lc3xixA5gtyog7TcXprTOFQZur5fR5zNOOvpFIeGAXjPWt5T3J0+tHgAAoKR
-VlM8vWq1udNF62/I7GUSwbpEGwzREt/5kGd9WAMBgM0WMMZSHP1t9wuEw73J2ZaPxOdBXL5MIhyH
-UyMk7slGWP6Um3dEHUk8ugYI5Xe3sG4GESoHf9vGYVBvBzB86sfmmuq8ru2luFT31JbTNMs0PkTx
-5MuFKW34TbS/BnxZ1wQfNMIb4B1gdwfMlnm/LmqCV56bdsR2AMM6WRszpL+DWczwAfSu3JALjAAA
-IABJREFU6B0kA4mHGrjNOab3EGfB3FqydN/Nyi/snNqOXfxW/BwzI973pG/kl33dyvsGivdGH+K0
-Qgp0xjVCt1m4pVneONaa5ENJ36Ds6rPF48oAw4DV1sNhDfKvxlDqE9RXKddVsF4XMPGH5BHmUuD5
-YC6heazMlNoNqgBfvTnF9ZVhfSISQ0kWsswhFj9O4PtFNuFLBdU+cmMAos5rPaxdNxnFSDqQ6aCr
-aWuVZA7k0+OVm/oxf61L+fGBP/fslkfBC0Z4341PaZz7ztdrgWkGYVkmFz7nsoFIMm3gHeTqmGb+
-rdz04xjPuWqmLcJ7SQPzpbiPNcJRJhUvqLoXPRzihr+45+hf8v393wK6JzAcYndLU3kv4NYBnjqB
-zOPirz7yBo+0USYRZZZfD2NC63tdoiVGflp5+QMY/wLhf899qBBOAuCVdjm0D3Z31FnC/qgV/j/J
-XXxvYLqXRwWBqTa5bOPn2083Q9cwZb/ewK+ijtNV+rH2i2h+gTg+rYcJPDIt2Wptv8QkUXmOMf5Y
-MCgbgpc2vs0sghphtQM+guBuB3wCyWkaEdrjKz4KcJmdAXEKg77pleHFtNV0uqyfMKET6GU8c9WH
-JUTM2srTQFXYob3HeYKjzJPaao7orPktYakit7tHaj8kDHLDGCIs5yQqcC3nvXBjAkLn9DD8H/Hp
-3tMCwSeQocXWLOeX8AJwxhNM9jRbAMcFqzFKirausYUZDoA2wi2+d7F7ZFPqC6IdEMvNaQPBX2nD
-v+K+chR1n+yp/fXgFQS//hUDukQbLB+5Wfm91FJmcLsA2n3bBMH1+F/BSeOZk4x+im/8dt8DO5kf
-0V3VY+c8n4LcHs/yM+2B8BvYXHnyfF0IeAQqft5gbMD3JU3HuoHiSuKaleQY8yRFaJPMPCeNL3Q8
-B7CsWuTmx6CBtmd/AsXjyU9sgk0r3H5lJnHfOyh2CDCOO0kqUThfKVuM8meA4imHPJ5S2dpqTL/c
-UwNccWEXzLP1ed5+zO2vjfD/T1xTaMbvdHRa2QqPF+bcUytcj7rF/QR6x+9bE1/ynX5JzCJo2osK
-yQC9eUutaB1Yjb5ywgw2XpSjhFjX+RJdi+ekQ8KgRjjlbV3JjxsAPvy+BAg/gNoJhjc74Ydyl2qD
-04Y4tMYhGLTPGBs/GZOXeFItcALcyZDEX/GiTWsa3U5nNIeAV7oBElficJ4xrBCvwPFuLlEUFTls
-lHkm/Vens9dAne4vpdOH+KOfdG7Y7XKb85bhmC/2S26jMeDW98jFv8vE5KBBT7Cl0HbWVqvQc1rr
-hwLaYgN2js8nGxGRmCQzr2zyouoEwNNWH9cCwfbPV4FiNY1Q3ovOhwFP+2C/vGmJS03lCSRW2GDO
-r0EubVWqmQdTcZjwxprnRjI6+36I29as1gBbvncw4I3Qjpx3gKkd9Kr/JzOJ3haZ7P7rH5noYHjV
-Ed8+/QkM57Xo6TFN5mAcwHFM84jqc8j8sc4PwLbOBn4AvkOT3NdA16Wvs29pcx2RphFwX8dzPv4D
-bljY5ZZybi9T+wiQfWUj3OhC5I3sQ4NzG5XSxG19AMiRH7BJ04jQCNuNpYEetPO33S8QDud+/5xp
-ZUxmugBL999CePQXMd4Rf2eaQT+BnI0cWIwX6JV8myspG1fLcPL3p2uwq2ZKkfJdhHDkb2opNu+e
-b5vWo6Gub9h2vXS9/J0lNJZi0j8bV1DjShkmWikxYWBcgVjr5g9p8sB4AtnQDmd5a21MrW/2ieNj
-RwU87KNX10EwJazaCxNFEvhC4j29ns0myHV2pbSTrCnJRuquONU5TsjlOU6FXx00l0Tq4ldH/TIn
-B7LfBM0RgWAgF28FLKO80oW+batYXa1nO1JIulC7QZo4DmiE2w3M7GvrQdv6P9arYevxrb8p2Egn
-pUXiQ9WKU/HqyS/rd5e0fEq779gnNxnNowZM13C0vDvBVARieRTdplW0YxyBwmrDYj9Vgwpa2ixr
-uvX+NbLKNO2s9AmSnm3seTdtsEvfxo58AsOA0FjUqQC3tA7lnx+daGlSftUp/baHq+bbyqHxmVxL
-AE3L+1Ma+nqwXdtiDs1vefR2lfXr/FabPa33SW/iWz+JJcjvx+9+imv7Q+h4tDP3z1tY6Yo44YqA
-MbNZmpJdvp6gw5c5xB1XNwu/bzztb7lfIEz34Qo4brjfcT0D4nv4z787gTABLgGfxs3WtbvreB+s
-O6rwP7xOHvXvwCIBY5wMYR7nzTsKUKKI3aR6bvki+qrT2UlyDdVutn49XKOSEzOy+TNebQO/PSzm
-CWkqoRpgBb2V/xX4Yl4FjJvMoU7cZPQybr1T35wwM0YQJHOO16WDZNUVqimEWdXB7jyC5OxiLcqu
-WZSReE+TJc2/K7zT5JyTHvOWv4rVjYPUs6GPU3rP28CvjT3UEIy4pPn1Z5sb/St58JRndto13Kjh
-0J9nvnbKOXVXjCu6qBuyBL7hb9rWVBjcsNuA64Z7PBm67wWUrnvN7231wtG1vjLF/ZLaqfvG+ixr
-8NZb2nbhxQLE215qWFHBJRJMJT8VgMwXeQo8CJc2IG0pdcl1fr23/0iCdAliJWLwijLVGAMzSY+4
-5CqilFjLp7vRJOUBGAftK8DdPyBxAL9PaTHvep0mKG+guJ/yIHMpc+AzTcJ7/opTbrbLnyV4dzDM
-2bMW39e4gN4u9WSu/ZTOLUU6R+6/flqEHutacbUrCizrPJX06fl1brTvXrnBYwgt75gNl4WCz335
-Y0ovWPhLg3wCxH/b/QJhuk9vRaYGg8A4fwfNxSBYgmG4jxfZCgg/ivsgtu0RUGUQMuqb8lvetCGo
-/QaOcRdMNMYmsj2AXaQ1KV9daIziT8na5jX6BvPUWnKSjiCYZU5a2qbdpYZYtMM2AbFqjzsAfgTF
-CoiF8TfN8MHtoGcA3QRCuw7VR5ZTHDW9msGh9yoiggP5nkByawMqbkV4pABSW9QuXE5Q74XyDwP8
-Ib/OneefgVCiVQ269nZoeDSTSWqUaduB6T5HegC+3nMcGtx9KqFU0h3qOdWifbGR13LtuxbYKPiG
-VlZB6UKR1O46YHfcXd/BuwzmN3CH3+4489dSCew393P06b7Xz+8FpIPXYvBYCmbXdWqjJsNgcICu
-CTIbEiIIA6jh1C8VHrbgUfs4V6KtiOmO2QHvEQQnkJxxnIcDuGW/fMRj5temPgC4h7R83N3S9v73
-sQgAHuvhh3IONBCMFi5W4cf8eq3d0JY+99wEwSWQtDeNxx1ow6OuCkfrj3m51RXgKraYaQGIMTXH
-1uIw0jDy93T2w7c6aBVeU0BZsyZ9fSl94Z3lNwHBng+JwOtfdr9AOJwfDBTOGXdgm9oPUDtBjXEB
-Y9UGd+3FDoTVJWl5ZXJ4HrumyVqKwDHDktNg+A4Qm6DRgW/KCALjrW/IxxynGSxttGby9teboN6v
-i39LWNJLfvWbhQTBzLMB1hGX2l85DYJaYpOwncPTPIIA+Jpg2HQeRbg9TeE+o/VXNHFMTZDsIyz5
-9EU6Lk0CHVkuBckFfM4geT50UGaYq+OTAmrdFJTs83DigvaSww95vGfaEIe/+rPfJGOtS+/ElJZt
-j1OR6tvYB9DIWMnhPWVzqf1kuot3jOvNHYgxacQHGCatRfu1rxcPvN3jq1IOv+NzqdQMx4s8CYIv
-g90CjhMor/2KwNIAlkb49tAMUyssfgXBMpNtDynTAA4gGA1M7VphDO3w+uNR/I3kioMd0oS3dwlg
-rW9IAClxEwTrWLAYO9OOoPcRMKPlJfBXW9kCv3gFv4i883zdHdjqWLHlqzHJRNvsK7oZRNY14kZY
-40b1K9Wtda8/5+ora9XZMZe1p7s8lD74S37y8gl8If7g5QlUvVjB0w8tXPRwTl8JM70UY57rePm6
-/81BmcVpEr7ivTTBCZJd1/3vuV8gTPfhbYhjPaJb2uCD9nfT/N5hBnEfATHwMwBWBu6os4f1rmx9
-kY7MWxkeSxWzUVne/AC+5Y53AV9LoKymEgTFl/XqDxN28m6ieRafzEjkhGiBB0hPAIoApmj2v02r
-qy+wXbs2eAO7m2b4aqA3QgWMk0EKII6+UTDYNtJiO5Mpag7vWXeBP0Gz0DYB7wI2EWd/CpKFAR56
-P1+GU8Cr49HXdvba5qg/z1P3CjIrfkiPxIQFLpEqAUz2JwViFqyRBa9f+xACfK3PWfXWR/hpzC9p
-Ke1O5ZQ4GoVUlo7Oc/3Vr2AYKG0w+Z9qhAl60wzC71XebYHheATmJiA4TCH0GN/UL3F6QyPcrqmB
-livFtczLNpOGArnRFv0xwp3ZAAWcI77ZCI/Zn9es95SnkXS9DfEzMGR9Cg7JvGPFvFYzzSM8V1bK
-TzAcVwP4QZemAT4A401L3PKgAeRTn/1xvOiAOcZXc2iydmN+XwDvMb+4aqHfsHdDsA6K6113bzJ7
-bkVlUa0vfvaTD5cmGBsQ7nmq7oxrY7VKG3Mx+1bpvqdTPiA+q2wyuWH+cHOtOSCzOFatNME3DJd5
-m4+/6X6BMN2n+vgkPjLf0gBr2v6iXNgF424vywFrIwmLk7ZmxAPTdaStMGspFllEmNArTCQMqgVe
-JhGhiIE7QsPZweY1+nUzbvTZuCky2ofvSWTIVpXNU+ewMlxgWEFmA8FNq2tlF0ytrtgIZ752zNk1
-APThTGCcXphbHeONg67IZKonV/MTvpM2eM4ZGUiPagST4Iw82iKODLcBPsYNkGy90mnuoKkqFJRu
-BUoeR64595Af07aa/CHw6K9ak18noTtS0uZkMCyTFxWtUzU6wtSt3Ec5bwY+z7MB4CAZ74N/rFVB
-8OqfP/oJLI+AWIFw8D6LF95oRoHQAmfc3bW/S3uMikMBYwBlI5zgWq/Vv7wOfpKhlNPWSShBmIJg
-TqlohFGaYo6l6p8W1vN6BsLpf9PuPoLgKld9rjBlQvsoQgPJuZoChnU8sjGQDHbT/D6aQRzzaJ/Z
-dm+XFw3vQLnPg7f4ESdpJ4mjZXrPJg+qHAqUkTyVPZb1ya1ZfHoC3uyLv5ehHFhh0QD7OV7YwouW
-9xyHVsYP6Z5t2/Y1S3Aj18kR3EOh/S3Q7GUugY+fzf9X3S8QDvfp9HfNh4JiakKGnTD2l+fc62U5
-oECmOtLVadPy+sU8rfBieKVvjEyLzy3wi0WD31gCm2GD5VnHVxCpaoP5qKOBYiAfaQj/amI0uzfk
-8JOb8skO/gorWCdoFRCMw8tuPM6paYgHAI58m1kEOujdzCHyX4zchnZ4DPII54LTzOlKBhR/lNmx
-QDLFEVYNJcFsB8QoW1bjGk6QjGS2Kizqb4f5/jDOsx74NBPecvhL2p7kMldofsQ4WNof/dKCxV7y
-DKwK82UpzlXMcjD21K8JaDqN4ux7ct79nTDa+J4qfYDWmXGtsbcYBcQKOgt4zhMh7lX4XoBsnUct
-WmE32O1wWyDYeViEITTGwttctcFeV2mrXtx7GKNuvpM/eGkDu4e8CowV/G42qmPqi7Ie0hIId7AL
-aasuE1AOEGwWc1H0CQzziPxwgstYXK6sz7PunzTAvJvpnyg+mUoMKm7AWMdTc6/8ZJtLk20wrpre
-4h7KSjOZqRQAk8OZmAse0rk9hZ/vgHZPIw/f0rz4mvL2aucUN8fe1/dVK5ztzjp6mZLxLjQZhfJu
-lgxVjlXjfuJcWKGHv+l+gTDdxxphviAnAmADxfXy3DxO7ck0Qt0EwY/djTw8Qz5BsBsw7tAMhm9+
-HENSqFn9jnYXKK4eEdBdqGPVllbYcFsB4F18snwXTF20ThFRYWM4EIlxYrwAsfLV/IGAkyAYO+hN
-s4c6Km2z/T0B4zctMP+1OILhmFNZaOPkz3VtM4QCT3L1lkEZZ8WrZqGvTtWh/Ilz7TnNnPOKA1xA
-8hjL6H/Gsz+DynsZHdBMrbQzYGa5KUjRxr77Z2Z0HtAm1EYznFxLmqR/zWWYRhjntguLp33tM2Sn
-+NHfpzE272HcLaX27WldToC4dnP4RUGgigILBYExK7XCt8PsXvQUgNcMiaWpOcybtjCH4Ity2kYH
-4xKv49CBDT8FsG/pBcYKHHdgzDNQdXqVWvcXsp6AsMl6vwBhiX+2tdV03hjT1GFosQ1DW4yeP/Od
-QC/bXr8/Asg6fmN75djmeauqrTTXZ8znG/DVONvTKnQAvTNczFFSI+yW7eRcDn5dpg21fSvsI4zk
-3RtQbvVoud5uxre5mGD4ARzX9hrlB+ZgQyJgDNxj7HPsJ/abe+mR2f1v3S8Qpvt4Bch8hzlE0wwX
-+J1geF6D9wPYRXy1OK5ejJsg+Is5hCAp2ggcgR343vTDwkzCsj8O4KLNcNA1tcKX5MlrMIW0KWN3
-TtP7MN3bTYH2nf232mMN+IYApXmCaob3I9DknOCmBT4D4Hmk2uNP+oPoR2mEkQKlhNxpXvqKzxsI
-stV5I5HhA2j2bKrALIJpdkCMZOwzzntS77Ks0Sm+wwM0QTZLzhdReg2+xdsW79l4m06NcGx5GTTJ
-lgqKZPJxTaLewXAfeN02HEneHuIPXRuD6JlcBpxA51TBy8YzzmKXdqr5LXOIiuuANLTATTMcee7Q
-CocZhLuA4LQdljiQTryD4Lve0/D0h/CntJbdUWFrpJPgtwHKaQIRE0OwFnGpFVY6mwAPO+B7As1M
-L/+fgeACh1Iu6HMzj2ja4sUPykxiB0Gt7R9Ab77oaJb5T9ph5NqyndO8cM4Oc7nN6172Ne6lzC6H
-p4lEhIUvNICcL9YpaOU2OMRBAa1svS1v5+ceFVTcSJ8guI3zA03wY9hlW8vqzJflPajJLI9SK1ry
-SOfN5KJP0uDfdr9AONynH9TYNcEvR6eB/jtNIaZW+G3Ju8iyInIroncEGHYEk+LG5Ob1gGWhFc6w
-2AeDYDi+igeszTywGrXCeXWkVpjgOHHDuJ5ZzsN1IucY1isoDsBZcRP49hflCIK7TfAb2D2YSBTk
-LRAcdxp7HkJk/LjPk9nMeGGWzKiMsjFDZqL2V2IfX5KLQGoxEzdUXIGlt76XO+L8Q0YFv77nxiZs
-pPZTfgqJilD/3qF87M9IjjcmbAk+Rz4tcUeiORJoSFh+lnxqhWUo0kc8upekkUmk6lZ47q+fW9BZ
-LJ3WnlZSe9cIUwtMcwfNvs4ORm1ogmC+NIcAygCMxBovx8UhqXlV2+S8Acghq+iGgDkUCNOFEdA1
-8xIYV1wB1g4WOsjbAJ6NcLSn/WO9aPWPPqHSq95RxoYtsOEZDG/9cbki29xMI+xg9tDS8FBG25N5
-3+J0Lp+ue7nNbzOuxjfHTA5XtdroXYHgkxZ4niyzeLQ8Pwk+ndtH05OMBWzOMmyR+aL5HKtn7Lg/
-Hmvt3VTmGRi3Zz9tDtle3pcFP7TAKDeUAVryWWqAE2uwjeSpf9f9AuF0H06+16O5fgyaguKpFaa/
-TpDIs4QP3dDHOisqbGiCKV8oTTA3ybUhE93QtZmZMrXFlEfUDlO26zUJ3uvA7Etmbm4cgyVe0HRl
-VCdn7bezOTvku4IHT9DbrgmA+2eT25nCdop70QAzDyZILoAOyofDOJ+cMjQyPKaIaBfhLxwPylAl
-jm3OL8mxbwTNxeezva413kH6p8D4XGaAX7lbUPC7m1eIwHlr2GXGfgDHZNQlMoR4DbUwiXALDJed
-cOVtxTADZ3dMPk2wSsBBD1rLII29hQEG57DT54ffOgC4HZt2+lEbvE7T9zVXd1zhy04YpMUrwU92
-LWyCqRnmxzWO7UVfn+bR6RE6U5tfgr6pEe5xALWur+D3BJQbKNOyCmp7X/4TEJxAV22Bn8DwyJNk
-xd1uwc8noLUCwY9AeQDm/wTY1nye45/87+kuceU/bbUJilcvFQSbmEl0cNzAbvqBpuF9SCsw7LIm
-c0/7CO97fgvrfDi2+fkpPNOuNj/yvoSYS8QtbgoeKgscoXyzMX1/0f0CYboP70Im6EXT/AbgjX/t
-yDTJU8eqFQB9bE/u1ObvcuArwLEPwEsNsLBIXOb4PoBfBcDan0uud9RK4KsgODfzwAbUONZG4258
-YoHPjsruBMcqG8JfWln+OmBNkwiCYrMOhmFbmZ8AMMx6WxsgllWwHlOD+2kWuP6ek53MUUoeT5eQ
-/FPjS6EyX5LLBWU61xdlRrH38GXtHkc1fUG7XnEKfifw1fJq137SlGyd/AEQp5929WnnE1cIoW+a
-4dAGJ5yOvck7jNe5EGc/ZZD409hO+8yfq9orjJ+C3hkX8Y54wiW/tBG++8tw7mgmEet6LTEZGmFi
-v6S/m8A3zCDUPwDw6WZgm0bZc6UdFpAqWLTbBoeO3Oq66hHAO7W+LWytzVcgLHV30Is9DILRkfYT
-0DUxoUBdp6a4gC4Au8hw8/d4QoQA5fxlWzv5+kPcn6b95+ldhnZQG3xbtTvc58ErWtm0EVbt7tT0
-upCvgl/f87DFA7vaeB2e8pOmir+WsqVjDZ2XPewSrvLdVKR4pVkco4Z6WrZeFyh6S/n1L7hfIEz3
-oWkEIFpgj+V8+KIcbYQ7AO4vy90DJhyJzsmMZ9zaaBf4WcIgNBDUit/WaRCGMoOYvzuItWaikFAe
-ji3hPAPQOiiunihb+Wxu80oEDQFA1LhZB8NktAWGCXxRWmI9G1hAsB6LhqkFxpN2eNcSQ9ol8K2+
-1ByrgH2bASRL6glKH2R87R5OmGfW42TMsR4UwoxXPg40kwn2NfVese5b91842CfMTaGC2nI+mUyM
-W4kU5i3W88/Bv3cuPyIS9eUcJO7l4MtUgkDCBHSkGUrJhbz5+HEuPgG/gIBv7NdtDs51aVLBngY/
-9w1MgiMofgKiaSsMwK+Mz5fkfIBhAGbXAs0oQJpz1jTCrF+v0oc5ukmsQ+MKoTeX9AK37E83h1DN
-pcazLMfQtcBlBtHBM3KiU8NruhckLHE+0zIcAFeBrtykFUjWcQspZV8dmPVuGl/s4Bcd/J60w9zt
-2uaUfdji/BD3Q5k/qP/Jndh1AuVkngMAGkqzG31PkAuU3/EezznyR9a1yYQebw9xNZc5T29bOtB4
-AXQkDfH2hnd4dZza4ohL6bf4472YKQyeykDuk/YU7i+6XyAc7uN7EX1jGQp8eZ7wDoZv7KdH0E92
-T5+eyXuJ6PTtt2uKCwAHUKTflpBXDfENAt8Cv7fH55WpuRr0qGD3foif/am+v+kAyilOtEOYPDl/
-Nvz5O2mDrX06OTXCeAG5EKB7+sm/BoZTc8NOy2Qaxuh0FkhXEfLDvLmEI6MC35ak0ZRnwVw7IEYB
-XpbP/J79Js8/75bP9tCJzXlLqUdrOi/W5qs63WMPAn6TAt78C/i+DIMaoOLtaIFUc2o0oaUA49Nk
-0OlmaRGHPOzTJ9cPxb1jv7mo/LF7EwBbSUfz4ScAvtbxZkat8ALAi3ksjrHMI+41ZbaKrqlbjKlB
-8gTAbEOv62cqoVNSy9z1LdfshBXkrlF3sFma21IvEGQ2ja8VLy+gGvWzvmxfgLSA8x3ckpccNL6j
-n9kv5n8BwwmGOA4UzapE9OzOu2mEaCYK/D6UKcri/Avp5vyp3Ci5l35nDow80u9Y52O6H/LmKkjE
-1PR2ykTnTWS0nrkIatsnjycA5hWHOO991X72sD2k+TFNgeeTJlj9bU28KITsUSgYNzXBWMC3aYRR
-9LZUiQaz3v7fdr9AmO6kTTlmC9CLp5fjztphPT1iaYLVRvgshHJTWD+lYX2nuwhnvbhmcZbZcgWC
-PUwKCgRbaLT0BTQFlLfucTZq/XpZ9e2n60l+H2Y2t2pu2YZQvPoa/SVzbuAXPNsXHQCHX0+MuKaN
-MBT0nk0mGiCW+JaWfUyIvIVjkT52U64rI1Iw7MwtQiLv2pn9h5fkKJi2I9My/WERP+Riz9m49hTk
-ZO+kiBJT7D8Ft4Lh8j74peyTv5GeoT4Q00BxEHgTnx38YqQeXVYxN905akPNGwie+U/u50wEUQCx
-wOI4rnMwwXD0g2YRqhn2+0KeJRyMy+2C+V0gGFj7CJ3t4C6TCGua4fXTr92p2D5CA0Mzh9BpXSUP
-4BLomloCXgHOzdzBCDSQ/lWH+n8Cwh34giDcMNI6eEaATxCk61XBzwkU8zrAMfu7mTko6D1ecdQW
-H0EXZUbEci1aPvK6NpcDxPmo98l/AODlSgg23iICzdhYrt1K149LFMDsIPeO6+dAmMqRorNy+622
-mpQxZsb1nXL2fxb2/NYAkhuvOeCNA83vEhjDcBs5pYMnRii9/U33C4TpPjWNILNNJn86MaKfJFHm
-EPVVufsuIOwI0wan9oFxneAIePm25YWFfS8LoroDFF5C8rJrChgrOAsbYLfUEn9nWTxKb5LrT79G
-1DnejyT1aseQgH216+IXAJ/g9wSA1TxivTCXoDg0wlBNsJhEYIsrELxpifmvAWUUapfZK1Av8QOs
-pTZEbgY6AJa5lilNYeKVh0Am/RCeTqBMQe7BWhsg5hD8uGyfsa63XMU+k4nmMKdQKi1ciZsKc+zZ
-mnvzZ4tzKF5wgH72LctaMHUDymYYS8BnfGw7CkUKsNP4Wc9pfo4AGbKAUqZtqbG/3m7yFXG2UnV7
-4Ql2F1JZYNZrApMnBldKFdYN3FdqhZGAl/bAWGBYRadhfYAjuxa0IEC4QLbyYeXLcquQApjjFe5h
-HGsBV27MqcElCN60w45WT0+f5bXeU9zoBwFt9Nuz/w9a41HmEQw/gmLS7UZBxcMML6D3k6uM2yF9
-CFIyrlmBZdU81hf+pIx30JkAV8bwBI65TskzZB9Y5ur770krPEEwvPj16sMLAJbrG0iendw/QFH9
-aZ+oHq7NTQuXdrjPlx/y1u+K8TbwC8dtcWU4engDeXxrhnH4Uu1fcr9AOFw9Wv7J7V+OW0xlPy2C
-ZhF7/N00wh0iRX+w6OpSYCyaYMNiAG7ADc+jy1ZhAVpO3r4+Y5ggOPjm5WhflrNosz3qiYtJX6g1
-PgFi3Zi5aVxju//AmrLPnBQFwyKv2lgKlBIMaxzBr5wXfHxRLuyFFdAawfKD9hjqhRq6AAAgAElE
-QVQm5QEFwakRpv/Am7aoYJ7kzmSiklisaoBkSLxLfqZTfjpwfElugZESPDM/Bc3R+eZ5dDNHiUPr
-YSt9sIogMt0SOxToyPFuDTW/N7+KkxNAFmRVG2E7KHvsGa1zyisO1Q8JOulaS1ZP4Og9z1j/zX3A
-4/ZZFUDM+AaGo16T9gl6/Vo0dseHM/yK+OVf9sAACIw5HdCbotAg+tIA2wTDh5MjDKUd1hXhGhQN
-E2T2uOaPzevSt+XXsgU8XeJIv5r/bDv8bA7RgXAyOnSwzLLDH7Sq1wS68wqOrbRy7Rr86wRq38Dv
-SXuc45e261cUyLBHAX0KqiZQZfpgjVeWPJI8PuKHX6gF6o7g17lkMy0Z6eq/o7S9WGBw1xSfQDKB
-sed9XmMLm4u2VaMxaTzz6VzrWs858WMY2v/eTM4c+TSfAdwP10X/dWP2MRT7L7pfIJzus9n31EAE
-OSsYPnxeWc0iup3wOjUC6OLzGlfdYn270b6Gpzoo+C1gXEB4fSKZoOzyOC3CqBGWl+VC0KZkCp57
-5wavftzEBcFUyWwZrmmd7Kbrx56w1ep7CdwcY/FUJOjMOAXDQ2sr2mB9UW6B3/ExjRAoR+CbZaYG
-uINgpit7sNcR18xMivQRSAYlc0wNxAp2ZlX80XN9HfjBZAJNQ3zs2Ij8cCe1jKXhLUEIIMD3SEum
-O/Jm4y7z0v1bB5t/JFQn2j7YwXD0JvzGffM2J0aNt2/xmPG6WJAxbX09je+0Gm9rVVLNsZ4eeZg+
-mICq0sBS8JOZLX5n8YJc+m8P0be41aKvC/XVuaVdtThHqXoRfl8M6xEARx8KBG+wpfwJSiWxxRGE
-VvupiQ3/avEZIJ+0vQnGpPxa1h+AcKtz5LFeT4+bYBios2QfTCLy2gER2zcz+FVPxPJ3cXP0uM1O
-+CIsEhDrvS3PX/Qz9lnOdwLg6Oss/xSfa1VttnFyS9eIW4jOWlpchcGWUnj18+ZYBeTe4l+k/QyA
-3T0tjLYuFVEjmQ+RqUmGw1BOc17+Mx045662nHCjtbK8jaUS6MYzGHaEqYQrTTzLxv+V+wXCdH9i
-GpEgdz9POO2H0bXAaiahGuMCRmhXxwDFIZBy63md1nDDcLnXqTYHjfBldWqEaoEvrw9r8GSI00xQ
-KHF78FFG95/uKM9XmdDtau1a7ZcSpgNiEPyig2BqaBX4XtZtg/lhjQTBWPF48Bf4rfwNHMsVMOkz
-UC/QWV/sw2wwdNLyTm3wdhctTL4vRoi6BnARfNIPJhMRf8if/Xvu/HATvM1ktawV6GsMS1o+Cq20
-HAMF59YvzzHUfHa/SVSDsQ6oicNKZCauaaW7VJjnab5QPp2lEGuRvZ+MYw1toeeYas7nzVVrxSou
-aybwFcG24lBAc2qFMa5+A65gl/7QDIuZxHo1+I5+TY1wTPcEwDfKnz8OKfoLoXmv5cplSRoLAaz+
-zFcaSvoR4K4BBIJlG+UeQfEZyLJs9m34N9MJ9bPdoKWudY11zLTKk8uW4yFHF9odmt0/sgse1wZE
-jUsnYHf4CdCXgkVA01jTGkfcwDGPrlOs/TH+4PK2LMGuC5NkHjruot7nm5ghxlPa3wPodUmPm79b
-O5cb1Xp7DQTXxCi70D6fwO5pPnKeI7LyCk9x1mg5XwWKkf4jCI7id5Z6Won/nfsFwuE+vQcxrMeC
-CcSCnBKcWY8nSxFWCL5RakQb2QNPElp7TQS7UbNlcf6e46I/6rjd1qkPLtpe+j1As+2AWPObv9no
-CDilkI/rHS/lqTaY2mKO66TpVGb4RP+KGdf+F0DJeCP4FRAMBcRdc1saYYNBbYGnphflZz9Yd7RO
-8K19re4RFO9j2Wf32b/jnQE8cADNLnMegEUxS66N15QGqQmwxMb3WzsPbl/p53zFp5P6a1fIeHkj
-UeBs9BklFLPU26Qeeqz+RpLZUZmM2XCmszBBjUhcvatA0RAEYKYTYGyHuBhhNpmrLesuUgqSseIy
-vYQn59p5c5I37J5PBzzHsOyAccWzqfsGrgC89/wWpY6k7ZToiohND+DrcTBk0HM/n5h5KJ2X3+6Z
-F6VJdofunTZ/IuR1qrgCDeCa0t8zwN3CLA807bGGG9gVf0qSVickDklf2U4Mxm1qh/X6cx72K3nj
-w2oqT1TeN/Pp8W1r7EC+MCUgVXlf5etpM530P2XLlEGtHOlBAWbY/NIMo4Ng3T8oeSjFV5WhOBKg
-e3PcDQRr+kMaym384ACCG6uxraSM/Q8AcfhmPlWM9V81vtfVOvavul8gHO6fDy2012cJDfdFwl2m
-BOsacVfF8TOGa5NbXuGOb7HXvSAAq3iggLwet0wf4i4rBa+De3Z1VgjS46i0kFs3gO8ls/B9Y2mT
-48B7u1aZr9jzvEaV8kln3ZriEiQg3iZdQX68w+XaNgeFSW6QWT/vMnWuBPTOnykjFuhEZmfB0q3K
-9L9oDCXjtXsJHqTHJWcXcxUtWTLcsUbl/PHqLbzPH9t0zRvhjuG8+kdMp/4odwbHVf7kesppDfeY
-yQ41nH6zc/xDeMX19lu7HU9C4B1kFh77vDccC7lNmhQ+aHz5ssu+lR76PtYlaQpoQLoAnqw1XvLC
-10ct7IZdYb9rF/y6M341vBiFGV94izK+8nKzmxv84jji6kAhGZpOOIzfab/61W+DXfz+spO43/0v
-INgdCyTfN/y+cYvf7/hA0k3gIeBDrroWqhFWENxMJcYvp0HjUN0vcFsAjC9e5gttSR4jDvPqxYPw
-H14HvdZqBt1KvG35eGszgXO/Ycu5UFpG3cjnPPmetwG4uT6tPsbp/I71ZR1yXrh7aDhPT2vURZGs
-hEA/6G8DufcI61glf46NHt37o33ybwWeFcxE4ac+5q1rYz1ze40t8ulcJNuTHzGLApeprLoynz65
-jeNb/7L7BcLhvj6c/PtaxPuPL63AfTn+cfLgAr63kdcTFMcd3hUE5wa7PejEdvCrAE3ilN6a4yYh
-UVN2hP8OP88Lzi/KeQBqAcG2VM4Bgtcm+Io2/DBNbcNFwAJd2eIn+UTz9t6XEggn8JFos4Ov0yTk
-3QJ3YOU1TmDMYM23SXWdYTP+OL8Ev0aA6D1dBfM+Sy28s+pdqM1SW05XcdAvKlBQXa/xkeeznuKX
-DRzrFJz69JP7KQ9XuYfDv/XBzmVGvl6/QTWz59X4tL99JprGJW5Gs0MYaXQUTt5WrijyeFhz7JG2
-10/g1kW46ZhduqFa3UiP02Y8+JKC4AWKsc79TSahd8/sdzKQnIMFRi2Od7yQqlUyKeNJE7YD42UP
-0MGsXPMOfwPH57gFQm74NwFwgN7by08ASD/CjzLK6s/50EBwGncxDno9/AI4rZYCQJlegTzqLMDZ
-zjm6Vk/X/ngj/nBVOmpXKx5MvqGPvTNeZuWsDS4zpwKzRbPkWTmvH+XBPvbGA2WMI8+RC5Oxxz5j
-XTUaqXwKifa0iCSo2l0EnVV8XXW8BYr7Gvb64V3cPfEt7eb80mE+beYQZIxaX/PbQ0JrrT8X2K6m
-L7T36992v0A43B9phC/Lu7kExGb45wrNr2iJXeIaKL7WayMJdtOvj+Bx9kP8Xo+MIXtFGcbt9XAy
-X4xDaYi/L3nUnycgOb4i0ln3yS99Cu7YHhGxXtUGbzJKfizoc3dZ2Ry1DcXNg/EzO+TVPLopTwxb
-Z7mY/2RC23UIEWWaKmw06SVixA+B1rKcGahWoeasun7mfkybvK7Okn3vbe+5CI4f8/YZdxST17Qn
-f4nkvd7nPp1o7bnLJTC8Nq10xDXT4fFpy39oRAH7qzjIqpW+dvpomt+mxUmCrO7dcSN0rXNo1pff
-JigGzG74dSUoNsMCyyBoNjiWHTAu2VUEwNSu+SWgw8CPcOQh5bfVi1kPQLeAsWp/57XGCgJe1QTH
-XXoDwNEMpNk29Y09mFx2EHyb8rgXQNyXtvpA8BtXBcVNKyygzbnKHDfpQ66VLrTxAIp595VgVofM
-q48wOg2X7SgS8OU4SYcufsa7jEfnx0dY+59hbO3MuC2cINhqn5N/HvhCnTGOomWujc/TIMp/H+I2
-gDzH1+bzHN/TB+gdOWqugw+SPSWRu5SdiqrRkVn/JpSRQKae4i4cpNrg3+PT/kX3dU0iObtF2AS+
-AxAn8KUGtOJaOGyh0mrOOhBLfwO/9hAPQMGw7GoH2osJBKN2L812fmr5DvtgQyp5dEMQELNOdcn7
-vcLJJL3CCbwxQbE8opHfVrlsTFaaj2NgKJQrnd02qPV0WJ/HjLFi7Ap01SWDtCbLV1ItgIc068KF
-ySpksojMQGeEs30VIK7FRGxQW0IevcCa19gOw3NJowCwrPFn95/kOQLbBOS2pW3+tj7CtMcjzdlu
-o7WhaXvrb8V7zxT02NajSawXsUWik/2r3Xts36ffpamTX6hM5yY1wli2kQ30ojTDBqxPvw17KtDP
-/bdA7NoDVgD3olaYxBXhBMECiG/u1xjbAyDeruhhAmb4BMLT7wWMD4K/wvoWCMrON4a6XgRSHoft
-N83DVlfX5lun7wQ9biAYG+gtjkEyHuvOgA+amQA482odHiPmyC1GBYnxRsI7X5WdfADB6RfaLC5W
-W2cDydXVLIdRD1r5GT7na0Kt+ffgvqNJ189A936Ib36ZH5I1n7TSVAydY3Y+vvWr59xAMEirnrw3
-63od72hAxTGsXwmC5zXTf4Hwv+r++VAd75fhnyRYE3vg97j76mHA6sMV2AVdwt6J6R7yl3AL0CBA
-djFd71rh22C0FdaSNMmL31eCUWnmMFWn2Ut75tzcJ8avTE+ALhTABNJEYV1ltNumM/rV5ETMSqzy
-UlhLTL0EqGPjuHX8HpklrYFgZbKNe3jnLDP54JQpnlODYbb5zI5USTJT1Fom/hq0VrN/YrlPvTi5
-khxPefT1N0cHtn3Kbffb3rfzLHUJdtLwVHD02Q55pNEWh9Kwa2LOpyJ9zfRCBLPvGTqCWgEzw690
-2fxA1/BeoeEF1mOjPJJmaXnLZMKizA13g10rnNpgmkSE9rcfwSYa4dsSBPvtMILgK54DCZhd96Ak
-9IfrS7q741YQ7KIJFm1c5Z9LQ7BQhJf70wbolfA90voTMm950eLW5vQAVgmU0W2Ea/U7PZ40wAp8
-f0rTvZXPXGg729Liqmw86ulmFCb97Hyha4aROTQ9yypfA0Y5nYOf80wgvWmFf3RCCMJQkyf7/FAG
-ks66XOzj0/ytf9IkSbGn99uULKKq/FGn1t3jAtRrQV/+nFkK17xIoAFekdUJhsVuGP2dnL/lfoFw
-uD83jRC7XxtxOLwsF+iywp1A6TLmk7331Mf40zWtPGEC+IanTPvu6HAxVtpsrL28GHADxodek+4d
-QuyxbawYP80yfPzY73xzOeuN7ReMt87StNpEualksyk4NoW6w1yiWmrXNpkytoZHBBg7GX8OqARp
-hoFNU7Ex6ceF9+aj8Dzm86qXzDW1CdGlIwB25HxrfuZpNwTHnr31/IA6D/l4czKb0fDZL2YRo6nz
-LO39aSHl9w95XCNsF1Lan1bOJRMONPeBU/pZYZXu/p6ODnoy1pAa4Hx0pKYPBL3o4dIKB7glWA5e
-2cAwwwQYNH8wAt9FeBnHJ3VtL8UYvQPjHl77o5lJIK7NNjiA8ADF+sIcF7PxK3ls3m2Cyeus8b00
-k4DmOfuhoDi1fwGIhD/O90CK5+sTidrPSpQKjita0hN59R3QwG4U2Xno2pWEu+3WVRhJ4/uD++U8
-aPgpn9BzLz/idOxbXLksqyD4yL6EEzmW7BxguIHeGc5faXwT9PqB5kaTEK//lNaYTIHUfDcnMp21
-xLt7shE2ChYC4GztAHxR/jre9PdluX/VffqyHE0f1otyC/CmhljAsL4s5+3OkOGlbxkysbyHjXfC
-PeebVYKg+Uiu4r8lb3e2Dq4P8Pu11W2HkB6rpnd968fj2Trj3zfdxoxiDgimAQubaAIm7URtuTR/
-aGtqveu0VZKe9m17Gi06s6GfHU/7WRGgNZofFAs+QlMEnPNuL1eIX1UNKfgeATDrwzZVg79uw/5s
-RNhK+UgB8Phi3OxH+m3vn7a19cFO6yD2caImntk2+lR//FEBoXv7ba4OO/AQOm3+aBPog/KXOI13
-39bAAdi95iKBsdk6SQKA4YbfBtUQ1xmxdbqEweJFLxPw60gQTHMIM4kjuLQCyLcAYUUG6ZfJVe0v
-yxw0xDeBb7suEEwksoGX7IMRZafZWd3oBL9V7I7gwzZPzOnAeL3vdwLA3OMBrLlmqRVmXVNzyiF7
-j8jpUaJ8uDFSMGyhARZN8AI3Hjza0OQAJg+dz3w6J5jczj+JH/sq03zEnfL5aL+VqTXOAXjzVN6m
-YUDJALeM2jS/Pn6QdI3LCjhmznExvd6bLrve0nKcEHpqtHLIs9XZqha/tEU5S/nNqFRiIbTAHSj/
-bfcLhMP9iUZ4AeA4LSLBL1+Cizynl+XEPAJu+L6E2C3I7bi5Sz7QL8VaPnLmYsKrXh5WbZna4V82
-PFFsUi74QfFWloCaVwNtjwVW2jgpwjH6V3yGd6b6gQJ9ecibxle0vXbucrM/yvzNEvjAvJdwVq3H
-EzJ09wJkoaJpgqYNUARLXF3rnEwW050o41B/RHLtG51I4EcArHHCeN9A8FN8pZ9LO4Y8we4n5XKN
-dtD80BfR7GwtH4GxUnkvdxK8Ld5H3pi847ycOrNFu6Ts5ZtmLy/jRkz8HQR1wZe49OIHLmS9m3mE
-AOTUCgPUDK+X5RTcBhhW0Ht5+W9JH37ud2IM7rfS8qJpgY/AWPaiw/vpEOOkCIYbIsGJX3ET7eud
-fA5hJ7yB4Odr+ykAhgCpoOfMZ2eeOtf4BIo7vSht1BxyjsmLLTYNaZKmEDF8iVPwi3ziVFD6oPzI
-63vaHnfs+p53sM2xBTIl8+XYOt965HO5l3S/CTlxnZ7CkLVv4eqP8nBu0CmeqvM7/yyw+nCjoaBY
-Ku2geGiMU9hHSxLen9iWzK6z/LtW+G+7XyAc7k9eljtpf28FxPpyHMQ2GB00x5dLk7jzERvbAlLQ
-1aMv7Uxd8nOtZlJ+tcvPLysgBpDa1UKORdiN2bPGOFbNrrIrXm7XAn+bJ8HDOwA+M//5KGZo6dJE
-gn2V3Z8aYPErd5Ds9VMwPBnGgYV4SyjOpOiNlyZAha3HYJnnyFZVYOXc7Nre/isNSUd1QgnKMElz
-4n8EwKMmLY9D+pPr6bNmE5BtGrv5M2ya1suc+tKFQ43mBFF9ZHXbsuiqbuVa2lHQPnXwOddp32fA
-9RptH+JmPm/pMjO3At14OW4Dv4aTVriBX5o2RDjzqHlE/NaNvoBhk/Jtcr37mcYbHQ9gDHRzCA8Y
-4OThXnx5gmH+uNcc2JbGpAtG/lV7MrtlFVd8zxoPfNQKo3j4qms1estNHU0ldp7A/jXG1OfykRY6
-XeRdhivLJRiuBPLInY9WvpJEUv3R/0Oe7civke7ncJU51K9lbPPkgLYb57wLs9YnXo5g16vf74BY
-15McrhpR3jcd8W7jZ1JD0ekLwOX1jLRbT7aGmzck7gTEYhpBbfAvEP4X3T8fTn57ES7A7y2mD7cb
-blicIuHYbIrFXIIM2oNru6Pd3QOR18ggsXiObMa1gZnbJH5tGn2cRkB8xw7UL3VVWzw2LTibYRyM
-73mEaMFID2BsBTgdeSoFN2PTButmz5pl03m/WswD4B3Uml4P9sECeXeTCdFXyC1sxvmKb6RBjilI
-LGY6+iusPAa5CZnmHJrsM+3VdVZezFMfs1FbFu0IF9QnUF5IdBuvzzg2bX/Ww2Oa9Tn35rdjvHT1
-WObTWezxIQxmcdvzU0zMNZsC5JT3/2nCTukuvfAe12zIVTV1jLPEphCcmqYOdkkcGjAuQHu1dDWF
-WC/RSQW5UQf4vUbaLU8BXPs8xzDn4DmtgeCjf+wf3VsAGuAZeyDzHQAwNcQT/G6KAQJb6+BaAW++
-ePcAgpMSW+d07WUSVWa85Dd+sYyZJvidYHlcp//ACfvfw97buz72+sMQ93K9wsdtpSknEKz5t6/i
-2C7vHFsctjg752N3rPZDw+l2mt+KIafVue1nUj8oo2QWWLbPXwfBue6xh7mdkTJaNMSRjy/K8Vzh
-v+1+gXC4f/5EI5wA2HFfiA9qdNMHNwkjCK6ZSyCOJOLmMAHBg7mxPmF60qPoVwQD+JLgHUvDEGZ/
-CwRv94Sr4cW8d3td3GELdjG3xfET/dB0M/laXoTvSFfg+3R0WoIRDRNckumetL/cOLGJcqNBQfH8
-dcOIyS4y5FtUCWUJrDfbc2ADhJQnGd+WdohooGwTBX/kTy3vGA/7Y4OJu6Bf08w2uvSwbR7kRU8b
-/TgD287AzargUxnW7Q3Vln8Du1ufJa/QZObxEZYcPX7mG40+TdJHkxeBbEAlvG9xBXJGPok7zxbx
-bNf6qklEgV95aS5/62hHbxpgjDwr7BY2wTOPDHGxA5pHFMcoM4k5L3u4NL5AB8EyT1M1l07AgdVm
-UOB7+u3HRsaLw3Y4Yx2iHWZc8maWfQDApt1entq/Z560hRvr0EDIgZAXFgxFWUcDvw0Yi/Z4MI0N
-zA7xdNoO8ynNBKiutE7fHP5P4RxIRR77snnCK8enrT7x5gXtuupV0OwtD5muo7YD+fns5uzfDow1
-dq3fy1aR6wMvbdXtHUqgC7UPDvkrAHieHPG33S8QDvf5y3K7ZrcAsOVb0vlSA9Uj1A5meJ2XR4aY
-J1BAGZ6LliDyDiHORywrEH+atqDXlX2x8i+Mbql1dtUGm8UXoaLxKyFJMLkllL6twt+8C4zxsGtT
-C3xm5NK/3Hc97qj9tYK2OTYFyuIvoBwlRBu8Sk4uK/5JJsKRTmYQqnWrtXIpCy11cL75NT/X9id/
-WeVZySIX3uUogGiVf6VZA8BtCgZDPo/hkHbYbk8a4PTb1tyxzM99GLlsF6RvFeyCYge8Gq4nLk8d
-OiS8SlyGXW7KOk11qaadlp61OE+zB4j2N1hVAs8JfHdwrPsNR8B7DiPBcD6F0bTZ/7YtehyfgDjQ
-ALIC5g42Ogieccq70gW/czsD0qPNr6Tlz5/BcKtH+vJU920934leelgnTYamtDHKN6D74i89fr3c
-pSD6jcU+xiufedoyL0N8LMu1P7Xrh7gPw3B0mRfyuGiM1zVPJ60wshzQuXJxQj3DXpNq23SAalmX
-Z3bmrhc9DdnRvN18GGdrQvcvlCCGOYSCX8jHNOxXI/xvuo9flgOgtsAbGAaANJcIoDnNI6IOw7qz
-X+f7Lo3w5XXY9jJlcHl0Zitd+pMggNw+EUsHwXwsVxtINNfiv+H5Hssy1qEphCWDTHsvB74JREJD
-8I0CwGYChIUhQPypFW5ghCgNtesItPVZnGiCmeVnTbC0EZtzn80Xf7sVH/64mVAu6lGmMzDkYP0Q
-JyVzDlTgEZRY4+iN4yYThmi/2vzlcKzmD8Mlg12tPrKnyYj35O4OwHmfUeuza+dZP5U5weEnLbAK
-hNY/WcaTAKg4b36gaJ0x3lIf+jETX/MwrICHe1M7Lj2fcQcNck6RPwNcQ2l3N+A7bzjjhtp1Y0L8
-c8Oi4px7M+KP2t85J0eQLB5vUyW8mPE97G1+pRz3ueFI+FzzO3j3jz+vvI6DdthGeKbT7z2s80Ae
-vXVUPNuTfaUbxFLQNCKUE3nDkn4cNMQ/8A91Y+9JJT3S9igNzPEfhvOaf3CPU1UPYa+/2d7Q9qJo
-mu8QtXDkW3H1FKA1YedpWa3t8foujJKs7vwi82KW/jLPD+w0rmKYOLZ7JOMCChALQP49Pu1fdJ+/
-LMeX4bqZgzsK7F6e5hIY5hH6At0CwmuT3ATAAn4NC/wqkMoPYiCEAnahwO/RL/5EEFyAuxiTbLZ4
-jLOEW9TIbzOHfx1yD9gV5UPwfoc/wa+HVtjLFENkSdvYx7DF3AGo0yPIb2XjoN9lHmGvESKNclDW
-/ASAu3tOifnSwYgGeGOST9z4UOv6ieZvqqYE9E5t8KQN1fgWM1XuvziWYv0as4zxAGSf+r+5E2M9
-lGugl2AItWajG1u44h8QMMe5dU4E2bG+HfhmvAgI1baopvFF2u7rujc+/N79XfpWHQ9aYhv5mhY4
-klLbFGnpl/iWhrjZSpAMpNTeNMUvcYh3AmwAqDHuJGcXsvZB/5JPp2xqT73Fo7WxmYsRsLRtP58E
-2JbGp3791wEzyyjAfTpz2KP8KW0n4jWYfT4B7pQeFyNdgmGVlc+Gm2yt5ke/dj95WSE510yzzzPu
-Cf31oWwJryx3ph/qetQY09+UGD2tmSqO7elSNF+sDz+SPq31kZr1XOPGq22fLtM+Wf5VWq3+Th7Y
-FQXOzS/1NaWNXrUD4waZ8njZBVu+LPd7asS/7D59WQ7XBMBWJ0Zg1xCvu8FRBktqLCAcWuHbkiEa
-wXBSft+YBMlJstxNVhnJfItQLcx6HTfv4G2B8Es+38mPPC2NMJbNXtgCUwPJj1ukPTBtg20dmH7H
-XuEjP+l6BwvCANjbnSmuylLYTayLIVOP17G4VjD4DIZxjtu46SyjYk/ihAMqvjnw8oh/4/IV0Nbs
-kNHz38iTz7rRwXF4jlvBgWVP9rBRZDqexvWWxnobAI4IP8VHmScwfG7nBeiKZPAZwT10WLtNuMnq
-dZv+0+PqIYGP0tjf01SqRpu2pddVwe8U4NxjKrfaC7oGpM0vM500xqEJ3gCuAuKMP4HgUWa6iVIe
-5+cc1ilRnrOvpaR7jyte20GC7kktv06K8NTu8sNC/H3jDHZvqasdQYnp3zXK7FrjDVvcit74h888
-LmBXX5iz9mSqYyDlJ5O3nHbo5NOSbW7sE22MKk/3kie6OLBWTC7sMqHblj2C4Ho6nPTgNRP9phnQ
-l9bKbxs9GoQHc69ubdshjinrr8f86XsU832I41gf6j25V7l8+F2Z9veR8C8QDvcnL8vdtA8OsNvO
-ExaAvOyFveyJSXgLfQJYYPe+Dfe1QKTdi9jXHZMnWPsOAZcyBvFzuSdzBOZ6IrEAACAASURBVJhd
-mfiN+tv5/W5PcwvH+qCH4qDbDRfLX54gGDxClCYSoR02X/V9O8vHV+ucQH6BYrBv5T1cQxOt+Wx4
-QsLXphJNcOLZmDFTzS9EG6wPiSYIHtcPdrvn7MXVkZpgeeZ6LHVgmy9NVjoZqz2mV6/Yj+xGoSQ0
-wZTDiMWMlzerDEuMiK0HL73/YIuluHwFwCd/CRnp7C4c3xI1JpIn89/CQ0BqnK7V+u+9cPqf4sXj
-Pbi6KPSVm8bHdf2xSQdRRk0pUiPM4obS7gYqNrPSBDsBshdy3swg5PoEjI9xVWYjTfTrkfDmZIlX
-j6msNe2PoF3yYeTLqqyXSxOzKMu4fmrEbi7xja4NJvidwLeuXbvcymhfx7xkz33finMeNS+ANIuA
-ITTEK6z6RxPP6QW6RYe2N575ii9tINhbRtQdWlbd3R+EH1h026+6b/rWHXlWpNDRotLtBisIiBrf
-+uIob7orDdsVufe2rnNPjkne89b8bSyn7RMf5R78Ijp1qzO+mT88Xa20w3/b/QLhcOdPB+/Or7Pt
-L5J4yxZ4Faj8CGFCIjRYgsfbDfft+A4tbNphkRkJMwh820ndAT6v9M16fjFPapp5ddHiGjzMGmIj
-Ue0MhEmEx/mfpR2222AB4BMAm7wA4sD3EEinTaSb0ynxxFH5YpQ+CXZr09Wmkk2oefGQV3ewXI/a
-hDHfnQUGo0pOIuONoHvFi8SERj+znMYGW7oK4gZ+RSOpjzcLRXiQ7Yn4p771dSr6NDyO4Dlei3V7
-YHvwn4Fxr3EIqaNAXZ4SXWeAPPuuGmKd/w6CFRhJ5uaH0JL3pnViD/HkMTZAb78CDRyHl2WyDhbJ
-jeJxZCE3GfGHJ9gt7e9iSC0MHK6oilD1boD4VGabkx7VALKfAbNeCwwH/IqnHbUkBB66Pzrs022b
-gMc6LRDYFNC1BLJPNsPuXbub+b3imgY4413Sq3MKRGOoMorh38pofOc/JpM6Pz/fwK+EpinWGfPU
-jlaTi9rwpDdJ60N8DbeoaeMt+3eFfwC+CWCf8h4AbbH+LOvRlyP4ddJoFZKHv+mUHzKmxdlTvqJ3
-ZjiDYNvHPzpg7ByKR1DDe3pSOwHw75fl/mX3qUY4CfNk+0ttcIJj5rEicl/5aQhzh3b1O21wfbub
-ir0TOLQ+zSwyi10bgJmSLYRX9MXCLMJA8LoKZnyAJuNbcwGCJ/jN8lFu2TlbguBbZZ3O4IMwS2di
-/6QpspGbiYSmpea3bzwFxVUdGUW3q4IrC+l4ZJ9o29OCsy2AS2YiYEVlQft7mBgUmDLJW1UN2z4F
-vy0XBCFY635qeHQsiSgGCHnHJP9hWmj9Hpo6++0xz97OlKYPzNzOET6jpWzZfpZOX8OVLq1RGB81
-wSKpT3Eaf9L+HuIMcoOm4fBn9YEsLFTDyjrypTeCF4uRNlC88pUglDnfgG7+eU7LBX17DnFwJwk9
-kgluPdqhaRt5j7Ndy2nrb9RL/7vWj+u+FBJ87yPNG7ZfgeRv4AyCvYPfI/D1/YU7eB+55ahqInr6
-8M/05BlWx9Q9lW1+tmvHeK51+2iR14rnVJNIrV6YPKI6YCQ+pYtnS+8mThh+3b/+UiZpo7Js25v0
-ktzc501Z1CQ3DtrdnALZJDUtJSg5j/tUFON7nCbsU3pgmG0Pty0ege3ECPDo8F+N8P8n3OemEQV4
-p73wYobCOVuY0qMQiCFeNAsQfN8LUH5fjjy7lxyCdkNrn+TRawMH5m6giYQ+uueLeCEKY2t4N+cj
-X+AbeUCaQTQQnJph1A/0F6AmYH6d06cE2UmN3/EOk//E7KHKKgMYZhItr/W/fuhPY5rBwre4Ahdw
-MsJgLgP5D8j0NPpTB7JcAwaumkey3QJgiXO3+qzFmPtBOxy0p9ETID/29MO0hr9Hnz7yd7H6iStI
-8FzKR7Lrz3u45ck0rssZDK/raOCHOE1Sre588e0JHBuKHs3RaDMPnZkAmDYTcXGuf6ilTEBy2m41
-iTykMwY92Tk9qVRo7V3baw/xcm0AOAAq45JVF6/lEzYfNNbDseP4ElPeBHUwXEB1N5H4RrwwHXXe
-6KYRCohVQ9y0y96BsVK5zGy/eqV0oGx7/hRHtQhNY4vO6/d2PXOhpTVu1shga0PFIQC+a9OcD8/c
-4nPDau8/Ab0EvKoBfgTOKQWEZpRL924UPXnz9xnUPrNPmeXI1ULSY9t7kltXritl4qn2iBuTh20N
-o6l2tn/GrfkuLXAB4g8P8Pqvul8gHO7Tm5DrAi43XNc6yuy6DF98YQ6ex6atL8yhNMOrlcUceX6D
-XTDzdRd0x0kL5rjuBXS/DWEzXCcy2B0mFRCtLCw+u9nvuRMuKj5sV29CpjM9ATs0A+FLdQyTOfMH
-0Qrfju8A0BOG/YnbSpoyiyfgwUhHmgiMH+470u8l/NrGVoYYQhFoyMh1trzi3O/4eXy69d76gLyy
-ugmnUOEZfUifbJU+c84KwQsTjBlyDAUWTuv1adxP0PKU+7GqV6dF6qbuKe+D5BsppzZ43X6mdNdX
-sNIKRPn/Ze9rtyTXUWUD1/u/8Wlzf4iAAMmZWT1716y5q9SdZQnJ+kQQxtj2Ako597oWKJofaM2t
-BVqOD6+h4ryjE2vOu0CIPIu6PNwZEihTQxEkhmJKDcagt2PGOYhz+DAO5LAp4aPQnTSBVm8Zayrq
-U5EAJPGWHOR8haw25Px5rNNGH63VLlPg0+VRWYrR7hbceU4HwTzvbnU81Dn50M+cvV0YSF+7yeS7
-29Fqabnkpln2TN/OsVFupFWfDUDXLMqADu45PfLyDTWylyw6kB9yidM89phzPN7nF+A+fGZdlbs+
-OkbNU5cXvp07x7Gtm6tMr5+FHHFXD/biXIXiPW+fOgDr7Q9A//maz3WMh+EYj2Ma0YzT9RfK4B8I
-v0A4wqeeEZctMPzlwH0ZvrCu3L8IcqkA6ddDpRLxAsG8CjL8MV/13qv+CYgXDQmKJ0g2LPAJtjf+
-rtgZtOy0wYhpJXRAwPDt6+0Td1h973s9RPjHo19hUVbhqNXaIZbTJcmu2iw29gGYuN7+pQuKCyD1
-QOs3cBOo3qXE84pexViBhAaU09Je/XfSbwLh+LHtBMB3pdl71WANUmkoUUgstPrcxafDl3CWauf1
-vQra0ojWNeQLYTT1yt+FUX9N7WPLvVczNcEw+aFX4CPNcystKketPNG3HfyWVbE+YFNp7ijum2Jg
-giugbsPMNeg032hU1qs8LVoFdLmUMTsC8Pi53ATDQOzzcn0Als+vgpL86AaiTILgOocnlPvl1NJj
-kZuQ6OVe8QPDBHDuxfM28usc7tvQwpxfR16IeM4Rcj6R9dTt6+QRR8IHz/SkebPqLiuvAOM4J98W
-4XgGyKc2pA+cyjYPvs/Xy7l8UbZa6NFyRetLW7fHR3nhh+3cIjY+nKylHbSRnvlddNvI82qTxqCQ
-A0USUGx4CXRXXYt51jxOYNtn9QSI97honTluHR9o6GqMK+cGB/v5xwdsDwzdgmOB3S8bQBgLHBte
-gGAeNf5qLv+l8AuEI1wf2uO/sMDfFSC4QK8owdAKZRUpC3BZgtdry/7YsgQvMOzL2hzA+LaI3/zy
-SoHiBZI9me2PMqezXYaH+IYKxqYjCJ4WYQXDoWDX2yOW+8Sf9saJ5ToCULcPoZbbvhR+6+3BarTm
-OeZd9m7f0x0Mb5bh++5AOIatj4P4A53ghvPsQr/9xh11d0B8p3V464uqrm0d5xqV1C0lFVzHsQPJ
-iVSmJTRNwFGtB8+qZXFdJGmtz4IP2guN2c58DN6L9OpmqoPfUjJ9Er2d3//22J72h9/a38Nax1vt
-lAdxAZwAuVmEOZxOS8t9K9dpfKC0gWCv+qnokha+hTW1tHKyTMeoHrcyaeF1BPjNNCpNsDqswskX
-baltpDF4DK0jp3V5BGfbnuhTp2xVgBf8I2ta65KW4KjZc/CHPOUDcM0rvQBuAd4CuB75Vb77BVf5
-lSf1uORnuurRefkkfAxAyItW6QnRtBhQ4HcDs8kmVYleaJmcdKpvZ4SVzvwhT1rZY56VLKXFNy2/
-JV0SFGeVJQ0VHCfvTVGKEyCuzpXzoshp6xK7Nq7zIOPh3R5p1yB3aA1wvSu6DCilQLVC1XBCEkb7
-wkI5X76+0rtwzIIA80jAyzdUNTCM/074BcIRPrUIu62Pb6TCMw/LMK0DYhUGkLtZdq6F4qBFmMCX
-LhK8ovpDvWIL+BrqausPkp3TZSI7KCBiPhXb+Fv3SWZY+0sFoO4QNx/YC2FtcvwDwG7gD32M1deY
-lmVQ/4unrjh+lZy1hsfKq/kETKiA0EAwiX4T/IYlOD/fR1XClgkc7YGOnJ2MK53WZrFEbz/2OtG7
-DAIjPkOs05T98FypLEYg4NEW5z/HFErdRKAqsD27SbwPCjwaPZTLR2EqKSFPndLTXhmVglp3G/3Q
-15n38mfIfYEDbaVLXuSYQosWuCJNNq/cvldaKexFJODlOuZtXaw9ZULjxa1FHttdVYawSXAbnU1L
-cKUL2SDPIQhOi7CuzFz2Ex9YL+iHIjN0MCJEoXHaSIPuklgrFvRJg6xBiagHHim+ajSvvJRRkbf5
-9mZ5BO0ZFBeI7mm28RR0vnTeJm3b/dbL9UpND20tCwTPMgMMC088guFDvY8CQeJHtwmNM8919ChQ
-O48CfSc4PoXSpDb4Wu7kfASMtcP78JPWxlTttEG7w8Q4kxZi/qZlOPvbW+d97mWUO7hIQLBK9K0s
-wnxGiQaarl1/KvwC4QifPqnoCVrDXxhlGe67ax4vWFqDyz/4Nj8A4gMojpoSAKc+WtbXC7WHF9/H
-lqHw8tWXKcB5pauKwHiiPKRHHyne8l3AN3yUPV6fFg/T/YHV69ZuT4tRfpAjr/BXh3WmbDRd+EAU
-JCyVAQei1l9PbbOD0KKHRVhaLzCTk5GKk/MVWlIUas1oXjCINTjbalfbXoLF+Ye0uUJjvVrKOzZS
-aoy33yKuSc3XqVUtaSnW8U4viQ3PbL18Fd7tsUO+z+yphqddeD/1OKO5tvHXJZ71VguiHvafKcAR
-Vwjut7QIR22pNTkuLqDcadANwDpycxSNgJeW3bIUl/+/T1rbW1JOgYmtuy6W4DYyGgie6aCFPDmq
-66PWPhC383tI3hTKBorHvkDmMxLzbTK/qPlhRIGL7qFcf93KgFiDJ6g95REEd8B79A2Ottrr03zP
-F8kwp7RYTubpRNvmuuVNgTDOED5i+W4R7u42WsUzGLaqb9YxO/ouDmC5Eu150wocwhLpRpRHbCpS
-p6TJ3MOMCnyWeex5ExjbtlKyHmR+iMzWsc6LcFizACcAVstw1s+5Upp025cVmFZh9RE+WoVxco/w
-OL562uPfC79AOMKnrhEOS5eEL1AAqQ8wBREViIn+iK9SJBguf+A/AYgJim2kr5ZeX6Ir/5sFPlUg
-Z9zR+kY5rrqWGyP3j2TWwz4FgpeiL5/IO65m/8DLCgxkvPwT13ZWABzqNntIRc0rxHxgJ0NZ3uEY
-yqJW6eQaAYLUe1lrEa4R+lUfFV/IdrzikpeKFDLHt7pCOFxuPx2twwqKsyLV8DIyOaSw9eLAvBwT
-QMCelwIfAtblwYmc6U80Cl7Q/tmQLYzxZ5ho3WuV1nRKXNevqtyOk5Y/3QM56xE3BSMnizCQ2px7
-DGgWycX8QAGwYHACZ65V7JF8MC7GqYCX+7gpVgemfzCBNKKv+pGa2n9qFZZyExR3tPJs1X1leLCu
-5veV6cnV/yLrhWFau1mWa4DBCVbyMy3xheRKJgocOXVnyiMHig/cC8S60OO8CXDPaSQv7fVPWchZ
-PEGoCW7f7/Q6SzMVuLI1Tp3pFA6LsALbMxjerb/P57wfiA+6AsMaSr1vvcBwSUxxf9hPHbTiEmoN
-XYdzno+aVj0tT0y9J060cxVdvjChINjvBMDcHVs6TmcXZOXPABhlDd6ALyQeR4hs+unwC4QjfMc1
-4jLDl3l3kwBQQnIx26oyLMErGwa6RPCNEcMKbL6sqzi7QVR6bRCTdArC4HU+sNeFY5Vpz+5kz2si
-FDwlGMZSggS/hmUJvi+PPlpZg+HwOz5WYgDfWazHNSee88JN4blJCBJUKfULjhzP9iug2R6a83hg
-7uaujtGPW9x9ZgKwvMwrSzPbme4RKYDkuNpmWhapMd5B/Y7iFmMuUByAiVUT+MhFCcUwhZDGyc9/
-I5ZUSRTls/C+zWEFds6iofMyoGuUNN/z+jk13XP/LP6vOjYwDAggJp+qRTgqN+Rcr04faLn/av3W
-+UqL9T0AXvUdLl9gBz+kg5kXYNbBB+a4cam5IOmGcKQsz5P5ToTzclGrflkHxnVlu373XIuGd7zW
-lsCY0q0Ar2VDZSVefXBtBN1ajDpN1v5wTB5BtwxHnO8Erteq7Q/L3VoH6560li5an8Hov8kwscdr
-Xvf4HiynUevf4nIBxTsMNvrTge0on0Ufzn3Z+b5nXpdZ/VA3ogLFKHf+2D/lZ4/tobld/iEbPINe
-5fIqeypDPk5a9Ftfa1cViaSUu0x0d1AQzLcMWTDU2tFLeSj4zWWV4xd2AMwH5K6YeuafwDDbA9M/
-HH6BcISPgfCFsAQvi/C69W9lBQWS8UqPhAX4Bi5cMCcAXpbg/7NKJxNZB8f6sFwxWoFhCvgUlMHz
-WxwH4dx2nwKHUD5iMVUQfOeGRFh/qxzuVe+XhbK1AYJDHGj/20/kkG4c4AEEr67vllYCYHlzRIHT
-G/wGNBV2x6JdKWfaqx96LoD+kNx9AMbZP3Y4Kxy1+STpymG+ygcSzyOVeM5WtyBW3LPFM/QtAXoK
-z0L/EB416+sNuJ82wHDSqjxQU+tbvhddFNhe7uFnuocUDAtfWs08a/VQWqUxhZYDPVuPO0hehSfQ
-LUVyAMG0cuncydImzdAemEuQDFqFE73g6Bphfd88hgeN93yeKP5ZmHggIjFl4Fp2H+Gi1+apMXgW
-9BonkBceVYWu+ZCvGSdQLavwE/3s8tBp8zPLG1CW9D5znLsOuJT12pSM4bZMjOWznkFZ3clJLRA7
-rL/JVoJyp4V4sw4zbMIwCCchWQzTywj4LVAM1EXjQ1PIqe0ehTN/TOWe5zEPaifuJcfqoSSM1KGA
-N5VB2+UN+M64AmDqZDzEDXQRrd90g2CcwPji+dTvEv+GNvnHwi8QjvCxawQBXpRPQKwLSEVxS1Lc
-IZIZ4kccXSDYBBh7Y7j8GeOejLie//IEvV1Iyq21oN+6k9E3bykCilAqgxUnCL51c91xsgJi1BYu
-QGtoX7cLN4gLc2Owg3q0nBEdC3SsHA/ng6BYgSkfmBM4P63B7YXq2K3F2i7Tt9cDed6Ab8URQmbG
-c8ZeyAFV6qL+UpCpJZgzVuvQhWyOihZCWOkBoPkR//dCqZxdMZ/AsOS29fOxXn1edlrP6z91exg0
-n+W0DhPtF736wA94t0Z6bM3gSbq3+HCNSFWK5vfo6XYUsyfMoq4R+cDcg2W4v0rN6se0zON3wrtz
-jlgmcmgUW3leYMS5S1FzrCHmzzXNmlU+7r1olP4bVuIXILjTKa9PYHcv90SboWQB5UD0m8svw5mj
-62fI3FiPl/VxtwJr/CUY1rKoso02yrYO5/6yBzogV39RMZnJYs+VhbXuoHAP+wC7pRu0yn0231mD
-d9DbzuN+bfmirzULIr9FqHcWXhLqBII3cAyhs90YuwG7fzDkzRHA+V3CEm/44G+Exn8YfoFwhM8f
-lgP8wgLEly8QDARPxiaVhTRDve+X7hB3WYCXdXQB3gS/NsCvyRXVw+8P4jaaWwpOyvuFAQlcKWTj
-7WbS/ZIXAj48odUGAm7Zec0LLR6Q87AGF+AP4BtKvFu0VzsJhmMer5xWCsXq7waCBfTWmxsg6XCH
-SMswH5ZTIFTuLUpTJenbOYgRYADgiuPQtwLDOqiqESM6wwQEnB9agnOlnOB4SRmOhx9iqFV8ldpa
-bT1pHmtbkXd7a88/tXIOOxj27W8/+iGv4p7zOfP7b1qBg2YCTFpZLm0oV1XS5C0rWlrvCCipxCln
-DN26zzWYYNgBurxsILj5CRNkWDVLAAwBxZQ4Am6eQLDO8XdCqvfc7E+1eGbTEnxOu+yTIA7EkvN/
-6rjtRH19GmURa2+vThNeUlp+Wc7lvcL+yi/49AaJknsnWpvDMRSV8To8dZ19DIe9nX9tFJhg1io+
-wXCdWlbfDRSfgHBXXjiDYC+FyHaybNApPBMMI3znC+Y2QMy/UddgqS09Z63nlbSd9mAO8giCt7FH
-X0401Niq2dIPNo9Rfzu2eEn9C0/uEfIsUzR/BMDwXMvTK1P/7fALhCN8xzXC87Vgtj0YtgqtQ4Jg
-2+PpL0OmMbEQIxjHzgC4vZJEGLQw3gLFl9MHbTHtHZ3z0eWVXwCgBtF3Uwn54fsIxx9McNDjNzy/
-NLPGXABGLcGQMcIW2E5jvVs8eGQPIDjSzvRKuKN92CK/+HbfyM+rBsDYQdKB1nyFR5n5MQ1aoQcY
-znVwr1+rtUJjTd/lOx7SnB/Kd/UR7m8bIB2KxUY4U1+FR0XQqvpenU99W3RR8z5zCUpqLTn/GR/H
-szVYfzsY5rnxVMBeDycYwMktYn40I3kkNG73M34AtRME06cRh/LpOxxzayghhbNVuIFiExr03D6f
-3wnnHXCGDSSrJbjW3/kIAJp1WKt9avRoltK1q9N0fbW6R6tv5ndLsH4uuQPok7tEnbfxpXM/OPwB
-2apkP9E17HvOOiH5YyXy+il2pPoF7yC48goM7y4S78EzOxq9nUJyCrZkGKEPSzCfGbGIq8tSAmKC
-N1ScXVKplE2MWVU3Jc+xaOlpKdZpP9QcQ2186tTeWn4VNifgVRAcuOJAezLG5XuEsb85wmDNTzhF
-TMZ5Z+q72uCfC79AOMJ3XCOIHhsoFiHZrqIMyzfY+itE/jjfBHE3IEzG0ngyzimOxeQGSwBsHgrv
-LsvyLWOgNZhxk23GflMwAEPh2wK1tWtXmc3NP4QEf2ZU1DyLV4qr3osCNOuqjbPAsOUUE4Rweyco
-zjhBcbe+QnyDCYztvnMEHGMODx0Ev6RRDhH8bq9QO1uEgTZreBlcIzy/1KuNdFqCc8ZegOAmjq2U
-x0ENcm3e9PYY+nmvxV613Ptw7hFyXfoh/iqP4Cm+g+L3P5P4AM62l0Eqa0DdIvgAI5Guy/5DgFoq
-SYLnl6DW0UFwuE+0ddbCbCfcHaIJvAPFuaEzbdvi5Lg/DKeitebs++QPDsPLz9B32qNxeWMoO3fk
-gWVzzb2kQ9GKP/hwnNLcT9ZesfK61N/OGXTv/Le6u2bJi9B6vTkWcfvPIVvLHpXFypieY+3Esgh/
-F+R+Ao5Hx1rct/2TzJ1zUjS9a9L3qde1kcmWCX6c87WzjmdO3oEBt/spT8HvkMPNRUKz4uw5/pwk
-nuJS1mtOsAPjBK5vwHB7h7DvD82l4Y/lnaJiya2sywB73KT/XvgFwhE+tQg3SzA3gwheM8MfWn4d
-CYL/3OH368v14ct9fSHOLvwf1CWCvsHLprS9hgSa9mRCw/rQxe1hEb7XJ6BXH9TVYm32P8KUalVu
-YMJkUzoVeggHW8B7Fa0PbNAlwuJBodvnl/JCQV+1HXkNYuO4T3V6Ku2AxKkwFASHchCrbPoG0084
-6y1hRvBCayGkHUjbOND8vpefsADeBYY7MOavv19YKs7Kt5GurBTEVbaJDxeBzCWlUkAHvynz7Sh2
-p/Z7wgIPCuDTjfUu9E4c9XpkuCT6lA6gewAOcxlOs7/9rHjmXVnYsNIFz1Ixp2J05N0KLtBaS5N1
-UoVmqUTmA3FpEEtLsYebktCAfkuSoDj22wTFBLz9K3QFdlSMnBdqX7cJKE5FZjXJu0yVTh+AeHbo
-A878gHUpezIutLowoiws+USgq8C4PrXc3yyx1zFAsLSl4Fk3MGHW88b59tAPhWT9Ebom+SbSwBnc
-PgLfD4FzDTX3hwozy31lIuC4cKTlRkk/8tU2+SraE+GrFxhaHapIk6nZSTEylN6dklfcI6SGbnRK
-IdIuRMgA1f9+TkoqLxxRFuBFuzLPB94ozECL7xdoDQ5A7BO7WILoBMQuoJrrOMfxQ+EXCEf4GAjD
-1ieDHWENLuFijvWZYYLhWNT7XsDwdiQwLoC4HAd2a/BV1mKIjsmf+teybYJgw58LBYDjwxYAUhZc
-EQc8X/3CTVChBACtSVTid2yOOzcVty+tjUgLEo3mBLQXLHysuzcJ4wsAr5HxnFvObwogfgQUGwgm
-4FTXCLoq3Hf216UfVBtsp9F90oeFWB7GKwB8tgbnw3KzFdfaJesQXPJKuInq9KIpQEqZjwJPCYyD
-h5GUczgD3w/Di7dQzPHZTPlTvtIFVDVQEqQNBA9r7sYTh58956nvfSt3sgKDFqvSpOoCoQ/LTaBc
-wBcFcMWqtVn8ZeH7e4RjLsUqXMAGAwRzbzM9yg100gDAE8PoPIyVRICS7XyXgwsgdsotT0FhQt/7
-FV145Ed7TDV+cYn3rmz81j6uIfx5fF1alHmyAO/0LhfY4RNgfN5B7+YipL2NdDZmuabMIX/QrUaP
-n1iFdz9h6fcYS1p0c79JPPeaVdzRwCknMF+bBkMBYiRPWk7yE2v3+VVD9MzfrcCVn3C4+T0cho86
-teuO3g4nghbkhSE889UyTBBMYEwrsVqG55fk0l8YuzEvQbQJhmEfvs2P/0z4BcIRro+dhEMiYa2u
-RdpoffUFSA3xEgUPq6Yv3EUQfLunj7DhaoC4rL/1DuLGQCgGKiZefVkg2OMzx1afXuYGqj0uAHiy
-XilkNuKx+z0f2Flg1VLJQG65c8NHfihOWoEdAXpvS1rzTIn5WkJ+TdIFtQbXliFUpAKAY4Bgiafb
-gtd7hEHXiBQLqcbYVzzS6xzS7+kj7P3XQGoq6qq78dlT8JmQ3vm5nGWrBZAOduEXDU4B/Vk4i+3v
-hcSL2ZfewpD/rYyCjMrdQS9a+oOfvcq3x/y2n0KRFwg+u0D0MigwEI/dRAAAIABJREFUncAXG+A9
-uU00vX/Ia2A3+lquEpZ4VD/FXMIpIxuLFCvveZPP6wJAztH1xKjCK+JRX0oECgXuL9kgp8u7zmd7
-I9m1bnob8iCbTN5qcdfjkk3rYTmxGIOyr+pqD8355N9zu9lj4ak2wqft7DhcFIgf+J7VlEjqpuSP
-ArukvwbBdWyg+QEQb+OR/fQyrzZDzU97dRKw/IRZLsbHjUCeUxadp2u29Kk7o2lfd6nMBSlNh1bP
-Fl7wcbms1H7gnZPCGAF+EwSjgV+9O73u6Hq+IaJbhgubvALEJUJKdv10+AXCET63CEdhDwaKe/fr
-dSOWgPSP0S0hbn95uAa443bffIQXM1zpEmEvadb9kIOZzeO1Zl5feUtrcD0Vl5vXKQM8mFA2RBOm
-cbAQ2lS+3DBAPYhnTos2LYurX/mmDYMAYk9f5VWNCO7o44Xq5xFwMO78EXSujG6BFZ9dPjAXk6GK
-CtIGRdam7J7y0hrswzp8AMSp+LwGUEOvcBQMHOBTuT5THkLegQaa2qvTgI+twU8hhf6TktXRvSrj
-e/IVSNGYv8jfAYPkeZut51/jRXugn89ZS2Y5UU6tlQp3dYRlnCcZoC4V84G404czTu8R3vzDm6tF
-+QY3qzD7dQDKJ3eJuY8T3CZBlqeVlQsWG+V0RZuil8WU/FxQBGdLWluaPJWfmR6hdSes6koreSk8
-lM3qw25e+ah3BzercJZFpNVyfPg98G3bNRnlSgOzSJuHw9jtNGFQIte/qLblvQfBlW9yWgC/4DU9
-t41Dx5M0h9z+QjH8kolZBmHoUTCWd2lqGHyvfpucMTWbDEp+OXWQmmTQjTxadx313L4U1ZG2NyRl
-pIWgSSMWUBbgGYenZbfHIfFwjXDb3CGegO+8y736RcZ52Pj/YvgFwhG+BYQzGHB1AFzxAr98iK1+
-AYgNWEBX3SPifcMQhoE1xqwra4DfQ08ADgtTtMEuxx++ISy667GR10cukFdrDmXKkhepdRzyxHsx
-6nSNSGUa59E6/MWHC4Fwi7BZVQojyH7o/peW219v/+kdz9R3A3TSV5ivTfPr3lwjulKjqJnKRfrj
-3sqvfpUvsr4tAvQNhvRndj7n4DmUO8WYu9HzXidCFnv1HR0EhRqAiKXQm15g5RBe4JW/Cy8qm6rg
-XTXzdrSCBS15BBdvf8Mf2A5l7OAznHrM0+Xh2QrsiT5YJi2bpv77JmCYe08Br9ASDItKDcXXrL1c
-cwLblzTRaKTpmHLx5PyYgwzRgaKU3DlxmaaSHwp5SkFPecCOKDSQ6a7yWqeCLW1z8Bak6TO/iKU3
-zlWf4fn6tCw76py+wbN8G+rqaI1GtvI+m88XvX0+UAgmQZyebVJmlaP/r4nmevIJ1vcMv3ShUGC8
-ddCx+f3qSBzYQDDvmD5Yeg3cR1Bim0gf6ZqvPenYTsfizbqQ3YBxxmeV7PuJVZ/W1mUYclcXhTOu
-Q/rpeaX5urSnX8Mx+pDc+P10+AXCET59a0QgScBNgG8HwPnaMvl5WGvvO45uuMMdIt0j+IBcuEQU
-4yjDqDVYmCnjBMqOP3fs3MuWBdQWAJYhxM/7BlezaPoTBp0ak9nQq8rVHsHvErzhs3gBX+IKUQ+v
-qCWM9YTQj7Zo+ZqgOJVPdE2Nqpt/cLxPrgPipVpUGFX9KzUVGjLfJS2/fDDv7g/peY93jSUd3/it
-Iioat0Jq7fJJpyqu22vuvH1fN+iw0Z7Cpnn+mfAwBU/FXvbgOJ8l/Cc4+Z41uAPc27SMSbn9PPaL
-F5WL9z3iBbrqwUak4l7x7j/Md3PzrRD64YwCvPF3WJHBC+IBXlJJ0y3CbCvzEVhmhPJjrEGhsgIW
-tWwnVCE0mdwj9miIVPeZNu/ZfEE6DMaKi4mR3WWG8kzV2a3CPZ3uDX5+WG6V6TxUD8I98Ogon/NU
-bBUXVLV/TcromD7a3Vx3TW5nK9+ggdgGghXkwqBAV8tsgDgHF0w2UaYOXK3CNAIMEGzjwoFDWBeV
-MkdPoHdOEZts0rt1DGlKsue83SYsc5x3Ph6Cv1hZyUuM4QJ+gQaApzW4fILljREnMGwdPHfgK77C
-sd9+OvwC4QjfsQivjbrArILhyy3f26tg2DVuy4p60yJsF64/N/4PAYDTVUKAsYmvcLuKss5IdzGW
-SgK/HV9xkusvbtlNpmyBlivVBPDWAgRgscSSJ0uzGdZG8cvLMhwuJfnid9nlZY1eQq8+e0Gr8K4A
-VlfV/SAUw8kqfMnX5XJECnAeQG7S7ECL33SNGAA4+wIv624NoB9b76SxjEdvG11GMZcyFTGVSolY
-AuNmKxRryitw/KwTTuWnIH5ZwcvwKN6HpT7X1CftUAYDdLz72eAd0/zdV1jBrb4z2HPKo+9iuuPt
-2rIcY4BaTiF97AiGF9hLizEOILi5RsSR4He4QXRgzIm3AwhWJa3jjIoSuEgdHlKl8QIH9oY5DqA3
-rcC6Q8VUGqJJJFcBY6gizn2ClEciqtr+53ECY13/e+Q9fzL5wIuOnSb0ybM6Pz7W5GQZ1j3kI/0y
-pPIIcBpg1zTeCk6Ae7AKs8wJEI8yzx0OYmNu8v6al3z4TEF0MolUMydpsmToK8044TnRymjA2DBA
-72xoSt+uE+yQdaZ70jvoHb9Bb69Bw/lX/sG7hZh3nrW+U7t7p38m/ALhb4YlCD0ZhTRxwV0g2PSN
-CAHjwk0CFMAX8OVLwXwFKPzyAojuiw735UoQZtHFz7F5LyB9B0UapNK0EIK2FNKXWoVt+Sm7Lev0
-pTSODT1yZNYAy1S0AOp2bEzQ7WvO7hBMdSGB9Gu+oy7e7uKbNhDgn3n5EGIc3T2/nJfgFwqIBwiV
-OIZ1RcOJPq3Bs1y1G5QD2M31AxL4vAu7nK9bzkl36VfSV1/Wrdyy+DrH4uMBCgHB+bGRAFjVoBWD
-mTQo6i7HaN2XEtCHQOTU/zBQx7Ey33JPZ0RMAEZb73do4HFzROKQLqDLP2tfIvpQQFlvz9Ji5LK/
-LPZD7P+0cmmndT2xr3GbC/bXEpx4A7lAPjRHy7ECYwIhIqGc29EHba9toqdyMU+5X5E/15ejY44b
-de5h/ctAKPdJnF0X6SfVLsv7iikg9uC7XJutNQ3aH3Uro45gndbOyKFEA3XPSvp3aH8tS1/v7SEz
-GyCrXehoD7TUJ3HpixV7aN6QMIOOvOCjPDqV6Q1+tAg1rFFO+9sncvLm6MAmjGs9XivPQ5ogHxzn
-Zp6qpg4sb4dxnVqy8btwtgBvb3uYP+8PwvXfWtlTfScwTNpPh18g/N3QzW8bjbcwioljm4csT1lg
-xVi06FwBSM1W/GqAFQvEBnjGtUDnlwPOV7hZCEvDesVbbKCvEDRf0ZevFD9oNAK5y+tGTN0SPU9H
-l6miQCRFDZNAEQD9lBeeD6Acc5Sfg6bSCcs13NZbMC6PW588YpUJa6gC5HYbMQFy7632vwEhGXTR
-fdBOoKKGrccn6eR6PBQpkFeEqQhqvaoOj4LKix2EpOZPrl3TWUpnXfSpph3S35U/DuppA8xV/I2u
-eht21usr51v+IbzsxEndvksHrWkcATW04mLcaTF02ZIXG5DBhNDwKuNeYLgsvRCfYEAfhmzXMFy+
-uNBMK7DZ7i/89BNgXMJMxqu8r/gUY7wNQ4yyBMG8vUZAbJMWe3sA5G1fRRennN5B2hluqSuKnxhI
-l5EAl6mwwlusUfnPSksBLOZQ8k06sg/5MDFfZETadqdvtLO1u6WrQpZvyorxbeh9zlLmCIp345yE
-ngne9bkgyrCyH7SFjDdeUgE/4g/82EYiwiONBH4o3NRdzFUMNXVO1JFD0XRWUUB3bfORnkDYx9TX
-JJzFsEsXpaILwJcdgO+Jhg5ijWkvwHsFtyv4tXFefk334cj4T4dfIJzhM7Vc+6mzqsbXnvV9L0ua
-zJnW5GSCBRBvA64Au/e1HNX9sgC+ZS12X24H3Ndf0pN36a+w5NJK/GUI67Q3i8S3XXbGU59pRXWU
-b2QIPr6CzbEeIlynC/CFJyCGc6N7uKQE+HWC30rXOzrVQkt5FnCcytNKVelKdiWXIn0ELTWlEzqX
-vGCxo2H4gSZyL4ulNUgEfWKmzAurMGL+GXfxRWs+chGf0rVMZ0VPpfAAHlQXkvYwxFN4ZEHfInv9
-bxpZ5UYhXbhP+N8kcogTUtRFMcFnrVN+7CKbFc/AxAHWLMhpb1+Vlb+/jdusAbzEIxwNDLP90EbT
-6jvjTz+9nc0LsT6nVO0YeX4qBvIxbo9xO+B3+RBEWd544Rc/iSCzLrIudH7laD3dOi0uEVXGj9ir
-VnvwU8g5utUlGEa/g+gY4Jdp2fTLUU4my6Rc0BKwmOBWWG5bTTOf/ax8JoN/oC4OQCt4nLuiawle
-SLS7GI9HJO8/bcMl/x4WQ3muCUMp4L3v1U7tNR2vtYv/7MB2NlO552ykR3526w0QFs3cOp537Vpl
-IdNH9+iq8AnwfQTE7s0FIn2KYQ0c8yNalxEoYz9a9MOKR38y/ALhCB/eoUbfSJuU3+JqIUYoQZ6r
-OiQtALYUUlmDAxzTXeJCuU9ggVkCEd08X5hpCh+Ui8S1W4RZjn5974Xbe5plnTGGAALNTzlAgFtY
-gk0twfGHwvAGbFiFCwyv8SQgBsRgJBZhlSFRP3VpEzWJ8bo4fwLOtKjuszKtzefgW6THOZ+63lQC
-pSxKSednlx/B61/ST/VlX+tBLc3/BAx/LAM/3K/tFvI8+aM6nnYANnq/wTz5g1owRryhUZ5mJYyi
-/D4CmbXYS7Qwl89rOmS1OU6F7IfmzdbrFo+A+DqD4KuXU4twswaPISw2mQT2cYAP9wD4vnz8CTxi
-w3tYhf2+ExESDHvMtYpr5zSzqy+YzmIua7L2VZhGEQVSIOiNPdHAMOjHXSAYvluDr9g30zp898YG
-FpMHkEyA7QaGcbAW6wiljShwAio+6HpflA9Rny5BXoPi0cg4XfUqWaI6MNbCz25jYxT7wNi0RW2O
-1I35HAzrNB337Pa8mBrgF2U1Bkq/uNSi462adYyoSWjimXzrNXYb4PddOn/ewXD8EvRK2giCcQC/
-XXy0Cze6lP5k+AXCGT7UrLk/d6SSQjPjSEUD1P7WeBNUwRh0h3BDAN8Q5kJzlItE79fD0eW86OcX
-wr3CIP7DwNflaZ3d69sFVFOySpexJgC1uP1OtwhYgF/ZwLAEyFkBXD4vR/Cr8bIE0zpcvsMTAJeX
-rKMsozWeLjQphLieVWpXfZ07zjxFa/Uzy/W5TycFr5wUit7nv5fbIfkrq/Ck51lPrhFPFmPmHTSm
-pSJpcOF7YZu3F3vXqw0/0LUP/pD3FDpeoB+v5Ix46tLNGhyZCYCtNu0UJLmn6k7KKm5p8VJ//VK+
-taarbakugc6VQkitwAl0r8h/AMTLGnypBn7AGnIL95DX0wj3D4f7DT78t159yDs9d0zdlWDYbyT4
-cqt50emde2mHMDO4LMnrkpY/AXeCTgxIEJxgwuNNOREu0ChQaRdasyC7AuiSGu/AcPbXpOxTeeHn
-dJU4zMOcx00/OB/05Hx4Z8gHnj/VrZnTasz9keksLnD1UQ6stgtXxqono1Dv+j4PIhZz7GaD56Z+
-Vflxzp+hUaQfpoNjX3I+kXLmc/C7A+Berj6oUeX0ncIFkpfVd6ajrJV1+KfDLxCOMIXh3Lw8+tyV
-A2r0c2WTyqYiY6YeQVgIqFOgrhJIAPxVT9+V5Kydk60RoNG6+2UI628pQ4LRr2v1usAxoC8M3+ZF
-ry5JP+q7nk/uLoswQkkREMc4PQR5VmgFEKJCgmAeaf1tgJhjFNeIBMDOPCR4WGu0C6AaRdDcWtd0
-rH0C1E50yD/RvR0eQxeoLjJPFbXEY4Id9M+TWjj+A50XCQ3onso/AOVuAa483SPfDtuJL2p6VHKf
-1P+6ZGLVxwLrj0mcvJQGddStVwPyAbjqLMETu9Nv1fKT2Sqs0gLs7ONnluFp0fXLALUCqxOfAOYT
-rT3JrxO7Cwm0XXBa29jn9WGQmw8PrM7fcXV833Ghb8sSbMB6bSRCxqzBEi+oTONFwjv9O2U75YPK
-3fN5wwIMWoUrvkDt+kqX4wRuq5HLDtZh2We8dW94B3CR1uA2SI2YZllV8kH5PleWfeMbG2Qj1Aly
-gfcWEI/TW6ONNvVykje4qaGaN/C9+PQNX3cpTERjCLsgbH7ANvef1Tg4S23uZs+6fG7tSr4pLfrD
-vlfjVm4NVqDWsI5fEj+C4ElzLNcHX/zUrb/iO2xCt4e0LWD80+EXCDM04CLkAy3BgNIFrCUQEeZT
-eUEZoBbhi9ZeINwhxDUiLLkee62+zGZJo6sE+5QeE/LQnIN+weFfPGnZXggsoHxrocpVBoLa/7Xf
-ulBhPQSaOQ50q3C5SdSDcpsCvS3dIuqHDnjFEpxuhfoTFaaKTBVzNi9zp5zgOnDJH1xR9Gdt/6xB
-Ja8EptAoyL3np1yOQlPkrnnfrcKrIiq0vXyjn6zB3s9rim+M6S2QfDEXp6BNvK13K1Cq5nhum3yW
-0xYLBEw45ZYZqwgvpJpvApJB0lKWDUWvpibPjpr4Hqq/calMl6o6nrCuSFMbBQBuT7AMa/A1QfIA
-x6d5nntgykbUPLRz3AHjJ9FRgjNAsONeavm+Abvgdq/nKOI96vn+dLKpDINNzBvPT6r4BKgQc9/H
-27nJQPcIxgMkeMkXBbjTX3hZhz07PX2HV3rtX9Zj/BnjJ+swRp8O+SiLsOox5fuahxq5RX8Wf07X
-CKRCoTtb5/3SL8+8/xBebBfVU5T1T2utQ82HSnkx1VC47eVHGg8WYU1bm8chR6Q2a1RLedr6E7I4
-jVCiFNif6wCGGzA2VJn2O9H2MgYcQLB3sEwREuNX8fGT4RcIfzs8SvdGa2vJh1tSp1Hp1RU/N8m0
-AnsIoA6ApWlxqKFf8LLqSlxpaRlGukYo7QoLcr3vskTG1nbt4gKOosi0PPXZAl7sX7hFiBJZAp8W
-krCOiNJf4Or0w/ZTK3D+c2+gOK/0s68iZhxcsFxVXe10t4jF9KRqIW/lZ3gEXuiCU7FDF4SldFJv
-8JY5p5XnOueUGq0EaaiivI3clFWU39VGMPWsa3OhGKdFEPY50vc5+YgobHjIn0DrfXV73Y95wz3C
-SK0SZQF+ZQ2eICDsj1t8Ag8FxQ9Hq/mJ3VSKnpbd1E4Ffm0Du1dzh7CMG4xfJ5prcBSdfqBpmgDW
-kC9Kv7UQ/aXu3J8Og9+WY9LXRfJhxKyeirfxfe9Ermv0hxCmS7pTCPDb0gSXahWu3xngrnSzBHvd
-OSTBci+WWsi6WzuvAHH1tU0U4wdrsA9y3/piHU0rqhe/NvA24mzvFMfO26eg3hafnqNN2eCZNdag
-Gd2PKAd7rclJQw54S0l6iMxd0k+KCa3LbMvNXhNg40r4HZith9+mG4S6SJQrBMtbpJvLBes0sT6b
-HK2D4Z8Ov0A4wtPt662cE9Sd/DxPdRTNNOJDABkBcAlnpiGg2PMkNBDcWnMkaObY3A3XJcDYFyhd
-tLWhL1v+wauOenjNczcXGkuLXtMd8zZnn4+0BgNpEb69rB+bv3CE9dogy2/AbSAY63gNq/BmCXb2
-sH5rLaWPppxgPT8fIOw7dQ3VD4LsBMR4fM1vJQjPQCGBzDjyzoaBSihGGetdYrNbhVVVlPuHi4Zb
-ZeriQOiigKVoJvg0c2tHom/l3mdbs5U/zFy2dazugzYaIEpiuT/MnZ4zxAKijLin8rVRUdcrMKzq
-cFnQzv2cyj5Bh6btAAoIfA+g1wX8gmB3Wou1zKMo9JF+UwZYr0xsiG3uYoLgC+53GBEGACb8Sssc
-/2lD3DNzYj33mPI9585byVZd/TVKEw6jP6B25bnLUqcPx6nrw8lv+I6L1QmUq92ha1p6jHXm55Dl
-ITuma3jZZMkd2/grZcpwrysQieLz3FS1B+riLfrwds/uG6Tx+6ch9l8C3lSAKgcjZOXksui7tKh8
-0fpik/a+l+kKIVe49dnpojf3iJhue7IKy89a2o9xy7Rt59XRAxAPC7EeDb9vjfjfCY/SG8Bk3c58
-xZwogZMMgAGAVwGn4L+eW9l6QrAZv3zlWpxbVmCklZa+wgTEBsdtluBx1WUSB9ZtLx/jk06g5NjS
-+6LAfH1cI0GwlWV43hbEbfDLy8px+/oFEL5uh1/qDnECxQ6i8GYRFsjqgmbKMsr+mwAWtY6X8Mri
-vnL1bFED24K9k+dnYDMVzyjr3o7MebIKlwW16FkrrcIx9lJQUse0iByUxEe66zT472W8rWvrx19U
-xVB1WRKa8hsgQm+xrrUXBQa8tAy7LvwD0Eg+sHPe9BVuA6HwodvDCeA+HrX82qn1FL+09ig+n8v4
-5cAfK2FJyTDFoN/AFaDXrQAxys0sb2sb1+gE+s593Dw+PkFUVkWO4NdK3h3dId7Etzzn2yQ824Pt
-Y2QcG20HuIDMi1Y4M4XW+a6s+imvlPdTCFKGoLODkGcL5yWw+OsH/pc9InvhtIyVtzrAd3U76DMc
-JUT2qb85hwTW0XpvkOyaVxndM3Op9b96n7ORoLfkTA0m+o7uCkF+PIHilXcAwaE/aRFuwNmqnAHt
-1WlpIZaj6fFh1P9m+AXCDG8sdL3sFmlAZEnNlt3KMK8JJyCviGBIa8YVHOLN6csf7x8QZF4ooEv/
-4SPoFUB8ZR5w32tT30DeUry18xP7NECnQLFANPdk5gmgVMsw2ykB7yXgY/gL7K5+8y0RSQPCa1Cd
-ItBSSWGnrfq6mqS1VGc2BJjiPVQF1ca+HtsaHVdvBP/escAvtuNaHy/rJPsqFpjmKxzjpBUNStcO
-0uLAdo5W36rP2ry9QRHf2JKn8m0LPtb1iMxeAJ1SYvPsshpbpqvGdcbCtzppeOEfKXEVHNqwsGQq
-bsa3PFHCmpcuEAvMuoBaewWAFfzG+fbSItwixySADqJj/JuvOTh/An6X8IwvcRIML4sq9/OUuW/D
-4INyqy7f+dk3bjPTNNY6b3I/+sGtclnd/XqyBmc8NpXmmXBBtbso2iZezMXWL9Q+3wZ1nKoBCLH6
-tVnVPfiRg891p3JhBVKTlWzbg+yRjTrdE5rwnzWAoDFLabtxgWXTADDqKVG4t9GZqtJ+yt/OnSV3
-hbymNGgKimNupwXYTAGwWnz9mQ4BxDatybbTbD/mtXYeP9mU/2z4BcIRPte5fow+1aJLqj6BzCMj
-LCZcFtZmFQ4Jlz58vFy64xbINdpwlPVY4uo37AAuL9C7ALEJIAZwFUhawHI90ayWiGYcTAXf58Ml
-prgt+wd6+ZnEUW4QMrU5b+7xwHhYhsNa7Fi3Ca9wi4iP0AUQ7K4SbJtWg3oYjP2jEN4BsQdd8d6R
-g/xlbi/qrfhzOZS7AwX2fvRqNSstC+8aL7Vm+AZLR7LmEM5Vl6iRzTWCzIC+aNNtIgL1Hk85DPTl
-LHxa3lqW73R/Vf6ZLqoGBB287U5yug8FCAnEGbSQBbFpPE9wOdlba2uvdUU/VGEDt4g+TjC8qp+u
-Ekvo+AH8ulqIk94BsU0r8pixXO9T8C3SounzvlWxBFyuY8isco0gCJb0CUF9rHe9T67uxDMWQvIG
-ClTmT0DxFdX1h+MWLQ0CIZuPluHckvHBDRf9Ir09p+2xTBJ5EN4GRmXVlQaGa6ZYtO5O8oxyXWWd
-LotdZ5d1eN/ZtNwaZBs1uBt76AiIH5jAUGAdniCtPk0v54aVuPUs5+5Uv43oqScjdejqyTpt7SHb
-6daBcI1AvPasgC558ewecQDBSQ+LL+sd5xrKAtzA8APtp8MvEM7wDqpEqa3Y+TzdaPn0ZmSQb21l
-dbcIlrHFoOscWy+Ih8d71SCAeOkitvXlBTTdyyeY1t+LZa7UHfGFurBAX4uW4kosVTdooSgYIHdh
-NmTBGMebyowyzuoUhwsI3o+4S7/yrRF8bdoFiQd9c42Qf+DRIXf91V/Yqm8cha81qbG5nLv7jqpN
-PGlD4X/GcXWGTUJU6kAqEn1ALkGO1zkJ8HnLWBYhwZgosOq44cjE0p8EfiffOa3r4DKxDfYb4Xwz
-R0CYf7vK750wFVPCAMt0+Ql7rA3nGfjMAlxxd2srlK9xQoFa5Re3AQis+tZRGkFtWYQtAK/blfEF
-diU+wLFRIB3n9DCxB2zaMLHfxcuapVePbnC/6gLeLT5CZOlv6yKDNlCKh1uyuoHk7gfztodLJTSA
-me1VHQsc1FoRLBzdIfxkGS7QewLMkLECfazaB5zS0mdIHW0C+vbPevuMiKxzG94+XtfWysGNRWQP
-VHWjjV60EUzdxiyqin3U4GmvraqSOsQwZASeqsTU0s39tnXwIZ1r9Fym5jPaJsBt8nhYqZ0PzvWB
-kd953EFvgdxG80nzcTx8WtkewDB5btIeVMe/GX6BMMPHis+T7xmmoi2+U2V1wBEIYeMhEAMw88ty
-S7KtAnn7iO8Ojq+r1UYsELJuE/q6PYgOfGkx1rdITBcJTyv06iitwcC6ZSf4vIGtAftytgpgIoVD
-6i/kcDbwSx0z5e7+eWX1C0b73bFxmyWYR4TXs6Pmlyu8pF2Hs95dC8BxyUN07nXGzlLeqa94TubL
-tKwXrYEd0KrC9fH0gS6ssM7wHGOI1vEQHRdoguYskzK3hG8JXav9ocrr4fbh94NvyVmrPyZ6eATI
-PuKjgdzfNtItb2XWF/ao4mI/B3+d/YHjluyxYu2a+B6DyjlZN6VPXqDAjnEDFpgOYOt8FzDj1wK5
-bhVXQKwA2QiQD2H385Q57pFMEoBsp4agWnx5rX3n3R3CzcQaXGBYDQ7VuZI9DemiCMnOSY49FBu0
-3rGOps1pdWUl820R+t5U+WZQfna5gd7oVgLmFqeVsg2rdUll6XcAMmZ6zN0pTJ0oEkUK2V5aG2/K
-7bmdBvaw5rjeuc19UOtJ2fgKdWlOVRMQV+p1Oz1geZinJ5D7QZlTLzea02JNvuSDyrHLm6U4HpRL
-/BEg9ykNtQYXIC4/YQLg+RBdgGM7gGGNG0Gw/VqE/5vhsEXcxFR/AAAgAElEQVRfFX4oXbergdJt
-udUNDQwDBL7FfLmjUypR0vl6RRE/LwwrQOrcngvl0fdTQWgDvqRdOy0twpfl08jAAsNkciCAqpfQ
-7NiBwqsmykc5+gSn8sEOghsglvMVCF/uuO+yBCfNxUfYcbYMt3Xs/mtrPjwbrgfHIG+WqLKQ2MRR
-ah32UWDy0a4oelTVhlqBNZ9tGKi0PXPXkkTcJA4Wllv32lpzi6ix70CxP4LykS/wywn4ftnJj0fa
-XIi/6kCqRRDq8K5BWyfyVHbCZPGoqFY8P5edDfT8dgvGZPppWYy1afHUtxI3623Y8qulm4PHK9EW
-GD5Zg9UivH7lTnE9r7j3iG30Q7j9IGc8j05Q7A7PI0Fx+AcTHLNKAj7rluCSR51v886y9IDvyHbd
-HsdAIKavLRMwPFwk6hkJvUu2Rt8swxJn82oVRrbFMr6B370MHsuM4Wxx175omSE+CKbJ6fvepFzd
-HwKuuP49BNG11TY7KMC13aF6qm2+L4YT1pT5XsdW1YTLdizTY1KXz1LeAa80nz7DAXoLvCPnhvy2
-W4KHFbjlM89bevkR7z7BVeYAhm3ELcD0f8Ek/AuEM3ymALfbsA8aN6GA6C5ggOHITzCMEsxuYYUl
-MA4wnJf/giIt3CMI+vh1OIJbdYNorhGOdJcoFwmkRfgEKQiIKST7j7eQCnoVEOxzV5apcrlgXBVC
-E8bxK0sw0heu/IH5ev3pGoGcDzgSEJqLtR3IspTmiZcU/DoPJWmnIpxuEC+Dj2ML+xock756S+C7
-zooec8xAswpTkDKuX53jbcQEsiZxDnBYHzoyA2VwdfJF/BGXPoWXZbuf4AcnfLsYlVrqFuHnui3a
-nx5Pn8Ik+AaG860Rn7hLsMt0jZhgmDo0gbi4UKTFes2WheU0j/QNTsBrDfSmBZhpdY3ge4S3GXsz
-v31YcmK91byEGn9LaCUIjjtZahV2W5bUtAjjAfzaSPcZB3ld2X7z+xrnFfg9geB+TJHuO9hdoHgR
-V7nVIJu83MNljVtRPNgTS+2uGjls6p9Bk4NEfKR7/LzE/qKAtdi3jFKncLy4nDLqsGDZlYPMbQKr
-06qWvc63qQ302TlmWqK3Y208cUdQ7tSZypHYQ4bd+st9UcDXj+DXJt0dV+CVlV+fT646o6ztb5fg
-HlA3ip8Ov0CY4T/ce0Btn1fHWTCd1oEEFDeWa8SN5QN0pfKL42XxkNhiclqHnX5xCGALAr6mMxCu
-dPi6FoDkJ5jLIrzcKtb9ueowx7B+ssFeTqhnVMEiLcIsc3NTMl/amqD4vssirBZgTadbRI7fS2Gq
-Uo32y6oToCGFCxIUTvCbAE7+CuSWOeigrOe8D8k7LnHW4RSMqAsulouFr5VYZxMs69Pa9SBdTkQK
-1xqdSaMC2BIMzw6P+Ivwqsg2V59M3qFMqjc/0N7Vy/36shitwlJ3WojLcgwCKK2tgV0caLEHrfb0
-KhLrlmAYua68TT5Bb8tjRrg9EPx6+gpP+rAOpzW4H9njbQ5l9s75c0az40tWfi2O5N2v3Lj3VVrd
-LT6oUVbgdJVA2RLURaKD3+4jr/yv6z8eN4Tug84nZIgJii3BgfahPRuhNBcLcDSpNgvu/dQdY56f
-wC/TFhXvtLkoSni3Gc/5g6vf1LGfWJJpTHaLn2SUyDHjmu5C6lkezRxvefZUbOYn6WAlPsVs0pju
-42mWbtCHOM50eXTRwoKLsv6albFsWomP8dA9GbdpNT74C7MdK35UQPzrI/xfDt/ZjAkstlP2FaRi
-pO5LsRmR9B9zb4yhoNfzaYglCQ1L7sORD8slEEYHvdfVLcDNVxieD9ddUvam9RlIS7QDCcg5jrRi
-z3lIYVSPpm2TZ1mk5s3KwptuGI22frcDduMAfssVJGnoVmGuG8e/HhK07JsPPzJql9XXAg8uA8lx
-jOWfoMu3zAP9RPA9aVJ5u+NAuCXjtRh/9tU68PW2GLQkjhuTAxxvGCA7MoCBHg9z9DwJFRokEUX4
-LnQw8r6db9XT8vcHY9T1IOPNUoUSBJvrw05zITULMkpx8ELOYnOyTYuj5ifP02pt9gh+sf0shMUp
-73q2CL+Z+8d8u4Of187jBvZL9jbfGEEAfFX85i/miGAYWA/+TtcsziuDx5rUg/f1Crxcl8bXDW42
-f2BesKyv8IXsF2txiPy3wLf7CIfxZJSP5e7ANkWtVw9bmerzHE0ZBnSoh92R4nQIOdvO7uENf7Rp
-9gGGZxj7q+7GWE0mq/KH8R6CbQXsENsrsaeyNnNOpYF0bziBXl6kUYZzn6e8XJNBOcDJSwDKeJSc
-FuJ1fLAG529Yg1nWrEBulrMBgE3cI2pv/mT4BcLfDL5Feu5U/5B4lxlL+rRn4rDA5h2CNoFnAlOD
-XQvkwa1cIhw7EB5HXrXRwX25QRjuA1D+CslAsecXcN31pHqzLHgOZUN+eb4SFFvlpj1MFiyswMMN
-w4E73hpBJViAGB0YDxCcH9agQs1+eVmDU3YQZNYtZs+xGE8T5dDg2jF+JpwCR9tPUz0iTbdq1V+Y
-FoDOjwKCVYPbAL4DHFfjowxB8rzVeNRPD0rr0yDjnbX4Y6IHnYe9gk8Wx3I6Use0utUdQrylBXiq
-NdgbTZspmh9omWabgmjKAhxp9nkhZPBVjFSMHnl0IygwvLSSxvFlZ+D7NUCzzMlpusfUbWUmCGg+
-TV/jGB/SWG+KUGswH5ZD/tbdtt0Fi8q3+cwnhfOcsy3XfCorq+8lzvSCpOac8Sv6SACyWYHHEQLa
-TkCZvqEKJszYb5lbsURaK5fS7RH0GTxl4ikc9xdC3mi/HI/z9lBFz/CSKOnaQlmUrwyLopJnUkbv
-enWpO2JjvIdp2fnZJq1bf7kODy32sjZrAlSeymV3YGHniai1L/kRIiAwQdx5FvC7A2Ch+Q6K66ty
-4Q+c5SsdN5420Jtvj+A5/4GK+NvwC4S/Gz7UlRPjZdyEiUWoLQajUBYliXjgKKQkBbWBgDBen3MD
-X5cA4QFu19HLfcK7tfhybxZhPkymktZzDCv/8hqPPvRnMfizhUdVjA0A7BJHjpSbhKC4u0Mg3xW8
-+QSTDqAelJu0wkBuU+nN1Tus8yn+VOZVucycldYE9Ru2feoaaHKBtK4jWevPeLvY4bhfWI6nP/H+
-/koAG03y2MFpmflkT+23X1oowNXv7ehOemrnrUKedGEJk+Q851XeKnB4OO5Vu0+ZbEc1NgEutbFZ
-B8RWADldI+Lop3i+T/gCjA/TCfAN+qNrxBPI3YdyGOy93lPsstmvO5xnDbgvuK1PK7PPBL4wyisF
-wwWcfDTZjl5vQSEf8QG5xerdr77xOfr6c2nU4soLE0MAYqil14sWgLf5C0dbjK/z1u3uWybSsi/W
-gFkWsQJNmWd5hqR1X5WjT575xty/3XF13UZ9Dxwv8OUcKA2IPV+7hNZfnw+Std1orZ4TT9qINMls
-h7Jt7ftJtucIva/NYzm521ZzpGMeaaAB39lfw3Rl+A6NLhH1sFwB4O4a8fT55Uv7YWodfpIQ/174
-BcLfDS+10sdFHjVtYgns27YEVGd28/UaFFoWlpANq/JVbg+XW6a/GjhGfIkJYREGcAHWpDDSXOFC
-8zRThNgNmisN5fahtwFFB9fEvZqodE1QT1zvIFfpkp41btOvkqxrgDWCtLIBKVpHOQDxZg+ro4CM
-VHwCNJg+ayntnD/GecHwEr7LKd3q9QIce9UPWHMb6conFCHR2OlWZOuy5MFr/t6A3WPwLbKFE9DN
-M7zTSDKJuAKiAfTXkNdc8QKQ8QmCs12OUzpgg0YgRlpaeA7nVu1D6SkTyDTPPIv9nleOd+2gNSaH
-3w7L7zzWdtG9lHHH4MYx/o1mjb6xP10htH/ef7levvps0ql8iBeleOtXQImQd25Diz6Y8qnI4pUk
-QI51d02bbI9qY4nRei1a8wWOtlfcjxZi32hsXTnBz+OB8kPJM67ELg4t8/RZipIcIgOF2dgbhXUO
-77wYjCTdqZyNWXzD3AbLO6ipV0D/1LK+n2hX5kHK9TZttmYtu0Wez939g5WiovXsNdzXcs93VX5L
-nvgDTWQO5U4e4S1e+0doKPCb58ReyHk0BAax1PFqEc74ZfKsraXn1U+HXyD8N+Ek5b8T5FxCjaYw
-bYJgggue0zcFP8mptxrom3M3mufdTbcOjue7hk+S2du9O5cyJm+aQDygshTBxc1CQSEbXUGwzXcx
-KhLJuYm/HlBhWwMfUW9nei9RaZPZ7BpBOqjKTqxCKhjN1kOMdifYVdDbALD18adiTVAs6vr0poHB
-hIp5iqsepkWmp8a/avAEYtEWax2381MXTlDL+g1Ci5niLe6N2T/YTKfBPISsXsAi5ya5QRXvqPXR
-uBUWQAParfHFA3XhkHwhO7vhfram1voBcE3zZRxq4Vc+eKU7tjxZ1lzifOdWak3Q7cDvPn8mJe1A
-e+qDS2pbD8w1klJ3gOAGgIEdDHurIEGNpJ+ONsonYPU1SUsG93heAJnIcFdQqXVUuua9ZN5nQPd1
-3qQRQFk2uIu1pDWRZ1veYVVQrm0FktdfOV9kSOVKflW702STFs02mkUPeExwKy4nCc5Cxl6Sv9KV
-X31HCzUX+546p+0hb8yv5vmL9lWJyTatjac7jKFrvlzKEwiWPWTN0tvzi77Wt8oKAM71QJUzGyAY
-glmsQPL5EYN/NfwC4b8NXbLP5H9cpSoYAO2uG7eXfvLYUMB3MZQnAHbr4DjfGhHxtATDMw5gPZjX
-pLAn8AVQ1l9KpAvhn7eEpKcVObYJhYgcN6WUOzhG78RaC3kQqK3/ww0CaO4O55+4Q1i1VJ2wdjQg
-bg9DQK+NsiiLodm6jRuffk5gHIvTwDHWYm20jSkoZgZ3JMMYGkBtgypCuT8EJf3jiNKAQkbhRkP1
-ZUUD4pawgFrj4hEhDjeXtXYdOqkP3z8aXGekh9Nt19XNDqJcy8uwAJSF2Avce67PspxvRvDWKBup
-xnr7le+Dpg9JPoWpXLsgQbGt0I39Wbdz6oI3IVZZiPkiL9W/NDQ5N+MAU6qii6t6f+edluTYJysw
-Nz3H4qqs1bJVv+C8RgMgYHXKJPqUapz9smQoy7sDxXdFYz8s+0uAWPYD8hJKrnv1Re0O7rtI9hGf
-c7sDPIGrKoeFecQGULNhfSU5+S4ncN5GKxuw2/mvD1pBofJv9Xutv759QwHvs0W4eOAa+cd5GN3e
-gOxD2Vfn7WUPeX7Oe6q/FCH3B3Jf7Hnk0QCxBMEhi5oF+EDLvUGaoQNgK1oDufHTh+QSDIdV+Pc9
-wv9j4VNjVgsHgKKKYsZ5Cq3AFLYsQYZMK0Aw1W31JOYtjPYVmOcKl4jLxE3CLOPLiY6KpgQ4LcTU
-l77uSYX/8hKUnu4RNna0CpqSgFP49CeBazYWjiKQHe4RTbmOSZ6LZFXKheaZR43IDln2uT15H0ct
-Z9cNi6uRaRVWy3DmIySBaoSgL7ApHGHAtLxOmHFSgkr3AVIKFCd3FWA1TXM9eauXa1Tgr5DAZ64R
-XibS7O1RBH53j43xd+zoW762c957Eg+CgmHYmg/PsR+xZ++DdMq1gy0ut/xP+a+CdoBjOnZK42pb
-rJ1l+dZaFr1wAsV1LFXJu1vRAygI5oqXTCvgrzZGT4Dum1WYt3eXwpY5yi1DaxXqTQrox52mgJfC
-gq4RwdMxoQS7S2SUWwSEliMVRqh6azuK95lYeledtTL7p+j9Ia5j2uO1Pknb4t1FQs/zHJBSk6NL
-ZuaZ5WK1tVeLvuo6bELTiAhtGlMU8Ja7wzOtQDJFbtH6eB/mBz1s82yVel3WHvO2/KVg0R7NfCkK
-CFTnL+oe4JfHDSTjGQQzn301hDtE8LjcAB0gWI8WX2y3tBT/dPgFwhmOKvgh/KVmhu5jf6APBd72
-pSXD6nkF1Ah8fQPABL1lDbb0B3YLMByNukpZBb6Gco/Y3CZCV11WZUNwTUvPLlzxdvqblSUtZQoO
-VrqpcN9FwCYOonGXecxdGzSCXsYByyftc+JzDa51ZRyAeAHgC+ouoWCYIJiAOkX3JhHtLQhOhmkA
-S0oK+EkrsFiAQQuwlZW4p2WqQYtu1HF0jWDVwbfDilwde9oVn+zKpxLFG7PEtnvnfprZjhyr51Pn
-eLAII+NtiId+Md6svO/iDvg+guf++4GotBlvVtc7kJmC4wmKSy1z9PWcuXbsNCfx18ir3caY++sm
-CL5H//rPoMo7fmOqbPyKtta3gZ2cG7pDOOpiPkCwqSHD5PnPAgfcNzaBY5TlZUV+Dh5y8y3KnUEy
-V0TjlnXI1P9VfLPMZac5dzETWcwbvfLIKTN4zoFeGKSeIM8nU5SeqwEW4JruDgbb3CKulg+kWwSq
-XB/BSV+JRp56bJ53TM9ZOpW1kebfodSAnn7xKwtx7Jmo6xUI5ppyj0zazOc+t9APPHLedxAM8Kvs
-CwT/Piz3PxFKgAO5SZ/1kpzDMIFLL5dkYqFRvAsn77fKAiutV/J0twgCYAW99Av+QqVxSZPT9ACk
-hG6gN10kVp882qOjWorBKVEkKaJTpItYH82SsvayWoaHO8SQFU/Lk9e5TSINjRB95pP2CxT2NMsa
-LcKX4bov3AGI1wdQBAzzX1qKo402GVa07dbDicZzBk2Fpeia0PsrTayV6VXYm0WYVt9o49B8+QAz
-s/ozDdtrDI4d3PfxKHDahtoGKBmPt2lUieupbzbwqGIDwzGWhfHXeAoGeU6FZQVVmWsfPozbU39l
-jc95Md/T3yP3l7pBLEi1LoLV6rs2dq1urGbqVqsxyV7KsqbnxV/r85YfCUmLuB9dI7q/MISRhQ7u
-pJqaTIuo6Yqd8H7JHvaH5wBiHQ4Bl9Ip67NaNxsA0elh1rlbXd0ewa4/A99pz9ewga4parCLvlN8
-yQFNN+cH6LCFO1oHQppw4ZPY1IMXoU2TV2EL4V2Sclp/8UjbLMJC66Mdc/WGfizXZ+ixLPyh/Zdh
-arpX+6T/Fj9TUj2AYNmDxnJ5FD9hoCzBxgsQ5PEJ/C7gG26bSXs76H88/AJhhvcct0KTMK+U+OHE
-D4varNZExzXZWZ2+4OIfXCDYbQDi+NE/OC3C1IOAuEbYAxgmzasLPP8OP2T2+0J8EASHOT5MepJK
-1XpKVwVZKyf1IK3EbeK8xeavt1t94WuYco7H/Z0Ep9zhYdVd5AV4YRZvjqj09sOkoegazc7ynBMv
-veBFKc5pzIsWuilwrvPNHDjMueUr5tpnS9PV4TClhzdE5PtEp/Lb+MG3WLMcPQ32Ff3T7Yrs7oo6
-YGXuKzAMpFuIwSQ++y2PxHo10B4M+iSeh9of29H7ujxOh57GQfJdjArBeEvpvqMH6z0HGfe7niuA
-tbWiHGBjeXdFQbD4oqfvNesyDBA8rcLY0tw2u1tEhyUmP0i5DeDo2if49RxhAUOx9AI59+II0QT5
-FfNzC12eOV4yPeO5Gm/jWaeOAU3E1UxYL9PKaV47V/blwXpnY2NPlakgOOdLeRAA/aWz7eD5lClC
-L2vvOloeXz8U92QlbsMd82ODuOft58xymRa5vs/i4Zxg8aMye1Rsz8GiMss4ziBYwG+Pjz0Gr7nO
-uKexR98isfkLXwWG+bCcarSp3U7z9Z+EXyD83bDhjTMw6cW+w53YV9z7JmtVSkZ/WG7Ji2YZlrdC
-NDAs3aTFON0egJSyy+pr4gYR9TWrsC9QjZR1cquD0uPRppVhzy8FtLYjQbC3MpkHtPhTG30puTtr
-TmnpDUmJHcDqebQIX8sS/MJXOH8IoTzVsmmavwKQNUeS9yKILhFLb0wpAN7eV99dvv0D9H8lg3BO
-QynZALq0MNK3uKaz8hRYg0/aH8cwtff3xGGW+MYW1LJyMyJ91Ot2t8f2j95bAVsFe63KdrWgcuMc
-t0nPwysHicN47IMjHLi4mWkJLgswjC/6qpGZY7lLkeqXuOAkbKi3bQBrT7BZ4yWCxfYOxxIrwOTu
-62L6ybrVAG+8YWJMs+4S2WXZP5VQZSkusEv+pnLX2jpbdmCcf1Neb4I779SpG8QJ6ALDRcKfylm+
-rWf0Qpel98JG3hCD/QTDlpWgf2+gUr4ThfeWWLIHOrABYO4/qBxdx2vQTg/FnWh5X06aepyrp+k5
-xEmZ8ugkwSbtuU6d80Fuvxf7hvMIPIPgLDPKQ8pYja+D37NVWA12RqvwBcz3CL+ez38u/ALhfySU
-0vuOvn27sFrh1KibICnmIgDWtJvla9MSwCLcIYSeoBgoAAzA7wDI8GYlpoXZIfEAxcsybENI7kNj
-8BbrmtolTRCcpQmOucePdQ/3iZeTvyavADBq9w6L7gTJV/oDd7/g00Nz0xpcwkNV82nS9Dr8E44r
-n2pacAoEB9ho/JSZ69wArgWUo49h2dVb4enGIhZjWp0JSPp4uL4QBaOldKH7Y5RHxar5iYdezZGo
-7odiCoYJgOudskxre2u87fqYdXTG7f3wPe4VAVq8FNK78KlkMt53J6S6LgHDDlw3/F4b3+LK2Hnn
-CFiWXb2t2RCFJdGMj2XSx9ZqDikv5C0M9A329elIsRDH6BzIdwxL0+al3Mk3ahXWXdZ33LL2W2er
-4JHia0Nd7ClKmmCnM77U2GT5sBDD+/uEo+mT5ffJKqxtTWnysDSSrsFr2dWzuZGrwG4glovmeVLu
-q7Xeyqb1RhmhG9JyWbKD4ndaf9dKdCvxuVyzEvehb3NX49slVZvTh/Nz3JN2KPdKJqmi2yzFGxKu
-U1RRLp6nXJYH4j61BIc868D4ZBUWAKzxCwKIw1J8/bpG/JfDiTVPwfuGxTOvZvlP6QJ04h5iba4m
-NFGSXeKKq9QNYv28XCDip6D4awpoukkkAEb5/IpPMF0q6mG6ii8gHGBJVNBpRjw2o3rTsVyCGd5C
-JUCDPhA3LGQ+gO9hxjk3J+Nr0frEnkCstbiC4PmQ3AXQbUJAb31cA9mBNX7RTBadhc2RjkGdk41P
-HWhgV3xGCygvLeWc6wTDAK29YtcDUV/zBaayEotxvlkhwTKEj/VxMwbVLHMTyIBEKfrMG8EO2f6U
-z2IuSwR0a3fOE891+SvDm74jB1Cc57fFQk8flv+VDMqpk+Xykc4HkxRxRbo+phH8S29Wm6DYciz6
-OsHVBK28cgTL8YG5AMnz4cqjS0Qo57QAh0J3XoDUfFof6vHHWUwwlJM6rcBB0zsgGDzDuUzi4KiN
-jWUvYn8wziHguKVfuUbst9ttJHq6W0JV9MwKtpq57Do4GftsN4vl7RPLC8lkVgHGq1/B92Zt/sxr
-zV5ZhD+mWe9vi9sDXSJP52baD7RZzvcS61wvMHuou/Qktp8nYK2Tm2tEnLc9EDfLoMo0P2GISoQj
-3SCSXq9RW/ik3mpF1Vi006z8u+EXCH87yO7cGPYzgJIP0Aw6axGJgCFvNau16sAAvvG7iv3pEqF+
-wsXFXnHxCXag3s5O0DzSfh8AscVDefEUH8er+ld1POVbWdGkJG89o17aVLrPpa7Dg3MopQhJizjI
-rdzdE+S3gd2Kb3mXrXch6i8A8P6+4FLDBq0T/TgB1JtwKq5uEH6gJQgeYBeG5irBi5GICpBSTeZ1
-fo6wspBDoluFZkbfUiGVspTRRH2dtqt+XeXT/PiW1886n2eSwbnT7TnPnctBpdYbVRjhchIVVE/P
-vm6qQ+e5vTcNJU+4tXLZbiAsvu0F4najXZQh1juswe4eflfBTBf3Dc7HqGfxkB69H8mTzQocvxtB
-AxIE576OW7je58cknmvhstWUDgHFctVgjS/pNnFQBZMbG2P0tSfMdnRrcKXLDYK0t2nWPQdtOk4b
-6TE/1st4o++Ky/rZOCW9lUVXCFGvCQheR8+TrNGQfNStvwVqy/pbVt/HcrJmnReKoHO3lXkTf0UD
-NrY4lt1kiig6cx9iJbRdXBw+uhSlnH7tH0y3CKZbXh7VIkyV1n2Dyz3C6qE5Q75H+Nci/D8SaM0Y
-xF3jfZ7dyxSW6GBRCzXhEeW52eN3hTWnuSwAycIKik6hWYOZNmwAWC3BM32FcGObOaSwAjgHxzas
-KzAOrm7AqG8wJ2e+NSKhXps/R8dTpKlwOVqHM1KgtwPjev/Lbg3eQfCTm8Quhk9icKrsom8yUAeZ
-9MVYBEh8ENFDuRwtwvkgXD0MVgDdwNXoiqoIdI3YwTI6INYxe90+z4GoRuJYNL29KLdOm3yuM+gH
-WuX5RifPHuuQ5hv20bIb01faqkA7wbWwjMnG6LYpAXJT+Zwe7jWtIr/coKA4Nr0A5HxIig8fZDm2
-R+RQRwMa8OW6+zwGCKaAePq0sqGswWlcSIvwVNp9V7Vd5ON27pikRi8/IJlEtPrbWrDtlmmtBq3y
-inFMi6/jyUoM8NPKpDF/b7FSbSuNpdLOKpCedbSteG5oo2U0H+bktuV+NZla8Rt2PW/R8g0euUaW
-8ffvEsZOs8pr/T2s76nMU/7TtDS6H2iDnmlHuUPMgmSouDj0lFOhPSW/7w1P2bNZheEy1zwn4uLy
-1n4GXBaW4ZzbVHX9C7ilPn8twv/18Onctx3/AkZSyTxXsDVf8rBY1yRT33y0gLI15XaFslO/Xb7K
-ZFqKmwXYjHfdy5TA/og7BF+zloDYAL980cUSfG3uEWtMickOCrnABS2Ti9BsbQ4BxNzz/QE57bv+
-dO47bcyzbucEqpC4HtcOnhZhu579hI9WZFqZDKjX453U9oRWRfeNthajqW2d61yLmu/+Jgmg3u5g
-UsGaC75Sbb5L2HM2y282+TfOPfsRk++lx4aeFmDRECfnSvN13CONMWNIWi+juuUkm7fyMkUsYaOs
-9tNGuqJVOiXBqCBdNbKP1uucbaKm08fUtYuXO94SoWD4Cnq6RhjcDuCXm10twq+Ary0erU8V13G1
-EYpWAHC5QdSvfCTFH5gXUl5D6zuq3//RAmMXjRm1KCO+zfsyDnnyEE4MF7QGdqPipI0yLmWfXp8G
-lF6ZXVrd7Hmmg1fmPw2HW/URzR1obn3csb9LZjgmKOkZUS0AACAASURBVIbLGIi+vKy63doLSQNn
-669Yk5Mm3T0Mu+VLwRN9m4J5h0yH/wl9iIEUyQF6j4veQLFqz6qw+QdjB8Elrw4AGQiLb6QT/MoF
-iiEs808/G3dSD+P4l8MvEP5PwtSyW5iZfiQ39Z8AxPobkFIgWJfqWgeZyksneWiCAsGUDEgLcZPI
-dHuIfOZ+3d3HmOde8UlltQRfAYL5YY+2/Xz1kft3Yp/ttn4CXQLp+Bsbu/VfaAWM66/+lBb6uCsK
-KrEEqyvdwPDVAe4CwAF+JY8f1YC8R7gBYLZDsRxrZHqB8qydz/LvIU2g1cBvswQDZT1kmRCkTFMg
-cr1SITkZGA0sK+LKNgUn+Iq0B6ZCAKd9Tqy9BDqltXe3CFGXjaY8MbdvvfGh15HzcqCTI895M7fa
-aDmCoGyk869r3aURj33Vtkyqq+kSMEz0gdr7afkF3AiASTcA8UrA5FvEelvco7+Kf2FbnOcRFCP2
-nltdWC25V/y03hpxb1+W4y3eBMHuBYJlKre1Pv1yjlXAegKdUv6VZ96hRV4XHNak1rbktfaHQ7rl
-vOnyAJytxE/0asJahzI60F5uP5WFvaJzetb1VG7qLbH85yRwcow87NhBsdw/C/4J6YngUEkfaBMY
-W+XpVGl3T/Gna4TzsPf5UXkAiW4llW+YPvC1guJ6DSH3Bc/zBMXkaQBoQNfR8p9AcKW5DhK37gpx
-feP30+EXCGf4cPabmXdjwwPlVVDtVFXbyC0wbE1vFb6g9SSswrbOubDASxprLEBN+v4q5wL5urSo
-mr7Ablh+viOvALAvAAzU2yJs3eJYlml1XbCumKms+27Gth4s4CauEVV60lrew8zvK4cCxNzVOFmC
-CWxJuwr4XpVv1z1ooy70uIlUJXx8UNmHEfWxzbSCH8fpSObrSin9glsa4/a159wRCxPcvnKNqE8S
-k8GBfFAKdX6rHNg3QKKd53mps3kL8AVYeUgrnX+7X9/5McYcj4qO7Xyp1aWfmT0At4dlda8xaTKL
-Lc12p2W4vyd8vSHCaA0OALwqo2vE4n1uevJxXryNfcQ49xf9gWEd+LZ4jP35t/L50JzKUBdlrvPw
-avfUDhOLlj/NZPHnnEp2e2vLZqSs2KTtgNYf6EpbF5JK10bP/VjUtm2mZXiUfUB4r2lPZQ4g2Ea6
-FCDze5r8ZMAOaqeVGHVUC7ACZVh110Z3G81e5evfXSKc+S/OKQFQwWu4cIw7zV5M0ZRo7Y2ir4Lu
-SB/gV5bgl1bil2n52Td+V/kJ/3T4BcLfDqKQN8qeV2GeQ6sFFZrsf8pXrW5agglaJDQAmvopwEvQ
-AMjDb+MGsRp/Zs/1IbgAvvlFOljzDyY45nuK/UZuq3tWbCUPOyjuykfBbgfWLnShZflqav6AAgNN
-4KdUs9zNHQRbWYOvAsRpEb7KJQJB2x6W0zjQwXCuqz1zVJSbeU/pPD4oGbrU7OAXYhFm2hKIcdH0
-XcJ5m74xtbDs4O3k9yCWdX7VyX1S5jbZCMqsXqQG+jLtLW/GWcWs0jQxZrjB0wQ+hzJC2oGx7EJv
-Zy36Q7tPFuz0OGEbnCqZb6ZdB3fH5udzcmr1Nb4pYm1yC2HjvOepV9v5BNqCGJQ/vBME4X+my2Ls
-uQeWXIhJusUafCvwnQ8eSx6VPvo6bsoaVPYnyXdgsMEt86G8PMv2GmZd47Kvy0hHfjWOv6NbhNd5
-u2tEyTPrf3rHKIeUbtYnDYf4kWZ7GXuZxATFZ0uxtf0h99OaRXexcHd9MGB8OMPSkjzdKdoUHPr8
-zgrcaafJqo1rBzKpc99vssurDpkWUXwhXTzm0zHcI8iDO+h9DZDnUd0gvP+YH2KkWX6v8TNLn+Gf
-Dr9A+G9CyPoU6uk7iQfUMsNHhQqnKIjg6VabxUN6kdHcwp8sFNCy6Pp6g4MtIFPKLpTNFVu2dF19
-ihk8lptCtwgHOLYdjNNNoglqX8ZoAA0EnwAKoE/CijU5lN3ROjzmudOQ0qdAgJXSEmlnEIKt+VUl
-vrtFCF3dJE4AmAADpFe/Cnywsy2zB6u8yVWPafKMA/3ht52e6dbG4HcDtlde2dk1YrMOR/m0bpLp
-5ZxsbCI8VFs1wj5PqyaX+Nb17cwj/BHFknnTAtNIkjd8A5NfHfus5tQOLpbbnBjnWBtlHweAAXiZ
-FhCXkxG3c+5ryR2REX7dML6cPEHr+qocLkt/3/bGfOXjBoy9pREyy1jHSK8Life/fGAOgp1yjmT8
-3/rpw1hCc5l3ioji9BcAWPjQBy1OoIxUCy8X9OQXzKAPzT1rGOsxE9qQh5mynTaq+iA92pjAF6hJ
-O4FgEFKKjIg8Bbzv3ghBePxoEZYuTp6ZU/BY7jAVPX+TLtv5HRSXcix/+MFHA/x2v2HKJrpHvHaN
-yLS9c4cYAFrujsiWB+GGioYOjC1fncbXqP10+AXCfxVUpX7zrONpqs5q05XRzkpQAKX4pXC6UQVj
-OfjMCgW13LLkk+EJklGfS77EChlfikPop/ZRDpvgd13JLdDsm5uEfINjDWEIwASh/jyztKz1B+MK
-EKgKKF/hoqsCaRCGMlpAbx37zt3BroXVN9IHALyAcp1PcNBew0ZxzLUGZIIqPeGQ5vW5qkjeoiV2
-1DIJhLrbQwfBQHvrQJbRexlV3kw6QD5uWybAjVn5+g4XiLxt3syZ2XgbctWxz8g7EHyiYcQnQ/Y7
-FkkoRf2QrzzbrblyftK1zlWH9pVlTv1l82pAm2BYTzA9mQ/JwfKrPG5XySRuCQCwOwGwCRhevu26
-Z4ACz9wD9EtXGgSgW7UXQNfDGtwAMMQqPMABh9RkJxQAnXbNYX9trhF7efJa4y3pz8ky2LDerDYo
-E/g6nv2CYzZQAHq22vdNJjTN9WI5O5wzhzKBy5Y/oslMkImKQk65IHnsYwPI3ENlyRUpugPdKDM/
-p3wCyq3LMj3z+B1aDG6L5gr5npd1lGBuPLYpMqEVZ0ZCbpuoa0S59nRTBy/2AHkgDr1sB8GWtHp4
-rliIoHqCYL5WLd0iIv7T4RcIj3BSjqe8v6lvhdNmINhAv81mAmKAumWsBeR8+jjRIrzicashHnZb
-Fp84KXyC7bZ0KrPbl1KLDpL9840RcIkvIZLgN98UYWWZpjJbXcjmFXunsDmMPbevAAf2o7lDpHW4
-ztN532SGFa3rBasjN+kAwB3QdlDcfIK3B+Z4S84SdO8uEXKMjuZDc9p5HY/tY53xNo8mR80nWBJQ
-nM05FwXlw4mw7rnh7BpBBhbrcHPirGpXPPK1g3yDgIfgTURddbfBkpHGbG1D0eIPtD6Z3uM5mTWx
-3vIeysHbHl998lZMVzf77Z2mfTyNAyilmXQuEcd6mhSxAiP2Lj8X7sL3CmpP1uAGhoGdlgA55q7t
-KyRvpn/u6V3CIiD0wxr8CECfu2fZbe1HJW/9xTrHGhYPBrfv/DMYqa9by8x+qkzb3MginNwievn+
-ieVTc2aDuIFZzWPRQcPTWG0f3lPa1S9YhBIZlHKyuUbITJrelj+/N3ixtV78TNoA03NsEv8bWuXN
-XavR2rUtqJBX2eKjEJlhpCk3CX4zHeX0obgls5n27I+B89/LtnyICwTTCohr28sDdPVp5XygLj63
-/NPhFwhn2EXdFHt+pL4Oa99+co6qsF1xVzwARZzjck4yZ+AHG/GLgINM51Ug92IAjWX5XUyplt+v
-AGVKJ/j9yng/vyyu5eOmM6IAOXWxKCHKVRfaFD7ccBj0uV7pr/lySR4UhILXCXDf/AAF0RBQjcyv
-Juq86madSwv/rpjsyL8BJYcIk+OG/vKkAZTHw3KFisvlIcBBgl/rVt+Ta0R+pc7KpxNAWZgzXXWp
-RTn3B/t2GP8+K98I0wWigdzeUnv47aFcTbdnsZp+AUxa19anOLKgPvnWJkIkCC9MIHQVMED2X7lL
-XxzRaEEwuYhWjdfveLBrvUz+/IHOPL8xvy5nQXdahsE8iF9kbXYCyDaOx18BYjyW9fG3L49K5j30
-8rZR1EXi4efALWdNgLzhLjX9b2lutHM6L8Ta5pQ2Tvyo4unUnzlyufq2V+UkzjsHF+LCxXhEHSG/
-4EFLn0PLNPXIXIinFXwfBk+0febHYj4Lyn7khR5dhTIda+Ox8JnGTHuU82qDDylv2oHLzwkxlOWX
-Fx9xDDemBWy7EWgB3YiH2wPzl/WXbhEWINh+H5b7/yq8Bb9NcnRSJihKO+D1F3nz5nkKdBEMl2qD
-kFkKigEI2MUDIN7pLnR9aA7XEk63V5+atWMMXQExIF4baMaqDRATMD87EEygQi1D8HAOddsHBW5h
-Pf0dUKxAGgTGaECZA6IVrUAx0YcMeqy4zmceQ/EJZGv4KeGC0oJPmvWYvOO+geG8dafAxoM/Tee+
-MNp8KYQNkDvL5q4h7+Y8+Ul/fHh8t1c5GVK7C/jJiZmKrPI3oORPPPqmOz5kAKc5slNhwqqc1YIu
-2v6X58qb0xpY5LMDFhuR8M9gwD0u8gRxUkkWqLUzGOYCb7ToxH3Dboffdz04Fz/TdADf9WjfWLMc
-j1h9xzjbz1C3kEf5TfbUkA/U96TswKdqI4J+PIPvHOZeLl90mccEQLanhY9XX04dyopaf9OtLBmR
-m9PaKYyblj0t06DZUzlgAN4AZ2Fp5ieYiyGXnFIw927idV173NuZj7UMvd7uCLUxjbNVnsmdkQLA
-HxyhwLnaaHdTqS+bGinpUHNbd+w2+viaKgEt75QWGFbAey1gnOdcee5Ph18g/JfBT2z/Roh542yN
-nLYZCoFo3lSEQLOKae/aVfHT0ZBuFJCjPgj3OSBm+XpjxHqjRClaA3Cf1IX39oFl7Qi9G2lRUAkk
-nxWTzrUBDQS7xOzhzFan9VZaHxLQnq3ApwfllgW0yiU4lvgmnRIgBL3pHnWfOI+ns9yBd42cc2Bl
-YcWM8pY2xMKCaQlGaoitrJ9B7wnkZtmqro00y7a1/d7xHJo2kjYVeHse2hPZk98cT7MbS3LqSUia
-Q1bOQ/4R0NEWTEHv6mM+YOsucxftybIdj3eA2+uhzA3wi1IEYYt1Q+i49XTjexFMpMET/CboPViI
-ax1Wmg8GzTnjcT4IV3T5ka2zfCn/RXxxQZOr0DZq39JQNtn7yiHwZvWrH99+6a1OhN818AiIlTm9
-+GTjU17ggsvCORj1yanlMjUHO5oYPJf9/UDHUrYn+2CwUubT8usyFmnj1D9ptmbl2Td/zpy6NI3D
-WKRDgxkvvtYPymzAl7C20eO87Lmmo+7JwHNTp1wJWY3a3zm/A+zaAL7rgxl8lkZpCpoLPP90+AXC
-/0B4rUjPBY9AIyMme1LTtdVO6S6oH+LmZRF+cVxWXR8A9wR8pUw+LBd+xfJmCcPweRszQL/hTrbM
-O49LBN2RVmVVyaTOVPClGkXxhPYmFXoo7xcg+PjDfh5GGWh9sHBjocqO7plYhw9Xz/5wZLzRRU/l
-bHQMBdh+TlN4FspBwHCBZIh/38rjGykshlBNrvM9FCDzFU/X+QJ6ow7VY++OLdiLPFFkFoRXAJiK
-pqpW7df9RJPvXkmQiUUa6SAPvMsOmUmYyaXEoV3WpA/JFL3GT2DXvqlhSGDb3X4UEIs1mP5ZgmIs
-aa6Vrn7eLq9QGxbg409ePeYJEXLGrI2HaZWlUoZdOpxHYfEEiBahMXAPugRW7RPDzIJcWxVhc087
-e7ftY/KiCDj3uoDmRYvKAPZju7Cdmy7OPQHiMTE2xz3nYgosOda5IhjQ2EjiBXzTZQJdFjXL9WHG
-T0tGGaDDOtWgF+XbeHX/HfMO+Qlu7xeAGNXqo5UYue7KS60jyvjgvBL40hWi3gbRQLCCXcaFdilN
-4vr76fALhP/T8EKHrfxXO77K8CZN5p8e8XaJgwJzbtcSmIauV2gxuJO2hNs1mB4Wr+Gx8vG95MG3
-Do6tXrN2V1l9p/BZmuzE+Hjrpoj672AJfrtvZL7ltnWCFncUEtbZzRZhD+kOgscr1Ao5v6SlRQyS
-jyrj1B4mk6mDVivxGHHTKaoYT+UGm/mc16ZYwyrIgqZATJUNsH04IxSPAtpVDqlb+3nSR2HTqaBT
-nz3MwUm/7onDlj5ps2kdzmxvq3DCQIpJHgWIv8gT+mfqWwAQqo8cgUkZBSmTq14fXeoKOi+ADGUZ
-DAVa/pncDwQmg9cnEHZPMGwutPlLBNfBhPbZtvTZDQLoAAvj3JfhZHGT/kzG2NezaOtnrVwHMpru
-DTR2kw3kZsWL5jVt2biNizXZjQbwwuv8JGasNfmgdWR0WrrwXO4wO951wAaGR3+W/FC3iCxwiI+m
-cu1F3vQZge7/xy2umGDLG8QhwCewfeUasbiAvaqJTVqKirP8XNMl+xVUXWvN89lY4Ogasd6i9Ax8
-N2AsVuGfDr9A+G/Ck35qCvF753dbEQv1tB22oEGYnEIhz+6gkiCCTMwmbojfsCCNejVafxguj/FB
-jv1jGtbKeTiy2T0V0GmidiVO2a3npv6M8nb47U2IlPWZdzyhBKlVmz1+tvx2X2KrcpKn1t8ECKj6
-az0Qa2/SIYhlWHqfA695fJK10xdY40P+Fq2V9RiDKsG4cege47GsoCzBhgLEFMAEtHSXUH29mJW+
-x9m3Vs1KNP87VN5Jr2r8ectm7aG/qiSfxtZ0lnAFxIuLpzfK3PWKUV4KkY2naw+oMmbPX9NTu69D
-7Iu07g+eb7yZcfoBz7SlZS75mzxD8JtgGFXhBMZwsQATADtcXCNMgAFi/juq03Wrox3SK+6HvNqj
-CbK4htwTco7nLI8wRTv7OYqu6ZpwrXw7uZb1jH+JtRqt114XS3AZRD0rIhDKV9dtyqw2nOkoTYQD
-gXOfhKzGlH46VsPnLTDK5/o4ZG1c1kofhONDuuxrjHXMsDbQ9+SizKWqs72lt316FMSa9pf5r8Dv
-Oa7nYKfZrgbJA03VGBL85haFbNXIv9QfOB+Ws+b/uwHjBMNXA8s/HX6B8L8Y/EVqBt2CtcnFGykE
-TbMc86oblDeeRxrkyLCXO18dXG9lsHJHUOsfdY9aftU9Io//j71vXXdjB5WEzrz/I5v5IQFVgLrb
-SXbOnPmWEi9LCN0lKGNaFj3yxK/LXZK/nnFtYfDxIdUNz8LHlY//OMhkLfYDi1ZinNNxCXCiqyQ4
-lHeoEf8C+EoHwocXgWFh62/N97ZM832N1X2BlXt5tArzLLQpGPKQgHojPiCZpLCy7WVNX7vuXeh7
-NizHqz/xIx6+mzHbh1J8glE/irfXyuSeYuFOsOA83tsA3FafEGA/3vZxFSzWBJ6OHTn0DIAJq+/6
-MO2pLoVkbUNjHP5gHObkuPSB5q4QIuwTbLG/CQw7kNrxtBDvGhEIm60H5vbXw/4Lc/4AUQfD+w+s
-l/cWT0uNn/Ny7r2L5ycMvLGy9iOK2mfbcsfnOk7rrTSWtPGt/sSwoR3akZ5nayDoJSAi+YFVK6/z
-7MguHx/6YG1DX3k8XKbK+KFbLgpOeS2UWybisxOmZe1D35O+x1U5HX2vjVvtMp9tiTxrxek73qHq
-2s4531n2vh+BsMxAOSoz6rmVJtq3fz4wP4KSxzT1Xp6D5hoBgBiBb9IY+KKv8I9F+H9zeNKqowIq
-0mqQEgSGN4/G0cu/LhxTZiUkCJ1iefOC77V6t6/awq2m299XJ4C7f3WuWYLzXa7d8kdCIjnEi0ed
-P3XSdo8HBbIAu4bsTbjICkta/CRhXPhYtNktdhLg0/+mYKiA2F9XebElOK1hxTrsI2o8Er62IeXb
-e9tVFCddglsO4t0qDN9u6FTWNeXKsLA2WVpZdh/DLw0AbKjwNcBcJ9BHuv8mcM5hG9SBIAHDaU6e
-8s6hgJLitsEWYU6HspTDPjv1BEjkc4j9cMaYk57vgIn733mkxO/P1Trs1Qq8trGJA2CtYNiFUZiY
-/cOSpHbFeHV7+CTotY+JAjhgMGzjGPUQf8q/Kyd7frsKV2TgxUdx74ItEll6jUQhDnveEhR7H7B0
-nGs/u75sQbdGr73Q+LuZyCrsNAmZQD9wA8BYsRoeZqdFHhSYyklZl5ATuafQ5x0/wM8nBftEpyvq
-ZR4RK7QqB3qVZQ+U+pi/8iL4nX2FN9fu3PzwHHDkrp2Uqa++H1V4Dz9hle4aUazDCIpr/BpulvjX
-4QcIfx2qOppCObgyHTM81SgsS1pkWwpY5LogwqPqyi7EE+IRT3vtlrdFRElT+aj/PPOqe/ILvgXB
-/sDcp7hGfLKfObAypj18tzar7Ade9hj802fE3QpczvGKszKJhzhcEHnSOm+8a5k7tNYSmHWhUCy7
-D5bhBA04ngTEYQkWb3uvVZMTXS3Tfjvsw65XwLIE23ByjbCdodEf17QST6nT147qe0z2/PkC+K4V
-VqJiBNgVaaHsvArX7LNKm8bszDNv35fThGZb7JKxZ4FxjnWQBBiFFeyDeKExWqXxBwPHIsmjrY70
-AUUOHAvEHeOA0mxW4bD47j2hQsB4rbNKAOAQVot2AsL+oxnx4xkfpiX4hReqe+OH5aS8n+J3+Sd5
-ljNihaC8OC6Lyr6uq5Hf9iXN4UwWZRcJPM++LKKxPHFkdc9/YFZfYz/SljXmnABD+PrDQGO9aTJo
-2HX62pSOPN5xzAMjBbWRt4KkmKoAWMRdqno/Jq3N8mAytJTpyo7WI34nqEJWlv7Y/qjTLML+INxE
-98qt9d9gMawukiqf82oJvpB2JZB1C3CxEhMALvTK+6/DDxD+w3Cvs4ZcPA/HwkVyiAvCrkpD/Jin
-BThNwFAXpclbQbZVWEJnyUeWFc4kgTC5RsgNCP50uojmzzrTYfMDuEdoXr+ly4Zo0MJdQpUFnr/r
-QMvJyQkHwcDpKXhbaBHW7IdbcyvgfaRtYZJShl8huVdaRdgyTPl7/iIbFWUG3HdsCfan64X0dC0D
-qA7aACVI4HbnhVUGtL8W39oACNX/N1W83dByb/OH1Pp1X9crJ4HLnH6mfIhW+ADDZA8MAU32t+MC
-6x170acT6Zl+yISJR6/UCtBRR2mlq0iC2/Qbjv2CwCn2PAAn38tHi7BAvAPjeJ/GVoCLd1cO729p
-eNZoTfssybyGug2mNS8OG+wZjf2E6awZJb/kOS/nGvPCY8HYnQ7HlOcMP9xk5WEZjg81We+YrtNR
-3+UbHs2/0Pc0juS5TVkuMUta+9TablKDSFYHZIV37POJFSTLWIYfhGuAVyAeAt3pEufHbcGrSW0y
-X2TPFKmmBMXL/1e2NXjHCezy/cAEhlXjIToCyw6IN6D+1+EHCP9JeKXA3jAOCCQOUW4Kxc1b+Mrn
-cSizqSr7kvmVDO8ESXAb6d2EGVh1FazCO3/9ypzJpTqD4H2bxLpHWKFjW2yDEhFJC6A/Ge0/FW2y
-AJDuMbiupHd4yfCOU+px+ppqELRR1oG3rnlWoD36AwO4JReJCpQl8xILa4BEtAxn57rAOOoS2GI5
-1AO48nXAeIF/cSMA5lZAvMHtzpJQ3aFZFy+BYteapChcATMNLTkG5DoZVIayePzvjnMCqTUPWRfo
-/U3ptKzivjUuYzJFsR9CfQG6eRz6bb1srbPF6QivhPoE+IcdQFdu6U0/4fQV9wfl/KGsVLoqDJQB
-PW0lnmB3f1PUXCIkwEDw13m6eb/LO/GYjz3Sml33fvp59TmDOY3zoPFYqFQLsK8hpnFV65luvvJ7
-aeJdZIGofUQRBIdnE9JivJmi22FwjZ/S2LGn9ykAD54T2rc20Hy2UIQ+Co7SEUKo9VwOeVZqqNU1
-hqEM1E83QpRvPgL8BtiVOA/pCCFhOd6nSKzs5ADA3jYo22UFltRTGxTjA3INAFc3iQC93VL884Ma
-/x+Eo36zGjnt9GkDMD0FoDJLEYlV6HtAa7CnDV4qun6Raeskk/WpDx+Uc99hB8G/4JaJyX0iPnZv
-F4kQ0mARNkvL77WF9SUaFp4rhLKmIIszq3lw4QCfv+6q68DSF2ebBSmA4BcvKWAXrb2ez5bgbEk2
-ODYaWA48rEF10II+g7z6LKZBccMWa3rI2EWBP4NBQVf6IsEUD8I5AEA4Fu4TXjj3kA+s+w07DMgi
-5Aiwq+VlZwV30q+d3hWjNj4j7gQuFkrWKM9K+vwe9dtUfwF2MObJIt15Up97f2lu6yz0Ze7KMtwh
-sutoPSQrMYBigXcNACz87vWLK/6VVnO3qa38TfoDc7BOuG8qePrd95yzvl+YMzcDAWUywSqVnyzA
-K9flZn7wZOkPXHBm0TXCv/2bQHBziVCR/BEcyeMLIJfuClelzzDJn+kxVOET73HYYU5hTXHblFmf
-aLj+Sbf2d1ToExBufE0IQVG7ZzN6KzTc2+wfLHJjLRbhPK9zWpQyMfztZ3nfrhGX+nsBtMXaO4Le
-ASTrlSeKZBZ37a+GHyD8bThp0t9jowLlOXmoxBqNL9UpB5jye08usQC502sJVw3rr/+S3C+V9RBc
-8Q9eND2AYNluEbuf7oshAhZhtgaHJdj2AROJr+6iylALSZP2bhRHQZifxNMH+bRoLghkz4sDYgK7
-AvckEvhdPT0C5lPekkLiwNm1uCqMTHHkPeCaMs1CQcYuaQoTdlQo8KzMYDKVCVA2C0WuA58q8HUr
-elA0McbydTf6BB+qjUqf/Pkrf74dNdkBlNpIn1p57kyv602Z1lTVppB2EBRxXCMEGjGQFaGrsfYm
-CquviAQQQp9gB8MiyedW5PqpVvJbEEKuvvlM0kdYZAPf3X+0CHtfLN3FcHTRBSu0L985MGDN+cAS
-xkDTreRw/jYF/nqdMyD2B+ac03YleLbx/Pq3feJy16cXjhXqAqxbC1WhhyLllojqI2w85/k+wZyy
-n2FJK70eV16bw4e7kTfLtHNFjZ957MhzqPKRVuSwmMRDcmKxxxMkS9CZx7N2WtYzQab+nmsuIrzR
-0SKsDILDTQIfkhvuBk5gfA0Py7HfMFqEn9fq74QfIPw3wqSpXuk+VpciQuJurrDQQznrwDtUc+if
-fxkXB0JlW2WFLL0maSF2v+AFem20BHs+OuWLamBkPAAAIABJREFUbqslACbPX4DMH9RL39UFiB20
-auhJjjMoXuNCgTpIm0kSYR0ANhO4IgiWB8vwU36CYUHwq27tgT6EctMEFNHZ5DmFZvlFBRmzkPOF
-2DbL4nxin8AyhnMa7QFQyhnd/QdBven5MB2WN8E3HL+RMk3QMc6DINtwbl6f3QqG6/sAjm069a1n
-NzwZyC3DePQOdDIuFK9jzfXAVRi6h1dR4WEzEXSDWPs3kY/6Pq5WYbIo7lbru8h2cchBxq0RsgYV
-WzOUPwwlPkhtyerbDpuR/s40O/B4BWu1k1724gCIk1WFFjCk8Y6rYCrHAaNSqDc+gGqZBi8HZ7tb
-h8s3CtC16P6Wx2hGpp9S9v7sShMce1nJ817lcJPLHJ7OxOLphadvB332xjACWjyfuF6Tjqkybai2
-0Sz/DqpqReD6NDFpQPhEk/zwSGPgA7yisckznT7CWzepgCsEu0aQdXcD39lKfLElGV7/OvwA4S/D
-Kx15KmMTtSAOcfkxqEI6IXWzsARZQms4rJICLo8IyClbYswc2DqvavgS/9pPraFfsKk2S7BdJr/i
-8mIT+TAA9oOWADzdIxwQ+y/ffUw2YATLsB/Iw4w0hdYkjIuCQWJJ6vnVBggJtAS/BLls+ZUQKGn1
-leQTtyYD0oi4loEhGumjR0UoMduYwumwqCqmSoHTct8o0CKVk7x7oKfNv78NSAWSliXZwNbrTRpW
-H7u8+EJgPYArSqiDPIUOWrx2lV7yzvVhCsXB6XVoD+y1BT0B4Il+Hj9Zf5vOhBHyU1cDwFUuQ+8C
-CteR0u4dAke08uI4fQ1D/E18+V5lRZMRwkDqmd/3mlI6c8s9HTFtBWHiHMbu1gBcbAV2q3lIL8EF
-il1oEg8949n16THN/WAi8RkmegsbLY8rjtPllu8BofUWYRBcH5gLcPrwPlmBQUhkMF6fKdzfzsK6
-gehWJGftFOYXudLUDgsx7ob1bhn+mUCvVLcI7OKmI5/T3CA1Sak4khoqJvTXJetsO4DdefGAHILh
-C/k4r7tEJDDOTtgQhw7+pfADhP8g3KnQzuTiatjpw0achWopY5m3YnD4QM9QvYoHK2WwQS88zhZg
-owflFkgGy/AWuPlwnZIvsVt9TSwUn5fJr2Zs93sB3rzGzQKEx68EDS8Z0oMUSoEhklrgKagkSJXE
-r7fuDeWVQLYDY/IhjsEALQY3jRqGVl6cbzdWYRDoWK1ZSbdmYS6FMqPO6D4UbNYJ59tgCjO3hnZ4
-gJu3+RhzF6h72Zm7Ba+DPQWLszSJa3yXiJ+/6zlN6VOgtpsy3pEKJIBGZUq9j217x3GwO66OsML6
-KymUzK3FqyV9AsS7zwmcGJDwEbcsWsHMMLaUGz1/4pvyc/14xVdq4GrzIbGXoxduSY/e+e5XyfXb
-dwnp1JM8DzhttifHIp06wZ/HcBnv/Wk+5ND3OH8q84eeuhe8hyB+BdLTvs9SPdPXXpnSGcZQz0ql
-ZafM5rxmGabzZMcmxp058ForeAOEPQ3xMoyoUwX7Cbt/VKZb66HOgx/O0EvY/YHyEAxfj37D/nBd
-htMp/LvhBwj/bpjO1nSgnjRaPQUupSqTFSAxNIrKGMWXSwqXRyIg/CR0k6A33RKG64C420NaiBO4
-xgN0+ym89Bu2tA67Sdef1LtE7GNRPwPitA6HX/AGxv7NK+JFeh3OyfzQ3AwAoNRMckFQrLe3AHi/
-0oKNwFi80gDZwb8bNJdA2KeStEiXjBzuMX2yBPtXqZmOAqGWo/vHlrLgGXBVFLD3YrsSArGRQj+o
-ssORu81s+a7rMddq3O7y27PYFCbaU+hQAOZsd4jOOMxPAF5Xgk2xZ6+qHyh1FrN8+QHw7gMrZCGM
-DYIPyYlUQNz8iClufenQzaaAEZq3g9U7ZCMM76R2OW6Uynqyr2QhNoH58drKbto8+LAj5iuVSyuw
-7nOAc4wji8/5CnI9p1/8w7HX7N/qFE8NIUt/tJBKJdbNRNBNQrPLdJnMNK8UALRhmt91XttRD3v0
-qAyA24jXaidQd5iXsFZ/c0WoXRnolNe2cgG88H6iH4GzbN9g2LZVloYlONKoxxzkCvkEs6tD/qBU
-c484XJ/GFuF/F36A8F8KJ/X6ezWdNgKepJlnCThj8FfkrivCxbutCiG7wG7lwlPd0pvAOB+a08A6
-bjWWSwEEy/oJ1Ap6VcU2WPY+BABW8BXeFskPguEAoMIvmV85dyY8hzi1LiSSrod/XnMHvddLWhEo
-8Ik7AXEOyOVCAOJiFTbto62jpvReXheJMXwojuBYokxDIvHGcEIgNZwMrbmhrYOiOhXlX5mbKp78
-AycIWXspUsYrUs7YGUzF0QKFMoHgu5P9bSDAK6iYvbE9j660DfgMy9vxg2LrrNPCurc3DdKsgF0H
-uJZx73R8sA8+GF0g07oJ+zycgM84rhjWtGPfgV8qQ+iuxE3KRvb5ETGyBgO/SM4f1Jk+8Hs+266C
-A7ytsdP5bjRJ2a1Ey3PI0+4f1nMfBQCOjaU0bHKdkKTXd8abe64O/OPSNqIVXjwjhXlwhYhZgI7x
-B0j4IH4AxMReu0p06OWNilp9i1WL+BEI3+W5vm0tXVI/reQ3l/jKbzPxZ5Prw3JxhVpxj6CH5eD2
-iB8f4f8vgk3yehbWRz4UqAe+yiMEYfM9wOOWRZYPjJhIe3DCAZJsfrQA/wJgbKLpErF9hUX94Tij
-2yTSYuwKVNhdorhOfHSDXtmHzNxPeAGdwILfBMOXC4MS33wNUgIgRSHAlt7TSxpNSl6tBy3CCH5V
-h61wmAcfaqaLb7AvvvQ6CVRFm6lUHPvMrZ5SsE8nRUSsCtYU1KgcjRhYjnutDIK5XOmHVa4+gugV
-rEVw924GxjPhWp/ib0K04+fb26evRGFS6IMeWP0OFtPHTqEAIaFjCY6ikQR+inPvgkmK1dNlkg7f
-g1nv0tTFM83G/J6e9s2pboMSA1AFa3B8WzCBYeelhoqV3kGxljbwwCqf+SX28Nu+1BchEqPvkqC5
-7I0EuAh2wdWjDMXvlsauEriG9wn0nk5g8q7K2g5mEVb2OOpoPhORwPQJ5IIOsVLOqFzpVhkbdqXx
-NrpJBcI20JhvmO/M2X8Z+G7lC/oo09Ul4qo/kDH8qAb9tDKCZi2gecf/dfgBwju8f2zlRmmISFW8
-d9zPiq+q2qlSPlWkj2BUo8wtBVKHLeHyURE1Xbc36KoD/XZN8+eQHdCy64RbjzVdIQIog4UYLcLR
-nm4rxop/zN0w1nVq+FJ6Gb1COIzTa03Qp/qAcoPi9anrCjIcHIDSObAGt25HesseFzwGo1PZFuCp
-8RgB51XhOgrbnTDlvTsCZhDM70XW3ZnQVJoEBADkFnr6mhsM2KDG3bNiFiSHhbauMwCO5ufhNL7W
-G+vg+BT3qt/ERaRZfF0p+4eNhb8spgl/aty/CYr6vGAVFndAucx7mYYm5LQSo7MKcaihtd2B8ddp
-vct/e93WIEAlgV+cmekhOHyAo2756F8BvVNnDIlwHnaFnqq++iLo+jDIPzx2d2sLo67zphDLNU/+
-+pCj4R8AbRXEMqBLXv+BldbnwdJL4/ZzAvlBwbyDVfgRDFObRfZiNwd6k9Pxx2QEw1ioVZgvxwVI
-JSXqYaerMUeqH7Dubz4R2KqSBbjxXyJ43egFbfy4RvyvCc/3k04B1TQ+zJDU20JvKoY0/dQACAwQ
-4VsAk6JFYLxla/rm6v6xi3RdiFseAuTytWpIE7hKbVmRV8VGdJNrW5GX68S+VxiA87UBuFtVL50A
-cY5XbQEAumzfPhG3z2eBy+tKGvJKSZswD0mfansdwiBsHAxG3NO+Tk1Q1dGCuhlBcN+zuENyFCrS
-OM/7vcOU3wlL4eetE0r0NSWFjuihfbKDEewJ6QDGU4wsWAbDnB6U1gSQx+M4rsm7+D3flOp/a8la
-boVvofiX8H7LjAA3yvmrRgDAOx9bTT7ufVnF+3yQee7+dV8f+3tjr9lNRCRuQxnAblpyc+u2Dxol
-CWSe1jHkmqSTUN24OPjDLos9zP2KvMD2S+4vYK9kqLb4QKUpwFwYx1eQ0A6KUOyrwHygMrMaxXHi
-7jYYFvNAC9SmlUbPZwrytuwiFzLvh85dD3qRGaYDr9OnI4Zj3a8jb2zVvVeqTtlGlvUG6+d8DdBe
-/EAcWIGnbz/pwfFN21af1cTvKZA/Dj9A+I9CBw2h9NCn6156ZdnCxso7hQcX4nhXfr2nBzXV4u6W
-sMAwuCXI3tjCADUsw5fJr/1QnP9ms4kUv2HpgHgLhl9bYOY1bv7jHrbBd36KvOAAjdbh8mlYANBa
-IPVTmgHwSvtCWc6o04yymGdYj4oaOwbTR2QZ+023ElbeNaf4Ke9t+tStyvterrnirC1sGBJKfNix
-26dnTVnd2V4H92qeJVkWvONoYL6D/8xdq3o4uo9xSiNgcqUOWpWV+SilhpxJCnwZdyCAa2lS1k24
-HOYHsJSsK8IoEYf0G3ALW43yjdM6lcc5U+iz8fAIGSYoOoKZI7hRxNBD6Cdy1gM52MlKPPJ6yoeo
-K7E+0GgcW2dS1W0F30Bn8zoYXuXQt7Y2iXu55vNhs4kue2ylgXoeaA1vzgnPYz0rsvRUWUP8QguG
-RLLDbmhIZxopF2qz7ymTfrn4rkeF7QoiCXadFwHx0ao7uAJONN8rgXmB5s0J/fnn4QcI/5Uwg10+
-UsnTOefA59vWJ+/nUsfkCQCLHwxJkVpfl/qFD/kAWwBVB8Dibg6SPmbFIiwiYf39VS3EYvvmCRO7
-lAGyg2tdYPzXrs6twftqw2Uplu0asRUHWoN1W4NtA16y9H5M7IJ0e0kpk2kCzS6sJmF+ClsI0I0U
-e47DRUIxDoszVObuFCg2Te6twtnVRWORm7RvQuV/3MGxQVFSQ9wgXniW/K+AyznugXH+HUaohWo+
-b73bCJywUNP3Na33fAe8IAGwEESUEkZad1DmrTcnoCupRU0C4AXogw8haUXt5d0/NerY45C939M3
-twLf+923eN6C5V7nlO8uBjCCvvJh1U2EQQ+bDmAELcFW8nIfaivnJTSI9UQZF6AODOm6UWt+fRjQ
-Qe6OxnV4WzaFRZSswZsGYLgjxKErhVC9HCJixAUxK0OD+T6BYsizoWPmc4I0zX5kWnoamp2svp1m
-ndaALmyacZ/NPK6fSYeETlF453gA4wvcIfClm34Aw/jQnUCeEDD+92D4Bwj/RsAN2RXqvbB+x3Nq
-F8spG0tqfmvCxtZJ8KccTxCpIpf5Xb5+pZkmwNT9i28OgMElQj67YrAIm5r8ujao+uj+cQ4Azyr9
-vuJiHQ5rsCZIR1yYLxNByOIgFYCvXAmKR7eIwTIsApZhco0Y5n5UNHuOff5RENHCaOiRsYKDwJj4
-k7bnXns371Rnpc2g4Dmc9l7mFiQ5WYBJw0BPFPKgBROmT8C4lqT88VihEoT8m61QATXFn7bQUH4C
-AGgVXrFB4VfF3lYT18EA6Ja8p7hJAqliIc6H4HrbFg9XGVRbXSSkpXEeTjzP6ZSTM4+1Hq/xlOYr
-MJEEy+3mkyKUa7HMy570sRvMca3y7mQ/pNFFYIOYXFeNNgMAgxtEtQZvpZGTZaXFwyEYRSi5NvTI
-dKLbzx/XElDHae7yLA5n7ASIs7sjsMUzGbS6HxQqaaB3S+8jGF48BnTcria5b0LeAgCOB+N8La8N
-XOF+4Le3JC2Y6+9d9f17CLzCDxD+OpyBLKra81F6rueh8p2un2Jnvg17Gi2LVYCw4wUMuwxzFwTb
-ysxd7y8Hq1tgith6SM6k/NqchBX48rSge0Rag00T/AbYVr81gsHw6pc/JLc6uPq8hYTth+dGS+8C
-w90NIsuuN0yDL7AJ1yfwHjwlXdYjPgUjOo7Jd0EiaQkupaeHPV2l1/3XVd+kNoEWuqvv2VrXe0F2
-2pVFxR8twJUXNEvUOPduUpIncNxSWtKkzJX5cRilyhM0acAa42N5y6E/fEX8JJHafIrIvC6HeLEO
-m8j2H53ra/kQ9/nMorlqWua1rnjdg3e7Budl5uHd0mWnZt9lC8iSJgBWj9CkBnCPWR8P93Eecbdj
-y5C+y+snVGTLQAK2Iu43oQXshuWXAPIgaw7dqGeM1qJ3bfXYOM2sd+ej8vd5avUAOLVKd04fyxEA
-G+VPbhVdEo2Df8gbRgDoc6UZBLvBSwDIHt0jriv8hf1hugC/B1BMAJneb4bwH4UfIPzb4Q2QdZ4v
-Qa98yX7LW2EOh2nPrR6r6L6vUs0i7a4RYQmWvJQ7jqH7AHuFV+ata9Zku0JI+gqrhLXYb5j45bdH
-bLeIsAiLhH+wg2H2D3Y1kGB4AsFmH5HPtX7+GZ/ss/uH5tDqloA55w68Jg7zjooRkK3mCNZ/LbuH
-89sLauZ3JRr19dyzkeZl7pRzDO029y4coAtpwDPAylIdIDClANfSP/ySPvgKgZLD7Rq9VlZCTd0O
-ZUhpUZ9A8eMerJ0kgEClpQeYa4W0TPHdsXADmEB00ezFQrwkyFSnJJiu6zisgcJemPYn19N9iJMH
-5zFHgeUqDcykCRTrQk5TI8L7aY+husDNs5C91UJjnlqmDO6BN/6GG0zZG+QqAWkAyM0a7C4Utb3D
-eRnp4zm0QqsCuHIN/Ic+HfMH0Et1V0CrkvpDJD4vRVHNeqz+peNkvEHpyFn2rR3FfCCY5MpeplQv
-vmYJij3dAPHJL/hKNwhXbS0N3cP3fx1+gPCXoSmovcPeqXiGpKiWZ6jyohMvGSehP3ZNJV0Ahc5C
-uEggCF7XqXk8LRF27fo2IA6LsIPg7SbBgFfEPttSjHmyQPCvsAgXa3DpZ4BhQhZ7UNWqW8Bu8/Ud
-LMPNoiwylPtyrbSPIxKnAo2kdRIicDdmX+EnGu73N2He6WOHJAEFMjykSZIXK+LYSAW2OSLkydyT
-ypRUcFibMUNJsuwYFfmOT+sWW2ro0wkE16+PKWZcuXfymIa4CQAim8vtPBONGwaObSCQRl4CyzAs
-6WsWVQgVpTDVE3uTwMcT+MW1cOSi0O/sV7oQeOsFoADruQ9Jn6UB5ES09qVrrndpkJ8Igj2t0q3E
-kQfrGTdLGG/wqatP9E2cznLGeqkRyDbOed5ObVF6kAlVTLkLidU8VBnodkF5Ze8U0FvduWd+mId9
-PklXkP5AEDxYcQ83RTzeob8bUIG6QG7/+Aj/bwhHHKBx4N+4RTTQUZQ61Ty2d9NA/7g99TYjAzsB
-TAe5mnFX9Ca2fgnmg6VBMG9AvI8rWYHRXSLplr9WF6A53SJ+IdbbYBitws0ybBIPy8WjY+6v4cj7
-c+2H5NIaPPkJs2UY5rmA4pyDO0kOq6CVpAEKVST97FBI0SpxM2kPr3TqVe3lI21KP4usLcKhgmMZ
-dIPwsprjmVuttC8fhBOo35KnnUSYc6OM09yYDNU2fjr2hZf4tK8zas9m/TKYh+JTUf2GOX6CnH2e
-78GxCLlIIBIooNdvBdE7sFwC99Q6zSCdf0ByCc3zVFdtufkP41ijkOaY6ppM4n0W+dFetwP3H+to
-/W8PVc/rbTd5KwkcrieObg8rHjdGhGU8hbUNrhGt1ZtzVboldfJOtU6xU6k39U28x/IIgEUC5OKc
-etzP8JhnWHe2ies+X5lmDQzT5zXUJaGOUteQu0K4QUhxiTgB4ivKVPeIAMreunrL/x4Ei/wA4f8w
-HBHz1+EkKEDXTpz3zZfuacljIOxb1Dauhfg+fabLVQGbxXeyCLsLhOeRdRjjbg2W/JGNfTiXVXoG
-v5cYpVPj2eG1ALAN6WYtFkgLWoGF24gJwPaHNWhpnPyMhx6S7fhBi6OnytpaYH63kLynTcpiFGGj
-gu9lurKqKPIAfvVQjsrWbmiJ9U42G7b1mqiUDrVYr8cm/oKXWEH5MErrhLOsVTb3stv8WxjdHGAt
-qgXYxLUY1DekT1Zf2kvuE9x56w7InvX5bTvFAPwCqCAQIZVW6HjEo7c+BQrzUhZzcpPwM3VQEbX/
-eozJyNnjMsRL+slVwtehWn1tW3mLRZiAMt4RDpbik3p6pENk5rX5PI6y4HCej72AvBcCz/cf3ixB
-/sDeTz/fDQAzXcS3VKe3Pgfo5b3WDPKhPlivEAiu1mBlwDu7R0iniQNsb88twtnu/0T4AcI7fPfL
-cm+svh53oJgS+O6ILYb3IBoV4dsRoAJoGXDGHfxOYNhVy+XXBUX5LSxN6OXRX07yH94YAHC4RsRD
-cxJuEXgrD/6wxukX5kRsuD7Ndj/hxoiPiVwmtt+blRfcItJVYqcj/wvFUtekOE35Z+OY/ebyUFax
-47aoj/ahyPHGiKnHJGNhWae9RvrfKmUOJwjSW5n43Pd14nPu+55mN8/guM0LVdktxFPp2s1ipGXc
-vyO5btIUO/oZnoGBcZ6VPIhjWw1ujeD4Jn2w+uqRN+P+rVOAKBj4G3eH0vNoK2hlrqb6pPHWGeJR
-S6AbQiqZFSXtVMFt/3s/TydwqumUtpJ90mKQLiDYyhqRj3CAYZXJUnySDPX0Nlrd8sxK+rDXPRD1
-mDP26DztNzW4Xtzl4xwOQNcKHfnhe4ZcB99TsfVYD+W2tL4FK/DcgJdNtRJg9fiwXL1KrfAxiE5d
-52B59RtcJvQ4yf9Z+AHCfxTeA9Ze5mXZk3waFGHIpZuWp8pO2y7Ar64DmNZgGQCxiH/EnbqAFuEa
-DzB8rcP+yyHsh4Hxr7AIOxBehyfuEJY827rnIudjC43BGny8MaKmo7xkeZEQcovM4ONxiV3x45yj
-74fiTgHJVcDwovUbIpzXZOrK2QLcFdJMG0MZ9ry/JmU7QZqswQZar6vu8nkNOkC2HjNJkFbpWP9U
-CynmTBC3nrdKgu30J+x5Xo46lMygQFsPoWEeOd7iO0Ey6Hh1ZbgFzLYx0AyAsVz/+h/HCnAd5vge
-DNstn0gB03agV/4YnnKO8bjP4Dfzp5luaZvyc814vabz5dGbvGF/RGwCwW7txXjw7DV2q3E8KDfJ
-nB7mb6L6GeMh9ZoseIc8LPcV/jrIvqEO9PgKK/A+px0AZ93eQqQCIFu2tfm11INrTF1SP27721Zn
-KC/y3wVwzC4SZ4A8ukKILLq4ldnxhc59/YfhBwj/jRDW3gS5byzGfxbmmiZh3884qWeW14BvAo9Z
-WoYvKrRYrwGcRK5/Dbbl4K+4QWLfQCGzldhBb43HTysDANbdBwfsZA2W2RocVmC1uDXizX3C/inc
-HIAQQHaRheAFFcu98PSvoIK4BYWhNZgKRCGoa1ppLJdCdlY+c5hVp7IKs85RydoZS5kBeMYc3oNf
-VB88C128Gq0LchZe6+q4FdMOmSbrqxG/SOsCgmeDMWnhs2luevoIkIF4gA2S4CRXOpL1GrsJAJsU
-oLvHY/7UOgiaoZx/gJmsQy5hsfgk97qHbR8d0e1AR/5p2yDwHVw+qvNmPqzEjQyzcQbHXymS8z7g
-PTKf8tqBBoLd+kv+w0aA2azkU7XTLrw5syKpVw79xXMzjntCXN1nhsMXdFpazW9jAvg24Fp4VaAM
-73UcS7ZjTXd39wmLvFHm1AE4WI0XAFwEw/U+4dEqLBR3QJyqDKzDh2n+L8MPEP6NYC/iKyQwnnIr
-5fUGaN+r3scrtKjtTOm4BlN8325L8FYWCUAPPffyux8BgAPs5nv8aIa6P7CMANhdKPxw4h3C3s9L
-0ZVDQgDE9Wn73TYwvn0obkwL1COhsCcXinG9TnOPQCIswQoCW3NdCHT0NCuaVO0GL863Ql+Lx/wP
-e9jGXTDwlz3YdG2FA1NrFU5jJRPUTkXyCiAfl0ubAjX+M7TtCg7Wpx5f7fR4spy7nyB67sTYn6lX
-HXzcwi5aF57hUsakAGDnSX7zv+VHNnKOsu3J9SHr4KFRFdDr007K8n3taIx2oJc+VWRD5+ZuO9/0
-rVrH0cXEDNeizx01YiXd8g77xfPC5IiAViRdICzlTtxG4O/uE53vvTeJgqzk3INigpx0npps6Y7e
-VAXJsrfKuIrhHUGZ60vCYLhagwEsY3+JBkLMtuQmsTxZhncRiPsOCV9lsvrmSw+081Vps0sEg2jH
-EjsuCIjdOvx28v9e+AHCXwfcXdIO27uybhXgCmYFd8o8b5dZefQyFC+HBn9M4/L260m/a8Q/1vrB
-tNklAm+IcDCcANl2HH2IJYDwCQyrSNwSEXcK2+4TgdqPqF1idHfwwTLsg5ADWBYfszeWwvq0RZRW
-QSO6xpDCwcdrKCQUy0oHaCfLMH2N67G+D8c+bz3Ylr4I8DdiDEHffIx6bfNJqa3ZofMDhCkfUCbA
-1dqtuKL1qCp0bouONCjsXB9XntA2dKx+694A7WTVI/1f56cKmdJY2WfeIvvxetGKQHCAvp9tJ/e3
-Ca1crVcH32BD7jb81eW+b2odDlJOdWfesPM2EPG/yXxQChWZ1PpyVsUleJmVoU7MmeJ1s5a1Hule
-L+fZbsvuQHC8Ix/yp7WYzov6mLEr+KETZ6mfd+p9mWI6P1XoeP6wQXBqj/JMOVplbpxRfJc9l5pt
-ND/h0J9IM+gs/jaAATDu8kDbLRX8uwL9wWt+BXA9gd/rkosswcUqXF4OduNbIUVA/D8XfoDwbwff
-Tn37vYn3eirTQaCiUEBBhgK9n/c5DgdoyndALBBH0HmN48kasIxd2bVf9L4sG2klxofjZlocJuH+
-5ItvjehXp5nEz9vB9Wn2+Yhcp+vTBNIuvFI4+Y0S4SPcLMPz6rdZw8EgGJ4AsA/eW6jgN8AYgwJ/
-TYEUVFHT40+zDA92YqqBjNZwUYpDj8Z8m/JPdcj+hcFT2PBj6NuZkmtQR88K3am4TlwZKms80w0o
-V0VNDUzgK1Rl6/2cRpoyzaSAS99ZhU9EZktvrWN4gM6tjUMdWbTtqOmj0FpvHejDUKcH4aYPLgr0
-zEPZXc9CP0Mt1D5Ct7G/GYd5sxMPDW5onc8huwkNGmtqxN1dDmDYXSHoXXaeCO2D7I7mOcBNr9k0
-z0QJMN10XmJLD7qzbt9BfCQZMks0boXbEBmZAAAgAElEQVSIPJcP1dXBCgjGPJH8gCEwL5bjMW8P
-19DSO3O3yVekWZRzeRXeTZO4bJbgTbv4RT+njD+zXKzCzTK82wi/Yck2ikr7Z+EHCH8ZBjGxg5/C
-ASw8hi/K3Omul/xa3iMOeshpi65xXaRKAuD9q8gMiP2w6sr3UN0dyD3iWsLdf4PjMueV5i5x7U7l
-J1UeD70cjIqJWN4pfOf6UPPqQ3PkFkFgGOf6TvmBBIbO8uFXeNNtDa50ZG/qj+oxeCWd+zj/oMZp
-DDmtTzKLYEGrqBJybmxkq/M6tW430QEZ9daG8pNLRAUadX6HHitjjaqsM803UZwsx4s2rZnJYQLH
-uZzDCSnsDjk42chNib7br5beVod/aHAL0cCHc2l7ZoZlTBKAJFD+bVTDBwfzRqQ2YW1KtaScz0qa
-eYb0sAwBVJjSGaO/0zxjF4rWGvaGPfFFp/IhODuCYM9nl4jKG1VXUKy854z2ks9yGSdXOU433sTA
-4+Kp4m2rwFYXa5DHLLo74PVh+vK7xTa2Q5zgHGuMrZgiCPDaEShTX/Y46OYgUppKtNur0C6V9kMa
-p1+Zqy8EvyqSFuFAHf80/ADh3wmvcCv4Q5lJ+TLjuzAprwM4ON0aUcHvcHb5XWU9JAfAVm0B0gqA
-PxvEYn3++xpeziRfv/bBvxAMI0h2zwT1+4Ol+Ahr1g1A0pXpIrlK2sICQaxYpgPgzncHE69I5rvQ
-ngB0rIZLKV6Uqpw9gdZg9KMKayIJqqoIkg4tFx5OHvmCPtwqgVY7KP8ouuDr0DteBCMTt51Sln2e
-S9Z6a5vHUlj5MF8DjNY2a7t5UOYjAPZ0ajDsG4LxeSgzyOFkLXhQ7i1/AMRAZ6Awg96MZwnn87VL
-S64WgQbtDF0y4ij0ATf1WrkofQ8xydrjPE/KYVYY0xJWSHuyEJ8rKVx1owxuD21fTO41IrCOeCPE
-CQQvnXfKW4P1D3wPaRjXmB5AbzszseBwRhwA1ykTCVg2rqUmZ5PPsqfF16GCYe+TSQfBlD5ZhVMX
-1fH4h0SYPipPX8z4CKoecv0j/GCbXB0MixYAfAOC0zLsdVTwm33+9zD4Bwj/lTCIlpvgp/Tm9L5p
-yAnfNT72xt8TAK+Ig1inOQi+Nuj6SIJhBCHxoJ2mVfgCwGuXLreIfegZDO/0tZ44/lXy42Ex5b6v
-d4t3V3D1vVl+PyZy3d8YYVM5BMMiLJzMIjvWpKyNtniOIoQwLQ4KiwMYToZosnYhX7zfKs8UsHz9
-wFV38jEzkoO339iwwV+o3SpHT1X9Xns3f2g0emMq9Fh7z7hNZZrHKrh1VjzKAQhKW1W5Y72TbKDk
-NNg72gMgDm3qmlUBdEJHB9Ab9Qygl39lDvvA66qlqpWc/Qx97rR2QQyjHUhb44J269zNwOmtOxE2
-qTVmdTXYPYLnt1RG4xmUReM5l4luVwDpcnynEwS7a4Qs6/B+F5ECiNcfw/0gOO+62+BxPlqGcZpx
-eRyo1m0J6NAU5dO0DxGxZb9VYfZgbqL/cFT5Pd0fRh9ilBEqIoZ3ChvgYQt9bRCPdiIvh4U6VUQS
-AK/EKhNgVtjyW35JroNgofdAui0P9Nr/QPgBwh7eLgKe/iOovSn+tj+vGZ/r6IAx38Pii3n7NKsg
-+BX52LrT9yPLfQE/sechU/lIgmC0CC++dcLD8gvl3VIcoHjnL9CcfsR4kER56XIcfvqn69M+Ih8V
-k886fHaJfGz/zPLOLw/N1buEm9uECNBwAUI8FUV0CDA+ESmDG/boy307XhdEPTwobRnuFX70hVzx
-yZKG6WP2iQxK+jSTJmsPnitkQHVbmYhMSnDEDgNUac2DlkRFRApOhAACtjX6FvdWSnLimkvWEcSb
-77E1uYcSZW5UhSzEo6VXuU6wHONPFCuAozoCpcT9tw7TN+OYYYU28THPMSG0uA8Buh9pwpwb1PiM
-JkzUkK3tDuF25JBQZVS2weS+l8KyuxGVmQPeNeb1lXuC4OoagS4SPk0WlhMRiTIi/kHKytj4vM3f
-vlQQfATHZdpCIUbWlBauJJSOcRoU69IP0BfyHTboY47HW2WJtUfv5XwY6AcsaU2OmyVEwh3IP5+S
-DELrb7wPLhHhH8wA+LpULnSVmNwm9vxNluCOTv5t+AHC3wY/xPCu8fVQYMjFKrn54z5xkP8RmuQD
-okFFVSHFji4SvmqDaEwbDfXcjG209223m91iMeybHNP+qe9Sk8+1gPRHN8DWZWX+XMvC7MAb3SfQ
-RSI/mULnI419AMHhcTOwWlWA++kAl0DvR8wfrKP8jxj8MEemsXxNwwv+kTVaYG0hjoLSHITTq9Nk
-b5dGH8JbHg9ddKlUBXUUb6FwZ6BTCXewIsY5tPzb4hW6NrXd8MaXPL8fV7mfjTu+uSyBIEXeQwCR
-1JZw77cOOlCuFRnnhbZgcT9CBwjjyBT6eJoO5WhWVwr8Nm2aoyKEaf9WAe15/PigmrvU5YmKjxcO
-cgpUqx8CTRbgItlgEN91kcxo6d2PDb4C0Hqv3NoLI8v684YkTuf4O9DlTVXnzCAPcx4POeSPn5V9
-DxLc3WktaeptVma+BthW2y+nE50zYS3tegy6G2B357gV2/Yu2WcDryrU6L/Jpbqfe/OH1DR/qVXz
-V1sZIyvEk34Mfs6P3w75xPqmtIcK/5vwA4QjvJv89QlrSWf1T8fb+uGCQeO6mAEYexx0QAt8Gnhz
-1I1lpREBbFjl7SS7QUB7Ne4awftVh64w2EWALCJyybYOSwJe3QesguG8KzgubiDf4Autw/sE0g0L
-sXxGfcjDZ3vNJOLp3uBA9ZLqK2zbOiwEdPfr8xHbbhX2sR3fgPdj8qkgWDowFqBFt+ifZLkpHqOr
-1hwOIYuVae1VAc2Bt2AeCnewgPKtc4wX/9nhnNRqjofJlRHX/XjiAyUc7g/+Kl0Byp/E/52iaC3R
-4i8Q1ORL03Mor4AnEF3KL3XZJiIG8WOnGsgY+NA3WxJK9WLTRjvQqHwdOPN2CHMuZ7u9tP7muYiv
-uyXzYzeYwyHItyIfHGtArzro5bQYlBd+/sBl9AR0wyWC2uM8kTvQiyAZ56iUURz1EL4Ax0g7bmUt
-aRhb6vR6H8r9qUagm11gEKzEDztKU8+HLgbl5yBYNffF3k0BgvFdVfKHqrbOxx+ucv0dYLio4HxZ
-gObscwW9mnGVtS/M4ssjnuMCieTvhh8g7OG1bkELsIU1NOT5XqngkfyEWBdWJAExAdoDEFl5qFSw
-zD58E+KY5PR00sdyrOhivNF3LV1mPz0/cGjlDUvw5TdEICDe7xv4XiJiliB5veAEYhf9FIqJW3zz
-BQO3nY+uEpbXpqEFWALkIoAtFmAHxJ+V/jS+G0swWKQD+rqcCB7IDR2MCjrjTfmVWWDlNYdWZhA9
-YJsay4eOOuVLz+9KQ4WGeajnfeCWDUmHPk3t3KV/l/d9HGd9Ugn3NL3l6yHlTiE+pgMVnIGxbKUd
-QlPSehbguGh5kaXYT71XGBmej9tBDvlmbVjjhpHGIDMwK7ue9nXmMdzr1l+voQLmPKUK/CAHXKZA
-WZIRkPYeTSB5rZGFVdjHGlIW5EtYjCsPlKsbhIGxSAXKIiJ2a+ERArLBw0qph7LQ8W1E9BQdfWo6
-+44jwTPWZdscx7VdWRaq3vNB7a13GiKA3xiHcd7uZwXBEzBGq/ClGxRLU787zeDXEW06Quxx4ddI
-JusbhS0zQjXfzNL3cv85/ADhHebHLHqwLaTDWLGFjjv/u8N3l/lgKZbcRPOi7kIAduPrfKAbgODb
-it/QGk/u9LUxIS0i0saC486My7ZPsRaLMABef+jOv2JD0Os87j/s1mCLE1lefSZ3xE/Y8F6sww6O
-rViGGfyiBdgB8GeBYrAKH0HwARBTWiRAcg4GwTIC5aK0JJVbm49QVvOLdFMpa5DN22kCy5jfK+vK
-obA0gAIA9ljfVGJgvsmZK//OKvw270nQc1xHeg16w/SdEoEB1yl7mdaIb0LImw0muqBMuQcyhzve
-gQ9nHWQ51XWanLKHbdh1N5PIe7r3o1s4ay6A3b3/EeyKCLk/oBlCof/+HeXZCgzyAs6iTfnQOxOF
-tMQHa9dHBmPgduG6NaBlTd0FYubDcwB5R3Bb94qW/KkMb+fQ36TjkgNjhmuBD8dZzr0TaY23PA96
-mRF8ENzfGyDGeAGqviMQlF6XFuCbrhGhUh0E7xrSBpUuEn5M69SytFKYKN9ggHFgo02fS//r8AOE
-PbzDwQvMrsi29O7NbwJGDGPLqZTjXWT+KYSSOLxrpYsLQ6h4QiN3tAMgjjFAx/uHh3WN2gcr2v3U
-7f9rwtZhAr7XEnuXpICNX5m7llvE6sLqED3t6nNPJ9DaawmbnXaAK9W3d7D2ovvDpn32Q3ZyfUQ+
-H7kuXdZgcouo9e6XzKBYJN8F0ulL7HKjxg0ECKovISuxCCrjDKTcxlcHulN5EdiDQ/6km+7qYvqc
-06kFMFZd2Hv0WGnUoz37FM90B/BTvNFAf3jEhvbrCN7rkZs1fcAYyTNMSsiQLRtTeKS2xDTuWxWQ
-MQZ5Qy9GkIxwZOaLbrfB9Lpi11ntgRFvc1eBjT67shzy3AKIgBgWPtI7VcEv1uM5KCukpFEyCtFA
-ZjhNpVmFfbarfCFrsEnc+IBAOTa08piylq6cclRKcxxhfKCYJhhoZYNjUrN97hGe5eTIU2CUNuCW
-L+KMGdD2TioveNEqnMNB8IvWWgM/YD1YhHdcBl9hf2ntCwJ0nruwDvtG0kVDjLP2iY53fP/X4QcI
-R7iDpMzmXz344VY46GSc3dZc1Bd83PcWx1MD79ij+GTslZ18hN0XaNLzXdbw8FEXoKAQEf86stH9
-aFnmacm7JK3BqhL+vnEf8Qa11QrswrVetWb7FKKLhClOwxblWr6qsu3BFg/KfdaHGbQK0wt/enml
-yUqMLhGXW4AZ8H4m1wjhuATIrRbhau1lQByAvuwTVnLFSuwvn0+o4Y34YV5U2Z2H94Ed8uaytTOn
-vtmYed8Kt3G6dGtuuzb1vYrrcewPWp2TV2/XqNN0oCbtnaRLLpqvabGfaJhull4HJgqNFZmGvVAY
-CeWX3TbJvalLJ6U70P3Mzfu97DnDZjmvnlgqB/XpBpESaThtlgAJtQkI783GVmHXJCgfvM0qM6Sm
-AcjifkyYlkahuDUiADDud9oQZVda6EyaG3XeqrxO0kcO+cyrTZ/1mkG1ZZ5iXv4ViPUrKu8lgRae
-/lihg06L/hAwDZ4NgNW1cYJg72t3g9i3OWA61Wv4D9NLhpdjH0lArPs4w6HLuHnSlVH7GPtPwg8Q
-3uHtg4rmgkZBUA1xVGpa4nGgrLfLSmcXVAHDyxIwDQTXywGxvRik8PkrMoUe7KxMg8BAycB3AGr8
-NdFwjXAQGy4SFwhlyKvAOB6gE39pgF/vQpwzoPvg/JN8QEJyN9hpB72CVtzqGnHtK9jqw3MOfDXd
-Ier7ZP09uEmIwbz4P9Qku985H8ibCg3XFgpWAlGpPhVuA0T1hDNOKkkH4qRspu4dYMor/urFdwp1
-RLcYCs/vwPNVHOTCmbeDJ+5TPdSd6R4qHMo741S40TZx3BRlwkIRspyCT9Ip2zxxuyCQccv3UMnj
-hjIi0d2sQwGr/RbfZ7UvWcLzEisYUrbOSBr2KNzXsC7Ds7tbuaVlD4+0vV2W/WDpH6YnkOF2Bmtw
-GcMtOI46B8kR0wnzCvI/5lU7W8w96K8EuezqKNBj9gcGyp4L9QnbDGHwQnpmRjy3Ps5A8qNqm0Bo
-5jMYdmDqPsPL0ovuEO4DnAAYLcMqy6AVLhLensp6EA/dKaiP01nb+ttFwz5QrorvpfV/E36AcIR3
-079wZwpy9UvDd1xGcNwV/ggAVMKKjGQGvHDINNtaT4ZCR0tDaSHuErwB4EmweNxSMMeEVB5hQc2H
-BoGuhttD3C4hKXDx9QvTu90AwvsVa9ME6ASGwzNZqqVX6eG5yVXiSiDr6Xigzq3ANw/JVcuwWAi/
-UECOfG1PiFSrcfJ1P+KM3r58nqUVfQw4y0iTO/od/hg6MPXn1Mcjb/x5Y/fdamdsBBQ8DL6otCE+
-W3OneKfpI98UzsB3Br19XiagNhC0ECd/L59/LRM2XZuD9wkjDXhyBKe2DqFuvtPGOw12LD6dAsiz
-7KCNHNMYqpUXQdQ9LfuK+iM/HDtHt/gCbfM2mrdZAG3G0VLscec/03gCMC/TNGfK6XkDdFArwvOK
-0Zw7jS2pO8P7WzWKj0eBCJTS88PJPwDimB2b6T6uDoIR/NY0WITB5SGvS6suEiL8oxj+SuBcgW+C
-X3j3iaB347Tw+zd66G+FHyDs4QuTcD5RuoR/B8dV/udXRgyWBSy9u3r/o1hetp7ZbQAtgLjigRHe
-ZEGUrqxGHhmIAuNOuhYeFxrIc6mM4JbcJTCPrL8pyH8h8BUJIK0OqHd+KArxZ603JawpFg9ABPgV
-E7cMm12rXfuIOiCOh+MSEEuktxvER4sVGN0j7oHx6ssaKF2r5uPfc0BfaYIgjfyYMy47LGyWgxx7
-fOXz67nG4BcGirxaAybVNXXtJAgb/SuhidaoVXaGMLMcqPL6TfzPaPqKD0McX/PUPDt1hLmaw9jP
-UzLkBSJoDdloEXCBpUDbkUQihwFYKTMwTRiJSDZ+ONsdPtBrhAdbS/UbDvp5xJ/e5pqUuqEjP98W
-wbSUCYvKsqTS/DyFfIHyvqXCigfnKeUHguEzjY0VOVaRbLcro+mWCc5PMtqdi76ja8Q2D+XxY7/R
-K9WxR7Mfce1v7eu7+PQxmIFn+gcHrcUHcKzSLMIJfrdVWBQswAisZwDsxzWmEnreaCZSvyaP/TjK
-5f8+/ADhHd5Ofnwi3YB4dHkQf5AODk3kI3AogtNQJ0CFtQ0jxuJ/I/UUzzSXfKWMCQB5qCBhjdJf
-KxRuBMpf/eD41Wn+08nzSwdwrACGhazBK897tZWJ7IbCSg9iuVl/t/sDWH4XIAbQu/OizOeSz/WR
-yy75fMA/uLlHPPgLB/A958UskNJKBYNWHNxcpTQpP9HG/hA6yHrccnbD+4BpJnrHKah2TtbfM2i5
-O//H9su8Ters92h6ziM5s0J80DsNIPjW35nzZgYq9qh5VBwIUx5aK0P2DGAmNid28d1GXaLXXm1o
-bGb+GveQqKzxq3bKLHAYKZ/WkFfTrNL3Ck/0GGbZzXCmThbgyJ94Nz3kBJVLQ84ycKx1pdsjyG3C
-810e78odPUF/TkqMwTOXbfykfrieqKFYoGpN7APsPSh15GiEXCVMRNsnrOnUV7g886RUuwHBijc7
-VICcD7pdkPdLJX4RLkExXJW29Wq9Z7jmd+swjg/ecSm2L41FPM/LsyT7++EHCHt4aRGOa042f0sL
-CIVX6QwkyuIEusU3mVRdIYI02ugV5QM+Udywi+7De5I/yIhxTVAMIy4iAgXMyscfzuiW33yYwgVz
-B8RQTiQBsiQtTiJ1YVe2B2ougOPUrRdZfwEQo1uEFr/gBMsfkc+1LcYIXicrMP+4hltQZ5oLBVBf
-FTBDGQyoxGhO5YvXoQxuk3pqJvqZNy1yk+g7icN+rVrl3UoLtn7n1kbZlQsC6Uk1fUv7jv/eHeKb
-ecrTd+Z7knpWI0+AOHhgjptg20xDmbRm9/Y0vm0YAgi+4k3R+SizcI4F7yrcHGWiqiWyrkK1AnM+
-S1P8lgXZ6sNyWRHX9gR0BeghFoE/61FatgDFJS5UtrhGoBEH6DyAns5e4HhnaaPxN7PrA23iKfcB
-Bn3RbnxC3U36Dnu7xtHoOKEivBhA55sSznEGuPni+33d6msElNe7xg9kLAB8Ar0aLhPkHiGHl+Ls
-puW4TBLPgfLUBJSRfx9+gPC3YW+0tYCWaXEF7WARZBLjWK8m08hjcCiJbuLX1Sx3jPUeINxEGqpw
-5XCSHyJda9AuTIEMz4DGvs6vi2YwnC4iGj+R3H2OhofjLoZ/a/h5Cj9bUC2QloD4g11nURb9YP8k
-20rG9kNwtgGvxws43r8Akr9CZ2Kfj3yuSy7/+eVqDf5df2HL0Vt2leYFWBq9zl8wTVLmRD+EqghE
-evGpyhPt1Eal2JxxqCN7VvOPX4ff1InKvPLU99/P6/7EkwX4tvugY068+OXvi6kY658wbuMBoDHn
-nSoykIEog0qoeOk0GoUI8hV2GtrJNYLKFXBFQ8k22p7RyjWfivahYKMGlLlIp04AOfduDj/lA8uY
-iGMZ0Em2O+Z32PvtEO8Asp8fT91qxQSpp3xUmnsv9Z0NNHd9aOukZYucfH0dPIuMoDd6irdH9ZOr
-MtNXFPcmrit/XEBwS7/0Rmm4Axh4AgirHl6pl8ONQqDu+trzQncLYz9xHOOZd8vwNNJ/E36A8A5a
-BfYhmG9QTVDof+gJU/NDUz6FFsWmWK8OPIU+gWTvUxxNkA/ZSN2Mnp9CbLbOYZ0OeGcwzJULnQj8
-dZrJ2lt/OQ7zRUV+XatnCBRsz+1nNxv3FENXQDSt8tFxn1CT/FllcIGQBYLD+mv+gFxahSUA8XqP
-q9LiGrWTJXiwCsN7tfYiLdSUuZrpvKjKpJf86lVD1jpYG41WP7ebDTRMHNpoMTvx3QVUZViuKMCb
-Msd5qACh8N+6OPwR7xQUapl57vK+DiTUzlks2KY6SkWtkwxr+gY7yO1pIz5uONwZpyKVAFZq6x+6
-EAzh1Wc8nmLZ5QncLHU/2mH8RuVJEkDW2TKcvTHMM2HXBskPhdFGAci3vJGqC9XTXR7cAGgHWrHl
-+MMrpTXZjYtJgGHlXmW8W4CF8uT4gXv+OD3HY9aMwW68lN/xQTYEyBQHWrUGo3FKBX9uuQLffFWV
-T3l1TQ1WNMh7z/i8zdP2n4YfIPxlWOdchdYSrozxDYs+SC4LUW4FregIlMn5YJ2JOwIrWYG3Mi/t
-c0egF+OptXqKIa0lQ+JrIwLFSiOFQW74ubuK/rwXujps4NtBsp9+5lNR+ex24e6HDYBZ7KFg856l
-f9tn9REswfiOYNfdJBT8hKW4RayfY/7Aj2l8YQm2VE2ZXxWHzJZh4ZD0tOhQ5heSZmrHq8htk6m6
-lTptABuFTwrfkNnG3Ggka0+DLkryoLhq/af3NzxzGf2NMhzqUcac6bGdCpxP4WFKzkyk+zaDDa0R
-CQegM19ryzpPkL8cnc05obiHcdY9a4AMDBhiBZRnnQ3Pw+kxKaunpVHsdB9vBb9eAuVCnu3Zaoyt
-pOzErqxzhPGsrVuDuysEjrlKC1dgz2V4p6fuWfPOUoqupGtp1uFa6RFnC/ARNOOEHk9xxmfnqGxf
-SzxeAEIDyAZ4VbLwukWYb4xAP+HqFqHsfqHcnqd7vxX6Wrys23z4/vj34QcIR3iPDvxc4lc2fPMP
-flZMoYEOBONRNrcqb2ER5UBgOug0kHs2fMq6RdiY1pIW4assFFh8GycgRZ4UG9JA9HpYzgK0+gyp
-CNCWX5MBj0Cez/eHhPB6d0AcP+e8c6IXOF84H/SwnDXA26zCAYAtfITF3SbUQTC6QyAYTkuwFDAs
-whbdEOjWednGC2zCLxxnzWuvPT9jHQ9hUN+kEEaadaVRa2xy8tAnazEd8xQ3zdArFsmoxAdAcNOP
-795LG8Pebu9lL5N8EKZPfZ2OwbfhsdzYaLF83lV0vO7sAJA3D4sym/nu2o38sZasCs5V1+swzrLl
-LJk6aMYWp/GOwAFJkOcPI0FOxKF6qzw4riiHZ6EA3XjeZQDAJmQQsuB5lhjWeN5ImUxX/+mwBLsu
-835FGmonQOuj2SnlHjGPt1R7XT5w2p4tXJAYnTEjtEEAWOEGBwe94gC3AF/P15rPt0ZUd4iwDov7
-FSsDYmibX+AmgWOir7NxxKzzx4dW/+PwA4Q9vHSNwEX033xHYUgK1LjqQYQBSN6lzSBd8zMtUqzD
-0D2Qr3nIvH8jQLY8/DSYAmbpIQINQa63vJ42udyiq2z9FbAOS3OL4Dw/fSrpE4wgGHth8b57YkjL
-+Ym7g2WBWtk/p6zXzkPAa9cGwAyMP7ZujSDg+3Ff4UXrP7tcrcMClugOfI36vRXawAMs46vpj5tw
-rINWO3lFigXSOs8ZiBXwccJCjVZz5gHWL97upuCtKK4+vN+9PwPtp3cGfpn6hn5ej3fhVRnjBJfR
-vhjHSlme9T1S3F0OfL2dA8NAbnuuuETgg1eJcZT0hMGfpOvQnpGC4e4OJ7GMB8987p034Df7Zlsx
-hC/w/oYyeVJ5jNZgd5kAOrd4ehiu0Lbei7mq6bEOhFYMs/jht/13BLp7lIqlJh4eSf2mrJ92jist
-xOw+cQSbCGBHEFx+OGO/funsKxx8UHcCZGUAPIDh7DrMkPLM+z5cBooi+/9x+AHCO7yFwbidGfz6
-/tXMIrl2shKvgmRJjvRWVVvIOMgwyFz5udk0KtwsFYWX/NZRz0dICYKGwHB1nVAuU32Jq/tDukdo
-0EUlbojwU2Ylz2cKV83BsJgDYvwEffN4ELk/5ENwKuz/K2AtRn/hj11yARi2zwLA9jkB3wd3CVc+
-oYQs3CHIOhx8dURGZXq+ULmn130Yn1un3TOmjdO1b22JTnxt5FzJyQO48vtNLk9837w/8+hI/6ad
-iAPhhPsmuksqQ3lQwrdKqfE/CNZYx9uGlN7mhiDjd5H9Le9dHx3MsgFk0YhDAjQXzGu0kDAAa5F5
-r9rw4cKy3XFPGtOcc8zfOuhkDb4DvVnvBIazfB/0HW1Ibz213BdN0jXPDvRdg6+F61OoOXWdlNHM
-N0QwkMZ7qs9STKBeirscs6TjEzmqFpbhdFuYfymOrb7508rpGlEejgO3CPoBjRsAjLOhsoB9YgRY
-YxtmOFw9RbC2fxV+gLCH1xZhXyYDoXcCv8mbgoKbi6Uvujh/Mdnvvs0K42ueai4OYU2nMSvE/lK+
-0R4d3RsKGEa6HeghfCwP5hEAX2tGTdbhZQHNM6qGN0TkRH7ED+f+tByC3IIzrOhR/74lQtwybILA
-WMrVaQFGN/hFK7GoDoD3AHxjZD3/N0AAACAASURBVGDLHflwFhj8oo3YJLo2zJmUchKuEI2p0CaX
-iYn1d9JCNDxPnY9pnXrmg7MZDWjhy3AL0KHOBiqGus48Mwg+vu8uT21gQqNuI5oU+gksvwnflJnW
-86t6teQe60p5pgL69FTnmz7dVgLdQnmPYBRAcdJy7xrI4dqS1ULYHkdI/3DXjTjj7FqlFT4r/PUd
-5AZ2h2lgPYbzVi3JPL4uEd7RfE+zLVZKHB+Sm+Kp0weAqxnP1q3UfgLDO3fPe1p9J0nR46lRU2oo
-9DEAaXnlncL1F+TgirSLf0b5Uv7FuQDEkvQEwApxiSPYgXHq49hACjMJLjxx//T/QPgBwr8dNDZA
-XTo+oiklAEuOx7n9KIcD4DhYaQ1W5DM4zlChX/VCwjLSu7RKukuIJAJX73O6QNAANN9jdCc+9R/P
-sIUd98FyYXuJiX0kXCfEwXFYhpVoMPg1BssDqYbXuKxP0ypuSYdFAikeYBjcEpTifnXaAsaK4Ncf
-mAM/4/Vrcl9Ygd2CCyonhXoFwDu2EW+4R4CVmEeGcLkHe3idyrBS4CmNtA35NgHksjalPh7RE8/c
-W+TjLzfv+abHyUaAUN7PYPdLEDzWLW3gE757T2PqW1X0n/M9FUSgbPPYFt8EJmWeyFcd47xYG2wm
-Nj98aLLKZ0Cr6LI3QHvRCg/RhjZJojC7T8Mxj973OdkKC+8pqdeoZbm/bQ2eaNbiCbGSzq4RIMuK
-esTlOLeQ48O63FLeRzwJOb8VJOtZ/Uhejp9fl+s9GUCvA+RG18Z7+mllBL4zjT9GKM7K3i8xazHh
-Hn8rJf6b8AOEd3h9fRr/WWVlXkYHiKeHpfPeYThiBodVC58IoOXi+0Tg0LUCnO5dd6aT3xQPI/b3
-BtxO7wf+IO0+6h4HHrJqLV7pTve2+GG7nGCfqbhwQhMcMweIeIOXqwOgOZQksCz7OWkAzGjR5bQQ
-jfkk2q1WYM9CtwjJN6nAV7AcvXabWuhbBqEC7LXNLw8xkyY0w7n6b9L7DNjcj7u+nWjjaXYZXEin
-cAIDd3n3YOIPfIJBd5zGXcfSaYOkMs57q45GvknP/0l9GKaFmoaiA+v4letNi990WnmNvJlK43t4
-J9qqjJpu/XDgrJVcornJH/el3eR5Obc0wB7EtggMj/H5DHFNz7tXBOU48+FfbF+Bh6mFFsh1j8qm
-s5NRHXJwtOnOIFs25prw0zOgHzcgdK2pWybq/lp1BrIz6D3T9/sl8ktVfl06+AmnNbhbguFdip+y
-+dzsjeLAeE9m+gKnvBeRMMaZ2L5+7+0B/HvhBwj/TihAsoe3C+kbYlssB7C7uDT9nMzVlTYAnTJA
-o3rS/Ns6kT7rngfAOyR1BbU4dmFazZtoyi8UFNHMPmVKIFmKFXkJrUvXOC3qWHNzKdIkDMhiDorz
-MDt4C2GJ0/YETWz6ktzEV4dBavKg0uH6kOTaKbk5xuUnlwnL7FR2Wz4Rb6W94ZHvQ0AwF+qtHtgU
-kn2utbxyixhkqWKkAYekV+XXV78od03aCVDc0vyoY95Ac94JDCvx2UDbfMOc8nR0oHi71kPmN3vj
-q30UMuuZL9c0mXuxE5h86FU/fm1N1vr120Byv5S+FlUSIrg2RX3kRqdeP+3BkAtTnmw5Mp0VAV1V
-9mrnPffv90IZu8qSJ+HEuxdEZRkuCl25oIiI1G9EnCLZUkqCoruQ+6QmXQ0HDQBvGocswHDYl3Y3
-nU9lcnMo1tyrA2L2CU7aAsEqv/SK+HqAThk4e7tbB18+H27JBQPbGquDeFC8aITDs/z/gDVY5AcI
-/1lQjqbQeJLWm8ufxPWDSXewwSnATVdoeC0MbrD8BApAdks/i6Ywz7JpPOKIILS8r0H0IVcpUrM2
-ElW4J9gBa3zlEnEXGBKfiNco/CEBFm0qUFd7d4G5uOPilpBWCH1iwoa0r5OFtTaWxCqfA4oZGCdX
-VUNTu8lbwW9tQaSC4wRZ0zVpjzyFduhehBT1HfSyCsKxwUaFvcOgNkvfis/SaK2i3xmsqdBL3gQU
-Gvi5if8WDeab8k3IKod5CgmiTXyF1sPAdZiXb8I3ZSZeWrcmXw4TI33OtOTfVDp0hgu6u1q1+rqI
-pTU05qu0w1QP9Cw08U77qrU55sHZIh6XsIf+vaE/qMWevdv0puv7VIcJgWECyWIBjN3aiiAtAa2n
-oV+6863QB36vK40t5eqz4LCSBnVq+SAclmsg99LQi7euEFe3FAcAvtIa/CveAfiq8JVpApZgHJ8D
-fB93jB/nWXN9YFclJvmfCT9A+C+F8aA/Liyc6vKzcWEFRgm5d5bGdT2w2+CrHQK5qNl9x8UbPNVa
-XSsAYDbwC91PciBJfsdp0ByqE9D668Oh16Win3UYRSWvXzOfJclDGe8MihFoRxsG/QmBVFWDx+++
-5NsrQW4VImHV9TzLurxcpk7tyk2aaaG4whVDyG+YfIXxaQ4vrNm76P4N7dyn+xBb0BpWJS4C9biX
-4qD1ko2iwv7vWH+pQjm7VRYrVIGN1FX9Lj/WTQ/03R8qBwCFlzE7DVLjFW0M0c7Xgu03ON/xU74l
-UHgCWaf1vC167AyXwHWZ1uoEhhttav7U753Z1qfUM+5HqzT+GF77E/3ADaS9v0+h8p92lSJhXBzI
-CLnuEaeZIBiWAKYueBx+7lbHr+RTTvo3pVW9VWuwineD3SPA5kNlnd+1bX5jqYUX7gm+ujW4WYiv
-k28wuEaERdhfA59mP+mnmiUBcU6hgXDXXJPJGgxTjAtvxzX/b8MPEP5LYVTNinkTMtyrTu4RwuBW
-8hCnr/D6tLjOZoLmEQDrpqGUAWc2K7zrDEPHKwguNBqzDnsYTzL0Qf00ubBSadZf8knyA72fsjPZ
-/sOWQsJnx90k8CumBoL9ELdFqe87itrjjhdUz/pqEXhAy6TluNeRtt3Dg3JQMvJtoN2lQRlHeVXm
-qzylx9jtsrxDHPa3PMtHpNY9hm0/KeO3gLfJZz+CUKiusr+/iSNt5PHjPvHsjX2q02PTbobuP9Ie
-J7OV5vDew7iW+5M2M6L3rI/t6/vOjOXrWt3RQhxorwMrrt2xwpQnRKVkHffZolnPazyc97fxybQf
-o33FERXd6TpMuUdawLCowb35ezJV0/3P9Y9UTeYPwhtZKRXzJMGgiDebVtLMnwDw6aUFaHK5SxIE
-n8DtyWVi5XEZB8BHS7CwVXgEwQJWbXeJEAW3FHgHrENWYV38+dTT395pz+EHCP9x+I1FCy3EG2Ll
-5abRvUlII5P/cB7ycPbHQ04AGKQqCosiEMx5C/BViNOQld8N8nWamjJUcl1Qhfi2AvswVNav030Y
-+F5xdOCrGj+wloJFXEaSdUCyM1LjmSZLMVl3y7tbY6WCAo4HCA5Ng0rJhlIcM+xHaNoaSmlYUju9
-6IE6PfCk8n4CDYhBOb4hsUq7zo57X1BszSs+xbcd2TyVTQ9jwX1cV3IEGH8hXgEv5e9EdU9h15I+
-5EaHryQn3hoelrgwv5OFX9WJ4aF6FHVvy2BnvlLBWK6cKzE5WnxH3r2Xg7/tRYgftzw/SNelk7HI
-Ke+17SkPZ+jtPDU+kOl1j1Zeoo2LAxbJOMdwd60J6MvNE5++NRcgxAz4EIN4QYMJukH4LU2ud/ZK
-EmCO1wHg1m8xu9UV6zj4/R78gvUaALGK6AbT/2e7RcQLwbDyD2tQ/+K1cYePXXM6fU6rm4RA3ije
-Z5H/n4cfIBzhtQj8e+0RCFY4qLB7APBWn2L162vwkPsuRK3gAhKEhXdhfrAOaVs0KNdBV7Uh4oVo
-2894AEo8hMUhjqB49Ue3e0SC4QWOla3DBvVY1rcO7J5HEGBxwINW1QbMEzzUxpZfyBd0TSAPPK5L
-RFgD1tnr0qFai7vvsIECXHvn/YNxYGkeLMW/G5o8jD15LnPOUvH5PSI/zBqEbKg1yJuqWmPXRs+8
-N/H+FXRLg94+5hf6NNY39BdTfx9eFPwTndbGMHX+qVxb03s5byeuu/05rZmLAJdXj/Tz0wkGhCOP
-JbXuDWp3yKviZ8rrYbrB4QBmtXNUOSBDutQydSGALtpuJmNHxrO1vidgs2xeBV7veQLTNakKnKhD
-RDZ4FdA94vro9FPFQFesr15xxu4PCXJfWor91gi90jUCwHC2xZbh6I+5brUcf30Gp1p/i9Ev9gZm
-91X+J+EHCP/noR98zMuH3dZuCNW+N1Raz3QfOrQSg9UYn5AVSa2B/qDt03DyhTVBJeoiAEGCiK9Y
-Y74SR8AL79UaHHi/5u/uLmvw+orHTAIEn/yCUYi4sPLh+bxymKBFVxuzmpluhwWtMrpVZHyx3LXr
-SfD/bbzYMoBhw3R1idBXD8alpVhhZLPImvb5HS1OB+3Dp9IPArMoPeKNr0YP5YY+1JU5AYVTnn2R
-3np7xfWBj7vd+nqmh5T5LvzHWuqpehrDtKFaSKLdsf1uh5DNDuvlul9K3kCfJIDvhUp/yqf2rPRp
-qsvOeTR3eWhpdvtFZXOYQG9dG6xj8IAIhuzPJhiqqj0g+vZzg1cRQQOQ4oFXgecKDPrlfw2+ZUTd
-47z80Nu1dY0qWFOtpLX64FYwrOkfrOgewQ/NtQflqi9xPFgHt0YQ+C0v0f3LdGeXjVgDARC8XR1o
-gds7Kn3bM4hX2/3b8AOE/3r4StRK3yHCm6S6QpDkKHlBEzroEwC2Jz5vBqSQEprM7luQm4Qch1iB
-bx54iUMnRFtCQtTip5RXce2+wR43rkMk/cUU584/queIhQ/iF7SNUlOxFeCMD9DJ9LCcSEdhlg/A
-tTaxHVaO7KZRXqG03eqrHQyPZZao6mKqU57AL3zE49wBvU2XrZ1aiqNxU2SyQtOWHcpOQMVV3zmP
-57CmT7Sg4/pp58f2pnHc030VbsJ/rI/+pHoem/bKDgOzU/ZXnUk5gudPTM5geBegNbZ5bQXyateI
-ZgOt8g31tH1kA+1Qrw5xUkmSZ/wu4BGc4j3syR2Y46YIJxioLYM9DvqPf2xqUbJttgA7v+snrAP1
-zmrCEuyK6y+Ty/L6M6dfUPbaddNPJYvrPig7PCjXbo6gB+XwF+QYEFdL8C+VeHWLsMct+ohz7RZh
-hCxxi0QDAJys7/+x2BnDDxD+K+Hp2N/xWQG+Eo7j7Y5hTIOP03pwzqUigNGDBdge+bIv2XX+kY/g
-55GU/DL0MtRM+7i3IPGu7bqu/AVj8AnmB+SQjsKpfyUV0yDkGoFSPUdTwqBqwESbisWEAW2Jk5/x
-VGfGrebteL0sTYQBsxW++xdaijWVtx54X+/3Dn6neU1qySf01sveCUzfi6m0bjo31aV5NOoKndL2
-RfqJ3vKs5MPxbeOWnjfTi4fxSw30fvV7278fbiFS55gaBRAFsOlY5/TV/8RHP5Ti6+TfcAivYQXD
-ftRjTWEZJgmC/Lc83l6hTzyNNvB5SJnZ553OuvI+o3xUR8prYaUeDklteSZp7RXljvtZKZZfunVE
-rAFjH2+0uAVC0zEuvfa3TRrlNkA1112rjAPJy/PjHQFwAcL7vd0ScT0D4qBVn+Fya0SCZZVf3pYk
-GNbjKw+UFleITMPCwgpXK7Dvj38dfoCwh9+afG0pu8lvIaRC7KLcKPUqEuF0gmWJMyziLK79UeJU
-AGxNGlkIg5Ri6vnUbR0AbxKMk32CYFi2nzTFZhkIa8QXIDb6eeZuFT6/3CViXhX/yuukWiBOPsBO
-tS1oobxbiPfcNiswbRZUnMY4mlrBYjbSSMk6DR6EE7lzifDyA0+kvZauKJ8Cb4FqFZ5rDdlJhPsy
-NWvKjb091sftVkt4Weljuu6gN/RXvJa03vOedzd9B2hxDIet+e/Coaun8Y4MkvvwseKhDt9xJjJa
-6237/DZAbFmG6DKvKdaL+/jp3Xmn/IjbQIv38vAeuMqlPGUg2+ezBzypRR1wWSCQewQmkI6dB7+Z
-1U9g0uz7jVQovVwge4Hc7g7hc6PGFmK8bzdArW0gaxpXk+WPVPgNDW4BhndFEDv4/t4C4gKgL781
-IsGw+wzzr8tp9IFcOfxl7nu9dXgDwbi4mmvnzzLt/IQ/v6NR/k74AcK/Fd4c95lK1rQNWlXKncHD
-fXzBI7mhVODhuQ2WV1GQss0FwqWpAkjwjanZL+FiIi50JsCrPGRsXovMoWH5A3/C153tiF4i12e7
-Q6is92sBNHfWv0wyHfWksPIm0wNizWHmd7Vz+9iKVXqxkcZg78pBHoDnBmkLsLbyN5RntUhLsQSD
-lkMge+8Ssd0gogwoclxfOYcKdifFN4u9U45vHqbOHxoG3/Vyq4G1fE46qe6GKV2BxonnxPs7r9p9
-KfRhaK/zToH3+d8J31Y1+njXPXHOopzjHra7/a3IVj5klnWKM1YelPTzqrye7d0O9Bf8E8/MZ40P
-+4mDpW/VpMdtiAcDhJMsmN57gJwdJfXTDkGVz9il/TdES7X4GoijKpe27ywYWNYLrcGWgNbYuhoA
-WBMABzh2IOv5t64QB79gtAjXh+gKGL5UhlsjukX4KmNce2IrBf+woHsNBtCba9NlPBb51+EHCH8d
-nlT/75SBo7+RIz0EJ77J8MG5Rc+bI1IiLKzrGxDAbfMzto23mcf3YX7aXmUsozCshI1ahwrKAff9
-BILTCK3xFZfXp7JO4vUR+QhbhFHwXq50LGllCHGIcV3wl3+Sd1Al5n+S7v13QBtuBhaprKO5S8DN
-DFKDQYwdIUyMCkFNBSRtSzD22gSUtu003AoB+QGGy3w3PSOzEpvDoExG7vlatbH2A6JrpUkQH6qW
-WRDXcU9zUWl3czbN6bev0zj1N/NqfuvwocxT2b+t1/j0PjfSP98DiJI7wFX4D32JD4wiR5cIEwbE
-dB5FGBCDPLh7J9qhDEuq7M9tXbVvm4Jgb80Kf8N2N494ar8DvUPlJnCQUcmgzoL6bKAF4bSxs8ds
-4YX01sm3eVvmXGIJbIVvYiArsPSfN2YgDNemVYALIJiuVCNA3O8XRreIuEeYwHB9mK/o0rAIC4Bf
-BMEV42AyAEtb5n8ZfoBwhD+Z/m8BcFFH6OrQT7yghVhVxazTyfQ6AmBpPBY8loIUvusKUZOSL3qF
-X5UFvYHkw7BhiAmCF8H86xbZNF1C5LPLd19gB8HZ/vgqwtAPcVjPBW9+sOBjVcIXaC15DKrCsDxo
-JpSylmUNiXbgp8DQsFmZgCcVsPOdXR5kW7QC/lO+dcuxb6fWoxKKUjopv9j3N1U9B0C5N/WcrmvT
-mjhUZTe0pOtAw/X4vdcn4jzrWkZAeeUc1r7XY3q3BZ8k5B8t32+EPgcyU8pavpP0Zy4EdW2dTi4R
-AuuvU7k8S/1M39DsBQ/S7Pf4fNz+qumkp9dnnUGct0kOyIE2BvO9XYwYd/t2qtAquQgIMFESIFb3
-Bx4AsGBeuXHBAGhKB8MMgtlv9wRw+SE6sBA36zCC5ORn/2AJ8Hv+QQ3/AJTva8H2XA0gOK3ESWur
-hN9W/+PwA4T/ONwhvk5vWwC/C8BNorIswIObxBEcbzCTUiQ3qAMe51nVuIaA69CsSAZF4RUIVXbt
-o6INFwqciqKdl1vHHgo2DVJLQUpdovK5RAR8hPEGiSMI3lWgMML+sMC+uSmWEILPW8bDdlr56AG2
-tBiHtqntlDRbgrMvNvC4QsU2sdbsQ7fyek1vwfDbMIm8An+ZTIOdS5/ppZ6m5GoV02r3fTvdlLHm
-4XxjRAUZ374+I33y/4Z9ESOAcZWOH4HvDc/v8P5rZZYy6h3vNA/6ugbeN+MDqfvbuw56Nb+lKXny
-EA+aDbSJr9Ls9/nEpFkDTTjNgHgISsfq+KBchC2DSGZXGQHporZ6sId8YkK3h6K5w13xDI7Rikqv
-CQCLwkvWg2qiHRijNfjJ4ot+wY22y0sBvxUM7zq7FbicAJM9doN32dby9CPm55IGmtf473HwDxD+
-s/ACBOsNHz3FioA40Wccv34OWeFjdftrinyYrtZrBATqA3YGmxVwafSj0miIEw3jZciLtAULViwL
-8H7AYnuJiF0q9rEQMiGM62G1QreSH2fQhHzIpILhQWWYLMt15OSDcgs09tsb+vftVQAU7VZ4fdnE
-+wzKNzjMqE3KNynAVg4uEWtGZxDWgV8N1R+Y86TlacutGeWcvAo6FNXOMezJU1UiQms6rOaY1+fx
-/vUGAONrkixWcip2uBniax6nIlA6883hN1f1lqHvq9+svXVurtEEz1FdJxX+tUagC8uI8dq1HTlI
-oSNtjNuc/w2fy85L8mv/23CTj7LA24DrgG/Xj9TKwybqKsge8lesus+tVWOf4BM9gLEkEMSH4ioA
-/iUViNa7fR3w1ruCy00QF4LkAn7Dr7gAYgTBIqX+E3Df4zOJsZP+CmxhA+CFeMhnzp9d5f7b8AOE
-fzvUo7rSgGEHnkVLcMuINvfPpvl1aVoeirPZRWJlOc0bMe5U1CV7I7oGcwBsrPwlq3O6iZAQjOoA
-vPD1Ocbjw2Bs+a0w6bpM7CPyAcHjD8x9PgXYit/hWIBxlawwF+zhVhkm4VlUBPrq0vndeVg20hOt
-Ap/y1LYlj1CJTjOKu5/w5OLgSrpagU8Pylko/T5n5zDfDTH5AE/wWQ5IbobaTYgewK7d8NTmYj5L
-htX8Q7m3rzsQHHnG7dF0UNRk2r0D+2P+4qlC4QTC/7sw7ZYTg93xCc6ODjTkO90yk2XS5WECxGkB
-/ogMoPi8rn8tbhm/5bU534ODO5+n+yu1Vj4HbfvUIO7t3canRSqk867f+ccsy/IA5tZ4FPLdLcCB
-IPsKuzXY3SQug4fNlMFwPJgmSpbg+iMXv5r/bwfBBIxHK/EAlEUIDEcfPU+U8lCnNp275yKAr4ic
-QLB/H9iW8qAC/uvwA4R/K1StMwPe5/Q+1eQbgzR4F0vgHF/NgOtEfMpytGrZN5OyGSWl3gbJ+HAd
-CSevDmWYggAr+vEOMIsMQkhrwnr8Urk+4OO6D9ClCSD8U3cKIAbBeVglHfvLOBFEHb/0Nkz7mMFe
-2sCvMC3e0G/3pg3D3NqnTQM2K5z8IN7JEgzWXpNXYPhNmKEqrnCu5etSDwjuKEN12HuNR3n6S4OG
-L5XywecAcIb+Ta8TCP6IEEhCfuph2XIZ04o/Cs80UoIE4qKhBqzj3Y74u4Hk1Nd8NzdGPNZa94TR
-WVvnCz/A6Aue7OcU/908389IH3ntPn8CulLSFfh+BloAWy3pmi+wr6fNOy4PH4IOjplPB94E0Ql2
-Xf6468Na0dQ1bsyZ3CTwF+IuWTKoguHlDuH+umApbtbhyZrL4Lf9gMYBIF9Xgt54R2BcaTKA4bZG
-ey5Cv84g+DYut0+d/GfhBwh/HapWeQN4e3ofLcldZHtPFL9gOISGjuh7AxG/yn7oTkKpxynf+Q6c
-mgU43CQSCVRLb+xbGEqcAc0yNT/KTtNB88KCB+nLD2KN/foogF+LrI8IXJ9mHQTv+fFq17QxXBm9
-QY3TAd4GgBtxsP6SApRloWX++vgTV1VhS7hGmHcv28Ey1cJjVEN1f+iuDyd/4qlPE3w95Z/ib4O2
-yLtK+EG5LDwe5zJ3p47w2s78J8By+zIbwXEFTSJz/2hMdQtvhhkc56G9m947YP2vQXHIoNd870Du
-m7y1FsUSbIv2iTu5nQeAL+4ZnfeAfJEe8+zMRzR7yN8hQJ2lTkAQ/IE4zo+Heu5lSDut7t869Xhp
-RMri0+rlRDzlLyZNPeGAuPgFi9QH5pJvzRGCZaEf1chr1MASrGkR9l92q0D41xH8SnebGK5Rq0C5
-Al//FvVyveq6E8Gw+Jq7DnS96ovhc+nWYVjghzh6U/zr8AOEI7yc/r8AgpkEwLaqm8kC7FKB7u6D
-r5knACwi/AlMBJ/qtCkf+hJyCLt/in8VylfZKsJfmficeGoDM3X/YZFmBQ7gu+dGQHDD1Gk2GXXn
-OCaoUdbGwBfWtpozcEMQVES7vJU6K5C2wmvInTXt5oHqpdwFwouz1Tletiblzgr88W0is0uF4yVU
-aG0bjMQaJijMNC00m/iiI1OD5evt0mRtnevLN37BBwQ/whA6/xm09PWZfYInYHya3np6p7Xps66V
-BfLm+qf2v3bpfgpKb2Po/TlCnhvQ1a3EKI0yzeV5TyDY7Q/MjR9uzPdRyvGn/fLEI0N8or3Nx/E7
-0P1QfLkESCkzxSdAjGd72mvNvW1qoPTzCI5DztY+eH72bumF1L+LisC4WoGTdoFrhIPhsMDKwWdY
-wRos/pPH04NzDmYL7XTH8PQQXQO6BqA49Wp3gWEQ7IuWxqdNtC1TFOZ0G/R0x8PlDz58/G3x8Sb8
-AOFvg9WEwkmqX/TW4+5iDngC8KQFLyWR0TtZDSvvXw97Mw/C5xW+edGxFIgO7HGm8oQ5WA6QvM0R
-Wg7fArxoCWba+uSqWS5QsIsuAZpEu0EKEyysy+PrM9JtfH0aTQ7vCXDBnxi7Jv2dlC/Mv8fxJZTu
-K07W9uC3VlZ8/od6vQzuh7dhhCbjlhtuahlb0iHVr0Gr8/gEUOilcNyP5WYQ3MrsD892Gg6FtycW
-9/sMim8B8X+swd6M4t1IM5AMovQdr6eZUtcKqt30XkNvrXwCe9WPQzrF6rG8iswP9JeeaK0g3lxG
-vwlQ07BQJkKW3sZ2KN6qsq6JqZHT3KK7YCkUVmDQ9z7+0EcjzVIXeTn1OrOWKBdt0t0vOUh4D/AI
-BrN1/agDTPdPB5BpLot061vLafF9AMC17SXYKNmMF3K+Pfs+TrqVSshWN5X55vz+rfADhP9GID08
-ieITQJY4tKnprLyXTfO3lU2TijNLCyiUDGSHWbpHNCl23weyeMOM+R23CMjw5x39IDnQDYDWaP4E
-r8BTzztPuTsRPyLLrfIAnOoEbD+F9ln0AMWfrKOnOzD2GylW0xZdcAv1E5gyQWvvBIjR8oFzwfs6
-Qe4BAEfcgO/Ecw7aUjbku0SeS/c6Ol8/sck+uT7gdliKJflk4J34busrfSGeaGevSQEPc5jk0h0n
-9uC0C5L4qgt/IbwfxZ+HgjcrNwAAIABJREFUgzikvGm9PLy5bhD3WW+hcd326a6jUznCVY5jDGil
-J/XcTj1l2ekE3qekHySzaAq8H2VejlgVy5SRUjdOs2c8x3UsNbguYl5wjXCaZl7j197X5AG9o5yf
-82nSQHDIwvXNqQJrWl3TKotX+q6oUf82e7pcei8iz8DFMNcYjL0SIFpE4sH+HTdY/PWt6r863Rx+
-gPBfCoejB3RWLHkmUXsavVvNv2nt+/BGpM4881Y1jmohl2pqzZPgtf0Vi/8GuTuHqMj+1Ctg7dU4
-vHG1C3y9g+A3rMR1XCClOI89ZdEy7AAYLbby+Ygcwe5H5ACOOV2twkJt5BVtDIDrlJ9AlkmC4A+M
-F4U2LqegsAO+yV8w4wCQjRVDgGSpcy1H2pgHSOIE3jI11/q0y9H1Ye+AeW4LyBXpc25jfX39jmu3
-F4PlimZ0Hj7UrCeWgd/5Bnlj84x+K4t+R+1VOTHl/069d/ugyqfz+qY7xFxrwo3Y/TdPcW4JCK3d
-93nsZwM7/T3y9vk0E2q1GQrgzJ/DtBKdFm3pwAWdo5LGfJk3nAwrhWp3qhyighW8sqEgwaySHBQp
-oNj5SNbueko57gP0zCfKpBmb0Bhlnm97PwJAjofNLW3BPo4EvTyLdR91EIxAN+cwLb85P4FpwCJt
-wbj08sGW/5+GHyD8F8IoxuITVIVUpUSgjFLOCh/xFBJWV4pkmMX8+3y50TDW804y8KHluAlD4NOr
-+FdNcEjA2ptW3koDQGwJfleepj8TmCurpaGBrlivfC3BsuNHKy7nNXBceStYHsCvCAJkKcDYcJvk
-SzkdYBjeRUC4t9XiXb3mXQoYNogLxdOlgq3I43yPtCVNT5D2VP6uxAQvKvSogKfSIq2+DnO5N69v
-2jsGzGybeBFr+TtgKVLxGkuyt1+On+qewr9Th72lE/DtZUru0OnTGid/1nEvhc+A+K7sBHCntTd8
-DxDTz3DtTU9bNhgkEBKgG0hN7E7VolFcuFztg2uHqPOwX7m9acYK2JUuDxsoVuA7WYURBKu308fR
-Ro+DL1bgpQPy2rLQlRv0Bn1fm+rA069NrXshvNQb6E3ZXUGwVwCuwYKWX/PFgLunDRaCXClgTe7O
-4N+WDT9A+I+D74ZGHWJaSRJgRkRGFOsbwwp9lbzp1wtg+6dhHwhO13yP7PfSJVQB65BKCF/bp0/F
-D25ezZLgFuqtgDcswEbgF32GqSeqclbpoMZIo5mkpXb2B04r8Q04/lh3i9g08hGWahVO4CuSW2gE
-VACCq0uEr4VQvC5oV0AEcCXnnvIobcB7UqzGaav52nmGMSTtPQieoIbP3Vr6erOG9LTyOoyvxnPv
-zpJiwKi9rOFGBo2DfF7bWq7lU14y3yr1L8KT4ptHfR/+VCpOyrmerbZuoxr/vhd118Mj0scWWkuw
-wRv4Hco4IEYQWgGxPKXbQtmwmUDilDwqnqK+VF8EHwVrdZyY51P0DIrj5ghFAGwk4zq47m36LLR+
-4CIMYPgWBEv5VhXyDRY81l1zDXh/8Rlv++pg+Y26CDSDG4TuvQxujVLqryv1J2d4Cj9A+E+CzQti
-R54hFhoUd16Jlx2BLH89VEn/R2GeID5EAXmh2S3iVeJGDN2n08C9wU/YHeB1qbnAGuRrCrRRstMY
-Vj0aEw+vI/A9WIILOG7AGMrJ8V0CAItIfLoPkGRFEY9Kel1Bh8oN76al8Tv4M2n3fybY9bWQIvxx
-eo3TW/jSBxJ7q2RTXM5gunMifQIOtTbkWWDHrxxEkFNBTwIjqesg0zrMdLmhV56k3oDhkRhaifKe
-jv/bfExNa/BNmEd3pr8JE6jluD7yVADcOR868G2ZsQJr1FpTA7sOcK3QpY/xeL600qbVKDRIgggX
-MqgAr/f1/7J3rUuO66wW8r3/I084PyRgcZPtdM/sOlVRddoyQuiOlglWSnY77lNBVNNGIgpftIci
-LvoaLNjYvs4CzIkPj1FTeniXAvUjtK2CX466Eeu8G5gt5STkFnXov7UnsAHQ/BC1izP9u/aNve8p
-sNX05l7L1TqEF+K0r5Llt3OLwN8x+JfhC4Q/DcOiMufvjm0TykaBO5zeYNxue2UxF3Yv7RMVbEKz
-7nNNdVk+E4WnRGJ9Wo1xZniqZbLVXy28W/mAdiguEuSA+CVM76BFbrZXdhmGOMU+2bf3ChyjFdjB
-8OBeQRTibiVWutZSrMvtA5tP9+lBcB68CBKzC4Qpd+nA8MBLcQ+cQe2Ull4usTRueJ1fAiXCtQwv
-5MN4BsMhLXya8WrzxO+A6pLKi3HiywyZI/boHcB7tYK6uuZx+c3Q9kRbxL1yI0CcN+tpfZUTIzjP
-QqzLtTbu6yMjz1FQU2QHKrOqZOp7z2cPx4FA94gcdCtpQe++Zyxv7w3HBkq4FHpHboGpA2CnNzR2
-q7DS9peMAfjOfVbLjfVj0hfM1kvITOqvoH7AwmD95Q14RY+B860R13arCxvQG10j+nuxTN4nk+V3
-JS9+0eaO5+T93fAFwp+EaR1JTJQUmdajgppIl5ihS/9ZdT18joSHAhXADrU4Kt34X7++8UVeAS36
-BNNFevcSnWmsrbTc06WDHUIKgKuFt95nn2Hl6azAy7XiDXkXrViDKfkLJ+uwkFUx13y7RERr8OVw
-2kfMKhxBbnO/C52OTss0v0qg3Y/PsCpvYT3A9v95KWiNhCiAW007xjmOBaadTpCQLK/hI6p1jVvc
-vWUdc9Rcfjd7AufN9E7oSulHKPLn1OPW+YM99Uotarq9IHfBb/PnsgTthbPzW5evy9OB2nJluJea
-bsAFPl2tDbgW3R8RbnEhxrw0pMMEQIltmzqjkXF3JN+MGTauoEegcRHYCqFf8MoXX5bz/JDelFH6
-rrvfmVY7AbFqx5LYCREKmtUvWGdINh+k7KFvtY7Wc4kXE/NpESor+xJHni3E+J/N+t8IXyD8NEgT
-7cZNhlvJ94PAsHN2PPfreT+kVUC65C8aGBbrRD/VKZahv6K3Fsz2OSI/jzAc3I0a8gLw9oDYNW7Y
-aIPeVvkC5e0PAl+q/r+9tbjxJW5ejssuEfH4tL316lQRss0Y21LBE9tB+Eg/WYSzDBzWCdiGQ9eJ
-7DnjlCeCIRrjPwHJeTav+x4E+z3H/rwAw4Wm80u6vqwAqutv/N/x1+CjdFIFnZwZO0aQ1cOFvrxn
-4BjnwQ+R7AdZ8izEeZHnSB4LpyVIakrlGjCnTKQz9GmeTJW71wEUMwobSh2DkOluQ3FDRlTpGfQS
-kX/hB3WNTfZZdJyZwN/Xf0koeoU17iVY/xSrsIT3sRlzMBXZrQ7DBiMKZWinjhOv1mQQrPfqLS0w
-sPmsaZtrCgcUuAZ44GeY4zHCi3+XYd3je7e2h4MVeEtDnn8cvkBYw+0RmBZTpAVlKTHRJkCRIVVm
-+2Tb8fW1fRruK9wUgvZK9GarKAp4Lza7kj65ujpaa99WDrwYN7wkR8OpEZi+Wy1bWYVqY11NGSma
-kV1RsfgCpvoByy688GbHp3XAtz1WbQLGCqzUIoyuERUw6Utelr43GAXACowREGv+V7rXcarAVtI9
-8NzIg9fforUbGcTPIDgCydx/uM5DWkOjnWcaozpec5rJTGCirj7dKOdt/op+0op5jQQiIoYHMqt8
-HCGnZxmBdnsz7cf+TOvzdGNkdO772mf+lcaNPHjXxln1J1E+/7WTerwGsBZBX1zDgIRsQgoo1U3P
-+4SsPh09JlTtwhwPEiXT+1Vwfpmu5qjfMHkJDnp3XIGcGV6UDrRUQn1ROFYslx77DcAwaR9r/0rK
-5qcv0d5R3U84dkm2CkNJFnFQvJSfzRHhwKfy3PIriUff+xl4/nH4AuHHAUY6rudKziBWmnXYZhyY
-y07403BHCTd1mDaatDEH4mUxXpewuHgrc95rr3vxTagCYtlpPPEu+WgRVs2Wla3XTkwhWRwswURS
-zwQOFuH00lxwk+h9hmkDYrsnB74KiLWe9jGaRLq2abcRLcPzyRGeF63Ifi5wfAEOQWmhiW8ci6e6
-TeSyn6RFnrx953gPkPN2LnBfrluJH3nSFceBSEb3CAp5mnFE9ZD1UFqfnR337qpvl/RULvJqJRt0
-WmtzXYcItg7hjsAHodOQPQh2qhDZjwDF0b4rcaqJSxmTC90RpEOhuQan6+Tfehlg6Pr55JO5A97I
-As1p6iKlzossxzxTfRlvQt5F69KN1gBg60N4EGCq0scVmhGqRtD6q0DXADDqlP5lOdK9FQQfXSUy
-KFYxmodIN2vby1Uf6Pn/I8/XR/j/U0gzgCjNW1jYkOYblxT2Lq+Vlne8vEaGtfwoPMLEHWOzKzYW
-YltwuIDIVUv+OtHA716U67fcyRbUWlwOcm1x8QZaQtVnOAFiUXlWDwpxtQYv0QLoRyhYgkmoO0JN
-X4Dr3SQ8T/UZHlwjoFyzCCeQnN0kTBkmEJwBMLb9lbbusjFuxvq13wCE9SPx7ekrYNtfz0C2B7+e
-C6d7jisf9t94BVVwhz9/Jjqmq0qY8mLIa6vcwteptzecpJKucgVeW1C9hNzvJ5kIhtvt8sH+eYaj
-VdCJ38aCI480eeb7uwrYVyi8cN/O53ANwIlM094Fxb8S7Cs/F1rk4zYCRzHiNDJ7bZAhgacVWCt0
-UV8q77lw1juqw0AZZt1n4HdnVBmxX/PM45LmdACznPMg0LXvBXzdQxqQvYRtRAol6mk5iMNV7yGA
-hePPzGpMvndbQSce0XX+b8MXCH8YgsLDxQu7Bm5Khd5FuvG3bM4zKaYq9y+FW9pRhnocVD8TWIEX
-r1uCdc3EjYAV2e2O8Z9OlvklOQTExC4TUFTZSHRb0+/qtluEAlCS965G59LwLnR1k5D3O7lOOHCu
-lmVyvI1x7RGMw1WgTTmNCEFxfYFD2/+i2ifGK6DwIX8EwtVybC/UKU/KH6880PtrpfX5M61+EU+7
-9oerbgpAn3jdAlytvP2neaCRvpxb9yGv1PSh/Sf5J37UgT6+eWPvKL2sDA8SvpvTi/C5hR041JSc
-VtZZijtEwzxTCXPJxmHruOe9ksxEZq12DCOhr6ruq+vZvoHjmhbDsFk0A17qoGuKswRfbJ10p8lQ
-+tDHgdlvum8jrP3Ai6DYXpbTOPXg1/sNAHZyb4i9EmpBiEZj/4FlmBY49pmH/1FaWou69+JsDhbd
-xezWY9HJVV6IM3Dd8iwhjHL/cfgC4U/CtAvBBPZo9AkWSLQ5MJYhDS3sK1d6cw6jzvWE43wsSiMn
-NkoqW9GH6piyhq9N7KgWom1RvHZ7UE3K9qTaAGItUGXbhposJkHLCJlvMK14/rGLzvobae9IC77E
-biH20yOoWoUJAXfdiPPmnEdLf1QDQfCL4o9tvCCu6bgxQvcl14d7LhNIx7UQ0jiCs9MV55vmwnRp
-8vg4c6Fhf536cvIZzvm6D12kZ74s9xS/uq9ypdDP+1LaPE/8vgcCTx6RjpLr9+FO+TBbBoNdmtZI
-NWZ+cKEmXiWc6aYPW17XVac2TLQlW0GxhHR8Ua4stkNYepaD66pfRRWEyz4PeGgAguK8RjPIvBRG
-tkVAvYnc2IJp+qklxYeBnR72Ri4y1hXldCtIUnVThzKWoCPo32WZzcZo/t4NUdR3xER6FCfx3jOJ
-wGWBHBTD3rzyYXqsXgbHSwfsDOL7rc4LnSL/OnyB8C8G6SK4iFugDFsPAIFecCJP/GOYlO+95FoB
-yHfFOMitSplgEXlcF9yK70UoQme3B3eXWPzxJTk89FwrEPW2A871VC37KVvIQDABCFZ6sP6e3R7K
-5x39iKeTJCr4FaitD88EuDAo8O3AqvZHBsRR8Sea6lPOvFV+puerbpQhja/z4TbTXeXA24GfE7gx
-mm4KBz6YKeP40AXfnTGd0k58U+i26JMEp+RXjsg6t6qOOxRfl4F6pX8O6VXlzcyfzg21vZ3Goa+w
-xNumLt1dpnT17tP9PGLVv/2aP4R2aBKxmVFbTa84xx4I8Ts8vqM6/XKvjL1Z673jQadVq3BIZ7AK
-q2JUBVbKTWM+0jxNj0PTuVXBML5wrv+jicetsj7Ya+xxz86+vkT5l+EKXZOEAvAlJgDUnPb6vZdM
-b07+xfAFwk/DuMPkHVD0r6RLjPTirxYuFMcN/d+F3I5Ugexw1JM8zSSkJ06i8JLVWkebMALiCo5V
-MqYR87aKOLSzxRnqlJqHWlahS2v9Tffb+lstxsmFIlmDnZcSbVdr0/TeNmPoQ6IIfF+pUZiGH98M
-71t6nwBiauI2Vjkd1kZ0x+CU9+oKY324ariK635CFPs7giIC94j5QzmPfvLYPqjfKX435DxX4CKs
-5YM8DpT7G+Ezbg01xzTmd/JQuLp9tpMTaQ5i+iLn2nT1u3PuMErs4hE0zf2b127lPNyHQY81FiK3
-RpLrfJS2eAb6sQcE+Co917W2MbUFEoKOA7cJ/wCNNw+T71umV/M9EY4UWnzzqMW+6KzAvI02mEaQ
-I8e2vAR6OzcHHIT40pyOlaRfkdv7197kESv/FybhLxD+YRD7hzSJNHH3CFNV0izIImeFE8/9Sk4h
-Kdt9+3xz6Qu+Y7U2Z3y7J8IuBLy7qwhuETu/dygC4s4nOKflyqxPtjh4zD/mH0w6rkIR8Eb3BwS8
-2TpcXCimF+asNLQAi80xSxes8bxFTsC3gtTGx7fwDGmiG8Q5Dz2IG21r2+yffL7yTT4Pj0EmLCsE
-S3esvMiHIfPkch/XcYg/WfvdrOqAg0BK92J41HWRoYdUFYy04UNFFiHGfKXm2tE6v2Dvk9qL0Rd4
-kpBzc+DDtCdxA01BFpsezn3KlRRD7jiN6voowygpyjEfTfGoqUeZt+hR0qj7EPRyBrv9fC73nFtC
-4d61FXSYaXoK6cE1wuaD63/dK4XYNtRFykebrQTbl/dLdER4HjARA4LF/KqTBZq2Tn9iyEs7n9iE
-Z23ePw5fIPxpkHgjSNP5KjlNIE2QlYgGZeJZRmVjc+1qTd8M9ZUQTOvpWRGNQVROVPRZDZjuE+fD
-b0wCmKX4YtxaVw3oJfQR9rhqte7XjPwmqpgKVzYw3e4Q0p4jvH45LoBfS08uFDdcI0goWYBT7YTK
-2bX9uNUPWbwecTbzXqQJbiB9XnoU97mj+jjz+PXsKpHBQ7cCJuA4prEve720nz1GbRrQc8hg64p2
-J727h+bcCrNOgw1aN8Ihb7R6dcAm5unA0e0KD7W9G3KfZleIiR7LBcU41m3Wr1GfSkP/JF7thnmt
-5jrEkEdNqpJVtiQkjqlEgwV3Jxc7QyxVSi36gi+qjkRODMjHmRf3H9Wn0epbR4DivaTRCfWKFt9l
-EIlWY6X43FtxtvRYXnyJjSgecebt7o5P8+Q11vWluV1bnPJM2zoMg/+PwxcI/zQkbRcBLiIQsNJl
-038CKncU/lSNT0NQn2edezNExXQpL61xW7CoNwX6AKzA/vYyvKRBDeglpvqjGkS4IxedrFZf1qdq
-GIWdhv/x+LQAXt8TmF150F1iAr+rSADB5NDXRCEN2qO1zgAoA4gIDiXdx/gEYG+l77HGAzs63jmN
-a9rWpZqC/PmqfYHXLk37BsP5nksf+3QBazDreHWfu1bjWod8fUK7Ch3viNtSHm6o0wkdysEpVtJP
-hY9pVzW+zpPnDh2uvayhJ5vjJrt6XL8cF8vIMOtpvOjhkIbBDjbT2yZfHM+ig9KE6XQUoi9hBH2d
-9Clc9XUtedZvydjCvrfYB1whsvzqa6wdIDbRJ2CrCsbAMFp8Cay4MGO9VC8PjyMVlaP7cXaNIKFs
-QTb+UK3Fx0S9fzF0hYFhwj76d+ELhHf4dbcUQbndFqSJvzPovYfYnn04CYHP536jnIN/2tq1fduq
-nXXck6TLoXU5tacWEo/cWsCWAg2AUgC9ECeC+7kB/pQqNkHM6stvEn6R0JtEXvueNqB90/utR6Pt
-OIBd5emOSJO3ECF/C4wVENMGyatmq8677tgOov2VVNwiPT75F+a3jOumlEHqlIbqOXxsevpGo5tn
-BqURrKIXHKSp8gaFn+VIkkVUy8I0bVMOmSaJKikSQKzcA7rZxeJUjzt1nGifhk7jTHy+UQ9pJoRb
-+i0ZrbXQ/+U5gPCgp9drV3a85pkwp13Ru9Kux28+oC0HcNscdENUkLnnu/GOtN1zYVBAYTcShChZ
-gJt0uzm3st9hYx7ON00mv21n1+3SZbvphd4WJmGFqgFJGl1ovdcSW+B7HBFTNrDxzo9NDpreqrBp
-hliJfA4pAJYNJRQAi/EGf9/2aDSi+BPLZPu2GDPikd/UUPfCFwh/EE5fNQNXm6dmahZLoVwF9ktT
-KZufEigxbS+0rk2++fQN5xBLi/FiTluOqd7n7KkODAsWAM1eiOtpPQIwfPp0xb9X8V7NSxHhVvUm
-odcCq3vjlW0xfv+JILj7vOHsYHkLvREcZ//hAQyrwjD3G4HNFIcpdGD8qjNu7vl9YgLqMEApaN48
-d+/QA+AVTEuAl3Oag5gOyGKbKFx74OP5PgCWcs03fUJIhQeePTVPgOnfbyODvmKPjPqM51QOkYHr
-lLbT5ZQeSyKie2AYh0hC3rha7qTNdLzeAcBV1tOAADhOQ18hGRRySN4AKWBF1akUuxoVVBzsGFQf
-XGyKUfzvrIJOb12XXhuq72kw8Ajxdl8D1wVZPW/AWODsIuwjtZ5St603ho29l60z46OG91mTRrz4
-NRDpy3aLJFtXD+4UDEOMP9QBSpyJAogOWPwfhi8Q/klQwPFgzeGmmZfLZ4HrreCUhskdHu4aDmZf
-FkLEaWF4GRt+SJKfKpJ9y6Zuup2WdWasjoMqa6fXIZwkQRRcJrKOFgTBtjK3MpJtCVYQLMsq/OYF
-yxTo4kfkHejqQyz5PrlQ5J9kRpeIDIK1f6bNNPeqwAy4AsN5JLp5e6LlvY8TXcgBb5blaVvhwtx2
-GfEt7QxYKF11NPs0rptu6sRprt6hS/pM9PDJg9EsmEsV9Jc2l1nskJIAT+HStXuqMN+XfwqTFTjy
-+GrpQDDyZbCagWyl5RxdHSPX+XpvIzpPp37VF50b+PNo3dUQKZmILk3BQJ4lnvrhTpq7OfTg9kkc
-rvaAsOeTAsA9w2R/c7k4eH/zqGAx7tnWB0E5sPWfY5KUR7AHWMGAy7ZLthJvjnQkhOz2WB5rsri4
-A/AlIgfReO7/Pw5fIPyLofj+3s9JP9mpOMQkzH2lhydEnZDEtmLicvIFRXmhGYDwRXSuU6S17F2V
-7+TTugKo0bbqXhnAMEUAjG9CK7+pLcFKpQ1O3kQIgmkdv/YmXsD2T7UAv9EVIoBicJcA94jWCpxd
-I8hBsFuJ97+mw6JqnsGwc98biTvbXgSuPU3hRga6HndAUnn8+CFsxTNwrJufT//T3HPuPnRpQrqp
-RRDTfUI+9nxasefa5vc2mNuSdO9vckQ8+wwYc3szg+MIUSrMbmDLyiqVlkubwKnHJ4Bc4esnwPaT
-mXDK3wHldY2r+jQHOL/8ZICJzxkD2B1AsVRSTXyW9jehV+w1BMH720QS+wEoBcCKExUAM6N+XzVm
-kvjTxdCWaM2FPCyVTKlnGN0sVr2Y9AQJR7T24pvl21bf5G/DwaBUgW94qa6M+78JXyD8MNwZotkN
-AuX8xjZWNwfJSXnhZH5w3o+4FkC1gs0RG2UYfVY4HwHehO0QQBnopQhgFvDdgFctwaQAWAq/wiwH
-w1qab0v2EXIQTOxXBbl/AOwGYAyuEJJ8hTUv+hOjm0QAxKsSCwwLVBPA8W5ZN9ccDDtHBAKz33Du
-kzO4ne8zmNXadi/CRTC8xzDlD0A25UMoNYHknEaQ3gc53HXcbHwnIKybTwbMWpnAl8q9E8fQtu+A
-Cp4Bhsauy3hJ+qtHtzSQ59pwnZcTbwTIdT74j0rorAM+W2cS5OEVU+N4XIPgnK+T/VnoV3UHOyuU
-6sDw7pvS6UDIYJgy71BFjgQfhciDa/+zEPXGPf48c6b4bjNnELw1gvkIsx0xpuDYfIStrb4Xh70a
-CFlf4Ogu14hIK8CX4jehpnXTXh+sw2j1VaOM8lv1xIqNP9Usm7b74lb//274AuGnIU+2O9tkjQQe
-TrGfBZ+oNm+t6LrtBW4G2LT5w7RsQC/h5H5Uw6TMurSxZZGGV601SnFAjB+2o9U0h294EFDh2BPw
-doOgBIKJNuiV1iL8fkvvP4y+wgZ800t0QslHWMEwmaIUmjZPfEmovvyGoDf2sfdjv3VSSMkzONxL
-vNd4v33EN5srGI48KGcGt1LSIk+EZsiL4QlorPniQwqOUbzfcwsqYOmpUqP2uQCzTzebWdxFB5Rk
-AMdXoJYHwHyrCpUYXFdBXw3QxaSgjkYXCVJgp/8l9usMkKWhxTxdWmrNDZ7Ij2HSpRONE62Xn8ZJ
-7F9KwU2Uxylk2UuhiShAG6dj1GpduG7XaY/u+XDGoJnF6ELLR1j1uIFGLnRsYgSqFBKk0GIrG3OY
-7RM+N7UiKT28NLfbyeQLqrP6KpYwmvObpdkm2lPN9DvhC4R3eNb9rvEe5fvVMb63A0elDROvwB2/
-YyLCry3C9tAsQG6kxLzPQsmV1vWUQwGvtsJdIBDogGWYOb2YtT9CSVnQBp+q3Fb6+hoLQDBxALzB
-JSKB43AqRPAbTu4R4Ui1Vbgdu0YS4gSX3G/YVxMY7udEBbwofvIjR52G/Yv3Oa3Go4Wekhy21uh4
-U8sr6UrttbqJXMAvb0CNjgHByylu97p5YP7rqjwIZz0ypt5QP9zFBvDLRWZvTZ7X/8M68gBdpKc7
-9BKIJxCruAz0a0jf/yuNQko3jpJ4uiCHuy48AcN4JQN3Z3qsS+pwTLMOPQwYdrpJFIj3fJF4Jfxp
-aBvUprVzCn2AFVpuK7BiRWF9id1PSLLaBtXs+3PrHwzxbltGV4oIcrVM14rLmCsmKPgGA0ao5wqv
-PTcAX5LNx9iEfx6+QPin4RIQN6rrFwFxnjQdqCl5WHm8MtXdN83KtNjaX2R70C5d6Mf0Q1p3xXRL
-40gzgExEr72WQ9tiLT8/AAAgAElEQVRtE2OIx4uowkrx6CMsySIs5v4gIgEsRz9heEHueHya91+I
-Qzty/7kyno5Fi64StVfnESnyoD73QO8JDPsWa+mSed1HuAOz5cqR58TrtZ/CfdV9Ar9jvAPDqUbd
-WB/rd1Hl39mMJuTK3SXGGsA81enJsaOX0IUrn/Z9DyuacUj+nAKp05hdu0pMsakln4c7ujfoSOxQ
-9A0GtpEf2bCjp1E6DWDiW+P5SV9czpKbaVm7iL8Qhy/Oifru6sty20+Y/YU50+faN8mHl6Drin9w
-GtDuzRDf3ut3g6xn6DfWYda6qlwTLRv4bpBr8hPw3YUbyP8PzhAm+gLhj0K7tACU/Ezyb00EmMhU
-6xxTifQ8wggIgdt+YlFJ+Lg3S5/rdo/rqj8dtDjAnWgFDG8dYldyKFiRJbn7gR5powBYlkX4LRyA
-rx+lJpFeXCLe9Eaw+85uEQvpuksE3Ks1mGDzTSA4xxEM53EwhdbOnastUi5B8BQvAFgyQK1AdwLO
-BPIzn6gMOYDkqaUjnryGDRoy0D3RMI9iiAqumvIeqpA8/s9y3q2CftPUUpu8kfH5/pi8WTM2S/3o
-8zGbCCI4JqLGaDCA3aBGehAr5X/k6dKntd3JuQrTii59kBMo9jAOT/Yj9lvVrVVWCDrRuZUW8p93
-TLmxpd7prZOQmKbfFHYpUUv5C2gIgvVlOZs7Bhq33O6pjAj8a3P6juz0OBMtt2puyg9xOS2/NLf8
-frmCWcGixV05lIaWZlDQ0xGufzt8gbCGiARqXO+bLJ2wyzczfzscd+7eRhyVXVJodq6YxAzFWemy
-En2YRDR1PCnqKe6A2I/CYSLwFV70F5EZMfzFOfXf8t0SX4A0ECzrXMYVX/V8/wFLrwFgdItIcelo
-aB2mCoYby3B4ea52c9f1BkI6QKyQs1OamqcCW+8z3ATiFtDEZU6nQosWYkwngjHkXg6Rbq0dveYJ
-6+K0ptt9sl9xGdDg9ZRGROmMzR6Gf6JnfvPXnDjfcdc93IDbG2D5XqEWJgjT/Zy6/wrXomQwqMVI
-zmhRqWTedCnsVg7Sr9Kn8BP4cNLBXd8dQS8RdCaMnLElFDsVYqxyCxAPmUe+fuXcDbkmqpmidrS4
-nanrb2PEU3uAqu4S6iIhFL+1RICLR5SFtHITgr8wF1vgcZWtwBm1r4+HnxrhYJgUzIZ6qrjdCDhV
-wk6xQKD8H4QvEM7hhCKkn1pHVdWSk1Z8PPhlRYd4XQK6meenvYkfqNnyOwLumjbsF1NJNcg5PTfd
-XpKD9RQBMfoM14/JxE7agJcAANMGpwsELyD8lqVgDMz+QeuvuHtEsAYnMCwYR5qWvcEwKWhy6IQg
-qpuRqJhT91q/lQ2/hQKRA4NufR1Ixq3DaILAVvNWQNrRSPDltwqKryy+gRevnHmm1V03/wajUmEi
-79dL0DteXfidrdw2yblivxJi+wcIxQ2vbpaNtCt8/pPWFDgT6oZv0se5izR/Gblbc/l/lnHtKjHn
-vdh3UjiBXU2/ktn7BZMDoEJLPRxue0BcwK6xyjyHwz76Kbi9Cj34vXe/49onBnJ5g1xxX+ACgtf+
-G4GlAxF3PWg2S8yTdXXa1wvY3eWwrQPcI3acPY9bMzhWEcCv29Z0LDlBDNn3f09HTeELhD8JeWf6
-YZ7sVvUbYQK3QXkDR8tfFlTm3ovlRj+4CG774I6iPoJh28R0VXVgd1uHWUoaiYIedn0qRGbq3TQ/
-wYHMEvzetPXryBEAS7q+BV6m68Bw6xdM4Bax6pTTrJ4Sq4xBeyUfUIPKrYe+c+8j6M0sGcgaTWpa
-VMfprGDJfFluPRECt+0pH9bLrlLzhG6A1mXgJjG53bcdaDh/pxqmtCsQbPteX+ln4Zh1AEUd56Tc
-uJPRgeU+f0ttiBVciV064GVqD8cNOjuvEbOSSaQHOMtRRrbgxrGXgZ7jvwH66ko/Se76vLUKq5AO
-DAdBQChpnTzXExUQ+6JraxSI09lELfNl6LlRQ8UzI1B3dpZgEger7lPrij24QgRFlZAw+vHY5r9/
-YQ4G+zjPYD066E76zor1wrpTIsJLdBRpq80UF8o/Cl8g/GEIiqgowMzXxDKzrZkHC/DDPS6A0iIO
-lfdeiLagds4r1Hoo7ZQtQLGGMYMXrKZaehdf8hfmBIgluUuET19DESJZR0MAEHZQ/Nbr+03yx10i
-KjCO7hDvbAUO4LgHwgKgFy1RwS0ibLqx57sTIjQ2b7AnVwmzIexrAshQpQ4E6xVV7NKb3PJR4Q97
-oI1jmy+BrCqvnj6BDUBadFM4AGPNx3EUsmUY4z2tAcEFY/xUd9xXKvdwcuLiztGBKxulgXpSdhMi
-xlLZMNulysQxgOPWRwgbj7trNL9u9u2uEOk1bUo58fWUOyHMc7uPqyL215Cmiz7nKAsMFdZh7EGU
-OyAEZXf+hj3U56cha7mqNdBJAgGk/eDIrs+6OAjWF+XQ4qs6pAXA+ILc8GMadd7ofp6tvkETR7q5
-vOi3y25Y6V+Wa45H27qdDCjrpsAOM/5x+ALhHe4vjYNK+q31dSecFruzjFXCpZlTwvY8CskJHWRp
-pJeN5H6dcyldBge02kGDOwQ70FKXiRBkKysDnAQgWAwYv422LcN/ZLAI97TJIqxWXzXv6i/HlTOF
-N83qiA049rXPgG4O9DmnWTOPWqMDBzVL5QU53GYKKAbgUkAxxbRwLWXkPL7h4PpSAJW7GOdhXA3N
-XdyvoRe8ghOoOVuCp8c3CrJr+GDHuaF3UDbHfzEv97QWKFvsZ0q2wjXXcwJMnNIFsxhPXTto0WpU
-SpVnaZL4cr6O87TqarjSrVOIa2g4Im0v4GZEodNj7zuIq8KWuMNks7x1P64/7NFlHIo+hk6wpB7p
-eTIARv1nVlJC8BhBMKW4tx8BMMU+1cU/WIXDPGKPFPBL7sVcNLO9xEfg78tBUZv1OihabKvzrur+
-eyT8BcIfhCtlYg9j8FR2SwHBGprX8u2dSLn7sjlcejCkMzR47OfFmBbaZW2uFfJdcQXcpsQFegDs
-svO9AJQxrTd2DTALft2PrhAOgO1+g+G3CL3flPyC0TcYrcQSfj3u/U4WYdH0pXLUHcLQlJZNUCeC
-ewJcvDtyPlLvPuj1OemK9AQqw1UO6RLvcWydR0IakY9tyAd7Ai6VLI+uruL3Li9voo3VdzPHPb7u
-+CcwczethYUNzugSw6jf1CfcxG4wxxuO95xoISXpqNtlh1B7IY5ZM/858rSlw9v1BagmkXKZNu8T
-OeVaL545rnTvveDQL6+ZFKnVmqzDiWQJtseMVenTCl3GtLLOj8Lv0Orqz04RRAvY8u4v2aBw0cW3
-3y0Gt14TXeKXOyvVGYXAVtLcd/CL2h6/G7S4nRzh9cdTItSF0stUcR1Q/vkMfRq+QPhpGMZoVmVz
-nn8V8vE/LQ/EfYPE1QiM99daDHKPDevztOuYKB2HRvAzy0gDt4gAelBhil8lXg0MvyVd088o/8F7
-tAR3wFdSegK8ehWK9cq78dhpT8FwTXsbNefJShZsCA9AcL4qwOx5JADfa/DdbN4JLGdZoWUS8/lm
-sP6HvR7WXJxbnYd2BbTxvktvUWIT+Hh7J0+b5RYWjnXMlkROoBiIVQYRfaZIJ5REsOm7nivcSREV
-OLonRwG0BfDWUR8kNvfdtzb3eLpw6JHLPHkut4IGMGxkotuA2FaHAr0D6C0lhrqcQwbD5+ndtf2C
-BgAvA2D9FkQB5ErawJAkgmAEvfUoiSG9b3H9FiKCYjjcwYFyAMUUXCoU/Lq/L9Zvl7qVfj5Ozd41
-UgvxPw5fIPxpOM0xZ+kJp4yDfnkcGo1+BT6NUxEKCmtWYzm4+1mF+vrIDZ6pBNi0AghGsMQKfnkd
-nUZiCzcowGWGDdZYMQAsIW6WYKF9drC0FuEMhsu9+QTjOcKUgHCKU8NDPizVElx7NEKArrc9zzQW
-BcAe0pbeczVcQKw0/MB7Asd0vHpLZ7CcZHHsHwTFDogXwXuQSzoCiO41nVO/Ftrlm7UAnH8AlD9X
-QxEA97RcUILJFy4SNTzYPbuf1tpKYFRnl8ujWR+mHhPggEl0BYCnIju+n4RbY12Y4sy2NbPJvt4G
-wGzMSXijRBwQ92BYXW5Lrmk/beiZjSfGUcSs/aoOWCXwrry5RQMABmwZ997i6gDXFgD3oFkgHR/q
-JdAUHBPE2TnCW24oFfBGPjZN9+R9fnKr/P9x+ALhh6EHbnniPZJg1M+UUZc+M2HKcbNUkHuwCnMm
-hEWTwtA/cUvq691BOQS5GRShTtCM8F5CBMTE5grhIGvDSUeeBogNAON5v3AfQa8UC/FEz77D9qty
-WgXCOJE40t1XvI+Krm625129H8FubPNLcc6DQFNLmsBmAJ7S802gVUvF+Zjng115yHu6WoMlgGLA
-MgkQOwBYdWZLi1vQfTBs6ce1zcBzFfZGxtMMuJf/lCHMCU735H0W7rVSTX4iqnroqhInpeMotRcz
-FdVY2YQEBzdhkB5czxbiiaPj7WJ/L/Rr5OAzbEyH3U2QB6SnW2f3Y8WK/iiyMsNvhkloR9dVn89V
-Btuq6QmpOqX1DQa6Eo4AOJJXMUz+X6uFWsq1JbZAmnR3n/S2kdBy9xCtqni9tUnNj3D8B54RXyD8
-GwHH7cpA+lfH+MkZbByXa1uvgiKEWmsK8lKACbfai6DixHMlLwAhTqA3rcHuBTqDiIIVElNO5Uct
-3ssa7NZfKaD3nS3Asiy/vfW3+RA5+CVQagCItbroKdH1cO275/Q6qmd41wFetPhSQ+vAaAtsqQfH
-I9CVga5X9rkQgavXkYj2L0Ap3V+Ew83L26zuG3wAqbHnYr+nhVrCSe61nNzGmFKID0IuL8vjlJQK
-6nRZq98OGmH65QuQF9ZUVGKz+JAGKyKpxit3iF78HQB8JeP3w3kaDKgVk3CBHLMnWSPe3P0evjmQ
-mf9H4QnodXpJRcsvKAqbQUGHMHSF+AUAsG7JxBl3DKA4ajPcShItxu2X4Si5Rey4DdvwstyqDns9
-Wwtxcpf4x+ELhH87DDjxiiGPfXvfnru57/nG+9SHCcbIsCdweOLDDcImsVBrMQap8fWAc/m3QHNe
-3zuvVYcoPEAzcwDEL1l+rm4R3i/RsXavkB6rRrJBmKHM/uMAmQqwpRzf1l7aABnzU5eHYscg8A0d
-oYRCO/dx1+eoDHu+5qs0aeQnGoLJy0qkNJyFHXCOVxnoFyGXxabmI7gVX4rxi0LYmEp5mXJ3x+5d
-KXaR98IJn2Ta3vB+vBd1An5COxbUT6DybW3lSCKa1bHThIb09m5aN4syL4GhHQfK1LS7s+tOwOcP
-vnn91UpBvlKepd3ZPW6G1N6qAZBtapQErkBmgJnG5tJUkTDmIXJXxK2czDVB/IWzo1VYf5wDxTJ+
-A9i8BEe0QXm0H+N/0xvmHyxuL8OvYoWOFmJ3l2i68y+HLxB+HABl7Ukdzv/rJhNMUv1ap3U3OG54
-U1oFx2UetVkbIpL2L93YGYOIMDuAbAsuX72a7V4DBV+6HOc1nmqg9/rRKigIdt+1TUdgTEQvYnox
-0ZuJmAXim5ect/905xJHZWpuF0oTUIbWQUKmZrDTBORBX6BrRxoRq1OnljPEijzS0GpfZ5kx7iXP
-PORKMKUZoZsTlyCnDxNALlfWe0ktqX3s2wW3PF44IOipYvWmZfRpIcB6kI2g74RGNu9hmZb8LQxw
-87n3494x7agoor2h09KfW9qa29LUdRjs2tE9X0jNyiTnuUjvfII7muXu6zTlmFswl9FxHkb6mNgN
-dbfeJyGBl3UFVRn1Wvnms7/Z9Tmdzob/7BPbwwPd9XGeqvjDayHsNE600NeXg7cZguLSfRfnR9qL
-cV8nPckhngXshoSoATOu0ZEyswN8PWdH3wF2QPCrL8YhSNZ3H47N/kvhC4SfhrDDpS0xPXnZjkrw
-BIVgGKZbLmJFLibGlSsEjzeX5MqiYF/b2bS3QApM2wvoQwDT8WewAd1twFerp8AXldCiMViEwTpM
-zYfTB9Ko44fKm5W5ybO0wAa/GwebFVr5dDqR/9M2GFcAyvCcj1iJfFNOeruFHHm4xrn6OF5/4a6T
-ezk98ySgB2C3u6a9ZRWBav8EiHH76HmIsLqSqENrTwAaBTJKnmRBhjv6AVOkRPyukcVEZBYo+wpb
-LT7w5jyRAWI7lmzv5qyTupkq9yFh4msArjetAcThtgOtB5Db8vd355Y8A8CX4c52cFx88Zcby5Wd
-48hn/L28HgQzlJE/ERD/JEQdfo/+qcx2/RelLFVBub8EtFg2EM37NcpQxEqW19Yn9RD4BIzBb2yD
-WsQGq54R/K664Iu/djrIExfPXwpfIPwwMFH704f6ZGX/TzyMT2JJvq7wUx0uJsqaaxeTqU1OUCi5
-P/S+v9zy7k6gvJpNwrC5nQJ+hZMqEQBNiEP1zCK871/k6+7F0dqbrcFV4VIFxcC36jQDYAXHZMok
-D0kEwdbrAHZN92weP6A91jPCrZMV2MvhIb2lBaAdAS7ms6ktDf0ULuZKeCa7EHUH/GJxOOMni2+M
-96DZK1BBrbRpqTWntCyIg1Qk9vx6M3TeLUgRsu8ewwd5dbVirY0D4vXngJiI1ks0WjaC9+tqtOBz
-4ITbHgBH7H8AyZJTJSfnSna1aOv5RE3+XRA8zwNd2zriqPPmK6d88VppMJ/4qorKK73+vkFDSVU/
-H2gFwDb17ZawWkvDum/4krYyTKvAuAXBDjJl6xL8BU8EuDV+DYxX1DXeGiMOWKB1h0Dwu9sht9bv
-74YvENZw9ynEBgl29NY6SmUihCNM9AlJRVFaADEJUERT9YYHt8yeeZYXJzW209M4LbSgrkN7dYV6
-ZWy5dEgh0IYFIbHjrGra9YMeQFD8kg10BVwemCq43Z9LYKxl0Fa+VL+io5RGKY02MFbloL94Z62F
-YfC+jD5VKk+HSgEyAQ1Li/cVJOd7SrRuI5jjTCOsgmnShm6udAXDVON8TQW3PBTbiX3wxE0i98xc
-/Qx4120rJ9x2o5GSQnJXetMhUx151BYmp2RXQwD0oKkCA8myMaMWoC5BqhSbSl3ph7YFGfhOMjq+
-ytNnlyZ2UY+x5OdgoMvR4qgmzNtC3E/K9c5+kiQFGdzL9rSL8i8/1WXibuj4W5rkfmjq1wLfiXZY
-90lZLUx7Ar/rGg93XAKGn8age8A45kN8YydAJHxU3CFI0yi4X/4HBuEvEH4cOof1lUD4E4P+1TS8
-dSnAYzt/XOi5KEqpPYi42qIINtfDLMvIJYP3vAphofkkTy/ZKY+JOCv4CCZ2aLL48qTd3wD+UnNM
-qUq+Mr1YCsAtwJji50WV7nncvYJCPrUAx3wGfkXgxbxNR4Ar3i8OuH0q1nsJFuMTCDa5qbtP9yZL
-YlqOn9OSe8SuaLsWsCFdPZTG836Tr1MaDbRFv3aToL05jZbhIzrpR8rWRGngBcA9JktPbu7SdKSS
-mLJpy9XHXb/p8j6Cb86gAHef2mvlrAbOdRkzFQ3jdwOwnWUP0DaI6xvxU+D7Ua5m3oXd5Zi+79MY
-t/q2XLnSuOflXcgojys/I0NbeV+FWZ9PH8+Z/IMF6xB6LyzfUp0LMGxlIFDURKFUgAPSXrvFfdit
-xaErQIbHMzCOcc2e/hcfZIJ939NkSGMEyN+fWP7/EbqX4CJYJGoBs8YRDNt/jKB6QVom3JgwoCHK
-hlyE5g0YygluD/vaQQFwxlceKbKA2lbqvnrXJa89ZrViQpxuRasS0ydPJgoAOFh+B5rSkaZ1CR/W
-IYrWYW8jgF+gOShVcAC9jICkpMVzPvRTQS+Z/B52dbz1PsfDvQHauAG1vE3aGHJl4PawxxTaCQxn
-WimWzy/G+f2EeE+9Tr7G2gAbxdhp0/rhOZmfAbPxXAkEgAwv1GzQSOx5F2nPDYYZaet18iKPaPJa
-W5w4JItr+Dv0WmXWpDs9+oT6w9AMWdiBxnTa4zYL4vZawSwRJSCNV0/o5VV9m+tw/qxVO32jV+o5
-lBHu01IN6TeUEjcyIh/OPY6K6nDFo9UcYK6GumvEkh/AsH3b24NkogkqUznVIn4DTrS+IWKvT2iT
-YgaCF2f/XfgC4U9C2vmWFUPsyUZAuZcTF3ac28EOamku+6iSer7MLwce47Sns2alhduBVy9M0RCM
-/fEo4KYV+8DfXPWisYk6DKtZi+9Fe+GT9GBXaRQVZlGkLP6hmOagFukZAJP1hSpGxcTYS9VNwoFs
-SCMqlmLtOoQWpmu5B3xhrnRpYQ3EMN9z6JPMy9Dgy3UwTJ+r/ecu8J3ii3/lyNWo98PxZxNGbtMH
-4JwLvgyH9eYK4UKnKPsNoGz7oCSaL1DbjuHri0K7HaSJdSxd6oEmtyQHxpYrrfd/v91j4Pu3XKnc
-8aXUzFMBY2P15SbfmOaW2k739s+Ss+IIejrVNeurk77r0gOvxLYQ0S3QXJPqvuzvIzFlFwn10WX7
-5tY1mL2vxCCDVFPn/5oH8u99dN1qmUSdq6idF6ydkcDwf4CDv0DYwkMf4QhyVQaVwUc+lmRJ7qox
-EEYLzCdt4SotzD1rEyyy8rVKXIDOA7yQtrokIqfYH0NdbWPJDFFLpOXsD6KwBjVBX4xTkP5iBsAr
-9eU5pgb8bl46HdeTLcGKcLEbQIYQ6ekRrnYWLUyzBvRq3PSJ8UsYuq737CEhp/EZEOf4nftKawb/
-NB86QQA+kMQDLafTDd6uWhnk1nQfg7qCfSurJe774AbRjWC+Tb19vm2CwP8uuUqYZObj0DLN1WCc
-VZyAco8ODuECgdbkJsODw679GLg71foPoS/Xm7GLA2vkndfzXetvXC/hyg2tpPXl1NXq1Kq3+ai3
-Y14usnKJdp+Uhr03wo38u8A30CKwDUrLSJmQwTEZ4Cxglojw/Z98RBrGiZI9OL3/VCy95HFz07AN
-BnWdYAtGff7bK+kLhJ+GYN7cQ7I0OpFPi3RWsPON4A/3PCRSR6eefgMAj0mQvuZzBr7kN6OrRCor
-pa2HAqLS8Mtp3W82IxDZa9Ebhve+CROns4E5Ka3m3gFyf9Qa2X1WtN09NKj0y75PU+Vk+dVzIVFh
-XILgFCe8l4YPdJeHVWLZGH5yf1PbCaVp3eEamtvbKdpc/Ng/kLNZzkfQXCrQEOQSDMdQHAnG21bZ
-DDIPArPMlLDmSzwOjYjIT4QgA9dqTUJezd8VeAvQjqEKmYHqJHeScc13JXkKN4fsx1Js1xlYOfPl
-tJR1euEt0LihWdpwtBrkdb06uz/UVvQjkPm7+ztpFqZl+wQMn4RPR6QRuD2Ul9nigWlEbtE19wia
-PYajbK0fU3xbHWkQt43EsZMAMLZ37SiOUEf7rfAFwjvcVTIAdW0QieIxaZ60J5ymdWA4lN0v1+uK
-X3AdAXJVeK629MijtNi65zLti9ZFAhcDUXloOFa+AYe4qVodd1Gavu9xjaJCMQC8q6cgl0l/VAOA
-K1MFyDTf+1fB2pfxPh8vtnx7Jd6LUNYCMCKu77BZAHpxBDiKsfs7gPgEjiml2X3j9nLOk16a0/Rh
-cpzgIOJG45P77Y5x/4/1yvc+KsADwjxPrnkGum2LDCxaY4YM9zcI11HnAG2/UjEtNIV1CoBWoD3o
-g4gT2fpMUn9dhMJx6V4xA90R2B5FSr27rPbEwEeuu3vWMUfR/WVTsmi3T1lay9/wwTXnZciDaVd5
-s46bFAeXzxk0Z7lTvKTdBbhTuOCNJ0VgFgTDuh07KFb3iPCDGlb7DWwF1iMNbhA7j++9uifnvV5p
-lNJ3jcFfmEv6vw1fIPxJYKL8AhxOnWi2YwfD5BNjgeHz2jAw0KTcWlMPAXAseU1KDi/FadvcJ7o+
-BTZXAMOuorCfYp8N+AeoseewJ0M+bWJGc5v2otWOF7ul1/2CmewEiJ316C5h9/mYnnqvIGTVDUCx
-SNq4o1uDXVM/MVHLl63BP43nMnOYaXFEs/00bn3Jbpor0IQGXsY2cKI3/RJrKSHudY64LLYCt4qJ
-Z6j9pdVXNxpowO1d1evRlHwRJESrDO44IQlOIklg3schLtKJfq7eZQ9fUgpdenoDc58UDEnnvFWb
-1d4/p+ZEviDBnpLmeHfX7U1c/keWQkuW3px+BwTn8v1Tj01rXRSGFaoyujZY+QJ1EGofBkKYlnaX
-YVQDSXNla7BB4nSS0wbDSFM+srYC36YWNwhxo4VXnb0DhneiSjppfOs1SP8PDo34AmELN32E7Vi0
-PcABDCdg57fsIBAmw6nESQHVuyH3yHQzrZu8EjghLS8sZYZFivRrdFADAkRdOI0mPryDmFDMir+I
-6a3KEhUlr48elTYeqZaUa6Rnuegekf2LU8WlguDc/wiMVVakBZX56/HcvcbQ8AQan/jq5GBkfDpv
-6AyMEeDpDMZiPM2ldFVh47raYu+A2AswTDRM9FyfmPse/1y3+4CS9rcCuORkpov3bE+/Ku1mjVuc
-e5J6AzBPlAFI3w05B5cZWfnnkbuab316NydmFwZOnDld42xy+vRzOTnPrHuxRn3/t3kPbWAi39Yu
-2pzlW9o0UIcl3zIfrMGiL8QRGQhmAKB6asQCvOtqcBj2cew/c4NghckpL05PPLJCwXvAEwRxeIL4
-viz3/ygcvuYnqucEH8FwFf6hykr1m3KOmbkKL/qDqbX0Ek5wfyOVYrfAaWvVyT4cCRZCR72AaNrM
-EyAGUUtxbusvUQC6L2J6s5Sj0oq/Lyje+dOAXoGKCPDZ3Ig7d+rx1o27tSBvOlHQn4+Ab9BzTVdO
-XTzRzrzz5hUyJWCTk6b7AClQL5PEtFSLyVUi8i0eJpjzl0HXE/Jf7IypezrOKUz78DEne+ROk/Al
-MgbdE18u4/hQQq4I/Di1W6Ucq97luynxufAbJ13cfyipeU7fI7YpPNMxwoUeb3prZxfr4tHa3PEy
-xrmh5Ty8pJZZkhp6qaPZVutcn2PbUrwoz0YuYL8QpiVvAriXjy+a7WNBFVQuFgeggm4IpIDX2y/c
-H5OmshUGe83wdlwAACAASURBVLoCZJcZgS8lYCzQFgfk2aL9r8MXCH8Q8Ig0oj0VwBocwTCBFZkT
-GAaZd8q9rtics03Kmy/SK8gN9BYUY/acluDUjH6bkBBPWixVd7imCQqqaSYqwxe4QoSj01CJIi3Q
-t4XXaB6PhbtiLC/MIRAAcFyA7UC3EzKgXwIIzvHN9AT4dmpq6mMud7gpcMkw8Y8hVWQ8/eLO/X6p
-C/snF5PdIjKf0+uLcfHuAHJDUgXeyJDbMoVpb73DWzPd+1KfPUp4NjB+Hb42V8zmaXic2q2Kl+o+
-YD7y3vgtuCdFPajNhJXIwEuffpp5dErzxRBT2v2hJtX4/g/bDI+8dODlSOOafj6951qX1LrFNXar
-7g1txHUS9487/MXKSvBo3gDJahkmO1YNtUgAtKJQt7EWNy/QrWPY2BuUX5azOPJ4B3ABz/Q9R/j/
-V9CBpTTgOU424BUMn9VULm26O+ZqWTlcerndBMbJnrIJ0QkwMxzjErqtCDmFmt69jd+CnaTZtTQh
-/NEMphdJ9QXG6614doGAuIK14BusygxqLahynK+AYr2KqbJAL8A3xwE85hN5pnyTrvbevUGH4Z5l
-1ZfoNLR14ETHJQj56r2EPiCJsiMo7sCpy3R6BKz3VPsMhju+CpBjXfqUnmfmPY1OF8T/my4EfoE5
-DQ/uvDOIMe7I2HEXPTokyynxjtw0wp+HPjfCLyl0zD2D4ctQFWJIzOuUG/oc98G+4mfgV/155nW5
-mb7e6SBq+5V3egbLnPKDHshldPUxWrNEsyvvuNigzccl34YIgLM1eLHIXkZ+bNk681fnkJbuL74p
-zQDy4QU6rz+nzYQoWntVtykPE/ouI8/XNeK/DDd9hP0oMEonQZBNJFJQcgTD3XZa6/ChqqOiibK0
-gkpStHuaQ56rEyI6UHy8xuh1MK8lqJbAf+Sc+nEB3+D2gFdVoI3bRFSis4IlpHc0OzdY5eiciUAZ
-AVzovQCOez/hXidzpUvi5Zpv6suqvHww5zk8A13P92hSEFHz8CO13t6fFRq1z3q6r6TcqZhSE9aD
-7Oda9vRQxASGa5jrEtON56JrbXyaqsIMPeffjJJ17DYIeHNDJ1P/o0N36n1/vlwD42fyfgceV5B7
-mgEdffXrpPU43VNaMFkwV1KJu7I48XBLp5K305VaRCcjljFZhyPYbcNQxkTL7cw0u78BhkP+acCD
-VXgT0RqMfsJERMx+ygQRHI22SsvW3UKj/AKd6kal6c6jpK2vR1cJduwzAuXjCP2V8AXCT8PkI6zj
-7ZG17BSR7LzhZbvD7vKjqdBOpEZLtYpP+QB9odxbLhJpMZj/8IyNQ3HaZ7rj5WvY7n1BZrAIujls
-LZZOWzki0MXrXRqmgRImitZgb57Gffs3v+CtJFiI9KxH7CrI1nVjC2h7/YtP/lGG9azEfpx0ebf3
-9/M3KtTYqFP+nnGsT07bkfpte/UJ1rw6JwOt1E83jhq8H3GjutsCoFsFJ9iTy6uiSttSrErkynUB
-PNtaceobMCJoORHUSlyzrihDWX0NjtW75uaYfg1nfwZ4uxDHCcFILDFSHu4U49bAMZlz6Z0IDlsN
-tzxbxzZpqjstfpLDPb3oZCtPsFltG6ZP7ou23L2mQ5ruZVNZvwCGHdQyYEawBpNs+tZQe98lPaVq
-K3V7gW7r1rXU4m6ja9P3Cd+sAw32fS6gFzZ4YsMDoo3RNDuGNrqd/qvwBcKfhOTecAbDBOZ/IvWR
-6wb7tHBDOM2TCQQXcrfy0pK/A3IRIgX+tnJRxuOAABLVMVD4anvg0NGoSNcJEXC2JHtaVbb66UE0
-AbgOgNfAcQTMOws54M9tpgBwM+hfc6paXPrHBqTFL7xaPp+6I6a4M217njoX7shKxY/7S0jbkZP7
-aUdTYpvWzMWYXnlqLW/sjgEQD+Ul1NrXJebr0wfdNNxNZXTAF8iEKN03f7YHkYPkQ+G1528JuGLk
-Z17HT+Yw1KLkxRNcke90/7xmPY0nDp5nA85MHtPiXtfxMPBwR4f4yYBY9TXo95ZbShm5jl1dQ5C5
-Th+B4amQPAvCV3j7tAhL0iML/Sxht/RuHazYBWl8ZTWGf+LuFQEMA1jmAI5rU/4jg/AXCOfQgYwu
-7WQZNk2ub4wmcLyyQ/5PKpnDfiKMdXdN43WPrw6sPHFjdRnxKRBB8To2brWhbYqlwXEt6bt6+2GR
-y9BtzM0vdYFiLWCu6TMlvRhcJHbVX+y+w0pb1xvW4/EjseyNXsxv2FokRj8CX6UZn7R8Kjl3Qewj
-CXwtgNZ65oVxCJc6zZfJDUkz42NALAdrMNZtSks1u+KxE1X6RJprzpWUYPhUn7IkD/W8Gqcn8LJ8
-rd8AX+PbafmYtfCA9ctWoo90ztXTxDn3ZejGhoGS1+8VGG5nVDP/2jk50JCaOXAHmdNcT1qcDnG+
-wZPu/QPf9NXGED4s5ny53tOViHwvO6SV0iewd/d52eIJ/FK0Blf/4D2ThHw/hj2+ukwsnuj+sOL5
-JAlsfPGPtrR4dFsAx9aWZ491vxW+QHgHVAinBR8QX3Z1sIFVtrjgMhhuV8NDDcopEhQSZ15I5Y5K
-e1FbA2zOlkIllxWBMsIs/LrDwTXKqoVE8gyC/ACYw5Xn9BXiaREv3mcLM21QzJsu9D9mktfqHnkt
-GQJD+pZ1/wa60pBXhOmdaMb32l97vZLPL5VniZVvd/sbaTv+bsB0vkYawuKoZ0O+tOt+orry3P3N
-cALXCq6W8h3qdJJ9M0e2hN6VeCvkQbmQdCrh6ozaJ9VZYUIAmfe86bH9k6CHfgaJezeOKnPWOT8Z
-rqe8nCh57mW57J2W5HSz9szn0zdad7t4OdkB0xjvD3yhDQ098Ti98Qne+5jO7Uj3tErvePOH6zeA
-1Fyt36a9K7ZpSl9BoPdiOTlPpfk6C/IGzGPrsgXsDpCJuBao2z9RxbTluLS913TuE/84fIHww1B9
-WADaMdksyEeomSvFtIO5uBoeb9bNBC27ULdRcYxnH56ji0QqKqMvlGn95EDPiilCaloWr+Ja+nD1
-bkrKcMtB8Ku/PPe/11IpLyF6vYj+t+zIoflv2TR6Ecl7g9f1cxxCLxKjqXrUNCL99S3Zct9vpjct
-cPqm1XVvjj8PLSoB+sgA8R6eNwzDvWu0tp8A9OrHRJFJ+f8rBbc6gw9F2raATJLTL8rIsWnN3ZRz
-Ky2TdBl+UILONg9nYHpH5inDmIePtyWhe8j56cy6bA8w5LIe98WNvHl2VQAEHKi2k0Sc51GGE7Cs
-6vPr/xHoMZTVpan4jq/wEhVAOca5owOo5ViOgeFC32k8geiufhzKP19Lb4e9JoeJ3qVN+TWCde7y
-hPhhXfZvj+QAPOmlN3MJJab8U8/15TjAFv+Bb8QXCH8SJreIyETF+lvy5HAGxyHHpPnLJOoydztP
-U5+hWS3ItfYmhuwSQUT26FD6juEBoi++DWnBh+XLeI9f88zK084QZgTEq6oLEC+ZQkT/kxfJa8FP
-Iaa3IOB1oOyWX6U1aRsc6wPUH2yH6g/y7nzxsvjiL+NhV7+xT2Aqnq4eV0jMtU9D5zcjdHrLrIS/
-C45tORyKsU2rO0TYeCB2qae5ic08OdQHjYuShoVyqua59Jr6fGtyOcd9DcxGhe2EDLCYgfXHM6v9
-OuxZXzzttwqfujQuKpvLf+AsW8AAeHkuA8FYPv3B0rjna3mbPIGO/G0dUpxBn6e8qsGmtNYyrGVa
-/TiUO32wb3O7bKE2bUaOSpedVtdmb+2N8jTCifhcPyh4vcjcZDO4o5sZ6WbFzsB7V/5ahP9/BF9W
-roXZXvIQIGc3gOxK0Unuy7tHHJi4oYXoacfZk/bWUWlNVrtF8JtYm+ygM4Znifq8WkVkdwiE3DFO
-TG4R3h8Evxg3QLzdJP5HREQvEhIAs3p9JXBMROAqQQqO5e2WY3EA+iahNzG9BKzAvNu2u17nnvIz
-LYCsSvy989wBwnq1uJ0hqT2KoVNYzTxQ+g399nsqMM6IO4DYtrvWr2JecHyD564sIq3rk50G2A/A
-sNKnzvjBGbW5rBu77ef6LfFNU68J4zQomVpFfZ3tB/w8xNc9t/321PVhynvKx8AT4pzuIX/h3ZGW
-nuNFbrzP5Z/cHQjTOMrr+P3DNV37v1zry8oE99zQc5szfQqnOZLXb8fLwBt30TtzuTGLXADcSMes
-kSf8CMg/DF8grOFu5z+0BBdXCuadLP2MG8jPAzeCuIlyw8KpnY2YlqZtx0WSgfKiLRIK8n4LDxAp
-b3m9juOSDKC4Tdv/99NnqxBpuRsI8XJFeOn9Ar16pdQNC2wOFl+6D46zRfgl7vqwfuzDz6ddL16I
-WYKZeLtGLHcK7X6Nt2C3uVqcMU087RPEqlPtkDdP2Z8B4zpR7wLiri5X/JdcfDo1+bm8vgwa2xal
-yiFN0z+rh6uWg4ygnhLfp83HfBcdzePN74YnoiNgaYAM13nJ5X/KH/q5WnkrDbg55kMQpXkQMIY0
-wrTIq3q8yNtltvQc50gPH640GuiLf7IKcymrKxP7bdpPPMS2x5Q6XzLvVXxOv16LfMVHZPtJpHVE
-ugmOoTg9Wu1rEf7/ENZgMine24O7x1YtZ9UtgihYqGh4ReVXHOC4mctpNWcGTrTHPsJKhyiQAj2X
-K33SKZtVseX1J2K5pEUFqhbfNy83jRcRWIIX+Pwf03phjhwQq+Dg4oAuEo2rBBGC4/xC3Up/0bbu
-0gbEGwSvbtf6O/jVtrxB4aIlWdvfgl+5SMexwOkjJZJCfXjxDhuyAOvPwTBVKRyr8ansdsuY1l6X
-1GT+yTNGuJEm7ZSvhHtg+F4fQNK1yCsRRHTrWWZmvlkHZz6PyhNx1+PBka9R5zijKvjZ/1GdH2iY
-b5QFZaquPPoIBz4vr48TgM0BIGuce7rr8MZFwmSfXpBDP97ZQIIfKnmx32LgQF/ziQ+8J9oUv8ub
-af187ExLN2Y5At6OP4Hj+Mtyi+E3vo16Gr5A+GlgomAJtnsiYolgmIgqaCbCM4b7Aq5IEi6BcdqE
-250Sb4dyB+A6pyUGsOhm3+BwGmH49QYJzxFEQUwoOpYmpOrZupoatwjOtH1ChLgyfbGYO4FsP2Fh
-WSc6ENH/3rRQKlbwLW7ptZfoojXYfk+dIuCl9JId0YvUqqtuEW9xEPwSWWBdiNiOsIuK3n4mWpZF
-megC8HKlncCxtx0oBdvG1VACX+NQX0kPQrc2Jil5iVwW1K2zge924CYWQ/sweCGyb9PFmJRSI+ek
-Yq7yT2om8N3t2otix+H/WG5SeDcG6U6RRyDDcVZUUMORX7mKynerb5uPXbrT6tf8yhFdDoa48Zxd
-Kiq94U/HoHXxCmqrG4TJ716QM16O+ZorAW/un1Iv7FOQgyHznGi6TmJapeUyc0In/zIIEdu3PA5m
-qYBZTd6mpg4cbxpbPpVHdPM8zV8NXyD8SWBKQA4SEMRtvgiGnY+IHu7uWAG6MZM7Pm6iSRDW0zYo
-oRHkFusxylCZygNyE0/NMn9NMluDyZRwBcv1vym53YwS34rzxezm2dfuLwTDOpySXCOKNdivlNwj
-1GfYa6cAGOok0VViHQ7P+/g0bdNW9ML0DmdZPwe8wYrcTtneVWV3BnDl4BQeeWLI86MLpmpHoZy4
-h4KGhHubx/0tpv+S9KdSG17rjyclalYHqZ+Eu/l/0zXwF0WB0HmmTqo08FxMXoRVZ8ttpuqc58RT
-wRrGGf4FWgPwjEcBYUq3OEf+lo8P+S9k6YrJPOWTLMMmW3U6VatxF6dONtaJPU/bv027NHfn9hCn
-T12r3fTqxlZfPIvhc2trrdspwA5rChkAb/lJ6Aiq/wPPiC8Q/jgwORgOO/Qe5OaEiMjLnh5CMal9
-XsFpN+SGZrfKMwBaukPbN83LdPnFOKblrxseEA74pDtsA4v2uL4m53EiMt9gIl+uHK7bDUJouULQ
-+vpGNvrc+NVcI+yIPDucfL2u1p0GoSbmlk5U3CPUR5jFP+ukiG0ZNhDsLhKrBky2Yezuz0eoZeA7
-Ad4Mik25kUPgTkEGH3ipPN0Q8yHtTjCdm4QyMrQlnmrV8U3hySbD8P9eLX6jBgFsTuusFdL27v26
-HLKy/Xtexl8PWT3a/+tRmtRvuE1iKqCq86QAY45U7jjZJUValN9ZlLNbRQSEEGdu6XbPTZ7HPOi3
-e7Lsuj7UPop8qj+b/FmO1ok1X20bQZ7Ydw0PxcApMqYf6AyEjr/wHhi6vmhDWa57DwzPvQpykRdp
-yWq8v9381+ELhHe4+6ZitVBupZh0Y3zhS9O0jJMS5X7mHR6TqsI9LKW86sJtXi46o9PTXAtyMz9U
-PYjE/kqdRqUbgW2lYHob59jLucdjHvv9HLe26vpk3udALKCuP4Yi5KBY36BjKGyB2GXlJT1LeLIS
-lxMmOABlJrcIv0XoLbxAMO2v90RBcGPhID8+zdCr3AO+OQ1HF3F/nmUCsZC2+9QZzqdp11nxS2Gu
-sG2yvyN4SubUD/ek3emLq5rf2fyuwz2gWvXRBd9BpKml+fn4V0NRg2fOC8qNtAvwMwEY7LueF2Y0
-N7RAgflf5GW3igyQ+BK8VheI5mW5wtPEWznkuo9j3Xhz6C+mBTrvNMyPMoos6Cto71hn1tpB2Yf1
-9uQ+xC/W8Clf7avpsMzmGn4FTu+JgtNhOO/TaRJo5GcMfy3C/5+CT26NmuUx/eqDHakGQJmJ9k8T
-Ez1S7TDjLdaB8mkX4oYWbptlBE29RQtp2mD2I+M2LXtJ8AZ/6DstwJSLxPscp3DvPwzRuUUYCNbP
-HjMmMUD8EjLLML14ue8S+TESb4+zULTq0rpf1thqJbafxjCgHI9V+0MUXCLKhxAEZwux28NpyzGf
-591JJ+BLkFb4KSpg/PIjwuE4ZqooMwD9DTB8D6I1hXSCngi4VWhauynPlSrIRZR+vVVyX8jjPmt6
-+ijDlZXdnFSR8nFO77q66bMnw3eux8P8D9OntAhcan9ZzzB2Td4b4D/7XQRCUXbnftFZgpWuOlPl
-wzB5PKRlazEnYCs1/50yTNbphzH2vlPoF/mxn7o6cJXnfej9G+mRJ+joA2+8745Hi+uylVPWUAa4
-h3CXJfDhzf455uCyEcGyvjj3wVL8cfgC4YdBX0paIQFc8kFdzOQmNCKKQA/oQXs/mAiXVuxu9+Am
-mpiKzy9ccfGM1mCmbBUuPhHeOVPNves61DvxErpE4DKksAjtyXWPFxP5CRGb/0XLFeL1hl9ae2VE
-uZr6R7afrhB1vsEk+ntwaiVO1mBiMr+LzbPfv6O3EP0JIFitwR0tzx91l9j9oICWAfB2oDgBX+JI
-wyC73zUh932hadkS+bLcbrjPs+YXwq3Fd3eF+hZ1yQUinwLjT3g+3Wy6TfduISdV1JI+aIROxVvh
-eRM+5p3SOjo3aQqrrlwnAkDiSGNIiGW41bfjRx6kHa24rEBvSst0hrgMPE2cPf98IsSWv08BQhlt
-fq17kdHJ9eByYr/lPb3LR8P9k7SaLjZITv+tUxlg3xc/jtTSCsDd9aG9kagxkBUg73R84e4fhi8Q
-/iA4ptsToezOToj4zyfHrPD3TQLHH9Ry3lW4oRkJtNtUbJfW0nzSxx8Syb7V64ncrcSh01Y6xZey
-YMkRkR/CXQHwtjClbmVMbZSbg+HFIJY79hkr4wakZukNLhGdz7Bag7Vi1ZJMQsRvr5P+uMbyHc4u
-ETgArnztJAytJgOYTaA4WIMnGt5baWLp1rctKJZ4z+lZieL9RLsMv4iUo6i7GpoPd5PsxK/T4mFb
-5hrKRfon8oZUnm4hz8iz+X7iKPgPs078Jzl38rTW4NJ1nV8wJdo9S7B3d5LJsS6WxrEclDf6Eo95
-Mo8Dp5mnlnG27Cr3lWV4ypva09KaftIeTSdfUBM/pV2FO3lLeXxP9goR+LYZw3nCi9/+JzcJQdcI
-A8hEhDjgH4YvEP402A7tAx9IYDWuuE4RwF2zj+6Ken+aKd0k7TafbifKS2VP0I98hK03kkjNT9hZ
-FGN06z6IDDy+4a7/vYuEptfPUsUv5XrtJiKK3MAX48sIDuBXS5R8njCeHQz+w7IPPNtlsYB7RPr8
-ESJ+45mXWtoGx9q38Kq6VVdBKvu9No/wXnkE1GC4j7Jx9qSTAm3kPhnjq1AgWaukw+V2wFl0l/tu
-OaG/Jh7YVy5W/aPyfoPP1/tDOXzBc78CvxIe98upvc0gTfI7egdcFMTFtDozL0HtdM8p/1CuAz4E
-bsnqC9OBxzx0mUclq/5maFcnh03ObBk2mcfj05r4gYb1RBo9iMcxeJ6/xPmCJ028ru4B+J5WyAav
-gUdxrWLdkox+xESKIfRdoO/Lcv9luNv76Leqg6z5JYNhjWhyB+NKAYc6NvnCEQwH1coNbZKJpDsm
-OpzsJY2hz6Jdt/ZV5Cem7Wfd5DlUSVsiQIi06mOlig6PBtZcQpkOhaKLApGD4A7gEu1JAy/LyaqN
-W4rBh3jLDK4Rb687v2i5Qry9rgZOu2EHwJsBcAHIIxj285URXOY+t3sWw+IlLXVl7trp/m4Iebr+
-KIzXOqCvR91Zekk8Alrkb8tgak5cvOdGdcXz0b5jmeIudylrb5ojX5J7kvfJnCjF3OUZMhRynmfS
-Jh/lBEDKSOkBjnJHtwlOPBzmZedXnMvoXCRa6zDUtdA58gRrb0qb5eaH/coTQSqbfM78DOltXhoB
-MQUZW07o86Y8SO0t3Eu71XGVlD/Hfd05vfMd9jp39Bw4yehDBMlRA+TdAOJCJMxbBQjZC3b2rfGi
-i1yV//vhC4SfBrDmlg16pxl0Swxsb0TKYefLk+BC3R8BfLcj5xXR5P81H2HogARwtYOyS4S/WNdU
-27KDLTJtPBEk4PLMG6u/wGcKT3rFGPzKlG8/uYajzYgiCKboCpFPinC/YXbECfFsEf7zJgO/9FaF
-Tna+MXvTSM/RtHt6CIADrxS3CJ06qtO07+M4wPnZm28CuGUtfRi6B6IYuvk+yziz9iXU/HHN4Tg9
-AsW67A716+XMLXq05YxdJ31ik/fIdXOzfsLzNPB4c11mm5bG+8RbQA83NHItFkBW6N/G8hv6FvIz
-JV6l99bjEbx2dE55Q5kovwGI3JSxF0BXRvg0NCq0HiyPVuFQH/+P6dDBQz/Efv438QvAjJMTGsEp
-ad0c1nnj97uswU5fsMDLiXsygurf2AmehS8Q/jjoROp2d9Q6MT2+bEc07mytlerBBCkAudtlOk3P
-fs0WbKb4MNiltfwuq/hXd1WnhKEH0cQDnWr35cWGtLVAIyB+NX0UvwGgCIZptesdrLyNKwR1fsM9
-YNap8UeE+M3EL3WJIKJgDablq7x/Po5NaZGPFwGw5c8AcOcjjOOA5wtz5tn5tWoTAPxtYOzS+nAF
-RCd598BylFTyXPQHSgjz+aJjrut/j6djPPfkkMpdT/SCL/n+QuDxZiRdpre0MpiVFwEKR0oBYUtm
-um/kZt/iSr+S6XoFZaCcSGerf6Bzk3fzqy4e/Yuh5as8KXymv0FmZ3l2PuAJead49CGmTi7UmUrc
-F+/M83n8ach1nXjKJhKUe12jCGmVUsxSCSjLlsWKF/5x+ALhTwJsRkxwLh6kO/5l4M9gUIVhkJ5M
-FAE28ubKlbzdLtNtRINKf/SrcgM/YVRlwCUB75XkDKE/myZJ2jRXc7rnS/cWzj2BIPgta0N4Qdpb
-327ddX4reBY1yqrF309+WJz6KxyqTJiiNRh5dzq9iN9vswj/oX0qxJvoz4uChZje3t684WA/ZRA8
-xonW2ckNANY4+ai64uMMiMPoh+E3HUhRVst/iM9hVqZyi6vjr3OmK/cK1OY62JKVJi3lxeUzyT6F
-W3zcRm+EuinOexrwdirrHwQukSbtlO8GvaUNE4FTegd4wpU7nvyy2+wX3PJf3Jc4L45A54afh/yQ
-pmWNLhBBru8QDOnhnojyL8y1PsyZJ3x4oMf65RD7Z2h34r+KV+K8hphCktOLQH/PpIbTg2iFu4Uf
-QC7bD3Th/aq0yH5YQVeJfxy+QPjTAJvRGkMOYJeYojFYB5oWT2dwdUYyvjFJCaHMgyrmhmbRYbIX
-14cYDTImWkhTdVd/jS94AltfRaGhC5viNFTgWzPh1zKrCxbQfLHCWAZZbMcFM/EyvG6grMDP3f/B
-qkvRwnu0BlP0D9ZymbYVmLZleFeKN25mWhUL9z7NQv9P1t0YPwNgIf/gWRpYnE5L0319dQ5r4LPg
-Zc1qfU6tfBpO2wRy1VgvL/PEeR/7a8onifcU7vAo423eMex1dVPQNd9pM/4sHFXeKc+DtNu0MNg3
-XBxAUOsHjPcHy+6Ynwd5BuicdnaHADp7HPmQp7bz+gU540vyNZ/fVz4qfNmCzFXOgzhBGad4HM+e
-964sItXJVQuWeFOAk+Kai6w7LSh2IXSLwFMlGlsw3MvOuvLKzvsfGIS/QFjD/V+Ws5htXC6EIlLb
-pv680dlENVbpd70yWxumUas3qwtvuKEZib2hWmem7dKhAFLAigtMozW4kak32b1kM1h3ToB5CHlY
-WkvVpuNrCqHWu1qqxNePyS3e1+YWctCs5b2Y6cVCrxfT/2SdrygvALwYFybRX+4gJv/9ZlVkr20J
-RsswuVWY3FXC7reVGK8KlCOglQBq/cMeF6E3axzympLzXjSwDT2u+tJGoOEx/dmMVAHO0M91jHva
-k/TIeQ6nrQJ5KPOJp7TAHFg6qSHPcTM7h5/sNWPeURc1+QZ9W9XVb++KV6N2Thv7fVLNF7Sshpmm
-qzNwpNR7PqSf8nPDn+6RVuLc0Lnh5ws5u7T8i3BtHMrkgQaNILrMi8aRlKGpx/GzzJylXbqKvc9c
-s+HsNBrUI1apmcvMtdqp7k13XNL7B9JKcwOJG4kUE/ieimcHr1xC+LLcWIm/Fr5A+GmweQyzK7lF
-RJBHYfOL6YmPKCZ2fHPiIC/no7g6Ap1j/PAinGSLbdcuy8qhj+KSj6AAeavF7QSCve74rHJY2VBy
-ZcLmAAM47wAAGqBJREFUMAEwJm+SgZWd/bUfjF+8XSUYgDEzvV6ywTGTvMROiVg4Ek6L2GcMq7J+
-vxfUXj+vLPRmSW4TcCW0IG+F/CZ6vxDQrp9rLq4RGSAzrwcAArrxQT/qV1rWrzB+wOsAmkJ6odE9
-Gh9omV9Dn84jzzTf8BWPKaQtD4RzbPw5c6jLYTrnki7DE94p8/Ub5g/5+YHMH4ZTKScA1fKmSTPy
-XdAiYIL7BohRx8tNXr3nhl/v+VS232N9sC5T/Cp9jN8CwfeePs6/OjfQunRo+6k8JI79wZmzGVtu
-ADI38U5WVjk5b6KvABrGoj3NHAwNIgicCEHb3QF1pPv/oquEfpPOHF9e/9fhC4Qt3O1+0HKGlBg2
-LambGBNlS/G8Bya+pvha32RbCk0ZVkRgGdpufsDUI4oAco1Ao1WYlAw0zR6E/gIIRsLF0GKp2eoY
-ROhXN5CR0zg7AF4gWBQEs9D/9F6txa8NIvdV9KftiIjoRczvBWJpdfOf7Qu8AO6LmIXe7wWY3wp+
-dzUzONZfqJP9EKOGaAXBb9EX47iAYt49IxSBMpEPfQSt7CCY43jeAsdp2AJNahoP/DiGma4pXZ7K
-helc0u/MyzZJLkq/qtztUj+u5YGp9sM5H1fSE56/EXgu5wm9a0+gycDX0HRWBlA0+flC/g74RBkD
-HfLeyTfFr9If8SYQXPg4W1kPn09A8Cntki+6VLj2xD5taMiX5k/bTzDgvnzuWYPx5s46c90Kyp5j
-av1PGxwTcecqsfcdPUXCzxy+/sb3b4QvENZwV/NOgHbNYwozD60+TLAXcU2/VanB1CB3Z3vKzA3N
-6pbgxJ1j1EIboYzywDDZ6e7SO55DUAQFvC1gDhLFn1aRbXeBBvUpVhnM21WCZYFNBcGv9Ws6/3sp
-SPTrlkTLCgzW4O108d6A+EVEf5jo9Xag+zZALMQUQbFdaYHnlwD43df3/irqtZVRB4pfG9Cun492
-1wl8OMGrg+DVm8Yr3ocBSKf8eUjCleN9niHdc4/WJ9sa7gHmlXqaiXXa8yGtYUvfrpQ8usxOstrS
-z7U85muFXK+1+kxd81S10/Dc1cc/CFMRT+i3aAhsip7J9/CffeY5GEr3kx/wD9NXWqQYEOMEyh7Q
-zj6/ZMB15DmBYM73H4JgvuBv+RoQarxr7U0v+xHI6AEyBet3GDNGXk3HelQGTpF+HnaalCBF8QC5
-9XfvE2b5JaquEGAcsW/VRN+Z4rhW/mH4AuGnYc1Vv7F7QEwBHAMx5KU4ky9BMfCGQhL5NOO7cgOd
-YzyfCqGLA81yeceezhGmxrtXJ396WBC8Ka2FeqRw7D0bo26ZRUswlhTrlLpuE/X8B2K3CKuLhITr
-Apb/237D/xMienm5AqCYwTXiz3aN+IMuEUxuLRa1/C7VYgBY/YQ3j8hSSO89pMJCrw123VqspS+Y
-q8BWe8jvV4NbUGwgGEAx7dNVlE/BOPJRHEPk63gy+O2AsalbzvSOd9PF25ZDmfJQyh2Q2gbTE9qT
-zexm8o3lgdjH9WjzHiQNSe0XnEV+p5cuZPximKTP9O4R6V7+ooql8l25OGCPHP2ATcbTdErynTby
-fUhr478Ggm/8ctxE63ggLXRKc4N8sW1du9aar8BVSr68GnyOOGOX3tfyQXA0i4SYvGPu5wvg2Hhk
-A+XIo/Uu7hL/OHyBsIUHna8jF+5xNifkpKasLm8oPtXh6DV+Ud9OHhH1QJAHcbvyrcVXbxJPZDJe
-Kectw4+LQAgn0BZRWGYMt/HB9mXqMi/pjfx9okQJQFJrq7pFyHaDCAD4tYGOMP2PdyaC7VWPY1sS
-zTWCt9wFfJcF+M92ibAf3CCiP5ROmaCVh18LFCsAXj/8AX7CQvRSv2FSgLxeDowuEsk9QhUa+bjJ
-nicGkDcoRrAv5DwGiONQ+MziMtPiVSqdKG4bV+DXaRwIHa/Sw4OZdPSJfwi6Xq/eFLklLGWBzfRZ
-Zob/hXzMY7FRr1ynXxb1aUjjO5eeaTXFKNLQTvKQCPPHVfGHL8NBOpbdnQAR04FrTKcA6PR6cq24
-ot2OJ+supjl4PfxaXPe54OkG7qkc77P4qDz1s3ISZ8swyor04CbR1TkKLzJvBQOvZO/HhBMiADDb
-DppcIzA1uELwbrOK+x6f9h+GuzPDdloG5deARLtvAN3oIpDrlCsl1/sYl8ggC+rZChiAaLb4dv7O
-TIGngOAq2PM0DYw/z1DDk2Wzun2CRgcbVKlCAsw7qtbfBf54A+PtamCAuOZnIaKX2ZaJiOjPdo1g
-fi/rLiHwZT2F2F+Koxe96b1dJpBf6A9Rsv5u8C7qHrGdMlhf4ltKawFj3lZmtQgzobVYW7OAsYPl
-CoL1eXHnByBtMoCXkvz2yjEvg0AHxWnoAo0bWgx1VnKk8YkXQjflM489UB7C410Msx1rOBfSqYqJ
-v4+2CVfpTU1+NQxa8TkN29OM8+28p6vxcUqHe75OP8oIdTlYjJtroPEhraGdXo67enHueYfHey8n
-pnNKa+MUT9Moy6YZ29g/6YW4MD7ZdYIq3+QmAfMgl4/tq+sQ96VzvP7faQH4Lpr/gAYt9wkiwpMk
-0F3i38PgLxCGcFPdHq29UndbuZFm4QbKDVm6Hb9px+VjIuTDlYKuESeLb+GZWuN9Uff66WC0pjwI
-j0Hwo5DAKlE/TZI1CBWmW4f3cKv5NpsRXpo/ukUsS/A+Ro3ei/ZefH+ULwBfDi/JMRH92b9KJ5vv
-pS4SROYecYyT/rTJGqP3rqkCXn2mK5ZfJhvT4C4hWxZ7mnZjB3DDVVzpdlfMyymv3cOazTxKOwLf
-kW8KuUJXQFd3jFvCPwhXZ16U3bFLvcWLpc3yrmXcSP4odDJ/TEuT6qm861MdmmsAVgNfAFknGV0d
-cnq9HusXyp/zXaWVeOcHnD98vsdK3spPkAkGsssX+2HDvNKPkR7HQNqtOo5OLAcn4T1rsFCLEQ4h
-wOLgI0ykbhDU0NQ1Qt3kdgujS8WjmvxO+ALhx2HPQt39g9JLaZt0TMPwCTDu5FjyoG453RcZWtF0
-DbdM1Q/Y80u2HGt7OOMA7ZceBE/vkP4eRohgF2o1llnSoAvNGswKfPfn5cwI+IIM3kqXl/sDv2VZ
-eGkB6j+8fm1OQS/zy45SMxq96C37pTmjLYuuiFuD3wKAmFdcXSUsTv4Sw2sDYPSmFlJwvO60Xdll
-IrhLBEC8LcN6D/2s/tKBRqZbY5r4KGa7RRwvNhnU8YRn3Fg+yjmDZqXVTcr4Lqy+qDL+rnkk218u
-9EnOe8nHFyK5qqMh/+1qPQhBXlDJ+THoIu9Ew+rLgS/Q0stpj192y9c8Bh1QruN0+3rId0fGaqO0
-eZiITn7B2V2Cd30e3V/xcC7T+2zKR4Evt6ceiXZ2ndBRk8Dr8VyveMWQ6zbxTQFPfAhnA2Mq6H6+
-Q5NNYyLaVuO/qvKG8AXCn4Y9cH4vcVedXpLLaVlmJgSWm1PkqRsENzRib5PVXyNwNR6d1Rv4hEZD
-cwRuoE1l62Gi/qcW7/ykRg13c+CijjSUVPvXeoSJmDm4SLzwdzL084pS3M83KtY3L19hPTrN3B92
-ngV2OVqAt6+x09iOT1OQqy/1idJo/6qeqLrzcyFFlde25DOMgYFfagAxKSh2V4hgFWYAyrANCFEF
-x7C8DBRDnxtN6ijhtpNnZbjXFzdgqJtZPMh4sqXsSiPyrgxeAW/EXwjcXQ7s/dy/4quq55B+wzr1
-sLevZYT2X9SdqPVjvAU+5MT305fdrl+Gw/jMMx/PVuitzHt83NyH+MEv+CMQ3H1u5tHCJznYwV2d
-Mb9qUKLcrwCGSx/yDHw5lmE5Gv7ARte0oEkDoI1p9rIbuf+v08hcI1oayFvpv7G6n4UvENZwt/PH
-XXLPxvLraTobM1CGtCoc6tXcjOCYm9k80CbZSrO62uzVx7cEjomQWEHqTuMMgr2fioV43PVV/o1+
-u52apeN24PlR0eQaYI15W0/xtAgiAMGvneON97R+Ill/HY62QtZj0ra7hLk/sL9Ih+kGfN9Mb5RF
-TH/e+6g32b+SJ+gGQXaqhILcF/PioXWYG8Dj3V4AwwZqabtETPTVm/EHPFZjFchaPuj7AnbJ50wG
-xdUtgssv2pUpDmo9jCenewzdy5MQTrM4cB3PFH8s8J6cT9i63TSzXPD8NH3M9zjHnPcuQCibdQNw
-+3zO3/EZ+HlsCYZ7bvgDV6xL5GloRV497/by2pS1ZPQWX4xf5cmV5+nDz+97mfXbkE4OUQS8ZSzA
-Em5XsBiHNOw/kBXGgLXM1DmFJwnPDWmhroaEhu2tt5Vk/r+ENFq7hYLchmawiWkwgP3d8AXCT4Mp
-Mp2ZaSPDF+HC5pU0YEjLPBR5j2w+4Vr6XVqIMs0/igG0ZDF2di6gGf0yexAMaYHe5CthTr+3pOpy
-Dy1mMgDe6Q4MdlTaFmIvhzH5OWssxhAswaIgmPe5wMsveAHgF/3hNwBk9yVWqzDG/QU6sdMk3gI/
-+CHZQqxW4e0WIRX4BjgstHyfSfvGQan9Sh43p0sgCIalEoAwQ5yogF7kUV2cZymq49ZnWHCbasYc
-4jru7TLP/I91+F5HkNdnYyr1ahkksb8RboFT4Gk5T+kcI3P6dfikyfk4uhEbXNCuAG6hRXUX0zvQ
-FMrpLcGet/Zlf2pE5Gtph/J8laWtI9G662V8Asin0yNSPTA8thaDzsj8WMSU5mPCVufQB9avAIZh
-XLR/UWg/N/oG9zx9uORBsKv1InhXY/sI4y5qZwOT+wRrm4SIGNwqlHflu+Ny9fvhC4QtPOl9BLk2
-mlFUB5SR3pkQPgXGhZWb5nDPF2hpmWfXiPaluJUozkRU4AS0GcS3FuLSSk+7ZWEb5dwPodZsnlEh
-eHrKJwsM64PBS78CRw2ogPglxPtFNiY9/WFZYs0qzAiAwS2ClqWWKZ4hbPE30Z/XUjj8WsW9FfSq
-KwQJvZkNDPP2Gc72X/XhwpfyJHyi6wOCUdnH4zgIrjRil4Wg2GYTz6DXpiXhs5ePVzf7lky2OGk9
-CGdQA34v7oNADHcnYivwBt/f2DiSTpjUyVQBPqTHdXM/7W54kivWMzm4wJy4kt9VuTvzuetHxrIM
-IEV+BEExjUNan9f1fKE1fNiW3rJM8IDUgLy71+bcYNW5uZ2ZHtI45j9ZdrHNpzw0pFHDi+KZXRf7
-uKR+8uEgoukFOqL8AxthbpnsLYMjnbo8HEnnZRaNQ9E/eKcLVJpckTvAJbN620kSCh9INkDmJK57
-kf7vhy8Q1nBXeyr4JaIIchk2p7RL6azQrCgDBU91CIDxxMAN24Fm0STX6og3gbhvmcoxasaus57q
-xC59AOB1SAsijgtlCbi3lnaFGVuZfGSnIobwAi0jxNv0SvYTdPzyM4ntAImtHPjN9GZZp0KYVfhN
-Lzgh4h0swRdx2f7CGxSrFVhfmjM3CXJsbnF2FwoGC7FajmX3cgTDVAAxgmSdC5J8hhXcivUbgGlJ
-95DOkMbEATBrXpxFumm04NiWL5dn18AH8Xzfp3GcL2kx+Fbn+oErWw139dXTMOkJvTukI4m79KB2
-Tnk/b9yTnJWXS/MQCugl5+vKDH01AOpAS213NXID6DZ5vSwuAKiTEWh5e8hXzvkk1aOvX07L8Y/S
-7pwakT95WjKm+36QP4uXIw3abPmhsTiORNL3EbhD5P61MU1uFNiIyW2ijjE2OhOkI5YQtniHxwZ6
-l16GSiSAq24Q5hssAjZDjtj6H4YvELZws/uDK0TatXSGZDqRj7xq0iempENSYcia9VPadF6wWYO9
-ERIExIblTX4RueT07LCC2tB2HpR3NwhcGa5ziVo9zmkceXgv+BdBn5HA7zGvuP7YBQvFOPM6MYKX
-Iqx+wBg/W4X5xeaNUQCwgK/wBrzBOkyq9BsLMW2Qi9ZdpWm6AV12Nwn9KC2A5vUP+dA3GL1t9Jfq
-dJl1YBSfTZm4sfpiHCzAMJ7dhOpnIB/SkI29kSepWJkHs/rjUKZ+Rgs5OqcroVMxP0u7H+5mvXKN
-4BzLG3UDjIsc2+iv6xhA1y6sgCK9h281WvCTHuSVu9KUP9NK64e8a46eeeq1jT+xEv/GqRF5Xl+k
-Yx0yL/ZfOyalf13G1lBpvDFffVDBMinH0/34kEKnkPfEfV+sDZzYAfQSxd/dUJcJdIEwsW4l/tfh
-C4QfBxtRIHEPcpG+s46+xVNZJRzAcZdQVstdmjaEoa6bFnyAmSawLMhHZG1v7bXmMF/T7FnY+p1b
-vlr3KUi9TQovPdQWmjF2t6EbvR+ZhFgBMJEDX9lWWACqb/bzgpnf+2ebMb4UCfoKqy9wjLP9why+
-n/cmtfAiXba7hWypYjIUAivtTQn4Guglt/gaTwLNgdeBrE4xBL6EMlCxAh1dI4KOtng8Ng1l6AbT
-AmlypRxmVANonkFVXFNntq68p6HF3MciMzoYuA5pGuXfTnsQ+lyxN7qXnvLzyFEmX+cxWhrPSbYB
-I073kP70ZbnCM5RV65FoEx+f+XPa1Y9kMN6fjlDDwCmdI+/VPTXpnbxcB+xzvK/9cLL8LqZ1f3gZ
-EeV26R3f0E+5z44BQC5ZLVcs+Ah3oJeSnzAoYNn9hlZiVfxMvV6+Vd+H4QuENdzt2acg1+jZHIWa
-A4b4I3CsGbOWSrxPaAVvMtXNO09Pp0mhUUPXZD1Cq020vH0l+9Q5zJzTwtMh5MzcxJci3NZg7bOt
-AN8q9bUsrrwtr6830xtoapVdoHN589qPZ1C0EDOtn0KOaRh3K7G6OiyLr1uFWa3CbyKmaPtdstwS
-zEQGlgPIJYrgN1l89fziApiTRZkQ5KoSpQiWsyW4LEny56/w0lwZT068OPZDvh0Js+9ToFqmdree
-ZvIUykp5tGvwIU/WLzna76p8TJt34p+4SNS69RS/iwDF0puHnvYexrKrddEdDZ+DGU4gx3tw1S+7
-NkRr4cn/N9CgsVWdHSzHLQ1XzYkvXkO8sQZP8QykOfFR5n/64Vy3NA4cy8plhzqHKa6uLqmv8niP
-L9h1/Ok+L6luMNqQ3oZRwJoyLctt6gAzeHFjjQCLRgbMsuWxinC5uPSyTvuhbaCELxC2cFPpMrmp
-isnBL+Fuyme+U9kWfQKOOxncJGda02ZbRThTbcZSfmFOtG3A5ydERIRRm7H66QSCz96+n50pPAf8
-uQhYy3nN4z2nYRd4F46I1CKMZy8w4Y9dEAlahsVdF/5s5bv49EW5zi3CeeY0cIl4qz8wWoW3tVoo
-uEcs67WfHsFw11l93wUgC0yXBY5bUAx9aMAanrvCC3cEUzHTQhq8iEE4FaMVWAdzLVnf7MaHohzP
-ywrqHehYJt7FhDYEa8rEo5JvqrK+hhTaw4c05OrUi2XgIWlM22PwcTs6aUNamgR5yw91OIBXJHTj
-dIdvAjIc0jil+f30oh3SovwyukVmSOeGluhd+kTL8b69vUtEzjdZe3PBV/xtXfjOPY/p5Wr8g+U3
-yLsx6Y4T/KIut8LSnvHwiKW8s1tg3OkcB1crMaWTI8heovt1lHsjfIHwJ6F1hdAZItd8OvLnQtro
-AVV6mUXMJ7Q4vSMtZ2xoyZ1CSvpqRN8Nqx4nkNsCig+C6H9At3ho2FJJsMwZ7hn6Z/e7ugGra8FL
-JfJ6ilYL7HoBjcx3N4Bg2j67ekzaPjWiAl61Ci+LMe0naqbkGiFE/BJ6v7e/8Estz2Ah3tbgJXHF
-mXi5b7z98DiGnkFL8JtUmUUXiHdxk9hAWvQHPdx/WJWt8bIDXW5oCPyQFkeMfMnRVr5p5vgM4vJF
-z90QZiEu3aNeh1ymPvJp1g1fEoht/FEYBfABlMaCI/iorfg8z8/CJOcMdJNNmjcw2bydXM6RBscc
-+ZK1t7o8xGwKvCjz2jU+pOAYZG2fqX1604YpvRnjtv7N/Zj2AAR393ST/1S33PfdWFgb85iFe6aT
-ZdgqC/qgPCSd7ossiiHPiQeLbUPjsG+aMiUiNZJp7SMYJgO9Ts9A+9+GLxDWcHcEOitvOT0iPear
-a8S+tHz3KxouiZqIDfUmDTCst2GwCnsNepAcQSvRjA58RfU9w0NqV3YX7vd3Vg4FbKRdQG//r727
-XU4UBsAwSvb+r1n3BwaSANaW1bq+58zsdlCQjh/haQb1UvoZ4bpd/Trh+bZus8JlG8TLpzg0p0aM
-wTsPxkPw3j4mbTw3eKqX149pq9F9qecDzwNanQ2+XvqZ33lwb+P4NmhN7cxumeo7htdzh9fZgPoN
-dt0s8O1OXd5MNzVPrWnaDeKuBYf12sehDefuJTf1MxDLbU075wkv65VmvR9onifzJ2/0rv0qj+1o
-eU6WdvGcezcyHtyXheONjr4d6ruXf3Xdd43fCDfe98vyYej24++XH5FWHlhn2HcbSn3g7ETy7nZ1
-m+3zo2z+7y/fXtbuf2c/3d1RX00Ht7HcVh+z88Lxm+HGdcu47bCPu/+2D+ju9Y8u16W9328af46P
-ValbrjPD3a+27KsMy+P1w852HG27tRmR1uidys7Vx2+MW9ddR9ZlTF5iuG5zbS7/d6/3R/15+R75
-PO3B/qk7eu6tv71+Mgrg1xiHMrw+S19PCHOeEREAPk7C4V0Ic17Cn4wAECbh8C6EOW84Jfp5El6S
-d4znhAH8EuNQBjPC8E3OEX4i5wgDb8I4xKcQwpxnagAAPk7C4V0Ic55TI17DqRHAmzAOZUiY+RfC
-nOfj017DqRHAmzAOZUj4g0cIc54REQA+TsLhvVx/9O1mAADwfzMjDABAJCEMAEAkIQwAQCQhDABA
-JCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQh
-DABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwA
-QCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAk
-IQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEM
-AEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABApL+Uk2g8s+79bwAAAABJ
-RU5ErkJggg==
-"
-width=353
-height=351
->
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;<a class="anchor-link" href="#&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#182;</a></h2><h3 id="&#26631;&#20934;&#21270;">&#26631;&#20934;&#21270;<a class="anchor-link" href="#&#26631;&#20934;&#21270;">&#182;</a></h3><p>在下面的单元中，实现 <code>normalize</code> 函数，传入图片数据 <code>x</code>，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 <code>x</code> 的形状一样。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[3]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Normalize a list of sample image data in the range of 0 to 1</span>
-<span class="sd">    : x: List of image data.  The image shape is (32, 32, 3)</span>
-<span class="sd">    : return: Numpy array of normalize data</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="mi">255</span><span class="p">)</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_normalize</span><span class="p">(</span><span class="n">normalize</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="One-hot-&#32534;&#30721;">One-hot &#32534;&#30721;<a class="anchor-link" href="#One-hot-&#32534;&#30721;">&#182;</a></h3><p>和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 <code>one_hot_encode</code> 函数。输入，也就是 <code>x</code>，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 <code>one_hot_encode</code> 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。</p>
-<p>提示：不要重复发明轮子。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[4]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">one_hot_encode</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.</span>
-<span class="sd">    : x: List of sample Labels</span>
-<span class="sd">    : return: Numpy array of one-hot encoded labels</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="kn">from</span> <span class="nn">tflearn.data_utils</span> <span class="k">import</span> <span class="n">to_categorical</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_one_hot_encode</span><span class="p">(</span><span class="n">one_hot_encode</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#38543;&#26426;&#21270;&#25968;&#25454;">&#38543;&#26426;&#21270;&#25968;&#25454;<a class="anchor-link" href="#&#38543;&#26426;&#21270;&#25968;&#25454;">&#182;</a></h3><p>之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;<a class="anchor-link" href="#&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#182;</a></h2><p>运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[5]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="c1"># Preprocess Training, Validation, and Testing Data</span>
-<span class="n">helper</span><span class="o">.</span><span class="n">preprocess_and_save_data</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">normalize</span><span class="p">,</span> <span class="n">one_hot_encode</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[6]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">import</span> <span class="nn">pickle</span>
-<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
-<span class="kn">import</span> <span class="nn">helper</span>
-
-<span class="c1"># Load the Preprocessed Validation data</span>
-<span class="n">valid_features</span><span class="p">,</span> <span class="n">valid_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_validation.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#26500;&#24314;&#32593;&#32476;">&#26500;&#24314;&#32593;&#32476;<a class="anchor-link" href="#&#26500;&#24314;&#32593;&#32476;">&#182;</a></h2><p>对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。</p>
-<blockquote><p><strong>注意</strong>：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。</p>
-<p>但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 <code>conv2d</code> 类 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/conv2d">tf.nn.conv2d</a>，而不是 TF Layers 版本的 <code>conv2d</code> 类 <a href="https://www.tensorflow.org/api_docs/python/tf/layers/conv2d">tf.layers.conv2d</a>。</p>
-</blockquote>
-<p>我们开始吧！</p>
-<h3 id="&#36755;&#20837;">&#36755;&#20837;<a class="anchor-link" href="#&#36755;&#20837;">&#182;</a></h3><p>神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：</p>
-<ul>
-<li>实现 <code>neural_net_image_input</code><ul>
-<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
-<li>使用 <code>image_shape</code> 设置形状，部分大小设为 <code>None</code></li>
-<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "x" 命名</li>
-</ul>
-</li>
-<li>实现 <code>neural_net_label_input</code><ul>
-<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
-<li>使用 <code>n_classes</code> 设置形状，部分大小设为 <code>None</code></li>
-<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "y" 命名</li>
-</ul>
-</li>
-<li>实现 <code>neural_net_keep_prob_input</code><ul>
-<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a>，用于丢弃保留概率</li>
-<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "keep_prob" 命名</li>
-</ul>
-</li>
-</ul>
-<p>这些名称将在项目结束时，用于加载保存的模型。</p>
-<p>注意：TensorFlow 中的 <code>None</code> 表示形状可以是动态大小。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[7]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-<span class="k">def</span> <span class="nf">neural_net_image_input</span><span class="p">(</span><span class="n">image_shape</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Return a Tensor for a batch of image input</span>
-<span class="sd">    : image_shape: Shape of the images</span>
-<span class="sd">    : return: Tensor for image input.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">neural_net_label_input</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Return a Tensor for a batch of label input</span>
-<span class="sd">    : n_classes: Number of classes</span>
-<span class="sd">    : return: Tensor for label input.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">neural_net_keep_prob_input</span><span class="p">():</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Return a Tensor for keep probability</span>
-<span class="sd">    : return: Tensor for keep probability.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;keep_prob&#39;</span><span class="p">)</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_image_inputs</span><span class="p">(</span><span class="n">neural_net_image_input</span><span class="p">)</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_label_inputs</span><span class="p">(</span><span class="n">neural_net_label_input</span><span class="p">)</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_keep_prob_inputs</span><span class="p">(</span><span class="n">neural_net_keep_prob_input</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Image Input Tests Passed.
-Label Input Tests Passed.
-Keep Prob Tests Passed.
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;">&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;<a class="anchor-link" href="#&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;">&#182;</a></h3><p>卷积层级适合处理图片。对于此代码单元，你应该实现函数 <code>conv2d_maxpool</code> 以便应用卷积然后进行最大池化：</p>
-<ul>
-<li>使用 <code>conv_ksize</code>、<code>conv_num_outputs</code> 和 <code>x_tensor</code> 的形状创建权重（weight）和偏置（bias）。</li>
-<li>使用权重和 <code>conv_strides</code> 对 <code>x_tensor</code> 应用卷积。<ul>
-<li>建议使用我们建议的间距（padding），当然也可以使用任何其他间距。</li>
-</ul>
-</li>
-<li>添加偏置</li>
-<li>向卷积中添加非线性激活（nonlinear activation）</li>
-<li>使用 <code>pool_ksize</code> 和 <code>pool_strides</code> 应用最大池化<ul>
-<li>建议使用我们建议的间距（padding），当然也可以使用任何其他间距。</li>
-</ul>
-</li>
-</ul>
-<p><strong>注意</strong>：对于<strong>此层</strong>，<strong>请勿使用</strong> <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a>，但是仍然可以使用 TensorFlow 的 <a href="https://www.tensorflow.org/api_docs/python/tf/nn">Neural Network</a> 包。对于所有<strong>其他层</strong>，你依然可以使用快捷方法。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[8]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv2d_maxpool</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">conv_num_outputs</span><span class="p">,</span> <span class="n">conv_ksize</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Apply convolution then max pooling to x_tensor</span>
-<span class="sd">    :param x_tensor: TensorFlow Tensor</span>
-<span class="sd">    :param conv_num_outputs: Number of outputs for the convolutional layer</span>
-<span class="sd">    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer</span>
-<span class="sd">    :param conv_strides: Stride 2-D Tuple for convolution</span>
-<span class="sd">    :param pool_ksize: kernal size 2-D Tuple for pool</span>
-<span class="sd">    :param pool_strides: Stride 2-D Tuple for pool</span>
-<span class="sd">    : return: A tensor that represents convolution and max pooling of x_tensor</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_ksize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conv_ksize</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">3</span><span class="p">],</span> <span class="n">conv_num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
-    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_num_outputs</span><span class="p">]))</span>
-    <span class="n">conv</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x_tensor</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conv_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span>
-    <span class="n">activate</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv</span><span class="p">)</span>
-    <span class="n">pool</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="n">activate</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pool_ksize</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pool_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span>
-    
-    <span class="k">return</span> <span class="n">pool</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_con_pool</span><span class="p">(</span><span class="n">conv2d_maxpool</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#25153;&#24179;&#21270;&#23618;">&#25153;&#24179;&#21270;&#23618;<a class="anchor-link" href="#&#25153;&#24179;&#21270;&#23618;">&#182;</a></h3><p>实现 <code>flatten</code> 函数，将 <code>x_tensor</code> 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（<em>部分大小（Batch Size）</em>，<em>扁平化图片大小（Flattened Image Size）</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[9]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">flatten</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Flatten x_tensor to (Batch Size, Flattened Image Size)</span>
-<span class="sd">    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.</span>
-<span class="sd">    : return: A tensor of size (Batch Size, Flattened Image Size).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="n">layer_shape</span> <span class="o">=</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span>
-    <span class="n">num_features</span> <span class="o">=</span> <span class="n">layer_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">num_elements</span><span class="p">()</span>
-    <span class="n">layer_flat</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_features</span><span class="p">])</span>
-    
-    <span class="k">return</span> <span class="n">layer_flat</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_flatten</span><span class="p">(</span><span class="n">flatten</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;">&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;<a class="anchor-link" href="#&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;">&#182;</a></h3><p>实现 <code>fully_conn</code> 函数，以向 <code>x_tensor</code> 应用完全连接的层级，形状为（<em>部分大小（Batch Size）</em>，<em>num_outputs</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[10]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fully_conn</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Apply a fully connected layer to x_tensor using weight and bias</span>
-<span class="sd">    : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
-<span class="sd">    : num_outputs: The number of output that the new tensor should be.</span>
-<span class="sd">    : return: A 2-D tensor where the second dimension is num_outputs.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
-    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
-    <span class="n">fc</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span><span class="p">)</span>
-    
-    <span class="k">return</span> <span class="n">fc</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_fully_conn</span><span class="p">(</span><span class="n">fully_conn</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#36755;&#20986;&#23618;">&#36755;&#20986;&#23618;<a class="anchor-link" href="#&#36755;&#20986;&#23618;">&#182;</a></h3><p>实现 <code>output</code> 函数，向 x_tensor 应用完全连接的层级，形状为（<em>部分大小（Batch Size）</em>，<em>num_outputs</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
-<p><strong>注意</strong>：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[11]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">output</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Apply a output layer to x_tensor using weight and bias</span>
-<span class="sd">    : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
-<span class="sd">    : num_outputs: The number of output that the new tensor should be.</span>
-<span class="sd">    : return: A 2-D tensor where the second dimension is num_outputs.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
-    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span>
-    
-    <span class="k">return</span> <span class="n">output</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_output</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;">&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;<a class="anchor-link" href="#&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;">&#182;</a></h3><p>实现函数 <code>conv_net</code>， 创建卷积神经网络模型。该函数传入一批图片 <code>x</code>，并输出对数（logits）。使用你在上方创建的层创建此模型：</p>
-<ul>
-<li>应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）</li>
-<li>应用一个扁平层（Flatten Layer）</li>
-<li>应用 1、2 或 3 个完全连接层（Fully Connected Layers）</li>
-<li>应用一个输出层（Output Layer）</li>
-<li>返回输出</li>
-<li>使用 <code>keep_prob</code> 向模型中的一个或多个层应用 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/dropout">TensorFlow 的 Dropout</a></li>
-</ul>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[12]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Create a convolutional neural network model</span>
-<span class="sd">    : x: Placeholder tensor that holds image data.</span>
-<span class="sd">    : keep_prob: Placeholder tensor that hold dropout keep probability.</span>
-<span class="sd">    : return: Tensor that represents logits</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Apply 1, 2, or 3 Convolution and Max Pool layers</span>
-    <span class="c1">#    Play around with different number of outputs, kernel size and stride</span>
-    <span class="c1"># Function Definition from Above:</span>
-    <span class="c1">#    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)</span>
-    <span class="n">conv_pool_1</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-    <span class="n">norm_layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">lrn</span><span class="p">(</span><span class="n">conv_pool_1</span><span class="p">,</span> <span class="mi">4</span> <span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.001</span> <span class="o">/</span> <span class="mf">9.0</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
-    <span class="n">conv_pool_2</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">norm_layer</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-
-    <span class="c1"># TODO: Apply a Flatten Layer</span>
-    <span class="c1"># Function Definition from Above:</span>
-    <span class="c1">#   flatten(x_tensor)</span>
-    <span class="n">flat_layer</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">conv_pool_2</span><span class="p">)</span>
-
-    <span class="c1"># TODO: Apply 1, 2, or 3 Fully Connected Layers</span>
-    <span class="c1">#    Play around with different number of outputs</span>
-    <span class="c1"># Function Definition from Above:</span>
-    <span class="c1">#   fully_conn(x_tensor, num_outputs)</span>
-    <span class="n">fc_layer1</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">flat_layer</span><span class="p">,</span> <span class="mi">384</span><span class="p">)</span>
-    <span class="n">dropout_layer_1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc_layer1</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
-    <span class="n">fc_layer2</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">dropout_layer_1</span><span class="p">,</span> <span class="mi">192</span><span class="p">)</span>
-    <span class="n">dropout_layer_2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc_layer2</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
-    
-    <span class="c1"># TODO: Apply an Output Layer</span>
-    <span class="c1">#    Set this to the number of classes</span>
-    <span class="c1"># Function Definition from Above:</span>
-    <span class="c1">#   output(x_tensor, num_outputs)</span>
-    <span class="n">logits</span> <span class="o">=</span> <span class="n">output</span><span class="p">(</span><span class="n">dropout_layer_2</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-    
-    <span class="c1"># TODO: return output</span>
-    <span class="k">return</span> <span class="n">logits</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1">##############################</span>
-<span class="c1">## Build the Neural Network ##</span>
-<span class="c1">##############################</span>
-
-<span class="c1"># Remove previous weights, bias, inputs, etc..</span>
-<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
-
-<span class="c1"># Inputs</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">neural_net_image_input</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">neural_net_label_input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">keep_prob</span> <span class="o">=</span> <span class="n">neural_net_keep_prob_input</span><span class="p">()</span>
-
-<span class="c1"># Model</span>
-<span class="n">logits</span> <span class="o">=</span> <span class="n">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
-
-<span class="c1"># Name logits Tensor, so that is can be loaded from disk after training</span>
-<span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;logits&#39;</span><span class="p">)</span>
-
-<span class="c1"># Loss and Optimizer</span>
-<span class="n">cost</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">))</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">()</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
-
-<span class="c1"># Accuracy</span>
-<span class="n">correct_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-<span class="n">accuracy</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">correct_pred</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">)</span>
-
-<span class="n">tests</span><span class="o">.</span><span class="n">test_conv_net</span><span class="p">(</span><span class="n">conv_net</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Neural Network Built!
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;<a class="anchor-link" href="#&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#182;</a></h2><h3 id="&#21333;&#27425;&#20248;&#21270;">&#21333;&#27425;&#20248;&#21270;<a class="anchor-link" href="#&#21333;&#27425;&#20248;&#21270;">&#182;</a></h3><p>实现函数 <code>train_neural_network</code> 以进行单次优化（single optimization）。该优化应该使用 <code>optimizer</code> 优化 <code>session</code>，其中 <code>feed_dict</code> 具有以下参数：</p>
-<ul>
-<li><code>x</code> 表示图片输入</li>
-<li><code>y</code> 表示标签</li>
-<li><code>keep_prob</code> 表示丢弃的保留率</li>
-</ul>
-<p>每个部分都会调用该函数，所以 <code>tf.global_variables_initializer()</code> 已经被调用。</p>
-<p>注意：不需要返回任何内容。该函数只是用来优化神经网络。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[13]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train_neural_network</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Optimize the session on a batch of images and labels</span>
-<span class="sd">    : session: Current TensorFlow session</span>
-<span class="sd">    : optimizer: TensorFlow optimizer function</span>
-<span class="sd">    : keep_probability: keep probability</span>
-<span class="sd">    : feature_batch: Batch of Numpy image data</span>
-<span class="sd">    : label_batch: Batch of Numpy label data</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">keep_prob</span><span class="p">:</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">label_batch</span><span class="p">})</span>
-
-
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">tests</span><span class="o">.</span><span class="n">test_train_nn</span><span class="p">(</span><span class="n">train_neural_network</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Tests Passed
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#26174;&#31034;&#25968;&#25454;">&#26174;&#31034;&#25968;&#25454;<a class="anchor-link" href="#&#26174;&#31034;&#25968;&#25454;">&#182;</a></h3><p>实现函数 <code>print_stats</code> 以输出损失和验证准确率。使用全局变量 <code>valid_features</code> 和 <code>valid_labels</code> 计算验证准确率。使用保留率 <code>1.0</code> 计算损失和验证准确率（loss and validation accuracy）。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[14]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">print_stats</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Print information about loss and validation accuracy</span>
-<span class="sd">    : session: Current TensorFlow session</span>
-<span class="sd">    : feature_batch: Batch of Numpy image data</span>
-<span class="sd">    : label_batch: Batch of Numpy label data</span>
-<span class="sd">    : cost: TensorFlow cost function</span>
-<span class="sd">    : accuracy: TensorFlow accuracy function</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># TODO: Implement Function</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Valid Loss: &#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">valid_features</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">valid_labels</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;, Valid Accuracy: &#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">valid_features</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">valid_labels</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#36229;&#21442;&#25968;">&#36229;&#21442;&#25968;<a class="anchor-link" href="#&#36229;&#21442;&#25968;">&#182;</a></h3><p>调试以下超参数：</p>
-<ul>
-<li>设置 <code>epochs</code> 表示神经网络停止学习或开始过拟合的迭代次数</li>
-<li><p>设置 <code>batch_size</code>，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：</p>
-<ul>
-<li>64</li>
-<li>128</li>
-<li>256</li>
-<li>...</li>
-</ul>
-</li>
-<li>设置 <code>keep_probability</code> 表示使用丢弃时保留节点的概率</li>
-</ul>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[21]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Tune Parameters</span>
-<span class="n">epochs</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">128</span>
-<span class="n">keep_probability</span> <span class="o">=</span> <span class="mf">0.75</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#22312;&#21333;&#20010;-CIFAR-10-&#37096;&#20998;&#19978;&#35757;&#32451;">&#22312;&#21333;&#20010; CIFAR-10 &#37096;&#20998;&#19978;&#35757;&#32451;<a class="anchor-link" href="#&#22312;&#21333;&#20010;-CIFAR-10-&#37096;&#20998;&#19978;&#35757;&#32451;">&#182;</a></h3><p>我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[22]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Checking the Training on a Single Batch...&#39;</span><span class="p">)</span>
-<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
-    <span class="c1"># Initializing the variables</span>
-    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
-    
-    <span class="c1"># Training cycle</span>
-    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
-        <span class="n">batch_i</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
-            <span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">:  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-        <span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Checking the Training on a Single Batch...
-Epoch  1, CIFAR-10 Batch 1:  Valid Loss: 1.82877, Valid Accuracy: 0.3608
-Epoch  2, CIFAR-10 Batch 1:  Valid Loss: 1.60118, Valid Accuracy: 0.429
-Epoch  3, CIFAR-10 Batch 1:  Valid Loss: 1.5015, Valid Accuracy: 0.4502
-Epoch  4, CIFAR-10 Batch 1:  Valid Loss: 1.38333, Valid Accuracy: 0.4996
-Epoch  5, CIFAR-10 Batch 1:  Valid Loss: 1.32223, Valid Accuracy: 0.5298
-Epoch  6, CIFAR-10 Batch 1:  Valid Loss: 1.34756, Valid Accuracy: 0.5206
-Epoch  7, CIFAR-10 Batch 1:  Valid Loss: 1.28294, Valid Accuracy: 0.5466
-Epoch  8, CIFAR-10 Batch 1:  Valid Loss: 1.31494, Valid Accuracy: 0.5374
-Epoch  9, CIFAR-10 Batch 1:  Valid Loss: 1.30606, Valid Accuracy: 0.5576
-Epoch 10, CIFAR-10 Batch 1:  Valid Loss: 1.32294, Valid Accuracy: 0.555
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;">&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;<a class="anchor-link" href="#&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;">&#182;</a></h3><p>现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[17]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">epochs</span> <span class="o">=</span> <span class="mi">8</span>
-<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./model/image_classification&#39;</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training...&#39;</span><span class="p">)</span>
-<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
-    <span class="c1"># Initializing the variables</span>
-    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
-    
-    <span class="c1"># Training cycle</span>
-    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
-        <span class="c1"># Loop over all batches</span>
-        <span class="n">n_batches</span> <span class="o">=</span> <span class="mi">5</span>
-        <span class="k">for</span> <span class="n">batch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_batches</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
-                <span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">:  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-            <span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
-            
-    <span class="c1"># Save Model</span>
-    <span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
-    <span class="n">save_path</span> <span class="o">=</span> <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Training...
-Epoch  1, CIFAR-10 Batch 1:  Valid Loss: 1.88313, Valid Accuracy: 0.3366
-Epoch  1, CIFAR-10 Batch 2:  Valid Loss: 1.60399, Valid Accuracy: 0.4198
-Epoch  1, CIFAR-10 Batch 3:  Valid Loss: 1.50717, Valid Accuracy: 0.4498
-Epoch  1, CIFAR-10 Batch 4:  Valid Loss: 1.41716, Valid Accuracy: 0.4816
-Epoch  1, CIFAR-10 Batch 5:  Valid Loss: 1.33043, Valid Accuracy: 0.5132
-Epoch  2, CIFAR-10 Batch 1:  Valid Loss: 1.28486, Valid Accuracy: 0.537
-Epoch  2, CIFAR-10 Batch 2:  Valid Loss: 1.30711, Valid Accuracy: 0.53
-Epoch  2, CIFAR-10 Batch 3:  Valid Loss: 1.22172, Valid Accuracy: 0.558
-Epoch  2, CIFAR-10 Batch 4:  Valid Loss: 1.18755, Valid Accuracy: 0.5712
-Epoch  2, CIFAR-10 Batch 5:  Valid Loss: 1.18258, Valid Accuracy: 0.576
-Epoch  3, CIFAR-10 Batch 1:  Valid Loss: 1.12699, Valid Accuracy: 0.5932
-Epoch  3, CIFAR-10 Batch 2:  Valid Loss: 1.13514, Valid Accuracy: 0.5916
-Epoch  3, CIFAR-10 Batch 3:  Valid Loss: 1.10883, Valid Accuracy: 0.5996
-Epoch  3, CIFAR-10 Batch 4:  Valid Loss: 1.06464, Valid Accuracy: 0.6178
-Epoch  3, CIFAR-10 Batch 5:  Valid Loss: 1.07656, Valid Accuracy: 0.6128
-Epoch  4, CIFAR-10 Batch 1:  Valid Loss: 1.10849, Valid Accuracy: 0.605
-Epoch  4, CIFAR-10 Batch 2:  Valid Loss: 1.08009, Valid Accuracy: 0.6166
-Epoch  4, CIFAR-10 Batch 3:  Valid Loss: 1.01519, Valid Accuracy: 0.637
-Epoch  4, CIFAR-10 Batch 4:  Valid Loss: 1.00247, Valid Accuracy: 0.643
-Epoch  4, CIFAR-10 Batch 5:  Valid Loss: 1.02331, Valid Accuracy: 0.6448
-Epoch  5, CIFAR-10 Batch 1:  Valid Loss: 0.995853, Valid Accuracy: 0.6502
-Epoch  5, CIFAR-10 Batch 2:  Valid Loss: 1.00064, Valid Accuracy: 0.65
-Epoch  5, CIFAR-10 Batch 3:  Valid Loss: 0.93919, Valid Accuracy: 0.6738
-Epoch  5, CIFAR-10 Batch 4:  Valid Loss: 0.991811, Valid Accuracy: 0.6504
-Epoch  5, CIFAR-10 Batch 5:  Valid Loss: 0.927782, Valid Accuracy: 0.6826
-Epoch  6, CIFAR-10 Batch 1:  Valid Loss: 0.969924, Valid Accuracy: 0.666
-Epoch  6, CIFAR-10 Batch 2:  Valid Loss: 1.01257, Valid Accuracy: 0.6408
-Epoch  6, CIFAR-10 Batch 3:  Valid Loss: 0.961456, Valid Accuracy: 0.6636
-Epoch  6, CIFAR-10 Batch 4:  Valid Loss: 0.935574, Valid Accuracy: 0.674
-Epoch  6, CIFAR-10 Batch 5:  Valid Loss: 0.904234, Valid Accuracy: 0.6824
-Epoch  7, CIFAR-10 Batch 1:  Valid Loss: 0.925582, Valid Accuracy: 0.6806
-Epoch  7, CIFAR-10 Batch 2:  Valid Loss: 0.962076, Valid Accuracy: 0.674
-Epoch  7, CIFAR-10 Batch 3:  Valid Loss: 0.935451, Valid Accuracy: 0.6754
-Epoch  7, CIFAR-10 Batch 4:  Valid Loss: 0.88064, Valid Accuracy: 0.6912
-Epoch  7, CIFAR-10 Batch 5:  Valid Loss: 0.912521, Valid Accuracy: 0.694
-Epoch  8, CIFAR-10 Batch 1:  Valid Loss: 0.932409, Valid Accuracy: 0.6876
-Epoch  8, CIFAR-10 Batch 2:  Valid Loss: 0.959626, Valid Accuracy: 0.6722
-Epoch  8, CIFAR-10 Batch 3:  Valid Loss: 0.958519, Valid Accuracy: 0.6904
-Epoch  8, CIFAR-10 Batch 4:  Valid Loss: 0.886022, Valid Accuracy: 0.7024
-Epoch  8, CIFAR-10 Batch 5:  Valid Loss: 0.947139, Valid Accuracy: 0.6868
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>模型已保存到本地。</p>
-<h2 id="&#27979;&#35797;&#27169;&#22411;">&#27979;&#35797;&#27169;&#22411;<a class="anchor-link" href="#&#27979;&#35797;&#27169;&#22411;">&#182;</a></h2><p>利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。</p>
-
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[18]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-<span class="kn">import</span> <span class="nn">pickle</span>
-<span class="kn">import</span> <span class="nn">helper</span>
-<span class="kn">import</span> <span class="nn">random</span>
-
-<span class="c1"># Set batch size if not already set</span>
-<span class="k">try</span><span class="p">:</span>
-    <span class="k">if</span> <span class="n">batch_size</span><span class="p">:</span>
-        <span class="k">pass</span>
-<span class="k">except</span> <span class="ne">NameError</span><span class="p">:</span>
-    <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
-
-<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./model/image_classification&#39;</span>
-<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">4</span>
-<span class="n">top_n_predictions</span> <span class="o">=</span> <span class="mi">3</span>
-
-<span class="k">def</span> <span class="nf">test_model</span><span class="p">():</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Test the saved model against the test dataset</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_test.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
-    <span class="n">loaded_graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
-
-    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">loaded_graph</span><span class="p">)</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
-        <span class="c1"># Load model</span>
-        <span class="n">loader</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">import_meta_graph</span><span class="p">(</span><span class="n">save_model_path</span> <span class="o">+</span> <span class="s1">&#39;.meta&#39;</span><span class="p">)</span>
-        <span class="n">loader</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
-
-        <span class="c1"># Get Tensors from loaded model</span>
-        <span class="n">loaded_x</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;x:0&#39;</span><span class="p">)</span>
-        <span class="n">loaded_y</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;y:0&#39;</span><span class="p">)</span>
-        <span class="n">loaded_keep_prob</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;keep_prob:0&#39;</span><span class="p">)</span>
-        <span class="n">loaded_logits</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;logits:0&#39;</span><span class="p">)</span>
-        <span class="n">loaded_acc</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;accuracy:0&#39;</span><span class="p">)</span>
-        
-        <span class="c1"># Get accuracy in batches for memory limitations</span>
-        <span class="n">test_batch_acc_total</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">test_batch_count</span> <span class="o">=</span> <span class="mi">0</span>
-        
-        <span class="k">for</span> <span class="n">test_feature_batch</span><span class="p">,</span> <span class="n">test_label_batch</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">batch_features_labels</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
-            <span class="n">test_batch_acc_total</span> <span class="o">+=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
-                <span class="n">loaded_acc</span><span class="p">,</span>
-                <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">test_feature_batch</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">test_label_batch</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
-            <span class="n">test_batch_count</span> <span class="o">+=</span> <span class="mi">1</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Testing Accuracy: </span><span class="si">{}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_batch_acc_total</span><span class="o">/</span><span class="n">test_batch_count</span><span class="p">))</span>
-
-        <span class="c1"># Print Random Samples</span>
-        <span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)),</span> <span class="n">n_samples</span><span class="p">)))</span>
-        <span class="n">random_test_predictions</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
-            <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">top_k</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">loaded_logits</span><span class="p">),</span> <span class="n">top_n_predictions</span><span class="p">),</span>
-            <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">random_test_features</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
-        <span class="n">helper</span><span class="o">.</span><span class="n">display_image_predictions</span><span class="p">(</span><span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">random_test_predictions</span><span class="p">)</span>
-
-
-<span class="n">test_model</span><span class="p">()</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>Testing Accuracy: 0.6769185126582279
-
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt"></div>
-
-
-<div class="output_png output_subarea ">
-<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA9oAAAN6CAYAAABxCzGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
-AAAewgAAHsIBbtB1PgAAIABJREFUeJzs3XecLFWZ//HPU9UzNxAFQQTTrgHMSzKhu2ZU1F3DGjGL
-cd11jetP17jGnzlgQEX9sWvAiAEUlTUioJgQcxYkZ7h3urvq+f3xnJqu6ame6e7pSXe+b17FzO2p
-OnW6uqamnjrnPMfcHRERERERERGZjGy1KyAiIiIiIiKyI1GgLSIiIiIiIjJBCrRFREREREREJkiB
-toiIiIiIiMgEKdAWERERERERmSAF2iIiIiIiIiITpEBbREREREREZIIUaIuIiIiIiIhMkAJtERER
-ERERkQlSoC0iIiIiIiIyQQq0RURERERERCZIgbaIiIiIiIjIBCnQFhEREREREZkgBdoiIiIiIiIi
-E6RAW0RERERERGSCFGiLiIiIiIiITJACbREREREREZEJUqAtIiIiIiIiMkEKtEVEREREREQmSIG2
-SAMz22xmjzGzD5jZj83sr2a2zczKvuXTq11XEVn/zOyZDdeXtw+57S0btv3Jctd5rTKzIxqOxwmr
-XS8REdlYWqtdAVlbzGw34CBgH2D3tJTAVWk5B/g98Cd376xWPZeTmT0deCWwZ3rJaz/2+VuIiEzM
-Uq4xuj7NpeMhIiKrRoG2YGa3BI4C7gvcBLAhNivM7Gzg+8AZwFfd/TfLV8uVYWZHA08jbtCqm7Rh
-jofImmBmrwJePOJm1wCXA5cRD9K+D5xO/F63J1tDGYIxfpCo69VcSzmWIiIiY1OgvYGZ2c2Bo4F/
-SC9539eFZMCtgFsDT0jl/R54s7u/a8JVXRFm9nh6Qfbsy4xwk2ZmewCH9r18kbv/YMkVFBnNKMHF
-lrTsA9wcuF96/WIz+xDwVnc/Z7LVk2XirJNg28yuDRzc9/IF7v7DCe5GQbaIiKwKBdoblJk9D3gV
-sIn5gWXdYq269W1vRATt6y7QNrMMeBnzj8XlwLHAt4ELgP7WvUv6/n0gcGLfa18F7j2xyooMb5yA
-q/47sAfwXODJZvYsdz9uMtWSZbIuAuya2wOf73vtC8ADJ1T+ejseIiKyA1GgvQGZ2SuA/6S5e3T1
-7+3Ab4lAcxuwG3HTvR8RnNO3XX3b9ejewA2ZezwuBQ5199+NUd56Phay4xnlfOy/FjiwK/ARM7up
-u79sojWTSen/jNfTNWg56rqej4eIiOwAFGhvMGb2JHpB9uzL6d9/Ao4BPgP8wt3n3ZiYWU50GT+E
-GNN9H6LLaVXOenWX2vfV8XjXmEF2fzkiq6XqRlwAhzH4d3Rn4NrE7/URwAH0zt/6efwSM/uzu79/
-OSsto3H3nwH5atdjiSZ2vXT3L7L+j4eIiKxzCrQ3kDR++PU0B9mvBV65WOIjdy+AH6flA2a2hRjP
-+XTgbstR7xVyYMNr31jxWogsE3c/Y4jVPgG8wMweArybCL7rLdsGvMnMPuPuFy9PTUVERETWP82j
-vbE8h+j+XamC7Oe7+0vGyS7s7tvc/VPufk8iMdpnJlPVFXfthtf+suK1EFkD3P1TxPjZCxp+vDOj
-ZzUXERER2VAUaG8sD2buGGQHTnP3N0+icHc/290/OomyVsFuzO+2uG01KiKyFrj774HHMj8PgwGP
-XJVKiYiIiKwTCrQ3CDO7ATHust8HVroua9T0aldAZK1x968ApzJ/bPfeZnabVaiSiIiIyLqgMdob
-xw0GvP6jFa3FCMzsOsTY6RsRLc4t4GrgHODnwM+aEratslVPCGdmU8TctDcH9gSmiAzqZwNnuPtI
-LfVpbP/BwE2Iz2EbcB7wY3f/xQSrXt9nBvwN8XDoukTW681EFvxL0v7PcPerlmP/MsdngTs2vH57
-4CdLLdzMDLgNcb7uA+xETKN3EfAZd79sjDKvTwxluSFx7kB0g7+AuG78Yan1XmT/W4E7APsDuxPJ
-6C4Bfgl8f9TfwbXAzG4E3BK4PnFMp4CrgMuIGSp+Psa4/VW/Xo7DzA4AbgFcjxhKUQJXAH8EfrQa
-c86b2fWI6/QNU52uAC4Eznb3ny7TPrcQv7s3Jf427Er0ermGODf+DPwB+L27d5ejDiIia5q7a9kA
-C/AQ4magSEv1/Y1Xu2599dwTeAlwVqrjQsvFwAeBQ0bcx7FDlD3M8thU3l8mVF4J7Dugzq9qWPf/
-9K1zQHpvly9Q/hXA0cDeQxyn+xNzgHcWKO+XwFMBm8BnfzsiI/7XiBu1xY5VB/g+8CJg1zH3+ZoB
-ZT9sCe/jnwaU+bYV+h2qzpX673p7CeXdraG8AnjxAtu8tuH9v6BvnVsSsxxcPOB4FcDtRqjnTYE3
-Ab8f4tz5WarjXhM+9ocAxxPTIw7a9zVE0rlD+7Z9Zu19V1/fPuR+b9mwn59M4P3cAXgvcO4Qx7Qk
-Hua9DThsgTIvGrKsYZY9BuzjiIZ1T1jisbg18B7iYc1i9foV8ErgOkvY36KfKZFZ/YnADxapzzlE
-ItTdJ3BO7Aw8Dfg20B3yc7oG+Fb6nTt0qXXQokWLlvWyqOv4xjHos957RWsxgJllZvZ84un3K4nW
-LV9k2R14PHC6mX3KzPYdYZdN5Q2zTtP6Sy2nntV5lHrPMrOXE5ngH0fcCA3az07ETdIvzexeTTsw
-s+uY2VeAE4C7E+fOoPJuQmSn/k5q+R6Zmb3UzH4LfA94BRHYbVpgn9WSAQcBrwb+ZGYvHGP3LwG+
-2VD2+8zsxmO8l78hHv70l3c68Nwx6rcWXDjg9aYEgv3mna9m1jKztxC9aZ5E/B4v9js0kJntZWYf
-Jnq5/DvRe2exc+cA4IXAb8zsRWnawrGZ2WYzezdwGvFQc2qBfW8CHgp8z8zemXqgTMpYx7CfmR1o
-Zt8AvgscBVyHxY+pEy34zwK+ZWZnpZb9heq4KtfLUZjZtc3sv4nz9SnEw+DF6ndj4try+3R9W8pn
-POiafysiwH4/8HeL1Gcf4PnAb83svuNWxMweBvyCeFh7J3q5XhZbNhHTC74QOM3M3jBuHURE1hMF
-2hvHRQNeX/UpucxsN+DLxBP3rfRuKKxv6X8Nen/IHwScaWZ3Gna3A8pfbJ1h1h2lnEHbLFb3+CZ8
-FHgpvaEg/WU2HbPdgC+Y2d3nFGx2U6KV+J7M/xwGvU8nWr6+ama7MrqnE8MD6jeTg45P/+vVNrsA
-r00PXJpu7hu5ewk8ivj9qL/HXYDjzWzosfvpZvoTxLGt1/cyooV8vXadXGr33vr5uhn4CvBv9P7+
-NH3GwxVsdlfgp8BjmDsP82LnDmndnYkHNZ9L3WBHZma7A18nenZU5S5Wh8ozgBPNbNM4+x5UJZbw
-maUHnqcBd2b830mIh6WbF6njil4vR2VmBxEBdpX8b9TzaxPwcuDrZjbMg6mBVanvy8zuAXyHaGX3
-hn03HS8HrgV81sweNHIFzF4MfIwYylOdFwt9LoOu1c7g80JEZIeiMdobx6/6/l39kXyWmb3H3S9Z
-hTpVYxm/AhzK3Cf29Ru284ju2dcA+xJBWYv5wfbewElmdri7n7rIrptaOPpvFEZpBemv+zjljNPq
-8lbg4fQ+TyfGt/6W6OK4mRjrXPVcqN/wTAEfN7MD3P1iM9ubCBjqPQOcGFv6R2Kc9x7EDfQm5n5G
-ALcluu4eNcb76P8sIbol/pEIVC8nPvPdiVb0rQO2exDRff7hw+7Y3c81syOBE+m9J0vv5+1ED4Bh
-vJkYI1m/GXfgCe7+p2HrswYNChAGPbxbyH8Dd2Xu+QrRk+V8YAbYjxj7umDwaWb/CHycXusxzD8f
-fke0yG8jfgduQu/cra93P+JB0d18hGkO04OYzxMPmgZdv/6Slm56bzdibmByN6IXxHeH3e9yMbN3
-EsF/0/Wseu1c4tpyCdFDZk/iGpP3rb+Q1bpeDs3MDiSGsVTjjmHusSiJ8/Zc4hy8HvH5VuvVW94P
-A75mZn/v7pcvsV4HE3kTtjL3en4hcZ5dSgTUNyUeJPUHulPAB83sdB9yLLmZPYIYkjLoHL+G+F27
-jBg2sTNx3G5InCNN24iI7PAUaG8Q7n6Omf0SuFnfj/YmbjAf4e79wfhKeDvNQXZBjId7v7v/uL5B
-6qL8MOBlRP3rNxE7E8HjbXxwEqVXEt2d6z5LdI+s9l8FbectUPffpq/3pxcU3C69p3ogcQbwrwuU
-U9c0b/Eg9yO671XH7mdE68mJ7n5NfUUzuyPwhrR+3R7E8XgmEbRUN4olcBzwTnf/fl9ZW4gWxNek
-7eutG08ws2Pc/fQR3gdp+xki0P880Y38bHfvNK1sZrcGHk20Iu7K3Bu4h5rZU9z9fUPv3P1kM3sN
-0d2z/n6OMrP/dfePLbS9mT2EOIb9QfZb3f2EYeuxRh064PVBXcoHeQwx7rQ6Rn8F/otIeDbn9yyd
-Yw9nwO+Dmd0S+B8iaJh9OZX9feKBz8nufmlDufchfk9uxdzz5g7A/yVa24f1MiKI6r9+dYC3AB9w
-91/31WE/4lj8HyIIMeAR9H73VkUaftIUZFcPLN4EfKnpoVHqqfB3xNjohxNdpwe5N73P7c7AG5l7
-vfwO8Lwhqz1yorxhpAfAn6CXSA969buUCDo/5u7n9213APBkovt8/QEQxPn2PkZ4CNjHU32Opxe8
-zhDduD/kfQnPzKwF3Bd4HfNnHNmVGEv/0MV2mn5n3sL882I7MX7/OHf/wQLb34j4u/iAVJ9rLbZP
-EZEdxmoPEteycgsxPqqe1Kie3GiGCKzuCbRWqD7/1FCfkri5vv0Q218LOKmvjOr7j49Yl983lHGD
-Md7TPWrbV1+/MoFj9aq+cvuTRr0ZFk5IRrQ4ndBwvK4C/qNW3iXAPwxRp1sRiaz6j9uxI763U4Fn
-AzuPcVyuSwTn/e/pPGBqxLKyAWVdDtx0ge1uTNzw95/H312p36UFzpVJJEP7zoDfsdsusM1raT5f
-qzKOB7aOWZ8txEOl/uN9DfDkIcswInBsel/3GrKM2xK9R/rrcQ5wmyG2vyGRtb3/OC0lGVq9nKGT
-oRG5GJr+LnTTtWGk8xi4F3AKcK1F1jui4T0vKWnZJMolxj03nRunMFwiyQPpJcnsL+OxS/hM6//+
-BXCTIcrZSuSh6K9LhwHJN/u2b0qkejlw0BifyzSRvO0ZS/2MtWjRomU9LBqjvbG8kwhAmrrqtYix
-ql8BLjazL5vZK83sAWZ23UlXJE3p8ybmPyW/Ejjc3U9brAyP1qoHEYFAvUufEa2ad55opdeWerfE
-d7r7c9x9wS557l4QNzn9XRe3EK3TEIHDvdz9G4tWwP0solWu6dgPPd7V3e/o7m/1Mabqcve/EjfV
-ZzD3vN6LOJ9HKaskxmKe31fWzsR47XldmVPX4U8QY7pnXyYeVjzc1++4bADM7HBiaq/+c+si7+tp
-soj69p8nxqxfM2jlRbyUGL5QMeK8PcLd3z9UZcJzgXcw/3r4iiHr8Ubm9goz4nfr3u6+6LRn7v5H
-4HDgT6xil9qUCO49/S8TQfaj3P11o57H7n6yu9/N+3oUrAcpydgTmd9d/AfAA9x90V5H7v5D4rOt
-v//q+viGMZOj1Xva/BG4k7v/Zoi6XEP0mLiy70cZ8Ngh9nt47fvqPH2Fu585TKX76tJ29w+6+9Gj
-bisish4p0N5A3P1q4un0DM3JS+rdr+9FdKP9HHCOmZ1jZp82s2eb2UEpUF6KBxDj+irVH/CXpJuU
-oXjMR3skMQaz37OWVMO1q7rZgsi0PGw3S9z9IqLnQtPn58DLfYFugA0+yPzum1uJbOArIp0DT234
-0aPHKOv8tF1ZvUQcq1sTD6r6vZVovapU5/Hj3f3Po+5/LUkZ1D9Mc1fij45QVH37S4GjFnsotECd
-qqmF+rvoP9/dTxmjyOcRU1JV9TTg9mY2qLt8VY8bE63A/fV4sbv/bNidpwdFTx+10hP2OGLseqV6
-L69x90+sTpVWVdMwnzZw5CgPA9N58DzmX2v3IgLfcVSfzaN9hLwq7n4uca3ur8thQ2x+/YbXvjjs
-vkVENjIF2huMR5KwI4jxldWN5aCgq77sQ3T1fjMxBvKPZvbalKV6HM9oeO1XwLtGLSi1DL2R+S2r
-/7QcrfFriAOv9wHjmBdQHzNcD3guJ8aXD1+B2PdJzD+HVizQTvX4EZF9up7M7HZjlvV1Ytx6//n0
-RDObDd7TVDdNQd+b3P0L4+x7rUhjzk8ngoJ+V9PrATF0kcSxeZe7jzq2u+6p9LK6V5/PLxnjugGz
-5+/rmX/+PnGRTY9q2ObXzM/9MEwdTiR6Eq1Wq3Y92V/9Ad6rVqEuqyrNmvAo5v9OH+1j5DBx92OJ
-lvD+c+VfRi2qVpeT3X2cxHn1hyZVeQcPsV3TTBJXjLF/EZENR4H2BpQCiQOJ5FclcwPuQYE3zA28
-9yPGfJ9tZseMEtCm7rZ3Zf7NzAdS991xvI9eK2SlRYyZ3lFtI6ZbGVV/t9bq+H9qzO68Td1k+5Pu
-rYQz+v69S0qaNY5XERmH+4Ptd5vZ/ukB0zHM7156KvCiMfe5rMzs9gssdzezh5nZG8zsbGIMdf+8
-6NV58sJhus8O8MGlvAfmJ5KqgvdxrxsQAUiVabz6nO+yyDb1uYir4/KhcVvqgQ+Mud2SmNkNgUOY
-G+A78LY01GSjuQu9GQ3qjllCmfWkjNX5dYiZjZsUbNzfoR8y/2/kdcxsp6aVa5oSzo31EFNEZKNR
-oL1Bufu57v4o4DbEjcDFzA2khw28M+BJwFlmds8hd38QkRSl38eHfgP9FYlpSr7J/Lrecdwy17DZ
-bOZjtGZX3aObpjD69pj1+UPDa7s1vLbcmoK/hbIfD5QCpkcRmbHr59ROwCeJc3WXvs0uJsZlr6UA
-pap7i3gIMGj5KvHQ5nnA/szvLl6dc29w95FbbZM/pt4nY0kBwYF9dQP4zLhlArj7DPPH+B9gZo3n
-cKpHPXt65ZNLqMYJRBbnlXavhtfaxDRsG1H970V1Ppzl7j9fQpkfJ8a797vDmOV9a5yN3H07MRVZ
-v8Wu1f1DIQx4vZldp2llERHpUaC9wbn72e7+dCJ78/2I6W1OI262hg28ncgAfmLqTruYphuM8ycw
-pvV7ff82dsxAu7KUm7/+xDhLKa+prLEDbTObNrP7m9krUl6As8zsz2Z2uZkVZlY2LUR25H67j1uP
-1MX5UUSmXegFVrckpjKqt2aXRDbhoealXQX9Q0EWWvp/1534jI9y93Fa66syRhn73+QwenM1V85L
-40+X6g99/67G5Tc5mPl/Oy8fJjHVICnYP4vBDzWXS73rcPU5/XAJierWu/6/TU78PRybu19BZAif
-xEPgK9K4/nE1dfle7FpdH49dXSNuBvzUzJ5rZnsuoT4iIjs0zaMtAKSssl9OCykr6m2I1udDiC51
-+6fV6zfgs0UQN8EfNLNfpTGzg9yw9v3szd0E3ka9jCpguMEEyl2rhk6G06Cp9Wzc8prK2jxqIakb
-638SCfvqN3/9LYcLdc/tv5kdO9AGcPdvmNnLiPme6+Mk6/tz4I1prO1aNUoAV39/lwEfAd48gQdh
-f1ni9reqfV8d9/PN7PZLLBdgXkZ55nedr9yooR6LZhkfwo+Ja+1KasqxsaTAcp27IfOvL5P623Sr
-vtfG+du0lGs+xHCj/mvYgtfqdA38LnCn2nYO7Ek8mH+dmX0bOJlobT8jtZ6LiGx4CrSlUeqS/IO0
-HAOQuor9M/AUolWvnniq+gO8BTia+KM8SNPYtKU8pa+M0y1uPZt0q9MkyxupZc7MXkRkud9Cr9Vk
-rLL6LDb+cFHu/hozuwsxzU39fK++foeY5mwtW2zs8DYiGd7lROvu94lkaCen1tZJ6J9WblT9LWdG
-9Cw4dYnlDjIo0G66fo07Zn3SZYxqP+afG0t9ILKereTfplHGaFfXwKsnUJdBZS/k0cQDmL1q61fX
-6Qz4h7QAdM3sh8D/Al8HTnH3pqFKIiI7PHUdl6G5+/nu/k53vw0xJczlzA86qulxFhqv3XSDMYks
-pk038lnKJCtrlJkdDbyaaFmpd8fuv6EbZplX/ISq+aEFfnbUEpNxLafqmBTuni+w7Ozu+7n7Ldz9
-fu7+Unf/wgSDbFj6g5ymwHeUc2PU82fQQ7qmXhLLdf1abjs3vNaU/GqjaPrMl+uzHTcZ2opLuRVu
-Tzx4779GV3/7qyUHDgWeD5wInGdm70vzk4uIbChq0ZaxuPtxZvYjIoHWLsy/SX0IkWCpyZaG15rm
-wR7VoDJ2QtORrElm9u/MnSILejduHeBMoiXlj0RL25VEV/XtzD/nngY8fhnqeD3gHbX99e/3xcBj
-J71fmacpwF3OMc3948ErTd3MJ9FiN8mHGsNqeo+TuBavO2k2jJz5v9/L8bfJmEBvm5WUgu3bmdmR
-wHOJoWWVprwtld2AJwNPMrOPAf/i7pcua2VFRNYIBdoyNnc/y8xeALyHuUGIAXdfYNOmoLc/g/M4
-BpWxGi1Fsog0FOHlzA+yrwZeARzj7kN/dmb2oIlWMMrMgY8C12ZuS049cdijzewb7r4qUzRtIE2Z
-m8edTmspms7JSVy/VqPnzWVAf/boDdkDyN3bZtYGpvp+tBx/m5x1+nfJ3Y8DjjOz2wD3B+5GJHar
-P0CvB9713iKPBO5sZnd199+vRH1FRFaTAm1ZqmOBNzL/6fzfLLBN09PsSdzcNXX762zgDLpr3eOY
-2xvCgKuAw9z9p2OUtxxdMV9NZLvuD7L7g+23mdn33L1/KhyZnP4HdA583d2bpqhaTk1dqyeRC2I1
-8klcwvzkWEtKILjOXQrs3ffacv1tWmpis1Xl7j8hkgC+Jj2QPJhImnoXIviuhiXUzy8Hrg98ycz+
-bsJDU0RE1hyN0ZYlSUnTTmd+17HczAYFPk2B9t9OoDpNcyari9ra9cDa99XN2MvGDLIhWp0nxszu
-R4wz7A+yj2P+/NpbgOPNbOsk6yBz9Gc9N+YnSFsJF9W+rx60NGXvHtXNJlDGqC5qeG0S72W90t+m
-Mbh74e6nu/ub3P2fiGvxEcBniakP++8Pbgb8ywpXU0RkxSnQlkk4f8Drg8Y4nl37vrpRve0E6nFg
-7fsqKDp7wLqy+g5mftff45ZQ3kEN5Y0ljcv+cMOPziTGGz6auIGE3jm8PzGMQpZH/3UDonVspf24
-4bWbmFlT7olR1OdmXyn90zAa8+eS3kjOZn5QeGDTiiM6kPmf7Q77t8ndO+5+krs/BLgr83uBGPCY
-Fa+YiMgKU6Atk9DUta5w96bWEmiejmdnMztoifW4K/NvZpZr6p9BVmPM6LqTMsH3J5W61N0vHLO8
-/Zg7v/HYUjfIjzG3tdSIMZUPSzeR/0uMI69nRq/Gaz9pEvWQeU5j/u/XHmms6Ipx998wv9tvxsJ5
-KRZkZjcFrreUeo3pG7Xvq2O7f3rQtBLW2vWy/vei+p2+81IKNLMbEfNzL7SvHZa7fxt4NnOvlQC3
-NrNJjH8XEVmzFGjLJOzP/BumQUE27v5LmsenHTluBVKQfsuGH630zUzTmLP+5DrSPGbxqiWU9+Ql
-bNvvtcQ88P1dxp/cl8Dnv4jM+v3B9tvMrOlclCVw9/OJ+b37WxwfvArVObWhHo9eQnmrlbX+VOZn
-TDfgqSu0/7V2vfxuw2v7mtnYD1GIXBT92sRUWRvF8fR6ANX1J+ITEdmhKNCWJTGzA4Gb1F8iAo6m
-G5a6TzE/QHmMmY2biOe5Da9dCnxtzPLGdWXfv9fdNC4rpCnj7l5mNvI1ycx2JgKDJbeOmdkRxLnU
-H2Qf7e6fqq/r7k48HDoPjddeKf+v9n113fjX1ENitevxIDO7yYD1BzKz3YCnsAqtu+6+Hfg486/F
-TzWzieY8GKD/egmre708jfm5AAD+fZzCzGwn4CjmX09OSPlNNgR33wY0JSUdNLxMRGSHoEB7gzCz
-g83s2HFuBBco04C3Dfjx5xbZ/B0Nr+0BvGaMevwDMW1I/83MMelGciXVE9xU9bnRCtdhzXP3K5h/
-47WZyFY7qrcD+6Tvx55X2cyuD3yo4UdnAs9p2sbdLwAeRfN47XePWxcZ6EPAxX2v7Qa8f4Xr8Rmg
-f5jDNHEujuq1wF7p++WcF3yQNzA/yN+TlTmm/ddLYxWvl+5eEr+3/Q8e7peSI47q5cC+Da83/f3b
-YZnZ3vSykNeds9J1ERFZSQq0N44W0YXt52b2kaWOhzazKeC/ifFr/TdpF7NIoO3uZxGtzU0tKUN3
-A05jGz/WUIc2cPSw5UyKu/+Z+a00e5pZU9bZje5bzA8sXjFKq3aax/3x9M6fsaRx2R9n/rjsK0jj
-sgdtm8Zrv5z55/KRZvbEcesk87n7VcBLmX+sH2JmR5vZkqasNLPMzB5sZrdapB4dIqjur8fhZvbm
-Efb3r8DTWOL5uxRpSrrPMv+9PMDM3jtOLxMAM9sz/V4t5HdA/8PQ/VZwjHiTY4Cra/+ujseHzezm
-wxZiZo8mHtD1/236QRq3vC6Y2TFmdoslFvPshtd+nX6fRUR2WAq0N56M6O76fTM7y8xeYGYLzXk9
-R7oR/Uci8+4jmHsTUbUkvzC1WC7m6TTf0LzHzF682E2amd2DCNbr855WdXhJCnpXww+Zf9P8n6tR
-kTXus7Xvq8/+jsD/LNbt2sx2M7N3A6+rbdtdQl1eR2RbXmxc9iCvBk5mfrDydo3Xnix3fzfzjzVE
-wPpdMxs5a7aZHWBmLyYCv+OBGwyx2RuAnzH/M3+2mX18oa7XZrbVzN4OvKW23dWD1l8BTwXOZf4x
-PQr48ijnsJndOD1s+COLzA3u7gUxF/OauV66+8VEgFyvkxMP4b5mZvddaPv0N/J5wLH9PyLGpK+3
-ZImPB84ysy+Z2SPSUJ2hWHgu8AKap0kUEdmhLenpv6xb1R+8mxMBxuvM7DxiXPUZxJjTi4lufZuI
-rOI3JqbgOpzo5ug0B9mfdvf+G4zmSrj/xsyeA7y3Vq/qpvNVwKPM7EPAicBfgG1EN7xDiIcFDxhQ
-h1Pc/U3D1GGZfBL4+/R99X4em25WP0FM63I50NRK+gN3X0rAuJ4cC7yYyLZcP58eBtzBzI4Gvgz8
-lmj12ovNNWLkAAAgAElEQVSY4/cfic+/Og+NOGc/xxhJnMzs/sxtearOo3e7+yeHKcPd3cyOJKZL
-um6trC3AJ8zskDROUSbjEUSPiKqlrTp/DiGC7TOAE4DvAL8hphfaBuxCBH/7Abchrml3Z+7c0UON
-lXb3Tsow/y0igVf9HP5norvx54iEeecABXH9ugvwEGKoTHX+XkT0wHnpCMdgYtz9IjN7BPB14r6g
-eh8O3AP4sZl9Afgi8XfifOLvw05EAHor4tgfQUyzB0MeR+J6ebvaNgYcZWa3JR56/Jy4XjZdF89I
-3b0nyt2PSfkaHsjcY7EP8EUzO4kIFL8H/JX4/K8H3At4AnFuNf1teom7/3TS9V0BDtwnLdvN7GvA
-6cSwml8R58JlxJjr3Ynfp78npvBqSpb6ZwYPOxMR2WEo0N54+p/SV38Ar0Nk710sg299m/6yPsGI
-mXfTDc0BzJ/+w4EDgNenZVBdKtWNzM+Ah49Sh2VwHNGVuLqRrup5cFoGcWJe4HOXs3LLbOiETu7e
-NrOnAp8nelrUb2ivT3oItMB+qpvy7URwfviolV1gXPYPGTEBkrtfaGaPIgKr6v0YcR6/h+bswzIG
-d7/UzO4GfIkI7PpbYg9Ny1DF0XxNG6Yep6dg+1h6iZ2q8rYS4/cftcA+jRjm8jCaZ00Yx1jd0N39
-2ynY/m9ivHl1Ta3Ke2BaJr3/DxEP3HZj7mdxO3oB+KD97EXzDBb9643jsUTPiUOYeyygF3Quts/6
-efmBJTwAnuTQgnGPR7XdJuKByhFDbtN/LK4GjnT3pkR4IiI7FHUd3zjOJzKqlsy9eaovPsRSX5/0
-2kXAU939keO0Lrj7c4EXEi0W9X1U5Q9TFwe+Avx96vq3atz9MqJVo0gvDXN8hzXJcZz14zzJ8oYu
-091PBP6FOFZN59Zin/3lwAP7xjwOtf/auOw9+vZ9BfDP42QFdvdv0Jtfu6pHNV57pbuMTvrzHbcO
-yyLNuX4X4iFGdV0b5Vo26DoCzb1NBtXjOKKF/aoB5TX9rlc/vwp4UBrnT+1n9a+jWNJn7u6fBu5K
-dPse5r0Mem+j7PMioot69bdj0tfLsY5JGv50d6KnzKjHor7PAniluz9l1Dr0vYdJGbe8Uc+FpmN2
-HnCP9TRGXURkKRRobxDu/gd3vyPRZfJpxM3DhQy+AWxa6Fv/LOD/ADdz9yVlqHX3NxItUN+k+Y90
-01Ktdw7wFHe/bwpyV527f57IoP1rhju+QxXbV9aSqznBMse+GXb39xCtQ39muM++2t/JwCHu/tUB
-9VjM64Db921TMvy47EFeTTz06a/LSs6vvZTgZDnqsDw7cN/u7s8kPseT6AXcsPj1o/+adi7wZuDv
-3P3kEevxKaJl/UTmvu+F9ndi2teJ9aIY/7hN5DN399OI1vX/IroED3s9rtfhz0SvnqZp/Jr2+Umi
-2/Xv+uo+qevlWMfD3a929wcTrdt/YPhjUa33XeAwd3/FqPue1HsYoszFPIcYUtBmuM+m6ThsA94K
-7O/up0/gPYiIrAsWU8HKRmVm+xNJoA4AbkaMxd6DGMu4E9Et90qile884KdE8ppTU7ba5ajTQUQX
-9HsQYzCbkqL9Ffg2MR/3Z5c6J6mZHUt0n6848PjUarYkZvb3wP2I8aA3I7pI7kyM66tz4Abuvp67
-jo8tZYx+LDG+9TDmz6frxHjbrwIf6b9hM7Om7sLfc/czl6fGa5OZ7UuMF61zdz9jNeqzkszsRsBD
-iWvHwczNIl83Q5xLZxPB0NcmdT1LGcsfSYxRvRkxZrUggtZfEtet49fDWN2UlPDBRJfxO9I8VRXE
-e/s58aD0y8C3fMybizQk4L7E9fKmRI6QQdfLvd19sa7jE5Gyrz+AOL/uQgxv6VcSn/HXgY+6+6kr
-UbeVkJKgHUacB7cnxl5fn+YGGyceTPyQGN7xSXUVF5GNSIG2rGmpe+8NiOA0I+ZePkd/tHds6ab2
-OkSg1CK62P7Z3WdWtWKyrpjZrkRyuq3Ezf+Vablw3EBwI0uB935E4Fsdz0tXKthdS8xsJ+KB1k5E
-gH0FcY1a0kPf9SQ9HN2HeDC/lXgwfzlwsZI/iogo0BYRERERERGZKI3RFhEREREREZkgBdoiIiIi
-IiIiE6RAW0RERERERGSCFGiLiIiIiIiITJACbREREREREZEJUqAtIiIiIiIiMkEKtEVEREREREQm
-SIG2iIiIiIiIyAQp0BYRERERERGZIAXaIiIiIiIiIhOkQFtERERERERkghRoi4iIiIiIiEyQAm0R
-ERERERGRCVKgLSIiIiIiIjJBCrRFREREREREJkiBtoiIiIiIiMgEKdAWERERERERmSAF2iIiIiIi
-IiITpEBbREREREREZIIUaIuIiIiIiIhMkAJtERERERERkQlSoC0iIiIiIiIyQQq0RURERERERCZI
-gbaIiIiIiIjIBCnQFhEREREREZkgBdoiIiIiIiIiE6RAW0RERERERGSCFGiLiIiIiIiITJACbRER
-EREREZEJUqAtIiIiIiIiMkEKtEVEREREREQmSIG2iIiIiIiIyAQp0BYRERERERGZIAXaIiIiIiIi
-IhOkQFtERERERERkghRoi4iIiIiIiEyQAm0RERERERGRCVKgLSIiIiIiIjJBCrRFREREREREJqi1
-2hVYr8ysTN+6u+erWhkRERERERFZM9SivTS+2hUQERERERGRtUWB9tLYaldARERERERE1hYF2iIi
-IiIiIiITpEBbREREREREZIIUaIuIiIiIiIhMkAJtERERERERkQlSoD1BZnYzM3urmZ1tZlea2eVm
-9iMze42Z7TliWbcwszeY2ZlmdqGZbTezc8zsFDN7gZntMUQZjzOzMi0fTK9lZvZwM/usmf3WzK5J
-P39g37YtMzvSzD6V1rvSzDpmdoWZ/drMTjKzV5jZoUO+nwPM7NVmdpqZnWdmM2Z2gZl9L5Vz3VGO
-j4iIiIiIyFpl7pqhahz982ib2dOAtwCbmDvtV5WZ/GLgcHc/c5Fy81TO04Fqfu6m8i4Dnu3uH1mg
-rMcBx6btPwy8GPgEcFhDuQ9y9xPSdjcDPgsc0LBefz0cuKm7/25AHaaBtwNPWuT9bANe4O7vGvR+
-RERERERE1oPWaldgR5AC2qOJAPIXwPeJwPEAIqg1YE/gBDO7ubtfOaAcAz4NPCCV5cAlwP+mr9cH
-7gZMA7sDHzKz3dz9HUNUczNwAnAw0AG+C/yWeDBwUK0OOwNfBfZL+y+BHwI/B64Ctqaf3Ra49iLH
-ZSvwFeBOtffzW+AHwKXAHun47AtsAd5hZru4++uGeD8iIiIiIiJrkgLtpalaZt8DXAA8xt1Prq9g
-ZncGvgDsClwX+DfgvwaU93x6QTbAa4GXu3u3Vt7eROv04Wm9N5rZ99z9jEXq+lCiRfkU4PHu/ue+
-ek6lb58IXC+VfTbR0v2bpgLN7GDgCcDMgH2+m16Q/Uvgqe7+rb4yDHgK0Yq/GXilmZ3i7qct8n5E
-RERERETWJHUdH1PqOu5Ea/V24FB3/9mAdZ8BvDOt/wt3v2XDOrsA5wA7pZf+r7v/x4DypoFvAYem
-Mk9x93s2rFfvOg7wE+AO7j4oMMbMjgcekra5p7ufMmjdhZjZXYBv0GvFvoO7X7LA+vW6nuTuR4yz
-XxERERERkdWmZGhL58B7BwXZyUeALhGU75+6Z/d7FLBzWud84GUDd+jeBv4l/dOAu5nZTRfYfzUO
-+oULBdnJrrXvL1pk3YU8p/79QkE2gLt/mOh2b8DhZnatJexbRERERERk1SjQnoxPLvRDd7+KaNWF
-CCRv2LDa3avVgY8uFhCnruI/rb10t0XqeClw8iLrANS7lD9tiPXnSQndqhb2K4AvDrlp1Xpu9BK2
-iYiIiIiIrCsao700RgTGP11sRSLreGXXhp8fWPv+u0Pu/zvArdP3By2wngM/8uHGCXyCGKdtwNPN
-7BBiTPiX3f23C27ZcxuiC7wTidfeHkOxF1WfKuz6Q+5LRERERERkTVGgPQHufsUQq3Vq3081/Hyv
-2vd/HHLXf6h9v2AGcODCYQp096+Y2duBZ6WXDk0LZnY+8G0iC/pn3f2cAcXs21evZw6z7z7qOi4i
-IiIiIuuSuo6vHfVx21cPuU19vV0WWXfbsBVx92cDDwZOpzctlwN7E4nS3gH8ycyON7Omlufd6sWN
-sYAeAomIiIiIyDqlYGbtuIpegLrTQivW1NdrnJt7XO7+OeBzZnY94K7ENF13AW5RW+0hwF3N7I59
-U4DVHwD8xN3r3eJFRERERER2aGrRXjvqXbtvMOQ2N6p9v5QM4QO5+1/c/Th3f4a735qo28uAa4jW
-5z2AN/dtdn76asA+y1EvERERERGRtUqB9trxw9r3dxpym/p6Z06wLgO5+znu/l/AU4lA2oB7m1l9
-3PmPgCpr+t5m9rcrUTcREREREZG1QIH22vH19NWAR5jZ9EIrp2zgt6m9dMqgdZfJCbXvp4iWbQDc
-fTu99wPwjJWqlIiIiIiIyGpToL12/A8xThvgukT37Eap9fgdtZe+7u6/nkQlzGzPIVetd28vmTt9
-GcDrqyKBZ5nZPUaow3WGXVdERERERGStUaC9Rrj7lcCr0j8N+A8ze2Vfl+wqCD0BuH16qQO8aIJV
-OdXM/tvM7tO/71odbgZ8qPbSV929W1/H3b9JzL8N0eL9RTP7DzNrTPRmZpvM7B/N7LPA55b8LkRE
-RERERFaJso6vLW8EDgMeQATbLwGebmanAJcC1wfuBmxK6zvwPHf//gTrMAU8Mi3bzOwnwO+AK4i5
-rf8WOKS2/jXA8waU9VQiGdq9gWngNcBLzOw04E/EOO7dgRsDt6L3vib5fkRERERERFaUAu01xN3d
-zB4MvAV4OpATY58fWl8tLZcD/+bu/2/C1biS3lzWm4mW89v3rVP9/HfAke7+s6aC3L1tZvcjusE/
-F9gKbCEeFsxbPS0d4NSlvAEREREREZHVpEB7fN73ddhtFlzf3Uvg38zsPcATgXsQLdm7AJcAvwK+
-CLzf3S9dhnreFrgDEQzfDtgf2JcIkq8BziOyip8AfMLdO4u8HwdebmbvAB4L3JOYi/vaROv5FcAf
-gZ8SCd2+5O79471FRERERETWDYs4SEREREREREQmQcnQRERERERERCZIgbaIiIiIiIjIBCnQFhER
-EREREZkgBdoiIiIiIiIiE6RAW0RERERERGSCFGiLiIiIiIiITJACbREREREREZEJUqAtIiIiIiIi
-MkEKtEVEREREREQmSIG2iIiIiIiIyAQp0BYRERERERGZIAXaIiIiIiIiIhPUWu0KiIiIrHdmdjWw
-CSiBC1a5OiIiIjuyvYkG4xl332m1KzOIuftq12FNuPLyOBDuzsBj4pA5GEBZUhRdim5BWXS5Zttl
-XL3tMq7edinbt13MTPsC2jPn0565APzK2cWsTZ4beW5kuVEUBd1ukb52Z5eiKMiyjCzPyPMcBwp3
-yrKkcKc906XdLmi3q+2doltSFCWll7jH102bpti6ZRNbtmxiy+ZppqdaTE21mJqeIrMMswww3KHT
-6dLtdOh0uhRFSVGWdEun2y3Y3u4w0+4y0+6SkZFZTkaeto8ywOgUJd0ylrIsgBKnBMq037T/qSla
-rSla+RR5PoWXRllmeJnRbhdsn+mwfXuHmXYHrAPWBeuS5cTxa2XkeXTIeOVLfm3LfX6IiCzEzLpA
-vtr1EBER2UAKd1+zDcdrtmKrycwag22r/jfnR45TUnqXopih291Gu7uNbnc73aJDUXSBAsMxN7AM
-T0VUuzCLfZpFvOgpoO7feXoUADiWgWWQpaUs078dDKN0wzyC0TzPaLVy8jzHsiz2445bVMLMUn2c
-0qEsnaIsI2juFnSLkm7hlCWUbnhaYh/gXuIe9SvNcXMyK7E8Vdxin61WRpaBWTpmZUEx+55zvIxC
-orx44OFllOfpOOeAWU6m50MisrY4xLV83333Xe26iMgQ2u02F154IXvttRfT09OrXR0RGdK5555b
-xWprOiJQoN1nYJBdtZm6zb4QIW+0HBdlh06xnZnONbQ7V9HpbKOTgm2zggwnwzC3FJSmQDJuzWqB
-dgSaZVGkn5eUnmGZxa7NMHPMnCxzLIugO8uZLcuBjAxwpqZypqZatNKSZxlksQ7us2+sqk9ZlhSl
-0+2WdLpdOt0iLU63APcMLzLKbgaFpZbokrIs8bIkm2J2yXOwzCK4zyDLjCwzLHPwkrLsxhEsS/Ac
-owXkKcAuI8h2j8DfS0ovcDeyLKtuaZfrNBARGdUlwN7Xvva1+ctf/rLadRGRIZx55pkcfPDBnHTS
-SRx00EGrXR0RGdLee+/NhRdeCPG3d81SoD1AFXBbLZYzLFqfiRZd6LW0FmWXTjFDu3M1M+2r6Xav
-oejO0C3b5BRgqeXYs9Rq2+uibhZBaLUvL52iKDB3SjeyssTyFLDmVVBebdcLsj2P+DMzwzAwmJrK
-aU3lTLVatFqt2e1iz15b6LVmFyXdoqTTKZjpRLBdlEaZFu9meMfwjlF2iW7mRUFZdpnaYkxnRms6
-o5UbWZ6RteIr+Gxs7KQHCUVJtPinugFlvVW7hCK1sHfLEsfIW9UDChERERERkbVHgXZS77Y9eCV6
-LdqJ47iVlHQpyzbdcjvdYjvdok1RtOkWXbASMsjSeOYIInt90Kt9Wy2qd6LVl9IoLLbNzMFT1+/U
-qm1ZKj4jlclsIJ5l0Grl0W08LZ76eJfumDPbOl5vzS6rFu1OQbvdpd0pcM8pPYuu4x0oZ8DbRtFx
-isIpU7Cd5Rk+nWE4WWbkOeR5RtbKesEzvQcNpRt4GSO8zcgsusFXreRzAv+iBIxiKl5f251FRERE
-RERko1KgvYha7+r5gZ1F1+jMI7GZ5dVXh25J4THG2fIyxegGqUu3VU27Vbyduo5nWUaWZeRZXuUX
-m1OX2dbwskwtv71gvWoVr1rII9DNybIcy6KwKsitxoCbOUaU1S1SoO3QLZx215mZiYRrsa8YR+3t
-FGjPGN5NY7PdwKM7ublFN/nZevfqGvVNLdK14+mUeFlE0rfCKKru6F5SFhH4dzsFRhbB/XQE+CIi
-a8lFF13E9a53vdWuhogMod1uA3Cf+9xHY7RF1ol99tlntaswNAXaA8xv2a5avOkFiFUX7swi4M6j
-G7dl4OaUXtItumSU5EYEorMFlLOlulkkS5stp8o0noLS2a9RAS9JmcUjcLVUlyyLEqsgO8t62bkz
-yyKhWhmt2UUZCcyMaCUuy16gXZRQFNDplMzMlMzMFBFgl2ksdwqymTG86HV5xzKsjEB79hnB7IOB
-avw5ta/pWAN4BNq4UxRGWUQ9Z1u0uyWdTgkG3W5J0XXKlgJtkXGY2cuAlwHu7sqUPUHuzjnnnLPa
-1RCREaSxniIiE6VAewi9pHZWb2COYDKL1tsIsuOr5Q7mlGVBtyjIU3doz6oW7ZJeU7Wl9GWpRduy
-2Wm9opt1CV51JffUMlxl+vZevQwsZfeOVvGYQqyV52RZhmVpGi+gKKObt1e9r1Mv9W7hqUXb6Bae
-Au2C7dsj0KZ0vMxhxmCmxLdn0W09T2PMc4tAu7TZ8ey91usFpk0jtXqnLuPRoj23C3mRAm3LoNtN
-3dXLgcWJiKyi/Va7AiIylDZwIbAXoBZtkbXtr1QNleuFAu0FeJr6qu9VfLbbdwoosyymnMpaZNkU
-WTaFWQusyqIdXacjVo2x0f0hZ2qvjm7jeWQKn00KVjolvWmuylQDs9QyboYRdTDrBdkRYOeRIT3t
-uyyihbhTlL35xdI82mWZpvAqPcZ7e0ZGHlNqkQM5Rh7vLWtBq4UVFl3jswKykpYZuVkkZKslNSuL
-crblO77GQwEj5ikrsRj/Xo1dr7rHl+VsgB5d46sA3pQQTUTWIAOUdVxkfTgTOBg4CVDWcZG17XrA
-+uoxpkB7EXOCba/aj0nRYtVCnWFZC5sNtKcxS8E2eZqjupoGLKbkyrzK++2zAW8E7TGuutp3dBFP
-gTolZWp+LjOwojc+OwLgjMxy8sxSwG5klhMJ2IixzmUkFut2u7VtSfNi94Lt6Faek1mLVpaR0SIn
-J6OF5S2s1cLKVkw7lhVgBZ4VTGVGK6vGaMeUXGVZUNrc+cKj9T5PrfB5HNPSUjf6EqdIXccL3Ms0
-nVl6eGBpBLj3PwQRERERERFZfQq0B6h3c66CbU9JzGYThtdakrMstWjn02TZdK1VO0+t0BFkZ1l9
-/mxmm7ZnW20tg1a0bJdlOTsWu/RIrmYpE3lkHi/iq2epJTsnszwlU6u6kEd7dJm6b1dTcXWLAqgy
-fRtOdNeOJaYvMzJya+HmtLIWLVrktMjyKTJvkXkrBcddSisoraCVES3aAFW3dy8pSS3SWcwtVgXY
-eZZHq7tH13ov0zMML1KLdhHH3VLX/Kyab7wvU5yIiIiIiMgaoUC7QVOX5Eg6Zr0WbYjvZoPtFlk2
-TZ5topVvmm3VjkMc03lF9+yy10pdlsxNENZr2Z7NaF5mlJTR2u21ADN97+lrL/1YlV08vpalp1zj
-kfys241M6N1uBNpZZpRZlBdjoyPgxqOVPM8ik3qrbDFFLNFFPifLW5GcDCiALk7mQGmU8QIlRmk2
-d0SFGTZ7jLM4frX3TbTbz8lSbpmRW0YrJXersqmLiKwRxWpXQERGdV0iL+R1V7siIjKCqvcva/xv
-b7baFVi7BreWRpKyqjE6uo5nWYs8n2aqtYVWayt5CrYza8X4ZrfUMt2bczqWcjb4LlPwXRQF3W6X
-ohvrxPpl6tbdW7wkLdXYamazdBdpHuxYunTaXTqdDt1Ol063iAzjqWW7Cry7abuiiDHaedZiOt/E
-ptZmNueb2JRNs8mm2JS10pIzbRktN6yMKc3KjtOdKenMOO0Zp9Nxul0oi2pxyq5TFFAUVQt7qn89
-o7v3Am0zI88zploxdn1qaiot00xNKXmJyFKZ2SYze76Z/cDMrkjLaWb2TDNb8ImWmd3QzN5iZmel
-7a42s1+Z2XvM7FaLbFum5aXp33c3s+PN7E9m1jaz3/Wtf10ze12q52VpnfPM7Cdm9j9m9jgz23mB
-/e1qZi8ys2+b2QVmNmNm55rZCWb2kFGOWYP0PFE9bUTWj+sCL0eBtsj6Ugu013R2NLVoj8Bn/2+9
-r2lubMumojW7tYVWvoVWvpncpjFaQIdofY4EX4WVWFliRUHViGsWPy/KXvDdm3M6BejVkgLsCE4d
-3MgskqRh1TzZNjuM3NLk2g50iy7dbkHRTfsume2KHdm8M7oF4K3oht7KyciZspwpMqY8IyurrupZ
-ZDAHshK8W+Idw9vAdsPLElqO5xDzm5EypqeI2h3yavqvGCfuqZXe04973cYzLCMF2r1gW0SWxsz2
-Br4M3Ja5eRoPTcu9gH8asO1jgfcCm/q2vTFwE+BJZvaf7v66BargqaxXAy/qK6e+r7sAnwd27Vtn
-r7TcCngEkUL4Sw3b3wP4OLBH3/bXAe4P3N/MvgQ8zN2vWaC+IiIiIotSoF037/auoWWi6uZNFW4b
-pKRheR6B9lRrC3m2OXUfTy3aVWZvjyC7KAqMopcgLKsC7V5rd71Sc4Jsr1q0I0DFndKii3ivT3at
-cSUieRynKLrRil2N0c7iWYF7llqYoSiMzI2Wtchb07SsxZRntEpjKk1BFt3aja5B16FdOBRO2XHK
-tlGmAJoSbCqCZYfoWp7Gi5P3HiS4W1Q5JTibnUHcncxizHneyuYE2dMKtEUm4dPAAcBbgS8AlwD7
-A/8J3AJ4gJkd5e7H1DcysyOAY9M/rwTeCHyNGE1yJyJo3gt4tZld6u7vXaAODwFuDfwYeAvwM2AL
-8HdpX9PAx4BdgCuAo4H/BS4g5uT5m7TPBzUVbmaHEcF3CzgPeEfa17nAvsDDgSOB+wIfBv55gbqK
-iIiILEqBdqUvOVmv1bqPkcZFVzGhgTmW5eT5FFOtTUxPbWHT1GY2TW1i09Qm8A6k7uMRVAOZ4Vls
-71btLQWd6b9qDmmvuounKcKKWsCdzSbfdqCgSOO/e+ObqzqmNYqCouymJGPxs2q6LGOKVraZ1vQm
-pnwzU2xi2jeRd3Mo29Du4O02ZZHGjpcZeEkry9jUmoLMKFtQZFA4lGVB0TWKlITNMk/HyslyyFsZ
-rVZJnpe1w2vpMUaMX49s45DnGa1WTivLI6u5NT4GEZHRGHAIcC93/1bt9R+Z2VeAs4G9gWcAs4G2
-xZQKVeB8FXBnd/9pbfvTzezTwKlEn8w3mtnx7n7JgHrcGjgZuL+7d2qvfzt9PSyV48Aj3f3Evu1P
-Bz5uZv8ObJ3zBqOuxxF/704EHuru2+vvFfiSmX0LeB/wYDO7h7t/bUBdRURERBalMdqNrPa1KckY
-s0FqtcQ0WNNMtzazaWoz01ObZpep1hStPE9Zt4HMIIss22WWpo9O82PPLql1u9staLc7zLTbtNsd
-2u02nXaHolvE+OUMsgzMqimxOhRlh063Tbszw0xnhnZnJm3XptNp0+1E9/FuJ5ZOu6TbAcop8mwL
-m6d2Zeum3dh587XYZese7Lx5dzbnW8jLjHKmi3e6lN0uXhTgTm4Z060ptk5vYktrE5uzaTYzzXQ5
-HUF6J6M7Y3S2Q3u7M7OtZGZ7SXumpNN2up1qnHaazqwaAW8lVIF2ltHKc1p5Rp5lpEnLyJp7mYrI
-cBx4e1+QHT9wv5RosTbg1ma2S+3HDyJaggFe1RdkV9v/CXh++udW4AkD6mBEMpMn9wXZdfvUvp9X
-19o+S3e/qu/lRwA3BLYDj+0Lsuvbvp8I2AEeP2gfIiIiIsNQi/ZA81u0Zxu9Z6fV7rUGZ1Zr0W7N
-bdEuihZFmTMbwM+2aNucruhe1lq13SmKgk63S7fTSQnKUks2kOV5ZN7OY/owL312vu1uLclZGv04
-m5V8drqwskzjobOU+IyYwivfwqbpXdmS78RW28qWbCuWlWy7uqD0bbTbHUqP+bpj/HROnht5lkOW
-0QQNspAAACAASURBVM08Fpx22aXrBU6Xgsg8XppTWkneigzs8aYzsryM6buq7uSWWr/T/Nl5njGV
-57TyPKYPMxRki0zG/yzwsx+kr0Z0z/5J+vc901en1328yfHAu4hx1fcE3tSwjgPfcfc/L1DOX2vf
-P4Ho+j2sB6av31igRb3yTeB2wB1HKF9ERERkHgXaC5rfObnew9yIoca9YDsjt5xW3qKVTzHVmmZ6
-apquTWFFjnuOk0GaJsyr/8Xg7TRvdDXdl1VN3bXM4j7785jPOwOv2thnU5DjXuBlkaYPozb1V7U/
-aq/nxBjzzUy3dmbLpmux89a92WJb2Gyb2cxmKNt07EqsgG67k5Kv5bNzdxtTZJalsdSxq+jSbkx5
-QdsLzAvcofCSrhNdyXEyS3OLw2y3ck/HpOpGT3q/GZGYrfov2rVFZIl+scDP6oFpvUW7yib+e3e/
-eNDG7t4xsx8Cd61t0+QnC/wMogv574C/Bd5mZkcCnyEC4zMWaAmH6BrvwH3MbNjspPssvsogJdHb
-fjF5WkRERCQUDJ6x6yIA2u32itVmqRRojyEaob3Xnlpl0q7mwTbSdFQtNk1PYTaF06L0nKLMcIyy
-tCijLGtLSmjmNhu4Vv9F8WkOrJLIKlaWUMQY7ypg782gXVuyCHwtjS8vyypn2hT4NM4mplu7sHXz
-nuy603XYfZd9mfZppoqcqSKnO3MNmU9RFtBpd2YfBLhlZHmLlpe0zMkzw7I86p1luBkdK5nyklYZ
-ida8LOh2Y77tPPfUsh1d6mczjafEb54SvlEalBmUOeYtjBYZU7R0kyqyZIO6Uif1wLT+C1dl7r5g
-iF2cV9tmkEsXKsDdu2Z2f+CTwM2J4PnQ9ONtZvZN4CPAx929P5iuot5RusBsHmHdBhcubXMRERFp
-dOGF6+dvrALtpaoC7irIxsnMYkzxVIvp6WmcKUqP7uOFpxbtEkocL0rKoqAsopt3nlktwC5nlxIw
-d6y06PZdgJvjVkZX9CrIrgXbkWstuoVnGWRmlJ4yjZcGtDA2gW9lempXtm7eg1122ptr7bovrW6O
-tZ2sXTJjjnmLsuu02+1oWcdwM7K8xTROmWVM5y1yz6Jrd94CyyLIToF4pygoiw7dDlgZQXariIcO
-aTg2VgXZ1VK18KdAOytzMm+Re4tcp6/IapvU+I1Bj697O3L/hZndGnhAWv6emEJsM3B4Wp5jZvf9
-/+y9a6xsW3bf9RtjzrVW1d7nnHvO7dO3uy8dg3lYQIyIcZwoJI4RQZYlEvOIiMQjshUEjWQQyAFC
-CGA7H2N/QCJfnIBMJ0YkliOMg4EQ83BCgDiOjUMwihLxiNtpku52uvveu3etNeccgw9jrqo6t++j
-H+c+jOevNW/tXbVq1aq16+ze//Uf4z/c/dNXT90vEPxXwL/5nI73TRERnj59+rbbpd7+MxgMBoPB
-IHh28tIb8/TpUz796U//khDcQ6l8BTxT4tzTvqNz29Ee3jVNE80mastRZm2KWzjUZo7VSOS22sJ9
-ThlNgqheXG1JaA9I643c1/ZvjNDqAWJCuMXhZkcPeRIhRQs1bRe1IggZ4YDILcscQvvR7Us8fvgy
-ujmmG24bTTfUc3e0a8z6Jv4y1pyxpHjOiDUgoQhJFFcJQexGwoANt5jXLe59bje0tov/OHa/EtoR
-QC7ging42uoTiZkk4+M7GLxH/CLxa+ZDX8S2exn22/VHvy0eoxR+tC9E5EPAtwDfAXw98A8Qaei/
-9eppnyESy2d3/7mv9BjejpdffplPfOIT7/TLDAaDwWDwy5aPfvSj7/UhfFEMpfIG7J3Zr7dq9tzx
-fcYztAgXs4a1jdZWrK7UtmFeI9ArIrORlENEW8a9XPqvu1B222d8RTeykBAxRBoqgqqiaqhGUHzS
-FOnbSUHoad0RLCZiXaDb1WgsidTzJlTAm5B0YUoPmPITbg9PuT2+yO3hMbeHF3AKdVOq2rnHe3/f
-EW8GNYrgwSvuBfNEbVAalOY0h5MXileax7my7vzvFxpqa5QaZeYetjtm3kehpXDiNdzrWBNZZrIu
-TDrmaA8G7xF/kQgM+2oR+cCb9Wn30VpfR/w6/YvP+yDc/a8DHxeR/wT4Xwih/ZtFZHH3tW/2M0RC
-+q8Wkezu9Xkfx2AwGAwGg8HrGeO93oI3G+6lAirdXbaCt43WNlrdKPVEbRvNa4jxpIgmJCW0i21U
-EZfot74ypqPZOoq+VTQEt+xrF9t9pVhJE6ox8kr37SSkenwdYjwnZcqJlMIhh0TShXl6wM3hCQ9u
-nvLg+AFuj0+4ObzAYX7AlA8kycge4ObE2DF3KkbFKN7YvLJ65WSFO9u4ayuv1pW7unJqG5sVqjWa
-2WVGeE8+b9UopVFrjDJrzWgWveRKImkmSUYlozKRZCbpQtYDUzoypeN79fEYDH458+P9VnjzsV0A
-/xTwwuue89zp4vkn+rcZeHz18I/22xd462MdDAaDwWAweG4Mof0mvOEkbeE8VkpxxBtYxaxgbaW1
-lVpXalsxa7g4ooKcxfaEakYk+vKi97ivXiItrrF2kf2M2L6I7HCz03mpXoWn7YIbIamQVZhyIud9
-24R0ob08I7Rf5PbwhNsutOe8kDQjcnG0zQlH253ixkZj9cbJC/deuLfCXdvO69QKxa4dbdunZNO6
-o11ro9RdbPfkdXZHO8eSjBJie3e0sx7I+hVmFg0Ggy+HHwH+GvGr8feIyBckiovIrwC+t397x1uP
-AXtLROQ3iMjf8RaPT8A39W9f5dk0so8DP9+P9ftE5Bvf5rV+vYj8xi/3WAeDwWAwGAxglI6/MXtv
-sDsuu+jex2J1mdhFtlsFK1E6Xk/Uck+p95S6UmuhttaFY/RFIxql2NLndHeLXCyBRMCYC9h57nQI
-WxeHJCG2XdApkfryfTxY8/P4b9G+JJLARRIqmSSJSROkxGF6xHF+gdvlCbfLCxymG+Y0k0RRN7CG
-d8ferGJuNMBEcAUXxbLSMpDA1WjaS927Q99oNDeatIh0ExCVPsvbKdUwKi4JFCT1s+1xjlRjBrif
-HfBKKZUtlagKGAwG7zp9bNe/CPxxwin+MyLyvcB/S8Q3/HrgdxGJ3w78zi9ihvVb8ZuAf1dE/jTw
-Y8Q4sE8BR+BrgH+JKBt34D+8Th53901Efhvw3wMPgP9ORP4IcbHg/yIuOH+E6PH+J4kxZP8yMTps
-MBgMBoPB4MtiCO3X08X0eVyX7/3Ye3922M9u7SyysRKhYXUNoV3uqa2L7bbRWo0xVYRQpotp7za5
-qCLZo0RbI0TMJHqhGyFSm9hZlKsoOqWz2DY3rCox6BpIe4J3bA8KnhGfyLIgaSExc5yecDuHyL6Z
-H7LkAwlBWohrbydavaO1e5pt8Tp9PrYniU9PVnwKse05ZmKLGLqXmmOY9F72fmFBVXAXmjmtNEpr
-vZ+dKImXfT55hLk5hnmj1o1NomPcrVHzL505eoPB/99w9/9SRL6dCB97APzevs6bEL+R/h13/wPP
-4SWFSBr/pjd4bC+U+RHg336DY/2zIvIPAT8E/Argn+3rzfbz+edwvIPBYDAYDH4ZM4T2F3DlZtsl
-yTvGeJ1rvHFr0CpYLL92tLc7ajtRWhfa3Q323cmW7sTuDd89HVwQSIrFNzSx3gvdMPFwd7sw13l3
-tDPiDZMeWuYeAjuBeBfxokBCfCLJkaQ3iNxyMz/hZn7M7fyYm/kRS1rIInHxoK1YPWH1nlbvMdsw
-bzHWSzU+OZPAJPis+CRYpge4yVWgnOEebraLd0dbwbpD3QwjSuw1KSkbKSmyJ6drWPRmjUpBHLyF
-s73pGI0zGHwF7KLyy97O3f+wiPwE8K8B3wx8FeEQ/zXC3f797v6/P4dj/V7gZ4F/hAhXe5nLfOz/
-F/hJ4OPu/l+/6Ztw/0kR+buAbyfGg30d8JRo3vkU8H8Qfd5/zN3/8nM45sFgMBgMBr+MGUJ7p/8Z
-uZcoW2shps8jtXrCNvG9e6PZirUt+rPrSishtGu5p7ZTXz2B3FuMALN9fBV9XNezUWvWk8itQTWn
-uVG9/50r0XMdKeZXyx3a3kAuoDFvW9R7f3UCy8BMkiM5PSTJI26mxxznRxznhxynI7Nm1AwvJ1q5
-o5VXKdsrlO1Vaj1RrUY5uxKO9qQwKzaFu+3Z+3naz9t+cSJGn5lfSsfFBXOnNqNaI2Ult0xt+yzy
-yw/G3GheI51dPMrHtw2VETEwGHw5uPv3AN/zRWz3E1zmUL/ZNn8V+M4v8zi+qH/E7n5HuNU/8uW8
-ztV+CvAH+xoMBoPBYDB4xxhC+3WYGbVs1FJopeAW7rV7C0Er4XC7G2YxZ9p8o6z31HLC6gmvuxu8
-0toJs1N3hDdq3ailUkujFcNdMBPi780Q07vhXZtRG1i7imbrpey1Wg9IsxDuRozE8kg0F7fwaSSB
-Z9AJ0YWcb1jSQ+b8mGN+yDEdWdLEJIK0DfNCaXesr/0id69+htde+TSvvfqL3N+/wlZOVG80BFPB
-U4hrT32p42Z9XJn18ns7i21zO/dpn9vUz0Q5OS6YS+yHRnMnnav4hebW3x+jR3swGAwGg8FgMBi8
-LxlC+3VYM2oprKcTZV0xq3irWKuXadICguFecN9wL2zdyW4lZmlbXbFywsqJZuvZ/a6tUEql1kYt
-3h3sLkyRKLUmxHS4wIQIFz+PATP33f+Ovm53zARMEU+I21mIRshahjZDWsjphlkfcjM95mZ6yCEf
-WXQiA9IKVgubF06vfYb7LrRfffUz3J9eYasnqlsEtamEyM4huC2Bpz62qwv/ENsWFQGvE9ii+xdx
-3vfa1LjwQAh0a716gPO7xaJ03Jv10v7BYDAYDAaDwWAweH8xhPbrMDNKF9rr6R6rhVZDgAp+DuiK
-YVflvGq7p9ZwtK2FwLbtRCsnWjvR9vFfbaPWLrSrnd1ssxDQdhbYjqiFg94FqUhoVtE+Xkx2+el9
-DreigLlE6bgZeEI8AxPYQppuWLrQPk4POeQbFp2YBLwWrL5Gq6+xvvaL3L/66Wcc7bWsVDNcFFeP
-pPFele7JMRWaGA3HmmHWRbaHYJakaJI+fozLDDVgT4dzNIR6i3241XiIHiBXjVoqrVSs2hf+AAeD
-wWAwGAwGg8HgPWYI7Y6799sQiGaNVutZZLe6hUssPZAMQyggIbRbjRFfVjesbLRtpW4n2hoCvJaV
-Wk+UeqKUlVIqpTasCc1CcDejl1eHK6zJ0eykxFmYingI7my0BC15nxRm537yVj0y2hp9xFaEk1mO
-KnLmCCinNrysNL2P0LbtFer6Cm17lbtXPsv9K5/n/tVXWO/uKNtKbbWnjsexuHqM+VLHe294OO+9
-TNxad7VDaCuOoyBxUcHdu3MPzYxSGqIFVe9OuCOuGNrfg/ZZ3oq50IahPRgMBoPBYDAYDN6HDKHd
-uQjtvf+6XVarWK0AqMRcZxEnxsU2hH2b0oX2Gm72ek893VG2E2VbL6ts1K1QSqM2oTWhVjCD1vuY
-zZ08QZ6ENEEEbPs5tNzUUHWqWne2odvaYSA3j95uIUaFidMmw3LD5wql4GmjiVI8RnaV0+fP6+7z
-n+X+1Vc4vfYa6/09Zdtozc5jyZ4R23sJuNJd6t6bbZfz6NZIpEgetyuh3cPfamtQSvRkq6Aa51pV
-ME+oJIzUnW0/jwMbDAaDwWAwGAwGg/cbQ2h3zkLbvAeg9RC01mit0nqPtqPd0e7p2jREGlZDjHsp
-WFlp10L7dGI7rZR1ZVu7yF4LZWvUGiK7FGKudC8bN4xpUaZFmWcJoX3laFeJZO49ofuSXU6EoLlE
-a7SAKEhybDZsNnxreKmYrlR3kjUMZ737HNvd59juP8fdK5/j/pVdaJ8oW6VZC0dZAPUrsc1ZZPve
-YX4ltFurIbbFSZIRjXT1i6tNjPmyitQY75VzX5KwLrLF4yTEPG5/tvR8MBgMBoPBYDAYDN4nDKHd
-aa2eb/dlfT0bhqbhaOMIFaiIVKwUrGzUstLWlbqu1PsT5f6eetr6KrS10lajbY5tQitO3aCWnjLu
-RvMQ3H4ADqCLkrKcE8/DTfdIQY9ibGSf7MVuOROCOzmSDEmN1hq2FOwQrnsDStugZLDGevc51tc+
-x3rXRfbdHdv9StkKpVqUuEceGRCjtvYYs+hZ74npsjvvl8ftnBZuNJOzkx0IZjEKzJuRLPVpa3Gu
-97p5Dys/etp7GflgMBgMBoPBYDAYvN8YQruzrfcAlO0+xHLZaHWjtQ1rBbfSZ21rr5s2nIJQcApt
-W6NEfD2xnk5s9yfWu5XtbqNuDduAqmjLqAnZIhF8T8/25lgVaH3EmMUP55xKnvxKuEb/ssVML5xe
-RS0h/89C2wVJDZsqlgvOiq3htLf1Nba2UUXYRPBWWe9eZb37POvdK9zf3bGuG7UZzaC5U90pez64
-Rwm4GNC8H0UcV1LQSdCw3nv5ejjrzi60w9VGFE2OXc0Xb+ZI9Z6m7iSM4kaixUUFJ8aYDUt7MBgM
-BoPBYDAYvA8ZQrtTtl1on6jb2sd0RRDa7mrv+lIQxB1hwynghbZt1NPKdrpnu79nvT+x3a2sdxte
-watDTUgVUlPEErKHhTXDmqEVqE6rTm3hl08ekl5TjBTbhfY+OsvNzq6vi3RHm3MjdcsNnSo+K8aG
-rSdsvaOtC15TiHx3Wi1sd6+x3b/Gevcq93f3bFvpyejQPDrSK7szHY66m3fH2WMMuDuqguRIF3fZ
-i+xDFFsoafYBZTE3XM9jvMwNaSHqzZzUDHVDrKEuJBGyQlZFR4/2YDAYDAaDwWAweB8yhHbn4mif
-qOUUbnbdsFrw1ldvDQ4Ra11kb0h3tOu6did7F9nhaIt1Ye2KNkWaI81QS7g1rMWq1WFzrBi1Rqq2
-9R5mUgjiENtdnPce6L2QXWNAdc9EC8Etk2CzYA1cFDt1R3ubQzi31md6F7b7O7a7e7b7cLPXrVCr
-03rCd3WndomsDro72l1k0/PJkkJSwVJks5sL6nKer23Weq97QkQR0XC3reF+mcUtrb+eGWKCWiMn
-xSdFsqI6HO3BYDAYDAaDwWDw/mMI7c5rr34OgLKe2O7v2e7uKesaKeItBLf0Ou4oXQ5/Fw9Hezu9
-ynp/x3Z/om4VDLLMyARKRj2jZLw5batYKjQptLZFj7f13nBrVGsxrxpFVJmSMmdQN5QQrW2PYuvl
-1tLvj+MLh9ldovbcLERsrVhZqes9232OSeC1UVuLPuzTiW09UbaNrVZqs56rpqAatd8WvdHNDKsg
-EvOxRRVJChr92WcR3A+hmdMa1Ba37oSbDSDhXscsccVMevVABNOFnW7QGodlIutEXhYOy/zuf1AG
-g8FgMBgMBoPB4G0YQrvz2qufBaBuG+X+RDmFQ21nZ3tD3MAccYuvu8jGK9vahfZppZaKmJJ1YZpn
-kkwkmUk64dWp6USRU3R4lxpzsa3SrFAtQseqGS7h2uacmLMQ2dtRSl2lRc90L/0GOKeDeYhsOfdS
-h9Cm9cC29Z5yn2g4pTa22mKu97pR1zVuzWnmGIKLhtj2ENsuUTLupU+3tihtTz0MTSV0uV+Vi9fm
-1Aq1eh9l1lPedyv8jGAWDrw1C1FewWrDK4glbpfElI8cDzfvzodjMBgMBoPBYDAYDL4EhtDu3J2F
-dqGu23lZ3bAagjvUXxfZFgpQroT2tt6xrSesVqaUmXQi54msMzkt5LTgzdg0hyh1Y81rOLpeqa3Q
-mlMtepSd6HeecmKehIyQPNbmjlgCi1A030PTehW3uOP9NcQEaUCTcLS3xHYSqjtbNbba2Ert771Q
-txIuNBpCWxUn0r5BMell69UQhzTl3ieuoOFSp7T3YHdHuzm1OluBUmLGdxxrHLRqXFRQDaFd+3at
-NFpx2ua04ky6YJbI6cDx8OA9/MQMBoPBYDAYDAaDwRszhHbndHoNAK8t3FOrqPQmZHGkB2+dXWJi
-zjYe2+4zt62G661JyTqzTAdymkk6k3TBqFRdwxkmnN1mRt1Hiu27J0RnSolpykyTkt3INHQX1hau
-L7b3cu9Plr6PcLZ9n1fdw9O894Sbe+8Pj15v633fMc9bMAlH2wiLWjWTVRCpNAXEEBWyKjllpjwx
-Zcip92gb4Ak3ibLxCrV4zAyv/Zi6I5+S9/cbbnYpfeTZRgjt4rRitKqozCzzA26OL7z7H5TBYDAY
-DAaDwWAweBuG0O7UsgIRdqYKeU7opHhTsARtir5sN8QMrNLqSqtKK4LbitVE0Uj7ThrCc55mRDKK
-9nFVRu2l2utaWEthK5W1NEozzCKFOyclp0TOiZwzOSvJjYRGAncixGly3BuOgRnGpZTcHdQvxdne
-XWdRRTWRHHJSjBYRZy2cZ1Hv4h2qQROBfixLnmhaYglIFuZ5Zl4W5nmO0V7qqEJFwRvW4hy1Aq3G
-7PBWw7mOcDRoupecezjaVagVWgWrirW4X5iY8g2H5SEPbp68dx+YwWAwGAwGg8FgMHgThtDulC60
-J02klJlTJosgnhDPiLdzb7a44a1QNqVuQkmOtYm6JVLvYU4pkfPEPM+4aV/QWgjtrVTWrbBulbVU
-ttp6+FhChO4SJ6b0eqFtiCnJQhSnFEFimF/GZ3OZSd0f2rPCQ2hLQjUBYKJkidLwVh3tUeJO9GhX
-i1HYSCLlxLIoTZWmQhPQCZZlZlkOLIfDpX4do4ggXvCWQmhXaAWsdsFdBWvhYEsX2SLhhLcGrT/m
-TfAWs8ZV5t6f/Yjbm8fv1cdlMBgMBoPBYDAYDN6UIbQ7u6OdpxmdEvOUWKaMYmgPIRO3GKTVhfZ6
-gk0NpVG3zJYSqhrznzX1cuo5BGYv37ZdaG+V01pCbJfK1h1tV0VkL8dWUs5noa1uJDdEDW2OJkNT
-zJ1GHMeihHzXuntyN1diWyQSwjWhIiRxXCLhvCZDNfbvLfZVzWgCkMhpIs8TLQkm0NTQSTgcFw6H
-A4fDTZSfN6PVhgL4GkK7Snend1c73Op9iUTJu0RwOq073WZCTDSLZm9hIqcjh1E6PhgMBoPBYDAY
-DN6nDKG94xUAIaHipBRl1UkiTVslxmeF0BZMo1+4ZSWlKHuWfVwV4H3Gda2VVv28tm1l2zbWbeO0
-baylsRVja+Eeq3IOBUt9aS/3lksNOKIKmkBbf5KELSxyFteOd0fbz73f7jHXOt6sICLn19OU0eRI
-cmgNo8VULRESCdFMShMpg2fwLORZORxvOR5vORweUEuLpY2yKVMqJF1Jmuhd6bh5lIK3XWg7oDEj
-XPoxmtBMdnP8/N5ba5RaWbeNdd3e5Q/JYDAYDAaDwWAwGLw9Q2jvWNu/CKGtwpSEpEoSJ4mgrj0a
-TGjVaCVR00WoisBuybo5rTVarbRq1GI9CCxEdgjtwqmXjZfmtBhaTRbISc5J3KLRty0C4hKiU7v4
-VkWuRTYSJeRdXBuX8vFLGXm424JGULhDSor2nm9NDhr7aeY0EZSESianGZkUZkHmxLQkjsdbjseH
-HI8PKWtl08pGZcswpZWcJlQySkN6Pbsb54sPtcSZFzSceY+QuH0EuBDvWYDaGrUUtq1wOq3vwQdl
-MBgMBoPBYDAYDN6aIbR3dkfbGypOVshZyeIkhSx7l7OE0MaouZd3a3eGrxxtc8O60A6X1yilUbaV
-rXSRvfYe7RqOtjso9NLxi5ut0sW2a5RYO4i27mpH33XMt6aL7BDYUTLuXyiyPQS5iKCEQFcu4Wqa
-CGcZp3mjIWQU0YmcZ3RS0qLokpiPEzfHW25uHnA8PmRNlSQF9cI6OTndk3UmSerH3tPPW7jarSeR
-7+dNIJLO7XLM+zlRLo72NhztwWAwGAwGg8Fg8D5lCO0zBtDFtJ9vVSBJL+fuPrC44BrCT2BXr+dx
-VdYM00aVSmGjlejLbrVRa+mrUpr15ZQWO5t6mXpOu9Duh9dFsp2/jn7rfXY1Et9fVZdj/fj28vFm
-4Zqbh6NsfWTZ2YV3OY/zak4cV4UqTqowNXDrTjipn5sJlQlhRglBnTVhKTOlStYZldyfo6gYiqLa
-Z20nkEmgPx6jyeTsxLv3cy9CEmWeMypCa41tG472YDB4f/HJT36Sj370o3z4wx/mp37qp97rwxkM
-BoPBYPAeMYR25+Kohro7z8vee6I9HtvX/pgbVzOtQ2S3ZlSpqCvqRJl5jfublSgpN6Puqd4Gpfdn
-QwjLqfeHC3sZuCP9uNydtrvWwqVcnPjaYtgX1o+7uaMG1XaxHUtb1GO7eBfiRm3ehb9TqkdZO0Kq
-xtTfh1SgglTp482UloWaBKsJ6ePNshZUJ4QELr0WQFF1clIUZdKE2UVk75cz9rJ3EFLSKOFX5fbB
-kWlOgFHKcLQHg/cjIvJtwA8Qv5a+2t3/6nt8SO8aZsYv/MIvvNeHMRgMBoPB4D1mCO1OBHVxFrOc
-R3lx7g+OILTLdnu8t9tlWXOsNpoL1WI7q3ZO47ZWaFYjzduc6k5xp5qTuoxPV6Xj53Jri5Fbu8a3
-c+DZ7mTHre3C20Nsi+/zsJ3UBba16IHe35hLjAjbBXg9i2xjLUZFmIpRayzpItuaYFVpValFqSq4
-KeKZrEJOhSQzugtt197/7pAFSVOkqpGhF+VzrieIiweIxDzxvm5vj0xTwr2dR7INBoPBYDAYDAaD
-wfuJIbR3dgHdx3ddhHQX2X7e8Cy2z462hVC15pgZZkYjxltJF8khsh2zill3tP3K0Q5VGf3SqkxZ
-SbqXflsEpfXj3HuwDS4haFcztPfHdkHezEG7kG7hXLdmOPtz4/5ajVIbW2ls+20xKjDXRilGK4Zk
-IAFZ0CRUhSJxV4SmpRhvphtJLo62aiInxacMUyLrgZQWks68Xmjv7wlVppyYcianzO3xwIPbW+Zl
-RtNehzAYDAaDwWAwGAwG7x+G0O64+fl2F88htK8ULNBTwsCuRHbbt+eqRBoi1gvc940b7hXzFmK7
-dYHu0RGOKCmFqFxyJongZtRScOul1RLH0Cwc7V1onxPHo7j84nr3/nG6U11bi1C2UpFmZ2FepXBE
-/AAAIABJREFUqnF3X3jtrvDqXdzer5W1VEyEbWvMW2Xb4vvqkAxqabStUddCnQvzrCxTJs8TSefe
-vz0hnjnMMzeHGU0T83zL7c1Dbo6POB5u4dzxfuk3j7s03OwcjvZhXrhZFo7LwmGe3p0Px2AwGAwG
-g8FgMBh8CQyhvdMd47PI7to4aq+jWfrsn+4a3DzSsVvcunOW2CF59x03oOF0oW2192hb77UWTARU
-0ZSZpsw8ZZKG0C7FaS2c7n3U11lEX4ns/QjPjrd7VIebg1i8ZrUIYisVRMLx9ujFvusC+9XXNu5O
-jdPaWEvDVZlLiOx5LTRXtAlShVIadauUuVKmwu3NRL5VZM4Xoe0ZmFjmW47HBxxuHvLo4WOePH7K
-k8dPefToyfmsnYX2PrJMo3Q8aSKlxNwvRMwpMaX0bnwyBoPBYDAYDAaDweBLYgjtzrWjTRfN1xHe
-l9Lx/phxFtmtOd66MPeLn31W7zSc+oyj3Tx6tPdQM0NBEro72lMGDPcYDSYqaIrHRfeD6Jxdbfao
-tt6j7cgeUy4eIW2t9fLwGGfWLAT4qdRwtF/bePW1jfvNuC/OVhzXxFa6o71WxBTp9eJpbdSpUqbK
-NhWSGIdZECaSTigXR3uZb3n08EUeP/kAT59+mA9/6CN86KWX+eDTl9gFtvT3cy20IwgtoUlJPTZN
-/XKWB4PBYDAYDAaDweD9xBDaHSstyqhzw2rDa8OqxczpRHeMrwLQKt2ovgyoFpEYRaWKqEeQmcTc
-aDOjWaO2vmrMgxZR5nlBsnB7mHn48MCDB0cePFgodaXUjVo3WrNujvt5HFeIf6eUyrZFP3WpRm3W
-y8S7z+2GmlNdyKUxlcr0jND23o/dWEvlVCqlRTm7JkGmiTxNpGki5ykuG1SnVUNTxVrDo36eVsFd
-UcnktDDPRw6HW26Pj3jh0Yu8+OJLPH36IT740od5qa+nH/jg1U+ilw7oXj7e55Pv88T3DLfz+x8M
-Bu82IvIY+LeAfwz4W4FXgL8AfL+7//AXuY8F+BeAfxz4lcCLwGf7fv5T4OPu3t6pfYjI/w18FfAf
-u/vvEJGvB/4V4DcCLwOzu+sbPXcwGAwGg8Hg7RhCu9O68LTUaFOjlYalhpliprh1/7Q73F4cr+Fk
-Y7s2FDSFONWemO2Es2zWBXCtlC6yS23olDksB26nAw9vFp48OvDCCwsPH8zcnxKcoLZKq5VG6254
-d9i7q117Wfe21Sjlbr0svQeeiTkgTA651LPYRqT3iIco36qxVWcrRiNBSuRpIs8z8+HIfDgwHw6s
-tVJKYS0VUQFvqBg5hciGhOpEzs6y3HB7fMijhyeePH7K0w98iA+99DJPP/ghPvCBD/L48RMePnoU
-p3avIDj3Z3dXG6JXnv19D6E9GLxXiMjfA/w48BEu6RUL8A8Dv0lEfgD4U2+zj78f+M8JoXv9L/np
-vh/gYyLyW9z9b7xD+zinb4jIx4D/gMh03LE3eM5gMBgMBoPBF8UQ2p22henRUsVKw7ZdaBOu9nmm
-9u5ox6L1de1oJ0Wu/kbbk7/Dxd5FdqW0ynE5cjweON4+5PGDGx4/Wnjh4cLDBxMolFbhdE+1fb51
-CPY9DR13Wo1ws1Ij6Ow8Osxi5jaEI18M8hxu9lxq7/WOMvNSnVItRnpVg5RJKZOmmeV4iLUcmJcD
-xU/YWti2hrmj0sjJaRO4CUJCJJOzcJhvuL15yLpVnrzwAZ5+4CVeeullnn7wJZ48ecILT57w8NEL
-5/N7rojfxTbRR34JqNtHq3FJfx8MBu8KIvIQ+BPAh4nfiH8E+EPA3wC+BvhO4NuBr32LffydwP8A
-PAI+B/x+4M8BPw98APhW4GPANwA/IiLf+HpX+nns44pfA/x24P8Bvg/488T/N37jF3NOBoPBYDAY
-DN6IIbQ7rZS4zRNWClZrlJB7Lxs/i+0oFffiUB3vpeP7rO0Q2nJ2Xt0jWby2KOsufRZ1beE455y5
-uTny+PELPHl0y+MHMw9vZ25vElsp5NMJ0YxTadbCca6G9B5lAVqF0qKcuzRoSCyRcNN7cFozY2tO
-sRgrJrpfN5CY+91nf4fOVjRn5sPCcjwwHxbmw8K0zKRaQTTc8L52QS+9zDulDJJYlgM3x1tKrTx6
-9JgXHr3Ik8cf4PELL/Lw4SNubx5wOB7PInrfz7WbHbO/jdYupfsyhPZg8F7w7wEfJUT273b333f1
-2M+IyA8DPwZ881vs4+PAC4Sg/WZ3/5uve/zHReTH+n5+LSHc/6N3YB87fy/ws8A3ufvnr+7/n9/i
-PQwGg8FgMBi8JaP/rNPKPa3cY+WE1Q1vJRSstavbrkKr4dXwZtCsi0SLGLI+pWqfCmYeTyu9JHst
-Tm1gLqCJeV54cHvLi49f4MUnj3n48BHH21vyckNejuT5SJoOaF6QtIDOuM5IWtB8IE03pHxEdMFl
-xslIWkjLken2AfnmFl2O+DTT8oRPMzLNpHkhTTOaZ0gT6ISkDDmWzhPTMrMcF5abhfkwMy0TecpM
-c2ZeJpbDxOGQWZbMsiTmWZgmZZqUlJScE/M8cTgsHI8HDsvMMsc+UkpIL61vZjEr3Nt5DvlFxNNF
-ttFaizL6fVmswWDwziMiE/A7iF9tf+F1IhuA7hr/80B5k338BuDX9X182xsI5H0/fwL4YeJa4rc/
-731c767v5zteJ7IHg8FgMBgMviKGo92p5R6ANiWsLdA2xOYIPBOgaR/5ZdA8hHa1UNLn2V49AE2B
-dmkArBal2evmbFu4zubh/M7LzIMHt7z44mOePHrAzaQcZ2FSmJYTeb7vQtuRViAp3hpoQjWRNeEU
-pF36wTVP6Dyhc6a0Rts2vJRwi/OETDM6zwiCN5DmkGoX2w1Si+CzQwjtw83CcgyhnebEVDPLIVPa
-hLtwOCTmJTHPyjSHwM5ZEdUutGeqHTgcFuZ5YsoZVY0e9u607051n1kGgLjGxQrbE9Nrd7MvE8MH
-g8G7xtcDT4hfax9/s43c/RdE5L8B/tE3ePhb++1fcvefe5vX+1PAbwO+QUTU3fd+nOexj2t+3t3/
-p7fZz2AwGAwGg8GXxBDanbYL7ZrxesSt4BaBYTQB9Jz67V1ox9dXIhH6lCrHxDHi7tq60C7GVoxa
-oXmM85rnJYT24xd48sLDENjqJCxE9tnRNqQpVMW1ISkjaSKlTPMNUcel4uKQFtKykI8LXitFE4aA
-NbwL7TQtIbTFMBxUIBmkhuSGTt3Rvlk4HBfmw8S05HC0l8zcJg5daIejrcxLONp5UlJOpCuhbX7g
-cJiZ54mcrx3t6GGPJuyL0BaPCwcg4Whbo7V2rhwYQnsweNf5+66+/nNvs+1P8sZC+1f3279bRL7Y
-sLGJSBP/9HPcx44TCeXPnW3b+Omf/uk3ffwjH/kIH/nIR96Jlx4MBoPB4Jckn/zkJ/nkJz/5tttt
-2/YuHM1XzhDaHZ0ibDZNik6KZkWzRAatgifvc6r7amDquIaodiVEbk8bV1Wk9z+rJByJMmgXJGWm
-ObEoLDe3HG9uOd4+4HD7IAQ2hnhF54U0LaR5YVqMWTJNZ2Q2lmk+r7JuTPNEmhLrtjLdHplvj0y3
-R9a6Md29RrrLtFo43C4sh4lpTliLQLfaKrXFfG/R6M/O3Z2eZmGahZwd1QYCKRnzDO4x1fpwSBwO
-ymER5tmZspG0IWqIVKASlaT7TDTj3NQu0dcdZQDn2HEg5pF7T30TolrAsfPXMkKBB4N3kxevvn7D
-JPAr/vqb3P8SfMlXyBy4ec77uOYNS8+/Uj71qU/x9V//9W/6+Hd913fx3d/93e/ESw8Gg8Fg8EuS
-7//+7+d7vud73uvDeG4Mod3JS5yKNGd0TuisyKyIaAjtvfeavjJ4AtMQ3PY64SiikBTEUQ1Bah4+
-rKbEJIrPmcPN7XktxxvUG+INrJLmw3nl5szZ8NnIDY7L4bzKupKXiTRlTuuJw8Mbloc3HB7ecr+d
-mA6ZNCtlW7k5ziyHiTwnylpxGrUVSi2YN1Anz4k8KbmL7Gl2Ug7hDI2UGssMKWVUEsuSWBZlmYVl
-dnJuJC24CMIGbJivuBeceM2LYN6FdkekJ6rLuXtSxGMuuV0EtogNR3sweO/4cv/x7eOzfhb4576E
-5/3Cc97HNW85q/tLRUR4+vQpT58+5Qd/8AffdLvhZg8Gg8Fg8Cwf+9jH+NZv/da33e5bvuVb+NSn
-PvUuHNFXxhDanTTH3246J9KcQmxPGiJQBBfHuyB06ZXOCSyFq+3qMep5F9oqqEsf+ZUAieegaJqY
-pglNSwjsmwcst7csN7c9eK3irZDmBe0rGzA5aiFRbw833B5jbetKmhIpK/Np5vjCA46PHnDz6AF3
-2z1pViTBekoc58wyZ6ZJiZZn60J7wzySyNP0Ojd7AlWL2eA4KTlJhZlESpl5ziyTMs8XR1tTjZ5w
-KbhvuG+YF9xDaJ+Hj4sgeo4/j7R2efZnIwpiINq3EUPFoh9+MBi8W1w7vx8C/spbbPuhN7n/M8S/
-8AdfRH/1m/E89vGO8fLLL/OJT3zivT6MwWAwGAx+yfHFtlXN8/wuHM1XzhDancPtQwCW4y35cETn
-BZlmQHFRHO1C2rsmbPikkBVy9HFLEqR1cd1Ln7X/1/d50ECaMjItzMsNh9vjOdU7LxPeIqDMqqHL
-zHRcWG6PeFYMoTkgyu3xyM3hhtvjkbJmJDUkG/OaOT56wPHRLcdHD5jWFEFnqXK6hxsVliQkFWQj
-erSt0ayBKCkrE8p8SCyHzPGYON6kcJK7uFXV88zwlDLTlJhzYpqEZXHyVFEtmDmqBU2VlIyUDE2O
-ahfP2qd4ARd1/XrxHDUE8fqxmYrQs9QGg8G7x/929fU3AH/mLbb9hje5/2eAfxD420XkJXd/uxL0
-d2ofg8FgMBgMBu8oQ2h3Hj4OA+YwH1gON+TDDdIDw+hCGXfcjAiudaQmtCa0JZIn1BSpvdy815jH
-iKp9fJXh4uEY38zk2wOHBxPTMaELyNxD1tRwMdJRWOrMDUemknGJ0nNEOMwLyzyxzIk0ZywtyHTk
-sCnLg4XldmK+VXTOmCxIuuF0gMmMyRraWjjE4rAL6ARZFVLmeJy4fTDz4NHC7e0SIlu9O8mCSJ8Z
-romcpkgaT8q8ODlXRFZEouR8XqB5Yunp5JFMrqiG8x/SOtzqmFW+/1TiwkT8R84l+SqgCqrD0R4M
-3kX+POFqPwZ+O/Dvv9FGIvK38OZztH8U+A7iytq/CvyeL+M4nsc+BoPBYDAYDN5RhtDuPHz8YQDm
-PHGYZvI0o2mKOnEi1AwaMSa24WrQMtIy2jLaUghvTZjU/hwHc8z7TGg3XIU0J+bbmeWFI8vtHEJ7
-FphC9LqGkM8HZfEJS0damyMZvJdaTykxpcyUlFoSMs3kw5FaMvk4Mx1iv6nEXO08G6ejIusK6wpr
-O/c+Iw7qpCTIpKQpcbiZuHkw8eDRwoMHB1QMFUdl76veHWklpUzShKowT7vQPiEKOdcITpMutOc+
-ZzsLmuTSn+1xvtzsLLS9N2nv6eMi2gU+pCRDaA8G7yLuvonIDwDfCfwqEfnX3f37rrcRkQT8QSLl
-+4328SdF5CeBXwP8GyLyM+7+w2/2miLytcDf5u7/xfPcx2AwGAwGg8E7zRDanYePXwIgS8ymzhrO
-tHRnOqZ3NXxPz9aG1Iy0cLS1JnTTXlatMdHawdtFZDe3KM+eleVm4vbRgcODmXxM6OLIZGeH2TGS
-K0uakOWIu8W+UzjBiqBEVnduKWZeF2htikC3KQLQcs2RWn6Aw0mpr0KlUep65Wh7d58hzQpL5niT
-ub2defBw4dGj5SyyVfwcYrb3o6skRFJ3uI2U6rkkPKVwtCUnloMyLZFonrOGWD7rbO9C26+Kx3uZ
-fv9Oepr75XXepQ/HYDDY+b3EXOqPAr9PRL4O+ENECvnXAL+TmLf9U7x5+fg/A/xZIsX8h0TkjwN/
-FPjLRDDZS8DXEfOyfy3wfcDrRfLz2MdgMBgMBoPBO8YQ2p3KCQjnuplSXVGRSLruehQqLg2n0trK
-yU4U26heqVZp1mJUVqm00mhbrG3bqN4ggSYlzUJeYDo4kjcad5zq5/FtwlrBWsVaoXqhUWhaoqxa
-w9V1icFWu9A2bViq4BXRBtnwVDFJkBqaK9kNM8cnsEyEtWUh5USecoStzRPMCeYuhhdlWoQ8C0nC
-UFd6b3QPIhPZ+6ctiuy1ISpdwAuanUkFMWXqaeYp7W42vTXbL0vsXCreX6AnkPdud3VUPQLURhja
-YPCu4u6fF5FvAf4k8GHgn+7rvAnwA8Cf7rdvtI//U0R+HfDHgK8FfjPwW95o074+907sozOSHgaD
-wWAwGLwjDKHd2YW27ZXUvTtb9iRsd8LRbrgYrW1sds/mG5VyEdutUWulbJVyKtS1spYSYWMJdBbS
-AtMBpqMjuYTQLhmThLWKWwuxbRWjYRql6HsAWYzNir8ejQg08xTHhluEn6niGnOoNRkJYzKwCVoG
-TSApws/ylJlMYMr4lGFKz6SOT4vEOPEutC9/mnZnm+uxW62PQYtLASkLuJKk73N3ss9haPtVjN3J
-vxbPMUn7IrYJR30X20NoDwbvOu7+cyLyK4HfBfwTwFcBrxBhaX/A3X9IRL6Ni8h9o338FRH5VYQ7
-/lsJ9/uDxOiuzwB/Cfgfgf/M3f/Xd2ofb3WMg8FgMBgMBl8JQ2h3KvfAbrA60sdNYVdfEyFlLoa1
-QrET1VaqV1oX2bvQ3raNbd1Y7zfWUqhukASdhLQI+QDzwZFpo3LHWp2GYhZ94G4xaxqJfm3EUeki
-u5dlG12janeC1bswjbnfLoAKkiFrzKeuE2gOkatpd7QnsivM+RlH+1psPyO0gXPvtNDnWRsXBe7g
-0svJc1QGaGI+O9q9/Ft4RrRfLh3sP4mIFg8xvyedX0S2jB7tweA9wd0/C/zuvt7o8Y8DH3+bfThR
-7v1Hv4Lj+LL34e5f/eW+7mAwGAwGg8HbMYR2p9jnu9bbk6/9WcHdk8ZdQmC6NWrbqLbSWqFYpbZC
-rVE6Xkpl3QrrVijWMAxPIJOQJsgz5AUkNVxWioE1uYhs7/3axG0Ia+n/65XZ7Dp1F9ix3fnW90fC
-PhaN5HDZBbgqmhKaEimBJwUVXOmOs4Ma0sPZdgc73OrLfY7vR9UT2eUcZCbE60ji/NrnknHZR2lH
-GJt7F89n/RyueDzHr0T3YDAYDAaDwWAwGLx/GUK7s9a/GV+chbY9K7R39ddHbLkZ1QqtVVor1LpR
-aqGUQqm1r8ZWG1WMtovbScPVnmCaIWXDpdIc3AQ8nOwoA+9l1d77off/+WXy9F7efhbb1/cDuOCu
-uAnN6Knp3kdl9SRvTV38xqxwJ8LbzCvmlWal39+T1Pde6rMLfXlF7/3VkdLuiAtiEZi2j0Y7X8jo
-BytdfWvqLryfB36xX15ALhcTzq/rZ0U+GAwGg8FgMBgMBu8bhtDurG0X2ldCcJ+XfRbae2Nxwg2a
-tXO5eKmFUgu1xColxPbWGlUdS+BJkNxDyCYhT6DJQCpm1r3hcIt9d4x9d7UlyrG9l1NzqbqWPmxa
-zquHlgF0ke2mmIHt7+ksshWVhOqVR+2Oe8O8dbGdzunffg4ve73QhmeFdve1XeOYvXXx3s/v/oxd
-aPdss/0Rv7pwAHsd/P69XQT/1asPBoPBYDAYDAaDwfuBIbQ7pUUo7e5mu1/EpJxFXwIyeMZNaWY0
-M6xZLxvvpeO1UlujNKO2cLNNpPdLKzrpRWhr9GEbcukD773Zfi1o/SKfpXdK7762+H5850Jx3HsP
-tBtuirthTjjKe2/1ldjeQ8n213TsLLSbp9jnLqQlzpHIXjp+JXW9y+OuiXUX2paj/9wMN4sLC97X
-9cUF6Y573/Nln3Let/TX3oX4pW98MBgMBoPBYDAYDN57htDuqGi4sOLdo73cPuPcdjd57y/W3rcd
-I67Ym477g4KrELOxYJ+RpRqBYDmlnvd16V32qzLsEJtdRvZAsKs89Mvd54bsLwzQPXvcLrj3ed5m
-4cS7sct5V8F747fvQWdX7/ziJYNbF9jeQmbL1Xk5e+n9+Pcqb5xmlVJX1u0OSQvNjNoqqWzgMZ7M
-vcLlqM5vy/fSevwssneh/fjh06/shz8YDAaDwWAwGAwGz5EhtDvSQ7c4SzzrzvEu+CzKxrvYRq4E
-8jOu96WXGxVI2vVxL49WjbFamkgpRe/xPuKqq2ZBr2TufruniO1iO7aF6x7t6+dwcZbPYtVxC7Fd
-rYUb7x5meRfLjkXgm3C+RfrYs95zbV2wRzL6pQxdRUCsn8ves70fizvWKrVubNsdyESuIbJTXvFd
-ZHu99Kd3we1GzAC3eEPSw9728V6Pn89HYDAYDAaDwWAwGAyeC0Nod1TCOd6dXBGuysdhHzV1icp+
-VmRzLsfmPJkqxLWcRfY56bunfaeUY//XZeKi7Gnbz5ZQX3de78XS+/H4OThNzup6Zy+/DnfY3C8l
-77ujLXIlri+Otj+7h91gprljLfbhbvF+1DFRVBS9MuH3Cwbu3dEuK9t2j3um5oKmFdUZp5wd7Ugu
-NyDS160LbYva92cucAwGg8FgMBgMBoPB+40htDuyz43y6xRt6LXUl6+vvnLfe6KdJJCSkKZEmhPJ
-QQ3UHMmCTOBZmKZEzomclKz7s8NBdxTZb+UideXcoy1feGz9VoQIS5N91JZfhK5fie1d+orjSoSz
-TZBEITmeBE/Sx3vBZZ6WnPPI3KGZU1v0sic33ENs70UASfZYtnDcHaO2ja28hp4+S60FTROqE6I5
-ytC9gvc54ti5Vz7cbMf8Wmj7ENqDwWAwGAwGg8HgfckQ2p1dlO6y8uLiPhv0tbdfu0d/9p5Orgo5
-K7ZkWnOKCBkn4ZAFsuBZmKfM1IW2qobA9itf3LX3hz8rJK9yur8gZftcRt5FtnzBY74b7r06vbvv
-iRg15kLOgO5XB0CzILqXyeszB7H3TFvbR6HJWcvLdYm77K8e56jWEyKv4AYp3SGaUc2I7EK7XQLS
-+ng1N+8p6CG0pTv/u9geDAaDwWAwGAwGg/cbQ2hfsfvAFwf52bLp0LJXUWTee7ndSAL/H3v3HmZZ
-VtZ5/vuutU9kVSGCWBRVJWKr6HjBC5algKAiNKIIXrunVbQUVBydfmwvra3TCqVP2w6D3Y6Xebps
-bCy151Hb9o4ioCi2jVKC19GmUURuKRSgUJfMiLPXeuePd629d0RGREZmRmRGVfw+yeaciLPP3vuc
-qqjI33nXepcPCd8YqBXWMAVty9Ft3AZjYyOzscoMQyLnWG+6tg1fNkTbPnDb2PbltoZs21/BufXu
-6ZX5/OVUzSaRzaiVFqgTbkbKRko2zbVensPd2nDuNnd6udSW9Rnk8/Pi8UIZN3Enqtm2wiy3LU3h
-urrjdRmymZZb67PnzWJouYK2iIiIiIgcRwrajfkcbJfheuqcjWHemqAthmZnWuE2G6wyVo2KsQJW
-OEMbOp6ykYbExmpoITuah9VWgzZfxuN5+Pe5NfYeaG3b7Xb9+dAX32ojy0nJSDmRhkTyGDLuObVG
-Y726nkg5KtrnBO3FCPu9tj5ZfXkN7hWvm5Q6ApvRaM5SLC9GWoTsVsGu0d28N0CbO7vFnPZYBqwi
-IiIiIiJy3ChoT3bOY14M1o7OaNOwcWgtyZJBzqQBkjvJoCaHVFgDW+4MpUZozRG2+5DxXfPx4ti+
-uBfV6x3Vamvzsqe55XscK7XytQMJhmFgY+X4KSMlJxXDRrDqWFvz2twYkpFtuZDY8v2I+ejVE6la
-a4YWwTyq4L0p3DIgW+vH1oZ+EwEbN9zSNAe7B+1aiLDtfdh7b6vW53D3pmkiIiIiIiLHi4L2ZFEx
-9mVAbLfnFHUjYFoGW0WjsJqgDgapsOXOZq2sSsFSryRbVLOnSvG5oX77OZZna1vv6N0qxr2i7S3U
-zh3K+9NTDLW2hCVYDQO+YVAzKVXSCJYcKxVqiaBdE0OKintaHqtda0qQcmIgR0M4M5LFkl49aM/v
-W78uorkZNoVr81gWDLcWtKOK3Zfy6lXtfiwzqLVQ60jxNV7Lwf7RioiIiIiIXEYK2pOdi1k5O4vO
-EXb7kG0iiOZMGhKk6NZNTVga2ayVjVIYxjH2bdXenNuwbNsZr70tKbb9fNP9Vk23XmU2mzZgapa+
-fP60xnVKrXubscoGq0TygZQq5BIN0EagZqwUsOiInnt12ufj9euMx+ag3Tuf98p2X4LL+wcWLWxP
-w8udGDbewnZt3cXrYsh4v2+L96DUkVLWjGVNreMF/RMWERERERG5HBS0m1rnYch+ToG5Rb3FUld9
-6HhOkNpEbTfDq5FqIvV52bkHY5iq11ParFOjL/C5c/eOhG/n3NnO2xN9x4UbfRh3LNfFQEuvfVGx
-EsuL1RLVZMbWnI02sNtIPs+0jv9vQ+hTPDZV3PvQ9kRrUrbjg4t5sjvzUPJ+2+ZnLwL99Epsnmce
-36tMa5jvN/5eRERERETkClHQbnaG1L1SrS3/tApuzuBm1JY7rc3JtmxtqPcchJfLV3lbX9p7+N7l
-3Lt3Fz/3mreF7cXOqQ1xz70CPjjmTnbHPVF9pFQoteI1UTGKx3LYqR0qVvemlbbb2uGtu5qzPfDO
-89gXcXn6kGFx20J2bc3Tenfx6SVt68HmU8wnebumNA2jFxEREREROU4UtJte0V522T63sjwvWxXz
-kttwcOaQXZNjJQJ2bDF+2qcK9vaNHr7Pmadt55x/qQ/Z3rWavS2s9iW8EskSyZ1M1LLdE7VCKVBz
-odYI5LHI1xyyl5nX23z1ZLS9dlx1D9XW54r74vvzxTltHvaiU/k5M9andunLkO4x590MPO/9BomI
-iIiIiFwhCto7uC9D4RwvZ72enZjaa5thqUXECqS5qp0Gw4vRxmbTK72x5NW8NvQ8xLp1Gt82QXv7
-JfRr3FmEX4ZtW04Cn8au93nd3uZS1+nDgmRGai8nYVjvY1YdLxW3SqVQKfPxL/i9ndddHsRUAAAg
-AElEQVTFXobs+H6fa26L/Zfv/vbJ67ZXmV9EREREROQKU9Bu5uBo+4TIOWRPTciIZamqxyDwiuNW
-ITlpMPKQcAMvMdO4L8s1zXveZTmxOVnbPGzc+u6+vci7sNtQ8v5UiCXI6ujUEerolDJSaqHWglPo
-DeAixDulFMb1SEpOoVAZKYw7jsp07XvNFV9+XjGH7X6dwGKu97zq944nW69595Z0O/cTERERERE5
-HhS0m+1BO267bRG4VVPNE7SqdK0tZHulmlOtxrDxbORVBO0+OHxa/sp2Cdi9vfd8KYvFveYr6U3E
-dvYtXzZU88Vw69LuVndqmbexlAjaXtqa1JWIulEur6VS1iNb5lTWlBa0twf6ft7lPPT56/mDgj6E
-fvm8xQudAnTa1nSO1vSsj+iPufF9QLsmaYuIiIiIyPGjoN0sg/Z59mxBL9G7hkc1O/64Lyra2RhW
-mQpY22+5NrUvq9k2B+N2QUxV7W2B2qdwu5tzA+98Lqs1KtljVLYjaI+4l6kp27KiXUtlHEeqVYqv
-GX1NYb0I1/NrWAbs+PChz3mP/+svZ/ul99EDbTa4tXW1+/dtMQKgjWtPFsuKxbrd5/lHJSLHnpnd
-AryI+C/DB7v7m67wJYmIiIhcMgXtc/RwuxebhkBTW6AsLai2arabRzV7SAw1gnbfck6xzjRMTdB8
-URGOkLuoqm/L2DEH/NyK8vaAXWudbhOJ5IlExtxibnSJLt/F67RVHEtGHgZIxjBE8K3V8bEwet/G
-7fPFFxfii/Wyp5EBPSi38fJpR0O5mGu9+PCih+3pOfPz49tt36mqLSJyfNx9991X+hJERETkGFDQ
-buagu6wkB8PawG+bHnIHrxUvJTareHLcYiNBXiVWDFQsNo/maCnNVWbvVXBvZ7B+xmWIjf/z2u63
-fVmE7GUlebmZG4kcQZsUQ97dMDfGHrI9ZpdbMnLKZHJ8GGBt2W0qYy2MdWRdxwi/TBfb3qOleZ57
-WgblHqhtewU7AneaH1tUs+egzTyMvIdzBW0ROWbuuuuuK30JIiIicgwoaO/Ql83afx+iwXiraNcy
-QvLo6UXcWo4h4oOlFrSj8XhKsa411tfU9hZ0fT42MHc/ax8CTH3TfO6fxty1O6rYEa5LKdNmnmKo
-dQvbyXJUuS1TaoTsXtHOKZNSiutrTd3cK2UsjLWwLoV1LS34pu1BuF3zVMVmGa6tzU2P194r0snm
-+1PgtrQtZNO6pGPbP4TQuHERERERETmuFLSbZUV7+xJfy53mKrPXc+cl90qzG5AiaGcMGxyrGatM
-S2hFOC7Uto52ndLzoglbP+YiXC9D9nIY+86gXUuJijZgbmARnrFYOsx7LzFvy5B5IudMzpkhD7GU
-lxeqG16dRCYzRKM3s1bx7uG4D+leDveOr6eAbWCpheu0M2gv52Yvl02jhXZv9/2cyrmIiIiIiMhx
-o6DdbF+DeroHU60ZeuOyc5avwqJC3Sva/altXnFKhuVEGpiDrlfGUqeQXZdrXrXz9zBfY/z2tpXA
-+hzlaZmxVh0HnyrH0aU7k2xoW1S0I+TGnG1LEbSBCNlpIOeMk6ieqV7JnsmeKazY8I1tleopWLc3
-bnslm6kSPQ8fXwwZn64/TcPBpxdvxrSkti0+XbDFhxHK2SJyfCTYZXlDETm2Tp8+zW233cZznvMc
-brjhhit9OSJyQKWUfvdYL0GkoN3s9pcjm3qR9f+fq9fmUfmeV6lahO3FNO++zJfXNLfcXgzJ7iE7
-GqNtt5xrzSJk4xGkk/Vh3sxV73bdliK89nCd0tC6dedpqDaesFxJNardOQ8MOZPz0I43z9+urZrt
-raI9Va3p89rb4PFtS3H1N9IXQdy27c+0JnZ/I21e9MyYm8XRJ6jbYhi5iBx3ZvZg4F8Bnwt8EHAX
-8KfAbe7+cwc8xgcB/wL4x8AjgAy8Ffgt4Ifd/c8PcIynA18H3AQ8AHgL8EvA97v7283sje3YP+7u
-z7qQ19jki3iOiFxBp0+f5tZbb+UZz3iGgrbIfcgiaB/r370K2pM5Utu27/T7LSxOw8Rb7doMN4sm
-aEw170WVlxhGngEyeKEmj2HZZZyXB/N6zhWVNvy7dxGPJmaAGzln2kEjuC4+KEhm0Dqbp5QxG0ip
-h+1ePU7gTnJrXc5bI7ScyWlYfKjQ5o+nvjGdc6po017oYgj59vfV5/dvW6hue8ypevosYjFrnejX
-bu0IfRj5tkOIyDFkZh8JvBy4gfk/qaeAzwCeZGYvAl55nmN8OXBbe97yP8sfCjwSeLaZfae7f98+
-x/gR4H9rX/ZjPBL4FuCZZvbZzB9lioiIiFwyBe1uHrE8VaWngeNt7nWE4UTqEdDasl1tmLi3tbV7
-ydV6GkyOtc9b3InAanMsN3NSq57bohxsyaKiXH0eMk50DM8ptfA8L3M1fUiwbc50hqmKvUymy0nf
-vQodYdpTaYG3zuPVE4t50rYIujsntM+Rev6WL/4GO6fpbX+jnceJTyF/sfhZdGenTvPBReR4M7MH
-Ar8BXE/81P808BPAO4APB74J+ArgUfsc42nEGtsQlfAXAL8JjMDjgG8HHgr8GzP7e3e/bZdjfCsR
-sh14M/BvgdcQwf0z23X8HHDNpbxeERERkSUF7a6P8F9kOLe+7NbcqKyvlx11Vqca1G1zhlO7G3OM
-+7ciAM/zsKdq8fTUHoxjne02kRv3NFV5k9sUtKeh49uqyu1Y07Bumz8AsPk4O9ftngrEieg03uea
-+7wfvXnatlHfO+v/NoXpaUm0/npZNHSbwva2q2bx0UYMW58C9lwVX+6uwC1yrH0X8HDip/3b3f35
-i8f+yMx+Dngx8JTdnmxmA1HJBrgbeLy7/9lil1eb2c8DryIq5i8ws//i7u9eHONhwPPaNfwV8Bh3
-//vFMX7PzH4deAWwgSraIiIickgUtLsdU+nngctzRTVq2W2+cAuuU0V7ql/Pk5NtcWzrQbUS4bN1
-CZ+qybTGZMlifnXafkFGC9pubXmu1s2bNBeUt61dHZtj7ZQ2n9rnoGu2HPLdK/UlArEtxnIvuoAv
-51z3d6sP/d72/s2F+Phem0e+LcBPl97ePbNpybNKzBGfK+dzsDc3lLNFjiczWwHPIn78/3RHyAbA
-3YuZPRt4A7Da5TCfD9zYjvE9O0J2P8abzOxfAj9FVKS/Evj+xS63AFe1Y3zDjpDdj/GqNrT8Gy/s
-VYqIiIjs7Vh3aruczPI0zDrelsVmO+73LWUsZSytsDzEbRrO6fKdpv0yluPrlAdSWpHTipRWpDxv
-eec2rMh5gzRskIcNct83rUh5wFJsqW22Y4vv9WtI7XUmSPE6bPmaWgXcW0ifbpcToz21YfJx6+R2
-2zbPsZGhbz4Qn+v093fx2PT9He+9t/2m29jPGMD68fRZkcgxdBPwfu3+7Xvt5O5vBV66x8NP7rsx
-Dx/fzX8B3rPjOTuP8U53f8k+x/iJfR4TERERuWBKKY0tCipTrbUPr27VVJtCYWsqliDleZ415pHJ
-p+prP06dh427RcXZEykNi0nVrYFZr2jbXBZ3ooKbfK6b9+d5H0C9raDcA3EPytCbs7l7e7ovjm+9
-zdjUsGwart0rz71hGUQ43zaOfHnync3OphZmzPVtZ/HyFq+nf6eCRZdxmyra/dE+bmDZrVxEjpmP
-Wdy/4zz7vhp42i7f73O3/8bd37XXk919bWZ/BHw65873fhTxH50/Ps81/Bmwxe6VdREREZELpqDd
-xHTAyMI9ZoZl2I5qqjFgKZG8NzCL/XrY3jFgun2vtqZgCWsV30R0GvfFsOw+P9uS9bP2HmTMh7dt
-wbeP7l6GVfc2HLuF7eV4dts537k/3eNa+lH7n+nY/dypBW1rYdtbnF6E7Z0heJqjvnhvpn1s+VzA
-KuZ90Ht7jxYBPv4kkoK2yHH1kMX9d5xn37fvcww/wPMB/m6X88JcVb9zvye7ezWzdwMPO8C59uXu
-vPa1rz3vfjfccIOWExIREVk4ffo0p0+fPu9+6/X6MlzNpVPQbnrQnqq4Lb3atn1yq2rHZmk5J7sH
-yHpOoPTWrRyr4AmzCuR5HvhyjnVvhJamWm+/rJ45t/Ug27VzT0//06cAfZJ4z8s+V7d3Pq1fue9o
-hrbsQ+YJt9yq2mmXC+mV9uW7199L36VCzbbvuVf67HI33/awkaagbaaZDyL3AZfaYOw+16Dspptu
-Ou8+z33uc3ne85539BcjIiJyH3Hbbbdx6623XunLODQK2rKrHUtzi4hciGXTsYcRHb/3slcV+d3E
-Z2wHqTJfv3jOzut4GLEE2J4sPrV7v/32OYCpmv7Qh+57OiD+MvHCF77wEk8pIpdia2sLgKc+9als
-bGxc4asRkVLKgX6H3nnnNFBt50i2Y0VBu/mCz32hxiGLiByOZYfwm4Hf22ffm/f4/p8DjwU+2Mze
-f6952m0ZsEcTle8/3/Hw/0eE8I8/z/V+DLGu9qV8vDj9Dln8BUBE7gP0Mytyn3Ws85uCtoiIHLbX
-ENXkBwNfBvzAbjuZ2QewxzrawMuBryZ+iX4l8II99vsnwIOIkPzyHY/9JvAk4Foz+yx3//U9jnHL
-Ht+/EJtEWK8cbF65iIiIXJzriPmrm1f6QvZjO+fpioiIXCozewHwTUQA/jZ3f8GOxzPwK8BnMreH
-+GB3f1N7fAX8DbGW9nuAJ7j7n+84xgcCr2r73AN8kLu/e/H4DcQ63RvE8PXH7ayMm9ljgVcwdxy/
-3d2fdclvgIiIiJxo6iYlIiJH4buBtxAh+vlm9p/N7DPN7NFm9r8SAfkzgT/c7cnuvga+hgjgDwJ+
-z8z+tZk91sw+ycy+kVg67Ma2zzcvQ3Y7xmng1nYNHwa8xsy+1sw+0cw+xcy+h6iCvxV4Z3/aYb4J
-IiIicjKpoi0iIkfCzD4KeBkxT3rnPCoHXgT8brvdVtFeHOPLgNuIYdm7HaMA/9rdn7/Pdfw/wHP6
-lzsefgfw2cAvAA8H/oO7f/1BXp+IiIjIXlTRFhGRI+HufwF8NPB84H8CZ4k1rX8L+GJ3/6q+K3ut
-Vuj+k8BHAP838BfA3cC9xFDwHwUevV/Ibsf4OuBzgZcC7wLOAK8n5o4/2t1fC7xv2/09F/NaRURE
-RJZU0RYRkROtNWV7MxH2v8rdX3SFL0lERETu41TRFhGRk+5LFvd//4pdhYiIiNxvqKItIiL3W2Z2
-DfC+7v53ezz+aOC3gQcCd7j7J1/GyxMREZH7Ka2jLSIi92cPBf7SzH4ReAnwOmLdzRuBzwKeBVxN
-rH/9TVfqIkVEROT+RRVtERG53zKzDyLW43bO7ThO+/4WMTf7P1/OaxMREZH7LwVtERG53zKzAfg8
-4KnAzUSF+yFE5/I3EsuP/bC7v/lKXaOIiIjc/yhoi4iIiIiIiBwidR0XEZETz8weYWbfb2Z/aWZ3
-m9m7zOzVZvYtZnb1IZ7ni83sN8zstJmdMbM3mtlPmtljDuscIifJUf7smtlzzawecPvUw3pNIvdX
-ZvZQM3uamd1qZr9mZncufob+0xGd84r93lVFW0RETjQzezrwk8D7EnO2tz0M/E/gae7+15dwjquA
-/0o0YNvtHBX4bnf/7os9h8hJc9Q/u2b2XOC5uxx7Jwc+w91feTHnETkpzKzu+NbyZ+t2d3/WIZ7r
-iv/eVUVbREROrLa8108Ty3vdBXwH8DjgScB/JH45fxjwq2b2gEs41YuYf9n/FjFv/JOAZwN/Rfw+
-fq6ZfdUlnEPkxLiMP7vdo4CP2WP7WOCOQziHyEngbftb4KXs3qj0MFzx37uqaIuIyIllZq8EHg+s
-gSe4+6t3PP7NwP9F/KK+9WI++TazzwBe3o7xy8AX+OKXr5m9P/Aa4BHA3wMf4u7vubhXJHIyXKaf
-3ami7e750q9a5GRrP1N3AHe4+507VgY5tIr2cfm9q4q2iIicSGZ2M/EXdQdeuPMv6s2/A/6S+MT9
-G8zsYv6y/c3tdgS+3nd8wu3u7wK+rX35YEBVbZF9XMafXRE5RO5+q7v/mrvfecSnOha/dxW0RUTk
-pPq8xf0f322H9sv5J9qXDwaeeCEnMLP3IYayOvByd3/bHrv+PPDedv/zL+QcIifQkf/sish903H6
-vaugLSIiJ9Xj2+09xBCyvfzO4v6nXOA5bgY2djnONu6+Bn6fqL7drOqbyL4ux8+uiNw3HZvfuwra
-IiJyUn0k8Yn3X7n7zk6oS/9jx3MuxEftcZz9zjMQTZxEZHeX42d3m7Y80NvNbLPdvsLMvs3MHnwp
-xxWRQ3dsfu8qaIuIyIljZqeAa9uXb9lvX3f/B6JyBvCBF3iqhy/u73se4M2L+xd6HpET4TL+7O70
-5Hbeod1+KvBvgTeY2TMu8dgicniOze/d4bAPKCIich/wwMX9uw+w/z3ANcD7HOF57lncv9DziJwU
-l+tnt/tT4BeBVwNvA1bA/wJ8KfAUYv73z5nZ0939Ny7yHCJyeI7N710FbREROYmuWtzfOsD+m8Q8
-rquP8Dybi/sXeh6Rk+Jy/ewC/Ht3v3WX798B/JSZfQ3wH4AMvNDMPtTdD3JNInJ0js3vXQ0dFxGR
-k+js4v7GnnvNThFzQs8c4XlOLe5f6HlETorL9bOLu7/3PI//KPBjRJC/EfjCCz2HiBy6Y/N7V0Fb
-REROorsW9w8yXOwB7fYgQ1Uv9jwPWNy/0POInBSX62f3oG5b3P+0IzqHiBzcsfm9q6AtIiInjrtv
-Au9qXz58v31bV+H+y/jN++27i2Ujln3Pw/ZGLBd6HpET4TL+7B7UXyzuf8ARnUNEDu7Y/N5V0BYR
-kZPqL4ghn480s/1+H37E4v5fXsQ5djvOfucZgddf4HlETpLL8bN7UH5ExxWRi3Nsfu8qaIuIyEn1
-39rtA4Cb9tlvORz09y7wHHcwN2PZc1ipma2AxxB/ab/D3csFnkfkJLkcP7sHtVyz921HdA4RObhj
-83tXQVtERE6qX1zc/8rddjAzA768ffkPwCsu5ATufjfwm0T17clmduMeu34h8L7t/s9fyDlETqAj
-/9m9AF+7uP87R3QOETmg4/R7V0FbREROJHe/A/hd4pfxs83sk3fZ7VuAjyQ+8f6BnZ94m9ktZlbb
-9l17nOoF7XYAfmTnUFczuxb4vvblPxBdjEVkD5fjZ9fMHmVmH7rfdbTlvZ7dvvw74Bcu/NWIyIW4
-L/3e1TraIiJykn0DMaT0auBlZva9ROXrauCLga9u+70O+Hf7HGfPeZru/goz+2ngnwGf287zA8Qw
-048FvgN4RDvGt7r7ey7pFYmcDEf9s3sTsTb2K4BfB/6MaMI2EPM6nwn847bvCHy1u2tZPpF9mNmn
-AI9cfOvaxf1Hmtkty/3d/fZ9Dnfsf+8qaIuIyInl7n9sZv8U+CliCNn37tyF+Iv609z9nks41bOA
-BwKfDXw68MQd5yjAd7u7qtkiB3CZfnYT8CTgyXtdBhG+n+Xuv3aR5xA5Sb4KuGWX7xvw+LZ1DuwX
-tM/niv/eVdAWEZETzd1fbGYfS1TInkYsB7IF/BXws8CPuPvZ/Q5xgHOcBZ5uZv8M+Arg44AHA28H
-XtnO8QeX8jpETpoj/tl9MTEs/LHAo4GHAe9PBIJ3A38CvAT48TYnVEQO5qCd+vfb7z7xe9fctSqB
-iIiIiIiIyGFRMzQRERERERGRQ6SgLSIiIiIiInKIFLRFREREREREDpGCtoiIiIiIiMghUtAWERER
-EREROUQK2iIiIiIiIiKHSEFbRERERERE5BApaIuIiIiIiIgcIgXtY8TMftvMats+9Upfj4iIiIiI
-iFw4Be3jxXfcioiIiIiIyH2MgraIiIiIiIjIIVLQFhERERERETlECtoiIiIiIiIih0hBW0RERERE
-ROQQKWiLiIiIiIiIHCIF7cvAwi1m9lIzO21mZ8zsb8zsF83scy/ymIOZPdPMfsbM/trM3mtmd5vZ
-G8zs/zWzz7uIY36Emf0bM/sDM/s7M9s0s3eY2e+b2a1mdsMBjnHOEmVmdr2ZfUc77mkzG83s3Rfz
-ukVERERERI47c9dKUkfJzB4G/BLwSYtv9zfd2u3PA18B/Arwae3xJ7r7K/c45qcDLwQ+ZMfxpl3a
-7e8DX+TubzvPNW4APwg8G8i7HLMf7wzwre7+I/sc6xXL1wC8H/Ai4ME7jvked3/IftclIiIiIiJy
-XzRc6Qu4PzOzBwGvAD6COWT+DfAqYBP4aCKAfz4HXDvbzP4J8FPEPzsnwu/vA28EKvDhwGPb448B
-/ruZ3ezud+5xvGuAlwKPa8dz4K+B1wB/DzwE+BTgRuBq4IfM7IHu/n0HuNxPAZ7XruWdwCvb7XXA
-xx/k9YqIiIiIiNzXqKJ9hMzsx4CvbF9uAl/r7rfv2OcTgZ8F/hGwBWywR0XbzD4aeDVwVdvnBcD3
-uvt7d+z3j4DbgSe0/X7d3T9nj2u8Hfiytt/rgOe4++/u2MeArwH+fTv3CDzB3f9gl+MtK9qFmJ7w
-ncDz3b0s9lu5+3q3axIREREREbkvU9A+Imb2YcD/WHzrFnf/qX32/SOiYmzsHbR/kxiO7cA3uvsP
-7nP+a4hQ/lFt/8e4+x079nkC8DvMVezHuPuec6fN7BZiGLgDL3H3p+2yTw/atP3+jwNWv0VERERE
-RO4X1Azt6DybeW7zq/cK2QDu/nrgBxb7n8PMPpY5ZL92v5Ddjnkv8D2Lb33pLrt90/L+fiG7HfN2
-4sMDAz7TzN5vv/2BtwHPP88+IiIiIiIi9ysK2kfniYv7P3mA/W8/z+Ofvbj/0we8ht9a3H/88gEz
-y8CT25fvBV58wGO+oh+CmIO9Fwd+zt3rAY8rIiIiIiJyv6BmaEfn4xb3X3W+nd399W3Jq706cT92
-cf8z2jzs87HF7QfueOxjgQcQgXgN/GBMxT6vmxf3dx5zp9cc5IAiIiIiIiL3JwraR6B1G99YfOtN
-B3zqm9g7aN+4uP9ZF3FZO4d5L493LfD1h3DMnXbtdC4iIiIiInJ/pqB9NN5nx9f3HvB59+zz2IOY
-lwC7mA52O6cJPGhx/2I74p3v358zF3lcERERERGR+ywF7aNx946vr+FgofMB+zx2D3NH8s9391++
-yGtbHq/7U3d/9CUeT0RERERERFAztCPh7u8h1sTuHnHAp+435/nti/s3XPBF7X08A64/hOOJiIiI
-iIgICtpH6U8W9x9zvp3N7JHA+++zyx8s7u/X7fug/hjYbPevM7MPOYRjioiIiIiInHgK2kfnFYv7
-zzzA/rec5/FfbbcGfIGZPfSirqpx97NsX/7r6y7leCIiIiIiIhIUtI/Ojy3uP8bMvmSvHVs1+1+w
-T1Myd78D+O325dXAT5rZ6iAXYmYrM3vwLg/9n30X4J+b2ZMOcrx2zIcddF8REREREZGTREH7iLj7
-64EfJ0KsAS80sy/fuZ+ZfSLwMqJh2tbOx3f450SjNQOeArzSzD5pr53N7MPM7DuBNwKP2+UaXwnc
-3r5cAS82s39lZrs2ZTOzU2b2uWb2i8AvnedaRURERERETiRzv9iVneR8WhX5VcCHE+EY4A3te5vA
-RwM9KP88sZ71pxGV7Se2ILzzmE8DfpoI5v2Yfw28Fng3cBVwHfCxwAe0xx14urv/2i7H2yBC81MW
-x7uXmBP+pnadDwY+FHgUcKrt84fufk7IN7NXnO81iIiIiIiI3J9pea8j5O7/YGZPJILsJ7Zvf0jb
-YB4q/kvAVzDPw97vmC82s8cRQ9Nvat/+0LZt23Vx/L8B3rLH8bbM7LOB5wLfTAT4q4En7rZ729bE
-hwUiIiIiIiKyg4L2EXP3vzOzxwBfDnwpUWl+ELG81p8AP+7uvwBgZrA9IO91zD8DPsnMngx8HtGF
-/Eai8rwJ3Am8jqhK/4a7//55jufA88zsh9p1Phn4KKLCvgLeC/wt8GdEk7dfc/d37XfI870GERER
-ERGR+ysNHRcRERERERE5RGqGJiIiIiIiInKIFLRFREREREREDpGCtoiIiIiIiMghUtAWERERERER
-OUQK2iIiIiIiIiKHSEFbRERERERE5BApaIuIiIiIiIgcIgVtERERERERkUOkoC0iIiIiIiJyiBS0
-RURERERERA6RgraIiIiIiIjIIVLQFhERERERETlEw5W+ABERkfs6M7sHOAVU4B1X+HJERETuz64j
-Csab7v6AK30xezF3v9LXcCz8xMs+zQG8OF7BC+CQLMdGIqdMypmcBiwZUHCrQKUC7rHVCuPojKNT
-Snt/rd1Mtw44Y12zLluMZQs8MdgpBjvFyq7igfkhPDC/Pw/MD8HKwObZNVuba7a21qzzmWnz1RZ2
-KjY21kCdrovkcS4DLF4bHrfmA9lXJFZYzXh1vFQojrOmMk63I2tGHymMcawUm+VETu29SYlEvFeJ
-BDXh1aatVqjVqQVKKazXhXFdGMfCarViWG2wWq1I2UjmWIrNGcEKzoglw3LCcpwP4JlP+F27PP+W
-iIjszsxGIF/p6xARETlBirsf28Lxsb2wy63WuvjKMDOMRLIWG6fAnbH2xzFwww3M43mYkQxScnJy
-5g8yFh9omLfAbZhBSkZyA4yEkczivCm1++0xi/MCeO2BPg5tyUg5YSlBctzAraV+83aBHv+r7Tne
-wrgXzMFrxd3x9n23irc/nfcX2w5HjUfd2gcNONXjOeYV97R49XH9yQw3Y0gGOcL5MAzkPLTAbph5
-2ypOjtdCbh9YpHjf9RmRiBwf8VvAjBtvvPFKX4uIHMDW1hZ33nknD33oQ9nY2LjSlyMiB/S2t72t
-Z6xjnQYUtBuvBZiiLmYJI5MXATtZwizH496CrNf4R9xCsJnhycjurbYRYdvpodtjX4vHLMUZsyXw
-RLaoCWeLSnEP2/PxI2t6gVJhXR0q5EjspJTx5HgqkCpuJYJyC82RiOMykht4wt2wVor3Gq/JKUCJ
-55m3UBuBnxaoaZ9NOFDNMaDimDtGndK3ewRj+vtqGUuQciaZ49nJOZOHTM7xGEqypS8AACAASURB
-VNYr8dYu2eN05hgJsAj9IiLHw7uB66699lre8pa3XOlrEZEDeO1rX8tNN93ES17yEj7hEz7hSl+O
-iBzQddddx5133gnxu/fYUtBuoooLtJqxWYqQTSbbQLLcwrdhFlVa84q3wGdYDGu2FJXuBNDCp0el
-uE6hPAIkFmHXLeEelfOBxECOoG1GTlHdnirlvZpeoKyjou0FPCUYcoT7IcK1W8VbaK69jN2uFTec
-gnvC3OK11FYm9wp96Dm9qh0V63heu2/xemp/nYC1knk/Rw/m8RFGjDdPluJDAYOhxvFSSjFkPKdW
-tY4PB7DU8rxHKG+HnMbii4iIiIiIHDMK2k0f4h0hO6rMZm3OsUXYNjNoYTuqvDYFTwBz66PHY0i4
-WQTfatQalV7vw7inMNn2hV5Ljwo3bfj4Yuh4Hzg+VbTXznrToTo2OLYyLM/X4xZhu7aQ7+4Rqvuf
-FraTG+apfdhQiMjsEZqnUNtfe7tu2rBwow3jbpXuXu32FrpJUS1nfg3JMkbub9L8IUWKIfDYPFzd
-pwp2wkn08eLHf7CIiIiIiIicVArajbWVzvqw5Kl2bG2etmUspRa2W9CuhWkotTuVAjWiaPUeR6NV
-WqVSrbQwOw8jr1ai6ZiVNqK7V5Pjq2nIeds/CuFteHep1LHEMO9VwTcrngpGifnNGZwUo9udaHbG
-FJvjlbYmaZiTHPC+4ptP34/H2nvi/braUHSLarSl+Ihgns/trdK/7aODmOPusTF9gDCdcRIV+X6c
-SvV4D6fqOqioLSIiIiIix5KCdmc9YNoctr03JsvkFEPHSS1oU6kpQbVpDvacFD2C9hS2K5XSgmJt
-IbvNfabgVqhWWvG4h+w4Tg/xNsXLNnfZI+iXdaH6iG8VfCjUVMhWSdnJbm1oe8Zriqp6C8nmHl3D
-abeLhm7x+uegG8O2ezhvVfl51nSbc95K80SF3X2uwU9N5Twq2dZu+9z2VhZn+mChhfz+JyryTmlV
-eWd7KBeR48XMbgFeRPyofrC7v+kKX9Jl8853vpOHP/zhV/oyROQAtra2AHjqU596v26Gdv311/OH
-f/iHV/oyRE4cBe1mWdG2bX+2V7T7cOeKYXXuqD2H7e1V1ylot2p2pfT42OZP19ZwrMZcbWpr+NWb
-jbVQ7d6Gtcete6WWSl0XSi14HvEcQdtzZdiYq9Depl1HRTtCtreh4d4atsWo+D403VrluzVya3PD
-515o24N2H3Qft8sPE1oTtz4Ef1HNtp7up+O2Dx6sB/k5dldi6HutMQy+f6ahruMicty4O29961uv
-9GWIyAVoTZVERA6VgnZjNg9grrXQO8ZPs6LdohqbYhj0NHjaphnLVC+4F2qfX9yP2AJ1LJfVm5O1
-BmWtYRktSFfbiMesUBgZ68hWLTA6m1tbbG1ucfbsJpubW2xtrlmv15Q6koeCrwt5VbGxwFhhLHF9
-1ajeVvqaXpXFnOdYDBtSnuaVm9k0XLuPO4+50ouh3g6t4xs9es8vui+PZvOyaDZEwI6J2HPH8J6W
-fbmI2KK23avY1adlyaYO6AraInIsfcCVvgAROZAt4E7gocD9saJ9mmmJGBG57BS0mz5yPKq+BWqh
-eqJPj46R1tETPMWqWK12C24WQ5upLUDHUmFTDrQ2W9uik3atMS+7Ms7DyGsM4c6sKTZSKIy1sK4j
-eVxT18bZrS3OnjnLmTNnuffMJmc3t9jaWuPDCKsKY8XGShkLrCN4xyjwCLY+DdGGqblYC9mkoXVN
-j07ntY54dWrtlWubXwrEBw+L0fJ9znafA86yoVwP2pbjfIvh4n0O+jQ0vj3WQ3b1Oofsam3qeK+4
-a5K2iBw3Bmh5L5H7htcCNwEvAe6Py3s9HNAIG5ErRUG7mzJbW4qr1tZNuzU6c8g42SClCJl9ASy3
-WEe61EKpI9XHbcft3bstteq2jVRfU3xkW2OxapQ0klsIH31kXUdSGSkjnNnc5N6zZzlz71nuPbvJ
-2c1NtrbW0YJ87RG0S9z6WEjrNeTe9iy1pcdaQDVorwhswCyCdspGSjHcnLEu5ogv3qRpTPz2Nma9
-OVocftFILsWwcVJuzdbSPCPb++ufm7T1Q87d0mtr5tbebPoSZYf6b4CIiIiIiMihUNBu+vJeEapL
-q6QCKUFNOBlPhnvUtXuH7Rg6Prcqi6p2G+PcmpbF3Opew+2N0Xrlu85V3TTGuSmtoj2yVdbYuMV6
-C+7d3OSee89yzz1nOLt5lrNno6JtVKwYqSX/WoHiUCLkTwVkLO5PkbZXjw232LDoIN4mZsfI8alR
-WjtGC7mGtW7rTK+R+VTRjbx3He/ri08hv8//jiAdb1Uct7b3sNbYtlW0+/m3x3IRkSutXOkLEJEL
-dQPw3HYrIvcVOed+91j/7k3n3+VkKKXGVp1aW+jbFkJpWxvubBGyY/3nhOUEud9aDC0375GZUkfG
-smasMad6DvatkdoiWJZaqG3/9XqLs1ubnNk8y71nznL3vWe56+4z3HPPJvee3eLs5sh6XSkF8ITZ
-imQDyVYkW5Ftg5xWDGnFkAayrchpINlAryxXrxRv11jjGmOeem9zthwCHsPAc4pjD6lteYNV2mCV
-4vg5ZVJfc7yNEJir1/EhhPUt9e7ihVJGShkZx74V6hjzzI28GL6/itdiqyv1r4zIiWVmDzaz7zOz
-vzSze83s7Wb2MjP7ogs4xikz+9/N7OVmdtrMNhfHeZaZ5aM8hpm90cyqmf2n9vVNZvbjZvYGMztr
-Zhc6sbEvw3CBTxORK+cG4HkoaIvctyyC9rFuQqCKdjOO7Z9Tm04dQ5lTWzM7gnMP2tUc63OuAbNW
-tfU0D432iteoXk9dvGoLmd6HTLfT1dYF3Fo1PRWKFRjX+HpN2drk7NkaQfueM9x191nW4xbjuGY9
-rtnIiY2SgR6EIVtfmixCsvVKNfOIa2vLcVUvrQzel+iyaOpW61S57mthZ8tTlTq149XW5K3aooN6
-H6Teht9Xr1hrgDY1nrO5N7sTYbxOgbu0DxwqvWe50cL7YhORy8vMPhJ4OfE30/6fk1PAZwBPMrMX
-Aa88zzE+Dvgl4BGLYwBc248DPMfMnu7u7ziiY0wrBZrZc4AfApbB/Fj/8hYREZHjTUG7KaV3v+4j
-m+eKdjQdX1S1qdE5G2L5qh5EPQIoWAzhtracVy1tnnGZgmsfxt0brVV3zOr0nGojXtbUcYtxvcmZ
-zco9Z85y9z1nueueM1H5rVH9tdWKUgHPrZJtJGvh1GjzpPvw7Xlxrh5w8UqtbUGvNhR+MSm6dRDv
-cXdoleoIvWCkaXmyGmuCt87q7u19q47XOsX1Pqze6GuGt2vwQmkV/ahsR9AeksW8eMuLsJ0VtEUu
-MzN7IPAbwPXED+9PAz8BvAP4cOCbgK8AHrXPMR4J/DbwvsB7gB8G7gDeDLw/8AzgOcDNwC+a2RPc
-vRz2MRY+Cfgy4G+BFwCvIX43PuEg74mIiIjIbhS0m1Li72DzElZtNWmveC0kW0cILoVCiurstOaz
-M9YtShsW3udZw3Ku9nx/mqvcu2v3iq5Htby6U9zJtc3d9mjCtpETV58aqFdtUGqi1EwtA6dODVw9
-nOKqtMEGG2Rfkb2SvZJqhG7zCN2OtzDv0fm8rqm+xr2QKqRq1EQLxdDXFad/gEBbHsx71Z/2YUOa
-Rkym+JwhXp21ofQW71n/w6JjOa3DeKy/3T6UYCo1tXPnmD9uUbm36b6IXEbfRbSxdeDb3f35i8f+
-yMx+Dngx8JR9jnE78CAi0D7F3f9+x+MvN7MXt+N8MhHcf+wIjtF9FPAnwKe5+3sX33/VPq9BRERE
-ZF8K2k2pc9CehoJjbfj3SLTyGimeSG5YWgRt86gu+0ipa6qPERgXDdGWw6S7WOO6h15ap20oOMkd
-c+IWGJJx1UbGr14xVKeUIeZzl8LGVQPXbGxwdT7FVazIwFAh19aQrEb372hsHhXnaoU1ZxmnKvJI
-tej9Ftk4A+15lplDNuAx07ovGIYzVb37N1Kr/pu1tdDSPLS+DyePQy1Ddm3V8HlIfhy7V69jebAp
-bKvFgMhlY2Yr4FnEfwb+dEfIBsDdi5k9G3gDcE4DBTN7PPDYdoxbdgnI/Ti/0UL7P2VHSD6MYywv
-qR3n63eEbBEREZFLoqDdlBpLchmJZD04RiXaaqyRbdDCL7Tp2/RvFgq1FoqPrZv49op270veQ2RU
-wo3am6t5W5fbW0W7hW3iVAw5cdVqIF/lnCLF/OVSKbWyWmVObWxwKm1wihWDJwZP5JpILWRbG/de
-bKTaSLERvFLqFrU6xUcsQUlg5niKRm8pzcuDOXNAhrbsl9GGg/du4P096dXumNxutPnrHvO2a/V2
-rOU87b6U1xS1I6gvQnavbhv9vohcJjcB70f8wN6+107u/lYzeynwtF0efka7fZ27/8V5zvdKIiTf
-bGbJ3fuc6cM4xtKb3f2/n+c4IiIiIhdEQbsppQVty1GtNZ8qqlgbC50iWFqa18WO5M1UjY2QvZjf
-THQwr3URUqe1sFq112nLgLXw6Mv2Y8YQE60ZNjKnHEpKrZodXcrzkFmtVqzyihUDg2eGOjCUeR61
-uUGG0ROlVeRHz1g1KN6athEl9eSYG5ncpqrPQ717JDaPweERoHvkXXbb7UuJRRM2a6+LGq/ZcKzS
-GsPVab3s6pXal/qa36z+FrWPO+L7Sd19RS6nj1ncv+M8+76a3YP2J7bbj7iArt4r4CHAOw/xGJ0D
-f3rAY4iIiIgcmIJ2M5Z1u1faMOU2J7kvgD3dtnWpk2+/32OotUptpQXLTG2Nx9vodFKOJcFSjuHn
-iagiJzJDRGUGEisSG5Y4lRIpZ3wwnISnPHXkrqWSspGHxJCNwaLeO1TIBcxjPW03x5OTUqGmiue5
-+Vsw3Aqk1i3dUptr3juKJxK1DR23qeJMH0Tu0S6tV/GnruPWu7K34ecpwraZt1zv7QODGMJeawRt
-PMX76I7VWCDcrUS/cyO6qW9rNCwiR+whi/u7dgJfePse378OLvgH14FrDvkYS7sOPb94lbjE88ls
-b3IuInLYTl/pCxC5IKdPn+b06fP/e7u1tXUZrubSKWg3Y42g3dfPjjDZg/YicBOhNQrSta0DXWOo
-ubUqricohhfDS6KOUEejjnHoPCSGIWGrRM/0KTvZImivGFiR2SBzyhKnLJFzm5OcHHKl1kytMVzd
-zEgtuCeDHLuQ2+h1bwG5tGslewTqRWV+apRWC9UKKSUqA9O619QI+dbbnNEq9DbN1nZv9eY+cXsx
-9Nsst8VyetiuUaGuHnPEy7yOeJ2mske1vLT1t6tXkpUYyG608C4iV8DFfsrVf2j/BHjmBTzvrYd8
-jKW9upFfgjsP/5AiIiL3c7fddhu33nrrlb6MQ6Og3fSKdl9uy1vDLu8ptC/nhcdwamI5rgjZlZQS
-ObVltDzj6wEfDV8nypppM4zVRsI2MqkkbIC0ioA8pLSoaOeoaJO4KiWGnKJRWY6sHSE7UfvQ897P
-29os5gp5tG3zvqFSUsVyjYMMDtmxHMPKI84Wiq/JnqeKdrVYy9p72G7BvdJ7vLUh8O2TCHNbfC4x
-r+Pdq9kQc+AdpoZuUzW7BfCpR1Ffc9wr5iVG8MP0OkXksllWfh8G/NU++z5sj++/i/jhfp8DzK/e
-y2Ec48iYGddee+1598s5k7M+LBSRo3f99ddf6UsQOZDnPOc5POMZzzjvfk996lO5887j/6G2gnbT
-19GOKiutE7a1im+KRGnzfOEpCFbDrZe6wWu0Dq+blbpplE3DR/Ax4WMiYdQxUdZG2ozAbacSeSMz
-DJmVDWyk2Fa+weAD2fsQcMcrEUrbutxeS2s8lpjW9G7zxL1Orb0xKoaT2rJfK5ziUN3a1pfxssjC
-vRu4F6q3Unyb772tf7rF0l1ujrd1u+f3aRGU8TaqPG6r+/QBQbRTi+jeB4R7O0FUyW1q3N6XE0u+
-eN9F5HL4s8X9m4Hf22ffm/f4/h8BjwM+xMyuc/fzDUE/qmMcmRtvvJG3vOUtV/oyRERE7nNuuOEG
-brjhhvPut7GxcRmu5tKpKNiU0eetVMZSGUtf+iqW36re2pt578IdFeXe7KyW9vytyvpsZfPeytm7
-Klt3w3hPop4Z8DMD9Z5MvTtT7kr4PQN27yny5lUMW9ewKtewUa9mw69hwzcYPJOqYdXxUinjyLge
-KeNIGWPouNcCXrCp0/mIe6yR7XULfI35SPKRTGFFZeXOhsPKjZUnVp4YPAJscsOmJmURtIuPFF9T
-iK0yEr3RS8zttkrrbsY8R7t1VK8Vry2410qdqtQwDS/vjdX63HjvS395VLnr3DDNPV6nzwujicjR
-ew1zVfvL9trJzD6AvdfR/uW+G/ANF3kdh3EMERERkSOloN2U0rcI2tNW+9DrPpw84d6Xqmoh241a
-4/m1OON6EbTvrmzd44z3GvVMpp4ZqPdmyt2J8a5EvWeAMxuks1czrK9mNV7NRr2GU341Kz81V7Rb
-0K5jiaC9HqllpJYWtj06rhkVvOA+4r6F+xrrQZtC9sLQKtorYIWxMQXtRHZrvd2ic3oP2ZVxW8iu
-xDJhbiNQYBG2fdEQrYdlr7HV2oN3X8arV7TTdBtLkdH2n0N2DGUvi4DdNxE5au6+BbyICLgfb2bf
-snMfi0Xu/yO7rKHdjvEyoiO5Af/SzL5ov3Oa2aPM7HMO+xgiIiIiR01Dx5t56Hjrs92WWzWrkFKs
-aW3eGpD3KmwsA9ZHkU89wNzAM5QMY8bqgNWB5JnkiR5Cq3kM6LaEp4GIvStSWpHzitSWw6oVvFXY
-x1bJtlY9NqtRgcYXw6+ZWhUtF8mal7m2thp1ZeVQs1E942nAU8GtYjVFJb3Quo7HC6zGNMR8Dset
-8Xq7CrO0vaJNH4buLb87xcu0T282Z4vqdp+fPU0EtxohPLWl19qtiFxW302sS/1w4Plm9mjgJ4gu
-5B8OfDOx3vYfsvfw8S8B/oDoYv6zZvYrwM8Arycak10HPJpYL/uTgRcAv3oExxARERE5MgraTS0R
-rBfRsM017iGxdxb3WF4qMbXrNqvRAIxoBFZrwoaBtBoYTg3k0UijkUoitTncvapbU2G0qBUP7qws
-UfOADxvRibs441jxsTCOEbTrWNo637GlPtc61W3LXm1fiTp4IZp5J7BcSNkZcsLzgCWPpcLygBda
-0zeL4dtWcavRkZxEshwNziyTUswATy10e/8AYupWXnBsasrmtbZ55i1s9wvsQ8jN2vW2fuYGlozU
-tpyMnCBrPIbIZeXu7zWzpwIvA64Hvrht0y5E1ft32+1ux3iDmT0W+K/Ao4DPAZ6+265te89RHKNR
-owcRERE5EgrazRy025rTvbP41Myr0BuCpeTRXdysZe3cmpDFxpDIqxXDxoqy3oiqczsWLTzXsVDH
-QkkjI5VEZTCnZKNuDLhvUOqIlxFGp65Lm5cdW2q9yVJb8SvVqDanqdt3C6i+CNxToG19zVrQXuWE
-5YE0GJmBgUqxEkPnqVF9tsLISLGRlDLZaoTtDLVGAHbvS3r5FLbd57Adw78rxds87b50WAvZUdie
-K9qtro0Z5AwpR7jOGXKy+LBDRC4rd/8LM/to4NuAzwceAdxFNEv7UXf/WTO7hUXPxF2O8Vdm9vFE
-dfwLier3Q4mlu94FvA74b8AvuPsfH9Ux9rtGERERkUuhoN3UPnTcYoi4Tz25omN3NO6KOdApZXKC
-lAxrgTu1ucXmCWomDxv4xgY+bgAF6gjjSPXKWJy6rvhWoVAYvUAtDAbjKlGuHvC6EYG8FMoYc7PL
-ulDHkTqOLWDHfOoI163S3oJrWwX83M0Wt9nJOarYachkzwzAymBd1mz5GnxNSSOVSrGRNVvkHMPM
-h+RUM2pK0xJcc1WaFqIruLXZ1JVS56W84v2O998WF7eswBvtw4QEORt5sBa042sRufzc/R+Ab2/b
-bo/fDtx+nmM4Mdz7Zy7hOi76GO7+wRd7XhEREZHzUdBuciuPxpLQPgVu6xOe2/BxvODV4vE2VDqn
-3CrH7U9KpJzxPMCwivW0U1ufihK5e6synh1JVqi5xj+J6tMa3tVhdCjVGYtTirfgHVtMHY9wbS1g
-J/MpcFv/2ha9vBdD3w3w0oawT8c1KIaNFtXtIeOrGGZezSlWwUawTKwrnmJwd7tOM2/N2BzzGutn
-R292jEylUK22be48PjVMa1X4lFuDNEu4Qx4SeZXIQybneG/7muUiIiIiIiLHjYJ2k3MG+rBxx61O
-1VZa9+zo5k2770AmmZFSInJ0Gzoe5VdsGGC1wkeoKeY4u1ush70ulLMjJReGVZ3WyI41pmPOdamw
-Ls66tCp4cbx1NjcDa7epDdPuYTu1MJ37fHKzbbf9A4QYvF3nZmdDrPXNkLANI9cMGD7AaIWURqyF
-7Bi3ndrc6/gModT4QIIEeMxdz+a4JZLFMPR+PsenZb5qjSHntLnYANlSjIV3ayE7kYZEzpmUEym1
-OeIiIiIiIiLHjIJ2sy1oU1vGW6wH7eBeYpkqS3jyqGJbIttiPnRNWMpYC9q2GqhbTs2tktuCdt2q
-jJsjw6riG1FVbsXgqdn26LCusFm9NUarU9i2dl2923iiRsDGScnJLWhH07BoHuYpqs/e5nK7V2ot
-lFpiLvSQYQDLRqqxXrilCNajFdYeQdtSW9qsnb1iUY2nJW4nwnyKyn9KiUyKmN2q2dVa5K6VWj06
-ipPa3PcUQ/AtY6RW0bYI2znFcP22n4iIiIiIyHGjoN3ktKxoR+drp63f3DuP1xSdt/ua2n0dZ2uV
-XCyapnmhz0rePi86Zh97jeZrZaswbo2M68IwFspYGEuNtbxrbKM7Y4Wx0p7n0bitz4n2iLpzNRuG
-BEOOId8TIxJ87zAGEXJLdAB3976KVgvIOTp950yyREojyUdSLlhtlfu+jnhtQ+7bEmjTsHsHb8uP
-eUrzOtre1ybv763Pjdra/O4+JL43QsvZyNmwaV58au+niIiIiIjI8aKg3aTU3oo2bDyl6DQ+1kLk
-2doCopF8Dow+LV/lPQlj1aklk+qA1zXuYwvlTMtcjcVZj4U0FtJ6xLa2GLbWbKzXjGWk1Epxp0xD
-yaHWWO+7jh6V6BJNxWxZxU6wMUSN29ra1EuLxcvaRwFOaUtute5vEXozWJ4rxylvkIfKKrVAXYHq
-bV1tosN4WwIM4r4lcIvu4DVZn+o+LZE9D8GH+FCiLZXWV+pObch7G63ev+7LqMUYdRERERERkeNF
-QbvpFe3oOJ7APBbdcijUCNv0+dvRMK16m29sFWqNJmkVqDVCdhmodRUJuQVKJ0JzKZVxrKT1SFqv
-sa01w3qLU+uR9VgYS+vO7R69zt1a8HbKWBlLjXW1S2lBO4aIDzmGc5sZmTQF7b62tk9bfERQ3dty
-WxUKbQh4bc3GokmcpUQi5lsPKVG9xFZLvF/T6jj/P3tvF3PdmqVlXWM8z1zv++2iEAjSVU2ZRu20
-MTRwACTGqOkGVALSAYLGH0IrJCJEMdoo9AF2VYyREKIHiqEOFOREtFsjIYo/zZ/REOlSFFAjahTs
-ygfdQqeqq/Z+15zPM4YHYzxzrW/3/q/dtd+qGtfOrPV+3/qba31779r3vMe477DEXQzEEIvvSjTO
-D08J7XGE0F5/AkKo9xTby9FWz5VwQTUD1jJ47lXLviiKoiiKoiiK4nlQQjtRya9C/TyE2F+WdHqX
-C2spEs9AL2YK6Pj1EtpqG2pHCkM/R6M9Hephjk5DjgHpaB/jYIzBmDlGbs504picCeT7YRxjsh8D
-Eaep0pqwWQjk3oXujWwpyzNcYttPwW/Ea1sK7NWF7Wr0BtoUtKPiIeS9MezA7QhHm7So12uL4TKJ
-kfqYDBCzTDvXcxebFNxZBpab3nm7ktRXT3gLV3ylqUM62l6j40VRFEVRFEVRPD9KaC9uE8zptFqO
-g4cQVNVwVUXRmGM+d5RnOsErRZvcP3Yb2DxQbzRptNZoF9geH3l47eDFPpHLhnRlYuxj5+n6RH/9
-y7AJx3VwXA/GfjD3gR8HPmMUHZmIOq2BqtJ7o/fG1ht9U7RHINtpIK907xS84Wfnx16L5Ol+r61v
-M2UeggsMF6YJY8LcBNvifm/hOqM5Pu7E52eGu+2OSAS1udwqyITsHD8997UgnpcFxHLYXhDviLWs
-DxMwRbzl84uiKIqiKIqiKJ4XJbQTt/xh1Xt5urXurwhtyVopifgxzASb3Bzv3H42n2AD5sHmQpOO
-9k6/SAjtjw3GNEwEU2H65Eihra93rBFBaUtkj4nMiUyLjupMGW9daE3Ztsa2bSG0u9KaIk1TO/t5
-bre/9Bwhj5H4lV+eWebemFNO03gYIbKHYFMwE9wV34CWjdotcuHO8DhmiGO1ENncxLai2eilkP3e
-K8M8zjic8rg2YCm2QVwRn7jFaHpRFEVRFEVRFMVzo4T2IoW2C1hWfEW9VyZfS+4qazsrr9L4Dkcb
-cg97jWDPdLR31DubCkpHLp3L42QOwwz2OdhtMufAx4Fen/DXhcFkHiOOfSJmdKDhNMjOaWhA60rf
-OtvDxmXrdJW8KJBesYdgxXIsPnu3zxwyIdxkOMW20cAUH3J+xjFgNHATzONCQzw9U8Zbdod5Vodh
-iERcupyOtoFo1ITlOL1k0viKPXdxYtg99s/FHbGbCy7WEbMMcCuKoiiKoiiKonhelNBOJEWjp4N9
-alCRHBUnK6WUSLtejw+xLZKp3dlN5WJMRoSYsTF5iIRuVfSysb14jPc4DvzYmQfQBHNjjAO5CnYM
-5hjYMbO+S2kpoJsoLZeZ+7bx8HDh4fGBy2Wjq9JU6aohVC1P1MINhxkXAiSC3qZr7JbfOdq4xKi5
-Raf4MYxdjF0NHwarz3sKbQIP8V25y9kDnnFrEYyWlV+RbHY3qb4OE0QzDO0uEx2ihkwkRgbUc+fb
-IoCuKIqiKIqiKIriuVFCO9GzKirkYeRs5z6zRLVUqMPYKz6bqdLBDZc2d2yqvwAAIABJREFUxKYp
-OYI+wpf1C8MPDh80OtKV7fGCtgbHFfaG7xoD3S2cXhsDmwOf4YwjEhq1NXqOhYe7rlwuIbIfHx+5
-PDyw9U5vna03JHem3RyfkzF25rHHrRvDhWERsJadWvE51745k+nG7oPdBrsf+IPDcDgcPaC/yGAz
-iZKuV+LR0ql2LJLNVuf4GT7uuMYIvXp8d5qfVfJPxl2zKTyEfIyQe140KIqiKIqiKIqieF6U0E6U
-W70XMfR8jjS7Kq7L8b13fcFX+rVHDZW0cF2HOiaDIYPGheYHzQ5Aka701ugPAnuDrtCFMUeoSw+h
-7XPgNsEmqKAitAZtkwhW653WOg8PFx5fhNB+fHzBtl24bBvb5ZLd1dGTPcfg+vQG1ydhuGFzMBAO
-iwR0WQJXVjK6hSNvznUMruNgnwMOhx1khzZIkS1ok9j1FjlFNLmXTTrSaIyOe46Imxjq2bUtMVfg
-oiiKaIsLHq7xHhk+p55CmxLaRVEURVEURVE8P0poJyrL0Y6xcEdvkloc13Rpzw7o+Nlt9VbHcyUd
-WtQwjT3lKQeTg+ETFaf3ztY2mm7INYX2phzHgc3BnAObM5zfKOaOaqysuuo9U8a3zpZj4y9ePPDi
-xWO42pdwti+XRwDcDDPnOA5c4LAR4+oow4XdojZMNf16Ecyirmxa1Ixd9xHHMeAAOQQZQrebyJ5d
-cFVMJb4vkdO5Bs/ucaI6LfvIJe+3FNuO4NogBTUobim2LVxvCKGttxLuoiiKoiiKoiiKZ0MJ7UQk
-HO1wSsMtFbdzB5sMRluuNoQYXQnlStZYYbg500NYygYyY/Rcm9CksfULD9sjl/7A9rDxMB547XjB
-MY4c6z4Yx4GNwRwH8xg0hcvW2bZO652+dfq20beNtvUYQ083GRVEM3WcEL9Y1o61zpDG7sLuwtXg
-Op0xHFULsW1grphHuvg0Z1qIYSdC4GQCu8S+dYuxc3OQLtA70h+jXqxNROPA7kLYxM9AN8+J/HXR
-Ig4FGkjLpPE88q8mchaDFUVRFEVRFEVRPCdKaCeaQpuspoqOZj0Dt1xWBdZ6VOSLrzqrbJ5GCEFp
-xNPZMv/LQE1oqlwuG48PjzxePhYOtg2mHczj4Niv5zGOg2PfGceOAFtb1V2dtm0hti8bvYfQFk1X
-XgSW0BaNMDSNye2pjYGyu3I12Cdcx5uEtmTtl0uOjsdo+fT4lJGn5rfQs+zpnhPaRWkPG+2i6NYQ
-n0gbiESKuPuETHSXnBJAHbF0wc8vLg9VyM5sQW9/ldAuiqIoiqIoiuKZUkI7WUI7xLOFQEz3Fp/p
-anO6xrGj7ZinMGeGC563lk6ydEFSSIrHbvX2eOHh8QWvPXyMW7r2ZI6D69Mb7Ncn9qcn9v3Kce3s
-1xaJ2xp93k013OzLcrQ3tGmmnZOOdrjaaFvKGZzT0T5c2O3e0bboChdD1cFv4WNLbJ+OtsUIuAtM
-y+bwCeNwLo9rxLsDG8JAZSA+cD8wjw34/PpIqY0rYIJrjOOTjrbQ8qJHOx1tFUHztiiKoiiKoiiK
-4rlRQjtRTUfbPIO6Gp7J2zHSHDvFEYpGCsW8PwPHwMAn4DBzjxs9R7k7jd43tssWSeEPD8BEJLK6
-5xAUo6U7ruK5Mx171iq5Dy1yBqG11lBdSejONGPfdxxhmDFdOKZzTOdpH3zxC1/kC1/4El/88de5
-Pl05rgfH4ZgJHbm54BIOsosiLtDClW9GlnZ5fifRc80wpjuDUOA+oR2gW6NdQC/pkkvUfaFAX21f
-crrV4opINIbLLZocSGEugoii2mlSf/sWRVEURVEURfH8KKWStBTakqPjSMfdw331GcnduaftGlVc
-97il8LaQoEw5NberoE3pumUa+Ma2Rf1WvGc8TlSZTbHesNnw2c/dajfL+rAIK2u90ZqGyM5RbzPD
-x2BO4+m6gyjXw3jaB2/sgzeeDr785dfPY44BNvE5Q+pqA2201s/qMJEQ3pbOtiNYOvmGZzL5wHxi
-YzBw5jD2J6Nt0B6EdlH6pUE3pDWkGbrlLjmCaornU+CHkx2uOqtHLW6z17xpp+vlq/c3SFEURVEU
-RVEUxXukhHbS1n4zjZZp3y4hIidZkaWE253Oag49x/9mHdZ0C6GeKdlujrfsv87Krcu90F7j0+6I
-C9YUbw3vDayfItPMQpBLOMCtt3Mv+6zjMsdtMIZxzMkYk9ffOPjSGztfev3Kl9/YeXraebruXK+x
-991V6CpsvUXSumz0vqEtXj8EdwMEl9ibPoW2OcMmx7FzHDs2YY6ZjneEpG2P0B+U/iDoxWhbQy8T
-EYGmqCtNboJeJD5PLJVLfjdkp7fFBQlpNN1orYR2URRFURRFURTPjxLaySm03bPnuaFimXYdv78S
-yF0MbI0zyykELcWu5Uh5xJKvwC9F++q+VlqLOqusmg5EaCr0plhreM+EbiSF9u3BTWNkXHJP2dOJ
-N3P2fefpenC97nzxy0988Utv8IUff+JLr185jskxJscx2Hrn4bLBZaNLQyTG0Xvf4jx7uNvS2imE
-kXaK7KgMG2CCDeeYlreTYzg0Z+zQH4TtQeiPSn9sdO+oel5MUNQjMV0yAC0y3W945pCH2I7vQ0TP
-KYSiKIqiKIqiKIrnRAntkyzvEkdUaB5d2k2UaZncTbjZc2QHNAKZhn2nqOPWsmPb4vFTJ0MGBzu7
-dbbZ2A7JSrAMULMZqWLuqMRYeGst3OqZPvHZN5b92M6dGwwqQu+dR4TWOi7xORxFW+M4BnseW+88
-Plx4uGw8Xi48XjqPDxsPl3662bqC1bI2jOzYnhIlZ9agNTKoLXuzDWw6Ng0zGAP23dkOuIzONrMi
-bNV1iURmWwfNqrD4w8gu7jU6jmEMph8Mu0bFWFEUzwYR+T7g+wB39w90JUxE/hTw9wF/yt1/yYd4
-eu/0nl/xeS9evnzJpz71qQ/nxL6O+MQnPsHnPve5j/o0iqIoiuKrRgntk1vImaaobKKoZcI1wvQY
-4Z4W49EqsUus4ufOdm4d4x5Ce84Q2kONIYPhB8e8cgxhbx6BZ+o0BXeLXWw89rC1RQp3C0HuNnMH
-PM/YHGMi4uGOt4aIsGVQ2uUBRGMkHAnRvu8H12Ow7wfb1nl8eDjF9mVrXHrj0jXd8nTMNfbCU8lj
-M349iX7tJbJVQzjHxQhjmsFwuMYY+WUoNhW3DTzKuSIdHfoWIWmiN9feRRDx7DCPiwvGwPxg2BoH
-KIri64yv6X+wzYzPf/7zH/VpFEVRFEXxEVNC+2Q52jkhjYIrLbKwI3nbHbfJnEfsQ2skg4v0EH0S
-z5MsmF4G9RRnyGQwOWwPka1OU6M3aE3OPwnPMXWV2IluLfaVp0xcBGOEwLYIInPzqA+TcNdVBbnb
-r1btZ1J3a8p137nsB9ersm0bLx4feHx84HLZYle7Cb2tjuo8JFztlcY2xYnF9Rh1V72JbSEvAEzn
-OOzc53acORWfDXwDNJ7XnGhWG0iD1gfgdyJbcLntZ5tPpu85YVCWdlF8nXK/VPM1yM/+qE/gGfES
-8kJpURRFUXwjUUI7GTbihxxTjp1ruznVKjnqvCabMwUbw07Bt8ae4xGyBLqBT2cyGPPgEOfKQGVn
-dqV3wXo4wmiLSi1pZ8q4KrgrJlHxZRLiNlK5HdVGa42eTra2ftZ/hdUdr71tneu+s+87132jt87l
-4cLlcmHrjaaSB7f/zHWiM/wU2kR9lwiu4aS37PbWu2OliEcvedagTcVGY+6d0eGqhhGBao9miBpt
-c9T9/HpNYmdeIgUu3G1xXOZaky+K4usId//Oj/ocvjIU+OGP+iSeEZ8CyuEviqIovvEooZ0c48if
-0kTxGFe2jOI666dUUdPojz4fN2FFlaXQFqKGKkK+BKbhPpnsHD5Qj87ovinbptimtN7RtiH9Eoni
-mTDuxDi7r+RvcSSqraFFMFqEl3V667QzzKwjLc65bY2Hxwv7NYT2vh+xA95bPKc1VMKZbpJp6e55
-S77v7TM2idqydo6Nyym2W2u0HqFpksFp7tCkId7woYyrMzF2O2hjYOLo5lxeZACc5/6557eZI/0i
-EuPlaiH8i6IoiqIoiqIonhkltJN97MCradeIYxI70SI3l1ZVo8KL2KvGJNuoUgyvcWtSnOfYudlg
-+s4xgem4wbY17KL4aPTLhX4J4Sx9S4HpKdslx8lv4WRLeKr2cLP7ul1Ce6N1o2+dh4eNYwzGdefY
-D459jwsIKhmklq+HoLLGvw2TFNvEx1xC21XQHHFvKbLbEtktbs1BzTEN8azSwRo2FGPi07B9wHVH
-u3B5BDM5x+fdHXHyggX5WSWdfqfS0IqiKIqiKIqieI7ouz/kG4MxB2MOjjkYNpl5mK/d7RyLlnaK
-7dsY+RoxX+PkSxQqTXuEq3mmhI/BPHbG9cr+9DrH9Q2O6xPH/sQ8dszGGYZ2E77p5J5HVFtFFdeF
-bdvoeSyxvY5t23h4uPDixSMfe+2Rn/KxF3z8p7zgp/6U1/j4xx557fGBF5k0ftk6ly1G0HtreSi9
-xX73bR87jpa/fz8yHq52u93X9BThIjFGb8MZh7NfJ9enwdMbB9c9+r/NbmP70+JwzwH+/MJjdNzO
-oyiK54mI/E0i8hkR+Ysi8uMi8tdF5E+IyD/6Ds/5UyJiIvIn3uK+b8n7TER+Q/7erxWR/1xEPi8i
-x9s872eLyO8Tkf9LRN7Ix/4REfmlH+4nLoqiKIqiCMrRTsYMd/TeKYboyXaJQLJwqBtNewhnjbCv
-lQRuEGPdREWYiiKt02ejzUgwP3WhLFEeI+iagrqliO7amL4WlS1G0kVxDYe7tRa72Bl81jRupTVc
-hOngc+JEUnocK7tbYgQ+X9/OajJPJz4uFqiAmMQIdzwr1qQNxByZEXS2BPXq9ZYcn/fVL57vLe6Y
-GsIEnaAD3QwujnbDNcbJh8M5N+6A6/n95x8SkgnnRVE8T0Tk5wA/CPxt3ILNXgO+A/gOEfnVwD/u
-azzoxnsJQotBI5E/BPz6d3q8iPy9wB8Ffurd4z4B/EPArxKRT7+nD1QURVEURfE+KKGdTFtCW3HN
-pPGzwlkz5yyqvJoYaCRghwh2LLPCHEfEw/2l0eh0bVEVNpYjfZc0RopYCWG+3N/eWhZSW4ykewaS
-uWJAO3ext0wYj11sEY3R7hS4N6Eb1WHufoajmVvsQPutu3qJbGmOaFxokAxEUwFDkOkhtBWmQ+sz
-ne0cRV9xcbnjbWbMaYhHcri4IM0QHWg39AG0O6gzPUQ86zsSzq7yOPK7wrHa0S6K58x/CHwL8O8A
-/zHwBeDnA78D+DbgHyZSsr7nLZ77Xv7h/hfy9f408PuBvwT8NODnnC8i8rcQIvvjRE3BZ990Lr8T
-+DRQBc9FURRFUXyolNBOlqOtmoaHCnq62WTvc+5pa3xtknlcUxwRi33qrPlSFbo2unb60WmuiK39
-4jM6LcfE478qNcV21wgowyauRE93uuqkY9z6Rts2+nZBtZ0VXginy25mWQEWP5M7zwKRcO6xYy4u
-cXv3n7brxwggT/G++sRb1nup091pe0N1ptC/TQMAtxHwOU8RD5Es3nWim9EfHOmko50p5XYT2sLM
-d25x/qoh+LWEdlE8UwT4RcA/5u7/0d3v/48i8v3Afwv8AuC3ici/6+7/6wd4j58H/EF3/43v8Jh/
-g5uT/U+8w7n8og/w/kVRFEVRFG9LCe3Es6LLM2Wc7LHmFMQQLdmN1S1NhpNFIvgEn7mrbZhPzAfm
-A2ciCq0r3RsNobnSmPS+5dFpbTt/vbUeNVo5Ri0a72WhyOmXB7btgX55QFs7zxnCnbc85ozbMN8n
-bkb2jTEs9tKHHcwltFcfeIaPndcZcnjcRZhEMNoU5xBjtIPZBt4PvA/YBmwTcaMpSFPaBtrXIbQH
-o72mtNcm7TXh8mi0Hufmd7Vi4hm+tgLaMgt+Il/TLbtF8XWOA3/0TcI27nD/soj808B/T1zL+2eA
-3/Y+X1+AHwP+ubd9gMg3Ab/6fZxLURRFURTFh0YJ7cRXgrWQadZyV4m9FF0miZ8xaIIT+8OZQY77
-xDGmDfA9ksV9C/dXla4bXYwmna7OtnX6luFl24XeLyG2ty3eI0Wn4qAx1i6t0S+PbA9xSDraGdbN
-tMGcA7PBGNHd7QNsxE65MTA72MeVfezs48qwgSjQQDQPkfN2iWxcMGKF2hwOnF0Go01mn/i24+NA
-LoMmRvPoABdv9IvQL0q/CPpg6ONAXwz08aA/TPoGIjnejsb0ODEuj0mK7Yieux93L4riWfIH3+4O
-d/8hEflfgJ8L/LIP8NpLPH/5HR7znUQLor+PcymKoiiKovhQKKG9SKF9uqdrnnvdncJaQ/IS/+2m
-KbQFSZc4DOgZSpUjwsj0BdKcpo2tbXScLtAl6r22rdO2niL7wtYvbH07HW33HB1vDWkdaZ3t8QWX
-xxdsjy8QbbdBdHfGPJjjYMwD2o4fYGIIAzNnMBhz52k88bS/wdPxBsfckU4I7c65b63OWQEW6Wix
-L56+OFNgV2M0Y26GjQPmgcyBqkdKOw2VxuVRuTw0Lo+N9mjwsCMXgQfQBtLuurF9xa+l0HbBTWIf
-PkfKvSztonjO/NC73P9nCXH7bSLS3X28z9f/8+9y/8/7AOdSFEVRFEXxoVBCeyGrliuDuOQcIicT
-uTKUK3alNWVexn6RRdopeKOeitg4huZoU/q2ceGBrsLWoDcJod3zuDyybZd0trcQ2edfRLd229C+
-nW725eEFnEI7RLmMCEZjCIYzfSKuMCXd9smRjvZ1PPHG/jq7XV/JH5MG6qvBW05XW0QzCD0uApjD
-EJjN8Q5cBmqT5o4YdFFa7p0/vOhcHhsPLxrtMvHN8MvE+8w98LhoEeegyApBc72JbcLVdomO76Io
-ni0/8i73/7W8FeCnAz/6Pl//x97l/p/xAc7lQ8CAn/UeHtfy+Hrn5Ud9AkVRFMXXCC9fvuTly3f/
-/419378KZ/OVU0I7aS0DzjLd2l0w91fqquC2rZ3pYOAphD2qrqLzeQWTNaCj+sDWX/Dw8DEe5WNc
-NmUbyjaVrWsKbWW7XLg8vGC7XNBto5E7yW7xmlnfJRpd3nGe3F0MeHWj/Jb+HfvNPh2bjk1jjhm3
-GZgWoWkZQpZBZOc1h3OMPB1ty2MStyiugmwSf0OpoJcGturQGk0a2wW2i9MuhvQJbeAM3I/ccfdM
-N1eU1Vnesptb4/clAutE9C5crSiKZ8hP9j+g83089qv8L4v3e82gKIqiKIrPfvazfOYzn/moT+ND
-o4R20toWPzi3ELL8tcutgou8XWPLS2RHlVXuLpPj1nREN1p7oG8htF+0j3OZjYspF2tsTek9BHff
-Ov1yoW0XWu+YG80N8x7nkyJbtIWQJ2u/XM5U8DzRqARb9WQx1Y5Px6dh07ARlVs2c287xbZZ/F6q
-89iFtrtgtNzTDrFNus5xAUC10VVom7KZnL3jKlFv1jq07vQ+8TaZMnAZxMTo6dsDikqnZWf5SmNX
-Wantfkt4L4riufJNRH3XO90P8W/dd3OnPwj3r/lez+UrRkT4mT/zZ77r41prtPaN4GgHn/jEJz7q
-UyiKoiieOb/5N/9mvuu7vutdH/fLf/kv50d/9Plf1C6hnbSs7Drd3xTNZyt0/nCK7XxwiGzDbP2c
-gteXo72h7YEthfbj9nEerPHgnQdv9KZsXehNaV2R3tHWkKan+A2xLYj2FNp6Cu1TbEP2bN+d7Jv6
-rE9H+15knzVgzrDJlDjc7ezxRvyVFHJ3idjx7BbvstF0i0ozaVmD1l5xpkUUFUfUUHVMBsYEwtU+
-JwSQ/HyNJhtdeuyKy62D/FajVkK7KJ4xv5h3Fre/OG//jw+wn/1e+Asf4Fy+Yr75m7+ZH/7hH/6w
-Xq4oiqIovmH45Cc/ySc/+cl3fdzlcvkqnM1XTgntZGuPAJgZ0w3Dcgw89Z9HV7ZqjJcjHk736bY2
-1BtKBwSVC00f6O0BlY67MKYxNfaREVDRcDV6o3dFmyJNiTdZHVsZAZ7nEf3Sho+BoUyXfHwEuBnG
-OHaOsTOOK8d+5dh3xjGYc+bzBdWWR6e1jtpEFezOtZc8B9HbCL1kGNpytgWlaY/xcF0iO/LYBQ9x
-zQxx3OJUWyPH3hXzhnu7NWwvsS1rN5ys+vJzFT5GBm7VY0VRPEu+G/hP3+oOEfnFwLcT/1T/4E/S
-+/9JYrxc3+O5FEVRFEVRfGiU0E62HkJ7zgk2XunF9hTdqjFSrin+4Ca0mzSm9EjpRunyQNcHentE
-6LjBOA52djYVXBvSYsRcVWm9o03PtHO/c6Vd9OaWu4HD9AMxhzlzVDz0p7uzH1eOsXMcV8ZxMI9I
-IZ9jhpAVRVtHfaMxUC6oWIxoq2S4eFxYEPWb0F5VX2tc3WMXvWsPsS3RMb4a0QS7c53jsbqce2m4
-Ncw6bnlRw+yViLlb6vv67Lk3LoIq5wWIoiieHQJ8l4j8Onf/gVfuEPkY8PvzlwZ89ifjBNz9r4rI
-HwF+7bucy2fh/l88RVEURVEUXzkltJPlaIsPXBVjx1YvtpMOd9Rd4auFylP0KeqNJk5DcGk0vdD1
-ga09otIxc45jcPjO3BousROuKmhr4So3zdqsW6jacrajNmyNtM8IOZ8GMm4iO4PT9uPKcVzZ92u4
-2HPmmPjEzFPwdppvNB9xiOEoJmtM26OeSx1pftuP1hTZOR4vohl2pqi2nL2/OxIXEO1oV9omqDR8
-dmwaTo6viyBuvDL2DueIPktkS3Rzl6VdFM8WBz4H/Aci8h3ADwBfBH4+8DuAvyMf82+7+198m+d/
-GHwP8PcDH3+Lc/kFwO8EvjXP9UMbHy+KoiiKoiihnahGKI1ICD1HWAHckfztWX8VQWFyVn7Fz7GP
-3GgSQlul525yw82Zc7L7ld07Qzes2ekQR2d1VnKZpbC02P12x8xjnH06ZsQ+OANb57l8d4/H7Smy
-j+OK2Qo24+51/Qx3k5XonWnrgrLms2N0O5vCU+DqClrzcNpjpFzz/AUsQ818udO3VDnPUXjRFvvu
-Lqgr6h2ICxprYh7I79zOJHTPiwSo3tz1oiieK/8I8MeB3wL81jfd54Tg/Z63ee6H8g+3u/9lEfku
-4I8QYvu3vulcHPhMvl8J7aIoiqIoPjRKaCfDDyBGskce0weefdoxL04mc78aN7bcbVWhSYx/a7rh
-c+4c1thniPAHa4x+wXlx1mUFOTptk7mOcTDGwRiDMSdzOnMa0zxd9hhlnx5d2WbGsMlx7Bzjyn7s
-iKeDLXlpwCfGjFs7cBu3dPH1+z6iOkvAxTGyOVxTYJ81ZnnBoUXHeEjy20UCUtAvUR3Pm7FXHt8m
-IuHqO4ouB3w1jGVg2wpyczOaNmgb0gRtNTpeFM+QuKwXIvcXAr8d+DXAtwAH8D8Dn3X3P/xur/EB
-7vuJD3b/0yLyc4HvBX4F8EkikfyHgH/L3X9QRL7v/b5uURRFURTFO1FCO5kexefDBzNF9vRxSxtX
-Od1gt3CyVcL7XWPVKtBFccmwtBTaA2WP+C+uNMZ8gbnddXRz9nGbTWwO5hwpsg+OcTDmZGRa+BiW
-QjsE6TRjzMGYg2mD4zhiR3sccU4aNTIqgjOjMzsFtduENbDuMV4+fWTWmWPLxXYiST13xaMSLL4e
-F8Wl0e+c+NinDiEuWTtmmZ5uNnO92s/vVl1AWyaoh5CfFt3adtaSOV1BtkYTIlmtKIpng7t/hnCI
-16+/APyuPN7ra3znO9z3l4H33Ynl7p8H/tl3uP+V8y6KoiiKovhKKaGdjDuhPU5Xe2Y9lcZOtGUq
-ecxv085U7lDjmuPTLpJCdGBmHLnbLe7sbIxx4FiMiishynMM/RTa4ziPENuDMeZ52OrsdmfMybEe
-M/PxeduacumdrW+0piGmmTmWfmS1lkVw2RLgNl9JVffcEU9ZnZ8tRtqj9nqN0OvtYoQb4iGyYyA9
-39vWSHh0kUsK+XDEHXXB5jz7vO97v30YdKGJ441Y/C6KoiiKoiiKonhmlNBODrsCMNzC1WbG2Pi9
-4ASmw5gWY94pqrOrC4jEccNinNtiTFosHFtxYZ87h+0cM8QwYhEAlkc40yGwj2NnP3b242A/Do5j
-sB+D45ghsi3E7k1oH4w5MBtMC8FsTRGL1zZTpuX9c0RnNoMhk8HBsJ3hO8OuuRcNMgVtQmud1oze
-Ou6e7rSn9BbMhWl3iePOzc322Pke5viYGPGaa687erPzK1Rwk3P/etV6RaXaTbaHu15CuyiKoiiK
-oiiK50cJ7WS3cLSnGRPDxDCJ/WbPRWN3YZqn0J64x97y2fucG35LkE+HYY6m0MaF3fYQ23lAA2+4
-TYQQ2jPF9jh2jj2Cza77wXU/2I/Bvo8ISDuF9uAYI4T2OMj88bjtLZO8J9OUMfY8Dkwm1gxXY8iM
-CwB+5bAdV8dn6FltSu/G5n0tX57ONURI3LRo24pO8dgJj4C1FMwu+AwnfJjTmtK2TpeeYWv31eES
-7bf5vLMzW0CkRfgaUkK7KIqiKIqiKIpnSQntZDna02HiTBxXyT5pYhycNaqdY81tVa9m2JjmcHUG
-lA0zDnPEUhRaCO1jhvt8zAN8giloA7dwopfY3q8c+xPX6xNP153rfvB0PbgeI+u6nDlTaB/xmnOO
-dInDIXZr8R5M1ORMI9/3K64G3aHBVGP4NRxt35niuIIJSFO2TBF3ydauO6GNRdc24uc+eNcewjhn
-7gXBpkHuhLeuXARa0xTP8qrYXsoaOC9kSAj5SEa/v78oiqIoiqIoiuL5UEI72UekjsceNpiHgy0W
-QWF4Jn7PELc2/S55PASomiNioalzl3vOyWEhsnHYjyvX44mn44mn6xuYNqwppg3Fcz85UsfHOBhH
-HMexs+871+vO034wMxxszhw3H3FMm7SW494aI9uIYRgyYb8+se9x0ByZIF3wZgwOJhEEZ+pMT6GN
-Zg3YiL3yMx18lYrn6DuO9BDJppZutmFoXoDIkXsfOI3Woj87Eszm4LtaAAAgAElEQVSBvKgh66KF
-hrst6YwLGqFuGv3dJbSLoiiKoiiKoniOlNBOjmMAWTltEocrzkSAgTOODCIb0Wft2WN91lQTDrfj
-4TjPSAmXOcBidPrJnnhje50v9y/xpfbAQ+s8tA1rnUYkc7tl+vcYzDnz18bMILTjOJjDovJrhJiP
-nexMDs9uaxdjGthwxCYuxrFfYxz9uCKTDGmL/uuhAxM7k9DPrm0k07+NKfPOZwYRv/1K7iu7Y789
-7orxcl/N2q650+2MYajmnPhyrTWmCVoLgS7Wom/blKadpre6sqIoiqIoiqIoiudGCe3kJrQzAdvD
-gZ5OjEZbiOY5DJshxo0Ulu2+gDUD1MYS25MxwGb8+mpv8EZ/g9fbl/mSPjD7hvcL9C2qwULpR73V
-mK8kcEfF1+A4BuMYdwnkK+nb0xUOke2q2Jy4OSaO+eQ4rnnsaIuU77Y+q8bONrrcejl7sN39FPsi
-gp7j3Ys7+e3rskM0cAsWlWee35dLTA0sR35MVOUceZecpI+R8YZ6o3lDraHSaNJKaBdFURRFURRF
-8WwpoZ0soR2CsRHx1+AjOpx9CmYREGa2HNpVsXV7HU3fNiqpZt46MkJQtqm80V7ny/rAg2zY9gDb
-QLYHTBtnmpp7JIOvequ5OrRzH/sIwX3sIzqr73abwxUO0T7dGD7jsBFBaMeVY+zohOZCJ4LcrEd3
-NllPLfdiOoU2PmJPWjR32Mm48FWqfXP3PT3/+Hosf1/i+zJhGug0RCat56h7inhyVFyFFNqdRkO8
-nYPkN1FfFEVRFEVRFEXxfCihnZjd/cLjF+Eup7t95+62FLNNlKaao8ycFVWGp/PqNAGyxooW4vTw
-yRvjypf21/HcbQa4tB7lVTmuPS1SusOpBlWh9c52uaCt0beN7cHO+5fYlRTLJs40OKZx+OCwEZVf
-EMnduoT5CiJLwY6nDx3J4XjuSXvWmYlkKJpjKjFA39YoOXffxZLD8XwXv2WY5fNnTguIKepOXKpY
-n0UiZM0cZ0admMRj1kuVrV0URVEURVEUxXOjhHbid0L7Vl8lSIq/lXytoURRlshuNG2IegpXEAwV
-R7XRnOjIahneJcrhxtPc0esbmdwdYnS60aXRRWmiTF9hYenvaqNvnQuO+YZ5hret7WcxDI/9bp+Y
-x573MONqk2MOJLuvSaGtuhxwiVCzOJlTZDur3Pp2m01lpyBe4jzEtb8ituWs+VLcc/9bwuU2HHFn
-mqG+dt1v6eOo4yZ5O5luOTHQ4vW/Kn9nFEVRFEVRFEVRvD9KaCdLaDuOO+kkg7imzF5iO0SpInRt
-IYy1pRbPHWlCaDYBV0lHXPF8jeHGG2NnLnGfo9jmcGmOaWfTNznaRBVW95aussbes4RTbFgIazeO
-uWPjiL1uh2MYezraLdq8aKqvpntLutzicaFgBb0tse3Lhk5Hm7saa4158dPJPhPDOce8BU2RbTmO
-nmLbDTHBXHME/y6JjRW2Ztgp0ONb9MwiL4qiKIqiKIqieG6U0E7WnrV5jkS7g0tUbomHU4vnGPWr
-bqrzZnc1x7FVQ4OqnsJYvOEIU5zDJ1c/6HOnj0Z44avqKh6DCtKURqOr4E0Rc6Q1RBtowzCGDaZP
-sIOxD9xhTFuFXRwex+nMsyznNT6+AtBi/3z1fpPnElcSUpHftXuJeK5Jr11pPw9Zo+zrr5Vgnq/h
-eZg7Zp694Bbu+t3utecDHcvvWVBRTEpoF0VRFEVRFEXx/CihnQw7N4NzdJxUkuEA3zqv/BwtN5vM
-u9cQiDHnHI1eYltEI5pcI0FbpSF5mAqHG2/MI4LC1ntpOsC90boi1hE3xJyWQhttSOsMGzAFm4YP
-Z4ix+8GT7exz50gRnm3a2NqDzqXqc1eb3IdOF90ldtRRyZ3ullb1TSX73WHmcWEh58qdnAaQFMaW
-2j6rvlYXmK8EcjVERux2i5zf6Zmovna081CtILSiKIqiKIqiKJ4fJbSTsRzU1fd85nb5OTV9S9S2
-GK6OBekU1alBo9HqFLKigrgCDaEhdFRuYtuAwyc+9gw1E7RpJG2rxri4NhxQM9Sc6YSj3TrSOvs8
-sMMZx4EbTInws+vcudrBboPhk4nRwqOPkfB7RzvmsW9R6h6Bap7utjRFaKi23F+3eJ2ll+221+65
-V85ZNxYp6CrLk5ZzDGB97WYwR7yurp7s3Il/K6EtGvVlRVEURVEURVEUz40S2sk8x6Fvc9FCVE3d
-3F8AT8fWWHnhvpxaE0L7rTSvHDNXzY3iENiqd0J7wj4mY2aqtirNos7qokrvjbZtITrNae6Yc4ps
-eoehHHIgrvhwpsyb0J47wweDmRcIwNC7ADW57Wjfi+x1LCNfw5VW7ZjNTB23HAHPUXvzU2SHWR1L
-6Geyebr08ZKWS9556cJyF9tB1VCJoDlf33k64BHmFuP8Z3hbURRFURRFURTFM6KEdtKkxQ8pOCV3
-i5toJIx73ras80JjB1sU1QZquDq0JWFzpDr3uj27qkNb+hnkbZPYuZ6ARXUYDjYnr/ECbcImG9qU
-1sITdwS0ReqYNg4DMKYNxjgY82B6HM5MB/j2WUMYR8CYCUxZ0tdiX3rGfW6Cy0wnf8XBgduMmrA4
-eZoaqCMZ3raC5PD1PdgpylWEpg0zid+39Neb59eSVrdIFIyJ3kb13VDlfJx6Ce2iKIqiKIqiKJ4f
-JbSTJvFVRGJ4ONmKIJ6Z2SY0aXTtdOk0TWe6KaqKycSYmIxM/47l4xixXnvJ6RpngJiLg5EiO0U5
-EQg250C6sl02XEG60qRlerfiqpjE+LdMwXGGDY65M+bOtAPzEUJZM+hM5dzBNpdTaFv2aNsS4Cl+
-XcIBJybkb6P0uZ9uZojaLSdNPUfIb3vVIbLDrZas+dJ0+6d7jLqnC649969RRGKfXCQqzsyW0I5z
-azja5pv/GIuiKIqiKIqiKD5ySmgny9FWCde4rWTumLUGhy6dTTcu/UJvHW0tD2X6wfCD6c4ww1Kh
-OvNM2CbTuV08e6gdZlaLTY/e7DkZEs709rDxwh8x9Uge10bTHuPbhHQ1AT0ET0f7GDtjHplCPjBm
-GMSN2150JnwvkW1E5dbpaFsK7nyKK5m47kg6y3FMNHQx0gQzwWzta9/2qm9i2xGJCwaIxGvMmWPj
-t31uX3VeWQs2zbBpTDNEod225L+qf48URVEURVEURVG8F0poJ5e2ARlZJkrLUemzQtqgt42tbXTd
-UvAqIi0StTHwkZ50utl3QnuJbfEU2SJRT5WuLhkoBhPzgY3BdX/kOHbGGHgP57ip0ltjEgFuywUP
-l3ncnGwfQM6j66rWArXYJRfz+NNvEYhu4sx0tWfugbsbtk7eYjQcs9tYvK975ZW17leP2/mRKeTr
-+kVGvGMzney8dRdcI71cJO6feXFAAOZ8c59aURRFURRFURTFs6GEdvKiPwC5lw2s+DJxgRZ70007
-KnGIK256isnplsdk2mT6TLE7ziCvm+jMne2MJ1+RYBCj13MCTMa+c1yvHE9PHNpoKLRLCHu3rOOy
-EL828xi4p8AWR9TPZHEkwtrU8zM2hS54j1qxbAljrNFvOCu4liUveaaiHhPjGsebK61PB//OzZfz
-+Y5mmZlbHDbjooHhqMZ5No0Jg9MlNzm/I5851l4URVEURVEURfHMKKGdPG4ptD2E6Ar+ip3i1YUd
-y8ji8XPme+HmDPcQ2cwY2fbY1XYm96nZIVNzqXm9QKaTk0FkTMMNjv3Kcb2yPz3x0DZcL8jFaSKR
-PL4Cx1Jkm41IBPeYR48wMQ9HW/2sy2ooXQRVQVpcSDB3psKA2J3OMfclsuXW9XX2b6vE85eIX6xK
-MM/j3uIWoqLL7ru47/bTcUOb0NZ9KvE13b2Wm2ES2elFURRFURRFURTPjRLayUNbjrYjEaKdAWLt
-TBY3E9wkbzO9O8eoJ+loM3O/OUR2jI7fUrjDk9WM87JwzP1cng7BPCY2/RVHe/QLtj0i5hHWBtnj
-Pe/c7BDbMYseIltIVztdYlWJfm7RqPVa6ee2EshDbK+C6xWAdn+OoppHvKbcO9p3LrYbt310ubnZ
-IsvRzmA2i9l5zxF18zhf2qpSE9zXiRLj5TJzHL0oiq9lROS7gT9A/Nvjb3X3v/IRn9JXxMuXL/nU
-pz71UZ/GV4VPfOITfO5zn/uoT6MoiqIoniUltJOzPhsQCdEn6zfcEJu4LSc7RJ/LuSEdo9YWNdSW
-zrVnuvYS5UsA+ynCNYUkZJ9W7j2H8FzhZtfjietx4XE8MuaOzQfmHHGki20eKeDLdVaF1tIpT3te
-MmwshKrH+eUHNzcGsZ89WfVmZAx7iOlwxuUU6KJZG7a6xhHmDPHrBtbBt+wTF1nmfbxfNnkDiEvu
-qcd54WBtCfw4gWWaSyp6cbm9WFEUxTPBzPj85z//UZ9GURRFURQfMSW0E/PbzyFIU0KbpVAGvCF5
-gKbJG9a3r7RuyyCxO2F5ikh38IlLIzxwzVTz1bPl59g3nkJ77uz7E/t+4TiuzLFjdjBtxC54Cu4Y
-GY90b8ne7FNosxxlTlfZFZaL7BmAtkS25ZM0xbYqeArs6D2Tm/g+r0bEdxRa35jT6RZfgEgI9LZO
-RiJ8zbGcRJf4bizOCI2LFbjgLXa2w5m/9ZtTQrsoimfLz/6oT+AnmZdQqztFURRF8Y6U0E5OoZ2C
-NBegz17pOaPaSnHUJZ1hIJ3vVWdlM19L7hzttUud49fOxDx2v2MtOUai3XIc/M7RHmPnejT245rV
-XTtzHthcIjvC11av9bkFLim0fcWshTYVWTPx4ZtHd3Z8zuHnsPv5+VRiNd1Pkc3paKN+E765z23m
-+aNkmrjEmHmL55Kvfaazkye2AtgsXtchE89DZNPWGPt6vyW2i6IonhMK/PBHfRI/yXwKKNe+KIqi
-KN6JEtrJHCN+EJDmiNpZQ3Vndp8p48s5RhzXFUhmeV88yXP22leoGDeBKXdSM56TQV9uGWQW4WqH
-Dfaxs4/97Mi2eYTAPo/BnHY66n4GrEVgmctytmP+WuTciM6c79yLFs9O7Lux8TU63iL4THs7nWXV
-5Smn+4yf7r9lf9ccxuyDMfPLW056Pj5eR+PqxPqiM+lcfP2RhCuumufutz+HoiiKoiiKoiiK54Z+
-1CfwXDiug+M6GMdgjpmCNZO6tdG106RHE7QvB9rCSZ7HK2nfK/wsdrblrPPyXJZewvHcgybGrc0t
-3OWUv9ONYZPDDo55MObBHK8eYyxX225j6x4p3Uslayant6anWBX87lxzL1wcbU7bQDtoy4sODbQJ
-2pXWW7xO03Nfezn7qxLsvNBgsa84pjHmYMwRn8MOpo8Ig1OhtUbTHodsNOk0OioNpcWtaPaWp7S/
-G3cviqJ4BuT/n9a/k4ria4WXL1/y6U9/mpcvX37Up1IUxftgRhcyPHMt+6xP7qvJsR8c+8E4DuaY
-p0MMkiJ1Q7VlzZfgFmnZNkccNvBMz14p3Wfytq1qqrvhZ5FTNJ4i1WNvecWlDbdwtFOgnmI7x8fP
-QLQ5sTzfWxVW3C5R37ShqlHJdU5cZ3P1ctHV0AatQ+uC9nS3myA93Oy2NbQ3tGUwmpye9u0z5Hub
-wzRnzskYI8V2fgYbsTe+hHZrIbClo7L9BJHdNIV2Lo5bppVHYnlRFM8VEflpIvK7ReR/E5HXReSv
-ich/LSK/7n28xreIyL8pIn9RRL4oIl8Wkb8kIr9fRL79Pb7GrxKRPyYiP5LP/99F5PeIyDfl/f+P
-iJiI/Hsf8KO2D/i8oig+Il6+fMlnPvOZEtpF8TXGndB+1v/fW6PjyTxydJysvWoaO8kxgI1KCyM7
-RSzZ/exieOR1p2C9H4G+/SiQI9y3zulX47zuxXZYwtON4ZNhwjGPFNcHc+So+DrGTWibrYiafHVZ
-Y9eR3L3c8pVsftrPOcYdDnWGk+XdouloN0VbI1PLAMPNz53r9ZnPT+XEBYnpxD8PTsyhG43sKF/f
-tUt0qpnG48QQsRgtzx5zVckJ8wxwK5FdFM8aEfk7gR8EPsntXxMPwC8BfqmI/AHgv3mX1/gNwGfz
-efd28d8OfCvwm0Tkd7n7736H1/h9wG/JX67X+FbgtwO/XkR+BetfhEVRFEVRFB8CJbSTLnFBREVo
-OW4tHqng5rmDPQUbgo9wtF1jkVh0DWmv2iyPELLMCIMVRPYWb3yX2h3aOJ+08r58ubeOTcPHxMfM
-4msHy/c6M9RWtniOqrtnmpmc6ehrJ3tdGJAU2Zk9Fm+f4vm2Gy2RQn5rBLuFmYkgoZwRVTDFc8y7
-qaPiyNq59pV8bvm+2fOdVWnm+Z457q4a4/vC2mHPaYGfsD1fFMVzQkQ+DvyXwCeIf1j/MPCHgB8B
-vg34F4F/EnhbR1pEfiXRsQ3w48DvBf44MIC/G/he4G8G/jUR+TF3/+xbvMa/TIhsB/5f4F8H/gdC
-uP+DeR4/ALz2lXzeoiiKoiiKe0poJ11DaAsptF2z31mw4fgMoe1T8RGiT1qGhwmoeJiyLKc7BfBS
-r3fVVm/JErErhUxu9opZpHn7NHwYjIw2d0dSYN+L7SWyz0IvlRwj55bu7Suh3Ffe2Sns1zmendWr
-VuvuYxh+PlbysSIS1WfRB5a1XpZC23M7PW3+U3kb8SE4R+3XJIFmJZjIq8nur4rtoiieKf8KEU/t
-wPe6+++5u+/PicgPAP8Z8A+81ZNFpBNONsCXgL/H3f/C3UP+rIj8J8CfIRzz3ysi3+/uf+PuNb4J
-+HSew/8J/F3u/mN3r/HficgfA/4kcKGu3hVFURRF8SFRO9pJk0aTRs/bRkNNYYINZ+zG2I25GzY8
-xTdnOrYQYls0j6zQWlVa8BZziZ7/s9xuuQlasofbs35r7YT7TEd7GkxHLI7T4M1ObjfBTV/dDV9B
-aRavd4p17jqzT+GcbV4KqvJqwvj9bHza36IRlhaBaUrvjd5a7IajIbJPVzuq0uTO1V7fkZ275TGu
-3zRC0c6rAOnI+3ke9d/FRfHcEJEN+I3EP6B//k0iGwB3n8BvAo63eZlfA3xz/vyvvklkr9f4K8C/
-lL98Dfin3vSQ7wYe8+d//k0ie73GnwF+3zt+oKIoiqIoivdJCe3ksm1cto3ee+wEZ7dzpIsvF/Xm
-3q4E7BW6LW/+S6JaS1RSqAqaZu9yciPyzFib06cSPQW33MamiXNZO9k+DXGPvmyROJCsupYlbXPk
-WjIB/JZIflvNlhTnmRI+Y6fa5wpwi1cjw83sfJ1bwjm5271SyM+LDGSqueVFiRxtF2LnWkSyl/u2
-H67rNVTyMZp1YcYYkzliLzx4dcu9KIpnwy8Efnr+/O+/3YPc/fPAf/U2d/+y9TBu4+NvxfcDX3jT
-c978Gv+fu/8X7/Aaf+gd7iuKoiiKonjf1Oh48nC5AEvyxl+nwCaF7xJ/riHx0vK9ieElIh3FcFEa
-dpscz1otcTt3r0Pv3tni67U0hbrErrIQ++I+HRsz3lcjjbtpCm0VmmgEtHn4xcBtXHx1bDuIx5D7
-Wrh2lsiOMXXVEOstx8At3X3u/GTgdqkmLypgxGcxw9K9Xm/qoaTzMkDLCxL5nSznvHFemJDThY9g
-uKgEm7iEuld91kGDRfGNzM+7+/mH3uWxfxb4lW/x+2t3+/9297/+dk9290NE/hzwHfzEfe9vJ/71
-9j+9yzn8BWAHtnd5XFEURVEUxXuihHZyuTwAMG0wbMSYtt1cbEjHFUE9a61u9m3+pa/8rGK3xeb7
-MLS0ht3tzC87PdpbMXW64ktoZxjYcrR7jnOnm931drgLJkJzPUPD3Dj3vm+j5CsBLWvBJszhzOm0
-Fp/ERQGNXXVC9JKBbwio3yWbt6wVy91pt/isvsxtBXqKbGl5UQLImi9p2d/Nq/VnZs60yTEHiEW3
-912ndlEUz46fcffzj7zLY//aO7yGv4fnA/zVt3hfuLnqP/pOT3Z3E5G/AXzTe3ivd8GBn/UeHtd4
-5q0k70BVIRVFURQfPi9fvnxPdXvH8XZbZ8+LEtrJZQsj4xhgbsgZGnZL3xbkTCUXWcnea3z5JrBj
-79hOoYzmHnI2V616qrVW7XC62et3JN9D0tU+3d0MRaP5+ZSW7vc6Wm4/n0HkfsrpFPTrooCfhvNt
-bBzmiNMxFVqOj0PufXt+Dvn/2bvzcMu2u6z339+Yc661dlN91TnJyYGIBC9oAEMMPdIKgYCAiIBc
-IYRGH3kUFb1cfbgXgvfx+iAg3AteaZSgMSI2xAaMGAitgpEoCokijZpzTpLTVLObtWYzxvjdP8bc
-u1btU7tqV51dtXfVfj/PM8+aa60x5xxr11l77XeNDgg+/gx2fwS7U4vv/OxwdruN+07Xca9ufH3s
-zp+Gh7H7e9hZ77uE95RK13FCHlvvy7raInLsvdCJFB7AiRhumetFRETkJr73e7+X17/+9UddjUOj
-oH0AO/FXREQOZHnSsUcpM37vZ79W5MuUX78HaWV+0dIxe+vxKGUJsH1Z6Up07lZlDmC3Nf2gvW0e
-9F45Tz/9NI8//vhRV0PkrvV9D8CrX/1qJuMQQhE5OiklLl265Uc2AM88s/uF9t6ebMeKgvboG//C
-zzzYf/GIiBwfyzOEvwr4hVuUfdU+j/8q8DHAB5jZhf3GaY/LgL2C8n3or+55+tcoIfz33qa+H0pZ
-V/uFfKe6+xmyM7fH7Ry03HGVc+bJJ5886mqIvGBLf7SLyIPlWOc3BW0RETlsv0xpTT4L/DHgO29W
-yMxewj7raANvBb6a8iH6FcC37VPuC4EzlJD81j3P/STwqcBFM/tMd/+X+5zjy/d5/E50lLCeOdi4
-chEREbk7j1AGs3ZHXZFbsQf9G3URETl+zOzbgD9HCcDf4O7ftuf5CvjnwGdwfYTOB4xrY++sxf3b
-lLW0rwGf4O6/uucc7wf827HMNvBSd7+89PyLgd8CJpTu6x+7t2XczD4GeBvXZxz/IXd/3Qv+AYiI
-iMiJptmkRETkXvgW4AlKiP5WM/t7ZvYZZvYKM/siSkD+DODf3+xgdx+Ar6EE8DPAL5jZN5rZx5jZ
-R5rZn6UsHfbYWObrl0P2eI73AK8f6/BBwC+b2Z8ws99nZh9nZn+Z0gr+JPDszmGH+UMQERGRk0kt
-2iIick+Y2e8G/jVlnPTecVQO/CDwc+PtDS3aS+f4Y8D3Urpl3+wcCfhGd//WW9TjbwB/fOfunqef
-Bj4L+FHgceBvuvvXHuT1iYiIiOxHLdoiInJPuPs7gd8DfCvw60BLWfvqp4Avcfev2inKPi3J7v53
-gQ8Gvgt4J7AFzCldwb8PeMWtQvZ4jj8JfC7wE8BzwAL4b5Sx469w93cAp8fi1+7mtYqIiIgsU4u2
-iIicaOOkbO+mhP2vcvcfPOIqiYiIyANOLdoiInLS/dGl/V88slqIiIjIQ0Mt2iIi8tAys1XgtLu/
-d5/nXwH8NHAKeLu7f9R9rJ6IiIg8pLSOtoiIPMwuAe8yszcDbwH+K2XdzceAzwReB6xQ1r/+c0dV
-SREREXm4qEVbREQeWmb2Usp63M7zZxxnfLynjM3+e/ezbiIiIvLwUtAWEZGHlpnVwOcBrwZeRWnh
-Pk+Zufy/U5Yf+253f/dR1VFEREQePgraIiJy4pnZ+wNfR1lT+/0o3ct/E/gR4HvcfXFI1/kS4LXA
-hwFngfdR1hL/HnfXRGwid+hevnfN7JuAbzpg8U9y95+922uJnARmdgn4yHF71bhdGJ9+g7u/7h5c
-88g+dxW0RUTkRDOzzwH+LmUt7b0fikZZA/w17v6bL+AaM+AfU8aF3+waGfgWd/+Wu72GyElzr9+7
-S0H7dn8sO/ApCtoit2Zmec9Dy++tHzrMoH0cPne1vJeIiJxY46zjP0yZdXwT+EvAxwKfCnw/5cP5
-g4B/YWZrL+BSP8j1D/ufonRn/0jgK4HfoHwef5OZfdULuIbIiXEf37s7Xg586D7bhwFvP4RriJwE
-Pm7/A/gJbj5/ymE48s9dtWiLiMiJZWY/C3w8MACf4O7/bs/zXw/8NcoH9evv5ptvM/sU4K3jOf4Z
-8Id86cPXzC4Avwy8P3AF+J3ufu3uXpHIyXCf3ru7LdruXr3wWoucbON76u2U5TSf2TNh6aG1aB+X
-z121aIuIyIlkZq+i/KHuwA/s/UN99B3AuyjfuH+dmd3NH9tfP95G4Gt9zzfc7v4c8A3j3bOAWrVF
-buE+vndF5BC5++vd/cfd/Zl7fKlj8bmroC0iIifV5y3tv+FmBcYP578z3j0LfPKdXMDM1ildWR14
-q7s/tU/RfwJsjPuffyfXEDmB7vl7V0QeTMfpc1dBW0RETqqPH2+3KV3I9vMzS/sfd4fXeBUwucl5
-buDuA/CLlNa3V6n1TeSW7sd7V0QeTMfmc1dBW0RETqoPoXzj/Rvuvncm1GX/Zc8xd+J373OeW12n
-pkziJCI3dz/euzcws39lZu8zs268fZuZfYOZnX0h5xWRQ3dsPncVtEVE5MQxsylwcbz7xK3KuvtV
-SssZlHV678TjS/u3vA7w7qX9O72OyIlwH9+7e33aeN16vP39wP8N/JaZ/cEXeG4ROTzH5nO3PuwT
-ioiIPABOLe1vHaD8NrAKrN/D62wv7d/pdUROivv13t3xn4A3A/8OeApogP8F+FLg0ynjv/+RmX2O
-u/+ru7yGiByeY/O5q6AtIiIn0Wxpvz9A+Y4yjmvlHl6nW9q/0+uInBT3670L8Nfd/fU3efztwBvN
-7GuAvwlUwA+Y2Qe6+0HqJCL3zrH53FXXcREROYnapf3JvqWum1LGhC7u4XWmS/t3eh2Rk+J+vXdx
-943bPP99wN+iBPnHgC+402uIyKE7Np+7CtoiInISbS7tH6S72Np4e5Cuqnd7nbWl/Tu9jshJcb/e
-uwf1vUv7n3iPriEiB3dsPncVtEVE5MRx9w54brz7+K3KjrMK73wYv/tWZW9ieSKWW16HGydiudPr
-iJwI9/G9e1DvXNp/yT26hogc3LH53FXQFhGRk+qdlC6fL+B1ivAAACAASURBVDOzW30efvDS/rvu
-4ho3O8+trhOB/3aH1xE5Se7He/eg/B6dV0TuzrH53FXQFhGRk+rnx9s14JW3KLfcHfQX7vAab+f6
-ZCz7dis1swb4aMof7W9393SH1xE5Se7He/egltfsfeoeXUNEDu7YfO4qaIuIyEn15qX9r7hZATMz
-4MvGu1eBt93JBdx9C/hJSuvbp5nZY/sU/QLg9Lj/T+7kGiIn0D1/796BP7G0/zP36BoickDH6XNX
-QVtERE4kd3878HOUD+OvNLOPukmxPw98COUb7+/c+423mX25meVx+z/3udS3jbc18D17u7qa2UXg
-r453r1JmMRaRfdyP966ZvdzMPvBW9RiX9/rK8e57gR+981cjInfiQfrc1TraIiJykn0dpUvpCvCv
-zeyvUFq+VoAvAb56LPdfge+4xXn2Hafp7m8zsx8Gvhj43PE630npZvphwF8C3n88x//m7tde0CsS
-ORnu9Xv3lZS1sd8G/EvgP1MmYasp4zr/V+APjGUj8NXurmX5RG7BzD4OeNnSQxeX9l9mZl++XN7d
-f+gWpzv2n7sK2iIicmK5+380sz8CvJHSheyv7C1C+UP9Ne6+/QIu9TrgFPBZwCcBn7znGgn4FndX
-a7bIAdyn924APhX4tP2qQQnfr3P3H7/La4icJF8FfPlNHjfg48dthwO3Ctq3c+SfuwraIiJyorn7
-j5nZh1FayF5DWQ6kB34D+BHge9y9vdUpDnCNFvgcM/ti4LXAhwNngfcBPzte45deyOsQOWnu8Xv3
-xyjdwj8GeAXwKHCBEgguA78CvAV4wzgmVEQO5qAz9d+q3APxuWvuWpVARERERERE5LBoMjQRERER
-ERGRQ6SgLSIiIiIiInKIFLRFREREREREDpGCtoiIiIiIiMghUtAWEREREREROUQK2iIiIiIiIiKH
-SEFbRERERERE5BApaIuIiIiIiIgcIgVtERERERERkUOkoH0EzOy8mX2Tmf2imV02s2hmedy+7Kjr
-JyIiIiIiInevPuoKnDRm9gHAzwGPjQ/5nlsRERERERF5gClo33/fRwnZDiyAtwJPAml8/l1HVC8R
-ERERERE5BOauhtT7xcxeBDxFCdkd8HJ3/62jrZWIiIiIiIgcJo3Rvr9esbT/cwrZIiIiIiIiDx8F
-7fvr3NL+e46sFiIiIiIiInLPKGjfX5Ol/XxktRAREREREZF7RkH7HjOzT9xZugv42zsPA69dWtJr
-Z/vbe48xs59aOtdnmtmbzOzXzWxzfP5P73PdNTP702b2FjN7t5ktxqXE/rOZ/b9m9pF3+DqCmX2l
-mf1rM3vveL7fNrM3m9nnLZX76aW6//47/oGJiIiIiIg84DTr+P2zM+uc7bl/22PM7DTwBuDzlh/f
-7xxm9tmU2c1ftKfcBDgL/B7ga83sTcBXu/viVpUws5cA/4zrY8x3zvf+wEuBP2hmbwa+7HZ1ExER
-ERERedgpaN97TwLfPe5/MPBplBD6X4Cf3FP2F29yvAFvBD6b0t383wPvHB9/OXsCrZl90Vg+jM8l
-4OeB3wDWgU/g+hrefxT4HWb2Ke7e36zyZnYeeBvwgUvX+k3glygzp38I8FHA53K9xV5EREREROTE
-UtC+x9z9N4A/DWBmX04J2gC/5O437fa9x8dR/p1+BfhSd3/n8pNm1izt/07g+7k+JOCXxmN+e88x
-fwb4a2O5jwG+Ffgz+1z/u4CXjfsL4Cvd/Yf3nO/DgR8B/jAlfIuIiIiIiJxYGqN9/NWUGco/ZW/I
-BnD3YenuN1FarY3Sgv0Ze0P2eMx3An9hLGeUbuQv3VvOzD4Y+FJKS7YDr90bssfz/QrwB4ANbpzw
-TURERERE5MRR0D7+HHi9u1+5VSEzOwP8kaVj/oK7b97ikO8Cfm3cD8DX3KTM65b2/427/8N9K+n+
-P4Fv5/oYdBERERERkRNJQft42wmtP3KAsh8LTMf9Z4F/cavC7u7cOKb6k29S7JOW9t94gDocpIyI
-iIiIiMhDTUH7eHPgt9396gHKLs8I/u/c/SDrdP/CeGtLxy/7sKX9X7rdycZu6s8e4LoiIiIiIiIP
-LQXt4++ZA5a7tLT/Pw54zH9f2p+Y2frOnbEr+vJ463cf8JxPHLCciIiIiIjIQ0lB+/i75RrXS9aX
-9rcPeMzecqf2OR/A/IDn3DpgORERERERkYeSgvbDYzngrh3wmL3llidP2xuYV+/ynCIiIiIiIieK
-gvbDY7mL+fsf8JjfsbTfu/tuuHb3a8Dy0mGPH/CcBy0nIiIiIiLyUFLQfnj8h/HWgI80s4Mss/Wx
-460vHb/sPy3tf9TtTjauxX3pduVEREREREQeZgraD49/A3Tj/iXgNbcqPAbxr1h66KduUuynl/a/
-9AB1+GMHKCMiIiIiIvJQU9B+SIxdvf/B0kN/zcxuNV76TwEfOu5n4PtuUmZ5ne2PN7Mv2O9kZvZ+
-wNdTWsdFREREREROLAXth8u3UCYxM+B3AT9hZh+wXMCKrwO+fXzIge929/+592Tu/i7gTeP5DPgh
-M/viveXM7MOBtwKnud6qLiIiIiIiciLVR10BOTzu/ltm9lXAG4EK+Bjgv5rZzwG/SVmy6xOAl+wc
-Avxb4BtucdqvAz4a+ADKzONvMrNvAX4R6IEPHq8D8I+AR4BPHO/nw3llIiIiIiIiDw4F7ePtIBOa
-3cDdf8TMtoAfAB6lBO5PHjco4Xqne/ebgK929/4W53vOzD4J+KfA7x0fftm47RYD3gy8DvhXS49v
-3Gn9RUREREREHnQK2vef77l9oeWef6D7j5vZyyjB97OB3wNcBBbAU8DbgL/j7m8/4PmeMLNXUSZP
-+xLg5cAZ4L3ArwBvcPc3A5jZ+aVDr95p3UVERERERB505q65q+RwmNkKcI3yBc6Wu58+4iqJiIiI
-iIjcd5oMTQ7TF1BCtgPvOOK6iIiIiIiIHAkFbTkUZnYO+MtLD/29o6qLiIiIiIjIUVLQltsysx82
-sy8ws+k+z38c8PPAS8eHnqBMtCYiIiIiInLiaIy23JaZ/TYlRG8B/wH4bcrEaueAj+DGGch74LPc
-/afudz1FRERERESOAwVtua0xaL//zt2bFNn5n+gp4Mvc/W33pWIiIiIiIiLHkIK23JaZvRT4fOAT
-gA+kLBV2ARiAZymt3G+hLBnWHVU9RUREREREjgMFbREREREREZFDpMnQRERERERERA6RgraIiIiI
-iIjIIVLQFhERERERETlECtoiIiIiIiIih0hBW0REREREROQQ1UddARERkQedmW0DUyADTx9xdURE
-RB5mj1AajDt3XzvqyuxHy3tdd7x/ELv/Tn6TbamYXS9Vbm3pSbDxGcOxGw61pW3nuLK57zy2VIU9
-zJZud/Z36ueOu9O1G3TdJl23QRw63CM5J9ydyXStbJM16npKqBqqqiFUzfUTYtcvhGHXX9vSixQR
-uf/MLALVUddDRETkBEnufmwbjo9txY7K8f7iwfeE3DHILmdph+tx+nqpZebluJ3IXXaWSy3nVr/h
-Ud/d8aWnffy5+Rj0d47JeM64J3JOtItrzOdXWGxfoR8W5JzIKeFkJpN1JtOyTadrTGarTKerNBbA
-AtgYrN1hjNi7r8CUs0XkyJVfj2Y89thjR10XETmAvu955plnuHTpEpPJ5KirIyIH9NRTT+1ktuMc
-3BS079bRBfK9Ldl7WrRvjKC7wZQ9pR3GFu2dR/aG1aXH7cawjY+BeidcewbP5RYfyzueEylHcoqk
-1LO99Syb155hY+Np+nablBM5xbFF+xTN7BST6WlW186wfuocZkbVTMavDQK+E7bxG6qooC0ix8Bl
-4JGLFy/yxBNPHHVdROQA3vGOd/DKV76St7zlLXzER3zEUVdHRA7okUce4ZlnnoHy2XtsKWjvY2+Q
-3glzR9vifWPA9htj83jPd/uP71vTpRbtUvJ6eLWl/9742GinxXr3HKXF2nPCPY2PZ5xMzpEUe2Ls
-GWLH9tZzXLv6Xq5cfop2vjmG8ETOmcnsDJPpaSazM5w602EWmExXmK2s4VZjYayjgbnh+HLXcRER
-ERERkWNDQfsOHH3I3rs33veblbpJwd2HbSmW3ziou8TvXFqtbwjUO63XO2OuM55Lq3XeDcwRPOGU
-0J3TwBA74tAxDC0b155m49r72LjyXhaLTXJKpJTI2WmmLc1kQTNdkLMxna6wtn6alM6MPccNs7D7
-JYIitoiIiIiIHFcK2gew05ptZkcYtndanXduAzebEM3c9hyzfLvs+spuu+3iuyHaS2t0iqQ8jPsD
-OQ2kFMu465zxlPGxXB67iOex/M4xMXbEoSfGlq2NZ9naeJa+3SD2C3Jycs54ht5b4lDRdVA3a6yf
-2qZdLOi7jqqBmoqwE7iP9WgMETnJnn32WR5//PGjroaIHEDf9wC8+tWv1hhtkQfIs88+e9RVOBAF
-7dvYO/736MP2Po/f2IucGzuB7z3Odrud39AR3fMYfBND7BhiS4wtw7BgGFqGvmUYWjylErJTIscS
-qFOKYxDviWkgxZ6UelIciLEnxY6u3aRfbNG3m8Shx93IGTwbOS5KR3RL1M0a89Nbu0F7QiCEmsqX
-Jh0XkWPJzN4P+EbgU4GXUJa8Avg8d/9nR1ax+8TdefLJJ4+6GiJyB8axniIih0pBex+3mmDraMM2
-3NhS7TeG7HGM9m6J570MY2fe7hsXDMtkh5QzOUWGoaPrt+n6Lbpum67bomu36dotcox4TOSYyHEo
-QXu8jUO32128BO1+93lP/fUtO+6lK3j2iphgyIkh9Uwma8y3S4v20Hdlqa8638Ofp4gchjFkvwO4
-AHt+xZwoLznqCojIgfTAM8AlQC3aIg+G9wAPRi5Q0L5LdzvT9d0E9BLsl+5fP1sZb708AziUGcB3
-1q/ec4tdH2+dcialNM7+nUhj63SOw/WQ3W/Rtpss5pss5hss5ht4ypATpJ3lu3IZm+2ZYRyPPfQd
-OQ1lojRPpbwnzCPBMlQ7ryngHrBQYbkiVBXBMrFfsL15hSvPrbC63rG6PgBO3UwIoSZYhQUtWSty
-zPwflJA9AH8J+Dlga3zufxxVpe4vAzTruMiD4R3AK4G3AJp1XOTB8DjwYPQcU9B+ALj784L9znzg
-O8ttjXdhXGorj8F3Z9ktz2Um8J0ZwSETh4FhGOiHnjgMJWjH0vrc9WMrdr9Fu9hge/sq21tX2d66
-BjljXtbjNjNCgMqMEIy+LyG771s8R8wgGARzzByzjNnOFxUBrAJqKq+pc0PyhrqCOMzZ2riMWWAY
-etwTIRjT6Sp1M6VppoCCtsgx86mUX09vdvdvP+rKiIiIiBwVBe0H1POX3GKpVTuBxzKG2mNZeisn
-sidg3DzS9z1d29K1LX3flbA9DMRhoN/tNr7FYnGNzY3LbG5eZnPjMiUiByozqhBompqmrqmbmr5r
-y9a3kDN1HbCqwupACJTNwEKZRdysAqvI1GSvyTRUwYn9gq2NKwxDJI8hu67r3ZdaVTWVcrbIcbPT
-Z/rXj7QWIiIiIkdMQftu7dcF/MbVsp7/xJ41qpdOePNT75wvZxgnLPOdQO1eWrDx3ZZs97w0Q/hA
-ThEfZwF331l+KwKJvmtp25Zu0dJ13W7IjkPP0M/p+236fk7fbtBvX6Xfvka/vYGNATuZ0VQ15g3B
-aypr8NjisSu3nnEqnBq3CieABSwEoHQdzz6uue2R5IHsRh8XLPqIbS+omi1iTqTsZM8MMbK+nglV
-Q13vjKcKiMixMKH8MhuOuiJHIB11BUTkTr0Y+KbxVkQeQMf6s1dB+7D57lLPd3bQ8/bHsdR5HFF9
-wzJa15fQKmtX56WgncoEZLEnxv7GoJ1vbNGOw8DQ9/R9z9D3u0G73Hbk2MLQEoaO2hNTM2JdE8bW
-6BACVRVoKqMOTkWiDhmvHK/GIeHmZI8MKVNbDVaatT0bQ0rEmBhiz5AqYgoMsbRuY2ULzZRFP7Dd
-dmzN51xYtFy4kAjVZOw+DlXVvNB/NRG5S2b25cAPjnd31j34ZjP75qVib3D315nZTwO/H/hpd/8U
-M/sg4OuAT6e0hq8Av8Pd/+eea3wO8GXAR1NmLdqitJr/U+C73X37NnVcAf488IeBDwRa4F3A33b3
-HzSzTwTeNhb/JHf/2Tv+QezOzKKlEUQeHC8GvvmoKyEid+9Yz4qmoH0EfPc/Zef68OvlyXl9HGt9
-vZU6Dh0xtsShJaZuDNQt7glbCtpx6Ha3naDty0HbM7CzNFckxkgaImkYSEMJ4DmWVnHSQIg9TY5M
-DbxpylrWIWDBCJVRhTFoW8KtBG1qSLm0WGcHj3kM2Y55IGZou8SijbRdohug68uWPOAE3AKhapi3
-LVvzOZtbm/RDJFQNK2unWV07BcB0iogcrZ1fXLfrsrP7S87M/iDwJkq43luOscwU+PvA5+15/hzw
-UZTg/afM7DXu/is3q5iZPQ78FPCypeNXgI8FPs7MPh/4f/apt4iIiMhdUdC+E0t/Su402/jNCjhl
-Xa3SpLtzc5Pe5vv9TVe6h2dPeC4Tm6XY0w+lO/fQzRmGOXHcco4laJPxHIl9t7vudU5DWfd6DNq2
-E7R3gnkuW86Z1A+7gdtzhnGzFEuLdjBC3Yyzm5WgXRqoM8EylWUIGSrH3LAEMWVScmJ2CDVWUYJ2
-chZdYmves7XdsegyizaxaBND2vlr3LBQsb1YsLm9zbXNDZzA6topzp1/hBj7w/l3FZEX4keBt4/7
-v0p5+/5/wN9YKnNlzzEvBd5IaZn+ZuDnKd1tXsX1WcoB/g4lZDvwH4HvoLREnwe+GHgt8BjwVjP7
-MHd/z/JFzKwGfozrIftfAD9AmRb8ceBrgNdQWslFREREDo2C9t3wG272KePjTYnkO7c3MtwzOadx
-Gazrodc9lZbmcax138/pus3dbei3GIZtYr+Ne8Twsnkmxp409Est2qlMjJZTqZf7OCN5qZFB6aKe
-IzmVidPMIVgJ0pU1NHWDz7wcbjAugY0Fp7JMFTIhODFGYhqIMdINA/PFwND29MPAkBPt0GMG3eBs
-z3vm857tRaQfnH5whmSk7KRx/LYZLNqOUG+DBTY3N9ne3mY+36ZtFwCcOXOY/7gicifcfQN4J9yw
-7OHT7v7OfQ4x4AMoa3N8tLsvr9Hx9t1CZq8BvpDyq/atwGvcPS6VfauZ/Vvg+ynB+zuAL9lzra8F
-PnQ8x1939z+/9Nx/AP65mX0X8KcO9mpFREREDkZB+x66dR/EsjRX9kxOkZwHUkrjfipduod+XJe6
-p+u2aNurLNqrtO01hn6TYdhk6DfB47iElmE4OQ5lPHccSnAeQ3a5HbN2trI0184Gu0HdPFNZhVlF
-FSqqUFNVDVXdUFU12bxMYIZDyNQhj2HbiSkyjN3Rt9uOwefQZ4Y4kHIsITolut5ZtAPzRek6HrMR
-M6RspExZ43tckiy0XZmZPMPm5iZb21ssFnPabnFf/h1F5NA58A17QvZef3K8HYCv2BOyy0nc/5aZ
-fRHwacAfMrNH3f19S0X++Hj7BPAX97nONwBfQGkZFxERETkUCtr3wI0B2/Y8Yzc86rkE7Rj7Mg47
-Xl/Puu9bum4xzg5+jfniMvPFZRaLywz9Nfp+g6G/BiSqUGYCD3C9BTslPOXrITs5ORs5GTkbIVTU
-oaKqqvF4qAyq4ISqjMWuqopJXTOdrjCdrTKdrpJIJE8kIja2ZtchUwdn2AnaKWJbC+Z9xkLPkJyu
-T3Rdouuh7TJdl8v47N5xC2VmcjNyhpidlPLYqt2Rc2kt32nRXizmuy3aIvLA6YF/tN+TZlYBn0j5
-pfkT7v7ULc71/ZSgXQOfBPyD8RyPAR88nuMfuvtNZ0J399bM/iFlUjYRERGRQ6GgfbeeN7X4nlHb
-N0xwdn3XfWxdzpmcI303p+u36bv52NW7tGqnFOnHkN11C7p2g0V7hUV7mba9WkL2sEHfb2Ik6qqi
-qgKVhdItPGc8OzklUozkmEpwTUZKRk6BYGXW8NJqHWjq61uwmqYOVPWEajIl1BMINdkqUnaGHIk5
-4yTq4KSdLRvJazIBQiZUU0I9o24GhpTAyutP2Ze2XJb8GlvlQx2oCbs/uaoqM5yTMznl8WdUNhF5
-IP03d7/VJAu/E1il/AL9pduca/n5lzMG7XF/xy/f5hz//jbP34EMPHKActW4iYiISLGzQtKtPHs/
-KnIoFLQPzU7ILt23bxyS7eOYbSfnYXeisr5fMJ9fY759lfn8GnFowR1zx3Nm6FuGvmPo2nFN6w26
-fpOh3yDGbWJsSUOPWTnGvMIqyhjsDPjYOpycGBNxSKU1O0FKhnkZU2lAFQKTScN00pAmDVVleJgQ
-JqtUkxmRijhk5kNLHzu62NLFjpwjdTAqM+oANq6TbcFo+4rsU5pJZnU9QOhxBrL3ZMZlx5KTk2OV
-jZOrBeqmYdI0NJOGuq7LlwUpk1Jm1jRMqpoqVJhp/WyRB9TeydH2Or+0//Rtyr53n+POLe0/c5tz
-3O75O3TIpxMREZEHjoL2oboxbF9XxjRDJueevt+mXWywWGywsfEsmxvPsnHtWYZhQWUltAYg9j1x
-6Il9R4wLYpwzxHm5TW1Z6iuVycXMfZzYzHYuCUB2SMkZYmYYIilBjpAT5Oxjy3cmhMBsNiNlx6mY
-TgMeGqrJCmGyQj8kuj7SDz2LfsGiW7Do56QUqS1QWUVtgbpuaJqGuqnJXuFMqSeBVRqcFveWnA3P
-Rk6+G6BDMKwqIX02bVhbW2N9bY3ZbErb9rSLjrbtmNYNTVWXbvJB69WKPKDupDvKA7Xklplx8eLF
-25arqjJsR0RERIqUEukAPVafffbZcaLp401B+xae/w9482B347rYhtn1buRmvjuLuHskxpau22R7
-+zJbW89x9cp7uXr1vVy98l6GvqWpAnWoqEMgx0ge17ROqSfnjpw7Uu5Iqd/dQggkyqRmCcMIYIZx
-fbzzEDN9zORYWpBzLP8z51i6lpsFshtYhVUTVjJkq7F6BvWUvluw3UW2thdst9u7WxwilVVUVNRU
-TGcrzGaZ2dQIdUWmxqrAZNYQkxETxFiW/Eopk2LGY7phubDZpObU6grnzpxibW2N7a0F29WC2ipW
-pjOmTUNTl1ZtEXkoXV7af/Q2ZV+0z3HLrea3W77r0Jb3euyxx3jiiScO63QiIiKyx+OPP86TT95q
-PtXjQUF7Hzf/luT5S3TdbB1tdx/DdrmfUs/QLxiGlsXiGtc2nmHj2tNsbDzN1tazbG09Q9teJsUO
-D4EcKnKoCDjBoG7Aax+XtTZSCthgZAeiX18HO5VWc8NLF24zkjvZS8u2Y4SqjMe2xohDIlo/1rls
-MfnYel22to/0uefKxjaXr25w+co1ttsF827BdjdnGCLBA8ED5hVrqyusjlszqaHKEMrs5H3XM8QB
-t0xVG5NJjbnRhJohZWJ0hmGgbzu66YLFfEIVaoIF1tbWWF1Z5+LFi5w9e4619XVmKyuH+48uIsfF
-bwFzYAX4qNuU/cil/V9d2v+1pf1XAn//Fuf4fXdUOxEREZHbUNC+Y9fD9s1C9o33S3qNsaPrtlks
-NtncfK60YF99D9euvpd2cYVFe4W2vYKnfjdk5xCY1A1N1TCpa4yy9FXOgZhK6/MQy5JcnstM4ykZ
-uI3jpEvYztnx7JSO5YGqqqlCTR1qhjCUJcbGGb4dIyXoh0w3ZNo+segjDHBlY5v3PXuF9z79XAna
-fce8axligmRl/p9srK+vcWp9lfVTq8xmDaFxqsYJtUN0SGXJrroybNrQhIZJ7czbjpx60jDQW0u7
-mDCpJ9ShYWVlldXVVWazVS5cusTZc2dZXz/FioK2yEPJ3ZOZ/QzwmcAfMLPHbjHz+FeNtxH46aVz
-PGlmvw78LuALzewv3mzmcTObUtbrFhERETk0Ctp3ZSe43vy5na2M086k2NO222xvXWVj41muXn0f
-Vy4/xeXLTxLjBnHYZBg2MI8lZFsgh5rGVgj1CpNmhcrK+tI5GzEFYjSqYGOWL7OJQwZP4+RogTJR
-9/UWbSwQqpqmmTCtpwSr8FwmSnOPuBsxOXnIY4t2pO3LDOElaF/lifc8zXbXMe975n1PPyRyAo+Q
-k3PmdMuZMy1n2p7VtYZmCs0UJlOoCTSEMqa7Ll3kranwaOSUaRc9cRggQ1e3tNWESTVhdXWdtdU1
-zp+/yIWLlzh79hzr6+sK2iIPt++hBO0J8LfM7HP2rqVtZq8DPp3yS/cf71lDG+B7gW8HHgf+KvD1
-N7nOt6E1tEVEROSQKWjfxH6D6/drwfad//pO1/GE54GcB7IPLOYbLObXmM+v0S02SUMLnkqrLjXB
-GqpqCrkiYFQ+LnMVKqq6oa4nBAuQAjDOFh4iIQyEUIE7IVQEC7tdxo0Swo0yOU+wAObUdc2kaZhO
-JmXKtrHbuVkFVoGFsQt5ousHFouWmI2u64kxkTNAwKyiqhqqHMAzKXhZsxtIDtEzfUxES/Q50Q6Z
-WTVhVjdY1VCFirqeUFuDNTXzNhKqDncrS4NZTaga6smMldVTnD57nvOXHuHs+QusnzrFdDajqvW/
-r8gD6ECzl7j7j4/rW38h8BnAL5rZdwD/hTKj+JcAXzEWf46bh+jvHsu8HPizZvZBlHW3n6CE7z8O
-fBZlibCdLurHf3YVEREROfaUVF6g5bjtY9jOKTIMLX0/ZxgWbG9fZb59jXaxwTAsMMtMpxNOnTpN
-SjU51aQ0wVOP51ySanbqZoWqWiFUq5gBXuF5Z9KyiIUJoY7gThUqQgjY0gRhhmFGCe9mWGXUVUVT
-N0x2graXpchCGEheuqanbKSU6fue+WJRuqynslb36soKVdPQpMQ0pRKmh0SMmTgkVtdmzGYTmkmN
-VeU8QxxwBnwCYRJoJg3eVITQ0DQr1NbQTDqqqsWthlBTT2ZMVtZZWT/D+plznD53gXMXH+HMufOs
-rq3TNBPMNOu4yAPoTt64X0ZZbPrzgVcAb9zzvFNCg9lodAAAIABJREFU82vc/T17D3b3wcxeA/wk
-8IHAZ4/b8vFvAb5zvAVo76B+IiIiIjeloH1H9h+V7eNsYu6ZlCJ9t2Cx2GKx2GAxv8J8fo3FYoNh
-mGM408mESX2alBtyakh5Soo9qR9IQyTHRFWv7G5laHi12yHdLUKIhKrB3KmqMLZq2zixmZdJ2cbZ
-yEMIBIemqmia0qodxtZrMEKoGWKZnTy7k8egDXOSB9IYtNdWZjQ5M81OnzNDSgxDZOgjwxBZWZ0x
-XZnQTBosZIY+0/U9w9BiK4HaG6YG1IEQJkya2bi1hGobp4LQUDU3Bu0z5y9y7uIjrJ86TTNboZlM
-CAraIg+i6zNF3q6gewf84TEsvxb4aOAisA38OvCjwPe4+/wW53i3mX04pcX7CymBu6O0jP+Qu3+f
-mX3u0iHX7vgViYiIiOyhoH0LN+1CvrNMtfmePxWd7AnPmZR6un7BYrHJ9tZVFosN2naDrt0kpZYQ
-MnUdqMKsTESWIadAjDWD9UTriURCNcGqBqzZGe1N8oqYQwmkVhNCjeOEKlBVpft46Q5eZiN3c0Iw
-6mC4G1UIu2tQhxCo63ocx23l/DkBZQ27rusZIrgHcoo0dWB9bZXoTnQnAUNMtH1P1/V0/cB0NmE2
-bZhManKOeHaGLjFveyZM6S0xVE5qDM8Bs5oqNFT1lKZZYTJbZTpbY3X9DKfOnOf0+YucPneR02cv
-cPrseVbW1rCqJlQ1WLjn/w+IyMG5+y3flO7+yXd53h8DfuyuKlWOXwD/17jdzMvH2wj897u9joiI
-iMgOBe07VtK1MYbt8bHskZQGUox03YK+m9N1c9p2m67fph8WDMNiN2gHy8SQ8dyRvSX7uDZ2Hkge
-yUSSDwypp4olDPexp4s9fRwYUiK74xZK9/BQjTOKB3Iq589krBpnIsfK7OOe6PuOnDM5eenyHcvS
-YDmX1mx3J8aIDxn3HqiwUDGb1KzMpiX0m+NAPwxszY0ty4Q8MK2MSRWYhoroTpUrLAXoAik4g2V6
-BhbeYd5Aruin5XWsnzrDi5tV1k6f5dEXvZhHXvRiHn30xVy4eJHV02cJkymEuownRyFbRA7NF423
-/9HLLz0RERGRF0RB+44sNWFb6cRdxmWDj0F7GDr6fkE7huy226Lv5mW8dmzJqcUsYZYxS7j3uHdk
-enIaQ7ZH3BLJB2Ku6GJZpqsfBvqhBO2YE8nL7OdmAavqMjlZqAiWStgmkcpIbYJBThn3TN+3dH2H
-ZyO74bks65Wyk3N5jTFGhiEThzJR2traGrPZlLW1VQhW1gkP0HU9k5AJaSD3xqQOTKqKaVXRZ6i9
-IsQK7wMpwGCJjkjwHs8L8mD0Myc0M9ZPr3Hq/IyzFy7y6Ise45EXP8alRx5lbW2dtfU1qmYGoR6/
-XFC3cRG5PTN7KfCEu6d9nv/LlBZtB95wH6smIiIiDzEF7bthNy7h5Va6jcfU0w8tXT+n7xd03TZt
-u80wbDMMC2IsLdpG6Z5tlsokYfTAQM6RnBOZRCaPLdqhzMSdnS4O9OOWPZGdEjpDRQilO3Vd1eQU
-CCTyGLIzEAwSZRx1P5Rbp8KstBC7j2tuj5OjxRjp2p52MVBVFauzKbNJxdnTa9QVhApCgLarsTyQ
-upZubkxqo6kCk6qGBJVXWAx4H8gGkUyXB0iBHI3YQz84p8+vsXbqDGfOXeLioy/mkRc/xqMvfgkX
-Lj1CVVW7G2Zj931NDCwiB/Ja4CvM7E3ALwBPAQ3wIeNznziW+zXgB46gfiIiIvIQUtC+rdJFukx0
-VkKweyKlSIwdw9AzDD1dt6BbLOjaBe1im3ZRJj9rF5sMw9a4zcneUwUnBKjCTlwPuAfcK3AjEAjm
-NGFCU09oqoaUvHT9zhnPpdt4ypC9rOidU6bPkWTlvgHBy9JfOZex1MMw0PcD/TDQ9QPBaqoKqiqM
-M5Czu+UEMWaGIZJS6UqeU4KcyDvj1Mcu5ilG4jj7uPtASoEhQooZc2c6aThzapXZdMJsUraV2QrT
-lTVmszVWVtc5d/HRpe0Rzp27wNr6KSbTWVmurKxTtvSvYsraInJQ7wf87/s858C7gM929+H+VUlE
-REQeZgra+9hp2YWdqJ1JOZJSX1qu+wWL+Rbz+Sbz+VYJ2G0J2kO3IMaWFEsrdmnRLhtEmrrM/F1Z
-hXuF42R38IB5aSUPwZnUUyb1lGkzLV3BnfK857IEV47Esbt3HNIYwocys3hVYVVNzokhOn0f6YaB
-ru/p+56+H6gqZ0KFWUMIY8B2I2dImTFgZ4JRlvAaBoZhICTAMpjTth1tO9B1kbZLhGHAuoxVA2Dg
-zspKw8qsZjqZlqA9nbCyss7q2hnW1s+wtn6Wcxcf5fzFF3Hu4qOcOnOO1fVTzKYrBKvYXal8N1iP
-wfuOVgkSkRPqB4CrwKcDLwMuAavAZeBXgH8C/KC7xyOroYiIiDx0FLRvyXdDtuOkHBliyzAsWMw3
-uXbtMlevPsfG1cu07Zy2benaBSn2mCXCuA3DNv0wZxjmBMswmVJZgKqGsSXbHXAnGFQGtcGkmjJr
-ZqxMpqSUxubmDDkRM8TkmHlZYism+i6TojObTLBJRW0Bz0ZMTjckFl0J2V3f0fc9TQ1mDVXlhJ1l
-w8oS3iVs7wRtvCzhNUTiMIA5TsbJLNqORduPYTuNjxuOUdcVK7MJK7MJs9mE6WTKdFJuV9dOc+r0
-BU6dvsip0xe4cOlFnL/4Is5ffJTZ6inqyYSqmRBChXv5+btnMKNM+G4apy0it+XuTwLfNW4iIiIi
-94WC9k3srEHtnsex0GmcrXtO227RtmXZrqtXnuHyc89w5fLTdF1L33X0fYfnRFNDXUNTG30/L7OQ
-99sEczw55oZ5IHsmeyZ5xnBqs9I124wcSuI2D9QBmqohNwnHqVJFlSpCqsipB8/EGOm7RB0qcu2Y
-Bcrs3EbGyA7Jx9bqDJWDuwFh7L7uZebxfP1x25l4zJ2cSvfz7GUZsJgT80UJ2f2QSal0Zd+ZuXxn
-+bDVtRlnTq8xm64wm5Vtbf0sp05f5PTpS5w6c4mz5y9x7vwlzp67QDOZ4RZgXBO8LIEWSSlhtrNE
-2ThIHKA6sv9VREREREREnkdBe4+ydvY4HjsnhtQTY0dMPfP5BvPtq2xvXWVz8wrXrjzHtSvPsXn1
-MjEO5JTIqcwo7mMLccYYuo52vqBdzHFPDItIPx1oJ11pK7edlnOncifgVAbDaiStZUhGXQewQDOZ
-UTUThhSJeaBOEbeWfnAs9PjYpTuM48DrumKSG5LPyjWsxr0ie0VVTwnVFKwB6vKlQi6t2BBomglr
-KxCCMWkaAIZxjHfb97TdwKIbmLeJFI26nuJjK7MbrKxMWT+1xukzpzh7dp319TOsr59mff00a+vn
-WFs/z+pa2dbWzzCbrVGFGgjjTO6lfby0wrf0fUcIgUlTxq7Xdfnfd9JMj+D/FBERERERkZtT0H4e
-H7sqZ7JHYmzp+gV9P2dr6zIb155jY+M5Nq9dZvPqFTauXmHz2tUydtoYW1xL1+ZMCdqx6+kWC+Zb
-26QY6ZuBtumZNFO8NDjjgdLhOmfwTOVOHhyyUVnNbDahmtQ0zYRQB+oUiTlSp0jKxqLtscpwStC2
-cbI1LNDQlHWvzXBKyE5eUYcJoZpiYYJT4+6knEmptIZPmoZJVVEFo2lqcCcOA/N5x9Z2y9a8pe0T
-KVckD9T1tCz9NW6rqzPW1tdL0D5/mrNnL3L27AXOnb3I6vo5Zitnmc7OMp2doZnMmDQzQmjGidnG
-Tvs503c9i8WC+XybqqpYma3gM9dkaCIiIiIiciwpaD/POP7YEzmXmcW7bptFu1lasa+VruLXrjzL
-9sZG2a5tUFeBpqlpmppQV6VF23eCdkc3XzDf3GboB+q6p6476mqCVQEqw6rSPdtzghQx3wnZFU3d
-EKqKlcmkBNKVKU2KDCnSpIEhZupmgQUbJynLmEGoDKPCbVK6WVd1Cdm5IqZAsOZ6i7ZXpRt7TqTk
-VFZeTx2MujKqYBilRXsxb9nYmHPl2jbd4DSTFerJCvVkSlUHQhUItbG6tsL6qTFonzvLpUuXuHjx
-RVy89GLW1s9TN6epJ6eom/XSBO47s6+Pk5/lTE6Zoe9ZzOdsbm5Q1zWeyxcBZaI0ERERERGR40VB
-e1cJdyknhqFjGFq6bs72/BrzxTW259fY2rrMYrFB32+Tc5nwrKphMiutvckTuU8McQynVbmNKVFV
-FbPZCpNmQl01VFW5HXIJzP0Qy9hrg8qMqgpkoI+RRddBFehzZhEjTduVycGsfC3QDZkhpdIaPY75
-vj623IgpE6Mz9E7bJbYXkc3tHjxRhUwVIhDou4Ghj/TdwGxSE+qGyaSmaSrqML6eYMyGyKyPzLqI
-VZl6MqFuGurJhOlsOm4TTp1Z59LFc1y4eJbzF85x6swlVtcvMJ2doW7WqOoZwRp2xpEzrvvtO/8e
-e+c6c663Yo/jwEVERERERI4bBe1dJbTlFOn7jrbdZrHYZHP7CltbV9javsx8u6yNvbMetoVM0wR8
-1oxLX0XiEHHP1FUZV11XRsqZqqpZWV3BHKqqoa5qqlCzvViUda27Dndn2tRUk4aqbnCgj4l52zF4
-php66rYdn68IdU1VV3RDYoiZ6JlM3p1gLedMdiPFzDA4fZdZLBJb2wPXNnuyG0bErMM9lDHmMZf1
-skP5AiE0Fc2soRlfT1MZXUzMhsSsT1jv1M2Uuilhe219jfVT66ytr3Pm7BkuXDzH+QvnOH/hPOun
-zrK6do5mepqqXiOEKWb12Jo9rv5tN/xz7N73Gx72PYVERERERESODwXtPVJODH3LYrHN1vY1tjav
-sLH5HBubz9J1WwzdnCHO8TFo141hNLgnuj7R9S0pxjFkl62qyuzbk0lDFQJ1qKmqimAVfRzwed4N
-2lUITMyomho3Y0iJedcS4gBthVUVVtdMp1MmsynT6ZRuSPQpEfMYsMkkT6WFPY0t2kOm751Fm9ia
-D2xsdQzJx67aoSyX5V7ald2pJxXJwJpAM2uY1IGmDkxqo/v/2bv7WNvyPK/r7+/vaa39cM65t6pr
-emqCEAQkJhCRdqIM+ASoHSc0GTUIBgVmJBMkBiVoJILdPagYBTHEIek4EZgIIRmCAR8YiYojISSA
-zcgQ/hAFA4zFTHdXd9U9Z++91u/h6x+/tffZ59xzH7qrputW1feVrD5Pa6+z9rld597P/v5+32+u
-rOba52Z7xYdEiAkfEtvtmkePHvHo8Ws8eu0xr732Go9fe51Hrz1mtbpgHLekdNH3cxNA+oiz24S9
-vBV5Zo5WPc42N8YYY4wxxphXjwXte7RVSsnM84HDfsd+d83u5l12N+8wzztK3lPygVpmtAHLXuiG
-kmvhsMyoDt73oB0cQ4ogER8CLgSc6yHbi8O54+EBJcRASok0jID2kNwKilDpe77VOVarFav1mnHV
-yHk6jdma5sIQCqvYm5rVCrlor2jPjcNc2U+Vm0NhLg1VoWnfT+5kOYCxROZaKDQKbdkzXqEKlYo6
-xUchIoTo8METgmO1Gri42PDo0RWPH7/G1aPXubh6ne3Fa6S0IsYVPoyIi4B/OmSfLRcX6bOyvfek
-GBmGAR/6+8EH3HG8lzHGGGOMMca8Qixo39OaUmvtS8HnPhd7nifm6cA07Sl5T857as0IDlGH4Jhr
-Ycozu2limqbboO09tSlNl07kCtUpQXr1WkWIKbHerHFOWK/XrNcrVuPInOe+X3yaybWSm5KbUoH1
-lFnPhdVcyPnAzfWO6+sDeZ5IbmCMjfVAD9oZ5gJzUebc93T3fd19aXnf+iz0unbvfn7ImX2e2c0T
-PiiOtowda0yHypQLjYrzDh+UEJUYYRg942pgs1mz2WxZrS8YxwuG4RIfIs4nIKJ4bvdlc3c/tiy7
-tZ3g1JFSYq395+OcY0gDKQ3EZeSYMcYYY4wxxrxKLGjfo9qDds7zErAn8nRgPhyYlzFfOe+pLeMk
-4l3Audir2Tmzmw4c9j1o+yVsV6XPl3Y9ylYHVSA4BRwxJTa+V25Xq5H1asUwJOpN4zAd2E8Th2nm
-UGrfj92Uw1Q4TIXNXCll5uZmx263p+SZMRTWqZFXSqtCLvSK9uloTKUH7eO8b4Vl6Xg/9jmzn3vQ
-FtegZagFbQWtoFVoCM4HXFBiVFJSxsGzWiXW2x601+st4+qCNF7gxCPiQPzZnuynq9migooi9GCd
-UjrN8hbn+v5235ffG2OMMcYYY8yrxoL2SU95qkqrlVIKec7keWaeekV7ng7M+cCU97RWeiXXgxdP
-bo15CduHecYfg7bz4Ja91T6gOKKDJkpzDhUIKeIlEqJnHEbGcSClxP5woDblMGVu9gd2U2Y/ZebS
-yLkfpSi1Zna7A/v9RC0zm6Ewz41SoLa+dHzOyjz3kD0X7deorb8AwBK0UdA+x3sqhUPJHPKMc42a
-p9MRXMC7SJBACA7v26mqnQbHuIqsV2Nf2j6uGcYNKW373uq+wfpOrzOVe38Kcqxzaw/YKfY53oyA
-LEvKHSLnZXBjjDHGGGOMeTVY0L7HicP7QAqJFBNePNqg5EotjVYVaYKow6ngcDiEISUuNhdIc0zr
-eRlUBSBE7/ESaBlya6gHdUpzDXXaD2EZzaXkkvHOs9vtmHMGpO/pdq2HWgXnlqB5LEcraBO0OVp1
-lOrIxVGKcJgau0Nhd8jMuaJNCeL6Zmx60O3VY8E5cCJcrNdcbrZcbjesBk+ePHlyZOG0XB7tS+1V
-G1ARqTSdqeXAnG+Y5mvivCLOG2Le0b+h6yF5Ccz9J3RLZXk+6J0g3cd+9Sr3va3cxhhjjDHGGPNK
-saB9jyxBO4aBGAacC6egXZaQ2kc8u+UQnAhjGnBbxxBHSq602k6HqqKt0YqSa0VdQ72jegdOwSnq
-G6UJOedesQXyPJNzb4QmzuNcW+Ze9zB8DPO6hOzWHK05anOU0oN2zsphbuwOmd1hJi/PwYsgp2Zi
-PdSGEAjBE4LnYrPmcrPhartlHDyTcxwA1/qLDdqW6r82VCvHoK2aKXXPPC9BO6+J+YaQ1zgJiIs4
-F3DHsA24pXqNnO3X1uOfx+3YL1nq7sYYY4wxxhjzKrOgfc9xD3CMiRQH3LGiPfeKtiqgsnTnvo3b
-Q0wMYeRi7ahVKbmQ50LJmXmamaaZucxoqzQvNK/40HrQ9n3JtqKnWdZaW78hBZa9ys45vPN9ubrr
-lfRTNVvlFLbrUtEuxTHnymFq7JeKds4VFIJzvRK9BFrnHCn2EWQpJS7Way42Gy63F4zJswNEG5RK
-oVC0UbSiraH0ijZSaW2m1KWiPV0T5w1p3hHzHu/T0lBN0LOf3/IUT/m6h+3bAdq3lW/H+WAvVRvx
-ZYwxxhhjjHn1WNA+6WHuuHQ8hESMA8EnvATA9VFU7Th3ulG00Uomz40UUg/nKSA4avCUECglMnlP
-8p7JO1qrON+XaXsvFCpVC/XYhG3uHc9rrr1zeQhLYzUhBKhNUOlLyYFeMW/9vmOIS6XYUXJjt8/s
-D4Wbm5nrm4ndbqK0Qq2th3RxyBJwYwishxXrzYr1Zs3VxZrtes0QE9715eKtwJyVWlgOAS+0djsi
-rJTCNE3sdjf4+C64ASVS1BHjmhhXxFQIIZ0axon3dxqgHReKnzdLk1Ml+/hWbY+2McYYY4wx5pVk
-QfseEcG5QDwG7ZDwPhFcpBJp2qB5am3kmqltprXGejWy3awZvSPFiIqjeUGbJ0dHXgXmnGhNESfL
-AYd54jBP7PNEKYWSlXlq5FwZksd5h/iId0pQoWnf4+ycW4JtRbXhvWMcE9oU5xw5V66f7NkdMk+e
-7HlyfeBmPyPSEGl9/rc4vCwV+ZC4WPf5148eXTGuIuPg8eKpJTNPjf2+cHM9L/uyBVUhiqdWT619
-b3jOrXddDzdApFTPlGE/lT7ma9wyjBcMw0iKA0NKOJHTUngsPBtjPsSur68/6FswxhhjzCvAgvY9
-xz3aIQ7EOBLDErZdwslMo6K1UDPMU6/ezvMMl5XRO9yYGHxAvCCxh+LaAqWmZZ5234usSyn53Zsb
-Ksp+ztQq5KxMcyNPFe+UlBzORRAICLo0EwOgQW0VEQjOE4MDBKon50I+7Lnezbx7feDJ9YH9NBMC
-xCjECF4EL54gjjEMXKy3vH71mDfeeAPvFShAWUadVfb7wvX1DOL6qC7Xj1KPYduT5x60RW6oTZgy
-DIfCbjex3hxYb2bWpdFaBW141/eG67HCfqcf+dNssbgx5lX25MmTD/oWjDHGGPMKsKB9j9xZOj4S
-40AKAzEkqk/QGk0qtEItjXnK7Hd71inQyojTQpTWx3s5h/eepp6q0BQqoAgNaNBHgs2Z4GecFJAC
-6mhL9bqp6+EaEDzOKc5pb7TWlFob3gsxemIMOCfMeyXnynwo3NxM3NwcuLmZ2M+Zceidv51zeEBc
-X4YeXGQ1rLjcXvL649doOi9zw/e0quS5MU2V3b7gfSRGT3AJkeMxIJJozVGKMs8Z5EBtgVxhzg1V
-AfG4kPAhEEOgtoJq689RleWJ3v553P8D0tP/GGPMN0xEfgbwt5YPf72q/uAHeT/GGGOM+WixoH1P
-3zvdQ+swJFarFZvNhouLC2KAnAPz7EnJ9cpwcgyDZ3MxMAyCc4WmB6QFRALSArk2clVybZTaqErf
-k61KKY0hRR4/umK1WrHfTxz2fSY29I7i19d7miqt9eZjrdVeHa89cPvg+mgw6UvIS+vXbkDTRm3a
-Z2nPimql1MY8C0NUhiCk4EiR09xtlaW5Gr31WENR6dV0xBPTivVmy2p9wXq1Yr2K/RgTwzAwjAPD
-MBKHkZjWhLQiDmvWqy3r1ZrVMDKkgRgC3vmzn74tGzfGfNPZK3fGGGOMed9Z0L5HxOGDByLDMCxB
-e83FxZYYlDl78uxIgyMlRxo94yqwHhzD4BBX+jgvbUtHbu0NwubKlAtzaZTWw25pSkwDKY1styO1
-KrslZO93B252e3a7PTe7PaX0WnhvMd7nVx/DdohCEw9OCThqg9pPo6lS6jFoN2qFnHtVPCco0dFS
-YByUXPvjoHcFV1Ga9Bnfvdm6Q8UT0orV5opHj15ju9myGiLjEFgN/Wc2DD1whzQQ4khIIyGOrFcb
-VuOacVgxxETwxzFfnEZ9PTtq22gvY4wxxhhjzIeDBe17nBMg4ATaOLBajWw2Gy4vtsSozLNnnh1p
-7sF6nD3T7Em+knzDuYJq6ZVnGgKUMjPNmd0hc8iZXCq5VEppXD1+zMV2y6NHl4jz7HcTu/3Ebren
-femrXN8cuL4+MM0z3i1Nw0RoTZewDbE51DXwjSaeVulLz0WoNGrrFfUpK5KXZmg0yiC0wYM2Vhly
-WbqaLz+LY0VbUZpID9oEQhpZb664evwGV5dXDNEzpsCwrAIY0kAaehd2HxIuREJMrJagvRpHUhoI
-IeKdO+so/iJWeDLGvG/slTtjjDHG/JSxoH1Pn9LcZ0OrVpAG0hDXCB5IgnO3Y6lwvbGZB0QaTStN
-Hd47QojEOJAVYlVCbQStNG00hUbDu4ZzDSe1z+Z2De8b3mn/2hKKhdanTkuf4V3py7xzbbSsqFea
-U3JraBUoDq3Sl60v1e/aTkO3UW0Ep5TQr9OaLpX2zFwmms7kMlHqTGsZ55QUPavVwHa74erqkseP
-X+PR1WNS8KTgidEzpF7RPgZp8QHnAuIDw7BhSCtSHIkx4nzAOQcid5uNn+dp+6ewMR8LIvIdwG8A
-/nHgTWAEfhL4UeBPA39EVd85O/9bge8CfinwDwHfRv877cvAXwb+KPBDqvrUK3TSX208fQj8IRH5
-Q/dO+5yqft/78uSMMcYY87FjQfs+1R6EW6G24/iufiAZHyrOK84LDV2qxRXRPmarasOrIN4ThoFx
-2FDFU3FUAAcuCD6Dr0qMirhCawdac7Q696NNoBknjehBw9LAzHtEhKoNldr3YxelzUqm4Ysgtc/7
-pjmm0iitUbW/hKAK2pTWoDROn2+iVC3MdWLKO1Rn5rwn5z2tzXhRhuTZbkYuL7Y8urri9dde4+rq
-NYJ3BO+J3pGGsS8fTwPOBxC37O12DMOGlFbEYwh3ri8dl2Wsl9gCcWM+bkRkBP5r4FcvnzoPxt+2
-HN8JfAL4vuUxDvhx+q+L+0H6TeAzy/E9IvJdqrq7d8798Qa2XMYYY4wx7ysL2vcoirbag3adqS3T
-dKbpDFLwriEefICivYLsa4VSl8dVGh7nPTENrNYbmvSQXWngFFcUHxq+NEJUnGRaPaAItWZqXYI9
-BSeV4Htzsh60+3ivXBXN9H3aTSmAVHAeRD0Oh6hnLrVXvZemZq0prYJWpVZdKuu9hl9aJZeJQ96D
-zpSyp+QDtc4414M2G8/VxYZHx4r2o9fxbnkRwDnSMDCkHradC73beutL2dMwElPv5H58wQDoFe3T
-Dm1Fn7mY/KF/UxtjPqyk/xL4U8Avp//H/TeAP0CvSO/oofk7gF91/6H0X11/Fvhh4MeALwEXwN8P
-/EbgFy3X/X56pfzcz6cH+D+zfN/fAfzJe+f85Ht9fsYYY4z5+LKgfY/eqWgXapnJeWKe9ygT3lc8
-laqZ1vLpPG1Ly+7a90CXuizXXpqSifQQ7IOQnO/jrRqk6HCuoTrTGtQy00qm1Qxa+izt2EO288uS
-dXFkVUJT/NJ5vLRGq735Wh/gpQjKlCul9ZgtfpmepT24ixNwgO+Nz0rLTOXA/nADOlPznloO1FxA
-Pd75vh97SKzXK7abNdvtdgnaHuc8aej7r9Mw4sRTa2/G1hqklIixV7Od67PA9V5wXsaMn7H6tjEf
-Yf8mtyH7TwD/iqrms68fl43/ThF58/hJVa0i8nNV9W8+cM0/B/xhEfks8FngXxWR/1BV/5+zx/91
-Ebk5e8yPq+pff/+eljHGGGM+7ixoP0VB21L5LczzxG5/w5PrJ9SyB8lAprWZwzxxmA5M8wStIaqI
-NkqGpjdMGW52M7kuR5lQWeZeDx7vAyJ+6bw5kpFBAAAgAElEQVSttNYDfqmZkmegEaIwriKtKT70
-oO2cxyVPGiLDqrI/lH5MmXmqoA3RBg1yadRacb6RBkUby/wuzzgIwwrioEgoFJ3YTzveuRZEM61M
-aJlotaI10kpAa6CWQqsF1YpIw/k+EzuERExLEzQfceIR119gaE0IMeC85xieH6pNv3xjNGPMh9lS
-zf5t9F8Ffxf4dfdC9h2q+ta9jx8K2ed+F/Cbgdfpy8h/33u6YWOMMcaYr4MF7fuU03iuWivTPLHf
-7Xjy5F3m+ea0f7q2TC6FXHsHcYfiRPFAdsqUb7jZzXh3Da6CFFQqMTlCGhjHgXGVaBVqgVobqscq
-eqaUjFCJQViN/Y/JR08Iy7L0HBizsirKu9eZ1ib2e2U+LGPFajt1Pm+03sxs0F7RXkZqDYNjGIQw
-NCRUSpvYT/DudUW0QJ3RmqEq0iq0BKpnQbsg9BcOQooMacCHgRASPkREAk5lmckteO/w7u4y8XMW
-sY35WPkFwE+j/yL4rx7YR/3SltD+rfSl4/H4aXqAf53eLM0YY4wx5pvGgvY9ip6Wj9dal4r2jusn
-77LfPyHnA6VMlJppClWFpuCdEBwELzjqshRcUYWQIEYlJFj7xMYHhpXj8nJkOlSmqdIOvULcaqHW
-TCkzoIQgPbA6R4ieED0uOIYq5Aq5CsjMfqdoK8wT1NJopVBr6Y3bIvjU95W7Jex674jRkSKEqIgv
-VIX9XOFmxrUKrSC1IA08rS9JV6GU3IN26xVt74UYA8M44lzE+YRzPWjL0ggNHCLa35Wnl4yfkwfe
-M8Z85PzDZ+//uW/kAiLya4HvBv5RYPWM05TeSO2bQlX54he/+MLz3nzzTd58880XnmeMMcZ8XLz1
-1lu89dZbLzxvnudvwt28dxa07xEEkd4N2zlPCIkhjYzjhjzPzFPmcKgcDjOlQalQmjIOnnEIrEaP
-eKGe9no3qNr3QheYZ+FwmIlxwrvAdKgcDoVpX5imSs6K4glhoFHRVqitUlqlaqVUwTl6eBVPFEdy
-hSiNKEp0wpACMngcER8hJMUP4CN9r/dyDMPAalyxWq2IKQAVpFJrpdQKRdHScCokD8kLPjjQyjzv
-2N18lesnA60VoOGcEMJIYKmai+txWpeRYqLQFET7Puxjs/FlNvjyB/D8Px+x8G3MR8R5+H3x36pn
-RGQA/lvg09wujXlep8RnhfD3naryqU996oXnffazn+Vzn/vcT/0NGWOMMR8SX/jCF/j85z//Qd/G
-+8aC9n0iS0j0eB9JcWQc1qzXF0yHid3NxDzB7qYy58aUG3NpbLcJcKQh4p1bgmWfld10mWFdwWVh
-v58Q8bQqS8juYbsWllZmkZgSpU00bczLUm2RZU+0KCF4QgyEEIgUoiskpwxeGKInJUeKjpggDNrD
-dgRxvZka4lhvtmy3l2y3l4QY2e137PY37PY7amnUXGgZvPagTnDE6FEtzNMNN0/eJkVHa7n3CndC
-GhqI9NnZ4kBbf07altUCDVAQ8N7hnCBuCeRiO7SNMS/ld3Absv83eqfyLwJ/T1X3x5NE5Efoc7m/
-ab9a3njjDX74h3/4hedZNdsYY4y563u/93v5zGc+88LzPv3pT/OlL33pm3BH740F7XtuK9o9aMc4
-MI4bNusLbp7cAE/IE+xuCvupsJ8qh6mgCilFqgo4hzZBRVH6DGuaQlGywF5mWhPy3Hp1fAna6LFr
-dyDGxJSVpplcGjkXaDNo37u9GiOeiPeJII0kleSU0QubMbLZRNbrSBp6s7Mw9KANvRKOOLYXV1w9
-ep2rR5/Ah8SX336b9hXh+iaTS6bMQp0VL5BCrzqH4EELeb7h+slXCGGpUDuHDx5wOB8IccCpR1V6
-yFbpe8a1H+IE8IBHTvu2bYq2MR8jXz57/03g//o6Hvs99F8Yf05Vf9lzznuNb/JMwJQSv/AX/sJv
-5rc0xhhjPhJedltVSumbcDfvnQXt+5aKtnMB7yIhDMQ4ktIa38u6lCLMkzJNynRoHKbG4dA4zJVp
-rjgPtRZq6R3EfQNfIfg+T1q1Lt3FHftdYb/L7HcFJ4FVC71q7YRSlJwb01TIOSNacFS8VKR5gsAY
-oCXPxVqYLjxelO02cbFJXGwTaXSEdKxoC0gEF0ACl1ev8/i1T/L4tW9BfELcwJwd1zeVmmFulcN8
-wLXK4Ct5aMve9UzOe+YpMh0iaejjvNI44n3Ah0gMaWngLv1QWfa+ax9B5hwichrz9bDjv48tfBvz
-EXS+kfmfAH7kZR4kIq/RG58p8EPPOW8D/NznXOqbGsCNMcYY8/FiQfseQXB94DXeR7yLiMQeUCWi
-GlA8iu9dxr0SQt+GPE+Z6+s9c5be1Kxlas3E4AjBEYOjvwBzuw98npX9vnJzPYMUavO05igVdrsD
-u/2B3X5CWyZ5cN4zxMBqHNmuBi7XI2P0eAdjgt0BNuvIeh3YrAMxOXwUQuwVZ/EjLow4P3Jx9Qke
-v/ZJHr32LeAStY3kHJmngLTAvC/M0xPKPBNdIPnAEAKr1UCtBShARVum1gN53jH72MO28xSfaU3Q
-KrTWl4gfDyT07u4v9adilW5jPoL+T+DvAH8f8K+LyO99yc7j539vbZ5z3m9czn3Wr5nD2fvDS3xf
-Y4wxxpiXZkH7HhHp8589+GP37GPYJvRD+xJpEY9bOo2jMM0FuW74SWmtLEclxUBKgSH2QN7DvOCd
-Y5qU/b5wfTOh6mjNo+qoDW52e3a7A/v9ASeNMAR8DAwpsB5GNqsVl5sVTQPj4LjYeubiGEfHauxv
-Q/D40OdX+5AIaYuP/bh89AaPXvsWHj3+JOoipSbmOXA4eA77wjtvP2GeYH8zk1wghcAQPfN2oNYM
-VISC6kwtPWh7H3DO9z3uLtOqUOsyRztEfIz4EHHO9f3aeu/fwM/M1Ba2jfkoUVUVkf8M+P30MV8/
-KCK/5qFZ2sfxXcss7S8BXwOugF8jIr/v/mNE5NuB7+P5VeuvADN9HNjPej+ekzHGGGPMkQXte0Rc
-H0GlQvAR5xLuWNEmwFLNRj0i4B2EICiVeS6UVhFXaa3RtKLaGIbGWIXW/NIoTAjeUfxtRfv6eu7N
-uftEbmqD3W5iv5/YHyaiF1YxEJxniCOrYcV2XHO5XuN84mIbyDVQCaQEQ4KUZAm+/cUCH0bi8Ig0
-PiIOj7h49AZXr32SR48/SZPYQ/besbuBr739BOd+gnmGm5uZIQTG6NklxzyvKMegLZXW8m3QdqG/
-UCEO5/IyI1xoVYjDSFIFEXw4r2jL8s9heTBLLw9B74Vy60BuzIfe9wO/AvjlwL8A/JiI/AHgLwM7
-+hLxXwT8auCPAN+3BPQ/Avxm+nzsPy8i/znwN+jh+zuB3wQ8AX6cZywfV9UqIn8J+MXAd4vIjwI/
-ChxD+9uq+tX3/ykbY4wx5uPAgvaDlsAnrnfp9hHxCRcTPiXCkEirREKXcKw0LTQyTTNVC0pFm6e0
-SmwD0PdAi0uIRFQCTftRm6NW18Omepx4og9sN2uGIXF5uSXFwOVmxcVmxeV6xdV24HIzst6OeB+p
-GihEFE+MEKIQI/iQ8H7A+5EQ18ThkjRcEodL1ttHjMMFzg+AYxzWXF5c8YnXD7z7tbd596tf5slX
-v0yksl55vPfUohymym6XeXI94fyeMXvmDOOslFkpudGyEuJqWQ2QiDESgydGT4yR4EN/EeA4WPs4
-7+t0dMdsfb/wbYz58FtC868E/jDwLwE/B/gvHjr13sf/PvAdwC8A/hHgj977+peBfxH4XTx/n/bv
-Bv4U8PoD1/gcvSpujDHGGPN1s6D9kNNMZ4Flv7YL8RS0Y4oMqwHvhOAF74Q5z8x5Ys7CnIWKR7VS
-q0c1oTL0oO0TuIQuobi1ftTqeudt9TgJBB9JQ+ojsLwwDgMXmzXbdT/WY2Q9RNZjxPke3FV6kzMf
-wAfBBQhhJMY1IayJaU1MF6RhSxwuSMOGYdzgfUJUGMcetPNcefK1t3nn7U/w7tufQFrGS8W7SimV
-aWrs9pl3rydwfU96npUyV2pWWlEoMIyVkNbE5Jemcn0kWQyREPo+bnGOPmT8ftg2xnwcqOoB+JdF
-5J8EfgPwS+iVbA/8BL3K/N8Bf+zsMe+KyC8Gfivwq+gBvdD3fP/3wO9X1f9vWfWiPGMJuar+jyLy
-y4DfAnw78AZ9KbkxxhhjzHtiQfuZ+pgujhXtkHDhrKI9JsbkGaJnTJ79fuJm5xCFWgSnvaJdW6Vp
-4ryi3RurBZp6mnpq7YeDHrTxxBBZrQfW63F5u2a73rBZb1mv1r2xmvfE4PE+IC4gfjkciBfECTGt
-iGlLWo6YNqS0IaYNPiScDzgfoSnjsObqsiI4nrzzVd59+0s8+eobaD6Q856S9+RcmObKbp+J1xMq
-njwrea6UOdOyokWh9pFmTjySRmJ0hOiJISzzvyPO973cdyvZFrKN+ThS1R/hJTuPL+cfgP94OZ51
-zj/9fn9fY4wxxpiXYUH7nvOyh4gQY2Rcrdlsr1hv32V892ukcUWYdvggeC84pwzJ42RkiIH1urKb
-+7GfG6t1ZL1OrMbEakgMQ2JIiSFG5o0wHxxl9jgRrq42XF1tubrccnG56cfFhvVmw2rcsF5tGIcV
-3jn8Mh7LO4/4gPMB8X0uNQ5wQogjKfZgHeOGEEdiXBHi2EOuuL4vXRopRdarFQK89viKd994nZsn
-nwDds7t5l90N3NxkQJnmzJObHbU18qpQcqHlAs0h9OXvzgV8HIjjitYq2ipNW9+brQ1Vt7yvyxxt
-lpxtYdsYY4wxxhjz4WVB+0G6NOAShmHk4uISbYXD/oYn736NYVjj/RNUMzlnWs6kFLnYjgxDQvHc
-TJXdVLmZKzE4YvSk6BmGxGocWa1WjONA8BtSumG1usKL49HVZjm2XF5ecHl1wcXlBevVhjSsGNKK
-FEfc0lTNifR95M73qrZzp6AtDpxP+ND3aPe3CXGR3tRtWbKtvZLsfWAYE+Lg0aML3viWx8zTtxDC
-xDvveL72tYrzEwiUmnlyXZimealkF7RWRHrg977PIA/pQJgOhLQH8SBhGZMmeAXfe7DTXMPhrMGZ
-McYYY4wx5kPPgvYDeuOthggMw8DFxSUxeK6vn/CVt7+yBO2IlkIumbnsGIdLLi5WvP7aFTGN3EyV
-m33h5lBP1xWUlBLrzYbNZsN6vSbFA6vxwGazxzvP1cWGq4sNl5cbrq6u+vHoinHc9OAaxh6WRc6O
-XpVm6faNWzKto4dvWUK4LO/L0jl9CdnHzt8heJxLxOi4erRlnh6h7Q1inBjGivMHmj5hf5g5TJnD
-LuOco9WCtoagPWD7SPCJEAfCNBKnkTiM4OLtIQ5d5ok38Tgc6qzjmTHGGGOMMebDz4L2iZ697cuZ
-ESENAz44VquRr73zVbbbK1brLTGtyHUm10qeJpDGZp34xOuXrLcbbvaV630P27U2aulHjJHt9oLN
-xQWb7ZYUZ8ZhYr2e8c5zebHhYrvhcrvh6upRPx49IqUVziXELbO9RZZl38tbbpeB92q2IP74jOR0
-9Kd3Nk7rjPeeEATEc3m1odVHOLcnxhnnJ5rumPO7qMBhLkw5o40+XztE5pSYc2EuhXJ+1H64knG+
-4HxBXB8N5lzDuWaVbGOMMcYYY8xHhgXtB52mO/fxUy4gOFbjlqvLx7z++ieZp4mbdxzXFOq8o9XG
-NO25uXmHqhP7qbE/VA5T7fuVxROTJ0Yh+obXgpYZrRO0CdqhV5vV4XA4cdQi7PcFZEeMA0ifh+1c
-WpZmp2V8V8K7vizc+YQce+w20LMAK+iyTPz00RlZPicIgveJYdyw3T6ilJk5Z2pTEMd6s2O73XF5
-tUcbXG7WXGzWXG43bLcXXGwvudhesNlcst5esd5estpcEtOmjxhLK0JMhBDx3uOcO1XnjTHGGGOM
-MebDzoL2id57CyCI+D5iyymr1ZbLy8d84vVPUuYZT6HMO/bXkdqUaTpwffMOue44zO10DHEgphVj
-HEkJkms4CtSpH+3Qg7ZzyBKynQilZPa7HTn3EV4s47ucS6S0Jg1rhrTpY7uCAh6RXok/Va6f81Tl
-9KEsn7zt/B1CYhjW6PYRqo1aG+DwPrLd7rm+OXB104P2dr3qx2rFZrPtx3rLar1lXG8ZVlvG1RYf
-Rnwc+1sf8cdZ2s7hnHu//0CNMcYYY4wx5gNhQfsBqvSl47AEwF5tXY0bLi8fc/jERC2ZMu/YPfkq
-4iK1Nqb5wM1NIWdhKsqcG1NuBLnADY4hJsYEzjecZrQI1CVk6x7Oqtk9aO/JWdE9vXGZeJSAc5HV
-+orV6oq6yoyqCB7vh37zDY4V69No6uP7d8rYcorXt188NkaLfV+4F7zvndW8T6Q0stsduNlP7HYH
-VJX1ON4eqzWrVe+OPowr0rAmjitiWuGWirvzCecCIm6pZn/9FW2rfhtjjDHGGGNeVRa0HyDLiKke
-tm8bjsU0sNlc8vhRoZVCzXu0HhCdiWFmNVZiWvYfu7o0I1OcV7xXfADv+8fOKU4aXiqegiPjcNBm
-WgnU7Gh9FDVNBRXXx3G5ivpGyQdKiOQQ8G4ZpyUBUcEFj/N9Hne/CVmq3OfLyAWVpZYt50m8h3px
-AR9GRARVoVZBJBLjmsM0cTjMHKYZFIaUGFNiSJFhWDEOI8MwEtPQG6LFAR+H3pHc9ZnfvXGbPHW8
-+M/GArYxxhhjjDHm1WZB++S8oqvcy56oQgiR9WqDqtInaGVSgM0qoe0Jwg0iN7R2QOeMupnmMiF6
-XBDEK7KE7OAV7yB7JbhGcBVHRdtMzZ48OVQ8xzFc4hzij/OpHU4aqplW95TSPy/q0NbwNRFiJGjq
-c7VPXckd4JbndFwwvjxvOfsxIEso7oE8JUdrAedGUrokl0LOhVwKKMTgCT4QgyeG/r1jiH15eAg4
-H/uM79Pcbg9nwdrCszHGGGOMMeajxIL2HceQ3avZ9wNgDJH1ekOMiSEmUoD1KnF1sWE6fIVpept5
-+grT/AR1B5oTqij+GLSdIq716raH4CA4JbiKl9KbmNVMzZ55cohLy1zsHk4dHr8EbaH1UF6hZEHU
-9eZnrRHTCAw4URwRccv8aie3LdDkdqP2aUX52TpzkT6TGwk4GXBuxTBcUGultkbTRmu965oT6cvd
-nSwvAnicuw34vaHccu1jhf34E7eMbYwxxhhjjPmIsaB9ctsaDJaV1nf2M+tSnQ2kNDKkRAywGiOX
-2zU31xuubwZurj27fcTPB/y8J8x7xpQYh5E0DMQ4EF0iuEh0kZICpQRK8b1S7vo3blXx4nBEvFst
-1WGPj8sRepjto8gqrWVqnU6VaO+F2vzpaTnnkLakahyn6r0s1eyzPdp6XC5/rH478Of/T3kwHOvZ
-/m85feqBs85OefHcbKt2G2OMMcYYYz5sLGh/I0QQ54lpZFxvcQ5CFIZVYrPZMs1PmPK0HDMpeKL3
-pOAJzuOPlWlxSEi4OBCGRGuN4FM/QiKmC2K6IKULfOzzvHt13CNO+tJuJ33clxvwbsT7ARci6oSm
-DdEGWkGPy8b1NlffybC3y+ZPnzn78KlTX+RlzjHGGGOMMcaYjyAL2s91TItyenMMnM735mjOXZBi
-ZBgTm7yl5MfkciDXzFwyuWZEFbfshnYoov1AFR8TYRxI00CrFZHYq9KSGFePGcdHjONjQhpxwS2N
-zhynYdmiyxLtgJN42sONczTOQrY2oOFU+giwO9FZn3rvPHafx29RufuIpwrO8lTIvrtWwBK4McYY
-Y4wx5qPNgvaD7ofBZTn5sqy6bzXuFe0YI4xr0C3aZlRnaivUVimtUmql1kwrmVYLrWaoBW0ZrYU4
-DMQ8MOSBWiraPKoB1chq8zrrzSfYbD5BTCvEO5z3iBeaVlQbTSu9pZksdygoDaTRqL0srR5aA9cQ
-HPdj9rOed9+3ffvp47ZuffAhcr8gfueUe/HcGGOMMcYYYz6yLGg/6G7n8aM+7Ut7VXcZ+XVKowp4
-DxpxWnGt4VTxrdFaodWC1kyrFW1lCduFWNaksqWUHbUWWnW0JrTmGVeXpGGNjwkXwhKye0XbqaI0
-VPVecFag9bBN7U3UxCPil87jy6ive0vH71Sv5Xa/tdz5opzmi995pNz98HQbd396d7/+Ml5yH/fX
-d1FjjDHGGGOM+allQfuZzuddHTtx03t+CX3p9+mTAvjlC4IQcG5Z2u3At4r6irYG2tBWobXexKwe
-qKej0CrUqrQmxLQmxAG89NXfDpyT3g0cEMKplt0dQ2lDpQK9gi0cg/bZXO3Tcng9a/2tp0899dMQ
-+isNwoPf8fSj0mOTtfMv6NPnGWOMMcYYY8xHlAXtpzxj/fPxq2eBU2HpGHYMrw7wp4KxY9nT7Hvo
-Fvq+7PND20zTmdYmWi19fNYyQquP2IqIW7qDu+WiTnDiYTnuxt5e0b49ABzS76ZXtZHjawLLUvPb
-pfG3ofhZafv4NTn9tI6f01PAPq+x23JxY4wxxhhjzMeLBe0HnZeuH/iqHLO29GXWyFPnyjG2ityu
-1D4rIsPyLTSDFlQzrS1Be3mrgGqffH07T/t4+NN8bLnz4oCC3Avael6ddzy9Y1qe+uhFLzg87P0P
-2A8sVH/6i1YhN8YYY4wxxrxCLGg/i56FbbiTH19u1/BS4V0eoPdz650mYg6IiHM4Wg/Prt0Wvumd
-xZ3rs7P7MvBj87MHLqpn1z37Ps/Mo8v+67tB+eFndX7r8lRg/6mny/9YtjbGGGOMMca8qixoP+QY
-ppcqtRzf5+sNlXobCPWBSdV6DKtLozI8zjXEaW92djr6A2+r2e6sy/hDd7V8V30ojvbH6f2zTy3G
-X+QZ+7Pvfe2n2lP3b4wxxhhjjDGvCAvaD7jde61nFezzpmEv56FF1OdNu5fe5sv+6aVP2Z1zFdV2
-t9P3shT9Nkw/dF/Pus+Hg7eeWqo9d6H2C7xMRdwYY+4SkV8H/EH6L5Cfqap/+wO+JWOMMcaY98yC
-9gNOe6/PK9qLp6PovaXl96jq3S/dO+/4+Gdfx3FqcibH7udfX+X57qflgTPkqc98YyxkG2M+3q6v
-rz/oWzDGGGPMK8CC9kNk6Rb+QP582Sh5rEK/TCh+avf0eeYWoY+1ltPH79m9a1g8NsaY98eTJ08+
-6FswxhhjzCvAgvazvMf0+XJV5xde5DZsv/erGWOMMcYYY4z5JrCgvTgc9qf39evreHbydYXheyc/
-1NxLH/riN/K9n7f2/QW+mRO0vtHu5avV6n29D2OMMcYYY4x5LyxoL9555x2gj9K6nZK1LP9+3gMf
-SIfy7HeeOvFZ4fLUmuxF6fNed7XbGdgv6c43kXspX++9APDwXPHnXPiB+79/Db3935dN2vfuwYK2
-MeYV4IC7zSuNMa+0t956iy984Qt87/d+L2+++eYHfTvGmJdUaz2+6z7I+3gRC9qLr73zteW9ZZyW
-6t1q7r2m3Odju57pqf5md0Pmgw+9X+l+IIHK+aceKIWf2qs9EIqPl9MHgvB5M/M73daP13vg/u4/
-9vzfmA9W5J83zPslfiB3ppAtb7/1W7/tWRc1xnzAROQR8O8BvxL4GcAT4K8CX1DVP/6S1/gZwL8F
-/DPATwc88OPA/wr8l6r6117iGr8C+DeATwEb4O8CfxL4var6EyLy/y7X/kOq+t1fz3Nc+G/gMcaY
-D9Bbb73F5z//eT7zmc9Y0DbmQ+QsaL/Sf/da0F68++5S0VZdQraeQuN5YJX7YfCh8P3gJ+6nbjmr
-aD9VN15C6/LVBwP+wxVjOX2be2PATtPA7o0se94LBXqvoi3y3L3ncn7P92/8/Cae06H9wfOf+kbP
-GBFujHmliMg/CPzPwJvc/rYZgF8K/DIR+YPA//6Ca/xrwBeWx53/kvhZwM8GvkdEfqeq/ifPucb3
-A79p+fB4jZ8N/Dbg14rIP89pvIMxxhhjzHtnQXuRp75Huwfs26r2g9nwOf8UOxWEl5B6G9zvdSJf
-QqveD7vHovcx0D6QPQXQdrzPp6vdp+s/UNW+W2W+u2z7OLP77jVvA/sxaN9+fHqyp2B9/jzvLMNX
-EHd+jeU4PVd9Opc/jwVtY15pInIB/E/At9J/E/wx4AeBnwT+AeC3Ar8e+HnPucZ30mdsQ6+E/x7g
-fwEK8B3AbwfeAP4jEfmqqn7hgWv8u/SQrcDfAX438H/Qg/s/t9zHHwfW7+X5GmOMMcacs6C9yPMS
-tPU2aL9wj/ZTIbgHx+M1miraGk0brfX3FXDOISI4586q1no3wMrthO3l0sdvAija9HTduy8IPB2I
-H9xarXJnB7aq0lqj1kpr7bbb+Z3jeN+3n+uPbaf7OS5LP/389DZ4O+dOhziHEzmF71Mmf5m0bSHb
-mA+D/wD4afT/tH+7qv6nZ1/7KyLyx4H/AfhnH3qwiAR6JRvgGvglqvpjZ6f8RRH5E8BfoFfMf4+I
-/JCqvn12jU8Cn1vu4f8G/jFV/erZNf68iPxp4M8CCatoG2OMMeZ9YkF7MS8VbVRpercae3Q/3z30
-LzJZ9im31pajUmsPsMf9BM45vPdnQftYRe7h+BRil7Ddm5DdrU7X1mi1f487FWihB9glyN/e1/PT
-aWtKLYVSC7VUOAVqd/bCwPHj27f9sctzrW35ud1Wtk+rA6A/Z+/wzp+efz/kTjB/FsvXxnw4iEgE
-vpv+X/RfvReyAVDVKiLfA/xNID5wme8Cvm25xu+6F7KP1/jbIvLvAP8NvSL9G4Dfe3bKrwPG5Rq/
-5V7IPl7jLyxLy//tr+9ZGmOMMcY8mwXtRQp9L/2pGnsWEJ8V8B7aJ3ysaNcGrSpVHKo9bKsWtCmo
-A63oErSPy8thCdnufOn3wxVqbW1Z6r1Uy09LvhW3hOPmbqvOd5ajc9oJfgr00GhaabVQSkZElsff
-VqFVexUaPN4rvf+Aoq3SaqWUsiyZ13sh+/g24iQg3uEceC94704vCLyoW+/LLt83xnzgPgU8pv+X
-+oefdZKq/riI/BngOx/48i8/nsbt8uECLKMAACAASURBVPGH/BDw/cDl8pjzoH28xpdV9Yefc40f
-xIK2McYYY95HFrQX203fnne+7PtlnHfBPkZa1UZtt1XseXJLxu2f6zlagQbHPdyqy6JwgXZW0RZB
-cKfKdl9Wvux3xuEEqvRqcm2Vpoq4XlF2y+Pu3u0xYutp+bZzDkWWwCz03KvLee30aKG/COBdD8gh
-+OX+G60dr9+W/ePttE/9yAn44EgpEGMihEAMAR/C6YUGfU6CfqlO78aYV8HPP3v/L73g3L/Iw0H7
-uHf7b6nqV571YFXNIvJXgH+Kp/d7/zz6b4wffcE9/Bgw83Bl3RhjjDHm62ZBe7G5F7TvNAU77ZeG
-p/qE3+8sLr1RWQ/Z5RSsoVeL0YYcm9vediE7Lbc+Lhbvh+vVb1rfH91vZWmiJoinn1P6nm2loa2i
-4pdMLRzD9e0d6uk9J4r3vbIMQqsO74VSzp9rv88e8hUn4LwQvCMGv+ztrpSyhOxjlV3bWZO0fivO
-KSE4UooMKRFTJMZIDPHs+72EF6w0MMZ84F47e/8nX3DuTzznGvoSjwf4ew98X+hVdYAvPe/BqtpE
-5G3gky/xvZ5LVfniF7/4wvPefPNNGydkjDHGnHnrrbd46623XnhezvmbcDfvnQXtxXo1ArdNwR5c
-xnyW7J766vnXtAftUvzSXKxSayZn1yu/S0fz2yXjt0vH74Rs2hJSHccK87GqfTu6S1DfqJWlslyX
-vdMOQZel4w8FWD3ts/a+L932vlez+7XPH9P69Zbve17RVlVKkdNjjkFbT0/0+DM5PrYH9JQCKaXl
-+DqKSHe3oxtjXn3vdQ3Kh24Ny6c+9akXnvPZz36Wz33ucz/1N2OMMcZ8SHzhC1/g85///Ad9G+8b
-C9rGGGPeb+dNxz5J7/j9LM+qIr9Nfz3tZarM33r2mPv38Un6CLBnkv5q5uPnnfMSTtX0N9547rcD
-+j8mfuAHfuA9fktjzHsxzzMAn/70p0kpfcB3Y4yptb7U36Ff+tJpodr9lWyvFAvai5/+cz5lBVJj
-jHl/nHcI/3bgzz/n3G9/xuf/GvCLgJ8p/z979x4t21rWd/77vO+cVbUue+8DHOBwPGC8pWO8RAQM
-oDEBTUAZXugYo4mKgkZbewyN2rGT0K3g6MRB7Gj3EEcwUUBjhsPYiokXohgS0jSJXEzihagkGAWO
-cgDPvqxL1Zzv+/Qf7zur5qq91tprn7PPXmvv9fucUdRt1pyz1mbt2r963vd5zZ5w1DztugzY0ymV
-799Ye/o3KSH8025wvp9CWVf70VTPl58ho38AiMgdQL+zInesM53fFLRFRORWeyelmnwP8JXA9x+2
-kZl9FEesow28Cfg6yofo1wDfe8R2fwW4RAnJb1p77leAzwHuNbPPc/dfPGIfLzni8Zsxp4T1zMnm
-lYuIiMgj8yTKPNv5aZ/IcexGSyqJiIjcLDP7XuBbKQH4O9z9e9eej8C/BF5AXRkR+Bh3//36fAu8
-l7KW9mXgz7n7b6zt46nA2+o2O8BHu/tHRs8/hbJO94QyfP2565VxM3sO8GZWHcdf7+4vfdQ/ABER
-ETnXwmmfgIiI3JVeCbyPEqJfZWY/bmYvMLOnm9lfpQTkFwDvOOzF7t4Bf4MSwC8BbzWzl5vZc8zs
-M8zsb1KWDru/bvNt45Bd9/Eg8Ip6Dp8AvNPMvsHMnmlmn2lm302pgr8f+NDwslv5QxAREZHzSRVt
-ERF5TJjZnwZ+mTJPen0elQOvBf5dvT5Q0R7t4yuB11CGZR+2jwS83N1fdcx5/CDw9cPdtac/CHw+
-8DPAA8A/cvdvOsn7ExERETmKKtoiIvKYcPffAj4JeBXwO8A+ZU3rfw18ubt/7bApR1SS3f3HgD8F
-/F/AbwHXgF3KUPAfAp5+XMiu+/hG4IuAXwI+DOwBv0uZO/50d38XcLFufvmRvFcRERGRMVW0RUTk
-XKtN2f6AEva/1t1fe8qnJCIiInc4VbRFROS8+2uj2//+1M5CRERE7hqqaIuIyF3LzDaBi+7+h0c8
-/3Tg3wAXgLe7+5+9jacnIiIidymtoy0iInezJwLvNrM3AG8Efpuy7ub9wOcBLwU2KOtff+tpnaSI
-iIjcXVTRFhGRu5aZfTRlPW7n+o7j1McXlLnZP347z01ERETuXgraIiJy1zKzBvhi4IXAsygV7sdT
-Opf/HmX5sR9w9z84rXMUERGRu4+CtoiIiIiIiMgtpK7jIiJy7pnZ08zs/zSzd5vZNTP7sJn9qpl9
-u5lt3MLjfLmZ/Ssze9DM9szs98zsx8zs2bfqGCLnyWP5u2tm32lm+YSXz75V70nkbmVmTzSzF5nZ
-K8zsF8zsodHv0I88Rsc8tc9dVbRFRORcM7MvAH4MuEiZs33gaeB3gBe5+399FMeYAf8PpQHbYcfI
-wCvd/ZWP9Bgi581j/btrZt8JfOch+17nwPPd/S2P5Dgi54WZ5bWHxr9br3f3l97CY536564q2iIi
-cm7V5b1+grK811Xg7wDPBT4H+MeUD+dPAH7OzLYexaFey+rD/l9T5o1/BvAy4D2Uz+PvNLOvfRTH
-EDk3buPv7uCTgU854vKpwNtvwTFEzgOvl/8O/BKHNyq9FU79c1cVbRERObfM7C3AZwEd8Ofc/VfX
-nv824B9QPqhf8Ui++Taz5wNvqvv4F8D/6KMPXzN7AvBO4GnAHwMf6+6XH9k7EjkfbtPv7rKi7e7x
-0Z+1yPlWf6feDrzd3R9aWxnkllW0z8rnriraIiJyLpnZsyj/UHfgn6z/Q736h8C7Kd+4f7OZPZJ/
-bH9bve6Bb/K1b7jd/cPAd9S79wCqaosc4zb+7orILeTur3D3X3D3hx7jQ52Jz10FbREROa++eHT7
-dYdtUD+cf7TevQd43s0cwMy2KUNZHXiTu3/giE1/GrhSb7/4Zo4hcg495r+7InJnOkufuwraIiJy
-Xn1Wvd6hDCE7yr8d3f7MmzzGs4DJIfs5wN074N9Tqm/PUvVN5Fi343dXRO5MZ+ZzV0FbRETOq0+k
-fOP9Hndf74Q69l/WXnMz/vQR+znuOA2liZOIHO52/O4eUJcH+iMzm9frN5vZd5jZPY9mvyJyy52Z
-z10FbREROXfMbArcW+++77ht3f1hSuUM4Kk3eagHRrePPQ7wB6PbN3sckXPhNv7urvvcetymXn82
-8PeB/2ZmX/go9y0it86Z+dxtbvUORURE7gAXRrevnWD7HWAT2H4Mj7Mzun2zxxE5L27X7+7gPwNv
-AH4V+ADQAv8D8NeBv0SZ//1TZvYF7v6vHuExROTWOTOfuwraIiJyHs1Gtxcn2H5Omce18RgeZz66
-fbPHETkvbtfvLsD3ufsrDnn87cA/NbO/AfwjIAL/xMw+zt1Pck4i8tg5M5+7GjouIiLn0f7o9uTI
-rVamlDmhe4/hcaaj2zd7HJHz4nb97uLuV27w/A8BP0wJ8vcDf/lmjyEit9yZ+dxV0BYRkfPo6uj2
-SYaLbdXrkwxVfaTH2RrdvtnjiJwXt+t396ReM7r95x+jY4jIyZ2Zz10FbREROXfcfQ58uN594Lht
-a1fh4cP4D47b9hDjRizHHoeDjVhu9jgi58Jt/N09qd8a3f6ox+gYInJyZ+ZzV0FbRETOq9+iDPn8
-eDM77vPwT41uv/sRHOOw/Rx3nB743Zs8jsh5cjt+d0/KH6P9isgjc2Y+dxW0RUTkvPp/6/UW8Ixj
-thsPB33rTR7j7ayasRw5rNTMWuDZlH+0v93d000eR+Q8uR2/uyc1XrP3A4/RMUTk5M7M566CtoiI
-nFdvGN3+msM2MDMDvqrefRh4880cwN2vAb9Cqb59rpndf8Smfxm4WG//9M0cQ+Qcesx/d2/CN4xu
-/9vH6BgickJn6XNXQVtERM4ld3878O8oH8YvM7M/e8hm3w58IuUb7+9f/8bbzF5iZrle/vcjDvW9
-9boBXr0+1NXM7gW+p959mNLFWESOcDt+d83sk83s4447j7q818vq3T8Efubm342I3Iw76XNX62iL
-iMh59s2UIaUbwC+b2d+jVL42gC8Hvq5u99vAPzxmP0fO03T3N5vZTwBfBnxRPc73U4aZfirwd4Cn
-1X38LXe//Kjekcj58Fj/7j6Dsjb2m4FfBH6d0oStoczr/ArgL9Zte+Dr3F3L8okcw8w+E/j40UP3
-jm5/vJm9ZLy9u7/+mN2d+c9dBW0RETm33P0/mtmXAv+UMoTs761vQvmH+ovcfedRHOqlwAXg84G/
-ADxv7RgJeKW7q5otcgK36Xc3AJ8DfO5Rp0EJ3y919194hMcQOU++FnjJIY8b8Fn1MnDguKB9I6f+
-uaugLSIi55q7/7yZfSqlQvYiynIgC+A9wE8Cr3b3/eN2cYJj7ANfYGZfBnw18GeAe4A/At5Sj/Ef
-Hs37EDlvHuPf3Z+nDAt/DvB04MnAEyiB4CPAfwLeCLyuzgkVkZM5aaf+47a7Iz53zV2rEoiIiIiI
-iIjcKmqGJiIiIiIiInILKWiLiIiIiIiI3EIK2iIiIiIiIiK3kIK2iIiIiIiIyC2koC0iIiIiIiJy
-Cyloi4iIiIiIiNxCCtoiIiIiIiIit5CCtoiIiIiIiMgtpKB9hzCzf2NmuV4++7TPR0RERERERA6n
-oH3n8LVrEREREREROYMUtEVERERERERuIQVtERERERERkVtIQVtERERERETkFlLQFhEREREREbmF
-FLRFREREREREbiEF7VNmxUvM7JfM7EEz2zOz95rZG8zsix7hPp9mZq8ws7eZ2R+a2bxev83MvsvM
-HrjJ/d1jZi83s7eb2UfM7KqZ/Rcz+8dm9szRdsPyY+mRnLeIiIiIiMjdwNy1WtRpMbMnAz8LfMbo
-4eEPxOr1TwNfDfxL4M/X55/n7m85Yp9/F/i7wGxtf+N97gPf5e6vOsE5Pg/4Z8CTj9hfBl7h7t9t
-Znl43t3jjfYtIiIiIiJyN2pO+wTOKzO7BLwZ+FOswut7gbcBc+CTKAH8xZxw7Wwz+wHgG+v2Dlyr
-x/hD4D7gecA2MAW+x8ye7O7fdsz+nk0J+Bujfb4d+E1gUs/vE4DvMrMPDy876fmKiIiIiIjcjVTR
-PiVm9sPA19S7c+Ab3P31a9s8E/hJ4E8AC0q4PbSibWZfCvwEq5D7WuBb3P3aaJtt4NXAV462+8vu
-/oZDzm8K/DrwcZTw/N+AL3X3d61t9yX1WE09PwNcFW0RERERETmvFLRPgZl9AvBfRg+9xN3/6THb
-/hqlqjxUiw8EbTMz4D2UQA7wk+7+5ccc/2eAL6r7+q/u/icP2eYbgB+sd3eAT3H33ztif19MGeLu
-KGiLiIiIiMg5p2Zop+NlrOZL/+pRIRvA3X8X+P7R9of5S8DH1G0WwDff4PjfBHR1+48zs794yDYv
-HU4B+L6jQnY9xzdQhqgfd44iIiIiIiLngoL26Xje6PaPnWD719/g+efXawd+wd0/eNzG7v4B4I1H
-nM8wxPzTRw/9+AnO8cgvC0RERERERM4TBe3T8WdGt992o41rVfsjx2zy9NHt/++E5/DW0e1PX3vu
-U1n9f+OKu//2Cfb3H054XBERERERkbuagvZtVruNT0YP/f4JX3rcdk8c3f7vJ9zf741u33vE/hx4
-3wn3d9LtRERERERE7moK2rff9tr93RO+bueE+zxuu6P2d+GY/Z30/K7deBMREREREZG7n4L27bce
-SDdP+LqtE+7zuO2O2t/VY/Z3K85PRERERETk3FDQvs3c/TKlM/jgaSd86VOPee6hR7C/PzG6/aG1
-54b7BnzUCff3wAm3ExERERERuaspaJ+O/zS6/ewbbWxmHw884ZhNfm10+7knPIfxdu9ae+4/A7ne
-vmRm162zfYjPOOFxRURERERE7moK2qfjzaPbX3GC7V9yg+f/db024PPNbL252QFm9hTg8w55PQDu
-fpWD4f2vn+AcT/I+RERERERE7noK2qfjh0e3n21mf+2oDWs1+1soHcCP8kvAe+vtKfD9Nzj+DwBt
-vf0ed3/TIdv8yHAKwLeY2Ucfc45fCHzODc5RRERERETkXFDQPgV1XezXUUKsAf/EzL5qfTszeybw
-y5SGZIv150f7c+B/HV4GfLmZ/ZCZHWhQZmbbZvY64MXDS4G/dcRuXwu8p97eBn7FzNbX28bMvgT4
-cWD/qPMTERERERE5T6xkNLndzOwe4G3An6SEY4D/Vh+bA5/Eat7zT1PWuv7zlHD8PHd/yyH7/L+B
-bxrt7yplmPofAU+iVJ2Hpbsc+D53//ZjzvG5lKC/MXrNfwB+i7IW+GfU83fgfwZeXbfL7t6c4Mcg
-IiIiIiJy11HQPkVmdh/ws8Azh4dGTw9/MD8LfCXwc9wgaNd9/h3g5ZQh5Eftcx94hbu/6gTn+Hzg
-nwFPPGJ/GXgF8D2squ4Pu/vjb7RvERERERGRu5GC9ikzMwO+itJw7FOBS5QK9H8CXufuP1O3ezPw
-2ZRw+/yjgnbd9qnA1wIvAD4GuAd4mFIxfyPww+7+vps4x3soFesvBj6WMr/7/cBbgNe4+zvN7EnA
-H9bz++/u/rEn3b+IiIiIiMjdREFbbgkz+1xKUzYH3ujuLzrlUxIRERERETkVaoYmt8qXjW6//dTO
-QkRERERE5JSpoi2Pmpn9Wcow8pZS0f5Ed/+d0z0rERERERGR06GKthzJzJ5qZj9pZp95xPPBzL6C
-Mu+7oYTsn1XIFhERERGR80wVbTmSmX008N5694PAO4EHgQQ8GXgOq27kUBqkPdPd/+h2nqeIiIiI
-iMhZoqAtRxoF7eH/JLa2yfj/PG8HvuRmupmLiIiIiIjcjRS05Vhm9kzgC4BnAw8A91KWC7tGWYbs
-bcBPu/vPn9pJioiIiIiInCEK2iIiIiIiIiK3kJqhiYiIiIiIiNxCCtoiIiIiIiIit5CCtoiIiIiI
-iMgtpKAtIiIiIiIicgspaIuIiIiIiIjcQs1pn4CIiMidzsx2gCmQgQ+e8umIiIjczZ5EKRjP3X3r
-tE/mKFreq/q0L/o6Bzj052Hjay+X+pjV62DloQAYhtXNDWhDpImRJgamkwmbGzM2N2ZszKZkd7Jn
-cs4sFgt29/bY3d9nd28Ph/o8LE/LyznmnJfXZkYIgRAjIdQzsHL0nJ0+OSk7KZXj5FxeF2OkaRra
-tiHGWN+PLY+D+/J4y+v6czCz5YX6njEj50TK5TgpJ/rUk/pEyokQArGeY4wRC6Gcd1gbWDF6j8s/
-Artuk6V3/Mxr1p4VEbm9zKwH4mmfh4iIyDmS3P3MFo7P7IndbkOIdHyU4pwhURvDw162YRX+hrAZ
-DAJGNKNtIm2MtLFhOmmZtm25nrTMJi3TyYTppKXrOrpuwaJLZHpC7qCbkxf7ZKcEZHfcV8dZZmB3
-3B2zUM7PDD+QSG11zge+QPDRZRTiD/u5LK+Hbb1+jbB6tJzXwZ/b8F95bP34RzPswM9X3wOJyB3C
-ofx9eP/995/2uYjICSwWCx566CGe+MQnMplMTvt0ROSEPvCBDwzZ4kwnBQXtKvuqesqBPzcHN9wA
-d9xWoTNQythGCYVmRjSYxMDGbMLmdMrmbMrmxoytjQ22ZjOmk5Y2BpoYaKKxt7vL7m5iL+2Tckfs
-57DYI+/v0mdfXpaB1sa1cnCMEGM5fjDcw+ot4MuKODWUew3t7uM3OwT2wwrDPro1bDs8Yqsob0NE
-9rWX+pG/AetHWw4cWAbs1dcbIiJn3EeAJ9177728733vO+1zEZETeNe73sUznvEM3vjGN/Lpn/7p
-p306InJCT3rSk3jooYegfPaeWQra1YGK61Dd9qFq7UPeLiHVVkOol2HbjGAQDdomsDWbcHF7k0vb
-m1zc3ubihW0ubW8xm7SYZwKOeeZKyDRpH+aZuY+C9t4OfXa65HQp4xgWAhYCBMMsgIV6gu1y+Lib
-jxKs47mG6wPvcRWuT/yzqZcSyFd7GcLwqu6/2v9xIXtwYFQ+tgzZqmaLiIiIiMidSkF7XQ2l47AN
-JUj7UNF2DjxXqs0QA0Qzpk1kc9ZycWvG4y9uc8/FC8vLtG3wviP3C3LfkXZhETJ7eUHs97FurwTt
-+R65z/Qps+jLcalB22IkhIiFiA1zq0Mg54iFIZ2u6stDJZu128P9g++fZfodF/aX0Xm0vY1u+TBv
-vVbMDybsY47HwUr2gcdtNKR/9Odw8NgiIiIiIiJni4J2ZasJ15gfXom15XDtet8p/WXdCWY0FpjG
-wKyNbLQNW5OW7WnLRhuYBCd6D32in+/R7++zmO+xd/Vh9q8+zPzaZbrdK6T9HXy+V+Zp95nUJfou
-kyjDs8tE8EDTtIS2JTalmu2pwZvMsvpOKQsfVbUehpCvbo8anR0VY48J6MuB44cezg69edg5HTw/
-lbRFREREROTOo6BdDZ2vV4Hz8LHLy4Lv8Fwuldzg0JgxiYFZ07A5BO3ZhM0mMjWIOUFKpP1d5rvX
-2Nu5yt6VP2Z+7WEW1y7T7Vwl7e3ii32sW+BdIi16ukWidydbDdtmNNMpbZ6WkB8isUl4bU/uZquw
-zXLU96Hzs4/NssuS9sES9TCf++iwfeMh48dRwBY5P8zsJcBrKX/JfIy7//4pn9Kj8qEPfYgHHnjg
-tE9DRE5gsVgA8MIXvlDN0ERO2X333cc73vGO0z6NW0pBu1oOSa4Bcr2qPYS/VXuug2PIDwbtoaLd
-cGHaMm1KRbvxntR3pPku850r7F15eK2ifY20P8fnc+jn+KInzRP9omeRMhkn4XgwpjmBOxYCsWnJ
-OYHn2unby1xtt7WmZ6vrVeA+JNRe95rrNzsqbB9M7ierZI/3OVyroi0idyJ35/3vf/9pn4aI3ITa
-VElE5JZS0K6Wbby8Dgmvjy77aNcq8YqXYJsz2TM9PQuP7PeBPRK7beBaE9iIxqRtmLSRSdvQd3Ou
-Xn6Ya1ce5uqVh7l2+TJXr1zm2uWr7OzssrvfsbffsVj09H1PTglPCXIdFj50Qe97ct+TFwtSbEix
-oY9NbZYWsdCURb3rudbG4wfq046Vi6+K1zaaa73ezGx43Xhxr/Ew8/WvIg78tHytI/kR1sP1ejd0
-hW8ROfs+6rRPQEROZAE8BDwRUEVb5HQ8SJmLe/dR0B4MFetRo7NVP+0aVMdZ2x33DKnHc0+XnP3O
-8eCEbpfWe2LusLRg0ja0Tbn03ZwrVy5z9XIN2levsnP1GjtXr7K3P2fRJRZdYt4luj6Tc1p2KXd3
-Al7+r5hzCdthQQqRFCJ9DKX7eNNiDYRQg/Ro3vYqbNvB0O1g5qsqtw/dxuvrRsPHh+Zv1J/JsNrY
-oauDsRbXj8jJxzVsExG5cxig5b1E7gzvAp4BvBHQ8l4ip+MB4O4cCaagPVgr3Y7rss7aCtH1SfeM
-5x76BV3uce/pc4/vR2LuCamDbkFTg3bTNHSLBVeuPMyVy5e5cvlhdnd32d3ZYWdnl27RkZKTs5Oy
-0+VMTqXjmrkToIZtICXoOzJGskgfIxZLV/KIE4JhBCAcCNPl2lZh2225OJc7YI4NIXv4GaxnXhuF
-7fUL1PS9Fq4PTvM++o9hLXAP14ev8S0iIiIiInL2KGhXo6WnR4/4aJj0cD2k1Yynntwt8G4fTx0p
-LehTB4tASybkhPcdTdMQYyxBu1tw9coVrl69wtUrV9nd22Nvb4/d3T36PtUDlSSb3MmeIfsyx8Z6
-LsEdUgZ6PHZ415BjJIVQqssx4DmW883DXO1hmLgfyL7jWrWNhniPly9bXqz+rIawfeCHOCyCzbIS
-fhKqXovIXSCd9gmIyM16CvCd9VpE7hSxLm/MGf/sDTfe5Jw5pH/XsubrucyVzj3eL0iLOf18j8Xu
-Dov9XfrFnNR39F3H/v6cnd0dLl+9yuXLl7l8+WEe/uOPcOXhh9nducZ8f5++70h9IqVMypCy02en
-z5kuZ1LO5OzL5mwRaEKgDZHWAo0ZEYjuZQh735EXC3JXL/2iPJZ6PCc85/IelpOyr7esY4/Wr14+
-Nl5fG1ZjxW1t3Pghy4ApTIucT2Z2j5l9j5m928x2zeyPzOyXzexLbmIfH21m32dmv2FmV8xsx8x+
-x8z+kZl98gn38QVm9otm9sH6+t82s1eZ2ZPr879nZtnMfuQRvtU6wUyjb0TuHE8BvgsFbZE7yyho
-n+nJ3apoH8bKXO2hYrtaPdvBE+RETh1psU+3v0e3t0NLLsOuzek9MJ8beCZ1PdGMGCCY4TmxWCxY
-LBakriOlnj7lGrJZLsO1bEZWb5sZweoc7FCGiJsZAcOyYynhXUc2sGDkEAgx4NbgHnFCnVc+zNvm
-4OhuH7/PYYj5KDCPLjauZq8N6fbRaw8bAi4i54eZfSLwJsq/Yoe/AKbA84HPMbPXAm+5wT6+CnhN
-fd34L5GPAz4eeJmZ/W/u/j3H7OPVwP9U7w77+Hjg24GvMLPP58QTXERERERuTEH7KDY0Rhv10vYM
-OeFDRbub0813WeztlCHV0QgBUg7s50zfdcz394lWZkoHAM/knMkpkXOmrxXtPlOD9mppK/M89AUn
-WCAYxBCINWQz9Pz2jKeEG2TKkl85lguhDgW3WMP1eBDDqs3ZcG/Z+m08bNztwL9AS9g+WdVG4Vrk
-fDKzC8C/Au6j/LXxE8CPAh8E/iTwrcBXA0dWpM3sRZQ1tgGuAt8L/ArQA88F/jalXfD/YWZ/7O6v
-OWQff4sSsh34A+DvA++kBPcX1PP4KWDz0bxfERERkTEF7cqHUdAw1HuBYWy91yHXCU8due+wbkFI
-Pa07BGMSjEkTmDaRJhjlPyfnRAwlgLfBiBYJoSUGwyyws79gZ3/BZH/B/ryj63q6rlyv9xkLlKp4
-DLacyr0c5u0ZErhZqWyHQMIgZjxkWC735ZQ6eGCYkz1Uz+veDq4VXq+W3djtYN+z201N0UTuGP87
-pZWoA3/b3V81eu7XzOyngJ8H/tJhLzazhlLJBrgGfJa7//pok181s58G3kapmH+vmf1zd//IaB9P
-powLdeA9wLPd/Y9H+3irmf0idA4RHwAAIABJREFU8GbK2j76ZlBERERuCc3RHqyl2jLtuAwFD3jp
-5J0Tlnqsm2PdnCb3tObMYmDWNszaltlkwnQyoW0iIZTh48GcNhrTJrA1a7lne4Mn3HORpzzxcTz5
-CffwxMdd4gmXLnLpwhabs/LaZSWbGrCxErKthO1gwxcCdZmxXNb09pTIXU+aL+j390nzfbybQ9+X
-c88JPGHk0RcKvsraXirY4yht9QdiowscDL3jSvyBH+tom8Ned+gfhcK0yB3NzFrgpZS/Vf7zWsgG
-wN0T8DKgO2I3Lwbur7e/ey1kD/v4feB/qXc3ga9Z2+QlwKze/ua1kD3s423Aq499QyIiIiI3SUH7
-KEPYxgmUdaxL0O6gX2D9gpgTE4NZrWTPJi3TyYTJZELTNITahTsYNMGYtpHN2YRL25vc+7gLPPne
-x/OkJ9zDvY+/xOPvucClC1tsbUyZNGE51HyZ/ev5rEI2lEHeGcjLsE1KtSnanLS/T5rPyYsFnjrI
-PeZlXW4bDU0/MEx8/YdQj2SjR+DwMLw+F3scrE8anhWyRe4KzwAeV2+//qiN3P39wC8d8fTnDpux
-Gj5+mH8OXF57zfo+PuTubzxmHz96zHMiIiIiN01DxysfQuF40LQNQ6tLiA25Dh3vFoR+QajV6hAj
-TROZxEgbI2aGWypt8DxjBGIwmhiYTho2N2dc3N7m4oULNJMpcbJPM5kRYoOnzGLRMd+fD53RalV8
-NIx9rWGZjwdy1y7l5IylRKhjzC0EsLK8l0UDG0L2sow96io+Gkc/unFYBL7ZYHxY+DazI+dyaw1t
-kTvSp4xuv/0G2/4q8KJDHh/mbr/X3T981IvdvTOzXwP+AtfP9/5kyl+V//EG5/DrwAJob7CdiIiI
-yIkoaK9ZzlEehWzPCU8J+g76jpAWkDoagzg0KLM66znnusx2rTKPA6QZFiKxmTCZbTLbukCKM2gX
-NBsdoZ2S3ehTpu96UurJfU/q+1pcLhXs5JC9LByXVpO1D8bh5FjOuAUI5RKw8iduAbNhRe7VUl7j
-sD066UN/TseF33FwXg/Wxw0/P84QuNf3KyJn0uNHtz94g23/6Jh9+AleD/CHhxwXVlX1h457sbtn
-M/sI8OQTHOsGMvCkE2wX60VEROQ8e3B168EHefDBB4/ZtlgsFo/lCd0yCtqHWk1Ydk94TpA6SB2W
-OkLfEVJHEyNtDLSxVovxGrC9vCaXYd0lyJZZ1xYiTTuhnW0y27yATRLNRmLWZWIzoe8zi8WC+Xyf
-bj5nsTA6MtlzXVYr4w7JvQRtALcynxwAgzx0D6+B1kJpkkZpwBZCA7EOH18u9jUaP76WYVeF7SPC
-rdlym8Mq0+uh+qiQfVRl+7CqtrqZi9wxHu0v6x34y35srhcREZFDvOY1r+EVr3jFaZ/GLaOgfZ1x
-VXcYNt7jfV+Cdt9haUHMPW2EiQUmMZAxeofkmZydnA6paNegHdspk+kGs60LNBmmDn12QjthvujY
-29tjb3eX/VA6l3vu6FNdisudjB8M2maYl4ZpRqhV+NFbMcOtDI+PocFjItSQzaHBdsjUwxDy46vX
-6/dvFLYPm7N92OsOu69KtsgdYdx07MmUjt9HOaqK/BHKX0InqTLfN3rN+nk8mbIE2JHMLLCqfj8q
-Zsa99957w+1ijMSoiraIiAjAfffdx9d//dfzhV/4hTfc9oUvfCEPPXT2v9RW0K58VDQpt3NZ8zol
-ct/j3QK6DlKPpVLl9hxwr4HaQm2eFggRzGKZXh0DTdNgIeCUQL3oe/a7jr35HEJTqtxNy2w6ZXNz
-gwvb2+xdvEisXctT3+GUKnaZk13nlC+D6Ko1WlmGjGECN9Q1uy2k0nk8dFhs8djjMdbth6q2jcvX
-ox/OweeWlfPlfVvezScM2euX8ZDw9dvr3c1F5Mwbdwh/FvDWY7Z91hGP/wbwHOBjzOwJR83TrsuA
-PZ3yt95vrD39m5QQ/mk3ON9Poayr/aj/grn//vt53/ve92h3IyIici495SlPueE2k8nkNpzJo6eu
-41Wp+Ja52cMyVdkzOffkviN1C1LfkfuenBM55/p8Xm5vBqE2PWvbhsmkZTqd0LQNFgMZ6FJm3vfs
-zxfs7O2z6DoyEJvIZDplc2OT7QvbXLp0ka2tLWazGU3bEmJcNTQbTnoZVGGYa+11ua8yxLwOZc/l
-y4Lc9+TUk+ta4J7K8Har7c+WedaM9eHjq/W6V13KS09yq5sPVWo7sM72uEv5SS6HbXvkn9khy4mJ
-yJnwTlZV7a88aiMz+yiOWEcbeNOwGdcv2zX2V4BLa68Z/Eq9vtfMPu+YfbzkmOdEREREbpqC9iG8
-LpuVPZFzIvV9CdpdV4JqbXQ2NDzLnks12UrfsRgDTRNp25bpbErTtliIZXh5ziy6nr3Fgt29fRZ9
-jzs0sWE6nbBRK9qXLl5ke3sI2g0xRkIIeK2M+7De14Eg6ssK9dCt3D2XYeypfGEwBG5P5YKnMlfb
-hmXEbC0qr3Y9nsp9WMge7h/VQG3sqOHjJwnYoMq2yFnm7gvKklwGfJqZffv6NlY6Mv5jju70/Qbg
-A3Uff9fM1juKY2ZPBf5BvbvL9cuAvR6Y19vfb2ZPOGQfzwG+kTtyLriIiIicVQrag5otfYjZNUin
-1JP6jr7rSF1XQndKpJzpc6bPiS4lUq1yew3dZkaMgdg0hBiwUOZJp5xZdB27e3tc3bnGzs4Ou3u7
-7O3vsZgvyCkB1GAdsTCqZJduaOVfg8vAO/xXHxyeHHcSH74USBlPqV56GDVss/G/MZcBvu53/M9P
-H+17tcVqve/lIxwM5UeE6MMapN2oU7mI3BFeCbyP8lfAq8zsx83sBWb2dDP7q8DbgBcA7zjsxe7e
-AX+D8jfJJeCtZvZyM3uOmX2Gmf1NytJh99dtvs3dP7K2jweBV9Rz+ATgnWb2DWb2TDP7TDP7bkoV
-/P3Ah4aX3cofgoiIiJxPmqN9nTL0mpzLEPG+VLT7boH1PSGlsmyWZwgJ7yEbZU1t9+U1TQnJ5ZuM
-YQa0k3JmvpgTdq+Rc+kwvr+/z+7ODjlndnd22N3ZZXd3l/35nK7vScnJufZlcy+Nzqzs8UDPsmXc
-tvr86rnlkO46b7vMMU9lne86HNyG8ePjQLtc78zXAvfq5vH5d4jgoznwNagft1TXcU3V1pf6EpGz
-x92vmNkLgV+mzJP+8npZbkKpQP87rq9ED/v4BTP7auA1wDYlvL9ybR898HJ3/6Ej9vE9ZvY04OuB
-pwI/uLbJBynDz3+m3t8/4VsUEREROZKC9jpnObc5p3Sgol06jveEnMiU6nA2yDjBnehO9EwELED0
-QInXvsy7OScWi3kN2XP29/fY3d1hOp2Bw3x/znw+Z79eL7qelJ3sNWT7EFBHIdsZ15GXS3stU/JQ
-IYZlhZs6P3tI8IaPwvn6sPFRq7hl/zU/eEyvr7tB9h2H7BtVqG80P1sVbpGzzd1/y8w+CfgO4MXA
-04CrlGZpP+TuP2lmL+HAxJTr9vFjZvZvgW+hzOd+GmU01gcoc7B/wN1/8wbn8Y1m9gvANwHPBDYp
-1fafB/6Buz9oZhfr5pcfzXsWERERAQXtJatV21wr2tlTCdp9T9/19IsSss0TIWeCZXKCZE6P0bgT
-cya601LmaecaUK2GXjcje6loz+dzzKBpWtp2QttMACP1iT5l+q4vFe2uI+VMdq+5uITtcbOxQwPn
-kMLt4EPjija1oo17eXz4QawF5mVR+5AH7cAGB5cyO6n1yvTwfo5aP1sBW+TO4e4PA3+7Xg57/vWU
-udTH7eP3gW99lOfxc8DPHfZcbcp2ifK32e8+muOIiIiIgIL20hA0zR3PjqUMdXh1zj0p91jusZzI
-ngiUYeLZjZSM1DhNzsQhCNf51SEmghkhGKEm0xIsy3H6vidnp+tKU7TUZ1LK9H2qQ8c7Uiprc/sN
-ysXLgDq8jwPPDQPYy1JeVudy23CxYftxYan+TDii1HT9CRy8u/b0uJo9DtdHBefDhodr6LiIPAb+
-2uj2vz+1sxAREZG7hoJ2NTQDM3dsrXlYTomcekgJ84TVTuPmYLnkyyY7KWaappR3Q4z10tDE0szM
-Qg2V7nUYeCb1GfcevFSrU3JSyqSUmM8XLLquNFrzslzXUYl3FVathuZVPDZbtQwfgjU1bK+amMEq
-ZK8i98Gf0Wjk+GHh+BYF36PmZq9vo8q2iNyImW0CF939D494/unAy+vdd7j7u2/byYmIiMhdS0G7
-WgZtMjbMXc6pBu2elHpIPea5hG0y5FXlN+VM0zTkYf50DdmhSWWIdTSihdrZvAzDzrlUr1Of6VO5
-neslJafrO7qup8+pBvPxUPRD3sNyyPVqlvaqUZqvwrSPKtk4gYPV5+Uo8uW7O2FV22wtuF9vqGbf
-TFBe70w+3peIyA08EXi3mb0BeCPw25Qlv+4HPg94KbABZB7l8HQRERGRgYJ2tZyjnL1WsxO568u6
-2bWq7TnVoJ1L0GY0DBzDPZXeYsugHbEYMYMQAw21pmy2rBtnd5JnUi5LhqWcSwDPJbynZcD2A4F3
-uazXCcLqaotRRdvH1etVtduXFe9lVD+wpzKF+4hj3mTwXW1+/euOmrd9o8dERA4xA/4q8GWHPOeU
-4P217v7W23pWIiIictdS0B7kEpxJCe/7smZ2tyDVsJ3qmtNDyLZxRzKoncHTsnFZGSdeLmZDc7Rm
-NY3ZDIJhMRCAxqyOQy+XTIIcsJCHdbdKj7LgWL7R2tJD+B+anA3zstcC9uj+MmQ7+LIKXkO9raL3
-DbuKjy6HObgu9hH7GM3hPslcbhGRY7wf+FLghcCzKBXuxwO7wO9Rlh/7AXf/g9M6QREREbn7KGgP
-hmCXM7nvyd2iBO2+I6WeXLt0D9Xg1VDsEhpzDerelznWJWCXudkxBtq2DCuPFvChs3cwAjWMh2HC
-d8JJlLsZkg2dzIajQfDxQ9cH0HHaNT/wgK0F7PFjJWQPFe36v3bLpl4vHRWY3YeTH+4fXApMQ8VF
-5Ga5ew/8VL2IiIiI3BYK2oOhop3rnOyuIy06ct+V4F0r2qvh16tluwzDc5lzPcy7JkQsBCwEJm1b
-5l8PncCHaraXOdvBSvEby7iViYLmjqXy+rqGVzm/5dzmEsAPm7fs46Q9dBe31ZDw8fzs5ZzqsvbX
-aj/L/72JKvIhef+op8fnvT6EfDyPe9h2PKf7sGq3iIiIiIjIWaGgPahBO6dErkPH+25B35WKtg9r
-TgOrQLgKukOALkt6RZrYlDWymwmxaQmhoSzwFYaMDIQSMkMJm2a5jug23ClN0UIiWCDbEDxL9Tws
-S9qj+Fqfx70WrYegWrYKBk0MxLYhTlpmsykbGzM2tjZophv02eky9DkxdG3zOo7cRtXmIZhb3fGB
-06j3zVchfjlM/cDZHtFibT08+yjsjzdXyBYRERERkTNKQbvyYeh3SqS+p+86+kUZOp6HoePu12Vb
-YBmAoczHDqEhxpammdC0E5rYEEJcDiUPFuvQcl9l0ANBGzw7KQRiCIRgWDLMrXYfL/OobRz0vS7p
-NQrZnus08Vq9jkATAm3T0E4mbM6mbG7O2NzcoJnOmHc9tujJuV82davfIFD+11ZfEpiBef3CwK77
-maxedXAZsfL4USF7+QOtl7UnvMwkXxXqFbZFREREROTsUdAe5FSvErkv1eyu6+j7npRSDeLXD4Yu
-Fd/a3CsYZoEQI7EZVbRjuwzatgzjdbj4MlwaZulA0O5jJIRAsECwTKY8l2t/NLfVeZR9+Cp7DhXt
-IcQDwYwmBiZtZDZt2ZxN2NqYsb1Vgrbt7Zf1uxd5NWd7CNsWlkPWy7UvK9qMz2NUfDYfhexlh7Tx
-z3BV5V5l5tXiZc4qbDvj17pCtoiIiIiInFkK2oMhuHmZZ13Wsy5LepXy7hC01+ZE1+sylDoQ6pDx
-2ExomwmTyZS2bctjoSHGSIhGjEaoQbtUtJ0wzPVOTg6JGAJNCPQhkEPAw6r6HSwQhpS7nl3Hbb8d
-AmWt7GDQBJjEwKSJbExbLmxOubi9STudYZ5J3YL5fsY90dX1vYcu6mZlzngIZdmyQJmHDmHVGZ1x
-o7hDCt2jKvWBOduHbGa1F/rw/DKAj96biIiIiIjIWaOgvW6tG7cNHcZrqDOcMEyFHl4CZUh4CMTY
-lJDdTmgn03JpG9qmoVleArEJxBjKmt117W7DSsC3RMKIFmhCpI0NZMqyYhjB8qruO1SJ/eDFvZx7
-WM7PdqI5TYAmGpNobEwaLmxMedzFTSbTDTx3dPM99oPT5wV50bGYL+j6tJx7biES25bYtMS2XT5W
-mrZFxkn/0DZqdvDrilWAHs/eHt7dELdX//mo27sraYuIiIiIyBmkoL3kB28ul8CCYTmvUnUeVVaX
-17XxV4jEpi3zsmvQnkymtJNI0zY0baRtyu1yHZeV85zK/OwcEskCwUrQjiHSxFiHf2cCoXYwz6Pu
-3Izz7XKoeDlvqxVtJ9aKdhtgEmFjEtnenPK4C5tMNzbpFnvs7USuxsx+7siLXeY7e8y7jhCbcmla
-mlTem1nGbEIZRt6U4fPk+rNa/TiX89qXWXoVtW0I1cZoLHyp7tdZ86vO6PUVDnV9cBERERERkbNH
-Qfs6a0Ofh+7Zy7nG9Tmv86TLVoARQiTWoeOrsD2hbUuobmIgNg1t2zBpW9q2IfV9CdchQXZyTPSh
-L9XsGPAYoWlKv3LLJMv0lsk5kXIN6XBdRXv4kmAYMj6E7CYYbTTaJjCbNGzPJlza2mC2ucHutQnX
-JpFZY+ySoF/Q7+8w318QmhKyQ2zwPCuV/VA6j8emHCRYWEZos/Uu4wf7jZdHbGghN/r5HwzY60uY
-HfiSQ1lbRERERETOIAXtQQ1zZmUOcgiRGCIWGrC+DIv2Wq1dJrxS3zazZcU3ti1xMiG2DaGJWAwQ
-A1YvIZZmaSFGYowlrNfu4qUpmC+7mzexoW16uq4tndBTJvWJvk90fUfXdXSeIGW8Lv/llmvnsRJh
-J01g0jZM24bZbMJs2jKbNEzbyCQGYgDzRPDMxqTl4vYW3eJxeHK6Rc/e7h7doivD27uOvksEjGhG
-snIdY2ASWiaThs4yPUaHgadS33ar52Z4netdFw6vUXsYT+7Larzb0Je8BvYauLV2toiIiIiInHUK
-2tVQNR26fMcatAkRt1jbicEwNns5e9hCCY+hdBqP7aTMXW4arIlQw7athe3YBGITCTlAyHiMZW3s
-PMwLN/qmJ/WJSVuu+1Sv+579fcc8kZOXSjg1bOPLdbPNjEkTmU4aZpOWjWkJ2tNJy7RtaGOgMcc8
-E3BmbcOlrU0Mp1907O3scbWdsG/79CnTZ6dPmUCpkMfaPj1MJkyCsdm2zEnMPZXu6Msh90Z2Vs3b
-hu7rjMP26AuMek5lCTMOVLRFRERERETOOgXtypYV7drULETiqJrtxNESU8Pg5tqJ2wIhNITY1mHj
-LaFtS9COAWuGkB2X1eyhok1gOdQ7WKgV3TIMO/WprOGdEimVgJ36nq5bgCc8dfQLcCvhPFvGcUJ9
-DyEYbVuD9rQ9WNGeNLRNIBoEzwTPzCYNtr3JdNIy35tz5fI1NtqWaxbInmpFuyMapFrR9qYh5sQ0
-GJttUyJzdnJ2es+YG54hm+EGbqXSXhqn1RZnQ6J2x2tVG7PyUB2zv1r0S0RERERE5GxT0K4s1KAd
-S1ftZjIh91NC7TyOe1nuyzNel/oKIdZqdiBOJrTTKe1sWq4nJXDHJo6Gi9eh42F1WTb68rLO9dDA
-LIZQm6SlGrR7+q6j7yMxGDmnOk870fSxNEfL5auAGMPyMptO2dyYsrExZXNjg82tLba2ttjc3OLi
-xYtsbm4ynUyY1A7ik3bKxkZmZ2efK/fscPXqLt0isbO7h7FL3/dl3jclnEecWRPZnk153IUtZoue
-2SSx2/VM5h078wXMF6S8KF8AmNWl0CgBGxtPdq/Kz8DNrwvYGj4uIiIiIiJnnYL2wMrQ8BAjsZ3Q
-zmbgTh9Wc4pz7pcB193LsPIQIQTa6Yx2NmNSL+1kUoN2s1zKq6ydHZbD00Oddxwo4TPUKm60QB/j
-KmjnUs2OMRK6BRaMVLuOY5BSWi3pZUbTxLqMWGRjNmNrc8bm5gZbW5tsbW2ztb3N1tYWW9sX2Nq+
-wMbmFtPZjIlD9jILfefSnGs7c/b3etyN9vIVMOi6nraJddg5TKKxNZtwaXuTe++5yF6X2O8Se4vE
-tf05zbUd3DNd1xEDRBuWHHNsCNLj9b4OydCqZouIiIiIyJ1EQXtQK9qhaYiTFnxWqq6hzCnGAil1
-pJSw1JPdIQao87ib2Yx2OmMy2yhhe9LSTuq62bE0DIuhVKqHYd0WhmW8asduK8PHc4i0TUOqITun
-TN93dHF4ndVQDRZDWRqMWhkPRtu2tG3LpG1GAXuT7a0tti9ss7V9ge3tbWazDabTGdPpjKad1GHw
-5YuDvf2evf2exSKTvXwh0Pc9e3t7NE2kjUYTStDenLbcs73JvY+7yH6X2e8z+11itrOHu7NYLNjb
-2yMY9bKaR46v2rerTi0iIiIiIncDBe3KQqloD0PHh2ZiQ8gmBKzvsFTmSZtnLEYsRIhxVc2eDmtn
-N8vKcoxGE4y4DNxDVbsGbyuPEUsHco8RT5mc8yhox+Wwc4tluHpoyrl6zsuu3MECk0nLZDJhMpmw
-tbXB1vYW28vLheWlbVtibJaXEGOda95wad4znyf6BO5Gyon5fM7u7g4hDFXzyGzSsr0x5eL2Jo+/
-dIF578z7zLx3Jm1L33fM9/fZ29st1X8LDCnbagX+4ELgB/5U1hb+GqZv27J6LyIiIiIictYoaA+G
-ZmghYE1TCtzB8GCloVlbK8ypJ6UydNxC6TZuITLd2KCdzojtZNnorImRNkTaYLSNMYmBNpaAGoem
-aFaq1CGEurpVCfduuc67DuSYCbWZWmwamn7CZNLT9R1935cGasGWTdCGkN1O2lLR3iwV7c2tzTIn
-e2OLdjqjGYfr+j4sRMwi7XTG1oVtHpfLOWW8HKOJdQ1tIwS4ePEily5dZHtrk9l0QjsxZtlIDpOm
-wXPC3Gmisegzi5SY95mUvfSAy7muXA5DN3ev2XsI1sByTe1hfXDN1RaRs+jBBx/kgQceuKnX3Hff
-fbzjHe94jM5IREREToOC9mAI2jEQrMGDYTEuQ3aYTup86VybojkWYgnIFplOJrTTCbGpVeIQaUKk
-CYE21ktTg3YN4gfma9fGaKUrGmXOdc5kd9wzIWdC09DkSQ38mVSr3cByXzGWoN22pao92yjzszc3
-N9jYqEPFZ1Payays/T0c2+r5WMRCYDKZsb0NIZblyghGbAJN0+CeGLqvb29tceniBba3NtiYTcAa
-3AIQmU0mGJkYyjJj1/b2ubq3z7W9OfOuJ2UnWVkGDNZq2qOUPYRs1a9F5KzLOfP+97//tE9DRERE
-TpmC9mAYhmwBC4YRSsW0bbCUCDnjOZFzCcBlOa5Y51kH2tjQNk3tMt4QYygV7Vgah01iZBJDrWaH
-6yvaFspSV4Ha5Ry8Njhzd6I7uV78wKU0UQsxEkPZbztpl0F7OpuysbHBxmYN2M2EdliCzGIZcl6X
-KQvDe7fAZDrFmpbpxiaTjRkhBpq27DulHq+N4TZmMy5dusDW1iaz6ZQY2+WXDVuzKdGgjYFZE/nw
-lauEAH0qr4WyDFipZNeq9rhSXVf9GodsVbJF5FYws48G3lvvfrW7/+it2/tHnXC7BxmWixQREZG7
-i4J2tZrvO5RSy9jlUNeiMnc8N4RhGS2nBFOLyznWIQ7ralvtKl4eb8IqXDfDUl8h1HnhZYmrVU23
-nIONjj085sPQ6nHO9DLcvRy/VMqHZmjtpGU6nTCdTmnbErKbWEJwCE0J9mbLmdA++h8zK+caAtmd
-ixe28ZxoYiDVtb1z7plM2rJM2MZGbfxWh803EdzZ3pyRPRGHTmh19Hcb99mdd+zRkxdpuTr5dQ3I
-xyG7/gxsbTsRkUfhFn9zF4D3nXDbBwBVv0VERO5GCtoDG08MttHQ5VCbopXwbR5xc/BahbZVuB6H
-daMG7Rq2Yx3WHcJ4yPgQcJ2US4AMNU4Gq13JQ6lUD8uPDaF8PJB6ub8a5tu2pWmaErabtoRrixix
-VK8JWO0kvur27eAZr8F7PDe6jYHN2QzzzLRtlkuc5Zxpmsjm5gaz2QZxOZQecCcGYzppuOAbNDEs
-G5kFC+W8ru3jeZ9usb+s6eRhnnr939W7rO+4nq/52hcOIiIiIiIiZ4SC9thy+PiqXmql/XipaNcl
-wIaOXTYO2vV1JRyWEDss51UucRm2LQyXMgw655IaS34vURhKRbtUwVtCKN26S5gPo9O11dDxWi1v
-mjKMvRkusSVYswrZQ4T1Emi9plevncCXleQautsYsI0p07Zhe2uTnDPupSt66UDe0Nbh4mVtcIBM
-CDCbtLRNZDYry4ctK/yhwXNgPk/s2YIEZV76qLhka7Xr5dcYXtumKWmLyCOnQTEiIiLymFHQXlN7
-Wo/u1bg3hFyvQ8nrf9gQXodZxsNrVlVtC7bqLG5W50GznI88hG1zXw1VH4agh0BsSjC1WuW2Wt0e
-qtrjanYIYRWw6+tiKMPbzW35JQFDWIXVnGdfxdzVsSAEI8YJNhmfc16e+xD4y+uGW2X4edtGJtYw
-gzLkvp6vu7FYJHZ3F+w0c7pUBo7nlFcjC0bv8Yg/KBG5S5jZc4GvAf4c8BRgBnwQ+I/ALwI/7u6X
-R9vfB7wYeD7wZ4D7KZ9pHwLeAfwz4J/7IU0dzGw8MdqA15nZ69Y2+y53f+UteXMiIiJy7ihoH2bt
-32U+fsxLkB6W4TJ3MGf1X5lrnGsmL5nWyF6GRYfaTbyE80yu62V7yiVSjhqxeQ2+y27kdvALgFXa
-9FG4XQXdIUyX6nMNwQY522pXcF1zsVI1txL+hy8YaqXavM4Vr43YcKfk51xfO4T0ISSvLk0MzKYT
-cnbmXWZ3v2N3v2feJfYaTWREAAAgAElEQVQWHfuLBSlnUq3uq94kcvczsxnwI8CX1YfGfyHdXy8v
-Au4FXllfEyiTm43rv3Z7CvCF9fIyM3uxu++ubXNg5cBD9iEiIiLyqChorxuHTveD//oawuW4Fdd4
-NPnQrKzWtvNQ567zit2dXBeJtpzrtOhV2C5xNODBDlSZh8OsWpatrzA9HHW8Tb32XIJwrd+YQUq2
-rEKX4xxswubOMmCPL6uq/urn4MswXy4hlIp98IMp2cyJTWQ2nRBCpEvO3rxjb16Ctu3ukT0zX/Tq
-wStyTlj5Vu9fAJ9L+Yvod4EfpFSkdymh+bnAl66/lPK32puBNwK/DjwEXAA+Fvg64Dl1v6+mVMrH
-PoUS4H+pHvflwM+ubfPBR/v+RERE5PxS0D7KELJHwXst+8I4VtuwsvRqPeghbJdLec7c///27j1M
-kuys7/z3PRGZWdWXGd3RaEE8RmCMH4TRCi0GydwkvMK6GJCNxZoFdLPs1e7iNazZxbDDCBv7eSQu
-axs/HtAuFhc/whjBw0WALZAlwbO2ZMtgsMSa2+4i1JIHSTPdXVWZEXHOu3+ccyIia6qru2eqe2qm
-fp+HoLKqIyMis1WT/Yv3nPdAysETgxTLsmExYWU/K/Oup0rzvHJ9uMx7uE93DeazYeEpkUoztxyy
-IzFOS2XV0Gy1am2G+/QY8tD3WtmeupNPS5ClMpS8NoJjnGteO6dPXcyXiyUxwcFm4GAzsO4jyRNd
-3xPs8GsSkcew/4EpZL8V+G/cvZ/9eR02/m1mdlf9obtHM/t0d/+9I475buDNZnY3cDfw35rZ33b3
-3509//1mtjd7zh+6+/tP7mWJiIjIWaegXfhRlewjm235oceOk7ZmaCfPXcSHlOhjbgrWxEAMdT5z
-CdxGGdbtuaJtRkpGMMfLz1KKxBSBWll2tqcXgnvIjdisyeE4lOOEVOaQ16tNuMdxGPn4amrVvDZ+
-w2aP58HbxuA9vQPb63onDwQS7gEfXyvj8UOZe75cNOzuLrl4YZduiAxxYN117O23JbRDwrf+CrSG
-tshjR6lmfxP5P0MfBL7uUMje4u6XDn1/VMie+w7gdcATycPIv+dhXbCIiIjITVDQLh4U3w4PG4dx
-lPYYls1zZbocYDtoJ4aY6OJQlvSCJjGrGpexj2lWDXYnlbAdMVJKxBiJQ8Sb2XDusUpcLylAnFWz
-zUjBSG407rjl9bqNgFOHks+vu1bB7eiwfWg7/IbMh48HcvCvw+i3+4YzVv/bxthdLbhwfpeYYNP1
-7B9sWC338zztMm/9iL8FEXls+GzyQtIO/MAR86hvWAntTyUPHV/UH5MD/BPJzdJEREREbhsF7cOO
-Cthz4/Tow3Oly9OB5InoRh8jzQBNyNvQWKnq1qZhpXt3CduGEZKTyI9jjMQQGMJAkydOz8I2Y8XZ
-CdPlGYSUw7p7oE7Bzr2DZuebDy2vVeJZxXpsajarao9N0TBmRe2xKl56o5V56Y674ebzXalzyZsm
-sLNccPEcYIG9gzVX9vZYLRf0Q4SBHLZLQ7bp9WlIuchjxLNmj9/9UA5gZl8DvBL4XGD3Grs5uZGa
-iIiIyG2joF08aOi4T9XhB5l39q5PmdVt3XNIHBL0g9OGXMGNKREs4AGa+qw67LpUmGvIDpaHjg8x
-5iHTjU9zpseKc2lSVruNmxNjWe7LQw7VWKkiTxVvBzzF8XU/6LXOKtrb1expznYN49vvQgnaXhc7
-S+N7kt+z6cZE28By2eIhYE3D5as7nNvdYXd3RR8jDgwpQVRrNJHHqHn4vXTNvY5gZivgJ4EXstX9
-8ZquFcJvgQQ85Qb2a1C/NRERkcmlS5e4dOn6/yTouu42XM3Dp6BdTF2+S2g+csmrQ93Qxg7d5ZsS
-th3yHO2YCDhtNBbRGKKN1Wyv1ejZ/OYcvKd1qmOKWLQyxDyV9bdzo7QQyvrc5GHhyROWDAuJ5BFP
-gZQCoZwwmNE0Yes1pUPrYU93EGoVmyl0z849hWyb3XSY3o/8GmYV9VqBL8PGDaMJxqIFp8Edzu+u
-uHB+lzsuXsij2m3NkBJdPxz+m3pof8Ei8ljyrUwh+1+RO5W/D/iwux/UnczsneR1uW/zUJj7bu/p
-REREHgPuvfde7rnnnkf6Mk6MgnY1BmxmoddzoN1edJopcDOGS2D8p1ydpz3E3Iq8bYw+BhYp0Xpt
-eMY4z7rOb86Hr+G3zNFmwD3lKrWVpbNCoPFAQ4OHebDNHcxzyM6N1NwD0JTn2bjMWH0dTpkjnqZq
-9/hSZkPUg09LfM0r6tSXMd5nmO8H5Fnb2/uSlwFbEMZq+bmdFRfOn+OOdc8QnSE6602P2fyO1XZz
-NBF5VPuj2eO7gP90E899Ffm/OO929+cfs98TuM1358yMJz3p+iPVm6ahafLYpqc+9am3+rJERERO
-vde+9rW89KUvve5+L3zhC7nvvtN/U1tBu9gOujnszn8O8+Hi82HmbJd0KX3IHQZPeMpzs4c2EGMg
-NnXprjAdwA8F7RK8U8rV5lA6koeQK9S5Mj11GB+DuYN5rm4njyQPJM/DykMwQhNIqQZs8OSl63kq
-xyhXP07ZtvFr7mKe1/geQ7Ydqnwbuau5G1bCPz41jJs3gTMzLASC5+s6t7vk4vldDrpIN0TWXU+7
-PxammO51KGmLPEa8b/b4C4B33siTzOwJ5MZnDvz4MfudBz79mEPdkv+YPO1pT+ODH/zgrTi0iIjI
-Y9pdd93FXXfddd39lsvlbbiah09Bu4hxFqxnQRsYq9peGnnVDuPzb8aR5Qa4jV23E3kIeT9ENgGM
-RGoC3oZchY456CbPxzZPWMpNxG1WHQ4hn8AskBI0oRmHhFupdtels/LryB3NpyA9DU+fWoDnanZd
-QsxLN7NpFPkUtEMIJE+EVCrr9Zpq2C7hO98CcIJBY+TrKuuCTx3TA+OzLN9wWC5adndWXDwfWXcd
-+wdrdlYLVsu2vD+lMRpeur3fqv8liMht8uvAHwCfBLzazL7rBjuPzz+3zh+z32vKvtcK1OvZ49UN
-nFdERETkhoXr73I2xJiIMZFiWVIr5iWmUkwlsNZh5bN/tY3V7sNzusuSV+6kup52P7Dpetabjk2X
-t67r6YfIEHPQrsuC5UZqOfxOW9knzSrOJagGC2UJsfx1Cttelg8rgdpTuQGQw3YdNh5TZBiGvMWB
-IfYMw0A/DAwx5m0YiGW96xiHPCw9RkgxV61xgjsBpzFozWjKFmwWtMv1TjcHcmBfLVrO7eSq9sXz
-u5w/t2J3Z8lq2bJYBNpgmKVxSbUQSpU+KHGLPBp5vvP3hvLtJwI/ZGaLo/a1rN7ivg+4vzz+6qOe
-Y2bPAV7P8VXrjwJ1bsozbvLyRURERI6linYRh/rvsTqMOo2NvvLw7Knr19ba0FM3NMBzc+9xfemc
-ymNMdDikiEfDUwMpgDeMPbzHhmG5I3ggh8hgVtbBZmxG5nUJsnE4ebP11QjgjCF7XtWeLWidz+VO
-jDlA15sD7j6u1W1lHnUIRkh5rjihDAkPpSJdhqwHCzSlkp03G6vaRj1WKMPMQ57HnRN/qWhDsoZ1
-13Hl6g7ndpbsLFu63ulSJMV8kyAASfla5LHg+4CXAC8AvhL4DTP7R8C/BfbJQ8Q/D3g58KPA693d
-zexHgdeR18f+VTP7buC3gTuBFwF/DbgC/CHXGD7u7tHM3gs8F3ilmf0a8GtAX3b5mLt//ORfsoiI
-iJwFCtrFNEea6as7Hg434JoW8prC9vQDnw0n97KqVowOnnDzsr61l47cuSlYEwLBan71Ovq8rENt
-eMhDvs0glSHiNcTDrFIcwjR8fFx7q1a1SxdzmzqGz4eNpxTLet5TB/I8rDvlY6cpbNOEXMEm1JW+
-MJr8GspbEYBAHQ4/e2PHDu1T5g9mtE1gtWiIbuyuFuwsW1aLhmUbSBEGc/AIaVoyzDRdW+RRrYTm
-Pw+8GfgLwKcB33vUroe+/1vA5wOfDXwO8E8P/fkfAS8DvoPj52n/XeCngScecYxvJ1fFRURERG6a
-gnYx74qdq9c5TFoIR84HLlF09nUKj7UJWA3bQx0WTiKmvG9ySG60IdA2TttYCap1BWqnKfOzPSVo
-avU7B+tU53Zvzb+uTcbKfO1QQrU7KUbiuHpX/nkaBlKMpVu5j1+Tp+1XZylfRwn+RkPASfW9mt7E
-8ubUKvo0XzyUarrV5mgW8ns8lvHzzYRAIpBoSDSWaC0SfMBih/cbUkylq7vaook8Frj7GvhLZvaF
-wCuA55Er2Q3wEXKV+WeAt8yec9nMngv8DeCryAF9IM/5/lng77v7h6wuc3CN/1y4+9vM7PnANwDP
-AZ4MHDl8XURERORmKGgXtSnXuNazp3Gpqtkq0A9+3tbyrDmM2nxfh+hO9IR5IpZh6MmN5M6idZb5
-qTSWm6cFTxj5/G6Wq+S1WRo5RNeQnTcnrxJT50DbOH/ZzMATKQKkaUi4Mc21rl3HU93K0HTS2Lgs
-zIJ2sDx0OyUjhTA2hrPSUjx3NE/5hgK58uzJIYAlrxk739QItQbuOYSTCF5CNonWEo0PEHt82JCG
-SKI0ezuhv3sReeS5+zu5wc7jZf818J1lu9Y+X3zS5xURERG5EQraxXytbJsPiR7Xg95ax4txB6gD
-pKc/d7ZqKCnloJlSyiHVIboRUw7clC7dhFzxTSVsegnfblbyv41BO8Y4q2inaWT2vOlYqKHfy5ra
-20t2xRhzRTvFPLTdZ1Xy8bXm6rZbWbKrBO1gRpPqEPbapnxe0c6v18r4ebM8f93KuPjglIp7qXDP
-QnataLclbAePWOpJfZer8FCGuJ/QX76IiIiIiMgJUtAuQpgq2k4izzL2rQB+ONmZ1fA7j9zbNW4Y
-67VQBmMnN2Jyhug0IdGY0Rt4gOCRQMqDx2tuNSD4tIRWCdrzLYRI0+S1urdnkNfEn8C8dCjPw8o9
-pbLOdQ3AZS55CeWO5XCO4cEIKU8kT2akYGWd7kRMTiib4zSW38N83FC6hU/D3q1Wui0f063MQa/L
-goV8g6EN+XEgYR4h9aXL+YP/LkRERERERE4LBe1iK1Afisrbf7a9hx2xHXXsWmWueTYlGEizBmK5
-8VqD01iiDoz2sdrtUwfwErSHYaBpasfxHLLN8vrcIZQO4dTgHIFEaBqa0NA0DXUSeSBXzROU5ml1
-znZtEleW0QoBUiBZIDWBmNIYsENKWHQaT3iAxo1QhoePYZs8f9w8byGlPJg9MA6ND/OQbdCa05Sq
-NilCGqalwJW1RURERETkFFLQLmpF20mYh1LVnq3cNTOG6jIUOmz93I8M2/Nat7uRSjevgZQboHmC
-AG6eK7yW17yuQ79pvATW7Yp2Ddv1+xq085ZDdkwDKfbgibZt8bYFb8fu5EZdgov8nBjzmttp6sZO
-CpTFq0lNIMWG1ORh5tFTnnudEk6g8QQh4J6wshpYCAFPeXi4MY0DyN/Xbuu1om1j2G4DBCvvTxrG
-oF3eVBERERERkVNHQbuYul/nocyHWpqND6fKdQ6GYyewmbo02Nh5POUSdl2SK+XT5OHTybAEBMMb
-8FLiDoEStNM4D3oMp2Zblez5ZrVpmefAneJAjD0p9mUu9zR5PJR1sPNzEikN0/7Jc1U75fKxh4bc
-BJgyh9sZB3yXynfMJfFStc7D7q2MYM/zw2sndh87k+M2m+ddlgUrxfM8hNzGij+eIKVZf3cRERER
-EZHTR0H7WmrGnq9eNa4LnTtk1yWpUl3T2nPDL5+F7Gkd6xJc6zJc9QRNnv/sDXgToG0IbSC0ZXmx
-0hwtH67MnfY0Dkev4bp+tbHjeChV5NzwLMYIHknGuMxXmlW0U3L6vqPv8lav1WvAb32sphuM58zn
-zatmjw3RxqHyZaj4uNzYbB8YA7qV4J7fmPr8fFwr88ln3eke/BcjIiIiIiJyiihoX8eDGpt5HsZs
-7lCWxqIsizVWr33avATs5FNwnRp5eW4uVjZvG8JyQcOCJlgeLl26cbvlGnJdisvmYXYWeoMZoQk0
-IeAh4J6DdipBO9rUrM1mRfuUEl3XlbDdb13rOGS9lpmZlhmzGrRtvA1RlhCr62SHsQHbGLDL/3fq
-ezQfO7AdssPs+Pkc9W/FjhzWLyIiIiIi8khT0H6Qmvqm5bymGqqP1WxLEY8DxPzVy3rWebh12q5o
-ew3IU3gdQ2bp4h2DwWKRm6EFY9GEci0JvCwLllIO5Sluh+tDQ8cbD3hoaBrHPeY51zE3RDODOFt7
-rF5TSom+6+j6Ple0680AZzx20zS5Al0KyznYN7OKdnm3SmfxwzcB8uFmYbvefJitimZlSbVcCZ+C
-vG1VsvNxNUVbREREREROIwXtwuexzWZhexzR7BBjDrkxQhzyms5xwIdhDNKkedV6mpPsJVxP85PL
-DGcv3cI8NxkbAy5l7nJpSOZ4HvadnBC3g2hoQu4m3jSEJuBlLjVliDgWsNASStdyx/La3mWetHsi
-xkg/DAxDTz/0Y+o1pkqyl2PVKeiprA3uuXcbyfJNiOCQ2vKyzEgpN5arxf5Ulw4bj12Pn0N01/V0
-3ZC3fmCIkbjVBV1EREREROT0UtAeHW5lPVV967xsUsKHgdQP+NDnoF2+4lPTsnFouE+DpKEOkZ5X
-tMlB2wMWnJSasWFa7iDGbG53XfLLCeaEJmChyQF7aGiagb5tCEMO2bWjeO6tZoSmwWjGBm6pDENP
-KeW1sEsH874f6Pp+mo9t+Txeg7eVVnBlmHyKaRzCbSQCDYE8wtzNSJbGEQEx+bjV483DdvL8tet6
-ur6n6/P19EMkxjR2aq/vnaZpi4iIiIjIaaSgfUiNxrXpeA2mRq7epiGS+o7U9Tlk9z2xBO0xnNfn
-zY7qZeHncch0DYu51ThGk6viNYSSlxgbK8ee8jDwFMGc0MxC9tDQtEMJ3E25M1CGbjd5rnQIOXTX
-Od6ecpO2mBIpRYYh0seBvlS0zQJNaAgNBK+3CijzrHPCTe5YdPINgNxBPJA7hacQxpCdyrsQY2SI
-iSEmUr2ZYIbnBc5I5I7pm34oVe0ctochkuI09N6nt1VEREREROTUUdAupvh2uKI9q06nlOdkD7FU
-s2PZhnFfGyvWs2PZdPyxxl2Gptd+ZMksh+lx/e5SBc9xO4f8FIlxAPJ62O0w5PWzU9k8r2kd6rxr
-IDh5DLfVRbI8v4yUSDHl58WBYRgY+kg/5NBtIS8u1pgRUmJIiTY5MTpDjIQhYhbxBvISXZSg7TRj
-Z/OGFBIh5Qp7PySGGBmGlF9lnXttoYTtgJvlaxnyUPa+BO1Yqu++9d6qpC0iIiIiIqePgvYRapSz
-ecg+1EWcusGD19wejzF11Z46bvvW6PJcfC5LXI3rZadxne4yqLqsc51DcZ5TPeTwOXYzP/QixvHc
-5ShueJnjnYeJ52PVbYg9XQm4Q0wEp0R8g5AIMdIMAyH04/GSQxParXsSxhIrw9vd41i5d5whJmKp
-aDt1jnkzboQGswZPToyJOOTh7LEsT+ZlTvj0zqmkLSIiIiIip4+CdrFd0fbxJz5fM9ungO3JZ+ES
-Dg8Wz1XrWXXbZ0eeNfUyPAdtK43J8sTt3F3MpnnZzhS0Y4oMcVkqvHWN7en2QJnePVtzOkueg3Yf
-U6kaD8TYM8TcBK2G7JhyEHZLuBmUCnYIA1hPKiE7JWhCnF5YYgzZTYCYAinFMg88MsQ8VH2IDmY0
-TUto2jIMfoE1EJowDmmPQ2ToI7EMHU+lqn3UjQ0REREREZHTQkG7mLpZT19tFrrLTlPH8Fp9LnON
-3X0cyFyDNV47hs+OOsvsudDteah37VZe5jublfPXsO2xBO3IEKch48mniE2+FKZ241N373pNqVSL
-+yHS96WaPfQldJcO3zHPCQ8YAQNLWBywocnDvOvrinmJr/kdhBByyG4aIyQbA/0Qh61maBYCTbug
-bSNNu6BxIxDASqCOsQwznyraqc5hZ3qvRUREREREThsF7cPG4DobKl6DZa1Ez3/GtHRXXdIrNzGb
-L+lVD17XgGbs3r1sGxaLluWiYXd3xc5qxWq5YLlo6Il5PjW1cp1KU7R8wPn61rU5WqjLfdV1tcN8
-yLrnedyphNgYyxzogb6vle0cvMc1rEOgaQdWQyIunRSduMhDwGPrtKHJHcoxghkx5aZqTZfDftd1
-dH1H13V5EHy5wRBCS7vIncRbh+gQEjTR83O6Ll/TWHmfwnbtqK6wLSIiIiIip5GCdjFWtA+H5sOh
-mllncmCrE/YspM+D9qy8nEN2Wc/aLLBoF+wsF+zsLNjdXbG7s2RnmYO3JyMG6D2BT2F7HHZuNobs
-pmloauiehWybTk0iV4tjaW7Wx9z8rK/rVQ99WUd7oJwAQh7iHYdETE7yMqy7LLcVm5Y25A7lhJBD
-/NDTlaHw6/Wa9WbDZrMZG7JhuZqdq/G54h4SZeh4YrPZ0HU9fdePYbuG7JRSrqLX6xMRERERETll
-FLSLKWhzZNCeh+ep0r0dwreDdhqryFmZye2GhbxGdQgNy7ZlZ7Xi3E4J2aslq9WCxSIwDEaw2gwt
-jstyzYN2CCEH7KMq2yEw1rPLdSZPJSiXdbOHgU0/5FA75GDb9/24JJcDTduUJcbyD1J00qIsEbaI
-eNvm4dyhJaaBYcixPsaBg4MD9g8OODg4IIQGaxostLSLmGefl+o0ybGYsBCvWdEeu46nBKF0LBcR
-ERERETllFLSLrTnaNVDPgvL050f0F58H8dqSzEJZVashWEMIeWtCKI9zFXh3d8W53WUZNr5guWxZ
-tA2N5ZDsJRTHoQ6djqS0XdFuDofruoa2laZhPg5oz5tPlemuH9h0HZtNR991dH1P1/f59Zamam3b
-jHPKoSwN5mms7JvlYeyOE1Osi4gx9D3rzYb1ugTtsfnZUOZfR7qup2k3eFniC4zLV/d44IH7uXr1
-Cvv7+2w2mzF0A3gINGZbfyciIiIiIiKnhYL2YceG7LGvN/Ph4FPILl3KDZqmoW3ztlguWC6WLBZL
-2rYtYTsH7dWyZbVasFoucsBuoAmWryM6w5CmIdT9kIdsz+ZoN7Nh43WoeJ7+PVV768zxrXnj5MDc
-D5HNpufgYM2mVJI3my7PgQ55a2NugoZZWU68nN+MEIwmBlIbgTbPA095aa9cHe9KkN8QmkjTRELT
-Qj8A63H5sSE5McKQnP39Ax64fIUHLl/h6tW9sco+DMNYxVY1W0RERERETisF7WoM1fOu4duLfh1u
-7j1+W3unlTAbQq40L5dLVqslu7s77O7usrOzw3K5GId6hxBYtA2LtqVtG4KBpwieSHHIjdCGSNcN
-dF1f1qGOY4jeqmiHQChzoK0sgIXV11Oq2KRS1aYEbacfBtZdx/56w2a9Yb3JodiCEZpA0wba2FI7
-q/u8mh4CTROIbZMr3JbK6me5G3vXT43Quq4jNGnckjvDUOaIDwNdH+mGSNcPHKw7Dg7W7O8fcLDe
-5CHvh+dni4iIiIiInFIK2oXPU/M4F3trca8Hr9zs84fzIedT0N7Z2eH8+fNcuJC31WqZA2oJqiHU
-edZGSpGh6xn6npggDYmhj/Sbnr4b8txq9zFshhBom7YMHZ81P6vb+Nq8zM/OW308xBxs1yXYHqw3
-rMsWmkC7KBX5RRqPk1Kp2AfLNwkWDTHFrTnpnhKkRF+6mfd9vxW0myY3YsvnK+fd9BxsOtbrLs8Z
-L9sQ87nrUPimaQ4NjReRm2VmdwN3A+7uzSN9PSIiIiKPNQraxazpeO2Dlr+fbVPlulSvZzOfD+1J
-Dew5806rWQcS5l6GYUdihDg4vcMwDHRlrvRmveHKlavs7e2zf7BhiENeaqtpaZcLlssdVssdVqsd
-VssVbbOgsZZAwNzy0tvUudh5PvYwJLpu4OBgw/7+AXt7B+zt7bO3f8De/joPG+86+n4gJMsdymMk
-prT18hZty3KR51inmEgxN0arL91qZ/FyI2DRLlguV6XlesiBPSbiUAJ1l4N4Xxug1SHypfnZONd8
-9r6OjelERE6Rq1evPtKXICIiIqeAgnaRZm22y6jxcXh4nZntbrMFvnJn7cNh28qf2XgwH38eSBgp
-h++SEVOMeU3qmHLQPZiqylevXmVv74CDgzUOLJdLwqJhuVixWqzGoL1c7NA0tct4yEHXyV3Bvax9
-nZx+SHk+9rrL4XrvgL39A/b3c2fwYVZJtmg0TSLEQIwpX2/ZlosFw3IoDdpKJ/Q8ZnwspJvlzupN
-07BYLFgtI7FUxKMzhvihzOPuy3JeXdflIfIp7+vusznx827vCU+qaIvI6XLlypVH+hJERETkFFDQ
-LrwE7Vq5rnO1A5SGXds/n1qKTdVsn4fsMk+5fjUcMydYymG8BPBa1e26ns2mY39/zUHZ9vdzEF4f
-bAhNQ9suCaFluViNFe2d5Q7LxWpsgmZmUzXeS9fysu71UOZ7H6w37O2tD4Xtda5el83MiDF3MI9t
-MwZtA7rlslSdS0U71WXH6qj1PEd8XtFOy9x4bfBITLFUtPMSY11XwnYZZh5jXkpstuJaUZrEeQIP
-+auIiIiIiMgpo6B9mM+D3fgjwOr/Ub+dyrf5STnj1srrtFZ1121YrwNNgKFvMZsq3+PQ6X6g2/S5
-mr3ZsO57BnesaVisViyWSy5cvMjFixe54847uPPOO7lw4QLnds+xWCxyM7JUh1unUj1OeR52WS/7
-YJMr2ft7uYp9cLBms+lyo7VhyGtlp2nNbUglcBvDEAlhIJjRbTrWiw3LxYJF09KGhjY0LEr387rl
-oFzmUpeO5TGlUjXv6YeBoVTFU6m+j29vmYPtZnmZtPJ9XsasztNWYzQRERERETl9FLQPGZeeHkO1
-YYd2KCtd5R0CkPLqV5RgCTVQ9uVJToyRTdfRtk0O2Zar3HU96aFUd/s+MgyJAQiLBTvtgtW5c5zb
-3eVxd97J4x53J3fOvp6/cJ5gRj/kJmop1WMOOeT3PeuuZ9P3HJR531f39tnf32e9zutTp5TmLy+H
-WOpryVXkWoHuMdJeJy0AABUJSURBVDbrLodqC7laX7aAsWhbFosFy0Wbh4knJ8a89cNUua9zwety
-ZU4OziE0jC3Ty5ucm7xZWU4sbK0ZLiJySgRAvSNEHkUuXbrEvffey2tf+1ruuuuuR/pyROQGxRjr
-w1MdBhS0q0PTfc1nResSPN0s/3WaQVlj2tL8uaXrN0aKiZ6elCIxDmy6jsVBm8NhPThlrnIqFd3S
-/CsPw7YSWBcsFgvuuHCBJzzh8Tzx8Y/n8Y9/HBfOn+f8+XOcP3+OlBK2yfO93Z0Yh3FJrYPNJncV
-33Tsr9fsXd3L294B6/WGrsthd/q3YVmLe1a+r2tnD32E5GxK1/Q6mb3OOQ8YO6sV7pSlxsjzz8tr
-zEPX+7KEWDd2Fa8jwK3M6TbPIdtK87Rgs7BdAnawgCloi5wIM1sB/yPwcuDTyo8/APwQ8I/dPR7z
-3E8G/jrwpcDTgQb4Q+CXgX/o7r95zHPrXb5vd/fXm9mXAH8N+FzgqcAH3f1TZvvfBXxDOdczgHPA
-x4D/DPwm8IvAT7j7kR3JzOwO4HXAi4A/DtwJfBT4t8Cb3f0nrnWtN0Dd20UeZS5dusQ999zDS1/6
-UgVtkUeRWdA+1Z+9CtqF2bSOdg3LWytlzeZAj3OhjRII6w6M486Tp7w8F5abi23CGBYxww8twYV7
-Xru6Bsm2Ybm7w/nd3bGa/eQnPoEnPeEJPOHxj2O1WrJcrlitVrlT99BjBilF+r5ns9lwcHDA/nqT
-u4yvN+wfrHMX8zJsvOu6MmR8ewmtrYpMqdKn5BiJwZ2u6wlmuflZcqYWbKE0lbM8vBtjGBL9UEJ2
-P7Dp8rVtun5W0S7vdwi1lVseFm6hhOtc7T78WEFb5OEzs6eQA+qfYnvmzHPK9qXAl1/juV8L3Aus
-Dj33GcCnAq8ys29z9793zCV4OdbfAf5XHjx7p57rzwA/A9xxaJ8nl+0zyTcK7gPedsTznw/8GPCE
-Q8//BODFwIvN7G3AV7n7/jHXKyIiInJdCtqj+WrZs20WhseGYyGMWw7Nedy45SW0x+pwzd3JwdL0
-55iXUdGW16lu2/x12bJcLlkuF6yWC3Z3dtjd3eXczg4XL1zgzjsvcvHCLrs7C5q2oWkAEjH2dN2G
-g/U+V/evjmF6b2+fg/VmXJ96vcnLhs2Hi4cQxrWpj3pL6j2EYCVMl9efHIaU2AwDzabDQkN06GOk
-G/JmZuOSYV2XA//BpmPdDXR9pC8dyN0CBCMAXt+kWUW73twIU8e3MlZfRE7AW4E/AXwv8LPkCvGn
-A98G/EngJWb2Gnf/gfmTzOxFwA+Wb68AbwR+CRiAzyeH5icDf8fMPu7u9x5zDS8Dngn8OvA9wH8E
-doHPLudaAm8BLgKXgX8E/CtyJXsJ/LFyzq846uBm9lxy+G6BDwP/oJzrQ8DTgL8EfA3wZcCbgb94
-zLWKiIiIXJeC9qiOXy7trsf1rMqPLYfAXHU2aAIpTWGbsvcYpktTNYNZczGm+dmlKt4splB97tx8
-22V3tWJnZ8Xuzopzu7ucO3ee8+d32dlZzqrPkSH2bPpcwd7b2+PK1T2uXr3Klat7rNddriJ3PV03
-5CW8ahXbyzWUOc9zh2ZpT1V+y9VmB4aYsH7AbEMqIbsfcvO1rs9V7650Eu/7acj4etMzxJiX8KLc
-qCjvSeOU+drzsF2r3DZeQ3knReThMeBzgC9193fPfv5rZvYvgPcDTwH+O2AM2mbWkivZAFeB57n7
-b8ye/x4zeyvwfwF3AW80sx93949d4zqeCfxL4MXu3s9+/ivl63PLcRz4anf/+UPPfw/wY2b2P5GH
-k08vMF/rj5A/734e+Avuvp6/VuBtZvZu4PuBrzSz57v7L13jWkVERESuS0F7NKto15DtXuYr58g8
-NUArFe0yvBkL48LbXirbPg4j97qS17iG2DQE3WktsLNYcuHcLnfccYE77zjPHXdc4I6L59lZLVmt
-VuzsLEule8VquaRtFyT3stb0VNHeLxXtK1evcvnyFS5fucp6nedCd31uuFbX1vY8XjvPdW5KqJ5V
-tesyXfXx9PP8OpM7HiMJL5XsxLrr6fo6RHwgBBubsg19Dt+bspRZTCmPBqBWp600YatN0GbD8+uV
-HFV1F5GHw4G/fyhk5z9w/7iZ/SDwvwDPNLOL7l4Xif4KciXYge84FLLr8/8/M/ufySH3HPAK4LuO
-uAYDIvDqQyF77qmzxw+61tk5Ezn4z70c+GTgAPjaQyF7/tw3mdmrycPlv55cnRcRERF5SDT+ttiq
-X8++cR9XxobSGdvKPOpczc4hcd6G3Ld+ViredQw2U5U4GDRmtMFYNIFlYyxDYBmM5exnixBYBKPB
-ISVSjHmJrf0Drl65wpXLZbtylcuXS1fxg3VpdpaX0Yox5mZruV48zQVvAm3b0rYtTdPQNs30fdvQ
-HtqatqVpWkLTYKEBa0gYQ3L6mIeSH3S5w/n+OlevN92QlxiLqVSxwQl5s4CHfCxr8haahtC2hGa2
-le+bpqVpF1ubiDws//SYP/t35auRh2dXLyhfnWn4+FF+HHjg0HMOc+BX3f0PjjnOpdnjVxyz31Fe
-Wr6+85iKevUu8mv9vJs8h4iIiMgWVbRHtcV2+dbrXGHKMPBaba2V7GasaI9ztJk9t3blHiviDsHH
-gF1GnxNCiZyeIEbS0BO7jn7d0OAM7rTk+dC+SKTohBDZX69zmD3Y8MDly3z84w9w//2XuXz5ap4L
-ve7phkRM4F6XztquUW81ceOoijalqn+4kjwbtm258Rkhvw/RjT6XuAkWZ+t758p3wsCaqXt7rWiX
-YeI2e1yv6fDjut+R88pF5Gb91jF/Ng+mF2ePP7N8/X13/+i1nuzuvZn9e+CLZs85yn+4zjX+CvB7
-wKcA/7uZfQ3wk+Rg/N5jKuGQh8Y78MJZl/Preer1dxERERG5NgXtymtxvy51lbuaeY3JdVgzAbMG
-gm+F7bwvpXv59pBrYzZkHGgMgjlNsPFx8ISliA89cbNhaIwBZwAGy83UUnRS6xAC+1f3ubK3x5W9
-fe5/4Aoff+Ay999/mQcu79GVYdp9n5cLw63MxWa8rjGk2oOHZU/XXRq4Hdov3zaoIXm6+eDBiG4Q
-E8kHgtW56Y67k9xyFTvUmxAPPkbtzD7Nx97ewqwzu6khmsjDdq2h1MU8mM6X0Kidu//zDZziw7Pn
-XMvHjzuAuw9m9mLgnwOfQQ7Pzyl/fGBm7yIvRfZjZfj43FPqYW7gWqudm9j38LXyvve977r73XXX
-XVpOSEREZObSpUtcunTpuvv1/XH3108PBe1RaWjmNlazyxTrsTrtBIJ5DpVjZbuGRcbn2/Yhy8NE
-jpkQQh4y3gRoLFe0jQQp4sNA6juGjRGDEUMglmWvxjW2LbA+yF3FL1++wuXLV7hy+SpXruyzt7fP
-kJwYE0PyMVTXrt01LI9hez4fev5ueL3FMLttMIbzUG5AWF5bfDZ0Pjl5HniK0+7lmPWGxfZQ+xq0
-bTb3/cEBewrauUv6UQ3cROS2O6mehNdcp3s8kftvmdkzgZeU7QvIS4jtAP912f6GmX2Zu//R7Kn1
-BsHPA3/zhK73WM9+9rOvu8/dd9/Nt3/7t9/6ixEREXmUuPfee7nnnnse6cs4MQraIiJysz5GvlP2
-CTewbx2Gfb350dfleYjMT5cNM/sE4IXA64BnA/8luRv6y2ZP+yi5Y/nS3d//cK/hGGPF/slPfvJ1
-d7733nt505vedAsvR0Sup+s6AF74wheyXC4f4asRkRjjDX2G3nffffXhcaPlHnEK2sVvv/3HNeFX
-ROTG/Ca5YdgfM7MnXmuedlla61nkyvdvnvRFuPtHgDeb2Y8C/5octF9sZit335Td/j25Q/rnmFnr
-7sNJX0cxfobM/gEgIo8C+p0VedQ61flNQVtERG7W24HXkD/gXgG88Rr7/UXgTnLQfvutupgyh/ud
-5KDdAo8DPlL++KeBF5XreAWz9cBP2AZYkee138jcdREREXlonkJurby53o6PJAVtERG5WT8FfIhc
-Kf5bZvYL7r5VsTazTwLeUL7d5/hlwI5lZs8DLrn7717jzxfAF5ZvrwLz8tSbgW8FPgl4o5n91lHr
-hs+O9Vygcfd33cw1uvv5m9lfREREHtsUtEVE5KaUZbv+CvAz5Erxr5rZG4BfIjc2ey7wzeQ7zg58
-4w2sYX2c5wPfZmbvBn6OvBzYfcAu8MeBv0quZjvwpnnncXfvzOyrgHcAF4BfNrO3kG8W/D75jvhd
-5DneX0lehuy/Jy8dJiIiIvKQKGiLiMhNc/e3mdnXk5uPXQBeX7ZxF2AAvtXdv/8ETmnkTuNfeMSf
-edl+CviWI67135jZFwH/jFzZ/stlu9ZxLp/A9YqIiMgZpqAtInI21VD5kPdz9x8uc6P/OvBngaeT
-K8QfIle3/6G7/8cTuNY3AL8OvIDcXO1pTOtjfxh4D/Bmd/+Fa74I9/eY2acBX09eHuxZwJPIc6rv
-Az4AvBP4CXf/7RO4ZhERETnDLK+WIiIiIiIiIiInITzSFyAiIiIiIiLyWKKgLSIiIiIiInKCFLRF
-RERERERETpCCtoiIiIiIiMgJUtAWEREREREROUEK2iIicuaZ2dPN7LvM7ANmdtXMPmpm7zGzbzKz
-3RM8z1eb2S+a2SUzOzCz/8fMftjM/vRJnUPkLLmVv7tmdreZpRvcvuCkXpPIY5WZPdnMXmRm95jZ
-28zsvtnv0P95i875iH3uankvERE508zsJcAPA3fw4DXDDfhPwIvc/Xcfxjl2gJ8Avuwa50jA6939
-9Q/1HCJnza3+3TWzu4G7jzj2YQ58ibu/66GcR+SsMLN06Efz3603u/srT/Bcj/jnriraIiJyZpnZ
-s4C3ABeBK8C3AJ8PPB/4AfKH86cBP2tm5x/GqX6Q6cP+l4EvB/4r4FXA75A/j+82s1c/jHOInBm3
-8Xe3+kzgmdfYPgt47wmcQ+Qs8LL9v8C/IIfeW+ER/9xVRVtERM4sM3sX8DygB/6Mu7/n0J9/I/AG
-8gf1PQ/lzreZfQnw9nKMnwa+0mcfvmb2RODfAU8HPg58irs/8NBekcjZcJt+d8eKtrs3D/+qRc62
-8jv1XuC97n6fmX0y8Pvk39MTq2ifls9dVbRFRORMMrPnkP+h7sCbDv9Dvfhu4APkO+7fYGYP5R/b
-31i+DsDr/NAdbnf/KPDN5dvHAapqixzjNv7uisgJcvd73P1t7n7fLT7VqfjcVdAWEZGz6stnj//J
-UTuUD+cfKt8+DvjimzmBmV0gD2V14O3u/qFr7PpW4HJ5/BU3cw6RM+iW/+6KyKPTafrcVdAWEZGz
-6nnl6x55CNm1vHP2+Lk3eY7nAMsjjrPF3XvgX5Orb89R9U3kWLfjd1dEHp1OzeeugraIiJxVn0G+
-4/077n64E+rcbx16zs34k9c4znHnaclNnETkaLfjd3dLWR7oI2a2KV/fYWbfbGaPezjHFZETd2o+
-dxW0RUTkzDGzFfCk8u0Hj9vX3e8nV84APukmT/WJs8fHngf4g9njmz2PyJlwG393D3tBOW9bvn4B
-8HeB3zOzlz7MY4vIyTk1n7vtSR9QRETkUeDi7PHVG9h/DzgHXLiF59mbPb7Z84icFbfrd7f6D8BP
-Ae8BPgQsgE8H/jLwZ8nzv/+5mb3E3X/xIZ5DRE7OqfncVdAWEZGzaGf2uLuB/TfkeVy7t/A8m9nj
-mz2PyFlxu353Ab7H3e854ufvBX7EzP4K8I+BBniTmT3D3W/kmkTk1jk1n7saOi4iImfRevZ4ec29
-JivynNCDW3ie1ezxzZ5H5Ky4Xb+7uPvl6/z59wP/BznIPw142c2eQ0RO3Kn53FXQFhGRs+jK7PGN
-DBc7X77eyFDVh3qe87PHN3sekbPidv3u3qh7Z4+/8BadQ0Ru3Kn53FXQFhGRM8fdN8BHy7efeNy+
-patw/TD+g+P2PcK8Ecux52G7EcvNnkfkTLiNv7s36v2zx//FLTqHiNy4U/O5q6AtIiJn1fvJQz4/
-1cyO+zz8E7PHH3gI5zjqOMedZwB++ybPI3KW3I7f3Rvlt+i4IvLQnJrPXQVtERE5q36lfD0PPPuY
-/ebDQX/1Js/xXqZmLNccVmpmC+BPk//R/l53jzd5HpGz5Hb87t6o+Zq9H7pF5xCRG3dqPncVtEVE
-5Kz6qdnjVxy1g5kZ8LXl2/uBd9zMCdz9KvBL5OrbC8zsadfY9WXAHeXxW2/mHCJn0C3/3b0Jf3X2
-+J236BwicoNO0+eugraIiJxJ7v5e4N3kD+NXmdnnHrHbNwGfQb7j/b2H73ib2deZWSrb/3aNU72x
-fG2B7zs81NXMngT8vfLt/eQuxiJyDbfjd9fMPtPMnnHcdZTlvV5Vvv0w8JM3/2pE5GY8mj53tY62
-iIicZd9AHlK6C/xLM/tOcuVrF/hq4DVlv/8b+O5jjnPNeZru/g4zewvwcuDPl/N8L3mY6WcB3wI8
-vRzjb7r7Aw/rFYmcDbf6d/fZ5LWx3wH8PPAb5CZsLXle59cAX1r2HYDXuLuW5RM5hpk9F/jU2Y+e
-NHv8qWb2dfP93f3Nxxzu1H/uKmiLiMiZ5e6/ZmZfBfwIeQjZdx7ehfwP9Re5+97DONUrgYvAnwO+
-CPjiQ+eIwOvdXdVskRtwm353A/B84AXXugxy+H6lu7/tIZ5D5Cx5NfB1R/zcgOeVrXLguKB9PY/4
-566CtoiInGnu/nNm9lnkCtmLyMuBdMDvAP8M+D53Xx93iBs4xxp4iZm9HPh64E8BjwM+AryrnOPf
-PJzXIXLW3OLf3Z8jDwv/POBZwCcATyQHgo8Bvw78AvBPypxQEbkxN9qp/7j9HhWfu+auVQlERERE
-REREToqaoYmIiIiIiIicIAVtERERERERkROkoC0iIiIiIiJyghS0RURERERERE6QgraIiIiIiIjI
-CVLQFhERERERETlBCtoiIiIiIiIiJ0hBW0REREREROQEKWiLiIiIiIiInCAFbREREREREZETpKAt
-IiIiIiIicoIUtEVEREREREROkIK2iIiIiIiIyAlS0BYRERERERE5QQraIiIiIiIiIidIQVtERERE
-RETkBCloi4iIiIiIiJwgBW0RERERERGRE6SgLSIiIiIiInKCFLRFRERERERETpCCtoiIiIiIiMgJ
-UtAWEREREREROUEK2iIiIiIiIiInSEFbRERERERE5AQpaIuIiIiIiIicoP8fEX4BoxSB8CgAAAAA
-SUVORK5CYII=
-"
-width=493
-height=445
->
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;">&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;<a class="anchor-link" href="#&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;">&#182;</a></h2><p>你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率<a href="http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130">远远超过 80%</a>。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。</p>
-<h2 id="&#25552;&#20132;&#39033;&#30446;">&#25552;&#20132;&#39033;&#30446;<a class="anchor-link" href="#&#25552;&#20132;&#39033;&#30446;">&#182;</a></h2><p>提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 "File" -&gt; "Download as" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。</p>
-
-</div>
-</div>
-</div>
-    </div>
-  </div>
-</body>
-</html>

From 0c36aa13d94d077c8dd4081ccbd49dba2966a153 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 9 Jul 2017 20:28:52 +0800
Subject: [PATCH 09/16] Delete dlnd_image_classification.ipynb

---
 .../dlnd_image_classification.ipynb           | 1107 -----------------
 1 file changed, 1107 deletions(-)
 delete mode 100644 image-classification/dlnd_image_classification.ipynb

diff --git a/image-classification/dlnd_image_classification.ipynb b/image-classification/dlnd_image_classification.ipynb
deleted file mode 100644
index 26b0065..0000000
--- a/image-classification/dlnd_image_classification.ipynb
+++ /dev/null
@@ -1,1107 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# 图像分类\n",
-    "\n",
-    "在此项目中，你将对 [CIFAR-10 数据集](https://www.cs.toronto.edu/~kriz/cifar.html) 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。\n",
-    "\n",
-    "\n",
-    "## 获取数据\n",
-    "\n",
-    "请运行以下单元，以下载 [CIFAR-10 数据集（Python版）](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All files found!\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "from urllib.request import urlretrieve\n",
-    "from os.path import isfile, isdir\n",
-    "from tqdm import tqdm\n",
-    "import problem_unittests as tests\n",
-    "import tarfile\n",
-    "\n",
-    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
-    "\n",
-    "# Use Floyd's cifar-10 dataset if present\n",
-    "floyd_cifar10_location = '/input/cifar-10/python.tar.gz'\n",
-    "if isfile(floyd_cifar10_location):\n",
-    "    tar_gz_path = floyd_cifar10_location\n",
-    "else:\n",
-    "    tar_gz_path = 'cifar-10-python.tar.gz'\n",
-    "\n",
-    "class DLProgress(tqdm):\n",
-    "    last_block = 0\n",
-    "\n",
-    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
-    "        self.total = total_size\n",
-    "        self.update((block_num - self.last_block) * block_size)\n",
-    "        self.last_block = block_num\n",
-    "\n",
-    "if not isfile(tar_gz_path):\n",
-    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
-    "        urlretrieve(\n",
-    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
-    "            tar_gz_path,\n",
-    "            pbar.hook)\n",
-    "\n",
-    "if not isdir(cifar10_dataset_folder_path):\n",
-    "    with tarfile.open(tar_gz_path) as tar:\n",
-    "        tar.extractall()\n",
-    "        tar.close()\n",
-    "\n",
-    "\n",
-    "tests.test_folder_path(cifar10_dataset_folder_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 探索数据\n",
-    "\n",
-    "该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 `data_batch_1`、`data_batch_2`，以此类推。每个部分都包含以下某个类别的标签和图片：\n",
-    "\n",
-    "* 飞机\n",
-    "* 汽车\n",
-    "* 鸟类\n",
-    "* 猫\n",
-    "* 鹿\n",
-    "* 狗\n",
-    "* 青蛙\n",
-    "* 马\n",
-    "* 船只\n",
-    "* 卡车\n",
-    "\n",
-    "了解数据集也是对数据进行预测的必经步骤。你可以通过更改 `batch_id` 和 `sample_id` 探索下面的代码单元。`batch_id` 是数据集一个部分的 ID（1 到 5）。`sample_id` 是该部分中图片和标签对（label pair）的 ID。\n",
-    "\n",
-    "问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Stats of batch 1:\n",
-      "Samples: 10000\n",
-      "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n",
-      "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n",
-      "\n",
-      "Example of Image 5:\n",
-      "Image - Min Value: 0 Max Value: 252\n",
-      "Image - Shape: (32, 32, 3)\n",
-      "Label - Label Id: 1 Name: automobile\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAK/CAYAAABjrqvGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21411d80e80>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 351,
-       "width": 353
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "\n",
-    "import helper\n",
-    "import numpy as np\n",
-    "\n",
-    "# Explore the dataset\n",
-    "batch_id = 1\n",
-    "sample_id = 5\n",
-    "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 实现预处理函数\n",
-    "\n",
-    "### 标准化\n",
-    "\n",
-    "在下面的单元中，实现 `normalize` 函数，传入图片数据 `x`，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 `x` 的形状一样。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def normalize(x):\n",
-    "    \"\"\"\n",
-    "    Normalize a list of sample image data in the range of 0 to 1\n",
-    "    : x: List of image data.  The image shape is (32, 32, 3)\n",
-    "    : return: Numpy array of normalize data\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return np.array(x/255)\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_normalize(normalize)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### One-hot 编码\n",
-    "\n",
-    "和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 `one_hot_encode` 函数。输入，也就是 `x`，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 `one_hot_encode` 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。\n",
-    "\n",
-    "提示：不要重复发明轮子。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def one_hot_encode(x):\n",
-    "    \"\"\"\n",
-    "    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n",
-    "    : x: List of sample Labels\n",
-    "    : return: Numpy array of one-hot encoded labels\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    from tflearn.data_utils import to_categorical\n",
-    "    return np.array(to_categorical(x, 10))\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_one_hot_encode(one_hot_encode)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 随机化数据\n",
-    "\n",
-    "之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 预处理所有数据并保存\n",
-    "\n",
-    "运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "# Preprocess Training, Validation, and Testing Data\n",
-    "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 检查点\n",
-    "\n",
-    "这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import pickle\n",
-    "import problem_unittests as tests\n",
-    "import helper\n",
-    "\n",
-    "# Load the Preprocessed Validation data\n",
-    "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 构建网络\n",
-    "\n",
-    "对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。\n",
-    "\n",
-    ">**注意**：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。\n",
-    "\n",
-    ">但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 `conv2d` 类 [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d)，而不是 TF Layers 版本的 `conv2d` 类 [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d)。\n",
-    "\n",
-    "我们开始吧！\n",
-    "\n",
-    "\n",
-    "### 输入\n",
-    "\n",
-    "神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：\n",
-    "\n",
-    "* 实现 `neural_net_image_input`\n",
-    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
-    " * 使用 `image_shape` 设置形状，部分大小设为 `None`\n",
-    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"x\" 命名\n",
-    "* 实现 `neural_net_label_input`\n",
-    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
-    " * 使用 `n_classes` 设置形状，部分大小设为 `None`\n",
-    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"y\" 命名\n",
-    "* 实现 `neural_net_keep_prob_input`\n",
-    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)，用于丢弃保留概率\n",
-    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"keep_prob\" 命名\n",
-    "\n",
-    "这些名称将在项目结束时，用于加载保存的模型。\n",
-    "\n",
-    "注意：TensorFlow 中的 `None` 表示形状可以是动态大小。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Image Input Tests Passed.\n",
-      "Label Input Tests Passed.\n",
-      "Keep Prob Tests Passed.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "def neural_net_image_input(image_shape):\n",
-    "    \"\"\"\n",
-    "    Return a Tensor for a batch of image input\n",
-    "    : image_shape: Shape of the images\n",
-    "    : return: Tensor for image input.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], name='x')\n",
-    "\n",
-    "\n",
-    "def neural_net_label_input(n_classes):\n",
-    "    \"\"\"\n",
-    "    Return a Tensor for a batch of label input\n",
-    "    : n_classes: Number of classes\n",
-    "    : return: Tensor for label input.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return tf.placeholder(tf.int32, [None, n_classes], name='y')\n",
-    "\n",
-    "\n",
-    "def neural_net_keep_prob_input():\n",
-    "    \"\"\"\n",
-    "    Return a Tensor for keep probability\n",
-    "    : return: Tensor for keep probability.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return tf.placeholder(tf.float32, name='keep_prob')\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tf.reset_default_graph()\n",
-    "tests.test_nn_image_inputs(neural_net_image_input)\n",
-    "tests.test_nn_label_inputs(neural_net_label_input)\n",
-    "tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 卷积和最大池化层\n",
-    "\n",
-    "卷积层级适合处理图片。对于此代码单元，你应该实现函数 `conv2d_maxpool` 以便应用卷积然后进行最大池化：\n",
-    "\n",
-    "* 使用 `conv_ksize`、`conv_num_outputs` 和 `x_tensor` 的形状创建权重（weight）和偏置（bias）。\n",
-    "* 使用权重和 `conv_strides` 对 `x_tensor` 应用卷积。\n",
-    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
-    "* 添加偏置\n",
-    "* 向卷积中添加非线性激活（nonlinear activation）\n",
-    "* 使用 `pool_ksize` 和 `pool_strides` 应用最大池化\n",
-    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
-    "\n",
-    "**注意**：对于**此层**，**请勿使用** [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers)，但是仍然可以使用 TensorFlow 的 [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) 包。对于所有**其他层**，你依然可以使用快捷方法。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n",
-    "    \"\"\"\n",
-    "    Apply convolution then max pooling to x_tensor\n",
-    "    :param x_tensor: TensorFlow Tensor\n",
-    "    :param conv_num_outputs: Number of outputs for the convolutional layer\n",
-    "    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n",
-    "    :param conv_strides: Stride 2-D Tuple for convolution\n",
-    "    :param pool_ksize: kernal size 2-D Tuple for pool\n",
-    "    :param pool_strides: Stride 2-D Tuple for pool\n",
-    "    : return: A tensor that represents convolution and max pooling of x_tensor\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    weights = tf.Variable(tf.truncated_normal(shape=[conv_ksize[0], conv_ksize[1], x_tensor.get_shape().as_list()[3], conv_num_outputs], stddev=0.1))\n",
-    "    bias = tf.Variable(tf.constant(0.1, shape=[conv_num_outputs]))\n",
-    "    conv = tf.nn.conv2d(input=x_tensor, filter=weights, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME') + bias\n",
-    "    activate = tf.nn.relu(conv)\n",
-    "    pool = tf.nn.max_pool(value=activate, ksize=[1, pool_ksize[0], pool_ksize[1], 1], strides=[1, pool_strides[0], pool_strides[1], 1], padding='SAME')\n",
-    "    \n",
-    "    return pool\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_con_pool(conv2d_maxpool)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 扁平化层\n",
-    "\n",
-    "实现 `flatten` 函数，将 `x_tensor` 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（*部分大小（Batch Size）*，*扁平化图片大小（Flattened Image Size）*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def flatten(x_tensor):\n",
-    "    \"\"\"\n",
-    "    Flatten x_tensor to (Batch Size, Flattened Image Size)\n",
-    "    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n",
-    "    : return: A tensor of size (Batch Size, Flattened Image Size).\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    layer_shape = x_tensor.get_shape()\n",
-    "    num_features = layer_shape[1:4].num_elements()\n",
-    "    layer_flat = tf.reshape(x_tensor, [-1, num_features])\n",
-    "    \n",
-    "    return layer_flat\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_flatten(flatten)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 完全连接的层\n",
-    "\n",
-    "实现 `fully_conn` 函数，以向 `x_tensor` 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def fully_conn(x_tensor, num_outputs):\n",
-    "    \"\"\"\n",
-    "    Apply a fully connected layer to x_tensor using weight and bias\n",
-    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
-    "    : num_outputs: The number of output that the new tensor should be.\n",
-    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n",
-    "    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n",
-    "    fc = tf.nn.relu(tf.matmul(x_tensor, weights) + bias)\n",
-    "    \n",
-    "    return fc\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_fully_conn(fully_conn)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 输出层\n",
-    "\n",
-    "实现 `output` 函数，向 x_tensor 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n",
-    "\n",
-    "**注意**：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def output(x_tensor, num_outputs):\n",
-    "    \"\"\"\n",
-    "    Apply a output layer to x_tensor using weight and bias\n",
-    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
-    "    : num_outputs: The number of output that the new tensor should be.\n",
-    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n",
-    "    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n",
-    "    output = tf.matmul(x_tensor, weights) + bias\n",
-    "    \n",
-    "    return output\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_output(output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 创建卷积模型\n",
-    "\n",
-    "实现函数 `conv_net`， 创建卷积神经网络模型。该函数传入一批图片 `x`，并输出对数（logits）。使用你在上方创建的层创建此模型：\n",
-    "\n",
-    "* 应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）\n",
-    "* 应用一个扁平层（Flatten Layer）\n",
-    "* 应用 1、2 或 3 个完全连接层（Fully Connected Layers）\n",
-    "* 应用一个输出层（Output Layer）\n",
-    "* 返回输出\n",
-    "* 使用 `keep_prob` 向模型中的一个或多个层应用 [TensorFlow 的 Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Neural Network Built!\n"
-     ]
-    }
-   ],
-   "source": [
-    "def conv_net(x, keep_prob):\n",
-    "    \"\"\"\n",
-    "    Create a convolutional neural network model\n",
-    "    : x: Placeholder tensor that holds image data.\n",
-    "    : keep_prob: Placeholder tensor that hold dropout keep probability.\n",
-    "    : return: Tensor that represents logits\n",
-    "    \"\"\"\n",
-    "    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers\n",
-    "    #    Play around with different number of outputs, kernel size and stride\n",
-    "    # Function Definition from Above:\n",
-    "    #    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
-    "    conv_pool_1 = conv2d_maxpool(x, 64, [5, 5], [1, 1], [3, 3], [2, 2])\n",
-    "    norm_layer = tf.nn.lrn(conv_pool_1, 4 , bias=1.0, alpha=0.001 / 9.0, beta=0.75)\n",
-    "    conv_pool_2 = conv2d_maxpool(norm_layer, 128, [5, 5], [1, 1], [3, 3], [2, 2])\n",
-    "\n",
-    "    # TODO: Apply a Flatten Layer\n",
-    "    # Function Definition from Above:\n",
-    "    #   flatten(x_tensor)\n",
-    "    flat_layer = flatten(conv_pool_2)\n",
-    "\n",
-    "    # TODO: Apply 1, 2, or 3 Fully Connected Layers\n",
-    "    #    Play around with different number of outputs\n",
-    "    # Function Definition from Above:\n",
-    "    #   fully_conn(x_tensor, num_outputs)\n",
-    "    fc_layer1 = fully_conn(flat_layer, 384)\n",
-    "    dropout_layer_1 = tf.nn.dropout(fc_layer1, keep_prob)\n",
-    "    fc_layer2 = fully_conn(dropout_layer_1, 192)\n",
-    "    dropout_layer_2 = tf.nn.dropout(fc_layer2, keep_prob)\n",
-    "    \n",
-    "    # TODO: Apply an Output Layer\n",
-    "    #    Set this to the number of classes\n",
-    "    # Function Definition from Above:\n",
-    "    #   output(x_tensor, num_outputs)\n",
-    "    logits = output(dropout_layer_2, 10)\n",
-    "    \n",
-    "    # TODO: return output\n",
-    "    return logits\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "\n",
-    "##############################\n",
-    "## Build the Neural Network ##\n",
-    "##############################\n",
-    "\n",
-    "# Remove previous weights, bias, inputs, etc..\n",
-    "tf.reset_default_graph()\n",
-    "\n",
-    "# Inputs\n",
-    "x = neural_net_image_input((32, 32, 3))\n",
-    "y = neural_net_label_input(10)\n",
-    "keep_prob = neural_net_keep_prob_input()\n",
-    "\n",
-    "# Model\n",
-    "logits = conv_net(x, keep_prob)\n",
-    "\n",
-    "# Name logits Tensor, so that is can be loaded from disk after training\n",
-    "logits = tf.identity(logits, name='logits')\n",
-    "\n",
-    "# Loss and Optimizer\n",
-    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
-    "optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
-    "\n",
-    "# Accuracy\n",
-    "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
-    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n",
-    "\n",
-    "tests.test_conv_net(conv_net)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 训练神经网络\n",
-    "\n",
-    "### 单次优化\n",
-    "\n",
-    "实现函数 `train_neural_network` 以进行单次优化（single optimization）。该优化应该使用 `optimizer` 优化 `session`，其中 `feed_dict` 具有以下参数：\n",
-    "\n",
-    "* `x` 表示图片输入\n",
-    "* `y` 表示标签\n",
-    "* `keep_prob` 表示丢弃的保留率\n",
-    "\n",
-    "每个部分都会调用该函数，所以 `tf.global_variables_initializer()` 已经被调用。\n",
-    "\n",
-    "注意：不需要返回任何内容。该函数只是用来优化神经网络。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tests Passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n",
-    "    \"\"\"\n",
-    "    Optimize the session on a batch of images and labels\n",
-    "    : session: Current TensorFlow session\n",
-    "    : optimizer: TensorFlow optimizer function\n",
-    "    : keep_probability: keep probability\n",
-    "    : feature_batch: Batch of Numpy image data\n",
-    "    : label_batch: Batch of Numpy label data\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    session.run(optimizer, feed_dict = {keep_prob: keep_probability, x: feature_batch, y: label_batch})\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_train_nn(train_neural_network)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 显示数据\n",
-    "\n",
-    "实现函数 `print_stats` 以输出损失和验证准确率。使用全局变量 `valid_features` 和 `valid_labels` 计算验证准确率。使用保留率 `1.0` 计算损失和验证准确率（loss and validation accuracy）。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def print_stats(session, feature_batch, label_batch, cost, accuracy):\n",
-    "    \"\"\"\n",
-    "    Print information about loss and validation accuracy\n",
-    "    : session: Current TensorFlow session\n",
-    "    : feature_batch: Batch of Numpy image data\n",
-    "    : label_batch: Batch of Numpy label data\n",
-    "    : cost: TensorFlow cost function\n",
-    "    : accuracy: TensorFlow accuracy function\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    print('Valid Loss: ', end='')\n",
-    "    print(session.run(cost, feed_dict = {x: valid_features, y: valid_labels, keep_prob: 1.0}), end='')\n",
-    "    print(', Valid Accuracy: ', end='')\n",
-    "    print(session.run(accuracy, feed_dict = {x: valid_features, y: valid_labels, keep_prob: 1.0}))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 超参数\n",
-    "\n",
-    "调试以下超参数：\n",
-    "* 设置 `epochs` 表示神经网络停止学习或开始过拟合的迭代次数\n",
-    "* 设置 `batch_size`，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：\n",
-    "\n",
-    " * 64\n",
-    " * 128\n",
-    " * 256\n",
-    " * ...\n",
-    "* 设置 `keep_probability` 表示使用丢弃时保留节点的概率"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# TODO: Tune Parameters\n",
-    "epochs = 10\n",
-    "batch_size = 128\n",
-    "keep_probability = 0.75"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 在单个 CIFAR-10 部分上训练\n",
-    "\n",
-    "我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Checking the Training on a Single Batch...\n",
-      "Epoch  1, CIFAR-10 Batch 1:  Valid Loss: 1.82877, Valid Accuracy: 0.3608\n",
-      "Epoch  2, CIFAR-10 Batch 1:  Valid Loss: 1.60118, Valid Accuracy: 0.429\n",
-      "Epoch  3, CIFAR-10 Batch 1:  Valid Loss: 1.5015, Valid Accuracy: 0.4502\n",
-      "Epoch  4, CIFAR-10 Batch 1:  Valid Loss: 1.38333, Valid Accuracy: 0.4996\n",
-      "Epoch  5, CIFAR-10 Batch 1:  Valid Loss: 1.32223, Valid Accuracy: 0.5298\n",
-      "Epoch  6, CIFAR-10 Batch 1:  Valid Loss: 1.34756, Valid Accuracy: 0.5206\n",
-      "Epoch  7, CIFAR-10 Batch 1:  Valid Loss: 1.28294, Valid Accuracy: 0.5466\n",
-      "Epoch  8, CIFAR-10 Batch 1:  Valid Loss: 1.31494, Valid Accuracy: 0.5374\n",
-      "Epoch  9, CIFAR-10 Batch 1:  Valid Loss: 1.30606, Valid Accuracy: 0.5576\n",
-      "Epoch 10, CIFAR-10 Batch 1:  Valid Loss: 1.32294, Valid Accuracy: 0.555\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "print('Checking the Training on a Single Batch...')\n",
-    "with tf.Session() as sess:\n",
-    "    # Initializing the variables\n",
-    "    sess.run(tf.global_variables_initializer())\n",
-    "    \n",
-    "    # Training cycle\n",
-    "    for epoch in range(epochs):\n",
-    "        batch_i = 1\n",
-    "        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
-    "            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
-    "        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
-    "        print_stats(sess, batch_features, batch_labels, cost, accuracy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 完全训练模型\n",
-    "\n",
-    "现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training...\n",
-      "Epoch  1, CIFAR-10 Batch 1:  Valid Loss: 1.88313, Valid Accuracy: 0.3366\n",
-      "Epoch  1, CIFAR-10 Batch 2:  Valid Loss: 1.60399, Valid Accuracy: 0.4198\n",
-      "Epoch  1, CIFAR-10 Batch 3:  Valid Loss: 1.50717, Valid Accuracy: 0.4498\n",
-      "Epoch  1, CIFAR-10 Batch 4:  Valid Loss: 1.41716, Valid Accuracy: 0.4816\n",
-      "Epoch  1, CIFAR-10 Batch 5:  Valid Loss: 1.33043, Valid Accuracy: 0.5132\n",
-      "Epoch  2, CIFAR-10 Batch 1:  Valid Loss: 1.28486, Valid Accuracy: 0.537\n",
-      "Epoch  2, CIFAR-10 Batch 2:  Valid Loss: 1.30711, Valid Accuracy: 0.53\n",
-      "Epoch  2, CIFAR-10 Batch 3:  Valid Loss: 1.22172, Valid Accuracy: 0.558\n",
-      "Epoch  2, CIFAR-10 Batch 4:  Valid Loss: 1.18755, Valid Accuracy: 0.5712\n",
-      "Epoch  2, CIFAR-10 Batch 5:  Valid Loss: 1.18258, Valid Accuracy: 0.576\n",
-      "Epoch  3, CIFAR-10 Batch 1:  Valid Loss: 1.12699, Valid Accuracy: 0.5932\n",
-      "Epoch  3, CIFAR-10 Batch 2:  Valid Loss: 1.13514, Valid Accuracy: 0.5916\n",
-      "Epoch  3, CIFAR-10 Batch 3:  Valid Loss: 1.10883, Valid Accuracy: 0.5996\n",
-      "Epoch  3, CIFAR-10 Batch 4:  Valid Loss: 1.06464, Valid Accuracy: 0.6178\n",
-      "Epoch  3, CIFAR-10 Batch 5:  Valid Loss: 1.07656, Valid Accuracy: 0.6128\n",
-      "Epoch  4, CIFAR-10 Batch 1:  Valid Loss: 1.10849, Valid Accuracy: 0.605\n",
-      "Epoch  4, CIFAR-10 Batch 2:  Valid Loss: 1.08009, Valid Accuracy: 0.6166\n",
-      "Epoch  4, CIFAR-10 Batch 3:  Valid Loss: 1.01519, Valid Accuracy: 0.637\n",
-      "Epoch  4, CIFAR-10 Batch 4:  Valid Loss: 1.00247, Valid Accuracy: 0.643\n",
-      "Epoch  4, CIFAR-10 Batch 5:  Valid Loss: 1.02331, Valid Accuracy: 0.6448\n",
-      "Epoch  5, CIFAR-10 Batch 1:  Valid Loss: 0.995853, Valid Accuracy: 0.6502\n",
-      "Epoch  5, CIFAR-10 Batch 2:  Valid Loss: 1.00064, Valid Accuracy: 0.65\n",
-      "Epoch  5, CIFAR-10 Batch 3:  Valid Loss: 0.93919, Valid Accuracy: 0.6738\n",
-      "Epoch  5, CIFAR-10 Batch 4:  Valid Loss: 0.991811, Valid Accuracy: 0.6504\n",
-      "Epoch  5, CIFAR-10 Batch 5:  Valid Loss: 0.927782, Valid Accuracy: 0.6826\n",
-      "Epoch  6, CIFAR-10 Batch 1:  Valid Loss: 0.969924, Valid Accuracy: 0.666\n",
-      "Epoch  6, CIFAR-10 Batch 2:  Valid Loss: 1.01257, Valid Accuracy: 0.6408\n",
-      "Epoch  6, CIFAR-10 Batch 3:  Valid Loss: 0.961456, Valid Accuracy: 0.6636\n",
-      "Epoch  6, CIFAR-10 Batch 4:  Valid Loss: 0.935574, Valid Accuracy: 0.674\n",
-      "Epoch  6, CIFAR-10 Batch 5:  Valid Loss: 0.904234, Valid Accuracy: 0.6824\n",
-      "Epoch  7, CIFAR-10 Batch 1:  Valid Loss: 0.925582, Valid Accuracy: 0.6806\n",
-      "Epoch  7, CIFAR-10 Batch 2:  Valid Loss: 0.962076, Valid Accuracy: 0.674\n",
-      "Epoch  7, CIFAR-10 Batch 3:  Valid Loss: 0.935451, Valid Accuracy: 0.6754\n",
-      "Epoch  7, CIFAR-10 Batch 4:  Valid Loss: 0.88064, Valid Accuracy: 0.6912\n",
-      "Epoch  7, CIFAR-10 Batch 5:  Valid Loss: 0.912521, Valid Accuracy: 0.694\n",
-      "Epoch  8, CIFAR-10 Batch 1:  Valid Loss: 0.932409, Valid Accuracy: 0.6876\n",
-      "Epoch  8, CIFAR-10 Batch 2:  Valid Loss: 0.959626, Valid Accuracy: 0.6722\n",
-      "Epoch  8, CIFAR-10 Batch 3:  Valid Loss: 0.958519, Valid Accuracy: 0.6904\n",
-      "Epoch  8, CIFAR-10 Batch 4:  Valid Loss: 0.886022, Valid Accuracy: 0.7024\n",
-      "Epoch  8, CIFAR-10 Batch 5:  Valid Loss: 0.947139, Valid Accuracy: 0.6868\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "epochs = 8\n",
-    "save_model_path = './model/image_classification'\n",
-    "\n",
-    "print('Training...')\n",
-    "with tf.Session() as sess:\n",
-    "    # Initializing the variables\n",
-    "    sess.run(tf.global_variables_initializer())\n",
-    "    \n",
-    "    # Training cycle\n",
-    "    for epoch in range(epochs):\n",
-    "        # Loop over all batches\n",
-    "        n_batches = 5\n",
-    "        for batch_i in range(1, n_batches + 1):\n",
-    "            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
-    "                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
-    "            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
-    "            print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
-    "            \n",
-    "    # Save Model\n",
-    "    saver = tf.train.Saver()\n",
-    "    save_path = saver.save(sess, save_model_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 检查点\n",
-    "\n",
-    "模型已保存到本地。\n",
-    "\n",
-    "## 测试模型\n",
-    "\n",
-    "利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Testing Accuracy: 0.6769185126582279\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9oAAAN6CAYAAABxCzGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAewgAAHsIBbtB1PgAAIABJREFUeJzs3XecLFWZ//HPU9UzNxAFQQTTrgHMSzKhu2ZU1F3DGjGL\ncd11jetP17jGnzlgQEX9sWvAiAEUlTUioJgQcxYkZ7h3urvq+f3xnJqu6ame6e7pSXe+b17FzO2p\nOnW6uqamnjrnPMfcHRERERERERGZjGy1KyAiIiIiIiKyI1GgLSIiIiIiIjJBCrRFREREREREJkiB\ntoiIiIiIiMgEKdAWERERERERmSAF2iIiIiIiIiITpEBbREREREREZIIUaIuIiIiIiIhMkAJtERER\nERERkQlSoC0iIiIiIiIyQQq0RURERERERCZIgbaIiIiIiIjIBCnQFhEREREREZkgBdoiIiIiIiIi\nE6RAW0RERERERGSCFGiLiIiIiIiITJACbREREREREZEJUqAtIiIiIiIiMkEKtEVEREREREQmSIG2\nSAMz22xmjzGzD5jZj83sr2a2zczKvuXTq11XEVn/zOyZDdeXtw+57S0btv3Jctd5rTKzIxqOxwmr\nXS8REdlYWqtdAVlbzGw34CBgH2D3tJTAVWk5B/g98Cd376xWPZeTmT0deCWwZ3rJaz/2+VuIiEzM\nUq4xuj7NpeMhIiKrRoG2YGa3BI4C7gvcBLAhNivM7Gzg+8AZwFfd/TfLV8uVYWZHA08jbtCqm7Rh\njofImmBmrwJePOJm1wCXA5cRD9K+D5xO/F63J1tDGYIxfpCo69VcSzmWIiIiY1OgvYGZ2c2Bo4F/\nSC9539eFZMCtgFsDT0jl/R54s7u/a8JVXRFm9nh6Qfbsy4xwk2ZmewCH9r18kbv/YMkVFBnNKMHF\nlrTsA9wcuF96/WIz+xDwVnc/Z7LVk2XirJNg28yuDRzc9/IF7v7DCe5GQbaIiKwKBdoblJk9D3gV\nsIn5gWXdYq269W1vRATt6y7QNrMMeBnzj8XlwLHAt4ELgP7WvUv6/n0gcGLfa18F7j2xyooMb5yA\nq/47sAfwXODJZvYsdz9uMtWSZbIuAuya2wOf73vtC8ADJ1T+ejseIiKyA1GgvQGZ2SuA/6S5e3T1\n7+3Ab4lAcxuwG3HTvR8RnNO3XX3b9ejewA2ZezwuBQ5199+NUd56Phay4xnlfOy/FjiwK/ARM7up\nu79sojWTSen/jNfTNWg56rqej4eIiOwAFGhvMGb2JHpB9uzL6d9/Ao4BPgP8wt3n3ZiYWU50GT+E\nGNN9H6LLaVXOenWX2vfV8XjXmEF2fzkiq6XqRlwAhzH4d3Rn4NrE7/URwAH0zt/6efwSM/uzu79/\nOSsto3H3nwH5atdjiSZ2vXT3L7L+j4eIiKxzCrQ3kDR++PU0B9mvBV65WOIjdy+AH6flA2a2hRjP\n+XTgbstR7xVyYMNr31jxWogsE3c/Y4jVPgG8wMweArybCL7rLdsGvMnMPuPuFy9PTUVERETWP82j\nvbE8h+j+XamC7Oe7+0vGyS7s7tvc/VPufk8iMdpnJlPVFXfthtf+suK1EFkD3P1TxPjZCxp+vDOj\nZzUXERER2VAUaG8sD2buGGQHTnP3N0+icHc/290/OomyVsFuzO+2uG01KiKyFrj774HHMj8PgwGP\nXJVKiYiIiKwTCrQ3CDO7ATHust8HVroua9T0aldAZK1x968ApzJ/bPfeZnabVaiSiIiIyLqgMdob\nxw0GvP6jFa3FCMzsOsTY6RsRLc4t4GrgHODnwM+aEratslVPCGdmU8TctDcH9gSmiAzqZwNnuPtI\nLfVpbP/BwE2Iz2EbcB7wY3f/xQSrXt9nBvwN8XDoukTW681EFvxL0v7PcPerlmP/MsdngTs2vH57\n4CdLLdzMDLgNcb7uA+xETKN3EfAZd79sjDKvTwxluSFx7kB0g7+AuG78Yan1XmT/W4E7APsDuxPJ\n6C4Bfgl8f9TfwbXAzG4E3BK4PnFMp4CrgMuIGSp+Psa4/VW/Xo7DzA4AbgFcjxhKUQJXAH8EfrQa\nc86b2fWI6/QNU52uAC4Eznb3ny7TPrcQv7s3Jf427Er0ermGODf+DPwB+L27d5ejDiIia5q7a9kA\nC/AQ4magSEv1/Y1Xu2599dwTeAlwVqrjQsvFwAeBQ0bcx7FDlD3M8thU3l8mVF4J7Dugzq9qWPf/\n9K1zQHpvly9Q/hXA0cDeQxyn+xNzgHcWKO+XwFMBm8BnfzsiI/7XiBu1xY5VB/g+8CJg1zH3+ZoB\nZT9sCe/jnwaU+bYV+h2qzpX673p7CeXdraG8AnjxAtu8tuH9v6BvnVsSsxxcPOB4FcDtRqjnTYE3\nAb8f4tz5WarjXhM+9ocAxxPTIw7a9zVE0rlD+7Z9Zu19V1/fPuR+b9mwn59M4P3cAXgvcO4Qx7Qk\nHua9DThsgTIvGrKsYZY9BuzjiIZ1T1jisbg18B7iYc1i9foV8ErgOkvY36KfKZFZ/YnADxapzzlE\nItTdJ3BO7Aw8Dfg20B3yc7oG+Fb6nTt0qXXQokWLlvWyqOv4xjHos957RWsxgJllZvZ84un3K4nW\nLV9k2R14PHC6mX3KzPYdYZdN5Q2zTtP6Sy2nntV5lHrPMrOXE5ngH0fcCA3az07ETdIvzexeTTsw\ns+uY2VeAE4C7E+fOoPJuQmSn/k5q+R6Zmb3UzH4LfA94BRHYbVpgn9WSAQcBrwb+ZGYvHGP3LwG+\n2VD2+8zsxmO8l78hHv70l3c68Nwx6rcWXDjg9aYEgv3mna9m1jKztxC9aZ5E/B4v9js0kJntZWYf\nJnq5/DvRe2exc+cA4IXAb8zsRWnawrGZ2WYzezdwGvFQc2qBfW8CHgp8z8zemXqgTMpYx7CfmR1o\nZt8AvgscBVyHxY+pEy34zwK+ZWZnpZb9heq4KtfLUZjZtc3sv4nz9SnEw+DF6ndj4try+3R9W8pn\nPOiafysiwH4/8HeL1Gcf4PnAb83svuNWxMweBvyCeFh7J3q5XhZbNhHTC74QOM3M3jBuHURE1hMF\n2hvHRQNeX/UpucxsN+DLxBP3rfRuKKxv6X8Nen/IHwScaWZ3Gna3A8pfbJ1h1h2lnEHbLFb3+CZ8\nFHgpvaEg/WU2HbPdgC+Y2d3nFGx2U6KV+J7M/xwGvU8nWr6+ama7MrqnE8MD6jeTg45P/+vVNrsA\nr00PXJpu7hu5ewk8ivj9qL/HXYDjzWzosfvpZvoTxLGt1/cyooV8vXadXGr33vr5uhn4CvBv9P7+\nNH3GwxVsdlfgp8BjmDsP82LnDmndnYkHNZ9L3WBHZma7A18nenZU5S5Wh8ozgBPNbNM4+x5UJZbw\nmaUHnqcBd2b830mIh6WbF6njil4vR2VmBxEBdpX8b9TzaxPwcuDrZjbMg6mBVanvy8zuAXyHaGX3\nhn03HS8HrgV81sweNHIFzF4MfIwYylOdFwt9LoOu1c7g80JEZIeiMdobx6/6/l39kXyWmb3H3S9Z\nhTpVYxm/AhzK3Cf29Ru284ju2dcA+xJBWYv5wfbewElmdri7n7rIrptaOPpvFEZpBemv+zjljNPq\n8lbg4fQ+TyfGt/6W6OK4mRjrXPVcqN/wTAEfN7MD3P1iM9ubCBjqPQOcGFv6R2Kc9x7EDfQm5n5G\nALcluu4eNcb76P8sIbol/pEIVC8nPvPdiVb0rQO2exDRff7hw+7Y3c81syOBE+m9J0vv5+1ED4Bh\nvJkYI1m/GXfgCe7+p2HrswYNChAGPbxbyH8Dd2Xu+QrRk+V8YAbYjxj7umDwaWb/CHycXusxzD8f\nfke0yG8jfgduQu/cra93P+JB0d18hGkO04OYzxMPmgZdv/6Slm56bzdibmByN6IXxHeH3e9yMbN3\nEsF/0/Wseu1c4tpyCdFDZk/iGpP3rb+Q1bpeDs3MDiSGsVTjjmHusSiJ8/Zc4hy8HvH5VuvVW94P\nA75mZn/v7pcvsV4HE3kTtjL3en4hcZ5dSgTUNyUeJPUHulPAB83sdB9yLLmZPYIYkjLoHL+G+F27\njBg2sTNx3G5InCNN24iI7PAUaG8Q7n6Omf0SuFnfj/YmbjAf4e79wfhKeDvNQXZBjId7v7v/uL5B\n6qL8MOBlRP3rNxE7E8HjbXxwEqVXEt2d6z5LdI+s9l8FbectUPffpq/3pxcU3C69p3ogcQbwrwuU\nU9c0b/Eg9yO671XH7mdE68mJ7n5NfUUzuyPwhrR+3R7E8XgmEbRUN4olcBzwTnf/fl9ZW4gWxNek\n7eutG08ws2Pc/fQR3gdp+xki0P880Y38bHfvNK1sZrcGHk20Iu7K3Bu4h5rZU9z9fUPv3P1kM3sN\n0d2z/n6OMrP/dfePLbS9mT2EOIb9QfZb3f2EYeuxRh064PVBXcoHeQwx7rQ6Rn8F/otIeDbn9yyd\nYw9nwO+Dmd0S+B8iaJh9OZX9feKBz8nufmlDufchfk9uxdzz5g7A/yVa24f1MiKI6r9+dYC3AB9w\n91/31WE/4lj8HyIIMeAR9H73VkUaftIUZFcPLN4EfKnpoVHqqfB3xNjohxNdpwe5N73P7c7AG5l7\nvfwO8Lwhqz1yorxhpAfAn6CXSA969buUCDo/5u7n9213APBkovt8/QEQxPn2PkZ4CNjHU32Opxe8\nzhDduD/kfQnPzKwF3Bd4HfNnHNmVGEv/0MV2mn5n3sL882I7MX7/OHf/wQLb34j4u/iAVJ9rLbZP\nEZEdxmoPEteycgsxPqqe1Kie3GiGCKzuCbRWqD7/1FCfkri5vv0Q218LOKmvjOr7j49Yl983lHGD\nMd7TPWrbV1+/MoFj9aq+cvuTRr0ZFk5IRrQ4ndBwvK4C/qNW3iXAPwxRp1sRiaz6j9uxI763U4Fn\nAzuPcVyuSwTn/e/pPGBqxLKyAWVdDtx0ge1uTNzw95/H312p36UFzpVJJEP7zoDfsdsusM1raT5f\nqzKOB7aOWZ8txEOl/uN9DfDkIcswInBsel/3GrKM2xK9R/rrcQ5wmyG2vyGRtb3/OC0lGVq9nKGT\noRG5GJr+LnTTtWGk8xi4F3AKcK1F1jui4T0vKWnZJMolxj03nRunMFwiyQPpJcnsL+OxS/hM6//+\nBXCTIcrZSuSh6K9LhwHJN/u2b0qkejlw0BifyzSRvO0ZS/2MtWjRomU9LBqjvbG8kwhAmrrqtYix\nql8BLjazL5vZK83sAWZ23UlXJE3p8ybmPyW/Ejjc3U9brAyP1qoHEYFAvUufEa2ad55opdeWerfE\nd7r7c9x9wS557l4QNzn9XRe3EK3TEIHDvdz9G4tWwP0solWu6dgPPd7V3e/o7m/1Mabqcve/EjfV\nZzD3vN6LOJ9HKaskxmKe31fWzsR47XldmVPX4U8QY7pnXyYeVjzc1++4bADM7HBiaq/+c+si7+tp\nsoj69p8nxqxfM2jlRbyUGL5QMeK8PcLd3z9UZcJzgXcw/3r4iiHr8Ubm9goz4nfr3u6+6LRn7v5H\n4HDgT6xil9qUCO49/S8TQfaj3P11o57H7n6yu9/N+3oUrAcpydgTmd9d/AfAA9x90V5H7v5D4rOt\nv//q+viGMZOj1Xva/BG4k7v/Zoi6XEP0mLiy70cZ8Ngh9nt47fvqPH2Fu585TKX76tJ29w+6+9Gj\nbisish4p0N5A3P1q4un0DM3JS+rdr+9FdKP9HHCOmZ1jZp82s2eb2UEpUF6KBxDj+irVH/CXpJuU\noXjMR3skMQaz37OWVMO1q7rZgsi0PGw3S9z9IqLnQtPn58DLfYFugA0+yPzum1uJbOArIp0DT234\n0aPHKOv8tF1ZvUQcq1sTD6r6vZVovapU5/Hj3f3Po+5/LUkZ1D9Mc1fij45QVH37S4GjFnsotECd\nqqmF+rvoP9/dTxmjyOcRU1JV9TTg9mY2qLt8VY8bE63A/fV4sbv/bNidpwdFTx+10hP2OGLseqV6\nL69x90+sTpVWVdMwnzZw5CgPA9N58DzmX2v3IgLfcVSfzaN9hLwq7n4uca3ur8thQ2x+/YbXvjjs\nvkVENjIF2huMR5KwI4jxldWN5aCgq77sQ3T1fjMxBvKPZvbalKV6HM9oeO1XwLtGLSi1DL2R+S2r\n/7QcrfFriAOv9wHjmBdQHzNcD3guJ8aXD1+B2PdJzD+HVizQTvX4EZF9up7M7HZjlvV1Ytx6//n0\nRDObDd7TVDdNQd+b3P0L4+x7rUhjzk8ngoJ+V9PrATF0kcSxeZe7jzq2u+6p9LK6V5/PLxnjugGz\n5+/rmX/+PnGRTY9q2ObXzM/9MEwdTiR6Eq1Wq3Y92V/9Ad6rVqEuqyrNmvAo5v9OH+1j5DBx92OJ\nlvD+c+VfRi2qVpeT3X2cxHn1hyZVeQcPsV3TTBJXjLF/EZENR4H2BpQCiQOJ5FclcwPuQYE3zA28\n9yPGfJ9tZseMEtCm7rZ3Zf7NzAdS991xvI9eK2SlRYyZ3lFtI6ZbGVV/t9bq+H9qzO68Td1k+5Pu\nrYQz+v69S0qaNY5XERmH+4Ptd5vZ/ukB0zHM7156KvCiMfe5rMzs9gssdzezh5nZG8zsbGIMdf+8\n6NV58sJhus8O8MGlvAfmJ5KqgvdxrxsQAUiVabz6nO+yyDb1uYir4/KhcVvqgQ+Mud2SmNkNgUOY\nG+A78LY01GSjuQu9GQ3qjllCmfWkjNX5dYiZjZsUbNzfoR8y/2/kdcxsp6aVa5oSzo31EFNEZKNR\noL1Bufu57v4o4DbEjcDFzA2khw28M+BJwFlmds8hd38QkRSl38eHfgP9FYlpSr7J/Lrecdwy17DZ\nbOZjtGZX3aObpjD69pj1+UPDa7s1vLbcmoK/hbIfD5QCpkcRmbHr59ROwCeJc3WXvs0uJsZlr6UA\npap7i3gIMGj5KvHQ5nnA/szvLl6dc29w95FbbZM/pt4nY0kBwYF9dQP4zLhlArj7DPPH+B9gZo3n\ncKpHPXt65ZNLqMYJRBbnlXavhtfaxDRsG1H970V1Ppzl7j9fQpkfJ8a797vDmOV9a5yN3H07MRVZ\nv8Wu1f1DIQx4vZldp2llERHpUaC9wbn72e7+dCJ78/2I6W1OI262hg28ncgAfmLqTruYphuM8ycw\npvV7ff82dsxAu7KUm7/+xDhLKa+prLEDbTObNrP7m9krUl6As8zsz2Z2uZkVZlY2LUR25H67j1uP\n1MX5UUSmXegFVrckpjKqt2aXRDbhoealXQX9Q0EWWvp/1534jI9y93Fa66syRhn73+QwenM1V85L\n40+X6g99/67G5Tc5mPl/Oy8fJjHVICnYP4vBDzWXS73rcPU5/XAJierWu/6/TU78PRybu19BZAif\nxEPgK9K4/nE1dfle7FpdH49dXSNuBvzUzJ5rZnsuoT4iIjs0zaMtAKSssl9OCykr6m2I1udDiC51\n+6fV6zfgs0UQN8EfNLNfpTGzg9yw9v3szd0E3ka9jCpguMEEyl2rhk6G06Cp9Wzc8prK2jxqIakb\n638SCfvqN3/9LYcLdc/tv5kdO9AGcPdvmNnLiPme6+Mk6/tz4I1prO1aNUoAV39/lwEfAd48gQdh\nf1ni9reqfV8d9/PN7PZLLBdgXkZ55nedr9yooR6LZhkfwo+Ja+1KasqxsaTAcp27IfOvL5P623Sr\nvtfG+du0lGs+xHCj/mvYgtfqdA38LnCn2nYO7Ek8mH+dmX0bOJlobT8jtZ6LiGx4CrSlUeqS/IO0\nHAOQuor9M/AUolWvnniq+gO8BTia+KM8SNPYtKU8pa+M0y1uPZt0q9MkyxupZc7MXkRkud9Cr9Vk\nrLL6LDb+cFHu/hozuwsxzU39fK++foeY5mwtW2zs8DYiGd7lROvu94lkaCen1tZJ6J9WblT9LWdG\n9Cw4dYnlDjIo0G66fo07Zn3SZYxqP+afG0t9ILKereTfplHGaFfXwKsnUJdBZS/k0cQDmL1q61fX\n6Qz4h7QAdM3sh8D/Al8HTnH3pqFKIiI7PHUdl6G5+/nu/k53vw0xJczlzA86qulxFhqv3XSDMYks\npk038lnKJCtrlJkdDbyaaFmpd8fuv6EbZplX/ISq+aEFfnbUEpNxLafqmBTuni+w7Ozu+7n7Ldz9\nfu7+Unf/wgSDbFj6g5ymwHeUc2PU82fQQ7qmXhLLdf1abjs3vNaU/GqjaPrMl+uzHTcZ2opLuRVu\nTzx4779GV3/7qyUHDgWeD5wInGdm70vzk4uIbChq0ZaxuPtxZvYjIoHWLsy/SX0IkWCpyZaG15rm\nwR7VoDJ2QtORrElm9u/MnSILejduHeBMoiXlj0RL25VEV/XtzD/nngY8fhnqeD3gHbX99e/3xcBj\nJ71fmacpwF3OMc3948ErTd3MJ9FiN8mHGsNqeo+TuBavO2k2jJz5v9/L8bfJmEBvm5WUgu3bmdmR\nwHOJoWWVprwtld2AJwNPMrOPAf/i7pcua2VFRNYIBdoyNnc/y8xeALyHuUGIAXdfYNOmoLc/g/M4\nBpWxGi1Fsog0FOHlzA+yrwZeARzj7kN/dmb2oIlWMMrMgY8C12ZuS049cdijzewb7r4qUzRtIE2Z\nm8edTmspms7JSVy/VqPnzWVAf/boDdkDyN3bZtYGpvp+tBx/m5x1+nfJ3Y8DjjOz2wD3B+5GJHar\nP0CvB9713iKPBO5sZnd199+vRH1FRFaTAm1ZqmOBNzL/6fzfLLBN09PsSdzcNXX762zgDLpr3eOY\n2xvCgKuAw9z9p2OUtxxdMV9NZLvuD7L7g+23mdn33L1/KhyZnP4HdA583d2bpqhaTk1dqyeRC2I1\n8klcwvzkWEtKILjOXQrs3ffacv1tWmpis1Xl7j8hkgC+Jj2QPJhImnoXIviuhiXUzy8Hrg98ycz+\nbsJDU0RE1hyN0ZYlSUnTTmd+17HczAYFPk2B9t9OoDpNcyari9ra9cDa99XN2MvGDLIhWp0nxszu\nR4wz7A+yj2P+/NpbgOPNbOsk6yBz9Gc9N+YnSFsJF9W+rx60NGXvHtXNJlDGqC5qeG0S72W90t+m\nMbh74e6nu/ub3P2fiGvxEcBniakP++8Pbgb8ywpXU0RkxSnQlkk4f8Drg8Y4nl37vrpRve0E6nFg\n7fsqKDp7wLqy+g5mftff45ZQ3kEN5Y0ljcv+cMOPziTGGz6auIGE3jm8PzGMQpZH/3UDonVspf24\n4bWbmFlT7olR1OdmXyn90zAa8+eS3kjOZn5QeGDTiiM6kPmf7Q77t8ndO+5+krs/BLgr83uBGPCY\nFa+YiMgKU6Atk9DUta5w96bWEmiejmdnMztoifW4K/NvZpZr6p9BVmPM6LqTMsH3J5W61N0vHLO8\n/Zg7v/HYUjfIjzG3tdSIMZUPSzeR/0uMI69nRq/Gaz9pEvWQeU5j/u/XHmms6Ipx998wv9tvxsJ5\nKRZkZjcFrreUeo3pG7Xvq2O7f3rQtBLW2vWy/vei+p2+81IKNLMbEfNzL7SvHZa7fxt4NnOvlQC3\nNrNJjH8XEVmzFGjLJOzP/BumQUE27v5LmsenHTluBVKQfsuGH630zUzTmLP+5DrSPGbxqiWU9+Ql\nbNvvtcQ88P1dxp/cl8Dnv4jM+v3B9tvMrOlclCVw9/OJ+b37WxwfvArVObWhHo9eQnmrlbX+VOZn\nTDfgqSu0/7V2vfxuw2v7mtnYD1GIXBT92sRUWRvF8fR6ANX1J+ITEdmhKNCWJTGzA4Gb1F8iAo6m\nG5a6TzE/QHmMmY2biOe5Da9dCnxtzPLGdWXfv9fdNC4rpCnj7l5mNvI1ycx2JgKDJbeOmdkRxLnU\nH2Qf7e6fqq/r7k48HDoPjddeKf+v9n113fjX1ENitevxIDO7yYD1BzKz3YCnsAqtu+6+Hfg486/F\nTzWzieY8GKD/egmre708jfm5AAD+fZzCzGwn4CjmX09OSPlNNgR33wY0JSUdNLxMRGSHoEB7gzCz\ng83s2HFuBBco04C3Dfjx5xbZ/B0Nr+0BvGaMevwDMW1I/83MMelGciXVE9xU9bnRCtdhzXP3K5h/\n47WZyFY7qrcD+6Tvx55X2cyuD3yo4UdnAs9p2sbdLwAeRfN47XePWxcZ6EPAxX2v7Qa8f4Xr8Rmg\nf5jDNHEujuq1wF7p++WcF3yQNzA/yN+TlTmm/ddLYxWvl+5eEr+3/Q8e7peSI47q5cC+Da83/f3b\nYZnZ3vSykNeds9J1ERFZSQq0N44W0YXt52b2kaWOhzazKeC/ifFr/TdpF7NIoO3uZxGtzU0tKUN3\nA05jGz/WUIc2cPSw5UyKu/+Z+a00e5pZU9bZje5bzA8sXjFKq3aax/3x9M6fsaRx2R9n/rjsK0jj\nsgdtm8Zrv5z55/KRZvbEcesk87n7VcBLmX+sH2JmR5vZkqasNLPMzB5sZrdapB4dIqjur8fhZvbm\nEfb3r8DTWOL5uxRpSrrPMv+9PMDM3jtOLxMAM9sz/V4t5HdA/8PQ/VZwjHiTY4Cra/+ujseHzezm\nwxZiZo8mHtD1/236QRq3vC6Y2TFmdoslFvPshtd+nX6fRUR2WAq0N56M6O76fTM7y8xeYGYLzXk9\nR7oR/Uci8+4jmHsTUbUkvzC1WC7m6TTf0LzHzF682E2amd2DCNbr855WdXhJCnpXww+Zf9P8n6tR\nkTXus7Xvq8/+jsD/LNbt2sx2M7N3A6+rbdtdQl1eR2RbXmxc9iCvBk5mfrDydo3Xnix3fzfzjzVE\nwPpdMxs5a7aZHWBmLyYCv+OBGwyx2RuAnzH/M3+2mX18oa7XZrbVzN4OvKW23dWD1l8BTwXOZf4x\nPQr48ijnsJndOD1s+COLzA3u7gUxF/OauV66+8VEgFyvkxMP4b5mZvddaPv0N/J5wLH9PyLGpK+3\nZImPB84ysy+Z2SPSUJ2hWHgu8AKap0kUEdmhLenpv6xb1R+8mxMBxuvM7DxiXPUZxJjTi4lufZuI\nrOI3JqbgOpzo5ug0B9mfdvf+G4zmSrj/xsyeA7y3Vq/qpvNVwKPM7EPAicBfgG1EN7xDiIcFDxhQ\nh1Pc/U3D1GGZfBL4+/R99X4em25WP0FM63I50NRK+gN3X0rAuJ4cC7yYyLZcP58eBtzBzI4Gvgz8\nlmj12ovNNWLkAAAgAElEQVSY4/cfic+/Og+NOGc/xxhJnMzs/sxtearOo3e7+yeHKcPd3cyOJKZL\num6trC3AJ8zskDROUSbjEUSPiKqlrTp/DiGC7TOAE4DvAL8hphfaBuxCBH/7Abchrml3Z+7c0UON\nlXb3Tsow/y0igVf9HP5norvx54iEeecABXH9ugvwEGKoTHX+XkT0wHnpCMdgYtz9IjN7BPB14r6g\neh8O3AP4sZl9Afgi8XfifOLvw05EAHor4tgfQUyzB0MeR+J6ebvaNgYcZWa3JR56/Jy4XjZdF89I\n3b0nyt2PSfkaHsjcY7EP8EUzO4kIFL8H/JX4/K8H3At4AnFuNf1teom7/3TS9V0BDtwnLdvN7GvA\n6cSwml8R58JlxJjr3Ynfp78npvBqSpb6ZwYPOxMR2WEo0N54+p/SV38Ar0Nk710sg299m/6yPsGI\nmXfTDc0BzJ/+w4EDgNenZVBdKtWNzM+Ah49Sh2VwHNGVuLqRrup5cFoGcWJe4HOXs3LLbOiETu7e\nNrOnAp8nelrUb2ivT3oItMB+qpvy7URwfviolV1gXPYPGTEBkrtfaGaPIgKr6v0YcR6/h+bswzIG\nd7/UzO4GfIkI7PpbYg9Ny1DF0XxNG6Yep6dg+1h6iZ2q8rYS4/cftcA+jRjm8jCaZ00Yx1jd0N39\n2ynY/m9ivHl1Ta3Ke2BaJr3/DxEP3HZj7mdxO3oB+KD97EXzDBb9643jsUTPiUOYeyygF3Quts/6\nefmBJTwAnuTQgnGPR7XdJuKByhFDbtN/LK4GjnT3pkR4IiI7FHUd3zjOJzKqlsy9eaovPsRSX5/0\n2kXAU939keO0Lrj7c4EXEi0W9X1U5Q9TFwe+Avx96vq3atz9MqJVo0gvDXN8hzXJcZz14zzJ8oYu\n091PBP6FOFZN59Zin/3lwAP7xjwOtf/auOw9+vZ9BfDP42QFdvdv0Jtfu6pHNV57pbuMTvrzHbcO\nyyLNuX4X4iFGdV0b5Vo26DoCzb1NBtXjOKKF/aoB5TX9rlc/vwp4UBrnT+1n9a+jWNJn7u6fBu5K\ndPse5r0Mem+j7PMioot69bdj0tfLsY5JGv50d6KnzKjHor7PAniluz9l1Dr0vYdJGbe8Uc+FpmN2\nHnCP9TRGXURkKRRobxDu/gd3vyPRZfJpxM3DhQy+AWxa6Fv/LOD/ADdz9yVlqHX3NxItUN+k+Y90\n01Ktdw7wFHe/bwpyV527f57IoP1rhju+QxXbV9aSqznBMse+GXb39xCtQ39muM++2t/JwCHu/tUB\n9VjM64Db921TMvy47EFeTTz06a/LSs6vvZTgZDnqsDw7cN/u7s8kPseT6AXcsPj1o/+adi7wZuDv\n3P3kEevxKaJl/UTmvu+F9ndi2teJ9aIY/7hN5DN399OI1vX/IroED3s9rtfhz0SvnqZp/Jr2+Umi\n2/Xv+uo+qevlWMfD3a929wcTrdt/YPhjUa33XeAwd3/FqPue1HsYoszFPIcYUtBmuM+m6ThsA94K\n7O/up0/gPYiIrAsWU8HKRmVm+xNJoA4AbkaMxd6DGMu4E9Et90qile884KdE8ppTU7ba5ajTQUQX\n9HsQYzCbkqL9Ffg2MR/3Z5c6J6mZHUt0n6848PjUarYkZvb3wP2I8aA3I7pI7kyM66tz4Abuvp67\njo8tZYx+LDG+9TDmz6frxHjbrwIf6b9hM7Om7sLfc/czl6fGa5OZ7UuMF61zdz9jNeqzkszsRsBD\niWvHwczNIl83Q5xLZxPB0NcmdT1LGcsfSYxRvRkxZrUggtZfEtet49fDWN2UlPDBRJfxO9I8VRXE\ne/s58aD0y8C3fMybizQk4L7E9fKmRI6QQdfLvd19sa7jE5Gyrz+AOL/uQgxv6VcSn/HXgY+6+6kr\nUbeVkJKgHUacB7cnxl5fn+YGGyceTPyQGN7xSXUVF5GNSIG2rGmpe+8NiOA0I+ZePkd/tHds6ab2\nOkSg1CK62P7Z3WdWtWKyrpjZrkRyuq3Ezf+Vablw3EBwI0uB935E4Fsdz0tXKthdS8xsJ+KB1k5E\ngH0FcY1a0kPf9SQ9HN2HeDC/lXgwfzlwsZI/iogo0BYRERERERGZKI3RFhEREREREZkgBdoiIiIi\nIiIiE6RAW0RERERERGSCFGiLiIiIiIiITJACbREREREREZEJUqAtIiIiIiIiMkEKtEVEREREREQm\nSIG2iIiIiIiIyAQp0BYRERERERGZIAXaIiIiIiIiIhOkQFtERERERERkghRoi4iIiIiIiEyQAm0R\nERERERGRCVKgLSIiIiIiIjJBCrRFREREREREJkiBtoiIiIiIiMgEKdAWERERERERmSAF2iIiIiIi\nIiITpEBbREREREREZIIUaIuIiIiIiIhMkAJtERERERERkQlSoC0iIiIiIiIyQQq0RURERERERCZI\ngbaIiIiIiIjIBCnQFhEREREREZkgBdoiIiIiIiIiE6RAW0RERERERGSCFGiLiIiIiIiITJACbRER\nEREREZEJUqAtIiIiIiIiMkEKtEVEREREREQmSIG2iIiIiIiIyAQp0BYRERERERGZIAXaIiIiIiIi\nIhOkQFtERERERERkghRoi4iIiIiIiEyQAm0RERERERGRCVKgLSIiIiIiIjJBCrRFREREREREJqi1\n2hVYr8ysTN+6u+erWhkRERERERFZM9SivTS+2hUQERERERGRtUWB9tLYaldARERERERE1hYF2iIi\nIiIiIiITpEBbREREREREZIIUaIuIiIiIiIhMkAJtERERERERkQlSoD1BZnYzM3urmZ1tZlea2eVm\n9iMze42Z7TliWbcwszeY2ZlmdqGZbTezc8zsFDN7gZntMUQZjzOzMi0fTK9lZvZwM/usmf3WzK5J\nP39g37YtMzvSzD6V1rvSzDpmdoWZ/drMTjKzV5jZoUO+nwPM7NVmdpqZnWdmM2Z2gZl9L5Vz3VGO\nj4iIiIiIyFpl7pqhahz982ib2dOAtwCbmDvtV5WZ/GLgcHc/c5Fy81TO04Fqfu6m8i4Dnu3uH1mg\nrMcBx6btPwy8GPgEcFhDuQ9y9xPSdjcDPgsc0LBefz0cuKm7/25AHaaBtwNPWuT9bANe4O7vGvR+\nRERERERE1oPWaldgR5AC2qOJAPIXwPeJwPEAIqg1YE/gBDO7ubtfOaAcAz4NPCCV5cAlwP+mr9cH\n7gZMA7sDHzKz3dz9HUNUczNwAnAw0AG+C/yWeDBwUK0OOwNfBfZL+y+BHwI/B64Ctqaf3Ra49iLH\nZSvwFeBOtffzW+AHwKXAHun47AtsAd5hZru4++uGeD8iIiIiIiJrkgLtpalaZt8DXAA8xt1Prq9g\nZncGvgDsClwX+DfgvwaU93x6QTbAa4GXu3u3Vt7eROv04Wm9N5rZ99z9jEXq+lCiRfkU4PHu/ue+\nek6lb58IXC+VfTbR0v2bpgLN7GDgCcDMgH2+m16Q/Uvgqe7+rb4yDHgK0Yq/GXilmZ3i7qct8n5E\nRERERETWJHUdH1PqOu5Ea/V24FB3/9mAdZ8BvDOt/wt3v2XDOrsA5wA7pZf+r7v/x4DypoFvAYem\nMk9x93s2rFfvOg7wE+AO7j4oMMbMjgcekra5p7ufMmjdhZjZXYBv0GvFvoO7X7LA+vW6nuTuR4yz\nXxERERERkdWmZGhL58B7BwXZyUeALhGU75+6Z/d7FLBzWud84GUDd+jeBv4l/dOAu5nZTRfYfzUO\n+oULBdnJrrXvL1pk3YU8p/79QkE2gLt/mOh2b8DhZnatJexbRERERERk1SjQnoxPLvRDd7+KaNWF\nCCRv2LDa3avVgY8uFhCnruI/rb10t0XqeClw8iLrANS7lD9tiPXnSQndqhb2K4AvDrlp1Xpu9BK2\niYiIiIiIrCsao700RgTGP11sRSLreGXXhp8fWPv+u0Pu/zvArdP3By2wngM/8uHGCXyCGKdtwNPN\n7BBiTPiX3f23C27ZcxuiC7wTidfeHkOxF1WfKuz6Q+5LRERERERkTVGgPQHufsUQq3Vq3081/Hyv\n2vd/HHLXf6h9v2AGcODCYQp096+Y2duBZ6WXDk0LZnY+8G0iC/pn3f2cAcXs21evZw6z7z7qOi4i\nIiIiIuuSuo6vHfVx21cPuU19vV0WWXfbsBVx92cDDwZOpzctlwN7E4nS3gH8ycyON7Omlufd6sWN\nsYAeAomIiIiIyDqlYGbtuIpegLrTQivW1NdrnJt7XO7+OeBzZnY94K7ENF13AW5RW+0hwF3N7I59\nU4DVHwD8xN3r3eJFRERERER2aGrRXjvqXbtvMOQ2N6p9v5QM4QO5+1/c/Th3f4a735qo28uAa4jW\n5z2AN/dtdn76asA+y1EvERERERGRtUqB9trxw9r3dxpym/p6Z06wLgO5+znu/l/AU4lA2oB7m1l9\n3PmPgCpr+t5m9rcrUTcREREREZG1QIH22vH19NWAR5jZ9EIrp2zgt6m9dMqgdZfJCbXvp4iWbQDc\nfTu99wPwjJWqlIiIiIiIyGpToL12/A8xThvgukT37Eap9fgdtZe+7u6/nkQlzGzPIVetd28vmTt9\nGcDrqyKBZ5nZPUaow3WGXVdERERERGStUaC9Rrj7lcCr0j8N+A8ze2Vfl+wqCD0BuH16qQO8aIJV\nOdXM/tvM7tO/71odbgZ8qPbSV929W1/H3b9JzL8N0eL9RTP7DzNrTPRmZpvM7B/N7LPA55b8LkRE\nRERERFaJso6vLW8EDgMeQATbLwGebmanAJcC1wfuBmxK6zvwPHf//gTrMAU8Mi3bzOwnwO+AK4i5\nrf8WOKS2/jXA8waU9VQiGdq9gWngNcBLzOw04E/EOO7dgRsDt6L3vib5fkRERERERFaUAu01xN3d\nzB4MvAV4OpATY58fWl8tLZcD/+bu/2/C1biS3lzWm4mW89v3rVP9/HfAke7+s6aC3L1tZvcjusE/\nF9gKbCEeFsxbPS0d4NSlvAEREREREZHVpEB7fN73ddhtFlzf3Uvg38zsPcATgXsQLdm7AJcAvwK+\nCLzf3S9dhnreFrgDEQzfDtgf2JcIkq8BziOyip8AfMLdO4u8HwdebmbvAB4L3JOYi/vaROv5FcAf\ngZ8SCd2+5O79471FRERERETWDYs4SEREREREREQmQcnQRERERERERCZIgbaIiIiIiIjIBCnQFhER\nEREREZkgBdoiIiIiIiIiE6RAW0RERERERGSCFGiLiIiIiIiITJACbREREREREZEJUqAtIiIiIiIi\nMkEKtEVEREREREQmSIG2iIiIiIiIyAQp0BYRERERERGZIAXaIiIiIiIiIhPUWu0KiIiIrHdmdjWw\nCSiBC1a5OiIiIjuyvYkG4xl332m1KzOIuftq12FNuPLyOBDuzsBj4pA5GEBZUhRdim5BWXS5Zttl\nXL3tMq7edinbt13MTPsC2jPn0565APzK2cWsTZ4beW5kuVEUBd1ukb52Z5eiKMiyjCzPyPMcBwp3\nyrKkcKc906XdLmi3q+2doltSFCWll7jH102bpti6ZRNbtmxiy+ZppqdaTE21mJqeIrMMswww3KHT\n6dLtdOh0uhRFSVGWdEun2y3Y3u4w0+4y0+6SkZFZTkaeto8ywOgUJd0ylrIsgBKnBMq037T/qSla\nrSla+RR5PoWXRllmeJnRbhdsn+mwfXuHmXYHrAPWBeuS5cTxa2XkeXTIeOVLfm3LfX6IiCzEzLpA\nvtr1EBER2UAKd1+zDcdrtmKrycwag22r/jfnR45TUnqXopih291Gu7uNbnc73aJDUXSBAsMxN7AM\nT0VUuzCLfZpFvOgpoO7feXoUADiWgWWQpaUs078dDKN0wzyC0TzPaLVy8jzHsiz2445bVMLMUn2c\n0qEsnaIsI2juFnSLkm7hlCWUbnhaYh/gXuIe9SvNcXMyK7E8Vdxin61WRpaBWTpmZUEx+55zvIxC\norx44OFllOfpOOeAWU6m50MisrY4xLV83333Xe26iMgQ2u02F154IXvttRfT09OrXR0RGdK5555b\nxWprOiJQoN1nYJBdtZm6zb4QIW+0HBdlh06xnZnONbQ7V9HpbKOTgm2zggwnwzC3FJSmQDJuzWqB\ndgSaZVGkn5eUnmGZxa7NMHPMnCxzLIugO8uZLcuBjAxwpqZypqZatNKSZxlksQ7us2+sqk9ZlhSl\n0+2WdLpdOt0iLU63APcMLzLKbgaFpZbokrIs8bIkm2J2yXOwzCK4zyDLjCwzLHPwkrLsxhEsS/Ac\nowXkKcAuI8h2j8DfS0ovcDeyLKtuaZfrNBARGdUlwN7Xvva1+ctf/rLadRGRIZx55pkcfPDBnHTS\nSRx00EGrXR0RGdLee+/NhRdeCPG3d81SoD1AFXBbLZYzLFqfiRZd6LW0FmWXTjFDu3M1M+2r6Xav\noejO0C3b5BRgqeXYs9Rq2+uibhZBaLUvL52iKDB3SjeyssTyFLDmVVBebdcLsj2P+DMzwzAwmJrK\naU3lTLVatFqt2e1iz15b6LVmFyXdoqTTKZjpRLBdlEaZFu9meMfwjlF2iW7mRUFZdpnaYkxnRms6\no5UbWZ6RteIr+Gxs7KQHCUVJtPinugFlvVW7hCK1sHfLEsfIW9UDChERERERkbVHgXZS77Y9eCV6\nLdqJ47iVlHQpyzbdcjvdYjvdok1RtOkWXbASMsjSeOYIInt90Kt9Wy2qd6LVl9IoLLbNzMFT1+/U\nqm1ZKj4jlclsIJ5l0Grl0W08LZ76eJfumDPbOl5vzS6rFu1OQbvdpd0pcM8pPYuu4x0oZ8DbRtFx\nisIpU7Cd5Rk+nWE4WWbkOeR5RtbKesEzvQcNpRt4GSO8zcgsusFXreRzAv+iBIxiKl5f251FRERE\nRERko1KgvYha7+r5gZ1F1+jMI7GZ5dVXh25J4THG2fIyxegGqUu3VU27Vbyduo5nWUaWZeRZXuUX\nm1OX2dbwskwtv71gvWoVr1rII9DNybIcy6KwKsitxoCbOUaU1S1SoO3QLZx215mZiYRrsa8YR+3t\nFGjPGN5NY7PdwKM7ublFN/nZevfqGvVNLdK14+mUeFlE0rfCKKru6F5SFhH4dzsFRhbB/XQE+CIi\na8lFF13E9a53vdWuhogMod1uA3Cf+9xHY7RF1ol99tlntaswNAXaA8xv2a5avOkFiFUX7swi4M6j\nG7dl4OaUXtItumSU5EYEorMFlLOlulkkS5stp8o0noLS2a9RAS9JmcUjcLVUlyyLEqsgO8t62bkz\nyyKhWhmt2UUZCcyMaCUuy16gXZRQFNDplMzMlMzMFBFgl2ksdwqymTG86HV5xzKsjEB79hnB7IOB\navw5ta/pWAN4BNq4UxRGWUQ9Z1u0uyWdTgkG3W5J0XXKlgJtkXGY2cuAlwHu7sqUPUHuzjnnnLPa\n1RCREaSxniIiE6VAewi9pHZWb2COYDKL1tsIsuOr5Q7mlGVBtyjIU3doz6oW7ZJeU7Wl9GWpRduy\n2Wm9opt1CV51JffUMlxl+vZevQwsZfeOVvGYQqyV52RZhmVpGi+gKKObt1e9r1Mv9W7hqUXb6Bae\nAu2C7dsj0KZ0vMxhxmCmxLdn0W09T2PMc4tAu7TZ8ey91usFpk0jtXqnLuPRoj23C3mRAm3LoNtN\n3dXLgcWJiKyi/Va7AiIylDZwIbAXoBZtkbXtr1QNleuFAu0FeJr6qu9VfLbbdwoosyymnMpaZNkU\nWTaFWQusyqIdXacjVo2x0f0hZ2qvjm7jeWQKn00KVjolvWmuylQDs9QyboYRdTDrBdkRYOeRIT3t\nuyyihbhTlL35xdI82mWZpvAqPcZ7e0ZGHlNqkQM5Rh7vLWtBq4UVFl3jswKykpYZuVkkZKslNSuL\ncrblO77GQwEj5ikrsRj/Xo1dr7rHl+VsgB5d46sA3pQQTUTWIAOUdVxkfTgTOBg4CVDWcZG17XrA\n+uoxpkB7EXOCba/aj0nRYtVCnWFZC5sNtKcxS8E2eZqjupoGLKbkyrzK++2zAW8E7TGuutp3dBFP\ngTolZWp+LjOwojc+OwLgjMxy8sxSwG5klhMJ2IixzmUkFut2u7VtSfNi94Lt6Faek1mLVpaR0SIn\nJ6OF5S2s1cLKVkw7lhVgBZ4VTGVGK6vGaMeUXGVZUNrc+cKj9T5PrfB5HNPSUjf6EqdIXccL3Ms0\nnVl6eGBpBLj3PwQRERERERFZfQq0B6h3c66CbU9JzGYThtdakrMstWjn02TZdK1VO0+t0BFkZ1l9\n/mxmm7ZnW20tg1a0bJdlOTsWu/RIrmYpE3lkHi/iq2epJTsnszwlU6u6kEd7dJm6b1dTcXWLAqgy\nfRtOdNeOJaYvMzJya+HmtLIWLVrktMjyKTJvkXkrBcddSisoraCVES3aAFW3dy8pSS3SWcwtVgXY\neZZHq7tH13ov0zMML1KLdhHH3VLX/Kyab7wvU5yIiIiIiMgaoUC7QVOX5Eg6Zr0WbYjvZoPtFlk2\nTZ5topVvmm3VjkMc03lF9+yy10pdlsxNENZr2Z7NaF5mlJTR2u21ADN97+lrL/1YlV08vpalp1zj\nkfys241M6N1uBNpZZpRZlBdjoyPgxqOVPM8ik3qrbDFFLNFFPifLW5GcDCiALk7mQGmU8QIlRmk2\nd0SFGTZ7jLM4frX3TbTbz8lSbpmRW0YrJXersqmLiKwRxWpXQERGdV0iL+R1V7siIjKCqvcva/xv\nb7baFVi7BreWRpKyqjE6uo5nWYs8n2aqtYVWayt5CrYza8X4ZrfUMt2bczqWcjb4LlPwXRQF3W6X\nohvrxPpl6tbdW7wkLdXYamazdBdpHuxYunTaXTqdDt1Ol063iAzjqWW7Cry7abuiiDHaedZiOt/E\nptZmNueb2JRNs8mm2JS10pIzbRktN6yMKc3KjtOdKenMOO0Zp9Nxul0oi2pxyq5TFFAUVQt7qn89\no7v3Am0zI88zploxdn1qaiot00xNKXmJyFKZ2SYze76Z/cDMrkjLaWb2TDNb8ImWmd3QzN5iZmel\n7a42s1+Z2XvM7FaLbFum5aXp33c3s+PN7E9m1jaz3/Wtf10ze12q52VpnfPM7Cdm9j9m9jgz23mB\n/e1qZi8ys2+b2QVmNmNm55rZCWb2kFGOWYP0PFE9bUTWj+sCL0eBtsj6Ugu013R2NLVoj8Bn/2+9\nr2lubMumojW7tYVWvoVWvpncpjFaQIdofY4EX4WVWFliRUHViGsWPy/KXvDdm3M6BejVkgLsCE4d\n3MgskqRh1TzZNjuM3NLk2g50iy7dbkHRTfsume2KHdm8M7oF4K3oht7KyciZspwpMqY8IyurrupZ\nZDAHshK8W+Idw9vAdsPLElqO5xDzm5EypqeI2h3yavqvGCfuqZXe04973cYzLCMF2r1gW0SWxsz2\nBr4M3Ja5eRoPTcu9gH8asO1jgfcCm/q2vTFwE+BJZvaf7v66BargqaxXAy/qK6e+r7sAnwd27Vtn\nr7TcCngEkUL4Sw3b3wP4OLBH3/bXAe4P3N/MvgQ8zN2vWaC+IiIiIotSoF037/auoWWi6uZNFW4b\npKRheR6B9lRrC3m2OXUfTy3aVWZvjyC7KAqMopcgLKsC7V5rd71Sc4Jsr1q0I0DFndKii3ivT3at\ncSUieRynKLrRil2N0c7iWYF7llqYoSiMzI2Wtchb07SsxZRntEpjKk1BFt3aja5B16FdOBRO2XHK\ntlGmAJoSbCqCZYfoWp7Gi5P3HiS4W1Q5JTibnUHcncxizHneyuYE2dMKtEUm4dPAAcBbgS8AlwD7\nA/8J3AJ4gJkd5e7H1DcysyOAY9M/rwTeCHyNGE1yJyJo3gt4tZld6u7vXaAODwFuDfwYeAvwM2AL\n8HdpX9PAx4BdgCuAo4H/BS4g5uT5m7TPBzUVbmaHEcF3CzgPeEfa17nAvsDDgSOB+wIfBv55gbqK\niIiILEqBdqUvOVmv1bqPkcZFVzGhgTmW5eT5FFOtTUxPbWHT1GY2TW1i09Qm8A6k7uMRVAOZ4Vls\n71btLQWd6b9qDmmvuounKcKKWsCdzSbfdqCgSOO/e+ObqzqmNYqCouymJGPxs2q6LGOKVraZ1vQm\npnwzU2xi2jeRd3Mo29Du4O02ZZHGjpcZeEkry9jUmoLMKFtQZFA4lGVB0TWKlITNMk/HyslyyFsZ\nrVZJnpe1w2vpMUaMX49s45DnGa1WTivLI6u5NT4GEZHRGHAIcC93/1bt9R+Z2VeAs4G9gWcAs4G2\nxZQKVeB8FXBnd/9pbfvTzezTwKlEn8w3mtnx7n7JgHrcGjgZuL+7d2qvfzt9PSyV48Aj3f3Evu1P\nBz5uZv8ObJ3zBqOuxxF/704EHuru2+vvFfiSmX0LeB/wYDO7h7t/bUBdRURERBalMdqNrPa1KckY\ns0FqtcQ0WNNMtzazaWoz01ObZpep1hStPE9Zt4HMIIss22WWpo9O82PPLql1u9staLc7zLTbtNsd\n2u02nXaHolvE+OUMsgzMqimxOhRlh063Tbszw0xnhnZnJm3XptNp0+1E9/FuJ5ZOu6TbAcop8mwL\nm6d2Zeum3dh587XYZese7Lx5dzbnW8jLjHKmi3e6lN0uXhTgTm4Z060ptk5vYktrE5uzaTYzzXQ5\nHUF6J6M7Y3S2Q3u7M7OtZGZ7SXumpNN2up1qnHaazqwaAW8lVIF2ltHKc1p5Rp5lpEnLyJp7mYrI\ncBx4e1+QHT9wv5RosTbg1ma2S+3HDyJaggFe1RdkV9v/CXh++udW4AkD6mBEMpMn9wXZdfvUvp9X\n19o+S3e/qu/lRwA3BLYDj+0Lsuvbvp8I2AEeP2gfIiIiIsNQi/ZA81u0Zxu9Z6fV7rUGZ1Zr0W7N\nbdEuihZFmTMbwM+2aNucruhe1lq13SmKgk63S7fTSQnKUks2kOV5ZN7OY/owL312vu1uLclZGv04\nm5V8drqwskzjobOU+IyYwivfwqbpXdmS78RW28qWbCuWlWy7uqD0bbTbHUqP+bpj/HROnht5lkOW\n0QQNspAAACAASURBVM08Fpx22aXrBU6Xgsg8XppTWkneigzs8aYzsryM6buq7uSWWr/T/Nl5njGV\n57TyPKYPMxRki0zG/yzwsx+kr0Z0z/5J+vc901en1328yfHAu4hx1fcE3tSwjgPfcfc/L1DOX2vf\nP4Ho+j2sB6av31igRb3yTeB2wB1HKF9ERERkHgXaC5rfObnew9yIoca9YDsjt5xW3qKVTzHVmmZ6\napquTWFFjnuOk0GaJsyr/8Xg7TRvdDXdl1VN3bXM4j7785jPOwOv2thnU5DjXuBlkaYPozb1V7U/\naq/nxBjzzUy3dmbLpmux89a92WJb2Gyb2cxmKNt07EqsgG67k5Kv5bNzdxtTZJalsdSxq+jSbkx5\nQdsLzAvcofCSrhNdyXEyS3OLw2y3ck/HpOpGT3q/GZGYrfov2rVFZIl+scDP6oFpvUW7yib+e3e/\neNDG7t4xsx8Cd61t0+QnC/wMogv574C/Bd5mZkcCnyEC4zMWaAmH6BrvwH3MbNjspPssvsogJdHb\nfjF5WkRERCQUDJ6x6yIA2u32itVmqRRojyEaob3Xnlpl0q7mwTbSdFQtNk1PYTaF06L0nKLMcIyy\ntCijLGtLSmjmNhu4Vv9F8WkOrJLIKlaWUMQY7ypg782gXVuyCHwtjS8vyypn2hT4NM4mplu7sHXz\nnuy603XYfZd9mfZppoqcqSKnO3MNmU9RFtBpd2YfBLhlZHmLlpe0zMkzw7I86p1luBkdK5nyklYZ\nida8LOh2Y77tPPfUsh1d6mczjafEb54SvlEalBmUOeYtjBYZU7R0kyqyZIO6Uif1wLT+C1dl7r5g\niF2cV9tmkEsXKsDdu2Z2f+CTwM2J4PnQ9ONtZvZN4CPAx929P5iuot5RusBsHmHdBhcubXMRERFp\ndOGF6+dvrALtpaoC7irIxsnMYkzxVIvp6WmcKUqP7uOFpxbtEkocL0rKoqAsopt3nlktwC5nlxIw\nd6y06PZdgJvjVkZX9CrIrgXbkWstuoVnGWRmlJ4yjZcGtDA2gW9lempXtm7eg1122ptr7bovrW6O\ntZ2sXTJjjnmLsuu02+1oWcdwM7K8xTROmWVM5y1yz6Jrd94CyyLIToF4pygoiw7dDlgZQXariIcO\naTg2VgXZ1VK18KdAOytzMm+Re4tcp6/IapvU+I1Bj697O3L/hZndGnhAWv6emEJsM3B4Wp5jZvf9\n/+y9a6xsW3bf9RtjzrVW1d7nnHvO7dO3uy8dg3lYQIyIcZwoJI4RQZYlEvOIiMQjshUEjWQQyAFC\nCGA7H2N/QCJfnIBMJ0YkliOMg4EQ83BCgDiOjUMwihLxiNtpku52uvveu3etNeccgw9jrqo6t++j\nH+c+jOevNW/tXbVq1aq16+ze//Uf4z/c/dNXT90vEPxXwL/5nI73TRERnj59+rbbpd7+MxgMBoPB\nIHh28tIb8/TpUz796U//khDcQ6l8BTxT4tzTvqNz29Ee3jVNE80mastRZm2KWzjUZo7VSOS22sJ9\nThlNgqheXG1JaA9I643c1/ZvjNDqAWJCuMXhZkcPeRIhRQs1bRe1IggZ4YDILcscQvvR7Us8fvgy\nujmmG24bTTfUc3e0a8z6Jv4y1pyxpHjOiDUgoQhJFFcJQexGwoANt5jXLe59bje0tov/OHa/EtoR\nQC7ging42uoTiZkk4+M7GLxH/CLxa+ZDX8S2exn22/VHvy0eoxR+tC9E5EPAtwDfAXw98A8Qaei/\n9eppnyESy2d3/7mv9BjejpdffplPfOIT7/TLDAaDwWDwy5aPfvSj7/UhfFEMpfIG7J3Zr7dq9tzx\nfcYztAgXs4a1jdZWrK7UtmFeI9ArIrORlENEW8a9XPqvu1B222d8RTeykBAxRBoqgqqiaqhGUHzS\nFOnbSUHoad0RLCZiXaDb1WgsidTzJlTAm5B0YUoPmPITbg9PuT2+yO3hMbeHF3AKdVOq2rnHe3/f\nEW8GNYrgwSvuBfNEbVAalOY0h5MXileax7my7vzvFxpqa5QaZeYetjtm3kehpXDiNdzrWBNZZrIu\nTDrmaA8G7xF/kQgM+2oR+cCb9Wn30VpfR/w6/YvP+yDc/a8DHxeR/wT4Xwih/ZtFZHH3tW/2M0RC\n+q8Wkezu9Xkfx2AwGAwGg8HrGeO93oI3G+6lAirdXbaCt43WNlrdKPVEbRvNa4jxpIgmJCW0i21U\nEZfot74ypqPZOoq+VTQEt+xrF9t9pVhJE6ox8kr37SSkenwdYjwnZcqJlMIhh0TShXl6wM3hCQ9u\nnvLg+AFuj0+4ObzAYX7AlA8kycge4ObE2DF3KkbFKN7YvLJ65WSFO9u4ayuv1pW7unJqG5sVqjWa\n2WVGeE8+b9UopVFrjDJrzWgWveRKImkmSUYlozKRZCbpQtYDUzoypeN79fEYDH458+P9VnjzsV0A\n/xTwwuue89zp4vkn+rcZeHz18I/22xd462MdDAaDwWAweG4Mof0mvOEkbeE8VkpxxBtYxaxgbaW1\nlVpXalsxa7g4ooKcxfaEakYk+vKi97ivXiItrrF2kf2M2L6I7HCz03mpXoWn7YIbIamQVZhyIud9\n24R0ob08I7Rf5PbwhNsutOe8kDQjcnG0zQlH253ixkZj9cbJC/deuLfCXdvO69QKxa4dbdunZNO6\no11ro9RdbPfkdXZHO8eSjBJie3e0sx7I+hVmFg0Ggy+HHwH+GvGr8feIyBckiovIrwC+t397x1uP\nAXtLROQ3iMjf8RaPT8A39W9f5dk0so8DP9+P9ftE5Bvf5rV+vYj8xi/3WAeDwWAwGAxglI6/MXtv\nsDsuu+jex2J1mdhFtlsFK1E6Xk/Uck+p95S6UmuhttaFY/RFIxql2NLndHeLXCyBRMCYC9h57nQI\nWxeHJCG2XdApkfryfTxY8/P4b9G+JJLARRIqmSSJSROkxGF6xHF+gdvlCbfLCxymG+Y0k0RRN7CG\nd8ferGJuNMBEcAUXxbLSMpDA1WjaS927Q99oNDeatIh0ExCVPsvbKdUwKi4JFCT1s+1xjlRjBrif\nHfBKKZUtlagKGAwG7zp9bNe/CPxxwin+MyLyvcB/S8Q3/HrgdxGJ3w78zi9ihvVb8ZuAf1dE/jTw\nY8Q4sE8BR+BrgH+JKBt34D+8Th53901Efhvw3wMPgP9ORP4IcbHg/yIuOH+E6PH+J4kxZP8yMTps\nMBgMBoPB4MtiCO3X08X0eVyX7/3Ye3922M9u7SyysRKhYXUNoV3uqa2L7bbRWo0xVYRQpotp7za5\nqCLZo0RbI0TMJHqhGyFSm9hZlKsoOqWz2DY3rCox6BpIe4J3bA8KnhGfyLIgaSExc5yecDuHyL6Z\nH7LkAwlBWohrbydavaO1e5pt8Tp9PrYniU9PVnwKse05ZmKLGLqXmmOY9F72fmFBVXAXmjmtNEpr\nvZ+dKImXfT55hLk5hnmj1o1NomPcrVHzL505eoPB/99w9/9SRL6dCB97APzevs6bEL+R/h13/wPP\n4SWFSBr/pjd4bC+U+RHg336DY/2zIvIPAT8E/Argn+3rzfbz+edwvIPBYDAYDH4ZM4T2F3DlZtsl\nyTvGeJ1rvHFr0CpYLL92tLc7ajtRWhfa3Q323cmW7sTuDd89HVwQSIrFNzSx3gvdMPFwd7sw13l3\ntDPiDZMeWuYeAjuBeBfxokBCfCLJkaQ3iNxyMz/hZn7M7fyYm/kRS1rIInHxoK1YPWH1nlbvMdsw\nbzHWSzU+OZPAJPis+CRYpge4yVWgnOEebraLd0dbwbpD3QwjSuw1KSkbKSmyJ6drWPRmjUpBHLyF\ns73pGI0zGHwF7KLyy97O3f+wiPwE8K8B3wx8FeEQ/zXC3f797v6/P4dj/V7gZ4F/hAhXe5nLfOz/\nF/hJ4OPu/l+/6Ztw/0kR+buAbyfGg30d8JRo3vkU8H8Qfd5/zN3/8nM45sFgMBgMBr+MGUJ7p/8Z\nuZcoW2shps8jtXrCNvG9e6PZirUt+rPrSishtGu5p7ZTXz2B3FuMALN9fBV9XNezUWvWk8itQTWn\nuVG9/50r0XMdKeZXyx3a3kAuoDFvW9R7f3UCy8BMkiM5PSTJI26mxxznRxznhxynI7Nm1AwvJ1q5\no5VXKdsrlO1Vaj1RrUY5uxKO9qQwKzaFu+3Z+3naz9t+cSJGn5lfSsfFBXOnNqNaI2Ult0xt+yzy\nyw/G3GheI51dPMrHtw2VETEwGHw5uPv3AN/zRWz3E1zmUL/ZNn8V+M4v8zi+qH/E7n5HuNU/8uW8\nztV+CvAH+xoMBoPBYDB4xxhC+3WYGbVs1FJopeAW7rV7C0Er4XC7G2YxZ9p8o6z31HLC6gmvuxu8\n0toJs1N3hDdq3ailUkujFcNdMBPi780Q07vhXZtRG1i7imbrpey1Wg9IsxDuRozE8kg0F7fwaSSB\nZ9AJ0YWcb1jSQ+b8mGN+yDEdWdLEJIK0DfNCaXesr/0id69+htde+TSvvfqL3N+/wlZOVG80BFPB\nU4hrT32p42Z9XJn18ns7i21zO/dpn9vUz0Q5OS6YS+yHRnMnnav4hebW3x+jR3swGAwGg8FgMBi8\nLxlC+3VYM2oprKcTZV0xq3irWKuXadICguFecN9wL2zdyW4lZmlbXbFywsqJZuvZ/a6tUEql1kYt\n3h3sLkyRKLUmxHS4wIQIFz+PATP33f+Ovm53zARMEU+I21mIRshahjZDWsjphlkfcjM95mZ6yCEf\nWXQiA9IKVgubF06vfYb7LrRfffUz3J9eYasnqlsEtamEyM4huC2Bpz62qwv/ENsWFQGvE9ii+xdx\n3vfa1LjwQAh0a716gPO7xaJ03Jv10v7BYDAYDAaDwWAweH8xhPbrMDNKF9rr6R6rhVZDgAp+DuiK\nYVflvGq7p9ZwtK2FwLbtRCsnWjvR9vFfbaPWLrSrnd1ssxDQdhbYjqiFg94FqUhoVtE+Xkx2+el9\nDreigLlE6bgZeEI8AxPYQppuWLrQPk4POeQbFp2YBLwWrL5Gq6+xvvaL3L/66Wcc7bWsVDNcFFeP\npPFele7JMRWaGA3HmmHWRbaHYJakaJI+fozLDDVgT4dzNIR6i3241XiIHiBXjVoqrVSs2hf+AAeD\nwWAwGAwGg8HgPWYI7Y6799sQiGaNVutZZLe6hUssPZAMQyggIbRbjRFfVjesbLRtpW4n2hoCvJaV\nWk+UeqKUlVIqpTasCc1CcDejl1eHK6zJ0eykxFmYingI7my0BC15nxRm537yVj0y2hp9xFaEk1mO\nKnLmCCinNrysNL2P0LbtFer6Cm17lbtXPsv9K5/n/tVXWO/uKNtKbbWnjsexuHqM+VLHe294OO+9\nTNxad7VDaCuOoyBxUcHdu3MPzYxSGqIFVe9OuCOuGNrfg/ZZ3oq50IahPRgMBoPBYDAYDN6HDKHd\nuQjtvf+6XVarWK0AqMRcZxEnxsU2hH2b0oX2Gm72ek893VG2E2VbL6ts1K1QSqM2oTWhVjCD1vuY\nzZ08QZ6ENEEEbPs5tNzUUHWqWne2odvaYSA3j95uIUaFidMmw3LD5wql4GmjiVI8RnaV0+fP6+7z\nn+X+1Vc4vfYa6/09Zdtozc5jyZ4R23sJuNJd6t6bbZfz6NZIpEgetyuh3cPfamtQSvRkq6Aa51pV\nME+oJIzUnW0/jwMbDAaDwWAwGAwGg/cbQ2h3zkLbvAeg9RC01mit0nqPtqPd0e7p2jREGlZDjHsp\nWFlp10L7dGI7rZR1ZVu7yF4LZWvUGiK7FGKudC8bN4xpUaZFmWcJoX3laFeJZO49ofuSXU6EoLlE\na7SAKEhybDZsNnxreKmYrlR3kjUMZ737HNvd59juP8fdK5/j/pVdaJ8oW6VZC0dZAPUrsc1ZZPve\nYX4ltFurIbbFSZIRjXT1i6tNjPmyitQY75VzX5KwLrLF4yTEPG5/tvR8MBgMBoPBYDAYDN4nDKHd\naa2eb/dlfT0bhqbhaOMIFaiIVKwUrGzUstLWlbqu1PsT5f6eetr6KrS10lajbY5tQitO3aCWnjLu\nRvMQ3H4ADqCLkrKcE8/DTfdIQY9ibGSf7MVuOROCOzmSDEmN1hq2FOwQrnsDStugZLDGevc51tc+\nx3rXRfbdHdv9StkKpVqUuEceGRCjtvYYs+hZ74npsjvvl8ftnBZuNJOzkx0IZjEKzJuRLPVpa3Gu\n97p5Dys/etp7GflgMBgMBoPBYDAYvN8YQruzrfcAlO0+xHLZaHWjtQ1rBbfSZ21rr5s2nIJQcApt\nW6NEfD2xnk5s9yfWu5XtbqNuDduAqmjLqAnZIhF8T8/25lgVaH3EmMUP55xKnvxKuEb/ssVML5xe\nRS0h/89C2wVJDZsqlgvOiq3htLf1Nba2UUXYRPBWWe9eZb37POvdK9zf3bGuG7UZzaC5U90pez64\nRwm4GNC8H0UcV1LQSdCw3nv5ejjrzi60w9VGFE2OXc0Xb+ZI9Z6m7iSM4kaixUUFJ8aYDUt7MBgM\nBoPBYDAYvA8ZQrtTtl1on6jb2sd0RRDa7mrv+lIQxB1hwynghbZt1NPKdrpnu79nvT+x3a2sdxte\nwatDTUgVUlPEErKHhTXDmqEVqE6rTm3hl08ekl5TjBTbhfY+OsvNzq6vi3RHm3MjdcsNnSo+K8aG\nrSdsvaOtC15TiHx3Wi1sd6+x3b/Gevcq93f3bFvpyejQPDrSK7szHY66m3fH2WMMuDuqguRIF3fZ\ni+xDFFsoafYBZTE3XM9jvMwNaSHqzZzUDHVDrKEuJBGyQlZFR4/2YDAYDAaDwWAweB8yhHbn4mif\nqOUUbnbdsFrw1ldvDQ4Ra11kb0h3tOu6did7F9nhaIt1Ye2KNkWaI81QS7g1rMWq1WFzrBi1Rqq2\n9R5mUgjiENtdnPce6L2QXWNAdc9EC8Etk2CzYA1cFDt1R3ubQzi31md6F7b7O7a7e7b7cLPXrVCr\n03rCd3WndomsDro72l1k0/PJkkJSwVJks5sL6nKer23Weq97QkQR0XC3reF+mcUtrb+eGWKCWiMn\nxSdFsqI6HO3BYDAYDAaDwWDw/mMI7c5rr34OgLKe2O7v2e7uKesaKeItBLf0Ou4oXQ5/Fw9Hezu9\nynp/x3Z/om4VDLLMyARKRj2jZLw5batYKjQptLZFj7f13nBrVGsxrxpFVJmSMmdQN5QQrW2PYuvl\n1tLvj+MLh9ldovbcLERsrVhZqes9232OSeC1UVuLPuzTiW09UbaNrVZqs56rpqAatd8WvdHNDKsg\nEvOxRRVJChr92WcR3A+hmdMa1Ba37oSbDSDhXscsccVMevVABNOFnW7QGodlIutEXhYOy/zuf1AG\ng8FgMBgMBoPB4G0YQrvz2qufBaBuG+X+RDmFQ21nZ3tD3MAccYuvu8jGK9vahfZppZaKmJJ1YZpn\nkkwkmUk64dWp6USRU3R4lxpzsa3SrFAtQseqGS7h2uacmLMQ2dtRSl2lRc90L/0GOKeDeYhsOfdS\nh9Cm9cC29Z5yn2g4pTa22mKu97pR1zVuzWnmGIKLhtj2ENsuUTLupU+3tihtTz0MTSV0uV+Vi9fm\n1Aq1eh9l1lPedyv8jGAWDrw1C1FewWrDK4glbpfElI8cDzfvzodjMBgMBoPBYDAYDL4EhtDu3J2F\ndqGu23lZ3bAagjvUXxfZFgpQroT2tt6xrSesVqaUmXQi54msMzkt5LTgzdg0hyh1Y81rOLpeqa3Q\nmlMtepSd6HeecmKehIyQPNbmjlgCi1A030PTehW3uOP9NcQEaUCTcLS3xHYSqjtbNbba2Ert771Q\ntxIuNBpCWxUn0r5BMell69UQhzTl3ieuoOFSp7T3YHdHuzm1OluBUmLGdxxrHLRqXFRQDaFd+3at\nNFpx2ua04ky6YJbI6cDx8OA9/MQMBoPBYDAYDAaDwRszhHbndHoNAK8t3FOrqPQmZHGkB2+dXWJi\nzjYe2+4zt62G661JyTqzTAdymkk6k3TBqFRdwxkmnN1mRt1Hiu27J0RnSolpykyTkt3INHQX1hau\nL7b3cu9Plr6PcLZ9n1fdw9O894Sbe+8Pj15v633fMc9bMAlH2wiLWjWTVRCpNAXEEBWyKjllpjwx\nZcip92gb4Ak3ibLxCrV4zAyv/Zi6I5+S9/cbbnYpfeTZRgjt4rRitKqozCzzA26OL7z7H5TBYDAY\nDAaDwWAweBuG0O7UsgIRdqYKeU7opHhTsARtir5sN8QMrNLqSqtKK4LbitVE0Uj7ThrCc55mRDKK\n9nFVRu2l2utaWEthK5W1NEozzCKFOyclp0TOiZwzOSvJjYRGAncixGly3BuOgRnGpZTcHdQvxdne\nXWdRRTWRHHJSjBYRZy2cZ1Hv4h2qQROBfixLnmhaYglIFuZ5Zl4W5nmO0V7qqEJFwRvW4hy1Aq3G\n7PBWw7mOcDRoupecezjaVagVWgWrirW4X5iY8g2H5SEPbp68dx+YwWAwGAwGg8FgMHgThtDulC60\nJ02klJlTJosgnhDPiLdzb7a44a1QNqVuQkmOtYm6JVLvYU4pkfPEPM+4aV/QWgjtrVTWrbBulbVU\nttp6+FhChO4SJ6b0eqFtiCnJQhSnFEFimF/GZ3OZSd0f2rPCQ2hLQjUBYKJkidLwVh3tUeJO9GhX\ni1HYSCLlxLIoTZWmQhPQCZZlZlkOLIfDpX4do4ggXvCWQmhXaAWsdsFdBWvhYEsX2SLhhLcGrT/m\nTfAWs8ZV5t6f/Yjbm8fv1cdlMBgMBoPBYDAYDN6UIbQ7u6OdpxmdEvOUWKaMYmgPIRO3GKTVhfZ6\ngk0NpVG3zJYSqhrznzX1cuo5BGYv37ZdaG+V01pCbJfK1h1tV0VkL8dWUs5noa1uJDdEDW2OJkNT\nzJ1GHMeihHzXuntyN1diWyQSwjWhIiRxXCLhvCZDNfbvLfZVzWgCkMhpIs8TLQkm0NTQSTgcFw6H\nA4fDTZSfN6PVhgL4GkK7Snend1c73Op9iUTJu0RwOq073WZCTDSLZm9hIqcjh1E6PhgMBoPBYDAY\nDN6nDKG94xUAIaHipBRl1UkiTVslxmeF0BZMo1+4ZSWlKHuWfVwV4H3Gda2VVv28tm1l2zbWbeO0\nbaylsRVja+Eeq3IOBUt9aS/3lksNOKIKmkBbf5KELSxyFteOd0fbz73f7jHXOt6sICLn19OU0eRI\ncmgNo8VULRESCdFMShMpg2fwLORZORxvOR5vORweUEuLpY2yKVMqJF1Jmuhd6bh5lIK3XWg7oDEj\nXPoxmtBMdnP8/N5ba5RaWbeNdd3e5Q/JYDAYDAaDwWAwGLw9Q2jvWNu/CKGtwpSEpEoSJ4mgrj0a\nTGjVaCVR00WoisBuybo5rTVarbRq1GI9CCxEdgjtwqmXjZfmtBhaTRbISc5J3KLRty0C4hKiU7v4\nVkWuRTYSJeRdXBuX8vFLGXm424JGULhDSor2nm9NDhr7aeY0EZSESianGZkUZkHmxLQkjsdbjseH\nHI8PKWtl08pGZcswpZWcJlQySkN6Pbsb54sPtcSZFzSceY+QuH0EuBDvWYDaGrUUtq1wOq3vwQdl\nMBgMBoPBYDAYDN6aIbR3dkfbGypOVshZyeIkhSx7l7OE0MaouZd3a3eGrxxtc8O60A6X1yilUbaV\nrXSRvfYe7RqOtjso9NLxi5ut0sW2a5RYO4i27mpH33XMt6aL7BDYUTLuXyiyPQS5iKCEQFcu4Wqa\nCGcZp3mjIWQU0YmcZ3RS0qLokpiPEzfHW25uHnA8PmRNlSQF9cI6OTndk3UmSerH3tPPW7jarSeR\n7+dNIJLO7XLM+zlRLo72NhztwWAwGAwGg8Fg8D5lCO0zBtDFtJ9vVSBJL+fuPrC44BrCT2BXr+dx\nVdYM00aVSmGjlejLbrVRa+mrUpr15ZQWO5t6mXpOu9Duh9dFsp2/jn7rfXY1Et9fVZdj/fj28vFm\n4Zqbh6NsfWTZ2YV3OY/zak4cV4UqTqowNXDrTjipn5sJlQlhRglBnTVhKTOlStYZldyfo6gYiqLa\nZ20nkEmgPx6jyeTsxLv3cy9CEmWeMypCa41tG472YDB4f/HJT36Sj370o3z4wx/mp37qp97rwxkM\nBoPBYPAeMYR25+Kohro7z8vee6I9HtvX/pgbVzOtQ2S3ZlSpqCvqRJl5jfublSgpN6Puqd4Gpfdn\nQwjLqfeHC3sZuCP9uNydtrvWwqVcnPjaYtgX1o+7uaMG1XaxHUtb1GO7eBfiRm3ehb9TqkdZO0Kq\nxtTfh1SgglTp482UloWaBKsJ6ePNshZUJ4QELr0WQFF1clIUZdKE2UVk75cz9rJ3EFLSKOFX5fbB\nkWlOgFHKcLQHg/cjIvJtwA8Qv5a+2t3/6nt8SO8aZsYv/MIvvNeHMRgMBoPB4D1mCO1OBHVxFrOc\nR3lx7g+OILTLdnu8t9tlWXOsNpoL1WI7q3ZO47ZWaFYjzduc6k5xp5qTuoxPV6Xj53Jri5Fbu8a3\nc+DZ7mTHre3C20Nsi+/zsJ3UBba16IHe35hLjAjbBXg9i2xjLUZFmIpRayzpItuaYFVpValFqSq4\nKeKZrEJOhSQzugtt197/7pAFSVOkqpGhF+VzrieIiweIxDzxvm5vj0xTwr2dR7INBoPBYDAYDAaD\nwfuJIbR3dgHdx3ddhHQX2X7e8Cy2z462hVC15pgZZkYjxltJF8khsh2zill3tP3K0Q5VGf3SqkxZ\nSbqXflsEpfXj3HuwDS4haFcztPfHdkHezEG7kG7hXLdmOPtz4/5ajVIbW2ls+20xKjDXRilGK4Zk\nIAFZ0CRUhSJxV4SmpRhvphtJLo62aiInxacMUyLrgZQWks68Xmjv7wlVppyYcianzO3xwIPbW+Zl\nRtNehzAYDAaDwWAwGAwG7x+G0O64+fl2F88htK8ULNBTwsCuRHbbt+eqRBoi1gvc940b7hXzFmK7\ndYHu0RGOKCmFqFxyJongZtRScOul1RLH0Cwc7V1onxPHo7j84nr3/nG6U11bi1C2UpFmZ2FepXBE\n/AAAIABJREFUqnF3X3jtrvDqXdzer5W1VEyEbWvMW2Xb4vvqkAxqabStUddCnQvzrCxTJs8TSefe\nvz0hnjnMMzeHGU0T83zL7c1Dbo6POB5u4dzxfuk3j7s03OwcjvZhXrhZFo7LwmGe3p0Px2AwGAwG\ng8FgMBh8CQyhvdMd47PI7to4aq+jWfrsn+4a3DzSsVvcunOW2CF59x03oOF0oW2192hb77UWTARU\n0ZSZpsw8ZZKG0C7FaS2c7n3U11lEX4ns/QjPjrd7VIebg1i8ZrUIYisVRMLx9ujFvusC+9XXNu5O\njdPaWEvDVZlLiOx5LTRXtAlShVIadauUuVKmwu3NRL5VZM4Xoe0ZmFjmW47HBxxuHvLo4WOePH7K\nk8dPefToyfmsnYX2PrJMo3Q8aSKlxNwvRMwpMaX0bnwyBoPBYDAYDAaDweBLYgjtzrWjTRfN1xHe\nl9Lx/phxFtmtOd66MPeLn31W7zSc+oyj3Tx6tPdQM0NBEro72lMGDPcYDSYqaIrHRfeD6Jxdbfao\ntt6j7cgeUy4eIW2t9fLwGGfWLAT4qdRwtF/bePW1jfvNuC/OVhzXxFa6o71WxBTp9eJpbdSpUqbK\nNhWSGIdZECaSTigXR3uZb3n08EUeP/kAT59+mA9/6CN86KWX+eDTl9gFtvT3cy20IwgtoUlJPTZN\n/XKWB4PBYDAYDAaDweD9xBDaHSstyqhzw2rDa8OqxczpRHeMrwLQKt2ovgyoFpEYRaWKqEeQmcTc\naDOjWaO2vmrMgxZR5nlBsnB7mHn48MCDB0cePFgodaXUjVo3WrNujvt5HFeIf6eUyrZFP3WpRm3W\ny8S7z+2GmlNdyKUxlcr0jND23o/dWEvlVCqlRTm7JkGmiTxNpGki5ykuG1SnVUNTxVrDo36eVsFd\nUcnktDDPRw6HW26Pj3jh0Yu8+OJLPH36IT740od5qa+nH/jg1U+ilw7oXj7e55Pv88T3DLfz+x8M\nBu82IvIY+LeAfwz4W4FXgL8AfL+7//AXuY8F+BeAfxz4lcCLwGf7fv5T4OPu3t6pfYjI/w18FfAf\nu/vvEJGvB/4V4DcCLwOzu+sbPXcwGAwGg8Hg7RhCu9O68LTUaFOjlYalhpliprh1/7Q73F4cr+Fk\nY7s2FDSFONWemO2Es2zWBXCtlC6yS23olDksB26nAw9vFp48OvDCCwsPH8zcnxKcoLZKq5VG6254\nd9i7q117Wfe21Sjlbr0svQeeiTkgTA651LPYRqT3iIco36qxVWcrRiNBSuRpIs8z8+HIfDgwHw6s\ntVJKYS0VUQFvqBg5hciGhOpEzs6y3HB7fMijhyeePH7K0w98iA+99DJPP/ghPvCBD/L48RMePnoU\np3avIDj3Z3dXG6JXnv19D6E9GLxXiMjfA/w48BEu6RUL8A8Dv0lEfgD4U2+zj78f+M8JoXv9L/np\nvh/gYyLyW9z9b7xD+zinb4jIx4D/gMh03LE3eM5gMBgMBoPBF8UQ2p22henRUsVKw7ZdaBOu9nmm\n9u5ox6L1de1oJ0Wu/kbbk7/Dxd5FdqW0ynE5cjweON4+5PGDGx4/Wnjh4cLDBxMolFbhdE+1fb51\nCPY9DR13Wo1ws1Ij6Ow8Osxi5jaEI18M8hxu9lxq7/WOMvNSnVItRnpVg5RJKZOmmeV4iLUcmJcD\nxU/YWti2hrmj0sjJaRO4CUJCJJOzcJhvuL15yLpVnrzwAZ5+4CVeeullnn7wJZ48ecILT57w8NEL\n5/N7rojfxTbRR34JqNtHq3FJfx8MBu8KIvIQ+BPAh4nfiH8E+EPA3wC+BvhO4NuBr32LffydwP8A\nPAI+B/x+4M8BPw98APhW4GPANwA/IiLf+HpX+nns44pfA/x24P8Bvg/488T/N37jF3NOBoPBYDAY\nDN6IIbQ7rZS4zRNWClZrlJB7Lxs/i+0oFffiUB3vpeP7rO0Q2nJ2Xt0jWby2KOsufRZ1beE455y5\nuTny+PELPHl0y+MHMw9vZ25vElsp5NMJ0YxTadbCca6G9B5lAVqF0qKcuzRoSCyRcNN7cFozY2tO\nsRgrJrpfN5CY+91nf4fOVjRn5sPCcjwwHxbmw8K0zKRaQTTc8L52QS+9zDulDJJYlgM3x1tKrTx6\n9JgXHr3Ik8cf4PELL/Lw4SNubx5wOB7PInrfz7WbHbO/jdYupfsyhPZg8F7w7wEfJUT273b333f1\n2M+IyA8DPwZ881vs4+PAC4Sg/WZ3/5uve/zHReTH+n5+LSHc/6N3YB87fy/ws8A3ufvnr+7/n9/i\nPQwGg8FgMBi8JaP/rNPKPa3cY+WE1Q1vJRSstavbrkKr4dXwZtCsi0SLGLI+pWqfCmYeTyu9JHst\nTm1gLqCJeV54cHvLi49f4MUnj3n48BHH21vyckNejuT5SJoOaF6QtIDOuM5IWtB8IE03pHxEdMFl\nxslIWkjLken2AfnmFl2O+DTT8oRPMzLNpHkhTTOaZ0gT6ISkDDmWzhPTMrMcF5abhfkwMy0TecpM\nc2ZeJpbDxOGQWZbMsiTmWZgmZZqUlJScE/M8cTgsHI8HDsvMMsc+UkpIL61vZjEr3Nt5DvlFxNNF\nttFaizL6fVmswWDwziMiE/A7iF9tf+F1IhuA7hr/80B5k338BuDX9X182xsI5H0/fwL4YeJa4rc/\n731c767v5zteJ7IHg8FgMBgMviKGo92p5R6ANiWsLdA2xOYIPBOgaR/5ZdA8hHa1UNLn2V49AE2B\ndmkArBal2evmbFu4zubh/M7LzIMHt7z44mOePHrAzaQcZ2FSmJYTeb7vQtuRViAp3hpoQjWRNeEU\npF36wTVP6Dyhc6a0Rts2vJRwi/OETDM6zwiCN5DmkGoX2w1Si+CzQwjtw83CcgyhnebEVDPLIVPa\nhLtwOCTmJTHPyjSHwM5ZEdUutGeqHTgcFuZ5YsoZVY0e9u607051n1kGgLjGxQrbE9Nrd7MvE8MH\ng8G7xtcDT4hfax9/s43c/RdE5L8B/tE3ePhb++1fcvefe5vX+1PAbwO+QUTU3fd+nOexj2t+3t3/\np7fZz2AwGAwGg8GXxBDanbYL7ZrxesSt4BaBYTQB9Jz67V1ox9dXIhH6lCrHxDHi7tq60C7GVoxa\noXmM85rnJYT24xd48sLDENjqJCxE9tnRNqQpVMW1ISkjaSKlTPMNUcel4uKQFtKykI8LXitFE4aA\nNbwL7TQtIbTFMBxUIBmkhuSGTt3Rvlk4HBfmw8S05HC0l8zcJg5daIejrcxLONp5UlJOpCuhbX7g\ncJiZ54mcrx3t6GGPJuyL0BaPCwcg4Whbo7V2rhwYQnsweNf5+66+/nNvs+1P8sZC+1f3279bRL7Y\nsLGJSBP/9HPcx44TCeXPnW3b+Omf/uk3ffwjH/kIH/nIR96Jlx4MBoPB4Jckn/zkJ/nkJz/5tttt\n2/YuHM1XzhDaHZ0ibDZNik6KZkWzRAatgifvc6r7amDquIaodiVEbk8bV1Wk9z+rJByJMmgXJGWm\nObEoLDe3HG9uOd4+4HD7IAQ2hnhF54U0LaR5YVqMWTJNZ2Q2lmk+r7JuTPNEmhLrtjLdHplvj0y3\nR9a6Md29RrrLtFo43C4sh4lpTliLQLfaKrXFfG/R6M/O3Z2eZmGahZwd1QYCKRnzDO4x1fpwSBwO\nymER5tmZspG0IWqIVKASlaT7TDTj3NQu0dcdZQDn2HEg5pF7T30TolrAsfPXMkKBB4N3kxevvn7D\nJPAr/vqb3P8SfMlXyBy4ec77uOYNS8+/Uj71qU/x9V//9W/6+Hd913fx3d/93e/ESw8Gg8Fg8EuS\n7//+7+d7vud73uvDeG4Mod3JS5yKNGd0TuisyKyIaAjtvfeavjJ4AtMQ3PY64SiikBTEUQ1Bah4+\nrKbEJIrPmcPN7XktxxvUG+INrJLmw3nl5szZ8NnIDY7L4bzKupKXiTRlTuuJw8Mbloc3HB7ecr+d\nmA6ZNCtlW7k5ziyHiTwnylpxGrUVSi2YN1Anz4k8KbmL7Gl2Ug7hDI2UGssMKWVUEsuSWBZlmYVl\ndnJuJC24CMIGbJivuBeceM2LYN6FdkekJ6rLuXtSxGMuuV0EtogNR3sweO/4cv/x7eOzfhb4576E\n5/3Cc97HNW85q/tLRUR4+vQpT58+5Qd/8AffdLvhZg8Gg8Fg8Cwf+9jH+NZv/da33e5bvuVb+NSn\nPvUuHNFXxhDanTTH3246J9KcQmxPGiJQBBfHuyB06ZXOCSyFq+3qMep5F9oqqEsf+ZUAieegaJqY\npglNSwjsmwcst7csN7c9eK3irZDmBe0rGzA5aiFRbw833B5jbetKmhIpK/Np5vjCA46PHnDz6AF3\n2z1pViTBekoc58wyZ6ZJiZZn60J7wzySyNP0Ojd7AlWL2eA4KTlJhZlESpl5ziyTMs8XR1tTjZ5w\nKbhvuG+YF9xDaJ+Hj4sgeo4/j7R2efZnIwpiINq3EUPFoh9+MBi8W1w7vx8C/spbbPuhN7n/M8S/\n8AdfRH/1m/E89vGO8fLLL/OJT3zivT6MwWAwGAx+yfHFtlXN8/wuHM1XzhDancPtQwCW4y35cETn\nBZlmQHFRHO1C2rsmbPikkBVy9HFLEqR1cd1Ln7X/1/d50ECaMjItzMsNh9vjOdU7LxPeIqDMqqHL\nzHRcWG6PeFYMoTkgyu3xyM3hhtvjkbJmJDUkG/OaOT56wPHRLcdHD5jWFEFnqXK6hxsVliQkFWQj\nerSt0ayBKCkrE8p8SCyHzPGYON6kcJK7uFXV88zwlDLTlJhzYpqEZXHyVFEtmDmqBU2VlIyUDE2O\nahfP2qd4ARd1/XrxHDUE8fqxmYrQs9QGg8G7x/929fU3AH/mLbb9hje5/2eAfxD420XkJXd/uxL0\nd2ofg8FgMBgMBu8oQ2h3Hj4OA+YwH1gON+TDDdIDw+hCGXfcjAiudaQmtCa0JZIn1BSpvdy815jH\niKp9fJXh4uEY38zk2wOHBxPTMaELyNxD1tRwMdJRWOrMDUemknGJ0nNEOMwLyzyxzIk0ZywtyHTk\nsCnLg4XldmK+VXTOmCxIuuF0gMmMyRraWjjE4rAL6ARZFVLmeJy4fTDz4NHC7e0SIlu9O8mCSJ8Z\nromcpkgaT8q8ODlXRFZEouR8XqB5Yunp5JFMrqiG8x/SOtzqmFW+/1TiwkT8R84l+SqgCqrD0R4M\n3kX+POFqPwZ+O/Dvv9FGIvK38OZztH8U+A7iytq/CvyeL+M4nsc+BoPBYDAYDN5RhtDuPHz8YQDm\nPHGYZvI0o2mKOnEi1AwaMSa24WrQMtIy2jLaUghvTZjU/hwHc8z7TGg3XIU0J+bbmeWFI8vtHEJ7\nFphC9LqGkM8HZfEJS0damyMZvJdaTykxpcyUlFoSMs3kw5FaMvk4Mx1iv6nEXO08G6ejIusK6wpr\nO/c+Iw7qpCTIpKQpcbiZuHkw8eDRwoMHB1QMFUdl76veHWklpUzShKowT7vQPiEKOdcITpMutOc+\nZzsLmuTSn+1xvtzsLLS9N2nv6eMi2gU+pCRDaA8G7yLuvonIDwDfCfwqEfnX3f37rrcRkQT8QSLl\n+4328SdF5CeBXwP8GyLyM+7+w2/2miLytcDf5u7/xfPcx2AwGAwGg8E7zRDanYePXwIgS8ymzhrO\ntHRnOqZ3NXxPz9aG1Iy0cLS1JnTTXlatMdHawdtFZDe3KM+eleVm4vbRgcODmXxM6OLIZGeH2TGS\nK0uakOWIu8W+UzjBiqBEVnduKWZeF2htikC3KQLQcs2RWn6Aw0mpr0KlUep65Wh7d58hzQpL5niT\nub2defBw4dGj5SyyVfwcYrb3o6skRFJ3uI2U6rkkPKVwtCUnloMyLZFonrOGWD7rbO9C26+Kx3uZ\nfv9Oepr75XXepQ/HYDDY+b3EXOqPAr9PRL4O+ENECvnXAL+TmLf9U7x5+fg/A/xZIsX8h0TkjwN/\nFPjLRDDZS8DXEfOyfy3wfcDrRfLz2MdgMBgMBoPBO8YQ2p3KCQjnuplSXVGRSLruehQqLg2n0trK\nyU4U26heqVZp1mJUVqm00mhbrG3bqN4ggSYlzUJeYDo4kjcad5zq5/FtwlrBWsVaoXqhUWhaoqxa\nw9V1icFWu9A2bViq4BXRBtnwVDFJkBqaK9kNM8cnsEyEtWUh5USecoStzRPMCeYuhhdlWoQ8C0nC\nUFd6b3QPIhPZ+6ctiuy1ISpdwAuanUkFMWXqaeYp7W42vTXbL0vsXCreX6AnkPdud3VUPQLURhja\nYPCu4u6fF5FvAf4k8GHgn+7rvAnwA8Cf7rdvtI//U0R+HfDHgK8FfjPwW95o074+907sozOSHgaD\nwWAwGLwjDKHd2YW27ZXUvTtb9iRsd8LRbrgYrW1sds/mG5VyEdutUWulbJVyKtS1spYSYWMJdBbS\nAtMBpqMjuYTQLhmThLWKWwuxbRWjYRql6HsAWYzNir8ejQg08xTHhluEn6niGnOoNRkJYzKwCVoG\nTSApws/ylJlMYMr4lGFKz6SOT4vEOPEutC9/mnZnm+uxW62PQYtLASkLuJKk73N3ss9haPtVjN3J\nvxbPMUn7IrYJR30X20NoDwbvOu7+cyLyK4HfBfwTwFcBrxBhaX/A3X9IRL6Ni8h9o338FRH5VYQ7\n/lsJ9/uDxOiuzwB/Cfgfgf/M3f/Xd2ofb3WMg8FgMBgMBl8JQ2h3KvfAbrA60sdNYVdfEyFlLoa1\nQrET1VaqV1oX2bvQ3raNbd1Y7zfWUqhukASdhLQI+QDzwZFpo3LHWp2GYhZ94G4xaxqJfm3EUeki\nu5dlG12janeC1bswjbnfLoAKkiFrzKeuE2gOkatpd7QnsivM+RlH+1psPyO0gXPvtNDnWRsXBe7g\n0svJc1QGaGI+O9q9/Ft4RrRfLh3sP4mIFg8xvyedX0S2jB7tweA9wd0/C/zuvt7o8Y8DH3+bfThR\n7v1Hv4Lj+LL34e5f/eW+7mAwGAwGg8HbMYR2p9jnu9bbk6/9WcHdk8ZdQmC6NWrbqLbSWqFYpbZC\nrVE6Xkpl3QrrVijWMAxPIJOQJsgz5AUkNVxWioE1uYhs7/3axG0Ia+n/65XZ7Dp1F9ix3fnW90fC\nPhaN5HDZBbgqmhKaEimBJwUVXOmOs4Ma0sPZdgc73OrLfY7vR9UT2eUcZCbE60ji/NrnknHZR2lH\nGJt7F89n/RyueDzHr0T3YDAYDAaDwWAwGLx/GUK7s9a/GV+chbY9K7R39ddHbLkZ1QqtVVor1LpR\naqGUQqm1r8ZWG1WMtovbScPVnmCaIWXDpdIc3AQ8nOwoA+9l1d77off/+WXy9F7efhbb1/cDuOCu\nuAnN6Knp3kdl9SRvTV38xqxwJ8LbzCvmlWal39+T1Pde6rMLfXlF7/3VkdLuiAtiEZi2j0Y7X8jo\nBytdfWvqLryfB36xX15ALhcTzq/rZ0U+GAwGg8FgMBgMBu8bhtDurG0X2ldCcJ+XfRbae2Nxwg2a\ntXO5eKmFUgu1xColxPbWGlUdS+BJkNxDyCYhT6DJQCpm1r3hcIt9d4x9d7UlyrG9l1NzqbqWPmxa\nzquHlgF0ke2mmIHt7+ksshWVhOqVR+2Oe8O8dbGdzunffg4ve73QhmeFdve1XeOYvXXx3s/v/oxd\naPdss/0Rv7pwAHsd/P69XQT/1asPBoPBYDAYDAaDwfuBIbQ7pUUo7e5mu1/EpJxFXwIyeMZNaWY0\nM6xZLxvvpeO1UlujNKO2cLNNpPdLKzrpRWhr9GEbcukD773Zfi1o/SKfpXdK7762+H5850Jx3HsP\ntBtuirthTjjKe2/1ldjeQ8n213TsLLSbp9jnLqQlzpHIXjp+JXW9y+OuiXUX2paj/9wMN4sLC97X\n9cUF6Y573/Nln3Let/TX3oX4pW98MBgMBoPBYDAYDN57htDuqGi4sOLdo73cPuPcdjd57y/W3rcd\nI67Ym477g4KrELOxYJ+RpRqBYDmlnvd16V32qzLsEJtdRvZAsKs89Mvd54bsLwzQPXvcLrj3ed5m\n4cS7sct5V8F747fvQWdX7/ziJYNbF9jeQmbL1Xk5e+n9+Pcqb5xmlVJX1u0OSQvNjNoqqWzgMZ7M\nvcLlqM5vy/fSevwssneh/fjh06/shz8YDAaDwWAwGAwGz5EhtDvSQ7c4SzzrzvEu+CzKxrvYRq4E\n8jOu96WXGxVI2vVxL49WjbFamkgpRe/xPuKqq2ZBr2TufruniO1iO7aF6x7t6+dwcZbPYtVxC7Fd\nrYUb7x5meRfLjkXgm3C+RfrYs95zbV2wRzL6pQxdRUCsn8ves70fizvWKrVubNsdyESuIbJTXvFd\nZHu99Kd3we1GzAC3eEPSw9728V6Pn89HYDAYDAaDwWAwGAyeC0Nod1TCOd6dXBGuysdhHzV1icp+\nVmRzLsfmPJkqxLWcRfY56bunfaeUY//XZeKi7Gnbz5ZQX3de78XS+/H4OThNzup6Zy+/DnfY3C8l\n77ujLXIlri+Otj+7h91gprljLfbhbvF+1DFRVBS9MuH3Cwbu3dEuK9t2j3um5oKmFdUZp5wd7Ugu\nNyDS160LbYva92cucAwGg8FgMBgMBoPB+40htDuyz43y6xRt6LXUl6+vvnLfe6KdJJCSkKZEmhPJ\nQQ3UHMmCTOBZmKZEzomclKz7s8NBdxTZb+UideXcoy1feGz9VoQIS5N91JZfhK5fie1d+orjSoSz\nTZBEITmeBE/Sx3vBZZ6WnPPI3KGZU1v0sic33ENs70UASfZYtnDcHaO2ja28hp4+S60FTROqE6I5\nytC9gvc54ti5Vz7cbMf8Wmj7ENqDwWAwGAwGg8HgfckQ2p1dlO6y8uLiPhv0tbdfu0d/9p5Orgo5\nK7ZkWnOKCBkn4ZAFsuBZmKfM1IW2qobA9itf3LX3hz8rJK9yur8gZftcRt5FtnzBY74b7r06vbvv\niRg15kLOgO5XB0CzILqXyeszB7H3TFvbR6HJWcvLdYm77K8e56jWEyKv4AYp3SGaUc2I7EK7XQLS\n+ng1N+8p6CG0pTv/u9geDAaDwWAwGAwGg/cbQ2hfsfvAFwf52bLp0LJXUWTee7ndSAL/H3v3HmZZ\nVtZ5/vuutU9kVSGCWBRVJWKr6HjBC5algKAiNKIIXrunVbQUVBydfmwvra3TCqVP2w6D3Y6Xebps\nbCy151Hb9o4ioCi2jVKC19GmUURuKRSgUJfMiLPXeuePd629d0RGREZmRmRGVfw+yeaciLPP3vuc\nqqjI33nXepcPCd8YqBXWMAVty9Ft3AZjYyOzscoMQyLnWG+6tg1fNkTbPnDb2PbltoZs21/BufXu\n6ZX5/OVUzSaRzaiVFqgTbkbKRko2zbVensPd2nDuNnd6udSW9Rnk8/Pi8UIZN3Enqtm2wiy3LU3h\nurrjdRmymZZb67PnzWJouYK2iIiIiIgcRwrajfkcbJfheuqcjWHemqAthmZnWuE2G6wyVo2KsQJW\nOEMbOp6ykYbExmpoITuah9VWgzZfxuN5+Pe5NfYeaG3b7Xb9+dAX32ojy0nJSDmRhkTyGDLuObVG\nY726nkg5KtrnBO3FCPu9tj5ZfXkN7hWvm5Q6ApvRaM5SLC9GWoTsVsGu0d28N0CbO7vFnPZYBqwi\nIiIiIiJy3ChoT3bOY14M1o7OaNOwcWgtyZJBzqQBkjvJoCaHVFgDW+4MpUZozRG2+5DxXfPx4ti+\nuBfV6x3Vamvzsqe55XscK7XytQMJhmFgY+X4KSMlJxXDRrDqWFvz2twYkpFtuZDY8v2I+ejVE6la\na4YWwTyq4L0p3DIgW+vH1oZ+EwEbN9zSNAe7B+1aiLDtfdh7b6vW53D3pmkiIiIiIiLHi4L2ZFEx\n9mVAbLfnFHUjYFoGW0WjsJqgDgapsOXOZq2sSsFSryRbVLOnSvG5oX77OZZna1vv6N0qxr2i7S3U\nzh3K+9NTDLW2hCVYDQO+YVAzKVXSCJYcKxVqiaBdE0OKintaHqtda0qQcmIgR0M4M5LFkl49aM/v\nW78uorkZNoVr81gWDLcWtKOK3Zfy6lXtfiwzqLVQ60jxNV7Lwf7RioiIiIiIXEYK2pOdi1k5O4vO\nEXb7kG0iiOZMGhKk6NZNTVga2ayVjVIYxjH2bdXenNuwbNsZr70tKbb9fNP9Vk23XmU2mzZgapa+\nfP60xnVKrXubscoGq0TygZQq5BIN0EagZqwUsOiInnt12ufj9euMx+ag3Tuf98p2X4LL+wcWLWxP\nw8udGDbewnZt3cXrYsh4v2+L96DUkVLWjGVNreMF/RMWERERERG5HBS0m1rnYch+ToG5Rb3FUld9\n6HhOkNpEbTfDq5FqIvV52bkHY5iq11ParFOjL/C5c/eOhG/n3NnO2xN9x4UbfRh3LNfFQEuvfVGx\nEsuL1RLVZMbWnI02sNtIPs+0jv9vQ+hTPDZV3PvQ9kRrUrbjg4t5sjvzUPJ+2+ZnLwL99Epsnmce\n36tMa5jvN/5eRERERETkClHQbnaG1L1SrS3/tApuzuBm1JY7rc3JtmxtqPcchJfLV3lbX9p7+N7l\n3Lt3Fz/3mreF7cXOqQ1xz70CPjjmTnbHPVF9pFQoteI1UTGKx3LYqR0qVvemlbbb2uGtu5qzPfDO\n89gXcXn6kGFx20J2bc3Tenfx6SVt68HmU8wnebumNA2jFxEREREROU4UtJte0V522T63sjwvWxXz\nkttwcOaQXZNjJQJ2bDF+2qcK9vaNHr7Pmadt55x/qQ/Z3rWavS2s9iW8EskSyZ1M1LLdE7VCKVBz\nodYI5LHI1xyyl5nX23z1ZLS9dlx1D9XW54r74vvzxTltHvaiU/k5M9andunLkO4x590MPO/9BomI\niIiIiFwhCto7uC9D4RwvZ72enZjaa5thqUXECqS5qp0Gw4vRxmbTK72x5NW8NvQ8xLp1Gt82QXv7\nJfRr3FmEX4ZtW04Cn8au93nd3uZS1+nDgmRGai8nYVjvY1YdLxW3SqVQKfPxL/i9ndddHsRUAAAg\nAElEQVTFXobs+H6fa26L/Zfv/vbJ67ZXmV9EREREROQKU9Bu5uBo+4TIOWRPTciIZamqxyDwiuNW\nITlpMPKQcAMvMdO4L8s1zXveZTmxOVnbPGzc+u6+vci7sNtQ8v5UiCXI6ujUEerolDJSaqHWglPo\nDeAixDulFMb1SEpOoVAZKYw7jsp07XvNFV9+XjGH7X6dwGKu97zq944nW69595Z0O/cTERERERE5\nHhS0m+1BO267bRG4VVPNE7SqdK0tZHulmlOtxrDxbORVBO0+OHxa/sp2Cdi9vfd8KYvFveYr6U3E\ndvYtXzZU88Vw69LuVndqmbexlAjaXtqa1JWIulEur6VS1iNb5lTWlBa0twf6ft7lPPT56/mDgj6E\nfvm8xQudAnTa1nSO1vSsj+iPufF9QLsmaYuIiIiIyPGjoN0sg/Z59mxBL9G7hkc1O/64Lyra2RhW\nmQpY22+5NrUvq9k2B+N2QUxV7W2B2qdwu5tzA+98Lqs1KtljVLYjaI+4l6kp27KiXUtlHEeqVYqv\nGX1NYb0I1/NrWAbs+PChz3mP/+svZ/ul99EDbTa4tXW1+/dtMQKgjWtPFsuKxbrd5/lHJSLHnpnd\nAryI+C/DB7v7m67wJYmIiIhcMgXtc/RwuxebhkBTW6AsLai2arabRzV7SAw1gnbfck6xzjRMTdB8\nURGOkLuoqm/L2DEH/NyK8vaAXWudbhOJ5IlExtxibnSJLt/F67RVHEtGHgZIxjBE8K3V8bEwet/G\n7fPFFxfii/Wyp5EBPSi38fJpR0O5mGu9+PCih+3pOfPz49tt36mqLSJyfNx9991X+hJERETkGFDQ\nbuagu6wkB8PawG+bHnIHrxUvJTareHLcYiNBXiVWDFQsNo/maCnNVWbvVXBvZ7B+xmWIjf/z2u63\nfVmE7GUlebmZG4kcQZsUQ97dMDfGHrI9ZpdbMnLKZHJ8GGBt2W0qYy2MdWRdxwi/TBfb3qOleZ57\nWgblHqhtewU7AneaH1tUs+egzTyMvIdzBW0ROWbuuuuuK30JIiIicgwoaO/Ql83afx+iwXiraNcy\nQvLo6UXcWo4h4oOlFrSj8XhKsa411tfU9hZ0fT42MHc/ax8CTH3TfO6fxty1O6rYEa5LKdNmnmKo\ndQvbyXJUuS1TaoTsXtHOKZNSiutrTd3cK2UsjLWwLoV1LS34pu1BuF3zVMVmGa6tzU2P194r0snm\n+1PgtrQtZNO6pGPbP4TQuHERERERETmuFLSbZUV7+xJfy53mKrPXc+cl90qzG5AiaGcMGxyrGatM\nS2hFOC7Uto52ndLzoglbP+YiXC9D9nIY+86gXUuJijZgbmARnrFYOsx7LzFvy5B5IudMzpkhD7GU\nlxeqG16dRCYzRKM3s1bx7uG4D+leDveOr6eAbWCpheu0M2gv52Yvl02jhXZv9/2cyrmIiIiIiMhx\no6DdbF+DeroHU60ZeuOyc5avwqJC3Sva/altXnFKhuVEGpiDrlfGUqeQXZdrXrXz9zBfY/z2tpXA\n+hzlaZmxVh0HnyrH0aU7k2xoW1S0I+TGnG1LEbSBCNlpIOeMk6ieqV7JnsmeKazY8I1tleopWLc3\nbnslm6kSPQ8fXwwZn64/TcPBpxdvxrSkti0+XbDFhxHK2SJyfCTYZXlDETm2Tp8+zW233cZznvMc\nbrjhhit9OSJyQKWUfvdYL0GkoN3s9pcjm3qR9f+fq9fmUfmeV6lahO3FNO++zJfXNLfcXgzJ7iE7\nGqNtt5xrzSJk4xGkk/Vh3sxV73bdliK89nCd0tC6dedpqDaesFxJNardOQ8MOZPz0I43z9+urZrt\nraI9Va3p89rb4PFtS3H1N9IXQdy27c+0JnZ/I21e9MyYm8XRJ6jbYhi5iBx3ZvZg4F8Bnwt8EHAX\n8KfAbe7+cwc8xgcB/wL4x8AjgAy8Ffgt4Ifd/c8PcIynA18H3AQ8AHgL8EvA97v7283sje3YP+7u\nz7qQ19jki3iOiFxBp0+f5tZbb+UZz3iGgrbIfcgiaB/r370K2pM5Utu27/T7LSxOw8Rb7doMN4sm\naEw170WVlxhGngEyeKEmj2HZZZyXB/N6zhWVNvy7dxGPJmaAGzln2kEjuC4+KEhm0Dqbp5QxG0ip\nh+1ePU7gTnJrXc5bI7ScyWlYfKjQ5o+nvjGdc6po017oYgj59vfV5/dvW6hue8ypevosYjFrnejX\nbu0IfRj5tkOIyDFkZh8JvBy4gfk/qaeAzwCeZGYvAl55nmN8OXBbe97yP8sfCjwSeLaZfae7f98+\nx/gR4H9rX/ZjPBL4FuCZZvbZzB9lioiIiFwyBe1uHrE8VaWngeNt7nWE4UTqEdDasl1tmLi3tbV7\nydV6GkyOtc9b3InAanMsN3NSq57bohxsyaKiXH0eMk50DM8ptfA8L3M1fUiwbc50hqmKvUymy0nf\nvQodYdpTaYG3zuPVE4t50rYIujsntM+Rev6WL/4GO6fpbX+jnceJTyF/sfhZdGenTvPBReR4M7MH\nAr8BXE/81P808BPAO4APB74J+ArgUfsc42nEGtsQlfAXAL8JjMDjgG8HHgr8GzP7e3e/bZdjfCsR\nsh14M/BvgdcQwf0z23X8HHDNpbxeERERkSUF7a6P8F9kOLe+7NbcqKyvlx11Vqca1G1zhlO7G3OM\n+7ciAM/zsKdq8fTUHoxjne02kRv3NFV5k9sUtKeh49uqyu1Y07Bumz8AsPk4O9ftngrEieg03uea\n+7wfvXnatlHfO+v/NoXpaUm0/npZNHSbwva2q2bx0UYMW58C9lwVX+6uwC1yrH0X8HDip/3b3f35\ni8f+yMx+Dngx8JTdnmxmA1HJBrgbeLy7/9lil1eb2c8DryIq5i8ws//i7u9eHONhwPPaNfwV8Bh3\n//vFMX7PzH4deAWwgSraIiIickgUtLsdU+nngctzRTVq2W2+cAuuU0V7ql/Pk5NtcWzrQbUS4bN1\nCZ+qybTGZMlifnXafkFGC9pubXmu1s2bNBeUt61dHZtj7ZQ2n9rnoGu2HPLdK/UlArEtxnIvuoAv\n51z3d6sP/d72/s2F+Phem0e+LcBPl97ePbNpybNKzBGfK+dzsDc3lLNFjiczWwHPIn78/3RHyAbA\n3YuZPRt4A7Da5TCfD9zYjvE9O0J2P8abzOxfAj9FVKS/Evj+xS63AFe1Y3zDjpDdj/GqNrT8Gy/s\nVYqIiIjs7Vh3aruczPI0zDrelsVmO+73LWUsZSytsDzEbRrO6fKdpv0yluPrlAdSWpHTipRWpDxv\neec2rMh5gzRskIcNct83rUh5wFJsqW22Y4vv9WtI7XUmSPE6bPmaWgXcW0ifbpcToz21YfJx6+R2\n2zbPsZGhbz4Qn+v093fx2PT9He+9t/2m29jPGMD68fRZkcgxdBPwfu3+7Xvt5O5vBV66x8NP7rsx\nDx/fzX8B3rPjOTuP8U53f8k+x/iJfR4TERERuWBKKY0tCipTrbUPr27VVJtCYWsqliDleZ415pHJ\np+prP06dh427RcXZEykNi0nVrYFZr2jbXBZ3ooKbfK6b9+d5H0C9raDcA3EPytCbs7l7e7ovjm+9\nzdjUsGwart0rz71hGUQ43zaOfHnync3OphZmzPVtZ/HyFq+nf6eCRZdxmyra/dE+bmDZrVxEjpmP\nWdy/4zz7vhp42i7f73O3/8bd37XXk919bWZ/BHw65873fhTxH50/Ps81/Bmwxe6VdREREZELpqDd\nxHTAyMI9ZoZl2I5qqjFgKZG8NzCL/XrY3jFgun2vtqZgCWsV30R0GvfFsOw+P9uS9bP2HmTMh7dt\nwbeP7l6GVfc2HLuF7eV4dts537k/3eNa+lH7n+nY/dypBW1rYdtbnF6E7Z0heJqjvnhvpn1s+VzA\nKuZ90Ht7jxYBPv4kkoK2yHH1kMX9d5xn37fvcww/wPMB/m6X88JcVb9zvye7ezWzdwMPO8C59uXu\nvPa1rz3vfjfccIOWExIREVk4ffo0p0+fPu9+6/X6MlzNpVPQbnrQnqq4Lb3atn1yq2rHZmk5J7sH\nyHpOoPTWrRyr4AmzCuR5HvhyjnVvhJamWm+/rJ45t/Ug27VzT0//06cAfZJ4z8s+V7d3Pq1fue9o\nhrbsQ+YJt9yq2mmXC+mV9uW7199L36VCzbbvuVf67HI33/awkaagbaaZDyL3AZfaYOw+16Dspptu\nOu8+z33uc3ne85539BcjIiJyH3Hbbbdx6623XunLODQK2rKrHUtzi4hciGXTsYcRHb/3slcV+d3E\nZ2wHqTJfv3jOzut4GLEE2J4sPrV7v/32OYCpmv7Qh+57OiD+MvHCF77wEk8pIpdia2sLgKc+9als\nbGxc4asRkVLKgX6H3nnnNFBt50i2Y0VBu/mCz32hxiGLiByOZYfwm4Hf22ffm/f4/p8DjwU+2Mze\nf6952m0ZsEcTle8/3/Hw/0eE8I8/z/V+DLGu9qV8vDj9Dln8BUBE7gP0Mytyn3Ws85uCtoiIHLbX\nENXkBwNfBvzAbjuZ2QewxzrawMuBryZ+iX4l8II99vsnwIOIkPzyHY/9JvAk4Foz+yx3//U9jnHL\nHt+/EJtEWK8cbF65iIiIXJzriPmrm1f6QvZjO+fpioiIXCozewHwTUQA/jZ3f8GOxzPwK8BnMreH\n+GB3f1N7fAX8DbGW9nuAJ7j7n+84xgcCr2r73AN8kLu/e/H4DcQ63RvE8PXH7ayMm9ljgVcwdxy/\n3d2fdclvgIiIiJxo6iYlIiJH4buBtxAh+vlm9p/N7DPN7NFm9r8SAfkzgT/c7cnuvga+hgjgDwJ+\nz8z+tZk91sw+ycy+kVg67Ma2zzcvQ3Y7xmng1nYNHwa8xsy+1sw+0cw+xcy+h6iCvxV4Z3/aYb4J\nIiIicjKpoi0iIkfCzD4KeBkxT3rnPCoHXgT8brvdVtFeHOPLgNuIYdm7HaMA/9rdn7/Pdfw/wHP6\nlzsefgfw2cAvAA8H/oO7f/1BXp+IiIjIXlTRFhGRI+HufwF8NPB84H8CZ4k1rX8L+GJ3/6q+K3ut\nVuj+k8BHAP838BfA3cC9xFDwHwUevV/Ibsf4OuBzgZcC7wLOAK8n5o4/2t1fC7xv2/09F/NaRURE\nRJZU0RYRkROtNWV7MxH2v8rdX3SFL0lERETu41TRFhGRk+5LFvd//4pdhYiIiNxvqKItIiL3W2Z2\nDfC+7v53ezz+aOC3gQcCd7j7J1/GyxMREZH7Ka2jLSIi92cPBf7SzH4ReAnwOmLdzRuBzwKeBVxN\nrH/9TVfqIkVEROT+RRVtERG53zKzDyLW43bO7ThO+/4WMTf7P1/OaxMREZH7LwVtERG53zKzAfg8\n4KnAzUSF+yFE5/I3EsuP/bC7v/lKXaOIiIjc/yhoi4iIiIiIiBwidR0XEZETz8weYWbfb2Z/aWZ3\nm9m7zOzVZvYtZnb1IZ7ni83sN8zstJmdMbM3mtlPmtljDuscIifJUf7smtlzzawecPvUw3pNIvdX\nZvZQM3uamd1qZr9mZncufob+0xGd84r93lVFW0RETjQzezrwk8D7EnO2tz0M/E/gae7+15dwjquA\n/0o0YNvtHBX4bnf/7os9h8hJc9Q/u2b2XOC5uxx7Jwc+w91feTHnETkpzKzu+NbyZ+t2d3/WIZ7r\niv/eVUVbREROrLa8108Ty3vdBXwH8DjgScB/JH45fxjwq2b2gEs41YuYf9n/FjFv/JOAZwN/Rfw+\nfq6ZfdUlnEPkxLiMP7vdo4CP2WP7WOCOQziHyEngbftb4KXs3qj0MFzx37uqaIuIyIllZq8EHg+s\ngSe4+6t3PP7NwP9F/KK+9WI++TazzwBe3o7xy8AX+OKXr5m9P/Aa4BHA3wMf4u7vubhXJHIyXKaf\n3ami7e750q9a5GRrP1N3AHe4+507VgY5tIr2cfm9q4q2iIicSGZ2M/EXdQdeuPMv6s2/A/6S+MT9\nG8zsYv6y/c3tdgS+3nd8wu3u7wK+rX35YEBVbZF9XMafXRE5RO5+q7v/mrvfecSnOha/dxW0RUTk\npPq8xf0f322H9sv5J9qXDwaeeCEnMLP3IYayOvByd3/bHrv+PPDedv/zL+QcIifQkf/sish903H6\nvaugLSIiJ9Xj2+09xBCyvfzO4v6nXOA5bgY2djnONu6+Bn6fqL7drOqbyL4ux8+uiNw3HZvfuwra\nIiJyUn0k8Yn3X7n7zk6oS/9jx3MuxEftcZz9zjMQTZxEZHeX42d3m7Y80NvNbLPdvsLMvs3MHnwp\nxxWRQ3dsfu8qaIuIyIljZqeAa9uXb9lvX3f/B6JyBvCBF3iqhy/u73se4M2L+xd6HpET4TL+7O70\n5Hbeod1+KvBvgTeY2TMu8dgicniOze/d4bAPKCIich/wwMX9uw+w/z3ANcD7HOF57lncv9DziJwU\nl+tnt/tT4BeBVwNvA1bA/wJ8KfAUYv73z5nZ0939Ny7yHCJyeI7N710FbREROYmuWtzfOsD+m8Q8\nrquP8Dybi/sXeh6Rk+Jy/ewC/Ht3v3WX798B/JSZfQ3wH4AMvNDMPtTdD3JNInJ0js3vXQ0dFxGR\nk+js4v7GnnvNThFzQs8c4XlOLe5f6HlETorL9bOLu7/3PI//KPBjRJC/EfjCCz2HiBy6Y/N7V0Fb\nREROorsW9w8yXOwB7fYgQ1Uv9jwPWNy/0POInBSX62f3oG5b3P+0IzqHiBzcsfm9q6AtIiInjrtv\nAu9qXz58v31bV+H+y/jN++27i2Ujln3Pw/ZGLBd6HpET4TL+7B7UXyzuf8ARnUNEDu7Y/N5V0BYR\nkZPqL4ghn480s/1+H37E4v5fXsQ5djvOfucZgddf4HlETpLL8bN7UH5ExxWRi3Nsfu8qaIuIyEn1\n39rtA4Cb9tlvORz09y7wHHcwN2PZc1ipma2AxxB/ab/D3csFnkfkJLkcP7sHtVyz921HdA4RObhj\n83tXQVtERE6qX1zc/8rddjAzA768ffkPwCsu5ATufjfwm0T17clmduMeu34h8L7t/s9fyDlETqAj\n/9m9AF+7uP87R3QOETmg4/R7V0FbREROJHe/A/hd4pfxs83sk3fZ7VuAjyQ+8f6BnZ94m9ktZlbb\n9l17nOoF7XYAfmTnUFczuxb4vvblPxBdjEVkD5fjZ9fMHmVmH7rfdbTlvZ7dvvw74Bcu/NWIyIW4\nL/3e1TraIiJykn0DMaT0auBlZva9ROXrauCLga9u+70O+Hf7HGfPeZru/goz+2ngnwGf287zA8Qw\n048FvgN4RDvGt7r7ey7pFYmcDEf9s3sTsTb2K4BfB/6MaMI2EPM6nwn847bvCHy1u2tZPpF9mNmn\nAI9cfOvaxf1Hmtkty/3d/fZ9Dnfsf+8qaIuIyInl7n9sZv8U+CliCNn37tyF+Iv609z9nks41bOA\nBwKfDXw68MQd5yjAd7u7qtkiB3CZfnYT8CTgyXtdBhG+n+Xuv3aR5xA5Sb4KuGWX7xvw+LZ1DuwX\ntM/niv/eVdAWEZETzd1fbGYfS1TInkYsB7IF/BXws8CPuPvZ/Q5xgHOcBZ5uZv8M+Arg44AHA28H\nXtnO8QeX8jpETpoj/tl9MTEs/LHAo4GHAe9PBIJ3A38CvAT48TYnVEQO5qCd+vfb7z7xe9fctSqB\niIiIiIiIyGFRMzQRERERERGRQ6SgLSIiIiIiInKIFLRFREREREREDpGCtoiIiIiIiMghUtAWERER\nEREROUQK2iIiIiIiIiKHSEFbRERERERE5BApaIuIiIiIiIgcIgXtY8TMftvMats+9Upfj4iIiIiI\niFw4Be3jxXfcioiIiIiIyH2MgraIiIiIiIjIIVLQFhERERERETlECtoiIiIiIiIih0hBW0RERERE\nROQQKWiLiIiIiIiIHCIF7cvAwi1m9lIzO21mZ8zsb8zsF83scy/ymIOZPdPMfsbM/trM3mtmd5vZ\nG8zs/zWzz7uIY36Emf0bM/sDM/s7M9s0s3eY2e+b2a1mdsMBjnHOEmVmdr2ZfUc77mkzG83s3Rfz\nukVERERERI47c9dKUkfJzB4G/BLwSYtv9zfd2u3PA18B/Arwae3xJ7r7K/c45qcDLwQ+ZMfxpl3a\n7e8DX+TubzvPNW4APwg8G8i7HLMf7wzwre7+I/sc6xXL1wC8H/Ai4ME7jvked3/IftclIiIiIiJy\nXzRc6Qu4PzOzBwGvAD6COWT+DfAqYBP4aCKAfz4HXDvbzP4J8FPEPzsnwu/vA28EKvDhwGPb448B\n/ruZ3ezud+5xvGuAlwKPa8dz4K+B1wB/DzwE+BTgRuBq4IfM7IHu/n0HuNxPAZ7XruWdwCvb7XXA\nxx/k9YqIiIiIiNzXqKJ9hMzsx4CvbF9uAl/r7rfv2OcTgZ8F/hGwBWywR0XbzD4aeDVwVdvnBcD3\nuvt7d+z3j4DbgSe0/X7d3T9nj2u8Hfiytt/rgOe4++/u2MeArwH+fTv3CDzB3f9gl+MtK9qFmJ7w\nncDz3b0s9lu5+3q3axIREREREbkvU9A+Imb2YcD/WHzrFnf/qX32/SOiYmzsHbR/kxiO7cA3uvsP\n7nP+a4hQ/lFt/8e4+x079nkC8DvMVezHuPuec6fN7BZiGLgDL3H3p+2yTw/atP3+jwNWv0VERERE\nRO4X1Azt6DybeW7zq/cK2QDu/nrgBxb7n8PMPpY5ZL92v5Ddjnkv8D2Lb33pLrt90/L+fiG7HfN2\n4sMDAz7TzN5vv/2BtwHPP88+IiIiIiIi9ysK2kfniYv7P3mA/W8/z+Ofvbj/0we8ht9a3H/88gEz\ny8CT25fvBV58wGO+oh+CmIO9Fwd+zt3rAY8rIiIiIiJyv6BmaEfn4xb3X3W+nd399W3Jq706cT92\ncf8z2jzs87HF7QfueOxjgQcQgXgN/GBMxT6vmxf3dx5zp9cc5IAiIiIiIiL3JwraR6B1G99YfOtN\nB3zqm9g7aN+4uP9ZF3FZO4d5L493LfD1h3DMnXbtdC4iIiIiInJ/pqB9NN5nx9f3HvB59+zz2IOY\nlwC7mA52O6cJPGhx/2I74p3v358zF3lcERERERGR+ywF7aNx946vr+FgofMB+zx2D3NH8s9391++\nyGtbHq/7U3d/9CUeT0RERERERFAztCPh7u8h1sTuHnHAp+435/nti/s3XPBF7X08A64/hOOJiIiI\niIgICtpH6U8W9x9zvp3N7JHA+++zyx8s7u/X7fug/hjYbPevM7MPOYRjioiIiIiInHgK2kfnFYv7\nzzzA/rec5/FfbbcGfIGZPfSirqpx97NsX/7r6y7leCIiIiIiIhIUtI/Ojy3uP8bMvmSvHVs1+1+w\nT1Myd78D+O325dXAT5rZ6iAXYmYrM3vwLg/9n30X4J+b2ZMOcrx2zIcddF8REREREZGTREH7iLj7\n64EfJ0KsAS80sy/fuZ+ZfSLwMqJh2tbOx3f450SjNQOeArzSzD5pr53N7MPM7DuBNwKP2+UaXwnc\n3r5cAS82s39lZrs2ZTOzU2b2uWb2i8AvnedaRURERERETiRzv9iVneR8WhX5VcCHE+EY4A3te5vA\nRwM9KP88sZ71pxGV7Se2ILzzmE8DfpoI5v2Yfw28Fng3cBVwHfCxwAe0xx14urv/2i7H2yBC81MW\nx7uXmBP+pnadDwY+FHgUcKrt84fufk7IN7NXnO81iIiIiIiI3J9pea8j5O7/YGZPJILsJ7Zvf0jb\nYB4q/kvAVzDPw97vmC82s8cRQ9Nvat/+0LZt23Vx/L8B3rLH8bbM7LOB5wLfTAT4q4En7rZ729bE\nhwUiIiIiIiKyg4L2EXP3vzOzxwBfDnwpUWl+ELG81p8AP+7uvwBgZrA9IO91zD8DPsnMngx8HtGF\n/Eai8rwJ3Am8jqhK/4a7//55jufA88zsh9p1Phn4KKLCvgLeC/wt8GdEk7dfc/d37XfI870GERER\nERGR+ysNHRcRERERERE5RGqGJiIiIiIiInKIFLRFREREREREDpGCtoiIiIiIiMghUtAWERERERER\nOUQK2iIiIiIiIiKHSEFbRERERERE5BApaIuIiIiIiIgcIgVtERERERERkUOkoC0iIiIiIiJyiBS0\nRURERERERA6RgraIiIiIiIjIIVLQFhERERERETlEw5W+ABERkfs6M7sHOAVU4B1X+HJERETuz64j\nCsab7v6AK30xezF3v9LXcCz8xMs+zQG8OF7BC+CQLMdGIqdMypmcBiwZUHCrQKUC7rHVCuPojKNT\nSnt/rd1Mtw44Y12zLluMZQs8MdgpBjvFyq7igfkhPDC/Pw/MD8HKwObZNVuba7a21qzzmWnz1RZ2\nKjY21kCdrovkcS4DLF4bHrfmA9lXJFZYzXh1vFQojrOmMk63I2tGHymMcawUm+VETu29SYlEvFeJ\nBDXh1aatVqjVqQVKKazXhXFdGMfCarViWG2wWq1I2UjmWIrNGcEKzoglw3LCcpwP4JlP+F27PP+W\niIjszsxGIF/p6xARETlBirsf28Lxsb2wy63WuvjKMDOMRLIWG6fAnbH2xzFwww3M43mYkQxScnJy\n5g8yFh9omLfAbZhBSkZyA4yEkczivCm1++0xi/MCeO2BPg5tyUg5YSlBctzAraV+83aBHv+r7Tne\nwrgXzMFrxd3x9n23irc/nfcX2w5HjUfd2gcNONXjOeYV97R49XH9yQw3Y0gGOcL5MAzkPLTAbph5\n2ypOjtdCbh9YpHjf9RmRiBwf8VvAjBtvvPFKX4uIHMDW1hZ33nknD33oQ9nY2LjSlyMiB/S2t72t\nZ6xjnQYUtBuvBZiiLmYJI5MXATtZwizH496CrNf4R9xCsJnhycjurbYRYdvpodtjX4vHLMUZsyXw\nRLaoCWeLSnEP2/PxI2t6gVJhXR0q5EjspJTx5HgqkCpuJYJyC82RiOMykht4wt2wVor3Gq/JKUCJ\n55m3UBuBnxaoaZ9NOFDNMaDimDtGndK3ewRj+vtqGUuQciaZ49nJOZOHTM7xGEqypS8AACAASURB\nVNYr8dYu2eN05hgJsAj9IiLHw7uB66699lre8pa3XOlrEZEDeO1rX8tNN93ES17yEj7hEz7hSl+O\niBzQddddx5133gnxu/fYUtBuoooLtJqxWYqQTSbbQLLcwrdhFlVa84q3wGdYDGu2FJXuBNDCp0el\nuE6hPAIkFmHXLeEelfOBxECOoG1GTlHdnirlvZpeoKyjou0FPCUYcoT7IcK1W8VbaK69jN2uFTec\ngnvC3OK11FYm9wp96Dm9qh0V63heu2/xemp/nYC1knk/Rw/m8RFGjDdPluJDAYOhxvFSSjFkPKdW\ntY4PB7DU8rxHKG+HnMbii4iIiIiIHDMK2k0f4h0hO6rMZm3OsUXYNjNoYTuqvDYFTwBz66PHY0i4\nWQTfatQalV7vw7inMNn2hV5Ljwo3bfj4Yuh4Hzg+VbTXznrToTo2OLYyLM/X4xZhu7aQ7+4Rqvuf\nFraTG+apfdhQiMjsEZqnUNtfe7tu2rBwow3jbpXuXu32FrpJUS1nfg3JMkbub9L8IUWKIfDYPFzd\npwp2wkn08eLHf7CIiIiIiIicVArajbWVzvqw5Kl2bG2etmUspRa2W9CuhWkotTuVAjWiaPUeR6NV\nWqVSrbQwOw8jr1ai6ZiVNqK7V5Pjq2nIeds/CuFteHep1LHEMO9VwTcrngpGifnNGZwUo9udaHbG\nFJvjlbYmaZiTHPC+4ptP34/H2nvi/braUHSLarSl+Ihgns/trdK/7aODmOPusTF9gDCdcRIV+X6c\nSvV4D6fqOqioLSIiIiIix5KCdmc9YNoctr03JsvkFEPHSS1oU6kpQbVpDvacFD2C9hS2K5XSgmJt\nIbvNfabgVqhWWvG4h+w4Tg/xNsXLNnfZI+iXdaH6iG8VfCjUVMhWSdnJbm1oe8Zriqp6C8nmHl3D\nabeLhm7x+uegG8O2ezhvVfl51nSbc95K80SF3X2uwU9N5Twq2dZu+9z2VhZn+mChhfz+JyryTmlV\neWd7KBeR48XMbgFeRPyofrC7v+kKX9Jl8853vpOHP/zhV/oyROQAtra2AHjqU596v26Gdv311/OH\nf/iHV/oyRE4cBe1mWdG2bX+2V7T7cOeKYXXuqD2H7e1V1ylot2p2pfT42OZP19ZwrMZcbWpr+NWb\njbVQ7d6Gtcete6WWSl0XSi14HvEcQdtzZdiYq9Depl1HRTtCtreh4d4atsWo+D403VrluzVya3PD\n515o24N2H3Qft8sPE1oTtz4Ef1HNtp7up+O2Dx6sB/k5dldi6HutMQy+f6ahruMicty4O29961uv\n9GWIyAVoTZVERA6VgnZjNg9grrXQO8ZPs6LdohqbYhj0NHjaphnLVC+4F2qfX9yP2AJ1LJfVm5O1\nBmWtYRktSFfbiMesUBgZ68hWLTA6m1tbbG1ucfbsJpubW2xtrlmv15Q6koeCrwt5VbGxwFhhLHF9\n1ajeVvqaXpXFnOdYDBtSnuaVm9k0XLuPO4+50ouh3g6t4xs9es8vui+PZvOyaDZEwI6J2HPH8J6W\nfbmI2KK23avY1adlyaYO6AraInIsfcCVvgAROZAt4E7gocD9saJ9mmmJGBG57BS0mz5yPKq+BWqh\neqJPj46R1tETPMWqWK12C24WQ5upLUDHUmFTDrQ2W9uik3atMS+7Ms7DyGsM4c6sKTZSKIy1sK4j\neVxT18bZrS3OnjnLmTNnuffMJmc3t9jaWuPDCKsKY8XGShkLrCN4xyjwCLY+DdGGqblYC9mkoXVN\nj07ntY54dWrtlWubXwrEBw+L0fJ9znafA86yoVwP2pbjfIvh4n0O+jQ0vj3WQ3b1Oofsam3qeK+4\na5K2iBw3Bmh5L5H7htcCNwEvAe6Py3s9HNAIG5ErRUG7mzJbW4qr1tZNuzU6c8g42SClCJl9ASy3\nWEe61EKpI9XHbcft3bstteq2jVRfU3xkW2OxapQ0klsIH31kXUdSGSkjnNnc5N6zZzlz71nuPbvJ\n2c1NtrbW0YJ87RG0S9z6WEjrNeTe9iy1pcdaQDVorwhswCyCdspGSjHcnLEu5ogv3qRpTPz2Nma9\nOVocftFILsWwcVJuzdbSPCPb++ufm7T1Q87d0mtr5tbebPoSZYf6b4CIiIiIiMihUNBu+vJeEapL\nq6QCKUFNOBlPhnvUtXuH7Rg6Prcqi6p2G+PcmpbF3Opew+2N0Xrlu85V3TTGuSmtoj2yVdbYuMV6\nC+7d3OSee89yzz1nOLt5lrNno6JtVKwYqSX/WoHiUCLkTwVkLO5PkbZXjw232LDoIN4mZsfI8alR\nWjtGC7mGtW7rTK+R+VTRjbx3He/ri08hv8//jiAdb1Uct7b3sNbYtlW0+/m3x3IRkSutXOkLEJEL\ndQPw3HYrIvcVOed+91j/7k3n3+VkKKXGVp1aW+jbFkJpWxvubBGyY/3nhOUEud9aDC0375GZUkfG\nsmasMad6DvatkdoiWJZaqG3/9XqLs1ubnNk8y71nznL3vWe56+4z3HPPJvee3eLs5sh6XSkF8ITZ\nimQDyVYkW5Ftg5xWDGnFkAayrchpINlAryxXrxRv11jjGmOeem9zthwCHsPAc4pjD6lteYNV2mCV\n4vg5ZVJfc7yNEJir1/EhhPUt9e7ihVJGShkZx74V6hjzzI28GL6/itdiqyv1r4zIiWVmDzaz7zOz\nvzSze83s7Wb2MjP7ogs4xikz+9/N7OVmdtrMNhfHeZaZ5aM8hpm90cyqmf2n9vVNZvbjZvYGMztr\nZhc6sbEvw3CBTxORK+cG4HkoaIvctyyC9rFuQqCKdjOO7Z9Tm04dQ5lTWzM7gnMP2tUc63OuAbNW\ntfU0D432iteoXk9dvGoLmd6HTLfT1dYF3Fo1PRWKFRjX+HpN2drk7NkaQfueM9x191nW4xbjuGY9\nrtnIiY2SgR6EIVtfmixCsvVKNfOIa2vLcVUvrQzel+iyaOpW61S57mthZ8tTlTq149XW5K3aooN6\nH6Teht9Xr1hrgDY1nrO5N7sTYbxOgbu0DxwqvWe50cL7YhORy8vMPhJ4OfE30/6fk1PAZwBPMrMX\nAa88zzE+Dvgl4BGLYwBc248DPMfMnu7u7ziiY0wrBZrZc4AfApbB/Fj/8hYREZHjTUG7KaV3v+4j\nm+eKdjQdX1S1qdE5G2L5qh5EPQIoWAzhtracVy1tnnGZgmsfxt0brVV3zOr0nGojXtbUcYtxvcmZ\nzco9Z85y9z1nueueM1H5rVH9tdWKUgHPrZJtJGvh1GjzpPvw7Xlxrh5w8UqtbUGvNhR+MSm6dRDv\ncXdoleoIvWCkaXmyGmuCt87q7u19q47XOsX1Pqze6GuGt2vwQmkV/ahsR9AeksW8eMuLsJ0VtEUu\nMzN7IPAbwPXED+9PAz8BvAP4cOCbgK8AHrXPMR4J/DbwvsB7gB8G7gDeDLw/8AzgOcDNwC+a2RPc\nvRz2MRY+Cfgy4G+BFwCvIX43PuEg74mIiIjIbhS0m1Li72DzElZtNWmveC0kW0cILoVCiurstOaz\nM9YtShsW3udZw3Ku9nx/mqvcu2v3iq5Htby6U9zJtc3d9mjCtpETV58aqFdtUGqi1EwtA6dODVw9\nnOKqtMEGG2Rfkb2SvZJqhG7zCN2OtzDv0fm8rqm+xr2QKqRq1EQLxdDXFad/gEBbHsx71Z/2YUOa\nRkym+JwhXp21ofQW71n/w6JjOa3DeKy/3T6UYCo1tXPnmD9uUbm36b6IXEbfRbSxdeDb3f35i8f+\nyMx+Dngx8JR9jnE78CAi0D7F3f9+x+MvN7MXt+N8MhHcf+wIjtF9FPAnwKe5+3sX33/VPq9BRERE\nZF8K2k2pc9CehoJjbfj3SLTyGimeSG5YWgRt86gu+0ipa6qPERgXDdGWw6S7WOO6h15ap20oOMkd\nc+IWGJJx1UbGr14xVKeUIeZzl8LGVQPXbGxwdT7FVazIwFAh19aQrEb372hsHhXnaoU1ZxmnKvJI\ntej9Ftk4A+15lplDNuAx07ovGIYzVb37N1Kr/pu1tdDSPLS+DyePQy1Ddm3V8HlIfhy7V69jebAp\nbKvFgMhlY2Yr4FnEfwb+dEfIBsDdi5k9G3gDcE4DBTN7PPDYdoxbdgnI/Ti/0UL7P2VHSD6MYywv\nqR3n63eEbBEREZFLoqDdlBpLchmJZD04RiXaaqyRbdDCL7Tp2/RvFgq1FoqPrZv49op270veQ2RU\nwo3am6t5W5fbW0W7hW3iVAw5cdVqIF/lnCLF/OVSKbWyWmVObWxwKm1wihWDJwZP5JpILWRbG/de\nbKTaSLERvFLqFrU6xUcsQUlg5niKRm8pzcuDOXNAhrbsl9GGg/du4P096dXumNxutPnrHvO2a/V2\nrOU87b6U1xS1I6gvQnavbhv9vohcJjcB70f8wN6+107u/lYzeynwtF0efka7fZ27/8V5zvdKIiTf\nbGbJ3fuc6cM4xtKb3f2/n+c4IiIiIhdEQbsppQVty1GtNZ8qqlgbC50iWFqa18WO5M1UjY2QvZjf\nTHQwr3URUqe1sFq112nLgLXw6Mv2Y8YQE60ZNjKnHEpKrZodXcrzkFmtVqzyihUDg2eGOjCUeR61\nuUGG0ROlVeRHz1g1KN6athEl9eSYG5ncpqrPQ717JDaPweERoHvkXXbb7UuJRRM2a6+LGq/ZcKzS\nGsPVab3s6pXal/qa36z+FrWPO+L7Sd19RS6nj1ncv+M8+76a3YP2J7bbj7iArt4r4CHAOw/xGJ0D\nf3rAY4iIiIgcmIJ2M5Z1u1faMOU2J7kvgD3dtnWpk2+/32OotUptpQXLTG2Nx9vodFKOJcFSjuHn\niagiJzJDRGUGEisSG5Y4lRIpZ3wwnISnPHXkrqWSspGHxJCNwaLeO1TIBcxjPW03x5OTUqGmiue5\n+Vsw3Aqk1i3dUptr3juKJxK1DR23qeJMH0Tu0S6tV/GnruPWu7K34ecpwraZt1zv7QODGMJeawRt\nPMX76I7VWCDcrUS/cyO6qW9rNCwiR+whi/u7dgJfePse378OLvgH14FrDvkYS7sOPb94lbjE88ls\nb3IuInLYTl/pCxC5IKdPn+b06fP/e7u1tXUZrubSKWg3Y42g3dfPjjDZg/YicBOhNQrSta0DXWOo\nubUqricohhfDS6KOUEejjnHoPCSGIWGrRM/0KTvZImivGFiR2SBzyhKnLJFzm5OcHHKl1kytMVzd\nzEgtuCeDHLuQ2+h1bwG5tGslewTqRWV+apRWC9UKKSUqA9O619QI+dbbnNEq9DbN1nZv9eY+cXsx\n9Nsst8VyetiuUaGuHnPEy7yOeJ2mske1vLT1t6tXkpUYyG608C4iV8DFfsrVf2j/BHjmBTzvrYd8\njKW9upFfgjsP/5AiIiL3c7fddhu33nrrlb6MQ6Og3fSKdl9uy1vDLu8ptC/nhcdwamI5rgjZlZQS\nObVltDzj6wEfDV8nypppM4zVRsI2MqkkbIC0ioA8pLSoaOeoaJO4KiWGnKJRWY6sHSE7UfvQ897P\n29os5gp5tG3zvqFSUsVyjYMMDtmxHMPKI84Wiq/JnqeKdrVYy9p72G7BvdJ7vLUh8O2TCHNbfC4x\nr+Pdq9kQc+AdpoZuUzW7BfCpR1Ffc9wr5iVG8MP0OkXksllWfh8G/NU++z5sj++/i/jhfp8DzK/e\ny2Ec48iYGddee+1598s5k7M+LBSRo3f99ddf6UsQOZDnPOc5POMZzzjvfk996lO5887j/6G2gnbT\n19GOKiutE7a1im+KRGnzfOEpCFbDrZe6wWu0Dq+blbpplE3DR/Ax4WMiYdQxUdZG2ozAbacSeSMz\nDJmVDWyk2Fa+weAD2fsQcMcrEUrbutxeS2s8lpjW9G7zxL1Orb0xKoaT2rJfK5ziUN3a1pfxssjC\nvRu4F6q3Unyb772tf7rF0l1ujrd1u+f3aRGU8TaqPG6r+/QBQbRTi+jeB4R7O0FUyW1q3N6XE0u+\neN9F5HL4s8X9m4Hf22ffm/f4/h8BjwM+xMyuc/fzDUE/qmMcmRtvvJG3vOUtV/oyRERE7nNuuOEG\nbrjhhvPut7GxcRmu5tKpKNiU0eetVMZSGUtf+iqW36re2pt578IdFeXe7KyW9vytyvpsZfPeytm7\nKlt3w3hPop4Z8DMD9Z5MvTtT7kr4PQN27yny5lUMW9ewKtewUa9mw69hwzcYPJOqYdXxUinjyLge\nKeNIGWPouNcCXrCp0/mIe6yR7XULfI35SPKRTGFFZeXOhsPKjZUnVp4YPAJscsOmJmURtIuPFF9T\niK0yEr3RS8zttkrrbsY8R7t1VK8Vry2410qdqtQwDS/vjdX63HjvS395VLnr3DDNPV6nzwujicjR\new1zVfvL9trJzD6AvdfR/uW+G/ANF3kdh3EMERERkSOloN2U0rcI2tNW+9DrPpw84d6Xqmoh241a\n4/m1OON6EbTvrmzd44z3GvVMpp4ZqPdmyt2J8a5EvWeAMxuks1czrK9mNV7NRr2GU341Kz81V7Rb\n0K5jiaC9HqllpJYWtj06rhkVvOA+4r6F+xrrQZtC9sLQKtorYIWxMQXtRHZrvd2ic3oP2ZVxW8iu\nxDJhbiNQYBG2fdEQrYdlr7HV2oN3X8arV7TTdBtLkdH2n0N2DGUvi4DdNxE5au6+BbyICLgfb2bf\nsnMfi0Xu/yO7rKHdjvEyoiO5Af/SzL5ov3Oa2aPM7HMO+xgiIiIiR01Dx5t56Hjrs92WWzWrkFKs\naW3eGpD3KmwsA9ZHkU89wNzAM5QMY8bqgNWB5JnkiR5Cq3kM6LaEp4GIvStSWpHzitSWw6oVvFXY\nx1bJtlY9NqtRgcYXw6+ZWhUtF8mal7m2thp1ZeVQs1E942nAU8GtYjVFJb3Quo7HC6zGNMR8Dset\n8Xq7CrO0vaJNH4buLb87xcu0T282Z4vqdp+fPU0EtxohPLWl19qtiFxW302sS/1w4Plm9mjgJ4gu\n5B8OfDOx3vYfsvfw8S8B/oDoYv6zZvYrwM8Arycak10HPJpYL/uTgRcAv3oExxARERE5MgraTS0R\nrBfRsM017iGxdxb3WF4qMbXrNqvRAIxoBFZrwoaBtBoYTg3k0UijkUoitTncvapbU2G0qBUP7qws\nUfOADxvRibs441jxsTCOEbTrWNo637GlPtc61W3LXm1fiTp4IZp5J7BcSNkZcsLzgCWPpcLygBda\n0zeL4dtWcavRkZxEshwNziyTUswATy10e/8AYupWXnBsasrmtbZ55i1s9wvsQ8jN2vW2fuYGlozU\ntpyMnCBrPIbIZeXu7zWzpwIvA64Hvrht0y5E1ft32+1ux3iDmT0W+K/Ao4DPAZ6+265te89RHKNR\nowcRERE5EgrazRy025rTvbP41Myr0BuCpeTRXdysZe3cmpDFxpDIqxXDxoqy3oiqczsWLTzXsVDH\nQkkjI5VEZTCnZKNuDLhvUOqIlxFGp65Lm5cdW2q9yVJb8SvVqDanqdt3C6i+CNxToG19zVrQXuWE\n5YE0GJmBgUqxEkPnqVF9tsLISLGRlDLZaoTtDLVGAHbvS3r5FLbd57Adw78rxds87b50WAvZUdie\nK9qtro0Z5AwpR7jOGXKy+LBDRC4rd/8LM/to4NuAzwceAdxFNEv7UXf/WTO7hUXPxF2O8Vdm9vFE\ndfwLier3Q4mlu94FvA74b8AvuPsfH9Ux9rtGERERkUuhoN3UPnTcYoi4Tz25omN3NO6KOdApZXKC\nlAxrgTu1ucXmCWomDxv4xgY+bgAF6gjjSPXKWJy6rvhWoVAYvUAtDAbjKlGuHvC6EYG8FMoYc7PL\nulDHkTqOLWDHfOoI163S3oJrWwX83M0Wt9nJOarYachkzwzAymBd1mz5GnxNSSOVSrGRNVvkHMPM\nh+RUM2pK0xJcc1WaFqIruLXZ1JVS56W84v2O998WF7eswBvtw4QEORt5sBa042sRufzc/R+Ab2/b\nbo/fDtx+nmM4Mdz7Zy7hOi76GO7+wRd7XhEREZHzUdBuciuPxpLQPgVu6xOe2/BxvODV4vE2VDqn\n3CrH7U9KpJzxPMCwivW0U1ufihK5e6synh1JVqi5xj+J6tMa3tVhdCjVGYtTirfgHVtMHY9wbS1g\nJ/MpcFv/2ha9vBdD3w3w0oawT8c1KIaNFtXtIeOrGGZezSlWwUawTKwrnmJwd7tOM2/N2BzzGutn\nR292jEylUK22be48PjVMa1X4lFuDNEu4Qx4SeZXIQybneG/7muUiIiIiIiLHjYJ2k3MG+rBxx61O\n1VZa9+zo5k2770AmmZFSInJ0Gzoe5VdsGGC1wkeoKeY4u1ush70ulLMjJReGVZ3WyI41pmPOdamw\nLs66tCp4cbx1NjcDa7epDdPuYTu1MJ37fHKzbbf9A4QYvF3nZmdDrPXNkLANI9cMGD7AaIWURqyF\n7Bi3ndrc6/gModT4QIIEeMxdz+a4JZLFMPR+PsenZb5qjSHntLnYANlSjIV3ayE7kYZEzpmUEym1\nOeIiIiIiIiLHjIJ2sy1oU1vGW6wH7eBeYpkqS3jyqGJbIttiPnRNWMpYC9q2GqhbTs2tktuCdt2q\njJsjw6riG1FVbsXgqdn26LCusFm9NUarU9i2dl2923iiRsDGScnJLWhH07BoHuYpqs/e5nK7V2ot\nlFpiLvSQYQDLRqqxXrilCNajFdYeQdtSW9qsnb1iUY2nJW4nwnyKyn9KiUyKmN2q2dVa5K6VWj06\nipPa3PcUQ/AtY6RW0bYI2znFcP22n4iIiIiIyHGjoN3ktKxoR+drp63f3DuP1xSdt/ua2n0dZ2uV\nXCyapnmhz0rePi86Zh97jeZrZaswbo2M68IwFspYGEuNtbxrbKM7Y4Wx0p7n0bitz4n2iLpzNRuG\nBEOOId8TIxJ87zAGEXJLdAB3976KVgvIOTp950yyREojyUdSLlhtlfu+jnhtQ+7bEmjTsHsHb8uP\neUrzOtre1ybv763Pjdra/O4+JL43QsvZyNmwaV58au+niIiIiIjI8aKg3aTU3oo2bDyl6DQ+1kLk\n2doCopF8Dow+LV/lPQlj1aklk+qA1zXuYwvlTMtcjcVZj4U0FtJ6xLa2GLbWbKzXjGWk1Epxp0xD\nyaHWWO+7jh6V6BJNxWxZxU6wMUSN29ra1EuLxcvaRwFOaUtute5vEXozWJ4rxylvkIfKKrVAXYHq\nbV1tosN4WwIM4r4lcIvu4DVZn+o+LZE9D8GH+FCiLZXWV+pObch7G63ev+7LqMUYdRERERERkeNF\nQbvpFe3oOJ7APBbdcijUCNv0+dvRMK16m29sFWqNJmkVqDVCdhmodRUJuQVKJ0JzKZVxrKT1SFqv\nsa01w3qLU+uR9VgYS+vO7R69zt1a8HbKWBlLjXW1S2lBO4aIDzmGc5sZmTQF7b62tk9bfERQ3dty\nWxUKbQh4bc3GokmcpUQi5lsPKVG9xFZLvF/T6jj/P3tvF3PdmqVlXWM8z1zv++2iEAjSVU2ZRu20\nMTRwACTGqOkGVALSAYLGH0IrJCJEMdoo9AF2VYyREKIHiqEOFOREtFsjIYo/zZ/REOlSFFAjahTs\nygfdQqeqq/Z+15zPM4YHYzxzrW/3/q/dtd+qGtfOrPV+3/qba31779r3vMe477DEXQzEEIvvSjTO\nD08J7XGE0F5/AkKo9xTby9FWz5VwQTUD1jJ47lXLviiKoiiKoiiK4nlQQjtRya9C/TyE2F+WdHqX\nC2spEs9AL2YK6Pj1EtpqG2pHCkM/R6M9Hephjk5DjgHpaB/jYIzBmDlGbs504picCeT7YRxjsh8D\nEaep0pqwWQjk3oXujWwpyzNcYttPwW/Ea1sK7NWF7Wr0BtoUtKPiIeS9MezA7QhHm7So12uL4TKJ\nkfqYDBCzTDvXcxebFNxZBpab3nm7ktRXT3gLV3ylqUM62l6j40VRFEVRFEVRPD9KaC9uE8zptFqO\ng4cQVNVwVUXRmGM+d5RnOsErRZvcP3Yb2DxQbzRptNZoF9geH3l47eDFPpHLhnRlYuxj5+n6RH/9\ny7AJx3VwXA/GfjD3gR8HPmMUHZmIOq2BqtJ7o/fG1ht9U7RHINtpIK907xS84Wfnx16L5Ol+r61v\nM2UeggsMF6YJY8LcBNvifm/hOqM5Pu7E52eGu+2OSAS1udwqyITsHD8997UgnpcFxHLYXhDviLWs\nDxMwRbzl84uiKIqiKIqiKJ4XJbQTt/xh1Xt5urXurwhtyVopifgxzASb3Bzv3H42n2AD5sHmQpOO\n9k6/SAjtjw3GNEwEU2H65Eihra93rBFBaUtkj4nMiUyLjupMGW9daE3Ztsa2bSG0u9KaIk1TO/t5\nbre/9Bwhj5H4lV+eWebemFNO03gYIbKHYFMwE9wV34CWjdotcuHO8DhmiGO1ENncxLai2eilkP3e\nK8M8zjic8rg2YCm2QVwRn7jFaHpRFEVRFEVRFMVzo4T2IoW2C1hWfEW9VyZfS+4qazsrr9L4Dkcb\ncg97jWDPdLR31DubCkpHLp3L42QOwwz2OdhtMufAx4Fen/DXhcFkHiOOfSJmdKDhNMjOaWhA60rf\nOtvDxmXrdJW8KJBesYdgxXIsPnu3zxwyIdxkOMW20cAUH3J+xjFgNHATzONCQzw9U8Zbdod5Vodh\niERcupyOtoFo1ITlOL1k0viKPXdxYtg99s/FHbGbCy7WEbMMcCuKoiiKoiiKonhelNBOJEWjp4N9\nalCRHBUnK6WUSLtejw+xLZKp3dlN5WJMRoSYsTF5iIRuVfSysb14jPc4DvzYmQfQBHNjjAO5CnYM\n5hjYMbO+S2kpoJsoLZeZ+7bx8HDh4fGBy2Wjq9JU6aohVC1P1MINhxkXAiSC3qZr7JbfOdq4xKi5\nRaf4MYxdjF0NHwarz3sKbQIP8V25y9kDnnFrEYyWlV+RbHY3qb4OE0QzDO0uEx2ihkwkRgbUc+fb\nIoCuKIqiKIqiKIriuVFCO9GzKirkYeRs5z6zRLVUqMPYKz6bqdLBDZc2d2yqvwAAIABJREFUxKYp\nOYI+wpf1C8MPDh80OtKV7fGCtgbHFfaG7xoD3S2cXhsDmwOf4YwjEhq1NXqOhYe7rlwuIbIfHx+5\nPDyw9U5vna03JHem3RyfkzF25rHHrRvDhWERsJadWvE51745k+nG7oPdBrsf+IPDcDgcPaC/yGAz\niZKuV+LR0ql2LJLNVuf4GT7uuMYIvXp8d5qfVfJPxl2zKTyEfIyQe140KIqiKIqiKIqieF6U0E6U\nW70XMfR8jjS7Kq7L8b13fcFX+rVHDZW0cF2HOiaDIYPGheYHzQ5Aka701ugPAnuDrtCFMUeoSw+h\n7XPgNsEmqKAitAZtkwhW653WOg8PFx5fhNB+fHzBtl24bBvb5ZLd1dGTPcfg+vQG1ydhuGFzMBAO\niwR0WQJXVjK6hSNvznUMruNgnwMOhx1khzZIkS1ok9j1FjlFNLmXTTrSaIyOe46Imxjq2bUtMVfg\noiiKaIsLHq7xHhk+p55CmxLaRVEURVEURVE8P0poJyrL0Y6xcEdvkloc13Rpzw7o+Nlt9VbHcyUd\nWtQwjT3lKQeTg+ETFaf3ztY2mm7INYX2phzHgc3BnAObM5zfKOaOaqysuuo9U8a3zpZj4y9ePPDi\nxWO42pdwti+XRwDcDDPnOA5c4LAR4+oow4XdojZMNf16Ecyirmxa1Ixd9xHHMeAAOQQZQrebyJ5d\ncFVMJb4vkdO5Bs/ucaI6LfvIJe+3FNuO4NogBTUobim2LVxvCKGttxLuoiiKoiiKoiiKZ0MJ7UQk\nHO1wSsMtFbdzB5sMRluuNoQYXQnlStZYYbg500NYygYyY/Rcm9CksfULD9sjl/7A9rDxMB547XjB\nMY4c6z4Yx4GNwRwH8xg0hcvW2bZO652+dfq20beNtvUYQ083GRVEM3WcEL9Y1o61zpDG7sLuwtXg\nOp0xHFULsW1grphHuvg0Z1qIYSdC4GQCu8S+dYuxc3OQLtA70h+jXqxNROPA7kLYxM9AN8+J/HXR\nIg4FGkjLpPE88q8mchaDFUVRFEVRFEVRPCdKaCeaQpuspoqOZj0Dt1xWBdZ6VOSLrzqrbJ5GCEFp\nxNPZMv/LQE1oqlwuG48PjzxePhYOtg2mHczj4Niv5zGOg2PfGceOAFtb1V2dtm0hti8bvYfQFk1X\nXgSW0BaNMDSNye2pjYGyu3I12Cdcx5uEtmTtl0uOjsdo+fT4lJGn5rfQs+zpnhPaRWkPG+2i6NYQ\nn0gbiESKuPuETHSXnBJAHbF0wc8vLg9VyM5sQW9/ldAuiqIoiqIoiuKZUkI7WUI7xLOFQEz3Fp/p\nanO6xrGj7ZinMGeGC563lk6ydEFSSIrHbvX2eOHh8QWvPXyMW7r2ZI6D69Mb7Ncn9qcn9v3Kce3s\n1xaJ2xp93k013OzLcrQ3tGmmnZOOdrjaaFvKGZzT0T5c2O3e0bboChdD1cFv4WNLbJ+OtsUIuAtM\ny+bwCeNwLo9rxLsDG8JAZSA+cD8wjw34/PpIqY0rYIJrjOOTjrbQ8qJHOx1tFUHztiiKoiiKoiiK\n4rlRQjtRTUfbPIO6Gp7J2zHSHDvFEYpGCsW8PwPHwMAn4DBzjxs9R7k7jd43tssWSeEPD8BEJLK6\n5xAUo6U7ruK5Mx171iq5Dy1yBqG11lBdSejONGPfdxxhmDFdOKZzTOdpH3zxC1/kC1/4El/88de5\nPl05rgfH4ZgJHbm54BIOsosiLtDClW9GlnZ5fifRc80wpjuDUOA+oR2gW6NdQC/pkkvUfaFAX21f\ncrrV4opINIbLLZocSGEugoii2mlSf/sWRVEURVEURfH8KKWStBTakqPjSMfdw331GcnduaftGlVc\n97il8LaQoEw5NberoE3pumUa+Ma2Rf1WvGc8TlSZTbHesNnw2c/dajfL+rAIK2u90ZqGyM5RbzPD\nx2BO4+m6gyjXw3jaB2/sgzeeDr785dfPY44BNvE5Q+pqA2201s/qMJEQ3pbOtiNYOvmGZzL5wHxi\nYzBw5jD2J6Nt0B6EdlH6pUE3pDWkGbrlLjmCaornU+CHkx2uOqtHLW6z17xpp+vlq/c3SFEURVEU\nRVEUxXukhHbS1n4zjZZp3y4hIidZkaWE253Oag49x/9mHdZ0C6GeKdlujrfsv87Krcu90F7j0+6I\nC9YUbw3vDayfItPMQpBLOMCtt3Mv+6zjMsdtMIZxzMkYk9ffOPjSGztfev3Kl9/YeXraebruXK+x\n991V6CpsvUXSumz0vqEtXj8EdwMEl9ibPoW2OcMmx7FzHDs2YY6ZjneEpG2P0B+U/iDoxWhbQy8T\nEYGmqCtNboJeJD5PLJVLfjdkp7fFBQlpNN1orYR2URRFURRFURTPjxLaySm03bPnuaFimXYdv78S\nyF0MbI0zyykELcWu5Uh5xJKvwC9F++q+VlqLOqusmg5EaCr0plhreM+EbiSF9u3BTWNkXHJP2dOJ\nN3P2fefpenC97nzxy0988Utv8IUff+JLr185jskxJscx2Hrn4bLBZaNLQyTG0Xvf4jx7uNvS2imE\nkXaK7KgMG2CCDeeYlreTYzg0Z+zQH4TtQeiPSn9sdO+oel5MUNQjMV0yAC0y3W945pCH2I7vQ0TP\nKYSiKIqiKIqiKIrnRAntkyzvEkdUaB5d2k2UaZncTbjZc2QHNAKZhn2nqOPWsmPb4vFTJ0MGBzu7\ndbbZ2A7JSrAMULMZqWLuqMRYeGst3OqZPvHZN5b92M6dGwwqQu+dR4TWOi7xORxFW+M4BnseW+88\nPlx4uGw8Xi48XjqPDxsPl3662bqC1bI2jOzYnhIlZ9agNTKoLXuzDWw6Ng0zGAP23dkOuIzONrMi\nbNV1iURmWwfNqrD4w8gu7jU6jmEMph8Mu0bFWFEUzwYR+T7g+wB39w90JUxE/hTw9wF/yt1/yYd4\neu/0nl/xeS9evnzJpz71qQ/nxL6O+MQnPsHnPve5j/o0iqIoiuKrRgntk1vImaaobKKoZcI1wvQY\n4Z4W49EqsUus4ufOdm4d4x5Ce84Q2kONIYPhB8e8cgxhbx6BZ+o0BXeLXWw89rC1RQp3C0HuNnMH\nPM/YHGMi4uGOt4aIsGVQ2uUBRGMkHAnRvu8H12Ow7wfb1nl8eDjF9mVrXHrj0jXd8nTMNfbCU8lj\nM349iX7tJbJVQzjHxQhjmsFwuMYY+WUoNhW3DTzKuSIdHfoWIWmiN9feRRDx7DCPiwvGwPxg2BoH\nKIri64yv6X+wzYzPf/7zH/VpFEVRFEXxEVNC+2Q52jkhjYIrLbKwI3nbHbfJnEfsQ2skg4v0EH0S\nz5MsmF4G9RRnyGQwOWwPka1OU6M3aE3OPwnPMXWV2IluLfaVp0xcBGOEwLYIInPzqA+TcNdVBbnb\nr1btZ1J3a8p137nsB9ersm0bLx4feHx84HLZYle7Cb2tjuo8JFztlcY2xYnF9Rh1V72JbSEvAEzn\nOOzc53acORWfDXwDNJ7XnGhWG0iD1gfgdyJbcLntZ5tPpu85YVCWdlF8nXK/VPM1yM/+qE/gGfES\n8kJpURRFUXwjUUI7GTbihxxTjp1ruznVKjnqvCabMwUbw07Bt8ae4xGyBLqBT2cyGPPgEOfKQGVn\ndqV3wXo4wmiLSi1pZ8q4KrgrJlHxZRLiNlK5HdVGa42eTra2ftZ/hdUdr71tneu+s+87132jt87l\n4cLlcmHrjaaSB7f/zHWiM/wU2kR9lwiu4aS37PbWu2OliEcvedagTcVGY+6d0eGqhhGBao9miBpt\nc9T9/HpNYmdeIgUu3G1xXOZaky+K4usId//Oj/ocvjIU+OGP+iSeEZ8CyuEviqIovvEooZ0c48if\n0kTxGFe2jOI666dUUdPojz4fN2FFlaXQFqKGKkK+BKbhPpnsHD5Qj87ovinbptimtN7RtiH9Eoni\nmTDuxDi7r+RvcSSqraFFMFqEl3V667QzzKwjLc65bY2Hxwv7NYT2vh+xA95bPKc1VMKZbpJp6e55\nS77v7TM2idqydo6Nyym2W2u0HqFpksFp7tCkId7woYyrMzF2O2hjYOLo5lxeZACc5/6557eZI/0i\nEuPlaiH8i6IoiqIoiqIonhkltJN97MCradeIYxI70SI3l1ZVo8KL2KvGJNuoUgyvcWtSnOfYudlg\n+s4xgem4wbY17KL4aPTLhX4J4Sx9S4HpKdslx8lv4WRLeKr2cLP7ul1Ce6N1o2+dh4eNYwzGdefY\nD459jwsIKhmklq+HoLLGvw2TFNvEx1xC21XQHHFvKbLbEtktbs1BzTEN8azSwRo2FGPi07B9wHVH\nu3B5BDM5x+fdHXHyggX5WSWdfqfS0IqiKIqiKIqieI7ouz/kG4MxB2MOjjkYNpl5mK/d7RyLlnaK\n7dsY+RoxX+PkSxQqTXuEq3mmhI/BPHbG9cr+9DrH9Q2O6xPH/sQ8dszGGYZ2E77p5J5HVFtFFdeF\nbdvoeSyxvY5t23h4uPDixSMfe+2Rn/KxF3z8p7zgp/6U1/j4xx557fGBF5k0ftk6ly1G0HtreSi9\nxX73bR87jpa/fz8yHq52u93X9BThIjFGb8MZh7NfJ9enwdMbB9c9+r/NbmP70+JwzwH+/MJjdNzO\noyiK54mI/E0i8hkR+Ysi8uMi8tdF5E+IyD/6Ds/5UyJiIvIn3uK+b8n7TER+Q/7erxWR/1xEPi8i\nx9s872eLyO8Tkf9LRN7Ix/4REfmlH+4nLoqiKIqiCMrRTsYMd/TeKYboyXaJQLJwqBtNewhnjbCv\nlQRuEGPdREWYiiKt02ejzUgwP3WhLFEeI+iagrqliO7amL4WlS1G0kVxDYe7tRa72Bl81jRupTVc\nhOngc+JEUnocK7tbYgQ+X9/OajJPJz4uFqiAmMQIdzwr1qQNxByZEXS2BPXq9ZYcn/fVL57vLe6Y\nGsIEnaAD3QwujnbDNcbJh8M5N+6A6/n95x8SkgnnRVE8T0Tk5wA/CPxt3ILNXgO+A/gOEfnVwD/u\nazzoxnsJQotBI5E/BPz6d3q8iPy9wB8Ffurd4z4B/EPArxKRT7+nD1QURVEURfE+KKGdTFtCW3HN\npPGzwlkz5yyqvJoYaCRghwh2LLPCHEfEw/2l0eh0bVEVNpYjfZc0RopYCWG+3N/eWhZSW4ykewaS\nuWJAO3ext0wYj11sEY3R7hS4N6Eb1WHufoajmVvsQPutu3qJbGmOaFxokAxEUwFDkOkhtBWmQ+sz\nne0cRV9xcbnjbWbMaYhHcri4IM0QHWg39AG0O6gzPUQ86zsSzq7yOPK7wrHa0S6K58x/CHwL8O8A\n/zHwBeDnA78D+DbgHyZSsr7nLZ77Xv7h/hfy9f408PuBvwT8NODnnC8i8rcQIvvjRE3BZ990Lr8T\n+DRQBc9FURRFUXyolNBOlqOtmoaHCnq62WTvc+5pa3xtknlcUxwRi33qrPlSFbo2unb60WmuiK39\n4jM6LcfE478qNcV21wgowyauRE93uuqkY9z6Rts2+nZBtZ0VXginy25mWQEWP5M7zwKRcO6xYy4u\ncXv3n7brxwggT/G++sRb1nup091pe0N1ptC/TQMAtxHwOU8RD5Es3nWim9EfHOmko50p5XYT2sLM\nd25x/qoh+LWEdlE8UwT4RcA/5u7/0d3v/48i8v3Afwv8AuC3ici/6+7/6wd4j58H/EF3/43v8Jh/\ng5uT/U+8w7n8og/w/kVRFEVRFG9LCe3Es6LLM2Wc7LHmFMQQLdmN1S1NhpNFIvgEn7mrbZhPzAfm\nA2ciCq0r3RsNobnSmPS+5dFpbTt/vbUeNVo5Ri0a72WhyOmXB7btgX55QFs7zxnCnbc85ozbMN8n\nbkb2jTEs9tKHHcwltFcfeIaPndcZcnjcRZhEMNoU5xBjtIPZBt4PvA/YBmwTcaMpSFPaBtrXIbQH\no72mtNcm7TXh8mi0Hufmd7Vi4hm+tgLaMgt+Il/TLbtF8XWOA3/0TcI27nD/soj808B/T1zL+2eA\n3/Y+X1+AHwP+ubd9gMg3Ab/6fZxLURRFURTFh0YJ7cRXgrWQadZyV4m9FF0miZ8xaIIT+8OZQY77\nxDGmDfA9ksV9C/dXla4bXYwmna7OtnX6luFl24XeLyG2ty3eI0Wn4qAx1i6t0S+PbA9xSDraGdbN\ntMGcA7PBGNHd7QNsxE65MTA72MeVfezs48qwgSjQQDQPkfN2iWxcMGKF2hwOnF0Go01mn/i24+NA\nLoMmRvPoABdv9IvQL0q/CPpg6ONAXwz08aA/TPoGIjnejsb0ODEuj0mK7Yieux93L4riWfIH3+4O\nd/8hEflfgJ8L/LIP8NpLPH/5HR7znUQLor+PcymKoiiKovhQKKG9SKF9uqdrnnvdncJaQ/IS/+2m\nKbQFSZc4DOgZSpUjwsj0BdKcpo2tbXScLtAl6r22rdO2niL7wtYvbH07HW33HB1vDWkdaZ3t8QWX\nxxdsjy8QbbdBdHfGPJjjYMwD2o4fYGIIAzNnMBhz52k88bS/wdPxBsfckU4I7c65b63OWQEW6Wix\nL56+OFNgV2M0Y26GjQPmgcyBqkdKOw2VxuVRuTw0Lo+N9mjwsCMXgQfQBtLuurF9xa+l0HbBTWIf\nPkfKvSztonjO/NC73P9nCXH7bSLS3X28z9f/8+9y/8/7AOdSFEVRFEXxoVBCeyGrliuDuOQcIicT\nuTKUK3alNWVexn6RRdopeKOeitg4huZoU/q2ceGBrsLWoDcJod3zuDyybZd0trcQ2edfRLd229C+\nnW725eEFnEI7RLmMCEZjCIYzfSKuMCXd9smRjvZ1PPHG/jq7XV/JH5MG6qvBW05XW0QzCD0uApjD\nEJjN8Q5cBmqT5o4YdFFa7p0/vOhcHhsPLxrtMvHN8MvE+8w98LhoEeegyApBc72JbcLVdomO76Io\nni0/8i73/7W8FeCnAz/6Pl//x97l/p/xAc7lQ8CAn/UeHtfy+Hrn5Ud9AkVRFMXXCC9fvuTly3f/\n/419378KZ/OVU0I7aS0DzjLd2l0w91fqquC2rZ3pYOAphD2qrqLzeQWTNaCj+sDWX/Dw8DEe5WNc\nNmUbyjaVrWsKbWW7XLg8vGC7XNBto5E7yW7xmlnfJRpd3nGe3F0MeHWj/Jb+HfvNPh2bjk1jjhm3\nGZgWoWkZQpZBZOc1h3OMPB1ty2MStyiugmwSf0OpoJcGturQGk0a2wW2i9MuhvQJbeAM3I/ccfdM\nN1eU1Vnesptb4/clAutE9C5crSiKZ8hP9j+g83089qv8L4v3e82gKIqiKIrPfvazfOYzn/moT+ND\no4R20toWPzi3ELL8tcutgou8XWPLS2RHlVXuLpPj1nREN1p7oG8htF+0j3OZjYspF2tsTek9BHff\nOv1yoW0XWu+YG80N8x7nkyJbtIWQJ2u/XM5U8DzRqARb9WQx1Y5Px6dh07ARlVs2c287xbZZ/F6q\n89iFtrtgtNzTDrFNus5xAUC10VVom7KZnL3jKlFv1jq07vQ+8TaZMnAZxMTo6dsDikqnZWf5SmNX\nWantfkt4L4riufJNRH3XO90P8W/dd3OnPwj3r/lez+UrRkT4mT/zZ77r41prtPaN4GgHn/jEJz7q\nUyiKoiieOb/5N/9mvuu7vutdH/fLf/kv50d/9Plf1C6hnbSs7Drd3xTNZyt0/nCK7XxwiGzDbP2c\ngteXo72h7YEthfbj9nEerPHgnQdv9KZsXehNaV2R3tHWkKan+A2xLYj2FNp6Cu1TbEP2bN+d7Jv6\nrE9H+15knzVgzrDJlDjc7ezxRvyVFHJ3idjx7BbvstF0i0ozaVmD1l5xpkUUFUfUUHVMBsYEwtU+\nJwSQ/HyNJhtdeuyKy62D/FajVkK7KJ4xv5h3Fre/OG//jw+wn/1e+Asf4Fy+Yr75m7+ZH/7hH/6w\nXq4oiqIovmH45Cc/ySc/+cl3fdzlcvkqnM1XTgntZGuPAJgZ0w3Dcgw89Z9HV7ZqjJcjHk736bY2\n1BtKBwSVC00f6O0BlY67MKYxNfaREVDRcDV6o3dFmyJNiTdZHVsZAZ7nEf3Sho+BoUyXfHwEuBnG\nOHaOsTOOK8d+5dh3xjGYc+bzBdWWR6e1jtpEFezOtZc8B9HbCL1kGNpytgWlaY/xcF0iO/LYBQ9x\nzQxx3OJUWyPH3hXzhnu7NWwvsS1rN5ys+vJzFT5GBm7VY0VRPEu+G/hP3+oOEfnFwLcT/1T/4E/S\n+/9JYrxc3+O5FEVRFEVRfGiU0E62HkJ7zgk2XunF9hTdqjFSrin+4Ca0mzSm9EjpRunyQNcHentE\n6LjBOA52djYVXBvSYsRcVWm9o03PtHO/c6Vd9OaWu4HD9AMxhzlzVDz0p7uzH1eOsXMcV8ZxMI9I\nIZ9jhpAVRVtHfaMxUC6oWIxoq2S4eFxYEPWb0F5VX2tc3WMXvWsPsS3RMb4a0QS7c53jsbqce2m4\nNcw6bnlRw+yViLlb6vv67Lk3LoIq5wWIoiieHQJ8l4j8Onf/gVfuEPkY8PvzlwZ89ifjBNz9r4rI\nHwF+7bucy2fh/l88RVEURVEUXzkltJPlaIsPXBVjx1YvtpMOd9Rd4auFylP0KeqNJk5DcGk0vdD1\nga09otIxc45jcPjO3BousROuKmhr4So3zdqsW6jacrajNmyNtM8IOZ8GMm4iO4PT9uPKcVzZ92u4\n2HPmmPjEzFPwdppvNB9xiOEoJmtM26OeSx1pftuP1hTZOR4vohl2pqi2nL2/OxIXEO1oV9omqDR8\ndmwaTo6viyBuvDL2DueIPktkS3Rzl6VdFM8WBz4H/Aci8h3ADwBfBH4+8DuAvyMf82+7+198m+d/\nGHwP8PcDH3+Lc/kFwO8EvjXP9UMbHy+KoiiKoiihnahGKI1ICD1HWAHckfztWX8VQWFyVn7Fz7GP\n3GgSQlul525yw82Zc7L7ld07Qzes2ekQR2d1VnKZpbC02P12x8xjnH06ZsQ+OANb57l8d4/H7Smy\nj+OK2Qo24+51/Qx3k5XonWnrgrLms2N0O5vCU+DqClrzcNpjpFzz/AUsQ818udO3VDnPUXjRFvvu\nLqgr6h2ICxprYh7I79zOJHTPiwSo3tz1oiieK/8I8MeB3wL81jfd54Tg/Z63ee6H8g+3u/9lEfku\n4I8QYvu3vulcHPhMvl8J7aIoiqIoPjRKaCfDDyBGskce0weefdoxL04mc78aN7bcbVWhSYx/a7rh\nc+4c1thniPAHa4x+wXlx1mUFOTptk7mOcTDGwRiDMSdzOnMa0zxd9hhlnx5d2WbGsMlx7Bzjyn7s\niKeDLXlpwCfGjFs7cBu3dPH1+z6iOkvAxTGyOVxTYJ81ZnnBoUXHeEjy20UCUtAvUR3Pm7FXHt8m\nIuHqO4ouB3w1jGVg2wpyczOaNmgb0gRtNTpeFM+QuKwXIvcXAr8d+DXAtwAH8D8Dn3X3P/xur/EB\n7vuJD3b/0yLyc4HvBX4F8EkikfyHgH/L3X9QRL7v/b5uURRFURTFO1FCO5kexefDBzNF9vRxSxtX\nOd1gt3CyVcL7XWPVKtBFccmwtBTaA2WP+C+uNMZ8gbnddXRz9nGbTWwO5hwpsg+OcTDmZGRa+BiW\nQjsE6TRjzMGYg2mD4zhiR3sccU4aNTIqgjOjMzsFtduENbDuMV4+fWTWmWPLxXYiST13xaMSLL4e\nF8Wl0e+c+NinDiEuWTtmmZ5uNnO92s/vVl1AWyaoh5CfFt3adtaSOV1BtkYTIlmtKIpng7t/hnCI\n16+/APyuPN7ra3znO9z3l4H33Ynl7p8H/tl3uP+V8y6KoiiKovhKKaGdjDuhPU5Xe2Y9lcZOtGUq\necxv085U7lDjmuPTLpJCdGBmHLnbLe7sbIxx4FiMiishynMM/RTa4ziPENuDMeZ52OrsdmfMybEe\nM/PxeduacumdrW+0piGmmTmWfmS1lkVw2RLgNl9JVffcEU9ZnZ8tRtqj9nqN0OvtYoQb4iGyYyA9\n39vWSHh0kUsK+XDEHXXB5jz7vO97v30YdKGJ441Y/C6KoiiKoiiKonhmlNBODrsCMNzC1WbG2Pi9\n4ASmw5gWY94pqrOrC4jEccNinNtiTFosHFtxYZ87h+0cM8QwYhEAlkc40yGwj2NnP3b242A/Do5j\nsB+D45ghsi3E7k1oH4w5MBtMC8FsTRGL1zZTpuX9c0RnNoMhk8HBsJ3hO8OuuRcNMgVtQmud1oze\nOu6e7rSn9BbMhWl3iePOzc322Pke5viYGPGaa687erPzK1Rwk3P/etV6RaXaTbaHu15CuyiKoiiK\noiiK50cJ7WS3cLSnGRPDxDCJ/WbPRWN3YZqn0J64x97y2fucG35LkE+HYY6m0MaF3fYQ23lAA2+4\nTYQQ2jPF9jh2jj2Cza77wXU/2I/Bvo8ISDuF9uAYI4T2OMj88bjtLZO8J9OUMfY8Dkwm1gxXY8iM\nCwB+5bAdV8dn6FltSu/G5n0tX57ONURI3LRo24pO8dgJj4C1FMwu+AwnfJjTmtK2TpeeYWv31eES\n7bf5vLMzW0CkRfgaUkK7KIqiKIqiKIpnSQntZDna02HiTBxXyT5pYhycNaqdY81tVa9m2JjmcHUG\nlA0zDnPEUhRaCO1jhvt8zAN8giloA7dwopfY3q8c+xPX6xNP153rfvB0PbgeI+u6nDlTaB/xmnOO\ndInDIXZr8R5M1ORMI9/3K64G3aHBVGP4NRxt35niuIIJSFO2TBF3ydauO6GNRdc24uc+eNcewjhn\n7gXBpkHuhLeuXARa0xTP8qrYXsoaOC9kSAj5SEa/v78oiqIoiqIoiuL5UEI72UekjsceNpiHgy0W\nQWF4Jn7PELc2/S55PASomiNioalzl3vOyWEhsnHYjyvX44mn44mn6xuYNqwppg3Fcz85UsfHOBhH\nHMexs+871+vO034wMxxszhw3H3FMm7SW494aI9uIYRgyYb8+se9x0ByZIF3wZgwOJhEEZ+pMT6GN\nZg3YiL3yMx18lYrn6DuO9BDJppZutmFoXoDIkXsfOI3Woj87Eszm4LtaAAAgAElEQVSBvKgh66KF\nhrst6YwLGqFuGv3dJbSLoiiKoiiKoniOlNBOjmMAWTltEocrzkSAgTOODCIb0Wft2WN91lQTDrfj\n4TjPSAmXOcBidPrJnnhje50v9y/xpfbAQ+s8tA1rnUYkc7tl+vcYzDnz18bMILTjOJjDovJrhJiP\nnexMDs9uaxdjGthwxCYuxrFfYxz9uCKTDGmL/uuhAxM7k9DPrm0k07+NKfPOZwYRv/1K7iu7Y789\n7orxcl/N2q650+2MYajmnPhyrTWmCVoLgS7Wom/blKadpre6sqIoiqIoiqIoiudGCe3kJrQzAdvD\ngZ5OjEZbiOY5DJshxo0Ulu2+gDUD1MYS25MxwGb8+mpv8EZ/g9fbl/mSPjD7hvcL9C2qwULpR73V\nmK8kcEfF1+A4BuMYdwnkK+nb0xUOke2q2Jy4OSaO+eQ4rnnsaIuU77Y+q8bONrrcejl7sN39FPsi\ngp7j3Ys7+e3rskM0cAsWlWee35dLTA0sR35MVOUceZecpI+R8YZ6o3lDraHSaNJKaBdFURRFURRF\n8WwpoZ0soR2CsRHx1+AjOpx9CmYREGa2HNpVsXV7HU3fNiqpZt46MkJQtqm80V7ny/rAg2zY9gDb\nQLYHTBtnmpp7JIOvequ5OrRzH/sIwX3sIzqr73abwxUO0T7dGD7jsBFBaMeVY+zohOZCJ4LcrEd3\nNllPLfdiOoU2PmJPWjR32Mm48FWqfXP3PT3/+Hosf1/i+zJhGug0RCat56h7inhyVFyFFNqdRkO8\nnYPkN1FfFEVRFEVRFEXxfCihnZjd/cLjF+Eup7t95+62FLNNlKaao8ycFVWGp/PqNAGyxooW4vTw\nyRvjypf21/HcbQa4tB7lVTmuPS1SusOpBlWh9c52uaCt0beN7cHO+5fYlRTLJs40OKZx+OCwEZVf\nEMnduoT5CiJLwY6nDx3J4XjuSXvWmYlkKJpjKjFA39YoOXffxZLD8XwXv2WY5fNnTguIKepOXKpY\nn0UiZM0cZ0admMRj1kuVrV0URVEURVEUxXOjhHbid0L7Vl8lSIq/lXytoURRlshuNG2IegpXEAwV\nR7XRnOjIahneJcrhxtPc0esbmdwdYnS60aXRRWmiTF9hYenvaqNvnQuO+YZ5hret7WcxDI/9bp+Y\nx573MONqk2MOJLuvSaGtuhxwiVCzOJlTZDur3Pp2m01lpyBe4jzEtb8ituWs+VLcc/9bwuU2HHFn\nmqG+dt1v6eOo4yZ5O5luOTHQ4vW/Kn9nFEVRFEVRFEVRvD9KaCdLaDuOO+kkg7imzF5iO0SpInRt\nIYy1pRbPHWlCaDYBV0lHXPF8jeHGG2NnLnGfo9jmcGmOaWfTNznaRBVW95aussbes4RTbFgIazeO\nuWPjiL1uh2MYezraLdq8aKqvpntLutzicaFgBb0tse3Lhk5Hm7saa4158dPJPhPDOce8BU2RbTmO\nnmLbDTHBXHME/y6JjRW2Ztgp0ONb9MwiL4qiKIqiKIqieG6U0E7WnrV5jkS7g0tUbomHU4vnGPWr\nbqrzZnc1x7FVQ4OqnsJYvOEIU5zDJ1c/6HOnj0Z44avqKh6DCtKURqOr4E0Rc6Q1RBtowzCGDaZP\nsIOxD9xhTFuFXRwex+nMsyznNT6+AtBi/3z1fpPnElcSUpHftXuJeK5Jr11pPw9Zo+zrr5Vgnq/h\neZg7Zp694Bbu+t3utecDHcvvWVBRTEpoF0VRFEVRFEXx/CihnQw7N4NzdJxUkuEA3zqv/BwtN5vM\nu9cQiDHnHI1eYltEI5pcI0FbpSF5mAqHG2/MI4LC1ntpOsC90boi1hE3xJyWQhttSOsMGzAFm4YP\nZ4ix+8GT7exz50gRnm3a2NqDzqXqc1eb3IdOF90ldtRRyZ3ullb1TSX73WHmcWEh58qdnAaQFMaW\n2j6rvlYXmK8EcjVERux2i5zf6Zmovna081CtILSiKIqiKIqiKJ4fJbSTsRzU1fd85nb5OTV9S9S2\nGK6OBekU1alBo9HqFLKigrgCDaEhdFRuYtuAwyc+9gw1E7RpJG2rxri4NhxQM9Sc6YSj3TrSOvs8\nsMMZx4EbTInws+vcudrBboPhk4nRwqOPkfB7RzvmsW9R6h6Bap7utjRFaKi23F+3eJ2ll+221+65\nV85ZNxYp6CrLk5ZzDGB97WYwR7yurp7s3Il/K6EtGvVlRVEURVEURVEUz40S2sk8x6Fvc9FCVE3d\n3F8AT8fWWHnhvpxaE0L7rTSvHDNXzY3iENiqd0J7wj4mY2aqtirNos7qokrvjbZtITrNae6Yc4ps\neoehHHIgrvhwpsyb0J47wweDmRcIwNC7ADW57Wjfi+x1LCNfw5VW7ZjNTB23HAHPUXvzU2SHWR1L\n6Geyebr08ZKWS9556cJyF9tB1VCJoDlf33k64BHmFuP8Z3hbURRFURRFURTFM6KEdtKkxQ8pOCV3\ni5toJIx73ras80JjB1sU1QZquDq0JWFzpDr3uj27qkNb+hnkbZPYuZ6ARXUYDjYnr/ECbcImG9qU\n1sITdwS0ReqYNg4DMKYNxjgY82B6HM5MB/j2WUMYR8CYCUxZ0tdiX3rGfW6Cy0wnf8XBgduMmrA4\neZoaqCMZ3raC5PD1PdgpylWEpg0zid+39Neb59eSVrdIFIyJ3kb13VDlfJx6Ce2iKIqiKIqiKJ4f\nJbSTJvFVRGJ4ONmKIJ6Z2SY0aXTtdOk0TWe6KaqKycSYmIxM/47l4xixXnvJ6RpngJiLg5EiO0U5\nEQg250C6sl02XEG60qRlerfiqpjE+LdMwXGGDY65M+bOtAPzEUJZM+hM5dzBNpdTaFv2aNsS4Cl+\nXcIBJybkb6P0uZ9uZojaLSdNPUfIb3vVIbLDrZas+dJ0+6d7jLqnC649969RRGKfXCQqzsyW0I5z\nazja5pv/GIuiKIqiKIqiKD5ySmgny9FWCde4rWTumLUGhy6dTTcu/UJvHW0tD2X6wfCD6c4ww1Kh\nOvNM2CbTuV08e6gdZlaLTY/e7DkZEs709rDxwh8x9Uge10bTHuPbhHQ1AT0ET0f7GDtjHplCPjBm\nGMSN2150JnwvkW1E5dbpaFsK7nyKK5m47kg6y3FMNHQx0gQzwWzta9/2qm9i2xGJCwaIxGvMmWPj\nt31uX3VeWQs2zbBpTDNEod225L+qf48URVEURVEURVG8F0poJ5e2ARlZJkrLUemzQtqgt42tbXTd\nUvAqIi0StTHwkZ50utl3QnuJbfEU2SJRT5WuLhkoBhPzgY3BdX/kOHbGGHgP57ip0ltjEgFuywUP\nl3ncnGwfQM6j66rWArXYJRfz+NNvEYhu4sx0tWfugbsbtk7eYjQcs9tYvK975ZW17leP2/mRKeTr\n+kVGvGMzney8dRdcI71cJO6feXFAAOZ8c59aURRFURRFURTFs6GEdvKiPwC5lw2s+DJxgRZ70007\nKnGIK256isnplsdk2mT6TLE7ziCvm+jMne2MJ1+RYBCj13MCTMa+c1yvHE9PHNpoKLRLCHu3rOOy\nEL828xi4p8AWR9TPZHEkwtrU8zM2hS54j1qxbAljrNFvOCu4liUveaaiHhPjGsebK61PB//OzZfz\n+Y5mmZlbHDbjooHhqMZ5No0Jg9MlNzm/I5851l4URVEURVEURfHMKKGdPG4ptD2E6Ar+ip3i1YUd\ny8ji8XPme+HmDPcQ2cwY2fbY1XYm96nZIVNzqXm9QKaTk0FkTMMNjv3Kcb2yPz3x0DZcL8jFaSKR\nPL4Cx1Jkm41IBPeYR48wMQ9HW/2sy2ooXQRVQVpcSDB3psKA2J3OMfclsuXW9XX2b6vE85eIX6xK\nMM/j3uIWoqLL7ru47/bTcUOb0NZ9KvE13b2Wm2ES2elFURRFURRFURTPjRLayUNbjrYjEaKdAWLt\nTBY3E9wkbzO9O8eoJ+loM3O/OUR2jI7fUrjDk9WM87JwzP1cng7BPCY2/RVHe/QLtj0i5hHWBtnj\nPe/c7BDbMYseIltIVztdYlWJfm7RqPVa6ee2EshDbK+C6xWAdn+OoppHvKbcO9p3LrYbt310ubnZ\nIsvRzmA2i9l5zxF18zhf2qpSE9zXiRLj5TJzHL0oiq9lROS7gT9A/Nvjb3X3v/IRn9JXxMuXL/nU\npz71UZ/GV4VPfOITfO5zn/uoT6MoiqIoniUltJOzPhsQCdEn6zfcEJu4LSc7RJ/LuSEdo9YWNdSW\nzrVnuvYS5UsA+ynCNYUkZJ9W7j2H8FzhZtfjietx4XE8MuaOzQfmHHGki20eKeDLdVaF1tIpT3te\nMmwshKrH+eUHNzcGsZ89WfVmZAx7iOlwxuUU6KJZG7a6xhHmDPHrBtbBt+wTF1nmfbxfNnkDiEvu\nqcd54WBtCfw4gWWaSyp6cbm9WFEUxTPBzPj85z//UZ9GURRFURQfMSW0E/PbzyFIU0KbpVAGvCF5\ngKbJG9a3r7RuyyCxO2F5ikh38IlLIzxwzVTz1bPl59g3nkJ77uz7E/t+4TiuzLFjdjBtxC54Cu4Y\nGY90b8ne7FNosxxlTlfZFZaL7BmAtkS25ZM0xbYqeArs6D2Tm/g+r0bEdxRa35jT6RZfgEgI9LZO\nRiJ8zbGcRJf4bizOCI2LFbjgLXa2w5m/9ZtTQrsoimfLz/6oT+AnmZdQqztFURRF8Y6U0E5OoZ2C\nNBegz17pOaPaSnHUJZ1hIJ3vVWdlM19L7hzttUud49fOxDx2v2MtOUai3XIc/M7RHmPnejT245rV\nXTtzHthcIjvC11av9bkFLim0fcWshTYVWTPx4ZtHd3Z8zuHnsPv5+VRiNd1Pkc3paKN+E765z23m\n+aNkmrjEmHmL55Kvfaazkye2AtgsXtchE89DZNPWGPt6vyW2i6IonhMK/PBHfRI/yXwKKNe+KIqi\nKN6JEtrJHCN+EJDmiNpZQ3Vndp8p48s5RhzXFUhmeV88yXP22leoGDeBKXdSM56TQV9uGWQW4WqH\nDfaxs4/97Mi2eYTAPo/BnHY66n4GrEVgmctytmP+WuTciM6c79yLFs9O7Lux8TU63iL4THs7nWXV\n5Smn+4yf7r9lf9ccxuyDMfPLW056Pj5eR+PqxPqiM+lcfP2RhCuumufutz+HoiiKoiiKoiiK54Z+\n1CfwXDiug+M6GMdgjpmCNZO6tdG106RHE7QvB9rCSZ7HK2nfK/wsdrblrPPyXJZewvHcgybGrc0t\n3OWUv9ONYZPDDo55MObBHK8eYyxX225j6x4p3Uslayant6anWBX87lxzL1wcbU7bQDtoy4sODbQJ\n2pXWW7xO03Nfezn7qxLsvNBgsa84pjHmYMwRn8MOpo8Ig1OhtUbTHodsNOk0OioNpcWtaPaWp7S/\nG3cviqJ4BuT/n9a/k4ria4WXL1/y6U9/mpcvX37Up1IUxftgRhcyPHMt+6xP7qvJsR8c+8E4DuaY\np0MMkiJ1Q7VlzZfgFmnZNkccNvBMz14p3Wfytq1qqrvhZ5FTNJ4i1WNvecWlDbdwtFOgnmI7x8fP\nQLQ5sTzfWxVW3C5R37ShqlHJdU5cZ3P1ctHV0AatQ+uC9nS3myA93Oy2NbQ3tGUwmpye9u0z5Hub\nwzRnzskYI8V2fgYbsTe+hHZrIbClo7L9BJHdNIV2Lo5bppVHYnlRFM8VEflpIvK7ReR/E5HXReSv\nich/LSK/7n28xreIyL8pIn9RRL4oIl8Wkb8kIr9fRL79Pb7GrxKRPyYiP5LP/99F5PeIyDfl/f+P\niJiI/Hsf8KO2D/i8oig+Il6+fMlnPvOZEtpF8TXGndB+1v/fW6PjyTxydJysvWoaO8kxgI1KCyM7\nRSzZ/exieOR1p2C9H4G+/SiQI9y3zulX47zuxXZYwtON4ZNhwjGPFNcHc+So+DrGTWibrYiafHVZ\nY9eR3L3c8pVsftrPOcYdDnWGk+XdouloN0VbI1PLAMPNz53r9ZnPT+XEBYnpxD8PTsyhG43sKF/f\ntUt0qpnG48QQsRgtzx5zVckJ8wxwK5FdFM8aEfk7gR8EPsntXxMPwC8BfqmI/AHgv3mX1/gNwGfz\nefd28d8OfCvwm0Tkd7n7736H1/h9wG/JX67X+FbgtwO/XkR+BetfhEVRFEVRFB8CJbSTLnFBREVo\nOW4tHqng5rmDPQUbgo9wtF1jkVh0DWmv2iyPELLMCIMVRPYWb3yX2h3aOJ+08r58ubeOTcPHxMfM\n4msHy/c6M9RWtniOqrtnmpmc6ehrJ3tdGJAU2Zk9Fm+f4vm2Gy2RQn5rBLuFmYkgoZwRVTDFc8y7\nqaPiyNq59pV8bvm+2fOdVWnm+Z457q4a4/vC2mHPaYGfsD1fFMVzQkQ+DvyXwCeIf1j/MPCHgB8B\nvg34F4F/EnhbR1pEfiXRsQ3w48DvBf44MIC/G/he4G8G/jUR+TF3/+xbvMa/TIhsB/5f4F8H/gdC\nuP+DeR4/ALz2lXzeoiiKoiiKe0poJ11DaAsptF2z31mw4fgMoe1T8RGiT1qGhwmoeJiyLKc7BfBS\nr3fVVm/JErErhUxu9opZpHn7NHwYjIw2d0dSYN+L7SWyz0IvlRwj55bu7Suh3Ffe2Sns1zmendWr\nVuvuYxh+PlbysSIS1WfRB5a1XpZC23M7PW3+U3kb8SE4R+3XJIFmJZjIq8nur4rtoiieKf8KEU/t\nwPe6+++5u+/PicgPAP8Z8A+81ZNFpBNONsCXgL/H3f/C3UP+rIj8J8CfIRzz3ysi3+/uf+PuNb4J\n+HSew/8J/F3u/mN3r/HficgfA/4kcKGu3hVFURRF8SFRO9pJk0aTRs/bRkNNYYINZ+zG2I25GzY8\nxTdnOrYQYls0j6zQWlVa8BZziZ7/s9xuuQlasofbs35r7YT7TEd7GkxHLI7T4M1ObjfBTV/dDV9B\naRavd4p17jqzT+GcbV4KqvJqwvj9bHza36IRlhaBaUrvjd5a7IajIbJPVzuq0uTO1V7fkZ275TGu\n3zRC0c6rAOnI+3ke9d/FRfHcEJEN+I3EP6B//k0iGwB3n8BvAo63eZlfA3xz/vyvvklkr9f4K8C/\nlL98Dfin3vSQ7wYe8+d//k0ie73GnwF+3zt+oKIoiqIoivdJCe3ksm1cto3ee+wEZ7dzpIsvF/Xm\n3q4E7BW6LW/+S6JaS1RSqAqaZu9yciPyzFib06cSPQW33MamiXNZO9k+DXGPvmyROJCsupYlbXPk\nWjIB/JZIflvNlhTnmRI+Y6fa5wpwi1cjw83sfJ1bwjm5271SyM+LDGSqueVFiRxtF2LnWkSyl/u2\nH67rNVTyMZp1YcYYkzliLzx4dcu9KIpnwy8Efnr+/O+/3YPc/fPAf/U2d/+y9TBu4+NvxfcDX3jT\nc978Gv+fu/8X7/Aaf+gd7iuKoiiKonjf1Oh48nC5AEvyxl+nwCaF7xJ/riHx0vK9ieElIh3FcFEa\ndpscz1otcTt3r0Pv3tni67U0hbrErrIQ++I+HRsz3lcjjbtpCm0VmmgEtHn4xcBtXHx1bDuIx5D7\nWrh2lsiOMXXVEOstx8At3X3u/GTgdqkmLypgxGcxw9K9Xm/qoaTzMkDLCxL5nSznvHFemJDThY9g\nuKgEm7iEuld91kGDRfGNzM+7+/mH3uWxfxb4lW/x+2t3+/9297/+dk9290NE/hzwHfzEfe9vJ/71\n9j+9yzn8BWAHtnd5XFEURVEUxXuihHZyuTwAMG0wbMSYtt1cbEjHFUE9a61u9m3+pa/8rGK3xeb7\nMLS0ht3tzC87PdpbMXW64ktoZxjYcrR7jnOnm931drgLJkJzPUPD3Dj3vm+j5CsBLWvBJszhzOm0\nFp/ERQGNXXVC9JKBbwio3yWbt6wVy91pt/isvsxtBXqKbGl5UQLImi9p2d/Nq/VnZs60yTEHiEW3\n912ndlEUz46fcffzj7zLY//aO7yGv4fnA/zVt3hfuLnqP/pOT3Z3E5G/AXzTe3ivd8GBn/UeHtd4\n5q0k70BVIRVFURQfPi9fvnxPdXvH8XZbZ8+LEtrJZQsj4xhgbsgZGnZL3xbkTCUXWcnea3z5JrBj\n79hOoYzmHnI2V616qrVW7XC62et3JN9D0tU+3d0MRaP5+ZSW7vc6Wm4/n0HkfsrpFPTrooCfhvNt\nbBzmiNMxFVqOj0PufXt+Dvn/2bvzcMu2u6z339+Yc661dlN91TnJyYGIBC9oAEMMPdIKgYCAiIBc\nIYRGH3kUFb1cfbgXgvfx+iAg3AteaZSgMSI2xAaMGAitgpEoCokijZpzTpLTVLObtWYzxvjdP8bc\nu1btU7tqV51dtXfVfj/PM8+aa60x5xxr11l77XeNDgg+/gx2fwS7U4vv/OxwdruN+07Xca9ufH3s\nzp+Gh7H7e9hZ77uE95RK13FCHlvvy7raInLsvdCJFB7AiRhumetFRETkJr73e7+X17/+9UddjUOj\noH0AO/FXREQOZHnSsUcpM37vZ79W5MuUX78HaWV+0dIxe+vxKGUJsH1Z6Up07lZlDmC3Nf2gvW0e\n9F45Tz/9NI8//vhRV0PkrvV9D8CrX/1qJuMQQhE5OiklLl265Uc2AM88s/uF9t6ebMeKgvboG//C\nzzzYf/GIiBwfyzOEvwr4hVuUfdU+j/8q8DHAB5jZhf3GaY/LgL2C8n3or+55+tcoIfz33qa+H0pZ\nV/uFfKe6+xmyM7fH7Ry03HGVc+bJJ5886mqIvGBLf7SLyIPlWOc3BW0RETlsv0xpTT4L/DHgO29W\nyMxewj7raANvBb6a8iH6FcC37VPuC4EzlJD81j3P/STwqcBFM/tMd/+X+5zjy/d5/E50lLCeOdi4\nchEREbk7j1AGs3ZHXZFbsQf9G3URETl+zOzbgD9HCcDf4O7ftuf5CvjnwGdwfYTOB4xrY++sxf3b\nlLW0rwGf4O6/uucc7wf827HMNvBSd7+89PyLgd8CJpTu6x+7t2XczD4GeBvXZxz/IXd/3Qv+AYiI\niMiJptmkRETkXvgW4AlKiP5WM/t7ZvYZZvYKM/siSkD+DODf3+xgdx+Ar6EE8DPAL5jZN5rZx5jZ\nR5rZn6UsHfbYWObrl0P2eI73AK8f6/BBwC+b2Z8ws99nZh9nZn+Z0gr+JPDszmGH+UMQERGRk0kt\n2iIick+Y2e8G/jVlnPTecVQO/CDwc+PtDS3aS+f4Y8D3Urpl3+wcCfhGd//WW9TjbwB/fOfunqef\nBj4L+FHgceBvuvvXHuT1iYiIiOxHLdoiInJPuPs7gd8DfCvw60BLWfvqp4Avcfev2inKPi3J7v53\ngQ8Gvgt4J7AFzCldwb8PeMWtQvZ4jj8JfC7wE8BzwAL4b5Sx469w93cAp8fi1+7mtYqIiIgsU4u2\niIicaOOkbO+mhP2vcvcfPOIqiYiIyANOLdoiInLS/dGl/V88slqIiIjIQ0Mt2iIi8tAys1XgtLu/\nd5/nXwH8NHAKeLu7f9R9rJ6IiIg8pLSOtoiIPMwuAe8yszcDbwH+K2XdzceAzwReB6xQ1r/+c0dV\nSREREXm4qEVbREQeWmb2Usp63M7zZxxnfLynjM3+e/ezbiIiIvLwUtAWEZGHlpnVwOcBrwZeRWnh\nPk+Zufy/U5Yf+253f/dR1VFEREQePgraIiJy4pnZ+wNfR1lT+/0o3ct/E/gR4HvcfXFI1/kS4LXA\nhwFngfdR1hL/HnfXRGwid+hevnfN7JuAbzpg8U9y95+922uJnARmdgn4yHF71bhdGJ9+g7u/7h5c\n88g+dxW0RUTkRDOzzwH+LmUt7b0fikZZA/w17v6bL+AaM+AfU8aF3+waGfgWd/+Wu72GyElzr9+7\nS0H7dn8sO/ApCtoit2Zmec9Dy++tHzrMoH0cPne1vJeIiJxY46zjP0yZdXwT+EvAxwKfCnw/5cP5\ng4B/YWZrL+BSP8j1D/ufonRn/0jgK4HfoHwef5OZfdULuIbIiXEf37s7Xg586D7bhwFvP4RriJwE\nPm7/A/gJbj5/ymE48s9dtWiLiMiJZWY/C3w8MACf4O7/bs/zXw/8NcoH9evv5ptvM/sU4K3jOf4Z\n8Id86cPXzC4Avwy8P3AF+J3ufu3uXpHIyXCf3ru7LdruXr3wWoucbON76u2U5TSf2TNh6aG1aB+X\nz121aIuIyIlkZq+i/KHuwA/s/UN99B3AuyjfuH+dmd3NH9tfP95G4Gt9zzfc7v4c8A3j3bOAWrVF\nbuE+vndF5BC5++vd/cfd/Zl7fKlj8bmroC0iIifV5y3tv+FmBcYP578z3j0LfPKdXMDM1ildWR14\nq7s/tU/RfwJsjPuffyfXEDmB7vl7V0QeTMfpc1dBW0RETqqPH2+3KV3I9vMzS/sfd4fXeBUwucl5\nbuDuA/CLlNa3V6n1TeSW7sd7V0QeTMfmc1dBW0RETqoPoXzj/Rvuvncm1GX/Zc8xd+J373OeW12n\npkziJCI3dz/euzcws39lZu8zs268fZuZfYOZnX0h5xWRQ3dsPncVtEVE5MQxsylwcbz7xK3KuvtV\nSssZlHV678TjS/u3vA7w7qX9O72OyIlwH9+7e33aeN16vP39wP8N/JaZ/cEXeG4ROTzH5nO3PuwT\nioiIPABOLe1vHaD8NrAKrN/D62wv7d/pdUROivv13t3xn4A3A/8OeApogP8F+FLg0ynjv/+RmX2O\nu/+ru7yGiByeY/O5q6AtIiIn0Wxpvz9A+Y4yjmvlHl6nW9q/0+uInBT3670L8Nfd/fU3efztwBvN\n7GuAvwlUwA+Y2Qe6+0HqJCL3zrH53FXXcREROYnapf3JvqWum1LGhC7u4XWmS/t3eh2Rk+J+vXdx\n943bPP99wN+iBPnHgC+402uIyKE7Np+7CtoiInISbS7tH6S72Np4e5Cuqnd7nbWl/Tu9jshJcb/e\nuwf1vUv7n3iPriEiB3dsPncVtEVE5MRx9w54brz7+K3KjrMK73wYv/tWZW9ieSKWW16HGydiudPr\niJwI9/G9e1DvXNp/yT26hogc3LH53FXQFhGRk+qdlC6fL+B1ivAAACAASURBVDOzW30efvDS/rvu\n4ho3O8+trhOB/3aH1xE5Se7He/eg/B6dV0TuzrH53FXQFhGRk+rnx9s14JW3KLfcHfQX7vAab+f6\nZCz7dis1swb4aMof7W9393SH1xE5Se7He/egltfsfeoeXUNEDu7YfO4qaIuIyEn15qX9r7hZATMz\n4MvGu1eBt93JBdx9C/hJSuvbp5nZY/sU/QLg9Lj/T+7kGiIn0D1/796BP7G0/zP36BoickDH6XNX\nQVtERE4kd3878HOUD+OvNLOPukmxPw98COUb7+/c+423mX25meVx+z/3udS3jbc18D17u7qa2UXg\nr453r1JmMRaRfdyP966ZvdzMPvBW9RiX9/rK8e57gR+981cjInfiQfrc1TraIiJykn0dpUvpCvCv\nzeyvUFq+VoAvAb56LPdfge+4xXn2Hafp7m8zsx8Gvhj43PE630npZvphwF8C3n88x//m7tde0CsS\nORnu9Xv3lZS1sd8G/EvgP1MmYasp4zr/V+APjGUj8NXurmX5RG7BzD4OeNnSQxeX9l9mZl++XN7d\nf+gWpzv2n7sK2iIicmK5+380sz8CvJHSheyv7C1C+UP9Ne6+/QIu9TrgFPBZwCcBn7znGgn4FndX\na7bIAdyn924APhX4tP2qQQnfr3P3H7/La4icJF8FfPlNHjfg48dthwO3Ctq3c+SfuwraIiJyorn7\nj5nZh1FayF5DWQ6kB34D+BHge9y9vdUpDnCNFvgcM/ti4LXAhwNngfcBPzte45deyOsQOWnu8Xv3\nxyjdwj8GeAXwKHCBEgguA78CvAV4wzgmVEQO5qAz9d+q3APxuWvuWpVARERERERE5LBoMjQRERER\nERGRQ6SgLSIiIiIiInKIFLRFREREREREDpGCtoiIiIiIiMghUtAWEREREREROUQK2iIiIiIiIiKH\nSEFbRERERERE5BApaIuIiIiIiIgcIgVtERERERERkUOkoH0EzOy8mX2Tmf2imV02s2hmedy+7Kjr\nJyIiIiIiInevPuoKnDRm9gHAzwGPjQ/5nlsRERERERF5gClo33/fRwnZDiyAtwJPAml8/l1HVC8R\nERERERE5BOauhtT7xcxeBDxFCdkd8HJ3/62jrZWIiIiIiIgcJo3Rvr9esbT/cwrZIiIiIiIiDx8F\n7fvr3NL+e46sFiIiIiIiInLPKGjfX5Ol/XxktRAREREREZF7RkH7HjOzT9xZugv42zsPA69dWtJr\nZ/vbe48xs59aOtdnmtmbzOzXzWxzfP5P73PdNTP702b2FjN7t5ktxqXE/rOZ/b9m9pF3+DqCmX2l\nmf1rM3vveL7fNrM3m9nnLZX76aW6//47/oGJiIiIiIg84DTr+P2zM+uc7bl/22PM7DTwBuDzlh/f\n7xxm9tmU2c1ftKfcBDgL/B7ga83sTcBXu/viVpUws5cA/4zrY8x3zvf+wEuBP2hmbwa+7HZ1ExER\nERERedgpaN97TwLfPe5/MPBplBD6X4Cf3FP2F29yvAFvBD6b0t383wPvHB9/OXsCrZl90Vg+jM8l\n4OeB3wDWgU/g+hrefxT4HWb2Ke7e36zyZnYeeBvwgUvX+k3glygzp38I8FHA53K9xV5EREREROTE\nUtC+x9z9N4A/DWBmX04J2gC/5O437fa9x8dR/p1+BfhSd3/n8pNm1izt/07g+7k+JOCXxmN+e88x\nfwb4a2O5jwG+Ffgz+1z/u4CXjfsL4Cvd/Yf3nO/DgR8B/jAlfIuIiIiIiJxYGqN9/NWUGco/ZW/I\nBnD3YenuN1FarY3Sgv0Ze0P2eMx3An9hLGeUbuQv3VvOzD4Y+FJKS7YDr90bssfz/QrwB4ANbpzw\nTURERERE5MRR0D7+HHi9u1+5VSEzOwP8kaVj/oK7b97ikO8Cfm3cD8DX3KTM65b2/427/8N9K+n+\nP4Fv5/oYdBERERERkRNJQft42wmtP3KAsh8LTMf9Z4F/cavC7u7cOKb6k29S7JOW9t94gDocpIyI\niIiIiMhDTUH7eHPgt9396gHKLs8I/u/c/SDrdP/CeGtLxy/7sKX9X7rdycZu6s8e4LoiIiIiIiIP\nLQXt4++ZA5a7tLT/Pw54zH9f2p+Y2frOnbEr+vJ463cf8JxPHLCciIiIiIjIQ0lB+/i75RrXS9aX\n9rcPeMzecqf2OR/A/IDn3DpgORERERERkYeSgvbDYzngrh3wmL3llidP2xuYV+/ynCIiIiIiIieK\ngvbDY7mL+fsf8JjfsbTfu/tuuHb3a8Dy0mGPH/CcBy0nIiIiIiLyUFLQfnj8h/HWgI80s4Mss/Wx\n460vHb/sPy3tf9TtTjauxX3pduVEREREREQeZgraD49/A3Tj/iXgNbcqPAbxr1h66KduUuynl/a/\n9AB1+GMHKCMiIiIiIvJQU9B+SIxdvf/B0kN/zcxuNV76TwEfOu5n4PtuUmZ5ne2PN7Mv2O9kZvZ+\nwNdTWsdFREREREROLAXth8u3UCYxM+B3AT9hZh+wXMCKrwO+fXzIge929/+592Tu/i7gTeP5DPgh\nM/viveXM7MOBtwKnud6qLiIiIiIiciLVR10BOTzu/ltm9lXAG4EK+Bjgv5rZzwG/SVmy6xOAl+wc\nAvxb4BtucdqvAz4a+ADKzONvMrNvAX4R6IEPHq8D8I+AR4BPHO/nw3llIiIiIiIiDw4F7ePtIBOa\n3cDdf8TMtoAfAB6lBO5PHjco4Xqne/ebgK929/4W53vOzD4J+KfA7x0fftm47RYD3gy8DvhXS49v\n3Gn9RUREREREHnQK2vef77l9oeWef6D7j5vZyyjB97OB3wNcBBbAU8DbgL/j7m8/4PmeMLNXUSZP\n+xLg5cAZ4L3ArwBvcPc3A5jZ+aVDr95p3UVERERERB505q65q+RwmNkKcI3yBc6Wu58+4iqJiIiI\niIjcd5oMTQ7TF1BCtgPvOOK6iIiIiIiIHAkFbTkUZnYO+MtLD/29o6qLiIiIiIjIUVLQltsysx82\nsy8ws+k+z38c8PPAS8eHnqBMtCYiIiIiInLiaIy23JaZ/TYlRG8B/wH4bcrEaueAj+DGGch74LPc\n/afudz1FRERERESOAwVtua0xaL//zt2bFNn5n+gp4Mvc/W33pWIiIiIiIiLHkIK23JaZvRT4fOAT\ngA+kLBV2ARiAZymt3G+hLBnWHVU9RUREREREjgMFbREREREREZFDpMnQRERERERERA6RgraIiIiI\niIjIIVLQFhERERERETlECtoiIiIiIiIih0hBW0REREREROQQ1UddARERkQedmW0DUyADTx9xdURE\nRB5mj1AajDt3XzvqyuxHy3tdd7x/ELv/Tn6TbamYXS9Vbm3pSbDxGcOxGw61pW3nuLK57zy2VIU9\nzJZud/Z36ueOu9O1G3TdJl23QRw63CM5J9ydyXStbJM16npKqBqqqiFUzfUTYtcvhGHXX9vSixQR\nuf/MLALVUddDRETkBEnufmwbjo9txY7K8f7iwfeE3DHILmdph+tx+nqpZebluJ3IXXaWSy3nVr/h\nUd/d8aWnffy5+Rj0d47JeM64J3JOtItrzOdXWGxfoR8W5JzIKeFkJpN1JtOyTadrTGarTKerNBbA\nAtgYrN1hjNi7r8CUs0XkyJVfj2Y89thjR10XETmAvu955plnuHTpEpPJ5KirIyIH9NRTT+1ktuMc\n3BS079bRBfK9Ldl7WrRvjKC7wZQ9pR3GFu2dR/aG1aXH7cawjY+BeidcewbP5RYfyzueEylHcoqk\n1LO99Syb155hY+Np+nablBM5xbFF+xTN7BST6WlW186wfuocZkbVTMavDQK+E7bxG6qooC0ix8Bl\n4JGLFy/yxBNPHHVdROQA3vGOd/DKV76St7zlLXzER3zEUVdHRA7okUce4ZlnnoHy2XtsKWjvY2+Q\n3glzR9vifWPA9htj83jPd/uP71vTpRbtUvJ6eLWl/9742GinxXr3HKXF2nPCPY2PZ5xMzpEUe2Ls\nGWLH9tZzXLv6Xq5cfop2vjmG8ETOmcnsDJPpaSazM5w602EWmExXmK2s4VZjYayjgbnh+HLXcRER\nERERkWNDQfsOHH3I3rs33veblbpJwd2HbSmW3ziou8TvXFqtbwjUO63XO2OuM55Lq3XeDcwRPOGU\n0J3TwBA74tAxDC0b155m49r72LjyXhaLTXJKpJTI2WmmLc1kQTNdkLMxna6wtn6alM6MPccNs7D7\nJYIitoiIiIiIHFcK2gew05ptZkcYtndanXduAzebEM3c9hyzfLvs+spuu+3iuyHaS2t0iqQ8jPsD\nOQ2kFMu465zxlPGxXB67iOex/M4xMXbEoSfGlq2NZ9naeJa+3SD2C3Jycs54ht5b4lDRdVA3a6yf\n2qZdLOi7jqqBmoqwE7iP9WgMETnJnn32WR5//PGjroaIHEDf9wC8+tWv1hhtkQfIs88+e9RVOBAF\n7dvYO/736MP2Po/f2IucGzuB7z3Odrud39AR3fMYfBND7BhiS4wtw7BgGFqGvmUYWjylErJTIscS\nqFOKYxDviWkgxZ6UelIciLEnxY6u3aRfbNG3m8Shx93IGTwbOS5KR3RL1M0a89Nbu0F7QiCEmsqX\nJh0XkWPJzN4P+EbgU4GXUJa8Avg8d/9nR1ax+8TdefLJJ4+6GiJyB8axniIih0pBex+3mmDraMM2\n3NhS7TeG7HGM9m6J570MY2fe7hsXDMtkh5QzOUWGoaPrt+n6Lbpum67bomu36dotcox4TOSYyHEo\nQXu8jUO32128BO1+93lP/fUtO+6lK3j2iphgyIkh9Uwma8y3S4v20Hdlqa8638Ofp4gchjFkvwO4\nAHt+xZwoLznqCojIgfTAM8AlQC3aIg+G9wAPRi5Q0L5LdzvT9d0E9BLsl+5fP1sZb708AziUGcB3\n1q/ec4tdH2+dcialNM7+nUhj63SOw/WQ3W/Rtpss5pss5hss5ht4ypATpJ3lu3IZm+2ZYRyPPfQd\nOQ1lojRPpbwnzCPBMlQ7ryngHrBQYbkiVBXBMrFfsL15hSvPrbC63rG6PgBO3UwIoSZYhQUtWSty\nzPwflJA9AH8J+Dlga3zufxxVpe4vAzTruMiD4R3AK4G3AJp1XOTB8DjwYPQcU9B+ALj784L9znzg\nO8ttjXdhXGorj8F3Z9ktz2Um8J0ZwSETh4FhGOiHnjgMJWjH0vrc9WMrdr9Fu9hge/sq21tX2d66\nBjljXtbjNjNCgMqMEIy+LyG771s8R8wgGARzzByzjNnOFxUBrAJqKq+pc0PyhrqCOMzZ2riMWWAY\netwTIRjT6Sp1M6VppoCCtsgx86mUX09vdvdvP+rKiIiIiBwVBe0H1POX3GKpVTuBxzKG2mNZeisn\nsidg3DzS9z1d29K1LX3flbA9DMRhoN/tNr7FYnGNzY3LbG5eZnPjMiUiByozqhBompqmrqmbmr5r\ny9a3kDN1HbCqwupACJTNwEKZRdysAqvI1GSvyTRUwYn9gq2NKwxDJI8hu67r3ZdaVTWVcrbIcbPT\nZ/rXj7QWIiIiIkdMQftu7dcF/MbVsp7/xJ41qpdOePNT75wvZxgnLPOdQO1eWrDx3ZZs97w0Q/hA\nThEfZwF331l+KwKJvmtp25Zu0dJ13W7IjkPP0M/p+236fk7fbtBvX6Xfvka/vYGNATuZ0VQ15g3B\naypr8NjisSu3nnEqnBq3CieABSwEoHQdzz6uue2R5IHsRh8XLPqIbS+omi1iTqTsZM8MMbK+nglV\nQ13vjKcKiMixMKH8MhuOuiJHIB11BUTkTr0Y+KbxVkQeQMf6s1dB+7D57lLPd3bQ8/bHsdR5HFF9\nwzJa15fQKmtX56WgncoEZLEnxv7GoJ1vbNGOw8DQ9/R9z9D3u0G73Hbk2MLQEoaO2hNTM2JdE8bW\n6BACVRVoKqMOTkWiDhmvHK/GIeHmZI8MKVNbDVaatT0bQ0rEmBhiz5AqYgoMsbRuY2ULzZRFP7Dd\ndmzN51xYtFy4kAjVZOw+DlXVvNB/NRG5S2b25cAPjnd31j34ZjP75qVib3D315nZTwO/H/hpd/8U\nM/sg4OuAT6e0hq8Av8Pd/+eea3wO8GXAR1NmLdqitJr/U+C73X37NnVcAf488IeBDwRa4F3A33b3\nHzSzTwTeNhb/JHf/2Tv+QezOzKKlEUQeHC8GvvmoKyEid+9Yz4qmoH0EfPc/Zef68OvlyXl9HGt9\nvZU6Dh0xtsShJaZuDNQt7glbCtpx6Ha3naDty0HbM7CzNFckxkgaImkYSEMJ4DmWVnHSQIg9TY5M\nDbxpylrWIWDBCJVRhTFoW8KtBG1qSLm0WGcHj3kM2Y55IGZou8SijbRdohug68uWPOAE3AKhapi3\nLVvzOZtbm/RDJFQNK2unWV07BcB0iogcrZ1fXLfrsrP7S87M/iDwJkq43luOscwU+PvA5+15/hzw\nUZTg/afM7DXu/is3q5iZPQ78FPCypeNXgI8FPs7MPh/4f/apt4iIiMhdUdC+E0t/Su402/jNCjhl\nXa3SpLtzc5Pe5vv9TVe6h2dPeC4Tm6XY0w+lO/fQzRmGOXHcco4laJPxHIl9t7vudU5DWfd6DNq2\nE7R3gnkuW86Z1A+7gdtzhnGzFEuLdjBC3Yyzm5WgXRqoM8EylWUIGSrH3LAEMWVScmJ2CDVWUYJ2\nchZdYmves7XdsegyizaxaBND2vlr3LBQsb1YsLm9zbXNDZzA6topzp1/hBj7w/l3FZEX4keBt4/7\nv0p5+/5/wN9YKnNlzzEvBd5IaZn+ZuDnKd1tXsX1WcoB/g4lZDvwH4HvoLREnwe+GHgt8BjwVjP7\nMHd/z/JFzKwGfozrIftfAD9AmRb8ceBrgNdQWslFREREDo2C9t3wG272KePjTYnkO7c3MtwzOadx\nGazrodc9lZbmcax138/pus3dbei3GIZtYr+Ne8Twsnkmxp409Est2qlMjJZTqZf7OCN5qZFB6aKe\nIzmVidPMIVgJ0pU1NHWDz7wcbjAugY0Fp7JMFTIhODFGYhqIMdINA/PFwND29MPAkBPt0GMG3eBs\nz3vm857tRaQfnH5whmSk7KRx/LYZLNqOUG+DBTY3N9ne3mY+36ZtFwCcOXOY/7gicifcfQN4J9yw\n7OHT7v7OfQ4x4AMoa3N8tLsvr9Hx9t1CZq8BvpDyq/atwGvcPS6VfauZ/Vvg+ynB+zuAL9lzra8F\nPnQ8x1939z+/9Nx/AP65mX0X8KcO9mpFREREDkZB+x66dR/EsjRX9kxOkZwHUkrjfipduod+XJe6\np+u2aNurLNqrtO01hn6TYdhk6DfB47iElmE4OQ5lPHccSnAeQ3a5HbN2trI0184Gu0HdPFNZhVlF\nFSqqUFNVDVXdUFU12bxMYIZDyNQhj2HbiSkyjN3Rt9uOwefQZ4Y4kHIsITolut5ZtAPzRek6HrMR\nM6RspExZ43tckiy0XZmZPMPm5iZb21ssFnPabnFf/h1F5NA58A17QvZef3K8HYCv2BOyy0nc/5aZ\nfRHwacAfMrNH3f19S0X++Hj7BPAX97nONwBfQGkZFxERETkUCtr3wI0B2/Y8Yzc86rkE7Rj7Mg47\nXl/Puu9bum4xzg5+jfniMvPFZRaLywz9Nfp+g6G/BiSqUGYCD3C9BTslPOXrITs5ORs5GTkbIVTU\noaKqqvF4qAyq4ISqjMWuqopJXTOdrjCdrTKdrpJIJE8kIja2ZtchUwdn2AnaKWJbC+Z9xkLPkJyu\nT3Rdouuh7TJdl8v47N5xC2VmcjNyhpidlPLYqt2Rc2kt32nRXizmuy3aIvLA6YF/tN+TZlYBn0j5\npfkT7v7ULc71/ZSgXQOfBPyD8RyPAR88nuMfuvtNZ0J399bM/iFlUjYRERGRQ6GgfbeeN7X4nlHb\nN0xwdn3XfWxdzpmcI303p+u36bv52NW7tGqnFOnHkN11C7p2g0V7hUV7mba9WkL2sEHfb2Ik6qqi\nqgKVhdItPGc8OzklUozkmEpwTUZKRk6BYGXW8NJqHWjq61uwmqYOVPWEajIl1BMINdkqUnaGHIk5\n4yTq4KSdLRvJazIBQiZUU0I9o24GhpTAyutP2Ze2XJb8GlvlQx2oCbs/uaoqM5yTMznl8WdUNhF5\nIP03d7/VJAu/E1il/AL9pduca/n5lzMG7XF/xy/f5hz//jbP34EMPHKActW4iYiISLGzQtKtPHs/\nKnIoFLQPzU7ILt23bxyS7eOYbSfnYXeisr5fMJ9fY759lfn8GnFowR1zx3Nm6FuGvmPo2nFN6w26\nfpOh3yDGbWJsSUOPWTnGvMIqyhjsDPjYOpycGBNxSKU1O0FKhnkZU2lAFQKTScN00pAmDVVleJgQ\nJqtUkxmRijhk5kNLHzu62NLFjpwjdTAqM+oANq6TbcFo+4rsU5pJZnU9QOhxBrL3ZMZlx5KTk2OV\njZOrBeqmYdI0NJOGuq7LlwUpk1Jm1jRMqpoqVJhp/WyRB9TeydH2Or+0//Rtyr53n+POLe0/c5tz\n3O75O3TIpxMREZEHjoL2oboxbF9XxjRDJueevt+mXWywWGywsfEsmxvPsnHtWYZhQWUltAYg9j1x\n6Il9R4wLYpwzxHm5TW1Z6iuVycXMfZzYzHYuCUB2SMkZYmYYIilBjpAT5Oxjy3cmhMBsNiNlx6mY\nTgMeGqrJCmGyQj8kuj7SDz2LfsGiW7Do56QUqS1QWUVtgbpuaJqGuqnJXuFMqSeBVRqcFveWnA3P\nRk6+G6BDMKwqIX02bVhbW2N9bY3ZbErb9rSLjrbtmNYNTVWXbvJB69WKPKDupDvKA7Xklplx8eLF\n25arqjJsR0RERIqUEukAPVafffbZcaLp401B+xae/w9482B347rYhtn1buRmvjuLuHskxpau22R7\n+zJbW89x9cp7uXr1vVy98l6GvqWpAnWoqEMgx0ge17ROqSfnjpw7Uu5Iqd/dQggkyqRmCcMIYIZx\nfbzzEDN9zORYWpBzLP8z51i6lpsFshtYhVUTVjJkq7F6BvWUvluw3UW2thdst9u7WxwilVVUVNRU\nTGcrzGaZ2dQIdUWmxqrAZNYQkxETxFiW/Eopk2LGY7phubDZpObU6grnzpxibW2N7a0F29WC2ipW\npjOmTUNTl1ZtEXkoXV7af/Q2ZV+0z3HLrea3W77r0Jb3euyxx3jiiScO63QiIiKyx+OPP86TT95q\nPtXjQUF7Hzf/luT5S3TdbB1tdx/DdrmfUs/QLxiGlsXiGtc2nmHj2tNsbDzN1tazbG09Q9teJsUO\nD4EcKnKoCDjBoG7Aax+XtTZSCthgZAeiX18HO5VWc8NLF24zkjvZS8u2Y4SqjMe2xohDIlo/1rls\nMfnYel22to/0uefKxjaXr25w+co1ttsF827BdjdnGCLBA8ED5hVrqyusjlszqaHKEMrs5H3XM8QB\nt0xVG5NJjbnRhJohZWJ0hmGgbzu66YLFfEIVaoIF1tbWWF1Z5+LFi5w9e4619XVmKyuH+48uIsfF\nbwFzYAX4qNuU/cil/V9d2v+1pf1XAn//Fuf4fXdUOxEREZHbUNC+Y9fD9s1C9o33S3qNsaPrtlks\nNtncfK60YF99D9euvpd2cYVFe4W2vYKnfjdk5xCY1A1N1TCpa4yy9FXOgZhK6/MQy5JcnstM4ykZ\nuI3jpEvYztnx7JSO5YGqqqlCTR1qhjCUJcbGGb4dIyXoh0w3ZNo+segjDHBlY5v3PXuF9z79XAna\nfce8axligmRl/p9srK+vcWp9lfVTq8xmDaFxqsYJtUN0SGXJrroybNrQhIZJ7czbjpx60jDQW0u7\nmDCpJ9ShYWVlldXVVWazVS5cusTZc2dZXz/FioK2yEPJ3ZOZ/QzwmcAfMLPHbjHz+FeNtxH46aVz\nPGlmvw78LuALzewv3mzmcTObUtbrFhERETk0Ctp3ZSe43vy5na2M086k2NO222xvXWVj41muXn0f\nVy4/xeXLTxLjBnHYZBg2MI8lZFsgh5rGVgj1CpNmhcrK+tI5GzEFYjSqYGOWL7OJQwZP4+RogTJR\n9/UWbSwQqpqmmTCtpwSr8FwmSnOPuBsxOXnIY4t2pO3LDOElaF/lifc8zXbXMe975n1PPyRyAo+Q\nk3PmdMuZMy1n2p7VtYZmCs0UJlOoCTSEMqa7Ll3kranwaOSUaRc9cRggQ1e3tNWESTVhdXWdtdU1\nzp+/yIWLlzh79hzr6+sK2iIPt++hBO0J8LfM7HP2rqVtZq8DPp3yS/cf71lDG+B7gW8HHgf+KvD1\nN7nOt6E1tEVEROSQKWjfxH6D6/drwfad//pO1/GE54GcB7IPLOYbLObXmM+v0S02SUMLnkqrLjXB\nGqpqCrkiYFQ+LnMVKqq6oa4nBAuQAjDOFh4iIQyEUIE7IVQEC7tdxo0Swo0yOU+wAObUdc2kaZhO\nJmXKtrHbuVkFVoGFsQt5ousHFouWmI2u64kxkTNAwKyiqhqqHMAzKXhZsxtIDtEzfUxES/Q50Q6Z\nWTVhVjdY1VCFirqeUFuDNTXzNhKqDncrS4NZTaga6smMldVTnD57nvOXHuHs+QusnzrFdDajqvW/\nr8gD6ECzl7j7j4/rW38h8BnAL5rZdwD/hTKj+JcAXzEWf46bh+jvHsu8HPizZvZBlHW3n6CE7z8O\nfBZlibCdLurHf3YVEREROfaUVF6g5bjtY9jOKTIMLX0/ZxgWbG9fZb59jXaxwTAsMMtMpxNOnTpN\nSjU51aQ0wVOP51ySanbqZoWqWiFUq5gBXuF5Z9KyiIUJoY7gThUqQgjY0gRhhmFGCe9mWGXUVUVT\nN0x2graXpchCGEheuqanbKSU6fue+WJRuqynslb36soKVdPQpMQ0pRKmh0SMmTgkVtdmzGYTmkmN\nVeU8QxxwBnwCYRJoJg3eVITQ0DQr1NbQTDqqqsWthlBTT2ZMVtZZWT/D+plznD53gXMXH+HMufOs\nrq3TNBPMNOu4yAPoTt64X0ZZbPrzgVcAb9zzvFNCg9lodAAAIABJREFU82vc/T17D3b3wcxeA/wk\n8IHAZ4/b8vFvAb5zvAVo76B+IiIiIjeloH1H9h+V7eNsYu6ZlCJ9t2Cx2GKx2GAxv8J8fo3FYoNh\nmGM408mESX2alBtyakh5Soo9qR9IQyTHRFWv7G5laHi12yHdLUKIhKrB3KmqMLZq2zixmZdJ2cbZ\nyEMIBIemqmia0qodxtZrMEKoGWKZnTy7k8egDXOSB9IYtNdWZjQ5M81OnzNDSgxDZOgjwxBZWZ0x\nXZnQTBosZIY+0/U9w9BiK4HaG6YG1IEQJkya2bi1hGobp4LQUDU3Bu0z5y9y7uIjrJ86TTNboZlM\nCAraIg+i6zNF3q6gewf84TEsvxb4aOAisA38OvCjwPe4+/wW53i3mX04pcX7CymBu6O0jP+Qu3+f\nmX3u0iHX7vgViYiIiOyhoH0LN+1CvrNMtfmePxWd7AnPmZR6un7BYrHJ9tZVFosN2naDrt0kpZYQ\nMnUdqMKsTESWIadAjDWD9UTriURCNcGqBqzZGe1N8oqYQwmkVhNCjeOEKlBVpft46Q5eZiN3c0Iw\n6mC4G1UIu2tQhxCo63ocx23l/DkBZQ27rusZIrgHcoo0dWB9bZXoTnQnAUNMtH1P1/V0/cB0NmE2\nbZhManKOeHaGLjFveyZM6S0xVE5qDM8Bs5oqNFT1lKZZYTJbZTpbY3X9DKfOnOf0+YucPneR02cv\ncPrseVbW1rCqJlQ1WLjn/w+IyMG5+y3flO7+yXd53h8DfuyuKlWOXwD/17jdzMvH2wj897u9joiI\niMgOBe07VtK1MYbt8bHskZQGUox03YK+m9N1c9p2m67fph8WDMNiN2gHy8SQ8dyRvSX7uDZ2Hkge\nyUSSDwypp4olDPexp4s9fRwYUiK74xZK9/BQjTOKB3Iq589krBpnIsfK7OOe6PuOnDM5eenyHcvS\nYDmX1mx3J8aIDxn3HqiwUDGb1KzMpiX0m+NAPwxszY0ty4Q8MK2MSRWYhoroTpUrLAXoAik4g2V6\nBhbeYd5Aruin5XWsnzrDi5tV1k6f5dEXvZhHXvRiHn30xVy4eJHV02cJkymEuownRyFbRA7NF423\n/9HLLz0RERGRF0RB+44sNWFb6cRdxmWDj0F7GDr6fkE7huy226Lv5mW8dmzJqcUsYZYxS7j3uHdk\nenIaQ7ZH3BLJB2Ku6GJZpqsfBvqhBO2YE8nL7OdmAavqMjlZqAiWStgmkcpIbYJBThn3TN+3dH2H\nZyO74bks65Wyk3N5jTFGhiEThzJR2traGrPZlLW1VQhW1gkP0HU9k5AJaSD3xqQOTKqKaVXRZ6i9\nIsQK7wMpwGCJjkjwHs8L8mD0Myc0M9ZPr3Hq/IyzFy7y6Ise45EXP8alRx5lbW2dtfU1qmYGoR6/\nXFC3cRG5PTN7KfCEu6d9nv/LlBZtB95wH6smIiIiDzEF7bthNy7h5Va6jcfU0w8tXT+n7xd03TZt\nu80wbDMMC2IsLdpG6Z5tlsokYfTAQM6RnBOZRCaPLdqhzMSdnS4O9OOWPZGdEjpDRQilO3Vd1eQU\nCCTyGLIzEAwSZRx1P5Rbp8KstBC7j2tuj5OjxRjp2p52MVBVFauzKbNJxdnTa9QVhApCgLarsTyQ\nupZubkxqo6kCk6qGBJVXWAx4H8gGkUyXB0iBHI3YQz84p8+vsXbqDGfOXeLioy/mkRc/xqMvfgkX\nLj1CVVW7G2Zj931NDCwiB/Ja4CvM7E3ALwBPAQ3wIeNznziW+zXgB46gfiIiIvIQUtC+rdJFukx0\nVkKweyKlSIwdw9AzDD1dt6BbLOjaBe1im3ZRJj9rF5sMw9a4zcneUwUnBKjCTlwPuAfcK3AjEAjm\nNGFCU09oqoaUvHT9zhnPpdt4ypC9rOidU6bPkWTlvgHBy9JfOZex1MMw0PcD/TDQ9QPBaqoKqiqM\nM5Czu+UEMWaGIZJS6UqeU4KcyDvj1Mcu5ilG4jj7uPtASoEhQooZc2c6aThzapXZdMJsUraV2QrT\nlTVmszVWVtc5d/HRpe0Rzp27wNr6KSbTWVmurKxTtvSvYsraInJQ7wf87/s858C7gM929+H+VUlE\nREQeZgra+9hp2YWdqJ1JOZJSX1qu+wWL+Rbz+Sbz+VYJ2G0J2kO3IMaWFEsrdmnRLhtEmrrM/F1Z\nhXuF42R38IB5aSUPwZnUUyb1lGkzLV3BnfK857IEV47Esbt3HNIYwocys3hVYVVNzokhOn0f6YaB\nru/p+56+H6gqZ0KFWUMIY8B2I2dImTFgZ4JRlvAaBoZhICTAMpjTth1tO9B1kbZLhGHAuoxVA2Dg\nzspKw8qsZjqZlqA9nbCyss7q2hnW1s+wtn6Wcxcf5fzFF3Hu4qOcOnOO1fVTzKYrBKvYXal8N1iP\nwfuOVgkSkRPqB4CrwKcDLwMuAavAZeBXgH8C/KC7xyOroYiIiDx0FLRvyXdDtuOkHBliyzAsWMw3\nuXbtMlevPsfG1cu07Zy2benaBSn2mCXCuA3DNv0wZxjmBMswmVJZgKqGsSXbHXAnGFQGtcGkmjJr\nZqxMpqSUxubmDDkRM8TkmHlZYism+i6TojObTLBJRW0Bz0ZMTjckFl0J2V3f0fc9TQ1mDVXlhJ1l\nw8oS3iVs7wRtvCzhNUTiMIA5TsbJLNqORduPYTuNjxuOUdcVK7MJK7MJs9mE6WTKdFJuV9dOc+r0\nBU6dvsip0xe4cOlFnL/4Is5ffJTZ6inqyYSqmRBChXv5+btnMKNM+G4apy0it+XuTwLfNW4iIiIi\n94WC9k3srEHtnsex0GmcrXtO227RtmXZrqtXnuHyc89w5fLTdF1L33X0fYfnRFNDXUNTG30/L7OQ\n99sEczw55oZ5IHsmeyZ5xnBqs9I124wcSuI2D9QBmqohNwnHqVJFlSpCqsipB8/EGOm7RB0qcu2Y\nBcrs3EbGyA7Jx9bqDJWDuwFh7L7uZebxfP1x25l4zJ2cSvfz7GUZsJgT80UJ2f2QSal0Zd+ZuXxn\n+bDVtRlnTq8xm64wm5Vtbf0sp05f5PTpS5w6c4mz5y9x7vwlzp67QDOZ4RZgXBO8LIEWSSlhtrNE\n2ThIHKA6sv9VREREREREnkdBe4+ydvY4HjsnhtQTY0dMPfP5BvPtq2xvXWVz8wrXrjzHtSvPsXn1\nMjEO5JTIqcwo7mMLccYYuo52vqBdzHFPDItIPx1oJ11pK7edlnOncifgVAbDaiStZUhGXQewQDOZ\nUTUThhSJeaBOEbeWfnAs9PjYpTuM48DrumKSG5LPyjWsxr0ie0VVTwnVFKwB6vKlQi6t2BBomglr\nKxCCMWkaAIZxjHfb97TdwKIbmLeJFI26nuJjK7MbrKxMWT+1xukzpzh7dp319TOsr59mff00a+vn\nWFs/z+pa2dbWzzCbrVGFGgjjTO6lfby0wrf0fUcIgUlTxq7Xdfnfd9JMj+D/FBERERERkZtT0H4e\nH7sqZ7JHYmzp+gV9P2dr6zIb155jY+M5Nq9dZvPqFTauXmHz2tUydtoYW1xL1+ZMCdqx6+kWC+Zb\n26QY6ZuBtumZNFO8NDjjgdLhOmfwTOVOHhyyUVnNbDahmtQ0zYRQB+oUiTlSp0jKxqLtscpwStC2\ncbI1LNDQlHWvzXBKyE5eUYcJoZpiYYJT4+6knEmptIZPmoZJVVEFo2lqcCcOA/N5x9Z2y9a8pe0T\nKVckD9T1tCz9NW6rqzPW1tdL0D5/mrNnL3L27AXOnb3I6vo5Zitnmc7OMp2doZnMmDQzQmjGidnG\nTvs503c9i8WC+XybqqpYma3gM9dkaCIiIiIiciwpaD/POP7YEzmXmcW7bptFu1lasa+VruLXrjzL\n9sZG2a5tUFeBpqlpmppQV6VF23eCdkc3XzDf3GboB+q6p6476mqCVQEqw6rSPdtzghQx3wnZFU3d\nEKqKlcmkBNKVKU2KDCnSpIEhZupmgQUbJynLmEGoDKPCbVK6WVd1Cdm5IqZAsOZ6i7ZXpRt7TqTk\nVFZeTx2MujKqYBilRXsxb9nYmHPl2jbd4DSTFerJCvVkSlUHQhUItbG6tsL6qTFonzvLpUuXuHjx\nRVy89GLW1s9TN6epJ6eom/XSBO47s6+Pk5/lTE6Zoe9ZzOdsbm5Q1zWeyxcBZaI0ERERERGR40VB\ne1cJdyknhqFjGFq6bs72/BrzxTW259fY2rrMYrFB32+Tc5nwrKphMiutvckTuU8McQynVbmNKVFV\nFbPZCpNmQl01VFW5HXIJzP0Qy9hrg8qMqgpkoI+RRddBFehzZhEjTduVycGsfC3QDZkhpdIaPY75\nvj623IgpE6Mz9E7bJbYXkc3tHjxRhUwVIhDou4Ghj/TdwGxSE+qGyaSmaSrqML6eYMyGyKyPzLqI\nVZl6MqFuGurJhOlsOm4TTp1Z59LFc1y4eJbzF85x6swlVtcvMJ2doW7WqOoZwRp2xpEzrvvtO/8e\ne+c6c663Yo/jwEVERERERI4bBe1dJbTlFOn7jrbdZrHYZHP7CltbV9javsx8u6yNvbMetoVM0wR8\n1oxLX0XiEHHP1FUZV11XRsqZqqpZWV3BHKqqoa5qqlCzvViUda27Dndn2tRUk4aqbnCgj4l52zF4\nphp66rYdn68IdU1VV3RDYoiZ6JlM3p1gLedMdiPFzDA4fZdZLBJb2wPXNnuyG0bErMM9lDHmMZf1\nskP5AiE0Fc2soRlfT1MZXUzMhsSsT1jv1M2Uuilhe219jfVT66ytr3Pm7BkuXDzH+QvnOH/hPOun\nzrK6do5mepqqXiOEKWb12Jo9rv5tN/xz7N73Gx72PYVERERERESODwXtPVJODH3LYrHN1vY1tjav\nsLH5HBubz9J1WwzdnCHO8TFo141hNLgnuj7R9S0pxjFkl62qyuzbk0lDFQJ1qKmqimAVfRzwed4N\n2lUITMyomho3Y0iJedcS4gBthVUVVtdMp1MmsynT6ZRuSPQpEfMYsMkkT6WFPY0t2kOm751Fm9ia\nD2xsdQzJx67aoSyX5V7ald2pJxXJwJpAM2uY1IGmDkxqo/v/2bv7WNvyPK/r7+/vaa39cM65t6pr\nemqCEAQkJhCRdqIM+ASoHSc0GTUIBgVmJBMkBiVoJILdPagYBTHEIek4EZgIIRmCAR8YiYojISSA\nzcgQ/hAFA4zFTHdXd9U9Z++91u/h6x+/tffZ59xzH7qrputW1feVrD5Pa6+z9rld597P/v5+32+u\nrOba52Z7xYdEiAkfEtvtmkePHvHo8Ws8eu0xr732Go9fe51Hrz1mtbpgHLekdNH3cxNA+oiz24S9\nvBV5Zo5WPc42N8YYY4wxxphXjwXte7RVSsnM84HDfsd+d83u5l12N+8wzztK3lPygVpmtAHLXuiG\nkmvhsMyoDt73oB0cQ4ogER8CLgSc6yHbi8O54+EBJcRASok0jID2kNwKilDpe77VOVarFav1mnHV\nyHk6jdma5sIQCqvYm5rVCrlor2jPjcNc2U+Vm0NhLg1VoWnfT+5kOYCxROZaKDQKbdkzXqEKlYo6\nxUchIoTo8METgmO1Gri42PDo0RWPH7/G1aPXubh6ne3Fa6S0IsYVPoyIi4B/OmSfLRcX6bOyvfek\nGBmGAR/6+8EH3HG8lzHGGGOMMca8Qixo39OaUmvtS8HnPhd7nifm6cA07Sl5T857as0IDlGH4Jhr\nYcozu2limqbboO09tSlNl07kCtUpQXr1WkWIKbHerHFOWK/XrNcrVuPInOe+X3yaybWSm5KbUoH1\nlFnPhdVcyPnAzfWO6+sDeZ5IbmCMjfVAD9oZ5gJzUebc93T3fd19aXnf+iz0unbvfn7ImX2e2c0T\nPiiOtowda0yHypQLjYrzDh+UEJUYYRg942pgs1mz2WxZrS8YxwuG4RIfIs4nIKJ4bvdlc3c/tiy7\ntZ3g1JFSYq395+OcY0gDKQ3EZeSYMcYYY4wxxrxKLGjfo9qDds7zErAn8nRgPhyYlzFfOe+pLeMk\n4l3Audir2Tmzmw4c9j1o+yVsV6XPl3Y9ylYHVSA4BRwxJTa+V25Xq5H1asUwJOpN4zAd2E8Th2nm\nUGrfj92Uw1Q4TIXNXCll5uZmx263p+SZMRTWqZFXSqtCLvSK9uloTKUH7eO8b4Vl6Xg/9jmzn3vQ\nFtegZagFbQWtoFVoCM4HXFBiVFJSxsGzWiXW2x601+st4+qCNF7gxCPiQPzZnuynq9migooi9GCd\nUjrN8hbn+v5235ffG2OMMcYYY8yrxoL2SU95qkqrlVIKec7keWaeekV7ng7M+cCU97RWeiXXgxdP\nbo15CduHecYfg7bz4Ja91T6gOKKDJkpzDhUIKeIlEqJnHEbGcSClxP5woDblMGVu9gd2U2Y/ZebS\nyLkfpSi1Zna7A/v9RC0zm6Ewz41SoLa+dHzOyjz3kD0X7deorb8AwBK0UdA+x3sqhUPJHPKMc42a\np9MRXMC7SJBACA7v26mqnQbHuIqsV2Nf2j6uGcYNKW373uq+wfpOrzOVe38Kcqxzaw/YKfY53oyA\nLEvKHSLnZXBjjDHGGGOMeTVY0L7HicP7QAqJFBNePNqg5EotjVYVaYKow6ngcDiEISUuNhdIc0zr\neRlUBSBE7/ESaBlya6gHdUpzDXXaD2EZzaXkkvHOs9vtmHMGpO/pdq2HWgXnlqB5LEcraBO0OVp1\nlOrIxVGKcJgau0Nhd8jMuaJNCeL6Zmx60O3VY8E5cCJcrNdcbrZcbjesBk+ePHlyZOG0XB7tS+1V\nG1ARqTSdqeXAnG+Y5mvivCLOG2Le0b+h6yF5Ccz9J3RLZXk+6J0g3cd+9Sr3va3cxhhjjDHGGPNK\nsaB9jyxBO4aBGAacC6egXZaQ2kc8u+UQnAhjGnBbxxBHSq602k6HqqKt0YqSa0VdQ72jegdOwSnq\nG6UJOedesQXyPJNzb4QmzuNcW+Ze9zB8DPO6hOzWHK05anOU0oN2zsphbuwOmd1hJi/PwYsgp2Zi\nPdSGEAjBE4LnYrPmcrPhartlHDyTcxwA1/qLDdqW6r82VCvHoK2aKXXPPC9BO6+J+YaQ1zgJiIs4\nF3DHsA24pXqNnO3X1uOfx+3YL1nq7sYYY4wxxhjzKrOgfc9xD3CMiRQH3LGiPfeKtiqgsnTnvo3b\nQ0wMYeRi7ahVKbmQ50LJmXmamaaZucxoqzQvNK/40HrQ9n3JtqKnWdZaW78hBZa9ys45vPN9ubrr\nlfRTNVvlFLbrUtEuxTHnymFq7JeKds4VFIJzvRK9BFrnHCn2EWQpJS7Way42Gy63F4zJswNEG5RK\noVC0UbSiraH0ijZSaW2m1KWiPV0T5w1p3hHzHu/T0lBN0LOf3/IUT/m6h+3bAdq3lW/H+WAvVRvx\nZYwxxhhjjHn1WNA+6WHuuHQ8hESMA8EnvATA9VFU7Th3ulG00Uomz40UUg/nKSA4avCUECglMnlP\n8p7JO1qrON+XaXsvFCpVC/XYhG3uHc9rrr1zeQhLYzUhBKhNUOlLyYFeMW/9vmOIS6XYUXJjt8/s\nD4Wbm5nrm4ndbqK0Qq2th3RxyBJwYwishxXrzYr1Zs3VxZrtes0QE9715eKtwJyVWlgOAS+0djsi\nrJTCNE3sdjf4+C64ASVS1BHjmhhXxFQIIZ0axon3dxqgHReKnzdLk1Ml+/hWbY+2McYYY4wx5pVk\nQfseEcG5QDwG7ZDwPhFcpBJp2qB5am3kmqltprXGejWy3awZvSPFiIqjeUGbJ0dHXgXmnGhNESfL\nAYd54jBP7PNEKYWSlXlq5FwZksd5h/iId0pQoWnf4+ycW4JtRbXhvWMcE9oU5xw5V66f7NkdMk+e\n7HlyfeBmPyPSEGl9/rc4vCwV+ZC4WPf5148eXTGuIuPg8eKpJTNPjf2+cHM9L/uyBVUhiqdWT619\nb3jOrXddDzdApFTPlGE/lT7ma9wyjBcMw0iKA0NKOJHTUngsPBtjPsSur68/6FswxhhjzCvAgvY9\nxz3aIQ7EOBLDErZdwslMo6K1UDPMU6/ezvMMl5XRO9yYGHxAvCCxh+LaAqWmZZ5234usSyn53Zsb\nKsp+ztQq5KxMcyNPFe+UlBzORRAICLo0EwOgQW0VEQjOE4MDBKon50I+7Lnezbx7feDJ9YH9NBMC\nxCjECF4EL54gjjEMXKy3vH71mDfeeAPvFShAWUadVfb7wvX1DOL6qC7Xj1KPYduT5x60RW6oTZgy\nDIfCbjex3hxYb2bWpdFaBW141/eG67HCfqcf+dNssbgx5lX25MmTD/oWjDHGGPMKsKB9j9xZOj4S\n40AKAzEkqk/QGk0qtEItjXnK7Hd71inQyojTQpTWx3s5h/eepp6q0BQqoAgNaNBHgs2Z4GecFJAC\n6mhL9bqp6+EaEDzOKc5pb7TWlFob3gsxemIMOCfMeyXnynwo3NxM3NwcuLmZ2M+Zceidv51zeEBc\nX4YeXGQ1rLjcXvL649doOi9zw/e0quS5MU2V3b7gfSRGT3AJkeMxIJJozVGKMs8Z5EBtgVxhzg1V\nAfG4kPAhEEOgtoJq689RleWJ3v553P8D0tP/GGPMN0xEfgbwt5YPf72q/uAHeT/GGGOM+WixoH1P\n3zvdQ+swJFarFZvNhouLC2KAnAPz7EnJ9cpwcgyDZ3MxMAyCc4WmB6QFRALSArk2clVybZTaqErf\nk61KKY0hRR4/umK1WrHfTxz2fSY29I7i19d7miqt9eZjrdVeHa89cPvg+mgw6UvIS+vXbkDTRm3a\nZ2nPimql1MY8C0NUhiCk4EiR09xtlaW5Gr31WENR6dV0xBPTivVmy2p9wXq1Yr2K/RgTwzAwjAPD\nMBKHkZjWhLQiDmvWqy3r1ZrVMDKkgRgC3vmzn74tGzfGfNPZK3fGGGOMed9Z0L5HxOGDByLDMCxB\ne83FxZYYlDl78uxIgyMlRxo94yqwHhzD4BBX+jgvbUtHbu0NwubKlAtzaZTWw25pSkwDKY1styO1\nKrslZO93B252e3a7PTe7PaX0WnhvMd7nVx/DdohCEw9OCThqg9pPo6lS6jFoN2qFnHtVPCco0dFS\nYByUXPvjoHcFV1Ga9Bnfvdm6Q8UT0orV5opHj15ju9myGiLjEFgN/Wc2DD1whzQQ4khIIyGOrFcb\nVuOacVgxxETwxzFfnEZ9PTtq22gvY4wxxhhjzIeDBe17nBMg4ATaOLBajWw2Gy4vtsSozLNnnh1p\n7sF6nD3T7Em+knzDuYJq6ZVnGgKUMjPNmd0hc8iZXCq5VEppXD1+zMV2y6NHl4jz7HcTu/3Ebren\nfemrXN8cuL4+MM0z3i1Nw0RoTZewDbE51DXwjSaeVulLz0WoNGrrFfUpK5KXZmg0yiC0wYM2Vhly\nWbqaLz+LY0VbUZpID9oEQhpZb664evwGV5dXDNEzpsCwrAIY0kAaehd2HxIuREJMrJagvRpHUhoI\nIeKdO+so/iJWeDLGvG/slTtjjDHG/JSxoH1Pn9LcZ0OrVpAG0hDXCB5IgnO3Y6lwvbGZB0QaTStN\nHd47QojEOJAVYlVCbQStNG00hUbDu4ZzDSe1z+Z2De8b3mn/2hKKhdanTkuf4V3py7xzbbSsqFea\nU3JraBUoDq3Sl60v1e/aTkO3UW0Ep5TQr9OaLpX2zFwmms7kMlHqTGsZ55QUPavVwHa74erqkseP\nX+PR1WNS8KTgidEzpF7RPgZp8QHnAuIDw7BhSCtSHIkx4nzAOQcid5uNn+dp+6ewMR8LIvIdwG8A\n/nHgTWAEfhL4UeBPA39EVd85O/9bge8CfinwDwHfRv877cvAXwb+KPBDqvrUK3TSX208fQj8IRH5\nQ/dO+5yqft/78uSMMcYY87FjQfs+1R6EW6G24/iufiAZHyrOK84LDV2qxRXRPmarasOrIN4ThoFx\n2FDFU3FUAAcuCD6Dr0qMirhCawdac7Q696NNoBknjehBw9LAzHtEhKoNldr3YxelzUqm4Ysgtc/7\npjmm0iitUbW/hKAK2pTWoDROn2+iVC3MdWLKO1Rn5rwn5z2tzXhRhuTZbkYuL7Y8urri9dde4+rq\nNYJ3BO+J3pGGsS8fTwPOBxC37O12DMOGlFbEYwh3ri8dl2Wsl9gCcWM+bkRkBP5r4FcvnzoPxt+2\nHN8JfAL4vuUxDvhx+q+L+0H6TeAzy/E9IvJdqrq7d8798Qa2XMYYY4wx7ysL2vcoirbag3adqS3T\ndKbpDFLwriEefICivYLsa4VSl8dVGh7nPTENrNYbmvSQXWngFFcUHxq+NEJUnGRaPaAItWZqXYI9\nBSeV4Htzsh60+3ivXBXN9H3aTSmAVHAeRD0Oh6hnLrVXvZemZq0prYJWpVZdKuu9hl9aJZeJQ96D\nzpSyp+QDtc4414M2G8/VxYZHx4r2o9fxbnkRwDnSMDCkHradC73beutL2dMwElPv5H58wQDoFe3T\nDm1Fn7mY/KF/UxtjPqyk/xL4U8Avp//H/TeAP0CvSO/oofk7gF91/6H0X11/Fvhh4MeALwEXwN8P\n/EbgFy3X/X56pfzcz6cH+D+zfN/fAfzJe+f85Ht9fsYYY4z5+LKgfY/eqWgXapnJeWKe9ygT3lc8\nlaqZ1vLpPG1Ly+7a90CXuizXXpqSifQQ7IOQnO/jrRqk6HCuoTrTGtQy00qm1Qxa+izt2EO288uS\ndXFkVUJT/NJ5vLRGq735Wh/gpQjKlCul9ZgtfpmepT24ixNwgO+Nz0rLTOXA/nADOlPznloO1FxA\nPd75vh97SKzXK7abNdvtdgnaHuc8aej7r9Mw4sRTa2/G1hqklIixV7Od67PA9V5wXsaMn7H6tjEf\nYf8mtyH7TwD/iqrms68fl43/ThF58/hJVa0i8nNV9W8+cM0/B/xhEfks8FngXxWR/1BV/5+zx/91\nEbk5e8yPq+pff/+eljHGGGM+7ixoP0VB21L5LczzxG5/w5PrJ9SyB8lAprWZwzxxmA5M8wStIaqI\nNkqGpjdMGW52M7kuR5lQWeZeDx7vAyJ+6bw5kpFBAAAgAElEQVSttNYDfqmZkmegEaIwriKtKT70\noO2cxyVPGiLDqrI/lH5MmXmqoA3RBg1yadRacb6RBkUby/wuzzgIwwrioEgoFJ3YTzveuRZEM61M\naJlotaI10kpAa6CWQqsF1YpIw/k+EzuERExLEzQfceIR119gaE0IMeC85xieH6pNv3xjNGPMh9lS\nzf5t9F8Ffxf4dfdC9h2q+ta9jx8K2ed+F/Cbgdfpy8h/33u6YWOMMcaYr4MF7fuU03iuWivTPLHf\n7Xjy5F3m+ea0f7q2TC6FXHsHcYfiRPFAdsqUb7jZzXh3Da6CFFQqMTlCGhjHgXGVaBVqgVobqscq\neqaUjFCJQViN/Y/JR08Iy7L0HBizsirKu9eZ1ib2e2U+LGPFajt1Pm+03sxs0F7RXkZqDYNjGIQw\nNCRUSpvYT/DudUW0QJ3RmqEq0iq0BKpnQbsg9BcOQooMacCHgRASPkREAk5lmckteO/w7u4y8XMW\nsY35WPkFwE+j/yL4rx7YR/3SltD+rfSl4/H4aXqAf53eLM0YY4wx5pvGgvY9ip6Wj9dal4r2jusn\n77LfPyHnA6VMlJppClWFpuCdEBwELzjqshRcUYWQIEYlJFj7xMYHhpXj8nJkOlSmqdIOvULcaqHW\nTCkzoIQgPbA6R4ieED0uOIYq5Aq5CsjMfqdoK8wT1NJopVBr6Y3bIvjU95W7Jex674jRkSKEqIgv\nVIX9XOFmxrUKrSC1IA08rS9JV6GU3IN26xVt74UYA8M44lzE+YRzPWjL0ggNHCLa35Wnl4yfkwfe\nM8Z85PzDZ+//uW/kAiLya4HvBv5RYPWM05TeSO2bQlX54he/+MLz3nzzTd58880XnmeMMcZ8XLz1\n1lu89dZbLzxvnudvwt28dxa07xEEkd4N2zlPCIkhjYzjhjzPzFPmcKgcDjOlQalQmjIOnnEIrEaP\neKGe9no3qNr3QheYZ+FwmIlxwrvAdKgcDoVpX5imSs6K4glhoFHRVqitUlqlaqVUwTl6eBVPFEdy\nhSiNKEp0wpACMngcER8hJMUP4CN9r/dyDMPAalyxWq2IKQAVpFJrpdQKRdHScCokD8kLPjjQyjzv\n2N18lesnA60VoOGcEMJIYKmai+txWpeRYqLQFET7Puxjs/FlNvjyB/D8Px+x8G3MR8R5+H3x36pn\nRGQA/lvg09wujXlep8RnhfD3naryqU996oXnffazn+Vzn/vcT/0NGWOMMR8SX/jCF/j85z//Qd/G\n+8aC9n0iS0j0eB9JcWQc1qzXF0yHid3NxDzB7qYy58aUG3NpbLcJcKQh4p1bgmWfld10mWFdwWVh\nv58Q8bQqS8juYbsWllZmkZgSpU00bczLUm2RZU+0KCF4QgyEEIgUoiskpwxeGKInJUeKjpggDNrD\ndgRxvZka4lhvtmy3l2y3l4QY2e137PY37PY7amnUXGgZvPagTnDE6FEtzNMNN0/eJkVHa7n3CndC\nGhqI9NnZ4kBbf07altUCDVAQ8N7hnCBuCeRiO7SNMS/ld3Absv83eqfyLwJ/T1X3x5NE5Efoc7m/\nab9a3njjDX74h3/4hedZNdsYY4y563u/93v5zGc+88LzPv3pT/OlL33pm3BH740F7XtuK9o9aMc4\nMI4bNusLbp7cAE/IE+xuCvupsJ8qh6mgCilFqgo4hzZBRVH6DGuaQlGywF5mWhPy3Hp1fAna6LFr\ndyDGxJSVpplcGjkXaDNo37u9GiOeiPeJII0kleSU0QubMbLZRNbrSBp6s7Mw9KANvRKOOLYXV1w9\nep2rR5/Ah8SX336b9hXh+iaTS6bMQp0VL5BCrzqH4EELeb7h+slXCGGpUDuHDx5wOB8IccCpR1V6\nyFbpe8a1H+IE8IBHTvu2bYq2MR8jXz57/03g//o6Hvs99F8Yf05Vf9lzznuNb/JMwJQSv/AX/sJv\n5rc0xhhjPhJedltVSumbcDfvnQXt+5aKtnMB7yIhDMQ4ktIa38u6lCLMkzJNynRoHKbG4dA4zJVp\nrjgPtRZq6R3EfQNfIfg+T1q1Lt3FHftdYb/L7HcFJ4FVC71q7YRSlJwb01TIOSNacFS8VKR5gsAY\noCXPxVqYLjxelO02cbFJXGwTaXSEdKxoC0gEF0ACl1ev8/i1T/L4tW9BfELcwJwd1zeVmmFulcN8\nwLXK4Ct5aMve9UzOe+YpMh0iaejjvNI44n3Ah0gMaWngLv1QWfa+ax9B5hwichrz9bDjv48tfBvz\nEXS+kfmfAH7kZR4kIq/RG58p8EPPOW8D/NznXOqbGsCNMcYY8/FiQfseQXB94DXeR7yLiMQeUCWi\nGlA8iu9dxr0SQt+GPE+Z6+s9c5be1Kxlas3E4AjBEYOjvwBzuw98npX9vnJzPYMUavO05igVdrsD\nu/2B3X5CWyZ5cN4zxMBqHNmuBi7XI2P0eAdjgt0BNuvIeh3YrAMxOXwUQuwVZ/EjLow4P3Jx9Qke\nv/ZJHr32LeAStY3kHJmngLTAvC/M0xPKPBNdIPnAEAKr1UCtBShARVum1gN53jH72MO28xSfaU3Q\nKrTWl4gfDyT07u4v9adilW5jPoL+T+DvAH8f8K+LyO99yc7j539vbZ5z3m9czn3Wr5nD2fvDS3xf\nY4wxxpiXZkH7HhHp8589+GP37GPYJvRD+xJpEY9bOo2jMM0FuW74SWmtLEclxUBKgSH2QN7DvOCd\nY5qU/b5wfTOh6mjNo+qoDW52e3a7A/v9ASeNMAR8DAwpsB5GNqsVl5sVTQPj4LjYeubiGEfHauxv\nQ/D40OdX+5AIaYuP/bh89AaPXvsWHj3+JOoipSbmOXA4eA77wjtvP2GeYH8zk1wghcAQPfN2oNYM\nVISC6kwtPWh7H3DO9z3uLtOqUOsyRztEfIz4EHHO9f3aeu/fwM/M1Ba2jfkoUVUVkf8M+P30MV8/\nKCK/5qFZ2sfxXcss7S8BXwOugF8jIr/v/mNE5NuB7+P5VeuvADN9HNjPej+ekzHGGGPMkQXte0Rc\nH0GlQvAR5xLuWNEmwFLNRj0i4B2EICiVeS6UVhFXaa3RtKLaGIbGWIXW/NIoTAjeUfxtRfv6eu7N\nuftEbmqD3W5iv5/YHyaiF1YxEJxniCOrYcV2XHO5XuN84mIbyDVQCaQEQ4KUZAm+/cUCH0bi8Ig0\nPiIOj7h49AZXr32SR48/SZPYQ/besbuBr739BOd+gnmGm5uZIQTG6NklxzyvKMegLZXW8m3QdqG/\nUCEO5/IyI1xoVYjDSFIFEXw4r2jL8s9heTBLLw9B74Vy60BuzIfe9wO/AvjlwL8A/JiI/AHgLwM7\n+hLxXwT8auCPAN+3BPQ/Avxm+nzsPy8i/znwN+jh+zuB3wQ8AX6cZywfV9UqIn8J+MXAd4vIjwI/\nChxD+9uq+tX3/ykbY4wx5uPAgvaDlsAnrnfp9hHxCRcTPiXCkEirREKXcKw0LTQyTTNVC0pFm6e0\nSmwD0PdAi0uIRFQCTftRm6NW18Omepx4og9sN2uGIXF5uSXFwOVmxcVmxeV6xdV24HIzst6OeB+p\nGihEFE+MEKIQI/iQ8H7A+5EQ18ThkjRcEodL1ttHjMMFzg+AYxzWXF5c8YnXD7z7tbd596tf5slX\nv0yksl55vPfUohymym6XeXI94fyeMXvmDOOslFkpudGyEuJqWQ2QiDESgydGT4yR4EN/EeA4WPs4\n7+t0dMdsfb/wbYz58FtC868E/jDwLwE/B/gvHjr13sf/PvAdwC8A/hHgj977+peBfxH4XTx/n/bv\nBv4U8PoD1/gcvSpujDHGGPN1s6D9kNNMZ4Flv7YL8RS0Y4oMqwHvhOAF74Q5z8x5Ys7CnIWKR7VS\nq0c1oTL0oO0TuIQuobi1ftTqeudt9TgJBB9JQ+ojsLwwDgMXmzXbdT/WY2Q9RNZjxPke3FV6kzMf\nwAfBBQhhJMY1IayJaU1MF6RhSxwuSMOGYdzgfUJUGMcetPNcefK1t3nn7U/w7tufQFrGS8W7SimV\naWrs9pl3rydwfU96npUyV2pWWlEoMIyVkNbE5Jemcn0kWQyREPo+bnGOPmT8ftg2xnwcqOoB+JdF\n5J8EfgPwS+iVbA/8BL3K/N8Bf+zsMe+KyC8Gfivwq+gBvdD3fP/3wO9X1f9vWfWiPGMJuar+jyLy\ny4DfAnw78AZ9KbkxxhhjzHtiQfuZ+pgujhXtkHDhrKI9JsbkGaJnTJ79fuJm5xCFWgSnvaJdW6Vp\n4ryi3RurBZp6mnpq7YeDHrTxxBBZrQfW63F5u2a73rBZb1mv1r2xmvfE4PE+IC4gfjkciBfECTGt\niGlLWo6YNqS0IaYNPiScDzgfoSnjsObqsiI4nrzzVd59+0s8+eobaD6Q856S9+RcmObKbp+J1xMq\nnjwrea6UOdOyokWh9pFmTjySRmJ0hOiJISzzvyPO973cdyvZFrKN+ThS1R/hJTuPL+cfgP94OZ51\nzj/9fn9fY4wxxpiXYUH7nvOyh4gQY2Rcrdlsr1hv32V892ukcUWYdvggeC84pwzJ42RkiIH1urKb\n+7GfG6t1ZL1OrMbEakgMQ2JIiSFG5o0wHxxl9jgRrq42XF1tubrccnG56cfFhvVmw2rcsF5tGIcV\n3jn8Mh7LO4/4gPMB8X0uNQ5wQogjKfZgHeOGEEdiXBHi2EOuuL4vXRopRdarFQK89viKd994nZsn\nnwDds7t5l90N3NxkQJnmzJObHbU18qpQcqHlAs0h9OXvzgV8HIjjitYq2ipNW9+brQ1Vt7yvyxxt\nlpxtYdsYY4wxxhjz4WVB+0G6NOAShmHk4uISbYXD/oYn736NYVjj/RNUMzlnWs6kFLnYjgxDQvHc\nTJXdVLmZKzE4YvSk6BmGxGocWa1WjONA8BtSumG1usKL49HVZjm2XF5ecHl1wcXlBevVhjSsGNKK\nFEfc0lTNifR95M73qrZzp6AtDpxP+ND3aPe3CXGR3tRtWbKtvZLsfWAYE+Lg0aML3viWx8zTtxDC\nxDvveL72tYrzEwiUmnlyXZimealkF7RWRHrg977PIA/pQJgOhLQH8SBhGZMmeAXfe7DTXMPhrMGZ\nMcYYY4wx5kPPgvYDeuOthggMw8DFxSUxeK6vn/CVt7+yBO2IlkIumbnsGIdLLi5WvP7aFTGN3EyV\nm33h5lBP1xWUlBLrzYbNZsN6vSbFA6vxwGazxzvP1cWGq4sNl5cbrq6u+vHoinHc9OAaxh6WRc6O\nXpVm6faNWzKto4dvWUK4LO/L0jl9CdnHzt8heJxLxOi4erRlnh6h7Q1inBjGivMHmj5hf5g5TJnD\nLuOco9WCtoagPWD7SPCJEAfCNBKnkTiM4OLtIQ5d5ok38Tgc6qzjmTHGGGOMMebDz4L2iZ697cuZ\nESENAz44VquRr73zVbbbK1brLTGtyHUm10qeJpDGZp34xOuXrLcbbvaV630P27U2aulHjJHt9oLN\nxQWb7ZYUZ8ZhYr2e8c5zebHhYrvhcrvh6upRPx49IqUVziXELbO9RZZl38tbbpeB92q2IP74jOR0\n9Kd3Nk7rjPeeEATEc3m1odVHOLcnxhnnJ5rumPO7qMBhLkw5o40+XztE5pSYc2EuhXJ+1H64knG+\n4HxBXB8N5lzDuWaVbGOMMcYYY8xHhgXtB52mO/fxUy4gOFbjlqvLx7z++ieZp4mbdxzXFOq8o9XG\nNO25uXmHqhP7qbE/VA5T7fuVxROTJ0Yh+obXgpYZrRO0CdqhV5vV4XA4cdQi7PcFZEeMA0ifh+1c\nWpZmp2V8V8K7vizc+YQce+w20LMAK+iyTPz00RlZPicIgveJYdyw3T6ilJk5Z2pTEMd6s2O73XF5\ntUcbXG7WXGzWXG43bLcXXGwvudhesNlcst5esd5estpcEtOmjxhLK0JMhBDx3uOcO1XnjTHGGGOM\nMebDzoL2id57CyCI+D5iyymr1ZbLy8d84vVPUuYZT6HMO/bXkdqUaTpwffMOue44zO10DHEgphVj\nHEkJkms4CtSpH+3Qg7ZzyBKynQilZPa7HTn3EV4s47ucS6S0Jg1rhrTpY7uCAh6RXok/Va6f81Tl\n9KEsn7zt/B1CYhjW6PYRqo1aG+DwPrLd7rm+OXB104P2dr3qx2rFZrPtx3rLar1lXG8ZVlvG1RYf\nRnwc+1sf8cdZ2s7hnHu//0CNMcYYY4wx5gNhQfsBqvSl47AEwF5tXY0bLi8fc/jERC2ZMu/YPfkq\n4iK1Nqb5wM1NIWdhKsqcG1NuBLnADY4hJsYEzjecZrQI1CVk6x7Oqtk9aO/JWdE9vXGZeJSAc5HV\n+orV6oq6yoyqCB7vh37zDY4V69No6uP7d8rYcorXt188NkaLfV+4F7zvndW8T6Q0stsduNlP7HYH\nVJX1ON4eqzWrVe+OPowr0rAmjitiWuGWirvzCecCIm6pZn/9FW2rfhtjjDHGGGNeVRa0HyDLiKke\ntm8bjsU0sNlc8vhRoZVCzXu0HhCdiWFmNVZiWvYfu7o0I1OcV7xXfADv+8fOKU4aXiqegiPjcNBm\nWgnU7Gh9FDVNBRXXx3G5ivpGyQdKiOQQ8G4ZpyUBUcEFj/N9Hne/CVmq3OfLyAWVpZYt50m8h3px\nAR9GRARVoVZBJBLjmsM0cTjMHKYZFIaUGFNiSJFhWDEOI8MwEtPQG6LFAR+H3pHc9ZnfvXGbPHW8\n+M/GArYxxhhjjDHm1WZB++S8oqvcy56oQgiR9WqDqtInaGVSgM0qoe0Jwg0iN7R2QOeMupnmMiF6\nXBDEK7KE7OAV7yB7JbhGcBVHRdtMzZ48OVQ8xzFc4hzij/OpHU4aqplW95TSPy/q0NbwNRFiJGjq\nc7VPXckd4JbndFwwvjxvOfsxIEso7oE8JUdrAedGUrokl0LOhVwKKMTgCT4QgyeG/r1jiH15eAg4\nH/uM79Pcbg9nwdrCszHGGGOMMeajxIL2HceQ3avZ9wNgDJH1ekOMiSEmUoD1KnF1sWE6fIVpept5\n+grT/AR1B5oTqij+GLSdIq716raH4CA4JbiKl9KbmNVMzZ55cohLy1zsHk4dHr8EbaH1UF6hZEHU\n9eZnrRHTCAw4URwRccv8aie3LdDkdqP2aUX52TpzkT6TGwk4GXBuxTBcUGultkbTRmu965oT6cvd\nnSwvAnicuw34vaHccu1jhf34E7eMbYwxxhhjjPmIsaB9ctsaDJaV1nf2M+tSnQ2kNDKkRAywGiOX\n2zU31xuubwZurj27fcTPB/y8J8x7xpQYh5E0DMQ4EF0iuEh0kZICpQRK8b1S7vo3blXx4nBEvFst\n1WGPj8sRepjto8gqrWVqnU6VaO+F2vzpaTnnkLakahyn6r0s1eyzPdp6XC5/rH478Of/T3kwHOvZ\n/m85feqBs85OefHcbKt2G2OMMcYYYz5sLGh/I0QQ54lpZFxvcQ5CFIZVYrPZMs1PmPK0HDMpeKL3\npOAJzuOPlWlxSEi4OBCGRGuN4FM/QiKmC2K6IKULfOzzvHt13CNO+tJuJ33clxvwbsT7ARci6oSm\nDdEGWkGPy8b1NlffybC3y+ZPnzn78KlTX+RlzjHGGGOMMcaYjyAL2s91TItyenMMnM735mjOXZBi\nZBgTm7yl5MfkciDXzFwyuWZEFbfshnYoov1AFR8TYRxI00CrFZHYq9KSGFePGcdHjONjQhpxwS2N\nzhynYdmiyxLtgJN42sONczTOQrY2oOFU+giwO9FZn3rvPHafx29RufuIpwrO8lTIvrtWwBK4McYY\nY4wx5qPNgvaD7ofBZTn5sqy6bzXuFe0YI4xr0C3aZlRnaivUVimtUmql1kwrmVYLrWaoBW0ZrYU4\nDMQ8MOSBWiraPKoB1chq8zrrzSfYbD5BTCvEO5z3iBeaVlQbTSu9pZksdygoDaTRqL0srR5aA9cQ\nHPdj9rOed9+3ffvp47ZuffAhcr8gfueUe/HcGGOMMcYYYz6yLGg/6G7n8aM+7Ut7VXcZ+XVKowp4\nDxpxWnGt4VTxrdFaodWC1kyrFW1lCduFWNaksqWUHbUWWnW0JrTmGVeXpGGNjwkXwhKye0XbqaI0\nVPVecFag9bBN7U3UxCPil87jy6ive0vH71Sv5Xa/tdz5opzmi995pNz98HQbd396d7/+Ml5yH/fX\nd1FjjDHGGGOM+allQfuZzuddHTtx03t+CX3p9+mTAvjlC4IQcG5Z2u3At4r6irYG2tBWobXexKwe\nqKej0CrUqrQmxLQmxAG89NXfDpyT3g0cEMKplt0dQ2lDpQK9gi0cg/bZXO3Tcng9a/2tp0899dMQ\n+isNwoPf8fSj0mOTtfMv6NPnGWOMMcYYY8xHlAXtpzxj/fPxq2eBU2HpGHYMrw7wp4KxY9nT7Hvo\nFvq+7PND20zTmdYmWi19fNYyQquP2IqIW7qDu+WiTnDiYTnuxt5e0b49ABzS76ZXtZHjawLLUvPb\npfG3ofhZafv4NTn9tI6f01PAPq+x23JxY4wxxhhjzMeLBe0HnZeuH/iqHLO29GXWyFPnyjG2ityu\n1D4rIsPyLTSDFlQzrS1Be3mrgGqffH07T/t4+NN8bLnz4oCC3Avael6ddzy9Y1qe+uhFLzg87P0P\n2A8sVH/6i1YhN8YYY4wxxrxCLGg/i56FbbiTH19u1/BS4V0eoPdz650mYg6IiHM4Wg/Prt0Wvumd\nxZ3rs7P7MvBj87MHLqpn1z37Ps/Mo8v+67tB+eFndX7r8lRg/6mny/9YtjbGGGOMMca8qixoP+QY\nppcqtRzf5+sNlXobCPWBSdV6DKtLozI8zjXEaW92djr6A2+r2e6sy/hDd7V8V30ojvbH6f2zTy3G\nX+QZ+7Pvfe2n2lP3b4wxxhhjjDGvCAvaD7jde61nFezzpmEv56FF1OdNu5fe5sv+6aVP2Z1zFdV2\nt9P3shT9Nkw/dF/Pus+Hg7eeWqo9d6H2C7xMRdwYY+4SkV8H/EH6L5Cfqap/+wO+JWOMMcaY98yC\n9gNOe6/PK9qLp6PovaXl96jq3S/dO+/4+Gdfx3FqcibH7udfX+X57qflgTPkqc98YyxkG2M+3q6v\nrz/oWzDGGGPMK8CC9kNk6Rb+QP582Sh5rEK/TCh+avf0eeYWoY+1ltPH79m9a1g8NsaY98eTJ08+\n6FswxhhjzCvAgvazvMf0+XJV5xde5DZsv/erGWOMMcYYY4z5JrCgvTgc9qf39evreHbydYXheyc/\n1NxLH/riN/K9n7f2/QW+mRO0vtHu5avV6n29D2OMMcYYY4x5LyxoL9555x2gj9K6nZK1LP9+3gMf\nSIfy7HeeOvFZ4fLUmuxF6fNed7XbGdgv6c43kXspX++9APDwXPHnXPiB+79/Db3935dN2vfuwYK2\nMeYV4IC7zSuNMa+0t956iy984Qt87/d+L2+++eYHfTvGmJdUaz2+6z7I+3gRC9qLr73zteW9ZZyW\n6t1q7r2m3Odju57pqf5md0Pmgw+9X+l+IIHK+aceKIWf2qs9EIqPl9MHgvB5M/M73daP13vg/u4/\n9vzfmA9W5J83zPslfiB3ppAtb7/1W7/tWRc1xnzAROQR8O8BvxL4GcAT4K8CX1DVP/6S1/gZwL8F\n/DPATwc88OPA/wr8l6r6117iGr8C+DeATwEb4O8CfxL4var6EyLy/y7X/kOq+t1fz3Nc+G/gMcaY\nD9Bbb73F5z//eT7zmc9Y0DbmQ+QsaL/Sf/da0F68++5S0VZdQraeQuN5YJX7YfCh8P3gJ+6nbjmr\naD9VN15C6/LVBwP+wxVjOX2be2PATtPA7o0se94LBXqvoi3y3L3ncn7P92/8/Cae06H9wfOf+kbP\nGBFujHmliMg/CPzPwJvc/rYZgF8K/DIR+YPA//6Ca/xrwBeWx53/kvhZwM8GvkdEfqeq/ifPucb3\nA79p+fB4jZ8N/Dbg14rIP89pvIMxxhhjzHtnQXuRp75Huwfs26r2g9nwOf8UOxWEl5B6G9zvdSJf\nQqveD7vHovcx0D6QPQXQdrzPp6vdp+s/UNW+W2W+u2z7OLP77jVvA/sxaN9+fHqyp2B9/jzvLMNX\nEHd+jeU4PVd9Opc/jwVtY15pInIB/E/At9J/E/wx4AeBnwT+AeC3Ar8e+HnPucZ30mdsQ6+E/x7g\nfwEK8B3AbwfeAP4jEfmqqn7hgWv8u/SQrcDfAX438H/Qg/s/t9zHHwfW7+X5GmOMMcacs6C9yPMS\ntPU2aL9wj/ZTIbgHx+M1miraGk0brfX3FXDOISI4586q1no3wMrthO3l0sdvAija9HTduy8IPB2I\nH9xarXJnB7aq0lqj1kpr7bbb+Z3jeN+3n+uPbaf7OS5LP/389DZ4O+dOhziHEzmF71Mmf5m0bSHb\nmA+D/wD4afT/tH+7qv6nZ1/7KyLyx4H/AfhnH3qwiAR6JRvgGvglqvpjZ6f8RRH5E8BfoFfMf4+I\n/JCqvn12jU8Cn1vu4f8G/jFV/erZNf68iPxp4M8CCatoG2OMMeZ9YkF7MS8VbVRpercae3Q/3z30\nLzJZ9im31pajUmsPsMf9BM45vPdnQftYRe7h+BRil7Ddm5DdrU7X1mi1f487FWihB9glyN/e1/PT\naWtKLYVSC7VUOAVqd/bCwPHj27f9sctzrW35ud1Wtk+rA6A/Z+/wzp+efz/kTjB/FsvXxnw4iEgE\nvpv+X/RfvReyAVDVKiLfA/xNID5wme8Cvm25xu+6F7KP1/jbIvLvAP8NvSL9G4Dfe3bKrwPG5Rq/\n5V7IPl7jLyxLy//tr+9ZGmOMMcY8mwXtRQp9L/2pGnsWEJ8V8B7aJ3ysaNcGrSpVHKo9bKsWtCmo\nA63oErSPy8thCdnufOn3wxVqbW1Z6r1Uy09LvhW3hOPmbqvOd5ajc9oJfgr00GhaabVQSkZElsff\nVqFVexUaPN4rvf+Aoq3SaqWUsiyZ13sh+/g24iQg3uEceC94704vCLyoW+/LLt83xnzgPgU8pv+X\n+oefdZKq/riI/BngOx/48i8/nsbt8uECLKMAACAASURBVPGH/BDw/cDl8pjzoH28xpdV9Yefc40f\nxIK2McYYY95HFrQX203fnne+7PtlnHfBPkZa1UZtt1XseXJLxu2f6zlagQbHPdyqy6JwgXZW0RZB\ncKfKdl9Wvux3xuEEqvRqcm2Vpoq4XlF2y+Pu3u0xYutp+bZzDkWWwCz03KvLee30aKG/COBdD8gh\n+OX+G60dr9+W/ePttE/9yAn44EgpEGMihEAMAR/C6YUGfU6CfqlO78aYV8HPP3v/L73g3L/Iw0H7\nuHf7b6nqV571YFXNIvJXgH+Kp/d7/zz6b4wffcE9/Bgw83Bl3RhjjDHm62ZBe7G5F7TvNAU77ZeG\np/qE3+8sLr1RWQ/Z5RSsoVeL0YYcm9vediE7Lbc+Lhbvh+vVb1rfH91vZWmiJoinn1P6nm2loa2i\n4pdMLRzD9e0d6uk9J4r3vbIMQqsO74VSzp9rv88e8hUn4LwQvCMGv+ztrpSyhOxjlV3bWZO0fivO\nKSE4UooMKRFTJMZIDPHs+72EF6w0MMZ84F47e/8nX3DuTzznGvoSjwf4ew98X+hVdYAvPe/BqtpE\n5G3gky/xvZ5LVfniF7/4wvPefPNNGydkjDHGnHnrrbd46623XnhezvmbcDfvnQXtxXo1ArdNwR5c\nxnyW7J766vnXtAftUvzSXKxSayZn1yu/S0fz2yXjt0vH74Rs2hJSHccK87GqfTu6S1DfqJWlslyX\nvdMOQZel4w8FWD3ts/a+L932vlez+7XPH9P69Zbve17RVlVKkdNjjkFbT0/0+DM5PrYH9JQCKaXl\n+DqKSHe3oxtjXn3vdQ3Kh24Ny6c+9akXnvPZz36Wz33ucz/1N2OMMcZ8SHzhC1/g85///Ad9G+8b\nC9rGGGPeb+dNxz5J7/j9LM+qIr9Nfz3tZarM33r2mPv38Un6CLBnkv5q5uPnnfMSTtX0N9547rcD\n+j8mfuAHfuA9fktjzHsxzzMAn/70p0kpfcB3Y4yptb7U36Ff+tJpodr9lWyvFAvai5/+cz5lBVJj\njHl/nHcI/3bgzz/n3G9/xuf/GvCLgJ8p/z979x4t21rWd/77vO+cVbUue+8DHOBwPGC8pWO8RAQM\noDEBTUAZXugYo4mKgkZbewyN2rGT0K3g6MRB7Gj3EEcwUUBjhsPYiokXohgS0jSJXEzihagkGAWO\ncgDPvqxL1Zzv+/Qf7zur5qq91tprn7PPXmvv9fucUdRt1pyz1mbt2r963vd5zZ5w1DztugzY0ymV\n799Ye/o3KSH8025wvp9CWVf70VTPl58ho38AiMgdQL+zInesM53fFLRFRORWeyelmnwP8JXA9x+2\nkZl9FEesow28Cfg6yofo1wDfe8R2fwW4RAnJb1p77leAzwHuNbPPc/dfPGIfLzni8Zsxp4T1zMnm\nlYuIiMgj8yTKPNv5aZ/IcexGSyqJiIjcLDP7XuBbKQH4O9z9e9eej8C/BF5AXRkR+Bh3//36fAu8\nl7KW9mXgz7n7b6zt46nA2+o2O8BHu/tHRs8/hbJO94QyfP2565VxM3sO8GZWHcdf7+4vfdQ/ABER\nETnXwmmfgIiI3JVeCbyPEqJfZWY/bmYvMLOnm9lfpQTkFwDvOOzF7t4Bf4MSwC8BbzWzl5vZc8zs\nM8zsb1KWDru/bvNt45Bd9/Eg8Ip6Dp8AvNPMvsHMnmlmn2lm302pgr8f+NDwslv5QxAREZHzSRVt\nERF5TJjZnwZ+mTJPen0elQOvBf5dvT5Q0R7t4yuB11CGZR+2jwS83N1fdcx5/CDw9cPdtac/CHw+\n8DPAA8A/cvdvOsn7ExERETmKKtoiIvKYcPffAj4JeBXwO8A+ZU3rfw18ubt/7bApR1SS3f3HgD8F\n/F/AbwHXgF3KUPAfAp5+XMiu+/hG4IuAXwI+DOwBv0uZO/50d38XcLFufvmRvFcRERGRMVW0RUTk\nXKtN2f6AEva/1t1fe8qnJCIiInc4VbRFROS8+2uj2//+1M5CRERE7hqqaIuIyF3LzDaBi+7+h0c8\n/3Tg3wAXgLe7+5+9jacnIiIidymtoy0iInezJwLvNrM3AG8Efpuy7ub9wOcBLwU2KOtff+tpnaSI\niIjcXVTRFhGRu5aZfTRlPW7n+o7j1McXlLnZP347z01ERETuXgraIiJy1zKzBvhi4IXAsygV7sdT\nOpf/HmX5sR9w9z84rXMUERGRu4+CtoiIiIiIiMgtpK7jIiJy7pnZ08zs/zSzd5vZNTP7sJn9qpl9\nu5lt3MLjfLmZ/Ssze9DM9szs98zsx8zs2bfqGCLnyWP5u2tm32lm+YSXz75V70nkbmVmTzSzF5nZ\nK8zsF8zsodHv0I88Rsc8tc9dVbRFRORcM7MvAH4MuEiZs33gaeB3gBe5+399FMeYAf8PpQHbYcfI\nwCvd/ZWP9Bgi581j/btrZt8JfOch+17nwPPd/S2P5Dgi54WZ5bWHxr9br3f3l97CY536564q2iIi\ncm7V5b1+grK811Xg7wDPBT4H+MeUD+dPAH7OzLYexaFey+rD/l9T5o1/BvAy4D2Uz+PvNLOvfRTH\nEDk3buPv7uCTgU854vKpwNtvwTFEzgOvl/8O/BKHNyq9FU79c1cVbRERObfM7C3AZwEd8Ofc/VfX\nnv824B9QPqhf8Ui++Taz5wNvqvv4F8D/6KMPXzN7AvBO4GnAHwMf6+6XH9k7EjkfbtPv7rKi7e7x\n0Z+1yPlWf6feDrzd3R9aWxnkllW0z8rnriraIiJyLpnZsyj/UHfgn6z/Q736h8C7Kd+4f7OZPZJ/\nbH9bve6Bb/K1b7jd/cPAd9S79wCqaosc4zb+7orILeTur3D3X3D3hx7jQ52Jz10FbREROa++eHT7\ndYdtUD+cf7TevQd43s0cwMy2KUNZHXiTu3/giE1/GrhSb7/4Zo4hcg495r+7InJnOkufuwraIiJy\nXn1Wvd6hDCE7yr8d3f7MmzzGs4DJIfs5wN074N9Tqm/PUvVN5Fi343dXRO5MZ+ZzV0FbRETOq0+k\nfOP9Hndf74Q69l/WXnMz/vQR+znuOA2liZOIHO52/O4eUJcH+iMzm9frN5vZd5jZPY9mvyJyy52Z\nz10FbREROXfMbArcW+++77ht3f1hSuUM4Kk3eagHRrePPQ7wB6PbN3sckXPhNv7urvvcetymXn82\n8PeB/2ZmX/go9y0it86Z+dxtbvUORURE7gAXRrevnWD7HWAT2H4Mj7Mzun2zxxE5L27X7+7gPwNv\nAH4V+ADQAv8D8NeBv0SZ//1TZvYF7v6vHuExROTWOTOfuwraIiJyHs1Gtxcn2H5Omce18RgeZz66\nfbPHETkvbtfvLsD3ufsrDnn87cA/NbO/AfwjIAL/xMw+zt1Pck4i8tg5M5+7GjouIiLn0f7o9uTI\nrVamlDmhe4/hcaaj2zd7HJHz4nb97uLuV27w/A8BP0wJ8vcDf/lmjyEit9yZ+dxV0BYRkfPo6uj2\nSYaLbdXrkwxVfaTH2RrdvtnjiJwXt+t396ReM7r95x+jY4jIyZ2Zz10FbREROXfcfQ58uN594Lht\na1fh4cP4D47b9hDjRizHHoeDjVhu9jgi58Jt/N09qd8a3f6ox+gYInJyZ+ZzV0FbRETOq9+iDPn8\neDM77vPwT41uv/sRHOOw/Rx3nB743Zs8jsh5cjt+d0/KH6P9isgjc2Y+dxW0RUTkvPp/6/UW8Ixj\nthsPB33rTR7j7ayasRw5rNTMWuDZlH+0v93d000eR+Q8uR2/uyc1XrP3A4/RMUTk5M7M566CtoiI\nnFdvGN3+msM2MDMDvqrefRh4880cwN2vAb9Cqb59rpndf8Smfxm4WG//9M0cQ+Qcesx/d2/CN4xu\n/9vH6BgickJn6XNXQVtERM4ld3878O8oH8YvM7M/e8hm3w58IuUb7+9f/8bbzF5iZrle/vcjDvW9\n9boBXr0+1NXM7gW+p959mNLFWESOcDt+d83sk83s4447j7q818vq3T8Efubm342I3Iw76XNX62iL\niMh59s2UIaUbwC+b2d+jVL42gC8Hvq5u99vAPzxmP0fO03T3N5vZTwBfBnxRPc73U4aZfirwd4Cn\n1X38LXe//Kjekcj58Fj/7j6Dsjb2m4FfBH6d0oStoczr/ArgL9Zte+Dr3F3L8okcw8w+E/j40UP3\njm5/vJm9ZLy9u7/+mN2d+c9dBW0RETm33P0/mtmXAv+UMoTs761vQvmH+ovcfedRHOqlwAXg84G/\nADxv7RgJeKW7q5otcgK36Xc3AJ8DfO5Rp0EJ3y919194hMcQOU++FnjJIY8b8Fn1MnDguKB9I6f+\nuaugLSIi55q7/7yZfSqlQvYiynIgC+A9wE8Cr3b3/eN2cYJj7ANfYGZfBnw18GeAe4A/At5Sj/Ef\nHs37EDlvHuPf3Z+nDAt/DvB04MnAEyiB4CPAfwLeCLyuzgkVkZM5aaf+47a7Iz53zV2rEoiIiIiI\niIjcKmqGJiIiIiIiInILKWiLiIiIiIiI3EIK2iIiIiIiIiK3kIK2iIiIiIiIyC2koC0iIiIiIiJy\nCyloi4iIiIiIiNxCCtoiIiIiIiIit5CCtoiIiIiIiMgtpKB9hzCzf2NmuV4++7TPR0RERERERA6n\noH3n8LVrEREREREROYMUtEVERERERERuIQVtERERERERkVtIQVtERERERETkFlLQFhEREREREbmF\nFLRFREREREREbiEF7VNmxUvM7JfM7EEz2zOz95rZG8zsix7hPp9mZq8ws7eZ2R+a2bxev83MvsvM\nHrjJ/d1jZi83s7eb2UfM7KqZ/Rcz+8dm9szRdsPyY+mRnLeIiIiIiMjdwNy1WtRpMbMnAz8LfMbo\n4eEPxOr1TwNfDfxL4M/X55/n7m85Yp9/F/i7wGxtf+N97gPf5e6vOsE5Pg/4Z8CTj9hfBl7h7t9t\nZnl43t3jjfYtIiIiIiJyN2pO+wTOKzO7BLwZ+FOswut7gbcBc+CTKAH8xZxw7Wwz+wHgG+v2Dlyr\nx/hD4D7gecA2MAW+x8ye7O7fdsz+nk0J+Bujfb4d+E1gUs/vE4DvMrMPDy876fmKiIiIiIjcjVTR\nPiVm9sPA19S7c+Ab3P31a9s8E/hJ4E8AC0q4PbSibWZfCvwEq5D7WuBb3P3aaJtt4NXAV462+8vu\n/oZDzm8K/DrwcZTw/N+AL3X3d61t9yX1WE09PwNcFW0RERERETmvFLRPgZl9AvBfRg+9xN3/6THb\n/hqlqjxUiw8EbTMz4D2UQA7wk+7+5ccc/2eAL6r7+q/u/icP2eYbgB+sd3eAT3H33ztif19MGeLu\nKGiLiIiIiMg5p2Zop+NlrOZL/+pRIRvA3X8X+P7R9of5S8DH1G0WwDff4PjfBHR1+48zs794yDYv\nHU4B+L6jQnY9xzdQhqgfd44iIiIiIiLngoL26Xje6PaPnWD719/g+efXawd+wd0/eNzG7v4B4I1H\nnM8wxPzTRw/9+AnO8cgvC0RERERERM4TBe3T8WdGt992o41rVfsjx2zy9NHt/++E5/DW0e1PX3vu\nU1n9f+OKu//2Cfb3H054XBERERERkbuagvZtVruNT0YP/f4JX3rcdk8c3f7vJ9zf741u33vE/hx4\n3wn3d9LtRERERERE7moK2rff9tr93RO+bueE+zxuu6P2d+GY/Z30/K7deBMREREREZG7n4L27bce\nSDdP+LqtE+7zuO2O2t/VY/Z3K85PRERERETk3FDQvs3c/TKlM/jgaSd86VOPee6hR7C/PzG6/aG1\n54b7BnzUCff3wAm3ExERERERuaspaJ+O/zS6/ewbbWxmHw884ZhNfm10+7knPIfxdu9ae+4/A7ne\nvmRm162zfYjPOOFxRURERERE7moK2qfjzaPbX3GC7V9yg+f/db024PPNbL252QFm9hTg8w55PQDu\nfpWD4f2vn+AcT/I+RERERERE7noK2qfjh0e3n21mf+2oDWs1+1soHcCP8kvAe+vtKfD9Nzj+DwBt\nvf0ed3/TIdv8yHAKwLeY2Ucfc45fCHzODc5RRERERETkXFDQPgV1XezXUUKsAf/EzL5qfTszeybw\ny5SGZIv150f7c+B/HV4GfLmZ/ZCZHWhQZmbbZvY64MXDS4G/dcRuXwu8p97eBn7FzNbX28bMvgT4\ncWD/qPMTERERERE5T6xkNLndzOwe4G3An6SEY4D/Vh+bA5/Eat7zT1PWuv7zlHD8PHd/yyH7/L+B\nbxrt7yplmPofAU+iVJ2Hpbsc+D53//ZjzvG5lKC/MXrNfwB+i7IW+GfU83fgfwZeXbfL7t6c4Mcg\nIiIiIiJy11HQPkVmdh/ws8Azh4dGTw9/MD8LfCXwc9wgaNd9/h3g5ZQh5Eftcx94hbu/6gTn+Hzg\nnwFPPGJ/GXgF8D2squ4Pu/vjb7RvERERERGRu5GC9ikzMwO+itJw7FOBS5QK9H8CXufuP1O3ezPw\n2ZRw+/yjgnbd9qnA1wIvAD4GuAd4mFIxfyPww+7+vps4x3soFesvBj6WMr/7/cBbgNe4+zvN7EnA\nH9bz++/u/rEn3b+IiIiIiMjdREFbbgkz+1xKUzYH3ujuLzrlUxIRERERETkVaoYmt8qXjW6//dTO\nQkRERERE5JSpoi2Pmpn9Wcow8pZS0f5Ed/+d0z0rERERERGR06GKthzJzJ5qZj9pZp95xPPBzL6C\nMu+7oYTsn1XIFhERERGR80wVbTmSmX008N5694PAO4EHgQQ8GXgOq27kUBqkPdPd/+h2nqeIiIiI\niMhZoqAtRxoF7eH/JLa2yfj/PG8HvuRmupmLiIiIiIjcjRS05Vhm9kzgC4BnAw8A91KWC7tGWYbs\nbcBPu/vPn9pJioiIiIiInCEK2iIiIiIiIiK3kJqhiYiIiIiIiNxCCtoiIiIiIiIit5CCtoiIiIiI\niMgtpKAtIiIiIiIicgspaIuIiIiIiIjcQs1pn4CIiMidzsx2gCmQgQ+e8umIiIjczZ5EKRjP3X3r\ntE/mKFreq/q0L/o6Bzj052Hjay+X+pjV62DloQAYhtXNDWhDpImRJgamkwmbGzM2N2ZszKZkd7Jn\ncs4sFgt29/bY3d9nd28Ph/o8LE/LyznmnJfXZkYIgRAjIdQzsHL0nJ0+OSk7KZXj5FxeF2OkaRra\ntiHGWN+PLY+D+/J4y+v6czCz5YX6njEj50TK5TgpJ/rUk/pEyokQArGeY4wRC6Gcd1gbWDF6j8s/\nArtuk6V3/Mxr1p4VEbm9zKwH4mmfh4iIyDmS3P3MFo7P7IndbkOIdHyU4pwhURvDw162YRX+hrAZ\nDAJGNKNtIm2MtLFhOmmZtm25nrTMJi3TyYTppKXrOrpuwaJLZHpC7qCbkxf7ZKcEZHfcV8dZZmB3\n3B2zUM7PDD+QSG11zge+QPDRZRTiD/u5LK+Hbb1+jbB6tJzXwZ/b8F95bP34RzPswM9X3wOJyB3C\nofx9eP/995/2uYjICSwWCx566CGe+MQnMplMTvt0ROSEPvCBDwzZ4kwnBQXtKvuqesqBPzcHN9wA\nd9xWoTNQythGCYVmRjSYxMDGbMLmdMrmbMrmxoytjQ22ZjOmk5Y2BpoYaKKxt7vL7m5iL+2Tckfs\n57DYI+/v0mdfXpaB1sa1cnCMEGM5fjDcw+ot4MuKODWUew3t7uM3OwT2wwrDPro1bDs8Yqsob0NE\n9rWX+pG/AetHWw4cWAbs1dcbIiJn3EeAJ9177728733vO+1zEZETeNe73sUznvEM3vjGN/Lpn/7p\np306InJCT3rSk3jooYegfPaeWQra1YGK61Dd9qFq7UPeLiHVVkOol2HbjGAQDdomsDWbcHF7k0vb\nm1zc3ubihW0ubW8xm7SYZwKOeeZKyDRpH+aZuY+C9t4OfXa65HQp4xgWAhYCBMMsgIV6gu1y+Lib\njxKs47mG6wPvcRWuT/yzqZcSyFd7GcLwqu6/2v9xIXtwYFQ+tgzZqmaLiIiIiMidSkF7XQ2l47AN\nJUj7UNF2DjxXqs0QA0Qzpk1kc9ZycWvG4y9uc8/FC8vLtG3wviP3C3LfkXZhETJ7eUHs97FurwTt\n+R65z/Qps+jLcalB22IkhIiFiA1zq0Mg54iFIZ2u6stDJZu128P9g++fZfodF/aX0Xm0vY1u+TBv\nvVbMDybsY47HwUr2gcdtNKR/9Odw8NgiIiIiIiJni4J2ZasJ15gfXom15XDtet8p/WXdCWY0FpjG\nwKyNbLQNW5OW7WnLRhuYBCd6D32in+/R7++zmO+xd/Vh9q8+zPzaZbrdK6T9HXy+V+Zp95nUJfou\nkyjDs8tE8EDTtIS2JTalmu2pwZvMsvpOKQsfVbUehpCvbo8anR0VY48J6MuB44cezg69edg5HTw/\nlbRFREREROTOo6BdDZ2vV4Hz8LHLy4Lv8Fwuldzg0JgxiYFZ07A5BO3ZhM0mMjWIOUFKpP1d5rvX\n2Nu5yt6VP2Z+7WEW1y7T7Vwl7e3ii32sW+BdIi16ukWidydbDdtmNNMpbZ6WkB8isUl4bU/uZquw\nzXLU96Hzs4/NssuS9sES9TCf++iwfeMh48dRwBY5P8zsJcBrKX/JfIy7//4pn9Kj8qEPfYgHHnjg\ntE9DRE5gsVgA8MIXvlDN0ERO2X333cc73vGO0z6NW0pBu1oOSa4Bcr2qPYS/VXuug2PIDwbtoaLd\ncGHaMm1KRbvxntR3pPku850r7F15eK2ifY20P8fnc+jn+KInzRP9omeRMhkn4XgwpjmBOxYCsWnJ\nOYHn2unby1xtt7WmZ6vrVeA+JNRe95rrNzsqbB9M7ierZI/3OVyroi0idyJ35/3vf/9pn4aI3ITa\nVElE5JZS0K6Wbby8Dgmvjy77aNcq8YqXYJsz2TM9PQuP7PeBPRK7beBaE9iIxqRtmLSRSdvQd3Ou\nXn6Ya1ce5uqVh7l2+TJXr1zm2uWr7OzssrvfsbffsVj09H1PTglPCXIdFj50Qe97ct+TFwtSbEix\noY9NbZYWsdCURb3rudbG4wfq046Vi6+K1zaaa73ezGx43Xhxr/Ew8/WvIg78tHytI/kR1sP1ejd0\nhW8ROfs+6rRPQEROZAE8BDwRUEVb5HQ8SJmLe/dR0B4MFetRo7NVP+0aVMdZ2x33DKnHc0+XnP3O\n8eCEbpfWe2LusLRg0ja0Tbn03ZwrVy5z9XIN2levsnP1GjtXr7K3P2fRJRZdYt4luj6Tc1p2KXd3\nAl7+r5hzCdthQQqRFCJ9DKX7eNNiDYRQg/Ro3vYqbNvB0O1g5qsqtw/dxuvrRsPHh+Zv1J/JsNrY\noauDsRbXj8jJxzVsExG5cxig5b1E7gzvAp4BvBHQ8l4ip+MB4O4cCaagPVgr3Y7rss7aCtH1SfeM\n5x76BV3uce/pc4/vR2LuCamDbkFTg3bTNHSLBVeuPMyVy5e5cvlhdnd32d3ZYWdnl27RkZKTs5Oy\n0+VMTqXjmrkToIZtICXoOzJGskgfIxZLV/KIE4JhBCAcCNPl2lZh2225OJc7YI4NIXv4GaxnXhuF\n7fUL1PS9Fq4PTvM++o9hLXAP14ev8S0iIiIiInL2KGhXo6WnR4/4aJj0cD2k1Yynntwt8G4fTx0p\nLehTB4tASybkhPcdTdMQYyxBu1tw9coVrl69wtUrV9nd22Nvb4/d3T36PtUDlSSb3MmeIfsyx8Z6\nLsEdUgZ6PHZ415BjJIVQqssx4DmW883DXO1hmLgfyL7jWrWNhniPly9bXqz+rIawfeCHOCyCzbIS\nfhKqXovIXSCd9gmIyM16CvCd9VpE7hSxLm/MGf/sDTfe5Jw5pH/XsubrucyVzj3eL0iLOf18j8Xu\nDov9XfrFnNR39F3H/v6cnd0dLl+9yuXLl7l8+WEe/uOPcOXhh9nducZ8f5++70h9IqVMypCy02en\nz5kuZ1LO5OzL5mwRaEKgDZHWAo0ZEYjuZQh735EXC3JXL/2iPJZ6PCc85/IelpOyr7esY4/Wr14+\nNl5fG1ZjxW1t3Pghy4ApTIucT2Z2j5l9j5m928x2zeyPzOyXzexLbmIfH21m32dmv2FmV8xsx8x+\nx8z+kZl98gn38QVm9otm9sH6+t82s1eZ2ZPr879nZtnMfuQRvtU6wUyjb0TuHE8BvgsFbZE7yyho\nn+nJ3apoH8bKXO2hYrtaPdvBE+RETh1psU+3v0e3t0NLLsOuzek9MJ8beCZ1PdGMGCCY4TmxWCxY\nLBakriOlnj7lGrJZLsO1bEZWb5sZweoc7FCGiJsZAcOyYynhXUc2sGDkEAgx4NbgHnFCnVc+zNvm\n4OhuH7/PYYj5KDCPLjauZq8N6fbRaw8bAi4i54eZfSLwJsq/Yoe/AKbA84HPMbPXAm+5wT6+CnhN\nfd34L5GPAz4eeJmZ/W/u/j3H7OPVwP9U7w77+Hjg24GvMLPP58QTXERERERuTEH7KDY0Rhv10vYM\nOeFDRbub0813WeztlCHV0QgBUg7s50zfdcz394lWZkoHAM/knMkpkXOmrxXtPlOD9mppK/M89AUn\nWCAYxBCINWQz9Pz2jKeEG2TKkl85lguhDgW3WMP1eBDDqs3ZcG/Z+m08bNztwL9AS9g+WdVG4Vrk\nfDKzC8C/Au6j/LXxE8CPAh8E/iTwrcBXA0dWpM3sRZQ1tgGuAt8L/ArQA88F/jalXfD/YWZ/7O6v\nOWQff4sSsh34A+DvA++kBPcX1PP4KWDz0bxfERERkTEF7cqHUdAw1HuBYWy91yHXCU8due+wbkFI\nPa07BGMSjEkTmDaRJhjlPyfnRAwlgLfBiBYJoSUGwyyws79gZ3/BZH/B/ryj63q6rlyv9xkLlKp4\nDLacyr0c5u0ZErhZqWyHQMIgZjxkWC735ZQ6eGCYkz1Uz+veDq4VXq+W3djtYN+z201N0UTuGP87\npZWoA3/b3V81eu7XzOyngJ8H/tJhLzazhlLJBrgGfJa7//pok181s58G3kapmH+vmf1zd//IaB9P\npowLdeA9wLPd/Y9H+3irmf0idA4RHwAAIABJREFU8GbK2j76ZlBERERuCc3RHqyl2jLtuAwFD3jp\n5J0Tlnqsm2PdnCb3tObMYmDWNszaltlkwnQyoW0iIZTh48GcNhrTJrA1a7lne4Mn3HORpzzxcTz5\nCffwxMdd4gmXLnLpwhabs/LaZSWbGrCxErKthO1gwxcCdZmxXNb09pTIXU+aL+j390nzfbybQ9+X\nc88JPGHk0RcKvsraXirY4yht9QdiowscDL3jSvyBH+tom8Ned+gfhcK0yB3NzFrgpZS/Vf7zWsgG\nwN0T8DKgO2I3Lwbur7e/ey1kD/v4feB/qXc3ga9Z2+QlwKze/ua1kD3s423Aq499QyIiIiI3SUH7\nKEPYxgmUdaxL0O6gX2D9gpgTE4NZrWTPJi3TyYTJZELTNITahTsYNMGYtpHN2YRL25vc+7gLPPne\nx/OkJ9zDvY+/xOPvucClC1tsbUyZNGE51HyZ/ev5rEI2lEHeGcjLsE1KtSnanLS/T5rPyYsFnjrI\nPeZlXW4bDU0/MEx8/YdQj2SjR+DwMLw+F3scrE8anhWyRe4KzwAeV2+//qiN3P39wC8d8fTnDpux\nGj5+mH8OXF57zfo+PuTubzxmHz96zHMiIiIiN01DxysfQuF40LQNQ6tLiA25Dh3vFoR+QajV6hAj\nTROZxEgbI2aGWypt8DxjBGIwmhiYTho2N2dc3N7m4oULNJMpcbJPM5kRYoOnzGLRMd+fD53RalV8\nNIx9rWGZjwdy1y7l5IylRKhjzC0EsLK8l0UDG0L2sow96io+Gkc/unFYBL7ZYHxY+DazI+dyaw1t\nkTvSp4xuv/0G2/4q8KJDHh/mbr/X3T981IvdvTOzXwP+AtfP9/5kyl+V//EG5/DrwAJob7CdiIiI\nyIkoaK9ZzlEehWzPCU8J+g76jpAWkDoagzg0KLM66znnusx2rTKPA6QZFiKxmTCZbTLbukCKM2gX\nNBsdoZ2S3ehTpu96UurJfU/q+1pcLhXs5JC9LByXVpO1D8bh5FjOuAUI5RKw8iduAbNhRe7VUl7j\nsD066UN/TseF33FwXg/Wxw0/P84QuNf3KyJn0uNHtz94g23/6Jh9+AleD/CHhxwXVlX1h457sbtn\nM/sI8OQTHOsGMvCkE2wX60VEROQ8e3B168EHefDBB4/ZtlgsFo/lCd0yCtqHWk1Ydk94TpA6SB2W\nOkLfEVJHEyNtDLSxVovxGrC9vCaXYd0lyJZZ1xYiTTuhnW0y27yATRLNRmLWZWIzoe8zi8WC+Xyf\nbj5nsTA6MtlzXVYr4w7JvQRtALcynxwAgzx0D6+B1kJpkkZpwBZCA7EOH18u9jUaP76WYVeF7SPC\nrdlym8Mq0+uh+qiQfVRl+7CqtrqZi9wxHu0v6x34y35srhcREZFDvOY1r+EVr3jFaZ/GLaOgfZ1x\nVXcYNt7jfV+Cdt9haUHMPW2EiQUmMZAxeofkmZydnA6paNegHdspk+kGs60LNBmmDn12QjthvujY\n29tjb3eX/VA6l3vu6FNdisudjB8M2maYl4ZpRqhV+NFbMcOtDI+PocFjItSQzaHBdsjUwxDy46vX\n6/dvFLYPm7N92OsOu69KtsgdYdx07MmUjt9HOaqK/BHKX0InqTLfN3rN+nk8mbIE2JHMLLCqfj8q\nZsa99957w+1ijMSoiraIiAjAfffdx9d//dfzhV/4hTfc9oUvfCEPPXT2v9RW0K58VDQpt3NZ8zol\nct/j3QK6DlKPpVLl9hxwr4HaQm2eFggRzGKZXh0DTdNgIeCUQL3oe/a7jr35HEJTqtxNy2w6ZXNz\ngwvb2+xdvEisXctT3+GUKnaZk13nlC+D6Ko1WlmGjGECN9Q1uy2k0nk8dFhs8djjMdbth6q2jcvX\nox/OweeWlfPlfVvezScM2euX8ZDw9dvr3c1F5Mwbdwh/FvDWY7Z91hGP/wbwHOBjzOwJR83TrsuA\nPZ3yt95vrD39m5QQ/mk3ON9Poayr/aj/grn//vt53/ve92h3IyIici495SlPueE2k8nkNpzJo6eu\n41Wp+Ja52cMyVdkzOffkviN1C1LfkfuenBM55/p8Xm5vBqE2PWvbhsmkZTqd0LQNFgMZ6FJm3vfs\nzxfs7O2z6DoyEJvIZDplc2OT7QvbXLp0ka2tLWazGU3bEmJcNTQbTnoZVGGYa+11ua8yxLwOZc/l\ny4Lc9+TUk+ta4J7K8Har7c+WedaM9eHjq/W6V13KS09yq5sPVWo7sM72uEv5SS6HbXvkn9khy4mJ\nyJnwTlZV7a88aiMz+yiOWEcbeNOwGdcv2zX2V4BLa68Z/Eq9vtfMPu+YfbzkmOdEREREbpqC9iG8\nLpuVPZFzIvV9CdpdV4JqbXQ2NDzLnks12UrfsRgDTRNp25bpbErTtliIZXh5ziy6nr3Fgt29fRZ9\njzs0sWE6nbBRK9qXLl5ke3sI2g0xRkIIeK2M+7De14Eg6ssK9dCt3D2XYeypfGEwBG5P5YKnMlfb\nhmXEbC0qr3Y9nsp9WMge7h/VQG3sqOHjJwnYoMq2yFnm7gvKklwGfJqZffv6NlY6Mv5jju70/Qbg\nA3Uff9fM1juKY2ZPBf5BvbvL9cuAvR6Y19vfb2ZPOGQfzwG+kTtyLriIiIicVQrag5otfYjZNUin\n1JP6jr7rSF1XQndKpJzpc6bPiS4lUq1yew3dZkaMgdg0hBiwUOZJp5xZdB27e3tc3bnGzs4Ou3u7\n7O3vsZgvyCkB1GAdsTCqZJduaOVfg8vAO/xXHxyeHHcSH74USBlPqV56GDVss/G/MZcBvu53/M9P\nH+17tcVqve/lIxwM5UeE6MMapN2oU7mI3BFeCbyP8lfAq8zsx83sBWb2dDP7q8DbgBcA7zjsxe7e\nAX+D8jfJJeCtZvZyM3uOmX2Gmf1NytJh99dtvs3dP7K2jweBV9Rz+ATgnWb2DWb2TDP7TDP7bkoV\n/P3Ah4aX3cofgoiIiJxPmqN9nTL0mpzLEPG+VLT7boH1PSGlsmyWZwgJ7yEbZU1t9+U1TQnJ5ZuM\nYQa0k3JmvpgTdq+Rc+kwvr+/z+7ODjlndnd22N3ZZXd3l/35nK7vScnJufZlcy+Nzqzs8UDPsmXc\ntvr86rnlkO46b7vMMU9lne86HNyG8ePjQLtc78zXAvfq5vH5d4jgoznwNagft1TXcU3V1pf6EpGz\nx92vmNkLgV+mzJP+8npZbkKpQP87rq9ED/v4BTP7auA1wDYlvL9ybR898HJ3/6Ej9vE9ZvY04OuB\npwI/uLbJBynDz3+m3t8/4VsUEREROZKC9jpnObc5p3Sgol06jveEnMiU6nA2yDjBnehO9EwELED0\nQInXvsy7OScWi3kN2XP29/fY3d1hOp2Bw3x/znw+Z79eL7qelJ3sNWT7EFBHIdsZ15GXS3stU/JQ\nIYZlhZs6P3tI8IaPwvn6sPFRq7hl/zU/eEyvr7tB9h2H7BtVqG80P1sVbpGzzd1/y8w+CfgO4MXA\n04CrlGZpP+TuP2lmL+HAxJTr9vFjZvZvgW+hzOd+GmU01gcoc7B/wN1/8wbn8Y1m9gvANwHPBDYp\n1fafB/6Buz9oZhfr5pcfzXsWERERAQXtJatV21wr2tlTCdp9T9/19IsSss0TIWeCZXKCZE6P0bgT\ncya601LmaecaUK2GXjcje6loz+dzzKBpWtp2QttMACP1iT5l+q4vFe2uI+VMdq+5uITtcbOxQwPn\nkMLt4EPjija1oo17eXz4QawF5mVR+5AH7cAGB5cyO6n1yvTwfo5aP1sBW+TO4e4PA3+7Xg57/vWU\nudTH7eP3gW99lOfxc8DPHfZcbcp2ifK32e8+muOIiIiIgIL20hA0zR3PjqUMdXh1zj0p91jusZzI\nngiUYeLZjZSM1DhNzsQhCNf51SEmghkhGKEm0xIsy3H6vidnp+tKU7TUZ1LK9H2qQ8c7Uiprc/sN\nysXLgDq8jwPPDQPYy1JeVudy23CxYftxYan+TDii1HT9CRy8u/b0uJo9DtdHBefDhodr6LiIPAb+\n2uj2vz+1sxAREZG7hoJ2NTQDM3dsrXlYTomcekgJ84TVTuPmYLnkyyY7KWaappR3Q4z10tDE0szM\nQg2V7nUYeCb1GfcevFSrU3JSyqSUmM8XLLquNFrzslzXUYl3FVathuZVPDZbtQwfgjU1bK+amMEq\nZK8i98Gf0Wjk+GHh+BYF36PmZq9vo8q2iNyImW0CF939D494/unAy+vdd7j7u2/byYmIiMhdS0G7\nWgZtMjbMXc6pBu2elHpIPea5hG0y5FXlN+VM0zTkYf50DdmhSWWIdTSihdrZvAzDzrlUr1Of6VO5\nneslJafrO7qup8+pBvPxUPRD3sNyyPVqlvaqUZqvwrSPKtk4gYPV5+Uo8uW7O2FV22wtuF9vqGbf\nTFBe70w+3peIyA08EXi3mb0BeCPw25Qlv+4HPg94KbABZB7l8HQRERGRgYJ2tZyjnL1WsxO568u6\n2bWq7TnVoJ1L0GY0DBzDPZXeYsugHbEYMYMQAw21pmy2rBtnd5JnUi5LhqWcSwDPJbynZcD2A4F3\nuazXCcLqaotRRdvH1etVtduXFe9lVD+wpzKF+4hj3mTwXW1+/euOmrd9o8dERA4xA/4q8GWHPOeU\n4P217v7W23pWIiIictdS0B7kEpxJCe/7smZ2tyDVsJ3qmtNDyLZxRzKoncHTsnFZGSdeLmZDc7Rm\nNY3ZDIJhMRCAxqyOQy+XTIIcsJCHdbdKj7LgWL7R2tJD+B+anA3zstcC9uj+MmQ7+LIKXkO9raL3\nDbuKjy6HObgu9hH7GM3hPslcbhGRY7wf+FLghcCzKBXuxwO7wO9Rlh/7AXf/g9M6QREREbn7KGgP\nhmCXM7nvyd2iBO2+I6WeXLt0D9Xg1VDsEhpzDerelznWJWCXudkxBtq2DCuPFvChs3cwAjWMh2HC\nd8JJlLsZkg2dzIajQfDxQ9cH0HHaNT/wgK0F7PFjJWQPFe36v3bLpl4vHRWY3YeTH+4fXApMQ8VF\n5Ga5ew/8VL2IiIiI3BYK2oOhop3rnOyuIy06ct+V4F0r2qvh16tluwzDc5lzPcy7JkQsBCwEJm1b\n5l8PncCHaraXOdvBSvEby7iViYLmjqXy+rqGVzm/5dzmEsAPm7fs46Q9dBe31ZDw8fzs5ZzqsvbX\naj/L/72JKvIhef+op8fnvT6EfDyPe9h2PKf7sGq3iIiIiIjIWaGgPahBO6dErkPH+25B35WKtg9r\nTgOrQLgKukOALkt6RZrYlDWymwmxaQmhoSzwFYaMDIQSMkMJm2a5jug23ClN0UIiWCDbEDxL9Tws\nS9qj+Fqfx70WrYegWrYKBk0MxLYhTlpmsykbGzM2tjZophv02eky9DkxdG3zOo7cRtXmIZhb3fGB\n06j3zVchfjlM/cDZHtFibT08+yjsjzdXyBYRERERkTNKQbvyYeh3SqS+p+86+kUZOp6HoePu12Vb\nYBmAoczHDqEhxpammdC0E5rYEEJcDiUPFuvQcl9l0ANBGzw7KQRiCIRgWDLMrXYfL/OobRz0vS7p\nNQrZnus08Vq9jkATAm3T0E4mbM6mbG7O2NzcoJnOmHc9tujJuV82davfIFD+11ZfEpiBef3CwK77\nmaxedXAZsfL4USF7+QOtl7UnvMwkXxXqFbZFREREROTsUdAe5FSvErkv1eyu6+j7npRSDeLXD4Yu\nFd/a3CsYZoEQI7EZVbRjuwzatgzjdbj4MlwaZulA0O5jJIRAsECwTKY8l2t/NLfVeZR9+Cp7DhXt\nIcQDwYwmBiZtZDZt2ZxN2NqYsb1Vgrbt7Zf1uxd5NWd7CNsWlkPWy7UvK9qMz2NUfDYfhexlh7Tx\nz3BV5V5l5tXiZc4qbDvj17pCtoiIiIiInFkK2oMhuHmZZ13Wsy5LepXy7hC01+ZE1+sylDoQ6pDx\n2ExomwmTyZS2bctjoSHGSIhGjEaoQbtUtJ0wzPVOTg6JGAJNCPQhkEPAw6r6HSwQhpS7nl3Hbb8d\nAmWt7GDQBJjEwKSJbExbLmxOubi9STudYZ5J3YL5fsY90dX1vYcu6mZlzngIZdmyQJmHDmHVGZ1x\no7hDCt2jKvWBOduHbGa1F/rw/DKAj96biIiIiIjIWaOgvW6tG7cNHcZrqDOcMEyFHl4CZUh4CMTY\nlJDdTmgn03JpG9qmoVleArEJxBjKmt117W7DSsC3RMKIFmhCpI0NZMqyYhjB8qruO1SJ/eDFvZx7\nWM7PdqI5TYAmGpNobEwaLmxMedzFTSbTDTx3dPM99oPT5wV50bGYL+j6tJx7biES25bYtMS2XT5W\nmrZFxkn/0DZqdvDrilWAHs/eHt7dELdX//mo27sraYuIiIiIyBmkoL3kB28ul8CCYTmvUnUeVVaX\n17XxV4jEpi3zsmvQnkymtJNI0zY0baRtyu1yHZeV85zK/OwcEskCwUrQjiHSxFiHf2cCoXYwz6Pu\n3Izz7XKoeDlvqxVtJ9aKdhtgEmFjEtnenPK4C5tMNzbpFnvs7USuxsx+7siLXeY7e8y7jhCbcmla\nmlTem1nGbEIZRt6U4fPk+rNa/TiX89qXWXoVtW0I1cZoLHyp7tdZ86vO6PUVDnV9cBERERERkbNH\nQfs6a0Ofh+7Zy7nG9Tmv86TLVoARQiTWoeOrsD2hbUuobmIgNg1t2zBpW9q2IfV9CdchQXZyTPSh\nL9XsGPAYoWlKv3LLJMv0lsk5kXIN6XBdRXv4kmAYMj6E7CYYbTTaJjCbNGzPJlza2mC2ucHutQnX\nJpFZY+ySoF/Q7+8w318QmhKyQ2zwPCuV/VA6j8emHCRYWEZos/Uu4wf7jZdHbGghN/r5HwzY60uY\nHfiSQ1lbRERERETOIAXtQQ1zZmUOcgiRGCIWGrC+DIv2Wq1dJrxS3zazZcU3ti1xMiG2DaGJWAwQ\nA1YvIZZmaSFGYowlrNfu4qUpmC+7mzexoW16uq4tndBTJvWJvk90fUfXdXSeIGW8Lv/llmvnsRJh\nJ01g0jZM24bZbMJs2jKbNEzbyCQGYgDzRPDMxqTl4vYW3eJxeHK6Rc/e7h7doivD27uOvksEjGhG\nsnIdY2ASWiaThs4yPUaHgadS33ar52Z4netdFw6vUXsYT+7Larzb0Je8BvYauLV2toiIiIiInHUK\n2tVQNR26fMcatAkRt1jbicEwNns5e9hCCY+hdBqP7aTMXW4arIlQw7athe3YBGITCTlAyHiMZW3s\nPMwLN/qmJ/WJSVuu+1Sv+579fcc8kZOXSjg1bOPLdbPNjEkTmU4aZpOWjWkJ2tNJy7RtaGOgMcc8\nE3BmbcOlrU0Mp1907O3scbWdsG/79CnTZ6dPmUCpkMfaPj1MJkyCsdm2zEnMPZXu6Msh90Z2Vs3b\nhu7rjMP26AuMek5lCTMOVLRFRERERETOOgXtypYV7drULETiqJrtxNESU8Pg5tqJ2wIhNITY1mHj\nLaFtS9COAWuGkB2X1eyhok1gOdQ7WKgV3TIMO/WprOGdEimVgJ36nq5bgCc8dfQLcCvhPFvGcUJ9\nDyEYbVuD9rQ9WNGeNLRNIBoEzwTPzCYNtr3JdNIy35tz5fI1NtqWaxbInmpFuyMapFrR9qYh5sQ0\nGJttUyJzdnJ2es+YG54hm+EGbqXSXhqn1RZnQ6J2x2tVG7PyUB2zv1r0S0RERERE5GxT0K4s1KAd\nS1ftZjIh91NC7TyOe1nuyzNel/oKIdZqdiBOJrTTKe1sWq4nJXDHJo6Gi9eh42F1WTb68rLO9dDA\nLIZQm6SlGrR7+q6j7yMxGDmnOk870fSxNEfL5auAGMPyMptO2dyYsrExZXNjg82tLba2ttjc3OLi\nxYtsbm4ynUyY1A7ik3bKxkZmZ2efK/fscPXqLt0isbO7h7FL3/dl3jclnEecWRPZnk153IUtZoue\n2SSx2/VM5h078wXMF6S8KF8AmNWl0CgBGxtPdq/Kz8DNrwvYGj4uIiIiIiJnnYL2wMrQ8BAjsZ3Q\nzmbgTh9Wc4pz7pcB193LsPIQIQTa6Yx2NmNSL+1kUoN2s1zKq6ydHZbD00Oddxwo4TPUKm60QB/j\nKmjnUs2OMRK6BRaMVLuOY5BSWi3pZUbTxLqMWGRjNmNrc8bm5gZbW5tsbW2ztb3N1tYWW9sX2Nq+\nwMbmFtPZjIlD9jILfefSnGs7c/b3etyN9vIVMOi6nraJddg5TKKxNZtwaXuTe++5yF6X2O8Se4vE\ntf05zbUd3DNd1xEDRBuWHHNsCNLj9b4OydCqZouIiIiIyJ1EQXtQK9qhaYiTFnxWqq6hzCnGAil1\npJSw1JPdIQao87ib2Yx2OmMy2yhhe9LSTuq62bE0DIuhVKqHYd0WhmW8asduK8PHc4i0TUOqITun\nTN93dHF4ndVQDRZDWRqMWhkPRtu2tG3LpG1GAXuT7a0tti9ss7V9ge3tbWazDabTGdPpjKad1GHw\n5YuDvf2evf2exSKTvXwh0Pc9e3t7NE2kjUYTStDenLbcs73JvY+7yH6X2e8z+11itrOHu7NYLNjb\n2yMY9bKaR46v2rerTi0iIiIiIncDBe3KQqloD0PHh2ZiQ8gmBKzvsFTmSZtnLEYsRIhxVc2eDmtn\nN8vKcoxGE4y4DNxDVbsGbyuPEUsHco8RT5mc8yhox+Wwc4tluHpoyrl6zsuu3MECk0nLZDJhMpmw\ntbXB1vYW28vLheWlbVtibJaXEGOda95wad4znyf6BO5Gyon5fM7u7g4hDFXzyGzSsr0x5eL2Jo+/\ndIF578z7zLx3Jm1L33fM9/fZ29st1X8LDCnbagX+4ELgB/5U1hb+GqZv27J6LyIiIiIictYoaA+G\nZmghYE1TCtzB8GCloVlbK8ypJ6UydNxC6TZuITLd2KCdzojtZNnorImRNkTaYLSNMYmBNpaAGoem\naFaq1CGEurpVCfduuc67DuSYCbWZWmwamn7CZNLT9R1935cGasGWTdCGkN1O2lLR3iwV7c2tzTIn\ne2OLdjqjGYfr+j4sRMwi7XTG1oVtHpfLOWW8HKOJdQ1tIwS4ePEily5dZHtrk9l0QjsxZtlIDpOm\nwXPC3Gmisegzi5SY95mUvfSAy7muXA5DN3ev2XsI1sByTe1hfXDN1RaRs+jBBx/kgQceuKnX3Hff\nfbzjHe94jM5IREREToOC9mAI2jEQrMGDYTEuQ3aYTup86VybojkWYgnIFplOJrTTCbGpVeIQaUKk\nCYE21ktTg3YN4gfma9fGaKUrGmXOdc5kd9wzIWdC09DkSQ38mVSr3cByXzGWoN22pao92yjzszc3\nN9jYqEPFZ1Payays/T0c2+r5WMRCYDKZsb0NIZblyghGbAJN0+CeGLqvb29tceniBba3NtiYTcAa\n3AIQmU0mGJkYyjJj1/b2ubq3z7W9OfOuJ2UnWVkGDNZq2qOUPYRs1a9F5KzLOfP+97//tE9DRERE\nTpmC9mAYhmwBC4YRSsW0bbCUCDnjOZFzCcBlOa5Y51kH2tjQNk3tMt4QYygV7Vgah01iZBJDrWaH\n6yvaFspSV4Ha5Ry8Njhzd6I7uV78wKU0UQsxEkPZbztpl0F7OpuysbHBxmYN2M2EdliCzGIZcl6X\nKQvDe7fAZDrFmpbpxiaTjRkhBpq27DulHq+N4TZmMy5dusDW1iaz6ZQY2+WXDVuzKdGgjYFZE/nw\nlauEAH0qr4WyDFipZNeq9rhSXVf9GodsVbJF5FYws48G3lvvfrW7/+it2/tHnXC7BxmWixQREZG7\ni4J2tZrvO5RSy9jlUNeiMnc8N4RhGS2nBFOLyznWIQ7ralvtKl4eb8IqXDfDUl8h1HnhZYmrVU23\nnIONjj085sPQ6nHO9DLcvRy/VMqHZmjtpGU6nTCdTmnbErKbWEJwCE0J9mbLmdA++h8zK+caAtmd\nixe28ZxoYiDVtb1z7plM2rJM2MZGbfxWh803EdzZ3pyRPRGHTmh19Hcb99mdd+zRkxdpuTr5dQ3I\nxyG7/gxsbTsRkUfhFn9zF4D3nXDbBwBVv0VERO5GCtoDG08MttHQ5VCbopXwbR5xc/BahbZVuB6H\ndaMG7Rq2Yx3WHcJ4yPgQcJ2US4AMNU4Gq13JQ6lUD8uPDaF8PJB6ub8a5tu2pWmaErabtoRrixix\nVK8JWO0kvur27eAZr8F7PDe6jYHN2QzzzLRtlkuc5Zxpmsjm5gaz2QZxOZQecCcGYzppuOAbNDEs\nG5kFC+W8ru3jeZ9usb+s6eRhnnr939W7rO+4nq/52hcOIiIiIiIiZ4SC9thy+PiqXmql/XipaNcl\nwIaOXTYO2vV1JRyWEDss51UucRm2LQyXMgw655IaS34vURhKRbtUwVtCKN26S5gPo9O11dDxWi1v\nmjKMvRkusSVYswrZQ4T1Emi9plevncCXleQautsYsI0p07Zhe2uTnDPupSt66UDe0Nbh4mVtcIBM\nCDCbtLRNZDYry4ctK/yhwXNgPk/s2YIEZV76qLhka7Xr5dcYXtumKWmLyCOnQTEiIiLymFHQXlN7\nWo/u1bg3hFyvQ8nrf9gQXodZxsNrVlVtC7bqLG5W50GznI88hG1zXw1VH4agh0BsSjC1WuW2Wt0e\nqtrjanYIYRWw6+tiKMPbzW35JQFDWIXVnGdfxdzVsSAEI8YJNhmfc16e+xD4y+uGW2X4edtGJtYw\ngzLkvp6vu7FYJHZ3F+w0c7pUBo7nlFcjC0bv8Yg/KBG5S5jZc4GvAf4c8BRgBnwQ+I/ALwI/7u6X\nR9vfB7wYeD7wZ4D7KZ9pHwLeAfwz4J/7IU0dzGw8MdqA15nZ69Y2+y53f+UteXMiIiJy7ihoH2bt\n32U+fsxLkB6W4TJ3MGf1X5lrnGsmL5nWyF6GRYfaTbyE80yu62V7yiVSjhqxeQ2+y27kdvALgFXa\n9FG4XQXdIUyX6nMNwQY522pXcF1zsVI1txL+hy8YaqXavM4Vr43YcKfk51xfO4T0ISSvLk0MzKYT\ncnbmXWZ3v2N3v2feJfYaTWREAAAgAElEQVQWHfuLBSlnUq3uq94kcvczsxnwI8CX1YfGfyHdXy8v\nAu4FXllfEyiTm43rv3Z7CvCF9fIyM3uxu++ubXNg5cBD9iEiIiLyqChorxuHTveD//oawuW4Fdd4\nNPnQrKzWtvNQ567zit2dXBeJtpzrtOhV2C5xNODBDlSZh8OsWpatrzA9HHW8Tb32XIJwrd+YQUq2\nrEKX4xxswubOMmCPL6uq/urn4MswXy4hlIp98IMp2cyJTWQ2nRBCpEvO3rxjb16Ctu3ukT0zX/Tq\nwStyTlj5Vu9fAJ9L+Yvod4EfpFSkdymh+bnAl66/lPK32puBNwK/DjwEXAA+Fvg64Dl1v6+mVMrH\nPoUS4H+pHvflwM+ubfPBR/v+RERE5PxS0D7KELJHwXst+8I4VtuwsvRqPeghbJdLec7c///27j1M\nkuys7/z3PRGZWdWXGd3RaEE8RmCMH4TRCi0GydwkvMK6GJCNxZoFdLPs1e7iNazZxbDDCBv7eSQu\naxs/HtAuFhc/whjBw0WALZAlwbO2ZMtgsMSa2+4i1JIHSTPdXVWZEXHOu3+ccyIia6qru2eqe2qm\nfp+HoLKqIyMis1WT/Yv3nPdAysETgxTLsmExYWU/K/Oup0rzvHJ9uMx7uE93DeazYeEpkUoztxyy\nIzFOS2XV0Gy1am2G+/QY8tD3WtmeupNPS5ClMpS8NoJjnGteO6dPXcyXiyUxwcFm4GAzsO4jyRNd\n3xPs8GsSkcew/4EpZL8V+G/cvZ/9eR02/m1mdlf9obtHM/t0d/+9I475buDNZnY3cDfw35rZ33b3\n3509//1mtjd7zh+6+/tP7mWJiIjIWaegXfhRlewjm235oceOk7ZmaCfPXcSHlOhjbgrWxEAMdT5z\nCdxGGdbtuaJtRkpGMMfLz1KKxBSBWll2tqcXgnvIjdisyeE4lOOEVOaQ16tNuMdxGPn4amrVvDZ+\nw2aP58HbxuA9vQPb63onDwQS7gEfXyvj8UOZe75cNOzuLrl4YZduiAxxYN117O23JbRDwrf+CrSG\ntshjR6lmfxP5P0MfBL7uUMje4u6XDn1/VMie+w7gdcATycPIv+dhXbCIiIjITVDQLh4U3w4PG4dx\nlPYYls1zZbocYDtoJ4aY6OJQlvSCJjGrGpexj2lWDXYnlbAdMVJKxBiJQ8Sb2XDusUpcLylAnFWz\nzUjBSG407rjl9bqNgFOHks+vu1bB7eiwfWg7/IbMh48HcvCvw+i3+4YzVv/bxthdLbhwfpeYYNP1\n7B9sWC338zztMm/9iL8FEXls+GzyQtIO/MAR86hvWAntTyUPHV/UH5MD/BPJzdJEREREbhsF7cOO\nCthz4/Tow3Oly9OB5InoRh8jzQBNyNvQWKnq1qZhpXt3CduGEZKTyI9jjMQQGMJAkydOz8I2Y8XZ\nCdPlGYSUw7p7oE7Bzr2DZuebDy2vVeJZxXpsajarao9N0TBmRe2xKl56o5V56Y674ebzXalzyZsm\nsLNccPEcYIG9gzVX9vZYLRf0Q4SBHLZLQ7bp9WlIuchjxLNmj9/9UA5gZl8DvBL4XGD3Grs5uZGa\niIiIyG2joF08aOi4T9XhB5l39q5PmdVt3XNIHBL0g9OGXMGNKREs4AGa+qw67LpUmGvIDpaHjg8x\n5iHTjU9zpseKc2lSVruNmxNjWe7LQw7VWKkiTxVvBzzF8XU/6LXOKtrb1expznYN49vvQgnaXhc7\nS+N7kt+z6cZE28By2eIhYE3D5as7nNvdYXd3RR8jDgwpQVRrNJHHqHn4vXTNvY5gZivgJ4EXstX9\n8ZquFcJvgQQ85Qb2a1C/NRERkcmlS5e4dOn6/yTouu42XM3Dp6BdTF2+S2g+csmrQ93Qxg7d5ZsS\nth3yHO2YCDhtNBbRGKKN1Wyv1ejZ/OYcvKd1qmOKWLQyxDyV9bdzo7QQyvrc5GHhyROWDAuJ5BFP\ngZQCoZwwmNE0Yes1pUPrYU93EGoVmyl0z849hWyb3XSY3o/8GmYV9VqBL8PGDaMJxqIFp8Edzu+u\nuHB+lzsuXsij2m3NkBJdPxz+m3pof8Ei8ljyrUwh+1+RO5W/D/iwux/UnczsneR1uW/zUJj7bu/p\nREREHgPuvfde7rnnnkf6Mk6MgnY1BmxmoddzoN1edJopcDOGS2D8p1ydpz3E3Iq8bYw+BhYp0Xpt\neMY4z7rOb86Hr+G3zNFmwD3lKrWVpbNCoPFAQ4OHebDNHcxzyM6N1NwD0JTn2bjMWH0dTpkjnqZq\n9/hSZkPUg09LfM0r6tSXMd5nmO8H5Fnb2/uSlwFbEMZq+bmdFRfOn+OOdc8QnSE6602P2fyO1XZz\nNBF5VPuj2eO7gP90E899Ffm/OO929+cfs98TuM1358yMJz3p+iPVm6ahafLYpqc+9am3+rJERERO\nvde+9rW89KUvve5+L3zhC7nvvtN/U1tBu9gOujnszn8O8+Hi82HmbJd0KX3IHQZPeMpzs4c2EGMg\nNnXprjAdwA8F7RK8U8rV5lA6koeQK9S5Mj11GB+DuYN5rm4njyQPJM/DykMwQhNIqQZs8OSl63kq\nxyhXP07ZtvFr7mKe1/geQ7Ydqnwbuau5G1bCPz41jJs3gTMzLASC5+s6t7vk4vldDrpIN0TWXU+7\nPxammO51KGmLPEa8b/b4C4B33siTzOwJ5MZnDvz4MfudBz79mEPdkv+YPO1pT+ODH/zgrTi0iIjI\nY9pdd93FXXfddd39lsvlbbiah09Bu4hxFqxnQRsYq9peGnnVDuPzb8aR5Qa4jV23E3kIeT9ENgGM\nRGoC3oZchY456CbPxzZPWMpNxG1WHQ4hn8AskBI0oRmHhFupdtels/LryB3NpyA9DU+fWoDnanZd\nQsxLN7NpFPkUtEMIJE+EVCrr9Zpq2C7hO98CcIJBY+TrKuuCTx3TA+OzLN9wWC5adndWXDwfWXcd\n+wdrdlYLVsu2vD+lMRpeur3fqv8liMht8uvAHwCfBLzazL7rBjuPzz+3zh+z32vKvtcK1OvZ49UN\nnFdERETkhoXr73I2xJiIMZFiWVIr5iWmUkwlsNZh5bN/tY3V7sNzusuSV+6kup52P7Dpetabjk2X\nt67r6YfIEHPQrsuC5UZqOfxOW9knzSrOJagGC2UJsfx1Cttelg8rgdpTuQGQw3YdNh5TZBiGvMWB\nIfYMw0A/DAwx5m0YiGW96xiHPCw9RkgxV61xgjsBpzFozWjKFmwWtMv1TjcHcmBfLVrO7eSq9sXz\nu5w/t2J3Z8lq2bJYBNpgmKVxSbUQSpU+KHGLPBp5vvP3hvLtJwI/ZGaLo/a1rN7ivg+4vzz+6qOe\nY2bPAV7P8VXrjwJ1bsozbvLyRURERI6linYRh/rvsTqMOo2NvvLw7Knr19ba0FM3NMBzc+9xfemc\nymNMdDikiEfDUwMpgDeMPbzHhmG5I3ggh8hgVtbBZmxG5nUJsnE4ebP11QjgjCF7XtWeLWidz+VO\njDlA15sD7j6u1W1lHnUIRkh5rjihDAkPpSJdhqwHCzSlkp03G6vaRj1WKMPMQ57HnRN/qWhDsoZ1\n13Hl6g7ndpbsLFu63ulSJMV8kyAASfla5LHg+4CXAC8AvhL4DTP7R8C/BfbJQ8Q/D3g58KPA693d\nzexHgdeR18f+VTP7buC3gTuBFwF/DbgC/CHXGD7u7tHM3gs8F3ilmf0a8GtAX3b5mLt//ORfsoiI\niJwFCtrFNEea6as7Hg434JoW8prC9vQDnw0n97KqVowOnnDzsr61l47cuSlYEwLBan71Ovq8rENt\neMhDvs0glSHiNcTDrFIcwjR8fFx7q1a1SxdzmzqGz4eNpxTLet5TB/I8rDvlY6cpbNOEXMEm1JW+\nMJr8GspbEYBAHQ4/e2PHDu1T5g9mtE1gtWiIbuyuFuwsW1aLhmUbSBEGc/AIaVoyzDRdW+RRrYTm\nPw+8GfgLwKcB33vUroe+/1vA5wOfDXwO8E8P/fkfAS8DvoPj52n/XeCngScecYxvJ1fFRURERG6a\ngnYx74qdq9c5TFoIR84HLlF09nUKj7UJWA3bQx0WTiKmvG9ySG60IdA2TttYCap1BWqnKfOzPSVo\navU7B+tU53Zvzb+uTcbKfO1QQrU7KUbiuHpX/nkaBlKMpVu5j1+Tp+1XZylfRwn+RkPASfW9mt7E\n8ubUKvo0XzyUarrV5mgW8ns8lvHzzYRAIpBoSDSWaC0SfMBih/cbUkylq7vaook8Frj7GvhLZvaF\nwCuA55Er2Q3wEXKV+WeAt8yec9nMngv8DeCryAF9IM/5/lng77v7h6wuc3CN/1y4+9vM7PnANwDP\nAZ4MHDl8XURERORmKGgXtSnXuNazp3Gpqtkq0A9+3tbyrDmM2nxfh+hO9IR5IpZh6MmN5M6idZb5\nqTSWm6cFTxj5/G6Wq+S1WRo5RNeQnTcnrxJT50DbOH/ZzMATKQKkaUi4Mc21rl3HU93K0HTS2Lgs\nzIJ2sDx0OyUjhTA2hrPSUjx3NE/5hgK58uzJIYAlrxk739QItQbuOYSTCF5CNonWEo0PEHt82JCG\nSKI0ezuhv3sReeS5+zu5wc7jZf818J1lu9Y+X3zS5xURERG5EQraxXytbJsPiR7Xg95ax4txB6gD\npKc/d7ZqKCnloJlSyiHVIboRUw7clC7dhFzxTSVsegnfblbyv41BO8Y4q2inaWT2vOlYqKHfy5ra\n20t2xRhzRTvFPLTdZ1Xy8bXm6rZbWbKrBO1gRpPqEPbapnxe0c6v18r4ebM8f93KuPjglIp7qXDP\nQnataLclbAePWOpJfZer8FCGuJ/QX76IiIiIiMgJUtAuQpgq2k4izzL2rQB+ONmZ1fA7j9zbNW4Y\n67VQBmMnN2Jyhug0IdGY0Rt4gOCRQMqDx2tuNSD4tIRWCdrzLYRI0+S1urdnkNfEn8C8dCjPw8o9\npbLOdQ3AZS55CeWO5XCO4cEIKU8kT2akYGWd7kRMTiib4zSW38N83FC6hU/D3q1Wui0f063MQa/L\ngoV8g6EN+XEgYR4h9aXL+YP/LkRERERERE4LBe1iK1Afisrbf7a9hx2xHXXsWmWueTYlGEizBmK5\n8VqD01iiDoz2sdrtUwfwErSHYaBpasfxHLLN8vrcIZQO4dTgHIFEaBqa0NA0DXUSeSBXzROU5ml1\nznZtEleW0QoBUiBZIDWBmNIYsENKWHQaT3iAxo1QhoePYZs8f9w8byGlPJg9MA6ND/OQbdCa05Sq\nNilCGqalwJW1RURERETkFFLQLmpF20mYh1LVnq3cNTOG6jIUOmz93I8M2/Nat7uRSjevgZQboHmC\nAG6eK7yW17yuQ79pvATW7Yp2Ddv1+xq085ZDdkwDKfbgibZt8bYFb8fu5EZdgov8nBjzmttp6sZO\nCpTFq0lNIMWG1ORh5tFTnnudEk6g8QQh4J6wshpYCAFPeXi4MY0DyN/Xbuu1om1j2G4DBCvvTxrG\noF3eVBERERERkVNHQbuYul/nocyHWpqND6fKdQ6GYyewmbo02Nh5POUSdl2SK+XT5OHTybAEBMMb\n8FLiDoEStNM4D3oMp2Zblez5ZrVpmefAneJAjD0p9mUu9zR5PJR1sPNzEikN0/7Jc1U75fKxh4bc\nBJgyh9sZB3yXynfMJfFStc7D7q2MYM/zw2sndh87k+M2m+ddlgUrxfM8hNzGij+eIKVZf3cRERER\nEZHTR0H7WmrGnq9eNa4LnTtk1yWpUl3T2nPDL5+F7Gkd6xJc6zJc9QRNnv/sDXgToG0IbSC0ZXmx\n0hwtH67MnfY0Dkev4bp+tbHjeChV5NzwLMYIHknGuMxXmlW0U3L6vqPv8lav1WvAb32sphuM58zn\nzatmjw3RxqHyZaj4uNzYbB8YA7qV4J7fmPr8fFwr88ln3eke/BcjIiIiIiJyiihoX8eDGpt5HsZs\n7lCWxqIsizVWr33avATs5FNwnRp5eW4uVjZvG8JyQcOCJlgeLl26cbvlGnJdisvmYXYWeoMZoQk0\nIeAh4J6DdipBO9rUrM1mRfuUEl3XlbDdb13rOGS9lpmZlhmzGrRtvA1RlhCr62SHsQHbGLDL/3fq\nezQfO7AdssPs+Pkc9W/FjhzWLyIiIiIi8khT0H6Qmvqm5bymGqqP1WxLEY8DxPzVy3rWebh12q5o\new3IU3gdQ2bp4h2DwWKRm6EFY9GEci0JvCwLllIO5Sluh+tDQ8cbD3hoaBrHPeY51zE3RDODOFt7\nrF5TSom+6+j6Ple0680AZzx20zS5Al0KyznYN7OKdnm3SmfxwzcB8uFmYbvefJitimZlSbVcCZ+C\nvG1VsvNxNUVbREREREROIwXtwuexzWZhexzR7BBjDrkxQhzyms5xwIdhDNKkedV6mpPsJVxP85PL\nDGcv3cI8NxkbAy5l7nJpSOZ4HvadnBC3g2hoQu4m3jSEJuBlLjVliDgWsNASStdyx/La3mWetHsi\nxkg/DAxDTz/0Y+o1pkqyl2PVKeiprA3uuXcbyfJNiOCQ2vKyzEgpN5arxf5Ulw4bj12Pn0N01/V0\n3ZC3fmCIkbjVBV1EREREROT0UtAeHW5lPVV967xsUsKHgdQP+NDnoF2+4lPTsnFouE+DpKEOkZ5X\ntMlB2wMWnJSasWFa7iDGbG53XfLLCeaEJmChyQF7aGiagb5tCEMO2bWjeO6tZoSmwWjGBm6pDENP\nKeW1sEsH874f6Pp+mo9t+Txeg7eVVnBlmHyKaRzCbSQCDYE8wtzNSJbGEQEx+bjV483DdvL8tet6\nur6n6/P19EMkxjR2aq/vnaZpi4iIiIjIaaSgfUiNxrXpeA2mRq7epiGS+o7U9Tlk9z2xBO0xnNfn\nzY7qZeHncch0DYu51ThGk6viNYSSlxgbK8ee8jDwFMGc0MxC9tDQtEMJ3E25M1CGbjd5rnQIOXTX\nOd6ecpO2mBIpRYYh0seBvlS0zQJNaAgNBK+3CijzrHPCTe5YdPINgNxBPJA7hacQxpCdyrsQY2SI\niSEmUr2ZYIbnBc5I5I7pm34oVe0ctochkuI09N6nt1VEREREROTUUdAupvh2uKI9q06nlOdkD7FU\ns2PZhnFfGyvWs2PZdPyxxl2Gptd+ZMksh+lx/e5SBc9xO4f8FIlxAPJ62O0w5PWzU9k8r2kd6rxr\nIDh5DLfVRbI8v4yUSDHl58WBYRgY+kg/5NBtIS8u1pgRUmJIiTY5MTpDjIQhYhbxBvISXZSg7TRj\nZ/OGFBIh5Qp7PySGGBmGlF9lnXttoYTtgJvlaxnyUPa+BO1Yqu++9d6qpC0iIiIiIqePgvYRapSz\necg+1EWcusGD19wejzF11Z46bvvW6PJcfC5LXI3rZadxne4yqLqsc51DcZ5TPeTwOXYzP/QixvHc\n5ShueJnjnYeJ52PVbYg9XQm4Q0wEp0R8g5AIMdIMAyH04/GSQxParXsSxhIrw9vd41i5d5whJmKp\naDt1jnkzboQGswZPToyJOOTh7LEsT+ZlTvj0zqmkLSIiIiIip4+CdrFd0fbxJz5fM9ungO3JZ+ES\nDg8Wz1XrWXXbZ0eeNfUyPAdtK43J8sTt3F3MpnnZzhS0Y4oMcVkqvHWN7en2QJnePVtzOkueg3Yf\nU6kaD8TYM8TcBK2G7JhyEHZLuBmUCnYIA1hPKiE7JWhCnF5YYgzZTYCYAinFMg88MsQ8VH2IDmY0\nTUto2jIMfoE1EJowDmmPQ2ToI7EMHU+lqn3UjQ0REREREZHTQkG7mLpZT19tFrrLTlPH8Fp9LnON\n3X0cyFyDNV47hs+OOsvsudDteah37VZe5jublfPXsO2xBO3IEKch48mniE2+FKZ241N373pNqVSL\n+yHS96WaPfQldJcO3zHPCQ8YAQNLWBywocnDvOvrinmJr/kdhBByyG4aIyQbA/0Qh61maBYCTbug\nbSNNu6BxIxDASqCOsQwznyraqc5hZ3qvRUREREREThsF7cPG4DobKl6DZa1Ez3/GtHRXXdIrNzGb\nL+lVD17XgGbs3r1sGxaLluWiYXd3xc5qxWq5YLlo6Il5PjW1cp1KU7R8wPn61rU5WqjLfdV1tcN8\nyLrnedyphNgYyxzogb6vle0cvMc1rEOgaQdWQyIunRSduMhDwGPrtKHJHcoxghkx5aZqTZfDftd1\ndH1H13V5EHy5wRBCS7vIncRbh+gQEjTR83O6Ll/TWHmfwnbtqK6wLSIiIiIip5GCdjFWtA+H5sOh\nmllncmCrE/YspM+D9qy8nEN2Wc/aLLBoF+wsF+zsLNjdXbG7s2RnmYO3JyMG6D2BT2F7HHZuNobs\npmloauiehWybTk0iV4tjaW7Wx9z8rK/rVQ99WUd7oJwAQh7iHYdETE7yMqy7LLcVm5Y25A7lhJBD\n/NDTlaHw6/Wa9WbDZrMZG7JhuZqdq/G54h4SZeh4YrPZ0HU9fdePYbuG7JRSrqLX6xMRERERETll\nFLSLKWhzZNCeh+ep0r0dwreDdhqryFmZye2GhbxGdQgNy7ZlZ7Xi3E4J2aslq9WCxSIwDEaw2gwt\njstyzYN2CCEH7KMq2yEw1rPLdSZPJSiXdbOHgU0/5FA75GDb9/24JJcDTduUJcbyD1J00qIsEbaI\neNvm4dyhJaaBYcixPsaBg4MD9g8OODg4IIQGaxostLSLmGefl+o0ybGYsBCvWdEeu46nBKF0LBcR\nERERETllFLSLrTnaNVDPgvL050f0F58H8dqSzEJZVashWEMIeWtCKI9zFXh3d8W53WUZNr5guWxZ\ntA2N5ZDsJRTHoQ6djqS0XdFuDofruoa2laZhPg5oz5tPlemuH9h0HZtNR991dH1P1/f59Zamam3b\njHPKoSwN5mms7JvlYeyOE1Osi4gx9D3rzYb1ugTtsfnZUOZfR7qup2k3eFniC4zLV/d44IH7uXr1\nCvv7+2w2mzF0A3gINGZbfyciIiIiIiKnhYL2YceG7LGvN/Ph4FPILl3KDZqmoW3ztlguWC6WLBZL\n2rYtYTsH7dWyZbVasFoucsBuoAmWryM6w5CmIdT9kIdsz+ZoN7Nh43WoeJ7+PVV768zxrXnj5MDc\nD5HNpufgYM2mVJI3my7PgQ55a2NugoZZWU68nN+MEIwmBlIbgTbPA095aa9cHe9KkN8QmkjTRELT\nQj8A63H5sSE5McKQnP39Ax64fIUHLl/h6tW9sco+DMNYxVY1W0RERERETisF7WoM1fOu4duLfh1u\n7j1+W3unlTAbQq40L5dLVqslu7s77O7usrOzw3K5GId6hxBYtA2LtqVtG4KBpwieSHHIjdCGSNcN\ndF1f1qGOY4jeqmiHQChzoK0sgIXV11Oq2KRS1aYEbacfBtZdx/56w2a9Yb3JodiCEZpA0wba2FI7\nq/u8mh4CTROIbZMr3JbK6me5G3vXT43Quq4jNGnckjvDUOaIDwNdH+mGSNcPHKw7Dg7W7O8fcLDe\n5CHvh+dni4iIiIiInFIK2oXPU/M4F3trca8Hr9zs84fzIedT0N7Z2eH8+fNcuJC31WqZA2oJqiHU\nedZGSpGh6xn6npggDYmhj/Sbnr4b8txq9zFshhBom7YMHZ81P6vb+Nq8zM/OW308xBxs1yXYHqw3\nrMsWmkC7KBX5RRqPk1Kp2AfLNwkWDTHFrTnpnhKkRF+6mfd9vxW0myY3YsvnK+fd9BxsOtbrLs8Z\nL9sQ87nrUPimaQ4NjReRm2VmdwN3A+7uzSN9PSIiIiKPNQraxazpeO2Dlr+fbVPlulSvZzOfD+1J\nDew5806rWQcS5l6GYUdihDg4vcMwDHRlrvRmveHKlavs7e2zf7BhiENeaqtpaZcLlssdVssdVqsd\nVssVbbOgsZZAwNzy0tvUudh5PvYwJLpu4OBgw/7+AXt7B+zt7bO3f8De/joPG+86+n4gJMsdymMk\nprT18hZty3KR51inmEgxN0arL91qZ/FyI2DRLlguV6XlesiBPSbiUAJ1l4N4Xxug1SHypfnZONd8\n9r6OjelERE6Rq1evPtKXICIiIqeAgnaRZm22y6jxcXh4nZntbrMFvnJn7cNh28qf2XgwH38eSBgp\nh++SEVOMeU3qmHLQPZiqylevXmVv74CDgzUOLJdLwqJhuVixWqzGoL1c7NA0tct4yEHXyV3Bvax9\nnZx+SHk+9rrL4XrvgL39A/b3c2fwYVZJtmg0TSLEQIwpX2/ZlosFw3IoDdpKJ/Q8ZnwspJvlzupN\n07BYLFgtI7FUxKMzhvihzOPuy3JeXdflIfIp7+vusznx827vCU+qaIvI6XLlypVH+hJERETkFFDQ\nLrwE7Vq5rnO1A5SGXds/n1qKTdVsn4fsMk+5fjUcMydYymG8BPBa1e26ns2mY39/zUHZ9vdzEF4f\nbAhNQ9suCaFluViNFe2d5Q7LxWpsgmZmUzXeS9fysu71UOZ7H6w37O2tD4Xtda5el83MiDF3MI9t\nMwZtA7rlslSdS0U71WXH6qj1PEd8XtFOy9x4bfBITLFUtPMSY11XwnYZZh5jXkpstuJaUZrEeQIP\n+auIiIiIiMgpo6B9mM+D3fgjwOr/Ub+dyrf5STnj1srrtFZ1121YrwNNgKFvMZsq3+PQ6X6g2/S5\nmr3ZsO57BnesaVisViyWSy5cvMjFixe54847uPPOO7lw4QLnds+xWCxyM7JUh1unUj1OeR52WS/7\nYJMr2ft7uYp9cLBms+lyo7VhyGtlp2nNbUglcBvDEAlhIJjRbTrWiw3LxYJF09KGhjY0LEr387rl\noFzmUpeO5TGlUjXv6YeBoVTFU6m+j29vmYPtZnmZtPJ9XsasztNWYzQRERERETl9FLQPGZeeHkO1\nYYd2KCtd5R0CkPLqV5RgCTVQ9uVJToyRTdfRtk0O2Zar3HU96aFUd/s+MgyJAQiLBTvtgtW5c5zb\n3eVxd97J4x53J3fOvp6/cJ5gRj/kJmop1WMOOeT3PeuuZ9P3HJR531f39tnf32e9zutTp5TmLy+H\nWOpryVXkWoHuMdJeJy0AABUJSURBVDbrLodqC7laX7aAsWhbFosFy0Wbh4knJ8a89cNUua9zwety\nZU4OziE0jC3Ty5ucm7xZWU4sbK0ZLiJySgRAvSNEHkUuXbrEvffey2tf+1ruuuuuR/pyROQGxRjr\nw1MdBhS0q0PTfc1nResSPN0s/3WaQVlj2tL8uaXrN0aKiZ6elCIxDmy6jsVBm8NhPThlrnIqFd3S\n/CsPw7YSWBcsFgvuuHCBJzzh8Tzx8Y/n8Y9/HBfOn+f8+XOcP3+OlBK2yfO93Z0Yh3FJrYPNJncV\n33Tsr9fsXd3L294B6/WGrsthd/q3YVmLe1a+r2tnD32E5GxK1/Q6mb3OOQ8YO6sV7pSlxsjzz8tr\nzEPX+7KEWDd2Fa8jwK3M6TbPIdtK87Rgs7BdAnawgCloi5wIM1sB/yPwcuDTyo8/APwQ8I/dPR7z\n3E8G/jrwpcDTgQb4Q+CXgX/o7r95zHPrXb5vd/fXm9mXAH8N+FzgqcAH3f1TZvvfBXxDOdczgHPA\nx4D/DPwm8IvAT7j7kR3JzOwO4HXAi4A/DtwJfBT4t8Cb3f0nrnWtN0Dd20UeZS5dusQ999zDS1/6\nUgVtkUeRWdA+1Z+9CtqF2bSOdg3LWytlzeZAj3OhjRII6w6M486Tp7w8F5abi23CGBYxww8twYV7\nXru6Bsm2Ybm7w/nd3bGa/eQnPoEnPeEJPOHxj2O1WrJcrlitVrlT99BjBilF+r5ns9lwcHDA/nqT\nu4yvN+wfrHMX8zJsvOu6MmR8ewmtrYpMqdKn5BiJwZ2u6wlmuflZcqYWbKE0lbM8vBtjGBL9UEJ2\nP7Dp8rVtun5W0S7vdwi1lVseFm6hhOtc7T78WEFb5OEzs6eQA+qfYnvmzHPK9qXAl1/juV8L3Aus\nDj33GcCnAq8ys29z9793zCV4OdbfAf5XHjx7p57rzwA/A9xxaJ8nl+0zyTcK7gPedsTznw/8GPCE\nQ8//BODFwIvN7G3AV7n7/jHXKyIiInJdCtqj+WrZs20WhseGYyGMWw7Nedy45SW0x+pwzd3JwdL0\n55iXUdGW16lu2/x12bJcLlkuF6yWC3Z3dtjd3eXczg4XL1zgzjsvcvHCLrs7C5q2oWkAEjH2dN2G\ng/U+V/evjmF6b2+fg/VmXJ96vcnLhs2Hi4cQxrWpj3pL6j2EYCVMl9efHIaU2AwDzabDQkN06GOk\nG/JmZuOSYV2XA//BpmPdDXR9pC8dyN0CBCMAXt+kWUW73twIU8e3MlZfRE7AW4E/AXwv8LPkCvGn\nA98G/EngJWb2Gnf/gfmTzOxFwA+Wb68AbwR+CRiAzyeH5icDf8fMPu7u9x5zDS8Dngn8OvA9wH8E\ndoHPLudaAm8BLgKXgX8E/CtyJXsJ/LFyzq846uBm9lxy+G6BDwP/oJzrQ8DTgL8EfA3wZcCbgb94\nzLWKiIiIXJeC9qiOXy7trsf1rMqPLYfAXHU2aAIpTWGbsvcYpktTNYNZczGm+dmlKt4splB97tx8\n22V3tWJnZ8Xuzopzu7ucO3ee8+d32dlZzqrPkSH2bPpcwd7b2+PK1T2uXr3Klat7rNddriJ3PV03\n5CW8ahXbyzWUOc9zh2ZpT1V+y9VmB4aYsH7AbEMqIbsfcvO1rs9V7650Eu/7acj4etMzxJiX8KLc\nqCjvSeOU+drzsF2r3DZeQ3knReThMeBzgC9193fPfv5rZvYvgPcDTwH+O2AM2mbWkivZAFeB57n7\nb8ye/x4zeyvwfwF3AW80sx93949d4zqeCfxL4MXu3s9+/ivl63PLcRz4anf/+UPPfw/wY2b2P5GH\nk08vMF/rj5A/734e+Avuvp6/VuBtZvZu4PuBrzSz57v7L13jWkVERESuS0F7NKto15DtXuYr58g8\nNUArFe0yvBkL48LbXirbPg4j97qS17iG2DQE3WktsLNYcuHcLnfccYE77zjPHXdc4I6L59lZLVmt\nVuzsLEule8VquaRtFyT3stb0VNHeLxXtK1evcvnyFS5fucp6nedCd31uuFbX1vY8XjvPdW5KqJ5V\ntesyXfXx9PP8OpM7HiMJL5XsxLrr6fo6RHwgBBubsg19Dt+bspRZTCmPBqBWp600YatN0GbD8+uV\nHFV1F5GHw4G/fyhk5z9w/7iZ/SDwvwDPNLOL7l4Xif4KciXYge84FLLr8/8/M/ufySH3HPAK4LuO\nuAYDIvDqQyF77qmzxw+61tk5Ezn4z70c+GTgAPjaQyF7/tw3mdmrycPlv55cnRcRERF5SDT+ttiq\nX8++cR9XxobSGdvKPOpczc4hcd6G3Ld+ViredQw2U5U4GDRmtMFYNIFlYyxDYBmM5exnixBYBKPB\nISVSjHmJrf0Drl65wpXLZbtylcuXS1fxg3VpdpaX0Yox5mZruV48zQVvAm3b0rYtTdPQNs30fdvQ\nHtqatqVpWkLTYKEBa0gYQ3L6mIeSH3S5w/n+OlevN92QlxiLqVSxwQl5s4CHfCxr8haahtC2hGa2\nle+bpqVpF1ubiDws//SYP/t35auRh2dXLyhfnWn4+FF+HHjg0HMOc+BX3f0PjjnOpdnjVxyz31Fe\nWr6+85iKevUu8mv9vJs8h4iIiMgWVbRHtcV2+dbrXGHKMPBaba2V7GasaI9ztJk9t3blHiviDsHH\ngF1GnxNCiZyeIEbS0BO7jn7d0OAM7rTk+dC+SKTohBDZX69zmD3Y8MDly3z84w9w//2XuXz5ap4L\nve7phkRM4F6XztquUW81ceOoijalqn+4kjwbtm258Rkhvw/RjT6XuAkWZ+t758p3wsCaqXt7rWiX\nYeI2e1yv6fDjut+R88pF5Gb91jF/Ng+mF2ePP7N8/X13/+i1nuzuvZn9e+CLZs85yn+4zjX+CvB7\nwKcA/7uZfQ3wk+Rg/N5jKuGQh8Y78MJZl/Preer1dxERERG5NgXtymtxvy51lbuaeY3JdVgzAbMG\ngm+F7bwvpXv59pBrYzZkHGgMgjlNsPFx8ISliA89cbNhaIwBZwAGy83UUnRS6xAC+1f3ubK3x5W9\nfe5/4Aoff+Ay999/mQcu79GVYdp9n5cLw63MxWa8rjGk2oOHZU/XXRq4Hdov3zaoIXm6+eDBiG4Q\nE8kHgtW56Y67k9xyFTvUmxAPPkbtzD7Nx97ewqwzu6khmsjDdq2h1MU8mM6X0Kidu//zDZziw7Pn\nXMvHjzuAuw9m9mLgnwOfQQ7Pzyl/fGBm7yIvRfZjZfj43FPqYW7gWqudm9j38LXyvve977r73XXX\nXVpOSEREZObSpUtcunTpuvv1/XH3108PBe1RaWjmNlazyxTrsTrtBIJ5DpVjZbuGRcbn2/Yhy8NE\njpkQQh4y3gRoLFe0jQQp4sNA6juGjRGDEUMglmWvxjW2LbA+yF3FL1++wuXLV7hy+SpXruyzt7fP\nkJwYE0PyMVTXrt01LI9hez4fev5ueL3FMLttMIbzUG5AWF5bfDZ0Pjl5HniK0+7lmPWGxfZQ+xq0\nbTb3/cEBewrauUv6UQ3cROS2O6mehNdcp3s8kftvmdkzgZeU7QvIS4jtAP912f6GmX2Zu//R7Kn1\nBsHPA3/zhK73WM9+9rOvu8/dd9/Nt3/7t9/6ixEREXmUuPfee7nnnnse6cs4MQraIiJysz5GvlP2\nCTewbx2Gfb350dfleYjMT5cNM/sE4IXA64BnA/8luRv6y2ZP+yi5Y/nS3d//cK/hGGPF/slPfvJ1\nd7733nt505vedAsvR0Sup+s6AF74wheyXC4f4asRkRjjDX2G3nffffXhcaPlHnEK2sVvv/3HNeFX\nROTG/Ca5YdgfM7MnXmuedlla61nkyvdvnvRFuPtHgDeb2Y8C/5octF9sZit335Td/j25Q/rnmFnr\n7sNJX0cxfobM/gEgIo8C+p0VedQ61flNQVtERG7W24HXkD/gXgG88Rr7/UXgTnLQfvutupgyh/ud\n5KDdAo8DPlL++KeBF5XreAWz9cBP2AZYkee138jcdREREXlonkJurby53o6PJAVtERG5WT8FfIhc\nKf5bZvYL7r5VsTazTwLeUL7d5/hlwI5lZs8DLrn7717jzxfAF5ZvrwLz8tSbgW8FPgl4o5n91lHr\nhs+O9Vygcfd33cw1uvv5m9lfREREHtsUtEVE5KaUZbv+CvAz5Erxr5rZG4BfIjc2ey7wzeQ7zg58\n4w2sYX2c5wPfZmbvBn6OvBzYfcAu8MeBv0quZjvwpnnncXfvzOyrgHcAF4BfNrO3kG8W/D75jvhd\n5DneX0lehuy/Jy8dJiIiIvKQKGiLiMhNc/e3mdnXk5uPXQBeX7ZxF2AAvtXdv/8ETmnkTuNfeMSf\nedl+CviWI67135jZFwH/jFzZ/stlu9ZxLp/A9YqIiMgZpqAtInI21VD5kPdz9x8uc6P/OvBngaeT\nK8QfIle3/6G7/8cTuNY3AL8OvIDcXO1pTOtjfxh4D/Bmd/+Fa74I9/eY2acBX09eHuxZwJPIc6rv\nAz4AvBP4CXf/7RO4ZhERETnDLK+WIiIiIiIiIiInITzSFyAiIiIiIiLyWKKgLSIiIiIiInKCFLRF\nRERERERETpCCtoiIiIiIiMgJUtAWEREREREROUEK2iIicuaZ2dPN7LvM7ANmdtXMPmpm7zGzbzKz\n3RM8z1eb2S+a2SUzOzCz/8fMftjM/vRJnUPkLLmVv7tmdreZpRvcvuCkXpPIY5WZPdnMXmRm95jZ\n28zsvtnv0P95i875iH3uankvERE508zsJcAPA3fw4DXDDfhPwIvc/Xcfxjl2gJ8Avuwa50jA6939\n9Q/1HCJnza3+3TWzu4G7jzj2YQ58ibu/66GcR+SsMLN06Efz3603u/srT/Bcj/jnriraIiJyZpnZ\ns4C3ABeBK8C3AJ8PPB/4AfKH86cBP2tm5x/GqX6Q6cP+l4EvB/4r4FXA75A/j+82s1c/jHOInBm3\n8Xe3+kzgmdfYPgt47wmcQ+Qs8LL9v8C/IIfeW+ER/9xVRVtERM4sM3sX8DygB/6Mu7/n0J9/I/AG\n8gf1PQ/lzreZfQnw9nKMnwa+0mcfvmb2RODfAU8HPg58irs/8NBekcjZcJt+d8eKtrs3D/+qRc62\n8jv1XuC97n6fmX0y8Pvk39MTq2ifls9dVbRFRORMMrPnkP+h7sCbDv9Dvfhu4APkO+7fYGYP5R/b\n31i+DsDr/NAdbnf/KPDN5dvHAapqixzjNv7uisgJcvd73P1t7n7fLT7VqfjcVdAWEZGz6stnj//J\nUTuUD+cfKt8+DvjimzmBmV0gD2V14O3u/qFr7PpW4HJ5/BU3cw6RM+iW/+6KyKPTafrcVdAWEZGz\n6nnl6x55CNm1vHP2+Lk3eY7nAMsjjrPF3XvgX5Orb89R9U3kWLfjd1dEHp1OzeeugraIiJxVn0G+\n4/077n64E+rcbx16zs34k9c4znHnaclNnETkaLfjd3dLWR7oI2a2KV/fYWbfbGaPezjHFZETd2o+\ndxW0RUTkzDGzFfCk8u0Hj9vX3e8nV84APukmT/WJs8fHngf4g9njmz2PyJlwG393D3tBOW9bvn4B\n8HeB3zOzlz7MY4vIyTk1n7vtSR9QRETkUeDi7PHVG9h/DzgHXLiF59mbPb7Z84icFbfrd7f6D8BP\nAe8BPgQsgE8H/jLwZ8nzv/+5mb3E3X/xIZ5DRE7OqfncVdAWEZGzaGf2uLuB/TfkeVy7t/A8m9nj\nmz2PyFlxu353Ab7H3e854ufvBX7EzP4K8I+BBniTmT3D3W/kmkTk1jk1n7saOi4iImfRevZ4ec29\nJivynNCDW3ie1ezxzZ5H5Ky4Xb+7uPvl6/z59wP/BznIPw142c2eQ0RO3Kn53FXQFhGRs+jK7PGN\nDBc7X77eyFDVh3qe87PHN3sekbPidv3u3qh7Z4+/8BadQ0Ru3Kn53FXQFhGRM8fdN8BHy7efeNy+\npatw/TD+g+P2PcK8Ecux52G7EcvNnkfkTLiNv7s36v2zx//FLTqHiNy4U/O5q6AtIiJn1fvJQz4/\n1cyO+zz8E7PHH3gI5zjqOMedZwB++ybPI3KW3I7f3Rvlt+i4IvLQnJrPXQVtERE5q36lfD0PPPuY\n/ebDQX/1Js/xXqZmLNccVmpmC+BPk//R/l53jzd5HpGz5Hb87t6o+Zq9H7pF5xCRG3dqPncVtEVE\n5Kz6qdnjVxy1g5kZ8LXl2/uBd9zMCdz9KvBL5OrbC8zsadfY9WXAHeXxW2/mHCJn0C3/3b0Jf3X2\n+J236BwicoNO0+eugraIiJxJ7v5e4N3kD+NXmdnnHrHbNwGfQb7j/b2H73ib2deZWSrb/3aNU72x\nfG2B7zs81NXMngT8vfLt/eQuxiJyDbfjd9fMPtPMnnHcdZTlvV5Vvv0w8JM3/2pE5GY8mj53tY62\niIicZd9AHlK6C/xLM/tOcuVrF/hq4DVlv/8b+O5jjnPNeZru/g4zewvwcuDPl/N8L3mY6WcB3wI8\nvRzjb7r7Aw/rFYmcDbf6d/fZ5LWx3wH8PPAb5CZsLXle59cAX1r2HYDXuLuW5RM5hpk9F/jU2Y+e\nNHv8qWb2dfP93f3Nxxzu1H/uKmiLiMiZ5e6/ZmZfBfwIeQjZdx7ehfwP9Re5+97DONUrgYvAnwO+\nCPjiQ+eIwOvdXdVskRtwm353A/B84AXXugxy+H6lu7/tIZ5D5Cx5NfB1R/zcgOeVrXLguKB9PY/4\n566CtoiInGnu/nNm9lnkCtmLyMuBdMDvAP8M+D53Xx93iBs4xxp4iZm9HPh64E8BjwM+AryrnOPf\nPJzXIXLW3OLf3Z8jDwv/POBZwCcATyQHgo8Bvw78AvBPypxQEbkxN9qp/7j9HhWfu+auVQlERERE\nREREToqaoYmIiIiIiIicIAVtERERERERkROkoC0iIiIiIiJyghS0RURERERERE6QgraIiIiIiIjI\nCVLQFhERERERETlBCtoiIiIiIiIiJ0hBW0REREREROQEKWiLiIiIiIiInCAFbREREREREZETpKAt\nIiIiIiIicoIUtEVEREREREROkIK2iIiIiIiIyAlS0BYRERERERE5QQraIiIiIiIiIidIQVtERERE\nRETkBCloi4iIiIiIiJwgBW0RERERERGRE6SgLSIiIiIiInKCFLRFRERERERETpCCtoiIiIiIiMgJ\nUtAWEREREREROUEK2iIiIiIiIiInSEFbRERERERE5AQpaIuIiIiIiIicoP8fEX4BoxSB8CgAAAAA\nSUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2140dfa1dd8>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 445,
-       "width": 493
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "import pickle\n",
-    "import helper\n",
-    "import random\n",
-    "\n",
-    "# Set batch size if not already set\n",
-    "try:\n",
-    "    if batch_size:\n",
-    "        pass\n",
-    "except NameError:\n",
-    "    batch_size = 64\n",
-    "\n",
-    "save_model_path = './model/image_classification'\n",
-    "n_samples = 4\n",
-    "top_n_predictions = 3\n",
-    "\n",
-    "def test_model():\n",
-    "    \"\"\"\n",
-    "    Test the saved model against the test dataset\n",
-    "    \"\"\"\n",
-    "\n",
-    "    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))\n",
-    "    loaded_graph = tf.Graph()\n",
-    "\n",
-    "    with tf.Session(graph=loaded_graph) as sess:\n",
-    "        # Load model\n",
-    "        loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
-    "        loader.restore(sess, save_model_path)\n",
-    "\n",
-    "        # Get Tensors from loaded model\n",
-    "        loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
-    "        loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
-    "        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
-    "        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
-    "        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
-    "        \n",
-    "        # Get accuracy in batches for memory limitations\n",
-    "        test_batch_acc_total = 0\n",
-    "        test_batch_count = 0\n",
-    "        \n",
-    "        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n",
-    "            test_batch_acc_total += sess.run(\n",
-    "                loaded_acc,\n",
-    "                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})\n",
-    "            test_batch_count += 1\n",
-    "\n",
-    "        print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
-    "\n",
-    "        # Print Random Samples\n",
-    "        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
-    "        random_test_predictions = sess.run(\n",
-    "            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n",
-    "            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n",
-    "        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
-    "\n",
-    "\n",
-    "test_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 为何准确率只有50-80%？\n",
-    "\n",
-    "你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率[远远超过 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130)。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。\n",
-    "\n",
-    "## 提交项目\n",
-    "\n",
-    "提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 \"File\" -> \"Download as\" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。\n"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python [default]",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

From 3978f5581fcaf9835bb1c48dbb31e182fd651f51 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 9 Jul 2017 20:29:30 +0800
Subject: [PATCH 10/16] Add files via upload

---
 .../dlnd_image_classification.html            | 19527 ++++++++++++++++
 .../dlnd_image_classification.ipynb           |  1107 +
 2 files changed, 20634 insertions(+)
 create mode 100644 image-classification/dlnd_image_classification.html
 create mode 100644 image-classification/dlnd_image_classification.ipynb

diff --git a/image-classification/dlnd_image_classification.html b/image-classification/dlnd_image_classification.html
new file mode 100644
index 0000000..87fc309
--- /dev/null
+++ b/image-classification/dlnd_image_classification.html
@@ -0,0 +1,19527 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>dlnd_image_classification</title>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.6 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: 1px dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    background: transparent !important;
+    color: #000 !important;
+    box-shadow: none !important;
+    text-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: 'Glyphicons Halflings';
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Glyphicons Halflings';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+  content: "\e156";
+}
+.glyphicon-unchecked:before {
+  content: "\e157";
+}
+.glyphicon-expand:before {
+  content: "\e158";
+}
+.glyphicon-collapse-down:before {
+  content: "\e159";
+}
+.glyphicon-collapse-up:before {
+  content: "\e160";
+}
+.glyphicon-log-in:before {
+  content: "\e161";
+}
+.glyphicon-flash:before {
+  content: "\e162";
+}
+.glyphicon-log-out:before {
+  content: "\e163";
+}
+.glyphicon-new-window:before {
+  content: "\e164";
+}
+.glyphicon-record:before {
+  content: "\e165";
+}
+.glyphicon-save:before {
+  content: "\e166";
+}
+.glyphicon-open:before {
+  content: "\e167";
+}
+.glyphicon-saved:before {
+  content: "\e168";
+}
+.glyphicon-import:before {
+  content: "\e169";
+}
+.glyphicon-export:before {
+  content: "\e170";
+}
+.glyphicon-send:before {
+  content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+  content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+  content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+  content: "\e174";
+}
+.glyphicon-floppy-save:before {
+  content: "\e175";
+}
+.glyphicon-floppy-open:before {
+  content: "\e176";
+}
+.glyphicon-credit-card:before {
+  content: "\e177";
+}
+.glyphicon-transfer:before {
+  content: "\e178";
+}
+.glyphicon-cutlery:before {
+  content: "\e179";
+}
+.glyphicon-header:before {
+  content: "\e180";
+}
+.glyphicon-compressed:before {
+  content: "\e181";
+}
+.glyphicon-earphone:before {
+  content: "\e182";
+}
+.glyphicon-phone-alt:before {
+  content: "\e183";
+}
+.glyphicon-tower:before {
+  content: "\e184";
+}
+.glyphicon-stats:before {
+  content: "\e185";
+}
+.glyphicon-sd-video:before {
+  content: "\e186";
+}
+.glyphicon-hd-video:before {
+  content: "\e187";
+}
+.glyphicon-subtitles:before {
+  content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+  content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+  content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+  content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+  content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+  content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
+.glyphicon-pawn:before {
+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
+.glyphicon-bitcoin:before {
+  content: "\e227";
+}
+.glyphicon-btc:before {
+  content: "\e227";
+}
+.glyphicon-xbt:before {
+  content: "\e227";
+}
+.glyphicon-yen:before {
+  content: "\00a5";
+}
+.glyphicon-jpy:before {
+  content: "\00a5";
+}
+.glyphicon-ruble:before {
+  content: "\20bd";
+}
+.glyphicon-rub:before {
+  content: "\20bd";
+}
+.glyphicon-scale:before {
+  content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+  content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+  content: "\e232";
+}
+.glyphicon-education:before {
+  content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+  content: "\e234";
+}
+.glyphicon-option-vertical:before {
+  content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+  content: "\e236";
+}
+.glyphicon-modal-window:before {
+  content: "\e237";
+}
+.glyphicon-oil:before {
+  content: "\e238";
+}
+.glyphicon-grain:before {
+  content: "\e239";
+}
+.glyphicon-sunglasses:before {
+  content: "\e240";
+}
+.glyphicon-text-size:before {
+  content: "\e241";
+}
+.glyphicon-text-color:before {
+  content: "\e242";
+}
+.glyphicon-text-background:before {
+  content: "\e243";
+}
+.glyphicon-object-align-top:before {
+  content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+  content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+  content: "\e246";
+}
+.glyphicon-object-align-left:before {
+  content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+  content: "\e248";
+}
+.glyphicon-object-align-right:before {
+  content: "\e249";
+}
+.glyphicon-triangle-right:before {
+  content: "\e250";
+}
+.glyphicon-triangle-left:before {
+  content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+  content: "\e252";
+}
+.glyphicon-triangle-top:before {
+  content: "\e253";
+}
+.glyphicon-console:before {
+  content: "\e254";
+}
+.glyphicon-superscript:before {
+  content: "\e255";
+}
+.glyphicon-subscript:before {
+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
+h5 small,
+h6 small,
+.h1 small,
+.h2 small,
+.h3 small,
+.h4 small,
+.h5 small,
+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 18px;
+  margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
+h2,
+.h2 {
+  font-size: 27px;
+}
+h3,
+.h3 {
+  font-size: 23px;
+}
+h4,
+.h4 {
+  font-size: 17px;
+}
+h5,
+.h5 {
+  font-size: 13px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
+address {
+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
+.col-xs-9 {
+  width: 75%;
+}
+.col-xs-8 {
+  width: 66.66666667%;
+}
+.col-xs-7 {
+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: thin dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+  content: "\f044";
+}
+.fa-share-square-o:before {
+  content: "\f045";
+}
+.fa-check-square-o:before {
+  content: "\f046";
+}
+.fa-arrows:before {
+  content: "\f047";
+}
+.fa-step-backward:before {
+  content: "\f048";
+}
+.fa-fast-backward:before {
+  content: "\f049";
+}
+.fa-backward:before {
+  content: "\f04a";
+}
+.fa-play:before {
+  content: "\f04b";
+}
+.fa-pause:before {
+  content: "\f04c";
+}
+.fa-stop:before {
+  content: "\f04d";
+}
+.fa-forward:before {
+  content: "\f04e";
+}
+.fa-fast-forward:before {
+  content: "\f050";
+}
+.fa-step-forward:before {
+  content: "\f051";
+}
+.fa-eject:before {
+  content: "\f052";
+}
+.fa-chevron-left:before {
+  content: "\f053";
+}
+.fa-chevron-right:before {
+  content: "\f054";
+}
+.fa-plus-circle:before {
+  content: "\f055";
+}
+.fa-minus-circle:before {
+  content: "\f056";
+}
+.fa-times-circle:before {
+  content: "\f057";
+}
+.fa-check-circle:before {
+  content: "\f058";
+}
+.fa-question-circle:before {
+  content: "\f059";
+}
+.fa-info-circle:before {
+  content: "\f05a";
+}
+.fa-crosshairs:before {
+  content: "\f05b";
+}
+.fa-times-circle-o:before {
+  content: "\f05c";
+}
+.fa-check-circle-o:before {
+  content: "\f05d";
+}
+.fa-ban:before {
+  content: "\f05e";
+}
+.fa-arrow-left:before {
+  content: "\f060";
+}
+.fa-arrow-right:before {
+  content: "\f061";
+}
+.fa-arrow-up:before {
+  content: "\f062";
+}
+.fa-arrow-down:before {
+  content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+  content: "\f064";
+}
+.fa-expand:before {
+  content: "\f065";
+}
+.fa-compress:before {
+  content: "\f066";
+}
+.fa-plus:before {
+  content: "\f067";
+}
+.fa-minus:before {
+  content: "\f068";
+}
+.fa-asterisk:before {
+  content: "\f069";
+}
+.fa-exclamation-circle:before {
+  content: "\f06a";
+}
+.fa-gift:before {
+  content: "\f06b";
+}
+.fa-leaf:before {
+  content: "\f06c";
+}
+.fa-fire:before {
+  content: "\f06d";
+}
+.fa-eye:before {
+  content: "\f06e";
+}
+.fa-eye-slash:before {
+  content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+  content: "\f071";
+}
+.fa-plane:before {
+  content: "\f072";
+}
+.fa-calendar:before {
+  content: "\f073";
+}
+.fa-random:before {
+  content: "\f074";
+}
+.fa-comment:before {
+  content: "\f075";
+}
+.fa-magnet:before {
+  content: "\f076";
+}
+.fa-chevron-up:before {
+  content: "\f077";
+}
+.fa-chevron-down:before {
+  content: "\f078";
+}
+.fa-retweet:before {
+  content: "\f079";
+}
+.fa-shopping-cart:before {
+  content: "\f07a";
+}
+.fa-folder:before {
+  content: "\f07b";
+}
+.fa-folder-open:before {
+  content: "\f07c";
+}
+.fa-arrows-v:before {
+  content: "\f07d";
+}
+.fa-arrows-h:before {
+  content: "\f07e";
+}
+.fa-bar-chart-o:before,
+.fa-bar-chart:before {
+  content: "\f080";
+}
+.fa-twitter-square:before {
+  content: "\f081";
+}
+.fa-facebook-square:before {
+  content: "\f082";
+}
+.fa-camera-retro:before {
+  content: "\f083";
+}
+.fa-key:before {
+  content: "\f084";
+}
+.fa-gears:before,
+.fa-cogs:before {
+  content: "\f085";
+}
+.fa-comments:before {
+  content: "\f086";
+}
+.fa-thumbs-o-up:before {
+  content: "\f087";
+}
+.fa-thumbs-o-down:before {
+  content: "\f088";
+}
+.fa-star-half:before {
+  content: "\f089";
+}
+.fa-heart-o:before {
+  content: "\f08a";
+}
+.fa-sign-out:before {
+  content: "\f08b";
+}
+.fa-linkedin-square:before {
+  content: "\f08c";
+}
+.fa-thumb-tack:before {
+  content: "\f08d";
+}
+.fa-external-link:before {
+  content: "\f08e";
+}
+.fa-sign-in:before {
+  content: "\f090";
+}
+.fa-trophy:before {
+  content: "\f091";
+}
+.fa-github-square:before {
+  content: "\f092";
+}
+.fa-upload:before {
+  content: "\f093";
+}
+.fa-lemon-o:before {
+  content: "\f094";
+}
+.fa-phone:before {
+  content: "\f095";
+}
+.fa-square-o:before {
+  content: "\f096";
+}
+.fa-bookmark-o:before {
+  content: "\f097";
+}
+.fa-phone-square:before {
+  content: "\f098";
+}
+.fa-twitter:before {
+  content: "\f099";
+}
+.fa-facebook:before {
+  content: "\f09a";
+}
+.fa-github:before {
+  content: "\f09b";
+}
+.fa-unlock:before {
+  content: "\f09c";
+}
+.fa-credit-card:before {
+  content: "\f09d";
+}
+.fa-rss:before {
+  content: "\f09e";
+}
+.fa-hdd-o:before {
+  content: "\f0a0";
+}
+.fa-bullhorn:before {
+  content: "\f0a1";
+}
+.fa-bell:before {
+  content: "\f0f3";
+}
+.fa-certificate:before {
+  content: "\f0a3";
+}
+.fa-hand-o-right:before {
+  content: "\f0a4";
+}
+.fa-hand-o-left:before {
+  content: "\f0a5";
+}
+.fa-hand-o-up:before {
+  content: "\f0a6";
+}
+.fa-hand-o-down:before {
+  content: "\f0a7";
+}
+.fa-arrow-circle-left:before {
+  content: "\f0a8";
+}
+.fa-arrow-circle-right:before {
+  content: "\f0a9";
+}
+.fa-arrow-circle-up:before {
+  content: "\f0aa";
+}
+.fa-arrow-circle-down:before {
+  content: "\f0ab";
+}
+.fa-globe:before {
+  content: "\f0ac";
+}
+.fa-wrench:before {
+  content: "\f0ad";
+}
+.fa-tasks:before {
+  content: "\f0ae";
+}
+.fa-filter:before {
+  content: "\f0b0";
+}
+.fa-briefcase:before {
+  content: "\f0b1";
+}
+.fa-arrows-alt:before {
+  content: "\f0b2";
+}
+.fa-group:before,
+.fa-users:before {
+  content: "\f0c0";
+}
+.fa-chain:before,
+.fa-link:before {
+  content: "\f0c1";
+}
+.fa-cloud:before {
+  content: "\f0c2";
+}
+.fa-flask:before {
+  content: "\f0c3";
+}
+.fa-cut:before,
+.fa-scissors:before {
+  content: "\f0c4";
+}
+.fa-copy:before,
+.fa-files-o:before {
+  content: "\f0c5";
+}
+.fa-paperclip:before {
+  content: "\f0c6";
+}
+.fa-save:before,
+.fa-floppy-o:before {
+  content: "\f0c7";
+}
+.fa-square:before {
+  content: "\f0c8";
+}
+.fa-navicon:before,
+.fa-reorder:before,
+.fa-bars:before {
+  content: "\f0c9";
+}
+.fa-list-ul:before {
+  content: "\f0ca";
+}
+.fa-list-ol:before {
+  content: "\f0cb";
+}
+.fa-strikethrough:before {
+  content: "\f0cc";
+}
+.fa-underline:before {
+  content: "\f0cd";
+}
+.fa-table:before {
+  content: "\f0ce";
+}
+.fa-magic:before {
+  content: "\f0d0";
+}
+.fa-truck:before {
+  content: "\f0d1";
+}
+.fa-pinterest:before {
+  content: "\f0d2";
+}
+.fa-pinterest-square:before {
+  content: "\f0d3";
+}
+.fa-google-plus-square:before {
+  content: "\f0d4";
+}
+.fa-google-plus:before {
+  content: "\f0d5";
+}
+.fa-money:before {
+  content: "\f0d6";
+}
+.fa-caret-down:before {
+  content: "\f0d7";
+}
+.fa-caret-up:before {
+  content: "\f0d8";
+}
+.fa-caret-left:before {
+  content: "\f0d9";
+}
+.fa-caret-right:before {
+  content: "\f0da";
+}
+.fa-columns:before {
+  content: "\f0db";
+}
+.fa-unsorted:before,
+.fa-sort:before {
+  content: "\f0dc";
+}
+.fa-sort-down:before,
+.fa-sort-desc:before {
+  content: "\f0dd";
+}
+.fa-sort-up:before,
+.fa-sort-asc:before {
+  content: "\f0de";
+}
+.fa-envelope:before {
+  content: "\f0e0";
+}
+.fa-linkedin:before {
+  content: "\f0e1";
+}
+.fa-rotate-left:before,
+.fa-undo:before {
+  content: "\f0e2";
+}
+.fa-legal:before,
+.fa-gavel:before {
+  content: "\f0e3";
+}
+.fa-dashboard:before,
+.fa-tachometer:before {
+  content: "\f0e4";
+}
+.fa-comment-o:before {
+  content: "\f0e5";
+}
+.fa-comments-o:before {
+  content: "\f0e6";
+}
+.fa-flash:before,
+.fa-bolt:before {
+  content: "\f0e7";
+}
+.fa-sitemap:before {
+  content: "\f0e8";
+}
+.fa-umbrella:before {
+  content: "\f0e9";
+}
+.fa-paste:before,
+.fa-clipboard:before {
+  content: "\f0ea";
+}
+.fa-lightbulb-o:before {
+  content: "\f0eb";
+}
+.fa-exchange:before {
+  content: "\f0ec";
+}
+.fa-cloud-download:before {
+  content: "\f0ed";
+}
+.fa-cloud-upload:before {
+  content: "\f0ee";
+}
+.fa-user-md:before {
+  content: "\f0f0";
+}
+.fa-stethoscope:before {
+  content: "\f0f1";
+}
+.fa-suitcase:before {
+  content: "\f0f2";
+}
+.fa-bell-o:before {
+  content: "\f0a2";
+}
+.fa-coffee:before {
+  content: "\f0f4";
+}
+.fa-cutlery:before {
+  content: "\f0f5";
+}
+.fa-file-text-o:before {
+  content: "\f0f6";
+}
+.fa-building-o:before {
+  content: "\f0f7";
+}
+.fa-hospital-o:before {
+  content: "\f0f8";
+}
+.fa-ambulance:before {
+  content: "\f0f9";
+}
+.fa-medkit:before {
+  content: "\f0fa";
+}
+.fa-fighter-jet:before {
+  content: "\f0fb";
+}
+.fa-beer:before {
+  content: "\f0fc";
+}
+.fa-h-square:before {
+  content: "\f0fd";
+}
+.fa-plus-square:before {
+  content: "\f0fe";
+}
+.fa-angle-double-left:before {
+  content: "\f100";
+}
+.fa-angle-double-right:before {
+  content: "\f101";
+}
+.fa-angle-double-up:before {
+  content: "\f102";
+}
+.fa-angle-double-down:before {
+  content: "\f103";
+}
+.fa-angle-left:before {
+  content: "\f104";
+}
+.fa-angle-right:before {
+  content: "\f105";
+}
+.fa-angle-up:before {
+  content: "\f106";
+}
+.fa-angle-down:before {
+  content: "\f107";
+}
+.fa-desktop:before {
+  content: "\f108";
+}
+.fa-laptop:before {
+  content: "\f109";
+}
+.fa-tablet:before {
+  content: "\f10a";
+}
+.fa-mobile-phone:before,
+.fa-mobile:before {
+  content: "\f10b";
+}
+.fa-circle-o:before {
+  content: "\f10c";
+}
+.fa-quote-left:before {
+  content: "\f10d";
+}
+.fa-quote-right:before {
+  content: "\f10e";
+}
+.fa-spinner:before {
+  content: "\f110";
+}
+.fa-circle:before {
+  content: "\f111";
+}
+.fa-mail-reply:before,
+.fa-reply:before {
+  content: "\f112";
+}
+.fa-github-alt:before {
+  content: "\f113";
+}
+.fa-folder-o:before {
+  content: "\f114";
+}
+.fa-folder-open-o:before {
+  content: "\f115";
+}
+.fa-smile-o:before {
+  content: "\f118";
+}
+.fa-frown-o:before {
+  content: "\f119";
+}
+.fa-meh-o:before {
+  content: "\f11a";
+}
+.fa-gamepad:before {
+  content: "\f11b";
+}
+.fa-keyboard-o:before {
+  content: "\f11c";
+}
+.fa-flag-o:before {
+  content: "\f11d";
+}
+.fa-flag-checkered:before {
+  content: "\f11e";
+}
+.fa-terminal:before {
+  content: "\f120";
+}
+.fa-code:before {
+  content: "\f121";
+}
+.fa-mail-reply-all:before,
+.fa-reply-all:before {
+  content: "\f122";
+}
+.fa-star-half-empty:before,
+.fa-star-half-full:before,
+.fa-star-half-o:before {
+  content: "\f123";
+}
+.fa-location-arrow:before {
+  content: "\f124";
+}
+.fa-crop:before {
+  content: "\f125";
+}
+.fa-code-fork:before {
+  content: "\f126";
+}
+.fa-unlink:before,
+.fa-chain-broken:before {
+  content: "\f127";
+}
+.fa-question:before {
+  content: "\f128";
+}
+.fa-info:before {
+  content: "\f129";
+}
+.fa-exclamation:before {
+  content: "\f12a";
+}
+.fa-superscript:before {
+  content: "\f12b";
+}
+.fa-subscript:before {
+  content: "\f12c";
+}
+.fa-eraser:before {
+  content: "\f12d";
+}
+.fa-puzzle-piece:before {
+  content: "\f12e";
+}
+.fa-microphone:before {
+  content: "\f130";
+}
+.fa-microphone-slash:before {
+  content: "\f131";
+}
+.fa-shield:before {
+  content: "\f132";
+}
+.fa-calendar-o:before {
+  content: "\f133";
+}
+.fa-fire-extinguisher:before {
+  content: "\f134";
+}
+.fa-rocket:before {
+  content: "\f135";
+}
+.fa-maxcdn:before {
+  content: "\f136";
+}
+.fa-chevron-circle-left:before {
+  content: "\f137";
+}
+.fa-chevron-circle-right:before {
+  content: "\f138";
+}
+.fa-chevron-circle-up:before {
+  content: "\f139";
+}
+.fa-chevron-circle-down:before {
+  content: "\f13a";
+}
+.fa-html5:before {
+  content: "\f13b";
+}
+.fa-css3:before {
+  content: "\f13c";
+}
+.fa-anchor:before {
+  content: "\f13d";
+}
+.fa-unlock-alt:before {
+  content: "\f13e";
+}
+.fa-bullseye:before {
+  content: "\f140";
+}
+.fa-ellipsis-h:before {
+  content: "\f141";
+}
+.fa-ellipsis-v:before {
+  content: "\f142";
+}
+.fa-rss-square:before {
+  content: "\f143";
+}
+.fa-play-circle:before {
+  content: "\f144";
+}
+.fa-ticket:before {
+  content: "\f145";
+}
+.fa-minus-square:before {
+  content: "\f146";
+}
+.fa-minus-square-o:before {
+  content: "\f147";
+}
+.fa-level-up:before {
+  content: "\f148";
+}
+.fa-level-down:before {
+  content: "\f149";
+}
+.fa-check-square:before {
+  content: "\f14a";
+}
+.fa-pencil-square:before {
+  content: "\f14b";
+}
+.fa-external-link-square:before {
+  content: "\f14c";
+}
+.fa-share-square:before {
+  content: "\f14d";
+}
+.fa-compass:before {
+  content: "\f14e";
+}
+.fa-toggle-down:before,
+.fa-caret-square-o-down:before {
+  content: "\f150";
+}
+.fa-toggle-up:before,
+.fa-caret-square-o-up:before {
+  content: "\f151";
+}
+.fa-toggle-right:before,
+.fa-caret-square-o-right:before {
+  content: "\f152";
+}
+.fa-euro:before,
+.fa-eur:before {
+  content: "\f153";
+}
+.fa-gbp:before {
+  content: "\f154";
+}
+.fa-dollar:before,
+.fa-usd:before {
+  content: "\f155";
+}
+.fa-rupee:before,
+.fa-inr:before {
+  content: "\f156";
+}
+.fa-cny:before,
+.fa-rmb:before,
+.fa-yen:before,
+.fa-jpy:before {
+  content: "\f157";
+}
+.fa-ruble:before,
+.fa-rouble:before,
+.fa-rub:before {
+  content: "\f158";
+}
+.fa-won:before,
+.fa-krw:before {
+  content: "\f159";
+}
+.fa-bitcoin:before,
+.fa-btc:before {
+  content: "\f15a";
+}
+.fa-file:before {
+  content: "\f15b";
+}
+.fa-file-text:before {
+  content: "\f15c";
+}
+.fa-sort-alpha-asc:before {
+  content: "\f15d";
+}
+.fa-sort-alpha-desc:before {
+  content: "\f15e";
+}
+.fa-sort-amount-asc:before {
+  content: "\f160";
+}
+.fa-sort-amount-desc:before {
+  content: "\f161";
+}
+.fa-sort-numeric-asc:before {
+  content: "\f162";
+}
+.fa-sort-numeric-desc:before {
+  content: "\f163";
+}
+.fa-thumbs-up:before {
+  content: "\f164";
+}
+.fa-thumbs-down:before {
+  content: "\f165";
+}
+.fa-youtube-square:before {
+  content: "\f166";
+}
+.fa-youtube:before {
+  content: "\f167";
+}
+.fa-xing:before {
+  content: "\f168";
+}
+.fa-xing-square:before {
+  content: "\f169";
+}
+.fa-youtube-play:before {
+  content: "\f16a";
+}
+.fa-dropbox:before {
+  content: "\f16b";
+}
+.fa-stack-overflow:before {
+  content: "\f16c";
+}
+.fa-instagram:before {
+  content: "\f16d";
+}
+.fa-flickr:before {
+  content: "\f16e";
+}
+.fa-adn:before {
+  content: "\f170";
+}
+.fa-bitbucket:before {
+  content: "\f171";
+}
+.fa-bitbucket-square:before {
+  content: "\f172";
+}
+.fa-tumblr:before {
+  content: "\f173";
+}
+.fa-tumblr-square:before {
+  content: "\f174";
+}
+.fa-long-arrow-down:before {
+  content: "\f175";
+}
+.fa-long-arrow-up:before {
+  content: "\f176";
+}
+.fa-long-arrow-left:before {
+  content: "\f177";
+}
+.fa-long-arrow-right:before {
+  content: "\f178";
+}
+.fa-apple:before {
+  content: "\f179";
+}
+.fa-windows:before {
+  content: "\f17a";
+}
+.fa-android:before {
+  content: "\f17b";
+}
+.fa-linux:before {
+  content: "\f17c";
+}
+.fa-dribbble:before {
+  content: "\f17d";
+}
+.fa-skype:before {
+  content: "\f17e";
+}
+.fa-foursquare:before {
+  content: "\f180";
+}
+.fa-trello:before {
+  content: "\f181";
+}
+.fa-female:before {
+  content: "\f182";
+}
+.fa-male:before {
+  content: "\f183";
+}
+.fa-gittip:before {
+  content: "\f184";
+}
+.fa-sun-o:before {
+  content: "\f185";
+}
+.fa-moon-o:before {
+  content: "\f186";
+}
+.fa-archive:before {
+  content: "\f187";
+}
+.fa-bug:before {
+  content: "\f188";
+}
+.fa-vk:before {
+  content: "\f189";
+}
+.fa-weibo:before {
+  content: "\f18a";
+}
+.fa-renren:before {
+  content: "\f18b";
+}
+.fa-pagelines:before {
+  content: "\f18c";
+}
+.fa-stack-exchange:before {
+  content: "\f18d";
+}
+.fa-arrow-circle-o-right:before {
+  content: "\f18e";
+}
+.fa-arrow-circle-o-left:before {
+  content: "\f190";
+}
+.fa-toggle-left:before,
+.fa-caret-square-o-left:before {
+  content: "\f191";
+}
+.fa-dot-circle-o:before {
+  content: "\f192";
+}
+.fa-wheelchair:before {
+  content: "\f193";
+}
+.fa-vimeo-square:before {
+  content: "\f194";
+}
+.fa-turkish-lira:before,
+.fa-try:before {
+  content: "\f195";
+}
+.fa-plus-square-o:before {
+  content: "\f196";
+}
+.fa-space-shuttle:before {
+  content: "\f197";
+}
+.fa-slack:before {
+  content: "\f198";
+}
+.fa-envelope-square:before {
+  content: "\f199";
+}
+.fa-wordpress:before {
+  content: "\f19a";
+}
+.fa-openid:before {
+  content: "\f19b";
+}
+.fa-institution:before,
+.fa-bank:before,
+.fa-university:before {
+  content: "\f19c";
+}
+.fa-mortar-board:before,
+.fa-graduation-cap:before {
+  content: "\f19d";
+}
+.fa-yahoo:before {
+  content: "\f19e";
+}
+.fa-google:before {
+  content: "\f1a0";
+}
+.fa-reddit:before {
+  content: "\f1a1";
+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+ul#tabs {
+  margin-bottom: 4px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+@-moz-document url-prefix() {
+  div.inner_cell {
+    overflow-x: hidden;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 20ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
+    display: none;
+  }
+  span.autosave_status {
+    font-size: x-small;
+  }
+}
+.toolbar {
+  padding: 0px;
+  margin-left: -5px;
+  margin-top: 2px;
+  margin-bottom: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+  width: auto;
+  vertical-align: middle;
+  margin-right: 2px;
+  margin-bottom: 0px;
+  display: inline;
+  font-size: 92%;
+  margin-left: 0.3em;
+  margin-right: 0.3em;
+  padding: 0px;
+  padding-top: 3px;
+}
+.toolbar .btn {
+  padding: 2px 8px;
+}
+.toolbar .btn-group {
+  margin-top: 0px;
+  margin-left: 5px;
+}
+#maintoolbar {
+  margin-bottom: -3px;
+  margin-top: -8px;
+  border: 0px;
+  min-height: 27px;
+  margin-left: 0px;
+  padding-top: 11px;
+  padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+  float: none;
+  vertical-align: middle;
+  text-align: right;
+  margin-left: 5px;
+  margin-right: 0px;
+  margin-top: 0px;
+}
+.select-xs {
+  height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+  background-color: #F37626;
+  color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-webkit-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-moz-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+@-webkit-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+  overflow: auto;
+  height: 200px;
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+  text-overflow: ellipsis;
+  overflow: hidden;
+  height: 80px;
+}
+.tooltipbuttons {
+  position: absolute;
+  padding-right: 15px;
+  top: 0px;
+  right: 0px;
+}
+.tooltiptext {
+  /*avoid the button to overlap on some docstring*/
+  padding-right: 30px;
+}
+.ipython_tooltip {
+  max-width: 700px;
+  /*fade-in animation when inserted*/
+  -webkit-animation: fadeOut 400ms;
+  -moz-animation: fadeOut 400ms;
+  animation: fadeOut 400ms;
+  -webkit-animation: fadeIn 400ms;
+  -moz-animation: fadeIn 400ms;
+  animation: fadeIn 400ms;
+  vertical-align: middle;
+  background-color: #f7f7f7;
+  overflow: visible;
+  border: #ababab 1px solid;
+  outline: none;
+  padding: 3px;
+  margin: 0px;
+  padding-left: 7px;
+  font-family: monospace;
+  min-height: 50px;
+  -moz-box-shadow: 0px 6px 10px -1px #adadad;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  border-radius: 2px;
+  position: absolute;
+  z-index: 1000;
+}
+.ipython_tooltip a {
+  float: right;
+}
+.ipython_tooltip .tooltiptext pre {
+  border: 0;
+  border-radius: 0;
+  font-size: 100%;
+  background-color: #f7f7f7;
+}
+.pretooltiparrow {
+  left: 0px;
+  margin: 0px;
+  top: -16px;
+  width: 40px;
+  height: 16px;
+  overflow: hidden;
+  position: absolute;
+}
+.pretooltiparrow:before {
+  background-color: #f7f7f7;
+  border: 1px #ababab solid;
+  z-index: 11;
+  content: "";
+  position: absolute;
+  left: 15px;
+  top: 10px;
+  width: 25px;
+  height: 25px;
+  -webkit-transform: rotate(45deg);
+  -moz-transform: rotate(45deg);
+  -ms-transform: rotate(45deg);
+  -o-transform: rotate(45deg);
+}
+ul.typeahead-list i {
+  margin-left: -10px;
+  width: 18px;
+}
+ul.typeahead-list {
+  max-height: 80vh;
+  overflow: auto;
+}
+ul.typeahead-list > li > a {
+  /** Firefox bug **/
+  /* see https://github.com/jupyter/notebook/issues/559 */
+  white-space: normal;
+}
+.cmd-palette .modal-body {
+  padding: 7px;
+}
+.cmd-palette form {
+  background: white;
+}
+.cmd-palette input {
+  outline: none;
+}
+.no-shortcut {
+  display: none;
+}
+.command-shortcut:before {
+  content: "(command)";
+  padding-right: 3px;
+  color: #777777;
+}
+.edit-shortcut:before {
+  content: "(edit)";
+  padding-right: 3px;
+  color: #777777;
+}
+#find-and-replace #replace-preview .match,
+#find-and-replace #replace-preview .insert {
+  background-color: #BBDEFB;
+  border-color: #90CAF9;
+  border-style: solid;
+  border-width: 1px;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .match {
+  background-color: #FFCDD2;
+  border-color: #EF9A9A;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .insert {
+  background-color: #C8E6C9;
+  border-color: #A5D6A7;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview {
+  max-height: 60vh;
+  overflow: auto;
+}
+#find-and-replace #replace-preview pre {
+  padding: 5px 10px;
+}
+.terminal-app {
+  background: #EEE;
+}
+.terminal-app #header {
+  background: #fff;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.terminal-app .terminal {
+  float: left;
+  font-family: monospace;
+  color: white;
+  background: black;
+  padding: 0.4em;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+}
+.terminal-app .terminal,
+.terminal-app .terminal dummy-screen {
+  line-height: 1em;
+  font-size: 14px;
+}
+.terminal-app .terminal-cursor {
+  color: black;
+  background: white;
+}
+.terminal-app #terminado-container {
+  margin-top: 20px;
+}
+/*# sourceMappingURL=style.min.css.map */
+    </style>
+<style type="text/css">
+    .highlight .hll { background-color: #ffffcc }
+.highlight  { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #7D9029 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #A0A000 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+    </style>
+<style type="text/css">
+    
+/* Temporary definitions which will become obsolete with Notebook release 5.0 */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-bold { font-weight: bold; }
+
+    </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+  overflow: visible;
+  padding: 8px;
+}
+
+div#notebook {
+  overflow: visible;
+  border-top: none;
+}
+
+@media print {
+  div.cell {
+    display: block;
+    page-break-inside: avoid;
+  } 
+  div.output_wrapper { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+  div.output { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+    <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    MathJax.Hub.Config({
+        tex2jax: {
+            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+            processEscapes: true,
+            processEnvironments: true
+        },
+        // Center justify equations in code and markdown cells. Elsewhere
+        // we use CSS to left justify single line equations in code cells.
+        displayAlign: 'center',
+        "HTML-CSS": {
+            styles: {'.MathJax_Display': {"margin": 0}},
+            linebreaks: { automatic: true }
+        }
+    });
+    </script>
+    <!-- End of mathjax configuration --></head>
+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#22270;&#20687;&#20998;&#31867;">&#22270;&#20687;&#20998;&#31867;<a class="anchor-link" href="#&#22270;&#20687;&#20998;&#31867;">&#182;</a></h1><p>在此项目中，你将对 <a href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10 数据集</a> 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。</p>
+<h2 id="&#33719;&#21462;&#25968;&#25454;">&#33719;&#21462;&#25968;&#25454;<a class="anchor-link" href="#&#33719;&#21462;&#25968;&#25454;">&#182;</a></h2><p>请运行以下单元，以下载 <a href="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz">CIFAR-10 数据集（Python版）</a>。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">urllib.request</span> <span class="k">import</span> <span class="n">urlretrieve</span>
+<span class="kn">from</span> <span class="nn">os.path</span> <span class="k">import</span> <span class="n">isfile</span><span class="p">,</span> <span class="n">isdir</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+<span class="kn">import</span> <span class="nn">tarfile</span>
+
+<span class="n">cifar10_dataset_folder_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-batches-py&#39;</span>
+
+<span class="c1"># Use Floyd&#39;s cifar-10 dataset if present</span>
+<span class="n">floyd_cifar10_location</span> <span class="o">=</span> <span class="s1">&#39;/input/cifar-10/python.tar.gz&#39;</span>
+<span class="k">if</span> <span class="n">isfile</span><span class="p">(</span><span class="n">floyd_cifar10_location</span><span class="p">):</span>
+    <span class="n">tar_gz_path</span> <span class="o">=</span> <span class="n">floyd_cifar10_location</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">tar_gz_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-python.tar.gz&#39;</span>
+
+<span class="k">class</span> <span class="nc">DLProgress</span><span class="p">(</span><span class="n">tqdm</span><span class="p">):</span>
+    <span class="n">last_block</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="k">def</span> <span class="nf">hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block_num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">block_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">total_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">total</span> <span class="o">=</span> <span class="n">total_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">block_num</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span><span class="p">)</span> <span class="o">*</span> <span class="n">block_size</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span> <span class="o">=</span> <span class="n">block_num</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">isfile</span><span class="p">(</span><span class="n">tar_gz_path</span><span class="p">):</span>
+    <span class="k">with</span> <span class="n">DLProgress</span><span class="p">(</span><span class="n">unit</span><span class="o">=</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">unit_scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">miniters</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;CIFAR-10 Dataset&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pbar</span><span class="p">:</span>
+        <span class="n">urlretrieve</span><span class="p">(</span>
+            <span class="s1">&#39;https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz&#39;</span><span class="p">,</span>
+            <span class="n">tar_gz_path</span><span class="p">,</span>
+            <span class="n">pbar</span><span class="o">.</span><span class="n">hook</span><span class="p">)</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">isdir</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">):</span>
+    <span class="k">with</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">tar_gz_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">tar</span><span class="p">:</span>
+        <span class="n">tar</span><span class="o">.</span><span class="n">extractall</span><span class="p">()</span>
+        <span class="n">tar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+
+<span class="n">tests</span><span class="o">.</span><span class="n">test_folder_path</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>All files found!
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#25506;&#32034;&#25968;&#25454;">&#25506;&#32034;&#25968;&#25454;<a class="anchor-link" href="#&#25506;&#32034;&#25968;&#25454;">&#182;</a></h2><p>该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 <code>data_batch_1</code>、<code>data_batch_2</code>，以此类推。每个部分都包含以下某个类别的标签和图片：</p>
+<ul>
+<li>飞机</li>
+<li>汽车</li>
+<li>鸟类</li>
+<li>猫</li>
+<li>鹿</li>
+<li>狗</li>
+<li>青蛙</li>
+<li>马</li>
+<li>船只</li>
+<li>卡车</li>
+</ul>
+<p>了解数据集也是对数据进行预测的必经步骤。你可以通过更改 <code>batch_id</code> 和 <code>sample_id</code> 探索下面的代码单元。<code>batch_id</code> 是数据集一个部分的 ID（1 到 5）。<code>sample_id</code> 是该部分中图片和标签对（label pair）的 ID。</p>
+<p>问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
+
+<span class="kn">import</span> <span class="nn">helper</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># Explore the dataset</span>
+<span class="n">batch_id</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">sample_id</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">display_stats</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">batch_id</span><span class="p">,</span> <span class="n">sample_id</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Stats of batch 1:
+Samples: 10000
+Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}
+First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]
+
+Example of Image 5:
+Image - Min Value: 0 Max Value: 252
+Image - Shape: (32, 32, 3)
+Label - Label Id: 1 Name: automobile
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsIAAAK/CAYAAABjrqvGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAAewgAAHsIBbtB1PgAAIABJREFUeJzsvWu23LjOJbhBf2PtnlbPocdQa9Wg7iHqB/HYAKmIOE6n
+M2+ZsOOIL1F8gMAWBFGiqrh06dKlS5cuXbp06U+j8U834NKlS5cuXbp06dKlf4IuEL506dKlS5cu
+Xbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLhS5cuXbp0
+6dKlS38kXSB86dKlS5cuXbp06Y+kC4QvXbp06dKlS5cu/ZF0gfClS5cuXbp06dKlP5IuEL506dKl
+S5cuXbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLhS5cu
+Xbp06dKlS38kXSB86dKlS5cuXbp06Y+kC4QvXbp06dKlS5cu/ZF0gfClS5cuXbp06dKlP5IuEL50
+6dKlS5cuXbr0R9IFwpcuXbp06dKlS5f+SLpA+NKlS5cuXbp06dIfSRcIX7p06dKlS5cuXfoj6QLh
+S5cuXbp06dKlS38kXSB86dKlS5cuXbp06Y+k//mnG/Bvof/v//1/9JNyqgqs/4CqHQG1RLUE9do4
+z895dSUpB4tIz24FABFZ5YYfB8UHpff4qOdRngJQu7ZSvKbveTjEf2BVO0Twwy43APwQsXTgB1b4
+h5VbaRS2c1Qn9Gtizq84zjmhX+04vzC/7EhldU5MP+/rC//5mvb7st/Ef+bM8IfHrzmhxhNq8578
+YfEIJ/9oy4fnP7GIbCkv8og/fhEJX8iOAiHe9bBQkSyfUc+X7QcPjz3v1S/Oa03c4ytRKP9Udohg
+WBvGEIwxIu3pmGU9Lc/5nx8/8OPHD/z4MfDjxw+K/8D/WNqPHz/wP/+TZWq5EeGNXB5ZGMAWj7Qm
+hFQ1edbDwZ89jcIP5+TE57xkPPN8vjy8+IMnYcmQOaf9tBy/5sRUbXkW1nX8aud+fX3hP//5Wsev
+L4pP/OfrP/j6z4z0rex/6jlH6kvttPZOy7HL+aYLMluO+bQErbytA+T6fxWXrOYQPzRdfHbOXRXo
+WZ9h572Sc+BdrSeU+KZPtV2BZaq25MO5n9HHBQHEjGVYntO3tOOVcwwUdbxqXFPf8Dk0xqF/SDeF
+HmppfATlvR1HJR54U/ZTTfX//6///cuU2rUIX/rj6Hsi7FKnO37PdMfmQ7oDdenSTv9F6+K/qKlv
+6QLhS38c/Trb6J9Jd/ye6Y7Nh3QH6tKlnf6L1sV/UVPf0gXC/0L6exnsH2Lff82q6Q/zLn2Xfsf4
+/WvY5Zv0r+EtevT9r6R/zUBduvQvor9pXexy4K9Lhv+blvAFwv9C+nsZ7B9i33/NqvnXNOTSC7qz
+9Bep+1leunTpj6VdDlzJwHSB8KU/jv61VrL/Errj90x3bD6kO1CXLu30X7Qu/oua+pYuEP4X0nWN
++Dvpukb8VbquEc/0r+Gt6xpx6dJ/H13XiH+ELhD+F9J1jfg7Sf+94OC/hH7H+P1r2OWb9K/hrX+7
+a8S/ZqAuXfoX0d+0Lv4O14j/m5bwBcKX/jj614KD/xK64/dMd2w+pDtQly7t9F+0Lv6LmvqW7gc1
+jHTOz8rRn9x02qwvuVt1buJd8vYPbZQNswWVuzwrTqId03s5VQCDdtXmXcQnoKN2IMrNdd7007Ps
+2ix7tXJtxS0ZV10f01CBiuWJ5ZUjbNN8ACoQUUxZG7ar+Gb8FvbzuJnbbac87OpNO4CD0mk39dyg
+vQ9giz/c6tpW//bxhn6U+uEIqlZ943mVOqdlo/1Dflz4xYMtqann9IdzOeWbt/dS2iVUhQf2D2fU
+y7x+dP+4+TplPmYZb8KGuAxluMbottxqglgpxVT/kAvaB1PyOO28wem2lOacwBCIDkxViP2GKqYd
+Ve06EdZVl9J146MVyT4sj2LIDqx94v0iy+Br0fvEH9PoaZSO8wc4Yn5dBmiygirxi7VNhGSmf1TA
+q9GtA3Xm9JDGcuGYX4vWgVK8cqAKrm+8dWRm7+RHC2yVa0smruUp0vJ47fVz+aMa/rEGsfo8jFau
+fEyDz6f+7OtYSxebWKJ4HQtVpY/9BDN4Zu2861KxVdyKF/5h9nBZYG3p/FjWx2manlnhI+Iqt7kD
+tSMOxAcmxLiLsh03SdZq1/i7jmvMGVawrFTAdDu1SzNNmwr2vGig1Zlz8GIA+9L4i2P9s3SBsJFu
+2uOx5AaAEbKWgW79qlgFpvwFMS0LMunzFZnLZAIqUIylhSHGoQMwZYw5gDGBKSWu8DBWWQeqauBX
+TD3wUWtaYFT/spzlAbCvS9WjCDBNqMUX6kTXV+v8yL0uinH9lJFB+dHg98/kgOIP4yrl51/DQggl
+8X/9y2bH+dmvEGlNEJxFWmqS4xVKnhzzHs99KvzmhLjWQUFX4MuN4/SfIVOErh+Puee2agttJ7J2
+Qa7LCUlwOwHFxBwCu5tbJ4wRS6o0zNbDiIbOwitTdN0YDgbGS4EMRQPGBsbpF2x8GIAqzvhrliSz
+KJCY8QB2t6/JPYBgBsIx1ActZ0rSs9eSlsIXC+hrPbd1nL+ZVe5MCCxnt7OcRhlt5Ttp+QULSy/z
+wNCflqOynYUABk91jDKcALjm0Y271dPTPi7H7erzki0Aj6VUNFT65TpRhG45WBYWQ4EjLsm86Kzm
+JW29SamHLsxNoXgHzhttc6bH4KvztzH040GuP6WX+EsB6lzh5iuxo68b09W87mlog+NpaBcOaOq0
+laHCRT7FxMQ84jAvJoceRPTfTRcIG6l+ZhGGKYCKpVKZdCYoYLmU8z8SvFEv8+TLekpNrlIdcECc
+lt5Z8+ZS3p6nHqe8sNQ6SD0CYg6vdp3ySbpuQHgBAlusqmcwLEsoxl3n5BXcjj4pYQlqeS1+ugES
+Dokf2LKyQFAojBdA+LSuX6WdZUBTgKcSJbOVfHPuQ40fXKdXfrACR5GTKshzntuXI6LqAr7naqQ/
+6b1TCuvaWINKitHz5rpH9ILDlte0JQOsG0dxgExH4ThGgF8ZGtbhOWvcwW9aiStLB2s3nMAd2wBg
+DZ7L0Zr4KRBM6Z1E6vzFww9rQ7fqBQ/VjuWhXIPWNgnYBMosrPPH7aRb9yhfAIvVz2mdzamyMxWE
+VstnTlpIKyCqMsCfPnmZTK/nDuSTqpRX7QgQCH440hhkozTb8GLlHYeEq4HJYbtWCduCXE8/1twu
+8GxzpKQXDuCqgFwfZ44rjXNfR5/QG8Eq21/U+eKyVWRSeuWbjYu4Ay3k4+YtKGJDqvoEhddT37ze
+uZw01apRccE4flF/ehzxKJQ3wmWCfh9dIGz0qWvEKpyLEvD517qQCiCuLhGtYPyta+pphb1iErYE
+u5YWU0BpFS4W4zmsqFmMrZzKAwhugHhGGA8A2foiKC4Ec1S3Age/7IoR4Lr3+pUVuFt/1RUipRXF
++XpIt8eH4pZg7o9giGDKPoPaYic1saUp4rF3EaEPLCFboCY8JH+DDl+83yVxCOTannZm383gQfjn
+GjkVSAAnyvDkPL5PyrlfrgKSxS78EEVt2TgoHpPAMD1o4bZKiU98yYAMhUwtFuFh8WGAOF0mKmuz
+xSbajcbWPHylzF5YOQwQuMUzCI70szXYAeY26gJsejDATwPInibRMuqMy1ZlLVw6r2Wt918fwBNv
+ZB8YuAS3OTqMa7xeo8y7J55OGUPxuK4c0qWWbwB4NS/l7REAy7nM6Hle8YHJUkIzGuWZ5zBLNDL1
+lDtSCkOJIQ5hIEHTqQllQesR9AqFA4g90s8BNBaDLEl33mp8JrKnx3rYT9rcvto1o78gS7ENZcyQ
+ydNYFSRv/EY246v2LJeFlQfV5ASPb0ocBufc+t9LFwgbfcc1gnGVxzUTyh1SlR0dEL++ThOZB5JY
+yIvYEjyhAWyR7g8GkLsVWF3jyzov/HYLCKZFInXBTHnOC+spAWHRdU0HwJPyAxQL1h3kks40NNp+
+hzQfwbpqc7AO+s91W8RJ+qTCQSgJbMDYH16fodeT3nwqdxCDL4Ajtgx5XeptRXvyoaAc8kzCl9JH
+t5GHC4fUpCQXj8aDLjiLbvSRi0Mqhlo/XZr1K/Y0f6Cidh9ZAO9oYFgAIWCsExjhP79OEJmY010j
+BGMMzGlgmAGw1TtUMUGgGMnKJ5bmrvLNepFTLd3lWD5EYWCLFxbiloe0ZMf0auVnH9dWpNz4FcDi
+faKOb3PaQG/mnNa/h7sAyDTZ0lZbTha7E/vz2Y/L792yJJ48gmAheUXuCy+tvyIJclluwW/mT8A4
+3SUqg8VsoUyUZpqW8eVByf7xUj9ZgnNx97AzvQ+Olrr3cGW8XgVbiffGfkin4kUWU7ilBxA8uaIA
+gKjJMy3n7wUtIjwr2uJmKVaEa0pClnfxXCFa4mteQn5AYl2qCMTD3tYyVjkRKvpPGIQvEA760DVi
+B7/xZ3tEsMXpDyuCqBs7f28A48RAQf6yXLUKL6ZciljJ8qtsIWawPMcCow0ET7ilFq+B8CFfBglY
+B5AqkMEAGA340ohYl+itpfyB46hhb0GuWouelWAf9VACcNcHcoUIpYJQJN7MPjN7mos1aoKcz+1c
+cRSEpzO2Mu+07+f1bwWeTnqsLG8uXtS6SJMP8sVDE+ggQa8+jA8A+A11YxSzHiaWtXahUwwIprk9
+jGlPA9wVgvjEmWLVOyEGfN0SPOesYNgA8GYRNp4tYNi1GQ8TqP2gJRBlOiB00IFi2fX89xbiHQSX
+dpwZuvC645tyTp0ZbyXSvzfTIxzXPpXR6M85ny9bbVL84txa61k+X+Zt0lteLKEXLhJ+qMC2guCa
+RkAV1VXiFfgdh6db4wCCHRwniuJxs2OMeb44DX2261UTz2ks1Bf6kpFb2K/hYwmUu1m7k4psiaQt
+HN2Qdd5fwmDHKc3ExxspqWmZ7tLMSavMPISjj5QulFlG3grHClDQ+z2WpzzTbP3t5VKGZHwdy/sA
+muPfx1qh5uryl2bhp+gCYaOf2TUCxCRppvHsrqCyMNfRWf3hihl8B1B0iVEtVmExFwhzlWgvyKn4
+i3QCFctTcnsI/CiY1gx2gYi0SK9pEMGXC7NBQFLsRaQhZgG231Do9DhiwwsAC5V00Ft8hjXuSitq
+IKXflGcf5qOQYuXiR1cuZOketapY8G/TRCoIqTDhY0z7vtx+4juWOhd4cdbR+tvOk2PqzuOq9pa4
+C8pVWkVdd6VQDWTyIEif9G8Ls9VSRDGGLJ6egjGWRXfxMjZLsBl+Cfyu9g/LmHOuF0QJ/IaP8Ew3
+CR2LrR9dJLzdfIQrIupLCagPaZ7kPFcszgcA/AoER94BCPPkauXqrpS3vCb6igCNm+BIoE5zf1te
+4Y1T3qb2rQu1UyIaip3r01e8X7L0lHgARgx065HdI9hKnOB48V0Jn8BwB8ElLvSyJ2LMi7uZ5Kzp
+tkWD2uhlmVihingSAKUX5yKdFmOETWMqAnSLajUMZ6UHRkOmgaqkFv4a+kkAfCjb008AuKdl/Ssj
+X1rPYjunM/B9AsTpTQ/4UvO5R8qR0MVSjz74vA0FTZKD4d9NFwgbfe9luTjpgzRljtvTPE6hRzYI
+Rf9E5BpRrMKAhgtEukYE6IUc8tIRXrFbgwN7diCMtRNEVysOgKdKhN2vVqAYAYLFro8EwBPQQSNz
+sgizYD5ZhkuYOvZqrLEUjP+D1GPfMaLrwBMke0yj7tUyb4TCGVe+LJvRvyBwZAt8dmE5DXvRtCjz
+LBQ2CR+WAyxAotqs6zgDrg6Bnsq4jA7dye+XHjZfwQBkrrb6UwMOexIwMefAlyhkTszmFrHu6c4+
+wivNdpGgoelWYNf/uR5YNvmh8r5SegfAcJCLTNtAcAHCqGCc54P5u7XfIZK0k0Jelplrjed+FWFL
+6z16wXkkGwqdV5+gW4qt5AN4eZW4SXzit2AXS0vwyxbfFj6UGV4Xg+AAx2bt5bQNFHv6uob6uFm7
+3ZXBx3M9clcalBxxxqEAGpAltVbq9HHplmCtQJtlRAG+UtOAZLpSjZbL/rxYfD5R/G+b58iPSF0F
+0q3C/eQ4HpiQjrKlC60GvwGp7/XwUW0xRpjPg+QNcMgGKWGJfIRVOMl457DafxddIGyk89Pht8kq
+svmkdJSLb8onldV51whfvHveK4hUXSO6VVi7O4SD3mFg2bdNGyOskwvkLiE4gWIhzjwUMJzhBZIh
+MDcIA74UHmLWNgMaalh+eXWYhSXewqchWOggw48Wohj9La8DhD78MQkMNkPxpHUlAP0HmLVfqqd9
+CwRziW8I7/2cvwCITzVTdadV9XS1ks6MT8pO1USm0CO3OPvpZbkHKtdoDaC4ufwuEEwbsUyzFs9p
+PEC7D0JAVmK3FA58zXSPCIuw7tZgVywBgNFY3doYYoTaXfma3QV6vsaxLiMCuzik9XINBNfdGAiD
+cFikYhik6t2mghtXJqiBskiqeTXf6+H8p/p7XdmPCEvPpda/WFZ16eU5QvEOuhPHvAbB8eLcCeSe
+jtjTwy1ChlmEGfBaez1sIDV0nD3d8jY4f0HbdmnbmDEcorxiCSarewHNfl2A96Rm4EvF+JJVDPxi
+EMzz3OfTm/e+rNbEDeAmv8mhzNO5rrvdqAAh0NuOSi1ZY9bTHAxLygBFxmeu/6qLcy6T6z+W4L+M
+LhA2+tgiDBTt8wSIs5i28yzA4Tb3XRF8RoLy4psDXSxUuRgwXSPCTcLBsVtjadeIBLQa4bQEvwfC
+fgRk7RKhdjSrg8qKj7BAp1U4+4H45ocGIlFrCKEC/TTe56EuTboXh9jj96Vc1h+x9A0QNyD8GuC+
+KPPB5L/Mfsj8mJ/eofkPqPfpVX4pV+S9K1s6mOKrYNiU8L6Mfq7hecnQs2EBhluEdfExpLhDiANg
+0fAXVkmrGjAh09wjhh3nqIA4fu4W4eByZ2VW5DywJzynXEgBbRkbyIVfy8DPCQA/gODzi8cSf+2K
+G0j2VNfdx6Wg3OHVj2LpBXUgpFfL74NT1n9ruxAIFSxAZoCDMVY9p7b8yJN7x8BjFFi6gZ2MJwj2
+fAfBkS8o8onB7hEQPx6HzVXKS7cGr6PzkAFWNVcFzec0ZW2uxbzWMK35lH2KtWXayRJ8AsFUgV2X
+tyeIl7WoEXEpAsXHufyIXpwV8uwwt/3sE1iOsloLCOUd0hgcP4HiuOmQCmpPQDhXxkMZBXSyLFgD
+qtNupkemoUAtmj8sHvtLTyp/ki4QNvqOa0QNuxAolR3Ka1mEHhYq9lemP98nbS/M9RfkAISbhCxA
+rFEOUN41Aq8Bbmyfdsjr7hKiY1mB3RpsoHigWoZVRi4c+iBe1WUNEVTNXeOb8rO0plDrWNaIizCB
+pnJxoHwAwh+DYJr0d6D427whx+DPVXJow8PQuY47pp/ac6ynLIamBDsYtnxWmvI8tftCo3i0XWpr
+NwswBOFWHwDZtjsSsf2EEyxB1NIO4NeVx7RfWINNoaAD0Mba6LLnnKYlkazEmnGDOARutS4xBsAB
+gs95TN1xha2orvQyzkISyJXRO0RW3jhhL7PLgexlvVCeHy4Qzgc+hx5Eo+MCS8bq8uRUsmeVa5kM
+cheFtAB7uoNkKWXSqotwg3gCwWOMF2A5hXC8bEjWYAfIvkfsWoapkbh/SscyTgRs84WpPOPkPlFc
+JhQkH4B4V6AD3dYIbqOE0H2YqL+goB8B8EG2CryhLd3HpAHeHQhnGbEyFUBnrSeQ6yvglJbn1HNV
+sV6wn1V2WEG4P6WAdLtl9ZcB/gEX4QuEnb7rGlFlrm7Znq5b+ist/VepbYdGvsLpd+vMO6DkK+wv
+zCnMYgx9BLcdHFcrsWxlAZjFSzDHgBj4nViAeIzVhglg2Ac+FAKYANZiDQaKdi6W4fZzha/1nCqE
+F20vw4DUmBDoDUDs4FdDceQLhFlLgwARXwJgSecn4MyC8jv0maL+tB45JSZQL6SlDKcyru19PVRf
+TyzWJymWYZAePLVftVbaoitNXclYPulVKGwHQt8pIoHFHBq7RMSNkPkLKwEJFVlePUIg2AHx0AeL
+cFqGdzcF6heLI80OqXfMcotS2sqDwG9fRnr+4Tl/qjbe0epCoAyAtZRM6xSixA5my+yh3tRqKRer
+7QUorvXmkSFCp8h7sbbCheDN+su+a4JYykgQTGmbBbiBYP93ALfDbtaGjEP62EDw8IvTDRD4GE9l
+NI/QwKJxJIalZV3mu6zXkm8Z6u4VVrHwEZnnNTIwLkDdL0hHb3Ncr8zOu9k759KkvQXAFGGHyZJe
+QDBcPVGcgXHynkiNe6EdCH8jXShdsUCwgeFZbpbpJt8BQXyMKPujFpZdsfztdIGw0c+6RhQ52SeQ
+hbOe09u6/1nMYme6FViw3CHS2rtotwQHIIZknkzbDi1B7iMopvxjGUMUbgGeUIgOTH8TXwQTA3PM
+5SIB81GeWG1USbMbKfrTb/MTLPk8/o4kymQcx17iZ9LSrX7FGoxiEeZz9UW8CvpWpjPDp8r0E/rp
+unoHK7/HQ622NArkcVncpuR83aVkw1JjNw7xqNPOylX0oY+w61Rq22nx+SWHvQg3BZBBTkdTwn1d
+hqmHSaBYnF0cELlFmAExgV6PT2ZdzbVF7M0iKMcyB/XA3kcrcJxJ13v+0fVflivoIoI5Q3ndXDPu
+a0gvpAmXrWubbidpEOpa1zpaVI/XdRgkjxMjMwyI/vjvxGwF5OyM9ama79ilp/ERHQQLyr7AI6zD
+DoIJ7I4EvGLW4cz3dNDcuqw14GPjmYAXKIba6LM2HrA+2Zrm0sUqTGu/ukPQkSfjdPHDsVfrmLnS
+QTC8mrBD4gnsPgPgDBQw7H/sZol5kIFvAGW3KBdGQT03OLsD3gMAFinxXnaxgEJ1pvqljxCVjxF1
+AOzbEZJc/t10gbDRt74sB5aTrGm4wjadSoEPZvoby68JXAO9/uU4ArpKrhFsCXbf4MjDco3ogJbj
+4RKhnLaXjxvAMcgCvIThxBLAcU2hNjgYBuwbs1bZkKrwYhXSryjNHFGNfBrkXMX7nOXgRjBAL8gi
+8wCE39EOlKv9+NWJ37zUL67T1RnZy0pH9jXBnl9qQOP0UahIOW2mz2AYiLAIX4dbuIdLuyQjiiqI
+hQpOa/sA7F7SngIMuvWcai9NOo+sCsV8if3GbuhoFt9ZXpxjq+rZQosNeMSQ9TFXznArMZenvKdr
+gX+vwS/XEReKNrpCz3R/E90bxNYz6kCbNH7KAw6UcttaLz3ZBumQv1NiinSTOC2avYbKUdspxHPH
+epv1twPezG8yisHvZhUeGwh2ANzB8BiuU6qVL+KiJU+EwvBni7mEHcCKuGNCDlM3DDD4Dfl4Mula
+OTV5kKcdQPGRXsldmr9vCd48R/bUFwCY0zXmlwsn4HV+EUqnMDxMYDjurt4D4bT6nsoQKFZgyoyn
+ySA+Ucz6MaJ4UG010t3B+Wbk76cLhJ0COPYVc0o7yeiW0EBYiZOOWNF+j/whbQubtk1zK2r5EgW9
+IAckALW9hHMvX/+gRgO/muF11B0I6w6KAeALyxK8fCyXC8WE0Ic4zEo9qa0LMdtNiluF32hrX4QB
+iKlMG/+T8Av54gpE3fJrRzUR4GnKrhLPU/Oz8bd8QWDuM9prPF7j0wvzMglwQbYMMf7WLO7W9bLl
+qBN3WvN8sGXYpb+tubADu+IrDfuEdAsyDvd7y1w96xrTdoTIrQCdb8wnGMuX2MusXSPsc8pmGdY5
+DPQiALDHi1tEWGTJFtoxXUvb4y2R0gxi7v98LX0AfpXauw9tF3p0G5WMgsoQ+1xq/PX1zTnZizyB
+GtcHbDsyHcp8KKRPxeqzv71Q73IBthyWGvbVVFwlGiAeQHGJCFDsIPgAhoubxLCX5ZgXHOyatUKB
+3BaWRbIAosNmhQAxD7Ov5aO1d605PVmAC+h1EJwuVH7D3DV4ls3L8vHlRH5M3vZWHc1vucTLsqtR
+FcxSWU5rgLim5UXEAK5v8ahxJSlHCPY0L88gWXW9CAy7MZrLMrzwwMDoYNheKI6XjXXpg+1G6DfR
+BcJGehSU2NNOIKrN3Objoi1SQMFe9JP1dy5TQW9+lKJaftmFQh8sxtNedlA0H2B9AYQ5HPEF84eu
+7aUWAHZfYnPDQL6JurZum1BdL/mpAeBt+6PTjyWwD6ZSgExJqawPy66jUkpMa0uC3wDIcj79bdzk
+e89/zwcvpPabk+WTQueT6qUbAPaDEMDyx3EOiF3ovrZ/u7LLa2ixEtvFCRDHHEWL3ohUqrtE6RIi
+MDBrn0vugLdYhs3H0wAwmvsMMHfXCCWXCCWX9+mWYWJxVNBZFDeLJ80OKOXVOOdb/18srde/k2sE
+iNEtcIpvAJg0Id896ckdwvsRA0RARtvPi/Z6clCE02I+vS15EAocHn5UKsCnLJqtfKm7nZ/hQDJx
+2EGw5FMrqb+08p6ODQzTcTWY53ga8B01HTPWEW+16R2I5Uogz58WBW/G3rlmIvK00w4SkVeTy1Af
+fIlZyLoVcq33J8n7QiJvybLHTgBY9tJHKzGlO+ewi4Mcj2cQzMcAseIz08CuHNKiMRU087pSe6FH
+4V/ObGBYYNtOenvXpK0x0YeF9PfSBcJG33WNWCetP9LS9BDpZVrg7WWOS3DLEJRt0ygOPFl+5xae
+tkjCBUI7uPVw3VZtUrpSechaCBOKLx0Yqpg61jkGzqe9jqR09I2FQ9hOH9Nv/HwOWMd1HbjNRQKq
+fDnOhEgobQfBpoDsTrer6m5lkOOVavhzOdBre13yzEQPwv2jCrGuz19KiEGVIyDmb8kvi4S2eQDx
+tWuqve4aRxmK85yeae0+kXVUqFIb5S4SKf6l7BiRN0ZmmRL31UyL8JyaVmH1XSNmhtlnuFiG24+7
+v2M66l8dpwCplp5A+hO3h/c/d+nIa9haOoJiBsQgoMPx0twW6KvF1zut/Z4X8qAKggQo+0tyxRXC
+2utp/Qa205kDfTWcKXmsuTxsYalhAzQsl4aA3CHkEegWyzCl/7AwgDbXawzZMryGfeTNkM4i0/hW
+I6283G9FfiL+AAAgAElEQVQCxb7mA9nm2FTLL8BA97zlWgx5iKuCq4MfWUk8zM53xWUDu3JIAx4A
+sPTzqt45gd5yHA/pJquULlLB7qr4lPYIllXNZWzd5AP+BMC3Z4V9E8CeEk9vi8tKLF9x7vRvpAuE
+jb7/shzJ76wkikgrq/S3WxF4nT5SKJKKqur7SgyC8wU5t/Y+ukaQVTgstfBHsS9A7xEIa1qNlVwj
+FMUq7PU64D6DYaVx0xeuEe2HF2ldifYxpeGOgAFgBsRuAS4uEyHlQsSUml/Fi/A/lN2pQraflh3y
+GHlZVDngWNU7UEAqCzlX//zI0v9KrTvGwhcQaw/lpUYtO82jlnHdSbdLRX9MWbvi8a+V5+Snv3CC
+D44zIF7XGW4RVn45LgHk7hvsljYtT2YSKDQWtnGOP5GvyIPmsIIrfbWsvgeIfc5c3ar6PBF4oXAo
+ei8XPFShVEwzRYF05Sg84ANlP93qWWEplaLW4c32dKnpbLQ+0YntlP42JFjrBv2OoLeFAwSvbSmr
+f3CGz6A4QfAPBsTuNwzmgVkAb5XXk0bZDBovRwP5slxZpxyhl+cUCOuCZP6j1ZeqilOK8KI8zuIK
+viNcpRaWQ9YpraQ3LCgtjUHw0Ro8ki86+IVguW2Jy2KTvl6BY4UOejswLuXNNSJmW+H77AAzrcEY
+kDntBePavrAKn/be/A10gbDR59unAam+IwqgAwU9pgO56MrJx6t8F+Cc/IEzzoBYKW895nKrMMxd
+oVqAl07RSDsDYaU8B7rmGgE1q3C6RygSFLPgVPHHbf66qSszvAgffjaIDBS0p9M8ORWBpK5w1h8G
+wFBSRPI0Vwz3euoe5vjz3H+fMz4m2QIvMCQpIwsrEHf2S1+55UzCZcjD3ou+b2SMR8EKDKioVZan
+bQ5LPY9r7Kj24uKucz23uj+0sJ5BMIcB2iXCwbD/GAwXYLz6vH1EkZpfLbCVx2ucx1Db+br/2yyA
+BVe+BsILOa1LHIFvhiEO3iXHm9xg6uy1RU5teyoTIDnKNQFRZIDWekgxF2ssmL9eAd6dpJVSS+11
+b5WW658A8AoPb6sI5dWdInjnCLYIdxDscQCFF86gmMLuI1rkO4VNdOxjRHOvbvkF6kdKQnIgF6mf
+R2l2txhPoWjCdqtwDafMqVp9m5QPxPAJ7H4HAHOY9cwZCMshLAl+KW0HuyPjByBc80bJ0+m3QADc
+Eozc83Ri6c58l0IK+F0GA5cJ78f0V9MFwkblBY/3pV9Gc7W9vOA6niaedQb24+nCK29YykRafQEt
+cRNWshb+lBm7Ncx+nsmUbuVla7Fqy4cJychb7UyLr7dTlysGWxbCN5htwRplYWWhCB9KkFAGK+KP
+w9nmUJxkVSu68wVJmSR/ROdzw96wCRAX0BbDCBR/KwleSd9v8LF4675PDG6X3nGrADajzJl3W5NJ
+A52WU6QRWj3NS1GXRYc53Dgr3+f29dYftGkglzNFm9gSrJpuEBMNAHvcjs6zJZ8gIjGrRl89TkDv
+cA6DaO0/xIp4+IftF/31+gkEh2sRAV/YC6d5Q2MKG45SkA16OcL6WE56OYp3UNrDCTwpj+b7DOb6
+dV+vSokSFQktvFIBb4AhcQBcgS4D3wS/o7woF7tGlJ0hGBD7L0ExkHI2gXAHvv40D9AxMN2QEW6H
+uXdWGHWz5w/yglY0o2dp/BFgmCvf0zz4nPZiojpSPd2pxEH2Uu+A7osyDITDimoJPt+P4QFKR9wI
+hfNfgFyrmwEurQBIBb8MjFd6NcRNFXuKZuXtaXC+gG43KWLuh/bvpxTSX6QLhI2+4yEskLSqlHRP
+qmpVStE9vTC+cJrmAlA65yHNldOEtPDKc1eEc96hnKayC/1JIDcEFWlAzwti7Ui/JUglTljAVEKZ
+FT80VdtKzS07cwnhubaf0rnik+JnwHGKT3xZ/GsqvjTzvuicL/Pj5DT++XlqYwQeIxozHsMso23s
+zhbOZ/pW4UUEULQlvzzHA6UPqbLO6QTSqG/1vL0vpy8MaawvPJbjqjqsLh4WVNpLfSKD9dPCfX06
+e1sd6QaxW4OdN9l94mtOjDi6xzKBWQeD1Mi8wfM0nwuNU3xOEuDs1uDAmGUNn3+OYb3T8dDAhy2Q
+R6Y/vqP0AQUu9cADGD79SgEKO6A85h/qBl6vwn76U1nGDY4JEvAetkM7gWEGweMVyJUFhsMFwsDx
+SFcJzweA4gc8mzsEA19dMnrYVlrb1xIt7P68vt3awqVpRMjdHVociVlfSr4Aw+ew14VWb5mxDdQe
+kOvbc1qWp1NE/O8BawuXL2A3eSHD1koGxN5uLh/tYqC79y1BcmubXSeAcD4qzcUskq5jILDrVuBI
+VTrvn6ELhI0+ngLti6UkJKOcKtRugeBz8oUS4fQtTR+B8VKycgS9HSA/lWNAzEo7zgtwB4DStj5z
+2tZpSwygKwR4JV/AIPCrArMUm8MFfZqWXzrK+DQgMQkczy19dhA8Z4LhBnwDMHN+B9kOBH3sGARq
+jitoXDOMAJKvgfDPCwzmqdPkbBivMFgt9R4A1/QM0xj44HD1flE99FTirK3RSmVO4ERKWd2VDvb+
+b9cnfXrIquMqFAbMSr6Dy3Jz1sDw15wYOjcQ/PU1zcJ6AMExvgyAvYynl4V8BL3xw/n3RB3YRpDm
++ZN7COFxfAC4QSYTX9bb0a/sWXLgiVKF1LLcJD7v3Qo98V2FW27dZf/zBDIMeE8AOIFwBbrs9+th
+4ZflhlB8xKeX3UXBZfDa3WfJ3GngdyhCJisAHcxTezj4VDX3Iu7gOMYm9pBIt3HSjVpGkLjHQW+E
+l747h3G4ITuBWwle6GkdBDso3eb9wGdbuagy695B8DpxSyfg68xK8BduTfak6HbcgdXGZPkdNCvs
+qSb7ETZgrNFe2lnnBJJxeCL4G+gCYaP5rQ9cs+aVEj9ivhqIYLy3Seu3g2Deii3TKI4E12p3tjsI
+9pfTNMIfAWKlcq4QCUiwEkhdyyCI+ku/tCwxCBZg6hKe00TbkBSg0wUrCdVJgLgBiKfjyeqWIHg/
+duDbQfHU6uvJNwrF0mtjk3kEGA9jmmP7a8VCB3pHYsH4Yl9en+lsc5b5PkjmeveFVMtl5nHZautn
+ADIFRbPcAyA+rmUCdzWPtjQ6thn8wAP5AlxahIM/2RKsucvE15wYXxNfY0LmAQhbO7hN57xD3NuF
+w0/f/Fp57rsD4LZBgEmiZLTjeP8kSftF4mEpud7Oxp7DHUO/a+yJnz4pH9ch3PEEfl8BYDmCYN4u
+rYNishrLWG4UVHaRb42TVl/YLkDFQjwe/IY1P7hQgS/iZTjhNCDdr9q2Wg/TmYNd7sQ0B9Xy1sH0
+TABqs2wWsNhmszGLdOZp4Hg7fYsTyG3d6Dfc3f83AG6AzxPwBSTaUQFzXsSBM/ch80rjiDn5nLVR
+hMc1XhAWA9GDLMVK6fECus8o4ZvfSRcIG31r87TySaeMl9Q+mbvmXPOe8x/pZ8DraVWZx8Jy0CqA
+6m759fAs6XbOAzguSi4AnkZaBXkJNEpfWTNu2rWD4EQKYSmgY1gaGiitYHc+gOBmCY5fAt1nMDyP
+1uDTr48THsdNKS/Hvpf/VeAgKHhlRbb7b2Ji7fkdnHqDI9z6hYPl91Tm1Miq98r1yxmnIMltbyKv
+oQLSLH93mWjgjMr0cwEcAbm0PAaOk+K8PdqcOHx2mS3CCvmaGMNdI7jnPGgNkiraXBPzWTyt1f0H
+sJvUcSlr8kC5RJ8LHzuVAzj+SX5/hYpMN7MrN+GGl1XWch0MWFAOlz605RNgzMa4bCtbhp8B70sg
+HCB4FHcHBsXVCiwEiLtrRPcJ3i3EGwi2dz4USCux8VoCX6WX10z/ODCmMcxxazpXgf0xBJ2pWPUB
+sSAzvsqo+6vHZOS8M6zcwC7HN6Dc+aAy3g6M+RpN91uF5QaJ2lCtwxyWOJ/O2IFvNDjdJzZQXNLT
+dWKlKSBrRwnfcz2B78EKLL5b0Fqg1yL8L6DvbBoRzNok/0kRtEBTPigKqqwn9WtFYAPG+eZ1Cond
+EvzdsCk9fxRmjeygeO8Kg5vaxW2QzDdYS4UJit1fmN0jAFSLcLhCmL9whPXBDeIMnL8srwPdfjz+
+OtgOIExj0eMxftX6u5fxkf91dFL8oSo6WmG185DH8/8xAAYe8lqrpMQoULXGcXw0i24grOnKqNFZ
+EPs4lXKcToULYEY9QUBrCgge7j7CG2/1j29Q/Otr+U5yK7MN5/R6y6Gb5YUBbVnvLh+ovU+MGYC4
+jf0T6Fx5XOJVac//kJ4AsuzhAj5Pl9/Swoa188U7NK/vecyBSjxdBjZrsBjAfQLFK1xBcLpD9C3S
+2ApMgNi/RAfgY+DrfOKubSPzpipE55LzbhUuYNi4zfRdrONT+GlwaYHHXuYOhgEDXZYfQFejfJnu
+BnIrUOX0HQhLraRmE0lL3MtkXY9gN0tWHuJrOObk9PibIDcszJEm1OUdEOeYrDkdkmHnRUVzgwiL
+cryyh3/EFGx0gbDR2Xv3XDL1jIlCpTAoKU+pEdLs5YYLzgsatUeetvL2h8sHqBUrGVacBnbFAdsL
+QNwtQA38BmBrAK7p2hp2GeXK0oAvDAhqPDtGCFLEixYgi3C+LFf8hAmUxpe5DmB1c3342WMHy275
+oH4/AmMeU23xjYF+DfXvFwTZsFfxqAkMN2SXnan8wH3X5/5S/7o9ODi/918O5TuQI/SVig47INZc
+P572BIaj/zRoUcch7XQtYJWNm7iwstYX5vg3zfcyX5ZbVmHhX1faTW48xnkto1rVCgjW9itjTf1A
+/lDKHOBtgJNfRK/A7gdryMHvY2YvS4D56RpPIPexACoGKBY8cUAsBQz3XSB4B4iwBsvpRbn6Ipwc
+yokBZZE8b3VE4e4QW9i/nxuc4B9EquWG8Yw47ziwdT6K+NqVdq0bf6GKgDLzLQ90LGQb6OC3Kjd8
+e0cXEnQGGOimnDiBYQa9JyC8M1ACz54Wk99LkEyTuOwOiBvY7fl2fuAGArTRPe9zEyxctnbRz3d9
+YfsLm0/wMICiYDCMDHubDOBIpPx+ukDY6GPXCHaLOEl+Tn4ChQAIMQTQ5cUaDGurlJlj5VWLzqri
+DGqxpVe3CFB4CScuQ0ox6kERHvqq32181JFF2THCEYkDhLUv4ZKdQi9f8KO5CnTzk7RPPsLTXByq
+tfgd2N39gHH0Dfa8R8uutniMIZUn0Mh1dPqrgqLgusbO7KtVGyAdQZX2Kv3pfXkFjo/9FKqTGx0N
+xhErc2UFbFlyBa7y7Re6QllSGTyl2XpWqRco68n4PXlKCfjab7hrzlhfo2NALPVBYpURz+na0n20
+4wUmJFBnoItDWtzUEGt4WQf/zjrdTeIEkoPvXjA5g1f5zs9BBMUPAxTHct6pER/SEx/1QtymFRZq
+w9k/uIPgsAjTC3H1Zbj9pTkhqzAD5UFAGTaX4eNbPqKhbWeIvi3mnheW4LAAG8i1+HpZbgRP+pg8
+ycQyuLzwARz9npA37UJ/fPcKQoFAgGYHhAlKPa0D4eJ6EHPcQB4JYdnSQNfNvHCHkGhapHsaA+Nu
+wOVrMaCV0jfudzJ/Bc+5QNIarvbSHAPi5SahMAAsaRFGyC+yFAPvn6j8DXSBsNHnL8u5RiN1QqA2
+AYEnlMim+MXKbLL4AHQd1jJWiTS4ElqZHnbwChAIVmNY7BZhUDj2BUYqP1C8dJXauo2Bx12S6epf
+2T7N6yQQDAUBYtijtplWs8POEeH7e9gd4rQ12lfbOWKz+L4Dyn3XiBibOteepjSI7yzDT/RWGRxL
+VyHcwRCn5xMIT1v+XKcLr25p4YPvAeBuE8YuCQvo7IxHUdIa5VGql3HFJ7VNrsROAPEpXMpRpryI
+hxXMxoEtwlNBwJf4dE7IHBCZGbZdI0beQRdgiBZmcJUrnPpkkVjnsSYrKI4yXM5GssuIPmantHf5
+dbS5Dxr9Oq6VE2LqaTuLVeB5KncYZL7P+XRhvtY0bvnNC1TwiwCvvh9s+TBGWHLTR/hH9/097Bxx
+BsoJtH3c2c3hM+Db4u7qJtrK9LQZY8Uvmfa0Pq7nKVBXOfvEgedOadwZ4Nm8ENitrhAdCDtYLZU1
+jLtzwcmC7Gd18Ltbh/dwORfn/NhKTfIi/EQir3VOKy8LGtBVUQyYrzDUXpxLn+DcNxgZpg7el+X+
+Qfp47EPYH1YifwyhawROauC4yldTbLHos2zwirY0Us0F+BrYTMCRrhPe1gKSFWbBerIIE7CjbkT6
+aRADAGsAHKUTHPx6WZ0aT9hip4hp1xiWpu5fmdui5fHsw5sgeBr4nRUE6+H40kq8+xB/qYPfBvaO
+aQfwqH0cf6VEWHNahfGai55WAQOhJC8QZbIPSg0vYaD2n/JPPKNySqwNdIC7d5Gu4UJbs3wHrhmv
+ViBV0lPqde15apkRD2UqpW/Sn7BoguDhR9sxpViFxwK+c05MGQmMvwQiM6zNqZyQcRAAJgDB08fH
+tRa1AI4A7bZgOwCOMqApUq7Tt61CtQZrslWA8A4UStuy/W9XBPHmqyJ+fG3tlZbWgAVdRVrxaME3
+lnC0JQCJt7G6RbA1eIgD4hHgeNseTU4+wdKswg6uz6B5dSiEclpvN7/gZil+YTleT0y4Hh7XEXlg
+ULwNaRFWj0lb8oO1ocvItILu8bT6HoBwA8HZlBLZ4tLO4Y506+4jCAbnS+a3Icl6DIASuM4+0HUD
+JEucly/LLUWiqNbgsAhbnggBYLYOgyzCx5n5e+kCYaPPX5YzoFiwbE5fw7gUbCinScnYH5jyOuPu
+VmLNfHGFZcxoamS3CD+FQx2CLckMcjOeq3S7F2j9tmZv2ldLmC2pLm8lhGamIQWpvRjnbhHl2EBw
+cYVgYOwW4S1NN4DM7g+bNZh+BSAS6K1jo6WvPnIRbsDxUzphw05FcVPa4iGBA3SO5wV8wuhcBp8N
+HH8CgLd+EsqNHNkKtLXlbaOmGrgKblXYYzlqG4MznMeE0ysIRnWHsLwY37g+NVkIOAbPJyieqhC2
+Ck+FDONJ3zJtCr5kkGuEXa8rOrH2IYG6ULuibzzFsRY11mhZ/3Tz1vP8EGWljo+08evj/G06acyW
+JkiAwLtGPOCM4zV4TL2+KP5okuYyLVH3Zp/OEwIeDmjybftmJW6uEU+W3gDEDHIPZd0/WMgqbE0H
+vzC3yP2CDayOV5biHl9CXbxeqek+UNKvWeZZKOkwsqfBluc5EFsUQpNfrJXdKvwSCHeQW+M9vzCL
+UPnIqmA4MEMwJ/F34tVSfU1znqr9lAZ+IVTOeNHbwgYEmDXYAbDYi3OxHwRZih30ooV/Uhr8ZbpA
+2Gh+PAGu4Qw8FvMILbACFnAoo6E8PeXZCsz53Y2iLul0jfAXDAAtXxDbgW+m1fwEvqTg7JxIA+BW
+t024KLG1IwdGGGQJ5rFRB1AmX8MSHBbhGWB3B8O0NVr7aMbRLUKrm8MGgj+xBkdeTvZbgKs5phs4
+fKFcX9HLswTlJkpbfAMWW/ys9Fe7NcMW15K//vDa2MbBr3nqSRTtkp3LVAQUPsAAWYc1XIJ82dHX
+fW07paycQdoW7iCPusAW5x5mq2u4ROh6iWgq1tEtwqIQswbLVExZvsLLTSIBygJOGfa5dtkhunrl
+ffX20hllLtT++FOiXPfJm8TqJcxjdQK5r/J+FXEf3xbsURqjChBen/udxj2xOTciwPATIJaTW8Ru
+6a3uDs314QEwc74D4sW/nwHcs+VYif/38/ycUA38ueYYG1cAPFwPuucVvSgcW6oRCASBQCCB4A6G
+saXxiQlIO+NVjV55K+M7yO3A2MPp4rHlozYtwGizCLuTUL1WdZvIFzlhX1dO1wi3AMeewXETl9uk
+5TsOPpaabfrNdIGw0afYowNdLYlPoPe5PLTYV5NZgQaAa36Ei5IXAiEV2OYZSkfOP7hQwIVSbvvU
+x8KB8Tr5BQuztCLAl8A3wS58t4gTCA6LsJZjguAKjBkE84tyCYLZIowKcg9W4SdrsJfJvmVHtfT7
+GfTGOcwrr0f1W/QKfGiolFVItcZrYyTaWVmZ+rISMmzxCoZLLvEXtbI0uA1MBJvyUONqVwa2Z+0K
+rz8MeWNcGjD2AidA3IFvWIot7FPJ4ahPl20rvsTlINiswUI8JVPxJdUS/CUC+XKry2qFsALXBFKr
+Da58ECDe3TfCGoNcx8HGSsBXCfhqfaIR7N3Cfu8kAtRbjDM9geRPgC1bfQsbaS3DrBKAl0EEn8vH
+hzTJwePDZ7TxbLbHY26dYxBc3CKG1PTtQxnnfYTZ6isnwNwANSFUvHSNeADGou1FZ3kG09UXjuMA
+iI9dN31G31DwR344WIA/AsKN7x9Bb5d5JyYjECymsRnkUl6cVgDvkgVee/aB+E4qzz3Hqb/eAFFM
+49PhgBg7OOYPbTA4ppb+drpA2OjzD2rYPrcOJt0t4gB0tSUoaQ+Gpuhh2udQsqY9bBZWrmMHt6my
+exq/MrNeVsi0ahFmwPrqDtyQxHnYdhTRjwSuOjjmY2w5RW4PSi4O1UeYP6LRrb+zAN6jdZjDMy14
+uzV4haM7ZC7TGijW4kgufLKN6k/RPhM7AAQSMCmFSyWaW9/Uj8l0N4fWp4hmheUGoXdO6KRTfkj4
+vV+1nvRNXTy9rKIQULqNRZRBGZcM79bjV8A3mug3ppzuINMA5mYJNuArsnzZRWRZg4d9SEMmvr4k
+lA9bWeCKxRSkz1UARMnxWGe5vGHpsToULhDKaahpwDEt+Ajt6OPXj2289xl4opRCnWU5kdUr13q0
+8n5AXJ8D/cjgVr9YtMeshoMS/KLNdf3VvYH3D2VUcPvKRzhdJzKc5Va7+Z/77Y4tXQ9pKH7Czk+n
+c3oYqNbhEdfeBu40yprl9nHfU07W3nQFaED4lLadR+3soLcDXqAxZjsnAG++0yAMbgP4skxAMGzs
+0LDxGlmEOe4g2XjQrb/lnBiulR8AOPLpBTmOuww6yaN/4G25C4SNPnaNUAKOnBTA2BN35VDCdD5b
+3oQKVxeJFPou4buleDWjAfU4S7kU9EUa0HeTSLUTVtyeTrWUxc3aEHSMcPbXt28DkLtFABUMtzeL
+T9umVfeI7tqw3r5PEOxfl6Mvep1AsGoD2OQzTHnOENr6WEAdg0NCFfWcv069mgB3b2UNoQjefFhr
+dl6H59ATtU71Zi2OMw8NfgF4ge1GYUM1qmmFDKCr4f/nwOwMhvlvvXIHUwyCC7ALwLn6LSKRluto
+/QoItp+7R4i4b/BSIl+uTL4oHMqwKRYfQgdTmkfvgD8FYKDIVt8Yawctyd45dyTPUgacx+uXEfHf
+EQTvxXb+RQsT5khlX0GveOanbXTS56yeLls4YYNb2tgCvG2l1sDx0TLcQPCrcvESHkCWWxxdIaSl
+d0vv2U0iP7Sx7VE8/fEgsHyRJ43Oi9F0Hd2ydygs7RzEOQ4geR09p78Dwg/A9wH06kP5vKaDYiUr
+bQLfU1tXvkNdTbmAg0W48H0C3npDlnEE+PU9gquvsMq6dUlArHkuSwnP+wf2T7tA2Ojzl+VA2E1S
+4TMQUBzTgAoIUiMb02oDuz0sFNZTmbT6PoFbxBna8vY61Eol0CVR0k0h1KVtKAt6sINZsx38Rtvc
+PQJin11mMGyWatUCcsNNIkBwBaxfZhnmXSJOPsGbL/DE0fL7ZA2eNPnbOJ3SOLilvVLvPwcu+jS4
+oFR9H04NobVpEd1BqrYE7n+pYkNL2k5wknPHOzI24Cnia1MCsMKBKXLdFCu5psLpYO40ftvRztet
+0BLwCQDyN6diDIoTKF6uEWrKY4UhCnwphoCUivXC05SsL8qgzsCv5fu4qtC8KIHdDn5LvAJfzpOY
+A6tPXo/XX6KCeLGzqeWHcn/4gY5b/Z+kvWofWhufygkCXCQA4R+BYH/xzb8IN8b+6+D25CMsJxBc
+64gmygnoJgCWLa+/HLfcJGYAXgCqGLHbBFl+Y994u8aUsEMfJ8IZUPqNbNNVTb6qnQPky+gBOj2y
+AVwCviYkz0D4GQRnC6TkxzW3/MQADmLj6XHIgFrdBoLLexAJaKPdWB/C6BbfwQB5403BFGCK7xCx
+wPAk/+AhFkfKIHiY274t5t9DFwgbfTr0So+Gu1LY0xgUsbWYnBMSHZSFewTADkwOILn0gkAm4Jq5
+Wof9qIXxahqDX42Ubgm2utoAlqjUfDXzWQWGBLxdPhIYBgwMC+Avy6V7BLtAdICabhEOfL9Uwyrc
+rb9nQOwAGHjyDa4WYQKHjBR8vFpa5RMfjydi2OlztpP0CAGXtzr8CHxbuLeF+sJx7lPv1XZT2Fsv
+PUuxVXJCUWoW4djaTCnsHE5ADWeQeyQHdQzucokFq59dATT5u/3cAnz+Lb/gjopUUpGFdacoLPpZ
+28bWMWqkOn+ytZd4czs2qzDqsY9nP7Yp+zYgPqlM6ekuAg9lj+i3p3GjDuWFO/lBg3nJn4sQGEIH
+xWJzKMU3OL4AJ6+svQ3gnsq1MsJWYSDBLBT+DHV3g7A8wcE6nNbgYUaLAL/DAe86znkY8DEMMItd
+hfIaA1XdWAfeX5j1uBCzKAEy9qFdc4Ed6L6LVwRL18m5rvks+w754WpgbXcA7/X1F+WsGqFzEYAU
+9S/Lkgi7FbjJF0mAy2A5LMCC2CHCAfACw9UiXAGwA+RvCoJfQBcIG33ngxpL+AuBmnSVUMiuIBgU
+tDTkWZHoTBth9TDDn3N4B7isArrF+DkvW8QAOuMVtmuJb6pub17N4P2XCfy6yIUDYE/T3QrM1rUN
+GHP6Bozna7B7BMo4u0wUINxAW2MEPaRl2QSY77myD+6euufGsNedFQLkHs6O5CW8atMroCV4vGH6
+oxvFRqcCch6M002DkEX4BIY3ELsStr1suRwejgx+vcmPhUE3eWss2E84rMDl564RCYpdibgf3nDF
+gq5gZAPAE6jWYuRxjZEPK8/f4Sb+ERzTFLZxkTYeR0B8SDvREwjWlnEExk91tjyCMREuPyH26+Lv
+RdmtFWEAACAASURBVLtLQPe8AkriWrL9fFeH8oU5fvlNHkBwA8T+5bnqH5yfYXaXi7T+JshFsQJX
+6zAMstZ/5AMcewpjGTQA+JvSw63Bc2KyW8SQcJlYf09A03UiAWBJ/czuhzBgWIo5uIu5qKA4ALEh
+QL5ZcSC8wuPQNskJjqQOglmO1/Jp9WVQm093OgiWVi6PT6DX+Y54Dwl4e9kR/UbsFzwlfYUnBAMG
+gLG2T5shdxBXYR/hkzf3300XCBt9/rIcwiqsDQTz+urW4q5QHBDkpCvxf897Clc4+qyyEeEjQOZN
+P+GgQONKLkL0Ic4teKQQ/FXVObB1IO7iE3MtqgKAwyLsoLeC2unC+ASACRh/mcX3iz6xvFt/093h
+1S4R7kIRQNjnmQfkA8Bb0cSHY7oN7wka1Lp49tHCfooqyV5TDPnY8YA9tYLhmt/GIoJ66i614wCR
+ToVPZkRNv1wHw0v2kxuEauwrvI2HooDio8UXfKQTelk6x+tS4ykx8OuuEJE+G+iFAjKRmsg87sxi
+t6aIlZOSUstHoeEiYU1darquRQa6PHeFhduRsuxI49rHslzxOe1joheGN+5vvCom5hzMlKOXobTA
+JWgF3rbJjp8s3mi4Ax0YrzroSotc+AS7pVbcAiy55+8Y+LF9RpnB7w6I64c4zmUWjQS25V0NrbtC
+0A8Ha/AWnsAcE0NHTcNcVmCd9mnnuYDvADAlWjRj4JyM550BbaBXVH2Akfyd4Ks4DgTAjdmpaU9A
+WASCkYuNJ7szk1Ae9SNgYQf4difPx3R90HKJ4Gs5HXOlJuhdoDXWgDAAloe486yD33SPcFeJKd0a
+zPIq49GHn5cGP00XCBt9CjgqCH4CvHS0P3kk60oHtErhYFQOV9gZebGuSUULNeIDgLyr9lDxdCVt
+rUuwXMNETSkQXMprhkWYr5ml4iU5EXuCSxZhsgzP0/ZpBwDsL9Jl2C2/OADoZi1+yHNLcYDbA+Ct
+A9CA3QMQ/pTOp/FjJy7ZxI0hhk/TTqCjgl0t3czDG/BbKmylToD3RTl+Sa1YfAkAO58zaOOhegRt
+Du7ouG4Q3P847E71RDtUQMDAWG2/4GoJhkxgSgDj1aqJoWwVNtAbyijjQ2DbGlUAvGxzpXkxdp6Y
+87i7S8R5D8CYJUofz9N4Pw56oxP/9YxepoDiU/2vrrnldQDTZSldNMbqsdVRLm5YPCmw0W4Nzpfi
+2CpMFt4nEMwfzmg7Q7yyHAMJYOHA13q0fsNuOvefuIFCQWCZLL5jrK0EzVViAWDBOIDfHfgmEFy0
+GEgag6mmL23q7wSPgNj6tekNGbFKFfArBB0bAF7HEedwO3OuT3Frg8QVD2WRfPJkCY48TmOgbNbX
+aG+CXinhBLl7WFp4XTOB8HKHWHLH4mD/4G4BJv73NfOb6QJho/po/3VJhq8ei7TEdqk0OB+vpnnP
+YacDWta1vF+H76heKpSW+YQxtsxDWEKcWDRBVoj2ECB5LCaZV42oPcUSYhrKOIFFAtDMr2Vg6RpZ
+HKa6Telznmbl23lK162NPQGGXgY7mNsKOH3Ko1k62g1syqKaLfFx2uYGo3trlfuK2vUVPIyDbIFe
+Yevgc7kFgq0PhlYXcOX+AA5bgVp2DVHvu+S2aC09rdAMAbkdqZCV+GwaqBCV2DJtfUTDgO+UWGNu
+efEhShBsIJ8Uk0ZYSzkR2NvdDprNbULq+CXYVUquc1BjPm40lgYUvPIIRx88T/L6BQH2+fbxTOAh
+muMiDEzG2nN5yOrgnAYY/R0tUfNLRW5U4I/qQWub5QT4x+4ifTyY0alv3KGNnRvQLXHEmAn2YWEq
+653AYJFTQrKZ2xXCugw4hYlNzDpsW0WEewQsjV+aWzJTioV4+ktwtqbmNP9SXTd9qrL2Gp5rDNWA
+91RZFmIA03aZGPYi9YydJhRj2At5UwFMjGE7U8zleiFj2mPg5WCxlrvrLiHwS3oLUsYl54V1mK3z
+AMuI8a/lHtJdRq1KLFl4KuvcblvVNNkFynf5RfziejrD7RIWSn7pJVDj20I5KAdkcf45+3xXz/0K
+ukD4m8Syz+fM+a3jIG3hPOojb9TST1fOkLS8tEUxW/fjokd2OwKSFITa0+0YOAIpUJTKycvQfs3o
+FYELNWEBdWVN4+HKmtej/cmwp2vUEaBWs54ALMi8mE8/j8+lvGg9T+MBSABPnPBKcjyTtBk9Xc2F
+7dlhk8cZtvduMveKelpeoHDklsZZNXXDttoCpTsHbj3eQDD4XO3VphwE5JMeIBYGsLwPCWbz+poK
+5sWxgOXW+v3xsD/NWO4QDobhR8wGUGh9mDWPQXAFxx62R6mjPwil2vrYx0RqzS6RHJNYo4GpHESl
+pcznJtKiW9YSEeqi1BYa6FVoAODYq9TzkZbSiUmA2MD+mJhz7cssul4AE7WPBJNc0BJv+rwMD7nF
+8ZDtkb0MD3ywogPfESAsxsv62IFpETF0SVXNDxoYez6gnKyPwyVqc6cIkBay3tcKA2KTix528Atv
+l7orxXpc7k9SpgrUnnx4eIqsF+R0+QMvN4mFd5cVWQ37KsYE5koEpgH1kduwCfcFw/KANNgsEF1B
+sFSepTjzJutBfsqUN0KmqU9xatfiqZqmzmsknnw+VdTWBSBxI7omWGEW402eZXhPCenfEAOHtIyl
+K1wpi4WU4iHM+Hjj4ddq7m+hC4SNPh5702/tOxrl+DZNQAL0/Kh5ryFD8pD3YfO32hPk7rC0K18P
+iy9MpCApR8dWXl6AvIt2hXhq1aGBwtE1mkoryYEFHJj66R3MWhoD47D4+LllVVIdqmV956Tadb08
+SuDQGQ6e5u75JulhlD44k2to2rBsKiyUdshnf4DGk/3Sp3HQvUBrdevh+YTPALJI7NQA/8Sw9939
+Sgkw1z6tMtWCvDchLckS1+RjsQYToHSe8hfk3C0ijv4xjblAsEyBv4nPIGWoQscJ+LJF2MK2D7e7
+THB+AqzG/CX4iiu1KUUGtR4mEOdAN4RBSpUsW6fX5yJcT6xc+HkHEB6ArNdxxhiYZhX2x++APZJn
+KzAMVMXcsLzg7q/2hk6HT2sdq2RtLewZ9/MOJKxOpSEUGcUqXABZGfMadu6Km33+mb5RsyxGGvWj
+16vI15kir091JJCcUFsVJI9DXtsgLFm6gK/IwHTL8HRgLMCcJFP9KYCBYJ8vXX7EQ9c8uz/xNJQ8
+/EW7YZ97tjKY/oqd84q9jEf92kBvrBEGqckPiy9pjpT4HWz5FeODlmcAVind+x46OMTTGu8UzS5j
+DuGYOy3R0J0NTaSGaG5jQNlRw+cyaFeMdMx8N0yVBRTp21m/jS4Q/iadVEWdet3TjAk19eFWj7Qa
+zm9OdgbrZU3dxx6hFSQUVSflUGoNJS+dKWlRlkqknBdHyXiAX9ZxIq2JrDXQrm+LHzSGWgUuC93q
+KoEUwH6+rtErCoPO5XWd67bmFWWoNPN9jg9Wo/Oqfy0C/rKAYNNQCGOz3jCIe8qPcK3y1DI9NlYf
+0jP/mRpvvDingldX1iu8/Bi9vib+uTyn9yPVz/ql1pXjyV+Z63uwTrVHw6bhxUH4nNTtXKzOYwvE
+LctoAcEjQXABxugAeXV1GBAUmpic331sZUupYxkgNoCE5ROgSKDbgEZpAJWFyxFz8wgQYOcHfw6I
+pH/p9EfisI+WGA7qIDfWeaTxS8FJZc0HqEk5w2u6r/01xhFKUKwmv208+CMZ4SIBtuL7+O6zEbzh
+WoiFm49vqAUSsFL7pVx9zJ/Pbmoc8blSRexGYHJU7PrRDm+bjcGcE6ITU8wtyHgTYRVeaZjsImF9
+FCxAHLhW13EhY4yhOzB2XrD0WF9D1runEOjwOUqLfPBhswoXPiV9aJ67xDu20MR5Bjn/4HSXD1m3
+xvnO/yCrMDkriK9fobDPDc1HmT3mmaOUQwHDJOt2bOIMxMoyj2mc4r82n7SolOO/mS4QNvrO2PNc
+lbBjCJMNEBQmOJ0DNIC6xTvYDbtWaY+0UM9NX8i8hudGnGRlFuVFzxkV6DpwTlHpd8pZNq8jpZnC
+Fz+0PQUgjUqiWZKzCbQYGKdA9vlgQLtbe9Xqd+AcTYn8h3OKBgQ1jHv0xGmn9F8pEXjGn8Pxwogz
+71P4qXU+/j1Xj0FLIEX9SK/Gop2r5OqAB7eHzZfO67F+VslPx6YumvW3VifZtwBM/QmDrhc+7UWj
+BMR2vvk49m4uHl3bXBXAqxUMh0vErAB5mJsEzHeW16cSW+yzog957NZgMbL4BohwYBHpiHLFn7jM
+rWYZhYFhpB+mxNUDEGNMiAMgIVA8ATHA4zcB66aiuUjA51pqWh8NWuMpb1jZu5tR/KH7TY2Xo5yl
+GATzE7QcThsHGqEOJgKAuAXY+2W66SXFJFrfaf54YoQZvdw8N6BVZKCPTbpCrCcfCXzDKjyz0+ki
+IWENXp6+9mVGLH/gYS/eYbpPsLlIsHuE2NHKxc2TvYbnumt/kkHHCOd8uOFn9TJvXGI0GhgO8Kso
+Y6w+yJphtesQC+XEh/UX5iZh5wgIECPllosvapsnhRoVFnnEv8QA3AxSrMRsp2NLy7NL6LWx5O+h
+C4S/ScFDiqITA4CBGOptmFP4+HFL2vEEcIj3WwnWRXFGASS82F/HDXJYtINhAa31PPMgYE/ddOGd
+IDcXjFqgv+Dmi7OuxT2tIN03gNirReRXl4oq8w9z2dOiwtPM/2ppENIypdqb8BKA5/C75uoxonuZ
+YN9X/X2nveu5TyBYQulUt4elJywsvSm0yFmRlEUlpJTW0V9KSYB8ACXq4FcN8CrY1xJutWo9TYWl
+a+eI8BU+W389vtwGFhDEJP0+Zrx0dpITvkyjy97VKCv7FBGI4PwdDDcA3UGHZa7x9LkTe0xrylwA
+f7ztoNf3ol2PyfPx+Rij6GMGv6BwiJAY8woOw3Ugbr7paUe5gc6bcC+z2MT5xOSSsVH9UEb9+SCV
+oXb903mEZdJavNGTJbMkgPG28rblJiTTqbxb4z1na1gDS3yQCdEveyl0gWG4VXiK34OYD2/OBeDg
+18Cw+wubr7Afl/sD3RCNCooF645IzZfc9ZXPcedDvgmpR0pHzlGTSNaHBLWRxkC5gV4Gq/niHJpN
+wn2a6aLFL7iNfyOe/5hfrSKuyoIHOV2YDsfjo/W3hf8JukDY6O2dchRsyuhb4QRUr+db3x4rU5KV
+uFjXmI0rRYkjGD4URGYG6JUEuxUMV2uxbMquhV+MAEnOFPi+cCygpajGoitlT2norhGe9gEg9uul
+lltJLV7a9S7tufc/R3yyUOLmA/ydMHLSjo1Lhfey7d/q2KeFTQn52DpaO1mCWXNs6JfKeVCAcucb
+9Wuew2ndYqb0uPjxtx4BK97sa25859bg5StMYBjN+oux3CBkQseyrK385Ss7fIti7y/1siJgiqqD
+1ihI+QYSDqC3lH+TLyE0FH5zgQC+zoNCMiRl0iICw7RzwFCEbzA4jLrmWdZ3WZ7ld6CbR59vAOIu
+a4gbI7cIM7AvPsI8hjw+D1TklLiDRAO+3njZzy0Ul01h7X93W4n06jbqAGqK2NwtMAyyBkOwXCEc
+CAqAmVZgOPiFfSDGzMTrPt5uesDuMHlDJHEUcxEWcpUIxox+7zdmNB8EfHMMqzU4e07H4g9sYT52
+azDcF954yJqYgFgz3MFvW7sJqN2Xu0i0whqfhPNSrDDVdGyNP4NjL0/8+ZvpAuFvkrZwF5ArrHue
+cZq6j2LXvb+sdZVFxdqTAPawNiKXw5KP7YAqhNkibIpHLb0D4yxjuvFUZden3Egat8RYJuA9SV0h
+ndMYLG+PpkHnKc9JLuwCiMu59CPL0DYlOFlRD5P/kwyRTX5xvgEXjTNIrClQH2+ew7H/7pvrHIKU
++J0+Es99dBoVcqusCf91UNMNgr6ThJ+DMoeVCSvGNcVE1+FdDXIf42pxLjtFBNMZ+o03uOhnVmHd
+fro+QOCuEQyGwRbhZfXybcRkjtTnAQgGJMAZAhSH8oWD2xyOdIFCzYfn2xw4mNssxBJDngHKJ5AR
+rfBz7K/a/CWXVJcIT/OvlM25dqnVMda3zeyluUHjGi/NEUd1mZ885oq7A+C0fmk8ieCjt92fTKy0
+NQwNDMNBsctQBmONgjmz4cFKbSnteIOs3G38fVeSUlryvPjb5XiPBw/psnxP/8jCF9IneBWZ0WEQ
+KCYwrOtLdAJ3kZjLL9ytwe4CsRgd4f7gH6OJ/FouGZzGIHgTkfYMkE+iioGtlxGc3R8on8/TlSa2
+DhIEtwsyKDbZV0lLyE/v4aoh6j5RZ3LlTAr4MBonMd7Tfk4L/jW6QNjoO4NfhIiQoLGjaird/oKc
+K7G8ZmeY3drrqfKmHH9OtrIy6XhKDyUmvXSLNOC7QgSU2RpsShKUz0KSQfFG0fY6Ihwvj1BMyrtv
+biiflraKVksvIs3L7IA45qrdsXr6bjnW2tgTV7miPGe8oBf5/bKP5UhCFmnpZxqHafoJbz7DfMqb
+i+qb/NcnffdEKr6BYI31uRhQI+xAryiUUCB6iNuxKKBD2oNl2HnRP60cHxbQtYVatwW7n+fIFOO9
+AR0JhoeBbh3D3CVyf2G3ECvGsgqL+RHPYZ9epi/YxerNcYst8/jxEQ1ZgmDKD+suAd+jBViibJU/
+DDj4RSzkVQVIu2C2ar0Qx2O6zhnDb2DXmI9BN8g+dVq5rrzgRMCIX5Zb51UQzNuFQV2OWFw83W/I
+nH8M9PrHLtgy3MBWzoC1s+geknelLw8y9SXMYUDIIFfalNd8Bs4uP1jcrJ1RCADLl/Uf8ZJcjnvt
+7pT1FMMpXSQ0XSXMb1hscBabT4h9olmKq0S6fsSFnP/KuJcBqOkPv0dge0pXvxk5lcn++821K+6Q
+3F2sK+GGIq+sHkrqiCF0XM5cpNcYn0C1WTz19Sktf8UQ9U2x/yvoAuFv0ulFuM5Mn8VdyXxn3nU7
+rqZ0cSeP8arOyUosRc1naelhVlzm0wWkYKS04tcmKR5MOpVq9xZa60lPrOxUMLH2fAwdAPt5dplS
+1vMorYLfF4A4FnhN57Vex72RPqRzfx/SH+mhyLkeFnM+4T4okuEXeQrhdxY/atte/CCRC71SzMcK
+69kEghfA8DBQdo0oVmEgAXM/jy5Y8knz9DSA8jPNt7FK6zDCR7h/BAAAvdTj/Cb0aN8eqTMYBsg9
+woAv/DO5DoKH+Qj7a/eJU305uywoz5F4Z4BY9glY1v/0p6x7BXOaYLMQb2nMBlxPl29ocbfqrTFj
+ULx26RgxfqBv64VcJnm8QMm0tERewQ2uA+JG2SRChGuev8DETwvyRi1l5g5+LT26WvufOkDimn4D
+lXy2y6597Lh/BPxYcNtReE7L0Xim5Uv0A9EfBsCrq7Lwr9Trrk/0+pRquEk4VAx/YV3bpZnx1z6o
+Yi9KytpCT8yXIttoT1QkvllHfZdzuOhFbOEYv+YC4enPYNffI9jL+8S4uILPbYlTe1hGHuP5zoJn
+86/w05Z+6DgB3Xq0dra0YpSKxed5+O10gbDRd8d+MXMFMFryEyRxfo8vLuTU5+NJBcTiAFBfKd2p
+g93tKO5/V86iA2emsGMwbKowyufnbL2OXK0hTB10PJHCfN5ACierKu4PsZDcIkKPKYGS5uU88wiI
+cUqvbStmZpqrUuaRw07p+rbEp3VtNanPm4+X5LyEVPWBFRPs+TbySxT8nUX0sqy+jL47TePxR909
+whUAAJSX5/zEyNca9n4/5fU0t0gDAXjiZTkHJQKzDK8XfNaXsfxln7Xvaex3OmTtAbw6Z4/z3Sq8
+Pn+qcH9gGAiydAPFsDI7IHZXAVLz6so1+xO8Y8lHGRHAKcHDtj0aBfm0DDfAERlK+U/XZ2KHB3KT
+gAIjc4atA39pzvPWCiHwm1iktM3dW1w2pJI3OWB58aU1543gz/Ukz62W3RLcQXFIU0mQwt/GYdec
+bJe3nWRXAxwZJBAmNS1feqabHYBArgTfrJsqaWE+fkV5P87CEJXcJUKx3FeSUbE+Se43dGNiIHcH
+AdwvGBD/SE0DxLz3NPeXLf/JuO0XIPfEsyZfXPccAC6HvfwzKG7i+U08unKUnxrXZ9SgcD7peam3
+88AMFAqSjggerFZDblDlyX+KLhD+JsVctrge4u/SggkembVfuR2JqRbrdgGy2FiolDNyU+N0Jmm5
+opw83sJkEQAkFAZYCCKcJcKyUe4s365ejUZG763v+ejRi6UCyHJIxVCGrrpGkMQi8EsWX1AaKZn8
+kUQqpIdQi31DGsRcflNwlPPYjMBxt3CmZt3iZcsnYOO6Nw34NWVf5dukL0BghQXhw5vsJaiPZJLv
+0i+VriXABobjgx3pRrDOk2Q+SaCjAVKcV9cxfRxn7IGbL/G4byMS7KrYo/7qIqEKjPUZNajMFZ4W
+Nh9anetTw+A9VQN0GTCBW9tIQmwTTVbKNBEiQJMDBWlp2NOK3zDoclKuFHPBUyabe0Rp4rKYk2VU
+kaAKOqG2m4YOrvvgNhH9mckSBQRrkQ8AhVXy+gSGY3cTl8sj/arrRzVyMGLp5mgV0KKFbbXIM+4X
+kCwKa0VUKSUQ85tuLkigDlCbbTYdwAc/VUCc1uDWoR1LBuWWaQkP7ZYvtk5bng7LBWLMgSkg94dh
+O6bYThHmMy/2EqnMBKMbH+MZFOshLdMBLeGsv4e5R/t5jf99fdB6EJ98UJkuJ6Wew4C34gAmbUzn
+MtPXqytCfvKgnbmqims/NjZ9V6/9CrpAmIlkflV+e5o6IKA8PaT7IzFOZ7/h7dpB2o4ZkzdlksUZ
+bq4zw1gWl0z4nHVLY3ygLJFQcnu8+v5xXdxyVqx9oI8dpW66kmlgly29aILfwWwsNk7rbhDN5ksL
+/AR8y7nbAn5a0Q8C4q0E2HnhO+UXSctzAas5XzYO8iJ+avVRiH5XqL0q/2me8aFGn9BAsN0csq+w
+rdVVmBeoaZMGgPklMn2yFjcAvNqZlvgJtT1R/chvtedLPAqE28M6KkSGgeHVRvcFXtPzBIgBfmEo
+Pik7oqdYZyTZq0XmS+wDzTJDKO4gqYIIlgkBmDpw4LRiReYbeZ/bOu+5fRqtTtG0wpZVux6Xr100
+ENZ1UIkF/hPsaozOrHFJcNtBcMgkqfECnA2GxM0RCFwykOQxzj+VXBYKgjfrTb7layn+Iix5YBN+
+ad/e3nThOKR7eYhZhD3+dbiWtabwnR2VZ2aFl5/wAsMCmHuEWXz9BVF/OVTEXtYzSzDxXnL3+jHI
+FTQwzD9qdh1PL+Oz7fXiISw2ZxSn+XAAnABXGyimeet44qBi91+61UQ82sRcyz1dN3aN6ewiNF+x
+PjJMU3gcv99FFwgbpSWzHVsazzGwM9I5/dlFopwhOKGpVquBkmMxZ9tdUPIa6GDYa97D0sbDlVOL
+R1pVYF4mrcC+7PuVuDVtpfrYCY0jry0GxbzAUMErXCForYPnp4LaCnyjQduE14nQQwi9xMPcnVMe
+6unD9EnZkJZCk+xXEdr0H1SGLyR4dI3QykN0wc/oxXB995zYm9VA6To4w6T1NhirfH5Ukrk2ZcLn
+8nkeXiesKrws1efNpnW+9kTVeJQ7sLZ6KlYsd2UA4F9WG0Ohsh7s54tfYwfEWODZPyerQFiJMRQ6
+zVIs6/pCPR0+Br6+IicVMo8NA6UiC0paFu8ipdTGYobL85B7hu7t6VPn06C6ADPLY48PioPOX6Bk
+Un0rDqC6RrDV2QSKgtPSZWFtF51xJ9/veQPDfANR2sFS1Hak8Owi66jPvhTqiNNBKJ9ke/s97Xcc
+P9oTmb+YF9UKykS7NZkvu9MaS4aJ/nGU5e2wPpbhDzxEbPs0LIvvGkcDy0JPQsBufFk7PcsEwGC4
+t7GWA5VbquIQdx/i7EmJu7bkPAbAgMuDNkRlOzVPI6VP/K3S1Bl64AkheJ9DEaPqtqJgSbFSMdav
+mqe8lPl/E10g/FMkCZ5Q563jpMzXQxrwNOsKXwa9jNp/F1NazjjTyiuQU2rNR2jaNFUaBljJHdJc
+S3XLsF2giplyxWPLAaS1znvulhiQYPdx0Zq2WXTjyBbdtAXnulU8WYzLXLLya/14N8+fpL+WC/pc
+pku2x+dp5zIKF7wOrPgG7MxrlQtft/zlS3c/P1zGu2K81kCvM2D0SwnPEmh1tOc9Ou0/vIFoOqeE
+XQmtdFWqYwJzrM3//Y13wbQvwa2tv1yBq+p65GvWYAnXiKUs10tgDRArlssEsHyBzdVCAxQPcpEw
+dqA9Wof1mdgg+KKAT17qYGsmGsaix83NIvxkKQZQ7zlsKHcwbteJaRRIWGQ1w9CwerNszlaS5VcE
+a2AQbeR4WJ1PIPiQHmH/gMpqZciUvocwg0cGac/rT+t4acoqEKsWMEzprFE2Awe6tVq2HS7GBoDH
++SMhXx7+KjziE7z3TuHSd7BcUwXs/VJjXWDOtS3bkPQXXvjX6s01BfvC4nKR8OukZXgHwK5Hn4Bv
+gl1YOaXeqJ5Bb/cXznKgummW3bjE8pA+fFRBsMYcMyednkzr4Veleuprb1tUHsykrRLtFVYGJP3q
+4d9NFwgbfTr2vGsE8cSRgbjOXMYJpJ5bcco9p+Wy7SBkpdiD1EgpsCd0jgOdJ/EKArWk1aRUUuUz
+lS+iQtrABRjeu3gcI1cyFnbwWbZMizwb9ZLG5YEN6CrlxY/AbynX1/gTF+3p+pD+oueU+sytqSMe
+eGhpW0qycgyI/dSeJoujqqj/gNpeloUHj+X/SpoGEkos6jxraRTPhYA2ZgxovYwew/7CU7EW27kC
+A7A+dnrgEwXMMSIU8lLcEzqrNU1UoGO5RoyFduFW4fWYP9enwl6s4311fV/hQbtTGBgOS5o1Lz9h
+K8Wty5vs4GC5bxCIDfzUwG2zEoPOCSkRoKuKGrWEAsA5XMigSuwQgriRANbuHO4Q4XtGpORNkAwR
+2rEjAXIKuon5AHQLEJ4s9w30jgqSobCXFBtg9H/Sxrb1mO/fANdTKbPgfEfL++lYBz8vKCTv5YkD
+cQAAIABJREFUl8V3lHDwqd2s9fAogBjAFzW4HF06Vi0a8+UyHMvVRADIXK4u5gURaeH6MNgibOB9
+ABo7pvhewi7jHsCwr//tR3NRgHGmVXDr2jnTllxqaXR977VANgAMO5+vGOPZd76x9MSi1U+4o4k9
+jZECpR0twF23b0ozlfM/SBcIf5vybqj8ngCype9HrcxRq6c0ZpCjyDqwrJbUp17UWqUYYo4St4Pc
+lI4WNYHRlF4VFQalGlCTAi4aueWXIUQI9gZ0PQ0VqGaabmuUl3pdyMh160qOi8S/UsM2smd6Kv3+
+nOcSLhBfVOEFeI5LGqWrEtCQVEAERJKeeY0LHkRyTe2ZR354kxYgONuc7FUB7nokynwnDRRX0Ps6
+nHU7AF5VSLbp0Gz/iMNaJzPBj1uCOzhSA9/2ueAExKbUFAaK/VvKgC4HjAqMB1bBqZa+QPHyV17t
+HiarlmyTvM/AQcI8rH8Gw5vf8EG+CNXFiWtbPNmt0b0NPHdIS3CA4YnlN718UVAtxJqnTr5lYX4g
+i/DcLb8OjqFqdxJFiOTgzWzj4hsDOQSC864ox0KOHadWkpqIG3+P4xRvE3A80lyaeTVdIEY5ju24
+gOcwUFym9WVfflB4cbDfYAjWPtzLPLw4sd9IhrsEEE87hiBeoDutr1WT39jBwCX7CtN4+C/kSnVp
+WO2uANh1bYBd3QEvA+PuNlFsvS6ElR0OSeYAbS1YvmMTnABwlCJt45ingRs+h/h78/1VFN1b9KpH
+Ke2lCvyb6AJho0/H/hn07Pnvfj+l5bUHUrS1ByJBO+h9ymeAS3Hpi4+KAkXB7ZYfL+wAeLUiXqoo
+dw9ZtneTF5zGqiHB3yzCcSMai5PT7KSTNdirs1ZoH/vT3Wxb+Nvclml5xWl73jOffJJ3ypdMrmaF
+lHGlz66x6jjs3PZuBUmZ5ZqzaeRzlY9l2hyZQmMzWfhOFl9hHEDwyVXCrL1UZukTBtESQCb4yxBj
++Ap7AzWHWpC+jJt+FUBEdythKG1TLoOsmwaKfSgUQknmBes+GM60I7eYggHFMSTWwzTUOQyIOhjm
+GawWRLK8FRlCSDeA7JuyPH9vwTBxmN98CBaYYTCMtARjAprP2nO9qyD9gj1sIHhSmrtbECAWAsMJ
+AA4W4uHAGVHnAsJIa3BYhV2cHmRyrFsPk2yEBj9uqoPGTbcskt0xHWxRrSB4jJFWYTty2gqTpVgO
+/WjqK9dwynxVA8BIQCyqSHw97amIYM62ZtZ+gsU6HGspbj68Ic7n3YnPfXu53bRgYwyr9RdcLxgA
+2xqldPS0SG8Lz2RT/kXmnwCw54dhqeORGt7jBzAsgaqLPswntP7L/FIn6c8Chn8zXSD8F2jDQ9/4
+gY6n2EqpnkFP5d6DkKQTVD6Dk1Z4swJXhVeBLxdJQQ511wgEt0ux4JwbxqAmxk4RC82tv7zuUnjm
+Olz5ZC0GNgvx0S9Yta3l3RKckPnQl+iHnifg5fzpnqvx53t5lhpwoSAJDR1KKOV9+lb/sYMEJDmN
+FUdvZkvthV7FGQAbwI+xV7+yNt9hoDyR6HzZLY0bAAb6fsWbq0Q2CqERWhdS7ZrSFilguICQYgk2
+HpQR8QC/Y+ZT/UhTaDz2Xy/bQeG+ELRoxL6AJ/WpFtiSZePcwHC1ZnpyA7xFrlAVdkK+XKUJhkG3
+/YojIOaxD9cVA61jVPuvW8IzBSRvFuCNNhAIhgjmnEs+SAXDcMDLLhAGgKdbiM1PeEChYwbwWsOU
+QLiOae2n800yUA5IvrcAOuY6rMcKfLc0mq/kRbIEjxH+wGOM9dviaSV+EBO1XaoAfhAvI8cW+WVG
+Mf/2Yc2czHpkgA4f/PIBmVxb7ne7XJh8HMgiHOvd0kJ2VABMsBRoVl0FSJd0y3CzDh8swqxL8gaJ
+JHEBwKkHYsh1z2M+qGGNsnF9KbUiDFn9iYcVCX18ulAvjxL8rXSBsNOLhVlI1YTNqyNAPBR6k+Nh
+CAVi9qMJYbUqF66FCPB4kjz83pHpp/ormRmWfjGPRrwqv3Idwgm0FiMsW7iDhU+Wia2ybQjJuts1
+wcf1fp8+O4sGow2bBrPU4sf0yHt94ciSgDAtT0N6yikdmceXcXVQ05qyBqjOE59/L/6Ov5nFnqrk
+B4uRp5WFa4nzdXaMkspD2noQBxn26+e78nrqB1s3l4IjsArXMw7CMn2Swhoi9uSebv4MNOZ5hzjM
+xaAjUGr0Ph5NKrnl2IEFyY7uWyzW81THXcC2NHZTaWligHWBJHeJEED9IbqhpDnjvAWUBkTnOo6J
+r7n2Xx5jQKdZgO3mwq3Cbi2eDpBlrpfkmrV4mhU+bwJoPCycK7EBL+PTfJnYgQviRb45l3vIVA0V
+Vd9x6MaBnM7wpy+U87Q+RmG/kaB4jdNIEDzMNcKAcK1WofhRwa6lOeAF3No+IGo3D2r7Z5fZ91sk
+u5Egy68edFLXlmzOUBpnB72B5xQtTFZ1bedGmpfrFmEu8wSanTOsj9EX53XvDvfRtmCMsII/LpP9
+Rwuf0ixM47DnE32k+A4Y4ZPT/ga6QDjoU6CTrPs8aRuLtx9CNifqOzSl6JkGfAg1Bvt2MJv65jGP
+gW/8SAkVE80uiylcx0+8QQCqpW1J32y+LXnxoeDwKp9fulrCfVWtcXMR+RSP9N4mYBvuz4iV7qs4
+CSi+lpQYCmA85NUstjDoOZ1A6npkv6cfGnQmas8TkATIKtda/TNp2/XfDTVelGnjKX18CXBsDZHa
+p7CCcViA8HV1EOfAABS2hRWPuD0eZRlQYLf4otXNIFFgFre0yjnI6GWLcqSww42idB0Mw10vZI+b
+7o29FdxU7ItQ7at2pKMJkiZ+dtkE2fqYL1pJyglhv2vNa2qp/cAMDto1TvFt6dYLgr5GLHM6+LXH
+7iKQocv/dKht0eUAd2JOs07aVwHV4lPMYhnuE7K+gjbXB1QcCLuPtttQ1jRJ5VGaO+U/0f/VT79B
+WnJ19QEyl1/5XG0SA+pQD8MM1AbgYbswOFin+StgDnkjFjcLkiD4+ecfhbHr40cAW3GwixUe9jGU
+NQ9+I6KA2sttMtCtus5YBVCWttf+TKybhBlP/siSjnR1Cq3vZVxGkojVF/EKenVvi9I5vi4LECY+
+CD0gYBbJ/V58bdlYio+GLL6PMUPKGGQYUsMEDrbwWv4SI7QUL424ZFrMivh84vD7OQ39V+gC4SB9
+XwRAhR6HJfbCKpxHZ5h+aQKIh6Yd+UNdWOrOr6wHxZXKQeH0n50fYKoD4hwMasQhvyujEB3eVwZ8
+OWYnMOxhEQQYPjVHvpHOeb/qxzVrjA12KoCLeOrEAEruDIUJlO4xXCBabEs/k2yBw/VLC1HazTa6
+XvbztA8E37ZWDmmnyS1AIuPS+9vZV1o5AqwJjh+AKtDAKAjY+hrsZd4D4P3cfAEptqgiMAxqDyRt
+Zc6buSpNMYtUBewYF2jxD6zG5Erh+HhhiHiga+O+yyQYCBbvF5C+2HEjTYo3hIU1EP5Ql8OVDSRe
+GlyuCjLF3EPmqmcyEDYrpIHfORaQdUtvAcRzYpplWIZC/ctlqmtLLwejBoqXe0paZztrP62OVCV+
+Q5o31mLAWAwAR1987HSBcQfoDoALIGbtFmCMHtE/KRr6CVuI3X3ixwLKy5rrbg8K1R8B0MXDouu8
+AO3D8hMQi1ofQ0cNhGWkCDeJceN+TaX+BRj2sjamWrWVjznA47PG/DMQTPnHcjsI7jwMkxFnubzG
+IPBIAcILAA9oAZ5+/7CmNOM+xyJo48nXlow9KF5nlaqMfa40+DP49DfTBcJG3a39idjDqgJfLXf0
+aHkbGLYi6+LNutx5quhy0y4h/P8Pe+e6JSmuc9slR7//C58K6/zQbUk2kVm9d1fvb4wkB4kxhjC+
+SBMhm1TtA2b7H1A+VB9kVwPngoDYegdojZluSLyEjoxHV+ZyiUtSeMCw1C2WMvXLshVYJLKhxeRa
+6UDpOP67dX4unJnbPt39AFcAON82Si/CQmiPlHasFKZDVqTXSnXGz4zdlscDbenc6QrgcoW4i4n8
+81euwvxepPf4oz9JPySUiPXjUe7toF+mAy24fzHk3kAVl7gb+H4Bv2f6uFYNTgoL6gHB1aHz3sIi
+WzB1A95ygZguEY/7BwwD/StVLFNmviqvOS9tzlGLlKPi127W4JsrRCKFy0wKp51wudXRZ3ZIdw9l
+VwILrwDGhQwbBO8GwntvLLcMyzZ3CBFx94SwCNtUeVDb2kdUesPOZjjaevYf5UMd9sOCGVZtBNBv
+s8Kp+KeHEzzLVaO7TzgsUolG+0mzS6tHqr9l7hJsHbYBcy8DMXW3B4LgpS/EYDhN6HWL79oOwhvQ
+gGMD4gApzPYeeaL2l1utFhLwvzVchyp9lGdzmdAJxVZjHXI7zFYeCqS1xXUIrjz0tab2Kwwof2bq
+SqgdYRgOII6iwiw2kpMcz4JS6tqctybYWa4KHeOmAr4+/9739NB/c/kB4Vy+D0UBwOHLVMo7Xk3p
+SR/DUiwwZTMFHre5+r2evd5OJDHkS8Cl61UjfFK8npKUJ6jBatwTpyG1k/vcKbm8qIwmDBf0nmGh
+n+1wq80Vove9/qAhHPii2oUSsgyoVfL6s/sewNuAFoiG0LJADaCVI8GvSMY2CWT1kpKY4rX9hlxC
+f2d5ht8z7qs08xhAdTuXdjG5x8f56MezdbZo6ecKoXs2OuorJMk/W2zZSvwQ/9D/no/zNc65Wtkq
+DDq/+rNUOWsVXypoYReIh3192D9guJR8F1rUS497XGgfaYgPHoS8zcFgIT+ikXCrmmGXTJ7H6qtm
+Gdsr4MsyG7M+hJuArHptb+PcGIQNfnVvbDEA3ttm4VjiH3YQswpvt8xuEdjsE4KamcJgmtu7csch
+fdJUy2M8AAlQ3FW+KgS/aNbgkJ9RmjluEgxqBIgDgAOCsTy83G84YfiVQGwQ/KJyDRcIAuC5FbfO
+52t1vzf/VnaH4Wpzym2e7mV7OF0icguqkwh3n91oU2U9pj5FEMzHznLkcz6ni2ULfeQmuoF0Y4Nl
+13WWl0kwi5Vd6dkqKjmLrivROgiKo99LRxpyi5hhyTxxfn4swv9DSzT3u0iNfUsZ4sC7hD9xVxeJ
+dqgJaMklqgmSXN/zIUgqS8fxlvQCfycEPyhZyKOVOH9QpO4HpTgLhiNeEygKgOMFW8B6iqIOg0CD
+4ScATlh2ARBpuL/Cjx2gfCs7LrOHVUf4bBV8LbIxTyu/jLMk/3GwWlA1NipXKoSc/ovjkAJRuXHp
+ISb/s2VcLF7O9jhO9n0XinmNZNQq2Oe85AVvnUVaOceFex3N+nDlMFvFE+w+Auv3wfc5DRDW0QOE
+AxhnnMQ9l4aremE4OMH16hLB4YTjOPYAw/6DDaKEy1ogWJ6/Vffgr9XDnassv0FrpGgbGFPnp37R
+BYHErMqASEJwzTJToKgIC6nDsh/fGltzb9h7Y799Kw7Fsh3Ydt7XjkF4EJSXtYV3M71y/7V86ohX
+Ji3Qm6IIbMF7bev/YRFeZQVmdwieAzmPgSzE0RbiJ6LnNBiOD2ywr3DA7wvysu1aK38vrMDTKpzu
+EVcwDquwWYnDgh/9JvODMoE0V4ZYtUA3LcJbqeWwRVezDOJ6B+hGPYQe0Pl73m9IT3Tg/WxFhgh9
+BtzjCfJLX5Hya+Br4ZVxTTR0/Q+UcGwKlsMkM1I5X0R1VAnF8e+cvII/vvyAsC9P4yCPOhEk9Gqe
+ly80ukDOllktW4QEmGv4/A0dv33T+XyqU180/Nao44+f9GZj47SHMq5SiVcV6lBQvsPntjzW4t5K
+KeXDQRzLcnGxc4Hhds+8H309yzz7fT9GBdYePCjb4HRfLB2JJEVtQH9cs2HwrGcZv8hJSVpoBr18
+lBWQ1qnxqo5uUEhQtxv/Yvl+yl5+820AX+vvxPU8Se8L10wKjiKnk0TOdHLbZ2mdx2/9qKA11ju8
+XuLX96H4a1D+MEDu0CrWUvVS0vZaXBwqygq8vUPF/L0bScIQiVfJXoYZ7jKgFP8QGJfyazBMvs88
+E4NZas26mnOKZztx6y0KCiIHIYqjjle6b5QlUBwot1PMThB2Ke/bnG3OIVjeDrybV6FPCQvaNHgk
+RdxRImetgz9M0CgD+ud3EhDmhKW+U/sOhjssqL66e0T43p4rsiwK0uqNkkb7Eas/xVl/EGkD45qf
+sPsIi75ythJJCL5ZhTsA2/2YhR2+rTZP/sEzj7hAqZZVOCzDW7Wn/RJ8CQQ1ymceu6WfEFz1qQ/n
+5bn+UKfZtuv+JBVd6KZQhiGPdGyzmKprUlwTh6Q0A4DzuFK6eQ6fmyJJqO7KSvxjEf4/ssQLAAAk
+dI26ehgZLgms1GB0gK82nSUV3dtUNCKigVDopxJu7e+qxGNU9kwTF1baCkjntTSh+Hzr1tDosLVE
+eVDeLzAsUVyZBs2nt/YtIVuHozzaMSrPhOXLcumzx/qpi+ZxqWvVnUslorzU8XnT3EziXALtrADN
+tJoV5r+Yuz3+afld8fNdqP1P4mKvydkz0DIvR0c6O5aMSmrnoIT1QwfK9RPofgbiE2Bv5z/+xidA
+xhl/CgMvNlLO0UwMCgCIYrl1d4sJtUVW3602BVtzhSBLMK9VRQzFvRzrXv0jDQFUXCnNEhx1pBXf
+KnrGISVVpgl5JHXJtAIj4jTLquDELYR74x1WYLIMv9ka7JAMeeNxUZ/jVkFwL2U0cZgBgRo0yjvy
+Z+UQFt5yIZAGvjHDhYrSPMfI8+qv12GX5B2IbzB8+gi/yiIMc46oGSMWlKzA4SesCfIxRdsmQDbL
+MMjSzXnp7x2l8oxejzVjhI46RksLbwt9P8LDX5av086Vdv2q3W4NplpHDYjkYzKup2ADVipFQUHv
+qvDiqgPp3aH/gXM/FVPIkJYf75tjK3Mriprx4xStf3r5AWFffm+wXFgcyNKQzRFZ2dkD2NIZ1kv7
+Ufp9T0r5YF0cMe24Ujp81NelIFGQjKc09EQdHc2sRGg/WKBVcTUQ56aUomxwwLBEWSELAvEdee50
+HZD1sABPIOaf5/4V5zIX0KWueY+weJitwdUO8g56WXEZtcyB6pis7e1YWHyoJUg+jtH9ah+oqHRu
+KwxavNy/1/rrFBn78ftxhEvsTPd8rR73JBEv8TfpKXQVuv+WVLoVu+0LMKHy43oA8N9MEzD4zbTg
+fhvgfYFMLrfeos0yZkBiVuAVSllIaYsr7DlY7gbD+TedoqJgS85IlDvfD89H67LAgM+V/AHEfDf1
+i9mnfQ7hkNpNTsQ9gtwAIr86885xPkfv3tjyxt4b73fB71veKVOrLZLE4c7ulVA9sUsi9R/mGL7T
+Nj9xWFoZgLfPtCA+S8awApc7BMJ9s1xBLmstQ5EsISuwYM4akStZhFXdhWJagXekJfjd4fNcluH4
+YlwnKdsqPwR6QR4Wbw/HYLm8x2tbON0euE3MY7f9p7i7FbneM3K55/zXQ4+0naxMaUVja3ePAKKY
+tE6la1oaTXUThgMN/ST1s19t5bKdHPKnlx8QzuW7KDAgOIQ05qt/YAJwWhkCYjTVgm2p/o/2M+Eu
+0xccc58/Ffg8DpcVlzR+LBRGdl4h8ZwZsY7Gz6xhKShngakGNbRTu+Hst6MQmlWXOy/pxey0kZbd
+UWa54bJzqX55WJW2PX1i5xUAMx3rv5ExucWnP7CecegFxhasKBQr6of2zdJO89+xzFhW11/FyWPo
+03kDUB/icInhAha+v6Y3pO8f0FznVKBrk28D7kegtSnQns/9YDkmCI6+DES813yDTfvH1qVWvooP
+gEuWXgl4oDQHDHeIBP/e7IwE8mlBZGtinB1uETFDRLMEx379Vt2lrWEw5MZx9OeHPJPkavHb3SC2
+LLzfb4hs3wrwFohsAO+qA5ZGSr/pX0CLr81RLz4qqiy4+nGVrTlNmuSAwOEHzFZi8DX94egGbKMf
+FHiutt7mFO4+wivdIsyiu9w9wv2E13oA4wUMNwk58lIS+8x/h097uCPLcNQ1tYXDUvuNY32/Hgpn
+mjSfjbKesCzeTkL/Zv4lqISWFF/CRVFVFl/cC0swqm+wa0L2l/Ip8s1U1L2RMiPI2Jrs9P58cYv4
+cY34V5fvFf4Bv+1cBryAEeoxUcGk+eN8qajWxrit9TgCYG+I8TSVUItq4P2Yr+iADEpjcIvyC/Zs
+K/WFCXH9BU7cVZRJL6fMF7/6ozI6rcEVH79v51P5RFKyurPlB5m+13VeC3ENRtrnJRBD6k5T3TVB
+QGEdF4ginE/UGc9l4tLviAMKdKm+utWd3yXoLIKR0Q4U51JCkYr2chQf07SfbOfJ2J9n3/Ytrsnm
+WZhjX7gCEIK60ohHHg+S2V8scAXdEbda3AVu13P8enCjiLxZNiUz3SyQTQsJvbGhnhqv1oVcGoSV
+7IRhpbQFzuE+YYOzShRq/oT0NSGeAep278jX/kggrmuaTKbjHSce2xGXQ7XBOj7dmfoxK1PzD377
+wDjfH1BseXx7Hdh+5J2Bx4A4vjS364fi/innVidoLg02cC9A1l0itllMZS/L41pt0Nza8ZtAzYqB
+vP4Gx3kZCIWjrc26Oz6q8coBc7JeEGizCOtrHe4RDYQ3gbEDMHwgYPiUiGzEA1UK9KzAcI2g1qE0
+SC73vdjZMkztmMO1T289jmOzNV7Ov17T6wBdbudvRXqSX6kZe5evKsqqMnm2GgQPvZr6mBqh0C9J
+vP9TkhWC+kRi9dXcDtCdzy3MKX96+QFhX37HNUJoiwsY63zVfwXg6lmDgeoJKuNDZFO8zvNOsGV5
+8HysK1VunCHs5rzA4sqxehyDkdYNNBWk962XncxjdOmmPiV+jsov4lDxqdzG+XwnUkkeFy7jWu+g
+bHXfzwPFRV4jQZ4vlV+Oq1uIA5ppWxxAoFv3r1wPyvcaP6gPN28FejcisytQBwOhEDX1luarOL0c
+l+N3pqAc+wGB/V+VfRPqdmA0caSVdUppsLKPKO4/51oQfJ/lYX0FxCMurhcaRNo9BdjJuLc6zvMI
+81KK94MV+ALDWyzBYstwgBpCaQ8IEc7/LDOyCq+A5bAGO5Xx1ehmZvupe/M+q/7mLtsEta1LWY2G
+gwCASBPgu90N4v02C7CF336Nd123bQtklyp0Adhq8wqLfSHMjN/m5qAatYQCrrTyMgz7Z57dEhxW
+4R3hZXMnB3SnRVjrYxorQKyBXPRwyftv9VikZe0z3VteA4hXwq/whzTSZWM166+F3T1ie38gIDb/
+a/tdCH2r2vPVLLGoNrk5rOQnnG222o5S+zqhWGn/AxTTuRVXcDvz1/a91Ujri3aPSo0+HfeyCIax
+yyE4LcKkU9KDWvhKQLfQdv1ufSYyoPCZobvCvSjehHRIwrHgxyL8P7B8r/DTF9SB6xP8Gq91uIu5
+Kf1iILGC1vjqlNpHQVgq6dZgq/2dCjniY+3xXd+7kDuUV+/EXeP0DpIJohzihriMqFyA02dXuADa
+ZSktKcEA4tnvMqDe2W8UQOny97UEUHfx6Nd3PMq2MO8w46THRLkrJYoi7Odr1ocdjDZHJ0ZcKnGl
+ZwWKQwlZjr+Wx7jlUWvt3v4bcXIJn1jToeT5GO0LlbjwYelnHQBNl6C+FJ3lyULL1uD1AMDriOv7
+5/HLdQ5Qazd33Bff/2yfqaRlbqerhMXFDBMQMWhiQIYcgHCvF9KKWX48hdrK/RN+tSjyagkO6KX9
+Ue/1+15eIVNllOtIxwLTQPiNd+Z/HxA8bt66m7JFVx2Ggb024F+jA2yrEkKCgQMFWgHD8WEPh2GI
+pG/wWhvbPxGdHwdJCA7Lb0Egu0XkfMINDqksmLQCgh8+prHWgrwIfl/kF8wW4AbBEQ6LdvkGyxYD
+7j3cI3wt2VoPMAWjBf/Nqu5CuaBU855tX2p/lEmlZdeKsux2wFXMeYirXk9XCkh8+hrt7U09nFEb
+87oR4TBBsAOstfnBIMKl5RkKPUHtTyTCgua7yWKZulCkj/5b4Durbcr1f375AeFcnsjoXMoaOKzD
+bkVrz3cNNLhB0S56+8mtfDiWYalGSw3pkE8Z361Z+aSY8ZL7DL67KdlzqflsGUa4M4XMpLKos+na
+UTZ1bnbUkLuKKgAtWcyXDCjOi7R6oXSXlTlxZq+v3S7M53IZzssh9NrQt+we0X9aMv9KJwjIanvE
+ufAjpZktRnRUARVWbqhwFZeSs1RpgRixYBDjchlx574cxw6AOa408iCz0HvbFS7gkV4ovXjDEuqI
+V/DleHaJmB+GELfmHgPhlsPvmb6+HneCcO9rT2UTGcf9uHq704BfsupKKO8etxU+9dgJyDWQrvg0
+FXz7bdaQVX5o9+kQFb/tmr8Gzm18tcw2RhXr9ZcVO+ra9/OYpUvY8jR71xzBAsEbPE/wnCWCXms3
+CC4QWxvuZrGgatcSWJkiyiHvLUCuw3CsEB/Mt2orykBc8LcUZB32eKAswxiwJoRPWSZRPjFQLsLT
+RYJdIRZ0LewbCB9W4bGVlRDclRltU4NQ+ccadQD+spyXLLdbnhEijud+yasTiil82/cd7h/X63hc
+PozEKg7cKTNDQYY8DDnGMsq2iyzCcxt6uEtidyKi0eklr708DpnquY/Rl7hBL692/R+L8L+4fN81
+wv6bXOzgq0BVIluC7QdyN4FJ+EkMI6xnXLRRyP04BuTG8dYJbvEBTdQgPY/bwzGRd1M93im6svGy
+YADhcvAykryf/jSaZ8k4j8gtIFc4rfb7ymKntPn7rdzu9S6XtXKnI13Zg6ma6148QCwKaftV/oev
+sCuiVMRAWh1M8fjvjbhUl3OgXf4oFcqI01Eks4SOe/sQx/sYZXQ7r58jtJk1SGE54w+gPdLagX5I
+RjKPaVDkxw8YvlmFLxDcBr/xQLkOx2uka6Ac8xAfpTvL5gaglxavcFnWH37LRaJguKZMw6MVeIeb
+BOpa9jPU+EvYEFyuA4bDMn6H3gW0X8lcI9FFFRqP8eLqXADI6nU7t5Avj4kI3jkbw9tMipLPAAAg
+AElEQVTl75vuh1YVQN+WxVeA6wAbh09j3voanFmESe6Mc3igl60b22da2Ko2mC9nj6gv6bFbRMCw
+WYOnXzBDHLkaeB0qCoCrP8yBcgbAK3yElVwhXoqlGxouE3tZfi8QjLX882oCbK6b+oJelL0i8lfN
+nO+n2jjIMhwQzNbYast3KL5Yey8wy9A79/t5pVc5XbSN/FCNCqVjeRBtF1UeXlcNikHwG7UrZ9zz
+9mYGonWKqKZ4QzH7Gr8d1feHlx8QzuUORHMJeEoFPlwk8lri2wCyOGdYSau9dLeIa1ge4lEW4ExH
+ylowLcDP8QVXQ4BITLFUnTKOMkAw2JwvRrWHuRBavyEn/liFjtOPNNDNETpnH0xMIH3Cy/w9nQfj
+mD6n4/347XmcyzUhluWXbxUcF4Nq4h66P/AtLs/JwYd9v35UKeNy7Gvb11mDaPXvcXKk+V7clH8K
+tLYofJZgnEFhuaTJjVwuISMpdzT/1QG9wBP83iy5w0WCXR8Y+hh+F/sOLzSXigHCvc9VBJdpU2PK
+cdUeroPhwg1CACjNGzxhWNTnHuYvtRHw5e9PCSYAFuohnnyD08dUkhKan/Bt3XdgVti5ZnEadRmw
+fXugIbgD1WfA1/IZIkKeMgC39jwg6fVSKh+FbsUOEFgwQBSu5y7RGMbyOrtbhMXnNpa1cpq3tVYC
+s2w1rgwYRvkK27W11V8Xm5QnKps28HFOmfZ6GRC//GtyL3PlUF3Y61VgHKAefsEBxCt8hG+WYfuA
+SfMTlmpr+XZjlF1MmRbuEbald76KBN3Q8wmeWS4XFwe13vfkA4x4eOT4lLE3P2Oa0jDzRECss41U
+3TT4zentIgVbhJuUQP0KKEcytn6frpu0N1PPSijpeox6cokQUax1UdD/8PIDwrn8TuF36A09waY0
+dpeoyBMjnuF27ifakJgdwBhtH6DGX78hrVNw46t4eBxwgu9CATA/tTWLOKokJyjyfR/7cjl2Ka8s
+M+lFO+cFjms2cKUMTfVyq/0o85YmySLKv9Ic4BF1G5AaoCX9jhy1jjSZJw2f4Lq6BKhSuYkLSH7Y
+qqmm+n5en463QrgUylMPucPvCci4putlVuVoGe7X5ji+UhZ0XU84jDojyjfPiaR5oF8zISj6F/cV
+GX1qAHHbDph12D0hmQA4jlOYrcjRZqxeuTPA31RxG8JxHDgfbNKemhZgcoMQEASfsLziODp0nFno
+SjpBqq2LVsBg+faSfoDvsk/u6lbIOKZweUDwy+UccMtuKQy/wsDs5+y18losmetHbS9g8qVwAHxh
+pWW4rLTwcq37DnCvsuM6NdDSBDpzj9g1iG+HFdgtxNtgM10m1GTJVqR7RFqIR0nX6mDpa4P/ReuH
+6dP2K+A2rMPbZpQ4wHdsjzgpX+E2WM7yZXkktPObmNbugOBwM0Hcp1YvaQPhCHAn2B7WYOX+Vfk4
+4fhzmrAINxeJQsusgxRWXg6nno8Htw7Bt3AWVkqTZy3PeWX4bca/I5uej5xVAv/K8gPCvnzXNSKW
+/lKgw1wooakGCm6GStC7neTr/Q5q0eAhMuKiwT3EkSyLuGiR1gE1IXhu87bH9tsAfNnPDin9fvO+
+GwAD/dPMcR9ooNyv/XmJNHc47ivnuqz+VT8H7FEhCaTvR/uQeY5fjQR0FETCryVwKMoz/Zpx4WiY
+SUKHTEtwxmy9Z1go5u4rPNM9x82zVc64BqkZHK1PPoU9wD/en+gKY6jtWX+Svh0wfIJcrTzwrSy6
+A4JXB+TpF7wIhMNNAgiLk/XbqOKo54ThIEA/FG0sFa3SftsqQLDbP5gh/irf2lZ8jEMRabW9Uj7a
+D2lFhmEQAK+Ap/jNIhaU9Jm4trP+3IaG/v4KwHgQEa6T8VByT0cgvMuPt7bVJ1WBl9cRHIJf+oLG
+vL7rhb3NClZ+vIDYCMQE7JLeyOvWa31yj9i1FQkXCfcLDreI4R7B8wunfI+ipnDWYxOgXHdWf7G2
+j6Ksqk9Z/jGNtROI13pBdXeXiBkOa7CHzT2Cp967+ArTOm2f2ZygCcF7EwRHvWX60PoFyNZ/Ahk1
+ZW22yN+CYK3for4a8XFuPLRYH+z3p6B7F2o/rR27bIFb4A8I1sooCmS5n2nqENIjIi0NtRCqDrP6
+xiwRoaNatX2pof/7yw8I5/J7IBzijs9PJT8JTLlRUDo/bwIf0Pc/xzEMl9UrG1Z0goyTM46UUZMh
+COgVgt8O7crb7OB6HONy4jI8n2Vv9XB2qswnl23Ej0vka5gPVcx50CM+yphqj/gqPTKSsZ4BuBSl
+HYz6CgEbXNYG2YUAlrq60gUyV37S/OqcQMtnOwH4KUwF4GGd4bincY+/G3fb17rNvkhYP6nguRdM
+AXoD3HaqUBh1Za6nENzjGh/B97AGD3eI1a26FZ7HBgBfQDnbTAKtehVLtsDqj1JArNXeUm4NC699
+YQ40JRrBMGjKtMNaDOzhR2nbm6Km8k4A7uUX1vEGugvIT+vCfIBrcTCWTffPQEzlzsCbdbDIAl8u
+Gu0BhtJ3EEbvIww8Dl0vVejLynUpDIiXuQos/9zxEoXKMj0SyoHkRr9mWTEtvN01gl0iOvg2q++I
+qwcYLV9h8Dp8UpvCIR9hfoigWSPkZX7CWwcQx9fmMjwtwOID49z6TmFe0fJVi17X4frBYZKx+R4i
+3R+GuwOVUbMMQwmSKR96B95Wv3xN35YlmNwiuC549cGbCcNSMiVlE8gnWHvJSMsh5cTdRgTalFs+
+8vrPN08N6r+s35k1wi2iZnn5s8sPCPtyVb6X5fxCV4e0sxL16JQywqQPbk2a4oUaKKfpll0LR+MH
+HZORhuKiDFKgnHcY46EXhTl/SoP/GgTrratOWq1fmulmWVSRarvv2yUPa3E7BnYrPo9f4u3YAGP/
+cSHhmOcOsCJ+tTsIIAsBksc9vVvlMr0L1wTmwEOHHK1fc7ip/fjsatYJhQOGJfJ0g+SHZULtd+Nu
+gNxjvJZZr82A9LTcdhsc83kcxeXvsYcsSGVS1+f+1ZXNd+D4AgtyA2WyFF8gOdVL/Iu6qk2VdUKw
+3//FWjvVXiQ/fIK9obZZItR6o021pWS1+tR8UgtWhbV1ZdmU7y9ZgNeGbotrll8FIPzOKuRQyDqu
+m1VQ3KA3AI7SkM9yhPe7/0YDmVgXsJbitRT6Mvhbaxn4+nbHlGAOwAYpbrToDdbAowFqHyyXX4/b
+OwfEpV/wAwyHpbFcJC4zRjTwCtnVlAnC7QQBq+2hwiy/NnBOsV/hA/wy14jp/sAPHTlnsF83Bsqt
+1dpMvl3wdmW+wXLWkdfN5nCUnxd2WX39fhlyGWaV04w+9QFy/05c9j1XFGWBZi1JEIzV6iXfTLkL
+Efvs8n4IgO4eoehjTLz10cN1pyHqEb1aPD/hK1w+w0tohqo/uPyA8G8uZZ3IGN/e1Hqp/qeqLbgt
+6DvjOxDieiwU5GoNvvyBbnH0JC3SfjMmlxdXaqDfY5cI26/ySLBC9zG+eDH1ONfj0sqyl3LzXQp/
+olkdoA77AMEQrde3KGHPYkfzf4/HQ/rcplInKMub5H0pJqN9wBXM0WD8F4/7ISedEejFQnej+nnL
+6WnLac5FjrI+2/yIUebTUPeSgMo9JwE00kjb87L8DLUnJF/CI7e3B2SuxtxKqEW5xgmdK54XAdLi
+Ga4QtT2tkUsIzmRZEYay5u2Iw4yLdk9x4Lw8fewjOxwvZUXb8Om/FuyjDkugW2x6rL2xt2CLYO+F
+Le7Lunx/2b5ugcqyL6SJnSPiPsk+Ty67ScRX1OqLahQXadoxYJmvhUGgE1/MmgCtQWNQxd4G9pXG
+ZRtbU3d3Uejh4SaiVEfa6yPLE1w/E976cVzWagOVRi9rwq4Ch4tE3hvK3WKAdm0/tbnLfaBAjzYN
+pFiUsR2RQWte4xN09nvX3lcIgLPM4NA7r3ndZwguM1VYbg/tF8LBzaYCcX1baVIHa2p3kg/1Rill
+Az8oHzPWnK49IV9LWk3PfhLm9sWwbJuiesxCpOwPnANz3NUHp/vFch1cyl8LjJ9UzD+4/IBwLL/1
+EBKKTVvcdy4l7eiEw2f4ZSy4QjAwLFDje+8DfAuGWaFXvCm2iLN83IGYRJPqtMGQFdn8z+6rHTvv
+rcpgFOKkPFt5ZJxyd3bBF52PhSf5P1VXR4EfxfN99dDYP2Re0g/CgnghowOIMR+67sHHmBZLIBv3
+92n7CZa5fBr4PQmwbwm2gtu6fW6ZuMAxpx9w3OC2ypSSjL44j3FYL6m1ElDo1vfnsQbDgrSQpJIL
+GI1Xyq7gymfWtkDVxyfQLSgpaK3XwAXC7IpgYeATBDMU2DiCjQ2BJPAKtrsPPO77p38Dkt9hGXUA
+lm15mqALAuK27g3c4hOcga0by10reJDaAb3+JYkE4O2Q7O+oN107Zmpgf90GxFzmDMyYxyasUZ1R
+y+P/fT1xpgCP89IhsID4HDRXsM8wjITHHufXyd8cUMqyFyfgXmdYmGG9xCvfJ63cR8B9hKzBozzO
+PCjatGkA7Qv9PkGwHzsg2I/f4HeuM008pK58K1Hygt2ojmkaVwFwHwxqN10P8J9akcUGBIu3zQRk
+7TBsz+nRoAiAoSiYKEtwQvNGPmz+yeUHhH9zaapApnKM4wUJHxWjCKJd3C2mT91E72lWB1+QEmtW
+4Phja3AoZUSXEAi0W3/F2vmEXQt2q0ZuXdCsOP96n0/3RivlLadLg3c6qbxRhvK41k35kcLb2u/5
+bpODn3ebEv0GyAxrafnNTKJtC4wvcb+56Ie9yrZeofYpLsMz7vgJPX8youSW/lymZbfglnD3gON7
+er4eZwHynWOPGcSEYrnF+c1WTCka4XwzAJNlOIE31rQC1VogzJAzFP8lDsrHCwyibBugQ0aY2nHe
+WVVy+JRCFNv9UxNyY3/FvkPwJ0j2WQ8gQFqCnbTukLsvcdosyZZpA+ugv0foXWQlVlfiWvMk47BC
+M+iOcj6OeelxHEmlqh8q4zhvgh6tbcm0BMDZFuyvimG6SwQUszW4W4cThlt+uO2RdCTxwPmc94CR
+JsrhiOfrEbhC7+eecBxpOf/8cChe8nJcr+3nbzL8jjxx3vOB0kH3C03PadaqgbITettMMzllI78F
+HjAsAoZdGnFAd2Z1aGNOwhLskiysxF6QzU0qZokguC3LcICuFhAHCKvp8ue3jv/c8gPCvvwud8gM
+NyUq9zQeYIVZgPcd6P0ibXONGINPLmtc6fYX7TxS7aNb06JuMeY0WuIiMGCpW4AfgPjRKpywEQWn
+BcA36T8swxkP6oCCGhlP+ew4fwHkzMNDWYyMM+i28GLwvYerrXyjcX6n/WpXUvhG+OlYFEuWDzvy
+Fm94/fRKempJca/NsouzHMpNojpdg2EqjysAPxwPgAX9Zl1VMWuD49rdCefGt9JTzt63JhAvXhmA
+XxkGWHkTiOgIH+kGoHGeEoarrLI+uGDpdvMti9ogOlEpoH0EXoJk2g/XCD4OkRNuVc39YsZ9sR/k
+spekJRhboeTQrESbAYOn9VjMz7UB7rASJwzegLfHAf06JJZyc1tnxA2O81IEg82HG/EVue5HnNsG
+vwrdoPur+2llwb+vl7xkuAOjpY+eWG1NpwX2dm5VL22rLBsURz+YfUUH/OqZ5/PYEwSTliMA7pp8
+tWP0fjfjY99kxIDh1S3Ax5crecaTpv8Xlab2cIo1yWNQPSzBMYYAkIyzfHsJ+dOUTehh2yX+5iXm
+7BMkAIuq+Qj/gPC/uXyHJEAK89wCoTS+c0yo6T8AILgb9LVDY/gIB3D1Bn8D4jMd2qpwpahP+esv
+fjaLiwY/JmCS+2D9ZMkNfC+/RQWYClpnbXknPEa+ddEV846HcrLzQpBX3kM5ZP7bfj/GdzqXAN8q
+b/hXs6THr54u4+gu+4PaaKtyxNwPMCAAHRL+xn7dPCnuGScIaZnAJJ6mzYFMPaZ4K1GMbiWOM5QG
+sFF5tWISDp4lOMtv7M8+fbuO+a1LP2/GZfVe+pz79iX8utJbwpbgV4PiKL0TgAlGjmN3OPas9nsa
+Zd7L1NuDiLvie19QQEQfgHe4QqzTNSL2F7lXPIHw9wGY3SVQMKu4+wj7dGZWOGID8g7rcc3W0L5I
+dt0GGI+6YGBMuZOlS/XU64r7mt7W5Bmh8880nK+V27qntassVGNqtj7fbpy/SW4ycE+QtDYiLf/G
+Wd9wiaC+xeVwS9/K7rqt/nFataM9S7923ssA3whTmX+p2ScYD+g1udE1ZMqHOSf5YQEevv0Uj1Xx
+nk0gZ5kZq6rrx5IDBcHq8tzTiliZKcDTrdmYVYfgaGPwrcO0TfUHu+b6cY34d5c7STwmC6Uw1Pcl
+3rek2PnY90H33qUWX6NZg29AHB2M/zgfknnUIEp7V9ELYEokFkMu7AWg+ZH1hF+/9LNvcFfMvB+R
+8hX80sVap20pK+8KNDjLY23D/1shZF4TelGgA4edfAChcFiIz3C76gF3cf2+yCWOcjlg4WYBrtfo
+Z1qO9yCq3lGC0KfUSmGq1bKitMy6wPfB2OVlxj2M7l0qQT9rwvDYmXGzqEq1xV6pgbp8d3voA/lC
+Td7jZvvuUFyW4bXWYQ1+LZt6KqA4cvpo7f10TAla8pZ15PkoPgi1f/FrxlJxZhk+3CAWD5AT+5Su
+PLtGrOYaoa09fg96L+eA3R9wWnmhfWDccJHolmGk7/JnSzDGPj9cIvMVFutb/WSphyzK63QI5LqI
+a1U76ddm8I38xQc2pnV4j/tMV4lDRnB+O1pBz3hc0uat6ojPvPd7svKQ9hv92Gz/sx/0MoFrsNIC
+JRWOvLbp0aZvcC2t5ycEu04e8dNKHGH2DV45MI7GFIyBc3e/4BpEV/qwy7sDih16xWU5FDWzUINi
+9xNO2ecl6HNjQ93lRuwBNNqwCLLdRdyfXn5A2JcneLimzS0BD6r6jzhS6D1uAu19XXqH5eO8dI14
+sATztCqkfJsy9lxquy4g/tRY9wl00Ru7BUsgUbJgQjfgdwGfYXiUYdtPqJ1gQzNJBMN08Wr5yRGr
+rngIzjL//J+ecPlmub9maiLgJ+s7hqBi+M245Q8med+d5PjY3H8CwlQCD2uDi6/SAp4+gkpbcflp
+5WEbL2vJYm8L57n9b/dxKQ/qbxzby2b81qWvyyVhvGmpQzp++fyN3F5+n/MmUvIj4Tfqv0GwGAAT
+BL9eL7cIS4ctDCUf+0PZt31Kn6o7+41vtd8/93O2BgPVDgpuY7vH/sJ7b4hcLMXSB82FRfgj4DrI
+3eJbHGCuENCcmHVaeQuMzccxLFlCluLyox0AnH7DGIDM/Wn0xSg3kkWzvhgC55rQeDmOdq0zD+wP
+HABs1l/Qvd9njIjrxAC5WMtaTG1MZ55PgMw3RyAca9ZjP96sx6cbQysLjkPPZ+sPWhZ75G9Td7iV
+6czXJRz6NiVHWkWk7VvYzVo3UBaCXyEAZotvm2ucwHjAcE45d4PeXGTEt5ZZaRXZP9mIZJdw2dEq
+IuYm721DqL38G8sPCP/mEgqbgVAe9iP9tAbP/RsIdsB9TtfjA6yGk3xYgS9QVgPoPE8OxKbkCn4f
+y+IiGlS5o1gHCtcIg2H5eD9z7YAHUsxATLnS6ap3VhssZ2n6rBG8r3wGq/YmHpT+Y3b8yNIgn1bW
+E4Ln0zsJOsgF/nL3d8OsBP+DFbCR+VHPWXz1AGQsrKbEQkB6uhCvfUsWVuo7CZ0XQM6yiXQ3GK4o
+3rQ6uwPw3E81RsvXcfIQlzJEcPTFsO6E0lsEwWYRfiUMA13Rf4LchKA8dqZHEhXaln2AW/kFOIxz
+VTWtvuEeoevuD7yFLMUJxGpz4Ep3jeiQW3LmI/hqB2QoDleIbuWVBsZ53ka3FKtZkAsMCaZucQRc
+mnmmepn11oqc4fgEhoYneZK0uFb/o330qdMMSmKdPsK3VdVm04iVkanlHcD1AxMP4Xl/t3juA8B5
+rWyarfxH+x/9os7XLMczHzLiJpwLpTMNmzNAXLYMvTKB2D+O0eRCwDD5C8+vUXbXiYuPcHtLelt9
+Q9bfYxttWSTLy4sD0OblbbcSsif1RW/zUZ9/evkB4VieaO+SsCkzMKyWqm5w/CkdTiD8e/vRcdg5
+PiD4MosE/0nkMfLpqp/h0huxLdVSQ9ixkkx0DEUFpGuEwbAm3H+5ZrqCunpIrt+9QQ4E9Xq+H6k8
+Av6RgPMIv6LR454fYovnuqV9CY4HEHqSv4YlAK/D7ae4gnCCL4/8BLh7P0xHdVsTTAqEEjzymFsr
+RfzLeAIJOE5BWzh84K9QOAqWAR+ocric14/0ZaYrVfZhGTAtLS7u4au4vI0uI7yt5FygAcPhDhFW
+YYLh12uZwmWFjq5kWlwq/QFF0YVCERFclkaKevJ0uRvKS8EQCsDnASb3iL1s7tDV/YFXAnHEmevE
+ojQQnKBLeawt8tgBw3ne3UcYyu4Qw2VC7WPN0JEuIOCYPeLW13qZh8h8AuJKUIDGQAbc4gnO+NzZ
+Flp7sG0A8Bp5nh/hOPxpP61NPva8W/5PqX31FVYK8/3NsuBttFWc8Ud/yLWXP5dny09uJ9Rbz47b
+ivOHmQyuvHLbPnoxgTiNWOt8SB6WXv7YS3OXmEax/C1knvrjiheI1H7UQ4lsqUYmJPsvV3Xjs53q
+BZxq2esiEYLFzh9cfkDYl4sd6SlhOyPANgHtGs8gzBDc3eF/B4Cvx7LjTJeICwQTqEVOMw4YDbrH
+3EuqpLvSfjTy5SCdMBz5tv4zfJ3771ixakGFhgji6gjLMBVK9DSMPHk6/qgGkN29wtLEQArXut7D
+0mVMwTBZhRdbgGP0b/ssKdVNg9yC3WxL39ya0t4Xvz770pUOGD7SHbDsJaUh4DwsEXYohuTXxxiD
+qeoaWeb/A4brXlpakRnji/JlPlTVbPHxW2dbBNgnODNObfUbcRLyodoEf8yi5gvus0YkDL8WXutF
+rxcvCh4c/xAHPj/q7L4Fl1DbvwNnzBixchaI7vuroqc/cFiG3VIsnqYervz6IOiN+JZ/XOPjwTYt
+vujA++gygYufMNRnjcC9r1Af+QTI4LIHQUHUJXDEN8DziiiouKyz7rONnPkLX820CruLhG5t/tBt
+vUElcI/jfF/CGGE7LhlR8XLCMDXPIw8U7mmiH/DDimavBZCv/OdUapEPjHjViCvgZDeJ1P5S+/km
+F2SxRcErwMYScoUgF4nFg+TYsHKdRUJAd88SFr11BeTe4uqmzWcYQ9jGdGgFwymPs93TNWYj+IPL
+DwjH8hscnNAIhlyKy3BYhDoI8/YJbnlQGbs+fEqPeOqjDlTW4L6fgBx5ofyK+HRp8Slflx69iKoR
+64gvwe4WBs6vMhRTuSBEB+1HnqLzJNQoJfJOqvX7XbxyOtBr+i529QjNNB4jFa5i0IwpMC/4bQ8h
+0p/k87OtA4TXWnUNv/HcEuBW+zrTcNsDXMivRdMgFdhu1bT0JhzrsBT78b133ncpdD63hGycc7xd
+9ypp8zUH6HNE1vkNhim9tLOOEP/yRzB+PKgzZ/1a8nUcz9VZCq+3C/7SHA+UYxgOII4r/h4AX9JG
+3I66pDl7/YOnpqQ07yD6f4GO93mfs7d9QW6NuYSlBsxx3BquE0vKNQLKD17+Wwm7GPuRN0oX5wHH
+dGlPX5g7pkxTmgP1Ar/TLaIGzRVgdfg66wLzeN53l2yR9hqPiq+dCXx1/ZoUo2AwLMH8tTmbRxjt
+nhtM81/83jVv3T+Y88rpuoTmttpuq9J+49i1X8x96b8/yz+PCe+fQMzx5fcbT8Fh+hmD2EM3XGA4
+B9BKAXCmv4DuszXY0qs62odF95DQVV9xjKG4AbL28rDTLD5lqpJs5HY6K/pfWH5A+HeXpu06kJxQ
+fAJmbDvECoUH6EqPv53HoDyfKiPcO5qc8TOvEPCLjjsflFLgbbPaWOhu+XUx8VQWHR4KkeLp8rAK
+cy9qkKyZj1IzHiK3iFSgdK12bPyOCcPRc30EoDjJx0M/P8GnRTjnhq0w71ddgervFr7HVf16rhl6
+94aulUC8HZIDdBOGRQ5wlgSTsiR1K5fBcCnLUnrT3mACVKsWpeo5IgpOpf/PUyg9Sd3eZkkaj+UT
+GNt1JgBTfkecPMZ5uLUJn0klALhNoTbg92V+wbGu1wsADZYDLtvfBGExiI15YhUbqv6mgMu2QQ61
+g/hwhQPk48cyHuLeW7B8wNw72n76CKPLF29ztV+g/AmSAXTgbRZfS3MfGKfmMoEaQBfg3wfJPfWL
+D+Goq+gbcZxa6zUOZ5ylpRkU4vrUBrbLzpx0h8C3VmQ9PvsI18C4/Eob/+b4/cxzy2vEDR9bBa4D
+6uK+RtlUedD9znLLssexv1u6uh4Qst9cHvg+SldguEOUSac9xMtlnuA0lAwD1txioc8m08NXGP5g
+FTa9kD04JEnWhEY/ItDNewxrEgP0RYgSIrUCPZJSg/gsi/+55QeEfXm2BI10F8gN5V2wW8fWRxDu
+IHtae+8W4Gcojk5VT5oJxwzCmHDc8x3AksAZD4SXRce2x7mSURssFwPlltbguVo7HIcAiQcBK2PK
+0+w9zS2ClCBKYWfeBOUeoaCjoNdgdTddMOpTUXiKCUj08HGxANaT/iIwXmUd9roB1ROD7nGM61Pi
+wcFRbEIvhVeE9y743bt8fD2sagOZnhT+CcjoULxneVVJNdAVLkehw9+A4XZNHTFPjfmMr3YYP63t
+l25xeIhLnUfXrXpCTZIf6wWI28C5F0+fNrdfwHHsN2CJAWymCrd9bxg+fQBEzUWh+pWXF9V9wPBW
+8gO+zRUst7hlwOVxS+aX5TQJZVp+K6x3YE4itLzPj2LEsQJeuQyMU6yYcorSTuvvnuXR9gnMWl1F
+vql+snKf4tDj6lCDNK5rzPbAbQF1H3JZ44tydY+V9z6bxGhvoN+m/jBne8A1Tb+P47xxjRbWfu61
+P7S4un8QFGoqwHEfT1A846eW845/6IMPELx8vM+cQ7ilYVeqKxTzdUs3hKSaML0JsKAAACAASURB
+VDzXCcZ5z0PsTu3XQiWu29EmJ/HvLD8gzEvoQtaJM66orAHv4fLwCMTiltGCvAJbaa4Q9+0dntNH
+uMEuTZfGHS3jCIAR/V1c312A4RKlc8+Vkqp9kjS24QphQ3w8z8ruEewWEWXDooSgh6C3vpdeeZCZ
+pwHJ8Yq0hB1oH8d+XZDuVsc+FxPJvgavCcAdeI99h50b6Fb90T4B72FFZqHHILw3tgOwQZA9oGyH
+3RZ2q12cK3GtQ9GTZfD4c5G6jK2ORkTSsoIP4jLulyvnAGLuwLPu7tB7Lp4xOdOcAj3aq2RT47jj
+x1h+SK/fUmqu/HLatPIPfoVrBAMVHhR+bqdFDGTJU8gWbAjUP6Ku25gQYtOc2WdVxcOjftUAWHWT
+lffiCiFzPmF3n2gf0fCZI+jLcgm5qP4bD11BIffweZ66FXhOl8a+v1FAe+uYWzgLLOfWvQFvWRjH
+sdhH7GeWqQ79HrjfTLBDf0Dnes04rv9xfW430y9YN6CrW4mnL/STb/DGKI/IjwL8gQ/OY/SSORND
+Ha/0fL8cb+V4+SzELCP0vB0PhpQ+oTbjrC2az7L0fVC8dI1Wciq1W9uvAe5dRxToUrjNKPMMvc1f
+mAGY3jQqbFyNhkwH2tfiov+kK0T0J9ep0YarFv0aq981330eEY470//p5QeEYwmle9dwtY0KZPgI
+MEmAK2BZFL8yvkD5ANoGx3Rc71DM+wW8dqQgSlqeejzdA3XRLUB9WBxIaUAhzf/RQWzPPt5YnSZe
+sefruCgLXAC4xXGn0SxHKDJdr0OletIC4FCE7R48vxJ7Hq9jn9K3fRn7Sr8NKueEHNSTeX4J6LT8
+Cc0XawJsAC7V2WFZQKXFiEeAbMDvstfSsV2xFemW4bAKj62ShcjWsCYCupIrgAm9sDbeFZZGJSNK
+jyqVDkmL7ilH/82vJfHxXkdNzY6mxMttABy7c1TcRWxc4rKnkaxgS3C9JYiZI2ou4XSTWC9T1MVs
+D1vtwDu2Evv+4AOpotgItxCxqRSlA0joSQRYBBxu7b7B0oH3+MqcyJhuzeJE2Ee4ZMpj2O/3Mewk
+2QbIKQ2eG3MEo/kNxzGY2wTs2GkRfYDjLGfQMYazSgOuJwZ5vkZUAYWzTkZzPmCYfjPmD+5uAqd7
+RHxUY84e0e49IBMkX0d+5j4uxyyulG7Fy8N9EUCT4FaKS16j8jvrgx4upK7hP51qUD/tR1xaQxCa
+CwUQtZ7yeo3PIgfQrmb15Q9jtPmFb9A7rMHxhhgJwe2Ri9Yq51YGqAfhAGIqJv5ncSHn6FDq9wbE
+tf+nlx8QjuWbpd8qMmGY4Rf1yhsFmYuPoQD5E9h2a+8FmhGALBcQnsA7rcHS8ox2Dw6tl/uPTp9S
+ANVl0vbHUOyWg8jrCghWnkKNAJg7jdAztdQvSYKu55sHzIVm5/wOAAaUrqct/yzQJhSPUuj7N9rJ
+vIaQIqhNAbaGBbC+HHZMuYZxjasgvacDcMKvQ2/bSrcMiwM0bw2OzXewXo+SQvc1BiWFJVrhXHzt
+awWyB/QmNI60XsD9ckL1kVRcxxrJTTjm5L2OGYBl5pDiMr65R5QqLNkBahNVV80ton1euU+d9nqZ
+n993wVfG/tyGpV8AvDXkiyXY/qGaaFtH6w+QyFkFNpbSBzUW+QKzFbj5DK8DnvnLciFTmBSbBZgJ
+B9Nq7PVJcez3C+iA4++7TcyPS0wgPl0lNMsMqPPi1gogS4bWMZJGBHoN+oBxPQo/QmBBLVuAI4zL
+V+UaBB/X49/S9pvVaKxXlOzl+Iqb9x+9qclonWkvDxUN9fSS1yp3CNUJkGIiwlnmJDPibckcNBfa
+TF0mIwGZdLEDbDdsPEFwuEKccwe3eYTjgfrBGmxvj6tk2F8YXgboJZYF2sbVUL2EnilRTqAr9CgQ
+YRlpKPynlx8Q9uXbhZ8wSZV6VOwNfAmARdI1YkGu1t45EK7AeMCyRPgJcs840LFmDWYlrYI2T69M
+iAiBHmGlw2HhKPHUwbffZ/gMn1biyJNS2K4f9XVRyz1MU6XxzBGqmtNPkXo5oLhdOZRYL4GeqDJ2
+Ausi0KGJzvmrYbY12EmBFvX0AMHfWoFyiXDLcISVQGVahkWk/IRFINtnEVAXiLterSoIAmySVrIS
+L3/NvO/uEV5o0spRzmM9QR2VnjZriDWZJazwbDp8ulYwEh+wy4BLce0+jruAt42SHzx1mpCrTFiD
+wy0ipk6z9vEaINwthgv+ej7ivtiKKhnv7ea32sAwUQFCcbLyj98lQNp7k0X3CXh5EN0YNPeuuDd9
+Wa6DsG0ZdjMu61xxA+RMr+H3S/sJu5puEzFzQqQXmkaN23oNmOtl0lwlvLzaXLz585X3Bo63fdA+
+0Oo960Z7fMOarC8TiRtA+4CGwvy11+kjfLpIVN7jHjf93gm2X31Qo3rRPA4d9+w96iiLdv4Ac165
+7ICsl/b7LC4mEEciocGJUifGBzRCctwGyk0YNqBdJRdCHqTV95xDOAGZwxN+hxVZ4rVclmFrIbhZ
+idnQlfsSIB3l4sAfgAR64A/+iTiE0RDESi6i//DyA8KxfLPwS5FNeBQC3AijxS0G4AgjrKQDcrX7
+Dj8BcXONYMC9QS/HHRDc4ySdhUq6NqGE6h4cNp/gCitAoNvBt8TELTwBmMIadZEB9HmENaE3lWHm
+fYjeeLrVOj7vMeNkHuDr9kaS0Jadu4Mpj/qVw+pHM0h8AbiL9z+lBzr03sBXumVYwhLsYaX7SF9j
+kA+hC0mbkQLAXmYR3tumaFqwDyRgY0kY47RJPqH/Jww7YkqPaUcFqaHMh08rnVIY2nj5WK4w/Cms
+kbsjLtIWMPPDTa/H7h5xmT2CfIUZArpCt62M+K9AuIqjYM/eCpi/bsgOIEb1T4hwOHLXiEW+wTq/
+ICdkBWZ3CZpOTaS7RgBs3UVmwIJBK173TI3jvBUy4KOPcBVOvN0o1wmpWSMYiBlyox4mFGc2OK7L
+IKX89v3I0t0i1/cLKLOu+bbiupmP+sxyWIax40GIwHdrTqF2fEoZ2rc6fh+Vt4jjfILScpVm+nGc
+z8t7p4Sch1YW4Dzqmd+ojLAMC+WnmqKHLaKsw/UuVUkC5FflAoLhD5c8jqetiyBYas5gMqJMH+Gn
+cHOLWDVdG5fGxB++774EIlPbIUFp+qLktDgglQ4sIF5j3/I9peifWX5A+HeXaEOIh7qh1DI+Klhy
+WyAc4WfQTYsyCHb1MxDfoRfP0NvSRJ7zNjPEnyW2pcNixJSg6jC8vaPlJ5u/guD4eEajkQhHr9Ki
+C+WO3EQj7Yf48xhhe7Uec0eGsrpdEUD6CLcDjd8ky3NahTsQl3Br0PN65fHfAeB1SStrJQgn9BIQ
+J/hKuUXk9RxiheEYyLB9GEEh/jUuSWuzpkU4/JI34ABsBbPgfqla9TqRmAqUdmUkGYIz23Roq9mG
+uFIJlqeClapYadnh/FbFRzbLBsRZL4WT8iHXs+6OD2nkIMqaQm2C8G8B8DzH6MbB5uVhgezKE+tw
+uyPvUw2CabAcf1Bj0WA4ublLnNOpidSX5ZiOFLQf9Ii7hdg2fZ99fqFi7Rd9ujSzBCPhWKbrhArW
+7gPm7h/VKDl4xuMAtsNVgkFOSWYxWFK47Ue5UJtu9a8Fv3wPPJ1aWMGPTyrTfbQvzjEMRwsZ6uMm
+odndIR+0xrntvMsX6Kakb+VB99geCtqxKuNQPe03Oc7o9rAAFxQzDJ8QnA+VYkD8lXznmSPaYLiA
+YQ7nOJSnGSNi/FCVnLZS1Co0Op5/cqS0WiuFZ/rPIlPGMewm/wQL/YDw/8ry3cK/jgMfarr+buDZ
+AEmL56i7tDWahkjnP1D6yEMMRosem6+MvbXKAbVPS0gIG0ceEqPNPxmKXWOAHAsJV/sOtQvRAbzB
+gyzE9OCQ952dguLlLBu+f1zC19XzZ/va6rPYitwvqLxCvJldk+ICyP1e8iEl9qPz51bw8v2Xg89r
+cTz5gE0heZkKZ35n/nbe9jwrUO4RHpfHPD7TxeoKotIp9oqteYFnGlUoNrYuE5wEx3tt6F60JYDJ
+8v0CdCNO5hHe81pVzQo3pRiVf4HiS1z015jTu7sj1XY5ALNLU9hslw80e8HgfzmIvTKd5vrytjXj
+F/x8P3chII+hp+9/2kbb5X7Lai+7PJVmrK2vz7BUmupzPBVXyKmKSxpbNk2FikOFCLDfUFn9Bv1X
+03J32Sb+HecBiHmRY/HXEy1OFVuXj4BH+gNzvlUMovd727rf2G9b3++37e+3H3tYdZe7Un71cdNc
+3vsyMHVAHSqu6oMglM5Fu06HeHmIC9jPbaQ58sUPBudvT8u4znqhJayWh6EppwsTkx/S35hsH1+x
+1sZruc/5S/Ha263ZNaAz8s95FpHsbdETVVaFYVZczd4tvk/mHJGsh6a3hpLp800MmTfPQZFHXF3z
+hJAF4nVc4QP0t/v8e9zzFxHvbeTpmOlAk6tLYC4TKXfLoi+cZ+kGsIUaVP+nlx8Q9uW7Ra+A+8y5
+mCcn+Qd1PQKMygXDkeS4hn56PgrhL7mfej4Qz3V+4l48wvpgHlXPiWiGBTHYxkWquuqMJ0QGYph4
+SAUp9i9/xv/S/SNAkMMBFnwMBJK071nt0HwJryOeVq1ZOCJtwAEDboTDnWpzWoJ/r4ksg7q3vhr4
+CF4c5/f78pUBuSD6Zi0AbsAbDxo3y0JY2lTKIqdSFroE3YhnID7iCnRzoFwD3hsk2wcadm5d0bc2
+PBd52H3qF6EYCoL7o472NLdjBM+hxMrnH/ct5rYeAEO1Lij+cth9AXipHutSxUt3hmvdEN25LzmI
+rBohzfrVoTdh8Dnt3jZNnm4DrwCm0KTR9aEGsXEh1u3vqJdlbfgvSLbrgP4X6p4CKDMccxaLz1ks
+An0LJDLLkBv7AMGu/WuAdQPh9MuJajfhpKD7XXJAb+RRVozstz70dvDldb8HFPP+3gXIA5KVAJnB
+WONrf7l/gm3dpmbR5C36/3IF+AzGBwQxmDc4n3OKa0/31bUjzykDCiTbW7RtsKtrQdfLzl2K18vl
+zsvBlvy0QQ8H2fZT1xJgI97cLrzXG/XouRzi5iMpx3WcnT7QBcEWa287PVVaZAIPdWTU26OYZFYs
+2FcfV6nivW0OboWXt6T7mUD8u5AhK0PjKfqXQL8PvPd09lsaio/If7m+0QUqK84LiqW8L6bx7g8u
+PyD8u0voSvRVgITiiV7XP3uEujWL2t7o/BIXT2SKeppiNV8wHNc36M1UeTA6dNxXCFgtJRGdmMIJ
+k2IKNts3AEMBJXAoC1uCnpcHO9I3AJYOtHXtJxi+rXWcoSXgDyDwpfgzrM9wjAG/GGseM1B4eZq0
+AHvcS+KBgOGWwLfFE/SCwgOGQyhtkTY4LuBWx35ahyN93KMLXhvwplC27OJiDVYk8G74l+tgkKxr
+Y+91upk8QjGn+JRGvKYeQJe3KtTR6ljAcz2IoLdhINtxbjEBOFQmIEoPPDog2OF4EQy38PaVgXgb
+LJ7QS7f6JSAjAXG5Qostg3DZ71jFe4klu1gbiweuaMfZzgVXC7g9UG8zLqjYhzvUygvpH5ySrIPN
+AcJauVMqCO1xZtXbddwHAmpYgFUhuoD4KqX71icEx1YW3logvH8F+O4rFL/9mO3vtBi/93tYih3u
+wlqsZb3kz6IX1AIBufkg4PeWMtwfZpiTGYgPS/BWqDhkSXxym6yqBME5ZR7l7Tal3ASsG7CDdMB0
+IdMdILyguoDXAnRBXy/Lg8NwWKsPEK6fuILwkoVfbwdhlWqhWsBrD/V07EibGtIkSIxdcVmSOkwm
+NnuaAcDq/QP+VkQdeA1kDYhtRh672ibNuPPXpPhEzfilH0CYH2C+hmL+2igKhEuZu5U93vIAuurh
+AZGvrA8fnPuHlx8Q9kVnh3xKF/UNdB372wtZhekyE4Ifofi27wPcJgJoKHxPqN7g2tMhqhtWd6y/
+UjwdgHldIcUuWcvp4hJ+ub/INa5Z21KIRKl9D3onAK9L+mkFfobgT2F+pc3g2y28rwYEDL4MDkKu
+JNPaS0CcgBbK4ozLeHwDgp8AeLhRfAt0BxDX9GqeTnzQDX3g5LFtH8tXnW4Cb+CBXI61R8Yzjh/K
+olxBch4Bv9SGOQ4RV3BbVmFfA3wpzcth97U31tpYvp/rMmW4ubz0GXpVByQj0nodOnAjLcJm0Uul
+7GuTU9Qn3942t9jnkV8i+EvCIiwDgtGs3WV53e6T7IXtv6ER/gC3FqSGc0sbgV3X13h6Z/AVSeUu
+ewMEv9jbXs8vexDZWw1w9wTd2u9AvOlYWIcLim/W4QOKJ6AgILc/vES1Rfs/IQZ3uIlZP1Ssv6qU
+dXq6bGzv0w2eyGK8P4FV9MuqN2tXBagxi4LKbjAM92PHi8G3rst8eZqhanaGmqHBwq+3W1UhbauQ
+AmCNsDykrYfMdIVMGaKI6RbvQIzKuKo/sJlcTYNBdsedULvzQXJZfeyp5UKmrehUVVdfwPAdiPtx
+iX4q4jNSmDDMsSUIy7BAdwFx5EXg5zbDxJ9bfkA4l+8RbeLftP7+jjW44RzqDUnsP0LveILWHpQr
+BPPWIRjqHZYtyX2rKaLCwtA7qvj1GCa5LEOXxW+n9RLoYJHpuhWY07SyaiVLv0VxN9CdABxxNwDG
+h2Nh+WYIZvE13SECiF+QBsjN8psWNLYSD+h6gt8b+PLW/Y4BA+K90SzDDXrJ/UGBfO1m1t/tg568
+LMIaHC4OPlhOc2YKtddhzS9Y66tVlG6S77cNAo9Pr9zikeGAnI8wrAFe8UjID2dnuNqwuoodgKzV
+D5prhD6smC4RekBjuUZoB1+G3rhthmT0NBHOmRO2wS8PCjsg2Mu8+mNZugyCra2+AoIFzT0i/Jwl
+tgOCbdSlbwE0IRI3cglrBT6mS0G7BLoVIqtgOKxu7hah3o8CaBoEb4O0994OwcNFYu8GvtMKnND7
+ntbg+DpfWF43AeWHNdouFwFJcKX7T4tsAjNDsObD6d5mzVTZ7lM7LMNXYNrNKptW44d8tyrLFuXb
+JtMIhuOLPQ7Dr6VmDb5AcFuU2yzDdg1WfgcIKwx2HXCNsw04t7/diePi6UVj5h1J/ZE6RzRlRLlG
+lMblDDcrsSl2Dzv4Clt1V3XRDez41SXAAcNhBXYgPdrV/hJ4qy5PEA4IFrXfF7WBsGkRXjAIDouw
+EjshIJi1/Z9bfkDYl+8+g+SIbLn3tUyHC1pLP1ZVPiwtHE5AJmHOAEwA3cG228Duyt9vJC8o7Wjc
+Y6kZ76hS145Xv+EWAP/tTVCqIgS8BAZj36BCxn4/3svvEwDfoTggmMPfAWCA3CEUDYIZig+XCIZh
+sgyfFuIAZSmrcAAwLvCLCwyTgF9jG+J3C/LJ3CyyZRW+huHuCwstnJ9XJT/gb1uDdfoN43nR1tz7
+gbQUXo9S6Al6BekrP/uFL9mWojxRAJxtKdonlNpXtb+l1ZanO8T0D2aXCNvuvg6LcIJvygYLjBnA
+LulquzSmzNpmFdYdc3/VSic1T7/sm2H5sjYW7T3bPIAlfdBfWMkLiDfMPVguI1Ep0w3q8HgMT8fE
+v5knPpZBpNrCEkDtjYUddwBey5R0grqB8NYC3gDgtATvYRWmYwXFTzBMUBk+w+l3uxvAgmE44Jeh
+WKOsyAr7CNUOW+EaoWwZ7jNHdKuwP9xegL2lz3wi7yHyV3akLsd0LfePD/cAtwZDAZi/8CvuLRv8
+idf5JzSLE1uDZeG9dkJw355xVi72zBYwHE/xqf+kHpJT13unCeBl81PJpKIL1QBnb9aezG4z6ivg
+1sMwCG0w7IBa7hF30G0PL1sf0vUHnCVoEJwPMigrsFI48pH58j4oWiX1J5cfEPblozLmdLOOiiHP
+hGQh7nZgyRPiaF5ClcLjNyicD4qDFK4QrBQ+Ms2Y7F3VT86J0T0zSqcI1OcLtCXGn5jFlGDY4fEG
+whOCr/Db1mkFvlmKn6F3Wuwky/DZ9SHuLQB4poWYpS/E1tNAuRowJ7muWAXkU9mtxQnA4ClmQpjP
+/edjBwjDPmxgggnDQuzgmy4RYdEFhbe/prwMllMrj2419kEeFzCGKriJk/Zu/Yqj2zL4tbf+z9v2
+/kQDrmu/z/LS2+VzOw3XiN7OYqaIF4C/CIjXBGKwS8TqfsIrgNEHlHnDu1l7c55mhl46nnAMuFsE
+Q/AmoAhQLQhu/S7LpQAmZ0TxcqoBczTsKOBX4av4LBKbq53qBCTuLuD7nf3I9Q6fxQBeA11V8dfX
+cdzWLG8R1CdqDU4DetP6y9bgWzwPltu0Tz7C7BIxX1+nRS7+EoaZAauCwyLM4FkgjXbdOXNEfi47
+Z1rYbXvOcsGg9AxZBcB9aXqB5Jo7b+cTnugr+5UuBZiNs9mK66rxJ4IFdon4ZdZgtwhvBd4bBcAe
+fqtkmOMhqOl4FNa28j5ChmiGE3pdKndF/xR2+SnFyVaHy8dbGGBuSFqDF8xCfUDwEix/uLk+qFzA
+N9vgExgL6jfgQLuqzM2thWDYXTkyb2QN/gHh/yOLq4asvNNbtlck73XFGVinlMY7x8kB5TIxAFj5
+GGMAXaMpj7hqo/9ylYhDKUsU5wNAdEjy4wi3ATvPn3MFJBQ+gPB3jnPcES9jezvWIWYCb9xDswgL
+wQIB75YBH6ixPYevMK8yrMFSljNew7f4hFoM4D2312N4gF/YK0f+/LLBLlmHD8twh9kE47AK8zkp
+qPvgOnOPQH6COVtyMGgj4Gpj7QXGsQRQR6Ln7bdhWXp7axbhW9scbbY/eJEVGGgW4ZpWbeMV8Bvr
+1vIT1o21V855e4Jvlc9hFY4HlKFrrR2TZmcIJosdF7x4v7f+4q+PTf/655FP3/jWD5QeGNRcPdzj
+mWrerYdybwt5D7NBzLi53yqwgDgqW8dxOeIKim3WiF3W3rYltwmeRm1C8p6zR9R8zB08J4yUJbjk
+O/WViA9C9HKYADyhNwbK6RZ3k+AZI6Y18A67py/zJV3kpSom6wD0UJXTpvnHPdYi4oXipS8LvrQV
+Qcp9AqyyCJsV+Jf8cmvwG78ChDfw3tqA+L0VawPvRWDs05Bhe5v1TqgLWJv0HrVp0/UmX/j9a8S1
+ekop6NLKfdjDNULgMAtpD7RKALzE4dj3VRyC3W3tBsDPdTbqnc5f4vDr61quW/yONxQrrMB7ZUno
+6ufZ1BOPAv4fW35A2JemeL9K51T45MuY49ByM1AtOmbIX4S9rtInHDf4RXaSua/aoTmSNW44AwOi
+pXSGFAQ2PvB8g+4RqO4c8BzlE2Gh8pige433grzBRZapPENuL/Eef/gLM8yC3B7ktAKnfzCdw3ET
+dh8twwEHl7g8hgLgGkBIMIwBxgsfgdj8c8VnathpAW4Q7L7ABa5IkC0Avll3w10i4OzuJhGWqa0F
+zpqKWqhPURsXB9fcP5PEQNE48MlTnreZjtwk2n4q4i/gN9vp/bHY2p2UW4Te1gsEp1tEWIbdUuwW
+yj7zwwTfCm+SGze/4u2/dUIw+/HWGiW1qVzy64QBwCiXnxwsF/clYRE2qcPW4aL1VfV91Y16j5+w
+jDjfcZErE7Nie1ymBQ6ABoQswgTAHi7AddeJPYF5WoIHDKc7ArklHJbXuQZCdYjq4Dm2DXTsnsoC
+Yq4gBegKtljHfLxsOcxZJEbebgP9QHmMug5Y1QBgCfhdNq+xp47pO/Gi9kGv1gt8FxZsTvZf4Qss
+b7zXG69fC7+Wh98vrxsD3/dWvDy8GIb9mIiFMzPbZcpWn0GJrcHnFmklrrYpdSMobeRbh950zwyZ
+CfsgjX0oyZSWUDsOdwVVuKuJwfAeMPsZhsfxeZ4A0JVlvpNhTE8ZDC8LL4UG9AYk+L5ofMHyzy4/
+IPy7C+tbTE71Co0n2o5tfdsgjqmSkDwvzlBcwl85fuiEGmCagfx90gl1SLkT0n1KAW5bghscxtTv
+qSBY8vy4oYKFvo+ECx37QmXVy5CvVdOr9e30BX4CFF7SSuwF1XyBZ5yUzg7r8W2wXANc1HzBS6S7
+Sgi5SyAswmwB/iKMOwQnCHtdbkFahefWXCTgVtvdtuUOEXAcVmI9BsGV9ZEV8QMQeztqsJJ+N9Gw
+zTrVG3kwT38qsyhFe2Ib+zqPP22F2tgE4slR5AvYV4djpfmDcfEVPgB547UXXkvTN9im71IbsKVy
+gVpFG3+o1GYpTcGxH3fIWnubyYsgOOYh5nuKiRdqLvGA4u6ec/jM5xqgH1ZhM6XJ9vcx4Ws16pZq
++AGOuc1cj1RbSCsZWmVqCiHfB0aFO0xPEGYAzn23Ck+rL/sFZ/y7IFP1tAQ/WuoCfgOO4l7HMe6L
+X61sFQbDUHd5CFDP6dOug/ru1mB4XriuxOsj+hskpn1cWKL2wQzvTzn5h8KE6ljYAsyW5bAGr/iE
+sYPw+5dvHXRtRbklvdWswL6+y6enINibl31QAh1+Xa7E+7myAltHktaoz21KJnUZ5jJT87jYQ4sI
+1lLs9GU3xSwLZREWUB1S/R5g/GQhDteYAuF0h1TBVntoWQTAlsdVoJBKtLT9j2vEv7zcjAjXdAF6
+GUPq4SazM+r28h50hCzCAQaF1/f+QaA8MkX3ow65lITvtZ0zKMMVhNbn6vo9EUgwQqQyaVaWE3Tj
+OjEQoJ7kGZD1iLOn/lGSUmnmegPieD0bxag4XSRavFyswF6Y7EJxdYUguK1PbdfxBskyrMXo06jZ
+Oc+uEJ/2zXIt7vMcYOzWYUwrccHwblBc0Ntgdg6CIwsz+w2bpTgsyr51ZahhkVUAPiCzLLXwNKPN
+tlap1Fp6PMOtjv3btoadzrZ6gi4QbXse89x4ew0raHOPIPA9LcGrhUXZIly/MMGXp0ULzpjgG/Bb
+x0zZ1+eHzToc1vr87C6VSZRWfqJbxWAl263k8YBfsxCpDyAMi/CGBATHVGUpJD4oxavMfmwcfQlr
+b4T992zrSCIPcZR26ybrLgHwAbg9TaZ9zynTHE6mVXhPEBlQGZWNqvs2dZo9cAAAIABJREFUhiR1
+hPe1bB83AL6F+8C46RpxTsN1c6O4zSV8qRruaSKAw6/6G4Jo7ikKXnXe8cdyEB2CX+EX/Mu3KyzC
+ivdb8WvvBOC1HYD3thlb3gTB2FWeghw3kQ/IIRvIEhy5j15cmq/dmUkiUtohu0JKwQfJWS9SB06z
+EIeve8h++8JlWNpxracE332rw5sPsbUL+ADqt1oPj7xsibozP3xrS66FzcHb9LkpHLu3p1ft/+Dy
+A8K+fNc1opptPZG19puputrsypSPaB3RaOT0evUGrSTYVLULvJkWWofa8ZvCGNeIef4o5+lJLNGR
+66azMycEU5ivJOj7vM1jgTR0bnvtfCvTv7fm7SKErHYoFgIKDMuwVHVcLcIP6wG8cvoK908yg4T6
+Le60FK9Fg+VCGIIAeG2H2tXAVddKi29ahNOSS9A7fX0TkGtO4YBf/uqcDf4m14lU4jKAWDv4Slhy
+K06bS0Spi1unvB9Tgu1wwaj9aJNXK3C2oUrLfVog7l0R7VZ8ejQ0K3B8MrmA+ITg5iKxfRopWMNr
+1l5vpwXH1XCfXCLymG6fQs2oOeE3IdiuG/JJrUoMurzd2YNWtEPkw5tIPIQq4pPTS+M7XbBXoggY
+FjcvR13IvVb1VtPnY44XU7WEbEdVU2EFPraRHkhwDhiO8nsnAL/TOsxA/N4dkk+f4A7C4QKRQOJ1
+coVhh+B4KO3uBhRPfc3gKs77YA1WsX4Lodki+mC+6RpxwFTA85GGZSopHRLMEo1oC+Jb1z6ph59H
+DzPVW0s/uBx8S8wb/DaZSBD8Wi9zk3jVzB+/3opf7n70623++fJWiGzIXpC3jYwTsa0q8PLZb8ww
+UA+EpQq7JVhkwi/Orfdfa4s0D5NyK4eBqAun7bC9XEDtlP/WbvmrpDUv9ZMPOPukzzcB8xyY2xQM
+yG3cgFnxE4K3PczYw5VPW6jIexIH4mFz+yPLDwj/5qLcRgHi1ye8KkAssz8rzQG++UP8oxQgCOan
+++pDI4MH8F7S3o4JhgNgRachOKA38i0U8PNiqphIVdbf+uEO00pp/fg4X3KvgNiEy9hv2+kjLNfB
+crEcFuHKQgGFEFB4mUyLcJs/OOPQ4LVNMyXnHMNhBW6Qm8dC4J/H7q4RBMBeEQqQiwRZhNlqOwa6
+MfSaFZh8fg83CRpIl9cjFwr3dw2wA8h1QdWUHbXVaHrcXK0XDMClLSuOJDdKc55LVe5lH+02FS51
+j2qPBMUabdfbbYDvgOA2aE4vQOzgG4PmwnrqvcmhQttguY8uE6Rvl5+rgLlGOISttNjZVmAWn7Nk
+7Y3RUlPCC2FxKmmX0ySiYFiU3tQ484rPgSosd6TDTkDT7Tn+JjLvYpSBNvpyShvSzQOEub/A37A4
+FE7ofQ/A7TCszQ3iCsLpbtAHnHWL6i6YdSCL+uKHgGgIHYjhRpQ4ly194gPnwv0moHdDLzDevx7H
+A+VGfi+D/FpFVs+hjhWyaxUAK9+W0Dib9/HfZi7aCcRmEbaZcV7rZb7Bbgnea+O93vbGZW388v72
+670NgmXb6DkIIBt4u9uI5+XlZZcQHA/A4ncVbhKhw0qRUo96aLUs6/jN2SG7TFZuAl6e2lAjP4Kq
+781t6wGK90N8vDUSEADPrcMvFnGF90OlbcrnP0/CPyDsy+iPz4vMZnqCbyTsuDuPUOfQUGlDzbA5
+jBVY259wXHEFFiMt9yoXmke8d5ywCks4/IoVQIJvbuh+pV5rNRCWKIm6kV5yOkqqoIIz1kv2u6sr
+5dhqV87MCDxTBB7A95pGpuVXcLMCx7RSNaVUuEVMi/AFfL0M+/VnmvFKUOwpW33cRXOLiI9geBhs
+EcawDvuI3uYC4ZDFFqkaJEdKFzr8hUkQo6ybbK1rEOyb7BqjlfTwHYZLaTynyd/m9ifVCkk/c7P2
+U6r9yiVs1lAcU6YZ/Br0vlDW4BOGzSViybYR2BCy/LqrQ4Kvt0+SEzmY7jhuF1hhDSZFF9Np5dRa
+UMdVq8MYypNvG6AFwsEzR1/U7HcC0CQM8eq4VqWy/+8sXf5oQm7UFOlnjGMZX8fCejsh+O1uDXc4
+1tNK3AaZlQU4oTIgMuMIRsEPLXWnHoMAYFGggWg87Iz97XErZID3i7QK04C5mNu4uXZcLcAnRHXd
+g+zzEOTHFRDw5A+ApaYkZUPI9w3BG2/YXNYLIm/zKX4H+G63Am/s18u+8Lde2Gtjv2p+59f7jfXe
+WO+NX2tD1huyCoZF3h2Cs1zN/3apWJtGtH/N/sDK0+JDN/E7aS2xFNNQpEiasky8hsQfvL3fKYEw
+AIZh8bKe7g0dhm8g/AzDpvPNGzgBOK3BalNyircLd5HAXg2IReMN2n+zv39v+QFhX77LwSUwe9Ot
+JcR97UWIAbgfDeALnzlaPwFw7hfgpiBMAHY0oDTPsEzH+Il8rT6dAuANn5NeQMF3ZHXlk3f+WOhR
+ynzeDaCfrb8MuU3peplyGvrJtjRAFs3i2ZSVdJvw7dOUaeUCIQ2QY/o0GzjHYBw+xeRjeQHemz/w
+PY1nOQE4rMKwT5buneGwCEM7BJsvGFl8yT3iOxbiT8es7QblhotC7iLcFUIPNChO94gH0FUpd4vZ
+nuK6BxTHbxYX10T4sR/oFMoljvHAGD+qpvTqy3HIadNuH9SwTyzbNGkFw5r7sq11HpZfNctMlN15
+/MlyzGB0vn4XB2C7W23AGABsD0CSejutUa2v1YN/xqcxIOL9wTtIIpe/qyD5PCWYjXuQHoeKA2qK
+WOBM+2YQ1g65AcO7wfCMI6twA92AzttUZBeYZRjOjkN9yNNJ6AP6myCMsArn7/JUbgW/aeW9Qu8T
+BFebsoxkiHpgPQBZ17f3dwr/sIl6a1QBdJfsdwPMll1A/N7YbgHee2OJDfrd7429Xg7Fb+z3C/tl
+ftzr/Yb8MsuwyBviUG3zpL29DbytJyigL8XOWV3izZ3bkDDkhFCbb3eNlB/V8qJADIb1SK6VOvqL
+b1UKKqMvIsrUt/3T2FGf+xmI8yHtBGKI+MA4ezzY4rMKYfnc9Q7AAnflWHUvGixhV0ijW8pjUHjK
+8f/O8gPCuXxPyF5nUMjmO69ReCYjPhukzifC0PLjKp8AWEHgQAJmgO+E4es5vk1rsPrEiUvcod07
+l19rKjoWShDYpNq3JzyNm5oN/UhE993hgkH2+P2RhiGY4fhUgLUy8HJllIVYGwSHEeM2c8QxlRQB
+cIFytwaXe8R/ui0QblbhGJ8AFAADQLMIA9juH+wwC4LYTYDLfsMNlh+OsctEKWTvFIq0+HTo7V2A
+txY+25RS27kJ1rtbhIedcJsVeMAwwEBMYBy/pgq4Ykr4Rc0d3OK0+wXXdGrbAdgs/Gu5XVbLzzdu
+q6ZRUz9OjRqn5Titwr4ur48CFyRElYX/BEf2mY+yYZYtK3lZsKKMEgDIPztOvkkGKt0vlrjeWB6i
+7lDcv5eQqxIIExB3v2F1QO5W4jYYrllXvV8N+N032ATXT6viaHII2M2HFwDTCszhGDRpTkxWfgvj
+d78BvreBVfvym2d1hRakWl8mqERXNeZlIId8gCoQjgew/d5Q8bKO7VoOwQ73r50QvN8b79fG+vU2
+AF5vICBYAn5DA1kZb/+0817uG7xgMLykfSXctkr9IcCUXf88fjZOBVQKEA8ZlzLTHvjNGuypGIRp
+Hw6kDLsJuU/xCcRnPMScUezrkZpWYQNggmBRQFa2WQusErXpI3yX7mfcf2f5AWFfbn3yW+ddzfiM
+aBXHFuF6JQKw8k1LSdDVuGpKYJLGyZQeH1Axw3DF6P9aGK7sojDUhcoCaISC1u9n/jXdDfLVS25r
+bR08gOZaoBcwyZ4bkMGA+wF6EcBrx6Y7xK2WeGnTpE3lT/DL2wa9ggNoyzXCobhB8PmBja8svd9a
+lzTohwAhruAwrEADYISP8DYANisRbMTvgNjbYLkDfjXmGaZjNAAvrbYNhjWtweGHllZjifYaD6bR
+bqKTSMY2CNZqT985Fk0v34CQIsmN9HYUv8xvNKrdkTVYyVVCu0W4PqaxHXwrzj6FXIN1bbBcAVGf
+HeK0GrM7RIHzBX5pH7P9z1X6fki2ggDfutKUkCEaZWadSGKKvHyFEe21Srj32UsPFg5oXWcsd6D3
+MhpxO9NqO7a33iE4Ybgsqg18H+Ki7Pnztpvq5LAKeyXWg2QBJktS9bJny3Cr2wjveOCN61m/b/MH
+e95qVotyiTi/hFdzIE+3iNQ9VEGtlrmPhQRbdJ4L3XLFcf/ftAK7BXtve2CPAYqL4rfWfliE1y8D
+4LXcGuxTfuXj2jvrZTsEr6V47Y29AoYNfDP/oS/znvQiM1DtlcUSHVc1I1XIvjzHZZbxsORbTx1y
+i8OboPYrID4sxGk59vpcMGv72yA43CHSGryi/lev9vDx9lVGafyp5QeEf3OpOitFVFaQQqtbKPek
+YM7iopF2iV3giykv+k4KloCDEGKWLi3BWgITruiezgt4Ne4wCI65Pi0sOUq0Q2d0+D5jwSXXtO+F
+iADwApMGNy4ASphQmY71ad7gGT+nULutOViOhNPT9tESLNM9Ij6aEfMIk4uEECCDrLsJ1zKu8w0Y
+5iaU8Os+v2u7lRjNJSKOqaJZhaEn9N5dIQKmOhBbHExxLn/tF1Cr1QYZfJslmPnG+dVbeiWQ0XZa
+ZUXbDxKjiwcE07Rt2XxDNw+4A10V3H7BPu/lF9sswrjA77EfbhLhIyympKMc9YMvsJc3W4W7O0SB
+c8IRahtyosXNPhL1RDBMxVQPD7l4zahUOOKz6OicdnKXkofi5Kq8KlRvT6h0eR8OlubiQTCsBb8B
+xD5fQAfhsT2g+LLdtC3oxQHAXx1r7gaeN/H+lPUqYRlmAAYSoMP1ya8fAyb3tjbIoNRgaQJxnJcA
+xXEF2Vz4VwCu7gogHpIy03ZegnAMgrM3Jvxlvu0zbxjw6hn/qvj93li/FiC/IP+vm0vMVxpU5qj7
+3PYxC+ufyLchAD8MEiWkDutscGhIEk/q56nr3arvEoLqD5mW1uIrH9xbwkeY623jAOIDlAmKyW9Y
+FOQWYWtYg7csuK0ji1Pj1Wz6B1ed/0yf9i8uE9A+L6OiciDZTDMtl9K15wWAp5V4Ngl+egoBFnEJ
+EdFrQuighE8Ivq/irCevfHUuDMSegbAYM2CGEKiZDsT9ukqBMrwgfloIUwhyBH5fYIzpDxO/byGu
+7XfqvfsKuyB82ObcwQ18OxTHfMEBvekrjCeLcIffO/iOY+ucXcK9Qwt4wxIcA+XUFAPiuALNNzjb
+yxfQG8cyjo7ponmEq+3ltR2Cap8UdlhCqH00q3C0K/IZrt5FGoVheMTVOdQnx5OXwXDtMBxXMBq1
+P7hpvJWY8wUz/M5p06Tvh1uEmqUYkHSL6FbfDrh2vKA50s1zQ3ZEfYHDrlytK8ZRVH3FvM+Yj/N9
+if5dZX0JXy/Q6yB2tB29yWDeDaLwFpFlUfcTAzoTeJUhuI7FgNCtA4T1BsPuEqEdfPncCE+43RSu
+fqStnlmOt4cVF+PZyuPWs3twn9aqZ2885h4hWEv7ILiv/IL32I41puIrXRJ5FEDiYUiqTqEOwQXD
+cT4AwOfM1YBgsbcoNge60jRvu6agSyDW8sPeZhGWtWAuEb9SD1pbk2ozCIvwwmsvsyb7w2rM4nO8
+DcFlX3S2UlCBgMWZPfjGeyZu+SXJzH+angfHQyjv3aG3u0Gcg+Tu4Aw1F5qXaFqFGxDvAmPTNWEZ
+Xtnfg2P8PfQfXX5AOJfvPoUcaJr/yzl9Nr0JZWfchOKK7+GZA4bmXBguEm5DCN7gmLYwYbpQadsr
+tbhQStQOxgaY5a8Vk+qnr58LEol5W9Vf9cCszApFWNXqNbiEprZjWq4Y6ZaB+D2kewb75dbHLApG
+bbS7JMgGiKn6CPHcp3S4xJGv8Iuun6tcVszVrbsj/jr4DoqXSgG2FmzXp2wJwFXNu4Wax+PW76ni
+Nd1YKp7Lx21MUWZxLOOU4mJUPo/Ol+/ly9sl/JzMj5RSUKX801zEQaoaZBAkneFodzjOycX3W/RQ
+dLw0GM5Sc9cIUF0eQMwwTEC8xV/T7uZulIAL9gW2iKvltwpzzCKhftuJwF5MJROyPdCxiov2U+2k
+liOix8dGL8daocpw9z3rJ5UqH88qoHYDf40cbYEz4MeV4qtYq7y7ZVbLejanGNuXMMf5fly4rPL5
+o5Q3jtfMq92m8q4HSk6TxaT2tVbzL3U57zJNlO9lj/tSiv8CiscKoCyX6PdRrjP1diXkfcKwn29R
+AtkbippH12Sy+ayq1FcxV4Cxw/3m+vDp0sDAm33L3tL8teth5/Va2LvmJF5rpxFCBDlYjp/PssVK
+1/XzGe721vOAXmoWCb7taLXdMq8pHRqA+xGEKW5Yg1VtKsctNpxoCzKsghxmlN6VHgbFdQFym9D0
+n11+QNiX38HgCbM1H+Z0EyiLaT+vFCEyTtv1QWktrA/xpXTb039kOF8zaO468/V0uaQarDkmC2FS
+SOcrtnGtOJvnp2z9kjti3EBECZCvfNLhn18128KzJYh/N33Oy2ufeF7kqO8QJMufWCxu8X3UXeY+
+l0imUYyBcgK4RewlC39JfTb5LwheCvylgpcK/lL4fn1VLMJ/bXuifsk2izB8qizYaz+zBvqcsrIN
+dpcDsQ/UsCl0lg3MUN/KytdkJYk8vLsy5DWUYqRJJflJwYdgZQV4KMNS9qX4vU14g0q00oNPUpHm
+PvpxbmDVq2ZLJ6tvHmZ1UuH2YEsk3GDrSNklSkiGfDBTh2Bo7kvEhSuEqK/bH+R2tfv4DSq4ANsd
+BdkUzN0qXJZGLlcCXI0YVH3VUf/pEaNV1nf5clv0G0m0lX8/p2QogHrIyWS9DYjW7BkCJfeQmnVD
+4FZzTpfhsKjf+5FewkoF39oy/UaVaZVrydnzwWOuXhRZVMcbu7HtcSYBYxt6qbZxjuYajUe4LEAy
+JsNhOfQwlamCewt16Oh/VHWSD6TldiRxYMEGqIUVZLuzLoAVX8m0PQRsrRivgJjrFvnRDV5fa+G1
+BO+X4LXFp2ELABa8fN0ZXvYBIVTfRxrJKOcUV5qd41H3SIEnXjkfwKMkeZYKLt7S8TN9GMyOOCJX
+lulQ2of2/f/P3rUtSo6jSFDN//9xm30QAQFCzqye7pp9OKrKY92tC4IwRjLkwGp+vz6Pa4+f/Rbi
+T7sfIOzueyB80fAOILjm2UAZAJjLQuyeQNfI1kgpj7XyCW7PThFoEAmN7G0QjNKxnEJoctjr4oWk
+kouAwXKUIgZn7b5sBApZN4Fg8XFUfw221DazMpNfuuTRDSb3AewmZhUMwy/QFOCcTGpZ3Tlu0aoi
+lKJMBcj/EQfBDsgjLHuxQdP7H9mAeP9M/mN5jNYvB8RLGPySiYQ1AExj0MdkPxxsLUkHssf1qeED
+EPefa7HyjUEDwC18MMagFYk6GQgkMLBCPsjP8rKkXRyD20Z8UoGxL4QmdT4C4i6tJAU3/qo4CBZr
+4Bcb4fau/aVkD+yaMdVHlOqJDhcURJpeEXrwoHVM4Bcb55J/EOiVPtY9jeelw+Mo3Kchk95m62Bm
+PoLJeCIuhT/nG5lh+B5v2wa+Hm8D0I20DpA9Y1sTloRLcekPWmaQQWkBKKRUUcLo7fRTSdvmK/BV
+ilMGv12GNfnlXE8JCAUgZq4ZIDg/093X/151OZ5CbZ7oQgkNq0gcrxec2z8IpOggjvskMCxuurDH
+0VUgujXFj7d7mcr6tfaD6AMgrCM4XgGCl/yl/LU6B82kEj4QQ/CF4d1wYTN3ZHIDvTmaMqRZ8dsQ
+ro9ZHtsBsCS/OPIJ5vqp4gKksVJ8PCu1yKZ+QNVLn/8t9wOE3b0RXM1Xf8uJ+hMIvmuHWdObCz6W
+BcwASr58RZvx+HsCBO4ZL48URhY79ptUFYaFAXAJ7HYArJzi9eEV2MngSNBoCUVfpkU+aYHXUjGD
+NniDwV+HRti1wa45FTX5S1YR4CPAPcI5xj0c9r2StsAMgAF+oRlm8PsfhUZ4h5dYaAFTK+yv3WAa
+AVtgg022a4DNYid1fKhgArPtpy64P4JmCDX6CEMBxGYBgoXj4idFMAbYCiDgo07EzBgDiZ3WiUoa
+k69pcdwQ8sSCcE+AYS6T/vZoVvw6xleAEQDFJAGwg2BVgGETfTD/+bC9hB5/J3TkYzOGI98OLxQn
+8MUj1+FthOxM72WKi8m9uVZqzJr9zFHuGeeHHbZFrTRjXG38Qf826GogONrgoDC0wTKvK8m0NEvL
+vJXuvS0xHwAd9BBiFD9cUR0Uo9r9ktpfbXIp/Tb8aESdR3ziJ/kAjc11rhmWUy51Tn+GSesPmata
+97Es2ajKX72XsIjk5oenhJfvWxATUQK565cSIE6Am9fVwip/uZLm13J0N4LeHke9Vpbo00i8uGPd
+GPn0msIUBHpNesp0Y9qVys+txPk+EMgB2oQZJ3WYbnrQPQ2POSj+AcL/O/ft0Cfp6gCC9QDBsE/l
+fGy7WupUEWhnsBSCASkvGc9juWBwbuBpel8FTBUfxtkKyaMkA1sVIX8uikPWsgw22SD0cGcctONH
+axX995KPyrM22FsMepftr3Ox1hcb9cgkQhwsSjGNyOdrE8ABHgsNgQQ71NjIRXapAYLJPCJ+VsHw
+/rTuCYh/qe0vjAnMQLZWOD6csRL0Lluh9V0OfJfa/rpRAGPNsW2gtQPfM42vThPO3Kr9YAPIUd/T
+wh0MS4Iqy0BltklMTF/ywZ/EY1KOnTioD2n9ChLsgoootiw3LWVOQaYFAO9nMgutMMCwOghez+M8
+BVeJY5Gi7mPxCQEy7wmmHes1cBmtX6sgivnGN/E1xznCiLqmcaZPWUawm/HDo3OpPjXA+dloaIYx
+vttvJW/Gb+7Qz1+O3wCMrYWTSSY4FpFYZ/F1Rsyb5BUmGjb9NPtynJAjCXAnOaWX9EhzGp21wPg9
+w5i0I9MepxyaFj08HDwfQgHuVaSA3v3tCfpMPMIGedU0wmZi8mxlidvHbOCrxSzir5VxrPmdrzCT
+wEawE/xu72zkOIWZm5w8zrLMBGRu686VX/wQi1wVFBNIDj7MdEvxHQSXH2mIn72p8VnLT90g2+Kx
+E/+u+wHC7r7XCJNdcIDbtP2rzOU0o1hUB/9Ek9nmYrcEwE5ouN/+T+FC4xy4Qd9JkNiRwyBMZZM/
+nskzvgPjSUN8G8vuOQLRkvKKmTTB1q6/VDdQdXvgfUyLH8/l2lG+PvHBWBbspAX2TWBi5ubWrAE+
+yyUIzq/D/UdyI1uCYUsA7MAnQPDzeFlzc4gEwQpTiQjvh4GwlwYgJoAcQJjmhoVyB7y7M3Pa5p2c
+F8A4hV0ImKeGAwSTWA8/AbkKDEQygWBYZhfKMQR68B30Gmeu7ygbj5jBsU5p5MMaD1BiAMES2uBt
+HsEgWMj0SqOeaDsv23hgwRTmeEFYxR4pYbCFljLQpbC2cE8fwuls9N5y17wk5G+VEH883QCaLUEt
+01EHvu9+871n9uFH9MzhWAeSD32W8CP9BIZ5qhEnMv46WDwB7wmOCwA2CbOJBMCZF7JIIg1x2Vd+
+EJYAQvt3PCSymGq7T3c79OyP0oosGmCRY3dwMZUw0QDEInHKjStswuzhF5lH/FL59Sw/GaJrhmv4
+8fCjGjJjBMLBUW7pgyv8qaf11TSM7bW65MmpeQC/zizW8gSvV/CSCQQDAK8z7hF/Y+ua4R8g/L91
+3w59LEYAVJlMHpp5hPLr0ATSoRHGLtlIt5CtSmdt5sK3ODkBgLgy/Mb8rVwGR8BDUjhVbXAHt2f8
+BG42E8wFeYgzPcdej8momrewEV66v+bjgJdNIX755jgjTTDAr6jFkW6Pz0LqWjxkssF0GQ3krOYS
+OD5KTPYrMQLEGwBDG+xhswDD+LhCAcWyN8ytB5pfdbBbQXGmmQPkHqc5VqJXkCsU3zXFInftsdIc
+c12T9reA4CEe0r0CN4xvUm8BxpiBj/Q9u1No2HBFWs8LOp3TDj/vIbDUCKvBVtgBxcOblR7iJfA3
+UVmRUe4BoHGEJp+w2Z5rASlUMBuAWGmcLFdC5NUMcfkuXqt7n6wiyq2mdJfnjd4qG0CwNGArecwc
+k5ZNec1NJDieQC/ouNK1BdBNvig1H48XzwWDYcGSqHNlJR/9GgDe8uM0hZjMIxIYT/FJt6Ah70wd
+C8HGOIzBszXBRmscZd0ubi8nfxpN4ZYTFRPIAO8Ghj3A2mILtiKW50MK8zgxSXOIZ7IP1kEz7GBY
+lzzFZIKPAcNi6qAX0h3h3mEC9H0cMPSUFiui8ckoy+yVKMYKHdIDGcJEv6DZYjcc9CwDCF50fNoG
+vI/L6v3hE/GPb2y/6s/xaf8zN34G+JJv0vTm76YFriCY82MphF+TySvCosF4tka4mUf4CQsWzDqF
+BJ5KlVZBF1XGPhOccx1rZ/Q3ps3mEgyWY6mbECaeEDBHtdfOlLZ0P20DDG+t7f6lXRY2xKWfzSL2
+w4x/Bcd7BICArwUe4Bgz4oyaT4tAj/dJD2kagaPUFsCwuXYYfgfB69kfWfilfqYsbETVN7fQiQGL
+wG2G7Ux3TXEcYefMsW5+63GYqBoH0FvAsMwAOGzCJ8ArPX+2qwB1oisi0wJWOmjpgSYG5A526aog
+UlpDSuW11zf5O30rxU48gLRutk+NiA/WmIk+BIKNgIjzBHQ2x93v5uObcac2OMCwV8JCtQ59AmOq
+LsBwgzbfhSZh3XIzZioa+VtZ88bz3azGWE3addsJjlU+aYUlgF791fgDIANMBK6gvJIgBnMU1RWA
+MoDf9pvlE0Ax7IS7HLOBRvPRn7XB4ScweQBiMwEgxqkRqRGuawaKh0yzI098uRR+8c5gQhgMu+Gp
+0kkFsvbh6crHefn4bqC8QbCuyTxCyUwiT4sAGH4WnybhskhcTqBPAPgIW/Z4AsF2DoEPFsaMkmMd
+S+656UtEz7i+QkFfINARMFMaNMKlrBAIbqAYH+TYLwzxlVGRx7aBrmQ9AAAgAElEQVSZxHrkj7sf
+IOzuOxh8Yyz8b9qhexOA+Yu6VQRHAWGxA4gUkwjaNVtMI/ypP9glHSvEQg4CMF1lsQkFLUBP9wP8
+xV/DOrNY/PXOKcoM7T4mQKN9gv5TmxG3XNtrDnbLUWnLHBwv+U9oRvkoMQk/m0b0nksZOQLA0UuJ
+MdhZHARLmsqkiUSaR2wtMF/zS2K/nsfBs4X2dwK/FfBqmD/UuDSL0CCJKqzZVKKC2503/JQ3zCMi
+bASqWABajM0EAlBvATwEhqe4g6ZKUufyHr5oBiN5jJ/qSn/Fwzr7j3K05i1/C+ubwDDAL3iCDmWz
+/9SJmDcShAXwNtMJzoviMS4+0oQikR4jY5kecPgmhOWMPHnQW3a7Ziso3Rt68HQvl4BWw0/ss8bZ
+CyD28AH8JNdMsY0NPFEBBnfHbJtnLDsgCBUHb3/JI9LoZwK9/ddth62VOwFwAcG4c+lzntMrAKPP
+E8oGjKMwjSlkVuX9SsIy33b5bQGApfoV59SGVjjthQW8Sy029i4R0vyyiYQeG+aqltjTFEeobe2w
+GRouMXKFVrXHhboqx6bF8dDEmNE4YU0FGxOKRJwTd6eeMRw0fU8Dj7e40oMcmUY8YRKR5hDQBm/T
+CMjkP+t+gLC7b5XxVbv7ov1Vf61N6X137hLdwNUgWq3UJW4yEX4XWunHE/FmVtAELzF5YFIRQiqF
+JwusDgTqMx1gXjOHMIqzlk5MvtiVeptNKhxmhFs+ARlP+y0ssj8va5aA2LXCVfPr40J+xCcgTtMI
+hwQ0BgR2Yfowpft8YNwqENY0h8CcW4Lf/indX+anBcgjv0R845sEcJ9AMIPfDEvNh3IhqOXiT4Sk
+JMwnP4NmAWgKwd/CIiMQriAYTFUCNMQDBkjF04zDzRkT9qtDnsuVgySQq794mv9Mi3VtBE4AfEVD
+06uuxVL/AkY5Ns1kpzGCRHNjPnPsDgBs4AUQVLuCMsxaux63YoCr59SISHt+CIZzmQ2bLtf0Gq0t
+Ld86TVVMZeNR1nwTnHTAq0Nc8uewMW4PfxX41rTUDs/lYin4uggwgaraXL39RO6KlxPgfhMWwUNV
+0l/vs+2ROcZiA6KwGfZ6RVOBsiPLwvN51Wj4lq1gipAZnr+DYQwEm0Z43F4Xmg/ykrxqOiJtMotg
+M4iHNMJLAYhXsjQRiQ+6sFAWTZI0hv8VRBN2TdK3KBa0jyKFd5YK0tnxF3WQEiKGMSNy6okuiblY
+zDV9YTQA8TaTePQJDbCZyPOQacTBR/999wOE3X07+B38nkfPOBhpedbhZwCdoDVsgh24iEj4QyMc
+/mRWIhso7bMvtzZQBHU4CD0WA0lKSWIHJLTw0aJDFUb5AHBQAowl4jQqUAeV8cwf6NYvLn1DEUyv
+v4IJPk8Bv6biKhSyCV4qYrAL1gKen7C3NX94yH5y74NJhT8RwdYO54CiDL5g90sTDAco9maFdthw
+fJY4ADb5tSz8rBFWn9NVwtASS8sHcCyZD30qYDP9DHDF51QlGdvoFwg4HykAXBIqExgOPxgug3C6
+WgsXBEBX6/Hsjrg3kNzB8Duw/f00AsOSD7Kx2chM9JEEwevZYdC+iW/0WdS0Oi7x0CIJdnOZN82w
+8NBbgtuQ05p+Gja2HGHhfJ0GG2OL8L3NCQvkCnSboL/5hzTwXAa/qelN8KtCH9po+bMeybU0flSD
+6H4AxPnLNYV5iZMjIjn1deDP4+kRYLeJH/OnCYJFOiD+EiCbHf7gLQ34JgCqH9xxVc5ug5v1JUmD
++de3hlvmAQAnz8tJ1gKAFZ+QXy53AHqNFTUYq9xDsX6prL9IE/wsqadHrLAjTkCc2mKAYmyWM/SJ
+/dTNMJ3Q9FtQYlsnevHHLcCPZZD3EjKs/k3q6Y9ZrCCxEgZvST4e9DmBX9vA154NdvfRabI1wLql
+8KP6A4T/1+7boWemwtrhsBvOtdryZF4wltXqBGDFma8AfupcbdIIb43yvu/jQA9CVsTCVghaZ1MS
+gMXxooDQbNpe6XGp402gnAukgGMR+ptg+JwBgGAGcugzAT4/JgynRYhrhqVohjVMIf4is4htkG/7
+lUwwAfR+BsGZJpQOBGDBxAoA1jSJwDm/4bdto4ezY38tDf9aOE9WyqkPcRxa+J2OGjAu40TjKILX
+4Bb0xXbBbNubaeQXiYccIdDFILiDZImyBAqYPgCC2nUGwcyk7y5mrGSbAPBeP1ViNDAc+biYXvzx
+p/pbKwDo9jzt+wNUiIhrhPdp8/uTylvVFQ/Ky8vxUFTVUzF3wLxi7nlDHUAWmmvU7AJ4kQC/p1nr
+4jQzdiScuewt3Qb/FPelH59uD1AbIQa/WijiEbx1k5i71Ah7BxjoBl9p2nbiifUqcQWoANmXnFbD
+Z435Y3ALDvBuIlFBr5R8AL00mPwASwBp+x2il+PU8HW57K/GXbPNKQpSkCpdeRNwAGPckp2D4gTB
+2TRdDRh7uohvkmvmEF0T/PiX4/6KeKX4BMablzEIltyDUpZwaoNz5JmXadX0BogWkvGW69dyCIPn
+DmzuBMHpP9IC9GY4wbEEAIYsSEAMTbAfU2dPmEbsj2xsAGwPHhx+gPD/zP2+Rjj/5evrCn77pjho
+iZdUEI2pTygIIQlAHPpTF4ZWwHAIU02zCJwrrFa1wbEWgAE8MoCHuLA8FiazbHqilmYWYQ0c+4JA
+e3nBi9+NzSACR4DheaADPJhELMPAu2aYAPDWBPjDAW+Q8/RHRSxOEu29leQyPAImGVdMJvbY7XON
+00Y4NcJOE0bAWDoglgDBa/mGqdDsMiBmv9PkoRkWYQAdY23eE59vBsYiMoNhjxc5wa56PRwvVH8F
+w1KE4Jl3ujbH0p/z9LwRvgBgjlOROxjOywls38IXgEycJgGxr+3HZG+YNv9UrINgfQgAO0+IndU8
+BixZMW9VSHYwzBrkENbeXt7jw/FZuzT/mVZyXKaUE8b0aY57n1s+HeJw4bdcmA9wgdzGZa4BTvAL
+vgYQrFGhrwfWiAIoFA0p8iELAecA0Fnlrh7aNskrxWWfJDTBCBeZkpiSfvSWUbrmlwGxtTKSWmFD
+eu13/T3Ux/1RDVks1aihcgSqO0Ax5WdtsN8aIDgG5dHYPLfzaJqGOUo+bIQH0wj4z010FRifIFgk
+tL4pRpy+lGTxua4iUie/xRnSWbdl8bJMrFzrvRAPekwtcAHHBIajJtC3pBZ4X0WKlviRbQYBMKzQ
+FC95Hhgk/Vn3A4TdHW9Bb/n818FvxIe/geUjvf4E5g/QAAs9KysDXok8+RW6ZCkAwZtZpQYY5avm
+Bc6Gv6f2dwvTfHpPrUUulEmLnLdJpnswO2WmrTkh8fSfgI8/sQzge/tpA8CPLvnLkx81seCeWvqf
+XMavZrGrOSkh8+AEiaoNzrlPrTAAsX/+2LzMY/scYHxeV1Y89BQALHUsMF4V+NJYYTxjDuy4MjAW
+uYPhCPu8F+Bb6sw8iUBaHiFapLiUGLc0moLs1Og9Xc6f9jgI1HmBZAmAxFLJl4DYJWLiy6Ql+AGA
+8eAmbh6z+YMQXQOW5XxB9pXXo2InGJY9pwByIRj5gVRcmIa6yYeG4uopErQ2QrAPjsb3Po02Bcf5
+11talezl+kT9Qc3eNI3YtA8GL652xLhu1pcgN/wAEAR0++kqgSNaE832fZcEtiAOK+WHNvb4sBtt
+8inkjbDsSuAr5D9NKOqPH57BRyYwnDaj+cn1fBCToC2A8jp5SZMafUptsPJ5wYdcqevdBCBYmqlE
+hnOz3LkRrsQpnRzhx6b9Io0wrvHA4uvFfA33BxmMgbVWx5rW2hPuKjZx15db/ja4LDIrxSeqSr7d
+lhA/3EW+phEWi2mXAL5VG/z4RsnQCOMq6seoqejT+em/736AsLtvh34Cv6OWmNLnc4Yn0wjKA/ns
+BA5CL6YShnwJoAMEARBbLjSuT0WqEqwtDRENcHTXAru22QCOaYEYLzIIU9wMzK0xLge8wbwZBJN2
+c8UkqEh8OQ5xfCoEA+flFhM7HqA4mQB6r+O1aI2BMqJIpvHpDvkGgPzm9KASIFhd6C3XCG7/UwAw
+xqyDXsF8tzQ58krM5x76qpk9wbAEU4xHBBZ8XjaVDlZoiesS8htz5tHPEuItLqtXikpXiPtDOoM4
+SArNJC5ycAq9pk2v+ObSGGd2gGIqeNuTD30rH27sHGP2TxpgFoSFlAsT0jvwbVqupH8W1rUdXR5z
+wMZ4ztIk83979RbyZrit+Y0RklzzO4aBcWiJvc4KeDk+0w/NcAEep0YYcxS5OFz8zLfr75ApHhtv
+mMCHKb0CXwbEnsdy/e8w3dH67zn6nfbCkJJgp5u+wcpDRsiOiLVBD2vVRlhyGbdf1fxKMZWQ0Bhv
+OfaIjCD4V5g7nCYSfzkwZvOIv3RriUMTGiB4t4W1wR0Ad384SuANchy3h4vWOBj/IOurDyuR6BK0
+SXylaoHZ73Qu6I81+hepGmKyHX50m0wAJH+rlfwH3Q8QdvetOl4lNX3pT3A7Ad7ur1rj1LGGZrdr
+hwvoJTAbJhGI91YBECOOjmMri+NwSeAB/2gna5C85UKJq6/2zFsZfHmFFUsrgS+P8I4HAE6Gl4wP
+QN9BMT5l6ZvkdtoCZpBlW/sL8PtsbCHPkv2aLnr3d6/ebrNCH10jXGjAEDaK3+YRqvTGIYQX04fT
+jibdBugVvaQnKNpTQPQQgg0M1MNGc2VpH5ikYwRCrVyyXIk86e43gLHe8l6cFp/9nv9A140/NGZd
+QO8Bii+8xSTGtMeLz39IO80IDWYgx5gFZRJACjDsEd3PtxUAX1TPT/0AwOgfBPvR1wnc1j7O7MeG
+nBRRrlaRwm9cVaSYPSQIPuMybHKcIiGYnsrrgj8yOAYooD6l9izHI0HRHsOHummUzno8zHnfOIdZ
+YXl0ht8AsYU/P/IC3gC/2w478FXumMeFbfDzZNyzxNbWEu7n/A2MNOiMe+BtozXBChISTpxb0iQQ
+IBjXLdcwT+H3W64nN8v9etY+0eBZ8usv1/T+WvLEJjo/NeKXykNfnfu1lph/8ClIFUPi7d23tQaS
+a1fGrhENnSIp+9F5b6xKl/8mJrAhsSNfpbJUcqEvWS6VG3sONwlU0Ls3yolrgVUe8VMiRENTvLXC
+7eHmD7kfIOzu26HXy+8d8N7juM4tf2/gWFIhhE1ySIur+AJLoMiguMh6ZPYIfjJFSjdzSC2w+JP1
+kB6LKrXEYOo5hlpaELghmBz8FQAD+D667bPE3B+vi7EZrmqDjdKWSgJhtX3KxMxRRC/xwcUwqKRW
+y6PMEszGhxCEAK+cD1ALcxVlsx2sJYH2JP3S/KQ1KaNNMMWZJOaSZz43WlkAh2CjxrDHymWKqzMf
+N7vGXcEu35erIN4+S4t3ByrNWi5+GsRD06t9hKlplzQILc+UkSz/Qz2WbwXKK5Nyoxz3WG/kV6/v
+5hf1ftHaA2nnKzCl26ENOU7FPIKbN83LJ9ArRDvMlEzOONQHWpjyWM9GexkUMQx+8yrJXQt1xEa7
+pgktr5EJIAdooPjy5gwaNfyo+wgzAO4/aWGQiNJUafQm+Q9G45A5AgWMBf/KvOZrMvu6r48k+KXx
+iP766RHyiNra+WRtkFqEVLYlBpzfdKkECGZOHcXj5xHOrrfcUm+PRj8AkkX8Yxr4utzLmcLFFELX
+VrSUTywvARgPuoNfoeXXY87qvBIRDCIoZM+QXuxPsMaJhuBO4MvrhOiZ6DTKWbbS/E/Q6qEB1hp+
+Kgg2wYlOE8P4d90PEHb3Oxrht39dG9j9NY5ADRalkByylhaaYX+Kla41drDoT5kJnCUYgQ6Lhgkd
+xM5saK+hCnolctGCQTxrRaooLmt1Ht86zq9g2FGjOXI03zina+0vQ2kCXxw9hqd0c+3wFoK1J4Ut
+YWzBeQZmzaw4TnLg2TY6z9eFCzTBKgJFdgisJRZzmW3TYBAd6Ma40SvETUOUFm31/gWvmbWD/RW6
+CABGh7ZWLnm/CzOzSxqD4RaGP8FuvecnEHz2vwO2M41HrrWMiLeBXmkAtaR3EMzXYUSh1ikAGH6u
+O+emzEx7uMje5byWh2fnJcl8xNcW3csyLV9f8hXU0le39SkbAzfSSLIlujWp/hJnr+mPxkjsqyFs
+43WfN77XeL3y/SroBR+8haEwMCrLXWSwYiWefi1OROqrdsaQkv6MS95V+Egpk8A4fgaeaUA8MQZz
+GKdF5Mc1bOVmKlXkl+C3TOvcvvJQtpLfwnynL+s4KYJkKd5eYi9OmkmY5Ga5YaPcAIKPUyUKINYc
+ApXT3+YL815dXefhHcGvjSC4mKO1xVA0wU7LAYyJb6Q8T1pGXphEgI9UEkgwvE+I2I+YpltGPxHe
+cvnxEz3+tPsBwu6+HXql36cNcD3tLf+uWxvIlQMM49URXpWLWTADA9giUwoGwVgkwWcGl8ukgV7s
+dPW/AYydiScUrNphPG2jP9mM9ugBWY9XIwQMOxgW/9DEs0yWg1oAYtj/QhNsqnsTmi3XAvsRw+73
+3UlSWsbbb8WotY3z+NjvFe/zJNAG82MPzbXlhrmtZbGWbvlJZGUB5WJbQSlJjx0Ax98QhA00SWeO
+mNNkhjktKaA53+lqfUeeg974HhydbZTeFg5bCur7fV8IPSh2ytvKMRj0CLumTSOUiCTSIPAtHztx
+X2MQ7Gun2s1p2SFehSSNnwul0mLD2s78uHMADLpaC3dIZS3MY1dGvgXsiMt8xgHOZ91vl3iqqKf5
+dWt7B3B7gN76uWUVf7AW2iwHMMBaYXkLU/MtmxratJduvP2khc+Z6nyiXuvbxXo9+JV3ILXDUuyF
+44QI+rgG4iSuK9ICxB2tj8bF27JuNhfyDdmVxraEU5YWuRRodN/zvlHuBMH4yhx/VKN8dtmB9xUM
+Y35ZQ6wS4DLyyq7n2EdGIin2/OQrPcEKLadIYH1GHCu3cg6qplhiDhMTZPkoZRZR+Rzktr/m4Bdx
+wgB4uWnPQye3ZDc6J59l0N93P0DY3bcDqy+/bwDxbBqRzCbv4YtUJTa+iaRGePsTBIvZBnhGzAIa
+Urcjrq+35KAukLNJAtudrWqDO+hNxvcCmEUkz0JMplsHP8OxK9j7lx+JSMG8/Mg07+a2+fUxyc8u
+QytsceYwAPLjoBot6qYQ3ToaDx54ktA2gGGTLantL1fMjaXgyTxW071qBmtgD/zqSMl3zG2JrbMM
+mdNfkmnJlQw1gBXlflszX6VxJuumD6cpBIO5UryEO8t8SzvDTPW9jSYi1dQhM0wzIlp6ceY1GufW
+kgRBmC9oZ9s9yhylwMqcXjcq5FZ4/Vltri2+4kHzSFuoze/MmjzuJPWnO2v52HskseoM8TlQ36fF
+SGnMKbgavsiJ1f2oHfnVHATjQaZJfj5nNW5viRlCi9a0ar2LvSvxsyGO84JNeUzV9lYTiNt1BsMW
+D6Mhr7xBxUSiaYXzIQGnRkBLaOWXdN17VtdcgmE9tYcFPflA0IlLcRvSCLPcWko2wpdNc0/zAwTz
+BjqkwTY5wLC4aR7PoXoep4YnOiLUGe9StD+zmBMlW+pF5shvJS5BLtNOj/P5i5bNcQzag+bHH8wj
+8IDgdsMigmNMn5CIteeddQys5L9yP0DY3ToE3D0ffpAFE8DtcQF0KE6KfxMQ8ha/J3c/l8t4obJc
+P2uL+eoaUnFTA3FzAepj+TjDUWftS10Fe5HksU17kwSeAEIIB1UbXRqps3BnDQPdL+xLS307jb+4
+abJ3jMdntZ1hBrB1MBqdMilpMc7BdF1gBGg/x5/nWYUeYswK+GWQfIDb3Xl/Y9Digx4U2YIYOmUb
+jSeX5xHvoDMB2d2NafqSbi2tADgpdMCgt7BJo/xdaLy09XfyTHk7I57HWCpudu0v4hJuubxywRx/
+g8g8h9OaHXOac1dsrEmoVe23xzOdU85eQoe03R6ARlofBYlks7VHUO5JqsXxbtw0o7xm5G91mJ3l
+KCJ5l7fcJwufgec3KNusyYJf5HFqFvwDtwDP59N46svnc3h4JYc9KdW5rIbN6z/irG2WU5+TUB78
+/lUANHV/KGj/ZH80SPPko/0zOiZyn4uu5fdAFbg3zbXx0M2U3TVurV4O7PypfWfeYEIDRrQm0FRy
+XPe3K5ZffQ5UV8qQ34GyNg3yr7XkAQAmIJzg2I++MzKhEAtzDnN+8UR5X2Vm+dVBw1uLBKXY4B73
+djrOMnt1P0+OH8wH7fE3H4q9NCrqH7xQFX+TurGDOT6AsulXOWv580/H35I4Iv0Puh8g7O5bs5TK
+ABJIAuhUEMzgFwKQ4kgQ5dO259mX9ItI1ZoxQMgn9Z3fyE8Ai6+SABgfY5ClYm4DJbHQV9kA1gEx
+2pAgWAOolvc/0TPb1ghLJD4CP4Yjcrvn2adDoO8AvtOPJW0Xsh7mg9P61OvF3wFyARE0tjEmtKFx
+ugrCBRR7ephatLbgdd7URm1tHwBG5k+QdNBZTS1lbKjz9Rky6Pt3nJ3zJhJ0NM3ZVOAE7ycxHCDt
+qPNDy3uWD+EtyNyveUfGR+F3PmHK8dWoqPaR11m9J4rw/JV4NEjzqrzIh3pLPdHRve7r/XM+S3yr
+jM11Krjhgo02it/m+BbZITs/xHJ/eKSDzwaMzr4BFIOfLL7Ge/m8YY67pumabG00AzwTGUHvQ2PJ
++Y5TIwBmpV0Z6N6usWlhBdgLMCwivwSgeP9wTjq+PqciFQg/7ergWJ+XFYYJekQOcEyfkStjDGIx
+XIheOE1ExNhEZ8eHVjPmKGmm8vYBEOv50Q3YJG8t6G5PAF8BKJYBFPs7WEvg3MEvA2L1vAUQtzjs
+IwCwTmArYYUVZoWuLDNVsaX7c9UeXkv9yDP4n00HS+U/bhryC/SyPv9WB9C/Jyz+EfcDhN2tL0X1
+/lgCA2I/HktZAwyw6+DRFxkAcbHhAWPEgiOuv9dhE+ZFU8JCAcBQIj/aUsIq2642APBmeOZtW5B+
+zgzLJjXR7JNmndEu3vnLWuH4Ylb2LcGvL1IKR7oS+BXZEmCJ1M92guNZuSfi85Wd0Filq4ASyMPD
+N3+vo6X1X4lnsMtgWMgOD4B2uB+DlyP+1r4WNvp7QiopNFmH7NQuU1Wf3csSA6MuYUJPAC5nfqP8
+c6POtOHe3yYGcLl1GyuJWlHqoIDtPsL+F0BXgjJ2JrwBiIMaJHXCvWlw2vof8VSgzPcb6KW0eZzS
+3lKcvx1AFlXZUfKk27FMW8PdT7QyOyvJHQzvuAH86gyIl+JkCVco4K2TUvtr5eTXaDN4x+arbJVJ
+rCx+e1bxDYn+A/uMoXFmcWh6h7hIW35GNc6qdk1PaoRTM1y/ntm0w2YJjO2hK2uEh0/Bsbq3fzIZ
+TkUkvlwmsacmSIDpJSbdMqqnZyGphdhsSGKc6DmiaYWhOFryS7dpBGuDVUhLa5v2GfgmcJVibgC/
+uv+RLN/jVIxANfgk4nAfL/NImBXur71JgmNLv4V/g2J7HCRDceYPBP9x85Ar4NVZM/yrx/0A4f+d
+W4XiJ+61w1v7O4HfBJ55IgQDYguAUX4hBFILDGaLFnTt73seZ5fUbF/DpS0LuXHSBHLuXWhbnhEI
+3nUA2Hs95eoOqxdP6WA6TeuLhU0JnzXBMK0IhmnxK6/yKd6MAdNVSp6g8prmA4k5CEEmAZCKmQTG
+ngBOaIYN+WAfnH6RClpKG2yOF8n79PIlT9HQp09vcQVj3MdwbJA02nhx2XIPWwuLyCdwLJynpPEa
+OhvT63ppVbuDCSHUo8hkyHBwFSwTRVrSinAYXdejBRJzRZUzF+s9ynh1sEeRmm2pDUff0UG/WqO1
+6f6WiUbhj+AXf3n5TgBmoi07PDQ8sAoWGufkqxP4FZSgBxf8lpcKcwmehwMEZ+fVtXt/4UQD7uYF
+8AaL815wvCgPFUDu9k+a4PCvNcSlf6lr+xwIL3GNsAzmEZZyiE0kYqcUgLBSww+zCNL44geR8uRs
+YCa3h4iA6e2gGyK28JKsasR0yGyFQijHJj6kVDTCsINWAq5pBhFg1dPgxxdcWTP8kD81u1yvj1qA
+ZvNw1l00wmIEgDWOXLXuH4Cv6ZK1nn3iw9qfzN6mEbMmOACvnnEJfn+A8P8L96tw8jssgkY4AKCe
+T8R8xBlAEH/pjTWAEsKeBHUwErQgFy7nLRKlxYeGUfoCRvwGYqEBjtqXg1ER1RWMMEEwA2OvF7IB
+yAUcq7XRWl+laYFNxDXD7ZU1NMEoVLS/boNM2uFuD1nqonEGy1Ma3NH04Us/5rJqy6HxTZB6guGc
+jy2zKiAW4faf4Q54GVxMPIX1vKC3I67lyy5OiKNUTvlvSPxTWStRn8HxWY0eMff8SLmnfbzBa54Y
+Y5oYgLASBhkq2pOAq4QZYMk5HsjANM+zxsCvxA1ERivzKF+TSCtMQJHNWeDp85n1Jp878ncwwwWl
+p/UZP7oVsWmR3ey1RQTAvzxcUr3LGQBocmnee9GdogUOgmONg087IH6KRjhfnzM0i/AIkrXkCVAr
+ALgSfnyYaAbF3b/N46qdsINgbR8MMgBgaWYRrBVeDoxvKl/ZNaqJ2bOnwWTQEC8x8U+QE8EU0ih+
+m7xVjlpuBGtCd9QEY8NegGDljXVr0AALgdcEuw/me8gXIHbSBstpDlHAr1zKmREAfvIhQ2Eq4UA4
+bIHh9w+hPLvfBhMJBsIEeteatcK/biB44a30n3U/QNjd16YRmmAYAPgAnOXHINiREzP/WGsMfCVW
+aoIpoXVZ04pwsAEAHz/S8FJ4cQvAGJt5hOhZn2SpeAiINrGWF+cPraYFPkwgHolvDSMTbbJjzUKC
+YAmbYSO/c4j0C8Se+wnAcj8++kmadj/PI2gAsDAALj2U5HF4KfTDTzcdcMp21tPPF+fZxMkWOGlK
+JIXE9HpdS6i71sKbkfKtuFwwpk15rIXnIt9ph6f7vzRSKpg6G+GBiPP5Bwlq1pF3IiAWtFR7qq3n
+vKdgAsXcizrnKpUONJrbF/VNJm2TBudnXjjy9jcObX0ly2VZnv8AACAASURBVJreOLzk79rffiV3
+zHsrWseew9UGm9NE0iQCsZtv1tssJg7wF80HXSVAnEddpkYY9VSwm1RZflaHAXn3XHaQu8ivH/1h
+HrFwRm41j1gKe+E9BqEdNpFDG+wgWJ40jdh6ZZMD4WL8wjb4BMzoq4q6jXDPYG/BMf+u01qa84oi
+GrdSKABcXLFR7skNdADCwpvXJMwlKjgFgNWS7yG/crz3C+YQuAfAL+jjkVpu55MDAAfoXepnPTvQ
+XSrr2UecAQTvuDVqhP+jJyAuoFhV1pIEw1qBsWpdf3/C/QBhd9+q49kkIu2E+dWQuo1Ugl/+Mk/8
+IEME1yqcu3lDEVyXVQ2glTCP4gN4JSBbwXF2zY8uWX6+o6EMMUel0ng9FPWz0B8EGXfGHhFdZCIh
+0kwgRA7wi7BKAFs1E3vI3zTEJ6DpY4qxfvEzUH4BzUjvIDj9YNsJtDRiiD60gpSOJa9geAy7UAdI
+6to5akuP6znr81uDDx2RX4LdAXj14kmRc/jbPOhXH5OzjB05DvptEWa94XprRHEgX0aOdbzJJMLz
+QTAU8NhmjtffOb/ZyjLeUnt+vBxCnqFPdTjQly1RI39bK9HvbGppQ7cRZz4XaS/XUqanl3bfbay1
+hT4BY6xt8PPFN2RAHHEabaynxuzTKYqpA3WFf8J5tHY1f2S0FvxbCOR+AMCrpilrg6UC4PM3nBrh
+yovtr6dGhIYYNg9Pn48EwyZ1nkwacGrT3+OP6CZj8Vax8D5cNVdn2TxOmuF69rAl4BXSAAubNQjl
+mfyawJY3zEkzkfB4vFk6bIRFTk2x2wiHeYSf96sPtMFP0QqvZXuT3FKxAor3uf4JgKWaR7AWuGl/
+AYrrA8UPEP6fua+PT8OPXgktbSBXHGh2cwgCxHAQCHosvNl0IvNUwLmfChOtsGkEFDfcvq3ZkGCX
+aYW7ROWJRc6MUHzxC/W3tNdEau+83Y+IrWYp6SYQxRUTCM/UwbAsOeyDAXy9/xBz8cWgC0C7gkhN
+wXvMVUsPvwu9CnQ1NAmgie1XsgPmNwFbSPLYxn3jT7c4/dAXBye7pJW0FAKnfXCGT4FQoaQcwzu6
+U1Zl84726xE3YcwpT97qaOU4bjfsOoGiv+UIoKKOHH+P0z7ec74q7+tc4g63uHqPdKVObRm1XHqH
+ZrBRjvf7wh648UOcHMF543IFwV7mhQ4PPEp9scEEIlN7CMAYZRrLGsGvhPkYP/B2EPwcGuFmM0w/
+8ZaUsNU8BdxKB7tDeE3hrUU+TSOgFd7y75ckj1OjfhZA/ITCI4BvUfae9sGcZi6XgFf3m78VNDM6
+G73nBHWmhBHencicLhcLCA7w+2xlkj4RZvvgTxrgcmqEDKDYm5J2vgC/8j345bLqbVCYRwD8iuC8
+3zjjWFXMLE+MWBYguGiED22wVPvgGyhW+TGN+P/gvj267jCJcLmR9sFGJgfdTliCiR5A0hIoRRwB
+FAC6Uyixd7PA+klVvo+/0vHFoJrmEI8SLvUjHtQ7mBrhqGVop5Zwb55Y2wQn8gXwneKeA/zm1cdw
+c4gcGArnVp9ZczYCX4xFEdpaTEB6ecy50GjlXPC8QlOT89+B8M4nh7uD3x5XwUUHTLPNMMJWyvT7
+HM268bAPGU/QMQPdUsoZeWi9PYPJ/LAwx79rjV/dWXAEvmM+IcxEE4L5aoYLOxOAks5zMJqy6JBv
+iIs6D0KWuGeJN3H6x9g7Q3FpjX5VkEt9D/9vgGUbEm2YrQ9rpSefU2gC06WS6l5+Wb/hmfN3ITYF
+fuFAN80p7AqCMS8Bbguw1ZJG3c80rWkMgifg280lRoC80jzuAMAiTTO8bYP3m1E/OUKaZtismEYw
+8M2R9hHGArEEvfI8ZVZMkjfzRH25gsPlePYR5qFUGia3C1ZXKmkCOwvziDWYO9zBLptRxCY78hdt
+sPxN8EvAeWuDVUwdsOsTwDg+foG0pf4xlG0CYvYECLZFD0qkDT4BsBQzCIDiBMQSGw//tPsBwu5+
+XyMMf7UT7mB4MwKkqwhsQScAZS9AVwgolbJsMlHwwAzOnHOobOD7KGuDu30bNMD1tZqSxKztR3sq
+ZzKReRMcgdyMfwHDGGA3h+g/aMU3Q2jg2Md3cgFE7bs4FvCY0/h6URsT1gKfaRivXme95p3O9k9U
+e4uzlv4N2EVKhFumA2BcJVAK63r3Wn5ehTrnwbSKHQ8KN3B7v8fpeLzi7f+1DoqdMnjcCcIs/vIN
+GYTxSlKqpNoF1znMJdrs4ammDcoobia8177WFZ2dLHQR/MpozrIIz2chLxqUQndWrzrEHWVe+zPT
+fk3VcmUep5pKhP64n6BUQisMJQTM5vbr73oWsYiOZg+gjB4/pUVPJoArE/BdsVHuMJ1YWu2DpR+h
+Jg6CSQY2bbA+G8jqwls9n+P41DJ9NMPWNqeQDnz3G8syK9r6OzKhykOuLuxRcmTrssAbUgwNADG+
+OGdiDoJ/LSugdNIMHwBZOggG+JWLNtitrB0gB/htoDjAL5d/JD/t/BjZCW+bCTMLTXDRDkMbbBZm
+EtAIh3lEB8UdEDdTiLx+j8X+SfcDhN39lo2wAJMp+RPcBCMQIe2wiBAoFpmu1gBRBVbidRyOtZ0B
+CBvrJkYslqYR0G6sXnsgQYA9CUla7E65WQpB7XdtGwO3BMibBQQ4lMUWG+tsNTgQ21/zh12woSkO
+YASuWscttcJCcaef+ethKoE4SpvKHoD3kud+rcz9RqZTfI8rNRmH7UynMl1jzCDmWv/BzDoCurhE
+cJG9PwDsuHNVaPEwKJzK18Z0ehhaXkEmgb4rsjqGIAVXTat2qAHg9Owpv+tRSMejbUN7tc2rRxoR
+Aq/rXtlt2hQNJm1wjDibObTpL/bAZf1Yzcvrjhsx5BnbRjc4qTivpzEO30iPpCUS561CK7yc38Xb
+tcYKsRs/LY/xdytOAL1oCCQ2vgkD3u/iu6Z3KzQqCB41wWQSAYAMTd0GwRIgOD6mET9zW2mXgSYB
+hsVPjJAnv9NX5mncGFftg8ETy+wFz6iMuKqVhhVShVf4D/Mc3APDISJsG7xB8N5Qto8cAyCGPFIC
+tF+aQbC/lVeRUxtM/gMUy4tGOEAuzDgeAr9b2yvPBIBdK+y2wgtvDCYzCI8PGvK4slFOsVHuBwj/
+T933H9SoP32JU5EExPST63XSDtqYr7qUCsnaHRBqrW/J/lyimO0jg4klLd1PhDPqI4bD8ZxNRAoC
+P1z/khzitGh8zb84HnlVZJ80gQzWTowAo/UHgOL3gQlJ4eNJUQfDs3N+Shr1Tz+Um3DF6zXqOJkx
+u9+JTxbfwzand9AiM9gqU0w0scnrBp1uDfVeW94PDrarVaxx2+uqqFVPpg8ik5kEQbjBueQA3jsL
+DkVALDXzpNWtYp6ixOKDGyXtMicRp9QXbrP2PJVwrVdW6up3odYwGOaxfQXANIctH68lXr4dDB/z
+AMoYpvE7Dl8B8j1+X1mR0BUKKsm+Nkez+HztHi6VZfsYq/0pZadK7eB2t77HsbnEGKd+sw8A+GYS
+wT9sllt0ZfOIqhXe/LhsmHtMdDnfDoXKBHzfOBj6qXUmDrmESGbSjWs5rZ6SFKNsR1EazgKAFSB4
++c8SCJcPYwCsOo9LsCs136UM/PiCIEwhAHDzFAkqJ3SSBNX52B6jbQJh1Rxi2TZ5cCPiEQCbiT1L
+ZD27v3KC4NAIt7g4Qk21mEyEVvi7hfqPuh8g7O53NcLl58R3/7F5RP1Jub6A3rJiT+0is0OAvAOE
+OVqDeYSIJCP2Wh/JQ+EPLlBuSIxnr0qW6tEebsf2+2kRfkJaz3+C5NW8D63otBHeXM31IZaM4pCc
+ZUx6uHb7o8aX8ojJO4j2+5c0q/mmcgVUDK4DjiN+iPsW4JIoiXbwbUp+66U/NMZ6hgG0FXQ2CTXk
+tSGu3uTMca6RW9xXDmugl/5QIW+6nCE76Fg/Vt3nUUSq+YRSHiK2Yo/cb0/hY2apAZuerVRQNL0D
+SOU1YCXfVL7dvYOY414v7f7kzqGmmtpiFwfBuuVAfORMvf2uXVNNkwjzM4M3ywQY3vG4CyQBg1yE
+p7i53JYy5Y3eb/3yIxt5Tq7EsWmnmSDkoYNgkWoeATBsvnHORAQa4kBqaTaRvw2+dAiHsiMewjCB
+WONgKNNqabJNpBKdNyDktfpoappGwD5Yl4o+S3TtfsJWeDSLEBk0wGQWIacGOdMkTCRMNDW9JPfD
+HMO7gzegp1Y4zSIEZg/QBDvdisvXtBE2MR9/We5/tlJtBr9SP88NDTEDX7INhlb4T7sfIOzut2yE
+zarmV9M+aselHXAsIknGP5k8jEAp/DV/F/KISy0c2XR6otITcj8Lk7csrCYYmbccN2QXzCYLGad1
+m1/SDO+W9LOFJYFvjw/OAqYaKzxAcRGcAZTl6m5gVClDB8bjXIG3smCe5psGZ6xnaOtXgPiWRt0H
+/XDYWv4OqjJsJbxFA9VGsqXM/6W15o3TktehrdZWAUSpWCI7YTCcd+5i78w3uS8gU6m43WW6KVCR
+trgB1e4unWkx4rGYd3zMa5fnauf8TYAYcRPYHfjhGVX7tueHKSMrzLmj0rxGynKl9TLxkqF+vkdv
+Yoc/UbbEarmGDTUv/Ml5MZhHiGy/0b3ghxlEfrDAgYj6UVhsVhLlezhXyxQucZOm9wvwO2qL6dU2
+wHD8hMwiGDgSCMZZXYrv+Lo2WEVE1tooDeGLOUQPx2xBxS4igaiN/DxRWD8T84vFwiPZ0hRVVNMI
+/gqb4Yty6wZqb7bBu4dPiKsKluOYNEF9KQ4f6cAXJhhnXgOtwuRBLbS8YhyHN66kKTbzI0tJM7ys
+aoQHQBw0Q1rfxTTFYPhNmP1L7gcIu1ufs0Q+fJIYT73VPjhBcMZbMOTNvL8DvOmftYKzRhNCsLHR
+IhH3lgOTrcngvrmF8SwwSMB2V4QQgcFoDWmAC7tvoBenG0daB8MiBxCOn0iYRWxOkvFnw0lgOzPs
+c3LOA11Rpl0jT9RpRx3X+e8g4uLejol6KxskQHR2yIB2j0leACAYZYz0t7Zpv0t6t9yq6ZupA2hl
+Swy3JYBSU6nO0oATIJ81jDD1e9e0olxpAaG3u2AQqY4DtBGqrbW0davD/HbwS3kIY1/cxaBkE8Jo
+Ft7pSYQwCwWYdrSV5bU51R3NtyPL4ZJHMvC997tRndTFXumVN7uZSOzLeBynYTMSNHQmGnaaMEuz
+qLFSJcxjbulM+Tmf34Lft98Gxura4QS/DIBzs1+YR7hWeLn2V02DN2s5NYJNIzh8ppkD5T3yBIxD
++6BgGntuaC0az18IqDLBITJ4lDlr/boqhdcG+EsJBNsSbA4/Nr/Juz1waHybDXEFspQmJvx57vjS
+nKQMzi/LIb8E2A3wy8C3AOFMMzPXBEMzbHeNsDZwrNVWuIPfH9OI/wfue42w5e5Y1QTDmsAYuK6a
+RFTzCAZDgjiZ4qqgvgJibeGbC5S26wQgnn4SufB3Og+ii4RTQoX4vGl6S3CIVxH+Gl1+NW763Tpg
+2dPj9VcOS9H6BpilofvimvXlWNzmkDcHFdE2Cf/mxvRh6n8LAA/hOS0Zswx5AaoKRVttHOcV0WD2
+O44gi1GFdHfeLMajx+Ni0sb1Nc8n5zk/FWjpxXSD5PRbRfF2Z+RLDH9qzBj2JscQoimEH+baz+a9
+biqzzBH5LdMrAKZ7Ma0T+yjg+WjYXKak9/I9Xeb0Sklvk022lyLtK10Zr8LaXyGNG8pppHFbzjdz
+n7TFndI1f661/C0Q3GyEFzSgIn64BINhZ9NmqQwSMotwABymFmQaocUUYsXxaqLbTMIe9IzHg06P
+UDAbvrb5K9PY0to0G/0Vkf1mCmtFhEAwaYQdAC/TMBeQtT+vctr/ztrg0TZYehlJUCynGYT5uGda
+8tAYYnONMINb8mOf0Jw2hF0jnCYQUuyCyxuEASifbxrkj7sfIOzuaxthwyYBB8R62gjjE5NariJC
+fmj/DlAU/nrd6/VMD9dARgi9HilygIdg5orFQi/ASchkKQg1vIRBE7BAuS3wz+D20AB7/KGjj08x
+o5E2H6OG5U5xRTQPEjHmplxpDnyAWBHUAfNkT8x1hKgiYJCAgGihzWOZw0maD/0oznp6lQk2pCH8
+W2na0qznPBpKbcx8W5ZpjoMmLbJtuxDlVeByQpdJj9kaMJS5w5+a4ZLTZMawXF6EBHZ1KbNJWF/a
+8KntZdg0p0ZbOO/9iRm+jSjTdSHviKclQLyD7n/TEvNdjnVGOfgekT61VGhocwHbdQQqgFKqgzsV
+/FSEQHFyxgTDCXok/NnaXXd9sPisCa5l40eAGL8ExmQOsVVzQLqRrh0Mq29n9vGrm8RZG+zXxzag
+fExUc3vhbm3T/oLHP96fULVvMAhbYfPXmQogjMlULAqVERTzeHI0E2YbXVSbABjDtcdxX9c29TMT
+fCX1I8DFnA9gdy6zw7eNcSapEQ46JA0xwPGW4Vs+oo0FGK8Z+N7y75NEcjMcA2I+KaJstNQ8VaKD
+4T/tfoCwu69PjVBrwFc3KBZ+MqYnYuSDtgsAeNQIE1trsm+yKeVytcym9tN2sHrAL2Kpa2XiQkA3
+FlYI5iptGASzSYS0a2FFj3wGvVOayAiCi6aYeiGSzLM7/aeujaEWk5Qy19ymJuKYER+C/uIG5n0r
+YxRvLc+RZjXfUU7bHUuAar0ikEs+15Yp0sHtBSYRJgyG8Unfqa9vZhIZHojiW3fK1UjgkyE+Aebs
+/22WzbPUjkb1XnW5HcJahnanK4WHseO6b047BbUCpW2IoyWBfv+uljjv3+9VMyn5bn3B/NcHqqxz
+v2lrACou9WGA+aaJxFt6kwRbsQHJ40ILDK1gGcpOnTr6z7xKc+9oTckvL5rhVcOcL7Sf4mBQAILp
+HH1z+Wcu3+Lqm+Me3SAYCt9mCqGjWcT2G+UP2nLAnBvinKDBI4JXNOYMoh+IaosOB30tA9ZV1wjH
+1+X8a2wLgFgkNKzhF7QZYdCJpSiTs0wC3Z0W9GZuJhFh0Ob2PygTadwuy/5KBb1imacCYRnBMX9w
+ZRU/m0dUkHwzi/gBwv9D9/2pEQ58/YtA1xMjEA+GIPUagJjq3gvN40em3thja3M/uIFBjrZKgg84
+U9vaYI28YWBPWCSPSGKm0liML+SQZrj2zynDERg+wMojm3GuBobL43EDwJ6WO4w9T7CKc3zYr80P
+zP/xKg5UGoKsU3QXZRgnBgmXokO9vZ7ZFXpo1Wrzf8xnZxlzwgIjFpH+vJS5GfnEzYAYdl2xWY6O
+9NsmERBw2UpKjfiDno6wfUi/jbO+ZjrKfJtHuF+Tw2BqDqHM81bCSmEtVZR0blWfnmuLysMclbcz
+D9fJNFS6NsXJOV5Fg8zxZwtpXJTynwDYKO1cuaCtM02ptjgtovvRNlVKt2ClyS6zbuTPfn3j3+GI
+b2DXAAwPILwynbXEMI9gTbCKg2EAY/BN1wALyz6rYNgHJcwhnptmeJZ6DJ5j/tBm8IUCgoX8jaOF
+5hhRzNHq3WPY0N8GgPcD+dZw7pMvIH6s+EMkyQvYpXwiVYMcoBdxoDsP8+Y4dA1hHLuG+4tJBbs+
+Vl37OwJhqUAapqL1lAhpoLgC4EkT/AOE/8fu26HPc4EJ2EoDu8gD6tzUw9yucnJuxBDf12qAqI6V
+DmSiY8cimw4ACLwhAO/QBmnlaBFlu6i/nDa5RySeHKZdi88TTFrV35MR6D01w9LCNS9bRasgTjIs
+JEqsXYvoaVdHff1lJl91iIMfbSlpFzIp7jq09kWeWu8X2TzztGKsVVDpj6mJycH8YSxQgSIPbcAi
+AjT/qwdFumBo3S5AqS2Jnj70aIA9bc18LHNGXssXkP/Sqr1Io47oMq9pzXuUs4iV0pXCpXED75ho
+8YVoso86xFHYhri2zmqIME5vz9jcYZzQKm2UBFoUpSGmVa5Z/nZnhlzW/LtdScFCeVCa57IC4woO
+B+5R/ZrtToCLpmrW0eLQptSmbsebwkPuSQW72wziScAIBMbOTAAWt4Ikz9nAQZ5iK2WKreDh2LzF
+8dG3ilSzT4ef8vUyJmL2iNiz5c6hZKlyJDdn17HhGWHzi7TS0JAXCcg5bH7Bgt58IU94opUUvMWK
+wQmoAXNYy2ajOr1K+HNj5yYH38dhIrnpEwozTbtxv6YJCV9vfskHmk/s719wP0DYnVlfrbd84k9Y
+Rv52NYn0eP3NV3D/Ks+ENwuNAkMlQafMwMUkF96skfAmYNHjJAjjBUuv8LwvgXFKfwj4GJ5MnSl4
+OZwfCjeBitJj2Am3nwWv84NiCPDG6xmEr+neD287f4K5XrPteZ3ysS0WmKGlcCFQm/OYcTkKlmlt
+UiegYS+hcZytXK4l7SXtqD356+FKPS/5dl7Q4ZytrwNOCE1nu0e/5T2cM3dbdzXubCEAZk/hMXjj
+6+/jfLqpXbXvdSBh/pACP8cNQsfeGsg36wD9fbAgm+u3DcCbkMcHP94iRTm9vN2aOUgZx+kBgfIn
+UNx/Ip8yKNh5wmY9hkBbbUMDylPelH6W62/zUH8FJ/ddIpwWfSSAl/UMk201EA8GAfyU/CZxvhf9
+NigeZOiZNXny42/7lm0zB/888Y57ZH9WdPsNcfv7vNuva3/Qomi2s88iQxzmj8KKdIzh84j89exz
+c/969scl/npE/rLw76uH40dh87AYi4EI01AcebBApjwhxzE/wnUjjeQy5Y10vhfmQiTkZNcE48aQ
+q91EAu2o0q5KujmOfkrlf5cp/gPuBwi7s+dLICxygN0RFF/SdyUVmfAr2i5bIqy5OJDehS0vkuIH
+IC4g2IY8Lhh5YcRNLcrkVaQ0irSnJgmGxSr7TX8Xa95IZ6vq6Nc0md3W2NSFqLRQWftrSEObKW+E
+pbYV+DUAbWE21trKVyrT0lO5/lam1l3mf7YvGMbzbF31aI06WvNdOOrKKt+alyTyIW+n/byVz4yd
+WOxWzr4OY0zezSTycYXupVryc+qBDb8Am73OyU1jZ0O52EkflWuC4gAD4nHtnhOCFK5O5wHlTDTn
+/KACPlRAsqRO9hjjEqdnnNaU7+L7l8kqcN6nPCR9WBaLik1aa8ryHQBySbcznfwV8FJnoo3U3t7h
+CHUt8Oah43zj3gforX59SAssG/gq3zl3W9MPoNf8XLUGfh87ga4SGPaNe4+ubXaAjXyxoW/tzwAL
+ZIRU0DsBZMVHRlzeUbyJONA1kb+2VphB7wGQGfxOYZqjGOLABSlCM91KmUojnc68rlbPTquSyPqN
+JOVnke9NPjIoFjlBcME61ITpevxYHphGHf8Dy4gfIAz3WxphYYJNQkiCkkr9lMaAeQt2K/Q9Mfo8
+pSHzFIGhdMsAuxKCpixEejKrmzhkAw7U4SXiGhRt7cr9m9PRJ+X4cXBFgrNqtgM2oQDEAKr89Iow
+Fm3sPGewXMLo1wl+FfnIn0KyaY05PgfEy9ZR3JGWY4LxFqNJBXC+j1PnEzb4pnytJR7TANS17jNc
+mO8LQC0RBSPomObTPTrMkPZCMRvnGnoLM8TIMM/koAfW2vYOQfheiPp8EsMwXl87BrZa4xnsHiAY
+cVTHVHubtzbscZ/SCQK7wU/A7yR51c6jtRrwL5QRCmOOWYjS/WMZNQJSmrMpT67jBMEHpXkd50hp
+nTzmhQdDp86UcQymcNR9PgdTuzXHoOYD8NV5MZHZBDsXAylQ/AzZ/VU4xJkfcUafYjLxvRxPVLR5
+7ZI45tDD4sDYlm6w+zDo9evTwvrIs5asApYdELOS5AJ8U/ubea/aYtb0/mV3MAxNcdEON60wicMy
+rDTgASIbDXF+TphAdJHB5T4sp8/KIX+K5jf8FhWxUqxuqrN+6yR/p68uGffbilqOr3+fD/599wOE
+3b0Bj56vP9WBUk9w3OKQl4gXt534pTpCuwl05AliJqZ9EBjyHUDZzSBKOuk2Lcn41Gjn4sA1QaUE
+gNzxviyccx97uVkoRNiSybnX1KqmOZ5U942PMPoAAEzh8MsMeNG1CpBnwMyjjVmoy3/7GbZJSx+B
+cyaG7euY3obPu3Z1NvreQfGrA+0MrSkj0Im5uLPMkY3vYyTPiX5GUrqE57U1aHSRbwArfZx0CPzO
+WH6GzLd7Aw0CAE9At6fpcEMdfHTDNuZa0rjDFnwGTWDNvrU6jjDdq5Tnrko1oYAZgIjzOMq/76Gl
+zljHkpuGp/hzMNrIEA8MbfKkGZ6kf8+Lex/H/njQDu4ZHSxg2F/77z57+sl5qV0uBCBUzHki83do
+hdf2x7nvCgC8J9aWiQL0Up37fF3d2uFH02/q2mENswcZQO82hfDj3Mg/g+H6Kw8GBIRLedEEt6T5
+laYFlmYaIQUAW4Ln4Nn+o8EvgJZII+KueU0q7aSng+w6v60M5gZ0S3GT+UMo+0ieGmhENp2doLdd
+wR6iP6kJnpbFn3I/QNjd75pG7IDF5QS8/qdcOV8Fg4fg17xPF9oiJG94xYB/OSfv4Be2v3cQ7DtX
+NftncT3jslG9n840S/sYvYiIEUsmgRd+NcFmKVNzAK3RBzaP0JyEXTwW+Hs6+pGCj7W9uzGRxoAZ
+Dc1hl2reci7pBLkcz/P7DpxFcl7Z3QAb3/NT7ESFU8kbgyqM90sUxyAnvAMdBMlc6j3WzaUp/204
+2/oCEmMma4ZvtME7Yyv/Ww5ltYQZHKdmWCsIxvV64z5RUrXEPAm5vH2Ny7T0Sx2cNs05jyeOkMu4
+uumNh1qVNoN5AgA4g9wI0zDklU7SkSkd/MHrozEsPD6WvLZx4zdS2XfpfQl2o5HEShR+QIi8qlRP
+90upv649C1mgJFCwIXxvJNtAVR8/9izeNGDS1BXGW7iYblCsMIaGxvfRwSRCxfQRXWvbDKueJhOI
+g5/NGw67Yfq1I+JUVeyhEzJE0jTiqdpeIS1wgOFiCjFohWlIjckAJ0Zc0jmuzBEnSpapANguZbaU
+QX6A4BMAp2xF3JsdMSvr8pYDwMXDUGsaX+mAoD/qKszfOwAAIABJREFUfoAw3G+YRuxrBb7mRCZH
+uhMfOBxR/iTEjZhRlzORV5nWKwm+aoVRv8lnkEwdDuZ8gOBk8smYm8aWKgMTHLZ1ZAPVe9F2y0Y8
+LabD3KG0t5lDIH8DwEUjLAx8MZ4s9GjOrObnOeBBiXoOyWU0l8aVhv9mGtzlF+c607qr1DTlyV5/
+qutSvc75a5wmnVF+7YVa2U/pnKf34m+FI+K8c++P1uAl3z/jku60xqoksA0tWfqn9PAP9zhJofXT
+8rac9w3k8ql4R14Pp4IUe9clKwFrIDMPEWg/qS+6W5ubA/t4ZdvrOsc1zWVumzl5DLbfanx0OtOZ
+x9iRTmPI1Wm251gMRmmtb6kJlTJ+U/uD73I/yLZOH922vTjWMjTBfpqP0QePoFXW5QoNc9Br+0MY
+YR+sEb/9G+yqqth6RJduEF0AsRaziBH4kslEjAHuhbgFDfGWMfr4OJVNcFa0w/IMaR0oPyZ7s9wT
+w2s0dztMgLDweQLIKV7LXCHdbpmsZS7586ZFrhcAjEalZlgMMh3AWMifNN1/cQvCFT2N8+znrutK
++9fcDxB2961GOCb8SwDM1D7Gyx2ajMI9JOAJfgqBgfDAx5KfxcKEjWo8qUWZ7ang/QTBPcyN1tZP
+tUO0ZP5JEqEStFPx6lIvADfvF/bC3i5t7T20yd673gyRLrRa3JCPpZfmqJX5KvmH9NAeaysmvPEu
+nbbhzHqyHMecFNbjEZriz/sccVTtV2VpzkGr8UGKBhCM/AdQgyarFZOzmunWNYz2NNRwgYu1X3qm
+/9OsvdwrTCH4Tko/5FEpwLeB5G/amGO+J6ObpvwOyEVdHM75xxqntin4FvJojRMRbJgy9Nnj+KSM
+c/3OLGgCv51eMqF1HJlF6tukkn4u2v7wW8wgGjEXza9Rf1QSZGPgoB0uroWxuFQkji402+e5LxER
+1wCrOAZ2cGoEVKHSWyv9DnxN1b8sp66Z9U1yqq4VVpECem9hPcJhErFA16uES36AYAfFqpbtExm0
+vZamDr6J7tT+Dlc/Kxnyljl9itYen5McvI7KcFylHat5jcpMGmKv6B0Ap5xM7fEQX7S8k9TTxEWU
+LzTGxNc5z590P0DY3dc2wpJ2OGwGweEdB0beiDEIcAeDFWmNL2lC/JUyFGEPgKR5z5qm2+TBpJ4a
+MYDgHAtr7UUf5ms/TzhecThAiQ8h2PFSsoxwMFBvs3oYuiEfWdrwJgFusVi1zME9nIKtmz5ILOlZ
+YNoRJyKFrWWveM7sSGXpp8o0cJnra5xlvJ1553cQ9/js/d1Nq+Zovn7I95L2dveggyFXzusZ7nXX
+sI4AaG6znr5LwY/c5b/Wgmj1H9re/btuEkKW21lqvnR5ICNngCbUQWNLoA3hQp9K68tyGA5bYm9j
+gF/W+oYJRE5cgl/NPEPXjrWtFQyLnLRU47wj2fDMEeyRKJTlQCmT8UXz2xqhVMUBeK31RzHfNLWR
+tfXoWCCNgTzqYNh5jB9zuUkLWmGiJzyUOiDdwNr5uGqAY3UNcGiJ9WmA9w6KTVV0JeCNc+ancgS+
+FfcLrTBkjg/AkxreBMRdE/wFEGaNsGn6ffK2P7kYy2xII+P57vNF04R8RnR05JVWGYHaEwAPJhOS
+8d1sgunuuAJbTOnKS6JKqT/pfoCwu987NcIKgR5HngyAOAi2aQFADKG55HvBQ5KiC/eajrb5oiMC
+PIiSCTDiYDPsBFkYIl1LP+r1/LgG4rPV11MtiRnvZuyGhklEsAfkIe0u6qYHkB2WKDdrh6lsbQIJ
+xWxrX6Rz+smF1AchxpSlf9zzBM/amMic9wR9tzgOzRSnLT+PyLeu1cHzerTs3U19ueURkXpUl5xj
+cA+nLejn+56xM37Ulzr+eZebopTuSnGIP14j53zVturg8zAyJ7twYKZ1DXvNDIjB8wKQIZ/SuFuO
+6U4j7bUQsI2w5AbAA/xSGPXzvWgtHTzg8PeVpIW3FUabjIoKMIGlEFDRUGT0G8dYGLWdGtw3z0XW
+Ps/Cv8F1orfKESr4df4MTazpeT8yO4BJgjn4FFUykdjgVB4H7gUQqxRQvHo9g4b40B6zPbL6B5pW
+aoWXudmHb5ZrZg7STSSsgd0hbwXCfRNZakNBUTdAnFOjJMtJGXdmrJN3SY9yJgX8il92WgPGgjQb
+0lgS1n7WplA+xS2rPfGf4pfsfoCwu9/aLCcSQLAS5B0QM6isACEjKovdLoTMALF2BooEg9RsV48T
+ETKXSO0vg+BJI4zru2bYm9GKx65jCEpIPkbqLAAgXcmfZgwOjGl8Rap2GLn2/6YNprx5a4xBG86j
+aWRK0fOGv7Ev5XbW9GoGQ+XoiLVejocN4X7XAB8cF1PEbGZiO7+hBdYh7pafB0r6KEkBsAc9VFLY
+nr7pKOqtGuLewzNcTwWY8qdT+lv7c7rahlf3X2uDh7YAJE7gdwLDH8VPpt9BcMZztxgsdnyoXnVf
+cwmUJSoLsNvD3lf1eD4urmuLd5jWEvUueAnaR71OPqEHjeSbJeoIv7kDUE1mGJ3cILcJgMb+MLbW
+G9YXYmUY5xhy9ut0W87L4MBbFJWgLep9G8DwFjVpvsDa2AS/jwBUG9sBq0oFxdoA8lNAsa3HNcR0
+2kRoklfaBdOpFfF1OqjMGeBa1QAfoNfsVSuM0cK0VcC4tdGcnrMgc1yJsJZ4qURE7MAi7inaX6Fw
+TUv80tPyjWw2gfqoKFvBbtgD9+Zr7dafcj9A2N3XGmHJidr+Zh5BcTzRQvnykoBndEoEOKSJCGkj
+8v4iUjBmpDmKMr9pAuQhLm5r87WB31tebl/9ylzb3tGliz9lAkh6beX5mesrAFckFuf2B9uhMSHw
+DOFsg/BjIc2tJwF2CpvpBAkpubSkGFUylFWRLivP+56bK2dAdwy0fAK+XMbm6OE+L1VR4yb+/daa
+b/LhgW4CNDWsH9Lnu9ysB3r+T6P65iaau9/KAYTQhwKATkbwWzcSfQTCGBBLcCvlcUPYnD/nNsn3
+tAOWtrawLht4CwBIQHbzpxZmQDyBYc3KS/20Dvu6q/TDf3lgqHPJlAqR8DGSx+Bc46SMeyeEGFNn
+UodtsfC4aR2X0VltS+k3Z7MY8z22e4LSLAKE4CZGhfYkwTBpidleF+YSqRmm32H/e5pJCEBwHMGm
+Uk6beJbfw/bmOdPYtKfP2kCMgWzR/joYvsY1IExyK68Ag+63jBMFWKy89lj/k0iJe0kq53A9yqSM
+jAwN4GY4ah0AcN6ot5WbxbcREcIm1HeO/28Y5990P0DY3W+dIyyV0Dg+eCP9QV4pZSo0ORxLDIrK
+NC5LC04rPwVvY20wiDBthtG2zcisVxJMklbYLe3Iu5kpA4/tG3rdoxRPm1VEgU3gISFSGfBGM63l
+r+lps4u0HLMitEWqMPdCLMyjbs17985FW6KimidYwysCOk+r6OAh78gPI+XuIjSub1Cw96TE9ao+
+5ceIH+U0bhvpPgelOeSvK6hbClfd8NnjSpHdLviwPP7InM8MX3GUD9rgz3U0oQnAx4A4wO4JgBWg
++NKHW+zGO1qAr1KaUdciy1u8DlOMeAd0J7gFIBu0w2/5ZQLAyb/nVVLNsmZCZGaaCUUjfMTlDNd8
+OY6vYSx8pXTcg7WwRXhcFmtZABlZ+AjGz+KPhCZYcvy9AeGPr/dRm2DiIKud8buobAPE56a4DoBV
+AgCXcDub2EGxLt1nHj/7KirVBtisaYgJ7JpVUwhr9sQjEIacZnOCnPdCPu0aP7v7U/4OFRxxM/gt
+J0VwHrmnq4l/4a+C4ld9GfdJsy//C/cDhN39HdOIToxS/FYntvg7U23uAEh57xMg2eErQtGynDX/
+zqMC7av42b1BykTFvBktb2KXqxD4qisw2HDpwwX1xa1m2+iwBwbj9zafLDzzFoFmXdhJCmqaL35N
+qSIVELNwKL04+y0i8fAxpqscD2RstpFC+pRd3Odbmgg19gifaZ81xVT/haDfGNtbuan8ka01v59G
+QNh6LDJU4eUhvF8af2/VV6P2jzJ8AjnKxDiB4QaKAT7woZscj3PwMK7KAwa69PJxW0n2EetCyW+X
+eJFTOxzgVj6D3XVLZ9MKo3blTBwPxbQW62qwyA0+sqOx0ZIZLQ1cicsyUadJGdMAtz2cU3LwnM3O
+QQMaY5M0wCUl2hIPfkVdT3fC/amMuOIEm5kzQ173eJxhzFMxlcD80RFnExgu2mU/Xm0DWhy7tjY4
+XjiK7fG4R8xWaoP9Qx4Ih4nEpA0+AHCCXXvx+6z4VGuG4y0sODbsaV1BJRyP8aXJruLjQ9ylbNH8
+Ev2ZzABZMl8ByO2tuGQ12U+E9bhVxGFsRiXZv+x+gLC73zGNYJ61y25GacITOvkn84jmWIKMaaUl
+R1onNgnGaAR6pRKfUr86KuDXKH4DLR2wcq2CIVeGi+OLuw2IDT4GLlYAtwnfA9rkTNtlaVEaAVSU
+pXEJzZWR3wUZa4LrkGVLlTo/fYFu38OIgbQ8VcodjwssyPu0qZztaz2N8GwV+fvO5huWds0FRb5p
+wkQDn6qbw1Xbm9pIHfOf7j31azb+QRv8RQXVD5pk7ZxofT0tKsU0YiUgvrdGxznSTI01IaKfNcJR
+ZTuRhXhWpV8lMNzA7ZLUJAJY3cDwahVL6uiY03QesttQ16ZxXL5Sq+M1xlEnPX2zfG+/y5IDUVC7
+A6N63MwXJMakTFbxN+f8P85I17o3Q7hd6v3X2jDFfeN2sBHOPpuq6JPzFtpgAsH50NN+rAmOeWXN
+bwJggN+qCbbtPwDxCkZvXRtMmt8AxwF6SRMM8Mt2xd5fwRz7JAfl+SuPRlH1quTpDyd8fYkzSXEM
+vBK0ZR530w5HpjNfOTWCxHgq1lqzDCus4hC6y/f88x90P0DY3e/aCFvzi3RCEKedjDx4Y3f6ktgY
++JTGRDRpgxFfgLGYnEC5MnbeXAbhEJCB0rQPgrS4mytru+khh6IVutG9EZZsH/HtGgbAV6nKGdQV
+Y6ZVOaJNeA9NnIRr6ZDmmB55nAa0lSTWcQL2lq+Mg/HdG5Ip957Sbv539w0gnnj3UeSlKdCaIH6y
+3uS4tx6P4cAL3/X5W/fvMPlso/JraYGQdZDA2l/lODaPeL+DtlHB2gEeKrjLclyN4o3qAi+Z0yUo
+XkUIOO0b2iLQFBpDITDsFRII5tNnRCR5XFmJSaXoB1oCwBK0ZaAV9yBrZypBYMyMaeB4YZQz3Py1
+Mw9kGVQfN6ucU6MabdcLDaLNgYSjAVki+kB91dIIqjAW0B5d5RQVW1LNIVT3EW0qMwDGPI5a4QqA
+D782EEx+teWv9iEHfX54oxxA7wF4m/a3g+TQlKZWN7TBofUVCQAeFJY8q8t28+zlKnbGWc2fFaa/
+A9+qHc4Koo4JJB8aYdIAW70tH82KviV+on1KE33+y+4HCLvDa4yP+ei3Jz4nlonv4G2dKCz5xCYK
+u1NA4V5W4ykK5yHG0yUvIua32tqoUsBwAZdE5Eqdgl1Q7Sh30tN7n3o/bEg8ypwDc2pCu/lEC9s9
+nVj5DrsU2XLLQggVGbB51yF8om0xZ0bp6WcIXM4RJnpCWtGU2dBe/9tBBNIqPERqTfldd18tdLfe
+mOF+hRwaE/yd1sVQk/y1Hm511jDB5sAzf398Prrf1Aa/sYb0OPV43XWjEvzLgYZvOGLtcK3xDPGA
+BUHmuggMbiW5FuOlIVhD5xyVhzwHQcXOl+1KGVTxK3cA5aI13ncH1Cir7QDFtF5vaV9ofEuaoEP3
+ssV6yjCXVrLzGAfv0MuaY6Jmf6fvGHwRBsOFs0T9O/NoAlfuIS1+jz40wmkzLAMAlg9h0AK0v0pA
+uIFh1+rKL//UM2mD9dcGrrp8sxwDWwK3wmYR1swk7ATKAYTFaZPMISoYPqUYr/eUdNYjz+uYdil3
+A77+E6sRHfzeNMJxm6xSjNI5XDYQavKHP+1+gLC7bzXCIjyJ8yTLEC54sYW/BcEVPMlRBoyx3JvD
+hf/SQjQ70kRI84unOKtgmDuh5abVZEG9Hiz5Eg4/3SeKDitLeKFUcwiUfg+fYBN8O4ATj5eF7CQb
+VGZWFnnqdGSobqCrabVP+boohXJtJ/ehy7w+FlXYc04eh/fSn9yNbEueRo9fVcrYwZHW52+fQbDU
+25w00sPTprp/hx3vMfjn6uZNUPn6eYMEbJ7dr6wZCMIcooHhw+lJCpZZFR3CK3HwC800FI9x1SFO
+cq41q6xlymt0ib7kcVjSNMEEgAGaYRohQuurcGWJB/+KGPLYR2nXbuPbAXHR+Pbj0pLpbhCrsfaL
+UhYoAXICPLfwlDaWxU/9dk/hV2WBJDOzWHPDKid+/NtOYS5RQfAtDBo+0kDzBH73Z5nXBryHFnht
+ze+yDYBNA9jpcpAHrSXA7wUUB9BlbTHHMxBWFZhAQN5mHIY9F9YpHSqZjde/k+a/AnyLlvcEvgx+
+2bZ4k4MxeVZtL5kAxjIhuVCUdnfK+dfcDxB297ePT2thmcIUlxUBZWXGgwBYQgzxE8FMRIbwRJTc
+lHx9Q7eBwPB2FHA8dphAsFV/NnLwcz9ZkEjzE2jvQC95hbdZozR63sI9ncIAxASQxWAmkfMG8FHn
+wqi+i4kEnRUsIrSJruWbypI7BZ+N8VlHCnIrcTeX6Z9y3VtZMwZNll58dsZNdf+tZO9V70WEVYbw
+CaZv9/ht9wUI/r16GcTngFTgC0AB4Jvgd4NhAsIH7tHTZzV7gDzEYRmrELVl3xzrnXGtCdEUFeHX
+6OqANjW+2gCvSJpD+HWdm+WS3xROnfyN8wRqqHGhHBCtvDERjhRip7T8+FBj6L5pQ8Hr/NbqA2QE
+iJWKo3yfQs1eZUSnbgZidrELbiNyjbFbCvM/lSQY0OTgl0s8/EKb435pguJnVe3wL3NA/KuZUNhO
+M6PzhAnwNtBbgTGdJHEDx6EJbmuxgGGPLyPl88FkISl2i//legz9UVn+Jo3vpvEpTbxM0ou1tuYb
+BYl+5DLINE4fqOmPuB8g7O73To3QYwLLb4g7CCGY8OVGwYNsjK9tskhkXtp56xEWLBZoh5NA0TQw
+dpMEw9z0qybYkvWq8WkRTfiUOvu15jeqL4qiXhHhc31RNNi9Vu1x6R+FRbbQDcDtmfIzz3zPCq07
+xKlzl/6riQTnUxKU8gIo6N49bwhK8nPJqbYr/FM5WvrmTqZ2A4BT/LdxZ5s+ze8Z1tfwP6q9/cdq
+EuGeGLURADiOq2JAHFo0hJfAVCI0bBMJlDjNOA8q5dvYRAvtTbRZNL5DPmlp1TSCANEEhpdrjVkT
+TPbEtQMQ+Ihr/CniAF6opXxaQjJIwStwHDFX+ST18s1ezTy9L2SjcrjYlkmVd9zG8mUdHgXAMxMg
+ox4eo1KM0JcNcSXrof1F4+UAuxUEC4FKCRthe5boL3MtsKU2+NcSsQS+upYYgV8xizA0wqwFBjDu
+2tA7QCbtcYB5Ar0GMNyHfUeQtAzSGEXJp6sPvV2uHQhfNb4i+6xlrsNBcJShabZ278PP7fD+RXhi
+An/A/QBhd9+O/Z7EfJKJn30f9i9VvrTh3ppUKJx5wJZwsoGRVJvXCh9t4rAqJY8kkwZjTXvVqLVJ
+sZQNLBySkQJkxns9zT51GdPxWeyoJpfrZj77AF2Z0o5J0AqEMprivOms7tJS4SmBtIVHpoY+GCVg
+7I8yjeOM/jPPm83r55R+Heo7QCM2OdW6Egw1TbpmnlI1lx9veStztsdaFdCiFnoRPbvypft3ePhL
+Y8BUtIVDqDv9hVDTEGDKcZJgLcuQZtwQn6st4mUAu3XJHP5PcTWNgRB+GmBoIM2kRa1tq4HLbPV0
+a3nHdK86/Nij4Zy1PKwQXzIAQIsxD54Y460x/mC5EChxP5p3NLfyvE9vOeYU7lNZFODhwhkk+frF
+BZgmkBu1Mi+NMa/IKESHOgUSkIyiPlBbuq2d5uPD4xrgF7+FNbPD5foQMJx+T/qV1ha/VcTLiJSR
+bnwSwJiIdbrGuPz+9QaE7YvfN/kWwKw/kD44m1lFnqXut50utr8o6H7DGc5Cb1X+sPsBwu74abcw
+dMqDMMuah+Mp/Mgm9tdwbYH0oB0UURlvfZrcCTMITmHX79Y1xTtuRwb4lWFt2hQPZMgJWNzgYPVa
+wHEA8zxL8M5SeX7uJzkcIJWDxX8TBNrCNW8ycQdU0yB3fwufZezI8x4vFchQnZWxXDjsQUcdWRxn
+BRCwYLpilMngNEunpnLPWgGcHX0ec9TvMeU7V5W2tBMQYJPZKW/+N2z5S9cFpEkCfcv0LcxI2+vh
+AMRERKEVxtoP8EDWooaVynOZc8rNe/PzGL8tFYBffr1cSVSFgW8t+MFZ8xzrjFpj1PIiL2Yu1emd
++XKMZcxXbm4W8bXs84G55Hk1bxtvSE6Ql81nkNm73bnYsS6sJAff3wHN8RjAb9RFgDl4fVRb13K0
+wlTwxbo6/iKJLH0sTUXlac3VmLqk3+3RAHdrp8FeeJl/UMNntGmE8yctzKC35tFQ9OAouk2nAXwF
+NuNSbMJJHGY8j0FfQNys8Wpj3m+B8GdQ/Mjj2v1H8HZp0775ujU3WbIGgAV+f+jeSkYmODn9SSz/
+mPsBwuGM/srhRziIq4UDEA/pAX5tAsE2+M52jenJbTxIDIKYW2T3Bh7CqvGxFGpSNMB7XVtN5/YA
++EoriESgMqtBbkfnf1XQTCNk9batXbWtPibadZnTqjp1w6zpVr6R2w5HJ8BtsonFn8KJB93G/GGO
+MtSz52cul2NrVDVOwmgdo/QbgyFDl5KXmVbaqNJxV9IBgdJc0evQqV1v4TfQfLizX8xfOd+/yGv/
+PdcJPh42qx/HRMXiAyCWrV3D+gbQ4uPNxIVVkHnwAg2a5rXGTTv9WoT7PT/RVjGJEKk2pqg2w8wT
+IvzNOIb/tjYv/rzVQUN1eelBe8H7PLDHN8c1lj+BoATAFvGRd+ADM8/W90EZ2S01hjXCBIZNtfKl
+477V6ZHRhcBlE7k5Dat3uDyQmQge+vLBwMRsxdgkGPb7MEiGaUTJIwkCJeMS+Na4vQk8j2RTvJmB
+aFBqq8oJhj/NBYnDwLgRl2M2AuNI8/78V7/cUCiq+whogF6RDY6XbhMS/4y2LatgGPbT9Mlr4ye5
+qf9T+L90P0DY3WOnhlNkZiIMbguhtTRogU3sAxjuzipxHxlJB9EaeoLgHbb0zmsqJMep30Cfbwy1
+xDEgLBlA3HvFG576BVphtJ+zzozwtgqO9mnmrm2eR167jwryK/iCx4bXWQl0iZJui9p6nF38vZwd
+8QxaUFRbOStPGgMABXM+gC/79QC+CUw5Lq8MehVlRVJDNE1Jl9M9jzbPZUG9CWGuv+ZpZhu/yXjv
+a/sb9+FmE3OS9Mdyw1yT39yvAACPhVYmj+1ywGuIAyhLMCxSeUI054Jf2DTgIGmni6vc63bBeAuh
+LQ15o87iKXbJx52s+1tDXgAwj8EOV6B/pNObCW5hhI0Clk1TqxkPANzz9z6wG+JGdtt4Dj6yMYLg
+i1lEX3/B7/ddaxuiQG3MycuMdC57Ys1ka3ZhDiGs/R3A72PxiWXYFCMJqNG8vQ1FSkGgeMBEOYGm
+PpU0Xb6FuXATTm9pZT6oOdmubBY/NIUpZ0sHmP17gPgp4SXi4FY3ABZxQNy0wtLAMECybN5kj6XZ
+zMDbyvUfdD9AGM5mvWOPM/5Z9Z9aYQvga3KaR5TbH3euifGUdG0+mFPdivUmYE5hJeiBxME5nqnQ
+oDX6PCVBJGaVmpyMpGWWz5etIeRw78JfZyH0dj0KvDo9srFumO2Y62s+uQ/uR8KyErfrsqPMm3YY
+Hp6bGF/k6SqHzmS4XBmnOhoJNvJaQTHlozhoC3YRLivzvOgQDfl7a97FjbRgr8F622+QrY3e33Zd
+a5hrsD6klo1tvDjpNbtSXEmnSU7bYA3a2+TCtsOpMcYnfCe51FnBDQCLC8eSFnNbB5uP0Aqi6CYS
+7C9vIkpVAgjKS6nezE4/854OPmrF4SnjoHqES57DfIIyW8aX27b4aBrFx5Kne30T5p6Uhx1rjRjA
+78BKkp+xKVwfuqkgO9AkNxDN0CX7pfymnbQH9iP0AgyT/3FTCIDkx/bGOrzWD+Ccgwnb4xD4xS+h
+BUZ/oWFWUcEGOe1XXXUYb8CP+0zNkli7lTYY/HIzKyieTB4qwP0WCG9Mo2Ea8YgINuBa1wo7GIad
+8G4f+JJWBdgk5/4b5npxP0DY3bdje2yUs9nPGmYGw8iTYHi+s3UfSZoj7Vqug+C6qgwVui/z6pAK
+pqnHWhWRapuI2MLvwHHrjupgMnoy3ipUa1+7wJ3SjquefOUlcMQd4NgrO3baj4bXXq4I2nYtgraX
+te/ijfoIJlnaJdlgI4AXk0xIsxrwSh8B5A/bTQbAqKv5Nzip8RqD2LpGtymuAWQb87XxlxbE3A3V
+T4T1iT9o/PmuvrcMEzns4AgxZmLHFGOOQypqu5KWtwHivQ/AuZRV8wkjEH09bbZpPndc7UffSFnn
+ctb8xkNX0Fvzf+Fqts5leB32dTekqR7PmPsepBlW4pvFhMP7jfZbA7h0myMNTQnecnTnv/OXe1mi
+tUkD/JZGVXXpc+Vno7Nepd/6CZpS27aoIibx1bi1nNQ97rE8U9jfiOzPLGsKiQCMpBXGOJCf102J
+93US69BZXLmKiNkT9sN5css69831ccJYoTlxayv0AsDLTY9+fQlyx9/TNMLq9sGSphG2VJ7H4xgM
+G8wkvB3QDrdh/JPuBwi7sy9H30VHA71JrKkVtqIhZtMIkQ5Rp+V/F47JEW85VbpWRcQkjmy53jPj
+GAoXMByiYjpfQRp44iZBaKPtrh0QCZsyEwgPOp1Cs0jRGLdbMgaY4t7lo15CdUdzYD86eoDTlZn/
+m1D6mEbC1v3a84EJog2lLpvjd+NTYivo/g1AbJShLZyb5ABQOvilc1tVKZ+PE4XtuD9RL2G9sWkX
+mih9ehGwNnm0x7VbvtUz5dOXjLdaP/IjGhj/Ydx5AAAgAElEQVSdrphngNYdVsn4vEIDjHWNNwvq
+aRUY88Oq0O36Wis9wDyXMPXjGhaiH9CTZ2B/H0bQGtpxDPHACDH1DCREzjVd1ic6T2A42ifB37If
+oH/uS8bVpe/5G59gcs40PdOE5qXwkLnvUVNjP6UM0NQbCKaiTAs6qD5He9Cjs70xtQ+pdRWxBftg
+3R9ZMdjnmmw7YXMtrIk8GgA4vjSn/oq+D2bhy/UKaQvwy8eMlqFRaSBY5kWjjhZsjelBk0bDYM7L
+yzSljABYBrBH5Dvonc9SHjXC6rJbYBrhNsDQCjMYhp8A8W4LHgTepfW/4X6AsLvTMvaSD4QlBIYj
+Thz02gCMZ5A8tST/nvHh4wyKMsGFz745Y2AJllVUKAHBAWJGDpGE70qr0yh9CldOrNkBF44BghUM
+88IXSws7QB/4CcfpkM6g/Rg2vYSazysNDTHipmuJs3a95ynC+RDCaMIcH1qL3v7XAZtoKMFIVqUJ
+UpAWmrsd178OVUHx/vXbHStAazMb8VfAM43RRiOZg/sdYaIkJqraqIMubyx7qiLbMuWcMt7TX8/7
+Pa4pkY2BiANb0JkZVryGMM/1maZSihTrj/MMPJMXFLAqcmyQDCqn/lhZmxq8IumopiUR6TEuxzF9
+o+s5mO/9H3vftmW5iisbIv//i0+XtR9QSCGBZ2X16VXrJZ1jpjFgDEKXsCxjJJIo4EGN3693TG8+
+8UAHxNQZM9/1mtxZa7R7fq26wyNl86GDUg37qHjbaz0Gz5J/Il0vYH5zIw+GjHYtXv1x7bB2xs96
+9KSmzeLeXb4mxzjhBcbIu9ErHB/WeLY+ciABI9Pa/zOt+obj2jr56ukNckLSTUw5RHuAiHdu8+Fy
+7TieIHgDS5k+HgfNPPNevMDPS/5LfcpniwUGejgEyvNLQAwCYrfs73edkv/L7QcIx/ZHHuGYsPfw
+B+/1QHC3AXBTdtHq/eqf4LmUqdWN3xY0F8CBQuKqV18sOb1I9X+3p77BbuxND1rDWU6pNCppmtTS
+r6oWd97dQHVtPS3fifOOuucp95SLjfUCbQpEeAdr2vlv7l3/XfbdKA7DBaRCbEZP8lvyAEnUyL2s
+VscwHChVmSwB8O0H7A82YHiA7z/ldOUw7xk0+xe5kNVDWjz67Hs/05NwIhS0SvPU3pWj+RsP+S2n
+dUNbvMzvLJcq1uZSmrjNb/KdTPIlVjib8k3RAsY+jselZrfbNuPATchtbYgaNqBE7zdTslcy2Thv
+zMhHDd9kT497Hm84C6wMGQJQMd6Snzd8we85Bq7kErIwWdZROtc129rx9Azr+bubnbc6IEbx+1VP
+ucTXygtztH+8yeJNNwGyNHGkk3bnePN62lkhug89mTG3C6GjN+CtF/sWiDLT80tv8GM7NvhZddPe
+CMVd2fSDk4auaB+Qsrw03B70pdMCjLMJ1cHGvCcOVr+eC1lk7ryVCQ6JMgWbz7dA75n3XPKWAY/v
+MRlfmkPIwrJddgPE7NcTFAtg/Le3HyAc25+FRpAX30IkAvAiZR2PePfciUnv13T5/538XjaBytiG
+nuwG+Wb85dUdGk4+4lMVJ7q6rTiQXVAtLsqG5zmvFHSiIrj3rHXRx1VdyvRyL8Ecc8Cv+aXApfs2
+yiAdvxqVvrfv1JvWQpWxkpVGZOR3LTvGdRnySevBU9bTNkDtf/MzuXJd3485Jfqr/D7b2zB7zQXP
+zuHPV87kAlm9hxHZqKZkaWXeio7mWZJY7TCqr1eKrMscajBhtkvL+5bn0tbkV0rUBfwiPFx5DMUz
+qSnaqgiiCpjQuNkauoJl1SkKGoVOmmfHRa4q8KSuX8gseTf5o4wRcGTDykAlIq58zoKUmVEeBG16
+Knm2d/FWR7pX4/U63jr2Mj4atJnHk9qLli6K2fqx5rX+es6vVUfE2VJavA3CpeORN19Y2/zm4d19
+6rPbjgiTYKjdClpzPCtBMT/2gBEaIaRrxD/ENvLJbjUOroAgL8QZ6r7bsPtMhwHFku0L2fNOZ86V
+AJAGguENKDfQCUk/E9TewfDzqc4ToRGrgG7GC8MrTZCbHuA4znjhYI1vYrH/5fYDhGP749CI2D+o
+eNYNcCsswkFw7FKOfreb1z9Tf57/BuSajfuwqSL3VFRq8UuRnRaGxrAZs9mv1M5lRFmg6/t2ENwO
+rr0WX9Zh8GauhPhysEdPzxypniQxEGPlsFN3R2dvRud172OPblRuhlnPnUVybsaGypRmxZn3zka1
+JcjICLmY+/lb/Tg/ddvrKT+XLfB2OQQAQ6urAGrXp/GrMdVgG/uQAYMu6e0hYLx4rCZpioSdqTpv
+z3Nc2lLLp9vId80f4S4+qre5FevqUlFvjmgwJ+glRPE7i1R4RM1DE1HRAzniFLDIH2BYz2uA2XSv
+9JllU7jv29QRR2LKZyGMpBdk/G1seQVrgNWvcgA0+XChlVy638SXwp11txjINd2LD3OeK28DD9TB
+GLf5CAeZvzZkO/KS48RmpJiF8kxdn3Zx9yOP86ajrqsvr+U7bukZtjh56x5b4aE2hkOEfjALj/AO
+l2ihEUKEFzHuQ0eXa/72sLkaxeoPEDJNT/Ea+ZOSsbuCX88pwgS+sy6YT2/wOxh+PoBjepIff7Ds
+BoIlROLTb3kDxj8e4X9x++5NSPMCh3A23gxvsBWvljfYx/Fo+Uz9N/kfO/8HW5g2Pu/XW9mXppp9
+umaUUWWF8taWkOrpfZTvA7gatsy3VvYJ5F7PLB2euQWGI18b9jqvaXoxMi7pq+Gd+UBnUpdEqyOF
+t+ocxOjva56qdaNh1x/SiOdLKxMAp9Ff7bjq9BliXwmPlQeKJ2ZnUcdtreQXGlzzygJNcHN7p+fG
+cy1vnDvPJFhvPHooohNcAPXoeXqQqjOewOpIZ+c0jTsIxgUeewe9J6X7OJOPOET1jh7lF4/xDehq
++irH3zWm51ocmTqAL4guKl/CxfQ+o3eF/F/y0D3BIRNR966fTNhg1pB6CoIRvBcgKHWS6hbVNxeg
+v2ODd9nmNysavADjGxl2X4RLePPr5LvITnWWHUc3uOoVdgG4T4Y21NJkEQbhS+ZgP4a3xTqWa9dy
+TpIkNsiD+3Y8fNG5MZkHe4J+K73DOWYDNiBePVotifmUzJLUAogJfhUEVx4SEKtHGAJmb2D41Qv8
+zPI9LgLgBoKtA9y3X43jou7+wvYDhGP7r1eNEMZqx5lXddM7nMLOFm9C9pLvH+ovBSjrqPEqybpd
+wREF0BLxXc2MSWIYt9kNwo2J2a5GSRXGKxSJM2z0zfqwTft4a+ItMzstRijy0m67qHTVoHPvcY4Y
+HpcyaFmeO9q91tHGLnUUOU2N3ayX8lHfqjUx7kAHww0Er6q3rMDvGvXUGGbapXvVQcIy9qiCd0ij
+GBCNtgH+BoyTz70Amsu5kCbZvzYVduXZlkce8Xut33uIR9+dVECfN1rN2US3qHIcB6lTfgeClVWm
+d/7CRtrrFBIb+xhL0xejXrKjjf2H9J9sMs/HDQ+RBDNVPgdNj5ljn0JW9Keg+HwyYtJANSaap+VX
+90b9YBD3GFfYpldwfAXCUnbxAs/5/l1ZidsZQ5wc6UJ26eu+/pP9YJ4jAO2ql3M7KO5Ad3uCVUd5
+nx/pC9BlX8neRmed/6lqLcIw2k1xLPdmsSpEB8Txgpw2xDJwHnROSBuM9DswTkxyeIM/eIIjhOK5
+1M1PLF8AcC6fNn6UiQ6II4TkX0DCP0A4tj8LjRAgDG/Lp7HsYWUUE2fscCiZm3z5BDIfezfy+TLc
+0gPx4j3rio93J7PJwkv5iiu28lbVRrBwa6QZtbuaTEw9qlzHyT5J9cPAftjOKvZScBr22p9eYO1w
+xgo3Y1n7BC2XMo5PjU/TBf+foLjRVOJnTea8BmUv1s36jwAGBMM0JDcQvMrgrxWgeIRMRGu7O2d4
+hLWj4Rl2dGBGF8sEtMI4B+myzPvKCoMCANq9RJ4/as+zQ4SS9r0XNboOdnTy+rElFY5m+vynMEvB
+6IO7HlWkfoJgo+R/Br+NjE14uiQdL8QZiXPbR9pGug0Y0dsaw1H/t3pChNPRATFBh9JP5XcMPuVB
+9IxzTNMLrLKRN/F9XHOsfsmr8feBKgiuPg8wpWPSMspWAqzz177Y9jy4bVd1Ev248g5u14ovoD37
+OEGwbz9k9wR/Z1/geMcIM5/Dr3SR1TpvAyIb6JFnZhV15U/qxnaPLqBXPcTwB2areE/5V4Cu7r2l
+Jwh2uX+ovHvYw/D0SvjDHTDv9P4uxkKGR9jFOwz1EK/dJ5Sq3u3+hEb8q9ufrRpxD4dIg+y93qNK
+Jc/z1ubNQvvMyPxbnsjnWHUl7eTbumQUzqzPsAiIdBMgABoHkGAq99PIvTC1A29rSN7OmN22zP2d
+0NhI3urfAc8t5/AC87+P+t7HNgGyS3rvu6Ey5kHqHgfjOC855nmCOs4ndK6AA+Exz04a5s2VCRhu
+vwmCV3mB49jSQyz9Zvcyrd5ha08FEpi15RM4DlqX09Q65yenLS1WlaHijEsuSALrx+y/D9JNUrKu
+V0uzZc//l/K3WGf0asdFkwkt+9kqPxTzUY+awIvWvWQY/9avyTOjX6kf6noFci3mRsoVDDSgi+Cz
+avNVI3wK7XJNlOxeATFBMSf8eOlAx2woD3AdNxDMl7Rs9QaavhIddOQdhEXy0vAWph5qALjqML3Z
+dObH7wXwYq2P5ckjXmEW7RjSl0zrdXsfyiMsoFY9v0L/1x/0GEivvCnolXCdS1mfBsoFV9BYA+Du
+eSc/7fjhDnq3mfW7eXs854OA9tvHBxgmoL14gj94i5l+vDzFvgg76utyJwBW8LtBL/iyHMvVNv7F
+7QcIx/ZnHmEMMKzhD2GkR/4bKObVtf2ZV0ef8+q91XV9UWffPQ+XMDFDthTAwkeB0SPapdNHUzth
+lfFqlYCuXfyS+s520xazQ5cadpacrbyUES+EEmx60Hu9MqBybgLdyDrK53UGeFEekeu1m7mjDjNk
+bki7UVTbzboXSLE0BDQq9PJe9gmCT0BMV+n+Ty4c3l+O7VgzTPbtGeOFXDPtnrzayGtS2arMRhuj
+2mvd9sJUy7m1BOSNaHbWRt0+7wdzXjwqXkw0Tuj6o7zBux5vlzjf2v+tDiSk5Nad5DcFJjGrdubj
+Lb+VoepYjcK0/NKT1qsLbzTZGwyU4QS3uk0nqtKRvNuN4urp8iSPc4+xVB2/1rH6L6DoEyj2HKMM
+snkdDQlyCXgDGNnzdGlc6x0sh8wl78h51ItaVsA35DUAmoJhAwT87v32sBYgK6CMfJfhHTgXf9Iz
+3HhW50ds3b7WFv7tnQYs7zKX6Ift8dXjXGoSI4/0uc5JzR1ppADYfR4TDGveCYZ1JYnnCpYjLWCZ
+YZ8PVtK2geBnwfGUR/jZN34JfB8AFrT68Qj/e9u3PcLJf3cgPAHvGyDuXhk1xCNvHF3zmJnOtScW
+Cb/YSAww3Bq0AhFbu+xcvj1PY4TRLpsSPdHvuPU6csHWCGMeP81Dsz7XGi18Y/StQ5L3NjT/Yn4a
+BWhQJ50TxKo91e5n2sXIestXmplPEgqdeG7LrhO6sb/Q520ys8wwKVHgyHD3CjMWeAEm4RACiG0A
+4d0z8jn91WGkbYZFBARwyWcdR4d0AxhfyZSAajP6MNHgwxC2zTZM2jvwl1/KoGOlbHgr4yjt2mHO
+zUvZ6MNN7O5ahCJvwmvRi+xm9NrQqeM485QOeazAUCp8BMEKPqTuEMz2cZysO4lyjt1uuY1XvP2O
++FUb1SYTqCf4BogXvcLrnT645VMLDUCmdcPoNBCMDYYYPyxG7EPa91JjQHyu2DvQvQBfX+sEyHHT
+ZNht1hdFqU3YRVGY2Q/+ngLDzz72GH+B29JNBYbx7i3OugBBuvOYgPjDDVuaSo7NIvTCC0TzRThO
+neMB5NjCn5rqeROp1EPymdDkzTt8yVfwmzcVL57e7g0escDMGyETK2iUtHteQHDmrw24CYwlRvjH
+I/wvbt8l/h3UXkDxxQPcLkSv4OjE2Y0PeVN7+xKj9oRBr7vKMsyestys1gidoOx7WMLsr+PESdLM
+B/TYC2/jDVB0grhZs7RDgl/rtW52kKDn/gDVLqnZjr2WbxoX0GmAxQf9vNKsoi+0HPySeZMw3nnX
+B8V8UOQKDnCS9zZfMrnN2zINfB7LKhEDENtS77A+fve4UnmE6IksysqyVVavcxXfOBr49dbifdhp
+kHkNyoNlPfJ6pv3EQpUeMdh6xUCNdUUVxO+kf7d5Uzm1mdZ4PbdJlqE8d/SeBy+7rpjw1sMg2slO
+BSSO48MbLMpG9cv0CDfdY203xzgJ9DZVx2+b8eTDsPz4+BKxgtUEwPoT73AhIx3o5XgqVq1T8wT3
+5unlTaUYrJHWvSFj/lacHCD4AMQ3L7DenLjnMVd6SF2npoe8dQPBAb4UDPvzIF/tvIDezaeqk/Ys
+KWCePDdBb/+sd+W3NgP8cjDbKbBtMqdkxgTrl+M22C0vMBBy13gaGSN9zFV6gwV/HN7f4IHHg5wd
+7M6wiPli3DNCJjTG+AkarKB3eYMHCKYXmCD4WSNGOPjqL28/QDi274dGvIPe7h0egPmSd1x9aOGb
+2Z71Wq1QUKyy5SzuNAEJl/C224rB5bgVp/wzfrLpNwVCvK4ol8NTE+PeClBMZ9+1Tnyam5vxVcAy
+m7sdvVjLax07ci4tuNDac8hiTAVIcUK1bObzGnrjcDIClIjeE1LJZP82gBs91PAOg33zBHPubcVj
+wPAANxAc6TSECkXD+zYWQS0ADAEkRktSFjVvQm5SdG6kVwPDciOjqUxTLmROlbKZDl40niSlr2CY
+4DPHVvmVvgzkwxh/p+Xm+PJJEAI0cJwWlKLHK8Z2PnWx3FX4BHkGDXzcQLC1OlIvj6XJvKjUGd24
+jngY3Zs+madQvnlDcG46rikjml49vayfewxOyy7pAYYrFCLmcYBifZQ+6+aeciAguMUCz7SA4QSi
+QAHUAMS1asSuUVwdN/bsA+dIwXB4gvHEMTmXN+ZGni2vbstTuhkOYExvcHqFpW4C5cucmsx5rlNs
+AdQTEEtMMFBMlIwUY3EUcNaipEXM3TPyxBvc84oH/OoRpjdYQK8A4RYPPEDy4+URfmIeFkHw8whN
+pxd4oV6QC2+4c5m4v7v9AOHY/uuX5TJ98Q7f8iCPKKBK9wJieokWHnWyxgTDwyuZqr5Z82o3FUVs
+p1HnHTNfmOpl7dGlIR6rCmMvKyWsV1iQfK2PrURuMWcNZKsB7GkbdSc4v5ju76WbIUy/RFVUgKSY
+rBkaOW5l2o7Mdv07jm8AuGoeM/lyrNsEL3OPNBL9ZZX4XOmyvl5ngGIXcJzeMR0H0YZXOkMdJC+Z
+eDN61UsjIAacdH3o0bOzHTlOkG1W3ZpbWe+e7XeK7pb1pMoxAo5Rzt7onEpjr136b/Ibh1jtAcqx
+VqTHzVBAYe7ReQbzeNQZx+Wtq1MbEJZ9qjbcnrRByY0+1x04vK2OMH8JJCjcrtowLpRy/SZfn8r+
+pNqtgl+OlAieNO/yMHShsL8B8XniyNDwh7VBjWEF4Kb3U3RZgGA8j+gPF1vAHtSKR3ioN7jywv7x
+Jdn0pQR/9XsGL8bNYVOmfVzT4eLZ3wN34deiTQ+/8xvHDQ1r+6YSOGVcl05y5FwUFqxrET7nV2mz
+PquK/lSdN3icda+8Ps+dZeCNypAXHZvvMZcDkOPTeuyb6P4S4r++/QDh2N4MxVFvgtqW916mL9E9
+yRrDuDWmHsdaZ5zD/ymmvNtubhwUozJtqgCoJ7yUitBFIxZTQabNK7Az07kqgCMU3gLw5Off89uK
+T1RYkhc/8wc+lSowAK01e9uPrdfqFn2kxFbe8n+X7l1Mu7hJ4Hmcha0ulYscH0k/j1sz4zhBtCjv
+HOAHpWPjdxRXWITeGKVHxgQcrwLFOH70ipWBzjV/9cU4KstjhQgvPtY89/3uBQh8o84KAxJKnF/O
+qmOkck8v2SQb0zSsSqISn4tS6QZUx1ExwcNQunrLKu/W8m275b+dnfGbCSYG2D2A7up5R10UkGhp
+gotZ5y0fZS+lKLcGGrp4HaybRrd+JyDAYfxFo18outv04+ZLVn+53JClfuaP+I/91HHpnUjKgqGx
+CutpWV4T7di1rFMNze4k0uT/ESO+rLzjz4IvlRu/jPXZ4VAPPasKioEWx0uesqdCBPh7uqSkdJim
+h32wWa+dqsMvGh6TIekAcoYP9Z30pGzd2zKg3hNrere86ntJsngl3g1L3vh0lBqlCBk8j8+XTDwt
+enrUmTLkWr5VJqyWT4pO0s3u7368EFtuXnbdi6H5S9sPEI7tzzzCoRa5j/OPMueyajtSl8fL9XrB
+cBeF5L0g8pShm8qqs7JpsdqiIPORM6UH4zGnXP9Ih7D2F6MgXC+/jHuLjV5fl8dprWyFEqVRebK+
+qfGSfilo196fADgEsk48MJ5xoOjd1gW+vyOqhmPaTvuZNnUYKhbm3XM3Q/P424A4zxEr9tb5Rrva
+e5vjsG2yVz7onuDzJukAyukpMXQAfMnTgY/QiQ1+QioXkmcIcPOTsQJMbqAYWg/CG9b5oal59xfb
+mSZkFHrJJYBaLaKb+F2oYRTVzNtTxJs2O3mB/ylDLnGVyJvcCXZ3HOOQ9VteNp2W7r/PY29Nem+k
+GtAI41IvSS0GnQB3gl4Fq8FPPvOlrGQ3JiKBs8n5Nq5hxZNtAfqS79Z3HVvKgk6hoxweQre85uzy
+oRSq3o1pgK4MAdHjwH5a5zA8++l/krZ0eQLiGH+uNHIAXtvhAKJLDt0iJDm7q7pfjscYmsW4CdQL
+WG36k3RPCaI8j96ZHt/avI9l55mAY3nF3eNlPOyumm1MsYsK/O5f8X1F7uo152o9kKOzHntPPZj1
+bJDytpHkpv0bhf/S9gOEY3vTAUc9BxoI5h5AeX83023Q++I1fgE2FDKfF9U+tmNpZwXjk1NTuQY7
+U2GGpctD4xWtCWUT8xDIMplsRkEOUF8Q0180NN+l4AsX+SPwpe5c4Sn29rKGiszWEV2c5isrHQDv
+49kGaXgFwPSUDSah0si097K0i9L+YW1cK3o/RxRNTfM731TL/alB8wZn5YvmOhAFCphomeHiteng
+tuftMIgMjRhxwk5XBsp3u8fOfCWuxg73Bb/y5ZILyFUwoqAYku4gWOIpeflQ/uqJM2i5GMMUnk77
+8g1ppZJVWawMguLqfF7XYw4GEk47PPPG0e1xbsk0AYjI/bx5gcaAc77lK4IhSPrm/e74ACBvoHfW
+y7wpULyG9+oChlUYWnjNAXKpmTu/DKHs5+S1ZR4H4D0BN5LH6gtLHuGgF9k8xDeZcc+jKmyer3yp
+IhRjF5UztqHE2GQeT90K+MWJQceFylH7+MZFdzSd0mxRh00HOD70clP293xcq6ATW+QvCcCbXZVd
+0bFW59loU/zCnX7o5zhqfJRDvrdovsMsCvgiwbCBafUEb51V1OO16kmZaoMExIZ01ik1SkOx7ZOW
+5eEd+4PWehaT77X+ye0HCMd26IPXehcQzD2oc/e6eivzLnuv9iba2U0M89WQzR0ol4IH+CiES9Qk
+MktL52MPWd6GglvCVCrBkle798ga5/fwCEOFPXz6SfhDxHISABdgrueHU6V0PX3z+E4AXCKuTgDR
+ZTotScNrGzoHojQ1L3Oa5wnjvEgcnhuv4joqHc3ceY7yVgKJeij2UenMImo1zboCXxMeWFXOEAkt
+b2/LixGXvgI0+AUAugUUngfE6Pa0guJbuoVFRFhFznl2x3MuJ5NsvrGscxrpaRC1Uq/cZsYHbcSE
+zdY10THyNHnjHPYgp0Ot2OWnS98dezai7CJxj2mhx8UbEJa+2q3n91EUXThPU8iARIHBHz3OUWRP
+0aJLmVAzUYsAYJP0HRhfrnPoA/K6HCYtxrApD3LjlB8/Yj0f5yiznKxUzeU1aQRGRyiODniM8QDB
+qc9kvCHyHz2+yX/lJaYNapUv/f8tlLrI5VkocmdlEWvuSQOVZ3Zc3Q93nabhhkCnBWt2alvSn/dK
+i97hIOmKeVMwvP0MvvspoJhhY8jejpV5OPyoY0d/hAT4Bs1PgW/yTmtqv2/of779AGFufpGmWzVs
+xriCYZfQB8nbst9B8AOXuymXLrhkqUdQlP0LSOYb9enx0vOu4JchESNCUXSwqoh6sxaYX0pKr5EA
+neYZHnG//PVQiPg93pfr8Xj5wgH41wXgqsKZQqkllWeXUvXuUQnQBpRR1TOaGhN4EgZh2hjHmSEG
+0LWOKKBuqyYgrgqf64AaEr/bPAeoWk6tlHh+QQVulb+YXgV2JVQCS7zCfFue4zXtMQcQ+4t3eGd7
+egU3/25i/BbsXtLpJYbXEwHOEQ0K+5Iv4kxDyLqkF8fQDeF177Uv7/BtgvTQ7sV5/SOo4sQOVsMB
+IF7gkPUFJNidIDiPZb1oaTeTJob/sInFa9c6KV0FEZp+mzyTOvWSnzIn6QGOJzAu3TzqH3NY8/YG
+eu2SF0YhaZ/bvPmbYBfBh9oHs6ZXWqMy9EaWt00V4OTEzKa+E95V+yO0zKX4fhncft2fLP3SlRli
++Cbq0/Rn0r527pZXRaXOOkG8Bo2krZH7SMM9ThMBzzW2gZqr1hcreyC2FfCUs8ZF+TRGQtLYmscC
+I2G7F7vpnTRQz7Dmi/xkH7KnMwSicMpRNkZ5R8JlI+y2J/jtQ/zr2w8Qju13+qAqVmjDAYa1POtJ
+OvYP+GijhFVBTynxb9SpiqJ8ukEtxRhgLR6vtLdkEySVUc8Xn2Lopfes9hbif/UYye83m8CIF8/x
+fFnO5L+2Y+O41zQIQGktRTq1EelWSoXhJL0PckPjRbncUnvjzHPJ0BsBNbhZzTWrrjLq3PLqv2hb
+UfLH1ggiCljnMoFSqDMDpmeY6faEIEIhGELji4BZzQBXiBDD3gxxGdbdl27Vb95gTR8A+SF15FHh
+kvNySba4Rq0VBoF2ZY0SsMiQdHP6hDK3YYIAACAASURBVPuYr3snLWraqp0yrJLVL/Xb47KUDCFq
+L80FCG7ee/UI83PZ7dPZsjzU4C/VJ5mh/TkFOgpGz9vYPecmDX0DwFWe6QF0Kw1UDDHr9fLWTluT
+16o9lglAZj2f9Zpeq8tNLHXQVPUHrGQowbLWYxlGPs78zFbl32XQ9DK5L56tEL1znzeuv9BsxHGj
+FPzXPKel0PPQdf5ybNQdGGP2QT+rOWwb9aPejoYcWtnHfYnSUXva66L1khwvOHXZGEvIoHabnmK+
+8rDMwjx5ixFuYRJOUnn7lYywd+wnIKPsZWHzWp/aeW26rvtzpGi2pGqNCf5L2w8Qju1PFnHWON8J
+hlv5qJcvzrFuKIbUU6lwq60EuZJm/nleXb8eSTE/NJd6EUJxDueWGEYR2eTTycQvnD8BMe3J608f
+KTroVcDtB4pLF5g8ch6/lMeA6m6ZysEkjQaKNYyi1xua1vthlo58zPxhoPIm6KagMVjhm3lTv7im
+GiCxUV+0Wcxtjt4goDfKFACnd9hOEJwxw6H8pmVN4zH2DSDvvDJDMWjy/UjrShKkk4WF0XjGY83V
+fOHH5GU57W/JbpLOpK+HAQxAlKZy1pkT3z2igsXr0tf5LeHuLKYmrJ9T81jplONjv/JjKQWO14E1
+ju2Sd9adgjTpIvKRb7JTeIoXCABS4ATQ8man60q0Oi2/hLPKTHnAkk+K/4S3D56M/Y6lOwjSaSJt
+kB9Ubg/5fyOyElXHVnI/IyD6OIMHidDUgCidczfzvOzDL944C//KDXfOOPlQupVeVA83lMt4aLQi
+XU6lUafRbNKuyysdIS7zsI8pk2FNGCpBvdFsrzZ7hkcoCC5P8b7uA4uHZ54rTi5p2l728BpF2bvU
+DsfxCXKntujHr7zWNpud6GWX5N/cfoBwbNPsvG18tPMGhskWt1CIDJmI/Lx2Kt86H5J39xaf5/RH
+uaKgthsMTUkzLEKFVQTzNM1WYDn35QmsfYVH+KoXaQYVBVjoD+1nxwsn+3e2Zs3Le9yFpiLoinSm
+1YGTOL/lhTIUwWd6riyhtrhtbXyjygXwC5egTTnzIuNTXu8GO3DXOIdHjgOCzKPxt/PSGAThrx7h
+AxCvWk7N4hGAelqyr0F8RX3WlTNHmHkBYj1etCxl7fuz4/7ZY8zzD0DcKKrSgZZuuKX18WJcsR+r
+1stxehmOfbTnwJSCVA12sp2WIWlRc5n1RW407l9vXhIE8+MoclyhLvJ1LF5fiDLwx9nbrCsgIfSU
+TdI3IRoKRBwDdUw9o7oSlf+b8qv3kfxJuRVnQ93cS18bELYKBXvGfOSEetFiyqeC4kSwU35uBJ+8
+2+vPm6xil9GBNAhS0dvMJe1an6kfeC3qGO7E1kxlfvCP0jh1hbfLqlVLKRUwnC/ZtjIhggJ96nuT
+qOEMMdyddjDG9sOcjCE3+xp8wHsNnRd6f2MlyHcwXKRsrKEhmTdA3Kfcsx+b1KXjtJ6FTm6hDnrt
+PB7lpvufGOF/fXtXzGe9Q59hgN0BlhsIlnK4GPOmtNmh4fX1EuN7ngDiMOoU0puSLkBhBd6smDz3
+FEpUKER6ixIA2eBqMZrNI8y+DMKa9PEAx6OMp6DppsxTa9IES5RDFrL90A7He1jGO30pMAG+v2Mc
+l9/ISO9VU+RatZvE1qiQkErMpWKVSRuc/6GLr5sNg2iab8kDnH8FTQV49XjHlCYglrjS+qLWGYeX
+Y5gT3Uav51TZDm/YhJrLpt1jhbvsEBxDzju9fZsGdSN0mGnZDubKPDU/9lrv3lyZpka2RqWZvh0D
+SOAR07xXolGrlZ8EXiPNrwd2IFy917GcPbj25Tv1tsIL+stPwW8DwpB5G/upe1ue8FYmWSYoYwDf
+4rnRFnnokScSc4Bk6zbe4o3+MiH7rLoKI60Ne+nvTwpBQFi21h5HIGzGnFdLfds30kbC7tRzYQBD
+JHxeX2xTv9xT9Iw5SFvmdcnsrvsJ9FsXvRuNGSIh5dsxxvIKWQDiBle85fu4BR6WpDe7y9DDfb10
+vGOAYJrZ6M4RKxzOpnt4BK0G8rhLqGeOaT3hyYodHtuwGXYtm4bl391+gHBsf7SOcAIRF70nedGe
+Y39Io4FgVP627X5e/w0Uq6aUayabilJNZSuPdAsEMJ5MLGkIaQnG8AwLfkpvkQDiY83YI0aYIJJG
+ATgArl3ydCxp2Ib4UPCFPKlbQ2vSjqvU5jllUnY+lSjPF53I+he1JWCrhomsSdoj62hfXBMD8O98
+b/VqT77TtqSuJwmOrWq9GUFFReigOOcbRWzhgfIWWwHiiAemF9jzk8tW/Mw+qSE6wO9ON4AVmtmA
+BLU3wLuNZPBQ7OdSalVW7eQyfm0G4kfevQJWkbHrePQcBTg0QuT7KFedILa/UYfiIteqXqnH/eSB
+HIbItH4A5fwwSl8KTz+b7dKh/gh4xF32zox+ScYkrY43yTfmJYBAv5EugWk6doLeJkCit6bAkeB5
+o6nH6McTLFOv80tsMh8+r0Ea2sg7bsLKQ3z1rpFfbi/ZpU4TYT/a6Hmz+DUkTdL7hTiA4RGpP5gP
+iBKnjbnoMsP+uAb1x7xR5YhiHjKkizce2SGV65LPspVJtLCTDE9iPDRpGNyevIDtKR729U3nNk9w
+7jmPVuZaXpI7Y4M3AO+rRhS56jbjuwA4Oa9rKiON+nRcR/aBDyFj+9DCP7r9AOHY3nTsUc8LZiiw
+JUh1rzr1GeUePvG4x5KRnm1CjhMUUOm2OnWdQ0mnZKjipxR0YU6t4oDJW+XysEfUqigTK0bVx6ev
+3mB6/hJUBCHTYICSXUbK6rjFDYN5NR8ZB0XaUJ5EYo9jvynnEa3V2pI4znRjSJ7ELjbTnmP7wF1a
+ZwDg+RnuavetvbewiPAU6YTqIHnUFNNICyj2JEzMPzDALzK989fgk5UvyZXXmBfrhug1PV06Msnd
++8tjTzorOK5ylu28rH9ZxxqP0l/la/CPHpTl+Ma4OIE3kLPTLTREycFaFBftBiYIvghFnmMl6inP
+6F8F/FpoYRH8fX2BBrJz8BjjHNek2Q3hH0nqDJUdb7/6OI/UOQTlQ/6nOsmyXnVMzmvOCDnPHbLE
+UKUnGUQfV9YtfChZBtqxlPtjC30n4PcgufXat3Y0/6Y2YPaSj3pZDlSrCvR7u6fGG/o2aavhTDEd
+TAMD+Kl8VZsnb/KJqYJgPh3dNKYNzXdzaN/mChIEyMcVSoYPT3Da3WKnZRGKEeyW4RFRdYJg9Qbr
+PLjkvwNgPyhSZUhvdQ2rrwSRaebbOO6Mdr95+4e3HyAc2599WY5KvoNhSLqFP0jd+FgwGLdWursU
+qV+P8wJoIDmTChrZRx5bGYEQRKueIgVdNaGUaK4DzQMM09jhyyoBJpxNKUrQG5liKDIuOD3H0nca
+Ua/mVHc1LzAvOSR4mJQ+saLrLeegRm6h3KwNRq8z8vTIbz89qGpVNhvoKZpNYYVR7q2ecGcN+GVr
+scJ27ismeP/yoyY3j3B6hekN1qcGDI1gHyXtNw9Kn6hbrHAHuzwuudh85imbeXPF48eLTwYAdnjE
+cwodlVY3i/GxMNIH/1wakAn2mR1kOfL/IM3NmvWytLTdQzxCI74EDH+t0cmdyMfI2dn7+PQ0bz2c
+NBFYX4ITxzJvCoJTj6Dq6UXH8XRAdIK5KMTQTTb2EwBD+C/7Gcl2gzVoAQDisCANu3xcZMbiGgOQ
+Athy6lWvafom/1KiKMWqIssECyXAMfln81yxHcd1yS1dKfcy5ueNq55N++V5ukv1G/dn+BiQdGvz
+TBuUT586CM6lSJ32ghcdOldVGl9alx4lGI6OO2LVCCu2MuxrtxjhqyfYs02CYnZsapxdW8FxlZ31
++nE7sONAKk4eopo5efRvbT9A+A+39mW4UNROAwsBvSjPbwPBAowhe+QxmvJ0KXs/5jn75271gk8D
+w2jH/TF9xMKWjKQAWs9qMpwibG+/Wic2tdYBcpEg8+5Jseoo+4rekQS8ov/bi25Z1UZeqUvRqrvm
+lF8BxjQ3TX35+H3cbud4p4E0osPLm6OZj1JdjS10WDJx7y/GXXqqOk28vrusz7neAPm65Ms6tLnu
+dLv47uh8vN9HWmWW4xYD5jjigo+8p4wCqWZAeulua1ubeoiZtqemqw/hhQ9mRYzKYnwBdA9qP615
+gpUEcpniiB6ekOBDN9NfzIHGdY+wiPIMbwBsa8G+Vuo1voNQnXMZz8zvdWo5MOR8HZTMgYoktBvM
+B3Uj9PAEdN0rBJvsdasju+zEAEl7eKLLdNzkLZ6bURGnvM+u9PKa8OJ/4WTDeESPqkOQJI/T9GnB
+gVmaMoy2qQsjr6pIufEUq7JswsIsmVynLpekaxTxrJbkeFByyvlXuVWdqCI2m25Zg1+piwT4wiEg
+2GPqSd/N2xsYRxsin6WzhuRbjZkOJ9L2ieusqKP3Wc0L3I5dbF/RonMKdaCDOnCSqWuse73bD6Od
+960ZmN/W/l9vP0A4tv9m+TTgxSMsiq4D50rzM8yqbGeIRDs+FLd4jrMNPhaiIqCkMBZI2XmfU0Jw
+Mh8FjKtCTE/vqwd4xhSKt7Ahs3wLPPINrc9pKAnSaVzFmBnQAa/oLM2r9qeoVUjE0MWiOTVi7qYi
+eDyUjNdv3nSIThrt8Ihzfi/XnORGn/laZvikX67cT40aFHMTyuUjTUuCzsXxOyjuH17I+GAzeblK
+jHgeT4t1t2AN4iV9g78evT1g6x6fhhWeasC58giCPd/uf4o+AGBrX98cjqfxXrfY3Zzs3YWPmi4q
+vq/6QieKiVziCJM40rcQibCceVmdX6sPakww/CVgWAEx54EGM0GKEObCsI1UgNDCgvZCUEf1WUEQ
+JJ0OCnqFnztRlOYqQBh91PzsGvWUzpWOj7qndFArG9eq/8mp1/TsVp/X3Z95s2PUsdkWYuq7fvCo
+bFGuAPi2p30wvO9TVwCwX9GNX9H2L3awzMBtS/IBY84op3t+PT5G5wuwx/pUYesLZa3im6JdD2tg
+nPvOo/cXCYLD+STAuDzL0kxmdCXBfrE7W59Wmm25hbcZQ9UGay0rEVEQ3DzFqQeV37qqOrVSB8rH
+sG7zZa3r1Wcpk8RH+/RPbz9AOLYrEHipuPUYmUg8vJKmoLJueoe9Pr9M5ZfGV87pADfal3p+KbcG
+gmkELOsfZaJtirlLSPQmlmKT9axhpPpx8X2V0ryFpSGwGt9hRDzLLenQx0oaNC/wPKbgp66xccz6
+OvMvoFhdbcIIJmfxeL4A1I8uW9y8pG6c421XLBL0/3NfARC9ZvHoR1DcJrYnqaB3WjRdznkd8wap
+vTjJmFJ+mSy+Mndaiz89rpGm15ePmtcIfcibq5mHDKFo84In3u73XO/VH0c+7rQCxrYtb3NuftfU
+9GMSux+3m2GeIaSoovJaJVDuIi98IZlNljlvaHNXnmDrIFjBMHvbAJ9XeETmU+6lU44NMvJte0S4
+ipLJL/R1aUD2DQQP2VJaTmGatJZ0m5boS33KPpRL02+jPTbA9YMfbL75Jsjt6UaYKiMgEvlVXkkH
+xeALxc656gv4YrRVJTpI0NPUA/aaDj3ZEFFo0XzKJEO3IutN65lRHh/gWWSYXfxgO2Pyq30V0kAb
+cWpZMRIO8QBHjDDtS+RtEOxRvUBw2tz2DkHx9bQNipuTBDF3bhoasZ+WbLaKdNQhC8YzmZgmHWSB
+Y8/jsAsj3cvuGkvT+bM+fTWYSujTAtNc6/z3t7YfIByb/75K1iy9JsCXPB7pBnxRaZc2qKgrHW1G
+25BrsPzdS0ylL7+Wl1rkHLhXgr7PzfjDVIpQknHvnmF0w0nJcCANn0Higa36yCVfjrIao3n0MsaT
+L78luEZ5OFhnzLK1PQFwN45ZJy7Ga16i7Zqeg+cwjvzso6bZSreuozunup59UPNw2F3VYjZabMMp
+I3pcYwJczjMZw3qd5hFun+W1AlTM+y9B771OGDu+iCTg9hMgTvnSuvmhA3qEt7E122l/sMdAF9S+
+MOLLHUW3g5jN3IkcK/H1JCudcJ6OBmY0zbaHNN/T1V297LsneB0g2OKn+i3RV9NJNaY2VNd0lEXo
+gD+WvNsEmDydQxhCl8BIgfCg4zXtZ/7tGECGcigIfgX81vjq1t45P/PS+1p6PMGxxZOYwUVHWzXf
+ovunuCnCobwqGBbAvIssfwVwxtPBX8iynHZV7HLp1G5NBkLOHyAQ7z73efYN9vPsVZSeCrPiy26J
+rIVvGm0b7BNbRN0iefQIA5BQFO9E9EvT4yrMIHld1KIhQiPMhZbevME9DIKE85q+5BCPEXTJt5H+
+TtlkE90ay6jV1ITV3uqsD63+M9sPEI7tu6ERFEo+nnjzCLPNfMQdSn0vpyYe4zQYEKNA4VRv8cVz
+rOeq0TlAcNyZgl4uqZPGohtrk1Jl/FKwlnehVIRHeETzCM+GqYREaQQY5vqHGlun37FXpcLHUdTe
+lnlFK/UKUwdqh5oXeOivw0hFu2qHq00Ozy4nftjavCLBmEsbUiynlSJTPXsrh9OTcR/YtbepkE/l
+lMYvATBqvgcPuB7nmrOVZyM04jTXUzG+5THpwRve6HkDxLPcRl2H5xv9Fmk3x3YxBSC2Bz7AsBGk
+kMfn+JKNlfKHWYxD4Sfvdd9Cmmb6d3k1h5Hf5sfucys/Yz0C4qAdV+zIR9HtyU+XZXraxqOo8JiG
+vkjvKUqHJXmmThsvyblXWMuNWG+E+lhGC+6YwKr0Evuq/ZS0rpymTfu8ZPF98c4LGCbb2bP1YshY
+8/wemnCkBihrgFd5ATH/NxAs6Q6Ea3d9MRc1rX4lPMP9tGiHJhlWgGGLG1UTQFzzRJNTPES+kTlN
+jc6nMTHGsFl8uS7DHDVP+U554bKdkk/oqHQG3Ptr7k9NifwmQIaA5Nq3p78v/cj5ibZSP6CrtmSN
+yzzqINWS2Cz8F7cfIBzbFQjc6nmpoBke0dMMi9C0t3S2FBV/G/7QAHIv68aF7cgvqmgYYCsbhFCZ
+pf3Kl+JYTslQT4HVsRuN6MIEvdY8wDs/X5hrgFdAC9CMZ+Vbjc2qjhlfGoz6DTZMMxD0MWkbl+NJ
+qD/a9Fo6RzLvmn4592YWKn2GRfhIoeXJZrdRlaZLD4UC42PeBSiFR8qu5auDYr0WIIw6FeSbNbnR
+NvhAjv2g94dyhkMAFQphgK2NXPJxrC3YAYZtWPnd75PuSnTvRQeAvoy42B8Jfkb6xi8NVEks5BZp
+sVgJguz0CnPd4Bka8fWF2814HePQS3qcN4IEJPuFim5B9Xfc1Mv18KC8wfKy3CTIx2P7UEfmrIFg
+q2u1G5m3dG+qLqN8s48Z8uKtlkl577OB/Atg1Uz3mtZAjfbh7WXom1wn4F0fgLDZjhHmNX9Noqor
+YVAkPZwSdpPFIZfbdxo3T3sunMo9EKK5eIatXyavq/aJ51hRZffTsq4HL6Ydax4Vntf18Nym/JXO
+lfvBoWZVPGKK416xg98ucH3Y9Y6DxxgEKDvL9gl5nqHjCeZJX95GWfdDlf7NSf/o9gOE/3BTM3yk
+RcfTI5IGCrikO2M2uX7zFosxmcC5Gxc1Bpe0S+WLXN7GyIQjDCO5HhoSgYvS1BbHRfySlvK5/Bkz
+K99wGDchQXYRDn1hrC91Q8zi1/4w2fehAr2UdoVDSKjM8DpmedLXx16OpF6VodW75890PbqbRE4e
+0kEctTDOQc3z77bJD0DegmR7Vxe8yX8tswsr+ah5zpiP42/tm5XxHRYBA54Ftw54W6iI05TXCLJ8
+8le+7IWiJ9mweRStn8e5F9kiHV3mppfv9Fs5vffp+RNPcL70qmuD58c1JEyC8cOw0jPuyJfU2H/9
+YeS3eUUDAx8ZM88Zem724yOw1bwhM6/X5ry5zCGVx9BPb+nsloY4cKrJw5Xrcl11toi2Q4ZGrH2B
+7SlFHCt0Vp1aANG0rYv+TpadMj5WijGTT3FbxBvnsoRr32x6HW9dHWWpSFfVgcNbmQExNsqoD0ho
+Aoo3n6fSRxqa4K8Q4Zp+yshQPae5mnroVueSMX+zfuQ/MCzbuvwxB8MjuKbwYxUuwZfmbj4IUcV9
+PF7Xy7SM4wiVlPGd3IHkoYIBSsCeZnnGkv/l7QcIx/Zd4q/wZlqck2mgPY4A0Pk7H6EgFVvTkQnK
+ytgbwVtIZ63DGfWjzLUhZbRzlJdfbBcENYXhkxGyl5Rlys8a45pCiiOvQCljsEPdedA8wKehjjkX
+j2u9Om/5HqRFg/qyoT3+UvZUnVDEJultNm5p/CYNqT9rzBK0dOX5tTxzm8dTfnL3P8ucAIbe/Gc/
+fmwalfXTQ3rTusUNj/2K7FB8PplB2MLeyzoFNMtl4JU+QiRaXTlHb0KL8SKPvKxeppBBvo25FGYQ
+rBAgzYkp+rU4YdJc52R0qUJOAILYtrqD9eXP9lrAcixpWwb7+oJ9fQHrS0Dt+Olnsc22Fxw1x3yc
+22/sih7HjaJMA4e6y132wOTL9x9+/3uDJwcreZV/YkGFBk0Pq4KPvAZ+Q1YYtpBF4wY4+Q6/zW84
+2xb4kSWzFeDP4oVO8iyBaMyl0aMp9W0h19dV7+9a4VwQ3jdDxlHYihsr3/zlda7/+gV/Hviz93ge
++LP1zd6XTOxpVZrEmG0D3uR7EP2F23uFZxgRJx1Pb6iKNjh/4qEBZSRumRMpihxb8HGCSKYZSpIF
+2cd2o9CepiBkFRd9iSqXOhb9uoLfZfGCXNAEwFd4xz32X7wxsP0OAx9yPU+aATwpg6XSEWqdvGmZ
+EssuXc8b6UYLQwFjK/qrfZgo/S9uP0A4tpVxip83cw9bY1gJgg1tqRL+glMa8NMDAblU0AaJY4rq
+fOSCuA7MBEAj42f5V5I6ezS2D8o9u+q/q2uSsmv+e877tRN/iOGncG6qdeDCwwTAAn6BDpiZ/sV8
+Ab3mTwLfyqs0PFSNlqOXtzjseZyQtVlmjqgR+zOw/V55GUiTCY0+XECGW+979f/ZxmJJ3g34MpZT
+88vyqKbMX43iI0d8r4ya+Qp2o75OxeSj0fS8ch7HOAogeBnkJ2TUgPxYyGP5Ls9uyKoTXt1MqzLB
+L/uj4JjtD0CcIHXZpU7PMwHJ7ctw87PJpiBY0mOOnfOcw+o3cHtvlT8MbwPJ3D+eEQ6cq/kiftcF
+H37XydT8m578TXkbJedP2lXkMAOCgQLEZu00l5M1DKDXscycdN7Okliv2xxcrnDz7Ir5395V8rCt
+VY4V258/9wwLWI2/4PHyWYDeyre8ZobGKQg22+D3V4DeAMHwBx66pW6EQo9xvEHa9gTGRuhDgGJ7
+yhsed6epbWDIJ4AbkD3gajBtXXPr82BWaX2aYlFYsyRyRuCbYjJA31CRd4B8yVLw+2yaeNyEUxwW
+08Z3evZLhI7tUX98h0E8T6xs5Q9Xi9xh1qRehF07kLe5LezZZMzZV0tS9M4jcYoFwD+dJn93+wHC
+sf2xR3iCYNr/UPIiSnUNOOACFy9eYEDjlyyPAVQsn8ejKqOCyIvnNefvW1saYtnf6nwoL5pUzh5G
+GPBEKpd2DtRxxlEzX8GOh4LbGE4AsHvbGyQd506g20HxPX0tv1vlk7DiuUqA/ApjJ8k/eYXP2GCM
+ckBBOZLfJnAHng24FAzLGp1wg4Xxdjeka4HgN5/JRV6CYJUKUXoToHQmqFPeyuYRkcJBkEueFyDT
+vE9XKqAAdDAMMciolQ4WACxs73rwfwPqsbuAY+Thpax5kAgCzr3dymfdZRsAJwj+kp+EPNxenJtG
+vZEzdBdKVu/74LPjePDms39HvvD1K0jGnNsbU3Ey+moNvfx3GtVfD3fyAoazb3JdFy3gvVUdRj5t
+YPvCH7wZY9yqCwCG0aO7AXDxMkMZFCRvz6NzKUy9CYq6hyc46zoIgPlS3Qa/4gl+tgOivMGh71xA
+MIeWPDbAE/n8Qd2AMmzCng2M6RF9ADPPl1/TRq3OH22FJKWxlY1zKU+fVhrjU0YO/1R0MT9h/lLP
+2NzamGM92B7htblmBSB+lmWTXwTzDyEtwHd2Hn/4vZnd+IM93/5w4Y30dQBORzIS5ZjGcnPIAkIA
+tPhwrdNuArTOT2jEv7r9iUeYTLYnTT2PFwAaXFY3R6JIqbWMxrGYyIACyFGH3k3GdBWo9vPKTYo0
+9QfbDVWBYvC5zu+vKSLUNPz78R6+gBnnyCMsQsCt7icgBgZADgP7Cfy+Ad8bSO6g8rTGNgfZvFXN
+al/BrWfJzOt0ankW12EMsPbR/Dje3hkr8LGkDj1Yj2O/JBYG7zGcYDg0bDNY+xz9m1sf3dv2Xu6U
+rRvxjn2v18DEvFqKatDGUDHOcaNK24uHwIENrPLsnZPzWuaJGLVMLOPNu5sg2Orrfq0eer21YOsr
+fuv45Seypyd46BlNcw63GNzCJYKWPvd7Ejy+GKZilCB3eIjr1+vzSnxq00EpD25a6rtlfuT+lm0B
+VFDwlp0Mc3GhUdN7v0vbmR8A1XxJGI3DBACn1zcAcAe8XBXF9lrfVuENDJvIeHH1+C6GV+wyriZE
+YOxmAYAFDMuNT0untht0LtIlD/rCXkc4ljjcN+4bAJrHyroW+t42n+1+PrHWsKJClKVOc122tHNB
+v2naIDFSao6Pl00pexMYop/HdOCNBaSHdy1v3uANkOkZBlbcgH8BAHa4iz/PnppYBvKhqxcx/Lh5
+Jwhey/E8DM0IG+ZALkXnDdOivT+k4iOAN8d23MxkwV/dfoBwbN+9C0n74fQMK99WiITWNwAhg7E5
+L7p3qZ9dlCHvoAL4ioBagGCnqFq1P3mv9+ICO24G329t3Ov2a8w6ahybBXrdkg6zjhixZk7T8BGI
+Mi3njONez3N/B7/AjgvGvTwUdUVm9d/5gZPykQ3Eg/xi3kEP1rwACS3zmce0g64NJy0DUSS4tTI+
+hUgCDKP6376uFsbOQmN6AmBHB78oY4V6MYI8qR6tc+Qvucfryi+nqoL/UH7Nm71Ij9kuUADsUQ4g
+PMEAlw10OgBJVlQaKdvSR6F5ce/zNwAAIABJREFUCwGSsjTiAm758RLNn6AYqzxzPT9CIrjqg3qC
+7QsZEpFL3wUgnoYsNSDHFP3dhCo+jZsscvRnECz8N+XpmTLW6zOvwLFfDK3O8lvZob0jZZ+qghN7
+5VaebqOe6rlbWvlT+DezI20BUHdYhPXjAMD7JrgAcN7weABivhTpXmB6hEFYhgVFmYBiRJus67Z2
+kh5gfz6AYpTqiYEVH9U12gufCwnk4Pt5/o5l3jrKnhWyE/S2sg375rxkUaexZtraNM8pP8ryhdIO
+cm9e4aunWDEi9hNph5cIPtsjvIDtTV9WIhAd8fCEu6HWPX8cj0V8dCosvntj2zEey0Y+D3V2hMIA
+qX8t5j8HLeq+ADF/RZxaX9ou9a7o4x/dfoBwbGt9j/gJgKGe4a7PTkDqE34GOKZ4VVzS9AJvgLvz
+KjRC2pygOBjq7mdrV69NDG0K/A0gSP0pr2fbduSMBylSKl6cS7/8yKNx2/vb53HVCNolD+PcKxB+
+8RJXHq7n8Trq+c10ltUA+9u4XgNHqf45HT3+90OZ5PH6LQ5YvcEsbxboKSPMUDsNm3jUAxyVxPOb
+ANj2iyst/i454/Y05gU8NEa4s+eocjnusZjv9e3kP1aLrm9xjdmUp4+enwNG0Axdzi4hEp6Wqya0
++mnZSIIfK3Bb+9VXeBieYT4Cn15k4zJoLRRixAqbgmBr6QQmqXeKq70BYKGjsBnlWQEr5dkfbMB7
+9QAP2jrKa4xL/SI0iXjOrgAr1/wPGnUXC8+2S9i4JvlGKhM8tjm/0CsSv6+DjAumJ5cAePPDW2xw
+ABt+Bt3Ko1xPFxgzLLwwwiAQN1fevMgRk7zRWcUFTwAcnlldblTlkAs9pOzZmJsVwM6BFD4Pr7Bt
+Hb1xMfXftukZ+qWT533mj/TQDw0cZ//qN5ece1uCrh/vhg3o4PfZesbD8b1gzbxp/3dssG+dzAgt
+hktoOnTN8wC2PK9liHSEQ3ChgBbxqERogJhZCnTPMR9rTf/F7QcIx2b2zdAI+AC/9AIXOEYrP9Vn
+OrO0IDTj1oXBWdSWgUxzlQjJIycWE/XevkHi6+Nol74dhbe8Gu0cr9Kn6tQdvWrv1jSNm2aIoaw6
+JfHnS18Qz5HkP71OgtIDzOICek8g3PMRHt0nwewNFLcyUHFWfvdEuJIEaRR47D2/lTGXdSxoQhB8
+A8ACkPNxPMMi0hO8x+gEum0v2i95NLzAOdriB+aT5z5B3xduecm5bUSuesYlDrR5py9XtQ6itbrD
+6+0SyMtxQuZrGATQgTG4sz4/WT/yG5ClV3jhDJFYWSe9wQMQ15rAOzTi9sJcgmF6/gLcIG9w9py3
+uOAEwXWzXjGgbcg11EdkOGWWxyJTKdeVV/pg1FG57xMufKqT7SgenlxgufNbviOZ2rXodm3EExGr
+ygpi5rE+IfDXOmw6AJ7Vi277aSJDJcIBI6tCmFUZxmoTBaj1RsgFBPNX8cb9i6MG9xVDdcDVKxx7
+mb+2bn4OT20gvZOGQTLwEYytJ9RdeIKjXd4YWPvoCmWzpr2mslxNpLVdysfUQleIsIwDRj5MIZiY
+3mBr6Z1BdWJWDm97rHD/ilBt6WMOi5E4z44P3mFt4VnmR2bMUnftk2P/hLc8Xn60x7uTl0BX89hM
+qnsBvNAQiRMU/3iE/8Xtjz3C/D1+BYD9HJr9YpzSoqpwQ/lgp7ewezFk1LWR1z2tBL69F7e8e2+l
+qRvC+Ig6bOzvNbrBebmE168rmCNaLD1HJwg+DeisewfCPTTitRz9B6DXiwFYDGKGUiDbSJPYBx5j
+nNPhgw632gNnlQeq9D0OcKwAOMHayzFXkbCnvMHpHS5g7HjqDn+AJdO+XZjhyCG4OGpqfQ40T5LS
+TzJ+1pvX0eMOgKsNBzIMosAv0LzoUOBC49snrICxZLeQCdTN76qXk/iWvl+8xPl4vAHlFecJ2CUY
+TuAr3mBJu34sBQKKcxiXuGAx0ArszvW2T9mdwLcD3qJ10x2xt9kBkzlW/mtGOOhvkx9c6nW+ytCZ
+vFnhiQRw6OelXEZ4QeikJhIuXRSa6XByrKjzCTxPr68jQa94ffec+iWMwqAeY3p900MsL9TR64us
+W7zpCszdmxe4eYhTTgoM32KEU3MyDl9+lk3I4xjR3wWKw7MJguJBw6S/JwhO++w5dTm/YJ5WHl5e
+/fpqrc09we8lHbsNfE3SsfeeTv7QtKHra6Jxe8orzBEyJOIAwXxqsHmpMA2ywwS8BXKlTo7tBRRH
+xY5quv79pMn/m+0HCMf2Jx5hnS6+Sdny9Pdmf5tCdjSwa2QE5t+9wMkw+eJcXahM0jmC32/enSf+
+qe6njeMogziPGoprSa2jj0zLSGZaFaYYR3e0N8zVczwN7J6rz97fuqERIHsDzdG5bC/PQSpKtsHP
+z2pwS1Fugl1vNKq95Oe8XQC0gN70/oYyewPAmo/nycf/28gOMIxIb21bYCEVHBe7V9406Jvab6x2
+8xbf6jYqUrZ4nQFamvK+1bPzOi0tFuC1jrTpcpk0nhMYQ+ejLlTzUOdu59u6gGH9gMGSF3IKBPta
+PS9fiqufXcMjCG4KBDN21ISzdz/Z3xi/eNxyHH7bhyw/Jef5djuBzgiT8OkpFuCjfJzRZUlwayyR
+MqHzN/KqROd+8I+jAJ+ynld6A5PQ55TDIbTlFZXxC22FLUQfRtoIsEz6Urzh4R02AcCwJWEUwRfi
+3fUGoA0tHEJihOkBPj3GK0Fi19UODI8wAeikMIY9OV4xUDqT/I49LvIIda+AbkS55cVK3gwKfEOD
+Rf+yfc6Rsse6gWAkPqy9HfHEBxjeVw3c4RnJsSJcLd8NFJ7YQ4v5pIwwniPXVN76m7qKTgouvWaw
+WJGig2DTF6QQY2jHOWzkk+kGeDm+0h/8AItO5S39v95+gHBs3/cIIxeXTj59A8Nv+jNOVqHaEkvL
+qwY7mC4EtRphzDCgFnZ6fvU4c3Mxxt+P9wTEEx3YtR2lA4/PaooMuqFzqezH/0239rW2lueoeMOZ
+5wGOObju7V0Ero47GH4rwwUgM0/BcR4LfcJQ3zzCV/Drb/k377GXUYy+FQj24j+mCZRlQnLpNHDV
+AxevL0GwS3p6hsXLkBDYYhzxuPXgjckv0xhG7iG2NE1xDYs9apyNh9v5Wk55G9dNdreahyYC1utO
+nk4RrrhZgDxcIKrs8Es9Dw+NWQPDGIAXR14HxukNJuh5jQ3Wn/VfA8AMFnPJE770D/uUWVxvbE3A
+7hn+MOgcrDxFimEanJ8ituXcJJic3uE6SbhCeCvyckWGwazeUtbzxQ50wKtEEr04QLOXYpAhMY7T
+ct5subwMF3LfVoTwCqNYFvpC5nxxbJz/78UIV4yxF38cQHjoHtXvQv6Uv0ZTmZ6sKwKYABjg0xnq
+apd00hZAxhIHT9Tekt5c6tRSl8U5Rj6wIzTiBowP0LuqrICwpUTt0Ij9Ups/2OsJL2D57tvKoVjx
+CsnxZKIuGh/dACpUx62+ZFehlw4FwdTlXB4tuToPOIAah3qBc4wKkg/Q9M9vP0A4tj/yCK8Ki9js
+/xxgWHWdIfQDZI69ynBZGWJr8tTM6MZ8l5uPuulJxoWhXoy/Aw0Yq9LRaleNcx5bS80azReclz+u
+JQnVY81QsqsKciFgN4yo/ugd1mMqRwXC6cX1AXozr3t/e/4JgFv5rWxSSwjgLUu8aRh1xO5ePcQ0
+iJku4tanpcUb3NIPnK6Gx/PzqObPXic3gK89QIJQE89vKLq3Yx83oZMnfsczfqTI+157gtwGgqrI
+W7njpowrWiHqJo1NDPQ+t4EZdiHnyNLYZp2Uf+4IinTCu2cYZlgCgrlfw9OLK0heEivcwyOOsIjx
+c35gAwuOndZPrO/xaliE9WP1DKuMx+Bpo0tGe34C3EfPF+/eAFcJcjwvEuQtXsibjQP8TsA1FahM
+jwu/0QvHeRWA1toieNQbHsdIo/o+jt+AMceS3vq5bnA8FSBY3N5egto41rAK23LaXpxLYB2Tfvng
+hoIbX/Q2W80Hap6S+ScYRpeVpF5DXSJnDQDvcpM26VTqx/sqpjwSfFX9Asi320MsNpvlIusFcLtX
+OIFxgsOYI9NzIKC52NLyx49pbCdegl9nv6rbaD+GVgad+NU9vvcRQNhBj3Bcx9HA8MSv2rnuAWa+
+DGzkERjDBBz/5e0HCMf2Jx5hvmhZC0wHy9iTKlKfEpBhqg2XY0u9XBcowELAUMbZRQN0pbx5i8xV
+Hcm0m+j5HjihLWW6KR7rlSddssakI2MGxRJkvUtDrU85ctm72DpNz5/n4uxHWo5pQDvQxRX4rnZ8
+8Q7jHQCvtzJJr66xBhXOHLGBZ71rnjfjvL2ZNDxRi/nwCoXAqpfl8HClnd2OxSvIzwiJ0GMAnrHv
+VsfZudXGM8d9ln2u2xTuAYqB8miNRiH1fNc7+7CBgach4dW7MfZsTgGOFW1d2ksjvY8V7Dq0DeYJ
+eFkBhgMIr7XwLMPK2N4Im0jwOwCxvgAn4Jf1bIJhATsNENk3PMJe83YFvxxigtg4zhUjXMpKdis/
+pm+Ikbe8rukI2mv6hU8caCv1pHIXvaxPEDiXpkygvKObJ23akwcR2BYCBu9jkmMNn2jHMU993WAJ
+bXA5JuCNmN/jZbpFz64LoNt1C+xSdvYxw2fq4xrlXZw6ZzJFu+ErhVZ0HDcsegPOG4/sb9CYT9yo
+LwmAWx+YDjpulnDxDpPXCK7FC0yyCx/Pzyo3YJyhECNfQfFCO17UI74dYSv6ucSZleCXbBXnlHE0
+0SWysk+g7z38DYyX1/yZe8OzBeL9BDtZTo3Q8UkHxtbG/uMR/he3P/MIPw0MZ9oWLAx/3jEBaVtT
+OdLQsjCMayrcpnW97pBcuVuMOkzKqqd5pGD4tP73zV/Sl3pni4Q8Jilvsvl2uaaXRKmroXT++b6P
+nZ5f/p52XJ/vfAiKh0f4BoYV/D5abwLhqLcGgCYA5uO3BMTgcW1cbnZSpWjjoyToM+v4oGeCklDi
+yZDq9bXuJU7DXNbX+bkhnucSCpEgGLVsGHTPdcXEM8wrqziMcerY4vQX6mhKFSrHWsq5idfkSAHB
+lEeXaup1YvynS6PZJ7MmruPdt0hXprMvXqPXlSTqvPKsZjhEeHTXWnhse4SftQIM0/MXH06gN/fm
+DR5e4lpZQsIicsUIAUBi9er5hnqEZX4Yp5nHBRo6GEaL++0hEiijH2kj8Ii6PfaTIIbTFF4zk07k
+vAfPCxiuOzYN4ek6WnkgQYYJb/VdVZ1tic7LNKsIjZInctxFR8SOH7XIdYPzBsbB2OANWF1igQvQ
+6st0GgpRL8lV3eKT3UaLD848vpCndOmaqgAahcKrjHs9/QaaZCp2sguhzXmiPFH3OYR/SHcPgFu8
+1UGw9RuSNOnq6bUW8qD5uiR3AczLcdjSFfJl2G441T+LhxoW4ZVnBMNJU4JfwPFsgI3wDK/w2DrB
+MMMg6LlNzRV70bOpF1A8kmOwAYxJNJPW/t72A4Rj+65HeNv6tR81PU/OMYAAxRsMq/OJGthE+Xb5
+bRZ2nojpxSgpQztv1+ig9/OoLqX+B8c+WnD2oOcPqBH9mgJav6H2DlVIHZV2Eh30Hunnkp4eYXRQ
+yxCJBn6lTvMOS50HBYb3IyUBwDG2FXRbwP7WO8ob7I0YRWjahK66tdbF6xYHaWxNPZJlvMszjCz3
+WjQ48iJGOL+5SaPCL8vRM7zrOgy8KXThw0xHLJsV27Rx9bF9SvuZf3gbMIy5ghygLKYY2kIpr/1K
+b6IVLQEr0KvYqBneGH9a8zLWXoMoEAQkKFbPscYFL1sb/ArwdfkinGlIQwPBGiohYRQKgGW5NAiY
+qZAICwf7pnnjTWce90GDYsPiUQG8uVegm6EQJbcd/KJ0iMt5Xm3nNFvRkZk1n1EpjbnIkGo2ZZEs
+S1gl860a0ItGoteVF3KMkHQqPo5N0iTgqKuxvRXCEHMcsZ8EwLt+8EEDzAPAjhUhunfYBfDstgsU
+7/ZyaT+Rrb7v1G4KTQk/30KfRiZBGj3BdATMuVCOJV0F+EKArzvaC3cXEGw5B9G38OiW1xMRL1zH
+6Q2Wl+TKQ4yGD2lbdzOWbGbxW+R3DtOB8gaLYnLSJD62AXHPeNy4LMt7Gf1Q6O139rH6XjpYKyhN
+ClgXwP672w8Qju27xFcPMNbay4mIh5h3PJzvvS8QXE/SKHy8rjfhTVRzlEc6QWfUMQo9Ys+6tT9T
+YyNWUOU6yl+o8lZw1DpDJO7Np6pKpS8eI2xls73CnoD4CSVEIFz75wqCn1i/0oDDG5xAFxeAjACw
+Uq6eYNc9PNd2pDcYVK4sRwFo9QoLVJqmIWjTj+FlUH0cV30Bw1a1C3SJNzgt7YP4sCcKID+lUEcY
+RM02XvMVHPOm7jr/l7w3emT6VWOjRIo/7dl0TdtsO4AO5VTjQKE3FnWt5sF2aecFFPM4Z85FZlIk
+t+FdbTWI8ARnegkAVk/wBRDzWLx66R0e+T7zCYYJdg2VTvABAXBCh6THpTz33lZ+qSXUql4+/b4C
+5GSvqtMAFOeRvNHLHaM+KEOV7nUh/MDByTgxj6sNJ1HaeNB0XwHge72i7x54glgn6PB8iQ0WHk25
+wTkBMz3GAYgZ40swncB2vlBHhFY3TVjiLb5ShKS42YeZZ1LX5NzZWJWPW7SRViYhLYOH5OuFdcPF
+8ARHB8EkvaNelrN8UY40BUkugHjndfDbPcOUrE37bZd2DG/SW9/34fDZJ+z6FoB+j3b3MwFwAmGS
+YR+vmLv6uqDO14XsOQbeHoe+1/OnJ7gh67+7/QDh2Nb6XmgEHm/e4Dvw7cd5kMzpMtkiyF7cS9H9
+rWdY0hbXKh612ldn7tuMmbxr7Xb8JgOZst6b1tBN1+m11GBm9fL0po3ENgQPNvDN4wGIe/pJQEzD
+mYAXBUh7yMMue6TuLWxCvcDuBR9XAONclSJGFfdVuVh6qCaFq+O/GtwPef7mNaYhJ4HrSnUVgcKP
+b9wLz54m0OdC7A3sktFPcKxeYu3rji+dff1srvqIR/nwNpQnE59FAFpuPT+yGngLmSqaalsl48Ql
+rvWznQBNeuw6thlKIKDZNuDVFSJWAF0Pj7B6h31xfeEe95vHSS9pc3iC58c00gvMc5MOv4EdLnu/
+7Qt4wD1WjIB4iAWoJPitNg5PcNYHKpiT4Sve9R/B4UVfn2pLPMCc51are/7rjAuAzvHrWBKRHMfv
+4FjGl6ELERoTgKl5eQMA18t0AZiNTxfO+OC+rvAExYa2xnR4l/ULc4ccKq4Vch7p45h2b1aggaI+
+0hM9kzZm9PAA59fnev7myeLXBoJzOWJPL2/KVnp9CxCq93dPVeWbob9Yh8j3+MBG0HURG5BfeQee
+RsSKL1LHGL5cnkV6hUM8LmEQ4RmeAH3i2AL0w+YL8M1z5e/qtPjL2w8Qju2PPcJrbU/YAuzZb80V
+1lywkIbtPBKGdz89TxD/WPNIkKFHXtMaAXZVED5Z+wOYWm9erlJ9+F1belT/6wph7Hk8mnRJ+KVM
+jWTq+VD89AgT7OZPvL9P2z91HJ/yNHSPbj9GguPPYBkJgund9QA4GzoWCEacQ4S04hzA801ggT4F
+pHRWIrPRjvUls87lrAwwXGfJGeoTjoXW7YlHrQ7n+oF8HMqF6DNeuDiisdYAKnxhjnF+N57oef6h
+TPYCgLchB/JzzxMUt/tf6y1ajSFzreoWjUp2mXY5KW1PkwMaI86Zzs3vjxGPLiEeYQ13cPldX3hb
+5zn9BqIfX5dNk9CITY+9t3ZzoGXFy+SDvheg8SA+rSx16Jmjh/jxDJlo5R/BcenJjAV2IJ9KtNCI
+4ocOXjk23bzNOWDlmc3/ldB5bGUJcP1bx6TVFQyb3Nwseihl3WAFtS022CqsIuyVy5zbsqId5cwM
+Nl6cq5fkWEdvtpJMsQ9dMU2X3pDMcuoNlml7F3ta5zpwXMcry33bdve93Nv4gEseWx176kCUV4Os
+topOG9gWHthzU+kEm80zLGlYqCzHY9gvycHxWIQxCI/tPgR+CCVUTzcpj44vGDy+vLeD3eIlufg9
+BMQHKLaCHOj7xLMCgtsENV1z/v729gOEY/sTjzDWE2GSGxXvCZc00IQ9ga9pmuawK9eyFCM/hJeg
+Mq1rcr1Ve7yOtH8NhxBhPfL0+DjvbMsk7/QCf2MTo9EOvY7TjgEBOBUAv6UVHAsI9ge/qNhwhkPQ
+A7y9vDg8wRXbizznibICwfRc9xUhHMAXkOAX8KY7XQFPniF0mYDQP5S7GnECNQFYY7/BWXl+zzqy
+nnB4hHeLhlzhPT/TKd5gD4MwPMK0fjeWq/0c3/seQK5oUHetBHIISyEBKA/i7RJphUIsslHtjzAG
+0tNYFiUypg6ApT2vNugl/ngMNEC8BNCaeIEf8fT6AMWwc43gXC4NZZimoUogDPH+CgjKeg38jrHG
+Y9uZDwFwHvxDsFeAl2AkyCvnqbfXtQzIECSELDLsq8JXPMHWVqmO8g4TPLLDMq8CsIbqimO/lx3p
+UnBFD09g/+bt/QiAeRyhEYfn1qxig61AbXqJ4wW5bauWgOAInZAX52CWL4BNcDxjhBlwWp/lJgmj
+zCC0j3x4FFuZx7Rxld4yaP1UIGXZTGjd8np9Pslj3DT8Ca+r1xcNbes+5TP4Cl57kofzIjZDIyDg
+GODLcgSORcIaJ2m1bHfDLEIcjGB49+UJ1lyiuxjCZsFSJORebWLr8i8PMAzHevYcPfFy3HKEhzim
+6dLH+lnbt6Kccxv1ZICZ/rvbDxCO7c88wttDpt7gjZbkMQ0ZIZWDpreyLTWqhljSCWYj3z/UNxyg
+91Oc8HWTy+SjnlsdafF3bc6y6Q1mmy2PyhzyYwJoALP0/wmAfw0gXMcPnsfxK75k1L2+yBCHlmYd
+7OtMrzDra+yyH8flIa4wCo5R/zjUoooe583ArVxp6VWjA13NGXujZ6sAccUI1x4PjaLnC3I546bH
+zDYQ+if+cOSngKufc+/X/HtdJixA3tPT2x2zOyBfs6ORyH5STjvlam+GPiM2XvgTgGTjXNbPubK6
+xjwGwyLIE1HGxgKE1DJM7wDYAwD7AYK/Gii6GqXLsWMAZgLfsG81TinLMZzyDK+88gwjFcAMiVCP
+b4HfT2W8KJK/2Vmu/qHKrziidOypDuvGspWXqFVNYQCt53qijJ399Tew2z4KNOtGnpXnN2N0Cczc
+e2yw2V4fPMBveYHj/HjyUCA4ytLj+ylGeCVYrhdWgVx7OhFThFCoTQumcrnxqPtUS7bMuWS5mD4W
+86COIedL3vOIJ9gAe+rGwp9Amys/CQ2ELuNNdahNe4I+EhvM5dBAki0BhzfPsFWekGWrsMjYnmGI
+YrXGe9t2WYBg2jLAsfAFB59D0gu8IjRiucVLchLSYNLfQbZzq3lV4Hu01yZifWzxn9p+gHBsf7Jq
+xOZCS2dTMsUF/G6lg1Qom0ODmVQjiuFNReAl/Kq4+wPnSPMuNsGzXJ85qSAiT5nw2I2yI33POWun
+DxttyOz6MBYJIAkexw9+9/T+CoD765Hjh4A38sIjnHU0NAId7M70eznKi4wdUrG9wgglU6BY0E4O
+fxyAXyoKLhEYVkCoqhvQatyB4iT565YAiwfRfnohbeRHrDDqpRGLtYcz9OGJ8rXXH84WTPpiK9Pf
+21/Aqe4zbIle30v6ecIT/FKX5Xx0bUJnTqHJbBx5nG4feZJyHU2H/Dz3Or6QYRop5y/KefwQ4BAk
+aN3Lb4Le5uFVdDHVD/tEVuZdtMhsATzPZdF4E9XLnl7/eeD+FAC+hj9EGp6Ic+MWTzpnd+dNzpvH
+l7rXZr0ARzkPJc9Tl9XBWV7yrtney13zoiLHO/IK3AtAbtPkZa+WTts2aI4l9uzpXv+1NsiT8CIb
+vOIKePQlMXH8JPCmrRUbWSAJya9s10Y9h4AxXi7PyWnMOcoXwD+a+EapIu0xbSZKLLQ05TucZHx5
+3uMGc4YZNdoFHQ5b22TMk4aNFyKZoudJgjw9w+FrlrfecLo4uLdYHcLi3nrP16f9F1+g81rD/CuW
+cVzx8m7tK02amPWPAGm9v739AOHYvruOsHp/yz6cgt3AbzKxi2KA8L2F0gaSja3SLXwilXAIIfMJ
+kqfARb9415wKQ8Awr1TtiWe75edltPOSb+cZsvsWCAMK+OIOhnPlh0c8vc8GuL+cYLe8vgV8I1/y
+aCTTG4wBfoEMj+jAVz27CRfwFUD4y7dKXUCm99giTUMoYzev+yyygyeK8jpuYMlGmRJzUt3GftDf
+WkKaYV4YNDhMwA5Q6QTFz442czpm52WFjfmi8wEi3/Yv9XKkHur9eeKrdTfgaxX7TC9OGO9Mo0Ak
+dA6FtHX9mtAD9wiIPlP+zXydSQveKvDLLnuAhzLiCnZJ6wK3BDAJhFOp3baOJjbgaEgOqaMOMHtL
+O9wF+A6AnHXpeZN1hfnFuQ6GycICYVKOXHTsHocLmOnAAyULPhhV0nPYmlYZnuWTZFU3bn1bw0al
+cU6Fc2TsK9s5eUaCdsq86FATEGsUapz7aHy9XfmlgKn83OoFuvxt2hHcutioCYppp8ijHfx225YY
+MWygS7Mpk+xW2sqgQfBB0kRurvRF1amD+g1RXc+5vKo4p2w8dckwgasudqTTLC62l61spkA6H9d2
+nW+kPJCVaQ1M8uevwPC+0W4AeDF2eOHLPfZxnWUJgr8OELzyZ7bu4DjCu4xPt/7y9gOEY/t2gDa9
+RRYvxFE44SXosTe/5NPTECxrki43sWUs25bYYu8WJqGPi/J81B00bKRZU4Exz7t7gE1ytFavaeOs
+32+U8R42UIqnsNVZ3laAoJc3Yn4n6CUQTmAcnmCCZn8qrCH3TpOgeeMYwysMxmB5xv9+eXmFCYa/
+OK1loZNmT8zv0yhaj8kLHKkZQ1y9m1ZPzVc8pZNzGPHpMpl5rrwYfMy3qfWtapR3jm9d+w4yg9kj
+4DCuse8sxMiUEWuGh3s64zsBAAAgAElEQVSK0JEvj98f7Hjl8HL5ituLZ0W+wTOkaed7pi0tgnPx
+TC/Ka/97/7z1rfXPfOTVmWlUjytITRv1s93qLsEtDfYb+CV9uzcYBVQ4PNJzCnOqL46Xum7WI6B9
+XtIOuABdHj+XsvQE1/ntc8qlLMQzzC6yfHbPOxKKdM8HMkyC8nADxn1yr+mUXpF35RUt1ydBb6Fk
+jT9CmbqMlXr01BQytKQFChgatiyI57LFCF/ALkzCKtpvoUBv/xXPyR7jeOT7yKfN9pZXeuIExLyu
+1/QRYOt05k1cZy+lY94gy2ZAPA2z1H1IDyjpJ4A47XCc3dQt45T3vLZLvfFZHKvGdz12seVeTzEZ
+0eEWL8oFTQmCNwCOWOI4dl+5LOgXdiz6+qrfVwLi1TzEX18THNMjvLBsr4e+vuuU/B9uP0A4tm/f
+hYTrRe9O68545HGfFiW8uwmKgQZyFSSbnDO8wNlOas7IS0UD9NCIUigqeHpHmv+t0lrSpd7mWWcV
+zUotv7cGC7wnZxjEzFMwrCtEcBWIR0CuguD/KDiWNF9kM6C8vmFAmxf4WoYDRDMM4gue3t8Nfges
+a0qJ7ZMGAmwzLCEI2dxFCoCLyD6OrxPztjWeYr/JFI4GrJuVCLASoNfjhVKX99V8rQ2I4WFs2b0e
+Q0pxaRQTdr/WITADcAPAbtggWNMExFmP8YxPpC3pQMqKKA/ICnRALyS1ytG6VcFbjHFucZ6/TKPz
+FwaeoPixur3u4DdorQDZ+jgh53bPcINOyPm3PUCF6JQDBHDdQLbS6gkuL7B6fBUQa516aekMi0Bd
+K2SUtKsX5CgxMo58CavSmn+IV/MgS/pl+hrYFZ5RwMJMC94+0Y2cNK+V6sTzuD1dYNt95qqrKurQ
+uptOXPLs8ASPn+GW7x/PmeDXgh9nfttLfgPTQAHfaJ5tbRbd/Smwb8kzFatMwlSoye2JjE/GkHN5
+E2OArA4RNNIPnAgYbg20CfLrfDXuGKxCnhMt3W2N7PkjGDaUR9hQYRAVErHgazuFfNl+wQ4VGvGl
+3uAL4F2rQig6CN7trx+P8L+/Lfsm8adw8vbJ7SzT0AhVDukp5k7YliEPTUt9yAOa8DCuL4UNSIFL
+4GsCZFO/ZE61K2LTSofg43Kmnn3fSqIJGKm1eawvzVy9wfkLb3D+TuB7A8L/uQFhfwe5Exjf8rPv
+2GC4WbqLYSIb5M9VlU4rTCWMD2WqGefxZVb8zKr5IXNwHOTPMJME6fTCZToAZ3qCNzBOQAyU4XOU
+UQOB275UUs3YBZZ5NwoBpNNrowA4VonwOMZi+gZ6V/Rzy4ivlcYI2Se/4hFkH73yeI6ClTkt5tXI
+ZIqGTHzUsXxykOE0sIoVfgG8TCtAaS8tRTvvwqv6SwbXaABoKIPGABPo0subx1dA7OFFHnkKgC+e
+4KpTVOfIdpagP8qacy4k7eRLkQVOV6Zl/iaguaQJVOrYpMqIwp9t6KWaXkEb6x5i2Aer45vm6Nfe
+WqhWesB4+h90WKGtBCT7AMwNJH8CwmIzNTYY5FNA6spe8ltfE1DTFkpa800BsOfoQbrnTT5pSVqR
+93M2sb3eVWbRt/TqCg3SPl82Ff8xsV3fze2iLjYYLt5i21Q3eyWkmgLCmPIOl95gqIRHTPDXWilm
+fMKpAFc9wT1MYh3AeIPgAsAs/9vbDxCO7duhEQZZbsm4lokYhRCyZDHHhU2lOTEqAi5S0Fy8yASb
+XvV3C7xuVwwxsJJZUYwFfEsw9b8mlTIT+E7SMDHPaQ91m2IpDb+Fi4Bql+lqCx0M11Jov5o3mIC3
+e4L/c4DiB//5tdspYAu8Ad3PIFg8wjEml/EMaARzxy+hmWEvgdPOA9TaYvINj/0oG/UmWPm0KUA4
+gEFkKuoTMLQ5foMaLgZn8Tlm50sk9kgsXzeEbPYOhCvCsYFidlW8wRvkogDws1eJqGMDrAAyQbCv
+1Y53aASvX+Rphkn72YzUu+fYLBvq6kG3nEL/WKcvuWcf0vb79GGgP+lD8oXotsmeA7CqN7iX3WJ/
+o+wpr3GFRdSLcw3wTk8wmI758AIBOQYFupBJjrs1T6cFkA6MnL+b/uONbpXdQqgFLmVh5bnkDwZS
+8ueubq4dpAMz5CYggJn2rsQ9qMVjCwpKWEP7ohjvvIIW70B3nqu/6EnIf9sDRxjEe11rABiGBLjp
+ETbbc9nShlxKztDSTaCpvw8VyvCzmkfTvqfdrqfHBMEm/WoOqzHJBLNeV7uxgfCK9k6sg/CHOqrU
+E5x2yLZe4Iu2Zt5AsK9d/pXptfu5rIHer3WLEZ4xwQWCK/+NHv/s9gOEY7Nv34XQY4Qm1E1R+Gag
+5gW+pRvYUMAbbaR0ebUpClSVGIC62zSkEEYBNDyiwDHLKSDRzpHuqTqnHY62Rj67OuFC6mxPpXMD
+v+oNnvHBujwaPcIdBAv4fR78+vVknjcP8FY4suTjBQCPOiOvAMh4qNa1VikfH4+mQJDCE9Q4Wmuj
+lb2mdSY+KJhkIzH4e2KKb7ROGmkBG/C9vvYiBXac7n7ZBtsDGx/j2OubVoy95yUGEA4DnnkegIHL
+vPGcTDv4Bnz3Bm+wu3v7ZPwvlTmepzykyzKGWO9FBucWCTDLCaJGHkrkcopkSo7ZGXVmeY/3xTfT
+9poW1DQ6cduEB/aA0SjRXnzTMIiRHt5ffVHuBpyvnuCjDEhgLJ5h7bfrGKiHBej6oZ+rjkedmkxp
+ngcSP8zqStMUtUHpvMz1vJFBZcOE8iIBqgBlro2rbMejGiaPLY9neMQBdEmL3/x0VaPfAd3yBkud
+1BN25J/A9yV/0fdLL3DxQ84YaerncV5fbpj6+xVit53Dsuwr7fDpGa60Jxao+X4PmbHki5t14H/X
+lFd5I2P03QwtLMLjJoEg2H07Er4AcN0JM8P6sh4X/PXiAX7ZW8QHf3sFr//h9gOEY/u+R9gAGnQK
+Np8rNAHdxrruii8gxfZ5HfBSWKiQtEzBMI/ZMe/KJhWBVb+hj0BVTErpaBDECWj5v4PiK/C1M3/a
+ikymsCMNGI3aBMMFgmVNYO9xv/+JJdMUBP/nefCfXwWGEwg/LoB3e3Yzbcy7eIdfzvH8nXfwpxKK
+F+yyHYuv0gH1qK6brZOa301ftm6v6xJyM1aX9jGJp9eNeQhPsBnBJOVlE26zvSM/cgHx7loofWOL
+ks66BMGWXlo3wNN4b/Cr3t/dryeVO6jUE/CqJ7gM6Xxxr0aO7NdZHv9v5VbinGKndEaSpBvSMVeh
+OtqHrI8widxPwHwBw+h9kJ70kTWGEbmdm4DbOwiWvT9S78ENLOvLmL8Dw/3mrHg0b7ynd1cAzfFl
+OYIg4xyMc5VGN0Koqr/UU/ECuzbOyJQ24JmS80MGqU+jfzZuym6aIeEWZcgKAKfn8wZugdcy2hzK
+fV+ij3k8H1n3nlft9HMhdg8Fgrk+eZbvNrbcRN8zPGKPj8s+9rm7cndRbopI9Llj4+54MhmXzQbE
+vEPB+pXVZCYbT5nUq5qzs815k4CXOiOe8R26YoeP8VU7an3GCO8X5bpXuC+p1r3E1vaW3uK/vf0A
+4dj+DAhzb5fjUhq68H7dFXoa9DDzyc8lLAP0yt39VsJ1V4sUlOrH9AZ3QZzCWONOAVJSDGGdoqRk
++358MEDNnIEDBL/YeQmA0b3Bj+/YQqa5dFrGCktIxH+8g97j90uWTwvyTSCsXt8GjF3rFEiOkfVx
+Ck1+gcDFK+3MQyyJpQEjnHdJM98lnRb3kv86GR9mqQFitquXIsRQXuR190j0gxwmq1b6kyvOp7zQ
+A3QA4QQgAFcocGd+Ke7dI4/Hd9Vtv3h/HYh1UQGP5dMYyuSPKP5lJ+BIspWnTcu1To8tLJJn6JJT
+JCnHOjNSp5TDAC4p/QJ8MQwXxLhhgOIT8CdPvII678XHUwp2IkBsgtPp4e0vz2GC2eYxvoRFQOqq
+9xdhmOkJZtkxIEUW5OV7/k6+5E+Eks2f+RaMcZDWOY91ToVZnOStefDc5SUz7eJbCZsRclzxwhHC
+5wiwbGFGmKYdCp1yAbps+w6AL7ay5Ud/gFa3H0O8w1bHIQ+M+XXD/vQzAXE8urfk7wC+BKAEwas0
+GB7Pa7WZayBzJoDukOrl3aaOMEVJn1vZevVBHHbhdtFmc1pHWz27/BYMD8Mhmie465BjFWKTmOCv
+24oRlzAJs5Y2AcR/e/sBwrF9l/iOtRWzKgObcVL5gGnkWeSG8vDa1+MUb+WavwEtz0t7XOmhYMob
+HGXc6Z269jM3NtIFaaqIDoIvabn8zQwxUeYrwIMjH4d3rzDKI0xg/EhIRHqCn+M3QfD/i/CIx89Q
+iAS9rh7g7amd9cw7YFY4OKlpeVwR5A9KnZSio8JRxdXT9pL/Of2yBUtdbTrzVBtTebvOHFBQXurb
+XmrQ8wkKinEOg1qgDUbQxjCEuEFIA8h4YXqIeY5vMKugl0N50IAvfxknTI+HYXs9HksSkJSCBWXc
+cg2xRVqas5E3y0j5LL6oCxEucRkzy3Y6GPa4ZguBAJfiw8WjU/y16SieOh3IaVP7WB29wgRqDfT2
+n2lIhKwm4dN7TID8EPTJDz0MIh/5i/7QpxVkx2MgZGPl7Ze941LGhvyUyE6w2hppvXL4MZ0rPW/k
+jn8OoUmma1xcjYgWSJvU4+QxOlsSiFnZpivgxTfzej7lmnLP8ybQ5Tm9ftkvrlVMbzYBsfGm2Mo7
+TFuXIDi+BusLNZ3/x94bblmO4kqjIff7v/Cd09b9gUIKCdiZ2Wem5jtrJVU7DQJjECCFZRk3fvQ1
+ynXT5tJQeHOenYxsk2bj4g6g9uiOeaHzYKtS9cLe4r13XS+VZRgJgs3WxzXmEyXE+xT8Ip0D7UW3
+Bnr/urhCyH7CbW9hW+k/HX6BcIRvf2LZYuFtflOoBU4QGgovAXCmWRmwgeC8jVeLMPLuPvGrAuNc
+BCp4qj11V8ppzyKVribZMX4vtz3cGeVOYQAoAbmkU6BfP6jRLMH8mtybX5XjC3P/4+tFuQTACoZj
+b+Fm+bWD9deGBTjGI0EzquyNB3qP8n7jR1/hQlQFjlIwZu0qnm9gOFuC07i3kRFg3ACyHiHIYpiu
+lt5lL7B6tEw2qE8dkylUipZrg0ouBXCUpVXYxXpV8bAGs/wF9NJlYge+cW1xq6h2FHN2TCJw2Dor
+BlfW2qWyDvlgMZHoAqWsyTF3KvAUGeAX45Z7BEfekn8Kip15Jn0a4NjMxIg5OnqYHx2RbZ1Ft/j2
+l97q5bmDVXj6DqdbxBtzSyzHmlagzJFpYFjSctNUaIoyGDi5TnjO+VGmddp68lPI5WNb8dsqnudr
+fJ+jNT60t+j8VJi0W4AjDdFHXKfTMjxB7wS8qpMaDVBw+xHwAvnkIvWpyXlhtfaH89jKOoygWdBe
+rP6FC1SC4NeXBbmNQnEoVahSc43WOalWD3rxqiftnFs3b3MGmGZCdUNBXA40Z9kOiBccKfcIM1tb
+o6HkAg14S1ZwVx3E+xaxL3xsC8eX5e67RtQWaXrkS3L14tyVU/+x8AuEI/zky3Jr0Rk1EmjxTRAc
+i59vYtsGiGkTjAmqIDi0kaVQAijNzEqqNdeIFE7I+ru51mRBoIRR5nPZ7mKA56vQ1jzrfy55DLuC
+LRhRyutkBXZfy27fQq3A76sWYe8vyJ2swf/6+++yCJt1a6+J9deXnTOtxKQFyys+eSo/72k+WNLf
+EkADW7gKua4iu3CTpxBfq9ERDmWo11WByrGhPz2/CWgFwEA9SZHNhRPQUclhB8KxLtKPjZYqoIAx
+wmJMMMhfNCF/D3o+XryY4HiAxgOXWlxcSK5lglW8uV29XrwzfqUStWy5e4zBGvZKloNzpfor3/8Q
+UFxKLenaP2jcc84VCBzD2xM15tr/NTCH3R3iN3yEbfgCg59RVlBMoHsAvwVw1Srs2bQs2/5atT1l
+JSd5MNsJtAQMKWBWYCzn2+k65GsLXd7m0vo4ieroI98lv/sH88a5AyZRLdKatb44Twl8S8dY8eAr
+IJzxPc31oL6/jYYPeTHv67pYIDbcIdL4Y8tX15/SudTdRteoZ60/0qjSa2DCacW5Dru/LyReN6uR
+Zpk2P3qYM2IO6pTk5xqGYICXoSuNS2x3zUnqIUfpOe648SDczh7DQxkau//geZdcfeLswC71stz5
+q3J8Ee62r3AB5F+L8H8tfHsT580SrL8SEDdAnFO2ST4kCG5uEfn6cFl+y6JRLzYAqMVpcgET9wih
+ZTIbomX4pxZ3kwvyd+Nh+7uXySUbipIrOwGvSnjemeq/Iwgu63CB4L592v/EC3TTGvwvdY24AmGL
+tBfgdWtguO8esf4Q+Co3ycsHwN/WwbDz6NYEXj3QdOGrx/8VL767lNpF5jmo8q/kqUgBBAwwkE1C
+2rN5jgEJgE9KEsNKCZQ1AuORnEs62lCWYBcwt76QtFmCsQDvewC8/L2hKLP8AfcBHdOktXCWmXGL
+dR6WYMs32GXFOUFyjGLc9HIeJEvjmC/HkXdQANxdJGhBm486024aY8odL7qSXb1QsJWDzrWbeS6/
+w8tyQa+PY6gbhO4xTJ9g+vLHuZhpuSbEDYJp7FO6AeBpDYbygfNV4hMYn2TdZd7cQ3kJF+UjhgoZ
+mUI0+u5SS9w0EhjJ0tXhMilLw0udZymLO9h1ic+8qPUCghF1cQ3p+l9pzbeR3stDQTCtwWIVNnjq
+43ZcX4+oz8A/xfPUje1GaYpI7VuMIUFx6lWv7Hy8pmP6xUSJMje5Um3Sa9UTDKuLr5Rc85G1QUDM
+9/7d6RaxCnh4hOIB3AsA59HqZTnuGHHeTu1Jf2C1EBMQW7hH/OnwC4QjfJf5udhstwpz0SkCSjma
+d6SctCFMxOK3g+AQTOOuXFtaABkieErY6C/Pm+Xy6qmOqz2a4qIHFbeNvOx2C6q8Bzd73JGCnMB3
+txDvbhH5Jbl3viznR2vwv8Ia/K+/AwjHGK1PPCoQXuOQabNwiyAotg0Mz6BcfaxeM3jc8DcEDMe8
+SMtw8qxE74r1HIj6TJDcmH3mfBuC2fAJfBtNAIAVDulBdqf0ALq0DDuQ5hgBwlS0+ki/pS36SfBr
+y1rlASw9+JDuERY8Hb/3AcpyvAPmzTosLEn2CEuLfldWOhT5dvtjsHg0a0+Nb3tyEPy2ACQm9Rm5
+HPzhrhHdJULTFhbjExjmzXr0I5+l985qX/NAkMQbW8YD4Np8OW7bNm3RG+DVMrqvMIYlWNJrOnZA
+aNnUbjXeALBw1QcYbi4QLa4M0UW0L6izZmEbKu5xzbTaHicfsv9JOpRLizC4Tufj87rqDoAxzsMA
+hSYT9BDn2p56SOIOFMANek9zbkZrrGgAxDKM1McbCOYRahGOLRS5k6OCYBeRV8oymu3gjfj2oQ6I
+wclMaOxIcJ3yE+NCAk67YJE1Bbqc1RhD16m0tsY3bqTJ0PFIKfVSnPME7x+Z5jkWWDxCLM0FiKOC
+19KaW24Rh/2D6S/819lqXO4Rvxbh/1r40a4RRx/h+tFHuFmF4QmCyzJMQUWQC3wJgmMB8UUGiBCj
+QNpcI9juAz1hGleFUFsp0xS08EXQS96lQKisBH4d9M70wU/43S3D+lll/YLc/xyswWoRXsNqHfQi
+QHICXh/gd91xT1/h+pV1L0G0lH1MrMFuAYb5U/uQWousvfxSo9UtX8659eUIjQHJCkRwNx1fwtlv
+51OQSno1I/b0NeFSKChVfHl0rieHWi8hFuAEtbE+VhlPgLsBXdkVgkB5HXmNYTEO9jU15b3LneQX
+OrtLSxQSBNtr6T2ygHAA5BgDg9XDJtTq3NxAgj8LIKu1twOK6pt161oCnbIIst1DJxfAZEYCYS93
+BO8+vTZcH/hynIlV2AaA9vcd4FfSA+Amttjy+vTsMQEjEwBnfAwm5XICvznQ/yzo2j3eYZ4mFfvm
+HA2Xc8vVZUqTLi9484OUGWo9XGuggF5Zz1V3SDyBsMTRy0wXB29xS9Yf4wJE66W4OG7g9wn3iBi2
+512uSLAQwHH0Zz24evpIVNSqe9GW0p1Wejf6aUynzKz11NbRNqBMyroCUDvQ1NhVWDQLmUlMsW4U
+YyznTRy6ruJuRbQIl4V4WcyfV1r5xPzkzxyw204RB9cI664Qa/eIXx/h/yfCd10jjBau7/xE0dcd
+qcgQRuTRXIFgj3QWFPVncgzhlcAcJZjkV72z/mO05aEd9dwNXyvIatdBtq6HgzDIx5oknS3Bx08s
+JwD2ZhH+2/VDGt7dI/4WIPwS7B6AsC3L7QLACJcIr3IJiOsFuuLsEkjpduHcnWIB3he+wC9QAJiy
+jxaTHOf5V3nvMjv4+FxArIrM797sNXCAGpusE1BAvI8u2rkJfmnWIfjN+GpbB8JqARZaukCURbhA
+8brwG+q8uZwEdyZAVqD8OuDmCYRfKt4D8P067ht9dXfNDAJi82EJ1mPe+J7GqIBuAl/2U+ZPWsOl
+fH9hTuQZONYCm2QK9R4FqG0TV4Hx4WU5tQwPcJzAN9wh8gU5jXMUCXAT9FWcyj/HQrBMWa+DwTnH
+L/Hsv4JeylqJJ1/+iQK/nROML3bPiOR5JXIoPGoRGSFDbKMK6pDqhcs9sOooF92mfBH9A5lPG0BG
+s+r2GzSkPOhxyyGZblQwxJPZJ3T4WlO0DlucQ5NEuUcsy7BxEyi8azcEPsxq3NuHid2Zurat73U3
+228qQbbFOlODwxhv/UJgAWK9+BTAQYtxUreIsh7XGBrKFSLjICjmq84hv2kBXhaKFofTRziA7l/d
+Erx/Zrl2jlifVn66q8S39dS/L/wC4QjfZv4TM+JiFS4fpNL97YgCxVCw6/TjGhZhqPVXy1JEFQxS
+GNaT2sZZLITHUSCfgG3FZl7Jj3NtDKlkh0wnAKbCokBvIPitTyy7WoP5wpwXCE4A7H3HiATDf6+P
+cawtW+qFuAWCvVmFk4YAtXnO/lNLMN/EzbrhC1wD+MvCLcIR4DiAihO8kLOl1PSRZ418twaj0U4C
+E11vtzgV3SEtGpSPUDkv5xU8QYKJTqfiXPQCw0CBshHfrMI9TkGd8wcFgl/OHzMBt2oJnnkEx54g
+eYEDl37hh3EFlFYbZ8Sj2BaH3FA5ygrMI8rNxoqtOVfS/QEFMgh8+w2G5gdQiLLag26Z3LlQf71K
+BChWcNtAsFiDFfB2S3E/4n1rZBX4KviVvGNa+rFNfAEmDQDfQG/bMaKvvI12FIQ7sdY2IIzeA9ea
+C6FFCzStlIyqTsXRkoBlEbfqclJQ6/YEgAnAshxK50waau7pjS6AmqdRtc7XWI5oT5DipNwP2J/Y
+/cGqHwSnCAvxgzX/NO1Y8zC2f8yH80ddtinRDQdwJ4WlqynQOR9rrGg1bjZeAk0OmujEPVjWwy/S
+0RyyQHjMLX2KDIu1t5L5why6ZZjjdHRU4E0R+2UH0PvXBQBf9hQ2eXnuT4dfIBzh+eauEd40lyUg
+pjuE3hVvDvopPBjq8fURDAM5eXPbJRGy011i3pkOeJrX3BbxFig2OqXOx8gTyuzi7SqipFIwhOD1
++HP6oEbzE3b5qIYHGH7vluEJhv/1xq4RvDs269ZgHlHgtwFkWAFbE4DijK/z/gdxnpfF+S8Af7vj
+L7MEw25qveRIU8CrI0RZfjvYJTBECsJdel7GfIIBS+IBKLhoUFWrMYZixvQc61KAZXUsJXIFwrxp
+jN6WmwNdIgIY5zosgFvglnxdFuFmDfZVXsHwa1KPQIUb2G1pw7kcFR+s/IMRX1LyJwGxO8GwdTAM
+0IbV6l7At6xoCXzBOBJIlIWYskj5HrNJOjJVb1unDeCOdAPA80Mas5y8KHfaX5hpyJGjP9MCAhun
+HDvnFOC2OX/IVwDc1sBNfp4mQIU8a5y/3bT6HJOSldlrkZk9PW9rrM1kzevlYuS9tzVdZ1LA61HA
+4HZUfTjP0zm7yuc8Nku2N7eocc5Sjw/c6SLhIZVD5zrKVQL8ducCXQl+Y/05EP768cpv9rm3u41f
+gv2yAps98cTnqbkuq6mNsh8mCEejra8aodsZ2UxOWZ8gGM2uIaaI3K7TjLcC6zrPqNwV1AsYpkvD
+t0BvukfoRzX6y3N/OvwC4Qg/2TViOeILGJbFUGhI6UiLMKcfb6ITyKLuDnOLpQAgDQSrpTghqAio
+/Nnlh4GF7PAbvElaXM/2HLQSFfnE1VKsBMRecQW+I617B6s1eLpHtC/L/V0/ukX8f/my3AUAm+3g
+V90jDPVDvQRn8qY/wfL/gNZlw18egMyAv32B4teWn/ACw2UVLqBpMeYc+R0MpyVARW6zXv0gNKXX
+JCtSouo4ZryuV3bqaiWVXdFXfAfBtt5UHiC5gze6RFhsk1ZAubalC+uud4DbgC/sUs6S40dwe+q/
+XehxfGJm2POk353hXWmP4fJ1A5XL1Zd/+RviRnZnztHOF+as0hsw1nTwulnk2lTxpqM954Qo48Sn
+HdzSL3h7WW77iMYrrhG1vzDj9nrzEVbbPzBcJKCgruRIKy8AMDurS+M45yNOxqqsFYBaBKnwHN0n
+BbQdreK9ea39muHVPrkZANB8faVHFZ+gF7UynecruPVVege2oie2PLlCm5s1LyHxnT5AcsT5tAi+
+bijX0QscP5SX1JJ0ixCLMF74s3yJixm00NfYKH/SUqyGJzPAnrWLRYBhmMWXNPvI5hTLtMgY7z9d
+X00dZOhaAdJbBcENvJqJJTg4IXXzdqJ13uXa6coROKVZgYc1eLMM64c2uI/ws3j2/FqE/08EkQOg
+X17SFS/qRB1yM+vIfO8rZGpaozBDPYUKIoWUUVhvF791BEdgrLJ5RL4VTiDBZr73o8pvyiKV8U3P
+AqDvMIHOsqj6/nvrl0BZwDKtybvKDGEMH3ETsKWquWh/w/GXG/42Htdnlf/C7gZRvqzz2nXNJnQ2
+UdppRSEojrT1sSEcqPQ8dlVYYJBgaVOVIsa7JVHng3NAt7qVlwJbYmsjR2/j/beu/Q76Gyxcc4ZW
+YeZFWqzJZTVmuzyemMYAACAASURBVHrY+iSxDTyO8Oaj1xdvKN8nJv17jK91Xt/sY57JWllz2Zxr
+g+n10zgtr+a2QKq9YVYmEI1K+WLbALjtUW2Lo+/wQADrBYxrkRMoo2hAukpkvM08jkaBCB6mfFHM
+VQPRYIyAts/jtVeshbnaQLQoBb2t23b6rSx5L/WrhbddVwXkiS55em0FvSpJ1o3DxqVSTQNEZZtt
+HIEAkRjKUo7en1QwT8Ev42d6B8Kl99gbjfPI/XlMRo20ymP7AcAt3hJ7lz+Tc4uJ54kPcGC588RO
+NEtcLz44yl2BgqitF10r8tSkr6m6eWz+wqyX/M0dLS5jIiDY3MW6D3knoazDkPj2s3VT3l5eCCxk
+aTiycAWsJ6orn8MVxgBLVjc73X8j/ALhCLu6+1QyJmeexYlKZahq2OP/oMV5DGony7QA4P6Ye7ZV
+6qsZ+62+6GTeDIfG/vbraLp61KkFZj3z07LbIAZppQe2uNBEl3Z2Fqvlp/aicWfS+rHn7fFP+Yvp
+eU0R2szf8rSNQktwbAS0gFlZf0txzjZNCzFHt1Regs4NGP/k2B+sDhtbXrXpY5lnfZacQXDOS6uy
+bH+fOTPNPvZ0zi8qX4x5Fnmv93bQSqxhyomT1LitzrXICFjxdfwF0CHwij+A+xvbvL14X4PFh0HM
+Epr1lhMURf3+11p3f/lf8L/Wkwq82MBuMUuVNDZXB5dyBXQJwOWlNxkxlXnk68ROruugjYA+6t+g
+Zg8B3Cr3vK4VHH5WyS5loj3bk5teWxZpZfXqZJ23Xwk7lNBTQbi1CVK2J3UFOkq3tN5454BrgSFZ
+2lH8T5NO0KX5cXRHArIEt5HtCfDC8MCy1IFETKSHq1HJu4rXUV0mYja4Y91p8xi02ObRc7uJRfPM
+c7zmeF4DHsf7Gp7HgdfwPuuJxmMP3qdeDKUP/LyBrLx3y8s4z2ty0dpNRNFu6UKczqOL7BR5CBcY
+oaC40UvrWfB4PRXlMznPMkDdiJ8+ssP3AHLBL8H3R8MvEM5wUmmnUiXIN0AnqpxCq9EYF9Bs23W/
+kbY6pFX41J9v3WINBWI4nnNq1U5zkdvsP1J2Ny4ILUtysVMRsD6W387Zf7cOn9rbwxko18++jFsI
+oAJ445F0o4W4UBBsPe/Nmkq5JNw1dZCIMn5SVCNlxYecgTdg3Cy/83zNq7YVH8ue96ncyQ71NeCd
+1lodu24/pBKtuRRxC/0yxrk+cOKor9Z1vqBd7Ryucy3mMR/DUhSkhTTeYHc8+RaLtXLLpzE/If0a
+1tfx1vzjsTXEkQBnKde/1vp6FiB+/Al/SIyF9jnewC96mWV1jmc2TsXINU4Q1lcum21cQ2FFqvkr
+AD8NA33svxZ49/x503MocCB0OI5cMyzcgWG/Ql+jnc9exMH7JSO1GZ4HXRc280njcHH1ZJrX05bJ
+icdJfZA5CooTgXcrcQPBiJtVgjbSw3KMDTBHnWnZtNqLEuLuIKA3/YXd4gaNL9V5lPe1rtxiI10v
+y6kRvEe/CIDN8LwOPIb39fVY/13O/u/zwl7xEZ7glunthtKhO6tsvvhqQsXXaQW+laaeqqV7HNYR
+NjCMcNMiiyK/thEVAEyp7PVEavIG+YTq1zXivxauiutYTgCbUEoEFXUT9lfavTUFq3r6u609+wbP
+cpdMkZz6t8700ZvZ8+rjEciqDj3mDQ53nfAtcNytlmcr8ek8G2VmXOvf4gS1JQLkkZ4+mh8gGPpR
+Dct+7z7A/Ugd00MHxx3wUkDus5D0E22fsXer8HfL1coZrhFtrOYc1WsMC3DOlQKy+VIdZM4EGG6f
+t/ZVH90neh/28NUqnPkWDfW4WKbfGJXcpmg1clmJl3IoLLIez/q7WlrzpnwRhXEtQUvjXwTD7rmV
+0Q5qIW1UuizArWzR0iIM1huj6q1xg0FxCHnlpjOeMyFoFjTvFuN9zX4JcXNs7uPZ6/UtZtkWpc0a
+P84Xr/FZN2I0LHTDAGKOFp1DkZnV4oF0als1NqYkNFxWVDuN/VI+KD92edQE0gDGfPehvEFCP3GN
+Gmm6dlF1JkiOBr8Bfp91Y9jAL0fm4C8sPhcoAGygFdhzO4mVfn29HwILwfEYXnvxvOVC8ZrBnrAW
+xxt4+1OWHRB/i0Zu/xAMX8Hxl0iC9vU+uoblmgXop5O8rMG67pMW8yv78wEMP78W4f9a8A/ToRe8
+W4LpErFbiPWHQxotXoB3tMlQCyJmJm+i+ZGFXtOs+ytA/HWoninHSgDPfDaLACMFuB4hHPFxBETY
++16O12ATvKfvoXhVnNtBryzlLa67hLQPMGjZlLMT6Bo6WI48Y5lhDfMCwWoLbu4Sjeo5NyBt0ll1
+tARbZ1uoonzE6TjPMbVBeaN2penSXnWxaErVIKieLfhkFT7kHaxNCoBnnL7CfbWGFahYuIVvSo3q
+c1MIyKeA9qzPW9XcXRalrP8JSzDz3wWA31b/jNWi8Nfhf2Edn/Xrm93rXQ6fMsliRLX5cxnmvZtL
+RD4aHVzeHmmJ9p0W4Ql21zrU+fP1GBwHbVbhM5Nz91RG57M32i1+au3RJWLegKSQaydW/Yd2Z9z1
+lEooWC5AzYM3nrvEs87p/sB+ukseck17xN2RLg+MpxoTVwnqrd0ajLQY5/yw6QYRlt58E6xcINZu
+Ek88cVkv29EKbPYsoB3W3wV+bblEPAFy37VF2suP4thyicDzLkBs3SLsbfxk3VxA8BkIf3J/OKcR
+cnCmKf90lCv0OasPl8W5JLdZS4CsR0e+AFxuESgZ4O8mClwb9QfDLxCO8G0gnCW76lVajaqeoT/0
+eBw+35+5lJnKLgjccu0jIDYpf8n7cP1J6z2bwJYpARhDXi+aWDsgMkLj4zhHTDm70nY+/9i3OwC2
+Q7n2woaUY7y97TxBtAqhoBH8Mu+N+NjbPfuVUNYBBb9NQTVBNnmDBnjzaN8tewK4esZ0f7jnsa7F
+Uxe+ngDvLX0Dx1SwqCOQ1qQTMK6X6daxcYPMEF59Lwj4SYHv+Z6O+gbDl44FAA9wDGcZeWz4oIBT
+swwHOBb9mW4Qvup0f9rvfZ/6xLzHaqVYkUWrwHf1RfoElxerSvEpIFZJMVeihSXQbPLWmo/8mjk6
+5/fwSZIZsLvo+uqct7TE88rjnO2KJEr8Is6P5AGA+s45wsKaRDk2xzaJrDTv9LxeVqn1ePYCPg0e
+XNOTSQdgnGAYFQfAl73K8osGcnN9xmBxrZY1GN3K+WD5mMKWu8Mru0L4yk+rsPvaIYJ57vFtAMYN
+8ADBZqhNhiNuyyd4fQ446OEOQQuxvbF9Wi5kv/xwAMc9H6EbM/1N8HtOc2ywGWHKSszz1MwSNwpR
+V5pUoi46MvQvq3bTEY8JoqOPJ59h/HmD8C8QrnCRWFspF+HgNVGLEqWGwN8UQZW+i/OSKXsZT9kz
+9EbcgvWXSXq9Nspf8ki1T9w55UwloDcMJdT3o3fuXMpQLjT5gf23NdEpxE83CjuwJZ1lCYDt0zkN
+6NZd904rwEvaK+Vf6PgNKy+vZwd1NIajAeds32DLoB9Ytp3T4ykuofPHJQ+t9dWvJaQ/WXkrjYwD
+3nvdWzRfzEmwPKzDAoC3H/Mxg1wjwuf3UgsUOf84ygonc9lCCfgzyrXPm76Al7J3rPJvKo+QSc96
+VBnoN/rjAX7XTirPw9+LN3cSRckUR82dAy13somO9XP7F+Raus2m4nCbQRaMtT77aybNZyKfoHEf
+hx0MrxoM0bzhI7HNAcqhzLyA4C0tYHC0BwA2izC88xBlMHAf7dLJNVdplvUqx3EatO0cb1dJXmnc
+J53Aca5H4YG6P6z1dgC5dH9wNPCsPsNBAPfqJ+gzwi4CP9Ba3K3E6TP81F7E6RaRL+hZxfkU8H3w
+2rIqL9BLdwhDbgrePkVMXqqVF31NTNqg5+r4R2BY+Cp6J8sCJcjybtRzZyxdVwTEjy8/acBhbmn5
+fbyn+441dJOYIPgVy89HgfofCb9AOIJvC/5SrqnnRWlgWEGyqG20v+s3IYOGI/CNDHMpM+Wulg+h
+sReZUxsDEKOsZ0Ke8F450dwWEEKdZ/i0zpZA2NQizxMryPE4f74arXpWuawyvqWvHBEgZiVQEXUW
+b0zK1n0wbKe1r3pJ+xzlI/xqebnGCQbU4Mvx0w4TohSUB9+3BK9YvgneIXaV87r2DfzW+ZPzfcZF
+w8e5lbq7SHwBfj+C4X7+OdToe2+mhL7yCBQRypfVND/hBoKx9iANK1VMkFUrXSne9arcE2P1pgUs
+6nzWWnrc0+q29s5+8fqD533is6ZviIzqscqZBnJHesW95ZU1uFZA/rwUZh9nraB+3CJynxuTdgg2
+2hxoq0amzs/H7umzSrDVq+wgWCNjvbXxHxWdogIMpq/wBo44U7ebi73+JHg/ut/z8piHE+9Z1nJC
+OPtKXjaelPLaQbAXCOZ6FfeH6Ufc4mGhXf7B3OosfIETqMkX58IfmLRsb34tdn4wS0BxusNFXMDv
+yw9pmMGeF/ZSiXrxoAHcPuZzDpzSZQlffzbwC5Sc/6qshfxsPsS54MAbjq77UuoBZulWzas/ohGt
+RqGBYcT67yD4WX4Ub3zq+g+HXyAcYXv09bFkFz4dHHdBdfvdXhgZsO2QN2kueTsw4YRVF8Ckn2rk
+Ar+Gc7tXjh850HMOeV+AXp5zkh3aGj3qfU2jK0G4bZIuWk8TaBWvBRAbBQv6Y6cTLfNONO57a+Ia
+QWlzs4HtcLIFsc7oyOVnTQefVh5prDlA4ZhHE9jquVsZlzItX1fODdh+J0/iG/jlI9Z61Mqxe8EP
+mtRLcrp7xJwbveV2Tc4zPJhDKy9Br7MRAW4LGFcZvO9S8vky3aKtvUwfuMXOETGm5i7Kfq2x/Azr
+Gx8QeB3vs172MXnSsYCsxKUvExR3usn5b4DdYRmW1d9npNeFfdViIYtYrvw/99vAFr7yGT6AW7Be
+rzZ8DYIJJhUgjnTmbxXtIQEwppCs5CWv1euS8C+OYKWQm7PLsXVFebz3m3O9WYLzuiJ7lvDcQXBa
+ZiUeZcGyOWfK+ktZTPmccYOA4LAEB4pLQDwtv3SZaAB4lGm/F+lCQYA8Aa3GcywOeady5K4C0wZi
+T+ng/QUMl/OvCRCO/pEfOcYm96M6p9P+LiC4LMJw+hh3i/AEwe7vGov3z/tG/ALhCD8Bwqp+u2Dv
+EqoDZpVQM/1RbMucK+3UHoHnybR0nFSwHfzvPimMOz+qt73H1QwtUSet+U8rRwn2oneBn/U79vJS
+DpMmzdg53G8dgjPSt/JkAgBuZWYtv/pbX/AqcZDlrJdvtDjn3WirDv1qmKH7STYQ7C59OJXp/U/e
+DPyWRzvQxjkTdveZUte+A2Qp472MCte62hegN/OoUEe5uIw7Aa+CXzk3x2mn1arq62UDyrVEtxuC
+BSDFEheVW0u/cPEDjkkSAHi5ReA10OxLa/AbQDa/TvkE6LX1NUS3BSRfjz1O3xf5JSd7QlEJkM3f
+iXYvy9F8Qsm9KMtwumoEk2KTOGFYRKNCzkdeqW8ZqPMmKBsA7mXaS11Qq7AUY75VOS3CpFPooBF7
+R0b21q5ZTATd/cW5g+7RstvF+kr2kd6sk6jjBowPazPTZI71/MLQ1ofGTay+tLxbrlOCY9ZHlwi1
+2ufwrAWA3GEkJtACu0vp2eMBgp9BjzoTBPP31Kccx289IXzRPqfcfm/Gky8N+HaabfmXshBeCD83
+8KvxD8A4jTH6w+qjJUAW3uhQOs/vs2KB3XWzv76wSjAcfeGNLkEw/cCcLwurU+CfC79AOMK3gfBm
+1fCNXoIKB5pe53JNyu1LlpbZaEaoN63Di5YK+jtzTdG26PkJBYBNNJf6CcG1i+8dSFNwNu5NgAzs
+ZTCGRfLgGNdgvXLOsW/W0ps12GhlYFk+lqqyCYpDwOy+wrQAL1p+8SzKqUX4CnBT8UTr1HoDzUO2
+pfGntVNpYw7ZgVajDNc2SC5G2Q0gpw8v58QOevu1DnFHPvanTm5WYY1zPlzjS2Hyq4WzPzZoE/Yc
+IELj87QGI3RAB71iGVbLMQ0lfDnosfBVXIr3AQBbb7abh4IOAGABhI2b/pvhsfX4Nh/pRnsJitn2
+tPQIvX7nPP2a3RML7fU1uk8uPJUcca0EQmtgeAMIdIswJLYxepK6Dm9zGgQa4PwhSO0Ctj1hUmDY
+Kus0P9BamDSxCM8PK+wfYRD5daxYrsnoydLrLnlZUI5eLGr120gHDxw4WWl88oovJx7Br6xl8RN2
+BXNqGX4dZvG05H3FChwzUgGeu4DgJy3Ea64FQKZFFCcATJDIX/gDYy9jOZcg7wTsfM4t7U5jMMcs
+5P0GhjNuJcc3MDzL1a+BfSx+FO2wvMbiCo6DrhAAXSJC9qBcohZ7K50vRtA6TCFyEvl6/DeGXyCc
+4fucTeE90NeNnvUfrMMH+X2mHQb/VC4z5K3Pa+CE+xgIS87ij2s14QstvknXcp4Ckflfpw/HuLje
+aM72rLSlsC3ldOultTW35HG3/lYvdwBscj0Hyu1B6sj8A63vI6yWSLZtSgAFA44EyBRyKMAw9dTk
+g4+8lv/pnI2HGiuptVuHu62a5/zI9YF5aRly1GN0OY+uEIzjDojLJaJblzHmxgTDt5BGRSk9rcEK
+dK0B5LUn6no828v1r8LW49gXryjhAL7h85hpM1iAhbXn6fIPXk8dBNw2YNvznlOej/P8Datw+CSL
+ktdb2zaPGggm1pH5bScQ7GkJ3AfgFL1r0TWNKMvrpALB+oRLrZ3IvrWrzZvSrNQHjTIt6jwBYOFa
+nTGNLrd1qULS87gZcVL+Cj35EQMjvGh93TpD9KQtElCMtR7V5UHBL48NBDfLcdSbIDUsiQR2oKsE
+AfACvC3PfQfBCQDpaN8BbgOLeu0GfrUceV2876C4jmubwZ0OH08vUsZLfAJjsQTXefOcaONjqJcD
+te3PhkPbmFtJd8rI9BgJK7uZWohRwpZzMvtMF4kw/3xSOufl+78Kv0A4wvd5u6la+UmZFF6HvOM5
+PwDFElFfvg1cbGC4lHqj2bxKn/ap/K3SEwpMmss/S+XXxTfAdSGW4O+kVa6j0sVR6xYc0qei2nrL
+e9rJrZ5WIJsAOORff1wk1l9Dxb9B46Pu5H2gBB1BHV3twzab5S6q+FbC7CprcryrFVWuen8GvXvY
+11id85Xl9wiOFeRmPSjFyrKhSDXfA1jRQFsfManfm01QvmNrW3G0eKSjoFzqE7fHXekJfN/Yy9SA
++KJcAuIWXvjfwxo1QDHskP+KEscAuuP4RNxH3uO0ClsownijnBZhdDD8Yr0XM2WogUCHILgHWtgU
+BJfsaivlMAVLWOp2dbqCuGazvlwE55mbdJUrXdCIHPoEjqWwowwGOYMZXQ3vuw70vN5UBes14WrL
+LiAB8c1K3MzO5xvYCmN9tLZwYIUNwwLcQG6s2XUk/ZTvBebemNfiKpGg1wLk0j+Y5Y4g2ArRKahV
+cDstxAMwr/VUc0m3HuSNibVFDxkD5V+AYIJl1dUbsI2E6J+MH6zCBL9545HWcFpl46kT6mM+pqDD
+S+MgAC8xB9dnWoR9lbfBi3wc5gAtw/tq+8+HXyAc4bu7RkCFxlSdIjR2kDzLqIDZg0yxoolMVjBc
+tFIGdZIqC7b5AFTO2EUe/7NXHY6wF2zGKQ1QRiug7ee3+FZ2pEnzXv/US1MvpAuAdFq5UfFJL4B8
+cocoy1UBWgqmtEqzfNBy7+AbDbqPMK29YgXj1VISVq9SYOVc7P3iuGr5Gb/n13zymTnLHYAy2z7T
+NQfO8W2UHLi7VQDlFhE8dZlfpOt8goMb+i/eF8Cu931qfV2WyyombMl3TmIMi8E4W3lv8fj0MpX8
+ml9D+UIVMRL4EgwoGEYC2aIZgIeW4ogrEHYTUEzgHFa3xxBKb33K4H19gV1ahLE2e3sSaC1GvKhv
+HSTPrGZHLjDUeNfss+DtmB/zxt7a4XNoemBfV9uEp8yZiyGjOzhuckrXb1qZGS8LcXeP0LYMqzoz
+XdMuWZ5lKh61KfB1CGA+SYiSp6e87Pux0zhYeHmMa2/5Skc7z2D5cpqCXwT4JcBTlwnmTRDMl55P
+ll9Nr+KaFvBrUia7vdq/5rQnrYFCHb8gpBEpF0fw3YSPSk+2ExhDALPI7QD7/siOGGAc64YBD/zF
++tgPvfmNhq0FcHUOGKp/FuOTINggVmFg7VIjAPh96sb/eyv13xp+gXCEKU4+lWySxidNpZAKkiad
+JJ+g5Xat27SQCdgK7C4RBHB1ylTlnKlCPlz0AEcgMCJ9nBZoVRud9v5sDV7prmoaF0U+NG6OtN5U
+bzLmUDc7u/VNhEbSCTaEri/TTWGzuUKY0ND9gYumgJjtaVfoI2wBkqMfoiWyb2xt6lyceYJozyde
+eaEThERDA71uLX+C3jmxvMX67JKZlTNpxpUn/cZqNXbxfdyWepXtn1euXSOqvHDf97WGzvkWjj0/
+Tm6J3wBxXH+zCDvAm10qyAS3mk5w0BW4hdJcPsOWO08QENMK/HCem4UhjXlRr8d5sPUpWrUIC6Bb
+L8jVMVlh0qVcw9HelBlsr8x4eUFurdvDIGwjgjZz1sEO5b1jwGzfkum6BZ6cUpU423oqs6ddAKmL
+FaAAqrQ5eSrXqFYfLqOW5UhdrL8uwlavfeCExE5a6nbeujGFAfkpZcfXFmAIskxEhQS2CX4V7Ao4
+TauwGWw99thoKeOzLnS3CNQNYQPBCYCR64Sudfn4HzXGBIwucXKsfRb7xPsbGNb4LHMq/6wtaBIM
+k6+xEw2eN8EwHnFt4DgE0FW5TSyzRJJTRYDW4BiyOHKeAcsabPD3kTp3ndCP/77wC4QzfJexlCR+
+jndVfKFL/pCSJhHzRllkq3NGjt6SjfMEOom8v/ZY5Pichqe4Ts0ZVwTQrcHKoUOeD3rSkCxXOk7l
+sB/lNlloaAC2L+sCsbUMy5JbfbWs00NYrvOEhp2W23UNsMz3ogLCZA/mA/gJgqUrI7/Psq4462Ib
+v+TKaVk4BK1jJ8651oXaDexWhV9bjbXO3YXivPpym0/UThKsU90l2m1SKoIzF0xK5zGEvxvOQPYr
+mvoE8wggLaRU6IynLCgwXCCyFHbPK5C7ALGAYTnmRwkCACxjWwAJsarBlyWIgBjOHSJqlL2lhV/G
+NcZbnVCvfMFK0t8GwSr3yNMatgJYTOt5Kk98FBkLxrXQrEvJ7bzoh3vKvQK6EYfE5xNJFYJ6se16
+sioUWKuVflxj36f4OPFR4zmasYVYo7lALJdUfs6cN9cylz3ocGxAOW8EBayqP2/uQZwAmDT5pVU4
+2tgswEVLf3QBwLzBTABM3WAFClN65zzxVNUlY3JA2jC2dLTFNZ4qTeSzaRmcwbJ7guF8F+GRhYIC
+w0350io8dmmxqDMxNRelR4sT0/C4vsKXW8fwReGGTk4L6OME+0fhFwhH+GcWYRUQlU6b4fZIyQ/l
+TmFHEzrdTOaJwjp9NDIfW5SaicO21dBMn4OWUoVfcd7lKrxREew97gfONDBccrjRGJd045gL55wc
+Uo7ufereTR34Khyq9S3WYBNaAizLNnz0Ef4AltlCXseoNZIK4XwHzaex2+MiSLcycS27nbtzqlmH
+reip7W7lxO3gBHb1Wh5C+Gwp3i3D+8r8AiC385XXnAOO+uTofd1I73upKTbi6BPwusRfL7cIKu18
+2agsM/2lGVVqCoT3I90dCHg1TouYS/ohyJB4Wp5jwjwe/sDB8dcLGD8omaszIpZqW5Ml7HR1Au3m
+dY5FHkT76qjY20kyNvnU4yaes2C0/yRYJP5VPuIJhTwaS6tsSkwHmvve5iJBOTku0gw2PTst25lP
+ADxBsTb4A1OGnP1QKPi85IICWic/lJYCt8rmF+cStMb6gJV/cM5LZDwB8DPyXc7JOqV+VB197YRM
+aDeJQLoSBTfhUw543iBvfPWdNtxyB9C9gF4r+ZVxLUMFyg9YGJACKF/gjQ9dTCsU16KzcaWJ2MkV
+92RdW7bZtdgSMnQs4suXfzr8AuEI3wbCw/pbD8pXLU2wbPlo57aJLkuiHnvjYBWuvIZgQGUi9Wxg
+eATOvy9C9xNmKzr0QMYtgKyloKafE+V0B8B+oNW6a76dAyAnN1zO8QMdO/1jf1GWYMq7FRfgixJs
+tMY1kBt86zQrmgkt8l/ji1u6dRrbU1KEo5pzJh+xqYQ5W4tLDNcFrvp5K+Oo2VnC78zDEQ5TuOo7
+WXaB0+yaluIdPCstrqJzLejq/vApzpkukKv3Q5JzXhTNWu+y8gF6U4lIGX9cwG/P071uEwDn4UM8
+22w1tw8A+LEHznQ8Nnahb4A4Xqx5sNbq+lDeOsKXz3CCX89YjRXbwvHKpqoW7f2pNero/WxdbPTl
+nmhoj11Wo+RS1tpXQyPAdI5XHNv01qPmN5rnkXIODpSPbpUptwyp7AuhVrKP9e2At+1O4Z3mrVO3
+i2yRQ0oIBmxfiyPfGZ/WYpbNxWJilbTYRq0AboJfw0YzsRJv24cJkF1tvYNfAl+AN4yx3q3rCUg8
+d4cYbLHB55r1ykW1AK9rAWg3wJ5tVhmuBo+IP09vRz5tekqCPg5w/3JHuDvIj7vqdFQfZUe6d6qK
+a/fed5z4Z8IvEI7wE4uwWnxVuJiUUdVrkp75fZlo2JFDB8sgSl6LOukD/LboxUp8m3eNPi293UKq
+PTsBAgpul9qmRQ6NJnEtJ4qkWYNHmVJkvbfFiTo20DsY4KOfSxYTKO+guATQbt3dgfGJViCZYLh6
+soNbZHukhwcfYc84SlhufJH4tczp5uo06vuxLMzUbr2GsvLWbNP6PwHjaRFu8+H2G2VugNja39Hn
+88SBSdtqNMpPrjE1dE0HxlQwVmW5/3xzpfAOEHjBrHoHhycaBAg/D4HuC4+PbrgTCK8v1S0AHL6F
+8enatQfom6A9LcJyM6vWYAVoa8p6rhda3d1qvvvWZpmJqjwHEAbipZ2NFsNnMd+S4HdBpkHurjkU
+wCj7ozxao+qSywAAIABJREFURAvo0sqbYFRvIFSmakUni5rv0e7rfADFrrRD/w91Hxnlp2JLDpS1
+FwXmHGWxJAiOeZ5WzSyDBmB3UBvA9hvgOJHaBQDni3kzHyWDy194tVWnEFDSc06vzq/Owzb9lAek
+U+fIBD9ZjlMa5TTn/IrqHEuuqFJ3wLjHr/8FLhNtW7W7CFlfFqxJsPdXT78I1P9w+AXCDJx8O4ao
+kJOvZomCXE8agEGvTypPReij+ib2AzfUMjIpXfP+k+eoh3DIjrYr6QK7anVM6CslRfYu5VV8EFsP
+GtWFKgI/OTTirP9kJYaU7S2uSHHNzmUyr+7wOycIevVxV8hvAcUUOiqokmYnYFy06SPMOKWYSyu0
+9X3koyy/6LNN5tlv62nrPPH8O24qWM47lz6HKfopUYdqiEl1cnuo9mr+sCTrE4T58wt95M2t1LT1
+pzVyExk6Um0utccT6IoIa34vKzA2y7G9HtZMUYZy47OPX8Rtp/cxLRD8vG/F7cXjZQH2+ECHP8+6
+cTGD4wlgDNnVYl0gLcKSJq/5ZTnKBbaTO36IBCt+KutPGrnlf4qbACpH+Zqi4s7CAlCjH82H9sBU
+n0ye9JbnLU/dIs7XYtvkmActO2SxWnlZJ/SawwqMvfwWLuRbXplCLHi/1iz5ntbedI9YZW+uEqsq
+k5/v4NZKPivtlM4X3FIWyTFBpFh+gQ0Er+apNu/HKQk/B2FitCOnZVwzSyZAR+uDJ01khlwhJa1s
+nVx5lNUOC8twfSmuu3eWDJttBrpxZgR+we+JcfJa7ye5Opr4bwm/QHiGmybJtCf9NBgDWhzLKXgu
+r1TNq0WkC2jGt7yvZscP8k3ayTber19206S5HBURMHLgs+sikvx6jKrKYhbrIif/0loaR91H9aGw
+eupFoOepPDP09K3cUy8N6X6sZtImEar1OKs4pyB6uUiI7zF2AFwzZYpTX/tkjrINTtjOtwmK9uFR
+gZsNG9c+if17GVruUxFl/aqc0IIqAaWenJC2oPPbR+t4SZSxlXPIPsZlzqdl/lIudamdulatz8fA
+BAVdv9hYA4B/8POuSKdbK2O6zSKtvViPKd2Rm+47XuQ2c6T5G/FVL2/0CIBpES5A7NKPIU+bldWT
+BTkl/BZfPdJ3fRKbSxwA8L5xs8Gjw993Ab489vh2Dssqk9s4eaMr+X4OcLUK86j5X5bzDm4/gdzZ
+3MA6VxCcffIj/RiUD2Ih1I1m1rUNDfCqNGO5XKAHiOQjDS8RjM/nsFq9F8rm0R8fF7uuyxo6+RN+
+YMuhtu1IBpBfC9gKb4IfTRZ8sBZv9VdNKnpgOnCO7H075eHew9Z/GGnZ0zx9ulnb25NHOXZJ/2/D
+LxD+B+E0v9tEaoJ9ljsN6QTDEzoUEBpTZ0ufF59M9QOI2BECHyVaq79fd62KCdz3SyxJZxkfl05R
+qlKSWSJmd71yWAyiyAVsJDgdIFj3Tj3mEewm6EXSTmCYbwyb1YgqMK62UYgJJ61z1M3w4gMA9nPe
+OowxlTHyVk55qPlobVP+zvx23W0W78dUdEApDeYZH1miBwNd1Pbqtc1bP8+t0qaf1tU+378Gw1UH
+x32vO+O63ExbfBhPKp5Ufr1zp/Ux+z/Tvv0N/h5B7fryXPpCulW+29oKlDd0SbPwD14Klb7WD+PR
+CZWP7p4vj7PPT/TpAQT0et7EpAFQ+OqHPIKYrP91uL9x9A5y3wCHCoy3cpU/rbkn4dTzuiDzcY5L
+/mYZHsdmoZ5laBE+geDNMnygg3Q2rcvnLbRsP+a1pMcgYVh4h0zyRGRrQEt+8bxIC/jNTxtLPdqX
+FRlP0hrgXlbP0azeDXfwk9+JTMVK7WZXD5tPx6+CszsoHZI0HzSy5iDv25NN731s7fDqeMsTOvmR
+4FaBry9a28XjiQpeoTdQ/B1O/HvDLxD+YTgNUSkyv5Y72atqyNcUu6VtlL/Rvtv+ue3JPv3PIJj9
+2Ky/oz1Zh7NcCfeKD8E6F5U0zVvcxzm959luAhF55FVAVdPYgC1B8ATIdgK+Axg/hzYkHDJtLwWW
+zJ4DGC5esFwA4EQR53FU95gZ5ixVa0ErYwfaKLePfM9LxaVjNfkACCLc01nHkND8WlxpASrLvd/7
+ejzf5NXPenPafL/HIedv8XjKkPSmVazHnX2soRbs0JfL6Jta6T4sqxZfCokvqjzLP1iBrj/w5405
+WfGct1H2DeWXm144PTtWo7kBBtDdJnI6R+sVBHNECWxjC9gOiDPtR4DcNo44WITXJ63l2MDyAQwz
+rsxkwnsSl6OfzpG0B2EeP+bxcAK9M30CyxjlMq3z5TKRJOjTimNezOOSgWSAyctyowYXmUjZwKcL
+03Lc6lRt5VKvtdLt8b3UL9QuV9MCzI4QPKvc/lkYrGlH7dYSp1aXsSrT3CUyz/Zy7RrRdld6t3sj
+5tXWYvewCK+133fkWKtQP+dsL/MRoBj1efn/UvgFwhHOkOEb56i0c6lHVm97tMBzNQ6qe2vprmT7
+70ZrGV82/nO2KusJgI+wJwQnmrWSYWhv0oBaXBcL8KpSrR+zKhvRaKFpPxQEIwEtUPGPLhAnF4l5
+VHCtHOB1WjvLHtFdJQJAJHg6zQfREiKii5e8HhWDCnhrZwnSE3rn50ZraRW6x1kRzR20cdQ3ofX0
+zUIsL9TNUPNi8CSiponRGh0zg27rW4D4O6C3r4/DzWLogJoT0qKmWIPqpaa6fu0c8JHwWx7T3jkF
+s/IDRihzdYUIZapvxk8Xidff2F1ibZhWIBhlDeYsUbzhNXPci/f0J+b7O1wLZfUNdwgv/m4gOM8T
+Lgi4LQA8XCEuILiVE4uwoottiEaZz9Zjjzpcyg/5eMmvD2GQ9g3Qq9dj23w0XfKroXrwjT6igzEi
+k0JcidjaLcT0U2A1BkBpeS1r10mbp+mlLa+eKBHRP6tz3LU0W2x7nA1v+1B3Kf3T4zX07nUdojIz
+hN2e1/2Lz0EGNgXqLm23nS8OYDg/bR1Cb4Jik+0gHQGQn+P34//j4RcI/8NwmkuddlbXtUT4U/W8
++wzznK+A8XFufzXh0+rGNnWoUNe9+wlDypT6lya0aru2OFqokt4FtJw2Tpgt1sPJEowt3UCwPf8M
+BPPI64Ls7cComlszgQLKgQaGu/BmadUWhzwb5ZAwqvPK6qxJq7PGJWD7ObNfSssmflEuD9aOfih+
+FuSb7QJVuvfraAm2SVvp8hWu0dtfgvkOONb0vkZaW0WxborSd4U514O3Gv1DXic4PD420N0jbHOF
+WNZiSNzd1oeo1DLsYfGFbIMM3Ra5oAtQOCLt/7HW1V97AV4fFuDKN5MyQlN/8NXZt/sFn0DuN8Ew
+F2oCQhFx89h4fhB+LnkuhJOVWONVr++y8wqC0UGu47NFWNuDPVxv0LbCcm2udfcN3FZ1sRYc6O4L
+8UdvHD3WV6tj5VurNOyfTTRY8m+TIEPkRkl2YNXmvZ4WPurhPWzrnlV46wJcAPeU5970C5o4JlDO
+uj367fdrn2QPSHfETjEe1uAHBrECh67lS75mBjz8fDPSN9gfwH63T/u/EU4K7DRsV5pIhjVFTtbT
+oajld8471VCFD0vz0lhd3NU2xSjVDgJkT8RC5cSqEtI5dBX2INYOLaBJPT1k9IduFMjNRQgCYHQ3
+CK/PxG6g93Yk6H2eMzgGunuEMth6S6tL3RJcn16G/FHoY8cxu8cv3BrZfqDNchug/hj/Hi0Vlkw0
+3Rczr33ozuY7PPu9uQGNbBmnGyBmud6LkWcf8vQ8uYCFdtvBMddUqaOTgsrSA4TM9dGAUi+a6aXs
+Foi1sOrQLcKaiwSWQpP4a7ZcGaz7B3NXiNeVN3F09h0FeH0AXyAtT9xGmTKG+RMY67k1ruM26eQK
+4Z12Bb4Exknr/GwMdwjvb0c/lBFg5T1fd6k4W42VfnGLYPmNfvARTjpamO4SkwV6gh8KfXJ/WHPL
+dUFUlQY0f2Gp3FIW1ArhPvwEvszNdeWD1r5eONcT55HKWA21VrUV/8RX+NN6B0oW1tNEyRtlANto
+q13zlb/Yi8ejze7hKy1Sn09B5CZwTbqnPvTzhPwKC3GCYhAQP8D7rm0ZExhbfNYZ/5XwC4QjfIIM
+9zDvNLtqT9rUPtv11OK6x2c7b79ZzjVxBEZCG8BqKZiD9Uzb5afrC0Bu1xNv2CnBWGIKVi2q6w49
+vvO/4gU8xDoci3N9ShadPt0kbH1A4NtuExcwVTyaIw8oEGY6HyPbeV48rS7vcVc6oEJdH6cx3YO1
+JvqtzFfxj7tKRNzH9WjF2FDTYbYYVU7ngUv8FMrVJLgiysIyf7+JMS3Tzvheeq9LujzWXu+P61Ph
+VFgdQVwA7gQjUmguwYU/Ar5avCR3jGNZop5D3AyvG56nW34NZQ1uQ+sY/Fwt48txDtnWNMquHSC6
+AjcfwDf6l7tFeM/DcIVoYDh9gE9gOOjTIqygcIq3PPpIH8alWYBn2qVuGddbnsco39wgPoLg0SVp
+5Gne9M7MtbrPtaxTBnG6PzTampzRRj2Pz7pqVfD8pKV6q3VV4LeumSuKMknrA+RsZc5Jn/aUfeP4
+01C7RlQbFOC2lpjShnxlG2Lqj3uTrWs3qzCAAL8KhldJs2ddNwHxkiVr7Az2PNkG4IU7P+bxZ8Mv
+EP6HwQ5myQR6R5oP2g+u9c3fl5V8VcS4SE4vy7EPu5vECuVCIaTW/saaS3zJ4q5V8uHcydR1XDPV
+wtM/WoHnrhFztwgLq+/RDYIW4QGGTa5+btep6VXa22+KnYovK5vjOeR9+fnsCWwnOL6UmX25xvNy
+di+bym3Ub3u6WaqHwOfFQpVnfGnD3uayGCEEM2q8or0W5dY8Ye13gPvjtNW1K1x2ACEQivYkSMm6
+h6UulWOhGO9/tvLFNVtuDrClvMLlwS6AVy3GjOcnmeMa+v7LiwsgFuWqe/lr3FDWXyrNDnxXLzZf
+YhxcJYCyCAvIVUvvfInuZhGGbJ/WwN4F9CpS81ueT+A4y35IQ2RnyE0kuPVGa+CXx+zHyT1itAkS
+dN7tXd3LZ74gOk53dXWA0NJCbFUbz2NdcdOltGnV3cGv9TZanHNQNafO2TXv+4Fz/KujNFUuVX6/
+2zQDPgBkG/XL7bWT3d3UkDW4liEtPsWMpRcdBMSrcYsmO9DQAoz1AR5/n3CTeGsPxD8YfoHwPwgT
+NnQYIzQ/0LLc9L39EDfbXwA5/E4t2shb1j7ViTvqdwO/kn96bg0fn14cGmP6P0wBqE2MA5/KdGHA
+Rq+fBbNobZsW32b9BQ4vve1+wgscP+ft0wQwH8fmyJvevd0qrCzRMaJEX2lusfbgUu46F/Yxb2W/
+Sm91i8S1A/2rMiqwGwiWKkz4IsG31A7sFQQrUGrb2+W8L2Csf6UZjXJPn8+biVQmjQmybuRDA2U0
+874GhoYkoEmuKDKZyw9hV7Py8eWLLr6+4hEuErJzhLxIR79ixGeY+S0QoEDvTCtP9vQOeMkd7hjB
+UVZA7JCt1uDHa5RFeLfyzpfo7lZg77tHCOv34xiTrYxv56gF9uNYzroPluIJdBlPkAxPebpZiqMN
+rHtbd8MwUf3zns64zL0N4Xke1mDWZG98i/NcAXOKxALJCejiBIoU5pq20bhKvY/T7ESE9l7cLSIH
++3DsFe/XamGfKsgX4w5tyqOd6blF3LhIoznW2PterkUD8MI9Xngj+F28ze3aYtcIxwN73gWAAeB5
+yzXixR8Pv0A4wvfN8b2cTbofaLPcFIRfrQBiPJy3NTvi2y/q62FvQz1Cvl1Tods41+u8rP60Ugd5
+DoErqxzb2tMOmXkjNouwefMPVheIF8Mi/DwHF4iD9VfBr5R/DrwSnLf3UY6LTYur6hrxtJIKdCve
+/NYa8BbR+1Owe0wP2k/ipXUOZajcKmsVP8xsIZU6k2uY9HmonQTECqgYN84YxNw4gdjvAOJV2c6B
+XFQba7U31Y0oIHP7qKRVqfOPt6wDrddp8NgRotwjLACuBcDlC03+UPmGfzCtwW547cHzvB8BMOmT
+Rn06aSZ09/US3vkc3+pY6WkRLkCr7hHpMiHx5jcsVuK0Jrdx6HxtoOqQdzrXT7RNRn6PtgNbYLcG
+Y7MOd/cISWO/jsy83t/qzZi7TNjmJ5wj2JbtAlf5zgAzEziH7M+aLZaLi6ix7HtKyXwqQx7EOrbe
+5i1+5P0pLcz4oJxbV2fXx3G/ZN0gf5xWPNpeD9eUxU8B7y49y384FRZ40+pI1whofF3Y4sbZsHyE
+7V1vENgTT57edZNt/mws/RPhFwj/wzBU+Nc0v5Wb4HIAKevld0Day342PorUkYPPvGxLtMfmtS7g
+2AUcH99sKh5crcE+030x70aYLiaqVQV+FyghIO5uEMuP8GAJVlCcIPg57CDxhKuEukaI0hXB0sDP
+7Bf7ZvVImdaMV3r16Dh5xUOsxIX2sdR9JBtEm4hM09aIY+LuMO+fxU/XZ7xf1zO++tenGF/vMaFU
+NS5HuiZ4zG0elyIM7qSi1L96VP7GUV4a7SyzjZXXINrHx82dhsrqE6m9xHSzGOZSk3QIkOkGgbAS
+g7tFZBr5VjgfXy+A7HBuDooAu5v8WJecoLeUrjXZp3sJ86U6y/PLgtVdKwr8Qq9BATKtwOomcQDD
+vZycn2BSxu/A609509Whlz2McxvT/RylfwV6d8sxWrkGoNvlK9WfLMw2uiZbbHN7iIFsDgdtPUi6
+XYYFomgswLQPUwZbk4TR7nCFoIzYeDg6uY3nnfdg03v3/vFx8rEd7UL/cOz3Ib7xRj+ljA9l14Kr
+TQ+bSwQoT8gNtQyvsTM8yyLsz1pXXwrJf3/4BcI/DX4eJpuFvkErQb2v7gSb3hXJPNew533VgVj6
+hxNziRzrvVmA69xNQkmdM17JWaynYwVOTTOqYgspFgmAgQK/cGuW4fayHC28Cnqnu8SwCB/9haGP
+Z2XspoaQmI84f9x/VcdliYkAvQIKV0zHYMWPglSRWkY76ATPbhNgjvuYkXmhQ7ljXsUbe0yez5jk
+y6lz+JWj563U+lWpg4Fwg0gAXP3maxsmDVFgxVhu/TVZipNVeCTYv6ZVDp0bmtHZ15w8QyG3tJ/p
+kueGZQ1ON4juImGO2i0i7igqvTq/XpR78Nq7yabd7c+6Rdi0vOeaYbd3n+GD9dfLVQJCT5kQdRaI
+HVbgAYbnjhITAPdPLBePj2Bpiq/LuPV4AZBb/tE6GSd41L9ZhDWNr0FxK9ua1uXYqU2uf7WtNkGz
+JTIrwGuomm21owHnOmetY9fSC4DBu1sULNdAuUuERnTk3N70jFjJhQGN30rSMfgsjb4TVE4KR601
+rzf3iyPjVCFq6R3PF3eI0H4ux2cdn0c0MZqLBDdVnOB3uUiECei/sHPELxCO8E8naapsWQF7XWea
+TrOuBC6gGCrcPwDhnyHjQ9m5Z3HtAnG2Dkv7vNMJPlu3dVmKwJjW3lxjl3SXpAnvQStDukG4L6AT
+8b57xOFlOfEF3i3DT6dPcOzR9wmAVaZDBXj0NxQA+5b7sAYgK67cQW+KSNk1wvXiO1JDgjVJd/Ap
+kO8Gco9xSadSO9DHNU9V+TFdjG3ToC54qM9SKXI/4AUAS2AD61PaL6zAcWuS/HU03m35Uo7x097I
+2ouc1yYJmTsb9JhKePMnioOU2/Pi1iG2WVjgt3aKSLCbL88FYHYD7MH7vOvjNLQOH152eTfZwRvW
+WieL3q3BwNxSzbc9hjfrbyjz08t3J4tw+gAfLb7DTeJiEd5A0aC55h3RyTbA2xCf6tWypYJklkyL
+cKAezpXvguLe/DkLvV8fVUbb3tapo4PaDAS8kHVgGJYE9B0lOM7xl+RYnI6x8wrXvtCrEhZ0PbRx
+/vjU5ZTXu92AcT9a73LSpzU+Wn+ZDt89av2bhvE1dwocq+Y54RmiV62Jn40LuftSNlLaKfglKPZf
+H+H/a+G+c8SnMgSIVZ4TTJXoCeCe6Kdy18ZIG45A4YCT1lZFBzeI6Mf+IhgFZwcMzaK7s6RHpklh
+SgEVXrMygt1YbsslYgBgIAHvAx/W3w50P4JefVkuLcehzEO+myH3a+yAxsDXUbZfKAKKm9MLccta
+rOOo7g8l6lzOqQHp4LftykAeKos5Q7OKA2r1XrbGa9RdF0UHxz1r0rK6QZtK2edJRhUjCsCqnrQK
+iUV49d+2/mzg1jovGgtlju73H54sO/QYQ/OUFTc70S1zWX6A22JKj/vhvDXDwvXB4u3u/ELU7haR
+luHQ2rQOvwY8NVFSwbebw2IzzEu2AJQ3iPVTNy4PwTogVt8Oii04k/lcgzIrNovwCfi6d7/gm0uE
+vzLpBiA6ACV8ord4l5FHq+83zvU4OUHpd0AxJgiGnNvnrE6hRpNrH8/RpirQbWrJqi2J2AwTONOa
+y7YYlu+xLJWgDWkYAkDL6qsF2v51k5SMEDZL5734VmqsP5tq3WO3O+UYnUFGrxX9aoq1mxN0Vq62
+OQ4aozSL7z+1JMddcJwlMndZFtZNtAPwB5ZrJ95DIAj+dY34vxNOw1Rgtg6braqvYTmnA+DK//xy
+3PwdWzcr1cZ8KJ+K5nj9vqSVbl3DZ/9KuVFJDY0xF34urEh6nTIXvIqZbJu5CFiCYaRf8AK36+zr
+55UjbVZuEw0c011CfIWzn2rxpYVam3rqLhAgmC/MeYEK5AOl7Cu3uqvR6mPaPeIkx1B0BbeqYGgl
+7X8EwJme3mJfpgc43thxyxtV3jBBp0oLrQ9J8s0kHWjNqlBcWuJS7bae6Sdsg35tWQcHzfpDkMJx
+zik1AIoq4BHXz/M2DSjllkJ0Ab5ISzB3ikBahsOPTyzD6RZhy7LzmsgMebzNYbfgpxllBpLnCYwJ
+XPz8kQ3xSIwl5fmpZVxptHCFwlVAewS5vrlKpGU4rcQTvCifv6BzLLaymi+k44Q/yE9tEsFvXOsG
+ds+W4aJXtd1FYAdXMjdbCfaFyvELoJsLRFZElqVcX+npApEWR1faknm5W0SKIAoFj2rlhT25Scj2
+b+C4BqjvzCHnbXLLClhid5+4HRufB2+3o39dzqSsvgC3A+RqeU+jTsrVGBX6AzhiZwqCY1qAV3zV
+Rq224utJ1J8Pv0D4h0EnRafd0zea5lV+WTL3/AlI++9juw2XF+n2qb2u44DtILjaKPR8fFbgiF+O
+6sivBEW7vOZ5X1quwrMJnX4cjQfibWLjEev4YH35ioCY20a1l+WaZVhfhnvKUvycfYUfh1xXlG9I
+5HZzZOwffwu4OuSTtNb3ClYwvAHjMawueYjxbDziLKMuOIBfj3jmtKkyb/X6PLKbNfiaPuhzeSS6
+5TF+AMhNZGu3ZbHQt5UuETXJYz9cm7d8GreRxhUgazc8chXwFkCf6iribQ0c1oN7jbcLLZlxUOyq
+MSOpX5QrF4lglNEtgg2Ot/7j61HLGux4nGlevrtE1M4crsXqSc1aJJtF2LCswlS3jtjDH+IC4bvv
+sK65lFUHYNtejPsmGJ43FH0IfaQvdAB+oG2q4rt5x7q9xTkHNkvxpAcqyrkpMqV3a/oFy2y+zd0Y
+1POLcR3oxuRs9ZBG1MYVxXkArDmltFU1IbHSKAn7+jgC4kmX8T+BZMehK+1oF/pZp8tofjG1zuCX
+zYWsK777Q14VQHZZLxDA7NnHvoMEK1cn30IytQpRsbixTksxFd8fDr9AOMLP7kLmkNYCtZ3UQTAX
+acweXQY3wItj/OQ3PIBIbwYORQ7tt5QxvR3V3g6K97aTbnR6nQyZtNbIEjKN5LLetk5FS2n9BcIi
+KyDYRdmmMo4zZW/gDn5P1t91nC4UuWuEQcA3waAXSNKXQgY72D++EKeuEUcw7GrxpeAj6EXLS1FP
+lGsyPwYAdslnhG1US2qNjw1Sm/GQmkqBfTuwLlWmt+DbNfdkTeytyQmI+VMxHsW8VaGXHt1Sq49e
+r0CCtewOOBROeDtPQUYp3lwzUzHrovFRVs8Fck9gkz2ByZe1x3D48iXwffB4pJ917mvLX3gNc8AT
+VgMC2wDBHlwyhOtS8M3Jfq7hAYK9A16myW/yuadDZiYPJqB1Abwd+FoDxeNcHeMD+O2Pz3W+7GUr
+Dz2kzvh+2RX1agMpVwswqUHZwHFVXt09zUyMc9ByU6w3iy5AC3EPkdkQYtyA9VJxzZJ9eUpYePWG
+Cs75OYEqz5J+b+vpRBMeuriUHK00x5Zfg/oKoy5bbcwW6yUvlmKZC6vf7ANy+7SUBZD59jHdW7Fv
+fzblrWwUbFxC8VqyhbvE++eR8C8Q/odhqvKm9CJSgst7uZn/RfwEjvffD8BFR60jsG0mRUtMKAhu
+oNhJ263ZUm3V74PmdRhN6af6rKrDCdP4AMYFiMvnNvcRDstuA7g2/YHrpTl7+HvQPrYBlI9y8sTi
+OIPCHw8hv4SrmxUA9h0ME6wqXURcXLHiykcCDnHlBGy3/ma+gN/2SHH0pYVUbkJyk/rjHO+nXBWD
+4Zh7UtCb1N6aaAnAxqQetL5Qrj2O623A+HDSzTq0zjmqr+qfghNRvHmuH44jz1te0Vdba9cIxJZp
+sBfcOYJApIAv0oUiJ8Zj6RrBOVXxsgLTJ5suRAmIo0zu8ILxcpz70SK8wDB5PC3HJW9PFmH7wk3C
+T1bir16WuyGR75TFnjb0KpL64ZycIwp/vEkc7BZg5uwgWOdpNbmvwKtVsoHm0d4Ew8Anf+G+cOIk
+K74ssBs3Xy7VUYNFu1MsCXBuTcq1Umur0cijtp6E11J2rnNqAsrnRrdZbmdFTZnOT+Xzeerd3SP0
+uV7Sjpbg6isyzQoLvJZM86I3UMOX4hzrvYQo+j5xoT//ttwvEP5h0BfdiqbBB81HuV1ymSyPAp07
+vecXIEmd/fPOXAFx4YICticQDCkHsQBXX6W/7Q7ZN5o+csqzW/4uYPc2K+i04IuC4MWxBMFA9wdO
+y+6Tx5NFuO0koRZkj5fynEp9CWweT8GFdXxA9Xq4ccDjJXwboJd1KZi2rLBbiinwFfkWfzxoHNEC
+wDXB1+8tAAAgAElEQVSKCYbbfPmINq9l9JZgz6WK0PJFx0a/KNhby2IS5yP+ICwgdvjpuU0577QJ
+jDcaEABwWqJO8zn6tYHfYTlWkIue/nhs5/PGqqy91nyBLUFy8iWsw974BSRQtmGJs3oisXjNXSNi
+pRIQ04Uo3Is45c6gt976NxQPuH1hzjSnSu4WYVp68wtxJ5CrYHlzn/jaIlwD/0WZXS2wV4tvpwne
+iFFWxW3Uuz1b8CFJs8zJQizHGwDeQNYNACuYBjYrcMjBzJvW4IMMqC0yIWtKnjxAquHac7S5uM7r
+T9SSH1xbCQpjvXAe4Z7Htdq6kLIYe/iGENNpdATDYyzO46Pt0bQfaB0on849td/kb4saCucalhx5
+PQbOyyfwD4dfIBzBvjEJoyR0gLtlc+SLMMskLK/lIfjXxFKIG4uZ9aXQ6PWtVEzLWN0Ff9qya2dS
+YGRcqqxJ3q27+Uaph/9rLPp609RaGZajsM7dM9od5YGmwrrdkUu+12GTHpawJhd2puOG8/EH7/uG
+Dy6Wb6+C3ucAek9uEY8A4PQR9u4n3ECwIV/iO0g9skRBcYkhxqm4alw8RyzGwar2LoSBtNLlFLWK
+CxguwTWFmZVV2A9lvkmzjab8sDYX/jdhCvZJU2B8FO5ZrC0u4ecHGrSfTAsYiMHR8VHlzTXoyQud
++MO6ewS4XxxR6VornAMiICLd1hLdfSaHczK5gFpUJyl8guEe5dyJfSpda0WUvvR93ch6+dP7SMMD
+QKPAgAPbdmkNGHujlbXPYYNWPBe+J8+Ux0q/0HSqS535jOu0FGQiWeOPFvHqO+ToI63zSstu5x7G
+YtKzSyqhHa2riVCp37RTlnltHsi8yXOzPFkhDMg7+jGX9e4oWahImmsLOfaLjX7kWQHfnQ9z6m/j
+fw07iNz5zW5UzX3Mepk5PfH62jXDffnmwtEd81xqfaMfJvQ3+2bBwrxXdsDeB48CXPMd9J7iX/Lm
+3x9+gfBPQ84mQ20dFppUFmR3O698G3kei5O+mx7ltwUkAoR1bCCnNbFmUylWSQ+8wzMS4PhS2gl4
+Y7HX1+NCeWZ+L1PK5cnFaceXTuSH76V5t26jj+0ngFitv1zqz/OE0kX3B/7r2f2BD4C3f0hjAGEg
+AHEHw8Y0vN+hjBHInSNS8HXQW9aO/jJXF7yqIOq8VdEuZE+At02OzZostKlQLjQ7lcuy6s5BvVSr
+xbOM2sKVK/2yt/SN9ils5Wz0Rbvj45xTQ7xFMjuLOud3KT9zUWXMj/VGhZ15k96O0hjJ6yBX1pHn
+bmmVn2Xjy4zgUxRLuj5BqGjNqfQHFhHZ5txIuyyZdnQ9ejuSl/wwDYHxJnd+9Jvnoxqog5lHH+kL
+Tc63pkcO9WpJ30qhn+LJh15SgW7Pu1mSWY/O2ntXveiH/K07c2AveT4nAaMJblWO8YIiu5LPa27l
+fAEKV3vwawJKXiLHpkkswBzebtxs69Zo+pdCqJWPjm7AF5020xuN7accE56z1fkCneh2ypuihVyK
+43MAu+/rcdMcZW7rCnr8zJP/RPgFwhH2/XCvJftiIwAGara6lMl8i0lsrY6cpDk5Bz2OuYhinuiN
+9El2SCWrBtPkeAQiAkUj9YJc2RzLKowBgteG2EWrvQGXjHqz7CmOrOOzIqrHmKPNkVAFrp88fmIo
+Xo/HrI7s3WbVTRB8t/xW2QGKQ9nWwo89UBsY5rzQEanfkteefpQKepdwmjdKnnOkzwbvgFMZ9kMw
+nIqCNBVWP7b+9vO7BdtSaQsM7hbsVOqQcr0rp/R3V3i263KeaebMFyzX8kPLpp+0T/Di27USELNs
+oglRGFcg3PNqF5c9D851I3MUFm7ACn65lkgr0PtkCuLuUAxJnh3SCoaLVxhWetkBgOx3xTbySFr4
+UKBu0V6RW1MR+wcZRIV/BMZtbHQAB63leadF2XardwIEoWMUbp3LKUjzKk2eSB5509IJmk5USTsu
++V+woIRXyZhJc5Ffk8Z+2Dg512VP19zp/rkrdCnaQDDnkdyo7MMZL/C51Gx6zV1k4kL7KnAXlxyV
+7F/1gdJzH5NOa7IGaC/MuVOHIvt/Ar/2Fmh+LdJynMD3jXPSUnxaZz+W1P/78AuEfxpyVinARca9
+AeMOevt5rM760WMx5hpW0CN3mK7KpDctI9aoBQoG6LdRju1UoAsvcJeKVdLML0D7yMTuyucWP94h
+elmAm+JBlbPoQ+5TnABYLF2R/9jqv0e9DhTo3YCw9Ty7gGUFx8GbsgiLVdjLtjv9rkr3idVXhIKC
+XbcSsZwfnCH8++mJQRI2H2DktWMKZHvrnH6utr3RXYp/5ToBxA1eiHArQU+fvxLvAL9IpdTpZ3sC
+wP4h75SedW1pssVPZa2xLWXE57flwBdsVNuWQo75zjXhpMeR58n6LM3oI0/yY7YYSvk9El/0HRhv
+lmILX/sxX2YaKLCs82nKM87nle3ZheZbzfaHsl6sKnlE3rnwoG8LNX+40He5VDcYg8dtzMfY4HP5
+uwU4VmKc17K8l6nKuIoOx3atr0Fx76ZXvJU6d+12bO2fi8zRrb8kTKHGfpiVDOFEYkMCPC4WsWGy
+c4RTspI3coOhQ3eLt36tNe5i6NIlflr2PwHEJ8tv8dWkRKRdwTHg3sssWswrEd3N+mtlAFNA7K/D
+ng6Qp9W3AV+1FPvZOszr/enwC4QjfNci7AlsTWbvovXhK8C7Fo41WstTgEvh76GerC+cTQ7oOs+V
+pS3ZQchxorX+rwmbjhy+hNAdGIv1d1iG4e+qR8rO+EnBdDpykZSAP2kcQF+yeLgLQwgmJwgO4Ude
+n10gPgPi7wDhLiBijoVg0JHZxzcUt+VDu/y7LKJgDAmcoTQddamVYAPo1rZPbg9K35FKLzumXV7N
+D2VR09Sl/GYNNgLDgro6378Dan+Sdwp2iWc62Wd9Kh4qoeHIlScjtBs/WReqjHRdiJY7AuAE1kKv
+L17GWo65s6w4JgBYgHH8ChDTPaK7RTwbyI0/AwxD5uPG1VG2wEpZzlXcsTtG0NvAb9HIk89g2IVX
+d2uw3rzpGMhAynGWk0IbTYLrjW6vqvFLx7pd9mLp5fEClk9dmiW2tPRpdunYRW9NP4Jc0rzRhHCL
+H/MAIIxM2e/dQqw3TcojdDIv0mYkLbaKLC9dAserzWN8DpSJtezPbhGu5T+Nh4tIdtS6CJ3ZLMQQ
+a3CA4LW23qXzXgBm6Q7xfAF8fay1XMefhON/KPwC4R+GvlgCFKd8taSVnikAnEtNgLQD6Q+pkzRd
+0rkY8zH5SYnHgjeMElJQUUMDKN5ITDTLr1Xr6oU4XKzDSg/LMBl2fCHloJCwl9no1YFU0GkVTkUt
+li4p8/jip2OBw3pB7uQCoSD5Ao5tAuFlBc54PD7KGfBhl5gmKF3hrj4Om3NBLcNV004Lbk1AAnwN
+fDc0eAa/CX8PdJbfAHj0dZEE8LqoCgOsgeFSCKx9XtIvcTbvu2D4BoRtxG7sKjATS/8rQe/SYIn7
+tg5QRyDXH+llrSxFo9fW8tv6yXWkIFh8goFhIbbcgeVkAd7AsIYrUN7ZQk679C9BhSrSUOoUss2l
+6gZ8b3LnIrPaDYk2Eq1hku8HWh+ToouN8jRdhuGCa25rSqvjZukVKJV13K3C1c0TwPKWbnnKFo75
+WHzJvjNyHFbiPV+EJwC7/AXSYJLndEDb1lxrWO9b72M1SmD1VRZtE3xbG3E97909Wn7xyS0idJ1r
+eWx8dBq/YppbyKoEx7nuwvxC+hsvqVq3CL++pxUMc/lwTU63xz8ZfoFwhG9bhPOlNwG7CrXcZFEE
+9GnAV2FZyWXKBC5VXd+8lgPN+HYExCepIhp6CUwRAG0lCD9Csl5BLvMx6Ify7kD63r1iDW6KJ/KV
+JnGfdLZTFTsEBLuDG/qXH2NUEb68DovtUucLcl/4At9cI8yGJRjrLdoAvivNW5w3vsG++pMjp8Ng
+K6cecZX1YgLk3SlCQ9T+LeCLQ9kLXefQFF4XsGwH+tJDpYDS0SEAWRaNtF7qBHKlxx/jp3DK/wyE
+7UOZyJsNY4nZmZbUuS4KwjHS5Bkyba0cmpX4Vq6tGQe468kjoLiBYJRPMN0hlo+81RjLvNJu30Dv
+ViY5oTO/clfTq4+qSMuKi66E85xvWIVxpjcL8ZCbyvdOQ117o1XExPTZlpQKBRK8pSQy6xhOEH7K
+2x0lMvYB8Hba4ejnsjOehJigroadQ9ypNGMiGN3FRvxmMVY52W6u2Sr2+dBgJ49Tj4Zu317gWW2c
+8qePG6994EfKCV6XIwVMsPtzcNzYvbOY6yPeGVguW2t+ulqD3xf2PAV63/jEurhDvHYGw7Umo65c
+b9/DYv/O8AuEfxjiJqlSww+4pnX5C7ucnTQflj6vibmMzNNCHHEPtWXISb6B4Lba5NFaapEuPo0L
+W1wmUnla9OACchMU+3SLCHeJd23IXwB45cHDRUBB8OHX3CFysVTbdVyK82ggmLtFOAyPlejx4PVH
+y68Z7K+n7xUcAPhoHX4XH8onmHfN0Q/u5/RGq+RDO+4iIXPIYpbQgt3EdzlC9LkAoQ5BrFj2BIZb
+/ABYMj7Ab8rsHfz2eSh5AorzJQnjnCwwjABpHm3Wx5owz/mrCpeK5RMAPtFmaKzwA/0Y38FxU3U6
+GEM52bjRW1Nf1oSshRx0AVfTyqtAEZFWH2EqP74ot7b+W+Xjmxm5hopGMMy4+A8HGAb9vDkvdFo0
+8FtzrEklnXfedwZRfq0iMvLRLwXB/FFZ25AvJnz9CI5xox0a5ZJodA6qzI9W1Hv5VqbmS4KWVigR
+U69PjqfYvqPEft5+9ui2+97Nw7HKow2405Vgo48KFKvmIpeCfKSfJxEshyw8yIt5k7V1YozxaXiy
+cXEtukjM5gM+Xlw/yEt4Y0NmhxXbs+8lI136VE2d4LiO7n1NLn/7cInyeOoYsmfdJK+aKHv5pBMP
+2gtyMIe9b9h4LC3Cr7wgtwAwZJ26xNHXzh8Kv0A4Ajdvzxk4F14c1yKzOiogOLhG5ARNUNwBs5/i
+nA/WZEI0w7JewRGiSPNPdU7vnqO0ZXtLIlutKAHAlj5DbHkHwgF+0Wm6Y8Rq0prspj7CB8WSSuiq
+dDYRgrTBi0KlAjdJ88MU7U7aPr38dvMP/lDGHHhLUNhDgREAOT6kYzociUZqbNmf2kqtbMBlBdZ5
+sepI4HyCeAPUuvCszw8c4nahR2TKrjE57ZJXrnuWwpgnqCsEAbAqhJOOhNAmGJ553wpScL9v2AGv
+8t026urPe5DzrT053X30k2sBckQ9FeFkEICr+TbLKy3iGwj2Ar/NTxgKgCPfykeYN+sAuoGngWBc
+AfI61gtNE4AoiFRepfpXmv54Q3oDuzfZk3z1vW5pSwe3Mi6NHqN5pM90l+MbsJrvdpyuM2Id3J5p
+Ps5VoDuP/Rp7E655uhAjrmxTPVyA2GXSCGATsJvwT4Cj7uWefzV/kxafxo5RGwXYbuv9kdL7u0Qh
+Fdow2qhZYxPcVppXOVl+FSjrWkp2psAUnoJPKGgRxjh6lQugSyvv6+Uj3GixlurjI7WOnIN/YOl/
+OvwCYQ3fWc0N+CrNMq8mpG3HNdYdELcJC1mKlL/6ktMow+vVZG65AWLr7AmAy4IXrc5H1AMA+6BR
+MfBoulNE0N4XeB45f7f8NmD8USlBaK2L0e4iF9C03DaNH6txIF/m8TjxS5eHzQI88tTHOEHHavuy
+Cq9vqNv7rEdKr683C/QLO7AyatgSLrxD9+iDOkR0wNvBMXs6y3wCu8cdIiafdzTbknn+EGQNbAfj
+bZxHQW18nBh3eimIWXbTT5Zvfs/W+eWo4VPeDCfA+xkE72WA5Zaj7uF1bZe0KDO5SZwvmIiWE6C7
+zizXB/T12spSWoVLhId1J9IEus36i05L1wioZTj6Rn/2CxjO/nLafByEkmPUmY2J0ffjNmdvxZvV
+F4eyCqo3nvdz9Kai6XA/EQ9tPgGuTK+Z2TBSlhONcJj7DRC5zKX8e0p9ju3d+KEVGIdu6sKTuLe0
+p4Bq407gxpNUB/qg6Uo3gE9kSUsL7umNtZlutHkdkn0r0+e3iyxrI5z9tHmN5PdXbhFx9FOepMX4
+x/f7kn3wvHlw0Aghx9fhj8Nik24Fv3SFSOAbH+6AOzzA8Q6EUety7Cj0J8IvEM7wPebXhFD/3xU4
+SRP8ZhnVV/LaFOmy+PXHteVSbiyZJityAWUBnVS8u0OA2wsARoCPWBVp/TUqBtYreWwghntE8gvQ
+F+Wmi0Qqli+UEu8YdRFP/Zp7mIJgOLZMQ/kJ8/RndfwKfLt/8GeQTGtxWm3ijnntmrE+I2kBfo2f
+tBNAkKBXBHm5QhTQrfF1UCCqi0TRuzxp8+JLl4hWeF8aG5rcQXFFvweMJ8I1KzCMPMgcpQC/NQmf
+wbAfzvkqDPw24gWD1Q96QmNeefq293YXzDBREiUMSnkMrSdp1NrEHSRTdizXiALA+rKcphP8Jiju
+lmHuIAGInj0wrD2As3HUc2JtEPRkNTqnJO4HHigIPv081+pZ7tzOS+swL3hpU7tLO5bxTudcagBW
+GOg7NceyzZ5+1L/Fontslt3zetNPXbu6UoxJf3SBAOVhz2tuFCzLggTBg+YJchVUKk36lU3uEkIw
+7Dk0MHyQm9QNke9a4bimZT97nfRDppGjsjX9Ka+PQ+tT8jUWuxeP0pXTq7bcGvV9lzp7F6/LFeLF
+Gy+Nl1UYlUa5Q/CGs31R9g+GXyAcwb7JewcfG0Bm1AK/Z39hTswAyAGOdT+/1PdWE5W/tpa8l9kV
+uQoIbyC4tnQhOIl4A8DBC0EKdHlIACsAeLMMoyZ1AkBfn2z81m4RU/kcjzJgvhobXC+rMBU48wxt
+SyeHpB87vCjX059A8kzjFQs6Xtj7LL9gxu2NNq54YqZs37AEo4AuBdwOivsM2OlC2UDJTu9+xLP8
+IXEBxqc6TsB4zbMQvPI2d4FhKyWS2QWN2UPVRP9uEKzd2Fli497CWp6eQ0vwY7Y+UJqWKfELdIC7
+IvCh59Lr00Ipa7atQyTQ/ZRnmofwD/ZhBZ7p6zEsw+EjPEHwBLfRzQ0Et/wPyCMpX91A6w3CtArf
+zjvR0XnPN+ePiHA2+Ysyeze9ndJL+VZ/AmAfGVKTt1S7wrWM5py68zXNr+WoklqGzgGue6D6JX40
+KQLk3NINcr5XZpl/Ko8317trhF4IUvEXQZ7eboNIv/kkSYOzL3rB0gnVFAG14tqB6IWC4T7edUk1
+KHAckmVAvSyXIFjBb/nrr5fmgmsBfN0t/YGnhXhagRfwRfSBtMmDPxN+gXCG76nGfKnnBn45YeWl
+uAK+5R5Rk3pOUgxA7K1MA+yiUHIJZyFmrjgfOSdIMypgBcCWjbBYESYCpVt+Vz18UQ4+QXN1KK+Z
+dfRt1HZA/MXLcymzduHVQHAALDNNBwgm+EyL8DeArj1fgmMgeJKOoL5eGHzkQ8+vL0D8rJcJ2RaP
+PtE3ODiNafUlZOKHNU6uEp8g32egewG4t3hopBtGVu12A8ZrqsSc3MAwQliXUkubl8jzk078akV/
+XbZyeveFJkBvz9vPfTwAcDtHjrSyIFS3A7s7hMalqVx7kbZcex0wq5U4QTGXBH88fwPB5Qecu0Rg
+HTWPcoxsSLE15pFnvu9l9RwlphgqZd6PB9nxyhGH/En7ThlV2C0+2t/aLlbB0fy946RNBqgeaE40
+Wx3Mc6H4iB3L2Cy9s/lO+945nOgznWWYYXsacOSHodIK7CIUxpGgkXptlPGWPq16iHydoZeuoZE2
+tHHhwpwdQy6AfushSBVLJ7DEJ9eHnrdbiRkv0EuZI5rGKXEpM/rZFiotXwg3Dyv7DoLf4UfsLy4g
++cDi/3D4BcIR/tEHNdDmp9AL/NISTIG1dNXuI6zLMeW7reut/A6IWY6KZK0eK+WCKNAWY1RMEEyB
+kOlan/XSHdISDHjRHah9guPlOKNbBF+gW0fe5dn79SeUT8pn+5BGdnr1Kx/pktNW4PfJDin41eNn
+a/DZP/jkJ1xAuAu34OHLfdRe+mQsXNzas8bEKbhz3HewexfKH8IBzOZc+Q7ozbTd80NxfwTGUU4B
+YOmvuvnKLZK4WgoLx/kJF5F81j5JfB5PofKaxm0xEwIlgJbK6daUo/Irxs2rTwlSsw2hgJyKCX0N
+NEBcjbct7o3egC9m3Av8+m4FLjeIYQm2dWM5gXH3be888zH4HLmzVdhrctQwF0elzw7lTfBH3SLe
+sART6WLchDf+XmhbWtvhrU1bG2tItrJbvDHBNRVFDyAqM2v2u5wRS3MAol4u86TuLslONR/yDR/z
+My0LsnVH5wJZNV0iwJtmlEwYMkLnTG895cqpoLxQx8Zgj5Kgsqat+BRsJGRH6owUbNonpWnjqRuA
+/oW4u+sDUNO0WYw9VUxxhUvMxOziehXrVmDmyD7CcAG+Xr7CSifgLStwAeB1/KFe+zeEXyD806BC
+bgDd/I2BLL1V/sEdCNcdnIqwVGwNfJwEn+e6yTtd97TwWktHGfpMccp7AcpVVhouLhAAChg7ytfR
+D+4RpA+F8zFORTMUTrOKCRrJBzUGAcAVRyjoAsYsABb8whJ8shZ/sAibx/PvuFXOI+BpEX5xAuau
+NK/RvP6Mc8e+PDYQqNPzFv8q71bwVM53yD6FsHn5v+1KrfjRreL9klNtTX3YmnTt0jmnAWE70KRP
+jW3DKhw7DuE9AWCxFtajSKTC0LgC4tSxh/gEvuoj3AAyDLXlX5doz0gvmlqChWZlEe6MUMF5YpSA
+q43ZO6JpUlD6VMo0hW6CYadcutxsX1+gQ68vfYNTHqKHoCdZ8y9le37NYBO6o+ft9dksDNgAuY7G
+Rd9izP9sFdb4sXt+77bLHHDN3MBvAN1Zp84b99jlYAkMdaX6+phiRaaYPHECXZ5EJvAEmcIqicp7
+Y8xxlzpY5wTE2TmXqFzBcATAGq9rn8qMMfPieXvVOuRMsYVjRZcIBBiuGZlWXl9+wZjW31h/Pc11
+6XnjlfLtD4dfIBzhuxZhqgK6OrQ33eSoLhEKfjW9rBJY22OB+qtAcaYpgw1S7qAMJgI45HUQXEpw
+WapW2XSLIDBRYBzHsj7d3CMW6DN/s68T+J6VzcFqjHk01HK3lEsiT1Mxv7RUBcB8FQw/61ig9ruW
+4M+uEcV2TgYC37AIm8ElvqZRtMm84uhgVqT15fgp+AY8WrjklTL4/9l7ty3LdRRYNHCu///inWY/
+iIAAyc5ZvbtrnTNGqmqmdb8iCGMsP5QbTawsPdMGeoXhVuR6JEcbaWrH8+isMdwymaj4+W7JnJnT
+bJ1m7hRnx2tpgHXUljRpLe+ai7UXbNSlfsoBtUNd8bWHtq8wCUNQkMuy02Z4mk00E4iIo82w+X5S
+BP39pTmkiUTyKkPSr2qHm/1v+k38J2Goj2grC+coeZXMFePUNOJoH/wYZp3jZhy8cT/IbR/9P6af
+8+v7G1tWBUVb/Mo92T5BTa9vAGA7p2lZbTX9up4Pwzv6SbY20mTj+Sik9OIqq8kHoO4nPnniEHVV
+AOx6Ez8m19TmF3yiIbs9H3GQYQGU+SW0ZNBWE5Ozbhy8rsMBABsaSD6aRXjEE3cA4yauP2fUGdlm
+616FzVb31rGhi2FcNrTDcWqEmkGsbbTy0J8f3/r0ha3/ovsFwv+Jm7v/AHoxf2kjXLw5w8FwzqYQ
+ov0i9xBCqXysbWzuABUm4QLBHRCvzVx5l8LYUiAkMKZpBDeyR9sOIMwheKxKzdGu/f3RNngIp/ZJ
+U0FXOmKj3W9MO/HubRZ2vlZA8zKcTSPetMNPL9N1ILym6V6ANx8l3bFKcYMQ/VikQfooZlyLXnFN
+09tuoHreHfLJZGmKCiMbuZ/Q4BZtr+kq9Wb9OWMb+O12e2UG8dwlH3F+8J/ipv88xrjaD2EBc5Wn
+ar68tMG5z1rf6glKylAMEJZMo/aXAkONm+YSBMUNBIPaYIYPHMz1xIjDV+biBnPdW+oExMw2OhPA
+pndJor3s8cinBbVexRON7CLj5hwF+D2dHoEH/0/h9GN3PgIzD9fnkL/zepmAp7pQZm29WH9qQv7A
+rdjSvLVcOX8Cuv4Qbw/xc1hrm/c0nQulH23LhD/mJgk5NUwlNvth00af+CU5j1XdKUtxcLWTs2ar
+m+RmxiKAmPlqcGSIE3qW/7DN2/VP0tQV/3Px1xTO2Vo9KprhF+W4PxLw3rvJBH9TO1xg+I0T/2/c
+LxAO97FGmOYQx5fl1D/4MfhTDXG0jQA3vsiQgHiSXNahe2N2L/eabHwsAqXmN8dL0wgXMwoRNiu7
+F/U7tcYihHIT0y5Y03lSApp98EkgnTTFu21wxB0wnqGYjwMCiNf1Cu2vX1dqhe2ydWrEhxrfNzBM
+O2GeEMt1Wq9GLfjgXBK74ZeFOUAwPwHobpZH55zNHdqKc/SZtjM6YcA6YU/hnwDwaascBHT+fdha
+qTF1bGYRQJc/TDMJ+CigzBu9qleQ2wdxztmAbgtLrI18tue/YwzrpbJ1nJACTtfw1ERiaoMFHTgE
+6ApIyDi5cn5d2jEDAfAldTWziNQMUwscn1e20gRTQ5yaYLM6Mzn4kt4sAEhaB9RO2LMM50RiJcy1
+j8erG9PVq5dmGHfyk+3jPk/+w3psdH8MV6RJ0Fuep3psz+BFVzUD5zJNoxtr0tv3Vqzl95aadcwR
+ae90uL3ew9CyjVO9L2nOflsfbm6gIJo2Ry0TBUaPY3cjzbH2RclUyS5+VhUSNPd9U+ATvHOCRFvs
+WUkxPr4sDcpsR47BJY9LX4Ge56gVlvg2HCHlyTd3E33WcCO5g18Cass8AnbjctsAMG7HdZU2eGqJ
+/7b7BcL/gcsnGCKE0pEJzBfnUOYU/WU5BTAExEFupgC4b1by9tUhidQXCjKeOzmEUOSjJij5iSMF
+otbHKimRGjhWTY0CYKpBuVleAO+rluYUn2O2bdPCkNpgBOC9xRb4DlDsAYqfgfCu8f0RCF9XrIGj
+FWIAACAASURBVFVNVzHvOz5tt9bfVDMtHV9rzgFM+hDhlbRxBsZMm+5V66tSeqadwppgP2Rpwnvv
+A58+5AtyosFpH9VgYe917BZtu8zCCD/2dR/dutpL2KrlAsGaL9LCBtFkDPPX5IBLvtwGui+YT9BC
+3LBO8KvLW5rgc74ykTAxgai0BMFA2glPG+E7unSlP9YnJ96DHk2GoABAV53qAqEF1zlBPlpdfEaE
+KgGwvCz3RwD44C+N/RDac/tsyb7FzXJZWoTM3Dk+2z2USZ95i04eIhXXo3PpUt6MS5wEMtW4//ah
+jO4cX6Krern3Z1rtVu0zyONSDimgLB7iGjfhXoLmiltUxrRRt3Szysg16DnpmjScL82JxlO0067t
+BX9v/bKgbQLgiP8ZAKNd1d80vboujpQq2QPpjkm7HMZtvt59Wd9WxmUOt10DDN+1wPP3C4T/Tffp
+3B80wAlcTnbBI+4OZnPDwGNLbit7VwKYG8HMsbSDJGJv+VLu9ZdTczgHi58EvvSHgBOwnCBDXipo
+NsPCMa12RwrgLU/kewLBx5dU8ByftUr/i6EjtcCr4QV8Cwwb/Fo2un7Z+vJd0wDb4UMan5tHEJs0
+AeBYaIAM1ELINLtglA2lCAK9GVJGXUC3VrprSIXBEkQM5FdajIOgbXHs04TWo8JHZ3vWk+DniOyQ
+bngExLMnKtYYtlOao80szPZxp2z22gOyq07X8tvWN5OftpVglD/vz46aFhLcN8zL/skAJyFFnGlb
+7tLexs22n5pDNLMIrD1HzTD347LR57jJX4T9bH5vT3VINuknf2KPcg8ptysB3kAygn4dqDOaURp2
+FhI/bbQbqG75utAu3idLMOL2xKc4oUuYNDMKtieZmiZ7xTXVOxiNDJ1cXNJq/k/dbvXbiH8YjW/x
+I821L4cyuYn3MiEAqnKKtBP/CZmV9dA72ehkCrboP/knadYMCoJ99CMBcRCKi39hB4BaJ9c2XbZz
+dNKDwF2A9FkDXEo2+lOGGHex8MU292hgmUqxRpkeN7NhH0w74ZOcV3MJN7nGDezdsMDfdb9A+A+d
+h1Di9Za7Hce4ajrzA7XoKJ7qAL6CXDtD8VL8GISoNc9yKoQH7tp+vXSy29yotW0YoOQgZ6FXd0pK
+nMqTQdkUKBthg7c0hlNbowOE8qXJtdblCpvbpfGlmYEB1wK+Pq4giKWt8NdX/2Tyq1Z4tyFOIOy+
+2nHpgwD0eiFuXxUZudz9k9n19ff8+cgTcUa2N/iLyZICZZrAZEPdA21zXwLzlA70269zvgJFa86E
+zsuzX1H9PkrUIIsx1BT42o81B1P3IVU60l65DejBP280qnFplQBXtIqqXylq6P/yBU8XOkLHgnnv
+J1vjKW29H1CCruyYaxBJntkxgfZW11Y2wnljPq5aNA5YDH/xPdoQ3yb0YHeZO32vl0xXu3f2yfix
+mliQajKeUIV/fQr+jrHHh204D8rvwp+aaNlEmaXt2/+FAD8RlcQ3gp75FfgwSrWImk/jrJU6bbPK
+05vU8gAGAJ1Pr1DEsLUj49sLHMsrTWZ7SQRSTh7lVDuW/SWIXVtXwG08rbModw73fIg6iGybdtpQ
+2m5bo1ZtcdMcQ8Evsh6XvBmXV0u+d9IYAyWX+NQqTfVQ455zn1FyE3FlnhSAqw+O0vYS+JrDfX18
+w/2G3wa3O19oF2Oqv+Z+gXC4T29CXNc5rgVuxQ+U9tfPcfAd/LJORJyCoiZ4NV74wPqtjLYVevCr
+ZrXFoyRle+yELuDzomAqzhZuE1cA4AkYJxiWDZWaMaZP5rf9rK7rGW5pggP8EgzjQ4Dbrnbh+ro2
+wFxA2OFxfvJ9ceyW/co7cYZlODlV6E5BMGliAuMNBAMJpAtoeb0zcl7GHQSLJoD5lYoAg+LoCexW
+vndwHIinE/zTFUWWJ+zRqJzkKjZ6JRKgg6zKWIF59ftpG/2h0y20xkENrAsYQwFY8GmjhdbVcJGW
+mDfrEtJnvPe6No0XULggC6kwlAxMsvJndOtIVVOAuBrR8lfQf5pRWPG9LO8OfIc2PvfOXc3ZCmuX
+CLkqHADYwm8ChnkWupZQbR0ny0nHASIm8Z20sye58mncI5HZHnx4x4WzwJviFUdO75XH2q7o5Vp9
+e9szrurb+7HLL5My5/Gd6mv5ihAkyfZyRwA8+/UB6D1oftcS7GCYI8jzjmNDllZbtfZe8sCHHxhA
+N/i9xKm2uMmEYOjTHBPg3tG5kn2aE7TPfZlLxggJdlP+rd9FYES7qxMotnu9Jy9z+7fdLxAOd+RD
+p3zEbyhQqyBXP2CUYYywyweOAHxZCUcHyjaL4GLw1xQUw6kQZDH9XacyUrFu3JKXspmdvEQ3NaqT
+goaWDIlzcxMEywTCJZ7op+JW1QqKJU82qvCqIwDa4KZmWIBvaoS/rtIIX/woxk9Hph1MI+SLc+sJ
+wAW/eGZimWUgNMSmAPgoHFAMA2RcNXq0uBJm9fORp1bX+zLlVOaDNEsevC3rqbyK1KIUJOUUmTyD
+40UW3q6QK07Xlx1rkm3JHy+gkJ3vvU0XA/EWDkAsGpCZ/9mtERII8gVQAmDdRrXn+Asb9zDJMEfa
+vKu4UADMFi1vGEc6/ToUGYNhDNEkrmEKAcnBE0wLR3wDzRGXNsOSPwFwCMOkxRDwBL8FhGOGmlaY
+Ta04Nps8KfmR+DOfdjTCQifFaXY6zjlsbhJR+D4VNLp/tM5Dvrz4jPccwgS/y28tpgG3kaenswmV
+HtFQ42XSN/EU7xnhyHPSQp7q2fphleb0a5kTz9U+8ObSHkAuwzGfDfQKOCbhpyI4+6R8qPOl+bEQ
+fUKXL5M6Qpu6AqnxpZZY+b6A33zuZOQ/Pa7mI/as0HzjKrkpK7rtDpXvKcpdfkv7eyUovtc7q9zz
+9wLFv0D4X3Tnlw8O+dDWOrXAet3iD78U9sFneHf3BZH53Bdem4HtT1px9De8IX5DsXyD40qiVvC7
+0mbFHgI7/6jkZsMsO6fQgbCGHlpglHZ3aIdL+4sWD1SaTgD3ZgljNYcA7CowDALirytBMb5oFvHz
+i3A/5rML5h43O75AtzvgV7woQBBMhioMFyqsitcctbwZJzSBAzPM5SmBmOsX89ZA7+b0hqhfcfRb
++idgKH89Hs0XjjiKlL4dDDcbzu3qLWxA3rCxjtTeUCtis+3T0L1Anm6L2DdNLX50OiO9Xn3p7bRf
+l19OlRAt8O3xiXCZ6yrjAnCtgd3d7zlfBaJrTLx3y0Yongbi7uW0cEQLUC6GZAJ8aco0/Ch/rsU3
+zR/YOQLhAB7ZQNcOJ48KfrRMI4D8MuatoLgNOteR4KHMIepx889uUoHL31MVdojWvfVQb+PRSFr/
+Efwa9rjWx2nSoLQvfVUaAISAZlvP5bKdDSRbu/R2Dn06+F37Z4f+ZL4H0EtenTTHfiqNF0BuT/wI
+QJOP7OGaCxQ2yODarE4sEDJGTSQSJCswNjSFCul4moToEY9p29xuMA5OeCFt7UtkhzZYgXAAXXeE
+Bjj2FLXCQM7x33a/QDjcJ+xsZXSo6r/5I3y749vX9YZeUeEQ7l8hx0srXBvAXf1QHvc4BoJhlTvr
+UC9LjbC3MqviYvcvlZv3MKQhASQqgJKlKtgdYJhxFWYD/YWhbMMYlsas/+yiYLUFhlMbXCB40wg3
+zfCLeYQ9mFEYAW99Qee2C2Zemjwroe19tmRa+yNK955SAJezpJB45hH29478NmB8Dlev6VOxqdCA
+olvDjlk+Ws9l94qoIdUc6MBbOxWd/XV0wWMmANhGbgm2aQkQJvtPREYv8LqBRp2cK+83G7l3k7wt
+Pl9KMBxCLIB4kr+X1pnKJUu/5xYtzbBlW1xkxay5eioMM10Fv4zZRHYSBCQotkofcZVmAxDbslpw
+B75XQ9QAbzhoOI4Xci1zmjtU0gF+xT6ja4ZJQELhpeZTzLK71zirCJ9ZH0fTqtjBwj4hNXzdeStD
+8gbreapcN4vQsuc2tY9CmAm49vyzTj+W6eV81tPakno1n3F8FdW+WDfKdPA7zR64705A+ZC30Q0O
+4VgZAn/maWGAJhS8gXbaBQd/WvjDUutMsDvBr4Z17p5uCh7Xe7DO2lsDD8UJEpTz/WW5G27AxYOl
+Yn2v47Pr/637BcJ0ryChZ2vaYH/Q/s6wLxD8LeHcCLzDAxZBiNBtwvUHQWuHq37kt66hFc4q+eh3
+9UUFcx/8iKG0eZg7xc0dBLvscAG5/DnOtsROFq5/vfgd8YFqXKkJNkuNMEGwXxfs6wRuBeQe48cL
+dhJXfb0SDOO2BMRdW2DZ5ynskuFFzJihipO7/ZlHNcclQGNtQ2tQYRzCwsqV6TX6OGl+d3CMVubw
+7MFLCHcNsNBX0pOIUK9suwttcIDE/vlVLaMj8s7k8+pILfAxvdfVd4rMTNwsUzYQoqh9cIjZdQ1U
+u26q1jm9qRVOWeY556yjA+Cyy+UpL6odngDlhEfYr11G7iYRJeMlTUHvVXGL/hfg1ack+RTHSA/d
+JKIeA1nr504r5TcSyn3F23kDEIumfTVsxcQGIE4wcaK7U/SRPnXPPzH3GVd8+6c8WjOpRPcrrMBu
+5XEpaT1/m2iZpw1AdXpq/TLtCw7hzgcPBPcQPqSF/wh6te1RpmmAbbcNZh3TJKLy6pM+GU+7M6d3
+xWmflK4qOxUZHiYRpQVe2T1NH3KdM7z4nscTsQLL+5y/zmf2pPrNTUc+68CSeVgMbr4od4W9cD85
+YtkIXzn+v+9+gXC4d21Zz+d4sA9+uH6jwO+3xJOWvpTouVcO2uAIaW9a33hSBO+nTOIJfktO1Gsf
+q3rGSNU2vWOSDnPWthSRd2zsI7AdceulHvZq/UrkaZz3p9Mpw0JYhyBdTOnKj2csLXBoiF81wrvZ
+g74UV3m7CcX6ml78GgBeYKx+CpmsTxwZngQXrU1grI+8UHGelUieEnOi1Kq50wiV95xSL1GteHAl
+W8OP2vfK96w5liFXDvpzEnxkRrd1Ztvkz7mPpBM5zpnw5EJgjOX52G2g25LGuR4cB4cl8AIAT1ZY
+gsyiP5fJq11RiGO3FEzAGQAXU5HtEgK+GreWaDmcGppolHkZgILggY+LFQDjKiCBSwBw+kMbnMQa
+5b5NGyrnI75tniCGywC/lgqKAPhCmEYYP0GJIqKosxnGW4KKvlLe29Wu7VGHhGl+0Cb0IfxJngPY
+tR7uvKaHhWsc2iDNfJDW/D3/J5rgM6Dey/wJ6G16HdEKl7b3DHjL/KED5JOGGMmPR5t5419jKb6t
++YpnLvZRZg9l8wvkMWRcLwW++Y9rqzc8NX8b/Y21le6hRwZ/WR0KTTWaeUQHxXFCxM01u2I8Cxj/
+nhrxLzrfyeAhX2K47Urwq1pfguATGIZLuyRq4fsFgL21D2had8UHvI4nQoHkW/wTaBEMd8VZn5dT
+s6/sORQt1iZLTSF6nIaXsJ697A94lpyq++4SvigwHCCYL8rhKjD8qhEeIPdoJ2zWAHIC4dvje7q+
++nArCA4BSzS8jTDmmQwPdYWGfZY951GNsGK/vJnwWtcK7y/XsahlahejUjXqhQukT1lvsyd26XN2
+VMbXBqr0WOCjty3zZw6LR4Y8Ks3isWLd/Mn8RJkOgIPJW7RvtUc+cTlSTmTQOrX+1vKFX+5WCIav
+EJB3xK0yri1EfUVXdc63STqi86WJTsyrPW54g3ESKVpgAghWok9lEGAXBA2Xxlna8a8r+hOcCYQP
+M9uc+04i5CsZpj8A8WXwOztek7E6TKbSzCJ2p5xy5PFj7N7117H9EdcddcYuJeBqu7bCmn+C572N
+w1WAk9qc1rr1PAt8nfJFfBHfihtl8VCWabumt/K6FJ9ttaeKGg762F6mG4C57Qupuyko8oZU+1V8
+NeMz6IkXUtNL/EAzCfLQAMVcx9IgKwg+3Xihz9NwyiPJuEkfKasa+LVmJnHFl+bcr3aG8HWXffCq
++kPG+l90v0A43OcaYbmzQdn71k/NILyAb1wTGMem+Aqicve0E/4KvqQarU2RNV0Q0bSuqfeiV3vU
+ChMoWQurYFVYcZyJj6ITNKnG96AVbucGQ1+Mg5R10UxuDUEft0IFsYDftA2On8XLcj/aANsLGBYT
+igTCcfUrNrqAYTLTZn95nMYJgkum10wRQJ61xeSoyVSpGcQEuQdQnFcxl4mlrAetNYQ/Acdd/Or+
+GwD/BIJd6j35Hfl4vmmGM4ONgqj9xg7mHlwAmiS8YbGn/XhwHX6c55BunaxQHeK+XSBYBGaMseGD
+7Jc843EvLS/kpS+TF/YGBmSFimOAku/plwE2EG4QHLmAbntSc3UQvJkw0c+J/9aODNCk8yFgAh40
+pISm4De0wLw59ex0bYSTbPD8MyPR1mYW9s1zSPtjEPwc1k+1r2tRm7eYTo17fq13Lr7V2ke8H9JX
+DhOwZdLVaE+J7yMt8JO/l9d6j1phE/6VsmPFKcBd7OOgEU6gLOWblPLqQ7ExAbpIWtniw8OX4da7
+Q+WHKwCmLKg4qGbYKl2qbu4k2WfuTtbBW6gJhoBfR/9wxp3wHPmCHG/1k2d/yFD/i+4XCP+hU6Ul
+j0Jz6GkRXrbAwALDWKD4G/US3bcwZ8cEv97BL87ECQhveHF8F6QAkoDfJiBzlFs4Y7IjihokpkVT
+kqwxtZfhVAvMAiegDBQoHiy79dT0Z/HpZKTWqYPhMo2wV23v/jLcCTBvQDg+NbmOillC/boLmNM0
+AkBphwUkcupqjWoVuIYAGVrFM0XBMn8GJ1kVo328+/NaUCljwUStcjRw+yfguMqIQNbuTHV3kx7l
+6X0petC9Qy2part3+t01w71CFOKc20Pzi9ORG+dSaJv3fs2WfivffRdCwzMzO2DWx0UAnzPtMlNi
+P8xFLiCjgJbgIBOqDYIbywwdhJjhrBnm/oynMhFXT2+uBMSLeXXgr3slex1zmffTbRMIz7mWqsDj
+KOH8AnyaCTvqVABDInn6gxvVnvVGnmNJ2tqdQYSkPm3HVs0kvOew7pDisj1MWKL5d22w7Jh5V8T1
+RwFBpZNWhmCx1VP+bE/HmnXjsVwvw3YqSV+U89me1h/ZOtB9MYc45YGA6Kb1lXWp6Pyz9SvLyThc
+tcEiCzYATNCrmmLVBgP1rLXL0vT7U1rjkplm0f4FR5lIrPZ5lrCHNnhpjG/4vV7jdwCXXevlOVAm
+nnm7TMd/1f0C4XAfa4TR+eonR6Z9y49mEt9k3AZ8sXETAZ6cx9Pf5BzdK0XsBVj1/nPZHFV+Ebky
+nwlQJomWmKJgNfg67/5JCxyTmaYR0SOD92PUoi4vabeEv/Jm+Z2EL+TYNF6fAe5uA9zTOlA2W/aH
+HsemXZfDrxt2XzB+GQsCCjb1Yk0rH4Wl8ETX/CooFkVpS8PIy1yl1RWcZ961wUFvWXJf5g3cFtNS
+hr4DZz+UT0HtPaxzMNvXcLNhZrtpCqHzSWDn257f2Pt8OQ7ql/TNHSaL8V5JDbK0JyLLpQlTFLu1
+Dt2SOoDom2qClXnk7Lv08SdsMbFL7C2baRRgtgo3++D4lfb3SjBMkyWNX8D4SiBcYGgCve6fes5F
+B3WMIeJicOBWTZ+1/voGgPdp89H4tuJbvPb9THzzNcuB8F7S9rwKempXzLQe1vzewqd2BuMVgrCI
+Sy2oRdyBwN7AcdWhZSyD6vcWP+vp+d+0wifTB9JzM4eQPDjE1YvRMU4hmJxb7dNJtNLvKI0vmYit
+Qo8AeMYPf44/eaNQic6nd3pNeqpidRPuaAC4jlRzXDdl0TL0umgm4XxZTvjHX3a/QDjcxpie8hVu
+a9f9hIgyhdAX5BQUN9mc8knQCEqOsZfTTZLxQyKrpzCluYJL/GpTBU4I04yXSllAGdRPdxI5WQPw
+qhY4J6HnSR1M5HnCHounWAFf8audsIJgu76a+YOC2mkasacVUCYwXkLd823Yy651J2wFhpeIqNsE
+WIEd/lFxTrA7BX974pt5pna46jB0QZ41GQYIHqA4r33ui5UitR8qQi3zmfhd8sniuQpeNOY7SWuC
+3lNf2ZbBsjzH1jovuduAMi7WB6phHQ1p3/aoMVpu/PjpXKn0OxRtT3a29imIZLZNZkXtrNKmvtJK
+HlvHKgJkgCGkRNMnxbNw24/td21Xe4j3q6+qy7UtRU7rWke1wUZ+3TGutwF2raW9gqrv/ghcNcAc
+Y5pN6Goe2J5rBo1rnmeb4rYvHt1Ms83fAe3J9OFPNMV9zmvNRctLrkYQZUIDQAJJlk/KVzmiRNQ0
+uzbynPLrFLBNGY9xHJIu5ajR3l5+GzdLUyOMGZd5pb1ox8XT+F1Lj3gJ1AkTK6G0wgJyDRJ/AMD5
+Qh0yjZW7yHhVrFA+uA/6GLLH2gty7EecIHEML43wFaDY5foLhP9F96lGGBBCcw+Ay7OCH06PcAHA
+qKuja4OTuJLD+1nwHoTwzgbp9/bxUIeCYRUsZOBezCUlDHe09DWEbmtU5zDCGS0Cqnbaz6B4sx32
+0oXOp3T5Ik/8bGiCTUwjECdC4CtOhzgBXTkerdkJN3vhnu4EwtQMX7eYQ6h2DHWVFaOsTo2ty9qg
+g90Cij28aYIJMg2p5U14kctYa9uB5a5B5o1RM4sYmtyu+VX9A2P6qDt4aJy4u3c1bjs7uMOAoHUd
+x1MjwuQTob4dHXHYj9liTRq4kqZjGENt1WhHTfpOgXkspIPgJlQwKfGtrRMYtmy62f5Km7UHhZZz
+D0qm1Jpdh/14LdswPq2ZcfMmidcQruYAviiQUQCY1wTCDver6BoGv9epLg0Atx/nXqlVQNQGINHp
+R6OfSHfYRFa96p44/HMa63nW/p7shHvebWyzLVn/An8EjkgzE9JTmp1kVRshlbjZ4t7KzfjeRz/k
+edIKs59qKwxTM4gnc4iKg44TnR7IDlrczhIOeZSvU/OLAr8TGKMA8E3NrKRThk9TjTqtSqXAyAPN
+QzbpIcbDHCJflkNohyEAuORTB8O01/+77hcIh/sUCLt3gMuFTkCMenHu29U2eIHf/+MuGuGSyg7A
+zfEFbAJu1/5U8iY40SPJsg2W301y+VEkTEGzNgR1AERG4odUtnVht09soFcBb4uTXjhf6JF0YdfM
+15RS8dNHsdaArwjg8bLc/Fxy1/oujdUOgodWuAHhBYLdLtzXBbvufFmoH6H2vOmbhneA2zIheA6r
+kDPJ0xbLX0CxxJnWg84eiwzeNb9qD3wgHa78AUg8IYvRH9/jex/2Sp76UW14CUN2JY7Cyyo/5Ntt
++wTt5xZ57Fhom6Ifx6332Ifae22mbIRZ0xFvmKQVMKx8qkEuwJM3pfPpzEUwrIC3wiZPazLdeev2
+3TmAk5JiJA74V9CqI+0Wl4qKZ3wz9zKXyOMVzQf4WaCGwO3AebJdqF/2WFvWAwn7lumJFu3Bv6d5
+i/fRv777+lh+SpN5bpJHGK+EFRTPD1HsoLcRXDMXOObNIZ/KI9vqUxT9V9A3tMIVJ2YO8pTgdDrE
+fLHuGAdpc9BB+u0c37XCJ+BLnr+wR4ZN8vgBHKekKM7eTq1C3Bzm3tNr71vWElrhAr1ep4neXqZe
+1zKJsPjkcr4sh7rZ+NvuFwj/oQtM1p+0Sdw8Nq1MIuQUCdTLcrkJp3ZVd0yEU2gepOeJPRrqo6Fx
+r3UEw51B6kiD8J0CziHGo0ih6mRwZ8Bi6KB3e2mO6ai4smXkqyneuEKzGDSk4FUBbHEOKU+OoAbK
+QvNk+rP9pbiuJX43n6Df7wLACwTf64U5qy/LtRfkkmcvJtrXZIJgkeUjPNOV6eoac87U/CEWINZR
+Vn6s56mcCd3sml/g3SyiP95G88v+mHlc6wi/9z3Q+ydjcKn/B1fiXyO22Be3tR40zJu8CVVmmwYe
+9cZBLvtV7Apqr9I+Xpobqwr4/sLLEZsMnNGxDjVl7KeUt8qj+w8mdsFmYp4kpkpfHSDbdQl/6sIX
+spbuWCA4hL99BU9NIIxk0u4L+OLi+RuWcU0zrJMgGm/df3rlBFR4zvJOdScq7Hz4jdZ6vtrLJnFK
+7QpknjXFJ5viU186wIVof/nEh5y7a4Tr6QIBLG1pT0Qo8U37+xIXRXNc23TayMO+Vz1HjbBofylr
+NJ9qwesc4cHDopkWR1mKA01pH48aYQJQBb44gN+1rgWaY264zsEe6kt12hevvIx3SNhT/nObXaoN
+vrGUQ9yKN+C4ceGK+9N7vUyOAMVPbw//D90vEA63acse83UMRxD8k0lE2gzLzx1LA4w4NSIbievg
+gd9BrMKi84dRxA7+uhtEA8TVZP0tTTCZEv16xTMANgS4ZT1rotrLb7GjNC4/qMH8uvNAdq/s2ftY
+U0grAFaBfMG+QvP0iUZYP66xxcnJEhJHbfB9rVMj7qYJZv+ys52Bcz1qmDnipuV1rmdnSMwHrrfE
+pxiUhTd4v7c5xCW7bHGdbpSWmlmETXCsZhEDKBzISNMenff26QI7ZgUtT5uGUVIyuVlh3wmCRzDr
+bXELzGqd+hJi8vxtfAJe8yzkEjpNkI6O8EmK6ypRuxNqaZM2aq0KIJQtJ/f4yJPlpWnLAiAgYrie
+0lztRpTmEKkJVj+BMYW7gIU1xBK8BL7+JbzlK0ypeJShX7A439txLf5k17q5uwztBbk12AacXMbm
+2Ciqa+9kYuZTFqXBE1k/k/qJ01d/GsjLuvoaf6L9LU6r8kDa3fhVSSIL4NbsZ7nnqV3fQKzMcwOq
+f5ivDd+26Ur6MY53lc3xixA5gtyog7TcXprTOFQZur5fR5zNOOvpFIeGAXjPWt5T3J0+tHgAAoKR
+VlM8vWq1udNF62/I7GUSwbpEGwzREt/5kGd9WAMBgM0WMMZSHP1t9wuEw73J2ZaPxOdBXL5MIhyH
+UyMk7slGWP6Um3dEHUk8ugYI5Xe3sG4GESoHf9vGYVBvBzB86sfmmuq8ru2luFT31JbTNMs0PkTx
+5MuFKW34TbS/BnxZ1wQfNMIb4B1gdwfMlnm/LmqCV56bdsR2AMM6WRszpL+DWczwAfSu3JALjAAA
+IABJREFU6B0kA4mHGrjNOab3EGfB3FqydN/Nyi/snNqOXfxW/BwzI973pG/kl33dyvsGivdGH+K0
+Qgp0xjVCt1m4pVneONaa5ENJ36Ds6rPF48oAw4DV1sNhDfKvxlDqE9RXKddVsF4XMPGH5BHmUuD5
+YC6heazMlNoNqgBfvTnF9ZVhfSISQ0kWsswhFj9O4PtFNuFLBdU+cmMAos5rPaxdNxnFSDqQ6aCr
+aWuVZA7k0+OVm/oxf61L+fGBP/fslkfBC0Z4341PaZz7ztdrgWkGYVkmFz7nsoFIMm3gHeTqmGb+
+rdz04xjPuWqmLcJ7SQPzpbiPNcJRJhUvqLoXPRzihr+45+hf8v393wK6JzAcYndLU3kv4NYBnjqB
+zOPirz7yBo+0USYRZZZfD2NC63tdoiVGflp5+QMY/wLhf899qBBOAuCVdjm0D3Z31FnC/qgV/j/J
+XXxvYLqXRwWBqTa5bOPn2083Q9cwZb/ewK+ijtNV+rH2i2h+gTg+rYcJPDIt2Wptv8QkUXmOMf5Y
+MCgbgpc2vs0sghphtQM+guBuB3wCyWkaEdrjKz4KcJmdAXEKg77pleHFtNV0uqyfMKET6GU8c9WH
+JUTM2srTQFXYob3HeYKjzJPaao7orPktYakit7tHaj8kDHLDGCIs5yQqcC3nvXBjAkLn9DD8H/Hp
+3tMCwSeQocXWLOeX8AJwxhNM9jRbAMcFqzFKirausYUZDoA2wi2+d7F7ZFPqC6IdEMvNaQPBX2nD
+v+K+chR1n+yp/fXgFQS//hUDukQbLB+5Wfm91FJmcLsA2n3bBMH1+F/BSeOZk4x+im/8dt8DO5kf
+0V3VY+c8n4LcHs/yM+2B8BvYXHnyfF0IeAQqft5gbMD3JU3HuoHiSuKaleQY8yRFaJPMPCeNL3Q8
+B7CsWuTmx6CBtmd/AsXjyU9sgk0r3H5lJnHfOyh2CDCOO0kqUThfKVuM8meA4imHPJ5S2dpqTL/c
+UwNccWEXzLP1ed5+zO2vjfD/T1xTaMbvdHRa2QqPF+bcUytcj7rF/QR6x+9bE1/ynX5JzCJo2osK
+yQC9eUutaB1Yjb5ywgw2XpSjhFjX+RJdi+ekQ8KgRjjlbV3JjxsAPvy+BAg/gNoJhjc74Ydyl2qD
+04Y4tMYhGLTPGBs/GZOXeFItcALcyZDEX/GiTWsa3U5nNIeAV7oBElficJ4xrBCvwPFuLlEUFTls
+lHkm/Vens9dAne4vpdOH+KOfdG7Y7XKb85bhmC/2S26jMeDW98jFv8vE5KBBT7Cl0HbWVqvQc1rr
+hwLaYgN2js8nGxGRmCQzr2zyouoEwNNWH9cCwfbPV4FiNY1Q3ovOhwFP+2C/vGmJS03lCSRW2GDO
+r0EubVWqmQdTcZjwxprnRjI6+36I29as1gBbvncw4I3Qjpx3gKkd9Kr/JzOJ3haZ7P7rH5noYHjV
+Ed8+/QkM57Xo6TFN5mAcwHFM84jqc8j8sc4PwLbOBn4AvkOT3NdA16Wvs29pcx2RphFwX8dzPv4D
+bljY5ZZybi9T+wiQfWUj3OhC5I3sQ4NzG5XSxG19AMiRH7BJ04jQCNuNpYEetPO33S8QDud+/5xp
+ZUxmugBL999CePQXMd4Rf2eaQT+BnI0cWIwX6JV8myspG1fLcPL3p2uwq2ZKkfJdhHDkb2opNu+e
+b5vWo6Gub9h2vXS9/J0lNJZi0j8bV1DjShkmWikxYWBcgVjr5g9p8sB4AtnQDmd5a21MrW/2ieNj
+RwU87KNX10EwJazaCxNFEvhC4j29ns0myHV2pbSTrCnJRuquONU5TsjlOU6FXx00l0Tq4ldH/TIn
+B7LfBM0RgWAgF28FLKO80oW+batYXa1nO1JIulC7QZo4DmiE2w3M7GvrQdv6P9arYevxrb8p2Egn
+pUXiQ9WKU/HqyS/rd5e0fEq779gnNxnNowZM13C0vDvBVARieRTdplW0YxyBwmrDYj9Vgwpa2ixr
+uvX+NbLKNO2s9AmSnm3seTdtsEvfxo58AsOA0FjUqQC3tA7lnx+daGlSftUp/baHq+bbyqHxmVxL
+AE3L+1Ma+nqwXdtiDs1vefR2lfXr/FabPa33SW/iWz+JJcjvx+9+imv7Q+h4tDP3z1tY6Yo44YqA
+MbNZmpJdvp6gw5c5xB1XNwu/bzztb7lfIEz34Qo4brjfcT0D4nv4z787gTABLgGfxs3WtbvreB+s
+O6rwP7xOHvXvwCIBY5wMYR7nzTsKUKKI3aR6bvki+qrT2UlyDdVutn49XKOSEzOy+TNebQO/PSzm
+CWkqoRpgBb2V/xX4Yl4FjJvMoU7cZPQybr1T35wwM0YQJHOO16WDZNUVqimEWdXB7jyC5OxiLcqu
+WZSReE+TJc2/K7zT5JyTHvOWv4rVjYPUs6GPU3rP28CvjT3UEIy4pPn1Z5sb/St58JRndto13Kjh
+0J9nvnbKOXVXjCu6qBuyBL7hb9rWVBjcsNuA64Z7PBm67wWUrnvN7231wtG1vjLF/ZLaqfvG+ixr
+8NZb2nbhxQLE215qWFHBJRJMJT8VgMwXeQo8CJc2IG0pdcl1fr23/0iCdAliJWLwijLVGAMzSY+4
+5CqilFjLp7vRJOUBGAftK8DdPyBxAL9PaTHvep0mKG+guJ/yIHMpc+AzTcJ7/opTbrbLnyV4dzDM
+2bMW39e4gN4u9WSu/ZTOLUU6R+6/flqEHutacbUrCizrPJX06fl1brTvXrnBYwgt75gNl4WCz335
+Y0ovWPhLg3wCxH/b/QJhuk9vRaYGg8A4fwfNxSBYgmG4jxfZCgg/ivsgtu0RUGUQMuqb8lvetCGo
+/QaOcRdMNMYmsj2AXaQ1KV9daIziT8na5jX6BvPUWnKSjiCYZU5a2qbdpYZYtMM2AbFqjzsAfgTF
+CoiF8TfN8MHtoGcA3QRCuw7VR5ZTHDW9msGh9yoiggP5nkByawMqbkV4pABSW9QuXE5Q74XyDwP8
+Ib/OneefgVCiVQ269nZoeDSTSWqUaduB6T5HegC+3nMcGtx9KqFU0h3qOdWifbGR13LtuxbYKPiG
+VlZB6UKR1O46YHfcXd/BuwzmN3CH3+4489dSCew393P06b7Xz+8FpIPXYvBYCmbXdWqjJsNgcICu
+CTIbEiIIA6jh1C8VHrbgUfs4V6KtiOmO2QHvEQQnkJxxnIcDuGW/fMRj5temPgC4h7R83N3S9v73
+sQgAHuvhh3IONBCMFi5W4cf8eq3d0JY+99wEwSWQtDeNxx1ow6OuCkfrj3m51RXgKraYaQGIMTXH
+1uIw0jDy93T2w7c6aBVeU0BZsyZ9fSl94Z3lNwHBng+JwOtfdr9AOJwfDBTOGXdgm9oPUDtBjXEB
+Y9UGd+3FDoTVJWl5ZXJ4HrumyVqKwDHDktNg+A4Qm6DRgW/KCALjrW/IxxynGSxttGby9teboN6v
+i39LWNJLfvWbhQTBzLMB1hGX2l85DYJaYpOwncPTPIIA+Jpg2HQeRbg9TeE+o/VXNHFMTZDsIyz5
+9EU6Lk0CHVkuBckFfM4geT50UGaYq+OTAmrdFJTs83DigvaSww95vGfaEIe/+rPfJGOtS+/ElJZt
+j1OR6tvYB9DIWMnhPWVzqf1kuot3jOvNHYgxacQHGCatRfu1rxcPvN3jq1IOv+NzqdQMx4s8CYIv
+g90CjhMor/2KwNIAlkb49tAMUyssfgXBMpNtDynTAA4gGA1M7VphDO3w+uNR/I3kioMd0oS3dwlg
+rW9IAClxEwTrWLAYO9OOoPcRMKPlJfBXW9kCv3gFv4i883zdHdjqWLHlqzHJRNvsK7oZRNY14kZY
+40b1K9Wtda8/5+ora9XZMZe1p7s8lD74S37y8gl8If7g5QlUvVjB0w8tXPRwTl8JM70UY57rePm6
+/81BmcVpEr7ivTTBCZJd1/3vuV8gTPfhbYhjPaJb2uCD9nfT/N5hBnEfATHwMwBWBu6os4f1rmx9
+kY7MWxkeSxWzUVne/AC+5Y53AV9LoKymEgTFl/XqDxN28m6ieRafzEjkhGiBB0hPAIoApmj2v02r
+qy+wXbs2eAO7m2b4aqA3QgWMk0EKII6+UTDYNtJiO5Mpag7vWXeBP0Gz0DYB7wI2EWd/CpKFAR56
+P1+GU8Cr49HXdvba5qg/z1P3CjIrfkiPxIQFLpEqAUz2JwViFqyRBa9f+xACfK3PWfXWR/hpzC9p
+Ke1O5ZQ4GoVUlo7Oc/3Vr2AYKG0w+Z9qhAl60wzC71XebYHheATmJiA4TCH0GN/UL3F6QyPcrqmB
+livFtczLNpOGArnRFv0xwp3ZAAWcI77ZCI/Zn9es95SnkXS9DfEzMGR9Cg7JvGPFvFYzzSM8V1bK
+TzAcVwP4QZemAT4A401L3PKgAeRTn/1xvOiAOcZXc2iydmN+XwDvMb+4aqHfsHdDsA6K6113bzJ7
+bkVlUa0vfvaTD5cmGBsQ7nmq7oxrY7VKG3Mx+1bpvqdTPiA+q2wyuWH+cHOtOSCzOFatNME3DJd5
+m4+/6X6BMN2n+vgkPjLf0gBr2v6iXNgF424vywFrIwmLk7ZmxAPTdaStMGspFllEmNArTCQMqgVe
+JhGhiIE7QsPZweY1+nUzbvTZuCky2ofvSWTIVpXNU+ewMlxgWEFmA8FNq2tlF0ytrtgIZ752zNk1
+APThTGCcXphbHeONg67IZKonV/MTvpM2eM4ZGUiPagST4Iw82iKODLcBPsYNkGy90mnuoKkqFJRu
+BUoeR64595Af07aa/CHw6K9ak18noTtS0uZkMCyTFxWtUzU6wtSt3Ec5bwY+z7MB4CAZ74N/rFVB
+8OqfP/oJLI+AWIFw8D6LF95oRoHQAmfc3bW/S3uMikMBYwBlI5zgWq/Vv7wOfpKhlNPWSShBmIJg
+TqlohFGaYo6l6p8W1vN6BsLpf9PuPoLgKld9rjBlQvsoQgPJuZoChnU8sjGQDHbT/D6aQRzzaJ/Z
+dm+XFw3vQLnPg7f4ESdpJ4mjZXrPJg+qHAqUkTyVPZb1ya1ZfHoC3uyLv5ehHFhh0QD7OV7YwouW
+9xyHVsYP6Z5t2/Y1S3Aj18kR3EOh/S3Q7GUugY+fzf9X3S8QDvfp9HfNh4JiakKGnTD2l+fc62U5
+oECmOtLVadPy+sU8rfBieKVvjEyLzy3wi0WD31gCm2GD5VnHVxCpaoP5qKOBYiAfaQj/amI0uzfk
+8JOb8skO/gorWCdoFRCMw8tuPM6paYgHAI58m1kEOujdzCHyX4zchnZ4DPII54LTzOlKBhR/lNmx
+QDLFEVYNJcFsB8QoW1bjGk6QjGS2Kizqb4f5/jDOsx74NBPecvhL2p7kMldofsQ4WNof/dKCxV7y
+DKwK82UpzlXMcjD21K8JaDqN4ux7ct79nTDa+J4qfYDWmXGtsbcYBcQKOgt4zhMh7lX4XoBsnUct
+WmE32O1wWyDYeViEITTGwttctcFeV2mrXtx7GKNuvpM/eGkDu4e8CowV/G42qmPqi7Ie0hIId7AL
+aasuE1AOEGwWc1H0CQzziPxwgstYXK6sz7PunzTAvJvpnyg+mUoMKm7AWMdTc6/8ZJtLk20wrpre
+4h7KSjOZqRQAk8OZmAse0rk9hZ/vgHZPIw/f0rz4mvL2aucUN8fe1/dVK5ztzjp6mZLxLjQZhfJu
+lgxVjlXjfuJcWKGHv+l+gTDdxxphviAnAmADxfXy3DxO7ck0Qt0EwY/djTw8Qz5BsBsw7tAMhm9+
+HENSqFn9jnYXKK4eEdBdqGPVllbYcFsB4F18snwXTF20ThFRYWM4EIlxYrwAsfLV/IGAkyAYO+hN
+s4c6Km2z/T0B4zctMP+1OILhmFNZaOPkz3VtM4QCT3L1lkEZZ8WrZqGvTtWh/Ilz7TnNnPOKA1xA
+8hjL6H/Gsz+DynsZHdBMrbQzYGa5KUjRxr77Z2Z0HtAm1EYznFxLmqR/zWWYRhjntguLp33tM2Sn
++NHfpzE272HcLaX27WldToC4dnP4RUGgigILBYExK7XCt8PsXvQUgNcMiaWpOcybtjCH4Ity2kYH
+4xKv49CBDT8FsG/pBcYKHHdgzDNQdXqVWvcXsp6AsMl6vwBhiX+2tdV03hjT1GFosQ1DW4yeP/Od
+QC/bXr8/Asg6fmN75djmeauqrTTXZ8znG/DVONvTKnQAvTNczFFSI+yW7eRcDn5dpg21fSvsI4zk
+3RtQbvVoud5uxre5mGD4ARzX9hrlB+ZgQyJgDNxj7HPsJ/abe+mR2f1v3S8Qpvt4Bch8hzlE0wwX
++J1geF6D9wPYRXy1OK5ejJsg+Is5hCAp2ggcgR343vTDwkzCsj8O4KLNcNA1tcKX5MlrMIW0KWN3
+TtP7MN3bTYH2nf232mMN+IYApXmCaob3I9DknOCmBT4D4Hmk2uNP+oPoR2mEkQKlhNxpXvqKzxsI
+stV5I5HhA2j2bKrALIJpdkCMZOwzzntS77Ks0Sm+wwM0QTZLzhdReg2+xdsW79l4m06NcGx5GTTJ
+lgqKZPJxTaLewXAfeN02HEneHuIPXRuD6JlcBpxA51TBy8YzzmKXdqr5LXOIiuuANLTATTMcee7Q
+CocZhLuA4LQdljiQTryD4Lve0/D0h/CntJbdUWFrpJPgtwHKaQIRE0OwFnGpFVY6mwAPO+B7As1M
+L/+fgeACh1Iu6HMzj2ja4sUPykxiB0Gt7R9Ab77oaJb5T9ph5NqyndO8cM4Oc7nN6172Ne6lzC6H
+p4lEhIUvNICcL9YpaOU2OMRBAa1svS1v5+ceFVTcSJ8guI3zA03wY9hlW8vqzJflPajJLI9SK1ry
+SOfN5KJP0uDfdr9AONynH9TYNcEvR6eB/jtNIaZW+G3Ju8iyInIroncEGHYEk+LG5Ob1gGWhFc6w
+2AeDYDi+igeszTywGrXCeXWkVpjgOHHDuJ5ZzsN1IucY1isoDsBZcRP49hflCIK7TfAb2D2YSBTk
+LRAcdxp7HkJk/LjPk9nMeGGWzKiMsjFDZqL2V2IfX5KLQGoxEzdUXIGlt76XO+L8Q0YFv77nxiZs
+pPZTfgqJilD/3qF87M9IjjcmbAk+Rz4tcUeiORJoSFh+lnxqhWUo0kc8upekkUmk6lZ47q+fW9BZ
+LJ3WnlZSe9cIUwtMcwfNvs4ORm1ogmC+NIcAygCMxBovx8UhqXlV2+S8Acghq+iGgDkUCNOFEdA1
+8xIYV1wB1g4WOsjbAJ6NcLSn/WO9aPWPPqHSq95RxoYtsOEZDG/9cbki29xMI+xg9tDS8FBG25N5
+3+J0Lp+ue7nNbzOuxjfHTA5XtdroXYHgkxZ4niyzeLQ8Pwk+ndtH05OMBWzOMmyR+aL5HKtn7Lg/
+Hmvt3VTmGRi3Zz9tDtle3pcFP7TAKDeUAVryWWqAE2uwjeSpf9f9AuF0H06+16O5fgyaguKpFaa/
+TpDIs4QP3dDHOisqbGiCKV8oTTA3ybUhE93QtZmZMrXFlEfUDlO26zUJ3uvA7Etmbm4cgyVe0HRl
+VCdn7bezOTvku4IHT9DbrgmA+2eT25nCdop70QAzDyZILoAOyofDOJ+cMjQyPKaIaBfhLxwPylAl
+jm3OL8mxbwTNxeezva413kH6p8D4XGaAX7lbUPC7m1eIwHlr2GXGfgDHZNQlMoR4DbUwiXALDJed
+cOVtxTADZ3dMPk2wSsBBD1rLII29hQEG57DT54ffOgC4HZt2+lEbvE7T9zVXd1zhy04YpMUrwU92
+LWyCqRnmxzWO7UVfn+bR6RE6U5tfgr6pEe5xALWur+D3BJQbKNOyCmp7X/4TEJxAV22Bn8DwyJNk
+xd1uwc8noLUCwY9AeQDm/wTY1nye45/87+kuceU/bbUJilcvFQSbmEl0cNzAbvqBpuF9SCsw7LIm
+c0/7CO97fgvrfDi2+fkpPNOuNj/yvoSYS8QtbgoeKgscoXyzMX1/0f0CYboP70Im6EXT/AbgjX/t
+yDTJU8eqFQB9bE/u1ObvcuArwLEPwEsNsLBIXOb4PoBfBcDan0uud9RK4KsgODfzwAbUONZG4258
+YoHPjsruBMcqG8JfWln+OmBNkwiCYrMOhmFbmZ8AMMx6WxsgllWwHlOD+2kWuP6ek53MUUoeT5eQ
+/FPjS6EyX5LLBWU61xdlRrH38GXtHkc1fUG7XnEKfifw1fJq137SlGyd/AEQp5929WnnE1cIoW+a
+4dAGJ5yOvck7jNe5EGc/ZZD409hO+8yfq9orjJ+C3hkX8Y54wiW/tBG++8tw7mgmEet6LTEZGmFi
+v6S/m8A3zCDUPwDw6WZgm0bZc6UdFpAqWLTbBoeO3Oq66hHAO7W+LWytzVcgLHV30Is9DILRkfYT
+0DUxoUBdp6a4gC4Au8hw8/d4QoQA5fxlWzv5+kPcn6b95+ldhnZQG3xbtTvc58ErWtm0EVbt7tT0
+upCvgl/f87DFA7vaeB2e8pOmir+WsqVjDZ2XPewSrvLdVKR4pVkco4Z6WrZeFyh6S/n1L7hfIEz3
+oWkEIFpgj+V8+KIcbYQ7AO4vy90DJhyJzsmMZ9zaaBf4WcIgNBDUit/WaRCGMoOYvzuItWaikFAe
+ji3hPAPQOiiunihb+Wxu80oEDQFA1LhZB8NktAWGCXxRWmI9G1hAsB6LhqkFxpN2eNcSQ9ol8K2+
+1ByrgH2bASRL6glKH2R87R5OmGfW42TMsR4UwoxXPg40kwn2NfVese5b91842CfMTaGC2nI+mUyM
+W4kU5i3W88/Bv3cuPyIS9eUcJO7l4MtUgkDCBHSkGUrJhbz5+HEuPgG/gIBv7NdtDs51aVLBngY/
+9w1MgiMofgKiaSsMwK+Mz5fkfIBhAGbXAs0oQJpz1jTCrF+v0oc5ukmsQ+MKoTeX9AK37E83h1DN
+pcazLMfQtcBlBtHBM3KiU8NruhckLHE+0zIcAFeBrtykFUjWcQspZV8dmPVuGl/s4Bcd/J60w9zt
+2uaUfdji/BD3Q5k/qP/Jndh1AuVkngMAGkqzG31PkAuU3/EezznyR9a1yYQebw9xNZc5T29bOtB4
+AXQkDfH2hnd4dZza4ohL6bf4472YKQyeykDuk/YU7i+6XyAc7uN7EX1jGQp8eZ7wDoZv7KdH0E92
+T5+eyXuJ6PTtt2uKCwAHUKTflpBXDfENAt8Cv7fH55WpuRr0qGD3foif/am+v+kAyilOtEOYPDl/
+Nvz5O2mDrX06OTXCeAG5EKB7+sm/BoZTc8NOy2Qaxuh0FkhXEfLDvLmEI6MC35ak0ZRnwVw7IEYB
+XpbP/J79Js8/75bP9tCJzXlLqUdrOi/W5qs63WMPAn6TAt78C/i+DIMaoOLtaIFUc2o0oaUA49Nk
+0OlmaRGHPOzTJ9cPxb1jv7mo/LF7EwBbSUfz4ScAvtbxZkat8ALAi3ksjrHMI+41ZbaKrqlbjKlB
+8gTAbEOv62cqoVNSy9z1LdfshBXkrlF3sFma21IvEGQ2ja8VLy+gGvWzvmxfgLSA8x3ckpccNL6j
+n9kv5n8BwwmGOA4UzapE9OzOu2mEaCYK/D6UKcri/Avp5vyp3Ci5l35nDow80u9Y52O6H/LmKkjE
+1PR2ykTnTWS0nrkIatsnjycA5hWHOO991X72sD2k+TFNgeeTJlj9bU28KITsUSgYNzXBWMC3aYRR
+9LZUiQaz3v7fdr9AmO6kTTlmC9CLp5fjztphPT1iaYLVRvgshHJTWD+lYX2nuwhnvbhmcZbZcgWC
+PUwKCgRbaLT0BTQFlLfucTZq/XpZ9e2n60l+H2Y2t2pu2YZQvPoa/SVzbuAXPNsXHQCHX0+MuKaN
+MBT0nk0mGiCW+JaWfUyIvIVjkT52U64rI1Iw7MwtQiLv2pn9h5fkKJi2I9My/WERP+Riz9m49hTk
+ZO+kiBJT7D8Ft4Lh8j74peyTv5GeoT4Q00BxEHgTnx38YqQeXVYxN905akPNGwie+U/u50wEUQCx
+wOI4rnMwwXD0g2YRqhn2+0KeJRyMy+2C+V0gGFj7CJ3t4C6TCGua4fXTr92p2D5CA0Mzh9BpXSUP
+4BLomloCXgHOzdzBCDSQ/lWH+n8Cwh34giDcMNI6eEaATxCk61XBzwkU8zrAMfu7mTko6D1ecdQW
+H0EXZUbEci1aPvK6NpcDxPmo98l/AODlSgg23iICzdhYrt1K149LFMDsIPeO6+dAmMqRorNy+622
+mpQxZsb1nXL2fxb2/NYAkhuvOeCNA83vEhjDcBs5pYMnRii9/U33C4TpPjWNILNNJn86MaKfJFHm
+EPVVufsuIOwI0wan9oFxneAIePm25YWFfS8LoroDFF5C8rJrChgrOAsbYLfUEn9nWTxKb5LrT79G
+1DnejyT1aseQgH216+IXAJ/g9wSA1TxivTCXoDg0wlBNsJhEYIsrELxpifmvAWUUapfZK1Av8QOs
+pTZEbgY6AJa5lilNYeKVh0Am/RCeTqBMQe7BWhsg5hD8uGyfsa63XMU+k4nmMKdQKi1ciZsKc+zZ
+mnvzZ4tzKF5wgH72LctaMHUDymYYS8BnfGw7CkUKsNP4Wc9pfo4AGbKAUqZtqbG/3m7yFXG2UnV7
+4Ql2F1JZYNZrApMnBldKFdYN3FdqhZGAl/bAWGBYRadhfYAjuxa0IEC4QLbyYeXLcquQApjjFe5h
+HGsBV27MqcElCN60w45WT0+f5bXeU9zoBwFt9Nuz/w9a41HmEQw/gmLS7UZBxcMML6D3k6uM2yF9
+CFIyrlmBZdU81hf+pIx30JkAV8bwBI65TskzZB9Y5ur770krPEEwvPj16sMLAJbrG0iendw/QFH9
+aZ+oHq7NTQuXdrjPlx/y1u+K8TbwC8dtcWU4engDeXxrhnH4Uu1fcr9AOFw9Wv7J7V+OW0xlPy2C
+ZhF7/N00wh0iRX+w6OpSYCyaYMNiAG7ADc+jy1ZhAVpO3r4+Y5ggOPjm5WhflrNosz3qiYtJX6g1
+PgFi3Zi5aVxju//AmrLPnBQFwyKv2lgKlBIMaxzBr5wXfHxRLuyFFdAawfKD9hjqhRq6AAAgAElE
+QVQm5QEFwakRpv/Am7aoYJ7kzmSiklisaoBkSLxLfqZTfjpwfElugZESPDM/Bc3R+eZ5dDNHiUPr
+YSt9sIogMt0SOxToyPFuDTW/N7+KkxNAFmRVG2E7KHvsGa1zyisO1Q8JOulaS1ZP4Og9z1j/zX3A
+4/ZZFUDM+AaGo16T9gl6/Vo0dseHM/yK+OVf9sAACIw5HdCbotAg+tIA2wTDh5MjDKUd1hXhGhQN
+E2T2uOaPzevSt+XXsgU8XeJIv5r/bDv8bA7RgXAyOnSwzLLDH7Sq1wS68wqOrbRy7Rr86wRq38Dv
+SXuc45e261cUyLBHAX0KqiZQZfpgjVeWPJI8PuKHX6gF6o7g17lkMy0Z6eq/o7S9WGBw1xSfQDKB
+sed9XmMLm4u2VaMxaTzz6VzrWs858WMY2v/eTM4c+TSfAdwP10X/dWP2MRT7L7pfIJzus9n31EAE
+OSsYPnxeWc0iup3wOjUC6OLzGlfdYn270b6Gpzoo+C1gXEB4fSKZoOzyOC3CqBGWl+VC0KZkCp57
+5wavftzEBcFUyWwZrmmd7Kbrx56w1ep7CdwcY/FUJOjMOAXDQ2sr2mB9UW6B3/ExjRAoR+CbZaYG
+uINgpit7sNcR18xMivQRSAYlc0wNxAp2ZlX80XN9HfjBZAJNQ3zs2Ij8cCe1jKXhLUEIIMD3SEum
+O/Jm4y7z0v1bB5t/JFQn2j7YwXD0JvzGffM2J0aNt2/xmPG6WJAxbX09je+0Gm9rVVLNsZ4eeZg+
+mICq0sBS8JOZLX5n8YJc+m8P0be41aKvC/XVuaVdtThHqXoRfl8M6xEARx8KBG+wpfwJSiWxxRGE
+VvupiQ3/avEZIJ+0vQnGpPxa1h+AcKtz5LFeT4+bYBios2QfTCLy2gER2zcz+FVPxPJ3cXP0uM1O
++CIsEhDrvS3PX/Qz9lnOdwLg6Oss/xSfa1VttnFyS9eIW4jOWlpchcGWUnj18+ZYBeTe4l+k/QyA
+3T0tjLYuFVEjmQ+RqUmGw1BOc17+Mx045662nHCjtbK8jaUS6MYzGHaEqYQrTTzLxv+V+wXCdH9i
+GpEgdz9POO2H0bXAaiahGuMCRmhXxwDFIZBy63md1nDDcLnXqTYHjfBldWqEaoEvrw9r8GSI00xQ
+KHF78FFG95/uKM9XmdDtau1a7ZcSpgNiEPyig2BqaBX4XtZtg/lhjQTBWPF48Bf4rfwNHMsVMOkz
+UC/QWV/sw2wwdNLyTm3wdhctTL4vRoi6BnARfNIPJhMRf8if/Xvu/HATvM1ktawV6GsMS1o+Cq20
+HAMF59YvzzHUfHa/SVSDsQ6oicNKZCauaaW7VJjnab5QPp2lEGuRvZ+MYw1toeeYas7nzVVrxSou
+aybwFcG24lBAc2qFMa5+A65gl/7QDIuZxHo1+I5+TY1wTPcEwDfKnz8OKfoLoXmv5cplSRoLAaz+
+zFcaSvoR4K4BBIJlG+UeQfEZyLJs9m34N9MJ9bPdoKWudY11zLTKk8uW4yFHF9odmt0/sgse1wZE
+jUsnYHf4CdCXgkVA01jTGkfcwDGPrlOs/TH+4PK2LMGuC5NkHjruot7nm5ghxlPa3wPodUmPm79b
+O5cb1Xp7DQTXxCi70D6fwO5pPnKeI7LyCk9x1mg5XwWKkf4jCI7id5Z6Won/nfsFwuE+vQcxrMeC
+CcSCnBKcWY8nSxFWCL5RakQb2QNPElp7TQS7UbNlcf6e46I/6rjd1qkPLtpe+j1As+2AWPObv9no
+CDilkI/rHS/lqTaY2mKO66TpVGb4RP+KGdf+F0DJeCP4FRAMBcRdc1saYYNBbYGnphflZz9Yd7RO
+8K19re4RFO9j2Wf32b/jnQE8cADNLnMegEUxS66N15QGqQmwxMb3WzsPbl/p53zFp5P6a1fIeHkj
+UeBs9BklFLPU26Qeeqz+RpLZUZmM2XCmszBBjUhcvatA0RAEYKYTYGyHuBhhNpmrLesuUgqSseIy
+vYQn59p5c5I37J5PBzzHsOyAccWzqfsGrgC89/wWpY6k7ZToiohND+DrcTBk0HM/n5h5KJ2X3+6Z
+F6VJdofunTZ/IuR1qrgCDeCa0t8zwN3CLA807bGGG9gVf0qSVickDklf2U4Mxm1qh/X6cx72K3nj
+w2oqT1TeN/Pp8W1r7EC+MCUgVXlf5etpM530P2XLlEGtHOlBAWbY/NIMo4Ng3T8oeSjFV5WhOBKg
+e3PcDQRr+kMaym384ACCG6uxraSM/Q8AcfhmPlWM9V81vtfVOvavul8gHO6fDy2012cJDfdFwl2m
+BOsacVfF8TOGa5NbXuGOb7HXvSAAq3iggLwet0wf4i4rBa+De3Z1VgjS46i0kFs3gO8ls/B9Y2mT
+48B7u1aZr9jzvEaV8kln3ZriEiQg3iZdQX68w+XaNgeFSW6QWT/vMnWuBPTOnykjFuhEZmfB0q3K
+9L9oDCXjtXsJHqTHJWcXcxUtWTLcsUbl/PHqLbzPH9t0zRvhjuG8+kdMp/4odwbHVf7kesppDfeY
+yQ41nH6zc/xDeMX19lu7HU9C4B1kFh77vDccC7lNmhQ+aHz5ssu+lR76PtYlaQpoQLoAnqw1XvLC
+10ct7IZdYb9rF/y6M341vBiFGV94izK+8nKzmxv84jji6kAhGZpOOIzfab/61W+DXfz+spO43/0v
+INgdCyTfN/y+cYvf7/hA0k3gIeBDrroWqhFWENxMJcYvp0HjUN0vcFsAjC9e5gttSR4jDvPqxYPw
+H14HvdZqBt1KvG35eGszgXO/Ycu5UFpG3cjnPPmetwG4uT6tPsbp/I71ZR1yXrh7aDhPT2vURZGs
+hEA/6G8DufcI61glf46NHt37o33ybwWeFcxE4ac+5q1rYz1ze40t8ulcJNuTHzGLApeprLoynz65
+jeNb/7L7BcLhvj6c/PtaxPuPL63AfTn+cfLgAr63kdcTFMcd3hUE5wa7PejEdvCrAE3ilN6a4yYh
+UVN2hP8OP88Lzi/KeQBqAcG2VM4Bgtcm+Io2/DBNbcNFwAJd2eIn+UTz9t6XEggn8JFos4Ov0yTk
+3QJ3YOU1TmDMYM23SXWdYTP+OL8Ev0aA6D1dBfM+Sy28s+pdqM1SW05XcdAvKlBQXa/xkeeznuKX
+DRzrFJz69JP7KQ9XuYfDv/XBzmVGvl6/QTWz59X4tL99JprGJW5Gs0MYaXQUTt5WrijyeFhz7JG2
+10/g1kW46ZhduqFa3UiP02Y8+JKC4AWKsc79TSahd8/sdzKQnIMFRi2Od7yQqlUyKeNJE7YD42UP
+0MGsXPMOfwPH57gFQm74NwFwgN7by08ASD/CjzLK6s/50EBwGncxDno9/AI4rZYCQJlegTzqLMDZ
+zjm6Vk/X/ngj/nBVOmpXKx5MvqGPvTNeZuWsDS4zpwKzRbPkWTmvH+XBPvbGA2WMI8+RC5Oxxz5j
+XTUaqXwKifa0iCSo2l0EnVV8XXW8BYr7Gvb64V3cPfEt7eb80mE+beYQZIxaX/PbQ0JrrT8X2K6m
+L7T36992v0A43B9phC/Lu7kExGb45wrNr2iJXeIaKL7WayMJdtOvj+Bx9kP8Xo+MIXtFGcbt9XAy
+X4xDaYi/L3nUnycgOb4i0ln3yS99Cu7YHhGxXtUGbzJKfizoc3dZ2Ry1DcXNg/EzO+TVPLopTwxb
+Z7mY/2RC23UIEWWaKmw06SVixA+B1rKcGahWoeasun7mfkybvK7Okn3vbe+5CI4f8/YZdxST17Qn
+f4nkvd7nPp1o7bnLJTC8Nq10xDXT4fFpy39oRAH7qzjIqpW+dvpomt+mxUmCrO7dcSN0rXNo1pff
+JigGzG74dSUoNsMCyyBoNjiWHTAu2VUEwNSu+SWgw8CPcOQh5bfVi1kPQLeAsWp/57XGCgJe1QTH
+XXoDwNEMpNk29Y09mFx2EHyb8rgXQNyXtvpA8BtXBcVNKyygzbnKHDfpQ66VLrTxAIp595VgVofM
+q48wOg2X7SgS8OU4SYcufsa7jEfnx0dY+59hbO3MuC2cINhqn5N/HvhCnTGOomWujc/TIMp/H+I2
+gDzH1+bzHN/TB+gdOWqugw+SPSWRu5SdiqrRkVn/JpSRQKae4i4cpNrg3+PT/kX3dU0iObtF2AS+
+AxAn8KUGtOJaOGyh0mrOOhBLfwO/9hAPQMGw7GoH2osJBKN2L812fmr5DvtgQyp5dEMQELNOdcn7
+vcLJJL3CCbwxQbE8opHfVrlsTFaaj2NgKJQrnd02qPV0WJ/HjLFi7Ap01SWDtCbLV1ItgIc068KF
+ySpksojMQGeEs30VIK7FRGxQW0IevcCa19gOw3NJowCwrPFn95/kOQLbBOS2pW3+tj7CtMcjzdlu
+o7WhaXvrb8V7zxT02NajSawXsUWik/2r3Xts36ffpamTX6hM5yY1wli2kQ30ojTDBqxPvw17KtDP
+/bdA7NoDVgD3olaYxBXhBMECiG/u1xjbAyDeruhhAmb4BMLT7wWMD4K/wvoWCMrON4a6XgRSHoft
+N83DVlfX5lun7wQ9biAYG+gtjkEyHuvOgA+amQA482odHiPmyC1GBYnxRsI7X5WdfADB6RfaLC5W
+W2cDydXVLIdRD1r5GT7na0Kt+ffgvqNJ189A936Ib36ZH5I1n7TSVAydY3Y+vvWr59xAMEirnrw3
+63od72hAxTGsXwmC5zXTf4Hwv+r++VAd75fhnyRYE3vg97j76mHA6sMV2AVdwt6J6R7yl3AL0CBA
+djFd71rh22C0FdaSNMmL31eCUWnmMFWn2Ut75tzcJ8avTE+ALhTABNJEYV1ltNumM/rV5ETMSqzy
+UlhLTL0EqGPjuHX8HpklrYFgZbKNe3jnLDP54JQpnlODYbb5zI5USTJT1Fom/hq0VrN/YrlPvTi5
+khxPefT1N0cHtn3Kbffb3rfzLHUJdtLwVHD02Q55pNEWh9Kwa2LOpyJ9zfRCBLPvGTqCWgEzw690
+2fxA1/BeoeEF1mOjPJJmaXnLZMKizA13g10rnNpgmkSE9rcfwSYa4dsSBPvtMILgK54DCZhd96Ak
+9IfrS7q741YQ7KIJFm1c5Z9LQ7BQhJf70wbolfA90voTMm950eLW5vQAVgmU0W2Ea/U7PZ40wAp8
+f0rTvZXPXGg729Liqmw86ulmFCb97Hyha4aROTQ9yypfA0Y5nYOf80wgvWmFf3RCCMJQkyf7/FAG
+ks66XOzj0/ytf9IkSbGn99uULKKq/FGn1t3jAtRrQV/+nFkK17xIoAFekdUJhsVuGP2dnL/lfoFw
+uD83jRC7XxtxOLwsF+iywp1A6TLmk7331Mf40zWtPGEC+IanTPvu6HAxVtpsrL28GHADxodek+4d
+QuyxbawYP80yfPzY73xzOeuN7ReMt87StNpEualksyk4NoW6w1yiWmrXNpkytoZHBBg7GX8OqARp
+hoFNU7Ex6ceF9+aj8Dzm86qXzDW1CdGlIwB25HxrfuZpNwTHnr31/IA6D/l4czKb0fDZL2YRo6nz
+LO39aSHl9w95XCNsF1Lan1bOJRMONPeBU/pZYZXu/p6ODnoy1pAa4Hx0pKYPBL3o4dIKB7glWA5e
+2cAwwwQYNH8wAt9FeBnHJ3VtL8UYvQPjHl77o5lJIK7NNjiA8ADF+sIcF7PxK3ls3m2Cyeus8b00
+k4DmOfuhoDi1fwGIhD/O90CK5+sTidrPSpQKjita0hN59R3QwG4U2Xno2pWEu+3WVRhJ4/uD++U8
+aPgpn9BzLz/idOxbXLksqyD4yL6EEzmW7BxguIHeGc5faXwT9PqB5kaTEK//lNaYTIHUfDcnMp21
+xLt7shE2ChYC4GztAHxR/jre9PdluX/VffqyHE0f1otyC/CmhljAsL4s5+3OkOGlbxkysbyHjXfC
+PeebVYKg+Uiu4r8lb3e2Dq4P8Pu11W2HkB6rpnd968fj2Trj3zfdxoxiDgimAQubaAIm7URtuTR/
+aGtqveu0VZKe9m17Gi06s6GfHU/7WRGgNZofFAs+QlMEnPNuL1eIX1UNKfgeATDrwzZVg79uw/5s
+RNhK+UgB8Phi3OxH+m3vn7a19cFO6yD2caImntk2+lR//FEBoXv7ba4OO/AQOm3+aBPog/KXOI13
+39bAAdi95iKBsdk6SQKA4YbfBtUQ1xmxdbqEweJFLxPw60gQTHMIM4kjuLQCyLcAYUUG6ZfJVe0v
+yxw0xDeBb7suEEwksoGX7IMRZafZWd3oBL9V7I7gwzZPzOnAeL3vdwLA3OMBrLlmqRVmXVNzyiF7
+j8jpUaJ8uDFSMGyhARZN8AI3Hjza0OQAJg+dz3w6J5jczj+JH/sq03zEnfL5aL+VqTXOAXjzVN6m
+YUDJALeM2jS/Pn6QdI3LCjhmznExvd6bLrve0nKcEHpqtHLIs9XZqha/tEU5S/nNqFRiIbTAHSj/
+bfcLhMP9iUZ4AeA4LSLBL1+Cizynl+XEPAJu+L6E2C3I7bi5Sz7QL8VaPnLmYsKrXh5WbZna4V82
+PFFsUi74QfFWloCaVwNtjwVW2jgpwjH6V3yGd6b6gQJ9ecibxle0vXbucrM/yvzNEvjAvJdwVq3H
+EzJ09wJkoaJpgqYNUARLXF3rnEwW050o41B/RHLtG51I4EcArHHCeN9A8FN8pZ9LO4Y8we4n5XKN
+dtD80BfR7GwtH4GxUnkvdxK8Ld5H3pi847ycOrNFu6Ts5ZtmLy/jRkz8HQR1wZe49OIHLmS9m3mE
+AOTUCgPUDK+X5RTcBhhW0Ht5+W9JH37ud2IM7rfS8qJpgY/AWPaiw/vpEOOkCIYbIsGJX3ET7eud
+fA5hJ7yB4Odr+ykAhgCpoOfMZ2eeOtf4BIo7vSht1BxyjsmLLTYNaZKmEDF8iVPwi3ziVFD6oPzI
+63vaHnfs+p53sM2xBTIl8+XYOt965HO5l3S/CTlxnZ7CkLVv4eqP8nBu0CmeqvM7/yyw+nCjoaBY
+Ku2geGiMU9hHSxLen9iWzK6z/LtW+G+7XyAc7k9eljtpf28FxPpyHMQ2GB00x5dLk7jzERvbAlLQ
+1aMv7Uxd8nOtZlJ+tcvPLysgBpDa1UKORdiN2bPGOFbNrrIrXm7XAn+bJ8HDOwA+M//5KGZo6dJE
+gn2V3Z8aYPErd5Ds9VMwPBnGgYV4SyjOpOiNlyZAha3HYJnnyFZVYOXc7Nre/isNSUd1QgnKMElz
+4n8EwKMmLY9D+pPr6bNmE5BtGrv5M2ya1suc+tKFQ43mBFF9ZHXbsuiqbuVa2lHQPnXwOddp32fA
+9RptH+JmPm/pMjO3At14OW4Dv4aTVriBX5o2RDjzqHlE/NaNvoBhk/Jtcr37mcYbHQ9gDHRzCA8Y
+4OThXnx5gmH+uNcc2JbGpAtG/lV7MrtlFVd8zxoPfNQKo3j4qms1estNHU0ldp7A/jXG1OfykRY6
+XeRdhivLJRiuBPLInY9WvpJEUv3R/0Oe7civke7ncJU51K9lbPPkgLYb57wLs9YnXo5g16vf74BY
+15McrhpR3jcd8W7jZ1JD0ekLwOX1jLRbT7aGmzck7gTEYhpBbfAvEP4X3T8fTn57ES7A7y2mD7cb
+blicIuHYbIrFXIIM2oNru6Pd3QOR18ggsXiObMa1gZnbJH5tGn2cRkB8xw7UL3VVWzw2LTibYRyM
+73mEaMFID2BsBTgdeSoFN2PTButmz5pl03m/WswD4B3Uml4P9sECeXeTCdFXyC1sxvmKb6RBjilI
+LGY6+iusPAa5CZnmHJrsM+3VdVZezFMfs1FbFu0IF9QnUF5IdBuvzzg2bX/Ww2Oa9Tn35rdjvHT1
+WObTWezxIQxmcdvzU0zMNZsC5JT3/2nCTukuvfAe12zIVTV1jLPEphCcmqYOdkkcGjAuQHu1dDWF
+WC/RSQW5UQf4vUbaLU8BXPs8xzDn4DmtgeCjf+wf3VsAGuAZeyDzHQAwNcQT/G6KAQJb6+BaAW++
+ePcAgpMSW+d07WUSVWa85Dd+sYyZJvidYHlcp//ACfvfw97buz72+sMQ93K9wsdtpSknEKz5t6/i
+2C7vHFsctjg752N3rPZDw+l2mt+KIafVue1nUj8oo2QWWLbPXwfBue6xh7mdkTJaNMSRjy/K8Vzh
+v+1+gXC4f/5EI5wA2HFfiA9qdNMHNwkjCK6ZSyCOJOLmMAHBg7mxPmF60qPoVwQD+JLgHUvDEGZ/
+CwRv94Sr4cW8d3td3GELdjG3xfET/dB0M/laXoTvSFfg+3R0WoIRDRNckumetL/cOLGJcqNBQfH8
+dcOIyS4y5FtUCWUJrDfbc2ADhJQnGd+WdohooGwTBX/kTy3vGA/7Y4OJu6Bf08w2uvSwbR7kRU8b
+/TgD287AzargUxnW7Q3Vln8Du1ufJa/QZObxEZYcPX7mG40+TdJHkxeBbEAlvG9xBXJGPok7zxbx
+bNf6qklEgV95aS5/62hHbxpgjDwr7BY2wTOPDHGxA5pHFMcoM4k5L3u4NL5AB8EyT1M1l07AgdVm
+UOB7+u3HRsaLw3Y4Yx2iHWZc8maWfQDApt1entq/Z560hRvr0EDIgZAXFgxFWUcDvw0Yi/Z4MI0N
+zA7xdNoO8ynNBKiutE7fHP5P4RxIRR77snnCK8enrT7x5gXtuupV0OwtD5muo7YD+fns5uzfDow1
+dq3fy1aR6wMvbdXtHUqgC7UPDvkrAHieHPG33S8QDvf5y3K7ZrcAsOVb0vlSA9Uj1A5meJ2XR4aY
+J1BAGZ6LliDyDiHORywrEH+atqDXlX2x8i+Mbql1dtUGm8UXoaLxKyFJMLkllL6twt+8C4zxsGtT
+C3xm5NK/3Hc97qj9tYK2OTYFyuIvoBwlRBu8Sk4uK/5JJsKRTmYQqnWrtXIpCy11cL75NT/X9id/
+WeVZySIX3uUogGiVf6VZA8BtCgZDPo/hkHbYbk8a4PTb1tyxzM99GLlsF6RvFeyCYge8Gq4nLk8d
+OiS8SlyGXW7KOk11qaadlp61OE+zB4j2N1hVAs8JfHdwrPsNR8B7DiPBcD6F0bTZ/7YtehyfgDjQ
+ALIC5g42Ogieccq70gW/czsD0qPNr6Tlz5/BcKtH+vJU920934leelgnTYamtDHKN6D74i89fr3c
+pSD6jcU+xiufedoyL0N8LMu1P7Xrh7gPw3B0mRfyuGiM1zVPJ60wshzQuXJxQj3DXpNq23SAalmX
+Z3bmrhc9DdnRvN18GGdrQvcvlCCGOYSCX8jHNOxXI/xvuo9flgOgtsAbGAaANJcIoDnNI6IOw7qz
+X+f7Lo3w5XXY9jJlcHl0Zitd+pMggNw+EUsHwXwsVxtINNfiv+H5Hssy1qEphCWDTHsvB74JREJD
+8I0CwGYChIUhQPypFW5ghCgNtesItPVZnGiCmeVnTbC0EZtzn80Xf7sVH/64mVAu6lGmMzDkYP0Q
+JyVzDlTgEZRY4+iN4yYThmi/2vzlcKzmD8Mlg12tPrKnyYj35O4OwHmfUeuza+dZP5U5weEnLbAK
+hNY/WcaTAKg4b36gaJ0x3lIf+jETX/MwrICHe1M7Lj2fcQcNck6RPwNcQ2l3N+A7bzjjhtp1Y0L8
+c8Oi4px7M+KP2t85J0eQLB5vUyW8mPE97G1+pRz3ueFI+FzzO3j3jz+vvI6DdthGeKbT7z2s80Ae
+vXVUPNuTfaUbxFLQNCKUE3nDkn4cNMQ/8A91Y+9JJT3S9igNzPEfhvOaf3CPU1UPYa+/2d7Q9qJo
+mu8QtXDkW3H1FKA1YedpWa3t8foujJKs7vwi82KW/jLPD+w0rmKYOLZ7JOMCChALQP49Pu1fdJ+/
+LMeX4bqZgzsK7F6e5hIY5hH6At0CwmuT3ATAAn4NC/wqkMoPYiCEAnahwO/RL/5EEFyAuxiTbLZ4
+jLOEW9TIbzOHfx1yD9gV5UPwfoc/wa+HVtjLFENkSdvYx7DF3AGo0yPIb2XjoN9lHmGvESKNclDW
+/ASAu3tOifnSwYgGeGOST9z4UOv6ieZvqqYE9E5t8KQN1fgWM1XuvziWYv0as4zxAGSf+r+5E2M9
+lGugl2AItWajG1u44h8QMMe5dU4E2bG+HfhmvAgI1baopvFF2u7rujc+/N79XfpWHQ9aYhv5mhY4
+klLbFGnpl/iWhrjZSpAMpNTeNMUvcYh3AmwAqDHuJGcXsvZB/5JPp2xqT73Fo7WxmYsRsLRtP58E
+2JbGp3791wEzyyjAfTpz2KP8KW0n4jWYfT4B7pQeFyNdgmGVlc+Gm2yt5ke/dj95WSE510yzzzPu
+Cf31oWwJryx3ph/qetQY09+UGD2tmSqO7elSNF+sDz+SPq31kZr1XOPGq22fLtM+Wf5VWq3+Th7Y
+FQXOzS/1NaWNXrUD4waZ8njZBVu+LPd7asS/7D59WQ7XBMBWJ0Zg1xCvu8FRBktqLCAcWuHbkiEa
+wXBSft+YBMlJstxNVhnJfItQLcx6HTfv4G2B8Es+38mPPC2NMJbNXtgCUwPJj1ukPTBtg20dmH7H
+XuEjP+l6BwvCANjbnSmuylLYTayLIVOP17G4VjD4DIZxjtu46SyjYk/ihAMqvjnw8oh/4/IV0Nbs
+kNHz38iTz7rRwXF4jlvBgWVP9rBRZDqexvWWxnobAI4IP8VHmScwfG7nBeiKZPAZwT10WLtNuMnq
+dZv+0+PqIYGP0tjf01SqRpu2pddVwe8U4NxjKrfaC7oGpM0vM500xqEJ3gCuAuKMP4HgUWa6iVIe
+5+cc1ilRnrOvpaR7jyte20GC7kktv06K8NTu8sNC/H3jDHZvqasdQYnp3zXK7FrjDVvcit74h888
+LmBXX5iz9mSqYyDlJ5O3nHbo5NOSbW7sE22MKk/3kie6OLBWTC7sMqHblj2C4Ho6nPTgNRP9phnQ
+l9bKbxs9GoQHc69ubdshjinrr8f86XsU832I41gf6j25V7l8+F2Z9veR8C8QDvcnL8vdtA8OsNvO
+ExaAvOyFveyJSXgLfQJYYPe+Dfe1QKTdi9jXHZMnWPsOAZcyBvFzuSdzBOZ6IrEAACAASURBVJhd
+mfiN+tv5/W5PcwvH+qCH4qDbDRfLX54gGDxClCYSoR02X/V9O8vHV+ucQH6BYrBv5T1cQxOt+Wx4
+QsLXphJNcOLZmDFTzS9EG6wPiSYIHtcPdrvn7MXVkZpgeeZ6LHVgmy9NVjoZqz2mV6/Yj+xGoSQ0
+wZTDiMWMlzerDEuMiK0HL73/YIuluHwFwCd/CRnp7C4c3xI1JpIn89/CQ0BqnK7V+u+9cPqf4sXj
+Pbi6KPSVm8bHdf2xSQdRRk0pUiPM4obS7gYqNrPSBDsBshdy3swg5PoEjI9xVWYjTfTrkfDmZIlX
+j6msNe2PoF3yYeTLqqyXSxOzKMu4fmrEbi7xja4NJvidwLeuXbvcymhfx7xkz33finMeNS+ANIuA
+ITTEK6z6RxPP6QW6RYe2N575ii9tINhbRtQdWlbd3R+EH1h026+6b/rWHXlWpNDRotLtBisIiBrf
++uIob7orDdsVufe2rnNPjkne89b8bSyn7RMf5R78Ijp1qzO+mT88Xa20w3/b/QLhcOdPB+/Or7Pt
+L5J4yxZ4Faj8CGFCIjRYgsfbDfft+A4tbNphkRkJMwh820ndAT6v9M16fjFPapp5ddHiGjzMGmIj
+Ue0MhEmEx/mfpR2222AB4BMAm7wA4sD3EEinTaSb0ynxxFH5YpQ+CXZr09Wmkk2oefGQV3ewXI/a
+hDHfnQUGo0pOIuONoHvFi8SERj+znMYGW7oK4gZ+RSOpjzcLRXiQ7Yn4p771dSr6NDyO4Dlei3V7
+YHvwn4Fxr3EIqaNAXZ4SXWeAPPuuGmKd/w6CFRhJ5uaH0JL3pnViD/HkMTZAb78CDRyHl2WyDhbJ
+jeJxZCE3GfGHJ9gt7e9iSC0MHK6oilD1boD4VGabkx7VALKfAbNeCwwH/IqnHbUkBB66Pzrs022b
+gMc6LRDYFNC1BLJPNsPuXbub+b3imgY4413Sq3MKRGOoMorh38pofOc/JpM6Pz/fwK+EpinWGfPU
+jlaTi9rwpDdJ60N8DbeoaeMt+3eFfwC+CWCf8h4AbbH+LOvRlyP4ddJoFZKHv+mUHzKmxdlTvqJ3
+ZjiDYNvHPzpg7ByKR1DDe3pSOwHw75fl/mX3qUY4CfNk+0ttcIJj5rEicl/5aQhzh3b1O21wfbub
+ir0TOLQ+zSwyi10bgJmSLYRX9MXCLMJA8LoKZnyAJuNbcwGCJ/jN8lFu2TlbguBbZZ3O4IMwS2di
+/6QpspGbiYSmpea3bzwFxVUdGUW3q4IrC+l4ZJ9o29OCsy2AS2YiYEVlQft7mBgUmDLJW1UN2z4F
+vy0XBCFY635qeHQsiSgGCHnHJP9hWmj9Hpo6++0xz97OlKYPzNzOET6jpWzZfpZOX8OVLq1RGB81
+wSKpT3Eaf9L+HuIMcoOm4fBn9YEsLFTDyjrypTeCF4uRNlC88pUglDnfgG7+eU7LBX17DnFwJwk9
+kgluPdqhaRt5j7Ndy2nrb9RL/7vWj+u+FBJ87yPNG7ZfgeRv4AyCvYPfI/D1/YU7eB+55ahqInr6
+8M/05BlWx9Q9lW1+tmvHeK51+2iR14rnVJNIrV6YPKI6YCQ+pYtnS+8mThh+3b/+UiZpo7Js25v0
+ktzc501Z1CQ3DtrdnALZJDUtJSg5j/tUFON7nCbsU3pgmG0Pty0ege3ECPDo8F+N8P8n3OemEQV4
+p73wYobCOVuY0qMQiCFeNAsQfN8LUH5fjjy7lxyCdkNrn+TRawMH5m6giYQ+uueLeCEKY2t4N+cj
+X+AbeUCaQTQQnJph1A/0F6AmYH6d06cE2UmN3/EOk//E7KHKKgMYZhItr/W/fuhPY5rBwre4Ahdw
+MsJgLgP5D8j0NPpTB7JcAwaumkey3QJgiXO3+qzFmPtBOxy0p9ETID/29MO0hr9Hnz7yd7H6iStI
+8FzKR7Lrz3u45ck0rssZDK/raOCHOE1Sre588e0JHBuKHs3RaDMPnZkAmDYTcXGuf6ilTEBy2m41
+iTykMwY92Tk9qVRo7V3baw/xcm0AOAAq45JVF6/lEzYfNNbDseP4ElPeBHUwXEB1N5H4RrwwHXXe
+6KYRCohVQ9y0y96BsVK5zGy/eqV0oGx7/hRHtQhNY4vO6/d2PXOhpTVu1shga0PFIQC+a9OcD8/c
+4nPDau8/Ab0EvKoBfgTOKQWEZpRL924UPXnz9xnUPrNPmeXI1ULSY9t7kltXritl4qn2iBuTh20N
+o6l2tn/GrfkuLXAB4g8P8Pqvul8gHO7Tm5DrAi43XNc6yuy6DF98YQ6ex6atL8yhNMOrlcUceX6D
+XTDzdRd0x0kL5rjuBXS/DWEzXCcy2B0mFRCtLCw+u9nvuRMuKj5sV29CpjM9ATs0A+FLdQyTOfMH
+0Qrfju8A0BOG/YnbSpoyiyfgwUhHmgiMH+470u8l/NrGVoYYQhFoyMh1trzi3O/4eXy69d76gLyy
+ugmnUOEZfUifbJU+c84KwQsTjBlyDAUWTuv1adxP0PKU+7GqV6dF6qbuKe+D5BsppzZ43X6mdNdX
+sNIKRPn/Ze9rtyTXUWUD1/u/8Wlzf4iAAMmZWT1716y5q9SdZQnJ+kQQxtj2Ako597oWKJofaM2t
+BVqOD6+h4ryjE2vOu0CIPIu6PNwZEihTQxEkhmJKDcagt2PGOYhz+DAO5LAp4aPQnTSBVm8Zayrq
+U5EAJPGWHOR8haw25Px5rNNGH63VLlPg0+VRWYrR7hbceU4HwTzvbnU81Dn50M+cvV0YSF+7yeS7
+29Fqabnkpln2TN/OsVFupFWfDUDXLMqADu45PfLyDTWylyw6kB9yidM89phzPN7nF+A+fGZdlbs+
+OkbNU5cXvp07x7Gtm6tMr5+FHHFXD/biXIXiPW+fOgDr7Q9A//maz3WMh+EYj2Ma0YzT9RfK4B8I
+v0A4wqeeEZctMPzlwH0ZvrCu3L8IcqkA6ddDpRLxAsG8CjL8MV/13qv+CYgXDQmKJ0g2LPAJtjf+
+rtgZtOy0wYhpJXRAwPDt6+0Td1h973s9RPjHo19hUVbhqNXaIZbTJcmu2iw29gGYuN7+pQuKCyD1
+QOs3cBOo3qXE84pexViBhAaU09Je/XfSbwLh+LHtBMB3pdl71WANUmkoUUgstPrcxafDl3CWauf1
+vQra0ojWNeQLYTT1yt+FUX9N7WPLvVczNcEw+aFX4CPNcystKketPNG3HfyWVbE+YFNp7ijum2Jg
+giugbsPMNeg032hU1qs8LVoFdLmUMTsC8Pi53ATDQOzzcn0Als+vgpL86AaiTILgOocnlPvl1NJj
+kZuQ6OVe8QPDBHDuxfM28usc7tvQwpxfR16IeM4Rcj6R9dTt6+QRR8IHz/SkebPqLiuvAOM4J98W
+4XgGyKc2pA+cyjYPvs/Xy7l8UbZa6NFyRetLW7fHR3nhh+3cIjY+nKylHbSRnvlddNvI82qTxqCQ
+A0USUGx4CXRXXYt51jxOYNtn9QSI97honTluHR9o6GqMK+cGB/v5xwdsDwzdgmOB3S8bQBgLHBte
+gGAeNf5qLv+l8AuEI1wf2uO/sMDfFSC4QK8owdAKZRUpC3BZgtdry/7YsgQvMOzL2hzA+LaI3/zy
+SoHiBZI9me2PMqezXYaH+IYKxqYjCJ4WYQXDoWDX2yOW+8Sf9saJ5ToCULcPoZbbvhR+6+3BarTm
+OeZd9m7f0x0Mb5bh++5AOIatj4P4A53ghvPsQr/9xh11d0B8p3V464uqrm0d5xqV1C0lFVzHsQPJ
+iVSmJTRNwFGtB8+qZXFdJGmtz4IP2guN2c58DN6L9OpmqoPfUjJ9Er2d3//22J72h9/a38Nax1vt
+lAdxAZwAuVmEOZxOS8t9K9dpfKC0gWCv+qnokha+hTW1tHKyTMeoHrcyaeF1BPjNNCpNsDqswskX
+baltpDF4DK0jp3V5BGfbnuhTp2xVgBf8I2ta65KW4KjZc/CHPOUDcM0rvQBuAd4CuB75Vb77BVf5
+lSf1uORnuurRefkkfAxAyItW6QnRtBhQ4HcDs8kmVYleaJmcdKpvZ4SVzvwhT1rZY56VLKXFNy2/
+JV0SFGeVJQ0VHCfvTVGKEyCuzpXzoshp6xK7Nq7zIOPh3R5p1yB3aA1wvSu6DCilQLVC1XBCEkb7
+wkI5X76+0rtwzIIA80jAyzdUNTCM/074BcIRPrUIu62Pb6TCMw/LMK0DYhUGkLtZdq6F4qBFmMCX
+LhK8ovpDvWIL+BrqausPkp3TZSI7KCBiPhXb+Fv3SWZY+0sFoO4QNx/YC2FtcvwDwG7gD32M1deY
+lmVQ/4unrjh+lZy1hsfKq/kETKiA0EAwiX4T/IYlOD/fR1XClgkc7YGOnJ2MK53WZrFEbz/2OtG7
+DAIjPkOs05T98FypLEYg4NEW5z/HFErdRKAqsD27SbwPCjwaPZTLR2EqKSFPndLTXhmVglp3G/3Q
+15n38mfIfYEDbaVLXuSYQosWuCJNNq/cvldaKexFJODlOuZtXaw9ZULjxa1FHttdVYawSXAbnU1L
+cKUL2SDPIQhOi7CuzFz2Ex9YL+iHIjN0MCJEoXHaSIPuklgrFvRJg6xBiagHHim+ajSvvJRRkbf5
+9mZ5BO0ZFBeI7mm28RR0vnTeJm3b/dbL9UpND20tCwTPMgMMC088guFDvY8CQeJHtwmNM8919ChQ
+O48CfSc4PoXSpDb4Wu7kfASMtcP78JPWxlTttEG7w8Q4kxZi/qZlOPvbW+d97mWUO7hIQLBK9K0s
+wnxGiQaarl1/KvwC4QifPqnoCVrDXxhlGe67ax4vWFqDyz/4Nj8A4gMojpoSAKc+WtbXC7WHF9/H
+lqHw8tWXKcB5pauKwHiiPKRHHyne8l3AN3yUPV6fFg/T/YHV69ZuT4tRfpAjr/BXh3WmbDRd+EAU
+JCyVAQei1l9PbbOD0KKHRVhaLzCTk5GKk/MVWlIUas1oXjCINTjbalfbXoLF+Ye0uUJjvVrKOzZS
+aoy33yKuSc3XqVUtaSnW8U4viQ3PbL18Fd7tsUO+z+yphqddeD/1OKO5tvHXJZ71VguiHvafKcAR
+Vwjut7QIR22pNTkuLqDcadANwDpycxSNgJeW3bIUl/+/T1rbW1JOgYmtuy6W4DYyGgie6aCFPDmq
+66PWPhC383tI3hTKBorHvkDmMxLzbTK/qPlhRIGL7qFcf93KgFiDJ6g95REEd8B79A2Ottrr03zP
+F8kwp7RYTubpRNvmuuVNgTDOED5i+W4R7u42WsUzGLaqb9YxO/ouDmC5Eu150wocwhLpRpRHbCpS
+p6TJ3MOMCnyWeex5ExjbtlKyHmR+iMzWsc6LcFizACcAVstw1s+5Upp025cVmFZh9RE+WoVxco/w
+OL562uPfC79AOMKnrhEOS5eEL1AAqQ8wBREViIn+iK9SJBguf+A/AYgJim2kr5ZeX6Ir/5sFPlUg
+Z9zR+kY5rrqWGyP3j2TWwz4FgpeiL5/IO65m/8DLCgxkvPwT13ZWABzqNntIRc0rxHxgJ0NZ3uEY
+yqJW6eQaAYLUe1lrEa4R+lUfFV/IdrzikpeKFDLHt7pCOFxuPx2twwqKsyLV8DIyOaSw9eLAvBwT
+QMCelwIfAtblwYmc6U80Cl7Q/tmQLYzxZ5ho3WuV1nRKXNevqtyOk5Y/3QM56xE3BSMnizCQ2px7
+DGgWycX8QAGwYHACZ65V7JF8MC7GqYCX+7gpVgemfzCBNKKv+pGa2n9qFZZyExR3tPJs1X1leLCu
+5veV6cnV/yLrhWFau1mWa4DBCVbyMy3xheRKJgocOXVnyiMHig/cC8S60OO8CXDPaSQv7fVPWchZ
+PEGoCW7f7/Q6SzMVuLI1Tp3pFA6LsALbMxjerb/P57wfiA+6AsMaSr1vvcBwSUxxf9hPHbTiEmoN
+XYdzno+aVj0tT0y9J060cxVdvjChINjvBMDcHVs6TmcXZOXPABhlDd6ALyQeR4hs+unwC4QjfMc1
+4jLDl3l3kwBQQnIx26oyLMErGwa6RPCNEcMKbL6sqzi7QVR6bRCTdArC4HU+sNeFY5Vpz+5kz2si
+FDwlGMZSggS/hmUJvi+PPlpZg+HwOz5WYgDfWazHNSee88JN4blJCBJUKfULjhzP9iug2R6a83hg
+7uaujtGPW9x9ZgKwvMwrSzPbme4RKYDkuNpmWhapMd5B/Y7iFmMuUByAiVUT+MhFCcUwhZDGyc9/
+I5ZUSRTls/C+zWEFds6iofMyoGuUNN/z+jk13XP/LP6vOjYwDAggJp+qRTgqN+Rcr04faLn/av3W
++UqL9T0AXvUdLl9gBz+kg5kXYNbBB+a4cam5IOmGcKQsz5P5ToTzclGrflkHxnVlu373XIuGd7zW
+lsCY0q0Ar2VDZSVefXBtBN1ajDpN1v5wTB5BtwxHnO8Erteq7Q/L3VoH6560li5an8Hov8kwscdr
+Xvf4HiynUevf4nIBxTsMNvrTge0on0Ufzn3Z+b5nXpdZ/VA3ogLFKHf+2D/lZ4/tobld/iEbPINe
+5fIqeypDPk5a9Ftfa1cViaSUu0x0d1AQzLcMWTDU2tFLeSj4zWWV4xd2AMwH5K6YeuafwDDbA9M/
+HH6BcISPgfCFsAQvi/C69W9lBQWS8UqPhAX4Bi5cMCcAXpbg/7NKJxNZB8f6sFwxWoFhCvgUlMHz
+WxwH4dx2nwKHUD5iMVUQfOeGRFh/qxzuVe+XhbK1AYJDHGj/20/kkG4c4AEEr67vllYCYHlzRIHT
+G/wGNBV2x6JdKWfaqx96LoD+kNx9AMbZP3Y4Kxy1+STpymG+ygcSzyOVeM5WtyBW3LPFM/QtAXoK
+z0L/EB416+sNuJ82wHDSqjxQU+tbvhddFNhe7uFnuocUDAtfWs08a/VQWqUxhZYDPVuPO0hehSfQ
+LUVyAMG0cuncydImzdAemEuQDFqFE73g6Bphfd88hgeN93yeKP5ZmHggIjFl4Fp2H+Gi1+apMXgW
+9BonkBceVYWu+ZCvGSdQLavwE/3s8tBp8zPLG1CW9D5znLsOuJT12pSM4bZMjOWznkFZ3clJLRA7
+rL/JVoJyp4V4sw4zbMIwCCchWQzTywj4LVAM1EXjQ1PIqe0ehTN/TOWe5zEPaifuJcfqoSSM1KGA
+N5VB2+UN+M64AmDqZDzEDXQRrd90g2CcwPji+dTvEv+GNvnHwi8QjvCxawQBXpRPQKwLSEVxS1Lc
+IZIZ4kccXSDYBBh7Y7j8GeOejLie//IEvV1Iyq21oN+6k9E3bykCilAqgxUnCL51c91xsgJi1BYu
+QGtoX7cLN4gLc2Owg3q0nBEdC3SsHA/ng6BYgSkfmBM4P63B7YXq2K3F2i7Tt9cDed6Ab8URQmbG
+c8ZeyAFV6qL+UpCpJZgzVuvQhWyOihZCWOkBoPkR//dCqZxdMZ/AsOS29fOxXn1edlrP6z91exg0
+n+W0DhPtF736wA94t0Z6bM3gSbq3+HCNSFWK5vfo6XYUsyfMoq4R+cDcg2W4v0rN6se0zON3wrtz
+jlgmcmgUW3leYMS5S1FzrCHmzzXNmlU+7r1olP4bVuIXILjTKa9PYHcv90SboWQB5UD0m8svw5mj
+62fI3FiPl/VxtwJr/CUY1rKoso02yrYO5/6yBzogV39RMZnJYs+VhbXuoHAP+wC7pRu0yn0231mD
+d9DbzuN+bfmirzULIr9FqHcWXhLqBII3cAyhs90YuwG7fzDkzRHA+V3CEm/44G+Exn8YfoFwhM8f
+lgP8wgLEly8QDARPxiaVhTRDve+X7hB3WYCXdXQB3gS/NsCvyRXVw+8P4jaaWwpOyvuFAQlcKWTj
+7WbS/ZIXAj48odUGAm7Zec0LLR6Q87AGF+AP4BtKvFu0VzsJhmMer5xWCsXq7waCBfTWmxsg6XCH
+SMswH5ZTIFTuLUpTJenbOYgRYADgiuPQtwLDOqiqESM6wwQEnB9agnOlnOB4SRmOhx9iqFV8ldpa
+bT1pHmtbkXd7a88/tXIOOxj27W8/+iGv4p7zOfP7b1qBg2YCTFpZLm0oV1XS5C0rWlrvCCipxCln
+DN26zzWYYNgBurxsILj5CRNkWDVLAAwBxZQ4Am6eQLDO8XdCqvfc7E+1eGbTEnxOu+yTIA7EkvN/
+6rjtRH19GmURa2+vThNeUlp+Wc7lvcL+yi/49AaJknsnWpvDMRSV8To8dZ19DIe9nX9tFJhg1io+
+wXCdWlbfDRSfgHBXXjiDYC+FyHaybNApPBMMI3znC+Y2QMy/UddgqS09Z63nlbSd9mAO8giCt7FH
+X0401Niq2dIPNo9Rfzu2eEn9C0/uEfIsUzR/BMDwXMvTK1P/7fALhCN8xzXC87Vgtj0YtgqtQ4Jg
+2+PpL0OmMbEQIxjHzgC4vZJEGLQw3gLFl9MHbTHtHZ3z0eWVXwCgBtF3Uwn54fsIxx9McNDjNzy/
+NLPGXABGLcGQMcIW2E5jvVs8eGQPIDjSzvRKuKN92CK/+HbfyM+rBsDYQdKB1nyFR5n5MQ1aoQcY
+znVwr1+rtUJjTd/lOx7SnB/Kd/UR7m8bIB2KxUY4U1+FR0XQqvpenU99W3RR8z5zCUpqLTn/GR/H
+szVYfzsY5rnxVMBeDycYwMktYn40I3kkNG73M34AtRME06cRh/LpOxxzayghhbNVuIFiExr03D6f
+3wnnHXCGDSSrJbjW3/kIAJp1WKt9avRoltK1q9N0fbW6R6tv5ndLsH4uuQPok7tEnbfxpXM/OPwB
+2apkP9E17HvOOiH5YyXy+il2pPoF7yC48goM7y4S78EzOxq9nUJyCrZkGKEPSzCfGbGIq8tSAmKC
+N1ScXVKplE2MWVU3Jc+xaOlpKdZpP9QcQ2186tTeWn4VNifgVRAcuOJAezLG5XuEsb85wmDNTzhF
+TMZ5Z+q72uCfC79AOMJ3XCOIHhsoFiHZrqIMyzfY+itE/jjfBHE3IEzG0ngyzimOxeQGSwBsHgrv
+LsvyLWOgNZhxk23GflMwAEPh2wK1tWtXmc3NP4QEf2ZU1DyLV4qr3osCNOuqjbPAsOUUE4Rweyco
+zjhBcbe+QnyDCYztvnMEHGMODx0Ev6RRDhH8bq9QO1uEgTZreBlcIzy/1KuNdFqCc8ZegOAmjq2U
+x0ENcm3e9PYY+nmvxV613Ptw7hFyXfoh/iqP4Cm+g+L3P5P4AM62l0Eqa0DdIvgAI5Guy/5DgFoq
+SYLnl6DW0UFwuE+0ddbCbCfcHaIJvAPFuaEzbdvi5Lg/DKeitebs++QPDsPLz9B32qNxeWMoO3fk
+gWVzzb2kQ9GKP/hwnNLcT9ZesfK61N/OGXTv/Le6u2bJi9B6vTkWcfvPIVvLHpXFypieY+3Esgh/
+F+R+Ao5Hx1rct/2TzJ1zUjS9a9L3qde1kcmWCX6c87WzjmdO3oEBt/spT8HvkMPNRUKz4uw5/pwk
+nuJS1mtOsAPjBK5vwHB7h7DvD82l4Y/lnaJiya2sywB73KT/XvgFwhE+tQg3SzA3gwheM8MfWn4d
+CYL/3OH368v14ct9fSHOLvwf1CWCvsHLprS9hgSa9mRCw/rQxe1hEb7XJ6BXH9TVYm32P8KUalVu
+YMJkUzoVeggHW8B7Fa0PbNAlwuJBodvnl/JCQV+1HXkNYuO4T3V6Ku2AxKkwFASHchCrbPoG0084
+6y1hRvBCayGkHUjbOND8vpefsADeBYY7MOavv19YKs7Kt5GurBTEVbaJDxeBzCWlUkAHvynz7Sh2
+p/Z7wgIPCuDTjfUu9E4c9XpkuCT6lA6gewAOcxlOs7/9rHjmXVnYsNIFz1Ixp2J05N0KLtBaS5N1
+UoVmqUTmA3FpEEtLsYebktCAfkuSoDj22wTFBLz9K3QFdlSMnBdqX7cJKE5FZjXJu0yVTh+AeHbo
+A878gHUpezIutLowoiws+USgq8C4PrXc3yyx1zFAsLSl4Fk3MGHW88b59tAPhWT9Ebom+SbSwBnc
+PgLfD4FzDTX3hwozy31lIuC4cKTlRkk/8tU2+SraE+GrFxhaHapIk6nZSTEylN6dklfcI6SGbnRK
+IdIuRMgA1f9+TkoqLxxRFuBFuzLPB94ozECL7xdoDQ5A7BO7WILoBMQuoJrrOMfxQ+EXCEf4GAjD
+1ieDHWENLuFijvWZYYLhWNT7XsDwdiQwLoC4HAd2a/BV1mKIjsmf+teybYJgw58LBYDjwxYAUhZc
+EQc8X/3CTVChBACtSVTid2yOOzcVty+tjUgLEo3mBLQXLHysuzcJ4wsAr5HxnFvObwogfgQUGwgm
+4FTXCLoq3Hf216UfVBtsp9F90oeFWB7GKwB8tgbnw3KzFdfaJesQXPJKuInq9KIpQEqZjwJPCYyD
+h5GUczgD3w/Di7dQzPHZTPlTvtIFVDVQEqQNBA9r7sYTh58956nvfSt3sgKDFqvSpOoCoQ/LTaBc
+wBcFcMWqtVn8ZeH7e4RjLsUqXMAGAwRzbzM9yg100gDAE8PoPIyVRICS7XyXgwsgdsotT0FhQt/7
+FV145Ed7TDV+cYn3rmz81j6uIfx5fF1alHmyAO/0LhfY4RNgfN5B7+YipL2NdDZmuabMIX/QrUaP
+n1iFdz9h6fcYS1p0c79JPPeaVdzRwCknMF+bBkMBYiRPWk7yE2v3+VVD9MzfrcCVn3C4+T0cho86
+teuO3g4nghbkhSE889UyTBBMYEwrsVqG55fk0l8YuzEvQbQJhmEfvs2P/0z4BcIRro+dhEMiYa2u
+RdpoffUFSA3xEgUPq6Yv3EUQfLunj7DhaoC4rL/1DuLGQCgGKiZefVkg2OMzx1afXuYGqj0uAHiy
+XilkNuKx+z0f2Flg1VLJQG65c8NHfihOWoEdAXpvS1rzTIn5WkJ+TdIFtQbXliFUpAKAY4Bgiafb
+gtd7hEHXiBQLqcbYVzzS6xzS7+kj7P3XQGoq6qq78dlT8JmQ3vm5nGWrBZAOduEXDU4B/Vk4i+3v
+hcSL2ZfewpD/rYyCjMrdQS9a+oOfvcq3x/y2n0KRFwg+u0D0MigwEI/dRAAAIABJREFUncAXG+A9
+uU00vX/Ia2A3+lquEpZ4VD/FXMIpIxuLFCvveZPP6wJAztH1xKjCK+JRX0oECgXuL9kgp8u7zmd7
+I9m1bnob8iCbTN5qcdfjkk3rYTmxGIOyr+pqD8355N9zu9lj4ak2wqft7DhcFIgf+J7VlEjqpuSP
+ArukvwbBdWyg+QEQb+OR/fQyrzZDzU97dRKw/IRZLsbHjUCeUxadp2u29Kk7o2lfd6nMBSlNh1bP
+Fl7wcbms1H7gnZPCGAF+EwSjgV+9O73u6Hq+IaJbhgubvALEJUJKdv10+AXCET63CEdhDwaKe/fr
+dSOWgPSP0S0hbn95uAa443bffIQXM1zpEmEvadb9kIOZzeO1Zl5feUtrcD0Vl5vXKQM8mFA2RBOm
+cbAQ2lS+3DBAPYhnTos2LYurX/mmDYMAYk9f5VWNCO7o44Xq5xFwMO78EXSujG6BFZ9dPjAXk6GK
+CtIGRdam7J7y0hrswzp8AMSp+LwGUEOvcBQMHOBTuT5THkLegQaa2qvTgI+twU8hhf6TktXRvSrj
+e/IVSNGYv8jfAYPkeZut51/jRXugn89ZS2Y5UU6tlQp3dYRlnCcZoC4V84G404czTu8R3vzDm6tF
++QY3qzD7dQDKJ3eJuY8T3CZBlqeVlQsWG+V0RZuil8WU/FxQBGdLWluaPJWfmR6hdSes6koreSk8
+lM3qw25e+ah3BzercJZFpNVyfPg98G3bNRnlSgOzSJuHw9jtNGFQIte/qLblvQfBlW9yWgC/4DU9
+t41Dx5M0h9z+QjH8kolZBmHoUTCWd2lqGHyvfpucMTWbDEp+OXWQmmTQjTxadx313L4U1ZG2NyRl
+pIWgSSMWUBbgGYenZbfHIfFwjXDb3CGegO+8y736RcZ52Pj/YvgFwhG+BYQzGHB1AFzxAr98iK1+
+AYgNWEBX3SPifcMQhoE1xqwra4DfQ08ADgtTtMEuxx++ISy667GR10cukFdrDmXKkhepdRzyxHsx
+6nSNSGUa59E6/MWHC4Fwi7BZVQojyH7o/peW219v/+kdz9R3A3TSV5ivTfPr3lwjulKjqJnKRfrj
+3sqvfpUvsr4tAvQNhvRndj7n4DmUO8WYu9HzXidCFnv1HR0EhRqAiKXQm15g5RBe4JW/Cy8qm6rg
+XTXzdrSCBS15BBdvf8Mf2A5l7OAznHrM0+Xh2QrsiT5YJi2bpv77JmCYe08Br9ASDItKDcXXrL1c
+cwLblzTRaKTpmHLx5PyYgwzRgaKU3DlxmaaSHwp5SkFPecCOKDSQ6a7yWqeCLW1z8Bak6TO/iKU3
+zlWf4fn6tCw76py+wbN8G+rqaI1GtvI+m88XvX0+UAgmQZyebVJmlaP/r4nmevIJ1vcMv3ShUGC8
+ddCx+f3qSBzYQDDvmD5Yeg3cR1Bim0gf6ZqvPenYTsfizbqQ3YBxxmeV7PuJVZ/W1mUYclcXhTOu
+Q/rpeaX5urSnX8Mx+pDc+P10+AXCET59a0QgScBNgG8HwPnaMvl5WGvvO45uuMMdIt0j+IBcuEQU
+4yjDqDVYmCnjBMqOP3fs3MuWBdQWAJYhxM/7BlezaPoTBp0ak9nQq8rVHsHvErzhs3gBX+IKUQ+v
+qCWM9YTQj7Zo+ZqgOJVPdE2Nqpt/cLxPrgPipVpUGFX9KzUVGjLfJS2/fDDv7g/peY93jSUd3/it
+Iioat0Jq7fJJpyqu22vuvH1fN+iw0Z7Cpnn+mfAwBU/FXvbgOJ8l/Cc4+Z41uAPc27SMSbn9PPaL
+F5WL9z3iBbrqwUak4l7x7j/Md3PzrRD64YwCvPF3WJHBC+IBXlJJ0y3CbCvzEVhmhPJjrEGhsgIW
+tWwnVCE0mdwj9miIVPeZNu/ZfEE6DMaKi4mR3WWG8kzV2a3CPZ3uDX5+WG6V6TxUD8I98Ogon/NU
+bBUXVLV/TcromD7a3Vx3TW5nK9+ggdgGghXkwqBAV8tsgDgHF0w2UaYOXK3CNAIMEGzjwoFDWBeV
+MkdPoHdOEZts0rt1DGlKsue83SYsc5x3Ph6Cv1hZyUuM4QJ+gQaApzW4fILljREnMGwdPHfgK77C
+sd9+OvwC4QjfsQivjbrArILhyy3f26tg2DVuy4p60yJsF64/N/4PAYDTVUKAsYmvcLuKss5IdzGW
+SgK/HV9xkusvbtlNpmyBlivVBPDWAgRgscSSJ0uzGdZG8cvLMhwuJfnid9nlZY1eQq8+e0Gr8K4A
+VlfV/SAUw8kqfMnX5XJECnAeQG7S7ECL33SNGAA4+wIv624NoB9b76SxjEdvG11GMZcyFTGVSolY
+AuNmKxRryitw/KwTTuWnIH5ZwcvwKN6HpT7X1CftUAYDdLz72eAd0/zdV1jBrb4z2HPKo+9iuuPt
+2rIcY4BaTiF97AiGF9hLizEOILi5RsSR4He4QXRgzIm3AwhWJa3jjIoSuEgdHlKl8QIH9oY5DqA3
+rcC6Q8VUGqJJJFcBY6gizn2ClEciqtr+53ECY13/e+Q9fzL5wIuOnSb0ybM6Pz7W5GQZ1j3kI/0y
+pPIIcBpg1zTeCk6Ae7AKs8wJEI8yzx0OYmNu8v6al3z4TEF0MolUMydpsmToK8044TnRymjA2DBA
+72xoSt+uE+yQdaZ70jvoHb9Bb69Bw/lX/sG7hZh3nrW+U7t7p38m/ALhb4YlCD0ZhTRxwV0g2PSN
+CAHjwk0CFMAX8OVLwXwFKPzyAojuiw735UoQZtHFz7F5LyB9B0UapNK0EIK2FNKXWoVt+Sm7Lev0
+pTSODT1yZNYAy1S0AOp2bEzQ7WvO7hBMdSGB9Gu+oy7e7uKbNhDgn3n5EGIc3T2/nJfgFwqIBwiV
+OIZ1RcOJPq3Bs1y1G5QD2M31AxL4vAu7nK9bzkl36VfSV1/Wrdyy+DrH4uMBCgHB+bGRAFjVoBWD
+mTQo6i7HaN2XEtCHQOTU/zBQx7Ey33JPZ0RMAEZb73do4HFzROKQLqDLP2tfIvpQQFlvz9Ji5LK/
+LPZD7P+0cmmndT2xr3GbC/bXEpx4A7lAPjRHy7ECYwIhIqGc29EHba9toqdyMU+5X5E/15ejY44b
+de5h/ctAKPdJnF0X6SfVLsv7iikg9uC7XJutNQ3aH3Uro45gndbOyKFEA3XPSvp3aH8tS1/v7SEz
+GyCrXehoD7TUJ3HpixV7aN6QMIOOvOCjPDqV6Q1+tAg1rFFO+9sncvLm6MAmjGs9XivPQ5ogHxzn
+Zp6qpg4sb4dxnVqy8btwtgBvb3uYP+8PwvXfWtlTfScwTNpPh18g/N3QzW8bjbcwioljm4csT1lg
+xVi06FwBSM1W/GqAFQvEBnjGtUDnlwPOV7hZCEvDesVbbKCvEDRf0ZevFD9oNAK5y+tGTN0SPU9H
+l6miQCRFDZNAEQD9lBeeD6Acc5Sfg6bSCcs13NZbMC6PW588YpUJa6gC5HYbMQFy7632vwEhGXTR
+fdBOoKKGrccn6eR6PBQpkFeEqQhqvaoOj4LKix2EpOZPrl3TWUpnXfSpph3S35U/DuppA8xV/I2u
+eht21usr51v+IbzsxEndvksHrWkcATW04mLcaTF02ZIXG5DBhNDwKuNeYLgsvRCfYEAfhmzXMFy+
+uNBMK7DZ7i/89BNgXMJMxqu8r/gUY7wNQ4yyBMG8vUZAbJMWe3sA5G1fRRennN5B2hluqSuKnxhI
+l5EAl6mwwlusUfnPSksBLOZQ8k06sg/5MDFfZETadqdvtLO1u6WrQpZvyorxbeh9zlLmCIp345yE
+ngne9bkgyrCyH7SFjDdeUgE/4g/82EYiwiONBH4o3NRdzFUMNXVO1JFD0XRWUUB3bfORnkDYx9TX
+JJzFsEsXpaILwJcdgO+Jhg5ijWkvwHsFtyv4tXFefk334cj4T4dfIJzhM7Vc+6mzqsbXnvV9L0ua
+zJnW5GSCBRBvA64Au/e1HNX9sgC+ZS12X24H3Ndf0pN36a+w5NJK/GUI67Q3i8S3XXbGU59pRXWU
+b2QIPr6CzbEeIlynC/CFJyCGc6N7uKQE+HWC30rXOzrVQkt5FnCcytNKVelKdiWXIn0ELTWlEzqX
+vGCxo2H4gSZyL4ulNUgEfWKmzAurMGL+GXfxRWs+chGf0rVMZ0VPpfAAHlQXkvYwxFN4ZEHfInv9
+bxpZ5UYhXbhP+N8kcogTUtRFMcFnrVN+7CKbFc/AxAHWLMhpb1+Vlb+/jdusAbzEIxwNDLP90EbT
+6jvjTz+9nc0LsT6nVO0YeX4qBvIxbo9xO+B3+RBEWd544Rc/iSCzLrIudH7laD3dOi0uEVXGj9ir
+VnvwU8g5utUlGEa/g+gY4Jdp2fTLUU4my6Rc0BKwmOBWWG5bTTOf/ax8JoN/oC4OQCt4nLuiawle
+SLS7GI9HJO8/bcMl/x4WQ3muCUMp4L3v1U7tNR2vtYv/7MB2NlO552ykR3526w0QFs3cOp537Vpl
+IdNH9+iq8AnwfQTE7s0FIn2KYQ0c8yNalxEoYz9a9MOKR38y/ALhCB/eoUbfSJuU3+JqIUYoQZ6r
+OiQtALYUUlmDAxzTXeJCuU9ggVkCEd08X5hpCh+Ui8S1W4RZjn5974Xbe5plnTGGAALNTzlAgFtY
+gk0twfGHwvAGbFiFCwyv8SQgBsRgJBZhlSFRP3VpEzWJ8bo4fwLOtKjuszKtzefgW6THOZ+63lQC
+pSxKSednlx/B61/ST/VlX+tBLc3/BAx/LAM/3K/tFvI8+aM6nnYANnq/wTz5g1owRryhUZ5mJYyi
+/D4CmbXYS7Qwl89rOmS1OU6F7IfmzdbrFo+A+DqD4KuXU4twswaPISw2mQT2cYAP9wD4vnz8CTxi
+w3tYhf2+ExESDHvMtYpr5zSzqy+YzmIua7L2VZhGEQVSIOiNPdHAMOjHXSAYvluDr9g30zp898YG
+FpMHkEyA7QaGcbAW6wiljShwAio+6HpflA9Rny5BXoPi0cg4XfUqWaI6MNbCz25jYxT7wNi0RW2O
+1I35HAzrNB337Pa8mBrgF2U1Bkq/uNSi462adYyoSWjimXzrNXYb4PddOn/ewXD8EvRK2giCcQC/
+XXy0Cze6lP5k+AXCGT7UrLk/d6SSQjPjSEUD1P7WeBNUwRh0h3BDAN8Q5kJzlItE79fD0eW86OcX
+wr3CIP7DwNflaZ3d69sFVFOySpexJgC1uP1OtwhYgF/ZwLAEyFkBXD4vR/Cr8bIE0zpcvsMTAJeX
+rKMsozWeLjQphLieVWpXfZ07zjxFa/Uzy/W5TycFr5wUit7nv5fbIfkrq/Ck51lPrhFPFmPmHTSm
+pSJpcOF7YZu3F3vXqw0/0LUP/pD3FDpeoB+v5Ix46tLNGhyZCYCtNu0UJLmn6k7KKm5p8VJ//VK+
+taarbakugc6VQkitwAl0r8h/AMTLGnypBn7AGnIL95DX0wj3D4f7DT78t159yDs9d0zdlWDYbyT4
+cqt50emde2mHMDO4LMnrkpY/AXeCTgxIEJxgwuNNOREu0ChQaRdasyC7AuiSGu/AcPbXpOxTeeHn
+dJU4zMOcx00/OB/05Hx4Z8gHnj/VrZnTasz9keksLnD1UQ6stgtXxqono1Dv+j4PIhZz7GaD56Z+
+Vflxzp+hUaQfpoNjX3I+kXLmc/C7A+Berj6oUeX0ncIFkpfVd6ajrJV1+KfDLxCOMIXh3Lw8+tyV
+A2r0c2WTyqYiY6YeQVgIqFOgrhJIAPxVT9+V5Kydk60RoNG6+2UI628pQ4LRr2v1usAxoC8M3+ZF
+ry5JP+q7nk/uLoswQkkREMc4PQR5VmgFEKJCgmAeaf1tgJhjFNeIBMDOPCR4WGu0C6AaRdDcWtd0
+rH0C1E50yD/RvR0eQxeoLjJPFbXEY4Id9M+TWjj+A50XCQ3onso/AOVuAa483SPfDtuJL2p6VHKf
+1P+6ZGLVxwLrj0mcvJQGddStVwPyAbjqLMETu9Nv1fKT2Sqs0gLs7ONnluFp0fXLALUCqxOfAOYT
+rT3JrxO7Cwm0XXBa29jn9WGQmw8PrM7fcXV833Ghb8sSbMB6bSRCxqzBEi+oTONFwjv9O2U75YPK
+3fN5wwIMWoUrvkDt+kqX4wRuq5HLDtZh2We8dW94B3CR1uA2SI2YZllV8kH5PleWfeMbG2Qj1Aly
+gfcWEI/TW6ONNvVykje4qaGaN/C9+PQNX3cpTERjCLsgbH7ANvef1Tg4S23uZs+6fG7tSr4pLfrD
+vlfjVm4NVqDWsI5fEj+C4ElzLNcHX/zUrb/iO2xCt4e0LWD80+EXCDM04CLkAy3BgNIFrCUQEeZT
+eUEZoBbhi9ZeINwhxDUiLLkee62+zGZJo6sE+5QeE/LQnIN+weFfPGnZXggsoHxrocpVBoLa/7Xf
+ulBhPQSaOQ50q3C5SdSDcpsCvS3dIuqHDnjFEpxuhfoTFaaKTBVzNi9zp5zgOnDJH1xR9Gdt/6xB
+Ja8EptAoyL3np1yOQlPkrnnfrcKrIiq0vXyjn6zB3s9rim+M6S2QfDEXp6BNvK13K1Cq5nhum3yW
+0xYLBEw45ZYZqwgvpJpvApJB0lKWDUWvpibPjpr4Hqq/calMl6o6nrCuSFMbBQBuT7AMa/A1QfIA
+x6d5nntgykbUPLRz3AHjJ9FRgjNAsONeavm+Abvgdq/nKOI96vn+dLKpDINNzBvPT6r4BKgQc9/H
+27nJQPcIxgMkeMkXBbjTX3hZhz07PX2HV3rtX9Zj/BnjJ+swRp8O+SiLsOox5fuahxq5RX8Wf07X
+CKRCoTtb5/3SL8+8/xBebBfVU5T1T2utQ82HSnkx1VC47eVHGg8WYU1bm8chR6Q2a1RLedr6E7I4
+jVCiFNif6wCGGzA2VJn2O9H2MgYcQLB3sEwREuNX8fGT4RcIfzs8SvdGa2vJh1tSp1Hp1RU/N8m0
+AnsIoA6ApWlxqKFf8LLqSlxpaRlGukYo7QoLcr3vskTG1nbt4gKOosi0PPXZAl7sX7hFiBJZAp8W
+krCOiNJf4Or0w/ZTK3D+c2+gOK/0s68iZhxcsFxVXe10t4jF9KRqIW/lZ3gEXuiCU7FDF4SldFJv
+8JY5p5XnOueUGq0EaaiivI3clFWU39VGMPWsa3OhGKdFEPY50vc5+YgobHjIn0DrfXV73Y95wz3C
+SK0SZQF+ZQ2eICDsj1t8Ag8FxQ9Hq/mJ3VSKnpbd1E4Ffm0Du1dzh7CMG4xfJ5prcBSdfqBpmgDW
+kC9Kv7UQ/aXu3J8Og9+WY9LXRfJhxKyeirfxfe9Ermv0hxCmS7pTCPDb0gSXahWu3xngrnSzBHvd
+OSTBci+WWsi6WzuvAHH1tU0U4wdrsA9y3/piHU0rqhe/NvA24mzvFMfO26eg3hafnqNN2eCZNdag
+Gd2PKAd7rclJQw54S0l6iMxd0k+KCa3LbMvNXhNg40r4HZith9+mG4S6SJQrBMtbpJvLBes0sT6b
+HK2D4Z8Ov0A4wtPt662cE9Sd/DxPdRTNNOJDABkBcAlnpiGg2PMkNBDcWnMkaObY3A3XJcDYFyhd
+tLWhL1v+wauOenjNczcXGkuLXtMd8zZnn4+0BgNpEb69rB+bv3CE9dogy2/AbSAY63gNq/BmCXb2
+sH5rLaWPppxgPT8fIOw7dQ3VD4LsBMR4fM1vJQjPQCGBzDjyzoaBSihGGetdYrNbhVVVlPuHi4Zb
+ZeriQOiigKVoJvg0c2tHom/l3mdbs5U/zFy2dazugzYaIEpiuT/MnZ4zxAKijLin8rVRUdcrMKzq
+cFnQzv2cyj5Bh6btAAoIfA+g1wX8gmB3Wou1zKMo9JF+UwZYr0xsiG3uYoLgC+53GBEGACb8Sssc
+/2lD3DNzYj33mPI9585byVZd/TVKEw6jP6B25bnLUqcPx6nrw8lv+I6L1QmUq92ha1p6jHXm55Dl
+ITuma3jZZMkd2/grZcpwrysQieLz3FS1B+riLfrwds/uG6Tx+6ch9l8C3lSAKgcjZOXksui7tKh8
+0fpik/a+l+kKIVe49dnpojf3iJhue7IKy89a2o9xy7Rt59XRAxAPC7EeDb9vjfjfCY/SG8Bk3c58
+xZwogZMMgAGAVwGn4L+eW9l6QrAZv3zlWpxbVmCklZa+wgTEBsdtluBx1WUSB9ZtLx/jk06g5NjS
++6LAfH1cI0GwlWV43hbEbfDLy8px+/oFEL5uh1/qDnECxQ6i8GYRFsjqgmbKMsr+mwAWtY6X8Mri
+vnL1bFED24K9k+dnYDMVzyjr3o7MebIKlwW16FkrrcIx9lJQUse0iByUxEe66zT472W8rWvrx19U
+xVB1WRKa8hsgQm+xrrUXBQa8tAy7LvwD0Eg+sHPe9BVuA6HwodvDCeA+HrX82qn1FL+09ig+n8v4
+5cAfK2FJyTDFoN/AFaDXrQAxys0sb2sb1+gE+s593Dw+PkFUVkWO4NdK3h3dId7Etzzn2yQ824Pt
+Y2QcG20HuIDMi1Y4M4XW+a6s+imvlPdTCFKGoLODkGcL5yWw+OsH/pc9InvhtIyVtzrAd3U76DMc
+JUT2qb85hwTW0XpvkOyaVxndM3Op9b96n7ORoLfkTA0m+o7uCkF+PIHilXcAwaE/aRFuwNmqnAHt
+1WlpIZaj6fFh1P9m+AXCDG8sdL3sFmlAZEnNlt3KMK8JJyCviGBIa8YVHOLN6csf7x8QZF4ooEv/
+4SPoFUB8ZR5w32tT30DeUry18xP7NECnQLFANPdk5gmgVMsw2ykB7yXgY/gL7K5+8y0RSQPCa1Cd
+ItBSSWGnrfq6mqS1VGc2BJjiPVQF1ca+HtsaHVdvBP/escAvtuNaHy/rJPsqFpjmKxzjpBUNStcO
+0uLAdo5W36rP2ry9QRHf2JKn8m0LPtb1iMxeAJ1SYvPsshpbpqvGdcbCtzppeOEfKXEVHNqwsGQq
+bsa3PFHCmpcuEAvMuoBaewWAFfzG+fbSItwixySADqJj/JuvOTh/An6X8IwvcRIML4sq9/OUuW/D
+4INyqy7f+dk3bjPTNNY6b3I/+sGtclnd/XqyBmc8NpXmmXBBtbso2iZezMXWL9Q+3wZ1nKoBCLH6
+tVnVPfiRg891p3JhBVKTlWzbg+yRjTrdE5rwnzWAoDFLabtxgWXTADDqKVG4t9GZqtJ+yt/OnSV3
+hbymNGgKimNupwXYTAGwWnz9mQ4BxDatybbTbD/mtXYeP9mU/2z4BcIRPte5fow+1aJLqj6BzCMj
+LCZcFtZmFQ4Jlz58vFy64xbINdpwlPVY4uo37AAuL9C7ALEJIAZwFUhawHI90ayWiGYcTAXf58Ml
+prgt+wd6+ZnEUW4QMrU5b+7xwHhYhsNa7Fi3Ca9wi4iP0AUQ7K4SbJtWg3oYjP2jEN4BsQdd8d6R
+g/xlbi/qrfhzOZS7AwX2fvRqNSstC+8aL7Vm+AZLR7LmEM5Vl6iRzTWCzIC+aNNtIgL1Hk85DPTl
+LHxa3lqW73R/Vf6ZLqoGBB287U5yug8FCAnEGbSQBbFpPE9wOdlba2uvdUU/VGEDt4g+TjC8qp+u
+Ekvo+AH8ulqIk94BsU0r8pixXO9T8C3SounzvlWxBFyuY8isco0gCJb0CUF9rHe9T67uxDMWQvIG
+ClTmT0DxFdX1h+MWLQ0CIZuPluHckvHBDRf9Ir09p+2xTBJ5EN4GRmXVlQaGa6ZYtO5O8oxyXWWd
+LotdZ5d1eN/ZtNwaZBs1uBt76AiIH5jAUGAdniCtPk0v54aVuPUs5+5Uv43oqScjdejqyTpt7SHb
+6daBcI1AvPasgC558ewecQDBSQ+LL+sd5xrKAtzA8APtp8MvEM7wDqpEqa3Y+TzdaPn0ZmSQb21l
+dbcIlrHFoOscWy+Ih8d71SCAeOkitvXlBTTdyyeY1t+LZa7UHfGFurBAX4uW4kosVTdooSgYIHdh
+NmTBGMebyowyzuoUhwsI3o+4S7/yrRF8bdoFiQd9c42Qf+DRIXf91V/Yqm8cha81qbG5nLv7jqpN
+PGlD4X/GcXWGTUJU6kAqEn1ALkGO1zkJ8HnLWBYhwZgosOq44cjE0p8EfiffOa3r4DKxDfYb4Xwz
+R0CYf7vK750wFVPCAMt0+Ql7rA3nGfjMAlxxd2srlK9xQoFa5Re3AQis+tZRGkFtWYQtAK/blfEF
+diU+wLFRIB3n9DCxB2zaMLHfxcuapVePbnC/6gLeLT5CZOlv6yKDNlCKh1uyuoHk7gfztodLJTSA
+me1VHQsc1FoRLBzdIfxkGS7QewLMkLECfazaB5zS0mdIHW0C+vbPevuMiKxzG94+XtfWysGNRWQP
+VHWjjV60EUzdxiyqin3U4GmvraqSOsQwZASeqsTU0s39tnXwIZ1r9Fym5jPaJsBt8nhYqZ0PzvWB
+kd953EFvgdxG80nzcTx8WtkewDB5btIeVMe/GX6BMMPHis+T7xmmoi2+U2V1wBEIYeMhEAMw88ty
+S7KtAnn7iO8Ojq+r1UYsELJuE/q6PYgOfGkx1rdITBcJTyv06iitwcC6ZSf4vIGtAftytgpgIoVD
+6i/kcDbwSx0z5e7+eWX1C0b73bFxmyWYR4TXs6Pmlyu8pF2Hs95dC8BxyUN07nXGzlLeqa94TubL
+tKwXrYEd0KrC9fH0gS6ssM7wHGOI1vEQHRdoguYskzK3hG8JXav9ocrr4fbh94NvyVmrPyZ6eATI
+PuKjgdzfNtItb2XWF/ao4mI/B3+d/YHjluyxYu2a+B6DyjlZN6VPXqDAjnEDFpgOYOt8FzDj1wK5
+bhVXQKwA2QiQD2H385Q57pFMEoBsp4agWnx5rX3n3R3CzcQaXGBYDQ7VuZI9DemiCMnOSY49FBu0
+3rGOps1pdWUl820R+t5U+WZQfna5gd7oVgLmFqeVsg2rdUll6XcAMmZ6zN0pTJ0oEkUK2V5aG2/K
+7bmdBvaw5rjeuc19UOtJ2fgKdWlOVRMQV+p1Oz1geZinJ5D7QZlTLzea02JNvuSDyrHLm6U4HpRL
+/BEg9ykNtQYXIC4/YQLg+RBdgGM7gGGNG0Gw/VqE/5vhsEXcxFR/AAAgAElEQVRfFX4oXbergdJt
+udUNDQwDBL7FfLmjUypR0vl6RRE/LwwrQOrcngvl0fdTQWgDvqRdOy0twpfl08jAAsNkciCAqpfQ
+7NiBwqsmykc5+gSn8sEOghsglvMVCF/uuO+yBCfNxUfYcbYMt3Xs/mtrPjwbrgfHIG+WqLKQ2MRR
+ah32UWDy0a4oelTVhlqBNZ9tGKi0PXPXkkTcJA4Wllv32lpzi6ix70CxP4LykS/wywn4ftnJj0fa
+XIi/6kCqRRDq8K5BWyfyVHbCZPGoqFY8P5edDfT8dgvGZPppWYy1afHUtxI3623Y8qulm4PHK9EW
+GD5Zg9UivH7lTnE9r7j3iG30Q7j9IGc8j05Q7A7PI0Fx+AcTHLNKAj7rluCSR51v886y9IDvyHbd
+HsdAIKavLRMwPFwk6hkJvUu2Rt8swxJn82oVRrbFMr6B370MHsuM4Wxx175omSE+CKbJ6fvepFzd
+HwKuuP49BNG11TY7KMC13aF6qm2+L4YT1pT5XsdW1YTLdizTY1KXz1LeAa80nz7DAXoLvCPnhvy2
+W4KHFbjlM89bevkR7z7BVeYAhm3ELcD0f8Ek/AuEM3ymALfbsA8aN6GA6C5ggOHITzCMEsxuYYUl
+MA4wnJf/giIt3CMI+vh1OIJbdYNorhGOdJcoFwmkRfgEKQiIKST7j7eQCnoVEOxzV5apcrlgXBVC
+E8bxK0sw0heu/IH5ev3pGoGcDzgSEJqLtR3IspTmiZcU/DoPJWmnIpxuEC+Dj2ML+xock756S+C7
+zooec8xAswpTkDKuX53jbcQEsiZxDnBYHzoyA2VwdfJF/BGXPoWXZbuf4AcnfLsYlVrqFuHnui3a
+nx5Pn8Ik+AaG860Rn7hLsMt0jZhgmDo0gbi4UKTFes2WheU0j/QNTsBrDfSmBZhpdY3ge4S3GXsz
+v31YcmK91byEGn9LaCUIjjtZahV2W5bUtAjjAfzaSPcZB3ld2X7z+xrnFfg9geB+TJHuO9hdoHgR
+V7nVIJu83MNljVtRPNgTS+2uGjls6p9Bk4NEfKR7/LzE/qKAtdi3jFKncLy4nDLqsGDZlYPMbQKr
+06qWvc63qQ302TlmWqK3Y208cUdQ7tSZypHYQ4bd+st9UcDXj+DXJt0dV+CVlV+fT646o6ztb5fg
+HlA3ip8Ov0CY4T/ce0Btn1fHWTCd1oEEFDeWa8SN5QN0pfKL42XxkNhiclqHnX5xCGALAr6mMxCu
+dPi6FoDkJ5jLIrzcKtb9ueowx7B+ssFeTqhnVMEiLcIsc3NTMl/amqD4vssirBZgTadbRI7fS2Gq
+Uo32y6oToCGFCxIUTvCbAE7+CuSWOeigrOe8D8k7LnHW4RSMqAsulouFr5VYZxMs69Pa9SBdTkQK
+1xqdSaMC2BIMzw6P+Ivwqsg2V59M3qFMqjc/0N7Vy/36shitwlJ3WojLcgwCKK2tgV0caLEHrfb0
+KhLrlmAYua68TT5Bb8tjRrg9EPx6+gpP+rAOpzW4H9njbQ5l9s75c0az40tWfi2O5N2v3Lj3VVrd
+LT6oUVbgdJVA2RLURaKD3+4jr/yv6z8eN4Tug84nZIgJii3BgfahPRuhNBcLcDSpNgvu/dQdY56f
+wC/TFhXvtLkoSni3Gc/5g6vf1LGfWJJpTHaLn2SUyDHjmu5C6lkezRxvefZUbOYn6WAlPsVs0pju
+42mWbtCHOM50eXTRwoKLsv6albFsWomP8dA9GbdpNT74C7MdK35UQPzrI/xfDt/ZjAkstlP2FaRi
+pO5LsRmR9B9zb4yhoNfzaYglCQ1L7sORD8slEEYHvdfVLcDNVxieD9ddUvam9RlIS7QDCcg5jrRi
+z3lIYVSPpm2TZ1mk5s3KwptuGI22frcDduMAfssVJGnoVmGuG8e/HhK07JsPPzJql9XXAg8uA8lx
+jOWfoMu3zAP9RPA9aVJ5u+NAuCXjtRh/9tU68PW2GLQkjhuTAxxvGCA7MoCBHg9z9DwJFRokEUX4
+LnQw8r6db9XT8vcHY9T1IOPNUoUSBJvrw05zITULMkpx8ELOYnOyTYuj5ifP02pt9gh+sf0shMUp
+73q2CL+Z+8d8u4Of187jBvZL9jbfGEEAfFX85i/miGAYWA/+TtcsziuDx5rUg/f1Crxcl8bXDW42
+f2BesKyv8IXsF2txiPy3wLf7CIfxZJSP5e7ANkWtVw9bmerzHE0ZBnSoh92R4nQIOdvO7uENf7Rp
+9gGGZxj7q+7GWE0mq/KH8R6CbQXsENsrsaeyNnNOpYF0bziBXl6kUYZzn6e8XJNBOcDJSwDKeJSc
+FuJ1fLAG529Yg1nWrEBulrMBgE3cI2pv/mT4BcLfDL5Feu5U/5B4lxlL+rRn4rDA5h2CNoFnAlOD
+XQvkwa1cIhw7EB5HXrXRwX25QRjuA1D+CslAsecXcN31pHqzLHgOZUN+eb4SFFvlpj1MFiyswMMN
+w4E73hpBJViAGB0YDxCcH9agQs1+eVmDU3YQZNYtZs+xGE8T5dDg2jF+JpwCR9tPUz0iTbdq1V+Y
+FoDOjwKCVYPbAL4DHFfjowxB8rzVeNRPD0rr0yDjnbX4Y6IHnYe9gk8Wx3I6Use0utUdQrylBXiq
+NdgbTZspmh9omWabgmjKAhxp9nkhZPBVjFSMHnl0IygwvLSSxvFlZ+D7NUCzzMlpusfUbWUmCGg+
+TV/jGB/SWG+KUGswH5ZD/tbdtt0Fi8q3+cwnhfOcsy3XfCorq+8lzvSCpOac8Sv6SACyWYHHEQLa
+TkCZvqEKJszYb5lbsURaK5fS7RH0GTxl4ikc9xdC3mi/HI/z9lBFz/CSKOnaQlmUrwyLopJnUkbv
+enWpO2JjvIdp2fnZJq1bf7kODy32sjZrAlSeymV3YGHniai1L/kRIiAwQdx5FvC7A2Ch+Q6K66ty
+4Q+c5SsdN5420Jtvj+A5/4GK+NvwC4S/Gz7UlRPjZdyEiUWoLQajUBYliXjgKKQkBbWBgDBen3MD
+X5cA4QFu19HLfcK7tfhybxZhPkymktZzDCv/8hqPPvRnMfizhUdVjA0A7BJHjpSbhKC4u0Mg3xW8
++QSTDqAelJu0wkBuU+nN1Tus8yn+VOZVucycldYE9Ru2feoaaHKBtK4jWevPeLvY4bhfWI6nP/H+
+/koAG03y2MFpmflkT+23X1oowNXv7ehOemrnrUKedGEJk+Q851XeKnB4OO5Vu0+ZbEc1NgEutbFZ
+B8RWADldI+Lop3i+T/gCjA/TCfAN+qNrxBPI3YdyGOy93lPsstmvO5xnDbgvuK1PK7PPBL4wyisF
+wwWcfDTZjl5vQSEf8QG5xerdr77xOfr6c2nU4soLE0MAYqil14sWgLf5C0dbjK/z1u3uWybSsi/W
+gFkWsQJNmWd5hqR1X5WjT575xty/3XF13UZ9Dxwv8OUcKA2IPV+7hNZfnw+Std1orZ4TT9qINMls
+h7Jt7ftJtucIva/NYzm521ZzpGMeaaAB39lfw3Rl+A6NLhH1sFwB4O4a8fT55Uv7YWodfpIQ/174
+BcLfDS+10sdFHjVtYgns27YEVGd28/UaFFoWlpANq/JVbg+XW6a/GjhGfIkJYREGcAHWpDDSXOFC
+8zRThNgNmisN5fahtwFFB9fEvZqodE1QT1zvIFfpkp41btOvkqxrgDWCtLIBKVpHOQDxZg+ro4CM
+VHwCNJg+ayntnD/GecHwEr7LKd3q9QIce9UPWHMb6conFCHR2OlWZOuy5MFr/t6A3WPwLbKFE9DN
+M7zTSDKJuAKiAfTXkNdc8QKQ8QmCs12OUzpgg0YgRlpaeA7nVu1D6SkTyDTPPIv9nleOd+2gNSaH
+3w7L7zzWdtG9lHHH4MYx/o1mjb6xP10htH/ef7levvps0ql8iBeleOtXQImQd25Diz6Y8qnI4pUk
+QI51d02bbI9qY4nRei1a8wWOtlfcjxZi32hsXTnBz+OB8kPJM67ELg4t8/RZipIcIgOF2dgbhXUO
+77wYjCTdqZyNWXzD3AbLO6ipV0D/1LK+n2hX5kHK9TZttmYtu0Wez939g5WiovXsNdzXcs93VX5L
+nvgDTWQO5U4e4S1e+0doKPCb58ReyHk0BAax1PFqEc74ZfKsraXn1U+HXyD8N+Ek5b8T5FxCjaYw
+bYJgggue0zcFP8mptxrom3M3mufdTbcOjue7hk+S2du9O5cyJm+aQDygshTBxc1CQSEbXUGwzXcx
+KhLJuYm/HlBhWwMfUW9nei9RaZPZ7BpBOqjKTqxCKhjN1kOMdifYVdDbALD18adiTVAs6vr0poHB
+hIp5iqsepkWmp8a/avAEYtEWax2381MXTlDL+g1Ci5niLe6N2T/YTKfBPISsXsAi5ya5QRXvqPXR
+uBUWQAParfHFA3XhkHwhO7vhfram1voBcE3zZRxq4Vc+eKU7tjxZ1lzifOdWak3Q7cDvPn8mJe1A
+e+qDS2pbD8w1klJ3gOAGgIEdDHurIEGNpJ+ONsonYPU1SUsG93heAJnIcFdQqXVUuua9ZN5nQPd1
+3qQRQFk2uIu1pDWRZ1veYVVQrm0FktdfOV9kSOVKflW702STFs02mkUPeExwKy4nCc5Cxl6Sv9KV
+X31HCzUX+546p+0hb8yv5vmL9lWJyTatjac7jKFrvlzKEwiWPWTN0tvzi77Wt8oKAM71QJUzGyAY
+glmsQPL5EYN/NfwC4b8NXbLP5H9cpSoYAO2uG7eXfvLYUMB3MZQnAHbr4DjfGhHxtATDMw5gPZjX
+pLAn8AVQ1l9KpAvhn7eEpKcVObYJhYgcN6WUOzhG78RaC3kQqK3/ww0CaO4O55+4Q1i1VJ2wdjQg
+bg9DQK+NsiiLodm6jRuffk5gHIvTwDHWYm20jSkoZgZ3JMMYGkBtgypCuT8EJf3jiNKAQkbhRkP1
+ZUUD4pawgFrj4hEhDjeXtXYdOqkP3z8aXGekh9Nt19XNDqJcy8uwAJSF2Avce67PspxvRvDWKBup
+xnr7le+Dpg9JPoWpXLsgQbGt0I39Wbdz6oI3IVZZiPkiL9W/NDQ5N+MAU6qii6t6f+edluTYJysw
+Nz3H4qqs1bJVv+C8RgMgYHXKJPqUapz9smQoy7sDxXdFYz8s+0uAWPYD8hJKrnv1Re0O7rtI9hGf
+c7sDPIGrKoeFecQGULNhfSU5+S4ncN5GKxuw2/mvD1pBofJv9Xutv759QwHvs0W4eOAa+cd5GN3e
+gOxD2Vfn7WUPeX7Oe6q/FCH3B3Jf7Hnk0QCxBMEhi5oF+EDLvUGaoQNgK1oDufHTh+QSDIdV+Pc9
+wv9j4VNjVgsHgKKKYsZ5Cq3AFLYsQYZMK0Aw1W31JOYtjPYVmOcKl4jLxE3CLOPLiY6KpgQ4LcTU
+l77uSYX/8hKUnu4RNna0CpqSgFP49CeBazYWjiKQHe4RTbmOSZ6LZFXKheaZR43IDln2uT15H0ct
+Z9cNi6uRaRVWy3DmIySBaoSgL7ApHGHAtLxOmHFSgkr3AVIKFCd3FWA1TXM9eauXa1Tgr5DAZ64R
+XibS7O1RBH53j43xd+zoW762c957Eg+CgmHYmg/PsR+xZ++DdMq1gy0ut/xP+a+CdoBjOnZK42pb
+rJ1l+dZaFr1wAsV1LFXJu1vRAygI5oqXTCvgrzZGT4Dum1WYt3eXwpY5yi1DaxXqTQrox52mgJfC
+gq4RwdMxoQS7S2SUWwSEliMVRqh6azuK95lYeledtTL7p+j9Ia5j2uO1Pknb4t1FQs/zHJBSk6NL
+ZuaZ5WK1tVeLvuo6bELTiAhtGlMU8Ja7wzOtQDJFbtH6eB/mBz1s82yVel3WHvO2/KVg0R7NfCkK
+CFTnL+oe4JfHDSTjGQQzn301hDtE8LjcAB0gWI8WX2y3tBT/dPgFwhmOKvgh/KVmhu5jf6APBd72
+pSXD6nkF1Ah8fQPABL1lDbb0B3YLMByNukpZBb6Gco/Y3CZCV11WZUNwTUvPLlzxdvqblSUtZQoO
+VrqpcN9FwCYOonGXecxdGzSCXsYByyftc+JzDa51ZRyAeAHgC+ouoWCYIJiAOkX3JhHtLQhOhmkA
+S0oK+EkrsFiAQQuwlZW4p2WqQYtu1HF0jWDVwbfDilwde9oVn+zKpxLFG7PEtnvnfprZjhyr51Pn
+eLAII+NtiId+Md6svO/iDvg+guf++4GotBlvVtc7kJmC4wmKSy1z9PWcuXbsNCfx18ir3caY++sm
+CL5H//rPoMo7fmOqbPyKtta3gZ2cG7pDOOpiPkCwqSHD5PnPAgfcNzaBY5TlZUV+Dh5y8y3KnUEy
+V0TjlnXI1P9VfLPMZac5dzETWcwbvfLIKTN4zoFeGKSeIM8nU5SeqwEW4JruDgbb3CKulg+kWwSq
+XB/BSV+JRp56bJ53TM9ZOpW1kebfodSAnn7xKwtx7Jmo6xUI5ppyj0zazOc+t9APPHLedxAM8Kvs
+CwT/Piz3PxFKgAO5SZ/1kpzDMIFLL5dkYqFRvAsn77fKAiutV/J0twgCYAW99Av+QqVxSZPT9ACk
+hG6gN10kVp882qOjWorBKVEkKaJTpItYH82SsvayWoaHO8SQFU/Lk9e5TSINjRB95pP2CxT2NMsa
+LcKX4bov3AGI1wdQBAzzX1qKo402GVa07dbDicZzBk2Fpeia0PsrTayV6VXYm0WYVt9o49B8+QAz
+s/ozDdtrDI4d3PfxKHDahtoGKBmPt2lUieupbzbwqGIDwzGWhfHXeAoGeU6FZQVVmWsfPozbU39l
+jc95Md/T3yP3l7pBLEi1LoLV6rs2dq1urGbqVqsxyV7KsqbnxV/r85YfCUmLuB9dI7q/MISRhQ7u
+pJqaTIuo6Yqd8H7JHvaH5wBiHQ4Bl9Ip67NaNxsA0elh1rlbXd0ewa4/A99pz9ewga4parCLvlN8
+yQFNN+cH6LCFO1oHQppw4ZPY1IMXoU2TV2EL4V2Sclp/8UjbLMJC66Mdc/WGfizXZ+ixLPyh/Zdh
+arpX+6T/Fj9TUj2AYNmDxnJ5FD9hoCzBxgsQ5PEJ/C7gG26bSXs76H88/AJhhvcct0KTMK+U+OHE
+D4varNZExzXZWZ2+4OIfXCDYbQDi+NE/OC3C1IOAuEbYAxgmzasLPP8OP2T2+0J8EASHOT5MepJK
+1XpKVwVZKyf1IK3EbeK8xeavt1t94WuYco7H/Z0Ep9zhYdVd5AV4YRZvjqj09sOkoegazc7ynBMv
+veBFKc5pzIsWuilwrvPNHDjMueUr5tpnS9PV4TClhzdE5PtEp/Lb+MG3WLMcPQ32Ff3T7Yrs7oo6
+YGXuKzAMpFuIwSQ++y2PxHo10B4M+iSeh9of29H7ujxOh57GQfJdjArBeEvpvqMH6z0HGfe7niuA
+tbWiHGBjeXdFQbD4oqfvNesyDBA8rcLY0tw2u1tEhyUmP0i5DeDo2if49RxhAUOx9AI59+II0QT5
+FfNzC12eOV4yPeO5Gm/jWaeOAU3E1UxYL9PKaV47V/blwXpnY2NPlakgOOdLeRAA/aWz7eD5lClC
+L2vvOloeXz8U92QlbsMd82ODuOft58xymRa5vs/i4Zxg8aMye1Rsz8GiMss4ziBYwG+Pjz0Gr7nO
+uKexR98isfkLXwWG+bCcarSp3U7z9Z+EXyD83bDhjTMw6cW+w53YV9z7JmtVSkZ/WG7Ji2YZlrdC
+NDAs3aTFON0egJSyy+pr4gYR9TWrsC9QjZR1cquD0uPRppVhzy8FtLYjQbC3MpkHtPhTG30puTtr
+TmnpDUmJHcDqebQIX8sS/MJXOH8IoTzVsmmavwKQNUeS9yKILhFLb0wpAN7eV99dvv0D9H8lg3BO
+QynZALq0MNK3uKaz8hRYg0/aH8cwtff3xGGW+MYW1LJyMyJ91Ot2t8f2j95bAVsFe63KdrWgcuMc
+t0nPwysHicN47IMjHLi4mWkJLgswjC/6qpGZY7lLkeqXuOAkbKi3bQBrT7BZ4yWCxfYOxxIrwOTu
+62L6ybrVAG+8YWJMs+4S2WXZP5VQZSkusEv+pnLX2jpbdmCcf1Neb4I779SpG8QJ6ALDRcKfylm+
+rWf0Qpel98JG3hCD/QTDlpWgf2+gUr4ThfeWWLIHOrABYO4/qBxdx2vQTg/FnWh5X06aepyrp+k5
+xEmZ8ugkwSbtuU6d80Fuvxf7hvMIPIPgLDPKQ8pYja+D37NVWA12RqvwBcz3CL+ez38u/ALhfySU
+0vuOvn27sFrh1KibICnmIgDWtJvla9MSwCLcIYSeoBgoAAzA7wDI8GYlpoXZIfEAxcsybENI7kNj
+8BbrmtolTRCcpQmOucePdQ/3iZeTvyavADBq9w6L7gTJV/oDd7/g00Nz0xpcwkNV82nS9Dr8E44r
+n2pacAoEB9ho/JSZ69wArgWUo49h2dVb4enGIhZjWp0JSPp4uL4QBaOldKH7Y5RHxar5iYdezZGo
+7odiCoYJgOudskxre2u87fqYdXTG7f3wPe4VAVq8FNK78KlkMt53J6S6LgHDDlw3/F4b3+LK2Hnn
+CFiWXb2t2RCFJdGMj2XSx9ZqDikv5C0M9A329elIsRDH6BzIdwxL0+al3Mk3ahXWXdZ33LL2W2er
+4JHia0Nd7ClKmmCnM77U2GT5sBDD+/uEo+mT5ffJKqxtTWnysDSSrsFr2dWzuZGrwG4glovmeVLu
+q7Xeyqb1RhmhG9JyWbKD4ndaf9dKdCvxuVyzEvehb3NX49slVZvTh/Nz3JN2KPdKJqmi2yzFGxKu
+U1RRLp6nXJYH4j61BIc868D4ZBUWAKzxCwKIw1J8/bpG/JfDiTVPwfuGxTOvZvlP6QJ04h5iba4m
+NFGSXeKKq9QNYv28XCDip6D4awpoukkkAEb5/IpPMF0q6mG6ii8gHGBJVNBpRjw2o3rTsVyCGd5C
+JUCDPhA3LGQ+gO9hxjk3J+Nr0frEnkCstbiC4PmQ3AXQbUJAb31cA9mBNX7RTBadhc2RjkGdk41P
+HWhgV3xGCygvLeWc6wTDAK29YtcDUV/zBaayEotxvlkhwTKEj/VxMwbVLHMTyIBEKfrMG8EO2f6U
+z2IuSwR0a3fOE891+SvDm74jB1Cc57fFQk8flv+VDMqpk+Xykc4HkxRxRbo+phH8S29Wm6DYciz6
+OsHVBK28cgTL8YG5AMnz4cqjS0Qo57QAh0J3XoDUfFof6vHHWUwwlJM6rcBB0zsgGDzDuUzi4KiN
+jWUvYn8wziHguKVfuUbst9ttJHq6W0JV9MwKtpq57Do4GftsN4vl7RPLC8lkVgHGq1/B92Zt/sxr
+zV5ZhD+mWe9vi9sDXSJP52baD7RZzvcS61wvMHuou/Qktp8nYK2Tm2tEnLc9EDfLoMo0P2GISoQj
+3SCSXq9RW/ik3mpF1Vi006z8u+EXCH87yO7cGPYzgJIP0Aw6axGJgCFvNau16sAAvvG7iv3pEqF+
+wsXFXnHxCXag3s5O0DzSfh8AscVDefEUH8er+ld1POVbWdGkJG89o17aVLrPpa7Dg3MopQhJizjI
+rdzdE+S3gd2Kb3mXrXch6i8A8P6+4FLDBq0T/TgB1JtwKq5uEH6gJQgeYBeG5irBi5GICpBSTeZ1
+fo6wspBDoluFZkbfUiGVspTRRH2dtqt+XeXT/PiW1886n2eSwbnT7TnPnctBpdYbVRjhchIVVE/P
+vm6qQ+e5vTcNJU+4tXLZbiAsvu0F4najXZQh1juswe4eflfBTBf3Dc7HqGfxkB69H8mTzQocvxtB
+AxIE576OW7je58cknmvhstWUDgHFctVgjS/pNnFQBZMbG2P0tSfMdnRrcKXLDYK0t2nWPQdtOk4b
+6TE/1st4o++Ky/rZOCW9lUVXCFGvCQheR8+TrNGQfNStvwVqy/pbVt/HcrJmnReKoHO3lXkTf0UD
+NrY4lt1kiig6cx9iJbRdXBw+uhSlnH7tH0y3CKZbXh7VIkyV1n2Dyz3C6qE5Q75H+Nci/D8SaM0Y
+xF3jfZ7dyxSW6GBRCzXhEeW52eN3hTWnuSwAycIKik6hWYOZNmwAWC3BM32FcGObOaSwAjgHxzas
+KzAOrm7AqG8wJ2e+NSKhXps/R8dTpKlwOVqHM1KgtwPjev/Lbg3eQfCTm8Quhk9icKrsom8yUAeZ
+9MVYBEh8ENFDuRwtwvkgXD0MVgDdwNXoiqoIdI3YwTI6INYxe90+z4GoRuJYNL29KLdOm3yuM+gH
+WuX5RifPHuuQ5hv20bIb01faqkA7wbWwjMnG6LYpAXJT+Zwe7jWtIr/coKA4Nr0A5HxIig8fZDm2
+R+RQRwMa8OW6+zwGCKaAePq0sqGswWlcSIvwVNp9V7Vd5ON27pikRi8/IJlEtPrbWrDtlmmtBq3y
+inFMi6/jyUoM8NPKpDF/b7FSbSuNpdLOKpCedbSteG5oo2U0H+bktuV+NZla8Rt2PW/R8g0euUaW
+8ffvEsZOs8pr/T2s76nMU/7TtDS6H2iDnmlHuUPMgmSouDj0lFOhPSW/7w1P2bNZheEy1zwn4uLy
+1n4GXBaW4ZzbVHX9C7ilPn8twv/18Onctx3/AkZSyTxXsDVf8rBY1yRT33y0gLI15XaFslO/Xb7K
+ZFqKmwXYjHfdy5TA/og7BF+zloDYAL980cUSfG3uEWtMickOCrnABS2Ti9BsbQ4BxNzz/QE57bv+
+dO47bcyzbucEqpC4HtcOnhZhu579hI9WZFqZDKjX453U9oRWRfeNthajqW2d61yLmu/+Jgmg3u5g
+UsGaC75Sbb5L2HM2y282+TfOPfsRk++lx4aeFmDRECfnSvN13CONMWNIWi+juuUkm7fyMkUsYaOs
+9tNGuqJVOiXBqCBdNbKP1uucbaKm08fUtYuXO94SoWD4Cnq6RhjcDuCXm10twq+Ary0erU8V13G1
+EYpWAHC5QdSvfCTFH5gXUl5D6zuq3//RAmMXjRm1KCO+zfsyDnnyEE4MF7QGdqPipI0yLmWfXp8G
+lF6ZXVrd7Hmmg1fmPw2HW/URzR1obn3csb9LZjgmKOkZUS0AACAASURBVIbLGIi+vKy63doLSQNn
+669Yk5Mm3T0Mu+VLwRN9m4J5h0yH/wl9iIEUyQF6j4veQLFqz6qw+QdjB8Elrw4AGQiLb6QT/MoF
+iiEs808/G3dSD+P4l8MvEP5PwtSyW5iZfiQ39Z8AxPobkFIgWJfqWgeZyksneWiCAsGUDEgLcZPI
+dHuIfOZ+3d3HmOde8UlltQRfAYL5YY+2/Xz1kft3Yp/ttn4CXQLp+Bsbu/VfaAWM66/+lBb6uCsK
+KrEEqyvdwPDVAe4CwAF+JY8f1YC8R7gBYLZDsRxrZHqB8qydz/LvIU2g1cBvswQDZT1kmRCkTFMg
+cr1SITkZGA0sK+LKNgUn+Iq0B6ZCAKd9Tqy9BDqltXe3CFGXjaY8MbdvvfGh15HzcqCTI895M7fa
+aDmCoGyk869r3aURj33Vtkyqq+kSMEz0gdr7afkF3AiASTcA8UrA5FvEelvco7+Kf2FbnOcRFCP2
+nltdWC25V/y03hpxb1+W4y3eBMHuBYJlKre1Pv1yjlXAegKdUv6VZ96hRV4XHNak1rbktfaHQ7rl
+vOnyAJytxE/0asJahzI60F5uP5WFvaJzetb1VG7qLbH85yRwcow87NhBsdw/C/4J6YngUEkfaBMY
+W+XpVGl3T/Gna4TzsPf5UXkAiW4llW+YPvC1guJ6DSH3Bc/zBMXkaQBoQNfR8p9AcKW5DhK37gpx
+feP30+EXCGf4cPabmXdjwwPlVVDtVFXbyC0wbE1vFb6g9SSswrbOubDASxprLEBN+v4q5wL5urSo
+mr7Ablh+viOvALAvAAzU2yJs3eJYlml1XbCumKms+27Gth4s4CauEVV60lrew8zvK4cCxNzVOFmC
+CWxJuwr4XpVv1z1ooy70uIlUJXx8UNmHEfWxzbSCH8fpSObrSin9glsa4/a159wRCxPcvnKNqE8S
+k8GBfFAKdX6rHNg3QKKd53mps3kL8AVYeUgrnX+7X9/5McYcj4qO7Xyp1aWfmT0At4dlda8xaTKL
+Lc12p2W4vyd8vSHCaA0OALwqo2vE4n1uevJxXryNfcQ49xf9gWEd+LZ4jP35t/L50JzKUBdlrvPw
+avfUDhOLlj/NZPHnnEp2e2vLZqSs2KTtgNYf6EpbF5JK10bP/VjUtm2mZXiUfUB4r2lPZQ4g2Ea6
+FCDze5r8ZMAOaqeVGHVUC7ACZVh110Z3G81e5evfXSKc+S/OKQFQwWu4cIw7zV5M0ZRo7Y2ir4Lu
+SB/gV5bgl1bil2n52Td+V/kJ/3T4BcLfDqKQN8qeV2GeQ6sFFZrsf8pXrW5agglaJDQAmvopwEvQ
+AMjDb+MGsRp/Zs/1IbgAvvlFOljzDyY45nuK/UZuq3tWbCUPOyjuykfBbgfWLnShZflqav6AAgNN
+4KdUs9zNHQRbWYOvAsRpEb7KJQJB2x6W0zjQwXCuqz1zVJSbeU/pPD4oGbrU7OAXYhFm2hKIcdH0
+XcJ5m74xtbDs4O3k9yCWdX7VyX1S5jbZCMqsXqQG+jLtLW/GWcWs0jQxZrjB0wQ+hzJC2oGx7EJv
+Zy36Q7tPFuz0OGEbnCqZb6ZdB3fH5udzcmr1Nb4pYm1yC2HjvOepV9v5BNqCGJQ/vBME4X+my2Ls
+uQeWXIhJusUafCvwnQ8eSx6VPvo6bsoaVPYnyXdgsMEt86G8PMv2GmZd47Kvy0hHfjWOv6NbhNd5
+u2tEyTPrf3rHKIeUbtYnDYf4kWZ7GXuZxATFZ0uxtf0h99OaRXexcHd9MGB8OMPSkjzdKdoUHPr8
+zgrcaafJqo1rBzKpc99vssurDpkWUXwhXTzm0zHcI8iDO+h9DZDnUd0gvP+YH2KkWX6v8TNLn+Gf
+Dr9A+G9CyPoU6uk7iQfUMsNHhQqnKIjg6VabxUN6kdHcwp8sFNCy6Pp6g4MtIFPKLpTNFVu2dF19
+ihk8lptCtwgHOLYdjNNNoglqX8ZoAA0EnwAKoE/CijU5lN3ROjzmudOQ0qdAgJXSEmlnEIKt+VUl
+vrtFCF3dJE4AmAADpFe/Cnywsy2zB6u8yVWPafKMA/3ht52e6dbG4HcDtlde2dk1YrMOR/m0bpLp
+5ZxsbCI8VFs1wj5PqyaX+Nb17cwj/BHFknnTAtNIkjd8A5NfHfus5tQOLpbbnBjnWBtlHweAAXiZ
+FhCXkxG3c+5ryR2REX7dML6cPEHr+qocLkt/3/bGfOXjBoy9pREyy1jHSK8Life/fGAOgp1yjmT8
+3/rpw1hCc5l3ioji9BcAWPjQBy1OoIxUCy8X9OQXzKAPzT1rGOsxE9qQh5mynTaq+iA92pjAF6hJ
+O4FgEFKKjIg8Bbzv3ghBePxoEZYuTp6ZU/BY7jAVPX+TLtv5HRSXcix/+MFHA/x2v2HKJrpHvHaN
+yLS9c4cYAFrujsiWB+GGioYOjC1fncbXqP10+AXCfxVUpX7zrONpqs5q05XRzkpQAKX4pXC6UQVj
+OfjMCgW13LLkk+EJklGfS77EChlfikPop/ZRDpvgd13JLdDsm5uEfINjDWEIwASh/jyztKz1B+MK
+EKgKKF/hoqsCaRCGMlpAbx37zt3BroXVN9IHALyAcp1PcNBew0ZxzLUGZIIqPeGQ5vW5qkjeoiV2
+1DIJhLrbQwfBQHvrQJbRexlV3kw6QD5uWybAjVn5+g4XiLxt3syZ2XgbctWxz8g7EHyiYcQnQ/Y7
+FkkoRf2QrzzbrblyftK1zlWH9pVlTv1l82pAm2BYTzA9mQ/JwfKrPG5XySRuCQCwOwGwCRhevu26
+Z4ACz9wD9EtXGgSgW7UXQNfDGtwAMMQqPMABh9RkJxQAnXbNYX9trhF7efJa4y3pz8ky2LDerDYo
+E/g6nv2CYzZQAHq22vdNJjTN9WI5O5wzhzKBy5Y/oslMkImKQk65IHnsYwPI3ENlyRUpugPdKDM/
+p3wCyq3LMj3z+B1aDG6L5gr5npd1lGBuPLYpMqEVZ0ZCbpuoa0S59nRTBy/2AHkgDr1sB8GWtHp4
+rliIoHqCYL5WLd0iIv7T4RcIj3BSjqe8v6lvhdNmINhAv81mAmKAumWsBeR8+jjRIrzicashHnZb
+Fp84KXyC7bZ0KrPbl1KLDpL9840RcIkvIZLgN98UYWWZpjJbXcjmFXunsDmMPbevAAf2o7lDpHW4
+ztN532SGFa3rBasjN+kAwB3QdlDcfIK3B+Z4S84SdO8uEXKMjuZDc9p5HY/tY53xNo8mR80nWBJQ
+nM05FwXlw4mw7rnh7BpBBhbrcHPirGpXPPK1g3yDgIfgTURddbfBkpHGbG1D0eIPtD6Z3uM5mTWx
+3vIeysHbHl998lZMVzf77Z2mfTyNAyilmXQuEcd6mhSxAiP2Lj8X7sL3CmpP1uAGhoGdlgA55q7t
+KyRvpn/u6V3CIiD0wxr8CECfu2fZbe1HJW/9xTrHGhYPBrfv/DMYqa9by8x+qkzb3MginNwievn+
+ieVTc2aDuIFZzWPRQcPTWG0f3lPa1S9YhBIZlHKyuUbITJrelj+/N3ixtV78TNoA03NsEv8bWuXN
+XavR2rUtqJBX2eKjEJlhpCk3CX4zHeX0obgls5n27I+B89/LtnyICwTTCohr28sDdPVp5XygLj63
+/NPhFwhn2EXdFHt+pL4Oa99+co6qsF1xVzwARZzjck4yZ+AHG/GLgINM51Ug92IAjWX5XUyplt+v
+AGVKJ/j9yng/vyyu5eOmM6IAOXWxKCHKVRfaFD7ccBj0uV7pr/lySR4UhILXCXDf/AAF0RBQjcyv
+Juq86madSwv/rpjsyL8BJYcIk+OG/vKkAZTHw3KFisvlIcBBgl/rVt+Ta0R+pc7KpxNAWZgzXXWp
+RTn3B/t2GP8+K98I0wWigdzeUnv47aFcTbdnsZp+AUxa19anOLKgPvnWJkIkCC9MIHQVMED2X7lL
+XxzRaEEwuYhWjdfveLBrvUz+/IHOPL8xvy5nQXdahsE8iF9kbXYCyDaOx18BYjyW9fG3L49K5j30
+8rZR1EXi4efALWdNgLzhLjX9b2lutHM6L8Ta5pQ2Tvyo4unUnzlyufq2V+UkzjsHF+LCxXhEHSG/
+4EFLn0PLNPXIXIinFXwfBk+0febHYj4Lyn7khR5dhTIda+Ox8JnGTHuU82qDDylv2oHLzwkxlOWX
+Fx9xDDemBWy7EWgB3YiH2wPzl/WXbhEWINh+H5b7/yq8Bb9NcnRSJihKO+D1F3nz5nkKdBEMl2qD
+kFkKigEI2MUDIN7pLnR9aA7XEk63V5+atWMMXQExIF4baMaqDRATMD87EEygQi1D8HAOddsHBW5h
+Pf0dUKxAGgTGaECZA6IVrUAx0YcMeqy4zmceQ/EJZGv4KeGC0oJPmvWYvOO+geG8dafAxoM/Tee+
+MNp8KYQNkDvL5q4h7+Y8+Ul/fHh8t1c5GVK7C/jJiZmKrPI3oORPPPqmOz5kAKc5slNhwqqc1YIu
+2v6X58qb0xpY5LMDFhuR8M9gwD0u8gRxUkkWqLUzGOYCb7ToxH3Dboffdz04Fz/TdADf9WjfWLMc
+j1h9xzjbz1C3kEf5TfbUkA/U96TswKdqI4J+PIPvHOZeLl90mccEQLanhY9XX04dyopaf9OtLBmR
+m9PaKYyblj0t06DZUzlgAN4AZ2Fp5ieYiyGXnFIw927idV173NuZj7UMvd7uCLUxjbNVnsmdkQLA
+HxyhwLnaaHdTqS+bGinpUHNbd+w2+viaKgEt75QWGFbAey1gnOdcee5Ph18g/JfBT2z/Roh542yN
+nLYZCoFo3lSEQLOKae/aVfHT0ZBuFJCjPgj3OSBm+XpjxHqjRClaA3Cf1IX39oFl7Qi9G2lRUAkk
+nxWTzrUBDQS7xOzhzFan9VZaHxLQnq3ApwfllgW0yiU4lvgmnRIgBL3pHnWfOI+ns9yBd42cc2Bl
+YcWM8pY2xMKCaQlGaoitrJ9B7wnkZtmqro00y7a1/d7xHJo2kjYVeHse2hPZk98cT7MbS3LqSUia
+Q1bOQ/4R0NEWTEHv6mM+YOsucxftybIdj3eA2+uhzA3wi1IEYYt1Q+i49XTjexFMpMET/CboPViI
+ax1Wmg8GzTnjcT4IV3T5ka2zfCn/RXxxQZOr0DZq39JQNtn7yiHwZvWrH99+6a1OhN818AiIlTm9
++GTjU17ggsvCORj1yanlMjUHO5oYPJf9/UDHUrYn+2CwUubT8usyFmnj1D9ptmbl2Td/zpy6NI3D
+WKRDgxkvvtYPymzAl7C20eO87Lmmo+7JwHNTp1wJWY3a3zm/A+zaAL7rgxl8lkZpCpoLPP90+AXC
+/0B4rUjPBY9AIyMme1LTtdVO6S6oH+LmZRF+cVxWXR8A9wR8pUw+LBd+xfJmCcPweRszQL/hTrbM
+O49LBN2RVmVVyaTOVPClGkXxhPYmFXoo7xcg+PjDfh5GGWh9sHBjocqO7plYhw9Xz/5wZLzRRU/l
+bHQMBdh+TlN4FspBwHCBZIh/38rjGykshlBNrvM9FCDzFU/X+QJ6ow7VY++OLdiLPFFkFoRXAJiK
+pqpW7df9RJPvXkmQiUUa6SAPvMsOmUmYyaXEoV3WpA/JFL3GT2DXvqlhSGDb3X4UEIs1mP5ZgmIs
+aa6Vrn7eLq9QGxbg409ePeYJEXLGrI2HaZWlUoZdOpxHYfEEiBahMXAPugRW7RPDzIJcWxVhc087
+e7ftY/KiCDj3uoDmRYvKAPZju7Cdmy7OPQHiMTE2xz3nYgosOda5IhjQ2EjiBXzTZQJdFjXL9WHG
+T0tGGaDDOtWgF+XbeHX/HfMO+Qlu7xeAGNXqo5UYue7KS60jyvjgvBL40hWi3gbRQLCCXcaFdilN
+4vr76fALhP/T8EKHrfxXO77K8CZN5p8e8XaJgwJzbtcSmIauV2gxuJO2hNs1mB4Wr+Gx8vG95MG3
+Do6tXrN2V1l9p/BZmuzE+Hjrpoj672AJfrtvZL7ltnWCFncUEtbZzRZhD+kOgscr1Ao5v6SlRQyS
+jyrj1B4mk6mDVivxGHHTKaoYT+UGm/mc16ZYwyrIgqZATJUNsH04IxSPAtpVDqlb+3nSR2HTqaBT
+nz3MwUm/7onDlj5ps2kdzmxvq3DCQIpJHgWIv8gT+mfqWwAQqo8cgUkZBSmTq14fXeoKOi+ADGUZ
+DAVa/pncDwQmg9cnEHZPMGwutPlLBNfBhPbZtvTZDQLoAAvj3JfhZHGT/kzG2NezaOtnrVwHMpru
+DTR2kw3kZsWL5jVt2biNizXZjQbwwuv8JGasNfmgdWR0WrrwXO4wO951wAaGR3+W/FC3iCxwiI+m
+cu1F3vQZge7/xy2umGDLG8QhwCewfeUasbiAvaqJTVqKirP8XNMl+xVUXWvN89lY4Ogasd6i9Ax8
+N2AsVuGfDr9A+G/Ck35qCvF753dbEQv1tB22oEGYnEIhz+6gkiCCTMwmbojfsCCNejVafxguj/FB
+jv1jGtbKeTiy2T0V0GmidiVO2a3npv6M8nb47U2IlPWZdzyhBKlVmz1+tvx2X2KrcpKn1t8ECKj6
+az0Qa2/SIYhlWHqfA695fJK10xdY40P+Fq2V9RiDKsG4cege47GsoCzBhgLEFMAEtHSXUH29mJW+
+x9m3Vs1KNP87VN5Jr2r8ectm7aG/qiSfxtZ0lnAFxIuLpzfK3PWKUV4KkY2naw+oMmbPX9NTu69D
+7Iu07g+eb7yZcfoBz7SlZS75mzxD8JtgGFXhBMZwsQATADtcXCNMgAFi/juq03Wrox3SK+6HvNqj
+CbK4htwTco7nLI8wRTv7OYqu6ZpwrXw7uZb1jH+JtRqt114XS3AZRD0rIhDKV9dtyqw2nOkoTYQD
+gXOfhKzGlH46VsPnLTDK5/o4ZG1c1kofhONDuuxrjHXMsDbQ9+SizKWqs72lt316FMSa9pf5r8Dv
+Oa7nYKfZrgbJA03VGBL85haFbNXIv9QfOB+Ws+b/uwHjBMNXA8s/HX6B8L8Y/EVqBt2CtcnFGykE
+TbMc86oblDeeRxrkyLCXO18dXG9lsHJHUOsfdY9aftU9Io//j71vXXdjB5WEzrz/I5v5IQFVgLrb
+SXbOnPmWEi9LCN0lKGNaFj3yxK/LXZK/nnFtYfDxIdUNz8LHlY//OMhkLfYDi1ZinNNxCXCiqyQ4
+lHeoEf8C+EoHwocXgWFh62/N97ZM832N1X2BlXt5tArzLLQpGPKQgHojPiCZpLCy7WVNX7vuXeh7
+NizHqz/xIx6+mzHbh1J8glE/irfXyuSeYuFOsOA83tsA3FafEGA/3vZxFSzWBJ6OHTn0DIAJq+/6
+MO2pLoVkbUNjHP5gHObkuPSB5q4QIuwTbLG/CQw7kNrxtBDvGhEIm60H5vbXw/4Lc/4AUQfD+w+s
+l/cWT0uNn/Ny7r2L5ycMvLGy9iOK2mfbcsfnOk7rrTSWtPGt/sSwoR3akZ5nayDoJSAi+YFVK6/z
+7MguHx/6YG1DX3k8XKbK+KFbLgpOeS2UWybisxOmZe1D35O+x1U5HX2vjVvtMp9tiTxrxek73qHq
+2s4531n2vh+BsMxAOSoz6rmVJtq3fz4wP4KSxzT1Xp6D5hoBgBiBb9IY+KKv8I9F+H9zeNKqowIq
+0mqQEgSGN4/G0cu/LhxTZiUkCJ1iefOC77V6t6/awq2m299XJ4C7f3WuWYLzXa7d8kdCIjnEi0ed
+P3XSdo8HBbIAu4bsTbjICkta/CRhXPhYtNktdhLg0/+mYKiA2F9XebElOK1hxTrsI2o8Er62IeXb
+e9tVFCddglsO4t0qDN9u6FTWNeXKsLA2WVpZdh/DLw0AbKjwNcBcJ9BHuv8mcM5hG9SBIAHDaU6e
+8s6hgJLitsEWYU6HspTDPjv1BEjkc4j9cMaYk57vgIn733mkxO/P1Trs1Qq8trGJA2CtYNiFUZiY
+/cOSpHbFeHV7+CTotY+JAjhgMGzjGPUQf8q/Kyd7frsKV2TgxUdx74ItEll6jUQhDnveEhR7H7B0
+nGs/u75sQbdGr73Q+LuZyCrsNAmZQD9wA8BYsRoeZqdFHhSYyklZl5ATuafQ5x0/wM8nBftEpyvq
+ZR4RK7QqB3qVZQ+U+pi/8iL4nX2FN9fu3PzwHHDkrp2Uqa++H1V4Dz9hle4aUazDCIpr/BpulvjX
+4QcIfx2qOppCObgyHTM81SgsS1pkWwpY5LogwqPqyi7EE+IRT3vtlrdFRElT+aj/PPOqe/ILvgXB
+/sDcp7hGfLKfObAypj18tzar7Ade9hj802fE3QpczvGKszKJhzhcEHnSOm+8a5k7tNYSmHWhUCy7
+D5bhBA04ngTEYQkWb3uvVZMTXS3Tfjvsw65XwLIE23ByjbCdodEf17QST6nT147qe0z2/PkC+K4V
+VqJiBNgVaaHsvArX7LNKm8bszDNv35fThGZb7JKxZ4FxjnWQBBiFFeyDeKExWqXxBwPHIsmjrY70
+AUUOHAvEHeOA0mxW4bD47j2hQsB4rbNKAOAQVot2AsL+oxnx4xkfpiX4hReqe+OH5aS8n+J3+Sd5
+ljNihaC8OC6Lyr6uq5Hf9iXN4UwWZRcJPM++LKKxPHFkdc9/YFZfYz/SljXmnABD+PrDQGO9aTJo
+2HX62pSOPN5xzAMjBbWRt4KkmKoAWMRdqno/Jq3N8mAytJTpyo7WI34nqEJWlv7Y/qjTLML+INxE
+98qt9d9gMawukiqf82oJvpB2JZB1C3CxEhMALvTK+6/DDxD+w3Cvs4ZcPA/HwkVyiAvCrkpD/Jin
+BThNwFAXpclbQbZVWEJnyUeWFc4kgTC5RsgNCP50uojmzzrTYfMDuEdoXr+ly4Zo0MJdQpUFnr/r
+QMvJyQkHwcDpKXhbaBHW7IdbcyvgfaRtYZJShl8huVdaRdgyTPl7/iIbFWUG3HdsCfan64X0dC0D
+qA7aACVI4HbnhVUGtL8W39oACNX/N1W83dByb/OH1Pp1X9crJ4HLnH6mfIhW+ADDZA8MAU32t+MC
+6x170acT6Zl+yISJR6/UCtBRR2mlq0iC2/Qbjv2CwCn2PAAn38tHi7BAvAPjeJ/GVoCLd1cO729p
+eNZoTfssybyGug2mNS8OG+wZjf2E6awZJb/kOS/nGvPCY8HYnQ7HlOcMP9xk5WEZjg81We+YrtNR
+3+UbHs2/0Pc0juS5TVkuMUta+9TablKDSFYHZIV37POJFSTLWIYfhGuAVyAeAt3pEufHbcGrSW0y
+X2TPFKmmBMXL/1e2NXjHCezy/cAEhlXjIToCyw6IN6D+1+EHCP9JeKXA3jAOCCQOUW4Kxc1b+Mrn
+cSizqSr7kvmVDO8ESXAb6d2EGVh1FazCO3/9ypzJpTqD4H2bxLpHWKFjW2yDEhFJC6A/Ge0/FW2y
+AJDuMbiupHd4yfCOU+px+ppqELRR1oG3rnlWoD36AwO4JReJCpQl8xILa4BEtAxn57rAOOoS2GI5
+1AO48nXAeIF/cSMA5lZAvMHtzpJQ3aFZFy+BYteapChcATMNLTkG5DoZVIayePzvjnMCqTUPWRfo
+/U3ptKzivjUuYzJFsR9CfQG6eRz6bb1srbPF6QivhPoE+IcdQFdu6U0/4fQV9wfl/KGsVLoqDJQB
+PW0lnmB3f1PUXCIkwEDw13m6eb/LO/GYjz3Sml33fvp59TmDOY3zoPFYqFQLsK8hpnFV65luvvJ7
+aeJdZIGofUQRBIdnE9JivJmi22FwjZ/S2LGn9ykAD54T2rc20Hy2UIQ+Co7SEUKo9VwOeVZqqNU1
+hqEM1E83QpRvPgL8BtiVOA/pCCFhOd6nSKzs5ADA3jYo22UFltRTGxTjA3INAFc3iQC93VL884Ma
+/x+Eo36zGjnt9GkDMD0FoDJLEYlV6HtAa7CnDV4qun6Raeskk/WpDx+Uc99hB8G/4JaJyX0iPnZv
+F4kQ0mARNkvL77WF9SUaFp4rhLKmIIszq3lw4QCfv+6q68DSF2ebBSmA4BcvKWAXrb2ez5bgbEk2
+ODYaWA48rEF10II+g7z6LKZBccMWa3rI2EWBP4NBQVf6IsEUD8I5AEA4Fu4TXjj3kA+s+w07DMgi
+5Aiwq+VlZwV30q+d3hWjNj4j7gQuFkrWKM9K+vwe9dtUfwF2MObJIt15Up97f2lu6yz0Ze7KMtwh
+sutoPSQrMYBigXcNACz87vWLK/6VVnO3qa38TfoDc7BOuG8qePrd95yzvl+YMzcDAWUywSqVnyzA
+K9flZn7wZOkPXHBm0TXCv/2bQHBziVCR/BEcyeMLIJfuClelzzDJn+kxVOET73HYYU5hTXHblFmf
+aLj+Sbf2d1ToExBufE0IQVG7ZzN6KzTc2+wfLHJjLRbhPK9zWpQyMfztZ3nfrhGX+nsBtMXaO4Le
+ASTrlSeKZBZ37a+GHyD8bThp0t9jowLlOXmoxBqNL9UpB5jye08usQC502sJVw3rr/+S3C+V9RBc
+8Q9eND2AYNluEbuf7oshAhZhtgaHJdj2AROJr+6iylALSZP2bhRHQZifxNMH+bRoLghkz4sDYgK7
+AvckEvhdPT0C5lPekkLiwNm1uCqMTHHkPeCaMs1CQcYuaQoTdlQo8KzMYDKVCVA2C0WuA58q8HUr
+elA0McbydTf6BB+qjUqf/Pkrf74dNdkBlNpIn1p57kyv602Z1lTVppB2EBRxXCMEGjGQFaGrsfYm
+CquviAQQQp9gB8MiyedW5PqpVvJbEEKuvvlM0kdYZAPf3X+0CHtfLN3FcHTRBSu0L985MGDN+cAS
+xkDTreRw/jYF/nqdMyD2B+ac03YleLbx/Pq3feJy16cXjhXqAqxbC1WhhyLllojqI2w85/k+wZyy
+n2FJK70eV16bw4e7kTfLtHNFjZ957MhzqPKRVuSwmMRDcmKxxxMkS9CZx7N2WtYzQab+nmsuIrzR
+0SKsDILDTQIfkhvuBk5gfA0Py7HfMFqEn9fq74QfIPw3wqSpXuk+VpciQuJurrDQQznrwDtUc+if
+fxkXB0JlW2WFLL0maSF2v+AFem20BHs+OuWLamBkPAAAIABJREFUbqslACbPX4DMH9RL39UFiB20
+auhJjjMoXuNCgTpIm0kSYR0ANhO4IgiWB8vwU36CYUHwq27tgT6EctMEFNHZ5DmFZvlFBRmzkPOF
+2DbL4nxin8AyhnMa7QFQyhnd/QdBven5MB2WN8E3HL+RMk3QMc6DINtwbl6f3QqG6/sAjm069a1n
+NzwZyC3DePQOdDIuFK9jzfXAVRi6h1dR4WEzEXSDWPs3kY/6Pq5WYbIo7lbru8h2cchBxq0RsgYV
+WzOUPwwlPkhtyerbDpuR/s40O/B4BWu1k1724gCIk1WFFjCk8Y6rYCrHAaNSqDc+gGqZBi8HZ7tb
+h8s3CtC16P6Wx2hGpp9S9v7sShMce1nJ817lcJPLHJ7OxOLphadvB332xjACWjyfuF6Tjqkybai2
+0Sz/DqpqReD6NDFpQPhEk/zwSGPgA7yisckznT7CWzepgCsEu0aQdXcD39lKfLElGV7/OvwA4S/D
+Kx15KmMTtSAOcfkxqEI6IXWzsARZQms4rJICLo8IyClbYswc2DqvavgS/9pPraFfsKk2S7BdJr/i
+8mIT+TAA9oOWADzdIxwQ+y/ffUw2YATLsB/Iw4w0hdYkjIuCQWJJ6vnVBggJtAS/BLls+ZUQKGn1
+leQTtyYD0oi4loEhGumjR0UoMduYwumwqCqmSoHTct8o0CKVk7x7oKfNv78NSAWSliXZwNbrTRpW
+H7u8+EJgPYArSqiDPIUOWrx2lV7yzvVhCsXB6XVoD+y1BT0B4Il+Hj9Zf5vOhBHyU1cDwFUuQ+8C
+CteR0u4dAke08uI4fQ1D/E18+V5lRZMRwkDqmd/3mlI6c8s9HTFtBWHiHMbu1gBcbAV2q3lIL8EF
+il1oEg8949n16THN/WAi8RkmegsbLY8rjtPllu8BofUWYRBcH5gLcPrwPlmBQUhkMF6fKdzfzsK6
+gehWJGftFOYXudLUDgsx7ob1bhn+mUCvVLcI7OKmI5/T3CA1Sak4khoqJvTXJetsO4DdefGAHILh
+C/k4r7tEJDDOTtgQhw7+pfADhP8g3KnQzuTiatjpw0achWopY5m3YnD4QM9QvYoHK2WwQS88zhZg
+owflFkgGy/AWuPlwnZIvsVt9TSwUn5fJr2Zs93sB3rzGzQKEx68EDS8Z0oMUSoEhklrgKagkSJXE
+r7fuDeWVQLYDY/IhjsEALQY3jRqGVl6cbzdWYRDoWK1ZSbdmYS6FMqPO6D4UbNYJ59tgCjO3hnZ4
+gJu3+RhzF6h72Zm7Ba+DPQWLszSJa3yXiJ+/6zlN6VOgtpsy3pEKJIBGZUq9j217x3GwO66OsML6
+KymUzK3FqyV9AsS7zwmcGJDwEbcsWsHMMLaUGz1/4pvyc/14xVdq4GrzIbGXoxduSY/e+e5XyfXb
+dwnp1JM8DzhttifHIp06wZ/HcBnv/Wk+5ND3OH8q84eeuhe8hyB+BdLTvs9SPdPXXpnSGcZQz0ql
+ZafM5rxmGabzZMcmxp058ForeAOEPQ3xMoyoUwX7Cbt/VKZb66HOgx/O0EvY/YHyEAxfj37D/nBd
+htMp/LvhBwj/bpjO1nSgnjRaPQUupSqTFSAxNIrKGMWXSwqXRyIg/CR0k6A33RKG64C420NaiBO4
+xgN0+ym89Bu2tA67Sdef1LtE7GNRPwPitA6HX/AGxv7NK+JFeh3OyfzQ3AwAoNRMckFQrLe3AHi/
+0oKNwFi80gDZwb8bNJdA2KeStEiXjBzuMX2yBPtXqZmOAqGWo/vHlrLgGXBVFLD3YrsSArGRQj+o
+ssORu81s+a7rMddq3O7y27PYFCbaU+hQAOZsd4jOOMxPAF5Xgk2xZ6+qHyh1FrN8+QHw7gMrZCGM
+DYIPyYlUQNz8iClufenQzaaAEZq3g9U7ZCMM76R2OW6Uynqyr2QhNoH58drKbto8+LAj5iuVSyuw
+7nOAc4wji8/5CnI9p1/8w7HX7N/qFE8NIUt/tJBKJdbNRNBNQrPLdJnMNK8UALRhmt91XttRD3v0
+qAyA24jXaidQd5iXsFZ/c0WoXRnolNe2cgG88H6iH4GzbN9g2LZVloYlONKoxxzkCvkEs6tD/qBU
+c484XJ/GFuF/F36A8F8KJ/X6ezWdNgKepJlnCThj8FfkrivCxbutCiG7wG7lwlPd0pvAOB+a08A6
+bjWWSwEEy/oJ1Ap6VcU2WPY+BABW8BXeFskPguEAoMIvmV85dyY8hzi1LiSSrod/XnMHvddLWhEo
+8Ik7AXEOyOVCAOJiFTbto62jpvReXheJMXwojuBYokxDIvHGcEIgNZwMrbmhrYOiOhXlX5mbKp78
+AycIWXspUsYrUs7YGUzF0QKFMoHgu5P9bSDAK6iYvbE9j660DfgMy9vxg2LrrNPCurc3DdKsgF0H
+uJZx73R8sA8+GF0g07oJ+zycgM84rhjWtGPfgV8qQ+iuxE3KRvb5ETGyBgO/SM4f1Jk+8Hs+266C
+A7ytsdP5bjRJ2a1Ey3PI0+4f1nMfBQCOjaU0bHKdkKTXd8abe64O/OPSNqIVXjwjhXlwhYhZgI7x
+B0j4IH4AxMReu0p06OWNilp9i1WL+BEI3+W5vm0tXVI/reQ3l/jKbzPxZ5Prw3JxhVpxj6CH5eD2
+iB8f4f8vgk3yehbWRz4UqAe+yiMEYfM9wOOWRZYPjJhIe3DCAZJsfrQA/wJgbKLpErF9hUX94Tij
+2yTSYuwKVNhdorhOfHSDXtmHzNxPeAGdwILfBMOXC4MS33wNUgIgRSHAlt7TSxpNSl6tBy3CCH5V
+h61wmAcfaqaLb7AvvvQ6CVRFm6lUHPvMrZ5SsE8nRUSsCtYU1KgcjRhYjnutDIK5XOmHVa4+gugV
+rEVw924GxjPhWp/ib0K04+fb26evRGFS6IMeWP0OFtPHTqEAIaFjCY6ikQR+inPvgkmK1dNlkg7f
+g1nv0tTFM83G/J6e9s2pboMSA1AFa3B8WzCBYeelhoqV3kGxljbwwCqf+SX28Nu+1BchEqPvkqC5
+7I0EuAh2wdWjDMXvlsauEriG9wn0nk5g8q7K2g5mEVb2OOpoPhORwPQJ5IIOsVLOqFzpVhkbdqXx
+NrpJBcI20JhvmO/M2X8Z+G7lC/oo09Ul4qo/kDH8qAb9tDKCZi2gecf/dfgBwju8f2zlRmmISFW8
+d9zPiq+q2qlSPlWkj2BUo8wtBVKHLeHyURE1Xbc36KoD/XZN8+eQHdCy64RbjzVdIQIog4UYLcLR
+nm4rxop/zN0w1nVq+FJ6Gb1COIzTa03Qp/qAcoPi9anrCjIcHIDSObAGt25HesseFzwGo1PZFuCp
+8RgB51XhOgrbnTDlvTsCZhDM70XW3ZnQVJoEBADkFnr6mhsM2KDG3bNiFiSHhbauMwCO5ufhNL7W
+G+vg+BT3qt/ERaRZfF0p+4eNhb8spgl/aty/CYr6vGAVFndAucx7mYYm5LQSo7MKcaihtd2B8ddp
+vct/e93WIEAlgV+cmekhOHyAo2756F8BvVNnDIlwHnaFnqq++iLo+jDIPzx2d2sLo67zphDLNU/+
++pCj4R8AbRXEMqBLXv+BldbnwdJL4/ZzAvlBwbyDVfgRDFObRfZiNwd6k9Pxx2QEw1ioVZgvxwVI
+JSXqYaerMUeqH7Dubz4R2KqSBbjxXyJ43egFbfy4RvyvCc/3k04B1TQ+zJDU20JvKoY0/dQACAwQ
+4VsAk6JFYLxla/rm6v6xi3RdiFseAuTytWpIE7hKbVmRV8VGdJNrW5GX68S+VxiA87UBuFtVL50A
+cY5XbQEAumzfPhG3z2eBy+tKGvJKSZswD0mfansdwiBsHAxG3NO+Tk1Q1dGCuhlBcN+zuENyFCrS
+OM/7vcOU3wlL4eetE0r0NSWFjuihfbKDEewJ6QDGU4wsWAbDnB6U1gSQx+M4rsm7+D3flOp/a8la
+boVvofiX8H7LjAA3yvmrRgDAOx9bTT7ufVnF+3yQee7+dV8f+3tjr9lNRCRuQxnAblpyc+u2Dxol
+CWSe1jHkmqSTUN24OPjDLos9zP2KvMD2S+4vYK9kqLb4QKUpwFwYx1eQ0A6KUOyrwHygMrMaxXHi
+7jYYFvNAC9SmlUbPZwrytuwiFzLvh85dD3qRGaYDr9OnI4Zj3a8jb2zVvVeqTtlGlvUG6+d8DdBe
+/EAcWIGnbz/pwfFN21af1cTvKZA/Dj9A+I9CBw2h9NCn6156ZdnCxso7hQcX4nhXfr2nBzXV4u6W
+sMAwuCXI3tjCADUsw5fJr/1QnP9ms4kUv2HpgHgLhl9bYOY1bv7jHrbBd36KvOAAjdbh8mlYANBa
+IPVTmgHwSvtCWc6o04yymGdYj4oaOwbTR2QZ+023ElbeNaf4Ke9t+tStyvterrnirC1sGBJKfNix
+26dnTVnd2V4H92qeJVkWvONoYL6D/8xdq3o4uo9xSiNgcqUOWpWV+SilhpxJCnwZdyCAa2lS1k24
+HOYHsJSsK8IoEYf0G3ALW43yjdM6lcc5U+iz8fAIGSYoOoKZI7hRxNBD6Cdy1gM52MlKPPJ6yoeo
+K7E+0GgcW2dS1W0F30Bn8zoYXuXQt7Y2iXu55vNhs4kue2ylgXoeaA1vzgnPYz0rsvRUWUP8QguG
+RLLDbmhIZxopF2qz7ymTfrn4rkeF7QoiCXadFwHx0ao7uAJONN8rgXmB5s0J/fnn4QcI/5Uwg10+
+UsnTOefA59vWJ+/nUsfkCQCLHwxJkVpfl/qFD/kAWwBVB8Dibg6SPmbFIiwiYf39VS3EYvvmCRO7
+lAGyg2tdYPzXrs6twftqw2Uplu0asRUHWoN1W4NtA16y9H5M7IJ0e0kpk2kCzS6sJmF+ClsI0I0U
+e47DRUIxDoszVObuFCg2Te6twtnVRWORm7RvQuV/3MGxQVFSQ9wgXniW/K+AyznugXH+HUaohWo+
+b73bCJywUNP3Na33fAe8IAGwEESUEkZad1DmrTcnoCupRU0C4AXogw8haUXt5d0/NerY45C939M3
+twLf+923eN6C5V7nlO8uBjCCvvJh1U2EQQ+bDmAELcFW8nIfaivnJTSI9UQZF6AODOm6UWt+fRjQ
+Qe6OxnV4WzaFRZSswZsGYLgjxKErhVC9HCJixAUxK0OD+T6BYsizoWPmc4I0zX5kWnoamp2svp1m
+ndaALmyacZ/NPK6fSYeETlF453gA4wvcIfClm34Aw/jQnUCeEDD+92D4Bwj/RsAN2RXqvbB+x3Nq
+F8spG0tqfmvCxtZJ8KccTxCpIpf5Xb5+pZkmwNT9i28OgMElQj67YrAIm5r8ujao+uj+cQ4Azyr9
+vuJiHQ5rsCZIR1yYLxNByOIgFYCvXAmKR7eIwTIsApZhco0Y5n5UNHuOff5RENHCaOiRsYKDwJj4
+k7bnXns371Rnpc2g4Dmc9l7mFiQ5WYBJw0BPFPKgBROmT8C4lqT88VihEoT8m61QATXFn7bQUH4C
+AGgVXrFB4VfF3lYT18EA6Ja8p7hJAqliIc6H4HrbFg9XGVRbXSSkpXEeTjzP6ZSTM4+1Hq/xlOYr
+MJEEy+3mkyKUa7HMy570sRvMca3y7mQ/pNFFYIOYXFeNNgMAgxtEtQZvpZGTZaXFwyEYRSi5NvTI
+dKLbzx/XElDHae7yLA5n7ASIs7sjsMUzGbS6HxQqaaB3S+8jGF48BnTcria5b0LeAgCOB+N8La8N
+XOF+4Le3JC2Y6+9d9f17CLzCDxD+OpyBLKra81F6rueh8p2un2Jnvg17Gi2LVYCw4wUMuwxzFwTb
+ysxd7y8Hq1tgith6SM6k/NqchBX48rSge0Rag00T/AbYVr81gsHw6pc/JLc6uPq8hYTth+dGS+8C
+w90NIsuuN0yDL7AJ1yfwHjwlXdYjPgUjOo7Jd0EiaQkupaeHPV2l1/3XVd+kNoEWuqvv2VrXe0F2
+2pVFxR8twJUXNEvUOPduUpIncNxSWtKkzJX5cRilyhM0acAa42N5y6E/fEX8JJHafIrIvC6HeLEO
+m8j2H53ra/kQ9/nMorlqWua1rnjdg3e7Budl5uHd0mWnZt9lC8iSJgBWj9CkBnCPWR8P93Eecbdj
+y5C+y+snVGTLQAK2Iu43oQXshuWXAPIgaw7dqGeM1qJ3bfXYOM2sd+ej8vd5avUAOLVKd04fyxEA
+G+VPbhVdEo2Df8gbRgDoc6UZBLvBSwDIHt0jriv8hf1hugC/B1BMAJneb4bwH4UfIPzb4Q2QdZ4v
+Qa98yX7LW2EOh2nPrR6r6L6vUs0i7a4RYQmWvJQ7jqH7AHuFV+ata9Zku0JI+gqrhLXYb5j45bdH
+bLeIsAiLhH+wg2H2D3Y1kGB4AsFmH5HPtX7+GZ/ss/uH5tDqloA55w68Jg7zjooRkK3mCNZ/LbuH
+89sLauZ3JRr19dyzkeZl7pRzDO029y4coAtpwDPAylIdIDClANfSP/ySPvgKgZLD7Rq9VlZCTd0O
+ZUhpUZ9A8eMerJ0kgEClpQeYa4W0TPHdsXADmEB00ezFQrwkyFSnJJiu6zisgcJemPYn19N9iJMH
+5zFHgeUqDcykCRTrQk5TI8L7aY+husDNs5C91UJjnlqmDO6BN/6GG0zZG+QqAWkAyM0a7C4Utb3D
+eRnp4zm0QqsCuHIN/Ic+HfMH0Et1V0CrkvpDJD4vRVHNeqz+peNkvEHpyFn2rR3FfCCY5MpeplQv
+vmYJij3dAPHJL/hKNwhXbS0N3cP3fx1+gPCXoSmovcPeqXiGpKiWZ6jyohMvGSehP3ZNJV0Ahc5C
+uEggCF7XqXk8LRF27fo2IA6LsIPg7SbBgFfEPttSjHmyQPCvsAgXa3DpZ4BhQhZ7UNWqW8Bu8/Ud
+LMPNoiwylPtyrbSPIxKnAo2kdRIicDdmX+EnGu73N2He6WOHJAEFMjykSZIXK+LYSAW2OSLkydyT
+ypRUcFibMUNJsuwYFfmOT+sWW2ro0wkE16+PKWZcuXfymIa4CQAim8vtPBONGwaObSCQRl4CyzAs
+6WsWVQgVpTDVE3uTwMcT+MW1cOSi0O/sV7oQeOsFoADruQ9Jn6UB5ES09qVrrndpkJ8Igj2t0q3E
+kQfrGTdLGG/wqatP9E2cznLGeqkRyDbOed5ObVF6kAlVTLkLidU8VBnodkF5Ze8U0FvduWd+mId9
+PklXkP5AEDxYcQ83RTzeob8bUIG6QG7/+Aj/bwhHHKBx4N+4RTTQUZQ61Ty2d9NA/7g99TYjAzsB
+TAe5mnFX9Ca2fgnmg6VBMG9AvI8rWYHRXSLplr9WF6A53SJ+IdbbYBitws0ybBIPy8WjY+6v4cj7
+c+2H5NIaPPkJs2UY5rmA4pyDO0kOq6CVpAEKVST97FBI0SpxM2kPr3TqVe3lI21KP4usLcKhgmMZ
+dIPwsprjmVuttC8fhBOo35KnnUSYc6OM09yYDNU2fjr2hZf4tK8zas9m/TKYh+JTUf2GOX6CnH2e
+78GxCLlIIBIooNdvBdE7sFwC99Q6zSCdf0ByCc3zVFdtufkP41ijkOaY6ppM4n0W+dFetwP3H+to
+/W8PVc/rbTd5KwkcrieObg8rHjdGhGU8hbUNrhGt1ZtzVboldfJOtU6xU6k39U28x/IIgEUC5OKc
+etzP8JhnWHe2ies+X5lmDQzT5zXUJaGOUteQu0K4QUhxiTgB4ivKVPeIAMreunrL/x4Ei/wA4f8w
+HBHz1+EkKEDXTpz3zZfuacljIOxb1Dauhfg+fabLVQGbxXeyCLsLhOeRdRjjbg2W/JGNfTiXVXoG
+v5cYpVPj2eG1ALAN6WYtFkgLWoGF24gJwPaHNWhpnPyMhx6S7fhBi6OnytpaYH63kLynTcpiFGGj
+gu9lurKqKPIAfvVQjsrWbmiJ9U42G7b1mqiUDrVYr8cm/oKXWEH5MErrhLOsVTb3stv8WxjdHGAt
+qgXYxLUY1DekT1Zf2kvuE9x56w7InvX5bTvFAPwCqCAQIZVW6HjEo7c+BQrzUhZzcpPwM3VQEbX/
+eozJyNnjMsRL+slVwtehWn1tW3mLRZiAMt4RDpbik3p6pENk5rX5PI6y4HCej72AvBcCz/cf3ixB
+/sDeTz/fDQAzXcS3VKe3Pgfo5b3WDPKhPlivEAiu1mBlwDu7R0iniQNsb88twtnu/0T4AcI7fPfL
+cm+svh53oJgS+O6ILYb3IBoV4dsRoAJoGXDGHfxOYNhVy+XXBUX5LSxN6OXRX07yH94YAHC4RsRD
+cxJuEXgrD/6wxukX5kRsuD7Ndj/hxoiPiVwmtt+blRfcItJVYqcj/wvFUtekOE35Z+OY/ebyUFax
+47aoj/ahyPHGiKnHJGNhWae9RvrfKmUOJwjSW5n43Pd14nPu+55mN8/guM0LVdktxFPp2s1ipGXc
+vyO5btIUO/oZnoGBcZ6VPIhjWw1ujeD4Jn2w+uqRN+P+rVOAKBj4G3eH0vNoK2hlrqb6pPHWGeJR
+S6AbQiqZFSXtVMFt/3s/TydwqumUtpJ90mKQLiDYyhqRj3CAYZXJUnySDPX0Nlrd8sxK+rDXPRD1
+mDP26DztNzW4Xtzl4xwOQNcKHfnhe4ZcB99TsfVYD+W2tL4FK/DcgJdNtRJg9fiwXL1KrfAxiE5d
+52B59RtcJvQ4yf9Z+AHCfxTeA9Ze5mXZk3waFGHIpZuWp8pO2y7Ar64DmNZgGQCxiH/EnbqAFuEa
+DzB8rcP+yyHsh4Hxr7AIOxBehyfuEJY827rnIudjC43BGny8MaKmo7xkeZEQcovM4ONxiV3x45yj
+74fiTgHJVcDwovUbIpzXZOrK2QLcFdJMG0MZ9ry/JmU7QZqswQZar6vu8nkNOkC2HjNJkFbpWP9U
+CynmTBC3nrdKgu30J+x5Xo46lMygQFsPoWEeOd7iO0Ey6Hh1ZbgFzLYx0AyAsVz/+h/HCnAd5vge
+DNstn0gB03agV/4YnnKO8bjP4Dfzp5luaZvyc814vabz5dGbvGF/RGwCwW7txXjw7DV2q3E8KDfJ
+nB7mb6L6GeMh9ZoseIc8LPcV/jrIvqEO9PgKK/A+px0AZ93eQqQCIFu2tfm11INrTF1SP27721Zn
+KC/y3wVwzC4SZ4A8ukKILLq4ldnxhc59/YfhBwj/jRDW3gS5byzGfxbmmiZh3884qWeW14BvAo9Z
+WoYvKrRYrwGcRK5/Dbbl4K+4QWLfQCGzldhBb43HTysDANbdBwfsZA2W2RocVmC1uDXizX3C/inc
+HIAQQHaRheAFFcu98PSvoIK4BYWhNZgKRCGoa1ppLJdCdlY+c5hVp7IKs85RydoZS5kBeMYc3oNf
+VB88C128Gq0LchZe6+q4FdMOmSbrqxG/SOsCgmeDMWnhs2luevoIkIF4gA2S4CRXOpL1GrsJAJsU
+oLvHY/7UOgiaoZx/gJmsQy5hsfgk97qHbR8d0e1AR/5p2yDwHVw+qvNmPqzEjQyzcQbHXymS8z7g
+PTKf8tqBBoLd+kv+w0aA2azkU7XTLrw5syKpVw79xXMzjntCXN1nhsMXdFpazW9jAvg24Fp4VaAM
+73UcS7ZjTXd39wmLvFHm1AE4WI0XAFwEw/U+4dEqLBR3QJyqDKzDh2n+L8MPEP6NYC/iKyQwnnIr
+5fUGaN+r3scrtKjtTOm4BlN8325L8FYWCUAPPffyux8BgAPs5nv8aIa6P7CMANhdKPxw4h3C3s9L
+0ZVDQgDE9Wn73TYwvn0obkwL1COhsCcXinG9TnOPQCIswQoCW3NdCHT0NCuaVO0GL863Ql+Lx/wP
+e9jGXTDwlz3YdG2FA1NrFU5jJRPUTkXyCiAfl0ubAjX+M7TtCg7Wpx5f7fR4spy7nyB67sTYn6lX
+HXzcwi5aF57hUsakAGDnSX7zv+VHNnKOsu3J9SHr4KFRFdDr007K8n3taIx2oJc+VWRD5+ZuO9/0
+rVrH0cXEDNeizx01YiXd8g77xfPC5IiAViRdICzlTtxG4O/uE53vvTeJgqzk3INigpx0npps6Y7e
+VAXJsrfKuIrhHUGZ60vCYLhagwEsY3+JBkLMtuQmsTxZhncRiPsOCV9lsvrmSw+081Vps0sEg2jH
+EjsuCIjdOvx28v9e+AHCXwfcXdIO27uybhXgCmYFd8o8b5dZefQyFC+HBn9M4/L260m/a8Q/1vrB
+tNklAm+IcDCcANl2HH2IJYDwCQyrSNwSEXcK2+4TgdqPqF1idHfwwTLsg5ADWBYfszeWwvq0RZRW
+QSO6xpDCwcdrKCQUy0oHaCfLMH2N67G+D8c+bz3Ylr4I8DdiDEHffIx6bfNJqa3ZofMDhCkfUCbA
+1dqtuKL1qCp0bouONCjsXB9XntA2dKx+694A7WTVI/1f56cKmdJY2WfeIvvxetGKQHCAvp9tJ/e3
+Ca1crVcH32BD7jb81eW+b2odDlJOdWfesPM2EPG/yXxQChWZ1PpyVsUleJmVoU7MmeJ1s5a1Hule
+L+fZbsvuQHC8Ix/yp7WYzov6mLEr+KETZ6mfd+p9mWI6P1XoeP6wQXBqj/JMOVplbpxRfJc9l5pt
+ND/h0J9IM+gs/jaAATDu8kDbLRX8uwL9wWt+BXA9gd/rkosswcUqXF4OduNbIUVA/D8XfoDwbwff
+Tn37vYn3eirTQaCiUEBBhgK9n/c5DgdoyndALBBH0HmN48kasIxd2bVf9L4sG2klxofjZlocJuH+
+5ItvjehXp5nEz9vB9Wn2+Yhcp+vTBNIuvFI4+Y0S4SPcLMPz6rdZw8EgGJ4AsA/eW6jgN8AYgwJ/
+TYEUVFHT40+zDA92YqqBjNZwUYpDj8Z8m/JPdcj+hcFT2PBj6NuZkmtQR88K3am4TlwZKms80w0o
+V0VNDUzgK1Rl6/2cRpoyzaSAS99ZhU9EZktvrWN4gM6tjUMdWbTtqOmj0FpvHejDUKcH4aYPLgr0
+zEPZXc9CP0Mt1D5Ct7G/GYd5sxMPDW5onc8huwkNGmtqxN1dDmDYXSHoXXaeCO2D7I7mOcBNr9k0
+z0QJMN10XmJLD7qzbt9BfCQZMks0boXbEBmZAAAgAElEQVSIPJcP1dXBCgjGPJH8gCEwL5bjMW8P
+19DSO3O3yVekWZRzeRXeTZO4bJbgTbv4RT+njD+zXKzCzTK82wi/Yck2ikr7Z+EHCH8ZBjGxg5/C
+ASw8hi/K3Omul/xa3iMOeshpi65xXaRKAuD9q8gMiP2w6sr3UN0dyD3iWsLdf4PjMueV5i5x7U7l
+J1UeD70cjIqJWN4pfOf6UPPqQ3PkFkFgGOf6TvmBBIbO8uFXeNNtDa50ZG/qj+oxeCWd+zj/oMZp
+DDmtTzKLYEGrqBJybmxkq/M6tW430QEZ9daG8pNLRAUadX6HHitjjaqsM803UZwsx4s2rZnJYQLH
+uZzDCSnsDjk42chNib7br5beVod/aHAL0cCHc2l7ZoZlTBKAJFD+bVTDBwfzRqQ2YW1KtaScz0qa
+eYb0sAwBVJjSGaO/0zxjF4rWGvaGPfFFp/IhODuCYM9nl4jKG1VXUKy854z2ks9yGSdXOU433sTA
+4+Kp4m2rwFYXa5DHLLo74PVh+vK7xTa2Q5zgHGuMrZgiCPDaEShTX/Y46OYgUppKtNur0C6V9kMa
+p1+Zqy8EvyqSFuFAHf80/ADh3wmvcCv4Q5lJ+TLjuzAprwM4ON0aUcHvcHb5XWU9JAfAVm0B0gqA
+PxvEYn3++xpeziRfv/bBvxAMI0h2zwT1+4Ol+Ahr1g1A0pXpIrlK2sICQaxYpgPgzncHE69I5rvQ
+ngB0rIZLKV6Uqpw9gdZg9KMKayIJqqoIkg4tFx5OHvmCPtwqgVY7KP8ouuDr0DteBCMTt51Sln2e
+S9Z6a5vHUlj5MF8DjNY2a7t5UOYjAPZ0ajDsG4LxeSgzyOFkLXhQ7i1/AMRAZ6Awg96MZwnn87VL
+S64WgQbtDF0y4ij0ATf1WrkofQ8xydrjPE/KYVYY0xJWSHuyEJ8rKVx1owxuD21fTO41IrCOeCPE
+CQQvnXfKW4P1D3wPaRjXmB5AbzszseBwRhwA1ykTCVg2rqUmZ5PPsqfF16GCYe+TSQfBlD5ZhVMX
+1fH4h0SYPipPX8z4CKoecv0j/GCbXB0MixYAfAOC0zLsdVTwm33+9zD4Bwj/lTCIlpvgp/Tm9L5p
+yAnfNT72xt8TAK+Ig1inOQi+Nuj6SIJhBCHxoJ2mVfgCwGuXLreIfegZDO/0tZ44/lXy42Ex5b6v
+d4t3V3D1vVl+PyZy3d8YYVM5BMMiLJzMIjvWpKyNtniOIoQwLQ4KiwMYToZosnYhX7zfKs8UsHz9
+wFV38jEzkoO339iwwV+o3SpHT1X9Xns3f2g0emMq9Fh7z7hNZZrHKrh1VjzKAQhKW1W5Y72TbKDk
+NNg72gMgDm3qmlUBdEJHB9Ab9Qygl39lDvvA66qlqpWc/Qx97rR2QQyjHUhb44J269zNwOmtOxE2
+qTVmdTXYPYLnt1RG4xmUReM5l4luVwDpcnynEwS7a4Qs6/B+F5ECiNcfw/0gOO+62+BxPlqGcZpx
+eRyo1m0J6NAU5dO0DxGxZb9VYfZgbqL/cFT5Pd0fRh9ilBEqIoZ3ChvgYQt9bRCPdiIvh4U6VUQS
+AK/EKhNgVtjyW35JroNgofdAui0P9Nr/QPgBwh7eLgKe/iOovSn+tj+vGZ/r6IAx38Pii3n7NKsg
++BX52LrT9yPLfQE/sechU/lIgmC0CC++dcLD8gvl3VIcoHjnL9CcfsR4kER56XIcfvqn69M+Ih8V
+k886fHaJfGz/zPLOLw/N1buEm9uECNBwAUI8FUV0CDA+ESmDG/boy307XhdEPTwobRnuFX70hVzx
+yZKG6WP2iQxK+jSTJmsPnitkQHVbmYhMSnDEDgNUac2DlkRFRApOhAACtjX6FvdWSnLimkvWEcSb
+77E1uYcSZW5UhSzEo6VXuU6wHONPFCuAozoCpcT9tw7TN+OYYYU28THPMSG0uA8Buh9pwpwb1PiM
+JkzUkK3tDuF25JBQZVS2weS+l8KyuxGVmQPeNeb1lXuC4OoagS4SPk0WlhMRiTIi/kHKytj4vM3f
+vlQQfATHZdpCIUbWlBauJJSOcRoU69IP0BfyHTboY47HW2WJtUfv5XwY6AcsaU2OmyVEwh3IP5+S
+DELrb7wPLhHhH8wA+LpULnSVmNwm9vxNluCOTv5t+AHC3wY/xPCu8fVQYMjFKrn54z5xkP8RmuQD
+okFFVSHFji4SvmqDaEwbDfXcjG209223m91iMeybHNP+qe9Sk8+1gPRHN8DWZWX+XMvC7MAb3SfQ
+RSI/mULnI419AMHhcTOwWlWA++kAl0DvR8wfrKP8jxj8MEemsXxNwwv+kTVaYG0hjoLSHITTq9Nk
+b5dGH8JbHg9ddKlUBXUUb6FwZ6BTCXewIsY5tPzb4hW6NrXd8MaXPL8fV7mfjTu+uSyBIEXeQwCR
+1JZw77cOOlCuFRnnhbZgcT9CBwjjyBT6eJoO5WhWVwr8Nm2aoyKEaf9WAe15/PigmrvU5YmKjxcO
+cgpUqx8CTRbgItlgEN91kcxo6d2PDb4C0Hqv3NoLI8v684YkTuf4O9DlTVXnzCAPcx4POeSPn5V9
+DxLc3WktaeptVma+BthW2y+nE50zYS3tegy6G2B357gV2/Yu2WcDryrU6L/Jpbqfe/OH1DR/qVXz
+V1sZIyvEk34Mfs6P3w75xPqmtIcK/5vwA4QjvJv89QlrSWf1T8fb+uGCQeO6mAEYexx0QAt8Gnhz
+1I1lpREBbFjl7SS7QUB7Ne4awftVh64w2EWALCJyybYOSwJe3QesguG8KzgubiDf4Autw/sE0g0L
+sXxGfcjDZ3vNJOLp3uBA9ZLqK2zbOiwEdPfr8xHbbhX2sR3fgPdj8qkgWDowFqBFt+ifZLkpHqOr
+1hwOIYuVae1VAc2Bt2AeCnewgPKtc4wX/9nhnNRqjofJlRHX/XjiAyUc7g/+Kl0Byp/E/52iaC3R
+4i8Q1ORL03Mor4AnEF3KL3XZJiIG8WOnGsgY+NA3WxJK9WLTRjvQqHwdOPN2CHMuZ7u9tP7muYiv
+uyXzYzeYwyHItyIfHGtArzro5bQYlBd+/sBl9AR0wyWC2uM8kTvQiyAZ56iUURz1EL4Ax0g7bmUt
+aRhb6vR6H8r9qUagm11gEKzEDztKU8+HLgbl5yBYNffF3k0BgvFdVfKHqrbOxx+ucv0dYLio4HxZ
+gObscwW9mnGVtS/M4ssjnuMCieTvhh8g7OG1bkELsIU1NOT5XqngkfyEWBdWJAExAdoDEFl5qFSw
+zD58E+KY5PR00sdyrOhivNF3LV1mPz0/cGjlDUvw5TdEICDe7xv4XiJiliB5veAEYhf9FIqJW3zz
+BQO3nY+uEpbXpqEFWALkIoAtFmAHxJ+V/jS+G0swWKQD+rqcCB7IDR2MCjrjTfmVWWDlNYdWZhA9
+YJsay4eOOuVLz+9KQ4WGeajnfeCWDUmHPk3t3KV/l/d9HGd9Ugn3NL3l6yHlTiE+pgMVnIGxbKUd
+QlPSehbguGh5kaXYT71XGBmej9tBDvlmbVjjhpHGIDMwK7ue9nXmMdzr1l+voQLmPKUK/CAHXKZA
+WZIRkPYeTSB5rZGFVdjHGlIW5EtYjCsPlKsbhIGxSAXKIiJ2a+ERArLBw0qph7LQ8W1E9BQdfWo6
++44jwTPWZdscx7VdWRaq3vNB7a13GiKA3xiHcd7uZwXBEzBGq/ClGxRLU787zeDXEW06Quxx4ddI
+JusbhS0zQjXfzNL3cv85/ADhHebHLHqwLaTDWLGFjjv/u8N3l/lgKZbcRPOi7kIAduPrfKAbgODb
+it/QGk/u9LUxIS0i0saC486My7ZPsRaLMABef+jOv2JD0Os87j/s1mCLE1lefSZ3xE/Y8F6sww6O
+rViGGfyiBdgB8GeBYrAKH0HwARBTWiRAcg4GwTIC5aK0JJVbm49QVvOLdFMpa5DN22kCy5jfK+vK
+obA0gAIA9ljfVGJgvsmZK//OKvw270nQc1xHeg16w/SdEoEB1yl7mdaIb0LImw0muqBMuQcyhzve
+gQ9nHWQ51XWanLKHbdh1N5PIe7r3o1s4ay6A3b3/EeyKCLk/oBlCof/+HeXZCgzyAs6iTfnQOxOF
+tMQHa9dHBmPgduG6NaBlTd0FYubDcwB5R3Bb94qW/KkMb+fQ36TjkgNjhmuBD8dZzr0TaY23PA96
+mRF8ENzfGyDGeAGqviMQlF6XFuCbrhGhUh0E7xrSBpUuEn5M69SytFKYKN9ggHFgo02fS//r8AOE
+PbzDwQvMrsi29O7NbwJGDGPLqZTjXWT+KYSSOLxrpYsLQ6h4QiN3tAMgjjFAx/uHh3WN2gcr2v3U
+7f9rwtZhAr7XEnuXpICNX5m7llvE6sLqED3t6nNPJ9DaawmbnXaAK9W3d7D2ovvDpn32Q3ZyfUQ+
+H7kuXdZgcouo9e6XzKBYJN8F0ulL7HKjxg0ECKovISuxCCrjDKTcxlcHulN5EdiDQ/6km+7qYvqc
+06kFMFZd2Hv0WGnUoz37FM90B/BTvNFAf3jEhvbrCN7rkZs1fcAYyTNMSsiQLRtTeKS2xDTuWxWQ
+MQZ5Qy9GkIxwZOaLbrfB9Lpi11ntgRFvc1eBjT67shzy3AKIgBgWPtI7VcEv1uM5KCukpFEyCtFA
+ZjhNpVmFfbarfCFrsEnc+IBAOTa08piylq6cclRKcxxhfKCYJhhoZYNjUrN97hGe5eTIU2CUNuCW
+L+KMGdD2TioveNEqnMNB8IvWWgM/YD1YhHdcBl9hf2ntCwJ0nruwDvtG0kVDjLP2iY53fP/X4QcI
+R7iDpMzmXz344VY46GSc3dZc1Bd83PcWx1MD79ij+GTslZ18hN0XaNLzXdbw8FEXoKAQEf86stH9
+aFnmacm7JK3BqhL+vnEf8Qa11QrswrVetWb7FKKLhClOwxblWr6qsu3BFg/KfdaHGbQK0wt/enml
+yUqMLhGXW4AZ8H4m1wjhuATIrRbhau1lQByAvuwTVnLFSuwvn0+o4Y34YV5U2Z2H94Ed8uaytTOn
+vtmYed8Kt3G6dGtuuzb1vYrrcewPWp2TV2/XqNN0oCbtnaRLLpqvabGfaJhull4HJgqNFZmGvVAY
+CeWX3TbJvalLJ6U70P3Mzfu97DnDZjmvnlgqB/XpBpESaThtlgAJtQkI783GVmHXJCgfvM0qM6Sm
+AcjifkyYlkahuDUiADDud9oQZVda6EyaG3XeqrxO0kcO+cyrTZ/1mkG1ZZ5iXv4ViPUrKu8lgRae
+/lihg06L/hAwDZ4NgNW1cYJg72t3g9i3OWA61Wv4D9NLhpdjH0lArPs4w6HLuHnSlVH7GPtPwg8Q
+3uHtg4rmgkZBUA1xVGpa4nGgrLfLSmcXVAHDyxIwDQTXywGxvRik8PkrMoUe7KxMg8BAycB3AGr8
+NdFwjXAQGy4SFwhlyKvAOB6gE39pgF/vQpwzoPvg/JN8QEJyN9hpB72CVtzqGnHtK9jqw3MOfDXd
+Ier7ZP09uEmIwbz4P9Qku985H8ibCg3XFgpWAlGpPhVuA0T1hDNOKkkH4qRspu4dYMor/urFdwp1
+RLcYCs/vwPNVHOTCmbeDJ+5TPdSd6R4qHMo741S40TZx3BRlwkIRspyCT9Ip2zxxuyCQccv3UMnj
+hjIi0d2sQwGr/RbfZ7UvWcLzEisYUrbOSBr2KNzXsC7Ds7tbuaVlD4+0vV2W/WDpH6YnkOF2Bmtw
+GcMtOI46B8kR0wnzCvI/5lU7W8w96K8EuezqKNBj9gcGyp4L9QnbDGHwQnpmRjy3Ps5A8qNqm0Bo
+5jMYdmDqPsPL0ovuEO4DnAAYLcMqy6AVLhLensp6EA/dKaiP01nb+ttFwz5QrorvpfV/E36AcIR3
+079wZwpy9UvDd1xGcNwV/ggAVMKKjGQGvHDINNtaT4ZCR0tDaSHuErwB4EmweNxSMMeEVB5hQc2H
+BoGuhttD3C4hKXDx9QvTu90AwvsVa9ME6ASGwzNZqqVX6eG5yVXiSiDr6Xigzq3ANw/JVcuwWAi/
+UECOfG1PiFSrcfJ1P+KM3r58nqUVfQw4y0iTO/od/hg6MPXn1Mcjb/x5Y/fdamdsBBQ8DL6otCE+
+W3OneKfpI98UzsB3Br19XiagNhC0ECd/L59/LRM2XZuD9wkjDXhyBKe2DqFuvtPGOw12LD6dAsiz
+7KCNHNMYqpUXQdQ9LfuK+iM/HDtHt/gCbfM2mrdZAG3G0VLscec/03gCMC/TNGfK6XkDdFArwvOK
+0Zw7jS2pO8P7WzWKj0eBCJTS88PJPwDimB2b6T6uDoIR/NY0WITB5SGvS6suEiL8oxj+SuBcgW+C
+X3j3iaB347Tw+zd66G+FHyDs4QuTcD5RuoR/B8dV/udXRgyWBSy9u3r/o1hetp7ZbQAtgLjigRHe
+ZEGUrqxGHhmIAuNOuhYeFxrIc6mM4JbcJTCPrL8pyH8h8BUJIK0OqHd+KArxZ603JawpFg9ABPgV
+E7cMm12rXfuIOiCOh+MSEEuktxvER4sVGN0j7oHx6ssaKF2r5uPfc0BfaYIgjfyYMy47LGyWgxx7
+fOXz67nG4BcGirxaAybVNXXtJAgb/SuhidaoVXaGMLMcqPL6TfzPaPqKD0McX/PUPDt1hLmaw9jP
+UzLkBSJoDdloEXCBpUDbkUQihwFYKTMwTRiJSDZ+ONsdPtBrhAdbS/UbDvp5xJ/e5pqUuqEjP98W
+wbSUCYvKsqTS/DyFfIHyvqXCigfnKeUHguEzjY0VOVaRbLcro+mWCc5PMtqdi76ja8Q2D+XxY7/R
+K9WxR7Mfce1v7eu7+PQxmIFn+gcHrcUHcKzSLMIJfrdVWBQswAisZwDsxzWmEnreaCZSvyaP/TjK
+5f8+/ADhHd5Ofnwi3YB4dHkQf5AODk3kI3AogtNQJ0CFtQ0jxuJ/I/UUzzSXfKWMCQB5qCBhjdJf
+KxRuBMpf/eD41Wn+08nzSwdwrACGhazBK897tZWJ7IbCSg9iuVl/t/sDWH4XIAbQu/OizOeSz/WR
+yy75fMA/uLlHPPgLB/A958UskNJKBYNWHNxcpTQpP9HG/hA6yHrccnbD+4BpJnrHKah2TtbfM2i5
+O//H9su8Ters92h6ziM5s0J80DsNIPjW35nzZgYq9qh5VBwIUx5aK0P2DGAmNid28d1GXaLXXm1o
+bGb+GveQqKzxq3bKLHAYKZ/WkFfTrNL3Ck/0GGbZzXCmThbgyJ94Nz3kBJVLQ84ycKx1pdsjyG3C
+810e78odPUF/TkqMwTOXbfykfrieqKFYoGpN7APsPSh15GiEXCVMRNsnrOnUV7g886RUuwHBijc7
+VICcD7pdkPdLJX4RLkExXJW29Wq9Z7jmd+swjg/ecSm2L41FPM/LsyT7++EHCHt4aRGOa042f0sL
+CIVX6QwkyuIEusU3mVRdIYI02ugV5QM+Udywi+7De5I/yIhxTVAMIy4iAgXMyscfzuiW33yYwgVz
+B8RQTiQBsiQtTiJ1YVe2B2ougOPUrRdZfwEQo1uEFr/gBMsfkc+1LcYIXicrMP+4hltQZ5oLBVBf
+FTBDGQyoxGhO5YvXoQxuk3pqJvqZNy1yk+g7icN+rVrl3UoLtn7n1kbZlQsC6Uk1fUv7jv/eHeKb
+ecrTd+Z7knpWI0+AOHhgjptg20xDmbRm9/Y0vm0YAgi+4k3R+SizcI4F7yrcHGWiqiWyrkK1AnM+
+S1P8lgXZ6sNyWRHX9gR0BeghFoE/61FatgDFJS5UtrhGoBEH6DyAns5e4HhnaaPxN7PrA23iKfcB
+Bn3RbnxC3U36Dnu7xtHoOKEivBhA55sSznEGuPni+33d6msElNe7xg9kLAB8Ar0aLhPkHiGHl+Ls
+puW4TBLPgfLUBJSRfx9+gPC3YW+0tYCWaXEF7WARZBLjWK8m08hjcCiJbuLX1Sx3jPUeINxEGqpw
+5XCSHyJda9AuTIEMz4DGvs6vi2YwnC4iGj+R3H2OhofjLoZ/a/h5Cj9bUC2QloD4g11nURb9YP8k
+20rG9kNwtgGvxws43r8Akr9CZ2Kfj3yuSy7/+eVqDf5df2HL0Vt2leYFWBq9zl8wTVLmRD+EqghE
+evGpyhPt1Eal2JxxqCN7VvOPX4ff1InKvPLU99/P6/7EkwX4tvugY068+OXvi6kY658wbuMBoDHn
+nSoykIEog0qoeOk0GoUI8hV2GtrJNYLKFXBFQ8k22p7RyjWfivahYKMGlLlIp04AOfduDj/lA8uY
+iGMZ0Em2O+Z32PvtEO8Asp8fT91qxQSpp3xUmnsv9Z0NNHd9aOukZYucfH0dPIuMoDd6irdH9ZOr
+MtNXFPcmrit/XEBwS7/0Rmm4Axh4AgirHl6pl8ONQqDu+trzQncLYz9xHOOZd8vwNNJ/E36A8A5a
+BfYhmG9QTVDof+gJU/NDUz6FFsWmWK8OPIU+gWTvUxxNkA/ZSN2Mnp9CbLbOYZ0OeGcwzJULnQj8
+dZrJ2lt/OQ7zRUV+XatnCBRsz+1nNxv3FENXQDSt8tFxn1CT/FllcIGQBYLD+mv+gFxahSUA8XqP
+q9LiGrWTJXiwCsN7tfYiLdSUuZrpvKjKpJf86lVD1jpYG41WP7ebDTRMHNpoMTvx3QVUZViuKMCb
+Msd5qACh8N+6OPwR7xQUapl57vK+DiTUzlks2KY6SkWtkwxr+gY7yO1pIz5uONwZpyKVAFZq6x+6
+EAzh1Wc8nmLZ5QncLHU/2mH8RuVJEkDW2TKcvTHMM2HXBskPhdFGAci3vJGqC9XTXR7cAGgHWrHl
++MMrpTXZjYtJgGHlXmW8W4CF8uT4gXv+OD3HY9aMwW68lN/xQTYEyBQHWrUGo3FKBX9uuQLffFWV
+T3l1TQ1WNMh7z/i8zdP2n4YfIPxlWOdchdYSrozxDYs+SC4LUW4FregIlMn5YJ2JOwIrWYG3Mi/t
+c0egF+OptXqKIa0lQ+JrIwLFSiOFQW74ubuK/rwXujps4NtBsp9+5lNR+ex24e6HDYBZ7KFg856l
+f9tn9REswfiOYNfdJBT8hKW4RayfY/7Aj2l8YQm2VE2ZXxWHzJZh4ZD0tOhQ5heSZmrHq8htk6m6
+lTptABuFTwrfkNnG3Ggka0+DLkryoLhq/af3NzxzGf2NMhzqUcac6bGdCpxP4WFKzkyk+zaDDa0R
+CQegM19ryzpPkL8cnc05obiHcdY9a4AMDBhiBZRnnQ3Pw+kxKaunpVHsdB9vBb9eAuVCnu3Zaoyt
+pOzErqxzhPGsrVuDuysEjrlKC1dgz2V4p6fuWfPOUoqupGtp1uFa6RFnC/ARNOOEHk9xxmfnqGxf
+SzxeAEIDyAZ4VbLwukWYb4xAP+HqFqHsfqHcnqd7vxX6Wrys23z4/vj34QcIR3iPDvxc4lc2fPMP
+flZMoYEOBONRNrcqb2ER5UBgOug0kHs2fMq6RdiY1pIW4assFFh8GycgRZ4UG9JA9HpYzgK0+gyp
+CNCWX5MBj0Cez/eHhPB6d0AcP+e8c6IXOF84H/SwnDXA26zCAYAtfITF3SbUQTC6QyAYTkuwFDAs
+whbdEOjWednGC2zCLxxnzWuvPT9jHQ9hUN+kEEaadaVRa2xy8tAnazEd8xQ3zdArFsmoxAdAcNOP
+795LG8Pebu9lL5N8EKZPfZ2OwbfhsdzYaLF83lV0vO7sAJA3D4sym/nu2o38sZasCs5V1+swzrLl
+LJk6aMYWp/GOwAFJkOcPI0FOxKF6qzw4riiHZ6EA3XjeZQDAJmQQsuB5lhjWeN5ImUxX/+mwBLsu
+835FGmonQOuj2SnlHjGPt1R7XT5w2p4tXJAYnTEjtEEAWOEGBwe94gC3AF/P15rPt0ZUd4iwDov7
+FSsDYmibX+AmgWOir7NxxKzzx4dW/+PwA4Q9vHSNwEX033xHYUgK1LjqQYQBSN6lzSBd8zMtUqzD
+0D2Qr3nIvH8jQLY8/DSYAmbpIQINQa63vJ42udyiq2z9FbAOS3OL4Dw/fSrpE4wgGHth8b57YkjL
++Ym7g2WBWtk/p6zXzkPAa9cGwAyMP7ZujSDg+3Ff4UXrP7tcrcMClugOfI36vRXawAMs46vpj5tw
+rINWO3lFigXSOs8ZiBXwccJCjVZz5gHWL97upuCtKK4+vN+9PwPtp3cGfpn6hn5ej3fhVRnjBJfR
+vhjHSlme9T1S3F0OfL2dA8NAbnuuuETgg1eJcZT0hMGfpOvQnpGC4e4OJ7GMB8987p034Df7Zlsx
+hC/w/oYyeVJ5jNZgd5kAOrd4ehiu0Lbei7mq6bEOhFYMs/jht/13BLp7lIqlJh4eSf2mrJ92jist
+xOw+cQSbCGBHEFx+OGO/funsKxx8UHcCZGUAPIDh7DrMkPLM+z5cBooi+/9x+AHCO7yFwbidGfz6
+/tXMIrl2shKvgmRJjvRWVVvIOMgwyFz5udk0KtwsFYWX/NZRz0dICYKGwHB1nVAuU32Jq/tDukdo
+0EUlbojwU2Ylz2cKV83BsJgDYvwEffN4ELk/5ENwKuz/K2AtRn/hj11yARi2zwLA9jkB3wd3CVc+
+oYQs3CHIOhx8dURGZXq+ULmn130Yn1un3TOmjdO1b22JTnxt5FzJyQO48vtNLk9837w/8+hI/6ad
+iAPhhPsmuksqQ3lQwrdKqfE/CNZYx9uGlN7mhiDjd5H9Le9dHx3MsgFk0YhDAjQXzGu0kDAAa5F5
+r9rw4cKy3XFPGtOcc8zfOuhkDb4DvVnvBIazfB/0HW1Ibz213BdN0jXPDvRdg6+F61OoOXWdlNHM
+N0QwkMZ7qs9STKBeirscs6TjEzmqFpbhdFuYfymOrb7508rpGlEejgO3CPoBjRsAjLOhsoB9YgRY
+YxtmOFw9RbC2fxV+gLCH1xZhXyYDoXcCv8mbgoKbi6Uvujh/Mdnvvs0K42ueai4OYU2nMSvE/lK+
+0R4d3RsKGEa6HeghfCwP5hEAX2tGTdbhZQHNM6qGN0TkRH7ED+f+tByC3IIzrOhR/74lQtwybILA
+WMrVaQFGN/hFK7GoDoD3AHxjZD3/N0AAACAASURBVGDLHflwFhj8oo3YJLo2zJmUchKuEI2p0CaX
+iYn1d9JCNDxPnY9pnXrmg7MZDWjhy3AL0KHOBiqGus48Mwg+vu8uT21gQqNuI5oU+gksvwnflJnW
+86t6teQe60p5pgL69FTnmz7dVgLdQnmPYBRAcdJy7xrI4dqS1ULYHkdI/3DXjTjj7FqlFT4r/PUd
+5AZ2h2lgPYbzVi3JPL4uEd7RfE+zLVZKHB+Sm+Kp0weAqxnP1q3UfgLDO3fPe1p9J0nR46lRU2oo
+9DEAaXnlncL1F+TgirSLf0b5Uv7FuQDEkvQEwApxiSPYgXHq49hACjMJLjxx//T/QPgBwr8dNDZA
+XTo+oiklAEuOx7n9KIcD4DhYaQ1W5DM4zlChX/VCwjLSu7RKukuIJAJX73O6QNAANN9jdCc+9R/P
+sIUd98FyYXuJiX0kXCfEwXFYhpVoMPg1BssDqYbXuKxP0ypuSYdFAikeYBjcEpTifnXaAsaK4Ncf
+mAM/4/Vrcl9Ygd2CCyonhXoFwDu2EW+4R4CVmEeGcLkHe3idyrBS4CmNtA35NgHksjalPh7RE8/c
+W+TjLzfv+abHyUaAUN7PYPdLEDzWLW3gE757T2PqW1X0n/M9FUSgbPPYFt8EJmWeyFcd47xYG2wm
+Nj98aLLKZ0Cr6LI3QHvRCg/RhjZJojC7T8Mxj973OdkKC+8pqdeoZbm/bQ2eaNbiCbGSzq4RIMuK
+esTlOLeQ48O63FLeRzwJOb8VJOtZ/Uhejp9fl+s9GUCvA+RG18Z7+mllBL4zjT9GKM7K3i8xazHh
+Hn8rJf6b8AOEd3h9fRr/WWVlXkYHiKeHpfPeYThiBodVC58IoOXi+0Tg0LUCnO5dd6aT3xQPI/b3
+BtxO7wf+IO0+6h4HHrJqLV7pTve2+GG7nGCfqbhwQhMcMweIeIOXqwOgOZQksCz7OWkAzGjR5bQQ
+jfkk2q1WYM9CtwjJN6nAV7AcvXabWuhbBqEC7LXNLw8xkyY0w7n6b9L7DNjcj7u+nWjjaXYZXEin
+cAIDd3n3YOIPfIJBd5zGXcfSaYOkMs57q45GvknP/0l9GKaFmoaiA+v4letNi990WnmNvJlK43t4
+J9qqjJpu/XDgrJVcornJH/el3eR5Obc0wB7EtggMj/H5DHFNz7tXBOU48+FfbF+Bh6mFFsh1j8qm
+s5NRHXJwtOnOIFs25prw0zOgHzcgdK2pWybq/lp1BrIz6D3T9/sl8ktVfl06+AmnNbhbguFdip+y
++dzsjeLAeE9m+gKnvBeRMMaZ2L5+7+0B/HvhBwj/TihAsoe3C+kbYlssB7C7uDT9nMzVlTYAnTJA
+o3rS/Ns6kT7rngfAOyR1BbU4dmFazZtoyi8UFNHMPmVKIFmKFXkJrUvXOC3qWHNzKdIkDMhiDorz
+MDt4C2GJ0/YETWz6ktzEV4dBavKg0uH6kOTaKbk5xuUnlwnL7FR2Wz4Rb6W94ZHvQ0AwF+qtHtgU
+kn2utbxyixhkqWKkAYekV+XXV78od03aCVDc0vyoY95Ac94JDCvx2UDbfMOc8nR0oHi71kPmN3vj
+q30UMuuZL9c0mXuxE5h86FU/fm1N1vr120Byv5S+FlUSIrg2RX3kRqdeP+3BkAtTnmw5Mp0VAV1V
+9mrnPffv90IZu8qSJ+HEuxdEZRkuCl25oIiI1G9EnCLZUkqCoruQ+6QmXQ0HDQBvGocswHDYl3Y3
+nU9lcnMo1tyrA2L2CU7aAsEqv/SK+HqAThk4e7tbB18+H27JBQPbGquDeFC8aITDs/z/gDVY5AcI
+/1lQjqbQeJLWm8ufxPWDSXewwSnATVdoeC0MbrD8BApAdks/i6Ywz7JpPOKIILS8r0H0IVcpUrM2
+ElW4J9gBa3zlEnEXGBKfiNco/CEBFm0qUFd7d4G5uOPilpBWCH1iwoa0r5OFtTaWxCqfA4oZGCdX
+VUNTu8lbwW9tQaSC4wRZ0zVpjzyFduhehBT1HfSyCsKxwUaFvcOgNkvfis/SaK2i3xmsqdBL3gQU
+Gvi5if8WDeab8k3IKod5CgmiTXyF1sPAdZiXb8I3ZSZeWrcmXw4TI33OtOTfVDp0hgu6u1q1+rqI
+pTU05qu0w1QP9Cw08U77qrU55sHZIh6XsIf+vaE/qMWevdv0puv7VIcJgWECyWIBjN3aiiAtAa2n
+oV+6863QB36vK40t5eqz4LCSBnVq+SAclmsg99LQi7euEFe3FAcAvtIa/CveAfiq8JVpApZgHJ8D
+fB93jB/nWXN9YFclJvmfCT9A+C+F8aA/Liyc6vKzcWEFRgm5d5bGdT2w2+CrHQK5qNl9x8UbPNVa
+XSsAYDbwC91PciBJfsdp0ByqE9D668Oh16Win3UYRSWvXzOfJclDGe8MihFoRxsG/QmBVFWDx+++
+5NsrQW4VImHV9TzLurxcpk7tyk2aaaG4whVDyG+YfIXxaQ4vrNm76P4N7dyn+xBb0BpWJS4C9biX
+4qD1ko2iwv7vWH+pQjm7VRYrVIGN1FX9Lj/WTQ/03R8qBwCFlzE7DVLjFW0M0c7Xgu03ON/xU74l
+UHgCWaf1vC167AyXwHWZ1uoEhhttav7U753Z1qfUM+5HqzT+GF77E/3ADaS9v0+h8p92lSJhXBzI
+CLnuEaeZIBiWAKYueBx+7lbHr+RTTvo3pVW9VWuwineD3SPA5kNlnd+1bX5jqYUX7gm+ujW4WYiv
+k28wuEaERdhfA59mP+mnmiUBcU6hgXDXXJPJGgxTjAtvxzX/b8MPEP5LYVTNinkTMtyrTu4RwuBW
+8hCnr/D6tLjOZoLmEQDrpqGUAWc2K7zrDEPHKwguNBqzDnsYTzL0Qf00ubBSadZf8knyA72fsjPZ
+/sOWQsJnx90k8CumBoL9ELdFqe87itrjjhdUz/pqEXhAy6TluNeRtt3Dg3JQMvJtoN2lQRlHeVXm
+qzylx9jtsrxDHPa3PMtHpNY9hm0/KeO3gLfJZz+CUKiusr+/iSNt5PHjPvHsjX2q02PTbobuP9Ie
+J7OV5vDew7iW+5M2M6L3rI/t6/vOjOXrWt3RQhxorwMrrt2xwpQnRKVkHffZolnPazyc97fxybQf
+o33FERXd6TpMuUdawLCowb35ezJV0/3P9Y9UTeYPwhtZKRXzJMGgiDebVtLMnwDw6aUFaHK5SxIE
+n8DtyWVi5XEZB8BHS7CwVXgEwQJWbXeJEAW3FHgHrENWYV38+dTT395pz+EHCP9x+I1FCy3EG2Ll
+5abRvUlII5P/cB7ycPbHQ04AGKQqCosiEMx5C/BViNOQld8N8nWamjJUcl1Qhfi2AvswVNav030Y
++F5xdOCrGj+wloJFXEaSdUCyM1LjmSZLMVl3y7tbY6WCAo4HCA5Ng0rJhlIcM+xHaNoaSmlYUju9
+6IE6PfCk8n4CDYhBOb4hsUq7zo57X1BszSs+xbcd2TyVTQ9jwX1cV3IEGH8hXgEv5e9EdU9h15I+
+5EaHryQn3hoelrgwv5OFX9WJ4aF6FHVvy2BnvlLBWK6cKzE5WnxH3r2Xg7/tRYgftzw/SNelk7HI
+Ke+17SkPZ+jtPDU+kOl1j1Zeoo2LAxbJOMdwd60J6MvNE5++NRcgxAz4EIN4QYMJukH4LU2ud/ZK
+EmCO1wHg1m8xu9UV6zj4/R78gvUaALGK6AbT/2e7RcQLwbDyD2tQ/+K1cYePXXM6fU6rm4RA3ije
+Z5H/n4cfIBzhtQj8e+0RCFY4qLB7APBWn2L162vwkPsuRK3gAhKEhXdhfrAOaVs0KNdBV7Uh4oVo
+2894AEo8hMUhjqB49Ue3e0SC4QWOla3DBvVY1rcO7J5HEGBxwINW1QbMEzzUxpZfyBd0TSAPPK5L
+RFgD1tnr0qFai7vvsIECXHvn/YNxYGkeLMW/G5o8jD15LnPOUvH5PSI/zBqEbKg1yJuqWmPXRs+8
+N/H+FXRLg94+5hf6NNY39BdTfx9eFPwTndbGMHX+qVxb03s5byeuu/05rZmLAJdXj/Tz0wkGhCOP
+JbXuDWp3yKviZ8rrYbrB4QBmtXNUOSBDutQydSGALtpuJmNHxrO1vidgs2xeBV7veQLTNakKnKhD
+RDZ4FdA94vro9FPFQFesr15xxu4PCXJfWor91gi90jUCwHC2xZbh6I+5brUcf30Gp1p/i9Ev9gZm
+91X+J+EHCP/noR98zMuH3dZuCNW+N1Raz3QfOrQSg9UYn5AVSa2B/qDt03DyhTVBJeoiAEGCiK9Y
+Y74SR8AL79UaHHi/5u/uLmvw+orHTAIEn/yCUYi4sPLh+bxymKBFVxuzmpluhwWtMrpVZHyx3LXr
+SfD/bbzYMoBhw3R1idBXD8alpVhhZLPImvb5HS1OB+3Dp9IPArMoPeKNr0YP5YY+1JU5AYVTnn2R
+3np7xfWBj7vd+nqmh5T5LvzHWuqpehrDtKFaSKLdsf1uh5DNDuvlul9K3kCfJIDvhUp/yqf2rPRp
+qsvOeTR3eWhpdvtFZXOYQG9dG6xj8IAIhuzPJhiqqj0g+vZzg1cRQQOQ4oFXgecKDPrlfw2+ZUTd
+47z80Nu1dY0qWFOtpLX64FYwrOkfrOgewQ/NtQflqi9xPFgHt0YQ+C0v0f3LdGeXjVgDARC8XR1o
+gds7Kn3bM4hX2/3b8AOE/3r4StRK3yHCm6S6QpDkKHlBEzroEwC2Jz5vBqSQEprM7luQm4Qch1iB
+bx54iUMnRFtCQtTip5RXce2+wR43rkMk/cUU584/queIhQ/iF7SNUlOxFeCMD9DJ9LCcSEdhlg/A
+tTaxHVaO7KZRXqG03eqrHQyPZZao6mKqU57AL3zE49wBvU2XrZ1aiqNxU2SyQtOWHcpOQMVV3zmP
+57CmT7Sg4/pp58f2pnHc030VbsJ/rI/+pHoem/bKDgOzU/ZXnUk5gudPTM5geBegNbZ5bQXyateI
+ZgOt8g31tH1kA+1Qrw5xUkmSZ/wu4BGc4j3syR2Y46YIJxioLYM9DvqPf2xqUbJttgA7v+snrAP1
+zmrCEuyK6y+Ty/L6M6dfUPbaddNPJYvrPig7PCjXbo6gB+XwF+QYEFdL8C+VeHWLsMct+ohz7RZh
+hCxxi0QDAJys7/+x2BnDDxD+K+Hp2N/xWQG+Eo7j7Y5hTIOP03pwzqUigNGDBdge+bIv2XX+kY/g
+55GU/DL0MtRM+7i3IPGu7bqu/AVj8AnmB+SQjsKpfyUV0yDkGoFSPUdTwqBqwESbisWEAW2Jk5/x
+VGfGrebteL0sTYQBsxW++xdaijWVtx54X+/3Dn6neU1qySf01sveCUzfi6m0bjo31aV5NOoKndL2
+RfqJ3vKs5MPxbeOWnjfTi4fxSw30fvV7278fbiFS55gaBRAFsOlY5/TV/8RHP5Ti6+TfcAivYQXD
+ftRjTWEZJgmC/Lc83l6hTzyNNvB5SJnZ553OuvI+o3xUR8prYaUeDklteSZp7RXljvtZKZZfunVE
+rAFjH2+0uAVC0zEuvfa3TRrlNkA1112rjAPJy/PjHQFwAcL7vd0ScT0D4qBVn+Fya0SCZZVf3pYk
+GNbjKw+UFleITMPCwgpXK7Dvj38dfoCwh9+afG0pu8lvIaRC7KLcKPUqEuF0gmWJMyziLK79UeJU
+AGxNGlkIg5Ri6vnUbR0AbxKMk32CYFi2nzTFZhkIa8QXIDb6eeZuFT6/3CViXhX/yuukWiBOPsBO
+tS1oobxbiPfcNiswbRZUnMY4mlrBYjbSSMk6DR6EE7lzifDyA0+kvZauKJ8Cb4FqFZ5rDdlJhPsy
+NWvKjb091sftVkt4Weljuu6gN/RXvJa03vOedzd9B2hxDIet+e/Coaun8Y4MkvvwseKhDt9xJjJa
+6237/DZAbFmG6DKvKdaL+/jp3Xmn/IjbQIv38vAeuMqlPGUg2+ezBzypRR1wWSCQewQmkI6dB7+Z
+1U9g0uz7jVQovVwge4Hc7g7hc6PGFmK8bzdArW0gaxpXk+WPVPgNDW4BhndFEDv4/t4C4gKgL781
+IsGw+wzzr8tp9IFcOfxl7nu9dXgDwbi4mmvnzzLt/IQ/v6NR/k74AcK/Fd4c95lK1rQNWlXKncHD
+fXzBI7mhVODhuQ2WV1GQss0FwqWpAkjwjanZL+FiIi50JsCrPGRsXovMoWH5A3/C153tiF4i12e7
+Q6is92sBNHfWv0wyHfWksPIm0wNizWHmd7Vz+9iKVXqxkcZg78pBHoDnBmkLsLbyN5RntUhLsQSD
+lkMge+8Ssd0gogwoclxfOYcKdifFN4u9U45vHqbOHxoG3/Vyq4G1fE46qe6GKV2BxonnxPs7r9p9
+KfRhaK/zToH3+d8J31Y1+njXPXHOopzjHra7/a3IVj5klnWKM1YelPTzqrye7d0O9Bf8E8/MZ40P
++4mDpW/VpMdtiAcDhJMsmN57gJwdJfXTDkGVz9il/TdES7X4GoijKpe27ywYWNYLrcGWgNbYuhoA
+WBMABzh2IOv5t64QB79gtAjXh+gKGL5UhlsjukX4KmNce2IrBf+woHsNBtCba9NlPBb51+EHCH8d
+nlT/75SBo7+RIz0EJ77J8MG5Rc+bI1IiLKzrGxDAbfMzto23mcf3YX7aXmUsozCshI1ahwrKAff9
+BILTCK3xFZfXp7JO4vUR+QhbhFHwXq50LGllCHGIcV3wl3+Sd1Al5n+S7v13QBtuBhaprKO5S8DN
+DFKDQYwdIUyMCkFNBSRtSzD22gSUtu003AoB+QGGy3w3PSOzEpvDoExG7vlatbH2A6JrpUkQH6qW
+WRDXcU9zUWl3czbN6bev0zj1N/NqfuvwocxT2b+t1/j0PjfSP98DiJI7wFX4D32JD4wiR5cIEwbE
+dB5FGBCDPLh7J9qhDEuq7M9tXbVvm4Jgb80Kf8N2N494ar8DvUPlJnCQUcmgzoL6bKAF4bSxs8ds
+4YX01sm3eVvmXGIJbIVvYiArsPSfN2YgDNemVYALIJiuVCNA3O8XRreIuEeYwHB9mK/o0rAIC4Bf
+BMEV42AyAEtb5n8ZfoBwhD+Z/m8BcFFH6OrQT7yghVhVxazTyfQ6AmBpPBY8loIUvusKUZOSL3qF
+X5UFvYHkw7BhiAmCF8H86xbZNF1C5LPLd19gB8HZ/vgqwtAPcVjPBW9+sOBjVcIXaC15DKrCsDxo
+JpSylmUNiXbgp8DQsFmZgCcVsPOdXR5kW7QC/lO+dcuxb6fWoxKKUjopv9j3N1U9B0C5N/WcrmvT
+mjhUZTe0pOtAw/X4vdcn4jzrWkZAeeUc1r7XY3q3BZ8k5B8t32+EPgcyU8pavpP0Zy4EdW2dTi4R
+AuuvU7k8S/1M39DsBQ/S7Pf4fNz+qumkp9dnnUGct0kOyIE2BvO9XYwYd/t2qtAquQgIMFESIFb3
+Bx4AsGBeuXHBAGhKB8MMgtlv9wRw+SE6sBA36zCC5ORn/2AJ8Hv+QQ3/AJTva8H2XA0gOK3ESWur
+hN9W/+PwA4T/ONwhvk5vWwC/C8BNorIswIObxBEcbzCTUiQ3qAMe51nVuIaA69CsSAZF4RUIVXbt
+o6INFwqciqKdl1vHHgo2DVJLQUpdovK5RAR8hPEGiSMI3lWgMML+sMC+uSmWEILPW8bDdlr56AG2
+tBiHtqntlDRbgrMvNvC4QsU2sdbsQ7fyek1vwfDbMIm8An+ZTIOdS5/ppZ6m5GoV02r3fTvdlLHm
+4XxjRAUZ374+I33y/4Z9ESOAcZWOH4HvDc/v8P5rZZYy6h3vNA/6ugbeN+MDqfvbuw56Nb+lKXny
+EA+aDbSJr9Ls9/nEpFkDTTjNgHgISsfq+KBchC2DSGZXGQHporZ6sId8YkK3h6K5w13xDI7Rikqv
+CQCLwkvWg2qiHRijNfjJ4ot+wY22y0sBvxUM7zq7FbicAJM9doN32dby9CPm55IGmtf473HwDxD+
+s/ACBOsNHz3FioA40Wccv34OWeFjdftrinyYrtZrBATqA3YGmxVwafSj0miIEw3jZciLtAULViwL
+8H7AYnuJiF0q9rEQMiGM62G1QreSH2fQhHzIpILhQWWYLMt15OSDcgs09tsb+vftVQAU7VZ4fdnE
++wzKNzjMqE3KNynAVg4uEWtGZxDWgV8N1R+Y86TlacutGeWcvAo6FNXOMezJU1UiQms6rOaY1+fx
+/vUGAONrkixWcip2uBniax6nIlA6883hN1f1lqHvq9+svXVurtEEz1FdJxX+tUagC8uI8dq1HTlI
+oSNtjNuc/w2fy85L8mv/23CTj7LA24DrgG/Xj9TKwybqKsge8lesus+tVWOf4BM9gLEkEMSH4ioA
+/iUViNa7fR3w1ruCy00QF4LkAn7Dr7gAYgTBIqX+E3Df4zOJsZP+CmxhA+CFeMhnzp9d5f7b8AOE
+fzvUo7rSgGEHnkVLcMuINvfPpvl1aVoeirPZRWJlOc0bMe5U1CV7I7oGcwBsrPwlq3O6iZAQjOoA
+vPD1Ocbjw2Bs+a0w6bpM7CPyAcHjD8x9PgXYit/hWIBxlawwF+zhVhkm4VlUBPrq0vndeVg20hOt
+Ap/y1LYlj1CJTjOKu5/w5OLgSrpagU8Pylko/T5n5zDfDTH5AE/wWQ5IbobaTYgewK7d8NTmYj5L
+htX8Q7m3rzsQHHnG7dF0UNRk2r0D+2P+4qlC4QTC/7sw7ZYTg93xCc6ODjTkO90yk2XS5WECxGkB
+/ogMoPi8rn8tbhm/5bU534ODO5+n+yu1Vj4HbfvUIO7t3canRSqk867f+ccsy/IA5tZ4FPLdLcCB
+IPsKuzXY3SQug4fNlMFwPJgmSpbg+iMXv5r/bwfBBIxHK/EAlEUIDEcfPU+U8lCnNp275yKAr4ic
+QLB/H9iW8qAC/uvwA4R/K1StMwPe5/Q+1eQbgzR4F0vgHF/NgOtEfMpytGrZN5OyGSWl3gbJ+HAd
+CSevDmWYggAr+vEOMIsMQkhrwnr8Urk+4OO6D9ClCSD8U3cKIAbBeVglHfvLOBFEHb/0Nkz7mMFe
+2sCvMC3e0G/3pg3D3NqnTQM2K5z8IN7JEgzWXpNXYPhNmKEqrnCu5etSDwjuKEN12HuNR3n6S4OG
+L5XywecAcIb+Ta8TCP6IEEhCfuph2XIZ04o/Cs80UoIE4qKhBqzj3Y74u4Hk1Nd8NzdGPNZa94TR
+WVvnCz/A6Aue7OcU/908389IH3ntPn8CulLSFfh+BloAWy3pmi+wr6fNOy4PH4IOjplPB94E0Ql2
+Xf6468Na0dQ1bsyZ3CTwF+IuWTKoguHlDuH+umApbtbhyZrL4Lf9gMYBIF9Xgt54R2BcaTKA4bZG
+ey5Cv84g+DYut0+d/GfhBwh/HapWeQN4e3ofLcldZHtPFL9gOISGjuh7AxG/yn7oTkKpxynf+Q6c
+mgU43CQSCVRLb+xbGEqcAc0yNT/KTtNB88KCB+nLD2KN/foogF+LrI8IXJ9mHQTv+fFq17QxXBm9
+QY3TAd4GgBtxsP6SApRloWX++vgTV1VhS7hGmHcv28Ey1cJjVEN1f+iuDyd/4qlPE3w95Z/ib4O2
+yLtK+EG5LDwe5zJ3p47w2s78J8By+zIbwXEFTSJz/2hMdQtvhhkc56G9m947YP2vQXHIoNd870Du
+m7y1FsUSbIv2iTu5nQeAL+4ZnfeAfJEe8+zMRzR7yN8hQJ2lTkAQ/IE4zo+Heu5lSDut7t869Xhp
+RMri0+rlRDzlLyZNPeGAuPgFi9QH5pJvzRGCZaEf1chr1MASrGkR9l92q0D41xH8SnebGK5Rq0C5
+Al//FvVyveq6E8Gw+Jq7DnS96ovhc+nWYVjghzh6U/zr8AOEI7yc/r8AgpkEwLaqm8kC7FKB7u6D
+r5knACwi/AlMBJ/qtCkf+hJyCLt/in8VylfZKsJfmficeGoDM3X/YZFmBQ7gu+dGQHDD1Gk2GXXn
+OCaoUdbGwBfWtpozcEMQVES7vJU6K5C2wmvInTXt5oHqpdwFwouz1Tletiblzgr88W0is0uF4yVU
+aG0bjMQaJijMNC00m/iiI1OD5evt0mRtnevLN37BBwQ/whA6/xm09PWZfYInYHya3np6p7Xps66V
+BfLm+qf2v3bpfgpKb2Po/TlCnhvQ1a3EKI0yzeV5TyDY7Q/MjR9uzPdRyvGn/fLEI0N8or3Nx/E7
+0P1QfLkESCkzxSdAjGd72mvNvW1qoPTzCI5DztY+eH72bumF1L+LisC4WoGTdoFrhIPhsMDKwWdY
+wRos/pPH04NzDmYL7XTH8PQQXQO6BqA49Wp3gWEQ7IuWxqdNtC1TFOZ0G/R0x8PlDz58/G3x8Sb8
+AOFvg9WEwkmqX/TW4+5iDngC8KQFLyWR0TtZDSvvXw97Mw/C5xW+edGxFIgO7HGm8oQ5WA6QvM0R
+Wg7fArxoCWba+uSqWS5QsIsuAZpEu0EKEyysy+PrM9JtfH0aTQ7vCXDBnxi7Jv2dlC/Mv8fxJZTu
+K07W9uC3VlZ8/od6vQzuh7dhhCbjlhtuahlb0iHVr0Gr8/gEUOilcNyP5WYQ3MrsD892Gg6FtycW
+9/sMim8B8X+swd6M4t1IM5AMovQdr6eZUtcKqt30XkNvrXwCe9WPQzrF6rG8iswP9JeeaK0g3lxG
+vwlQ07BQJkKW3sZ2KN6qsq6JqZHT3KK7YCkUVmDQ9z7+0EcjzVIXeTn1OrOWKBdt0t0vOUh4D/AI
+BrN1/agDTPdPB5BpLot061vLafF9AMC17SXYKNmMF3K+Pfs+TrqVSshWN5X55vz+rfADhP9GID08
+ieITQJY4tKnprLyXTfO3lU2TijNLCyiUDGSHWbpHNCl23weyeMOM+R23CMjw5x39IDnQDYDWaP4E
+r8BTzztPuTsRPyLLrfIAnOoEbD+F9ln0AMWfrKOnOzD2GylW0xZdcAv1E5gyQWvvBIjR8oFzwfs6
+Qe4BAEfcgO/Ecw7aUjbku0SeS/c6Ol8/sck+uT7gdliKJflk4J34busrfSGeaGevSQEPc5jk0h0n
+9uC0C5L4qgt/IbwfxZ+HgjcrNwAAIABJREFUgzikvGm9PLy5bhD3WW+hcd326a6jUznCVY5jDGil
+J/XcTj1l2ekE3qekHySzaAq8H2VejlgVy5SRUjdOs2c8x3UsNbguYl5wjXCaZl7j197X5AG9o5yf
+82nSQHDIwvXNqQJrWl3TKotX+q6oUf82e7pcei8iz8DFMNcYjL0SIFpE4sH+HTdY/PWt6r863Rx+
+gPBfCoejB3RWLHkmUXsavVvNv2nt+/BGpM4881Y1jmohl2pqzZPgtf0Vi/8GuTuHqMj+1Ctg7dU4
+vHG1C3y9g+A3rMR1XCClOI89ZdEy7AAYLbby+Ygcwe5H5ACOOV2twkJt5BVtDIDrlJ9AlkmC4A+M
+F4U2LqegsAO+yV8w4wCQjRVDgGSpcy1H2pgHSOIE3jI11/q0y9H1Ye+AeW4LyBXpc25jfX39jmu3
+F4PlimZ0Hj7UrCeWgd/5Bnlj84x+K4t+R+1VOTHl/069d/ugyqfz+qY7xFxrwo3Y/TdPcW4JCK3d
+93nsZwM7/T3y9vk0E2q1GQrgzJ/DtBKdFm3pwAWdo5LGfJk3nAwrhWp3qhyighW8sqEgwaySHBQp
+oNj5SNbueko57gP0zCfKpBmb0Bhlnm97PwJAjofNLW3BPo4EvTyLdR91EIxAN+cwLb85P4FpwCJt
+wbj08sGW/5+GHyD8F8IoxuITVIVUpUSgjFLOCh/xFBJWV4pkmMX8+3y50TDW804y8KHluAlD4NOr
++FdNcEjA2ptW3koDQGwJfleepj8TmCurpaGBrlivfC3BsuNHKy7nNXBceStYHsCvCAJkKcDYcJvk
+SzkdYBjeRUC4t9XiXb3mXQoYNogLxdOlgq3I43yPtCVNT5D2VP6uxAQvKvSogKfSIq2+DnO5N69v
+2jsGzGybeBFr+TtgKVLxGkuyt1+On+qewr9Th72lE/DtZUru0OnTGid/1nEvhc+A+K7sBHCntTd8
+DxDTz3DtTU9bNhgkEBKgG0hN7E7VolFcuFztg2uHqPOwX7m9acYK2JUuDxsoVuA7WYURBKu308fR
+Ro+DL1bgpQPy2rLQlRv0Bn1fm+rA069NrXshvNQb6E3ZXUGwVwCuwYKWX/PFgLunDRaCXClgTe7O
+4N+WDT9A+I+D74ZGHWJaSRJgRkRGFOsbwwp9lbzp1wtg+6dhHwhO13yP7PfSJVQB65BKCF/bp0/F
+D25ezZLgFuqtgDcswEbgF32GqSeqclbpoMZIo5mkpXb2B04r8Q04/lh3i9g08hGWahVO4CuSW2gE
+VACCq0uEr4VQvC5oV0AEcCXnnvIobcB7UqzGaav52nmGMSTtPQieoIbP3Vr6erOG9LTyOoyvxnPv
+zpJiwKi9rOFGBo2DfF7bWq7lU14y3yr1L8KT4ptHfR/+VCpOyrmerbZuoxr/vhd118Mj0scWWkuw
+wRv4Hco4IEYQWgGxPKXbQtmwmUDilDwqnqK+VF8EHwVrdZyY51P0DIrj5ghFAGwk4zq47m36LLR+
+4CIMYPgWBEv5VhXyDRY81l1zDXh/8Rlv++pg+Y26CDSDG4TuvQxujVLqryv1J2d4Cj9A+E+CzQti
+R54hFhoUd16Jlx2BLH89VEn/R2GeID5EAXmh2S3iVeJGDN2n08C9wU/YHeB1qbnAGuRrCrRRstMY
+Vj0aEw+vI/A9WIILOG7AGMrJ8V0CAItIfLoPkGRFEY9Kel1Bh8oN76al8Tv4M2n3fybY9bWQIvxx
+eo3TW/jSBxJ7q2RTXM5gunMifQIOtTbkWWDHrxxEkFNBTwIjqesg0zrMdLmhV56k3oDhkRhaifKe
+jv/bfExNa/BNmEd3pr8JE6jluD7yVADcOR868G2ZsQJr1FpTA7sOcK3QpY/xeL600qbVKDRIgggX
+MqgAr/f1/7J3rUuO66wW8r3/I084PyRgcZPtdM/sOlVRddoyQuiOlglWSnY77lNBVNNGIgpftIci
+LvoaLNjYvs4CzIkPj1FTeniXAvUjtK2CX466Eeu8G5gt5STkFnXov7UnsAHQ/BC1izP9u/aNve8p
+sNX05l7L1TqEF+K0r5Llt3OLwN8x+JfhC4Q/DcOiMufvjm0TykaBO5zeYNxue2UxF3Yv7RMVbEKz
+7nNNdVk+E4WnRGJ9Wo1xZniqZbLVXy28W/mAdiguEuSA+CVM76BFbrZXdhmGOMU+2bf3ChyjFdjB
+8OBeQRTibiVWutZSrMvtA5tP9+lBcB68CBKzC4Qpd+nA8MBLcQ+cQe2Ull4usTRueJ1fAiXCtQwv
+5MN4BsMhLXya8WrzxO+A6pLKi3HiywyZI/boHcB7tYK6uuZx+c3Q9kRbxL1yI0CcN+tpfZUTIzjP
+QqzLtTbu6yMjz1FQU2QHKrOqZOp7z2cPx4FA94gcdCtpQe++Zyxv7w3HBkq4FHpHboGpA2CnNzR2
+q7DS9peMAfjOfVbLjfVj0hfM1kvITOqvoH7AwmD95Q14RY+B860R13arCxvQG10j+nuxTN4nk+V3
+JS9+0eaO5+T93fAFwp+EaR1JTJQUmdajgppIl5ihS/9ZdT18joSHAhXADrU4Kt34X7++8UVeAS36
+BNNFevcSnWmsrbTc06WDHUIKgKuFt95nn2Hl6azAy7XiDXkXrViDKfkLJ+uwkFUx13y7RERr8OVw
+2kfMKhxBbnO/C52OTss0v0qg3Y/PsCpvYT3A9v95KWiNhCiAW007xjmOBaadTpCQLK/hI6p1jVvc
+vWUdc9Rcfjd7AufN9E7oSulHKPLn1OPW+YM99Uotarq9IHfBb/PnsgTthbPzW5evy9OB2nJluJea
+bsAFPl2tDbgW3R8RbnEhxrw0pMMEQIltmzqjkXF3JN+MGTauoEegcRHYCqFf8MoXX5bz/JDelFH6
+rrvfmVY7AbFqx5LYCREKmtUvWGdINh+k7KFvtY7Wc4kXE/NpESor+xJHni3E+J/N+t8IXyD8NEgT
+7cZNhlvJ94PAsHN2PPfreT+kVUC65C8aGBbrRD/VKZahv6K3Fsz2OSI/jzAc3I0a8gLw9oDYNW7Y
+aIPeVvkC5e0PAl+q/r+9tbjxJW5ejssuEfH4tL316lQRss0Y21LBE9tB+Eg/WYSzDBzWCdiGQ9eJ
+7DnjlCeCIRrjPwHJeTav+x4E+z3H/rwAw4Wm80u6vqwAqutv/N/x1+CjdFIFnZwZO0aQ1cOFvrxn
+4BjnwQ+R7AdZ8izEeZHnSB4LpyVIakrlGjCnTKQz9GmeTJW71wEUMwobSh2DkOluQ3FDRlTpGfQS
+kX/hB3WNTfZZdJyZwN/Xf0koeoU17iVY/xSrsIT3sRlzMBXZrQ7DBiMKZWinjhOv1mQQrPfqLS0w
+sPmsaZtrCgcUuAZ44GeY4zHCi3+XYd3je7e2h4MVeEtDnn8cvkBYw+0RmBZTpAVlKTHRJkCRIVVm
++2Tb8fW1fRruK9wUgvZK9GarKAp4Lza7kj65ujpaa99WDrwYN7wkR8OpEZi+Wy1bWYVqY11NGSma
+kV1RsfgCpvoByy688GbHp3XAtz1WbQLGCqzUIoyuERUw6Utelr43GAXACowREGv+V7rXcarAVtI9
+8NzIg9fforUbGcTPIDgCydx/uM5DWkOjnWcaozpec5rJTGCirj7dKOdt/op+0op5jQQiIoYHMqt8
+HCGnZxmBdnsz7cf+TOvzdGNkdO772mf+lcaNPHjXxln1J1E+/7WTerwGsBZBX1zDgIRsQgoo1U3P
++4SsPh09JlTtwhwPEiXT+1Vwfpmu5qjfMHkJDnp3XIGcGV6UDrRUQn1ROFYslx77DcAwaR9r/0rK
+5qcv0d5R3U84dkm2CkNJFnFQvJSfzRHhwKfy3PIriUff+xl4/nH4AuHHAUY6rudKziBWmnXYZhyY
+y07403BHCTd1mDaatDEH4mUxXpewuHgrc95rr3vxTagCYtlpPPEu+WgRVs2Wla3XTkwhWRwswURS
+zwQOFuH00lxwk+h9hmkDYrsnB74KiLWe9jGaRLq2abcRLcPzyRGeF63Ifi5wfAEOQWmhiW8ci6e6
+TeSyn6RFnrx953gPkPN2LnBfrluJH3nSFceBSEb3CAp5mnFE9ZD1UFqfnR337qpvl/RULvJqJRt0
+WmtzXYcItg7hjsAHodOQPQh2qhDZjwDF0b4rcaqJSxmTC90RpEOhuQan6+Tfehlg6Pr55JO5A97I
+As1p6iKlzossxzxTfRlvQt5F69KN1gBg60N4EGCq0scVmhGqRtD6q0DXADDqlP5lOdK9FQQfXSUy
+KFYxmodIN2vby1Uf6Pn/I8/XR/j/U0gzgCjNW1jYkOYblxT2Lq+Vlne8vEaGtfwoPMLEHWOzKzYW
+YltwuIDIVUv+OtHA716U67fcyRbUWlwOcm1x8QZaQtVnOAFiUXlWDwpxtQYv0QLoRyhYgkmoO0JN
+X4Dr3SQ8T/UZHlwjoFyzCCeQnN0kTBkmEJwBMLb9lbbusjFuxvq13wCE9SPx7ekrYNtfz0C2B7+e
+C6d7jisf9t94BVVwhz9/Jjqmq0qY8mLIa6vcwteptzecpJKucgVeW1C9hNzvJ5kIhtvt8sH+eYaj
+VdCJ38aCI480eeb7uwrYVyi8cN/O53ANwIlM094Fxb8S7Cs/F1rk4zYCRzHiNDJ7bZAhgacVWCt0
+UV8q77lw1juqw0AZZt1n4HdnVBmxX/PM45LmdACznPMg0LXvBXzdQxqQvYRtRAol6mk5iMNV7yGA
+hePPzGpMvndbQSce0XX+b8MXCH8YgsLDxQu7Bm5Khd5FuvG3bM4zKaYq9y+FW9pRhnocVD8TWIEX
+r1uCdc3EjYAV2e2O8Z9OlvklOQTExC4TUFTZSHRb0+/qtluEAlCS965G59LwLnR1k5D3O7lOOHCu
+lmVyvI1x7RGMw1WgTTmNCEFxfYFD2/+i2ifGK6DwIX8EwtVybC/UKU/KH6880PtrpfX5M61+EU+7
+9oerbgpAn3jdAlytvP2neaCRvpxb9yGv1PSh/Sf5J37UgT6+eWPvKL2sDA8SvpvTi/C5hR041JSc
+VtZZijtEwzxTCXPJxmHruOe9ksxEZq12DCOhr6ruq+vZvoHjmhbDsFk0A17qoGuKswRfbJ10p8lQ
++tDHgdlvum8jrP3Ai6DYXpbTOPXg1/sNAHZyb4i9EmpBiEZj/4FlmBY49pmH/1FaWou69+JsDhbd
+xezWY9HJVV6IM3Dd8iwhjHL/cfgC4U/CtAvBBPZo9AkWSLQ5MJYhDS3sK1d6cw6jzvWE43wsSiMn
+NkoqW9GH6piyhq9N7KgWom1RvHZ7UE3K9qTaAGItUGXbhposJkHLCJlvMK14/rGLzvobae9IC77E
+biH20yOoWoUJAXfdiPPmnEdLf1QDQfCL4o9tvCCu6bgxQvcl14d7LhNIx7UQ0jiCs9MV55vmwnRp
+8vg4c6Fhf536cvIZzvm6D12kZ74s9xS/uq9ypdDP+1LaPE/8vgcCTx6RjpLr9+FO+TBbBoNdmtZI
+NWZ+cKEmXiWc6aYPW17XVac2TLQlW0GxhHR8Ua4stkNYepaD66pfRRWEyz4PeGgAguK8RjPIvBRG
+tkVAvYnc2IJp+qklxYeBnR72Ri4y1hXldCtIUnVThzKWoCPo32WZzcZo/t4NUdR3xER6FCfx3jOJ
+wGWBHBTD3rzyYXqsXgbHSwfsDOL7rc4LnSL/OnyB8C8G6SK4iFugDFsPAIFecCJP/GOYlO+95FoB
+yHfFOMitSplgEXlcF9yK70UoQme3B3eXWPzxJTk89FwrEPW2A871VC37KVvIQDABCFZ6sP6e3R7K
+5x39iKeTJCr4FaitD88EuDAo8O3AqvZHBsRR8Sea6lPOvFV+puerbpQhja/z4TbTXeXA24GfE7gx
+mm4KBz6YKeP40AXfnTGd0k58U+i26JMEp+RXjsg6t6qOOxRfl4F6pX8O6VXlzcyfzg21vZ3Goa+w
+xNumLt1dpnT17tP9PGLVv/2aP4R2aBKxmVFbTa84xx4I8Ts8vqM6/XKvjL1Z673jQadVq3BIZ7AK
+q2JUBVbKTWM+0jxNj0PTuVXBML5wrv+jicetsj7Ya+xxz86+vkT5l+EKXZOEAvAlJgDUnPb6vZdM
+b07+xfAFwk/DuMPkHVD0r6RLjPTirxYuFMcN/d+F3I5Ugexw1JM8zSSkJ06i8JLVWkebMALiCo5V
+MqYR87aKOLSzxRnqlJqHWlahS2v9Tffb+lstxsmFIlmDnZcSbVdr0/TeNmPoQ6IIfF+pUZiGH98M
+71t6nwBiauI2Vjkd1kZ0x+CU9+oKY324ariK635CFPs7giIC94j5QzmPfvLYPqjfKX435DxX4CKs
+5YM8DpT7G+Ezbg01xzTmd/JQuLp9tpMTaQ5i+iLn2nT1u3PuMErs4hE0zf2b127lPNyHQY81FiK3
+RpLrfJS2eAb6sQcE+Co917W2MbUFEoKOA7cJ/wCNNw+T71umV/M9EY4UWnzzqMW+6KzAvI02mEaQ
+I8e2vAR6OzcHHIT40pyOlaRfkdv7197kESv/FybhLxD+YRD7hzSJNHH3CFNV0izIImeFE8/9Sk4h
+Kdt9+3xz6Qu+Y7U2Z3y7J8IuBLy7qwhuETu/dygC4s4nOKflyqxPtjh4zD/mH0w6rkIR8Eb3BwS8
+2TpcXCimF+asNLQAi80xSxes8bxFTsC3gtTGx7fwDGmiG8Q5Dz2IG21r2+yffL7yTT4Pj0EmLCsE
+S3esvMiHIfPkch/XcYg/WfvdrOqAg0BK92J41HWRoYdUFYy04UNFFiHGfKXm2tE6v2Dvk9qL0Rd4
+kpBzc+DDtCdxA01BFpsezn3KlRRD7jiN6voowygpyjEfTfGoqUeZt+hR0qj7EPRyBrv9fC73nFtC
+4d61FXSYaXoK6cE1wuaD63/dK4XYNtRFykebrQTbl/dLdER4HjARA4LF/KqTBZq2Tn9iyEs7n9iE
+Z23ePw5fIPxpkHgjSNP5KjlNIE2QlYgGZeJZRmVjc+1qTd8M9ZUQTOvpWRGNQVROVPRZDZjuE+fD
+b0wCmKX4YtxaVw3oJfQR9rhqte7XjPwmqpgKVzYw3e4Q0p4jvH45LoBfS08uFDdcI0goWYBT7YTK
+2bX9uNUPWbwecTbzXqQJbiB9XnoU97mj+jjz+PXsKpHBQ7cCJuA4prEve720nz1GbRrQc8hg64p2
+J727h+bcCrNOgw1aN8Ihb7R6dcAm5unA0e0KD7W9G3KfZleIiR7LBcU41m3Wr1GfSkP/JF7thnmt
+5jrEkEdNqpJVtiQkjqlEgwV3Jxc7QyxVSi36gi+qjkRODMjHmRf3H9Wn0epbR4DivaTRCfWKFt9l
+EIlWY6X43FtxtvRYXnyJjSgecebt7o5P8+Q11vWluV1bnPJM2zoMg/+PwxcI/zQkbRcBLiIQsNJl
+038CKncU/lSNT0NQn2edezNExXQpL61xW7CoNwX6AKzA/vYyvKRBDeglpvqjGkS4IxedrFZf1qdq
+GIWdhv/x+LQAXt8TmF150F1iAr+rSADB5NDXRCEN2qO1zgAoA4gIDiXdx/gEYG+l77HGAzs63jmN
+a9rWpZqC/PmqfYHXLk37BsP5nksf+3QBazDreHWfu1bjWod8fUK7Ch3viNtSHm6o0wkdysEpVtJP
+hY9pVzW+zpPnDh2uvayhJ5vjJrt6XL8cF8vIMOtpvOjhkIbBDjbT2yZfHM+ig9KE6XQUoi9hBH2d
+9Clc9XUtedZvydjCvrfYB1whsvzqa6wdIDbRJ2CrCsbAMFp8Cay4MGO9VC8PjyMVlaP7cXaNIKFs
+QTb+UK3Fx0S9fzF0hYFhwj76d+ELhHf4dbcUQbndFqSJvzPovYfYnn04CYHP536jnIN/2tq1fduq
+nXXck6TLoXU5tacWEo/cWsCWAg2AUgC9ECeC+7kB/pQqNkHM6stvEn6R0JtEXvueNqB90/utR6Pt
+OIBd5emOSJO3ECF/C4wVENMGyatmq8677tgOov2VVNwiPT75F+a3jOumlEHqlIbqOXxsevpGo5tn
+BqURrKIXHKSp8gaFn+VIkkVUy8I0bVMOmSaJKikSQKzcA7rZxeJUjzt1nGifhk7jTHy+UQ9pJoRb
++i0ZrbXQ/+U5gPCgp9drV3a85pkwp13Ru9Kux28+oC0HcNscdENUkLnnu/GOtN1zYVBAYTcShChZ
+gJt0uzm3st9hYx7ON00mv21n1+3SZbvphd4WJmGFqgFJGl1ovdcSW+B7HBFTNrDxzo9NDpreqrBp
+hliJfA4pAJYNJRQAi/EGf9/2aDSi+BPLZPu2GDPikd/UUPfCFwh/EE5fNQNXm6dmahZLoVwF9ktT
+KZufEigxbS+0rk2++fQN5xBLi/FiTluOqd7n7KkODAsWAM1eiOtpPQIwfPp0xb9X8V7NSxHhVvUm
+odcCq3vjlW0xfv+JILj7vOHsYHkLvREcZ//hAQyrwjD3G4HNFIcpdGD8qjNu7vl9YgLqMEApaN48
+d+/QA+AVTEuAl3Oag5gOyGKbKFx74OP5PgCWcs03fUJIhQeePTVPgOnfbyODvmKPjPqM51QOkYHr
+lLbT5ZQeSyKie2AYh0hC3rha7qTNdLzeAcBV1tOAADhOQ18hGRRySN4AKWBF1akUuxoVVBzsGFQf
+XGyKUfzvrIJOb12XXhuq72kw8Ajxdl8D1wVZPW/AWODsIuwjtZ5St603ho29l60z46OG91mTRrz4
+NRDpy3aLJFtXD+4UDEOMP9QBSpyJAogOWPwfhi8Q/klQwPFgzeGmmZfLZ4HrreCUhskdHu4aDmZf
+FkLEaWF4GRt+SJKfKpJ9y6Zuup2WdWasjoMqa6fXIZwkQRRcJrKOFgTBtjK3MpJtCVYQLMsq/OYF
+yxTo4kfkHejqQyz5PrlQ5J9kRpeIDIK1f6bNNPeqwAy4AsN5JLp5e6LlvY8TXcgBb5blaVvhwtx2
+GfEt7QxYKF11NPs0rptu6sRprt6hS/pM9PDJg9EsmEsV9Jc2l1nskJIAT+HStXuqMN+XfwqTFTjy
++GrpQDDyZbCagWyl5RxdHSPX+XpvIzpPp37VF50b+PNo3dUQKZmILk3BQJ4lnvrhTpq7OfTg9kkc
+rvaAsOeTAsA9w2R/c7k4eH/zqGAx7tnWB0E5sPWfY5KUR7AHWMGAy7ZLthJvjnQkhOz2WB5rsri4
+A/AlIgfReO7/Pw5fIPyLofj+3s9JP9mpOMQkzH2lhydEnZDEtmLicvIFRXmhGYDwRXSuU6S17F2V
+7+TTugKo0bbqXhnAMEUAjG9CK7+pLcFKpQ1O3kQIgmkdv/YmXsD2T7UAv9EVIoBicJcA94jWCpxd
+I8hBsFuJ97+mw6JqnsGwc98biTvbXgSuPU3hRga6HndAUnn8+CFsxTNwrJufT//T3HPuPnRpQrqp
+RRDTfUI+9nxasefa5vc2mNuSdO9vckQ8+wwYc3szg+MIUSrMbmDLyiqVlkubwKnHJ4Bc4esnwPaT
+mXDK3wHldY2r+jQHOL/8ZICJzxkD2B1AsVRSTXyW9jehV+w1BMH720QS+wEoBcCKExUAM6N+XzVm
+kvjTxdCWaM2FPCyVTKlnGN0sVr2Y9AQJR7T24pvl21bf5G/DwaBUgW94qa6M+78JXyD8MNwZotkN
+AuX8xjZWNwfJSXnhZH5w3o+4FkC1gs0RG2UYfVY4HwHehO0QQBnopQhgFvDdgFctwaQAWAq/wiwH
+w1qab0v2EXIQTOxXBbl/AOwGYAyuEJJ8hTUv+hOjm0QAxKsSCwwLVBPA8W5ZN9ccDDtHBAKz33Du
+kzO4ne8zmNXadi/CRTC8xzDlD0A25UMoNYHknEaQ3gc53HXcbHwnIKybTwbMWpnAl8q9E8fQtu+A
+Cp4Bhsauy3hJ+qtHtzSQ59pwnZcTbwTIdT74j0rorAM+W2cS5OEVU+N4XIPgnK+T/VnoV3UHOyuU
+6sDw7pvS6UDIYJgy71BFjgQfhciDa/+zEPXGPf48c6b4bjNnELw1gvkIsx0xpuDYfIStrb4Xh70a
+CFlf4Ogu14hIK8CX4jehpnXTXh+sw2j1VaOM8lv1xIqNP9Usm7b74lb//274AuGnIU+2O9tkjQQe
+TrGfBZ+oNm+t6LrtBW4G2LT5w7RsQC/h5H5Uw6TMurSxZZGGV601SnFAjB+2o9U0h294EFDh2BPw
+doOgBIKJNuiV1iL8fkvvP4y+wgZ800t0QslHWMEwmaIUmjZPfEmovvyGoDf2sfdjv3VSSMkzONxL
+vNd4v33EN5srGI48KGcGt1LSIk+EZsiL4QlorPniQwqOUbzfcwsqYOmpUqP2uQCzTzebWdxFB5Rk
+AMdXoJYHwHyrCpUYXFdBXw3QxaSgjkYXCVJgp/8l9usMkKWhxTxdWmrNDZ7Ij2HSpRONE62Xn8ZJ
+7F9KwU2Uxylk2UuhiShAG6dj1GpduG7XaY/u+XDGoJnF6ELLR1j1uIFGLnRsYgSqFBKk0GIrG3OY
+7RM+N7UiKT28NLfbyeQLqrP6KpYwmvObpdkm2lPN9DvhC4R3eNb9rvEe5fvVMb63A0elDROvwB2/
+YyLCry3C9tAsQG6kxLzPQsmV1vWUQwGvtsJdIBDogGWYOb2YtT9CSVnQBp+q3Fb6+hoLQDBxALzB
+JSKB43AqRPAbTu4R4Ui1Vbgdu0YS4gSX3G/YVxMY7udEBbwofvIjR52G/Yv3Oa3Go4Wekhy21uh4
+U8sr6UrttbqJXMAvb0CNjgHByylu97p5YP7rqjwIZz0ypt5QP9zFBvDLRWZvTZ7X/8M68gBdpKc7
+9BKIJxCruAz0a0jf/yuNQko3jpJ4uiCHuy48AcN4JQN3Z3qsS+pwTLMOPQwYdrpJFIj3fJF4Jfxp
+aBvUprVzCn2AFVpuK7BiRWF9id1PSLLaBtXs+3PrHwzxbltGV4oIcrVM14rLmCsmKPgGA0ao5wqv
+PTcAX5LNx9iEfx6+QPin4RIQN6rrFwFxnjQdqCl5WHm8MtXdN83KtNjaX2R70C5d6Mf0Q1p3xXRL
+40gzgExEr72WQ9tiLT8/AAAgAElEQVRtE2OIx4uowkrx6CMsySIs5v4gIgEsRz9heEHueHya91+I
+Qzty/7kyno5Fi64StVfnESnyoD73QO8JDPsWa+mSed1HuAOz5cqR58TrtZ/CfdV9Ar9jvAPDqUbd
+WB/rd1Hl39mMJuTK3SXGGsA81enJsaOX0IUrn/Z9DyuacUj+nAKp05hdu0pMsakln4c7ujfoSOxQ
+9A0GtpEf2bCjp1E6DWDiW+P5SV9czpKbaVm7iL8Qhy/Oifru6sty20+Y/YU50+faN8mHl6Drin9w
+GtDuzRDf3ut3g6xn6DfWYda6qlwTLRv4bpBr8hPw3YUbyP8PzhAm+gLhj0K7tACU/Ezyb00EmMhU
+6xxTifQ8wggIgdt+YlFJ+Lg3S5/rdo/rqj8dtDjAnWgFDG8dYldyKFiRJbn7gR5powBYlkX4LRyA
+rx+lJpFeXCLe9Eaw+85uEQvpuksE3Ks1mGDzTSA4xxEM53EwhdbOnastUi5B8BQvAFgyQK1AdwLO
+BPIzn6gMOYDkqaUjnryGDRoy0D3RMI9iiAqumvIeqpA8/s9y3q2CftPUUpu8kfH5/pi8WTM2S/3o
+8zGbCCI4JqLGaDCA3aBGehAr5X/k6dKntd3JuQrTii59kBMo9jAOT/Yj9lvVrVVWCDrRuZUW8p93
+TLmxpd7prZOQmKbfFHYpUUv5C2gIgvVlOZs7Bhq33O6pjAj8a3P6juz0OBMtt2puyg9xOS2/NLf8
+frmCWcGixV05lIaWZlDQ0xGufzt8gbCGiARqXO+bLJ2wyzczfzscd+7eRhyVXVJodq6YxAzFWemy
+En2YRDR1PCnqKe6A2I/CYSLwFV70F5EZMfzFOfXf8t0SX4A0ECzrXMYVX/V8/wFLrwFgdItIcelo
+aB2mCoYby3B4ea52c9f1BkI6QKyQs1OamqcCW+8z3ATiFtDEZU6nQosWYkwngjHkXg6Rbq0dveYJ
+6+K0ptt9sl9xGdDg9ZRGROmMzR6Gf6JnfvPXnDjfcdc93IDbG2D5XqEWJgjT/Zy6/wrXomQwqMVI
+zmhRqWTedCnsVg7Sr9Kn8BP4cNLBXd8dQS8RdCaMnLElFDsVYqxyCxAPmUe+fuXcDbkmqpmidrS4
+nanrb2PEU3uAqu4S6iIhFL+1RICLR5SFtHITgr8wF1vgcZWtwBm1r4+HnxrhYJgUzIZ6qrjdCDhV
+wk6xQKD8H4QvEM7hhCKkn1pHVdWSk1Z8PPhlRYd4XQK6meenvYkfqNnyOwLumjbsF1NJNcg5PTfd
+XpKD9RQBMfoM14/JxE7agJcAANMGpwsELyD8lqVgDMz+QeuvuHtEsAYnMCwYR5qWvcEwKWhy6IQg
+qpuRqJhT91q/lQ2/hQKRA4NufR1Ixq3DaILAVvNWQNrRSPDltwqKryy+gRevnHmm1V03/wajUmEi
+79dL0DteXfidrdw2yblivxJi+wcIxQ2vbpaNtCt8/pPWFDgT6oZv0se5izR/Gblbc/l/lnHtKjHn
+vdh3UjiBXU2/ktn7BZMDoEJLPRxue0BcwK6xyjyHwz76Kbi9Cj34vXe/49onBnJ5g1xxX+ACgtf+
+G4GlAxF3PWg2S8yTdXXa1wvY3eWwrQPcI3acPY9bMzhWEcCv29Z0LDlBDNn3f09HTeELhD8JeWf6
+YZ7sVvUbYQK3QXkDR8tfFlTm3ovlRj+4CG774I6iPoJh28R0VXVgd1uHWUoaiYIedn0qRGbq3TQ/
+wYHMEvzetPXryBEAS7q+BV6m68Bw6xdM4Bax6pTTrJ4Sq4xBeyUfUIPKrYe+c+8j6M0sGcgaTWpa
+VMfprGDJfFluPRECt+0pH9bLrlLzhG6A1mXgJjG53bcdaDh/pxqmtCsQbPteX+ln4Zh1AEUd56Tc
+uJPRgeU+f0ttiBVciV064GVqD8cNOjuvEbOSSaQHOMtRRrbgxrGXgZ7jvwH66ko/Se76vLUKq5AO
+DAdBQChpnTzXExUQ+6JraxSI09lELfNl6LlRQ8UzI1B3dpZgEger7lPrij24QgRFlZAw+vHY5r9/
+YQ4G+zjPYD066E76zor1wrpTIsJLdBRpq80UF8o/Cl8g/GEIiqgowMzXxDKzrZkHC/DDPS6A0iIO
+lfdeiLagds4r1Hoo7ZQtQLGGMYMXrKZaehdf8hfmBIgluUuET19DESJZR0MAEHZQ/Nbr+03yx10i
+KjCO7hDvbAUO4LgHwgKgFy1RwS0ibLqx57sTIjQ2b7AnVwmzIexrAshQpQ4E6xVV7NKb3PJR4Q97
+oI1jmy+BrCqvnj6BDUBadFM4AGPNx3EUsmUY4z2tAcEFY/xUd9xXKvdwcuLiztGBKxulgXpSdhMi
+xlLZMNulysQxgOPWRwgbj7trNL9u9u2uEOk1bUo58fWUOyHMc7uPqyL215Cmiz7nKAsMFdZh7EGU
+OyAEZXf+hj3U56cha7mqNdBJAgGk/eDIrs+6OAjWF+XQ4qs6pAXA+ILc8GMadd7ofp6tvkETR7q5
+vOi3y25Y6V+Wa45H27qdDCjrpsAOM/5x+ALhHe4vjYNK+q31dSecFruzjFXCpZlTwvY8CskJHWRp
+pJeN5H6dcyldBge02kGDOwQ70FKXiRBkKysDnAQgWAwYv422LcN/ZLAI97TJIqxWXzXv6i/HlTOF
+N83qiA049rXPgG4O9DmnWTOPWqMDBzVL5QU53GYKKAbgUkAxxbRwLWXkPL7h4PpSAJW7GOdhXA3N
+XdyvoRe8ghOoOVuCp8c3CrJr+GDHuaF3UDbHfzEv97QWKFvsZ0q2wjXXcwJMnNIFsxhPXTto0WpU
+SpVnaZL4cr6O87TqarjSrVOIa2g4Im0v4GZEodNj7zuIq8KWuMNks7x1P64/7NFlHIo+hk6wpB7p
+eTIARv1nVlJC8BhBMKW4tx8BMMU+1cU/WIXDPGKPFPBL7sVcNLO9xEfg78tBUZv1OihabKvzrur+
+eyT8BcIfhCtlYg9j8FR2SwHBGprX8u2dSLn7sjlcejCkMzR47OfFmBbaZW2uFfJdcQXcpsQFegDs
+svO9AJQxrTd2DTALft2PrhAOgO1+g+G3CL3flPyC0TcYrcQSfj3u/U4WYdH0pXLUHcLQlJZNUCeC
+ewJcvDtyPlLvPuj1OemK9AQqw1UO6RLvcWydR0IakY9tyAd7Ai6VLI+uruL3Li9voo3VdzPHPb7u
++CcwczethYUNzugSw6jf1CfcxG4wxxuO95xoISXpqNtlh1B7IY5ZM/858rSlw9v1BagmkXKZNu8T
+OeVaL545rnTvveDQL6+ZFKnVmqzDiWQJtseMVenTCl3GtLLOj8Lv0Orqz04RRAvY8u4v2aBw0cW3
+3y0Gt14TXeKXOyvVGYXAVtLcd/CL2h6/G7S4nRzh9cdTItSF0stUcR1Q/vkMfRq+QPhpGMZoVmVz
+nn8V8vE/LQ/EfYPE1QiM99daDHKPDevztOuYKB2HRvAzy0gDt4gAelBhil8lXg0MvyVd088o/8F7
+tAR3wFdSegK8ehWK9cq78dhpT8FwTXsbNefJShZsCA9AcL4qwOx5JADfa/DdbN4JLGdZoWUS8/lm
+sP6HvR7WXJxbnYd2BbTxvktvUWIT+Hh7J0+b5RYWjnXMlkROoBiIVQYRfaZIJ5REsOm7nivcSREV
+OLonRwG0BfDWUR8kNvfdtzb3eLpw6JHLPHkut4IGMGxkotuA2FaHAr0D6C0lhrqcQwbD5+ndtf2C
+BgAvA2D9FkQB5ErawJAkgmAEvfUoiSG9b3H9FiKCYjjcwYFyAMUUXCoU/Lq/L9Zvl7qVfj5Ozd41
+UgvxPw5fIPxpOM0xZ+kJp4yDfnkcGo1+BT6NUxEKCmtWYzm4+1mF+vrIDZ6pBNi0AghGsMQKfnkd
+nUZiCzcowGWGDdZYMQAsIW6WYKF9drC0FuEMhsu9+QTjOcKUgHCKU8NDPizVElx7NEKArrc9zzQW
+BcAe0pbeczVcQKw0/MB7Asd0vHpLZ7CcZHHsHwTFDogXwXuQSzoCiO41nVO/Ftrlm7UAnH8AlD9X
+QxEA97RcUILJFy4SNTzYPbuf1tpKYFRnl8ujWR+mHhPggEl0BYCnIju+n4RbY12Y4sy2NbPJvt4G
+wGzMSXijRBwQ92BYXW5Lrmk/beiZjSfGUcSs/aoOWCXwrry5RQMABmwZ997i6gDXFgD3oFkgHR/q
+JdAUHBPE2TnCW24oFfBGPjZN9+R9fnKr/P9x+ALhh6EHbnniPZJg1M+UUZc+M2HKcbNUkHuwCnMm
+hEWTwtA/cUvq691BOQS5GRShTtCM8F5CBMTE5grhIGvDSUeeBogNAON5v3AfQa8UC/FEz77D9qty
+WgXCOJE40t1XvI+Krm625129H8FubPNLcc6DQFNLmsBmAJ7S802gVUvF+Zjng115yHu6WoMlgGLA
+MgkQOwBYdWZLi1vQfTBs6ce1zcBzFfZGxtMMuJf/lCHMCU735H0W7rVSTX4iqnroqhInpeMotRcz
+FdVY2YQEBzdhkB5czxbiiaPj7WJ/L/Rr5OAzbEyH3U2QB6SnW2f3Y8WK/iiyMsNvhkloR9dVn89V
+Btuq6QmpOqX1DQa6Eo4AOJJXMUz+X6uFWsq1JbZAmnR3n/S2kdBy9xCtqni9tUnNj3D8B54RXyD8
+GwHH7cpA+lfH+MkZbByXa1uvgiKEWmsK8lKACbfai6DixHMlLwAhTqA3rcHuBTqDiIIVElNO5Uct
+3ssa7NZfKaD3nS3Asiy/vfW3+RA5+CVQagCItbroKdH1cO275/Q6qmd41wFetPhSQ+vAaAtsqQfH
+I9CVga5X9rkQgavXkYj2L0Ap3V+Ew83L26zuG3wAqbHnYr+nhVrCSe61nNzGmFKID0IuL8vjlJQK
+6nRZq98OGmH65QuQF9ZUVGKz+JAGKyKpxit3iF78HQB8JeP3w3kaDKgVk3CBHLMnWSPe3P0evjmQ
+mf9H4QnodXpJRcsvKAqbQUGHMHSF+AUAsG7JxBl3DKA4ajPcShItxu2X4Si5Rey4DdvwstyqDns9
+Wwtxcpf4x+ELhH87DDjxiiGPfXvfnru57/nG+9SHCcbIsCdweOLDDcImsVBrMQap8fWAc/m3QHNe
+3zuvVYcoPEAzcwDEL1l+rm4R3i/RsXavkB6rRrJBmKHM/uMAmQqwpRzf1l7aABnzU5eHYscg8A0d
+oYRCO/dx1+eoDHu+5qs0aeQnGoLJy0qkNJyFHXCOVxnoFyGXxabmI7gVX4rxi0LYmEp5mXJ3x+5d
+KXaR98IJn2Ta3vB+vBd1An5COxbUT6DybW3lSCKa1bHThIb09m5aN4syL4GhHQfK1LS7s+tOwOcP
+vnn91UpBvlKepd3ZPW6G1N6qAZBtapQErkBmgJnG5tJUkTDmIXJXxK2czDVB/IWzo1VYf5wDxTJ+
+A9i8BEe0QXm0H+N/0xvmHyxuL8OvYoWOFmJ3l2i68y+HLxB+HABl7Ukdzv/rJhNMUv1ap3U3OG54
+U1oFx2UetVkbIpL2L93YGYOIMDuAbAsuX72a7V4DBV+6HOc1nmqg9/rRKigIdt+1TUdgTEQvYnox
+0ZuJmAXim5ect/905xJHZWpuF0oTUIbWQUKmZrDTBORBX6BrRxoRq1OnljPEijzS0GpfZ5kx7iXP
+PORKMKUZoZsTlyCnDxNALlfWe0ktqX3s2wW3PF44IOipYvWmZfRpIcB6kI2g74RGNu9hmZb8LQxw
+87n3494x7agoor2h09KfW9qa29LUdRjs2tE9X0jNyiTnuUjvfII7muXu6zTlmFswl9FxHkb6mNgN
+dbfeJyGBl3UFVRn1Wvnms7/Z9Tmdzob/7BPbwwPd9XGeqvjDayHsNE600NeXg7cZguLSfRfnR9qL
+cV8nPckhngXshoSoATOu0ZEyswN8PWdH3wF2QPCrL8YhSNZ3H47N/kvhC4SfhrDDpS0xPXnZjkrw
+BIVgGKZbLmJFLibGlSsEjzeX5MqiYF/b2bS3QApM2wvoQwDT8WewAd1twFerp8AXldCiMViEwTpM
+zYfTB9Ko44fKm5W5ybO0wAa/GwebFVr5dDqR/9M2GFcAyvCcj1iJfFNOeruFHHm4xrn6OF5/4a6T
+ezk98ySgB2C3u6a9ZRWBav8EiHH76HmIsLqSqENrTwAaBTJKnmRBhjv6AVOkRPyukcVEZBYo+wpb
+LT7w5jyRAWI7lmzv5qyTupkq9yFh4msArjetAcThtgOtB5Db8vd355Y8A8CX4c52cFx88Zcby5Wd
+48hn/L28HgQzlJE/ERD/JEQdfo/+qcx2/RelLFVBub8EtFg2EM37NcpQxEqW19Yn9RD4BIzBb2yD
+WsQGq54R/K664Iu/djrIExfPXwpfIPwwMFH704f6ZGX/TzyMT2JJvq7wUx0uJsqaaxeTqU1OUCi5
+P/S+v9zy7k6gvJpNwrC5nQJ+hZMqEQBNiEP1zCK871/k6+7F0dqbrcFV4VIFxcC36jQDYAXHZMok
+D0kEwdbrAHZN92weP6A91jPCrZMV2MvhIb2lBaAdAS7ms6ktDf0ULuZKeCa7EHUH/GJxOOMni2+M
+96DZK1BBrbRpqTWntCyIg1Qk9vx6M3TeLUgRsu8ewwd5dbVirY0D4vXngJiI1ks0WjaC9+tqtOBz
+4ITbHgBH7H8AyZJTJSfnSna1aOv5RE3+XRA8zwNd2zriqPPmK6d88VppMJ/4qorKK73+vkFDSVU/
+H2gFwDb17ZawWkvDum/4krYyTKvAuAXBDjJl6xL8BU8EuDV+DYxX1DXeGiMOWKB1h0Dwu9sht9bv
+74YvENZw9ynEBgl29NY6SmUihCNM9AlJRVFaADEJUERT9YYHt8yeeZYXJzW209M4LbSgrkN7dYV6
+ZWy5dEgh0IYFIbHjrGra9YMeQFD8kg10BVwemCq43Z9LYKxl0Fa+VL+io5RGKY02MFbloL94Z62F
+YfC+jD5VKk+HSgEyAQ1Li/cVJOd7SrRuI5jjTCOsgmnShm6udAXDVON8TQW3PBTbiX3wxE0i98xc
+/Qx4120rJ9x2o5GSQnJXetMhUx151BYmp2RXQwD0oKkCA8myMaMWoC5BqhSbSl3ph7YFGfhOMjq+
+ytNnlyZ2UY+x5OdgoMvR4qgmzNtC3E/K9c5+kiQFGdzL9rSL8i8/1WXibuj4W5rkfmjq1wLfiXZY
+90lZLUx7Ar/rGg93XAKGn8age8A45kN8YydAJHxU3CFI0yi4X/4HBuEvEH4cOof1lUD4E4P+1TS8
+dSnAYzt/XOi5KEqpPYi42qIINtfDLMvIJYP3vAphofkkTy/ZKY+JOCv4CCZ2aLL48qTd3wD+UnNM
+qUq+Mr1YCsAtwJji50WV7nncvYJCPrUAx3wGfkXgxbxNR4Ar3i8OuH0q1nsJFuMTCDa5qbtP9yZL
+YlqOn9OSe8SuaLsWsCFdPZTG836Tr1MaDbRFv3aToL05jZbhIzrpR8rWRGngBcA9JktPbu7SdKSS
+mLJpy9XHXb/p8j6Cb86gAHef2mvlrAbOdRkzFQ3jdwOwnWUP0DaI6xvxU+D7Ua5m3oXd5Zi+79MY
+t/q2XLnSuOflXcgojys/I0NbeV+FWZ9PH8+Z/IMF6xB6LyzfUp0LMGxlIFDURKFUgAPSXrvFfdit
+xaErQIbHMzCOcc2e/hcfZIJ939NkSGMEyN+fWP7/EbqX4CJYJGoBs8YRDNt/jKB6QVom3JgwoCHK
+hlyE5g0YygluD/vaQQFwxlceKbKA2lbqvnrXJa89ZrViQpxuRasS0ydPJgoAOFh+B5rSkaZ1CR/W
+IYrWYW8jgF+gOShVcAC9jICkpMVzPvRTQS+Z/B52dbz1PsfDvQHauAG1vE3aGHJl4PawxxTaCQxn
+WimWzy/G+f2EeE+9Tr7G2gAbxdhp0/rhOZmfAbPxXAkEgAwv1GzQSOx5F2nPDYYZaet18iKPaPJa
+W5w4JItr+Dv0WmXWpDs9+oT6w9AMWdiBxnTa4zYL4vZawSwRJSCNV0/o5VV9m+tw/qxVO32jV+o5
+lBHu01IN6TeUEjcyIh/OPY6K6nDFo9UcYK6GumvEkh/AsH3b24NkogkqUznVIn4DTrS+IWKvT2iT
+YgaCF2f/XfgC4U9C2vmWFUPsyUZAuZcTF3ac28EOamku+6iSer7MLwce47Sns2alhduBVy9M0RCM
+/fEo4KYV+8DfXPWisYk6DKtZi+9Fe+GT9GBXaRQVZlGkLP6hmOagFukZAJP1hSpGxcTYS9VNwoFs
+SCMqlmLtOoQWpmu5B3xhrnRpYQ3EMN9z6JPMy9Dgy3UwTJ+r/ecu8J3ii3/lyNWo98PxZxNGbtMH
+4JwLvgyH9eYK4UKnKPsNoGz7oCSaL1DbjuHri0K7HaSJdSxd6oEmtyQHxpYrrfd/v91j4Pu3XKnc
+8aXUzFMBY2P15SbfmOaW2k739s+Ss+IIejrVNeurk77r0gOvxLYQ0S3QXJPqvuzvIzFlFwn10WX7
+5tY1mL2vxCCDVFPn/5oH8u99dN1qmUSdq6idF6ydkcDwf4CDv0DYwkMf4QhyVQaVwUc+lmRJ7qox
+EEYLzCdt4SotzD1rEyyy8rVKXIDOA7yQtrokIqfYH0NdbWPJDFFLpOXsD6KwBjVBX4xTkP5iBsAr
+9eU5pgb8bl46HdeTLcGKcLEbQIYQ6ekRrnYWLUyzBvRq3PSJ8UsYuq737CEhp/EZEOf4nftKawb/
+NB86QQA+kMQDLafTDd6uWhnk1nQfg7qCfSurJe774AbRjWC+Tb19vm2CwP8uuUqYZObj0DLN1WCc
+VZyAco8ODuECgdbkJsODw679GLg71foPoS/Xm7GLA2vkndfzXetvXC/hyg2tpPXl1NXq1Kq3+ai3
+Y14usnKJdp+Uhr03wo38u8A30CKwDUrLSJmQwTEZ4Cxglojw/Z98RBrGiZI9OL3/VCy95HFz07AN
+BnWdYAtGff7bK+kLhJ+GYN7cQ7I0OpFPi3RWsPON4A/3PCRSR6eefgMAj0mQvuZzBr7kN6OrRCor
+pa2HAqLS8Mtp3W82IxDZa9Ebhve+CROns4E5Ka3m3gFyf9Qa2X1WtN09NKj0y75PU+Vk+dVzIVFh
+XILgFCe8l4YPdJeHVWLZGH5yf1PbCaVp3eEamtvbKdpc/Ng/kLNZzkfQXCrQEOQSDMdQHAnG21bZ
+DDIPArPMlLDmSzwOjYjIT4QgA9dqTUJezd8VeAvQjqEKmYHqJHeScc13JXkKN4fsx1Js1xlYOfPl
+tJR1euEt0LihWdpwtBrkdb06uz/UVvQjkPm7+ztpFqZl+wQMn4RPR6QRuD2Ul9nigWlEbtE19wia
+PYajbK0fU3xbHWkQt43EsZMAMLZ37SiOUEf7rfAFwjvcVTIAdW0QieIxaZ60J5ymdWA4lN0v1+uK
+X3AdAXJVeK629MijtNi65zLti9ZFAhcDUXloOFa+AYe4qVodd1Gavu9xjaJCMQC8q6cgl0l/VAOA
+K1MFyDTf+1fB2pfxPh8vtnx7Jd6LUNYCMCKu77BZAHpxBDiKsfs7gPgEjiml2X3j9nLOk16a0/Rh
+cpzgIOJG45P77Y5x/4/1yvc+KsADwjxPrnkGum2LDCxaY4YM9zcI11HnAG2/UjEtNIV1CoBWoD3o
+g4gT2fpMUn9dhMJx6V4xA90R2B5FSr27rPbEwEeuu3vWMUfR/WVTsmi3T1lay9/wwTXnZciDaVd5
+s46bFAeXzxk0Z7lTvKTdBbhTuOCNJ0VgFgTDuh07KFb3iPCDGlb7DWwF1iMNbhA7j++9uifnvV5p
+lNJ3jcFfmEv6vw1fIPxJYKL8AhxOnWi2YwfD5BNjgeHz2jAw0KTcWlMPAXAseU1KDi/FadvcJ7o+
+BTZXAMOuorCfYp8N+AeoseewJ0M+bWJGc5v2otWOF7ul1/2CmewEiJ316C5h9/mYnnqvIGTVDUCx
+SNq4o1uDXVM/MVHLl63BP43nMnOYaXFEs/00bn3Jbpor0IQGXsY2cKI3/RJrKSHudY64LLYCt4qJ
+Z6j9pdVXNxpowO1d1evRlHwRJESrDO44IQlOIklg3schLtKJfq7eZQ9fUgpdenoDc58UDEnnvFWb
+1d4/p+ZEviDBnpLmeHfX7U1c/keWQkuW3px+BwTn8v1Tj01rXRSGFaoyujZY+QJ1EGofBkKYlnaX
+YVQDSXNla7BB4nSS0wbDSFM+srYC36YWNwhxo4VXnb0DhneiSjppfOs1SP8PDo34AmELN32E7Vi0
+PcABDCdg57fsIBAmw6nESQHVuyH3yHQzrZu8EjghLS8sZYZFivRrdFADAkRdOI0mPryDmFDMir+I
+6a3KEhUlr48elTYeqZaUa6Rnuegekf2LU8WlguDc/wiMVVakBZX56/HcvcbQ8AQan/jq5GBkfDpv
+6AyMEeDpDMZiPM2ldFVh47raYu+A2AswTDRM9FyfmPse/1y3+4CS9rcCuORkpov3bE+/Ku1mjVuc
+e5J6AzBPlAFI3w05B5cZWfnnkbuab316NydmFwZOnDld42xy+vRzOTnPrHuxRn3/t3kPbWAi39Yu
+2pzlW9o0UIcl3zIfrMGiL8QRGQhmAKB6asQCvOtqcBj2cew/c4NghckpL05PPLJCwXvAEwRxeIL4
+viz3/ygcvuYnqucEH8FwFf6hykr1m3KOmbkKL/qDqbX0Ek5wfyOVYrfAaWvVyT4cCRZCR72AaNrM
+EyAGUUtxbusvUQC6L2J6s5Sj0oq/Lyje+dOAXoGKCPDZ3Ig7d+rx1o27tSBvOlHQn4+Ab9BzTVdO
+XTzRzrzz5hUyJWCTk6b7AClQL5PEtFSLyVUi8i0eJpjzl0HXE/Jf7IypezrOKUz78DEne+ROk/Al
+MgbdE18u4/hQQq4I/Di1W6Ucq97luynxufAbJ13cfyipeU7fI7YpPNMxwoUeb3prZxfr4tHa3PEy
+xrmh5Ty8pJZZkhp6qaPZVutcn2PbUrwoz0YuYL8QpiVvAriXjy+a7WNBFVQuFgeggm4IpIDX2y/c
+H5OmshUGe83wdlwAACAASURBVLoCZJcZgS8lYCzQFgfk2aL9r8MXCH8Q8Ig0oj0VwBocwTCBFZkT
+GAaZd8q9rtics03Kmy/SK8gN9BYUY/acluDUjH6bkBBPWixVd7imCQqqaSYqwxe4QoSj01CJIi3Q
+t4XXaB6PhbtiLC/MIRAAcFyA7UC3EzKgXwIIzvHN9AT4dmpq6mMud7gpcMkw8Y8hVWQ8/eLO/X6p
+C/snF5PdIjKf0+uLcfHuAHJDUgXeyJDbMoVpb73DWzPd+1KfPUp4NjB+Hb42V8zmaXic2q2Kl+o+
+YD7y3vgtuCdFPajNhJXIwEuffpp5dErzxRBT2v2hJtX4/g/bDI+8dODlSOOafj6951qX1LrFNXar
+7g1txHUS9487/MXKSvBo3gDJahkmO1YNtUgAtKJQt7EWNy/QrWPY2BuUX5azOPJ4B3ABz/Q9R/j/
+V9CBpTTgOU424BUMn9VULm26O+ZqWTlcerndBMbJnrIJ0QkwMxzjErqtCDmFmt69jd+CnaTZtTQh
+/NEMphdJ9QXG6614doGAuIK14BusygxqLahynK+AYr2KqbJAL8A3xwE85hN5pnyTrvbevUGH4Z5l
+1ZfoNLR14ETHJQj56r2EPiCJsiMo7sCpy3R6BKz3VPsMhju+CpBjXfqUnmfmPY1OF8T/my4EfoE5
+DQ/uvDOIMe7I2HEXPTokyynxjtw0wp+HPjfCLyl0zD2D4ctQFWJIzOuUG/oc98G+4mfgV/155nW5
+mb7e6SBq+5V3egbLnPKDHshldPUxWrNEsyvvuNigzccl34YIgLM1eLHIXkZ+bNk681fnkJbuL74p
+zQDy4QU6rz+nzYQoWntVtykPE/ouI8/XNeK/DDd9hP0oMEonQZBNJFJQcgTD3XZa6/ChqqOiibK0
+gkpStHuaQ56rEyI6UHy8xuh1MK8lqJbAf+Sc+nEB3+D2gFdVoI3bRFSis4IlpHc0OzdY5eiciUAZ
+AVzovQCOez/hXidzpUvi5Zpv6suqvHww5zk8A13P92hSEFHz8CO13t6fFRq1z3q6r6TcqZhSE9aD
+7Oda9vRQxASGa5jrEtON56JrbXyaqsIMPeffjJJ17DYIeHNDJ1P/o0N36n1/vlwD42fyfgceV5B7
+mgEdffXrpPU43VNaMFkwV1KJu7I48XBLp5K305VaRCcjljFZhyPYbcNQxkTL7cw0u78BhkP+acCD
+VXgT0RqMfsJERMx+ygQRHI22SsvW3UKj/AKd6kal6c6jpK2vR1cJduwzAuXjCP2V8AXCT8PkI6zj
+7ZG17BSR7LzhZbvD7vKjqdBOpEZLtYpP+QB9odxbLhJpMZj/8IyNQ3HaZ7rj5WvY7n1BZrAIujls
+LZZOWzki0MXrXRqmgRImitZgb57Gffs3v+CtJFiI9KxH7CrI1nVjC2h7/YtP/lGG9azEfpx0ebf3
+9/M3KtTYqFP+nnGsT07bkfpte/UJ1rw6JwOt1E83jhq8H3GjutsCoFsFJ9iTy6uiSttSrErkynUB
+PNtaceobMCJoORHUSlyzrihDWX0NjtW75uaYfg1nfwZ4uxDHCcFILDFSHu4U49bAMZlz6Z0IDlsN
+tzxbxzZpqjstfpLDPb3oZCtPsFltG6ZP7ou23L2mQ5ruZVNZvwCGHdQyYEawBpNs+tZQe98lPaVq
+K3V7gW7r1rXU4m6ja9P3Cd+sAw32fS6gFzZ4YsMDoo3RNDuGNrqd/qvwBcKfhOTecAbDBOZ/IvWR
+6wb7tHBDOM2TCQQXcrfy0pK/A3IRIgX+tnJRxuOAABLVMVD4anvg0NGoSNcJEXC2JHtaVbb66UE0
+AbgOgNfAcQTMOws54M9tpgBwM+hfc6paXPrHBqTFL7xaPp+6I6a4M217njoX7shKxY/7S0jbkZP7
+aUdTYpvWzMWYXnlqLW/sjgEQD+Ul1NrXJebr0wfdNNxNZXTAF8iEKN03f7YHkYPkQ+G1528JuGLk
+Z17HT+Yw1KLkxRNcke90/7xmPY0nDp5nA85MHtPiXtfxMPBwR4f4yYBY9TXo95ZbShm5jl1dQ5C5
+Th+B4amQPAvCV3j7tAhL0iML/Sxht/RuHazYBWl8ZTWGf+LuFQEMA1jmAI5rU/4jg/AXCOfQgYwu
+7WQZNk2ub4wmcLyyQ/5PKpnDfiKMdXdN43WPrw6sPHFjdRnxKRBB8To2brWhbYqlwXEt6bt6+2GR
+y9BtzM0vdYFiLWCu6TMlvRhcJHbVX+y+w0pb1xvW4/EjseyNXsxv2FokRj8CX6UZn7R8Kjl3Qewj
+CXwtgNZ65oVxCJc6zZfJDUkz42NALAdrMNZtSks1u+KxE1X6RJprzpWUYPhUn7IkD/W8Gqcn8LJ8
+rd8AX+PbafmYtfCA9ctWoo90ztXTxDn3ZejGhoGS1+8VGG5nVDP/2jk50JCaOXAHmdNcT1qcDnG+
+wZPu/QPf9NXGED4s5ny53tOViHwvO6SV0iewd/d52eIJ/FK0Blf/4D2ThHw/hj2+ukwsnuj+sOL5
+JAlsfPGPtrR4dFsAx9aWZ491vxW+QHgHVAinBR8QX3Z1sIFVtrjgMhhuV8NDDcopEhQSZ15I5Y5K
+e1FbA2zOlkIllxWBMsIs/LrDwTXKqoVE8gyC/ACYw5Xn9BXiaREv3mcLM21QzJsu9D9mktfqHnkt
+GQJD+pZ1/wa60pBXhOmdaMb32l97vZLPL5VniZVvd/sbaTv+bsB0vkYawuKoZ0O+tOt+orry3P3N
+cALXCq6W8h3qdJJ9M0e2hN6VeCvkQbmQdCrh6ozaJ9VZYUIAmfe86bH9k6CHfgaJezeOKnPWOT8Z
+rqe8nCh57mW57J2W5HSz9szn0zdad7t4OdkB0xjvD3yhDQ098Ti98Qne+5jO7Uj3tErvePOH6zeA
+1Fyt36a9K7ZpSl9BoPdiOTlPpfk6C/IGzGPrsgXsDpCJuBao2z9RxbTluLS913TuE/84fIHww1B9
+WADaMdksyEeomSvFtIO5uBoeb9bNBC27ULdRcYxnH56ji0QqKqMvlGn95EDPiilCaloWr+Ja+nD1
+bkrKcMtB8Ku/PPe/11IpLyF6vYj+t+zIoflv2TR6Ecl7g9f1cxxCLxKjqXrUNCL99S3Zct9vpjct
+cPqm1XVvjj8PLSoB+sgA8R6eNwzDvWu0tp8A9OrHRJFJ+f8rBbc6gw9F2raATJLTL8rIsWnN3ZRz
+Ky2TdBl+UILONg9nYHpH5inDmIePtyWhe8j56cy6bA8w5LIe98WNvHl2VQAEHKi2k0Sc51GGE7Cs
+6vPr/xHoMZTVpan4jq/wEhVAOca5owOo5ViOgeFC32k8geiufhzKP19Lb4e9JoeJ3qVN+TWCde7y
+hPhhXfZvj+QAPOmlN3MJJab8U8/15TjAFv+Bb8QXCH8SJreIyETF+lvy5HAGxyHHpPnLJOoydztP
+U5+hWS3ItfYmhuwSQUT26FD6juEBoi++DWnBh+XLeI9f88zK084QZgTEq6oLEC+ZQkT/kxfJa8FP
+Iaa3IOB1oOyWX6U1aRsc6wPUH2yH6g/y7nzxsvjiL+NhV7+xT2Aqnq4eV0jMtU9D5zcjdHrLrIS/
+C45tORyKsU2rO0TYeCB2qae5ic08OdQHjYuShoVyqua59Jr6fGtyOcd9DcxGhe2EDLCYgfXHM6v9
+OuxZXzzttwqfujQuKpvLf+AsW8AAeHkuA8FYPv3B0rjna3mbPIGO/G0dUpxBn6e8qsGmtNYyrGVa
+/TiUO32wb3O7bKE2bUaOSpedVtdmb+2N8jTCifhcPyh4vcjcZDO4o5sZ6WbFzsB7V/5ahP9/BF9W
+roXZXvIQIGc3gOxK0Unuy7tHHJi4oYXoacfZk/bWUWlNVrtF8JtYm+ygM4Znifq8WkVkdwiE3DFO
+TG4R3h8Evxg3QLzdJP5HREQvEhIAs3p9JXBMROAqQQqO5e2WY3EA+iahNzG9BKzAvNu2u17nnvIz
+LYCsSvy989wBwnq1uJ0hqT2KoVNYzTxQ+g399nsqMM6IO4DYtrvWr2JecHyD564sIq3rk50G2A/A
+sNKnzvjBGbW5rBu77ef6LfFNU68J4zQomVpFfZ3tB/w8xNc9t/321PVhynvKx8AT4pzuIX/h3ZGW
+nuNFbrzP5Z/cHQjTOMrr+P3DNV37v1zry8oE99zQc5szfQqnOZLXb8fLwBt30TtzuTGLXADcSMes
+kSf8CMg/DF8grOFu5z+0BBdXCuadLP2MG8jPAzeCuIlyw8KpnY2YlqZtx0WSgfKiLRIK8n4LDxAp
+b3m9juOSDKC4Tdv/99NnqxBpuRsI8XJFeOn9Ar16pdQNC2wOFl+6D46zRfgl7vqwfuzDz6ddL16I
+WYKZeLtGLHcK7X6Nt2C3uVqcMU087RPEqlPtkDdP2Z8B4zpR7wLiri5X/JdcfDo1+bm8vgwa2xal
+yiFN0z+rh6uWg4ygnhLfp83HfBcdzePN74YnoiNgaYAM13nJ5X/KH/q5WnkrDbg55kMQpXkQMIY0
+wrTIq3q8yNtltvQc50gPH640GuiLf7IKcymrKxP7bdpPPMS2x5Q6XzLvVXxOv16LfMVHZPtJpHVE
+ugmOoTg9Wu1rEf7/ENZgMine24O7x1YtZ9UtgihYqGh4ReVXHOC4mctpNWcGTrTHPsJKhyiQAj2X
+K33SKZtVseX1J2K5pEUFqhbfNy83jRcRWIIX+Pwf03phjhwQq+Dg4oAuEo2rBBGC4/xC3Up/0bbu
+0gbEGwSvbtf6O/jVtrxB4aIlWdvfgl+5SMexwOkjJZJCfXjxDhuyAOvPwTBVKRyr8ansdsuY1l6X
+1GT+yTNGuJEm7ZSvhHtg+F4fQNK1yCsRRHTrWWZmvlkHZz6PyhNx1+PBka9R5zijKvjZ/1GdH2iY
+b5QFZaquPPoIBz4vr48TgM0BIGuce7rr8MZFwmSfXpBDP97ZQIIfKnmx32LgQF/ziQ+8J9oUv8ub
+af187ExLN2Y5At6OP4Hj+Mtyi+E3vo16Gr5A+GlgomAJtnsiYolgmIgqaCbCM4b7Aq5IEi6BcdqE
+250Sb4dyB+A6pyUGsOhm3+BwGmH49QYJzxFEQUwoOpYmpOrZupoatwjOtH1ChLgyfbGYO4FsP2Fh
+WSc6ENH/3rRQKlbwLW7ptZfoojXYfk+dIuCl9JId0YvUqqtuEW9xEPwSWWBdiNiOsIuK3n4mWpZF
+megC8HKlncCxtx0oBdvG1VACX+NQX0kPQrc2Jil5iVwW1K2zge924CYWQ/sweCGyb9PFmJRSI+ek
+Yq7yT2om8N3t2otix+H/WG5SeDcG6U6RRyDDcVZUUMORX7mKynerb5uPXbrT6tf8yhFdDoa48Zxd
+Kiq94U/HoHXxCmqrG4TJ716QM16O+ZorAW/un1Iv7FOQgyHznGi6TmJapeUyc0In/zIIEdu3PA5m
+qYBZTd6mpg4cbxpbPpVHdPM8zV8NXyD8SWBKQA4SEMRtvgiGnY+IHu7uWAG6MZM7Pm6iSRDW0zYo
+oRHkFusxylCZygNyE0/NMn9NMluDyZRwBcv1vym53YwS34rzxezm2dfuLwTDOpySXCOKNdivlNwj
+1GfYa6cAGOok0VViHQ7P+/g0bdNW9ML0DmdZPwe8wYrcTtneVWV3BnDl4BQeeWLI86MLpmpHoZy4
+h4KGhHubx/0tpv+S9KdSG17rjyclalYHqZ+Eu/l/0zXwF0WB0HmmTqo08FxMXoRVZ8ttpuqc58RT
+wRrGGf4FWgPwjEcBYUq3OEf+lo8P+S9k6YrJPOWTLMMmW3U6VatxF6dONtaJPU/bv027NHfn9hCn
+T12r3fTqxlZfPIvhc2trrdspwA5rChkAb/lJ6Aiq/wPPiC8Q/jgwORgOO/Qe5OaEiMjLnh5CMal9
+XsFpN+SGZrfKMwBaukPbN83LdPnFOKblrxseEA74pDtsA4v2uL4m53EiMt9gIl+uHK7bDUJouULQ
++vpGNvrc+NVcI+yIPDucfL2u1p0GoSbmlk5U3CPUR5jFP+ukiG0ZNhDsLhKrBky2Yezuz0eoZeA7
+Ad4Mik25kUPgTkEGH3ipPN0Q8yHtTjCdm4QyMrQlnmrV8U3hySbD8P9eLX6jBgFsTuusFdL27v26
+HLKy/Xtexl8PWT3a/+tRmtRvuE1iKqCq86QAY45U7jjZJUValN9ZlLNbRQSEEGdu6XbPTZ7HPOi3
+e7Lsuj7UPop8qj+b/FmO1ok1X20bQZ7Ydw0PxcApMqYf6AyEjr/wHhi6vmhDWa57DwzPvQpykRdp
+yWq8v9381+ELhHe4+6ZitVBupZh0Y3zhS9O0jJMS5X7mHR6TqsI9LKW86sJtXi46o9PTXAtyMz9U
+PYjE/kqdRqUbgW2lYHob59jLucdjHvv9HLe26vpk3udALKCuP4Yi5KBY36BjKGyB2GXlJT1LeLIS
+lxMmOABlJrcIv0XoLbxAMO2v90RBcGPhID8+zdCr3AO+OQ1HF3F/nmUCsZC2+9QZzqdp11nxS2Gu
+sG2yvyN4SubUD/ek3emLq5rf2fyuwz2gWvXRBd9BpKml+fn4V0NRg2fOC8qNtAvwMwEY7LueF2Y0
+N7RAgflf5GW3igyQ+BK8VheI5mW5wtPEWznkuo9j3Xhz6C+mBTrvNMyPMoos6Cto71hn1tpB2Yf1
+9uQ+xC/W8Clf7avpsMzmGn4FTu+JgtNhOO/TaRJo5GcMfy3C/5+CT26NmuUx/eqDHakGQJmJ9k8T
+Ez1S7TDjLdaB8mkX4oYWbptlBE29RQtp2mD2I+M2LXtJ8AZ/6DstwJSLxPscp3DvPwzRuUUYCNbP
+HjMmMUD8EjLLML14ue8S+TESb4+zULTq0rpf1thqJbafxjCgHI9V+0MUXCLKhxAEZwux28NpyzGf
+591JJ+BLkFb4KSpg/PIjwuE4ZqooMwD9DTB8D6I1hXSCngi4VWhauynPlSrIRZR+vVVyX8jjPmt6
++ijDlZXdnFSR8nFO77q66bMnw3eux8P8D9OntAhcan9ZzzB2Td4b4D/7XQRCUXbnftFZgpWuOlPl
+wzB5PKRlazEnYCs1/50yTNbphzH2vlPoF/mxn7o6cJXnfej9G+mRJ+joA2+8745Hi+uylVPWUAa4
+h3CXJfDhzf455uCyEcGyvjj3wVL8cfgC4YdBX0paIQFc8kFdzOQmNCKKQA/oQXs/mAiXVuxu9+Am
+mpiKzy9ccfGM1mCmbBUuPhHeOVPNves61DvxErpE4DKksAjtyXWPFxP5CRGb/0XLFeL1hl9ae2VE
+uZr6R7afrhB1vsEk+ntwaiVO1mBiMr+LzbPfv6O3EP0JIFitwR0tzx91l9j9oICWAfB2oDgBX+JI
+wyC73zUh932hadkS+bLcbrjPs+YXwq3Fd3eF+hZ1yQUinwLjT3g+3Wy6TfduISdV1JI+aIROxVvh
+eRM+5p3SOjo3aQqrrlwnAkDiSGNIiGW41bfjRx6kHa24rEBvSst0hrgMPE2cPf98IsSWv08BQhlt
+fq17kdHJ9eByYr/lPb3LR8P9k7SaLjZITv+tUxlg3xc/jtTSCsDd9aG9kagxkBUg73R84e4fhi8Q
+/iA4ptsToezOToj4zyfHrPD3TQLHH9Ry3lW4oRkJtNtUbJfW0nzSxx8Syb7V64ncrcSh01Y6xZey
+YMkRkR/CXQHwtjClbmVMbZSbg+HFIJY79hkr4wakZukNLhGdz7Bag7Vi1ZJMQsRvr5P+uMbyHc4u
+ETgArnztJAytJgOYTaA4WIMnGt5baWLp1rctKJZ4z+lZieL9RLsMv4iUo6i7GpoPd5PsxK/T4mFb
+5hrKRfon8oZUnm4hz8iz+X7iKPgPs078Jzl38rTW4NJ1nV8wJdo9S7B3d5LJsS6WxrEclDf6Eo95
+Mo8Dp5mnlnG27Cr3lWV4ypva09KaftIeTSdfUBM/pV2FO3lLeXxP9goR+LYZw3nCi9/+JzcJQdcI
+A8hEhDjgH4YvEP402A7tAx9IYDWuuE4RwF2zj+6Ken+aKd0k7TafbifKS2VP0I98hK03kkjNT9hZ
+FGN06z6IDDy+4a7/vYuEptfPUsUv5XrtJiKK3MAX48sIDuBXS5R8njCeHQz+w7IPPNtlsYB7RPr8
+ESJ+45mXWtoGx9q38Kq6VVdBKvu9No/wXnkE1GC4j7Jx9qSTAm3kPhnjq1AgWaukw+V2wFl0l/tu
+OaG/Jh7YVy5W/aPyfoPP1/tDOXzBc78CvxIe98upvc0gTfI7egdcFMTFtDozL0HtdM8p/1CuAz4E
+bsnqC9OBxzx0mUclq/5maFcnh03ObBk2mcfj05r4gYb1RBo9iMcxeJ6/xPmCJ028ru4B+J5WyAav
+gUdxrWLdkox+xESKIfRdoO/Lcv9luNv76Leqg6z5JYNhjWhyB+NKAYc6NvnCEQwH1coNbZKJpDsm
+OpzsJY2hz6Jdt/ZV5Cem7Wfd5DlUSVsiQIi06mOlig6PBtZcQpkOhaKLApGD4A7gEu1JAy/LyaqN
+W4rBh3jLDK4Rb687v2i5Qry9rgZOu2EHwJsBcAHIIxj285URXOY+t3sWw+IlLXVl7trp/m4Iebr+
+KIzXOqCvR91Zekk8Alrkb8tgak5cvOdGdcXz0b5jmeIudylrb5ojX5J7kvfJnCjF3OUZMhRynmfS
+Jh/lBEDKSOkBjnJHtwlOPBzmZedXnMvoXCRa6zDUtdA58gRrb0qb5eaH/coTQSqbfM78DOltXhoB
+MQUZW07o86Y8SO0t3Eu71XGVlD/Hfd05vfMd9jp39Bw4yehDBMlRA+TdAOJCJMxbBQjZC3b2rfGi
+i1yV//vhC4SfBrDmlg16pxl0Swxsb0TKYefLk+BC3R8BfLcj5xXR5P81H2HogARwtYOyS4S/WNdU
+27KDLTJtPBEk4PLMG6u/wGcKT3rFGPzKlG8/uYajzYgiCKboCpFPinC/YXbECfFsEf7zJgO/9FaF
+Tna+MXvTSM/RtHt6CIADrxS3CJ06qtO07+M4wPnZm28CuGUtfRi6B6IYuvk+yziz9iXU/HHN4Tg9
+AsW67A716+XMLXq05YxdJ31ik/fIdXOzfsLzNPB4c11mm5bG+8RbQA83NHItFkBW6N/G8hv6FvIz
+JV6l99bjEbx2dE55Q5kovwGI3JSxF0BXRvg0NCq0HiyPVuFQH/+P6dDBQz/Efv438QvAjJMTGsEp
+ad0c1nnj97uswU5fsMDLiXsygurf2AmehS8Q/jjoROp2d9Q6MT2+bEc07mytlerBBCkAudtlOk3P
+fs0WbKb4MNiltfwuq/hXd1WnhKEH0cQDnWr35cWGtLVAIyB+NX0UvwGgCIZptesdrLyNKwR1fsM9
+YNap8UeE+M3EL3WJIKJgDablq7x/Po5NaZGPFwGw5c8AcOcjjOOA5wtz5tn5tWoTAPxtYOzS+nAF
+RCd598BylFTyXPQHSgjz+aJjrut/j6djPPfkkMpdT/SCL/n+QuDxZiRdpre0MpiVFwEKR0oBYUtm
+um/kZt/iSr+S6XoFZaCcSGerf6Bzk3fzqy4e/Yuh5as8KXymv0FmZ3l2PuAJead49CGmTi7UmUrc
+F+/M83n8ach1nXjKJhKUe12jCGmVUsxSCSjLlsWKF/5x+ALhTwJsRkxwLh6kO/5l4M9gUIVhkJ5M
+FAE28ubKlbzdLtNtRINKf/SrcgM/YVRlwCUB75XkDKE/myZJ2jRXc7rnS/cWzj2BIPgta0N4Qdpb
+327ddX4reBY1yqrF309+WJz6KxyqTJiiNRh5dzq9iN9vswj/oX0qxJvoz4uChZje3t684WA/ZRA8
+xonW2ckNANY4+ai64uMMiMPoh+E3HUhRVst/iM9hVqZyi6vjr3OmK/cK1OY62JKVJi3lxeUzyT6F
+W3zcRm+EuinOexrwdirrHwQukSbtlO8GvaUNE4FTegd4wpU7nvyy2+wX3PJf3Jc4L45A54afh/yQ
+pmWNLhBBru8QDOnhnojyL8y1PsyZJ3x4oMf65RD7Z2h34r+KV+K8hphCktOLQH/PpIbTg2iFu4Uf
+QC7bD3Th/aq0yH5YQVeJfxy+QPjTAJvRGkMOYJeYojFYB5oWT2dwdUYyvjFJCaHMgyrmhmbRYbIX
+14cYDTImWkhTdVd/jS94AltfRaGhC5viNFTgWzPh1zKrCxbQfLHCWAZZbMcFM/EyvG6grMDP3f/B
+qkvRwnu0BlP0D9ZymbYVmLZleFeKN25mWhUL9z7NQv9P1t0YPwNgIf/gWRpYnE5L0319dQ5r4LPg
+Zc1qfU6tfBpO2wRy1VgvL/PEeR/7a8onifcU7vAo423eMex1dVPQNd9pM/4sHFXeKc+DtNu0MNg3
+XBxAUOsHjPcHy+6Ynwd5BuicdnaHADp7HPmQp7bz+gU540vyNZ/fVz4qfNmCzFXOgzhBGad4HM+e
+964sItXJVQuWeFOAk+Kai6w7LSh2IXSLwFMlGlsw3MvOuvLKzvsfGIS/QFjD/V+Ws5htXC6EIlLb
+pv680dlENVbpd70yWxumUas3qwtvuKEZib2hWmem7dKhAFLAigtMozW4kak32b1kM1h3ToB5CHlY
+WkvVpuNrCqHWu1qqxNePyS3e1+YWctCs5b2Y6cVCrxfT/2SdrygvALwYFybRX+4gJv/9ZlVkr20J
+RsswuVWY3FXC7reVGK8KlCOglQBq/cMeF6E3axzympLzXjSwDT2u+tJGoOEx/dmMVAHO0M91jHva
+k/TIeQ6nrQJ5KPOJp7TAHFg6qSHPcTM7h5/sNWPeURc1+QZ9W9XVb++KV6N2Thv7fVLNF7Sshpmm
+qzNwpNR7PqSf8nPDn+6RVuLc0Lnh5ws5u7T8i3BtHMrkgQaNILrMi8aRlKGpx/GzzJylXbqKvc9c
+s+HsNBrUI1apmcvMtdqp7k13XNL7B9JKcwOJG4kUE/ieimcHr1xC+LLcWIm/Fr5A+GmweQyzK7lF
+RJBHYfOL6YmPKCZ2fHPiIC/no7g6Ap1j/PAinGSLbdcuy8qhj+KSj6AAeavF7QSCve74rHJY2VBy
+ZcLmAAM47wAAGqBJREFUMAEwJm+SgZWd/bUfjF+8XSUYgDEzvV6ywTGTvMROiVg4Ek6L2GcMq7J+
+vxfUXj+vLPRmSW4TcCW0IG+F/CZ6vxDQrp9rLq4RGSAzrwcAArrxQT/qV1rWrzB+wOsAmkJ6odE9
+Gh9omV9Dn84jzzTf8BWPKaQtD4RzbPw5c6jLYTrnki7DE94p8/Ub5g/5+YHMH4ZTKScA1fKmSTPy
+XdAiYIL7BohRx8tNXr3nhl/v+VS232N9sC5T/Cp9jN8CwfeePs6/OjfQunRo+6k8JI79wZmzGVtu
+ADI38U5WVjk5b6KvABrGoj3NHAwNIgicCEHb3QF1pPv/oquEfpPOHF9e/9fhC4Qt3O1+0HKGlBg2
+LambGBNlS/G8Bya+pvha32RbCk0ZVkRgGdpufsDUI4oAco1Ao1WYlAw0zR6E/gIIRsLF0GKp2eoY
+ROhXN5CR0zg7AF4gWBQEs9D/9F6txa8NIvdV9KftiIjoRczvBWJpdfOf7Qu8AO6LmIXe7wWY3wp+
+dzUzONZfqJP9EKOGaAXBb9EX47iAYt49IxSBMpEPfQSt7CCY43jeAsdp2AJNahoP/DiGma4pXZ7K
+helc0u/MyzZJLkq/qtztUj+u5YGp9sM5H1fSE56/EXgu5wm9a0+gycDX0HRWBlA0+flC/g74RBkD
+HfLeyTfFr9If8SYQXPg4W1kPn09A8Cntki+6VLj2xD5taMiX5k/bTzDgvnzuWYPx5s46c90Kyp5j
+av1PGxwTcecqsfcdPUXCzxy+/sb3b4QvENZwV/NOgHbNYwozD60+TLAXcU2/VanB1CB3Z3vKzA3N
+6pbgxJ1j1EIboYzywDDZ6e7SO55DUAQFvC1gDhLFn1aRbXeBBvUpVhnM21WCZYFNBcGv9Ws6/3sp
+SPTrlkTLCgzW4O108d6A+EVEf5jo9Xag+zZALMQUQbFdaYHnlwD43df3/irqtZVRB4pfG9Cun492
+1wl8OMGrg+DVm8Yr3ocBSKf8eUjCleN9niHdc4/WJ9sa7gHmlXqaiXXa8yGtYUvfrpQ8usxOstrS
+z7U85muFXK+1+kxd81S10/Dc1cc/CFMRT+i3aAhsip7J9/CffeY5GEr3kx/wD9NXWqQYEOMEyh7Q
+zj6/ZMB15DmBYM73H4JgvuBv+RoQarxr7U0v+xHI6AEyBet3GDNGXk3HelQGTpF+HnaalCBF8QC5
+9XfvE2b5JaquEGAcsW/VRN+Z4rhW/mH4AuGnYc1Vv7F7QEwBHAMx5KU4ky9BMfCGQhL5NOO7cgOd
+YzyfCqGLA81yeceezhGmxrtXJ396WBC8Ka2FeqRw7D0bo26ZRUswlhTrlLpuE/X8B2K3CKuLhITr
+Apb/237D/xMienm5AqCYwTXiz3aN+IMuEUxuLRa1/C7VYgBY/YQ3j8hSSO89pMJCrw123VqspS+Y
+q8BWe8jvV4NbUGwgGEAx7dNVlE/BOPJRHEPk63gy+O2AsalbzvSOd9PF25ZDmfJQyh2Q2gbTE9qT
+zexm8o3lgdjH9WjzHiQNSe0XnEV+p5cuZPximKTP9O4R6V7+ooql8l25OGCPHP2ATcbTdErynTby
+fUhr478Ggm/8ctxE63ggLXRKc4N8sW1du9aar8BVSr68GnyOOGOX3tfyQXA0i4SYvGPu5wvg2Hhk
+A+XIo/Uu7hL/OHyBsIUHna8jF+5xNifkpKasLm8oPtXh6DV+Ud9OHhH1QJAHcbvyrcVXbxJPZDJe
+Kectw4+LQAgn0BZRWGYMt/HB9mXqMi/pjfx9okQJQFJrq7pFyHaDCAD4tYGOMP2PdyaC7VWPY1sS
+zTWCt9wFfJcF+M92ibAf3CCiP5ROmaCVh18LFCsAXj/8AX7CQvRSv2FSgLxeDowuEsk9QhUa+bjJ
+nicGkDcoRrAv5DwGiONQ+MziMtPiVSqdKG4bV+DXaRwIHa/Sw4OZdPSJfwi6Xq/eFLklLGWBzfRZ
+Zob/hXzMY7FRr1ynXxb1aUjjO5eeaTXFKNLQTvKQCPPHVfGHL8NBOpbdnQAR04FrTKcA6PR6cq24
+ot2OJ+supjl4PfxaXPe54OkG7qkc77P4qDz1s3ISZ8swyor04CbR1TkKLzJvBQOvZO/HhBMiADDb
+DppcIzA1uELwbrOK+x6f9h+GuzPDdloG5deARLtvAN3oIpDrlCsl1/sYl8ggC+rZChiAaLb4dv7O
+TIGngOAq2PM0DYw/z1DDk2Wzun2CRgcbVKlCAsw7qtbfBf54A+PtamCAuOZnIaKX2ZaJiOjPdo1g
+fi/rLiHwZT2F2F+Koxe96b1dJpBf6A9Rsv5u8C7qHrGdMlhf4ltKawFj3lZmtQgzobVYW7OAsYPl
+CoL1eXHnByBtMoCXkvz2yjEvg0AHxWnoAo0bWgx1VnKk8YkXQjflM489UB7C410Msx1rOBfSqYqJ
+v4+2CVfpTU1+NQxa8TkN29OM8+28p6vxcUqHe75OP8oIdTlYjJtroPEhraGdXo67enHueYfHey8n
+pnNKa+MUT9Moy6YZ29g/6YW4MD7ZdYIq3+QmAfMgl4/tq+sQ96VzvP7faQH4Lpr/gAYt9wkiwpMk
+0F3i38PgLxCGcFPdHq29UndbuZFm4QbKDVm6Hb9px+VjIuTDlYKuESeLb+GZWuN9Uff66WC0pjwI
+j0Hwo5DAKlE/TZI1CBWmW4f3cKv5NpsRXpo/ukUsS/A+Ro3ei/ZefH+ULwBfDi/JMRH92b9KJ5vv
+pS4SROYecYyT/rTJGqP3rqkCXn2mK5ZfJhvT4C4hWxZ7mnZjB3DDVVzpdlfMyymv3cOazTxKOwLf
+kW8KuUJXQFd3jFvCPwhXZ16U3bFLvcWLpc3yrmXcSP4odDJ/TEuT6qm861MdmmsAVgNfAFknGV0d
+cnq9HusXyp/zXaWVeOcHnD98vsdK3spPkAkGsssX+2HDvNKPkR7HQNqtOo5OLAcn4T1rsFCLEQ4h
+wOLgI0ykbhDU0NQ1Qt3kdgujS8WjmvxO+ALhx2HPQt39g9JLaZt0TMPwCTDu5FjyoG453RcZWtF0
+DbdM1Q/Y80u2HGt7OOMA7ZceBE/vkP4eRohgF2o1llnSoAvNGswKfPfn5cwI+IIM3kqXl/sDv2VZ
+eGkB6j+8fm1OQS/zy45SMxq96C37pTmjLYuuiFuD3wKAmFdcXSUsTv4Sw2sDYPSmFlJwvO60Xdll
+IrhLBEC8LcN6D/2s/tKBRqZbY5r4KGa7RRwvNhnU8YRn3Fg+yjmDZqXVTcr4Lqy+qDL+rnkk218u
+9EnOe8nHFyK5qqMh/+1qPQhBXlDJ+THoIu9Ew+rLgS/Q0stpj192y9c8Bh1QruN0+3rId0fGaqO0
+eZiITn7B2V2Cd30e3V/xcC7T+2zKR4Evt6ceiXZ2ndBRk8Dr8VyveMWQ6zbxTQFPfAhnA2Mq6H6+
+Q5NNYyLaVuO/qvKG8AXCn4Y9cH4vcVedXpLLaVlmJgSWm1PkqRsENzRib5PVXyNwNR6d1Rv4hEZD
+cwRuoE1l62Gi/qcW7/ykRg13c+CijjSUVPvXeoSJmDm4SLzwdzL084pS3M83KtY3L19hPTrN3B92
+ngV2OVqAt6+x09iOT1OQqy/1idJo/6qeqLrzcyFFlde25DOMgYFfagAxKSh2V4hgFWYAyrANCFEF
+x7C8DBRDnxtN6ijhtpNnZbjXFzdgqJtZPMh4sqXsSiPyrgxeAW/EXwjcXQ7s/dy/4quq55B+wzr1
+sLevZYT2X9SdqPVjvAU+5MT305fdrl+Gw/jMMx/PVuitzHt83NyH+MEv+CMQ3H1u5tHCJznYwV2d
+Mb9qUKLcrwCGSx/yDHw5lmE5Gv7ARte0oEkDoI1p9rIbuf+v08hcI1oayFvpv7G6n4UvENZwt/PH
+XXLPxvLraTobM1CGtCoc6tXcjOCYm9k80CbZSrO62uzVx7cEjomQWEHqTuMMgr2fioV43PVV/o1+
+u52apeN24PlR0eQaYI15W0/xtAgiAMGvneON97R+Ill/HY62QtZj0ra7hLk/sL9Ih+kGfN9Mb5RF
+TH/e+6g32b+SJ+gGQXaqhILcF/PioXWYG8Dj3V4AwwZqabtETPTVm/EHPFZjFchaPuj7AnbJ50wG
+xdUtgssv2pUpDmo9jCenewzdy5MQTrM4cB3PFH8s8J6cT9i63TSzXPD8NH3M9zjHnPcuQCibdQNw
++3zO3/EZ+HlsCYZ7bvgDV6xL5GloRV497/by2pS1ZPQWX4xf5cmV5+nDz+97mfXbkE4OUQS8ZSzA
+Em5XsBiHNOw/kBXGgLXM1DmFJwnPDWmhroaEhu2tt5Vk/r+ENFq7hYLchmawiWkwgP3d8AXCT4Mp
+Mp2ZaSPDF+HC5pU0YEjLPBR5j2w+4Vr6XVqIMs0/igG0ZDF2di6gGf0yexAMaYHe5CthTr+3pOpy
+Dy1mMgDe6Q4MdlTaFmIvhzH5OWssxhAswaIgmPe5wMsveAHgF/3hNwBk9yVWqzDG/QU6sdMk3gI/
++CHZQqxW4e0WIRX4BjgstHyfSfvGQan9Sh43p0sgCIalEoAwQ5yogF7kUV2cZymq49ZnWHCbasYc
+4jru7TLP/I91+F5HkNdnYyr1ahkksb8RboFT4Gk5T+kcI3P6dfikyfk4uhEbXNCuAG6hRXUX0zvQ
+FMrpLcGet/Zlf2pE5Gtph/J8laWtI9G662V8Asin0yNSPTA8thaDzsj8WMSU5mPCVufQB9avAIZh
+XLR/UWg/N/oG9zx9uORBsKv1InhXY/sI4y5qZwOT+wRrm4SIGNwqlHflu+Ny9fvhC4QtPOl9BLk2
+mlFUB5SR3pkQPgXGhZWb5nDPF2hpmWfXiPaluJUozkRU4AS0GcS3FuLSSk+7ZWEb5dwPodZsnlEh
+eHrKJwsM64PBS78CRw2ogPglxPtFNiY9/WFZYs0qzAiAwS2ClqWWKZ4hbPE30Z/XUjj8WsW9FfSq
+KwQJvZkNDPP2Gc72X/XhwpfyJHyi6wOCUdnH4zgIrjRil4Wg2GYTz6DXpiXhs5ePVzf7lky2OGk9
+CGdQA34v7oNADHcnYivwBt/f2DiSTpjUyVQBPqTHdXM/7W54kivWMzm4wJy4kt9VuTvzuetHxrIM
+IEV+BEExjUNan9f1fKE1fNiW3rJM8IDUgLy71+bcYNW5uZ2ZHtI45j9ZdrHNpzw0pFHDi+KZXRf7
+uKR+8uEgoukFOqL8AxthbpnsLYMjnbo8HEnnZRaNQ9E/eKcLVJpckTvAJbN620kSCh9INkDmJK57
+kf7vhy8Q1nBXeyr4JaIIchk2p7RL6azQrCgDBU91CIDxxMAN24Fm0STX6og3gbhvmcoxasaus57q
+xC59AOB1SAsijgtlCbi3lnaFGVuZfGSnIobwAi0jxNv0SvYTdPzyM4ntAImtHPjN9GZZp0KYVfhN
+Lzgh4h0swRdx2f7CGxSrFVhfmjM3CXJsbnF2FwoGC7FajmX3cgTDVAAxgmSdC5J8hhXcivUbgGlJ
+95DOkMbEATBrXpxFumm04NiWL5dn18AH8Xzfp3GcL2kx+Fbn+oErWw139dXTMOkJvTukI4m79KB2
+Tnk/b9yTnJWXS/MQCugl5+vKDH01AOpAS213NXID6DZ5vSwuAKiTEWh5e8hXzvkk1aOvX07L8Y/S
+7pwakT95WjKm+36QP4uXIw3abPmhsTiORNL3EbhD5P61MU1uFNiIyW2ijjE2OhOkI5YQtniHxwZ6
+l16GSiSAq24Q5hssAjZDjtj6H4YvELZws/uDK0TatXSGZDqRj7xq0iempENSYcia9VPadF6wWYO9
+ERIExIblTX4RueT07LCC2tB2HpR3NwhcGa5ziVo9zmkceXgv+BdBn5HA7zGvuP7YBQvFOPM6MYKX
+Iqx+wBg/W4X5xeaNUQCwgK/wBrzBOkyq9BsLMW2Qi9ZdpWm6AV12Nwn9KC2A5vUP+dA3GL1t9Jfq
+dJl1YBSfTZm4sfpiHCzAMJ7dhOpnIB/SkI29kSepWJkHs/rjUKZ+Rgs5OqcroVMxP0u7H+5mvXKN
+4BzLG3UDjIsc2+iv6xhA1y6sgCK9h281WvCTHuSVu9KUP9NK64e8a46eeeq1jT+xEv/GqRF5Xl+k
+Yx0yL/ZfOyalf13G1lBpvDFffVDBMinH0/34kEKnkPfEfV+sDZzYAfQSxd/dUJcJdIEwsW4l/tfh
+C4QfBxtRIHEPcpG+s46+xVNZJRzAcZdQVstdmjaEoa6bFnyAmSawLMhHZG1v7bXmMF/T7FnY+p1b
+vlr3KUi9TQovPdQWmjF2t6EbvR+ZhFgBMJEDX9lWWACqb/bzgpnf+2ebMb4UCfoKqy9wjLP9why+
+n/cmtfAiXba7hWypYjIUAivtTQn4Guglt/gaTwLNgdeBrE4xBL6EMlCxAh1dI4KOtng8Ng1l6AbT
+AmlypRxmVANonkFVXFNntq68p6HF3MciMzoYuA5pGuXfTnsQ+lyxN7qXnvLzyFEmX+cxWhrPSbYB
+I073kP70ZbnCM5RV65FoEx+f+XPa1Y9kMN6fjlDDwCmdI+/VPTXpnbxcB+xzvK/9cLL8LqZ1f3gZ
+EeV26R3f0E+5z44BQC5ZLVcs+Ah3oJeSnzAoYNn9hlZiVfxMvV6+Vd+H4QuENdzt2acg1+jZHIWa
+A4b4I3CsGbOWSrxPaAVvMtXNO09Pp0mhUUPXZD1Cq020vH0l+9Q5zJzTwtMh5MzcxJci3NZg7bOt
+AN8q9bUsrrwtr6830xtoapVdoHN589qPZ1C0EDOtn0KOaRh3K7G6OiyLr1uFWa3CbyKmaPtdstwS
+zEQGlgPIJYrgN1l89fziApiTRZkQ5KoSpQiWsyW4LEny56/w0lwZT068OPZDvh0Js+9ToFqmdree
+ZvIUykp5tGvwIU/WLzna76p8TJt34p+4SNS69RS/iwDF0puHnvYexrKrddEdDZ+DGU4gx3tw1S+7
+NkRr4cn/N9CgsVWdHSzHLQ1XzYkvXkO8sQZP8QykOfFR5n/64Vy3NA4cy8plhzqHKa6uLqmv8niP
+L9h1/Ok+L6luMNqQ3oZRwJoyLctt6gAzeHFjjQCLRgbMsuWxinC5uPSyTvuhbaCELxC2cFPpMrmp
+isnBL+Fuyme+U9kWfQKOOxncJGda02ZbRThTbcZSfmFOtG3A5ydERIRRm7H66QSCz96+n50pPAf8
+uQhYy3nN4z2nYRd4F46I1CKMZy8w4Y9dEAlahsVdF/5s5bv49EW5zi3CeeY0cIl4qz8wWoW3tVoo
+uEcs67WfHsFw11l93wUgC0yXBY5bUAx9aMAanrvCC3cEUzHTQhq8iEE4FaMVWAdzLVnf7MaHohzP
+ywrqHehYJt7FhDYEa8rEo5JvqrK+hhTaw4c05OrUi2XgIWlM22PwcTs6aUNamgR5yw91OIBXJHTj
+dIdvAjIc0jil+f30oh3SovwyukVmSOeGluhd+kTL8b69vUtEzjdZe3PBV/xtXfjOPY/p5Wr8g+U3
+yLsx6Y4T/KIut8LSnvHwiKW8s1tg3OkcB1crMaWTI8heovt1lHsjfIHwJ6F1hdAZItd8OvLnQtro
+AVV6mUXMJ7Q4vSMtZ2xoyZ1CSvpqRN8Nqx4nkNsCig+C6H9At3ho2FJJsMwZ7hn6Z/e7ugGra8FL
+JfJ6ilYL7HoBjcx3N4Bg2j67ekzaPjWiAl61Ci+LMe0naqbkGiFE/BJ6v7e/8Estz2Ah3tbgJXHF
+mXi5b7z98DiGnkFL8JtUmUUXiHdxk9hAWvQHPdx/WJWt8bIDXW5oCPyQFkeMfMnRVr5p5vgM4vJF
+z90QZiEu3aNeh1ymPvJp1g1fEoht/FEYBfABlMaCI/iorfg8z8/CJOcMdJNNmjcw2bydXM6RBscc
++ZK1t7o8xGwKvCjz2jU+pOAYZG2fqX1604YpvRnjtv7N/Zj2AAR393ST/1S33PfdWFgb85iFe6aT
+ZdgqC/qgPCSd7ossiiHPiQeLbUPjsG+aMiUiNZJp7SMYJgO9Ts9A+9+GLxDWcHcEOitvOT0iPear
+a8S+tHz3KxouiZqIDfUmDTCst2GwCnsNepAcQSvRjA58RfU9w0NqV3YX7vd3Vg4FbKRdQG//r727
+XU4UBsAwSvb+r1n3BwaSANaW1bq+58zsdlCQjh/haQb1UvoZ4bpd/Trh+bZus8JlG8TLpzg0p0aM
+wTsPxkPw3j4mbTw3eKqX149pq9F9qecDzwNanQ2+XvqZ33lwb+P4NmhN7cxumeo7htdzh9fZgPoN
+dt0s8O1OXd5MNzVPrWnaDeKuBYf12sehDefuJTf1MxDLbU075wkv65VmvR9onifzJ2/0rv0qj+1o
+eU6WdvGcezcyHtyXheONjr4d6ruXf3Xdd43fCDfe98vyYej24++XH5FWHlhn2HcbSn3g7ETy7nZ1
+m+3zo2z+7y/fXtbuf2c/3d1RX00Ht7HcVh+z88Lxm+HGdcu47bCPu/+2D+ju9Y8u16W9328af46P
+ValbrjPD3a+27KsMy+P1w852HG27tRmR1uidys7Vx2+MW9ddR9ZlTF5iuG5zbS7/d6/3R/15+R75
+PO3B/qk7eu6tv71+Mgrg1xiHMrw+S19PCHOeEREAPk7C4V0Ic17Cn4wAECbh8C6EOW84Jfp5El6S
+d4znhAH8EuNQBjPC8E3OEX4i5wgDb8I4xKcQwpxnagAAPk7C4V0Ic55TI17DqRHAmzAOZUiY+RfC
+nOfj017DqRHAmzAOZUj4g0cIc54REQA+TsLhvVx/9O1mAADwfzMjDABAJCEMAEAkIQwAQCQhDABA
+JCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQh
+DABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwA
+QCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAk
+IQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEM
+AEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABAJCEMAEAkIQwAQCQhDABApL+Uk2g8s+79bwAAAABJ
+RU5ErkJggg==
+"
+width=353
+height=351
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;<a class="anchor-link" href="#&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#182;</a></h2><h3 id="&#26631;&#20934;&#21270;">&#26631;&#20934;&#21270;<a class="anchor-link" href="#&#26631;&#20934;&#21270;">&#182;</a></h3><p>在下面的单元中，实现 <code>normalize</code> 函数，传入图片数据 <code>x</code>，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 <code>x</code> 的形状一样。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Normalize a list of sample image data in the range of 0 to 1</span>
+<span class="sd">    : x: List of image data.  The image shape is (32, 32, 3)</span>
+<span class="sd">    : return: Numpy array of normalize data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="mi">255</span><span class="p">)</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_normalize</span><span class="p">(</span><span class="n">normalize</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="One-hot-&#32534;&#30721;">One-hot &#32534;&#30721;<a class="anchor-link" href="#One-hot-&#32534;&#30721;">&#182;</a></h3><p>和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 <code>one_hot_encode</code> 函数。输入，也就是 <code>x</code>，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 <code>one_hot_encode</code> 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。</p>
+<p>提示：不要重复发明轮子。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">one_hot_encode</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.</span>
+<span class="sd">    : x: List of sample Labels</span>
+<span class="sd">    : return: Numpy array of one-hot encoded labels</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="kn">from</span> <span class="nn">tflearn.data_utils</span> <span class="k">import</span> <span class="n">to_categorical</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_one_hot_encode</span><span class="p">(</span><span class="n">one_hot_encode</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#38543;&#26426;&#21270;&#25968;&#25454;">&#38543;&#26426;&#21270;&#25968;&#25454;<a class="anchor-link" href="#&#38543;&#26426;&#21270;&#25968;&#25454;">&#182;</a></h3><p>之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;<a class="anchor-link" href="#&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#182;</a></h2><p>运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="c1"># Preprocess Training, Validation, and Testing Data</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">preprocess_and_save_data</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">normalize</span><span class="p">,</span> <span class="n">one_hot_encode</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+
+<span class="c1"># Load the Preprocessed Validation data</span>
+<span class="n">valid_features</span><span class="p">,</span> <span class="n">valid_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_validation.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#26500;&#24314;&#32593;&#32476;">&#26500;&#24314;&#32593;&#32476;<a class="anchor-link" href="#&#26500;&#24314;&#32593;&#32476;">&#182;</a></h2><p>对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。</p>
+<blockquote><p><strong>注意</strong>：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。</p>
+<p>但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 <code>conv2d</code> 类 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/conv2d">tf.nn.conv2d</a>，而不是 TF Layers 版本的 <code>conv2d</code> 类 <a href="https://www.tensorflow.org/api_docs/python/tf/layers/conv2d">tf.layers.conv2d</a>。</p>
+</blockquote>
+<p>我们开始吧！</p>
+<h3 id="&#36755;&#20837;">&#36755;&#20837;<a class="anchor-link" href="#&#36755;&#20837;">&#182;</a></h3><p>神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：</p>
+<ul>
+<li>实现 <code>neural_net_image_input</code><ul>
+<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
+<li>使用 <code>image_shape</code> 设置形状，部分大小设为 <code>None</code></li>
+<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "x" 命名</li>
+</ul>
+</li>
+<li>实现 <code>neural_net_label_input</code><ul>
+<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
+<li>使用 <code>n_classes</code> 设置形状，部分大小设为 <code>None</code></li>
+<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "y" 命名</li>
+</ul>
+</li>
+<li>实现 <code>neural_net_keep_prob_input</code><ul>
+<li>返回 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a>，用于丢弃保留概率</li>
+<li>使用 <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> 中的 TensorFlow <code>name</code> 参数对 TensorFlow 占位符 "keep_prob" 命名</li>
+</ul>
+</li>
+</ul>
+<p>这些名称将在项目结束时，用于加载保存的模型。</p>
+<p>注意：TensorFlow 中的 <code>None</code> 表示形状可以是动态大小。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+
+<span class="k">def</span> <span class="nf">neural_net_image_input</span><span class="p">(</span><span class="n">image_shape</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return a Tensor for a batch of image input</span>
+<span class="sd">    : image_shape: Shape of the images</span>
+<span class="sd">    : return: Tensor for image input.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">neural_net_label_input</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return a Tensor for a batch of label input</span>
+<span class="sd">    : n_classes: Number of classes</span>
+<span class="sd">    : return: Tensor for label input.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">neural_net_keep_prob_input</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return a Tensor for keep probability</span>
+<span class="sd">    : return: Tensor for keep probability.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;keep_prob&#39;</span><span class="p">)</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_image_inputs</span><span class="p">(</span><span class="n">neural_net_image_input</span><span class="p">)</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_label_inputs</span><span class="p">(</span><span class="n">neural_net_label_input</span><span class="p">)</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_keep_prob_inputs</span><span class="p">(</span><span class="n">neural_net_keep_prob_input</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Image Input Tests Passed.
+Label Input Tests Passed.
+Keep Prob Tests Passed.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;">&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;<a class="anchor-link" href="#&#21367;&#31215;&#21644;&#26368;&#22823;&#27744;&#21270;&#23618;">&#182;</a></h3><p>卷积层级适合处理图片。对于此代码单元，你应该实现函数 <code>conv2d_maxpool</code> 以便应用卷积然后进行最大池化：</p>
+<ul>
+<li>使用 <code>conv_ksize</code>、<code>conv_num_outputs</code> 和 <code>x_tensor</code> 的形状创建权重（weight）和偏置（bias）。</li>
+<li>使用权重和 <code>conv_strides</code> 对 <code>x_tensor</code> 应用卷积。<ul>
+<li>建议使用我们建议的间距（padding），当然也可以使用任何其他间距。</li>
+</ul>
+</li>
+<li>添加偏置</li>
+<li>向卷积中添加非线性激活（nonlinear activation）</li>
+<li>使用 <code>pool_ksize</code> 和 <code>pool_strides</code> 应用最大池化<ul>
+<li>建议使用我们建议的间距（padding），当然也可以使用任何其他间距。</li>
+</ul>
+</li>
+</ul>
+<p><strong>注意</strong>：对于<strong>此层</strong>，<strong>请勿使用</strong> <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a>，但是仍然可以使用 TensorFlow 的 <a href="https://www.tensorflow.org/api_docs/python/tf/nn">Neural Network</a> 包。对于所有<strong>其他层</strong>，你依然可以使用快捷方法。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv2d_maxpool</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">conv_num_outputs</span><span class="p">,</span> <span class="n">conv_ksize</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Apply convolution then max pooling to x_tensor</span>
+<span class="sd">    :param x_tensor: TensorFlow Tensor</span>
+<span class="sd">    :param conv_num_outputs: Number of outputs for the convolutional layer</span>
+<span class="sd">    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer</span>
+<span class="sd">    :param conv_strides: Stride 2-D Tuple for convolution</span>
+<span class="sd">    :param pool_ksize: kernal size 2-D Tuple for pool</span>
+<span class="sd">    :param pool_strides: Stride 2-D Tuple for pool</span>
+<span class="sd">    : return: A tensor that represents convolution and max pooling of x_tensor</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_ksize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conv_ksize</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">3</span><span class="p">],</span> <span class="n">conv_num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
+    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_num_outputs</span><span class="p">]))</span>
+    <span class="n">conv</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x_tensor</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">conv_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span>
+    <span class="n">activate</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv</span><span class="p">)</span>
+    <span class="n">pool</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="n">activate</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pool_ksize</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pool_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">pool</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_con_pool</span><span class="p">(</span><span class="n">conv2d_maxpool</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#25153;&#24179;&#21270;&#23618;">&#25153;&#24179;&#21270;&#23618;<a class="anchor-link" href="#&#25153;&#24179;&#21270;&#23618;">&#182;</a></h3><p>实现 <code>flatten</code> 函数，将 <code>x_tensor</code> 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（<em>部分大小（Batch Size）</em>，<em>扁平化图片大小（Flattened Image Size）</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">flatten</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Flatten x_tensor to (Batch Size, Flattened Image Size)</span>
+<span class="sd">    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.</span>
+<span class="sd">    : return: A tensor of size (Batch Size, Flattened Image Size).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">layer_shape</span> <span class="o">=</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span>
+    <span class="n">num_features</span> <span class="o">=</span> <span class="n">layer_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">num_elements</span><span class="p">()</span>
+    <span class="n">layer_flat</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_features</span><span class="p">])</span>
+    
+    <span class="k">return</span> <span class="n">layer_flat</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_flatten</span><span class="p">(</span><span class="n">flatten</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;">&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;<a class="anchor-link" href="#&#23436;&#20840;&#36830;&#25509;&#30340;&#23618;">&#182;</a></h3><p>实现 <code>fully_conn</code> 函数，以向 <code>x_tensor</code> 应用完全连接的层级，形状为（<em>部分大小（Batch Size）</em>，<em>num_outputs</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fully_conn</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Apply a fully connected layer to x_tensor using weight and bias</span>
+<span class="sd">    : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
+<span class="sd">    : num_outputs: The number of output that the new tensor should be.</span>
+<span class="sd">    : return: A 2-D tensor where the second dimension is num_outputs.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
+    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
+    <span class="n">fc</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">fc</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_fully_conn</span><span class="p">(</span><span class="n">fully_conn</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#36755;&#20986;&#23618;">&#36755;&#20986;&#23618;<a class="anchor-link" href="#&#36755;&#20986;&#23618;">&#182;</a></h3><p>实现 <code>output</code> 函数，向 x_tensor 应用完全连接的层级，形状为（<em>部分大小（Batch Size）</em>，<em>num_outputs</em>）。快捷方法：对于此层，你可以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> 或 <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。</p>
+<p><strong>注意</strong>：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">output</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Apply a output layer to x_tensor using weight and bias</span>
+<span class="sd">    : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
+<span class="sd">    : num_outputs: The number of output that the new tensor should be.</span>
+<span class="sd">    : return: A 2-D tensor where the second dimension is num_outputs.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
+    <span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
+    <span class="n">output</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">bias</span>
+    
+    <span class="k">return</span> <span class="n">output</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_output</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;">&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;<a class="anchor-link" href="#&#21019;&#24314;&#21367;&#31215;&#27169;&#22411;">&#182;</a></h3><p>实现函数 <code>conv_net</code>， 创建卷积神经网络模型。该函数传入一批图片 <code>x</code>，并输出对数（logits）。使用你在上方创建的层创建此模型：</p>
+<ul>
+<li>应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）</li>
+<li>应用一个扁平层（Flatten Layer）</li>
+<li>应用 1、2 或 3 个完全连接层（Fully Connected Layers）</li>
+<li>应用一个输出层（Output Layer）</li>
+<li>返回输出</li>
+<li>使用 <code>keep_prob</code> 向模型中的一个或多个层应用 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/dropout">TensorFlow 的 Dropout</a></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a convolutional neural network model</span>
+<span class="sd">    : x: Placeholder tensor that holds image data.</span>
+<span class="sd">    : keep_prob: Placeholder tensor that hold dropout keep probability.</span>
+<span class="sd">    : return: Tensor that represents logits</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Apply 1, 2, or 3 Convolution and Max Pool layers</span>
+    <span class="c1">#    Play around with different number of outputs, kernel size and stride</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)</span>
+    <span class="n">conv_pool_1</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+    <span class="n">norm_layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">lrn</span><span class="p">(</span><span class="n">conv_pool_1</span><span class="p">,</span> <span class="mi">4</span> <span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.001</span> <span class="o">/</span> <span class="mf">9.0</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
+    <span class="n">conv_pool_2</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">norm_layer</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+
+    <span class="c1"># TODO: Apply a Flatten Layer</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#   flatten(x_tensor)</span>
+    <span class="n">flat_layer</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">conv_pool_2</span><span class="p">)</span>
+
+    <span class="c1"># TODO: Apply 1, 2, or 3 Fully Connected Layers</span>
+    <span class="c1">#    Play around with different number of outputs</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#   fully_conn(x_tensor, num_outputs)</span>
+    <span class="n">fc_layer1</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">flat_layer</span><span class="p">,</span> <span class="mi">384</span><span class="p">)</span>
+    <span class="n">dropout_layer_1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc_layer1</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+    <span class="n">fc_layer2</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">dropout_layer_1</span><span class="p">,</span> <span class="mi">192</span><span class="p">)</span>
+    <span class="n">dropout_layer_2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc_layer2</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+    
+    <span class="c1"># TODO: Apply an Output Layer</span>
+    <span class="c1">#    Set this to the number of classes</span>
+    <span class="c1"># Function Definition from Above:</span>
+    <span class="c1">#   output(x_tensor, num_outputs)</span>
+    <span class="n">logits</span> <span class="o">=</span> <span class="n">output</span><span class="p">(</span><span class="n">dropout_layer_2</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+    
+    <span class="c1"># TODO: return output</span>
+    <span class="k">return</span> <span class="n">logits</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="c1">##############################</span>
+<span class="c1">## Build the Neural Network ##</span>
+<span class="c1">##############################</span>
+
+<span class="c1"># Remove previous weights, bias, inputs, etc..</span>
+<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
+
+<span class="c1"># Inputs</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">neural_net_image_input</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">neural_net_label_input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">keep_prob</span> <span class="o">=</span> <span class="n">neural_net_keep_prob_input</span><span class="p">()</span>
+
+<span class="c1"># Model</span>
+<span class="n">logits</span> <span class="o">=</span> <span class="n">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+
+<span class="c1"># Name logits Tensor, so that is can be loaded from disk after training</span>
+<span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;logits&#39;</span><span class="p">)</span>
+
+<span class="c1"># Loss and Optimizer</span>
+<span class="n">cost</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">))</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">()</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
+
+<span class="c1"># Accuracy</span>
+<span class="n">correct_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">accuracy</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">correct_pred</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">)</span>
+
+<span class="n">tests</span><span class="o">.</span><span class="n">test_conv_net</span><span class="p">(</span><span class="n">conv_net</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Neural Network Built!
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;<a class="anchor-link" href="#&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#182;</a></h2><h3 id="&#21333;&#27425;&#20248;&#21270;">&#21333;&#27425;&#20248;&#21270;<a class="anchor-link" href="#&#21333;&#27425;&#20248;&#21270;">&#182;</a></h3><p>实现函数 <code>train_neural_network</code> 以进行单次优化（single optimization）。该优化应该使用 <code>optimizer</code> 优化 <code>session</code>，其中 <code>feed_dict</code> 具有以下参数：</p>
+<ul>
+<li><code>x</code> 表示图片输入</li>
+<li><code>y</code> 表示标签</li>
+<li><code>keep_prob</code> 表示丢弃的保留率</li>
+</ul>
+<p>每个部分都会调用该函数，所以 <code>tf.global_variables_initializer()</code> 已经被调用。</p>
+<p>注意：不需要返回任何内容。该函数只是用来优化神经网络。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[13]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train_neural_network</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Optimize the session on a batch of images and labels</span>
+<span class="sd">    : session: Current TensorFlow session</span>
+<span class="sd">    : optimizer: TensorFlow optimizer function</span>
+<span class="sd">    : keep_probability: keep probability</span>
+<span class="sd">    : feature_batch: Batch of Numpy image data</span>
+<span class="sd">    : label_batch: Batch of Numpy label data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">keep_prob</span><span class="p">:</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">label_batch</span><span class="p">})</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_train_nn</span><span class="p">(</span><span class="n">train_neural_network</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#26174;&#31034;&#25968;&#25454;">&#26174;&#31034;&#25968;&#25454;<a class="anchor-link" href="#&#26174;&#31034;&#25968;&#25454;">&#182;</a></h3><p>实现函数 <code>print_stats</code> 以输出损失和验证准确率。使用全局变量 <code>valid_features</code> 和 <code>valid_labels</code> 计算验证准确率。使用保留率 <code>1.0</code> 计算损失和验证准确率（loss and validation accuracy）。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[14]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">print_stats</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Print information about loss and validation accuracy</span>
+<span class="sd">    : session: Current TensorFlow session</span>
+<span class="sd">    : feature_batch: Batch of Numpy image data</span>
+<span class="sd">    : label_batch: Batch of Numpy label data</span>
+<span class="sd">    : cost: TensorFlow cost function</span>
+<span class="sd">    : accuracy: TensorFlow accuracy function</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training Loss: &#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">label_batch</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;, Valid Accuracy: &#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">valid_features</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">valid_labels</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#36229;&#21442;&#25968;">&#36229;&#21442;&#25968;<a class="anchor-link" href="#&#36229;&#21442;&#25968;">&#182;</a></h3><p>调试以下超参数：</p>
+<ul>
+<li>设置 <code>epochs</code> 表示神经网络停止学习或开始过拟合的迭代次数</li>
+<li><p>设置 <code>batch_size</code>，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：</p>
+<ul>
+<li>64</li>
+<li>128</li>
+<li>256</li>
+<li>...</li>
+</ul>
+</li>
+<li>设置 <code>keep_probability</code> 表示使用丢弃时保留节点的概率</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[15]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Tune Parameters</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">128</span>
+<span class="n">keep_probability</span> <span class="o">=</span> <span class="mf">0.75</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#22312;&#21333;&#20010;-CIFAR-10-&#37096;&#20998;&#19978;&#35757;&#32451;">&#22312;&#21333;&#20010; CIFAR-10 &#37096;&#20998;&#19978;&#35757;&#32451;<a class="anchor-link" href="#&#22312;&#21333;&#20010;-CIFAR-10-&#37096;&#20998;&#19978;&#35757;&#32451;">&#182;</a></h3><p>我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[16]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Checking the Training on a Single Batch...&#39;</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+    <span class="c1"># Initializing the variables</span>
+    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
+    
+    <span class="c1"># Training cycle</span>
+    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
+        <span class="n">batch_i</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+            <span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">:  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+        <span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Checking the Training on a Single Batch...
+Epoch  1, CIFAR-10 Batch 1:  Training Loss: 2.02859, Valid Accuracy: 0.3288
+Epoch  2, CIFAR-10 Batch 1:  Training Loss: 1.73148, Valid Accuracy: 0.3978
+Epoch  3, CIFAR-10 Batch 1:  Training Loss: 1.43727, Valid Accuracy: 0.4584
+Epoch  4, CIFAR-10 Batch 1:  Training Loss: 1.20958, Valid Accuracy: 0.488
+Epoch  5, CIFAR-10 Batch 1:  Training Loss: 1.13607, Valid Accuracy: 0.4956
+Epoch  6, CIFAR-10 Batch 1:  Training Loss: 0.906183, Valid Accuracy: 0.5094
+Epoch  7, CIFAR-10 Batch 1:  Training Loss: 0.814033, Valid Accuracy: 0.5296
+Epoch  8, CIFAR-10 Batch 1:  Training Loss: 0.686904, Valid Accuracy: 0.5408
+Epoch  9, CIFAR-10 Batch 1:  Training Loss: 0.580816, Valid Accuracy: 0.5502
+Epoch 10, CIFAR-10 Batch 1:  Training Loss: 0.497506, Valid Accuracy: 0.5612
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;">&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;<a class="anchor-link" href="#&#23436;&#20840;&#35757;&#32451;&#27169;&#22411;">&#182;</a></h3><p>现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[17]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">8</span>
+<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./model/image_classification&#39;</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training...&#39;</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+    <span class="c1"># Initializing the variables</span>
+    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
+    
+    <span class="c1"># Training cycle</span>
+    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
+        <span class="c1"># Loop over all batches</span>
+        <span class="n">n_batches</span> <span class="o">=</span> <span class="mi">5</span>
+        <span class="k">for</span> <span class="n">batch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_batches</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+                <span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">:  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+            <span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
+            
+    <span class="c1"># Save Model</span>
+    <span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
+    <span class="n">save_path</span> <span class="o">=</span> <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Training...
+Epoch  1, CIFAR-10 Batch 1:  Training Loss: 2.06226, Valid Accuracy: 0.3212
+Epoch  1, CIFAR-10 Batch 2:  Training Loss: 1.57472, Valid Accuracy: 0.4404
+Epoch  1, CIFAR-10 Batch 3:  Training Loss: 1.28891, Valid Accuracy: 0.4616
+Epoch  1, CIFAR-10 Batch 4:  Training Loss: 1.4297, Valid Accuracy: 0.4844
+Epoch  1, CIFAR-10 Batch 5:  Training Loss: 1.43679, Valid Accuracy: 0.518
+Epoch  2, CIFAR-10 Batch 1:  Training Loss: 1.38818, Valid Accuracy: 0.5218
+Epoch  2, CIFAR-10 Batch 2:  Training Loss: 1.09959, Valid Accuracy: 0.5522
+Epoch  2, CIFAR-10 Batch 3:  Training Loss: 0.961711, Valid Accuracy: 0.551
+Epoch  2, CIFAR-10 Batch 4:  Training Loss: 1.12144, Valid Accuracy: 0.5856
+Epoch  2, CIFAR-10 Batch 5:  Training Loss: 1.02137, Valid Accuracy: 0.586
+Epoch  3, CIFAR-10 Batch 1:  Training Loss: 1.05762, Valid Accuracy: 0.5896
+Epoch  3, CIFAR-10 Batch 2:  Training Loss: 0.814285, Valid Accuracy: 0.6132
+Epoch  3, CIFAR-10 Batch 3:  Training Loss: 0.825072, Valid Accuracy: 0.605
+Epoch  3, CIFAR-10 Batch 4:  Training Loss: 0.865807, Valid Accuracy: 0.616
+Epoch  3, CIFAR-10 Batch 5:  Training Loss: 0.840993, Valid Accuracy: 0.636
+Epoch  4, CIFAR-10 Batch 1:  Training Loss: 0.74788, Valid Accuracy: 0.6272
+Epoch  4, CIFAR-10 Batch 2:  Training Loss: 0.652433, Valid Accuracy: 0.6234
+Epoch  4, CIFAR-10 Batch 3:  Training Loss: 0.572802, Valid Accuracy: 0.6414
+Epoch  4, CIFAR-10 Batch 4:  Training Loss: 0.697387, Valid Accuracy: 0.657
+Epoch  4, CIFAR-10 Batch 5:  Training Loss: 0.599844, Valid Accuracy: 0.6582
+Epoch  5, CIFAR-10 Batch 1:  Training Loss: 0.706912, Valid Accuracy: 0.6546
+Epoch  5, CIFAR-10 Batch 2:  Training Loss: 0.458327, Valid Accuracy: 0.6452
+Epoch  5, CIFAR-10 Batch 3:  Training Loss: 0.491401, Valid Accuracy: 0.6734
+Epoch  5, CIFAR-10 Batch 4:  Training Loss: 0.484113, Valid Accuracy: 0.6714
+Epoch  5, CIFAR-10 Batch 5:  Training Loss: 0.494395, Valid Accuracy: 0.6666
+Epoch  6, CIFAR-10 Batch 1:  Training Loss: 0.506375, Valid Accuracy: 0.6906
+Epoch  6, CIFAR-10 Batch 2:  Training Loss: 0.337756, Valid Accuracy: 0.6578
+Epoch  6, CIFAR-10 Batch 3:  Training Loss: 0.353094, Valid Accuracy: 0.687
+Epoch  6, CIFAR-10 Batch 4:  Training Loss: 0.373179, Valid Accuracy: 0.677
+Epoch  6, CIFAR-10 Batch 5:  Training Loss: 0.38469, Valid Accuracy: 0.694
+Epoch  7, CIFAR-10 Batch 1:  Training Loss: 0.428482, Valid Accuracy: 0.6932
+Epoch  7, CIFAR-10 Batch 2:  Training Loss: 0.269606, Valid Accuracy: 0.677
+Epoch  7, CIFAR-10 Batch 3:  Training Loss: 0.308877, Valid Accuracy: 0.7042
+Epoch  7, CIFAR-10 Batch 4:  Training Loss: 0.290038, Valid Accuracy: 0.688
+Epoch  7, CIFAR-10 Batch 5:  Training Loss: 0.30759, Valid Accuracy: 0.6892
+Epoch  8, CIFAR-10 Batch 1:  Training Loss: 0.376015, Valid Accuracy: 0.6916
+Epoch  8, CIFAR-10 Batch 2:  Training Loss: 0.207914, Valid Accuracy: 0.6826
+Epoch  8, CIFAR-10 Batch 3:  Training Loss: 0.22358, Valid Accuracy: 0.7052
+Epoch  8, CIFAR-10 Batch 4:  Training Loss: 0.205842, Valid Accuracy: 0.7022
+Epoch  8, CIFAR-10 Batch 5:  Training Loss: 0.201466, Valid Accuracy: 0.6862
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>模型已保存到本地。</p>
+<h2 id="&#27979;&#35797;&#27169;&#22411;">&#27979;&#35797;&#27169;&#22411;<a class="anchor-link" href="#&#27979;&#35797;&#27169;&#22411;">&#182;</a></h2><p>利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[18]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
+
+<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+<span class="kn">import</span> <span class="nn">random</span>
+
+<span class="c1"># Set batch size if not already set</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">batch_size</span><span class="p">:</span>
+        <span class="k">pass</span>
+<span class="k">except</span> <span class="ne">NameError</span><span class="p">:</span>
+    <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
+
+<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./model/image_classification&#39;</span>
+<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">top_n_predictions</span> <span class="o">=</span> <span class="mi">3</span>
+
+<span class="k">def</span> <span class="nf">test_model</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Test the saved model against the test dataset</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_test.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
+    <span class="n">loaded_graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
+
+    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">loaded_graph</span><span class="p">)</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+        <span class="c1"># Load model</span>
+        <span class="n">loader</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">import_meta_graph</span><span class="p">(</span><span class="n">save_model_path</span> <span class="o">+</span> <span class="s1">&#39;.meta&#39;</span><span class="p">)</span>
+        <span class="n">loader</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
+
+        <span class="c1"># Get Tensors from loaded model</span>
+        <span class="n">loaded_x</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;x:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_y</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;y:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_keep_prob</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;keep_prob:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_logits</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;logits:0&#39;</span><span class="p">)</span>
+        <span class="n">loaded_acc</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;accuracy:0&#39;</span><span class="p">)</span>
+        
+        <span class="c1"># Get accuracy in batches for memory limitations</span>
+        <span class="n">test_batch_acc_total</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">test_batch_count</span> <span class="o">=</span> <span class="mi">0</span>
+        
+        <span class="k">for</span> <span class="n">test_feature_batch</span><span class="p">,</span> <span class="n">test_label_batch</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">batch_features_labels</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+            <span class="n">test_batch_acc_total</span> <span class="o">+=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+                <span class="n">loaded_acc</span><span class="p">,</span>
+                <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">test_feature_batch</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">test_label_batch</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
+            <span class="n">test_batch_count</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Testing Accuracy: </span><span class="si">{}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_batch_acc_total</span><span class="o">/</span><span class="n">test_batch_count</span><span class="p">))</span>
+
+        <span class="c1"># Print Random Samples</span>
+        <span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)),</span> <span class="n">n_samples</span><span class="p">)))</span>
+        <span class="n">random_test_predictions</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+            <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">top_k</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">loaded_logits</span><span class="p">),</span> <span class="n">top_n_predictions</span><span class="p">),</span>
+            <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">random_test_features</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
+        <span class="n">helper</span><span class="o">.</span><span class="n">display_image_predictions</span><span class="p">(</span><span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">random_test_predictions</span><span class="p">)</span>
+
+
+<span class="n">test_model</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Testing Accuracy: 0.6764240506329114
+
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA9oAAAN6CAYAAABxCzGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAAewgAAHsIBbtB1PgAAIABJREFUeJzs3XecK1d5//HPc2a0u/faYIq7TcmPZgKYuFFCCSWETigJ
+vYMpIfAj1CQQbCABwg8IEGKKAybESaiBAMH0EqoLEMCY0EJzwdjGGGxf70ozz++P54yk1Uq7Wq22
+3f2+/RrvXu3ozNFoNJpnzjnPMXdHRERERERERKYjbXYFRERERERERPYmCrRFREREREREpkiBtoiI
+iIiIiMgUKdAWERERERERmSIF2iIiIiIiIiJTpEBbREREREREZIoUaIuIiIiIiIhMkQJtERERERER
+kSlSoC0iIiIiIiIyRQq0RURERERERKZIgbaIiIiIiIjIFCnQFhEREREREZkiBdoiIiIiIiIiU6RA
+W0RERERERGSKFGiLiIiIiIiITJECbREREREREZEpUqAtIiIiIiIiMkUKtEVERERERESmSIG2iIiI
+iIiIyBQp0BYZwszmzOxRZvZWM/uGmV1gZnvMrB5Y/n2z6yoi25+ZPW3I+eX1Yz73ZkOe+831rvNW
+ZWb3HrI/PrjZ9RIRkZ2l3OwKyNZiZvsBRwMHA9fISw1cnpfzgB8BP3X39mbVcz2Z2VOBlwDXzg95
+35996TNERKZmLecYnZ8W0/4QEZFNo0BbMLObAccD9wRuCNgYT6vM7BzgLOBM4JPu/oP1q+XGMLOT
+gKcQF2jNRdo4+0NkSzCzlwIvWOXTrgQuA35F3Eg7CziD+FwvTLeGMgZj8iBR56vF1rIvRUREJqZA
+ewczs5sCJwG/lx/ygZ/LScDNgVsAj8vl/Qh4jbv/w5SruiHM7LH0guzuw6ziIs3MrgUcN/Dwxe7+
+1TVXUGR1VhNc7MrLwcBNgXvlxy8xs7cDr3X386ZbPVknzjYJts1sf+CYgYd/4e5fn+JmFGSLiMim
+UKC9Q5nZc4CXArMsDSz7rdSq2//c6xNB+7YLtM0sASewdF9cBpwCfAH4BTDYuvfLgX8fBZw28Ngn
+gT+YWmVFxjdJwNX/GbgW8GzgiWb2dHc/dTrVknWyLQLsPrcGPjTw2IeB+02p/O22P0REZC+iQHsH
+MrMXA3/F8O7Rzb+vAn5IBJp7gP2Ii+7DiOCcgef1P3c7+gPgeizeH5cCx7n7/05Q3nbeF7L3Wc3x
+OHgucODqwDvM7EbufsJUaybTMvgeb6dz0HrUdTvvDxER2Qso0N5hzOwJ9ILs7sP53z8FTgbeD/yP
+uy+5MDGzgugyfiwxpvseRJfTppzt6g59vzf74x8mDLIHyxHZLE034gq4HaM/o/sC+xOf63sDR9A7
+fvuP4xea2c/c/R/Xs9KyOu7+baDY7Hqs0dTOl+7+n2z//SEiItucAu0dJI8f/luGB9kvB16yUuIj
+d6+Ab+TlrWa2ixjP+VTgzutR7w1y1JDHPrfhtRBZJ+5+5hirvRt4npk9CHgjEXz3t2wb8Goze7+7
+X7I+NRURERHZ/jSP9s7yLKL7d6MJsp/r7i+cJLuwu+9x9/e5++8TidHeP52qbrj9hzx27obXQmQL
+cPf3EeNnfzHkz/uy+qzmIiIiIjuKAu2d5YEsHoPswOnu/pppFO7u57j7v02jrE2wH0u7Le7ZjIqI
+bAXu/iPg0SzNw2DAwzalUiIiIiLbhALtHcLMrkuMuxz01o2uyxY1s9kVENlq3P3jwJdZOrb7QDM7
+chOqJCIiIrItaIz2znHdEY//94bWYhXM7CBi7PT1iRbnErgCOA/4DvDtYQnbNtmmJ4QzsxYxN+1N
+gWsDLSKD+jnAme6+qpb6PLb/GOCGxPuwB/g58A13/58pVr1/mwn4LeLm0CFE1us5Igv+L/P2z3T3
+y9dj+7LIB4DbDnn81sA311q4mRlwJHG8HgzsQ0yjdzHwfnf/1QRlXocYynI94tiB6Ab/C+K88eO1
+1nuF7e8GbgPcBLgGkYzul8B3gbNW+xncCszs+sDNgOsQ+7QFXA78ipih4jsTjNvf9PPlJMzsCOC3
+gcOJoRQ18GvgJ8B/b8ac82Z2OHGevl6u06+Bi4Bz3P1b67TNXcRn90bEd8PViV4vVxLHxs+AHwM/
+cvfOetRBRGRLc3ctO2ABHkRcDFR5aX6/wWbXbaCe1wZeCJyd67jccgnwNuDYVW7jlDHKHmd5dC7v
+3CmVVwOHjqjzS4es+5cD6xyRX9tly5T/a+Ak4MAx9tN9iDnA28uU913gyYBN4b2/FZER/1PEhdpK
++6oNnAX8BXD1Cbf5shFlP3gNr+P+I8p83QZ9hppjpf+zvrCG8u48pLwKeMEyz3n5kNf/vIF1bkbM
+cnDJiP1VAbdaRT1vBLwa+NEYx863cx0PmPK+PxZ4DzE94qhtX0kknTtu4LlP63vdzc/Xj7ndmw3Z
+zjen8HpuA7wZOH+MfVoTN/NeB9xumTIvHrOscZZrjdjGvYes+8E17otbAG8ibtasVK/vAS8BDlrD
+9lZ8T4nM6o8HvrpCfc4jEqFeYwrHxL7AU4AvAJ0x36crgc/nz9xxa62DFi1atGyXRV3Hd45R7/WB
+G1qLEcwsmdlzibvfLyFat3yF5RrAY4EzzOx9ZnboKjY5rLxx1hm2/lrL6c/qvJp6d5nZiUQm+McQ
+F0KjtrMPcZH0XTO727ANmNlBZvZx4IPAXYhjZ1R5NySyU38xt3yvmpm9yMx+CHwFeDER2M0us81m
+ScDRwN8APzWz50+w+RcC/zWk7LeY2Q0meC2/Rdz8GSzvDODZE9RvK7hoxOPDEggOWnK8mllpZn9H
+9KZ5AvE5XukzNJKZHWBm/0T0cvkzovfOSsfOEcDzgR+Y2V/kaQsnZmZzZvZG4HTipmZrmW3PAn8E
+fMXM3pB7oEzLRPtwkJkdZWafA74EHA8cxMr71IkW/KcDnzezs3PL/nJ13JTz5WqY2f5m9i/E8fok
+4mbwSvW7AXFu+VE+v63lPR51zr85EWD/I/A7K9TnYOC5wA/N7J6TVsTMHgz8D3Gz9nfp5XpZaZkl
+phd8PnC6mb1y0jqIiGwnCrR3jotHPL7pU3KZ2X7Ax4g77rvpXVDYwDL4GPS+yB8AfM3MfnfczY4o
+f6V1xll3NeWMes5KdY9fwr8BL6I3FGSwzGH7bD/gw2Z2l0UFm92IaCX+fZa+D6NepxMtX580s6uz
+ek8lhgf0X0yO2j+DjzfPuRrw8nzDZdjF/VDuXgMPJz4f/a/xasB7zGzssfv5YvrdxL7tr++viBby
+7dp1cq3de/uP1zng48D/pff9M+w9Hq9gszsB3wIexeJ5mFc6dsjr7kvcqPmP3A121czsGsCniZ4d
+Tbkr1aHxJ8BpZjY7ybZHVYk1vGf5hufpwO2Z/DMJcbN0boU6buj5crXM7GgiwG6S/632+JoFTgQ+
+bWbj3JgaWZX+bZnZXYEvEq3sPmTbw/aXA9cEPmBmD1h1BcxeALyTGMrTHBfLvS+jztXO6ONCRGSv
+ojHaO8f3Bv7dfEk+3cze5O6/3IQ6NWMZPw4cx+I79v0XbD8numdfCRxKBGUlS4PtA4GPmtnd3f3L
+K2x6WAvH4IXCalpBBus+STmTtLq8FngIvffTifGtPyS6OM4RY52bngv9Fzwt4F1mdoS7X2JmBxIB
+Q3/PACfGlv6EGOd9LeICepbF7xHALYmuu8dP8DoG30uIbok/IQLVy4j3/BpEK/ruEc97ANF9/iHj
+btjdzzezRwKn0XtNll/P64keAON4DTFGsv9i3IHHuftPx63PFjQqQBh18245/wLcicXHK0RPlguB
+eeAwYuzrssGnmf0h8C56rcew9Hj4X6JFfg/xGbghvWO3f717ETeK7uyrmOYw34j5EHGjadT569y8
+dPJruz6LA5M7E70gvjTudteLmb2BCP6Hnc+ax84nzi2/JHrIXJs4xxQD6y9ns86XYzOzo4hhLM24
+Y1i8L2riuD2fOAYPJ97fZr3+lvfbAZ8yszu6+2VrrNcxRN6E3Sw+n19EHGeXEgH1jYgbSYOBbgt4
+m5md4WOOJTezhxJDUkYd41cSn7VfEcMm9iX22/WIY2TYc0RE9noKtHcIdz/PzL4L3HjgTwcSF5gP
+dffBYHwjvJ7hQXZFjIf7R3f/Rv8TchflBwMnEPXvv4jYlwgej/TRSZReQnR37vcBontks/0maPv5
+MnX/Yf55H3pBwa3ya+oPJM4EnrFMOf2GzVs8yr2I7nvNvvs20Xpymrtf2b+imd0WeGVev9+1iP3x
+NCJoaS4Ua+BU4A3uftZAWbuIFsSX5ef3t248zsxOdvczVvE6yM+fJwL9DxHdyM9x9/awlc3sFsAj
+iFbEq7P4Au6PzOxJ7v6WsTfu/gkzexnR3bP/9RxvZp9193cu93wzexCxDweD7Ne6+wfHrccWddyI
+x0d1KR/lUcS402YfXQD8NZHwbNHnLB9jD2HE58HMbgb8KxE0dB/OZZ9F3PD5hLtfOqTcexCfk5uz
++Li5DfD/iNb2cZ1ABFGD56828HfAW939+wN1OIzYF39JBCEGPJTeZ29T5OEnw4Ls5obFq4GPDLtp
+lHsq/A4xNvohRNfpUf6A3vt2e+BVLD5ffhF4zpjVXnWivHHkG8DvppdID3r1u5QIOt/p7hcOPO8I
+4IlE9/n+G0AQx9tbWMVNwAGe6/MeesHrPNGN++0+kPDMzErgnsArWDrjyNWJsfR/tNJG82fm71h6
+XFxFjN8/1d2/uszzr098L9431+eaK21TRGSvsdmDxLVs3EKMj+pPatSf3GieCKx+Hyg3qD73H1Kf
+mri4vvUYz78m8NGBMprf37XKuvxoSBnXneA13bXv+c3Pj09hX710oNzBpFGvgeUTkhEtTh8csr8u
+B/68r7xfAr83Rp1uTiSyGtxvp6zytX0ZeCaw7wT75RAiOB98TT8HWqssK40o6zLgRss87wbEBf/g
+cfyljfosLXOsTCMZ2hdHfMZuucxzXs7w47Up4z3A7gnrs4u4qTS4v68EnjhmGUYEjsNe193GLOOW
+RO+RwXqcBxw5xvOvR2RtH9xPa0mG1l/O2MnQiFwMw74XOvncsKrjGLgb8Bngmiusd+8hr3lNScum
+US4x7nnYsfEZxkskeRS9JJmDZTx6De9p/7//B7jhGOXsJvJQDNalzYjkmwPPH5ZI9TLg6Anelxki
+edufrPU91qJFi5btsGiM9s7yBiIAGdZVryTGqn4cuMTMPmZmLzGz+5rZIdOuSJ7S59UsvUv+G+Du
+7n76SmV4tFY9gAgE+rv0GdGqefupVnpr6e+W+AZ3f5a7L9slz90r4iJnsOviLqJ1GiJwuJu7f27F
+CrifTbTKDdv3Y493dffbuvtrfYKputz9AuKi+kwWH9cHEMfzasqqibGYFw6UtS8xXntJV+bcdfjd
+xJju7sPEzYqH+PYdlw2Amd2dmNpr8Ni62Ad6mqyg//kfIsasXzlq5RW8iBi+0DDiuL23u//jWJUJ
+zwb+nqXnwxePWY9XsbhXmBGfrT9w9xWnPXP3nwB3B37KJnapzYng3jT4MBFkP9zdX7Ha49jdP+Hu
+d/aBHgXbQU4y9niWdhf/KnBfd1+x15G7f514b/tff3N+fOWEydH6e9r8BPhdd//BGHW5kugx8ZuB
+PyXg0WNs9+59vzfH6Yvd/WvjVHqgLgvu/jZ3P2m1zxUR2Y4UaO8g7n4FcXd6nuHJS/q7X9+N6Eb7
+H8B5Znaemf27mT3TzI7OgfJa3JcY19dovsBfmC9SxuIxH+0jiTGYg56+phpuXc3FFkSm5XG7WeLu
+FxM9F4a9fw6c6Mt0AxzibSztvrmbyAa+IfIx8OQhf3rEBGVdmJ9XNw8R++oWxI2qQa8lWq8azXH8
+WHf/2Wq3v5XkDOr/xPCuxP+2iqL6n38pcPxKN4WWqVMztdBgF/3nuvtnJijyOcSUVE09Dbi1mY3q
+Lt/U4wZEK/BgPV7g7t8ed+P5RtFTV1vpKXsMMXa90byWl7n7uzenSptq2DCfBeCRq7kZmI+D57D0
+XHsAEfhOonlvHuGryKvi7ucT5+rButxujKdfZ8hj/znutkVEdjIF2juMR5KwexPjK5sLy1FBV/9y
+MNHV+zXEGMifmNnLc5bqSfzJkMe+B/zDagvKLUOvYmnL6v3XozV+C3Hgb33EOOZl9I8Z7g94LiPG
+l49fgdj2R1l6DG1YoJ3r8d9E9un+ZGa3mrCsTxPj1gePp8ebWTd4z1PdDAv6Xu3uH55k21tFHnN+
+BhEUDLqCXg+IsYsk9s0/uPtqx3b3ezK9rO7N+/NdJjhvQPf4/VuWHr+PX+Gpxw95zvdZmvthnDqc
+RvQk2qxW7f5kf/038F66CXXZVHnWhIez9DN9kk+Qw8TdTyFawgePlT9dbVF9dfmEu0+SOK//pklT
+3jFjPG/YTBK/nmD7IiI7jgLtHSgHEkcRya9qFgfcowJvWBx4H0aM+T7HzE5eTUCbu9veiaUXM2/N
+3Xcn8RZ6rZCNkhgzvbfaQ0y3slqD3Vqb/f++CbvzDusmO5h0byOcOfDvq+WkWZN4KZFxeDDYfqOZ
+3STfYDqZpd1Lvwz8xYTbXFdmdutllruY2YPN7JVmdg4xhnpwXvTmOHn+ON1nR3jbWl4DSxNJNcH7
+pOcNiACkyTTevM93WOE5/XMRN/vl7ZO21ANvnfB5a2Jm1wOOZXGA78Dr8lCTneYO9GY06HfyGsrs
+T8rYHF/HmtmkScEm/Qx9naXfkQeZ2T7DVu4zLOHcRDcxRUR2GgXaO5S7n+/uDweOJC4ELmFxID1u
+4J2AJwBnm9nvj7n5o4mkKIPeNfYLGKxITFPyXyyt620nLXML62Yzn6A1u+kePWwKoy9MWJ8fD3ls
+vyGPrbdhwd9y2Y9HygHTw4nM2P3H1D7Ae4lj9WoDT7uEGJe9lQKUpu4lcRNg1PJJ4qbNc4CbsLS7
+eHPMvdLdV91qm/0k9z6ZSA4IjhqoG8D7Jy0TwN3nWTrG/wgzG3oM53r0Z09vvHcN1fggkcV5o91t
+yGMLxDRsO1H/90VzPJzt7t9ZQ5nvIsa7D7rNhOV9fpInuftVxFRkg1Y6Vw8OhTDgb83soGEri4hI
+jwLtHc7dz3H3pxLZm+9FTG9zOnGxNW7g7UQG8NNyd9qVDLvAuHAKY1q/MvBvY+8MtBtrufgbTIyz
+lvKGlTVxoG1mM2Z2HzN7cc4LcLaZ/czMLjOzyszqYQuRHXnQNSatR+7i/HAi0y70AqubEVMZ9bdm
+10Q24bHmpd0Eg0NBllsGP+tOvMfHu/skrfVNGasZ+z/M7ejN1dz4eR5/ulY/Hvh3My5/mGNY+t15
+2TiJqUbJwf7ZjL6puV76uw4379PX15Cobrsb/G5y4vtwYu7+ayJD+DRuAv86j+uf1LAu3yudq/vH
+YzfniBsD3zKzZ5vZtddQHxGRvZrm0RYAclbZj+WFnBX1SKL1+ViiS91N8ur9F+DdIoiL4LeZ2ffy
+mNlRrtf3e/fibgovo7+MJmC47hTK3arGToYzxLDWs0nLG1bW3GoLyd1Y/4pI2Nd/8TfYcrhc99zB
+i9mJA20Ad/+cmZ1AzPfcP06yf3sOvCqPtd2qVhPA9b++XwHvAF4zhRth567x+Tfv+73Z7xea2a3X
+WC7AkozyLO0637j+kHqsmGV8DN8gzrUbaViOjTUFltvc9Vh6fpnWd9PNBx6b5LtpLed8iOFGg+ew
+Zc/V+Rz4JeB3+57nwLWJG/OvMLMvAJ8gWtvPzK3nIiI7ngJtGSp3Sf5qXk4GyF3F/hh4EtGq1594
+qvkC3gWcRHwpjzJsbNpa7tI3JukWt51Nu9VpmuWtqmXOzP6CyHK/i16ryURlDVhp/OGK3P1lZnYH
+Ypqb/uO9+flFYpqzrWylscN7iGR4lxGtu2cRydA+kVtbp2FwWrnVGmw5M6JnwZfXWO4oowLtYeev
+ScesT7uM1TqMpcfGWm+IbGcb+d20mjHazTnwiinUZVTZy3kEcQPmgL71m/N0An4vLwAdM/s68Fng
+08Bn3H3YUCURkb2euo7L2Nz9Qnd/g7sfSUwJcxlLg45mepzlxmsPu8CYRhbTYRfyKWeSlS3KzE4C
+/oZoWenvjj14QTfOsqT4KVXz7cv87fg1JuNaT80+qdy9WGbZ190Pc/ffdvd7ufuL3P3DUwyyYe03
+coYFvqs5NlZ7/Iy6STesl8R6nb/W275DHhuW/GqnGPaer9d7O2kytA2XcyvcmrjxPniObr77m6UA
+jgOeC5wG/NzM3pLnJxcR2VHUoi0TcfdTzey/iQRaV2PpReqDiARLw+wa8tiwebBXa1QZ+6DpSLYk
+M/szFk+RBb0LtzbwNaIl5SdES9tviK7qV7H0mHsK8Nh1qOPhwN/3bW9wuy8AHj3t7coSwwLc9RzT
+PDgevDGsm/k0WuymeVNjXMNe4zTOxdtOng2jYOnnez2+m4wp9LbZSDnYvpWZPRJ4NjG0rDEsb0tj
+P+CJwBPM7J3An7r7petaWRGRLUKBtkzM3c82s+cBb2JxEGLAXZZ56rCgdzCD8yRGlbEZLUWygjwU
+4USWBtlXAC8GTnb3sd87M3vAVCsYZRbAvwH7s7glpz9x2CPM7HPuvilTNO0gwzI3Tzqd1loMOyan
+cf7ajJ43vwIGs0fvyB5A7r5gZgtAa+BP6/Hd5GzT7yV3PxU41cyOBO4D3JlI7NZ/A70/8O7vLfIw
+4PZmdid3/9FG1FdEZDMp0Ja1OgV4FUvvzv/WMs8Zdjd7Ghd3w7r9tXdwBt2t7jEs7g1hwOXA7dz9
+WxOUtx5dMf+GyHY9GGQPBtuvM7OvuPvgVDgyPYM36Bz4tLsPm6JqPQ3rWj2NXBCbkU/ilyxNjrWm
+BILb3KXAgQOPrdd301oTm20qd/8mkQTwZfmG5DFE0tQ7EMF3Myyh//hy4DrAR8zsd6Y8NEVEZMvR
+GG1Zk5w07QyWdh0rzGxU4DMs0P4/U6jOsDmT1UVt67pf3+/NxdgJEwbZEK3OU2Nm9yLGGQ4G2aey
+dH7tXcB7zGz3NOsgiwxmPTeWJkjbCBf3/d7caBmWvXu1bjyFMlbr4iGPTeO1bFf6bpqAu1fufoa7
+v9rd70+ci+8NfICY+nDw+uDGwJ9ucDVFRDacAm2ZhgtHPD5qjOM5fb83F6q3nEI9jur7vQmKzhmx
+rmy+Y1ja9ffUNZR39JDyJpLHZf/TkD99jRhv+AjiAhJ6x/BNiGEUsj4GzxsQrWMb7RtDHruhmQ3L
+PbEa/XOzb5TBaRiNpXNJ7yTnsDQoPGrYiqt0FEvf2732u8nd2+7+UXd/EHAnlvYCMeBRG14xEZEN
+pkBbpmFY17rK3Ye1lsDw6Xj2NbOj11iPO7H0Yma9pv4ZZTPGjG47ORP8YFKpS939ognLO4zF8xtP
+LHeDfCeLW0uNGFP54HwR+VliHHl/ZvRmvPYTplEPWeJ0ln6+rpXHim4Yd/8BS7v9JpbPS7EsM7sR
+cPha6jWhz/X93uzbm+QbTRthq50v+78vms/07ddSoJldn5ife7lt7bXc/QvAM1l8rgS4hZlNY/y7
+iMiWpUBbpuEmLL1gGhVk4+7fZfj4tEdOWoEcpN9syJ82+mJm2JizweQ6MnzM4uVrKO+Ja3juoJcT
+88APdhl/4kACn78mMusPBtuvM7Nhx6KsgbtfSMzvPdji+MBNqM6Xh9TjEWsob7Oy1n+ZpRnTDXjy
+Bm1/q50vvzTksUPNbOKbKEQuikELxFRZO8V76PUA6jeYiE9EZK+iQFvWxMyOAm7Y/xARcAy7YOn3
+PpYGKI8ys0kT8Tx7yGOXAp+asLxJ/Wbg39tuGpcNMizj7gFmtupzkpntSwQGa24dM7N7E8fSYJB9
+kru/r39dd3fi5tDP0XjtjfLPfb83541n5B4Sm12PB5jZDUesP5KZ7Qc8iU1o3XX3q4B3sfRc/GQz
+m2rOgxEGz5ewuefL01maCwDgzyYpzMz2AY5n6fnkgzm/yY7g7nuAYUlJRw0vExHZKyjQ3iHM7Bgz
+O2WSC8FlyjTgdSP+/B8rPP3vhzx2LeBlE9Tj94hpQwYvZk7OF5IbqT/BTVOf629wHbY8d/81Sy+8
+5ohstav1euDg/PvE8yqb2XWAtw/509eAZw17jrv/Ang4w8drv3HSushIbwcuGXhsP+AfN7ge7wcG
+hznMEMfiar0cOCD/vp7zgo/ySpYG+ddmY/bp4PnS2MTzpbvXxOd28MbDvXJyxNU6ETh0yOPDvv/2
+WmZ2IL0s5P3O2+i6iIhsJAXaO0dJdGH7jpm9Y63joc2sBfwLMX5t8CLtElYItN39bKK1eVhLytjd
+gPPYxncOqcMCcNK45UyLu/+Mpa001zazYVlnd7rPszSwePFqWrXzPO6PpXf8TCSPy34XS8dl/5o8
+LnvUc/N47RNZeiw/0sweP2mdZCl3vxx4EUv39YPM7CQzW9OUlWaWzOyBZnbzFerRJoLqwXrc3cxe
+s4rtPQN4Cms8ftciT0n3AZa+lvua2Zsn6WUCYGbXzp+r5fwvMHgz9LANHCM+zMnAFX3/bvbHP5nZ
+TcctxMweQdygG/xu+moet7wtmNnJZvbbayzmmUMe+37+PIuI7LUUaO88iejuepaZnW1mzzOz5ea8
+XiRfiP4hkXn3oSy+iGhakp+fWyxX8lSGX9C8ycxesNJFmpndlQjW++c9berwwhz0boavs/Si+a82
+oyJb3Af6fm/e+9sC/7pSt2sz28/M3gi8ou+5nTXU5RVEtuWVxmWP8jfAJ1garLxe47Wny93fyNJ9
+DRGwfsnMVp0128yOMLMXEIHfe4DrjvG0VwLfZul7/kwze9dyXa/NbLeZvR74u77nXTFq/Q3wZOB8
+lu7T44GPreYYNrMb5JsNP2GFucHdvSLmYt4y50t3v4QIkPvr5MRNuE+Z2T2Xe37+jnwOcMrgn4gx
+6dstWeJjgbPN7CNm9tA8VGcsFp4NPI/h0ySKiOzV1nT3X7at5gvvpkSA8Qoz+zkxrvpMYszpJUS3
+vlkiq/gNiCm47k50c3SGB9n/7u6DFxjDK+H+AzN7FvDmvno1F50vBR5uZm8HTgPOBfYQ3fCOJW4W
+3HdEHT5D7akNAAAgAElEQVTj7q8epw7r5L3AHfPvzet5dL5YfTcxrctlwLBW0q+6+1oCxu3kFOAF
+RLbl/uPpwcBtzOwk4GPAD4lWrwOIOX7/kHj/m+PQiGP2P5ggiZOZ3YfFLU/NcfRGd3/vOGW4u5vZ
+I4npkg7pK2sX8G4zOzaPU5TpeCjRI6JpaWuOn2OJYPtM4IPAF4EfENML7QGuRgR/hwFHEue0u7B4
+7uixxkq7eztnmP88kcCr/xj+Y6K78X8QCfPOAyri/HUH4EHEUJnm+L2Y6IHzolXsg6lx94vN7KHA
+p4nrguZ1OHBX4Btm9mHgP4nviQuJ74d9iAD05sS+vzcxzR6MuR+J8+Wt+p5jwPFmdkvipsd3iPPl
+sPPimbm791S5+8k5X8P9WLwvDgb+08w+SgSKXwEuIN7/w4G7AY8jjq1h300vdPdvTbu+G8CBe+Tl
+KjP7FHAGMazme8Sx8CtizPU1iM/THYkpvIYlS/0Zo4ediYjsNRRo7zyDd+mbL8CDiOy9K2Xw7X/O
+YFnvZpWZd/MFzREsnf7DgSOAv83LqLo0mguZbwMPWU0d1sGpRFfi5kK6qecxeRnFiXmBz1/Pyq2z
+sRM6ufuCmT0Z+BDR06L/gvY65JtAy2ynuSi/igjO777ayi4zLvvrrDIBkrtfZGYPJwKr5vUYcRy/
+ieHZh2UC7n6pmd0Z+AgR2A22xB6Xl7GKY/g5bZx6nJGD7VPoJXZqyttNjN9/+DLbNGKYy4MZPmvC
+JCbqhu7uX8jB9r8Q482bc2pT3v3yMu3tv5244bYfi9+LW9ELwEdt5wCGz2AxuN4kHk30nDiWxfsC
+ekHnStvsPy7fuoYbwNMcWjDp/mieN0vcULn3mM8Z3BdXAI9092GJ8ERE9irqOr5zXEhkVK1ZfPHU
+v/gYS//65McuBp7s7g+bpHXB3Z8NPJ9osejfRlP+OHVx4OPAHXPXv03j7r8iWjWq/NA4+3dc0xzH
+2b+fp1ne2GW6+2nAnxL7atixtdJ7fxlwv4Exj2Ntv29c9rUGtv1r4I8nyQrs7p+jN792U49mvPZG
+dxmd9vs7aR3WRZ5z/Q7ETYzmvLaac9mo8wgM720yqh6nEi3sl48ob9hnvfn75cAD8jh/+v7W/3M1
+1vSeu/u/A3ciun2P81pGvbbVbPNioot6890x7fPlRPskD3+6C9FTZrX7on+bFfASd3/Sausw8Bqm
+ZdLyVnssDNtnPwfuup3GqIuIrIUC7R3C3X/s7rclukw+hbh4uIjRF4DDFgbWPxv4S+DG7r6mDLXu
+/iqiBeq/GP4lPWxp1jsPeJK73zMHuZvO3T9EZND+PuPt37GKHShrzdWcYpkTXwy7+5uI1qGfMd57
+32zvE8Cx7v7JEfVYySuAWw88p2b8cdmj/A1x02ewLhs5v/ZagpP1qMP6bMD9Knd/GvE+fpRewA0r
+nz8Gz2nnA68BfsfdP7HKeryPaFk/jcWve7ntnZa3dVp/UUy+36bynrv76UTr+l8TXYLHPR/31+Fn
+RK+eYdP4Ddvme4lu1/87UPdpnS8n2h/ufoW7P5Bo3f4x4++LZr0vAbdz9xevdtvTeg1jlLmSZxFD
+ChYY770Zth/2AK8FbuLuZ0zhNYiIbAsWU8HKTmVmNyGSQB0B3JgYi30tYizjPkS33N8QrXw/B75F
+JK/5cs5Wux51Oprogn5XYgzmsKRoFwBfIObj/sBa5yQ1s1OI7vMNBx6bW83WxMzuCNyLGA96Y6KL
+5L7EuL5+DlzX3bdz1/GJ5YzRjybGt96OpfPpOjHe9pPAOwYv2MxsWHfhr7j719anxluTmR1KjBft
+5+5+5mbUZyOZ2fWBPyLOHcewOIt8v3niWDqHCIY+Na3zWc5Y/jBijOqNiTGrFRG0fpc4b71nO4zV
+zUkJH0h0Gb8tw6eqgnht3yFulH4M+LxPeHGRhwTckzhf3ojIETLqfHmgu6/UdXwqcvb1+xLH1x2I
+4S2DauI9/jTwb+7+5Y2o20bISdBuRxwHtybGXl+H4Q02TtyY+DoxvOO96iouIjuRAm3Z0nL33usS
+wWki5l4+T1/ae7d8UXsQESiVRBfbn7n7/KZWTLYVM7s6kZxuN3Hx/5u8XDRpILiT5cD7MCLwbfbn
+pRsV7G4lZrYPcUNrHyLA/jVxjlrTTd/tJN8cPZi4Mb+buDF/GXCJkj+KiCjQFhEREREREZkqjdEW
+ERERERERmSIF2iIiIiIiIiJTpEBbREREREREZIoUaIuIiIiIiIhMkQJtERERERERkSlSoC0iIiIi
+IiIyRQq0RURERERERKZIgbaIiIiIiIjIFCnQFhEREREREZkiBdoiIiIiIiIiU6RAW0RERERERGSK
+FGiLiIiIiIiITJECbREREREREZEpUqAtIiIiIiIiMkUKtEVERERERESmSIG2iIiIiIiIyBQp0BYR
+ERERERGZIgXaIiIiIiIiIlOkQFtERERERERkihRoi4iIiIiIiEyRAm0RERERERGRKVKgLSIiIiIi
+IjJFCrRFREREREREpkiBtoiIiIiIiMgUKdAWERERERERmSIF2iIiIiIiIiJTpEBbREREREREZIoU
+aIuIiIiIiIhMkQJtERERERERkSlSoC0iIiIiIiIyRQq0RURERERERKZIgbaIiIiIiIjIFCnQFhER
+EREREZkiBdoiIiIiIiIiU6RAW0RERERERGSKFGiLiIiIiIiITJECbREREREREZEpUqAtIiIiIiIi
+MkUKtEVERERERESmSIH2BjKzOi/VFMs8pa/cR0+r3AnrckJfXV60mXURERERERHZLAq0N55vs3In
+sZXqIiIiIiIisqEUaG882+wKiIiIiIiIyPpRoL13cNSKLCIiIiIisiWUm10BWRt3fxzwuM2uh4iI
+iIiIiAS1aIuIiIiIiIhMkQJtERERERERkSlSoL3JzOxYMzvZzL5rZpeb2SVmdrqZ/bmZXW2M5684
+vdewabfMbM7MnmBmHzOzn5jZfP77kSPKuLOZ/YuZ/djM9pjZ+Wb2X2b2VDPbtba9ICIiIiIisvfQ
+GO1NZGYnAi8kbng0ycx2Acfl5Wlm9sfu/pUxihsnGZrn7R4BvBf47YHnLinDzArgLSweB+7AQcDB
+wO1zPR84xvZFRERERET2egq0N14T7D4deFH+9/eB04EF4BbAsXndw4DTzOz33P2bU9r+/sBHgesA
+e4AvAD8B9gVuM2T9fwYeSi8I/xXwGeAS4LrAnYCbAh8BPjilOoqIiIiIiGxbCrQ3z/8jAt0nuPs7
++/9gZrcF3gUcDlwdeIeZHePu1RS2+xSgAN4DPM3dLxnYdtH3+6NYHGT/PfB8d5/vW+cg4FTgrsCf
+TKF+IiIiIiIi25rGaG8OA1rAYwaDbAB3/zJwD2A+r3sL4FFT2nYBfMzdHzoYZOdtVwBmZsBf0wuy
+T3H3Z/YH2Xn9C4H7At/Mr0lERERERGRHU6C9ORz4vLu/d+QK7ucA/9D30PFT2K7ln88cY927E93L
+jWh5f+6oFd39KuA5ed1xxoqLiIiIiIjstRRob553jLHOP+WfBhw3hezeDnzT3b83xrp37nvOR9z9
+0mULdv8kcB69YF5ERERERGRHUqC98ZpA9Msrreju3wIuz/8sgKFTb63SV8dc76i+31esa3b6Kusi
+IiIiIiKy11GgvXl+OuZ65/b9fsAUtnvRmOv1b2vcuo67noiIiIiIyF5LgfbmuXLM9a7o+/1qU9ju
+njHX27fv90nqKiIiIiIisiMp0N48u8dcb5++33+zHhUZ4fK+3yepq4iIiIiIyI6kQHvzXHfM9Q7r
++/3i9ajICP1dzMet63XWoyIiIiIiIiLbiQLtjddMf3WblVY0s5vT6y5eAd9Yr0oN8fW+31esa3br
+9aiIiIiIiIjIdqJAe/M8cox1HpN/OnCmu487vnoaPpN/GnAvM7vGciub2V2Bw9E82iIiIiIissMp
+0N4cBtzJzB44cgWzmwJPoxe4nrwRFevzceBn+ffdwCtHrWhms8Crm3+uc71ERERERES2NAXam8OB
+BeCfzeyhg380s9sCHwVmicD1bODUDa2gew38VVMl4Alm9nc5qO6v68HAh4k5vuc3so4iIiIiIiJb
+kQLtzfM8YBfwr2b2XTN7h5m91cxOB75IJBYzItP4Y9y9s9EVdPd3AO8mbgwY8H+B883svWb2ZjP7
+CPAj4K7A/wInbXQdRUREREREtppysyuwAxng7v73ZrY/8ALghsCN+tZpuoufBzzY3f97g+vY7xHE
+PNrNePFrAv1d3h04Jz/2sI2tmoiIiIiIyNajFu2N5X0L7n4C8LvAKcD3gSuAXwFfBf4SuJm7f2UV
+5a60zuor7F65++OJVut3EeO254GfA18AngHcyt2/t5btiIiIiIiI7C3MXXGRiIiIiIiIyLSoRVtE
+RERERERkihRoi4iIiIiIiEyRAm0RERERERGRKVKgLSIiIiIiIjJFCrRFREREREREpkiBtoiIiIiI
+iMgUKdAWERERERERmSIF2iIiIiIiIiJTpEBbREREREREZIoUaIuIiIiIiIhMkQJtERERERERkSlS
+oC0iIiIiIiIyReVmV0BERGS7M7MrgFmgBn6xydURERHZmx1INBjPu/s+m12ZUczdN7sOW8KRjzs8
+dkQNuEENyRJFWVK2WhRliRlY9xk1Xld4XUFdkQzKZBTJsATgOE2R3l2qTsXCQof2Qod2u8LMMLPY
+VkrMlAWtoqQsEp2qpqpqOnVNVTt17VTu4DA7UzLbKpltFZRFgaWCVCTAWOh0mG+3WWi36XQqOp0o
+p64dw8AShtFqtZhptZhttZiZmWF2dra7tDtt2gsLLLTnqaoKS6lbV8dxj1fU6XSYv2qe+fl5FuYX
+6CzUVO2aTrsGN1KZKPKy766SfXaX7LOrRZHIrynqNl85852ahcqpHVLK+yUZyYyCRDLDHSp3qrqm
+OXTPfPvPDBGRTWRmHaDY7HqIiIjsIJW7b9mG4y1bsY1WdWoAvAYqoAbzmroFdQVl6ViyCLYNwMEr
+3GvwGjdwN2oHqyFuYHjvP4uAu6prandyvIw5gNG93ZF/MTfMoz7eiZ9182936hSLF06doOg+MeqY
+ki1aKs9VBsziFkAT/jf/r3344u6Ye74hEHV1M5wouyhSd/HS8doo6giKLcWLdJyaeA21x+t27/27
+2R/9933ixobl3RJ1ronXH8/VTSIR2TLibG7GoYceutl1EZExLCwscNFFF3HAAQcwMzOz2dURkTGd
+f/75OdZiSwcDCrSzTqcCclDbAe84VhtFyylKpyprUmFYMlKKANK9JkK/OoJbnI7lQLYJtD2CbCyO
+hNqdKrfaRoBtvVbyHAxbnYP5yqCK+tQ1eG7VdicC7SL+ndy7R5kZuY5GURbUTZDcF8XG2tYXaOeg
+mprKKyqvqOvmZ/wtWsKj7LwljEQqorW6rBNep+5rwKGuPTp1GHgOtn0gwK6dRQF43X1H4o6GJcs3
+I5oA26k9B+1b+7MlIjvLL4ED999/f84999zNrouIjOFrX/saxxxzDB/96Ec5+uijN7s6IjKmAw88
+kIsuugjiu3fLUqCdddq5Rbvj1AuOLzheQdGqKcqaolWQCouljGA7R69gHm3Szc8cYJMDwybYxCwC
+xboXaEMT/3ov0M4LNXhlEfhXEWx3A+3CqcumrAi2yQG9mUUAjFPVCfMaqy1a6+lGrbjlIJuKmqLb
+gh3Bdh3duusar2tSirx5llvMnQRWU5Coi0RZFnhddJulvfboGWDRE79p2a+J+pJb//uX2GUe+ym3
+ZptZd382rdjdgF2BtoiIiIiIbEEKtLO6yuOpOzX1glPP1xHcVhHQVp2aoowgu6gi4CaBpRzgNh2b
++1qym5+WrBtoQ3cVmkDbAKutu4oBycE6YG2HDtBxvIoAtu4LtKvKSfkxd88t2rm92fONgTrGjVtB
+LxDuBr85uK4rOnWHVCVSJ1FVHaq6ymOxowXcLJFSQa89udfK3XQhrwsn5SVay0O05sfSdGOvFgXa
++XXVsUNSE6Dn5u/mZ+0ePftzq7aIiIiIiMhWo0A763aI7rbI1ngnh6LdKNHwvKSC6EpeNN2p60Xj
+svtC6NxibU2cHX/rBtRGqmO7RU0E8Sm6oRdtp1hwyrZHS3sVC+54iqC2KiElJ6UIvK0bGHsOgom6
+ulFEUzQeEWxumXbqqqLTtB67x7js2qmrGvfcRTwVpFRSpBaVd7qt3Z38s+n27bk7fDfi98Wt0VXt
+dGrvdqOPIDtat6sqbhz0t+7H/s7J13LAXedx4rVSoInIFnPxxRdz+OGHb3Y1ZC9w8MEHc9ZZZ212
+NUREZEIKtDPzHLXlAcNeRaDZNMV6nbpBtleGl+Bl6rYa97o35/bf3IU7R7MYvURfloNuA1IdrddF
+BYUbBZG2NjkRaLeduu3UnRyEdnKwWThV4XRauQW5dIraSU3asNz/vAm0C7dIYBbRcATQuRU8Ep51
+IsiuKjpVByPlfRJBtllJspKUWtSV496hqmqqqooAufYImMnbsaYmkR28xqnryBjeqev4e9MN3qGq
+o/dA1Wm6tse+r5PFTY+cNb3bFT11s9KJyBZhZicAJwDu7hNl4DazzwJ3BD7r7neZYvWW2+aa691w
+d84777zpVExERES2LQXa2aIW7Rz5eaeOzN61kSrHi9zCWkTAjTtGojsyuz/QTim6VDetyBABt1kz
+lDonUMst2bk1u6gheXQHTx2naOdx452a1K6xTm5tLpyqdNKC0ymhqHKSMO9NKxZdyOMmQipzJvC6
+yVxOzq7uVHWFUVPXVbfreJFbr8tUYFaQrK9Fu65wt2idruqcMK1phI7gPPeHB8/TgdXRVbyqnU4e
+w910oXeiy3hVkwNtxz3hXpOMKL+KseKeE6RRpDyNmojsZfaC5AuHbXYFZFu7ADQ4SkRk21OgnbXK
+2BWpZVgVMWJKVZ7Sy7rjkFNpWGEUhZFaiaI0ijJFkNtkBQeSGZYSKSXcmpZY7wbZiWi9LjFaZrQw
+WpYoU6JVx88iGUWqaCfHLQJUq4hW7fmKujQ6ecqxZE4qAGsm7OpN2dWbK92ajGN4RYz5riLRGh43
+B1IRic9Sq6QoC2ZmZplpzeafM8zMzFJ5B18wOp2adrvpRp4zlOeW9yZJW288evxsWq89R9ies6l3
+Ok6n7VTtyCVeeA7SC+t2mfeaPC8YGLX6jovsvfrPHNuMAco6LmtxOKBeESIi250C7axsRW/B5GDu
+JKAurTsvNZYD7SJ1s49HcrQItqP7dI1VEVxaYRGw5kC7aWfO7b0UDgXGDIkZCmYtMWMFpRmlJQo3
+Ot6sF13Z29TRst6uqecNUhUJ0Igx2qkELyJZmVtN3cxf3dfKDRHYxhjs6JJed2L8eSrq6CKfHEoo
+U8nszCxzs7tptWa6y0JnATCqqmah3RuvXddVN4GcV81UXrl1nd6Vc+3ea1nP63faTqdd02l7r3sB
+0U28rmP9uvJuQeZ5R4rIXsXd77zZdRARERFZKwXaWasVu6LGSAZVMupObxywGb1AO/WC7ZRbty3V
+WIfoFu3eDbKLIvVmq/aYvTq5dYPtFok5CnZZi5mUKEiUGIUbCx6Bf3KnSjWlEy3aC05tudW6jm7a
+VoK1gDJav+tUR4CfW9PJ02XVHmOd66qmyl3Sq3ZkMkt1ItU1qYj5sIuiZHZmjl27dlOWLVrlDGWr
+xZ75K4EULdoLnRxkR7DdtFJ3+5LTJFiP/dhkHTfPAXknuou3c5Ddadc50E7d8dhN4N4E2taL2Dfs
++BARERERERmXAu2sKKN5tDAoCsPLhNcl3SRn5EA7B9ARbDdZvQ06OcN4Tv7VXbdIuenWukFiiUWX
+cRKz1mKuaLHbZpihoMgt2gkoUqJMRpGg03FaqaKgE0OfOzlzd11hBVQzRjVrWMvwIprCvWhak63X
+SuwWSc6afGkkihyIFymRioKiTMy0Wrk1e4652bl4PJWklDAiE3jVqWi3q26QHWO9I8ka+WeTFK67
++Zx9HCe6mXci2VunHVOo1VVMh1a7k/qatpsx7t05x0Et2iKylVSbXQERWZ1DDjmEE044gUMOOWSz
+qyIiq1AU3bylW/q7V4F2llLzM5FaBYlWtLp6k+yrjoDRIsi2lOembvqCp5j82c2iRdtSHqcdga15
+pE1LnpjxRMsLZijYlWbYzSy7mWHWSgqzGJttULYLFtqJtJBod6DVqinLilRU0WCcu2fX8041X1PN
+19iMYS16idhSL1jNudFzF3anLFIkbSsThSXKVknRKihbBfvuczX22b2bXbtmmZkpaW4iVFVNp+rQ
+6XTodGo67bx/6oqqrvNuiG1bd6E7zh3rH5ddUy1Ei3rVqfLUZfl9sNxjoEw0EXZuvF/UtVxEti4z
+2w94FvAg4HrAAvAN4C3u/s4Rz/ksI7KOm9n1gB/lfz7W3d9hZg8EngjcEjgQ+PyQ5x0G/CVwD+BQ
+4JfAWcDr3f1TU3ip0M1epROUyHZxyCGHcOKJJ252NURklfoC7S2dOVKBdta8X2UqaJUFrSJalztV
+RVV16NQVMRd2BNDdlOFN1+xOM62VUdeOkYNN8lRaGO7RJbxFyayXzNFil82ym1n2sTlmU0HRJFpL
+RrFQUMxHUL/QcWZmOpRlQZFifuoqj7G2sqZeaALtRGGGlZGMrX8GrCbA7mY7LwrKVFJai1bZojVT
+5qXF7t37sHv3bnbNzdFqtehUFZ0qt153OnQ6FZ12RdXuS4RWV1DkgDr19lWTSK65/qw9xl1XnTwu
+e77K47Bzt3t6zykKwzxBM7UXdLvCi8jWZWbXBz4J/B96ic12A3cC7mRm9wce7u6DX5LjJEKLWRTN
+3gE8crn1zewOwIeAq/etdzBwH+C+ZnbiWC9IREREZBUUaGcxFzaUrcTcTIu5mRnKItHudGh32rQ7
+bSLQ7k1f5eQx0NBrzTbDqsXZxWParxhwXHokP5ujxRwz7Eqz7E672J3mmC1KyibJWkE34CbBfLum
+1WpTFm2KlKg7NVTE/NLzUM3XpPkam62xsohx4BZBenPZ2nSBb5Yilcy2ZrvLzGyLmbkWM7MzzM3t
+Ym4uWrRTKvEFp1N1qKp2LJ0OnXZFp9MLsr2uo+W+MLDUC+oTFEWK/UPuPl55tI63K9oLVbd+5O7m
+yeI5RRlBtifDUx6fbdDcNhCRLetdRCv2ScD7gMuAI4HnAzcG/phIrfzsIc8d58P9Z7m8zwFvAr4H
+XAO4frcQs+sQQfbViO5lbx6oy58DJxKt2yIiIiJTo0A7a67qCkvR0luWtMqCbuN1bqmuvZc1m5Rb
+qnPX6KKIVuvUN41XM145nuARaHuLFq34aS2KVOSu5imPZ27GNBd5KYESs5JUFKSiiFmu6praLbKc
+V2AdhwWnmDOMgiK1YsqvJog1SNQUJCqrmS1nmJuZY641x9zsLDOzM7HMzTDTmqFVlpgZdV3Rbi8w
+P7+HK/fs4aqrrqLdbue5rcnTbcUrLlIEx2UZP4uiyJnai0iERp5hDJoB1/kNaMaTdweP00yy3SSi
+Iydpo8lRZwq0RbYoA44FHubu7+57/Gtm9h7gC0RX72eY2Vvd/ZwJtnEL4O3u/vhl1nkNvZbsRyxT
+l2Mn2L6IiIjISAq0s5SDtpQSZZFolQUzrRYpB9HJjE5V06nqCDDdI7FYHoecLFpdyzLhdQ603XPA
+7NH9GaflBS1vMeMzzDBDi5LSSgoreoG2xYBxWxRoF1gqSKkklWW08lqiporRCR2HNlgb6ioC7bIo
+SYXlunqeYzvhOGWqmW3NMjc7y66c8Gx2LoLt2bmZ2E4qMKCqK9qdBa6av4orrryCq67aw8LCAlVV
+Lx6VmKdAK4tEWRaUZSKVRQTbKdFpphVr5vJe1L4erd9unuPnXhBuuVyKSI/W3PwwBdoiW5UDHxoI
+bOMP7leY2ZOA04lRLE8BnrHK8g24FHj6yBXMDgLuv4q6iIiIiEyNAu2sCbSLZBSpoFWWuUW3F2hD
+h7omgkT3yN4dI4pJKZpazTy6OOM50O7vRk4kQfMZZuoWLW9R0qKgIJFiTHMuL1qIC/ASvMIsWrSt
+adFOFY5RORHsVkDbsbbjVa9Fu0hNoF1DM91XioB3dmaGublZds3tYtfcrl6gPTsL5Bm63HOLdpur
+rtrDlVdezp6r9uQW7ao3T3ZOetZMadYqI6laKiLYTilFN/dOjdWLA+zefF25F0A32IYmACcZRc5D
+binnnksKtEW2sLeP+oO7n2lm3wZuBvz+BGU3wfMVy6xzZ+JOpa+iLiIiIiJToUA7S91W5DxNV3cq
+qaZFNWcbtzqPM85BnpMD7giyC/Jcz93AMSdGy2UXXlDWJSUlZV1QYBR5vmyrHScCaK+hrvKc1244
+BakoYz7r1gztyinKmtSOebq9jsRoVdupK6CKehmJZHVEp96XY8gi63iRLDKuN2PO802EOgfYVV2z
+0F6g3Z5nYWGe+YV52p02VdXptdxbk4HdKIuCsixotZpAu6AoElYU8dqaRGjdzOSDPcD7u4333oSm
+q3hKOSN5U28R2arOXOHvZxDB7Y3NrHT3zirL/+YKf7/FBHWZgppIfr6SpseSyKALNrsCIiKb4oIL
+LuCCC1Y+By4sLGxAbdZOgXaWmkmZ3airmnY7rvlqr3MSMesumEW3ZzeoY8y2WQ6mLc+XnTuNW/49
+Qt4ItBMFyVNfkO2kutOdn7pjNU5k5K6qmqp23IxUlLRmZpibczq10+5UFGUbLPpve+3U7RrP81F7
+1UTw5C7wUSFLUfeE43VFp1rA2oZTUdUdOlWnG2y718wvLDC/MB8BdzfIjhZys0jclpJhRSSTi273
+BWWr7I7PTr00/BE/V44XRl0YVeqbJ5tewF/XNXXV7L9ed/FoNbduLwQR2ZJ+scLfL8w/DbgmcNEq
+y790hb9fa4K6TMlqX4qIiIi8+c1v5sUvfvFmV2NqFGhnTYs2DlVd0+50qJtuy+T5n8mZxZuAu2kg
+tvioyAcAACAASURBVNzdOVl3Hmn6nhetyrl1uU4UXlDUKTqMO6S6JtXNuOWKyisqajodo1NBlVvM
+U1HSas0yO2ssdCoW2m3KhZRboSN4rTs1dcdzsF3n6ba8Wy/L01JbTYwr94pOp43jVF5RVB0W2u3Y
+FXkc9cLCAvPzV9FuL9DptGM6r5wFrUlU1mRKb7UiyG7lZHKpKEhljC9verB7DXVRUxWp2zLtnod7
+54jb3SPZW0137DrNePiUg211HRfZylaaomutqlWsu9516TIz9t9//xXXK4qifx5QkSUOPvjgza6C
+iMiGevKTn8z97ne/Fde7xz3uwUUXbf2b2gq0s6YFOqaeqmnToarraJFNiVQkcmjZDbjp6z4eGcih
+yPNn96Urz23ZOdCmINU5y3g33ZmTvMbqmto7uDfBbJNtPMY6t1qJOQpIJZ2qEwnKFgq8qqkgWr77
+gmzvOF7W3db2wpopqHNXbyLQrmqn7lR0qg4pxfZ6U2hBu9PutmZ3qk5M5ZWnvrUUU6MVhVG0Eq2y
+16LdakWWdCsK0kCgXRU1VVHRKWI6sGggz9nc8xsRwbbjuVW+STxnZpEd3tR3XGQLO4iYvmu5v0Oc
+aFZqnZ5Ef5nj1mXNDj30UM4999xpFSciIrJjHHLIIRxyyCErrjczM7MBtVk7BdpZVUdXcSfhJGpP
+0b3bo+tzAb3EXymR3BbNQpVSHnOcYvHu9FPWGxrtNV4bVkdLdmmxtAxmUsw3XeJUwAzGXDFDlXZR
+pd20PdFu1yx0aq5a6LBrtqTVMpJV7FmYZ6GuWKhqanNSDd6p6Sy0KVJB0Upg0aqeaLKk0+0C715T
+5/pVdRUjypus3mZUdQf3CqfuZfxOvXsJRbIcYCdmyiKWItFKCcsLKVGmRF0k6qKgVdRUeTx3VUaw
+b7V3ewZYaoLq6C0QXQpydvL8TonIlnYcywe3x+Wf359gfPY4vjVBXURERESmQoF21gTaEWAbdZ1I
+VaIuS8o8F3adx2onSzH2uS/QbrpQp9ziiuVW7xwgujtU0T/a6kThUJjRMqeVYNaiZbtOUJtRJ8Nm
+ZmB2X5jZj5oW7cppd5z5doeZllGkGuoFWlcae9pt0kKbNhXJHTo1nYUOZQG1ebQ8p4Jksd1kCTeo
+LLqHe93JWcbzTGAWAXJKRlXV1HXde6HdYDuC3rKAsjBmWomZMi9FQVkUUES3cYpEXRXUyamLaNEu
+y4KySHTKAqudqqpzoE23S3m3F36eDKypoDXJykVkq3oM8IFhfzCz44CbE3fMPrlO2/8M0b08jVkX
+ERERkalRoJ11qhjuZ1aT6tzqWyRyjA3JcM+ZyVN0Am8CPvBugB0tsXSbeyMU9Zj3uqpjlq3cbl5i
+lAlm3Jlxp7Q8m1dORlvunqHcvS/F7mtCmqVTQ1XBfLtDYRVeL9Bp/3/23jbUtjXL7/qN8TxzzrX3
+OdUNXdVS1bYEfKE/iCaiQdLxLSZCY7AkMbSJKJrE0CRRBBNQMaHTHdEPopFogoUgSaBjIEHpKCqi
+aNP4KUXoEMkHg0qwyxvTrVZXnXPWmvN5xhh+GM9ce9/bdW9Vdd2699yu51fMWvtlvcy9zuHc/Z//
+Mf7/16g4es0gNfrpaBt2CFYgtEJVNOI+cl1VsZTYOI7HU/CamyOaaeFaykg+zyqve0r4s+n4UuQ9
+bnY62rUooQpFCS3UErgXfIyO3/u2LV873/9U0PrM0X4+xv6ufu1v+9+KyWTyS0SAz4vIb4mIP/eu
+b4i8AP7j8akDX/h2nEBE/HUR+UngN3+dc/kC97SNyWQymUwmkw+HKbQHd/kWgTN+4/J4ChqLp6Zn
+QZ79ShZf41e0MTL+rEX7qe5rPMdwlRWnDNG9qAx7WJBFWB8fWV++ZH353YRe6OZ0c5aj8er1Iy8f
+H3j1cOE4Do5u1L1ldro5fgjt2qkiuCpRI/+05dkPe56zjFx0zfOJSCf7mcp9FkKW+9hRcu5dEZai
+LEVYi7AUoQxH+iwQzwRzz8qwkWSeQWp5MaNWvSeKm2WauRYZzvZ5qs+rwGK889PSnkzeUgL4IvCf
+icg/Avw54CvA3w38a8APjPv8RxHxv7zP4z8Mfh/wjwGf+hrn8iuBfx3428e5zvHxyWQymUwmHxpT
+aA/iSXXm/0eGnUWcYvMeI/ZMMj/9LngK8TjXid/3lYZTS85F6xC5KpEO8qJoFXQtbA8v2B4/xfby
+uwldOVqntU4gXLaVbVu5XDbW68KyHxTNvfFogd0MCDqKlUIsQSwQEoTECHbjnuSdAWOR4+UaiIzm
+b1FCfPSIZ3BZcYUaSAQFWKqMnWyhjCoxxki6h+OAe04NnId5VpiJykjeFUbueNaGKUgZu+A8XRM4
+Z8ljyuzJ5G3nh4H/AfjdwO95z/eCFLy/730e+6G4yxHx10Tk88BPkmL797znXAL4sfF6U2hPJpPJ
+ZDL50JhCe+Dj9zodKjnuY99DPD+Xe2d/c5wBXePTOPeIz3TyfB65i/Tnqd8pYnO3O0fPSxXqUiir
+UreVy8Mjlxcvubz8LkJWyrGj+45H3EX25ZKCe3mzU0SRAO+B7zkCvohii+Nb7oiHQmjgxD2wTXTU
+m43zLnni42cRXPL7Z3+1uKAhlFCKkCK7CKsqdexWI0FIimwLsAi6G+aWt2Ew9tpr1efXMvIN1CG2
+R+Lc815y5Kwwn1J7MnkLyWuOKXL/XuD3A78J+BVAA/4S8IWI+DNf7zl+Cd/7xXeO+CkR+TuBfwP4
+x4HPkYnkfwH4DyPivxeRH/1mn3cymUwmk8nkg5hC+072mZ5T4PcqqiE4I4aufpe/erqqwZmH/eRo
+x11syxDqMsRqBo0VRCtBxzCa5254RIFQJAqiG3V95PLwktAVNJ1fM2NdV9Z1ZVs3tmVhXbK3uqrS
+I4iWVV++OL47fjje7gXa4E6IEvrUT82ziwLhgA8xG+8eH5eS+97nu7YUebebffZgx6gdi6CH0j0r
+xLplddp5jUKGs5/nwz3dHIl7bRrnu/4ukT1/J55M3iYi4sdIh/j8/BeAPziOb/Q5ft0HfO+vcf5j
+/c2d15eAf+kDvv+u855MJpPJZDL5VplCe1BkvBVjITvcU9BZOsNWjJCA0Kfd61E3dcrtZ8PPKdIZ
+lVgxAshKpcpClQtFL6hs7IfTducr+40SQVkqdVlYNuMztcHL4EEWSlnR2im1U2ql1oVaK7VWlmXl
+sl148dAxg+6d5kaLzuqCtsCvnUNBrVIiMClDuGb1V5yOdl5NwDy7uM0cs04zw93HmLsgpSAoBdLF
+HpMAbo5LRv2GCkZgZPDauWPezbDu907t8Hzc+ZoIRBmp7u9Zgffzj4iYjvZkMplMJpPJZDJ5K5lC
+e3AK7QgnwnAXnFE55QJuz7YGdbix94LspwC18eXnIWqIoJoie9H1XUL7dj3Yr8btKzeid0qpaF1Y
+N4MXBw+fhu+RhaWuqDVKbdRan8R2WViXjcul86KlS3y0RmsHRwN10MOxa+eQoBCYBqVyD2QTJUfF
+nXvFV+9B70bvDTMjouMRI4BdKZp1ZIURfgbggQuEZfBZaL6HLoGj2PMd7ZHAHvYktMMdt8gnG+Fo
+MTz2U2xL5G54fn5e1phMJpPJZDKZTCaTt4cptAen0HYM86zj8ojR7xyZ5nVf0R573MTd1VZ4JvtS
+ZLsH4UHUikqh1oWlXKiyUfSCyMbur/jy1fjyl68ctwMtC0Ury+o8fLrxPTuYVOSZ0H4usmtdWJeV
+h82wnmPe++3GEVB6ildaYGJ4OEWgVPBVRsBZ3OvMfPyY7tC7j/C1hllHZHRcjx7uKmNcnCF+x3vh
+nj6+ObhKTgWMce/uPsbHh9C2J6Ed7oTlJAGRCehRniLnzs7ss0t79vBMJpPJZDKZTCaTt5UptE98
+BJ+Z4JZCM3DCcp9ZYki+iOGkylNQ1z0JG3zEj4efMeRQUBatPNSNx3rhQR/GceH1ekHKislKwxEv
+aCje4TDoo2IMHBUomp3Vl23lxYtHvuu7vhvVwuVy4fHhgev1xpvXr7m+LlwrNG90yd1oP5w4FN8V
+Fs9ReB21X5HVWudYd++N1jq9G+42KrvSzRbJUXEVRca5hUOPwDzoEnTJ0XHGERr3ULnc9dZ7eFy+
+b/n+jusWRIC45HPEeA8iMg0tMq18qu3JZDKZTCaTyWTyNjKF9sB7qjY3MAt6DxzPTuuQDP+KQOLc
+DNYUnqNbOjjrrJ52i8lKaKooW1lSaC8XXpQHHksK7cv6wLI+oMsFafm8HopR8Bij12GAI+IUhaUo
+D5eNly9fYtZZ15Vj39n3G7fbjVdfqXx1FV6V4Lpf2fvBrXVaczic2B1fPOfKC8QioNDcaa3TjhFY
+1jtmHeJpNzvVrfJUap0BcB5GWEYKd4JOjo5LEfTek83o4S4IuQMf9pTU7pKOfIzMNs7087PP3Mcb
+K+P9lam0J5PJZDKZTCaTydvHFNqDsCG0O1jP4C7HEBfElTLGo2MsMYt4WrySYpJwfIyb4zHEudwd
+7VUrD2XjxfLAY73woj5wKQ9s2yPL+kBZLlBjjFELnYIhuescBhjKqLyqysO2YS9fICI8PDzQ204/
+dvb9ypdXYS1BoVFeO/LG6Af0ZrCUFNk3J0oQS4CnWdzM2VtnPw7cDPdOuCHEcKCz/zt7tzUrysaI
+vIdgw9VuEbTIdHNFKRoUBZAhtDVTxi3GaPkYsb+PBYxZ8zF3Hn6OtPuTzs+nm0wmk8lkMplMJpO3
+jim0B9Zyw9pGIFe4p0d97ln7034w7lnVNbq8ZAyTRwTuTnhQQggUCaFIYSsLj8uFl+sDj+sjj8sL
+HuoDLx5f8PDwgu3hBYfJSDkHkYKWOurAcjdai5ARZAuXhwuoUpaVdtzox41+XNlvheg71q60faO3
+nWPX7Ma2IFo62qEG1WF16I6L3N3sY2+435enUU2hG3q+W2clmHLmrjuODbFtQe65B1ACHY501nYr
+pUDI6PEezysuiKWw9ghACR+TAqeY97NTmymyJ5PJZDKZTCaTyVvLFNqD49iBDEOLsAz9UqHcq6uG
+snPuI8wpwH10bp9VWDb2iQsZknYK7ZUX6wOfenjBi8tLXmwvedweeX3beX298fq6U8pC7471QER4
+fHxg3RZqVUpVVBekKiwV6kJZO8ulcdzesF+FXTrehVqEqqOze/R23wuoLYjDCTGsdKIUvDS8G31v
+tL3Rjpbj4kK6+MNpjjHWfXaJh+RIeKgTqoSOBWsfu9TvXaOWsxNb0KJIGQ55CF0MU6OXDErrEfRw
+rDsew3LPh957v0WZTCaTyWQymUwmk7eOKbQHrR35geTuskrk/vUQhzIc7aztSis217YVH2nb7tkT
+jcc9MCzgLrQf1wdeXl7w8vElL198iheXF7y63nj95sarNzuqld6M3o0I4fHhwjaEdq2KokgsOcq9
+GIs7Fzf2N4UiHbEb/aYsVXM8+37+p6CF6MN/dkA7URSqYqb0veVxtJzQHvvn5GT8CHlLBzpECR3d
+26rpUKvneyMy1qflLrjHZ3cnXEQoUilaUFEWNaxkX/nROrSONce735vKAxlPdwaqTVt7MplMJpPJ
+ZDKZvH1MoT04HW0tWX9VFsYuMU+O9pmIPUaY3cdYtDvu6WabGXjkDrIoIVApbPXJ0f7Ui5d86lOf
+4sWLT/H6uvPmuvP6mkK7HZ3WOuHw+PhwF9qlFoooOo5RLgYEtyqI7cTxiqOmo13KWd917lTnzxAd
+cCd6utAUJapiJrQj3ex+NERT3AsKovck8DM5PO4iWkYcuY+Px3v1iwT2s48RihaWWqklD1fHi+HV
+0duBebAfHe8+zPi4P9EpsmUK7clkMplMJpPJZPIWMoX2oCzjtkApT2JVNIWtoPc9bM5aqlE7FQHe
+HWtO2w0s0FIoCqKChlKoLGVhrStLXSilUkph3VYeHh54+fIl4XAcLXekzdm2hWWpKfhL1mmdDnCK
+eyPccmccRkBZRaRA6F0YxxDGZ/v0fef8MHzvRBGsCW6dGBcKIgyLIMyhOCoLVdP9Pp/LydFxRwjJ
+Ki7VwlqUhZphaEXRWtCiI9ht7GuLPJ2f5/RAWRakACJYZCCduZP/CwwfLnteQChlzo5PJpPJZDKZ
+TCaTt48ptAf1LrTHjnPJdGzuO846+pyf72jLcLXJveLDaLcOHWp1omZCuHqhSmUpK0tdqXWh1oqW
+wrKuXB4uvHzxAgL2285eD3oztm1lHUK76BDZpaBS8N4Qj7vQhsiANi15rggRgg8H+hzjPi8MRATe
+DL9lhVhfBI9OjAjyIHBxHKAWSlG81uHsK4GSpV+kyB7udtGSe+3DcZai9y5tH8LZ3Dm97YhMaq9a
+qKVQtYAq3YLWjW6Wu9phRICO16glxftkMplMJpPJZDKZvG1MoT2oa94WhUWFOmqo4i6ydbjZds/7
+yjRt7o52P5x2NTBYl0wflxDUnxztpSzUUp8c7WXh4XLh5csXeR61UEqhtZaOdh2OtipalFJKimnv
+OTzuBm5ZLy2KSjrawekYj9uzAzsYXVmBN8foKX6r4GqE5o762Qme/dXOUiuxZsr6k0M+UsdF8/lV
+KLWwLpWllqwEkyHEgS4dzMY0wDg3ByfQRVmWylZXVJTWc1e7NUO85/laPHOz832aTCaTyWQymUwm
+k7eNKbQH6yUd1iLKIiVTu6Xg6LM+67jXTDmOuSBdMOHuZh/XjnSwLevCVIXiSpGSjvQIEDMzWmuY
+GxCo5E54LYWlFojc83a33J1eDrymsFcNvHesd7x33EaHt4OHjNCwdLdrLSxLZV0rrS+5/90CC4ee
+YtrM6VXwJZCVTAQ/He0gk8tLPp+WimgKaycrvJy472trKdS6sK0LWgo+dsl9VKFFSJ4nTkRgYbnf
+XirBGLWvhWWtbJcVc0e7ZvVXFyhCKSXH5GfH12QymUwmk8lkMnkLmUJ78PCY7mihUKRQqQiFwwIz
+6OZ0d8wNGx3b9Hysh9D2FNnH65biOxxKUBahhFACioBE0HuDK7TeefPqNW/eXLndbhz7kYnjHgiC
+dWO/7rz66mvcnGVZWJaVWgvRO9EbdMO60ZrRmnP0TP4upbCulcu2YtaJ6BRxrjfhGmA9HfeIwCww
+S0daUJYquJ5d10GpheWyUC8Ly7ZwtoX5SEiLsbPOEPelVGpdKUNo+3DHnRTcFo73wMNxy1H1ojk2
+XrXiBKUUtsuaLvlRkVagHVk2pnkC7h/P35XJZDKZTCaTyWQy+SCm0B48PKTQViqFSmEBV+wwjpEm
+/l6hfY5Aq5Fu9pvG/rqhXTE1WKH4ENqMPm6c3hrdOnG78fr1lesptI+WdVaesWW9GbfrzquvvsJ6
+Z1tX1m1lWRbEHHFD3end6M1pzWktcqxdC+u68HBZIFaUTpF0oHt39v1pHLt70ItQ1koRpSw1a8A1
+CIWyFNbLyrIt1HXJerBwYhz3BHSRIbQXal0zTXwEmXmkO5477Y5IOtrdOt6Nounm1+GYay1sqpRl
+QZcddgHl6b2/J6BPJpPJZDKZTCaTydvFFNqD7SGDtTQKJRY0FsKUvUNgdDfMPN3Y8KdRcgORoO19
+iO1ONcW3HM0uLmgISmaCEVkD1oZD/ubNjeubG7frldbsXiMmKNY6+23ntQreO33b6L2zrQsF0iUn
+3enePV3t4WhrUdZ1wW1FcIo6RYLejX3vqIL0ICzSXXYoBopSS4VFoELUFNr1LrQr5p2w/DkiHOIc
+4j5H1iu1pPOejna+Z+ZO94J6gd6zd7x3eu/UXmm902qnSo6d11VZyXHxGA566z3/HEal2mQymUwm
+k8lkMpm8bUyhPehjDFwJIpwSjhspsL3TveNkLVVRpQDhnp3UFuBQVFiWQq1KXbOHWqsgGiCOR8es
+0cxpPce899vO7bZzve70ZoAgZJXXuhR677jZGO/OfWqzM2E896KfdqZzNFtUqLWyrhvhdmaO4xZs
+m7GujWVRlghcIDRo4+dSF+KIsSudu9mlFkRzT7333Cs363ehLeMVhByZ7240y7C2kJFgPoS2j+kA
+93GxAu473GciubrnzwT3DnB51gkeHvSWI/OTyWQymUwmk8lk8rYxhfbALOeQ49lItFuONnczuvWn
+sK9S7snj5kG0QAJUU2AvFOqiuZ9dQTTGuHUK9t6NozlHM/b9SLF9vdGaZWe3CCqFba30vmCWu8xu
+jvXACmjJtG/J4u8RKJ6vIyqUWlm3DYYQFrKKbLs11rWyLMpKjobToYhkwrql0NYq6JZ1YqUWRAQP
+p/WOe8c8LwCcndhC1qCZO92M1jt3GT3EtpkNkT5uw/H7WY9gNjc0ChKBQraSqabQLgVphjvZWd6m
+0J5MJpPJZDKZTCZvH1NoD3pPoa0RFE6hzdjNTmGppSCandKC0C1wN6IH8szRXrRQ10JZJB3tEoTY
+k6M9wsv23dj3Z452T6GtozP7aAu99yFM/e5mmwVFBdAU2VruKeApfJVaFyQMjUDHfrhbsK17Cu1V
+MLLjWjS/byKYQz+ATe5j5GUpMJz+boa74dbzZ49ApSCannaOhxtt1HiJBCIpuP25yPan8fvcdX9y
+vYuPHXDS0c7d73xfGI62daft/eP66zKZTCaTyWQymUwm78sU2oNjz1slKAQFI0IwTwEuKncBXEtF
+QgnJCizB8nu1ULcYQlvRRZEieb9wunVab3fB7H7Wcp0uerq/IqCawr0UpdZ00Wut1FopNTu4pSii
+cneM3dN5z4AyEC2/6EDk3mudQna81nDosYBwYjfsprCMGi9y/B0JIlJsh48gtDTUUc3RcXs2Oi6S
+jdsQ9NaxkZLunmnjokqRUSk2hPQ5At/d0GBMFNh9L/v+nvlMQ5tMJm8X77zzDt///d//cZ/G5BPI
+Zz/7Wb74xS9+3KcxmUwmkw+JKbQH+y1vVaCIUyXjvTxIh3hZqFpZykLVBQkh1HENrDhaoQqgQdVC
+WXM/mwIuQY8cp2690y3Tt8+95ruILwxRXbP7ess+6m1buWwr62Vl21bWdaUUoRZBlBxL93TLez8I
+t9wd9zx/D7AY6eIeNHOOnh9nu1ZKYfwpytuu4JIXB2RTqCAFpObrneP1IqAhlJJBaH4ms4eBB3LG
+g0cGmfWerna4IyI5hi95EaGUchfb5o63DhhHOzhao7WWjx9u96zRnkwmbxvuzpe+9KWP+zQmk8lk
+Mpl8zEyhPbgNoV00qBq4Wu4lj97mqkLVhVoWliG0XYNSAlWj1AAFCtSilFWRRaGmo21uHL2z9o6Z
+ECGZ1i3kuHhJgVnrENlrZdsW1m1l2xa2y8q2bWyXjWXdUPFnbrHjYfQhtImhruPpQ3smtpsFh+Wt
+nyI7MtDtFMWO4yb4IbAKsgq6Sopu/O6iq2Z1GaKI+3C0c3w8ImA41+FxTxj3buOx6fhryRF1LbkT
+DmP3PTrucRfZR+8062MCINKOn0wmk7eOv/njPoHJJ4p3yP8ATyaTyeSXE1NoD87R8aKBF8dKjm6H
+CpRTaFeKFJSCRAaWZfp3IUZHtURQyzk2TorsM4m7N47WCVciCoy0blWhlIJKUJeSe95LZVlTcK/L
+wrqex8q6LYR3PIxww2Mko1ujW0NG3ZZGCnqPwD3e42rnwRlFFhmWFmMk2zzoLeh7EIugD0q5FDQ0
+LyhIIApecvZc3PGQHF/3jnTBRXO8O1PjcvzbDDeHvK5AGWFrRUuOj4tk2Nm4f+/2JLJPR9tOR3sK
+7clk8ktDRH4F8H+MT/+FiPhTH84zK/CzH85TTb5D+H5gTkFMJpPJLzem0B54ZIJ1pAGLebq1WgtK
+RUUJA8OJaBBkAnd4JneHZPJ4CCpCRFZxtdZpR+PYG/t+sHCAVIQgoo4daaEWJUpQiqJFUAW9V1s9
+3er42MJp7aD3nevtxn7sKUTNKKIUyf3tHC1n7Igb3Z1ufq8Yy0AyUiSPfnAfI+U+OrbDZCxhC1RB
+KkjJr4lk//gZaNa7ESMVXETuol+C4UQ/VXapKEWEcj6H5V629aAdfbx3PXfbbYyde6bBx6gxm0wm
+k2+RGfYwmUwmk8nkQ2cK7YF5Cm2RrJrCUgAvCFUCDU0Bbo55ilGPsS889J7c/wd44GY0OkdrHEdj
+vx2scqAa5Cqywjl+XQtBuuHlFNQKomRXtpxfy1t3p/eD237ltl85jj2D1rohhRTsQ5RD3EPKunm6
+2pa72uaMcLFRD5Zz5JluDhhBdEmTpqbQVjTPf6SWI5J1Zw7hRseR6HnOoxM8LxqMYLkhrovq/Xsp
+9vMqRzuM49a43Q6OvY0QtKek8ruTPXX2ZDKZTCaTyWQyeQuZQnvw5GiP1O3TMZWSorIqYVkr5S2T
+r0ODkOyizgYrQUJR0t21bjRvtKOz7ykaD22Umg52qRnXrSrUqgRDIBd9crMl97hPV/sUzx45in4K
+7SdHu2cNFtyDxYKn6qy72Lag9bw9Lx4Ip4aN+063Oym0i0B1WDTrynwI7LwScHe0s/M7CHMgR+LL
+qEQrKhSFMirMUmTnz+qng+5OPzr79eD2Zud2PUYF2BDZKkhRtOp0tCeTybfC/AdkMplMJpPJt40p
+tAc+KrHCsy/a3VFXvGR6N54ObpFAi+LqWBhG4OaoRGahCZTh5J6/xnlkSFhzo3nuVYsa4kaEEeIg
+jkqmd9eaO9p1qZT6VMuVB0CGq+1t57pfuR17hoRFDAd7bF6PPmp7tqNtIxgtV6fPMfEYKegpmUXO
+9yF/bgSiOX44cTPOH1Q8EI/RCBaESF6I6Ib1HB2PCizpaodA5Lx5OtgehNlTZZel2G5Hp7dOPzp2
+ZKhaiu3IkfUAR5Dykf4VmUwm30ZE5AeB3w78g8DngAvwN4CfAf4b4Cci4hee3f+zwG8C/lHgVwLf
+R/437eeBLwJ/GvizEfGLRsNF5HnylAB/QkT+xHvu9oci4sc/lB9uMplMJpPJdxxTaA/uQjtSOLsP
+F7VGCu1IsShVkSqEB0c/cDvuQlt0jIFrCm0Z4tgFnKCPUDRxQ91QHTveYQSGSEGLsCyFbV1Za2fX
+vAAAIABJREFUlkopFSnKmNXOc8Xp1tj7wZv9yq3tdOu5M13SJfeIdLDN3z3uPgR07m3LfVz8HBk/
+a77iLNoOEAvogR92T1aXKsgYKdeU57gE1gxrjjVL952sLRt+OZz3BdwMbIys+xDrTvZtt4Y3I7qN
+8xvn6Rk4pyPAbTKZfLIRkQvwnwK/dXzpuTD+vnH8RuAzwI+PxyiZHiXvuT+kSP/8OH6niPymiHjz
+nvucj5H3fD6ZTCaTyWTyoTCF9uA0PU531boTomOcOQWgVqWUStWSghyn9UZ0J0pOURfNtHI9XW2E
+kNx17mH0MCQMjU5xxcNwnMBB09Felsq2rSzLQqkla7BUQLMqLHCad/a2p9A+9qzTgrwvZxhbpnzb
+cK3N496r7XexfVaAjS+cnWDjJsPUhehB7HkxQirYKogJWL6WkMFnvTl2GL3lCLtqxWu+tzFENmdY
+nPu4qGH318OD3gxrljVgze8XAsIZf2MDF0dCP+q/JpPJ5ENEcgTnzwO/gfwX6K8Cf5x0pN+QovkH
+gR9+70PJeZv/Efhvgb8M/BzwKeBvBX4X8GvG8/4x0il/zt9FCvj/brzuHwB+8j33+Rvf6s83mUwm
+k8nkO5cptAchpxjkPqp8jlaHpcsdClqEUipEoCiMhO0CuTt83z8eDq6cQtvpOC0MDcND8Rw8JzAQ
+R7RQqg6hnXVedVkotSKao9ceTrhnEndvHO2gWyMiE8ul1rEvnj9Djo2f+9nPBfcZ6JZHjDC0u919
+fzMAh+iBj+5sVkGO3FVH5b6fTgh2+BDbucNu1bHFsX4qZc3R9LHPbdYxs7vfDTJcfr+Psd9F+HlO
+HuBC6DShJpNPOP8yTyL7Pwf+mYhoz75/jo3/QRH53PnFiDAR+YGI+N+/xnP+NPAnReRHgR8F/jkR
++bci4n979vi/IiKvnz3mSxHxVz68H2symUwmk8l3OlNoD0rNhV/BUzg6KeY8x6GbtAw6C0VJZ9ea
+Y8c40OyUJse8Q5QQwRV6CZo6hzQOGpqFYelii2catwalQK3Kula2bWPbVrZ1ZV1XSq1A0Lxnmrkd
+eKRArarIUhFRoizpYo++aSTHtLNDe1R8mdF6HjbC0OLc0R51XE8J5Oe+N4Tlc0mLDIQ7bAxuDpkc
+ghmEjfcOsMNpYoQdlKrUKvSlAIFHx92IcIoqUiRvF+CSKl8KuIFZ7nBn+nmO70uZo+OTySeV4Wb/
+flJk/yzwz79HZL+LiHjnPZ9/LZH9nD8M/F7g0+QY+R/5lk54MplMJpPJ5JtgCu1BKUNop/0M+jTK
+bN04oiFRUApFMiHMWtCPoO9OUUnDFgUtQ2yn0LbidDUOOgedSsGpd6GNRu4dF6jLKbSHyN7yEFUs
+Ot0azY4cWXdDCZaiFCmUIkiNrBOTRtBG6jhYPO/PHkLbLEfjLR1tQTiDvHOPOu4Of6aPjzHzu9A+
+w9lkjIVrjpi7EKFg0I8gvNMPoy6FviilOqIx3PxMmpNlJJSXkonr5Ci+LmA9kB5Yz8A1ycS5mTo+
+mXyy+VXA95NC+z/5GnvU3zBDtH+WHB1fzi+TAv7TZFjaZDKZTCaTyUfGFNqDUkclVgAlo7kznCt3
+q6MLSqdIxYbQ7sPN7ruzVE2BiSJSCJV7cJiVoIlxSOeQxioFE4NTaEogMoR2VZZTaD9ztJ2g90az
+zq3tNGu4G0JQVVm1sEpFQ1BVIoJu6Tg7OS7efLjZZhzd6N1wewohU8k6MJXcBfenM8zxbcvp7xTZ
+4C3fsJAhshnT4QZEJoubO9ZyDLyvhbIUyuJoIS8wSIruWkr2a9cCaHaI10AXaN2hObQR1qYyqss+
+6r8lk8nkQ+TvefbxT/9SnkBE/lngdwB/P/DwPncLMkjtI8KBv+kbuF8Zx2Tyzte/y2QymXwH8M47
+7/DOO1//38TjOD6Cs/nWmUJ7UM5cLb2vGxPyFGomw2UlMjCNEMIyzCtGune3FK/SI/eaFehQpFCl
+ULWwSOVSNkIFXXMc/PSD67pQt411u7BtF9ZtY1kXlqXmyHgz9nbjenuN9QMh2GpF1KlRKGga5CL3
+3m3R3BkvpVBrZVkXtm3l8eHCoR03xyz30DMLLeuznoWOj93tMSEeQZjk/dvZJS6Ejj33eFZDNtLC
+GZVpkK6/W15UEIl0rQuYBl4Cr6OaOwvGU2zDqD5zguf94lNpTyafYJ6L329KaYjIBvwXwA/x7lSJ
+9+P9RPi3iZ/7aF9uMplMJpNfBnzhC1/gx37sxz7u0/jQmEJ7cB+ZlhTI56GStV5FSo4qj/ovPOup
+wvwelta7cfSGNx2OcBASFDSFtlS2stLDiaLouvBcaJd1ZVlX1m1Lob1urENoW+uYN47jyvX2GnpD
+JdiWilikkz2s5zSmBZFToJZ7N/dliOwXj42lNHp37Dzcn3a2OYPhzmounv06G9CFaKTQLuPe+tQd
+LprVYfThmFtkjZk53hTJ6XpUIYrgGlgBX7I67F4HpoJUpZQMQGMIbRWZjvZk8p3LH+BJZP9PZFL5
+XwT+ekRczzuJyE+Rvdwf2b8WIsJnPvP1DfRzVWYyOfnsZz/7cZ/CZDKZfKz8yI/8CJ///Oe/7v1+
+6Id+iJ/7ubf/ovYU2gO9O9pxd7RdYojB0aEt53i0Zwq3Oe4ZJObmWDdaB1fBcDwcw9PR1krVysOy
+0XNOG10XRpM1EkFdV5bhaK+XS4rsdaGuBXXw6MPRfsWCsAJLrSlWPcfdPfwushnOtqpSamVZ0s1+
+uGy8eOwspdKa0dvY2W6dwLCzs3oY1Fnf9QwfY+Q9ckSe4XYPJ1ruiet59/DICjSToZX96X0tWWlm
+NfAlA89UIFQIERBFywhqIwBBGRcSptKeTD7J/Pyzjz8H/K/fxGN/Jymyfzoifv0H3O97+Ig7sr/v
++76Pn/3Zn/0oX3IymUwmk18WfO5zn+Nzn/vc173fuq4fwdl860yhfWLjdzFPfahj7Dpdh0rR7M9O
+Z1rxCMo5Vh4pJrsZ0oIO+NmOLU5rnaOOY+xIt8i6LyBfcLweoy87O7NzHDvCcO+YNXrbOdpO1YqW
+wloq6mA9+6idse8tgqpQS2GplXVZuGwbjw9G64GHsO+N1jrt6BxH43o7CGlYOJ4ZbdnNHU+SNvW0
+IA508sLE+XusnGJ4fDp23LOOa4ygy0g0P3fYIwPovMUIl/PsH1dPt1wj7yfcx+EVGYFpU2hPJp9g
+/uKzj/8h4Ke+kQeJyPeQwWcB/NkPuN8L4Ac+4KlmP+BkMplMJpNvG1NoD/otRW844IqgaFFqXal1
+odaFRRfWUll1wbvTW2O/HahK9kL3gN0yUE3TlaXIUx31CCjbe+d67Ly6XSmqlKIUVbp3unWOdrAf
+NyzK/diPK63tmB0QHUEpUlm0DHHr2YedhdgZLKa5l72tG90Cd4EoqC4sy8axHxzHwXE0breduhRQ
+sOiIORaCeoahjaHtexq4AGJAP7/nY9b8lNoyXHZn5L7dHW/ONe5xIOAe9G7o0VGHUL+LbSmZNC4q
+ub+t+UARZTKZfGL5S8D/CfwtwL8oIv/eN5g8/vy/Wy8+4H6/a9z3/QT17dnH2zfwupPJZDKZTCbf
+MFNoD9otfxdTGSJbFNVCLQvLksdWV7aat96dfT9Y3hRUNDuhu2PiWdW1KFoVHTt4HpFC2529N97s
+O+vyhrVW1rqw1Iq50Xpj7zu3dqNSsFAslP14Q+s3zLLWS0pQBFZVIhwXkPD8HjnyXouy1gVbwV0Q
+CkUXlrpx2S7sx86xH+zHzptrRYbIPvqOdNIp9xwjP51kOV12hlPdcsZcHMTiKTgNRhcY4JG1aTn5
+naPlw8VnBJp5BNadtnekky8uASXQqkhVtJLVXgFSdDrak8knmIgIEfl3gT9K1nz9KRH5bV+rS/us
+7xpd2j8HfBn4buC3icgfee9jRORXAz/OB7vW/w9wkHVgf9uH8TNNJpPJZDKZnEyhPTgd7VKUWhUp
+lVJqOtnLwroubOvCZdl4WDa8O9fXV2qtqAoeYGbYKJuuVKrmPjGMqquAbs7RGtdjZ7lVbF0JAlXB
+zGjWOPrBre3UEMwFc33maDciDMEpAkvRzCYD4Ez3ztHxopqOtgsMkV2XjW1rPLbGfuzs+439uLFt
+ioWx953Xt7Ss1dO1jjhD4VLa+tg/91GBJhF5X8+LCYwd7/zJz/+Xd//Ke6aGj8Pd6W2klCuIRnaM
+FygLFAeJks9bzkT1KbQnk084fwz4J4DfAPxm4C+LyB8Hvgi8IUfEfw3wW4GfAH58CPSfAH4v2Y/9
+P4vIvw/8VVJ8/0bgdwNfBb7E+4yPR4SJyF8Afi3wO0TkZ4CfAU7R/v9GxP/34f/Ik8lkMplMvhOY
+QnvgniqwVCi1sF1WlmXJTmqc1o8Ur6JUVdwyVVw07+8YIYoPAVlKoZbKUvM5RHK8vJlx3W/gQT8a
+Lx4u2OUBzFhU2ZeVY1k4auVojkiAOr0f3I4rEXm/KkoBZIyLC45EuukywtXOdmsVR/Fxa8h5xLOP
+8XFf7vvdEVl7Fv7U2HV+HwohmqFmdYy/V8U9R9jdHfM0vS0Cixyl95HqLirD8Ve0PgnnYXoDT8np
+o1UMN0c0E8o9Ine5J5PJJ5Yhmv9J4E8CvwX4O4D/4Gvd9T2f/5vADwK/Cvj7gD/9nu//PPBPAX+Y
+D97T/neAPw98+ms8xx8iXfHJZDKZTCaTb5optAfm6WgvAmUpbJeFdV2xkSzeWkORFLiq4GA4FKEs
+BfdCYCgKEu/qrtYsjSYi6L1z9aAfnf16w/oLxIxCsJXKMYT2vlQsGhadHp3wTrhBOEsp6ZaLpMAe
+wpqIJ8F9fh0fIjpFtmJovEdkn/eXFNpFBJensDI/d7JH6ZZKpoWrCqUKtRZqVZaqoyLM0qG2oHnc
+b13AVDBl7F3rfcT+LplHp5gg5yp2Mh4vNrq7I4/JZPLJJiJuwD8tIv8w8NuBf4B0sgvwf5Mu838J
+/Jlnj/mKiPxa4F8FfpgU6J3c+f6vgD8aEf9XTpzfYzK+1mv/1yLy64F/BfjVwPeSo+STyWQymUwm
+3xJTaA/cRgI4KRy3y8p2WdlvB/ve6e1A4R5eBorjoKRYNEVCcxdZ5F1Cu4x94gjovdO9cYtAPMBT
+ZK+qPNSFY13Y14VtL+y2s9uNm+0osGph08JaCkXl7mgTDkMsE0+93BLDpSZQsbuzfXez4xTZ9iTG
+BYoKPhztuLd0jYqvyO/XWliqstTCsihrVdal0M0w0xyD78beg2MEpfVzgnwEmmlVylLQpRAW4J7i
+Oc4V7hTb9z5vC0QddyFCn9LOJ5PJJ56I+Cm+weTxcf8b8G+P4/3u8+s+7NedTCaTyWQy+UaYQnug
+S25T17WwXha2h5XtsmFmHDtY73RRejFazcAxj5R6MkatC0pEQSrUUqmlULUgKOFgYZhDmOHWCXPW
+ksJ5K4VtWXhYV/p2YFvjaDfetCuv2xuqKiwby7qhLEiMADH3u+uend6W/d5mo9u73Y/e8nj62k5v
+O60dtNbSiTZLt9jj7gOFQ+5/x7iVFPRj3LuojOT0LCAXz1HwUMXVidAMNSvQS6aWh+b9dRz+fK97
+OOa1CmUZo+cEHrnDfR9Ntym0J5PJZDKZTCaTydvHFNqD7XEZtyvb48rlcWPdVo79QCRHy9VS1Jql
+Y2wjDCyG6CyiiFa0Ckut1DLcbINwp1sGiLl1zDpuxptlYauVtRQuy8rLywO999zhNuPoGZxWVVml
+4gUoijv0cA7vWDda78NNNsw6vfXRkd042s5+HHm0xn50jta57Tu3/eC671yvO/u+01rHev6M5kG3
+sb8eT0L77LRWd9SF7uPjyPumEM7E8SLKqpHTAAW6Bl0DP/vCh00unLOdGQxXq7JelLoqzQzGxYQY
+gW9m8LTNPZlMJpPJZDKZTCZvD1NoD9aHU2gPN/txY10Xrq9LBnCZYZJCtptn+rZn+jbhGYpWCqUK
+uijLUnN3WQtuWV3lLbuizbIv263zpu6spbCo8rBu3B53zIwYu85HOzKhvBQeyoYvOc/tAR3nIIV7
+O0W2W46nt4N+NNqxcxy3TBg/dvbWOY7O3ox9P7jddq63g+v1YN+PFNrmuOeFBBtCO13uHO2+i2wL
+tARlBJ+Zj/fEgxF+TiEd66pyF9mtBDaC0VyyAez5GqWoUBZl2SrLpUATvHV6yyf1yJS1mEJ7MplM
+JpPJZDKZvIVMoT24vNjy9nHL42Gl1oVSRw/2EL42XG1BhqB8crS1CFI1RWKtGVpWCq0ZMQLQWkuR
+3a3TvbPWnWWMXT9uG7d95zhautSts7fG7TjwUmm107vjNejP+qr9/pw5+m29jzHxg3bcOPYrx3Fl
+33f2Zhytc3Rj39tdZKfQ7nehnSL7SWj7ENnugRRHiqPFUBM0V9ZBg7AgzHOfOnJMvIiOMfNANFAN
+uubOdod0qU/ZLOTY+FJYt8r6UAkNejhq54h5DPH/8fxdmUwmk8lkMplMJpMPYgrtwWc+82kALpeN
+y8MFLZXwuItMN0Ok3HehVSXDuDSQQh4VdEk3tq6aQnGrYIE3yTCw++g1KIKFs/fG65vwlTdv+PKr
+VzxeHqi18qpf2VvL/W6C2974ir8hDmfTwiYZjoYZ3nMsPZ1lH4fRLY/WO6032hDae+vse0tXez+4
+7TlS3lo69maB+wghG7Fj59p2N0ea3T8+mlBLpo7f7+QxasIU1UCL4AQmKdxNIExAAgllEZAqiCjb
+ZeHxYeXxxZaTBjIi3CLS7R8J6+8TJDyZTCaTyWQymUwmHytTaA++93tTaKsWSi0ULfRmd2Ftbogb
+HkaEZyCYBGiKbC2gddRdLUJdC8uWNWHenb73vP+ZCD76ts09xXQ4S608bA9s60bRykHjoOGAhXPd
+DziCpgePdeWxrFhdKAGRc9vjwkCMei2ju9Gt03ruZR+tsx+NvXVue8sd7dvB7dbYm3M0p3cb/deC
+k9r5mX4GCwKjuVPaCIIbRyaFC0J+riUodQTFEYSMI4RwQXrep1allkJZlMvDwuPjxuPjA+vjchfZ
+rWehd9yD3/z9/0Ank8lkMplMJpPJ5GNiCu3BZz7zPQBjXPp0dPuTo+05lp172YaEEHg6sppurI6j
+LMoyHO3tUul7Zy+CnMndY7VYVPBw9ua0fqCiXJZXrHVFpeSfTpXxpxTc/KD5wRWlLQ/4YrDCIpI1
+XZEBZOZxd7TNcmf7dLRTbLcU20cK7dvt4LZ39pZitlvgIfeB7pE1niFnMarQHKLHvYZLTnFdFC2S
+NWhVqRFUlCKnkz/6vmMsaAcomuFnVVi3wsPDcLQfL6wvNsyhmbG0ThBYz/TxuaM9mUwmk8lkMplM
+3kam0B5sS4ah7dFou3G7Hrx+/Yb9tmcKOGQHNCMUTIbILoIseh8ZL6tQ1kpZKnUp1KWmu71Wlsvy
+TLA6Fn53Z82do3de7zd+4fUrRIS61nGUdIktu7dLkMvNPaDDWkrqcUkxbxF0H6nh7nQPujmtO62P
+He2jZ/r4YRzN7wI7U8YzoOwczo54HlWWu9EpdId4BmR0ZEsBFUVqQYoSCsaoCJMAG65+BAyBru54
+Ofe7c8c7q8nsPlUAIKrj8HzSOTo+mUwmk8lkMplM3kKm0B7ouI1m7G9uvPrqa776lddc31xTaJ91
+VCmVifAhLAUlR571udBeK7pUtBaWdWG9GNadUuQ+ju6RY9qtdVoPLJzbcfCVN29wC7bLyrZtbNs6
+OqoDCUcBcckR7hZcamWrha1WipDCOsbhQ3Rb0C0d69ac/TBasyeB3WG0aGEhdwkbMuTsu1ainwtc
+SZWtOgLhCnWtrGullNMVzy7s8PNCRY7c69ND0eJocUSdsht6bchy0IkMhzO/v1Qq+7mjPZlMJpPJ
+ZDKZTN5OptAelHHrvbNfb7z6hVd85Rdecew7rXdEBVFAYuxo293RVi3oqpQ1Q9DqUinL2PWu6Wpv
+l4XwoBa97xe7O/t+5Di0Oz2C63HgFux740V74LFlyFkpSvrMKbTpQRTH1ejrhq8rgrAWfeZmj8Oe
+blt3jmYcR+doRmtO6+lk97OiazjYT+XWp1x+NzJGt8/RcVWl1HTv10tFi97r0Nxyd9wcfIyRn9cu
+zl1uVUfU0FuHpRFVqeE073ehnT988B7lP5lMJpPJZDKZTCZvDVNoD06hHWa028GbV294/dXXGX5G
+UJZKKQVVQYazKmRKtiJoSZFZFs371orWipZCWSrL5im0VXDLFHO3FNvNHNGOW9C6ET3oh0EIEopG
+oVYd4+qOSKadhwYu+TxEpnxHVA4zDsuwsj4E/Nlz/Vxwtx4psofQNpccG3+XYf3skyG8RYazH+d7
+kO+DqKL1FNsLok/haT6mvY3AhkDW4WqHBtoyyR0RKJ2oShSh4hhBF3+X2M8A9ym0J5PJZDKZTCaT
+ydvHFNqDKjk8XsgR7XNPuFRFa800ca1UqVRZAKG5ESN0jBAEpUilaqVIpUhBKJTieElnm3O32cEj
+u6mFQFOysmhl1coilYUKXbA9xTfFCQ1EgqKGSHuaeR+743sttHbQ+kFvncMcC3BR0EJQRoq33MfE
+zQXzeEoXj7uV/SxubFxgUDlfjWC42arp+D8T3Iz3Eze8B3Y4Lo7LeKSOkDUFJx13mufXisDSYU+x
+beKYBC6B2zhJf/c1gMlkMplMJpPJZDJ5W5hCe1D1SWiLx3CcjWVd2baV7WGhUClRKFFy37g3zLPn
+mhBUSgpsXShaUSl5qFNq9nJHZIK59QxEC2JUawsqyqqVraxsZaFIRSyrvVz/f/buPVyyrKzz/Pdd
+a+0d52RWFsidspRR8Q44iCgi9oig0PKgrfZMa4+Kgorg9IMXvPXYjYXtZWi8jJceq8dpLuqMj9A2
+Mo0iItVi0yj3AcUbogJFAgXFpfJyzt57rXf+WGtHRB7ynDxZeTLPycrfp56oiBOxY+8dpzIq8hfv
+Wu8qeASPrct5yHX+tLXmbLnO916kQM5TvZSpVrXdcbMatC1QPLRwbUylDRn3WtFeLlFthvmqhjx3
+FV/OU/c5aFPnZ88XW10AvBg+QRkKJbSlvQLtCwfwUJcQy7nOfS9eKBHYDtBNeLL2PCjBKbnguQVt
+dR0XEREREZEjSEG76VowDK2iXaYaXmMILBY9x49vEomEKWDZyGMdgj14hkwd4k0ghlbRDolgCZuD
+dix4VyglMk2hhmQv4KVNPa5BuwuJRezYSAvIBhmmMdeO54m25BfQ1qQuVpfwym35ru0Qls3a3Etd
+qmxZ0Q41aGMUt7Vqdhs27lC8hddlyG6LaM1LeIXAXM+ugZxlNZu5ut2CtpfaAK1MThlb0A51qDip
+hWyv1fypFMypXc9Dq2gPAU8GoVa+PQJtn+qFJiIiIiIiR5WCdrO9VZfwGofaHXxe7zqEujZ06hLR
+Q13/2Q0PpXXNXgXTWpr1uTyMhxrWPZcWOtslF3xy8uRQjOCBBAQiwQ0rhk817OepUKZa0SWBJ8Mi
+ZDMmjNGM0QJDNLoQSK3izLJa3kJ3KUxTXeqruOEWaoQ2w83mgeBzezPmqxqujZgiISZiiq2ZW11f
+3Fcb0tY/a2tvr4J3SJHQRTCnWKmb0oaA5/qFg0XqcHJbdTpfLi3WupUv72gjCGw1bl5EREREROTI
+UNBuzp4ZgRq4p6ngDsGMEIwQrc7VzgFCDZ8FapCkZUynBUfHpxayQ8angE+ZsrwU8lhDdpkcstVm
+ZxaIBEIJuDuFtvTW2NaSBkgG0bBo1NnfRsKI9W6iObENJ5+jajAnUF9LKYWpVbhpjdxqc7VQG6xR
+72dZqQYsEJLVdcH7jtSn5RrXecqU4nVO9zzk3Nvw83mB7RSxLhJLB+TaQM3Lcskwn2pjt3lYeojW
+OrzX59fKP/jautnWQvbySw4REREREZEjREG7OXNmApzt7YlprF28Ldiqoh1ruzLPMKfr9ZhnXiu0
+tVqd8WDLS8lrIXsqlFalLmNdDzu4Ea1WtoMblLbc1zAxDCPbw0QBLAZIrUoMRK9hu9aPC0ZmrifP
+Y6tTO/cu1eeMU216RhtKTgvbmLW54vMLmpufUV9/H+k3OrqNnmnM2DDVX0QulEJdG7vMRWfDvQXm
+GAh9Iprj2Wp1Otc53q3zGp7B22uqz7FW3a6vzN1bZdtbx/f5Cw4FbREREREROXoUtJs7Tp0B4MyZ
+bba3R6Yp1+HRPjf+8rXmW6ugF4IR5vnL1HxrO6vby3Bd6lzlyfGpVmmtDR0PpQ0Zz97Wns5sDyPD
+MLE91qAdUiR4xCJYcaYClufh4XXhrLpToIXtRddxbKMjhtiq0gEsEmKHh5HCSPERLONtreuSHaJj
+0SFA7AOxT8Q+khapFu9LganOIacdrjZSq7+v4oVAgGgEIjFSt53qlxFM3ka4+9rC3axVs1t387k4
+34aZ10ZwdTm1EBS0RURERETk6FHQbm7/yEcAOH36NKdPn2HrzMAwTQzbE8P2yPYwEj0SSm16NjcH
+CyEQYqyXEAhhHtJsy4BYcptvPWTyWOdnM0HIc8CmLveVC3ms20xjYcyZacqMuUAMdBawVOdL52Fi
+ypkyTJScV3OxW1W7csLxiIWOjWPHOX5ss4Xg2lDs9JmBU6e2uePUNmfPDIxjZhwmhjHjoUB0PJZ6
+zD5gneHBcWvdwUttxOZlDtlQSqaUiZxD60YOMRnBEmECy0aYII+BMkzkoYZ7oI0UYPVFwRywW8im
+gIVWoe/q2uIiIiIiIiJHjYJ2c/tHa9A+e3aL06fOcPbsQM6ZYXtie5hYjAOJjlRSbYE9N/oKgRgj
+cQ7c8zJXc2OwUqvaZSx1LemhUAvIRigBy3XkNrl25h6GGuyHYSK7k92Z3EkhEkPCYiB2kWmsQXsY
+RvI0QVl1Gp+X3wJn0S9q0N48zvV3u54wD+eOxulT22x+bIvFxllOn9ri7NbA1tZA3NomW8FjocQC
+EUIXsWSwDNq5djTPZW1+NnjJ5DKRs2EpYamer3WRMFFD9gRTNKbW3f2cgG2rnE37AmK5IR1DAAAg
+AElEQVQO2RQIXtcz77tE1+uPr4iIiIiIHD1KKs3tH/0wAMPWxNaZga2zA+7eQm+taHsIGG0ucVvC
+KoRIjGUVtkMgWKtqzx2z54r2mCljGzY9QWjLd9Uu5VDGzLg1cnZrm7NbQ1s72lrHcaMzlsEVg6lk
+trYHpnHCSy0r17Ddhrt74fhxJ4SOzc3jXH+3u9P1iW4R6frEqVNbbGyeZtGfYdGf4dTps6R0FjNj
+CpkcMznktna3YYHa5A1fVrRLbmPHW4O1ep+RM4QCKXbERSBupBa0jTDV+d8+V/kt76hmt5veOri3
++d9W6jJoKUb6rqNfdFf4T4mIHDQzexLwPOr/RT7F3d91yKd0SU6ePMmNN9542KchV8j97nc/3vCG
+Nxz2aYiIyBGkoN3kqQB17rH7au1osDYkGnLJDNnIk+PFGEtmKBO5ZKZcq7W1U7dDTLVTeUhYLIRY
+CKlgk9fh1wGyFUpp87bHwjhODGUkhwx9waIRAhCNuIB4zEjHjLQwupLYoIfgjNsjeZyYxomSvX4B
+0JqZbRzv6TYTcRHr0O/kFHMmCpMXpjnLmhG6RHdswWaE0SZymJhCplim5Lo0We043pq5Td6OBzUa
+r4as08J4njIM47zyNrgTYqjzvXNX18QOgdAHQmrnXX85tUmaeW2elh0vhdGMmEYsQqm92EVEjoxS
+Crfeeuthn4aIiIgcMgXtJucWtHOdGzzPs14OAXeYcmEaRmyY8AkmK2QrZGqAtuDLbmjBAm4dIaYW
+sjMhRyxlGAvFnExhLBPTNDFu1aHgmYkSc21ElowQgWTEDSMdg3gM4sJYWMKiEzpj2AqMW4ZtOdNQ
+O43HGEkxsnF8Qb/ZERYB68CD1/MuMJbCVApjcQo1aPcRwkYk2cg0X8rEtD0xbdc1xkteXTx7W/KM
+uib3+j9eKFOdM15KqUt3xdrIjC7hpS7TFVOq+4j1v0XwgJVa7XdquC/tuDgMoQbwXPIu/zVFRA7T
+Jx72CchldxL0Za+IiOxBQbuZh0B7aWs8W+0mzrzEVHGmqVC2C+WsU0bHE5QIHuta0IwFp8457lJX
++5S3oG0pE3LGUoSYKaEF7ZzZHke2t4caHFPBkmOdt2vDOogb6xXtgMVISEZaJNLpwFasa287ma5L
+9F1H16W1inaADkqb/OzFGVvIngpkN6yLdDHSRSeRGC0yErCxvf45aLeLT7XSDLYK20u1Al0mpxTD
+pkxcJGJItaId2zD8EIldDeV4PS8rrIK2Oz75cmk0d8dDoVhmKmqGJiJHTQDec9gnIZfdjYBGLoiI
+yO4UtJsQWjk1BaAQLYLVDt9Qh5aX0ZmGXJuaTY55m1QcjJxbBbdkcKcLiRQSXezIU106C28V8nlI
+OjX41oZnde5ziIHQQ+wNkkFHrWx3hqVakSYUQhfoLdJ1RkqBmIyYjGmjhuy+7+j7nmMnNug2I0Rn
+8pGcnZKhFOfMmYGzZ7c5u7XF1jgRQyB1gbgIJKslZiNBLOTtjIW2pnVbjst99fszoy51FowQrQ4D
+jzUIu9d56kbt0p5SwkLAg1O6utxXHjN5mihjBvO6XJrVeeGE+jueu6QVd6ac8UnVBBEREREROXoU
+tJu+74H1taCpGboLGM44DOTByVPttO2lDXGmXuZh1GUEnzJxXuN6Kqv1uItDMSg1cJuF2gE81TWu
+3ZxuEUgbgdQHSqzzuUsoGEbJTh4LY8hES6QuEvuOvkts9Injxxb4NJG6jtSlWtHe7EkbkWwTZ8ez
+TNulXTKnTg3c8dFtPnbHNttjpvdEHxO9p1qhjxBDJJGIaawdy1sAnpc3MzOChRaya6O21Cf6RUeI
+YTm/3d1IsaNLPX23wGKArv5OvDjj1sC47Yw5Q2hDzLtASC1kB7BoWHAsWR1mrmW0ReToaENsfO+t
+ROTIOHnyJDfffDNPfepTuf/973/YpyMi+5TzcvrokR7eqqDd9IsatNdDJOZkz2TPreFYIU/eGqZB
+8ER0iG71vpLxnCkWatOzsVC2J0KIteGXBSxbrYR7gNa1PKRC7Ls65HwR6ReJbiMwkes/ngHDszON
+GbOJ0Cdil1j0C+ImWF5AzlhxYheJKRJTIkYjBig2sTWMDGcmhtMTw5mJU6cGPnbHNnecGhhyYTP2
++GKB4cRQw2yIAafuL8SwnItt65fQljqLgZgiXdfRLTpiCuSJWkHPLIN21/fLkQJzadyoTYSmYawV
+7bkq3odl8C65tR6P83z4Q/rDIiL7ZmZ3B34Y+BrgAcAdwFuBm939xfvcxwOA7wG+AvhkIFLH7b4K
++CV3/7N97OOJwNOBhwHHqeO7fwf4GXd/v5n9fdv38939yRfzGpt44U1E5Cg5efIkN910E1/91V+t
+oC1yFVkL2kf6s1dBu+m6GrTnYc8xBRxna3uLvD0xDuOq23Zp5e5Wz4bQuodPlHEkO7WSPWZyN9Gl
+jtT1dd72vD50G0Zubf3t2IEFo9/oWGx09BuJsQyMZYTSlpUuc3f0TN9BjJHFRk+fIok60jwFsFgr
+5ZYCJU/kcWQaB6ZhZOvswPapka07Rk6dGjh1ZuDU6YHJHdsoxAyJgFkkhUDsAkYkxrp0mbVu5ucE
+7BayQwyk1CraGx0xRqbg5PoSSKmj62pFO/YRsxaW3clTZhoGhracWUi1oh27WCvZuV7AcCt17TBT
+5UjkKDOzzwZeCdyfVal3AXw58Bgzex7w6gvs41uAm9vz1t/0nwY8EHiKmf0rd//pPfbxy8DT2o/z
+Ph4IPBP4JjP7KublEkREREQOgIL2Ui2PulPnMXvGKUxjZprycvmv1EWsq+GyVnkjIUWKweSllm7d
+W1fswpQnSnHGsbBtIzhMU+0wPk0ZHGIKWDBiF9k41rNxvGex0TFMoa453aromNWZ3V6r57lMTNNA
+tK42Fwt1P7l1+85TIU9TvYwjeZiYcqEYhBTpN3uOxYD1HROwcaJnsdnTxZ4Y23xrMzw4MUVSF+kW
+HXl08lAIbT576AKpj8Qu0G92bFy3YPPEBjFF8uhMQx0J0G32dBuphvdQK9i1e/lUf09e1+uugT3R
+9Ym0iHVJsZxrZbyt1+1YbTwnIkeSmZ0Afh+4HzXA/ibwQuADwGcA3wd8K/CgPfbxBOoa21Ar4c8F
+/hCYgEcCPwLcG/gJM/uwu998nn38IDVkO/Bu4KeAN1KD++PaebwYOHYpr1dERERknYJ2496CdnHc
+HUoN2+PUgnbOdcmsrg6NTinVOdatyjvhbYx0m69t1Odn6v15xIu16nQ9huMQavU8xUC3SGxet2Dz
++IKNYx1xG2wosF2PX8p8frXpWs4jU06kaHisDcYsBso0sj2NDHlkGifylFu1vdS/ngKxi/QhYIuO
+dLyuo50WibSRSCkRY10T3AwseH3tfapBeyjkLjOmCahV79gHuj7Rb9YvCo6d2CB2iWksTENhGgtp
+0ZH6ROrqaIGcC9M0Mo0jeZrq+uXBiDGQulg7p/eRKWdChpyhFMO9zpF3jR0XOcr+NbU1swM/4u7P
+WXvszWb2YuBlwFee78lmlqiVbIBTwKPc/W1rm7zOzH4beC21Yv5cM3uRu9++to/7Aj/WzuEdwCPc
+/cNr+3iNmf0ecAvQo4q2iIiIHBAF7aUa2kpxCnWZrlxyrWiPmTxlUgvaG5sL+r4NA2/PMy94jpQc
+liE6e6kNzIZ6KaPXJmrBahO0YKRF7WyeFh2LYz2b121w7LoFG8d77KzDVsZtYpgbsXlbgqxMdVj4
+NFJSwKlzmUM08lgYpoHT21tMw9SW48r4BKlEokVSivRdqGtth4C3kE4Iq6ZjwTFz3AIhhdpgbSMx
+bWdiPxHnDu2top0WkX6zY3F8weaJTVKfGIfMNGSmsRBSrf7HFJnyhHsN2sMw1C8SKJjVofRzRbtf
+JMJkTNlbZb8NGrC65JiIHD1m1gFPpgbXt+4I2QC4ezazpwDvpK6vsNPXAje0ffz4jpA97+NdZvYD
+wK9TK9LfBvzM2iZPAjbaPp6xI2TP+3htG1r+vRf3KkVERER2d6Q7tV1Jw9ZYL9v1st2up3Ei51Kr
+ydS5ySFaW04r1GZjbV3o2pU7LNfeLg7Z2xDykhmmiTFPTJ7rmtetg3ZaRPqNRL+R6hDsNpTcQlsO
+DAO32jes1GHkpRSKFwqZ4plcMjlPjNPEMI5sDQNbW9tsDQPDONbKfMlMXsgUJhyPdbh6v9GzcWyD
+xcaCxWLBot8gpR4LEccpXtcGt9i+FOhbsN6owbvb7Og3O/pjPd2xjn6zp9vsSRsdqY+1c3sCzCkl
+L6vY0zSSl8PG6zBwa93LzULt5+7zb8CWFfa5w/k8L1xEjpyHAZ/Qbr9gt43c/VbgFbs8/Nh5M1bD
+x8/nRcBHdzxn5z4+6O4v32MfL9zjMREREZGLpop2c/bUFgBujluh2KqqXZYLRrd1pL3gXmorNKth
+eLIash3D/dzwV8woVveNUbtmJwidE3sjLiLdZkdaJEI0CqXO4x4z09SaiY116HTOXivaXudqG457
+DdnDaEzTxPawzfb2wNb2Nl7qet9GnW+d2/lnzyQinVnrFp5ad/REiLE2YvPCUJyp5Ba2vX7J0EXS
+RqTPCc9Ot9HRbXT0GzVkp0VdXsxiXZar/h5HcGvLpxk5Z8ZxJOeMe6nN4awu5WUWMAfPUKbVEmCl
+eB3XaUaI1C8fROQoevDa7ddfYNvXAU84z/3z3O2/c/cP7fZkdx/N7M3Al/Hx870fRA3qb7nAObwN
+GDh/ZV1ERETkoiloN2dP16CN1bWrPRQIbR61tS7jPs+vbkHbanidL3PtdZ47XOO51dBuUEJ7JDok
+xzqIfa1odxuJtKjh1N3JeapDxcdCGSBP1KXFcl2Tu5TSzq1+ITDlqS0CTq1kb9ewjde1sGOoncRL
+G9JOAfdCNLA2VDvGjpg6YuxhgmkaKLkwlakOp5+r2l2dT+5tmbN+o6fb6JdBu1t0xC61+d2GUxu3
+ldzOf6pV/lqFb0G7rUc+r9WNG57Bp/Z6vc2dx+tcdNp2InIU3WPt9gcusO3799iH7+P5AO87z3Fh
+VVW/ba8nu3sxs9uB++7jWBfgwH32sV3kiK9KIns6edgnICJyl3Py5ElOnrzw/1/HcbwCZ3PpFLSb
+4ewAgAenLY4NASzShnG34Oy0qnYN4K0IS1iGvrWg3e5y6pJVbrQg3/abIHRG7ENrEhZpUb6G7NyW
+E5uol8wyaC+bqdlcoW6V7kIdKj6MDONYw3+qw63rymL1uaU40b0upRVql+/UQnZKCyYGLBvFS51r
+Tq3Gh2CE1M7Z6x+frg177xZ1/ezUJ2KK7fxqJb94HYKfx3bJdS78fD5G/V1aC9z1mwnwaV5zx5f7
+M1bDx0XkyLvUZgpXYTOGPXO9iIiInMfNN9/MTTfddNincWAUtI8qFWtF5Oq13nTsvtSO37vZrYp8
+O/X/hPupMt9v7Tk7z+O+1CXAdmVmgVX1+85aVtP3O9pGo3Kufh/4wAe48cYbD/s05E4ahlpkefzj
+H0/f94d8NiKSc+be997zIxuA225bfqG9cyTbkaKg3bzrTe/T33hERA7GeofwhwOv2WPbh+9y/58B
+Xwx8ipndc7d52m0ZsIdSK99/tuPhP6eG8P/+Auf7YOq62pdSPV9+hrjvbzf73U6OrlIKt95662Gf
+hlyitb+0i8jV5UjnNwVtERE5aG+kVpPvDnwz8PPn28jMPpFd1tEGXgl8B/VD9NuA5+6y3f8I3I0a
+kl+547E/BB4D3MvM/rG7/94u+3jSLvdfjG1qWC/sb165iIiI3Dn3oa6etX3YJ7IX0zfqIiJy0Mzs
+ucD3UQPwD7n7c3c8HoH/F3gcNUw78Cnu/q72eAf8HXUt7Y8CX+ruf7ZjH58EvLZtcxp4gLvfvvb4
+/anrdPfU4euP3FkZN7MvBm5h1XH8Be7+5Ev+BYiIiMg1Td2kRETkcng28B5qiH6Omf2GmT3OzB5q
+Zv+MGpAfB7zhfE929xH4TmoAvxvwGjP7UTP7YjP7QjP7XurSYTe0bb5/PWS3fZwEbmrn8OnAG83s
+u8zsC8zsS8zsx6lV8FuBD85PO8hfgoiIiFybVNEWEZHLwsw+B/gD6jzpnfOoHHge8Mft+pyK9to+
+vhm4mTos+3z7yMCPuvtz9jiPfwc8df5xx8MfAL4K+E/AjcCvuPt37+f1iYiIiOxGFW0REbks3P3t
+wOcCzwH+Gtiirn31KuAb3f3b503ZpZLs7r8GfBbwvwNvB04BZ6hDwf898NC9Qnbbx9OBrwFeAXwI
+OAv8DXXu+EPd/U3A9W3zj96Z1yoiIiKyThVtERG5prWmbO+mhv1vd/fnHfIpiYiIyFVOFW0REbnW
+/fO1239yaGchIiIidxmqaIuIyF2WmR0Drnf39+3y+EOB/wKcAF7v7l90BU9PRERE7qK0jraIiNyV
+3Rv4CzN7CfBy4K+o627eAPxj4MnAJnX96+87rJMUERGRuxZVtEVE5C7LzB5AXY/b+fiO47T7B+rc
+7N+4kucmIiIid10K2iIicpdlZgn4J8DjgYdTK9z3oHYu/3vq8mO/5O7vPqxzFBERkbseBW0RERER
+ERGRA6Su4yIics0zs082s58xs78ws1Nm9iEze52ZPdPMNg/wON9oZr9vZifN7KyZ/b2Z/ZqZPeKg
+jiFyLbmc710ze5aZlX1e/tFBvSaRuyozu7eZPcHMbjKz3zWz29beQ//hMh3z0D53VdEWEZFrmpk9
+Efg14HrqnO1zHgb+GniCu//tJRxjA/iP1AZs5ztGAZ7t7s++s8cQudZc7veumT0LeNZ59r2TA1/u
+7q++M8cRuVaYWdlx1/p76wXu/uQDPNahf+6qoi0iItestrzXb1KX97oD+JfAI4HHAP8n9cP504H/
+bGbHL+FQz2P1Yf8q6rzxLwSeAryD+nn8LDP79ks4hsg14wq+d2cPAh68y+UhwOsP4Bgi1wJvl38A
+XsH5G5UehEP/3FVFW0RErllm9mrgUcAIfKm7v27H498P/FvqB/VNd+abbzP7cuCVbR8vBb7O1z58
+zeyewBuBTwY+DHyqu3/0zr0ikWvDFXrvLiva7h4v/axFrm3tPfV64PXuftuOlUEOrKJ9VD53VdEW
+EZFrkpk9nPoXdQd+dedf1JufBf6C+o37M8zszvxl+/vb9QR8t+/4htvdPwT8UPvx7oCq2iJ7uILv
+XRE5QO5+k7v/rrvfdpkPdSQ+dxW0RUTkWvVP1m4//3wbtA/nF7Yf7w48+mIOYGbXUYeyOvBKd3/v
+Lpv+NvCxdvtrL+YYItegy/7eFZGr01H63FXQFhGRa9Wj2vVp6hCy3fzR2u0vuchjPBzoz7Ofc7j7
+CPwJtfr2cFXfRPZ0Jd67InJ1OjKfuwraIiJyrfps6jfe73D3nZ1Q1/3ljudcjM/ZZT97HSdRmziJ
+yPldiffuOdryQO83s+12fYuZ/ZCZ3f1S9isiB+7IfO4qaIuIyDXHzBbAvdqP79lrW3f/CLVyBvBJ
+F3moG9du73kc4N1rty/2OCLXhCv43t3pse24qV3/I+CngHea2Vdf4r5F5OAcmc/ddNA7FBERuQqc
+WLt9ah/bnwaOAdddxuOcXrt9sccRuVZcqffu7K3AS4DXAe8FOuAzgf8Z+Erq/O8Xm9kT3f337+Qx
+ROTgHJnPXQVtERG5Fm2s3R72sf02dR7X5mU8zvba7Ys9jsi14kq9dwF+zt1vOs/9rwd+3cy+E/gV
+IAK/amaf5u77OScRuXyOzOeuho6LiMi1aGvtdr/rVisL6pzQs5fxOIu12xd7HJFrxZV67+LuH7vA
+4/8e+L+oQf4G4Osv9hgicuCOzOeugraIiFyL7li7vZ/hYsfb9X6Gqt7Z4xxfu32xxxG5Vlyp9+5+
+3bx2+3+4TMcQkf07Mp+7CtoiInLNcfdt4EPtxxv32rZ1FZ4/jN+917bnsd6IZc/jcG4jlos9jsg1
+4Qq+d/fr7Wu3P/EyHUNE9u/IfO4qaIuIyLXq7dQhnw80s70+Dz9r7fZf3IljnG8/ex1nAv7mIo8j
+ci25Eu/d/fLLtF8RuXOOzOeugraIiFyr/mu7Pg48bI/t1oeDvuYij/F6Vs1Ydh1WamYd8AjqX9pf
+7+75Io8jci25Eu/d/Vpfs/e9l+kYIrJ/R+ZzV0FbRESuVS9Zu/1t59vAzAz4lvbjR4BbLuYA7n4K
++ENq9e2xZnbDLpt+PXB9u/3bF3MMkWvQZX/vXoTvWrv9R5fpGCKyT0fpc1dBW0RErknu/nrgj6kf
+xk8xsy86z2bPBD6b+o33z+/8xtvMnmRmpV3+9S6Hem67TsAv7xzqamb3An66/fgRahdjEdnFlXjv
+mtmDzOzT9jqPtrzXU9qP7wP+08W/GhG5GFfT567W0RYRkWvZM6hDSjeBPzCzn6RWvjaBbwS+o233
+V8DP7rGfXedpuvstZvabwDcAX9OO8/PUYaYPAf4l8MltHz/o7h+9pFckcm243O/dh1HXxr4F+D3g
+bdQmbIk6r/ObgK9o207Ad7i7luUT2YOZfQnwwLW77rV2+4Fm9qT17d39BXvs7sh/7ipoi4jINcvd
+32Jm/xPw69QhZD+5cxPqX9Sf4O6nL+FQTwZOAF8FfBnw6B3HyMCz3V3VbJF9uELv3QA8BnjsbqdB
+Dd9PdvffvZPHELmWfDvwpPPcb8Cj2mXmwF5B+0IO/XNXQVtERK5p7v4yM3sItUL2BOpyIAPwDuC3
+gF929629drGPY2wBTzSzbwC+Ffg84O7A+4FXt2P86aW8DpFrzWV+776MOiz8i4GHAvcF7kkNBLcD
+/x/wcuD5bU6oiOzPfjv177XdVfG5a+5alUBERERERETkoKgZmoiIiIiIiMgBUtAWEREREREROUAK
+2iIiIiIiIiIHSEFbRERERERE5AApaIuIiIiIiIgcIAVtERERERERkQOkoC0iIiIiIiJygBS0RURE
+RERERA5QOuwTuFqZWWk33d3joZ6MiIiIiIiIHBmqaF8aP+wTEBERERERkaNFQfvS2GGfgIiIiIiI
+iBwtCtoiIiIiIiIiB0hBW0REREREROQAKWiLiIiIiIiIHCAFbREREREREZEDpKB9gMzsM8zs583s
+7WZ2h5l91MzeYmY/aWb3vMh9fY6ZPcfM3mRmt5nZlpndama3mNkPmtk99rGPJ5lZaZf/0O4LZvbP
+zOwlZva3ZnamPf7VO56bzOybzOw/tu3uMLPRzD5mZn9jZi83s5vM7OH7fD2fZWY/YWZ/ambvM7Nt
+M/uAmf1J28/9L+b3IyIiIiIiclSZu1aoujN2rqNtZt8F/Byw4Nxlv+bO5B8CHufub7rAfmPbz9OA
+eX3u8+3vI8D3uPsL99jXk4Dntee/APhfgd8CvuQ8+/1ad39pe95nAC8BPus82+08Dwc+3d3fucs5
+9MAvAE+5wOs5C/ygu//ybq9HRERERETkapAO+wTuClqg/XfUAPmXwBuowfGzqKHWgHsCLzWzz3b3
+O3bZjwG/DTyx7cuB24H/0q4/CXg00AN3B55vZndz91/cx2luAC8FHgaMwH8D/pb6xcDnr53DdcAr
+gU9sxy/Am4G/AE4Bx9pjnwfc6wK/l2PAK4BHrr2evwXeCHwYuEf7/dwAbAK/aGYn3P2n9/F6RERE
+REREjiQF7UszV2Z/BfgA8M3u/gfrG5jZo4D/DFwP3B94BvBvdtnfD7AK2QA/BfyYu09r+7sPtTr9
+uLbdc83sT9z99Rc4139KrSjfAnyru797x3l27eaTgRvbvt9OrXS/43w7NLOHAd8GbO9yzP+DVcj+
+K+Cp7v7HO/ZhwHdSq/gbwLPN7BZ3/9MLvB4REREREZEjSUPH76Q2dNyp1eot4OHu/ue7bPt04Jfa
+9n/p7p97nm1OALcCx9td/9bdf3iX/fXAHwMPb/u8xd0fe57t1oeOA7wVeIS77xaMMbMXAV/fnvNY
+d79lt233YmZfCvwRqyr2I9z99j22Xz/Xl7v7E+7McUVERERERA6bmqFdOgdu3i1kNy8EJmoo/8w2
+PHunfw5c17Z5P/CsXQ/oPgD/S/vRgEeb2afvcfx5HvQP7RWym+vXbn/wAtvu5fvWb+8VsgHc/QXU
+YfcGPM7MPuESji0iIiIiInJoFLQPxov3etDdT1GrulCD5APOs9mXz5sD/8+FAnEbKv62tbsefYFz
+/DDwBxfYBmB9SPl37WP7j9Maus0V9o8BL9vnU+fqubFq2CYiIiIiInJV0RztS2PUYPy2C21I7To+
+u/48jz907fZ/2+fxXwM8uN3+/D22c+Atvr95Ar9FnadtwNPM7Auoc8J/393/ds9nrjyEOgTeqY3X
+fqFOxb6g9aXCPmmfxxIRERERETlSFLQPgLt/bB+bjWu3u/M8fu+12/+wz0P//drtPTuAA7ftZ4fu
+/goz+wXgX7S7Ht4umNn7gf9K7YL+Ene/dZfd3LDjvL57P8feQUPHRURERETkqqSh40fH+rzt0/t8
+zvp2Jy6w7dn9noi7fw/wdcDrWC3L5cB9qI3SfhF4l5m9yMzOV3m+2/ru7sQF9CWQiIiIiIhcpRRm
+jo5TrALq8b02XLO+3XnX5r6z3P13gN8xsxuBL6Mu0/WlwOesbfb1wJeZ2RfvWAJs/QuAt7r7+rB4
+ERERERGRuzRVtI+O9aHdn7zP5/x3a7cvpUP4rtz9Pe7+6+7+dHd/MPXcngWcoVaf7wH87I6nvb9d
+G3C/y3FeIiIiIiIiR5WC9tHx5rXbj9znc9a3e9MBnsuu3P1Wd/83wFOpQdqArzSz9XnnbwHmrun3
+MbNPvRLnJiIiIiIichQoaB8dr2rXBnyDmfV7bdy6gT9k7a5bdtv2Mnnp2u2OWtkGwN23WL0egKdf
+qZMSERERERE5bAraR8f/TZ2nDXB/6vDs82rV419cu+tV7v43B3ESZnbPfW66Pry9cO7yZQD/27xL
+4F+Y2WMu4hzuu99tRUREREREjhoF7SPC3e8Afrz9aMAPm9mzdwzJnkPoS4EvahAq7lAAACAASURB
+VHeNwI8c4Km81sx+w8wev/PYa+fwGcDz1+56pbtP69u4+6up629DrXi/zMx+2MzO2+jNzBZm9jVm
+9hLgdy75VYiIiIiIiBwSdR0/Wp4LfAnwRGrY/lHgaWZ2C/Bh4JOARwOLtr0Dz3T3NxzgOXTAN7bL
+WTN7K/BO4GPUta0/FfiCte3PAM/cZV9PpTZD+0qgB34S+FEz+1PgXdR53HcHPg14EKvXdZCvR0RE
+RERE5IpS0D5C3N3N7OuAnwOeBkTq3Od/ur5Zu3wUeIa7/9oBn8YdrNay3qBWzr9oxzbz4+8Evsnd
+//x8O3L3wcy+ijoM/vuBY8Am9cuCj9u8XUbgtZfyAkRERERERA6Tgvad5zuu9/ucPbd39wI8w8x+
+BXgy8BhqJfsEcDvw18DLgF919w9fhvP8POAR1DD8hcBnAjdQQ/IZ4H3UruIvBX7L3ccLvB4HfszM
+fhH4FuCx1LW470Wtnn8M+AfgbdSGbr/r7jvne4uIiIiIiFw1rOYgERERERERETkIaoYmIiIiIiIi
+coAUtEVEREREREQOkIK2iIiIiIiIyAFS0BYRERERERE5QAraIiIiIiIiIgdIQVtERERERETkAClo
+i4iIiIiIiBwgBW0RERERERGRA6SgLSIiIiIiInKAFLRFREREREREDpCCtoiIiIiIiMgBUtAWERER
+EREROUDpsE9ARETkamdmp4EFUIAPHPLpiIiI3JXdh1ow3nb344d9Mrsxdz/sczgSPvfhD3EAy5nQ
+Lj3O9X3k+kXk+j6w2UcWfWTRJ0IKbGNsmbENbJdSLzmzXQpjcQZ3RgfHcAIQsBAIIbZLwAF3p3i9
+nv+ZWbsO7XbACBgGmLXrYFgwQgxYMFI0YoAUjWBGNIgGwQIpJmJIxJgobkxu5AIZAwt4CPXaVr+b
+UjLTODANY7semMaRaRgopbSTM8wgpI4YEyElQghYADMw8/Y6C8UL7rSLgUMITgiFEErdHgjtHEp2
+ci6U7EDBaNdWf0+v/K03r52tiMiVZ2YTEA/7PERERK4h2d2PbOH4yJ7YlRZjDW1zwAvW8mN03AoF
+mDwQ3YkUghvFAPcaZnG6FpTNIEYjupFw3AJOwC2AxRa2jRACxb0GcXfcDV/7x5iDtrUs267PE7RD
+MCwaIUAMEIMRY30dEZaBO4T54oARvb5gc8ON1aWFfccJ5sRA3ZEbgUi0QgodxQs2J2MzQoyEmOp1
+CC1k13NdhexCcWAZtuegXX8nYX5O3SUlO16c0p5kLWQb+pJIRI6M+mWtGTfccMNhn4uI7MMwDNx2
+223c+973pu/7wz4dEdmn9773vbRi8ZEOAwraTUotaAcnZMeCE92hBe3JqQEbI3ggUiheo3Bw6NwJ
+OBGnN2c0YwrGZIFigWKREmrYNquVbZuD9rKaXYNt/YPjNcAyh85ana5HtOV9NWhTg3aAEFuoboE6
+WquCW62EhwDBang2HPO6D3coNteKfXUe7hQrEJyQIFqghIgnKDmw/PPdknEIEQsRi5FgYRmyMWrA
+LnPYbq+3PT2Eek4h1C8q1sO2u+OlXtfw7e3xI/3eEpFry+3Afe51r3vxnve857DPRUT24U1vehMP
+e9jDePnLX87nf/7nH/bpiMg+3ec+9+G2226D+tl7ZCloN6n9JqzUgGoZIjVwF4OJQiS3odtlOYDZ
+vG4X3EkUenMykKMxxcAUAyUEskVyqKGbEDAz3Gr5eB4+vrqeK+O2DKo1XNtayGZV1TZvYXvtMlev
+WQ03D2vPnyPyPBy9uFGoIXsVbkurrBdCADA8GKQIPpe4G6v/shAwi8svE1ieIy1k+7KqXb+HqmG5
+Bu32u18G6dXQeagnbLZ6faYB4yIiIiIicgQpaDeLvlWP3bBiWDaC16Ha7k7GGYtBKXjOJGhzpes/
+kYJ5rRJ7gBIgJyOnQI6RHCJTqFVt5pBtdk5ldzk7ex6CHmq4Dmatum2r8L127nXYdavG7wii8zmG
+tX/T/g20KnobkO21i0+eh3SXdnEgGuYtODMPB187jxa0sRbt2+TsOdhDGzq+VtFeveL6WpdD9m0t
+bM+7XR7X1l6fkraIiIiIiBw9CtrNddfXuTlWCuSMlQy5EEYnTIUyOdPkFJ+YxkIME8kC0YxkAcyJ
+85DsVJufJQOPNWyXFrg91IZj3sL2PLtgjp2sXe8M2rYWuM+JyDaH7XZZhtEWRL3OwWYZtmsYhtap
+jLAK2m6tMRu4xzllM7d0Y547fk7QXpstPQdt5pKzLbeZA/Zq6Lgvr8PcTG1tbvYyaDMH7tXQ8fla
+RERERETkqFHQbk5c39UbuUCOeC4wZcrZTPGJMmZKLtgEeCYadCHQt/KqBSPG2jQsWgCvAZhklGSU
+LlC6iMdYA/YctgGWEXae79zutdZNfBmywzJs16HXNRHbWsiOwVvV15ZBu1alDXdWQdtW1e05EK8H
+bXzVim0esr0M2MxP21lRnp8T1m6vHiulBWz3Og/cV0F7NTR+/Thz9X698dmq6/hygreIXLXM7EnA
+86jfHH6Ku7/rkE/pknzwgx/kxhtvPOzTEJF9GIYBgMc//vFqhnYR7ne/+/GGN7zhsE9D5MhT0G6u
+OzEHbcdzwadCGTJDGRnGQvFMmZySa+AODhu1vTcWAykZloyQ6vJagTqcO0TD55DdRzy1qrbVudsw
+t6pdXdOuw/mCdqjDsZ1VB2+zGrCXQbs9J4QarktpFwejzp+uVef1ec5t2zlorw01Xw1XX80ZPzdo
+t0C/fCGtSr4jB5c5WPu8pNla0zWsNWazteHic/CeK+kO5PolwzyuXUTkCHF3br311sM+DRG5CK2p
+kojIgVLQbrrFot7IpQbt5JSQKaNRBsgdQMYsEKwQHRZd5FiKHOsii+gsIiyik/pASDUUB3zZAKyU
+QimBYuBmtcv3cgh5m7Ntq6r2emXawqqaXYN2aMtk1aBdh4yX1kwMVsPMW1dy5hrzWtC2VagF8GKY
+Q3Bbza/G2nzrVdCuO13ddp8Hh8/rYre6tM+Ps3wcm8elg7Wu5vNSZqsh6DvOa9mgrW2zXBpMc7RF
+5Cj6xMM+ARHZlwG4Dbg3oIr2hZ2kdvMRkf1Q0G5i1/4HG70NH3eyZfJglMHIA3gokApMhQ7juj5x
+3SJxoo/0odCHQmeZlMC6utyW4UxeyKUw5UzGKBiFSLGAB1tWt1f51PFW0Q6tSdg8bHyubhev63Ob
+lxbKHayshp+3SjbUBm+roNw6gltkNcO6dVAPqxxcg/aqodk5Xcnas1bzsn0ZppdfGPi529RQ3NYM
+X/5T/3e9vszXcvT82jrixrxBWYb0eQ1uEZGjxQAt7yVydXgT8DDg5YCW97qwGwGN2BHZLwXtJs5z
+c4pj2eu1ZfIC8gBxBGImTI5Fpzc4vtFx/UbH3TY6epvoGElMxFCok7VrYBzdGUthnAqTF4o52aAE
+o1DX2DaLeGgZ0hy31tTM2vrYLfiuhpG3YdjWFuqyVWxddfSeg+r60mB1nevQgvZ6Bdnn3bSgvQrb
+cwu0c1eFX972+sXA3NStPjY3elufh74Wtm01P7ssk3M7h9bgzZzVUZdl7DoGfnmeIiIiIiIiR4yC
+dmOx/ipqZbgG7VgCsXNiD6kHC07sjFRgIwSu2+y5frPjbps9HQOpbJF8m+BTW6cKMGMwZ8BJXhi9
+MJVCzpnJQqtu17W3C4a3UFvs3AZhsN5xvJWVzbBz5jmzDNnWQvdyfvVcpV7fx3IY+np38Dndtm2t
+DTpfC9v1OMtbrSlb+4KAVdF5Obva10J6u72sZLdzrkPOV6/DWjM5m8M1pVbv3c+9iIgcDfmwT0BE
+Ltb9gWe1axG5WsQY55tH+rNXQbuZh1nP45mttDBoAYuJ0EHXRXrrWISOY6nj+mMdJzZ7Tmx2pHKW
+OJ0hTmcIvr3sjG3mDBgjxoAzkBkdhlwIZSLHxBQ6iBkLkRJrR/LaybzlyTbvuixHcLdgy2qO8xx4
+fR5ivTz+3MXbMCtry4CthqPX+dqs/QIAq8F/VTYObVh4aF3IV4uRrY69/tPata1n4jl8tzN0r7N9
+lo3S6ms6twHa/MWBgrbI1cbM7g78MPA1wAOAO4C3Aje7+4v3uY8HAN8DfAXwydQ1B28FXgX8krv/
+2T728UTg6dRxosep47t/B/gZd3+/mf192/fz3f3JF/Mam3myzp14qogcjvsDP3bYJyEiF2ktaB/p
+pgEK2k0uq85dyyZdbeKyxUTsAn1csNkf41i3yXWLDU5sdlx/rAXt6RRh6LEhEHLEPBN8wsgMxWu4
+dmcomS0vhDzVwBp6CBlCBzFBSngCYlwLq3NaXusq1tLqcph4W+7L10L2/GdvWbM2I0YIgXptcdlk
+bWltberlfO9lBXxee3v9NNaGrFOWgXu+ZzUnez4TX+67dh2fM3Nb9quUHUF7rsyvz9FW0Ba5GpjZ
+ZwOvpP5tdn7DLoAvBx5jZs8DXn2BfXwLcHN73vqb/tOABwJPMbN/5e4/vcc+fhl4Wvtx3scDgWcC
+32RmX8VqkI2IiIjIJVPQbvL816vCOfN/a0U71Ir24jibmyc4sXk91x87zvWbHSda0I7DAtuK2JZj
+oxHKSCgDwUfGXBhyYczOFoUw1YZqZXIsZAiZEhyPXivpbkBsa0232viORmn1PFcV7fU1qaG0wF3W
+AmurinsN2e51GnlwIwRfjhBnGYTXQn5jzE3UZr6Mzk5ZzscuRu2IThtO7utbrg0ZZ5563UJ3qZ3Z
+l3Ox18K22Sp8+zJoH+AfABE5UGZ2Avh94H7Ud+tvAi8EPgB8BvB9wLcCD9pjH0+grrENtRL+XOAP
+gQl4JPAj1HbBP2FmH3b3m8+zjx+khmwH3g38FPBGanB/XDuPFwPHLuX1ioiIiKxT0G7OXRHazi3i
+zrdjIHYdabFBt9ikW/SkRU9adAQfYDoLscdy13qLR8gTwSDVJbNreC0OwQmhMJDZziMhO1OeyD6R
+S71YCliKGLGuiR3WlwJjWdmuFeT1V7BqYnbu8OtVB++6DreBT1AKzrwuGKu53K07uVkkhIRZqtfz
+b2kO1KWtM26ZXAq5ZLKtjQwAmIP48guNHZXp5drYeVmlX7644Mum56u55Gv/0UTkKPrX1Ba1DvyI
+uz9n7bE3m9mLgZcBX3m+J5tZolayAU4Bj3L3t61t8joz+23gtdSK+XPN7EXufvvaPu5LHRfqwDuA
+R7j7h9f28Roz+z3gFuraPvr6TkRERA6EgnYT1sLjzNdaX7t5HWYdE7HriP2C2PfEriemBZa28LiA
+0IN1QMZ9XO4rttBuAAVCgeSwnTOx1KHko0XGMmHTCFOH9YngHW6JYhH3gIe2JNicQ23ttrfGY/M6
+XcC5c53Bgi3X8HIypThupS3hFbBQh4fX7uaBECIhdMTQteu0bNAWMJwWrMtELhmbpvZX1VqRLoB5
+aVVoVsuHzXOx5yp8C9m0hmfz/HOYG7nRhrj7somb/koscjSZWQc8mfoufeuOkA2Au2czewrwTqA7
+z26+Frih7ePHd4TseR/vMrMfAH6dWpH+NuBn1jZ5ErDR9vGMHSF73sdr29Dy7724VykiIiKyu3DY
+J3BUtNnHrdP3ag1rs7l7OBACISVC6kndgthtENMGsdsgpA3CWtB2izihLldldV50itAH2IxwLMJ1
+EY6R2Swjm9MWi+Es/fYZ0vZZ4tYZ0rBFGgdSnuhKpvNCwkkGySAGW621vbwd2sWIMRBiqNftvvmL
+hFrRLhSfyGVcBuVcSmtQNle0EzF0xNjRxZ4+LejTgkXaYNG167Sgjz197OhiIoVAsLDsGm6+HqTX
+LiV/3H3eLpRa4fa503izXEs8GBbrRUSOnIcBn9Buv2C3jdz9VuAVuzz82HkzVsPHz+dFwEd3PGfn
+Pj7o7i/fYx8v3OMxERERkYuminYTynnKozYvJz2H7VA7dYdQ16IOkZASMXV4THhMECIeAlbCsjw+
+V4BrVm/rYkdIhVrathpCzQ3LLZCWhEeva2vHNteZuga32zz7ug4lL3NP7rYOdz1YaA3EVuXuWtW2
+tULwapkwJxAsEUJHiIkYN+jSgpQWpLigiz2phe0QjNgCL+5MZSTnOtx9GLcZpoFh3GbMI1OemPJU
+O6znGu7dvbZ5b83PvDil5DoEvf08LxlWz7JW131ejszOGUAuIkfPg9duv/4C274OeMJ57p/nbv+d
+u39otye7+2hmbwa+jI+f7/0g6v9J3nKBc3gbMHD+yrqIiIjIRVPQnpW6DFudBrxcR6suhx2M2KrB
+7oWSMzlPuCcCTgxAhBIdSzUYz826rCxXwW7Ds2tAbf3OSBEWiTac3AlkoheS12ZpORQKE547Stfh
+qaOkhMeIh3axQDGjBKuNyFjvQL72mpYJu4Z0swAW25cGiZg2arBOG3TzpauBu48dXVpVrGMI9XeC
+kz1TykT2zDBusz1utevtej1ssz0ODOPAOI6MeSAXarDOrYo+V9PzXMWGeV65F8NLIJTQliWz5frg
+InIk3WPt9gcusO3799iH7+P5AO87z3FhVVW/ba8nu3sxs9uB++7jWBdQgPvsY7vYLiIiV4uTh30C
+chd38uRJTp688J+zYRiuwNlcOgXtxvLUblitWM+zm62GbQ+hXruTcyZPE3jthp3a35dyBIuOJ181
+zg6t+uqroG3zsHSHroVycydmJ5ZCLEb0WgHOQybnkZI7Su7xlJeBmwSeAiU6JYQWtOcVpwullbfN
+V3Ob55DtbljosJCw0BHjgq47Tt8fo++O03eb9N1GvaQFi9TRp0SfEikGUgx0MbRO5v8/e+8fdFua
+3XV91nr2Pue9d5IJBRMyPSShAMFfpCRKysKgUAZ0UoQpAfmlCEGBUbAUQaWQUDixsCiTKlBTlBOo
+okbU4jcpfiRRCBpCSoyhEE0IFqVYMEnHjEmmu++973vOfp61/GOtZ5/z3u7bd2a6M30zWZ+pZ/Z5
+37PP2T9u95357u9a3zXCqWZwOt9x2u44bSfuzrc8uXvCk7sntLsnCLe4Qd86boMxnDGMPjo2bC9d
+dzck74kA3hR3oXlc4146XkK7KH408FbTFH4UpjG8qa4viqIoiuIN+PCHP8yHPvShd/o03jZKaE+m
+oy0So6REEaJcualAuzjaY/RwtG2gYrTm0BxpHqK3ebQkW7ixss/mCrGtKnsC99qiKn0BFnFaN5o7
+iznbGHRr9N4YfcX7wA4DN9tnfHuLXvBwtJXRlOEw3BmeLrZcxL6j6RNrCu0j0o4sy0OO62dzPHw2
+N8d3czw84Lg+4Hh4wM165LAsHNfGcV1YW+PQlHUJh3m65y6WQvuW0/mOx3ePOSyv0XQFV2xA3wZw
+inLxYfQ+2Hq/pJWbgV0LbcdNc0UPumsIbdVS2kXxgnIdOvZ5ROL3s3iWi/xDREHOJ+Iyv/fqM0+f
+x+cRI8CeiYgoF/f7LSEivOc973nufq01WitHuyiKH328973vff5ORfEp8MEPfpAPfOADz93v/e9/
+Px/72Iv/ULuEdqIZCyezmXr2N6sj6kgDaaBNkJZ92k2R1tAla8AXjdUEH1HKnZo2XWXdZ2FHQFgD
+dRh2/1xwGr6XUIc8zoRxIllc25IzsFdYb+BwgMMRXxc2G3SPZVnC7rN5O8d1IQ1pR1SPaDvS2g2H
+9eFlLRl81hYOqhwUVoEFY4nhYRHI7uksS3y3txUBmjRUGk0aaztwsz7gwXrDw+XIg7Zyuz7h7nTL
+3emWk8NGZ3Nnw7DZG+9Zcj8Pxgxxk7xtJbSL4gXlOiH8S4Bvf5N9v+QZv/8u4OcAP0VEfsKz+rRz
+DNgXE48Vv+upt7+bEOE/6znn+0XEXO237J6/733v46Mf/ehb/ZqiKIqi+DHHSy+9xEsvvfTc/Q6H
+w6fhbN46JbQTvU6v1hzphYXDraDuKbIFbYosii4hsrXdF9k0xRpIjxCz2R+9l47nYRxg2B5oluZz
+JqA7zWNElrrRyAC0HOG1rjesLqy6ossD9Pgu9MFDON5wtsHmg7OF0DZPhx1B2hqzsNuKtiPaDqge
+ae3IogeWdmDRA2sL53ptjaXBqiGym3vcHnfEPCR3lnELAm1FRVnasoen3awP+aybOx6uNzxejrxr
+OfB4eZVH2njs8GQMToC6gcllJng+9NBccy64u2M2fy6K4gXkbxBu8o8D/jXgD7zRTiLyk3jGHG3g
+LwO/kfg3/dcDX/uM/X458DnEX6l/+an3vgX4MuA9IvLl7v5Nz/iOX/eM3xdFURRFUXxKlNBOpqM9
+xzP7nEMt4Wgr09WeY6VauNktBLe0Bu3K0darsWCQfd/gNt3sNGl14KIpoOfM64jIERxNURmCGXw4
+mHBzYxwRjrqyrg9Ybj6b9vBz0Afv4mQhsk8+6OaYOcM9nPCc+63LgZZCu7UDKgdUlPmfJUvmF5G8
+JI9FPnSwxrwIcUW95dztENkAthoP1gf0m87WOw+XI4+XA4+Xlde0cQCW3pHzKUV29GAPI5LQL6Ho
+2Y/t6czfTyUviuLFwt3PIvJHgN8G/CwR+ffd/Z5QFpEG/CGenfT9DcD3EbO0f5eIfLO733OsReQL
+gK/JH5/w+jFgHwF+D3AA/oCIfMfTzriI/BzgN1N/oxRFURRF8TZSQjuR7Pfdx2NNWgo9FXQR2qK0
+pdEWRZcsH0/LVTQV4ZXA9vCRw63OEC+/nrolDafHiC6fa47AIoLSLL/SepRVu9L6xtI7a+8chrEY
+rK4oC00XFnVWZq92TtMSRdcjbTnEth1obQ1XW5YsBY9ycN2FdWxVPZzlfBAg6WpjeV1u+xiuOQLN
+gbWtmBnDIkl9xTngLGZo3+B8gvOJJiAY7oOu8aDDssx+//OQqIH3dPbr/xcXxQvNVwO/Avh84D8T
+kS8m5lX/APAzgN9OzNv+Tt6gfDzHdv0m4M8TjvW3i8jXEC71AL4U+B1ExLcDv93df+ip73hZRD4E
+/KfATwf+hoj8vjzmEXg/8TDge4HPAt5D/cVSFEVRFMXbQAntZJYr+yW3jEjsTtHoIbCXtbEcGuth
+CbGd4toB81jDYJjHGo4qaM68BkCiV1s0uq/NG92UbQhjwOgwBvuIsCBKppERwvx8ot8+YWsryIKR
+5zgcWxa05SguWcIxbzG8W9uKLAd0WWltiVngKjE2K6z8S/m6hLM+nx8Ijsp03R2wuFGzB3z2TOd3
+SV6viiIKh2XFDzfo6Eg/4+c7/HyHbGday3MQ52ydIc4Qy0Flcf1k6vv1KorixcTdXxWR9wN/ieiT
+/tW59l0IB/rbeL0TPb/jG0XkK4EPE0L4q3Ndf0cHvsrdv/4Z3/H7ROQLgQ8CXwD8wad2+QGi/PzP
+5s93n+AlFkVRFEVRPJMS2onJfaEdS3P6tSI02qq0tbEeQnC3pkhYsbiQY7X8ykWOsm1moFfY2ul6
+C7ji0hmudJNYu9j2/TSiqj2TvQHxzjif6O0JIg2n7UJ7MdDDA/QoLMcV2oLIArogbUGWXG1FW0M1
+HhbMHmtJS38K7RDXcY9kCmyxODHXuGKDaTvn84kYHxZXnmX5DV9X1I6sGDo2/HwH5ztkO6HR+I3L
+YBnKJkZn0MVnoTgw+81zldAuihcad//bIvKPE87zLwG+EHiNCEv7enf/EyLy69hrfN7wO/6oiHwr
+8FuJfu4vJP5a/D7C3f46d//u55zHbxaRbwR+C/CzgYfAR4G/CHxNOt/vzt1feSvXXBRFURRFASW0
+d0ZuRWQX2lEKHmO+REJkL4fGui6sa7s42lz+X2KM1gqRPV1tUqx6yvY4kIL6LpKvHe2ejvYUuxci
+HM3dw9EWDVPZwVxwi7U+hFVW1uUhTRsiGXy2rDD7yZcQ2XI1l5prF5qr5w33rtDzv0P0X/0azONB
+hWUw2i7ic3b4srL6DaZCGz1E9vkO7SdEHRfD6TQTzgxOKMLYj+rEfXVzbI44K4rihcbdPw78zlxv
+9P5HiF7qN/uOv0+UeL+V8/gLwF94o/cylG0Gqv3dt3KcoiiKoigKKKG90+eILZt92pEW3jRmNzdx
+xjC8DzzVsJ03tkU4K2y3d5xuz5xuN/ptx+4GdhrY3WBdYF2Ug3vO0CbLrYXhwjDJcvN4bS64X8LA
+EN8Ty8k+6Clgo2/ZwDo+Tth2yzi1PXzNljukrTEze1nRdUEOsfV1pS0rLCtCuzj4mYwu056G3eq/
++NbpfscMrn3/eGe/wBhRNn8rIE1RX2jryno4cDgcuTkeuRsnDn1hbcqG0BHUrx5MILg7IoKLx4OB
++1PRiqIoPlX+lavXf/0dO4uiKIqiKD5jKKGdbCm0PR3jCOESlqa0prg2bBg2DPrAt441oatzxjjd
+3nF7e+L2buN81/G7ENt+Mo6m3Lgj4izXLmz2dHcjS8e5CkSTORXsEqQmmoFq0Tc+S7vxgduG9XMI
+bU15agPNHm7J0vF2OKLHA+14gOMNeri5uPf3jhOnGEXkMeYMVyTVrfilzF4uzwDyfEL8h+Odxe9T
+iWtDFmjrgWU9cDweGecjx37g2BvrWVlcaW7olZ9+7baLZlDcTIoviqJ4BiLyEHi3u3//M97/YuCr
+8sfvdPfv+bSdXFEURVEUn7GU0E56DwFpHgFclk6sLY3VDZozuuM9XG16Z5yhi3Hyzu3ticd3Zx7f
+TqE98FMIbXNDMFQbNN8dY0hHO8X2MMFMcNe9QDt3gyzvFtWn+qqno72Fo90Vwek28LFFf7Yo0BBd
+aA9uWPoNPm4Q63Gc1qC1mBN+JbrnsYUU2WJI9l57mum7uJbrsPY9HQ2ffdwekl2yXH062uNwwI5H
+jtvK4RyO9mJCc4lWcN9PgumVC4TIrtLxoiiez+cC3yMi3wB8M/B/AidibNiXA/868IB4fPeWytOL
+oiiKoigmJbSTXbNNG/mqQ3mO43IzRt8YpxMbjc2UkzWW0Tidbrk7n7ntg9MwfBjeHetzZpYhMrAF
+VLIvWoQ+PMLPLEdw7dp6lol7poKnwG4t1rJerYXWMj0cwuHuZ8wdUDxdtM1SHwAAIABJREFUZZEG
+dgI7g51RjKaCLzkDfApmvVbNOU88x3rtt4nZrR37yNXrS/M28RBA9HKDZ1m6KtqUpbVYqrFEaBJC
+W0XSUZ/TxS+F5LvgLoqieD43wK8EftUbvOeE8P4N7v7tn9azKoqiKIriM5YS2smyxK1wuJSOIzmT
+OkSs26CfT5zsEbd9Q3tDe6P1xt35Mad+ZsM4i2Bc+q8Zjm+Ge+fYjaYaS4TzNujDIuTrSmSLEMK5
+xWpNacuKtoW2LDkP+0hbb9D1iK43yHpEl0OKUsfHxjS8cQdRhnSgwzijOEOE0RQVAT/SyETxK7G9
+h6HFF8V377o6Jbh7lpVnmfgU5nFD9zTy+asL4X6Lg5rTcqkL6joL1/OKSloXRfFJ873EPO/3E/O6
+Pxf48cAT4P8hxo99nbv/g3fqBIuiKIqi+MyjhHaytMutyHHSMZpKWi7FbTDsjtNmLHpHS5Gto3HX
+z5zHmTPGWWKwa8+ScO8RCubD6C2d26YsKpy7XQltnxlsORYrVmtCWyJArC0rbT3Q1huW9YZ2uEGX
+I7ockfWItIUxBjYGNjpuaZUPS0HcYWzQz3SB1hRbGtay/zoDy0Iwyx57vgec7WXh5FzvCCzzPSzN
+r/aPBwa70J7k3PH5PeG3O+pzEYs5Wk32kLY55kv82lEviqJ4Y9y9A38qV1EURVEUxaeFEtrJdLQB
+LklggnjbxR4j3OfzOKMoOhpqC2qNkw9OPji7sYlwRthcOFukZbsZJrCJcWjKmmvbBlt3RprFU5Cq
+ZNp503C0U2gvhyPLemQ5PEih/SBEdjuEmy0N/ISPHn3bc1bY6BG+ZhuMM97PqMJYGmNZaEuLEWRN
+YYme7ujt1gwhu57jRQaeRSKaZxm8i6ShrZmU7nlNU3RfBoVdf4/giMfaHe04MirhmMtMW8vPesns
+oiiKoiiKoiheUEpoJ4f1UjrO/mrGfmeKtsJw4ZxzopUQsGNrbBinXGc3zjhngZMqZsY2nDt3jhgH
+dQ5qrKIxLqwbbr4LSxWlqbMsC+2wsBwW1sOB5XDDcrxhOdzQlsO+RBsu4D7CObcN9w4+gJGvO26O
+DMcYyNiQRoaqOXjH+gm3DXyg6wFZFmRZQRtgIW3dcRuYGWYRGCe6ILLECDFtsTtCpJmxJ5O7X70e
+Z7AtxpJZjwcDc1lnn9ctjqOYSCwn5mh7VAAURVEURVEURVG8aJTQTtZ13oqLgHMct+zZztLuITle
+ygFXxhA2UbrAhtMFzgxOOGcRTqr04dwNWLqxmnMU56DCQQZtGM2MZs4K4aI3QRXaurAcDizHA8vx
+hvX4gPX4kPX4ANUIRRNdAMHcMR9Rgm4hrGHktmPewQ0bIwQuimiMHIMe4tw2YpQXNL9BuaFlr7Zj
++1xsGz3L03ukiesa39VAW9w7JcrOxR2X2XzuIbIFGBs+tjxuimvboqx99L3PGwmxbcyxZ/HnUUK7
+KIqiKIqiKIoXlRLayXS0ccexENdumDhmzsgwMHNnc2MYDIfNhPMmDBVMY7u5cRLnLoW2YOgAOTvL
+MI7AETgIHAmX+ygewd8iiEJbYgRWO6TIvnnI4eZdHG7exXp8EKXUEj3QZo6NgfeBWTjY7uOeo+30
+2JrsVd9TiLudIziNkSFskUAumUguqpFk7pZCe8O2jdE33MGaoQo6s9KIWdwzmxzs4m4T4t7tvIts
+30V2Cu6xzUb1cOrFcNHd0barBx9FURRFURRFURQvGiW0J361uR5PBenEOmhWkQM+w7XFsRSEln3K
+poq1BVbAwIYyegjj7oNhRnfnbIZF6hfRGi24CroobVXa8cj64OFl3bwr1uFBlnGH8KV3sIH5YEyH
+2Ecc/F5vtV/ceXOsnxm9oVv0aY/tyNjuGNsdui7YWGlTuFuIeLPBdjqxne7YTndR8t4ijE3bTfSQ
+H47ghs6ANSxnbXvM4hbHtlOsfs61YWPD+4aPkfeTS8m4REK65Zi1crSLoiiKoiiKonhRKaGd9D7y
+VSaEX42xcnLUlQjSPEuiScdVwnVVjfAwVcShubLQcFkZvmG+MEbH6PSt473TB6gbirOIsCJ4a8iy
+0I4L681D1oefxeFdnxVO9s1DluND2uEGHx2zDR8pgnHMe4hVH+AhaMP4nmFlilwHic0JXkLM+nZL
+Mb1hs3faO5hiY2OMzuid0+1jTo8fcffkEaMbS44Za8uRw4OHHB48BH8XbWlZmh5CWySEv4hh59sQ
+9ec7xvnE2M7YtmG9x7onsoUhhqGMWWlQjnZRFEVRFEVRFC8oJbST3i1fRen4xQXO9K4s6SZnOodA
+nf3Lgqviu9AW1BuLONKczRe6LVFybRt9CN1ButGasIhwEKcjuC7IukZf9oMHrA/fxfFd72Z98C7W
+40OW4wPaemRsJ7wLbI6PdHzT0Z6juHLCdQptjZJtsUxV34d2hyCfDxh8MEanjZ5iO8rLx9jo20bv
+Z863j3ny6BWevPoK/dxZD0eW9YZlPWL93RGmJoAfiJ7vmLEd5xNl5ON8m2J7iux0tXehPUU2mGjM
+JRcLob072kZRFEVRFEVRFMWLRgnt5NrR3pc4orrPcJ490dMlhiwVF8LN3oW20vJX2gQfDesNtoZt
+ip0dc8OHsuCsIhwEBlF2zrKghwPteBOl4g8/m8ODd9GOD1gOD9B1xZtg53Ch2c7hqmOYD4QYj7XP
+C5NrsT1/L/uDgn2L4z4wi/A0txHuuGkEoPUz/Xzi7u4Jt49e49HHf5jtfOZwuGFdj6yHG/CBiNOa
+gvd01f11Yjvc7FM42tuJ0TfG1rE+YmUZvmfZuIlkIJpH4nm62kVRFEVRFEVRFC8aJbSTi2YTLq8E
+txDZ4jF6iwwsix1yQvS0uDX2c7i0R0PGbKcwT7k5XBgGXYVusBlsQximmCnmDfeGM9cCLKAN0RVd
+Bot7nNNMAk9X2OfoLbMMDYvjuii0mNGNGbqsuZbYrgu6LLRliUTzpunYO8OMbRh32+D2tPHobuPV
+2zPb3R2H8+CwnDmsd3tgmnhn3BxpS6MtSlvaxT0X6L2z9Y3ztnE6nTmfO9s26JszBphKzPQmQ9X8
+MiMsp63hVo52URRFURRFURQvHiW0k12zySwOB/eLk02WMqvL7hLPPmfIXZzQhfv8be5lqrncF9rd
+U2QL9AHdoJswXC8C2xv4AjSQBrIgutCWAyqRCu5ujO3Msh4Y2ylKr58ODJMYt4VHCby6h7BOcT1X
+WxbamkJb4zOOM9w598HdNnhy6jy+O/PakxPb7R2H5cyxNQ5LSwXcETtj2w3r4cB6PMBxjSRyFUSV
+PlJonzdOpy2FttG7Y5aevGTiOVcPKszA4jCls4uiKIqiKIqieBEpoZ1MR1syVTzGUwEe5cuCoJqv
+MwCNq2AxFVAHSSHuEba9i22f4l0iGmwA3YXNJGdwh9geJphrrourTTraIgvogipIa8CK26Cf7miH
+W9p5jaAwBiPTxePBQZSNzz5zFWjr+npXezrai6IanzGgm3Eexmnr3J43Ht2dee32xPnxLccmnBsc
+VZAU2Won6A/wBw/AHyDyIM63NfDGGJ1t6yG0z1eOdneGZXyaaDxYiD+Zy5+Hk/PCf6T/qSiKoiiK
+oiiKovjkKaGdOCO3IUvdZSruEM9IlGBLlDVHnNjVyCxVzB11Q9B9VrV59E4jRpi0Ak1ynlfDxRmi
+dDE2h9Mw7rbO7WlD7k7oeoesT3BRDgI0yXZwR8XRdIilNVTD7RYdIA2XFuo/e8zn0uzZbuuBdjjQ
+Diu6rrR1idndy4JowwWGG+cxuDufeHx7yyuPHvPKoye8+viWVx/fcX5yx4Mm9AajEQ8AGIifsR6j
+wmyc8HFClgO6rMi60s8bfTvTR2cMY5hjnl3c+URApKGqaD4YUEDVsOtKg6IoiqIoiqIoiheMEtoT
+yTA0B5siG8ikMyBCz0SjfDzahWN0lbujrogNVDV7iTP72wXDcDGkObKAroIeGtoXpis+cM4Od73z
++E5o4pi0PQBsZGI5dEQ6rWn0MC96Sd+O9LUQ283Qlud2LbJVwqlWCZG9rhfBvR5SbC940xgZNjqn
+baTIfo0feuUVfvi1V/n4a4945fEt290dY1FsEViU1u7Crx/nTBS/y+CzB7TDDe1wpB1v6H1g/RyB
+a9mC7Sq4trj1rSGLok1REVr+UZiBYmhmqxdFURRFURRFUbxolNCepND2LB2fjvQMMnMycXyGosnc
+wcCjr1hUEZv12VcC3SPBnBTasipyUHQ0xByzDBtz464PlpMhtu0i23O2NXREB60Znu6zyBqOOkTi
+eQuRvS+f7dlxfq2F0NamLIcDy+FwJbgva4gwcPoY3G0nHt0+4ZVHr/GDr3ycj7/6Kh9/9IhXHj9h
+nE7YQfG1IaYIAx8n/KyM88o43zDOt4zTTYwou3nIOkZc87ZhYwCeIjsC0BwuIntp4WTjtDlb2+Nh
+h1GOdlEURVEURVEULx4ltHfCFXaJ3t892ZqLOz3He9lespypXPieTD7HaM0ochFNESxoA10EXYU2
+Wny0O94HA+XsRtsGMgzfsjDdHWwgDNrirAfoB4AjIoY1YgQX7OXW6IKoIRrnphoPB6bAbkuL7WFF
+Dwd0XdH1gCwLsjRoijlsNjhb5/Z0x6O7J7z6+BE//NqrvPLoEa8+ueW1uxN2PiO0yEWXhtCR4cjm
+UUs+Nhhn6CdsDNxCHhvK6Bvu09HOWeStxZ+D5qg0lRhXJrPTPEamqYOW0C6KoiiKoiiK4gWkhPbO
+VeTWxYzOXuyrJHJmyXgULsv1qK+ZNrbPrFZUFJZ0lVWQTFuTaPaOmdKmnLrggxhfZY6bodtg7Ru9
+L4xxxvoJ2+6wbcEjUww3CbEaH45U73kamtc0+7g1HeKlocuCrCuyxtbXxlAiWK2fuO3Gbe882Tqv
+PrnllSev8er5CY/7iZNvbOLYouANbwvWGkMb3kBWaCssa2NZldZAxREfYJ0xNlwa5sZAGbpgalgz
+xuIMcwbC5s42Bh1niGNCjA7DUIWlerSL4lNCRH4P8HsAd/f2Tp/PZxIvv/wyn//5n/9On8aPWt77
+3vfynd/5ne/0aRRFURTFW6aE9tP4U4Kb+yL7KgLt6kMX93qqXNEQ2qJKy/nbboKKZ7Oxgg3MOrY1
+ugk2x1ZhmBjrOrjZOr2fGX1l9BPWV7wvWBd8EdxazpM2fNaJz6Vx7qIaLnCWYssc57VGMBmHBRZl
+iNO94915fN547XTi0enMK4+f8PHb13jt9IQn/Y4762xi2KKIL9gSIrtrwxdBVmE5SojsRWkqaBTC
+49axvmGa6eIimDasLYzmWHOGGB2jm7PZCJGtjonvd7tpOdpFUbx4mBnf+73f+06fRlEURVEU7zAl
+tHfk3ivfB3LJHm5tzN7tCDATkQwmz1FUzLRsBYnxWKotWrY95LopIbRdwZWtd/ppY3PYBhF85s7A
+OB4GD3und91Ftm0Lvi0hsofio8X4sOloZ3216Cxjj1JraSG2Jd1sXVdkmY72gi8tR44Neh88Pt3y
+6u0trzy55eOPH/PKk0c8Oj3mcb/j7D0c7RaJ4LbolaOtyKq0g7KsQlvI0nUDH9FvPjaGg3nDRDFt
+u8i2BUbfUmQbmw9MHHfD1LNaoFLHi6J4kflJ7/QJ/CjkZWYLV1EURVF8JlBCO+kjHeq9KvzinsZo
+bNmnZs/Z2WFkX+Zs7yPAkL3HW32foB1C22GI4+qwOL6AL4qtC+LGMBges7RdF1zbXvYtkqO9JJPK
+3cA6fcx+6sHZB92MYYPu0Q+tDs0juVvcUDfUDPoWzwbcsdbobnR3Njce3Z147e6OV29veXS647Zv
+bG64xmgyXRfaENQc1ejrniPLyD5vFkEakbauRP94PpywPM7J4c6cu2HcmXOb6zScuxHX5OK4hmMv
+baam614aXxRF8eKgwEff6ZP4UcjnA1UJUBRFUXzmUEI76SOfpD81M0okJmtLTvwyMriLnLktM5Uc
+yOA0AWyAiF2GUKXgdsuSaZzRCKG9KhyWqPi2howBDGRd031eaEuutqAtBOtwZwxjG85pDO5G5zw6
+3TqbDbr1mJctRPm2CYwRDwXcsTEi+VuVLnJPaN9tG0/OZ27PZ55sZzYbmIC2xrJm6TsNHdA03Pto
+nG54a1hrmEo8UGiONqAtIIpnovlmIa4fd3jcO09653Hv3G0jrqcbZ4sZ5K4OYmgLl7w1odWAr6Io
+iqIoiqIoXkBKaCdbn8nd2ZM9W52nV53mqctlBNg+9iu3s78bzx5uhzn+SzJAbU8zz+pxW4CDImOJ
+cu/RkGHhPK8LurYU2SttWbLHWjGVmHNtg9Mw7kbnzkJs99HZRqePEXO/RWgW23DbOwxjY+PssOUM
+7+7hgneMcx9sY8S2D85mmAhtWfaxZiKOGiwZ+iYi+LKkyE6h3Xx3tUmHPh4SwNmN2+E87p4ie/C4
+D+764JRrGwPEwpYXoy3KOu93GdpFURRFURRFUbyAlNBOdkdbMkk8k7sVnbliAFcC76LyQnhfp5Jn
+H7d5JJTv6nrGqF2Syk0dXyQCyaTBsBiPZYasDV0yKbwtSGtoi1Jyc2cbxuaduz647RtPto27PoX2
+oNtAXFEZIbQRzBwXxUQ4m3FnxmmEc9x9sKXQNvPoF7f5Oq5NW0t3vkWSuAktfor7tix49msPEUwi
+xMzVQVvGobOXvm95/Lsxonx8GCczTuach7ONnEFusV1wRB1pjpahXRRvCyJyBP4d4FcBPz1//T3A
+fw38V77PEHzDz/5k4LcCvxD4QqARNcB/Bfg6d/+uN/nsbMr9j939q0Xknwf+LeCfBt4LfNTdf+rV
+/i8B/24e66cBD4EfAn4A+C7gvwf+tLs/esbx3g38FuAXAT8D+BzgB4HvBD7i7n/6WedaFEVRFEXx
+yVBCOxmzR1tA1C+jvZQouY4I79zlKpmcS3iaT+HnlxFgu+iOxuTcb+aXX0S6qyKLo9JQdcIAFqRJ
+9CJnufpw6Obc+eDWjScu3PbBk/PG7Xnj9tyjP3sMxrB8WCA0H4h6lL1nkNhmHn3dw0JgZwjbcN8D
+3zw17uxJ38eUtbgP6sKC0lAaAqoMhLMJy4AFp+E0jCZCU6GJosy2boky8CYxY9wEQeMzsmBDgIG7
+RfCc6lXgXFnaRfFWEZGfSAjUf4L7zTNfkusXAv/SMz77a4EPA8enPvvTgH8I+DdE5He7++97k1Pw
+/K7fC/zOp77n+lj/LPDngXc/tc/n5vqZxIOCjwHf+Aaf/zLgjwM//qnPfx7wFcBXiMg3Ar/C3Z+8
+yfkWRVEURVE8lxLaybArR9vZdbUDrgpuiMs9cT3LzO/JPc+gtBTXTrrB5uEm3xOxDtZCNLYZdiao
+kUIbdPEYDZYC2QixfdcHj7rxancebzHv+vE5HO2ZXG7uWXUd3zkfIGRNPN2cbuEqd7MoRc/lcO+6
+9h70DCSbgjtC1qbQboAwUDaDE/EPWMNoGIemiIcrHX3juZrE+DEDWQRF0ewrd42AOfOB+2UmOCJP
+nWFRFJ8ifwb4R4A/APwFwiH+h4HfDfxjwC8Wkd/o7n/o+kMi8ouAP5I/vgZ8LfAtQAf+GUI0fy7w
+e0Xkh939w29yDr8M+CLgbwG/H/hu4AHws/JYB+CPAZ8NvAr8QeB/IpzsA/BT8pi/5I2+XES+lBDf
+C/D9wH+Zx/o+4H3ArwR+DfDlwEeAX/4m51oURVEURfFcSmgnr3O0uS/j5F4J+cwYB8UjKM0zKI3L
+f+1V4+4Mc8xIsW2Yx3aKSm0NVFFXNMuxdTWkGaKWjnYI7e4eQvvU+fip89q583jbeLx17sbAkF2Y
+i9ouskVtH7ENgrkxLFc6xvMaRAS9fpCQyvsypjtGhjUXFtpFaOc88LPJRVTjLBi6GAuKitNEUIXW
+oFmKbcuFpshWaI6Z7N8bk9PK0S6KtwkBfjbwC939265+/7+JyP8A/G3gJwK/GdiFtogshJMN8Aj4
+ue7+f1x9/jtE5M8A/zPwEvC1IvIn3f2HnnEeXwT8JeAr3H27+v1fy+2X5vc48Kvd/Zue+vx3AH9c
+RP49opz8coFxrv8N8b933wT8y+5+d32twDeKyLcBXw/8UhH5Mnf/lmeca1EURVEUxXPR5+/yY5GQ
+0VNOT6dZrlxUn1Jbrt7XnBnd0u1tEkJyEZZVWVdlXRvL2nK77K/XQ2M9LLE9LqyHlSXTxrU1tGWq
+t2gKbqEjnB3OwBnhjHBCOItwFthkvgdnnLM7mzsbzhBj4PdXOuF9F98hvA2J0d+X8d9puRveHG8x
+43qI0RmcPHqub3Pd9ei/PpsxhmE2EDeaGyvOUWFVZ1VozWmaa75uc2qY0haN0vV9TniJ7aJ4Czjw
+XzwlsuMN9x8mHGsBvkhEPvvq7V9COMEA/8lTInt+/u8D/0H++BD49c84h+gPgd/wlMi+5r1Xr193
+rlfHtDfoz/5VwE8G7oBf+5TIvv7sHyYEO8BXPusYRVEURVEUnwgltHfuC2cVQSTGVqlcloiy3za5
+sng1ypq1kSOohLZeBPbhoByOjeOxcTwuHI8rN8eVm8PC4bjkexqvD0uK7RDibckQtBTbroqpYqKY
+NoZk+Jjqvkw1w8iEIYSQFn+dYI7lkYAujrmF0z3Lz4E0lON9BVOPELdmuA6GxOo6ODM4eefWOre9
+c9sHt2bcDuM8onfcxwAbNIxVnKM4R3VWjZ8XNZoaKoaqpdgWlkVCbDfJZw5Ss7SL4q3z373Je38j
+t0KUZ09+QW6dS/n4G/EngVee+szTOPDt7v4P3uR7Xr56/SzB/iw+kNtvfRNHffJXiWv9OZ/kMYqi
+KIqiKO5RpeNPMWPOBFCRENiqqDb2iV1c5mhPoS0Sws8lInejzTv3ux77dVVO7g7qmdntyuqNgzdW
+b6yuISzXmBsdaePt4mpPka1+tcKFJmd++z6ibD4LuPRoi+Y+2Y/tdunPHuaoOvNqp5YVAdxRQrDP
+a5rzzmyGvLnDiBJ3xVGJHu3jELqNKAEXp7lwQOgqHMQ5pNjunoJfZ7+4o3m/VSQc7bznRVG8Zf7O
+m7x3LUyvHe2fmdu/5+4/+KwPu/smIn8T+PlXn3kj/vfnnONfA/5v4KcC/7mI/BrgzxLC+H99Eycc
+ojTegfdfpZw/j/c+f5eiKIqiKIpnU0I7Ec3u61n+nfOnw8luqIYyNYtyal4naC+zt6cCFLkKEUuN
+O4U2LhHuhSIeo7cWF5oriyuLtxTY5BxqjXNMRztGdHm42hLu9XSwXVLw7iLb92A3meO+Rbiej+Ue
+vzfP0DaZTxXijac/Px81uEj0m5shhPPtHgFw5s7izmLOIvDAoFvcw6bxQGIVuBG41Xi9CDRxmjh9
+FsnPpwVcysWrZLwo3h6eVUqdXAvTdvV6Jnf/wCdwiO+/+syz+OE3+wJ37yLyFcCfAv5RQjx/Sb59
+KyJ/lRhF9sfd/Wkx/RPn13wC5zq5+ST2fQq7OuSb0bh/S3+s8/LzdymKoig+o3n55Zd5+eXn/+/B
++Xz+NJzNW6eEdrKsK3AJ+lKdfdaX8nG/UsshSqPc3GdIl3Gxf5kjvmAGo+3bOf7LQbHsu87vJ5xb
+E80S77ltsaTl+7vUxbAYf2XR/xwC+OJoayrkGdoW53Z/TWd6uu2McJPFHVNDrx8izEhyuRp0pnEf
+XMCaYjESnC7OGViBjYVNFrosEWhGCHAH1uEsYiggeW+mYJc8oKjsd2g/56Io3knern8Lnzmnez+Q
++98RkS8CfnGuf44YIXYD/Iu5fpuIfLm7/39XH51q9puA//BtOt/n8LFPz2GKoiiK4jOID3/4w3zo
+Qx96p0/jbaOEdrIscStmSfJ0TSPcLEWepwiUGM1lzN+l7lRQgxnTHcNhr0Z67WLbL4o3csfxKayR
+zOmeM69DZJukyE4HeyaQ2/xuM8wHNkYcc+a2SYwGuxfk5pey8at6eDySz2IrhIR3UFdc857MLbIL
+4DnjG5Xo9VbBFAZCz2C2BeHMysZCl5VFBFVHxFBxDsNYRHJAGLvoN/c41jS1ibne1+ddFMWnnR8i
+/pX8vE9g31mG/bz+6Ofi8fTyz+VCRD4PeD/wW4B/CvgniTT0X3b1sR8kEssP7v633+o5PA8R4T3v
+ec9z92ut0Vo52k/z3vdW1X5RFMWPVT74wQ/ygQ984Ln7vf/97+djH3vxH2qX0E6mow1chN0U2ulu
+e6jOUKK+m8DxGYue7Ov527Mp29yu5mfbfRs5A9Y8A9ZcdJ9k7ZLOtgquyz2x7bujTY4LC0fbbaSb
+7amB50MCvRLY8/AX8b9rbvcU2p4l8nG94oo3LvXocaPY1fwU8Tn72lpI8S7KhnBy5SwrXVaGrJgK
+TY2mA2Swaoz0UtKBn462RVs6XHT17roXRfFO8V1EYNhPEZGf8Kw+7Ryt9cXEv77f9XafhLv/v8BH
+ROS/Bf46IbS/QkSO7n7K3f4mkZD+s0Vkcff+dp/HNe973/v46Ec/+iN5iKIoiqL4jOSll17ipZde
+eu5+h8Ph03A2b51KHU/6sFwx3qoPpw9nG8bWjW0b9J5r3zfGVY3huR30bvQ+GLl6H4xxWTaXPbUd
+nTE6PZO5tzHo5gwE4+JomyhGYzgMM3rvbKPHd9vAPS3pVNVXhewhrC2WmTOGY90ZHcbGZbs5Y3Ns
+i/e9Oz4i4AwDRh5ibvcB4rMKQGmqMRs8z7d7Y7PGyRp3Qzlbo9NwXZHlgLQVbSvSVqQtSGuItL0v
+XeR6Sfz+MhS8KIpPL385t8Kbp4D/cuBznvrM206K52/NHxfgx129/edy+zl88onlRVEURVEUnxLl
+aCfnHi2Cch24xSyV9uxvjkRuS9f34uOG3os0bUFk942ZXdRRMm5cWce5zR7tPJ56i0Vja0p3YUgL
+cZ1bQRkmbMM4907fOj3FtttIYZrhbFwdEjJdPH624fmQAHp3+hZrbE5MMRNEHW9ylZQeZeHqEXwm
+Oo1tQSWS2hGJ8DZifpiZ0k05m3Iawu0Q2hC8LRGi3oh/EpujzZFQkxupAAAgAElEQVSRo7vaiPT3
+OTd79r/PhwhSrnZRvEN8A/B9hFP8u0Tkm939nmMtIl8AfE3++IQ3HwP2pojIzwVedvf/6xnvr8DP
+yx8fcb9J+iPAVwFfAHytiPydN5obfvVdXwo0d/+rn+r5FkVRFEVRlNBOti2qCe+5piIwPFO7fQ/n
+ihLwKbTlUjy9G6zZuL2LbXtqG0Jb8EwJtxgVhiC+EN3Ng80WhguDhsmCsVyEtocLv/XO1sMNn452
+HFrvnduscQ+xTYzxGjC60zv07eJq981pAtIcaaB72fulx9s8Hj6IewhhSGEvNFUif0iREf3kA2Uz
+5TSU2y4sS/Smr9pYFoHmcbzF0QHqgliEsb0uaXwmolf5eFG8I+TYrt8E/HnCKf52Efka4FuI2pcv
+BX4HEb/twG//BGZYvxlfBvxuEfk24C8S48A+BjwAfgbwbxJl4w784evkcXc/i8ivAP5H4LOAvyIi
+f4x4WPD3iMqul4ge719KjCH7t4nRYUVRFEVRFJ8SJbSTbXe0HcR2sU1EnnGdvDXbmpWrOdXzzd1l
+tXtiW6bITtE+Y88kR4iZaMyndsPcGUKKbMUk3WwJwS0Iw4RuvgvtPkvTfSB4hJ8bGagmu9OuIpEm
+joSjfVUu3jdndGNscXHiHs61z0z0mURO9qvPfmrL3nRBXPeHCPPaDcVd6C5sLpxNOXvjMEvim+LN
+oBmiA2kWIlvnNkX2HJs2v7vKxoviHcPdv1FEvpIIH/ss4Ktz7bsAHfgqd//6t+GQQiSN/7w3eG/+
+hfMNwH/0Buf6v4jIzwf+BOFs/6u5nvU9r74N51sURVEUxY9hSmgnfZvTZQbX0jnCuv2eYz3D0iyK
+o4GrBO/8iRSmqUqZbrZcfZdKlEgzk7xFoy+ZBrJAWzIEbcFkDaHNwpT/w2ZveTjaY2x0G4g21CxG
+aGUZuc5QtD3BW0Jk93C1R3dsGKMb1g1pmWqOomLYmK69x6WoI+a4ppjXEPQRnNYQ13DnrUVSOwsu
+l4A304bpgumCixL14i2Sz6Jef38IsafAcy2s5epeF0XxKXD/CeKnsJ+7/1ER+VbgtwL/AvCFxDPI
+7yPc7a9z9+9+G871a4C/BfwCIlztfVyGVX8/8B3AR9z9m595Ee7fISI/HfhKYjzYFwPvIf5G+xjw
+PUSf959297/7NpxzURRFURQ/himhnfQepeNPz5aWmbml0X+sIpGOnW6q79ovx3ZNge1+Jb5n2rjt
+rrKmHS6iwBTCitIQWRBZQKfQXrN0PBxtxBmuDPM9PK2P7NO2DlciW2VuI6TMp+gWwQYXV7v7LrLH
+sHC8iT5sG4rszr4iYXPvYtuyPzvE8EA9ytvFDbEVcdK1j++L2eCaCepLzgjfck54hp/Ncxe7KoHP
+e76PFgMoV7soPlnc/UPAcwdVuvu3cplD/ax9/j7w2z7F8/iEAjnd/QnhVn/Dp3Kcq+/ZgD+UqyiK
+oiiK4keMEtrJdLQtE7nnOK4ZwqUaAnvJFGw0xGWGbUOWfF9GeF1cbk9H290QgaYCLZOzzffZ1Dod
+bWmITkd7DedXlnS04//zDhf67mgPep+OdkckAtYk07pVL6tl8JpqCO3RQ2xbCu1IIjfMs9hdYtnu
+8l+LbMsRYhGwFk55PixA0fxUdItfZoObRDm86cWxd1nC0ZZ26ZNXjfvPVX82l/C1GvFVFEVRFEVR
+FMWLSAntxEYIbXcP4WmRpSMektFJWzv7j7OiPErAIb3e3c++V0Qe33sVNM4Ur5FUbgZjkD3bIbzV
+o2zbpYGu0FZoLUdfOdoWtDW0NVpTltnvbJEMHq3TIYQjJd3wWUreUgirsCwzwExYprO9CK0JS4tt
+a3mdejm/2M6J4XM7HywMZlycygBiVrZoXluTOHeNBwqxWj4YIMU7u0if7PPJp8AuoV0URVEURVEU
+xQtICe3ELUrHzUKg+vAUzzm/aorsXOrRY51DrC4yUwSbaWkAhCXsnnXT4pHanePBbDhDQsAOPMQo
+ThOPYm1VaAvM+dJLyzTwFW0LrTWW1iK5XBcYzjAYc162X8aWmQiyLjSd4hp0DUHtBmO0eMgwIkxN
+Wwjw+2nq2Vs++8vJhwv7DbiUzpsISk93vaFqqEJrSmu6i+0Q3O0qXfzpFHdSVMue/l5udlEURVEU
+RVEULyoltJPd0c7S8SgfD6HnQsyU9qu0cEL2KY4iUWJNVlWTjrWHB7vrbifdZtlzyEVAUmxDCnni
+Z0PC0W4r0g5Ia9Aamo52S6HdlhbutVmUdPcRM7+znD11b/aXg2t0YGtrKWjjIs2yjNyuw9ryBqWA
+dp895pdQNfMsuZ9Xm+PP1NmDzkQ6ooY2aLujrelkL1dj1S4iW6+qBeYt3Md67SUCRVEURVEURVEU
+LxYltJM9XCsFHimc9UrwSe43953DpmTOdZ6/E8BnYJfs//FZXO7hIDtpcgswnOiENoTBEItRX4Dt
+Sd0zRMz3HmxVpWk42syQstyYZTK4h7sd4jVEsmqUhWv2cItonFM+YNgvRlI4O7grbhb95BmwBiDm
+ec3poke9+qV/XbKnewaKa5StSzrZ95PGr9Pd/XJv5bo8nadeF0VRFEVRFEVRvDiU0E6WJazb3SyN
+Fm20aSzNUutZQj0Rsn87f0yRPbOxo6d7ju6aLqyk+53l5Ewn2BAfSOt0OUeSuA26hTs9LOZni/ge
+vBZmu9BEwVvO7g4R3rSlo53uPMJhXTisC+u60K7C0sK6z2vyvRs6e84v5druFqnlmZTuSAhtzwRy
+c0hXPMruG6SY9itB7ftWLvdvXzkWbY5WS5EtU8zv11RCuyiKoiiKoiiKF48S2skU2vu8LpcM5bqk
+jmcFechkn7vKJaXrYmcz+4mZc6s9BPLrwtHI4V9ueDOgg3eELUd2DboZ3Ty3AnoZJTbPKcR1OO+q
+URbeFktRGqckAktbWJfG2mb4mFy281wz0XsX2IC7pdC+3jfE+XAQg2FRBo8aDI9Z2tJApsiWENxp
+bU+R7VNwXwehZZq5pODeb+0sTbdytIuiKIqiKIqieDEpoZ0sLYS2yKX7ek/A5mp7PSsbzVdXjva9
+bw3RrSkixad7PUW2h0PtpNtt4APahojmXOzBcKO7MVwYbuEcu6VLncncKZZVBG1O83S9Z8x5XtWi
+mVKe4WPkgwSZJeW59fy87d+jl5FnU2iTvr3BsIhRF/VU3PNJRIzsuohsuRLZV2J73q658HSz897o
+VSBblqqXoV0URVEURVEUxYtICe2ktSmRZReS0TOc/dgp8q614BypvX80Zba73MvrsgwVM0un2Lkk
+Z88e7dwnR3TTRJgm8AwIC+c71jBnpNN9HsYYhsnAsN2UJ1O8NZfMcm2EMZ1u8RDIu3OvqMl9kb3P
+B58l6L4HobkLltdrJgzLbTagu+S1SpznNoxzH2x90MeIBwkmmBljDMbIeeBjo/cz27blxcw+8Pxz
+KJVdFEVRFEVRFMULSgntRFtsw72+Ko2Ge+XLu98tF1GsGY0trxPaKUA1w8lcMs3cM3RsWs2xnYI4
+wsou3z2FeHx3jO4a7nRzNjPOw+je6T4YPjLNewaezbFZmfINu1u9z/zOeHVRQcVR1YvQtmtn/JLE
+Pu+Dp9iOpftccDPJsWY5LxzfRfa5d869cxgLfRiLWQruzrAeJfP9zNbPbNv53sMJ4N7DjqIoiqIo
+iqIoiheNEtqJZov2Pst5OtqwlzJrCsw5R1qFDEjLXuyr+mfziwDdRXYmgbs5Jrb3GU8HehfYV2uf
+Ky0XR5t0tHuubRjnMTjbRvfB0rI8fGmgEUom2tCmKfAz0dwdN1JIzzTwKP+egv6eo33lIgvT5dcQ
+1MSccXNwy1nimWmGO8rVefbBeQy2dLTNNIX2Gznap33cmplnmb3kn0VJ7aIoiqIoiqIoXjxKaL+O
+WdLt2WY8O4gvJeSS5eLXIvjaCXeXnK0dr/ch2/l5lxDVZvOIM1X7Mnorxoo9Ne4qHwJE+fec1R1r
+TKfbIkBMdG8n3+dou11K1i0F9sWxvopxcw3BnMLZrr/jdSXbBq7TG88Sct/d/Fk7LzinbXBqnVPr
+HLee5ePGsCh9HyPKyfsY9N7Z+sbWtzjHEaPHcHaRXTK7KIqiKIqiKIoXkRLaiU3Vu8u3OZbLL0JX
+iVCumUh+9fk5tGsvbr5KIp89zeoxgSuCvGJc2EULR9l4S1d7Du8Wlat1GTWmraGa5eCqqCvqjQY0
+bTTRcLI9BOroA/SSQn5fMMsl0NvYS9khgtaEKP+OXVJMpxt+yVGf9zHXFPZzLrfBKisHXTnoxmFZ
+uemDrRtjeKzu9G70Pujd2Hr0dHvUnsfWr0ahvZU/8KIoiqIoiqIoih8hSmgnY+yDt/ZZ13AJJFMV
+FCI1e9rJT6ltv3oVVeSXN+Oze/x37jbnSKcmn+J4d7Il+7ND5atqCmzPbYpsTZGdw7+bKJpLXPBh
+4TJfl1tfpXbLJZj8klK+J4tLXrOF2HaJ0nebpeXzyg2Ey+8NbPheKu9GCOxcN+t0tJ1hcf/7sIvA
+vhLb4hIPN9LCD5FvFTteFEVRFEVRFMULSQntZIyLo73PniYDxTRUqaExs1quCpenw/qG/up1Yvks
+L78EeoHsVeUZh4bGYDFkD0GTPRF8OtqtgbaGzFnY2lA31FsMHROlidBm37jHOLA4l8s4r9dFis0S
+cTz3ifL1eK4giHuWxmeP+LAU2nHuSDrTWcI+hmPDsBH96Id24NA2jm3j1DvnWTo+PAR3d0aK7T5C
+ZPdueVfCXXd3fDhmFmK7KIqiKIqiKIriBaOEdmIjttHDLJfRV9O9dsn0bGFABH2F+ttHf4W565ch
+YP7/s3f3UZZlZZ3nv88+59wbmVkFJRRFAQWKIqMtvmABgq8NqF3KoDI6LJlWS0EbR3otHaG1fRmx
+dI3NUmxZM9JryjdepB2HphFaQRSUFptmpHhRVNSWBkWKkiqoysrMiLj3nLP3M3/sfc49ERURGVkZ
+mRFV8fsUl3sj7ol9zo2qyMjfffZ+9jABPbPSWW2sVuNlzGFqdiKUWwqprF/2sXI8RPQhAOdbDtVe
+urMFjDoYdQjUIYzB2cu+2+Mi8ck6bx+6pY99yMFIuQpfrszw3PTNHEKeCl+F3PTNWLVGjwH6BDFC
+NCeaES2QklNVuQI/7J2d300IpSN6hYWaEBpCaDDryPtt5+OD5UnsFgLJnJACrpwtIiIiIiJHkIJ2
+Eft87+MaYIC8TpqK0kHbVtOYhzXUiZKpV/tM+xCyS2A3K2udJw3NhsfJnYiPlWGzVdDuhkrvpGHZ
+UB4f98cuFXf3AAxBO1BboLEKx4meSNg49Rsr24+VsI0N+10PoTx3CR96meVrzRdYhdV68zrY+KbC
+sEd3jNBH6IMTg9EH6APEmK/Thpbtw40ctM1WITuEGgs1WFXeQAjj9mM4hBDw6OU1i4iIiIiIHC0K
+2sUYtBm6a5dwOVS3q1y9TUB0y23Hk5f9vsoqZc/1X/cEaei6XarPrKrQFkJZdm1E9/EGk6BdpVVH
+7mHbrBL+h5AezPIU8VLNNjdSCDTBaEpFO3nunt6X6/LSpM2HtdcGHmzLntpp0vrMVu3dxmxclf2z
+c2gPq/XcFuhDojfozOjHNxZKBbw0cqNUtL2E7FVVu6YKM0JoSsheVbSp8vcNEpYMD0NbdRERERER
+kaNFQbvo2sk85DLFO3cOH3qClzDs4CE3+crp00sWdxJD0PaxP9gYtEsgDhawkPK9Ob3ninNf9tAK
+peLtqRqbgfWlK3cq49mWf4ry7oB5YixPl725UszbZ/WpvAEQVluPeWLyRkEqU8wTwfJUcS9F/TBO
+dyc3Zhu+TyVEg+c3CcwJIVEN24j5ajq+hWELsFTOFUkp5vXW47r4MnDZgzzmHmtEgzxxP78mH16f
+iIiIiIjIEaOgXXTLski7VJrHpmHkfaljidLJrexVDSX14jbuIr1qbFZK4+65C3hlIYfWcbswJ1gO
+v73nyvWwxZcFw2Ok6/L2V12XO3Cn0r07T19nVXpPuQt3iomUEiHkYGoBYsphvY2RPqUcsMcbqxuM
+ITt5ojKnKvuFVyGvxw4WcuXcUlljnRukJRs2ALPJNPehEj5pCOc97j0pdaTYEWNHH1v6viLGlpR6
+vDQ5S6WZWoy5a3mMZQa5l27jrq7jIiIiIiJyNCloF+0izx23KhAqy/tVQ5n2nMuqHnNNddydC3JF
+e/h47IG2qs66O8mcZOAWVkG7rG6OqYTtFMcO4yEYXpWg3cWyDVbuzj1WfodbIofT0t07xUgMq72/
++5S3ympjpCtBmyFoMxTdfQzaySPuaQzX9XBfGXXleRV4uU6Gdd6l+gyMFWzK94bypkJ+NuKpJ1mf
+g3bfEvslsQ/E2BFjj3ss24eVRnC9E22Ywp7vrQRtU9AWEREREZEjSEG7aEtFu6qdqh6abAXMUmn0
+Vaq4w/RxX1Wx8VXIZtqfq1R2vayDxowwtg/Lkb1PuZrdx1LRDgkLiVRHui7Rd+W+j2WbLFYLyVOp
+7Jbp4amPxBjHgEtwujgJ2jGu1jybjdPch/tpRbsOUFVGqqCuDLzKQ47rril7bOemb2ma/yePx3AM
++Z0K73PYTqWi3ZeKdt+NFe1UKtqpNILzEqydVLYYy1PkTTlb5EgysxuBV5D/GHi0u3/0kC/psrnt
+ttu47rrrDvsyLtq1117Le97znsO+DBERkfssBe3BUB2dpMRgNuxqnadNM0wLd8oC7lXoHr58y5ZT
+OYzHvMU0MeTgnSvleVq6J8qU8LEunoNk8rJHVg99h3ctqQvEzoihInZtuXXErqNve7qup+97QmlK
+FizQJ6eNkWWZOm6WclOxYXNvz9V1dx8rySklYmVYgpSMlFZbkMXVdwFIq8w/fOts9SqS59cWyzh5
+u7Tc0dzcIUVS7On7tlS3W2Ls8NQTSDTBmQUbv7/utpotoJAtIkdQSolbb731sC9DREREDpmCduEp
+J2QvbbmHRmOhdNM2C6vF18O+2eNy4dVWWOWIbLIddwwlIA7Nut0J2Biyp0GbErQtJugj3nd435H6
+adBeEruW1PXErs9Bu+3p+g6zQAj5mnt3uhjpUqR3p7JACIkQwpaLNXeICY85bFs0Uh2IyQie13wH
+KHtrD43OfKzsj+uyS4D3sq92DtulA3vwsehvJDzFMoXciH033jz1mEdqg1np9u6pfK8owbvci4gc
+PY847Au4CLcB6bxHiYiIyN4UtAdDaEs+zrzOW1bZ2DXctx3K0AE7raraw17a08BpZYstCw7mefo1
+jAF1bHK2uphc0U5pW0UbUgd9CMRuSRrCadvRdx1d29N2fd4uK+TGZdGdrqwBTzgeAlUKWPDyVsLw
+usGHoN0nrApEz5XniBEsd1WPlq83eRrv8/TzMsW7NJIb2pK7D5PlA54cC7miHdzBS0W7gz7manaa
+VLTr4DlopzzRPmKk6GN1XTlbRI6eAHzssC/iIlwHqCIvIiJysRS0i6pUeKsQyr7XpaZdKtYplhXH
+Zc3w0JTMyr7W+ckcsssM6S3bg3mZZp2GKnf5J8VITLmJWT5rIlgFqVS93alwzCNeQmlnRtct6buW
+2PfEGIl9IvaRvncIed22m5Fwes+dzb0E/jAU58s661Cq7NW4znyyHRmlw/m02Xe55aC9qmYnL28Q
+BMsv1IYp9cObDqls5xWJqafvA11nLC3RdR1939PHmJu7lbXYYfx+lWr6dI346p0JERERERGRI0NB
+u2iaBoBQBaoq5H2u3XLIpuybPbYSH/8PM6gs70edcs+0stHVZAq55+p0/jg3PRsaecXSLTymsgc1
+FW4OVQ65IRh1MKoAeE/sIy2eg3bp1B2HBmLDevFE3nLMjOR5a7I0NG/zoRqdu4lb8HFLsWBGFXID
+tBDK50J+HMoS62Fv8HyzVWIvc+c9v8QyjZzSxCwAiWiRSKSnpwuBZYA6OE7Psmvphtfi0zczhjcq
+VluGYVYq55fivwQRkXtl63ocETnybrvtNm6++Wae//zn87CHPeywL0dE9inGODwMex132I70xV1O
+s6Zm1tQ0dUUVKkIoVdjS0Tv2kdhHUh9JMeJxaOxV9pgOpQJcbkMFNs8Wz8d6zF+b+p4Ye/q+I/a5
+gVnf9ZPO4qXyXKrKdWVU5rj39LGlbTdz0O474jDm0KnbydtiRSeWAJ9KhXhcV51WHcYNJwSnCk5T
+G7MmsDarmDWBWR3GkD9Md98Sshn2M1vdxrXUZRZAviW8H7qi9/QxryVv+5ZFt2SzXbLsO7q+v+c1
+swrceRKBjVPjLVRYqA7nPxiRY8zMrjKzl5jZX5nZhpl9wszeambfcgFjzM3sX5rZ28zsNjNbTsZ5
+rpmd94f7YsYws78zs2Rmv1Y+vt7MXmlmHzazhZld6EJl/WEkch9z2223cdNNN3Hbbbcd9qWIyAWY
+BO0j/btXFe1iVpdvRamWjvkxrdYkW3nayJVg84CVgG0h77a1SqND064ybJkKPVSVh3XdYyCOuWV3
+CqkEYcB93FILEin2dHQ4LW23pOtbYopEz1XgxBBGGR8n8zEbj43Sy5TuYdp7KHtl29hlvXRYn15/
+uV91AKd8g3zLvY/X4ENT9tUy8JSnjsfY0QWo+oRZJHlFF3u61BNTzEHdV9+DlaHZWpnWH/Q+kcjl
+ZmafC7wNeBir8u0ceBrwdDN7BfCO84zxhcAbgUdNxgC4ehgHeL6ZPdPdb79EY6z+aDR7PvB/sfUX
+tjqCiYiIyL2moF3MZnnq+DgLmiGw5s7ZcWyQVsL2WL0GC3lZNFUO6GGyLhlYbedV1myntGqC1lvK
+zcNwhl22Y3K6PrLsOhbLBeubG6QUCKGnqnqclo3lgmXf03siWT53SKF09B66g6+Kz8nyNdUhT3Wv
+DKoqUNVGXQWqKm9hNtzytlx5WvtQDfdYOrNTtvAeA/X2QG7jCw9DWzkzQnCwSLKeiNN5haVAiiHv
+JV7eNPCyWXh+kyGM+4J7yFPuywZh5QgRuVzM7Erg94BryX8U/CbwauB24LHADwLfCTxujzEeA/xn
+4AHA3cAvArcA/wA8GPgG4PnAE4E3mNlXuHs86DEmngR8O/D3wEuB95J/N37Ffr4nIiIiIjtR0C7m
+8xy0U6mkpjLN2ig5L5UwbJOwPaxdLsk2jLtbWWkytrofQnleru15endKedswSrgsm1Cn5PR9z7Lt
+2FguWd/YIMZACB2h6nHLn2/7LgfTIYTWYZzyPrxj4GZllne+0FAK9hVGVRl1PQnbFsoU+ECMEeuh
+L1X3cep5abI2fTNiCNvlrOP/5TcXJrcAhIRbRyTSeYAUiISyxnw1eyA3brcxaHswSIlUAnbeU1tB
+W+Qy+wlyW2oHfsTdf3by3PvN7HXAm4Cv3WOMVwEPJAfar3X3u7Y9/zYze1MZ50vIwf1XL8EYg38C
+/BnwVe5+ZvL5d+3xGkRERET2pKBdDBXtlPI+0nG4t9zgrKzYXt3bZJb5MM28rCHOBea8l3WuHodx
+vbW705c11H3KG1W5572iU8p/e42e8BhZdi2biwXn6kAXjRA6rOrBOhZ9xzJ29Kwq2mZGNfQLK6Ed
+Cwzzw62sHR/eLKgro6oDdR3y4xCoLd/3fQ7qKSVSJFfiY35zYNgjexxoy6xNG8N2rmKX25aKdiSW
+ufkpBSq3oefZuDf50EttrGiXaeNmjCFb23uJXD5m1gDPJf/Af2BbyAbA3aOZPQ/4MNDsMMaXA08p
+Y9y4Q0Aexvm9EtqfzbaQfBBjTC+pjPOCbSFbRERE5KIoaBdjIbjs/5yDYsAckhkpDAucwch7eFkA
+q3KWnVZ3jdy9uzKjtlAe53scquTECurkmEWs7Awdo+dmYp4DeRcji65lfRnoEliIhKoH6+k80qZE
+WdpdQi3jlmRDkdls0jysBO3hDQMrFfdh267kTgyAp0lTMi/V/bzuO21ftD1+kMcc8z2rHD4G73Ju
+GLqfW96GLFjuVD6GbBvXmk8zvFl5bcM6bRW0RS6n64FPI/8Uvmq3g9z9VjP7feAZOzz9DeX+b9z9
+g+c53zvIIfmJZhY8ryk5qDGm/sHd/+t5xhERERG5IArao2Hqcw7QwUujMA+kAO6rVc9DezEb2ooH
+SqOzPO18mCZehbxsux626CpTqKuU13xHL2HeElii63Olm1jWb6fIsu8IS1hGCFUihISFSLSybRfk
+Sm8Z34cmbENoZZjmPlljPnnNXqZj9ym/xxDL8zEm+hjpY67uj43RpunZhu/Y0AVua2V7+PTq2dVa
+7qEi7QYMe3InVo3gxqtcVa5tOH+Zlq6Ctshl9fmTx7ec59h3s3PQfkK5/5wL6OrdAA8CPnmAYwwc
++MA+xxARERHZNwXtwocGs5YbeOXKbMCByoeInUrjr2Eqs+OBcWpz/ieN08SrYNQGdchhuwkBs5Cb
+q5Er10YO2ZAwS3QWcU9lHXdk0UO0SB2NUHne97rc573FVlO0rcrTxFPplE4sbx6M09xt7GSOe2ks
+5njMATuWgE7Za3vY03rY13r4/owD2iS2b8vYW7+5vnp+8pxPnhrXrce8Vt22Va3dVwHbMLzci8hl
+86DJ4x07gU98YpfPXwMX/B6ZAycPeIypHaee33tOvsTzqTiau5JomyMRETkct91227622+u67jJc
+zcVT0B6t/t42NO9azU0uk5592LIr5bBtjlteO+ylsGJlc6zcVKw0HqtsrGqHYCQCVQnauYN2Kvta
+R1KCGPIC5d4jHhPRjVAZoco7WoUKqhqqOu9xbWWxeKhyZZuYp3mvwu0QUKevhtKZrexhU9Zgp5S3
+F0vj2wbb/j47djvLt7EFmnl59dv//jvUs3f4cLzEHOZjyuE+zwrIU91XfdS3nn7rvx8Ruczu7YSS
+IVn+GfBtF/B1tx7wGFO7dSO/CHcc/JAiIiL3czfffDM33XTTYV/GgVHQFhGR/ZhWfh8KfGiPYx+6
+y+c/RX6/7Ip9rK/ezUGMcSmMFX/b55uA+z3uMNx+++1cd80mvw4AACAASURBVN11h30ZIpdU27YA
+3HDDDcxms0O+GhGJMfKQhzzkvMfdccf4hvaD9jrusCloF7/9G+84un/jERE5fH8+efxE4J17HPvE
+XT7/fuBLgc80s2vc/XxT0C/VGJfCZLLQ/gr++z3uMKSUuPXW3SYBiNy/TP7SLiL3LUc6vyloi4jI
+fryXXNW+Cvh24GU7HWRmj2D3fbT/E/AC8i/G7wd+7F5cx0GMcSksgTl5Nc5RCf8iIiL3R9eQu1Ut
+D/tC9qKgLSIi5+XurZm9AvhB4IvM7EXu/tLpMWZWAb/MDntolzHeambvBp4E/Csze7+7v263c5rZ
+44DPcPffOcgxLgV3P3UpxxcREZH7FjvKU9dEROToMLMHAH8BDIt3/x/g1eQK7mOBF5L3234Pefq4
+A492949OxvhM4E/I66oM+G3g/wX+ltyY7Brg8eT9sr8EeKm7/9C26ziIMT4CPAp4lbs/9+K+MyIi
+IiJbqaItIiL74u5nzOwG4K3AtcBzym08BHgF8MflfqcxPmxmTwH+I/A44H8EnrnToeV296UYozjS
+a7tERETkvktBW0RE9s3dP2hmnwf8MPAsclX4LLlZ2i+5+2vN7EZWIXenMT5kZl8EPBv4ZnL1+yHk
+rbs+BfwN8F+A33L3P71UY+x1jSIiIiIXQ1PHRURERERERA5QOOwLEBEREREREbk/UdAWERERERER
+OUAK2iIiIiIiIiIHSEFbRERERERE5AApaIuIiIiIiIgcIAVtERE59szsUWb282b2V2Z2zsw+ZWbv
+NrMXmdmJAzzPc8zs98zsNjPbNLO/M7NfN7MnH9Q5RI6TS/mza2YvNrO0z9tXHtRrErm/MrOHmNkz
+zOwmM3uzmd0x+Rn6tUt0zkP7vavtvURE5Fgzs2cCvw48gHvuq23AfwOe4e7//SLOsQb8R+DrdjlH
+An7K3X/q3p5D5Li51D+7ZvZi4MU7jL2dA09z93fcm/OIHBdmlrZ9avqz9Sp3f+4BnuvQf++qoi0i
+IseWmT0e+E3gSuAs8KPAlwJPB36Z/Mv5s4HfMbNTF3GqV7D6Zf+HwDcBTwKeB3yI/Pv4xWb23Rdx
+DpFj4zL+7A4eB3z+LrcvAG45gHOIHAdebn8P/D459F4Kh/57VxVtERE5tszsHcCXAx3wFe7+7m3P
+vxD4OfIv6pvuzTvfZvY04G1ljP8E/E8++eVrZg8G3gs8CrgL+Ex3v/vevSKR4+Ey/eyOFW13ry7+
+qkWOt/IzdQtwi7vfYWafDnyE/HN6YBXto/J7VxVtERE5lszsieS/qDvwK9v/ol78W+CvyO+4f7+Z
+3Zu/bL+w3PfAC3zbO9zu/ingh8uHVwGqaovs4TL+7IrIAXL3m9z9ze5+xyU+1ZH4vaugLSIix9U3
+TR6/cqcDyi/nV5cPrwKeeiEnMLMryFNZHXibu398l0NfD5wpj591IecQOYYu+c+uiNw3HaXfuwra
+IiJyXH15uV8nTyHbzR9NHn/ZBZ7jicBsh3G2cPcO+P/I1bcnqvomsqfL8bMrIvdNR+b3roK2iIgc
+V59Lfsf7Q+6+vRPq1F9v+5oL8U92GWev89TkJk4isrPL8bO7Rdke6BNmtiz3bzezHzazqy5mXBE5
+cEfm966CtoiIHDtmNgeuLh9+bK9j3f00uXIG8MgLPNV1k8d7ngf4h8njCz2PyLFwGX92t/vqct66
+3H8l8G+AD5vZN1zk2CJycI7M7936oAcUERG5D7hy8vjcPo5fB04CV1zC86xPHl/oeUSOi8v1szv4
+APAG4N3Ax4EG+B+Afw58LXn99+vM7Jnu/nv38hwicnCOzO9dBW0RETmO1iaP230cvySv4zpxCc+z
+nDy+0POIHBeX62cX4Bfc/aYdPn8L8Boz+xfA/w1UwK+Y2We5+36uSUQunSPze1dTx0VE5DhaTB7P
+dj1qZU5eE7p5Cc8znzy+0POIHBeX62cXdz9znud/CfhVcpB/OPDNF3oOETlwR+b3roK2iIgcR2cn
+j/czXexUud/PVNV7e55Tk8cXeh6R4+Jy/ezu182Tx191ic4hIvt3ZH7vKmiLiMix4+5L4FPlw+v2
+OrZ0FR5+Gf/DXsfuYNqIZc/zsLURy4WeR+RYuIw/u/v1wcnjR1yic4jI/h2Z37sK2iIiclx9kDzl
+8zFmttfvw8+ZPP6re3GOncbZ6zw98LcXeB6R4+Ry/Ozul1+icUXk3jkyv3cVtEVE5Lj6L+X+FHD9
+HsdNp4O+8wLPcQurZiy7Tis1swZ4Mvkv7be4e7zA84gcJ5fjZ3e/pnv2fvwSnUNE9u/I/N5V0BYR
+kePqDZPH37XTAWZmwHeUD08Db7+QE7j7OeAPyNW3rzazh+9y6DcDDyiPX38h5xA5hi75z+4F+N7J
+4z+6ROcQkX06Sr93FbRFRORYcvdbgD8m/zJ+npl9yQ6HvQj4XPI73i/b/o63md1oZqncfmKXU720
+3NfAy7dPdTWzq4GXlA9Pk7sYi8guLsfPrpk9zsw+a6/rKNt7Pa98+I/Ab134qxGRC3Ff+r2rfbRF
+ROQ4+37ylNITwFvN7GfIla8TwHOA7ynH/Q3wb/cYZ9d1mu7+djP7TeBbgW8s53kZeZrpFwA/Cjyq
+jPFD7n73Rb0ikePhUv/sXk/eG/vtwO8Cf05uwlaT13V+G/A15dge+B5317Z8Inswsy8DHjP51NWT
+x48xsxunx7v7q/YY7sj/3lXQFhGRY8vd/9TMng28hjyF7Ge2H0L+i/oz3H39Ik71XOBK4OuBfwo8
+dds5IvBT7q5qtsg+XKaf3QA8Hfjq3S6DHL6f6+5vvpfnEDlOvhu4cYfPG/Dl5TZwYK+gfT6H/ntX
+QVtERI41d3+TmX0BuUL2DPJ2IC3wIeC1wMvdfbHXEPs4xwJ4ppl9K/CdwBcCVwGfAN5RzvEnF/M6
+RI6bS/yz+ybytPCnAI8HHgo8mBwI7gT+DHgL8MqyJlRE9me/nfr3Ou4+8XvX3LUrgYiIiIiIiMhB
+UTM0ERERERERkQOkoC0iIiIiIiJygBS0RURERERERA6QgraIiIiIiIjIAVLQFhERERERETlACtoi
+IiIiIiIiB0hBW0REREREROQAKWiLiIiIiIiIHCAFbREREREREZEDpKB9H2Fm/9nMUrl95WFfj4iI
+iIiIiOxMQfu+w7fdi4iIiIiIyBGkoC0iIiIiIiJygBS0RURERERERA6QgraIiIiIiIjIAVLQFhER
+ERERETlACtoiIiIiIiIiB0hB+5BZdqOZ/b6Z3WZmm2b2ETN7g5l9470c81FmdpOZvcvM/tHMluX+
+XWb2k2Z23QWOd5WZ/biZ3WJmd5rZWTP7azP7ZTN7wuS4YfuxeG+uW0RERERE5P7A3LVb1GExs4cC
+bwSeNPn08C/Eyv3rge8Efhv4qvL8U939HbuM+WPAjwFr28abjrkAftLdf3Yf1/hU4DeAh+4yXgJu
+cvefNrM0PO/u1fnGFhERERERuT+qD/sCjiszeyDwduBzWIXXjwDvApbA55ED+LPY597ZZvaLwPeV
+4x04V87xj8C1wFOBK4A58BIze6i7v3CP8Z5MDvgnJmPeAvwlMCvX99nAT5rZp4Yv2+/1ioiIiIiI
+3B+pon1IzOxXge8qHy6B73X3V2075gnAa4HPAFpyuN2xom1mzwZ+k1XIfQXwA+5+bnLMFcDLgW+f
+HPfN7v6GHa5vDvw58Fnk8Pxh4Nnu/r5tx31LOVddrs8AV0VbRERERESOKwXtQ2Bmnw389eRTN7r7
+a/Y49v3kqvJQLd4StM3MgA+RAznAa939OXuc/7eAbyxj/Xd3f+wOx3wv8O/Kh+vA57v73+0y3jeR
+p7g7CtoiIiIiInLMqRna4Xgeq/XS794tZAO4+98CL5scv5OvBR5djmmB7z/P+V8AdOX4zzKzr9nh
+mOcOlwD8wm4hu1zjG8hT1Pe6RhERERERkWNBQftwPHXy+Nf3cfyrzvP808q9A29299v3OtjdPw68
+ZZfrGaaYf/HkU/9+H9e465sFIiIiIiIix4mC9uH4wsnjd53v4FLVvnOPQx4/efxf93kN75w8/uJt
+z30Bq/82zrj73+xjvD/Z53lFRERERETu1xS0L7PSbXw2+dRH9/mlex33kMnjv9/neH83eXz1LuM5
+8LF9jrff40RERERERO7XFLQvvyu2fbyxz69b3+eYex2323hX7jHefq/v3PkPERERERERuf9T0L78
+tgfSk/v8ulP7HHOv43Yb7+we4x3E9YmIiIiIiBwbCtqXmbvfTe4MPnjUPr/0kXs8d8e9GO8zJo8/
+ue254WMDHrHP8a7b53EiIiIiIiL3awrah+PPJo+ffL6DzewxwIP3OOT9k8dfus9rmB73vm3PfQBI
+5fEDzewe+2zv4En7PK+IiIiIiMj9moL24Xj75PG37eP4G8/z/B+WewO+3sy2NzfbwsweBnzdDl8P
+gLufZWt4/+f7uMb9vA4REREREZH7PQXtw/Grk8dPNrP/ZbcDSzX7B8gdwHfz+8BHyuM58LLznP8X
+gaY8/pC7v22HY35tuATgB8zs0/e4xm8Ann6eaxQRERERETkWFLQPQdkX+5XkEGvAr5jZd2w/zsye
+ALyV3JCs3f78ZDwH/vXwZcBzzOyXzGxLgzIzu8LMXgk8a/hS4Id2GfYVwIfK4yuAPzCz7fttY2bf
+Avx7YLHb9YmIiIiIiBwnljOaXG5mdhXwLuCx5HAM8OHyuSXweazWPb+evNf1V5HD8VPd/R07jPl/
+Ai+YjHeWPE39E8A15KrzsHWXA7/g7i/a4xq/lBz0T0y+5k+AD5L3An9SuX4H/iXw8nJccvd6H98G
+ERERERGR+x0F7UNkZtcCbwSeMHxq8vTwL+aNwLcDv8N5gnYZ80eBHydPId9tzAVwk7v/7D6u8WnA
+bwAP2WW8BNwEvIRV1f20uz/ofGOLiIiIiIjcHyloHzIzM+A7yA3HvgB4ILkC/WfAK939t8pxbwe+
+khxun7Zb0C7HPhL4buCfAY8GrgJOkyvmbwF+1d0/dgHXeBW5Yv1NwGeS13ffCrwDuNnd32tm1wD/
+WK7v7939M/c7voiIiIiIyP2JgrYcCDP7anJTNgfe4u7POORLEhERERERORRqhiYH5Vsnj285tKsQ
+ERERERE5ZKpoy0Uzsy8hTyNvyBXtz3X3/3a4VyUiIiIiInI4VNGWXZnZI83stWb2Zbs8H8zs28jr
+vmtyyH6jQraIiIiIiBxnqmjLrszs04GPlA9vB94L3AZE4KHAU1h1I4fcIO0J7v6Jy3mdIiIiIiIi
+R4mCtuxqErSH/0hs2yHT/3huAb7lQrqZi4iIiIiI3B8paMuezOwJwDOBJwPXAVeTtws7R96G7F3A
+6939TYd2kSIiIiIiIkeIgraIiIiIiIjIAVIzNBEREREREZEDpKAtIiIiIiIicoAUtEVEREREREQO
+kIK2iIiIiIiIyAFS0BYRERERERE5QPVhX4CIiMh9nZmtA3MgAbcf8uWIiIjcn11DLhgv3f3UYV/M
+brS9V/HqN/+0A2z/fpjZeHN3Ukrjzd3xcj8eC4QQyq0ihADYdEQgjweGe8In44Hnfzzlw3e5nuEc
+VRXyWAZYPk9KiZgiKaVy7Oprpra8Dvd8jIVyjoCVawVIcfW6kyfch3sHX117SpHk+ThwAkYo44Yq
+UIV8jznJI8njeK0xJWJMmAVCVVNVNaGqgeFawur6Qh4T4Mavf7EhInKIzKwHqsO+DhERkWMkuvuR
+LRwf2Qu73KYBe2vYtiG/bmHkDOz3OJ4SXKefd5iEVsrXTsfIg/oYWIfHww0Yw7m5DXEcx1dBmxzT
+U3kDIHkO2qmE3eSpHJdj6xCUcygePksO2Z5WQdsZw3V+AyBfn5VrJpUze8ICBB+CumPkYBwogT/k
+a3HL5zMCIXi59uF7VN7YAFJy8gksB3oLeQwPuKXz/WsVEblcHPKf0w9/+MMP+1pEZB/atuWOO+7g
+IQ95CLPZ7LAvR0T26eMf//iQj450xVhBu9gatGH492aWQ7NN0raZ4Wb5GHc8DakZMCMlL1/n5bEN
++ZZVInYSkzDNJGBTAvBQNU6pDG1jtdmTkXBCCc85sJdxfGsINy9fF0qYLtcznGO4hhACjhM8vzEw
+/JOHTPl6yUHbzMfvjYfyZkByzHL9OZ/DMJ+MFFZhGyCQK9t4qfqb59dWnh0CfnlJOOTqeDI8hC3/
+TkREDtmdwDVXX301H/vYxw77WkRkH973vvdx/fXX85a3vIUv/uIvPuzLEZF9uuaaa7jjjjsg/+49
+shS0txkCt5ewOUyp3lpVXt2PleuhaD0ZZ1qNxmzsPOc++boSXFcxeQjcCSeNU6vzEMO0bieZ5aqz
+BRiq2MP5rAR/W1W7twTuUmEeg3YapqsPFfoSjn1r0M5vCKRc4bccqs0huWPJcXPCMK07DNO9yeN4
+CfrDmwVAMsth2/NrMndCcFKC5OWWfMsU/VSmwFelai4iIiIiInLUKGgXw/RpWE39LjVUwBmWN2+Z
+Jr6toLr6GptMH99aiV59UTkOYMuUbh9q6WNYHj7jnvJUbFIJp7aqnPtqvfVwmvF+PH8O66FU5LdW
+00uYtmEK93b5DYA8xnBPngKeWH2+rBsPoSoh3YZCfa5y27CUfIzhBEuTqfR57CFpuw0V+uF7P3xf
+hykEIiIiIiIiR4uCdhFjrhpvDdngHsj9be5ZPbWy7hgPq8hXHqyq1pR1yDV1Veep0ZMQnpuH9cRk
+QCphNId7JgE9lXKzbznHUIneOgV9fDNgDMB5XTQhh9OykLC8iMmU+LCqeBulMRrDNPMyP51Uwjtj
+yJ8G3hCqUnmvGGv4q0Xo9/j+lQS+ZRq4V1ClXClPqXyP0qrBmm0J7CIiR8cnP/lJrrvuusO+DBHZ
+h7ZtAbjhhhu0RlvkiLr22mt5z3vec9iXca8oaBcx9sBqSvcwdbyq7tn1e/VBqRBXOzVAszFMY7nC
+W1UNIVSrNccOfeohGimnXywEzByCY9FIFrBQrTqEp7y2m7ImmzSstZ5Up71UgsvU8RCMRCB4rqG7
+2VBL3/Ka8jru0mxs8s8Yz8v3JP9v1bBt/GZgBKvGmw3fg+k3DEoxv7yRsOUNhVUTtPGNCB+6o8ct
+ndlXNxG5LzOzG4FXkH+gH+3uHz3kS7oo7s6tt9562JchIhegrPUUETlQCtrF1or2KmhPd2uZhsHx
+45Cr3kMAdltVq1fHlYp23VBVdakOl9zaB9whJCcRCeZYBRZyYzBLieCJGGO+kSDGSRgdrne1hnqs
+ag9rtAmEkPJ1muew7balvmylRDx9jWHYTmvo9Oal+/c4F3zYhmxVuQ5hCNp1njZenlhV4ocO6V5C
+dhoDfhi27LLx2VL1T1uq2r6lA7qIyFHziMO+ABHZlxa4A3gIoIq2yNFyG2Vro/ssBe1i2GN66BQ+
+rZYOW2Bt34d6Opd7rPgOiXIIwKnkUzfcA0beH7oKubrddkuqbklrC6J35P+gIu4R89zBewidaXIO
+K5c3VoDLlHTGVd7T5mSrEGuWa9R5+62JEqy3NEEbOofb9ONVmMcTTsgvsHQOTzHgZsQInpzYJ2Lv
+pJjG6d5mECrDKggVhJDvcaCavobh5W6vvg+Lw9UMTUSOGgPUdVzkvuF9wPXAWwB1HRc5Wq4D7tsz
+xBS0i2nQNvOx2/XwuXGv6WlVe5yqPZluPoZd8rTukNc3ewqQctBu6jVmzQma2Zx6uUkVNjFq+rgk
+ppbkXd4yzMHGrbbIXcY9jttKh7Fh2ipc52njMMTTLV3Ay1ptG6rUY9IedswO2FCFHkN2GLuE21gN
+L/tqDx3JKevOk5fXmqeMx97p20jXRfquL9PYc9CuaqNuAvUsUDdhS9dzs6H4vZqOP+1BN0xTFxER
+EREROYoUtIuqylPEV6F6uv549fnt08dX1etVc7KhAGswVrQ95bBt1NTVGvP5KU6cOEVlDUYNXrHs
+AtYbfXRS6jFymddKxdw8QMrV3JKvtzQUG8vc45V5mbZuk/t8Zdvvy0eUedtlCfWw7rrU1UsTsuSG
+pUkALo3S3CFFI0YjRehap11G2mVHu+zGkB0C1LPAfF7jDA3ichO0odP6OEvAyrr3ezRUU9gWERER
+EZGjSUG7CEPQHrf58rH5lm8tp24L2Tt9PpRKcOm+XW5YjdksB+3mJGvzK0u6rDCqPJW8dBz3NKxB
+zuc391zNHrbLKtXpEFZrnrcG7ck68kkn8fHZUjo2n1S2y6LoVcz1caJ4mO4RnsjdwKORohMjxB5i
+hL5z+t6JHbTLyGKzZbG5ZLFY5iniIc/4ns0r+pPzXJy3QFUbFYGypJxV0N76PV41OQ+l2i8iciTE
+w74AEblQDwNeXO5F5L5iKJByxH/3KmgX49TxUil1IoFA8gRlq6lc/LWxmDqdfm1jgzPLU7CtJliN
+WU0VZuXWUFUzqmpOXa/RVGv4zDAq6tDQ1A2LUOXp3W6k1I/bfyWGPbQD5nk1dTCjCmGsAA/hdFhP
+vWo8NnT7tjG0jttQT7ejnnZOD4aHSFUZniBS9rcmT6vPjcmcGJ2+c7ou5fvW6btczV4uOjbWN9lY
+32RzY5MQvARtZ+1EQ99HnLyOvJ5VNIBZRShd0500ubhJx/LxXmu0RY46M7sK+NfANwKfDpwFPgDc
+7O6v2+cYnw78APA1wKPIXSpvBf4Q+EV3/4t9jPFM4PvICzJPkRdSvxH4eXf/hJn9XRn7le7+3At5
+jUV5l1YzbUTuOx4G/ORhX4SIXKBJ0D7S3dIUtIsq5H9hqfz7yjEvYclKKM0JL8fXoUK8YhZKes3B
+2awpQXsI1zOqMKcKM+pqTl2t0dRrmOWQ3TdrNPUs73dNrqz3sSP2HX0a1mdXmOf11cEClQWqsh2Y
+e1qt0y5l4aFR2lB4n+4PPs2vw+tIZZ11SimvLa8MPJLGqdx5EE9O8tzsrO+hXSa6NtG2kXbpdMtE
+2zqbG0vWz22wfnad9fUNQnAsJEJwTp6a4+6EEKjrmjm5+l5V5Q2P8ZyTTudjTX6ozusvtCJHmZl9
+LvA28t9mhz915sDTgKeb2SuAd5xnjO8Abi5fN53G8lnAY4Dnmdn/7u4v2WOMlwP/a/lwGOMxwIuA
+bzOzr2d4t1JERETkAChoF6GqcvArRd8wdr1e/d1r2J1qLBgPwW9oHEZgnAZuNcEags2oq1LRHqrZ
+5dZUa1Rhhte5y/iyakqQTaS+J7CkTUayhBG3VLSNQLBqErSN5GX2RJm2zrD2edIFHWzrVPhJXvXo
+ky20DPeIB8r405uNa8/73mmXieUi0i4jy0VkuUi0y8TG+pKzZzY5e2ads2fObgnay+UaIQSaWcN8
+PitrtwNN46vmbpNK/RC2vUwvH97sEJGjycyuBH4PuJb8Q/ybwKuB24HHAj8IfCfwuD3GeAZ5j23I
+lfCXAn8A9MCXAj9C3pfn/zCzu9z95h3G+CFyyHbgH4B/A7yXHNz/WbmO1wEnL+b1ioiIiEwpaBep
+7Mk8NDTbEiwZ9ou23I28BL5ggRACIeQ12KFMFw/WEMKcymZUNmM2O8l8for57CQn1k6xNjvJrJ7n
+bb7GLbIC5mt4PIXh1KGm7Ra03ZKuXbDsFiyXG9TtjK5bEMpe26GsZ8YiwS1XgCd7YlOeJhlhDMy5
+Gp2fSmUZdK7gOykH9rTa5iwl6Lqeru3p2lgq9jWBGjyQUo3FhsYCKXSk0JOsJ3iPd0636Fisb4Ll
+rcuwROy7XL13iF3k5Kk1Tl4xZ7lYYzavqBqoG6Oqy9p3Y9L9HBWzRY6+nyDvzeHAj7j7z06ee7+Z
+vQ54E/C1O32xmdXkSjbAOeDL3f3PJ4e828xeD7yLXDF/qZn9B3e/czLGQ8nzQh34EPBkd79rMsY7
+zex3gbeTN9FVRVtEREQOhIJ2MVR50zRcswrZTq7ykgwjARWhyi20Q5XXYYfQ5HAd5lRhTh3m1GGN
++fwkayVsr81PsjY/wayZU4eKvM64jEfezquqambNfAzZbbdg0W7Q1A1VVbOsKrCEl9DqlCZpRMa5
+4KVJWl5WXUrweV8uLKWyBjpXulNO4nnLLk85cKdhu65E3yU21ls2N5ZsrLdl+vsaTZiXan1DXc2Y
+hYZk7XirWOI9JWgvcO9J3uP09O0yV9n7RN/2LK44wXJxknYZOXFyxvxExdrJOk/pL93Sww7LzkXk
+6DGzBnguObh+YFvIBsDdo5k9D/gw0OwwzLOAh5cxfnpbyB7G+KiZ/SvgNeSK9HcBPz855EZgrYzx
+/dtC9jDGu8rU8v/twl6liIiIyO4UtIvpntn3uOGlGVoiYliZoe1V3poqVPW4Bruu1qhDbnRWVydo
+whpr81OsrZ3M97M1ZrMZs3pGXdVlx+2yd3cIY8ju5ydpS8huuwXNMlfALQQsBFLqiN6RUkeihOwh
+gZYtsrz0CrOyHZi7Ecua5zw1fPJGwuSWSKQEsU+kaCwXkbNnNzlzeoMzd28yq08yb5xZHVibzzgx
+r6nnazRrJ0lhQQoVKRiV1yVo9yzObRJTN96Wmw2pd2IbaZc9y82Odhnp2kTsIu5z6iowa+r8csJY
+qB92IFPYFjm6rgc+jfyj+qrdDnL3W83s94Fn7PD0Vw+HsZo+vpP/ALwceED5mmnQHsb4pLu/ZY8x
+Xo2CtoiIiBwgBe1i3J15rJzmVOehhNKxjDpduz2sFc7Tx+uqoalzR/FZdYKmOkFTnWQ+O8m8Ocm8
+WaOp5zRVrtSGoamaeTlvbnKWQkWdGqpQUdc1TdNQVRUhhPFzfWzp+5YuLompI3lPTKVibKk0EStd
+uyeB1Cyvfbahoj10K/fV+uwUc+Btl07bJjbXO07fuc7dd21w+q51ZnXL2qxn3iROnQSurGnsBGFe
+U9mMpnJooKlnGIHUJ9plS0r9GLRJzkbYxB36PtF1kbbtWS47uvYkMZ7ASFhwmlmgdqOxgJdF9EMd
+XkSOpM+fPL7lPMe+m52D9rB2+yPu/qndvtjdOzN78L8bTgAAIABJREFUP/BPued678eR/5D70/Nc
+w58DLTtX1i9QAq7Zx3FVuYmIiEgWWe3Y9UkA2rblfe9735aj2ra9vJd1LyloF8N65kAoDcDy9llm
+juEYValwJ/BhE7Dp1+dqdF03NPWsBOo1ZvWcpplR1w1VqPN67hyxV1873CzkgnTIjw0nVEZdV1RV
+oKoqmqZh3pZp5d2Ctl/Q9Uu6vqXrl/SxJdGTvBsr1viqk3re/svz+u5U4mrpRu7uxD7R95GNjY6N
+cy2b6x3nziw4fdcGd9+1zt13rdPUC+azJfNmk+UVHcEr5vUa6ZQTLNDUDcGMtfkadVXnddj9MDUd
+8Lz/drvscDf6LtK1PYvNJevnNlksNun6U7liT8faiYa1kzUWGqyyPL0fHxrBi8jR86DJ49vPc+wn
+9hjD9/H1AP+4w3khV9UB7tjri909mdmdwEP3ca592PN0IiIisk933HEH119//WFfxr2ioF2ESQdr
+t5DXLhtAhZWicO7Gne/HcMywJDoQqoqqbmiaOU09Z1bPmVVrOWhXJWhbNWlSttqbeyiY5/28A8Gd
+EIzKK5I31HVD3TTMuzltd4JFu8lycgvtJmB52y13zGMpVqcxYI8dvId1zsFzfzLz8vo8V5fbxOZG
+y9kzG5w5vcmZIWSfzlXtpl4wqxfMmg26RWLerHHlySvxlHJVvmlompr5ELQx+j6O5zeGoN3TdYnF
+omWxaJmtbzJba1i0p0iphRCxEIlpDao59SxPg0/uJE8kJW2R+4KL/UG9T/2gmxlXX331eY+rqmq6
+D6iIiMixF2Mkxrjlc1dffTWvec1rtnzuhhtu4I47jv6b2graxRi0J1PEgxuGE8eA6KQ0dMDe2pQr
+V8ArqlDlyvakul1XTelMHjC33Jxs2Nh6WFI9dAmfBn4CTg0kYtPQxIY+rtHHE8wWcxbLHOir0GBW
+kdyIKTc8c4+Qygpwz83OvOwRns8FKeVp8ql0/07R6bvIctnnrblOb3DXJ89x+s5znDm9zt2nNzhz
+ep26WjCrN2maNVIPV568gs0HXEXXttR1XW4Vs2aW15VjpJjKFPm8x7d7yj9M3pHcqRZVrtzXga5v
+CVWimkHdgFWJegZrJ/J0+5giMaWxU7yIHDnTpmMPJXf83s1uVeQ7yX887qfKfO3ka7Zfx0PJW4Dt
+yswCq+r3RXn4wx/Oxz72sYMYSkRERHYwm80O+xL2RUG7aEplYdg328n7WbvlieMJxjXVIVTj9lnm
+4CkRY0/XtQRbYt5gqSLQUDHHI0RL9Bapq4q6CjR1INaBuq5o6qpUuofSjU3+P0/2Ng8EKqrS5Kxp
+hj2layxUWKgJoaaqGtpunbbLndBS6nCLOH3ZoozVmwNe3kRwzxXmzcT62Y6zZza4+1Ob3H3XgrOn
+F5y7e8livaNf9HifiKmjjU7f9TRVxZ13nmA2qzFzTp06xamTpzh56hR97HMTOQvU9QxImOW148lD
+rrZ7KJX9snVaTHRdT7vsWC5aFosla4uatm3ouo7gQ9DuFbRFjq5ph/AnAu/c49gn7vL5vwCeAjza
+zB682zrtsg3Y48l/fP7Ftqf/khzCv+g81/v55H2171PVcxERETm6FLSLadD2MjU5F55zVTg448dD
+o7SQ987CUyL1PT0t+AJShXlNZXNq60gkeo+Yd9ShomkqUl2Rmgq8IQSoCWUK+diWLYd+HxqwWd6r
+ezi2NoLVeXutqqEKw23GZhhCdiISSHSloVuevj1U4mNadVRPfWK5SKyfaTl95yan79zgzJ2bnLlr
+k/W7lywWHd0yknon0dFbX6bNJ2azCrNE3y940Kddzac96MGEqqLvI+7kRnH1DIgl7Me8bryE7KFp
+W/IhaEeWbZenlG8uWS6bErRnVECfemLsib51aomIHBnvJVeTrwK+HXjZTgeZ2SPYZR9t4G3A95Df
+Gvwu4KW7HPc/Aw8k/0H5tm3P/QHwdOBqM/s6d//dXca4cZfPi4iIiNwrCtpFXktM3t4qJYJDxEtD
+NErcNkIo1eYSts1zs7EYe/CWlAKkioo5dWhpQg8p4jHvwV1ZRZzVpKbGU02w3OxsWLucTcI2OWwb
+oVTUA1Y5FmrqKk+frqs8fTyEHLohh+y+68AhuJHKNtoMr8XALU9ON3c8RtrNxLmzLac/NVS0Nzl7
+14KNs0u6rqPvezwmUorEFEke6fu2hOwlG5vn6PqeUAVOnDhJ1/d5dnwJ2u497hF3w0pTOchV7ZRi
+vnmcVLSXbG42nFw0tO2Mvu9IlKCdOmLqL99/ICKyb+7emtkrgB8EvsjMXuTuW4KymVXAL7N7p+83
+AB8n76X9Y2b2FnffUrE2s0cCP1c+3OCe24C9CngxMANeZmbv3l4ZN7OnAN+HqtkiIiJygBS0izD0
+JysV61R6gwdb3ZyyxngoCbvjJFLsSanNe2ybkyqDWJF6Iy4t7xfdO6lzqqrm5Ik1Tp6Y42mtrFt2
+gjlV3TM0AXcva6lDWbs9VNIxAvm5FALmThWcKkTq4NQhEmyB+QxPDbHr6Hro+0hMHVUVCJVRBaPr
+oF06y2Vi41zP+tme9TMd5+7u2DjXs9yIdEsn9kAyzCsqS2BOIuYp56mna5dsbq4TzNh8wANZLhf0
+XTu+hqZuWJvPiTHkgBydmEplvaxXt7K1GdRU1XBrqOuaUFVbmtVNt2ITkSPrp4BnA9cBP2tmjyfv
+V3078FjgheT9tt/DDtPHy7Zd/wL4bXLF+p1m9nPkKnUEvgz4YfJeWg680N3v3DbGbWZ2E/AzwGcD
+7zWzl5RzzoEbyG8G3ApcAVyNAreIiIgcAAXtYrVD9nS7rWFf7VzJ9i3HlinXKZJIY2DME6udfgnL
+KrJRdfRtom8jfZto6oYHXHkF6QFXUiZ4ly3DEqEKZT/r3CN81Vgsh00YQjfj2uZ8Cxh1vnkDqSb1
+NamraRewWEQ2F0u6fsGsaWhmNbOmpm2dxWZksdlz9kzHuTMd62dy4F5sRPoleMzT4AN52zE3w2Ku
+iuc3CPIbDqnPgbvvOmLXEWPE3KhCoGka1k6s0bVG2+XvWX7dZYsudywEqhCoDWazOWvzNdbW1lhb
+O8FsNqeuG0IIhJCbxFUWMFfHXpGjyt3PmNkNwFvJ66SfU27jIeQK9B9zz0r0MMabzew7gZvJQfin
+ym06Rg/8uLv/0i5jvMTMHgU8H3gk8O+2HXI7efr5b5WPF/t8iSIiIiK7UtCeGJqPYWVNNqwq2sHG
+ajMMa7kTKUFMeWssT4mUevBISyLQUrGgXfT5tuyZNzNi3xEMZk3uKO4p4SkSQsjrqlMOofP5GvO1
+eZ4ubgEL+S2AHLZzyM7ruCvwCqMGGkgNHmtiV9NuGhvnImfPLVm2C06ccNbWjLRW0y6djY3IxnrH
+ubtbzt3dsX6mY+Nsz3Ij0bWG94HgdZlqnjuGDyGb0kmcUtXvuiV91+Yp3rEvXdiN2azhxNqJ3LXd
+I33fjd/zYU18PjYQ6sB8Nmc+zyH7xNoJ5rNZfrMhVITgZfu1vO2aiBxd7v5BM/s8cuX5WcCjgLPk
+Zmm/5O6vNbMbGfce3HGMXzezPwJ+gLye+1FAIE8r/wPgF939L89zHd9nZm8GXgA8ATgJfAx4E/Bz
+pfL9gHL43RfzmkVERERAQXs0TkN2xj2np5XtYIYPs8bLYudcyXY8eZ4O3SdiH0ixx2OHx02IDYuN
+juVmy3KzZd6s4SlRGdRVYLFomM/yzYLRx7J1VUqcOnUFp644RUpeqro1VZ2nVQ97e+dLNTwZKRqp
+N/oWuiW0m87meuTsmZbTpzdZLNZpTzndqZCr3W1kfT2ysd6zfrZj41zHxnrPYqOnWyRi63jK68Ox
+avxeQAWUYG+eq8tY3lYsJUgJjxGrK5q65sTaGldccapUoxMxdiSPeR9sz29S5Ip5WG2LNpsxm82Y
+zec0TUNV16WiPVTwg4K2yH2Au58GfqTcdnr+VeS11HuN8VHyFO+LuY7fAX5np+dKU7ahodrfXsx5
+REREREBBe2VYo22lyppXX4+hOzcRs/HeoGz55QQSFh1PkT5GumVHv1zSLY1uaSw2liw2WhYbLU09
+o+9b2uUmG+vnqKpAXQfqKk8k71MkxlzRfuBVV3HVVVdx1VWfxhVXXMHJU6c4efIUayeqUslm3Bar
+a3sWm0vWz21w9sw5Tt91ljvvvJu77rqb06fv5q7TZ1gs1llcEVluJtor8rrx5aJnuShrsTvPXcVj
+fvNgeO1Onto+TnPHqELugB6qwHwIxbMZJ9dOMGsaKjOaEDh5Yg3SA5nVDWfPzZjNKurKWN8I/P/s
+vWuoZeua3/V7L+MyL2vVWlW1b6dPFBTJJzEhBkniBU0+tARbopIbiuZGkCCC+M1IdzqiH0QjwQSC
+IElIghDxEkWDXzSEgGDTRAIJGrxAuj3Zu2pXrducc1ze93388LxjzFl773P6pPucfeqc8/yKcea6
+zDnXmLNX167/+D/P/38cBlJO5JLx2VGCo4irP0PW0X3ndaLABxXaDhX93lLHDcP43vC7Lz7+X39g
+Z2EYhmEYxo8MJrQXLpe0QaumRFapDWfR55zua4e6k13EgdME8JwK01g4HRKnQ2Y4ZE6HUY/jSPCR
+cVCR/Xh/D07OfdlSyLmQi9ZivfzgAz744AMOHxx5/uIFt7kQQqTr+qWkS0+7FOZ5ZjgNHJ4OPNw/
+cf/2gbef3/N5Fdtv395zOh0YjoVxgGl0II40CykV5lFIUw1tK7K+fqnVWyKlCu6M9+BCxLtA00Td
+pa471bt+QxcbAo4meHabDV3TsN9t6TrtEMfp86aSGEZHzgkfHL44SvGr0OZSZHuP954Q9AKHk7Wo
+zDAM49vinNsC1yLyd7/N938t8Ifrpz8nIn/razs5wzAMwzB+ZDGhfUmdFV/9bFFneRF91BTw4P2a
+DF4EgtdBcimFlBLTNHE8jjzejzzeDxwPI6cnvfV4TocDTw8P3O935Jw0UC0nch0bzzkjwCf39xyO
+R6ZpppSaWL7d1nM6n3ApQnrH0T5w//aRN68fePP5PW/e6HEajkwDpMmRp4D3AakhbvNYSHMhJ0EW
+R7tWcC2XG6Soo+29J3gVzV3bsu23bDcbtpst235LGxsdjfeedtPUIDdPCA6ckGVmThOnccA5KDlT
+sqcURxGvP0tkHdl3VWhrYnq9uCCuXgQwDMP4jnwA/C3n3H8D/GXg/wBGtDbsnwV+L7BBr+H9isbT
+DcMwDMMwFkxof4HzwLQepfZq55K0gkq0VkuAlMt6FMmAEKoodHWBWvusJ4Zh4Hg4UrIwTxOn45HH
+hx5Z+qPXQyh1PNv7gBRIcyKljHOOtgaFxdgQY0OIje6IFw0Zm8aJ0/HE0+MT9/d3PNw/8PR04Hgc
+GIeJJowEP+BoiKGp5+pJcyKnpPvlcj4K2m2N5PW9Aa/hZSHStRuu9s+4vXnGzc0N11fXXF1dcb2/
+ot/oeYZGxba4jLhCoZByZpxGng4HvPe1LU3IOZNSIqWZNM/MaSbNnpQ88+zr0Hjh3VkDwzCM70gP
+/A7gd37F9wQV3r9fRP7a13pWhmEYhmH8yGJCu1KqaFulW+3LLiKkouIPzqPMoEI7p0LOQin6kBAD
+TSPEEOq4s6PU0e7TaWAeZ8bTwCFGmlhTx1fnuNZd1Z/vfCClzGkYyDkTm4bNZstuu6XfbOn7DX0I
+LII+58ycZobhxNPhifu7ex4fHjkejozDxDQmhjgR/Ign0jRJ68NCWAVuKTMiemhgWUbIVdTqu+Sc
+XgQIoaHreq6vn/HBBx/x8Ucfsel7NpsNm76n6zqatiU2LbGJ+nz1z5xmDqcj3cMDIXodx1+Ftgrs
+aZ6ZpolpckyTY558HdOX82EYhvGd+UW0z/sn0b7uD4DnwBH4f9H6sf9URP7OD+oEDcMwDMP40cOE
+dkUubsWttdir0J5TQhCcc2tCec6FUhPHS9Fk7hg8MQZCDDrq7NzFDvWJ03E4p5kLWo/lll5qTf9y
+zuFdIKfMcDrx+PhIKYXNZsv19TXPnj3TpG4f6LpOd6lLJqdU3fMTh6cnHu7veXh84HQ8MZ5G5pQJ
+fsYxIMXTtZm21V5tkUJOcxXai6OdKZLqhYCLN8l5nAsEr472s+tbPvzgI37VN/8+mhiJMdDEQNM0
+tH1H23XEptFwOSeIg2EauXu4p+s6Qgi6ty2FkkUd7XlmnicV2rMK7Wn2xCWQzpvQNgzjl0ZEEvBf
+1sMwDMMwDONrwYR2pVQruUgNAiu6f11Ew8GWbmucimOptVSlCCUXkAiE6vbWuiuWcWghp8Q8J6Zx
+omTtzZZSCMFrf3RwhBCqUxyQAOM4ITwyjiNd1/Hi+XNevHjB7e0tIuBjpOv7OmqdqxucmaeZcRw5
+nU4MJx0Zn+ZETpk5JGJIBJ+0ImvpCOd8TlITxqHgkFprtvR3u9pnrUeMDX3fs99fcXN7Swye4J0e
+MdK0LU3XEprIZtywm/dMKbF/umK327PdbdlstqSSyDmRSqqj8GV12XOKGhKXMs57nK8XKvwP5nfF
+MAzDMAzDMAzjO2FCu5JrU9TZoc6kpI61iAMXWMRnERXQi9AW0fYrKRokllMhpUyaM/Nc67pqH7V3
+AeedqkQntG2jrnLX0DbaH900un+di1ZflZIYTkce7u95/foV+/2enAvOB9q2Z5wS45SYU6HUCwQ6
+jV6T00upgWM6ou6cU4EffN3Rrh57ddP18DXkzREEnPN49OtN7bRen3tNZxcNi2siTXX18Y4kmTQL
+WXS+PjQNXb9ht99zfX3D7e2JcRoYpoFxGmpndkDwGnpWD72Q4fEOrRYzoW0YhmEYhmEYxnuICe1K
+Kepo5yzqntbbUrRGyrklDdudRbbImjy+iOxSw9FyKsxzZp4TORWksAptvMeJgIeuqzvN257Npqfr
+9WjblsPhicPhkcNh4nQ68fBwx+evX7Hpe7wPNG3PZnetlWJTIqVCLug5157td8R2yTXN+1yX5Ze6
+MlnawfV/vfOI1xF2/TzgvMe76sDXQLgiNZ19CSfzTvfUuxYfPFlUYKecSKWA98QqtLe7K66vbzid
+Jo6nJ/wpIAghqlsOrk4VOMADOrLuPeqYhx/Ub4thGIZhGIZhGMa3x4R2JZflVkjVlc5Z3Vqqo+vq
+WPkyUi6Xt4u7XS4c7ZSZp7wK9tXR1jVjPNB3G3bbPVdXe3b7nY5S73b0fcfr16+Y54nHx/uzo933
+xBCryL7i+tlzBM84zZp+Xs61YwJQzo52ztpP7Rx1TF0Tx92ydF7Pi3phwS8j4+hYe/CLAIZlaXsR
+2upqF1xw+CbS9C3OOfI8k+bCOE/qaPsLR3u35/rZLdOUCTFQnDDnmRg1CA58fR0OQXfgHQHvqtA2
+R9swDMMwDMMwjPcQE9oLF93U3jnEeyDg8SDqtJaSa9d1ouRCcVCctjk7H3AlUFx9DA2UgBSPk4D3
+kRAaYqzj26LysWl0x3m723J1/YxnN8949uwZu/0e7z2lJMbxBAjznHl4eMT7SL+9ot9csdle433D
+4XTidBrVQc9aDxZ8wIe4CmTvIYRI23R0XUfXtWtwWZFM2/a07UjbjpSclzS45V3REXnRRPUl8G27
+3dZAM3X8cQ4fvI5/h4AEDzHgm0hsEk2baVNCxDFPiTTrSLuP4IIAmdDBfr9ht9+w227U6e86mqah
+aTwhQIyYo20YhmEYhmEYxnuJCe0v4OAcaKZry3V3GRXZOZFSILu6D511cztLgKKHut8tIhEpVbC7
+SAwNJYo+SJe6aWJD13dstzuunz3jxYuXvHj5kmc3N5SSmaaBp6cnhuFISonHh0fmKb0jtJu2Y5oT
+06SBa6UIDqciO8Q1uCx4aGJD27b0/YaubQhRRXMpma4dGduOru1rp3ahuDqLvoS7lYJvG9qupe87
+dvsdbdcSYqhj6eCDJ8ZIaCKueHW4SyGl2jueCs4FHXXPBZEqsl2hkAiNcHW1Yb/v2e17NpuWvuto
+m4YYPSEIMYAPljpuGIZhGIZhGMb7hwntL6Di2tegLd1hXnaZNQU74F0guULOmsudQUW2D7CEhtGo
+E148ENXRjg0xC5IdUtQxjo12UW92O66vr3nx4iUff/wJL16+ZBoHDk+PvH37Vjuyp4nj8cTDwxP9
+9opNFdr9ZreOi58dbU/wKrRVbHvEQ1yEdtfTVqEdgqeUdx3t5ALZJRXc4tCFbx0R997Tti3b3Zbt
+bkfXd+pea8k23ntCE2jahiCRiI7b5yL14oTgfdTd9Vx3yV2mkEgy4aOwv+pVaO96NptI10WaJtJE
+FdghCN6b0DYMwzAMwzAM4/3DhHallLqkjah3W13sZTc5hIBzGcQj4msVloZ1UUCypyRPnjxp9uTk
+kRwQCTo6TiT4huyXfG7tjXbe6eG0DqurVVnPbm71eHbLs+sbpnHiIWXGcWacJu7uHthfvWW7e812
+N+BDQwhag5WShp5pVdgithuQfPH5cvj62hxN09L3PTknpnFknmZmHBnt0hZxOCl0XcfV1RW3z295
++cELnj9/zs3NDVfXV+z2OzbbLV3X07QNS1GYuCWkTYPXQoggQnCBJkZCFdDOF8TPbPctm11D10e6
+NtA0dVy9CmzvytpnbhiGYRiGYRiG8T5hQrtSSgKWzG1qMJiv7rYKVhFH8RAcZOegQEmQRmEaYR4K
+0yAMh8w8Qk4eJw2OhHMzzgWcCwgZcQ4RyEWTycdxZJomck3mVtG7ZX91zc3Nc4ZxZBxnHAfmKXE8
+HLm/e6DfvGY3jLRtT9N2OOeZp4lS1HkOPmp4WmzwTl+HqwXUay2ZE8ARY6TvNzjnGEJk9MPyVlSh
+rQ/Y7XY8f/6cT77xMR999CEffPiSDz/4gA8+fEnXtev+t4++Cm2pq94ecIh4Yt0d79uO3W5D03ti
+5wgtpDIQOyF20EQIcUlHryej2+AXe/WGYRiGYRiGYRjvDya0K6Vokbbj3CONA+9c3W+OSHCU7Cje
+aeBZEUoqpEmYB2E4FsZTYTgWpgFKCiANMOOIuNoDrc+tEd+5FKY0M4wDYxXaznli07LZbNnvr7m5
+fc7xeOTx4QDOM42Jw+HE/f09IbYMw8Rms2Oz3RFiZJpmDRhz6laH2BCbFucy3us5LMFmRcCJ7qXH
+2FRnuyF4rfXKOSM1ylzQ291ux/MXz/nkk0/4iW9+g5cvX/DBBy95+cGLdczee71gUfSygv4cp/Vc
+4Om7jr7r2O92PHt2RdM5YguhEYbpieImxE0QZu37Dg7veVdsm9A2DMMwDMMwDOM9xIR2pWR1tL2v
+fc3izsFoi6tdHNmz1l5JKepoT4VpUJF9OmROh8Q8Cjm5KrAjzi2H7nGDhnrnUkhJx8GnedYgM+eJ
+sVZg7fc8e3bDw/0DXfcGh2eeE6fjwMP9I0JgmhL7K6336rqeaZwoVbCH8GVHm5ogXgRcEcQJLqjQ
+bpoG7/UCQymZOenYOCLr7W6/4/b5LR9/8jE/8RPf4MXLF7x8+YIXL56fk9lzokghL5VoSO3AVlcf
+gbIt2js+z/goOjoeCk/HljE9MaYDqRRCYHW0nYlswzAMwzAMwzDec0xoV6Q62kUE50oVlUXHx0XF
+d05CnoWcICeYp8I4ZE7HxPFp5viUOD4lTseJ4TSRU6kd3K6K3oAPQcPFlv1u5/AhEBsVuSFGQtT7
++eBXB3pNDq+71YioQB8GQgjgHKUIYzcwDgPjOJCqSHb1Z1DdehEhpYRzIMEjBBoXaLqGtm3ouqbu
+Q2t6+DhsWHqzEeHm5obr62v2+x2b7Yaupo47r53bHn1OaoiaE/AIOL+6+oherAje4Z3UBPMN19MV
+hBk3zJRhIM9y4WC7egFhEdyGYRiGYRiGYRjvHya0K+vouNMqK1c84gXEr0fOKrBL1iNNhfGUOB0m
+joeJw9PM4XFiOM6klElzBqni03vtl/ae7LzKVll6pyMxNsSmITYRH6OKch9w/lJkn4W2AGlODMMA
+OIoIOSeasSPNM2lOpJwQndnWLm2nY90iZRXaQQIgxKA72pvNhv1+S9M0xBhpmoZxHHGL0Ea4eX7L
+9bNrdvs9m82GtmuJTUCn7fVPcHUqoIpsfVd1R3s5nBcQHTPfbHp205Yp7xE/UtyJKXtckmVrXhPP
+ZTkPvYBhGIZhGIZhGIbxvmFCu7IIbVgEnK+7yVVoF0cpnpx1T/vsaKuDfTyMHB5Hnh5GxiG9k9Pl
+nD8L5hBwWYWmjlPr11Vkt4TYVOfb133ncPF4HQOPIYKgjvUwUEohl0SaZ5q20YAz0dqsIqWmp6vI
+XRztnPM6hg2B0kRiCGw2G66vr+m6lqaJtE3DOI3rfZ1DHe1n1+yv9my2m7VHG38etdeEcsGjqeMe
+QFwNMatj+fhqVhd1tPOWzESWgTE/chgDbgScrDvly/tqGtswDMMwDMMwjPcVE9ortd5LtIIKhOLA
+kykUMkXrvLI7O9pJSHNmGhPTODNNs1ZizUnHxaucFCksItV5rbaKsUHo2Gy3XF1fc/P8OTe3N+z2
+F73UDlLOTPNc+7H1YoDukQsl5yo+CyKZlGbi2Gg1Wf0j4paJ7xqCJirMc67utsc5iCHQ9R37/Y7b
+2xumaWKz6TluN8zzpDnf1U2+ub3h6uqa7XZL1/c68h5CfR/dWQTXD/Rs6zmIFqitvrZzIG4dU2/b
+hqaNNDEQo/aZa/3ZuzvZIh7T2oZhvG9861vf4pvf/OaXvv7xxx/zcz/3cz+AMzIMwzAM4weBCe2V
+pUe7yjfRr4kIUgrihJIFyULJXsV2KqQ5k+aZnBIlJ4pkkKx90VV0imQVw4j2ZYdA23U0beD6+hkv
+Xr7k448/5sMPP+Lm5obNdqPVWCLM88TxeOR0OjHPc90h13MUKZRSkFQokpnTpI45ft0Ld87ja61Y
+8IFSMkUyIhnQce8QPE0b2W43XF/vub29IeXEMGwZh5F51nA1FfSFm9ubs5vdtsQY173rM471C+7y
+K+7d+1WXuq6r45wGn2nKuK/VXlLH0mV9srNHSB9MAAAgAElEQVRUNwzDeH8opfCLv/iLP+jTMAzD
+MAzjB4wJ7RU530oVceKqYyyUshzokSGnQk5Z96FTUoe5pCpiFzGsY+mLSHUOYoxE53Gu4/rZM168
+eMnHn3zChx9/xLPbGzabDSF4RArTPHM8HTlWoS2laCJ4Fe7qTgskV3+kClTt7F5SxxtCaHCxPq6U
+OioveOcI0dM2DZtNz9X1Fc+f31JKYZompmlinmdyyZScySVxc3PL1dUVm02/7mery768g06t61UU
+a6CZTgpUo7uKa+pI+IJzrtaDeUJUp9v5snSQ1Xv58/+NDMMw3jt+4uLjb3G+kGsYhmEYxo8LJrQr
+jneXf+WsAlnzt8SthxRHKSq205woKat4lcK77riOPfvFqfURHxwhOGITuHl2y/MXL/nwo494+fIl
++6sr2q4FJ6ScGKeRQ3W0p3kil1ytYVnd6VwKRfRYRsSXGq0mNpSm0LQ6cu6zJ3u/CnnqecUm0G86
+9vstz26uAZjnpI59SuQ8k3Ii5Zlnz67riPsyNq775Mtbdn7tGmQm7isE9vL21qouWd9/qa42BK8X
+AWp0e33yOgpfR84NwzB+OTjn/n7g/6mf/msi8me/N8/sgV+4+PybgDnchmEYhvHjhgntSoh1x1jO
+e80QCC6oOHbah+1dg3eR1CSCj2u4mFBwTnBeCBF87Yz23pNL1G7ponvRTRNp2kjXNrz84ANevHjO
+7e0tu/2eGCMpJw6HA4fDE0+HRw6HJ46nA8NwYp4nck7qMEvS3uoqtPW2Bqw5dbX13LgQwXVX3Dma
+NlJKUwW3qOCOgabR1PO2bauLr2I75ZmUEtvd9lzp5dyFseyWJewz74yNn1nPSkQvEhR1y/Myfu8E
+Hzwx+lWIl7roLeh45ruj6oZhGL8s7G8SwzAMwzC+55jQroRQ34olDE0AqbVaLuBdANcgrgHfkqLu
+PDuc7nDXpC+doFYnNoZIjHFNzC4ihBjo+06PTc/LD17y/MULbm5u2e12VdTOjNPE4fC0iu3j8cAw
+DszzRMozRfKFo61iO+esQhtfO6s9pcg5rVtYRbZ3npybdazdwSq021Y7vZc971I7u/VIbLc9XdcR
+Qu3OXsV29aW/oKq/SmRrKroGuelrSZSSyGWmSMI5Udc/BkoRcim4WmMmpR72z2PDMAzDMAzDMN5D
+TGhXFkdbBCiuJmQHvItVbEeQiPMtSEcbfRXnrrqrOjLuvBAcNE2gbSNN09XwMhWjTdOy223Zbrfs
+9rvqaL/g9vaWru84HA+Mx7EK7PNxOB0YxhNTGsklrSJbxWkmVce85ALOAx5HqIJU1lF4t1RweU9K
+rQptLhzt4GnbSNd1xBgJUX9FUkrMaSbNM23XaKVXCFWMuy/3bX3bqe7zcvayt15kuWBQHe2yONoq
+tFMuCJ6Scx2ZFw2jK6a0DcP4ZWO7J4ZhGIZhfN8woV2Ri39zLfu/oqp0dYi9i3jX4ukoXWTTb9lu
+d+z3V7TtrMFoKeO9p+t6+n5D3/dViGoSeIwaOtb3Pf1mw7Nn12y2G2ITNfxsGnl6euLu7o6np0eG
+4URKE0US+IILEBoHGU1BX9xhVw9/rs1yONXcCCKJnL263qXuk18IbD1Yx7JdFbpNE7TDu+6Vp+iJ
+MRBjrKFrl+9hvb2wmt8NGBfO4XJFXWzJ5DwzpZFpPjHOR6b5xDyP5DxXpztX5z3Xxwm5aAq8YRg/
+GjjnfiPwe4B/AvgE6IHPgL8O/I/AnxeR+4v7fwz8NuCfAf4R4Bvof9NeAz8H/AXgL4p8efbFOXeZ
+TuaAP+2c+9NfuNvPiMjPfk9enGEYhmEYP3aY0K68I7QvU8fdeQzb+0j0LTH0UITt9oqr64nhlMk5
+a5q3CDFEttttPXa1Zktd3xACTdOsx9X1nq5rcQjzPHM8Hnl4uOfNm895fHpgGE9kSeCrw9t6hACp
+UGbISUewHeDrCLevlV4aiObxbqkC01T0UuvG1tRx79cebJGy7oA36Ch8iA7nAt6DL5oErgFo1cm+
+FNv137TLbvjlN6VQd77LOiaey0xKE9N8YpiPDOOBYTwyziemeSSluTrdywWCQs56mNA2jB9+nHM9
+8J8Dv7N+6fL/sb9Rj98KvAR+tj7GowljX9xMARXpP1WP3+ec+20icvzCfZbHuC98bhiGYRiG8T3B
+hHblbG9c1kbpx4KHGooWY0sTezyO7W7iap+YR6mBYtqb3bYt+92e/dWe/X6PX8S6c+oO191n7z37
+KxXaOJjTzOl0qEL7DY+Pjyq0S1aHOULEIy5QXCKLwxXBVaHtnCN43b8O3hN8APG1p/rsIEtZerSX
+Xm8VzhrsVki5VpS5gvcQg8d51NUuKrC98zj/1ZOXZ+d6mSh3F1/XcfFcw89SnpjzwJROjNOR03hg
+mA5M08CcRt1Hr452LrkK7kVoW2WOYfww43Sv5i8BvwUVu38b+JOoI31ERfNvBH77Fx+K/rX9PwN/
+GfgbwCvgCvgHgD8A/Ib6vH8Cdcov+YdRAf8/1Z/7h4H/9gv3+exX+voMwzAMw/jxxYR2ZR0uvKin
+Oo98+9pJ3RBjQ9d2lBDZ7zN5BkcEByE4fPB0Xct+v+dqf8XV1R63jKDrHPc50Auha9t1bHyeRk6n
+E4enJx4f7hnHE6VkYuNo+4ZYIGdPmwNhdPgRXCjkXNPFi5539M3an10y5FxIqYBUUR0DoaaLd33L
+ZrNhu9nQtVrVRe3odoDzDh8cTjzFg4i+BndZ2VXfuCX0reacnSvSlnuUUkWy1oXNaWTOA+N8ZJiO
+DKM62tN0Yk5fHh0veRkdL3VH3Uwow/gh59/gLLL/K+B3i8h88f1lbPzfdc59snxRRLJz7leLyP/9
+Fc/5V4E/45z7aeCngX/FOffvicj/dfH4v+mcO1w85hdF5G9+716WYRiGYRg/7pjQrqxrfOLe2XH2
+daw6hEhsIm3b0nU9jhYngbbp2e2u1OWt49Rt27DZbNhserbbDWq+eMDV/eQ6Pi1ShWphGkeG4cQ0
+DkzzSM4TIcB225LzFdtdi1wEoA3DwDgODMNAznlpysLhaZqWNnY0sWOeM+MwMgwTORW6fkPfbej7
+Dc9urnn54jkfffQBH330IbfPb9jttjRtQwwBH97dwV6brJfKrndGxs+3shaP189rAJo62ImUZqZZ
+BfY4qYN9PD7qyPg0MKeJUhKg++NnAV/W43LuwDCMHz6qm/1vo39Z/ALwr35BZL+DiHzrC59/lci+
+5I8Cfwh4gY6R/7Ff0QkbhmEYhmH8PWBCuyI1wdo5QRPFdDza+0AIgRgiTWyq0O6IYbOK7HRb8F7d
+4iUpu231vm3brJVhGjQGJZe1jmueZ+Z5YponhuHEOJ2Y54GUJkJwbLYdIV6Ty1yd5oJIZhgGhmFk
+HBeh7Wp9l6drN3RtT9duGIeJp8cDT08HxnGm7zZVbPfc3Fzz4uVzPvroQz76+ENub2/Y7bfaox1D
+DTu7GKN3yya7+1LIOHyVwD5fwCgipJJIaWJOE+N84jQcOI1PnIZHDqcnTsOBcTqR8oS4tAay6YS4
+XpyQUli6ykxoG8YPNb8G+Cb6V8V/9hV71N81VbR/jI6ON8uXUQH/Ag1LMwzDMAzD+NowoV25nELW
+fK+6Tx08Piyj1meh3bVbvAs4F3CEOpLt677z4oSr+C5FBbYI5CzknOs4d+ZwODDPI9N0drTndxzt
+ju22AVdU3DoBShXai6OtHdOI/txNt2PTb+n7LcenE3f9PTFGjseBvu/pqqN9c/OMFy+e8+FHH/LR
+Rx+y223Z7ba0i6Pt1dXXNwWWkuxvL7IvHW1qT3ZNGZd6YaGOjE/TwGk8cjg+cjw9cDw9cRqOjNOJ
+IhM+iB51711YpgCKXlD4/v0qGIbx9fBrLz7+q7+cJ3DO/cvA7wX+MWDzbe4maJDa10QBPrz4/DUA
+0zTx8z//8+tXP/nkEz755BMMwzAMw1C+9a1v8a1vfeuXvN80TV/D2fzKMaFdkaLBWhpYpmJb26iX
+SDQVfMv3Q3AErwFpITQaQlZrsLSnetljXhSqVMGtgjOlTErV4Z0n5lmDv6AQArRdQIPYLlK76/OI
+aE83riNGDTvzyx65j/TdVsfDuy1d19M0Dd1G3W2tHNPjo48+4sOPPuDFy1ue3VzT9x1939G0zepo
+nzN5z0vsGnJ2mdIu76aNf0Fca4hZ4jRo2NlpOHI8PXI4PnA4PXA8PTBOhzpGfgKX8FEIUXBe1kR3
+5+qovQdXBML37/fBMIzvO5fi95f+r+oFzrkO+K+Bn+SiWfA7POTbifDvE6++/JVXr/h1v+7XrZ//
+9E//ND/zMz/zNZ6TYRiGYbzf/Kk/9af4I3/kj/ygT+N7hgntyjI6jj+PJeuuNmiLlVShLfp5TeGO
+wRPjEpiGVl6tKdtaZ5Vz7X6ubnZKqY6MJ6a5Cu00kYvWeMXG0/dNreFKa3haEQ0CEzIhenxoaNuo
+KeMhEnwghEbHw1s9NtvMpt+wv7ompaxO92bDpt/y4sULPvrwA54/v+X62RVNE7V2rI3rjjbVTV7G
+wEWkppNfhJx9QWQXKYjoa53TUt818XR85HB44OnwyNPxgePxgcPpkePwSMon5jyQ8oDzmZDRI6JJ
+4yKAw7v6cwNWyGMYP778Yc4i+39Bk8p/Hvi7InJa7uSc+ytoL/fXNgTjnOPlyy8b6C9fvuTP/bk/
+t35ubrZhGIZhvMsf/IN/kJ/6qZ/6Je/3kz/5k7x69eWL2u8bJrQXqqON8+tosgainQV3DSFXkV1T
+xmP0xBguUrjdmra9jFEvYlvHxReRPTNNM/M0rWK75BnnhKbxdH1DKXXUvBRyBsk6No6UNaBt2R+P
+URPRm9jStj1t09M1G0qBtFcHXcSx6bdsNnrc3Nzw/PY5z5/fsr/ar4588L7WkPl3Xs95/1ouXutX
+iOxydrNTmhinkXEaOBweuX+44/7hjqfDvYrs0wPH4QlhpsiMMOODEBtHLI5Q97ORZXTe4dbR9O//
+r4VhGN83Xl98/Anwf/49PPb3oX8D/FUR+c3f4X7P+Zr/pvjGN77BL/zCL3ydP9IwDMMwfiT4bteq
+2rb9Gs7mV44J7S8iKuqkFCRnistkl3DMpGYmp5mUZnKTCBLBaTL2WZBePM0iPi8qqXQ/exkbn9dj
+TpP2V5NxHmL0pOTUya2PTVmD01JONLEBGhX4Qeq4ul+Tz3WvuRCiinDnPDG07HY7tts9u+2eq6sr
+rq6v2O139H2veWfu3H19Hg+vQWT1ogG8Ozqur7eGnuWy9l1P01THxA8cTwfe3n3O2/s3vL37nKfD
+A6fhidPwxDAdwWecyzhfCA3E5GmyIzYe78B7CM7hqr/uLoS/YRg/lPz8xcf/JPBXvpsHOeeeo8Fn
+AvzF73C/HfCrv8NT2V8ghmEYhmF83zChXfHLZKGoWKQkpEDJgZwcwQuOFu86vO/xvgV0JzpGwX0b
+kf3Vh1ZUnV3fRJqrgM8ZkYKK26xVWNOkVV6jVnrN80SMi4sdaZuOtq1H0xHiQAwtMbSaPt5ppVfT
+Rrq+Z7fbcXV1xXa3o+s6fAycrXu+MGQpdQRew9tyzu+491/8OOdcHfqZ4/HIw9M9D4/3PDze8fZO
+Rfbbu885HB8Z55qynoZz+Fl1s5vWE7tA03iaqOP5TVTRDQVX3yPDMH5o+d+BvwP8KuD3O+f+o+8y
+efzyv1u773C/P1Dv++3+ohguPu6+i59rGIZhGIbxXWNCu7Kma1chnCk6to1jdoJ3GUdL8H09OryP
+NLGjlIJzvj7Pdye0l6oqdX/nuss8U3LW8Wv0e/OcGKeR03DidDpyPB0Zh0Gd6hBXod11fQ0+6wg+
+akibj+x2V1zj6Lqe2DT0fc92v+P62TVd19O2DSEEHcm+ENrvXjgo7+yWL6J6Obz365FSYhonhnHg
+6emJu7dv+fzt57x5+5o3b1Vkv7n7nNNwIOWJlCdymQjRqZMdHbH1tF2gmQNtF+jaSNdFnIsErxMH
+kHGUr/m3xDCM7xUiIs65/xD442jN1591zv2ur+rSXuq7apf2K+AOeAb8LufcH/viY5xzvx74Wb7z
+1bjPgQmtA/sHvxevyTAMwzAMY8GEdsVXZ7YUdZJFBCkZdXQLkHCuI/gNMWyIsaeJHalNlCJ4r8XR
+a/ztV7rYl7d1pPxLjnbSsXV519EehoHD8cDT0xOn07HuZ2sAWtf19N1E1090TV+D2bR6rBTouh4R
+oWkiXd+x2225ur6maZpVKNdOs3N1V71isISgqdDW3XLgHYEdQlhHx1NKer4nFdpv397x6tUrPnv1
+KW/uPq9i+zXDeNSwNxJCUQe79epgd4F2jrRdYE5NvZABIfiafp71EBPahvFDzp8A/jngtwD/AvA3
+nHN/Evg54IiOiP8G4HcCfx742SrQ/zzwh9B+7L/mnPuPgb+Niu/fCvzrwCPwi3yb8XERyc65/w34
+TcDvdc79deCvA4tofyMib7/3L9kwDMMwjB8HTGhXFn3pa+BW0ZDrcxCYaAiZoAcI4pZ95V/iuRcx
+C+TsERFSroJ0HDidThwOBw7HA9N0ZJyOjOOR4+nE6XTiNAwMw8g0JtKcyUkFevbgnQalpVkYx0wT
+J3XXncfhkOIIoSGGjqbp6Psd+/1ISkn7wX1Yz3GtzHZ157o68/OcOJ1OPB0OHJ4OXxLZMda08qbh
+/v6eh4cH7u/v+fzNGz579Xf59NWnfPbqUx4PDzw+PfD4dGSeR8Tl+l4WUvE0xZOyJ5VCyoU5ReYk
+9eIH5AJNdIQAMfj1PTUM44eTKpr/eeDPAP8S8A8B/8lX3fULn/87wG8Efg3wjwJ/4Qvffw38i8Af
+5Tvvaf8HwF8CXnzFc/wM6oobhmEYhmH8PWNCu+IuFpO10ksoVWkL7zrRy5+LePL1sXJpay+p5d6z
+DJZ7n9QhTolpHhmGd4X2OB2ZphPjeOI0nOr3B8Zh0pTyOZOS4FC33eGY58IUCjHMeB8v+rtBxBNj
+S9N0tN2G/dUzxnEi5UxcnPgl+MydX44miAtSCnOaOR5PPNw/cHd3h3OOELRnO8ZI13W0bUvbttzd
+3fHmzRs+//xzXr1+zWevPlWh/fozhvHEaTxyGk7kMoMr9RBiFdlN9lVkF5pUSKnU/nE9ujbSth7a
+QONMaBvGDzsiMgC/wzn3TwG/B/jHUSc7AJ+iLvN/B/wXF495cM79JuDfAn47KtATuvP93wN/XET+
+v5oh8W2TE0Xkf3DO/Wbg3wR+PfABOkpuGIZhGIbxK8KEdmWRbMu/xopzuEUzrwJb3dd3/9120Sd9
+KbIXLsS2dnG7uvOc1pCz4+nI4XDg6fDENJ2YpoFxOjGMJ4YqtsdxZBrn1dHWPfCCFHAu4dyMQ/u8
+11Nw7wrtvt9xc3NgGEdSyjVJPKzq+p1rBtXNzqUwz4nj6cjDwwOff/75KrRDCLRtS9/3dF1H3/fc
+3d3z6tVrPv30Uz579RmfvvqUT199xqvXn5FKIuWZVBIiGecFnGjKevbE7Ek5EFMhpsI865EvhHbJ
+AA0+eEK0X1/D+FFBRP4K32XyeL3/APz79fh29/mnv9c/1zAMwzAM47vBlEpldbSdfuyd1K94FndY
+q6/cV94uz/FVYnvZx5ZSmOeZYRh4Ohx0xPrhgYd7vT0cHlVoz4Me41hF98Q0JeYpk5KGtGlXdV1T
+FvW2kYvZ73o79hPTqJ3diyOec6nj2LLuVq92NudAt1IKOWXmaeZ0PPHw8MCbN2/0XfHasd3UgLWu
+7+i7nk8/+4zPPvuUzz77lFevX/PmzVse7h94OhwRyZT6B6dJ7c4LHu0Lxwnisg7nL93jpV5UKLon
+XrKWezmvO+iGYRiGYRiGYRjvGya0v4RDV3894sA5T3YehyeEenjtql76s2Hpla6L3ch5xxkh50Sa
+EykljkcV2G/fvuHVq9e8ffuGN2/f8PbtWw7HJ+Z5ZJ4H5nnUju2caghZIqV3hba62lTHnfXw3quD
+vkaIO93ZdiqO15Xzi8fAxemjbnnJpYabjRyPRx4fH3n79u3aCy4ieO91dLzT0fHPP/98Pd7e33E4
+PDFOI1IK50KuizF1lgsV2kMuGQpCkkJZLlBIoeTEPAdyLvV8HTn/EsvxhmEYhmEYhmEYPwBMaK9c
+9kHD0nV1GSwWvCd4DQILfhGu9TGAuNXTXmbO6z52Zp4npmnSbunHB96+ecvr1695e/eGu7u33N3d
+cTwdqsCemNOElFr1VUfNU1qC0KrYXIVyTTAvKmNFIh5H8E6FLH4dKwe3JonLFwT6Zaibjo1n3SWf
+Jk6nI4+PuqM9TZMmpacEoEForYah3d3fcX9/x93dPU+HJ47DiWmadN972WmvZ7VeAzg3q6nLvnZ3
+Q8lOk9lnRxi9Ot/iwQVKMUfbMAzDMAzDMIz3DxPaK1Voc+6G1m5pv3yVUJOug3d4p8fl6Pg5OG0R
+vjqmPc8j4zi+MzL+9k6F9v3DnR7395yGIylNteZrqdGCpWIsZz1Kviy5rrvaF2Ft3geV1z5on3bt
+2w4h4l1A3ePqipeLCjJWHayvoVaP6QWCE4+PT9zd3a074+M4UqToc0dNH386PPH09MTh8MQwDMwp
+MWcNgMPVEff6k/R6hltfI3VEHIQsOlpeipALuFQT4akp6rHFuS/V7RqGYRiGYRiGYfzAMaFd0X1f
+wVXH2lU3WNxS+SU49LiY2V6zytc+7JSY55l50v7r9Rj19v7+jof7ex4fH3g6PHI6HRmHgWkeSXmu
+3dKL83sxjC6XLnSpY+tL/7evO9oqXNt2Q9v2tO2G57c33N7ecHt7y83NDZvthhhi7cVOdQS++tze
+Q3AE578wOj4xTeeE9OPxyDAMDONAzlkTyIMnRM8wDozDwJwmCgVx9T0NTnewl9fkZHWz9dBQNOeF
+EPSiRoj6uMv79V2kbTwxQHBfGSRsGIZhGIZhGIbxA8WEdmXpZHYXTjUOvFB3tZeh69qnvSw5o0ni
+pWSmeVLn+jRwOh05HU+cjkfSnJjTTJoT9w8P3N/f8fjwwNPTE8fjgWEcmOeJlJM6006qul/2vLmo
+GCsUKXh0TzzGKrbr/nXwgb7v6fs9fb/n9vkNz29vef78OTfPbthutoSgu86L0HZVtBM1YMz5Gj5W
+8nrhYHHkT0cV2kvHd0ozLrh1Zz3lTC6JnBNFypoqvoh3QWvDnL8Q2E41vq9CO0ZH0wSaNhBjWO+D
+E7ou0rWeGCF4E9qGYRiGYRiGYbx/mNCuLAnW6qqqaNQvCOLVSXZOcHJOEnMiS9wYpRTmeaqd2E88
+PTzy9PjI4+MjOWVyyZRceHx85OFBHe3D4VG7sscTUxXaWnmFhp1fhJQt3d1LOBhO3d4YL0bag/Za
+b7cbdts92+0zdbOf3/L89pabm2dstxtiCHXvOeHduTfcOQi+OuTL6Hiedb98nBiHgePxyOFw5Hg8
+cDgdmefpnV3r5f3TifvzHnbAUcRRRNDq7ncdbb+I7QAxOtrW0/eRtm1Y+sqdE5pGHe0mmNA2DMMw
+DMMwDOP9xIR2ZU0Ph2ohL8Fil/HcuYpsLXQWyZSixzSNHA8HHh7uebi/5+H+od7e191tfeLj8cQw
+HJjTiEjCubyKSxeqk17dXlnSvcuS1e3wLiACfR/pukjfNcQY1kT0pmnZbq/Y7a7Yba+5udlzdbVh
+u+vo+4YmepwXSkmU4sgFQnaU7JFQLuq+5J097iJlrSgrRS8c5KzOtfZ0AU7w4jV6bUk4d+f31tf3
+Ufe1ZXWxvYMQHTF6YuP1dW06NpuWtlOh7dDHxBho2oa2CcTGYxiGYRiGYRiG8b5hQruy7Fq7RWCX
+s9CWskRgF0RyHeHOlJwpOZHTzDCceHp65O7tG968ecPD/T33d3c83N3VsWrdY56mkXk6IczECEU8
+BbVyS6nhZ17d25zQALSkopTo6x52YLft2G5bttuOpomEEKrQbthur/XY7Hn2bMt+39H3gaYBHwqO
+TCkzpTikeEoJZ6dc1ll1Rc7vjzrWTt1nt4x6c746cTEKDmV5VBXJrgrxJX1cqpuuY+QxRtou0HaR
+Td+y2fZsNh193yKU+nyFED1NE4lNIAZLHTcMwzAMwzAM4/3DhHblnUbmRQzWjikpS6J3rkXPekhJ
+KrRzYhwHnp60H/vz16+4v7vj7u1b7u/eEmOkaSNN09Rd7hNIIkatBMN7fIwUcatYBSF5IaVlV1s7
+q73zxOi52vfs91uu9hvatiFETwyB2DRsN9dsNldst1dc7bfs9i19H4hNHbd2GZGZUnx1soO+xtrN
+vb4jXxLbKrKX0fpFbJ/fwEVkLw9aarycjsSj4/a1BhvvdRzfe3W023YR2R27Xc9229NvOkCnB4Si
+QWlR34MQzNE2DMMwDMMwDOP9w4T2glyqymWEWqrLuzi99WMKQnW1SyLnmXE8cTw88fBwx93dG+7v
+7ri/f8vD/R1t29D2LV3XgoNSJnwotF3Al0IsDU126ty685j0NME8CyEUwBODCsy2iVxdbbm62nJ9
+taPtGt3PDoEYGzb9ln6zYdN3bDeRrvOEUHAurdVjUjLeQQ6OUgJS4oWj/UWRLVpn5l39Ob6OeQcE
+vzrUQtG6rrqfXSvIz6732k9+dsedE0JAx8X7yKa69JutCu6+b4GMkIGiwWpVbK979IZhGIZhGIZh
+GO8RJrQrUop+4Ja56epq824qth4F5zIiiVJmUhpJ88A0nxjHI9M8IDITgtC2nqZ1NBFCEHWCQ6CR
+lr4EsjQUybXWq+jPXhzteda08jTjQPeTo6eJke22W48YQx1P91WECs7NFBlJOTCOAJmURrxv8K7B
++1Z3xL0QvD7nKrbX/Wzdx9Zubqdj3dseIeF8xoXMPHut8ZJSLxSgrjxooNs6Cn8W3foeLO+lI0TH
+ZtPSb3Uvu+8bmsbjvQAZXKlj5q4GptUucxPahmEYhmEYhmG8h5jQrhSpQrumiy+Wrqv/s4htV8ed
+nStAopSJnCfmNDBPJ6bxyDydKGXGe0H7IY8AACAASURBVKHtAk3jiQ3EKJoU7gO4oEKUgrhSRX3h
+3DRdSHUsPaeE844mhiq2A10XaVs9QvA1vVtDyEIsOD8jMjAnARI5T0zxSPAdMXSE0OEoVWQ35NKu
+olpqGNzifiM6st22DdttBy7hQibEwjSH9UJBkaxv4dJvXUPSpN6uTnS9vRwD7/uGrq8ie6n1CoKQ
+z4nky8h6cIRaS2YYhmEYhmEYhvG+YUK7sjranBO03Tt7xzoK7Z3U3eKC1FCxnEfSPDLNA+N0YppP
+FJl1PLz1xKh91yEIMQqhCeuxjFUvO8yr4JZzuncpGV+FdtPEmjLu1mM5z7oWjXc6Jl5ESClR8sTk
+TgTfEsOGGHti6PGuhpDlllI6RNo6Gi/vjJiLFIKvO9QbFdo+FGIUptmTlxH6kmpg+3kaQJwgTkfi
+YxP0iPW28fVjT9s1dG2k6xp80JFyHUdPOOfXsfVFbIclod0wDMMwDMMwDOM9w4R2ZSngWj5bAsgW
+N1vFZyIXda99ahDn1gM30zSw27WkeUvJMzm3lDwTvCdEpyPPweGjX49VyLvzyPiyI14kU0qhSK6i
+OBBiIAa/pnu7y1Ft9GNdkS5rMJmuWRdyWfbOtZrL+0WsShXUWTuvgyfnCSTjPVUIR7ablv2+p2mF
+fvbMc2TOE7nuqb8rtOtlg+rWixMNMIuesArssO56N1VwN03QgLX6XjiP7oTX+jLnHCavDcMwDMMw
+DMN4nzGhXZF3Pro86teckCUx5wmfTjB5vceiZl2i7wPX1xtC0Nqv5TiPPWsAGJ7zceFirz/TqQAX
+8XWc21/sNl+EgImK2GVsXAW3usEOwVHWDLLluUtRp9oxswjslGbSPKlz7RxNjKQ0ISR8gLYJ9H3D
+ZtOy3/V0vSOXhlw6cpnriPtMKulCZC+v7Cy0fXDnsfFYxXa91Z1tRwh1l9ud97qjD2vYG0ApsibB
+G4ZhGIZhGIZhvG+Y0K5ctlotbrZcJIA7B0USqUy4+cQiZYWlkivR9YHrZ1u6zlNyWuu/1iCw6lyX
+mlq+hIgVKYh4VGwv9V7USiuvAWX1OXxVzqUIRZaRd1frsnSMXO9StFprvVagoreUjJQZEU/OiTTP
+jOPIPE94T93/bslZk76Dh6YJ9F1ku23Z7XtEAuL0+0USKc9VcM/v1HBfyGwKRc+vCukQ/HmMPHgd
+L68XHM778PqaVGhHYghIEVLK5FTIeRn3NwzDMAzDMAzDeH8woV1Zw9DWpPGyBqO5WvVFmZF5IBch
+l0IRIUshSyaXRIiFzSbSNB2SYxXaue4bg6uCM5dMlkwqiVKEXKihY+dxcMfilmtXtNTHq3aW5aT1
+6xdhYc5VcS1aTeYoIGqfizhKcbU/21FcoeS8utl917LZ9IzTBj2DQojQdYF+07Lb9YzjBqEBn8FV
+oV1Ftgrts39ehHpRQQU3TsDLelFg6cIOwa+p5cv4+jIBoAnjgegjIQRKEpBUe8zz1/cLYhiGYRiG
+YRiG8V1iQrsy51k/cFzsPwsU7W/GFVIpeDRoLPiZZp5pppEmnlQYi+C8Bp6JFyRXjYurO8d6I0h1
+YwslJx3dTgmRgndL6FrtoXa6pwx1FFsudrmdVnq5euu9x+G0Kkx0LBxRwazu9iLcPc55cCqMcxFS
+CszpyDwfmKYtMTQ1wdxDH9ltO+Z5Qy57TVpnpshEEUgZctFbwZ1dbVkbxymLo15l97u777mGuAGE
+LyeMu6B77s6DF32tNZTOMAzDMAzDMAzjfcOEdmXOCXi379ktIhAdk0ay7jZLwLlInEdiGIihpYmR
+JjY0MRKiQxYjubiLXWKn7rUIrhSoTnhKE9M0IaWc+7CryPTe4Ze0syVYTDRe3DmHc76mcvv6MbhS
+dLQ85zo67lRo11JrR32BqBjWcDRPSkfmWcW2oyc0LU1sCb5hu2vJZQPsmfNAKgMpCymrI59FxbZI
+Fdqig/AqsvU2l0wuOgGgbruAKxRZLiy4s8D2bu3K9i6sh16M8Hhf8MV/rb8jhmEYhmEYhmEY3w3/
+P3t3HidJetd3/vN7IiKzqrp7JIOO6UEc5r4kDJJYgTh0gYV1WJwGwyIOg1hYHyDb2Nh4NPhlljWs
+YbHxMrZYkGFZbgQYITAgNOAFHYjTgLmRJbVgkDQzXUdmRjzPb/94noiMyq6zu6q7uuv7FklmVUZF
+PFlSTc43f8/zexS0i3HQDn0sLdtLuXf5PkGKhqccWCubEUKdg+jaOhvr60yadeq6ydO6E+XYHDzB
+IUKXcqMy90iKuRHZYj4jpbyvdVUFUigNw5qAhapUzPv6sJfvGRbGQbsqE75zkE0xV7b7Tt25w3iF
+UeVqfels7l7RRStBe4vFYp26ArOKpjGsqYlpCqwTqo5Fayw6Z95Gui4RPU8T3xW0KVPH80vOW43F
+jjZGuggpMdq3O4FVuYJeppIvw3Z5feU/nhiq98FU0RY5S8zsXuBewN29us5z/ALwCcAvuPuzTnB4
+B13zhsctIiIiMqagXSzaPHU8BMNSv97Z8VLN9n4Nt/cxvJ/KnUipo+0WzBe5CVlT11i/HffKdVLp
+FJ6bmxmhqqjqmqZpSDENlV0zI1iuPg/nMcPI3c77SvZQrfZl//IcXvtw37NyLLm5WxlDyLt7UVUO
+Fsv2ZdvDa3HvMGA236Jtd4hxTowLYlyQ4oKUWpJ3JTTHErKtXHvUjI0c9KuQw34ySAFSyseGsjC9
+37+7rEjPFWwCyQKBUqlP/X8fCtoid6Db+g97c3PzVg9BREREzgAF7aLtctC2YQpzv4a4TBsn5spq
+2UorR9ay7tih6xbMy5rrOlS5GmshT3MewnPIa5VLaA8WqEKgrmpSnUhhuR1XrkCHEqNtGZOHPbf7
++e3L5mPmXkJ2SdnLGevLPbfpm6rlsB3KGvKqpjQ3W9B2O/lDBu/o4hzDWCx2WCxmdN0sh+1ukffO
+jouy33csHyKUK/ioK3v5XgAIAQt9UzYjWST5qAGc5y3IPM8qJ1n+yVCq2n3QHj6wEJE70Xh/xdvK
+1atXb/UQRERE5AxQ0C4W3WjquPXrnwGLuXO3xbLlVICq7IftZTq3O60nUmxpFzaE57qqqKu6THWu
+CCEsu4aPKtp1ncNxynOvh9uuiD3eKztQthUznNFaaxxP5VZy9u6Kug1hG3LjtlDOV9WG9UE77uBE
+YpzThgbD6LoFXbcgxjldN6frw3Zqc9D2VKa2l48J3IYxDtV0M6pSqfdQ1quXW7/2fPigoG8eB5jl
+qnZfuU/DHtra3kvkTuPuz7zVYxARERG5UQrahS0TaA6IXuZU94GRHBD7jt25Prucqp08b/dl5KCe
+6khKDe5OCBVV5QTy0r80VG1LmAyBqqoJlpbtut3L9lZ98O8bteXHjuFmuPWH+zCOvD1Z6UheHvfT
+0W0cvYcMnKfIR29p4xxbQBcWVFYRrMagVK+7/GFCzKE775/d5WuTyrWXTdfcIZkPlW0L/e8xV6rz
+l/mFJfdh6njumL5s/BbKlPnQb11WPki4TQteIiIiIiJyh1PQLiaTaX7QZ7cS5PI070hukVZh1ARq
+glkJmF6OozQXc9wCFsvPmRFKB+4cs3OoTJ6GKdKQw7aFMPQF75uxDfej/bX7zwF8CKkJT/0WWmXt
+ddkWq1xyOe182OfahrFYSnR00C3yZHiPuct3+Q+ep2vH2JFSvsXU5bXZ/RTufk32sD7bhyp7pKwX
+L9PGza38epcLyY3h4bBOO6ZlM7dQgnZf1V75yEBE5FYLwGiXCRE5665cucL999/PS17yEi5fvnyr
+hyMiRxRj7B+e6S2IzvTgbqbJZMJkMqGZTKjrhqpqqEJDFWqCNRhNCdhV6e5dsYzEfVUbUnJiyntu
+tzHSdh1djPlxjHQp0sVIFxMxpVLJLVt6VRV13dA0EyaTKU0zoambPA09VFShGgJwZVWuOPfbegGQ
+p29bgKoK1E2db1VNqPL09SHQh9xULZEDbRtb2nbOvJ0xm2+zM9tke3aVrZ1Hhtv27CrbO5vM5tss
+2jld7PI07r4672VbMyd3aE9Oik7qEjFGUuzKLeJ9QzP3XYE5b61dgnb/++s6utjf99PU+484ROSs
+MrNHmdl9ZvbbZnbVzN5hZj9vZp99wM/8gpklM/v5PZ577/JcMrPPL9/7NDN7lZm91czafX7uPczs
+28zsj8xspxz7Y2b27BN8uepWLnKbuXLlCvfddx9Xrly51UMRkWMYBe0z/d6rinbRV7S9D4fJ8ZhI
+HvI0Zo+lA3g1rBe2vmrbT9tOefp0X7Edtrki/68gF33DsK7b+8psCdohBOpQUVcVVcjXzYuYI8tY
+WdYvm+HBIJSqcWKYwt13LK/DcmuxoXpuDOnUyxrzlFJflyd6pIttXiLdv4D+mHJbNoyzMuW+38/b
+hiH2ry9PqS/T6j1Xs4P7ro94+kq9Da8w//5jTHSxKw3l+v2zHawqVX7VtEXOKjN7H+Bngfdl+ZnY
+BvAM4Blm9iLgb/uwpcPgKJ+h5c0XzP4T8HkHHW9mHw/8BHDX6Li7gecDLzCzlx3pBYmIiIgcg4J2
+MZ3moJ1ibkqWopOqZbj0FHNfL3e6LgEpd79OkZgiuZqcn++3pUop0nVGVdfUVaSqc2O0vkXZrnXh
+nsCXTc9CqTjnyeBW1iX7MkwThk7mfehcrsHum7mVRmk4FsiJ373cjSrCNq7KJzyVzuSj9eLgedZ5
+WLYw75ud2agDeh+S3SGWgN2fN3gYtiEzz4u0Q3++ZLka755f2/B6wvBa+rXxtqvJmoicUd8PvDfw
+74EfBh4GngR8NfCBwGcCbwVeusfPHuWP+yvL+V4LfDvw+8CjgfcZTmL2nuSQfYm8Buj+lbH8E+Bl
+wBuP99JEREREDqagXUxLRbuvpKYukWLppF3Cdop5bfIwDdpjWa8cd62fBobp1O5Qp5pUN9SeCFVV
+KrM5OA49vaw8sLyHd1UFEo67kaw0PisVYveUA6qzDKS2DKBDdbkE6OHfWPuO5+7s2hyrH/Ow1jyV
+oJ0Ddv+68n0J2aNu4liZjm4BPOEWieRrJJZhu7+ie7+Hd/k99N3Yk+MxgKVRyC4fKvS3PtSjoC1y
+hhnwFOBz3P0HRt9/k5n9IPBLwEcAf8/MvsPdf+c6rvFE4Lvc/YsOOObfsKxkf+4BY3nKdVxfRERE
+ZF8K2sWyop2I0YkhEWOuZPdTuFucGCOxi3RdmxuDeSSmjlCqs30AjmWNcYyJFJsyrdypvaaq6hw0
+S+dy6O9K1/KQK9p9czUrMyuH6ekpQZV7mPdTz4eQ3f+nVICXKd5x67uo+1DRHofVvoM6w0zOZdAO
+ed+zYao4jCvaxtAi3R1PfcO3cit1+b6qnTxRUS2nzFdVaSbupQHcHpXs8uHB+PUpaIucWQ78xEqw
+zU+4b5nZlwKvIy8i+TLg7x3z/Aa8C/i7+x5g9njgRccYi4iIiMiJUdAu+k6xfZW3D4Gw7O7dxfx1
+36gr9t23U1uamQWcUDpo507heaZ4Dqx5qnleb0zYrw9dXwlPQxV4WLdMDq7RlxtQW1pWjJeZfRTQ
+d2395cQUy4cAJUwPU77zPmF9p3B23fLvIJTV6ePFkONmcJ4S0Z1IKtXsPN08h/NAv4R7+AAhJSzl
+323/oUQX4+gDirxe3szx4Hgoa9+tr4oraIucYd+13xPu/gYz+2/AhwHPuY5z9+F564BjnsmyPcZR
+xyIiIiJyIhS0i8ViUR4Z7v0a6uH/5a2pyvzwvA45kWIkxpYutlSV4VTUVKWxWQ6XdV1j1n9dAvQo
+qg5bSZev3T2f13KgX4btZcjug7bHiIf8MzH1jdmMlMhTuBMM22yV8L4MsnE05bvKYduX/c/61eF5
+YTe4h34U+efK6K1Uqi3mbchiivmDCBI+Wjse+j2yywcJsTRgSzhWmp51XUfb5W3Eui7SdbFU7/tr
+kV9vcnI+X+2hJCJnyBsOef715HD7gWZWu3t3zPP/5iHPP/E6xiIiIiJyIhS0i0XbAmAEwtB4y5Z7
+UDNqXuZ9uI0lIC5wz93GcmOyihBqqqrK08TJAbgPvP3a5/6cy+nfuSlaTBE6H9ZTD9Xscouep7SH
+uKzu5inly7ElfNd/KA3Jun7qe+ywUBG8IlR5qvY4aPdN13IX89H3LU9p78c7NEwjt9nvO6+nUtG3
+YGVrsZA/QEh5/3A8Dk3SwErILlt4dbmiHWMsx+b/DoKFHLKDVmiL3Ab+4pDn/7zcG/BXgAePef53
+HfL8u13HWG6Yu/OmN73p0OMuX76sfXtFRERGrly5cqTt9tqS2846Be2i63IxJViAUFP165H7cD2s
+D1525+6nkHddC1RYgJDAKxv2sm6aujRY66eDLyvaw8znoaLdbxMWWd2tJvXPlVtfsfayjVw/Pbzv
+6p2nwqchMFMqzzFGupT3pQ5lvy+zvN3WOGinlZAe3MC8TIn3skK7b262nHqfhteYcjgOYCEQqjCs
+/x7G5xFSfuVt19G1OWwvp47nUB4s5KnjKU8hJ6Ed4EXOvsO26LpR8fBDBqc9ll2e/OQnH3rMvffe
+y8te9rLTH4yIiMht4v777+e+++671cM4MQra58Bo6+xT/ZkjnfQsnUdETtPjydt3HfQ85H/UHFad
+vh7jcx51LDdiqKA/9rGPPfTg+++/n5e//OUncFkRuV79ssHnPve5TCaTWzwaEYkxHuk99MEHh0lw
+73bQcbeagnbxRZ/69YpvIiIn56kcHG6fWu7/4DrWZx/Fb13HWG7E8B4y+hcAEbkN6G9W5LZ1pvOb
+graIiJyGFwOv3OsJM3sq8OHkavbPntL1X0OeXh6OOJYbNQem5MUth60JFxERkev3OPL7+/xWD+Qg
+CtoiInLSDHihmX2Gu//QrifMLgDfXr5MwP2nMQB3f7uZ/RjwaYeM5X5y4L+hT8Xd/cKN/LyIiIjc
+WRS0RUTkpDnwRuD/NbNnAD8EPAI8Cfhq4IPKMf/O3X97n58/CS8FPgm4tMdYPgL4J8D7l7GexPRx
+EREREUBBW0RETsdnAT8H/C/Al6885+TA+9J9fvZE1ly5+5+Z2QuBHyOH7S9fGYsD95XrKWiLiIjI
+idEmSSIicpIccHf/M+DJwNcDvwNsAQ8BrwU+193/lrung85xHc9de7D7a4EPA/4v4E/J67neDvwE
+8Nfd/euu57wiIiIiB7F+/2MRERERERERuXGqaIuIiIiIiIicIAVtERERERERkROkoC0iIiIiIiJy
+ghS0RURERERERE6QgraIiIiIiIjICVLQFhERERERETlBCtoiInLumdl7mdn/YWa/a2abZvYOM3u9
+mf1DM1s/wet8jpn9tJldMbMdM/tTM/tuM3vaSV1D5Dw5zb9dM7vXzNIRb59wUq9J5E5lZo81s+eZ
+2X1m9ioze3D0N/R/n9I1b9n7rvbRFhGRc83MXgB8N3AXsPqmaMDvA89z9z+6gWusAT8MfMo+10jA
+17n7113vNUTOm9P+2zWze4F79zj3Kgee5e4PXM91RM4LM0sr3xr/bb3C3b/oBK91y993VdEWEZFz
+y8w+Evg+4BJwFfga4GOBZwP/kfzm/AHAfzazCzdwqe9k+Wb/88CLgI8Gvhj4Q/L78b1m9ndu4Boi
+58ZN/NvtfTjwxH1uTwLecALXEDkPvNz+DPgZcug9Dbf8fVcVbRERObfM7AHg44AW+Hh3f/3K8y8F
+vpH8Rn3f9XzybWbPAn62nOPHgU/z0Zuvmb078KvAewHvAt7X3R++vlckcj7cpL/doaLt7tWNj1rk
+fCt/U28A3uDuD5rZewN/Qv47PbGK9ll531VFW0REziUzeyr5X9QdePnqv6gX/wb4XfIn7n/fzK7n
+X7ZfWu474Ct85RNud38H8NXly0cDqmqLHOAm/u2KyAly9/vc/VXu/uApX+pMvO8qaIuIyHn1otHj
+79rrgPLm/J/Kl48GnnmcC5jZRfJUVgd+1t3fts+hPwI8Uh5/6nGuIXIOnfrfrojcns7S+66CtoiI
+nFcfV+63yFPI9vPa0eOnH/MaTwUme5xnF3dvgV8hV9+equqbyIFuxt+uiNyezsz7roK2iIicVx9C
+/sT7D919tRPq2O+t/MxxfOg+5znoOjW5iZOI7O1m/O3uUrYH+nMzm5f715jZV5vZo2/kvCJy4s7M
++66CtoiInDtmNgUeU758y0HHuvtD5MoZwHse81JPGD0+8DrA/xg9Pu51RM6Fm/i3u+o55bp1uf8E
+4H8D/tjMXniD5xaRk3Nm3nfrkz6hiIjIbeDS6PHmEY7fAjaAi6d4na3R4+NeR+S8uFl/u73fBF4J
+vB54G9AAHwR8LvDJ5PXfP2RmL3D3n77Oa4jIyTkz77sK2iIich6tjR4vjnD8nLyOa/0UrzMfPT7u
+dUTOi5v1twvwze5+3x7ffwPwPWb2pcC3AxXwcjN7P3c/yphE5PScmfddTR0XEZHzaDZ6PNn3qKUp
+eU3ozileZzp6fNzriJwXN+tvF3d/5JDn/wPwHeQgfw/w6ce9hoicuDPzvqugLSIi59HV0eOjTBe7
+UO6PMlX1eq9zYfT4uNcROS9u1t/uUd0/evyJp3QNETm6M/O+q6AtIiLnjrvPgXeUL59w0LGlq3D/
+Zvw/Djp2D+NGLAdeh92NWI57HZFz4Sb+7R7V74wev8cpXUNEju7MvO8qaIuIyHn1O+Qpn+9vZge9
+H37w6PHvXsc19jrPQdfpgD845nVEzpOb8bd7VH5K5xWR63Nm3ncVtEVE5Lz6pXJ/AXjyAceNp4P+
+12Ne4w0sm7HsO63UzBrgaeR/aX+Du8djXkfkPLkZf7tHNd6z922ndA0ROboz876roC0iIufVK0eP
+v3CvA8zMgM8vXz4EvOY4F3D3TeDnyNW355jZPfsc+unAXeXxjxznGiLn0Kn/7R7Dl40ev/aUriEi
+R3SW3ncVtEVE5Fxy9zcAv0h+M/5iM/uf9jjsHwIfQv7E+1tWP/E2sxebWSq3f7HPpb6p3NfAt61O
+dTWzxwDfUL58iNzFWET2cTP+ds3sw83s/Q4aR9ne64vLl28HfvT4r0ZEjuN2et/VPtoiInKe/X3y
+lNJ14L+Y2deTK1/rwOcAX1KO++/AvzngPPuu03T315jZ9wGfDfzNcp1vIU8zfRLwNcB7lXP8Y3d/
++IZekcj5cNp/u08m7439GuCngN8iN2Gryes6Pw/4pHJsB3yJu2tbPpEDmNnTgfcffesxo8fvb2Yv
+Hh/v7q844HRn/n1XQVtERM4td/91M/ss4HvIU8i+fvUQ8r+oP8/dt27gUl8EXAL+BvAM4Jkr14jA
+17m7qtkiR3CT/nYD8GzgOfsNgxy+v8jdX3Wd1xA5T/4O8OI9vm/Ax5Vbz4GDgvZhbvn7roK2iIic
+a+7+k2b2JHKF7Hnk7UAWwB8CPwB8m7vPDjrFEa4xA15gZp8NfAHwEcCjgT8HHijXeN2NvA6R8+aU
+/3Z/kjwt/GOAjwQeD7w7ORC8E/gN4NXAd5U1oSJyNEft1H/QcbfF+665a1cCERERERERkZOiZmgi
+IiIiIiIiJ0hBW0REREREROQEKWiLiIiIiIiInCAFbREREREREZETpKAtIiIiIiIicoIUtEVERERE
+REROkIK2iIiIiIiIyAlS0BYRERERERE5QQraIiIiIiIiIidIQfsMMbNfMLNUbp9wq8cjIiIiIiIi
+x6egfbb4yr2IiIiIiIjcZhS0RURERERERE6QgraIiIiIiIjICVLQFhERERERETlBCtoiIiIiIiIi
+J0hBW0REREREROQEKWjfBJa92Mx+xsyumNmOmf2Jmb3SzP7mdZ6zNrPPM7PvN7M/MrNHzGzTzP7Y
+zL7XzF50Hef8YDP7V2b2OjN7u5nNzewvzOxXzOw+M7t8hHNcs0WZmd1tZl9TznvFzDoze+f1vG4R
+EREREZGzzty1k9RpMrPHAz8GfPTo2/0v3cr9jwBfAPwE8Inl+We6+wP7nPMZwMuB910533BIuf8V
+4DPc/W2HjHECfCvwxUC1xzn78+0A/9jdv+2Ac71m/BqAvwJ8J/DolXM+7O7vdtC4REREREREbkf1
+rR7AnczMHgW8BvhgliHzT4BfBubAh5ED+KdyxL2zzewzge8h/3fn5PD7K8CfAgn4QOBjyvNPA/4/
+M3uquz+4z/k2gJ8BPracz4E/An4VeBfwbsDTgXuAdeDfmtkld/+GIwz36cDLylj+Enig3D8O+GtH
+eb0iIiIiIiK3G1W0T5GZfQfwheXLOfBl7v6KlWOeAvwA8D7AApiwT0XbzD4MeD2wVo75JuDr3f2R
+lePeB3gF8PHluJ9y9+fvM8ZXAP9zOe6/Ay9x919cOcaALwW+uVy7Az7e3V+3x/nGFe1IXp7wtcC/
+dvc4Oq5x93avMYmIiIiIiNzOFLRPiZl9APB7o2+92N2/54Bjf41cMTb2D9o/R56O7cBXuvu3HnD9
+DXIo/9By/NPc/Q0rx3w88FqWVeynufu+a6fN7MXkaeAOvNrdn7fHMX3Qphz3z45Y/RYREREREbkj
+qBna6flilmubX79fyAZw9z8AvmV0/DXM7EksQ/abDgrZ5ZzbwL8cfetz9zjsq8aPDwrZ5ZyvIH94
+YMBfN7O/ctDxwNuAf33IMSIiIiIiIncUBe3T88zR4+8+wvGvOOT5vzF6/H1HHMPPjx5/3PgJM6uA
+55QvHwF+8ojnfE1/CvIa7P048EPuno54XhERERERkTuCmqGdno8YPf7lww529z8oW17t14n7Y0aP
+n1XWYR/GRvfvufLck4AL5EDcAt+al2If6qmjx6vnXPWrRzmhiIiIiIjInURB+xSUbuOT0bfefMQf
+fTP7B+17Ro8/5TqGtTrNe3y+xwBfcQLnXLVnp3MREREREZE7mYL26bi48vX2EX9u64DnHsVyC7Dr
+6WC3ukzgUaPH19sR77D//exc53lFRERERERuWwrap2Nz5esNjhY6Lxzw3BbLjuSf6u4/fp1jG5+v
+95vu/pE3eD4RERERERFBzdBODc0W6gAAIABJREFUhbs/TN4Tu/deR/zRg9Y8//no8eVjD2r/8xlw
+9wmcT0RERERERFDQPk2/MXr8tMMONrP3B979gENeN3p8ULfvo/p1YF4eP87M3vcEzikiIiIiInLu
+KWifnteMHn/eEY5/8SHP/+dyb8Cnmdljr2tUhbvP2L3915ffyPlEREREREQkU9A+Pd8xevw0M/vb
++x1Yqtn/gAOakrn7G4BfKF+uA99tZs1RBmJmjZk9eo+n/vf+EODvmtmzj3K+cs7HH/VYERERERGR
+80RB+5S4+x8A30UOsQa83Mw+f/U4M3sK8F/IDdMWq8+v+LvkRmsGfDLwgJl99H4Hm9kHmNnXAn8K
+fOweY3wAeEX5sgF+0sz+iZnt2ZTNzKZm9jfN7JXAjx0yVhERERERkXPJ3K93Zyc5TKki/zLwgeRw
+DPDH5Xtz4MOAPij/CHk/608kV7afWYLw6jmfB3wfOZj35/wj4E3AO4E14HHAk4D3KM878AJ3f9Ue
+55uQQ/Mnj863TV4T/uYyzkcD7wd8ODAtx7zR3a8J+Wb2msNeg4iIiIiIyJ1M23udInd/yMyeSQ6y
+Tynfft9yg+VU8R8DvoDlOuyDzvmTZvax5KnpTy7ffr9y23Xo6Px/Arxln/MtzOxvAPcCLyUH+HXg
+mXsdXm4t+cMCERERERERWaGgfcrc/e1m9jTg84HPJVeaH0XeXus3gO9y9x8FMDPYHZD3O+dvAR9t
+Zs8BXkTuQn4PufI8Bx4E/ju5Kv3T7v4rh5zPgZeZ2b8t43wO8KHkCnsDPAL8GfBb5CZvr3L3dxx0
+ysNeg4iIiIiIyJ1KU8dFRERERERETpCaoYmIiIiIiIicIAVtERERERERkROkoC0iIiIiIiJyghS0
+RURERERERE6QgraIiIiIiIjICVLQFhERERERETlBCtoiIiIiIiIiJ0hBW0REREREROQEKWiLiIiI
+iIiInCAFbREREREREZETpKAtIiIiIiIicoLqWz0AERGR252ZbQFTIAF/cYuHIyIicid7HLlgPHf3
+C7d6MPsxd7/VYzgTHnjgAQdwd67nd3LUn7n+37dhBhbyPThYApy2XTCb7TCbzei6josX7sq3i5cA
+o+siXReJMQL96/PhvEd1lLGbGRaMEAzLAx3G68lJ5dafKt/3YxiPxUe3gz3rWc86+osQETkFZtYB
+1a0eh4iIyDkS3f3MFo7P7MButhDyLPo+TF5v4B7b6+f78Hm8c5eQbTbc3CM4OImUEjEmYsxhOqWU
+x89xYvQ+VzY7dKzjcYUQCMEIVSgBeymlhMUEJFJannP36Q32HLk+EBKRMy1/bGjGPffcc6vHIiJH
+sFgsePDBB3nsYx/LZDK51cMRkSN629ve1ueTMx0QFLSLus6/CndfBtVjhOFxQO/tF6rdfXjuqIYg
+aznAJqxUhn0I2F3X0bVdDtue9s6rB5z/oOf6Ma++lj5cV1VV7kMO21UgmOEsS9ddjEQijgH97xiW
+A7XhnNf+3ahoLSJn2juBxz3mMY/hLW95y60ei4gcwZve9Cae/OQn8+pXv5qP+qiPutXDEZEjetzj
+HseDDz4I+b33zFLQLqoqz/jrA2UftntHqeruF0b3c/yqdpmWbYbHCOQqcYqRWEJ223XEOK4YHxRQ
+935uHLqP8rqrqqKqKuq6Lo9z8B6mjJcPLcwCuOHegUNKjlnCnVH1e7/xnukPrERERERERAYK2sXq
+1HHIIbZ3nAA9tlq9Hofxg6rIq5fqp47nirYN44tdlyvZXQ7ZXdeNpo/D/gF1/5A9fq2rHyCsjnlc
+0c5BOwyVbTNK4HcgjaaYB8x8NB1+93j2/rWooi0iIiIiIrcHBe1idW329a7PPqgKvtf08uMYr4VO
+KdG2LfP5nPl8zmKxoGvbYa12SonkiUAYvR6/JsAfdK3jr80OpdlZR9d5mYZfxlJmCKR0479jERER
+ERGRs0xBe8U4BF7PGu2jfn18eQ3zMmg7Xdsyn82YzWYsFgvariuN0SIpRTwlPID70dacH7ZufK+q
+9jhs9x8AxNiVWxw1aIuEEDCrhtkD+1MAFzlNZnYvcC/g7q5O2SfoL//yL3nCE55wq4chZ9Tdd9/N
+G9/4xls9DBERuQkUtIsbrTYfdI7jrPU+yLjzeEqJtuuYrVS03SHFSIq5irysTB/tukdt0rbcumt3
+RbvrWrquY7GY07YtMS6nszdNQ103NM1k5Tq7twETEblduTtvfetbb/UwRERE5BZT0C5iaS7WV37H
+zdD2Cp97BebDAvXJTZV2kufKddvmYNtXmoMt969erc73l199OXutId/Pfr+Lfnp423bM5zN2dnZy
++O86uq4lxsja2hrTqRNCbp7mDu62HM+uX08/2GP+akREbrn3uNUDkDPnCpAOPUpERO4cCtpF3/hs
+dXuv1enSB4XlkwzXZtc2RMvnW443lm7j/XhzVbkqe1jnLbLGAXvv6+ydZI9a4R9/MJEr2Qt2dmZs
+b28zm812Be2+83iuaAf23zNbROR2ZYC295JVTwA000FE5DxR0C5WK9rjoL1XY7DTbuS1/+lzcPYS
+tNsyLbufvl3XVVkLvfparj3Tcffy3k8f+gHadsFsNmNra4vZbIe2XQZts0BdN6ytxbKdWjhkDJpG
+LiIiIiIitx8F7eKwruPXu73X9bo2f/qux2mYrh2HanbeYqspe1j325Ud9XrX7p293+tdXZ/dH5ur
+2rFUtlvm8wVtuximt0+na3RDBR7M+mr26otVwBaR20681QMQkeO5fPky9957L5cvX77VQxGRY8gF
+O+CMv/ce1v753Kiq6rpufROw/rbahfukqsbXNgobbddlefxN0zCZTGhGYXv3WMYBfrnl1/HHYiXU
+7/495Kq6Dfto999bTscf70sOmjIucjaY2dTM/pGZ/aqZPVJurzOzrzCzA7uSm9l7m9k3m9lvl5/b
+MrPfN7NvN7MPP+RnU7n9i/L1s8zsB83szWa2MLM/Xjn+spl9QxnnQ+WYt5vZb5rZ95rZi83s4gHX
+u8vM/qmZ/ZKZ/YWZzc3sbWb242b26cf5ne2h/ANO/1wTuV1cvnyZl73sZQraIreZUdA+080vVNEu
++i2nVqu5q4F5v8r3auO002uG5sNdPl1pglaFIWjXTbMr/OaGY457vybah4ryrmZkRxjruMt4VYWh
+cr58LgzV9aoaB+04mo4PYLv/dVRFbJFbwsweB/w08BHs/kt8arl9EvCifX7284H7genKz74f8P7A
+F5vZ17r7NxwwBC/n+lfAP2WffxqY2ccDPwHctXLMY8vtw4HPBh4EXrXHzz8b+H7g3VZ+/vHA84Hn
+m9mrgM9y9+0DxisiIiJyKAXtYry38zhkHha0+0Zkq8/3AXO1odqJhm08txIrFeamaZhOJjR1TVXV
+o4q2D/fLy+8fsvfTv8a+et+H+X4NeJ7CbqPnKvqGZ31X8r752a6CtkK2yK30I8AHA98C/GfgncAH
+AV8LfCjwAjP7Enf/j+MfMrPnAd9ZvrwKfBPwc0AHfCw5ND8W+Fdm9i53v/+AMXw68ETgN4BvBv4b
+sA78tXKtCfB9wCXgEeDfA78A/AUwAf5quean7nVyM3s6OXzXwNuBf1uu9TbgHuBvAZ8HfArwCuAz
+DxiriIiIyKEUtItxRXs83XuvoN0fk1IaBc29t/a63nB9bfO13YnYLI+5CoEQjLqqqauKuq6p6pqq
+DkNF2QxSsnLLHwykMo3b9rne8sLL6/X3hhOs39fbh++7G1UVmDQ10+mEGLtSyc7LJyaTSfkAoJ/D
+3t9Wr63kLXKTGPAU4JPc/RdH3/91M/sZ4HeAxwFfDgxB28xqciUbYBP4OHf/rdHPv97MfgT4ZeAy
+8E1m9oPu/s59xvFE4L8Az3f3dvT9Xyr3Ty/nceBz3P2nVn7+9cD3m9lXAhu7XmAe6/eQ3+9+CvgM
+d5+NXyvwKjP7ReA/AJ9mZs9295/bZ6wiIiIih9Ia7WI8JXr1ttd67P7YvdZij5uFrX7voOsfLE/5
+NpwAhGBUIeRwXUJ2VUJ2PawhD+WW11P3t1DlcB5yah7OvecllyMcYnHOyF4q5U4Y3dd1RTNpWJtO
+WV9bY319eZtOJ9R1vWv2wF6vcY+Li8jpcOBbV0J2fsL9XeSKtQFPNLNLo6c/lVwJBviXKyG7//k3
+A/+ofLkBfOE+YzByM5O/sxKyx+4ePb5mrKNrJnffXPn2ZwPvDcyAz18J2eOffTk5sAN8wX7XEBER
+ETkKBe1itYHZ6vTovW57he3jhuz9fmZpdwg2+lDbB+1lJXsI3FVV7nPQrutQQnZdvpeblpmtrJMe
+X8uv/da4P7iNgrYZucIdoO4r2mtT1oaQvc76xjqT6ZS6qYff2bWv89rXKyKn7nsPeO5Xy72Rp2f3
+nlPuneX08b38IPDwys+scuC/uvv/OOA8V0aP9wvs+3lhuX/tARX13gPk1/oxx7yGiIiIyC6aOl6s
+NjNb/f5+x69OGT/O1liHjWXlu5gvw2gwqIbu3uNwXRFCno7tnnDv14onUvKyqjtXxBMObpB277Pt
+fSvzIWAb/U5cOWznynpu9LecAm5ACFDXFSnV+KQhpWnuNm556nhd19goaLszjAnrz7vrN7bv70lE
+TsTvHfDcOJiOK9p9N/E/cfd37PfD7t6a2a8Bzxj9zF5+85Ax/hLwx8D7Av+nmX0e8KPkYPyGAyrh
+kKfGO/BcMztqd9K7Dz9EREREZH8K2kWMu7dhGwficSgedxvf69Yfs5fV6vVeoX7cYG30DMNa5tHa
+6rw2Ozcdq0O+Dxbw5HRdNzQoizESR/tt57XdOZB7clIA0sq4h0xvQ9Yd/RZyiE+O90Hby8R2T0Ai
+WBlfmUqOOZNJk6vqod/ne7y23Xe9UhG5OfabSl2Mg+l4m6++c/dfHOESbx/9zH7eddAJ3L0zs+cD
+PwR8CDk8P7U8vWNmDwD/Cfh+H+8jmD2uP80RxtpbO8axK9Lokgep2P0rlTvblcMPERE5565cucKV
+K4f/83KxWNyE0dw4Be1iNWj3xsF4teP4Xo/7+9UQvRqw95o+3Xfm7qd0j/+t0IY9r/O+Xnm6tuXA
+PN7X23Jztq5t6bqOGCNd19HFDndnMpkwmUypqwrDSAFCglRK1n0H8VxBL+Mb0q+Xu1wJdwdPfQ3a
+cij33FnczAlVnrLuNIRgTJqGpt49dXy1E/ryVStui9wGTmqdx97/AB5fyP33zOyJwAvK7RPIW4it
+AX+93L7KzD7F3f9y9KN9mv0p4B+f0HgP8eDNuYyIiMgd5P777+e+++671cM4MQraRR+0D5vyvV93
+8Wu7hPs1Veu9Gq6tHj+E9WtGkAO2e6LvYRZsWdGuQlU6kIdSwc6hvW1bFm1L2y1KiL6QN3m3SR5L
+IodtyFPLj9SLrFTwk5OGfbrKpPI+aAeo3PC6yo+riqYuFe0qb/sVYxrOt9c1FLZFzqx3kv9AH3+E
+Y/tp2Ietjz6U539A/ni5YWaPB54LfAXwZOCjyN3QP330Y+8gdyyfuPvv3OgYDmNmPOYxjzn0uP7D
+UTlf7r5bqxJERPbzkpe8hBe+8IWHHvfc5z6XBx88+x9qK2jvY78tuo66VddRmqGtnvPaKeOjY1lW
+jh0Hs7zWeVQdTinRxY7FYjHcZrMZO7MZO7MdUoxcuusuLl26i8ViwaTJ221V1Won8H7t9ajLeB4E
+2Chklwp0jGm4tW1H23W0bZs/PAh5DXmoAik5aajMj34n5bwictv4bXLDsL9qZu++3zrtsrXWR5L/
+yn/7pAfh7n8OvMLM/h/gV8hB+/lmNnX3eTns18gd0p9iZrW7dyc9jrF77rmHt7zlLad5CRERkTvS
+5cuXuXz58qHHTSaTmzCaG6egvYfVKeB7hd/V7+0Vpg8K2HtNPe+D9kFhPs/qLs3HLOS1zmWMnhKx
+65jPZuzs7LC9vc3m1habm5tc3dykix2P3t5mPp/TdS0bGxdYW1tnfW2DEKphT+w8+F2vhBy+y9hK
+yDaclCLtomWxyJXz2WzOfD5nNp+DGdPplOl0ymS6RgiRqlTNd4X33a9ORM6+nwW+hPwPhy8Evmmf
+4z4TeBT5j/tnT2swZQ33a8lBuwYeDfx5efrHgeeVcXwho/3ARURERE6LgvY+VqvNe00dP8h+a7R7
+/Vrs1cC99/nLtbFlSDVbVrVLg/DkidQl5vMZW1ubXL16lYcffpiHHn6Yhx56iHnbMp/NSqO0XIk2
+ApNmStNMyOHdd13VWG4B1q/dhhzqrTRam89L5Xxnxvb2Nts722xtbxNCxcWLF0mXLlHVDSkmvB73
+KVp5rZotLnK7eCXwNnKl+J+Z2avdfVfF2szeE/jG8uU2B28DdiAz+zjgirv/0T7PN8Anli832b1I
++hXAPwfeE/gmM/u9vfYNH53r6UDl7g9c73hFREREFLSP6KgB+6Cf36s6fm3n8vz9a48ddrBeNh8z
+G3XvTsQu4p5YLBa5qr29zdbmJlcfeYSHH36Y+WLBdDplY2OD2WzG+voGMcZDXpuPwm//QcDy+BgT
+8/mCra1tNjc32dzaYmsr39d1jZnRTKf5teFDJ/QDdjcTkTOubNv1pcBPkCvF/9XMvhH4OXJjs6cD
+X01uv+3AS4+wh/VBng18rZn9IvCT5O3AHgTWgQ8EvoxczXbg5ePO4+6+MLPPAl4DXAR+3sy+j/xh
+wZ+QW1RcJq/x/jTyNmT/K3nrMBEREZHroqB9SsaV8L3C9X7Hw2rYzt/LO2hZP2t8ua2WgadcnU4p
+kWKkXSxoFwu6dkHX9d3HO1KKuWN5CNR1Q1Xt3tM6j2M0JsbV7RyU+27ofZ07JWexyEH74YcfLmF7
+k83NTSbTKWvr61zqYumkvtwqbM9wr/Atcttw91eZ2ReQm49dBL6u3IZDgA745+7+H07gkkbuNP6J
+ezzXfwL4SuBr9hjr68zsGcAPkCvbn1tu+53nkRMYr4iIiJxjCtqn7LBK+LUhvH+UI60ZuI8TaL+p
+daloYyQHL/tkx67NQbttaduWru2IXUfXxaHLdwhh6P7dN0Hbf5xe/s93he3+/8XopaK9xUMPPVxC
+9lU2NzdZ21jnrrseRYxdWU9uu7O0r04eX93UTERO0e7pKddxnLt/d1kb/Q+ATwbei1whfhu5uv3v
+3P2/ncBYvxH4DeA55OZq97DcrPrtwOuBV7j7q/d9Ee6vN7MPAL6AvD3YRwKPITegeBD4XeC1wA+7
++x+cwJhFRETkHFPQHrmx6eGHLzA+rNHZku26t7LHdVmcvavOjOX4m6vZHd1iQdvmW95LO9/6iraz
+DNp1nffdHtaA++r1x69tWd/Oe2nn6nSuaLdDRfvq5lU2N69ydfMqF7uWnXffoYuREEbj993nWyZ3
+hWyRm8Hd7wMO3ajS3V/Lch/q/Y55M/BV1zmOcPhR4O7b5Gr1K6/nOqPztORmaGqIJiIiIqdKQbu4
+0TXYJ8eG9cv9kIZJ5Q7JvUzbzvtxV1WFp4SnVI7NRwfKPtt1xdraGhdKA7S7Ll3k0sWLXLx4kbW1
+dZpJg4Vl+M17fecz9btjXzOIUp0OZqVbed6/u98jvKpqmqahmUyYTCZMy31d1YRqPFX9rPzORURE
+RERETo6C9hmx51ZgK0Xefv6m+3If7VBVhBQJ0UiQ12CXKefBoKlr1tamOaCHwKVLl7h46dIQtOu6
+IaysI+/HMvQ697Kj2MoxFvLU8z5kmwWqUFHXVQ7aTcOkD9vTCWZ1ad62ey24iIiIiIjInURBe8V+
+Tcz220/7uPY6777H0ufs0hTNneT9OucSbKuaFCMWRtuPOaW6bDR1zfp0Sqgqqrrm0qVLQ0W739IL
++kq4lf/rr+DDjHgbrafuQ3UY3fo12KEKVHVN3dRMJg3NpGEynTCZTMED7ssGbwrbIiIiIiJyJ1LQ
+3sfuENiHw2vXES9zcr+eev/9tle36xpn7CFw++5u36Ub2rC+GcDNoFSzcxhO4BFSwlNHO2loFzVt
+U+PuOWTHSNU0rK+vM51OaZomTzt3J6Xx2CyPpYRhs35Muw1hO1TDVPHJZIKTsGDUdeDixYtsDNer
+iXmIeGSYqr5c267ULSIiIiIidwYF7X1dG/xs2W575Xt7fX935frgavjBTdTMQtkz24Zu4yFU+adC
+IJhRBaOpAk0V8BRJsSN1LSFU1DFSxUTVNDR1TQhhuW936ve2HvUkK/dlQ64h+C+r2gaEoaLeTCZ5
+HfiFDaZrDV1cp4stFy9e5OKlS0yn03LN3Dxtdc/w3Q5vKiciIiIiInKWKWjvYWhC5rB7Zndftd59
+fB/AdwdHP+J0c7vmfOXHy/ZdDOE630ozsZC7hxsJqoDXAW8qurrCY0dqW2I7x0LIITslQpXXTS+D
+dspbg5XCspsxNDhnGbyNXFXvd9G2EvpzRbumaSasTdfY2NjASXkTMEtcuHCBS5cuMl3LU9dTSsMW
+YcP+4Lt+tT5+8Yf83kRERERERM4mBe1dlkFvNWz3YbgP1Xv2LtvVLXwZSo+9tnvXDPNSyQ45ZPdh
+OwQj5LyNEbFUYV6TmorUtXTtnG4xz13AY6KKCatq6roexjSuZuNlTfY1L2y0ha5RuqIZ/RrxECqa
+umE6nbK+vkFVQ6gCoTbW19e5eOniUNE282GWuHu/R/gelxQREREREbmNKWgPRmESSuW15Fr2Ctt7
+Ga3TLuG6D5JDt/Bygj7UmpXtuoYdr3LFl9IlPOdqGzI2IQfvUMaY73MHcvOKRE3T1EyahmZS08aI
+d5G2bfN91xJTyuPpK+bl1aeUcI94Kuu+yTcDqqqiqirqSZWDOom2W7BYzPO+3WWv7qrOVfPp2oS1
+9XWausEwYpfyXt+p30vccV/9RWqttoiIiIiI3P4UtHujoGuWw+44YBslFFup/LJ7a+lllduW51sW
+cHdHyDI/O1d4bVeIH5qhuWPBc+U6OFZZeZxveD6NuRMwzA2jIpkzaXLYrpuGsGhxd9quI7rTtR0p
+xuX8+NGFU0rEriN2XQ7c5VYFYzpdo64DTVOTkpNSJHaRts1BuytB22xCU5qura1v0DQTzAIxRlJM
+pYp+2BRxlbhFREREROT2paC9ysZ3/XpkzzO3yxO7QrF7+X6/l/TyuZ674zaagn1NyPbS6IxlRRsn
+BKeqStiuoAoQKqMKAVLKeT15aU0WCEAK5KBdttYKs0DyRNe2tDHtqmgPr9UsNy33SNe1tPMFKbWk
+1OGpo67z3thmU5pJRdtGYox0XTdUtLuuo+s6zKwE7Q3W1tepQqlox1Qq5rt/D9eGbYVsERERERG5
+vSloF26pPHAg4eZYCdohhBK0h12tS0OvvM45uWNWESyAVcP2WDbawsqGAM2w5npXj7MSuJ1+yrZT
+Vey6hWA5bJvjw3ZeucGYlW25zBMBpwrkcBwgpchiMWe26NjZ2WF7Z5vtnR0mKVFXDVVd56npkF+7
+RzzF3FQttUSLpNiSPFe6U2xp2wXz+YL5YkZMETNjMpkwaSZMmimTZkpTT+hXkafYTxvf9Vtn/+ni
+CtwiIiIiInJ7UtAucsCFfl0ynsA8b2EVnCpUgOMpP+cp4TGRYqJLiWAVHmpCyM3GjDK1Gy8BfVnJ
+NctbY1lpENZXzUuJGixhJOrGqWqnrqEyJ1gq08Rz9TnFjtTGoWM4FvDkmLcES9RVDvMxdswXc7a3
+Z2xubjLduMpkbZ2NCxdZW1tnvQpUoSrVcqMOEFOeuo6nUmjvSHFB181YtAvm8zk7sxmL+QJITCYN
+ZhdYW19nMplSVQ1mFe6Wt/lmdb/u/Shgi4iIiIjI7U1Bu1gG7YgRgQhAsIoQoK5C3g6LiMcIHvHU
+5SnUbSSEmqpqoMqh28oC7WEKel/ZLpXs0Dc5G6aOU4J9AouYJeraaRqoG6jMCO45vEen85aubOPl
+KYd3typH+dQSzKnqHOBj6pjPZ2xvb3N1c5NmbZ1qMiUBFgKTtSl1VQ8Vcw/kQnSZ8m5E3FtiXNB2
+FYvFjNl8h52dGW3bYZbXbk8mE9bXN4agHawmuRPL/tkAh+ZsERERERGR25yC9mBc0c5BO08dh2BG
+CAlPEeiIntcup9gR25Zu0RGqJle5U8rV71LV9hKksbKW2o1AP9XchuZqyzXhEQsJC4mmdiY1NI1T
+WQ7YFhNuCfcFKS6wdoEnyyHbKhIBUkcgV7SDOZ7y2uvZfMb29hbN5hrWNISqoplM2IgbYE1+rcGo
+g+GBPC2+VPdT19K2M2wO8/mM+XyH2XyHlJzJZI3JZMpkssZ0bY2m6SvaoUyxT9cEbNuzdbuq2SIi
+IiIicvtT0C6aqt8EO7cWMy9rnz2SukQX2xysu0jsOtq2ZT5vmS8WzOZtrmiHmlA1VKGiLoG1Dn1Q
+hyoYoQpUdal+15FhSnnfZbxKhOCE2rGqJtSlmo3nKronPHZ4zEE7dYu89pkKLJAImDt1cKYNrE8r
+LqxPuHhhjfligZHY2d7M2365U1XGZNoQAoTYEcxpJjUxLYgxMp/PaWNLmC8I2zWhrvEEKUFdN4RQ
+54BdgnbdNIRQ4QliWcOuHbtEREREROQ8UdAu6ioAfQOzhHluaJabgiXafuurNm9/tVi07MwW7Mzm
+zGYLLFSEUJUp5BVNZTTBaKpAXQXqyqiqQF3XeGygieBNqfbmmwWoK4c6bydGkyvgNYG8c3Weru4x
+r5fOYXtOio4Tyi3vqV2HgFWB9WnNxnrDpQtrzOZz2pjY3t6i3dwCg+m0YX1jjUlTMbW8RrtpahYL
+I6VY1mLvkMxIGG4wmayzNl1jOl1nOl1j0qwNVe1+/Xk/VTz5agM0ERERERGRO5uCdjGuaJsbYJCg
+i5HYtcR2Qdd2dG2kazsW85bZzoztnRnbO/Pc0KyE7ToEJnW5VYGmqWjqfPOmgTTBPGKeSCmSUr4P
+waExzAOVGRbB3Kiockdyj5A6Uiohu5vjcU7q8q7f7oYTsKahrhrqptpV0d6ezXjo6g4729s8vLmD
+GWxsrHHp0gU21qdUkwmHWeAZAAAgAElEQVTTakIzqQg7EGNkNp+ztbXNIkYWMdJ2iUc96tGER9dc
+uNCwtpbXZOewPS17bDtxtF/2tTlbU8RFREREROTOpaBd1KGskk55TXNKLalb0M3ntPMF7WJOt+iD
+dmQxb9ne3mF7e8bW9g4WQp4WHkqgntTYpKJqaoJVVNQkKhKJ6An67bNiJPZBuwJLFRUVHiqIFZZS
+3rLLHfMEHnNHdBJWupMHSyTvN8UOhL6jeaiY1rA+rbm4MWV7ts6ii8wWC5rawCPtYs7O9hbbm1Pq
+jQ2mwWDS5GnjizlbW1s8cvUqbfK8D3d01tYv4m7U9YTJZEpdT6iqGrMKSHkbM0+7O4zvuSZbRERE
+RETkzqOgXfRBOyanix3dYk632GExm9POZyxm8yFkd21iPmvZ2d5ma2uHre0dQhWo64qqhOzaJng1
+wRobdu0iJTx6DtZdRxcWZX/pSEyRug5U3lCHJk8fjynfUtlqrF83jmPmBMv7axtGyMu8cTw3U7OE
+EZlUsD4NXNiYcFe7TvSUo7AZ6+sTjMRstsXm1ZrGnPWmIqUpi3bB9s4Oj1zd5OFHrpIIeFkDnhKY
+VdTNhLqZ5jXZBGJy0jAO2H+PbBERERERkTuXgnZRl6nj3iVSbOkWM+Y7Oyx2dljM8uMcsj0H7XnH
+9tY221tbbG7tUNd5i6tmUmE+YVolfAJWmpOZJ0gV7in3M/cAGHEI2onUVNSWSDXQVBAjpBK2A1jy
+YQ15MHIDs8owyjpodxyGruVmkaZ21iYVFzYmLGIiet7IzC0QqhojMtvZZtOMtaZisTYleceizRX7
+Pmhb1RDqhlBNctAOFVU9oWmm5dpGisugvQzZCtsiIiIiInK+KGgXoYRBT5HUtrSzGYud7SFot7MZ
+7SKVoO254/jOjPn2jNn2Dk1TgdeY1aTa8VRhRKrgBCvBGMPcSdGJefY4KaUcsmPEUk1qAt7VWIow
+VLTz9l99NTsYJPPSybxUkMeztCtyRdtSqWjXLOKUzo0y6Ry3ii7m6d2L2Q5bnriwNmG2vs5iscj7
+bu9ss7m5xdXNbZrJGs3UaKZNbrhmNXXdUNVNDtjRSWUbryFoGyhki4iIiIjIeaOgXcxnMwBmOzts
+b2+xvbXJbHsT73JVuakqrA5lrbSTIkybhsV0wnrsaCY1a+s107WGjY0pFy5M2djItzoEagtUIYBD
+7JzYOZ05KRkhGSkEmrqiqiqC5Wr3rv8YhHIOLzf6x2XKOF6ODYFQV4S6ZuKBaTTWU6D1QNs5i85p
+ozNfdLRtR2oj7XzB9tYWD9c1wZ13PfQwW9s7LLoOxwhVnio+na7RNBOqqgLC8PvzPILMQK3GReQo
+zOzFwHeSP5X7q+7+5ls8pBty5coVnvCEJ9zqYcgJuPvuu3njG994q4chIiK3KQXtYjbbyfc7O+xs
+bbG1ucliZ4vajNoCTVURnCFoe4TJpGEtTogpMlmrWVtvWFtvuLAx5cKFNS5srLGxMaGyQGU5bHuC
+NiQ6S2AJT6F0Hfe8xjtUBNsdsvMEdHAzsAChKiHbCMFIfcB2Byt7dVcVoa6YEJimQEuio8pBOzpd
+5+DzsnVZS9dFtre2MXe6xSIH7a1t2rbDgRBqmnrCdLpegnaNmY3WY2eukC0i51hKibe+9a23ehgi
+IiJyiyloF8uK9jY723nt9WJni/XJhHoyoaknw9Rv3EkJprHJ2395ZLrWsL7esH5hwoWNKRsX1ti4
+MGV9fZqDNoGKvI45WMSIeTuu4Dlsewnao4p2f8sVbaPqu4mTK9kpBFIox3neCgwz6qoiVP9/e/ce
+ZOl21vf9+6y13tve3T1zztHlSAg5gEyMLTAqLO74AoQIK1JMcAgkYGEJW4lJFQlgKGwMHFzBKpBt
+qgIpC2yEgKoQLAOxETeJEIQVbF2Kq1EwCmAkMUiydM5Md+/Le1krf6z33b2nT3fPzJmey5n+fU69
+Z+/e++33fXdX7d7z62etZ+VjFWZUJAaXGAi0U0W7T/R9pFt3pBjp2o7FGLKXhwv2Dw45XCxyRTvl
+pcuKoqSuasqi2gTtLG2t2DWGbAVuEbmwPupeX4DclivkDqYiIiJPnYL2qO8HAIYh5gZlQyTFhCVw
+5GW7LEF0iegSwXvKMjBYCQGaWcFsXjHfGYeM1yVNU1LXZR7ynQyHIw6JZAORvKVx3WkXE8473FSt
+nmZkJ4gJ3Lhfmh6Y2Bi07ajTd9x0OQeS4Z2jLBw1kbZLtN0YtLuYlypr+zzHOiZWqzWr1Zrlck3f
+D5g5yjLQNDPmO7tcunSZ2XxOUZZgRkyRmBIxJdL4D5Ojq1PQFpGLxgHvu9cXIbfleYBGJYiIyO1R
+0B7FsfoaU67OmjOcy8F3uiWBcxHnIj44CitIwfCVZzav2Nmt2dltmM1KqjJQFzmMuzFkOxxDn8aQ
+3ROTI8aETWHbGTifl9FKxpDI25CboaUYIeXGaSmmoyW0zG2akCXyMts2JCwNueJtgRACtRltNQVt
+Nh3UuzYSB+jalrZraduWth+ICUIoCGXFfGeHS5cv8fAzHmFnZ4eqrjBnedh7yn8ASArWIiIiIiIi
+CtqTmMZq7Ngx29n1IduZAzctqQUeKH3Am6ekYGevYfdSw97ejGZWUjgjeKNwhiU3LvNlOA+RniE5
+YsrLetmQcofxcWHsvF51nns9xLy2t5EgxrwW97jlXOtIRDZF7HG3nLYNC0DhCL7AgqeeQvZgOWR3
+kb6NDH2k73q6buBwscyB3Tw+FNR1w87ODpcuX+bhRx6mqirKssTGinZK+QoSeQTAEdu6Z4iIiIiI
+iFwE7sa7XAxxe2g2wDgvOv+IxqZkmxCZq96+8JRVoJ7nOdk7OzN29+bs7s6ZzRvquqIoC0IZCEXA
+h4DzHvMB8x68zxXsqXu45eW3hgR9gj6mvA2JfhzSPgwDcRg2Q73z8tk5kPcRugHaLrJuI6u2p+0j
+KRrOeYpQUJYlVVlRVxVN1dBUNXXVUBYVzjwxJtq2YxgSzhxlWdHMZsx35uzu7nLp0iVmsxlFWeSK
+doo5bI9j1dPWf2w2EbmozOyymb3GzN5tZgsz+4CZvdnM/uotHONPmNk/NrPfMrNrZnZoZv/ezP6J
+mb3wJo/xMjP7GTP74Pj9v2Nm32lmzx6f/wMzi2b2A0/1tYqIiIhMVNEeDeO85ynU9kNk6Ad6l7fO
+Bvoh0vYDbTcwkPDO4wtPCI5Q+hyox81ixFLCUiTlJbFJMdH1A+uuZ9l2rFYdQ0ybOeGYEULuGB5i
+xAWHbx0+OKJLWBw2Ww7eOYR3Q6TrY553PUTSWA1PyShqR20ldZHwzjDzeB8oQ6IseopQUoSOIpSU
+xbiVFb4oqOqGqm7Y2d2jmc0oyxLvHWb57zNpK0gbY8dxEpsJ45amB0XkAjKzTwDeAjyHo7+6VcDn
+Ap9nZq8H3nqDY/w14HXj923/5e7jgBcArzKzv5dSes0Zx/he4H8Yv5yO8QLg64EvN7O/jP4yKCIi
+IudIQXvUD3no+BAjfR/p+yF35XYDnetpnafvI23Xs+57IomyKI/CduEJpacYq9fEmNN1PJrH3A+J
+th9Ydx2rtmWx7hhiJA4pDw83IwwOHyIh+hy0gyN0nuQTLg64oc9BO6YctGNubLZuI+uup+0iwzTK
+PBl18lD0hCrigmE4gg+UhVGEnjJ0lEVBWRQURUlR5q2sambzOc1sh53dXWazGWVV5oo8HM3JTkcd
+x48aj6ft5C0iF5CZ7QI/BzxKDrA/CvwQ8EHg44GvBb4SOLUibWYvJa+xDbAPvBb4BaAHPhP4JuCZ
+wP9iZo+nlF53wjG+gRyyE/Be4B8A7yIH9/98vI43ArPbeb0iIiIi2xS0R1NFe+o63g9j2HY9nXmC
+9Ztq9LrrSA588uMQcocvc9guykBRBlI/EM1Ifa78DiltKuKrsaK9WK8ZYtoMAzczfPCEIRJibrgW
+Ck/RB1JMuNjjhwEXe/o4DjGPRtdF1u3AYt2zaodcJR/ndg8W8NVANUuEBGYO7wJF8JRFT1l0Y0W7
+oCiKTUW7bhpm8x3mu7vs7u3RNLNc6fY+h+w4wFYDNIOxA/oojf+z6b5Ct8gF8y3k9s0J+KaU0ndu
+PferZvZG4E3AF5z0zWYWyJVsgAPgs1NKv7m1y9vN7MeBXyFXzF9rZv88pfSRrWM8G/i28RreA3x6
+SunxrWO8zcx+BvhFoEQVbRERETknCtobUxB0YHl4tNm0zFZuMtYNA8u25XC1YiAxBIPC4+pA1/UM
+fZ4/nYZcpY593rousVoPrNc9h4uW/YMV+4dL9g+W4xD1HO6nNbBDcIQisG4buqFnSJGmcJQWKS1R
+4HLIHvKc7FULh8ue/UXLYtVuhr53Q2I9GBQ1oW5wocpF9pS7qpvZOMc7NzTzzlGWBU1T0zQNs6Zh
+1sxo6mYcNu63lsnOQ9MhjxAfVxg79q9U29pE5KIwswJ4JflXwm8cC9kApJQGM3sV8HtAccJhvgh4
+7niMv38sZE/H+EMz+9vAj5Ar0n8d+Idbu7wCqMdjfM2xkD0d41fGoeX/8629ShEREZHTKWiPHB7I
+Fd/ccTxv5tymC3jbRxbrlquHS/oUicGgcLjKM1tXdG1H3/YMzufh4H0k9on1emC56lksW/YP11zd
+X/LEtUOu7h/mTt/9QN/ldby9d4SQq+Or9Q7d0BGJDHXBrHC4wvDeM5CbpeWgnThYDlzdb7l2uKQb
+8jG7YaBNhqtqimaOL1sMD0x/RICUBoahJ8Ye54yyLJjTUM9mNLOGpmmoq4oiFDhzuVlcmrqzg6Wc
+sG2c3ZiHlXNd4j4pZps9+dGjRnQi8jT3KcBD5N8Ebzhtp5TS+83s54GXnvD050+7cTR8/CT/HPhe
+YG/8nu2gPR3jP6aUfvaMY/wQCtoiIiJyjhS0RzY2YHfmcc7jnSd6nxt/jd3A2yGyWHdcWyxphz6H
+7DIQqoKdVUu77hm6nugDQ5/GjU3QPlh0XDtY88T+ko9cPeDxq/t0bU/bdrRtT0ppbIbmKIpAN/RE
+xmW/UoOlgsIXlIUnpkQXE20PyxYOFgNXD9Y8cW3Juu9ox603R9HMqHdWlM2M4BPBFQQ/VupjGoP2
+gHNQlgUuOOpZrmg3TUNV15ugTRyboKUcsnPPs2N17PHx/HM9qmifFK6nxxSyRR4on7h1/x032Pft
+nBy0p7nbv59S+vBp35xS6szsV4G/yJPne7+QHNR/7QbX8JtAy8mVdREREZFbpqA9mjppO+cJPhBC
+gcWe4APeFzgfSGabsL3qWoqmolq31OuWddvTdz1DNzCEgb5L9F0eNr5cdhwcrrl2sOKJ/SWPXz3k
+I1cP+MgT+7TrjvW6o123efj2GLRD4UkWMQfOG2aRQENdeOrC6CJ5aHiXWK4jh8sc4h+/tmTdrVl1
+a9ZdS3KeemeHncWSZr6mKhKpMMy5cWmugZgGUoq5GVvwODxVWY5LgZWURYEPeU1x4ISQfUKAhuua
+oR0P2ad9rcAt8kB4eOv+B2+w7wfOOEa6ie8H+OMTzgu5qg7wobO+OaUUzewjwLNv4lw3EIFn3cR+
+ftzk/nPlXl+AiMiFdOXKFa5cufHv4LZt78LV3D4F7ZH3OUQWRYC6wqWGWHqKMXQXPnDYdoSDQ8x7
+YmcM4xrXXZ87lQ9DIkUjDtB1A6tVz3rVs3+w4Oq1JY9fW+RK9hPXeOKJfZ64uk/Xdrmive6IKeG9
+4bzL87QDOJcwG7DUE4hUwVEGx2odWa4Si1VksVxzuFhzsFhzcLhk2S5Zrlcs2yW+COzs73KwOGS+
+nBHjQEwRSPR9xxCHMdzmgJviuBr2OER8amRmuHE18VyhTrm0nRuMH8/Z13UbP72KvX1fAVvkgXW7
+b+6n4S+HM3O9iIiInOB1r3sdjz322L2+jHOjoD2agraFgK9KCjeDoSCEguALilBQL9cUZQUuMCRj
+iOQlwLppXetxWa0IXTuwWrUsDlv29xc8cfWAjzxxyEeu7vP41X0ev3qNq1f3abs+h+2uJ8WIc4Z5
+CN7hfMIsgg04i1TBmFcFTVWwbCPLdWS5iiyWLYfLNYeLFQeLFYerRd6Wh/iyYO9gj/3DQ3ZW802A
+dlie/x0HIOWwnBIxJWLKQ8rTJmTnxmnT/PUcxG2cjG3bK3zdkpOGkovIA2G76dizyR2/T3NaFfkj
+5F8tN1NlfnTre45fx7PJS4CdyvKQpofO2udmmRnPeMYzbrif9z43mJT71qOPPnrjnURE5Ny8+tWv
+5uUvf/kN93vJS17Chz50//9RW0F75Mdh0b4I4CoowFJJCCVFyIG7PjgklBXmPBFjSHn4dq5op7Gi
+DXFItF0O2oeLJdf2F1y9esDjT+zzkSeu8cS1A564us8T1/bp+p6uy1uMEZenhOO9w1wEBmLqCC4x
+rwouzRt2Zg2r9ZCr2ashV7SXaw4WKw4Ol+wvFhws99lfHFDUBQ8d7HOwOGSxXGJjBdo5O6poj13M
+UsyV5Ryyt5bushy23Ri2LcXcdZwpbOeS05Nz8+nVbIVskQfadofwFwNvO2PfF5/y+G8BnwF8jJk9
+cto87XEZsBeRfw391rGn/x05hH/yDa73E8nrat929fy5z30u73vf+273MCIiIhfOc57zHJ7znOfc
+cL+yLO/C1dw+d68v4H7hvOF8nqNclAVVVVHXNWVVEIqA8w7nPc57fBiHkxclVVVT1zPKqqYoK3wo
+caHEhxIfKnxR4UPAnB9HYo8B0/I8aXMuN1/znlAUFGVFVdc0s4a6OdrKuqYoSpwPefkx53Eh4EOB
+C+GoQzoQU14DvOs61us1q9WSxfKQw8N99g+vsX9wjf39qxwuDmjbdR5KbtMyZolhiMSUMBzOB7wP
++LExXK5snxaS7dg2vdTT52dPx1PwFnmgvIujqvZXnLaTmX0Up6yjDbxl2o28bNdp/mvg0rHvmfzC
+ePsMM/vCM47xijOeExEREbllCtoj7yxvwVGUgaouqeqKYuzCjTPMGd47fPAURUFV1TTNjNl8TtPM
+KasGX9b4UBHKmrKuqeqGoqwJRYH3ORDnsO4JIVAUBUVZUJYldVUxmzXs7Oywu7fH3t4ee3u77O3t
+sTPfoWoafFliPuCKglAUlFVJUZb4oshheKzMx5Tohxy2V6sVi8UhB4f77B9c49q1J7h67XEOFwes
+2zUxxs2a2jEm+iGSs7fDj3PU3Xjtkxstz3VSgN7+WuFa5MGVUmrJS3IZ8Mlm9vXH9zEzD3w/p3f6
+/kngj8Zj/F0zO95RHDP7aOC7xi8XPHkZsDcA6/H+d5vZIycc4zOAv8XTci64iIiI3K80dHzkfA59
+wTkKbwTn8TYufxXzcGqcy43KfBiDdkXdNMxmO9TNjLJqCEWFLwJFmSh6qAZHUS0JRZkrz96P4TUQ
+ijDOe/YMbsB7T1WVVHVJXZfs7u6wuztnd3eH+c4OVd0Qihy0fYBQ5EBdlGWuugeP845kuao99ANt
+17Jar1gsDzk4LOnaiq4saVclzgUcBc7CUUU7jut/J2AK2kWBc7nr+PGwfDxcT7fHQ/Rp+20/f/x4
+IvK09u3AlwDPA77TzF5EXq/6g8DHA19HXm/7nZwwfHxctutvAv+KXLF+m5l9F7lKPQCfBXwjucV3
+Ar4upfSRY8e4YmaPAd8B/EngXWb2mvGcFfAS4GuB9wM7wDNQ4BYREZFzoKC9Mc5HdtMQcsNZInUD
+KUb6YWAYhhy6pzBoDuem5b/yZi5gvsCFRCgglFCUFaEsc/W6KCmrjrKrqLqeWCTikIgxEkKgbmqa
+pqKZ1Vy6NGN3b87OzoxmNqducnXcFyU+QZFyg++iLPAhD2/f7uIdUyQOkX7o6buOtmtxBs4SjkTw
+eW66jcPCnXN5WHy0PIx93IIvrgvPJ4Xm7a+PB+0pRB8P6ce/TyFb5MGRUrpmZi8B3kyeJ/1l47bZ
+hVyB/mWeXImejvHTZvaVwOvIQfjbx237GD3wzSml7zvlGK8xs+cDrwY+Gvjfju3yQfLw858Yv17d
+5EsUEREROZWC9miIAwBuMKIzYnKQEm3fs1q3Y2OzBYvlkuVqxXK5HrcVi+Wa1bpj3Q10A4RkRBzJ
+eZwvCEVJWdXUzYzZvGMABoxkjjQukZVSXlqsaWpmszxHe2enYmdeUc/yEPSybja3uatZIlrCl3md
+a3MG7vqwm4e7e0IR8rrYVU1dVTRlhbMCSx6SA3NUVUFZeeY4di9dZjbfoazyHPOUcoO0OAyb+6cx
+s+uGmR/fV8t7iVwMKaXfNrM/Q648fxHwfGCf3Czt+1JKP2Zmr2BrMcETjvHDZvZLwP9Ens/9fPK0
+pz8iV7e/J6X0725wHX/LzH4a+GrgzwEz4H3Am4DvGivfe+PuV2/nNYuIiIiAgvbGMPQAOHMM0RGT
+AZGu71mt89JZh4sli8WS5XLFarVmucohezYG7bYb6GNiSDlEYx7z4MegXTUNTdszYERz4DxT4zDD
+KMqC2axhNm9y2K4DTVNQ14GqOQrbZT0DG0gWiRY3w8bNuWP9yHLg9d7n5m1lSVlV1HVDU9cQHbE3
+hgFIjqKsx/nkNfOdXebzOVVVEYJn6CNDHIgxnhm0rwv5Y5X6tGq15miLPPhSSk8A3zRuJz3/BvJc
+6rOO8YfkId63cx0/BfzUSc+NTdkukcP+797OeURERERAQXtjGHJF27tEjJCSMZBou57Vas3hYsHh
+csFiuWK5XLFctUdV7dVqE7S7IQftiAeX536HoqSoauqmoekHohnJeXA+z302h5mjqkrm8xmznYbZ
+rKEsjLJ0lIVRjY3VqiZXtRMDkZ6BAV8UOWh7uy5kG7mzeZ5nHSjKctNNvZnNiX2iW8e8lrY5qqpi
+Nt9lNt+jambUdUNV1jjvibGDAWKMZ/4cp2r2NJ/7RtVvEZH7wH+7df/f3LOrEBERkQeGgvbGNIQ5
+rw8dU14juh8i625gte5YLlsWy9Wmuh18wDCGYcAAb4bzjsVyzTBEhiEx9IlrB3kN63Xb0fV5nndK
+aVzeyzbzo83lRmZ5LeuBIRp9nzuCd/1AN0S6Po5hHpIZ5t1mc1Oztq1GbU0zp5nNmc12mc12c3iu
+G8qyIbqEEXEWAZ+DfN1Q1TUhFGCOIebGaHGIm2r2dT+1k7qPn/TT3apqK3iLyN1iZjNgL6X0x6c8
+/yLgm8cv35lSevdduzgRERF5YClob9jmNqVpg35ItN3Aqu03VezFYsn+/mF+vu9ZrVYwNfxycLhY
+kiLEce71/uGSg8WC5XpN23X0Q8+Q0lEgHSvQjN3C+2Gg63sS+RpiMsoi0HY9bT/ksJ4SEQN3FLJt
+XOs7rwNe08w6ZvM58/kus/ku8/kedVlRlxVlWRM9OIsEF4FAXc+oqjx83HkPTJX+yDAMN6xmb5xR
+xT6twq3Kt4jcIc8E3m1mPwn8LPA75CW/ngt8IfBKoAEitzk8XURERGSioL1hW7c54A4R+iHmoL3u
+WK47FmPQPjhY0PcDq9V6DN0pV6edcbhcA7lSDcZytWYxzuluu45uK7Qmsxyy3bi81tb611PYj8lY
+d0dBux2X38pBOw8PzyHb5XXAi4KqrpgNA/PZDrP5DvP5HrP5LlUoqYqSoihJwzhU3ifMPFWVlygr
+qyqfNyaGIRJTJMVE2qpo3+786uOhWiFbRO6gGvhvgC894blEDt5flVJ62129KhEREXlgKWhPxpwX
+IwxDonc58K7X/dhxfM1q1dJ1w2YOd4ww9IneR9brjsPFiv2DBckchsF4u25b1m3Hqm3p+j4vFRYH
+YooQbXN6M7AezBIQ89DxCGEwgncsihVlUeDNYy7gfMCcz2tzj+tyl2XJEFNeAzsU7O5dYndnj/nO
+HrNmj8KHzYaD6CB6MDxlWRFCXjM7xkRiyEPH8wvO2038GBMnB+epan1S9VpBW0TukPeT1/N+CXm9
+7mcCDwML4A/Iy499T0rpvffqAkVEROTBo6A9OhoVHSFFYjTarh3D85Kr1w5YrTvMPE0zBytompqm
+rqmbmr1Lu9TNDFzYzMFOaSAlNkG7nYJ2jAxDpI8RswEbm6GFwTHEwDAE+iHkda5DXtObFLFkDP3A
+etVRNw11M6OpZ7iQh4vXdc1sNqMoS5pmRtf3PPzww1y69DA7O5doZrsEc/hxIxnJQfK5+u5DgZkj
+xnTifGzMzg7b22t4A3ZCmN4O2QrbInKnpZR64I3jJiIiInJXKGiPpqAdY2KwiPWJ9XrN4eGS/f0F
+164dslp1YJ5mNqeq58xms7wc12xGM6uo6gpzgX5IuRI8NhBr245119K24/zsGMdtCpY5oPa9px96
++iHQ954QLAdtb8ShJ/aRdt2yWq65dPky5vK8ah/GjuJj0I7paH74Qw8/xKXLD7O7c4lZs5uX38Zw
+CUiWO5UnAIc5D2ZjFTvlqvY4l3x7oPipw8anAD3eT8f2VTVbREREREQuAgXt0RS0U4q5Eh0HlquW
+w8M8HPzqtQPaLm4q2iEU7OzssLMzZ2dnBx8czoM5o4+Jvo8MfU/f97TdWM3uujxsPEViSnnbVL/B
+OUcx+FzRDmPQ9ob30HWedt2xWCxZVCvMB6p6RjLbBO26rpnNZ3lYufM4F7j00GUuXXqInfllmmYX
+hjzfmpjG0D0Oc8eIkJusjRXtOIXi7bB9xtzsTajeavS2Paf7rGq2iIiIiIjIg0JBe7RcrAAYhp5h
+aOn7juVqyeHhkvW6YxgSZVmNnblnNM0sV7TnDU0zw3nAAZZIKTIMPX0/0Pc9XddttqOgHcd50GPo
+HIN2KDxFCPjgGIZus61XHathTRoi3nlScnhfUtWzfOy2I8aYjxECoSgJoaCuasqixHuPkZcPMxKJ
+SCJXs6cKdOToeqaAfSNp7LY+3d92uw3TREREREREno4UtEeLMWj3fUvbrmnbNcvlkoODHLRjhFlZ
+c2nvMpcfeoTdvT3quqauK+q6zgF7jKoxDjlQ930eCt51dF1P1+egnavFU8X46BpySPaE4PHesVgc
+slwesOh61quO9YR94/QAABl4SURBVGLFarkkDpEQSqq6YWdnl5SgbTtSSpugXZYFZVlTVSVFKPDO
+j5Vrx1RvPgr5+TryKyC/imNB+6zIvB22T7L93PZ62iIiIiIiIg8iBe3RVNFu2xXL1ZLVasliO2gP
+UJY1ly5d5jmPPoeHH3mEoizGQFuOFeqBIQ0Mw7T1DONSXX2fA3c/DGPIzhXtI4bzY9D2HueMx51n
+6HsWcUG76rh27YCrT1xlvVpT1Q3z2Q6XLz2E95627YhxO2iXNE1NXVUURe4knpcbSySLgNusdz1t
+cQzbUwyfbm+mLn2jsC0iIiIiInJRKGiPhmEAoG171quWxWLNcrUmxkQIJfO5Z3d3j0uXLnP5octc
+vnw5V55DIARPTDEv2RXH23EJr2HIc7WnoeRxXNYrxpSX9zIbg6xtKtree8wZXduxWiwJvoTkGfp8
+favlmtWyZb3uWK87go90bUfb5iHkRdGTUhxnXtvRbcqDxklH86Uj203KeFLIvhUK2yIiIiIiIgra
+G855gLy8VbLczGysYjezEhcKHnnkES7t7dHUDaEIuDFUbpbCmjp6m8N8vvUuEb0nxiJ3IU/x+u7b
+ZmN/sRy0vc/Dxs2MZbNgUS+oq0OaaklTr5jVK4hGVdYEX+Asr3ndth3LRa7EM15DcJ6qrBj6nhQj
+eUJ2guka2Bo2nidqb/1E7szw7pO6jouIiIiIiDxIFLRHm6CNI0UYhkSM0FR5HvR8d49Lly6xd+kS
+dVNThLAJq7mp2aYcjDOXJzWPxzyaB52uW/oqkXKl2Qwsz9H2zuF8/r7D5pCmntFUM+oxaK/qNSnl
+PwCEUGLmGPp+E7QPF4c54PvcVK2rW4a+I8UBtq9jK/CzuRq43YB9UlVblW4REREREblIFLRHzly+
+Y0ZM0A+RGKEa52U/45nPYr4zp5nNaOoaHzxxOBounr/VNsH5pO0k28875zZbSolZM88hu2zyVs1o
+mhy0tyvaXezp2o7lcsnhwQHBe4oiUJUFXbveVLSNKWtvzc3e+u+66zrnn6+q2CIiIiIiclEoaI+m
+OdoAIQTquqGsErP5nNlsTjNrKMsK70Ou/45V7O1gDVx3u/348aC5/fW037Q8l3O5UdkwDAwxEmPE
+nFGWJfP5nKIo2NndZTabUVc1MQ6YGcMwsF63LFcriqLA+0Bdz9hZr+n7fhzifv3Q9TsRgKcK9vYa
+2tPjCtwiIiIiIvKgU9Ae9UO/uR+KQNPUYI75fMZsPmPWzCjKvB41jPOy86TsTTX8eMjefmw7ZB4P
+udM+zrlN2IYc/uMYts1y0IY5dV2zu7PDbJ7vt10LZvTDQNu2rFYrQsidy2fzOeu23QTtaT758es5
+b2etry0iIiIiIvIgU9AePami3RjeF2NFe8ZsNsN5vxlkHcfwa2aYu36d6OO320FzCrqbBmpb3zeF
+7CkE931/VNE2R1mWFEXAzDYV7aquKVbLo4p2u8av/Cas7yyWrK+raF9/7jsZgo+/PgVuERERERG5
+CBS0R9M8a8woioJQlhRFRV3XlGWJDwFzdl1FGIPN4lzH5mM7564bEj5t2+farmhP97efizFu1uRO
+KRFCwDlHUQSapqGuKsqyJISAd35TWY/DQNe1rFbGar2ibVu6Pq/pHSNbIfvu/GxFREREREQuEgXt
+URxTp/M+B9eioKxqqqrChbGSnab+3EemdamdO2pm5r2/7nYKy/0YdoETh45vjjmtcR0jfd/TdR3D
+MBCCpygK6rqiKsv8BwGf190OIVAUBUUosHEIetfldbW7rqPvOvq+h3FVbcjLit2tsK1qtoiIiIiI
+XBQK2qMpaAdnFGVJVddUdUNZVfhxyDgcLYLFdeF4DOnOEULIQX0MvyGEHHT7/knN0abh5ydJUzO0
+YaDruk3Dsxy08x8ApoZn03mKIodtjE3Qbts2B+6uH6/Bj9V2N163wW0u6SUiIiIiIiJHFLRHm+qy
+c4SioK4b6tmMsiw3c7NJT64AT+tiby/P5X2uPE/bFLC3K9Wb7uKnXMt2Rbvv+01zsSlol1NFO/i8
+nFcIFEV+bIi5iVofe9quG4N2Dvu5l5ttbs3SZsmvbWete32za2Krii1yMZnZK4DXk39FfkxK6Q/v
+8SXdNVeuXOF5z3vevb4MeQoeffRR3vnOd97ryxARkQeEgvYoFAUAPgR88Dg/BmFnYJCOrSydxv/Z
+uMjXNJ/6pKr1NPx7Gjp+M8tcbXcGP1qn222Gj09V86mKXtUVs9mM+c6c9XpNu17TDx3D0G+Cdtt1
+FMkoCr8pyN9KFlZDMxGRs8UYef/733+vL0NERETuMQXtUSjyjyIH2IDzHhsbmnFSyJ7uJzA7alyW
+H0vXNUCbqtLTUPCTOo4fHS9dd/+oAp7ngXsfjgXtPES9qnLQ3pnPAej7jhjTZm54O87XdpaD+dEf
+A46/ousdr17fbDVbROTi+qh7fQFyS64A8V5fhIiIPGAUtEebinYRcMFj3mFuXLrL7IQomo4eS0/u
+Fj5t2+F6uj0etOHJ1eLtkJ3ncnvM7FhFO+C92wraDfOdHbq+Z7VaklLcBO1c0W7xIVBsNWGbho6f
+5Hh1XkREbsQB77vXFyG35HmARiGIiMj5UtAeTfOlj6rYT+4wnncYq8DpqM69aZR2whrV22tXnxSy
+j8Js7gL+5IA+bCraeR6436pm5ytw3lOWFbPZnJ2dJev1isWi2Cwt1g89bbtmvV5TFCVpaw3wyTQH
+/DTbc8yfSlX7rKB+1vEU8EVERERE5Onm5G5cF9HWCPFEIqa8pZTy0l5Mz23/fyuKn1LJPm24+JNO
+Pwb4qdt4Hmre0/cDw9BvKuZ5+Ljf/GEgxoQzoywLZvMZu7u7zMYmbj53PKPve9brNavViq5trwvu
+pw1bP+6kavvNhmCFZRG5AMbPU/2+E3m6uHLlCt/2bd/GlStX7vWliMgtmKbrcp9n2fv64u4qZ0eN
+z8aQHWPMYXvcJZ1Q6c7V7TFyH2tgtr129nY1+/rgOVWz87G3vy+H7GHTQA2mJcT81pDuXJ0uy5L5
+LAftphm7pTufg3vf067XrJarcamwiJm7blj4dhX+VtxK4BaRB4OZXTaz15jZu81sYWYfMLM3m9lf
+vYVjVGb2P5rZW8zsipmtt47zSjPzd/IYZvYHZhbN7AfGrz/FzH7QzH7PzFZmdquTdm94vSJyf7ly
+5QqPPfaYgrbI08xW0L6vP3s1dPyYKURPYXsTqp80vDmNq1AfVbQZvy/Z9dXi04ZG532mQ09B+/qK
+doxTyE6barb3YVwLOw/nNucoipJmlmvvs1lDXVcURcCNw8fbtmO9XtF1R9XxWx0+fubP7Ta+V0Se
+PszsE4C3AM/h6G+OFfC5wOeZ2euBt97gGH8W+D+B53N9CfgZ03GAV5vZy1JKH7xDx9j69W6vBv5X
+rv/AVncsERERecoUtEcxbgXlTbOw/JxxFIbT+HUC7AbhcgqfJ4fQ6d+FUxifHtvuYJ67jJdlSVlW
+W2tn5znaZo6pGu59oCQRq4HZbIednT0uXXoIM0dZVpjZVoU8btbynq7teOg+7vTXcWMnLQumUC7y
+9GNmu8DPAY+Sf2H9KPBDwAeBjwe+FvhK4IVnHOMFwP8N7AFXge8B3gG8F3gEeDnwauDFwE+a2eek
+lIbzPsaWTwW+AvgPwGuBd5E/Gz/nZn4mIiIiIidR0B5NVd4Yt7qJcxRAbWuJryls58fPdhROp++a
+jnBkqmrnedqRlCIxDphBCJ6qqqiqirKsKIoctqdqeBqvIwSP9/n489mcvd09FpcWDEMkhBKwreZq
+w+b1Mn7/NOf7LMfD8dEfJG48z/upBmuFdJH7yreQWzQn4JtSSt+59dyvmtkbgTcBX3DGMd4AXCIH
+2i9IKT1+7Pm3mNmbxuN8Gjm4/7M7cIzJnwZ+HfgLKaVrW4//yhmvQURERORMmqM9ijHlkJ3ycldj
+hMz/2bGNo6DNsedOMh3z5D7m163KfWxJrzxUvCzLMWiXlGVBCEWuaE+d0s2N62tX1HXNfD5nd3eP
+y5cvs7OzSzVWtI+as13f/fz465sem6598qSfwwmv+UZV8Rt9/41sX99T+X4ReWrMrABeSf6l9RvH
+QjYAY9X4VUB3yjE+G/iM8RivOCEgT8f5OeCN5F+1X3nex9g+3Hicrz4WskVERERuiyrao36cVO/6
+Ad/1eN/jXJfnQjs3DrOGFOM4bxqcWd62lgTbbmq2HZhzJt8Orzl8e5+P7b3fzMue5mbDVNEuc3Mz
+n68hN0eLm47oZrlJmnNGikbwBXU9Y3f3MmW5pu8Gum7AuTzcPKVE3/dPamR2UlO07SZs26/ztM7j
+N7OM1/GwfLPH2x66roAtctd9CvAQOZi+4bSdUkrvN7OfB156wtMvH29/J6X02zc431uBLwFebGYu
+pTQNwzmPY2x7b0rp/7nBcURERERuiYL2qO+m6XvdZu7zpqP4WIielt4ahh4SY2Myh3d+s+TWNAR7
+amjWdd0JQfuo+7gPnuDz9w/DQNe1m6BtLgftsiopirCpSq/X66NgOgZtvzl3wsxTVQ27u5GybOna
+jrbNBaYQAiml8Ty57880PPt41/TpFvJrneaGb1fdTwrn2yH4pPtTwJ5+ZjY2bJu27WNN96+fO6+w
+LXIPfOLW/XfcYN+3c3LQ/nPj7Z+6ha7eBfAw8B/P8RiTBPzGTR5DRERE5KYpaI+6vgcgYWPVl03A
+ZpyhPcTI0Pf0fUdKUBSBIhSb8BpC2IS/vu9p25a2bcGOOozDWBVPuZt48IEQcoiNMdH3Hf0UtA18
+yEPHi7LADIYhHzduhc+pqu69y+cwR1XVeBeoynZcQ3tNjJEQApBo2w7v45PmV0/heRpm3o8/l7Is
+iTFSFMWmWdt22J5uzxqGvv341IjteHifljLbDtrb55lMx9HSYiJ3zcNb90/sBL7lA6c8/ixufaHp
+BMzO+RjbThx6/tQl8iXeiOc+X5XkAtHSTiIi94MrV67c1HJ7XXfiDLX7joL2aBimwkg/BjjYNEPb
+VF0Huq6j63pyd/DiuirrNAR8Co1T2L4+aE/V4Dz8ewhhc5yY4tGyXumoklyWOYwbxjBE2rY9mks+
+hlvvHcOQK+yGoywqyqIihGIzZLzruvH68h8Cps7jx4e7T0PLu67bBO3t1zm9tpPWB98O0icNEd+u
+Zm9X0qfjTMecbD93vIGbKtoi98xT/QvXlCx/HfjyW/i+95/zMbad1o38Nnzo/A8pIiLygHvd617H
+Y489dq8v49woaN/HbjVHXpTcqYAtck9sV36fDbznjH2ffcrjHyY3INu5ifnVpzmPY9wJm4r/zf6O
+0u+y+8sHP/hBnve8593ry5C7qG1bAF7ykpdQluU9vhoRGYaBZz7zmTfc70Mf2vxB++Gz9rvXFLRH
+3/zNf0//4hEROd1vbt1/MfC2M/Z98SmP/yrwmcDHmtmzUko3GoJ+p45xJ2w+Q252SoumvtxfYoy8
+//2nDXyQB9nWP9pF5Onlvs5vCtoiInIz3kWual8GvgL47pN2MrOP4vR1tP8l8NXkD8avAf7uU7iO
+8zjGnbAGKiBy4znsIiIi8tQ9i7xM9fpeX8hZFLRFROSGUkqtmb0e+Frgk83s61NKr93ex8w88P3k
+Lt8nHePNZvZ24FOBv21mv5pSeuNp5zSzFwL/SUrpp87zGHdCSml+J48vIiIiTy+moWsiInIzzGwP
++C1gmsj6vwM/RK7gfjzwdeT1tt9JHj6egI9JKf3h1jE+Fvi35HlVBvwr4P8AfpfcmOxZwIvI62V/
+GvDalNI3HLuO8zjG7wPPB96QUnrl7f1kRERERK6niraIiNyUlNI1M3sJ8GbgUeDLxm2zC/B64JfH
+25OO8Xtm9hnAvwBeCPwXwMtO2nXcrt6JY4zu67ldIiIi8vSloC0iIjctpfTbZvZngG8EvohcFd4n
+N0v7vpTSj5nZKzgKuScd4z1m9snAlwBfTK5+P5O8dNeHgd8B/jXwEymlX7tTxzjrGkVERERuh4aO
+i4iIiIiIiJwjd68vQERERERERORBoqAtIiIiIiIico4UtEVERERERETOkYK2iIiIiIiIyDlS0BYR
+ERERERE5RwraIiJy4ZnZ883sH5rZu83swMw+bGZvN7OvN7PmHM/zZWb2c2Z2xcyWZvYHZvbDZvbp
+53UOkYvkTr53zexbzSze5Pbnz+s1iTyozOyZZvZSM3vMzH7azD609R76gTt0znv2uavlvURE5EIz
+s5cBPwzs8eR1tQ3498BLU0r/322cowb+BfCFp5wjAt+eUvr2p3oOkYvmTr93zexbgW894djHJeBz
+U0pvfSrnEbkozCwee2j7vfWGlNIrz/Fc9/xzVxVtERG5sMzsRcCPArvAPvB3gM8EPg/4fvKH858E
+fsrM5rdxqtdz9GH/fwF/BfhU4FXAe8ifx99qZl91G+cQuTDu4nt38kLgE0/ZPgl4xzmcQ+QiSOP2
+H4CfJ4feO+Gef+6qoi0iIheWmb0V+GygAz4npfT2Y89/HfBd5A/qx57KX77N7HOBt4zH+JfAf5W2
+PnzN7BHgXcDzgceBj00pXX1qr0jkYrhL791NRTul5G//qkUutvE99Q7gHSmlD5nZnwB+n/w+PbeK
+9v3yuauKtoiIXEhm9mLyP9QT8E+P/0N99I+Ad5P/4v41ZvZU/rH9deNtD3x1OvYX7pTSh4FvHL+8
+DKiqLXKGu/jeFZFzlFJ6LKX00ymlD93hU90Xn7sK2iIiclH9la37P3jSDuOH8w+NX14G/tKtnMDM
+dshDWRPwlpTSH52y648D18b7X3Qr5xC5gO74e1dEnp7up89dBW0REbmoPnu8PSQPITvNL23d/6xb
+PMeLgfKE41wnpdQB/4ZcfXuxqm8iZ7ob710ReXq6bz53FbRFROSi+gTyX7zfk1I63gl12/977Htu
+xZ8+5ThnnSeQmziJyMnuxnv3OuPyQB8ws/V4+4tm9o1mdvl2jisi5+6++dxV0BYRkQvHzCrgGeOX
+7ztr35TSE+TKGcBH3+Kpnrd1/8zzAO/dun+r5xG5EO7ie/e4zx/PG8bbPw/8A+D3zOzlt3lsETk/
+983nbjjvA4qIiDwN7G7dP7iJ/Q+BGbBzB89zuHX/Vs8jclHcrffu5DeAnwTeDvwRUAD/KfDfAV9A
+nv/9RjN7WUrp557iOUTk/Nw3n7sK2iIichHVW/fbm9h/TZ7H1dzB86y37t/qeUQuirv13gX4xyml
+x054/B3Aj5jZ3wT+CeCBf2pmH5dSuplrEpE757753NXQcRERuYhWW/fLU/c6UpHnhC7v4Hmqrfu3
+eh6Ri+JuvXdJKV27wfPfB/wzcpB/LvDFt3oOETl3983nroK2iIhcRPtb929muNh8vL2ZoapP9Tzz
+rfu3eh6Ri+JuvXdv1uu27v+FO3QOEbl5983nroK2iIhcOCmlNfDh8cvnnbXv2FV4+jB+71n7nmC7
+EcuZ5+H6Riy3eh6RC+Euvndv1m9v3f+oO3QOEbl5983nroK2iIhcVL9NHvL5AjM76/PwT23df/dT
+OMdJxznrPD3wu7d4HpGL5G68d29WukPHFZGn5r753FXQFhGRi+pfj7dz4FPO2G97OOjbbvEc7+Co
+Gcupw0rNrAA+nfyP9neklIZbPI/IRXI33rs3a3vN3j+6Q+cQkZt333zuKmiLiMhF9ZNb9//6STuY
+mQF/bfzyCeAXb+UEKaUD4BfI1bfPN7PnnrLrFwN74/0fv5VziFxAd/y9ewv++637v3SHziEiN+l+
++txV0BYRkQsppfQO4JfJH8avMrNPO2G3rwc+gfwX7+8+/hdvM3uFmcVx+5ZTTvXa8TYA33t8qKuZ
+PQN4zfjlE+QuxiJyirvx3jWzF5rZx511HePyXq8av/xj4Cdu/dWIyK14On3uah1tERG5yL6GPKS0
+Ad5sZt9Brnw1wJcBf2Pc73eAf3TGcU6dp5lS+kUz+1HgS4H/cjzPd5OHmX4S8HeA54/H+IaU0tXb
+ekUiF8Odfu9+Cnlt7F8Efgb4TXITtkCe1/nlwH827tsDfyOlpGX5RM5gZp8FvGDroWds3X+Bmb1i
+e/+U0hvOONx9/7mroC0iIhdWSunXzOxLgB8hDyH7juO7kP+h/tKU0uFtnOqVwC7wl4G/CPylY+cY
+gG9PKamaLXIT7tJ71wGfB3z+aZdBDt+vTCn99FM8h8hF8lXAK0543IDPHrdJAs4K2jdyzz93FbRF
+RORCSym9ycw+iVwheyl5OZAWeA/wY8D3ppRWZx3iJs6xAl5mZl8KfCXwZ4HLwAeAt47n+Le38zpE
+Lpo7/N59E3lY+GcALwKeDTxCDgQfAX4d+FngB8c5oSJyc262U/9Z+z0tPnctJa1KICIiIiIiInJe
+1AxNRERERERE5BwpaIuIiIiIiIicIwVtERERERERkXOkoC0iIiIiIiJyjhS0RURERERERM6RgraI
+iIiIiIjIOVLQFhERERERETlHCtoiIiIiIiIi50hBW0REREREROQcKWiLiIiIiIiInCMFbRERERER
+EZFzpKAtIiIiIiIico4UtEVERERERETOkYK2iIiIiIiIyDlS0BYRERERERE5RwraIiIiIiIiIudI
+QVtERERERETkHCloi4iIiIiIiJwjBW0RERERERGRc6SgLSIiIiIiInKOFLRFREREREREzpGCtoiI
+iIiIiMg5UtAWEREREREROUcK2iIiIiIiIiLnSEFbRERERERE5Bz9/xxVPCjMDi4EAAAAAElFTkSu
+QmCC
+"
+width=493
+height=445
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;">&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;<a class="anchor-link" href="#&#20026;&#20309;&#20934;&#30830;&#29575;&#21482;&#26377;50-80%&#65311;">&#182;</a></h2><p>你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率<a href="http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130">远远超过 80%</a>。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。</p>
+<h2 id="&#25552;&#20132;&#39033;&#30446;">&#25552;&#20132;&#39033;&#30446;<a class="anchor-link" href="#&#25552;&#20132;&#39033;&#30446;">&#182;</a></h2><p>提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 "File" -&gt; "Download as" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。</p>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+</html>
diff --git a/image-classification/dlnd_image_classification.ipynb b/image-classification/dlnd_image_classification.ipynb
new file mode 100644
index 0000000..8338fe9
--- /dev/null
+++ b/image-classification/dlnd_image_classification.ipynb
@@ -0,0 +1,1107 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# 图像分类\n",
+    "\n",
+    "在此项目中，你将对 [CIFAR-10 数据集](https://www.cs.toronto.edu/~kriz/cifar.html) 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。\n",
+    "\n",
+    "\n",
+    "## 获取数据\n",
+    "\n",
+    "请运行以下单元，以下载 [CIFAR-10 数据集（Python版）](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All files found!\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "from urllib.request import urlretrieve\n",
+    "from os.path import isfile, isdir\n",
+    "from tqdm import tqdm\n",
+    "import problem_unittests as tests\n",
+    "import tarfile\n",
+    "\n",
+    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
+    "\n",
+    "# Use Floyd's cifar-10 dataset if present\n",
+    "floyd_cifar10_location = '/input/cifar-10/python.tar.gz'\n",
+    "if isfile(floyd_cifar10_location):\n",
+    "    tar_gz_path = floyd_cifar10_location\n",
+    "else:\n",
+    "    tar_gz_path = 'cifar-10-python.tar.gz'\n",
+    "\n",
+    "class DLProgress(tqdm):\n",
+    "    last_block = 0\n",
+    "\n",
+    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
+    "        self.total = total_size\n",
+    "        self.update((block_num - self.last_block) * block_size)\n",
+    "        self.last_block = block_num\n",
+    "\n",
+    "if not isfile(tar_gz_path):\n",
+    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
+    "        urlretrieve(\n",
+    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
+    "            tar_gz_path,\n",
+    "            pbar.hook)\n",
+    "\n",
+    "if not isdir(cifar10_dataset_folder_path):\n",
+    "    with tarfile.open(tar_gz_path) as tar:\n",
+    "        tar.extractall()\n",
+    "        tar.close()\n",
+    "\n",
+    "\n",
+    "tests.test_folder_path(cifar10_dataset_folder_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 探索数据\n",
+    "\n",
+    "该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 `data_batch_1`、`data_batch_2`，以此类推。每个部分都包含以下某个类别的标签和图片：\n",
+    "\n",
+    "* 飞机\n",
+    "* 汽车\n",
+    "* 鸟类\n",
+    "* 猫\n",
+    "* 鹿\n",
+    "* 狗\n",
+    "* 青蛙\n",
+    "* 马\n",
+    "* 船只\n",
+    "* 卡车\n",
+    "\n",
+    "了解数据集也是对数据进行预测的必经步骤。你可以通过更改 `batch_id` 和 `sample_id` 探索下面的代码单元。`batch_id` 是数据集一个部分的 ID（1 到 5）。`sample_id` 是该部分中图片和标签对（label pair）的 ID。\n",
+    "\n",
+    "问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Stats of batch 1:\n",
+      "Samples: 10000\n",
+      "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n",
+      "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n",
+      "\n",
+      "Example of Image 5:\n",
+      "Image - Min Value: 0 Max Value: 252\n",
+      "Image - Shape: (32, 32, 3)\n",
+      "Label - Label Id: 1 Name: automobile\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAK/CAYAAABjrqvGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21febbaaf98>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 351,
+       "width": 353
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "import helper\n",
+    "import numpy as np\n",
+    "\n",
+    "# Explore the dataset\n",
+    "batch_id = 1\n",
+    "sample_id = 5\n",
+    "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 实现预处理函数\n",
+    "\n",
+    "### 标准化\n",
+    "\n",
+    "在下面的单元中，实现 `normalize` 函数，传入图片数据 `x`，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 `x` 的形状一样。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def normalize(x):\n",
+    "    \"\"\"\n",
+    "    Normalize a list of sample image data in the range of 0 to 1\n",
+    "    : x: List of image data.  The image shape is (32, 32, 3)\n",
+    "    : return: Numpy array of normalize data\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return np.array(x/255)\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_normalize(normalize)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### One-hot 编码\n",
+    "\n",
+    "和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 `one_hot_encode` 函数。输入，也就是 `x`，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 `one_hot_encode` 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。\n",
+    "\n",
+    "提示：不要重复发明轮子。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def one_hot_encode(x):\n",
+    "    \"\"\"\n",
+    "    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n",
+    "    : x: List of sample Labels\n",
+    "    : return: Numpy array of one-hot encoded labels\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    from tflearn.data_utils import to_categorical\n",
+    "    return np.array(to_categorical(x, 10))\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_one_hot_encode(one_hot_encode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 随机化数据\n",
+    "\n",
+    "之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 预处理所有数据并保存\n",
+    "\n",
+    "运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "# Preprocess Training, Validation, and Testing Data\n",
+    "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 检查点\n",
+    "\n",
+    "这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import pickle\n",
+    "import problem_unittests as tests\n",
+    "import helper\n",
+    "\n",
+    "# Load the Preprocessed Validation data\n",
+    "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 构建网络\n",
+    "\n",
+    "对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。\n",
+    "\n",
+    ">**注意**：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。\n",
+    "\n",
+    ">但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 `conv2d` 类 [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d)，而不是 TF Layers 版本的 `conv2d` 类 [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d)。\n",
+    "\n",
+    "我们开始吧！\n",
+    "\n",
+    "\n",
+    "### 输入\n",
+    "\n",
+    "神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：\n",
+    "\n",
+    "* 实现 `neural_net_image_input`\n",
+    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
+    " * 使用 `image_shape` 设置形状，部分大小设为 `None`\n",
+    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"x\" 命名\n",
+    "* 实现 `neural_net_label_input`\n",
+    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
+    " * 使用 `n_classes` 设置形状，部分大小设为 `None`\n",
+    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"y\" 命名\n",
+    "* 实现 `neural_net_keep_prob_input`\n",
+    " * 返回 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)，用于丢弃保留概率\n",
+    " * 使用 [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) 中的 TensorFlow `name` 参数对 TensorFlow 占位符 \"keep_prob\" 命名\n",
+    "\n",
+    "这些名称将在项目结束时，用于加载保存的模型。\n",
+    "\n",
+    "注意：TensorFlow 中的 `None` 表示形状可以是动态大小。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image Input Tests Passed.\n",
+      "Label Input Tests Passed.\n",
+      "Keep Prob Tests Passed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "def neural_net_image_input(image_shape):\n",
+    "    \"\"\"\n",
+    "    Return a Tensor for a batch of image input\n",
+    "    : image_shape: Shape of the images\n",
+    "    : return: Tensor for image input.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], name='x')\n",
+    "\n",
+    "\n",
+    "def neural_net_label_input(n_classes):\n",
+    "    \"\"\"\n",
+    "    Return a Tensor for a batch of label input\n",
+    "    : n_classes: Number of classes\n",
+    "    : return: Tensor for label input.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return tf.placeholder(tf.int32, [None, n_classes], name='y')\n",
+    "\n",
+    "\n",
+    "def neural_net_keep_prob_input():\n",
+    "    \"\"\"\n",
+    "    Return a Tensor for keep probability\n",
+    "    : return: Tensor for keep probability.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return tf.placeholder(tf.float32, name='keep_prob')\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tf.reset_default_graph()\n",
+    "tests.test_nn_image_inputs(neural_net_image_input)\n",
+    "tests.test_nn_label_inputs(neural_net_label_input)\n",
+    "tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 卷积和最大池化层\n",
+    "\n",
+    "卷积层级适合处理图片。对于此代码单元，你应该实现函数 `conv2d_maxpool` 以便应用卷积然后进行最大池化：\n",
+    "\n",
+    "* 使用 `conv_ksize`、`conv_num_outputs` 和 `x_tensor` 的形状创建权重（weight）和偏置（bias）。\n",
+    "* 使用权重和 `conv_strides` 对 `x_tensor` 应用卷积。\n",
+    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
+    "* 添加偏置\n",
+    "* 向卷积中添加非线性激活（nonlinear activation）\n",
+    "* 使用 `pool_ksize` 和 `pool_strides` 应用最大池化\n",
+    " * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。\n",
+    "\n",
+    "**注意**：对于**此层**，**请勿使用** [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers)，但是仍然可以使用 TensorFlow 的 [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) 包。对于所有**其他层**，你依然可以使用快捷方法。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n",
+    "    \"\"\"\n",
+    "    Apply convolution then max pooling to x_tensor\n",
+    "    :param x_tensor: TensorFlow Tensor\n",
+    "    :param conv_num_outputs: Number of outputs for the convolutional layer\n",
+    "    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n",
+    "    :param conv_strides: Stride 2-D Tuple for convolution\n",
+    "    :param pool_ksize: kernal size 2-D Tuple for pool\n",
+    "    :param pool_strides: Stride 2-D Tuple for pool\n",
+    "    : return: A tensor that represents convolution and max pooling of x_tensor\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    weights = tf.Variable(tf.truncated_normal(shape=[conv_ksize[0], conv_ksize[1], x_tensor.get_shape().as_list()[3], conv_num_outputs], stddev=0.1))\n",
+    "    bias = tf.Variable(tf.constant(0.1, shape=[conv_num_outputs]))\n",
+    "    conv = tf.nn.conv2d(input=x_tensor, filter=weights, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME') + bias\n",
+    "    activate = tf.nn.relu(conv)\n",
+    "    pool = tf.nn.max_pool(value=activate, ksize=[1, pool_ksize[0], pool_ksize[1], 1], strides=[1, pool_strides[0], pool_strides[1], 1], padding='SAME')\n",
+    "    \n",
+    "    return pool\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_con_pool(conv2d_maxpool)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 扁平化层\n",
+    "\n",
+    "实现 `flatten` 函数，将 `x_tensor` 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（*部分大小（Batch Size）*，*扁平化图片大小（Flattened Image Size）*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def flatten(x_tensor):\n",
+    "    \"\"\"\n",
+    "    Flatten x_tensor to (Batch Size, Flattened Image Size)\n",
+    "    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n",
+    "    : return: A tensor of size (Batch Size, Flattened Image Size).\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    layer_shape = x_tensor.get_shape()\n",
+    "    num_features = layer_shape[1:4].num_elements()\n",
+    "    layer_flat = tf.reshape(x_tensor, [-1, num_features])\n",
+    "    \n",
+    "    return layer_flat\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_flatten(flatten)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 完全连接的层\n",
+    "\n",
+    "实现 `fully_conn` 函数，以向 `x_tensor` 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def fully_conn(x_tensor, num_outputs):\n",
+    "    \"\"\"\n",
+    "    Apply a fully connected layer to x_tensor using weight and bias\n",
+    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
+    "    : num_outputs: The number of output that the new tensor should be.\n",
+    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n",
+    "    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n",
+    "    fc = tf.nn.relu(tf.matmul(x_tensor, weights) + bias)\n",
+    "    \n",
+    "    return fc\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_fully_conn(fully_conn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 输出层\n",
+    "\n",
+    "实现 `output` 函数，向 x_tensor 应用完全连接的层级，形状为（*部分大小（Batch Size）*，*num_outputs*）。快捷方法：对于此层，你可以使用 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) 或 [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。\n",
+    "\n",
+    "**注意**：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def output(x_tensor, num_outputs):\n",
+    "    \"\"\"\n",
+    "    Apply a output layer to x_tensor using weight and bias\n",
+    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
+    "    : num_outputs: The number of output that the new tensor should be.\n",
+    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n",
+    "    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n",
+    "    output = tf.matmul(x_tensor, weights) + bias\n",
+    "    \n",
+    "    return output\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_output(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 创建卷积模型\n",
+    "\n",
+    "实现函数 `conv_net`， 创建卷积神经网络模型。该函数传入一批图片 `x`，并输出对数（logits）。使用你在上方创建的层创建此模型：\n",
+    "\n",
+    "* 应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）\n",
+    "* 应用一个扁平层（Flatten Layer）\n",
+    "* 应用 1、2 或 3 个完全连接层（Fully Connected Layers）\n",
+    "* 应用一个输出层（Output Layer）\n",
+    "* 返回输出\n",
+    "* 使用 `keep_prob` 向模型中的一个或多个层应用 [TensorFlow 的 Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Network Built!\n"
+     ]
+    }
+   ],
+   "source": [
+    "def conv_net(x, keep_prob):\n",
+    "    \"\"\"\n",
+    "    Create a convolutional neural network model\n",
+    "    : x: Placeholder tensor that holds image data.\n",
+    "    : keep_prob: Placeholder tensor that hold dropout keep probability.\n",
+    "    : return: Tensor that represents logits\n",
+    "    \"\"\"\n",
+    "    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers\n",
+    "    #    Play around with different number of outputs, kernel size and stride\n",
+    "    # Function Definition from Above:\n",
+    "    #    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
+    "    conv_pool_1 = conv2d_maxpool(x, 64, [5, 5], [1, 1], [3, 3], [2, 2])\n",
+    "    norm_layer = tf.nn.lrn(conv_pool_1, 4 , bias=1.0, alpha=0.001 / 9.0, beta=0.75)\n",
+    "    conv_pool_2 = conv2d_maxpool(norm_layer, 128, [5, 5], [1, 1], [3, 3], [2, 2])\n",
+    "\n",
+    "    # TODO: Apply a Flatten Layer\n",
+    "    # Function Definition from Above:\n",
+    "    #   flatten(x_tensor)\n",
+    "    flat_layer = flatten(conv_pool_2)\n",
+    "\n",
+    "    # TODO: Apply 1, 2, or 3 Fully Connected Layers\n",
+    "    #    Play around with different number of outputs\n",
+    "    # Function Definition from Above:\n",
+    "    #   fully_conn(x_tensor, num_outputs)\n",
+    "    fc_layer1 = fully_conn(flat_layer, 384)\n",
+    "    dropout_layer_1 = tf.nn.dropout(fc_layer1, keep_prob)\n",
+    "    fc_layer2 = fully_conn(dropout_layer_1, 192)\n",
+    "    dropout_layer_2 = tf.nn.dropout(fc_layer2, keep_prob)\n",
+    "    \n",
+    "    # TODO: Apply an Output Layer\n",
+    "    #    Set this to the number of classes\n",
+    "    # Function Definition from Above:\n",
+    "    #   output(x_tensor, num_outputs)\n",
+    "    logits = output(dropout_layer_2, 10)\n",
+    "    \n",
+    "    # TODO: return output\n",
+    "    return logits\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "\n",
+    "##############################\n",
+    "## Build the Neural Network ##\n",
+    "##############################\n",
+    "\n",
+    "# Remove previous weights, bias, inputs, etc..\n",
+    "tf.reset_default_graph()\n",
+    "\n",
+    "# Inputs\n",
+    "x = neural_net_image_input((32, 32, 3))\n",
+    "y = neural_net_label_input(10)\n",
+    "keep_prob = neural_net_keep_prob_input()\n",
+    "\n",
+    "# Model\n",
+    "logits = conv_net(x, keep_prob)\n",
+    "\n",
+    "# Name logits Tensor, so that is can be loaded from disk after training\n",
+    "logits = tf.identity(logits, name='logits')\n",
+    "\n",
+    "# Loss and Optimizer\n",
+    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
+    "optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
+    "\n",
+    "# Accuracy\n",
+    "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n",
+    "\n",
+    "tests.test_conv_net(conv_net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 训练神经网络\n",
+    "\n",
+    "### 单次优化\n",
+    "\n",
+    "实现函数 `train_neural_network` 以进行单次优化（single optimization）。该优化应该使用 `optimizer` 优化 `session`，其中 `feed_dict` 具有以下参数：\n",
+    "\n",
+    "* `x` 表示图片输入\n",
+    "* `y` 表示标签\n",
+    "* `keep_prob` 表示丢弃的保留率\n",
+    "\n",
+    "每个部分都会调用该函数，所以 `tf.global_variables_initializer()` 已经被调用。\n",
+    "\n",
+    "注意：不需要返回任何内容。该函数只是用来优化神经网络。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n",
+    "    \"\"\"\n",
+    "    Optimize the session on a batch of images and labels\n",
+    "    : session: Current TensorFlow session\n",
+    "    : optimizer: TensorFlow optimizer function\n",
+    "    : keep_probability: keep probability\n",
+    "    : feature_batch: Batch of Numpy image data\n",
+    "    : label_batch: Batch of Numpy label data\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    session.run(optimizer, feed_dict = {keep_prob: keep_probability, x: feature_batch, y: label_batch})\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_train_nn(train_neural_network)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 显示数据\n",
+    "\n",
+    "实现函数 `print_stats` 以输出损失和验证准确率。使用全局变量 `valid_features` 和 `valid_labels` 计算验证准确率。使用保留率 `1.0` 计算损失和验证准确率（loss and validation accuracy）。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def print_stats(session, feature_batch, label_batch, cost, accuracy):\n",
+    "    \"\"\"\n",
+    "    Print information about loss and validation accuracy\n",
+    "    : session: Current TensorFlow session\n",
+    "    : feature_batch: Batch of Numpy image data\n",
+    "    : label_batch: Batch of Numpy label data\n",
+    "    : cost: TensorFlow cost function\n",
+    "    : accuracy: TensorFlow accuracy function\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    print('Training Loss: ', end='')\n",
+    "    print(session.run(cost, feed_dict = {x: feature_batch, y: label_batch, keep_prob: 1.0}), end='')\n",
+    "    print(', Valid Accuracy: ', end='')\n",
+    "    print(session.run(accuracy, feed_dict = {x: valid_features, y: valid_labels, keep_prob: 1.0}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 超参数\n",
+    "\n",
+    "调试以下超参数：\n",
+    "* 设置 `epochs` 表示神经网络停止学习或开始过拟合的迭代次数\n",
+    "* 设置 `batch_size`，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：\n",
+    "\n",
+    " * 64\n",
+    " * 128\n",
+    " * 256\n",
+    " * ...\n",
+    "* 设置 `keep_probability` 表示使用丢弃时保留节点的概率"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# TODO: Tune Parameters\n",
+    "epochs = 10\n",
+    "batch_size = 128\n",
+    "keep_probability = 0.75"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 在单个 CIFAR-10 部分上训练\n",
+    "\n",
+    "我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking the Training on a Single Batch...\n",
+      "Epoch  1, CIFAR-10 Batch 1:  Training Loss: 2.02859, Valid Accuracy: 0.3288\n",
+      "Epoch  2, CIFAR-10 Batch 1:  Training Loss: 1.73148, Valid Accuracy: 0.3978\n",
+      "Epoch  3, CIFAR-10 Batch 1:  Training Loss: 1.43727, Valid Accuracy: 0.4584\n",
+      "Epoch  4, CIFAR-10 Batch 1:  Training Loss: 1.20958, Valid Accuracy: 0.488\n",
+      "Epoch  5, CIFAR-10 Batch 1:  Training Loss: 1.13607, Valid Accuracy: 0.4956\n",
+      "Epoch  6, CIFAR-10 Batch 1:  Training Loss: 0.906183, Valid Accuracy: 0.5094\n",
+      "Epoch  7, CIFAR-10 Batch 1:  Training Loss: 0.814033, Valid Accuracy: 0.5296\n",
+      "Epoch  8, CIFAR-10 Batch 1:  Training Loss: 0.686904, Valid Accuracy: 0.5408\n",
+      "Epoch  9, CIFAR-10 Batch 1:  Training Loss: 0.580816, Valid Accuracy: 0.5502\n",
+      "Epoch 10, CIFAR-10 Batch 1:  Training Loss: 0.497506, Valid Accuracy: 0.5612\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "print('Checking the Training on a Single Batch...')\n",
+    "with tf.Session() as sess:\n",
+    "    # Initializing the variables\n",
+    "    sess.run(tf.global_variables_initializer())\n",
+    "    \n",
+    "    # Training cycle\n",
+    "    for epoch in range(epochs):\n",
+    "        batch_i = 1\n",
+    "        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
+    "            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
+    "        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
+    "        print_stats(sess, batch_features, batch_labels, cost, accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 完全训练模型\n",
+    "\n",
+    "现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training...\n",
+      "Epoch  1, CIFAR-10 Batch 1:  Training Loss: 2.06226, Valid Accuracy: 0.3212\n",
+      "Epoch  1, CIFAR-10 Batch 2:  Training Loss: 1.57472, Valid Accuracy: 0.4404\n",
+      "Epoch  1, CIFAR-10 Batch 3:  Training Loss: 1.28891, Valid Accuracy: 0.4616\n",
+      "Epoch  1, CIFAR-10 Batch 4:  Training Loss: 1.4297, Valid Accuracy: 0.4844\n",
+      "Epoch  1, CIFAR-10 Batch 5:  Training Loss: 1.43679, Valid Accuracy: 0.518\n",
+      "Epoch  2, CIFAR-10 Batch 1:  Training Loss: 1.38818, Valid Accuracy: 0.5218\n",
+      "Epoch  2, CIFAR-10 Batch 2:  Training Loss: 1.09959, Valid Accuracy: 0.5522\n",
+      "Epoch  2, CIFAR-10 Batch 3:  Training Loss: 0.961711, Valid Accuracy: 0.551\n",
+      "Epoch  2, CIFAR-10 Batch 4:  Training Loss: 1.12144, Valid Accuracy: 0.5856\n",
+      "Epoch  2, CIFAR-10 Batch 5:  Training Loss: 1.02137, Valid Accuracy: 0.586\n",
+      "Epoch  3, CIFAR-10 Batch 1:  Training Loss: 1.05762, Valid Accuracy: 0.5896\n",
+      "Epoch  3, CIFAR-10 Batch 2:  Training Loss: 0.814285, Valid Accuracy: 0.6132\n",
+      "Epoch  3, CIFAR-10 Batch 3:  Training Loss: 0.825072, Valid Accuracy: 0.605\n",
+      "Epoch  3, CIFAR-10 Batch 4:  Training Loss: 0.865807, Valid Accuracy: 0.616\n",
+      "Epoch  3, CIFAR-10 Batch 5:  Training Loss: 0.840993, Valid Accuracy: 0.636\n",
+      "Epoch  4, CIFAR-10 Batch 1:  Training Loss: 0.74788, Valid Accuracy: 0.6272\n",
+      "Epoch  4, CIFAR-10 Batch 2:  Training Loss: 0.652433, Valid Accuracy: 0.6234\n",
+      "Epoch  4, CIFAR-10 Batch 3:  Training Loss: 0.572802, Valid Accuracy: 0.6414\n",
+      "Epoch  4, CIFAR-10 Batch 4:  Training Loss: 0.697387, Valid Accuracy: 0.657\n",
+      "Epoch  4, CIFAR-10 Batch 5:  Training Loss: 0.599844, Valid Accuracy: 0.6582\n",
+      "Epoch  5, CIFAR-10 Batch 1:  Training Loss: 0.706912, Valid Accuracy: 0.6546\n",
+      "Epoch  5, CIFAR-10 Batch 2:  Training Loss: 0.458327, Valid Accuracy: 0.6452\n",
+      "Epoch  5, CIFAR-10 Batch 3:  Training Loss: 0.491401, Valid Accuracy: 0.6734\n",
+      "Epoch  5, CIFAR-10 Batch 4:  Training Loss: 0.484113, Valid Accuracy: 0.6714\n",
+      "Epoch  5, CIFAR-10 Batch 5:  Training Loss: 0.494395, Valid Accuracy: 0.6666\n",
+      "Epoch  6, CIFAR-10 Batch 1:  Training Loss: 0.506375, Valid Accuracy: 0.6906\n",
+      "Epoch  6, CIFAR-10 Batch 2:  Training Loss: 0.337756, Valid Accuracy: 0.6578\n",
+      "Epoch  6, CIFAR-10 Batch 3:  Training Loss: 0.353094, Valid Accuracy: 0.687\n",
+      "Epoch  6, CIFAR-10 Batch 4:  Training Loss: 0.373179, Valid Accuracy: 0.677\n",
+      "Epoch  6, CIFAR-10 Batch 5:  Training Loss: 0.38469, Valid Accuracy: 0.694\n",
+      "Epoch  7, CIFAR-10 Batch 1:  Training Loss: 0.428482, Valid Accuracy: 0.6932\n",
+      "Epoch  7, CIFAR-10 Batch 2:  Training Loss: 0.269606, Valid Accuracy: 0.677\n",
+      "Epoch  7, CIFAR-10 Batch 3:  Training Loss: 0.308877, Valid Accuracy: 0.7042\n",
+      "Epoch  7, CIFAR-10 Batch 4:  Training Loss: 0.290038, Valid Accuracy: 0.688\n",
+      "Epoch  7, CIFAR-10 Batch 5:  Training Loss: 0.30759, Valid Accuracy: 0.6892\n",
+      "Epoch  8, CIFAR-10 Batch 1:  Training Loss: 0.376015, Valid Accuracy: 0.6916\n",
+      "Epoch  8, CIFAR-10 Batch 2:  Training Loss: 0.207914, Valid Accuracy: 0.6826\n",
+      "Epoch  8, CIFAR-10 Batch 3:  Training Loss: 0.22358, Valid Accuracy: 0.7052\n",
+      "Epoch  8, CIFAR-10 Batch 4:  Training Loss: 0.205842, Valid Accuracy: 0.7022\n",
+      "Epoch  8, CIFAR-10 Batch 5:  Training Loss: 0.201466, Valid Accuracy: 0.6862\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "epochs = 8\n",
+    "save_model_path = './model/image_classification'\n",
+    "\n",
+    "print('Training...')\n",
+    "with tf.Session() as sess:\n",
+    "    # Initializing the variables\n",
+    "    sess.run(tf.global_variables_initializer())\n",
+    "    \n",
+    "    # Training cycle\n",
+    "    for epoch in range(epochs):\n",
+    "        # Loop over all batches\n",
+    "        n_batches = 5\n",
+    "        for batch_i in range(1, n_batches + 1):\n",
+    "            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
+    "                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
+    "            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
+    "            print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
+    "            \n",
+    "    # Save Model\n",
+    "    saver = tf.train.Saver()\n",
+    "    save_path = saver.save(sess, save_model_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 检查点\n",
+    "\n",
+    "模型已保存到本地。\n",
+    "\n",
+    "## 测试模型\n",
+    "\n",
+    "利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing Accuracy: 0.6764240506329114\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9oAAAN6CAYAAABxCzGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21f802e8be0>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 445,
+       "width": 493
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "import pickle\n",
+    "import helper\n",
+    "import random\n",
+    "\n",
+    "# Set batch size if not already set\n",
+    "try:\n",
+    "    if batch_size:\n",
+    "        pass\n",
+    "except NameError:\n",
+    "    batch_size = 64\n",
+    "\n",
+    "save_model_path = './model/image_classification'\n",
+    "n_samples = 4\n",
+    "top_n_predictions = 3\n",
+    "\n",
+    "def test_model():\n",
+    "    \"\"\"\n",
+    "    Test the saved model against the test dataset\n",
+    "    \"\"\"\n",
+    "\n",
+    "    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))\n",
+    "    loaded_graph = tf.Graph()\n",
+    "\n",
+    "    with tf.Session(graph=loaded_graph) as sess:\n",
+    "        # Load model\n",
+    "        loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
+    "        loader.restore(sess, save_model_path)\n",
+    "\n",
+    "        # Get Tensors from loaded model\n",
+    "        loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
+    "        loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
+    "        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
+    "        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
+    "        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
+    "        \n",
+    "        # Get accuracy in batches for memory limitations\n",
+    "        test_batch_acc_total = 0\n",
+    "        test_batch_count = 0\n",
+    "        \n",
+    "        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n",
+    "            test_batch_acc_total += sess.run(\n",
+    "                loaded_acc,\n",
+    "                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})\n",
+    "            test_batch_count += 1\n",
+    "\n",
+    "        print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
+    "\n",
+    "        # Print Random Samples\n",
+    "        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
+    "        random_test_predictions = sess.run(\n",
+    "            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n",
+    "            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n",
+    "        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
+    "\n",
+    "\n",
+    "test_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 为何准确率只有50-80%？\n",
+    "\n",
+    "你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率[远远超过 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130)。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。\n",
+    "\n",
+    "## 提交项目\n",
+    "\n",
+    "提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 \"File\" -> \"Download as\" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From 4bc4103da562b65c6d85a05df28403742369c0d8 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Mon, 7 Aug 2017 13:57:51 +0800
Subject: [PATCH 11/16] Delete dlnd_tv_script_generation.ipynb

---
 .../dlnd_tv_script_generation.ipynb           | 961 ------------------
 1 file changed, 961 deletions(-)
 delete mode 100644 tv-script-generation/dlnd_tv_script_generation.ipynb

diff --git a/tv-script-generation/dlnd_tv_script_generation.ipynb b/tv-script-generation/dlnd_tv_script_generation.ipynb
deleted file mode 100644
index ecc4005..0000000
--- a/tv-script-generation/dlnd_tv_script_generation.ipynb
+++ /dev/null
@@ -1,961 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "# TV Script Generation\n",
-    "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs.  You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons.  The Neural Network you'll build will generate a new TV script for a scene at [Moe's Tavern](https://simpsonswiki.com/wiki/Moe's_Tavern).\n",
-    "## Get the Data\n",
-    "The data is already provided for you.  You'll be using a subset of the original dataset.  It consists of only the scenes in Moe's Tavern.  This doesn't include other versions of the tavern, like \"Moe's Cavern\", \"Flaming Moe's\", \"Uncle Moe's Family Feed-Bag\", etc.."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import helper\n",
-    "\n",
-    "data_dir = './data/simpsons/moes_tavern_lines.txt'\n",
-    "text = helper.load_data(data_dir)\n",
-    "# Ignore notice, since we don't use it for analysing the data\n",
-    "text = text[81:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Explore the Data\n",
-    "Play around with `view_sentence_range` to view different parts of the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "view_sentence_range = (0, 10)\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import numpy as np\n",
-    "\n",
-    "print('Dataset Stats')\n",
-    "print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))\n",
-    "scenes = text.split('\\n\\n')\n",
-    "print('Number of scenes: {}'.format(len(scenes)))\n",
-    "sentence_count_scene = [scene.count('\\n') for scene in scenes]\n",
-    "print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))\n",
-    "\n",
-    "sentences = [sentence for scene in scenes for sentence in scene.split('\\n')]\n",
-    "print('Number of lines: {}'.format(len(sentences)))\n",
-    "word_count_sentence = [len(sentence.split()) for sentence in sentences]\n",
-    "print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))\n",
-    "\n",
-    "print()\n",
-    "print('The sentences {} to {}:'.format(*view_sentence_range))\n",
-    "print('\\n'.join(text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Implement Preprocessing Functions\n",
-    "The first thing to do to any dataset is preprocessing.  Implement the following preprocessing functions below:\n",
-    "- Lookup Table\n",
-    "- Tokenize Punctuation\n",
-    "\n",
-    "### Lookup Table\n",
-    "To create a word embedding, you first need to transform the words to ids.  In this function, create two dictionaries:\n",
-    "- Dictionary to go from the words to an id, we'll call `vocab_to_int`\n",
-    "- Dictionary to go from the id to word, we'll call `int_to_vocab`\n",
-    "\n",
-    "Return these dictionaries in the following tuple `(vocab_to_int, int_to_vocab)`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import problem_unittests as tests\n",
-    "\n",
-    "def create_lookup_tables(text):\n",
-    "    \"\"\"\n",
-    "    Create lookup tables for vocabulary\n",
-    "    :param text: The text of tv scripts split into words\n",
-    "    :return: A tuple of dicts (vocab_to_int, int_to_vocab)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_create_lookup_tables(create_lookup_tables)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Tokenize Punctuation\n",
-    "We'll be splitting the script into a word array using spaces as delimiters.  However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word \"bye\" and \"bye!\".\n",
-    "\n",
-    "Implement the function `token_lookup` to return a dict that will be used to tokenize symbols like \"!\" into \"||Exclamation_Mark||\".  Create a dictionary for the following symbols where the symbol is the key and value is the token:\n",
-    "- Period ( . )\n",
-    "- Comma ( , )\n",
-    "- Quotation Mark ( \" )\n",
-    "- Semicolon ( ; )\n",
-    "- Exclamation mark ( ! )\n",
-    "- Question mark ( ? )\n",
-    "- Left Parentheses ( ( )\n",
-    "- Right Parentheses ( ) )\n",
-    "- Dash ( -- )\n",
-    "- Return ( \\n )\n",
-    "\n",
-    "This dictionary will be used to token the symbols and add the delimiter (space) around it.  This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token \"dash\", try using something like \"||dash||\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def token_lookup():\n",
-    "    \"\"\"\n",
-    "    Generate a dict to turn punctuation into a token.\n",
-    "    :return: Tokenize dictionary where the key is the punctuation and the value is the token\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_tokenize(token_lookup)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Preprocess all the data and save it\n",
-    "Running the code cell below will preprocess all the data and save it to file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "# Preprocess Training, Validation, and Testing Data\n",
-    "helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "# Check Point\n",
-    "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import helper\n",
-    "import numpy as np\n",
-    "import problem_unittests as tests\n",
-    "\n",
-    "int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Build the Neural Network\n",
-    "You'll build the components necessary to build a RNN by implementing the following functions below:\n",
-    "- get_inputs\n",
-    "- get_init_cell\n",
-    "- get_embed\n",
-    "- build_rnn\n",
-    "- build_nn\n",
-    "- get_batches\n",
-    "\n",
-    "### Check the Version of TensorFlow and Access to GPU"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "from distutils.version import LooseVersion\n",
-    "import warnings\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# Check TensorFlow Version\n",
-    "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'\n",
-    "print('TensorFlow Version: {}'.format(tf.__version__))\n",
-    "\n",
-    "# Check for a GPU\n",
-    "if not tf.test.gpu_device_name():\n",
-    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
-    "else:\n",
-    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Input\n",
-    "Implement the `get_inputs()` function to create TF Placeholders for the Neural Network.  It should create the following placeholders:\n",
-    "- Input text placeholder named \"input\" using the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) `name` parameter.\n",
-    "- Targets placeholder\n",
-    "- Learning Rate placeholder\n",
-    "\n",
-    "Return the placeholders in the following tuple `(Input, Targets, LearningRate)`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def get_inputs():\n",
-    "    \"\"\"\n",
-    "    Create TF Placeholders for input, targets, and learning rate.\n",
-    "    :return: Tuple (input, targets, learning rate)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None, None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_get_inputs(get_inputs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Build RNN Cell and Initialize\n",
-    "Stack one or more [`BasicLSTMCells`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell) in a [`MultiRNNCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell).\n",
-    "- The Rnn size should be set using `rnn_size`\n",
-    "- Initalize Cell State using the MultiRNNCell's [`zero_state()`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell#zero_state) function\n",
-    "    - Apply the name \"initial_state\" to the initial state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n",
-    "\n",
-    "Return the cell and initial state in the following tuple `(Cell, InitialState)`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def get_init_cell(batch_size, rnn_size):\n",
-    "    \"\"\"\n",
-    "    Create an RNN Cell and initialize it.\n",
-    "    :param batch_size: Size of batches\n",
-    "    :param rnn_size: Size of RNNs\n",
-    "    :return: Tuple (cell, initialize state)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_get_init_cell(get_init_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Word Embedding\n",
-    "Apply embedding to `input_data` using TensorFlow.  Return the embedded sequence."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def get_embed(input_data, vocab_size, embed_dim):\n",
-    "    \"\"\"\n",
-    "    Create embedding for <input_data>.\n",
-    "    :param input_data: TF placeholder for text input.\n",
-    "    :param vocab_size: Number of words in vocabulary.\n",
-    "    :param embed_dim: Number of embedding dimensions\n",
-    "    :return: Embedded input.\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_get_embed(get_embed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Build RNN\n",
-    "You created a RNN Cell in the `get_init_cell()` function.  Time to use the cell to create a RNN.\n",
-    "- Build the RNN using the [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)\n",
-    " - Apply the name \"final_state\" to the final state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n",
-    "\n",
-    "Return the outputs and final_state state in the following tuple `(Outputs, FinalState)` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def build_rnn(cell, inputs):\n",
-    "    \"\"\"\n",
-    "    Create a RNN using a RNN Cell\n",
-    "    :param cell: RNN Cell\n",
-    "    :param inputs: Input text data\n",
-    "    :return: Tuple (Outputs, Final State)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_build_rnn(build_rnn)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Build the Neural Network\n",
-    "Apply the functions you implemented above to:\n",
-    "- Apply embedding to `input_data` using your `get_embed(input_data, vocab_size, embed_dim)` function.\n",
-    "- Build RNN using `cell` and your `build_rnn(cell, inputs)` function.\n",
-    "- Apply a fully connected layer with a linear activation and `vocab_size` as the number of outputs.\n",
-    "\n",
-    "Return the logits and final state in the following tuple (Logits, FinalState) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):\n",
-    "    \"\"\"\n",
-    "    Build part of the neural network\n",
-    "    :param cell: RNN cell\n",
-    "    :param rnn_size: Size of rnns\n",
-    "    :param input_data: Input data\n",
-    "    :param vocab_size: Vocabulary size\n",
-    "    :param embed_dim: Number of embedding dimensions\n",
-    "    :return: Tuple (Logits, FinalState)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_build_nn(build_nn)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Batches\n",
-    "Implement `get_batches` to create batches of input and targets using `int_text`.  The batches should be a Numpy array with the shape `(number of batches, 2, batch size, sequence length)`. Each batch contains two elements:\n",
-    "- The first element is a single batch of **input** with the shape `[batch size, sequence length]`\n",
-    "- The second element is a single batch of **targets** with the shape `[batch size, sequence length]`\n",
-    "\n",
-    "If you can't fill the last batch with enough data, drop the last batch.\n",
-    "\n",
-    "For exmple, `get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3)` would return a Numpy array of the following:\n",
-    "```\n",
-    "[\n",
-    "  # First Batch\n",
-    "  [\n",
-    "    # Batch of Input\n",
-    "    [[ 1  2  3], [ 7  8  9]],\n",
-    "    # Batch of targets\n",
-    "    [[ 2  3  4], [ 8  9 10]]\n",
-    "  ],\n",
-    " \n",
-    "  # Second Batch\n",
-    "  [\n",
-    "    # Batch of Input\n",
-    "    [[ 4  5  6], [10 11 12]],\n",
-    "    # Batch of targets\n",
-    "    [[ 5  6  7], [11 12 13]]\n",
-    "  ]\n",
-    "]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def get_batches(int_text, batch_size, seq_length):\n",
-    "    \"\"\"\n",
-    "    Return batches of input and target\n",
-    "    :param int_text: Text with the words replaced by their ids\n",
-    "    :param batch_size: The size of batch\n",
-    "    :param seq_length: The length of sequence\n",
-    "    :return: Batches as a Numpy array\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_get_batches(get_batches)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Neural Network Training\n",
-    "### Hyperparameters\n",
-    "Tune the following parameters:\n",
-    "\n",
-    "- Set `num_epochs` to the number of epochs.\n",
-    "- Set `batch_size` to the batch size.\n",
-    "- Set `rnn_size` to the size of the RNNs.\n",
-    "- Set `embed_dim` to the size of the embedding.\n",
-    "- Set `seq_length` to the length of sequence.\n",
-    "- Set `learning_rate` to the learning rate.\n",
-    "- Set `show_every_n_batches` to the number of batches the neural network should print progress."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "# Number of Epochs\n",
-    "num_epochs = None\n",
-    "# Batch Size\n",
-    "batch_size = None\n",
-    "# RNN Size\n",
-    "rnn_size = None\n",
-    "# Embedding Dimension Size\n",
-    "embed_dim = None\n",
-    "# Sequence Length\n",
-    "seq_length = None\n",
-    "# Learning Rate\n",
-    "learning_rate = None\n",
-    "# Show stats for every n number of batches\n",
-    "show_every_n_batches = None\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "save_dir = './save'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Build the Graph\n",
-    "Build the graph using the neural network you implemented."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "from tensorflow.contrib import seq2seq\n",
-    "\n",
-    "train_graph = tf.Graph()\n",
-    "with train_graph.as_default():\n",
-    "    vocab_size = len(int_to_vocab)\n",
-    "    input_text, targets, lr = get_inputs()\n",
-    "    input_data_shape = tf.shape(input_text)\n",
-    "    cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)\n",
-    "    logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size, embed_dim)\n",
-    "\n",
-    "    # Probabilities for generating words\n",
-    "    probs = tf.nn.softmax(logits, name='probs')\n",
-    "\n",
-    "    # Loss function\n",
-    "    cost = seq2seq.sequence_loss(\n",
-    "        logits,\n",
-    "        targets,\n",
-    "        tf.ones([input_data_shape[0], input_data_shape[1]]))\n",
-    "\n",
-    "    # Optimizer\n",
-    "    optimizer = tf.train.AdamOptimizer(lr)\n",
-    "\n",
-    "    # Gradient Clipping\n",
-    "    gradients = optimizer.compute_gradients(cost)\n",
-    "    capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n",
-    "    train_op = optimizer.apply_gradients(capped_gradients)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Train\n",
-    "Train the neural network on the preprocessed data.  If you have a hard time getting a good loss, check the [forms](https://discussions.udacity.com/) to see if anyone is having the same problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "batches = get_batches(int_text, batch_size, seq_length)\n",
-    "\n",
-    "with tf.Session(graph=train_graph) as sess:\n",
-    "    sess.run(tf.global_variables_initializer())\n",
-    "\n",
-    "    for epoch_i in range(num_epochs):\n",
-    "        state = sess.run(initial_state, {input_text: batches[0][0]})\n",
-    "\n",
-    "        for batch_i, (x, y) in enumerate(batches):\n",
-    "            feed = {\n",
-    "                input_text: x,\n",
-    "                targets: y,\n",
-    "                initial_state: state,\n",
-    "                lr: learning_rate}\n",
-    "            train_loss, state, _ = sess.run([cost, final_state, train_op], feed)\n",
-    "\n",
-    "            # Show every <show_every_n_batches> batches\n",
-    "            if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:\n",
-    "                print('Epoch {:>3} Batch {:>4}/{}   train_loss = {:.3f}'.format(\n",
-    "                    epoch_i,\n",
-    "                    batch_i,\n",
-    "                    len(batches),\n",
-    "                    train_loss))\n",
-    "\n",
-    "    # Save Model\n",
-    "    saver = tf.train.Saver()\n",
-    "    saver.save(sess, save_dir)\n",
-    "    print('Model Trained and Saved')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Save Parameters\n",
-    "Save `seq_length` and `save_dir` for generating a new TV script."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "# Save parameters for checkpoint\n",
-    "helper.save_params((seq_length, save_dir))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "# Checkpoint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "import helper\n",
-    "import problem_unittests as tests\n",
-    "\n",
-    "_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()\n",
-    "seq_length, load_dir = helper.load_params()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Implement Generate Functions\n",
-    "### Get Tensors\n",
-    "Get tensors from `loaded_graph` using the function [`get_tensor_by_name()`](https://www.tensorflow.org/api_docs/python/tf/Graph#get_tensor_by_name).  Get the tensors using the following names:\n",
-    "- \"input:0\"\n",
-    "- \"initial_state:0\"\n",
-    "- \"final_state:0\"\n",
-    "- \"probs:0\"\n",
-    "\n",
-    "Return the tensors in the following tuple `(InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def get_tensors(loaded_graph):\n",
-    "    \"\"\"\n",
-    "    Get input, initial state, final state, and probabilities tensor from <loaded_graph>\n",
-    "    :param loaded_graph: TensorFlow graph loaded from file\n",
-    "    :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None, None, None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_get_tensors(get_tensors)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Choose Word\n",
-    "Implement the `pick_word()` function to select the next word using `probabilities`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def pick_word(probabilities, int_to_vocab):\n",
-    "    \"\"\"\n",
-    "    Pick the next word in the generated text\n",
-    "    :param probabilities: Probabilites of the next word\n",
-    "    :param int_to_vocab: Dictionary of word ids as the keys and words as the values\n",
-    "    :return: String of the predicted word\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_pick_word(pick_word)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Generate TV Script\n",
-    "This will generate the TV script for you.  Set `gen_length` to the length of TV script you want to generate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "gen_length = 200\n",
-    "# homer_simpson, moe_szyslak, or Barney_Gumble\n",
-    "prime_word = 'moe_szyslak'\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "loaded_graph = tf.Graph()\n",
-    "with tf.Session(graph=loaded_graph) as sess:\n",
-    "    # Load saved model\n",
-    "    loader = tf.train.import_meta_graph(load_dir + '.meta')\n",
-    "    loader.restore(sess, load_dir)\n",
-    "\n",
-    "    # Get Tensors from loaded model\n",
-    "    input_text, initial_state, final_state, probs = get_tensors(loaded_graph)\n",
-    "\n",
-    "    # Sentences generation setup\n",
-    "    gen_sentences = [prime_word + ':']\n",
-    "    prev_state = sess.run(initial_state, {input_text: np.array([[1]])})\n",
-    "\n",
-    "    # Generate sentences\n",
-    "    for n in range(gen_length):\n",
-    "        # Dynamic Input\n",
-    "        dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]\n",
-    "        dyn_seq_length = len(dyn_input[0])\n",
-    "\n",
-    "        # Get Prediction\n",
-    "        probabilities, prev_state = sess.run(\n",
-    "            [probs, final_state],\n",
-    "            {input_text: dyn_input, initial_state: prev_state})\n",
-    "        \n",
-    "        pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)\n",
-    "\n",
-    "        gen_sentences.append(pred_word)\n",
-    "    \n",
-    "    # Remove tokens\n",
-    "    tv_script = ' '.join(gen_sentences)\n",
-    "    for key, token in token_dict.items():\n",
-    "        ending = ' ' if key in ['\\n', '(', '\"'] else ''\n",
-    "        tv_script = tv_script.replace(' ' + token.lower(), key)\n",
-    "    tv_script = tv_script.replace('\\n ', '\\n')\n",
-    "    tv_script = tv_script.replace('( ', '(')\n",
-    "        \n",
-    "    print(tv_script)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "# The TV Script is Nonsensical\n",
-    "It's ok if the TV script doesn't make any sense.  We trained on less than a megabyte of text.  In order to get good results, you'll have to use a smaller vocabulary or get more data.  Luckly there's more data!  As we mentioned in the begging of this project, this is a subset of [another dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data).  We didn't have you train on all the data, because that would take too long.  However, you are free to train your neural network on all the data.  After you complete the project, of course.\n",
-    "# Submitting This Project\n",
-    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_tv_script_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  },
-  "widgets": {
-   "state": {},
-   "version": "1.1.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

From 6abe37243df341af8b5b58e42bc345f5415e4884 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Mon, 7 Aug 2017 13:58:48 +0800
Subject: [PATCH 12/16] Add files via upload

---
 .../dlnd_tv_script_generation.html            | 13077 ++++++++++++++++
 .../dlnd_tv_script_generation.ipynb           |  1084 ++
 2 files changed, 14161 insertions(+)
 create mode 100644 tv-script-generation/dlnd_tv_script_generation.html
 create mode 100644 tv-script-generation/dlnd_tv_script_generation.ipynb

diff --git a/tv-script-generation/dlnd_tv_script_generation.html b/tv-script-generation/dlnd_tv_script_generation.html
new file mode 100644
index 0000000..b6367a1
--- /dev/null
+++ b/tv-script-generation/dlnd_tv_script_generation.html
@@ -0,0 +1,13077 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>dlnd_tv_script_generation</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: 1px dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    background: transparent !important;
+    color: #000 !important;
+    box-shadow: none !important;
+    text-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: 'Glyphicons Halflings';
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Glyphicons Halflings';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+  content: "\e156";
+}
+.glyphicon-unchecked:before {
+  content: "\e157";
+}
+.glyphicon-expand:before {
+  content: "\e158";
+}
+.glyphicon-collapse-down:before {
+  content: "\e159";
+}
+.glyphicon-collapse-up:before {
+  content: "\e160";
+}
+.glyphicon-log-in:before {
+  content: "\e161";
+}
+.glyphicon-flash:before {
+  content: "\e162";
+}
+.glyphicon-log-out:before {
+  content: "\e163";
+}
+.glyphicon-new-window:before {
+  content: "\e164";
+}
+.glyphicon-record:before {
+  content: "\e165";
+}
+.glyphicon-save:before {
+  content: "\e166";
+}
+.glyphicon-open:before {
+  content: "\e167";
+}
+.glyphicon-saved:before {
+  content: "\e168";
+}
+.glyphicon-import:before {
+  content: "\e169";
+}
+.glyphicon-export:before {
+  content: "\e170";
+}
+.glyphicon-send:before {
+  content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+  content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+  content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+  content: "\e174";
+}
+.glyphicon-floppy-save:before {
+  content: "\e175";
+}
+.glyphicon-floppy-open:before {
+  content: "\e176";
+}
+.glyphicon-credit-card:before {
+  content: "\e177";
+}
+.glyphicon-transfer:before {
+  content: "\e178";
+}
+.glyphicon-cutlery:before {
+  content: "\e179";
+}
+.glyphicon-header:before {
+  content: "\e180";
+}
+.glyphicon-compressed:before {
+  content: "\e181";
+}
+.glyphicon-earphone:before {
+  content: "\e182";
+}
+.glyphicon-phone-alt:before {
+  content: "\e183";
+}
+.glyphicon-tower:before {
+  content: "\e184";
+}
+.glyphicon-stats:before {
+  content: "\e185";
+}
+.glyphicon-sd-video:before {
+  content: "\e186";
+}
+.glyphicon-hd-video:before {
+  content: "\e187";
+}
+.glyphicon-subtitles:before {
+  content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+  content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+  content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+  content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+  content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+  content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
+.glyphicon-pawn:before {
+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
+.glyphicon-bitcoin:before {
+  content: "\e227";
+}
+.glyphicon-btc:before {
+  content: "\e227";
+}
+.glyphicon-xbt:before {
+  content: "\e227";
+}
+.glyphicon-yen:before {
+  content: "\00a5";
+}
+.glyphicon-jpy:before {
+  content: "\00a5";
+}
+.glyphicon-ruble:before {
+  content: "\20bd";
+}
+.glyphicon-rub:before {
+  content: "\20bd";
+}
+.glyphicon-scale:before {
+  content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+  content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+  content: "\e232";
+}
+.glyphicon-education:before {
+  content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+  content: "\e234";
+}
+.glyphicon-option-vertical:before {
+  content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+  content: "\e236";
+}
+.glyphicon-modal-window:before {
+  content: "\e237";
+}
+.glyphicon-oil:before {
+  content: "\e238";
+}
+.glyphicon-grain:before {
+  content: "\e239";
+}
+.glyphicon-sunglasses:before {
+  content: "\e240";
+}
+.glyphicon-text-size:before {
+  content: "\e241";
+}
+.glyphicon-text-color:before {
+  content: "\e242";
+}
+.glyphicon-text-background:before {
+  content: "\e243";
+}
+.glyphicon-object-align-top:before {
+  content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+  content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+  content: "\e246";
+}
+.glyphicon-object-align-left:before {
+  content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+  content: "\e248";
+}
+.glyphicon-object-align-right:before {
+  content: "\e249";
+}
+.glyphicon-triangle-right:before {
+  content: "\e250";
+}
+.glyphicon-triangle-left:before {
+  content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+  content: "\e252";
+}
+.glyphicon-triangle-top:before {
+  content: "\e253";
+}
+.glyphicon-console:before {
+  content: "\e254";
+}
+.glyphicon-superscript:before {
+  content: "\e255";
+}
+.glyphicon-subscript:before {
+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
+h5 small,
+h6 small,
+.h1 small,
+.h2 small,
+.h3 small,
+.h4 small,
+.h5 small,
+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 18px;
+  margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
+h2,
+.h2 {
+  font-size: 27px;
+}
+h3,
+.h3 {
+  font-size: 23px;
+}
+h4,
+.h4 {
+  font-size: 17px;
+}
+h5,
+.h5 {
+  font-size: 13px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
+address {
+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
+.col-xs-9 {
+  width: 75%;
+}
+.col-xs-8 {
+  width: 66.66666667%;
+}
+.col-xs-7 {
+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+  content: "\f044";
+}
+.fa-share-square-o:before {
+  content: "\f045";
+}
+.fa-check-square-o:before {
+  content: "\f046";
+}
+.fa-arrows:before {
+  content: "\f047";
+}
+.fa-step-backward:before {
+  content: "\f048";
+}
+.fa-fast-backward:before {
+  content: "\f049";
+}
+.fa-backward:before {
+  content: "\f04a";
+}
+.fa-play:before {
+  content: "\f04b";
+}
+.fa-pause:before {
+  content: "\f04c";
+}
+.fa-stop:before {
+  content: "\f04d";
+}
+.fa-forward:before {
+  content: "\f04e";
+}
+.fa-fast-forward:before {
+  content: "\f050";
+}
+.fa-step-forward:before {
+  content: "\f051";
+}
+.fa-eject:before {
+  content: "\f052";
+}
+.fa-chevron-left:before {
+  content: "\f053";
+}
+.fa-chevron-right:before {
+  content: "\f054";
+}
+.fa-plus-circle:before {
+  content: "\f055";
+}
+.fa-minus-circle:before {
+  content: "\f056";
+}
+.fa-times-circle:before {
+  content: "\f057";
+}
+.fa-check-circle:before {
+  content: "\f058";
+}
+.fa-question-circle:before {
+  content: "\f059";
+}
+.fa-info-circle:before {
+  content: "\f05a";
+}
+.fa-crosshairs:before {
+  content: "\f05b";
+}
+.fa-times-circle-o:before {
+  content: "\f05c";
+}
+.fa-check-circle-o:before {
+  content: "\f05d";
+}
+.fa-ban:before {
+  content: "\f05e";
+}
+.fa-arrow-left:before {
+  content: "\f060";
+}
+.fa-arrow-right:before {
+  content: "\f061";
+}
+.fa-arrow-up:before {
+  content: "\f062";
+}
+.fa-arrow-down:before {
+  content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+  content: "\f064";
+}
+.fa-expand:before {
+  content: "\f065";
+}
+.fa-compress:before {
+  content: "\f066";
+}
+.fa-plus:before {
+  content: "\f067";
+}
+.fa-minus:before {
+  content: "\f068";
+}
+.fa-asterisk:before {
+  content: "\f069";
+}
+.fa-exclamation-circle:before {
+  content: "\f06a";
+}
+.fa-gift:before {
+  content: "\f06b";
+}
+.fa-leaf:before {
+  content: "\f06c";
+}
+.fa-fire:before {
+  content: "\f06d";
+}
+.fa-eye:before {
+  content: "\f06e";
+}
+.fa-eye-slash:before {
+  content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+  content: "\f071";
+}
+.fa-plane:before {
+  content: "\f072";
+}
+.fa-calendar:before {
+  content: "\f073";
+}
+.fa-random:before {
+  content: "\f074";
+}
+.fa-comment:before {
+  content: "\f075";
+}
+.fa-magnet:before {
+  content: "\f076";
+}
+.fa-chevron-up:before {
+  content: "\f077";
+}
+.fa-chevron-down:before {
+  content: "\f078";
+}
+.fa-retweet:before {
+  content: "\f079";
+}
+.fa-shopping-cart:before {
+  content: "\f07a";
+}
+.fa-folder:before {
+  content: "\f07b";
+}
+.fa-folder-open:before {
+  content: "\f07c";
+}
+.fa-arrows-v:before {
+  content: "\f07d";
+}
+.fa-arrows-h:before {
+  content: "\f07e";
+}
+.fa-bar-chart-o:before,
+.fa-bar-chart:before {
+  content: "\f080";
+}
+.fa-twitter-square:before {
+  content: "\f081";
+}
+.fa-facebook-square:before {
+  content: "\f082";
+}
+.fa-camera-retro:before {
+  content: "\f083";
+}
+.fa-key:before {
+  content: "\f084";
+}
+.fa-gears:before,
+.fa-cogs:before {
+  content: "\f085";
+}
+.fa-comments:before {
+  content: "\f086";
+}
+.fa-thumbs-o-up:before {
+  content: "\f087";
+}
+.fa-thumbs-o-down:before {
+  content: "\f088";
+}
+.fa-star-half:before {
+  content: "\f089";
+}
+.fa-heart-o:before {
+  content: "\f08a";
+}
+.fa-sign-out:before {
+  content: "\f08b";
+}
+.fa-linkedin-square:before {
+  content: "\f08c";
+}
+.fa-thumb-tack:before {
+  content: "\f08d";
+}
+.fa-external-link:before {
+  content: "\f08e";
+}
+.fa-sign-in:before {
+  content: "\f090";
+}
+.fa-trophy:before {
+  content: "\f091";
+}
+.fa-github-square:before {
+  content: "\f092";
+}
+.fa-upload:before {
+  content: "\f093";
+}
+.fa-lemon-o:before {
+  content: "\f094";
+}
+.fa-phone:before {
+  content: "\f095";
+}
+.fa-square-o:before {
+  content: "\f096";
+}
+.fa-bookmark-o:before {
+  content: "\f097";
+}
+.fa-phone-square:before {
+  content: "\f098";
+}
+.fa-twitter:before {
+  content: "\f099";
+}
+.fa-facebook:before {
+  content: "\f09a";
+}
+.fa-github:before {
+  content: "\f09b";
+}
+.fa-unlock:before {
+  content: "\f09c";
+}
+.fa-credit-card:before {
+  content: "\f09d";
+}
+.fa-rss:before {
+  content: "\f09e";
+}
+.fa-hdd-o:before {
+  content: "\f0a0";
+}
+.fa-bullhorn:before {
+  content: "\f0a1";
+}
+.fa-bell:before {
+  content: "\f0f3";
+}
+.fa-certificate:before {
+  content: "\f0a3";
+}
+.fa-hand-o-right:before {
+  content: "\f0a4";
+}
+.fa-hand-o-left:before {
+  content: "\f0a5";
+}
+.fa-hand-o-up:before {
+  content: "\f0a6";
+}
+.fa-hand-o-down:before {
+  content: "\f0a7";
+}
+.fa-arrow-circle-left:before {
+  content: "\f0a8";
+}
+.fa-arrow-circle-right:before {
+  content: "\f0a9";
+}
+.fa-arrow-circle-up:before {
+  content: "\f0aa";
+}
+.fa-arrow-circle-down:before {
+  content: "\f0ab";
+}
+.fa-globe:before {
+  content: "\f0ac";
+}
+.fa-wrench:before {
+  content: "\f0ad";
+}
+.fa-tasks:before {
+  content: "\f0ae";
+}
+.fa-filter:before {
+  content: "\f0b0";
+}
+.fa-briefcase:before {
+  content: "\f0b1";
+}
+.fa-arrows-alt:before {
+  content: "\f0b2";
+}
+.fa-group:before,
+.fa-users:before {
+  content: "\f0c0";
+}
+.fa-chain:before,
+.fa-link:before {
+  content: "\f0c1";
+}
+.fa-cloud:before {
+  content: "\f0c2";
+}
+.fa-flask:before {
+  content: "\f0c3";
+}
+.fa-cut:before,
+.fa-scissors:before {
+  content: "\f0c4";
+}
+.fa-copy:before,
+.fa-files-o:before {
+  content: "\f0c5";
+}
+.fa-paperclip:before {
+  content: "\f0c6";
+}
+.fa-save:before,
+.fa-floppy-o:before {
+  content: "\f0c7";
+}
+.fa-square:before {
+  content: "\f0c8";
+}
+.fa-navicon:before,
+.fa-reorder:before,
+.fa-bars:before {
+  content: "\f0c9";
+}
+.fa-list-ul:before {
+  content: "\f0ca";
+}
+.fa-list-ol:before {
+  content: "\f0cb";
+}
+.fa-strikethrough:before {
+  content: "\f0cc";
+}
+.fa-underline:before {
+  content: "\f0cd";
+}
+.fa-table:before {
+  content: "\f0ce";
+}
+.fa-magic:before {
+  content: "\f0d0";
+}
+.fa-truck:before {
+  content: "\f0d1";
+}
+.fa-pinterest:before {
+  content: "\f0d2";
+}
+.fa-pinterest-square:before {
+  content: "\f0d3";
+}
+.fa-google-plus-square:before {
+  content: "\f0d4";
+}
+.fa-google-plus:before {
+  content: "\f0d5";
+}
+.fa-money:before {
+  content: "\f0d6";
+}
+.fa-caret-down:before {
+  content: "\f0d7";
+}
+.fa-caret-up:before {
+  content: "\f0d8";
+}
+.fa-caret-left:before {
+  content: "\f0d9";
+}
+.fa-caret-right:before {
+  content: "\f0da";
+}
+.fa-columns:before {
+  content: "\f0db";
+}
+.fa-unsorted:before,
+.fa-sort:before {
+  content: "\f0dc";
+}
+.fa-sort-down:before,
+.fa-sort-desc:before {
+  content: "\f0dd";
+}
+.fa-sort-up:before,
+.fa-sort-asc:before {
+  content: "\f0de";
+}
+.fa-envelope:before {
+  content: "\f0e0";
+}
+.fa-linkedin:before {
+  content: "\f0e1";
+}
+.fa-rotate-left:before,
+.fa-undo:before {
+  content: "\f0e2";
+}
+.fa-legal:before,
+.fa-gavel:before {
+  content: "\f0e3";
+}
+.fa-dashboard:before,
+.fa-tachometer:before {
+  content: "\f0e4";
+}
+.fa-comment-o:before {
+  content: "\f0e5";
+}
+.fa-comments-o:before {
+  content: "\f0e6";
+}
+.fa-flash:before,
+.fa-bolt:before {
+  content: "\f0e7";
+}
+.fa-sitemap:before {
+  content: "\f0e8";
+}
+.fa-umbrella:before {
+  content: "\f0e9";
+}
+.fa-paste:before,
+.fa-clipboard:before {
+  content: "\f0ea";
+}
+.fa-lightbulb-o:before {
+  content: "\f0eb";
+}
+.fa-exchange:before {
+  content: "\f0ec";
+}
+.fa-cloud-download:before {
+  content: "\f0ed";
+}
+.fa-cloud-upload:before {
+  content: "\f0ee";
+}
+.fa-user-md:before {
+  content: "\f0f0";
+}
+.fa-stethoscope:before {
+  content: "\f0f1";
+}
+.fa-suitcase:before {
+  content: "\f0f2";
+}
+.fa-bell-o:before {
+  content: "\f0a2";
+}
+.fa-coffee:before {
+  content: "\f0f4";
+}
+.fa-cutlery:before {
+  content: "\f0f5";
+}
+.fa-file-text-o:before {
+  content: "\f0f6";
+}
+.fa-building-o:before {
+  content: "\f0f7";
+}
+.fa-hospital-o:before {
+  content: "\f0f8";
+}
+.fa-ambulance:before {
+  content: "\f0f9";
+}
+.fa-medkit:before {
+  content: "\f0fa";
+}
+.fa-fighter-jet:before {
+  content: "\f0fb";
+}
+.fa-beer:before {
+  content: "\f0fc";
+}
+.fa-h-square:before {
+  content: "\f0fd";
+}
+.fa-plus-square:before {
+  content: "\f0fe";
+}
+.fa-angle-double-left:before {
+  content: "\f100";
+}
+.fa-angle-double-right:before {
+  content: "\f101";
+}
+.fa-angle-double-up:before {
+  content: "\f102";
+}
+.fa-angle-double-down:before {
+  content: "\f103";
+}
+.fa-angle-left:before {
+  content: "\f104";
+}
+.fa-angle-right:before {
+  content: "\f105";
+}
+.fa-angle-up:before {
+  content: "\f106";
+}
+.fa-angle-down:before {
+  content: "\f107";
+}
+.fa-desktop:before {
+  content: "\f108";
+}
+.fa-laptop:before {
+  content: "\f109";
+}
+.fa-tablet:before {
+  content: "\f10a";
+}
+.fa-mobile-phone:before,
+.fa-mobile:before {
+  content: "\f10b";
+}
+.fa-circle-o:before {
+  content: "\f10c";
+}
+.fa-quote-left:before {
+  content: "\f10d";
+}
+.fa-quote-right:before {
+  content: "\f10e";
+}
+.fa-spinner:before {
+  content: "\f110";
+}
+.fa-circle:before {
+  content: "\f111";
+}
+.fa-mail-reply:before,
+.fa-reply:before {
+  content: "\f112";
+}
+.fa-github-alt:before {
+  content: "\f113";
+}
+.fa-folder-o:before {
+  content: "\f114";
+}
+.fa-folder-open-o:before {
+  content: "\f115";
+}
+.fa-smile-o:before {
+  content: "\f118";
+}
+.fa-frown-o:before {
+  content: "\f119";
+}
+.fa-meh-o:before {
+  content: "\f11a";
+}
+.fa-gamepad:before {
+  content: "\f11b";
+}
+.fa-keyboard-o:before {
+  content: "\f11c";
+}
+.fa-flag-o:before {
+  content: "\f11d";
+}
+.fa-flag-checkered:before {
+  content: "\f11e";
+}
+.fa-terminal:before {
+  content: "\f120";
+}
+.fa-code:before {
+  content: "\f121";
+}
+.fa-mail-reply-all:before,
+.fa-reply-all:before {
+  content: "\f122";
+}
+.fa-star-half-empty:before,
+.fa-star-half-full:before,
+.fa-star-half-o:before {
+  content: "\f123";
+}
+.fa-location-arrow:before {
+  content: "\f124";
+}
+.fa-crop:before {
+  content: "\f125";
+}
+.fa-code-fork:before {
+  content: "\f126";
+}
+.fa-unlink:before,
+.fa-chain-broken:before {
+  content: "\f127";
+}
+.fa-question:before {
+  content: "\f128";
+}
+.fa-info:before {
+  content: "\f129";
+}
+.fa-exclamation:before {
+  content: "\f12a";
+}
+.fa-superscript:before {
+  content: "\f12b";
+}
+.fa-subscript:before {
+  content: "\f12c";
+}
+.fa-eraser:before {
+  content: "\f12d";
+}
+.fa-puzzle-piece:before {
+  content: "\f12e";
+}
+.fa-microphone:before {
+  content: "\f130";
+}
+.fa-microphone-slash:before {
+  content: "\f131";
+}
+.fa-shield:before {
+  content: "\f132";
+}
+.fa-calendar-o:before {
+  content: "\f133";
+}
+.fa-fire-extinguisher:before {
+  content: "\f134";
+}
+.fa-rocket:before {
+  content: "\f135";
+}
+.fa-maxcdn:before {
+  content: "\f136";
+}
+.fa-chevron-circle-left:before {
+  content: "\f137";
+}
+.fa-chevron-circle-right:before {
+  content: "\f138";
+}
+.fa-chevron-circle-up:before {
+  content: "\f139";
+}
+.fa-chevron-circle-down:before {
+  content: "\f13a";
+}
+.fa-html5:before {
+  content: "\f13b";
+}
+.fa-css3:before {
+  content: "\f13c";
+}
+.fa-anchor:before {
+  content: "\f13d";
+}
+.fa-unlock-alt:before {
+  content: "\f13e";
+}
+.fa-bullseye:before {
+  content: "\f140";
+}
+.fa-ellipsis-h:before {
+  content: "\f141";
+}
+.fa-ellipsis-v:before {
+  content: "\f142";
+}
+.fa-rss-square:before {
+  content: "\f143";
+}
+.fa-play-circle:before {
+  content: "\f144";
+}
+.fa-ticket:before {
+  content: "\f145";
+}
+.fa-minus-square:before {
+  content: "\f146";
+}
+.fa-minus-square-o:before {
+  content: "\f147";
+}
+.fa-level-up:before {
+  content: "\f148";
+}
+.fa-level-down:before {
+  content: "\f149";
+}
+.fa-check-square:before {
+  content: "\f14a";
+}
+.fa-pencil-square:before {
+  content: "\f14b";
+}
+.fa-external-link-square:before {
+  content: "\f14c";
+}
+.fa-share-square:before {
+  content: "\f14d";
+}
+.fa-compass:before {
+  content: "\f14e";
+}
+.fa-toggle-down:before,
+.fa-caret-square-o-down:before {
+  content: "\f150";
+}
+.fa-toggle-up:before,
+.fa-caret-square-o-up:before {
+  content: "\f151";
+}
+.fa-toggle-right:before,
+.fa-caret-square-o-right:before {
+  content: "\f152";
+}
+.fa-euro:before,
+.fa-eur:before {
+  content: "\f153";
+}
+.fa-gbp:before {
+  content: "\f154";
+}
+.fa-dollar:before,
+.fa-usd:before {
+  content: "\f155";
+}
+.fa-rupee:before,
+.fa-inr:before {
+  content: "\f156";
+}
+.fa-cny:before,
+.fa-rmb:before,
+.fa-yen:before,
+.fa-jpy:before {
+  content: "\f157";
+}
+.fa-ruble:before,
+.fa-rouble:before,
+.fa-rub:before {
+  content: "\f158";
+}
+.fa-won:before,
+.fa-krw:before {
+  content: "\f159";
+}
+.fa-bitcoin:before,
+.fa-btc:before {
+  content: "\f15a";
+}
+.fa-file:before {
+  content: "\f15b";
+}
+.fa-file-text:before {
+  content: "\f15c";
+}
+.fa-sort-alpha-asc:before {
+  content: "\f15d";
+}
+.fa-sort-alpha-desc:before {
+  content: "\f15e";
+}
+.fa-sort-amount-asc:before {
+  content: "\f160";
+}
+.fa-sort-amount-desc:before {
+  content: "\f161";
+}
+.fa-sort-numeric-asc:before {
+  content: "\f162";
+}
+.fa-sort-numeric-desc:before {
+  content: "\f163";
+}
+.fa-thumbs-up:before {
+  content: "\f164";
+}
+.fa-thumbs-down:before {
+  content: "\f165";
+}
+.fa-youtube-square:before {
+  content: "\f166";
+}
+.fa-youtube:before {
+  content: "\f167";
+}
+.fa-xing:before {
+  content: "\f168";
+}
+.fa-xing-square:before {
+  content: "\f169";
+}
+.fa-youtube-play:before {
+  content: "\f16a";
+}
+.fa-dropbox:before {
+  content: "\f16b";
+}
+.fa-stack-overflow:before {
+  content: "\f16c";
+}
+.fa-instagram:before {
+  content: "\f16d";
+}
+.fa-flickr:before {
+  content: "\f16e";
+}
+.fa-adn:before {
+  content: "\f170";
+}
+.fa-bitbucket:before {
+  content: "\f171";
+}
+.fa-bitbucket-square:before {
+  content: "\f172";
+}
+.fa-tumblr:before {
+  content: "\f173";
+}
+.fa-tumblr-square:before {
+  content: "\f174";
+}
+.fa-long-arrow-down:before {
+  content: "\f175";
+}
+.fa-long-arrow-up:before {
+  content: "\f176";
+}
+.fa-long-arrow-left:before {
+  content: "\f177";
+}
+.fa-long-arrow-right:before {
+  content: "\f178";
+}
+.fa-apple:before {
+  content: "\f179";
+}
+.fa-windows:before {
+  content: "\f17a";
+}
+.fa-android:before {
+  content: "\f17b";
+}
+.fa-linux:before {
+  content: "\f17c";
+}
+.fa-dribbble:before {
+  content: "\f17d";
+}
+.fa-skype:before {
+  content: "\f17e";
+}
+.fa-foursquare:before {
+  content: "\f180";
+}
+.fa-trello:before {
+  content: "\f181";
+}
+.fa-female:before {
+  content: "\f182";
+}
+.fa-male:before {
+  content: "\f183";
+}
+.fa-gittip:before {
+  content: "\f184";
+}
+.fa-sun-o:before {
+  content: "\f185";
+}
+.fa-moon-o:before {
+  content: "\f186";
+}
+.fa-archive:before {
+  content: "\f187";
+}
+.fa-bug:before {
+  content: "\f188";
+}
+.fa-vk:before {
+  content: "\f189";
+}
+.fa-weibo:before {
+  content: "\f18a";
+}
+.fa-renren:before {
+  content: "\f18b";
+}
+.fa-pagelines:before {
+  content: "\f18c";
+}
+.fa-stack-exchange:before {
+  content: "\f18d";
+}
+.fa-arrow-circle-o-right:before {
+  content: "\f18e";
+}
+.fa-arrow-circle-o-left:before {
+  content: "\f190";
+}
+.fa-toggle-left:before,
+.fa-caret-square-o-left:before {
+  content: "\f191";
+}
+.fa-dot-circle-o:before {
+  content: "\f192";
+}
+.fa-wheelchair:before {
+  content: "\f193";
+}
+.fa-vimeo-square:before {
+  content: "\f194";
+}
+.fa-turkish-lira:before,
+.fa-try:before {
+  content: "\f195";
+}
+.fa-plus-square-o:before {
+  content: "\f196";
+}
+.fa-space-shuttle:before {
+  content: "\f197";
+}
+.fa-slack:before {
+  content: "\f198";
+}
+.fa-envelope-square:before {
+  content: "\f199";
+}
+.fa-wordpress:before {
+  content: "\f19a";
+}
+.fa-openid:before {
+  content: "\f19b";
+}
+.fa-institution:before,
+.fa-bank:before,
+.fa-university:before {
+  content: "\f19c";
+}
+.fa-mortar-board:before,
+.fa-graduation-cap:before {
+  content: "\f19d";
+}
+.fa-yahoo:before {
+  content: "\f19e";
+}
+.fa-google:before {
+  content: "\f1a0";
+}
+.fa-reddit:before {
+  content: "\f1a1";
+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+[dir="rtl"] #ipython_notebook {
+  float: right !important;
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+[dir="rtl"] #tabs li {
+  float: right;
+}
+ul#tabs {
+  margin-bottom: 4px;
+}
+[dir="rtl"] ul#tabs {
+  margin-right: 0px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+[dir="rtl"] .list_toolbar .tree-buttons {
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-right {
+  padding-top: 1px;
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-left {
+  float: right !important;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+[dir="rtl"] #tree-selector a {
+  float: right;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+[dir="rtl"] #new-menu {
+  text-align: right;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+[dir="rtl"] #running .col-sm-8 {
+  float: right !important;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  min-width: 0;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 21ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
+    display: none;
+  }
+  span.autosave_status {
+    font-size: x-small;
+  }
+}
+.toolbar {
+  padding: 0px;
+  margin-left: -5px;
+  margin-top: 2px;
+  margin-bottom: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+  width: auto;
+  vertical-align: middle;
+  margin-right: 2px;
+  margin-bottom: 0px;
+  display: inline;
+  font-size: 92%;
+  margin-left: 0.3em;
+  margin-right: 0.3em;
+  padding: 0px;
+  padding-top: 3px;
+}
+.toolbar .btn {
+  padding: 2px 8px;
+}
+.toolbar .btn-group {
+  margin-top: 0px;
+  margin-left: 5px;
+}
+#maintoolbar {
+  margin-bottom: -3px;
+  margin-top: -8px;
+  border: 0px;
+  min-height: 27px;
+  margin-left: 0px;
+  padding-top: 11px;
+  padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+  float: none;
+  vertical-align: middle;
+  text-align: right;
+  margin-left: 5px;
+  margin-right: 0px;
+  margin-top: 0px;
+}
+.select-xs {
+  height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+  background-color: #F37626;
+  color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-webkit-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-moz-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+@-webkit-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+  overflow: auto;
+  height: 200px;
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+  text-overflow: ellipsis;
+  overflow: hidden;
+  height: 80px;
+}
+.tooltipbuttons {
+  position: absolute;
+  padding-right: 15px;
+  top: 0px;
+  right: 0px;
+}
+.tooltiptext {
+  /*avoid the button to overlap on some docstring*/
+  padding-right: 30px;
+}
+.ipython_tooltip {
+  max-width: 700px;
+  /*fade-in animation when inserted*/
+  -webkit-animation: fadeOut 400ms;
+  -moz-animation: fadeOut 400ms;
+  animation: fadeOut 400ms;
+  -webkit-animation: fadeIn 400ms;
+  -moz-animation: fadeIn 400ms;
+  animation: fadeIn 400ms;
+  vertical-align: middle;
+  background-color: #f7f7f7;
+  overflow: visible;
+  border: #ababab 1px solid;
+  outline: none;
+  padding: 3px;
+  margin: 0px;
+  padding-left: 7px;
+  font-family: monospace;
+  min-height: 50px;
+  -moz-box-shadow: 0px 6px 10px -1px #adadad;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  border-radius: 2px;
+  position: absolute;
+  z-index: 1000;
+}
+.ipython_tooltip a {
+  float: right;
+}
+.ipython_tooltip .tooltiptext pre {
+  border: 0;
+  border-radius: 0;
+  font-size: 100%;
+  background-color: #f7f7f7;
+}
+.pretooltiparrow {
+  left: 0px;
+  margin: 0px;
+  top: -16px;
+  width: 40px;
+  height: 16px;
+  overflow: hidden;
+  position: absolute;
+}
+.pretooltiparrow:before {
+  background-color: #f7f7f7;
+  border: 1px #ababab solid;
+  z-index: 11;
+  content: "";
+  position: absolute;
+  left: 15px;
+  top: 10px;
+  width: 25px;
+  height: 25px;
+  -webkit-transform: rotate(45deg);
+  -moz-transform: rotate(45deg);
+  -ms-transform: rotate(45deg);
+  -o-transform: rotate(45deg);
+}
+ul.typeahead-list i {
+  margin-left: -10px;
+  width: 18px;
+}
+ul.typeahead-list {
+  max-height: 80vh;
+  overflow: auto;
+}
+ul.typeahead-list > li > a {
+  /** Firefox bug **/
+  /* see https://github.com/jupyter/notebook/issues/559 */
+  white-space: normal;
+}
+.cmd-palette .modal-body {
+  padding: 7px;
+}
+.cmd-palette form {
+  background: white;
+}
+.cmd-palette input {
+  outline: none;
+}
+.no-shortcut {
+  display: none;
+}
+.command-shortcut:before {
+  content: "(command)";
+  padding-right: 3px;
+  color: #777777;
+}
+.edit-shortcut:before {
+  content: "(edit)";
+  padding-right: 3px;
+  color: #777777;
+}
+#find-and-replace #replace-preview .match,
+#find-and-replace #replace-preview .insert {
+  background-color: #BBDEFB;
+  border-color: #90CAF9;
+  border-style: solid;
+  border-width: 1px;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .match {
+  background-color: #FFCDD2;
+  border-color: #EF9A9A;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .insert {
+  background-color: #C8E6C9;
+  border-color: #A5D6A7;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview {
+  max-height: 60vh;
+  overflow: auto;
+}
+#find-and-replace #replace-preview pre {
+  padding: 5px 10px;
+}
+.terminal-app {
+  background: #EEE;
+}
+.terminal-app #header {
+  background: #fff;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.terminal-app .terminal {
+  width: 100%;
+  float: left;
+  font-family: monospace;
+  color: white;
+  background: black;
+  padding: 0.4em;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+}
+.terminal-app .terminal,
+.terminal-app .terminal dummy-screen {
+  line-height: 1em;
+  font-size: 14px;
+}
+.terminal-app .terminal .xterm-rows {
+  padding: 10px;
+}
+.terminal-app .terminal-cursor {
+  color: black;
+  background: white;
+}
+.terminal-app #terminado-container {
+  margin-top: 20px;
+}
+/*# sourceMappingURL=style.min.css.map */
+    </style>
+<style type="text/css">
+    .highlight .hll { background-color: #ffffcc }
+.highlight  { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #7D9029 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #A0A000 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #0000FF } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+    </style>
+<style type="text/css">
+    
+/* Temporary definitions which will become obsolete with Notebook release 5.0 */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-bold { font-weight: bold; }
+
+    </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+  overflow: visible;
+  padding: 8px;
+}
+
+div#notebook {
+  overflow: visible;
+  border-top: none;
+}@media print {
+  div.cell {
+    display: block;
+    page-break-inside: avoid;
+  } 
+  div.output_wrapper { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+  div.output { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS_HTML"></script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    MathJax.Hub.Config({
+        tex2jax: {
+            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+            processEscapes: true,
+            processEnvironments: true
+        },
+        // Center justify equations in code and markdown cells. Elsewhere
+        // we use CSS to left justify single line equations in code cells.
+        displayAlign: 'center',
+        "HTML-CSS": {
+            styles: {'.MathJax_Display': {"margin": 0}},
+            linebreaks: { automatic: true }
+        }
+    });
+    </script>
+    <!-- End of mathjax configuration --></head>
+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="TV-Script-Generation">TV Script Generation<a class="anchor-link" href="#TV-Script-Generation">&#182;</a></h1><p>In this project, you'll generate your own <a href="https://en.wikipedia.org/wiki/The_Simpsons">Simpsons</a> TV scripts using RNNs.  You'll be using part of the <a href="https://www.kaggle.com/wcukierski/the-simpsons-by-the-data">Simpsons dataset</a> of scripts from 27 seasons.  The Neural Network you'll build will generate a new TV script for a scene at <a href="https://simpsonswiki.com/wiki/Moe&#39;s_Tavern">Moe's Tavern</a>.</p>
+<h2 id="Get-the-Data">Get the Data<a class="anchor-link" href="#Get-the-Data">&#182;</a></h2><p>The data is already provided for you.  You'll be using a subset of the original dataset.  It consists of only the scenes in Moe's Tavern.  This doesn't include other versions of the tavern, like "Moe's Cavern", "Flaming Moe's", "Uncle Moe's Family Feed-Bag", etc..</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+
+<span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;./data/simpsons/moes_tavern_lines.txt&#39;</span>
+<span class="n">text</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_data</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span>
+<span class="c1"># Ignore notice, since we don&#39;t use it for analysing the data</span>
+<span class="n">text</span> <span class="o">=</span> <span class="n">text</span><span class="p">[</span><span class="mi">81</span><span class="p">:]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Explore-the-Data">Explore the Data<a class="anchor-link" href="#Explore-the-Data">&#182;</a></h2><p>Play around with <code>view_sentence_range</code> to view different parts of the data.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">view_sentence_range</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Dataset Stats&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Roughly the number of unique words: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">({</span><span class="n">word</span><span class="p">:</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">text</span><span class="o">.</span><span class="n">split</span><span class="p">()})))</span>
+<span class="n">scenes</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n\n</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Number of scenes: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">scenes</span><span class="p">)))</span>
+<span class="n">sentence_count_scene</span> <span class="o">=</span> <span class="p">[</span><span class="n">scene</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">scene</span> <span class="ow">in</span> <span class="n">scenes</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Average number of sentences in each scene: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">sentence_count_scene</span><span class="p">)))</span>
+
+<span class="n">sentences</span> <span class="o">=</span> <span class="p">[</span><span class="n">sentence</span> <span class="k">for</span> <span class="n">scene</span> <span class="ow">in</span> <span class="n">scenes</span> <span class="k">for</span> <span class="n">sentence</span> <span class="ow">in</span> <span class="n">scene</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)]</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Number of lines: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sentences</span><span class="p">)))</span>
+<span class="n">word_count_sentence</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">sentence</span><span class="o">.</span><span class="n">split</span><span class="p">())</span> <span class="k">for</span> <span class="n">sentence</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Average number of words in each line: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">word_count_sentence</span><span class="p">)))</span>
+
+<span class="nb">print</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The sentences </span><span class="si">{}</span><span class="s1"> to </span><span class="si">{}</span><span class="s1">:&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">view_sentence_range</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)[</span><span class="n">view_sentence_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span><span class="n">view_sentence_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]]))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Dataset Stats
+Roughly the number of unique words: 11492
+Number of scenes: 262
+Average number of sentences in each scene: 15.248091603053435
+Number of lines: 4257
+Average number of words in each line: 11.50434578341555
+
+The sentences 0 to 10:
+Moe_Szyslak: (INTO PHONE) Moe&#39;s Tavern. Where the elite meet to drink.
+Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch.
+Moe_Szyslak: (INTO PHONE) Hold on, I&#39;ll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately?
+Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I&#39;m gonna catch you, and I&#39;m gonna carve my name on your back with an ice pick.
+Moe_Szyslak: What&#39;s the matter Homer? You&#39;re not your normal effervescent self.
+Homer_Simpson: I got my problems, Moe. Give me another one.
+Moe_Szyslak: Homer, hey, you should not drink to forget your problems.
+Barney_Gumble: Yeah, you should only drink to enhance your social skills.
+
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Implement-Preprocessing-Functions">Implement Preprocessing Functions<a class="anchor-link" href="#Implement-Preprocessing-Functions">&#182;</a></h2><p>The first thing to do to any dataset is preprocessing.  Implement the following preprocessing functions below:</p>
+<ul>
+<li>Lookup Table</li>
+<li>Tokenize Punctuation</li>
+</ul>
+<h3 id="Lookup-Table">Lookup Table<a class="anchor-link" href="#Lookup-Table">&#182;</a></h3><p>To create a word embedding, you first need to transform the words to ids.  In this function, create two dictionaries:</p>
+<ul>
+<li>Dictionary to go from the words to an id, we'll call <code>vocab_to_int</code></li>
+<li>Dictionary to go from the id to word, we'll call <code>int_to_vocab</code></li>
+</ul>
+<p>Return these dictionaries in the following tuple <code>(vocab_to_int, int_to_vocab)</code></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">Counter</span>
+
+<span class="k">def</span> <span class="nf">create_lookup_tables</span><span class="p">(</span><span class="n">text</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create lookup tables for vocabulary</span>
+<span class="sd">    :param text: The text of tv scripts split into words</span>
+<span class="sd">    :return: A tuple of dicts (vocab_to_int, int_to_vocab)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">word_counts</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
+    <span class="n">sorted_vocab</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">word_counts</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">word_counts</span><span class="o">.</span><span class="n">get</span><span class="p">,</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">int_to_vocab</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">word</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sorted_vocab</span><span class="p">)}</span>
+    <span class="n">vocab_to_int</span> <span class="o">=</span> <span class="p">{</span><span class="n">word</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">int_to_vocab</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+
+    <span class="k">return</span> <span class="n">vocab_to_int</span><span class="p">,</span> <span class="n">int_to_vocab</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_create_lookup_tables</span><span class="p">(</span><span class="n">create_lookup_tables</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Tokenize-Punctuation">Tokenize Punctuation<a class="anchor-link" href="#Tokenize-Punctuation">&#182;</a></h3><p>We'll be splitting the script into a word array using spaces as delimiters.  However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word "bye" and "bye!".</p>
+<p>Implement the function <code>token_lookup</code> to return a dict that will be used to tokenize symbols like "!" into "||Exclamation_Mark||".  Create a dictionary for the following symbols where the symbol is the key and value is the token:</p>
+<ul>
+<li>Period ( . )</li>
+<li>Comma ( , )</li>
+<li>Quotation Mark ( " )</li>
+<li>Semicolon ( ; )</li>
+<li>Exclamation mark ( ! )</li>
+<li>Question mark ( ? )</li>
+<li>Left Parentheses ( ( )</li>
+<li>Right Parentheses ( ) )</li>
+<li>Dash ( -- )</li>
+<li>Return ( \n )</li>
+</ul>
+<p>This dictionary will be used to token the symbols and add the delimiter (space) around it.  This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token "dash", try using something like "||dash||".</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">token_lookup</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generate a dict to turn punctuation into a token.</span>
+<span class="sd">    :return: Tokenize dictionary where the key is the punctuation and the value is the token</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="nb">dict</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="nb">dict</span><span class="p">[</span><span class="s1">&#39;.&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;||Period||&#39;</span>
+    <span class="nb">dict</span><span class="p">[</span><span class="s1">&#39;,&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;||Comma||&#39;</span>
+    <span class="nb">dict</span><span class="p">[</span><span class="s1">&#39;&quot;&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;||Quotation_Mark||&#39;</span>
+    <span class="nb">dict</span><span class="p">[</span><span class="s1">&#39;;&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;||Semicolon||&#39;</span>
+    <span class="nb">dict</span><span class="p">[</span><span class="s1">&#39;!&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;||Exclamation_Mark||&#39;</span>
+    <span class="nb">dict</span><span class="p">[</span><span class="s1">&#39;?&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;||Question_Mark||&#39;</span>
+    <span class="nb">dict</span><span class="p">[</span><span class="s1">&#39;(&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;||Left_Parentheses||&#39;</span>
+    <span class="nb">dict</span><span class="p">[</span><span class="s1">&#39;)&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;||Right_Parentheses||&#39;</span>
+    <span class="nb">dict</span><span class="p">[</span><span class="s1">&#39;--&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;||Dash||&#39;</span>
+    <span class="nb">dict</span><span class="p">[</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;||Return||&#39;</span>
+    
+    <span class="k">return</span> <span class="nb">dict</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_tokenize</span><span class="p">(</span><span class="n">token_lookup</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Preprocess-all-the-data-and-save-it">Preprocess all the data and save it<a class="anchor-link" href="#Preprocess-all-the-data-and-save-it">&#182;</a></h2><p>Running the code cell below will preprocess all the data and save it to file.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="c1"># Preprocess Training, Validation, and Testing Data</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">preprocess_and_save_data</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">token_lookup</span><span class="p">,</span> <span class="n">create_lookup_tables</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Check-Point">Check Point<a class="anchor-link" href="#Check-Point">&#182;</a></h1><p>This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+
+<span class="n">int_text</span><span class="p">,</span> <span class="n">vocab_to_int</span><span class="p">,</span> <span class="n">int_to_vocab</span><span class="p">,</span> <span class="n">token_dict</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Build-the-Neural-Network">Build the Neural Network<a class="anchor-link" href="#Build-the-Neural-Network">&#182;</a></h2><p>You'll build the components necessary to build a RNN by implementing the following functions below:</p>
+<ul>
+<li>get_inputs</li>
+<li>get_init_cell</li>
+<li>get_embed</li>
+<li>build_rnn</li>
+<li>build_nn</li>
+<li>get_batches</li>
+</ul>
+<h3 id="Check-the-Version-of-TensorFlow-and-Access-to-GPU">Check the Version of TensorFlow and Access to GPU<a class="anchor-link" href="#Check-the-Version-of-TensorFlow-and-Access-to-GPU">&#182;</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">distutils.version</span> <span class="k">import</span> <span class="n">LooseVersion</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+
+<span class="c1"># Check TensorFlow Version</span>
+<span class="k">assert</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="s1">&#39;1.0&#39;</span><span class="p">),</span> <span class="s1">&#39;Please use TensorFlow version 1.0 or newer&#39;</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;TensorFlow Version: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">))</span>
+
+<span class="c1"># Check for a GPU</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">gpu_device_name</span><span class="p">():</span>
+    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;No GPU found. Please use a GPU to train your neural network.&#39;</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Default GPU Device: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">gpu_device_name</span><span class="p">()))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>TensorFlow Version: 1.0.1
+Default GPU Device: /gpu:0
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Input">Input<a class="anchor-link" href="#Input">&#182;</a></h3><p>Implement the <code>get_inputs()</code> function to create TF Placeholders for the Neural Network.  It should create the following placeholders:</p>
+<ul>
+<li>Input text placeholder named "input" using the <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> <code>name</code> parameter.</li>
+<li>Targets placeholder</li>
+<li>Learning Rate placeholder</li>
+</ul>
+<p>Return the placeholders in the following tuple <code>(Input, Targets, LearningRate)</code></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_inputs</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create TF Placeholders for input, targets, and learning rate.</span>
+<span class="sd">    :return: Tuple (input, targets, learning rate)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">inputs</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;input&#39;</span><span class="p">)</span>
+    <span class="n">targets</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;targets&#39;</span><span class="p">)</span>
+    <span class="n">learning_rate</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;learning_rate&#39;</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">learning_rate</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_get_inputs</span><span class="p">(</span><span class="n">get_inputs</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Build-RNN-Cell-and-Initialize">Build RNN Cell and Initialize<a class="anchor-link" href="#Build-RNN-Cell-and-Initialize">&#182;</a></h3><p>Stack one or more <a href="https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell"><code>BasicLSTMCells</code></a> in a <a href="https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell"><code>MultiRNNCell</code></a>.</p>
+<ul>
+<li>The Rnn size should be set using <code>rnn_size</code></li>
+<li>Initalize Cell State using the MultiRNNCell's <a href="https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell#zero_state"><code>zero_state()</code></a> function<ul>
+<li>Apply the name "initial_state" to the initial state using <a href="https://www.tensorflow.org/api_docs/python/tf/identity"><code>tf.identity()</code></a></li>
+</ul>
+</li>
+</ul>
+<p>Return the cell and initial state in the following tuple <code>(Cell, InitialState)</code></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_init_cell</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">rnn_size</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create an RNN Cell and initialize it.</span>
+<span class="sd">    :param batch_size: Size of batches</span>
+<span class="sd">    :param rnn_size: Size of RNNs</span>
+<span class="sd">    :return: Tuple (cell, initialize state)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">num_layers</span> <span class="o">=</span> <span class="mi">2</span>
+    <span class="c1"># keep_prob = 0.7</span>
+    
+    <span class="n">lstm</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">BasicLSTMCell</span><span class="p">(</span><span class="n">rnn_size</span><span class="p">)</span>
+    <span class="c1"># drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)</span>
+    <span class="n">cell</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">MultiRNNCell</span><span class="p">([</span><span class="n">lstm</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_layers</span><span class="p">)</span>
+    <span class="n">initial_state</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">cell</span><span class="o">.</span><span class="n">zero_state</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;initial_state&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">cell</span><span class="p">,</span> <span class="n">initial_state</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_get_init_cell</span><span class="p">(</span><span class="n">get_init_cell</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Word-Embedding">Word Embedding<a class="anchor-link" href="#Word-Embedding">&#182;</a></h3><p>Apply embedding to <code>input_data</code> using TensorFlow.  Return the embedded sequence.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_embed</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create embedding for &lt;input_data&gt;.</span>
+<span class="sd">    :param input_data: TF placeholder for text input.</span>
+<span class="sd">    :param vocab_size: Number of words in vocabulary.</span>
+<span class="sd">    :param embed_dim: Number of embedding dimensions</span>
+<span class="sd">    :return: Embedded input.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">embedding</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">((</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="n">embed</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">embedding_lookup</span><span class="p">(</span><span class="n">embedding</span><span class="p">,</span> <span class="n">input_data</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">embed</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_get_embed</span><span class="p">(</span><span class="n">get_embed</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Build-RNN">Build RNN<a class="anchor-link" href="#Build-RNN">&#182;</a></h3><p>You created a RNN Cell in the <code>get_init_cell()</code> function.  Time to use the cell to create a RNN.</p>
+<ul>
+<li>Build the RNN using the <a href="https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn"><code>tf.nn.dynamic_rnn()</code></a><ul>
+<li>Apply the name "final_state" to the final state using <a href="https://www.tensorflow.org/api_docs/python/tf/identity"><code>tf.identity()</code></a></li>
+</ul>
+</li>
+</ul>
+<p>Return the outputs and final_state state in the following tuple <code>(Outputs, FinalState)</code></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">build_rnn</span><span class="p">(</span><span class="n">cell</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a RNN using a RNN Cell</span>
+<span class="sd">    :param cell: RNN Cell</span>
+<span class="sd">    :param inputs: Input text data</span>
+<span class="sd">    :return: Tuple (Outputs, Final State)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">outputs</span><span class="p">,</span> <span class="n">final_state</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dynamic_rnn</span><span class="p">(</span><span class="n">cell</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+    <span class="n">final_state</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">final_state</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;final_state&#39;</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">final_state</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_build_rnn</span><span class="p">(</span><span class="n">build_rnn</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Build-the-Neural-Network">Build the Neural Network<a class="anchor-link" href="#Build-the-Neural-Network">&#182;</a></h3><p>Apply the functions you implemented above to:</p>
+<ul>
+<li>Apply embedding to <code>input_data</code> using your <code>get_embed(input_data, vocab_size, embed_dim)</code> function.</li>
+<li>Build RNN using <code>cell</code> and your <code>build_rnn(cell, inputs)</code> function.</li>
+<li>Apply a fully connected layer with a linear activation and <code>vocab_size</code> as the number of outputs.</li>
+</ul>
+<p>Return the logits and final state in the following tuple (Logits, FinalState)</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">build_nn</span><span class="p">(</span><span class="n">cell</span><span class="p">,</span> <span class="n">rnn_size</span><span class="p">,</span> <span class="n">input_data</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Build part of the neural network</span>
+<span class="sd">    :param cell: RNN cell</span>
+<span class="sd">    :param rnn_size: Size of rnns</span>
+<span class="sd">    :param input_data: Input data</span>
+<span class="sd">    :param vocab_size: Vocabulary size</span>
+<span class="sd">    :param embed_dim: Number of embedding dimensions</span>
+<span class="sd">    :return: Tuple (Logits, FinalState)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">embed_data</span> <span class="o">=</span> <span class="n">get_embed</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">)</span>
+    <span class="n">outputs</span><span class="p">,</span> <span class="n">final_state</span> <span class="o">=</span> <span class="n">build_rnn</span><span class="p">(</span><span class="n">cell</span><span class="p">,</span> <span class="n">embed_data</span><span class="p">)</span>
+    <span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fully_connected</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">activation_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">logits</span><span class="p">,</span> <span class="n">final_state</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_build_nn</span><span class="p">(</span><span class="n">build_nn</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Batches">Batches<a class="anchor-link" href="#Batches">&#182;</a></h3><p>Implement <code>get_batches</code> to create batches of input and targets using <code>int_text</code>.  The batches should be a Numpy array with the shape <code>(number of batches, 2, batch size, sequence length)</code>. Each batch contains two elements:</p>
+<ul>
+<li>The first element is a single batch of <strong>input</strong> with the shape <code>[batch size, sequence length]</code></li>
+<li>The second element is a single batch of <strong>targets</strong> with the shape <code>[batch size, sequence length]</code></li>
+</ul>
+<p>If you can't fill the last batch with enough data, drop the last batch.</p>
+<p>For exmple, <code>get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3)</code> would return a Numpy array of the following:</p>
+
+<pre><code>[
+  # First Batch
+  [
+    # Batch of Input
+    [[ 1  2  3], [ 7  8  9]],
+    # Batch of targets
+    [[ 2  3  4], [ 8  9 10]]
+  ],
+
+  # Second Batch
+  [
+    # Batch of Input
+    [[ 4  5  6], [10 11 12]],
+    # Batch of targets
+    [[ 5  6  7], [11 12 13]]
+  ]
+]</code></pre>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[13]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_batches</span><span class="p">(</span><span class="n">int_text</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return batches of input and target</span>
+<span class="sd">    :param int_text: Text with the words replaced by their ids</span>
+<span class="sd">    :param batch_size: The size of batch</span>
+<span class="sd">    :param seq_length: The length of sequence</span>
+<span class="sd">    :return: Batches as a Numpy array</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">n_batches</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">int_text</span><span class="p">)</span> <span class="o">//</span> <span class="p">(</span><span class="n">batch_size</span> <span class="o">*</span> <span class="n">seq_length</span><span class="p">)</span>
+    
+    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">int_text</span><span class="p">[:</span><span class="n">n_batches</span> <span class="o">*</span> <span class="n">batch_size</span> <span class="o">*</span> <span class="n">seq_length</span><span class="p">])</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">int_text</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="n">n_batches</span> <span class="o">*</span> <span class="n">batch_size</span> <span class="o">*</span> <span class="n">seq_length</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="n">y</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">x_batches</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">n_batches</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">y_batches</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">n_batches</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">x_batches</span><span class="p">,</span> <span class="n">y_batches</span><span class="p">)))</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_get_batches</span><span class="p">(</span><span class="n">get_batches</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Neural-Network-Training">Neural Network Training<a class="anchor-link" href="#Neural-Network-Training">&#182;</a></h2><h3 id="Hyperparameters">Hyperparameters<a class="anchor-link" href="#Hyperparameters">&#182;</a></h3><p>Tune the following parameters:</p>
+<ul>
+<li>Set <code>num_epochs</code> to the number of epochs.</li>
+<li>Set <code>batch_size</code> to the batch size.</li>
+<li>Set <code>rnn_size</code> to the size of the RNNs.</li>
+<li>Set <code>embed_dim</code> to the size of the embedding.</li>
+<li>Set <code>seq_length</code> to the length of sequence.</li>
+<li>Set <code>learning_rate</code> to the learning rate.</li>
+<li>Set <code>show_every_n_batches</code> to the number of batches the neural network should print progress.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[14]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Number of Epochs</span>
+<span class="n">num_epochs</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="c1"># Batch Size</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">128</span>
+<span class="c1"># RNN Size</span>
+<span class="n">rnn_size</span> <span class="o">=</span> <span class="mi">256</span>
+<span class="c1"># Embedding Dimension Size</span>
+<span class="n">embed_dim</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="c1"># Sequence Length</span>
+<span class="n">seq_length</span> <span class="o">=</span> <span class="mi">32</span>
+<span class="c1"># Learning Rate</span>
+<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.01</span>
+<span class="c1"># Show stats for every n number of batches</span>
+<span class="n">show_every_n_batches</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">save_dir</span> <span class="o">=</span> <span class="s1">&#39;./save&#39;</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Build-the-Graph">Build the Graph<a class="anchor-link" href="#Build-the-Graph">&#182;</a></h3><p>Build the graph using the neural network you implemented.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[15]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">tensorflow.contrib</span> <span class="k">import</span> <span class="n">seq2seq</span>
+
+<span class="n">train_graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
+<span class="k">with</span> <span class="n">train_graph</span><span class="o">.</span><span class="n">as_default</span><span class="p">():</span>
+    <span class="n">vocab_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">int_to_vocab</span><span class="p">)</span>
+    <span class="n">input_text</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="n">get_inputs</span><span class="p">()</span>
+    <span class="n">input_data_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">input_text</span><span class="p">)</span>
+    <span class="n">cell</span><span class="p">,</span> <span class="n">initial_state</span> <span class="o">=</span> <span class="n">get_init_cell</span><span class="p">(</span><span class="n">input_data_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rnn_size</span><span class="p">)</span>
+    <span class="n">logits</span><span class="p">,</span> <span class="n">final_state</span> <span class="o">=</span> <span class="n">build_nn</span><span class="p">(</span><span class="n">cell</span><span class="p">,</span> <span class="n">rnn_size</span><span class="p">,</span> <span class="n">input_text</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">)</span>
+
+    <span class="c1"># Probabilities for generating words</span>
+    <span class="n">probs</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;probs&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Loss function</span>
+    <span class="n">cost</span> <span class="o">=</span> <span class="n">seq2seq</span><span class="o">.</span><span class="n">sequence_loss</span><span class="p">(</span>
+        <span class="n">logits</span><span class="p">,</span>
+        <span class="n">targets</span><span class="p">,</span>
+        <span class="n">tf</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="n">input_data_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">input_data_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]))</span>
+
+    <span class="c1"># Optimizer</span>
+    <span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span>
+
+    <span class="c1"># Gradient Clipping</span>
+    <span class="n">gradients</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">compute_gradients</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
+    <span class="n">capped_gradients</span> <span class="o">=</span> <span class="p">[(</span><span class="n">tf</span><span class="o">.</span><span class="n">clip_by_value</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">),</span> <span class="n">var</span><span class="p">)</span> <span class="k">for</span> <span class="n">grad</span><span class="p">,</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">gradients</span> <span class="k">if</span> <span class="n">grad</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span>
+    <span class="n">train_op</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">apply_gradients</span><span class="p">(</span><span class="n">capped_gradients</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Train">Train<a class="anchor-link" href="#Train">&#182;</a></h2><p>Train the neural network on the preprocessed data.  If you have a hard time getting a good loss, check the <a href="https://discussions.udacity.com/">forms</a> to see if anyone is having the same problem.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[16]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">batches</span> <span class="o">=</span> <span class="n">get_batches</span><span class="p">(</span><span class="n">int_text</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">train_graph</span><span class="p">)</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
+
+    <span class="k">for</span> <span class="n">epoch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_epochs</span><span class="p">):</span>
+        <span class="n">state</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">initial_state</span><span class="p">,</span> <span class="p">{</span><span class="n">input_text</span><span class="p">:</span> <span class="n">batches</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]})</span>
+
+        <span class="k">for</span> <span class="n">batch_i</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">batches</span><span class="p">):</span>
+            <span class="n">feed</span> <span class="o">=</span> <span class="p">{</span>
+                <span class="n">input_text</span><span class="p">:</span> <span class="n">x</span><span class="p">,</span>
+                <span class="n">targets</span><span class="p">:</span> <span class="n">y</span><span class="p">,</span>
+                <span class="n">initial_state</span><span class="p">:</span> <span class="n">state</span><span class="p">,</span>
+                <span class="n">lr</span><span class="p">:</span> <span class="n">learning_rate</span><span class="p">}</span>
+            <span class="n">train_loss</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">cost</span><span class="p">,</span> <span class="n">final_state</span><span class="p">,</span> <span class="n">train_op</span><span class="p">],</span> <span class="n">feed</span><span class="p">)</span>
+
+            <span class="c1"># Show every &lt;show_every_n_batches&gt; batches</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">epoch_i</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">batches</span><span class="p">)</span> <span class="o">+</span> <span class="n">batch_i</span><span class="p">)</span> <span class="o">%</span> <span class="n">show_every_n_batches</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;3}</span><span class="s1"> Batch </span><span class="si">{:&gt;4}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">   train_loss = </span><span class="si">{:.3f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">epoch_i</span><span class="p">,</span>
+                    <span class="n">batch_i</span><span class="p">,</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="n">batches</span><span class="p">),</span>
+                    <span class="n">train_loss</span><span class="p">))</span>
+
+    <span class="c1"># Save Model</span>
+    <span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
+    <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_dir</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Model Trained and Saved&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch   0 Batch    0/16   train_loss = 8.822
+Epoch   6 Batch    4/16   train_loss = 5.457
+Epoch  12 Batch    8/16   train_loss = 4.972
+Epoch  18 Batch   12/16   train_loss = 4.116
+Epoch  25 Batch    0/16   train_loss = 3.257
+Epoch  31 Batch    4/16   train_loss = 2.535
+Epoch  37 Batch    8/16   train_loss = 2.091
+Epoch  43 Batch   12/16   train_loss = 1.477
+Epoch  50 Batch    0/16   train_loss = 1.024
+Epoch  56 Batch    4/16   train_loss = 0.852
+Epoch  62 Batch    8/16   train_loss = 0.566
+Epoch  68 Batch   12/16   train_loss = 0.429
+Epoch  75 Batch    0/16   train_loss = 0.395
+Epoch  81 Batch    4/16   train_loss = 0.316
+Epoch  87 Batch    8/16   train_loss = 0.201
+Epoch  93 Batch   12/16   train_loss = 0.206
+Model Trained and Saved
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Save-Parameters">Save Parameters<a class="anchor-link" href="#Save-Parameters">&#182;</a></h2><p>Save <code>seq_length</code> and <code>save_dir</code> for generating a new TV script.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[17]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="c1"># Save parameters for checkpoint</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">save_params</span><span class="p">((</span><span class="n">seq_length</span><span class="p">,</span> <span class="n">save_dir</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Checkpoint">Checkpoint<a class="anchor-link" href="#Checkpoint">&#182;</a></h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[18]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">vocab_to_int</span><span class="p">,</span> <span class="n">int_to_vocab</span><span class="p">,</span> <span class="n">token_dict</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess</span><span class="p">()</span>
+<span class="n">seq_length</span><span class="p">,</span> <span class="n">load_dir</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_params</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Implement-Generate-Functions">Implement Generate Functions<a class="anchor-link" href="#Implement-Generate-Functions">&#182;</a></h2><h3 id="Get-Tensors">Get Tensors<a class="anchor-link" href="#Get-Tensors">&#182;</a></h3><p>Get tensors from <code>loaded_graph</code> using the function <a href="https://www.tensorflow.org/api_docs/python/tf/Graph#get_tensor_by_name"><code>get_tensor_by_name()</code></a>.  Get the tensors using the following names:</p>
+<ul>
+<li>"input:0"</li>
+<li>"initial_state:0"</li>
+<li>"final_state:0"</li>
+<li>"probs:0"</li>
+</ul>
+<p>Return the tensors in the following tuple <code>(InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)</code></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[19]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_tensors</span><span class="p">(</span><span class="n">loaded_graph</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get input, initial state, final state, and probabilities tensor from &lt;loaded_graph&gt;</span>
+<span class="sd">    :param loaded_graph: TensorFlow graph loaded from file</span>
+<span class="sd">    :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">InputTensor</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;input:0&#39;</span><span class="p">)</span>
+    <span class="n">InitialStateTensor</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;initial_state:0&#39;</span><span class="p">)</span>
+    <span class="n">FinalStateTensor</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;final_state:0&#39;</span><span class="p">)</span>
+    <span class="n">ProbsTensor</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;probs:0&#39;</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">InputTensor</span><span class="p">,</span> <span class="n">InitialStateTensor</span><span class="p">,</span> <span class="n">FinalStateTensor</span><span class="p">,</span> <span class="n">ProbsTensor</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_get_tensors</span><span class="p">(</span><span class="n">get_tensors</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Choose-Word">Choose Word<a class="anchor-link" href="#Choose-Word">&#182;</a></h3><p>Implement the <code>pick_word()</code> function to select the next word using <code>probabilities</code>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[20]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">pick_word</span><span class="p">(</span><span class="n">probabilities</span><span class="p">,</span> <span class="n">int_to_vocab</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Pick the next word in the generated text</span>
+<span class="sd">    :param probabilities: Probabilites of the next word</span>
+<span class="sd">    :param int_to_vocab: Dictionary of word ids as the keys and words as the values</span>
+<span class="sd">    :return: String of the predicted word</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">return</span> <span class="n">int_to_vocab</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">probabilities</span><span class="p">))]</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_pick_word</span><span class="p">(</span><span class="n">pick_word</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Generate-TV-Script">Generate TV Script<a class="anchor-link" href="#Generate-TV-Script">&#182;</a></h2><p>This will generate the TV script for you.  Set <code>gen_length</code> to the length of TV script you want to generate.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[21]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">gen_length</span> <span class="o">=</span> <span class="mi">800</span>
+<span class="c1"># homer_simpson, moe_szyslak, or Barney_Gumble</span>
+<span class="n">prime_word</span> <span class="o">=</span> <span class="s1">&#39;moe_szyslak&#39;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">loaded_graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">loaded_graph</span><span class="p">)</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+    <span class="c1"># Load saved model</span>
+    <span class="n">loader</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">import_meta_graph</span><span class="p">(</span><span class="n">load_dir</span> <span class="o">+</span> <span class="s1">&#39;.meta&#39;</span><span class="p">)</span>
+    <span class="n">loader</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">load_dir</span><span class="p">)</span>
+
+    <span class="c1"># Get Tensors from loaded model</span>
+    <span class="n">input_text</span><span class="p">,</span> <span class="n">initial_state</span><span class="p">,</span> <span class="n">final_state</span><span class="p">,</span> <span class="n">probs</span> <span class="o">=</span> <span class="n">get_tensors</span><span class="p">(</span><span class="n">loaded_graph</span><span class="p">)</span>
+
+    <span class="c1"># Sentences generation setup</span>
+    <span class="n">gen_sentences</span> <span class="o">=</span> <span class="p">[</span><span class="n">prime_word</span> <span class="o">+</span> <span class="s1">&#39;:&#39;</span><span class="p">]</span>
+    <span class="n">prev_state</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">initial_state</span><span class="p">,</span> <span class="p">{</span><span class="n">input_text</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">]])})</span>
+
+    <span class="c1"># Generate sentences</span>
+    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">gen_length</span><span class="p">):</span>
+        <span class="c1"># Dynamic Input</span>
+        <span class="n">dyn_input</span> <span class="o">=</span> <span class="p">[[</span><span class="n">vocab_to_int</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">gen_sentences</span><span class="p">[</span><span class="o">-</span><span class="n">seq_length</span><span class="p">:]]]</span>
+        <span class="n">dyn_seq_length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dyn_input</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="c1"># Get Prediction</span>
+        <span class="n">probabilities</span><span class="p">,</span> <span class="n">prev_state</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+            <span class="p">[</span><span class="n">probs</span><span class="p">,</span> <span class="n">final_state</span><span class="p">],</span>
+            <span class="p">{</span><span class="n">input_text</span><span class="p">:</span> <span class="n">dyn_input</span><span class="p">,</span> <span class="n">initial_state</span><span class="p">:</span> <span class="n">prev_state</span><span class="p">})</span>
+        
+        <span class="n">pred_word</span> <span class="o">=</span> <span class="n">pick_word</span><span class="p">(</span><span class="n">probabilities</span><span class="p">[</span><span class="n">dyn_seq_length</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">int_to_vocab</span><span class="p">)</span>
+
+        <span class="n">gen_sentences</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pred_word</span><span class="p">)</span>
+    
+    <span class="c1"># Remove tokens</span>
+    <span class="n">tv_script</span> <span class="o">=</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">gen_sentences</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">token_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">ending</span> <span class="o">=</span> <span class="s1">&#39; &#39;</span> <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="s1">&#39;(&#39;</span><span class="p">,</span> <span class="s1">&#39;&quot;&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="s1">&#39;&#39;</span>
+        <span class="n">tv_script</span> <span class="o">=</span> <span class="n">tv_script</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">token</span><span class="o">.</span><span class="n">lower</span><span class="p">(),</span> <span class="n">key</span><span class="p">)</span>
+    <span class="n">tv_script</span> <span class="o">=</span> <span class="n">tv_script</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> &#39;</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="n">tv_script</span> <span class="o">=</span> <span class="n">tv_script</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;( &#39;</span><span class="p">,</span> <span class="s1">&#39;(&#39;</span><span class="p">)</span>
+        
+    <span class="nb">print</span><span class="p">(</span><span class="n">tv_script</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>moe_szyslak:(eyeing homer&#39;s ass) oh yeah, that would look so good on me.
+
+
+moe_szyslak:(hostile) hey homer, i told you not to come out.
+
+
+lisa_simpson: your love of you, stupid more funny.
+moe_szyslak: i gotta join a girl for moe&#39;s bar it&#39;ll let him.
+moe_szyslak: so, uh, you got it, shouldn&#39;t it be. i&#39;m talkin&#39; malfeasance here.
+bart_simpson: sir, well, thank you, i&#39;ve never seen this again.
+homer_simpson:(chuckles) oh yeah.
+bart_simpson:(finishing in) you know about that!
+duffman:(small frustrated noise) that&#39;s good...(then) just said that was i could do it?
+homer_simpson: i don&#39;t pay you to have. i&#39;m talkin&#39; about you.
+moe_szyslak: okay, well, you&#39;re the love of jeff!
+lenny_leonard:(reading)&#34; i don&#39;t know.
+moe_szyslak: sorry, it&#39;s probably gonna do worse...(sighs)
+moe_szyslak: that&#39;s the big day of innocent.(sadly) are you doing?
+moe_szyslak: and now, homer.
+lisa_simpson: my barney is, moe.
+moe_szyslak: yeah. but who was great. now, i can&#39;t believe marge comes down.
+moe_szyslak:(tough) homer, i can&#39;t talk on the future.
+homer_simpson: all right! the fire twins!
+homer_simpson:(moans) yeah, that. it&#39;s my love!
+homer_simpson:(ominous) it&#39;s it.
+moe_szyslak: my sweet friend? he makes &#39;em nice in the world here.
+
+
+moe_szyslak: i got a big shot who again... that&#39;s the grammy judges.(laughs) all right. you&#39;re a free beer.
+lenny_leonard: are you home? i&#39;m just a guy, that&#39;s a yes!
+homer_simpson:(chuckles)
+moe_szyslak: oh my god...
+thanks for that...
+moe_szyslak: oh, no.(as beer) it&#39;s all day.
+lenny_leonard: hey, moe! i wrote a little girl!
+duffman:(laughs) we gotta get you a job?
+homer_simpson: wow.(laughs)
+homer_simpson:(grunt) hey!(laughs)
+marge_simpson:(party laugh) oh, no!
+homer_simpson:(chuckles) hey, maggie, i&#39;m not, barney.
+homer_simpson: guys, i love you, barney!
+homer_simpson:(flatly) yeah.
+marge_simpson:(chanting) are you done to the little girl you&#39;re disappointing.
+lisa_simpson:(worried) hey, you know what about you, huh?!
+moe_szyslak: you will have to go home from a way to be nine of a jar.
+carl_carlson: and the second way, a lot of people bad-mouth you and me...
+homer_simpson:(to moe) wow, that&#39;s a coaster.
+homer_simpson: hey, i&#39;ve got a lot to mull.
+moe_szyslak: hey, hey, hey, hey! hey ain&#39;t work?
+moe_szyslak: that&#39;s the beauty part. you need a pal. i gotta go.
+
+
+moe_szyslak:(laughs) the cop,(points off pain) lisa_simpson: moe, moe. but then you can say the most poor bucks-- it&#39;s?
+moe_szyslak: it&#39;s true, a&#34; forget-me-shot in my old man.
+moe_szyslak: okay, this is like a!
+homer_simpson:(amazed) him...
+moe_szyslak:(to barney) i think when i&#39;ve gonna get some professional help. no one could you no real good.
+moe_szyslak: ah, this is a pal. gotta be how how much that how you know a&#34; business problems...
+
+
+moe_szyslak: he&#39;s the greatest gift of all, so a little bit. it was all the other day / a problem. that&#39;s sweet.
+moe_szyslak:(ominous) one, for you...
+homer_simpson:(to homer) come on, sweet...
+moe_szyslak: oh, boy...
+fat_tony:(sings) full-blooded...
+barney_gumble:(realizing) yeah, i was sure the little girl.
+bart_simpson: oh, you know, that was philip glass.
+david_byrne: yeah, have you go, ain&#39;t you little worried.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="The-TV-Script-is-Nonsensical">The TV Script is Nonsensical<a class="anchor-link" href="#The-TV-Script-is-Nonsensical">&#182;</a></h1><p>It's ok if the TV script doesn't make any sense.  We trained on less than a megabyte of text.  In order to get good results, you'll have to use a smaller vocabulary or get more data.  Luckly there's more data!  As we mentioned in the begging of this project, this is a subset of <a href="https://www.kaggle.com/wcukierski/the-simpsons-by-the-data">another dataset</a>.  We didn't have you train on all the data, because that would take too long.  However, you are free to train your neural network on all the data.  After you complete the project, of course.</p>
+<h1 id="Submitting-This-Project">Submitting This Project<a class="anchor-link" href="#Submitting-This-Project">&#182;</a></h1><p>When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_tv_script_generation.ipynb" and save it as a HTML file under "File" -&gt; "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.</p>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+
+ 
+
+
+</html>
diff --git a/tv-script-generation/dlnd_tv_script_generation.ipynb b/tv-script-generation/dlnd_tv_script_generation.ipynb
new file mode 100644
index 0000000..ce2a97b
--- /dev/null
+++ b/tv-script-generation/dlnd_tv_script_generation.ipynb
@@ -0,0 +1,1084 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TV Script Generation\n",
+    "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs.  You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons.  The Neural Network you'll build will generate a new TV script for a scene at [Moe's Tavern](https://simpsonswiki.com/wiki/Moe's_Tavern).\n",
+    "## Get the Data\n",
+    "The data is already provided for you.  You'll be using a subset of the original dataset.  It consists of only the scenes in Moe's Tavern.  This doesn't include other versions of the tavern, like \"Moe's Cavern\", \"Flaming Moe's\", \"Uncle Moe's Family Feed-Bag\", etc.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import helper\n",
+    "\n",
+    "data_dir = './data/simpsons/moes_tavern_lines.txt'\n",
+    "text = helper.load_data(data_dir)\n",
+    "# Ignore notice, since we don't use it for analysing the data\n",
+    "text = text[81:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Data\n",
+    "Play around with `view_sentence_range` to view different parts of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset Stats\n",
+      "Roughly the number of unique words: 11492\n",
+      "Number of scenes: 262\n",
+      "Average number of sentences in each scene: 15.248091603053435\n",
+      "Number of lines: 4257\n",
+      "Average number of words in each line: 11.50434578341555\n",
+      "\n",
+      "The sentences 0 to 10:\n",
+      "Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink.\n",
+      "Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch.\n",
+      "Moe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately?\n",
+      "Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick.\n",
+      "Moe_Szyslak: What's the matter Homer? You're not your normal effervescent self.\n",
+      "Homer_Simpson: I got my problems, Moe. Give me another one.\n",
+      "Moe_Szyslak: Homer, hey, you should not drink to forget your problems.\n",
+      "Barney_Gumble: Yeah, you should only drink to enhance your social skills.\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "view_sentence_range = (0, 10)\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import numpy as np\n",
+    "\n",
+    "print('Dataset Stats')\n",
+    "print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))\n",
+    "scenes = text.split('\\n\\n')\n",
+    "print('Number of scenes: {}'.format(len(scenes)))\n",
+    "sentence_count_scene = [scene.count('\\n') for scene in scenes]\n",
+    "print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))\n",
+    "\n",
+    "sentences = [sentence for scene in scenes for sentence in scene.split('\\n')]\n",
+    "print('Number of lines: {}'.format(len(sentences)))\n",
+    "word_count_sentence = [len(sentence.split()) for sentence in sentences]\n",
+    "print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))\n",
+    "\n",
+    "print()\n",
+    "print('The sentences {} to {}:'.format(*view_sentence_range))\n",
+    "print('\\n'.join(text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implement Preprocessing Functions\n",
+    "The first thing to do to any dataset is preprocessing.  Implement the following preprocessing functions below:\n",
+    "- Lookup Table\n",
+    "- Tokenize Punctuation\n",
+    "\n",
+    "### Lookup Table\n",
+    "To create a word embedding, you first need to transform the words to ids.  In this function, create two dictionaries:\n",
+    "- Dictionary to go from the words to an id, we'll call `vocab_to_int`\n",
+    "- Dictionary to go from the id to word, we'll call `int_to_vocab`\n",
+    "\n",
+    "Return these dictionaries in the following tuple `(vocab_to_int, int_to_vocab)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import problem_unittests as tests\n",
+    "from collections import Counter\n",
+    "\n",
+    "def create_lookup_tables(text):\n",
+    "    \"\"\"\n",
+    "    Create lookup tables for vocabulary\n",
+    "    :param text: The text of tv scripts split into words\n",
+    "    :return: A tuple of dicts (vocab_to_int, int_to_vocab)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    word_counts = Counter(text)\n",
+    "    sorted_vocab = sorted(word_counts, key=word_counts.get, reverse=True)\n",
+    "    int_to_vocab = {i: word for i, word in enumerate(sorted_vocab)}\n",
+    "    vocab_to_int = {word: i for i, word in int_to_vocab.items()}\n",
+    "\n",
+    "    return vocab_to_int, int_to_vocab\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_create_lookup_tables(create_lookup_tables)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tokenize Punctuation\n",
+    "We'll be splitting the script into a word array using spaces as delimiters.  However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word \"bye\" and \"bye!\".\n",
+    "\n",
+    "Implement the function `token_lookup` to return a dict that will be used to tokenize symbols like \"!\" into \"||Exclamation_Mark||\".  Create a dictionary for the following symbols where the symbol is the key and value is the token:\n",
+    "- Period ( . )\n",
+    "- Comma ( , )\n",
+    "- Quotation Mark ( \" )\n",
+    "- Semicolon ( ; )\n",
+    "- Exclamation mark ( ! )\n",
+    "- Question mark ( ? )\n",
+    "- Left Parentheses ( ( )\n",
+    "- Right Parentheses ( ) )\n",
+    "- Dash ( -- )\n",
+    "- Return ( \\n )\n",
+    "\n",
+    "This dictionary will be used to token the symbols and add the delimiter (space) around it.  This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token \"dash\", try using something like \"||dash||\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def token_lookup():\n",
+    "    \"\"\"\n",
+    "    Generate a dict to turn punctuation into a token.\n",
+    "    :return: Tokenize dictionary where the key is the punctuation and the value is the token\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    dict = {}\n",
+    "    dict['.'] = '||Period||'\n",
+    "    dict[','] = '||Comma||'\n",
+    "    dict['\"'] = '||Quotation_Mark||'\n",
+    "    dict[';'] = '||Semicolon||'\n",
+    "    dict['!'] = '||Exclamation_Mark||'\n",
+    "    dict['?'] = '||Question_Mark||'\n",
+    "    dict['('] = '||Left_Parentheses||'\n",
+    "    dict[')'] = '||Right_Parentheses||'\n",
+    "    dict['--'] = '||Dash||'\n",
+    "    dict['\\n'] = '||Return||'\n",
+    "    \n",
+    "    return dict\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_tokenize(token_lookup)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocess all the data and save it\n",
+    "Running the code cell below will preprocess all the data and save it to file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "# Preprocess Training, Validation, and Testing Data\n",
+    "helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Check Point\n",
+    "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import helper\n",
+    "import numpy as np\n",
+    "import problem_unittests as tests\n",
+    "\n",
+    "int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build the Neural Network\n",
+    "You'll build the components necessary to build a RNN by implementing the following functions below:\n",
+    "- get_inputs\n",
+    "- get_init_cell\n",
+    "- get_embed\n",
+    "- build_rnn\n",
+    "- build_nn\n",
+    "- get_batches\n",
+    "\n",
+    "### Check the Version of TensorFlow and Access to GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TensorFlow Version: 1.0.1\n",
+      "Default GPU Device: /gpu:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "from distutils.version import LooseVersion\n",
+    "import warnings\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Check TensorFlow Version\n",
+    "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'\n",
+    "print('TensorFlow Version: {}'.format(tf.__version__))\n",
+    "\n",
+    "# Check for a GPU\n",
+    "if not tf.test.gpu_device_name():\n",
+    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
+    "else:\n",
+    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Input\n",
+    "Implement the `get_inputs()` function to create TF Placeholders for the Neural Network.  It should create the following placeholders:\n",
+    "- Input text placeholder named \"input\" using the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) `name` parameter.\n",
+    "- Targets placeholder\n",
+    "- Learning Rate placeholder\n",
+    "\n",
+    "Return the placeholders in the following tuple `(Input, Targets, LearningRate)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_inputs():\n",
+    "    \"\"\"\n",
+    "    Create TF Placeholders for input, targets, and learning rate.\n",
+    "    :return: Tuple (input, targets, learning rate)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    inputs = tf.placeholder(tf.int32, [None, None], name='input')\n",
+    "    targets = tf.placeholder(tf.int32, [None, None], name='targets')\n",
+    "    learning_rate = tf.placeholder(tf.float32, name='learning_rate')\n",
+    "    \n",
+    "    return inputs, targets, learning_rate\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_get_inputs(get_inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Build RNN Cell and Initialize\n",
+    "Stack one or more [`BasicLSTMCells`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell) in a [`MultiRNNCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell).\n",
+    "- The Rnn size should be set using `rnn_size`\n",
+    "- Initalize Cell State using the MultiRNNCell's [`zero_state()`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell#zero_state) function\n",
+    "    - Apply the name \"initial_state\" to the initial state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n",
+    "\n",
+    "Return the cell and initial state in the following tuple `(Cell, InitialState)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_init_cell(batch_size, rnn_size):\n",
+    "    \"\"\"\n",
+    "    Create an RNN Cell and initialize it.\n",
+    "    :param batch_size: Size of batches\n",
+    "    :param rnn_size: Size of RNNs\n",
+    "    :return: Tuple (cell, initialize state)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    num_layers = 2\n",
+    "    # keep_prob = 0.7\n",
+    "    \n",
+    "    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n",
+    "    # drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n",
+    "    cell = tf.contrib.rnn.MultiRNNCell([lstm] * num_layers)\n",
+    "    initial_state = tf.identity(cell.zero_state(batch_size, tf.float32), name='initial_state')\n",
+    "\n",
+    "    return cell, initial_state\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_get_init_cell(get_init_cell)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Word Embedding\n",
+    "Apply embedding to `input_data` using TensorFlow.  Return the embedded sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_embed(input_data, vocab_size, embed_dim):\n",
+    "    \"\"\"\n",
+    "    Create embedding for <input_data>.\n",
+    "    :param input_data: TF placeholder for text input.\n",
+    "    :param vocab_size: Number of words in vocabulary.\n",
+    "    :param embed_dim: Number of embedding dimensions\n",
+    "    :return: Embedded input.\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))\n",
+    "    embed = tf.nn.embedding_lookup(embedding, input_data)\n",
+    "    \n",
+    "    return embed\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_get_embed(get_embed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Build RNN\n",
+    "You created a RNN Cell in the `get_init_cell()` function.  Time to use the cell to create a RNN.\n",
+    "- Build the RNN using the [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)\n",
+    " - Apply the name \"final_state\" to the final state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n",
+    "\n",
+    "Return the outputs and final_state state in the following tuple `(Outputs, FinalState)` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def build_rnn(cell, inputs):\n",
+    "    \"\"\"\n",
+    "    Create a RNN using a RNN Cell\n",
+    "    :param cell: RNN Cell\n",
+    "    :param inputs: Input text data\n",
+    "    :return: Tuple (Outputs, Final State)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)\n",
+    "    final_state = tf.identity(final_state, name='final_state')\n",
+    "    \n",
+    "    return outputs, final_state\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_build_rnn(build_rnn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Build the Neural Network\n",
+    "Apply the functions you implemented above to:\n",
+    "- Apply embedding to `input_data` using your `get_embed(input_data, vocab_size, embed_dim)` function.\n",
+    "- Build RNN using `cell` and your `build_rnn(cell, inputs)` function.\n",
+    "- Apply a fully connected layer with a linear activation and `vocab_size` as the number of outputs.\n",
+    "\n",
+    "Return the logits and final state in the following tuple (Logits, FinalState) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):\n",
+    "    \"\"\"\n",
+    "    Build part of the neural network\n",
+    "    :param cell: RNN cell\n",
+    "    :param rnn_size: Size of rnns\n",
+    "    :param input_data: Input data\n",
+    "    :param vocab_size: Vocabulary size\n",
+    "    :param embed_dim: Number of embedding dimensions\n",
+    "    :return: Tuple (Logits, FinalState)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    embed_data = get_embed(input_data, vocab_size, embed_dim)\n",
+    "    outputs, final_state = build_rnn(cell, embed_data)\n",
+    "    logits = tf.contrib.layers.fully_connected(outputs, vocab_size, activation_fn=None)\n",
+    "    \n",
+    "    return logits, final_state\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_build_nn(build_nn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Batches\n",
+    "Implement `get_batches` to create batches of input and targets using `int_text`.  The batches should be a Numpy array with the shape `(number of batches, 2, batch size, sequence length)`. Each batch contains two elements:\n",
+    "- The first element is a single batch of **input** with the shape `[batch size, sequence length]`\n",
+    "- The second element is a single batch of **targets** with the shape `[batch size, sequence length]`\n",
+    "\n",
+    "If you can't fill the last batch with enough data, drop the last batch.\n",
+    "\n",
+    "For exmple, `get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3)` would return a Numpy array of the following:\n",
+    "```\n",
+    "[\n",
+    "  # First Batch\n",
+    "  [\n",
+    "    # Batch of Input\n",
+    "    [[ 1  2  3], [ 7  8  9]],\n",
+    "    # Batch of targets\n",
+    "    [[ 2  3  4], [ 8  9 10]]\n",
+    "  ],\n",
+    " \n",
+    "  # Second Batch\n",
+    "  [\n",
+    "    # Batch of Input\n",
+    "    [[ 4  5  6], [10 11 12]],\n",
+    "    # Batch of targets\n",
+    "    [[ 5  6  7], [11 12 13]]\n",
+    "  ]\n",
+    "]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_batches(int_text, batch_size, seq_length):\n",
+    "    \"\"\"\n",
+    "    Return batches of input and target\n",
+    "    :param int_text: Text with the words replaced by their ids\n",
+    "    :param batch_size: The size of batch\n",
+    "    :param seq_length: The length of sequence\n",
+    "    :return: Batches as a Numpy array\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    n_batches = len(int_text) // (batch_size * seq_length)\n",
+    "    \n",
+    "    x = np.array(int_text[:n_batches * batch_size * seq_length])\n",
+    "    y = np.array(int_text[1:n_batches * batch_size * seq_length + 1])\n",
+    "    y[-1] = x[0]\n",
+    "    x_batches = np.split(x.reshape(batch_size, -1), n_batches, 1)\n",
+    "    y_batches = np.split(y.reshape(batch_size, -1), n_batches, 1)\n",
+    "    \n",
+    "    return np.array(list(zip(x_batches, y_batches)))\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_get_batches(get_batches)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neural Network Training\n",
+    "### Hyperparameters\n",
+    "Tune the following parameters:\n",
+    "\n",
+    "- Set `num_epochs` to the number of epochs.\n",
+    "- Set `batch_size` to the batch size.\n",
+    "- Set `rnn_size` to the size of the RNNs.\n",
+    "- Set `embed_dim` to the size of the embedding.\n",
+    "- Set `seq_length` to the length of sequence.\n",
+    "- Set `learning_rate` to the learning rate.\n",
+    "- Set `show_every_n_batches` to the number of batches the neural network should print progress."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Number of Epochs\n",
+    "num_epochs = 100\n",
+    "# Batch Size\n",
+    "batch_size = 128\n",
+    "# RNN Size\n",
+    "rnn_size = 256\n",
+    "# Embedding Dimension Size\n",
+    "embed_dim = 200\n",
+    "# Sequence Length\n",
+    "seq_length = 32\n",
+    "# Learning Rate\n",
+    "learning_rate = 0.01\n",
+    "# Show stats for every n number of batches\n",
+    "show_every_n_batches = 100\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "save_dir = './save'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Build the Graph\n",
+    "Build the graph using the neural network you implemented."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "from tensorflow.contrib import seq2seq\n",
+    "\n",
+    "train_graph = tf.Graph()\n",
+    "with train_graph.as_default():\n",
+    "    vocab_size = len(int_to_vocab)\n",
+    "    input_text, targets, lr = get_inputs()\n",
+    "    input_data_shape = tf.shape(input_text)\n",
+    "    cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)\n",
+    "    logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size, embed_dim)\n",
+    "\n",
+    "    # Probabilities for generating words\n",
+    "    probs = tf.nn.softmax(logits, name='probs')\n",
+    "\n",
+    "    # Loss function\n",
+    "    cost = seq2seq.sequence_loss(\n",
+    "        logits,\n",
+    "        targets,\n",
+    "        tf.ones([input_data_shape[0], input_data_shape[1]]))\n",
+    "\n",
+    "    # Optimizer\n",
+    "    optimizer = tf.train.AdamOptimizer(lr)\n",
+    "\n",
+    "    # Gradient Clipping\n",
+    "    gradients = optimizer.compute_gradients(cost)\n",
+    "    capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n",
+    "    train_op = optimizer.apply_gradients(capped_gradients)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train\n",
+    "Train the neural network on the preprocessed data.  If you have a hard time getting a good loss, check the [forms](https://discussions.udacity.com/) to see if anyone is having the same problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch    0/16   train_loss = 8.822\n",
+      "Epoch   6 Batch    4/16   train_loss = 5.457\n",
+      "Epoch  12 Batch    8/16   train_loss = 4.972\n",
+      "Epoch  18 Batch   12/16   train_loss = 4.116\n",
+      "Epoch  25 Batch    0/16   train_loss = 3.257\n",
+      "Epoch  31 Batch    4/16   train_loss = 2.535\n",
+      "Epoch  37 Batch    8/16   train_loss = 2.091\n",
+      "Epoch  43 Batch   12/16   train_loss = 1.477\n",
+      "Epoch  50 Batch    0/16   train_loss = 1.024\n",
+      "Epoch  56 Batch    4/16   train_loss = 0.852\n",
+      "Epoch  62 Batch    8/16   train_loss = 0.566\n",
+      "Epoch  68 Batch   12/16   train_loss = 0.429\n",
+      "Epoch  75 Batch    0/16   train_loss = 0.395\n",
+      "Epoch  81 Batch    4/16   train_loss = 0.316\n",
+      "Epoch  87 Batch    8/16   train_loss = 0.201\n",
+      "Epoch  93 Batch   12/16   train_loss = 0.206\n",
+      "Model Trained and Saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "batches = get_batches(int_text, batch_size, seq_length)\n",
+    "\n",
+    "with tf.Session(graph=train_graph) as sess:\n",
+    "    sess.run(tf.global_variables_initializer())\n",
+    "\n",
+    "    for epoch_i in range(num_epochs):\n",
+    "        state = sess.run(initial_state, {input_text: batches[0][0]})\n",
+    "\n",
+    "        for batch_i, (x, y) in enumerate(batches):\n",
+    "            feed = {\n",
+    "                input_text: x,\n",
+    "                targets: y,\n",
+    "                initial_state: state,\n",
+    "                lr: learning_rate}\n",
+    "            train_loss, state, _ = sess.run([cost, final_state, train_op], feed)\n",
+    "\n",
+    "            # Show every <show_every_n_batches> batches\n",
+    "            if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:\n",
+    "                print('Epoch {:>3} Batch {:>4}/{}   train_loss = {:.3f}'.format(\n",
+    "                    epoch_i,\n",
+    "                    batch_i,\n",
+    "                    len(batches),\n",
+    "                    train_loss))\n",
+    "\n",
+    "    # Save Model\n",
+    "    saver = tf.train.Saver()\n",
+    "    saver.save(sess, save_dir)\n",
+    "    print('Model Trained and Saved')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save Parameters\n",
+    "Save `seq_length` and `save_dir` for generating a new TV script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "# Save parameters for checkpoint\n",
+    "helper.save_params((seq_length, save_dir))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import helper\n",
+    "import problem_unittests as tests\n",
+    "\n",
+    "_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()\n",
+    "seq_length, load_dir = helper.load_params()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implement Generate Functions\n",
+    "### Get Tensors\n",
+    "Get tensors from `loaded_graph` using the function [`get_tensor_by_name()`](https://www.tensorflow.org/api_docs/python/tf/Graph#get_tensor_by_name).  Get the tensors using the following names:\n",
+    "- \"input:0\"\n",
+    "- \"initial_state:0\"\n",
+    "- \"final_state:0\"\n",
+    "- \"probs:0\"\n",
+    "\n",
+    "Return the tensors in the following tuple `(InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_tensors(loaded_graph):\n",
+    "    \"\"\"\n",
+    "    Get input, initial state, final state, and probabilities tensor from <loaded_graph>\n",
+    "    :param loaded_graph: TensorFlow graph loaded from file\n",
+    "    :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    InputTensor = loaded_graph.get_tensor_by_name('input:0')\n",
+    "    InitialStateTensor = loaded_graph.get_tensor_by_name('initial_state:0')\n",
+    "    FinalStateTensor = loaded_graph.get_tensor_by_name('final_state:0')\n",
+    "    ProbsTensor = loaded_graph.get_tensor_by_name('probs:0')\n",
+    "    \n",
+    "    return InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_get_tensors(get_tensors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose Word\n",
+    "Implement the `pick_word()` function to select the next word using `probabilities`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def pick_word(probabilities, int_to_vocab):\n",
+    "    \"\"\"\n",
+    "    Pick the next word in the generated text\n",
+    "    :param probabilities: Probabilites of the next word\n",
+    "    :param int_to_vocab: Dictionary of word ids as the keys and words as the values\n",
+    "    :return: String of the predicted word\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    return int_to_vocab[int(np.argmax(probabilities))]\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_pick_word(pick_word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate TV Script\n",
+    "This will generate the TV script for you.  Set `gen_length` to the length of TV script you want to generate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "moe_szyslak:(eyeing homer's ass) oh yeah, that would look so good on me.\n",
+      "\n",
+      "\n",
+      "moe_szyslak:(hostile) hey homer, i told you not to come out.\n",
+      "\n",
+      "\n",
+      "lisa_simpson: your love of you, stupid more funny.\n",
+      "moe_szyslak: i gotta join a girl for moe's bar it'll let him.\n",
+      "moe_szyslak: so, uh, you got it, shouldn't it be. i'm talkin' malfeasance here.\n",
+      "bart_simpson: sir, well, thank you, i've never seen this again.\n",
+      "homer_simpson:(chuckles) oh yeah.\n",
+      "bart_simpson:(finishing in) you know about that!\n",
+      "duffman:(small frustrated noise) that's good...(then) just said that was i could do it?\n",
+      "homer_simpson: i don't pay you to have. i'm talkin' about you.\n",
+      "moe_szyslak: okay, well, you're the love of jeff!\n",
+      "lenny_leonard:(reading)\" i don't know.\n",
+      "moe_szyslak: sorry, it's probably gonna do worse...(sighs)\n",
+      "moe_szyslak: that's the big day of innocent.(sadly) are you doing?\n",
+      "moe_szyslak: and now, homer.\n",
+      "lisa_simpson: my barney is, moe.\n",
+      "moe_szyslak: yeah. but who was great. now, i can't believe marge comes down.\n",
+      "moe_szyslak:(tough) homer, i can't talk on the future.\n",
+      "homer_simpson: all right! the fire twins!\n",
+      "homer_simpson:(moans) yeah, that. it's my love!\n",
+      "homer_simpson:(ominous) it's it.\n",
+      "moe_szyslak: my sweet friend? he makes 'em nice in the world here.\n",
+      "\n",
+      "\n",
+      "moe_szyslak: i got a big shot who again... that's the grammy judges.(laughs) all right. you're a free beer.\n",
+      "lenny_leonard: are you home? i'm just a guy, that's a yes!\n",
+      "homer_simpson:(chuckles)\n",
+      "moe_szyslak: oh my god...\n",
+      "thanks for that...\n",
+      "moe_szyslak: oh, no.(as beer) it's all day.\n",
+      "lenny_leonard: hey, moe! i wrote a little girl!\n",
+      "duffman:(laughs) we gotta get you a job?\n",
+      "homer_simpson: wow.(laughs)\n",
+      "homer_simpson:(grunt) hey!(laughs)\n",
+      "marge_simpson:(party laugh) oh, no!\n",
+      "homer_simpson:(chuckles) hey, maggie, i'm not, barney.\n",
+      "homer_simpson: guys, i love you, barney!\n",
+      "homer_simpson:(flatly) yeah.\n",
+      "marge_simpson:(chanting) are you done to the little girl you're disappointing.\n",
+      "lisa_simpson:(worried) hey, you know what about you, huh?!\n",
+      "moe_szyslak: you will have to go home from a way to be nine of a jar.\n",
+      "carl_carlson: and the second way, a lot of people bad-mouth you and me...\n",
+      "homer_simpson:(to moe) wow, that's a coaster.\n",
+      "homer_simpson: hey, i've got a lot to mull.\n",
+      "moe_szyslak: hey, hey, hey, hey! hey ain't work?\n",
+      "moe_szyslak: that's the beauty part. you need a pal. i gotta go.\n",
+      "\n",
+      "\n",
+      "moe_szyslak:(laughs) the cop,(points off pain) lisa_simpson: moe, moe. but then you can say the most poor bucks-- it's?\n",
+      "moe_szyslak: it's true, a\" forget-me-shot in my old man.\n",
+      "moe_szyslak: okay, this is like a!\n",
+      "homer_simpson:(amazed) him...\n",
+      "moe_szyslak:(to barney) i think when i've gonna get some professional help. no one could you no real good.\n",
+      "moe_szyslak: ah, this is a pal. gotta be how how much that how you know a\" business problems...\n",
+      "\n",
+      "\n",
+      "moe_szyslak: he's the greatest gift of all, so a little bit. it was all the other day / a problem. that's sweet.\n",
+      "moe_szyslak:(ominous) one, for you...\n",
+      "homer_simpson:(to homer) come on, sweet...\n",
+      "moe_szyslak: oh, boy...\n",
+      "fat_tony:(sings) full-blooded...\n",
+      "barney_gumble:(realizing) yeah, i was sure the little girl.\n",
+      "bart_simpson: oh, you know, that was philip glass.\n",
+      "david_byrne: yeah, have you go, ain't you little worried.\n"
+     ]
+    }
+   ],
+   "source": [
+    "gen_length = 800\n",
+    "# homer_simpson, moe_szyslak, or Barney_Gumble\n",
+    "prime_word = 'moe_szyslak'\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "loaded_graph = tf.Graph()\n",
+    "with tf.Session(graph=loaded_graph) as sess:\n",
+    "    # Load saved model\n",
+    "    loader = tf.train.import_meta_graph(load_dir + '.meta')\n",
+    "    loader.restore(sess, load_dir)\n",
+    "\n",
+    "    # Get Tensors from loaded model\n",
+    "    input_text, initial_state, final_state, probs = get_tensors(loaded_graph)\n",
+    "\n",
+    "    # Sentences generation setup\n",
+    "    gen_sentences = [prime_word + ':']\n",
+    "    prev_state = sess.run(initial_state, {input_text: np.array([[1]])})\n",
+    "\n",
+    "    # Generate sentences\n",
+    "    for n in range(gen_length):\n",
+    "        # Dynamic Input\n",
+    "        dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]\n",
+    "        dyn_seq_length = len(dyn_input[0])\n",
+    "\n",
+    "        # Get Prediction\n",
+    "        probabilities, prev_state = sess.run(\n",
+    "            [probs, final_state],\n",
+    "            {input_text: dyn_input, initial_state: prev_state})\n",
+    "        \n",
+    "        pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)\n",
+    "\n",
+    "        gen_sentences.append(pred_word)\n",
+    "    \n",
+    "    # Remove tokens\n",
+    "    tv_script = ' '.join(gen_sentences)\n",
+    "    for key, token in token_dict.items():\n",
+    "        ending = ' ' if key in ['\\n', '(', '\"'] else ''\n",
+    "        tv_script = tv_script.replace(' ' + token.lower(), key)\n",
+    "    tv_script = tv_script.replace('\\n ', '\\n')\n",
+    "    tv_script = tv_script.replace('( ', '(')\n",
+    "        \n",
+    "    print(tv_script)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The TV Script is Nonsensical\n",
+    "It's ok if the TV script doesn't make any sense.  We trained on less than a megabyte of text.  In order to get good results, you'll have to use a smaller vocabulary or get more data.  Luckly there's more data!  As we mentioned in the begging of this project, this is a subset of [another dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data).  We didn't have you train on all the data, because that would take too long.  However, you are free to train your neural network on all the data.  After you complete the project, of course.\n",
+    "# Submitting This Project\n",
+    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_tv_script_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From bf424ff5a1bfe921598a29cd8b1dc6fceb1e263f Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 27 Aug 2017 19:19:30 +0800
Subject: [PATCH 13/16] Delete dlnd_language_translation.ipynb

---
 .../dlnd_language_translation.ipynb           | 963 ------------------
 1 file changed, 963 deletions(-)
 delete mode 100644 language-translation/dlnd_language_translation.ipynb

diff --git a/language-translation/dlnd_language_translation.ipynb b/language-translation/dlnd_language_translation.ipynb
deleted file mode 100644
index 4fad7ba..0000000
--- a/language-translation/dlnd_language_translation.ipynb
+++ /dev/null
@@ -1,963 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "# 语言翻译\n",
-    "\n",
-    "在此项目中，你将了解神经网络机器翻译这一领域。你将用由英语和法语语句组成的数据集，训练一个序列到序列模型（sequence to sequence model），该模型能够将新的英语句子翻译成法语。\n",
-    "\n",
-    "## 获取数据\n",
-    "\n",
-    "因为将整个英语语言内容翻译成法语需要大量训练时间，所以我们提供了一小部分的英语语料库。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import helper\n",
-    "import problem_unittests as tests\n",
-    "\n",
-    "source_path = 'data/small_vocab_en'\n",
-    "target_path = 'data/small_vocab_fr'\n",
-    "source_text = helper.load_data(source_path)\n",
-    "target_text = helper.load_data(target_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## 探索数据\n",
-    "\n",
-    "研究 view_sentence_range，查看并熟悉该数据的不同部分。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "view_sentence_range = (0, 10)\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import numpy as np\n",
-    "\n",
-    "print('Dataset Stats')\n",
-    "print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\n",
-    "\n",
-    "sentences = source_text.split('\\n')\n",
-    "word_counts = [len(sentence.split()) for sentence in sentences]\n",
-    "print('Number of sentences: {}'.format(len(sentences)))\n",
-    "print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\n",
-    "\n",
-    "print()\n",
-    "print('English sentences {} to {}:'.format(*view_sentence_range))\n",
-    "print('\\n'.join(source_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))\n",
-    "print()\n",
-    "print('French sentences {} to {}:'.format(*view_sentence_range))\n",
-    "print('\\n'.join(target_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## 实现预处理函数\n",
-    "\n",
-    "### 文本到单词 id\n",
-    "\n",
-    "和之前的 RNN 一样，你必须首先将文本转换为数字，这样计算机才能读懂。在函数 `text_to_ids()` 中，你需要将单词中的 `source_text` 和 `target_text` 转为 id。但是，你需要在 `target_text` 中每个句子的末尾，添加 `<EOS>` 单词 id。这样可以帮助神经网络预测句子应该在什么地方结束。\n",
-    "\n",
-    "\n",
-    "你可以通过以下代码获取  `<EOS> ` 单词ID：\n",
-    "\n",
-    "```python\n",
-    "target_vocab_to_int['<EOS>']\n",
-    "```\n",
-    "\n",
-    "你可以使用 `source_vocab_to_int` 和 `target_vocab_to_int` 获得其他单词 id。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n",
-    "    \"\"\"\n",
-    "    Convert source and target text to proper word ids\n",
-    "    :param source_text: String that contains all the source text.\n",
-    "    :param target_text: String that contains all the target text.\n",
-    "    :param source_vocab_to_int: Dictionary to go from the source words to an id\n",
-    "    :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
-    "    :return: A tuple of lists (source_id_text, target_id_text)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None, None\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_text_to_ids(text_to_ids)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 预处理所有数据并保存\n",
-    "\n",
-    "运行以下代码单元，预处理所有数据，并保存到文件中。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "helper.preprocess_and_save_data(source_path, target_path, text_to_ids)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "# 检查点\n",
-    "\n",
-    "这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，可以从这里继续。预处理的数据已保存到磁盘上。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import numpy as np\n",
-    "import helper\n",
-    "\n",
-    "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 检查 TensorFlow 版本，确认可访问 GPU\n",
-    "\n",
-    "这一检查步骤，可以确保你使用的是正确版本的 TensorFlow，并且能够访问 GPU。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "from distutils.version import LooseVersion\n",
-    "import warnings\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# Check TensorFlow Version\n",
-    "assert LooseVersion(tf.__version__) in [LooseVersion('1.0.0'), LooseVersion('1.0.1')], 'This project requires TensorFlow version 1.0  You are using {}'.format(tf.__version__)\n",
-    "print('TensorFlow Version: {}'.format(tf.__version__))\n",
-    "\n",
-    "# Check for a GPU\n",
-    "if not tf.test.gpu_device_name():\n",
-    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
-    "else:\n",
-    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## 构建神经网络\n",
-    "\n",
-    "你将通过实现以下函数，构建出要构建一个序列到序列模型所需的组件：\n",
-    "\n",
-    "- `model_inputs`\n",
-    "- `process_decoding_input`\n",
-    "- `encoding_layer`\n",
-    "- `decoding_layer_train`\n",
-    "- `decoding_layer_infer`\n",
-    "- `decoding_layer`\n",
-    "- `seq2seq_model`\n",
-    "\n",
-    "### 输入\n",
-    "\n",
-    "实现 `model_inputs()` 函数，为神经网络创建 TF 占位符。该函数应该创建以下占位符：\n",
-    "\n",
-    "- 名为 “input” 的输入文本占位符，并使用 TF Placeholder 名称参数（等级（Rank）为 2）。\n",
-    "- 目标占位符（等级为 2）。\n",
-    "- 学习速率占位符（等级为 0）。\n",
-    "- 名为 “keep_prob” 的保留率占位符，并使用 TF Placeholder 名称参数（等级为 0）。\n",
-    "\n",
-    "在以下元祖（tuple）中返回占位符：（输入、目标、学习速率、保留率）\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def model_inputs():\n",
-    "    \"\"\"\n",
-    "    Create TF Placeholders for input, targets, and learning rate.\n",
-    "    :return: Tuple (input, targets, learning rate, keep probability)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None, None, None, None\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_model_inputs(model_inputs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 处理解码输入\n",
-    "\n",
-    "使用 TensorFlow 实现 `process_decoding_input`，以便删掉 `target_data` 中每个批次的最后一个单词 ID，并将 GO ID 放到每个批次的开头。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def process_decoding_input(target_data, target_vocab_to_int, batch_size):\n",
-    "    \"\"\"\n",
-    "    Preprocess target data for dencoding\n",
-    "    :param target_data: Target Placehoder\n",
-    "    :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
-    "    :param batch_size: Batch Size\n",
-    "    :return: Preprocessed target data\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_process_decoding_input(process_decoding_input)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 编码\n",
-    "\n",
-    "实现 `encoding_layer()`，以使用 [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn) 创建编码器 RNN 层级。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob):\n",
-    "    \"\"\"\n",
-    "    Create encoding layer\n",
-    "    :param rnn_inputs: Inputs for the RNN\n",
-    "    :param rnn_size: RNN Size\n",
-    "    :param num_layers: Number of layers\n",
-    "    :param keep_prob: Dropout keep probability\n",
-    "    :return: RNN state\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_encoding_layer(encoding_layer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 解码 - 训练\n",
-    "\n",
-    "使用 [`tf.contrib.seq2seq.simple_decoder_fn_train()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_train) 和 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 创建训练分对数（training logits）。将 `output_fn` 应用到 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 输出上。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,\n",
-    "                         output_fn, keep_prob):\n",
-    "    \"\"\"\n",
-    "    Create a decoding layer for training\n",
-    "    :param encoder_state: Encoder State\n",
-    "    :param dec_cell: Decoder RNN Cell\n",
-    "    :param dec_embed_input: Decoder embedded input\n",
-    "    :param sequence_length: Sequence Length\n",
-    "    :param decoding_scope: TenorFlow Variable Scope for decoding\n",
-    "    :param output_fn: Function to apply the output layer\n",
-    "    :param keep_prob: Dropout keep probability\n",
-    "    :return: Train Logits\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_decoding_layer_train(decoding_layer_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 解码 - 推论\n",
-    "\n",
-    "使用 [`tf.contrib.seq2seq.simple_decoder_fn_inference()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_inference) 和 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 创建推论分对数（inference logits）。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id,\n",
-    "                         maximum_length, vocab_size, decoding_scope, output_fn, keep_prob):\n",
-    "    \"\"\"\n",
-    "    Create a decoding layer for inference\n",
-    "    :param encoder_state: Encoder state\n",
-    "    :param dec_cell: Decoder RNN Cell\n",
-    "    :param dec_embeddings: Decoder embeddings\n",
-    "    :param start_of_sequence_id: GO ID\n",
-    "    :param end_of_sequence_id: EOS Id\n",
-    "    :param maximum_length: The maximum allowed time steps to decode\n",
-    "    :param vocab_size: Size of vocabulary\n",
-    "    :param decoding_scope: TensorFlow Variable Scope for decoding\n",
-    "    :param output_fn: Function to apply the output layer\n",
-    "    :param keep_prob: Dropout keep probability\n",
-    "    :return: Inference Logits\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_decoding_layer_infer(decoding_layer_infer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 构建解码层级\n",
-    "\n",
-    "实现 `decoding_layer()` 以创建解码器 RNN 层级。\n",
-    "\n",
-    "- 使用 `rnn_size` 和 `num_layers` 创建解码 RNN 单元。\n",
-    "- 使用 [`lambda`](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions) 创建输出函数，将输入，也就是分对数转换为类分对数（class logits）。\n",
-    "- 使用 `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob)` 函数获取训练分对数。\n",
-    "- 使用 `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, decoding_scope, output_fn, keep_prob)` 函数获取推论分对数。\n",
-    "\n",
-    "注意：你将需要使用 [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 在训练和推论分对数间分享变量。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size,\n",
-    "                   num_layers, target_vocab_to_int, keep_prob):\n",
-    "    \"\"\"\n",
-    "    Create decoding layer\n",
-    "    :param dec_embed_input: Decoder embedded input\n",
-    "    :param dec_embeddings: Decoder embeddings\n",
-    "    :param encoder_state: The encoded state\n",
-    "    :param vocab_size: Size of vocabulary\n",
-    "    :param sequence_length: Sequence Length\n",
-    "    :param rnn_size: RNN Size\n",
-    "    :param num_layers: Number of layers\n",
-    "    :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
-    "    :param keep_prob: Dropout keep probability\n",
-    "    :return: Tuple of (Training Logits, Inference Logits)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_decoding_layer(decoding_layer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 构建神经网络\n",
-    "\n",
-    "应用你在上方实现的函数，以：\n",
-    "\n",
-    "- 向编码器的输入数据应用嵌入。\n",
-    "- 使用 `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)` 编码输入。\n",
-    "- 使用 `process_decoding_input(target_data, target_vocab_to_int, batch_size)` 函数处理目标数据。\n",
-    "- 向解码器的目标数据应用嵌入。\n",
-    "- 使用 `decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)` 解码编码的输入数据。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def seq2seq_model(input_data, target_data, keep_prob, batch_size, sequence_length, source_vocab_size, target_vocab_size,\n",
-    "                  enc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int):\n",
-    "    \"\"\"\n",
-    "    Build the Sequence-to-Sequence part of the neural network\n",
-    "    :param input_data: Input placeholder\n",
-    "    :param target_data: Target placeholder\n",
-    "    :param keep_prob: Dropout keep probability placeholder\n",
-    "    :param batch_size: Batch Size\n",
-    "    :param sequence_length: Sequence Length\n",
-    "    :param source_vocab_size: Source vocabulary size\n",
-    "    :param target_vocab_size: Target vocabulary size\n",
-    "    :param enc_embedding_size: Decoder embedding size\n",
-    "    :param dec_embedding_size: Encoder embedding size\n",
-    "    :param rnn_size: RNN Size\n",
-    "    :param num_layers: Number of layers\n",
-    "    :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
-    "    :return: Tuple of (Training Logits, Inference Logits)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_seq2seq_model(seq2seq_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## 训练神经网络\n",
-    "\n",
-    "### 超参数\n",
-    "\n",
-    "调试以下参数：\n",
-    "\n",
-    "- 将 `epochs` 设为 epoch 次数。\n",
-    "- 将 `batch_size` 设为批次大小。\n",
-    "- 将 `rnn_size` 设为 RNN 的大小。\n",
-    "- 将 `num_layers` 设为层级数量。\n",
-    "- 将 `encoding_embedding_size` 设为编码器嵌入大小。\n",
-    "- 将 `decoding_embedding_size` 设为解码器嵌入大小\n",
-    "- 将 `learning_rate` 设为训练速率。\n",
-    "- 将 `keep_probability` 设为丢弃保留率（Dropout keep probability）。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "# Number of Epochs\n",
-    "epochs = None\n",
-    "# Batch Size\n",
-    "batch_size = None\n",
-    "# RNN Size\n",
-    "rnn_size = None\n",
-    "# Number of Layers\n",
-    "num_layers = None\n",
-    "# Embedding Size\n",
-    "encoding_embedding_size = None\n",
-    "decoding_embedding_size = None\n",
-    "# Learning Rate\n",
-    "learning_rate = None\n",
-    "# Dropout Keep Probability\n",
-    "keep_probability = None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 构建图表\n",
-    "\n",
-    "使用你实现的神经网络构建图表。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "save_path = 'checkpoints/dev'\n",
-    "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\n",
-    "max_source_sentence_length = max([len(sentence) for sentence in source_int_text])\n",
-    "\n",
-    "train_graph = tf.Graph()\n",
-    "with train_graph.as_default():\n",
-    "    input_data, targets, lr, keep_prob = model_inputs()\n",
-    "    sequence_length = tf.placeholder_with_default(max_source_sentence_length, None, name='sequence_length')\n",
-    "    input_shape = tf.shape(input_data)\n",
-    "    \n",
-    "    train_logits, inference_logits = seq2seq_model(\n",
-    "        tf.reverse(input_data, [-1]), targets, keep_prob, batch_size, sequence_length, len(source_vocab_to_int), len(target_vocab_to_int),\n",
-    "        encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, target_vocab_to_int)\n",
-    "\n",
-    "    tf.identity(inference_logits, 'logits')\n",
-    "    with tf.name_scope(\"optimization\"):\n",
-    "        # Loss function\n",
-    "        cost = tf.contrib.seq2seq.sequence_loss(\n",
-    "            train_logits,\n",
-    "            targets,\n",
-    "            tf.ones([input_shape[0], sequence_length]))\n",
-    "\n",
-    "        # Optimizer\n",
-    "        optimizer = tf.train.AdamOptimizer(lr)\n",
-    "\n",
-    "        # Gradient Clipping\n",
-    "        gradients = optimizer.compute_gradients(cost)\n",
-    "        capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n",
-    "        train_op = optimizer.apply_gradients(capped_gradients)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 训练\n",
-    "\n",
-    "利用预处理的数据训练神经网络。如果很难获得低损失值，请访问我们的论坛，看看其他人是否遇到了相同的问题。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import time\n",
-    "\n",
-    "def get_accuracy(target, logits):\n",
-    "    \"\"\"\n",
-    "    Calculate accuracy\n",
-    "    \"\"\"\n",
-    "    max_seq = max(target.shape[1], logits.shape[1])\n",
-    "    if max_seq - target.shape[1]:\n",
-    "        target = np.pad(\n",
-    "            target,\n",
-    "            [(0,0),(0,max_seq - target.shape[1])],\n",
-    "            'constant')\n",
-    "    if max_seq - logits.shape[1]:\n",
-    "        logits = np.pad(\n",
-    "            logits,\n",
-    "            [(0,0),(0,max_seq - logits.shape[1]), (0,0)],\n",
-    "            'constant')\n",
-    "\n",
-    "    return np.mean(np.equal(target, np.argmax(logits, 2)))\n",
-    "\n",
-    "train_source = source_int_text[batch_size:]\n",
-    "train_target = target_int_text[batch_size:]\n",
-    "\n",
-    "valid_source = helper.pad_sentence_batch(source_int_text[:batch_size])\n",
-    "valid_target = helper.pad_sentence_batch(target_int_text[:batch_size])\n",
-    "\n",
-    "with tf.Session(graph=train_graph) as sess:\n",
-    "    sess.run(tf.global_variables_initializer())\n",
-    "\n",
-    "    for epoch_i in range(epochs):\n",
-    "        for batch_i, (source_batch, target_batch) in enumerate(\n",
-    "                helper.batch_data(train_source, train_target, batch_size)):\n",
-    "            start_time = time.time()\n",
-    "            \n",
-    "            _, loss = sess.run(\n",
-    "                [train_op, cost],\n",
-    "                {input_data: source_batch,\n",
-    "                 targets: target_batch,\n",
-    "                 lr: learning_rate,\n",
-    "                 sequence_length: target_batch.shape[1],\n",
-    "                 keep_prob: keep_probability})\n",
-    "            \n",
-    "            batch_train_logits = sess.run(\n",
-    "                inference_logits,\n",
-    "                {input_data: source_batch, keep_prob: 1.0})\n",
-    "            batch_valid_logits = sess.run(\n",
-    "                inference_logits,\n",
-    "                {input_data: valid_source, keep_prob: 1.0})\n",
-    "                \n",
-    "            train_acc = get_accuracy(target_batch, batch_train_logits)\n",
-    "            valid_acc = get_accuracy(np.array(valid_target), batch_valid_logits)\n",
-    "            end_time = time.time()\n",
-    "            print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.3f}, Validation Accuracy: {:>6.3f}, Loss: {:>6.3f}'\n",
-    "                  .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\n",
-    "\n",
-    "    # Save Model\n",
-    "    saver = tf.train.Saver()\n",
-    "    saver.save(sess, save_path)\n",
-    "    print('Model Trained and Saved')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### 保存参数\n",
-    "\n",
-    "保存 `batch_size` 和 `save_path` 参数以进行推论（for inference）。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "# Save parameters for checkpoint\n",
-    "helper.save_params(save_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "# 检查点"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "import helper\n",
-    "import problem_unittests as tests\n",
-    "\n",
-    "_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\n",
-    "load_path = helper.load_params()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## 句子到序列\n",
-    "\n",
-    "要向模型提供要翻译的句子，你首先需要预处理该句子。实现函数 `sentence_to_seq()` 以预处理新的句子。\n",
-    "\n",
-    "- 将句子转换为小写形式\n",
-    "- 使用 `vocab_to_int` 将单词转换为 id\n",
-    " - 如果单词不在词汇表中，将其转换为`<UNK>` 单词 id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def sentence_to_seq(sentence, vocab_to_int):\n",
-    "    \"\"\"\n",
-    "    Convert a sentence to a sequence of ids\n",
-    "    :param sentence: String\n",
-    "    :param vocab_to_int: Dictionary to go from the words to an id\n",
-    "    :return: List of word ids\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_sentence_to_seq(sentence_to_seq)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## 翻译\n",
-    "\n",
-    "将 `translate_sentence` 从英语翻译成法语。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "translate_sentence = 'he saw a old yellow truck .'\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\n",
-    "\n",
-    "loaded_graph = tf.Graph()\n",
-    "with tf.Session(graph=loaded_graph) as sess:\n",
-    "    # Load saved model\n",
-    "    loader = tf.train.import_meta_graph(load_path + '.meta')\n",
-    "    loader.restore(sess, load_path)\n",
-    "\n",
-    "    input_data = loaded_graph.get_tensor_by_name('input:0')\n",
-    "    logits = loaded_graph.get_tensor_by_name('logits:0')\n",
-    "    keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
-    "\n",
-    "    translate_logits = sess.run(logits, {input_data: [translate_sentence], keep_prob: 1.0})[0]\n",
-    "\n",
-    "print('Input')\n",
-    "print('  Word Ids:      {}'.format([i for i in translate_sentence]))\n",
-    "print('  English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\n",
-    "\n",
-    "print('\\nPrediction')\n",
-    "print('  Word Ids:      {}'.format([i for i in np.argmax(translate_logits, 1)]))\n",
-    "print('  French Words: {}'.format([target_int_to_vocab[i] for i in np.argmax(translate_logits, 1)]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## 不完美的翻译\n",
-    "\n",
-    "你可能注意到了，某些句子的翻译质量比其他的要好。因为你使用的数据集只有 227 个英语单词，但实际生活中有数千个单词，只有使用这些单词的句子结果才会比较理想。对于此项目，不需要达到完美的翻译。但是，如果你想创建更好的翻译模型，则需要更好的数据。\n",
-    "\n",
-    "你可以使用 [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar) 语料库训练模型。该数据集拥有更多的词汇，讨论的话题也更丰富。但是，训练时间要好多天的时间，所以确保你有 GPU 并且对于我们提供的数据集，你的神经网络性能很棒。提交此项目后，别忘了研究下 WMT10 语料库。\n",
-    "\n",
-    "\n",
-    "## 提交项目\n",
-    "\n",
-    "提交项目时，确保先运行所有单元，然后再保存记事本。保存记事本文件为 “dlnd_language_translation.ipynb”，再通过菜单中的“文件” ->“下载为”将其另存为 HTML 格式。提交的项目文档中需包含“helper.py”和“problem_unittests.py”文件。\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

From a257aeedf9976db7c84aa20db0904b61c3fd7491 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 27 Aug 2017 19:20:09 +0800
Subject: [PATCH 14/16] Add files via upload

---
 .../dlnd_language_translation.html            | 18349 ++++++++++++++++
 .../dlnd_language_translation.ipynb           |  6781 ++++++
 .../dlnd_language_translation.py              |   697 +
 3 files changed, 25827 insertions(+)
 create mode 100644 language-translation/dlnd_language_translation.html
 create mode 100644 language-translation/dlnd_language_translation.ipynb
 create mode 100644 language-translation/dlnd_language_translation.py

diff --git a/language-translation/dlnd_language_translation.html b/language-translation/dlnd_language_translation.html
new file mode 100644
index 0000000..261ae70
--- /dev/null
+++ b/language-translation/dlnd_language_translation.html
@@ -0,0 +1,18349 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>dlnd_language_translation</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: 1px dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    background: transparent !important;
+    color: #000 !important;
+    box-shadow: none !important;
+    text-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: 'Glyphicons Halflings';
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Glyphicons Halflings';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+  content: "\e156";
+}
+.glyphicon-unchecked:before {
+  content: "\e157";
+}
+.glyphicon-expand:before {
+  content: "\e158";
+}
+.glyphicon-collapse-down:before {
+  content: "\e159";
+}
+.glyphicon-collapse-up:before {
+  content: "\e160";
+}
+.glyphicon-log-in:before {
+  content: "\e161";
+}
+.glyphicon-flash:before {
+  content: "\e162";
+}
+.glyphicon-log-out:before {
+  content: "\e163";
+}
+.glyphicon-new-window:before {
+  content: "\e164";
+}
+.glyphicon-record:before {
+  content: "\e165";
+}
+.glyphicon-save:before {
+  content: "\e166";
+}
+.glyphicon-open:before {
+  content: "\e167";
+}
+.glyphicon-saved:before {
+  content: "\e168";
+}
+.glyphicon-import:before {
+  content: "\e169";
+}
+.glyphicon-export:before {
+  content: "\e170";
+}
+.glyphicon-send:before {
+  content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+  content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+  content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+  content: "\e174";
+}
+.glyphicon-floppy-save:before {
+  content: "\e175";
+}
+.glyphicon-floppy-open:before {
+  content: "\e176";
+}
+.glyphicon-credit-card:before {
+  content: "\e177";
+}
+.glyphicon-transfer:before {
+  content: "\e178";
+}
+.glyphicon-cutlery:before {
+  content: "\e179";
+}
+.glyphicon-header:before {
+  content: "\e180";
+}
+.glyphicon-compressed:before {
+  content: "\e181";
+}
+.glyphicon-earphone:before {
+  content: "\e182";
+}
+.glyphicon-phone-alt:before {
+  content: "\e183";
+}
+.glyphicon-tower:before {
+  content: "\e184";
+}
+.glyphicon-stats:before {
+  content: "\e185";
+}
+.glyphicon-sd-video:before {
+  content: "\e186";
+}
+.glyphicon-hd-video:before {
+  content: "\e187";
+}
+.glyphicon-subtitles:before {
+  content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+  content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+  content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+  content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+  content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+  content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
+.glyphicon-pawn:before {
+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
+.glyphicon-bitcoin:before {
+  content: "\e227";
+}
+.glyphicon-btc:before {
+  content: "\e227";
+}
+.glyphicon-xbt:before {
+  content: "\e227";
+}
+.glyphicon-yen:before {
+  content: "\00a5";
+}
+.glyphicon-jpy:before {
+  content: "\00a5";
+}
+.glyphicon-ruble:before {
+  content: "\20bd";
+}
+.glyphicon-rub:before {
+  content: "\20bd";
+}
+.glyphicon-scale:before {
+  content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+  content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+  content: "\e232";
+}
+.glyphicon-education:before {
+  content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+  content: "\e234";
+}
+.glyphicon-option-vertical:before {
+  content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+  content: "\e236";
+}
+.glyphicon-modal-window:before {
+  content: "\e237";
+}
+.glyphicon-oil:before {
+  content: "\e238";
+}
+.glyphicon-grain:before {
+  content: "\e239";
+}
+.glyphicon-sunglasses:before {
+  content: "\e240";
+}
+.glyphicon-text-size:before {
+  content: "\e241";
+}
+.glyphicon-text-color:before {
+  content: "\e242";
+}
+.glyphicon-text-background:before {
+  content: "\e243";
+}
+.glyphicon-object-align-top:before {
+  content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+  content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+  content: "\e246";
+}
+.glyphicon-object-align-left:before {
+  content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+  content: "\e248";
+}
+.glyphicon-object-align-right:before {
+  content: "\e249";
+}
+.glyphicon-triangle-right:before {
+  content: "\e250";
+}
+.glyphicon-triangle-left:before {
+  content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+  content: "\e252";
+}
+.glyphicon-triangle-top:before {
+  content: "\e253";
+}
+.glyphicon-console:before {
+  content: "\e254";
+}
+.glyphicon-superscript:before {
+  content: "\e255";
+}
+.glyphicon-subscript:before {
+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
+h5 small,
+h6 small,
+.h1 small,
+.h2 small,
+.h3 small,
+.h4 small,
+.h5 small,
+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 18px;
+  margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
+h2,
+.h2 {
+  font-size: 27px;
+}
+h3,
+.h3 {
+  font-size: 23px;
+}
+h4,
+.h4 {
+  font-size: 17px;
+}
+h5,
+.h5 {
+  font-size: 13px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
+address {
+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
+.col-xs-9 {
+  width: 75%;
+}
+.col-xs-8 {
+  width: 66.66666667%;
+}
+.col-xs-7 {
+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+  content: "\f044";
+}
+.fa-share-square-o:before {
+  content: "\f045";
+}
+.fa-check-square-o:before {
+  content: "\f046";
+}
+.fa-arrows:before {
+  content: "\f047";
+}
+.fa-step-backward:before {
+  content: "\f048";
+}
+.fa-fast-backward:before {
+  content: "\f049";
+}
+.fa-backward:before {
+  content: "\f04a";
+}
+.fa-play:before {
+  content: "\f04b";
+}
+.fa-pause:before {
+  content: "\f04c";
+}
+.fa-stop:before {
+  content: "\f04d";
+}
+.fa-forward:before {
+  content: "\f04e";
+}
+.fa-fast-forward:before {
+  content: "\f050";
+}
+.fa-step-forward:before {
+  content: "\f051";
+}
+.fa-eject:before {
+  content: "\f052";
+}
+.fa-chevron-left:before {
+  content: "\f053";
+}
+.fa-chevron-right:before {
+  content: "\f054";
+}
+.fa-plus-circle:before {
+  content: "\f055";
+}
+.fa-minus-circle:before {
+  content: "\f056";
+}
+.fa-times-circle:before {
+  content: "\f057";
+}
+.fa-check-circle:before {
+  content: "\f058";
+}
+.fa-question-circle:before {
+  content: "\f059";
+}
+.fa-info-circle:before {
+  content: "\f05a";
+}
+.fa-crosshairs:before {
+  content: "\f05b";
+}
+.fa-times-circle-o:before {
+  content: "\f05c";
+}
+.fa-check-circle-o:before {
+  content: "\f05d";
+}
+.fa-ban:before {
+  content: "\f05e";
+}
+.fa-arrow-left:before {
+  content: "\f060";
+}
+.fa-arrow-right:before {
+  content: "\f061";
+}
+.fa-arrow-up:before {
+  content: "\f062";
+}
+.fa-arrow-down:before {
+  content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+  content: "\f064";
+}
+.fa-expand:before {
+  content: "\f065";
+}
+.fa-compress:before {
+  content: "\f066";
+}
+.fa-plus:before {
+  content: "\f067";
+}
+.fa-minus:before {
+  content: "\f068";
+}
+.fa-asterisk:before {
+  content: "\f069";
+}
+.fa-exclamation-circle:before {
+  content: "\f06a";
+}
+.fa-gift:before {
+  content: "\f06b";
+}
+.fa-leaf:before {
+  content: "\f06c";
+}
+.fa-fire:before {
+  content: "\f06d";
+}
+.fa-eye:before {
+  content: "\f06e";
+}
+.fa-eye-slash:before {
+  content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+  content: "\f071";
+}
+.fa-plane:before {
+  content: "\f072";
+}
+.fa-calendar:before {
+  content: "\f073";
+}
+.fa-random:before {
+  content: "\f074";
+}
+.fa-comment:before {
+  content: "\f075";
+}
+.fa-magnet:before {
+  content: "\f076";
+}
+.fa-chevron-up:before {
+  content: "\f077";
+}
+.fa-chevron-down:before {
+  content: "\f078";
+}
+.fa-retweet:before {
+  content: "\f079";
+}
+.fa-shopping-cart:before {
+  content: "\f07a";
+}
+.fa-folder:before {
+  content: "\f07b";
+}
+.fa-folder-open:before {
+  content: "\f07c";
+}
+.fa-arrows-v:before {
+  content: "\f07d";
+}
+.fa-arrows-h:before {
+  content: "\f07e";
+}
+.fa-bar-chart-o:before,
+.fa-bar-chart:before {
+  content: "\f080";
+}
+.fa-twitter-square:before {
+  content: "\f081";
+}
+.fa-facebook-square:before {
+  content: "\f082";
+}
+.fa-camera-retro:before {
+  content: "\f083";
+}
+.fa-key:before {
+  content: "\f084";
+}
+.fa-gears:before,
+.fa-cogs:before {
+  content: "\f085";
+}
+.fa-comments:before {
+  content: "\f086";
+}
+.fa-thumbs-o-up:before {
+  content: "\f087";
+}
+.fa-thumbs-o-down:before {
+  content: "\f088";
+}
+.fa-star-half:before {
+  content: "\f089";
+}
+.fa-heart-o:before {
+  content: "\f08a";
+}
+.fa-sign-out:before {
+  content: "\f08b";
+}
+.fa-linkedin-square:before {
+  content: "\f08c";
+}
+.fa-thumb-tack:before {
+  content: "\f08d";
+}
+.fa-external-link:before {
+  content: "\f08e";
+}
+.fa-sign-in:before {
+  content: "\f090";
+}
+.fa-trophy:before {
+  content: "\f091";
+}
+.fa-github-square:before {
+  content: "\f092";
+}
+.fa-upload:before {
+  content: "\f093";
+}
+.fa-lemon-o:before {
+  content: "\f094";
+}
+.fa-phone:before {
+  content: "\f095";
+}
+.fa-square-o:before {
+  content: "\f096";
+}
+.fa-bookmark-o:before {
+  content: "\f097";
+}
+.fa-phone-square:before {
+  content: "\f098";
+}
+.fa-twitter:before {
+  content: "\f099";
+}
+.fa-facebook:before {
+  content: "\f09a";
+}
+.fa-github:before {
+  content: "\f09b";
+}
+.fa-unlock:before {
+  content: "\f09c";
+}
+.fa-credit-card:before {
+  content: "\f09d";
+}
+.fa-rss:before {
+  content: "\f09e";
+}
+.fa-hdd-o:before {
+  content: "\f0a0";
+}
+.fa-bullhorn:before {
+  content: "\f0a1";
+}
+.fa-bell:before {
+  content: "\f0f3";
+}
+.fa-certificate:before {
+  content: "\f0a3";
+}
+.fa-hand-o-right:before {
+  content: "\f0a4";
+}
+.fa-hand-o-left:before {
+  content: "\f0a5";
+}
+.fa-hand-o-up:before {
+  content: "\f0a6";
+}
+.fa-hand-o-down:before {
+  content: "\f0a7";
+}
+.fa-arrow-circle-left:before {
+  content: "\f0a8";
+}
+.fa-arrow-circle-right:before {
+  content: "\f0a9";
+}
+.fa-arrow-circle-up:before {
+  content: "\f0aa";
+}
+.fa-arrow-circle-down:before {
+  content: "\f0ab";
+}
+.fa-globe:before {
+  content: "\f0ac";
+}
+.fa-wrench:before {
+  content: "\f0ad";
+}
+.fa-tasks:before {
+  content: "\f0ae";
+}
+.fa-filter:before {
+  content: "\f0b0";
+}
+.fa-briefcase:before {
+  content: "\f0b1";
+}
+.fa-arrows-alt:before {
+  content: "\f0b2";
+}
+.fa-group:before,
+.fa-users:before {
+  content: "\f0c0";
+}
+.fa-chain:before,
+.fa-link:before {
+  content: "\f0c1";
+}
+.fa-cloud:before {
+  content: "\f0c2";
+}
+.fa-flask:before {
+  content: "\f0c3";
+}
+.fa-cut:before,
+.fa-scissors:before {
+  content: "\f0c4";
+}
+.fa-copy:before,
+.fa-files-o:before {
+  content: "\f0c5";
+}
+.fa-paperclip:before {
+  content: "\f0c6";
+}
+.fa-save:before,
+.fa-floppy-o:before {
+  content: "\f0c7";
+}
+.fa-square:before {
+  content: "\f0c8";
+}
+.fa-navicon:before,
+.fa-reorder:before,
+.fa-bars:before {
+  content: "\f0c9";
+}
+.fa-list-ul:before {
+  content: "\f0ca";
+}
+.fa-list-ol:before {
+  content: "\f0cb";
+}
+.fa-strikethrough:before {
+  content: "\f0cc";
+}
+.fa-underline:before {
+  content: "\f0cd";
+}
+.fa-table:before {
+  content: "\f0ce";
+}
+.fa-magic:before {
+  content: "\f0d0";
+}
+.fa-truck:before {
+  content: "\f0d1";
+}
+.fa-pinterest:before {
+  content: "\f0d2";
+}
+.fa-pinterest-square:before {
+  content: "\f0d3";
+}
+.fa-google-plus-square:before {
+  content: "\f0d4";
+}
+.fa-google-plus:before {
+  content: "\f0d5";
+}
+.fa-money:before {
+  content: "\f0d6";
+}
+.fa-caret-down:before {
+  content: "\f0d7";
+}
+.fa-caret-up:before {
+  content: "\f0d8";
+}
+.fa-caret-left:before {
+  content: "\f0d9";
+}
+.fa-caret-right:before {
+  content: "\f0da";
+}
+.fa-columns:before {
+  content: "\f0db";
+}
+.fa-unsorted:before,
+.fa-sort:before {
+  content: "\f0dc";
+}
+.fa-sort-down:before,
+.fa-sort-desc:before {
+  content: "\f0dd";
+}
+.fa-sort-up:before,
+.fa-sort-asc:before {
+  content: "\f0de";
+}
+.fa-envelope:before {
+  content: "\f0e0";
+}
+.fa-linkedin:before {
+  content: "\f0e1";
+}
+.fa-rotate-left:before,
+.fa-undo:before {
+  content: "\f0e2";
+}
+.fa-legal:before,
+.fa-gavel:before {
+  content: "\f0e3";
+}
+.fa-dashboard:before,
+.fa-tachometer:before {
+  content: "\f0e4";
+}
+.fa-comment-o:before {
+  content: "\f0e5";
+}
+.fa-comments-o:before {
+  content: "\f0e6";
+}
+.fa-flash:before,
+.fa-bolt:before {
+  content: "\f0e7";
+}
+.fa-sitemap:before {
+  content: "\f0e8";
+}
+.fa-umbrella:before {
+  content: "\f0e9";
+}
+.fa-paste:before,
+.fa-clipboard:before {
+  content: "\f0ea";
+}
+.fa-lightbulb-o:before {
+  content: "\f0eb";
+}
+.fa-exchange:before {
+  content: "\f0ec";
+}
+.fa-cloud-download:before {
+  content: "\f0ed";
+}
+.fa-cloud-upload:before {
+  content: "\f0ee";
+}
+.fa-user-md:before {
+  content: "\f0f0";
+}
+.fa-stethoscope:before {
+  content: "\f0f1";
+}
+.fa-suitcase:before {
+  content: "\f0f2";
+}
+.fa-bell-o:before {
+  content: "\f0a2";
+}
+.fa-coffee:before {
+  content: "\f0f4";
+}
+.fa-cutlery:before {
+  content: "\f0f5";
+}
+.fa-file-text-o:before {
+  content: "\f0f6";
+}
+.fa-building-o:before {
+  content: "\f0f7";
+}
+.fa-hospital-o:before {
+  content: "\f0f8";
+}
+.fa-ambulance:before {
+  content: "\f0f9";
+}
+.fa-medkit:before {
+  content: "\f0fa";
+}
+.fa-fighter-jet:before {
+  content: "\f0fb";
+}
+.fa-beer:before {
+  content: "\f0fc";
+}
+.fa-h-square:before {
+  content: "\f0fd";
+}
+.fa-plus-square:before {
+  content: "\f0fe";
+}
+.fa-angle-double-left:before {
+  content: "\f100";
+}
+.fa-angle-double-right:before {
+  content: "\f101";
+}
+.fa-angle-double-up:before {
+  content: "\f102";
+}
+.fa-angle-double-down:before {
+  content: "\f103";
+}
+.fa-angle-left:before {
+  content: "\f104";
+}
+.fa-angle-right:before {
+  content: "\f105";
+}
+.fa-angle-up:before {
+  content: "\f106";
+}
+.fa-angle-down:before {
+  content: "\f107";
+}
+.fa-desktop:before {
+  content: "\f108";
+}
+.fa-laptop:before {
+  content: "\f109";
+}
+.fa-tablet:before {
+  content: "\f10a";
+}
+.fa-mobile-phone:before,
+.fa-mobile:before {
+  content: "\f10b";
+}
+.fa-circle-o:before {
+  content: "\f10c";
+}
+.fa-quote-left:before {
+  content: "\f10d";
+}
+.fa-quote-right:before {
+  content: "\f10e";
+}
+.fa-spinner:before {
+  content: "\f110";
+}
+.fa-circle:before {
+  content: "\f111";
+}
+.fa-mail-reply:before,
+.fa-reply:before {
+  content: "\f112";
+}
+.fa-github-alt:before {
+  content: "\f113";
+}
+.fa-folder-o:before {
+  content: "\f114";
+}
+.fa-folder-open-o:before {
+  content: "\f115";
+}
+.fa-smile-o:before {
+  content: "\f118";
+}
+.fa-frown-o:before {
+  content: "\f119";
+}
+.fa-meh-o:before {
+  content: "\f11a";
+}
+.fa-gamepad:before {
+  content: "\f11b";
+}
+.fa-keyboard-o:before {
+  content: "\f11c";
+}
+.fa-flag-o:before {
+  content: "\f11d";
+}
+.fa-flag-checkered:before {
+  content: "\f11e";
+}
+.fa-terminal:before {
+  content: "\f120";
+}
+.fa-code:before {
+  content: "\f121";
+}
+.fa-mail-reply-all:before,
+.fa-reply-all:before {
+  content: "\f122";
+}
+.fa-star-half-empty:before,
+.fa-star-half-full:before,
+.fa-star-half-o:before {
+  content: "\f123";
+}
+.fa-location-arrow:before {
+  content: "\f124";
+}
+.fa-crop:before {
+  content: "\f125";
+}
+.fa-code-fork:before {
+  content: "\f126";
+}
+.fa-unlink:before,
+.fa-chain-broken:before {
+  content: "\f127";
+}
+.fa-question:before {
+  content: "\f128";
+}
+.fa-info:before {
+  content: "\f129";
+}
+.fa-exclamation:before {
+  content: "\f12a";
+}
+.fa-superscript:before {
+  content: "\f12b";
+}
+.fa-subscript:before {
+  content: "\f12c";
+}
+.fa-eraser:before {
+  content: "\f12d";
+}
+.fa-puzzle-piece:before {
+  content: "\f12e";
+}
+.fa-microphone:before {
+  content: "\f130";
+}
+.fa-microphone-slash:before {
+  content: "\f131";
+}
+.fa-shield:before {
+  content: "\f132";
+}
+.fa-calendar-o:before {
+  content: "\f133";
+}
+.fa-fire-extinguisher:before {
+  content: "\f134";
+}
+.fa-rocket:before {
+  content: "\f135";
+}
+.fa-maxcdn:before {
+  content: "\f136";
+}
+.fa-chevron-circle-left:before {
+  content: "\f137";
+}
+.fa-chevron-circle-right:before {
+  content: "\f138";
+}
+.fa-chevron-circle-up:before {
+  content: "\f139";
+}
+.fa-chevron-circle-down:before {
+  content: "\f13a";
+}
+.fa-html5:before {
+  content: "\f13b";
+}
+.fa-css3:before {
+  content: "\f13c";
+}
+.fa-anchor:before {
+  content: "\f13d";
+}
+.fa-unlock-alt:before {
+  content: "\f13e";
+}
+.fa-bullseye:before {
+  content: "\f140";
+}
+.fa-ellipsis-h:before {
+  content: "\f141";
+}
+.fa-ellipsis-v:before {
+  content: "\f142";
+}
+.fa-rss-square:before {
+  content: "\f143";
+}
+.fa-play-circle:before {
+  content: "\f144";
+}
+.fa-ticket:before {
+  content: "\f145";
+}
+.fa-minus-square:before {
+  content: "\f146";
+}
+.fa-minus-square-o:before {
+  content: "\f147";
+}
+.fa-level-up:before {
+  content: "\f148";
+}
+.fa-level-down:before {
+  content: "\f149";
+}
+.fa-check-square:before {
+  content: "\f14a";
+}
+.fa-pencil-square:before {
+  content: "\f14b";
+}
+.fa-external-link-square:before {
+  content: "\f14c";
+}
+.fa-share-square:before {
+  content: "\f14d";
+}
+.fa-compass:before {
+  content: "\f14e";
+}
+.fa-toggle-down:before,
+.fa-caret-square-o-down:before {
+  content: "\f150";
+}
+.fa-toggle-up:before,
+.fa-caret-square-o-up:before {
+  content: "\f151";
+}
+.fa-toggle-right:before,
+.fa-caret-square-o-right:before {
+  content: "\f152";
+}
+.fa-euro:before,
+.fa-eur:before {
+  content: "\f153";
+}
+.fa-gbp:before {
+  content: "\f154";
+}
+.fa-dollar:before,
+.fa-usd:before {
+  content: "\f155";
+}
+.fa-rupee:before,
+.fa-inr:before {
+  content: "\f156";
+}
+.fa-cny:before,
+.fa-rmb:before,
+.fa-yen:before,
+.fa-jpy:before {
+  content: "\f157";
+}
+.fa-ruble:before,
+.fa-rouble:before,
+.fa-rub:before {
+  content: "\f158";
+}
+.fa-won:before,
+.fa-krw:before {
+  content: "\f159";
+}
+.fa-bitcoin:before,
+.fa-btc:before {
+  content: "\f15a";
+}
+.fa-file:before {
+  content: "\f15b";
+}
+.fa-file-text:before {
+  content: "\f15c";
+}
+.fa-sort-alpha-asc:before {
+  content: "\f15d";
+}
+.fa-sort-alpha-desc:before {
+  content: "\f15e";
+}
+.fa-sort-amount-asc:before {
+  content: "\f160";
+}
+.fa-sort-amount-desc:before {
+  content: "\f161";
+}
+.fa-sort-numeric-asc:before {
+  content: "\f162";
+}
+.fa-sort-numeric-desc:before {
+  content: "\f163";
+}
+.fa-thumbs-up:before {
+  content: "\f164";
+}
+.fa-thumbs-down:before {
+  content: "\f165";
+}
+.fa-youtube-square:before {
+  content: "\f166";
+}
+.fa-youtube:before {
+  content: "\f167";
+}
+.fa-xing:before {
+  content: "\f168";
+}
+.fa-xing-square:before {
+  content: "\f169";
+}
+.fa-youtube-play:before {
+  content: "\f16a";
+}
+.fa-dropbox:before {
+  content: "\f16b";
+}
+.fa-stack-overflow:before {
+  content: "\f16c";
+}
+.fa-instagram:before {
+  content: "\f16d";
+}
+.fa-flickr:before {
+  content: "\f16e";
+}
+.fa-adn:before {
+  content: "\f170";
+}
+.fa-bitbucket:before {
+  content: "\f171";
+}
+.fa-bitbucket-square:before {
+  content: "\f172";
+}
+.fa-tumblr:before {
+  content: "\f173";
+}
+.fa-tumblr-square:before {
+  content: "\f174";
+}
+.fa-long-arrow-down:before {
+  content: "\f175";
+}
+.fa-long-arrow-up:before {
+  content: "\f176";
+}
+.fa-long-arrow-left:before {
+  content: "\f177";
+}
+.fa-long-arrow-right:before {
+  content: "\f178";
+}
+.fa-apple:before {
+  content: "\f179";
+}
+.fa-windows:before {
+  content: "\f17a";
+}
+.fa-android:before {
+  content: "\f17b";
+}
+.fa-linux:before {
+  content: "\f17c";
+}
+.fa-dribbble:before {
+  content: "\f17d";
+}
+.fa-skype:before {
+  content: "\f17e";
+}
+.fa-foursquare:before {
+  content: "\f180";
+}
+.fa-trello:before {
+  content: "\f181";
+}
+.fa-female:before {
+  content: "\f182";
+}
+.fa-male:before {
+  content: "\f183";
+}
+.fa-gittip:before {
+  content: "\f184";
+}
+.fa-sun-o:before {
+  content: "\f185";
+}
+.fa-moon-o:before {
+  content: "\f186";
+}
+.fa-archive:before {
+  content: "\f187";
+}
+.fa-bug:before {
+  content: "\f188";
+}
+.fa-vk:before {
+  content: "\f189";
+}
+.fa-weibo:before {
+  content: "\f18a";
+}
+.fa-renren:before {
+  content: "\f18b";
+}
+.fa-pagelines:before {
+  content: "\f18c";
+}
+.fa-stack-exchange:before {
+  content: "\f18d";
+}
+.fa-arrow-circle-o-right:before {
+  content: "\f18e";
+}
+.fa-arrow-circle-o-left:before {
+  content: "\f190";
+}
+.fa-toggle-left:before,
+.fa-caret-square-o-left:before {
+  content: "\f191";
+}
+.fa-dot-circle-o:before {
+  content: "\f192";
+}
+.fa-wheelchair:before {
+  content: "\f193";
+}
+.fa-vimeo-square:before {
+  content: "\f194";
+}
+.fa-turkish-lira:before,
+.fa-try:before {
+  content: "\f195";
+}
+.fa-plus-square-o:before {
+  content: "\f196";
+}
+.fa-space-shuttle:before {
+  content: "\f197";
+}
+.fa-slack:before {
+  content: "\f198";
+}
+.fa-envelope-square:before {
+  content: "\f199";
+}
+.fa-wordpress:before {
+  content: "\f19a";
+}
+.fa-openid:before {
+  content: "\f19b";
+}
+.fa-institution:before,
+.fa-bank:before,
+.fa-university:before {
+  content: "\f19c";
+}
+.fa-mortar-board:before,
+.fa-graduation-cap:before {
+  content: "\f19d";
+}
+.fa-yahoo:before {
+  content: "\f19e";
+}
+.fa-google:before {
+  content: "\f1a0";
+}
+.fa-reddit:before {
+  content: "\f1a1";
+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+[dir="rtl"] #ipython_notebook {
+  float: right !important;
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+[dir="rtl"] #tabs li {
+  float: right;
+}
+ul#tabs {
+  margin-bottom: 4px;
+}
+[dir="rtl"] ul#tabs {
+  margin-right: 0px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+[dir="rtl"] .list_toolbar .tree-buttons {
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-right {
+  padding-top: 1px;
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-left {
+  float: right !important;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+[dir="rtl"] #tree-selector a {
+  float: right;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+[dir="rtl"] #new-menu {
+  text-align: right;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+[dir="rtl"] #running .col-sm-8 {
+  float: right !important;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  min-width: 0;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 21ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
+    display: none;
+  }
+  span.autosave_status {
+    font-size: x-small;
+  }
+}
+.toolbar {
+  padding: 0px;
+  margin-left: -5px;
+  margin-top: 2px;
+  margin-bottom: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+  width: auto;
+  vertical-align: middle;
+  margin-right: 2px;
+  margin-bottom: 0px;
+  display: inline;
+  font-size: 92%;
+  margin-left: 0.3em;
+  margin-right: 0.3em;
+  padding: 0px;
+  padding-top: 3px;
+}
+.toolbar .btn {
+  padding: 2px 8px;
+}
+.toolbar .btn-group {
+  margin-top: 0px;
+  margin-left: 5px;
+}
+#maintoolbar {
+  margin-bottom: -3px;
+  margin-top: -8px;
+  border: 0px;
+  min-height: 27px;
+  margin-left: 0px;
+  padding-top: 11px;
+  padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+  float: none;
+  vertical-align: middle;
+  text-align: right;
+  margin-left: 5px;
+  margin-right: 0px;
+  margin-top: 0px;
+}
+.select-xs {
+  height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+  background-color: #F37626;
+  color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-webkit-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-moz-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+@-webkit-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+  overflow: auto;
+  height: 200px;
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+  text-overflow: ellipsis;
+  overflow: hidden;
+  height: 80px;
+}
+.tooltipbuttons {
+  position: absolute;
+  padding-right: 15px;
+  top: 0px;
+  right: 0px;
+}
+.tooltiptext {
+  /*avoid the button to overlap on some docstring*/
+  padding-right: 30px;
+}
+.ipython_tooltip {
+  max-width: 700px;
+  /*fade-in animation when inserted*/
+  -webkit-animation: fadeOut 400ms;
+  -moz-animation: fadeOut 400ms;
+  animation: fadeOut 400ms;
+  -webkit-animation: fadeIn 400ms;
+  -moz-animation: fadeIn 400ms;
+  animation: fadeIn 400ms;
+  vertical-align: middle;
+  background-color: #f7f7f7;
+  overflow: visible;
+  border: #ababab 1px solid;
+  outline: none;
+  padding: 3px;
+  margin: 0px;
+  padding-left: 7px;
+  font-family: monospace;
+  min-height: 50px;
+  -moz-box-shadow: 0px 6px 10px -1px #adadad;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  border-radius: 2px;
+  position: absolute;
+  z-index: 1000;
+}
+.ipython_tooltip a {
+  float: right;
+}
+.ipython_tooltip .tooltiptext pre {
+  border: 0;
+  border-radius: 0;
+  font-size: 100%;
+  background-color: #f7f7f7;
+}
+.pretooltiparrow {
+  left: 0px;
+  margin: 0px;
+  top: -16px;
+  width: 40px;
+  height: 16px;
+  overflow: hidden;
+  position: absolute;
+}
+.pretooltiparrow:before {
+  background-color: #f7f7f7;
+  border: 1px #ababab solid;
+  z-index: 11;
+  content: "";
+  position: absolute;
+  left: 15px;
+  top: 10px;
+  width: 25px;
+  height: 25px;
+  -webkit-transform: rotate(45deg);
+  -moz-transform: rotate(45deg);
+  -ms-transform: rotate(45deg);
+  -o-transform: rotate(45deg);
+}
+ul.typeahead-list i {
+  margin-left: -10px;
+  width: 18px;
+}
+ul.typeahead-list {
+  max-height: 80vh;
+  overflow: auto;
+}
+ul.typeahead-list > li > a {
+  /** Firefox bug **/
+  /* see https://github.com/jupyter/notebook/issues/559 */
+  white-space: normal;
+}
+.cmd-palette .modal-body {
+  padding: 7px;
+}
+.cmd-palette form {
+  background: white;
+}
+.cmd-palette input {
+  outline: none;
+}
+.no-shortcut {
+  display: none;
+}
+.command-shortcut:before {
+  content: "(command)";
+  padding-right: 3px;
+  color: #777777;
+}
+.edit-shortcut:before {
+  content: "(edit)";
+  padding-right: 3px;
+  color: #777777;
+}
+#find-and-replace #replace-preview .match,
+#find-and-replace #replace-preview .insert {
+  background-color: #BBDEFB;
+  border-color: #90CAF9;
+  border-style: solid;
+  border-width: 1px;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .match {
+  background-color: #FFCDD2;
+  border-color: #EF9A9A;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .insert {
+  background-color: #C8E6C9;
+  border-color: #A5D6A7;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview {
+  max-height: 60vh;
+  overflow: auto;
+}
+#find-and-replace #replace-preview pre {
+  padding: 5px 10px;
+}
+.terminal-app {
+  background: #EEE;
+}
+.terminal-app #header {
+  background: #fff;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.terminal-app .terminal {
+  width: 100%;
+  float: left;
+  font-family: monospace;
+  color: white;
+  background: black;
+  padding: 0.4em;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+}
+.terminal-app .terminal,
+.terminal-app .terminal dummy-screen {
+  line-height: 1em;
+  font-size: 14px;
+}
+.terminal-app .terminal .xterm-rows {
+  padding: 10px;
+}
+.terminal-app .terminal-cursor {
+  color: black;
+  background: white;
+}
+.terminal-app #terminado-container {
+  margin-top: 20px;
+}
+/*# sourceMappingURL=style.min.css.map */
+    </style>
+<style type="text/css">
+    .highlight .hll { background-color: #ffffcc }
+.highlight  { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #7D9029 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #A0A000 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #0000FF } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+    </style>
+<style type="text/css">
+    
+/* Temporary definitions which will become obsolete with Notebook release 5.0 */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-bold { font-weight: bold; }
+
+    </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+  overflow: visible;
+  padding: 8px;
+}
+
+div#notebook {
+  overflow: visible;
+  border-top: none;
+}@media print {
+  div.cell {
+    display: block;
+    page-break-inside: avoid;
+  } 
+  div.output_wrapper { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+  div.output { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS_HTML"></script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    MathJax.Hub.Config({
+        tex2jax: {
+            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+            processEscapes: true,
+            processEnvironments: true
+        },
+        // Center justify equations in code and markdown cells. Elsewhere
+        // we use CSS to left justify single line equations in code cells.
+        displayAlign: 'center',
+        "HTML-CSS": {
+            styles: {'.MathJax_Display': {"margin": 0}},
+            linebreaks: { automatic: true }
+        }
+    });
+    </script>
+    <!-- End of mathjax configuration --></head>
+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#35821;&#35328;&#32763;&#35793;">&#35821;&#35328;&#32763;&#35793;<a class="anchor-link" href="#&#35821;&#35328;&#32763;&#35793;">&#182;</a></h1><p>在此项目中，你将了解神经网络机器翻译这一领域。你将用由英语和法语语句组成的数据集，训练一个序列到序列模型（sequence to sequence model），该模型能够将新的英语句子翻译成法语。</p>
+<h2 id="&#33719;&#21462;&#25968;&#25454;">&#33719;&#21462;&#25968;&#25454;<a class="anchor-link" href="#&#33719;&#21462;&#25968;&#25454;">&#182;</a></h2><p>因为将整个英语语言内容翻译成法语需要大量训练时间，所以我们提供了一小部分的英语语料库。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+
+<span class="n">source_path</span> <span class="o">=</span> <span class="s1">&#39;data/small_vocab_en&#39;</span>
+<span class="n">target_path</span> <span class="o">=</span> <span class="s1">&#39;data/small_vocab_fr&#39;</span>
+<span class="n">source_text</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_data</span><span class="p">(</span><span class="n">source_path</span><span class="p">)</span>
+<span class="n">target_text</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_data</span><span class="p">(</span><span class="n">target_path</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#25506;&#32034;&#25968;&#25454;">&#25506;&#32034;&#25968;&#25454;<a class="anchor-link" href="#&#25506;&#32034;&#25968;&#25454;">&#182;</a></h2><p>研究 view_sentence_range，查看并熟悉该数据的不同部分。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">view_sentence_range</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Dataset Stats&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Roughly the number of unique words: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">({</span><span class="n">word</span><span class="p">:</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">source_text</span><span class="o">.</span><span class="n">split</span><span class="p">()})))</span>
+
+<span class="n">sentences</span> <span class="o">=</span> <span class="n">source_text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="n">word_counts</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">sentence</span><span class="o">.</span><span class="n">split</span><span class="p">())</span> <span class="k">for</span> <span class="n">sentence</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Number of sentences: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sentences</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Average number of words in a sentence: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">word_counts</span><span class="p">)))</span>
+
+<span class="nb">print</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;English sentences </span><span class="si">{}</span><span class="s1"> to </span><span class="si">{}</span><span class="s1">:&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">view_sentence_range</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">source_text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)[</span><span class="n">view_sentence_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span><span class="n">view_sentence_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]]))</span>
+<span class="nb">print</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;French sentences </span><span class="si">{}</span><span class="s1"> to </span><span class="si">{}</span><span class="s1">:&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">view_sentence_range</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">target_text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)[</span><span class="n">view_sentence_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span><span class="n">view_sentence_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]]))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Dataset Stats
+Roughly the number of unique words: 227
+Number of sentences: 137861
+Average number of words in a sentence: 13.225277634719028
+
+English sentences 0 to 10:
+new jersey is sometimes quiet during autumn , and it is snowy in april .
+the united states is usually chilly during july , and it is usually freezing in november .
+california is usually quiet during march , and it is usually hot in june .
+the united states is sometimes mild during june , and it is cold in september .
+your least liked fruit is the grape , but my least liked is the apple .
+his favorite fruit is the orange , but my favorite is the grape .
+paris is relaxing during december , but it is usually chilly in july .
+new jersey is busy during spring , and it is never hot in march .
+our least liked fruit is the lemon , but my least liked is the grape .
+the united states is sometimes busy during january , and it is sometimes warm in november .
+
+French sentences 0 to 10:
+new jersey est parfois calme pendant l&#39; automne , et il est neigeux en avril .
+les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .
+california est généralement calme en mars , et il est généralement chaud en juin .
+les états-unis est parfois légère en juin , et il fait froid en septembre .
+votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .
+son fruit préféré est l&#39;orange , mais mon préféré est le raisin .
+paris est relaxant en décembre , mais il est généralement froid en juillet .
+new jersey est occupé au printemps , et il est jamais chaude en mars .
+notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .
+les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;<a class="anchor-link" href="#&#23454;&#29616;&#39044;&#22788;&#29702;&#20989;&#25968;">&#182;</a></h2><h3 id="&#25991;&#26412;&#21040;&#21333;&#35789;-id">&#25991;&#26412;&#21040;&#21333;&#35789; id<a class="anchor-link" href="#&#25991;&#26412;&#21040;&#21333;&#35789;-id">&#182;</a></h3><p>和之前的 RNN 一样，你必须首先将文本转换为数字，这样计算机才能读懂。在函数 <code>text_to_ids()</code> 中，你需要将单词中的 <code>source_text</code> 和 <code>target_text</code> 转为 id。但是，你需要在 <code>target_text</code> 中每个句子的末尾，添加 <code>&lt;EOS&gt;</code> 单词 id。这样可以帮助神经网络预测句子应该在什么地方结束。</p>
+<p>你可以通过以下代码获取  <code>&lt;EOS&gt;</code> 单词ID：</p>
+<div class="highlight"><pre><span></span><span class="n">target_vocab_to_int</span><span class="p">[</span><span class="s1">&#39;&lt;EOS&gt;&#39;</span><span class="p">]</span>
+</pre></div>
+<p>你可以使用 <code>source_vocab_to_int</code> 和 <code>target_vocab_to_int</code> 获得其他单词 id。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">text_to_ids</span><span class="p">(</span><span class="n">source_text</span><span class="p">,</span> <span class="n">target_text</span><span class="p">,</span> <span class="n">source_vocab_to_int</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Convert source and target text to proper word ids</span>
+<span class="sd">    :param source_text: String that contains all the source text.</span>
+<span class="sd">    :param target_text: String that contains all the target text.</span>
+<span class="sd">    :param source_vocab_to_int: Dictionary to go from the source words to an id</span>
+<span class="sd">    :param target_vocab_to_int: Dictionary to go from the target words to an id</span>
+<span class="sd">    :return: A tuple of lists (source_id_text, target_id_text)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">source_letter_ids</span> <span class="o">=</span> <span class="p">[[</span><span class="n">source_vocab_to_int</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">letter</span><span class="p">,</span> <span class="n">source_vocab_to_int</span><span class="p">[</span><span class="s1">&#39;&lt;UNK&gt;&#39;</span><span class="p">])</span> <span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">source_text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)]</span>
+    <span class="n">target_letter_ids</span> <span class="o">=</span> <span class="p">[[</span><span class="n">target_vocab_to_int</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">letter</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">[</span><span class="s1">&#39;&lt;UNK&gt;&#39;</span><span class="p">])</span> <span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span> <span class="o">+</span> <span class="p">[</span><span class="n">target_vocab_to_int</span><span class="p">[</span><span class="s1">&#39;&lt;EOS&gt;&#39;</span><span class="p">]]</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">target_text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)]</span> 
+    
+    <span class="k">return</span> <span class="n">source_letter_ids</span><span class="p">,</span> <span class="n">target_letter_ids</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_text_to_ids</span><span class="p">(</span><span class="n">text_to_ids</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;<a class="anchor-link" href="#&#39044;&#22788;&#29702;&#25152;&#26377;&#25968;&#25454;&#24182;&#20445;&#23384;">&#182;</a></h3><p>运行以下代码单元，预处理所有数据，并保存到文件中。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">preprocess_and_save_data</span><span class="p">(</span><span class="n">source_path</span><span class="p">,</span> <span class="n">target_path</span><span class="p">,</span> <span class="n">text_to_ids</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1><p>这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，可以从这里继续。预处理的数据已保存到磁盘上。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+
+<span class="p">(</span><span class="n">source_int_text</span><span class="p">,</span> <span class="n">target_int_text</span><span class="p">),</span> <span class="p">(</span><span class="n">source_vocab_to_int</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">),</span> <span class="n">_</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#26816;&#26597;-TensorFlow-&#29256;&#26412;&#65292;&#30830;&#35748;&#21487;&#35775;&#38382;-GPU">&#26816;&#26597; TensorFlow &#29256;&#26412;&#65292;&#30830;&#35748;&#21487;&#35775;&#38382; GPU<a class="anchor-link" href="#&#26816;&#26597;-TensorFlow-&#29256;&#26412;&#65292;&#30830;&#35748;&#21487;&#35775;&#38382;-GPU">&#182;</a></h3><p>这一检查步骤，可以确保你使用的是正确版本的 TensorFlow，并且能够访问 GPU。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">distutils.version</span> <span class="k">import</span> <span class="n">LooseVersion</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+
+<span class="c1"># Check TensorFlow Version</span>
+<span class="k">assert</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="n">LooseVersion</span><span class="p">(</span><span class="s1">&#39;1.0.0&#39;</span><span class="p">),</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="s1">&#39;1.0.1&#39;</span><span class="p">)],</span> <span class="s1">&#39;This project requires TensorFlow version 1.0  You are using </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;TensorFlow Version: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">))</span>
+
+<span class="c1"># Check for a GPU</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">gpu_device_name</span><span class="p">():</span>
+    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;No GPU found. Please use a GPU to train your neural network.&#39;</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Default GPU Device: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">gpu_device_name</span><span class="p">()))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>TensorFlow Version: 1.0.1
+Default GPU Device: /gpu:0
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#26500;&#24314;&#31070;&#32463;&#32593;&#32476;">&#26500;&#24314;&#31070;&#32463;&#32593;&#32476;<a class="anchor-link" href="#&#26500;&#24314;&#31070;&#32463;&#32593;&#32476;">&#182;</a></h2><p>你将通过实现以下函数，构建出要构建一个序列到序列模型所需的组件：</p>
+<ul>
+<li><code>model_inputs</code></li>
+<li><code>process_decoding_input</code></li>
+<li><code>encoding_layer</code></li>
+<li><code>decoding_layer_train</code></li>
+<li><code>decoding_layer_infer</code></li>
+<li><code>decoding_layer</code></li>
+<li><code>seq2seq_model</code></li>
+</ul>
+<h3 id="&#36755;&#20837;">&#36755;&#20837;<a class="anchor-link" href="#&#36755;&#20837;">&#182;</a></h3><p>实现 <code>model_inputs()</code> 函数，为神经网络创建 TF 占位符。该函数应该创建以下占位符：</p>
+<ul>
+<li>名为 “input” 的输入文本占位符，并使用 TF Placeholder 名称参数（等级（Rank）为 2）。</li>
+<li>目标占位符（等级为 2）。</li>
+<li>学习速率占位符（等级为 0）。</li>
+<li>名为 “keep_prob” 的保留率占位符，并使用 TF Placeholder 名称参数（等级为 0）。</li>
+</ul>
+<p>在以下元祖（tuple）中返回占位符：（输入、目标、学习速率、保留率）</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+<span class="k">def</span> <span class="nf">model_inputs</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create TF Placeholders for input, targets, and learning rate.</span>
+<span class="sd">    :return: Tuple (input, targets, learning rate, keep probability)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">inputs</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;input&#39;</span><span class="p">)</span>
+    <span class="n">targets</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;targets&#39;</span><span class="p">)</span>
+    <span class="n">learning_rate</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;learning_rate&#39;</span><span class="p">)</span>
+    <span class="n">keep_prob</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;keep_prob&#39;</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">keep_prob</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_model_inputs</span><span class="p">(</span><span class="n">model_inputs</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#22788;&#29702;&#35299;&#30721;&#36755;&#20837;">&#22788;&#29702;&#35299;&#30721;&#36755;&#20837;<a class="anchor-link" href="#&#22788;&#29702;&#35299;&#30721;&#36755;&#20837;">&#182;</a></h3><p>使用 TensorFlow 实现 <code>process_decoding_input</code>，以便删掉 <code>target_data</code> 中每个批次的最后一个单词 ID，并将 GO ID 放到每个批次的开头。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">process_decoding_input</span><span class="p">(</span><span class="n">target_data</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Preprocess target data for dencoding</span>
+<span class="sd">    :param target_data: Target Placehoder</span>
+<span class="sd">    :param target_vocab_to_int: Dictionary to go from the target words to an id</span>
+<span class="sd">    :param batch_size: Batch Size</span>
+<span class="sd">    :return: Preprocessed target data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">ending</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">strided_slice</span><span class="p">(</span><span class="n">target_data</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="n">dec_input</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">tf</span><span class="o">.</span><span class="n">fill</span><span class="p">([</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">target_vocab_to_int</span><span class="p">[</span><span class="s1">&#39;&lt;GO&gt;&#39;</span><span class="p">]),</span> <span class="n">ending</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">dec_input</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_process_decoding_input</span><span class="p">(</span><span class="n">process_decoding_input</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#32534;&#30721;">&#32534;&#30721;<a class="anchor-link" href="#&#32534;&#30721;">&#182;</a></h3><p>实现 <code>encoding_layer()</code>，以使用 <a href="https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn"><code>tf.nn.dynamic_rnn()</code></a> 创建编码器 RNN 层级。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">encoding_layer</span><span class="p">(</span><span class="n">rnn_inputs</span><span class="p">,</span> <span class="n">rnn_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create encoding layer</span>
+<span class="sd">    :param rnn_inputs: Inputs for the RNN</span>
+<span class="sd">    :param rnn_size: RNN Size</span>
+<span class="sd">    :param num_layers: Number of layers</span>
+<span class="sd">    :param keep_prob: Dropout keep probability</span>
+<span class="sd">    :return: RNN state</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">lstm</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">BasicLSTMCell</span><span class="p">(</span><span class="n">rnn_size</span><span class="p">)</span>
+    <span class="n">drop</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">DropoutWrapper</span><span class="p">(</span><span class="n">lstm</span><span class="p">,</span> <span class="n">output_keep_prob</span><span class="o">=</span><span class="n">keep_prob</span><span class="p">)</span>
+    <span class="n">enc_cell</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">MultiRNNCell</span><span class="p">([</span><span class="n">drop</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_layers</span><span class="p">)</span>
+    <span class="n">_</span><span class="p">,</span> <span class="n">enc_state</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dynamic_rnn</span><span class="p">(</span><span class="n">enc_cell</span><span class="p">,</span> <span class="n">rnn_inputs</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">enc_state</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_encoding_layer</span><span class="p">(</span><span class="n">encoding_layer</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#35299;&#30721;---&#35757;&#32451;">&#35299;&#30721; - &#35757;&#32451;<a class="anchor-link" href="#&#35299;&#30721;---&#35757;&#32451;">&#182;</a></h3><p>使用 <a href="https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_train"><code>tf.contrib.seq2seq.simple_decoder_fn_train()</code></a> 和 <a href="https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder"><code>tf.contrib.seq2seq.dynamic_rnn_decoder()</code></a> 创建训练分对数（training logits）。将 <code>output_fn</code> 应用到 <a href="https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder"><code>tf.contrib.seq2seq.dynamic_rnn_decoder()</code></a> 输出上。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">decoding_layer_train</span><span class="p">(</span><span class="n">encoder_state</span><span class="p">,</span> <span class="n">dec_cell</span><span class="p">,</span> <span class="n">dec_embed_input</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="n">decoding_scope</span><span class="p">,</span>
+                         <span class="n">output_fn</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a decoding layer for training</span>
+<span class="sd">    :param encoder_state: Encoder State</span>
+<span class="sd">    :param dec_cell: Decoder RNN Cell</span>
+<span class="sd">    :param dec_embed_input: Decoder embedded input</span>
+<span class="sd">    :param sequence_length: Sequence Length</span>
+<span class="sd">    :param decoding_scope: TenorFlow Variable Scope for decoding</span>
+<span class="sd">    :param output_fn: Function to apply the output layer</span>
+<span class="sd">    :param keep_prob: Dropout keep probability</span>
+<span class="sd">    :return: Train Logits</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="c1"># Training Decoder</span>
+    <span class="n">train_decoder_fn</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">seq2seq</span><span class="o">.</span><span class="n">simple_decoder_fn_train</span><span class="p">(</span><span class="n">encoder_state</span><span class="p">)</span>
+    <span class="n">train_pred</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">seq2seq</span><span class="o">.</span><span class="n">dynamic_rnn_decoder</span><span class="p">(</span>
+    <span class="n">dec_cell</span><span class="p">,</span> <span class="n">train_decoder_fn</span><span class="p">,</span> <span class="n">dec_embed_input</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="n">scope</span><span class="o">=</span><span class="n">decoding_scope</span><span class="p">)</span>
+    
+    <span class="c1"># Apply output function</span>
+    <span class="n">train_logits</span> <span class="o">=</span>  <span class="n">output_fn</span><span class="p">(</span><span class="n">train_pred</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">train_logits</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_decoding_layer_train</span><span class="p">(</span><span class="n">decoding_layer_train</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#35299;&#30721;---&#25512;&#35770;">&#35299;&#30721; - &#25512;&#35770;<a class="anchor-link" href="#&#35299;&#30721;---&#25512;&#35770;">&#182;</a></h3><p>使用 <a href="https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_inference"><code>tf.contrib.seq2seq.simple_decoder_fn_inference()</code></a> 和 <a href="https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder"><code>tf.contrib.seq2seq.dynamic_rnn_decoder()</code></a> 创建推论分对数（inference logits）。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">decoding_layer_infer</span><span class="p">(</span><span class="n">encoder_state</span><span class="p">,</span> <span class="n">dec_cell</span><span class="p">,</span> <span class="n">dec_embeddings</span><span class="p">,</span> <span class="n">start_of_sequence_id</span><span class="p">,</span> <span class="n">end_of_sequence_id</span><span class="p">,</span>
+                         <span class="n">maximum_length</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">decoding_scope</span><span class="p">,</span> <span class="n">output_fn</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a decoding layer for inference</span>
+<span class="sd">    :param encoder_state: Encoder state</span>
+<span class="sd">    :param dec_cell: Decoder RNN Cell</span>
+<span class="sd">    :param dec_embeddings: Decoder embeddings</span>
+<span class="sd">    :param start_of_sequence_id: GO ID</span>
+<span class="sd">    :param end_of_sequence_id: EOS Id</span>
+<span class="sd">    :param maximum_length: The maximum allowed time steps to decode</span>
+<span class="sd">    :param vocab_size: Size of vocabulary</span>
+<span class="sd">    :param decoding_scope: TensorFlow Variable Scope for decoding</span>
+<span class="sd">    :param output_fn: Function to apply the output layer</span>
+<span class="sd">    :param keep_prob: Dropout keep probability</span>
+<span class="sd">    :return: Inference Logits</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="c1"># Inference Decoder</span>
+    <span class="n">infer_decoder_fn</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">seq2seq</span><span class="o">.</span><span class="n">simple_decoder_fn_inference</span><span class="p">(</span>
+        <span class="n">output_fn</span><span class="p">,</span> <span class="n">encoder_state</span><span class="p">,</span> <span class="n">dec_embeddings</span><span class="p">,</span> <span class="n">start_of_sequence_id</span><span class="p">,</span> <span class="n">end_of_sequence_id</span><span class="p">,</span> 
+        <span class="n">maximum_length</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">)</span>
+    <span class="n">inference_logits</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">seq2seq</span><span class="o">.</span><span class="n">dynamic_rnn_decoder</span><span class="p">(</span><span class="n">dec_cell</span><span class="p">,</span> <span class="n">infer_decoder_fn</span><span class="p">,</span> <span class="n">scope</span><span class="o">=</span><span class="n">decoding_scope</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">inference_logits</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_decoding_layer_infer</span><span class="p">(</span><span class="n">decoding_layer_infer</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#26500;&#24314;&#35299;&#30721;&#23618;&#32423;">&#26500;&#24314;&#35299;&#30721;&#23618;&#32423;<a class="anchor-link" href="#&#26500;&#24314;&#35299;&#30721;&#23618;&#32423;">&#182;</a></h3><p>实现 <code>decoding_layer()</code> 以创建解码器 RNN 层级。</p>
+<ul>
+<li>使用 <code>rnn_size</code> 和 <code>num_layers</code> 创建解码 RNN 单元。</li>
+<li>使用 <a href="https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions"><code>lambda</code></a> 创建输出函数，将输入，也就是分对数转换为类分对数（class logits）。</li>
+<li>使用 <code>decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob)</code> 函数获取训练分对数。</li>
+<li>使用 <code>decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, decoding_scope, output_fn, keep_prob)</code> 函数获取推论分对数。</li>
+</ul>
+<p>注意：你将需要使用 <a href="https://www.tensorflow.org/api_docs/python/tf/variable_scope">tf.variable_scope</a> 在训练和推论分对数间分享变量。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">decoding_layer</span><span class="p">(</span><span class="n">dec_embed_input</span><span class="p">,</span> <span class="n">dec_embeddings</span><span class="p">,</span> <span class="n">encoder_state</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="n">rnn_size</span><span class="p">,</span>
+                   <span class="n">num_layers</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create decoding layer</span>
+<span class="sd">    :param dec_embed_input: Decoder embedded input</span>
+<span class="sd">    :param dec_embeddings: Decoder embeddings</span>
+<span class="sd">    :param encoder_state: The encoded state</span>
+<span class="sd">    :param vocab_size: Size of vocabulary</span>
+<span class="sd">    :param sequence_length: Sequence Length</span>
+<span class="sd">    :param rnn_size: RNN Size</span>
+<span class="sd">    :param num_layers: Number of layers</span>
+<span class="sd">    :param target_vocab_to_int: Dictionary to go from the target words to an id</span>
+<span class="sd">    :param keep_prob: Dropout keep probability</span>
+<span class="sd">    :return: Tuple of (Training Logits, Inference Logits)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">lstm</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">BasicLSTMCell</span><span class="p">(</span><span class="n">rnn_size</span><span class="p">)</span>
+    <span class="n">dropout</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">DropoutWrapper</span><span class="p">(</span><span class="n">lstm</span><span class="p">,</span> <span class="n">output_keep_prob</span><span class="o">=</span><span class="n">keep_prob</span><span class="p">)</span>
+    <span class="n">dec_cell</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">MultiRNNCell</span><span class="p">([</span><span class="n">dropout</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_layers</span><span class="p">)</span>
+    
+    <span class="n">output_fn</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fully_connected</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">activation_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scope</span><span class="o">=</span><span class="n">decoding_scope</span><span class="p">)</span>           
+
+    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">variable_scope</span><span class="p">(</span><span class="s2">&quot;decoding&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">decoding_scope</span><span class="p">:</span>
+        <span class="n">training_decoder_output</span> <span class="o">=</span> <span class="n">decoding_layer_train</span><span class="p">(</span><span class="n">encoder_state</span><span class="p">,</span> <span class="n">dec_cell</span><span class="p">,</span> <span class="n">dec_embed_input</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="n">decoding_scope</span><span class="p">,</span>
+                         <span class="n">output_fn</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">variable_scope</span><span class="p">(</span><span class="s2">&quot;decoding&quot;</span><span class="p">,</span> <span class="n">reuse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">decoding_scope</span><span class="p">:</span>
+        <span class="n">start_of_sequence_id</span> <span class="o">=</span> <span class="n">target_vocab_to_int</span><span class="p">[</span><span class="s2">&quot;&lt;GO&gt;&quot;</span><span class="p">]</span>
+        <span class="n">end_of_sequence_id</span> <span class="o">=</span> <span class="n">target_vocab_to_int</span><span class="p">[</span><span class="s2">&quot;&lt;EOS&gt;&quot;</span><span class="p">]</span>
+        <span class="n">inference_decoder_output</span> <span class="o">=</span> <span class="n">decoding_layer_infer</span><span class="p">(</span><span class="n">encoder_state</span><span class="p">,</span> <span class="n">dec_cell</span><span class="p">,</span> <span class="n">dec_embeddings</span><span class="p">,</span> <span class="n">start_of_sequence_id</span><span class="p">,</span> <span class="n">end_of_sequence_id</span><span class="p">,</span> 
+                         <span class="n">sequence_length</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">decoding_scope</span><span class="p">,</span> <span class="n">output_fn</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">training_decoder_output</span><span class="p">,</span> <span class="n">inference_decoder_output</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_decoding_layer</span><span class="p">(</span><span class="n">decoding_layer</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#26500;&#24314;&#31070;&#32463;&#32593;&#32476;">&#26500;&#24314;&#31070;&#32463;&#32593;&#32476;<a class="anchor-link" href="#&#26500;&#24314;&#31070;&#32463;&#32593;&#32476;">&#182;</a></h3><p>应用你在上方实现的函数，以：</p>
+<ul>
+<li>向编码器的输入数据应用嵌入。</li>
+<li>使用 <code>encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)</code> 编码输入。</li>
+<li>使用 <code>process_decoding_input(target_data, target_vocab_to_int, batch_size)</code> 函数处理目标数据。</li>
+<li>向解码器的目标数据应用嵌入。</li>
+<li>使用 <code>decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)</code> 解码编码的输入数据。</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[13]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">seq2seq_model</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">target_data</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="n">source_vocab_size</span><span class="p">,</span> <span class="n">target_vocab_size</span><span class="p">,</span>
+                  <span class="n">enc_embedding_size</span><span class="p">,</span> <span class="n">dec_embedding_size</span><span class="p">,</span> <span class="n">rnn_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Build the Sequence-to-Sequence part of the neural network</span>
+<span class="sd">    :param input_data: Input placeholder</span>
+<span class="sd">    :param target_data: Target placeholder</span>
+<span class="sd">    :param keep_prob: Dropout keep probability placeholder</span>
+<span class="sd">    :param batch_size: Batch Size</span>
+<span class="sd">    :param sequence_length: Sequence Length</span>
+<span class="sd">    :param source_vocab_size: Source vocabulary size</span>
+<span class="sd">    :param target_vocab_size: Target vocabulary size</span>
+<span class="sd">    :param enc_embedding_size: Decoder embedding size</span>
+<span class="sd">    :param dec_embedding_size: Encoder embedding size</span>
+<span class="sd">    :param rnn_size: RNN Size</span>
+<span class="sd">    :param num_layers: Number of layers</span>
+<span class="sd">    :param target_vocab_to_int: Dictionary to go from the target words to an id</span>
+<span class="sd">    :return: Tuple of (Training Logits, Inference Logits)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">rnn_inputs</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">embed_sequence</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">source_vocab_size</span><span class="p">,</span> <span class="n">enc_embedding_size</span><span class="p">)</span>
+    
+    <span class="n">encoder_state</span> <span class="o">=</span> <span class="n">encoding_layer</span><span class="p">(</span><span class="n">rnn_inputs</span><span class="p">,</span> <span class="n">rnn_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+    
+    <span class="n">dec_input</span> <span class="o">=</span> <span class="n">process_decoding_input</span><span class="p">(</span><span class="n">target_data</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
+    <span class="n">dec_embeddings</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">([</span><span class="n">target_vocab_size</span><span class="p">,</span> <span class="n">dec_embedding_size</span><span class="p">]))</span>
+    <span class="n">dec_embed_input</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">embedding_lookup</span><span class="p">(</span><span class="n">dec_embeddings</span><span class="p">,</span> <span class="n">dec_input</span><span class="p">)</span>
+    
+    <span class="n">train_logits</span><span class="p">,</span> <span class="n">inference_logits</span> <span class="o">=</span> <span class="n">decoding_layer</span><span class="p">(</span><span class="n">dec_embed_input</span><span class="p">,</span> <span class="n">dec_embeddings</span><span class="p">,</span> <span class="n">encoder_state</span><span class="p">,</span> <span class="n">target_vocab_size</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span>
+                       <span class="n">rnn_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">train_logits</span><span class="p">,</span> <span class="n">inference_logits</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_seq2seq_model</span><span class="p">(</span><span class="n">seq2seq_model</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;<a class="anchor-link" href="#&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;">&#182;</a></h2><h3 id="&#36229;&#21442;&#25968;">&#36229;&#21442;&#25968;<a class="anchor-link" href="#&#36229;&#21442;&#25968;">&#182;</a></h3><p>调试以下参数：</p>
+<ul>
+<li>将 <code>epochs</code> 设为 epoch 次数。</li>
+<li>将 <code>batch_size</code> 设为批次大小。</li>
+<li>将 <code>rnn_size</code> 设为 RNN 的大小。</li>
+<li>将 <code>num_layers</code> 设为层级数量。</li>
+<li>将 <code>encoding_embedding_size</code> 设为编码器嵌入大小。</li>
+<li>将 <code>decoding_embedding_size</code> 设为解码器嵌入大小</li>
+<li>将 <code>learning_rate</code> 设为训练速率。</li>
+<li>将 <code>keep_probability</code> 设为丢弃保留率（Dropout keep probability）。</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[14]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Number of Epochs</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="c1"># Batch Size</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">128</span>
+<span class="c1"># RNN Size</span>
+<span class="n">rnn_size</span> <span class="o">=</span> <span class="mi">128</span>
+<span class="c1"># Number of Layers</span>
+<span class="n">num_layers</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="c1"># Embedding Size</span>
+<span class="n">encoding_embedding_size</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">decoding_embedding_size</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="c1"># Learning Rate</span>
+<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.01</span>
+<span class="c1"># Dropout Keep Probability</span>
+<span class="n">keep_probability</span> <span class="o">=</span> <span class="mf">0.8</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#26500;&#24314;&#22270;&#34920;">&#26500;&#24314;&#22270;&#34920;<a class="anchor-link" href="#&#26500;&#24314;&#22270;&#34920;">&#182;</a></h3><p>使用你实现的神经网络构建图表。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[15]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">save_path</span> <span class="o">=</span> <span class="s1">&#39;checkpoints/dev&#39;</span>
+<span class="p">(</span><span class="n">source_int_text</span><span class="p">,</span> <span class="n">target_int_text</span><span class="p">),</span> <span class="p">(</span><span class="n">source_vocab_to_int</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">),</span> <span class="n">_</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess</span><span class="p">()</span>
+<span class="n">max_source_sentence_length</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">sentence</span><span class="p">)</span> <span class="k">for</span> <span class="n">sentence</span> <span class="ow">in</span> <span class="n">source_int_text</span><span class="p">])</span>
+
+<span class="n">train_graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
+<span class="k">with</span> <span class="n">train_graph</span><span class="o">.</span><span class="n">as_default</span><span class="p">():</span>
+    <span class="n">input_data</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">keep_prob</span> <span class="o">=</span> <span class="n">model_inputs</span><span class="p">()</span>
+    <span class="n">sequence_length</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder_with_default</span><span class="p">(</span><span class="n">max_source_sentence_length</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;sequence_length&#39;</span><span class="p">)</span>
+    <span class="n">input_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
+    
+    <span class="n">train_logits</span><span class="p">,</span> <span class="n">inference_logits</span> <span class="o">=</span> <span class="n">seq2seq_model</span><span class="p">(</span>
+        <span class="n">tf</span><span class="o">.</span><span class="n">reverse</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">targets</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">source_vocab_to_int</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">target_vocab_to_int</span><span class="p">),</span>
+        <span class="n">encoding_embedding_size</span><span class="p">,</span> <span class="n">decoding_embedding_size</span><span class="p">,</span> <span class="n">rnn_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">)</span>
+
+    <span class="n">tf</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">inference_logits</span><span class="p">,</span> <span class="s1">&#39;logits&#39;</span><span class="p">)</span>
+    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">name_scope</span><span class="p">(</span><span class="s2">&quot;optimization&quot;</span><span class="p">):</span>
+        <span class="c1"># Loss function</span>
+        <span class="n">cost</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">seq2seq</span><span class="o">.</span><span class="n">sequence_loss</span><span class="p">(</span>
+            <span class="n">train_logits</span><span class="p">,</span>
+            <span class="n">targets</span><span class="p">,</span>
+            <span class="n">tf</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="n">input_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sequence_length</span><span class="p">]))</span>
+
+        <span class="c1"># Optimizer</span>
+        <span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span>
+
+        <span class="c1"># Gradient Clipping</span>
+        <span class="n">gradients</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">compute_gradients</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
+        <span class="n">capped_gradients</span> <span class="o">=</span> <span class="p">[(</span><span class="n">tf</span><span class="o">.</span><span class="n">clip_by_value</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">),</span> <span class="n">var</span><span class="p">)</span> <span class="k">for</span> <span class="n">grad</span><span class="p">,</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">gradients</span> <span class="k">if</span> <span class="n">grad</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span>
+        <span class="n">train_op</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">apply_gradients</span><span class="p">(</span><span class="n">capped_gradients</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#35757;&#32451;">&#35757;&#32451;<a class="anchor-link" href="#&#35757;&#32451;">&#182;</a></h3><p>利用预处理的数据训练神经网络。如果很难获得低损失值，请访问我们的论坛，看看其他人是否遇到了相同的问题。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[16]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">time</span>
+
+<span class="k">def</span> <span class="nf">get_accuracy</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">logits</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculate accuracy</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">max_seq</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">logits</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="k">if</span> <span class="n">max_seq</span> <span class="o">-</span> <span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+        <span class="n">target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span>
+            <span class="n">target</span><span class="p">,</span>
+            <span class="p">[(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="n">max_seq</span> <span class="o">-</span> <span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])],</span>
+            <span class="s1">&#39;constant&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">max_seq</span> <span class="o">-</span> <span class="n">logits</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+        <span class="n">logits</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span>
+            <span class="n">logits</span><span class="p">,</span>
+            <span class="p">[(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="n">max_seq</span> <span class="o">-</span> <span class="n">logits</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)],</span>
+            <span class="s1">&#39;constant&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+
+<span class="n">train_source</span> <span class="o">=</span> <span class="n">source_int_text</span><span class="p">[</span><span class="n">batch_size</span><span class="p">:]</span>
+<span class="n">train_target</span> <span class="o">=</span> <span class="n">target_int_text</span><span class="p">[</span><span class="n">batch_size</span><span class="p">:]</span>
+
+<span class="n">valid_source</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">pad_sentence_batch</span><span class="p">(</span><span class="n">source_int_text</span><span class="p">[:</span><span class="n">batch_size</span><span class="p">])</span>
+<span class="n">valid_target</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">pad_sentence_batch</span><span class="p">(</span><span class="n">target_int_text</span><span class="p">[:</span><span class="n">batch_size</span><span class="p">])</span>
+
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">train_graph</span><span class="p">)</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
+
+    <span class="k">for</span> <span class="n">epoch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">batch_i</span><span class="p">,</span> <span class="p">(</span><span class="n">source_batch</span><span class="p">,</span> <span class="n">target_batch</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
+                <span class="n">helper</span><span class="o">.</span><span class="n">batch_data</span><span class="p">(</span><span class="n">train_source</span><span class="p">,</span> <span class="n">train_target</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)):</span>
+            <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            
+            <span class="n">_</span><span class="p">,</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">train_op</span><span class="p">,</span> <span class="n">cost</span><span class="p">],</span>
+                <span class="p">{</span><span class="n">input_data</span><span class="p">:</span> <span class="n">source_batch</span><span class="p">,</span>
+                 <span class="n">targets</span><span class="p">:</span> <span class="n">target_batch</span><span class="p">,</span>
+                 <span class="n">lr</span><span class="p">:</span> <span class="n">learning_rate</span><span class="p">,</span>
+                 <span class="n">sequence_length</span><span class="p">:</span> <span class="n">target_batch</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                 <span class="n">keep_prob</span><span class="p">:</span> <span class="n">keep_probability</span><span class="p">})</span>
+            
+            <span class="n">batch_train_logits</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+                <span class="n">inference_logits</span><span class="p">,</span>
+                <span class="p">{</span><span class="n">input_data</span><span class="p">:</span> <span class="n">source_batch</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
+            <span class="n">batch_valid_logits</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+                <span class="n">inference_logits</span><span class="p">,</span>
+                <span class="p">{</span><span class="n">input_data</span><span class="p">:</span> <span class="n">valid_source</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
+                
+            <span class="n">train_acc</span> <span class="o">=</span> <span class="n">get_accuracy</span><span class="p">(</span><span class="n">target_batch</span><span class="p">,</span> <span class="n">batch_train_logits</span><span class="p">)</span>
+            <span class="n">valid_acc</span> <span class="o">=</span> <span class="n">get_accuracy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">valid_target</span><span class="p">),</span> <span class="n">batch_valid_logits</span><span class="p">)</span>
+            <span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;3}</span><span class="s1"> Batch </span><span class="si">{:&gt;4}</span><span class="s1">/</span><span class="si">{}</span><span class="s1"> - Train Accuracy: </span><span class="si">{:&gt;6.3f}</span><span class="s1">, Validation Accuracy: </span><span class="si">{:&gt;6.3f}</span><span class="s1">, Loss: </span><span class="si">{:&gt;6.3f}</span><span class="s1">&#39;</span>
+                  <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch_i</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">source_int_text</span><span class="p">)</span> <span class="o">//</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">train_acc</span><span class="p">,</span> <span class="n">valid_acc</span><span class="p">,</span> <span class="n">loss</span><span class="p">))</span>
+
+    <span class="c1"># Save Model</span>
+    <span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
+    <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_path</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Model Trained and Saved&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch   0 Batch    0/1077 - Train Accuracy:  0.294, Validation Accuracy:  0.305, Loss:  5.889
+Epoch   0 Batch    1/1077 - Train Accuracy:  0.221, Validation Accuracy:  0.305, Loss:  5.074
+Epoch   0 Batch    2/1077 - Train Accuracy:  0.244, Validation Accuracy:  0.335, Loss:  4.336
+Epoch   0 Batch    3/1077 - Train Accuracy:  0.275, Validation Accuracy:  0.337, Loss:  3.832
+Epoch   0 Batch    4/1077 - Train Accuracy:  0.263, Validation Accuracy:  0.336, Loss:  3.669
+Epoch   0 Batch    5/1077 - Train Accuracy:  0.295, Validation Accuracy:  0.336, Loss:  3.552
+Epoch   0 Batch    6/1077 - Train Accuracy:  0.282, Validation Accuracy:  0.342, Loss:  3.504
+Epoch   0 Batch    7/1077 - Train Accuracy:  0.268, Validation Accuracy:  0.342, Loss:  3.515
+Epoch   0 Batch    8/1077 - Train Accuracy:  0.275, Validation Accuracy:  0.341, Loss:  3.414
+Epoch   0 Batch    9/1077 - Train Accuracy:  0.283, Validation Accuracy:  0.341, Loss:  3.324
+Epoch   0 Batch   10/1077 - Train Accuracy:  0.273, Validation Accuracy:  0.363, Loss:  3.444
+Epoch   0 Batch   11/1077 - Train Accuracy:  0.334, Validation Accuracy:  0.370, Loss:  3.166
+Epoch   0 Batch   12/1077 - Train Accuracy:  0.314, Validation Accuracy:  0.380, Loss:  3.323
+Epoch   0 Batch   13/1077 - Train Accuracy:  0.353, Validation Accuracy:  0.373, Loss:  3.069
+Epoch   0 Batch   14/1077 - Train Accuracy:  0.338, Validation Accuracy:  0.377, Loss:  3.096
+Epoch   0 Batch   15/1077 - Train Accuracy:  0.333, Validation Accuracy:  0.391, Loss:  3.169
+Epoch   0 Batch   16/1077 - Train Accuracy:  0.350, Validation Accuracy:  0.392, Loss:  3.136
+Epoch   0 Batch   17/1077 - Train Accuracy:  0.341, Validation Accuracy:  0.382, Loss:  3.051
+Epoch   0 Batch   18/1077 - Train Accuracy:  0.343, Validation Accuracy:  0.407, Loss:  3.059
+Epoch   0 Batch   19/1077 - Train Accuracy:  0.361, Validation Accuracy:  0.402, Loss:  2.920
+Epoch   0 Batch   20/1077 - Train Accuracy:  0.341, Validation Accuracy:  0.400, Loss:  2.897
+Epoch   0 Batch   21/1077 - Train Accuracy:  0.330, Validation Accuracy:  0.409, Loss:  2.971
+Epoch   0 Batch   22/1077 - Train Accuracy:  0.359, Validation Accuracy:  0.417, Loss:  2.950
+Epoch   0 Batch   23/1077 - Train Accuracy:  0.354, Validation Accuracy:  0.412, Loss:  2.924
+Epoch   0 Batch   24/1077 - Train Accuracy:  0.360, Validation Accuracy:  0.413, Loss:  2.818
+Epoch   0 Batch   25/1077 - Train Accuracy:  0.361, Validation Accuracy:  0.425, Loss:  2.867
+Epoch   0 Batch   26/1077 - Train Accuracy:  0.359, Validation Accuracy:  0.420, Loss:  2.829
+Epoch   0 Batch   27/1077 - Train Accuracy:  0.410, Validation Accuracy:  0.427, Loss:  2.581
+Epoch   0 Batch   28/1077 - Train Accuracy:  0.394, Validation Accuracy:  0.436, Loss:  2.710
+Epoch   0 Batch   29/1077 - Train Accuracy:  0.395, Validation Accuracy:  0.441, Loss:  2.660
+Epoch   0 Batch   30/1077 - Train Accuracy:  0.397, Validation Accuracy:  0.448, Loss:  2.664
+Epoch   0 Batch   31/1077 - Train Accuracy:  0.399, Validation Accuracy:  0.453, Loss:  2.683
+Epoch   0 Batch   32/1077 - Train Accuracy:  0.453, Validation Accuracy:  0.460, Loss:  2.471
+Epoch   0 Batch   33/1077 - Train Accuracy:  0.429, Validation Accuracy:  0.468, Loss:  2.467
+Epoch   0 Batch   34/1077 - Train Accuracy:  0.405, Validation Accuracy:  0.462, Loss:  2.541
+Epoch   0 Batch   35/1077 - Train Accuracy:  0.410, Validation Accuracy:  0.460, Loss:  2.516
+Epoch   0 Batch   36/1077 - Train Accuracy:  0.425, Validation Accuracy:  0.466, Loss:  2.453
+Epoch   0 Batch   37/1077 - Train Accuracy:  0.427, Validation Accuracy:  0.480, Loss:  2.505
+Epoch   0 Batch   38/1077 - Train Accuracy:  0.378, Validation Accuracy:  0.474, Loss:  2.650
+Epoch   0 Batch   39/1077 - Train Accuracy:  0.420, Validation Accuracy:  0.480, Loss:  2.496
+Epoch   0 Batch   40/1077 - Train Accuracy:  0.410, Validation Accuracy:  0.466, Loss:  2.415
+Epoch   0 Batch   41/1077 - Train Accuracy:  0.455, Validation Accuracy:  0.483, Loss:  2.359
+Epoch   0 Batch   42/1077 - Train Accuracy:  0.414, Validation Accuracy:  0.467, Loss:  2.375
+Epoch   0 Batch   43/1077 - Train Accuracy:  0.439, Validation Accuracy:  0.497, Loss:  2.402
+Epoch   0 Batch   44/1077 - Train Accuracy:  0.355, Validation Accuracy:  0.436, Loss:  2.547
+Epoch   0 Batch   45/1077 - Train Accuracy:  0.406, Validation Accuracy:  0.469, Loss:  2.402
+Epoch   0 Batch   46/1077 - Train Accuracy:  0.419, Validation Accuracy:  0.492, Loss:  2.394
+Epoch   0 Batch   47/1077 - Train Accuracy:  0.406, Validation Accuracy:  0.458, Loss:  2.299
+Epoch   0 Batch   48/1077 - Train Accuracy:  0.407, Validation Accuracy:  0.443, Loss:  2.303
+Epoch   0 Batch   49/1077 - Train Accuracy:  0.404, Validation Accuracy:  0.462, Loss:  2.302
+Epoch   0 Batch   50/1077 - Train Accuracy:  0.403, Validation Accuracy:  0.478, Loss:  2.336
+Epoch   0 Batch   51/1077 - Train Accuracy:  0.434, Validation Accuracy:  0.457, Loss:  2.179
+Epoch   0 Batch   52/1077 - Train Accuracy:  0.432, Validation Accuracy:  0.492, Loss:  2.245
+Epoch   0 Batch   53/1077 - Train Accuracy:  0.474, Validation Accuracy:  0.518, Loss:  2.199
+Epoch   0 Batch   54/1077 - Train Accuracy:  0.439, Validation Accuracy:  0.523, Loss:  2.408
+Epoch   0 Batch   55/1077 - Train Accuracy:  0.479, Validation Accuracy:  0.507, Loss:  2.111
+Epoch   0 Batch   56/1077 - Train Accuracy:  0.455, Validation Accuracy:  0.513, Loss:  2.172
+Epoch   0 Batch   57/1077 - Train Accuracy:  0.520, Validation Accuracy:  0.512, Loss:  1.935
+Epoch   0 Batch   58/1077 - Train Accuracy:  0.445, Validation Accuracy:  0.493, Loss:  2.165
+Epoch   0 Batch   59/1077 - Train Accuracy:  0.462, Validation Accuracy:  0.532, Loss:  2.256
+Epoch   0 Batch   60/1077 - Train Accuracy:  0.487, Validation Accuracy:  0.525, Loss:  2.097
+Epoch   0 Batch   61/1077 - Train Accuracy:  0.454, Validation Accuracy:  0.509, Loss:  2.091
+Epoch   0 Batch   62/1077 - Train Accuracy:  0.464, Validation Accuracy:  0.531, Loss:  2.211
+Epoch   0 Batch   63/1077 - Train Accuracy:  0.517, Validation Accuracy:  0.535, Loss:  1.980
+Epoch   0 Batch   64/1077 - Train Accuracy:  0.446, Validation Accuracy:  0.494, Loss:  2.087
+Epoch   0 Batch   65/1077 - Train Accuracy:  0.462, Validation Accuracy:  0.544, Loss:  2.220
+Epoch   0 Batch   66/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.528, Loss:  2.058
+Epoch   0 Batch   67/1077 - Train Accuracy:  0.497, Validation Accuracy:  0.507, Loss:  1.945
+Epoch   0 Batch   68/1077 - Train Accuracy:  0.479, Validation Accuracy:  0.534, Loss:  2.047
+Epoch   0 Batch   69/1077 - Train Accuracy:  0.516, Validation Accuracy:  0.546, Loss:  1.991
+Epoch   0 Batch   70/1077 - Train Accuracy:  0.474, Validation Accuracy:  0.534, Loss:  2.054
+Epoch   0 Batch   71/1077 - Train Accuracy:  0.486, Validation Accuracy:  0.534, Loss:  1.950
+Epoch   0 Batch   72/1077 - Train Accuracy:  0.491, Validation Accuracy:  0.543, Loss:  1.957
+Epoch   0 Batch   73/1077 - Train Accuracy:  0.475, Validation Accuracy:  0.521, Loss:  1.968
+Epoch   0 Batch   74/1077 - Train Accuracy:  0.507, Validation Accuracy:  0.535, Loss:  1.823
+Epoch   0 Batch   75/1077 - Train Accuracy:  0.526, Validation Accuracy:  0.539, Loss:  1.826
+Epoch   0 Batch   76/1077 - Train Accuracy:  0.502, Validation Accuracy:  0.529, Loss:  1.857
+Epoch   0 Batch   77/1077 - Train Accuracy:  0.462, Validation Accuracy:  0.527, Loss:  1.920
+Epoch   0 Batch   78/1077 - Train Accuracy:  0.455, Validation Accuracy:  0.527, Loss:  2.023
+Epoch   0 Batch   79/1077 - Train Accuracy:  0.480, Validation Accuracy:  0.521, Loss:  1.887
+Epoch   0 Batch   80/1077 - Train Accuracy:  0.482, Validation Accuracy:  0.540, Loss:  1.808
+Epoch   0 Batch   81/1077 - Train Accuracy:  0.509, Validation Accuracy:  0.534, Loss:  1.829
+Epoch   0 Batch   82/1077 - Train Accuracy:  0.532, Validation Accuracy:  0.542, Loss:  1.644
+Epoch   0 Batch   83/1077 - Train Accuracy:  0.471, Validation Accuracy:  0.533, Loss:  1.863
+Epoch   0 Batch   84/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.537, Loss:  1.749
+Epoch   0 Batch   85/1077 - Train Accuracy:  0.495, Validation Accuracy:  0.535, Loss:  1.639
+Epoch   0 Batch   86/1077 - Train Accuracy:  0.489, Validation Accuracy:  0.530, Loss:  1.738
+Epoch   0 Batch   87/1077 - Train Accuracy:  0.489, Validation Accuracy:  0.525, Loss:  1.735
+Epoch   0 Batch   88/1077 - Train Accuracy:  0.509, Validation Accuracy:  0.533, Loss:  1.659
+Epoch   0 Batch   89/1077 - Train Accuracy:  0.499, Validation Accuracy:  0.548, Loss:  1.632
+Epoch   0 Batch   90/1077 - Train Accuracy:  0.471, Validation Accuracy:  0.535, Loss:  1.699
+Epoch   0 Batch   91/1077 - Train Accuracy:  0.511, Validation Accuracy:  0.518, Loss:  1.448
+Epoch   0 Batch   92/1077 - Train Accuracy:  0.490, Validation Accuracy:  0.524, Loss:  1.569
+Epoch   0 Batch   93/1077 - Train Accuracy:  0.476, Validation Accuracy:  0.520, Loss:  1.581
+Epoch   0 Batch   94/1077 - Train Accuracy:  0.485, Validation Accuracy:  0.526, Loss:  1.457
+Epoch   0 Batch   95/1077 - Train Accuracy:  0.525, Validation Accuracy:  0.550, Loss:  1.486
+Epoch   0 Batch   96/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.533, Loss:  1.471
+Epoch   0 Batch   97/1077 - Train Accuracy:  0.467, Validation Accuracy:  0.525, Loss:  1.476
+Epoch   0 Batch   98/1077 - Train Accuracy:  0.497, Validation Accuracy:  0.508, Loss:  1.370
+Epoch   0 Batch   99/1077 - Train Accuracy:  0.458, Validation Accuracy:  0.501, Loss:  1.478
+Epoch   0 Batch  100/1077 - Train Accuracy:  0.490, Validation Accuracy:  0.514, Loss:  1.390
+Epoch   0 Batch  101/1077 - Train Accuracy:  0.474, Validation Accuracy:  0.501, Loss:  1.334
+Epoch   0 Batch  102/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.529, Loss:  1.319
+Epoch   0 Batch  103/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.538, Loss:  1.417
+Epoch   0 Batch  104/1077 - Train Accuracy:  0.458, Validation Accuracy:  0.547, Loss:  1.394
+Epoch   0 Batch  105/1077 - Train Accuracy:  0.510, Validation Accuracy:  0.512, Loss:  1.274
+Epoch   0 Batch  106/1077 - Train Accuracy:  0.468, Validation Accuracy:  0.512, Loss:  1.405
+Epoch   0 Batch  107/1077 - Train Accuracy:  0.510, Validation Accuracy:  0.526, Loss:  1.229
+Epoch   0 Batch  108/1077 - Train Accuracy:  0.556, Validation Accuracy:  0.542, Loss:  1.149
+Epoch   0 Batch  109/1077 - Train Accuracy:  0.515, Validation Accuracy:  0.548, Loss:  1.224
+Epoch   0 Batch  110/1077 - Train Accuracy:  0.536, Validation Accuracy:  0.543, Loss:  1.191
+Epoch   0 Batch  111/1077 - Train Accuracy:  0.496, Validation Accuracy:  0.547, Loss:  1.237
+Epoch   0 Batch  112/1077 - Train Accuracy:  0.494, Validation Accuracy:  0.531, Loss:  1.218
+Epoch   0 Batch  113/1077 - Train Accuracy:  0.475, Validation Accuracy:  0.522, Loss:  1.197
+Epoch   0 Batch  114/1077 - Train Accuracy:  0.526, Validation Accuracy:  0.529, Loss:  1.110
+Epoch   0 Batch  115/1077 - Train Accuracy:  0.504, Validation Accuracy:  0.532, Loss:  1.182
+Epoch   0 Batch  116/1077 - Train Accuracy:  0.488, Validation Accuracy:  0.524, Loss:  1.181
+Epoch   0 Batch  117/1077 - Train Accuracy:  0.459, Validation Accuracy:  0.544, Loss:  1.173
+Epoch   0 Batch  118/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.558, Loss:  1.164
+Epoch   0 Batch  119/1077 - Train Accuracy:  0.507, Validation Accuracy:  0.550, Loss:  1.075
+Epoch   0 Batch  120/1077 - Train Accuracy:  0.482, Validation Accuracy:  0.533, Loss:  1.114
+Epoch   0 Batch  121/1077 - Train Accuracy:  0.502, Validation Accuracy:  0.533, Loss:  1.056
+Epoch   0 Batch  122/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.539, Loss:  1.036
+Epoch   0 Batch  123/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.550, Loss:  1.013
+Epoch   0 Batch  124/1077 - Train Accuracy:  0.495, Validation Accuracy:  0.543, Loss:  1.080
+Epoch   0 Batch  125/1077 - Train Accuracy:  0.529, Validation Accuracy:  0.537, Loss:  1.015
+Epoch   0 Batch  126/1077 - Train Accuracy:  0.515, Validation Accuracy:  0.537, Loss:  0.966
+Epoch   0 Batch  127/1077 - Train Accuracy:  0.502, Validation Accuracy:  0.545, Loss:  1.023
+Epoch   0 Batch  128/1077 - Train Accuracy:  0.536, Validation Accuracy:  0.521, Loss:  0.953
+Epoch   0 Batch  129/1077 - Train Accuracy:  0.525, Validation Accuracy:  0.534, Loss:  1.014
+Epoch   0 Batch  130/1077 - Train Accuracy:  0.539, Validation Accuracy:  0.547, Loss:  0.926
+Epoch   0 Batch  131/1077 - Train Accuracy:  0.499, Validation Accuracy:  0.558, Loss:  0.993
+Epoch   0 Batch  132/1077 - Train Accuracy:  0.464, Validation Accuracy:  0.545, Loss:  1.016
+Epoch   0 Batch  133/1077 - Train Accuracy:  0.471, Validation Accuracy:  0.548, Loss:  0.994
+Epoch   0 Batch  134/1077 - Train Accuracy:  0.534, Validation Accuracy:  0.560, Loss:  0.927
+Epoch   0 Batch  135/1077 - Train Accuracy:  0.532, Validation Accuracy:  0.576, Loss:  0.994
+Epoch   0 Batch  136/1077 - Train Accuracy:  0.527, Validation Accuracy:  0.565, Loss:  0.944
+Epoch   0 Batch  137/1077 - Train Accuracy:  0.573, Validation Accuracy:  0.572, Loss:  0.864
+Epoch   0 Batch  138/1077 - Train Accuracy:  0.528, Validation Accuracy:  0.570, Loss:  0.924
+Epoch   0 Batch  139/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.555, Loss:  0.947
+Epoch   0 Batch  140/1077 - Train Accuracy:  0.457, Validation Accuracy:  0.557, Loss:  0.986
+Epoch   0 Batch  141/1077 - Train Accuracy:  0.508, Validation Accuracy:  0.552, Loss:  0.943
+Epoch   0 Batch  142/1077 - Train Accuracy:  0.546, Validation Accuracy:  0.557, Loss:  0.855
+Epoch   0 Batch  143/1077 - Train Accuracy:  0.522, Validation Accuracy:  0.550, Loss:  0.941
+Epoch   0 Batch  144/1077 - Train Accuracy:  0.481, Validation Accuracy:  0.558, Loss:  0.944
+Epoch   0 Batch  145/1077 - Train Accuracy:  0.584, Validation Accuracy:  0.559, Loss:  0.890
+Epoch   0 Batch  146/1077 - Train Accuracy:  0.527, Validation Accuracy:  0.549, Loss:  0.908
+Epoch   0 Batch  147/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.543, Loss:  0.933
+Epoch   0 Batch  148/1077 - Train Accuracy:  0.518, Validation Accuracy:  0.564, Loss:  0.885
+Epoch   0 Batch  149/1077 - Train Accuracy:  0.507, Validation Accuracy:  0.572, Loss:  0.904
+Epoch   0 Batch  150/1077 - Train Accuracy:  0.574, Validation Accuracy:  0.568, Loss:  0.849
+Epoch   0 Batch  151/1077 - Train Accuracy:  0.528, Validation Accuracy:  0.576, Loss:  0.812
+Epoch   0 Batch  152/1077 - Train Accuracy:  0.529, Validation Accuracy:  0.572, Loss:  0.877
+Epoch   0 Batch  153/1077 - Train Accuracy:  0.522, Validation Accuracy:  0.568, Loss:  0.907
+Epoch   0 Batch  154/1077 - Train Accuracy:  0.511, Validation Accuracy:  0.574, Loss:  0.873
+Epoch   0 Batch  155/1077 - Train Accuracy:  0.539, Validation Accuracy:  0.572, Loss:  0.864
+Epoch   0 Batch  156/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.584, Loss:  0.836
+Epoch   0 Batch  157/1077 - Train Accuracy:  0.557, Validation Accuracy:  0.574, Loss:  0.846
+Epoch   0 Batch  158/1077 - Train Accuracy:  0.536, Validation Accuracy:  0.591, Loss:  0.881
+Epoch   0 Batch  159/1077 - Train Accuracy:  0.548, Validation Accuracy:  0.581, Loss:  0.768
+Epoch   0 Batch  160/1077 - Train Accuracy:  0.532, Validation Accuracy:  0.576, Loss:  0.831
+Epoch   0 Batch  161/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.566, Loss:  0.835
+Epoch   0 Batch  162/1077 - Train Accuracy:  0.534, Validation Accuracy:  0.568, Loss:  0.866
+Epoch   0 Batch  163/1077 - Train Accuracy:  0.525, Validation Accuracy:  0.571, Loss:  0.891
+Epoch   0 Batch  164/1077 - Train Accuracy:  0.533, Validation Accuracy:  0.586, Loss:  0.841
+Epoch   0 Batch  165/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.587, Loss:  0.804
+Epoch   0 Batch  166/1077 - Train Accuracy:  0.580, Validation Accuracy:  0.586, Loss:  0.824
+Epoch   0 Batch  167/1077 - Train Accuracy:  0.558, Validation Accuracy:  0.574, Loss:  0.835
+Epoch   0 Batch  168/1077 - Train Accuracy:  0.520, Validation Accuracy:  0.568, Loss:  0.844
+Epoch   0 Batch  169/1077 - Train Accuracy:  0.577, Validation Accuracy:  0.575, Loss:  0.822
+Epoch   0 Batch  170/1077 - Train Accuracy:  0.553, Validation Accuracy:  0.566, Loss:  0.861
+Epoch   0 Batch  171/1077 - Train Accuracy:  0.592, Validation Accuracy:  0.577, Loss:  0.755
+Epoch   0 Batch  172/1077 - Train Accuracy:  0.599, Validation Accuracy:  0.583, Loss:  0.743
+Epoch   0 Batch  173/1077 - Train Accuracy:  0.543, Validation Accuracy:  0.578, Loss:  0.845
+Epoch   0 Batch  174/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.580, Loss:  0.779
+Epoch   0 Batch  175/1077 - Train Accuracy:  0.598, Validation Accuracy:  0.589, Loss:  0.784
+Epoch   0 Batch  176/1077 - Train Accuracy:  0.565, Validation Accuracy:  0.605, Loss:  0.783
+Epoch   0 Batch  177/1077 - Train Accuracy:  0.537, Validation Accuracy:  0.591, Loss:  0.837
+Epoch   0 Batch  178/1077 - Train Accuracy:  0.564, Validation Accuracy:  0.582, Loss:  0.763
+Epoch   0 Batch  179/1077 - Train Accuracy:  0.586, Validation Accuracy:  0.578, Loss:  0.807
+Epoch   0 Batch  180/1077 - Train Accuracy:  0.559, Validation Accuracy:  0.581, Loss:  0.773
+Epoch   0 Batch  181/1077 - Train Accuracy:  0.555, Validation Accuracy:  0.580, Loss:  0.803
+Epoch   0 Batch  182/1077 - Train Accuracy:  0.596, Validation Accuracy:  0.570, Loss:  0.752
+Epoch   0 Batch  183/1077 - Train Accuracy:  0.567, Validation Accuracy:  0.559, Loss:  0.767
+Epoch   0 Batch  184/1077 - Train Accuracy:  0.568, Validation Accuracy:  0.566, Loss:  0.718
+Epoch   0 Batch  185/1077 - Train Accuracy:  0.580, Validation Accuracy:  0.580, Loss:  0.760
+Epoch   0 Batch  186/1077 - Train Accuracy:  0.569, Validation Accuracy:  0.596, Loss:  0.777
+Epoch   0 Batch  187/1077 - Train Accuracy:  0.573, Validation Accuracy:  0.588, Loss:  0.741
+Epoch   0 Batch  188/1077 - Train Accuracy:  0.569, Validation Accuracy:  0.604, Loss:  0.744
+Epoch   0 Batch  189/1077 - Train Accuracy:  0.567, Validation Accuracy:  0.611, Loss:  0.724
+Epoch   0 Batch  190/1077 - Train Accuracy:  0.618, Validation Accuracy:  0.621, Loss:  0.726
+Epoch   0 Batch  191/1077 - Train Accuracy:  0.630, Validation Accuracy:  0.620, Loss:  0.663
+Epoch   0 Batch  192/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.620, Loss:  0.747
+Epoch   0 Batch  193/1077 - Train Accuracy:  0.629, Validation Accuracy:  0.617, Loss:  0.712
+Epoch   0 Batch  194/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.596, Loss:  0.676
+Epoch   0 Batch  195/1077 - Train Accuracy:  0.561, Validation Accuracy:  0.592, Loss:  0.712
+Epoch   0 Batch  196/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.583, Loss:  0.718
+Epoch   0 Batch  197/1077 - Train Accuracy:  0.602, Validation Accuracy:  0.586, Loss:  0.708
+Epoch   0 Batch  198/1077 - Train Accuracy:  0.646, Validation Accuracy:  0.581, Loss:  0.667
+Epoch   0 Batch  199/1077 - Train Accuracy:  0.570, Validation Accuracy:  0.590, Loss:  0.717
+Epoch   0 Batch  200/1077 - Train Accuracy:  0.579, Validation Accuracy:  0.599, Loss:  0.735
+Epoch   0 Batch  201/1077 - Train Accuracy:  0.597, Validation Accuracy:  0.594, Loss:  0.686
+Epoch   0 Batch  202/1077 - Train Accuracy:  0.601, Validation Accuracy:  0.592, Loss:  0.718
+Epoch   0 Batch  203/1077 - Train Accuracy:  0.563, Validation Accuracy:  0.582, Loss:  0.688
+Epoch   0 Batch  204/1077 - Train Accuracy:  0.582, Validation Accuracy:  0.596, Loss:  0.732
+Epoch   0 Batch  205/1077 - Train Accuracy:  0.584, Validation Accuracy:  0.583, Loss:  0.720
+Epoch   0 Batch  206/1077 - Train Accuracy:  0.623, Validation Accuracy:  0.574, Loss:  0.690
+Epoch   0 Batch  207/1077 - Train Accuracy:  0.587, Validation Accuracy:  0.583, Loss:  0.712
+Epoch   0 Batch  208/1077 - Train Accuracy:  0.597, Validation Accuracy:  0.600, Loss:  0.688
+Epoch   0 Batch  209/1077 - Train Accuracy:  0.629, Validation Accuracy:  0.608, Loss:  0.634
+Epoch   0 Batch  210/1077 - Train Accuracy:  0.613, Validation Accuracy:  0.611, Loss:  0.682
+Epoch   0 Batch  211/1077 - Train Accuracy:  0.580, Validation Accuracy:  0.626, Loss:  0.682
+Epoch   0 Batch  212/1077 - Train Accuracy:  0.609, Validation Accuracy:  0.636, Loss:  0.659
+Epoch   0 Batch  213/1077 - Train Accuracy:  0.623, Validation Accuracy:  0.631, Loss:  0.642
+Epoch   0 Batch  214/1077 - Train Accuracy:  0.579, Validation Accuracy:  0.620, Loss:  0.675
+Epoch   0 Batch  215/1077 - Train Accuracy:  0.579, Validation Accuracy:  0.615, Loss:  0.702
+Epoch   0 Batch  216/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.605, Loss:  0.700
+Epoch   0 Batch  217/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.596, Loss:  0.664
+Epoch   0 Batch  218/1077 - Train Accuracy:  0.582, Validation Accuracy:  0.583, Loss:  0.766
+Epoch   0 Batch  219/1077 - Train Accuracy:  0.639, Validation Accuracy:  0.603, Loss:  0.670
+Epoch   0 Batch  220/1077 - Train Accuracy:  0.593, Validation Accuracy:  0.608, Loss:  0.685
+Epoch   0 Batch  221/1077 - Train Accuracy:  0.660, Validation Accuracy:  0.605, Loss:  0.705
+Epoch   0 Batch  222/1077 - Train Accuracy:  0.561, Validation Accuracy:  0.600, Loss:  0.712
+Epoch   0 Batch  223/1077 - Train Accuracy:  0.615, Validation Accuracy:  0.610, Loss:  0.634
+Epoch   0 Batch  224/1077 - Train Accuracy:  0.623, Validation Accuracy:  0.606, Loss:  0.677
+Epoch   0 Batch  225/1077 - Train Accuracy:  0.618, Validation Accuracy:  0.606, Loss:  0.695
+Epoch   0 Batch  226/1077 - Train Accuracy:  0.615, Validation Accuracy:  0.601, Loss:  0.668
+Epoch   0 Batch  227/1077 - Train Accuracy:  0.593, Validation Accuracy:  0.600, Loss:  0.721
+Epoch   0 Batch  228/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.605, Loss:  0.645
+Epoch   0 Batch  229/1077 - Train Accuracy:  0.630, Validation Accuracy:  0.610, Loss:  0.654
+Epoch   0 Batch  230/1077 - Train Accuracy:  0.631, Validation Accuracy:  0.621, Loss:  0.659
+Epoch   0 Batch  231/1077 - Train Accuracy:  0.589, Validation Accuracy:  0.613, Loss:  0.668
+Epoch   0 Batch  232/1077 - Train Accuracy:  0.601, Validation Accuracy:  0.615, Loss:  0.708
+Epoch   0 Batch  233/1077 - Train Accuracy:  0.621, Validation Accuracy:  0.619, Loss:  0.706
+Epoch   0 Batch  234/1077 - Train Accuracy:  0.640, Validation Accuracy:  0.616, Loss:  0.661
+Epoch   0 Batch  235/1077 - Train Accuracy:  0.633, Validation Accuracy:  0.618, Loss:  0.606
+Epoch   0 Batch  236/1077 - Train Accuracy:  0.590, Validation Accuracy:  0.622, Loss:  0.689
+Epoch   0 Batch  237/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.616, Loss:  0.619
+Epoch   0 Batch  238/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.602, Loss:  0.673
+Epoch   0 Batch  239/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.605, Loss:  0.596
+Epoch   0 Batch  240/1077 - Train Accuracy:  0.656, Validation Accuracy:  0.611, Loss:  0.621
+Epoch   0 Batch  241/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.606, Loss:  0.611
+Epoch   0 Batch  242/1077 - Train Accuracy:  0.603, Validation Accuracy:  0.598, Loss:  0.641
+Epoch   0 Batch  243/1077 - Train Accuracy:  0.578, Validation Accuracy:  0.603, Loss:  0.677
+Epoch   0 Batch  244/1077 - Train Accuracy:  0.654, Validation Accuracy:  0.604, Loss:  0.614
+Epoch   0 Batch  245/1077 - Train Accuracy:  0.638, Validation Accuracy:  0.604, Loss:  0.600
+Epoch   0 Batch  246/1077 - Train Accuracy:  0.620, Validation Accuracy:  0.611, Loss:  0.644
+Epoch   0 Batch  247/1077 - Train Accuracy:  0.653, Validation Accuracy:  0.631, Loss:  0.609
+Epoch   0 Batch  248/1077 - Train Accuracy:  0.665, Validation Accuracy:  0.623, Loss:  0.616
+Epoch   0 Batch  249/1077 - Train Accuracy:  0.596, Validation Accuracy:  0.621, Loss:  0.615
+Epoch   0 Batch  250/1077 - Train Accuracy:  0.626, Validation Accuracy:  0.614, Loss:  0.585
+Epoch   0 Batch  251/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.588, Loss:  0.639
+Epoch   0 Batch  252/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.588, Loss:  0.620
+Epoch   0 Batch  253/1077 - Train Accuracy:  0.636, Validation Accuracy:  0.602, Loss:  0.600
+Epoch   0 Batch  254/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.605, Loss:  0.642
+Epoch   0 Batch  255/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.613, Loss:  0.625
+Epoch   0 Batch  256/1077 - Train Accuracy:  0.602, Validation Accuracy:  0.612, Loss:  0.685
+Epoch   0 Batch  257/1077 - Train Accuracy:  0.641, Validation Accuracy:  0.614, Loss:  0.624
+Epoch   0 Batch  258/1077 - Train Accuracy:  0.646, Validation Accuracy:  0.598, Loss:  0.613
+Epoch   0 Batch  259/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.605, Loss:  0.613
+Epoch   0 Batch  260/1077 - Train Accuracy:  0.643, Validation Accuracy:  0.606, Loss:  0.593
+Epoch   0 Batch  261/1077 - Train Accuracy:  0.625, Validation Accuracy:  0.604, Loss:  0.620
+Epoch   0 Batch  262/1077 - Train Accuracy:  0.633, Validation Accuracy:  0.609, Loss:  0.612
+Epoch   0 Batch  263/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.615, Loss:  0.601
+Epoch   0 Batch  264/1077 - Train Accuracy:  0.626, Validation Accuracy:  0.630, Loss:  0.619
+Epoch   0 Batch  265/1077 - Train Accuracy:  0.617, Validation Accuracy:  0.625, Loss:  0.626
+Epoch   0 Batch  266/1077 - Train Accuracy:  0.638, Validation Accuracy:  0.617, Loss:  0.590
+Epoch   0 Batch  267/1077 - Train Accuracy:  0.656, Validation Accuracy:  0.608, Loss:  0.576
+Epoch   0 Batch  268/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.623, Loss:  0.614
+Epoch   0 Batch  269/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.627, Loss:  0.658
+Epoch   0 Batch  270/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.622, Loss:  0.650
+Epoch   0 Batch  271/1077 - Train Accuracy:  0.672, Validation Accuracy:  0.624, Loss:  0.607
+Epoch   0 Batch  272/1077 - Train Accuracy:  0.635, Validation Accuracy:  0.630, Loss:  0.633
+Epoch   0 Batch  273/1077 - Train Accuracy:  0.629, Validation Accuracy:  0.633, Loss:  0.586
+Epoch   0 Batch  274/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.623, Loss:  0.596
+Epoch   0 Batch  275/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.617, Loss:  0.576
+Epoch   0 Batch  276/1077 - Train Accuracy:  0.602, Validation Accuracy:  0.628, Loss:  0.632
+Epoch   0 Batch  277/1077 - Train Accuracy:  0.643, Validation Accuracy:  0.639, Loss:  0.557
+Epoch   0 Batch  278/1077 - Train Accuracy:  0.620, Validation Accuracy:  0.665, Loss:  0.639
+Epoch   0 Batch  279/1077 - Train Accuracy:  0.611, Validation Accuracy:  0.671, Loss:  0.646
+Epoch   0 Batch  280/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.655, Loss:  0.627
+Epoch   0 Batch  281/1077 - Train Accuracy:  0.612, Validation Accuracy:  0.647, Loss:  0.627
+Epoch   0 Batch  282/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.639, Loss:  0.630
+Epoch   0 Batch  283/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.631, Loss:  0.620
+Epoch   0 Batch  284/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.630, Loss:  0.634
+Epoch   0 Batch  285/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.625, Loss:  0.586
+Epoch   0 Batch  286/1077 - Train Accuracy:  0.673, Validation Accuracy:  0.624, Loss:  0.567
+Epoch   0 Batch  287/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.623, Loss:  0.582
+Epoch   0 Batch  288/1077 - Train Accuracy:  0.612, Validation Accuracy:  0.640, Loss:  0.623
+Epoch   0 Batch  289/1077 - Train Accuracy:  0.665, Validation Accuracy:  0.643, Loss:  0.583
+Epoch   0 Batch  290/1077 - Train Accuracy:  0.639, Validation Accuracy:  0.638, Loss:  0.617
+Epoch   0 Batch  291/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.638, Loss:  0.606
+Epoch   0 Batch  292/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.636, Loss:  0.568
+Epoch   0 Batch  293/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.629, Loss:  0.611
+Epoch   0 Batch  294/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.637, Loss:  0.540
+Epoch   0 Batch  295/1077 - Train Accuracy:  0.622, Validation Accuracy:  0.616, Loss:  0.636
+Epoch   0 Batch  296/1077 - Train Accuracy:  0.696, Validation Accuracy:  0.647, Loss:  0.544
+Epoch   0 Batch  297/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.637, Loss:  0.623
+Epoch   0 Batch  298/1077 - Train Accuracy:  0.636, Validation Accuracy:  0.659, Loss:  0.615
+Epoch   0 Batch  299/1077 - Train Accuracy:  0.641, Validation Accuracy:  0.667, Loss:  0.562
+Epoch   0 Batch  300/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.659, Loss:  0.566
+Epoch   0 Batch  301/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.643, Loss:  0.556
+Epoch   0 Batch  302/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.652, Loss:  0.564
+Epoch   0 Batch  303/1077 - Train Accuracy:  0.649, Validation Accuracy:  0.636, Loss:  0.579
+Epoch   0 Batch  304/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.598, Loss:  0.534
+Epoch   0 Batch  305/1077 - Train Accuracy:  0.656, Validation Accuracy:  0.592, Loss:  0.552
+Epoch   0 Batch  306/1077 - Train Accuracy:  0.644, Validation Accuracy:  0.607, Loss:  0.547
+Epoch   0 Batch  307/1077 - Train Accuracy:  0.640, Validation Accuracy:  0.610, Loss:  0.559
+Epoch   0 Batch  308/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.610, Loss:  0.609
+Epoch   0 Batch  309/1077 - Train Accuracy:  0.674, Validation Accuracy:  0.615, Loss:  0.521
+Epoch   0 Batch  310/1077 - Train Accuracy:  0.612, Validation Accuracy:  0.617, Loss:  0.575
+Epoch   0 Batch  311/1077 - Train Accuracy:  0.666, Validation Accuracy:  0.617, Loss:  0.530
+Epoch   0 Batch  312/1077 - Train Accuracy:  0.637, Validation Accuracy:  0.614, Loss:  0.590
+Epoch   0 Batch  313/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.624, Loss:  0.547
+Epoch   0 Batch  314/1077 - Train Accuracy:  0.675, Validation Accuracy:  0.639, Loss:  0.552
+Epoch   0 Batch  315/1077 - Train Accuracy:  0.661, Validation Accuracy:  0.643, Loss:  0.519
+Epoch   0 Batch  316/1077 - Train Accuracy:  0.673, Validation Accuracy:  0.627, Loss:  0.522
+Epoch   0 Batch  317/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.619, Loss:  0.604
+Epoch   0 Batch  318/1077 - Train Accuracy:  0.637, Validation Accuracy:  0.617, Loss:  0.560
+Epoch   0 Batch  319/1077 - Train Accuracy:  0.639, Validation Accuracy:  0.608, Loss:  0.547
+Epoch   0 Batch  320/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.636, Loss:  0.557
+Epoch   0 Batch  321/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.631, Loss:  0.547
+Epoch   0 Batch  322/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.634, Loss:  0.529
+Epoch   0 Batch  323/1077 - Train Accuracy:  0.665, Validation Accuracy:  0.622, Loss:  0.538
+Epoch   0 Batch  324/1077 - Train Accuracy:  0.652, Validation Accuracy:  0.630, Loss:  0.547
+Epoch   0 Batch  325/1077 - Train Accuracy:  0.672, Validation Accuracy:  0.636, Loss:  0.527
+Epoch   0 Batch  326/1077 - Train Accuracy:  0.683, Validation Accuracy:  0.614, Loss:  0.548
+Epoch   0 Batch  327/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.610, Loss:  0.558
+Epoch   0 Batch  328/1077 - Train Accuracy:  0.685, Validation Accuracy:  0.615, Loss:  0.535
+Epoch   0 Batch  329/1077 - Train Accuracy:  0.634, Validation Accuracy:  0.630, Loss:  0.574
+Epoch   0 Batch  330/1077 - Train Accuracy:  0.658, Validation Accuracy:  0.635, Loss:  0.545
+Epoch   0 Batch  331/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.635, Loss:  0.574
+Epoch   0 Batch  332/1077 - Train Accuracy:  0.625, Validation Accuracy:  0.640, Loss:  0.505
+Epoch   0 Batch  333/1077 - Train Accuracy:  0.676, Validation Accuracy:  0.640, Loss:  0.561
+Epoch   0 Batch  334/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.640, Loss:  0.553
+Epoch   0 Batch  335/1077 - Train Accuracy:  0.684, Validation Accuracy:  0.632, Loss:  0.509
+Epoch   0 Batch  336/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.635, Loss:  0.544
+Epoch   0 Batch  337/1077 - Train Accuracy:  0.620, Validation Accuracy:  0.635, Loss:  0.549
+Epoch   0 Batch  338/1077 - Train Accuracy:  0.652, Validation Accuracy:  0.613, Loss:  0.565
+Epoch   0 Batch  339/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.613, Loss:  0.512
+Epoch   0 Batch  340/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.618, Loss:  0.547
+Epoch   0 Batch  341/1077 - Train Accuracy:  0.667, Validation Accuracy:  0.619, Loss:  0.577
+Epoch   0 Batch  342/1077 - Train Accuracy:  0.644, Validation Accuracy:  0.619, Loss:  0.511
+Epoch   0 Batch  343/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.627, Loss:  0.545
+Epoch   0 Batch  344/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.636, Loss:  0.526
+Epoch   0 Batch  345/1077 - Train Accuracy:  0.706, Validation Accuracy:  0.628, Loss:  0.502
+Epoch   0 Batch  346/1077 - Train Accuracy:  0.647, Validation Accuracy:  0.636, Loss:  0.551
+Epoch   0 Batch  347/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.632, Loss:  0.491
+Epoch   0 Batch  348/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.630, Loss:  0.518
+Epoch   0 Batch  349/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.627, Loss:  0.529
+Epoch   0 Batch  350/1077 - Train Accuracy:  0.646, Validation Accuracy:  0.620, Loss:  0.544
+Epoch   0 Batch  351/1077 - Train Accuracy:  0.653, Validation Accuracy:  0.650, Loss:  0.545
+Epoch   0 Batch  352/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.655, Loss:  0.523
+Epoch   0 Batch  353/1077 - Train Accuracy:  0.635, Validation Accuracy:  0.670, Loss:  0.567
+Epoch   0 Batch  354/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.661, Loss:  0.548
+Epoch   0 Batch  355/1077 - Train Accuracy:  0.652, Validation Accuracy:  0.649, Loss:  0.516
+Epoch   0 Batch  356/1077 - Train Accuracy:  0.686, Validation Accuracy:  0.643, Loss:  0.528
+Epoch   0 Batch  357/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.644, Loss:  0.501
+Epoch   0 Batch  358/1077 - Train Accuracy:  0.636, Validation Accuracy:  0.648, Loss:  0.546
+Epoch   0 Batch  359/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.662, Loss:  0.524
+Epoch   0 Batch  360/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.648, Loss:  0.515
+Epoch   0 Batch  361/1077 - Train Accuracy:  0.680, Validation Accuracy:  0.657, Loss:  0.545
+Epoch   0 Batch  362/1077 - Train Accuracy:  0.670, Validation Accuracy:  0.636, Loss:  0.505
+Epoch   0 Batch  363/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.641, Loss:  0.519
+Epoch   0 Batch  364/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.641, Loss:  0.542
+Epoch   0 Batch  365/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.636, Loss:  0.502
+Epoch   0 Batch  366/1077 - Train Accuracy:  0.638, Validation Accuracy:  0.635, Loss:  0.520
+Epoch   0 Batch  367/1077 - Train Accuracy:  0.703, Validation Accuracy:  0.645, Loss:  0.452
+Epoch   0 Batch  368/1077 - Train Accuracy:  0.686, Validation Accuracy:  0.657, Loss:  0.506
+Epoch   0 Batch  369/1077 - Train Accuracy:  0.668, Validation Accuracy:  0.666, Loss:  0.485
+Epoch   0 Batch  370/1077 - Train Accuracy:  0.684, Validation Accuracy:  0.662, Loss:  0.483
+Epoch   0 Batch  371/1077 - Train Accuracy:  0.711, Validation Accuracy:  0.673, Loss:  0.487
+Epoch   0 Batch  372/1077 - Train Accuracy:  0.711, Validation Accuracy:  0.668, Loss:  0.480
+Epoch   0 Batch  373/1077 - Train Accuracy:  0.699, Validation Accuracy:  0.667, Loss:  0.463
+Epoch   0 Batch  374/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.657, Loss:  0.524
+Epoch   0 Batch  375/1077 - Train Accuracy:  0.694, Validation Accuracy:  0.662, Loss:  0.468
+Epoch   0 Batch  376/1077 - Train Accuracy:  0.675, Validation Accuracy:  0.654, Loss:  0.471
+Epoch   0 Batch  377/1077 - Train Accuracy:  0.661, Validation Accuracy:  0.657, Loss:  0.496
+Epoch   0 Batch  378/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.658, Loss:  0.473
+Epoch   0 Batch  379/1077 - Train Accuracy:  0.688, Validation Accuracy:  0.661, Loss:  0.514
+Epoch   0 Batch  380/1077 - Train Accuracy:  0.676, Validation Accuracy:  0.674, Loss:  0.480
+Epoch   0 Batch  381/1077 - Train Accuracy:  0.669, Validation Accuracy:  0.661, Loss:  0.509
+Epoch   0 Batch  382/1077 - Train Accuracy:  0.689, Validation Accuracy:  0.672, Loss:  0.521
+Epoch   0 Batch  383/1077 - Train Accuracy:  0.688, Validation Accuracy:  0.661, Loss:  0.467
+Epoch   0 Batch  384/1077 - Train Accuracy:  0.678, Validation Accuracy:  0.654, Loss:  0.481
+Epoch   0 Batch  385/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.657, Loss:  0.477
+Epoch   0 Batch  386/1077 - Train Accuracy:  0.683, Validation Accuracy:  0.652, Loss:  0.472
+Epoch   0 Batch  387/1077 - Train Accuracy:  0.708, Validation Accuracy:  0.662, Loss:  0.458
+Epoch   0 Batch  388/1077 - Train Accuracy:  0.681, Validation Accuracy:  0.672, Loss:  0.460
+Epoch   0 Batch  389/1077 - Train Accuracy:  0.702, Validation Accuracy:  0.681, Loss:  0.489
+Epoch   0 Batch  390/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.669, Loss:  0.494
+Epoch   0 Batch  391/1077 - Train Accuracy:  0.696, Validation Accuracy:  0.652, Loss:  0.472
+Epoch   0 Batch  392/1077 - Train Accuracy:  0.690, Validation Accuracy:  0.669, Loss:  0.463
+Epoch   0 Batch  393/1077 - Train Accuracy:  0.697, Validation Accuracy:  0.681, Loss:  0.443
+Epoch   0 Batch  394/1077 - Train Accuracy:  0.669, Validation Accuracy:  0.698, Loss:  0.478
+Epoch   0 Batch  395/1077 - Train Accuracy:  0.710, Validation Accuracy:  0.696, Loss:  0.442
+Epoch   0 Batch  396/1077 - Train Accuracy:  0.653, Validation Accuracy:  0.696, Loss:  0.495
+Epoch   0 Batch  397/1077 - Train Accuracy:  0.712, Validation Accuracy:  0.679, Loss:  0.460
+Epoch   0 Batch  398/1077 - Train Accuracy:  0.704, Validation Accuracy:  0.674, Loss:  0.496
+Epoch   0 Batch  399/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.692, Loss:  0.478
+Epoch   0 Batch  400/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.664, Loss:  0.478
+Epoch   0 Batch  401/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.639, Loss:  0.459
+Epoch   0 Batch  402/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.647, Loss:  0.449
+Epoch   0 Batch  403/1077 - Train Accuracy:  0.693, Validation Accuracy:  0.689, Loss:  0.473
+Epoch   0 Batch  404/1077 - Train Accuracy:  0.701, Validation Accuracy:  0.670, Loss:  0.438
+Epoch   0 Batch  405/1077 - Train Accuracy:  0.717, Validation Accuracy:  0.688, Loss:  0.506
+Epoch   0 Batch  406/1077 - Train Accuracy:  0.730, Validation Accuracy:  0.711, Loss:  0.462
+Epoch   0 Batch  407/1077 - Train Accuracy:  0.669, Validation Accuracy:  0.707, Loss:  0.490
+Epoch   0 Batch  408/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.703, Loss:  0.471
+Epoch   0 Batch  409/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.707, Loss:  0.478
+Epoch   0 Batch  410/1077 - Train Accuracy:  0.662, Validation Accuracy:  0.701, Loss:  0.485
+Epoch   0 Batch  411/1077 - Train Accuracy:  0.729, Validation Accuracy:  0.688, Loss:  0.457
+Epoch   0 Batch  412/1077 - Train Accuracy:  0.697, Validation Accuracy:  0.710, Loss:  0.438
+Epoch   0 Batch  413/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.718, Loss:  0.438
+Epoch   0 Batch  414/1077 - Train Accuracy:  0.668, Validation Accuracy:  0.710, Loss:  0.463
+Epoch   0 Batch  415/1077 - Train Accuracy:  0.693, Validation Accuracy:  0.697, Loss:  0.420
+Epoch   0 Batch  416/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.696, Loss:  0.452
+Epoch   0 Batch  417/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.689, Loss:  0.474
+Epoch   0 Batch  418/1077 - Train Accuracy:  0.695, Validation Accuracy:  0.696, Loss:  0.449
+Epoch   0 Batch  419/1077 - Train Accuracy:  0.707, Validation Accuracy:  0.697, Loss:  0.446
+Epoch   0 Batch  420/1077 - Train Accuracy:  0.710, Validation Accuracy:  0.696, Loss:  0.435
+Epoch   0 Batch  421/1077 - Train Accuracy:  0.675, Validation Accuracy:  0.704, Loss:  0.465
+Epoch   0 Batch  422/1077 - Train Accuracy:  0.715, Validation Accuracy:  0.690, Loss:  0.433
+Epoch   0 Batch  423/1077 - Train Accuracy:  0.695, Validation Accuracy:  0.684, Loss:  0.465
+Epoch   0 Batch  424/1077 - Train Accuracy:  0.679, Validation Accuracy:  0.685, Loss:  0.444
+Epoch   0 Batch  425/1077 - Train Accuracy:  0.723, Validation Accuracy:  0.670, Loss:  0.429
+Epoch   0 Batch  426/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.688, Loss:  0.442
+Epoch   0 Batch  427/1077 - Train Accuracy:  0.714, Validation Accuracy:  0.681, Loss:  0.433
+Epoch   0 Batch  428/1077 - Train Accuracy:  0.711, Validation Accuracy:  0.686, Loss:  0.416
+Epoch   0 Batch  429/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.683, Loss:  0.433
+Epoch   0 Batch  430/1077 - Train Accuracy:  0.707, Validation Accuracy:  0.681, Loss:  0.427
+Epoch   0 Batch  431/1077 - Train Accuracy:  0.658, Validation Accuracy:  0.670, Loss:  0.429
+Epoch   0 Batch  432/1077 - Train Accuracy:  0.708, Validation Accuracy:  0.669, Loss:  0.440
+Epoch   0 Batch  433/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.680, Loss:  0.433
+Epoch   0 Batch  434/1077 - Train Accuracy:  0.707, Validation Accuracy:  0.692, Loss:  0.420
+Epoch   0 Batch  435/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.699, Loss:  0.468
+Epoch   0 Batch  436/1077 - Train Accuracy:  0.718, Validation Accuracy:  0.704, Loss:  0.412
+Epoch   0 Batch  437/1077 - Train Accuracy:  0.710, Validation Accuracy:  0.711, Loss:  0.430
+Epoch   0 Batch  438/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.700, Loss:  0.426
+Epoch   0 Batch  439/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.709, Loss:  0.446
+Epoch   0 Batch  440/1077 - Train Accuracy:  0.701, Validation Accuracy:  0.705, Loss:  0.441
+Epoch   0 Batch  441/1077 - Train Accuracy:  0.691, Validation Accuracy:  0.699, Loss:  0.414
+Epoch   0 Batch  442/1077 - Train Accuracy:  0.730, Validation Accuracy:  0.701, Loss:  0.412
+Epoch   0 Batch  443/1077 - Train Accuracy:  0.728, Validation Accuracy:  0.702, Loss:  0.403
+Epoch   0 Batch  444/1077 - Train Accuracy:  0.741, Validation Accuracy:  0.677, Loss:  0.426
+Epoch   0 Batch  445/1077 - Train Accuracy:  0.686, Validation Accuracy:  0.675, Loss:  0.439
+Epoch   0 Batch  446/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.666, Loss:  0.391
+Epoch   0 Batch  447/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.670, Loss:  0.408
+Epoch   0 Batch  448/1077 - Train Accuracy:  0.691, Validation Accuracy:  0.669, Loss:  0.430
+Epoch   0 Batch  449/1077 - Train Accuracy:  0.672, Validation Accuracy:  0.686, Loss:  0.430
+Epoch   0 Batch  450/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.696, Loss:  0.410
+Epoch   0 Batch  451/1077 - Train Accuracy:  0.756, Validation Accuracy:  0.699, Loss:  0.397
+Epoch   0 Batch  452/1077 - Train Accuracy:  0.727, Validation Accuracy:  0.691, Loss:  0.420
+Epoch   0 Batch  453/1077 - Train Accuracy:  0.725, Validation Accuracy:  0.692, Loss:  0.381
+Epoch   0 Batch  454/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.694, Loss:  0.409
+Epoch   0 Batch  455/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.690, Loss:  0.391
+Epoch   0 Batch  456/1077 - Train Accuracy:  0.738, Validation Accuracy:  0.702, Loss:  0.405
+Epoch   0 Batch  457/1077 - Train Accuracy:  0.724, Validation Accuracy:  0.694, Loss:  0.370
+Epoch   0 Batch  458/1077 - Train Accuracy:  0.683, Validation Accuracy:  0.693, Loss:  0.413
+Epoch   0 Batch  459/1077 - Train Accuracy:  0.751, Validation Accuracy:  0.689, Loss:  0.391
+Epoch   0 Batch  460/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.720, Loss:  0.425
+Epoch   0 Batch  461/1077 - Train Accuracy:  0.713, Validation Accuracy:  0.725, Loss:  0.407
+Epoch   0 Batch  462/1077 - Train Accuracy:  0.729, Validation Accuracy:  0.722, Loss:  0.403
+Epoch   0 Batch  463/1077 - Train Accuracy:  0.693, Validation Accuracy:  0.717, Loss:  0.404
+Epoch   0 Batch  464/1077 - Train Accuracy:  0.746, Validation Accuracy:  0.716, Loss:  0.403
+Epoch   0 Batch  465/1077 - Train Accuracy:  0.714, Validation Accuracy:  0.718, Loss:  0.428
+Epoch   0 Batch  466/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.707, Loss:  0.383
+Epoch   0 Batch  467/1077 - Train Accuracy:  0.767, Validation Accuracy:  0.696, Loss:  0.393
+Epoch   0 Batch  468/1077 - Train Accuracy:  0.758, Validation Accuracy:  0.712, Loss:  0.400
+Epoch   0 Batch  469/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.717, Loss:  0.404
+Epoch   0 Batch  470/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.712, Loss:  0.423
+Epoch   0 Batch  471/1077 - Train Accuracy:  0.762, Validation Accuracy:  0.721, Loss:  0.375
+Epoch   0 Batch  472/1077 - Train Accuracy:  0.722, Validation Accuracy:  0.725, Loss:  0.387
+Epoch   0 Batch  473/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.738, Loss:  0.405
+Epoch   0 Batch  474/1077 - Train Accuracy:  0.723, Validation Accuracy:  0.739, Loss:  0.391
+Epoch   0 Batch  475/1077 - Train Accuracy:  0.749, Validation Accuracy:  0.716, Loss:  0.390
+Epoch   0 Batch  476/1077 - Train Accuracy:  0.749, Validation Accuracy:  0.705, Loss:  0.383
+Epoch   0 Batch  477/1077 - Train Accuracy:  0.756, Validation Accuracy:  0.703, Loss:  0.378
+Epoch   0 Batch  478/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.713, Loss:  0.407
+Epoch   0 Batch  479/1077 - Train Accuracy:  0.728, Validation Accuracy:  0.721, Loss:  0.404
+Epoch   0 Batch  480/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.714, Loss:  0.396
+Epoch   0 Batch  481/1077 - Train Accuracy:  0.730, Validation Accuracy:  0.708, Loss:  0.393
+Epoch   0 Batch  482/1077 - Train Accuracy:  0.704, Validation Accuracy:  0.701, Loss:  0.413
+Epoch   0 Batch  483/1077 - Train Accuracy:  0.709, Validation Accuracy:  0.713, Loss:  0.392
+Epoch   0 Batch  484/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.723, Loss:  0.385
+Epoch   0 Batch  485/1077 - Train Accuracy:  0.756, Validation Accuracy:  0.718, Loss:  0.395
+Epoch   0 Batch  486/1077 - Train Accuracy:  0.740, Validation Accuracy:  0.721, Loss:  0.377
+Epoch   0 Batch  487/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.717, Loss:  0.389
+Epoch   0 Batch  488/1077 - Train Accuracy:  0.731, Validation Accuracy:  0.709, Loss:  0.393
+Epoch   0 Batch  489/1077 - Train Accuracy:  0.739, Validation Accuracy:  0.700, Loss:  0.364
+Epoch   0 Batch  490/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.689, Loss:  0.388
+Epoch   0 Batch  491/1077 - Train Accuracy:  0.728, Validation Accuracy:  0.705, Loss:  0.379
+Epoch   0 Batch  492/1077 - Train Accuracy:  0.749, Validation Accuracy:  0.709, Loss:  0.394
+Epoch   0 Batch  493/1077 - Train Accuracy:  0.750, Validation Accuracy:  0.713, Loss:  0.357
+Epoch   0 Batch  494/1077 - Train Accuracy:  0.739, Validation Accuracy:  0.726, Loss:  0.354
+Epoch   0 Batch  495/1077 - Train Accuracy:  0.733, Validation Accuracy:  0.740, Loss:  0.359
+Epoch   0 Batch  496/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.725, Loss:  0.396
+Epoch   0 Batch  497/1077 - Train Accuracy:  0.743, Validation Accuracy:  0.735, Loss:  0.410
+Epoch   0 Batch  498/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.727, Loss:  0.372
+Epoch   0 Batch  499/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.728, Loss:  0.354
+Epoch   0 Batch  500/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.722, Loss:  0.357
+Epoch   0 Batch  501/1077 - Train Accuracy:  0.723, Validation Accuracy:  0.728, Loss:  0.364
+Epoch   0 Batch  502/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.727, Loss:  0.377
+Epoch   0 Batch  503/1077 - Train Accuracy:  0.748, Validation Accuracy:  0.733, Loss:  0.369
+Epoch   0 Batch  504/1077 - Train Accuracy:  0.745, Validation Accuracy:  0.724, Loss:  0.366
+Epoch   0 Batch  505/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.722, Loss:  0.332
+Epoch   0 Batch  506/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.707, Loss:  0.377
+Epoch   0 Batch  507/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.740, Loss:  0.361
+Epoch   0 Batch  508/1077 - Train Accuracy:  0.758, Validation Accuracy:  0.734, Loss:  0.353
+Epoch   0 Batch  509/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.719, Loss:  0.385
+Epoch   0 Batch  510/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.719, Loss:  0.349
+Epoch   0 Batch  511/1077 - Train Accuracy:  0.748, Validation Accuracy:  0.735, Loss:  0.360
+Epoch   0 Batch  512/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.731, Loss:  0.355
+Epoch   0 Batch  513/1077 - Train Accuracy:  0.759, Validation Accuracy:  0.737, Loss:  0.356
+Epoch   0 Batch  514/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.720, Loss:  0.379
+Epoch   0 Batch  515/1077 - Train Accuracy:  0.734, Validation Accuracy:  0.725, Loss:  0.380
+Epoch   0 Batch  516/1077 - Train Accuracy:  0.780, Validation Accuracy:  0.732, Loss:  0.347
+Epoch   0 Batch  517/1077 - Train Accuracy:  0.768, Validation Accuracy:  0.756, Loss:  0.353
+Epoch   0 Batch  518/1077 - Train Accuracy:  0.794, Validation Accuracy:  0.756, Loss:  0.342
+Epoch   0 Batch  519/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.754, Loss:  0.342
+Epoch   0 Batch  520/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.745, Loss:  0.331
+Epoch   0 Batch  521/1077 - Train Accuracy:  0.745, Validation Accuracy:  0.752, Loss:  0.345
+Epoch   0 Batch  522/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.735, Loss:  0.352
+Epoch   0 Batch  523/1077 - Train Accuracy:  0.754, Validation Accuracy:  0.743, Loss:  0.374
+Epoch   0 Batch  524/1077 - Train Accuracy:  0.781, Validation Accuracy:  0.745, Loss:  0.351
+Epoch   0 Batch  525/1077 - Train Accuracy:  0.734, Validation Accuracy:  0.736, Loss:  0.352
+Epoch   0 Batch  526/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.730, Loss:  0.337
+Epoch   0 Batch  527/1077 - Train Accuracy:  0.729, Validation Accuracy:  0.723, Loss:  0.364
+Epoch   0 Batch  528/1077 - Train Accuracy:  0.746, Validation Accuracy:  0.724, Loss:  0.349
+Epoch   0 Batch  529/1077 - Train Accuracy:  0.725, Validation Accuracy:  0.728, Loss:  0.349
+Epoch   0 Batch  530/1077 - Train Accuracy:  0.761, Validation Accuracy:  0.752, Loss:  0.358
+Epoch   0 Batch  531/1077 - Train Accuracy:  0.751, Validation Accuracy:  0.731, Loss:  0.346
+Epoch   0 Batch  532/1077 - Train Accuracy:  0.717, Validation Accuracy:  0.741, Loss:  0.376
+Epoch   0 Batch  533/1077 - Train Accuracy:  0.760, Validation Accuracy:  0.744, Loss:  0.347
+Epoch   0 Batch  534/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.729, Loss:  0.345
+Epoch   0 Batch  535/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.743, Loss:  0.329
+Epoch   0 Batch  536/1077 - Train Accuracy:  0.740, Validation Accuracy:  0.745, Loss:  0.348
+Epoch   0 Batch  537/1077 - Train Accuracy:  0.760, Validation Accuracy:  0.762, Loss:  0.339
+Epoch   0 Batch  538/1077 - Train Accuracy:  0.788, Validation Accuracy:  0.749, Loss:  0.299
+Epoch   0 Batch  539/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.742, Loss:  0.347
+Epoch   0 Batch  540/1077 - Train Accuracy:  0.773, Validation Accuracy:  0.734, Loss:  0.317
+Epoch   0 Batch  541/1077 - Train Accuracy:  0.762, Validation Accuracy:  0.739, Loss:  0.337
+Epoch   0 Batch  542/1077 - Train Accuracy:  0.807, Validation Accuracy:  0.767, Loss:  0.325
+Epoch   0 Batch  543/1077 - Train Accuracy:  0.782, Validation Accuracy:  0.761, Loss:  0.332
+Epoch   0 Batch  544/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.773, Loss:  0.302
+Epoch   0 Batch  545/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.770, Loss:  0.349
+Epoch   0 Batch  546/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.763, Loss:  0.362
+Epoch   0 Batch  547/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.777, Loss:  0.326
+Epoch   0 Batch  548/1077 - Train Accuracy:  0.776, Validation Accuracy:  0.771, Loss:  0.341
+Epoch   0 Batch  549/1077 - Train Accuracy:  0.727, Validation Accuracy:  0.761, Loss:  0.362
+Epoch   0 Batch  550/1077 - Train Accuracy:  0.731, Validation Accuracy:  0.750, Loss:  0.340
+Epoch   0 Batch  551/1077 - Train Accuracy:  0.747, Validation Accuracy:  0.743, Loss:  0.339
+Epoch   0 Batch  552/1077 - Train Accuracy:  0.771, Validation Accuracy:  0.747, Loss:  0.334
+Epoch   0 Batch  553/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.752, Loss:  0.340
+Epoch   0 Batch  554/1077 - Train Accuracy:  0.768, Validation Accuracy:  0.753, Loss:  0.322
+Epoch   0 Batch  555/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.759, Loss:  0.310
+Epoch   0 Batch  556/1077 - Train Accuracy:  0.763, Validation Accuracy:  0.762, Loss:  0.293
+Epoch   0 Batch  557/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.767, Loss:  0.314
+Epoch   0 Batch  558/1077 - Train Accuracy:  0.797, Validation Accuracy:  0.775, Loss:  0.297
+Epoch   0 Batch  559/1077 - Train Accuracy:  0.793, Validation Accuracy:  0.766, Loss:  0.316
+Epoch   0 Batch  560/1077 - Train Accuracy:  0.775, Validation Accuracy:  0.756, Loss:  0.305
+Epoch   0 Batch  561/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.763, Loss:  0.309
+Epoch   0 Batch  562/1077 - Train Accuracy:  0.814, Validation Accuracy:  0.776, Loss:  0.276
+Epoch   0 Batch  563/1077 - Train Accuracy:  0.771, Validation Accuracy:  0.766, Loss:  0.319
+Epoch   0 Batch  564/1077 - Train Accuracy:  0.776, Validation Accuracy:  0.767, Loss:  0.320
+Epoch   0 Batch  565/1077 - Train Accuracy:  0.779, Validation Accuracy:  0.760, Loss:  0.319
+Epoch   0 Batch  566/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.752, Loss:  0.325
+Epoch   0 Batch  567/1077 - Train Accuracy:  0.765, Validation Accuracy:  0.755, Loss:  0.310
+Epoch   0 Batch  568/1077 - Train Accuracy:  0.808, Validation Accuracy:  0.756, Loss:  0.295
+Epoch   0 Batch  569/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.762, Loss:  0.307
+Epoch   0 Batch  570/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.762, Loss:  0.317
+Epoch   0 Batch  571/1077 - Train Accuracy:  0.790, Validation Accuracy:  0.777, Loss:  0.278
+Epoch   0 Batch  572/1077 - Train Accuracy:  0.788, Validation Accuracy:  0.779, Loss:  0.285
+Epoch   0 Batch  573/1077 - Train Accuracy:  0.765, Validation Accuracy:  0.769, Loss:  0.328
+Epoch   0 Batch  574/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.779, Loss:  0.315
+Epoch   0 Batch  575/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.785, Loss:  0.292
+Epoch   0 Batch  576/1077 - Train Accuracy:  0.794, Validation Accuracy:  0.794, Loss:  0.302
+Epoch   0 Batch  577/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.795, Loss:  0.330
+Epoch   0 Batch  578/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.791, Loss:  0.304
+Epoch   0 Batch  579/1077 - Train Accuracy:  0.779, Validation Accuracy:  0.783, Loss:  0.302
+Epoch   0 Batch  580/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.786, Loss:  0.281
+Epoch   0 Batch  581/1077 - Train Accuracy:  0.775, Validation Accuracy:  0.792, Loss:  0.282
+Epoch   0 Batch  582/1077 - Train Accuracy:  0.793, Validation Accuracy:  0.784, Loss:  0.287
+Epoch   0 Batch  583/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.787, Loss:  0.317
+Epoch   0 Batch  584/1077 - Train Accuracy:  0.794, Validation Accuracy:  0.797, Loss:  0.289
+Epoch   0 Batch  585/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.786, Loss:  0.250
+Epoch   0 Batch  586/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.782, Loss:  0.297
+Epoch   0 Batch  587/1077 - Train Accuracy:  0.825, Validation Accuracy:  0.786, Loss:  0.274
+Epoch   0 Batch  588/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.780, Loss:  0.273
+Epoch   0 Batch  589/1077 - Train Accuracy:  0.814, Validation Accuracy:  0.780, Loss:  0.289
+Epoch   0 Batch  590/1077 - Train Accuracy:  0.750, Validation Accuracy:  0.781, Loss:  0.304
+Epoch   0 Batch  591/1077 - Train Accuracy:  0.805, Validation Accuracy:  0.778, Loss:  0.275
+Epoch   0 Batch  592/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.781, Loss:  0.292
+Epoch   0 Batch  593/1077 - Train Accuracy:  0.811, Validation Accuracy:  0.781, Loss:  0.293
+Epoch   0 Batch  594/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.784, Loss:  0.302
+Epoch   0 Batch  595/1077 - Train Accuracy:  0.796, Validation Accuracy:  0.779, Loss:  0.301
+Epoch   0 Batch  596/1077 - Train Accuracy:  0.824, Validation Accuracy:  0.786, Loss:  0.284
+Epoch   0 Batch  597/1077 - Train Accuracy:  0.775, Validation Accuracy:  0.789, Loss:  0.275
+Epoch   0 Batch  598/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.782, Loss:  0.271
+Epoch   0 Batch  599/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.767, Loss:  0.311
+Epoch   0 Batch  600/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.777, Loss:  0.284
+Epoch   0 Batch  601/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.798, Loss:  0.268
+Epoch   0 Batch  602/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.784, Loss:  0.282
+Epoch   0 Batch  603/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.781, Loss:  0.255
+Epoch   0 Batch  604/1077 - Train Accuracy:  0.796, Validation Accuracy:  0.792, Loss:  0.299
+Epoch   0 Batch  605/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.782, Loss:  0.318
+Epoch   0 Batch  606/1077 - Train Accuracy:  0.817, Validation Accuracy:  0.787, Loss:  0.262
+Epoch   0 Batch  607/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.785, Loss:  0.247
+Epoch   0 Batch  608/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.785, Loss:  0.306
+Epoch   0 Batch  609/1077 - Train Accuracy:  0.783, Validation Accuracy:  0.795, Loss:  0.267
+Epoch   0 Batch  610/1077 - Train Accuracy:  0.800, Validation Accuracy:  0.786, Loss:  0.287
+Epoch   0 Batch  611/1077 - Train Accuracy:  0.801, Validation Accuracy:  0.775, Loss:  0.265
+Epoch   0 Batch  612/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.772, Loss:  0.246
+Epoch   0 Batch  613/1077 - Train Accuracy:  0.784, Validation Accuracy:  0.779, Loss:  0.280
+Epoch   0 Batch  614/1077 - Train Accuracy:  0.818, Validation Accuracy:  0.766, Loss:  0.257
+Epoch   0 Batch  615/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.771, Loss:  0.273
+Epoch   0 Batch  616/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.767, Loss:  0.284
+Epoch   0 Batch  617/1077 - Train Accuracy:  0.806, Validation Accuracy:  0.771, Loss:  0.262
+Epoch   0 Batch  618/1077 - Train Accuracy:  0.802, Validation Accuracy:  0.778, Loss:  0.270
+Epoch   0 Batch  619/1077 - Train Accuracy:  0.789, Validation Accuracy:  0.779, Loss:  0.266
+Epoch   0 Batch  620/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.777, Loss:  0.254
+Epoch   0 Batch  621/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.780, Loss:  0.259
+Epoch   0 Batch  622/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.774, Loss:  0.280
+Epoch   0 Batch  623/1077 - Train Accuracy:  0.787, Validation Accuracy:  0.766, Loss:  0.270
+Epoch   0 Batch  624/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.771, Loss:  0.253
+Epoch   0 Batch  625/1077 - Train Accuracy:  0.818, Validation Accuracy:  0.782, Loss:  0.259
+Epoch   0 Batch  626/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.790, Loss:  0.231
+Epoch   0 Batch  627/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.787, Loss:  0.250
+Epoch   0 Batch  628/1077 - Train Accuracy:  0.808, Validation Accuracy:  0.783, Loss:  0.253
+Epoch   0 Batch  629/1077 - Train Accuracy:  0.789, Validation Accuracy:  0.788, Loss:  0.273
+Epoch   0 Batch  630/1077 - Train Accuracy:  0.820, Validation Accuracy:  0.779, Loss:  0.245
+Epoch   0 Batch  631/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.784, Loss:  0.248
+Epoch   0 Batch  632/1077 - Train Accuracy:  0.807, Validation Accuracy:  0.792, Loss:  0.251
+Epoch   0 Batch  633/1077 - Train Accuracy:  0.826, Validation Accuracy:  0.783, Loss:  0.269
+Epoch   0 Batch  634/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.793, Loss:  0.236
+Epoch   0 Batch  635/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.782, Loss:  0.271
+Epoch   0 Batch  636/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.792, Loss:  0.234
+Epoch   0 Batch  637/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.800, Loss:  0.248
+Epoch   0 Batch  638/1077 - Train Accuracy:  0.817, Validation Accuracy:  0.800, Loss:  0.237
+Epoch   0 Batch  639/1077 - Train Accuracy:  0.825, Validation Accuracy:  0.795, Loss:  0.266
+Epoch   0 Batch  640/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.790, Loss:  0.251
+Epoch   0 Batch  641/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.792, Loss:  0.248
+Epoch   0 Batch  642/1077 - Train Accuracy:  0.785, Validation Accuracy:  0.795, Loss:  0.246
+Epoch   0 Batch  643/1077 - Train Accuracy:  0.800, Validation Accuracy:  0.788, Loss:  0.226
+Epoch   0 Batch  644/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.790, Loss:  0.249
+Epoch   0 Batch  645/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.795, Loss:  0.247
+Epoch   0 Batch  646/1077 - Train Accuracy:  0.803, Validation Accuracy:  0.789, Loss:  0.248
+Epoch   0 Batch  647/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.788, Loss:  0.246
+Epoch   0 Batch  648/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.794, Loss:  0.217
+Epoch   0 Batch  649/1077 - Train Accuracy:  0.806, Validation Accuracy:  0.798, Loss:  0.256
+Epoch   0 Batch  650/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.790, Loss:  0.249
+Epoch   0 Batch  651/1077 - Train Accuracy:  0.853, Validation Accuracy:  0.794, Loss:  0.215
+Epoch   0 Batch  652/1077 - Train Accuracy:  0.832, Validation Accuracy:  0.799, Loss:  0.257
+Epoch   0 Batch  653/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.795, Loss:  0.227
+Epoch   0 Batch  654/1077 - Train Accuracy:  0.830, Validation Accuracy:  0.802, Loss:  0.241
+Epoch   0 Batch  655/1077 - Train Accuracy:  0.824, Validation Accuracy:  0.803, Loss:  0.260
+Epoch   0 Batch  656/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.797, Loss:  0.245
+Epoch   0 Batch  657/1077 - Train Accuracy:  0.840, Validation Accuracy:  0.792, Loss:  0.247
+Epoch   0 Batch  658/1077 - Train Accuracy:  0.829, Validation Accuracy:  0.786, Loss:  0.215
+Epoch   0 Batch  659/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.784, Loss:  0.234
+Epoch   0 Batch  660/1077 - Train Accuracy:  0.812, Validation Accuracy:  0.781, Loss:  0.238
+Epoch   0 Batch  661/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.779, Loss:  0.215
+Epoch   0 Batch  662/1077 - Train Accuracy:  0.807, Validation Accuracy:  0.781, Loss:  0.235
+Epoch   0 Batch  663/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.802, Loss:  0.221
+Epoch   0 Batch  664/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.806, Loss:  0.233
+Epoch   0 Batch  665/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.803, Loss:  0.202
+Epoch   0 Batch  666/1077 - Train Accuracy:  0.836, Validation Accuracy:  0.798, Loss:  0.254
+Epoch   0 Batch  667/1077 - Train Accuracy:  0.796, Validation Accuracy:  0.814, Loss:  0.258
+Epoch   0 Batch  668/1077 - Train Accuracy:  0.831, Validation Accuracy:  0.816, Loss:  0.230
+Epoch   0 Batch  669/1077 - Train Accuracy:  0.836, Validation Accuracy:  0.819, Loss:  0.220
+Epoch   0 Batch  670/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.822, Loss:  0.223
+Epoch   0 Batch  671/1077 - Train Accuracy:  0.824, Validation Accuracy:  0.832, Loss:  0.251
+Epoch   0 Batch  672/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.836, Loss:  0.216
+Epoch   0 Batch  673/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.838, Loss:  0.212
+Epoch   0 Batch  674/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.836, Loss:  0.217
+Epoch   0 Batch  675/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.828, Loss:  0.240
+Epoch   0 Batch  676/1077 - Train Accuracy:  0.825, Validation Accuracy:  0.819, Loss:  0.225
+Epoch   0 Batch  677/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.813, Loss:  0.256
+Epoch   0 Batch  678/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.821, Loss:  0.198
+Epoch   0 Batch  679/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.823, Loss:  0.236
+Epoch   0 Batch  680/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.824, Loss:  0.210
+Epoch   0 Batch  681/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.820, Loss:  0.239
+Epoch   0 Batch  682/1077 - Train Accuracy:  0.800, Validation Accuracy:  0.823, Loss:  0.212
+Epoch   0 Batch  683/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.827, Loss:  0.212
+Epoch   0 Batch  684/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.818, Loss:  0.217
+Epoch   0 Batch  685/1077 - Train Accuracy:  0.793, Validation Accuracy:  0.821, Loss:  0.234
+Epoch   0 Batch  686/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.810, Loss:  0.202
+Epoch   0 Batch  687/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.814, Loss:  0.237
+Epoch   0 Batch  688/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.814, Loss:  0.207
+Epoch   0 Batch  689/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.815, Loss:  0.200
+Epoch   0 Batch  690/1077 - Train Accuracy:  0.848, Validation Accuracy:  0.820, Loss:  0.209
+Epoch   0 Batch  691/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.824, Loss:  0.245
+Epoch   0 Batch  692/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.819, Loss:  0.204
+Epoch   0 Batch  693/1077 - Train Accuracy:  0.760, Validation Accuracy:  0.818, Loss:  0.267
+Epoch   0 Batch  694/1077 - Train Accuracy:  0.838, Validation Accuracy:  0.822, Loss:  0.240
+Epoch   0 Batch  695/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.828, Loss:  0.195
+Epoch   0 Batch  696/1077 - Train Accuracy:  0.817, Validation Accuracy:  0.816, Loss:  0.233
+Epoch   0 Batch  697/1077 - Train Accuracy:  0.847, Validation Accuracy:  0.811, Loss:  0.214
+Epoch   0 Batch  698/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.815, Loss:  0.208
+Epoch   0 Batch  699/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.821, Loss:  0.212
+Epoch   0 Batch  700/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.817, Loss:  0.208
+Epoch   0 Batch  701/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.808, Loss:  0.239
+Epoch   0 Batch  702/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.812, Loss:  0.227
+Epoch   0 Batch  703/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.811, Loss:  0.219
+Epoch   0 Batch  704/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.820, Loss:  0.235
+Epoch   0 Batch  705/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.819, Loss:  0.250
+Epoch   0 Batch  706/1077 - Train Accuracy:  0.806, Validation Accuracy:  0.828, Loss:  0.244
+Epoch   0 Batch  707/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.832, Loss:  0.211
+Epoch   0 Batch  708/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.837, Loss:  0.216
+Epoch   0 Batch  709/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.838, Loss:  0.238
+Epoch   0 Batch  710/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.836, Loss:  0.197
+Epoch   0 Batch  711/1077 - Train Accuracy:  0.820, Validation Accuracy:  0.838, Loss:  0.229
+Epoch   0 Batch  712/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.845, Loss:  0.196
+Epoch   0 Batch  713/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.825, Loss:  0.172
+Epoch   0 Batch  714/1077 - Train Accuracy:  0.829, Validation Accuracy:  0.815, Loss:  0.209
+Epoch   0 Batch  715/1077 - Train Accuracy:  0.830, Validation Accuracy:  0.813, Loss:  0.225
+Epoch   0 Batch  716/1077 - Train Accuracy:  0.849, Validation Accuracy:  0.831, Loss:  0.198
+Epoch   0 Batch  717/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.831, Loss:  0.206
+Epoch   0 Batch  718/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.831, Loss:  0.203
+Epoch   0 Batch  719/1077 - Train Accuracy:  0.818, Validation Accuracy:  0.837, Loss:  0.220
+Epoch   0 Batch  720/1077 - Train Accuracy:  0.847, Validation Accuracy:  0.834, Loss:  0.215
+Epoch   0 Batch  721/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.849, Loss:  0.221
+Epoch   0 Batch  722/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.843, Loss:  0.193
+Epoch   0 Batch  723/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.842, Loss:  0.207
+Epoch   0 Batch  724/1077 - Train Accuracy:  0.838, Validation Accuracy:  0.829, Loss:  0.226
+Epoch   0 Batch  725/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.822, Loss:  0.172
+Epoch   0 Batch  726/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.822, Loss:  0.201
+Epoch   0 Batch  727/1077 - Train Accuracy:  0.832, Validation Accuracy:  0.827, Loss:  0.190
+Epoch   0 Batch  728/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.841, Loss:  0.210
+Epoch   0 Batch  729/1077 - Train Accuracy:  0.814, Validation Accuracy:  0.835, Loss:  0.225
+Epoch   0 Batch  730/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.837, Loss:  0.221
+Epoch   0 Batch  731/1077 - Train Accuracy:  0.826, Validation Accuracy:  0.841, Loss:  0.190
+Epoch   0 Batch  732/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.833, Loss:  0.216
+Epoch   0 Batch  733/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.826, Loss:  0.208
+Epoch   0 Batch  734/1077 - Train Accuracy:  0.862, Validation Accuracy:  0.844, Loss:  0.210
+Epoch   0 Batch  735/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.849, Loss:  0.202
+Epoch   0 Batch  736/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.848, Loss:  0.178
+Epoch   0 Batch  737/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.833, Loss:  0.219
+Epoch   0 Batch  738/1077 - Train Accuracy:  0.860, Validation Accuracy:  0.838, Loss:  0.165
+Epoch   0 Batch  739/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.841, Loss:  0.183
+Epoch   0 Batch  740/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.852, Loss:  0.185
+Epoch   0 Batch  741/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.850, Loss:  0.202
+Epoch   0 Batch  742/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.839, Loss:  0.186
+Epoch   0 Batch  743/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.834, Loss:  0.201
+Epoch   0 Batch  744/1077 - Train Accuracy:  0.836, Validation Accuracy:  0.840, Loss:  0.179
+Epoch   0 Batch  745/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.843, Loss:  0.188
+Epoch   0 Batch  746/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.839, Loss:  0.174
+Epoch   0 Batch  747/1077 - Train Accuracy:  0.856, Validation Accuracy:  0.837, Loss:  0.165
+Epoch   0 Batch  748/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.842, Loss:  0.174
+Epoch   0 Batch  749/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.837, Loss:  0.191
+Epoch   0 Batch  750/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.837, Loss:  0.170
+Epoch   0 Batch  751/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.835, Loss:  0.177
+Epoch   0 Batch  752/1077 - Train Accuracy:  0.853, Validation Accuracy:  0.828, Loss:  0.164
+Epoch   0 Batch  753/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.829, Loss:  0.170
+Epoch   0 Batch  754/1077 - Train Accuracy:  0.862, Validation Accuracy:  0.836, Loss:  0.201
+Epoch   0 Batch  755/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.850, Loss:  0.174
+Epoch   0 Batch  756/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.834, Loss:  0.179
+Epoch   0 Batch  757/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.846, Loss:  0.181
+Epoch   0 Batch  758/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.846, Loss:  0.174
+Epoch   0 Batch  759/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.851, Loss:  0.170
+Epoch   0 Batch  760/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.858, Loss:  0.191
+Epoch   0 Batch  761/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.863, Loss:  0.172
+Epoch   0 Batch  762/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.856, Loss:  0.167
+Epoch   0 Batch  763/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.855, Loss:  0.159
+Epoch   0 Batch  764/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.850, Loss:  0.185
+Epoch   0 Batch  765/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.840, Loss:  0.171
+Epoch   0 Batch  766/1077 - Train Accuracy:  0.816, Validation Accuracy:  0.838, Loss:  0.191
+Epoch   0 Batch  767/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.846, Loss:  0.171
+Epoch   0 Batch  768/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.867, Loss:  0.175
+Epoch   0 Batch  769/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.876, Loss:  0.186
+Epoch   0 Batch  770/1077 - Train Accuracy:  0.823, Validation Accuracy:  0.877, Loss:  0.168
+Epoch   0 Batch  771/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.871, Loss:  0.194
+Epoch   0 Batch  772/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.868, Loss:  0.153
+Epoch   0 Batch  773/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.866, Loss:  0.179
+Epoch   0 Batch  774/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.862, Loss:  0.186
+Epoch   0 Batch  775/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.865, Loss:  0.190
+Epoch   0 Batch  776/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.858, Loss:  0.160
+Epoch   0 Batch  777/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.859, Loss:  0.174
+Epoch   0 Batch  778/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.862, Loss:  0.163
+Epoch   0 Batch  779/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.856, Loss:  0.186
+Epoch   0 Batch  780/1077 - Train Accuracy:  0.809, Validation Accuracy:  0.853, Loss:  0.219
+Epoch   0 Batch  781/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.852, Loss:  0.153
+Epoch   0 Batch  782/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.854, Loss:  0.173
+Epoch   0 Batch  783/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.858, Loss:  0.182
+Epoch   0 Batch  784/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.849, Loss:  0.157
+Epoch   0 Batch  785/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.855, Loss:  0.166
+Epoch   0 Batch  786/1077 - Train Accuracy:  0.813, Validation Accuracy:  0.854, Loss:  0.180
+Epoch   0 Batch  787/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.858, Loss:  0.165
+Epoch   0 Batch  788/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.854, Loss:  0.148
+Epoch   0 Batch  789/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.836, Loss:  0.183
+Epoch   0 Batch  790/1077 - Train Accuracy:  0.832, Validation Accuracy:  0.847, Loss:  0.175
+Epoch   0 Batch  791/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.849, Loss:  0.182
+Epoch   0 Batch  792/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.869, Loss:  0.185
+Epoch   0 Batch  793/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.871, Loss:  0.172
+Epoch   0 Batch  794/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.881, Loss:  0.152
+Epoch   0 Batch  795/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.876, Loss:  0.178
+Epoch   0 Batch  796/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.874, Loss:  0.168
+Epoch   0 Batch  797/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.864, Loss:  0.159
+Epoch   0 Batch  798/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.857, Loss:  0.175
+Epoch   0 Batch  799/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.853, Loss:  0.194
+Epoch   0 Batch  800/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.863, Loss:  0.172
+Epoch   0 Batch  801/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.851, Loss:  0.168
+Epoch   0 Batch  802/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.855, Loss:  0.177
+Epoch   0 Batch  803/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.861, Loss:  0.174
+Epoch   0 Batch  804/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.857, Loss:  0.144
+Epoch   0 Batch  805/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.852, Loss:  0.166
+Epoch   0 Batch  806/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.851, Loss:  0.150
+Epoch   0 Batch  807/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.850, Loss:  0.154
+Epoch   0 Batch  808/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.848, Loss:  0.184
+Epoch   0 Batch  809/1077 - Train Accuracy:  0.831, Validation Accuracy:  0.839, Loss:  0.191
+Epoch   0 Batch  810/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.847, Loss:  0.146
+Epoch   0 Batch  811/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.845, Loss:  0.152
+Epoch   0 Batch  812/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.846, Loss:  0.155
+Epoch   0 Batch  813/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.863, Loss:  0.159
+Epoch   0 Batch  814/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.868, Loss:  0.175
+Epoch   0 Batch  815/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.859, Loss:  0.151
+Epoch   0 Batch  816/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.863, Loss:  0.192
+Epoch   0 Batch  817/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.867, Loss:  0.173
+Epoch   0 Batch  818/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.866, Loss:  0.161
+Epoch   0 Batch  819/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.867, Loss:  0.143
+Epoch   0 Batch  820/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.860, Loss:  0.144
+Epoch   0 Batch  821/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.850, Loss:  0.140
+Epoch   0 Batch  822/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.843, Loss:  0.165
+Epoch   0 Batch  823/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.847, Loss:  0.166
+Epoch   0 Batch  824/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.858, Loss:  0.167
+Epoch   0 Batch  825/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.856, Loss:  0.150
+Epoch   0 Batch  826/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.858, Loss:  0.149
+Epoch   0 Batch  827/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.862, Loss:  0.154
+Epoch   0 Batch  828/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.871, Loss:  0.164
+Epoch   0 Batch  829/1077 - Train Accuracy:  0.829, Validation Accuracy:  0.865, Loss:  0.182
+Epoch   0 Batch  830/1077 - Train Accuracy:  0.864, Validation Accuracy:  0.870, Loss:  0.157
+Epoch   0 Batch  831/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.875, Loss:  0.159
+Epoch   0 Batch  832/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.884, Loss:  0.152
+Epoch   0 Batch  833/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.884, Loss:  0.163
+Epoch   0 Batch  834/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.871, Loss:  0.160
+Epoch   0 Batch  835/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.876, Loss:  0.160
+Epoch   0 Batch  836/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.874, Loss:  0.172
+Epoch   0 Batch  837/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.876, Loss:  0.165
+Epoch   0 Batch  838/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.878, Loss:  0.140
+Epoch   0 Batch  839/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.880, Loss:  0.126
+Epoch   0 Batch  840/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.882, Loss:  0.132
+Epoch   0 Batch  841/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.880, Loss:  0.157
+Epoch   0 Batch  842/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.873, Loss:  0.139
+Epoch   0 Batch  843/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.873, Loss:  0.128
+Epoch   0 Batch  844/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.882, Loss:  0.132
+Epoch   0 Batch  845/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.886, Loss:  0.141
+Epoch   0 Batch  846/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.874, Loss:  0.162
+Epoch   0 Batch  847/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.869, Loss:  0.166
+Epoch   0 Batch  848/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.868, Loss:  0.138
+Epoch   0 Batch  849/1077 - Train Accuracy:  0.860, Validation Accuracy:  0.862, Loss:  0.141
+Epoch   0 Batch  850/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.866, Loss:  0.169
+Epoch   0 Batch  851/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.849, Loss:  0.154
+Epoch   0 Batch  852/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.851, Loss:  0.171
+Epoch   0 Batch  853/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.852, Loss:  0.139
+Epoch   0 Batch  854/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.861, Loss:  0.158
+Epoch   0 Batch  855/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.862, Loss:  0.139
+Epoch   0 Batch  856/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.856, Loss:  0.145
+Epoch   0 Batch  857/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.858, Loss:  0.139
+Epoch   0 Batch  858/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.861, Loss:  0.130
+Epoch   0 Batch  859/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.866, Loss:  0.166
+Epoch   0 Batch  860/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.861, Loss:  0.156
+Epoch   0 Batch  861/1077 - Train Accuracy:  0.849, Validation Accuracy:  0.856, Loss:  0.137
+Epoch   0 Batch  862/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.864, Loss:  0.139
+Epoch   0 Batch  863/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.864, Loss:  0.136
+Epoch   0 Batch  864/1077 - Train Accuracy:  0.862, Validation Accuracy:  0.863, Loss:  0.136
+Epoch   0 Batch  865/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.870, Loss:  0.134
+Epoch   0 Batch  866/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.864, Loss:  0.148
+Epoch   0 Batch  867/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.863, Loss:  0.192
+Epoch   0 Batch  868/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.858, Loss:  0.144
+Epoch   0 Batch  869/1077 - Train Accuracy:  0.864, Validation Accuracy:  0.868, Loss:  0.145
+Epoch   0 Batch  870/1077 - Train Accuracy:  0.831, Validation Accuracy:  0.857, Loss:  0.148
+Epoch   0 Batch  871/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.856, Loss:  0.124
+Epoch   0 Batch  872/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.859, Loss:  0.150
+Epoch   0 Batch  873/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.864, Loss:  0.141
+Epoch   0 Batch  874/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.867, Loss:  0.169
+Epoch   0 Batch  875/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.862, Loss:  0.155
+Epoch   0 Batch  876/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.864, Loss:  0.144
+Epoch   0 Batch  877/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.864, Loss:  0.133
+Epoch   0 Batch  878/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.861, Loss:  0.143
+Epoch   0 Batch  879/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.865, Loss:  0.127
+Epoch   0 Batch  880/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.868, Loss:  0.152
+Epoch   0 Batch  881/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.864, Loss:  0.146
+Epoch   0 Batch  882/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.868, Loss:  0.163
+Epoch   0 Batch  883/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.859, Loss:  0.183
+Epoch   0 Batch  884/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.865, Loss:  0.131
+Epoch   0 Batch  885/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.869, Loss:  0.118
+Epoch   0 Batch  886/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.861, Loss:  0.139
+Epoch   0 Batch  887/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.861, Loss:  0.168
+Epoch   0 Batch  888/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.865, Loss:  0.134
+Epoch   0 Batch  889/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.858, Loss:  0.139
+Epoch   0 Batch  890/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.854, Loss:  0.137
+Epoch   0 Batch  891/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.854, Loss:  0.139
+Epoch   0 Batch  892/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.871, Loss:  0.128
+Epoch   0 Batch  893/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.869, Loss:  0.146
+Epoch   0 Batch  894/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.876, Loss:  0.131
+Epoch   0 Batch  895/1077 - Train Accuracy:  0.860, Validation Accuracy:  0.876, Loss:  0.128
+Epoch   0 Batch  896/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.863, Loss:  0.143
+Epoch   0 Batch  897/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.860, Loss:  0.115
+Epoch   0 Batch  898/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.862, Loss:  0.113
+Epoch   0 Batch  899/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.863, Loss:  0.147
+Epoch   0 Batch  900/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.864, Loss:  0.156
+Epoch   0 Batch  901/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.888, Loss:  0.166
+Epoch   0 Batch  902/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.885, Loss:  0.145
+Epoch   0 Batch  903/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.882, Loss:  0.130
+Epoch   0 Batch  904/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.887, Loss:  0.134
+Epoch   0 Batch  905/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.880, Loss:  0.113
+Epoch   0 Batch  906/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.881, Loss:  0.130
+Epoch   0 Batch  907/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.859, Loss:  0.132
+Epoch   0 Batch  908/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.857, Loss:  0.144
+Epoch   0 Batch  909/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.855, Loss:  0.142
+Epoch   0 Batch  910/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.851, Loss:  0.132
+Epoch   0 Batch  911/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.850, Loss:  0.129
+Epoch   0 Batch  912/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.853, Loss:  0.133
+Epoch   0 Batch  913/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.863, Loss:  0.162
+Epoch   0 Batch  914/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.862, Loss:  0.144
+Epoch   0 Batch  915/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.858, Loss:  0.131
+Epoch   0 Batch  916/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.867, Loss:  0.150
+Epoch   0 Batch  917/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.867, Loss:  0.127
+Epoch   0 Batch  918/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.866, Loss:  0.119
+Epoch   0 Batch  919/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.865, Loss:  0.119
+Epoch   0 Batch  920/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.869, Loss:  0.133
+Epoch   0 Batch  921/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.870, Loss:  0.139
+Epoch   0 Batch  922/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.871, Loss:  0.145
+Epoch   0 Batch  923/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.873, Loss:  0.117
+Epoch   0 Batch  924/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.865, Loss:  0.148
+Epoch   0 Batch  925/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.863, Loss:  0.123
+Epoch   0 Batch  926/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.865, Loss:  0.125
+Epoch   0 Batch  927/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.860, Loss:  0.142
+Epoch   0 Batch  928/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.851, Loss:  0.130
+Epoch   0 Batch  929/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.849, Loss:  0.129
+Epoch   0 Batch  930/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.855, Loss:  0.120
+Epoch   0 Batch  931/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.862, Loss:  0.108
+Epoch   0 Batch  932/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.861, Loss:  0.143
+Epoch   0 Batch  933/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.862, Loss:  0.124
+Epoch   0 Batch  934/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.859, Loss:  0.109
+Epoch   0 Batch  935/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.873, Loss:  0.118
+Epoch   0 Batch  936/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.866, Loss:  0.135
+Epoch   0 Batch  937/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.862, Loss:  0.147
+Epoch   0 Batch  938/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.861, Loss:  0.137
+Epoch   0 Batch  939/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.865, Loss:  0.133
+Epoch   0 Batch  940/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.866, Loss:  0.116
+Epoch   0 Batch  941/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.866, Loss:  0.124
+Epoch   0 Batch  942/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.860, Loss:  0.125
+Epoch   0 Batch  943/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.862, Loss:  0.135
+Epoch   0 Batch  944/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.857, Loss:  0.116
+Epoch   0 Batch  945/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.862, Loss:  0.114
+Epoch   0 Batch  946/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.881, Loss:  0.121
+Epoch   0 Batch  947/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.874, Loss:  0.131
+Epoch   0 Batch  948/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.868, Loss:  0.118
+Epoch   0 Batch  949/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.864, Loss:  0.100
+Epoch   0 Batch  950/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.873, Loss:  0.114
+Epoch   0 Batch  951/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.876, Loss:  0.132
+Epoch   0 Batch  952/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.891, Loss:  0.107
+Epoch   0 Batch  953/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.891, Loss:  0.111
+Epoch   0 Batch  954/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.894, Loss:  0.127
+Epoch   0 Batch  955/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.886, Loss:  0.133
+Epoch   0 Batch  956/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.882, Loss:  0.134
+Epoch   0 Batch  957/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.870, Loss:  0.102
+Epoch   0 Batch  958/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.870, Loss:  0.123
+Epoch   0 Batch  959/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.868, Loss:  0.125
+Epoch   0 Batch  960/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.871, Loss:  0.111
+Epoch   0 Batch  961/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.876, Loss:  0.117
+Epoch   0 Batch  962/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.880, Loss:  0.123
+Epoch   0 Batch  963/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.872, Loss:  0.144
+Epoch   0 Batch  964/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.872, Loss:  0.101
+Epoch   0 Batch  965/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.878, Loss:  0.124
+Epoch   0 Batch  966/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.880, Loss:  0.109
+Epoch   0 Batch  967/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.881, Loss:  0.125
+Epoch   0 Batch  968/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.881, Loss:  0.140
+Epoch   0 Batch  969/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.885, Loss:  0.133
+Epoch   0 Batch  970/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.886, Loss:  0.128
+Epoch   0 Batch  971/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.886, Loss:  0.122
+Epoch   0 Batch  972/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.883, Loss:  0.114
+Epoch   0 Batch  973/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.877, Loss:  0.098
+Epoch   0 Batch  974/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.873, Loss:  0.105
+Epoch   0 Batch  975/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.876, Loss:  0.112
+Epoch   0 Batch  976/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.877, Loss:  0.105
+Epoch   0 Batch  977/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.884, Loss:  0.105
+Epoch   0 Batch  978/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.886, Loss:  0.122
+Epoch   0 Batch  979/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.883, Loss:  0.127
+Epoch   0 Batch  980/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.882, Loss:  0.126
+Epoch   0 Batch  981/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.888, Loss:  0.112
+Epoch   0 Batch  982/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.888, Loss:  0.122
+Epoch   0 Batch  983/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.887, Loss:  0.121
+Epoch   0 Batch  984/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.887, Loss:  0.122
+Epoch   0 Batch  985/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.887, Loss:  0.106
+Epoch   0 Batch  986/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.886, Loss:  0.116
+Epoch   0 Batch  987/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.890, Loss:  0.101
+Epoch   0 Batch  988/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.885, Loss:  0.126
+Epoch   0 Batch  989/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.879, Loss:  0.121
+Epoch   0 Batch  990/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.878, Loss:  0.121
+Epoch   0 Batch  991/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.877, Loss:  0.119
+Epoch   0 Batch  992/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.882, Loss:  0.118
+Epoch   0 Batch  993/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.882, Loss:  0.087
+Epoch   0 Batch  994/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.892, Loss:  0.114
+Epoch   0 Batch  995/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.892, Loss:  0.114
+Epoch   0 Batch  996/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.890, Loss:  0.108
+Epoch   0 Batch  997/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.894, Loss:  0.111
+Epoch   0 Batch  998/1077 - Train Accuracy:  0.856, Validation Accuracy:  0.889, Loss:  0.107
+Epoch   0 Batch  999/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.903, Loss:  0.115
+Epoch   0 Batch 1000/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.897, Loss:  0.107
+Epoch   0 Batch 1001/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.900, Loss:  0.092
+Epoch   0 Batch 1002/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.898, Loss:  0.096
+Epoch   0 Batch 1003/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.895, Loss:  0.116
+Epoch   0 Batch 1004/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.898, Loss:  0.130
+Epoch   0 Batch 1005/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.895, Loss:  0.095
+Epoch   0 Batch 1006/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.896, Loss:  0.091
+Epoch   0 Batch 1007/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.876, Loss:  0.089
+Epoch   0 Batch 1008/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.875, Loss:  0.132
+Epoch   0 Batch 1009/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.888, Loss:  0.089
+Epoch   0 Batch 1010/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.888, Loss:  0.105
+Epoch   0 Batch 1011/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.882, Loss:  0.104
+Epoch   0 Batch 1012/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.887, Loss:  0.085
+Epoch   0 Batch 1013/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.890, Loss:  0.094
+Epoch   0 Batch 1014/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.898, Loss:  0.114
+Epoch   0 Batch 1015/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.895, Loss:  0.127
+Epoch   0 Batch 1016/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.904, Loss:  0.128
+Epoch   0 Batch 1017/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.906, Loss:  0.112
+Epoch   0 Batch 1018/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.895, Loss:  0.101
+Epoch   0 Batch 1019/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.907, Loss:  0.119
+Epoch   0 Batch 1020/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.901, Loss:  0.102
+Epoch   0 Batch 1021/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.898, Loss:  0.099
+Epoch   0 Batch 1022/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.898, Loss:  0.092
+Epoch   0 Batch 1023/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.894, Loss:  0.111
+Epoch   0 Batch 1024/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.899, Loss:  0.130
+Epoch   0 Batch 1025/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.901, Loss:  0.113
+Epoch   0 Batch 1026/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.892, Loss:  0.113
+Epoch   0 Batch 1027/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.888, Loss:  0.101
+Epoch   0 Batch 1028/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.888, Loss:  0.102
+Epoch   0 Batch 1029/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.890, Loss:  0.089
+Epoch   0 Batch 1030/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.887, Loss:  0.121
+Epoch   0 Batch 1031/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.883, Loss:  0.122
+Epoch   0 Batch 1032/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.882, Loss:  0.129
+Epoch   0 Batch 1033/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.885, Loss:  0.108
+Epoch   0 Batch 1034/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.886, Loss:  0.111
+Epoch   0 Batch 1035/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.881, Loss:  0.081
+Epoch   0 Batch 1036/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.874, Loss:  0.117
+Epoch   0 Batch 1037/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.877, Loss:  0.121
+Epoch   0 Batch 1038/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.878, Loss:  0.126
+Epoch   0 Batch 1039/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.883, Loss:  0.112
+Epoch   0 Batch 1040/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.886, Loss:  0.121
+Epoch   0 Batch 1041/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.888, Loss:  0.120
+Epoch   0 Batch 1042/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.888, Loss:  0.102
+Epoch   0 Batch 1043/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.892, Loss:  0.122
+Epoch   0 Batch 1044/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.888, Loss:  0.119
+Epoch   0 Batch 1045/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.874, Loss:  0.095
+Epoch   0 Batch 1046/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.881, Loss:  0.085
+Epoch   0 Batch 1047/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.891, Loss:  0.100
+Epoch   0 Batch 1048/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.891, Loss:  0.110
+Epoch   0 Batch 1049/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.895, Loss:  0.097
+Epoch   0 Batch 1050/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.898, Loss:  0.101
+Epoch   0 Batch 1051/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.883, Loss:  0.108
+Epoch   0 Batch 1052/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.894, Loss:  0.103
+Epoch   0 Batch 1053/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.901, Loss:  0.114
+Epoch   0 Batch 1054/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.891, Loss:  0.100
+Epoch   0 Batch 1055/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.890, Loss:  0.111
+Epoch   0 Batch 1056/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.892, Loss:  0.099
+Epoch   0 Batch 1057/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.886, Loss:  0.119
+Epoch   0 Batch 1058/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.883, Loss:  0.116
+Epoch   0 Batch 1059/1077 - Train Accuracy:  0.847, Validation Accuracy:  0.876, Loss:  0.138
+Epoch   0 Batch 1060/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.876, Loss:  0.100
+Epoch   0 Batch 1061/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.880, Loss:  0.123
+Epoch   0 Batch 1062/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.879, Loss:  0.107
+Epoch   0 Batch 1063/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.885, Loss:  0.107
+Epoch   0 Batch 1064/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.888, Loss:  0.107
+Epoch   0 Batch 1065/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.889, Loss:  0.096
+Epoch   0 Batch 1066/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.887, Loss:  0.089
+Epoch   0 Batch 1067/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.886, Loss:  0.116
+Epoch   0 Batch 1068/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.876, Loss:  0.091
+Epoch   0 Batch 1069/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.883, Loss:  0.079
+Epoch   0 Batch 1070/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.871, Loss:  0.100
+Epoch   0 Batch 1071/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.875, Loss:  0.105
+Epoch   0 Batch 1072/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.880, Loss:  0.106
+Epoch   0 Batch 1073/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.876, Loss:  0.123
+Epoch   0 Batch 1074/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.875, Loss:  0.117
+Epoch   0 Batch 1075/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.876, Loss:  0.108
+Epoch   1 Batch    0/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.878, Loss:  0.086
+Epoch   1 Batch    1/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.875, Loss:  0.094
+Epoch   1 Batch    2/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.875, Loss:  0.111
+Epoch   1 Batch    3/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.883, Loss:  0.106
+Epoch   1 Batch    4/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.880, Loss:  0.093
+Epoch   1 Batch    5/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.889, Loss:  0.136
+Epoch   1 Batch    6/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.886, Loss:  0.103
+Epoch   1 Batch    7/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.883, Loss:  0.088
+Epoch   1 Batch    8/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.881, Loss:  0.104
+Epoch   1 Batch    9/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.885, Loss:  0.093
+Epoch   1 Batch   10/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.892, Loss:  0.098
+Epoch   1 Batch   11/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.893, Loss:  0.106
+Epoch   1 Batch   12/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.902, Loss:  0.103
+Epoch   1 Batch   13/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.898, Loss:  0.121
+Epoch   1 Batch   14/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.892, Loss:  0.080
+Epoch   1 Batch   15/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.896, Loss:  0.093
+Epoch   1 Batch   16/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.899, Loss:  0.116
+Epoch   1 Batch   17/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.894, Loss:  0.091
+Epoch   1 Batch   18/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.897, Loss:  0.098
+Epoch   1 Batch   19/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.889, Loss:  0.097
+Epoch   1 Batch   20/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.892, Loss:  0.086
+Epoch   1 Batch   21/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.888, Loss:  0.110
+Epoch   1 Batch   22/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.892, Loss:  0.101
+Epoch   1 Batch   23/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.896, Loss:  0.104
+Epoch   1 Batch   24/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.904, Loss:  0.096
+Epoch   1 Batch   25/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.897, Loss:  0.078
+Epoch   1 Batch   26/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.902, Loss:  0.102
+Epoch   1 Batch   27/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.897, Loss:  0.088
+Epoch   1 Batch   28/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.887, Loss:  0.095
+Epoch   1 Batch   29/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.883, Loss:  0.099
+Epoch   1 Batch   30/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.892, Loss:  0.088
+Epoch   1 Batch   31/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.890, Loss:  0.088
+Epoch   1 Batch   32/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.892, Loss:  0.093
+Epoch   1 Batch   33/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.894, Loss:  0.092
+Epoch   1 Batch   34/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.892, Loss:  0.084
+Epoch   1 Batch   35/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.891, Loss:  0.090
+Epoch   1 Batch   36/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.892, Loss:  0.091
+Epoch   1 Batch   37/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.896, Loss:  0.102
+Epoch   1 Batch   38/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.898, Loss:  0.127
+Epoch   1 Batch   39/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.885, Loss:  0.123
+Epoch   1 Batch   40/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.892, Loss:  0.082
+Epoch   1 Batch   41/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.892, Loss:  0.092
+Epoch   1 Batch   42/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.890, Loss:  0.106
+Epoch   1 Batch   43/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.889, Loss:  0.070
+Epoch   1 Batch   44/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.888, Loss:  0.093
+Epoch   1 Batch   45/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.891, Loss:  0.099
+Epoch   1 Batch   46/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.887, Loss:  0.107
+Epoch   1 Batch   47/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.888, Loss:  0.096
+Epoch   1 Batch   48/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.899, Loss:  0.125
+Epoch   1 Batch   49/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.908, Loss:  0.103
+Epoch   1 Batch   50/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.906, Loss:  0.090
+Epoch   1 Batch   51/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.903, Loss:  0.096
+Epoch   1 Batch   52/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.907, Loss:  0.111
+Epoch   1 Batch   53/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.897, Loss:  0.091
+Epoch   1 Batch   54/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.894, Loss:  0.130
+Epoch   1 Batch   55/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.895, Loss:  0.093
+Epoch   1 Batch   56/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.889, Loss:  0.080
+Epoch   1 Batch   57/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.894, Loss:  0.102
+Epoch   1 Batch   58/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.891, Loss:  0.087
+Epoch   1 Batch   59/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.889, Loss:  0.091
+Epoch   1 Batch   60/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.895, Loss:  0.079
+Epoch   1 Batch   61/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.899, Loss:  0.100
+Epoch   1 Batch   62/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.904, Loss:  0.104
+Epoch   1 Batch   63/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.903, Loss:  0.076
+Epoch   1 Batch   64/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.903, Loss:  0.089
+Epoch   1 Batch   65/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.907, Loss:  0.088
+Epoch   1 Batch   66/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   1 Batch   67/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.905, Loss:  0.086
+Epoch   1 Batch   68/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.906, Loss:  0.092
+Epoch   1 Batch   69/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.912, Loss:  0.109
+Epoch   1 Batch   70/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.906, Loss:  0.094
+Epoch   1 Batch   71/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.064
+Epoch   1 Batch   72/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.901, Loss:  0.097
+Epoch   1 Batch   73/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.901, Loss:  0.093
+Epoch   1 Batch   74/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.898, Loss:  0.082
+Epoch   1 Batch   75/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.902, Loss:  0.122
+Epoch   1 Batch   76/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.892, Loss:  0.076
+Epoch   1 Batch   77/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.892, Loss:  0.085
+Epoch   1 Batch   78/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.891, Loss:  0.090
+Epoch   1 Batch   79/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.890, Loss:  0.080
+Epoch   1 Batch   80/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.880, Loss:  0.089
+Epoch   1 Batch   81/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.876, Loss:  0.070
+Epoch   1 Batch   82/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.881, Loss:  0.089
+Epoch   1 Batch   83/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.878, Loss:  0.097
+Epoch   1 Batch   84/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.877, Loss:  0.095
+Epoch   1 Batch   85/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.884, Loss:  0.092
+Epoch   1 Batch   86/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.892, Loss:  0.093
+Epoch   1 Batch   87/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.893, Loss:  0.118
+Epoch   1 Batch   88/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.892, Loss:  0.098
+Epoch   1 Batch   89/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.891, Loss:  0.098
+Epoch   1 Batch   90/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.902, Loss:  0.101
+Epoch   1 Batch   91/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.907, Loss:  0.077
+Epoch   1 Batch   92/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.904, Loss:  0.105
+Epoch   1 Batch   93/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.891, Loss:  0.084
+Epoch   1 Batch   94/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.890, Loss:  0.075
+Epoch   1 Batch   95/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.103
+Epoch   1 Batch   96/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.892, Loss:  0.105
+Epoch   1 Batch   97/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.893, Loss:  0.099
+Epoch   1 Batch   98/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.890, Loss:  0.100
+Epoch   1 Batch   99/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.896, Loss:  0.098
+Epoch   1 Batch  100/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.899, Loss:  0.093
+Epoch   1 Batch  101/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.885, Loss:  0.077
+Epoch   1 Batch  102/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.883, Loss:  0.102
+Epoch   1 Batch  103/1077 - Train Accuracy:  0.856, Validation Accuracy:  0.874, Loss:  0.112
+Epoch   1 Batch  104/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.873, Loss:  0.102
+Epoch   1 Batch  105/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.887, Loss:  0.087
+Epoch   1 Batch  106/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.896, Loss:  0.114
+Epoch   1 Batch  107/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.896, Loss:  0.103
+Epoch   1 Batch  108/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.895, Loss:  0.099
+Epoch   1 Batch  109/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.892, Loss:  0.087
+Epoch   1 Batch  110/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.899, Loss:  0.082
+Epoch   1 Batch  111/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.896, Loss:  0.082
+Epoch   1 Batch  112/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.900, Loss:  0.098
+Epoch   1 Batch  113/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.895, Loss:  0.087
+Epoch   1 Batch  114/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.898, Loss:  0.075
+Epoch   1 Batch  115/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.896, Loss:  0.110
+Epoch   1 Batch  116/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.896, Loss:  0.096
+Epoch   1 Batch  117/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.903, Loss:  0.090
+Epoch   1 Batch  118/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.905, Loss:  0.087
+Epoch   1 Batch  119/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.911, Loss:  0.094
+Epoch   1 Batch  120/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.915, Loss:  0.097
+Epoch   1 Batch  121/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.914, Loss:  0.092
+Epoch   1 Batch  122/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.914, Loss:  0.081
+Epoch   1 Batch  123/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.903, Loss:  0.074
+Epoch   1 Batch  124/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.892, Loss:  0.104
+Epoch   1 Batch  125/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.897, Loss:  0.091
+Epoch   1 Batch  126/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.898, Loss:  0.073
+Epoch   1 Batch  127/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.897, Loss:  0.091
+Epoch   1 Batch  128/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.892, Loss:  0.090
+Epoch   1 Batch  129/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.902, Loss:  0.111
+Epoch   1 Batch  130/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.895, Loss:  0.081
+Epoch   1 Batch  131/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.889, Loss:  0.088
+Epoch   1 Batch  132/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.902, Loss:  0.093
+Epoch   1 Batch  133/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.898, Loss:  0.078
+Epoch   1 Batch  134/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.890, Loss:  0.087
+Epoch   1 Batch  135/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.896, Loss:  0.094
+Epoch   1 Batch  136/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.898, Loss:  0.086
+Epoch   1 Batch  137/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.067
+Epoch   1 Batch  138/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.900, Loss:  0.086
+Epoch   1 Batch  139/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.896, Loss:  0.110
+Epoch   1 Batch  140/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.901, Loss:  0.096
+Epoch   1 Batch  141/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.896, Loss:  0.094
+Epoch   1 Batch  142/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.891, Loss:  0.080
+Epoch   1 Batch  143/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.888, Loss:  0.101
+Epoch   1 Batch  144/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.888, Loss:  0.113
+Epoch   1 Batch  145/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.897, Loss:  0.088
+Epoch   1 Batch  146/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.891, Loss:  0.104
+Epoch   1 Batch  147/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.902, Loss:  0.088
+Epoch   1 Batch  148/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.894, Loss:  0.098
+Epoch   1 Batch  149/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.892, Loss:  0.096
+Epoch   1 Batch  150/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.898, Loss:  0.095
+Epoch   1 Batch  151/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.898, Loss:  0.077
+Epoch   1 Batch  152/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.897, Loss:  0.118
+Epoch   1 Batch  153/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.891, Loss:  0.110
+Epoch   1 Batch  154/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.881, Loss:  0.079
+Epoch   1 Batch  155/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.887, Loss:  0.093
+Epoch   1 Batch  156/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.887, Loss:  0.079
+Epoch   1 Batch  157/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.894, Loss:  0.086
+Epoch   1 Batch  158/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.888, Loss:  0.111
+Epoch   1 Batch  159/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.892, Loss:  0.071
+Epoch   1 Batch  160/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.892, Loss:  0.076
+Epoch   1 Batch  161/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.893, Loss:  0.081
+Epoch   1 Batch  162/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.896, Loss:  0.103
+Epoch   1 Batch  163/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.893, Loss:  0.106
+Epoch   1 Batch  164/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.893, Loss:  0.087
+Epoch   1 Batch  165/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.903, Loss:  0.065
+Epoch   1 Batch  166/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.901, Loss:  0.102
+Epoch   1 Batch  167/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.902, Loss:  0.083
+Epoch   1 Batch  168/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.906, Loss:  0.094
+Epoch   1 Batch  169/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.901, Loss:  0.108
+Epoch   1 Batch  170/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.900, Loss:  0.088
+Epoch   1 Batch  171/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.896, Loss:  0.083
+Epoch   1 Batch  172/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.901, Loss:  0.066
+Epoch   1 Batch  173/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.911, Loss:  0.095
+Epoch   1 Batch  174/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.897, Loss:  0.077
+Epoch   1 Batch  175/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.896, Loss:  0.094
+Epoch   1 Batch  176/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.900, Loss:  0.085
+Epoch   1 Batch  177/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.910, Loss:  0.107
+Epoch   1 Batch  178/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.903, Loss:  0.089
+Epoch   1 Batch  179/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.896, Loss:  0.095
+Epoch   1 Batch  180/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.895, Loss:  0.079
+Epoch   1 Batch  181/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.899, Loss:  0.102
+Epoch   1 Batch  182/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.899, Loss:  0.104
+Epoch   1 Batch  183/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.894, Loss:  0.088
+Epoch   1 Batch  184/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.896, Loss:  0.090
+Epoch   1 Batch  185/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.892, Loss:  0.096
+Epoch   1 Batch  186/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.888, Loss:  0.099
+Epoch   1 Batch  187/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.889, Loss:  0.071
+Epoch   1 Batch  188/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.885, Loss:  0.081
+Epoch   1 Batch  189/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.895, Loss:  0.077
+Epoch   1 Batch  190/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.892, Loss:  0.075
+Epoch   1 Batch  191/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.888, Loss:  0.072
+Epoch   1 Batch  192/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.895, Loss:  0.090
+Epoch   1 Batch  193/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.897, Loss:  0.080
+Epoch   1 Batch  194/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.897, Loss:  0.072
+Epoch   1 Batch  195/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.900, Loss:  0.074
+Epoch   1 Batch  196/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.903, Loss:  0.077
+Epoch   1 Batch  197/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.898, Loss:  0.095
+Epoch   1 Batch  198/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.898, Loss:  0.087
+Epoch   1 Batch  199/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.900, Loss:  0.070
+Epoch   1 Batch  200/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.897, Loss:  0.102
+Epoch   1 Batch  201/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.893, Loss:  0.072
+Epoch   1 Batch  202/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.897, Loss:  0.082
+Epoch   1 Batch  203/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.894, Loss:  0.081
+Epoch   1 Batch  204/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.889, Loss:  0.108
+Epoch   1 Batch  205/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.880, Loss:  0.102
+Epoch   1 Batch  206/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.886, Loss:  0.079
+Epoch   1 Batch  207/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.877, Loss:  0.077
+Epoch   1 Batch  208/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.879, Loss:  0.090
+Epoch   1 Batch  209/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.880, Loss:  0.075
+Epoch   1 Batch  210/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.880, Loss:  0.094
+Epoch   1 Batch  211/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.890, Loss:  0.082
+Epoch   1 Batch  212/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.897, Loss:  0.073
+Epoch   1 Batch  213/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.866, Loss:  0.074
+Epoch   1 Batch  214/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.873, Loss:  0.074
+Epoch   1 Batch  215/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.870, Loss:  0.090
+Epoch   1 Batch  216/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.873, Loss:  0.092
+Epoch   1 Batch  217/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.877, Loss:  0.072
+Epoch   1 Batch  218/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.877, Loss:  0.102
+Epoch   1 Batch  219/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.877, Loss:  0.086
+Epoch   1 Batch  220/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.874, Loss:  0.081
+Epoch   1 Batch  221/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.896, Loss:  0.085
+Epoch   1 Batch  222/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.900, Loss:  0.078
+Epoch   1 Batch  223/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.900, Loss:  0.070
+Epoch   1 Batch  224/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.898, Loss:  0.097
+Epoch   1 Batch  225/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.905, Loss:  0.095
+Epoch   1 Batch  226/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.905, Loss:  0.084
+Epoch   1 Batch  227/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.910, Loss:  0.104
+Epoch   1 Batch  228/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.071
+Epoch   1 Batch  229/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.891, Loss:  0.098
+Epoch   1 Batch  230/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.883, Loss:  0.092
+Epoch   1 Batch  231/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.893, Loss:  0.085
+Epoch   1 Batch  232/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.890, Loss:  0.075
+Epoch   1 Batch  233/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.892, Loss:  0.108
+Epoch   1 Batch  234/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.891, Loss:  0.093
+Epoch   1 Batch  235/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.888, Loss:  0.088
+Epoch   1 Batch  236/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.892, Loss:  0.095
+Epoch   1 Batch  237/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.905, Loss:  0.075
+Epoch   1 Batch  238/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.902, Loss:  0.080
+Epoch   1 Batch  239/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.884, Loss:  0.061
+Epoch   1 Batch  240/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.887, Loss:  0.071
+Epoch   1 Batch  241/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.886, Loss:  0.063
+Epoch   1 Batch  242/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.886, Loss:  0.065
+Epoch   1 Batch  243/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.891, Loss:  0.085
+Epoch   1 Batch  244/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.889, Loss:  0.076
+Epoch   1 Batch  245/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.907, Loss:  0.077
+Epoch   1 Batch  246/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.903, Loss:  0.072
+Epoch   1 Batch  247/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.905, Loss:  0.076
+Epoch   1 Batch  248/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.905, Loss:  0.090
+Epoch   1 Batch  249/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.909, Loss:  0.080
+Epoch   1 Batch  250/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.903, Loss:  0.073
+Epoch   1 Batch  251/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.902, Loss:  0.090
+Epoch   1 Batch  252/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.909, Loss:  0.081
+Epoch   1 Batch  253/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.908, Loss:  0.080
+Epoch   1 Batch  254/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.906, Loss:  0.089
+Epoch   1 Batch  255/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.897, Loss:  0.079
+Epoch   1 Batch  256/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.894, Loss:  0.103
+Epoch   1 Batch  257/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.898, Loss:  0.087
+Epoch   1 Batch  258/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.903, Loss:  0.083
+Epoch   1 Batch  259/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.911, Loss:  0.072
+Epoch   1 Batch  260/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   1 Batch  261/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.086
+Epoch   1 Batch  262/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.907, Loss:  0.070
+Epoch   1 Batch  263/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.904, Loss:  0.071
+Epoch   1 Batch  264/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.900, Loss:  0.077
+Epoch   1 Batch  265/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.900, Loss:  0.075
+Epoch   1 Batch  266/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.899, Loss:  0.088
+Epoch   1 Batch  267/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.899, Loss:  0.076
+Epoch   1 Batch  268/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.900, Loss:  0.088
+Epoch   1 Batch  269/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.901, Loss:  0.099
+Epoch   1 Batch  270/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.897, Loss:  0.082
+Epoch   1 Batch  271/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.897, Loss:  0.072
+Epoch   1 Batch  272/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.893, Loss:  0.111
+Epoch   1 Batch  273/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.887, Loss:  0.072
+Epoch   1 Batch  274/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.898, Loss:  0.082
+Epoch   1 Batch  275/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.898, Loss:  0.082
+Epoch   1 Batch  276/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.896, Loss:  0.122
+Epoch   1 Batch  277/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.897, Loss:  0.072
+Epoch   1 Batch  278/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.895, Loss:  0.089
+Epoch   1 Batch  279/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.890, Loss:  0.090
+Epoch   1 Batch  280/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.892, Loss:  0.090
+Epoch   1 Batch  281/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.889, Loss:  0.100
+Epoch   1 Batch  282/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.896, Loss:  0.106
+Epoch   1 Batch  283/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.900, Loss:  0.087
+Epoch   1 Batch  284/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.899, Loss:  0.089
+Epoch   1 Batch  285/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.898, Loss:  0.084
+Epoch   1 Batch  286/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.081
+Epoch   1 Batch  287/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.891, Loss:  0.076
+Epoch   1 Batch  288/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.889, Loss:  0.089
+Epoch   1 Batch  289/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.892, Loss:  0.078
+Epoch   1 Batch  290/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.902, Loss:  0.109
+Epoch   1 Batch  291/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.902, Loss:  0.103
+Epoch   1 Batch  292/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.902, Loss:  0.085
+Epoch   1 Batch  293/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.908, Loss:  0.089
+Epoch   1 Batch  294/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.906, Loss:  0.078
+Epoch   1 Batch  295/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.907, Loss:  0.091
+Epoch   1 Batch  296/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.907, Loss:  0.085
+Epoch   1 Batch  297/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.896, Loss:  0.094
+Epoch   1 Batch  298/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.898, Loss:  0.092
+Epoch   1 Batch  299/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.911, Loss:  0.085
+Epoch   1 Batch  300/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.080
+Epoch   1 Batch  301/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.906, Loss:  0.070
+Epoch   1 Batch  302/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.898, Loss:  0.084
+Epoch   1 Batch  303/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.898, Loss:  0.094
+Epoch   1 Batch  304/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.900, Loss:  0.079
+Epoch   1 Batch  305/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.892, Loss:  0.074
+Epoch   1 Batch  306/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.901, Loss:  0.089
+Epoch   1 Batch  307/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.881, Loss:  0.072
+Epoch   1 Batch  308/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.885, Loss:  0.097
+Epoch   1 Batch  309/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.882, Loss:  0.073
+Epoch   1 Batch  310/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.884, Loss:  0.080
+Epoch   1 Batch  311/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.885, Loss:  0.074
+Epoch   1 Batch  312/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.895, Loss:  0.095
+Epoch   1 Batch  313/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.894, Loss:  0.064
+Epoch   1 Batch  314/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.895, Loss:  0.076
+Epoch   1 Batch  315/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.901, Loss:  0.070
+Epoch   1 Batch  316/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.884, Loss:  0.075
+Epoch   1 Batch  317/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.890, Loss:  0.104
+Epoch   1 Batch  318/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.891, Loss:  0.068
+Epoch   1 Batch  319/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.881, Loss:  0.094
+Epoch   1 Batch  320/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.877, Loss:  0.088
+Epoch   1 Batch  321/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.879, Loss:  0.070
+Epoch   1 Batch  322/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.880, Loss:  0.073
+Epoch   1 Batch  323/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.877, Loss:  0.086
+Epoch   1 Batch  324/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.891, Loss:  0.065
+Epoch   1 Batch  325/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.883, Loss:  0.091
+Epoch   1 Batch  326/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.888, Loss:  0.073
+Epoch   1 Batch  327/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.889, Loss:  0.093
+Epoch   1 Batch  328/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.892, Loss:  0.087
+Epoch   1 Batch  329/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.889, Loss:  0.087
+Epoch   1 Batch  330/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.890, Loss:  0.086
+Epoch   1 Batch  331/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.891, Loss:  0.091
+Epoch   1 Batch  332/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.878, Loss:  0.066
+Epoch   1 Batch  333/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.882, Loss:  0.063
+Epoch   1 Batch  334/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.885, Loss:  0.074
+Epoch   1 Batch  335/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.888, Loss:  0.077
+Epoch   1 Batch  336/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.884, Loss:  0.108
+Epoch   1 Batch  337/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.880, Loss:  0.081
+Epoch   1 Batch  338/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.881, Loss:  0.106
+Epoch   1 Batch  339/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.881, Loss:  0.070
+Epoch   1 Batch  340/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.896, Loss:  0.070
+Epoch   1 Batch  341/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.901, Loss:  0.093
+Epoch   1 Batch  342/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.905, Loss:  0.069
+Epoch   1 Batch  343/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.897, Loss:  0.073
+Epoch   1 Batch  344/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.901, Loss:  0.073
+Epoch   1 Batch  345/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.903, Loss:  0.062
+Epoch   1 Batch  346/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.902, Loss:  0.082
+Epoch   1 Batch  347/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.065
+Epoch   1 Batch  348/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.077
+Epoch   1 Batch  349/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.907, Loss:  0.073
+Epoch   1 Batch  350/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.914, Loss:  0.088
+Epoch   1 Batch  351/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.912, Loss:  0.073
+Epoch   1 Batch  352/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   1 Batch  353/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.901, Loss:  0.082
+Epoch   1 Batch  354/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.903, Loss:  0.081
+Epoch   1 Batch  355/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.898, Loss:  0.072
+Epoch   1 Batch  356/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.901, Loss:  0.083
+Epoch   1 Batch  357/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.890, Loss:  0.074
+Epoch   1 Batch  358/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.894, Loss:  0.089
+Epoch   1 Batch  359/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.898, Loss:  0.076
+Epoch   1 Batch  360/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.893, Loss:  0.066
+Epoch   1 Batch  361/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.892, Loss:  0.074
+Epoch   1 Batch  362/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.887, Loss:  0.085
+Epoch   1 Batch  363/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.890, Loss:  0.079
+Epoch   1 Batch  364/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.888, Loss:  0.083
+Epoch   1 Batch  365/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.885, Loss:  0.064
+Epoch   1 Batch  366/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.885, Loss:  0.069
+Epoch   1 Batch  367/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.878, Loss:  0.055
+Epoch   1 Batch  368/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.877, Loss:  0.083
+Epoch   1 Batch  369/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.880, Loss:  0.081
+Epoch   1 Batch  370/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.876, Loss:  0.074
+Epoch   1 Batch  371/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.887, Loss:  0.062
+Epoch   1 Batch  372/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.885, Loss:  0.060
+Epoch   1 Batch  373/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.887, Loss:  0.056
+Epoch   1 Batch  374/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.903, Loss:  0.085
+Epoch   1 Batch  375/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.907, Loss:  0.081
+Epoch   1 Batch  376/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.906, Loss:  0.076
+Epoch   1 Batch  377/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.902, Loss:  0.069
+Epoch   1 Batch  378/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.911, Loss:  0.058
+Epoch   1 Batch  379/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.909, Loss:  0.083
+Epoch   1 Batch  380/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.914, Loss:  0.061
+Epoch   1 Batch  381/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.918, Loss:  0.098
+Epoch   1 Batch  382/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.925, Loss:  0.117
+Epoch   1 Batch  383/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.073
+Epoch   1 Batch  384/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.910, Loss:  0.074
+Epoch   1 Batch  385/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.917, Loss:  0.067
+Epoch   1 Batch  386/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.066
+Epoch   1 Batch  387/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.073
+Epoch   1 Batch  388/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.907, Loss:  0.065
+Epoch   1 Batch  389/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.905, Loss:  0.073
+Epoch   1 Batch  390/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.907, Loss:  0.081
+Epoch   1 Batch  391/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.908, Loss:  0.082
+Epoch   1 Batch  392/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.892, Loss:  0.084
+Epoch   1 Batch  393/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.069
+Epoch   1 Batch  394/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.899, Loss:  0.069
+Epoch   1 Batch  395/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.900, Loss:  0.082
+Epoch   1 Batch  396/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.899, Loss:  0.078
+Epoch   1 Batch  397/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.899, Loss:  0.061
+Epoch   1 Batch  398/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.902, Loss:  0.077
+Epoch   1 Batch  399/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.898, Loss:  0.067
+Epoch   1 Batch  400/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.904, Loss:  0.094
+Epoch   1 Batch  401/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.892, Loss:  0.068
+Epoch   1 Batch  402/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.896, Loss:  0.069
+Epoch   1 Batch  403/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.896, Loss:  0.091
+Epoch   1 Batch  404/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.891, Loss:  0.078
+Epoch   1 Batch  405/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.897, Loss:  0.068
+Epoch   1 Batch  406/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.896, Loss:  0.074
+Epoch   1 Batch  407/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.895, Loss:  0.083
+Epoch   1 Batch  408/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.899, Loss:  0.084
+Epoch   1 Batch  409/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.079
+Epoch   1 Batch  410/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.904, Loss:  0.085
+Epoch   1 Batch  411/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.085
+Epoch   1 Batch  412/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.903, Loss:  0.056
+Epoch   1 Batch  413/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.066
+Epoch   1 Batch  414/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.902, Loss:  0.080
+Epoch   1 Batch  415/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.899, Loss:  0.074
+Epoch   1 Batch  416/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.902, Loss:  0.076
+Epoch   1 Batch  417/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.909, Loss:  0.113
+Epoch   1 Batch  418/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.069
+Epoch   1 Batch  419/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.913, Loss:  0.061
+Epoch   1 Batch  420/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.916, Loss:  0.059
+Epoch   1 Batch  421/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.915, Loss:  0.083
+Epoch   1 Batch  422/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   1 Batch  423/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.910, Loss:  0.086
+Epoch   1 Batch  424/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.902, Loss:  0.073
+Epoch   1 Batch  425/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.900, Loss:  0.064
+Epoch   1 Batch  426/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.901, Loss:  0.089
+Epoch   1 Batch  427/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.905, Loss:  0.070
+Epoch   1 Batch  428/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.906, Loss:  0.059
+Epoch   1 Batch  429/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.902, Loss:  0.067
+Epoch   1 Batch  430/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.905, Loss:  0.067
+Epoch   1 Batch  431/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.907, Loss:  0.060
+Epoch   1 Batch  432/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.900, Loss:  0.071
+Epoch   1 Batch  433/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.902, Loss:  0.079
+Epoch   1 Batch  434/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.898, Loss:  0.064
+Epoch   1 Batch  435/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.899, Loss:  0.092
+Epoch   1 Batch  436/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.900, Loss:  0.074
+Epoch   1 Batch  437/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.900, Loss:  0.062
+Epoch   1 Batch  438/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.899, Loss:  0.071
+Epoch   1 Batch  439/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.901, Loss:  0.090
+Epoch   1 Batch  440/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.901, Loss:  0.091
+Epoch   1 Batch  441/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.909, Loss:  0.072
+Epoch   1 Batch  442/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.901, Loss:  0.081
+Epoch   1 Batch  443/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.901, Loss:  0.066
+Epoch   1 Batch  444/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.906, Loss:  0.085
+Epoch   1 Batch  445/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.897, Loss:  0.082
+Epoch   1 Batch  446/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.902, Loss:  0.056
+Epoch   1 Batch  447/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.901, Loss:  0.074
+Epoch   1 Batch  448/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.908, Loss:  0.093
+Epoch   1 Batch  449/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.909, Loss:  0.075
+Epoch   1 Batch  450/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.908, Loss:  0.072
+Epoch   1 Batch  451/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.914, Loss:  0.076
+Epoch   1 Batch  452/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.913, Loss:  0.078
+Epoch   1 Batch  453/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.911, Loss:  0.067
+Epoch   1 Batch  454/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.076
+Epoch   1 Batch  455/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.897, Loss:  0.072
+Epoch   1 Batch  456/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.896, Loss:  0.086
+Epoch   1 Batch  457/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.895, Loss:  0.056
+Epoch   1 Batch  458/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.897, Loss:  0.080
+Epoch   1 Batch  459/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.895, Loss:  0.069
+Epoch   1 Batch  460/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.901, Loss:  0.076
+Epoch   1 Batch  461/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.896, Loss:  0.073
+Epoch   1 Batch  462/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.895, Loss:  0.072
+Epoch   1 Batch  463/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.898, Loss:  0.080
+Epoch   1 Batch  464/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.889, Loss:  0.070
+Epoch   1 Batch  465/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.874, Loss:  0.074
+Epoch   1 Batch  466/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.879, Loss:  0.065
+Epoch   1 Batch  467/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.887, Loss:  0.080
+Epoch   1 Batch  468/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.886, Loss:  0.071
+Epoch   1 Batch  469/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.890, Loss:  0.074
+Epoch   1 Batch  470/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.893, Loss:  0.075
+Epoch   1 Batch  471/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.904, Loss:  0.055
+Epoch   1 Batch  472/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.908, Loss:  0.070
+Epoch   1 Batch  473/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.907, Loss:  0.072
+Epoch   1 Batch  474/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   1 Batch  475/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.922, Loss:  0.073
+Epoch   1 Batch  476/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.917, Loss:  0.057
+Epoch   1 Batch  477/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.075
+Epoch   1 Batch  478/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.912, Loss:  0.068
+Epoch   1 Batch  479/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.911, Loss:  0.087
+Epoch   1 Batch  480/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.903, Loss:  0.058
+Epoch   1 Batch  481/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.907, Loss:  0.085
+Epoch   1 Batch  482/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.903, Loss:  0.092
+Epoch   1 Batch  483/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.898, Loss:  0.071
+Epoch   1 Batch  484/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.893, Loss:  0.090
+Epoch   1 Batch  485/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.888, Loss:  0.087
+Epoch   1 Batch  486/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.889, Loss:  0.066
+Epoch   1 Batch  487/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.887, Loss:  0.062
+Epoch   1 Batch  488/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.886, Loss:  0.069
+Epoch   1 Batch  489/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.887, Loss:  0.065
+Epoch   1 Batch  490/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.897, Loss:  0.073
+Epoch   1 Batch  491/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.901, Loss:  0.076
+Epoch   1 Batch  492/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.900, Loss:  0.084
+Epoch   1 Batch  493/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.901, Loss:  0.058
+Epoch   1 Batch  494/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.902, Loss:  0.064
+Epoch   1 Batch  495/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.902, Loss:  0.072
+Epoch   1 Batch  496/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.069
+Epoch   1 Batch  497/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.891, Loss:  0.072
+Epoch   1 Batch  498/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.891, Loss:  0.074
+Epoch   1 Batch  499/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.896, Loss:  0.057
+Epoch   1 Batch  500/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.900, Loss:  0.057
+Epoch   1 Batch  501/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.906, Loss:  0.063
+Epoch   1 Batch  502/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.909, Loss:  0.079
+Epoch   1 Batch  503/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.905, Loss:  0.064
+Epoch   1 Batch  504/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.919, Loss:  0.072
+Epoch   1 Batch  505/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.059
+Epoch   1 Batch  506/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.913, Loss:  0.079
+Epoch   1 Batch  507/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.913, Loss:  0.072
+Epoch   1 Batch  508/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.916, Loss:  0.059
+Epoch   1 Batch  509/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.914, Loss:  0.079
+Epoch   1 Batch  510/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.901, Loss:  0.072
+Epoch   1 Batch  511/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.906, Loss:  0.071
+Epoch   1 Batch  512/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.909, Loss:  0.055
+Epoch   1 Batch  513/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.900, Loss:  0.068
+Epoch   1 Batch  514/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.904, Loss:  0.080
+Epoch   1 Batch  515/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.910, Loss:  0.079
+Epoch   1 Batch  516/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.072
+Epoch   1 Batch  517/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.911, Loss:  0.068
+Epoch   1 Batch  518/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   1 Batch  519/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.901, Loss:  0.072
+Epoch   1 Batch  520/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.894, Loss:  0.068
+Epoch   1 Batch  521/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.889, Loss:  0.081
+Epoch   1 Batch  522/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.897, Loss:  0.081
+Epoch   1 Batch  523/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.079
+Epoch   1 Batch  524/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.899, Loss:  0.079
+Epoch   1 Batch  525/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.895, Loss:  0.075
+Epoch   1 Batch  526/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.891, Loss:  0.065
+Epoch   1 Batch  527/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.884, Loss:  0.072
+Epoch   1 Batch  528/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.888, Loss:  0.074
+Epoch   1 Batch  529/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.885, Loss:  0.075
+Epoch   1 Batch  530/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.893, Loss:  0.073
+Epoch   1 Batch  531/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.888, Loss:  0.067
+Epoch   1 Batch  532/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.897, Loss:  0.085
+Epoch   1 Batch  533/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.906, Loss:  0.069
+Epoch   1 Batch  534/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.910, Loss:  0.081
+Epoch   1 Batch  535/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.906, Loss:  0.069
+Epoch   1 Batch  536/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.897, Loss:  0.071
+Epoch   1 Batch  537/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.894, Loss:  0.067
+Epoch   1 Batch  538/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.891, Loss:  0.054
+Epoch   1 Batch  539/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.877, Loss:  0.105
+Epoch   1 Batch  540/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.879, Loss:  0.063
+Epoch   1 Batch  541/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.885, Loss:  0.056
+Epoch   1 Batch  542/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.885, Loss:  0.071
+Epoch   1 Batch  543/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.891, Loss:  0.071
+Epoch   1 Batch  544/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.884, Loss:  0.052
+Epoch   1 Batch  545/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.888, Loss:  0.075
+Epoch   1 Batch  546/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.898, Loss:  0.078
+Epoch   1 Batch  547/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.902, Loss:  0.066
+Epoch   1 Batch  548/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.907, Loss:  0.077
+Epoch   1 Batch  549/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.911, Loss:  0.084
+Epoch   1 Batch  550/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.898, Loss:  0.078
+Epoch   1 Batch  551/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.912, Loss:  0.080
+Epoch   1 Batch  552/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.081
+Epoch   1 Batch  553/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.913, Loss:  0.087
+Epoch   1 Batch  554/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.908, Loss:  0.068
+Epoch   1 Batch  555/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.066
+Epoch   1 Batch  556/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   1 Batch  557/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.898, Loss:  0.073
+Epoch   1 Batch  558/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.900, Loss:  0.061
+Epoch   1 Batch  559/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   1 Batch  560/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.898, Loss:  0.065
+Epoch   1 Batch  561/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.900, Loss:  0.066
+Epoch   1 Batch  562/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.890, Loss:  0.059
+Epoch   1 Batch  563/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.891, Loss:  0.066
+Epoch   1 Batch  564/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.891, Loss:  0.082
+Epoch   1 Batch  565/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.886, Loss:  0.075
+Epoch   1 Batch  566/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.883, Loss:  0.083
+Epoch   1 Batch  567/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.892, Loss:  0.075
+Epoch   1 Batch  568/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.894, Loss:  0.068
+Epoch   1 Batch  569/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.891, Loss:  0.073
+Epoch   1 Batch  570/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.884, Loss:  0.085
+Epoch   1 Batch  571/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.885, Loss:  0.052
+Epoch   1 Batch  572/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.885, Loss:  0.065
+Epoch   1 Batch  573/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.884, Loss:  0.079
+Epoch   1 Batch  574/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.882, Loss:  0.081
+Epoch   1 Batch  575/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.889, Loss:  0.054
+Epoch   1 Batch  576/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.896, Loss:  0.063
+Epoch   1 Batch  577/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.898, Loss:  0.078
+Epoch   1 Batch  578/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.071
+Epoch   1 Batch  579/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.903, Loss:  0.062
+Epoch   1 Batch  580/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   1 Batch  581/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.896, Loss:  0.052
+Epoch   1 Batch  582/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.898, Loss:  0.073
+Epoch   1 Batch  583/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.898, Loss:  0.068
+Epoch   1 Batch  584/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   1 Batch  585/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.048
+Epoch   1 Batch  586/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.903, Loss:  0.078
+Epoch   1 Batch  587/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.900, Loss:  0.068
+Epoch   1 Batch  588/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.896, Loss:  0.055
+Epoch   1 Batch  589/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.889, Loss:  0.066
+Epoch   1 Batch  590/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.894, Loss:  0.083
+Epoch   1 Batch  591/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.069
+Epoch   1 Batch  592/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.888, Loss:  0.069
+Epoch   1 Batch  593/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.880, Loss:  0.094
+Epoch   1 Batch  594/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.875, Loss:  0.077
+Epoch   1 Batch  595/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.880, Loss:  0.071
+Epoch   1 Batch  596/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.875, Loss:  0.070
+Epoch   1 Batch  597/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.893, Loss:  0.059
+Epoch   1 Batch  598/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.907, Loss:  0.080
+Epoch   1 Batch  599/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.907, Loss:  0.089
+Epoch   1 Batch  600/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.900, Loss:  0.078
+Epoch   1 Batch  601/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.902, Loss:  0.074
+Epoch   1 Batch  602/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.905, Loss:  0.070
+Epoch   1 Batch  603/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.910, Loss:  0.067
+Epoch   1 Batch  604/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.912, Loss:  0.079
+Epoch   1 Batch  605/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.905, Loss:  0.089
+Epoch   1 Batch  606/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.907, Loss:  0.064
+Epoch   1 Batch  607/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.907, Loss:  0.069
+Epoch   1 Batch  608/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.909, Loss:  0.075
+Epoch   1 Batch  609/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.911, Loss:  0.067
+Epoch   1 Batch  610/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.909, Loss:  0.084
+Epoch   1 Batch  611/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.914, Loss:  0.057
+Epoch   1 Batch  612/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.062
+Epoch   1 Batch  613/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.911, Loss:  0.084
+Epoch   1 Batch  614/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.896, Loss:  0.058
+Epoch   1 Batch  615/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.903, Loss:  0.069
+Epoch   1 Batch  616/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.917, Loss:  0.076
+Epoch   1 Batch  617/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.922, Loss:  0.069
+Epoch   1 Batch  618/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.911, Loss:  0.067
+Epoch   1 Batch  619/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.915, Loss:  0.055
+Epoch   1 Batch  620/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.067
+Epoch   1 Batch  621/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.916, Loss:  0.065
+Epoch   1 Batch  622/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.912, Loss:  0.083
+Epoch   1 Batch  623/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.911, Loss:  0.074
+Epoch   1 Batch  624/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.906, Loss:  0.080
+Epoch   1 Batch  625/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.911, Loss:  0.063
+Epoch   1 Batch  626/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.904, Loss:  0.068
+Epoch   1 Batch  627/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.908, Loss:  0.069
+Epoch   1 Batch  628/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.071
+Epoch   1 Batch  629/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.908, Loss:  0.074
+Epoch   1 Batch  630/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.912, Loss:  0.063
+Epoch   1 Batch  631/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.911, Loss:  0.071
+Epoch   1 Batch  632/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.063
+Epoch   1 Batch  633/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.913, Loss:  0.074
+Epoch   1 Batch  634/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   1 Batch  635/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.913, Loss:  0.078
+Epoch   1 Batch  636/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   1 Batch  637/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   1 Batch  638/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   1 Batch  639/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.911, Loss:  0.091
+Epoch   1 Batch  640/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.923, Loss:  0.065
+Epoch   1 Batch  641/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.916, Loss:  0.069
+Epoch   1 Batch  642/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.922, Loss:  0.083
+Epoch   1 Batch  643/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   1 Batch  644/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.920, Loss:  0.073
+Epoch   1 Batch  645/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.915, Loss:  0.080
+Epoch   1 Batch  646/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.911, Loss:  0.071
+Epoch   1 Batch  647/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   1 Batch  648/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.927, Loss:  0.046
+Epoch   1 Batch  649/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.927, Loss:  0.063
+Epoch   1 Batch  650/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.071
+Epoch   1 Batch  651/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.062
+Epoch   1 Batch  652/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.908, Loss:  0.065
+Epoch   1 Batch  653/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.911, Loss:  0.067
+Epoch   1 Batch  654/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.910, Loss:  0.058
+Epoch   1 Batch  655/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.906, Loss:  0.076
+Epoch   1 Batch  656/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.067
+Epoch   1 Batch  657/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.909, Loss:  0.058
+Epoch   1 Batch  658/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.906, Loss:  0.055
+Epoch   1 Batch  659/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.902, Loss:  0.073
+Epoch   1 Batch  660/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.908, Loss:  0.069
+Epoch   1 Batch  661/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.909, Loss:  0.053
+Epoch   1 Batch  662/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.904, Loss:  0.075
+Epoch   1 Batch  663/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.906, Loss:  0.057
+Epoch   1 Batch  664/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.901, Loss:  0.061
+Epoch   1 Batch  665/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.903, Loss:  0.058
+Epoch   1 Batch  666/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.903, Loss:  0.073
+Epoch   1 Batch  667/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.911, Loss:  0.082
+Epoch   1 Batch  668/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.909, Loss:  0.071
+Epoch   1 Batch  669/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.906, Loss:  0.071
+Epoch   1 Batch  670/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.908, Loss:  0.078
+Epoch   1 Batch  671/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.913, Loss:  0.069
+Epoch   1 Batch  672/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   1 Batch  673/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.904, Loss:  0.062
+Epoch   1 Batch  674/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.901, Loss:  0.072
+Epoch   1 Batch  675/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.906, Loss:  0.082
+Epoch   1 Batch  676/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.907, Loss:  0.063
+Epoch   1 Batch  677/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.918, Loss:  0.091
+Epoch   1 Batch  678/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.911, Loss:  0.057
+Epoch   1 Batch  679/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.907, Loss:  0.062
+Epoch   1 Batch  680/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.904, Loss:  0.061
+Epoch   1 Batch  681/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.894, Loss:  0.066
+Epoch   1 Batch  682/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.903, Loss:  0.062
+Epoch   1 Batch  683/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.903, Loss:  0.057
+Epoch   1 Batch  684/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.909, Loss:  0.067
+Epoch   1 Batch  685/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.914, Loss:  0.075
+Epoch   1 Batch  686/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   1 Batch  687/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.913, Loss:  0.091
+Epoch   1 Batch  688/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.914, Loss:  0.066
+Epoch   1 Batch  689/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.904, Loss:  0.047
+Epoch   1 Batch  690/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.909, Loss:  0.068
+Epoch   1 Batch  691/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.906, Loss:  0.085
+Epoch   1 Batch  692/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.906, Loss:  0.061
+Epoch   1 Batch  693/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.901, Loss:  0.084
+Epoch   1 Batch  694/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.885, Loss:  0.072
+Epoch   1 Batch  695/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.884, Loss:  0.049
+Epoch   1 Batch  696/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.886, Loss:  0.085
+Epoch   1 Batch  697/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.890, Loss:  0.069
+Epoch   1 Batch  698/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.900, Loss:  0.059
+Epoch   1 Batch  699/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.908, Loss:  0.056
+Epoch   1 Batch  700/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.906, Loss:  0.067
+Epoch   1 Batch  701/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.900, Loss:  0.074
+Epoch   1 Batch  702/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.904, Loss:  0.087
+Epoch   1 Batch  703/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.070
+Epoch   1 Batch  704/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.910, Loss:  0.083
+Epoch   1 Batch  705/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.911, Loss:  0.074
+Epoch   1 Batch  706/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.914, Loss:  0.114
+Epoch   1 Batch  707/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.913, Loss:  0.067
+Epoch   1 Batch  708/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.909, Loss:  0.081
+Epoch   1 Batch  709/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.907, Loss:  0.089
+Epoch   1 Batch  710/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.905, Loss:  0.059
+Epoch   1 Batch  711/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.893, Loss:  0.083
+Epoch   1 Batch  712/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.896, Loss:  0.058
+Epoch   1 Batch  713/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.899, Loss:  0.056
+Epoch   1 Batch  714/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   1 Batch  715/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.895, Loss:  0.074
+Epoch   1 Batch  716/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.891, Loss:  0.056
+Epoch   1 Batch  717/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.887, Loss:  0.047
+Epoch   1 Batch  718/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.887, Loss:  0.056
+Epoch   1 Batch  719/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.896, Loss:  0.084
+Epoch   1 Batch  720/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.901, Loss:  0.081
+Epoch   1 Batch  721/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.902, Loss:  0.074
+Epoch   1 Batch  722/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.904, Loss:  0.065
+Epoch   1 Batch  723/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.899, Loss:  0.075
+Epoch   1 Batch  724/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.907, Loss:  0.073
+Epoch   1 Batch  725/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.059
+Epoch   1 Batch  726/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.914, Loss:  0.073
+Epoch   1 Batch  727/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   1 Batch  728/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.909, Loss:  0.078
+Epoch   1 Batch  729/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.909, Loss:  0.082
+Epoch   1 Batch  730/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.893, Loss:  0.081
+Epoch   1 Batch  731/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.899, Loss:  0.063
+Epoch   1 Batch  732/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.899, Loss:  0.072
+Epoch   1 Batch  733/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.902, Loss:  0.082
+Epoch   1 Batch  734/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.896, Loss:  0.064
+Epoch   1 Batch  735/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.901, Loss:  0.061
+Epoch   1 Batch  736/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   1 Batch  737/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.914, Loss:  0.082
+Epoch   1 Batch  738/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.047
+Epoch   1 Batch  739/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.061
+Epoch   1 Batch  740/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.054
+Epoch   1 Batch  741/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.073
+Epoch   1 Batch  742/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   1 Batch  743/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.913, Loss:  0.066
+Epoch   1 Batch  744/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.077
+Epoch   1 Batch  745/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.075
+Epoch   1 Batch  746/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.054
+Epoch   1 Batch  747/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   1 Batch  748/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.059
+Epoch   1 Batch  749/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.917, Loss:  0.069
+Epoch   1 Batch  750/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.063
+Epoch   1 Batch  751/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   1 Batch  752/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.916, Loss:  0.064
+Epoch   1 Batch  753/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.060
+Epoch   1 Batch  754/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.910, Loss:  0.070
+Epoch   1 Batch  755/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.067
+Epoch   1 Batch  756/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.924, Loss:  0.056
+Epoch   1 Batch  757/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   1 Batch  758/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   1 Batch  759/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.919, Loss:  0.054
+Epoch   1 Batch  760/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.915, Loss:  0.069
+Epoch   1 Batch  761/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.059
+Epoch   1 Batch  762/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.912, Loss:  0.063
+Epoch   1 Batch  763/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.903, Loss:  0.063
+Epoch   1 Batch  764/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.067
+Epoch   1 Batch  765/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.918, Loss:  0.083
+Epoch   1 Batch  766/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.063
+Epoch   1 Batch  767/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.061
+Epoch   1 Batch  768/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.924, Loss:  0.062
+Epoch   1 Batch  769/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.065
+Epoch   1 Batch  770/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.908, Loss:  0.064
+Epoch   1 Batch  771/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.906, Loss:  0.078
+Epoch   1 Batch  772/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   1 Batch  773/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.909, Loss:  0.068
+Epoch   1 Batch  774/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.909, Loss:  0.067
+Epoch   1 Batch  775/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.907, Loss:  0.072
+Epoch   1 Batch  776/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.924, Loss:  0.058
+Epoch   1 Batch  777/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.926, Loss:  0.070
+Epoch   1 Batch  778/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.924, Loss:  0.064
+Epoch   1 Batch  779/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.920, Loss:  0.069
+Epoch   1 Batch  780/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.916, Loss:  0.091
+Epoch   1 Batch  781/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   1 Batch  782/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   1 Batch  783/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.918, Loss:  0.076
+Epoch   1 Batch  784/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.053
+Epoch   1 Batch  785/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.051
+Epoch   1 Batch  786/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.922, Loss:  0.063
+Epoch   1 Batch  787/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.913, Loss:  0.065
+Epoch   1 Batch  788/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.905, Loss:  0.060
+Epoch   1 Batch  789/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.901, Loss:  0.078
+Epoch   1 Batch  790/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.907, Loss:  0.065
+Epoch   1 Batch  791/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   1 Batch  792/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.913, Loss:  0.072
+Epoch   1 Batch  793/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.917, Loss:  0.061
+Epoch   1 Batch  794/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.920, Loss:  0.055
+Epoch   1 Batch  795/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.068
+Epoch   1 Batch  796/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   1 Batch  797/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.928, Loss:  0.065
+Epoch   1 Batch  798/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.069
+Epoch   1 Batch  799/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.935, Loss:  0.079
+Epoch   1 Batch  800/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.064
+Epoch   1 Batch  801/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.072
+Epoch   1 Batch  802/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.068
+Epoch   1 Batch  803/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.929, Loss:  0.073
+Epoch   1 Batch  804/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.929, Loss:  0.047
+Epoch   1 Batch  805/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.928, Loss:  0.067
+Epoch   1 Batch  806/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.931, Loss:  0.059
+Epoch   1 Batch  807/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.931, Loss:  0.056
+Epoch   1 Batch  808/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.937, Loss:  0.090
+Epoch   1 Batch  809/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.930, Loss:  0.089
+Epoch   1 Batch  810/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.925, Loss:  0.058
+Epoch   1 Batch  811/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.059
+Epoch   1 Batch  812/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.933, Loss:  0.060
+Epoch   1 Batch  813/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.929, Loss:  0.061
+Epoch   1 Batch  814/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.934, Loss:  0.084
+Epoch   1 Batch  815/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.934, Loss:  0.075
+Epoch   1 Batch  816/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.934, Loss:  0.076
+Epoch   1 Batch  817/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.921, Loss:  0.072
+Epoch   1 Batch  818/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.925, Loss:  0.067
+Epoch   1 Batch  819/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.925, Loss:  0.069
+Epoch   1 Batch  820/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   1 Batch  821/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   1 Batch  822/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.918, Loss:  0.060
+Epoch   1 Batch  823/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.070
+Epoch   1 Batch  824/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.930, Loss:  0.063
+Epoch   1 Batch  825/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   1 Batch  826/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.920, Loss:  0.059
+Epoch   1 Batch  827/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.917, Loss:  0.066
+Epoch   1 Batch  828/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.917, Loss:  0.056
+Epoch   1 Batch  829/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.920, Loss:  0.077
+Epoch   1 Batch  830/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.924, Loss:  0.076
+Epoch   1 Batch  831/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.919, Loss:  0.069
+Epoch   1 Batch  832/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.916, Loss:  0.062
+Epoch   1 Batch  833/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.918, Loss:  0.071
+Epoch   1 Batch  834/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.068
+Epoch   1 Batch  835/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.931, Loss:  0.071
+Epoch   1 Batch  836/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.063
+Epoch   1 Batch  837/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.930, Loss:  0.086
+Epoch   1 Batch  838/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.937, Loss:  0.058
+Epoch   1 Batch  839/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.938, Loss:  0.057
+Epoch   1 Batch  840/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.937, Loss:  0.055
+Epoch   1 Batch  841/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.941, Loss:  0.070
+Epoch   1 Batch  842/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.934, Loss:  0.053
+Epoch   1 Batch  843/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.932, Loss:  0.045
+Epoch   1 Batch  844/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.932, Loss:  0.053
+Epoch   1 Batch  845/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.053
+Epoch   1 Batch  846/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.922, Loss:  0.071
+Epoch   1 Batch  847/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.078
+Epoch   1 Batch  848/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.053
+Epoch   1 Batch  849/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.920, Loss:  0.052
+Epoch   1 Batch  850/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.924, Loss:  0.098
+Epoch   1 Batch  851/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.076
+Epoch   1 Batch  852/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.920, Loss:  0.091
+Epoch   1 Batch  853/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.076
+Epoch   1 Batch  854/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.086
+Epoch   1 Batch  855/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.906, Loss:  0.064
+Epoch   1 Batch  856/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.903, Loss:  0.070
+Epoch   1 Batch  857/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.070
+Epoch   1 Batch  858/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.907, Loss:  0.059
+Epoch   1 Batch  859/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.903, Loss:  0.079
+Epoch   1 Batch  860/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.900, Loss:  0.081
+Epoch   1 Batch  861/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.900, Loss:  0.067
+Epoch   1 Batch  862/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.901, Loss:  0.067
+Epoch   1 Batch  863/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.909, Loss:  0.054
+Epoch   1 Batch  864/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.898, Loss:  0.073
+Epoch   1 Batch  865/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.896, Loss:  0.069
+Epoch   1 Batch  866/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.913, Loss:  0.078
+Epoch   1 Batch  867/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.911, Loss:  0.115
+Epoch   1 Batch  868/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.069
+Epoch   1 Batch  869/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.910, Loss:  0.067
+Epoch   1 Batch  870/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.911, Loss:  0.069
+Epoch   1 Batch  871/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   1 Batch  872/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.067
+Epoch   1 Batch  873/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.918, Loss:  0.069
+Epoch   1 Batch  874/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.922, Loss:  0.078
+Epoch   1 Batch  875/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.919, Loss:  0.082
+Epoch   1 Batch  876/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.929, Loss:  0.064
+Epoch   1 Batch  877/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   1 Batch  878/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   1 Batch  879/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.921, Loss:  0.055
+Epoch   1 Batch  880/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.923, Loss:  0.071
+Epoch   1 Batch  881/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.912, Loss:  0.073
+Epoch   1 Batch  882/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.915, Loss:  0.075
+Epoch   1 Batch  883/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.921, Loss:  0.092
+Epoch   1 Batch  884/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.913, Loss:  0.067
+Epoch   1 Batch  885/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.908, Loss:  0.042
+Epoch   1 Batch  886/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.064
+Epoch   1 Batch  887/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.906, Loss:  0.083
+Epoch   1 Batch  888/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.049
+Epoch   1 Batch  889/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.900, Loss:  0.057
+Epoch   1 Batch  890/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.901, Loss:  0.069
+Epoch   1 Batch  891/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.905, Loss:  0.057
+Epoch   1 Batch  892/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.908, Loss:  0.054
+Epoch   1 Batch  893/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.903, Loss:  0.073
+Epoch   1 Batch  894/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.911, Loss:  0.069
+Epoch   1 Batch  895/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   1 Batch  896/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.923, Loss:  0.064
+Epoch   1 Batch  897/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   1 Batch  898/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.057
+Epoch   1 Batch  899/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.076
+Epoch   1 Batch  900/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.917, Loss:  0.076
+Epoch   1 Batch  901/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.075
+Epoch   1 Batch  902/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.929, Loss:  0.081
+Epoch   1 Batch  903/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.062
+Epoch   1 Batch  904/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.918, Loss:  0.060
+Epoch   1 Batch  905/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.912, Loss:  0.054
+Epoch   1 Batch  906/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.904, Loss:  0.069
+Epoch   1 Batch  907/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.900, Loss:  0.078
+Epoch   1 Batch  908/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.909, Loss:  0.074
+Epoch   1 Batch  909/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.901, Loss:  0.071
+Epoch   1 Batch  910/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.903, Loss:  0.065
+Epoch   1 Batch  911/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.074
+Epoch   1 Batch  912/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.061
+Epoch   1 Batch  913/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.900, Loss:  0.092
+Epoch   1 Batch  914/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.902, Loss:  0.091
+Epoch   1 Batch  915/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.898, Loss:  0.057
+Epoch   1 Batch  916/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.903, Loss:  0.086
+Epoch   1 Batch  917/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.903, Loss:  0.065
+Epoch   1 Batch  918/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.058
+Epoch   1 Batch  919/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.904, Loss:  0.051
+Epoch   1 Batch  920/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   1 Batch  921/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.906, Loss:  0.066
+Epoch   1 Batch  922/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.897, Loss:  0.073
+Epoch   1 Batch  923/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.911, Loss:  0.044
+Epoch   1 Batch  924/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.905, Loss:  0.086
+Epoch   1 Batch  925/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.908, Loss:  0.066
+Epoch   1 Batch  926/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.908, Loss:  0.055
+Epoch   1 Batch  927/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.908, Loss:  0.077
+Epoch   1 Batch  928/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   1 Batch  929/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.062
+Epoch   1 Batch  930/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.901, Loss:  0.060
+Epoch   1 Batch  931/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.905, Loss:  0.053
+Epoch   1 Batch  932/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.898, Loss:  0.067
+Epoch   1 Batch  933/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.896, Loss:  0.062
+Epoch   1 Batch  934/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.906, Loss:  0.051
+Epoch   1 Batch  935/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.898, Loss:  0.055
+Epoch   1 Batch  936/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   1 Batch  937/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.917, Loss:  0.075
+Epoch   1 Batch  938/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.912, Loss:  0.082
+Epoch   1 Batch  939/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.910, Loss:  0.072
+Epoch   1 Batch  940/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.044
+Epoch   1 Batch  941/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.059
+Epoch   1 Batch  942/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.908, Loss:  0.065
+Epoch   1 Batch  943/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.912, Loss:  0.063
+Epoch   1 Batch  944/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.910, Loss:  0.056
+Epoch   1 Batch  945/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.057
+Epoch   1 Batch  946/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.916, Loss:  0.044
+Epoch   1 Batch  947/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.910, Loss:  0.059
+Epoch   1 Batch  948/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.059
+Epoch   1 Batch  949/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.915, Loss:  0.052
+Epoch   1 Batch  950/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.917, Loss:  0.058
+Epoch   1 Batch  951/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.923, Loss:  0.068
+Epoch   1 Batch  952/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   1 Batch  953/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   1 Batch  954/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.925, Loss:  0.067
+Epoch   1 Batch  955/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.928, Loss:  0.074
+Epoch   1 Batch  956/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.933, Loss:  0.066
+Epoch   1 Batch  957/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   1 Batch  958/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.067
+Epoch   1 Batch  959/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.918, Loss:  0.068
+Epoch   1 Batch  960/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.916, Loss:  0.060
+Epoch   1 Batch  961/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.918, Loss:  0.068
+Epoch   1 Batch  962/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.916, Loss:  0.054
+Epoch   1 Batch  963/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.912, Loss:  0.094
+Epoch   1 Batch  964/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   1 Batch  965/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.074
+Epoch   1 Batch  966/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   1 Batch  967/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.064
+Epoch   1 Batch  968/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.917, Loss:  0.076
+Epoch   1 Batch  969/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.916, Loss:  0.085
+Epoch   1 Batch  970/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.913, Loss:  0.066
+Epoch   1 Batch  971/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.908, Loss:  0.057
+Epoch   1 Batch  972/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.908, Loss:  0.059
+Epoch   1 Batch  973/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.908, Loss:  0.057
+Epoch   1 Batch  974/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   1 Batch  975/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   1 Batch  976/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.050
+Epoch   1 Batch  977/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   1 Batch  978/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.064
+Epoch   1 Batch  979/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.913, Loss:  0.068
+Epoch   1 Batch  980/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.056
+Epoch   1 Batch  981/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.917, Loss:  0.065
+Epoch   1 Batch  982/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.909, Loss:  0.065
+Epoch   1 Batch  983/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.907, Loss:  0.063
+Epoch   1 Batch  984/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.915, Loss:  0.076
+Epoch   1 Batch  985/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.911, Loss:  0.059
+Epoch   1 Batch  986/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   1 Batch  987/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.918, Loss:  0.048
+Epoch   1 Batch  988/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.913, Loss:  0.072
+Epoch   1 Batch  989/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.915, Loss:  0.073
+Epoch   1 Batch  990/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.066
+Epoch   1 Batch  991/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.065
+Epoch   1 Batch  992/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.067
+Epoch   1 Batch  993/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.044
+Epoch   1 Batch  994/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   1 Batch  995/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.912, Loss:  0.066
+Epoch   1 Batch  996/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   1 Batch  997/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.912, Loss:  0.054
+Epoch   1 Batch  998/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.918, Loss:  0.063
+Epoch   1 Batch  999/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.065
+Epoch   1 Batch 1000/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.920, Loss:  0.056
+Epoch   1 Batch 1001/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   1 Batch 1002/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   1 Batch 1003/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.059
+Epoch   1 Batch 1004/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.917, Loss:  0.070
+Epoch   1 Batch 1005/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.050
+Epoch   1 Batch 1006/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   1 Batch 1007/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.911, Loss:  0.057
+Epoch   1 Batch 1008/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.085
+Epoch   1 Batch 1009/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.059
+Epoch   1 Batch 1010/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.925, Loss:  0.058
+Epoch   1 Batch 1011/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   1 Batch 1012/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   1 Batch 1013/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   1 Batch 1014/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.064
+Epoch   1 Batch 1015/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.929, Loss:  0.075
+Epoch   1 Batch 1016/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.922, Loss:  0.066
+Epoch   1 Batch 1017/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.921, Loss:  0.059
+Epoch   1 Batch 1018/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.921, Loss:  0.048
+Epoch   1 Batch 1019/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.917, Loss:  0.075
+Epoch   1 Batch 1020/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.917, Loss:  0.058
+Epoch   1 Batch 1021/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.915, Loss:  0.060
+Epoch   1 Batch 1022/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   1 Batch 1023/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.910, Loss:  0.062
+Epoch   1 Batch 1024/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.906, Loss:  0.071
+Epoch   1 Batch 1025/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.912, Loss:  0.059
+Epoch   1 Batch 1026/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.919, Loss:  0.080
+Epoch   1 Batch 1027/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.919, Loss:  0.067
+Epoch   1 Batch 1028/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.922, Loss:  0.061
+Epoch   1 Batch 1029/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.051
+Epoch   1 Batch 1030/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.060
+Epoch   1 Batch 1031/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.063
+Epoch   1 Batch 1032/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.915, Loss:  0.064
+Epoch   1 Batch 1033/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.913, Loss:  0.057
+Epoch   1 Batch 1034/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.913, Loss:  0.055
+Epoch   1 Batch 1035/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.918, Loss:  0.036
+Epoch   1 Batch 1036/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.919, Loss:  0.076
+Epoch   1 Batch 1037/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.924, Loss:  0.061
+Epoch   1 Batch 1038/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.076
+Epoch   1 Batch 1039/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.929, Loss:  0.066
+Epoch   1 Batch 1040/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.928, Loss:  0.061
+Epoch   1 Batch 1041/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.928, Loss:  0.067
+Epoch   1 Batch 1042/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.052
+Epoch   1 Batch 1043/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.076
+Epoch   1 Batch 1044/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.081
+Epoch   1 Batch 1045/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.058
+Epoch   1 Batch 1046/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.911, Loss:  0.046
+Epoch   1 Batch 1047/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.909, Loss:  0.055
+Epoch   1 Batch 1048/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.908, Loss:  0.050
+Epoch   1 Batch 1049/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.903, Loss:  0.046
+Epoch   1 Batch 1050/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.903, Loss:  0.059
+Epoch   1 Batch 1051/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.900, Loss:  0.066
+Epoch   1 Batch 1052/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.900, Loss:  0.060
+Epoch   1 Batch 1053/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.907, Loss:  0.068
+Epoch   1 Batch 1054/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.912, Loss:  0.062
+Epoch   1 Batch 1055/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.065
+Epoch   1 Batch 1056/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.909, Loss:  0.054
+Epoch   1 Batch 1057/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   1 Batch 1058/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.906, Loss:  0.071
+Epoch   1 Batch 1059/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.901, Loss:  0.074
+Epoch   1 Batch 1060/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.051
+Epoch   1 Batch 1061/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.914, Loss:  0.076
+Epoch   1 Batch 1062/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.907, Loss:  0.062
+Epoch   1 Batch 1063/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.909, Loss:  0.066
+Epoch   1 Batch 1064/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.909, Loss:  0.063
+Epoch   1 Batch 1065/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.909, Loss:  0.071
+Epoch   1 Batch 1066/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.909, Loss:  0.055
+Epoch   1 Batch 1067/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.919, Loss:  0.074
+Epoch   1 Batch 1068/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.910, Loss:  0.060
+Epoch   1 Batch 1069/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.045
+Epoch   1 Batch 1070/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.913, Loss:  0.070
+Epoch   1 Batch 1071/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.913, Loss:  0.058
+Epoch   1 Batch 1072/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   1 Batch 1073/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.909, Loss:  0.068
+Epoch   1 Batch 1074/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.903, Loss:  0.070
+Epoch   1 Batch 1075/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.900, Loss:  0.070
+Epoch   2 Batch    0/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.907, Loss:  0.046
+Epoch   2 Batch    1/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.910, Loss:  0.060
+Epoch   2 Batch    2/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.906, Loss:  0.068
+Epoch   2 Batch    3/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.907, Loss:  0.061
+Epoch   2 Batch    4/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.910, Loss:  0.056
+Epoch   2 Batch    5/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.918, Loss:  0.072
+Epoch   2 Batch    6/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.065
+Epoch   2 Batch    7/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.905, Loss:  0.052
+Epoch   2 Batch    8/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.060
+Epoch   2 Batch    9/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.930, Loss:  0.065
+Epoch   2 Batch   10/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.921, Loss:  0.062
+Epoch   2 Batch   11/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.916, Loss:  0.072
+Epoch   2 Batch   12/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.058
+Epoch   2 Batch   13/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.922, Loss:  0.063
+Epoch   2 Batch   14/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   2 Batch   15/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.060
+Epoch   2 Batch   16/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.064
+Epoch   2 Batch   17/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   2 Batch   18/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.929, Loss:  0.065
+Epoch   2 Batch   19/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.065
+Epoch   2 Batch   20/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.918, Loss:  0.060
+Epoch   2 Batch   21/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.066
+Epoch   2 Batch   22/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.930, Loss:  0.067
+Epoch   2 Batch   23/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.066
+Epoch   2 Batch   24/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.924, Loss:  0.069
+Epoch   2 Batch   25/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   2 Batch   26/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.916, Loss:  0.066
+Epoch   2 Batch   27/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.052
+Epoch   2 Batch   28/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   2 Batch   29/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.913, Loss:  0.063
+Epoch   2 Batch   30/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   2 Batch   31/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.052
+Epoch   2 Batch   32/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.054
+Epoch   2 Batch   33/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.907, Loss:  0.055
+Epoch   2 Batch   34/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.900, Loss:  0.063
+Epoch   2 Batch   35/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.900, Loss:  0.066
+Epoch   2 Batch   36/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.898, Loss:  0.056
+Epoch   2 Batch   37/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.903, Loss:  0.063
+Epoch   2 Batch   38/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.907, Loss:  0.081
+Epoch   2 Batch   39/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   2 Batch   40/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.906, Loss:  0.049
+Epoch   2 Batch   41/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.908, Loss:  0.058
+Epoch   2 Batch   42/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.908, Loss:  0.075
+Epoch   2 Batch   43/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.904, Loss:  0.044
+Epoch   2 Batch   44/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.905, Loss:  0.049
+Epoch   2 Batch   45/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.908, Loss:  0.052
+Epoch   2 Batch   46/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.904, Loss:  0.063
+Epoch   2 Batch   47/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.906, Loss:  0.068
+Epoch   2 Batch   48/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.912, Loss:  0.084
+Epoch   2 Batch   49/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.904, Loss:  0.078
+Epoch   2 Batch   50/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.902, Loss:  0.058
+Epoch   2 Batch   51/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.906, Loss:  0.073
+Epoch   2 Batch   52/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.907, Loss:  0.065
+Epoch   2 Batch   53/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.058
+Epoch   2 Batch   54/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.910, Loss:  0.083
+Epoch   2 Batch   55/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.911, Loss:  0.056
+Epoch   2 Batch   56/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.907, Loss:  0.044
+Epoch   2 Batch   57/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.054
+Epoch   2 Batch   58/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.901, Loss:  0.062
+Epoch   2 Batch   59/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.907, Loss:  0.050
+Epoch   2 Batch   60/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.901, Loss:  0.041
+Epoch   2 Batch   61/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.901, Loss:  0.076
+Epoch   2 Batch   62/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.910, Loss:  0.067
+Epoch   2 Batch   63/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.911, Loss:  0.043
+Epoch   2 Batch   64/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.901, Loss:  0.056
+Epoch   2 Batch   65/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.902, Loss:  0.054
+Epoch   2 Batch   66/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.904, Loss:  0.046
+Epoch   2 Batch   67/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.905, Loss:  0.060
+Epoch   2 Batch   68/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.906, Loss:  0.073
+Epoch   2 Batch   69/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.911, Loss:  0.070
+Epoch   2 Batch   70/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.914, Loss:  0.059
+Epoch   2 Batch   71/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   2 Batch   72/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   2 Batch   73/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.921, Loss:  0.052
+Epoch   2 Batch   74/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.916, Loss:  0.050
+Epoch   2 Batch   75/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.074
+Epoch   2 Batch   76/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   2 Batch   77/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.061
+Epoch   2 Batch   78/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.064
+Epoch   2 Batch   79/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.059
+Epoch   2 Batch   80/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.057
+Epoch   2 Batch   81/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   2 Batch   82/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.932, Loss:  0.044
+Epoch   2 Batch   83/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.928, Loss:  0.071
+Epoch   2 Batch   84/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.057
+Epoch   2 Batch   85/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.937, Loss:  0.054
+Epoch   2 Batch   86/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.057
+Epoch   2 Batch   87/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.932, Loss:  0.062
+Epoch   2 Batch   88/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.930, Loss:  0.068
+Epoch   2 Batch   89/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.925, Loss:  0.075
+Epoch   2 Batch   90/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.931, Loss:  0.057
+Epoch   2 Batch   91/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   2 Batch   92/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.919, Loss:  0.067
+Epoch   2 Batch   93/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.920, Loss:  0.051
+Epoch   2 Batch   94/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.932, Loss:  0.050
+Epoch   2 Batch   95/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.936, Loss:  0.067
+Epoch   2 Batch   96/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.940, Loss:  0.064
+Epoch   2 Batch   97/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   2 Batch   98/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.065
+Epoch   2 Batch   99/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.938, Loss:  0.068
+Epoch   2 Batch  100/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   2 Batch  101/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.932, Loss:  0.056
+Epoch   2 Batch  102/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.058
+Epoch   2 Batch  103/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.923, Loss:  0.071
+Epoch   2 Batch  104/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.919, Loss:  0.064
+Epoch   2 Batch  105/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   2 Batch  106/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.078
+Epoch   2 Batch  107/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.915, Loss:  0.062
+Epoch   2 Batch  108/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.919, Loss:  0.061
+Epoch   2 Batch  109/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.913, Loss:  0.050
+Epoch   2 Batch  110/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   2 Batch  111/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.066
+Epoch   2 Batch  112/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.915, Loss:  0.057
+Epoch   2 Batch  113/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   2 Batch  114/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.911, Loss:  0.044
+Epoch   2 Batch  115/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.912, Loss:  0.065
+Epoch   2 Batch  116/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.916, Loss:  0.061
+Epoch   2 Batch  117/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.916, Loss:  0.059
+Epoch   2 Batch  118/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.917, Loss:  0.049
+Epoch   2 Batch  119/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   2 Batch  120/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.067
+Epoch   2 Batch  121/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.909, Loss:  0.052
+Epoch   2 Batch  122/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.916, Loss:  0.053
+Epoch   2 Batch  123/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.047
+Epoch   2 Batch  124/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.926, Loss:  0.077
+Epoch   2 Batch  125/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.920, Loss:  0.067
+Epoch   2 Batch  126/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.924, Loss:  0.051
+Epoch   2 Batch  127/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.057
+Epoch   2 Batch  128/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.909, Loss:  0.061
+Epoch   2 Batch  129/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.894, Loss:  0.059
+Epoch   2 Batch  130/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.897, Loss:  0.057
+Epoch   2 Batch  131/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.901, Loss:  0.063
+Epoch   2 Batch  132/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.894, Loss:  0.051
+Epoch   2 Batch  133/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.899, Loss:  0.048
+Epoch   2 Batch  134/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.903, Loss:  0.046
+Epoch   2 Batch  135/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.908, Loss:  0.065
+Epoch   2 Batch  136/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.904, Loss:  0.050
+Epoch   2 Batch  137/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.904, Loss:  0.041
+Epoch   2 Batch  138/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.907, Loss:  0.054
+Epoch   2 Batch  139/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.903, Loss:  0.059
+Epoch   2 Batch  140/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   2 Batch  141/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   2 Batch  142/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.917, Loss:  0.052
+Epoch   2 Batch  143/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.925, Loss:  0.062
+Epoch   2 Batch  144/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.927, Loss:  0.087
+Epoch   2 Batch  145/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.923, Loss:  0.070
+Epoch   2 Batch  146/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.926, Loss:  0.091
+Epoch   2 Batch  147/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.064
+Epoch   2 Batch  148/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.935, Loss:  0.066
+Epoch   2 Batch  149/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   2 Batch  150/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   2 Batch  151/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.909, Loss:  0.049
+Epoch   2 Batch  152/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.911, Loss:  0.074
+Epoch   2 Batch  153/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.907, Loss:  0.075
+Epoch   2 Batch  154/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   2 Batch  155/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.920, Loss:  0.056
+Epoch   2 Batch  156/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   2 Batch  157/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.920, Loss:  0.042
+Epoch   2 Batch  158/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.932, Loss:  0.073
+Epoch   2 Batch  159/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   2 Batch  160/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.916, Loss:  0.045
+Epoch   2 Batch  161/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.916, Loss:  0.054
+Epoch   2 Batch  162/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.076
+Epoch   2 Batch  163/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.925, Loss:  0.072
+Epoch   2 Batch  164/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   2 Batch  165/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.914, Loss:  0.047
+Epoch   2 Batch  166/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.909, Loss:  0.063
+Epoch   2 Batch  167/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.908, Loss:  0.055
+Epoch   2 Batch  168/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.908, Loss:  0.070
+Epoch   2 Batch  169/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.913, Loss:  0.078
+Epoch   2 Batch  170/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.915, Loss:  0.070
+Epoch   2 Batch  171/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.051
+Epoch   2 Batch  172/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   2 Batch  173/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.921, Loss:  0.066
+Epoch   2 Batch  174/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.051
+Epoch   2 Batch  175/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.916, Loss:  0.055
+Epoch   2 Batch  176/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.912, Loss:  0.066
+Epoch   2 Batch  177/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.911, Loss:  0.062
+Epoch   2 Batch  178/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.064
+Epoch   2 Batch  179/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.057
+Epoch   2 Batch  180/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.911, Loss:  0.048
+Epoch   2 Batch  181/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.909, Loss:  0.063
+Epoch   2 Batch  182/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.914, Loss:  0.057
+Epoch   2 Batch  183/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.059
+Epoch   2 Batch  184/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.055
+Epoch   2 Batch  185/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.912, Loss:  0.058
+Epoch   2 Batch  186/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.909, Loss:  0.056
+Epoch   2 Batch  187/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   2 Batch  188/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   2 Batch  189/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   2 Batch  190/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.919, Loss:  0.051
+Epoch   2 Batch  191/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.918, Loss:  0.050
+Epoch   2 Batch  192/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   2 Batch  193/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.903, Loss:  0.047
+Epoch   2 Batch  194/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.908, Loss:  0.045
+Epoch   2 Batch  195/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   2 Batch  196/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.920, Loss:  0.045
+Epoch   2 Batch  197/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   2 Batch  198/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.925, Loss:  0.059
+Epoch   2 Batch  199/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.925, Loss:  0.048
+Epoch   2 Batch  200/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.065
+Epoch   2 Batch  201/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.929, Loss:  0.041
+Epoch   2 Batch  202/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.928, Loss:  0.044
+Epoch   2 Batch  203/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.048
+Epoch   2 Batch  204/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.923, Loss:  0.073
+Epoch   2 Batch  205/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.926, Loss:  0.060
+Epoch   2 Batch  206/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.918, Loss:  0.049
+Epoch   2 Batch  207/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.048
+Epoch   2 Batch  208/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   2 Batch  209/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   2 Batch  210/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   2 Batch  211/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.905, Loss:  0.045
+Epoch   2 Batch  212/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.911, Loss:  0.046
+Epoch   2 Batch  213/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.913, Loss:  0.052
+Epoch   2 Batch  214/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   2 Batch  215/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   2 Batch  216/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.911, Loss:  0.056
+Epoch   2 Batch  217/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.906, Loss:  0.044
+Epoch   2 Batch  218/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.905, Loss:  0.069
+Epoch   2 Batch  219/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.901, Loss:  0.042
+Epoch   2 Batch  220/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.900, Loss:  0.051
+Epoch   2 Batch  221/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.897, Loss:  0.056
+Epoch   2 Batch  222/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.904, Loss:  0.045
+Epoch   2 Batch  223/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.903, Loss:  0.043
+Epoch   2 Batch  224/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.912, Loss:  0.064
+Epoch   2 Batch  225/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.058
+Epoch   2 Batch  226/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   2 Batch  227/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.911, Loss:  0.073
+Epoch   2 Batch  228/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   2 Batch  229/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.930, Loss:  0.058
+Epoch   2 Batch  230/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   2 Batch  231/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.050
+Epoch   2 Batch  232/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.921, Loss:  0.041
+Epoch   2 Batch  233/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.911, Loss:  0.075
+Epoch   2 Batch  234/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.915, Loss:  0.062
+Epoch   2 Batch  235/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   2 Batch  236/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.923, Loss:  0.064
+Epoch   2 Batch  237/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   2 Batch  238/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   2 Batch  239/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.041
+Epoch   2 Batch  240/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.916, Loss:  0.050
+Epoch   2 Batch  241/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.915, Loss:  0.045
+Epoch   2 Batch  242/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.909, Loss:  0.044
+Epoch   2 Batch  243/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.904, Loss:  0.055
+Epoch   2 Batch  244/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.904, Loss:  0.051
+Epoch   2 Batch  245/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.917, Loss:  0.043
+Epoch   2 Batch  246/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.917, Loss:  0.044
+Epoch   2 Batch  247/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.056
+Epoch   2 Batch  248/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.045
+Epoch   2 Batch  249/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.924, Loss:  0.052
+Epoch   2 Batch  250/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.056
+Epoch   2 Batch  251/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.916, Loss:  0.057
+Epoch   2 Batch  252/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.917, Loss:  0.061
+Epoch   2 Batch  253/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.056
+Epoch   2 Batch  254/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.910, Loss:  0.057
+Epoch   2 Batch  255/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.910, Loss:  0.054
+Epoch   2 Batch  256/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.906, Loss:  0.073
+Epoch   2 Batch  257/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.905, Loss:  0.055
+Epoch   2 Batch  258/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.907, Loss:  0.054
+Epoch   2 Batch  259/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.906, Loss:  0.045
+Epoch   2 Batch  260/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.901, Loss:  0.048
+Epoch   2 Batch  261/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.901, Loss:  0.057
+Epoch   2 Batch  262/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.911, Loss:  0.048
+Epoch   2 Batch  263/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.912, Loss:  0.051
+Epoch   2 Batch  264/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.913, Loss:  0.053
+Epoch   2 Batch  265/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   2 Batch  266/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.057
+Epoch   2 Batch  267/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.925, Loss:  0.050
+Epoch   2 Batch  268/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.927, Loss:  0.062
+Epoch   2 Batch  269/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.926, Loss:  0.082
+Epoch   2 Batch  270/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.925, Loss:  0.065
+Epoch   2 Batch  271/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.048
+Epoch   2 Batch  272/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.915, Loss:  0.093
+Epoch   2 Batch  273/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   2 Batch  274/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.917, Loss:  0.066
+Epoch   2 Batch  275/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.917, Loss:  0.055
+Epoch   2 Batch  276/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.910, Loss:  0.088
+Epoch   2 Batch  277/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   2 Batch  278/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.917, Loss:  0.067
+Epoch   2 Batch  279/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.068
+Epoch   2 Batch  280/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.072
+Epoch   2 Batch  281/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.072
+Epoch   2 Batch  282/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.912, Loss:  0.082
+Epoch   2 Batch  283/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.919, Loss:  0.080
+Epoch   2 Batch  284/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.928, Loss:  0.062
+Epoch   2 Batch  285/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.917, Loss:  0.061
+Epoch   2 Batch  286/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.053
+Epoch   2 Batch  287/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   2 Batch  288/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.904, Loss:  0.067
+Epoch   2 Batch  289/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.901, Loss:  0.056
+Epoch   2 Batch  290/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.915, Loss:  0.088
+Epoch   2 Batch  291/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.922, Loss:  0.076
+Epoch   2 Batch  292/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.918, Loss:  0.067
+Epoch   2 Batch  293/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.917, Loss:  0.067
+Epoch   2 Batch  294/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.917, Loss:  0.052
+Epoch   2 Batch  295/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.913, Loss:  0.067
+Epoch   2 Batch  296/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.913, Loss:  0.060
+Epoch   2 Batch  297/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.065
+Epoch   2 Batch  298/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.079
+Epoch   2 Batch  299/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.921, Loss:  0.067
+Epoch   2 Batch  300/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.056
+Epoch   2 Batch  301/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.908, Loss:  0.045
+Epoch   2 Batch  302/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.901, Loss:  0.053
+Epoch   2 Batch  303/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.906, Loss:  0.067
+Epoch   2 Batch  304/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.906, Loss:  0.059
+Epoch   2 Batch  305/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.904, Loss:  0.046
+Epoch   2 Batch  306/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.905, Loss:  0.075
+Epoch   2 Batch  307/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.910, Loss:  0.049
+Epoch   2 Batch  308/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.908, Loss:  0.075
+Epoch   2 Batch  309/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.053
+Epoch   2 Batch  310/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.912, Loss:  0.058
+Epoch   2 Batch  311/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.903, Loss:  0.058
+Epoch   2 Batch  312/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.904, Loss:  0.077
+Epoch   2 Batch  313/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.899, Loss:  0.049
+Epoch   2 Batch  314/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.903, Loss:  0.049
+Epoch   2 Batch  315/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.899, Loss:  0.049
+Epoch   2 Batch  316/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.893, Loss:  0.068
+Epoch   2 Batch  317/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.895, Loss:  0.082
+Epoch   2 Batch  318/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.898, Loss:  0.066
+Epoch   2 Batch  319/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.900, Loss:  0.071
+Epoch   2 Batch  320/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.893, Loss:  0.067
+Epoch   2 Batch  321/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.898, Loss:  0.054
+Epoch   2 Batch  322/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.903, Loss:  0.057
+Epoch   2 Batch  323/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.908, Loss:  0.066
+Epoch   2 Batch  324/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.903, Loss:  0.055
+Epoch   2 Batch  325/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   2 Batch  326/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.913, Loss:  0.059
+Epoch   2 Batch  327/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   2 Batch  328/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.904, Loss:  0.070
+Epoch   2 Batch  329/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.902, Loss:  0.071
+Epoch   2 Batch  330/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.894, Loss:  0.059
+Epoch   2 Batch  331/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.886, Loss:  0.061
+Epoch   2 Batch  332/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.888, Loss:  0.049
+Epoch   2 Batch  333/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.879, Loss:  0.054
+Epoch   2 Batch  334/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.878, Loss:  0.058
+Epoch   2 Batch  335/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.878, Loss:  0.058
+Epoch   2 Batch  336/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.893, Loss:  0.096
+Epoch   2 Batch  337/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.897, Loss:  0.065
+Epoch   2 Batch  338/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.895, Loss:  0.073
+Epoch   2 Batch  339/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.909, Loss:  0.049
+Epoch   2 Batch  340/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.908, Loss:  0.061
+Epoch   2 Batch  341/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.072
+Epoch   2 Batch  342/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.922, Loss:  0.053
+Epoch   2 Batch  343/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   2 Batch  344/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.912, Loss:  0.068
+Epoch   2 Batch  345/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.907, Loss:  0.052
+Epoch   2 Batch  346/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.056
+Epoch   2 Batch  347/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.906, Loss:  0.050
+Epoch   2 Batch  348/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.897, Loss:  0.052
+Epoch   2 Batch  349/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.897, Loss:  0.056
+Epoch   2 Batch  350/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.904, Loss:  0.065
+Epoch   2 Batch  351/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.903, Loss:  0.060
+Epoch   2 Batch  352/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.906, Loss:  0.058
+Epoch   2 Batch  353/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.902, Loss:  0.067
+Epoch   2 Batch  354/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.903, Loss:  0.074
+Epoch   2 Batch  355/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.898, Loss:  0.057
+Epoch   2 Batch  356/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.896, Loss:  0.063
+Epoch   2 Batch  357/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.896, Loss:  0.057
+Epoch   2 Batch  358/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.904, Loss:  0.076
+Epoch   2 Batch  359/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.059
+Epoch   2 Batch  360/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   2 Batch  361/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.908, Loss:  0.055
+Epoch   2 Batch  362/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.914, Loss:  0.066
+Epoch   2 Batch  363/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.913, Loss:  0.068
+Epoch   2 Batch  364/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.915, Loss:  0.077
+Epoch   2 Batch  365/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   2 Batch  366/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   2 Batch  367/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.908, Loss:  0.047
+Epoch   2 Batch  368/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.068
+Epoch   2 Batch  369/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.913, Loss:  0.059
+Epoch   2 Batch  370/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.061
+Epoch   2 Batch  371/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.917, Loss:  0.055
+Epoch   2 Batch  372/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.931, Loss:  0.053
+Epoch   2 Batch  373/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   2 Batch  374/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.071
+Epoch   2 Batch  375/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   2 Batch  376/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   2 Batch  377/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.929, Loss:  0.053
+Epoch   2 Batch  378/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.044
+Epoch   2 Batch  379/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.068
+Epoch   2 Batch  380/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   2 Batch  381/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.929, Loss:  0.066
+Epoch   2 Batch  382/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.932, Loss:  0.078
+Epoch   2 Batch  383/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   2 Batch  384/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.058
+Epoch   2 Batch  385/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.052
+Epoch   2 Batch  386/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.924, Loss:  0.049
+Epoch   2 Batch  387/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   2 Batch  388/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.913, Loss:  0.061
+Epoch   2 Batch  389/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.907, Loss:  0.058
+Epoch   2 Batch  390/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.904, Loss:  0.070
+Epoch   2 Batch  391/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.912, Loss:  0.057
+Epoch   2 Batch  392/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   2 Batch  393/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.916, Loss:  0.053
+Epoch   2 Batch  394/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   2 Batch  395/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   2 Batch  396/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.932, Loss:  0.054
+Epoch   2 Batch  397/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.937, Loss:  0.049
+Epoch   2 Batch  398/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.938, Loss:  0.059
+Epoch   2 Batch  399/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.938, Loss:  0.052
+Epoch   2 Batch  400/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.943, Loss:  0.071
+Epoch   2 Batch  401/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   2 Batch  402/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   2 Batch  403/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.927, Loss:  0.077
+Epoch   2 Batch  404/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.929, Loss:  0.050
+Epoch   2 Batch  405/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.930, Loss:  0.065
+Epoch   2 Batch  406/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.051
+Epoch   2 Batch  407/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.062
+Epoch   2 Batch  408/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.917, Loss:  0.052
+Epoch   2 Batch  409/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.066
+Epoch   2 Batch  410/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.077
+Epoch   2 Batch  411/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.067
+Epoch   2 Batch  412/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   2 Batch  413/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   2 Batch  414/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.910, Loss:  0.071
+Epoch   2 Batch  415/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.059
+Epoch   2 Batch  416/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.052
+Epoch   2 Batch  417/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.092
+Epoch   2 Batch  418/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   2 Batch  419/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.059
+Epoch   2 Batch  420/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   2 Batch  421/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.070
+Epoch   2 Batch  422/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   2 Batch  423/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.923, Loss:  0.068
+Epoch   2 Batch  424/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.928, Loss:  0.062
+Epoch   2 Batch  425/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.932, Loss:  0.045
+Epoch   2 Batch  426/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.931, Loss:  0.057
+Epoch   2 Batch  427/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.931, Loss:  0.060
+Epoch   2 Batch  428/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   2 Batch  429/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.926, Loss:  0.048
+Epoch   2 Batch  430/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   2 Batch  431/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.924, Loss:  0.050
+Epoch   2 Batch  432/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.924, Loss:  0.065
+Epoch   2 Batch  433/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.065
+Epoch   2 Batch  434/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.050
+Epoch   2 Batch  435/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.907, Loss:  0.075
+Epoch   2 Batch  436/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.056
+Epoch   2 Batch  437/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.911, Loss:  0.046
+Epoch   2 Batch  438/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.906, Loss:  0.054
+Epoch   2 Batch  439/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.897, Loss:  0.081
+Epoch   2 Batch  440/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.898, Loss:  0.064
+Epoch   2 Batch  441/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.892, Loss:  0.058
+Epoch   2 Batch  442/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.895, Loss:  0.064
+Epoch   2 Batch  443/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.899, Loss:  0.046
+Epoch   2 Batch  444/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.904, Loss:  0.052
+Epoch   2 Batch  445/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.895, Loss:  0.061
+Epoch   2 Batch  446/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.893, Loss:  0.051
+Epoch   2 Batch  447/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.890, Loss:  0.056
+Epoch   2 Batch  448/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.894, Loss:  0.067
+Epoch   2 Batch  449/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.060
+Epoch   2 Batch  450/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.906, Loss:  0.063
+Epoch   2 Batch  451/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   2 Batch  452/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   2 Batch  453/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.048
+Epoch   2 Batch  454/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.058
+Epoch   2 Batch  455/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.929, Loss:  0.052
+Epoch   2 Batch  456/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.059
+Epoch   2 Batch  457/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.050
+Epoch   2 Batch  458/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.939, Loss:  0.062
+Epoch   2 Batch  459/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.048
+Epoch   2 Batch  460/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.061
+Epoch   2 Batch  461/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.935, Loss:  0.052
+Epoch   2 Batch  462/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.934, Loss:  0.056
+Epoch   2 Batch  463/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.932, Loss:  0.064
+Epoch   2 Batch  464/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   2 Batch  465/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.062
+Epoch   2 Batch  466/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   2 Batch  467/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.908, Loss:  0.063
+Epoch   2 Batch  468/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   2 Batch  469/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   2 Batch  470/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.902, Loss:  0.052
+Epoch   2 Batch  471/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.903, Loss:  0.046
+Epoch   2 Batch  472/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.051
+Epoch   2 Batch  473/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.906, Loss:  0.054
+Epoch   2 Batch  474/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.906, Loss:  0.053
+Epoch   2 Batch  475/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.907, Loss:  0.052
+Epoch   2 Batch  476/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.913, Loss:  0.041
+Epoch   2 Batch  477/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.912, Loss:  0.054
+Epoch   2 Batch  478/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.047
+Epoch   2 Batch  479/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.914, Loss:  0.054
+Epoch   2 Batch  480/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.919, Loss:  0.050
+Epoch   2 Batch  481/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.913, Loss:  0.060
+Epoch   2 Batch  482/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.923, Loss:  0.064
+Epoch   2 Batch  483/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.055
+Epoch   2 Batch  484/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   2 Batch  485/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.065
+Epoch   2 Batch  486/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   2 Batch  487/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   2 Batch  488/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.914, Loss:  0.050
+Epoch   2 Batch  489/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.909, Loss:  0.048
+Epoch   2 Batch  490/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.903, Loss:  0.058
+Epoch   2 Batch  491/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.905, Loss:  0.060
+Epoch   2 Batch  492/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.909, Loss:  0.062
+Epoch   2 Batch  493/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.910, Loss:  0.033
+Epoch   2 Batch  494/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.910, Loss:  0.038
+Epoch   2 Batch  495/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   2 Batch  496/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.920, Loss:  0.061
+Epoch   2 Batch  497/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   2 Batch  498/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.053
+Epoch   2 Batch  499/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.932, Loss:  0.044
+Epoch   2 Batch  500/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.934, Loss:  0.041
+Epoch   2 Batch  501/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   2 Batch  502/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   2 Batch  503/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   2 Batch  504/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   2 Batch  505/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.044
+Epoch   2 Batch  506/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.058
+Epoch   2 Batch  507/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   2 Batch  508/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.921, Loss:  0.046
+Epoch   2 Batch  509/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.925, Loss:  0.073
+Epoch   2 Batch  510/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.930, Loss:  0.051
+Epoch   2 Batch  511/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.935, Loss:  0.042
+Epoch   2 Batch  512/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.930, Loss:  0.044
+Epoch   2 Batch  513/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.928, Loss:  0.060
+Epoch   2 Batch  514/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   2 Batch  515/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   2 Batch  516/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   2 Batch  517/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.062
+Epoch   2 Batch  518/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   2 Batch  519/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.047
+Epoch   2 Batch  520/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.909, Loss:  0.051
+Epoch   2 Batch  521/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   2 Batch  522/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.905, Loss:  0.057
+Epoch   2 Batch  523/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   2 Batch  524/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.910, Loss:  0.054
+Epoch   2 Batch  525/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.056
+Epoch   2 Batch  526/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.039
+Epoch   2 Batch  527/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.915, Loss:  0.065
+Epoch   2 Batch  528/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.908, Loss:  0.051
+Epoch   2 Batch  529/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.908, Loss:  0.058
+Epoch   2 Batch  530/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.066
+Epoch   2 Batch  531/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.904, Loss:  0.060
+Epoch   2 Batch  532/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   2 Batch  533/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.058
+Epoch   2 Batch  534/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.919, Loss:  0.061
+Epoch   2 Batch  535/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.913, Loss:  0.047
+Epoch   2 Batch  536/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   2 Batch  537/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.038
+Epoch   2 Batch  538/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.910, Loss:  0.034
+Epoch   2 Batch  539/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.905, Loss:  0.071
+Epoch   2 Batch  540/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.900, Loss:  0.045
+Epoch   2 Batch  541/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.905, Loss:  0.046
+Epoch   2 Batch  542/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.047
+Epoch   2 Batch  543/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.896, Loss:  0.044
+Epoch   2 Batch  544/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.896, Loss:  0.038
+Epoch   2 Batch  545/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.898, Loss:  0.055
+Epoch   2 Batch  546/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.894, Loss:  0.060
+Epoch   2 Batch  547/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.894, Loss:  0.042
+Epoch   2 Batch  548/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.898, Loss:  0.060
+Epoch   2 Batch  549/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.903, Loss:  0.074
+Epoch   2 Batch  550/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.906, Loss:  0.049
+Epoch   2 Batch  551/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.906, Loss:  0.060
+Epoch   2 Batch  552/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.908, Loss:  0.058
+Epoch   2 Batch  553/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.906, Loss:  0.072
+Epoch   2 Batch  554/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.901, Loss:  0.049
+Epoch   2 Batch  555/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.901, Loss:  0.041
+Epoch   2 Batch  556/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.902, Loss:  0.048
+Epoch   2 Batch  557/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.903, Loss:  0.050
+Epoch   2 Batch  558/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   2 Batch  559/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.910, Loss:  0.049
+Epoch   2 Batch  560/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.900, Loss:  0.053
+Epoch   2 Batch  561/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.904, Loss:  0.046
+Epoch   2 Batch  562/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.908, Loss:  0.046
+Epoch   2 Batch  563/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.907, Loss:  0.053
+Epoch   2 Batch  564/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.907, Loss:  0.058
+Epoch   2 Batch  565/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.907, Loss:  0.056
+Epoch   2 Batch  566/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.903, Loss:  0.049
+Epoch   2 Batch  567/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.904, Loss:  0.054
+Epoch   2 Batch  568/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.904, Loss:  0.047
+Epoch   2 Batch  569/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.903, Loss:  0.060
+Epoch   2 Batch  570/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.908, Loss:  0.066
+Epoch   2 Batch  571/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.917, Loss:  0.037
+Epoch   2 Batch  572/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.912, Loss:  0.046
+Epoch   2 Batch  573/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.060
+Epoch   2 Batch  574/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.919, Loss:  0.059
+Epoch   2 Batch  575/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.039
+Epoch   2 Batch  576/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   2 Batch  577/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   2 Batch  578/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   2 Batch  579/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   2 Batch  580/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   2 Batch  581/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.922, Loss:  0.037
+Epoch   2 Batch  582/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.045
+Epoch   2 Batch  583/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.913, Loss:  0.057
+Epoch   2 Batch  584/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.913, Loss:  0.050
+Epoch   2 Batch  585/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.912, Loss:  0.044
+Epoch   2 Batch  586/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.914, Loss:  0.044
+Epoch   2 Batch  587/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.910, Loss:  0.052
+Epoch   2 Batch  588/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.909, Loss:  0.041
+Epoch   2 Batch  589/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.901, Loss:  0.045
+Epoch   2 Batch  590/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.902, Loss:  0.058
+Epoch   2 Batch  591/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.897, Loss:  0.055
+Epoch   2 Batch  592/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.902, Loss:  0.051
+Epoch   2 Batch  593/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.897, Loss:  0.078
+Epoch   2 Batch  594/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.894, Loss:  0.064
+Epoch   2 Batch  595/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.895, Loss:  0.048
+Epoch   2 Batch  596/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.895, Loss:  0.050
+Epoch   2 Batch  597/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.896, Loss:  0.045
+Epoch   2 Batch  598/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.897, Loss:  0.056
+Epoch   2 Batch  599/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.912, Loss:  0.061
+Epoch   2 Batch  600/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.921, Loss:  0.057
+Epoch   2 Batch  601/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   2 Batch  602/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   2 Batch  603/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.928, Loss:  0.048
+Epoch   2 Batch  604/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.941, Loss:  0.065
+Epoch   2 Batch  605/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.936, Loss:  0.062
+Epoch   2 Batch  606/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.935, Loss:  0.046
+Epoch   2 Batch  607/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.055
+Epoch   2 Batch  608/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.924, Loss:  0.062
+Epoch   2 Batch  609/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   2 Batch  610/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.895, Loss:  0.067
+Epoch   2 Batch  611/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.893, Loss:  0.045
+Epoch   2 Batch  612/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.889, Loss:  0.046
+Epoch   2 Batch  613/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.892, Loss:  0.061
+Epoch   2 Batch  614/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.892, Loss:  0.040
+Epoch   2 Batch  615/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.900, Loss:  0.047
+Epoch   2 Batch  616/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.907, Loss:  0.059
+Epoch   2 Batch  617/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   2 Batch  618/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.056
+Epoch   2 Batch  619/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.035
+Epoch   2 Batch  620/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.918, Loss:  0.040
+Epoch   2 Batch  621/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   2 Batch  622/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.923, Loss:  0.058
+Epoch   2 Batch  623/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.918, Loss:  0.064
+Epoch   2 Batch  624/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   2 Batch  625/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.042
+Epoch   2 Batch  626/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   2 Batch  627/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   2 Batch  628/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   2 Batch  629/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.920, Loss:  0.055
+Epoch   2 Batch  630/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.921, Loss:  0.045
+Epoch   2 Batch  631/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.049
+Epoch   2 Batch  632/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.929, Loss:  0.038
+Epoch   2 Batch  633/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.927, Loss:  0.056
+Epoch   2 Batch  634/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   2 Batch  635/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.054
+Epoch   2 Batch  636/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.936, Loss:  0.055
+Epoch   2 Batch  637/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.932, Loss:  0.050
+Epoch   2 Batch  638/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.041
+Epoch   2 Batch  639/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.930, Loss:  0.073
+Epoch   2 Batch  640/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.046
+Epoch   2 Batch  641/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.931, Loss:  0.044
+Epoch   2 Batch  642/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   2 Batch  643/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   2 Batch  644/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   2 Batch  645/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.924, Loss:  0.066
+Epoch   2 Batch  646/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   2 Batch  647/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   2 Batch  648/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.033
+Epoch   2 Batch  649/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   2 Batch  650/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.054
+Epoch   2 Batch  651/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.920, Loss:  0.053
+Epoch   2 Batch  652/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.054
+Epoch   2 Batch  653/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.053
+Epoch   2 Batch  654/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.927, Loss:  0.049
+Epoch   2 Batch  655/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.063
+Epoch   2 Batch  656/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.057
+Epoch   2 Batch  657/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.932, Loss:  0.051
+Epoch   2 Batch  658/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.040
+Epoch   2 Batch  659/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.053
+Epoch   2 Batch  660/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   2 Batch  661/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   2 Batch  662/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   2 Batch  663/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.904, Loss:  0.042
+Epoch   2 Batch  664/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.900, Loss:  0.052
+Epoch   2 Batch  665/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.899, Loss:  0.048
+Epoch   2 Batch  666/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.899, Loss:  0.063
+Epoch   2 Batch  667/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.897, Loss:  0.060
+Epoch   2 Batch  668/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.905, Loss:  0.042
+Epoch   2 Batch  669/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   2 Batch  670/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.912, Loss:  0.058
+Epoch   2 Batch  671/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.908, Loss:  0.059
+Epoch   2 Batch  672/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.045
+Epoch   2 Batch  673/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.914, Loss:  0.048
+Epoch   2 Batch  674/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.916, Loss:  0.051
+Epoch   2 Batch  675/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.921, Loss:  0.060
+Epoch   2 Batch  676/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.921, Loss:  0.046
+Epoch   2 Batch  677/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.068
+Epoch   2 Batch  678/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.042
+Epoch   2 Batch  679/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   2 Batch  680/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   2 Batch  681/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   2 Batch  682/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.923, Loss:  0.055
+Epoch   2 Batch  683/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.919, Loss:  0.037
+Epoch   2 Batch  684/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   2 Batch  685/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.919, Loss:  0.059
+Epoch   2 Batch  686/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.044
+Epoch   2 Batch  687/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.913, Loss:  0.056
+Epoch   2 Batch  688/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   2 Batch  689/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.046
+Epoch   2 Batch  690/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   2 Batch  691/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.059
+Epoch   2 Batch  692/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.043
+Epoch   2 Batch  693/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.909, Loss:  0.070
+Epoch   2 Batch  694/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.053
+Epoch   2 Batch  695/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.919, Loss:  0.042
+Epoch   2 Batch  696/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   2 Batch  697/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   2 Batch  698/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.046
+Epoch   2 Batch  699/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.910, Loss:  0.044
+Epoch   2 Batch  700/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   2 Batch  701/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.915, Loss:  0.052
+Epoch   2 Batch  702/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   2 Batch  703/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.913, Loss:  0.055
+Epoch   2 Batch  704/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.063
+Epoch   2 Batch  705/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.923, Loss:  0.061
+Epoch   2 Batch  706/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.073
+Epoch   2 Batch  707/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.928, Loss:  0.056
+Epoch   2 Batch  708/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.929, Loss:  0.057
+Epoch   2 Batch  709/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   2 Batch  710/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.921, Loss:  0.040
+Epoch   2 Batch  711/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.068
+Epoch   2 Batch  712/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.922, Loss:  0.037
+Epoch   2 Batch  713/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.036
+Epoch   2 Batch  714/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   2 Batch  715/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.932, Loss:  0.059
+Epoch   2 Batch  716/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.938, Loss:  0.047
+Epoch   2 Batch  717/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.939, Loss:  0.035
+Epoch   2 Batch  718/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.939, Loss:  0.053
+Epoch   2 Batch  719/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.938, Loss:  0.057
+Epoch   2 Batch  720/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.938, Loss:  0.053
+Epoch   2 Batch  721/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.934, Loss:  0.060
+Epoch   2 Batch  722/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.935, Loss:  0.042
+Epoch   2 Batch  723/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.058
+Epoch   2 Batch  724/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.939, Loss:  0.056
+Epoch   2 Batch  725/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.039
+Epoch   2 Batch  726/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.934, Loss:  0.052
+Epoch   2 Batch  727/1077 - Train Accuracy:  0.969, Validation Accuracy:  0.934, Loss:  0.041
+Epoch   2 Batch  728/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.934, Loss:  0.058
+Epoch   2 Batch  729/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.931, Loss:  0.063
+Epoch   2 Batch  730/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.932, Loss:  0.058
+Epoch   2 Batch  731/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   2 Batch  732/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.935, Loss:  0.054
+Epoch   2 Batch  733/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.933, Loss:  0.053
+Epoch   2 Batch  734/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   2 Batch  735/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.923, Loss:  0.044
+Epoch   2 Batch  736/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.046
+Epoch   2 Batch  737/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.921, Loss:  0.051
+Epoch   2 Batch  738/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.038
+Epoch   2 Batch  739/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.041
+Epoch   2 Batch  740/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.038
+Epoch   2 Batch  741/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   2 Batch  742/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.036
+Epoch   2 Batch  743/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.933, Loss:  0.056
+Epoch   2 Batch  744/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   2 Batch  745/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.053
+Epoch   2 Batch  746/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.932, Loss:  0.052
+Epoch   2 Batch  747/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.927, Loss:  0.041
+Epoch   2 Batch  748/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.931, Loss:  0.043
+Epoch   2 Batch  749/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   2 Batch  750/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.049
+Epoch   2 Batch  751/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.938, Loss:  0.050
+Epoch   2 Batch  752/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.942, Loss:  0.047
+Epoch   2 Batch  753/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.938, Loss:  0.055
+Epoch   2 Batch  754/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.933, Loss:  0.061
+Epoch   2 Batch  755/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.942, Loss:  0.062
+Epoch   2 Batch  756/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.942, Loss:  0.049
+Epoch   2 Batch  757/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.939, Loss:  0.045
+Epoch   2 Batch  758/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.934, Loss:  0.047
+Epoch   2 Batch  759/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.045
+Epoch   2 Batch  760/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   2 Batch  761/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.049
+Epoch   2 Batch  762/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   2 Batch  763/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   2 Batch  764/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   2 Batch  765/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.933, Loss:  0.060
+Epoch   2 Batch  766/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.935, Loss:  0.050
+Epoch   2 Batch  767/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.938, Loss:  0.044
+Epoch   2 Batch  768/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.931, Loss:  0.053
+Epoch   2 Batch  769/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.045
+Epoch   2 Batch  770/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.933, Loss:  0.040
+Epoch   2 Batch  771/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.931, Loss:  0.057
+Epoch   2 Batch  772/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.929, Loss:  0.053
+Epoch   2 Batch  773/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   2 Batch  774/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   2 Batch  775/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.937, Loss:  0.056
+Epoch   2 Batch  776/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.947, Loss:  0.043
+Epoch   2 Batch  777/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.936, Loss:  0.056
+Epoch   2 Batch  778/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.936, Loss:  0.047
+Epoch   2 Batch  779/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.935, Loss:  0.057
+Epoch   2 Batch  780/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.947, Loss:  0.062
+Epoch   2 Batch  781/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.945, Loss:  0.044
+Epoch   2 Batch  782/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.940, Loss:  0.047
+Epoch   2 Batch  783/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.942, Loss:  0.053
+Epoch   2 Batch  784/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.938, Loss:  0.045
+Epoch   2 Batch  785/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.935, Loss:  0.050
+Epoch   2 Batch  786/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.931, Loss:  0.041
+Epoch   2 Batch  787/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.053
+Epoch   2 Batch  788/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   2 Batch  789/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.931, Loss:  0.051
+Epoch   2 Batch  790/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   2 Batch  791/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   2 Batch  792/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   2 Batch  793/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.918, Loss:  0.050
+Epoch   2 Batch  794/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.916, Loss:  0.047
+Epoch   2 Batch  795/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   2 Batch  796/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   2 Batch  797/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.920, Loss:  0.044
+Epoch   2 Batch  798/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.051
+Epoch   2 Batch  799/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.933, Loss:  0.078
+Epoch   2 Batch  800/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.940, Loss:  0.050
+Epoch   2 Batch  801/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.940, Loss:  0.068
+Epoch   2 Batch  802/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.940, Loss:  0.058
+Epoch   2 Batch  803/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.944, Loss:  0.051
+Epoch   2 Batch  804/1077 - Train Accuracy:  0.975, Validation Accuracy:  0.944, Loss:  0.037
+Epoch   2 Batch  805/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.939, Loss:  0.048
+Epoch   2 Batch  806/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.940, Loss:  0.043
+Epoch   2 Batch  807/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.041
+Epoch   2 Batch  808/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.928, Loss:  0.069
+Epoch   2 Batch  809/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.936, Loss:  0.069
+Epoch   2 Batch  810/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.944, Loss:  0.046
+Epoch   2 Batch  811/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   2 Batch  812/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   2 Batch  813/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.936, Loss:  0.045
+Epoch   2 Batch  814/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.936, Loss:  0.063
+Epoch   2 Batch  815/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.926, Loss:  0.049
+Epoch   2 Batch  816/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.935, Loss:  0.062
+Epoch   2 Batch  817/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   2 Batch  818/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.933, Loss:  0.059
+Epoch   2 Batch  819/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.050
+Epoch   2 Batch  820/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   2 Batch  821/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.049
+Epoch   2 Batch  822/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   2 Batch  823/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.056
+Epoch   2 Batch  824/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   2 Batch  825/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.917, Loss:  0.040
+Epoch   2 Batch  826/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   2 Batch  827/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.912, Loss:  0.056
+Epoch   2 Batch  828/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.048
+Epoch   2 Batch  829/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.904, Loss:  0.064
+Epoch   2 Batch  830/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.904, Loss:  0.061
+Epoch   2 Batch  831/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   2 Batch  832/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.913, Loss:  0.049
+Epoch   2 Batch  833/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.913, Loss:  0.054
+Epoch   2 Batch  834/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.918, Loss:  0.059
+Epoch   2 Batch  835/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.914, Loss:  0.051
+Epoch   2 Batch  836/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.050
+Epoch   2 Batch  837/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.923, Loss:  0.062
+Epoch   2 Batch  838/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   2 Batch  839/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.036
+Epoch   2 Batch  840/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.924, Loss:  0.039
+Epoch   2 Batch  841/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.924, Loss:  0.050
+Epoch   2 Batch  842/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   2 Batch  843/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   2 Batch  844/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.039
+Epoch   2 Batch  845/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.926, Loss:  0.039
+Epoch   2 Batch  846/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.063
+Epoch   2 Batch  847/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   2 Batch  848/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.045
+Epoch   2 Batch  849/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   2 Batch  850/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.913, Loss:  0.085
+Epoch   2 Batch  851/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.913, Loss:  0.062
+Epoch   2 Batch  852/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.913, Loss:  0.061
+Epoch   2 Batch  853/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.922, Loss:  0.055
+Epoch   2 Batch  854/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.921, Loss:  0.062
+Epoch   2 Batch  855/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   2 Batch  856/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.068
+Epoch   2 Batch  857/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.048
+Epoch   2 Batch  858/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   2 Batch  859/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.917, Loss:  0.067
+Epoch   2 Batch  860/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.922, Loss:  0.057
+Epoch   2 Batch  861/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.926, Loss:  0.051
+Epoch   2 Batch  862/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   2 Batch  863/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   2 Batch  864/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   2 Batch  865/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   2 Batch  866/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.913, Loss:  0.056
+Epoch   2 Batch  867/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.908, Loss:  0.085
+Epoch   2 Batch  868/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.920, Loss:  0.056
+Epoch   2 Batch  869/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   2 Batch  870/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.933, Loss:  0.058
+Epoch   2 Batch  871/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   2 Batch  872/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.939, Loss:  0.056
+Epoch   2 Batch  873/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.935, Loss:  0.056
+Epoch   2 Batch  874/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.931, Loss:  0.061
+Epoch   2 Batch  875/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.932, Loss:  0.058
+Epoch   2 Batch  876/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   2 Batch  877/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.939, Loss:  0.046
+Epoch   2 Batch  878/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.939, Loss:  0.039
+Epoch   2 Batch  879/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.935, Loss:  0.047
+Epoch   2 Batch  880/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.053
+Epoch   2 Batch  881/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.941, Loss:  0.063
+Epoch   2 Batch  882/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.941, Loss:  0.058
+Epoch   2 Batch  883/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.936, Loss:  0.079
+Epoch   2 Batch  884/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.927, Loss:  0.052
+Epoch   2 Batch  885/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.920, Loss:  0.031
+Epoch   2 Batch  886/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   2 Batch  887/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.922, Loss:  0.063
+Epoch   2 Batch  888/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   2 Batch  889/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   2 Batch  890/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.911, Loss:  0.049
+Epoch   2 Batch  891/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.046
+Epoch   2 Batch  892/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.045
+Epoch   2 Batch  893/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.043
+Epoch   2 Batch  894/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   2 Batch  895/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   2 Batch  896/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.914, Loss:  0.052
+Epoch   2 Batch  897/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.907, Loss:  0.037
+Epoch   2 Batch  898/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.904, Loss:  0.044
+Epoch   2 Batch  899/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.907, Loss:  0.057
+Epoch   2 Batch  900/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.917, Loss:  0.065
+Epoch   2 Batch  901/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.068
+Epoch   2 Batch  902/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.057
+Epoch   2 Batch  903/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   2 Batch  904/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   2 Batch  905/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.912, Loss:  0.046
+Epoch   2 Batch  906/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.902, Loss:  0.056
+Epoch   2 Batch  907/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.908, Loss:  0.052
+Epoch   2 Batch  908/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.926, Loss:  0.061
+Epoch   2 Batch  909/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.931, Loss:  0.061
+Epoch   2 Batch  910/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.920, Loss:  0.057
+Epoch   2 Batch  911/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.929, Loss:  0.057
+Epoch   2 Batch  912/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.933, Loss:  0.043
+Epoch   2 Batch  913/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.067
+Epoch   2 Batch  914/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.919, Loss:  0.079
+Epoch   2 Batch  915/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   2 Batch  916/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.909, Loss:  0.053
+Epoch   2 Batch  917/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.901, Loss:  0.051
+Epoch   2 Batch  918/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.911, Loss:  0.048
+Epoch   2 Batch  919/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.907, Loss:  0.047
+Epoch   2 Batch  920/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.052
+Epoch   2 Batch  921/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.912, Loss:  0.056
+Epoch   2 Batch  922/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   2 Batch  923/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.923, Loss:  0.033
+Epoch   2 Batch  924/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.914, Loss:  0.071
+Epoch   2 Batch  925/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.042
+Epoch   2 Batch  926/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.917, Loss:  0.042
+Epoch   2 Batch  927/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   2 Batch  928/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.909, Loss:  0.046
+Epoch   2 Batch  929/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.906, Loss:  0.060
+Epoch   2 Batch  930/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.909, Loss:  0.047
+Epoch   2 Batch  931/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.908, Loss:  0.038
+Epoch   2 Batch  932/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.050
+Epoch   2 Batch  933/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.056
+Epoch   2 Batch  934/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.928, Loss:  0.047
+Epoch   2 Batch  935/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.044
+Epoch   2 Batch  936/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.051
+Epoch   2 Batch  937/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.918, Loss:  0.074
+Epoch   2 Batch  938/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.058
+Epoch   2 Batch  939/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.917, Loss:  0.058
+Epoch   2 Batch  940/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.048
+Epoch   2 Batch  941/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.043
+Epoch   2 Batch  942/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.059
+Epoch   2 Batch  943/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   2 Batch  944/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.927, Loss:  0.052
+Epoch   2 Batch  945/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   2 Batch  946/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.920, Loss:  0.037
+Epoch   2 Batch  947/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.916, Loss:  0.057
+Epoch   2 Batch  948/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   2 Batch  949/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.919, Loss:  0.043
+Epoch   2 Batch  950/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.039
+Epoch   2 Batch  951/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.913, Loss:  0.063
+Epoch   2 Batch  952/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.917, Loss:  0.045
+Epoch   2 Batch  953/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.046
+Epoch   2 Batch  954/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.913, Loss:  0.054
+Epoch   2 Batch  955/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   2 Batch  956/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   2 Batch  957/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.914, Loss:  0.042
+Epoch   2 Batch  958/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.903, Loss:  0.048
+Epoch   2 Batch  959/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.901, Loss:  0.041
+Epoch   2 Batch  960/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   2 Batch  961/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.044
+Epoch   2 Batch  962/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   2 Batch  963/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.064
+Epoch   2 Batch  964/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   2 Batch  965/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.051
+Epoch   2 Batch  966/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   2 Batch  967/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.923, Loss:  0.059
+Epoch   2 Batch  968/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.922, Loss:  0.064
+Epoch   2 Batch  969/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.926, Loss:  0.062
+Epoch   2 Batch  970/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   2 Batch  971/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.920, Loss:  0.055
+Epoch   2 Batch  972/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.916, Loss:  0.052
+Epoch   2 Batch  973/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.916, Loss:  0.033
+Epoch   2 Batch  974/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.037
+Epoch   2 Batch  975/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.914, Loss:  0.045
+Epoch   2 Batch  976/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.037
+Epoch   2 Batch  977/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.925, Loss:  0.037
+Epoch   2 Batch  978/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.054
+Epoch   2 Batch  979/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.932, Loss:  0.056
+Epoch   2 Batch  980/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.938, Loss:  0.057
+Epoch   2 Batch  981/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.042
+Epoch   2 Batch  982/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.056
+Epoch   2 Batch  983/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.931, Loss:  0.048
+Epoch   2 Batch  984/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.936, Loss:  0.067
+Epoch   2 Batch  985/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.938, Loss:  0.049
+Epoch   2 Batch  986/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.938, Loss:  0.055
+Epoch   2 Batch  987/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.954, Loss:  0.046
+Epoch   2 Batch  988/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.952, Loss:  0.062
+Epoch   2 Batch  989/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.938, Loss:  0.048
+Epoch   2 Batch  990/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.910, Loss:  0.061
+Epoch   2 Batch  991/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.910, Loss:  0.049
+Epoch   2 Batch  992/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.912, Loss:  0.073
+Epoch   2 Batch  993/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.908, Loss:  0.040
+Epoch   2 Batch  994/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.917, Loss:  0.050
+Epoch   2 Batch  995/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.057
+Epoch   2 Batch  996/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.048
+Epoch   2 Batch  997/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.052
+Epoch   2 Batch  998/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   2 Batch  999/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.942, Loss:  0.057
+Epoch   2 Batch 1000/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.946, Loss:  0.054
+Epoch   2 Batch 1001/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.940, Loss:  0.046
+Epoch   2 Batch 1002/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.930, Loss:  0.035
+Epoch   2 Batch 1003/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   2 Batch 1004/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.934, Loss:  0.069
+Epoch   2 Batch 1005/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.934, Loss:  0.041
+Epoch   2 Batch 1006/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.041
+Epoch   2 Batch 1007/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.934, Loss:  0.051
+Epoch   2 Batch 1008/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.932, Loss:  0.074
+Epoch   2 Batch 1009/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.928, Loss:  0.038
+Epoch   2 Batch 1010/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.050
+Epoch   2 Batch 1011/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.043
+Epoch   2 Batch 1012/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.926, Loss:  0.032
+Epoch   2 Batch 1013/1077 - Train Accuracy:  0.974, Validation Accuracy:  0.917, Loss:  0.037
+Epoch   2 Batch 1014/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   2 Batch 1015/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.933, Loss:  0.058
+Epoch   2 Batch 1016/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   2 Batch 1017/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.055
+Epoch   2 Batch 1018/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.941, Loss:  0.041
+Epoch   2 Batch 1019/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.948, Loss:  0.074
+Epoch   2 Batch 1020/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.941, Loss:  0.042
+Epoch   2 Batch 1021/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   2 Batch 1022/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   2 Batch 1023/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.932, Loss:  0.052
+Epoch   2 Batch 1024/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.936, Loss:  0.066
+Epoch   2 Batch 1025/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.940, Loss:  0.055
+Epoch   2 Batch 1026/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.944, Loss:  0.052
+Epoch   2 Batch 1027/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.943, Loss:  0.049
+Epoch   2 Batch 1028/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.943, Loss:  0.048
+Epoch   2 Batch 1029/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.942, Loss:  0.045
+Epoch   2 Batch 1030/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.941, Loss:  0.042
+Epoch   2 Batch 1031/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.059
+Epoch   2 Batch 1032/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.921, Loss:  0.070
+Epoch   2 Batch 1033/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.062
+Epoch   2 Batch 1034/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   2 Batch 1035/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.911, Loss:  0.030
+Epoch   2 Batch 1036/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.907, Loss:  0.061
+Epoch   2 Batch 1037/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.907, Loss:  0.047
+Epoch   2 Batch 1038/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.903, Loss:  0.051
+Epoch   2 Batch 1039/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.049
+Epoch   2 Batch 1040/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.058
+Epoch   2 Batch 1041/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.913, Loss:  0.050
+Epoch   2 Batch 1042/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   2 Batch 1043/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.061
+Epoch   2 Batch 1044/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.910, Loss:  0.070
+Epoch   2 Batch 1045/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.910, Loss:  0.056
+Epoch   2 Batch 1046/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.914, Loss:  0.042
+Epoch   2 Batch 1047/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   2 Batch 1048/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   2 Batch 1049/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.918, Loss:  0.041
+Epoch   2 Batch 1050/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   2 Batch 1051/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   2 Batch 1052/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.913, Loss:  0.049
+Epoch   2 Batch 1053/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.926, Loss:  0.069
+Epoch   2 Batch 1054/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.047
+Epoch   2 Batch 1055/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   2 Batch 1056/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.042
+Epoch   2 Batch 1057/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.917, Loss:  0.059
+Epoch   2 Batch 1058/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.056
+Epoch   2 Batch 1059/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.929, Loss:  0.070
+Epoch   2 Batch 1060/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.928, Loss:  0.051
+Epoch   2 Batch 1061/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.935, Loss:  0.070
+Epoch   2 Batch 1062/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.936, Loss:  0.056
+Epoch   2 Batch 1063/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.058
+Epoch   2 Batch 1064/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.920, Loss:  0.050
+Epoch   2 Batch 1065/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.923, Loss:  0.043
+Epoch   2 Batch 1066/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.921, Loss:  0.036
+Epoch   2 Batch 1067/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.053
+Epoch   2 Batch 1068/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.926, Loss:  0.043
+Epoch   2 Batch 1069/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.037
+Epoch   2 Batch 1070/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.920, Loss:  0.043
+Epoch   2 Batch 1071/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.920, Loss:  0.046
+Epoch   2 Batch 1072/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   2 Batch 1073/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.913, Loss:  0.053
+Epoch   2 Batch 1074/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   2 Batch 1075/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   3 Batch    0/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.913, Loss:  0.045
+Epoch   3 Batch    1/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.911, Loss:  0.032
+Epoch   3 Batch    2/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.053
+Epoch   3 Batch    3/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   3 Batch    4/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.911, Loss:  0.043
+Epoch   3 Batch    5/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.904, Loss:  0.072
+Epoch   3 Batch    6/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.904, Loss:  0.054
+Epoch   3 Batch    7/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.906, Loss:  0.040
+Epoch   3 Batch    8/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.896, Loss:  0.051
+Epoch   3 Batch    9/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.892, Loss:  0.041
+Epoch   3 Batch   10/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.898, Loss:  0.053
+Epoch   3 Batch   11/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.905, Loss:  0.068
+Epoch   3 Batch   12/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.907, Loss:  0.053
+Epoch   3 Batch   13/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.054
+Epoch   3 Batch   14/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.912, Loss:  0.036
+Epoch   3 Batch   15/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.912, Loss:  0.044
+Epoch   3 Batch   16/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.919, Loss:  0.060
+Epoch   3 Batch   17/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   3 Batch   18/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.922, Loss:  0.067
+Epoch   3 Batch   19/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   3 Batch   20/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.936, Loss:  0.040
+Epoch   3 Batch   21/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.934, Loss:  0.056
+Epoch   3 Batch   22/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.933, Loss:  0.062
+Epoch   3 Batch   23/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.933, Loss:  0.051
+Epoch   3 Batch   24/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   3 Batch   25/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.922, Loss:  0.038
+Epoch   3 Batch   26/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.922, Loss:  0.055
+Epoch   3 Batch   27/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.923, Loss:  0.042
+Epoch   3 Batch   28/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.926, Loss:  0.053
+Epoch   3 Batch   29/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.056
+Epoch   3 Batch   30/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.036
+Epoch   3 Batch   31/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   3 Batch   32/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   3 Batch   33/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.055
+Epoch   3 Batch   34/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.050
+Epoch   3 Batch   35/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   3 Batch   36/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   3 Batch   37/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   3 Batch   38/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.930, Loss:  0.078
+Epoch   3 Batch   39/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.930, Loss:  0.062
+Epoch   3 Batch   40/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.918, Loss:  0.040
+Epoch   3 Batch   41/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.906, Loss:  0.044
+Epoch   3 Batch   42/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.908, Loss:  0.064
+Epoch   3 Batch   43/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.913, Loss:  0.028
+Epoch   3 Batch   44/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.916, Loss:  0.040
+Epoch   3 Batch   45/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.916, Loss:  0.053
+Epoch   3 Batch   46/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.046
+Epoch   3 Batch   47/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   3 Batch   48/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.922, Loss:  0.087
+Epoch   3 Batch   49/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.060
+Epoch   3 Batch   50/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   3 Batch   51/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.058
+Epoch   3 Batch   52/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.065
+Epoch   3 Batch   53/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.922, Loss:  0.039
+Epoch   3 Batch   54/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.925, Loss:  0.078
+Epoch   3 Batch   55/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   3 Batch   56/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.919, Loss:  0.051
+Epoch   3 Batch   57/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.916, Loss:  0.059
+Epoch   3 Batch   58/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.912, Loss:  0.046
+Epoch   3 Batch   59/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.912, Loss:  0.043
+Epoch   3 Batch   60/1077 - Train Accuracy:  0.970, Validation Accuracy:  0.910, Loss:  0.035
+Epoch   3 Batch   61/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.060
+Epoch   3 Batch   62/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.910, Loss:  0.061
+Epoch   3 Batch   63/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   3 Batch   64/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.913, Loss:  0.045
+Epoch   3 Batch   65/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   3 Batch   66/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.031
+Epoch   3 Batch   67/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   3 Batch   68/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.053
+Epoch   3 Batch   69/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.915, Loss:  0.061
+Epoch   3 Batch   70/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.915, Loss:  0.050
+Epoch   3 Batch   71/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.919, Loss:  0.035
+Epoch   3 Batch   72/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   3 Batch   73/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.055
+Epoch   3 Batch   74/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.045
+Epoch   3 Batch   75/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.915, Loss:  0.065
+Epoch   3 Batch   76/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.926, Loss:  0.038
+Epoch   3 Batch   77/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   3 Batch   78/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.928, Loss:  0.042
+Epoch   3 Batch   79/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   3 Batch   80/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   3 Batch   81/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   3 Batch   82/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.922, Loss:  0.038
+Epoch   3 Batch   83/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.044
+Epoch   3 Batch   84/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.043
+Epoch   3 Batch   85/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.047
+Epoch   3 Batch   86/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   3 Batch   87/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.917, Loss:  0.065
+Epoch   3 Batch   88/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.054
+Epoch   3 Batch   89/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.050
+Epoch   3 Batch   90/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   3 Batch   91/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   3 Batch   92/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.910, Loss:  0.060
+Epoch   3 Batch   93/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.910, Loss:  0.038
+Epoch   3 Batch   94/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   3 Batch   95/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.909, Loss:  0.054
+Epoch   3 Batch   96/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.909, Loss:  0.053
+Epoch   3 Batch   97/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.050
+Epoch   3 Batch   98/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.907, Loss:  0.058
+Epoch   3 Batch   99/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.052
+Epoch   3 Batch  100/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.919, Loss:  0.047
+Epoch   3 Batch  101/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.048
+Epoch   3 Batch  102/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.932, Loss:  0.042
+Epoch   3 Batch  103/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.929, Loss:  0.061
+Epoch   3 Batch  104/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   3 Batch  105/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   3 Batch  106/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   3 Batch  107/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.925, Loss:  0.048
+Epoch   3 Batch  108/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   3 Batch  109/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.929, Loss:  0.059
+Epoch   3 Batch  110/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.929, Loss:  0.035
+Epoch   3 Batch  111/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.922, Loss:  0.055
+Epoch   3 Batch  112/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.921, Loss:  0.047
+Epoch   3 Batch  113/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   3 Batch  114/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.911, Loss:  0.035
+Epoch   3 Batch  115/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   3 Batch  116/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.920, Loss:  0.066
+Epoch   3 Batch  117/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   3 Batch  118/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   3 Batch  119/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   3 Batch  120/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.940, Loss:  0.058
+Epoch   3 Batch  121/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   3 Batch  122/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   3 Batch  123/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.941, Loss:  0.045
+Epoch   3 Batch  124/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.936, Loss:  0.073
+Epoch   3 Batch  125/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.934, Loss:  0.056
+Epoch   3 Batch  126/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.934, Loss:  0.045
+Epoch   3 Batch  127/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   3 Batch  128/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.909, Loss:  0.047
+Epoch   3 Batch  129/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.066
+Epoch   3 Batch  130/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.048
+Epoch   3 Batch  131/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.901, Loss:  0.055
+Epoch   3 Batch  132/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.903, Loss:  0.040
+Epoch   3 Batch  133/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.909, Loss:  0.039
+Epoch   3 Batch  134/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.917, Loss:  0.051
+Epoch   3 Batch  135/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.046
+Epoch   3 Batch  136/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   3 Batch  137/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.030
+Epoch   3 Batch  138/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   3 Batch  139/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.919, Loss:  0.062
+Epoch   3 Batch  140/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   3 Batch  141/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.913, Loss:  0.047
+Epoch   3 Batch  142/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.043
+Epoch   3 Batch  143/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.049
+Epoch   3 Batch  144/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.068
+Epoch   3 Batch  145/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   3 Batch  146/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.923, Loss:  0.083
+Epoch   3 Batch  147/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.052
+Epoch   3 Batch  148/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   3 Batch  149/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.918, Loss:  0.045
+Epoch   3 Batch  150/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.057
+Epoch   3 Batch  151/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.046
+Epoch   3 Batch  152/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.910, Loss:  0.073
+Epoch   3 Batch  153/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.908, Loss:  0.064
+Epoch   3 Batch  154/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.905, Loss:  0.045
+Epoch   3 Batch  155/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.911, Loss:  0.056
+Epoch   3 Batch  156/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   3 Batch  157/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.909, Loss:  0.041
+Epoch   3 Batch  158/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.903, Loss:  0.065
+Epoch   3 Batch  159/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.908, Loss:  0.040
+Epoch   3 Batch  160/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.047
+Epoch   3 Batch  161/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.913, Loss:  0.041
+Epoch   3 Batch  162/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.913, Loss:  0.063
+Epoch   3 Batch  163/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.064
+Epoch   3 Batch  164/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   3 Batch  165/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   3 Batch  166/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.917, Loss:  0.056
+Epoch   3 Batch  167/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   3 Batch  168/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.919, Loss:  0.071
+Epoch   3 Batch  169/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.918, Loss:  0.064
+Epoch   3 Batch  170/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.065
+Epoch   3 Batch  171/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   3 Batch  172/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.914, Loss:  0.038
+Epoch   3 Batch  173/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.908, Loss:  0.052
+Epoch   3 Batch  174/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.909, Loss:  0.040
+Epoch   3 Batch  175/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.911, Loss:  0.059
+Epoch   3 Batch  176/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   3 Batch  177/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.915, Loss:  0.057
+Epoch   3 Batch  178/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   3 Batch  179/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.932, Loss:  0.047
+Epoch   3 Batch  180/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.935, Loss:  0.044
+Epoch   3 Batch  181/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.058
+Epoch   3 Batch  182/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.935, Loss:  0.051
+Epoch   3 Batch  183/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   3 Batch  184/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.046
+Epoch   3 Batch  185/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.935, Loss:  0.054
+Epoch   3 Batch  186/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.933, Loss:  0.064
+Epoch   3 Batch  187/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.932, Loss:  0.039
+Epoch   3 Batch  188/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.052
+Epoch   3 Batch  189/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.044
+Epoch   3 Batch  190/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.051
+Epoch   3 Batch  191/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.932, Loss:  0.043
+Epoch   3 Batch  192/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.927, Loss:  0.069
+Epoch   3 Batch  193/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   3 Batch  194/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   3 Batch  195/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.927, Loss:  0.034
+Epoch   3 Batch  196/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.927, Loss:  0.041
+Epoch   3 Batch  197/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   3 Batch  198/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.062
+Epoch   3 Batch  199/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   3 Batch  200/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.932, Loss:  0.059
+Epoch   3 Batch  201/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.927, Loss:  0.040
+Epoch   3 Batch  202/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.927, Loss:  0.049
+Epoch   3 Batch  203/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.935, Loss:  0.046
+Epoch   3 Batch  204/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.074
+Epoch   3 Batch  205/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.932, Loss:  0.077
+Epoch   3 Batch  206/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.040
+Epoch   3 Batch  207/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   3 Batch  208/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   3 Batch  209/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.927, Loss:  0.030
+Epoch   3 Batch  210/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   3 Batch  211/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   3 Batch  212/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.043
+Epoch   3 Batch  213/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.043
+Epoch   3 Batch  214/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   3 Batch  215/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.927, Loss:  0.051
+Epoch   3 Batch  216/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.927, Loss:  0.052
+Epoch   3 Batch  217/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   3 Batch  218/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.069
+Epoch   3 Batch  219/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.041
+Epoch   3 Batch  220/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.928, Loss:  0.042
+Epoch   3 Batch  221/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.065
+Epoch   3 Batch  222/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   3 Batch  223/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.927, Loss:  0.039
+Epoch   3 Batch  224/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   3 Batch  225/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   3 Batch  226/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.052
+Epoch   3 Batch  227/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.908, Loss:  0.056
+Epoch   3 Batch  228/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   3 Batch  229/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.921, Loss:  0.048
+Epoch   3 Batch  230/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.917, Loss:  0.047
+Epoch   3 Batch  231/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.065
+Epoch   3 Batch  232/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.909, Loss:  0.040
+Epoch   3 Batch  233/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.908, Loss:  0.065
+Epoch   3 Batch  234/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.927, Loss:  0.067
+Epoch   3 Batch  235/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.051
+Epoch   3 Batch  236/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.933, Loss:  0.061
+Epoch   3 Batch  237/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.917, Loss:  0.045
+Epoch   3 Batch  238/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   3 Batch  239/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.922, Loss:  0.038
+Epoch   3 Batch  240/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.911, Loss:  0.044
+Epoch   3 Batch  241/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.911, Loss:  0.032
+Epoch   3 Batch  242/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.924, Loss:  0.040
+Epoch   3 Batch  243/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.060
+Epoch   3 Batch  244/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   3 Batch  245/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.039
+Epoch   3 Batch  246/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   3 Batch  247/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   3 Batch  248/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   3 Batch  249/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   3 Batch  250/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   3 Batch  251/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.920, Loss:  0.061
+Epoch   3 Batch  252/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   3 Batch  253/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   3 Batch  254/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  255/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.928, Loss:  0.047
+Epoch   3 Batch  256/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.920, Loss:  0.076
+Epoch   3 Batch  257/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.925, Loss:  0.043
+Epoch   3 Batch  258/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   3 Batch  259/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.922, Loss:  0.040
+Epoch   3 Batch  260/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   3 Batch  261/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.904, Loss:  0.059
+Epoch   3 Batch  262/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.899, Loss:  0.048
+Epoch   3 Batch  263/1077 - Train Accuracy:  0.973, Validation Accuracy:  0.898, Loss:  0.033
+Epoch   3 Batch  264/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.895, Loss:  0.044
+Epoch   3 Batch  265/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.896, Loss:  0.048
+Epoch   3 Batch  266/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.901, Loss:  0.054
+Epoch   3 Batch  267/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.903, Loss:  0.039
+Epoch   3 Batch  268/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.909, Loss:  0.058
+Epoch   3 Batch  269/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   3 Batch  270/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.060
+Epoch   3 Batch  271/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.040
+Epoch   3 Batch  272/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.923, Loss:  0.073
+Epoch   3 Batch  273/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.921, Loss:  0.043
+Epoch   3 Batch  274/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.924, Loss:  0.051
+Epoch   3 Batch  275/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   3 Batch  276/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.923, Loss:  0.073
+Epoch   3 Batch  277/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.043
+Epoch   3 Batch  278/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.929, Loss:  0.062
+Epoch   3 Batch  279/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.932, Loss:  0.074
+Epoch   3 Batch  280/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.926, Loss:  0.061
+Epoch   3 Batch  281/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.065
+Epoch   3 Batch  282/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.929, Loss:  0.080
+Epoch   3 Batch  283/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.924, Loss:  0.068
+Epoch   3 Batch  284/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.928, Loss:  0.061
+Epoch   3 Batch  285/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   3 Batch  286/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.051
+Epoch   3 Batch  287/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   3 Batch  288/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.059
+Epoch   3 Batch  289/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.928, Loss:  0.053
+Epoch   3 Batch  290/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.935, Loss:  0.074
+Epoch   3 Batch  291/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.934, Loss:  0.076
+Epoch   3 Batch  292/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.929, Loss:  0.052
+Epoch   3 Batch  293/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.932, Loss:  0.072
+Epoch   3 Batch  294/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.042
+Epoch   3 Batch  295/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.931, Loss:  0.055
+Epoch   3 Batch  296/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   3 Batch  297/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.928, Loss:  0.068
+Epoch   3 Batch  298/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.065
+Epoch   3 Batch  299/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.924, Loss:  0.052
+Epoch   3 Batch  300/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.914, Loss:  0.042
+Epoch   3 Batch  301/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   3 Batch  302/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.906, Loss:  0.046
+Epoch   3 Batch  303/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.055
+Epoch   3 Batch  304/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   3 Batch  305/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.918, Loss:  0.046
+Epoch   3 Batch  306/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.916, Loss:  0.058
+Epoch   3 Batch  307/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   3 Batch  308/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.059
+Epoch   3 Batch  309/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   3 Batch  310/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.916, Loss:  0.055
+Epoch   3 Batch  311/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.050
+Epoch   3 Batch  312/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.062
+Epoch   3 Batch  313/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.924, Loss:  0.040
+Epoch   3 Batch  314/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   3 Batch  315/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.922, Loss:  0.039
+Epoch   3 Batch  316/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.925, Loss:  0.051
+Epoch   3 Batch  317/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.920, Loss:  0.059
+Epoch   3 Batch  318/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.915, Loss:  0.042
+Epoch   3 Batch  319/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.920, Loss:  0.068
+Epoch   3 Batch  320/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.917, Loss:  0.066
+Epoch   3 Batch  321/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   3 Batch  322/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.931, Loss:  0.054
+Epoch   3 Batch  323/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   3 Batch  324/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.037
+Epoch   3 Batch  325/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   3 Batch  326/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.925, Loss:  0.049
+Epoch   3 Batch  327/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.925, Loss:  0.055
+Epoch   3 Batch  328/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.920, Loss:  0.064
+Epoch   3 Batch  329/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.919, Loss:  0.068
+Epoch   3 Batch  330/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.910, Loss:  0.058
+Epoch   3 Batch  331/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.058
+Epoch   3 Batch  332/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.903, Loss:  0.040
+Epoch   3 Batch  333/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.903, Loss:  0.053
+Epoch   3 Batch  334/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.907, Loss:  0.052
+Epoch   3 Batch  335/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.905, Loss:  0.052
+Epoch   3 Batch  336/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.900, Loss:  0.067
+Epoch   3 Batch  337/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.907, Loss:  0.065
+Epoch   3 Batch  338/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   3 Batch  339/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.909, Loss:  0.039
+Epoch   3 Batch  340/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.046
+Epoch   3 Batch  341/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.068
+Epoch   3 Batch  342/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.046
+Epoch   3 Batch  343/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.055
+Epoch   3 Batch  344/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   3 Batch  345/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   3 Batch  346/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.916, Loss:  0.052
+Epoch   3 Batch  347/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.907, Loss:  0.033
+Epoch   3 Batch  348/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.900, Loss:  0.039
+Epoch   3 Batch  349/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.906, Loss:  0.053
+Epoch   3 Batch  350/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   3 Batch  351/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   3 Batch  352/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   3 Batch  353/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.915, Loss:  0.067
+Epoch   3 Batch  354/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.920, Loss:  0.062
+Epoch   3 Batch  355/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.041
+Epoch   3 Batch  356/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.924, Loss:  0.057
+Epoch   3 Batch  357/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   3 Batch  358/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.919, Loss:  0.066
+Epoch   3 Batch  359/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.051
+Epoch   3 Batch  360/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   3 Batch  361/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.050
+Epoch   3 Batch  362/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.050
+Epoch   3 Batch  363/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.922, Loss:  0.064
+Epoch   3 Batch  364/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.925, Loss:  0.060
+Epoch   3 Batch  365/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.919, Loss:  0.047
+Epoch   3 Batch  366/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.913, Loss:  0.052
+Epoch   3 Batch  367/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.912, Loss:  0.038
+Epoch   3 Batch  368/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.055
+Epoch   3 Batch  369/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.915, Loss:  0.060
+Epoch   3 Batch  370/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   3 Batch  371/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   3 Batch  372/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   3 Batch  373/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.037
+Epoch   3 Batch  374/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   3 Batch  375/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.929, Loss:  0.046
+Epoch   3 Batch  376/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   3 Batch  377/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.935, Loss:  0.044
+Epoch   3 Batch  378/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   3 Batch  379/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.933, Loss:  0.057
+Epoch   3 Batch  380/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.933, Loss:  0.045
+Epoch   3 Batch  381/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.947, Loss:  0.055
+Epoch   3 Batch  382/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.947, Loss:  0.073
+Epoch   3 Batch  383/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.947, Loss:  0.046
+Epoch   3 Batch  384/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.947, Loss:  0.031
+Epoch   3 Batch  385/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.956, Loss:  0.046
+Epoch   3 Batch  386/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.954, Loss:  0.042
+Epoch   3 Batch  387/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.953, Loss:  0.038
+Epoch   3 Batch  388/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.955, Loss:  0.050
+Epoch   3 Batch  389/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.945, Loss:  0.047
+Epoch   3 Batch  390/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.945, Loss:  0.056
+Epoch   3 Batch  391/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.941, Loss:  0.054
+Epoch   3 Batch  392/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.941, Loss:  0.052
+Epoch   3 Batch  393/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.044
+Epoch   3 Batch  394/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.936, Loss:  0.045
+Epoch   3 Batch  395/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.930, Loss:  0.045
+Epoch   3 Batch  396/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   3 Batch  397/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.927, Loss:  0.048
+Epoch   3 Batch  398/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.054
+Epoch   3 Batch  399/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.946, Loss:  0.057
+Epoch   3 Batch  400/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.943, Loss:  0.055
+Epoch   3 Batch  401/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.943, Loss:  0.048
+Epoch   3 Batch  402/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.939, Loss:  0.044
+Epoch   3 Batch  403/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.938, Loss:  0.077
+Epoch   3 Batch  404/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.938, Loss:  0.052
+Epoch   3 Batch  405/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   3 Batch  406/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.928, Loss:  0.039
+Epoch   3 Batch  407/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.935, Loss:  0.074
+Epoch   3 Batch  408/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.936, Loss:  0.052
+Epoch   3 Batch  409/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.941, Loss:  0.062
+Epoch   3 Batch  410/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.936, Loss:  0.062
+Epoch   3 Batch  411/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.934, Loss:  0.058
+Epoch   3 Batch  412/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.934, Loss:  0.042
+Epoch   3 Batch  413/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.039
+Epoch   3 Batch  414/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.940, Loss:  0.044
+Epoch   3 Batch  415/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.940, Loss:  0.052
+Epoch   3 Batch  416/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.943, Loss:  0.046
+Epoch   3 Batch  417/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.943, Loss:  0.078
+Epoch   3 Batch  418/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.945, Loss:  0.046
+Epoch   3 Batch  419/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.948, Loss:  0.045
+Epoch   3 Batch  420/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.946, Loss:  0.042
+Epoch   3 Batch  421/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.940, Loss:  0.061
+Epoch   3 Batch  422/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.940, Loss:  0.042
+Epoch   3 Batch  423/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.936, Loss:  0.068
+Epoch   3 Batch  424/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.935, Loss:  0.047
+Epoch   3 Batch  425/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.922, Loss:  0.037
+Epoch   3 Batch  426/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  427/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.933, Loss:  0.047
+Epoch   3 Batch  428/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.932, Loss:  0.036
+Epoch   3 Batch  429/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   3 Batch  430/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.941, Loss:  0.047
+Epoch   3 Batch  431/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.942, Loss:  0.041
+Epoch   3 Batch  432/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.951, Loss:  0.048
+Epoch   3 Batch  433/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.943, Loss:  0.057
+Epoch   3 Batch  434/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.938, Loss:  0.044
+Epoch   3 Batch  435/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.056
+Epoch   3 Batch  436/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.918, Loss:  0.054
+Epoch   3 Batch  437/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.918, Loss:  0.034
+Epoch   3 Batch  438/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.916, Loss:  0.049
+Epoch   3 Batch  439/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.916, Loss:  0.057
+Epoch   3 Batch  440/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.920, Loss:  0.057
+Epoch   3 Batch  441/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.926, Loss:  0.046
+Epoch   3 Batch  442/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.060
+Epoch   3 Batch  443/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   3 Batch  444/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.925, Loss:  0.037
+Epoch   3 Batch  445/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.925, Loss:  0.057
+Epoch   3 Batch  446/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   3 Batch  447/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   3 Batch  448/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.922, Loss:  0.069
+Epoch   3 Batch  449/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   3 Batch  450/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.920, Loss:  0.054
+Epoch   3 Batch  451/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   3 Batch  452/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   3 Batch  453/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.920, Loss:  0.053
+Epoch   3 Batch  454/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   3 Batch  455/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.926, Loss:  0.057
+Epoch   3 Batch  456/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   3 Batch  457/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.042
+Epoch   3 Batch  458/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.055
+Epoch   3 Batch  459/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   3 Batch  460/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.923, Loss:  0.061
+Epoch   3 Batch  461/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   3 Batch  462/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  463/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  464/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   3 Batch  465/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.916, Loss:  0.056
+Epoch   3 Batch  466/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   3 Batch  467/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.917, Loss:  0.058
+Epoch   3 Batch  468/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.057
+Epoch   3 Batch  469/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.061
+Epoch   3 Batch  470/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   3 Batch  471/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   3 Batch  472/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.909, Loss:  0.049
+Epoch   3 Batch  473/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.046
+Epoch   3 Batch  474/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.916, Loss:  0.047
+Epoch   3 Batch  475/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.051
+Epoch   3 Batch  476/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.917, Loss:  0.039
+Epoch   3 Batch  477/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   3 Batch  478/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.934, Loss:  0.052
+Epoch   3 Batch  479/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.934, Loss:  0.056
+Epoch   3 Batch  480/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.934, Loss:  0.054
+Epoch   3 Batch  481/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.935, Loss:  0.064
+Epoch   3 Batch  482/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.935, Loss:  0.065
+Epoch   3 Batch  483/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.934, Loss:  0.051
+Epoch   3 Batch  484/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.048
+Epoch   3 Batch  485/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.049
+Epoch   3 Batch  486/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.046
+Epoch   3 Batch  487/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.046
+Epoch   3 Batch  488/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.927, Loss:  0.053
+Epoch   3 Batch  489/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   3 Batch  490/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.931, Loss:  0.051
+Epoch   3 Batch  491/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.056
+Epoch   3 Batch  492/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.921, Loss:  0.059
+Epoch   3 Batch  493/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.921, Loss:  0.042
+Epoch   3 Batch  494/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   3 Batch  495/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.924, Loss:  0.051
+Epoch   3 Batch  496/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.920, Loss:  0.050
+Epoch   3 Batch  497/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   3 Batch  498/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.052
+Epoch   3 Batch  499/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.039
+Epoch   3 Batch  500/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.933, Loss:  0.040
+Epoch   3 Batch  501/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.933, Loss:  0.040
+Epoch   3 Batch  502/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.935, Loss:  0.053
+Epoch   3 Batch  503/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.935, Loss:  0.044
+Epoch   3 Batch  504/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.939, Loss:  0.042
+Epoch   3 Batch  505/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.935, Loss:  0.036
+Epoch   3 Batch  506/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.939, Loss:  0.059
+Epoch   3 Batch  507/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.937, Loss:  0.055
+Epoch   3 Batch  508/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.941, Loss:  0.046
+Epoch   3 Batch  509/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.941, Loss:  0.069
+Epoch   3 Batch  510/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.938, Loss:  0.057
+Epoch   3 Batch  511/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.936, Loss:  0.048
+Epoch   3 Batch  512/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.930, Loss:  0.056
+Epoch   3 Batch  513/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   3 Batch  514/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.934, Loss:  0.055
+Epoch   3 Batch  515/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   3 Batch  516/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   3 Batch  517/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.925, Loss:  0.063
+Epoch   3 Batch  518/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.047
+Epoch   3 Batch  519/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.046
+Epoch   3 Batch  520/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.916, Loss:  0.050
+Epoch   3 Batch  521/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.913, Loss:  0.058
+Epoch   3 Batch  522/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.921, Loss:  0.063
+Epoch   3 Batch  523/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.054
+Epoch   3 Batch  524/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   3 Batch  525/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.061
+Epoch   3 Batch  526/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   3 Batch  527/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   3 Batch  528/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.056
+Epoch   3 Batch  529/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.934, Loss:  0.049
+Epoch   3 Batch  530/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.937, Loss:  0.063
+Epoch   3 Batch  531/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.935, Loss:  0.057
+Epoch   3 Batch  532/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.931, Loss:  0.066
+Epoch   3 Batch  533/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.058
+Epoch   3 Batch  534/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.059
+Epoch   3 Batch  535/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   3 Batch  536/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   3 Batch  537/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.923, Loss:  0.039
+Epoch   3 Batch  538/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.928, Loss:  0.037
+Epoch   3 Batch  539/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.061
+Epoch   3 Batch  540/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.914, Loss:  0.050
+Epoch   3 Batch  541/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   3 Batch  542/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.923, Loss:  0.052
+Epoch   3 Batch  543/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   3 Batch  544/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.918, Loss:  0.035
+Epoch   3 Batch  545/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.057
+Epoch   3 Batch  546/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.924, Loss:  0.056
+Epoch   3 Batch  547/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.925, Loss:  0.045
+Epoch   3 Batch  548/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.930, Loss:  0.067
+Epoch   3 Batch  549/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.926, Loss:  0.069
+Epoch   3 Batch  550/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.049
+Epoch   3 Batch  551/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   3 Batch  552/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   3 Batch  553/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.070
+Epoch   3 Batch  554/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   3 Batch  555/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   3 Batch  556/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.907, Loss:  0.052
+Epoch   3 Batch  557/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   3 Batch  558/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.901, Loss:  0.038
+Epoch   3 Batch  559/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.908, Loss:  0.046
+Epoch   3 Batch  560/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.904, Loss:  0.049
+Epoch   3 Batch  561/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.912, Loss:  0.042
+Epoch   3 Batch  562/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.046
+Epoch   3 Batch  563/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.904, Loss:  0.057
+Epoch   3 Batch  564/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.901, Loss:  0.057
+Epoch   3 Batch  565/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.068
+Epoch   3 Batch  566/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.902, Loss:  0.057
+Epoch   3 Batch  567/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.060
+Epoch   3 Batch  568/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.907, Loss:  0.041
+Epoch   3 Batch  569/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.908, Loss:  0.049
+Epoch   3 Batch  570/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.071
+Epoch   3 Batch  571/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.913, Loss:  0.041
+Epoch   3 Batch  572/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   3 Batch  573/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.918, Loss:  0.070
+Epoch   3 Batch  574/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.920, Loss:  0.054
+Epoch   3 Batch  575/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.036
+Epoch   3 Batch  576/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   3 Batch  577/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.925, Loss:  0.060
+Epoch   3 Batch  578/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   3 Batch  579/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.921, Loss:  0.052
+Epoch   3 Batch  580/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.920, Loss:  0.046
+Epoch   3 Batch  581/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.920, Loss:  0.041
+Epoch   3 Batch  582/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   3 Batch  583/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.054
+Epoch   3 Batch  584/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   3 Batch  585/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.921, Loss:  0.032
+Epoch   3 Batch  586/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   3 Batch  587/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.921, Loss:  0.063
+Epoch   3 Batch  588/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.921, Loss:  0.043
+Epoch   3 Batch  589/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   3 Batch  590/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.921, Loss:  0.069
+Epoch   3 Batch  591/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.934, Loss:  0.055
+Epoch   3 Batch  592/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.935, Loss:  0.055
+Epoch   3 Batch  593/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.932, Loss:  0.073
+Epoch   3 Batch  594/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   3 Batch  595/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   3 Batch  596/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.053
+Epoch   3 Batch  597/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   3 Batch  598/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.051
+Epoch   3 Batch  599/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   3 Batch  600/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.916, Loss:  0.055
+Epoch   3 Batch  601/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.057
+Epoch   3 Batch  602/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   3 Batch  603/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.917, Loss:  0.054
+Epoch   3 Batch  604/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.058
+Epoch   3 Batch  605/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.078
+Epoch   3 Batch  606/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.933, Loss:  0.041
+Epoch   3 Batch  607/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.923, Loss:  0.042
+Epoch   3 Batch  608/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.065
+Epoch   3 Batch  609/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.050
+Epoch   3 Batch  610/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  611/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.920, Loss:  0.044
+Epoch   3 Batch  612/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.905, Loss:  0.042
+Epoch   3 Batch  613/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.905, Loss:  0.067
+Epoch   3 Batch  614/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.906, Loss:  0.041
+Epoch   3 Batch  615/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.912, Loss:  0.047
+Epoch   3 Batch  616/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   3 Batch  617/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   3 Batch  618/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.931, Loss:  0.054
+Epoch   3 Batch  619/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   3 Batch  620/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.931, Loss:  0.046
+Epoch   3 Batch  621/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.930, Loss:  0.059
+Epoch   3 Batch  622/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.916, Loss:  0.054
+Epoch   3 Batch  623/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.907, Loss:  0.061
+Epoch   3 Batch  624/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.912, Loss:  0.051
+Epoch   3 Batch  625/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.912, Loss:  0.052
+Epoch   3 Batch  626/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.051
+Epoch   3 Batch  627/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   3 Batch  628/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.061
+Epoch   3 Batch  629/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.919, Loss:  0.066
+Epoch   3 Batch  630/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.926, Loss:  0.040
+Epoch   3 Batch  631/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.931, Loss:  0.054
+Epoch   3 Batch  632/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.935, Loss:  0.045
+Epoch   3 Batch  633/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   3 Batch  634/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.933, Loss:  0.041
+Epoch   3 Batch  635/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.928, Loss:  0.059
+Epoch   3 Batch  636/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.931, Loss:  0.043
+Epoch   3 Batch  637/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.932, Loss:  0.048
+Epoch   3 Batch  638/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.937, Loss:  0.051
+Epoch   3 Batch  639/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.938, Loss:  0.068
+Epoch   3 Batch  640/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.933, Loss:  0.046
+Epoch   3 Batch  641/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   3 Batch  642/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   3 Batch  643/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   3 Batch  644/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   3 Batch  645/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.061
+Epoch   3 Batch  646/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.048
+Epoch   3 Batch  647/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.931, Loss:  0.057
+Epoch   3 Batch  648/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.931, Loss:  0.031
+Epoch   3 Batch  649/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.931, Loss:  0.048
+Epoch   3 Batch  650/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.040
+Epoch   3 Batch  651/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.931, Loss:  0.044
+Epoch   3 Batch  652/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.936, Loss:  0.065
+Epoch   3 Batch  653/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.934, Loss:  0.054
+Epoch   3 Batch  654/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   3 Batch  655/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.924, Loss:  0.058
+Epoch   3 Batch  656/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.928, Loss:  0.053
+Epoch   3 Batch  657/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.929, Loss:  0.045
+Epoch   3 Batch  658/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.934, Loss:  0.033
+Epoch   3 Batch  659/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.929, Loss:  0.053
+Epoch   3 Batch  660/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.938, Loss:  0.034
+Epoch   3 Batch  661/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   3 Batch  662/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.046
+Epoch   3 Batch  663/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.927, Loss:  0.045
+Epoch   3 Batch  664/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   3 Batch  665/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.038
+Epoch   3 Batch  666/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.052
+Epoch   3 Batch  667/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   3 Batch  668/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.911, Loss:  0.060
+Epoch   3 Batch  669/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   3 Batch  670/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.924, Loss:  0.057
+Epoch   3 Batch  671/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.933, Loss:  0.061
+Epoch   3 Batch  672/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.937, Loss:  0.046
+Epoch   3 Batch  673/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.941, Loss:  0.046
+Epoch   3 Batch  674/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   3 Batch  675/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.932, Loss:  0.059
+Epoch   3 Batch  676/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   3 Batch  677/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.927, Loss:  0.067
+Epoch   3 Batch  678/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.933, Loss:  0.046
+Epoch   3 Batch  679/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.930, Loss:  0.045
+Epoch   3 Batch  680/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.058
+Epoch   3 Batch  681/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   3 Batch  682/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.917, Loss:  0.047
+Epoch   3 Batch  683/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.049
+Epoch   3 Batch  684/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.041
+Epoch   3 Batch  685/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   3 Batch  686/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.914, Loss:  0.047
+Epoch   3 Batch  687/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.909, Loss:  0.075
+Epoch   3 Batch  688/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.907, Loss:  0.050
+Epoch   3 Batch  689/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.913, Loss:  0.035
+Epoch   3 Batch  690/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.910, Loss:  0.056
+Epoch   3 Batch  691/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   3 Batch  692/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.918, Loss:  0.050
+Epoch   3 Batch  693/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.918, Loss:  0.063
+Epoch   3 Batch  694/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.917, Loss:  0.062
+Epoch   3 Batch  695/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.917, Loss:  0.042
+Epoch   3 Batch  696/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   3 Batch  697/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   3 Batch  698/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.901, Loss:  0.051
+Epoch   3 Batch  699/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.908, Loss:  0.063
+Epoch   3 Batch  700/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.917, Loss:  0.047
+Epoch   3 Batch  701/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.916, Loss:  0.051
+Epoch   3 Batch  702/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.924, Loss:  0.073
+Epoch   3 Batch  703/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   3 Batch  704/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.929, Loss:  0.075
+Epoch   3 Batch  705/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.934, Loss:  0.066
+Epoch   3 Batch  706/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.931, Loss:  0.081
+Epoch   3 Batch  707/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.933, Loss:  0.058
+Epoch   3 Batch  708/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.932, Loss:  0.065
+Epoch   3 Batch  709/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.933, Loss:  0.067
+Epoch   3 Batch  710/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.935, Loss:  0.048
+Epoch   3 Batch  711/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.064
+Epoch   3 Batch  712/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   3 Batch  713/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.048
+Epoch   3 Batch  714/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   3 Batch  715/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.059
+Epoch   3 Batch  716/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.048
+Epoch   3 Batch  717/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.923, Loss:  0.044
+Epoch   3 Batch  718/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.039
+Epoch   3 Batch  719/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   3 Batch  720/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.927, Loss:  0.058
+Epoch   3 Batch  721/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.931, Loss:  0.052
+Epoch   3 Batch  722/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.934, Loss:  0.048
+Epoch   3 Batch  723/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.938, Loss:  0.049
+Epoch   3 Batch  724/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.049
+Epoch   3 Batch  725/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.933, Loss:  0.039
+Epoch   3 Batch  726/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  727/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.928, Loss:  0.044
+Epoch   3 Batch  728/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.926, Loss:  0.071
+Epoch   3 Batch  729/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.065
+Epoch   3 Batch  730/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.066
+Epoch   3 Batch  731/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.053
+Epoch   3 Batch  732/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.918, Loss:  0.059
+Epoch   3 Batch  733/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.904, Loss:  0.046
+Epoch   3 Batch  734/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.907, Loss:  0.054
+Epoch   3 Batch  735/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   3 Batch  736/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.041
+Epoch   3 Batch  737/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.063
+Epoch   3 Batch  738/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.039
+Epoch   3 Batch  739/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.053
+Epoch   3 Batch  740/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   3 Batch  741/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   3 Batch  742/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   3 Batch  743/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.932, Loss:  0.053
+Epoch   3 Batch  744/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.941, Loss:  0.053
+Epoch   3 Batch  745/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.936, Loss:  0.058
+Epoch   3 Batch  746/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   3 Batch  747/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.933, Loss:  0.039
+Epoch   3 Batch  748/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   3 Batch  749/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.045
+Epoch   3 Batch  750/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.937, Loss:  0.048
+Epoch   3 Batch  751/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.937, Loss:  0.053
+Epoch   3 Batch  752/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.936, Loss:  0.048
+Epoch   3 Batch  753/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.932, Loss:  0.048
+Epoch   3 Batch  754/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.936, Loss:  0.060
+Epoch   3 Batch  755/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.927, Loss:  0.058
+Epoch   3 Batch  756/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   3 Batch  757/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   3 Batch  758/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   3 Batch  759/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.927, Loss:  0.042
+Epoch   3 Batch  760/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.938, Loss:  0.059
+Epoch   3 Batch  761/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.942, Loss:  0.048
+Epoch   3 Batch  762/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.939, Loss:  0.043
+Epoch   3 Batch  763/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.936, Loss:  0.049
+Epoch   3 Batch  764/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.941, Loss:  0.053
+Epoch   3 Batch  765/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.938, Loss:  0.058
+Epoch   3 Batch  766/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.942, Loss:  0.043
+Epoch   3 Batch  767/1077 - Train Accuracy:  0.971, Validation Accuracy:  0.939, Loss:  0.046
+Epoch   3 Batch  768/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.939, Loss:  0.051
+Epoch   3 Batch  769/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   3 Batch  770/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.919, Loss:  0.049
+Epoch   3 Batch  771/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.917, Loss:  0.050
+Epoch   3 Batch  772/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.918, Loss:  0.048
+Epoch   3 Batch  773/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   3 Batch  774/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.063
+Epoch   3 Batch  775/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.057
+Epoch   3 Batch  776/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.925, Loss:  0.045
+Epoch   3 Batch  777/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.928, Loss:  0.054
+Epoch   3 Batch  778/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   3 Batch  779/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.062
+Epoch   3 Batch  780/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.916, Loss:  0.077
+Epoch   3 Batch  781/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.935, Loss:  0.041
+Epoch   3 Batch  782/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.939, Loss:  0.045
+Epoch   3 Batch  783/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.945, Loss:  0.053
+Epoch   3 Batch  784/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.945, Loss:  0.040
+Epoch   3 Batch  785/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.947, Loss:  0.037
+Epoch   3 Batch  786/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.947, Loss:  0.043
+Epoch   3 Batch  787/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.939, Loss:  0.055
+Epoch   3 Batch  788/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.938, Loss:  0.045
+Epoch   3 Batch  789/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.938, Loss:  0.060
+Epoch   3 Batch  790/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.933, Loss:  0.053
+Epoch   3 Batch  791/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.933, Loss:  0.059
+Epoch   3 Batch  792/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.060
+Epoch   3 Batch  793/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.932, Loss:  0.044
+Epoch   3 Batch  794/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.931, Loss:  0.037
+Epoch   3 Batch  795/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.068
+Epoch   3 Batch  796/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.930, Loss:  0.037
+Epoch   3 Batch  797/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.923, Loss:  0.052
+Epoch   3 Batch  798/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.053
+Epoch   3 Batch  799/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   3 Batch  800/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.041
+Epoch   3 Batch  801/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.056
+Epoch   3 Batch  802/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   3 Batch  803/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.061
+Epoch   3 Batch  804/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.912, Loss:  0.040
+Epoch   3 Batch  805/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.913, Loss:  0.043
+Epoch   3 Batch  806/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.059
+Epoch   3 Batch  807/1077 - Train Accuracy:  0.969, Validation Accuracy:  0.905, Loss:  0.044
+Epoch   3 Batch  808/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.905, Loss:  0.068
+Epoch   3 Batch  809/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.901, Loss:  0.056
+Epoch   3 Batch  810/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.900, Loss:  0.043
+Epoch   3 Batch  811/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.906, Loss:  0.051
+Epoch   3 Batch  812/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.910, Loss:  0.050
+Epoch   3 Batch  813/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.920, Loss:  0.054
+Epoch   3 Batch  814/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.920, Loss:  0.061
+Epoch   3 Batch  815/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   3 Batch  816/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.910, Loss:  0.056
+Epoch   3 Batch  817/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.920, Loss:  0.061
+Epoch   3 Batch  818/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.925, Loss:  0.056
+Epoch   3 Batch  819/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.932, Loss:  0.048
+Epoch   3 Batch  820/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.927, Loss:  0.048
+Epoch   3 Batch  821/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   3 Batch  822/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.918, Loss:  0.040
+Epoch   3 Batch  823/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.922, Loss:  0.058
+Epoch   3 Batch  824/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.064
+Epoch   3 Batch  825/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.919, Loss:  0.043
+Epoch   3 Batch  826/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.048
+Epoch   3 Batch  827/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   3 Batch  828/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.051
+Epoch   3 Batch  829/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.918, Loss:  0.069
+Epoch   3 Batch  830/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.920, Loss:  0.059
+Epoch   3 Batch  831/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.915, Loss:  0.052
+Epoch   3 Batch  832/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.915, Loss:  0.044
+Epoch   3 Batch  833/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.920, Loss:  0.057
+Epoch   3 Batch  834/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.927, Loss:  0.045
+Epoch   3 Batch  835/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   3 Batch  836/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.923, Loss:  0.047
+Epoch   3 Batch  837/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.928, Loss:  0.059
+Epoch   3 Batch  838/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.923, Loss:  0.046
+Epoch   3 Batch  839/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.918, Loss:  0.037
+Epoch   3 Batch  840/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.043
+Epoch   3 Batch  841/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   3 Batch  842/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.935, Loss:  0.037
+Epoch   3 Batch  843/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.050
+Epoch   3 Batch  844/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.945, Loss:  0.044
+Epoch   3 Batch  845/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.950, Loss:  0.048
+Epoch   3 Batch  846/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.940, Loss:  0.059
+Epoch   3 Batch  847/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.944, Loss:  0.057
+Epoch   3 Batch  848/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.944, Loss:  0.039
+Epoch   3 Batch  849/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.940, Loss:  0.051
+Epoch   3 Batch  850/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.940, Loss:  0.078
+Epoch   3 Batch  851/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.935, Loss:  0.063
+Epoch   3 Batch  852/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.933, Loss:  0.053
+Epoch   3 Batch  853/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.934, Loss:  0.059
+Epoch   3 Batch  854/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.935, Loss:  0.052
+Epoch   3 Batch  855/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   3 Batch  856/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   3 Batch  857/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.055
+Epoch   3 Batch  858/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.049
+Epoch   3 Batch  859/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.054
+Epoch   3 Batch  860/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.044
+Epoch   3 Batch  861/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.936, Loss:  0.061
+Epoch   3 Batch  862/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.936, Loss:  0.049
+Epoch   3 Batch  863/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.037
+Epoch   3 Batch  864/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   3 Batch  865/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.922, Loss:  0.048
+Epoch   3 Batch  866/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.065
+Epoch   3 Batch  867/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.926, Loss:  0.095
+Epoch   3 Batch  868/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.923, Loss:  0.063
+Epoch   3 Batch  869/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.049
+Epoch   3 Batch  870/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   3 Batch  871/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.918, Loss:  0.036
+Epoch   3 Batch  872/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   3 Batch  873/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.926, Loss:  0.051
+Epoch   3 Batch  874/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.919, Loss:  0.066
+Epoch   3 Batch  875/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.067
+Epoch   3 Batch  876/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.043
+Epoch   3 Batch  877/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.933, Loss:  0.043
+Epoch   3 Batch  878/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.942, Loss:  0.043
+Epoch   3 Batch  879/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.933, Loss:  0.042
+Epoch   3 Batch  880/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.933, Loss:  0.049
+Epoch   3 Batch  881/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.933, Loss:  0.050
+Epoch   3 Batch  882/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   3 Batch  883/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.063
+Epoch   3 Batch  884/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   3 Batch  885/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.923, Loss:  0.036
+Epoch   3 Batch  886/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.914, Loss:  0.041
+Epoch   3 Batch  887/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.908, Loss:  0.061
+Epoch   3 Batch  888/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.908, Loss:  0.048
+Epoch   3 Batch  889/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   3 Batch  890/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   3 Batch  891/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.909, Loss:  0.045
+Epoch   3 Batch  892/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.912, Loss:  0.042
+Epoch   3 Batch  893/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.043
+Epoch   3 Batch  894/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.041
+Epoch   3 Batch  895/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.044
+Epoch   3 Batch  896/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.916, Loss:  0.053
+Epoch   3 Batch  897/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.922, Loss:  0.037
+Epoch   3 Batch  898/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.040
+Epoch   3 Batch  899/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.930, Loss:  0.044
+Epoch   3 Batch  900/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.934, Loss:  0.066
+Epoch   3 Batch  901/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.935, Loss:  0.067
+Epoch   3 Batch  902/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.933, Loss:  0.055
+Epoch   3 Batch  903/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   3 Batch  904/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.939, Loss:  0.043
+Epoch   3 Batch  905/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.933, Loss:  0.038
+Epoch   3 Batch  906/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.931, Loss:  0.053
+Epoch   3 Batch  907/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.046
+Epoch   3 Batch  908/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.052
+Epoch   3 Batch  909/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.052
+Epoch   3 Batch  910/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   3 Batch  911/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   3 Batch  912/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.930, Loss:  0.047
+Epoch   3 Batch  913/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.926, Loss:  0.078
+Epoch   3 Batch  914/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.927, Loss:  0.067
+Epoch   3 Batch  915/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.916, Loss:  0.045
+Epoch   3 Batch  916/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.912, Loss:  0.050
+Epoch   3 Batch  917/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   3 Batch  918/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.913, Loss:  0.045
+Epoch   3 Batch  919/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.908, Loss:  0.038
+Epoch   3 Batch  920/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.043
+Epoch   3 Batch  921/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.918, Loss:  0.058
+Epoch   3 Batch  922/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   3 Batch  923/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.920, Loss:  0.037
+Epoch   3 Batch  924/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   3 Batch  925/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.050
+Epoch   3 Batch  926/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.032
+Epoch   3 Batch  927/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.064
+Epoch   3 Batch  928/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   3 Batch  929/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   3 Batch  930/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   3 Batch  931/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.914, Loss:  0.046
+Epoch   3 Batch  932/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.905, Loss:  0.055
+Epoch   3 Batch  933/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.911, Loss:  0.049
+Epoch   3 Batch  934/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.917, Loss:  0.042
+Epoch   3 Batch  935/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   3 Batch  936/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   3 Batch  937/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.924, Loss:  0.058
+Epoch   3 Batch  938/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.920, Loss:  0.056
+Epoch   3 Batch  939/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   3 Batch  940/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.926, Loss:  0.041
+Epoch   3 Batch  941/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.042
+Epoch   3 Batch  942/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.927, Loss:  0.053
+Epoch   3 Batch  943/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   3 Batch  944/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   3 Batch  945/1077 - Train Accuracy:  0.969, Validation Accuracy:  0.930, Loss:  0.041
+Epoch   3 Batch  946/1077 - Train Accuracy:  0.973, Validation Accuracy:  0.927, Loss:  0.029
+Epoch   3 Batch  947/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   3 Batch  948/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   3 Batch  949/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   3 Batch  950/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.044
+Epoch   3 Batch  951/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.926, Loss:  0.055
+Epoch   3 Batch  952/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.929, Loss:  0.037
+Epoch   3 Batch  953/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.037
+Epoch   3 Batch  954/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   3 Batch  955/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.926, Loss:  0.073
+Epoch   3 Batch  956/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   3 Batch  957/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.034
+Epoch   3 Batch  958/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.042
+Epoch   3 Batch  959/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   3 Batch  960/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.919, Loss:  0.044
+Epoch   3 Batch  961/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.920, Loss:  0.041
+Epoch   3 Batch  962/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   3 Batch  963/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.919, Loss:  0.066
+Epoch   3 Batch  964/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.920, Loss:  0.044
+Epoch   3 Batch  965/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.064
+Epoch   3 Batch  966/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.043
+Epoch   3 Batch  967/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.914, Loss:  0.067
+Epoch   3 Batch  968/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.057
+Epoch   3 Batch  969/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.916, Loss:  0.074
+Epoch   3 Batch  970/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   3 Batch  971/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.916, Loss:  0.060
+Epoch   3 Batch  972/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   3 Batch  973/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   3 Batch  974/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.929, Loss:  0.035
+Epoch   3 Batch  975/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   3 Batch  976/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.931, Loss:  0.039
+Epoch   3 Batch  977/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.038
+Epoch   3 Batch  978/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.047
+Epoch   3 Batch  979/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.046
+Epoch   3 Batch  980/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   3 Batch  981/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   3 Batch  982/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.936, Loss:  0.048
+Epoch   3 Batch  983/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.936, Loss:  0.049
+Epoch   3 Batch  984/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.933, Loss:  0.066
+Epoch   3 Batch  985/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.046
+Epoch   3 Batch  986/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.936, Loss:  0.049
+Epoch   3 Batch  987/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   3 Batch  988/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.932, Loss:  0.066
+Epoch   3 Batch  989/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.933, Loss:  0.055
+Epoch   3 Batch  990/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.059
+Epoch   3 Batch  991/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   3 Batch  992/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.926, Loss:  0.049
+Epoch   3 Batch  993/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.919, Loss:  0.040
+Epoch   3 Batch  994/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   3 Batch  995/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.917, Loss:  0.048
+Epoch   3 Batch  996/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.052
+Epoch   3 Batch  997/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   3 Batch  998/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   3 Batch  999/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.054
+Epoch   3 Batch 1000/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.041
+Epoch   3 Batch 1001/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.925, Loss:  0.035
+Epoch   3 Batch 1002/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.928, Loss:  0.036
+Epoch   3 Batch 1003/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.933, Loss:  0.050
+Epoch   3 Batch 1004/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   3 Batch 1005/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.931, Loss:  0.043
+Epoch   3 Batch 1006/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.047
+Epoch   3 Batch 1007/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.047
+Epoch   3 Batch 1008/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.925, Loss:  0.063
+Epoch   3 Batch 1009/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.927, Loss:  0.038
+Epoch   3 Batch 1010/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   3 Batch 1011/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.927, Loss:  0.037
+Epoch   3 Batch 1012/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.924, Loss:  0.037
+Epoch   3 Batch 1013/1077 - Train Accuracy:  0.971, Validation Accuracy:  0.923, Loss:  0.034
+Epoch   3 Batch 1014/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   3 Batch 1015/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.059
+Epoch   3 Batch 1016/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   3 Batch 1017/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   3 Batch 1018/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.921, Loss:  0.042
+Epoch   3 Batch 1019/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   3 Batch 1020/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.923, Loss:  0.040
+Epoch   3 Batch 1021/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   3 Batch 1022/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   3 Batch 1023/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   3 Batch 1024/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.931, Loss:  0.068
+Epoch   3 Batch 1025/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.050
+Epoch   3 Batch 1026/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.938, Loss:  0.053
+Epoch   3 Batch 1027/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.934, Loss:  0.048
+Epoch   3 Batch 1028/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   3 Batch 1029/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.931, Loss:  0.038
+Epoch   3 Batch 1030/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.935, Loss:  0.043
+Epoch   3 Batch 1031/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   3 Batch 1032/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   3 Batch 1033/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.051
+Epoch   3 Batch 1034/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.934, Loss:  0.049
+Epoch   3 Batch 1035/1077 - Train Accuracy:  0.975, Validation Accuracy:  0.929, Loss:  0.029
+Epoch   3 Batch 1036/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.929, Loss:  0.046
+Epoch   3 Batch 1037/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.930, Loss:  0.038
+Epoch   3 Batch 1038/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.935, Loss:  0.067
+Epoch   3 Batch 1039/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.938, Loss:  0.050
+Epoch   3 Batch 1040/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.938, Loss:  0.053
+Epoch   3 Batch 1041/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   3 Batch 1042/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.935, Loss:  0.041
+Epoch   3 Batch 1043/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.054
+Epoch   3 Batch 1044/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.937, Loss:  0.053
+Epoch   3 Batch 1045/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   3 Batch 1046/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.929, Loss:  0.035
+Epoch   3 Batch 1047/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.933, Loss:  0.048
+Epoch   3 Batch 1048/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.942, Loss:  0.051
+Epoch   3 Batch 1049/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.938, Loss:  0.045
+Epoch   3 Batch 1050/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.935, Loss:  0.037
+Epoch   3 Batch 1051/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.934, Loss:  0.053
+Epoch   3 Batch 1052/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.939, Loss:  0.046
+Epoch   3 Batch 1053/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.941, Loss:  0.045
+Epoch   3 Batch 1054/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.941, Loss:  0.053
+Epoch   3 Batch 1055/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.940, Loss:  0.045
+Epoch   3 Batch 1056/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.943, Loss:  0.046
+Epoch   3 Batch 1057/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.936, Loss:  0.055
+Epoch   3 Batch 1058/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.936, Loss:  0.052
+Epoch   3 Batch 1059/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   3 Batch 1060/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   3 Batch 1061/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.918, Loss:  0.049
+Epoch   3 Batch 1062/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   3 Batch 1063/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.929, Loss:  0.061
+Epoch   3 Batch 1064/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.930, Loss:  0.046
+Epoch   3 Batch 1065/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.042
+Epoch   3 Batch 1066/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.039
+Epoch   3 Batch 1067/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.938, Loss:  0.057
+Epoch   3 Batch 1068/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.940, Loss:  0.043
+Epoch   3 Batch 1069/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.937, Loss:  0.035
+Epoch   3 Batch 1070/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.937, Loss:  0.041
+Epoch   3 Batch 1071/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.940, Loss:  0.048
+Epoch   3 Batch 1072/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.040
+Epoch   3 Batch 1073/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.929, Loss:  0.047
+Epoch   3 Batch 1074/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   3 Batch 1075/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.051
+Epoch   4 Batch    0/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   4 Batch    1/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.036
+Epoch   4 Batch    2/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   4 Batch    3/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   4 Batch    4/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.034
+Epoch   4 Batch    5/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.069
+Epoch   4 Batch    6/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   4 Batch    7/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   4 Batch    8/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.914, Loss:  0.041
+Epoch   4 Batch    9/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.045
+Epoch   4 Batch   10/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   4 Batch   11/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.919, Loss:  0.058
+Epoch   4 Batch   12/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   4 Batch   13/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   4 Batch   14/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.039
+Epoch   4 Batch   15/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.917, Loss:  0.038
+Epoch   4 Batch   16/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.060
+Epoch   4 Batch   17/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   4 Batch   18/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   4 Batch   19/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   4 Batch   20/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.037
+Epoch   4 Batch   21/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.926, Loss:  0.054
+Epoch   4 Batch   22/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.933, Loss:  0.061
+Epoch   4 Batch   23/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.048
+Epoch   4 Batch   24/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.042
+Epoch   4 Batch   25/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   4 Batch   26/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.916, Loss:  0.056
+Epoch   4 Batch   27/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.916, Loss:  0.037
+Epoch   4 Batch   28/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   4 Batch   29/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   4 Batch   30/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.926, Loss:  0.038
+Epoch   4 Batch   31/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.931, Loss:  0.044
+Epoch   4 Batch   32/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.934, Loss:  0.048
+Epoch   4 Batch   33/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   4 Batch   34/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   4 Batch   35/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   4 Batch   36/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   4 Batch   37/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.932, Loss:  0.048
+Epoch   4 Batch   38/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.936, Loss:  0.060
+Epoch   4 Batch   39/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.926, Loss:  0.061
+Epoch   4 Batch   40/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.928, Loss:  0.040
+Epoch   4 Batch   41/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.038
+Epoch   4 Batch   42/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   4 Batch   43/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.925, Loss:  0.029
+Epoch   4 Batch   44/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.926, Loss:  0.030
+Epoch   4 Batch   45/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.043
+Epoch   4 Batch   46/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   4 Batch   47/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.924, Loss:  0.048
+Epoch   4 Batch   48/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.063
+Epoch   4 Batch   49/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.060
+Epoch   4 Batch   50/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   4 Batch   51/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.046
+Epoch   4 Batch   52/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.933, Loss:  0.051
+Epoch   4 Batch   53/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.935, Loss:  0.042
+Epoch   4 Batch   54/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.931, Loss:  0.085
+Epoch   4 Batch   55/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.932, Loss:  0.049
+Epoch   4 Batch   56/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.042
+Epoch   4 Batch   57/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   4 Batch   58/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.911, Loss:  0.037
+Epoch   4 Batch   59/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.908, Loss:  0.038
+Epoch   4 Batch   60/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.035
+Epoch   4 Batch   61/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.917, Loss:  0.051
+Epoch   4 Batch   62/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.913, Loss:  0.045
+Epoch   4 Batch   63/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.917, Loss:  0.035
+Epoch   4 Batch   64/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.913, Loss:  0.040
+Epoch   4 Batch   65/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   4 Batch   66/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.909, Loss:  0.026
+Epoch   4 Batch   67/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.039
+Epoch   4 Batch   68/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.909, Loss:  0.056
+Epoch   4 Batch   69/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.906, Loss:  0.060
+Epoch   4 Batch   70/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.051
+Epoch   4 Batch   71/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.914, Loss:  0.028
+Epoch   4 Batch   72/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   4 Batch   73/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.909, Loss:  0.043
+Epoch   4 Batch   74/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   4 Batch   75/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.062
+Epoch   4 Batch   76/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.914, Loss:  0.032
+Epoch   4 Batch   77/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.912, Loss:  0.043
+Epoch   4 Batch   78/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.038
+Epoch   4 Batch   79/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.929, Loss:  0.035
+Epoch   4 Batch   80/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   4 Batch   81/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.043
+Epoch   4 Batch   82/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   4 Batch   83/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch   84/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   4 Batch   85/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   4 Batch   86/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   4 Batch   87/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.908, Loss:  0.054
+Epoch   4 Batch   88/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.908, Loss:  0.047
+Epoch   4 Batch   89/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   4 Batch   90/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.044
+Epoch   4 Batch   91/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.908, Loss:  0.035
+Epoch   4 Batch   92/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   4 Batch   93/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch   94/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.034
+Epoch   4 Batch   95/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch   96/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.045
+Epoch   4 Batch   97/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.050
+Epoch   4 Batch   98/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   4 Batch   99/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.043
+Epoch   4 Batch  100/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   4 Batch  101/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   4 Batch  102/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.042
+Epoch   4 Batch  103/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.940, Loss:  0.049
+Epoch   4 Batch  104/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   4 Batch  105/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.929, Loss:  0.047
+Epoch   4 Batch  106/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   4 Batch  107/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.928, Loss:  0.041
+Epoch   4 Batch  108/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   4 Batch  109/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.931, Loss:  0.048
+Epoch   4 Batch  110/1077 - Train Accuracy:  0.976, Validation Accuracy:  0.931, Loss:  0.028
+Epoch   4 Batch  111/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   4 Batch  112/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.929, Loss:  0.041
+Epoch   4 Batch  113/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   4 Batch  114/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.925, Loss:  0.037
+Epoch   4 Batch  115/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch  116/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.914, Loss:  0.054
+Epoch   4 Batch  117/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.914, Loss:  0.034
+Epoch   4 Batch  118/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.916, Loss:  0.041
+Epoch   4 Batch  119/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.914, Loss:  0.037
+Epoch   4 Batch  120/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   4 Batch  121/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   4 Batch  122/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.036
+Epoch   4 Batch  123/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.910, Loss:  0.036
+Epoch   4 Batch  124/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.052
+Epoch   4 Batch  125/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.914, Loss:  0.058
+Epoch   4 Batch  126/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.035
+Epoch   4 Batch  127/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   4 Batch  128/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.916, Loss:  0.043
+Epoch   4 Batch  129/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.914, Loss:  0.048
+Epoch   4 Batch  130/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch  131/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   4 Batch  132/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.913, Loss:  0.038
+Epoch   4 Batch  133/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.918, Loss:  0.037
+Epoch   4 Batch  134/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.918, Loss:  0.039
+Epoch   4 Batch  135/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.046
+Epoch   4 Batch  136/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.914, Loss:  0.041
+Epoch   4 Batch  137/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.913, Loss:  0.036
+Epoch   4 Batch  138/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.906, Loss:  0.043
+Epoch   4 Batch  139/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.906, Loss:  0.052
+Epoch   4 Batch  140/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.906, Loss:  0.047
+Epoch   4 Batch  141/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.908, Loss:  0.038
+Epoch   4 Batch  142/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.913, Loss:  0.037
+Epoch   4 Batch  143/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.041
+Epoch   4 Batch  144/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.906, Loss:  0.059
+Epoch   4 Batch  145/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.907, Loss:  0.052
+Epoch   4 Batch  146/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.906, Loss:  0.078
+Epoch   4 Batch  147/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.906, Loss:  0.046
+Epoch   4 Batch  148/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.906, Loss:  0.050
+Epoch   4 Batch  149/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.903, Loss:  0.043
+Epoch   4 Batch  150/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.909, Loss:  0.049
+Epoch   4 Batch  151/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.909, Loss:  0.041
+Epoch   4 Batch  152/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.070
+Epoch   4 Batch  153/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.907, Loss:  0.067
+Epoch   4 Batch  154/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.911, Loss:  0.044
+Epoch   4 Batch  155/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.915, Loss:  0.042
+Epoch   4 Batch  156/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.039
+Epoch   4 Batch  157/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   4 Batch  158/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.066
+Epoch   4 Batch  159/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.917, Loss:  0.039
+Epoch   4 Batch  160/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.046
+Epoch   4 Batch  161/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   4 Batch  162/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.054
+Epoch   4 Batch  163/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.060
+Epoch   4 Batch  164/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.927, Loss:  0.045
+Epoch   4 Batch  165/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.044
+Epoch   4 Batch  166/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.060
+Epoch   4 Batch  167/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.931, Loss:  0.047
+Epoch   4 Batch  168/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.929, Loss:  0.062
+Epoch   4 Batch  169/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.929, Loss:  0.059
+Epoch   4 Batch  170/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   4 Batch  171/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.917, Loss:  0.055
+Epoch   4 Batch  172/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.917, Loss:  0.044
+Epoch   4 Batch  173/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   4 Batch  174/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   4 Batch  175/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.921, Loss:  0.049
+Epoch   4 Batch  176/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.922, Loss:  0.057
+Epoch   4 Batch  177/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   4 Batch  178/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   4 Batch  179/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.924, Loss:  0.058
+Epoch   4 Batch  180/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.043
+Epoch   4 Batch  181/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   4 Batch  182/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   4 Batch  183/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.055
+Epoch   4 Batch  184/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.904, Loss:  0.050
+Epoch   4 Batch  185/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.911, Loss:  0.060
+Epoch   4 Batch  186/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.909, Loss:  0.053
+Epoch   4 Batch  187/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.908, Loss:  0.038
+Epoch   4 Batch  188/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.908, Loss:  0.049
+Epoch   4 Batch  189/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.909, Loss:  0.040
+Epoch   4 Batch  190/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   4 Batch  191/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.924, Loss:  0.039
+Epoch   4 Batch  192/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.925, Loss:  0.049
+Epoch   4 Batch  193/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.931, Loss:  0.043
+Epoch   4 Batch  194/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.036
+Epoch   4 Batch  195/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.930, Loss:  0.039
+Epoch   4 Batch  196/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.935, Loss:  0.039
+Epoch   4 Batch  197/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.933, Loss:  0.059
+Epoch   4 Batch  198/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.932, Loss:  0.048
+Epoch   4 Batch  199/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.933, Loss:  0.051
+Epoch   4 Batch  200/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.933, Loss:  0.057
+Epoch   4 Batch  201/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.938, Loss:  0.040
+Epoch   4 Batch  202/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.934, Loss:  0.045
+Epoch   4 Batch  203/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.933, Loss:  0.046
+Epoch   4 Batch  204/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.069
+Epoch   4 Batch  205/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.930, Loss:  0.069
+Epoch   4 Batch  206/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   4 Batch  207/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   4 Batch  208/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   4 Batch  209/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.916, Loss:  0.042
+Epoch   4 Batch  210/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   4 Batch  211/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   4 Batch  212/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.917, Loss:  0.036
+Epoch   4 Batch  213/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.040
+Epoch   4 Batch  214/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.044
+Epoch   4 Batch  215/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.918, Loss:  0.060
+Epoch   4 Batch  216/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.058
+Epoch   4 Batch  217/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.912, Loss:  0.042
+Epoch   4 Batch  218/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.924, Loss:  0.061
+Epoch   4 Batch  219/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.042
+Epoch   4 Batch  220/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.043
+Epoch   4 Batch  221/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.056
+Epoch   4 Batch  222/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   4 Batch  223/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.931, Loss:  0.049
+Epoch   4 Batch  224/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.046
+Epoch   4 Batch  225/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.922, Loss:  0.062
+Epoch   4 Batch  226/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   4 Batch  227/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   4 Batch  228/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.917, Loss:  0.047
+Epoch   4 Batch  229/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   4 Batch  230/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   4 Batch  231/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.050
+Epoch   4 Batch  232/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.919, Loss:  0.041
+Epoch   4 Batch  233/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   4 Batch  234/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   4 Batch  235/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.047
+Epoch   4 Batch  236/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.912, Loss:  0.064
+Epoch   4 Batch  237/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.912, Loss:  0.055
+Epoch   4 Batch  238/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.903, Loss:  0.047
+Epoch   4 Batch  239/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.923, Loss:  0.036
+Epoch   4 Batch  240/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.920, Loss:  0.043
+Epoch   4 Batch  241/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.922, Loss:  0.036
+Epoch   4 Batch  242/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   4 Batch  243/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   4 Batch  244/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   4 Batch  245/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.037
+Epoch   4 Batch  246/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   4 Batch  247/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   4 Batch  248/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.052
+Epoch   4 Batch  249/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   4 Batch  250/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.038
+Epoch   4 Batch  251/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   4 Batch  252/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.907, Loss:  0.061
+Epoch   4 Batch  253/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.903, Loss:  0.048
+Epoch   4 Batch  254/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.900, Loss:  0.062
+Epoch   4 Batch  255/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.905, Loss:  0.045
+Epoch   4 Batch  256/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.902, Loss:  0.058
+Epoch   4 Batch  257/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.898, Loss:  0.050
+Epoch   4 Batch  258/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.898, Loss:  0.057
+Epoch   4 Batch  259/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.896, Loss:  0.041
+Epoch   4 Batch  260/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.906, Loss:  0.036
+Epoch   4 Batch  261/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.905, Loss:  0.066
+Epoch   4 Batch  262/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.896, Loss:  0.040
+Epoch   4 Batch  263/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.042
+Epoch   4 Batch  264/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.910, Loss:  0.052
+Epoch   4 Batch  265/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.043
+Epoch   4 Batch  266/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.924, Loss:  0.048
+Epoch   4 Batch  267/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.036
+Epoch   4 Batch  268/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   4 Batch  269/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.932, Loss:  0.062
+Epoch   4 Batch  270/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.050
+Epoch   4 Batch  271/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.049
+Epoch   4 Batch  272/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.932, Loss:  0.080
+Epoch   4 Batch  273/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.941, Loss:  0.046
+Epoch   4 Batch  274/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   4 Batch  275/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.936, Loss:  0.050
+Epoch   4 Batch  276/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.924, Loss:  0.080
+Epoch   4 Batch  277/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.926, Loss:  0.038
+Epoch   4 Batch  278/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.926, Loss:  0.067
+Epoch   4 Batch  279/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.063
+Epoch   4 Batch  280/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.058
+Epoch   4 Batch  281/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.926, Loss:  0.059
+Epoch   4 Batch  282/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.935, Loss:  0.074
+Epoch   4 Batch  283/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.063
+Epoch   4 Batch  284/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.940, Loss:  0.053
+Epoch   4 Batch  285/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.056
+Epoch   4 Batch  286/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   4 Batch  287/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.045
+Epoch   4 Batch  288/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.925, Loss:  0.061
+Epoch   4 Batch  289/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   4 Batch  290/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.071
+Epoch   4 Batch  291/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.070
+Epoch   4 Batch  292/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.052
+Epoch   4 Batch  293/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.931, Loss:  0.052
+Epoch   4 Batch  294/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.926, Loss:  0.049
+Epoch   4 Batch  295/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.921, Loss:  0.069
+Epoch   4 Batch  296/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   4 Batch  297/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.058
+Epoch   4 Batch  298/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.912, Loss:  0.072
+Epoch   4 Batch  299/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.062
+Epoch   4 Batch  300/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.044
+Epoch   4 Batch  301/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.044
+Epoch   4 Batch  302/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.918, Loss:  0.045
+Epoch   4 Batch  303/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.913, Loss:  0.054
+Epoch   4 Batch  304/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   4 Batch  305/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.923, Loss:  0.046
+Epoch   4 Batch  306/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.059
+Epoch   4 Batch  307/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch  308/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   4 Batch  309/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.923, Loss:  0.041
+Epoch   4 Batch  310/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   4 Batch  311/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.930, Loss:  0.047
+Epoch   4 Batch  312/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.931, Loss:  0.062
+Epoch   4 Batch  313/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.930, Loss:  0.035
+Epoch   4 Batch  314/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.045
+Epoch   4 Batch  315/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.934, Loss:  0.039
+Epoch   4 Batch  316/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.934, Loss:  0.051
+Epoch   4 Batch  317/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.930, Loss:  0.056
+Epoch   4 Batch  318/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.931, Loss:  0.036
+Epoch   4 Batch  319/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.071
+Epoch   4 Batch  320/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.929, Loss:  0.058
+Epoch   4 Batch  321/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.043
+Epoch   4 Batch  322/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.933, Loss:  0.040
+Epoch   4 Batch  323/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.929, Loss:  0.044
+Epoch   4 Batch  324/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.929, Loss:  0.046
+Epoch   4 Batch  325/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.044
+Epoch   4 Batch  326/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.042
+Epoch   4 Batch  327/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.933, Loss:  0.056
+Epoch   4 Batch  328/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.062
+Epoch   4 Batch  329/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.060
+Epoch   4 Batch  330/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.921, Loss:  0.048
+Epoch   4 Batch  331/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.052
+Epoch   4 Batch  332/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.038
+Epoch   4 Batch  333/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   4 Batch  334/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.907, Loss:  0.049
+Epoch   4 Batch  335/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.900, Loss:  0.050
+Epoch   4 Batch  336/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.909, Loss:  0.080
+Epoch   4 Batch  337/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.914, Loss:  0.050
+Epoch   4 Batch  338/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.918, Loss:  0.070
+Epoch   4 Batch  339/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.041
+Epoch   4 Batch  340/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   4 Batch  341/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.933, Loss:  0.064
+Epoch   4 Batch  342/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.933, Loss:  0.051
+Epoch   4 Batch  343/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   4 Batch  344/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   4 Batch  345/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.930, Loss:  0.034
+Epoch   4 Batch  346/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.929, Loss:  0.052
+Epoch   4 Batch  347/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.924, Loss:  0.033
+Epoch   4 Batch  348/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.918, Loss:  0.049
+Epoch   4 Batch  349/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   4 Batch  350/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.911, Loss:  0.050
+Epoch   4 Batch  351/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.905, Loss:  0.046
+Epoch   4 Batch  352/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.903, Loss:  0.043
+Epoch   4 Batch  353/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.903, Loss:  0.056
+Epoch   4 Batch  354/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.067
+Epoch   4 Batch  355/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   4 Batch  356/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   4 Batch  357/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.042
+Epoch   4 Batch  358/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.054
+Epoch   4 Batch  359/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.916, Loss:  0.043
+Epoch   4 Batch  360/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.039
+Epoch   4 Batch  361/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   4 Batch  362/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.924, Loss:  0.051
+Epoch   4 Batch  363/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.928, Loss:  0.059
+Epoch   4 Batch  364/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.923, Loss:  0.057
+Epoch   4 Batch  365/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.044
+Epoch   4 Batch  366/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   4 Batch  367/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.915, Loss:  0.040
+Epoch   4 Batch  368/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   4 Batch  369/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.909, Loss:  0.048
+Epoch   4 Batch  370/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.918, Loss:  0.054
+Epoch   4 Batch  371/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch  372/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.919, Loss:  0.041
+Epoch   4 Batch  373/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.919, Loss:  0.036
+Epoch   4 Batch  374/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.059
+Epoch   4 Batch  375/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   4 Batch  376/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   4 Batch  377/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   4 Batch  378/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.931, Loss:  0.041
+Epoch   4 Batch  379/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.933, Loss:  0.062
+Epoch   4 Batch  380/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.933, Loss:  0.041
+Epoch   4 Batch  381/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.057
+Epoch   4 Batch  382/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.068
+Epoch   4 Batch  383/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.931, Loss:  0.046
+Epoch   4 Batch  384/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.041
+Epoch   4 Batch  385/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   4 Batch  386/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.925, Loss:  0.055
+Epoch   4 Batch  387/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.044
+Epoch   4 Batch  388/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.937, Loss:  0.059
+Epoch   4 Batch  389/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.932, Loss:  0.049
+Epoch   4 Batch  390/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.929, Loss:  0.064
+Epoch   4 Batch  391/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.059
+Epoch   4 Batch  392/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.929, Loss:  0.050
+Epoch   4 Batch  393/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.923, Loss:  0.044
+Epoch   4 Batch  394/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.924, Loss:  0.042
+Epoch   4 Batch  395/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.049
+Epoch   4 Batch  396/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   4 Batch  397/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   4 Batch  398/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   4 Batch  399/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   4 Batch  400/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.066
+Epoch   4 Batch  401/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.918, Loss:  0.041
+Epoch   4 Batch  402/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.918, Loss:  0.042
+Epoch   4 Batch  403/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.919, Loss:  0.077
+Epoch   4 Batch  404/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.915, Loss:  0.046
+Epoch   4 Batch  405/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.055
+Epoch   4 Batch  406/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.913, Loss:  0.049
+Epoch   4 Batch  407/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.904, Loss:  0.073
+Epoch   4 Batch  408/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.904, Loss:  0.048
+Epoch   4 Batch  409/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.906, Loss:  0.062
+Epoch   4 Batch  410/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.061
+Epoch   4 Batch  411/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.914, Loss:  0.060
+Epoch   4 Batch  412/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   4 Batch  413/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   4 Batch  414/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   4 Batch  415/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.916, Loss:  0.058
+Epoch   4 Batch  416/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.916, Loss:  0.042
+Epoch   4 Batch  417/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.920, Loss:  0.085
+Epoch   4 Batch  418/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.051
+Epoch   4 Batch  419/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.921, Loss:  0.052
+Epoch   4 Batch  420/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.920, Loss:  0.041
+Epoch   4 Batch  421/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.920, Loss:  0.062
+Epoch   4 Batch  422/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   4 Batch  423/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.070
+Epoch   4 Batch  424/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   4 Batch  425/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.039
+Epoch   4 Batch  426/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.922, Loss:  0.055
+Epoch   4 Batch  427/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch  428/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.919, Loss:  0.036
+Epoch   4 Batch  429/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.917, Loss:  0.043
+Epoch   4 Batch  430/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.912, Loss:  0.043
+Epoch   4 Batch  431/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   4 Batch  432/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.055
+Epoch   4 Batch  433/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   4 Batch  434/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.036
+Epoch   4 Batch  435/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.909, Loss:  0.059
+Epoch   4 Batch  436/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.909, Loss:  0.041
+Epoch   4 Batch  437/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch  438/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch  439/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   4 Batch  440/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.913, Loss:  0.059
+Epoch   4 Batch  441/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   4 Batch  442/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.911, Loss:  0.060
+Epoch   4 Batch  443/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.913, Loss:  0.041
+Epoch   4 Batch  444/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.916, Loss:  0.047
+Epoch   4 Batch  445/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.912, Loss:  0.055
+Epoch   4 Batch  446/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.907, Loss:  0.038
+Epoch   4 Batch  447/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.907, Loss:  0.047
+Epoch   4 Batch  448/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.909, Loss:  0.067
+Epoch   4 Batch  449/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.905, Loss:  0.066
+Epoch   4 Batch  450/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.916, Loss:  0.045
+Epoch   4 Batch  451/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.910, Loss:  0.053
+Epoch   4 Batch  452/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   4 Batch  453/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.907, Loss:  0.049
+Epoch   4 Batch  454/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.057
+Epoch   4 Batch  455/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.910, Loss:  0.064
+Epoch   4 Batch  456/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.916, Loss:  0.061
+Epoch   4 Batch  457/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.912, Loss:  0.050
+Epoch   4 Batch  458/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.910, Loss:  0.068
+Epoch   4 Batch  459/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.912, Loss:  0.054
+Epoch   4 Batch  460/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.907, Loss:  0.060
+Epoch   4 Batch  461/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.908, Loss:  0.048
+Epoch   4 Batch  462/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.906, Loss:  0.053
+Epoch   4 Batch  463/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.058
+Epoch   4 Batch  464/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   4 Batch  465/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.905, Loss:  0.062
+Epoch   4 Batch  466/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   4 Batch  467/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   4 Batch  468/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.062
+Epoch   4 Batch  469/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.051
+Epoch   4 Batch  470/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.913, Loss:  0.054
+Epoch   4 Batch  471/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.912, Loss:  0.037
+Epoch   4 Batch  472/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   4 Batch  473/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.054
+Epoch   4 Batch  474/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.053
+Epoch   4 Batch  475/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.051
+Epoch   4 Batch  476/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.922, Loss:  0.040
+Epoch   4 Batch  477/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   4 Batch  478/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   4 Batch  479/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.920, Loss:  0.071
+Epoch   4 Batch  480/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   4 Batch  481/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.930, Loss:  0.060
+Epoch   4 Batch  482/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.061
+Epoch   4 Batch  483/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   4 Batch  484/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.926, Loss:  0.059
+Epoch   4 Batch  485/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.925, Loss:  0.068
+Epoch   4 Batch  486/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.920, Loss:  0.045
+Epoch   4 Batch  487/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.042
+Epoch   4 Batch  488/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.915, Loss:  0.061
+Epoch   4 Batch  489/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   4 Batch  490/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   4 Batch  491/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.914, Loss:  0.067
+Epoch   4 Batch  492/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.060
+Epoch   4 Batch  493/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   4 Batch  494/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.051
+Epoch   4 Batch  495/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   4 Batch  496/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.911, Loss:  0.071
+Epoch   4 Batch  497/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.904, Loss:  0.053
+Epoch   4 Batch  498/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.921, Loss:  0.060
+Epoch   4 Batch  499/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.923, Loss:  0.055
+Epoch   4 Batch  500/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.927, Loss:  0.054
+Epoch   4 Batch  501/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.053
+Epoch   4 Batch  502/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   4 Batch  503/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.939, Loss:  0.052
+Epoch   4 Batch  504/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.938, Loss:  0.055
+Epoch   4 Batch  505/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.932, Loss:  0.046
+Epoch   4 Batch  506/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.931, Loss:  0.084
+Epoch   4 Batch  507/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.931, Loss:  0.064
+Epoch   4 Batch  508/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.047
+Epoch   4 Batch  509/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.923, Loss:  0.076
+Epoch   4 Batch  510/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.061
+Epoch   4 Batch  511/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.049
+Epoch   4 Batch  512/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.908, Loss:  0.047
+Epoch   4 Batch  513/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   4 Batch  514/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   4 Batch  515/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.915, Loss:  0.055
+Epoch   4 Batch  516/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   4 Batch  517/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.913, Loss:  0.060
+Epoch   4 Batch  518/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   4 Batch  519/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.916, Loss:  0.062
+Epoch   4 Batch  520/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   4 Batch  521/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   4 Batch  522/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.918, Loss:  0.069
+Epoch   4 Batch  523/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.922, Loss:  0.059
+Epoch   4 Batch  524/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.917, Loss:  0.068
+Epoch   4 Batch  525/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.062
+Epoch   4 Batch  526/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.909, Loss:  0.052
+Epoch   4 Batch  527/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   4 Batch  528/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   4 Batch  529/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.914, Loss:  0.058
+Epoch   4 Batch  530/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.070
+Epoch   4 Batch  531/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.910, Loss:  0.055
+Epoch   4 Batch  532/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.910, Loss:  0.072
+Epoch   4 Batch  533/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.898, Loss:  0.056
+Epoch   4 Batch  534/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.898, Loss:  0.059
+Epoch   4 Batch  535/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.061
+Epoch   4 Batch  536/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.911, Loss:  0.057
+Epoch   4 Batch  537/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   4 Batch  538/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.921, Loss:  0.041
+Epoch   4 Batch  539/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.922, Loss:  0.070
+Epoch   4 Batch  540/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   4 Batch  541/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.917, Loss:  0.061
+Epoch   4 Batch  542/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.056
+Epoch   4 Batch  543/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   4 Batch  544/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.051
+Epoch   4 Batch  545/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.908, Loss:  0.056
+Epoch   4 Batch  546/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.904, Loss:  0.056
+Epoch   4 Batch  547/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.911, Loss:  0.041
+Epoch   4 Batch  548/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   4 Batch  549/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.910, Loss:  0.080
+Epoch   4 Batch  550/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.055
+Epoch   4 Batch  551/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.912, Loss:  0.068
+Epoch   4 Batch  552/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   4 Batch  553/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.919, Loss:  0.068
+Epoch   4 Batch  554/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   4 Batch  555/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.922, Loss:  0.043
+Epoch   4 Batch  556/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   4 Batch  557/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   4 Batch  558/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.928, Loss:  0.046
+Epoch   4 Batch  559/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   4 Batch  560/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.055
+Epoch   4 Batch  561/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.925, Loss:  0.047
+Epoch   4 Batch  562/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.055
+Epoch   4 Batch  563/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   4 Batch  564/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.921, Loss:  0.056
+Epoch   4 Batch  565/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.925, Loss:  0.066
+Epoch   4 Batch  566/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   4 Batch  567/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.929, Loss:  0.057
+Epoch   4 Batch  568/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   4 Batch  569/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.926, Loss:  0.071
+Epoch   4 Batch  570/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.926, Loss:  0.057
+Epoch   4 Batch  571/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.053
+Epoch   4 Batch  572/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   4 Batch  573/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.929, Loss:  0.065
+Epoch   4 Batch  574/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   4 Batch  575/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.033
+Epoch   4 Batch  576/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.931, Loss:  0.069
+Epoch   4 Batch  577/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.055
+Epoch   4 Batch  578/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.930, Loss:  0.051
+Epoch   4 Batch  579/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.938, Loss:  0.054
+Epoch   4 Batch  580/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.942, Loss:  0.044
+Epoch   4 Batch  581/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.939, Loss:  0.048
+Epoch   4 Batch  582/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.938, Loss:  0.063
+Epoch   4 Batch  583/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.056
+Epoch   4 Batch  584/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.060
+Epoch   4 Batch  585/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   4 Batch  586/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.056
+Epoch   4 Batch  587/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.925, Loss:  0.066
+Epoch   4 Batch  588/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.927, Loss:  0.053
+Epoch   4 Batch  589/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.934, Loss:  0.059
+Epoch   4 Batch  590/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.927, Loss:  0.072
+Epoch   4 Batch  591/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   4 Batch  592/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.928, Loss:  0.062
+Epoch   4 Batch  593/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.923, Loss:  0.084
+Epoch   4 Batch  594/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.922, Loss:  0.067
+Epoch   4 Batch  595/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   4 Batch  596/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.927, Loss:  0.059
+Epoch   4 Batch  597/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.927, Loss:  0.061
+Epoch   4 Batch  598/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.934, Loss:  0.062
+Epoch   4 Batch  599/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.934, Loss:  0.080
+Epoch   4 Batch  600/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.932, Loss:  0.060
+Epoch   4 Batch  601/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.931, Loss:  0.067
+Epoch   4 Batch  602/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.936, Loss:  0.056
+Epoch   4 Batch  603/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.935, Loss:  0.046
+Epoch   4 Batch  604/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.072
+Epoch   4 Batch  605/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.940, Loss:  0.072
+Epoch   4 Batch  606/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.935, Loss:  0.043
+Epoch   4 Batch  607/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.939, Loss:  0.052
+Epoch   4 Batch  608/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.064
+Epoch   4 Batch  609/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.938, Loss:  0.052
+Epoch   4 Batch  610/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.061
+Epoch   4 Batch  611/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.935, Loss:  0.052
+Epoch   4 Batch  612/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.931, Loss:  0.042
+Epoch   4 Batch  613/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.935, Loss:  0.062
+Epoch   4 Batch  614/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.935, Loss:  0.046
+Epoch   4 Batch  615/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.937, Loss:  0.048
+Epoch   4 Batch  616/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.044
+Epoch   4 Batch  617/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.937, Loss:  0.048
+Epoch   4 Batch  618/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.938, Loss:  0.054
+Epoch   4 Batch  619/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.948, Loss:  0.043
+Epoch   4 Batch  620/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.944, Loss:  0.047
+Epoch   4 Batch  621/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.939, Loss:  0.056
+Epoch   4 Batch  622/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.939, Loss:  0.058
+Epoch   4 Batch  623/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.937, Loss:  0.071
+Epoch   4 Batch  624/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.053
+Epoch   4 Batch  625/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.047
+Epoch   4 Batch  626/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.934, Loss:  0.057
+Epoch   4 Batch  627/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.934, Loss:  0.049
+Epoch   4 Batch  628/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.931, Loss:  0.054
+Epoch   4 Batch  629/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.931, Loss:  0.064
+Epoch   4 Batch  630/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.052
+Epoch   4 Batch  631/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.942, Loss:  0.050
+Epoch   4 Batch  632/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.946, Loss:  0.043
+Epoch   4 Batch  633/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.941, Loss:  0.048
+Epoch   4 Batch  634/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   4 Batch  635/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.941, Loss:  0.059
+Epoch   4 Batch  636/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.938, Loss:  0.042
+Epoch   4 Batch  637/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.934, Loss:  0.051
+Epoch   4 Batch  638/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.934, Loss:  0.056
+Epoch   4 Batch  639/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.935, Loss:  0.075
+Epoch   4 Batch  640/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.928, Loss:  0.053
+Epoch   4 Batch  641/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.927, Loss:  0.042
+Epoch   4 Batch  642/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.057
+Epoch   4 Batch  643/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch  644/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   4 Batch  645/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.066
+Epoch   4 Batch  646/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   4 Batch  647/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.056
+Epoch   4 Batch  648/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.920, Loss:  0.039
+Epoch   4 Batch  649/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   4 Batch  650/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.055
+Epoch   4 Batch  651/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   4 Batch  652/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.919, Loss:  0.058
+Epoch   4 Batch  653/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   4 Batch  654/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   4 Batch  655/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.058
+Epoch   4 Batch  656/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   4 Batch  657/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.924, Loss:  0.052
+Epoch   4 Batch  658/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.916, Loss:  0.037
+Epoch   4 Batch  659/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.047
+Epoch   4 Batch  660/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.914, Loss:  0.044
+Epoch   4 Batch  661/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.914, Loss:  0.046
+Epoch   4 Batch  662/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   4 Batch  663/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.039
+Epoch   4 Batch  664/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   4 Batch  665/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.928, Loss:  0.039
+Epoch   4 Batch  666/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.065
+Epoch   4 Batch  667/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.063
+Epoch   4 Batch  668/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   4 Batch  669/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   4 Batch  670/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   4 Batch  671/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   4 Batch  672/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.045
+Epoch   4 Batch  673/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.926, Loss:  0.049
+Epoch   4 Batch  674/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.929, Loss:  0.039
+Epoch   4 Batch  675/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.926, Loss:  0.060
+Epoch   4 Batch  676/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.921, Loss:  0.042
+Epoch   4 Batch  677/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.924, Loss:  0.061
+Epoch   4 Batch  678/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.925, Loss:  0.035
+Epoch   4 Batch  679/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.049
+Epoch   4 Batch  680/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.926, Loss:  0.058
+Epoch   4 Batch  681/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.929, Loss:  0.054
+Epoch   4 Batch  682/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   4 Batch  683/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   4 Batch  684/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.928, Loss:  0.054
+Epoch   4 Batch  685/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.925, Loss:  0.065
+Epoch   4 Batch  686/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   4 Batch  687/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.058
+Epoch   4 Batch  688/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.923, Loss:  0.047
+Epoch   4 Batch  689/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.035
+Epoch   4 Batch  690/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  691/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.926, Loss:  0.063
+Epoch   4 Batch  692/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.040
+Epoch   4 Batch  693/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.918, Loss:  0.064
+Epoch   4 Batch  694/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.918, Loss:  0.055
+Epoch   4 Batch  695/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   4 Batch  696/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   4 Batch  697/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.050
+Epoch   4 Batch  698/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.910, Loss:  0.046
+Epoch   4 Batch  699/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.910, Loss:  0.042
+Epoch   4 Batch  700/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.911, Loss:  0.039
+Epoch   4 Batch  701/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.063
+Epoch   4 Batch  702/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   4 Batch  703/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   4 Batch  704/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.920, Loss:  0.057
+Epoch   4 Batch  705/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.922, Loss:  0.067
+Epoch   4 Batch  706/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.921, Loss:  0.089
+Epoch   4 Batch  707/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.059
+Epoch   4 Batch  708/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.057
+Epoch   4 Batch  709/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.050
+Epoch   4 Batch  710/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.036
+Epoch   4 Batch  711/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.930, Loss:  0.058
+Epoch   4 Batch  712/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   4 Batch  713/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.049
+Epoch   4 Batch  714/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.048
+Epoch   4 Batch  715/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   4 Batch  716/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.932, Loss:  0.041
+Epoch   4 Batch  717/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.927, Loss:  0.044
+Epoch   4 Batch  718/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.934, Loss:  0.047
+Epoch   4 Batch  719/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.939, Loss:  0.063
+Epoch   4 Batch  720/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.939, Loss:  0.053
+Epoch   4 Batch  721/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.065
+Epoch   4 Batch  722/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.940, Loss:  0.037
+Epoch   4 Batch  723/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.944, Loss:  0.059
+Epoch   4 Batch  724/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.939, Loss:  0.051
+Epoch   4 Batch  725/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.039
+Epoch   4 Batch  726/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   4 Batch  727/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.926, Loss:  0.046
+Epoch   4 Batch  728/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.926, Loss:  0.062
+Epoch   4 Batch  729/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.920, Loss:  0.059
+Epoch   4 Batch  730/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.069
+Epoch   4 Batch  731/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   4 Batch  732/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.912, Loss:  0.062
+Epoch   4 Batch  733/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.916, Loss:  0.070
+Epoch   4 Batch  734/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   4 Batch  735/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.919, Loss:  0.039
+Epoch   4 Batch  736/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.923, Loss:  0.030
+Epoch   4 Batch  737/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   4 Batch  738/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   4 Batch  739/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  740/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   4 Batch  741/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   4 Batch  742/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.916, Loss:  0.031
+Epoch   4 Batch  743/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.917, Loss:  0.052
+Epoch   4 Batch  744/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.046
+Epoch   4 Batch  745/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.054
+Epoch   4 Batch  746/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.047
+Epoch   4 Batch  747/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.936, Loss:  0.047
+Epoch   4 Batch  748/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.940, Loss:  0.047
+Epoch   4 Batch  749/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.938, Loss:  0.052
+Epoch   4 Batch  750/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.934, Loss:  0.032
+Epoch   4 Batch  751/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.929, Loss:  0.049
+Epoch   4 Batch  752/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.048
+Epoch   4 Batch  753/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.044
+Epoch   4 Batch  754/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.931, Loss:  0.059
+Epoch   4 Batch  755/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.932, Loss:  0.063
+Epoch   4 Batch  756/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   4 Batch  757/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.930, Loss:  0.044
+Epoch   4 Batch  758/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.934, Loss:  0.039
+Epoch   4 Batch  759/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.935, Loss:  0.037
+Epoch   4 Batch  760/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.931, Loss:  0.061
+Epoch   4 Batch  761/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   4 Batch  762/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.048
+Epoch   4 Batch  763/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.931, Loss:  0.049
+Epoch   4 Batch  764/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.932, Loss:  0.042
+Epoch   4 Batch  765/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   4 Batch  766/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   4 Batch  767/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.917, Loss:  0.042
+Epoch   4 Batch  768/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.920, Loss:  0.052
+Epoch   4 Batch  769/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.916, Loss:  0.052
+Epoch   4 Batch  770/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.038
+Epoch   4 Batch  771/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.914, Loss:  0.046
+Epoch   4 Batch  772/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.901, Loss:  0.050
+Epoch   4 Batch  773/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   4 Batch  774/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.920, Loss:  0.050
+Epoch   4 Batch  775/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  776/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  777/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  778/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  779/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.943, Loss:  0.054
+Epoch   4 Batch  780/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.937, Loss:  0.070
+Epoch   4 Batch  781/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.933, Loss:  0.044
+Epoch   4 Batch  782/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   4 Batch  783/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.053
+Epoch   4 Batch  784/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.933, Loss:  0.037
+Epoch   4 Batch  785/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.042
+Epoch   4 Batch  786/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.920, Loss:  0.036
+Epoch   4 Batch  787/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   4 Batch  788/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   4 Batch  789/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch  790/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.912, Loss:  0.053
+Epoch   4 Batch  791/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.927, Loss:  0.051
+Epoch   4 Batch  792/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   4 Batch  793/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.931, Loss:  0.040
+Epoch   4 Batch  794/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.937, Loss:  0.044
+Epoch   4 Batch  795/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.931, Loss:  0.051
+Epoch   4 Batch  796/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.931, Loss:  0.043
+Epoch   4 Batch  797/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.047
+Epoch   4 Batch  798/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.936, Loss:  0.053
+Epoch   4 Batch  799/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.930, Loss:  0.064
+Epoch   4 Batch  800/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.049
+Epoch   4 Batch  801/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.052
+Epoch   4 Batch  802/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.931, Loss:  0.052
+Epoch   4 Batch  803/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.942, Loss:  0.045
+Epoch   4 Batch  804/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.929, Loss:  0.038
+Epoch   4 Batch  805/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.936, Loss:  0.047
+Epoch   4 Batch  806/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.934, Loss:  0.045
+Epoch   4 Batch  807/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   4 Batch  808/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.932, Loss:  0.062
+Epoch   4 Batch  809/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.073
+Epoch   4 Batch  810/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.039
+Epoch   4 Batch  811/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.929, Loss:  0.050
+Epoch   4 Batch  812/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.922, Loss:  0.037
+Epoch   4 Batch  813/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   4 Batch  814/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   4 Batch  815/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.048
+Epoch   4 Batch  816/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.926, Loss:  0.059
+Epoch   4 Batch  817/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.049
+Epoch   4 Batch  818/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   4 Batch  819/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   4 Batch  820/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.933, Loss:  0.037
+Epoch   4 Batch  821/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   4 Batch  822/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.929, Loss:  0.052
+Epoch   4 Batch  823/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.927, Loss:  0.057
+Epoch   4 Batch  824/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.927, Loss:  0.056
+Epoch   4 Batch  825/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   4 Batch  826/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.927, Loss:  0.044
+Epoch   4 Batch  827/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   4 Batch  828/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.926, Loss:  0.040
+Epoch   4 Batch  829/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.064
+Epoch   4 Batch  830/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.924, Loss:  0.061
+Epoch   4 Batch  831/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.919, Loss:  0.051
+Epoch   4 Batch  832/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.052
+Epoch   4 Batch  833/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   4 Batch  834/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.049
+Epoch   4 Batch  835/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.934, Loss:  0.049
+Epoch   4 Batch  836/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.040
+Epoch   4 Batch  837/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.933, Loss:  0.063
+Epoch   4 Batch  838/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.930, Loss:  0.046
+Epoch   4 Batch  839/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.938, Loss:  0.033
+Epoch   4 Batch  840/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.944, Loss:  0.037
+Epoch   4 Batch  841/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.938, Loss:  0.058
+Epoch   4 Batch  842/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.940, Loss:  0.043
+Epoch   4 Batch  843/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.042
+Epoch   4 Batch  844/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.037
+Epoch   4 Batch  845/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.933, Loss:  0.031
+Epoch   4 Batch  846/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.933, Loss:  0.067
+Epoch   4 Batch  847/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.931, Loss:  0.053
+Epoch   4 Batch  848/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   4 Batch  849/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.926, Loss:  0.043
+Epoch   4 Batch  850/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.926, Loss:  0.074
+Epoch   4 Batch  851/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.925, Loss:  0.061
+Epoch   4 Batch  852/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.929, Loss:  0.067
+Epoch   4 Batch  853/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.939, Loss:  0.049
+Epoch   4 Batch  854/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.933, Loss:  0.055
+Epoch   4 Batch  855/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.931, Loss:  0.048
+Epoch   4 Batch  856/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.941, Loss:  0.043
+Epoch   4 Batch  857/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.941, Loss:  0.050
+Epoch   4 Batch  858/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   4 Batch  859/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.065
+Epoch   4 Batch  860/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   4 Batch  861/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.934, Loss:  0.044
+Epoch   4 Batch  862/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.943, Loss:  0.053
+Epoch   4 Batch  863/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.945, Loss:  0.046
+Epoch   4 Batch  864/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   4 Batch  865/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.930, Loss:  0.053
+Epoch   4 Batch  866/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.060
+Epoch   4 Batch  867/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.922, Loss:  0.092
+Epoch   4 Batch  868/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.921, Loss:  0.058
+Epoch   4 Batch  869/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.938, Loss:  0.046
+Epoch   4 Batch  870/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.938, Loss:  0.042
+Epoch   4 Batch  871/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.939, Loss:  0.034
+Epoch   4 Batch  872/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.943, Loss:  0.051
+Epoch   4 Batch  873/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.038
+Epoch   4 Batch  874/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.942, Loss:  0.065
+Epoch   4 Batch  875/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.942, Loss:  0.055
+Epoch   4 Batch  876/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   4 Batch  877/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.936, Loss:  0.050
+Epoch   4 Batch  878/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.938, Loss:  0.043
+Epoch   4 Batch  879/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.943, Loss:  0.039
+Epoch   4 Batch  880/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.051
+Epoch   4 Batch  881/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.941, Loss:  0.056
+Epoch   4 Batch  882/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.933, Loss:  0.041
+Epoch   4 Batch  883/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.933, Loss:  0.065
+Epoch   4 Batch  884/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.933, Loss:  0.048
+Epoch   4 Batch  885/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.035
+Epoch   4 Batch  886/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.918, Loss:  0.046
+Epoch   4 Batch  887/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.910, Loss:  0.069
+Epoch   4 Batch  888/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.910, Loss:  0.040
+Epoch   4 Batch  889/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   4 Batch  890/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.050
+Epoch   4 Batch  891/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   4 Batch  892/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.909, Loss:  0.042
+Epoch   4 Batch  893/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.910, Loss:  0.048
+Epoch   4 Batch  894/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.908, Loss:  0.039
+Epoch   4 Batch  895/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.044
+Epoch   4 Batch  896/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   4 Batch  897/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   4 Batch  898/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.049
+Epoch   4 Batch  899/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.934, Loss:  0.063
+Epoch   4 Batch  900/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.934, Loss:  0.055
+Epoch   4 Batch  901/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.935, Loss:  0.068
+Epoch   4 Batch  902/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.945, Loss:  0.052
+Epoch   4 Batch  903/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.942, Loss:  0.046
+Epoch   4 Batch  904/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.942, Loss:  0.057
+Epoch   4 Batch  905/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.942, Loss:  0.034
+Epoch   4 Batch  906/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.942, Loss:  0.049
+Epoch   4 Batch  907/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.942, Loss:  0.045
+Epoch   4 Batch  908/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.940, Loss:  0.051
+Epoch   4 Batch  909/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.934, Loss:  0.057
+Epoch   4 Batch  910/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.934, Loss:  0.050
+Epoch   4 Batch  911/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.938, Loss:  0.049
+Epoch   4 Batch  912/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.938, Loss:  0.042
+Epoch   4 Batch  913/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.938, Loss:  0.072
+Epoch   4 Batch  914/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.083
+Epoch   4 Batch  915/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.038
+Epoch   4 Batch  916/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   4 Batch  917/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.040
+Epoch   4 Batch  918/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.919, Loss:  0.039
+Epoch   4 Batch  919/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.907, Loss:  0.036
+Epoch   4 Batch  920/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.912, Loss:  0.037
+Epoch   4 Batch  921/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   4 Batch  922/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   4 Batch  923/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.924, Loss:  0.040
+Epoch   4 Batch  924/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.930, Loss:  0.060
+Epoch   4 Batch  925/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   4 Batch  926/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.936, Loss:  0.048
+Epoch   4 Batch  927/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.930, Loss:  0.055
+Epoch   4 Batch  928/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.930, Loss:  0.055
+Epoch   4 Batch  929/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.046
+Epoch   4 Batch  930/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.937, Loss:  0.037
+Epoch   4 Batch  931/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.940, Loss:  0.039
+Epoch   4 Batch  932/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.940, Loss:  0.040
+Epoch   4 Batch  933/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.936, Loss:  0.045
+Epoch   4 Batch  934/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.042
+Epoch   4 Batch  935/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.047
+Epoch   4 Batch  936/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.051
+Epoch   4 Batch  937/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.058
+Epoch   4 Batch  938/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.061
+Epoch   4 Batch  939/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   4 Batch  940/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.043
+Epoch   4 Batch  941/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.038
+Epoch   4 Batch  942/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   4 Batch  943/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.927, Loss:  0.044
+Epoch   4 Batch  944/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   4 Batch  945/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.942, Loss:  0.044
+Epoch   4 Batch  946/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.936, Loss:  0.030
+Epoch   4 Batch  947/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.931, Loss:  0.056
+Epoch   4 Batch  948/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.941, Loss:  0.040
+Epoch   4 Batch  949/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.941, Loss:  0.040
+Epoch   4 Batch  950/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.941, Loss:  0.045
+Epoch   4 Batch  951/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.945, Loss:  0.060
+Epoch   4 Batch  952/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.946, Loss:  0.042
+Epoch   4 Batch  953/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.946, Loss:  0.042
+Epoch   4 Batch  954/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.946, Loss:  0.053
+Epoch   4 Batch  955/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.956, Loss:  0.056
+Epoch   4 Batch  956/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.961, Loss:  0.058
+Epoch   4 Batch  957/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.942, Loss:  0.031
+Epoch   4 Batch  958/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.939, Loss:  0.050
+Epoch   4 Batch  959/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   4 Batch  960/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.048
+Epoch   4 Batch  961/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   4 Batch  962/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.928, Loss:  0.041
+Epoch   4 Batch  963/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.066
+Epoch   4 Batch  964/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.924, Loss:  0.043
+Epoch   4 Batch  965/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.921, Loss:  0.053
+Epoch   4 Batch  966/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.934, Loss:  0.042
+Epoch   4 Batch  967/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.934, Loss:  0.053
+Epoch   4 Batch  968/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.930, Loss:  0.061
+Epoch   4 Batch  969/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.064
+Epoch   4 Batch  970/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.936, Loss:  0.050
+Epoch   4 Batch  971/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.931, Loss:  0.044
+Epoch   4 Batch  972/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.925, Loss:  0.050
+Epoch   4 Batch  973/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.929, Loss:  0.037
+Epoch   4 Batch  974/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.933, Loss:  0.035
+Epoch   4 Batch  975/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.932, Loss:  0.050
+Epoch   4 Batch  976/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.931, Loss:  0.038
+Epoch   4 Batch  977/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.934, Loss:  0.032
+Epoch   4 Batch  978/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.938, Loss:  0.048
+Epoch   4 Batch  979/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.934, Loss:  0.042
+Epoch   4 Batch  980/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.935, Loss:  0.046
+Epoch   4 Batch  981/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.050
+Epoch   4 Batch  982/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.930, Loss:  0.041
+Epoch   4 Batch  983/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.939, Loss:  0.047
+Epoch   4 Batch  984/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.935, Loss:  0.050
+Epoch   4 Batch  985/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.935, Loss:  0.044
+Epoch   4 Batch  986/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   4 Batch  987/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.936, Loss:  0.033
+Epoch   4 Batch  988/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.940, Loss:  0.068
+Epoch   4 Batch  989/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.938, Loss:  0.066
+Epoch   4 Batch  990/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.938, Loss:  0.053
+Epoch   4 Batch  991/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.925, Loss:  0.050
+Epoch   4 Batch  992/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   4 Batch  993/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.925, Loss:  0.037
+Epoch   4 Batch  994/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.044
+Epoch   4 Batch  995/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.921, Loss:  0.051
+Epoch   4 Batch  996/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   4 Batch  997/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   4 Batch  998/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.913, Loss:  0.049
+Epoch   4 Batch  999/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.049
+Epoch   4 Batch 1000/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.908, Loss:  0.041
+Epoch   4 Batch 1001/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.907, Loss:  0.041
+Epoch   4 Batch 1002/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.907, Loss:  0.035
+Epoch   4 Batch 1003/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.911, Loss:  0.050
+Epoch   4 Batch 1004/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.917, Loss:  0.051
+Epoch   4 Batch 1005/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.044
+Epoch   4 Batch 1006/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.916, Loss:  0.056
+Epoch   4 Batch 1007/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.916, Loss:  0.039
+Epoch   4 Batch 1008/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.904, Loss:  0.059
+Epoch   4 Batch 1009/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.911, Loss:  0.040
+Epoch   4 Batch 1010/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.906, Loss:  0.054
+Epoch   4 Batch 1011/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.906, Loss:  0.038
+Epoch   4 Batch 1012/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   4 Batch 1013/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.912, Loss:  0.039
+Epoch   4 Batch 1014/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.914, Loss:  0.062
+Epoch   4 Batch 1015/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.918, Loss:  0.053
+Epoch   4 Batch 1016/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.049
+Epoch   4 Batch 1017/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   4 Batch 1018/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.049
+Epoch   4 Batch 1019/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.915, Loss:  0.073
+Epoch   4 Batch 1020/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch 1021/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   4 Batch 1022/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   4 Batch 1023/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.922, Loss:  0.057
+Epoch   4 Batch 1024/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.921, Loss:  0.059
+Epoch   4 Batch 1025/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   4 Batch 1026/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.920, Loss:  0.055
+Epoch   4 Batch 1027/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.920, Loss:  0.046
+Epoch   4 Batch 1028/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   4 Batch 1029/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.040
+Epoch   4 Batch 1030/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   4 Batch 1031/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.054
+Epoch   4 Batch 1032/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.061
+Epoch   4 Batch 1033/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.934, Loss:  0.060
+Epoch   4 Batch 1034/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   4 Batch 1035/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.926, Loss:  0.032
+Epoch   4 Batch 1036/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.060
+Epoch   4 Batch 1037/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.927, Loss:  0.053
+Epoch   4 Batch 1038/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   4 Batch 1039/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   4 Batch 1040/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.927, Loss:  0.068
+Epoch   4 Batch 1041/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.925, Loss:  0.047
+Epoch   4 Batch 1042/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.041
+Epoch   4 Batch 1043/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.937, Loss:  0.052
+Epoch   4 Batch 1044/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   4 Batch 1045/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.925, Loss:  0.047
+Epoch   4 Batch 1046/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.929, Loss:  0.040
+Epoch   4 Batch 1047/1077 - Train Accuracy:  0.974, Validation Accuracy:  0.919, Loss:  0.036
+Epoch   4 Batch 1048/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.915, Loss:  0.040
+Epoch   4 Batch 1049/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.913, Loss:  0.040
+Epoch   4 Batch 1050/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.909, Loss:  0.036
+Epoch   4 Batch 1051/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   4 Batch 1052/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   4 Batch 1053/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   4 Batch 1054/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.045
+Epoch   4 Batch 1055/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.928, Loss:  0.044
+Epoch   4 Batch 1056/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   4 Batch 1057/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.058
+Epoch   4 Batch 1058/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   4 Batch 1059/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.939, Loss:  0.055
+Epoch   4 Batch 1060/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.935, Loss:  0.040
+Epoch   4 Batch 1061/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.056
+Epoch   4 Batch 1062/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.926, Loss:  0.054
+Epoch   4 Batch 1063/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.931, Loss:  0.068
+Epoch   4 Batch 1064/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.940, Loss:  0.050
+Epoch   4 Batch 1065/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.050
+Epoch   4 Batch 1066/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.032
+Epoch   4 Batch 1067/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.927, Loss:  0.059
+Epoch   4 Batch 1068/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.927, Loss:  0.041
+Epoch   4 Batch 1069/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.923, Loss:  0.038
+Epoch   4 Batch 1070/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.041
+Epoch   4 Batch 1071/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.042
+Epoch   4 Batch 1072/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   4 Batch 1073/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   4 Batch 1074/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   4 Batch 1075/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.062
+Model Trained and Saved
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#20445;&#23384;&#21442;&#25968;">&#20445;&#23384;&#21442;&#25968;<a class="anchor-link" href="#&#20445;&#23384;&#21442;&#25968;">&#182;</a></h3><p>保存 <code>batch_size</code> 和 <code>save_path</code> 参数以进行推论（for inference）。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[17]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="c1"># Save parameters for checkpoint</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">save_params</span><span class="p">(</span><span class="n">save_path</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#26816;&#26597;&#28857;">&#26816;&#26597;&#28857;<a class="anchor-link" href="#&#26816;&#26597;&#28857;">&#182;</a></h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[18]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">source_vocab_to_int</span><span class="p">,</span> <span class="n">target_vocab_to_int</span><span class="p">),</span> <span class="p">(</span><span class="n">source_int_to_vocab</span><span class="p">,</span> <span class="n">target_int_to_vocab</span><span class="p">)</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess</span><span class="p">()</span>
+<span class="n">load_path</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_params</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#21477;&#23376;&#21040;&#24207;&#21015;">&#21477;&#23376;&#21040;&#24207;&#21015;<a class="anchor-link" href="#&#21477;&#23376;&#21040;&#24207;&#21015;">&#182;</a></h2><p>要向模型提供要翻译的句子，你首先需要预处理该句子。实现函数 <code>sentence_to_seq()</code> 以预处理新的句子。</p>
+<ul>
+<li>将句子转换为小写形式</li>
+<li>使用 <code>vocab_to_int</code> 将单词转换为 id<ul>
+<li>如果单词不在词汇表中，将其转换为<code>&lt;UNK&gt;</code> 单词 id</li>
+</ul>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[19]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">sentence_to_seq</span><span class="p">(</span><span class="n">sentence</span><span class="p">,</span> <span class="n">vocab_to_int</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Convert a sentence to a sequence of ids</span>
+<span class="sd">    :param sentence: String</span>
+<span class="sd">    :param vocab_to_int: Dictionary to go from the words to an id</span>
+<span class="sd">    :return: List of word ids</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">word_ids</span> <span class="o">=</span> <span class="p">[</span><span class="n">vocab_to_int</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">vocab_to_int</span><span class="p">[</span><span class="s2">&quot;&lt;UNK&gt;&quot;</span><span class="p">])</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">sentence</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span>
+    
+    <span class="k">return</span> <span class="n">word_ids</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_sentence_to_seq</span><span class="p">(</span><span class="n">sentence_to_seq</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#32763;&#35793;">&#32763;&#35793;<a class="anchor-link" href="#&#32763;&#35793;">&#182;</a></h2><p>将 <code>translate_sentence</code> 从英语翻译成法语。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[20]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">translate_sentence</span> <span class="o">=</span> <span class="s1">&#39;he saw a old yellow truck .&#39;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">translate_sentence</span> <span class="o">=</span> <span class="n">sentence_to_seq</span><span class="p">(</span><span class="n">translate_sentence</span><span class="p">,</span> <span class="n">source_vocab_to_int</span><span class="p">)</span>
+
+<span class="n">loaded_graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="n">loaded_graph</span><span class="p">)</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+    <span class="c1"># Load saved model</span>
+    <span class="n">loader</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">import_meta_graph</span><span class="p">(</span><span class="n">load_path</span> <span class="o">+</span> <span class="s1">&#39;.meta&#39;</span><span class="p">)</span>
+    <span class="n">loader</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">load_path</span><span class="p">)</span>
+
+    <span class="n">input_data</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;input:0&#39;</span><span class="p">)</span>
+    <span class="n">logits</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;logits:0&#39;</span><span class="p">)</span>
+    <span class="n">keep_prob</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;keep_prob:0&#39;</span><span class="p">)</span>
+
+    <span class="n">translate_logits</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="p">{</span><span class="n">input_data</span><span class="p">:</span> <span class="p">[</span><span class="n">translate_sentence</span><span class="p">],</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})[</span><span class="mi">0</span><span class="p">]</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Input&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  Word Ids:      </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">translate_sentence</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  English Words: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">([</span><span class="n">source_int_to_vocab</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">translate_sentence</span><span class="p">]))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Prediction&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  Word Ids:      </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">translate_logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  French Words: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">([</span><span class="n">target_int_to_vocab</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">translate_logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Input
+  Word Ids:      [226, 218, 43, 30, 138, 202, 171]
+  English Words: [&#39;he&#39;, &#39;saw&#39;, &#39;a&#39;, &#39;old&#39;, &#39;yellow&#39;, &#39;truck&#39;, &#39;.&#39;]
+
+Prediction
+  Word Ids:      [286, 17, 192, 281, 138, 94, 89, 60, 1]
+  French Words: [&#39;il&#39;, &#39;a&#39;, &#39;vu&#39;, &#39;un&#39;, &#39;petit&#39;, &#39;camion&#39;, &#39;jaune&#39;, &#39;.&#39;, &#39;&lt;EOS&gt;&#39;]
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#19981;&#23436;&#32654;&#30340;&#32763;&#35793;">&#19981;&#23436;&#32654;&#30340;&#32763;&#35793;<a class="anchor-link" href="#&#19981;&#23436;&#32654;&#30340;&#32763;&#35793;">&#182;</a></h2><p>你可能注意到了，某些句子的翻译质量比其他的要好。因为你使用的数据集只有 227 个英语单词，但实际生活中有数千个单词，只有使用这些单词的句子结果才会比较理想。对于此项目，不需要达到完美的翻译。但是，如果你想创建更好的翻译模型，则需要更好的数据。</p>
+<p>你可以使用 <a href="http://www.statmt.org/wmt10/training-giga-fren.tar">WMT10 French-English corpus</a> 语料库训练模型。该数据集拥有更多的词汇，讨论的话题也更丰富。但是，训练时间要好多天的时间，所以确保你有 GPU 并且对于我们提供的数据集，你的神经网络性能很棒。提交此项目后，别忘了研究下 WMT10 语料库。</p>
+<h2 id="&#25552;&#20132;&#39033;&#30446;">&#25552;&#20132;&#39033;&#30446;<a class="anchor-link" href="#&#25552;&#20132;&#39033;&#30446;">&#182;</a></h2><p>提交项目时，确保先运行所有单元，然后再保存记事本。保存记事本文件为 “dlnd_language_translation.ipynb”，再通过菜单中的“文件” -&gt;“下载为”将其另存为 HTML 格式。提交的项目文档中需包含“helper.py”和“problem_unittests.py”文件。</p>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+
+ 
+
+
+</html>
diff --git a/language-translation/dlnd_language_translation.ipynb b/language-translation/dlnd_language_translation.ipynb
new file mode 100644
index 0000000..c10e6e3
--- /dev/null
+++ b/language-translation/dlnd_language_translation.ipynb
@@ -0,0 +1,6781 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# 语言翻译\n",
+    "\n",
+    "在此项目中，你将了解神经网络机器翻译这一领域。你将用由英语和法语语句组成的数据集，训练一个序列到序列模型（sequence to sequence model），该模型能够将新的英语句子翻译成法语。\n",
+    "\n",
+    "## 获取数据\n",
+    "\n",
+    "因为将整个英语语言内容翻译成法语需要大量训练时间，所以我们提供了一小部分的英语语料库。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import helper\n",
+    "import problem_unittests as tests\n",
+    "\n",
+    "source_path = 'data/small_vocab_en'\n",
+    "target_path = 'data/small_vocab_fr'\n",
+    "source_text = helper.load_data(source_path)\n",
+    "target_text = helper.load_data(target_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 探索数据\n",
+    "\n",
+    "研究 view_sentence_range，查看并熟悉该数据的不同部分。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset Stats\n",
+      "Roughly the number of unique words: 227\n",
+      "Number of sentences: 137861\n",
+      "Average number of words in a sentence: 13.225277634719028\n",
+      "\n",
+      "English sentences 0 to 10:\n",
+      "new jersey is sometimes quiet during autumn , and it is snowy in april .\n",
+      "the united states is usually chilly during july , and it is usually freezing in november .\n",
+      "california is usually quiet during march , and it is usually hot in june .\n",
+      "the united states is sometimes mild during june , and it is cold in september .\n",
+      "your least liked fruit is the grape , but my least liked is the apple .\n",
+      "his favorite fruit is the orange , but my favorite is the grape .\n",
+      "paris is relaxing during december , but it is usually chilly in july .\n",
+      "new jersey is busy during spring , and it is never hot in march .\n",
+      "our least liked fruit is the lemon , but my least liked is the grape .\n",
+      "the united states is sometimes busy during january , and it is sometimes warm in november .\n",
+      "\n",
+      "French sentences 0 to 10:\n",
+      "new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\n",
+      "les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\n",
+      "california est généralement calme en mars , et il est généralement chaud en juin .\n",
+      "les états-unis est parfois légère en juin , et il fait froid en septembre .\n",
+      "votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\n",
+      "son fruit préféré est l'orange , mais mon préféré est le raisin .\n",
+      "paris est relaxant en décembre , mais il est généralement froid en juillet .\n",
+      "new jersey est occupé au printemps , et il est jamais chaude en mars .\n",
+      "notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\n",
+      "les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\n"
+     ]
+    }
+   ],
+   "source": [
+    "view_sentence_range = (0, 10)\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import numpy as np\n",
+    "\n",
+    "print('Dataset Stats')\n",
+    "print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\n",
+    "\n",
+    "sentences = source_text.split('\\n')\n",
+    "word_counts = [len(sentence.split()) for sentence in sentences]\n",
+    "print('Number of sentences: {}'.format(len(sentences)))\n",
+    "print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\n",
+    "\n",
+    "print()\n",
+    "print('English sentences {} to {}:'.format(*view_sentence_range))\n",
+    "print('\\n'.join(source_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))\n",
+    "print()\n",
+    "print('French sentences {} to {}:'.format(*view_sentence_range))\n",
+    "print('\\n'.join(target_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 实现预处理函数\n",
+    "\n",
+    "### 文本到单词 id\n",
+    "\n",
+    "和之前的 RNN 一样，你必须首先将文本转换为数字，这样计算机才能读懂。在函数 `text_to_ids()` 中，你需要将单词中的 `source_text` 和 `target_text` 转为 id。但是，你需要在 `target_text` 中每个句子的末尾，添加 `<EOS>` 单词 id。这样可以帮助神经网络预测句子应该在什么地方结束。\n",
+    "\n",
+    "\n",
+    "你可以通过以下代码获取  `<EOS> ` 单词ID：\n",
+    "\n",
+    "```python\n",
+    "target_vocab_to_int['<EOS>']\n",
+    "```\n",
+    "\n",
+    "你可以使用 `source_vocab_to_int` 和 `target_vocab_to_int` 获得其他单词 id。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n",
+    "    \"\"\"\n",
+    "    Convert source and target text to proper word ids\n",
+    "    :param source_text: String that contains all the source text.\n",
+    "    :param target_text: String that contains all the target text.\n",
+    "    :param source_vocab_to_int: Dictionary to go from the source words to an id\n",
+    "    :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
+    "    :return: A tuple of lists (source_id_text, target_id_text)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    source_letter_ids = [[source_vocab_to_int.get(letter, source_vocab_to_int['<UNK>']) for letter in line.split()] for line in source_text.split('\\n')]\n",
+    "    target_letter_ids = [[target_vocab_to_int.get(letter, target_vocab_to_int['<UNK>']) for letter in line.split()] + [target_vocab_to_int['<EOS>']] for line in target_text.split('\\n')] \n",
+    "    \n",
+    "    return source_letter_ids, target_letter_ids\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_text_to_ids(text_to_ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 预处理所有数据并保存\n",
+    "\n",
+    "运行以下代码单元，预处理所有数据，并保存到文件中。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "helper.preprocess_and_save_data(source_path, target_path, text_to_ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 检查点\n",
+    "\n",
+    "这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，可以从这里继续。预处理的数据已保存到磁盘上。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import numpy as np\n",
+    "import helper\n",
+    "\n",
+    "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 检查 TensorFlow 版本，确认可访问 GPU\n",
+    "\n",
+    "这一检查步骤，可以确保你使用的是正确版本的 TensorFlow，并且能够访问 GPU。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TensorFlow Version: 1.0.1\n",
+      "Default GPU Device: /gpu:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "from distutils.version import LooseVersion\n",
+    "import warnings\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Check TensorFlow Version\n",
+    "assert LooseVersion(tf.__version__) in [LooseVersion('1.0.0'), LooseVersion('1.0.1')], 'This project requires TensorFlow version 1.0  You are using {}'.format(tf.__version__)\n",
+    "print('TensorFlow Version: {}'.format(tf.__version__))\n",
+    "\n",
+    "# Check for a GPU\n",
+    "if not tf.test.gpu_device_name():\n",
+    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
+    "else:\n",
+    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 构建神经网络\n",
+    "\n",
+    "你将通过实现以下函数，构建出要构建一个序列到序列模型所需的组件：\n",
+    "\n",
+    "- `model_inputs`\n",
+    "- `process_decoding_input`\n",
+    "- `encoding_layer`\n",
+    "- `decoding_layer_train`\n",
+    "- `decoding_layer_infer`\n",
+    "- `decoding_layer`\n",
+    "- `seq2seq_model`\n",
+    "\n",
+    "### 输入\n",
+    "\n",
+    "实现 `model_inputs()` 函数，为神经网络创建 TF 占位符。该函数应该创建以下占位符：\n",
+    "\n",
+    "- 名为 “input” 的输入文本占位符，并使用 TF Placeholder 名称参数（等级（Rank）为 2）。\n",
+    "- 目标占位符（等级为 2）。\n",
+    "- 学习速率占位符（等级为 0）。\n",
+    "- 名为 “keep_prob” 的保留率占位符，并使用 TF Placeholder 名称参数（等级为 0）。\n",
+    "\n",
+    "在以下元祖（tuple）中返回占位符：（输入、目标、学习速率、保留率）\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "def model_inputs():\n",
+    "    \"\"\"\n",
+    "    Create TF Placeholders for input, targets, and learning rate.\n",
+    "    :return: Tuple (input, targets, learning rate, keep probability)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    inputs = tf.placeholder(tf.int32, [None, None], name='input')\n",
+    "    targets = tf.placeholder(tf.int32, [None, None], name='targets')\n",
+    "    learning_rate = tf.placeholder(tf.float32, name='learning_rate')\n",
+    "    keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n",
+    "    \n",
+    "    return inputs, targets, learning_rate, keep_prob\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_model_inputs(model_inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 处理解码输入\n",
+    "\n",
+    "使用 TensorFlow 实现 `process_decoding_input`，以便删掉 `target_data` 中每个批次的最后一个单词 ID，并将 GO ID 放到每个批次的开头。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def process_decoding_input(target_data, target_vocab_to_int, batch_size):\n",
+    "    \"\"\"\n",
+    "    Preprocess target data for dencoding\n",
+    "    :param target_data: Target Placehoder\n",
+    "    :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
+    "    :param batch_size: Batch Size\n",
+    "    :return: Preprocessed target data\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\n",
+    "    dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), ending], 1)\n",
+    "    \n",
+    "    return dec_input\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_process_decoding_input(process_decoding_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 编码\n",
+    "\n",
+    "实现 `encoding_layer()`，以使用 [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn) 创建编码器 RNN 层级。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob):\n",
+    "    \"\"\"\n",
+    "    Create encoding layer\n",
+    "    :param rnn_inputs: Inputs for the RNN\n",
+    "    :param rnn_size: RNN Size\n",
+    "    :param num_layers: Number of layers\n",
+    "    :param keep_prob: Dropout keep probability\n",
+    "    :return: RNN state\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n",
+    "    drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n",
+    "    enc_cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)\n",
+    "    _, enc_state = tf.nn.dynamic_rnn(enc_cell, rnn_inputs, dtype=tf.float32)\n",
+    "    \n",
+    "    return enc_state\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_encoding_layer(encoding_layer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 解码 - 训练\n",
+    "\n",
+    "使用 [`tf.contrib.seq2seq.simple_decoder_fn_train()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_train) 和 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 创建训练分对数（training logits）。将 `output_fn` 应用到 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 输出上。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,\n",
+    "                         output_fn, keep_prob):\n",
+    "    \"\"\"\n",
+    "    Create a decoding layer for training\n",
+    "    :param encoder_state: Encoder State\n",
+    "    :param dec_cell: Decoder RNN Cell\n",
+    "    :param dec_embed_input: Decoder embedded input\n",
+    "    :param sequence_length: Sequence Length\n",
+    "    :param decoding_scope: TenorFlow Variable Scope for decoding\n",
+    "    :param output_fn: Function to apply the output layer\n",
+    "    :param keep_prob: Dropout keep probability\n",
+    "    :return: Train Logits\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    # Training Decoder\n",
+    "    train_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_train(encoder_state)\n",
+    "    train_pred, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(\n",
+    "    dec_cell, train_decoder_fn, dec_embed_input, sequence_length, scope=decoding_scope)\n",
+    "    \n",
+    "    # Apply output function\n",
+    "    train_logits =  output_fn(train_pred)\n",
+    "\n",
+    "    return train_logits\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_decoding_layer_train(decoding_layer_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 解码 - 推论\n",
+    "\n",
+    "使用 [`tf.contrib.seq2seq.simple_decoder_fn_inference()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_inference) 和 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 创建推论分对数（inference logits）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id,\n",
+    "                         maximum_length, vocab_size, decoding_scope, output_fn, keep_prob):\n",
+    "    \"\"\"\n",
+    "    Create a decoding layer for inference\n",
+    "    :param encoder_state: Encoder state\n",
+    "    :param dec_cell: Decoder RNN Cell\n",
+    "    :param dec_embeddings: Decoder embeddings\n",
+    "    :param start_of_sequence_id: GO ID\n",
+    "    :param end_of_sequence_id: EOS Id\n",
+    "    :param maximum_length: The maximum allowed time steps to decode\n",
+    "    :param vocab_size: Size of vocabulary\n",
+    "    :param decoding_scope: TensorFlow Variable Scope for decoding\n",
+    "    :param output_fn: Function to apply the output layer\n",
+    "    :param keep_prob: Dropout keep probability\n",
+    "    :return: Inference Logits\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    # Inference Decoder\n",
+    "    infer_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_inference(\n",
+    "        output_fn, encoder_state, dec_embeddings, start_of_sequence_id, end_of_sequence_id, \n",
+    "        maximum_length - 1, vocab_size)\n",
+    "    inference_logits, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(dec_cell, infer_decoder_fn, scope=decoding_scope)\n",
+    "\n",
+    "    return inference_logits\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_decoding_layer_infer(decoding_layer_infer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 构建解码层级\n",
+    "\n",
+    "实现 `decoding_layer()` 以创建解码器 RNN 层级。\n",
+    "\n",
+    "- 使用 `rnn_size` 和 `num_layers` 创建解码 RNN 单元。\n",
+    "- 使用 [`lambda`](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions) 创建输出函数，将输入，也就是分对数转换为类分对数（class logits）。\n",
+    "- 使用 `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob)` 函数获取训练分对数。\n",
+    "- 使用 `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, decoding_scope, output_fn, keep_prob)` 函数获取推论分对数。\n",
+    "\n",
+    "注意：你将需要使用 [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 在训练和推论分对数间分享变量。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size,\n",
+    "                   num_layers, target_vocab_to_int, keep_prob):\n",
+    "    \"\"\"\n",
+    "    Create decoding layer\n",
+    "    :param dec_embed_input: Decoder embedded input\n",
+    "    :param dec_embeddings: Decoder embeddings\n",
+    "    :param encoder_state: The encoded state\n",
+    "    :param vocab_size: Size of vocabulary\n",
+    "    :param sequence_length: Sequence Length\n",
+    "    :param rnn_size: RNN Size\n",
+    "    :param num_layers: Number of layers\n",
+    "    :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
+    "    :param keep_prob: Dropout keep probability\n",
+    "    :return: Tuple of (Training Logits, Inference Logits)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n",
+    "    dropout = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n",
+    "    dec_cell = tf.contrib.rnn.MultiRNNCell([dropout] * num_layers)\n",
+    "    \n",
+    "    output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, activation_fn=None, scope=decoding_scope)           \n",
+    "\n",
+    "    with tf.variable_scope(\"decoding\") as decoding_scope:\n",
+    "        training_decoder_output = decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,\n",
+    "                         output_fn, keep_prob)\n",
+    "\n",
+    "    with tf.variable_scope(\"decoding\", reuse=True) as decoding_scope:\n",
+    "        start_of_sequence_id = target_vocab_to_int[\"<GO>\"]\n",
+    "        end_of_sequence_id = target_vocab_to_int[\"<EOS>\"]\n",
+    "        inference_decoder_output = decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, \n",
+    "                         sequence_length, vocab_size, decoding_scope, output_fn, keep_prob)\n",
+    "    \n",
+    "    return training_decoder_output, inference_decoder_output\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_decoding_layer(decoding_layer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 构建神经网络\n",
+    "\n",
+    "应用你在上方实现的函数，以：\n",
+    "\n",
+    "- 向编码器的输入数据应用嵌入。\n",
+    "- 使用 `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)` 编码输入。\n",
+    "- 使用 `process_decoding_input(target_data, target_vocab_to_int, batch_size)` 函数处理目标数据。\n",
+    "- 向解码器的目标数据应用嵌入。\n",
+    "- 使用 `decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)` 解码编码的输入数据。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def seq2seq_model(input_data, target_data, keep_prob, batch_size, sequence_length, source_vocab_size, target_vocab_size,\n",
+    "                  enc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int):\n",
+    "    \"\"\"\n",
+    "    Build the Sequence-to-Sequence part of the neural network\n",
+    "    :param input_data: Input placeholder\n",
+    "    :param target_data: Target placeholder\n",
+    "    :param keep_prob: Dropout keep probability placeholder\n",
+    "    :param batch_size: Batch Size\n",
+    "    :param sequence_length: Sequence Length\n",
+    "    :param source_vocab_size: Source vocabulary size\n",
+    "    :param target_vocab_size: Target vocabulary size\n",
+    "    :param enc_embedding_size: Decoder embedding size\n",
+    "    :param dec_embedding_size: Encoder embedding size\n",
+    "    :param rnn_size: RNN Size\n",
+    "    :param num_layers: Number of layers\n",
+    "    :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
+    "    :return: Tuple of (Training Logits, Inference Logits)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    rnn_inputs = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, enc_embedding_size)\n",
+    "    \n",
+    "    encoder_state = encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)\n",
+    "    \n",
+    "    dec_input = process_decoding_input(target_data, target_vocab_to_int, batch_size)\n",
+    "    dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, dec_embedding_size]))\n",
+    "    dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\n",
+    "    \n",
+    "    train_logits, inference_logits = decoding_layer(dec_embed_input, dec_embeddings, encoder_state, target_vocab_size, sequence_length,\n",
+    "                       rnn_size, num_layers, target_vocab_to_int, keep_prob)\n",
+    "    \n",
+    "    return train_logits, inference_logits\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_seq2seq_model(seq2seq_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 训练神经网络\n",
+    "\n",
+    "### 超参数\n",
+    "\n",
+    "调试以下参数：\n",
+    "\n",
+    "- 将 `epochs` 设为 epoch 次数。\n",
+    "- 将 `batch_size` 设为批次大小。\n",
+    "- 将 `rnn_size` 设为 RNN 的大小。\n",
+    "- 将 `num_layers` 设为层级数量。\n",
+    "- 将 `encoding_embedding_size` 设为编码器嵌入大小。\n",
+    "- 将 `decoding_embedding_size` 设为解码器嵌入大小\n",
+    "- 将 `learning_rate` 设为训练速率。\n",
+    "- 将 `keep_probability` 设为丢弃保留率（Dropout keep probability）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Number of Epochs\n",
+    "epochs = 5\n",
+    "# Batch Size\n",
+    "batch_size = 128\n",
+    "# RNN Size\n",
+    "rnn_size = 128\n",
+    "# Number of Layers\n",
+    "num_layers = 2\n",
+    "# Embedding Size\n",
+    "encoding_embedding_size = 100\n",
+    "decoding_embedding_size = 100\n",
+    "# Learning Rate\n",
+    "learning_rate = 0.01\n",
+    "# Dropout Keep Probability\n",
+    "keep_probability = 0.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 构建图表\n",
+    "\n",
+    "使用你实现的神经网络构建图表。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "save_path = 'checkpoints/dev'\n",
+    "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\n",
+    "max_source_sentence_length = max([len(sentence) for sentence in source_int_text])\n",
+    "\n",
+    "train_graph = tf.Graph()\n",
+    "with train_graph.as_default():\n",
+    "    input_data, targets, lr, keep_prob = model_inputs()\n",
+    "    sequence_length = tf.placeholder_with_default(max_source_sentence_length, None, name='sequence_length')\n",
+    "    input_shape = tf.shape(input_data)\n",
+    "    \n",
+    "    train_logits, inference_logits = seq2seq_model(\n",
+    "        tf.reverse(input_data, [-1]), targets, keep_prob, batch_size, sequence_length, len(source_vocab_to_int), len(target_vocab_to_int),\n",
+    "        encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, target_vocab_to_int)\n",
+    "\n",
+    "    tf.identity(inference_logits, 'logits')\n",
+    "    with tf.name_scope(\"optimization\"):\n",
+    "        # Loss function\n",
+    "        cost = tf.contrib.seq2seq.sequence_loss(\n",
+    "            train_logits,\n",
+    "            targets,\n",
+    "            tf.ones([input_shape[0], sequence_length]))\n",
+    "\n",
+    "        # Optimizer\n",
+    "        optimizer = tf.train.AdamOptimizer(lr)\n",
+    "\n",
+    "        # Gradient Clipping\n",
+    "        gradients = optimizer.compute_gradients(cost)\n",
+    "        capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n",
+    "        train_op = optimizer.apply_gradients(capped_gradients)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 训练\n",
+    "\n",
+    "利用预处理的数据训练神经网络。如果很难获得低损失值，请访问我们的论坛，看看其他人是否遇到了相同的问题。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch    0/1077 - Train Accuracy:  0.294, Validation Accuracy:  0.305, Loss:  5.889\n",
+      "Epoch   0 Batch    1/1077 - Train Accuracy:  0.221, Validation Accuracy:  0.305, Loss:  5.074\n",
+      "Epoch   0 Batch    2/1077 - Train Accuracy:  0.244, Validation Accuracy:  0.335, Loss:  4.336\n",
+      "Epoch   0 Batch    3/1077 - Train Accuracy:  0.275, Validation Accuracy:  0.337, Loss:  3.832\n",
+      "Epoch   0 Batch    4/1077 - Train Accuracy:  0.263, Validation Accuracy:  0.336, Loss:  3.669\n",
+      "Epoch   0 Batch    5/1077 - Train Accuracy:  0.295, Validation Accuracy:  0.336, Loss:  3.552\n",
+      "Epoch   0 Batch    6/1077 - Train Accuracy:  0.282, Validation Accuracy:  0.342, Loss:  3.504\n",
+      "Epoch   0 Batch    7/1077 - Train Accuracy:  0.268, Validation Accuracy:  0.342, Loss:  3.515\n",
+      "Epoch   0 Batch    8/1077 - Train Accuracy:  0.275, Validation Accuracy:  0.341, Loss:  3.414\n",
+      "Epoch   0 Batch    9/1077 - Train Accuracy:  0.283, Validation Accuracy:  0.341, Loss:  3.324\n",
+      "Epoch   0 Batch   10/1077 - Train Accuracy:  0.273, Validation Accuracy:  0.363, Loss:  3.444\n",
+      "Epoch   0 Batch   11/1077 - Train Accuracy:  0.334, Validation Accuracy:  0.370, Loss:  3.166\n",
+      "Epoch   0 Batch   12/1077 - Train Accuracy:  0.314, Validation Accuracy:  0.380, Loss:  3.323\n",
+      "Epoch   0 Batch   13/1077 - Train Accuracy:  0.353, Validation Accuracy:  0.373, Loss:  3.069\n",
+      "Epoch   0 Batch   14/1077 - Train Accuracy:  0.338, Validation Accuracy:  0.377, Loss:  3.096\n",
+      "Epoch   0 Batch   15/1077 - Train Accuracy:  0.333, Validation Accuracy:  0.391, Loss:  3.169\n",
+      "Epoch   0 Batch   16/1077 - Train Accuracy:  0.350, Validation Accuracy:  0.392, Loss:  3.136\n",
+      "Epoch   0 Batch   17/1077 - Train Accuracy:  0.341, Validation Accuracy:  0.382, Loss:  3.051\n",
+      "Epoch   0 Batch   18/1077 - Train Accuracy:  0.343, Validation Accuracy:  0.407, Loss:  3.059\n",
+      "Epoch   0 Batch   19/1077 - Train Accuracy:  0.361, Validation Accuracy:  0.402, Loss:  2.920\n",
+      "Epoch   0 Batch   20/1077 - Train Accuracy:  0.341, Validation Accuracy:  0.400, Loss:  2.897\n",
+      "Epoch   0 Batch   21/1077 - Train Accuracy:  0.330, Validation Accuracy:  0.409, Loss:  2.971\n",
+      "Epoch   0 Batch   22/1077 - Train Accuracy:  0.359, Validation Accuracy:  0.417, Loss:  2.950\n",
+      "Epoch   0 Batch   23/1077 - Train Accuracy:  0.354, Validation Accuracy:  0.412, Loss:  2.924\n",
+      "Epoch   0 Batch   24/1077 - Train Accuracy:  0.360, Validation Accuracy:  0.413, Loss:  2.818\n",
+      "Epoch   0 Batch   25/1077 - Train Accuracy:  0.361, Validation Accuracy:  0.425, Loss:  2.867\n",
+      "Epoch   0 Batch   26/1077 - Train Accuracy:  0.359, Validation Accuracy:  0.420, Loss:  2.829\n",
+      "Epoch   0 Batch   27/1077 - Train Accuracy:  0.410, Validation Accuracy:  0.427, Loss:  2.581\n",
+      "Epoch   0 Batch   28/1077 - Train Accuracy:  0.394, Validation Accuracy:  0.436, Loss:  2.710\n",
+      "Epoch   0 Batch   29/1077 - Train Accuracy:  0.395, Validation Accuracy:  0.441, Loss:  2.660\n",
+      "Epoch   0 Batch   30/1077 - Train Accuracy:  0.397, Validation Accuracy:  0.448, Loss:  2.664\n",
+      "Epoch   0 Batch   31/1077 - Train Accuracy:  0.399, Validation Accuracy:  0.453, Loss:  2.683\n",
+      "Epoch   0 Batch   32/1077 - Train Accuracy:  0.453, Validation Accuracy:  0.460, Loss:  2.471\n",
+      "Epoch   0 Batch   33/1077 - Train Accuracy:  0.429, Validation Accuracy:  0.468, Loss:  2.467\n",
+      "Epoch   0 Batch   34/1077 - Train Accuracy:  0.405, Validation Accuracy:  0.462, Loss:  2.541\n",
+      "Epoch   0 Batch   35/1077 - Train Accuracy:  0.410, Validation Accuracy:  0.460, Loss:  2.516\n",
+      "Epoch   0 Batch   36/1077 - Train Accuracy:  0.425, Validation Accuracy:  0.466, Loss:  2.453\n",
+      "Epoch   0 Batch   37/1077 - Train Accuracy:  0.427, Validation Accuracy:  0.480, Loss:  2.505\n",
+      "Epoch   0 Batch   38/1077 - Train Accuracy:  0.378, Validation Accuracy:  0.474, Loss:  2.650\n",
+      "Epoch   0 Batch   39/1077 - Train Accuracy:  0.420, Validation Accuracy:  0.480, Loss:  2.496\n",
+      "Epoch   0 Batch   40/1077 - Train Accuracy:  0.410, Validation Accuracy:  0.466, Loss:  2.415\n",
+      "Epoch   0 Batch   41/1077 - Train Accuracy:  0.455, Validation Accuracy:  0.483, Loss:  2.359\n",
+      "Epoch   0 Batch   42/1077 - Train Accuracy:  0.414, Validation Accuracy:  0.467, Loss:  2.375\n",
+      "Epoch   0 Batch   43/1077 - Train Accuracy:  0.439, Validation Accuracy:  0.497, Loss:  2.402\n",
+      "Epoch   0 Batch   44/1077 - Train Accuracy:  0.355, Validation Accuracy:  0.436, Loss:  2.547\n",
+      "Epoch   0 Batch   45/1077 - Train Accuracy:  0.406, Validation Accuracy:  0.469, Loss:  2.402\n",
+      "Epoch   0 Batch   46/1077 - Train Accuracy:  0.419, Validation Accuracy:  0.492, Loss:  2.394\n",
+      "Epoch   0 Batch   47/1077 - Train Accuracy:  0.406, Validation Accuracy:  0.458, Loss:  2.299\n",
+      "Epoch   0 Batch   48/1077 - Train Accuracy:  0.407, Validation Accuracy:  0.443, Loss:  2.303\n",
+      "Epoch   0 Batch   49/1077 - Train Accuracy:  0.404, Validation Accuracy:  0.462, Loss:  2.302\n",
+      "Epoch   0 Batch   50/1077 - Train Accuracy:  0.403, Validation Accuracy:  0.478, Loss:  2.336\n",
+      "Epoch   0 Batch   51/1077 - Train Accuracy:  0.434, Validation Accuracy:  0.457, Loss:  2.179\n",
+      "Epoch   0 Batch   52/1077 - Train Accuracy:  0.432, Validation Accuracy:  0.492, Loss:  2.245\n",
+      "Epoch   0 Batch   53/1077 - Train Accuracy:  0.474, Validation Accuracy:  0.518, Loss:  2.199\n",
+      "Epoch   0 Batch   54/1077 - Train Accuracy:  0.439, Validation Accuracy:  0.523, Loss:  2.408\n",
+      "Epoch   0 Batch   55/1077 - Train Accuracy:  0.479, Validation Accuracy:  0.507, Loss:  2.111\n",
+      "Epoch   0 Batch   56/1077 - Train Accuracy:  0.455, Validation Accuracy:  0.513, Loss:  2.172\n",
+      "Epoch   0 Batch   57/1077 - Train Accuracy:  0.520, Validation Accuracy:  0.512, Loss:  1.935\n",
+      "Epoch   0 Batch   58/1077 - Train Accuracy:  0.445, Validation Accuracy:  0.493, Loss:  2.165\n",
+      "Epoch   0 Batch   59/1077 - Train Accuracy:  0.462, Validation Accuracy:  0.532, Loss:  2.256\n",
+      "Epoch   0 Batch   60/1077 - Train Accuracy:  0.487, Validation Accuracy:  0.525, Loss:  2.097\n",
+      "Epoch   0 Batch   61/1077 - Train Accuracy:  0.454, Validation Accuracy:  0.509, Loss:  2.091\n",
+      "Epoch   0 Batch   62/1077 - Train Accuracy:  0.464, Validation Accuracy:  0.531, Loss:  2.211\n",
+      "Epoch   0 Batch   63/1077 - Train Accuracy:  0.517, Validation Accuracy:  0.535, Loss:  1.980\n",
+      "Epoch   0 Batch   64/1077 - Train Accuracy:  0.446, Validation Accuracy:  0.494, Loss:  2.087\n",
+      "Epoch   0 Batch   65/1077 - Train Accuracy:  0.462, Validation Accuracy:  0.544, Loss:  2.220\n",
+      "Epoch   0 Batch   66/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.528, Loss:  2.058\n",
+      "Epoch   0 Batch   67/1077 - Train Accuracy:  0.497, Validation Accuracy:  0.507, Loss:  1.945\n",
+      "Epoch   0 Batch   68/1077 - Train Accuracy:  0.479, Validation Accuracy:  0.534, Loss:  2.047\n",
+      "Epoch   0 Batch   69/1077 - Train Accuracy:  0.516, Validation Accuracy:  0.546, Loss:  1.991\n",
+      "Epoch   0 Batch   70/1077 - Train Accuracy:  0.474, Validation Accuracy:  0.534, Loss:  2.054\n",
+      "Epoch   0 Batch   71/1077 - Train Accuracy:  0.486, Validation Accuracy:  0.534, Loss:  1.950\n",
+      "Epoch   0 Batch   72/1077 - Train Accuracy:  0.491, Validation Accuracy:  0.543, Loss:  1.957\n",
+      "Epoch   0 Batch   73/1077 - Train Accuracy:  0.475, Validation Accuracy:  0.521, Loss:  1.968\n",
+      "Epoch   0 Batch   74/1077 - Train Accuracy:  0.507, Validation Accuracy:  0.535, Loss:  1.823\n",
+      "Epoch   0 Batch   75/1077 - Train Accuracy:  0.526, Validation Accuracy:  0.539, Loss:  1.826\n",
+      "Epoch   0 Batch   76/1077 - Train Accuracy:  0.502, Validation Accuracy:  0.529, Loss:  1.857\n",
+      "Epoch   0 Batch   77/1077 - Train Accuracy:  0.462, Validation Accuracy:  0.527, Loss:  1.920\n",
+      "Epoch   0 Batch   78/1077 - Train Accuracy:  0.455, Validation Accuracy:  0.527, Loss:  2.023\n",
+      "Epoch   0 Batch   79/1077 - Train Accuracy:  0.480, Validation Accuracy:  0.521, Loss:  1.887\n",
+      "Epoch   0 Batch   80/1077 - Train Accuracy:  0.482, Validation Accuracy:  0.540, Loss:  1.808\n",
+      "Epoch   0 Batch   81/1077 - Train Accuracy:  0.509, Validation Accuracy:  0.534, Loss:  1.829\n",
+      "Epoch   0 Batch   82/1077 - Train Accuracy:  0.532, Validation Accuracy:  0.542, Loss:  1.644\n",
+      "Epoch   0 Batch   83/1077 - Train Accuracy:  0.471, Validation Accuracy:  0.533, Loss:  1.863\n",
+      "Epoch   0 Batch   84/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.537, Loss:  1.749\n",
+      "Epoch   0 Batch   85/1077 - Train Accuracy:  0.495, Validation Accuracy:  0.535, Loss:  1.639\n",
+      "Epoch   0 Batch   86/1077 - Train Accuracy:  0.489, Validation Accuracy:  0.530, Loss:  1.738\n",
+      "Epoch   0 Batch   87/1077 - Train Accuracy:  0.489, Validation Accuracy:  0.525, Loss:  1.735\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch   88/1077 - Train Accuracy:  0.509, Validation Accuracy:  0.533, Loss:  1.659\n",
+      "Epoch   0 Batch   89/1077 - Train Accuracy:  0.499, Validation Accuracy:  0.548, Loss:  1.632\n",
+      "Epoch   0 Batch   90/1077 - Train Accuracy:  0.471, Validation Accuracy:  0.535, Loss:  1.699\n",
+      "Epoch   0 Batch   91/1077 - Train Accuracy:  0.511, Validation Accuracy:  0.518, Loss:  1.448\n",
+      "Epoch   0 Batch   92/1077 - Train Accuracy:  0.490, Validation Accuracy:  0.524, Loss:  1.569\n",
+      "Epoch   0 Batch   93/1077 - Train Accuracy:  0.476, Validation Accuracy:  0.520, Loss:  1.581\n",
+      "Epoch   0 Batch   94/1077 - Train Accuracy:  0.485, Validation Accuracy:  0.526, Loss:  1.457\n",
+      "Epoch   0 Batch   95/1077 - Train Accuracy:  0.525, Validation Accuracy:  0.550, Loss:  1.486\n",
+      "Epoch   0 Batch   96/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.533, Loss:  1.471\n",
+      "Epoch   0 Batch   97/1077 - Train Accuracy:  0.467, Validation Accuracy:  0.525, Loss:  1.476\n",
+      "Epoch   0 Batch   98/1077 - Train Accuracy:  0.497, Validation Accuracy:  0.508, Loss:  1.370\n",
+      "Epoch   0 Batch   99/1077 - Train Accuracy:  0.458, Validation Accuracy:  0.501, Loss:  1.478\n",
+      "Epoch   0 Batch  100/1077 - Train Accuracy:  0.490, Validation Accuracy:  0.514, Loss:  1.390\n",
+      "Epoch   0 Batch  101/1077 - Train Accuracy:  0.474, Validation Accuracy:  0.501, Loss:  1.334\n",
+      "Epoch   0 Batch  102/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.529, Loss:  1.319\n",
+      "Epoch   0 Batch  103/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.538, Loss:  1.417\n",
+      "Epoch   0 Batch  104/1077 - Train Accuracy:  0.458, Validation Accuracy:  0.547, Loss:  1.394\n",
+      "Epoch   0 Batch  105/1077 - Train Accuracy:  0.510, Validation Accuracy:  0.512, Loss:  1.274\n",
+      "Epoch   0 Batch  106/1077 - Train Accuracy:  0.468, Validation Accuracy:  0.512, Loss:  1.405\n",
+      "Epoch   0 Batch  107/1077 - Train Accuracy:  0.510, Validation Accuracy:  0.526, Loss:  1.229\n",
+      "Epoch   0 Batch  108/1077 - Train Accuracy:  0.556, Validation Accuracy:  0.542, Loss:  1.149\n",
+      "Epoch   0 Batch  109/1077 - Train Accuracy:  0.515, Validation Accuracy:  0.548, Loss:  1.224\n",
+      "Epoch   0 Batch  110/1077 - Train Accuracy:  0.536, Validation Accuracy:  0.543, Loss:  1.191\n",
+      "Epoch   0 Batch  111/1077 - Train Accuracy:  0.496, Validation Accuracy:  0.547, Loss:  1.237\n",
+      "Epoch   0 Batch  112/1077 - Train Accuracy:  0.494, Validation Accuracy:  0.531, Loss:  1.218\n",
+      "Epoch   0 Batch  113/1077 - Train Accuracy:  0.475, Validation Accuracy:  0.522, Loss:  1.197\n",
+      "Epoch   0 Batch  114/1077 - Train Accuracy:  0.526, Validation Accuracy:  0.529, Loss:  1.110\n",
+      "Epoch   0 Batch  115/1077 - Train Accuracy:  0.504, Validation Accuracy:  0.532, Loss:  1.182\n",
+      "Epoch   0 Batch  116/1077 - Train Accuracy:  0.488, Validation Accuracy:  0.524, Loss:  1.181\n",
+      "Epoch   0 Batch  117/1077 - Train Accuracy:  0.459, Validation Accuracy:  0.544, Loss:  1.173\n",
+      "Epoch   0 Batch  118/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.558, Loss:  1.164\n",
+      "Epoch   0 Batch  119/1077 - Train Accuracy:  0.507, Validation Accuracy:  0.550, Loss:  1.075\n",
+      "Epoch   0 Batch  120/1077 - Train Accuracy:  0.482, Validation Accuracy:  0.533, Loss:  1.114\n",
+      "Epoch   0 Batch  121/1077 - Train Accuracy:  0.502, Validation Accuracy:  0.533, Loss:  1.056\n",
+      "Epoch   0 Batch  122/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.539, Loss:  1.036\n",
+      "Epoch   0 Batch  123/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.550, Loss:  1.013\n",
+      "Epoch   0 Batch  124/1077 - Train Accuracy:  0.495, Validation Accuracy:  0.543, Loss:  1.080\n",
+      "Epoch   0 Batch  125/1077 - Train Accuracy:  0.529, Validation Accuracy:  0.537, Loss:  1.015\n",
+      "Epoch   0 Batch  126/1077 - Train Accuracy:  0.515, Validation Accuracy:  0.537, Loss:  0.966\n",
+      "Epoch   0 Batch  127/1077 - Train Accuracy:  0.502, Validation Accuracy:  0.545, Loss:  1.023\n",
+      "Epoch   0 Batch  128/1077 - Train Accuracy:  0.536, Validation Accuracy:  0.521, Loss:  0.953\n",
+      "Epoch   0 Batch  129/1077 - Train Accuracy:  0.525, Validation Accuracy:  0.534, Loss:  1.014\n",
+      "Epoch   0 Batch  130/1077 - Train Accuracy:  0.539, Validation Accuracy:  0.547, Loss:  0.926\n",
+      "Epoch   0 Batch  131/1077 - Train Accuracy:  0.499, Validation Accuracy:  0.558, Loss:  0.993\n",
+      "Epoch   0 Batch  132/1077 - Train Accuracy:  0.464, Validation Accuracy:  0.545, Loss:  1.016\n",
+      "Epoch   0 Batch  133/1077 - Train Accuracy:  0.471, Validation Accuracy:  0.548, Loss:  0.994\n",
+      "Epoch   0 Batch  134/1077 - Train Accuracy:  0.534, Validation Accuracy:  0.560, Loss:  0.927\n",
+      "Epoch   0 Batch  135/1077 - Train Accuracy:  0.532, Validation Accuracy:  0.576, Loss:  0.994\n",
+      "Epoch   0 Batch  136/1077 - Train Accuracy:  0.527, Validation Accuracy:  0.565, Loss:  0.944\n",
+      "Epoch   0 Batch  137/1077 - Train Accuracy:  0.573, Validation Accuracy:  0.572, Loss:  0.864\n",
+      "Epoch   0 Batch  138/1077 - Train Accuracy:  0.528, Validation Accuracy:  0.570, Loss:  0.924\n",
+      "Epoch   0 Batch  139/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.555, Loss:  0.947\n",
+      "Epoch   0 Batch  140/1077 - Train Accuracy:  0.457, Validation Accuracy:  0.557, Loss:  0.986\n",
+      "Epoch   0 Batch  141/1077 - Train Accuracy:  0.508, Validation Accuracy:  0.552, Loss:  0.943\n",
+      "Epoch   0 Batch  142/1077 - Train Accuracy:  0.546, Validation Accuracy:  0.557, Loss:  0.855\n",
+      "Epoch   0 Batch  143/1077 - Train Accuracy:  0.522, Validation Accuracy:  0.550, Loss:  0.941\n",
+      "Epoch   0 Batch  144/1077 - Train Accuracy:  0.481, Validation Accuracy:  0.558, Loss:  0.944\n",
+      "Epoch   0 Batch  145/1077 - Train Accuracy:  0.584, Validation Accuracy:  0.559, Loss:  0.890\n",
+      "Epoch   0 Batch  146/1077 - Train Accuracy:  0.527, Validation Accuracy:  0.549, Loss:  0.908\n",
+      "Epoch   0 Batch  147/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.543, Loss:  0.933\n",
+      "Epoch   0 Batch  148/1077 - Train Accuracy:  0.518, Validation Accuracy:  0.564, Loss:  0.885\n",
+      "Epoch   0 Batch  149/1077 - Train Accuracy:  0.507, Validation Accuracy:  0.572, Loss:  0.904\n",
+      "Epoch   0 Batch  150/1077 - Train Accuracy:  0.574, Validation Accuracy:  0.568, Loss:  0.849\n",
+      "Epoch   0 Batch  151/1077 - Train Accuracy:  0.528, Validation Accuracy:  0.576, Loss:  0.812\n",
+      "Epoch   0 Batch  152/1077 - Train Accuracy:  0.529, Validation Accuracy:  0.572, Loss:  0.877\n",
+      "Epoch   0 Batch  153/1077 - Train Accuracy:  0.522, Validation Accuracy:  0.568, Loss:  0.907\n",
+      "Epoch   0 Batch  154/1077 - Train Accuracy:  0.511, Validation Accuracy:  0.574, Loss:  0.873\n",
+      "Epoch   0 Batch  155/1077 - Train Accuracy:  0.539, Validation Accuracy:  0.572, Loss:  0.864\n",
+      "Epoch   0 Batch  156/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.584, Loss:  0.836\n",
+      "Epoch   0 Batch  157/1077 - Train Accuracy:  0.557, Validation Accuracy:  0.574, Loss:  0.846\n",
+      "Epoch   0 Batch  158/1077 - Train Accuracy:  0.536, Validation Accuracy:  0.591, Loss:  0.881\n",
+      "Epoch   0 Batch  159/1077 - Train Accuracy:  0.548, Validation Accuracy:  0.581, Loss:  0.768\n",
+      "Epoch   0 Batch  160/1077 - Train Accuracy:  0.532, Validation Accuracy:  0.576, Loss:  0.831\n",
+      "Epoch   0 Batch  161/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.566, Loss:  0.835\n",
+      "Epoch   0 Batch  162/1077 - Train Accuracy:  0.534, Validation Accuracy:  0.568, Loss:  0.866\n",
+      "Epoch   0 Batch  163/1077 - Train Accuracy:  0.525, Validation Accuracy:  0.571, Loss:  0.891\n",
+      "Epoch   0 Batch  164/1077 - Train Accuracy:  0.533, Validation Accuracy:  0.586, Loss:  0.841\n",
+      "Epoch   0 Batch  165/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.587, Loss:  0.804\n",
+      "Epoch   0 Batch  166/1077 - Train Accuracy:  0.580, Validation Accuracy:  0.586, Loss:  0.824\n",
+      "Epoch   0 Batch  167/1077 - Train Accuracy:  0.558, Validation Accuracy:  0.574, Loss:  0.835\n",
+      "Epoch   0 Batch  168/1077 - Train Accuracy:  0.520, Validation Accuracy:  0.568, Loss:  0.844\n",
+      "Epoch   0 Batch  169/1077 - Train Accuracy:  0.577, Validation Accuracy:  0.575, Loss:  0.822\n",
+      "Epoch   0 Batch  170/1077 - Train Accuracy:  0.553, Validation Accuracy:  0.566, Loss:  0.861\n",
+      "Epoch   0 Batch  171/1077 - Train Accuracy:  0.592, Validation Accuracy:  0.577, Loss:  0.755\n",
+      "Epoch   0 Batch  172/1077 - Train Accuracy:  0.599, Validation Accuracy:  0.583, Loss:  0.743\n",
+      "Epoch   0 Batch  173/1077 - Train Accuracy:  0.543, Validation Accuracy:  0.578, Loss:  0.845\n",
+      "Epoch   0 Batch  174/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.580, Loss:  0.779\n",
+      "Epoch   0 Batch  175/1077 - Train Accuracy:  0.598, Validation Accuracy:  0.589, Loss:  0.784\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch  176/1077 - Train Accuracy:  0.565, Validation Accuracy:  0.605, Loss:  0.783\n",
+      "Epoch   0 Batch  177/1077 - Train Accuracy:  0.537, Validation Accuracy:  0.591, Loss:  0.837\n",
+      "Epoch   0 Batch  178/1077 - Train Accuracy:  0.564, Validation Accuracy:  0.582, Loss:  0.763\n",
+      "Epoch   0 Batch  179/1077 - Train Accuracy:  0.586, Validation Accuracy:  0.578, Loss:  0.807\n",
+      "Epoch   0 Batch  180/1077 - Train Accuracy:  0.559, Validation Accuracy:  0.581, Loss:  0.773\n",
+      "Epoch   0 Batch  181/1077 - Train Accuracy:  0.555, Validation Accuracy:  0.580, Loss:  0.803\n",
+      "Epoch   0 Batch  182/1077 - Train Accuracy:  0.596, Validation Accuracy:  0.570, Loss:  0.752\n",
+      "Epoch   0 Batch  183/1077 - Train Accuracy:  0.567, Validation Accuracy:  0.559, Loss:  0.767\n",
+      "Epoch   0 Batch  184/1077 - Train Accuracy:  0.568, Validation Accuracy:  0.566, Loss:  0.718\n",
+      "Epoch   0 Batch  185/1077 - Train Accuracy:  0.580, Validation Accuracy:  0.580, Loss:  0.760\n",
+      "Epoch   0 Batch  186/1077 - Train Accuracy:  0.569, Validation Accuracy:  0.596, Loss:  0.777\n",
+      "Epoch   0 Batch  187/1077 - Train Accuracy:  0.573, Validation Accuracy:  0.588, Loss:  0.741\n",
+      "Epoch   0 Batch  188/1077 - Train Accuracy:  0.569, Validation Accuracy:  0.604, Loss:  0.744\n",
+      "Epoch   0 Batch  189/1077 - Train Accuracy:  0.567, Validation Accuracy:  0.611, Loss:  0.724\n",
+      "Epoch   0 Batch  190/1077 - Train Accuracy:  0.618, Validation Accuracy:  0.621, Loss:  0.726\n",
+      "Epoch   0 Batch  191/1077 - Train Accuracy:  0.630, Validation Accuracy:  0.620, Loss:  0.663\n",
+      "Epoch   0 Batch  192/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.620, Loss:  0.747\n",
+      "Epoch   0 Batch  193/1077 - Train Accuracy:  0.629, Validation Accuracy:  0.617, Loss:  0.712\n",
+      "Epoch   0 Batch  194/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.596, Loss:  0.676\n",
+      "Epoch   0 Batch  195/1077 - Train Accuracy:  0.561, Validation Accuracy:  0.592, Loss:  0.712\n",
+      "Epoch   0 Batch  196/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.583, Loss:  0.718\n",
+      "Epoch   0 Batch  197/1077 - Train Accuracy:  0.602, Validation Accuracy:  0.586, Loss:  0.708\n",
+      "Epoch   0 Batch  198/1077 - Train Accuracy:  0.646, Validation Accuracy:  0.581, Loss:  0.667\n",
+      "Epoch   0 Batch  199/1077 - Train Accuracy:  0.570, Validation Accuracy:  0.590, Loss:  0.717\n",
+      "Epoch   0 Batch  200/1077 - Train Accuracy:  0.579, Validation Accuracy:  0.599, Loss:  0.735\n",
+      "Epoch   0 Batch  201/1077 - Train Accuracy:  0.597, Validation Accuracy:  0.594, Loss:  0.686\n",
+      "Epoch   0 Batch  202/1077 - Train Accuracy:  0.601, Validation Accuracy:  0.592, Loss:  0.718\n",
+      "Epoch   0 Batch  203/1077 - Train Accuracy:  0.563, Validation Accuracy:  0.582, Loss:  0.688\n",
+      "Epoch   0 Batch  204/1077 - Train Accuracy:  0.582, Validation Accuracy:  0.596, Loss:  0.732\n",
+      "Epoch   0 Batch  205/1077 - Train Accuracy:  0.584, Validation Accuracy:  0.583, Loss:  0.720\n",
+      "Epoch   0 Batch  206/1077 - Train Accuracy:  0.623, Validation Accuracy:  0.574, Loss:  0.690\n",
+      "Epoch   0 Batch  207/1077 - Train Accuracy:  0.587, Validation Accuracy:  0.583, Loss:  0.712\n",
+      "Epoch   0 Batch  208/1077 - Train Accuracy:  0.597, Validation Accuracy:  0.600, Loss:  0.688\n",
+      "Epoch   0 Batch  209/1077 - Train Accuracy:  0.629, Validation Accuracy:  0.608, Loss:  0.634\n",
+      "Epoch   0 Batch  210/1077 - Train Accuracy:  0.613, Validation Accuracy:  0.611, Loss:  0.682\n",
+      "Epoch   0 Batch  211/1077 - Train Accuracy:  0.580, Validation Accuracy:  0.626, Loss:  0.682\n",
+      "Epoch   0 Batch  212/1077 - Train Accuracy:  0.609, Validation Accuracy:  0.636, Loss:  0.659\n",
+      "Epoch   0 Batch  213/1077 - Train Accuracy:  0.623, Validation Accuracy:  0.631, Loss:  0.642\n",
+      "Epoch   0 Batch  214/1077 - Train Accuracy:  0.579, Validation Accuracy:  0.620, Loss:  0.675\n",
+      "Epoch   0 Batch  215/1077 - Train Accuracy:  0.579, Validation Accuracy:  0.615, Loss:  0.702\n",
+      "Epoch   0 Batch  216/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.605, Loss:  0.700\n",
+      "Epoch   0 Batch  217/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.596, Loss:  0.664\n",
+      "Epoch   0 Batch  218/1077 - Train Accuracy:  0.582, Validation Accuracy:  0.583, Loss:  0.766\n",
+      "Epoch   0 Batch  219/1077 - Train Accuracy:  0.639, Validation Accuracy:  0.603, Loss:  0.670\n",
+      "Epoch   0 Batch  220/1077 - Train Accuracy:  0.593, Validation Accuracy:  0.608, Loss:  0.685\n",
+      "Epoch   0 Batch  221/1077 - Train Accuracy:  0.660, Validation Accuracy:  0.605, Loss:  0.705\n",
+      "Epoch   0 Batch  222/1077 - Train Accuracy:  0.561, Validation Accuracy:  0.600, Loss:  0.712\n",
+      "Epoch   0 Batch  223/1077 - Train Accuracy:  0.615, Validation Accuracy:  0.610, Loss:  0.634\n",
+      "Epoch   0 Batch  224/1077 - Train Accuracy:  0.623, Validation Accuracy:  0.606, Loss:  0.677\n",
+      "Epoch   0 Batch  225/1077 - Train Accuracy:  0.618, Validation Accuracy:  0.606, Loss:  0.695\n",
+      "Epoch   0 Batch  226/1077 - Train Accuracy:  0.615, Validation Accuracy:  0.601, Loss:  0.668\n",
+      "Epoch   0 Batch  227/1077 - Train Accuracy:  0.593, Validation Accuracy:  0.600, Loss:  0.721\n",
+      "Epoch   0 Batch  228/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.605, Loss:  0.645\n",
+      "Epoch   0 Batch  229/1077 - Train Accuracy:  0.630, Validation Accuracy:  0.610, Loss:  0.654\n",
+      "Epoch   0 Batch  230/1077 - Train Accuracy:  0.631, Validation Accuracy:  0.621, Loss:  0.659\n",
+      "Epoch   0 Batch  231/1077 - Train Accuracy:  0.589, Validation Accuracy:  0.613, Loss:  0.668\n",
+      "Epoch   0 Batch  232/1077 - Train Accuracy:  0.601, Validation Accuracy:  0.615, Loss:  0.708\n",
+      "Epoch   0 Batch  233/1077 - Train Accuracy:  0.621, Validation Accuracy:  0.619, Loss:  0.706\n",
+      "Epoch   0 Batch  234/1077 - Train Accuracy:  0.640, Validation Accuracy:  0.616, Loss:  0.661\n",
+      "Epoch   0 Batch  235/1077 - Train Accuracy:  0.633, Validation Accuracy:  0.618, Loss:  0.606\n",
+      "Epoch   0 Batch  236/1077 - Train Accuracy:  0.590, Validation Accuracy:  0.622, Loss:  0.689\n",
+      "Epoch   0 Batch  237/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.616, Loss:  0.619\n",
+      "Epoch   0 Batch  238/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.602, Loss:  0.673\n",
+      "Epoch   0 Batch  239/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.605, Loss:  0.596\n",
+      "Epoch   0 Batch  240/1077 - Train Accuracy:  0.656, Validation Accuracy:  0.611, Loss:  0.621\n",
+      "Epoch   0 Batch  241/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.606, Loss:  0.611\n",
+      "Epoch   0 Batch  242/1077 - Train Accuracy:  0.603, Validation Accuracy:  0.598, Loss:  0.641\n",
+      "Epoch   0 Batch  243/1077 - Train Accuracy:  0.578, Validation Accuracy:  0.603, Loss:  0.677\n",
+      "Epoch   0 Batch  244/1077 - Train Accuracy:  0.654, Validation Accuracy:  0.604, Loss:  0.614\n",
+      "Epoch   0 Batch  245/1077 - Train Accuracy:  0.638, Validation Accuracy:  0.604, Loss:  0.600\n",
+      "Epoch   0 Batch  246/1077 - Train Accuracy:  0.620, Validation Accuracy:  0.611, Loss:  0.644\n",
+      "Epoch   0 Batch  247/1077 - Train Accuracy:  0.653, Validation Accuracy:  0.631, Loss:  0.609\n",
+      "Epoch   0 Batch  248/1077 - Train Accuracy:  0.665, Validation Accuracy:  0.623, Loss:  0.616\n",
+      "Epoch   0 Batch  249/1077 - Train Accuracy:  0.596, Validation Accuracy:  0.621, Loss:  0.615\n",
+      "Epoch   0 Batch  250/1077 - Train Accuracy:  0.626, Validation Accuracy:  0.614, Loss:  0.585\n",
+      "Epoch   0 Batch  251/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.588, Loss:  0.639\n",
+      "Epoch   0 Batch  252/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.588, Loss:  0.620\n",
+      "Epoch   0 Batch  253/1077 - Train Accuracy:  0.636, Validation Accuracy:  0.602, Loss:  0.600\n",
+      "Epoch   0 Batch  254/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.605, Loss:  0.642\n",
+      "Epoch   0 Batch  255/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.613, Loss:  0.625\n",
+      "Epoch   0 Batch  256/1077 - Train Accuracy:  0.602, Validation Accuracy:  0.612, Loss:  0.685\n",
+      "Epoch   0 Batch  257/1077 - Train Accuracy:  0.641, Validation Accuracy:  0.614, Loss:  0.624\n",
+      "Epoch   0 Batch  258/1077 - Train Accuracy:  0.646, Validation Accuracy:  0.598, Loss:  0.613\n",
+      "Epoch   0 Batch  259/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.605, Loss:  0.613\n",
+      "Epoch   0 Batch  260/1077 - Train Accuracy:  0.643, Validation Accuracy:  0.606, Loss:  0.593\n",
+      "Epoch   0 Batch  261/1077 - Train Accuracy:  0.625, Validation Accuracy:  0.604, Loss:  0.620\n",
+      "Epoch   0 Batch  262/1077 - Train Accuracy:  0.633, Validation Accuracy:  0.609, Loss:  0.612\n",
+      "Epoch   0 Batch  263/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.615, Loss:  0.601\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch  264/1077 - Train Accuracy:  0.626, Validation Accuracy:  0.630, Loss:  0.619\n",
+      "Epoch   0 Batch  265/1077 - Train Accuracy:  0.617, Validation Accuracy:  0.625, Loss:  0.626\n",
+      "Epoch   0 Batch  266/1077 - Train Accuracy:  0.638, Validation Accuracy:  0.617, Loss:  0.590\n",
+      "Epoch   0 Batch  267/1077 - Train Accuracy:  0.656, Validation Accuracy:  0.608, Loss:  0.576\n",
+      "Epoch   0 Batch  268/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.623, Loss:  0.614\n",
+      "Epoch   0 Batch  269/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.627, Loss:  0.658\n",
+      "Epoch   0 Batch  270/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.622, Loss:  0.650\n",
+      "Epoch   0 Batch  271/1077 - Train Accuracy:  0.672, Validation Accuracy:  0.624, Loss:  0.607\n",
+      "Epoch   0 Batch  272/1077 - Train Accuracy:  0.635, Validation Accuracy:  0.630, Loss:  0.633\n",
+      "Epoch   0 Batch  273/1077 - Train Accuracy:  0.629, Validation Accuracy:  0.633, Loss:  0.586\n",
+      "Epoch   0 Batch  274/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.623, Loss:  0.596\n",
+      "Epoch   0 Batch  275/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.617, Loss:  0.576\n",
+      "Epoch   0 Batch  276/1077 - Train Accuracy:  0.602, Validation Accuracy:  0.628, Loss:  0.632\n",
+      "Epoch   0 Batch  277/1077 - Train Accuracy:  0.643, Validation Accuracy:  0.639, Loss:  0.557\n",
+      "Epoch   0 Batch  278/1077 - Train Accuracy:  0.620, Validation Accuracy:  0.665, Loss:  0.639\n",
+      "Epoch   0 Batch  279/1077 - Train Accuracy:  0.611, Validation Accuracy:  0.671, Loss:  0.646\n",
+      "Epoch   0 Batch  280/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.655, Loss:  0.627\n",
+      "Epoch   0 Batch  281/1077 - Train Accuracy:  0.612, Validation Accuracy:  0.647, Loss:  0.627\n",
+      "Epoch   0 Batch  282/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.639, Loss:  0.630\n",
+      "Epoch   0 Batch  283/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.631, Loss:  0.620\n",
+      "Epoch   0 Batch  284/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.630, Loss:  0.634\n",
+      "Epoch   0 Batch  285/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.625, Loss:  0.586\n",
+      "Epoch   0 Batch  286/1077 - Train Accuracy:  0.673, Validation Accuracy:  0.624, Loss:  0.567\n",
+      "Epoch   0 Batch  287/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.623, Loss:  0.582\n",
+      "Epoch   0 Batch  288/1077 - Train Accuracy:  0.612, Validation Accuracy:  0.640, Loss:  0.623\n",
+      "Epoch   0 Batch  289/1077 - Train Accuracy:  0.665, Validation Accuracy:  0.643, Loss:  0.583\n",
+      "Epoch   0 Batch  290/1077 - Train Accuracy:  0.639, Validation Accuracy:  0.638, Loss:  0.617\n",
+      "Epoch   0 Batch  291/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.638, Loss:  0.606\n",
+      "Epoch   0 Batch  292/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.636, Loss:  0.568\n",
+      "Epoch   0 Batch  293/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.629, Loss:  0.611\n",
+      "Epoch   0 Batch  294/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.637, Loss:  0.540\n",
+      "Epoch   0 Batch  295/1077 - Train Accuracy:  0.622, Validation Accuracy:  0.616, Loss:  0.636\n",
+      "Epoch   0 Batch  296/1077 - Train Accuracy:  0.696, Validation Accuracy:  0.647, Loss:  0.544\n",
+      "Epoch   0 Batch  297/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.637, Loss:  0.623\n",
+      "Epoch   0 Batch  298/1077 - Train Accuracy:  0.636, Validation Accuracy:  0.659, Loss:  0.615\n",
+      "Epoch   0 Batch  299/1077 - Train Accuracy:  0.641, Validation Accuracy:  0.667, Loss:  0.562\n",
+      "Epoch   0 Batch  300/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.659, Loss:  0.566\n",
+      "Epoch   0 Batch  301/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.643, Loss:  0.556\n",
+      "Epoch   0 Batch  302/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.652, Loss:  0.564\n",
+      "Epoch   0 Batch  303/1077 - Train Accuracy:  0.649, Validation Accuracy:  0.636, Loss:  0.579\n",
+      "Epoch   0 Batch  304/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.598, Loss:  0.534\n",
+      "Epoch   0 Batch  305/1077 - Train Accuracy:  0.656, Validation Accuracy:  0.592, Loss:  0.552\n",
+      "Epoch   0 Batch  306/1077 - Train Accuracy:  0.644, Validation Accuracy:  0.607, Loss:  0.547\n",
+      "Epoch   0 Batch  307/1077 - Train Accuracy:  0.640, Validation Accuracy:  0.610, Loss:  0.559\n",
+      "Epoch   0 Batch  308/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.610, Loss:  0.609\n",
+      "Epoch   0 Batch  309/1077 - Train Accuracy:  0.674, Validation Accuracy:  0.615, Loss:  0.521\n",
+      "Epoch   0 Batch  310/1077 - Train Accuracy:  0.612, Validation Accuracy:  0.617, Loss:  0.575\n",
+      "Epoch   0 Batch  311/1077 - Train Accuracy:  0.666, Validation Accuracy:  0.617, Loss:  0.530\n",
+      "Epoch   0 Batch  312/1077 - Train Accuracy:  0.637, Validation Accuracy:  0.614, Loss:  0.590\n",
+      "Epoch   0 Batch  313/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.624, Loss:  0.547\n",
+      "Epoch   0 Batch  314/1077 - Train Accuracy:  0.675, Validation Accuracy:  0.639, Loss:  0.552\n",
+      "Epoch   0 Batch  315/1077 - Train Accuracy:  0.661, Validation Accuracy:  0.643, Loss:  0.519\n",
+      "Epoch   0 Batch  316/1077 - Train Accuracy:  0.673, Validation Accuracy:  0.627, Loss:  0.522\n",
+      "Epoch   0 Batch  317/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.619, Loss:  0.604\n",
+      "Epoch   0 Batch  318/1077 - Train Accuracy:  0.637, Validation Accuracy:  0.617, Loss:  0.560\n",
+      "Epoch   0 Batch  319/1077 - Train Accuracy:  0.639, Validation Accuracy:  0.608, Loss:  0.547\n",
+      "Epoch   0 Batch  320/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.636, Loss:  0.557\n",
+      "Epoch   0 Batch  321/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.631, Loss:  0.547\n",
+      "Epoch   0 Batch  322/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.634, Loss:  0.529\n",
+      "Epoch   0 Batch  323/1077 - Train Accuracy:  0.665, Validation Accuracy:  0.622, Loss:  0.538\n",
+      "Epoch   0 Batch  324/1077 - Train Accuracy:  0.652, Validation Accuracy:  0.630, Loss:  0.547\n",
+      "Epoch   0 Batch  325/1077 - Train Accuracy:  0.672, Validation Accuracy:  0.636, Loss:  0.527\n",
+      "Epoch   0 Batch  326/1077 - Train Accuracy:  0.683, Validation Accuracy:  0.614, Loss:  0.548\n",
+      "Epoch   0 Batch  327/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.610, Loss:  0.558\n",
+      "Epoch   0 Batch  328/1077 - Train Accuracy:  0.685, Validation Accuracy:  0.615, Loss:  0.535\n",
+      "Epoch   0 Batch  329/1077 - Train Accuracy:  0.634, Validation Accuracy:  0.630, Loss:  0.574\n",
+      "Epoch   0 Batch  330/1077 - Train Accuracy:  0.658, Validation Accuracy:  0.635, Loss:  0.545\n",
+      "Epoch   0 Batch  331/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.635, Loss:  0.574\n",
+      "Epoch   0 Batch  332/1077 - Train Accuracy:  0.625, Validation Accuracy:  0.640, Loss:  0.505\n",
+      "Epoch   0 Batch  333/1077 - Train Accuracy:  0.676, Validation Accuracy:  0.640, Loss:  0.561\n",
+      "Epoch   0 Batch  334/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.640, Loss:  0.553\n",
+      "Epoch   0 Batch  335/1077 - Train Accuracy:  0.684, Validation Accuracy:  0.632, Loss:  0.509\n",
+      "Epoch   0 Batch  336/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.635, Loss:  0.544\n",
+      "Epoch   0 Batch  337/1077 - Train Accuracy:  0.620, Validation Accuracy:  0.635, Loss:  0.549\n",
+      "Epoch   0 Batch  338/1077 - Train Accuracy:  0.652, Validation Accuracy:  0.613, Loss:  0.565\n",
+      "Epoch   0 Batch  339/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.613, Loss:  0.512\n",
+      "Epoch   0 Batch  340/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.618, Loss:  0.547\n",
+      "Epoch   0 Batch  341/1077 - Train Accuracy:  0.667, Validation Accuracy:  0.619, Loss:  0.577\n",
+      "Epoch   0 Batch  342/1077 - Train Accuracy:  0.644, Validation Accuracy:  0.619, Loss:  0.511\n",
+      "Epoch   0 Batch  343/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.627, Loss:  0.545\n",
+      "Epoch   0 Batch  344/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.636, Loss:  0.526\n",
+      "Epoch   0 Batch  345/1077 - Train Accuracy:  0.706, Validation Accuracy:  0.628, Loss:  0.502\n",
+      "Epoch   0 Batch  346/1077 - Train Accuracy:  0.647, Validation Accuracy:  0.636, Loss:  0.551\n",
+      "Epoch   0 Batch  347/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.632, Loss:  0.491\n",
+      "Epoch   0 Batch  348/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.630, Loss:  0.518\n",
+      "Epoch   0 Batch  349/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.627, Loss:  0.529\n",
+      "Epoch   0 Batch  350/1077 - Train Accuracy:  0.646, Validation Accuracy:  0.620, Loss:  0.544\n",
+      "Epoch   0 Batch  351/1077 - Train Accuracy:  0.653, Validation Accuracy:  0.650, Loss:  0.545\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch  352/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.655, Loss:  0.523\n",
+      "Epoch   0 Batch  353/1077 - Train Accuracy:  0.635, Validation Accuracy:  0.670, Loss:  0.567\n",
+      "Epoch   0 Batch  354/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.661, Loss:  0.548\n",
+      "Epoch   0 Batch  355/1077 - Train Accuracy:  0.652, Validation Accuracy:  0.649, Loss:  0.516\n",
+      "Epoch   0 Batch  356/1077 - Train Accuracy:  0.686, Validation Accuracy:  0.643, Loss:  0.528\n",
+      "Epoch   0 Batch  357/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.644, Loss:  0.501\n",
+      "Epoch   0 Batch  358/1077 - Train Accuracy:  0.636, Validation Accuracy:  0.648, Loss:  0.546\n",
+      "Epoch   0 Batch  359/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.662, Loss:  0.524\n",
+      "Epoch   0 Batch  360/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.648, Loss:  0.515\n",
+      "Epoch   0 Batch  361/1077 - Train Accuracy:  0.680, Validation Accuracy:  0.657, Loss:  0.545\n",
+      "Epoch   0 Batch  362/1077 - Train Accuracy:  0.670, Validation Accuracy:  0.636, Loss:  0.505\n",
+      "Epoch   0 Batch  363/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.641, Loss:  0.519\n",
+      "Epoch   0 Batch  364/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.641, Loss:  0.542\n",
+      "Epoch   0 Batch  365/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.636, Loss:  0.502\n",
+      "Epoch   0 Batch  366/1077 - Train Accuracy:  0.638, Validation Accuracy:  0.635, Loss:  0.520\n",
+      "Epoch   0 Batch  367/1077 - Train Accuracy:  0.703, Validation Accuracy:  0.645, Loss:  0.452\n",
+      "Epoch   0 Batch  368/1077 - Train Accuracy:  0.686, Validation Accuracy:  0.657, Loss:  0.506\n",
+      "Epoch   0 Batch  369/1077 - Train Accuracy:  0.668, Validation Accuracy:  0.666, Loss:  0.485\n",
+      "Epoch   0 Batch  370/1077 - Train Accuracy:  0.684, Validation Accuracy:  0.662, Loss:  0.483\n",
+      "Epoch   0 Batch  371/1077 - Train Accuracy:  0.711, Validation Accuracy:  0.673, Loss:  0.487\n",
+      "Epoch   0 Batch  372/1077 - Train Accuracy:  0.711, Validation Accuracy:  0.668, Loss:  0.480\n",
+      "Epoch   0 Batch  373/1077 - Train Accuracy:  0.699, Validation Accuracy:  0.667, Loss:  0.463\n",
+      "Epoch   0 Batch  374/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.657, Loss:  0.524\n",
+      "Epoch   0 Batch  375/1077 - Train Accuracy:  0.694, Validation Accuracy:  0.662, Loss:  0.468\n",
+      "Epoch   0 Batch  376/1077 - Train Accuracy:  0.675, Validation Accuracy:  0.654, Loss:  0.471\n",
+      "Epoch   0 Batch  377/1077 - Train Accuracy:  0.661, Validation Accuracy:  0.657, Loss:  0.496\n",
+      "Epoch   0 Batch  378/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.658, Loss:  0.473\n",
+      "Epoch   0 Batch  379/1077 - Train Accuracy:  0.688, Validation Accuracy:  0.661, Loss:  0.514\n",
+      "Epoch   0 Batch  380/1077 - Train Accuracy:  0.676, Validation Accuracy:  0.674, Loss:  0.480\n",
+      "Epoch   0 Batch  381/1077 - Train Accuracy:  0.669, Validation Accuracy:  0.661, Loss:  0.509\n",
+      "Epoch   0 Batch  382/1077 - Train Accuracy:  0.689, Validation Accuracy:  0.672, Loss:  0.521\n",
+      "Epoch   0 Batch  383/1077 - Train Accuracy:  0.688, Validation Accuracy:  0.661, Loss:  0.467\n",
+      "Epoch   0 Batch  384/1077 - Train Accuracy:  0.678, Validation Accuracy:  0.654, Loss:  0.481\n",
+      "Epoch   0 Batch  385/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.657, Loss:  0.477\n",
+      "Epoch   0 Batch  386/1077 - Train Accuracy:  0.683, Validation Accuracy:  0.652, Loss:  0.472\n",
+      "Epoch   0 Batch  387/1077 - Train Accuracy:  0.708, Validation Accuracy:  0.662, Loss:  0.458\n",
+      "Epoch   0 Batch  388/1077 - Train Accuracy:  0.681, Validation Accuracy:  0.672, Loss:  0.460\n",
+      "Epoch   0 Batch  389/1077 - Train Accuracy:  0.702, Validation Accuracy:  0.681, Loss:  0.489\n",
+      "Epoch   0 Batch  390/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.669, Loss:  0.494\n",
+      "Epoch   0 Batch  391/1077 - Train Accuracy:  0.696, Validation Accuracy:  0.652, Loss:  0.472\n",
+      "Epoch   0 Batch  392/1077 - Train Accuracy:  0.690, Validation Accuracy:  0.669, Loss:  0.463\n",
+      "Epoch   0 Batch  393/1077 - Train Accuracy:  0.697, Validation Accuracy:  0.681, Loss:  0.443\n",
+      "Epoch   0 Batch  394/1077 - Train Accuracy:  0.669, Validation Accuracy:  0.698, Loss:  0.478\n",
+      "Epoch   0 Batch  395/1077 - Train Accuracy:  0.710, Validation Accuracy:  0.696, Loss:  0.442\n",
+      "Epoch   0 Batch  396/1077 - Train Accuracy:  0.653, Validation Accuracy:  0.696, Loss:  0.495\n",
+      "Epoch   0 Batch  397/1077 - Train Accuracy:  0.712, Validation Accuracy:  0.679, Loss:  0.460\n",
+      "Epoch   0 Batch  398/1077 - Train Accuracy:  0.704, Validation Accuracy:  0.674, Loss:  0.496\n",
+      "Epoch   0 Batch  399/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.692, Loss:  0.478\n",
+      "Epoch   0 Batch  400/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.664, Loss:  0.478\n",
+      "Epoch   0 Batch  401/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.639, Loss:  0.459\n",
+      "Epoch   0 Batch  402/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.647, Loss:  0.449\n",
+      "Epoch   0 Batch  403/1077 - Train Accuracy:  0.693, Validation Accuracy:  0.689, Loss:  0.473\n",
+      "Epoch   0 Batch  404/1077 - Train Accuracy:  0.701, Validation Accuracy:  0.670, Loss:  0.438\n",
+      "Epoch   0 Batch  405/1077 - Train Accuracy:  0.717, Validation Accuracy:  0.688, Loss:  0.506\n",
+      "Epoch   0 Batch  406/1077 - Train Accuracy:  0.730, Validation Accuracy:  0.711, Loss:  0.462\n",
+      "Epoch   0 Batch  407/1077 - Train Accuracy:  0.669, Validation Accuracy:  0.707, Loss:  0.490\n",
+      "Epoch   0 Batch  408/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.703, Loss:  0.471\n",
+      "Epoch   0 Batch  409/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.707, Loss:  0.478\n",
+      "Epoch   0 Batch  410/1077 - Train Accuracy:  0.662, Validation Accuracy:  0.701, Loss:  0.485\n",
+      "Epoch   0 Batch  411/1077 - Train Accuracy:  0.729, Validation Accuracy:  0.688, Loss:  0.457\n",
+      "Epoch   0 Batch  412/1077 - Train Accuracy:  0.697, Validation Accuracy:  0.710, Loss:  0.438\n",
+      "Epoch   0 Batch  413/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.718, Loss:  0.438\n",
+      "Epoch   0 Batch  414/1077 - Train Accuracy:  0.668, Validation Accuracy:  0.710, Loss:  0.463\n",
+      "Epoch   0 Batch  415/1077 - Train Accuracy:  0.693, Validation Accuracy:  0.697, Loss:  0.420\n",
+      "Epoch   0 Batch  416/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.696, Loss:  0.452\n",
+      "Epoch   0 Batch  417/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.689, Loss:  0.474\n",
+      "Epoch   0 Batch  418/1077 - Train Accuracy:  0.695, Validation Accuracy:  0.696, Loss:  0.449\n",
+      "Epoch   0 Batch  419/1077 - Train Accuracy:  0.707, Validation Accuracy:  0.697, Loss:  0.446\n",
+      "Epoch   0 Batch  420/1077 - Train Accuracy:  0.710, Validation Accuracy:  0.696, Loss:  0.435\n",
+      "Epoch   0 Batch  421/1077 - Train Accuracy:  0.675, Validation Accuracy:  0.704, Loss:  0.465\n",
+      "Epoch   0 Batch  422/1077 - Train Accuracy:  0.715, Validation Accuracy:  0.690, Loss:  0.433\n",
+      "Epoch   0 Batch  423/1077 - Train Accuracy:  0.695, Validation Accuracy:  0.684, Loss:  0.465\n",
+      "Epoch   0 Batch  424/1077 - Train Accuracy:  0.679, Validation Accuracy:  0.685, Loss:  0.444\n",
+      "Epoch   0 Batch  425/1077 - Train Accuracy:  0.723, Validation Accuracy:  0.670, Loss:  0.429\n",
+      "Epoch   0 Batch  426/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.688, Loss:  0.442\n",
+      "Epoch   0 Batch  427/1077 - Train Accuracy:  0.714, Validation Accuracy:  0.681, Loss:  0.433\n",
+      "Epoch   0 Batch  428/1077 - Train Accuracy:  0.711, Validation Accuracy:  0.686, Loss:  0.416\n",
+      "Epoch   0 Batch  429/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.683, Loss:  0.433\n",
+      "Epoch   0 Batch  430/1077 - Train Accuracy:  0.707, Validation Accuracy:  0.681, Loss:  0.427\n",
+      "Epoch   0 Batch  431/1077 - Train Accuracy:  0.658, Validation Accuracy:  0.670, Loss:  0.429\n",
+      "Epoch   0 Batch  432/1077 - Train Accuracy:  0.708, Validation Accuracy:  0.669, Loss:  0.440\n",
+      "Epoch   0 Batch  433/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.680, Loss:  0.433\n",
+      "Epoch   0 Batch  434/1077 - Train Accuracy:  0.707, Validation Accuracy:  0.692, Loss:  0.420\n",
+      "Epoch   0 Batch  435/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.699, Loss:  0.468\n",
+      "Epoch   0 Batch  436/1077 - Train Accuracy:  0.718, Validation Accuracy:  0.704, Loss:  0.412\n",
+      "Epoch   0 Batch  437/1077 - Train Accuracy:  0.710, Validation Accuracy:  0.711, Loss:  0.430\n",
+      "Epoch   0 Batch  438/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.700, Loss:  0.426\n",
+      "Epoch   0 Batch  439/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.709, Loss:  0.446\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch  440/1077 - Train Accuracy:  0.701, Validation Accuracy:  0.705, Loss:  0.441\n",
+      "Epoch   0 Batch  441/1077 - Train Accuracy:  0.691, Validation Accuracy:  0.699, Loss:  0.414\n",
+      "Epoch   0 Batch  442/1077 - Train Accuracy:  0.730, Validation Accuracy:  0.701, Loss:  0.412\n",
+      "Epoch   0 Batch  443/1077 - Train Accuracy:  0.728, Validation Accuracy:  0.702, Loss:  0.403\n",
+      "Epoch   0 Batch  444/1077 - Train Accuracy:  0.741, Validation Accuracy:  0.677, Loss:  0.426\n",
+      "Epoch   0 Batch  445/1077 - Train Accuracy:  0.686, Validation Accuracy:  0.675, Loss:  0.439\n",
+      "Epoch   0 Batch  446/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.666, Loss:  0.391\n",
+      "Epoch   0 Batch  447/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.670, Loss:  0.408\n",
+      "Epoch   0 Batch  448/1077 - Train Accuracy:  0.691, Validation Accuracy:  0.669, Loss:  0.430\n",
+      "Epoch   0 Batch  449/1077 - Train Accuracy:  0.672, Validation Accuracy:  0.686, Loss:  0.430\n",
+      "Epoch   0 Batch  450/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.696, Loss:  0.410\n",
+      "Epoch   0 Batch  451/1077 - Train Accuracy:  0.756, Validation Accuracy:  0.699, Loss:  0.397\n",
+      "Epoch   0 Batch  452/1077 - Train Accuracy:  0.727, Validation Accuracy:  0.691, Loss:  0.420\n",
+      "Epoch   0 Batch  453/1077 - Train Accuracy:  0.725, Validation Accuracy:  0.692, Loss:  0.381\n",
+      "Epoch   0 Batch  454/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.694, Loss:  0.409\n",
+      "Epoch   0 Batch  455/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.690, Loss:  0.391\n",
+      "Epoch   0 Batch  456/1077 - Train Accuracy:  0.738, Validation Accuracy:  0.702, Loss:  0.405\n",
+      "Epoch   0 Batch  457/1077 - Train Accuracy:  0.724, Validation Accuracy:  0.694, Loss:  0.370\n",
+      "Epoch   0 Batch  458/1077 - Train Accuracy:  0.683, Validation Accuracy:  0.693, Loss:  0.413\n",
+      "Epoch   0 Batch  459/1077 - Train Accuracy:  0.751, Validation Accuracy:  0.689, Loss:  0.391\n",
+      "Epoch   0 Batch  460/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.720, Loss:  0.425\n",
+      "Epoch   0 Batch  461/1077 - Train Accuracy:  0.713, Validation Accuracy:  0.725, Loss:  0.407\n",
+      "Epoch   0 Batch  462/1077 - Train Accuracy:  0.729, Validation Accuracy:  0.722, Loss:  0.403\n",
+      "Epoch   0 Batch  463/1077 - Train Accuracy:  0.693, Validation Accuracy:  0.717, Loss:  0.404\n",
+      "Epoch   0 Batch  464/1077 - Train Accuracy:  0.746, Validation Accuracy:  0.716, Loss:  0.403\n",
+      "Epoch   0 Batch  465/1077 - Train Accuracy:  0.714, Validation Accuracy:  0.718, Loss:  0.428\n",
+      "Epoch   0 Batch  466/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.707, Loss:  0.383\n",
+      "Epoch   0 Batch  467/1077 - Train Accuracy:  0.767, Validation Accuracy:  0.696, Loss:  0.393\n",
+      "Epoch   0 Batch  468/1077 - Train Accuracy:  0.758, Validation Accuracy:  0.712, Loss:  0.400\n",
+      "Epoch   0 Batch  469/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.717, Loss:  0.404\n",
+      "Epoch   0 Batch  470/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.712, Loss:  0.423\n",
+      "Epoch   0 Batch  471/1077 - Train Accuracy:  0.762, Validation Accuracy:  0.721, Loss:  0.375\n",
+      "Epoch   0 Batch  472/1077 - Train Accuracy:  0.722, Validation Accuracy:  0.725, Loss:  0.387\n",
+      "Epoch   0 Batch  473/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.738, Loss:  0.405\n",
+      "Epoch   0 Batch  474/1077 - Train Accuracy:  0.723, Validation Accuracy:  0.739, Loss:  0.391\n",
+      "Epoch   0 Batch  475/1077 - Train Accuracy:  0.749, Validation Accuracy:  0.716, Loss:  0.390\n",
+      "Epoch   0 Batch  476/1077 - Train Accuracy:  0.749, Validation Accuracy:  0.705, Loss:  0.383\n",
+      "Epoch   0 Batch  477/1077 - Train Accuracy:  0.756, Validation Accuracy:  0.703, Loss:  0.378\n",
+      "Epoch   0 Batch  478/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.713, Loss:  0.407\n",
+      "Epoch   0 Batch  479/1077 - Train Accuracy:  0.728, Validation Accuracy:  0.721, Loss:  0.404\n",
+      "Epoch   0 Batch  480/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.714, Loss:  0.396\n",
+      "Epoch   0 Batch  481/1077 - Train Accuracy:  0.730, Validation Accuracy:  0.708, Loss:  0.393\n",
+      "Epoch   0 Batch  482/1077 - Train Accuracy:  0.704, Validation Accuracy:  0.701, Loss:  0.413\n",
+      "Epoch   0 Batch  483/1077 - Train Accuracy:  0.709, Validation Accuracy:  0.713, Loss:  0.392\n",
+      "Epoch   0 Batch  484/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.723, Loss:  0.385\n",
+      "Epoch   0 Batch  485/1077 - Train Accuracy:  0.756, Validation Accuracy:  0.718, Loss:  0.395\n",
+      "Epoch   0 Batch  486/1077 - Train Accuracy:  0.740, Validation Accuracy:  0.721, Loss:  0.377\n",
+      "Epoch   0 Batch  487/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.717, Loss:  0.389\n",
+      "Epoch   0 Batch  488/1077 - Train Accuracy:  0.731, Validation Accuracy:  0.709, Loss:  0.393\n",
+      "Epoch   0 Batch  489/1077 - Train Accuracy:  0.739, Validation Accuracy:  0.700, Loss:  0.364\n",
+      "Epoch   0 Batch  490/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.689, Loss:  0.388\n",
+      "Epoch   0 Batch  491/1077 - Train Accuracy:  0.728, Validation Accuracy:  0.705, Loss:  0.379\n",
+      "Epoch   0 Batch  492/1077 - Train Accuracy:  0.749, Validation Accuracy:  0.709, Loss:  0.394\n",
+      "Epoch   0 Batch  493/1077 - Train Accuracy:  0.750, Validation Accuracy:  0.713, Loss:  0.357\n",
+      "Epoch   0 Batch  494/1077 - Train Accuracy:  0.739, Validation Accuracy:  0.726, Loss:  0.354\n",
+      "Epoch   0 Batch  495/1077 - Train Accuracy:  0.733, Validation Accuracy:  0.740, Loss:  0.359\n",
+      "Epoch   0 Batch  496/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.725, Loss:  0.396\n",
+      "Epoch   0 Batch  497/1077 - Train Accuracy:  0.743, Validation Accuracy:  0.735, Loss:  0.410\n",
+      "Epoch   0 Batch  498/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.727, Loss:  0.372\n",
+      "Epoch   0 Batch  499/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.728, Loss:  0.354\n",
+      "Epoch   0 Batch  500/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.722, Loss:  0.357\n",
+      "Epoch   0 Batch  501/1077 - Train Accuracy:  0.723, Validation Accuracy:  0.728, Loss:  0.364\n",
+      "Epoch   0 Batch  502/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.727, Loss:  0.377\n",
+      "Epoch   0 Batch  503/1077 - Train Accuracy:  0.748, Validation Accuracy:  0.733, Loss:  0.369\n",
+      "Epoch   0 Batch  504/1077 - Train Accuracy:  0.745, Validation Accuracy:  0.724, Loss:  0.366\n",
+      "Epoch   0 Batch  505/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.722, Loss:  0.332\n",
+      "Epoch   0 Batch  506/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.707, Loss:  0.377\n",
+      "Epoch   0 Batch  507/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.740, Loss:  0.361\n",
+      "Epoch   0 Batch  508/1077 - Train Accuracy:  0.758, Validation Accuracy:  0.734, Loss:  0.353\n",
+      "Epoch   0 Batch  509/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.719, Loss:  0.385\n",
+      "Epoch   0 Batch  510/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.719, Loss:  0.349\n",
+      "Epoch   0 Batch  511/1077 - Train Accuracy:  0.748, Validation Accuracy:  0.735, Loss:  0.360\n",
+      "Epoch   0 Batch  512/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.731, Loss:  0.355\n",
+      "Epoch   0 Batch  513/1077 - Train Accuracy:  0.759, Validation Accuracy:  0.737, Loss:  0.356\n",
+      "Epoch   0 Batch  514/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.720, Loss:  0.379\n",
+      "Epoch   0 Batch  515/1077 - Train Accuracy:  0.734, Validation Accuracy:  0.725, Loss:  0.380\n",
+      "Epoch   0 Batch  516/1077 - Train Accuracy:  0.780, Validation Accuracy:  0.732, Loss:  0.347\n",
+      "Epoch   0 Batch  517/1077 - Train Accuracy:  0.768, Validation Accuracy:  0.756, Loss:  0.353\n",
+      "Epoch   0 Batch  518/1077 - Train Accuracy:  0.794, Validation Accuracy:  0.756, Loss:  0.342\n",
+      "Epoch   0 Batch  519/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.754, Loss:  0.342\n",
+      "Epoch   0 Batch  520/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.745, Loss:  0.331\n",
+      "Epoch   0 Batch  521/1077 - Train Accuracy:  0.745, Validation Accuracy:  0.752, Loss:  0.345\n",
+      "Epoch   0 Batch  522/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.735, Loss:  0.352\n",
+      "Epoch   0 Batch  523/1077 - Train Accuracy:  0.754, Validation Accuracy:  0.743, Loss:  0.374\n",
+      "Epoch   0 Batch  524/1077 - Train Accuracy:  0.781, Validation Accuracy:  0.745, Loss:  0.351\n",
+      "Epoch   0 Batch  525/1077 - Train Accuracy:  0.734, Validation Accuracy:  0.736, Loss:  0.352\n",
+      "Epoch   0 Batch  526/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.730, Loss:  0.337\n",
+      "Epoch   0 Batch  527/1077 - Train Accuracy:  0.729, Validation Accuracy:  0.723, Loss:  0.364\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch  528/1077 - Train Accuracy:  0.746, Validation Accuracy:  0.724, Loss:  0.349\n",
+      "Epoch   0 Batch  529/1077 - Train Accuracy:  0.725, Validation Accuracy:  0.728, Loss:  0.349\n",
+      "Epoch   0 Batch  530/1077 - Train Accuracy:  0.761, Validation Accuracy:  0.752, Loss:  0.358\n",
+      "Epoch   0 Batch  531/1077 - Train Accuracy:  0.751, Validation Accuracy:  0.731, Loss:  0.346\n",
+      "Epoch   0 Batch  532/1077 - Train Accuracy:  0.717, Validation Accuracy:  0.741, Loss:  0.376\n",
+      "Epoch   0 Batch  533/1077 - Train Accuracy:  0.760, Validation Accuracy:  0.744, Loss:  0.347\n",
+      "Epoch   0 Batch  534/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.729, Loss:  0.345\n",
+      "Epoch   0 Batch  535/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.743, Loss:  0.329\n",
+      "Epoch   0 Batch  536/1077 - Train Accuracy:  0.740, Validation Accuracy:  0.745, Loss:  0.348\n",
+      "Epoch   0 Batch  537/1077 - Train Accuracy:  0.760, Validation Accuracy:  0.762, Loss:  0.339\n",
+      "Epoch   0 Batch  538/1077 - Train Accuracy:  0.788, Validation Accuracy:  0.749, Loss:  0.299\n",
+      "Epoch   0 Batch  539/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.742, Loss:  0.347\n",
+      "Epoch   0 Batch  540/1077 - Train Accuracy:  0.773, Validation Accuracy:  0.734, Loss:  0.317\n",
+      "Epoch   0 Batch  541/1077 - Train Accuracy:  0.762, Validation Accuracy:  0.739, Loss:  0.337\n",
+      "Epoch   0 Batch  542/1077 - Train Accuracy:  0.807, Validation Accuracy:  0.767, Loss:  0.325\n",
+      "Epoch   0 Batch  543/1077 - Train Accuracy:  0.782, Validation Accuracy:  0.761, Loss:  0.332\n",
+      "Epoch   0 Batch  544/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.773, Loss:  0.302\n",
+      "Epoch   0 Batch  545/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.770, Loss:  0.349\n",
+      "Epoch   0 Batch  546/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.763, Loss:  0.362\n",
+      "Epoch   0 Batch  547/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.777, Loss:  0.326\n",
+      "Epoch   0 Batch  548/1077 - Train Accuracy:  0.776, Validation Accuracy:  0.771, Loss:  0.341\n",
+      "Epoch   0 Batch  549/1077 - Train Accuracy:  0.727, Validation Accuracy:  0.761, Loss:  0.362\n",
+      "Epoch   0 Batch  550/1077 - Train Accuracy:  0.731, Validation Accuracy:  0.750, Loss:  0.340\n",
+      "Epoch   0 Batch  551/1077 - Train Accuracy:  0.747, Validation Accuracy:  0.743, Loss:  0.339\n",
+      "Epoch   0 Batch  552/1077 - Train Accuracy:  0.771, Validation Accuracy:  0.747, Loss:  0.334\n",
+      "Epoch   0 Batch  553/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.752, Loss:  0.340\n",
+      "Epoch   0 Batch  554/1077 - Train Accuracy:  0.768, Validation Accuracy:  0.753, Loss:  0.322\n",
+      "Epoch   0 Batch  555/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.759, Loss:  0.310\n",
+      "Epoch   0 Batch  556/1077 - Train Accuracy:  0.763, Validation Accuracy:  0.762, Loss:  0.293\n",
+      "Epoch   0 Batch  557/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.767, Loss:  0.314\n",
+      "Epoch   0 Batch  558/1077 - Train Accuracy:  0.797, Validation Accuracy:  0.775, Loss:  0.297\n",
+      "Epoch   0 Batch  559/1077 - Train Accuracy:  0.793, Validation Accuracy:  0.766, Loss:  0.316\n",
+      "Epoch   0 Batch  560/1077 - Train Accuracy:  0.775, Validation Accuracy:  0.756, Loss:  0.305\n",
+      "Epoch   0 Batch  561/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.763, Loss:  0.309\n",
+      "Epoch   0 Batch  562/1077 - Train Accuracy:  0.814, Validation Accuracy:  0.776, Loss:  0.276\n",
+      "Epoch   0 Batch  563/1077 - Train Accuracy:  0.771, Validation Accuracy:  0.766, Loss:  0.319\n",
+      "Epoch   0 Batch  564/1077 - Train Accuracy:  0.776, Validation Accuracy:  0.767, Loss:  0.320\n",
+      "Epoch   0 Batch  565/1077 - Train Accuracy:  0.779, Validation Accuracy:  0.760, Loss:  0.319\n",
+      "Epoch   0 Batch  566/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.752, Loss:  0.325\n",
+      "Epoch   0 Batch  567/1077 - Train Accuracy:  0.765, Validation Accuracy:  0.755, Loss:  0.310\n",
+      "Epoch   0 Batch  568/1077 - Train Accuracy:  0.808, Validation Accuracy:  0.756, Loss:  0.295\n",
+      "Epoch   0 Batch  569/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.762, Loss:  0.307\n",
+      "Epoch   0 Batch  570/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.762, Loss:  0.317\n",
+      "Epoch   0 Batch  571/1077 - Train Accuracy:  0.790, Validation Accuracy:  0.777, Loss:  0.278\n",
+      "Epoch   0 Batch  572/1077 - Train Accuracy:  0.788, Validation Accuracy:  0.779, Loss:  0.285\n",
+      "Epoch   0 Batch  573/1077 - Train Accuracy:  0.765, Validation Accuracy:  0.769, Loss:  0.328\n",
+      "Epoch   0 Batch  574/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.779, Loss:  0.315\n",
+      "Epoch   0 Batch  575/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.785, Loss:  0.292\n",
+      "Epoch   0 Batch  576/1077 - Train Accuracy:  0.794, Validation Accuracy:  0.794, Loss:  0.302\n",
+      "Epoch   0 Batch  577/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.795, Loss:  0.330\n",
+      "Epoch   0 Batch  578/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.791, Loss:  0.304\n",
+      "Epoch   0 Batch  579/1077 - Train Accuracy:  0.779, Validation Accuracy:  0.783, Loss:  0.302\n",
+      "Epoch   0 Batch  580/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.786, Loss:  0.281\n",
+      "Epoch   0 Batch  581/1077 - Train Accuracy:  0.775, Validation Accuracy:  0.792, Loss:  0.282\n",
+      "Epoch   0 Batch  582/1077 - Train Accuracy:  0.793, Validation Accuracy:  0.784, Loss:  0.287\n",
+      "Epoch   0 Batch  583/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.787, Loss:  0.317\n",
+      "Epoch   0 Batch  584/1077 - Train Accuracy:  0.794, Validation Accuracy:  0.797, Loss:  0.289\n",
+      "Epoch   0 Batch  585/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.786, Loss:  0.250\n",
+      "Epoch   0 Batch  586/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.782, Loss:  0.297\n",
+      "Epoch   0 Batch  587/1077 - Train Accuracy:  0.825, Validation Accuracy:  0.786, Loss:  0.274\n",
+      "Epoch   0 Batch  588/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.780, Loss:  0.273\n",
+      "Epoch   0 Batch  589/1077 - Train Accuracy:  0.814, Validation Accuracy:  0.780, Loss:  0.289\n",
+      "Epoch   0 Batch  590/1077 - Train Accuracy:  0.750, Validation Accuracy:  0.781, Loss:  0.304\n",
+      "Epoch   0 Batch  591/1077 - Train Accuracy:  0.805, Validation Accuracy:  0.778, Loss:  0.275\n",
+      "Epoch   0 Batch  592/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.781, Loss:  0.292\n",
+      "Epoch   0 Batch  593/1077 - Train Accuracy:  0.811, Validation Accuracy:  0.781, Loss:  0.293\n",
+      "Epoch   0 Batch  594/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.784, Loss:  0.302\n",
+      "Epoch   0 Batch  595/1077 - Train Accuracy:  0.796, Validation Accuracy:  0.779, Loss:  0.301\n",
+      "Epoch   0 Batch  596/1077 - Train Accuracy:  0.824, Validation Accuracy:  0.786, Loss:  0.284\n",
+      "Epoch   0 Batch  597/1077 - Train Accuracy:  0.775, Validation Accuracy:  0.789, Loss:  0.275\n",
+      "Epoch   0 Batch  598/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.782, Loss:  0.271\n",
+      "Epoch   0 Batch  599/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.767, Loss:  0.311\n",
+      "Epoch   0 Batch  600/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.777, Loss:  0.284\n",
+      "Epoch   0 Batch  601/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.798, Loss:  0.268\n",
+      "Epoch   0 Batch  602/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.784, Loss:  0.282\n",
+      "Epoch   0 Batch  603/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.781, Loss:  0.255\n",
+      "Epoch   0 Batch  604/1077 - Train Accuracy:  0.796, Validation Accuracy:  0.792, Loss:  0.299\n",
+      "Epoch   0 Batch  605/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.782, Loss:  0.318\n",
+      "Epoch   0 Batch  606/1077 - Train Accuracy:  0.817, Validation Accuracy:  0.787, Loss:  0.262\n",
+      "Epoch   0 Batch  607/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.785, Loss:  0.247\n",
+      "Epoch   0 Batch  608/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.785, Loss:  0.306\n",
+      "Epoch   0 Batch  609/1077 - Train Accuracy:  0.783, Validation Accuracy:  0.795, Loss:  0.267\n",
+      "Epoch   0 Batch  610/1077 - Train Accuracy:  0.800, Validation Accuracy:  0.786, Loss:  0.287\n",
+      "Epoch   0 Batch  611/1077 - Train Accuracy:  0.801, Validation Accuracy:  0.775, Loss:  0.265\n",
+      "Epoch   0 Batch  612/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.772, Loss:  0.246\n",
+      "Epoch   0 Batch  613/1077 - Train Accuracy:  0.784, Validation Accuracy:  0.779, Loss:  0.280\n",
+      "Epoch   0 Batch  614/1077 - Train Accuracy:  0.818, Validation Accuracy:  0.766, Loss:  0.257\n",
+      "Epoch   0 Batch  615/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.771, Loss:  0.273\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch  616/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.767, Loss:  0.284\n",
+      "Epoch   0 Batch  617/1077 - Train Accuracy:  0.806, Validation Accuracy:  0.771, Loss:  0.262\n",
+      "Epoch   0 Batch  618/1077 - Train Accuracy:  0.802, Validation Accuracy:  0.778, Loss:  0.270\n",
+      "Epoch   0 Batch  619/1077 - Train Accuracy:  0.789, Validation Accuracy:  0.779, Loss:  0.266\n",
+      "Epoch   0 Batch  620/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.777, Loss:  0.254\n",
+      "Epoch   0 Batch  621/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.780, Loss:  0.259\n",
+      "Epoch   0 Batch  622/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.774, Loss:  0.280\n",
+      "Epoch   0 Batch  623/1077 - Train Accuracy:  0.787, Validation Accuracy:  0.766, Loss:  0.270\n",
+      "Epoch   0 Batch  624/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.771, Loss:  0.253\n",
+      "Epoch   0 Batch  625/1077 - Train Accuracy:  0.818, Validation Accuracy:  0.782, Loss:  0.259\n",
+      "Epoch   0 Batch  626/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.790, Loss:  0.231\n",
+      "Epoch   0 Batch  627/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.787, Loss:  0.250\n",
+      "Epoch   0 Batch  628/1077 - Train Accuracy:  0.808, Validation Accuracy:  0.783, Loss:  0.253\n",
+      "Epoch   0 Batch  629/1077 - Train Accuracy:  0.789, Validation Accuracy:  0.788, Loss:  0.273\n",
+      "Epoch   0 Batch  630/1077 - Train Accuracy:  0.820, Validation Accuracy:  0.779, Loss:  0.245\n",
+      "Epoch   0 Batch  631/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.784, Loss:  0.248\n",
+      "Epoch   0 Batch  632/1077 - Train Accuracy:  0.807, Validation Accuracy:  0.792, Loss:  0.251\n",
+      "Epoch   0 Batch  633/1077 - Train Accuracy:  0.826, Validation Accuracy:  0.783, Loss:  0.269\n",
+      "Epoch   0 Batch  634/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.793, Loss:  0.236\n",
+      "Epoch   0 Batch  635/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.782, Loss:  0.271\n",
+      "Epoch   0 Batch  636/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.792, Loss:  0.234\n",
+      "Epoch   0 Batch  637/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.800, Loss:  0.248\n",
+      "Epoch   0 Batch  638/1077 - Train Accuracy:  0.817, Validation Accuracy:  0.800, Loss:  0.237\n",
+      "Epoch   0 Batch  639/1077 - Train Accuracy:  0.825, Validation Accuracy:  0.795, Loss:  0.266\n",
+      "Epoch   0 Batch  640/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.790, Loss:  0.251\n",
+      "Epoch   0 Batch  641/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.792, Loss:  0.248\n",
+      "Epoch   0 Batch  642/1077 - Train Accuracy:  0.785, Validation Accuracy:  0.795, Loss:  0.246\n",
+      "Epoch   0 Batch  643/1077 - Train Accuracy:  0.800, Validation Accuracy:  0.788, Loss:  0.226\n",
+      "Epoch   0 Batch  644/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.790, Loss:  0.249\n",
+      "Epoch   0 Batch  645/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.795, Loss:  0.247\n",
+      "Epoch   0 Batch  646/1077 - Train Accuracy:  0.803, Validation Accuracy:  0.789, Loss:  0.248\n",
+      "Epoch   0 Batch  647/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.788, Loss:  0.246\n",
+      "Epoch   0 Batch  648/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.794, Loss:  0.217\n",
+      "Epoch   0 Batch  649/1077 - Train Accuracy:  0.806, Validation Accuracy:  0.798, Loss:  0.256\n",
+      "Epoch   0 Batch  650/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.790, Loss:  0.249\n",
+      "Epoch   0 Batch  651/1077 - Train Accuracy:  0.853, Validation Accuracy:  0.794, Loss:  0.215\n",
+      "Epoch   0 Batch  652/1077 - Train Accuracy:  0.832, Validation Accuracy:  0.799, Loss:  0.257\n",
+      "Epoch   0 Batch  653/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.795, Loss:  0.227\n",
+      "Epoch   0 Batch  654/1077 - Train Accuracy:  0.830, Validation Accuracy:  0.802, Loss:  0.241\n",
+      "Epoch   0 Batch  655/1077 - Train Accuracy:  0.824, Validation Accuracy:  0.803, Loss:  0.260\n",
+      "Epoch   0 Batch  656/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.797, Loss:  0.245\n",
+      "Epoch   0 Batch  657/1077 - Train Accuracy:  0.840, Validation Accuracy:  0.792, Loss:  0.247\n",
+      "Epoch   0 Batch  658/1077 - Train Accuracy:  0.829, Validation Accuracy:  0.786, Loss:  0.215\n",
+      "Epoch   0 Batch  659/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.784, Loss:  0.234\n",
+      "Epoch   0 Batch  660/1077 - Train Accuracy:  0.812, Validation Accuracy:  0.781, Loss:  0.238\n",
+      "Epoch   0 Batch  661/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.779, Loss:  0.215\n",
+      "Epoch   0 Batch  662/1077 - Train Accuracy:  0.807, Validation Accuracy:  0.781, Loss:  0.235\n",
+      "Epoch   0 Batch  663/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.802, Loss:  0.221\n",
+      "Epoch   0 Batch  664/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.806, Loss:  0.233\n",
+      "Epoch   0 Batch  665/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.803, Loss:  0.202\n",
+      "Epoch   0 Batch  666/1077 - Train Accuracy:  0.836, Validation Accuracy:  0.798, Loss:  0.254\n",
+      "Epoch   0 Batch  667/1077 - Train Accuracy:  0.796, Validation Accuracy:  0.814, Loss:  0.258\n",
+      "Epoch   0 Batch  668/1077 - Train Accuracy:  0.831, Validation Accuracy:  0.816, Loss:  0.230\n",
+      "Epoch   0 Batch  669/1077 - Train Accuracy:  0.836, Validation Accuracy:  0.819, Loss:  0.220\n",
+      "Epoch   0 Batch  670/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.822, Loss:  0.223\n",
+      "Epoch   0 Batch  671/1077 - Train Accuracy:  0.824, Validation Accuracy:  0.832, Loss:  0.251\n",
+      "Epoch   0 Batch  672/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.836, Loss:  0.216\n",
+      "Epoch   0 Batch  673/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.838, Loss:  0.212\n",
+      "Epoch   0 Batch  674/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.836, Loss:  0.217\n",
+      "Epoch   0 Batch  675/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.828, Loss:  0.240\n",
+      "Epoch   0 Batch  676/1077 - Train Accuracy:  0.825, Validation Accuracy:  0.819, Loss:  0.225\n",
+      "Epoch   0 Batch  677/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.813, Loss:  0.256\n",
+      "Epoch   0 Batch  678/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.821, Loss:  0.198\n",
+      "Epoch   0 Batch  679/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.823, Loss:  0.236\n",
+      "Epoch   0 Batch  680/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.824, Loss:  0.210\n",
+      "Epoch   0 Batch  681/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.820, Loss:  0.239\n",
+      "Epoch   0 Batch  682/1077 - Train Accuracy:  0.800, Validation Accuracy:  0.823, Loss:  0.212\n",
+      "Epoch   0 Batch  683/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.827, Loss:  0.212\n",
+      "Epoch   0 Batch  684/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.818, Loss:  0.217\n",
+      "Epoch   0 Batch  685/1077 - Train Accuracy:  0.793, Validation Accuracy:  0.821, Loss:  0.234\n",
+      "Epoch   0 Batch  686/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.810, Loss:  0.202\n",
+      "Epoch   0 Batch  687/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.814, Loss:  0.237\n",
+      "Epoch   0 Batch  688/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.814, Loss:  0.207\n",
+      "Epoch   0 Batch  689/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.815, Loss:  0.200\n",
+      "Epoch   0 Batch  690/1077 - Train Accuracy:  0.848, Validation Accuracy:  0.820, Loss:  0.209\n",
+      "Epoch   0 Batch  691/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.824, Loss:  0.245\n",
+      "Epoch   0 Batch  692/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.819, Loss:  0.204\n",
+      "Epoch   0 Batch  693/1077 - Train Accuracy:  0.760, Validation Accuracy:  0.818, Loss:  0.267\n",
+      "Epoch   0 Batch  694/1077 - Train Accuracy:  0.838, Validation Accuracy:  0.822, Loss:  0.240\n",
+      "Epoch   0 Batch  695/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.828, Loss:  0.195\n",
+      "Epoch   0 Batch  696/1077 - Train Accuracy:  0.817, Validation Accuracy:  0.816, Loss:  0.233\n",
+      "Epoch   0 Batch  697/1077 - Train Accuracy:  0.847, Validation Accuracy:  0.811, Loss:  0.214\n",
+      "Epoch   0 Batch  698/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.815, Loss:  0.208\n",
+      "Epoch   0 Batch  699/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.821, Loss:  0.212\n",
+      "Epoch   0 Batch  700/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.817, Loss:  0.208\n",
+      "Epoch   0 Batch  701/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.808, Loss:  0.239\n",
+      "Epoch   0 Batch  702/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.812, Loss:  0.227\n",
+      "Epoch   0 Batch  703/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.811, Loss:  0.219\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch  704/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.820, Loss:  0.235\n",
+      "Epoch   0 Batch  705/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.819, Loss:  0.250\n",
+      "Epoch   0 Batch  706/1077 - Train Accuracy:  0.806, Validation Accuracy:  0.828, Loss:  0.244\n",
+      "Epoch   0 Batch  707/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.832, Loss:  0.211\n",
+      "Epoch   0 Batch  708/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.837, Loss:  0.216\n",
+      "Epoch   0 Batch  709/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.838, Loss:  0.238\n",
+      "Epoch   0 Batch  710/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.836, Loss:  0.197\n",
+      "Epoch   0 Batch  711/1077 - Train Accuracy:  0.820, Validation Accuracy:  0.838, Loss:  0.229\n",
+      "Epoch   0 Batch  712/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.845, Loss:  0.196\n",
+      "Epoch   0 Batch  713/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.825, Loss:  0.172\n",
+      "Epoch   0 Batch  714/1077 - Train Accuracy:  0.829, Validation Accuracy:  0.815, Loss:  0.209\n",
+      "Epoch   0 Batch  715/1077 - Train Accuracy:  0.830, Validation Accuracy:  0.813, Loss:  0.225\n",
+      "Epoch   0 Batch  716/1077 - Train Accuracy:  0.849, Validation Accuracy:  0.831, Loss:  0.198\n",
+      "Epoch   0 Batch  717/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.831, Loss:  0.206\n",
+      "Epoch   0 Batch  718/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.831, Loss:  0.203\n",
+      "Epoch   0 Batch  719/1077 - Train Accuracy:  0.818, Validation Accuracy:  0.837, Loss:  0.220\n",
+      "Epoch   0 Batch  720/1077 - Train Accuracy:  0.847, Validation Accuracy:  0.834, Loss:  0.215\n",
+      "Epoch   0 Batch  721/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.849, Loss:  0.221\n",
+      "Epoch   0 Batch  722/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.843, Loss:  0.193\n",
+      "Epoch   0 Batch  723/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.842, Loss:  0.207\n",
+      "Epoch   0 Batch  724/1077 - Train Accuracy:  0.838, Validation Accuracy:  0.829, Loss:  0.226\n",
+      "Epoch   0 Batch  725/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.822, Loss:  0.172\n",
+      "Epoch   0 Batch  726/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.822, Loss:  0.201\n",
+      "Epoch   0 Batch  727/1077 - Train Accuracy:  0.832, Validation Accuracy:  0.827, Loss:  0.190\n",
+      "Epoch   0 Batch  728/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.841, Loss:  0.210\n",
+      "Epoch   0 Batch  729/1077 - Train Accuracy:  0.814, Validation Accuracy:  0.835, Loss:  0.225\n",
+      "Epoch   0 Batch  730/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.837, Loss:  0.221\n",
+      "Epoch   0 Batch  731/1077 - Train Accuracy:  0.826, Validation Accuracy:  0.841, Loss:  0.190\n",
+      "Epoch   0 Batch  732/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.833, Loss:  0.216\n",
+      "Epoch   0 Batch  733/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.826, Loss:  0.208\n",
+      "Epoch   0 Batch  734/1077 - Train Accuracy:  0.862, Validation Accuracy:  0.844, Loss:  0.210\n",
+      "Epoch   0 Batch  735/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.849, Loss:  0.202\n",
+      "Epoch   0 Batch  736/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.848, Loss:  0.178\n",
+      "Epoch   0 Batch  737/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.833, Loss:  0.219\n",
+      "Epoch   0 Batch  738/1077 - Train Accuracy:  0.860, Validation Accuracy:  0.838, Loss:  0.165\n",
+      "Epoch   0 Batch  739/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.841, Loss:  0.183\n",
+      "Epoch   0 Batch  740/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.852, Loss:  0.185\n",
+      "Epoch   0 Batch  741/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.850, Loss:  0.202\n",
+      "Epoch   0 Batch  742/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.839, Loss:  0.186\n",
+      "Epoch   0 Batch  743/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.834, Loss:  0.201\n",
+      "Epoch   0 Batch  744/1077 - Train Accuracy:  0.836, Validation Accuracy:  0.840, Loss:  0.179\n",
+      "Epoch   0 Batch  745/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.843, Loss:  0.188\n",
+      "Epoch   0 Batch  746/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.839, Loss:  0.174\n",
+      "Epoch   0 Batch  747/1077 - Train Accuracy:  0.856, Validation Accuracy:  0.837, Loss:  0.165\n",
+      "Epoch   0 Batch  748/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.842, Loss:  0.174\n",
+      "Epoch   0 Batch  749/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.837, Loss:  0.191\n",
+      "Epoch   0 Batch  750/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.837, Loss:  0.170\n",
+      "Epoch   0 Batch  751/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.835, Loss:  0.177\n",
+      "Epoch   0 Batch  752/1077 - Train Accuracy:  0.853, Validation Accuracy:  0.828, Loss:  0.164\n",
+      "Epoch   0 Batch  753/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.829, Loss:  0.170\n",
+      "Epoch   0 Batch  754/1077 - Train Accuracy:  0.862, Validation Accuracy:  0.836, Loss:  0.201\n",
+      "Epoch   0 Batch  755/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.850, Loss:  0.174\n",
+      "Epoch   0 Batch  756/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.834, Loss:  0.179\n",
+      "Epoch   0 Batch  757/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.846, Loss:  0.181\n",
+      "Epoch   0 Batch  758/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.846, Loss:  0.174\n",
+      "Epoch   0 Batch  759/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.851, Loss:  0.170\n",
+      "Epoch   0 Batch  760/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.858, Loss:  0.191\n",
+      "Epoch   0 Batch  761/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.863, Loss:  0.172\n",
+      "Epoch   0 Batch  762/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.856, Loss:  0.167\n",
+      "Epoch   0 Batch  763/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.855, Loss:  0.159\n",
+      "Epoch   0 Batch  764/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.850, Loss:  0.185\n",
+      "Epoch   0 Batch  765/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.840, Loss:  0.171\n",
+      "Epoch   0 Batch  766/1077 - Train Accuracy:  0.816, Validation Accuracy:  0.838, Loss:  0.191\n",
+      "Epoch   0 Batch  767/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.846, Loss:  0.171\n",
+      "Epoch   0 Batch  768/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.867, Loss:  0.175\n",
+      "Epoch   0 Batch  769/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.876, Loss:  0.186\n",
+      "Epoch   0 Batch  770/1077 - Train Accuracy:  0.823, Validation Accuracy:  0.877, Loss:  0.168\n",
+      "Epoch   0 Batch  771/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.871, Loss:  0.194\n",
+      "Epoch   0 Batch  772/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.868, Loss:  0.153\n",
+      "Epoch   0 Batch  773/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.866, Loss:  0.179\n",
+      "Epoch   0 Batch  774/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.862, Loss:  0.186\n",
+      "Epoch   0 Batch  775/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.865, Loss:  0.190\n",
+      "Epoch   0 Batch  776/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.858, Loss:  0.160\n",
+      "Epoch   0 Batch  777/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.859, Loss:  0.174\n",
+      "Epoch   0 Batch  778/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.862, Loss:  0.163\n",
+      "Epoch   0 Batch  779/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.856, Loss:  0.186\n",
+      "Epoch   0 Batch  780/1077 - Train Accuracy:  0.809, Validation Accuracy:  0.853, Loss:  0.219\n",
+      "Epoch   0 Batch  781/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.852, Loss:  0.153\n",
+      "Epoch   0 Batch  782/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.854, Loss:  0.173\n",
+      "Epoch   0 Batch  783/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.858, Loss:  0.182\n",
+      "Epoch   0 Batch  784/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.849, Loss:  0.157\n",
+      "Epoch   0 Batch  785/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.855, Loss:  0.166\n",
+      "Epoch   0 Batch  786/1077 - Train Accuracy:  0.813, Validation Accuracy:  0.854, Loss:  0.180\n",
+      "Epoch   0 Batch  787/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.858, Loss:  0.165\n",
+      "Epoch   0 Batch  788/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.854, Loss:  0.148\n",
+      "Epoch   0 Batch  789/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.836, Loss:  0.183\n",
+      "Epoch   0 Batch  790/1077 - Train Accuracy:  0.832, Validation Accuracy:  0.847, Loss:  0.175\n",
+      "Epoch   0 Batch  791/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.849, Loss:  0.182\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch  792/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.869, Loss:  0.185\n",
+      "Epoch   0 Batch  793/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.871, Loss:  0.172\n",
+      "Epoch   0 Batch  794/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.881, Loss:  0.152\n",
+      "Epoch   0 Batch  795/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.876, Loss:  0.178\n",
+      "Epoch   0 Batch  796/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.874, Loss:  0.168\n",
+      "Epoch   0 Batch  797/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.864, Loss:  0.159\n",
+      "Epoch   0 Batch  798/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.857, Loss:  0.175\n",
+      "Epoch   0 Batch  799/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.853, Loss:  0.194\n",
+      "Epoch   0 Batch  800/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.863, Loss:  0.172\n",
+      "Epoch   0 Batch  801/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.851, Loss:  0.168\n",
+      "Epoch   0 Batch  802/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.855, Loss:  0.177\n",
+      "Epoch   0 Batch  803/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.861, Loss:  0.174\n",
+      "Epoch   0 Batch  804/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.857, Loss:  0.144\n",
+      "Epoch   0 Batch  805/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.852, Loss:  0.166\n",
+      "Epoch   0 Batch  806/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.851, Loss:  0.150\n",
+      "Epoch   0 Batch  807/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.850, Loss:  0.154\n",
+      "Epoch   0 Batch  808/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.848, Loss:  0.184\n",
+      "Epoch   0 Batch  809/1077 - Train Accuracy:  0.831, Validation Accuracy:  0.839, Loss:  0.191\n",
+      "Epoch   0 Batch  810/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.847, Loss:  0.146\n",
+      "Epoch   0 Batch  811/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.845, Loss:  0.152\n",
+      "Epoch   0 Batch  812/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.846, Loss:  0.155\n",
+      "Epoch   0 Batch  813/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.863, Loss:  0.159\n",
+      "Epoch   0 Batch  814/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.868, Loss:  0.175\n",
+      "Epoch   0 Batch  815/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.859, Loss:  0.151\n",
+      "Epoch   0 Batch  816/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.863, Loss:  0.192\n",
+      "Epoch   0 Batch  817/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.867, Loss:  0.173\n",
+      "Epoch   0 Batch  818/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.866, Loss:  0.161\n",
+      "Epoch   0 Batch  819/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.867, Loss:  0.143\n",
+      "Epoch   0 Batch  820/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.860, Loss:  0.144\n",
+      "Epoch   0 Batch  821/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.850, Loss:  0.140\n",
+      "Epoch   0 Batch  822/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.843, Loss:  0.165\n",
+      "Epoch   0 Batch  823/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.847, Loss:  0.166\n",
+      "Epoch   0 Batch  824/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.858, Loss:  0.167\n",
+      "Epoch   0 Batch  825/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.856, Loss:  0.150\n",
+      "Epoch   0 Batch  826/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.858, Loss:  0.149\n",
+      "Epoch   0 Batch  827/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.862, Loss:  0.154\n",
+      "Epoch   0 Batch  828/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.871, Loss:  0.164\n",
+      "Epoch   0 Batch  829/1077 - Train Accuracy:  0.829, Validation Accuracy:  0.865, Loss:  0.182\n",
+      "Epoch   0 Batch  830/1077 - Train Accuracy:  0.864, Validation Accuracy:  0.870, Loss:  0.157\n",
+      "Epoch   0 Batch  831/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.875, Loss:  0.159\n",
+      "Epoch   0 Batch  832/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.884, Loss:  0.152\n",
+      "Epoch   0 Batch  833/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.884, Loss:  0.163\n",
+      "Epoch   0 Batch  834/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.871, Loss:  0.160\n",
+      "Epoch   0 Batch  835/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.876, Loss:  0.160\n",
+      "Epoch   0 Batch  836/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.874, Loss:  0.172\n",
+      "Epoch   0 Batch  837/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.876, Loss:  0.165\n",
+      "Epoch   0 Batch  838/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.878, Loss:  0.140\n",
+      "Epoch   0 Batch  839/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.880, Loss:  0.126\n",
+      "Epoch   0 Batch  840/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.882, Loss:  0.132\n",
+      "Epoch   0 Batch  841/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.880, Loss:  0.157\n",
+      "Epoch   0 Batch  842/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.873, Loss:  0.139\n",
+      "Epoch   0 Batch  843/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.873, Loss:  0.128\n",
+      "Epoch   0 Batch  844/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.882, Loss:  0.132\n",
+      "Epoch   0 Batch  845/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.886, Loss:  0.141\n",
+      "Epoch   0 Batch  846/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.874, Loss:  0.162\n",
+      "Epoch   0 Batch  847/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.869, Loss:  0.166\n",
+      "Epoch   0 Batch  848/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.868, Loss:  0.138\n",
+      "Epoch   0 Batch  849/1077 - Train Accuracy:  0.860, Validation Accuracy:  0.862, Loss:  0.141\n",
+      "Epoch   0 Batch  850/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.866, Loss:  0.169\n",
+      "Epoch   0 Batch  851/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.849, Loss:  0.154\n",
+      "Epoch   0 Batch  852/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.851, Loss:  0.171\n",
+      "Epoch   0 Batch  853/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.852, Loss:  0.139\n",
+      "Epoch   0 Batch  854/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.861, Loss:  0.158\n",
+      "Epoch   0 Batch  855/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.862, Loss:  0.139\n",
+      "Epoch   0 Batch  856/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.856, Loss:  0.145\n",
+      "Epoch   0 Batch  857/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.858, Loss:  0.139\n",
+      "Epoch   0 Batch  858/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.861, Loss:  0.130\n",
+      "Epoch   0 Batch  859/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.866, Loss:  0.166\n",
+      "Epoch   0 Batch  860/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.861, Loss:  0.156\n",
+      "Epoch   0 Batch  861/1077 - Train Accuracy:  0.849, Validation Accuracy:  0.856, Loss:  0.137\n",
+      "Epoch   0 Batch  862/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.864, Loss:  0.139\n",
+      "Epoch   0 Batch  863/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.864, Loss:  0.136\n",
+      "Epoch   0 Batch  864/1077 - Train Accuracy:  0.862, Validation Accuracy:  0.863, Loss:  0.136\n",
+      "Epoch   0 Batch  865/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.870, Loss:  0.134\n",
+      "Epoch   0 Batch  866/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.864, Loss:  0.148\n",
+      "Epoch   0 Batch  867/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.863, Loss:  0.192\n",
+      "Epoch   0 Batch  868/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.858, Loss:  0.144\n",
+      "Epoch   0 Batch  869/1077 - Train Accuracy:  0.864, Validation Accuracy:  0.868, Loss:  0.145\n",
+      "Epoch   0 Batch  870/1077 - Train Accuracy:  0.831, Validation Accuracy:  0.857, Loss:  0.148\n",
+      "Epoch   0 Batch  871/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.856, Loss:  0.124\n",
+      "Epoch   0 Batch  872/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.859, Loss:  0.150\n",
+      "Epoch   0 Batch  873/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.864, Loss:  0.141\n",
+      "Epoch   0 Batch  874/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.867, Loss:  0.169\n",
+      "Epoch   0 Batch  875/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.862, Loss:  0.155\n",
+      "Epoch   0 Batch  876/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.864, Loss:  0.144\n",
+      "Epoch   0 Batch  877/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.864, Loss:  0.133\n",
+      "Epoch   0 Batch  878/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.861, Loss:  0.143\n",
+      "Epoch   0 Batch  879/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.865, Loss:  0.127\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch  880/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.868, Loss:  0.152\n",
+      "Epoch   0 Batch  881/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.864, Loss:  0.146\n",
+      "Epoch   0 Batch  882/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.868, Loss:  0.163\n",
+      "Epoch   0 Batch  883/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.859, Loss:  0.183\n",
+      "Epoch   0 Batch  884/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.865, Loss:  0.131\n",
+      "Epoch   0 Batch  885/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.869, Loss:  0.118\n",
+      "Epoch   0 Batch  886/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.861, Loss:  0.139\n",
+      "Epoch   0 Batch  887/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.861, Loss:  0.168\n",
+      "Epoch   0 Batch  888/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.865, Loss:  0.134\n",
+      "Epoch   0 Batch  889/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.858, Loss:  0.139\n",
+      "Epoch   0 Batch  890/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.854, Loss:  0.137\n",
+      "Epoch   0 Batch  891/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.854, Loss:  0.139\n",
+      "Epoch   0 Batch  892/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.871, Loss:  0.128\n",
+      "Epoch   0 Batch  893/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.869, Loss:  0.146\n",
+      "Epoch   0 Batch  894/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.876, Loss:  0.131\n",
+      "Epoch   0 Batch  895/1077 - Train Accuracy:  0.860, Validation Accuracy:  0.876, Loss:  0.128\n",
+      "Epoch   0 Batch  896/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.863, Loss:  0.143\n",
+      "Epoch   0 Batch  897/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.860, Loss:  0.115\n",
+      "Epoch   0 Batch  898/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.862, Loss:  0.113\n",
+      "Epoch   0 Batch  899/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.863, Loss:  0.147\n",
+      "Epoch   0 Batch  900/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.864, Loss:  0.156\n",
+      "Epoch   0 Batch  901/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.888, Loss:  0.166\n",
+      "Epoch   0 Batch  902/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.885, Loss:  0.145\n",
+      "Epoch   0 Batch  903/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.882, Loss:  0.130\n",
+      "Epoch   0 Batch  904/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.887, Loss:  0.134\n",
+      "Epoch   0 Batch  905/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.880, Loss:  0.113\n",
+      "Epoch   0 Batch  906/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.881, Loss:  0.130\n",
+      "Epoch   0 Batch  907/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.859, Loss:  0.132\n",
+      "Epoch   0 Batch  908/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.857, Loss:  0.144\n",
+      "Epoch   0 Batch  909/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.855, Loss:  0.142\n",
+      "Epoch   0 Batch  910/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.851, Loss:  0.132\n",
+      "Epoch   0 Batch  911/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.850, Loss:  0.129\n",
+      "Epoch   0 Batch  912/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.853, Loss:  0.133\n",
+      "Epoch   0 Batch  913/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.863, Loss:  0.162\n",
+      "Epoch   0 Batch  914/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.862, Loss:  0.144\n",
+      "Epoch   0 Batch  915/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.858, Loss:  0.131\n",
+      "Epoch   0 Batch  916/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.867, Loss:  0.150\n",
+      "Epoch   0 Batch  917/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.867, Loss:  0.127\n",
+      "Epoch   0 Batch  918/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.866, Loss:  0.119\n",
+      "Epoch   0 Batch  919/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.865, Loss:  0.119\n",
+      "Epoch   0 Batch  920/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.869, Loss:  0.133\n",
+      "Epoch   0 Batch  921/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.870, Loss:  0.139\n",
+      "Epoch   0 Batch  922/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.871, Loss:  0.145\n",
+      "Epoch   0 Batch  923/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.873, Loss:  0.117\n",
+      "Epoch   0 Batch  924/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.865, Loss:  0.148\n",
+      "Epoch   0 Batch  925/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.863, Loss:  0.123\n",
+      "Epoch   0 Batch  926/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.865, Loss:  0.125\n",
+      "Epoch   0 Batch  927/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.860, Loss:  0.142\n",
+      "Epoch   0 Batch  928/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.851, Loss:  0.130\n",
+      "Epoch   0 Batch  929/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.849, Loss:  0.129\n",
+      "Epoch   0 Batch  930/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.855, Loss:  0.120\n",
+      "Epoch   0 Batch  931/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.862, Loss:  0.108\n",
+      "Epoch   0 Batch  932/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.861, Loss:  0.143\n",
+      "Epoch   0 Batch  933/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.862, Loss:  0.124\n",
+      "Epoch   0 Batch  934/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.859, Loss:  0.109\n",
+      "Epoch   0 Batch  935/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.873, Loss:  0.118\n",
+      "Epoch   0 Batch  936/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.866, Loss:  0.135\n",
+      "Epoch   0 Batch  937/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.862, Loss:  0.147\n",
+      "Epoch   0 Batch  938/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.861, Loss:  0.137\n",
+      "Epoch   0 Batch  939/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.865, Loss:  0.133\n",
+      "Epoch   0 Batch  940/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.866, Loss:  0.116\n",
+      "Epoch   0 Batch  941/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.866, Loss:  0.124\n",
+      "Epoch   0 Batch  942/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.860, Loss:  0.125\n",
+      "Epoch   0 Batch  943/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.862, Loss:  0.135\n",
+      "Epoch   0 Batch  944/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.857, Loss:  0.116\n",
+      "Epoch   0 Batch  945/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.862, Loss:  0.114\n",
+      "Epoch   0 Batch  946/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.881, Loss:  0.121\n",
+      "Epoch   0 Batch  947/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.874, Loss:  0.131\n",
+      "Epoch   0 Batch  948/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.868, Loss:  0.118\n",
+      "Epoch   0 Batch  949/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.864, Loss:  0.100\n",
+      "Epoch   0 Batch  950/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.873, Loss:  0.114\n",
+      "Epoch   0 Batch  951/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.876, Loss:  0.132\n",
+      "Epoch   0 Batch  952/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.891, Loss:  0.107\n",
+      "Epoch   0 Batch  953/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.891, Loss:  0.111\n",
+      "Epoch   0 Batch  954/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.894, Loss:  0.127\n",
+      "Epoch   0 Batch  955/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.886, Loss:  0.133\n",
+      "Epoch   0 Batch  956/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.882, Loss:  0.134\n",
+      "Epoch   0 Batch  957/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.870, Loss:  0.102\n",
+      "Epoch   0 Batch  958/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.870, Loss:  0.123\n",
+      "Epoch   0 Batch  959/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.868, Loss:  0.125\n",
+      "Epoch   0 Batch  960/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.871, Loss:  0.111\n",
+      "Epoch   0 Batch  961/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.876, Loss:  0.117\n",
+      "Epoch   0 Batch  962/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.880, Loss:  0.123\n",
+      "Epoch   0 Batch  963/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.872, Loss:  0.144\n",
+      "Epoch   0 Batch  964/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.872, Loss:  0.101\n",
+      "Epoch   0 Batch  965/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.878, Loss:  0.124\n",
+      "Epoch   0 Batch  966/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.880, Loss:  0.109\n",
+      "Epoch   0 Batch  967/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.881, Loss:  0.125\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch  968/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.881, Loss:  0.140\n",
+      "Epoch   0 Batch  969/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.885, Loss:  0.133\n",
+      "Epoch   0 Batch  970/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.886, Loss:  0.128\n",
+      "Epoch   0 Batch  971/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.886, Loss:  0.122\n",
+      "Epoch   0 Batch  972/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.883, Loss:  0.114\n",
+      "Epoch   0 Batch  973/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.877, Loss:  0.098\n",
+      "Epoch   0 Batch  974/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.873, Loss:  0.105\n",
+      "Epoch   0 Batch  975/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.876, Loss:  0.112\n",
+      "Epoch   0 Batch  976/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.877, Loss:  0.105\n",
+      "Epoch   0 Batch  977/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.884, Loss:  0.105\n",
+      "Epoch   0 Batch  978/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.886, Loss:  0.122\n",
+      "Epoch   0 Batch  979/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.883, Loss:  0.127\n",
+      "Epoch   0 Batch  980/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.882, Loss:  0.126\n",
+      "Epoch   0 Batch  981/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.888, Loss:  0.112\n",
+      "Epoch   0 Batch  982/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.888, Loss:  0.122\n",
+      "Epoch   0 Batch  983/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.887, Loss:  0.121\n",
+      "Epoch   0 Batch  984/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.887, Loss:  0.122\n",
+      "Epoch   0 Batch  985/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.887, Loss:  0.106\n",
+      "Epoch   0 Batch  986/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.886, Loss:  0.116\n",
+      "Epoch   0 Batch  987/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.890, Loss:  0.101\n",
+      "Epoch   0 Batch  988/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.885, Loss:  0.126\n",
+      "Epoch   0 Batch  989/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.879, Loss:  0.121\n",
+      "Epoch   0 Batch  990/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.878, Loss:  0.121\n",
+      "Epoch   0 Batch  991/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.877, Loss:  0.119\n",
+      "Epoch   0 Batch  992/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.882, Loss:  0.118\n",
+      "Epoch   0 Batch  993/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.882, Loss:  0.087\n",
+      "Epoch   0 Batch  994/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.892, Loss:  0.114\n",
+      "Epoch   0 Batch  995/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.892, Loss:  0.114\n",
+      "Epoch   0 Batch  996/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.890, Loss:  0.108\n",
+      "Epoch   0 Batch  997/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.894, Loss:  0.111\n",
+      "Epoch   0 Batch  998/1077 - Train Accuracy:  0.856, Validation Accuracy:  0.889, Loss:  0.107\n",
+      "Epoch   0 Batch  999/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.903, Loss:  0.115\n",
+      "Epoch   0 Batch 1000/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.897, Loss:  0.107\n",
+      "Epoch   0 Batch 1001/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.900, Loss:  0.092\n",
+      "Epoch   0 Batch 1002/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.898, Loss:  0.096\n",
+      "Epoch   0 Batch 1003/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.895, Loss:  0.116\n",
+      "Epoch   0 Batch 1004/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.898, Loss:  0.130\n",
+      "Epoch   0 Batch 1005/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.895, Loss:  0.095\n",
+      "Epoch   0 Batch 1006/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.896, Loss:  0.091\n",
+      "Epoch   0 Batch 1007/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.876, Loss:  0.089\n",
+      "Epoch   0 Batch 1008/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.875, Loss:  0.132\n",
+      "Epoch   0 Batch 1009/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.888, Loss:  0.089\n",
+      "Epoch   0 Batch 1010/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.888, Loss:  0.105\n",
+      "Epoch   0 Batch 1011/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.882, Loss:  0.104\n",
+      "Epoch   0 Batch 1012/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.887, Loss:  0.085\n",
+      "Epoch   0 Batch 1013/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.890, Loss:  0.094\n",
+      "Epoch   0 Batch 1014/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.898, Loss:  0.114\n",
+      "Epoch   0 Batch 1015/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.895, Loss:  0.127\n",
+      "Epoch   0 Batch 1016/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.904, Loss:  0.128\n",
+      "Epoch   0 Batch 1017/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.906, Loss:  0.112\n",
+      "Epoch   0 Batch 1018/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.895, Loss:  0.101\n",
+      "Epoch   0 Batch 1019/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.907, Loss:  0.119\n",
+      "Epoch   0 Batch 1020/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.901, Loss:  0.102\n",
+      "Epoch   0 Batch 1021/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.898, Loss:  0.099\n",
+      "Epoch   0 Batch 1022/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.898, Loss:  0.092\n",
+      "Epoch   0 Batch 1023/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.894, Loss:  0.111\n",
+      "Epoch   0 Batch 1024/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.899, Loss:  0.130\n",
+      "Epoch   0 Batch 1025/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.901, Loss:  0.113\n",
+      "Epoch   0 Batch 1026/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.892, Loss:  0.113\n",
+      "Epoch   0 Batch 1027/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.888, Loss:  0.101\n",
+      "Epoch   0 Batch 1028/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.888, Loss:  0.102\n",
+      "Epoch   0 Batch 1029/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.890, Loss:  0.089\n",
+      "Epoch   0 Batch 1030/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.887, Loss:  0.121\n",
+      "Epoch   0 Batch 1031/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.883, Loss:  0.122\n",
+      "Epoch   0 Batch 1032/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.882, Loss:  0.129\n",
+      "Epoch   0 Batch 1033/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.885, Loss:  0.108\n",
+      "Epoch   0 Batch 1034/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.886, Loss:  0.111\n",
+      "Epoch   0 Batch 1035/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.881, Loss:  0.081\n",
+      "Epoch   0 Batch 1036/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.874, Loss:  0.117\n",
+      "Epoch   0 Batch 1037/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.877, Loss:  0.121\n",
+      "Epoch   0 Batch 1038/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.878, Loss:  0.126\n",
+      "Epoch   0 Batch 1039/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.883, Loss:  0.112\n",
+      "Epoch   0 Batch 1040/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.886, Loss:  0.121\n",
+      "Epoch   0 Batch 1041/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.888, Loss:  0.120\n",
+      "Epoch   0 Batch 1042/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.888, Loss:  0.102\n",
+      "Epoch   0 Batch 1043/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.892, Loss:  0.122\n",
+      "Epoch   0 Batch 1044/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.888, Loss:  0.119\n",
+      "Epoch   0 Batch 1045/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.874, Loss:  0.095\n",
+      "Epoch   0 Batch 1046/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.881, Loss:  0.085\n",
+      "Epoch   0 Batch 1047/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.891, Loss:  0.100\n",
+      "Epoch   0 Batch 1048/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.891, Loss:  0.110\n",
+      "Epoch   0 Batch 1049/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.895, Loss:  0.097\n",
+      "Epoch   0 Batch 1050/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.898, Loss:  0.101\n",
+      "Epoch   0 Batch 1051/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.883, Loss:  0.108\n",
+      "Epoch   0 Batch 1052/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.894, Loss:  0.103\n",
+      "Epoch   0 Batch 1053/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.901, Loss:  0.114\n",
+      "Epoch   0 Batch 1054/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.891, Loss:  0.100\n",
+      "Epoch   0 Batch 1055/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.890, Loss:  0.111\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   0 Batch 1056/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.892, Loss:  0.099\n",
+      "Epoch   0 Batch 1057/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.886, Loss:  0.119\n",
+      "Epoch   0 Batch 1058/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.883, Loss:  0.116\n",
+      "Epoch   0 Batch 1059/1077 - Train Accuracy:  0.847, Validation Accuracy:  0.876, Loss:  0.138\n",
+      "Epoch   0 Batch 1060/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.876, Loss:  0.100\n",
+      "Epoch   0 Batch 1061/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.880, Loss:  0.123\n",
+      "Epoch   0 Batch 1062/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.879, Loss:  0.107\n",
+      "Epoch   0 Batch 1063/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.885, Loss:  0.107\n",
+      "Epoch   0 Batch 1064/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.888, Loss:  0.107\n",
+      "Epoch   0 Batch 1065/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.889, Loss:  0.096\n",
+      "Epoch   0 Batch 1066/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.887, Loss:  0.089\n",
+      "Epoch   0 Batch 1067/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.886, Loss:  0.116\n",
+      "Epoch   0 Batch 1068/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.876, Loss:  0.091\n",
+      "Epoch   0 Batch 1069/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.883, Loss:  0.079\n",
+      "Epoch   0 Batch 1070/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.871, Loss:  0.100\n",
+      "Epoch   0 Batch 1071/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.875, Loss:  0.105\n",
+      "Epoch   0 Batch 1072/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.880, Loss:  0.106\n",
+      "Epoch   0 Batch 1073/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.876, Loss:  0.123\n",
+      "Epoch   0 Batch 1074/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.875, Loss:  0.117\n",
+      "Epoch   0 Batch 1075/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.876, Loss:  0.108\n",
+      "Epoch   1 Batch    0/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.878, Loss:  0.086\n",
+      "Epoch   1 Batch    1/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.875, Loss:  0.094\n",
+      "Epoch   1 Batch    2/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.875, Loss:  0.111\n",
+      "Epoch   1 Batch    3/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.883, Loss:  0.106\n",
+      "Epoch   1 Batch    4/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.880, Loss:  0.093\n",
+      "Epoch   1 Batch    5/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.889, Loss:  0.136\n",
+      "Epoch   1 Batch    6/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.886, Loss:  0.103\n",
+      "Epoch   1 Batch    7/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.883, Loss:  0.088\n",
+      "Epoch   1 Batch    8/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.881, Loss:  0.104\n",
+      "Epoch   1 Batch    9/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.885, Loss:  0.093\n",
+      "Epoch   1 Batch   10/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.892, Loss:  0.098\n",
+      "Epoch   1 Batch   11/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.893, Loss:  0.106\n",
+      "Epoch   1 Batch   12/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.902, Loss:  0.103\n",
+      "Epoch   1 Batch   13/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.898, Loss:  0.121\n",
+      "Epoch   1 Batch   14/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.892, Loss:  0.080\n",
+      "Epoch   1 Batch   15/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.896, Loss:  0.093\n",
+      "Epoch   1 Batch   16/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.899, Loss:  0.116\n",
+      "Epoch   1 Batch   17/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.894, Loss:  0.091\n",
+      "Epoch   1 Batch   18/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.897, Loss:  0.098\n",
+      "Epoch   1 Batch   19/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.889, Loss:  0.097\n",
+      "Epoch   1 Batch   20/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.892, Loss:  0.086\n",
+      "Epoch   1 Batch   21/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.888, Loss:  0.110\n",
+      "Epoch   1 Batch   22/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.892, Loss:  0.101\n",
+      "Epoch   1 Batch   23/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.896, Loss:  0.104\n",
+      "Epoch   1 Batch   24/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.904, Loss:  0.096\n",
+      "Epoch   1 Batch   25/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.897, Loss:  0.078\n",
+      "Epoch   1 Batch   26/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.902, Loss:  0.102\n",
+      "Epoch   1 Batch   27/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.897, Loss:  0.088\n",
+      "Epoch   1 Batch   28/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.887, Loss:  0.095\n",
+      "Epoch   1 Batch   29/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.883, Loss:  0.099\n",
+      "Epoch   1 Batch   30/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.892, Loss:  0.088\n",
+      "Epoch   1 Batch   31/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.890, Loss:  0.088\n",
+      "Epoch   1 Batch   32/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.892, Loss:  0.093\n",
+      "Epoch   1 Batch   33/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.894, Loss:  0.092\n",
+      "Epoch   1 Batch   34/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.892, Loss:  0.084\n",
+      "Epoch   1 Batch   35/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.891, Loss:  0.090\n",
+      "Epoch   1 Batch   36/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.892, Loss:  0.091\n",
+      "Epoch   1 Batch   37/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.896, Loss:  0.102\n",
+      "Epoch   1 Batch   38/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.898, Loss:  0.127\n",
+      "Epoch   1 Batch   39/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.885, Loss:  0.123\n",
+      "Epoch   1 Batch   40/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.892, Loss:  0.082\n",
+      "Epoch   1 Batch   41/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.892, Loss:  0.092\n",
+      "Epoch   1 Batch   42/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.890, Loss:  0.106\n",
+      "Epoch   1 Batch   43/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.889, Loss:  0.070\n",
+      "Epoch   1 Batch   44/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.888, Loss:  0.093\n",
+      "Epoch   1 Batch   45/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.891, Loss:  0.099\n",
+      "Epoch   1 Batch   46/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.887, Loss:  0.107\n",
+      "Epoch   1 Batch   47/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.888, Loss:  0.096\n",
+      "Epoch   1 Batch   48/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.899, Loss:  0.125\n",
+      "Epoch   1 Batch   49/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.908, Loss:  0.103\n",
+      "Epoch   1 Batch   50/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.906, Loss:  0.090\n",
+      "Epoch   1 Batch   51/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.903, Loss:  0.096\n",
+      "Epoch   1 Batch   52/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.907, Loss:  0.111\n",
+      "Epoch   1 Batch   53/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.897, Loss:  0.091\n",
+      "Epoch   1 Batch   54/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.894, Loss:  0.130\n",
+      "Epoch   1 Batch   55/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.895, Loss:  0.093\n",
+      "Epoch   1 Batch   56/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.889, Loss:  0.080\n",
+      "Epoch   1 Batch   57/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.894, Loss:  0.102\n",
+      "Epoch   1 Batch   58/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.891, Loss:  0.087\n",
+      "Epoch   1 Batch   59/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.889, Loss:  0.091\n",
+      "Epoch   1 Batch   60/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.895, Loss:  0.079\n",
+      "Epoch   1 Batch   61/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.899, Loss:  0.100\n",
+      "Epoch   1 Batch   62/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.904, Loss:  0.104\n",
+      "Epoch   1 Batch   63/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.903, Loss:  0.076\n",
+      "Epoch   1 Batch   64/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.903, Loss:  0.089\n",
+      "Epoch   1 Batch   65/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.907, Loss:  0.088\n",
+      "Epoch   1 Batch   66/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.912, Loss:  0.067\n",
+      "Epoch   1 Batch   67/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.905, Loss:  0.086\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch   68/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.906, Loss:  0.092\n",
+      "Epoch   1 Batch   69/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.912, Loss:  0.109\n",
+      "Epoch   1 Batch   70/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.906, Loss:  0.094\n",
+      "Epoch   1 Batch   71/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.064\n",
+      "Epoch   1 Batch   72/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.901, Loss:  0.097\n",
+      "Epoch   1 Batch   73/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.901, Loss:  0.093\n",
+      "Epoch   1 Batch   74/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.898, Loss:  0.082\n",
+      "Epoch   1 Batch   75/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.902, Loss:  0.122\n",
+      "Epoch   1 Batch   76/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.892, Loss:  0.076\n",
+      "Epoch   1 Batch   77/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.892, Loss:  0.085\n",
+      "Epoch   1 Batch   78/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.891, Loss:  0.090\n",
+      "Epoch   1 Batch   79/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.890, Loss:  0.080\n",
+      "Epoch   1 Batch   80/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.880, Loss:  0.089\n",
+      "Epoch   1 Batch   81/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.876, Loss:  0.070\n",
+      "Epoch   1 Batch   82/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.881, Loss:  0.089\n",
+      "Epoch   1 Batch   83/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.878, Loss:  0.097\n",
+      "Epoch   1 Batch   84/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.877, Loss:  0.095\n",
+      "Epoch   1 Batch   85/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.884, Loss:  0.092\n",
+      "Epoch   1 Batch   86/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.892, Loss:  0.093\n",
+      "Epoch   1 Batch   87/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.893, Loss:  0.118\n",
+      "Epoch   1 Batch   88/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.892, Loss:  0.098\n",
+      "Epoch   1 Batch   89/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.891, Loss:  0.098\n",
+      "Epoch   1 Batch   90/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.902, Loss:  0.101\n",
+      "Epoch   1 Batch   91/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.907, Loss:  0.077\n",
+      "Epoch   1 Batch   92/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.904, Loss:  0.105\n",
+      "Epoch   1 Batch   93/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.891, Loss:  0.084\n",
+      "Epoch   1 Batch   94/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.890, Loss:  0.075\n",
+      "Epoch   1 Batch   95/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.103\n",
+      "Epoch   1 Batch   96/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.892, Loss:  0.105\n",
+      "Epoch   1 Batch   97/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.893, Loss:  0.099\n",
+      "Epoch   1 Batch   98/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.890, Loss:  0.100\n",
+      "Epoch   1 Batch   99/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.896, Loss:  0.098\n",
+      "Epoch   1 Batch  100/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.899, Loss:  0.093\n",
+      "Epoch   1 Batch  101/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.885, Loss:  0.077\n",
+      "Epoch   1 Batch  102/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.883, Loss:  0.102\n",
+      "Epoch   1 Batch  103/1077 - Train Accuracy:  0.856, Validation Accuracy:  0.874, Loss:  0.112\n",
+      "Epoch   1 Batch  104/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.873, Loss:  0.102\n",
+      "Epoch   1 Batch  105/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.887, Loss:  0.087\n",
+      "Epoch   1 Batch  106/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.896, Loss:  0.114\n",
+      "Epoch   1 Batch  107/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.896, Loss:  0.103\n",
+      "Epoch   1 Batch  108/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.895, Loss:  0.099\n",
+      "Epoch   1 Batch  109/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.892, Loss:  0.087\n",
+      "Epoch   1 Batch  110/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.899, Loss:  0.082\n",
+      "Epoch   1 Batch  111/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.896, Loss:  0.082\n",
+      "Epoch   1 Batch  112/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.900, Loss:  0.098\n",
+      "Epoch   1 Batch  113/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.895, Loss:  0.087\n",
+      "Epoch   1 Batch  114/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.898, Loss:  0.075\n",
+      "Epoch   1 Batch  115/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.896, Loss:  0.110\n",
+      "Epoch   1 Batch  116/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.896, Loss:  0.096\n",
+      "Epoch   1 Batch  117/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.903, Loss:  0.090\n",
+      "Epoch   1 Batch  118/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.905, Loss:  0.087\n",
+      "Epoch   1 Batch  119/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.911, Loss:  0.094\n",
+      "Epoch   1 Batch  120/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.915, Loss:  0.097\n",
+      "Epoch   1 Batch  121/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.914, Loss:  0.092\n",
+      "Epoch   1 Batch  122/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.914, Loss:  0.081\n",
+      "Epoch   1 Batch  123/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.903, Loss:  0.074\n",
+      "Epoch   1 Batch  124/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.892, Loss:  0.104\n",
+      "Epoch   1 Batch  125/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.897, Loss:  0.091\n",
+      "Epoch   1 Batch  126/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.898, Loss:  0.073\n",
+      "Epoch   1 Batch  127/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.897, Loss:  0.091\n",
+      "Epoch   1 Batch  128/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.892, Loss:  0.090\n",
+      "Epoch   1 Batch  129/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.902, Loss:  0.111\n",
+      "Epoch   1 Batch  130/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.895, Loss:  0.081\n",
+      "Epoch   1 Batch  131/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.889, Loss:  0.088\n",
+      "Epoch   1 Batch  132/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.902, Loss:  0.093\n",
+      "Epoch   1 Batch  133/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.898, Loss:  0.078\n",
+      "Epoch   1 Batch  134/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.890, Loss:  0.087\n",
+      "Epoch   1 Batch  135/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.896, Loss:  0.094\n",
+      "Epoch   1 Batch  136/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.898, Loss:  0.086\n",
+      "Epoch   1 Batch  137/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.067\n",
+      "Epoch   1 Batch  138/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.900, Loss:  0.086\n",
+      "Epoch   1 Batch  139/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.896, Loss:  0.110\n",
+      "Epoch   1 Batch  140/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.901, Loss:  0.096\n",
+      "Epoch   1 Batch  141/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.896, Loss:  0.094\n",
+      "Epoch   1 Batch  142/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.891, Loss:  0.080\n",
+      "Epoch   1 Batch  143/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.888, Loss:  0.101\n",
+      "Epoch   1 Batch  144/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.888, Loss:  0.113\n",
+      "Epoch   1 Batch  145/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.897, Loss:  0.088\n",
+      "Epoch   1 Batch  146/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.891, Loss:  0.104\n",
+      "Epoch   1 Batch  147/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.902, Loss:  0.088\n",
+      "Epoch   1 Batch  148/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.894, Loss:  0.098\n",
+      "Epoch   1 Batch  149/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.892, Loss:  0.096\n",
+      "Epoch   1 Batch  150/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.898, Loss:  0.095\n",
+      "Epoch   1 Batch  151/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.898, Loss:  0.077\n",
+      "Epoch   1 Batch  152/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.897, Loss:  0.118\n",
+      "Epoch   1 Batch  153/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.891, Loss:  0.110\n",
+      "Epoch   1 Batch  154/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.881, Loss:  0.079\n",
+      "Epoch   1 Batch  155/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.887, Loss:  0.093\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch  156/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.887, Loss:  0.079\n",
+      "Epoch   1 Batch  157/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.894, Loss:  0.086\n",
+      "Epoch   1 Batch  158/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.888, Loss:  0.111\n",
+      "Epoch   1 Batch  159/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.892, Loss:  0.071\n",
+      "Epoch   1 Batch  160/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.892, Loss:  0.076\n",
+      "Epoch   1 Batch  161/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.893, Loss:  0.081\n",
+      "Epoch   1 Batch  162/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.896, Loss:  0.103\n",
+      "Epoch   1 Batch  163/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.893, Loss:  0.106\n",
+      "Epoch   1 Batch  164/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.893, Loss:  0.087\n",
+      "Epoch   1 Batch  165/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.903, Loss:  0.065\n",
+      "Epoch   1 Batch  166/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.901, Loss:  0.102\n",
+      "Epoch   1 Batch  167/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.902, Loss:  0.083\n",
+      "Epoch   1 Batch  168/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.906, Loss:  0.094\n",
+      "Epoch   1 Batch  169/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.901, Loss:  0.108\n",
+      "Epoch   1 Batch  170/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.900, Loss:  0.088\n",
+      "Epoch   1 Batch  171/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.896, Loss:  0.083\n",
+      "Epoch   1 Batch  172/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.901, Loss:  0.066\n",
+      "Epoch   1 Batch  173/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.911, Loss:  0.095\n",
+      "Epoch   1 Batch  174/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.897, Loss:  0.077\n",
+      "Epoch   1 Batch  175/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.896, Loss:  0.094\n",
+      "Epoch   1 Batch  176/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.900, Loss:  0.085\n",
+      "Epoch   1 Batch  177/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.910, Loss:  0.107\n",
+      "Epoch   1 Batch  178/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.903, Loss:  0.089\n",
+      "Epoch   1 Batch  179/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.896, Loss:  0.095\n",
+      "Epoch   1 Batch  180/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.895, Loss:  0.079\n",
+      "Epoch   1 Batch  181/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.899, Loss:  0.102\n",
+      "Epoch   1 Batch  182/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.899, Loss:  0.104\n",
+      "Epoch   1 Batch  183/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.894, Loss:  0.088\n",
+      "Epoch   1 Batch  184/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.896, Loss:  0.090\n",
+      "Epoch   1 Batch  185/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.892, Loss:  0.096\n",
+      "Epoch   1 Batch  186/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.888, Loss:  0.099\n",
+      "Epoch   1 Batch  187/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.889, Loss:  0.071\n",
+      "Epoch   1 Batch  188/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.885, Loss:  0.081\n",
+      "Epoch   1 Batch  189/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.895, Loss:  0.077\n",
+      "Epoch   1 Batch  190/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.892, Loss:  0.075\n",
+      "Epoch   1 Batch  191/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.888, Loss:  0.072\n",
+      "Epoch   1 Batch  192/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.895, Loss:  0.090\n",
+      "Epoch   1 Batch  193/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.897, Loss:  0.080\n",
+      "Epoch   1 Batch  194/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.897, Loss:  0.072\n",
+      "Epoch   1 Batch  195/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.900, Loss:  0.074\n",
+      "Epoch   1 Batch  196/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.903, Loss:  0.077\n",
+      "Epoch   1 Batch  197/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.898, Loss:  0.095\n",
+      "Epoch   1 Batch  198/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.898, Loss:  0.087\n",
+      "Epoch   1 Batch  199/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.900, Loss:  0.070\n",
+      "Epoch   1 Batch  200/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.897, Loss:  0.102\n",
+      "Epoch   1 Batch  201/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.893, Loss:  0.072\n",
+      "Epoch   1 Batch  202/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.897, Loss:  0.082\n",
+      "Epoch   1 Batch  203/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.894, Loss:  0.081\n",
+      "Epoch   1 Batch  204/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.889, Loss:  0.108\n",
+      "Epoch   1 Batch  205/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.880, Loss:  0.102\n",
+      "Epoch   1 Batch  206/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.886, Loss:  0.079\n",
+      "Epoch   1 Batch  207/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.877, Loss:  0.077\n",
+      "Epoch   1 Batch  208/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.879, Loss:  0.090\n",
+      "Epoch   1 Batch  209/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.880, Loss:  0.075\n",
+      "Epoch   1 Batch  210/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.880, Loss:  0.094\n",
+      "Epoch   1 Batch  211/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.890, Loss:  0.082\n",
+      "Epoch   1 Batch  212/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.897, Loss:  0.073\n",
+      "Epoch   1 Batch  213/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.866, Loss:  0.074\n",
+      "Epoch   1 Batch  214/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.873, Loss:  0.074\n",
+      "Epoch   1 Batch  215/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.870, Loss:  0.090\n",
+      "Epoch   1 Batch  216/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.873, Loss:  0.092\n",
+      "Epoch   1 Batch  217/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.877, Loss:  0.072\n",
+      "Epoch   1 Batch  218/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.877, Loss:  0.102\n",
+      "Epoch   1 Batch  219/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.877, Loss:  0.086\n",
+      "Epoch   1 Batch  220/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.874, Loss:  0.081\n",
+      "Epoch   1 Batch  221/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.896, Loss:  0.085\n",
+      "Epoch   1 Batch  222/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.900, Loss:  0.078\n",
+      "Epoch   1 Batch  223/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.900, Loss:  0.070\n",
+      "Epoch   1 Batch  224/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.898, Loss:  0.097\n",
+      "Epoch   1 Batch  225/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.905, Loss:  0.095\n",
+      "Epoch   1 Batch  226/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.905, Loss:  0.084\n",
+      "Epoch   1 Batch  227/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.910, Loss:  0.104\n",
+      "Epoch   1 Batch  228/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.071\n",
+      "Epoch   1 Batch  229/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.891, Loss:  0.098\n",
+      "Epoch   1 Batch  230/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.883, Loss:  0.092\n",
+      "Epoch   1 Batch  231/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.893, Loss:  0.085\n",
+      "Epoch   1 Batch  232/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.890, Loss:  0.075\n",
+      "Epoch   1 Batch  233/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.892, Loss:  0.108\n",
+      "Epoch   1 Batch  234/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.891, Loss:  0.093\n",
+      "Epoch   1 Batch  235/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.888, Loss:  0.088\n",
+      "Epoch   1 Batch  236/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.892, Loss:  0.095\n",
+      "Epoch   1 Batch  237/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.905, Loss:  0.075\n",
+      "Epoch   1 Batch  238/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.902, Loss:  0.080\n",
+      "Epoch   1 Batch  239/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.884, Loss:  0.061\n",
+      "Epoch   1 Batch  240/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.887, Loss:  0.071\n",
+      "Epoch   1 Batch  241/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.886, Loss:  0.063\n",
+      "Epoch   1 Batch  242/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.886, Loss:  0.065\n",
+      "Epoch   1 Batch  243/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.891, Loss:  0.085\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch  244/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.889, Loss:  0.076\n",
+      "Epoch   1 Batch  245/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.907, Loss:  0.077\n",
+      "Epoch   1 Batch  246/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.903, Loss:  0.072\n",
+      "Epoch   1 Batch  247/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.905, Loss:  0.076\n",
+      "Epoch   1 Batch  248/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.905, Loss:  0.090\n",
+      "Epoch   1 Batch  249/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.909, Loss:  0.080\n",
+      "Epoch   1 Batch  250/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.903, Loss:  0.073\n",
+      "Epoch   1 Batch  251/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.902, Loss:  0.090\n",
+      "Epoch   1 Batch  252/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.909, Loss:  0.081\n",
+      "Epoch   1 Batch  253/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.908, Loss:  0.080\n",
+      "Epoch   1 Batch  254/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.906, Loss:  0.089\n",
+      "Epoch   1 Batch  255/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.897, Loss:  0.079\n",
+      "Epoch   1 Batch  256/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.894, Loss:  0.103\n",
+      "Epoch   1 Batch  257/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.898, Loss:  0.087\n",
+      "Epoch   1 Batch  258/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.903, Loss:  0.083\n",
+      "Epoch   1 Batch  259/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.911, Loss:  0.072\n",
+      "Epoch   1 Batch  260/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.067\n",
+      "Epoch   1 Batch  261/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.086\n",
+      "Epoch   1 Batch  262/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.907, Loss:  0.070\n",
+      "Epoch   1 Batch  263/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.904, Loss:  0.071\n",
+      "Epoch   1 Batch  264/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.900, Loss:  0.077\n",
+      "Epoch   1 Batch  265/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.900, Loss:  0.075\n",
+      "Epoch   1 Batch  266/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.899, Loss:  0.088\n",
+      "Epoch   1 Batch  267/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.899, Loss:  0.076\n",
+      "Epoch   1 Batch  268/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.900, Loss:  0.088\n",
+      "Epoch   1 Batch  269/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.901, Loss:  0.099\n",
+      "Epoch   1 Batch  270/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.897, Loss:  0.082\n",
+      "Epoch   1 Batch  271/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.897, Loss:  0.072\n",
+      "Epoch   1 Batch  272/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.893, Loss:  0.111\n",
+      "Epoch   1 Batch  273/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.887, Loss:  0.072\n",
+      "Epoch   1 Batch  274/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.898, Loss:  0.082\n",
+      "Epoch   1 Batch  275/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.898, Loss:  0.082\n",
+      "Epoch   1 Batch  276/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.896, Loss:  0.122\n",
+      "Epoch   1 Batch  277/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.897, Loss:  0.072\n",
+      "Epoch   1 Batch  278/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.895, Loss:  0.089\n",
+      "Epoch   1 Batch  279/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.890, Loss:  0.090\n",
+      "Epoch   1 Batch  280/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.892, Loss:  0.090\n",
+      "Epoch   1 Batch  281/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.889, Loss:  0.100\n",
+      "Epoch   1 Batch  282/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.896, Loss:  0.106\n",
+      "Epoch   1 Batch  283/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.900, Loss:  0.087\n",
+      "Epoch   1 Batch  284/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.899, Loss:  0.089\n",
+      "Epoch   1 Batch  285/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.898, Loss:  0.084\n",
+      "Epoch   1 Batch  286/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.081\n",
+      "Epoch   1 Batch  287/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.891, Loss:  0.076\n",
+      "Epoch   1 Batch  288/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.889, Loss:  0.089\n",
+      "Epoch   1 Batch  289/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.892, Loss:  0.078\n",
+      "Epoch   1 Batch  290/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.902, Loss:  0.109\n",
+      "Epoch   1 Batch  291/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.902, Loss:  0.103\n",
+      "Epoch   1 Batch  292/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.902, Loss:  0.085\n",
+      "Epoch   1 Batch  293/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.908, Loss:  0.089\n",
+      "Epoch   1 Batch  294/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.906, Loss:  0.078\n",
+      "Epoch   1 Batch  295/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.907, Loss:  0.091\n",
+      "Epoch   1 Batch  296/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.907, Loss:  0.085\n",
+      "Epoch   1 Batch  297/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.896, Loss:  0.094\n",
+      "Epoch   1 Batch  298/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.898, Loss:  0.092\n",
+      "Epoch   1 Batch  299/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.911, Loss:  0.085\n",
+      "Epoch   1 Batch  300/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.080\n",
+      "Epoch   1 Batch  301/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.906, Loss:  0.070\n",
+      "Epoch   1 Batch  302/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.898, Loss:  0.084\n",
+      "Epoch   1 Batch  303/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.898, Loss:  0.094\n",
+      "Epoch   1 Batch  304/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.900, Loss:  0.079\n",
+      "Epoch   1 Batch  305/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.892, Loss:  0.074\n",
+      "Epoch   1 Batch  306/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.901, Loss:  0.089\n",
+      "Epoch   1 Batch  307/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.881, Loss:  0.072\n",
+      "Epoch   1 Batch  308/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.885, Loss:  0.097\n",
+      "Epoch   1 Batch  309/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.882, Loss:  0.073\n",
+      "Epoch   1 Batch  310/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.884, Loss:  0.080\n",
+      "Epoch   1 Batch  311/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.885, Loss:  0.074\n",
+      "Epoch   1 Batch  312/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.895, Loss:  0.095\n",
+      "Epoch   1 Batch  313/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.894, Loss:  0.064\n",
+      "Epoch   1 Batch  314/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.895, Loss:  0.076\n",
+      "Epoch   1 Batch  315/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.901, Loss:  0.070\n",
+      "Epoch   1 Batch  316/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.884, Loss:  0.075\n",
+      "Epoch   1 Batch  317/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.890, Loss:  0.104\n",
+      "Epoch   1 Batch  318/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.891, Loss:  0.068\n",
+      "Epoch   1 Batch  319/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.881, Loss:  0.094\n",
+      "Epoch   1 Batch  320/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.877, Loss:  0.088\n",
+      "Epoch   1 Batch  321/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.879, Loss:  0.070\n",
+      "Epoch   1 Batch  322/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.880, Loss:  0.073\n",
+      "Epoch   1 Batch  323/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.877, Loss:  0.086\n",
+      "Epoch   1 Batch  324/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.891, Loss:  0.065\n",
+      "Epoch   1 Batch  325/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.883, Loss:  0.091\n",
+      "Epoch   1 Batch  326/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.888, Loss:  0.073\n",
+      "Epoch   1 Batch  327/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.889, Loss:  0.093\n",
+      "Epoch   1 Batch  328/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.892, Loss:  0.087\n",
+      "Epoch   1 Batch  329/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.889, Loss:  0.087\n",
+      "Epoch   1 Batch  330/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.890, Loss:  0.086\n",
+      "Epoch   1 Batch  331/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.891, Loss:  0.091\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch  332/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.878, Loss:  0.066\n",
+      "Epoch   1 Batch  333/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.882, Loss:  0.063\n",
+      "Epoch   1 Batch  334/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.885, Loss:  0.074\n",
+      "Epoch   1 Batch  335/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.888, Loss:  0.077\n",
+      "Epoch   1 Batch  336/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.884, Loss:  0.108\n",
+      "Epoch   1 Batch  337/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.880, Loss:  0.081\n",
+      "Epoch   1 Batch  338/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.881, Loss:  0.106\n",
+      "Epoch   1 Batch  339/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.881, Loss:  0.070\n",
+      "Epoch   1 Batch  340/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.896, Loss:  0.070\n",
+      "Epoch   1 Batch  341/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.901, Loss:  0.093\n",
+      "Epoch   1 Batch  342/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.905, Loss:  0.069\n",
+      "Epoch   1 Batch  343/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.897, Loss:  0.073\n",
+      "Epoch   1 Batch  344/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.901, Loss:  0.073\n",
+      "Epoch   1 Batch  345/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.903, Loss:  0.062\n",
+      "Epoch   1 Batch  346/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.902, Loss:  0.082\n",
+      "Epoch   1 Batch  347/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.065\n",
+      "Epoch   1 Batch  348/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.077\n",
+      "Epoch   1 Batch  349/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.907, Loss:  0.073\n",
+      "Epoch   1 Batch  350/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.914, Loss:  0.088\n",
+      "Epoch   1 Batch  351/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.912, Loss:  0.073\n",
+      "Epoch   1 Batch  352/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.912, Loss:  0.069\n",
+      "Epoch   1 Batch  353/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.901, Loss:  0.082\n",
+      "Epoch   1 Batch  354/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.903, Loss:  0.081\n",
+      "Epoch   1 Batch  355/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.898, Loss:  0.072\n",
+      "Epoch   1 Batch  356/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.901, Loss:  0.083\n",
+      "Epoch   1 Batch  357/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.890, Loss:  0.074\n",
+      "Epoch   1 Batch  358/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.894, Loss:  0.089\n",
+      "Epoch   1 Batch  359/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.898, Loss:  0.076\n",
+      "Epoch   1 Batch  360/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.893, Loss:  0.066\n",
+      "Epoch   1 Batch  361/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.892, Loss:  0.074\n",
+      "Epoch   1 Batch  362/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.887, Loss:  0.085\n",
+      "Epoch   1 Batch  363/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.890, Loss:  0.079\n",
+      "Epoch   1 Batch  364/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.888, Loss:  0.083\n",
+      "Epoch   1 Batch  365/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.885, Loss:  0.064\n",
+      "Epoch   1 Batch  366/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.885, Loss:  0.069\n",
+      "Epoch   1 Batch  367/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.878, Loss:  0.055\n",
+      "Epoch   1 Batch  368/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.877, Loss:  0.083\n",
+      "Epoch   1 Batch  369/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.880, Loss:  0.081\n",
+      "Epoch   1 Batch  370/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.876, Loss:  0.074\n",
+      "Epoch   1 Batch  371/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.887, Loss:  0.062\n",
+      "Epoch   1 Batch  372/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.885, Loss:  0.060\n",
+      "Epoch   1 Batch  373/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.887, Loss:  0.056\n",
+      "Epoch   1 Batch  374/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.903, Loss:  0.085\n",
+      "Epoch   1 Batch  375/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.907, Loss:  0.081\n",
+      "Epoch   1 Batch  376/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.906, Loss:  0.076\n",
+      "Epoch   1 Batch  377/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.902, Loss:  0.069\n",
+      "Epoch   1 Batch  378/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.911, Loss:  0.058\n",
+      "Epoch   1 Batch  379/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.909, Loss:  0.083\n",
+      "Epoch   1 Batch  380/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.914, Loss:  0.061\n",
+      "Epoch   1 Batch  381/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.918, Loss:  0.098\n",
+      "Epoch   1 Batch  382/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.925, Loss:  0.117\n",
+      "Epoch   1 Batch  383/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.073\n",
+      "Epoch   1 Batch  384/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.910, Loss:  0.074\n",
+      "Epoch   1 Batch  385/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.917, Loss:  0.067\n",
+      "Epoch   1 Batch  386/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.066\n",
+      "Epoch   1 Batch  387/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.073\n",
+      "Epoch   1 Batch  388/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.907, Loss:  0.065\n",
+      "Epoch   1 Batch  389/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.905, Loss:  0.073\n",
+      "Epoch   1 Batch  390/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.907, Loss:  0.081\n",
+      "Epoch   1 Batch  391/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.908, Loss:  0.082\n",
+      "Epoch   1 Batch  392/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.892, Loss:  0.084\n",
+      "Epoch   1 Batch  393/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.069\n",
+      "Epoch   1 Batch  394/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.899, Loss:  0.069\n",
+      "Epoch   1 Batch  395/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.900, Loss:  0.082\n",
+      "Epoch   1 Batch  396/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.899, Loss:  0.078\n",
+      "Epoch   1 Batch  397/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.899, Loss:  0.061\n",
+      "Epoch   1 Batch  398/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.902, Loss:  0.077\n",
+      "Epoch   1 Batch  399/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.898, Loss:  0.067\n",
+      "Epoch   1 Batch  400/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.904, Loss:  0.094\n",
+      "Epoch   1 Batch  401/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.892, Loss:  0.068\n",
+      "Epoch   1 Batch  402/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.896, Loss:  0.069\n",
+      "Epoch   1 Batch  403/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.896, Loss:  0.091\n",
+      "Epoch   1 Batch  404/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.891, Loss:  0.078\n",
+      "Epoch   1 Batch  405/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.897, Loss:  0.068\n",
+      "Epoch   1 Batch  406/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.896, Loss:  0.074\n",
+      "Epoch   1 Batch  407/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.895, Loss:  0.083\n",
+      "Epoch   1 Batch  408/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.899, Loss:  0.084\n",
+      "Epoch   1 Batch  409/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.079\n",
+      "Epoch   1 Batch  410/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.904, Loss:  0.085\n",
+      "Epoch   1 Batch  411/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.085\n",
+      "Epoch   1 Batch  412/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.903, Loss:  0.056\n",
+      "Epoch   1 Batch  413/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.066\n",
+      "Epoch   1 Batch  414/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.902, Loss:  0.080\n",
+      "Epoch   1 Batch  415/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.899, Loss:  0.074\n",
+      "Epoch   1 Batch  416/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.902, Loss:  0.076\n",
+      "Epoch   1 Batch  417/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.909, Loss:  0.113\n",
+      "Epoch   1 Batch  418/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.069\n",
+      "Epoch   1 Batch  419/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.913, Loss:  0.061\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch  420/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.916, Loss:  0.059\n",
+      "Epoch   1 Batch  421/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.915, Loss:  0.083\n",
+      "Epoch   1 Batch  422/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.912, Loss:  0.069\n",
+      "Epoch   1 Batch  423/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.910, Loss:  0.086\n",
+      "Epoch   1 Batch  424/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.902, Loss:  0.073\n",
+      "Epoch   1 Batch  425/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.900, Loss:  0.064\n",
+      "Epoch   1 Batch  426/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.901, Loss:  0.089\n",
+      "Epoch   1 Batch  427/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.905, Loss:  0.070\n",
+      "Epoch   1 Batch  428/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.906, Loss:  0.059\n",
+      "Epoch   1 Batch  429/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.902, Loss:  0.067\n",
+      "Epoch   1 Batch  430/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.905, Loss:  0.067\n",
+      "Epoch   1 Batch  431/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.907, Loss:  0.060\n",
+      "Epoch   1 Batch  432/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.900, Loss:  0.071\n",
+      "Epoch   1 Batch  433/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.902, Loss:  0.079\n",
+      "Epoch   1 Batch  434/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.898, Loss:  0.064\n",
+      "Epoch   1 Batch  435/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.899, Loss:  0.092\n",
+      "Epoch   1 Batch  436/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.900, Loss:  0.074\n",
+      "Epoch   1 Batch  437/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.900, Loss:  0.062\n",
+      "Epoch   1 Batch  438/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.899, Loss:  0.071\n",
+      "Epoch   1 Batch  439/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.901, Loss:  0.090\n",
+      "Epoch   1 Batch  440/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.901, Loss:  0.091\n",
+      "Epoch   1 Batch  441/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.909, Loss:  0.072\n",
+      "Epoch   1 Batch  442/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.901, Loss:  0.081\n",
+      "Epoch   1 Batch  443/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.901, Loss:  0.066\n",
+      "Epoch   1 Batch  444/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.906, Loss:  0.085\n",
+      "Epoch   1 Batch  445/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.897, Loss:  0.082\n",
+      "Epoch   1 Batch  446/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.902, Loss:  0.056\n",
+      "Epoch   1 Batch  447/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.901, Loss:  0.074\n",
+      "Epoch   1 Batch  448/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.908, Loss:  0.093\n",
+      "Epoch   1 Batch  449/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.909, Loss:  0.075\n",
+      "Epoch   1 Batch  450/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.908, Loss:  0.072\n",
+      "Epoch   1 Batch  451/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.914, Loss:  0.076\n",
+      "Epoch   1 Batch  452/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.913, Loss:  0.078\n",
+      "Epoch   1 Batch  453/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.911, Loss:  0.067\n",
+      "Epoch   1 Batch  454/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.076\n",
+      "Epoch   1 Batch  455/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.897, Loss:  0.072\n",
+      "Epoch   1 Batch  456/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.896, Loss:  0.086\n",
+      "Epoch   1 Batch  457/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.895, Loss:  0.056\n",
+      "Epoch   1 Batch  458/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.897, Loss:  0.080\n",
+      "Epoch   1 Batch  459/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.895, Loss:  0.069\n",
+      "Epoch   1 Batch  460/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.901, Loss:  0.076\n",
+      "Epoch   1 Batch  461/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.896, Loss:  0.073\n",
+      "Epoch   1 Batch  462/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.895, Loss:  0.072\n",
+      "Epoch   1 Batch  463/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.898, Loss:  0.080\n",
+      "Epoch   1 Batch  464/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.889, Loss:  0.070\n",
+      "Epoch   1 Batch  465/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.874, Loss:  0.074\n",
+      "Epoch   1 Batch  466/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.879, Loss:  0.065\n",
+      "Epoch   1 Batch  467/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.887, Loss:  0.080\n",
+      "Epoch   1 Batch  468/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.886, Loss:  0.071\n",
+      "Epoch   1 Batch  469/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.890, Loss:  0.074\n",
+      "Epoch   1 Batch  470/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.893, Loss:  0.075\n",
+      "Epoch   1 Batch  471/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.904, Loss:  0.055\n",
+      "Epoch   1 Batch  472/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.908, Loss:  0.070\n",
+      "Epoch   1 Batch  473/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.907, Loss:  0.072\n",
+      "Epoch   1 Batch  474/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.065\n",
+      "Epoch   1 Batch  475/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.922, Loss:  0.073\n",
+      "Epoch   1 Batch  476/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.917, Loss:  0.057\n",
+      "Epoch   1 Batch  477/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.075\n",
+      "Epoch   1 Batch  478/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.912, Loss:  0.068\n",
+      "Epoch   1 Batch  479/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.911, Loss:  0.087\n",
+      "Epoch   1 Batch  480/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.903, Loss:  0.058\n",
+      "Epoch   1 Batch  481/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.907, Loss:  0.085\n",
+      "Epoch   1 Batch  482/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.903, Loss:  0.092\n",
+      "Epoch   1 Batch  483/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.898, Loss:  0.071\n",
+      "Epoch   1 Batch  484/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.893, Loss:  0.090\n",
+      "Epoch   1 Batch  485/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.888, Loss:  0.087\n",
+      "Epoch   1 Batch  486/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.889, Loss:  0.066\n",
+      "Epoch   1 Batch  487/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.887, Loss:  0.062\n",
+      "Epoch   1 Batch  488/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.886, Loss:  0.069\n",
+      "Epoch   1 Batch  489/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.887, Loss:  0.065\n",
+      "Epoch   1 Batch  490/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.897, Loss:  0.073\n",
+      "Epoch   1 Batch  491/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.901, Loss:  0.076\n",
+      "Epoch   1 Batch  492/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.900, Loss:  0.084\n",
+      "Epoch   1 Batch  493/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.901, Loss:  0.058\n",
+      "Epoch   1 Batch  494/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.902, Loss:  0.064\n",
+      "Epoch   1 Batch  495/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.902, Loss:  0.072\n",
+      "Epoch   1 Batch  496/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.069\n",
+      "Epoch   1 Batch  497/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.891, Loss:  0.072\n",
+      "Epoch   1 Batch  498/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.891, Loss:  0.074\n",
+      "Epoch   1 Batch  499/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.896, Loss:  0.057\n",
+      "Epoch   1 Batch  500/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.900, Loss:  0.057\n",
+      "Epoch   1 Batch  501/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.906, Loss:  0.063\n",
+      "Epoch   1 Batch  502/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.909, Loss:  0.079\n",
+      "Epoch   1 Batch  503/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.905, Loss:  0.064\n",
+      "Epoch   1 Batch  504/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.919, Loss:  0.072\n",
+      "Epoch   1 Batch  505/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.059\n",
+      "Epoch   1 Batch  506/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.913, Loss:  0.079\n",
+      "Epoch   1 Batch  507/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.913, Loss:  0.072\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch  508/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.916, Loss:  0.059\n",
+      "Epoch   1 Batch  509/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.914, Loss:  0.079\n",
+      "Epoch   1 Batch  510/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.901, Loss:  0.072\n",
+      "Epoch   1 Batch  511/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.906, Loss:  0.071\n",
+      "Epoch   1 Batch  512/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.909, Loss:  0.055\n",
+      "Epoch   1 Batch  513/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.900, Loss:  0.068\n",
+      "Epoch   1 Batch  514/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.904, Loss:  0.080\n",
+      "Epoch   1 Batch  515/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.910, Loss:  0.079\n",
+      "Epoch   1 Batch  516/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.072\n",
+      "Epoch   1 Batch  517/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.911, Loss:  0.068\n",
+      "Epoch   1 Batch  518/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.906, Loss:  0.056\n",
+      "Epoch   1 Batch  519/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.901, Loss:  0.072\n",
+      "Epoch   1 Batch  520/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.894, Loss:  0.068\n",
+      "Epoch   1 Batch  521/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.889, Loss:  0.081\n",
+      "Epoch   1 Batch  522/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.897, Loss:  0.081\n",
+      "Epoch   1 Batch  523/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.079\n",
+      "Epoch   1 Batch  524/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.899, Loss:  0.079\n",
+      "Epoch   1 Batch  525/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.895, Loss:  0.075\n",
+      "Epoch   1 Batch  526/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.891, Loss:  0.065\n",
+      "Epoch   1 Batch  527/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.884, Loss:  0.072\n",
+      "Epoch   1 Batch  528/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.888, Loss:  0.074\n",
+      "Epoch   1 Batch  529/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.885, Loss:  0.075\n",
+      "Epoch   1 Batch  530/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.893, Loss:  0.073\n",
+      "Epoch   1 Batch  531/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.888, Loss:  0.067\n",
+      "Epoch   1 Batch  532/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.897, Loss:  0.085\n",
+      "Epoch   1 Batch  533/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.906, Loss:  0.069\n",
+      "Epoch   1 Batch  534/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.910, Loss:  0.081\n",
+      "Epoch   1 Batch  535/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.906, Loss:  0.069\n",
+      "Epoch   1 Batch  536/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.897, Loss:  0.071\n",
+      "Epoch   1 Batch  537/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.894, Loss:  0.067\n",
+      "Epoch   1 Batch  538/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.891, Loss:  0.054\n",
+      "Epoch   1 Batch  539/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.877, Loss:  0.105\n",
+      "Epoch   1 Batch  540/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.879, Loss:  0.063\n",
+      "Epoch   1 Batch  541/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.885, Loss:  0.056\n",
+      "Epoch   1 Batch  542/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.885, Loss:  0.071\n",
+      "Epoch   1 Batch  543/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.891, Loss:  0.071\n",
+      "Epoch   1 Batch  544/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.884, Loss:  0.052\n",
+      "Epoch   1 Batch  545/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.888, Loss:  0.075\n",
+      "Epoch   1 Batch  546/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.898, Loss:  0.078\n",
+      "Epoch   1 Batch  547/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.902, Loss:  0.066\n",
+      "Epoch   1 Batch  548/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.907, Loss:  0.077\n",
+      "Epoch   1 Batch  549/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.911, Loss:  0.084\n",
+      "Epoch   1 Batch  550/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.898, Loss:  0.078\n",
+      "Epoch   1 Batch  551/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.912, Loss:  0.080\n",
+      "Epoch   1 Batch  552/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.081\n",
+      "Epoch   1 Batch  553/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.913, Loss:  0.087\n",
+      "Epoch   1 Batch  554/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.908, Loss:  0.068\n",
+      "Epoch   1 Batch  555/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.066\n",
+      "Epoch   1 Batch  556/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.902, Loss:  0.068\n",
+      "Epoch   1 Batch  557/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.898, Loss:  0.073\n",
+      "Epoch   1 Batch  558/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.900, Loss:  0.061\n",
+      "Epoch   1 Batch  559/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.906, Loss:  0.056\n",
+      "Epoch   1 Batch  560/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.898, Loss:  0.065\n",
+      "Epoch   1 Batch  561/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.900, Loss:  0.066\n",
+      "Epoch   1 Batch  562/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.890, Loss:  0.059\n",
+      "Epoch   1 Batch  563/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.891, Loss:  0.066\n",
+      "Epoch   1 Batch  564/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.891, Loss:  0.082\n",
+      "Epoch   1 Batch  565/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.886, Loss:  0.075\n",
+      "Epoch   1 Batch  566/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.883, Loss:  0.083\n",
+      "Epoch   1 Batch  567/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.892, Loss:  0.075\n",
+      "Epoch   1 Batch  568/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.894, Loss:  0.068\n",
+      "Epoch   1 Batch  569/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.891, Loss:  0.073\n",
+      "Epoch   1 Batch  570/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.884, Loss:  0.085\n",
+      "Epoch   1 Batch  571/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.885, Loss:  0.052\n",
+      "Epoch   1 Batch  572/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.885, Loss:  0.065\n",
+      "Epoch   1 Batch  573/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.884, Loss:  0.079\n",
+      "Epoch   1 Batch  574/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.882, Loss:  0.081\n",
+      "Epoch   1 Batch  575/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.889, Loss:  0.054\n",
+      "Epoch   1 Batch  576/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.896, Loss:  0.063\n",
+      "Epoch   1 Batch  577/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.898, Loss:  0.078\n",
+      "Epoch   1 Batch  578/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.071\n",
+      "Epoch   1 Batch  579/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.903, Loss:  0.062\n",
+      "Epoch   1 Batch  580/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.906, Loss:  0.056\n",
+      "Epoch   1 Batch  581/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.896, Loss:  0.052\n",
+      "Epoch   1 Batch  582/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.898, Loss:  0.073\n",
+      "Epoch   1 Batch  583/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.898, Loss:  0.068\n",
+      "Epoch   1 Batch  584/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.902, Loss:  0.068\n",
+      "Epoch   1 Batch  585/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.048\n",
+      "Epoch   1 Batch  586/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.903, Loss:  0.078\n",
+      "Epoch   1 Batch  587/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.900, Loss:  0.068\n",
+      "Epoch   1 Batch  588/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.896, Loss:  0.055\n",
+      "Epoch   1 Batch  589/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.889, Loss:  0.066\n",
+      "Epoch   1 Batch  590/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.894, Loss:  0.083\n",
+      "Epoch   1 Batch  591/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.069\n",
+      "Epoch   1 Batch  592/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.888, Loss:  0.069\n",
+      "Epoch   1 Batch  593/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.880, Loss:  0.094\n",
+      "Epoch   1 Batch  594/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.875, Loss:  0.077\n",
+      "Epoch   1 Batch  595/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.880, Loss:  0.071\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch  596/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.875, Loss:  0.070\n",
+      "Epoch   1 Batch  597/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.893, Loss:  0.059\n",
+      "Epoch   1 Batch  598/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.907, Loss:  0.080\n",
+      "Epoch   1 Batch  599/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.907, Loss:  0.089\n",
+      "Epoch   1 Batch  600/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.900, Loss:  0.078\n",
+      "Epoch   1 Batch  601/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.902, Loss:  0.074\n",
+      "Epoch   1 Batch  602/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.905, Loss:  0.070\n",
+      "Epoch   1 Batch  603/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.910, Loss:  0.067\n",
+      "Epoch   1 Batch  604/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.912, Loss:  0.079\n",
+      "Epoch   1 Batch  605/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.905, Loss:  0.089\n",
+      "Epoch   1 Batch  606/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.907, Loss:  0.064\n",
+      "Epoch   1 Batch  607/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.907, Loss:  0.069\n",
+      "Epoch   1 Batch  608/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.909, Loss:  0.075\n",
+      "Epoch   1 Batch  609/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.911, Loss:  0.067\n",
+      "Epoch   1 Batch  610/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.909, Loss:  0.084\n",
+      "Epoch   1 Batch  611/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.914, Loss:  0.057\n",
+      "Epoch   1 Batch  612/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.062\n",
+      "Epoch   1 Batch  613/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.911, Loss:  0.084\n",
+      "Epoch   1 Batch  614/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.896, Loss:  0.058\n",
+      "Epoch   1 Batch  615/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.903, Loss:  0.069\n",
+      "Epoch   1 Batch  616/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.917, Loss:  0.076\n",
+      "Epoch   1 Batch  617/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.922, Loss:  0.069\n",
+      "Epoch   1 Batch  618/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.911, Loss:  0.067\n",
+      "Epoch   1 Batch  619/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.915, Loss:  0.055\n",
+      "Epoch   1 Batch  620/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.067\n",
+      "Epoch   1 Batch  621/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.916, Loss:  0.065\n",
+      "Epoch   1 Batch  622/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.912, Loss:  0.083\n",
+      "Epoch   1 Batch  623/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.911, Loss:  0.074\n",
+      "Epoch   1 Batch  624/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.906, Loss:  0.080\n",
+      "Epoch   1 Batch  625/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.911, Loss:  0.063\n",
+      "Epoch   1 Batch  626/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.904, Loss:  0.068\n",
+      "Epoch   1 Batch  627/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.908, Loss:  0.069\n",
+      "Epoch   1 Batch  628/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.071\n",
+      "Epoch   1 Batch  629/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.908, Loss:  0.074\n",
+      "Epoch   1 Batch  630/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.912, Loss:  0.063\n",
+      "Epoch   1 Batch  631/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.911, Loss:  0.071\n",
+      "Epoch   1 Batch  632/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.063\n",
+      "Epoch   1 Batch  633/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.913, Loss:  0.074\n",
+      "Epoch   1 Batch  634/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.915, Loss:  0.056\n",
+      "Epoch   1 Batch  635/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.913, Loss:  0.078\n",
+      "Epoch   1 Batch  636/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.063\n",
+      "Epoch   1 Batch  637/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.911, Loss:  0.066\n",
+      "Epoch   1 Batch  638/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.911, Loss:  0.061\n",
+      "Epoch   1 Batch  639/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.911, Loss:  0.091\n",
+      "Epoch   1 Batch  640/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.923, Loss:  0.065\n",
+      "Epoch   1 Batch  641/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.916, Loss:  0.069\n",
+      "Epoch   1 Batch  642/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.922, Loss:  0.083\n",
+      "Epoch   1 Batch  643/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.053\n",
+      "Epoch   1 Batch  644/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.920, Loss:  0.073\n",
+      "Epoch   1 Batch  645/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.915, Loss:  0.080\n",
+      "Epoch   1 Batch  646/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.911, Loss:  0.071\n",
+      "Epoch   1 Batch  647/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.915, Loss:  0.063\n",
+      "Epoch   1 Batch  648/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.927, Loss:  0.046\n",
+      "Epoch   1 Batch  649/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.927, Loss:  0.063\n",
+      "Epoch   1 Batch  650/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.071\n",
+      "Epoch   1 Batch  651/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.062\n",
+      "Epoch   1 Batch  652/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.908, Loss:  0.065\n",
+      "Epoch   1 Batch  653/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.911, Loss:  0.067\n",
+      "Epoch   1 Batch  654/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.910, Loss:  0.058\n",
+      "Epoch   1 Batch  655/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.906, Loss:  0.076\n",
+      "Epoch   1 Batch  656/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.067\n",
+      "Epoch   1 Batch  657/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.909, Loss:  0.058\n",
+      "Epoch   1 Batch  658/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.906, Loss:  0.055\n",
+      "Epoch   1 Batch  659/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.902, Loss:  0.073\n",
+      "Epoch   1 Batch  660/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.908, Loss:  0.069\n",
+      "Epoch   1 Batch  661/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.909, Loss:  0.053\n",
+      "Epoch   1 Batch  662/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.904, Loss:  0.075\n",
+      "Epoch   1 Batch  663/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.906, Loss:  0.057\n",
+      "Epoch   1 Batch  664/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.901, Loss:  0.061\n",
+      "Epoch   1 Batch  665/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.903, Loss:  0.058\n",
+      "Epoch   1 Batch  666/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.903, Loss:  0.073\n",
+      "Epoch   1 Batch  667/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.911, Loss:  0.082\n",
+      "Epoch   1 Batch  668/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.909, Loss:  0.071\n",
+      "Epoch   1 Batch  669/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.906, Loss:  0.071\n",
+      "Epoch   1 Batch  670/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.908, Loss:  0.078\n",
+      "Epoch   1 Batch  671/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.913, Loss:  0.069\n",
+      "Epoch   1 Batch  672/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.065\n",
+      "Epoch   1 Batch  673/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.904, Loss:  0.062\n",
+      "Epoch   1 Batch  674/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.901, Loss:  0.072\n",
+      "Epoch   1 Batch  675/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.906, Loss:  0.082\n",
+      "Epoch   1 Batch  676/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.907, Loss:  0.063\n",
+      "Epoch   1 Batch  677/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.918, Loss:  0.091\n",
+      "Epoch   1 Batch  678/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.911, Loss:  0.057\n",
+      "Epoch   1 Batch  679/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.907, Loss:  0.062\n",
+      "Epoch   1 Batch  680/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.904, Loss:  0.061\n",
+      "Epoch   1 Batch  681/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.894, Loss:  0.066\n",
+      "Epoch   1 Batch  682/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.903, Loss:  0.062\n",
+      "Epoch   1 Batch  683/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.903, Loss:  0.057\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch  684/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.909, Loss:  0.067\n",
+      "Epoch   1 Batch  685/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.914, Loss:  0.075\n",
+      "Epoch   1 Batch  686/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.060\n",
+      "Epoch   1 Batch  687/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.913, Loss:  0.091\n",
+      "Epoch   1 Batch  688/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.914, Loss:  0.066\n",
+      "Epoch   1 Batch  689/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.904, Loss:  0.047\n",
+      "Epoch   1 Batch  690/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.909, Loss:  0.068\n",
+      "Epoch   1 Batch  691/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.906, Loss:  0.085\n",
+      "Epoch   1 Batch  692/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.906, Loss:  0.061\n",
+      "Epoch   1 Batch  693/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.901, Loss:  0.084\n",
+      "Epoch   1 Batch  694/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.885, Loss:  0.072\n",
+      "Epoch   1 Batch  695/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.884, Loss:  0.049\n",
+      "Epoch   1 Batch  696/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.886, Loss:  0.085\n",
+      "Epoch   1 Batch  697/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.890, Loss:  0.069\n",
+      "Epoch   1 Batch  698/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.900, Loss:  0.059\n",
+      "Epoch   1 Batch  699/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.908, Loss:  0.056\n",
+      "Epoch   1 Batch  700/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.906, Loss:  0.067\n",
+      "Epoch   1 Batch  701/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.900, Loss:  0.074\n",
+      "Epoch   1 Batch  702/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.904, Loss:  0.087\n",
+      "Epoch   1 Batch  703/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.070\n",
+      "Epoch   1 Batch  704/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.910, Loss:  0.083\n",
+      "Epoch   1 Batch  705/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.911, Loss:  0.074\n",
+      "Epoch   1 Batch  706/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.914, Loss:  0.114\n",
+      "Epoch   1 Batch  707/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.913, Loss:  0.067\n",
+      "Epoch   1 Batch  708/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.909, Loss:  0.081\n",
+      "Epoch   1 Batch  709/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.907, Loss:  0.089\n",
+      "Epoch   1 Batch  710/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.905, Loss:  0.059\n",
+      "Epoch   1 Batch  711/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.893, Loss:  0.083\n",
+      "Epoch   1 Batch  712/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.896, Loss:  0.058\n",
+      "Epoch   1 Batch  713/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.899, Loss:  0.056\n",
+      "Epoch   1 Batch  714/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.902, Loss:  0.068\n",
+      "Epoch   1 Batch  715/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.895, Loss:  0.074\n",
+      "Epoch   1 Batch  716/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.891, Loss:  0.056\n",
+      "Epoch   1 Batch  717/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.887, Loss:  0.047\n",
+      "Epoch   1 Batch  718/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.887, Loss:  0.056\n",
+      "Epoch   1 Batch  719/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.896, Loss:  0.084\n",
+      "Epoch   1 Batch  720/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.901, Loss:  0.081\n",
+      "Epoch   1 Batch  721/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.902, Loss:  0.074\n",
+      "Epoch   1 Batch  722/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.904, Loss:  0.065\n",
+      "Epoch   1 Batch  723/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.899, Loss:  0.075\n",
+      "Epoch   1 Batch  724/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.907, Loss:  0.073\n",
+      "Epoch   1 Batch  725/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.059\n",
+      "Epoch   1 Batch  726/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.914, Loss:  0.073\n",
+      "Epoch   1 Batch  727/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.909, Loss:  0.064\n",
+      "Epoch   1 Batch  728/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.909, Loss:  0.078\n",
+      "Epoch   1 Batch  729/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.909, Loss:  0.082\n",
+      "Epoch   1 Batch  730/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.893, Loss:  0.081\n",
+      "Epoch   1 Batch  731/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.899, Loss:  0.063\n",
+      "Epoch   1 Batch  732/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.899, Loss:  0.072\n",
+      "Epoch   1 Batch  733/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.902, Loss:  0.082\n",
+      "Epoch   1 Batch  734/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.896, Loss:  0.064\n",
+      "Epoch   1 Batch  735/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.901, Loss:  0.061\n",
+      "Epoch   1 Batch  736/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.914, Loss:  0.053\n",
+      "Epoch   1 Batch  737/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.914, Loss:  0.082\n",
+      "Epoch   1 Batch  738/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.047\n",
+      "Epoch   1 Batch  739/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.061\n",
+      "Epoch   1 Batch  740/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.054\n",
+      "Epoch   1 Batch  741/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.073\n",
+      "Epoch   1 Batch  742/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.051\n",
+      "Epoch   1 Batch  743/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.913, Loss:  0.066\n",
+      "Epoch   1 Batch  744/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.077\n",
+      "Epoch   1 Batch  745/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.075\n",
+      "Epoch   1 Batch  746/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.054\n",
+      "Epoch   1 Batch  747/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.040\n",
+      "Epoch   1 Batch  748/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.059\n",
+      "Epoch   1 Batch  749/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.917, Loss:  0.069\n",
+      "Epoch   1 Batch  750/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.063\n",
+      "Epoch   1 Batch  751/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.920, Loss:  0.058\n",
+      "Epoch   1 Batch  752/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.916, Loss:  0.064\n",
+      "Epoch   1 Batch  753/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.060\n",
+      "Epoch   1 Batch  754/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.910, Loss:  0.070\n",
+      "Epoch   1 Batch  755/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.067\n",
+      "Epoch   1 Batch  756/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.924, Loss:  0.056\n",
+      "Epoch   1 Batch  757/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.050\n",
+      "Epoch   1 Batch  758/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.056\n",
+      "Epoch   1 Batch  759/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.919, Loss:  0.054\n",
+      "Epoch   1 Batch  760/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.915, Loss:  0.069\n",
+      "Epoch   1 Batch  761/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.059\n",
+      "Epoch   1 Batch  762/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.912, Loss:  0.063\n",
+      "Epoch   1 Batch  763/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.903, Loss:  0.063\n",
+      "Epoch   1 Batch  764/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.067\n",
+      "Epoch   1 Batch  765/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.918, Loss:  0.083\n",
+      "Epoch   1 Batch  766/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.063\n",
+      "Epoch   1 Batch  767/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.061\n",
+      "Epoch   1 Batch  768/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.924, Loss:  0.062\n",
+      "Epoch   1 Batch  769/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.065\n",
+      "Epoch   1 Batch  770/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.908, Loss:  0.064\n",
+      "Epoch   1 Batch  771/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.906, Loss:  0.078\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch  772/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.909, Loss:  0.060\n",
+      "Epoch   1 Batch  773/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.909, Loss:  0.068\n",
+      "Epoch   1 Batch  774/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.909, Loss:  0.067\n",
+      "Epoch   1 Batch  775/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.907, Loss:  0.072\n",
+      "Epoch   1 Batch  776/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.924, Loss:  0.058\n",
+      "Epoch   1 Batch  777/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.926, Loss:  0.070\n",
+      "Epoch   1 Batch  778/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.924, Loss:  0.064\n",
+      "Epoch   1 Batch  779/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.920, Loss:  0.069\n",
+      "Epoch   1 Batch  780/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.916, Loss:  0.091\n",
+      "Epoch   1 Batch  781/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.054\n",
+      "Epoch   1 Batch  782/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.917, Loss:  0.053\n",
+      "Epoch   1 Batch  783/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.918, Loss:  0.076\n",
+      "Epoch   1 Batch  784/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.053\n",
+      "Epoch   1 Batch  785/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.051\n",
+      "Epoch   1 Batch  786/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.922, Loss:  0.063\n",
+      "Epoch   1 Batch  787/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.913, Loss:  0.065\n",
+      "Epoch   1 Batch  788/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.905, Loss:  0.060\n",
+      "Epoch   1 Batch  789/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.901, Loss:  0.078\n",
+      "Epoch   1 Batch  790/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.907, Loss:  0.065\n",
+      "Epoch   1 Batch  791/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.911, Loss:  0.066\n",
+      "Epoch   1 Batch  792/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.913, Loss:  0.072\n",
+      "Epoch   1 Batch  793/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.917, Loss:  0.061\n",
+      "Epoch   1 Batch  794/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.920, Loss:  0.055\n",
+      "Epoch   1 Batch  795/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.068\n",
+      "Epoch   1 Batch  796/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.924, Loss:  0.055\n",
+      "Epoch   1 Batch  797/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.928, Loss:  0.065\n",
+      "Epoch   1 Batch  798/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.069\n",
+      "Epoch   1 Batch  799/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.935, Loss:  0.079\n",
+      "Epoch   1 Batch  800/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.064\n",
+      "Epoch   1 Batch  801/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.072\n",
+      "Epoch   1 Batch  802/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.068\n",
+      "Epoch   1 Batch  803/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.929, Loss:  0.073\n",
+      "Epoch   1 Batch  804/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.929, Loss:  0.047\n",
+      "Epoch   1 Batch  805/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.928, Loss:  0.067\n",
+      "Epoch   1 Batch  806/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.931, Loss:  0.059\n",
+      "Epoch   1 Batch  807/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.931, Loss:  0.056\n",
+      "Epoch   1 Batch  808/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.937, Loss:  0.090\n",
+      "Epoch   1 Batch  809/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.930, Loss:  0.089\n",
+      "Epoch   1 Batch  810/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.925, Loss:  0.058\n",
+      "Epoch   1 Batch  811/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.059\n",
+      "Epoch   1 Batch  812/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.933, Loss:  0.060\n",
+      "Epoch   1 Batch  813/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.929, Loss:  0.061\n",
+      "Epoch   1 Batch  814/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.934, Loss:  0.084\n",
+      "Epoch   1 Batch  815/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.934, Loss:  0.075\n",
+      "Epoch   1 Batch  816/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.934, Loss:  0.076\n",
+      "Epoch   1 Batch  817/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.921, Loss:  0.072\n",
+      "Epoch   1 Batch  818/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.925, Loss:  0.067\n",
+      "Epoch   1 Batch  819/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.925, Loss:  0.069\n",
+      "Epoch   1 Batch  820/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.054\n",
+      "Epoch   1 Batch  821/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.912, Loss:  0.069\n",
+      "Epoch   1 Batch  822/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.918, Loss:  0.060\n",
+      "Epoch   1 Batch  823/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.070\n",
+      "Epoch   1 Batch  824/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.930, Loss:  0.063\n",
+      "Epoch   1 Batch  825/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.914, Loss:  0.056\n",
+      "Epoch   1 Batch  826/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.920, Loss:  0.059\n",
+      "Epoch   1 Batch  827/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.917, Loss:  0.066\n",
+      "Epoch   1 Batch  828/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.917, Loss:  0.056\n",
+      "Epoch   1 Batch  829/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.920, Loss:  0.077\n",
+      "Epoch   1 Batch  830/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.924, Loss:  0.076\n",
+      "Epoch   1 Batch  831/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.919, Loss:  0.069\n",
+      "Epoch   1 Batch  832/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.916, Loss:  0.062\n",
+      "Epoch   1 Batch  833/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.918, Loss:  0.071\n",
+      "Epoch   1 Batch  834/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.068\n",
+      "Epoch   1 Batch  835/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.931, Loss:  0.071\n",
+      "Epoch   1 Batch  836/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.063\n",
+      "Epoch   1 Batch  837/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.930, Loss:  0.086\n",
+      "Epoch   1 Batch  838/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.937, Loss:  0.058\n",
+      "Epoch   1 Batch  839/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.938, Loss:  0.057\n",
+      "Epoch   1 Batch  840/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.937, Loss:  0.055\n",
+      "Epoch   1 Batch  841/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.941, Loss:  0.070\n",
+      "Epoch   1 Batch  842/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.934, Loss:  0.053\n",
+      "Epoch   1 Batch  843/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.932, Loss:  0.045\n",
+      "Epoch   1 Batch  844/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.932, Loss:  0.053\n",
+      "Epoch   1 Batch  845/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.053\n",
+      "Epoch   1 Batch  846/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.922, Loss:  0.071\n",
+      "Epoch   1 Batch  847/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.078\n",
+      "Epoch   1 Batch  848/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.053\n",
+      "Epoch   1 Batch  849/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.920, Loss:  0.052\n",
+      "Epoch   1 Batch  850/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.924, Loss:  0.098\n",
+      "Epoch   1 Batch  851/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.076\n",
+      "Epoch   1 Batch  852/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.920, Loss:  0.091\n",
+      "Epoch   1 Batch  853/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.076\n",
+      "Epoch   1 Batch  854/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.086\n",
+      "Epoch   1 Batch  855/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.906, Loss:  0.064\n",
+      "Epoch   1 Batch  856/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.903, Loss:  0.070\n",
+      "Epoch   1 Batch  857/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.070\n",
+      "Epoch   1 Batch  858/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.907, Loss:  0.059\n",
+      "Epoch   1 Batch  859/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.903, Loss:  0.079\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch  860/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.900, Loss:  0.081\n",
+      "Epoch   1 Batch  861/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.900, Loss:  0.067\n",
+      "Epoch   1 Batch  862/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.901, Loss:  0.067\n",
+      "Epoch   1 Batch  863/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.909, Loss:  0.054\n",
+      "Epoch   1 Batch  864/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.898, Loss:  0.073\n",
+      "Epoch   1 Batch  865/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.896, Loss:  0.069\n",
+      "Epoch   1 Batch  866/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.913, Loss:  0.078\n",
+      "Epoch   1 Batch  867/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.911, Loss:  0.115\n",
+      "Epoch   1 Batch  868/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.069\n",
+      "Epoch   1 Batch  869/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.910, Loss:  0.067\n",
+      "Epoch   1 Batch  870/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.911, Loss:  0.069\n",
+      "Epoch   1 Batch  871/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.911, Loss:  0.066\n",
+      "Epoch   1 Batch  872/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.067\n",
+      "Epoch   1 Batch  873/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.918, Loss:  0.069\n",
+      "Epoch   1 Batch  874/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.922, Loss:  0.078\n",
+      "Epoch   1 Batch  875/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.919, Loss:  0.082\n",
+      "Epoch   1 Batch  876/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.929, Loss:  0.064\n",
+      "Epoch   1 Batch  877/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.936, Loss:  0.057\n",
+      "Epoch   1 Batch  878/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.930, Loss:  0.054\n",
+      "Epoch   1 Batch  879/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.921, Loss:  0.055\n",
+      "Epoch   1 Batch  880/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.923, Loss:  0.071\n",
+      "Epoch   1 Batch  881/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.912, Loss:  0.073\n",
+      "Epoch   1 Batch  882/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.915, Loss:  0.075\n",
+      "Epoch   1 Batch  883/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.921, Loss:  0.092\n",
+      "Epoch   1 Batch  884/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.913, Loss:  0.067\n",
+      "Epoch   1 Batch  885/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.908, Loss:  0.042\n",
+      "Epoch   1 Batch  886/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.064\n",
+      "Epoch   1 Batch  887/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.906, Loss:  0.083\n",
+      "Epoch   1 Batch  888/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.049\n",
+      "Epoch   1 Batch  889/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.900, Loss:  0.057\n",
+      "Epoch   1 Batch  890/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.901, Loss:  0.069\n",
+      "Epoch   1 Batch  891/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.905, Loss:  0.057\n",
+      "Epoch   1 Batch  892/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.908, Loss:  0.054\n",
+      "Epoch   1 Batch  893/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.903, Loss:  0.073\n",
+      "Epoch   1 Batch  894/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.911, Loss:  0.069\n",
+      "Epoch   1 Batch  895/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.909, Loss:  0.064\n",
+      "Epoch   1 Batch  896/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.923, Loss:  0.064\n",
+      "Epoch   1 Batch  897/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.053\n",
+      "Epoch   1 Batch  898/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.057\n",
+      "Epoch   1 Batch  899/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.076\n",
+      "Epoch   1 Batch  900/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.917, Loss:  0.076\n",
+      "Epoch   1 Batch  901/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.075\n",
+      "Epoch   1 Batch  902/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.929, Loss:  0.081\n",
+      "Epoch   1 Batch  903/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.062\n",
+      "Epoch   1 Batch  904/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.918, Loss:  0.060\n",
+      "Epoch   1 Batch  905/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.912, Loss:  0.054\n",
+      "Epoch   1 Batch  906/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.904, Loss:  0.069\n",
+      "Epoch   1 Batch  907/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.900, Loss:  0.078\n",
+      "Epoch   1 Batch  908/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.909, Loss:  0.074\n",
+      "Epoch   1 Batch  909/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.901, Loss:  0.071\n",
+      "Epoch   1 Batch  910/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.903, Loss:  0.065\n",
+      "Epoch   1 Batch  911/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.074\n",
+      "Epoch   1 Batch  912/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.061\n",
+      "Epoch   1 Batch  913/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.900, Loss:  0.092\n",
+      "Epoch   1 Batch  914/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.902, Loss:  0.091\n",
+      "Epoch   1 Batch  915/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.898, Loss:  0.057\n",
+      "Epoch   1 Batch  916/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.903, Loss:  0.086\n",
+      "Epoch   1 Batch  917/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.903, Loss:  0.065\n",
+      "Epoch   1 Batch  918/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.058\n",
+      "Epoch   1 Batch  919/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.904, Loss:  0.051\n",
+      "Epoch   1 Batch  920/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.912, Loss:  0.069\n",
+      "Epoch   1 Batch  921/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.906, Loss:  0.066\n",
+      "Epoch   1 Batch  922/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.897, Loss:  0.073\n",
+      "Epoch   1 Batch  923/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.911, Loss:  0.044\n",
+      "Epoch   1 Batch  924/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.905, Loss:  0.086\n",
+      "Epoch   1 Batch  925/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.908, Loss:  0.066\n",
+      "Epoch   1 Batch  926/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.908, Loss:  0.055\n",
+      "Epoch   1 Batch  927/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.908, Loss:  0.077\n",
+      "Epoch   1 Batch  928/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.902, Loss:  0.068\n",
+      "Epoch   1 Batch  929/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.062\n",
+      "Epoch   1 Batch  930/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.901, Loss:  0.060\n",
+      "Epoch   1 Batch  931/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.905, Loss:  0.053\n",
+      "Epoch   1 Batch  932/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.898, Loss:  0.067\n",
+      "Epoch   1 Batch  933/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.896, Loss:  0.062\n",
+      "Epoch   1 Batch  934/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.906, Loss:  0.051\n",
+      "Epoch   1 Batch  935/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.898, Loss:  0.055\n",
+      "Epoch   1 Batch  936/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.912, Loss:  0.069\n",
+      "Epoch   1 Batch  937/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.917, Loss:  0.075\n",
+      "Epoch   1 Batch  938/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.912, Loss:  0.082\n",
+      "Epoch   1 Batch  939/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.910, Loss:  0.072\n",
+      "Epoch   1 Batch  940/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.044\n",
+      "Epoch   1 Batch  941/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.059\n",
+      "Epoch   1 Batch  942/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.908, Loss:  0.065\n",
+      "Epoch   1 Batch  943/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.912, Loss:  0.063\n",
+      "Epoch   1 Batch  944/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.910, Loss:  0.056\n",
+      "Epoch   1 Batch  945/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.057\n",
+      "Epoch   1 Batch  946/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.916, Loss:  0.044\n",
+      "Epoch   1 Batch  947/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.910, Loss:  0.059\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch  948/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.059\n",
+      "Epoch   1 Batch  949/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.915, Loss:  0.052\n",
+      "Epoch   1 Batch  950/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.917, Loss:  0.058\n",
+      "Epoch   1 Batch  951/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.923, Loss:  0.068\n",
+      "Epoch   1 Batch  952/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.055\n",
+      "Epoch   1 Batch  953/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.930, Loss:  0.049\n",
+      "Epoch   1 Batch  954/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.925, Loss:  0.067\n",
+      "Epoch   1 Batch  955/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.928, Loss:  0.074\n",
+      "Epoch   1 Batch  956/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.933, Loss:  0.066\n",
+      "Epoch   1 Batch  957/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.922, Loss:  0.050\n",
+      "Epoch   1 Batch  958/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.067\n",
+      "Epoch   1 Batch  959/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.918, Loss:  0.068\n",
+      "Epoch   1 Batch  960/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.916, Loss:  0.060\n",
+      "Epoch   1 Batch  961/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.918, Loss:  0.068\n",
+      "Epoch   1 Batch  962/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.916, Loss:  0.054\n",
+      "Epoch   1 Batch  963/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.912, Loss:  0.094\n",
+      "Epoch   1 Batch  964/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.915, Loss:  0.054\n",
+      "Epoch   1 Batch  965/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.074\n",
+      "Epoch   1 Batch  966/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.052\n",
+      "Epoch   1 Batch  967/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.064\n",
+      "Epoch   1 Batch  968/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.917, Loss:  0.076\n",
+      "Epoch   1 Batch  969/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.916, Loss:  0.085\n",
+      "Epoch   1 Batch  970/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.913, Loss:  0.066\n",
+      "Epoch   1 Batch  971/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.908, Loss:  0.057\n",
+      "Epoch   1 Batch  972/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.908, Loss:  0.059\n",
+      "Epoch   1 Batch  973/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.908, Loss:  0.057\n",
+      "Epoch   1 Batch  974/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.913, Loss:  0.046\n",
+      "Epoch   1 Batch  975/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.911, Loss:  0.065\n",
+      "Epoch   1 Batch  976/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.050\n",
+      "Epoch   1 Batch  977/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.913, Loss:  0.048\n",
+      "Epoch   1 Batch  978/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.064\n",
+      "Epoch   1 Batch  979/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.913, Loss:  0.068\n",
+      "Epoch   1 Batch  980/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.056\n",
+      "Epoch   1 Batch  981/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.917, Loss:  0.065\n",
+      "Epoch   1 Batch  982/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.909, Loss:  0.065\n",
+      "Epoch   1 Batch  983/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.907, Loss:  0.063\n",
+      "Epoch   1 Batch  984/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.915, Loss:  0.076\n",
+      "Epoch   1 Batch  985/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.911, Loss:  0.059\n",
+      "Epoch   1 Batch  986/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.918, Loss:  0.056\n",
+      "Epoch   1 Batch  987/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.918, Loss:  0.048\n",
+      "Epoch   1 Batch  988/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.913, Loss:  0.072\n",
+      "Epoch   1 Batch  989/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.915, Loss:  0.073\n",
+      "Epoch   1 Batch  990/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.066\n",
+      "Epoch   1 Batch  991/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.065\n",
+      "Epoch   1 Batch  992/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.067\n",
+      "Epoch   1 Batch  993/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.044\n",
+      "Epoch   1 Batch  994/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.061\n",
+      "Epoch   1 Batch  995/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.912, Loss:  0.066\n",
+      "Epoch   1 Batch  996/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.917, Loss:  0.060\n",
+      "Epoch   1 Batch  997/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.912, Loss:  0.054\n",
+      "Epoch   1 Batch  998/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.918, Loss:  0.063\n",
+      "Epoch   1 Batch  999/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.065\n",
+      "Epoch   1 Batch 1000/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.920, Loss:  0.056\n",
+      "Epoch   1 Batch 1001/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.915, Loss:  0.053\n",
+      "Epoch   1 Batch 1002/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.915, Loss:  0.047\n",
+      "Epoch   1 Batch 1003/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.059\n",
+      "Epoch   1 Batch 1004/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.917, Loss:  0.070\n",
+      "Epoch   1 Batch 1005/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.050\n",
+      "Epoch   1 Batch 1006/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.914, Loss:  0.055\n",
+      "Epoch   1 Batch 1007/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.911, Loss:  0.057\n",
+      "Epoch   1 Batch 1008/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.085\n",
+      "Epoch   1 Batch 1009/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.059\n",
+      "Epoch   1 Batch 1010/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.925, Loss:  0.058\n",
+      "Epoch   1 Batch 1011/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.924, Loss:  0.055\n",
+      "Epoch   1 Batch 1012/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.051\n",
+      "Epoch   1 Batch 1013/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.928, Loss:  0.052\n",
+      "Epoch   1 Batch 1014/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.064\n",
+      "Epoch   1 Batch 1015/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.929, Loss:  0.075\n",
+      "Epoch   1 Batch 1016/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.922, Loss:  0.066\n",
+      "Epoch   1 Batch 1017/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.921, Loss:  0.059\n",
+      "Epoch   1 Batch 1018/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.921, Loss:  0.048\n",
+      "Epoch   1 Batch 1019/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.917, Loss:  0.075\n",
+      "Epoch   1 Batch 1020/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.917, Loss:  0.058\n",
+      "Epoch   1 Batch 1021/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.915, Loss:  0.060\n",
+      "Epoch   1 Batch 1022/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.053\n",
+      "Epoch   1 Batch 1023/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.910, Loss:  0.062\n",
+      "Epoch   1 Batch 1024/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.906, Loss:  0.071\n",
+      "Epoch   1 Batch 1025/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.912, Loss:  0.059\n",
+      "Epoch   1 Batch 1026/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.919, Loss:  0.080\n",
+      "Epoch   1 Batch 1027/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.919, Loss:  0.067\n",
+      "Epoch   1 Batch 1028/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.922, Loss:  0.061\n",
+      "Epoch   1 Batch 1029/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.051\n",
+      "Epoch   1 Batch 1030/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.060\n",
+      "Epoch   1 Batch 1031/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.063\n",
+      "Epoch   1 Batch 1032/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.915, Loss:  0.064\n",
+      "Epoch   1 Batch 1033/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.913, Loss:  0.057\n",
+      "Epoch   1 Batch 1034/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.913, Loss:  0.055\n",
+      "Epoch   1 Batch 1035/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.918, Loss:  0.036\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 Batch 1036/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.919, Loss:  0.076\n",
+      "Epoch   1 Batch 1037/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.924, Loss:  0.061\n",
+      "Epoch   1 Batch 1038/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.076\n",
+      "Epoch   1 Batch 1039/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.929, Loss:  0.066\n",
+      "Epoch   1 Batch 1040/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.928, Loss:  0.061\n",
+      "Epoch   1 Batch 1041/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.928, Loss:  0.067\n",
+      "Epoch   1 Batch 1042/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.052\n",
+      "Epoch   1 Batch 1043/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.076\n",
+      "Epoch   1 Batch 1044/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.081\n",
+      "Epoch   1 Batch 1045/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.058\n",
+      "Epoch   1 Batch 1046/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.911, Loss:  0.046\n",
+      "Epoch   1 Batch 1047/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.909, Loss:  0.055\n",
+      "Epoch   1 Batch 1048/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.908, Loss:  0.050\n",
+      "Epoch   1 Batch 1049/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.903, Loss:  0.046\n",
+      "Epoch   1 Batch 1050/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.903, Loss:  0.059\n",
+      "Epoch   1 Batch 1051/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.900, Loss:  0.066\n",
+      "Epoch   1 Batch 1052/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.900, Loss:  0.060\n",
+      "Epoch   1 Batch 1053/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.907, Loss:  0.068\n",
+      "Epoch   1 Batch 1054/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.912, Loss:  0.062\n",
+      "Epoch   1 Batch 1055/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.065\n",
+      "Epoch   1 Batch 1056/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.909, Loss:  0.054\n",
+      "Epoch   1 Batch 1057/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.902, Loss:  0.068\n",
+      "Epoch   1 Batch 1058/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.906, Loss:  0.071\n",
+      "Epoch   1 Batch 1059/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.901, Loss:  0.074\n",
+      "Epoch   1 Batch 1060/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.051\n",
+      "Epoch   1 Batch 1061/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.914, Loss:  0.076\n",
+      "Epoch   1 Batch 1062/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.907, Loss:  0.062\n",
+      "Epoch   1 Batch 1063/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.909, Loss:  0.066\n",
+      "Epoch   1 Batch 1064/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.909, Loss:  0.063\n",
+      "Epoch   1 Batch 1065/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.909, Loss:  0.071\n",
+      "Epoch   1 Batch 1066/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.909, Loss:  0.055\n",
+      "Epoch   1 Batch 1067/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.919, Loss:  0.074\n",
+      "Epoch   1 Batch 1068/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.910, Loss:  0.060\n",
+      "Epoch   1 Batch 1069/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.045\n",
+      "Epoch   1 Batch 1070/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.913, Loss:  0.070\n",
+      "Epoch   1 Batch 1071/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.913, Loss:  0.058\n",
+      "Epoch   1 Batch 1072/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.919, Loss:  0.055\n",
+      "Epoch   1 Batch 1073/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.909, Loss:  0.068\n",
+      "Epoch   1 Batch 1074/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.903, Loss:  0.070\n",
+      "Epoch   1 Batch 1075/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.900, Loss:  0.070\n",
+      "Epoch   2 Batch    0/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.907, Loss:  0.046\n",
+      "Epoch   2 Batch    1/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.910, Loss:  0.060\n",
+      "Epoch   2 Batch    2/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.906, Loss:  0.068\n",
+      "Epoch   2 Batch    3/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.907, Loss:  0.061\n",
+      "Epoch   2 Batch    4/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.910, Loss:  0.056\n",
+      "Epoch   2 Batch    5/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.918, Loss:  0.072\n",
+      "Epoch   2 Batch    6/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.065\n",
+      "Epoch   2 Batch    7/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.905, Loss:  0.052\n",
+      "Epoch   2 Batch    8/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.060\n",
+      "Epoch   2 Batch    9/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.930, Loss:  0.065\n",
+      "Epoch   2 Batch   10/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.921, Loss:  0.062\n",
+      "Epoch   2 Batch   11/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.916, Loss:  0.072\n",
+      "Epoch   2 Batch   12/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.058\n",
+      "Epoch   2 Batch   13/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.922, Loss:  0.063\n",
+      "Epoch   2 Batch   14/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.925, Loss:  0.054\n",
+      "Epoch   2 Batch   15/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.060\n",
+      "Epoch   2 Batch   16/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.064\n",
+      "Epoch   2 Batch   17/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.929, Loss:  0.055\n",
+      "Epoch   2 Batch   18/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.929, Loss:  0.065\n",
+      "Epoch   2 Batch   19/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.065\n",
+      "Epoch   2 Batch   20/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.918, Loss:  0.060\n",
+      "Epoch   2 Batch   21/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.066\n",
+      "Epoch   2 Batch   22/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.930, Loss:  0.067\n",
+      "Epoch   2 Batch   23/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.066\n",
+      "Epoch   2 Batch   24/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.924, Loss:  0.069\n",
+      "Epoch   2 Batch   25/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.915, Loss:  0.047\n",
+      "Epoch   2 Batch   26/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.916, Loss:  0.066\n",
+      "Epoch   2 Batch   27/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.052\n",
+      "Epoch   2 Batch   28/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.056\n",
+      "Epoch   2 Batch   29/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.913, Loss:  0.063\n",
+      "Epoch   2 Batch   30/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.918, Loss:  0.051\n",
+      "Epoch   2 Batch   31/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.052\n",
+      "Epoch   2 Batch   32/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.054\n",
+      "Epoch   2 Batch   33/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.907, Loss:  0.055\n",
+      "Epoch   2 Batch   34/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.900, Loss:  0.063\n",
+      "Epoch   2 Batch   35/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.900, Loss:  0.066\n",
+      "Epoch   2 Batch   36/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.898, Loss:  0.056\n",
+      "Epoch   2 Batch   37/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.903, Loss:  0.063\n",
+      "Epoch   2 Batch   38/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.907, Loss:  0.081\n",
+      "Epoch   2 Batch   39/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.068\n",
+      "Epoch   2 Batch   40/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.906, Loss:  0.049\n",
+      "Epoch   2 Batch   41/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.908, Loss:  0.058\n",
+      "Epoch   2 Batch   42/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.908, Loss:  0.075\n",
+      "Epoch   2 Batch   43/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.904, Loss:  0.044\n",
+      "Epoch   2 Batch   44/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.905, Loss:  0.049\n",
+      "Epoch   2 Batch   45/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.908, Loss:  0.052\n",
+      "Epoch   2 Batch   46/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.904, Loss:  0.063\n",
+      "Epoch   2 Batch   47/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.906, Loss:  0.068\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch   48/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.912, Loss:  0.084\n",
+      "Epoch   2 Batch   49/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.904, Loss:  0.078\n",
+      "Epoch   2 Batch   50/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.902, Loss:  0.058\n",
+      "Epoch   2 Batch   51/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.906, Loss:  0.073\n",
+      "Epoch   2 Batch   52/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.907, Loss:  0.065\n",
+      "Epoch   2 Batch   53/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.058\n",
+      "Epoch   2 Batch   54/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.910, Loss:  0.083\n",
+      "Epoch   2 Batch   55/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.911, Loss:  0.056\n",
+      "Epoch   2 Batch   56/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.907, Loss:  0.044\n",
+      "Epoch   2 Batch   57/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.054\n",
+      "Epoch   2 Batch   58/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.901, Loss:  0.062\n",
+      "Epoch   2 Batch   59/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.907, Loss:  0.050\n",
+      "Epoch   2 Batch   60/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.901, Loss:  0.041\n",
+      "Epoch   2 Batch   61/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.901, Loss:  0.076\n",
+      "Epoch   2 Batch   62/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.910, Loss:  0.067\n",
+      "Epoch   2 Batch   63/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.911, Loss:  0.043\n",
+      "Epoch   2 Batch   64/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.901, Loss:  0.056\n",
+      "Epoch   2 Batch   65/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.902, Loss:  0.054\n",
+      "Epoch   2 Batch   66/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.904, Loss:  0.046\n",
+      "Epoch   2 Batch   67/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.905, Loss:  0.060\n",
+      "Epoch   2 Batch   68/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.906, Loss:  0.073\n",
+      "Epoch   2 Batch   69/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.911, Loss:  0.070\n",
+      "Epoch   2 Batch   70/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.914, Loss:  0.059\n",
+      "Epoch   2 Batch   71/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.043\n",
+      "Epoch   2 Batch   72/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.918, Loss:  0.056\n",
+      "Epoch   2 Batch   73/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.921, Loss:  0.052\n",
+      "Epoch   2 Batch   74/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.916, Loss:  0.050\n",
+      "Epoch   2 Batch   75/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.074\n",
+      "Epoch   2 Batch   76/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.914, Loss:  0.043\n",
+      "Epoch   2 Batch   77/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.061\n",
+      "Epoch   2 Batch   78/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.064\n",
+      "Epoch   2 Batch   79/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.059\n",
+      "Epoch   2 Batch   80/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.057\n",
+      "Epoch   2 Batch   81/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.046\n",
+      "Epoch   2 Batch   82/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.932, Loss:  0.044\n",
+      "Epoch   2 Batch   83/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.928, Loss:  0.071\n",
+      "Epoch   2 Batch   84/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.057\n",
+      "Epoch   2 Batch   85/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.937, Loss:  0.054\n",
+      "Epoch   2 Batch   86/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.057\n",
+      "Epoch   2 Batch   87/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.932, Loss:  0.062\n",
+      "Epoch   2 Batch   88/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.930, Loss:  0.068\n",
+      "Epoch   2 Batch   89/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.925, Loss:  0.075\n",
+      "Epoch   2 Batch   90/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.931, Loss:  0.057\n",
+      "Epoch   2 Batch   91/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.055\n",
+      "Epoch   2 Batch   92/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.919, Loss:  0.067\n",
+      "Epoch   2 Batch   93/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.920, Loss:  0.051\n",
+      "Epoch   2 Batch   94/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.932, Loss:  0.050\n",
+      "Epoch   2 Batch   95/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.936, Loss:  0.067\n",
+      "Epoch   2 Batch   96/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.940, Loss:  0.064\n",
+      "Epoch   2 Batch   97/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.057\n",
+      "Epoch   2 Batch   98/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.065\n",
+      "Epoch   2 Batch   99/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.938, Loss:  0.068\n",
+      "Epoch   2 Batch  100/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.051\n",
+      "Epoch   2 Batch  101/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.932, Loss:  0.056\n",
+      "Epoch   2 Batch  102/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.058\n",
+      "Epoch   2 Batch  103/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.923, Loss:  0.071\n",
+      "Epoch   2 Batch  104/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.919, Loss:  0.064\n",
+      "Epoch   2 Batch  105/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.919, Loss:  0.055\n",
+      "Epoch   2 Batch  106/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.078\n",
+      "Epoch   2 Batch  107/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.915, Loss:  0.062\n",
+      "Epoch   2 Batch  108/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.919, Loss:  0.061\n",
+      "Epoch   2 Batch  109/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.913, Loss:  0.050\n",
+      "Epoch   2 Batch  110/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.926, Loss:  0.042\n",
+      "Epoch   2 Batch  111/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.066\n",
+      "Epoch   2 Batch  112/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.915, Loss:  0.057\n",
+      "Epoch   2 Batch  113/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.911, Loss:  0.065\n",
+      "Epoch   2 Batch  114/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.911, Loss:  0.044\n",
+      "Epoch   2 Batch  115/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.912, Loss:  0.065\n",
+      "Epoch   2 Batch  116/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.916, Loss:  0.061\n",
+      "Epoch   2 Batch  117/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.916, Loss:  0.059\n",
+      "Epoch   2 Batch  118/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.917, Loss:  0.049\n",
+      "Epoch   2 Batch  119/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.911, Loss:  0.055\n",
+      "Epoch   2 Batch  120/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.067\n",
+      "Epoch   2 Batch  121/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.909, Loss:  0.052\n",
+      "Epoch   2 Batch  122/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.916, Loss:  0.053\n",
+      "Epoch   2 Batch  123/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.047\n",
+      "Epoch   2 Batch  124/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.926, Loss:  0.077\n",
+      "Epoch   2 Batch  125/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.920, Loss:  0.067\n",
+      "Epoch   2 Batch  126/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.924, Loss:  0.051\n",
+      "Epoch   2 Batch  127/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.057\n",
+      "Epoch   2 Batch  128/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.909, Loss:  0.061\n",
+      "Epoch   2 Batch  129/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.894, Loss:  0.059\n",
+      "Epoch   2 Batch  130/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.897, Loss:  0.057\n",
+      "Epoch   2 Batch  131/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.901, Loss:  0.063\n",
+      "Epoch   2 Batch  132/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.894, Loss:  0.051\n",
+      "Epoch   2 Batch  133/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.899, Loss:  0.048\n",
+      "Epoch   2 Batch  134/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.903, Loss:  0.046\n",
+      "Epoch   2 Batch  135/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.908, Loss:  0.065\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch  136/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.904, Loss:  0.050\n",
+      "Epoch   2 Batch  137/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.904, Loss:  0.041\n",
+      "Epoch   2 Batch  138/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.907, Loss:  0.054\n",
+      "Epoch   2 Batch  139/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.903, Loss:  0.059\n",
+      "Epoch   2 Batch  140/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.053\n",
+      "Epoch   2 Batch  141/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.926, Loss:  0.052\n",
+      "Epoch   2 Batch  142/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.917, Loss:  0.052\n",
+      "Epoch   2 Batch  143/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.925, Loss:  0.062\n",
+      "Epoch   2 Batch  144/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.927, Loss:  0.087\n",
+      "Epoch   2 Batch  145/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.923, Loss:  0.070\n",
+      "Epoch   2 Batch  146/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.926, Loss:  0.091\n",
+      "Epoch   2 Batch  147/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.064\n",
+      "Epoch   2 Batch  148/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.935, Loss:  0.066\n",
+      "Epoch   2 Batch  149/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.914, Loss:  0.049\n",
+      "Epoch   2 Batch  150/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.914, Loss:  0.056\n",
+      "Epoch   2 Batch  151/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.909, Loss:  0.049\n",
+      "Epoch   2 Batch  152/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.911, Loss:  0.074\n",
+      "Epoch   2 Batch  153/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.907, Loss:  0.075\n",
+      "Epoch   2 Batch  154/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.911, Loss:  0.047\n",
+      "Epoch   2 Batch  155/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.920, Loss:  0.056\n",
+      "Epoch   2 Batch  156/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.920, Loss:  0.049\n",
+      "Epoch   2 Batch  157/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.920, Loss:  0.042\n",
+      "Epoch   2 Batch  158/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.932, Loss:  0.073\n",
+      "Epoch   2 Batch  159/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   2 Batch  160/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.916, Loss:  0.045\n",
+      "Epoch   2 Batch  161/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.916, Loss:  0.054\n",
+      "Epoch   2 Batch  162/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.076\n",
+      "Epoch   2 Batch  163/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.925, Loss:  0.072\n",
+      "Epoch   2 Batch  164/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.056\n",
+      "Epoch   2 Batch  165/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.914, Loss:  0.047\n",
+      "Epoch   2 Batch  166/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.909, Loss:  0.063\n",
+      "Epoch   2 Batch  167/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.908, Loss:  0.055\n",
+      "Epoch   2 Batch  168/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.908, Loss:  0.070\n",
+      "Epoch   2 Batch  169/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.913, Loss:  0.078\n",
+      "Epoch   2 Batch  170/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.915, Loss:  0.070\n",
+      "Epoch   2 Batch  171/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.051\n",
+      "Epoch   2 Batch  172/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.046\n",
+      "Epoch   2 Batch  173/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.921, Loss:  0.066\n",
+      "Epoch   2 Batch  174/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.051\n",
+      "Epoch   2 Batch  175/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.916, Loss:  0.055\n",
+      "Epoch   2 Batch  176/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.912, Loss:  0.066\n",
+      "Epoch   2 Batch  177/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.911, Loss:  0.062\n",
+      "Epoch   2 Batch  178/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.064\n",
+      "Epoch   2 Batch  179/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.057\n",
+      "Epoch   2 Batch  180/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.911, Loss:  0.048\n",
+      "Epoch   2 Batch  181/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.909, Loss:  0.063\n",
+      "Epoch   2 Batch  182/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.914, Loss:  0.057\n",
+      "Epoch   2 Batch  183/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.059\n",
+      "Epoch   2 Batch  184/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.055\n",
+      "Epoch   2 Batch  185/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.912, Loss:  0.058\n",
+      "Epoch   2 Batch  186/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.909, Loss:  0.056\n",
+      "Epoch   2 Batch  187/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.914, Loss:  0.043\n",
+      "Epoch   2 Batch  188/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.056\n",
+      "Epoch   2 Batch  189/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.919, Loss:  0.056\n",
+      "Epoch   2 Batch  190/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.919, Loss:  0.051\n",
+      "Epoch   2 Batch  191/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.918, Loss:  0.050\n",
+      "Epoch   2 Batch  192/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.067\n",
+      "Epoch   2 Batch  193/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.903, Loss:  0.047\n",
+      "Epoch   2 Batch  194/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.908, Loss:  0.045\n",
+      "Epoch   2 Batch  195/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.913, Loss:  0.051\n",
+      "Epoch   2 Batch  196/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.920, Loss:  0.045\n",
+      "Epoch   2 Batch  197/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.920, Loss:  0.060\n",
+      "Epoch   2 Batch  198/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.925, Loss:  0.059\n",
+      "Epoch   2 Batch  199/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.925, Loss:  0.048\n",
+      "Epoch   2 Batch  200/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.065\n",
+      "Epoch   2 Batch  201/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.929, Loss:  0.041\n",
+      "Epoch   2 Batch  202/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.928, Loss:  0.044\n",
+      "Epoch   2 Batch  203/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.048\n",
+      "Epoch   2 Batch  204/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.923, Loss:  0.073\n",
+      "Epoch   2 Batch  205/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.926, Loss:  0.060\n",
+      "Epoch   2 Batch  206/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.918, Loss:  0.049\n",
+      "Epoch   2 Batch  207/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.048\n",
+      "Epoch   2 Batch  208/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.056\n",
+      "Epoch   2 Batch  209/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.045\n",
+      "Epoch   2 Batch  210/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.911, Loss:  0.065\n",
+      "Epoch   2 Batch  211/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.905, Loss:  0.045\n",
+      "Epoch   2 Batch  212/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.911, Loss:  0.046\n",
+      "Epoch   2 Batch  213/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.913, Loss:  0.052\n",
+      "Epoch   2 Batch  214/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.913, Loss:  0.051\n",
+      "Epoch   2 Batch  215/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.917, Loss:  0.060\n",
+      "Epoch   2 Batch  216/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.911, Loss:  0.056\n",
+      "Epoch   2 Batch  217/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.906, Loss:  0.044\n",
+      "Epoch   2 Batch  218/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.905, Loss:  0.069\n",
+      "Epoch   2 Batch  219/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.901, Loss:  0.042\n",
+      "Epoch   2 Batch  220/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.900, Loss:  0.051\n",
+      "Epoch   2 Batch  221/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.897, Loss:  0.056\n",
+      "Epoch   2 Batch  222/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.904, Loss:  0.045\n",
+      "Epoch   2 Batch  223/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.903, Loss:  0.043\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch  224/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.912, Loss:  0.064\n",
+      "Epoch   2 Batch  225/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.058\n",
+      "Epoch   2 Batch  226/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.921, Loss:  0.050\n",
+      "Epoch   2 Batch  227/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.911, Loss:  0.073\n",
+      "Epoch   2 Batch  228/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.915, Loss:  0.053\n",
+      "Epoch   2 Batch  229/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.930, Loss:  0.058\n",
+      "Epoch   2 Batch  230/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.056\n",
+      "Epoch   2 Batch  231/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.050\n",
+      "Epoch   2 Batch  232/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.921, Loss:  0.041\n",
+      "Epoch   2 Batch  233/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.911, Loss:  0.075\n",
+      "Epoch   2 Batch  234/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.915, Loss:  0.062\n",
+      "Epoch   2 Batch  235/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.922, Loss:  0.056\n",
+      "Epoch   2 Batch  236/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.923, Loss:  0.064\n",
+      "Epoch   2 Batch  237/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.048\n",
+      "Epoch   2 Batch  238/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.913, Loss:  0.048\n",
+      "Epoch   2 Batch  239/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.041\n",
+      "Epoch   2 Batch  240/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.916, Loss:  0.050\n",
+      "Epoch   2 Batch  241/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.915, Loss:  0.045\n",
+      "Epoch   2 Batch  242/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.909, Loss:  0.044\n",
+      "Epoch   2 Batch  243/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.904, Loss:  0.055\n",
+      "Epoch   2 Batch  244/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.904, Loss:  0.051\n",
+      "Epoch   2 Batch  245/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.917, Loss:  0.043\n",
+      "Epoch   2 Batch  246/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.917, Loss:  0.044\n",
+      "Epoch   2 Batch  247/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.056\n",
+      "Epoch   2 Batch  248/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.045\n",
+      "Epoch   2 Batch  249/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.924, Loss:  0.052\n",
+      "Epoch   2 Batch  250/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.056\n",
+      "Epoch   2 Batch  251/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.916, Loss:  0.057\n",
+      "Epoch   2 Batch  252/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.917, Loss:  0.061\n",
+      "Epoch   2 Batch  253/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.056\n",
+      "Epoch   2 Batch  254/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.910, Loss:  0.057\n",
+      "Epoch   2 Batch  255/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.910, Loss:  0.054\n",
+      "Epoch   2 Batch  256/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.906, Loss:  0.073\n",
+      "Epoch   2 Batch  257/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.905, Loss:  0.055\n",
+      "Epoch   2 Batch  258/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.907, Loss:  0.054\n",
+      "Epoch   2 Batch  259/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.906, Loss:  0.045\n",
+      "Epoch   2 Batch  260/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.901, Loss:  0.048\n",
+      "Epoch   2 Batch  261/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.901, Loss:  0.057\n",
+      "Epoch   2 Batch  262/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.911, Loss:  0.048\n",
+      "Epoch   2 Batch  263/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.912, Loss:  0.051\n",
+      "Epoch   2 Batch  264/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.913, Loss:  0.053\n",
+      "Epoch   2 Batch  265/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.918, Loss:  0.051\n",
+      "Epoch   2 Batch  266/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.057\n",
+      "Epoch   2 Batch  267/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.925, Loss:  0.050\n",
+      "Epoch   2 Batch  268/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.927, Loss:  0.062\n",
+      "Epoch   2 Batch  269/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.926, Loss:  0.082\n",
+      "Epoch   2 Batch  270/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.925, Loss:  0.065\n",
+      "Epoch   2 Batch  271/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.048\n",
+      "Epoch   2 Batch  272/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.915, Loss:  0.093\n",
+      "Epoch   2 Batch  273/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.049\n",
+      "Epoch   2 Batch  274/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.917, Loss:  0.066\n",
+      "Epoch   2 Batch  275/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.917, Loss:  0.055\n",
+      "Epoch   2 Batch  276/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.910, Loss:  0.088\n",
+      "Epoch   2 Batch  277/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.049\n",
+      "Epoch   2 Batch  278/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.917, Loss:  0.067\n",
+      "Epoch   2 Batch  279/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.068\n",
+      "Epoch   2 Batch  280/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.072\n",
+      "Epoch   2 Batch  281/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.072\n",
+      "Epoch   2 Batch  282/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.912, Loss:  0.082\n",
+      "Epoch   2 Batch  283/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.919, Loss:  0.080\n",
+      "Epoch   2 Batch  284/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.928, Loss:  0.062\n",
+      "Epoch   2 Batch  285/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.917, Loss:  0.061\n",
+      "Epoch   2 Batch  286/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.053\n",
+      "Epoch   2 Batch  287/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.060\n",
+      "Epoch   2 Batch  288/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.904, Loss:  0.067\n",
+      "Epoch   2 Batch  289/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.901, Loss:  0.056\n",
+      "Epoch   2 Batch  290/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.915, Loss:  0.088\n",
+      "Epoch   2 Batch  291/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.922, Loss:  0.076\n",
+      "Epoch   2 Batch  292/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.918, Loss:  0.067\n",
+      "Epoch   2 Batch  293/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.917, Loss:  0.067\n",
+      "Epoch   2 Batch  294/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.917, Loss:  0.052\n",
+      "Epoch   2 Batch  295/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.913, Loss:  0.067\n",
+      "Epoch   2 Batch  296/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.913, Loss:  0.060\n",
+      "Epoch   2 Batch  297/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.065\n",
+      "Epoch   2 Batch  298/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.079\n",
+      "Epoch   2 Batch  299/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.921, Loss:  0.067\n",
+      "Epoch   2 Batch  300/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.056\n",
+      "Epoch   2 Batch  301/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.908, Loss:  0.045\n",
+      "Epoch   2 Batch  302/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.901, Loss:  0.053\n",
+      "Epoch   2 Batch  303/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.906, Loss:  0.067\n",
+      "Epoch   2 Batch  304/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.906, Loss:  0.059\n",
+      "Epoch   2 Batch  305/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.904, Loss:  0.046\n",
+      "Epoch   2 Batch  306/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.905, Loss:  0.075\n",
+      "Epoch   2 Batch  307/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.910, Loss:  0.049\n",
+      "Epoch   2 Batch  308/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.908, Loss:  0.075\n",
+      "Epoch   2 Batch  309/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.053\n",
+      "Epoch   2 Batch  310/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.912, Loss:  0.058\n",
+      "Epoch   2 Batch  311/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.903, Loss:  0.058\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch  312/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.904, Loss:  0.077\n",
+      "Epoch   2 Batch  313/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.899, Loss:  0.049\n",
+      "Epoch   2 Batch  314/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.903, Loss:  0.049\n",
+      "Epoch   2 Batch  315/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.899, Loss:  0.049\n",
+      "Epoch   2 Batch  316/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.893, Loss:  0.068\n",
+      "Epoch   2 Batch  317/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.895, Loss:  0.082\n",
+      "Epoch   2 Batch  318/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.898, Loss:  0.066\n",
+      "Epoch   2 Batch  319/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.900, Loss:  0.071\n",
+      "Epoch   2 Batch  320/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.893, Loss:  0.067\n",
+      "Epoch   2 Batch  321/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.898, Loss:  0.054\n",
+      "Epoch   2 Batch  322/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.903, Loss:  0.057\n",
+      "Epoch   2 Batch  323/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.908, Loss:  0.066\n",
+      "Epoch   2 Batch  324/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.903, Loss:  0.055\n",
+      "Epoch   2 Batch  325/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.069\n",
+      "Epoch   2 Batch  326/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.913, Loss:  0.059\n",
+      "Epoch   2 Batch  327/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.066\n",
+      "Epoch   2 Batch  328/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.904, Loss:  0.070\n",
+      "Epoch   2 Batch  329/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.902, Loss:  0.071\n",
+      "Epoch   2 Batch  330/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.894, Loss:  0.059\n",
+      "Epoch   2 Batch  331/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.886, Loss:  0.061\n",
+      "Epoch   2 Batch  332/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.888, Loss:  0.049\n",
+      "Epoch   2 Batch  333/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.879, Loss:  0.054\n",
+      "Epoch   2 Batch  334/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.878, Loss:  0.058\n",
+      "Epoch   2 Batch  335/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.878, Loss:  0.058\n",
+      "Epoch   2 Batch  336/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.893, Loss:  0.096\n",
+      "Epoch   2 Batch  337/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.897, Loss:  0.065\n",
+      "Epoch   2 Batch  338/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.895, Loss:  0.073\n",
+      "Epoch   2 Batch  339/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.909, Loss:  0.049\n",
+      "Epoch   2 Batch  340/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.908, Loss:  0.061\n",
+      "Epoch   2 Batch  341/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.072\n",
+      "Epoch   2 Batch  342/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.922, Loss:  0.053\n",
+      "Epoch   2 Batch  343/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.911, Loss:  0.055\n",
+      "Epoch   2 Batch  344/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.912, Loss:  0.068\n",
+      "Epoch   2 Batch  345/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.907, Loss:  0.052\n",
+      "Epoch   2 Batch  346/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.056\n",
+      "Epoch   2 Batch  347/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.906, Loss:  0.050\n",
+      "Epoch   2 Batch  348/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.897, Loss:  0.052\n",
+      "Epoch   2 Batch  349/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.897, Loss:  0.056\n",
+      "Epoch   2 Batch  350/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.904, Loss:  0.065\n",
+      "Epoch   2 Batch  351/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.903, Loss:  0.060\n",
+      "Epoch   2 Batch  352/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.906, Loss:  0.058\n",
+      "Epoch   2 Batch  353/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.902, Loss:  0.067\n",
+      "Epoch   2 Batch  354/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.903, Loss:  0.074\n",
+      "Epoch   2 Batch  355/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.898, Loss:  0.057\n",
+      "Epoch   2 Batch  356/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.896, Loss:  0.063\n",
+      "Epoch   2 Batch  357/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.896, Loss:  0.057\n",
+      "Epoch   2 Batch  358/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.904, Loss:  0.076\n",
+      "Epoch   2 Batch  359/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.059\n",
+      "Epoch   2 Batch  360/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.048\n",
+      "Epoch   2 Batch  361/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.908, Loss:  0.055\n",
+      "Epoch   2 Batch  362/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.914, Loss:  0.066\n",
+      "Epoch   2 Batch  363/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.913, Loss:  0.068\n",
+      "Epoch   2 Batch  364/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.915, Loss:  0.077\n",
+      "Epoch   2 Batch  365/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.916, Loss:  0.048\n",
+      "Epoch   2 Batch  366/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.914, Loss:  0.056\n",
+      "Epoch   2 Batch  367/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.908, Loss:  0.047\n",
+      "Epoch   2 Batch  368/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.068\n",
+      "Epoch   2 Batch  369/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.913, Loss:  0.059\n",
+      "Epoch   2 Batch  370/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.061\n",
+      "Epoch   2 Batch  371/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.917, Loss:  0.055\n",
+      "Epoch   2 Batch  372/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.931, Loss:  0.053\n",
+      "Epoch   2 Batch  373/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.915, Loss:  0.043\n",
+      "Epoch   2 Batch  374/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.071\n",
+      "Epoch   2 Batch  375/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.057\n",
+      "Epoch   2 Batch  376/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.054\n",
+      "Epoch   2 Batch  377/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.929, Loss:  0.053\n",
+      "Epoch   2 Batch  378/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.044\n",
+      "Epoch   2 Batch  379/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.068\n",
+      "Epoch   2 Batch  380/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.930, Loss:  0.054\n",
+      "Epoch   2 Batch  381/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.929, Loss:  0.066\n",
+      "Epoch   2 Batch  382/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.932, Loss:  0.078\n",
+      "Epoch   2 Batch  383/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.930, Loss:  0.049\n",
+      "Epoch   2 Batch  384/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.058\n",
+      "Epoch   2 Batch  385/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.052\n",
+      "Epoch   2 Batch  386/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.924, Loss:  0.049\n",
+      "Epoch   2 Batch  387/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.918, Loss:  0.057\n",
+      "Epoch   2 Batch  388/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.913, Loss:  0.061\n",
+      "Epoch   2 Batch  389/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.907, Loss:  0.058\n",
+      "Epoch   2 Batch  390/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.904, Loss:  0.070\n",
+      "Epoch   2 Batch  391/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.912, Loss:  0.057\n",
+      "Epoch   2 Batch  392/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.911, Loss:  0.061\n",
+      "Epoch   2 Batch  393/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.916, Loss:  0.053\n",
+      "Epoch   2 Batch  394/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.914, Loss:  0.053\n",
+      "Epoch   2 Batch  395/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.054\n",
+      "Epoch   2 Batch  396/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.932, Loss:  0.054\n",
+      "Epoch   2 Batch  397/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.937, Loss:  0.049\n",
+      "Epoch   2 Batch  398/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.938, Loss:  0.059\n",
+      "Epoch   2 Batch  399/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.938, Loss:  0.052\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch  400/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.943, Loss:  0.071\n",
+      "Epoch   2 Batch  401/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.935, Loss:  0.049\n",
+      "Epoch   2 Batch  402/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.050\n",
+      "Epoch   2 Batch  403/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.927, Loss:  0.077\n",
+      "Epoch   2 Batch  404/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.929, Loss:  0.050\n",
+      "Epoch   2 Batch  405/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.930, Loss:  0.065\n",
+      "Epoch   2 Batch  406/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.051\n",
+      "Epoch   2 Batch  407/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.062\n",
+      "Epoch   2 Batch  408/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.917, Loss:  0.052\n",
+      "Epoch   2 Batch  409/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.066\n",
+      "Epoch   2 Batch  410/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.077\n",
+      "Epoch   2 Batch  411/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.067\n",
+      "Epoch   2 Batch  412/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.915, Loss:  0.048\n",
+      "Epoch   2 Batch  413/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.049\n",
+      "Epoch   2 Batch  414/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.910, Loss:  0.071\n",
+      "Epoch   2 Batch  415/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.059\n",
+      "Epoch   2 Batch  416/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.052\n",
+      "Epoch   2 Batch  417/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.092\n",
+      "Epoch   2 Batch  418/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.050\n",
+      "Epoch   2 Batch  419/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.059\n",
+      "Epoch   2 Batch  420/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.927, Loss:  0.050\n",
+      "Epoch   2 Batch  421/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.070\n",
+      "Epoch   2 Batch  422/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.051\n",
+      "Epoch   2 Batch  423/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.923, Loss:  0.068\n",
+      "Epoch   2 Batch  424/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.928, Loss:  0.062\n",
+      "Epoch   2 Batch  425/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.932, Loss:  0.045\n",
+      "Epoch   2 Batch  426/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.931, Loss:  0.057\n",
+      "Epoch   2 Batch  427/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.931, Loss:  0.060\n",
+      "Epoch   2 Batch  428/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.931, Loss:  0.045\n",
+      "Epoch   2 Batch  429/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.926, Loss:  0.048\n",
+      "Epoch   2 Batch  430/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.928, Loss:  0.057\n",
+      "Epoch   2 Batch  431/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.924, Loss:  0.050\n",
+      "Epoch   2 Batch  432/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.924, Loss:  0.065\n",
+      "Epoch   2 Batch  433/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.065\n",
+      "Epoch   2 Batch  434/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.050\n",
+      "Epoch   2 Batch  435/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.907, Loss:  0.075\n",
+      "Epoch   2 Batch  436/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.056\n",
+      "Epoch   2 Batch  437/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.911, Loss:  0.046\n",
+      "Epoch   2 Batch  438/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.906, Loss:  0.054\n",
+      "Epoch   2 Batch  439/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.897, Loss:  0.081\n",
+      "Epoch   2 Batch  440/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.898, Loss:  0.064\n",
+      "Epoch   2 Batch  441/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.892, Loss:  0.058\n",
+      "Epoch   2 Batch  442/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.895, Loss:  0.064\n",
+      "Epoch   2 Batch  443/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.899, Loss:  0.046\n",
+      "Epoch   2 Batch  444/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.904, Loss:  0.052\n",
+      "Epoch   2 Batch  445/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.895, Loss:  0.061\n",
+      "Epoch   2 Batch  446/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.893, Loss:  0.051\n",
+      "Epoch   2 Batch  447/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.890, Loss:  0.056\n",
+      "Epoch   2 Batch  448/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.894, Loss:  0.067\n",
+      "Epoch   2 Batch  449/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.060\n",
+      "Epoch   2 Batch  450/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.906, Loss:  0.063\n",
+      "Epoch   2 Batch  451/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.063\n",
+      "Epoch   2 Batch  452/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.928, Loss:  0.055\n",
+      "Epoch   2 Batch  453/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.048\n",
+      "Epoch   2 Batch  454/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.058\n",
+      "Epoch   2 Batch  455/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.929, Loss:  0.052\n",
+      "Epoch   2 Batch  456/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.059\n",
+      "Epoch   2 Batch  457/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.050\n",
+      "Epoch   2 Batch  458/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.939, Loss:  0.062\n",
+      "Epoch   2 Batch  459/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.048\n",
+      "Epoch   2 Batch  460/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.061\n",
+      "Epoch   2 Batch  461/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.935, Loss:  0.052\n",
+      "Epoch   2 Batch  462/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.934, Loss:  0.056\n",
+      "Epoch   2 Batch  463/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.932, Loss:  0.064\n",
+      "Epoch   2 Batch  464/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.921, Loss:  0.050\n",
+      "Epoch   2 Batch  465/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.062\n",
+      "Epoch   2 Batch  466/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.913, Loss:  0.048\n",
+      "Epoch   2 Batch  467/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.908, Loss:  0.063\n",
+      "Epoch   2 Batch  468/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.060\n",
+      "Epoch   2 Batch  469/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.046\n",
+      "Epoch   2 Batch  470/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.902, Loss:  0.052\n",
+      "Epoch   2 Batch  471/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.903, Loss:  0.046\n",
+      "Epoch   2 Batch  472/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.051\n",
+      "Epoch   2 Batch  473/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.906, Loss:  0.054\n",
+      "Epoch   2 Batch  474/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.906, Loss:  0.053\n",
+      "Epoch   2 Batch  475/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.907, Loss:  0.052\n",
+      "Epoch   2 Batch  476/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.913, Loss:  0.041\n",
+      "Epoch   2 Batch  477/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.912, Loss:  0.054\n",
+      "Epoch   2 Batch  478/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.047\n",
+      "Epoch   2 Batch  479/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.914, Loss:  0.054\n",
+      "Epoch   2 Batch  480/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.919, Loss:  0.050\n",
+      "Epoch   2 Batch  481/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.913, Loss:  0.060\n",
+      "Epoch   2 Batch  482/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.923, Loss:  0.064\n",
+      "Epoch   2 Batch  483/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.055\n",
+      "Epoch   2 Batch  484/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.923, Loss:  0.060\n",
+      "Epoch   2 Batch  485/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.065\n",
+      "Epoch   2 Batch  486/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.914, Loss:  0.049\n",
+      "Epoch   2 Batch  487/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.052\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch  488/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.914, Loss:  0.050\n",
+      "Epoch   2 Batch  489/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.909, Loss:  0.048\n",
+      "Epoch   2 Batch  490/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.903, Loss:  0.058\n",
+      "Epoch   2 Batch  491/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.905, Loss:  0.060\n",
+      "Epoch   2 Batch  492/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.909, Loss:  0.062\n",
+      "Epoch   2 Batch  493/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.910, Loss:  0.033\n",
+      "Epoch   2 Batch  494/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.910, Loss:  0.038\n",
+      "Epoch   2 Batch  495/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.913, Loss:  0.051\n",
+      "Epoch   2 Batch  496/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.920, Loss:  0.061\n",
+      "Epoch   2 Batch  497/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.058\n",
+      "Epoch   2 Batch  498/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.053\n",
+      "Epoch   2 Batch  499/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.932, Loss:  0.044\n",
+      "Epoch   2 Batch  500/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.934, Loss:  0.041\n",
+      "Epoch   2 Batch  501/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.925, Loss:  0.044\n",
+      "Epoch   2 Batch  502/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.054\n",
+      "Epoch   2 Batch  503/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.045\n",
+      "Epoch   2 Batch  504/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.922, Loss:  0.045\n",
+      "Epoch   2 Batch  505/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.044\n",
+      "Epoch   2 Batch  506/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.058\n",
+      "Epoch   2 Batch  507/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.054\n",
+      "Epoch   2 Batch  508/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.921, Loss:  0.046\n",
+      "Epoch   2 Batch  509/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.925, Loss:  0.073\n",
+      "Epoch   2 Batch  510/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.930, Loss:  0.051\n",
+      "Epoch   2 Batch  511/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.935, Loss:  0.042\n",
+      "Epoch   2 Batch  512/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.930, Loss:  0.044\n",
+      "Epoch   2 Batch  513/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.928, Loss:  0.060\n",
+      "Epoch   2 Batch  514/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.922, Loss:  0.052\n",
+      "Epoch   2 Batch  515/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.050\n",
+      "Epoch   2 Batch  516/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.054\n",
+      "Epoch   2 Batch  517/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.062\n",
+      "Epoch   2 Batch  518/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.914, Loss:  0.043\n",
+      "Epoch   2 Batch  519/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.047\n",
+      "Epoch   2 Batch  520/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.909, Loss:  0.051\n",
+      "Epoch   2 Batch  521/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.911, Loss:  0.047\n",
+      "Epoch   2 Batch  522/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.905, Loss:  0.057\n",
+      "Epoch   2 Batch  523/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.911, Loss:  0.054\n",
+      "Epoch   2 Batch  524/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.910, Loss:  0.054\n",
+      "Epoch   2 Batch  525/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.056\n",
+      "Epoch   2 Batch  526/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.039\n",
+      "Epoch   2 Batch  527/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.915, Loss:  0.065\n",
+      "Epoch   2 Batch  528/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.908, Loss:  0.051\n",
+      "Epoch   2 Batch  529/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.908, Loss:  0.058\n",
+      "Epoch   2 Batch  530/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.066\n",
+      "Epoch   2 Batch  531/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.904, Loss:  0.060\n",
+      "Epoch   2 Batch  532/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.067\n",
+      "Epoch   2 Batch  533/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.058\n",
+      "Epoch   2 Batch  534/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.919, Loss:  0.061\n",
+      "Epoch   2 Batch  535/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.913, Loss:  0.047\n",
+      "Epoch   2 Batch  536/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.909, Loss:  0.060\n",
+      "Epoch   2 Batch  537/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.038\n",
+      "Epoch   2 Batch  538/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.910, Loss:  0.034\n",
+      "Epoch   2 Batch  539/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.905, Loss:  0.071\n",
+      "Epoch   2 Batch  540/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.900, Loss:  0.045\n",
+      "Epoch   2 Batch  541/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.905, Loss:  0.046\n",
+      "Epoch   2 Batch  542/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.047\n",
+      "Epoch   2 Batch  543/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.896, Loss:  0.044\n",
+      "Epoch   2 Batch  544/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.896, Loss:  0.038\n",
+      "Epoch   2 Batch  545/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.898, Loss:  0.055\n",
+      "Epoch   2 Batch  546/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.894, Loss:  0.060\n",
+      "Epoch   2 Batch  547/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.894, Loss:  0.042\n",
+      "Epoch   2 Batch  548/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.898, Loss:  0.060\n",
+      "Epoch   2 Batch  549/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.903, Loss:  0.074\n",
+      "Epoch   2 Batch  550/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.906, Loss:  0.049\n",
+      "Epoch   2 Batch  551/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.906, Loss:  0.060\n",
+      "Epoch   2 Batch  552/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.908, Loss:  0.058\n",
+      "Epoch   2 Batch  553/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.906, Loss:  0.072\n",
+      "Epoch   2 Batch  554/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.901, Loss:  0.049\n",
+      "Epoch   2 Batch  555/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.901, Loss:  0.041\n",
+      "Epoch   2 Batch  556/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.902, Loss:  0.048\n",
+      "Epoch   2 Batch  557/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.903, Loss:  0.050\n",
+      "Epoch   2 Batch  558/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.914, Loss:  0.043\n",
+      "Epoch   2 Batch  559/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.910, Loss:  0.049\n",
+      "Epoch   2 Batch  560/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.900, Loss:  0.053\n",
+      "Epoch   2 Batch  561/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.904, Loss:  0.046\n",
+      "Epoch   2 Batch  562/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.908, Loss:  0.046\n",
+      "Epoch   2 Batch  563/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.907, Loss:  0.053\n",
+      "Epoch   2 Batch  564/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.907, Loss:  0.058\n",
+      "Epoch   2 Batch  565/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.907, Loss:  0.056\n",
+      "Epoch   2 Batch  566/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.903, Loss:  0.049\n",
+      "Epoch   2 Batch  567/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.904, Loss:  0.054\n",
+      "Epoch   2 Batch  568/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.904, Loss:  0.047\n",
+      "Epoch   2 Batch  569/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.903, Loss:  0.060\n",
+      "Epoch   2 Batch  570/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.908, Loss:  0.066\n",
+      "Epoch   2 Batch  571/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.917, Loss:  0.037\n",
+      "Epoch   2 Batch  572/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.912, Loss:  0.046\n",
+      "Epoch   2 Batch  573/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.060\n",
+      "Epoch   2 Batch  574/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.919, Loss:  0.059\n",
+      "Epoch   2 Batch  575/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.039\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch  576/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.915, Loss:  0.048\n",
+      "Epoch   2 Batch  577/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.922, Loss:  0.056\n",
+      "Epoch   2 Batch  578/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.922, Loss:  0.045\n",
+      "Epoch   2 Batch  579/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.922, Loss:  0.045\n",
+      "Epoch   2 Batch  580/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.042\n",
+      "Epoch   2 Batch  581/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.922, Loss:  0.037\n",
+      "Epoch   2 Batch  582/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.045\n",
+      "Epoch   2 Batch  583/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.913, Loss:  0.057\n",
+      "Epoch   2 Batch  584/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.913, Loss:  0.050\n",
+      "Epoch   2 Batch  585/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.912, Loss:  0.044\n",
+      "Epoch   2 Batch  586/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.914, Loss:  0.044\n",
+      "Epoch   2 Batch  587/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.910, Loss:  0.052\n",
+      "Epoch   2 Batch  588/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.909, Loss:  0.041\n",
+      "Epoch   2 Batch  589/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.901, Loss:  0.045\n",
+      "Epoch   2 Batch  590/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.902, Loss:  0.058\n",
+      "Epoch   2 Batch  591/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.897, Loss:  0.055\n",
+      "Epoch   2 Batch  592/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.902, Loss:  0.051\n",
+      "Epoch   2 Batch  593/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.897, Loss:  0.078\n",
+      "Epoch   2 Batch  594/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.894, Loss:  0.064\n",
+      "Epoch   2 Batch  595/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.895, Loss:  0.048\n",
+      "Epoch   2 Batch  596/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.895, Loss:  0.050\n",
+      "Epoch   2 Batch  597/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.896, Loss:  0.045\n",
+      "Epoch   2 Batch  598/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.897, Loss:  0.056\n",
+      "Epoch   2 Batch  599/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.912, Loss:  0.061\n",
+      "Epoch   2 Batch  600/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.921, Loss:  0.057\n",
+      "Epoch   2 Batch  601/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.922, Loss:  0.056\n",
+      "Epoch   2 Batch  602/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.923, Loss:  0.050\n",
+      "Epoch   2 Batch  603/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.928, Loss:  0.048\n",
+      "Epoch   2 Batch  604/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.941, Loss:  0.065\n",
+      "Epoch   2 Batch  605/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.936, Loss:  0.062\n",
+      "Epoch   2 Batch  606/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.935, Loss:  0.046\n",
+      "Epoch   2 Batch  607/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.055\n",
+      "Epoch   2 Batch  608/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.924, Loss:  0.062\n",
+      "Epoch   2 Batch  609/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.924, Loss:  0.047\n",
+      "Epoch   2 Batch  610/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.895, Loss:  0.067\n",
+      "Epoch   2 Batch  611/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.893, Loss:  0.045\n",
+      "Epoch   2 Batch  612/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.889, Loss:  0.046\n",
+      "Epoch   2 Batch  613/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.892, Loss:  0.061\n",
+      "Epoch   2 Batch  614/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.892, Loss:  0.040\n",
+      "Epoch   2 Batch  615/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.900, Loss:  0.047\n",
+      "Epoch   2 Batch  616/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.907, Loss:  0.059\n",
+      "Epoch   2 Batch  617/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.050\n",
+      "Epoch   2 Batch  618/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.056\n",
+      "Epoch   2 Batch  619/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.035\n",
+      "Epoch   2 Batch  620/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.918, Loss:  0.040\n",
+      "Epoch   2 Batch  621/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.918, Loss:  0.052\n",
+      "Epoch   2 Batch  622/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.923, Loss:  0.058\n",
+      "Epoch   2 Batch  623/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.918, Loss:  0.064\n",
+      "Epoch   2 Batch  624/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.053\n",
+      "Epoch   2 Batch  625/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.042\n",
+      "Epoch   2 Batch  626/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.916, Loss:  0.048\n",
+      "Epoch   2 Batch  627/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.918, Loss:  0.047\n",
+      "Epoch   2 Batch  628/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.053\n",
+      "Epoch   2 Batch  629/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.920, Loss:  0.055\n",
+      "Epoch   2 Batch  630/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.921, Loss:  0.045\n",
+      "Epoch   2 Batch  631/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.049\n",
+      "Epoch   2 Batch  632/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.929, Loss:  0.038\n",
+      "Epoch   2 Batch  633/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.927, Loss:  0.056\n",
+      "Epoch   2 Batch  634/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.926, Loss:  0.045\n",
+      "Epoch   2 Batch  635/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.054\n",
+      "Epoch   2 Batch  636/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.936, Loss:  0.055\n",
+      "Epoch   2 Batch  637/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.932, Loss:  0.050\n",
+      "Epoch   2 Batch  638/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.041\n",
+      "Epoch   2 Batch  639/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.930, Loss:  0.073\n",
+      "Epoch   2 Batch  640/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.046\n",
+      "Epoch   2 Batch  641/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.931, Loss:  0.044\n",
+      "Epoch   2 Batch  642/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.927, Loss:  0.050\n",
+      "Epoch   2 Batch  643/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.926, Loss:  0.044\n",
+      "Epoch   2 Batch  644/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.922, Loss:  0.047\n",
+      "Epoch   2 Batch  645/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.924, Loss:  0.066\n",
+      "Epoch   2 Batch  646/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.050\n",
+      "Epoch   2 Batch  647/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.916, Loss:  0.048\n",
+      "Epoch   2 Batch  648/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.033\n",
+      "Epoch   2 Batch  649/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.923, Loss:  0.051\n",
+      "Epoch   2 Batch  650/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.054\n",
+      "Epoch   2 Batch  651/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.920, Loss:  0.053\n",
+      "Epoch   2 Batch  652/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.054\n",
+      "Epoch   2 Batch  653/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.053\n",
+      "Epoch   2 Batch  654/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.927, Loss:  0.049\n",
+      "Epoch   2 Batch  655/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.063\n",
+      "Epoch   2 Batch  656/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.057\n",
+      "Epoch   2 Batch  657/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.932, Loss:  0.051\n",
+      "Epoch   2 Batch  658/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.040\n",
+      "Epoch   2 Batch  659/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.053\n",
+      "Epoch   2 Batch  660/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.924, Loss:  0.047\n",
+      "Epoch   2 Batch  661/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.924, Loss:  0.046\n",
+      "Epoch   2 Batch  662/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.053\n",
+      "Epoch   2 Batch  663/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.904, Loss:  0.042\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch  664/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.900, Loss:  0.052\n",
+      "Epoch   2 Batch  665/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.899, Loss:  0.048\n",
+      "Epoch   2 Batch  666/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.899, Loss:  0.063\n",
+      "Epoch   2 Batch  667/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.897, Loss:  0.060\n",
+      "Epoch   2 Batch  668/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.905, Loss:  0.042\n",
+      "Epoch   2 Batch  669/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.915, Loss:  0.049\n",
+      "Epoch   2 Batch  670/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.912, Loss:  0.058\n",
+      "Epoch   2 Batch  671/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.908, Loss:  0.059\n",
+      "Epoch   2 Batch  672/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.045\n",
+      "Epoch   2 Batch  673/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.914, Loss:  0.048\n",
+      "Epoch   2 Batch  674/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.916, Loss:  0.051\n",
+      "Epoch   2 Batch  675/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.921, Loss:  0.060\n",
+      "Epoch   2 Batch  676/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.921, Loss:  0.046\n",
+      "Epoch   2 Batch  677/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.068\n",
+      "Epoch   2 Batch  678/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.042\n",
+      "Epoch   2 Batch  679/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.925, Loss:  0.042\n",
+      "Epoch   2 Batch  680/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.918, Loss:  0.052\n",
+      "Epoch   2 Batch  681/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.919, Loss:  0.052\n",
+      "Epoch   2 Batch  682/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.923, Loss:  0.055\n",
+      "Epoch   2 Batch  683/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.919, Loss:  0.037\n",
+      "Epoch   2 Batch  684/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.046\n",
+      "Epoch   2 Batch  685/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.919, Loss:  0.059\n",
+      "Epoch   2 Batch  686/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.044\n",
+      "Epoch   2 Batch  687/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.913, Loss:  0.056\n",
+      "Epoch   2 Batch  688/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.913, Loss:  0.046\n",
+      "Epoch   2 Batch  689/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.046\n",
+      "Epoch   2 Batch  690/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.053\n",
+      "Epoch   2 Batch  691/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.059\n",
+      "Epoch   2 Batch  692/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.043\n",
+      "Epoch   2 Batch  693/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.909, Loss:  0.070\n",
+      "Epoch   2 Batch  694/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.053\n",
+      "Epoch   2 Batch  695/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.919, Loss:  0.042\n",
+      "Epoch   2 Batch  696/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.914, Loss:  0.053\n",
+      "Epoch   2 Batch  697/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.048\n",
+      "Epoch   2 Batch  698/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.046\n",
+      "Epoch   2 Batch  699/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.910, Loss:  0.044\n",
+      "Epoch   2 Batch  700/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.915, Loss:  0.048\n",
+      "Epoch   2 Batch  701/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.915, Loss:  0.052\n",
+      "Epoch   2 Batch  702/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.066\n",
+      "Epoch   2 Batch  703/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.913, Loss:  0.055\n",
+      "Epoch   2 Batch  704/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.063\n",
+      "Epoch   2 Batch  705/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.923, Loss:  0.061\n",
+      "Epoch   2 Batch  706/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.073\n",
+      "Epoch   2 Batch  707/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.928, Loss:  0.056\n",
+      "Epoch   2 Batch  708/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.929, Loss:  0.057\n",
+      "Epoch   2 Batch  709/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.924, Loss:  0.053\n",
+      "Epoch   2 Batch  710/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.921, Loss:  0.040\n",
+      "Epoch   2 Batch  711/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.068\n",
+      "Epoch   2 Batch  712/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.922, Loss:  0.037\n",
+      "Epoch   2 Batch  713/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.036\n",
+      "Epoch   2 Batch  714/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.052\n",
+      "Epoch   2 Batch  715/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.932, Loss:  0.059\n",
+      "Epoch   2 Batch  716/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.938, Loss:  0.047\n",
+      "Epoch   2 Batch  717/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.939, Loss:  0.035\n",
+      "Epoch   2 Batch  718/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.939, Loss:  0.053\n",
+      "Epoch   2 Batch  719/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.938, Loss:  0.057\n",
+      "Epoch   2 Batch  720/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.938, Loss:  0.053\n",
+      "Epoch   2 Batch  721/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.934, Loss:  0.060\n",
+      "Epoch   2 Batch  722/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.935, Loss:  0.042\n",
+      "Epoch   2 Batch  723/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.058\n",
+      "Epoch   2 Batch  724/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.939, Loss:  0.056\n",
+      "Epoch   2 Batch  725/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.039\n",
+      "Epoch   2 Batch  726/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.934, Loss:  0.052\n",
+      "Epoch   2 Batch  727/1077 - Train Accuracy:  0.969, Validation Accuracy:  0.934, Loss:  0.041\n",
+      "Epoch   2 Batch  728/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.934, Loss:  0.058\n",
+      "Epoch   2 Batch  729/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.931, Loss:  0.063\n",
+      "Epoch   2 Batch  730/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.932, Loss:  0.058\n",
+      "Epoch   2 Batch  731/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.931, Loss:  0.045\n",
+      "Epoch   2 Batch  732/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.935, Loss:  0.054\n",
+      "Epoch   2 Batch  733/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.933, Loss:  0.053\n",
+      "Epoch   2 Batch  734/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.044\n",
+      "Epoch   2 Batch  735/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.923, Loss:  0.044\n",
+      "Epoch   2 Batch  736/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.046\n",
+      "Epoch   2 Batch  737/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.921, Loss:  0.051\n",
+      "Epoch   2 Batch  738/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.038\n",
+      "Epoch   2 Batch  739/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.041\n",
+      "Epoch   2 Batch  740/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.038\n",
+      "Epoch   2 Batch  741/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.930, Loss:  0.057\n",
+      "Epoch   2 Batch  742/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.036\n",
+      "Epoch   2 Batch  743/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.933, Loss:  0.056\n",
+      "Epoch   2 Batch  744/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.931, Loss:  0.050\n",
+      "Epoch   2 Batch  745/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.053\n",
+      "Epoch   2 Batch  746/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.932, Loss:  0.052\n",
+      "Epoch   2 Batch  747/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.927, Loss:  0.041\n",
+      "Epoch   2 Batch  748/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.931, Loss:  0.043\n",
+      "Epoch   2 Batch  749/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.924, Loss:  0.053\n",
+      "Epoch   2 Batch  750/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.049\n",
+      "Epoch   2 Batch  751/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.938, Loss:  0.050\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch  752/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.942, Loss:  0.047\n",
+      "Epoch   2 Batch  753/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.938, Loss:  0.055\n",
+      "Epoch   2 Batch  754/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.933, Loss:  0.061\n",
+      "Epoch   2 Batch  755/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.942, Loss:  0.062\n",
+      "Epoch   2 Batch  756/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.942, Loss:  0.049\n",
+      "Epoch   2 Batch  757/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.939, Loss:  0.045\n",
+      "Epoch   2 Batch  758/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.934, Loss:  0.047\n",
+      "Epoch   2 Batch  759/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.045\n",
+      "Epoch   2 Batch  760/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.055\n",
+      "Epoch   2 Batch  761/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.049\n",
+      "Epoch   2 Batch  762/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.929, Loss:  0.051\n",
+      "Epoch   2 Batch  763/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.043\n",
+      "Epoch   2 Batch  764/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.046\n",
+      "Epoch   2 Batch  765/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.933, Loss:  0.060\n",
+      "Epoch   2 Batch  766/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.935, Loss:  0.050\n",
+      "Epoch   2 Batch  767/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.938, Loss:  0.044\n",
+      "Epoch   2 Batch  768/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.931, Loss:  0.053\n",
+      "Epoch   2 Batch  769/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.045\n",
+      "Epoch   2 Batch  770/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.933, Loss:  0.040\n",
+      "Epoch   2 Batch  771/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.931, Loss:  0.057\n",
+      "Epoch   2 Batch  772/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.929, Loss:  0.053\n",
+      "Epoch   2 Batch  773/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.924, Loss:  0.047\n",
+      "Epoch   2 Batch  774/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.057\n",
+      "Epoch   2 Batch  775/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.937, Loss:  0.056\n",
+      "Epoch   2 Batch  776/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.947, Loss:  0.043\n",
+      "Epoch   2 Batch  777/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.936, Loss:  0.056\n",
+      "Epoch   2 Batch  778/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.936, Loss:  0.047\n",
+      "Epoch   2 Batch  779/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.935, Loss:  0.057\n",
+      "Epoch   2 Batch  780/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.947, Loss:  0.062\n",
+      "Epoch   2 Batch  781/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.945, Loss:  0.044\n",
+      "Epoch   2 Batch  782/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.940, Loss:  0.047\n",
+      "Epoch   2 Batch  783/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.942, Loss:  0.053\n",
+      "Epoch   2 Batch  784/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.938, Loss:  0.045\n",
+      "Epoch   2 Batch  785/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.935, Loss:  0.050\n",
+      "Epoch   2 Batch  786/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.931, Loss:  0.041\n",
+      "Epoch   2 Batch  787/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.053\n",
+      "Epoch   2 Batch  788/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.049\n",
+      "Epoch   2 Batch  789/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.931, Loss:  0.051\n",
+      "Epoch   2 Batch  790/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.933, Loss:  0.052\n",
+      "Epoch   2 Batch  791/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.918, Loss:  0.057\n",
+      "Epoch   2 Batch  792/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.056\n",
+      "Epoch   2 Batch  793/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.918, Loss:  0.050\n",
+      "Epoch   2 Batch  794/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.916, Loss:  0.047\n",
+      "Epoch   2 Batch  795/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.911, Loss:  0.066\n",
+      "Epoch   2 Batch  796/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.045\n",
+      "Epoch   2 Batch  797/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.920, Loss:  0.044\n",
+      "Epoch   2 Batch  798/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.051\n",
+      "Epoch   2 Batch  799/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.933, Loss:  0.078\n",
+      "Epoch   2 Batch  800/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.940, Loss:  0.050\n",
+      "Epoch   2 Batch  801/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.940, Loss:  0.068\n",
+      "Epoch   2 Batch  802/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.940, Loss:  0.058\n",
+      "Epoch   2 Batch  803/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.944, Loss:  0.051\n",
+      "Epoch   2 Batch  804/1077 - Train Accuracy:  0.975, Validation Accuracy:  0.944, Loss:  0.037\n",
+      "Epoch   2 Batch  805/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.939, Loss:  0.048\n",
+      "Epoch   2 Batch  806/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.940, Loss:  0.043\n",
+      "Epoch   2 Batch  807/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.041\n",
+      "Epoch   2 Batch  808/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.928, Loss:  0.069\n",
+      "Epoch   2 Batch  809/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.936, Loss:  0.069\n",
+      "Epoch   2 Batch  810/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.944, Loss:  0.046\n",
+      "Epoch   2 Batch  811/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.929, Loss:  0.055\n",
+      "Epoch   2 Batch  812/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.930, Loss:  0.057\n",
+      "Epoch   2 Batch  813/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.936, Loss:  0.045\n",
+      "Epoch   2 Batch  814/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.936, Loss:  0.063\n",
+      "Epoch   2 Batch  815/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.926, Loss:  0.049\n",
+      "Epoch   2 Batch  816/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.935, Loss:  0.062\n",
+      "Epoch   2 Batch  817/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.054\n",
+      "Epoch   2 Batch  818/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.933, Loss:  0.059\n",
+      "Epoch   2 Batch  819/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.050\n",
+      "Epoch   2 Batch  820/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.935, Loss:  0.049\n",
+      "Epoch   2 Batch  821/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.049\n",
+      "Epoch   2 Batch  822/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.925, Loss:  0.052\n",
+      "Epoch   2 Batch  823/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.056\n",
+      "Epoch   2 Batch  824/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.919, Loss:  0.055\n",
+      "Epoch   2 Batch  825/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.917, Loss:  0.040\n",
+      "Epoch   2 Batch  826/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.917, Loss:  0.046\n",
+      "Epoch   2 Batch  827/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.912, Loss:  0.056\n",
+      "Epoch   2 Batch  828/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.048\n",
+      "Epoch   2 Batch  829/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.904, Loss:  0.064\n",
+      "Epoch   2 Batch  830/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.904, Loss:  0.061\n",
+      "Epoch   2 Batch  831/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.909, Loss:  0.060\n",
+      "Epoch   2 Batch  832/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.913, Loss:  0.049\n",
+      "Epoch   2 Batch  833/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.913, Loss:  0.054\n",
+      "Epoch   2 Batch  834/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.918, Loss:  0.059\n",
+      "Epoch   2 Batch  835/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.914, Loss:  0.051\n",
+      "Epoch   2 Batch  836/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.050\n",
+      "Epoch   2 Batch  837/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.923, Loss:  0.062\n",
+      "Epoch   2 Batch  838/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.924, Loss:  0.044\n",
+      "Epoch   2 Batch  839/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.036\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch  840/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.924, Loss:  0.039\n",
+      "Epoch   2 Batch  841/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.924, Loss:  0.050\n",
+      "Epoch   2 Batch  842/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.926, Loss:  0.044\n",
+      "Epoch   2 Batch  843/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.044\n",
+      "Epoch   2 Batch  844/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.039\n",
+      "Epoch   2 Batch  845/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.926, Loss:  0.039\n",
+      "Epoch   2 Batch  846/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.063\n",
+      "Epoch   2 Batch  847/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.057\n",
+      "Epoch   2 Batch  848/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.045\n",
+      "Epoch   2 Batch  849/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.042\n",
+      "Epoch   2 Batch  850/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.913, Loss:  0.085\n",
+      "Epoch   2 Batch  851/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.913, Loss:  0.062\n",
+      "Epoch   2 Batch  852/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.913, Loss:  0.061\n",
+      "Epoch   2 Batch  853/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.922, Loss:  0.055\n",
+      "Epoch   2 Batch  854/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.921, Loss:  0.062\n",
+      "Epoch   2 Batch  855/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.920, Loss:  0.058\n",
+      "Epoch   2 Batch  856/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.068\n",
+      "Epoch   2 Batch  857/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.048\n",
+      "Epoch   2 Batch  858/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   2 Batch  859/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.917, Loss:  0.067\n",
+      "Epoch   2 Batch  860/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.922, Loss:  0.057\n",
+      "Epoch   2 Batch  861/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.926, Loss:  0.051\n",
+      "Epoch   2 Batch  862/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.049\n",
+      "Epoch   2 Batch  863/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.914, Loss:  0.040\n",
+      "Epoch   2 Batch  864/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.913, Loss:  0.051\n",
+      "Epoch   2 Batch  865/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.917, Loss:  0.053\n",
+      "Epoch   2 Batch  866/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.913, Loss:  0.056\n",
+      "Epoch   2 Batch  867/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.908, Loss:  0.085\n",
+      "Epoch   2 Batch  868/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.920, Loss:  0.056\n",
+      "Epoch   2 Batch  869/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.056\n",
+      "Epoch   2 Batch  870/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.933, Loss:  0.058\n",
+      "Epoch   2 Batch  871/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.929, Loss:  0.048\n",
+      "Epoch   2 Batch  872/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.939, Loss:  0.056\n",
+      "Epoch   2 Batch  873/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.935, Loss:  0.056\n",
+      "Epoch   2 Batch  874/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.931, Loss:  0.061\n",
+      "Epoch   2 Batch  875/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.932, Loss:  0.058\n",
+      "Epoch   2 Batch  876/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.935, Loss:  0.049\n",
+      "Epoch   2 Batch  877/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.939, Loss:  0.046\n",
+      "Epoch   2 Batch  878/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.939, Loss:  0.039\n",
+      "Epoch   2 Batch  879/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.935, Loss:  0.047\n",
+      "Epoch   2 Batch  880/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.053\n",
+      "Epoch   2 Batch  881/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.941, Loss:  0.063\n",
+      "Epoch   2 Batch  882/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.941, Loss:  0.058\n",
+      "Epoch   2 Batch  883/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.936, Loss:  0.079\n",
+      "Epoch   2 Batch  884/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.927, Loss:  0.052\n",
+      "Epoch   2 Batch  885/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.920, Loss:  0.031\n",
+      "Epoch   2 Batch  886/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.050\n",
+      "Epoch   2 Batch  887/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.922, Loss:  0.063\n",
+      "Epoch   2 Batch  888/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.045\n",
+      "Epoch   2 Batch  889/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.923, Loss:  0.045\n",
+      "Epoch   2 Batch  890/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.911, Loss:  0.049\n",
+      "Epoch   2 Batch  891/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.046\n",
+      "Epoch   2 Batch  892/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.045\n",
+      "Epoch   2 Batch  893/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.043\n",
+      "Epoch   2 Batch  894/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.053\n",
+      "Epoch   2 Batch  895/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.048\n",
+      "Epoch   2 Batch  896/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.914, Loss:  0.052\n",
+      "Epoch   2 Batch  897/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.907, Loss:  0.037\n",
+      "Epoch   2 Batch  898/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.904, Loss:  0.044\n",
+      "Epoch   2 Batch  899/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.907, Loss:  0.057\n",
+      "Epoch   2 Batch  900/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.917, Loss:  0.065\n",
+      "Epoch   2 Batch  901/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.068\n",
+      "Epoch   2 Batch  902/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.057\n",
+      "Epoch   2 Batch  903/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.920, Loss:  0.049\n",
+      "Epoch   2 Batch  904/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.928, Loss:  0.049\n",
+      "Epoch   2 Batch  905/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.912, Loss:  0.046\n",
+      "Epoch   2 Batch  906/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.902, Loss:  0.056\n",
+      "Epoch   2 Batch  907/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.908, Loss:  0.052\n",
+      "Epoch   2 Batch  908/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.926, Loss:  0.061\n",
+      "Epoch   2 Batch  909/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.931, Loss:  0.061\n",
+      "Epoch   2 Batch  910/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.920, Loss:  0.057\n",
+      "Epoch   2 Batch  911/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.929, Loss:  0.057\n",
+      "Epoch   2 Batch  912/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.933, Loss:  0.043\n",
+      "Epoch   2 Batch  913/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.067\n",
+      "Epoch   2 Batch  914/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.919, Loss:  0.079\n",
+      "Epoch   2 Batch  915/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.914, Loss:  0.040\n",
+      "Epoch   2 Batch  916/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.909, Loss:  0.053\n",
+      "Epoch   2 Batch  917/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.901, Loss:  0.051\n",
+      "Epoch   2 Batch  918/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.911, Loss:  0.048\n",
+      "Epoch   2 Batch  919/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.907, Loss:  0.047\n",
+      "Epoch   2 Batch  920/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.052\n",
+      "Epoch   2 Batch  921/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.912, Loss:  0.056\n",
+      "Epoch   2 Batch  922/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.919, Loss:  0.056\n",
+      "Epoch   2 Batch  923/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.923, Loss:  0.033\n",
+      "Epoch   2 Batch  924/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.914, Loss:  0.071\n",
+      "Epoch   2 Batch  925/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.042\n",
+      "Epoch   2 Batch  926/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.917, Loss:  0.042\n",
+      "Epoch   2 Batch  927/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.064\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch  928/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.909, Loss:  0.046\n",
+      "Epoch   2 Batch  929/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.906, Loss:  0.060\n",
+      "Epoch   2 Batch  930/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.909, Loss:  0.047\n",
+      "Epoch   2 Batch  931/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.908, Loss:  0.038\n",
+      "Epoch   2 Batch  932/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.050\n",
+      "Epoch   2 Batch  933/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.056\n",
+      "Epoch   2 Batch  934/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.928, Loss:  0.047\n",
+      "Epoch   2 Batch  935/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.044\n",
+      "Epoch   2 Batch  936/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.051\n",
+      "Epoch   2 Batch  937/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.918, Loss:  0.074\n",
+      "Epoch   2 Batch  938/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.058\n",
+      "Epoch   2 Batch  939/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.917, Loss:  0.058\n",
+      "Epoch   2 Batch  940/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.048\n",
+      "Epoch   2 Batch  941/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.043\n",
+      "Epoch   2 Batch  942/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.059\n",
+      "Epoch   2 Batch  943/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.931, Loss:  0.050\n",
+      "Epoch   2 Batch  944/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.927, Loss:  0.052\n",
+      "Epoch   2 Batch  945/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.924, Loss:  0.046\n",
+      "Epoch   2 Batch  946/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.920, Loss:  0.037\n",
+      "Epoch   2 Batch  947/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.916, Loss:  0.057\n",
+      "Epoch   2 Batch  948/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.042\n",
+      "Epoch   2 Batch  949/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.919, Loss:  0.043\n",
+      "Epoch   2 Batch  950/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.039\n",
+      "Epoch   2 Batch  951/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.913, Loss:  0.063\n",
+      "Epoch   2 Batch  952/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.917, Loss:  0.045\n",
+      "Epoch   2 Batch  953/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.046\n",
+      "Epoch   2 Batch  954/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.913, Loss:  0.054\n",
+      "Epoch   2 Batch  955/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.920, Loss:  0.060\n",
+      "Epoch   2 Batch  956/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.918, Loss:  0.057\n",
+      "Epoch   2 Batch  957/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.914, Loss:  0.042\n",
+      "Epoch   2 Batch  958/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.903, Loss:  0.048\n",
+      "Epoch   2 Batch  959/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.901, Loss:  0.041\n",
+      "Epoch   2 Batch  960/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.915, Loss:  0.043\n",
+      "Epoch   2 Batch  961/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.044\n",
+      "Epoch   2 Batch  962/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.917, Loss:  0.053\n",
+      "Epoch   2 Batch  963/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.064\n",
+      "Epoch   2 Batch  964/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.048\n",
+      "Epoch   2 Batch  965/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.051\n",
+      "Epoch   2 Batch  966/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.922, Loss:  0.050\n",
+      "Epoch   2 Batch  967/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.923, Loss:  0.059\n",
+      "Epoch   2 Batch  968/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.922, Loss:  0.064\n",
+      "Epoch   2 Batch  969/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.926, Loss:  0.062\n",
+      "Epoch   2 Batch  970/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.924, Loss:  0.046\n",
+      "Epoch   2 Batch  971/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.920, Loss:  0.055\n",
+      "Epoch   2 Batch  972/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.916, Loss:  0.052\n",
+      "Epoch   2 Batch  973/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.916, Loss:  0.033\n",
+      "Epoch   2 Batch  974/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.037\n",
+      "Epoch   2 Batch  975/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.914, Loss:  0.045\n",
+      "Epoch   2 Batch  976/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.037\n",
+      "Epoch   2 Batch  977/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.925, Loss:  0.037\n",
+      "Epoch   2 Batch  978/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.054\n",
+      "Epoch   2 Batch  979/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.932, Loss:  0.056\n",
+      "Epoch   2 Batch  980/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.938, Loss:  0.057\n",
+      "Epoch   2 Batch  981/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.042\n",
+      "Epoch   2 Batch  982/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.056\n",
+      "Epoch   2 Batch  983/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.931, Loss:  0.048\n",
+      "Epoch   2 Batch  984/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.936, Loss:  0.067\n",
+      "Epoch   2 Batch  985/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.938, Loss:  0.049\n",
+      "Epoch   2 Batch  986/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.938, Loss:  0.055\n",
+      "Epoch   2 Batch  987/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.954, Loss:  0.046\n",
+      "Epoch   2 Batch  988/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.952, Loss:  0.062\n",
+      "Epoch   2 Batch  989/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.938, Loss:  0.048\n",
+      "Epoch   2 Batch  990/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.910, Loss:  0.061\n",
+      "Epoch   2 Batch  991/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.910, Loss:  0.049\n",
+      "Epoch   2 Batch  992/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.912, Loss:  0.073\n",
+      "Epoch   2 Batch  993/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.908, Loss:  0.040\n",
+      "Epoch   2 Batch  994/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.917, Loss:  0.050\n",
+      "Epoch   2 Batch  995/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.057\n",
+      "Epoch   2 Batch  996/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.048\n",
+      "Epoch   2 Batch  997/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.052\n",
+      "Epoch   2 Batch  998/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.926, Loss:  0.042\n",
+      "Epoch   2 Batch  999/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.942, Loss:  0.057\n",
+      "Epoch   2 Batch 1000/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.946, Loss:  0.054\n",
+      "Epoch   2 Batch 1001/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.940, Loss:  0.046\n",
+      "Epoch   2 Batch 1002/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.930, Loss:  0.035\n",
+      "Epoch   2 Batch 1003/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.933, Loss:  0.052\n",
+      "Epoch   2 Batch 1004/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.934, Loss:  0.069\n",
+      "Epoch   2 Batch 1005/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.934, Loss:  0.041\n",
+      "Epoch   2 Batch 1006/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.041\n",
+      "Epoch   2 Batch 1007/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.934, Loss:  0.051\n",
+      "Epoch   2 Batch 1008/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.932, Loss:  0.074\n",
+      "Epoch   2 Batch 1009/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.928, Loss:  0.038\n",
+      "Epoch   2 Batch 1010/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.050\n",
+      "Epoch   2 Batch 1011/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.043\n",
+      "Epoch   2 Batch 1012/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.926, Loss:  0.032\n",
+      "Epoch   2 Batch 1013/1077 - Train Accuracy:  0.974, Validation Accuracy:  0.917, Loss:  0.037\n",
+      "Epoch   2 Batch 1014/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.049\n",
+      "Epoch   2 Batch 1015/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.933, Loss:  0.058\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   2 Batch 1016/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.933, Loss:  0.054\n",
+      "Epoch   2 Batch 1017/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.055\n",
+      "Epoch   2 Batch 1018/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.941, Loss:  0.041\n",
+      "Epoch   2 Batch 1019/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.948, Loss:  0.074\n",
+      "Epoch   2 Batch 1020/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.941, Loss:  0.042\n",
+      "Epoch   2 Batch 1021/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.936, Loss:  0.057\n",
+      "Epoch   2 Batch 1022/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.050\n",
+      "Epoch   2 Batch 1023/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.932, Loss:  0.052\n",
+      "Epoch   2 Batch 1024/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.936, Loss:  0.066\n",
+      "Epoch   2 Batch 1025/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.940, Loss:  0.055\n",
+      "Epoch   2 Batch 1026/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.944, Loss:  0.052\n",
+      "Epoch   2 Batch 1027/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.943, Loss:  0.049\n",
+      "Epoch   2 Batch 1028/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.943, Loss:  0.048\n",
+      "Epoch   2 Batch 1029/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.942, Loss:  0.045\n",
+      "Epoch   2 Batch 1030/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.941, Loss:  0.042\n",
+      "Epoch   2 Batch 1031/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.059\n",
+      "Epoch   2 Batch 1032/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.921, Loss:  0.070\n",
+      "Epoch   2 Batch 1033/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.062\n",
+      "Epoch   2 Batch 1034/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   2 Batch 1035/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.911, Loss:  0.030\n",
+      "Epoch   2 Batch 1036/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.907, Loss:  0.061\n",
+      "Epoch   2 Batch 1037/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.907, Loss:  0.047\n",
+      "Epoch   2 Batch 1038/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.903, Loss:  0.051\n",
+      "Epoch   2 Batch 1039/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.049\n",
+      "Epoch   2 Batch 1040/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.058\n",
+      "Epoch   2 Batch 1041/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.913, Loss:  0.050\n",
+      "Epoch   2 Batch 1042/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.911, Loss:  0.047\n",
+      "Epoch   2 Batch 1043/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.061\n",
+      "Epoch   2 Batch 1044/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.910, Loss:  0.070\n",
+      "Epoch   2 Batch 1045/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.910, Loss:  0.056\n",
+      "Epoch   2 Batch 1046/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.914, Loss:  0.042\n",
+      "Epoch   2 Batch 1047/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.918, Loss:  0.047\n",
+      "Epoch   2 Batch 1048/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.923, Loss:  0.049\n",
+      "Epoch   2 Batch 1049/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.918, Loss:  0.041\n",
+      "Epoch   2 Batch 1050/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.913, Loss:  0.042\n",
+      "Epoch   2 Batch 1051/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.909, Loss:  0.064\n",
+      "Epoch   2 Batch 1052/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.913, Loss:  0.049\n",
+      "Epoch   2 Batch 1053/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.926, Loss:  0.069\n",
+      "Epoch   2 Batch 1054/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.047\n",
+      "Epoch   2 Batch 1055/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.051\n",
+      "Epoch   2 Batch 1056/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.042\n",
+      "Epoch   2 Batch 1057/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.917, Loss:  0.059\n",
+      "Epoch   2 Batch 1058/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.056\n",
+      "Epoch   2 Batch 1059/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.929, Loss:  0.070\n",
+      "Epoch   2 Batch 1060/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.928, Loss:  0.051\n",
+      "Epoch   2 Batch 1061/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.935, Loss:  0.070\n",
+      "Epoch   2 Batch 1062/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.936, Loss:  0.056\n",
+      "Epoch   2 Batch 1063/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.058\n",
+      "Epoch   2 Batch 1064/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.920, Loss:  0.050\n",
+      "Epoch   2 Batch 1065/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.923, Loss:  0.043\n",
+      "Epoch   2 Batch 1066/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.921, Loss:  0.036\n",
+      "Epoch   2 Batch 1067/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.053\n",
+      "Epoch   2 Batch 1068/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.926, Loss:  0.043\n",
+      "Epoch   2 Batch 1069/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.037\n",
+      "Epoch   2 Batch 1070/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.920, Loss:  0.043\n",
+      "Epoch   2 Batch 1071/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.920, Loss:  0.046\n",
+      "Epoch   2 Batch 1072/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.052\n",
+      "Epoch   2 Batch 1073/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.913, Loss:  0.053\n",
+      "Epoch   2 Batch 1074/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.915, Loss:  0.063\n",
+      "Epoch   2 Batch 1075/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.919, Loss:  0.053\n",
+      "Epoch   3 Batch    0/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.913, Loss:  0.045\n",
+      "Epoch   3 Batch    1/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.911, Loss:  0.032\n",
+      "Epoch   3 Batch    2/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.053\n",
+      "Epoch   3 Batch    3/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.911, Loss:  0.047\n",
+      "Epoch   3 Batch    4/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.911, Loss:  0.043\n",
+      "Epoch   3 Batch    5/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.904, Loss:  0.072\n",
+      "Epoch   3 Batch    6/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.904, Loss:  0.054\n",
+      "Epoch   3 Batch    7/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.906, Loss:  0.040\n",
+      "Epoch   3 Batch    8/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.896, Loss:  0.051\n",
+      "Epoch   3 Batch    9/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.892, Loss:  0.041\n",
+      "Epoch   3 Batch   10/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.898, Loss:  0.053\n",
+      "Epoch   3 Batch   11/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.905, Loss:  0.068\n",
+      "Epoch   3 Batch   12/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.907, Loss:  0.053\n",
+      "Epoch   3 Batch   13/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.054\n",
+      "Epoch   3 Batch   14/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.912, Loss:  0.036\n",
+      "Epoch   3 Batch   15/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.912, Loss:  0.044\n",
+      "Epoch   3 Batch   16/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.919, Loss:  0.060\n",
+      "Epoch   3 Batch   17/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.919, Loss:  0.046\n",
+      "Epoch   3 Batch   18/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.922, Loss:  0.067\n",
+      "Epoch   3 Batch   19/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.931, Loss:  0.050\n",
+      "Epoch   3 Batch   20/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.936, Loss:  0.040\n",
+      "Epoch   3 Batch   21/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.934, Loss:  0.056\n",
+      "Epoch   3 Batch   22/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.933, Loss:  0.062\n",
+      "Epoch   3 Batch   23/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.933, Loss:  0.051\n",
+      "Epoch   3 Batch   24/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.926, Loss:  0.052\n",
+      "Epoch   3 Batch   25/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.922, Loss:  0.038\n",
+      "Epoch   3 Batch   26/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.922, Loss:  0.055\n",
+      "Epoch   3 Batch   27/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.923, Loss:  0.042\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch   28/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.926, Loss:  0.053\n",
+      "Epoch   3 Batch   29/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.056\n",
+      "Epoch   3 Batch   30/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.036\n",
+      "Epoch   3 Batch   31/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.041\n",
+      "Epoch   3 Batch   32/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.045\n",
+      "Epoch   3 Batch   33/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.055\n",
+      "Epoch   3 Batch   34/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.050\n",
+      "Epoch   3 Batch   35/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.929, Loss:  0.051\n",
+      "Epoch   3 Batch   36/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.043\n",
+      "Epoch   3 Batch   37/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.934, Loss:  0.043\n",
+      "Epoch   3 Batch   38/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.930, Loss:  0.078\n",
+      "Epoch   3 Batch   39/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.930, Loss:  0.062\n",
+      "Epoch   3 Batch   40/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.918, Loss:  0.040\n",
+      "Epoch   3 Batch   41/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.906, Loss:  0.044\n",
+      "Epoch   3 Batch   42/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.908, Loss:  0.064\n",
+      "Epoch   3 Batch   43/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.913, Loss:  0.028\n",
+      "Epoch   3 Batch   44/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.916, Loss:  0.040\n",
+      "Epoch   3 Batch   45/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.916, Loss:  0.053\n",
+      "Epoch   3 Batch   46/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.046\n",
+      "Epoch   3 Batch   47/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.053\n",
+      "Epoch   3 Batch   48/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.922, Loss:  0.087\n",
+      "Epoch   3 Batch   49/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.060\n",
+      "Epoch   3 Batch   50/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.919, Loss:  0.046\n",
+      "Epoch   3 Batch   51/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.058\n",
+      "Epoch   3 Batch   52/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.065\n",
+      "Epoch   3 Batch   53/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.922, Loss:  0.039\n",
+      "Epoch   3 Batch   54/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.925, Loss:  0.078\n",
+      "Epoch   3 Batch   55/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.925, Loss:  0.042\n",
+      "Epoch   3 Batch   56/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.919, Loss:  0.051\n",
+      "Epoch   3 Batch   57/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.916, Loss:  0.059\n",
+      "Epoch   3 Batch   58/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.912, Loss:  0.046\n",
+      "Epoch   3 Batch   59/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.912, Loss:  0.043\n",
+      "Epoch   3 Batch   60/1077 - Train Accuracy:  0.970, Validation Accuracy:  0.910, Loss:  0.035\n",
+      "Epoch   3 Batch   61/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.060\n",
+      "Epoch   3 Batch   62/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.910, Loss:  0.061\n",
+      "Epoch   3 Batch   63/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.913, Loss:  0.042\n",
+      "Epoch   3 Batch   64/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.913, Loss:  0.045\n",
+      "Epoch   3 Batch   65/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.917, Loss:  0.053\n",
+      "Epoch   3 Batch   66/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.031\n",
+      "Epoch   3 Batch   67/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.047\n",
+      "Epoch   3 Batch   68/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.053\n",
+      "Epoch   3 Batch   69/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.915, Loss:  0.061\n",
+      "Epoch   3 Batch   70/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.915, Loss:  0.050\n",
+      "Epoch   3 Batch   71/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.919, Loss:  0.035\n",
+      "Epoch   3 Batch   72/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.052\n",
+      "Epoch   3 Batch   73/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.055\n",
+      "Epoch   3 Batch   74/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.045\n",
+      "Epoch   3 Batch   75/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.915, Loss:  0.065\n",
+      "Epoch   3 Batch   76/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.926, Loss:  0.038\n",
+      "Epoch   3 Batch   77/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.045\n",
+      "Epoch   3 Batch   78/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.928, Loss:  0.042\n",
+      "Epoch   3 Batch   79/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.042\n",
+      "Epoch   3 Batch   80/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.922, Loss:  0.050\n",
+      "Epoch   3 Batch   81/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.041\n",
+      "Epoch   3 Batch   82/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.922, Loss:  0.038\n",
+      "Epoch   3 Batch   83/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.044\n",
+      "Epoch   3 Batch   84/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.043\n",
+      "Epoch   3 Batch   85/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.047\n",
+      "Epoch   3 Batch   86/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.923, Loss:  0.051\n",
+      "Epoch   3 Batch   87/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.917, Loss:  0.065\n",
+      "Epoch   3 Batch   88/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.054\n",
+      "Epoch   3 Batch   89/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.050\n",
+      "Epoch   3 Batch   90/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.911, Loss:  0.055\n",
+      "Epoch   3 Batch   91/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.915, Loss:  0.043\n",
+      "Epoch   3 Batch   92/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.910, Loss:  0.060\n",
+      "Epoch   3 Batch   93/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.910, Loss:  0.038\n",
+      "Epoch   3 Batch   94/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.914, Loss:  0.040\n",
+      "Epoch   3 Batch   95/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.909, Loss:  0.054\n",
+      "Epoch   3 Batch   96/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.909, Loss:  0.053\n",
+      "Epoch   3 Batch   97/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.050\n",
+      "Epoch   3 Batch   98/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.907, Loss:  0.058\n",
+      "Epoch   3 Batch   99/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.052\n",
+      "Epoch   3 Batch  100/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.919, Loss:  0.047\n",
+      "Epoch   3 Batch  101/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.048\n",
+      "Epoch   3 Batch  102/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.932, Loss:  0.042\n",
+      "Epoch   3 Batch  103/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.929, Loss:  0.061\n",
+      "Epoch   3 Batch  104/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.920, Loss:  0.049\n",
+      "Epoch   3 Batch  105/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.919, Loss:  0.045\n",
+      "Epoch   3 Batch  106/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.056\n",
+      "Epoch   3 Batch  107/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.925, Loss:  0.048\n",
+      "Epoch   3 Batch  108/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.915, Loss:  0.056\n",
+      "Epoch   3 Batch  109/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.929, Loss:  0.059\n",
+      "Epoch   3 Batch  110/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.929, Loss:  0.035\n",
+      "Epoch   3 Batch  111/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.922, Loss:  0.055\n",
+      "Epoch   3 Batch  112/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.921, Loss:  0.047\n",
+      "Epoch   3 Batch  113/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.055\n",
+      "Epoch   3 Batch  114/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.911, Loss:  0.035\n",
+      "Epoch   3 Batch  115/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.915, Loss:  0.056\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  116/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.920, Loss:  0.066\n",
+      "Epoch   3 Batch  117/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.049\n",
+      "Epoch   3 Batch  118/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.925, Loss:  0.044\n",
+      "Epoch   3 Batch  119/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.046\n",
+      "Epoch   3 Batch  120/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.940, Loss:  0.058\n",
+      "Epoch   3 Batch  121/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.936, Loss:  0.057\n",
+      "Epoch   3 Batch  122/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.936, Loss:  0.043\n",
+      "Epoch   3 Batch  123/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.941, Loss:  0.045\n",
+      "Epoch   3 Batch  124/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.936, Loss:  0.073\n",
+      "Epoch   3 Batch  125/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.934, Loss:  0.056\n",
+      "Epoch   3 Batch  126/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.934, Loss:  0.045\n",
+      "Epoch   3 Batch  127/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.923, Loss:  0.053\n",
+      "Epoch   3 Batch  128/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.909, Loss:  0.047\n",
+      "Epoch   3 Batch  129/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.066\n",
+      "Epoch   3 Batch  130/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.048\n",
+      "Epoch   3 Batch  131/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.901, Loss:  0.055\n",
+      "Epoch   3 Batch  132/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.903, Loss:  0.040\n",
+      "Epoch   3 Batch  133/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.909, Loss:  0.039\n",
+      "Epoch   3 Batch  134/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.917, Loss:  0.051\n",
+      "Epoch   3 Batch  135/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.046\n",
+      "Epoch   3 Batch  136/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.055\n",
+      "Epoch   3 Batch  137/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.030\n",
+      "Epoch   3 Batch  138/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.923, Loss:  0.054\n",
+      "Epoch   3 Batch  139/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.919, Loss:  0.062\n",
+      "Epoch   3 Batch  140/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.915, Loss:  0.049\n",
+      "Epoch   3 Batch  141/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.913, Loss:  0.047\n",
+      "Epoch   3 Batch  142/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.043\n",
+      "Epoch   3 Batch  143/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.049\n",
+      "Epoch   3 Batch  144/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.068\n",
+      "Epoch   3 Batch  145/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.915, Loss:  0.048\n",
+      "Epoch   3 Batch  146/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.923, Loss:  0.083\n",
+      "Epoch   3 Batch  147/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.052\n",
+      "Epoch   3 Batch  148/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.051\n",
+      "Epoch   3 Batch  149/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.918, Loss:  0.045\n",
+      "Epoch   3 Batch  150/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.057\n",
+      "Epoch   3 Batch  151/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.046\n",
+      "Epoch   3 Batch  152/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.910, Loss:  0.073\n",
+      "Epoch   3 Batch  153/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.908, Loss:  0.064\n",
+      "Epoch   3 Batch  154/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.905, Loss:  0.045\n",
+      "Epoch   3 Batch  155/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.911, Loss:  0.056\n",
+      "Epoch   3 Batch  156/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.913, Loss:  0.042\n",
+      "Epoch   3 Batch  157/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.909, Loss:  0.041\n",
+      "Epoch   3 Batch  158/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.903, Loss:  0.065\n",
+      "Epoch   3 Batch  159/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.908, Loss:  0.040\n",
+      "Epoch   3 Batch  160/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.047\n",
+      "Epoch   3 Batch  161/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.913, Loss:  0.041\n",
+      "Epoch   3 Batch  162/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.913, Loss:  0.063\n",
+      "Epoch   3 Batch  163/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.064\n",
+      "Epoch   3 Batch  164/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.922, Loss:  0.052\n",
+      "Epoch   3 Batch  165/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.922, Loss:  0.042\n",
+      "Epoch   3 Batch  166/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.917, Loss:  0.056\n",
+      "Epoch   3 Batch  167/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   3 Batch  168/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.919, Loss:  0.071\n",
+      "Epoch   3 Batch  169/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.918, Loss:  0.064\n",
+      "Epoch   3 Batch  170/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.065\n",
+      "Epoch   3 Batch  171/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.055\n",
+      "Epoch   3 Batch  172/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.914, Loss:  0.038\n",
+      "Epoch   3 Batch  173/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.908, Loss:  0.052\n",
+      "Epoch   3 Batch  174/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.909, Loss:  0.040\n",
+      "Epoch   3 Batch  175/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.911, Loss:  0.059\n",
+      "Epoch   3 Batch  176/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.911, Loss:  0.054\n",
+      "Epoch   3 Batch  177/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.915, Loss:  0.057\n",
+      "Epoch   3 Batch  178/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.053\n",
+      "Epoch   3 Batch  179/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.932, Loss:  0.047\n",
+      "Epoch   3 Batch  180/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.935, Loss:  0.044\n",
+      "Epoch   3 Batch  181/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.058\n",
+      "Epoch   3 Batch  182/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.935, Loss:  0.051\n",
+      "Epoch   3 Batch  183/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.935, Loss:  0.049\n",
+      "Epoch   3 Batch  184/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.046\n",
+      "Epoch   3 Batch  185/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.935, Loss:  0.054\n",
+      "Epoch   3 Batch  186/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.933, Loss:  0.064\n",
+      "Epoch   3 Batch  187/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.932, Loss:  0.039\n",
+      "Epoch   3 Batch  188/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.052\n",
+      "Epoch   3 Batch  189/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.044\n",
+      "Epoch   3 Batch  190/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.051\n",
+      "Epoch   3 Batch  191/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.932, Loss:  0.043\n",
+      "Epoch   3 Batch  192/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.927, Loss:  0.069\n",
+      "Epoch   3 Batch  193/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.925, Loss:  0.046\n",
+      "Epoch   3 Batch  194/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.042\n",
+      "Epoch   3 Batch  195/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.927, Loss:  0.034\n",
+      "Epoch   3 Batch  196/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.927, Loss:  0.041\n",
+      "Epoch   3 Batch  197/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.053\n",
+      "Epoch   3 Batch  198/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.062\n",
+      "Epoch   3 Batch  199/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.931, Loss:  0.045\n",
+      "Epoch   3 Batch  200/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.932, Loss:  0.059\n",
+      "Epoch   3 Batch  201/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.927, Loss:  0.040\n",
+      "Epoch   3 Batch  202/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.927, Loss:  0.049\n",
+      "Epoch   3 Batch  203/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.935, Loss:  0.046\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  204/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.074\n",
+      "Epoch   3 Batch  205/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.932, Loss:  0.077\n",
+      "Epoch   3 Batch  206/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.040\n",
+      "Epoch   3 Batch  207/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.928, Loss:  0.045\n",
+      "Epoch   3 Batch  208/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.928, Loss:  0.049\n",
+      "Epoch   3 Batch  209/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.927, Loss:  0.030\n",
+      "Epoch   3 Batch  210/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.060\n",
+      "Epoch   3 Batch  211/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.046\n",
+      "Epoch   3 Batch  212/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.043\n",
+      "Epoch   3 Batch  213/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.043\n",
+      "Epoch   3 Batch  214/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.049\n",
+      "Epoch   3 Batch  215/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.927, Loss:  0.051\n",
+      "Epoch   3 Batch  216/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.927, Loss:  0.052\n",
+      "Epoch   3 Batch  217/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.045\n",
+      "Epoch   3 Batch  218/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.069\n",
+      "Epoch   3 Batch  219/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.041\n",
+      "Epoch   3 Batch  220/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.928, Loss:  0.042\n",
+      "Epoch   3 Batch  221/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.065\n",
+      "Epoch   3 Batch  222/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.045\n",
+      "Epoch   3 Batch  223/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.927, Loss:  0.039\n",
+      "Epoch   3 Batch  224/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.057\n",
+      "Epoch   3 Batch  225/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.061\n",
+      "Epoch   3 Batch  226/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.052\n",
+      "Epoch   3 Batch  227/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.908, Loss:  0.056\n",
+      "Epoch   3 Batch  228/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.921, Loss:  0.050\n",
+      "Epoch   3 Batch  229/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.921, Loss:  0.048\n",
+      "Epoch   3 Batch  230/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.917, Loss:  0.047\n",
+      "Epoch   3 Batch  231/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.065\n",
+      "Epoch   3 Batch  232/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.909, Loss:  0.040\n",
+      "Epoch   3 Batch  233/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.908, Loss:  0.065\n",
+      "Epoch   3 Batch  234/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.927, Loss:  0.067\n",
+      "Epoch   3 Batch  235/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.051\n",
+      "Epoch   3 Batch  236/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.933, Loss:  0.061\n",
+      "Epoch   3 Batch  237/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.917, Loss:  0.045\n",
+      "Epoch   3 Batch  238/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.913, Loss:  0.048\n",
+      "Epoch   3 Batch  239/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.922, Loss:  0.038\n",
+      "Epoch   3 Batch  240/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.911, Loss:  0.044\n",
+      "Epoch   3 Batch  241/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.911, Loss:  0.032\n",
+      "Epoch   3 Batch  242/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.924, Loss:  0.040\n",
+      "Epoch   3 Batch  243/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.060\n",
+      "Epoch   3 Batch  244/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.046\n",
+      "Epoch   3 Batch  245/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.039\n",
+      "Epoch   3 Batch  246/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.929, Loss:  0.043\n",
+      "Epoch   3 Batch  247/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.933, Loss:  0.054\n",
+      "Epoch   3 Batch  248/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.915, Loss:  0.056\n",
+      "Epoch   3 Batch  249/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   3 Batch  250/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.053\n",
+      "Epoch   3 Batch  251/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.920, Loss:  0.061\n",
+      "Epoch   3 Batch  252/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.053\n",
+      "Epoch   3 Batch  253/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.919, Loss:  0.045\n",
+      "Epoch   3 Batch  254/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.928, Loss:  0.057\n",
+      "Epoch   3 Batch  255/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.928, Loss:  0.047\n",
+      "Epoch   3 Batch  256/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.920, Loss:  0.076\n",
+      "Epoch   3 Batch  257/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.925, Loss:  0.043\n",
+      "Epoch   3 Batch  258/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.925, Loss:  0.053\n",
+      "Epoch   3 Batch  259/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.922, Loss:  0.040\n",
+      "Epoch   3 Batch  260/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.048\n",
+      "Epoch   3 Batch  261/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.904, Loss:  0.059\n",
+      "Epoch   3 Batch  262/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.899, Loss:  0.048\n",
+      "Epoch   3 Batch  263/1077 - Train Accuracy:  0.973, Validation Accuracy:  0.898, Loss:  0.033\n",
+      "Epoch   3 Batch  264/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.895, Loss:  0.044\n",
+      "Epoch   3 Batch  265/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.896, Loss:  0.048\n",
+      "Epoch   3 Batch  266/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.901, Loss:  0.054\n",
+      "Epoch   3 Batch  267/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.903, Loss:  0.039\n",
+      "Epoch   3 Batch  268/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.909, Loss:  0.058\n",
+      "Epoch   3 Batch  269/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.915, Loss:  0.063\n",
+      "Epoch   3 Batch  270/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.060\n",
+      "Epoch   3 Batch  271/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.040\n",
+      "Epoch   3 Batch  272/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.923, Loss:  0.073\n",
+      "Epoch   3 Batch  273/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.921, Loss:  0.043\n",
+      "Epoch   3 Batch  274/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.924, Loss:  0.051\n",
+      "Epoch   3 Batch  275/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.922, Loss:  0.045\n",
+      "Epoch   3 Batch  276/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.923, Loss:  0.073\n",
+      "Epoch   3 Batch  277/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.043\n",
+      "Epoch   3 Batch  278/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.929, Loss:  0.062\n",
+      "Epoch   3 Batch  279/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.932, Loss:  0.074\n",
+      "Epoch   3 Batch  280/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.926, Loss:  0.061\n",
+      "Epoch   3 Batch  281/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.065\n",
+      "Epoch   3 Batch  282/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.929, Loss:  0.080\n",
+      "Epoch   3 Batch  283/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.924, Loss:  0.068\n",
+      "Epoch   3 Batch  284/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.928, Loss:  0.061\n",
+      "Epoch   3 Batch  285/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.050\n",
+      "Epoch   3 Batch  286/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.051\n",
+      "Epoch   3 Batch  287/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.045\n",
+      "Epoch   3 Batch  288/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.059\n",
+      "Epoch   3 Batch  289/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.928, Loss:  0.053\n",
+      "Epoch   3 Batch  290/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.935, Loss:  0.074\n",
+      "Epoch   3 Batch  291/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.934, Loss:  0.076\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  292/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.929, Loss:  0.052\n",
+      "Epoch   3 Batch  293/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.932, Loss:  0.072\n",
+      "Epoch   3 Batch  294/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.042\n",
+      "Epoch   3 Batch  295/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.931, Loss:  0.055\n",
+      "Epoch   3 Batch  296/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.057\n",
+      "Epoch   3 Batch  297/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.928, Loss:  0.068\n",
+      "Epoch   3 Batch  298/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.065\n",
+      "Epoch   3 Batch  299/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.924, Loss:  0.052\n",
+      "Epoch   3 Batch  300/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.914, Loss:  0.042\n",
+      "Epoch   3 Batch  301/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.914, Loss:  0.043\n",
+      "Epoch   3 Batch  302/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.906, Loss:  0.046\n",
+      "Epoch   3 Batch  303/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.055\n",
+      "Epoch   3 Batch  304/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.914, Loss:  0.055\n",
+      "Epoch   3 Batch  305/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.918, Loss:  0.046\n",
+      "Epoch   3 Batch  306/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.916, Loss:  0.058\n",
+      "Epoch   3 Batch  307/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.912, Loss:  0.048\n",
+      "Epoch   3 Batch  308/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.059\n",
+      "Epoch   3 Batch  309/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.044\n",
+      "Epoch   3 Batch  310/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.916, Loss:  0.055\n",
+      "Epoch   3 Batch  311/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.050\n",
+      "Epoch   3 Batch  312/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.062\n",
+      "Epoch   3 Batch  313/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.924, Loss:  0.040\n",
+      "Epoch   3 Batch  314/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.920, Loss:  0.047\n",
+      "Epoch   3 Batch  315/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.922, Loss:  0.039\n",
+      "Epoch   3 Batch  316/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.925, Loss:  0.051\n",
+      "Epoch   3 Batch  317/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.920, Loss:  0.059\n",
+      "Epoch   3 Batch  318/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.915, Loss:  0.042\n",
+      "Epoch   3 Batch  319/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.920, Loss:  0.068\n",
+      "Epoch   3 Batch  320/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.917, Loss:  0.066\n",
+      "Epoch   3 Batch  321/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.914, Loss:  0.049\n",
+      "Epoch   3 Batch  322/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.931, Loss:  0.054\n",
+      "Epoch   3 Batch  323/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.923, Loss:  0.050\n",
+      "Epoch   3 Batch  324/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.037\n",
+      "Epoch   3 Batch  325/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.930, Loss:  0.054\n",
+      "Epoch   3 Batch  326/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.925, Loss:  0.049\n",
+      "Epoch   3 Batch  327/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.925, Loss:  0.055\n",
+      "Epoch   3 Batch  328/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.920, Loss:  0.064\n",
+      "Epoch   3 Batch  329/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.919, Loss:  0.068\n",
+      "Epoch   3 Batch  330/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.910, Loss:  0.058\n",
+      "Epoch   3 Batch  331/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.058\n",
+      "Epoch   3 Batch  332/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.903, Loss:  0.040\n",
+      "Epoch   3 Batch  333/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.903, Loss:  0.053\n",
+      "Epoch   3 Batch  334/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.907, Loss:  0.052\n",
+      "Epoch   3 Batch  335/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.905, Loss:  0.052\n",
+      "Epoch   3 Batch  336/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.900, Loss:  0.067\n",
+      "Epoch   3 Batch  337/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.907, Loss:  0.065\n",
+      "Epoch   3 Batch  338/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.909, Loss:  0.064\n",
+      "Epoch   3 Batch  339/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.909, Loss:  0.039\n",
+      "Epoch   3 Batch  340/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.046\n",
+      "Epoch   3 Batch  341/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.068\n",
+      "Epoch   3 Batch  342/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.046\n",
+      "Epoch   3 Batch  343/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.055\n",
+      "Epoch   3 Batch  344/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.048\n",
+      "Epoch   3 Batch  345/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.047\n",
+      "Epoch   3 Batch  346/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.916, Loss:  0.052\n",
+      "Epoch   3 Batch  347/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.907, Loss:  0.033\n",
+      "Epoch   3 Batch  348/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.900, Loss:  0.039\n",
+      "Epoch   3 Batch  349/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.906, Loss:  0.053\n",
+      "Epoch   3 Batch  350/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.047\n",
+      "Epoch   3 Batch  351/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.915, Loss:  0.054\n",
+      "Epoch   3 Batch  352/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.046\n",
+      "Epoch   3 Batch  353/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.915, Loss:  0.067\n",
+      "Epoch   3 Batch  354/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.920, Loss:  0.062\n",
+      "Epoch   3 Batch  355/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.041\n",
+      "Epoch   3 Batch  356/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.924, Loss:  0.057\n",
+      "Epoch   3 Batch  357/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.924, Loss:  0.053\n",
+      "Epoch   3 Batch  358/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.919, Loss:  0.066\n",
+      "Epoch   3 Batch  359/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.051\n",
+      "Epoch   3 Batch  360/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.046\n",
+      "Epoch   3 Batch  361/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.050\n",
+      "Epoch   3 Batch  362/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.050\n",
+      "Epoch   3 Batch  363/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.922, Loss:  0.064\n",
+      "Epoch   3 Batch  364/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.925, Loss:  0.060\n",
+      "Epoch   3 Batch  365/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.919, Loss:  0.047\n",
+      "Epoch   3 Batch  366/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.913, Loss:  0.052\n",
+      "Epoch   3 Batch  367/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.912, Loss:  0.038\n",
+      "Epoch   3 Batch  368/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.055\n",
+      "Epoch   3 Batch  369/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.915, Loss:  0.060\n",
+      "Epoch   3 Batch  370/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.055\n",
+      "Epoch   3 Batch  371/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.919, Loss:  0.045\n",
+      "Epoch   3 Batch  372/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.044\n",
+      "Epoch   3 Batch  373/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.037\n",
+      "Epoch   3 Batch  374/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.923, Loss:  0.050\n",
+      "Epoch   3 Batch  375/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.929, Loss:  0.046\n",
+      "Epoch   3 Batch  376/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.049\n",
+      "Epoch   3 Batch  377/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.935, Loss:  0.044\n",
+      "Epoch   3 Batch  378/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.045\n",
+      "Epoch   3 Batch  379/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.933, Loss:  0.057\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  380/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.933, Loss:  0.045\n",
+      "Epoch   3 Batch  381/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.947, Loss:  0.055\n",
+      "Epoch   3 Batch  382/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.947, Loss:  0.073\n",
+      "Epoch   3 Batch  383/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.947, Loss:  0.046\n",
+      "Epoch   3 Batch  384/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.947, Loss:  0.031\n",
+      "Epoch   3 Batch  385/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.956, Loss:  0.046\n",
+      "Epoch   3 Batch  386/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.954, Loss:  0.042\n",
+      "Epoch   3 Batch  387/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.953, Loss:  0.038\n",
+      "Epoch   3 Batch  388/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.955, Loss:  0.050\n",
+      "Epoch   3 Batch  389/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.945, Loss:  0.047\n",
+      "Epoch   3 Batch  390/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.945, Loss:  0.056\n",
+      "Epoch   3 Batch  391/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.941, Loss:  0.054\n",
+      "Epoch   3 Batch  392/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.941, Loss:  0.052\n",
+      "Epoch   3 Batch  393/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.044\n",
+      "Epoch   3 Batch  394/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.936, Loss:  0.045\n",
+      "Epoch   3 Batch  395/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.930, Loss:  0.045\n",
+      "Epoch   3 Batch  396/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.049\n",
+      "Epoch   3 Batch  397/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.927, Loss:  0.048\n",
+      "Epoch   3 Batch  398/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.054\n",
+      "Epoch   3 Batch  399/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.946, Loss:  0.057\n",
+      "Epoch   3 Batch  400/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.943, Loss:  0.055\n",
+      "Epoch   3 Batch  401/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.943, Loss:  0.048\n",
+      "Epoch   3 Batch  402/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.939, Loss:  0.044\n",
+      "Epoch   3 Batch  403/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.938, Loss:  0.077\n",
+      "Epoch   3 Batch  404/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.938, Loss:  0.052\n",
+      "Epoch   3 Batch  405/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.928, Loss:  0.050\n",
+      "Epoch   3 Batch  406/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.928, Loss:  0.039\n",
+      "Epoch   3 Batch  407/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.935, Loss:  0.074\n",
+      "Epoch   3 Batch  408/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.936, Loss:  0.052\n",
+      "Epoch   3 Batch  409/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.941, Loss:  0.062\n",
+      "Epoch   3 Batch  410/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.936, Loss:  0.062\n",
+      "Epoch   3 Batch  411/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.934, Loss:  0.058\n",
+      "Epoch   3 Batch  412/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.934, Loss:  0.042\n",
+      "Epoch   3 Batch  413/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.039\n",
+      "Epoch   3 Batch  414/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.940, Loss:  0.044\n",
+      "Epoch   3 Batch  415/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.940, Loss:  0.052\n",
+      "Epoch   3 Batch  416/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.943, Loss:  0.046\n",
+      "Epoch   3 Batch  417/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.943, Loss:  0.078\n",
+      "Epoch   3 Batch  418/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.945, Loss:  0.046\n",
+      "Epoch   3 Batch  419/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.948, Loss:  0.045\n",
+      "Epoch   3 Batch  420/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.946, Loss:  0.042\n",
+      "Epoch   3 Batch  421/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.940, Loss:  0.061\n",
+      "Epoch   3 Batch  422/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.940, Loss:  0.042\n",
+      "Epoch   3 Batch  423/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.936, Loss:  0.068\n",
+      "Epoch   3 Batch  424/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.935, Loss:  0.047\n",
+      "Epoch   3 Batch  425/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.922, Loss:  0.037\n",
+      "Epoch   3 Batch  426/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.057\n",
+      "Epoch   3 Batch  427/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.933, Loss:  0.047\n",
+      "Epoch   3 Batch  428/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.932, Loss:  0.036\n",
+      "Epoch   3 Batch  429/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.936, Loss:  0.043\n",
+      "Epoch   3 Batch  430/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.941, Loss:  0.047\n",
+      "Epoch   3 Batch  431/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.942, Loss:  0.041\n",
+      "Epoch   3 Batch  432/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.951, Loss:  0.048\n",
+      "Epoch   3 Batch  433/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.943, Loss:  0.057\n",
+      "Epoch   3 Batch  434/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.938, Loss:  0.044\n",
+      "Epoch   3 Batch  435/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.056\n",
+      "Epoch   3 Batch  436/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.918, Loss:  0.054\n",
+      "Epoch   3 Batch  437/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.918, Loss:  0.034\n",
+      "Epoch   3 Batch  438/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.916, Loss:  0.049\n",
+      "Epoch   3 Batch  439/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.916, Loss:  0.057\n",
+      "Epoch   3 Batch  440/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.920, Loss:  0.057\n",
+      "Epoch   3 Batch  441/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.926, Loss:  0.046\n",
+      "Epoch   3 Batch  442/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.060\n",
+      "Epoch   3 Batch  443/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.925, Loss:  0.041\n",
+      "Epoch   3 Batch  444/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.925, Loss:  0.037\n",
+      "Epoch   3 Batch  445/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.925, Loss:  0.057\n",
+      "Epoch   3 Batch  446/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.046\n",
+      "Epoch   3 Batch  447/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.926, Loss:  0.044\n",
+      "Epoch   3 Batch  448/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.922, Loss:  0.069\n",
+      "Epoch   3 Batch  449/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.920, Loss:  0.047\n",
+      "Epoch   3 Batch  450/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.920, Loss:  0.054\n",
+      "Epoch   3 Batch  451/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.920, Loss:  0.049\n",
+      "Epoch   3 Batch  452/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   3 Batch  453/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.920, Loss:  0.053\n",
+      "Epoch   3 Batch  454/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.930, Loss:  0.057\n",
+      "Epoch   3 Batch  455/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.926, Loss:  0.057\n",
+      "Epoch   3 Batch  456/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.923, Loss:  0.060\n",
+      "Epoch   3 Batch  457/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.042\n",
+      "Epoch   3 Batch  458/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.055\n",
+      "Epoch   3 Batch  459/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.045\n",
+      "Epoch   3 Batch  460/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.923, Loss:  0.061\n",
+      "Epoch   3 Batch  461/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.928, Loss:  0.049\n",
+      "Epoch   3 Batch  462/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.928, Loss:  0.057\n",
+      "Epoch   3 Batch  463/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.057\n",
+      "Epoch   3 Batch  464/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.922, Loss:  0.047\n",
+      "Epoch   3 Batch  465/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.916, Loss:  0.056\n",
+      "Epoch   3 Batch  466/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.912, Loss:  0.048\n",
+      "Epoch   3 Batch  467/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.917, Loss:  0.058\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  468/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.057\n",
+      "Epoch   3 Batch  469/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.061\n",
+      "Epoch   3 Batch  470/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.917, Loss:  0.053\n",
+      "Epoch   3 Batch  471/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.913, Loss:  0.046\n",
+      "Epoch   3 Batch  472/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.909, Loss:  0.049\n",
+      "Epoch   3 Batch  473/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.046\n",
+      "Epoch   3 Batch  474/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.916, Loss:  0.047\n",
+      "Epoch   3 Batch  475/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.051\n",
+      "Epoch   3 Batch  476/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.917, Loss:  0.039\n",
+      "Epoch   3 Batch  477/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.052\n",
+      "Epoch   3 Batch  478/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.934, Loss:  0.052\n",
+      "Epoch   3 Batch  479/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.934, Loss:  0.056\n",
+      "Epoch   3 Batch  480/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.934, Loss:  0.054\n",
+      "Epoch   3 Batch  481/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.935, Loss:  0.064\n",
+      "Epoch   3 Batch  482/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.935, Loss:  0.065\n",
+      "Epoch   3 Batch  483/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.934, Loss:  0.051\n",
+      "Epoch   3 Batch  484/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.048\n",
+      "Epoch   3 Batch  485/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.049\n",
+      "Epoch   3 Batch  486/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.046\n",
+      "Epoch   3 Batch  487/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.046\n",
+      "Epoch   3 Batch  488/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.927, Loss:  0.053\n",
+      "Epoch   3 Batch  489/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   3 Batch  490/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.931, Loss:  0.051\n",
+      "Epoch   3 Batch  491/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.056\n",
+      "Epoch   3 Batch  492/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.921, Loss:  0.059\n",
+      "Epoch   3 Batch  493/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.921, Loss:  0.042\n",
+      "Epoch   3 Batch  494/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.922, Loss:  0.049\n",
+      "Epoch   3 Batch  495/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.924, Loss:  0.051\n",
+      "Epoch   3 Batch  496/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.920, Loss:  0.050\n",
+      "Epoch   3 Batch  497/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.924, Loss:  0.055\n",
+      "Epoch   3 Batch  498/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.052\n",
+      "Epoch   3 Batch  499/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.039\n",
+      "Epoch   3 Batch  500/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.933, Loss:  0.040\n",
+      "Epoch   3 Batch  501/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.933, Loss:  0.040\n",
+      "Epoch   3 Batch  502/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.935, Loss:  0.053\n",
+      "Epoch   3 Batch  503/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.935, Loss:  0.044\n",
+      "Epoch   3 Batch  504/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.939, Loss:  0.042\n",
+      "Epoch   3 Batch  505/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.935, Loss:  0.036\n",
+      "Epoch   3 Batch  506/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.939, Loss:  0.059\n",
+      "Epoch   3 Batch  507/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.937, Loss:  0.055\n",
+      "Epoch   3 Batch  508/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.941, Loss:  0.046\n",
+      "Epoch   3 Batch  509/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.941, Loss:  0.069\n",
+      "Epoch   3 Batch  510/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.938, Loss:  0.057\n",
+      "Epoch   3 Batch  511/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.936, Loss:  0.048\n",
+      "Epoch   3 Batch  512/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.930, Loss:  0.056\n",
+      "Epoch   3 Batch  513/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.050\n",
+      "Epoch   3 Batch  514/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.934, Loss:  0.055\n",
+      "Epoch   3 Batch  515/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.933, Loss:  0.052\n",
+      "Epoch   3 Batch  516/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.055\n",
+      "Epoch   3 Batch  517/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.925, Loss:  0.063\n",
+      "Epoch   3 Batch  518/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.047\n",
+      "Epoch   3 Batch  519/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.046\n",
+      "Epoch   3 Batch  520/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.916, Loss:  0.050\n",
+      "Epoch   3 Batch  521/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.913, Loss:  0.058\n",
+      "Epoch   3 Batch  522/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.921, Loss:  0.063\n",
+      "Epoch   3 Batch  523/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.054\n",
+      "Epoch   3 Batch  524/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.920, Loss:  0.060\n",
+      "Epoch   3 Batch  525/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.061\n",
+      "Epoch   3 Batch  526/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.049\n",
+      "Epoch   3 Batch  527/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.056\n",
+      "Epoch   3 Batch  528/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.056\n",
+      "Epoch   3 Batch  529/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.934, Loss:  0.049\n",
+      "Epoch   3 Batch  530/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.937, Loss:  0.063\n",
+      "Epoch   3 Batch  531/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.935, Loss:  0.057\n",
+      "Epoch   3 Batch  532/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.931, Loss:  0.066\n",
+      "Epoch   3 Batch  533/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.058\n",
+      "Epoch   3 Batch  534/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.059\n",
+      "Epoch   3 Batch  535/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.931, Loss:  0.050\n",
+      "Epoch   3 Batch  536/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.929, Loss:  0.048\n",
+      "Epoch   3 Batch  537/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.923, Loss:  0.039\n",
+      "Epoch   3 Batch  538/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.928, Loss:  0.037\n",
+      "Epoch   3 Batch  539/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.061\n",
+      "Epoch   3 Batch  540/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.914, Loss:  0.050\n",
+      "Epoch   3 Batch  541/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.047\n",
+      "Epoch   3 Batch  542/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.923, Loss:  0.052\n",
+      "Epoch   3 Batch  543/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.048\n",
+      "Epoch   3 Batch  544/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.918, Loss:  0.035\n",
+      "Epoch   3 Batch  545/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.057\n",
+      "Epoch   3 Batch  546/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.924, Loss:  0.056\n",
+      "Epoch   3 Batch  547/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.925, Loss:  0.045\n",
+      "Epoch   3 Batch  548/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.930, Loss:  0.067\n",
+      "Epoch   3 Batch  549/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.926, Loss:  0.069\n",
+      "Epoch   3 Batch  550/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.049\n",
+      "Epoch   3 Batch  551/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.047\n",
+      "Epoch   3 Batch  552/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.915, Loss:  0.054\n",
+      "Epoch   3 Batch  553/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.070\n",
+      "Epoch   3 Batch  554/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.914, Loss:  0.043\n",
+      "Epoch   3 Batch  555/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.914, Loss:  0.049\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  556/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.907, Loss:  0.052\n",
+      "Epoch   3 Batch  557/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.906, Loss:  0.056\n",
+      "Epoch   3 Batch  558/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.901, Loss:  0.038\n",
+      "Epoch   3 Batch  559/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.908, Loss:  0.046\n",
+      "Epoch   3 Batch  560/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.904, Loss:  0.049\n",
+      "Epoch   3 Batch  561/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.912, Loss:  0.042\n",
+      "Epoch   3 Batch  562/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.046\n",
+      "Epoch   3 Batch  563/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.904, Loss:  0.057\n",
+      "Epoch   3 Batch  564/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.901, Loss:  0.057\n",
+      "Epoch   3 Batch  565/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.068\n",
+      "Epoch   3 Batch  566/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.902, Loss:  0.057\n",
+      "Epoch   3 Batch  567/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.060\n",
+      "Epoch   3 Batch  568/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.907, Loss:  0.041\n",
+      "Epoch   3 Batch  569/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.908, Loss:  0.049\n",
+      "Epoch   3 Batch  570/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.071\n",
+      "Epoch   3 Batch  571/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.913, Loss:  0.041\n",
+      "Epoch   3 Batch  572/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.915, Loss:  0.048\n",
+      "Epoch   3 Batch  573/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.918, Loss:  0.070\n",
+      "Epoch   3 Batch  574/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.920, Loss:  0.054\n",
+      "Epoch   3 Batch  575/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.036\n",
+      "Epoch   3 Batch  576/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.926, Loss:  0.045\n",
+      "Epoch   3 Batch  577/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.925, Loss:  0.060\n",
+      "Epoch   3 Batch  578/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.051\n",
+      "Epoch   3 Batch  579/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.921, Loss:  0.052\n",
+      "Epoch   3 Batch  580/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.920, Loss:  0.046\n",
+      "Epoch   3 Batch  581/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.920, Loss:  0.041\n",
+      "Epoch   3 Batch  582/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.918, Loss:  0.047\n",
+      "Epoch   3 Batch  583/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.054\n",
+      "Epoch   3 Batch  584/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.919, Loss:  0.046\n",
+      "Epoch   3 Batch  585/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.921, Loss:  0.032\n",
+      "Epoch   3 Batch  586/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.925, Loss:  0.052\n",
+      "Epoch   3 Batch  587/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.921, Loss:  0.063\n",
+      "Epoch   3 Batch  588/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.921, Loss:  0.043\n",
+      "Epoch   3 Batch  589/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.914, Loss:  0.055\n",
+      "Epoch   3 Batch  590/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.921, Loss:  0.069\n",
+      "Epoch   3 Batch  591/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.934, Loss:  0.055\n",
+      "Epoch   3 Batch  592/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.935, Loss:  0.055\n",
+      "Epoch   3 Batch  593/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.932, Loss:  0.073\n",
+      "Epoch   3 Batch  594/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.060\n",
+      "Epoch   3 Batch  595/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.045\n",
+      "Epoch   3 Batch  596/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.053\n",
+      "Epoch   3 Batch  597/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.046\n",
+      "Epoch   3 Batch  598/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.051\n",
+      "Epoch   3 Batch  599/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.912, Loss:  0.067\n",
+      "Epoch   3 Batch  600/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.916, Loss:  0.055\n",
+      "Epoch   3 Batch  601/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.057\n",
+      "Epoch   3 Batch  602/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.055\n",
+      "Epoch   3 Batch  603/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.917, Loss:  0.054\n",
+      "Epoch   3 Batch  604/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.058\n",
+      "Epoch   3 Batch  605/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.078\n",
+      "Epoch   3 Batch  606/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.933, Loss:  0.041\n",
+      "Epoch   3 Batch  607/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.923, Loss:  0.042\n",
+      "Epoch   3 Batch  608/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.065\n",
+      "Epoch   3 Batch  609/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.050\n",
+      "Epoch   3 Batch  610/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.057\n",
+      "Epoch   3 Batch  611/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.920, Loss:  0.044\n",
+      "Epoch   3 Batch  612/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.905, Loss:  0.042\n",
+      "Epoch   3 Batch  613/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.905, Loss:  0.067\n",
+      "Epoch   3 Batch  614/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.906, Loss:  0.041\n",
+      "Epoch   3 Batch  615/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.912, Loss:  0.047\n",
+      "Epoch   3 Batch  616/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.046\n",
+      "Epoch   3 Batch  617/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.922, Loss:  0.050\n",
+      "Epoch   3 Batch  618/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.931, Loss:  0.054\n",
+      "Epoch   3 Batch  619/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.926, Loss:  0.042\n",
+      "Epoch   3 Batch  620/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.931, Loss:  0.046\n",
+      "Epoch   3 Batch  621/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.930, Loss:  0.059\n",
+      "Epoch   3 Batch  622/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.916, Loss:  0.054\n",
+      "Epoch   3 Batch  623/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.907, Loss:  0.061\n",
+      "Epoch   3 Batch  624/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.912, Loss:  0.051\n",
+      "Epoch   3 Batch  625/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.912, Loss:  0.052\n",
+      "Epoch   3 Batch  626/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.051\n",
+      "Epoch   3 Batch  627/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.915, Loss:  0.054\n",
+      "Epoch   3 Batch  628/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.061\n",
+      "Epoch   3 Batch  629/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.919, Loss:  0.066\n",
+      "Epoch   3 Batch  630/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.926, Loss:  0.040\n",
+      "Epoch   3 Batch  631/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.931, Loss:  0.054\n",
+      "Epoch   3 Batch  632/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.935, Loss:  0.045\n",
+      "Epoch   3 Batch  633/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.055\n",
+      "Epoch   3 Batch  634/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.933, Loss:  0.041\n",
+      "Epoch   3 Batch  635/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.928, Loss:  0.059\n",
+      "Epoch   3 Batch  636/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.931, Loss:  0.043\n",
+      "Epoch   3 Batch  637/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.932, Loss:  0.048\n",
+      "Epoch   3 Batch  638/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.937, Loss:  0.051\n",
+      "Epoch   3 Batch  639/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.938, Loss:  0.068\n",
+      "Epoch   3 Batch  640/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.933, Loss:  0.046\n",
+      "Epoch   3 Batch  641/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.928, Loss:  0.050\n",
+      "Epoch   3 Batch  642/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.929, Loss:  0.048\n",
+      "Epoch   3 Batch  643/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.053\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  644/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.052\n",
+      "Epoch   3 Batch  645/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.061\n",
+      "Epoch   3 Batch  646/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.048\n",
+      "Epoch   3 Batch  647/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.931, Loss:  0.057\n",
+      "Epoch   3 Batch  648/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.931, Loss:  0.031\n",
+      "Epoch   3 Batch  649/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.931, Loss:  0.048\n",
+      "Epoch   3 Batch  650/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.040\n",
+      "Epoch   3 Batch  651/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.931, Loss:  0.044\n",
+      "Epoch   3 Batch  652/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.936, Loss:  0.065\n",
+      "Epoch   3 Batch  653/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.934, Loss:  0.054\n",
+      "Epoch   3 Batch  654/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.044\n",
+      "Epoch   3 Batch  655/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.924, Loss:  0.058\n",
+      "Epoch   3 Batch  656/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.928, Loss:  0.053\n",
+      "Epoch   3 Batch  657/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.929, Loss:  0.045\n",
+      "Epoch   3 Batch  658/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.934, Loss:  0.033\n",
+      "Epoch   3 Batch  659/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.929, Loss:  0.053\n",
+      "Epoch   3 Batch  660/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.938, Loss:  0.034\n",
+      "Epoch   3 Batch  661/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.936, Loss:  0.046\n",
+      "Epoch   3 Batch  662/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.046\n",
+      "Epoch   3 Batch  663/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.927, Loss:  0.045\n",
+      "Epoch   3 Batch  664/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.047\n",
+      "Epoch   3 Batch  665/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.038\n",
+      "Epoch   3 Batch  666/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.052\n",
+      "Epoch   3 Batch  667/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.911, Loss:  0.054\n",
+      "Epoch   3 Batch  668/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.911, Loss:  0.060\n",
+      "Epoch   3 Batch  669/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.911, Loss:  0.054\n",
+      "Epoch   3 Batch  670/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.924, Loss:  0.057\n",
+      "Epoch   3 Batch  671/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.933, Loss:  0.061\n",
+      "Epoch   3 Batch  672/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.937, Loss:  0.046\n",
+      "Epoch   3 Batch  673/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.941, Loss:  0.046\n",
+      "Epoch   3 Batch  674/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.933, Loss:  0.054\n",
+      "Epoch   3 Batch  675/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.932, Loss:  0.059\n",
+      "Epoch   3 Batch  676/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.929, Loss:  0.048\n",
+      "Epoch   3 Batch  677/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.927, Loss:  0.067\n",
+      "Epoch   3 Batch  678/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.933, Loss:  0.046\n",
+      "Epoch   3 Batch  679/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.930, Loss:  0.045\n",
+      "Epoch   3 Batch  680/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.058\n",
+      "Epoch   3 Batch  681/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.926, Loss:  0.052\n",
+      "Epoch   3 Batch  682/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.917, Loss:  0.047\n",
+      "Epoch   3 Batch  683/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.049\n",
+      "Epoch   3 Batch  684/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.041\n",
+      "Epoch   3 Batch  685/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.051\n",
+      "Epoch   3 Batch  686/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.914, Loss:  0.047\n",
+      "Epoch   3 Batch  687/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.909, Loss:  0.075\n",
+      "Epoch   3 Batch  688/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.907, Loss:  0.050\n",
+      "Epoch   3 Batch  689/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.913, Loss:  0.035\n",
+      "Epoch   3 Batch  690/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.910, Loss:  0.056\n",
+      "Epoch   3 Batch  691/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.912, Loss:  0.067\n",
+      "Epoch   3 Batch  692/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.918, Loss:  0.050\n",
+      "Epoch   3 Batch  693/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.918, Loss:  0.063\n",
+      "Epoch   3 Batch  694/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.917, Loss:  0.062\n",
+      "Epoch   3 Batch  695/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.917, Loss:  0.042\n",
+      "Epoch   3 Batch  696/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.917, Loss:  0.060\n",
+      "Epoch   3 Batch  697/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.915, Loss:  0.054\n",
+      "Epoch   3 Batch  698/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.901, Loss:  0.051\n",
+      "Epoch   3 Batch  699/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.908, Loss:  0.063\n",
+      "Epoch   3 Batch  700/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.917, Loss:  0.047\n",
+      "Epoch   3 Batch  701/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.916, Loss:  0.051\n",
+      "Epoch   3 Batch  702/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.924, Loss:  0.073\n",
+      "Epoch   3 Batch  703/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.933, Loss:  0.054\n",
+      "Epoch   3 Batch  704/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.929, Loss:  0.075\n",
+      "Epoch   3 Batch  705/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.934, Loss:  0.066\n",
+      "Epoch   3 Batch  706/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.931, Loss:  0.081\n",
+      "Epoch   3 Batch  707/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.933, Loss:  0.058\n",
+      "Epoch   3 Batch  708/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.932, Loss:  0.065\n",
+      "Epoch   3 Batch  709/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.933, Loss:  0.067\n",
+      "Epoch   3 Batch  710/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.935, Loss:  0.048\n",
+      "Epoch   3 Batch  711/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.064\n",
+      "Epoch   3 Batch  712/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.925, Loss:  0.044\n",
+      "Epoch   3 Batch  713/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.048\n",
+      "Epoch   3 Batch  714/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.060\n",
+      "Epoch   3 Batch  715/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.059\n",
+      "Epoch   3 Batch  716/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.048\n",
+      "Epoch   3 Batch  717/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.923, Loss:  0.044\n",
+      "Epoch   3 Batch  718/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.039\n",
+      "Epoch   3 Batch  719/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.053\n",
+      "Epoch   3 Batch  720/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.927, Loss:  0.058\n",
+      "Epoch   3 Batch  721/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.931, Loss:  0.052\n",
+      "Epoch   3 Batch  722/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.934, Loss:  0.048\n",
+      "Epoch   3 Batch  723/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.938, Loss:  0.049\n",
+      "Epoch   3 Batch  724/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.049\n",
+      "Epoch   3 Batch  725/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.933, Loss:  0.039\n",
+      "Epoch   3 Batch  726/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.057\n",
+      "Epoch   3 Batch  727/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.928, Loss:  0.044\n",
+      "Epoch   3 Batch  728/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.926, Loss:  0.071\n",
+      "Epoch   3 Batch  729/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.065\n",
+      "Epoch   3 Batch  730/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.066\n",
+      "Epoch   3 Batch  731/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.053\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  732/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.918, Loss:  0.059\n",
+      "Epoch   3 Batch  733/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.904, Loss:  0.046\n",
+      "Epoch   3 Batch  734/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.907, Loss:  0.054\n",
+      "Epoch   3 Batch  735/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.911, Loss:  0.047\n",
+      "Epoch   3 Batch  736/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.041\n",
+      "Epoch   3 Batch  737/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.063\n",
+      "Epoch   3 Batch  738/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.039\n",
+      "Epoch   3 Batch  739/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.053\n",
+      "Epoch   3 Batch  740/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.929, Loss:  0.043\n",
+      "Epoch   3 Batch  741/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.058\n",
+      "Epoch   3 Batch  742/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.933, Loss:  0.054\n",
+      "Epoch   3 Batch  743/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.932, Loss:  0.053\n",
+      "Epoch   3 Batch  744/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.941, Loss:  0.053\n",
+      "Epoch   3 Batch  745/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.936, Loss:  0.058\n",
+      "Epoch   3 Batch  746/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.043\n",
+      "Epoch   3 Batch  747/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.933, Loss:  0.039\n",
+      "Epoch   3 Batch  748/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.043\n",
+      "Epoch   3 Batch  749/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.045\n",
+      "Epoch   3 Batch  750/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.937, Loss:  0.048\n",
+      "Epoch   3 Batch  751/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.937, Loss:  0.053\n",
+      "Epoch   3 Batch  752/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.936, Loss:  0.048\n",
+      "Epoch   3 Batch  753/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.932, Loss:  0.048\n",
+      "Epoch   3 Batch  754/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.936, Loss:  0.060\n",
+      "Epoch   3 Batch  755/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.927, Loss:  0.058\n",
+      "Epoch   3 Batch  756/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.928, Loss:  0.049\n",
+      "Epoch   3 Batch  757/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.924, Loss:  0.044\n",
+      "Epoch   3 Batch  758/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.917, Loss:  0.046\n",
+      "Epoch   3 Batch  759/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.927, Loss:  0.042\n",
+      "Epoch   3 Batch  760/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.938, Loss:  0.059\n",
+      "Epoch   3 Batch  761/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.942, Loss:  0.048\n",
+      "Epoch   3 Batch  762/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.939, Loss:  0.043\n",
+      "Epoch   3 Batch  763/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.936, Loss:  0.049\n",
+      "Epoch   3 Batch  764/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.941, Loss:  0.053\n",
+      "Epoch   3 Batch  765/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.938, Loss:  0.058\n",
+      "Epoch   3 Batch  766/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.942, Loss:  0.043\n",
+      "Epoch   3 Batch  767/1077 - Train Accuracy:  0.971, Validation Accuracy:  0.939, Loss:  0.046\n",
+      "Epoch   3 Batch  768/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.939, Loss:  0.051\n",
+      "Epoch   3 Batch  769/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.045\n",
+      "Epoch   3 Batch  770/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.919, Loss:  0.049\n",
+      "Epoch   3 Batch  771/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.917, Loss:  0.050\n",
+      "Epoch   3 Batch  772/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.918, Loss:  0.048\n",
+      "Epoch   3 Batch  773/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.923, Loss:  0.049\n",
+      "Epoch   3 Batch  774/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.063\n",
+      "Epoch   3 Batch  775/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.057\n",
+      "Epoch   3 Batch  776/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.925, Loss:  0.045\n",
+      "Epoch   3 Batch  777/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.928, Loss:  0.054\n",
+      "Epoch   3 Batch  778/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   3 Batch  779/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.062\n",
+      "Epoch   3 Batch  780/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.916, Loss:  0.077\n",
+      "Epoch   3 Batch  781/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.935, Loss:  0.041\n",
+      "Epoch   3 Batch  782/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.939, Loss:  0.045\n",
+      "Epoch   3 Batch  783/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.945, Loss:  0.053\n",
+      "Epoch   3 Batch  784/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.945, Loss:  0.040\n",
+      "Epoch   3 Batch  785/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.947, Loss:  0.037\n",
+      "Epoch   3 Batch  786/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.947, Loss:  0.043\n",
+      "Epoch   3 Batch  787/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.939, Loss:  0.055\n",
+      "Epoch   3 Batch  788/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.938, Loss:  0.045\n",
+      "Epoch   3 Batch  789/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.938, Loss:  0.060\n",
+      "Epoch   3 Batch  790/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.933, Loss:  0.053\n",
+      "Epoch   3 Batch  791/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.933, Loss:  0.059\n",
+      "Epoch   3 Batch  792/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.060\n",
+      "Epoch   3 Batch  793/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.932, Loss:  0.044\n",
+      "Epoch   3 Batch  794/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.931, Loss:  0.037\n",
+      "Epoch   3 Batch  795/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.068\n",
+      "Epoch   3 Batch  796/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.930, Loss:  0.037\n",
+      "Epoch   3 Batch  797/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.923, Loss:  0.052\n",
+      "Epoch   3 Batch  798/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.053\n",
+      "Epoch   3 Batch  799/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.060\n",
+      "Epoch   3 Batch  800/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.041\n",
+      "Epoch   3 Batch  801/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.056\n",
+      "Epoch   3 Batch  802/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.913, Loss:  0.048\n",
+      "Epoch   3 Batch  803/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.061\n",
+      "Epoch   3 Batch  804/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.912, Loss:  0.040\n",
+      "Epoch   3 Batch  805/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.913, Loss:  0.043\n",
+      "Epoch   3 Batch  806/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.059\n",
+      "Epoch   3 Batch  807/1077 - Train Accuracy:  0.969, Validation Accuracy:  0.905, Loss:  0.044\n",
+      "Epoch   3 Batch  808/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.905, Loss:  0.068\n",
+      "Epoch   3 Batch  809/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.901, Loss:  0.056\n",
+      "Epoch   3 Batch  810/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.900, Loss:  0.043\n",
+      "Epoch   3 Batch  811/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.906, Loss:  0.051\n",
+      "Epoch   3 Batch  812/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.910, Loss:  0.050\n",
+      "Epoch   3 Batch  813/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.920, Loss:  0.054\n",
+      "Epoch   3 Batch  814/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.920, Loss:  0.061\n",
+      "Epoch   3 Batch  815/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.053\n",
+      "Epoch   3 Batch  816/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.910, Loss:  0.056\n",
+      "Epoch   3 Batch  817/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.920, Loss:  0.061\n",
+      "Epoch   3 Batch  818/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.925, Loss:  0.056\n",
+      "Epoch   3 Batch  819/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.932, Loss:  0.048\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  820/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.927, Loss:  0.048\n",
+      "Epoch   3 Batch  821/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.923, Loss:  0.054\n",
+      "Epoch   3 Batch  822/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.918, Loss:  0.040\n",
+      "Epoch   3 Batch  823/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.922, Loss:  0.058\n",
+      "Epoch   3 Batch  824/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.064\n",
+      "Epoch   3 Batch  825/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.919, Loss:  0.043\n",
+      "Epoch   3 Batch  826/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.048\n",
+      "Epoch   3 Batch  827/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   3 Batch  828/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.051\n",
+      "Epoch   3 Batch  829/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.918, Loss:  0.069\n",
+      "Epoch   3 Batch  830/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.920, Loss:  0.059\n",
+      "Epoch   3 Batch  831/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.915, Loss:  0.052\n",
+      "Epoch   3 Batch  832/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.915, Loss:  0.044\n",
+      "Epoch   3 Batch  833/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.920, Loss:  0.057\n",
+      "Epoch   3 Batch  834/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.927, Loss:  0.045\n",
+      "Epoch   3 Batch  835/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.052\n",
+      "Epoch   3 Batch  836/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.923, Loss:  0.047\n",
+      "Epoch   3 Batch  837/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.928, Loss:  0.059\n",
+      "Epoch   3 Batch  838/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.923, Loss:  0.046\n",
+      "Epoch   3 Batch  839/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.918, Loss:  0.037\n",
+      "Epoch   3 Batch  840/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.043\n",
+      "Epoch   3 Batch  841/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.936, Loss:  0.057\n",
+      "Epoch   3 Batch  842/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.935, Loss:  0.037\n",
+      "Epoch   3 Batch  843/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.050\n",
+      "Epoch   3 Batch  844/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.945, Loss:  0.044\n",
+      "Epoch   3 Batch  845/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.950, Loss:  0.048\n",
+      "Epoch   3 Batch  846/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.940, Loss:  0.059\n",
+      "Epoch   3 Batch  847/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.944, Loss:  0.057\n",
+      "Epoch   3 Batch  848/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.944, Loss:  0.039\n",
+      "Epoch   3 Batch  849/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.940, Loss:  0.051\n",
+      "Epoch   3 Batch  850/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.940, Loss:  0.078\n",
+      "Epoch   3 Batch  851/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.935, Loss:  0.063\n",
+      "Epoch   3 Batch  852/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.933, Loss:  0.053\n",
+      "Epoch   3 Batch  853/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.934, Loss:  0.059\n",
+      "Epoch   3 Batch  854/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.935, Loss:  0.052\n",
+      "Epoch   3 Batch  855/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.933, Loss:  0.052\n",
+      "Epoch   3 Batch  856/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.050\n",
+      "Epoch   3 Batch  857/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.055\n",
+      "Epoch   3 Batch  858/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.049\n",
+      "Epoch   3 Batch  859/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.054\n",
+      "Epoch   3 Batch  860/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.044\n",
+      "Epoch   3 Batch  861/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.936, Loss:  0.061\n",
+      "Epoch   3 Batch  862/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.936, Loss:  0.049\n",
+      "Epoch   3 Batch  863/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.037\n",
+      "Epoch   3 Batch  864/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.928, Loss:  0.052\n",
+      "Epoch   3 Batch  865/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.922, Loss:  0.048\n",
+      "Epoch   3 Batch  866/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.065\n",
+      "Epoch   3 Batch  867/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.926, Loss:  0.095\n",
+      "Epoch   3 Batch  868/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.923, Loss:  0.063\n",
+      "Epoch   3 Batch  869/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.049\n",
+      "Epoch   3 Batch  870/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.923, Loss:  0.051\n",
+      "Epoch   3 Batch  871/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.918, Loss:  0.036\n",
+      "Epoch   3 Batch  872/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.926, Loss:  0.052\n",
+      "Epoch   3 Batch  873/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.926, Loss:  0.051\n",
+      "Epoch   3 Batch  874/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.919, Loss:  0.066\n",
+      "Epoch   3 Batch  875/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.067\n",
+      "Epoch   3 Batch  876/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.043\n",
+      "Epoch   3 Batch  877/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.933, Loss:  0.043\n",
+      "Epoch   3 Batch  878/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.942, Loss:  0.043\n",
+      "Epoch   3 Batch  879/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.933, Loss:  0.042\n",
+      "Epoch   3 Batch  880/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.933, Loss:  0.049\n",
+      "Epoch   3 Batch  881/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.933, Loss:  0.050\n",
+      "Epoch   3 Batch  882/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.050\n",
+      "Epoch   3 Batch  883/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.063\n",
+      "Epoch   3 Batch  884/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.053\n",
+      "Epoch   3 Batch  885/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.923, Loss:  0.036\n",
+      "Epoch   3 Batch  886/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.914, Loss:  0.041\n",
+      "Epoch   3 Batch  887/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.908, Loss:  0.061\n",
+      "Epoch   3 Batch  888/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.908, Loss:  0.048\n",
+      "Epoch   3 Batch  889/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.048\n",
+      "Epoch   3 Batch  890/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.914, Loss:  0.053\n",
+      "Epoch   3 Batch  891/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.909, Loss:  0.045\n",
+      "Epoch   3 Batch  892/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.912, Loss:  0.042\n",
+      "Epoch   3 Batch  893/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.043\n",
+      "Epoch   3 Batch  894/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.041\n",
+      "Epoch   3 Batch  895/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.044\n",
+      "Epoch   3 Batch  896/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.916, Loss:  0.053\n",
+      "Epoch   3 Batch  897/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.922, Loss:  0.037\n",
+      "Epoch   3 Batch  898/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.040\n",
+      "Epoch   3 Batch  899/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.930, Loss:  0.044\n",
+      "Epoch   3 Batch  900/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.934, Loss:  0.066\n",
+      "Epoch   3 Batch  901/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.935, Loss:  0.067\n",
+      "Epoch   3 Batch  902/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.933, Loss:  0.055\n",
+      "Epoch   3 Batch  903/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.933, Loss:  0.054\n",
+      "Epoch   3 Batch  904/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.939, Loss:  0.043\n",
+      "Epoch   3 Batch  905/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.933, Loss:  0.038\n",
+      "Epoch   3 Batch  906/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.931, Loss:  0.053\n",
+      "Epoch   3 Batch  907/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.046\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  908/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.052\n",
+      "Epoch   3 Batch  909/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.052\n",
+      "Epoch   3 Batch  910/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.935, Loss:  0.049\n",
+      "Epoch   3 Batch  911/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.928, Loss:  0.045\n",
+      "Epoch   3 Batch  912/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.930, Loss:  0.047\n",
+      "Epoch   3 Batch  913/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.926, Loss:  0.078\n",
+      "Epoch   3 Batch  914/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.927, Loss:  0.067\n",
+      "Epoch   3 Batch  915/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.916, Loss:  0.045\n",
+      "Epoch   3 Batch  916/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.912, Loss:  0.050\n",
+      "Epoch   3 Batch  917/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.047\n",
+      "Epoch   3 Batch  918/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.913, Loss:  0.045\n",
+      "Epoch   3 Batch  919/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.908, Loss:  0.038\n",
+      "Epoch   3 Batch  920/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.043\n",
+      "Epoch   3 Batch  921/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.918, Loss:  0.058\n",
+      "Epoch   3 Batch  922/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.049\n",
+      "Epoch   3 Batch  923/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.920, Loss:  0.037\n",
+      "Epoch   3 Batch  924/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.924, Loss:  0.055\n",
+      "Epoch   3 Batch  925/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.050\n",
+      "Epoch   3 Batch  926/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.032\n",
+      "Epoch   3 Batch  927/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.064\n",
+      "Epoch   3 Batch  928/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.925, Loss:  0.052\n",
+      "Epoch   3 Batch  929/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.924, Loss:  0.053\n",
+      "Epoch   3 Batch  930/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.923, Loss:  0.050\n",
+      "Epoch   3 Batch  931/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.914, Loss:  0.046\n",
+      "Epoch   3 Batch  932/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.905, Loss:  0.055\n",
+      "Epoch   3 Batch  933/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.911, Loss:  0.049\n",
+      "Epoch   3 Batch  934/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.917, Loss:  0.042\n",
+      "Epoch   3 Batch  935/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.049\n",
+      "Epoch   3 Batch  936/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.927, Loss:  0.050\n",
+      "Epoch   3 Batch  937/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.924, Loss:  0.058\n",
+      "Epoch   3 Batch  938/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.920, Loss:  0.056\n",
+      "Epoch   3 Batch  939/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.919, Loss:  0.052\n",
+      "Epoch   3 Batch  940/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.926, Loss:  0.041\n",
+      "Epoch   3 Batch  941/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.042\n",
+      "Epoch   3 Batch  942/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.927, Loss:  0.053\n",
+      "Epoch   3 Batch  943/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.930, Loss:  0.049\n",
+      "Epoch   3 Batch  944/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.044\n",
+      "Epoch   3 Batch  945/1077 - Train Accuracy:  0.969, Validation Accuracy:  0.930, Loss:  0.041\n",
+      "Epoch   3 Batch  946/1077 - Train Accuracy:  0.973, Validation Accuracy:  0.927, Loss:  0.029\n",
+      "Epoch   3 Batch  947/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.924, Loss:  0.047\n",
+      "Epoch   3 Batch  948/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.047\n",
+      "Epoch   3 Batch  949/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.922, Loss:  0.041\n",
+      "Epoch   3 Batch  950/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.044\n",
+      "Epoch   3 Batch  951/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.926, Loss:  0.055\n",
+      "Epoch   3 Batch  952/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.929, Loss:  0.037\n",
+      "Epoch   3 Batch  953/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.037\n",
+      "Epoch   3 Batch  954/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.045\n",
+      "Epoch   3 Batch  955/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.926, Loss:  0.073\n",
+      "Epoch   3 Batch  956/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.052\n",
+      "Epoch   3 Batch  957/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.034\n",
+      "Epoch   3 Batch  958/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.042\n",
+      "Epoch   3 Batch  959/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.919, Loss:  0.045\n",
+      "Epoch   3 Batch  960/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.919, Loss:  0.044\n",
+      "Epoch   3 Batch  961/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.920, Loss:  0.041\n",
+      "Epoch   3 Batch  962/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   3 Batch  963/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.919, Loss:  0.066\n",
+      "Epoch   3 Batch  964/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.920, Loss:  0.044\n",
+      "Epoch   3 Batch  965/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.064\n",
+      "Epoch   3 Batch  966/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.043\n",
+      "Epoch   3 Batch  967/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.914, Loss:  0.067\n",
+      "Epoch   3 Batch  968/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.057\n",
+      "Epoch   3 Batch  969/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.916, Loss:  0.074\n",
+      "Epoch   3 Batch  970/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.042\n",
+      "Epoch   3 Batch  971/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.916, Loss:  0.060\n",
+      "Epoch   3 Batch  972/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.925, Loss:  0.052\n",
+      "Epoch   3 Batch  973/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.926, Loss:  0.042\n",
+      "Epoch   3 Batch  974/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.929, Loss:  0.035\n",
+      "Epoch   3 Batch  975/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.045\n",
+      "Epoch   3 Batch  976/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.931, Loss:  0.039\n",
+      "Epoch   3 Batch  977/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.038\n",
+      "Epoch   3 Batch  978/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.047\n",
+      "Epoch   3 Batch  979/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.046\n",
+      "Epoch   3 Batch  980/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.935, Loss:  0.049\n",
+      "Epoch   3 Batch  981/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.936, Loss:  0.043\n",
+      "Epoch   3 Batch  982/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.936, Loss:  0.048\n",
+      "Epoch   3 Batch  983/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.936, Loss:  0.049\n",
+      "Epoch   3 Batch  984/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.933, Loss:  0.066\n",
+      "Epoch   3 Batch  985/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.046\n",
+      "Epoch   3 Batch  986/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.936, Loss:  0.049\n",
+      "Epoch   3 Batch  987/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.931, Loss:  0.045\n",
+      "Epoch   3 Batch  988/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.932, Loss:  0.066\n",
+      "Epoch   3 Batch  989/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.933, Loss:  0.055\n",
+      "Epoch   3 Batch  990/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.059\n",
+      "Epoch   3 Batch  991/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.041\n",
+      "Epoch   3 Batch  992/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.926, Loss:  0.049\n",
+      "Epoch   3 Batch  993/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.919, Loss:  0.040\n",
+      "Epoch   3 Batch  994/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.917, Loss:  0.046\n",
+      "Epoch   3 Batch  995/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.917, Loss:  0.048\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   3 Batch  996/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.052\n",
+      "Epoch   3 Batch  997/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.922, Loss:  0.045\n",
+      "Epoch   3 Batch  998/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.926, Loss:  0.050\n",
+      "Epoch   3 Batch  999/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.054\n",
+      "Epoch   3 Batch 1000/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.041\n",
+      "Epoch   3 Batch 1001/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.925, Loss:  0.035\n",
+      "Epoch   3 Batch 1002/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.928, Loss:  0.036\n",
+      "Epoch   3 Batch 1003/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.933, Loss:  0.050\n",
+      "Epoch   3 Batch 1004/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.933, Loss:  0.052\n",
+      "Epoch   3 Batch 1005/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.931, Loss:  0.043\n",
+      "Epoch   3 Batch 1006/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.047\n",
+      "Epoch   3 Batch 1007/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.047\n",
+      "Epoch   3 Batch 1008/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.925, Loss:  0.063\n",
+      "Epoch   3 Batch 1009/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.927, Loss:  0.038\n",
+      "Epoch   3 Batch 1010/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.043\n",
+      "Epoch   3 Batch 1011/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.927, Loss:  0.037\n",
+      "Epoch   3 Batch 1012/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.924, Loss:  0.037\n",
+      "Epoch   3 Batch 1013/1077 - Train Accuracy:  0.971, Validation Accuracy:  0.923, Loss:  0.034\n",
+      "Epoch   3 Batch 1014/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.055\n",
+      "Epoch   3 Batch 1015/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.059\n",
+      "Epoch   3 Batch 1016/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.046\n",
+      "Epoch   3 Batch 1017/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.042\n",
+      "Epoch   3 Batch 1018/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.921, Loss:  0.042\n",
+      "Epoch   3 Batch 1019/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.923, Loss:  0.060\n",
+      "Epoch   3 Batch 1020/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.923, Loss:  0.040\n",
+      "Epoch   3 Batch 1021/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.046\n",
+      "Epoch   3 Batch 1022/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.924, Loss:  0.044\n",
+      "Epoch   3 Batch 1023/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.047\n",
+      "Epoch   3 Batch 1024/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.931, Loss:  0.068\n",
+      "Epoch   3 Batch 1025/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.050\n",
+      "Epoch   3 Batch 1026/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.938, Loss:  0.053\n",
+      "Epoch   3 Batch 1027/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.934, Loss:  0.048\n",
+      "Epoch   3 Batch 1028/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.048\n",
+      "Epoch   3 Batch 1029/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.931, Loss:  0.038\n",
+      "Epoch   3 Batch 1030/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.935, Loss:  0.043\n",
+      "Epoch   3 Batch 1031/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.050\n",
+      "Epoch   3 Batch 1032/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.930, Loss:  0.050\n",
+      "Epoch   3 Batch 1033/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.051\n",
+      "Epoch   3 Batch 1034/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.934, Loss:  0.049\n",
+      "Epoch   3 Batch 1035/1077 - Train Accuracy:  0.975, Validation Accuracy:  0.929, Loss:  0.029\n",
+      "Epoch   3 Batch 1036/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.929, Loss:  0.046\n",
+      "Epoch   3 Batch 1037/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.930, Loss:  0.038\n",
+      "Epoch   3 Batch 1038/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.935, Loss:  0.067\n",
+      "Epoch   3 Batch 1039/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.938, Loss:  0.050\n",
+      "Epoch   3 Batch 1040/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.938, Loss:  0.053\n",
+      "Epoch   3 Batch 1041/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.058\n",
+      "Epoch   3 Batch 1042/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.935, Loss:  0.041\n",
+      "Epoch   3 Batch 1043/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.054\n",
+      "Epoch   3 Batch 1044/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.937, Loss:  0.053\n",
+      "Epoch   3 Batch 1045/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.044\n",
+      "Epoch   3 Batch 1046/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.929, Loss:  0.035\n",
+      "Epoch   3 Batch 1047/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.933, Loss:  0.048\n",
+      "Epoch   3 Batch 1048/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.942, Loss:  0.051\n",
+      "Epoch   3 Batch 1049/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.938, Loss:  0.045\n",
+      "Epoch   3 Batch 1050/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.935, Loss:  0.037\n",
+      "Epoch   3 Batch 1051/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.934, Loss:  0.053\n",
+      "Epoch   3 Batch 1052/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.939, Loss:  0.046\n",
+      "Epoch   3 Batch 1053/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.941, Loss:  0.045\n",
+      "Epoch   3 Batch 1054/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.941, Loss:  0.053\n",
+      "Epoch   3 Batch 1055/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.940, Loss:  0.045\n",
+      "Epoch   3 Batch 1056/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.943, Loss:  0.046\n",
+      "Epoch   3 Batch 1057/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.936, Loss:  0.055\n",
+      "Epoch   3 Batch 1058/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.936, Loss:  0.052\n",
+      "Epoch   3 Batch 1059/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.920, Loss:  0.058\n",
+      "Epoch   3 Batch 1060/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.044\n",
+      "Epoch   3 Batch 1061/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.918, Loss:  0.049\n",
+      "Epoch   3 Batch 1062/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.920, Loss:  0.047\n",
+      "Epoch   3 Batch 1063/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.929, Loss:  0.061\n",
+      "Epoch   3 Batch 1064/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.930, Loss:  0.046\n",
+      "Epoch   3 Batch 1065/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.042\n",
+      "Epoch   3 Batch 1066/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.039\n",
+      "Epoch   3 Batch 1067/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.938, Loss:  0.057\n",
+      "Epoch   3 Batch 1068/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.940, Loss:  0.043\n",
+      "Epoch   3 Batch 1069/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.937, Loss:  0.035\n",
+      "Epoch   3 Batch 1070/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.937, Loss:  0.041\n",
+      "Epoch   3 Batch 1071/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.940, Loss:  0.048\n",
+      "Epoch   3 Batch 1072/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.040\n",
+      "Epoch   3 Batch 1073/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.929, Loss:  0.047\n",
+      "Epoch   3 Batch 1074/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.929, Loss:  0.055\n",
+      "Epoch   3 Batch 1075/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.051\n",
+      "Epoch   4 Batch    0/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.926, Loss:  0.050\n",
+      "Epoch   4 Batch    1/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.036\n",
+      "Epoch   4 Batch    2/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.050\n",
+      "Epoch   4 Batch    3/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.918, Loss:  0.044\n",
+      "Epoch   4 Batch    4/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.034\n",
+      "Epoch   4 Batch    5/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.069\n",
+      "Epoch   4 Batch    6/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.918, Loss:  0.051\n",
+      "Epoch   4 Batch    7/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.915, Loss:  0.047\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch    8/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.914, Loss:  0.041\n",
+      "Epoch   4 Batch    9/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.045\n",
+      "Epoch   4 Batch   10/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.912, Loss:  0.048\n",
+      "Epoch   4 Batch   11/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.919, Loss:  0.058\n",
+      "Epoch   4 Batch   12/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.922, Loss:  0.046\n",
+      "Epoch   4 Batch   13/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.058\n",
+      "Epoch   4 Batch   14/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.039\n",
+      "Epoch   4 Batch   15/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.917, Loss:  0.038\n",
+      "Epoch   4 Batch   16/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.060\n",
+      "Epoch   4 Batch   17/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.924, Loss:  0.044\n",
+      "Epoch   4 Batch   18/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.046\n",
+      "Epoch   4 Batch   19/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.052\n",
+      "Epoch   4 Batch   20/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.037\n",
+      "Epoch   4 Batch   21/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.926, Loss:  0.054\n",
+      "Epoch   4 Batch   22/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.933, Loss:  0.061\n",
+      "Epoch   4 Batch   23/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.048\n",
+      "Epoch   4 Batch   24/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.042\n",
+      "Epoch   4 Batch   25/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.042\n",
+      "Epoch   4 Batch   26/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.916, Loss:  0.056\n",
+      "Epoch   4 Batch   27/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.916, Loss:  0.037\n",
+      "Epoch   4 Batch   28/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.053\n",
+      "Epoch   4 Batch   29/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.052\n",
+      "Epoch   4 Batch   30/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.926, Loss:  0.038\n",
+      "Epoch   4 Batch   31/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.931, Loss:  0.044\n",
+      "Epoch   4 Batch   32/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.934, Loss:  0.048\n",
+      "Epoch   4 Batch   33/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.926, Loss:  0.045\n",
+      "Epoch   4 Batch   34/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.930, Loss:  0.049\n",
+      "Epoch   4 Batch   35/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.926, Loss:  0.042\n",
+      "Epoch   4 Batch   36/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.050\n",
+      "Epoch   4 Batch   37/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.932, Loss:  0.048\n",
+      "Epoch   4 Batch   38/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.936, Loss:  0.060\n",
+      "Epoch   4 Batch   39/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.926, Loss:  0.061\n",
+      "Epoch   4 Batch   40/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.928, Loss:  0.040\n",
+      "Epoch   4 Batch   41/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.038\n",
+      "Epoch   4 Batch   42/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.050\n",
+      "Epoch   4 Batch   43/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.925, Loss:  0.029\n",
+      "Epoch   4 Batch   44/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.926, Loss:  0.030\n",
+      "Epoch   4 Batch   45/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.043\n",
+      "Epoch   4 Batch   46/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.923, Loss:  0.045\n",
+      "Epoch   4 Batch   47/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.924, Loss:  0.048\n",
+      "Epoch   4 Batch   48/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.063\n",
+      "Epoch   4 Batch   49/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.060\n",
+      "Epoch   4 Batch   50/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.920, Loss:  0.047\n",
+      "Epoch   4 Batch   51/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.046\n",
+      "Epoch   4 Batch   52/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.933, Loss:  0.051\n",
+      "Epoch   4 Batch   53/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.935, Loss:  0.042\n",
+      "Epoch   4 Batch   54/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.931, Loss:  0.085\n",
+      "Epoch   4 Batch   55/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.932, Loss:  0.049\n",
+      "Epoch   4 Batch   56/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.042\n",
+      "Epoch   4 Batch   57/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.918, Loss:  0.047\n",
+      "Epoch   4 Batch   58/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.911, Loss:  0.037\n",
+      "Epoch   4 Batch   59/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.908, Loss:  0.038\n",
+      "Epoch   4 Batch   60/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.035\n",
+      "Epoch   4 Batch   61/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.917, Loss:  0.051\n",
+      "Epoch   4 Batch   62/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.913, Loss:  0.045\n",
+      "Epoch   4 Batch   63/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.917, Loss:  0.035\n",
+      "Epoch   4 Batch   64/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.913, Loss:  0.040\n",
+      "Epoch   4 Batch   65/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.051\n",
+      "Epoch   4 Batch   66/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.909, Loss:  0.026\n",
+      "Epoch   4 Batch   67/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.039\n",
+      "Epoch   4 Batch   68/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.909, Loss:  0.056\n",
+      "Epoch   4 Batch   69/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.906, Loss:  0.060\n",
+      "Epoch   4 Batch   70/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.051\n",
+      "Epoch   4 Batch   71/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.914, Loss:  0.028\n",
+      "Epoch   4 Batch   72/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.913, Loss:  0.042\n",
+      "Epoch   4 Batch   73/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.909, Loss:  0.043\n",
+      "Epoch   4 Batch   74/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.049\n",
+      "Epoch   4 Batch   75/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.062\n",
+      "Epoch   4 Batch   76/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.914, Loss:  0.032\n",
+      "Epoch   4 Batch   77/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.912, Loss:  0.043\n",
+      "Epoch   4 Batch   78/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.038\n",
+      "Epoch   4 Batch   79/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.929, Loss:  0.035\n",
+      "Epoch   4 Batch   80/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.045\n",
+      "Epoch   4 Batch   81/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.043\n",
+      "Epoch   4 Batch   82/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.923, Loss:  0.045\n",
+      "Epoch   4 Batch   83/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   4 Batch   84/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   4 Batch   85/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.044\n",
+      "Epoch   4 Batch   86/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.917, Loss:  0.046\n",
+      "Epoch   4 Batch   87/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.908, Loss:  0.054\n",
+      "Epoch   4 Batch   88/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.908, Loss:  0.047\n",
+      "Epoch   4 Batch   89/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.913, Loss:  0.046\n",
+      "Epoch   4 Batch   90/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.044\n",
+      "Epoch   4 Batch   91/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.908, Loss:  0.035\n",
+      "Epoch   4 Batch   92/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.055\n",
+      "Epoch   4 Batch   93/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.918, Loss:  0.043\n",
+      "Epoch   4 Batch   94/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.034\n",
+      "Epoch   4 Batch   95/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.918, Loss:  0.043\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch   96/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.045\n",
+      "Epoch   4 Batch   97/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.050\n",
+      "Epoch   4 Batch   98/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.052\n",
+      "Epoch   4 Batch   99/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.043\n",
+      "Epoch   4 Batch  100/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.048\n",
+      "Epoch   4 Batch  101/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.048\n",
+      "Epoch   4 Batch  102/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.042\n",
+      "Epoch   4 Batch  103/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.940, Loss:  0.049\n",
+      "Epoch   4 Batch  104/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.925, Loss:  0.054\n",
+      "Epoch   4 Batch  105/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.929, Loss:  0.047\n",
+      "Epoch   4 Batch  106/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.053\n",
+      "Epoch   4 Batch  107/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.928, Loss:  0.041\n",
+      "Epoch   4 Batch  108/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.050\n",
+      "Epoch   4 Batch  109/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.931, Loss:  0.048\n",
+      "Epoch   4 Batch  110/1077 - Train Accuracy:  0.976, Validation Accuracy:  0.931, Loss:  0.028\n",
+      "Epoch   4 Batch  111/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.928, Loss:  0.045\n",
+      "Epoch   4 Batch  112/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.929, Loss:  0.041\n",
+      "Epoch   4 Batch  113/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.921, Loss:  0.050\n",
+      "Epoch   4 Batch  114/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.925, Loss:  0.037\n",
+      "Epoch   4 Batch  115/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.043\n",
+      "Epoch   4 Batch  116/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.914, Loss:  0.054\n",
+      "Epoch   4 Batch  117/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.914, Loss:  0.034\n",
+      "Epoch   4 Batch  118/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.916, Loss:  0.041\n",
+      "Epoch   4 Batch  119/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.914, Loss:  0.037\n",
+      "Epoch   4 Batch  120/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.919, Loss:  0.053\n",
+      "Epoch   4 Batch  121/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.919, Loss:  0.045\n",
+      "Epoch   4 Batch  122/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.036\n",
+      "Epoch   4 Batch  123/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.910, Loss:  0.036\n",
+      "Epoch   4 Batch  124/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.052\n",
+      "Epoch   4 Batch  125/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.914, Loss:  0.058\n",
+      "Epoch   4 Batch  126/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.035\n",
+      "Epoch   4 Batch  127/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.916, Loss:  0.048\n",
+      "Epoch   4 Batch  128/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.916, Loss:  0.043\n",
+      "Epoch   4 Batch  129/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.914, Loss:  0.048\n",
+      "Epoch   4 Batch  130/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.040\n",
+      "Epoch   4 Batch  131/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.915, Loss:  0.048\n",
+      "Epoch   4 Batch  132/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.913, Loss:  0.038\n",
+      "Epoch   4 Batch  133/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.918, Loss:  0.037\n",
+      "Epoch   4 Batch  134/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.918, Loss:  0.039\n",
+      "Epoch   4 Batch  135/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.046\n",
+      "Epoch   4 Batch  136/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.914, Loss:  0.041\n",
+      "Epoch   4 Batch  137/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.913, Loss:  0.036\n",
+      "Epoch   4 Batch  138/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.906, Loss:  0.043\n",
+      "Epoch   4 Batch  139/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.906, Loss:  0.052\n",
+      "Epoch   4 Batch  140/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.906, Loss:  0.047\n",
+      "Epoch   4 Batch  141/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.908, Loss:  0.038\n",
+      "Epoch   4 Batch  142/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.913, Loss:  0.037\n",
+      "Epoch   4 Batch  143/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.041\n",
+      "Epoch   4 Batch  144/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.906, Loss:  0.059\n",
+      "Epoch   4 Batch  145/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.907, Loss:  0.052\n",
+      "Epoch   4 Batch  146/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.906, Loss:  0.078\n",
+      "Epoch   4 Batch  147/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.906, Loss:  0.046\n",
+      "Epoch   4 Batch  148/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.906, Loss:  0.050\n",
+      "Epoch   4 Batch  149/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.903, Loss:  0.043\n",
+      "Epoch   4 Batch  150/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.909, Loss:  0.049\n",
+      "Epoch   4 Batch  151/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.909, Loss:  0.041\n",
+      "Epoch   4 Batch  152/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.070\n",
+      "Epoch   4 Batch  153/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.907, Loss:  0.067\n",
+      "Epoch   4 Batch  154/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.911, Loss:  0.044\n",
+      "Epoch   4 Batch  155/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.915, Loss:  0.042\n",
+      "Epoch   4 Batch  156/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.039\n",
+      "Epoch   4 Batch  157/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.915, Loss:  0.043\n",
+      "Epoch   4 Batch  158/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.066\n",
+      "Epoch   4 Batch  159/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.917, Loss:  0.039\n",
+      "Epoch   4 Batch  160/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.046\n",
+      "Epoch   4 Batch  161/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.926, Loss:  0.042\n",
+      "Epoch   4 Batch  162/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.054\n",
+      "Epoch   4 Batch  163/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.060\n",
+      "Epoch   4 Batch  164/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.927, Loss:  0.045\n",
+      "Epoch   4 Batch  165/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.044\n",
+      "Epoch   4 Batch  166/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.060\n",
+      "Epoch   4 Batch  167/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.931, Loss:  0.047\n",
+      "Epoch   4 Batch  168/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.929, Loss:  0.062\n",
+      "Epoch   4 Batch  169/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.929, Loss:  0.059\n",
+      "Epoch   4 Batch  170/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.054\n",
+      "Epoch   4 Batch  171/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.917, Loss:  0.055\n",
+      "Epoch   4 Batch  172/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.917, Loss:  0.044\n",
+      "Epoch   4 Batch  173/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   4 Batch  174/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.922, Loss:  0.042\n",
+      "Epoch   4 Batch  175/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.921, Loss:  0.049\n",
+      "Epoch   4 Batch  176/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.922, Loss:  0.057\n",
+      "Epoch   4 Batch  177/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.920, Loss:  0.060\n",
+      "Epoch   4 Batch  178/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.048\n",
+      "Epoch   4 Batch  179/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.924, Loss:  0.058\n",
+      "Epoch   4 Batch  180/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.043\n",
+      "Epoch   4 Batch  181/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.920, Loss:  0.058\n",
+      "Epoch   4 Batch  182/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.057\n",
+      "Epoch   4 Batch  183/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.055\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch  184/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.904, Loss:  0.050\n",
+      "Epoch   4 Batch  185/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.911, Loss:  0.060\n",
+      "Epoch   4 Batch  186/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.909, Loss:  0.053\n",
+      "Epoch   4 Batch  187/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.908, Loss:  0.038\n",
+      "Epoch   4 Batch  188/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.908, Loss:  0.049\n",
+      "Epoch   4 Batch  189/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.909, Loss:  0.040\n",
+      "Epoch   4 Batch  190/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.916, Loss:  0.048\n",
+      "Epoch   4 Batch  191/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.924, Loss:  0.039\n",
+      "Epoch   4 Batch  192/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.925, Loss:  0.049\n",
+      "Epoch   4 Batch  193/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.931, Loss:  0.043\n",
+      "Epoch   4 Batch  194/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.036\n",
+      "Epoch   4 Batch  195/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.930, Loss:  0.039\n",
+      "Epoch   4 Batch  196/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.935, Loss:  0.039\n",
+      "Epoch   4 Batch  197/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.933, Loss:  0.059\n",
+      "Epoch   4 Batch  198/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.932, Loss:  0.048\n",
+      "Epoch   4 Batch  199/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.933, Loss:  0.051\n",
+      "Epoch   4 Batch  200/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.933, Loss:  0.057\n",
+      "Epoch   4 Batch  201/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.938, Loss:  0.040\n",
+      "Epoch   4 Batch  202/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.934, Loss:  0.045\n",
+      "Epoch   4 Batch  203/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.933, Loss:  0.046\n",
+      "Epoch   4 Batch  204/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.069\n",
+      "Epoch   4 Batch  205/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.930, Loss:  0.069\n",
+      "Epoch   4 Batch  206/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.928, Loss:  0.045\n",
+      "Epoch   4 Batch  207/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.047\n",
+      "Epoch   4 Batch  208/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.053\n",
+      "Epoch   4 Batch  209/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.916, Loss:  0.042\n",
+      "Epoch   4 Batch  210/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.918, Loss:  0.047\n",
+      "Epoch   4 Batch  211/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.915, Loss:  0.049\n",
+      "Epoch   4 Batch  212/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.917, Loss:  0.036\n",
+      "Epoch   4 Batch  213/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.040\n",
+      "Epoch   4 Batch  214/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.044\n",
+      "Epoch   4 Batch  215/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.918, Loss:  0.060\n",
+      "Epoch   4 Batch  216/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.058\n",
+      "Epoch   4 Batch  217/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.912, Loss:  0.042\n",
+      "Epoch   4 Batch  218/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.924, Loss:  0.061\n",
+      "Epoch   4 Batch  219/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.042\n",
+      "Epoch   4 Batch  220/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.043\n",
+      "Epoch   4 Batch  221/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.056\n",
+      "Epoch   4 Batch  222/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.925, Loss:  0.052\n",
+      "Epoch   4 Batch  223/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.931, Loss:  0.049\n",
+      "Epoch   4 Batch  224/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.046\n",
+      "Epoch   4 Batch  225/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.922, Loss:  0.062\n",
+      "Epoch   4 Batch  226/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.041\n",
+      "Epoch   4 Batch  227/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.922, Loss:  0.056\n",
+      "Epoch   4 Batch  228/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.917, Loss:  0.047\n",
+      "Epoch   4 Batch  229/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.917, Loss:  0.053\n",
+      "Epoch   4 Batch  230/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.917, Loss:  0.053\n",
+      "Epoch   4 Batch  231/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.050\n",
+      "Epoch   4 Batch  232/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.919, Loss:  0.041\n",
+      "Epoch   4 Batch  233/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.915, Loss:  0.063\n",
+      "Epoch   4 Batch  234/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.052\n",
+      "Epoch   4 Batch  235/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.047\n",
+      "Epoch   4 Batch  236/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.912, Loss:  0.064\n",
+      "Epoch   4 Batch  237/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.912, Loss:  0.055\n",
+      "Epoch   4 Batch  238/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.903, Loss:  0.047\n",
+      "Epoch   4 Batch  239/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.923, Loss:  0.036\n",
+      "Epoch   4 Batch  240/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.920, Loss:  0.043\n",
+      "Epoch   4 Batch  241/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.922, Loss:  0.036\n",
+      "Epoch   4 Batch  242/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.922, Loss:  0.049\n",
+      "Epoch   4 Batch  243/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.052\n",
+      "Epoch   4 Batch  244/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.047\n",
+      "Epoch   4 Batch  245/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.037\n",
+      "Epoch   4 Batch  246/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.046\n",
+      "Epoch   4 Batch  247/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.053\n",
+      "Epoch   4 Batch  248/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.052\n",
+      "Epoch   4 Batch  249/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.922, Loss:  0.046\n",
+      "Epoch   4 Batch  250/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.038\n",
+      "Epoch   4 Batch  251/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.055\n",
+      "Epoch   4 Batch  252/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.907, Loss:  0.061\n",
+      "Epoch   4 Batch  253/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.903, Loss:  0.048\n",
+      "Epoch   4 Batch  254/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.900, Loss:  0.062\n",
+      "Epoch   4 Batch  255/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.905, Loss:  0.045\n",
+      "Epoch   4 Batch  256/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.902, Loss:  0.058\n",
+      "Epoch   4 Batch  257/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.898, Loss:  0.050\n",
+      "Epoch   4 Batch  258/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.898, Loss:  0.057\n",
+      "Epoch   4 Batch  259/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.896, Loss:  0.041\n",
+      "Epoch   4 Batch  260/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.906, Loss:  0.036\n",
+      "Epoch   4 Batch  261/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.905, Loss:  0.066\n",
+      "Epoch   4 Batch  262/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.896, Loss:  0.040\n",
+      "Epoch   4 Batch  263/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.042\n",
+      "Epoch   4 Batch  264/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.910, Loss:  0.052\n",
+      "Epoch   4 Batch  265/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.043\n",
+      "Epoch   4 Batch  266/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.924, Loss:  0.048\n",
+      "Epoch   4 Batch  267/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.036\n",
+      "Epoch   4 Batch  268/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.931, Loss:  0.058\n",
+      "Epoch   4 Batch  269/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.932, Loss:  0.062\n",
+      "Epoch   4 Batch  270/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.050\n",
+      "Epoch   4 Batch  271/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.049\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch  272/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.932, Loss:  0.080\n",
+      "Epoch   4 Batch  273/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.941, Loss:  0.046\n",
+      "Epoch   4 Batch  274/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.936, Loss:  0.057\n",
+      "Epoch   4 Batch  275/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.936, Loss:  0.050\n",
+      "Epoch   4 Batch  276/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.924, Loss:  0.080\n",
+      "Epoch   4 Batch  277/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.926, Loss:  0.038\n",
+      "Epoch   4 Batch  278/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.926, Loss:  0.067\n",
+      "Epoch   4 Batch  279/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.063\n",
+      "Epoch   4 Batch  280/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.058\n",
+      "Epoch   4 Batch  281/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.926, Loss:  0.059\n",
+      "Epoch   4 Batch  282/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.935, Loss:  0.074\n",
+      "Epoch   4 Batch  283/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.063\n",
+      "Epoch   4 Batch  284/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.940, Loss:  0.053\n",
+      "Epoch   4 Batch  285/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.056\n",
+      "Epoch   4 Batch  286/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.043\n",
+      "Epoch   4 Batch  287/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.045\n",
+      "Epoch   4 Batch  288/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.925, Loss:  0.061\n",
+      "Epoch   4 Batch  289/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   4 Batch  290/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.071\n",
+      "Epoch   4 Batch  291/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.070\n",
+      "Epoch   4 Batch  292/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.052\n",
+      "Epoch   4 Batch  293/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.931, Loss:  0.052\n",
+      "Epoch   4 Batch  294/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.926, Loss:  0.049\n",
+      "Epoch   4 Batch  295/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.921, Loss:  0.069\n",
+      "Epoch   4 Batch  296/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.915, Loss:  0.048\n",
+      "Epoch   4 Batch  297/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.058\n",
+      "Epoch   4 Batch  298/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.912, Loss:  0.072\n",
+      "Epoch   4 Batch  299/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.062\n",
+      "Epoch   4 Batch  300/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.044\n",
+      "Epoch   4 Batch  301/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.044\n",
+      "Epoch   4 Batch  302/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.918, Loss:  0.045\n",
+      "Epoch   4 Batch  303/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.913, Loss:  0.054\n",
+      "Epoch   4 Batch  304/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.922, Loss:  0.052\n",
+      "Epoch   4 Batch  305/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.923, Loss:  0.046\n",
+      "Epoch   4 Batch  306/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.059\n",
+      "Epoch   4 Batch  307/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.043\n",
+      "Epoch   4 Batch  308/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.052\n",
+      "Epoch   4 Batch  309/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.923, Loss:  0.041\n",
+      "Epoch   4 Batch  310/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.926, Loss:  0.050\n",
+      "Epoch   4 Batch  311/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.930, Loss:  0.047\n",
+      "Epoch   4 Batch  312/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.931, Loss:  0.062\n",
+      "Epoch   4 Batch  313/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.930, Loss:  0.035\n",
+      "Epoch   4 Batch  314/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.045\n",
+      "Epoch   4 Batch  315/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.934, Loss:  0.039\n",
+      "Epoch   4 Batch  316/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.934, Loss:  0.051\n",
+      "Epoch   4 Batch  317/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.930, Loss:  0.056\n",
+      "Epoch   4 Batch  318/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.931, Loss:  0.036\n",
+      "Epoch   4 Batch  319/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.071\n",
+      "Epoch   4 Batch  320/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.929, Loss:  0.058\n",
+      "Epoch   4 Batch  321/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.043\n",
+      "Epoch   4 Batch  322/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.933, Loss:  0.040\n",
+      "Epoch   4 Batch  323/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.929, Loss:  0.044\n",
+      "Epoch   4 Batch  324/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.929, Loss:  0.046\n",
+      "Epoch   4 Batch  325/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.044\n",
+      "Epoch   4 Batch  326/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.042\n",
+      "Epoch   4 Batch  327/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.933, Loss:  0.056\n",
+      "Epoch   4 Batch  328/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.062\n",
+      "Epoch   4 Batch  329/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.060\n",
+      "Epoch   4 Batch  330/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.921, Loss:  0.048\n",
+      "Epoch   4 Batch  331/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.052\n",
+      "Epoch   4 Batch  332/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.038\n",
+      "Epoch   4 Batch  333/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.913, Loss:  0.042\n",
+      "Epoch   4 Batch  334/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.907, Loss:  0.049\n",
+      "Epoch   4 Batch  335/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.900, Loss:  0.050\n",
+      "Epoch   4 Batch  336/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.909, Loss:  0.080\n",
+      "Epoch   4 Batch  337/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.914, Loss:  0.050\n",
+      "Epoch   4 Batch  338/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.918, Loss:  0.070\n",
+      "Epoch   4 Batch  339/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.041\n",
+      "Epoch   4 Batch  340/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.925, Loss:  0.041\n",
+      "Epoch   4 Batch  341/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.933, Loss:  0.064\n",
+      "Epoch   4 Batch  342/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.933, Loss:  0.051\n",
+      "Epoch   4 Batch  343/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.930, Loss:  0.050\n",
+      "Epoch   4 Batch  344/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.929, Loss:  0.051\n",
+      "Epoch   4 Batch  345/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.930, Loss:  0.034\n",
+      "Epoch   4 Batch  346/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.929, Loss:  0.052\n",
+      "Epoch   4 Batch  347/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.924, Loss:  0.033\n",
+      "Epoch   4 Batch  348/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.918, Loss:  0.049\n",
+      "Epoch   4 Batch  349/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.916, Loss:  0.048\n",
+      "Epoch   4 Batch  350/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.911, Loss:  0.050\n",
+      "Epoch   4 Batch  351/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.905, Loss:  0.046\n",
+      "Epoch   4 Batch  352/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.903, Loss:  0.043\n",
+      "Epoch   4 Batch  353/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.903, Loss:  0.056\n",
+      "Epoch   4 Batch  354/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.067\n",
+      "Epoch   4 Batch  355/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   4 Batch  356/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.922, Loss:  0.056\n",
+      "Epoch   4 Batch  357/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.042\n",
+      "Epoch   4 Batch  358/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.054\n",
+      "Epoch   4 Batch  359/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.916, Loss:  0.043\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch  360/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.039\n",
+      "Epoch   4 Batch  361/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.919, Loss:  0.055\n",
+      "Epoch   4 Batch  362/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.924, Loss:  0.051\n",
+      "Epoch   4 Batch  363/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.928, Loss:  0.059\n",
+      "Epoch   4 Batch  364/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.923, Loss:  0.057\n",
+      "Epoch   4 Batch  365/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.044\n",
+      "Epoch   4 Batch  366/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.925, Loss:  0.044\n",
+      "Epoch   4 Batch  367/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.915, Loss:  0.040\n",
+      "Epoch   4 Batch  368/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.913, Loss:  0.051\n",
+      "Epoch   4 Batch  369/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.909, Loss:  0.048\n",
+      "Epoch   4 Batch  370/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.918, Loss:  0.054\n",
+      "Epoch   4 Batch  371/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.914, Loss:  0.040\n",
+      "Epoch   4 Batch  372/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.919, Loss:  0.041\n",
+      "Epoch   4 Batch  373/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.919, Loss:  0.036\n",
+      "Epoch   4 Batch  374/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.059\n",
+      "Epoch   4 Batch  375/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.049\n",
+      "Epoch   4 Batch  376/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.049\n",
+      "Epoch   4 Batch  377/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.926, Loss:  0.052\n",
+      "Epoch   4 Batch  378/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.931, Loss:  0.041\n",
+      "Epoch   4 Batch  379/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.933, Loss:  0.062\n",
+      "Epoch   4 Batch  380/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.933, Loss:  0.041\n",
+      "Epoch   4 Batch  381/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.057\n",
+      "Epoch   4 Batch  382/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.068\n",
+      "Epoch   4 Batch  383/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.931, Loss:  0.046\n",
+      "Epoch   4 Batch  384/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.041\n",
+      "Epoch   4 Batch  385/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.924, Loss:  0.047\n",
+      "Epoch   4 Batch  386/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.925, Loss:  0.055\n",
+      "Epoch   4 Batch  387/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.044\n",
+      "Epoch   4 Batch  388/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.937, Loss:  0.059\n",
+      "Epoch   4 Batch  389/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.932, Loss:  0.049\n",
+      "Epoch   4 Batch  390/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.929, Loss:  0.064\n",
+      "Epoch   4 Batch  391/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.059\n",
+      "Epoch   4 Batch  392/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.929, Loss:  0.050\n",
+      "Epoch   4 Batch  393/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.923, Loss:  0.044\n",
+      "Epoch   4 Batch  394/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.924, Loss:  0.042\n",
+      "Epoch   4 Batch  395/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.049\n",
+      "Epoch   4 Batch  396/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.053\n",
+      "Epoch   4 Batch  397/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.923, Loss:  0.051\n",
+      "Epoch   4 Batch  398/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.055\n",
+      "Epoch   4 Batch  399/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.915, Loss:  0.048\n",
+      "Epoch   4 Batch  400/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.066\n",
+      "Epoch   4 Batch  401/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.918, Loss:  0.041\n",
+      "Epoch   4 Batch  402/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.918, Loss:  0.042\n",
+      "Epoch   4 Batch  403/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.919, Loss:  0.077\n",
+      "Epoch   4 Batch  404/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.915, Loss:  0.046\n",
+      "Epoch   4 Batch  405/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.055\n",
+      "Epoch   4 Batch  406/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.913, Loss:  0.049\n",
+      "Epoch   4 Batch  407/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.904, Loss:  0.073\n",
+      "Epoch   4 Batch  408/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.904, Loss:  0.048\n",
+      "Epoch   4 Batch  409/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.906, Loss:  0.062\n",
+      "Epoch   4 Batch  410/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.061\n",
+      "Epoch   4 Batch  411/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.914, Loss:  0.060\n",
+      "Epoch   4 Batch  412/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.049\n",
+      "Epoch   4 Batch  413/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.049\n",
+      "Epoch   4 Batch  414/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.914, Loss:  0.049\n",
+      "Epoch   4 Batch  415/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.916, Loss:  0.058\n",
+      "Epoch   4 Batch  416/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.916, Loss:  0.042\n",
+      "Epoch   4 Batch  417/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.920, Loss:  0.085\n",
+      "Epoch   4 Batch  418/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.051\n",
+      "Epoch   4 Batch  419/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.921, Loss:  0.052\n",
+      "Epoch   4 Batch  420/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.920, Loss:  0.041\n",
+      "Epoch   4 Batch  421/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.920, Loss:  0.062\n",
+      "Epoch   4 Batch  422/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.918, Loss:  0.044\n",
+      "Epoch   4 Batch  423/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.070\n",
+      "Epoch   4 Batch  424/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.054\n",
+      "Epoch   4 Batch  425/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.039\n",
+      "Epoch   4 Batch  426/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.922, Loss:  0.055\n",
+      "Epoch   4 Batch  427/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.040\n",
+      "Epoch   4 Batch  428/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.919, Loss:  0.036\n",
+      "Epoch   4 Batch  429/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.917, Loss:  0.043\n",
+      "Epoch   4 Batch  430/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.912, Loss:  0.043\n",
+      "Epoch   4 Batch  431/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.920, Loss:  0.047\n",
+      "Epoch   4 Batch  432/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.055\n",
+      "Epoch   4 Batch  433/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.049\n",
+      "Epoch   4 Batch  434/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.036\n",
+      "Epoch   4 Batch  435/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.909, Loss:  0.059\n",
+      "Epoch   4 Batch  436/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.909, Loss:  0.041\n",
+      "Epoch   4 Batch  437/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.914, Loss:  0.040\n",
+      "Epoch   4 Batch  438/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.914, Loss:  0.040\n",
+      "Epoch   4 Batch  439/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.061\n",
+      "Epoch   4 Batch  440/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.913, Loss:  0.059\n",
+      "Epoch   4 Batch  441/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.912, Loss:  0.048\n",
+      "Epoch   4 Batch  442/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.911, Loss:  0.060\n",
+      "Epoch   4 Batch  443/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.913, Loss:  0.041\n",
+      "Epoch   4 Batch  444/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.916, Loss:  0.047\n",
+      "Epoch   4 Batch  445/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.912, Loss:  0.055\n",
+      "Epoch   4 Batch  446/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.907, Loss:  0.038\n",
+      "Epoch   4 Batch  447/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.907, Loss:  0.047\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch  448/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.909, Loss:  0.067\n",
+      "Epoch   4 Batch  449/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.905, Loss:  0.066\n",
+      "Epoch   4 Batch  450/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.916, Loss:  0.045\n",
+      "Epoch   4 Batch  451/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.910, Loss:  0.053\n",
+      "Epoch   4 Batch  452/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.911, Loss:  0.054\n",
+      "Epoch   4 Batch  453/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.907, Loss:  0.049\n",
+      "Epoch   4 Batch  454/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.057\n",
+      "Epoch   4 Batch  455/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.910, Loss:  0.064\n",
+      "Epoch   4 Batch  456/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.916, Loss:  0.061\n",
+      "Epoch   4 Batch  457/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.912, Loss:  0.050\n",
+      "Epoch   4 Batch  458/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.910, Loss:  0.068\n",
+      "Epoch   4 Batch  459/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.912, Loss:  0.054\n",
+      "Epoch   4 Batch  460/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.907, Loss:  0.060\n",
+      "Epoch   4 Batch  461/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.908, Loss:  0.048\n",
+      "Epoch   4 Batch  462/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.906, Loss:  0.053\n",
+      "Epoch   4 Batch  463/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.058\n",
+      "Epoch   4 Batch  464/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.906, Loss:  0.056\n",
+      "Epoch   4 Batch  465/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.905, Loss:  0.062\n",
+      "Epoch   4 Batch  466/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.056\n",
+      "Epoch   4 Batch  467/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.049\n",
+      "Epoch   4 Batch  468/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.062\n",
+      "Epoch   4 Batch  469/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.051\n",
+      "Epoch   4 Batch  470/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.913, Loss:  0.054\n",
+      "Epoch   4 Batch  471/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.912, Loss:  0.037\n",
+      "Epoch   4 Batch  472/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.915, Loss:  0.053\n",
+      "Epoch   4 Batch  473/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.054\n",
+      "Epoch   4 Batch  474/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.053\n",
+      "Epoch   4 Batch  475/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.051\n",
+      "Epoch   4 Batch  476/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.922, Loss:  0.040\n",
+      "Epoch   4 Batch  477/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.923, Loss:  0.053\n",
+      "Epoch   4 Batch  478/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.915, Loss:  0.048\n",
+      "Epoch   4 Batch  479/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.920, Loss:  0.071\n",
+      "Epoch   4 Batch  480/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.041\n",
+      "Epoch   4 Batch  481/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.930, Loss:  0.060\n",
+      "Epoch   4 Batch  482/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.061\n",
+      "Epoch   4 Batch  483/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.925, Loss:  0.053\n",
+      "Epoch   4 Batch  484/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.926, Loss:  0.059\n",
+      "Epoch   4 Batch  485/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.925, Loss:  0.068\n",
+      "Epoch   4 Batch  486/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.920, Loss:  0.045\n",
+      "Epoch   4 Batch  487/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.042\n",
+      "Epoch   4 Batch  488/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.915, Loss:  0.061\n",
+      "Epoch   4 Batch  489/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.043\n",
+      "Epoch   4 Batch  490/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.053\n",
+      "Epoch   4 Batch  491/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.914, Loss:  0.067\n",
+      "Epoch   4 Batch  492/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.060\n",
+      "Epoch   4 Batch  493/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.915, Loss:  0.047\n",
+      "Epoch   4 Batch  494/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.051\n",
+      "Epoch   4 Batch  495/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.911, Loss:  0.054\n",
+      "Epoch   4 Batch  496/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.911, Loss:  0.071\n",
+      "Epoch   4 Batch  497/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.904, Loss:  0.053\n",
+      "Epoch   4 Batch  498/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.921, Loss:  0.060\n",
+      "Epoch   4 Batch  499/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.923, Loss:  0.055\n",
+      "Epoch   4 Batch  500/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.927, Loss:  0.054\n",
+      "Epoch   4 Batch  501/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.053\n",
+      "Epoch   4 Batch  502/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.935, Loss:  0.049\n",
+      "Epoch   4 Batch  503/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.939, Loss:  0.052\n",
+      "Epoch   4 Batch  504/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.938, Loss:  0.055\n",
+      "Epoch   4 Batch  505/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.932, Loss:  0.046\n",
+      "Epoch   4 Batch  506/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.931, Loss:  0.084\n",
+      "Epoch   4 Batch  507/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.931, Loss:  0.064\n",
+      "Epoch   4 Batch  508/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.047\n",
+      "Epoch   4 Batch  509/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.923, Loss:  0.076\n",
+      "Epoch   4 Batch  510/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.061\n",
+      "Epoch   4 Batch  511/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.049\n",
+      "Epoch   4 Batch  512/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.908, Loss:  0.047\n",
+      "Epoch   4 Batch  513/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.915, Loss:  0.054\n",
+      "Epoch   4 Batch  514/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.063\n",
+      "Epoch   4 Batch  515/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.915, Loss:  0.055\n",
+      "Epoch   4 Batch  516/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.906, Loss:  0.056\n",
+      "Epoch   4 Batch  517/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.913, Loss:  0.060\n",
+      "Epoch   4 Batch  518/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.912, Loss:  0.069\n",
+      "Epoch   4 Batch  519/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.916, Loss:  0.062\n",
+      "Epoch   4 Batch  520/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.920, Loss:  0.049\n",
+      "Epoch   4 Batch  521/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.920, Loss:  0.060\n",
+      "Epoch   4 Batch  522/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.918, Loss:  0.069\n",
+      "Epoch   4 Batch  523/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.922, Loss:  0.059\n",
+      "Epoch   4 Batch  524/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.917, Loss:  0.068\n",
+      "Epoch   4 Batch  525/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.062\n",
+      "Epoch   4 Batch  526/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.909, Loss:  0.052\n",
+      "Epoch   4 Batch  527/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.909, Loss:  0.060\n",
+      "Epoch   4 Batch  528/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.914, Loss:  0.055\n",
+      "Epoch   4 Batch  529/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.914, Loss:  0.058\n",
+      "Epoch   4 Batch  530/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.070\n",
+      "Epoch   4 Batch  531/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.910, Loss:  0.055\n",
+      "Epoch   4 Batch  532/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.910, Loss:  0.072\n",
+      "Epoch   4 Batch  533/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.898, Loss:  0.056\n",
+      "Epoch   4 Batch  534/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.898, Loss:  0.059\n",
+      "Epoch   4 Batch  535/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.061\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch  536/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.911, Loss:  0.057\n",
+      "Epoch   4 Batch  537/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.922, Loss:  0.041\n",
+      "Epoch   4 Batch  538/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.921, Loss:  0.041\n",
+      "Epoch   4 Batch  539/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.922, Loss:  0.070\n",
+      "Epoch   4 Batch  540/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.918, Loss:  0.052\n",
+      "Epoch   4 Batch  541/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.917, Loss:  0.061\n",
+      "Epoch   4 Batch  542/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.056\n",
+      "Epoch   4 Batch  543/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.056\n",
+      "Epoch   4 Batch  544/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.051\n",
+      "Epoch   4 Batch  545/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.908, Loss:  0.056\n",
+      "Epoch   4 Batch  546/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.904, Loss:  0.056\n",
+      "Epoch   4 Batch  547/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.911, Loss:  0.041\n",
+      "Epoch   4 Batch  548/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.064\n",
+      "Epoch   4 Batch  549/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.910, Loss:  0.080\n",
+      "Epoch   4 Batch  550/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.055\n",
+      "Epoch   4 Batch  551/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.912, Loss:  0.068\n",
+      "Epoch   4 Batch  552/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.058\n",
+      "Epoch   4 Batch  553/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.919, Loss:  0.068\n",
+      "Epoch   4 Batch  554/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.055\n",
+      "Epoch   4 Batch  555/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.922, Loss:  0.043\n",
+      "Epoch   4 Batch  556/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.044\n",
+      "Epoch   4 Batch  557/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.922, Loss:  0.047\n",
+      "Epoch   4 Batch  558/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.928, Loss:  0.046\n",
+      "Epoch   4 Batch  559/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.927, Loss:  0.050\n",
+      "Epoch   4 Batch  560/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.055\n",
+      "Epoch   4 Batch  561/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.925, Loss:  0.047\n",
+      "Epoch   4 Batch  562/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.055\n",
+      "Epoch   4 Batch  563/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.054\n",
+      "Epoch   4 Batch  564/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.921, Loss:  0.056\n",
+      "Epoch   4 Batch  565/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.925, Loss:  0.066\n",
+      "Epoch   4 Batch  566/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.055\n",
+      "Epoch   4 Batch  567/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.929, Loss:  0.057\n",
+      "Epoch   4 Batch  568/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.045\n",
+      "Epoch   4 Batch  569/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.926, Loss:  0.071\n",
+      "Epoch   4 Batch  570/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.926, Loss:  0.057\n",
+      "Epoch   4 Batch  571/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.053\n",
+      "Epoch   4 Batch  572/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.926, Loss:  0.044\n",
+      "Epoch   4 Batch  573/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.929, Loss:  0.065\n",
+      "Epoch   4 Batch  574/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.922, Loss:  0.056\n",
+      "Epoch   4 Batch  575/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.033\n",
+      "Epoch   4 Batch  576/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.931, Loss:  0.069\n",
+      "Epoch   4 Batch  577/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.055\n",
+      "Epoch   4 Batch  578/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.930, Loss:  0.051\n",
+      "Epoch   4 Batch  579/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.938, Loss:  0.054\n",
+      "Epoch   4 Batch  580/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.942, Loss:  0.044\n",
+      "Epoch   4 Batch  581/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.939, Loss:  0.048\n",
+      "Epoch   4 Batch  582/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.938, Loss:  0.063\n",
+      "Epoch   4 Batch  583/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.056\n",
+      "Epoch   4 Batch  584/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.060\n",
+      "Epoch   4 Batch  585/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.041\n",
+      "Epoch   4 Batch  586/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.056\n",
+      "Epoch   4 Batch  587/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.925, Loss:  0.066\n",
+      "Epoch   4 Batch  588/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.927, Loss:  0.053\n",
+      "Epoch   4 Batch  589/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.934, Loss:  0.059\n",
+      "Epoch   4 Batch  590/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.927, Loss:  0.072\n",
+      "Epoch   4 Batch  591/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.055\n",
+      "Epoch   4 Batch  592/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.928, Loss:  0.062\n",
+      "Epoch   4 Batch  593/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.923, Loss:  0.084\n",
+      "Epoch   4 Batch  594/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.922, Loss:  0.067\n",
+      "Epoch   4 Batch  595/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.931, Loss:  0.058\n",
+      "Epoch   4 Batch  596/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.927, Loss:  0.059\n",
+      "Epoch   4 Batch  597/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.927, Loss:  0.061\n",
+      "Epoch   4 Batch  598/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.934, Loss:  0.062\n",
+      "Epoch   4 Batch  599/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.934, Loss:  0.080\n",
+      "Epoch   4 Batch  600/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.932, Loss:  0.060\n",
+      "Epoch   4 Batch  601/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.931, Loss:  0.067\n",
+      "Epoch   4 Batch  602/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.936, Loss:  0.056\n",
+      "Epoch   4 Batch  603/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.935, Loss:  0.046\n",
+      "Epoch   4 Batch  604/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.072\n",
+      "Epoch   4 Batch  605/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.940, Loss:  0.072\n",
+      "Epoch   4 Batch  606/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.935, Loss:  0.043\n",
+      "Epoch   4 Batch  607/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.939, Loss:  0.052\n",
+      "Epoch   4 Batch  608/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.064\n",
+      "Epoch   4 Batch  609/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.938, Loss:  0.052\n",
+      "Epoch   4 Batch  610/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.061\n",
+      "Epoch   4 Batch  611/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.935, Loss:  0.052\n",
+      "Epoch   4 Batch  612/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.931, Loss:  0.042\n",
+      "Epoch   4 Batch  613/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.935, Loss:  0.062\n",
+      "Epoch   4 Batch  614/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.935, Loss:  0.046\n",
+      "Epoch   4 Batch  615/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.937, Loss:  0.048\n",
+      "Epoch   4 Batch  616/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.044\n",
+      "Epoch   4 Batch  617/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.937, Loss:  0.048\n",
+      "Epoch   4 Batch  618/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.938, Loss:  0.054\n",
+      "Epoch   4 Batch  619/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.948, Loss:  0.043\n",
+      "Epoch   4 Batch  620/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.944, Loss:  0.047\n",
+      "Epoch   4 Batch  621/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.939, Loss:  0.056\n",
+      "Epoch   4 Batch  622/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.939, Loss:  0.058\n",
+      "Epoch   4 Batch  623/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.937, Loss:  0.071\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch  624/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.053\n",
+      "Epoch   4 Batch  625/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.047\n",
+      "Epoch   4 Batch  626/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.934, Loss:  0.057\n",
+      "Epoch   4 Batch  627/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.934, Loss:  0.049\n",
+      "Epoch   4 Batch  628/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.931, Loss:  0.054\n",
+      "Epoch   4 Batch  629/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.931, Loss:  0.064\n",
+      "Epoch   4 Batch  630/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.052\n",
+      "Epoch   4 Batch  631/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.942, Loss:  0.050\n",
+      "Epoch   4 Batch  632/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.946, Loss:  0.043\n",
+      "Epoch   4 Batch  633/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.941, Loss:  0.048\n",
+      "Epoch   4 Batch  634/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.936, Loss:  0.043\n",
+      "Epoch   4 Batch  635/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.941, Loss:  0.059\n",
+      "Epoch   4 Batch  636/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.938, Loss:  0.042\n",
+      "Epoch   4 Batch  637/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.934, Loss:  0.051\n",
+      "Epoch   4 Batch  638/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.934, Loss:  0.056\n",
+      "Epoch   4 Batch  639/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.935, Loss:  0.075\n",
+      "Epoch   4 Batch  640/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.928, Loss:  0.053\n",
+      "Epoch   4 Batch  641/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.927, Loss:  0.042\n",
+      "Epoch   4 Batch  642/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.057\n",
+      "Epoch   4 Batch  643/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.918, Loss:  0.043\n",
+      "Epoch   4 Batch  644/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.913, Loss:  0.048\n",
+      "Epoch   4 Batch  645/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.066\n",
+      "Epoch   4 Batch  646/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.924, Loss:  0.045\n",
+      "Epoch   4 Batch  647/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.056\n",
+      "Epoch   4 Batch  648/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.920, Loss:  0.039\n",
+      "Epoch   4 Batch  649/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.055\n",
+      "Epoch   4 Batch  650/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.055\n",
+      "Epoch   4 Batch  651/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.919, Loss:  0.046\n",
+      "Epoch   4 Batch  652/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.919, Loss:  0.058\n",
+      "Epoch   4 Batch  653/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.060\n",
+      "Epoch   4 Batch  654/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.919, Loss:  0.048\n",
+      "Epoch   4 Batch  655/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.058\n",
+      "Epoch   4 Batch  656/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.920, Loss:  0.048\n",
+      "Epoch   4 Batch  657/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.924, Loss:  0.052\n",
+      "Epoch   4 Batch  658/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.916, Loss:  0.037\n",
+      "Epoch   4 Batch  659/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.047\n",
+      "Epoch   4 Batch  660/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.914, Loss:  0.044\n",
+      "Epoch   4 Batch  661/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.914, Loss:  0.046\n",
+      "Epoch   4 Batch  662/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.919, Loss:  0.048\n",
+      "Epoch   4 Batch  663/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.039\n",
+      "Epoch   4 Batch  664/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.055\n",
+      "Epoch   4 Batch  665/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.928, Loss:  0.039\n",
+      "Epoch   4 Batch  666/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.065\n",
+      "Epoch   4 Batch  667/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.063\n",
+      "Epoch   4 Batch  668/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.923, Loss:  0.053\n",
+      "Epoch   4 Batch  669/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.043\n",
+      "Epoch   4 Batch  670/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.055\n",
+      "Epoch   4 Batch  671/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.052\n",
+      "Epoch   4 Batch  672/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.045\n",
+      "Epoch   4 Batch  673/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.926, Loss:  0.049\n",
+      "Epoch   4 Batch  674/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.929, Loss:  0.039\n",
+      "Epoch   4 Batch  675/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.926, Loss:  0.060\n",
+      "Epoch   4 Batch  676/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.921, Loss:  0.042\n",
+      "Epoch   4 Batch  677/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.924, Loss:  0.061\n",
+      "Epoch   4 Batch  678/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.925, Loss:  0.035\n",
+      "Epoch   4 Batch  679/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.049\n",
+      "Epoch   4 Batch  680/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.926, Loss:  0.058\n",
+      "Epoch   4 Batch  681/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.929, Loss:  0.054\n",
+      "Epoch   4 Batch  682/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.050\n",
+      "Epoch   4 Batch  683/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.929, Loss:  0.043\n",
+      "Epoch   4 Batch  684/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.928, Loss:  0.054\n",
+      "Epoch   4 Batch  685/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.925, Loss:  0.065\n",
+      "Epoch   4 Batch  686/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.926, Loss:  0.044\n",
+      "Epoch   4 Batch  687/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.058\n",
+      "Epoch   4 Batch  688/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.923, Loss:  0.047\n",
+      "Epoch   4 Batch  689/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.035\n",
+      "Epoch   4 Batch  690/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   4 Batch  691/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.926, Loss:  0.063\n",
+      "Epoch   4 Batch  692/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.040\n",
+      "Epoch   4 Batch  693/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.918, Loss:  0.064\n",
+      "Epoch   4 Batch  694/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.918, Loss:  0.055\n",
+      "Epoch   4 Batch  695/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.046\n",
+      "Epoch   4 Batch  696/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.911, Loss:  0.065\n",
+      "Epoch   4 Batch  697/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.050\n",
+      "Epoch   4 Batch  698/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.910, Loss:  0.046\n",
+      "Epoch   4 Batch  699/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.910, Loss:  0.042\n",
+      "Epoch   4 Batch  700/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.911, Loss:  0.039\n",
+      "Epoch   4 Batch  701/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.063\n",
+      "Epoch   4 Batch  702/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.911, Loss:  0.061\n",
+      "Epoch   4 Batch  703/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.920, Loss:  0.049\n",
+      "Epoch   4 Batch  704/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.920, Loss:  0.057\n",
+      "Epoch   4 Batch  705/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.922, Loss:  0.067\n",
+      "Epoch   4 Batch  706/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.921, Loss:  0.089\n",
+      "Epoch   4 Batch  707/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.059\n",
+      "Epoch   4 Batch  708/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.057\n",
+      "Epoch   4 Batch  709/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.050\n",
+      "Epoch   4 Batch  710/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.036\n",
+      "Epoch   4 Batch  711/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.930, Loss:  0.058\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch  712/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.925, Loss:  0.041\n",
+      "Epoch   4 Batch  713/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.049\n",
+      "Epoch   4 Batch  714/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.048\n",
+      "Epoch   4 Batch  715/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.049\n",
+      "Epoch   4 Batch  716/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.932, Loss:  0.041\n",
+      "Epoch   4 Batch  717/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.927, Loss:  0.044\n",
+      "Epoch   4 Batch  718/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.934, Loss:  0.047\n",
+      "Epoch   4 Batch  719/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.939, Loss:  0.063\n",
+      "Epoch   4 Batch  720/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.939, Loss:  0.053\n",
+      "Epoch   4 Batch  721/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.065\n",
+      "Epoch   4 Batch  722/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.940, Loss:  0.037\n",
+      "Epoch   4 Batch  723/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.944, Loss:  0.059\n",
+      "Epoch   4 Batch  724/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.939, Loss:  0.051\n",
+      "Epoch   4 Batch  725/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.039\n",
+      "Epoch   4 Batch  726/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.926, Loss:  0.045\n",
+      "Epoch   4 Batch  727/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.926, Loss:  0.046\n",
+      "Epoch   4 Batch  728/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.926, Loss:  0.062\n",
+      "Epoch   4 Batch  729/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.920, Loss:  0.059\n",
+      "Epoch   4 Batch  730/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.069\n",
+      "Epoch   4 Batch  731/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.911, Loss:  0.054\n",
+      "Epoch   4 Batch  732/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.912, Loss:  0.062\n",
+      "Epoch   4 Batch  733/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.916, Loss:  0.070\n",
+      "Epoch   4 Batch  734/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.919, Loss:  0.045\n",
+      "Epoch   4 Batch  735/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.919, Loss:  0.039\n",
+      "Epoch   4 Batch  736/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.923, Loss:  0.030\n",
+      "Epoch   4 Batch  737/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.924, Loss:  0.045\n",
+      "Epoch   4 Batch  738/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.925, Loss:  0.042\n",
+      "Epoch   4 Batch  739/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   4 Batch  740/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.044\n",
+      "Epoch   4 Batch  741/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.053\n",
+      "Epoch   4 Batch  742/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.916, Loss:  0.031\n",
+      "Epoch   4 Batch  743/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.917, Loss:  0.052\n",
+      "Epoch   4 Batch  744/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.046\n",
+      "Epoch   4 Batch  745/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.054\n",
+      "Epoch   4 Batch  746/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.047\n",
+      "Epoch   4 Batch  747/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.936, Loss:  0.047\n",
+      "Epoch   4 Batch  748/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.940, Loss:  0.047\n",
+      "Epoch   4 Batch  749/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.938, Loss:  0.052\n",
+      "Epoch   4 Batch  750/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.934, Loss:  0.032\n",
+      "Epoch   4 Batch  751/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.929, Loss:  0.049\n",
+      "Epoch   4 Batch  752/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.048\n",
+      "Epoch   4 Batch  753/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.044\n",
+      "Epoch   4 Batch  754/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.931, Loss:  0.059\n",
+      "Epoch   4 Batch  755/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.932, Loss:  0.063\n",
+      "Epoch   4 Batch  756/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.930, Loss:  0.054\n",
+      "Epoch   4 Batch  757/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.930, Loss:  0.044\n",
+      "Epoch   4 Batch  758/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.934, Loss:  0.039\n",
+      "Epoch   4 Batch  759/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.935, Loss:  0.037\n",
+      "Epoch   4 Batch  760/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.931, Loss:  0.061\n",
+      "Epoch   4 Batch  761/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.046\n",
+      "Epoch   4 Batch  762/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.048\n",
+      "Epoch   4 Batch  763/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.931, Loss:  0.049\n",
+      "Epoch   4 Batch  764/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.932, Loss:  0.042\n",
+      "Epoch   4 Batch  765/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.928, Loss:  0.052\n",
+      "Epoch   4 Batch  766/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.050\n",
+      "Epoch   4 Batch  767/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.917, Loss:  0.042\n",
+      "Epoch   4 Batch  768/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.920, Loss:  0.052\n",
+      "Epoch   4 Batch  769/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.916, Loss:  0.052\n",
+      "Epoch   4 Batch  770/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.038\n",
+      "Epoch   4 Batch  771/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.914, Loss:  0.046\n",
+      "Epoch   4 Batch  772/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.901, Loss:  0.050\n",
+      "Epoch   4 Batch  773/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.048\n",
+      "Epoch   4 Batch  774/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.920, Loss:  0.050\n",
+      "Epoch   4 Batch  775/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   4 Batch  776/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   4 Batch  777/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   4 Batch  778/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.047\n",
+      "Epoch   4 Batch  779/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.943, Loss:  0.054\n",
+      "Epoch   4 Batch  780/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.937, Loss:  0.070\n",
+      "Epoch   4 Batch  781/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.933, Loss:  0.044\n",
+      "Epoch   4 Batch  782/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.922, Loss:  0.041\n",
+      "Epoch   4 Batch  783/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.053\n",
+      "Epoch   4 Batch  784/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.933, Loss:  0.037\n",
+      "Epoch   4 Batch  785/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.042\n",
+      "Epoch   4 Batch  786/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.920, Loss:  0.036\n",
+      "Epoch   4 Batch  787/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.918, Loss:  0.047\n",
+      "Epoch   4 Batch  788/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.047\n",
+      "Epoch   4 Batch  789/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.918, Loss:  0.043\n",
+      "Epoch   4 Batch  790/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.912, Loss:  0.053\n",
+      "Epoch   4 Batch  791/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.927, Loss:  0.051\n",
+      "Epoch   4 Batch  792/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.931, Loss:  0.058\n",
+      "Epoch   4 Batch  793/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.931, Loss:  0.040\n",
+      "Epoch   4 Batch  794/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.937, Loss:  0.044\n",
+      "Epoch   4 Batch  795/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.931, Loss:  0.051\n",
+      "Epoch   4 Batch  796/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.931, Loss:  0.043\n",
+      "Epoch   4 Batch  797/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.047\n",
+      "Epoch   4 Batch  798/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.936, Loss:  0.053\n",
+      "Epoch   4 Batch  799/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.930, Loss:  0.064\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch  800/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.049\n",
+      "Epoch   4 Batch  801/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.052\n",
+      "Epoch   4 Batch  802/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.931, Loss:  0.052\n",
+      "Epoch   4 Batch  803/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.942, Loss:  0.045\n",
+      "Epoch   4 Batch  804/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.929, Loss:  0.038\n",
+      "Epoch   4 Batch  805/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.936, Loss:  0.047\n",
+      "Epoch   4 Batch  806/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.934, Loss:  0.045\n",
+      "Epoch   4 Batch  807/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.043\n",
+      "Epoch   4 Batch  808/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.932, Loss:  0.062\n",
+      "Epoch   4 Batch  809/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.073\n",
+      "Epoch   4 Batch  810/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.039\n",
+      "Epoch   4 Batch  811/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.929, Loss:  0.050\n",
+      "Epoch   4 Batch  812/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.922, Loss:  0.037\n",
+      "Epoch   4 Batch  813/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.919, Loss:  0.055\n",
+      "Epoch   4 Batch  814/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.917, Loss:  0.060\n",
+      "Epoch   4 Batch  815/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.048\n",
+      "Epoch   4 Batch  816/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.926, Loss:  0.059\n",
+      "Epoch   4 Batch  817/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.049\n",
+      "Epoch   4 Batch  818/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.929, Loss:  0.051\n",
+      "Epoch   4 Batch  819/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.052\n",
+      "Epoch   4 Batch  820/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.933, Loss:  0.037\n",
+      "Epoch   4 Batch  821/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.929, Loss:  0.055\n",
+      "Epoch   4 Batch  822/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.929, Loss:  0.052\n",
+      "Epoch   4 Batch  823/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.927, Loss:  0.057\n",
+      "Epoch   4 Batch  824/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.927, Loss:  0.056\n",
+      "Epoch   4 Batch  825/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.929, Loss:  0.043\n",
+      "Epoch   4 Batch  826/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.927, Loss:  0.044\n",
+      "Epoch   4 Batch  827/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.049\n",
+      "Epoch   4 Batch  828/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.926, Loss:  0.040\n",
+      "Epoch   4 Batch  829/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.064\n",
+      "Epoch   4 Batch  830/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.924, Loss:  0.061\n",
+      "Epoch   4 Batch  831/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.919, Loss:  0.051\n",
+      "Epoch   4 Batch  832/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.052\n",
+      "Epoch   4 Batch  833/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.046\n",
+      "Epoch   4 Batch  834/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.049\n",
+      "Epoch   4 Batch  835/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.934, Loss:  0.049\n",
+      "Epoch   4 Batch  836/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.040\n",
+      "Epoch   4 Batch  837/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.933, Loss:  0.063\n",
+      "Epoch   4 Batch  838/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.930, Loss:  0.046\n",
+      "Epoch   4 Batch  839/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.938, Loss:  0.033\n",
+      "Epoch   4 Batch  840/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.944, Loss:  0.037\n",
+      "Epoch   4 Batch  841/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.938, Loss:  0.058\n",
+      "Epoch   4 Batch  842/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.940, Loss:  0.043\n",
+      "Epoch   4 Batch  843/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.042\n",
+      "Epoch   4 Batch  844/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.037\n",
+      "Epoch   4 Batch  845/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.933, Loss:  0.031\n",
+      "Epoch   4 Batch  846/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.933, Loss:  0.067\n",
+      "Epoch   4 Batch  847/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.931, Loss:  0.053\n",
+      "Epoch   4 Batch  848/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.926, Loss:  0.045\n",
+      "Epoch   4 Batch  849/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.926, Loss:  0.043\n",
+      "Epoch   4 Batch  850/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.926, Loss:  0.074\n",
+      "Epoch   4 Batch  851/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.925, Loss:  0.061\n",
+      "Epoch   4 Batch  852/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.929, Loss:  0.067\n",
+      "Epoch   4 Batch  853/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.939, Loss:  0.049\n",
+      "Epoch   4 Batch  854/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.933, Loss:  0.055\n",
+      "Epoch   4 Batch  855/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.931, Loss:  0.048\n",
+      "Epoch   4 Batch  856/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.941, Loss:  0.043\n",
+      "Epoch   4 Batch  857/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.941, Loss:  0.050\n",
+      "Epoch   4 Batch  858/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.043\n",
+      "Epoch   4 Batch  859/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.065\n",
+      "Epoch   4 Batch  860/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.927, Loss:  0.050\n",
+      "Epoch   4 Batch  861/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.934, Loss:  0.044\n",
+      "Epoch   4 Batch  862/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.943, Loss:  0.053\n",
+      "Epoch   4 Batch  863/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.945, Loss:  0.046\n",
+      "Epoch   4 Batch  864/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.931, Loss:  0.045\n",
+      "Epoch   4 Batch  865/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.930, Loss:  0.053\n",
+      "Epoch   4 Batch  866/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.060\n",
+      "Epoch   4 Batch  867/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.922, Loss:  0.092\n",
+      "Epoch   4 Batch  868/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.921, Loss:  0.058\n",
+      "Epoch   4 Batch  869/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.938, Loss:  0.046\n",
+      "Epoch   4 Batch  870/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.938, Loss:  0.042\n",
+      "Epoch   4 Batch  871/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.939, Loss:  0.034\n",
+      "Epoch   4 Batch  872/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.943, Loss:  0.051\n",
+      "Epoch   4 Batch  873/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.038\n",
+      "Epoch   4 Batch  874/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.942, Loss:  0.065\n",
+      "Epoch   4 Batch  875/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.942, Loss:  0.055\n",
+      "Epoch   4 Batch  876/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.936, Loss:  0.043\n",
+      "Epoch   4 Batch  877/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.936, Loss:  0.050\n",
+      "Epoch   4 Batch  878/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.938, Loss:  0.043\n",
+      "Epoch   4 Batch  879/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.943, Loss:  0.039\n",
+      "Epoch   4 Batch  880/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.051\n",
+      "Epoch   4 Batch  881/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.941, Loss:  0.056\n",
+      "Epoch   4 Batch  882/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.933, Loss:  0.041\n",
+      "Epoch   4 Batch  883/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.933, Loss:  0.065\n",
+      "Epoch   4 Batch  884/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.933, Loss:  0.048\n",
+      "Epoch   4 Batch  885/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.035\n",
+      "Epoch   4 Batch  886/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.918, Loss:  0.046\n",
+      "Epoch   4 Batch  887/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.910, Loss:  0.069\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch  888/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.910, Loss:  0.040\n",
+      "Epoch   4 Batch  889/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.913, Loss:  0.048\n",
+      "Epoch   4 Batch  890/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.050\n",
+      "Epoch   4 Batch  891/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.042\n",
+      "Epoch   4 Batch  892/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.909, Loss:  0.042\n",
+      "Epoch   4 Batch  893/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.910, Loss:  0.048\n",
+      "Epoch   4 Batch  894/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.908, Loss:  0.039\n",
+      "Epoch   4 Batch  895/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.044\n",
+      "Epoch   4 Batch  896/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.052\n",
+      "Epoch   4 Batch  897/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.929, Loss:  0.043\n",
+      "Epoch   4 Batch  898/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.049\n",
+      "Epoch   4 Batch  899/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.934, Loss:  0.063\n",
+      "Epoch   4 Batch  900/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.934, Loss:  0.055\n",
+      "Epoch   4 Batch  901/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.935, Loss:  0.068\n",
+      "Epoch   4 Batch  902/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.945, Loss:  0.052\n",
+      "Epoch   4 Batch  903/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.942, Loss:  0.046\n",
+      "Epoch   4 Batch  904/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.942, Loss:  0.057\n",
+      "Epoch   4 Batch  905/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.942, Loss:  0.034\n",
+      "Epoch   4 Batch  906/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.942, Loss:  0.049\n",
+      "Epoch   4 Batch  907/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.942, Loss:  0.045\n",
+      "Epoch   4 Batch  908/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.940, Loss:  0.051\n",
+      "Epoch   4 Batch  909/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.934, Loss:  0.057\n",
+      "Epoch   4 Batch  910/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.934, Loss:  0.050\n",
+      "Epoch   4 Batch  911/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.938, Loss:  0.049\n",
+      "Epoch   4 Batch  912/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.938, Loss:  0.042\n",
+      "Epoch   4 Batch  913/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.938, Loss:  0.072\n",
+      "Epoch   4 Batch  914/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.083\n",
+      "Epoch   4 Batch  915/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.038\n",
+      "Epoch   4 Batch  916/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.918, Loss:  0.057\n",
+      "Epoch   4 Batch  917/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.040\n",
+      "Epoch   4 Batch  918/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.919, Loss:  0.039\n",
+      "Epoch   4 Batch  919/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.907, Loss:  0.036\n",
+      "Epoch   4 Batch  920/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.912, Loss:  0.037\n",
+      "Epoch   4 Batch  921/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.917, Loss:  0.053\n",
+      "Epoch   4 Batch  922/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.053\n",
+      "Epoch   4 Batch  923/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.924, Loss:  0.040\n",
+      "Epoch   4 Batch  924/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.930, Loss:  0.060\n",
+      "Epoch   4 Batch  925/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.936, Loss:  0.043\n",
+      "Epoch   4 Batch  926/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.936, Loss:  0.048\n",
+      "Epoch   4 Batch  927/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.930, Loss:  0.055\n",
+      "Epoch   4 Batch  928/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.930, Loss:  0.055\n",
+      "Epoch   4 Batch  929/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.046\n",
+      "Epoch   4 Batch  930/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.937, Loss:  0.037\n",
+      "Epoch   4 Batch  931/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.940, Loss:  0.039\n",
+      "Epoch   4 Batch  932/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.940, Loss:  0.040\n",
+      "Epoch   4 Batch  933/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.936, Loss:  0.045\n",
+      "Epoch   4 Batch  934/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.042\n",
+      "Epoch   4 Batch  935/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.047\n",
+      "Epoch   4 Batch  936/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.051\n",
+      "Epoch   4 Batch  937/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.058\n",
+      "Epoch   4 Batch  938/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.061\n",
+      "Epoch   4 Batch  939/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.056\n",
+      "Epoch   4 Batch  940/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.043\n",
+      "Epoch   4 Batch  941/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.038\n",
+      "Epoch   4 Batch  942/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.052\n",
+      "Epoch   4 Batch  943/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.927, Loss:  0.044\n",
+      "Epoch   4 Batch  944/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.046\n",
+      "Epoch   4 Batch  945/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.942, Loss:  0.044\n",
+      "Epoch   4 Batch  946/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.936, Loss:  0.030\n",
+      "Epoch   4 Batch  947/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.931, Loss:  0.056\n",
+      "Epoch   4 Batch  948/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.941, Loss:  0.040\n",
+      "Epoch   4 Batch  949/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.941, Loss:  0.040\n",
+      "Epoch   4 Batch  950/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.941, Loss:  0.045\n",
+      "Epoch   4 Batch  951/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.945, Loss:  0.060\n",
+      "Epoch   4 Batch  952/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.946, Loss:  0.042\n",
+      "Epoch   4 Batch  953/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.946, Loss:  0.042\n",
+      "Epoch   4 Batch  954/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.946, Loss:  0.053\n",
+      "Epoch   4 Batch  955/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.956, Loss:  0.056\n",
+      "Epoch   4 Batch  956/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.961, Loss:  0.058\n",
+      "Epoch   4 Batch  957/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.942, Loss:  0.031\n",
+      "Epoch   4 Batch  958/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.939, Loss:  0.050\n",
+      "Epoch   4 Batch  959/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.052\n",
+      "Epoch   4 Batch  960/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.048\n",
+      "Epoch   4 Batch  961/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.929, Loss:  0.043\n",
+      "Epoch   4 Batch  962/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.928, Loss:  0.041\n",
+      "Epoch   4 Batch  963/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.066\n",
+      "Epoch   4 Batch  964/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.924, Loss:  0.043\n",
+      "Epoch   4 Batch  965/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.921, Loss:  0.053\n",
+      "Epoch   4 Batch  966/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.934, Loss:  0.042\n",
+      "Epoch   4 Batch  967/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.934, Loss:  0.053\n",
+      "Epoch   4 Batch  968/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.930, Loss:  0.061\n",
+      "Epoch   4 Batch  969/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.064\n",
+      "Epoch   4 Batch  970/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.936, Loss:  0.050\n",
+      "Epoch   4 Batch  971/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.931, Loss:  0.044\n",
+      "Epoch   4 Batch  972/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.925, Loss:  0.050\n",
+      "Epoch   4 Batch  973/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.929, Loss:  0.037\n",
+      "Epoch   4 Batch  974/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.933, Loss:  0.035\n",
+      "Epoch   4 Batch  975/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.932, Loss:  0.050\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch  976/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.931, Loss:  0.038\n",
+      "Epoch   4 Batch  977/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.934, Loss:  0.032\n",
+      "Epoch   4 Batch  978/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.938, Loss:  0.048\n",
+      "Epoch   4 Batch  979/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.934, Loss:  0.042\n",
+      "Epoch   4 Batch  980/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.935, Loss:  0.046\n",
+      "Epoch   4 Batch  981/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.050\n",
+      "Epoch   4 Batch  982/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.930, Loss:  0.041\n",
+      "Epoch   4 Batch  983/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.939, Loss:  0.047\n",
+      "Epoch   4 Batch  984/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.935, Loss:  0.050\n",
+      "Epoch   4 Batch  985/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.935, Loss:  0.044\n",
+      "Epoch   4 Batch  986/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.046\n",
+      "Epoch   4 Batch  987/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.936, Loss:  0.033\n",
+      "Epoch   4 Batch  988/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.940, Loss:  0.068\n",
+      "Epoch   4 Batch  989/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.938, Loss:  0.066\n",
+      "Epoch   4 Batch  990/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.938, Loss:  0.053\n",
+      "Epoch   4 Batch  991/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.925, Loss:  0.050\n",
+      "Epoch   4 Batch  992/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.057\n",
+      "Epoch   4 Batch  993/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.925, Loss:  0.037\n",
+      "Epoch   4 Batch  994/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.044\n",
+      "Epoch   4 Batch  995/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.921, Loss:  0.051\n",
+      "Epoch   4 Batch  996/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.922, Loss:  0.052\n",
+      "Epoch   4 Batch  997/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.050\n",
+      "Epoch   4 Batch  998/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.913, Loss:  0.049\n",
+      "Epoch   4 Batch  999/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.049\n",
+      "Epoch   4 Batch 1000/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.908, Loss:  0.041\n",
+      "Epoch   4 Batch 1001/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.907, Loss:  0.041\n",
+      "Epoch   4 Batch 1002/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.907, Loss:  0.035\n",
+      "Epoch   4 Batch 1003/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.911, Loss:  0.050\n",
+      "Epoch   4 Batch 1004/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.917, Loss:  0.051\n",
+      "Epoch   4 Batch 1005/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.044\n",
+      "Epoch   4 Batch 1006/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.916, Loss:  0.056\n",
+      "Epoch   4 Batch 1007/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.916, Loss:  0.039\n",
+      "Epoch   4 Batch 1008/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.904, Loss:  0.059\n",
+      "Epoch   4 Batch 1009/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.911, Loss:  0.040\n",
+      "Epoch   4 Batch 1010/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.906, Loss:  0.054\n",
+      "Epoch   4 Batch 1011/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.906, Loss:  0.038\n",
+      "Epoch   4 Batch 1012/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.912, Loss:  0.048\n",
+      "Epoch   4 Batch 1013/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.912, Loss:  0.039\n",
+      "Epoch   4 Batch 1014/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.914, Loss:  0.062\n",
+      "Epoch   4 Batch 1015/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.918, Loss:  0.053\n",
+      "Epoch   4 Batch 1016/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.049\n",
+      "Epoch   4 Batch 1017/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.057\n",
+      "Epoch   4 Batch 1018/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.049\n",
+      "Epoch   4 Batch 1019/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.915, Loss:  0.073\n",
+      "Epoch   4 Batch 1020/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.040\n",
+      "Epoch   4 Batch 1021/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.044\n",
+      "Epoch   4 Batch 1022/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.919, Loss:  0.045\n",
+      "Epoch   4 Batch 1023/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.922, Loss:  0.057\n",
+      "Epoch   4 Batch 1024/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.921, Loss:  0.059\n",
+      "Epoch   4 Batch 1025/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.050\n",
+      "Epoch   4 Batch 1026/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.920, Loss:  0.055\n",
+      "Epoch   4 Batch 1027/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.920, Loss:  0.046\n",
+      "Epoch   4 Batch 1028/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.046\n",
+      "Epoch   4 Batch 1029/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.040\n",
+      "Epoch   4 Batch 1030/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.924, Loss:  0.046\n",
+      "Epoch   4 Batch 1031/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.054\n",
+      "Epoch   4 Batch 1032/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.061\n",
+      "Epoch   4 Batch 1033/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.934, Loss:  0.060\n",
+      "Epoch   4 Batch 1034/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.050\n",
+      "Epoch   4 Batch 1035/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.926, Loss:  0.032\n",
+      "Epoch   4 Batch 1036/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.060\n",
+      "Epoch   4 Batch 1037/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.927, Loss:  0.053\n",
+      "Epoch   4 Batch 1038/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.051\n",
+      "Epoch   4 Batch 1039/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.927, Loss:  0.050\n",
+      "Epoch   4 Batch 1040/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.927, Loss:  0.068\n",
+      "Epoch   4 Batch 1041/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.925, Loss:  0.047\n",
+      "Epoch   4 Batch 1042/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.041\n",
+      "Epoch   4 Batch 1043/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.937, Loss:  0.052\n",
+      "Epoch   4 Batch 1044/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.930, Loss:  0.057\n",
+      "Epoch   4 Batch 1045/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.925, Loss:  0.047\n",
+      "Epoch   4 Batch 1046/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.929, Loss:  0.040\n",
+      "Epoch   4 Batch 1047/1077 - Train Accuracy:  0.974, Validation Accuracy:  0.919, Loss:  0.036\n",
+      "Epoch   4 Batch 1048/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.915, Loss:  0.040\n",
+      "Epoch   4 Batch 1049/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.913, Loss:  0.040\n",
+      "Epoch   4 Batch 1050/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.909, Loss:  0.036\n",
+      "Epoch   4 Batch 1051/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.919, Loss:  0.056\n",
+      "Epoch   4 Batch 1052/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.918, Loss:  0.051\n",
+      "Epoch   4 Batch 1053/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.923, Loss:  0.053\n",
+      "Epoch   4 Batch 1054/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.045\n",
+      "Epoch   4 Batch 1055/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.928, Loss:  0.044\n",
+      "Epoch   4 Batch 1056/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.924, Loss:  0.044\n",
+      "Epoch   4 Batch 1057/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.058\n",
+      "Epoch   4 Batch 1058/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.930, Loss:  0.054\n",
+      "Epoch   4 Batch 1059/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.939, Loss:  0.055\n",
+      "Epoch   4 Batch 1060/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.935, Loss:  0.040\n",
+      "Epoch   4 Batch 1061/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.056\n",
+      "Epoch   4 Batch 1062/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.926, Loss:  0.054\n",
+      "Epoch   4 Batch 1063/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.931, Loss:  0.068\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   4 Batch 1064/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.940, Loss:  0.050\n",
+      "Epoch   4 Batch 1065/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.050\n",
+      "Epoch   4 Batch 1066/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.032\n",
+      "Epoch   4 Batch 1067/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.927, Loss:  0.059\n",
+      "Epoch   4 Batch 1068/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.927, Loss:  0.041\n",
+      "Epoch   4 Batch 1069/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.923, Loss:  0.038\n",
+      "Epoch   4 Batch 1070/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.041\n",
+      "Epoch   4 Batch 1071/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.042\n",
+      "Epoch   4 Batch 1072/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.045\n",
+      "Epoch   4 Batch 1073/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.053\n",
+      "Epoch   4 Batch 1074/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.054\n",
+      "Epoch   4 Batch 1075/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.062\n",
+      "Model Trained and Saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import time\n",
+    "\n",
+    "def get_accuracy(target, logits):\n",
+    "    \"\"\"\n",
+    "    Calculate accuracy\n",
+    "    \"\"\"\n",
+    "    max_seq = max(target.shape[1], logits.shape[1])\n",
+    "    if max_seq - target.shape[1]:\n",
+    "        target = np.pad(\n",
+    "            target,\n",
+    "            [(0,0),(0,max_seq - target.shape[1])],\n",
+    "            'constant')\n",
+    "    if max_seq - logits.shape[1]:\n",
+    "        logits = np.pad(\n",
+    "            logits,\n",
+    "            [(0,0),(0,max_seq - logits.shape[1]), (0,0)],\n",
+    "            'constant')\n",
+    "\n",
+    "    return np.mean(np.equal(target, np.argmax(logits, 2)))\n",
+    "\n",
+    "train_source = source_int_text[batch_size:]\n",
+    "train_target = target_int_text[batch_size:]\n",
+    "\n",
+    "valid_source = helper.pad_sentence_batch(source_int_text[:batch_size])\n",
+    "valid_target = helper.pad_sentence_batch(target_int_text[:batch_size])\n",
+    "\n",
+    "with tf.Session(graph=train_graph) as sess:\n",
+    "    sess.run(tf.global_variables_initializer())\n",
+    "\n",
+    "    for epoch_i in range(epochs):\n",
+    "        for batch_i, (source_batch, target_batch) in enumerate(\n",
+    "                helper.batch_data(train_source, train_target, batch_size)):\n",
+    "            start_time = time.time()\n",
+    "            \n",
+    "            _, loss = sess.run(\n",
+    "                [train_op, cost],\n",
+    "                {input_data: source_batch,\n",
+    "                 targets: target_batch,\n",
+    "                 lr: learning_rate,\n",
+    "                 sequence_length: target_batch.shape[1],\n",
+    "                 keep_prob: keep_probability})\n",
+    "            \n",
+    "            batch_train_logits = sess.run(\n",
+    "                inference_logits,\n",
+    "                {input_data: source_batch, keep_prob: 1.0})\n",
+    "            batch_valid_logits = sess.run(\n",
+    "                inference_logits,\n",
+    "                {input_data: valid_source, keep_prob: 1.0})\n",
+    "                \n",
+    "            train_acc = get_accuracy(target_batch, batch_train_logits)\n",
+    "            valid_acc = get_accuracy(np.array(valid_target), batch_valid_logits)\n",
+    "            end_time = time.time()\n",
+    "            print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.3f}, Validation Accuracy: {:>6.3f}, Loss: {:>6.3f}'\n",
+    "                  .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\n",
+    "\n",
+    "    # Save Model\n",
+    "    saver = tf.train.Saver()\n",
+    "    saver.save(sess, save_path)\n",
+    "    print('Model Trained and Saved')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 保存参数\n",
+    "\n",
+    "保存 `batch_size` 和 `save_path` 参数以进行推论（for inference）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "# Save parameters for checkpoint\n",
+    "helper.save_params(save_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 检查点"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import helper\n",
+    "import problem_unittests as tests\n",
+    "\n",
+    "_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\n",
+    "load_path = helper.load_params()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 句子到序列\n",
+    "\n",
+    "要向模型提供要翻译的句子，你首先需要预处理该句子。实现函数 `sentence_to_seq()` 以预处理新的句子。\n",
+    "\n",
+    "- 将句子转换为小写形式\n",
+    "- 使用 `vocab_to_int` 将单词转换为 id\n",
+    " - 如果单词不在词汇表中，将其转换为`<UNK>` 单词 id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def sentence_to_seq(sentence, vocab_to_int):\n",
+    "    \"\"\"\n",
+    "    Convert a sentence to a sequence of ids\n",
+    "    :param sentence: String\n",
+    "    :param vocab_to_int: Dictionary to go from the words to an id\n",
+    "    :return: List of word ids\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    word_ids = [vocab_to_int.get(word, vocab_to_int[\"<UNK>\"]) for word in sentence.lower().split()]\n",
+    "    \n",
+    "    return word_ids\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_sentence_to_seq(sentence_to_seq)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 翻译\n",
+    "\n",
+    "将 `translate_sentence` 从英语翻译成法语。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input\n",
+      "  Word Ids:      [226, 218, 43, 30, 138, 202, 171]\n",
+      "  English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\n",
+      "\n",
+      "Prediction\n",
+      "  Word Ids:      [286, 17, 192, 281, 138, 94, 89, 60, 1]\n",
+      "  French Words: ['il', 'a', 'vu', 'un', 'petit', 'camion', 'jaune', '.', '<EOS>']\n"
+     ]
+    }
+   ],
+   "source": [
+    "translate_sentence = 'he saw a old yellow truck .'\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\n",
+    "\n",
+    "loaded_graph = tf.Graph()\n",
+    "with tf.Session(graph=loaded_graph) as sess:\n",
+    "    # Load saved model\n",
+    "    loader = tf.train.import_meta_graph(load_path + '.meta')\n",
+    "    loader.restore(sess, load_path)\n",
+    "\n",
+    "    input_data = loaded_graph.get_tensor_by_name('input:0')\n",
+    "    logits = loaded_graph.get_tensor_by_name('logits:0')\n",
+    "    keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
+    "\n",
+    "    translate_logits = sess.run(logits, {input_data: [translate_sentence], keep_prob: 1.0})[0]\n",
+    "\n",
+    "print('Input')\n",
+    "print('  Word Ids:      {}'.format([i for i in translate_sentence]))\n",
+    "print('  English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\n",
+    "\n",
+    "print('\\nPrediction')\n",
+    "print('  Word Ids:      {}'.format([i for i in np.argmax(translate_logits, 1)]))\n",
+    "print('  French Words: {}'.format([target_int_to_vocab[i] for i in np.argmax(translate_logits, 1)]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 不完美的翻译\n",
+    "\n",
+    "你可能注意到了，某些句子的翻译质量比其他的要好。因为你使用的数据集只有 227 个英语单词，但实际生活中有数千个单词，只有使用这些单词的句子结果才会比较理想。对于此项目，不需要达到完美的翻译。但是，如果你想创建更好的翻译模型，则需要更好的数据。\n",
+    "\n",
+    "你可以使用 [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar) 语料库训练模型。该数据集拥有更多的词汇，讨论的话题也更丰富。但是，训练时间要好多天的时间，所以确保你有 GPU 并且对于我们提供的数据集，你的神经网络性能很棒。提交此项目后，别忘了研究下 WMT10 语料库。\n",
+    "\n",
+    "\n",
+    "## 提交项目\n",
+    "\n",
+    "提交项目时，确保先运行所有单元，然后再保存记事本。保存记事本文件为 “dlnd_language_translation.ipynb”，再通过菜单中的“文件” ->“下载为”将其另存为 HTML 格式。提交的项目文档中需包含“helper.py”和“problem_unittests.py”文件。\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/language-translation/dlnd_language_translation.py b/language-translation/dlnd_language_translation.py
new file mode 100644
index 0000000..4eef684
--- /dev/null
+++ b/language-translation/dlnd_language_translation.py
@@ -0,0 +1,697 @@
+
+# coding: utf-8
+
+# # 语言翻译
+# 
+# 在此项目中，你将了解神经网络机器翻译这一领域。你将用由英语和法语语句组成的数据集，训练一个序列到序列模型（sequence to sequence model），该模型能够将新的英语句子翻译成法语。
+# 
+# ## 获取数据
+# 
+# 因为将整个英语语言内容翻译成法语需要大量训练时间，所以我们提供了一小部分的英语语料库。
+# 
+
+# In[1]:
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import helper
+import problem_unittests as tests
+
+source_path = 'data/small_vocab_en'
+target_path = 'data/small_vocab_fr'
+source_text = helper.load_data(source_path)
+target_text = helper.load_data(target_path)
+
+
+# ## 探索数据
+# 
+# 研究 view_sentence_range，查看并熟悉该数据的不同部分。
+# 
+
+# In[2]:
+
+
+view_sentence_range = (0, 10)
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import numpy as np
+
+print('Dataset Stats')
+print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))
+
+sentences = source_text.split('\n')
+word_counts = [len(sentence.split()) for sentence in sentences]
+print('Number of sentences: {}'.format(len(sentences)))
+print('Average number of words in a sentence: {}'.format(np.average(word_counts)))
+
+print()
+print('English sentences {} to {}:'.format(*view_sentence_range))
+print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
+print()
+print('French sentences {} to {}:'.format(*view_sentence_range))
+print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
+
+
+# ## 实现预处理函数
+# 
+# ### 文本到单词 id
+# 
+# 和之前的 RNN 一样，你必须首先将文本转换为数字，这样计算机才能读懂。在函数 `text_to_ids()` 中，你需要将单词中的 `source_text` 和 `target_text` 转为 id。但是，你需要在 `target_text` 中每个句子的末尾，添加 `<EOS>` 单词 id。这样可以帮助神经网络预测句子应该在什么地方结束。
+# 
+# 
+# 你可以通过以下代码获取  `<EOS> ` 单词ID：
+# 
+# ```python
+# target_vocab_to_int['<EOS>']
+# ```
+# 
+# 你可以使用 `source_vocab_to_int` 和 `target_vocab_to_int` 获得其他单词 id。
+# 
+
+# In[3]:
+
+
+def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):
+    """
+    Convert source and target text to proper word ids
+    :param source_text: String that contains all the source text.
+    :param target_text: String that contains all the target text.
+    :param source_vocab_to_int: Dictionary to go from the source words to an id
+    :param target_vocab_to_int: Dictionary to go from the target words to an id
+    :return: A tuple of lists (source_id_text, target_id_text)
+    """
+    # TODO: Implement Function
+    source_letter_ids = [[source_vocab_to_int.get(letter, source_vocab_to_int['<UNK>']) for letter in line.split()] for line in source_text.split('\n')]
+    target_letter_ids = [[target_vocab_to_int.get(letter, target_vocab_to_int['<UNK>']) for letter in line.split()] + [target_vocab_to_int['<EOS>']] for line in target_text.split('\n')] 
+    
+    return source_letter_ids, target_letter_ids
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_text_to_ids(text_to_ids)
+
+
+# ### 预处理所有数据并保存
+# 
+# 运行以下代码单元，预处理所有数据，并保存到文件中。
+# 
+
+# In[4]:
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+helper.preprocess_and_save_data(source_path, target_path, text_to_ids)
+
+
+# # 检查点
+# 
+# 这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，可以从这里继续。预处理的数据已保存到磁盘上。
+
+# In[5]:
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import numpy as np
+import helper
+
+(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()
+
+
+# ### 检查 TensorFlow 版本，确认可访问 GPU
+# 
+# 这一检查步骤，可以确保你使用的是正确版本的 TensorFlow，并且能够访问 GPU。
+# 
+
+# In[6]:
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+from distutils.version import LooseVersion
+import warnings
+import tensorflow as tf
+
+# Check TensorFlow Version
+assert LooseVersion(tf.__version__) in [LooseVersion('1.0.0'), LooseVersion('1.0.1')], 'This project requires TensorFlow version 1.0  You are using {}'.format(tf.__version__)
+print('TensorFlow Version: {}'.format(tf.__version__))
+
+# Check for a GPU
+if not tf.test.gpu_device_name():
+    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
+else:
+    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
+
+
+# ## 构建神经网络
+# 
+# 你将通过实现以下函数，构建出要构建一个序列到序列模型所需的组件：
+# 
+# - `model_inputs`
+# - `process_decoding_input`
+# - `encoding_layer`
+# - `decoding_layer_train`
+# - `decoding_layer_infer`
+# - `decoding_layer`
+# - `seq2seq_model`
+# 
+# ### 输入
+# 
+# 实现 `model_inputs()` 函数，为神经网络创建 TF 占位符。该函数应该创建以下占位符：
+# 
+# - 名为 “input” 的输入文本占位符，并使用 TF Placeholder 名称参数（等级（Rank）为 2）。
+# - 目标占位符（等级为 2）。
+# - 学习速率占位符（等级为 0）。
+# - 名为 “keep_prob” 的保留率占位符，并使用 TF Placeholder 名称参数（等级为 0）。
+# 
+# 在以下元祖（tuple）中返回占位符：（输入、目标、学习速率、保留率）
+# 
+
+# In[7]:
+
+
+import tensorflow as tf
+def model_inputs():
+    """
+    Create TF Placeholders for input, targets, and learning rate.
+    :return: Tuple (input, targets, learning rate, keep probability)
+    """
+    # TODO: Implement Function
+    inputs = tf.placeholder(tf.int32, [None, None], name='input')
+    targets = tf.placeholder(tf.int32, [None, None], name='targets')
+    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
+    keep_prob = tf.placeholder(tf.float32, name='keep_prob')
+    
+    return inputs, targets, learning_rate, keep_prob
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_model_inputs(model_inputs)
+
+
+# ### 处理解码输入
+# 
+# 使用 TensorFlow 实现 `process_decoding_input`，以便删掉 `target_data` 中每个批次的最后一个单词 ID，并将 GO ID 放到每个批次的开头。
+
+# In[8]:
+
+
+def process_decoding_input(target_data, target_vocab_to_int, batch_size):
+    """
+    Preprocess target data for dencoding
+    :param target_data: Target Placehoder
+    :param target_vocab_to_int: Dictionary to go from the target words to an id
+    :param batch_size: Batch Size
+    :return: Preprocessed target data
+    """
+    # TODO: Implement Function
+    ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])
+    dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), ending], 1)
+    
+    return dec_input
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_process_decoding_input(process_decoding_input)
+
+
+# ### 编码
+# 
+# 实现 `encoding_layer()`，以使用 [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn) 创建编码器 RNN 层级。
+
+# In[9]:
+
+
+def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob):
+    """
+    Create encoding layer
+    :param rnn_inputs: Inputs for the RNN
+    :param rnn_size: RNN Size
+    :param num_layers: Number of layers
+    :param keep_prob: Dropout keep probability
+    :return: RNN state
+    """
+    # TODO: Implement Function
+    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
+    drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
+    enc_cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)
+    _, enc_state = tf.nn.dynamic_rnn(enc_cell, rnn_inputs, dtype=tf.float32)
+    
+    return enc_state
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_encoding_layer(encoding_layer)
+
+
+# ### 解码 - 训练
+# 
+# 使用 [`tf.contrib.seq2seq.simple_decoder_fn_train()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_train) 和 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 创建训练分对数（training logits）。将 `output_fn` 应用到 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 输出上。
+
+# In[10]:
+
+
+def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,
+                         output_fn, keep_prob):
+    """
+    Create a decoding layer for training
+    :param encoder_state: Encoder State
+    :param dec_cell: Decoder RNN Cell
+    :param dec_embed_input: Decoder embedded input
+    :param sequence_length: Sequence Length
+    :param decoding_scope: TenorFlow Variable Scope for decoding
+    :param output_fn: Function to apply the output layer
+    :param keep_prob: Dropout keep probability
+    :return: Train Logits
+    """
+    # TODO: Implement Function
+    # Training Decoder
+    train_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_train(encoder_state)
+    train_pred, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(
+    dec_cell, train_decoder_fn, dec_embed_input, sequence_length, scope=decoding_scope)
+    
+    # Apply output function
+    train_logits =  output_fn(train_pred)
+
+    return train_logits
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_decoding_layer_train(decoding_layer_train)
+
+
+# ### 解码 - 推论
+# 
+# 使用 [`tf.contrib.seq2seq.simple_decoder_fn_inference()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_inference) 和 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 创建推论分对数（inference logits）。
+
+# In[11]:
+
+
+def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id,
+                         maximum_length, vocab_size, decoding_scope, output_fn, keep_prob):
+    """
+    Create a decoding layer for inference
+    :param encoder_state: Encoder state
+    :param dec_cell: Decoder RNN Cell
+    :param dec_embeddings: Decoder embeddings
+    :param start_of_sequence_id: GO ID
+    :param end_of_sequence_id: EOS Id
+    :param maximum_length: The maximum allowed time steps to decode
+    :param vocab_size: Size of vocabulary
+    :param decoding_scope: TensorFlow Variable Scope for decoding
+    :param output_fn: Function to apply the output layer
+    :param keep_prob: Dropout keep probability
+    :return: Inference Logits
+    """
+    # TODO: Implement Function
+    # Inference Decoder
+    infer_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_inference(
+        output_fn, encoder_state, dec_embeddings, start_of_sequence_id, end_of_sequence_id, 
+        maximum_length - 1, vocab_size)
+    inference_logits, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(dec_cell, infer_decoder_fn, scope=decoding_scope)
+
+    return inference_logits
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_decoding_layer_infer(decoding_layer_infer)
+
+
+# ### 构建解码层级
+# 
+# 实现 `decoding_layer()` 以创建解码器 RNN 层级。
+# 
+# - 使用 `rnn_size` 和 `num_layers` 创建解码 RNN 单元。
+# - 使用 [`lambda`](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions) 创建输出函数，将输入，也就是分对数转换为类分对数（class logits）。
+# - 使用 `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob)` 函数获取训练分对数。
+# - 使用 `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, decoding_scope, output_fn, keep_prob)` 函数获取推论分对数。
+# 
+# 注意：你将需要使用 [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 在训练和推论分对数间分享变量。
+
+# In[12]:
+
+
+def decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size,
+                   num_layers, target_vocab_to_int, keep_prob):
+    """
+    Create decoding layer
+    :param dec_embed_input: Decoder embedded input
+    :param dec_embeddings: Decoder embeddings
+    :param encoder_state: The encoded state
+    :param vocab_size: Size of vocabulary
+    :param sequence_length: Sequence Length
+    :param rnn_size: RNN Size
+    :param num_layers: Number of layers
+    :param target_vocab_to_int: Dictionary to go from the target words to an id
+    :param keep_prob: Dropout keep probability
+    :return: Tuple of (Training Logits, Inference Logits)
+    """
+    # TODO: Implement Function
+    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
+    dropout = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
+    dec_cell = tf.contrib.rnn.MultiRNNCell([dropout] * num_layers)
+    
+    output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, activation_fn=None, scope=decoding_scope)           
+
+    with tf.variable_scope("decoding") as decoding_scope:
+        training_decoder_output = decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,
+                         output_fn, keep_prob)
+
+    with tf.variable_scope("decoding", reuse=True) as decoding_scope:
+        start_of_sequence_id = target_vocab_to_int["<GO>"]
+        end_of_sequence_id = target_vocab_to_int["<EOS>"]
+        inference_decoder_output = decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, 
+                         sequence_length, vocab_size, decoding_scope, output_fn, keep_prob)
+    
+    return training_decoder_output, inference_decoder_output
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_decoding_layer(decoding_layer)
+
+
+# ### 构建神经网络
+# 
+# 应用你在上方实现的函数，以：
+# 
+# - 向编码器的输入数据应用嵌入。
+# - 使用 `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)` 编码输入。
+# - 使用 `process_decoding_input(target_data, target_vocab_to_int, batch_size)` 函数处理目标数据。
+# - 向解码器的目标数据应用嵌入。
+# - 使用 `decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)` 解码编码的输入数据。
+
+# In[13]:
+
+
+def seq2seq_model(input_data, target_data, keep_prob, batch_size, sequence_length, source_vocab_size, target_vocab_size,
+                  enc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int):
+    """
+    Build the Sequence-to-Sequence part of the neural network
+    :param input_data: Input placeholder
+    :param target_data: Target placeholder
+    :param keep_prob: Dropout keep probability placeholder
+    :param batch_size: Batch Size
+    :param sequence_length: Sequence Length
+    :param source_vocab_size: Source vocabulary size
+    :param target_vocab_size: Target vocabulary size
+    :param enc_embedding_size: Decoder embedding size
+    :param dec_embedding_size: Encoder embedding size
+    :param rnn_size: RNN Size
+    :param num_layers: Number of layers
+    :param target_vocab_to_int: Dictionary to go from the target words to an id
+    :return: Tuple of (Training Logits, Inference Logits)
+    """
+    # TODO: Implement Function
+    rnn_inputs = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, enc_embedding_size)
+    
+    encoder_state = encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)
+    
+    dec_input = process_decoding_input(target_data, target_vocab_to_int, batch_size)
+    dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, dec_embedding_size]))
+    dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)
+    
+    train_logits, inference_logits = decoding_layer(dec_embed_input, dec_embeddings, encoder_state, target_vocab_size, sequence_length,
+                       rnn_size, num_layers, target_vocab_to_int, keep_prob)
+    
+    return train_logits, inference_logits
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_seq2seq_model(seq2seq_model)
+
+
+# ## 训练神经网络
+# 
+# ### 超参数
+# 
+# 调试以下参数：
+# 
+# - 将 `epochs` 设为 epoch 次数。
+# - 将 `batch_size` 设为批次大小。
+# - 将 `rnn_size` 设为 RNN 的大小。
+# - 将 `num_layers` 设为层级数量。
+# - 将 `encoding_embedding_size` 设为编码器嵌入大小。
+# - 将 `decoding_embedding_size` 设为解码器嵌入大小
+# - 将 `learning_rate` 设为训练速率。
+# - 将 `keep_probability` 设为丢弃保留率（Dropout keep probability）。
+
+# In[14]:
+
+
+# Number of Epochs
+epochs = 5
+# Batch Size
+batch_size = 128
+# RNN Size
+rnn_size = 128
+# Number of Layers
+num_layers = 2
+# Embedding Size
+encoding_embedding_size = 100
+decoding_embedding_size = 100
+# Learning Rate
+learning_rate = 0.01
+# Dropout Keep Probability
+keep_probability = 0.8
+
+
+# ### 构建图表
+# 
+# 使用你实现的神经网络构建图表。
+
+# In[15]:
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+save_path = 'checkpoints/dev'
+(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()
+max_source_sentence_length = max([len(sentence) for sentence in source_int_text])
+
+train_graph = tf.Graph()
+with train_graph.as_default():
+    input_data, targets, lr, keep_prob = model_inputs()
+    sequence_length = tf.placeholder_with_default(max_source_sentence_length, None, name='sequence_length')
+    input_shape = tf.shape(input_data)
+    
+    train_logits, inference_logits = seq2seq_model(
+        tf.reverse(input_data, [-1]), targets, keep_prob, batch_size, sequence_length, len(source_vocab_to_int), len(target_vocab_to_int),
+        encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, target_vocab_to_int)
+
+    tf.identity(inference_logits, 'logits')
+    with tf.name_scope("optimization"):
+        # Loss function
+        cost = tf.contrib.seq2seq.sequence_loss(
+            train_logits,
+            targets,
+            tf.ones([input_shape[0], sequence_length]))
+
+        # Optimizer
+        optimizer = tf.train.AdamOptimizer(lr)
+
+        # Gradient Clipping
+        gradients = optimizer.compute_gradients(cost)
+        capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]
+        train_op = optimizer.apply_gradients(capped_gradients)
+
+
+# ### 训练
+# 
+# 利用预处理的数据训练神经网络。如果很难获得低损失值，请访问我们的论坛，看看其他人是否遇到了相同的问题。
+
+# In[16]:
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import time
+
+def get_accuracy(target, logits):
+    """
+    Calculate accuracy
+    """
+    max_seq = max(target.shape[1], logits.shape[1])
+    if max_seq - target.shape[1]:
+        target = np.pad(
+            target,
+            [(0,0),(0,max_seq - target.shape[1])],
+            'constant')
+    if max_seq - logits.shape[1]:
+        logits = np.pad(
+            logits,
+            [(0,0),(0,max_seq - logits.shape[1]), (0,0)],
+            'constant')
+
+    return np.mean(np.equal(target, np.argmax(logits, 2)))
+
+train_source = source_int_text[batch_size:]
+train_target = target_int_text[batch_size:]
+
+valid_source = helper.pad_sentence_batch(source_int_text[:batch_size])
+valid_target = helper.pad_sentence_batch(target_int_text[:batch_size])
+
+with tf.Session(graph=train_graph) as sess:
+    sess.run(tf.global_variables_initializer())
+
+    for epoch_i in range(epochs):
+        for batch_i, (source_batch, target_batch) in enumerate(
+                helper.batch_data(train_source, train_target, batch_size)):
+            start_time = time.time()
+            
+            _, loss = sess.run(
+                [train_op, cost],
+                {input_data: source_batch,
+                 targets: target_batch,
+                 lr: learning_rate,
+                 sequence_length: target_batch.shape[1],
+                 keep_prob: keep_probability})
+            
+            batch_train_logits = sess.run(
+                inference_logits,
+                {input_data: source_batch, keep_prob: 1.0})
+            batch_valid_logits = sess.run(
+                inference_logits,
+                {input_data: valid_source, keep_prob: 1.0})
+                
+            train_acc = get_accuracy(target_batch, batch_train_logits)
+            valid_acc = get_accuracy(np.array(valid_target), batch_valid_logits)
+            end_time = time.time()
+            print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.3f}, Validation Accuracy: {:>6.3f}, Loss: {:>6.3f}'
+                  .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))
+
+    # Save Model
+    saver = tf.train.Saver()
+    saver.save(sess, save_path)
+    print('Model Trained and Saved')
+
+
+# ### 保存参数
+# 
+# 保存 `batch_size` 和 `save_path` 参数以进行推论（for inference）。
+
+# In[17]:
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+# Save parameters for checkpoint
+helper.save_params(save_path)
+
+
+# # 检查点
+
+# In[18]:
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import tensorflow as tf
+import numpy as np
+import helper
+import problem_unittests as tests
+
+_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()
+load_path = helper.load_params()
+
+
+# ## 句子到序列
+# 
+# 要向模型提供要翻译的句子，你首先需要预处理该句子。实现函数 `sentence_to_seq()` 以预处理新的句子。
+# 
+# - 将句子转换为小写形式
+# - 使用 `vocab_to_int` 将单词转换为 id
+#  - 如果单词不在词汇表中，将其转换为`<UNK>` 单词 id
+
+# In[19]:
+
+
+def sentence_to_seq(sentence, vocab_to_int):
+    """
+    Convert a sentence to a sequence of ids
+    :param sentence: String
+    :param vocab_to_int: Dictionary to go from the words to an id
+    :return: List of word ids
+    """
+    # TODO: Implement Function
+    word_ids = [vocab_to_int.get(word, vocab_to_int["<UNK>"]) for word in sentence.lower().split()]
+    
+    return word_ids
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_sentence_to_seq(sentence_to_seq)
+
+
+# ## 翻译
+# 
+# 将 `translate_sentence` 从英语翻译成法语。
+
+# In[20]:
+
+
+translate_sentence = 'he saw a old yellow truck .'
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)
+
+loaded_graph = tf.Graph()
+with tf.Session(graph=loaded_graph) as sess:
+    # Load saved model
+    loader = tf.train.import_meta_graph(load_path + '.meta')
+    loader.restore(sess, load_path)
+
+    input_data = loaded_graph.get_tensor_by_name('input:0')
+    logits = loaded_graph.get_tensor_by_name('logits:0')
+    keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
+
+    translate_logits = sess.run(logits, {input_data: [translate_sentence], keep_prob: 1.0})[0]
+
+print('Input')
+print('  Word Ids:      {}'.format([i for i in translate_sentence]))
+print('  English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))
+
+print('\nPrediction')
+print('  Word Ids:      {}'.format([i for i in np.argmax(translate_logits, 1)]))
+print('  French Words: {}'.format([target_int_to_vocab[i] for i in np.argmax(translate_logits, 1)]))
+
+
+# ## 不完美的翻译
+# 
+# 你可能注意到了，某些句子的翻译质量比其他的要好。因为你使用的数据集只有 227 个英语单词，但实际生活中有数千个单词，只有使用这些单词的句子结果才会比较理想。对于此项目，不需要达到完美的翻译。但是，如果你想创建更好的翻译模型，则需要更好的数据。
+# 
+# 你可以使用 [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar) 语料库训练模型。该数据集拥有更多的词汇，讨论的话题也更丰富。但是，训练时间要好多天的时间，所以确保你有 GPU 并且对于我们提供的数据集，你的神经网络性能很棒。提交此项目后，别忘了研究下 WMT10 语料库。
+# 
+# 
+# ## 提交项目
+# 
+# 提交项目时，确保先运行所有单元，然后再保存记事本。保存记事本文件为 “dlnd_language_translation.ipynb”，再通过菜单中的“文件” ->“下载为”将其另存为 HTML 格式。提交的项目文档中需包含“helper.py”和“problem_unittests.py”文件。
+# 

From f17fbd627b87d4c6ef3430324f1babb9f9373061 Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 24 Sep 2017 19:36:22 +0800
Subject: [PATCH 15/16] Delete dlnd_face_generation.ipynb

---
 face-generation/dlnd_face_generation.ipynb | 585 ---------------------
 1 file changed, 585 deletions(-)
 delete mode 100755 face-generation/dlnd_face_generation.ipynb

diff --git a/face-generation/dlnd_face_generation.ipynb b/face-generation/dlnd_face_generation.ipynb
deleted file mode 100755
index 59bd77f..0000000
--- a/face-generation/dlnd_face_generation.ipynb
+++ /dev/null
@@ -1,585 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "# Face Generation\n",
-    "In this project, you'll use generative adversarial networks to generate new images of faces.\n",
-    "### Get the Data\n",
-    "You'll be using two datasets in this project:\n",
-    "- MNIST\n",
-    "- CelebA\n",
-    "\n",
-    "Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA.  Running the GANs on MNIST will allow you to see how well your model trains sooner.\n",
-    "\n",
-    "If you're using [FloydHub](https://www.floydhub.com/), set `data_dir` to \"/input\" and use the [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) \"R5KrjnANiKVhLWAkpXhNBe\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "data_dir = './data'\n",
-    "\n",
-    "# FloydHub - Use with data ID \"R5KrjnANiKVhLWAkpXhNBe\"\n",
-    "#data_dir = '/input'\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import helper\n",
-    "\n",
-    "helper.download_extract('mnist', data_dir)\n",
-    "helper.download_extract('celeba', data_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Explore the Data\n",
-    "### MNIST\n",
-    "As you're aware, the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset contains images of handwritten digits. You can view the first number of examples by changing `show_n_images`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "show_n_images = 25\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "%matplotlib inline\n",
-    "import os\n",
-    "from glob import glob\n",
-    "from matplotlib import pyplot\n",
-    "\n",
-    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')\n",
-    "pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### CelebA\n",
-    "The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations.  Since you're going to be generating faces, you won't need the annotations.  You can view the first number of examples by changing `show_n_images`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "show_n_images = 25\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')\n",
-    "pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Preprocess the Data\n",
-    "Since the project's main focus is on building the GANs, we'll preprocess the data for you.  The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images.  The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.\n",
-    "\n",
-    "The MNIST images are black and white images with a single [color channel](https://en.wikipedia.org/wiki/Channel_(digital_image%29) while the CelebA images have [3 color channels (RGB color channel)](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images).\n",
-    "## Build the Neural Network\n",
-    "You'll build the components necessary to build a GANs by implementing the following functions below:\n",
-    "- `model_inputs`\n",
-    "- `discriminator`\n",
-    "- `generator`\n",
-    "- `model_loss`\n",
-    "- `model_opt`\n",
-    "- `train`\n",
-    "\n",
-    "### Check the Version of TensorFlow and Access to GPU\n",
-    "This will check to make sure you have the correct version of TensorFlow and access to a GPU"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "from distutils.version import LooseVersion\n",
-    "import warnings\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# Check TensorFlow Version\n",
-    "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)\n",
-    "print('TensorFlow Version: {}'.format(tf.__version__))\n",
-    "\n",
-    "# Check for a GPU\n",
-    "if not tf.test.gpu_device_name():\n",
-    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
-    "else:\n",
-    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Input\n",
-    "Implement the `model_inputs` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n",
-    "- Real input images placeholder with rank 4 using `image_width`, `image_height`, and `image_channels`.\n",
-    "- Z input placeholder with rank 2 using `z_dim`.\n",
-    "- Learning rate placeholder with rank 0.\n",
-    "\n",
-    "Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "import problem_unittests as tests\n",
-    "\n",
-    "def model_inputs(image_width, image_height, image_channels, z_dim):\n",
-    "    \"\"\"\n",
-    "    Create the model inputs\n",
-    "    :param image_width: The input image width\n",
-    "    :param image_height: The input image height\n",
-    "    :param image_channels: The number of image channels\n",
-    "    :param z_dim: The dimension of Z\n",
-    "    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "\n",
-    "    return None, None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_model_inputs(model_inputs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Discriminator\n",
-    "Implement `discriminator` to create a discriminator neural network that discriminates on `images`.  This function should be able to reuse the variabes in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"discriminator\" to allow the variables to be reused.  The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def discriminator(images, reuse=False):\n",
-    "    \"\"\"\n",
-    "    Create the discriminator network\n",
-    "    :param image: Tensor of input image(s)\n",
-    "    :param reuse: Boolean if the weights should be reused\n",
-    "    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "\n",
-    "    return None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_discriminator(discriminator, tf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Generator\n",
-    "Implement `generator` to generate an image using `z`. This function should be able to reuse the variabes in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"generator\" to allow the variables to be reused. The function should return the generated 28 x 28 x `out_channel_dim` images."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def generator(z, out_channel_dim, is_train=True):\n",
-    "    \"\"\"\n",
-    "    Create the generator network\n",
-    "    :param z: Input z\n",
-    "    :param out_channel_dim: The number of channels in the output image\n",
-    "    :param is_train: Boolean if generator is being used for training\n",
-    "    :return: The tensor output of the generator\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    \n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_generator(generator, tf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Loss\n",
-    "Implement `model_loss` to build the GANs for training and calculate the loss.  The function should return a tuple of (discriminator loss, generator loss).  Use the following functions you implemented:\n",
-    "- `discriminator(images, reuse=False)`\n",
-    "- `generator(z, out_channel_dim, is_train=True)`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def model_loss(input_real, input_z, out_channel_dim):\n",
-    "    \"\"\"\n",
-    "    Get the loss for the discriminator and generator\n",
-    "    :param input_real: Images from the real dataset\n",
-    "    :param input_z: Z input\n",
-    "    :param out_channel_dim: The number of channels in the output image\n",
-    "    :return: A tuple of (discriminator loss, generator loss)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    \n",
-    "    return None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_model_loss(model_loss)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Optimization\n",
-    "Implement `model_opt` to create the optimization operations for the GANs. Use [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) to get all the trainable variables.  Filter the variables with names that are in the discriminator and generator scope names.  The function should return a tuple of (discriminator training operation, generator training operation)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
-    "    \"\"\"\n",
-    "    Get optimization operations\n",
-    "    :param d_loss: Discriminator loss Tensor\n",
-    "    :param g_loss: Generator loss Tensor\n",
-    "    :param learning_rate: Learning Rate Placeholder\n",
-    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
-    "    :return: A tuple of (discriminator training operation, generator training operation)\n",
-    "    \"\"\"\n",
-    "    # TODO: Implement Function\n",
-    "    \n",
-    "    return None, None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "tests.test_model_opt(model_opt, tf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Neural Network Training\n",
-    "### Show Output\n",
-    "Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL\n",
-    "\"\"\"\n",
-    "import numpy as np\n",
-    "\n",
-    "def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):\n",
-    "    \"\"\"\n",
-    "    Show example output for the generator\n",
-    "    :param sess: TensorFlow session\n",
-    "    :param n_images: Number of Images to display\n",
-    "    :param input_z: Input Z Tensor\n",
-    "    :param out_channel_dim: The number of channels in the output image\n",
-    "    :param image_mode: The mode to use for images (\"RGB\" or \"L\")\n",
-    "    \"\"\"\n",
-    "    cmap = None if image_mode == 'RGB' else 'gray'\n",
-    "    z_dim = input_z.get_shape().as_list()[-1]\n",
-    "    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])\n",
-    "\n",
-    "    samples = sess.run(\n",
-    "        generator(input_z, out_channel_dim, False),\n",
-    "        feed_dict={input_z: example_z})\n",
-    "\n",
-    "    images_grid = helper.images_square_grid(samples, image_mode)\n",
-    "    pyplot.imshow(images_grid, cmap=cmap)\n",
-    "    pyplot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Train\n",
-    "Implement `train` to build and train the GANs.  Use the following functions you implemented:\n",
-    "- `model_inputs(image_width, image_height, image_channels, z_dim)`\n",
-    "- `model_loss(input_real, input_z, out_channel_dim)`\n",
-    "- `model_opt(d_loss, g_loss, learning_rate, beta1)`\n",
-    "\n",
-    "Use the `show_generator_output` to show `generator` output while you train. Running `show_generator_output` for every batch will drastically increase training time and increase the size of the notebook.  It's recommended to print the `generator` output every 100 batches."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):\n",
-    "    \"\"\"\n",
-    "    Train the GAN\n",
-    "    :param epoch_count: Number of epochs\n",
-    "    :param batch_size: Batch Size\n",
-    "    :param z_dim: Z dimension\n",
-    "    :param learning_rate: Learning Rate\n",
-    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
-    "    :param get_batches: Function to get batches\n",
-    "    :param data_shape: Shape of the data\n",
-    "    :param data_image_mode: The image mode to use for images (\"RGB\" or \"L\")\n",
-    "    \"\"\"\n",
-    "    # TODO: Build Model\n",
-    "    \n",
-    "    \n",
-    "    with tf.Session() as sess:\n",
-    "        sess.run(tf.global_variables_initializer())\n",
-    "        for epoch_i in range(epoch_count):\n",
-    "            for batch_images in get_batches(batch_size):\n",
-    "                # TODO: Train Model\n",
-    "                \n",
-    "                "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### MNIST\n",
-    "Test your GANs architecture on MNIST.  After 2 epochs, the GANs should be able to generate images that look like handwritten digits.  Make sure the loss of the generator is lower than the loss of the discriminator or close to 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "batch_size = None\n",
-    "z_dim = None\n",
-    "learning_rate = None\n",
-    "beta1 = None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "epochs = 2\n",
-    "\n",
-    "mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))\n",
-    "with tf.Graph().as_default():\n",
-    "    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,\n",
-    "          mnist_dataset.shape, mnist_dataset.image_mode)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### CelebA\n",
-    "Run your GANs on CelebA.  It will take around 20 minutes on the average GPU to run one epoch.  You can run the whole epoch or stop when it starts to generate realistic faces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "batch_size = None\n",
-    "z_dim = None\n",
-    "learning_rate = None\n",
-    "beta1 = None\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
-    "\"\"\"\n",
-    "epochs = 1\n",
-    "\n",
-    "celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))\n",
-    "with tf.Graph().as_default():\n",
-    "    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,\n",
-    "          celeba_dataset.shape, celeba_dataset.image_mode)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "### Submitting This Project\n",
-    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

From be495d377f1e9486cab08a74168f3f540a16bfdf Mon Sep 17 00:00:00 2001
From: YCG09 <18601969997@126.com>
Date: Sun, 24 Sep 2017 19:37:13 +0800
Subject: [PATCH 16/16] Add files via upload

---
 face-generation/dlnd_face_generation.html  | 58623 +++++++++++++++++++
 face-generation/dlnd_face_generation.ipynb |  2032 +
 face-generation/dlnd_face_generation.py    |   459 +
 3 files changed, 61114 insertions(+)
 create mode 100644 face-generation/dlnd_face_generation.html
 create mode 100644 face-generation/dlnd_face_generation.ipynb
 create mode 100644 face-generation/dlnd_face_generation.py

diff --git a/face-generation/dlnd_face_generation.html b/face-generation/dlnd_face_generation.html
new file mode 100644
index 0000000..b3dc1c4
--- /dev/null
+++ b/face-generation/dlnd_face_generation.html
@@ -0,0 +1,58623 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>dlnd_face_generation</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: 1px dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    background: transparent !important;
+    color: #000 !important;
+    box-shadow: none !important;
+    text-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: 'Glyphicons Halflings';
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Glyphicons Halflings';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+  content: "\e156";
+}
+.glyphicon-unchecked:before {
+  content: "\e157";
+}
+.glyphicon-expand:before {
+  content: "\e158";
+}
+.glyphicon-collapse-down:before {
+  content: "\e159";
+}
+.glyphicon-collapse-up:before {
+  content: "\e160";
+}
+.glyphicon-log-in:before {
+  content: "\e161";
+}
+.glyphicon-flash:before {
+  content: "\e162";
+}
+.glyphicon-log-out:before {
+  content: "\e163";
+}
+.glyphicon-new-window:before {
+  content: "\e164";
+}
+.glyphicon-record:before {
+  content: "\e165";
+}
+.glyphicon-save:before {
+  content: "\e166";
+}
+.glyphicon-open:before {
+  content: "\e167";
+}
+.glyphicon-saved:before {
+  content: "\e168";
+}
+.glyphicon-import:before {
+  content: "\e169";
+}
+.glyphicon-export:before {
+  content: "\e170";
+}
+.glyphicon-send:before {
+  content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+  content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+  content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+  content: "\e174";
+}
+.glyphicon-floppy-save:before {
+  content: "\e175";
+}
+.glyphicon-floppy-open:before {
+  content: "\e176";
+}
+.glyphicon-credit-card:before {
+  content: "\e177";
+}
+.glyphicon-transfer:before {
+  content: "\e178";
+}
+.glyphicon-cutlery:before {
+  content: "\e179";
+}
+.glyphicon-header:before {
+  content: "\e180";
+}
+.glyphicon-compressed:before {
+  content: "\e181";
+}
+.glyphicon-earphone:before {
+  content: "\e182";
+}
+.glyphicon-phone-alt:before {
+  content: "\e183";
+}
+.glyphicon-tower:before {
+  content: "\e184";
+}
+.glyphicon-stats:before {
+  content: "\e185";
+}
+.glyphicon-sd-video:before {
+  content: "\e186";
+}
+.glyphicon-hd-video:before {
+  content: "\e187";
+}
+.glyphicon-subtitles:before {
+  content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+  content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+  content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+  content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+  content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+  content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
+.glyphicon-pawn:before {
+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
+.glyphicon-bitcoin:before {
+  content: "\e227";
+}
+.glyphicon-btc:before {
+  content: "\e227";
+}
+.glyphicon-xbt:before {
+  content: "\e227";
+}
+.glyphicon-yen:before {
+  content: "\00a5";
+}
+.glyphicon-jpy:before {
+  content: "\00a5";
+}
+.glyphicon-ruble:before {
+  content: "\20bd";
+}
+.glyphicon-rub:before {
+  content: "\20bd";
+}
+.glyphicon-scale:before {
+  content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+  content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+  content: "\e232";
+}
+.glyphicon-education:before {
+  content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+  content: "\e234";
+}
+.glyphicon-option-vertical:before {
+  content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+  content: "\e236";
+}
+.glyphicon-modal-window:before {
+  content: "\e237";
+}
+.glyphicon-oil:before {
+  content: "\e238";
+}
+.glyphicon-grain:before {
+  content: "\e239";
+}
+.glyphicon-sunglasses:before {
+  content: "\e240";
+}
+.glyphicon-text-size:before {
+  content: "\e241";
+}
+.glyphicon-text-color:before {
+  content: "\e242";
+}
+.glyphicon-text-background:before {
+  content: "\e243";
+}
+.glyphicon-object-align-top:before {
+  content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+  content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+  content: "\e246";
+}
+.glyphicon-object-align-left:before {
+  content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+  content: "\e248";
+}
+.glyphicon-object-align-right:before {
+  content: "\e249";
+}
+.glyphicon-triangle-right:before {
+  content: "\e250";
+}
+.glyphicon-triangle-left:before {
+  content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+  content: "\e252";
+}
+.glyphicon-triangle-top:before {
+  content: "\e253";
+}
+.glyphicon-console:before {
+  content: "\e254";
+}
+.glyphicon-superscript:before {
+  content: "\e255";
+}
+.glyphicon-subscript:before {
+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
+h5 small,
+h6 small,
+.h1 small,
+.h2 small,
+.h3 small,
+.h4 small,
+.h5 small,
+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 18px;
+  margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
+h2,
+.h2 {
+  font-size: 27px;
+}
+h3,
+.h3 {
+  font-size: 23px;
+}
+h4,
+.h4 {
+  font-size: 17px;
+}
+h5,
+.h5 {
+  font-size: 13px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
+address {
+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
+.col-xs-9 {
+  width: 75%;
+}
+.col-xs-8 {
+  width: 66.66666667%;
+}
+.col-xs-7 {
+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+  content: "\f044";
+}
+.fa-share-square-o:before {
+  content: "\f045";
+}
+.fa-check-square-o:before {
+  content: "\f046";
+}
+.fa-arrows:before {
+  content: "\f047";
+}
+.fa-step-backward:before {
+  content: "\f048";
+}
+.fa-fast-backward:before {
+  content: "\f049";
+}
+.fa-backward:before {
+  content: "\f04a";
+}
+.fa-play:before {
+  content: "\f04b";
+}
+.fa-pause:before {
+  content: "\f04c";
+}
+.fa-stop:before {
+  content: "\f04d";
+}
+.fa-forward:before {
+  content: "\f04e";
+}
+.fa-fast-forward:before {
+  content: "\f050";
+}
+.fa-step-forward:before {
+  content: "\f051";
+}
+.fa-eject:before {
+  content: "\f052";
+}
+.fa-chevron-left:before {
+  content: "\f053";
+}
+.fa-chevron-right:before {
+  content: "\f054";
+}
+.fa-plus-circle:before {
+  content: "\f055";
+}
+.fa-minus-circle:before {
+  content: "\f056";
+}
+.fa-times-circle:before {
+  content: "\f057";
+}
+.fa-check-circle:before {
+  content: "\f058";
+}
+.fa-question-circle:before {
+  content: "\f059";
+}
+.fa-info-circle:before {
+  content: "\f05a";
+}
+.fa-crosshairs:before {
+  content: "\f05b";
+}
+.fa-times-circle-o:before {
+  content: "\f05c";
+}
+.fa-check-circle-o:before {
+  content: "\f05d";
+}
+.fa-ban:before {
+  content: "\f05e";
+}
+.fa-arrow-left:before {
+  content: "\f060";
+}
+.fa-arrow-right:before {
+  content: "\f061";
+}
+.fa-arrow-up:before {
+  content: "\f062";
+}
+.fa-arrow-down:before {
+  content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+  content: "\f064";
+}
+.fa-expand:before {
+  content: "\f065";
+}
+.fa-compress:before {
+  content: "\f066";
+}
+.fa-plus:before {
+  content: "\f067";
+}
+.fa-minus:before {
+  content: "\f068";
+}
+.fa-asterisk:before {
+  content: "\f069";
+}
+.fa-exclamation-circle:before {
+  content: "\f06a";
+}
+.fa-gift:before {
+  content: "\f06b";
+}
+.fa-leaf:before {
+  content: "\f06c";
+}
+.fa-fire:before {
+  content: "\f06d";
+}
+.fa-eye:before {
+  content: "\f06e";
+}
+.fa-eye-slash:before {
+  content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+  content: "\f071";
+}
+.fa-plane:before {
+  content: "\f072";
+}
+.fa-calendar:before {
+  content: "\f073";
+}
+.fa-random:before {
+  content: "\f074";
+}
+.fa-comment:before {
+  content: "\f075";
+}
+.fa-magnet:before {
+  content: "\f076";
+}
+.fa-chevron-up:before {
+  content: "\f077";
+}
+.fa-chevron-down:before {
+  content: "\f078";
+}
+.fa-retweet:before {
+  content: "\f079";
+}
+.fa-shopping-cart:before {
+  content: "\f07a";
+}
+.fa-folder:before {
+  content: "\f07b";
+}
+.fa-folder-open:before {
+  content: "\f07c";
+}
+.fa-arrows-v:before {
+  content: "\f07d";
+}
+.fa-arrows-h:before {
+  content: "\f07e";
+}
+.fa-bar-chart-o:before,
+.fa-bar-chart:before {
+  content: "\f080";
+}
+.fa-twitter-square:before {
+  content: "\f081";
+}
+.fa-facebook-square:before {
+  content: "\f082";
+}
+.fa-camera-retro:before {
+  content: "\f083";
+}
+.fa-key:before {
+  content: "\f084";
+}
+.fa-gears:before,
+.fa-cogs:before {
+  content: "\f085";
+}
+.fa-comments:before {
+  content: "\f086";
+}
+.fa-thumbs-o-up:before {
+  content: "\f087";
+}
+.fa-thumbs-o-down:before {
+  content: "\f088";
+}
+.fa-star-half:before {
+  content: "\f089";
+}
+.fa-heart-o:before {
+  content: "\f08a";
+}
+.fa-sign-out:before {
+  content: "\f08b";
+}
+.fa-linkedin-square:before {
+  content: "\f08c";
+}
+.fa-thumb-tack:before {
+  content: "\f08d";
+}
+.fa-external-link:before {
+  content: "\f08e";
+}
+.fa-sign-in:before {
+  content: "\f090";
+}
+.fa-trophy:before {
+  content: "\f091";
+}
+.fa-github-square:before {
+  content: "\f092";
+}
+.fa-upload:before {
+  content: "\f093";
+}
+.fa-lemon-o:before {
+  content: "\f094";
+}
+.fa-phone:before {
+  content: "\f095";
+}
+.fa-square-o:before {
+  content: "\f096";
+}
+.fa-bookmark-o:before {
+  content: "\f097";
+}
+.fa-phone-square:before {
+  content: "\f098";
+}
+.fa-twitter:before {
+  content: "\f099";
+}
+.fa-facebook:before {
+  content: "\f09a";
+}
+.fa-github:before {
+  content: "\f09b";
+}
+.fa-unlock:before {
+  content: "\f09c";
+}
+.fa-credit-card:before {
+  content: "\f09d";
+}
+.fa-rss:before {
+  content: "\f09e";
+}
+.fa-hdd-o:before {
+  content: "\f0a0";
+}
+.fa-bullhorn:before {
+  content: "\f0a1";
+}
+.fa-bell:before {
+  content: "\f0f3";
+}
+.fa-certificate:before {
+  content: "\f0a3";
+}
+.fa-hand-o-right:before {
+  content: "\f0a4";
+}
+.fa-hand-o-left:before {
+  content: "\f0a5";
+}
+.fa-hand-o-up:before {
+  content: "\f0a6";
+}
+.fa-hand-o-down:before {
+  content: "\f0a7";
+}
+.fa-arrow-circle-left:before {
+  content: "\f0a8";
+}
+.fa-arrow-circle-right:before {
+  content: "\f0a9";
+}
+.fa-arrow-circle-up:before {
+  content: "\f0aa";
+}
+.fa-arrow-circle-down:before {
+  content: "\f0ab";
+}
+.fa-globe:before {
+  content: "\f0ac";
+}
+.fa-wrench:before {
+  content: "\f0ad";
+}
+.fa-tasks:before {
+  content: "\f0ae";
+}
+.fa-filter:before {
+  content: "\f0b0";
+}
+.fa-briefcase:before {
+  content: "\f0b1";
+}
+.fa-arrows-alt:before {
+  content: "\f0b2";
+}
+.fa-group:before,
+.fa-users:before {
+  content: "\f0c0";
+}
+.fa-chain:before,
+.fa-link:before {
+  content: "\f0c1";
+}
+.fa-cloud:before {
+  content: "\f0c2";
+}
+.fa-flask:before {
+  content: "\f0c3";
+}
+.fa-cut:before,
+.fa-scissors:before {
+  content: "\f0c4";
+}
+.fa-copy:before,
+.fa-files-o:before {
+  content: "\f0c5";
+}
+.fa-paperclip:before {
+  content: "\f0c6";
+}
+.fa-save:before,
+.fa-floppy-o:before {
+  content: "\f0c7";
+}
+.fa-square:before {
+  content: "\f0c8";
+}
+.fa-navicon:before,
+.fa-reorder:before,
+.fa-bars:before {
+  content: "\f0c9";
+}
+.fa-list-ul:before {
+  content: "\f0ca";
+}
+.fa-list-ol:before {
+  content: "\f0cb";
+}
+.fa-strikethrough:before {
+  content: "\f0cc";
+}
+.fa-underline:before {
+  content: "\f0cd";
+}
+.fa-table:before {
+  content: "\f0ce";
+}
+.fa-magic:before {
+  content: "\f0d0";
+}
+.fa-truck:before {
+  content: "\f0d1";
+}
+.fa-pinterest:before {
+  content: "\f0d2";
+}
+.fa-pinterest-square:before {
+  content: "\f0d3";
+}
+.fa-google-plus-square:before {
+  content: "\f0d4";
+}
+.fa-google-plus:before {
+  content: "\f0d5";
+}
+.fa-money:before {
+  content: "\f0d6";
+}
+.fa-caret-down:before {
+  content: "\f0d7";
+}
+.fa-caret-up:before {
+  content: "\f0d8";
+}
+.fa-caret-left:before {
+  content: "\f0d9";
+}
+.fa-caret-right:before {
+  content: "\f0da";
+}
+.fa-columns:before {
+  content: "\f0db";
+}
+.fa-unsorted:before,
+.fa-sort:before {
+  content: "\f0dc";
+}
+.fa-sort-down:before,
+.fa-sort-desc:before {
+  content: "\f0dd";
+}
+.fa-sort-up:before,
+.fa-sort-asc:before {
+  content: "\f0de";
+}
+.fa-envelope:before {
+  content: "\f0e0";
+}
+.fa-linkedin:before {
+  content: "\f0e1";
+}
+.fa-rotate-left:before,
+.fa-undo:before {
+  content: "\f0e2";
+}
+.fa-legal:before,
+.fa-gavel:before {
+  content: "\f0e3";
+}
+.fa-dashboard:before,
+.fa-tachometer:before {
+  content: "\f0e4";
+}
+.fa-comment-o:before {
+  content: "\f0e5";
+}
+.fa-comments-o:before {
+  content: "\f0e6";
+}
+.fa-flash:before,
+.fa-bolt:before {
+  content: "\f0e7";
+}
+.fa-sitemap:before {
+  content: "\f0e8";
+}
+.fa-umbrella:before {
+  content: "\f0e9";
+}
+.fa-paste:before,
+.fa-clipboard:before {
+  content: "\f0ea";
+}
+.fa-lightbulb-o:before {
+  content: "\f0eb";
+}
+.fa-exchange:before {
+  content: "\f0ec";
+}
+.fa-cloud-download:before {
+  content: "\f0ed";
+}
+.fa-cloud-upload:before {
+  content: "\f0ee";
+}
+.fa-user-md:before {
+  content: "\f0f0";
+}
+.fa-stethoscope:before {
+  content: "\f0f1";
+}
+.fa-suitcase:before {
+  content: "\f0f2";
+}
+.fa-bell-o:before {
+  content: "\f0a2";
+}
+.fa-coffee:before {
+  content: "\f0f4";
+}
+.fa-cutlery:before {
+  content: "\f0f5";
+}
+.fa-file-text-o:before {
+  content: "\f0f6";
+}
+.fa-building-o:before {
+  content: "\f0f7";
+}
+.fa-hospital-o:before {
+  content: "\f0f8";
+}
+.fa-ambulance:before {
+  content: "\f0f9";
+}
+.fa-medkit:before {
+  content: "\f0fa";
+}
+.fa-fighter-jet:before {
+  content: "\f0fb";
+}
+.fa-beer:before {
+  content: "\f0fc";
+}
+.fa-h-square:before {
+  content: "\f0fd";
+}
+.fa-plus-square:before {
+  content: "\f0fe";
+}
+.fa-angle-double-left:before {
+  content: "\f100";
+}
+.fa-angle-double-right:before {
+  content: "\f101";
+}
+.fa-angle-double-up:before {
+  content: "\f102";
+}
+.fa-angle-double-down:before {
+  content: "\f103";
+}
+.fa-angle-left:before {
+  content: "\f104";
+}
+.fa-angle-right:before {
+  content: "\f105";
+}
+.fa-angle-up:before {
+  content: "\f106";
+}
+.fa-angle-down:before {
+  content: "\f107";
+}
+.fa-desktop:before {
+  content: "\f108";
+}
+.fa-laptop:before {
+  content: "\f109";
+}
+.fa-tablet:before {
+  content: "\f10a";
+}
+.fa-mobile-phone:before,
+.fa-mobile:before {
+  content: "\f10b";
+}
+.fa-circle-o:before {
+  content: "\f10c";
+}
+.fa-quote-left:before {
+  content: "\f10d";
+}
+.fa-quote-right:before {
+  content: "\f10e";
+}
+.fa-spinner:before {
+  content: "\f110";
+}
+.fa-circle:before {
+  content: "\f111";
+}
+.fa-mail-reply:before,
+.fa-reply:before {
+  content: "\f112";
+}
+.fa-github-alt:before {
+  content: "\f113";
+}
+.fa-folder-o:before {
+  content: "\f114";
+}
+.fa-folder-open-o:before {
+  content: "\f115";
+}
+.fa-smile-o:before {
+  content: "\f118";
+}
+.fa-frown-o:before {
+  content: "\f119";
+}
+.fa-meh-o:before {
+  content: "\f11a";
+}
+.fa-gamepad:before {
+  content: "\f11b";
+}
+.fa-keyboard-o:before {
+  content: "\f11c";
+}
+.fa-flag-o:before {
+  content: "\f11d";
+}
+.fa-flag-checkered:before {
+  content: "\f11e";
+}
+.fa-terminal:before {
+  content: "\f120";
+}
+.fa-code:before {
+  content: "\f121";
+}
+.fa-mail-reply-all:before,
+.fa-reply-all:before {
+  content: "\f122";
+}
+.fa-star-half-empty:before,
+.fa-star-half-full:before,
+.fa-star-half-o:before {
+  content: "\f123";
+}
+.fa-location-arrow:before {
+  content: "\f124";
+}
+.fa-crop:before {
+  content: "\f125";
+}
+.fa-code-fork:before {
+  content: "\f126";
+}
+.fa-unlink:before,
+.fa-chain-broken:before {
+  content: "\f127";
+}
+.fa-question:before {
+  content: "\f128";
+}
+.fa-info:before {
+  content: "\f129";
+}
+.fa-exclamation:before {
+  content: "\f12a";
+}
+.fa-superscript:before {
+  content: "\f12b";
+}
+.fa-subscript:before {
+  content: "\f12c";
+}
+.fa-eraser:before {
+  content: "\f12d";
+}
+.fa-puzzle-piece:before {
+  content: "\f12e";
+}
+.fa-microphone:before {
+  content: "\f130";
+}
+.fa-microphone-slash:before {
+  content: "\f131";
+}
+.fa-shield:before {
+  content: "\f132";
+}
+.fa-calendar-o:before {
+  content: "\f133";
+}
+.fa-fire-extinguisher:before {
+  content: "\f134";
+}
+.fa-rocket:before {
+  content: "\f135";
+}
+.fa-maxcdn:before {
+  content: "\f136";
+}
+.fa-chevron-circle-left:before {
+  content: "\f137";
+}
+.fa-chevron-circle-right:before {
+  content: "\f138";
+}
+.fa-chevron-circle-up:before {
+  content: "\f139";
+}
+.fa-chevron-circle-down:before {
+  content: "\f13a";
+}
+.fa-html5:before {
+  content: "\f13b";
+}
+.fa-css3:before {
+  content: "\f13c";
+}
+.fa-anchor:before {
+  content: "\f13d";
+}
+.fa-unlock-alt:before {
+  content: "\f13e";
+}
+.fa-bullseye:before {
+  content: "\f140";
+}
+.fa-ellipsis-h:before {
+  content: "\f141";
+}
+.fa-ellipsis-v:before {
+  content: "\f142";
+}
+.fa-rss-square:before {
+  content: "\f143";
+}
+.fa-play-circle:before {
+  content: "\f144";
+}
+.fa-ticket:before {
+  content: "\f145";
+}
+.fa-minus-square:before {
+  content: "\f146";
+}
+.fa-minus-square-o:before {
+  content: "\f147";
+}
+.fa-level-up:before {
+  content: "\f148";
+}
+.fa-level-down:before {
+  content: "\f149";
+}
+.fa-check-square:before {
+  content: "\f14a";
+}
+.fa-pencil-square:before {
+  content: "\f14b";
+}
+.fa-external-link-square:before {
+  content: "\f14c";
+}
+.fa-share-square:before {
+  content: "\f14d";
+}
+.fa-compass:before {
+  content: "\f14e";
+}
+.fa-toggle-down:before,
+.fa-caret-square-o-down:before {
+  content: "\f150";
+}
+.fa-toggle-up:before,
+.fa-caret-square-o-up:before {
+  content: "\f151";
+}
+.fa-toggle-right:before,
+.fa-caret-square-o-right:before {
+  content: "\f152";
+}
+.fa-euro:before,
+.fa-eur:before {
+  content: "\f153";
+}
+.fa-gbp:before {
+  content: "\f154";
+}
+.fa-dollar:before,
+.fa-usd:before {
+  content: "\f155";
+}
+.fa-rupee:before,
+.fa-inr:before {
+  content: "\f156";
+}
+.fa-cny:before,
+.fa-rmb:before,
+.fa-yen:before,
+.fa-jpy:before {
+  content: "\f157";
+}
+.fa-ruble:before,
+.fa-rouble:before,
+.fa-rub:before {
+  content: "\f158";
+}
+.fa-won:before,
+.fa-krw:before {
+  content: "\f159";
+}
+.fa-bitcoin:before,
+.fa-btc:before {
+  content: "\f15a";
+}
+.fa-file:before {
+  content: "\f15b";
+}
+.fa-file-text:before {
+  content: "\f15c";
+}
+.fa-sort-alpha-asc:before {
+  content: "\f15d";
+}
+.fa-sort-alpha-desc:before {
+  content: "\f15e";
+}
+.fa-sort-amount-asc:before {
+  content: "\f160";
+}
+.fa-sort-amount-desc:before {
+  content: "\f161";
+}
+.fa-sort-numeric-asc:before {
+  content: "\f162";
+}
+.fa-sort-numeric-desc:before {
+  content: "\f163";
+}
+.fa-thumbs-up:before {
+  content: "\f164";
+}
+.fa-thumbs-down:before {
+  content: "\f165";
+}
+.fa-youtube-square:before {
+  content: "\f166";
+}
+.fa-youtube:before {
+  content: "\f167";
+}
+.fa-xing:before {
+  content: "\f168";
+}
+.fa-xing-square:before {
+  content: "\f169";
+}
+.fa-youtube-play:before {
+  content: "\f16a";
+}
+.fa-dropbox:before {
+  content: "\f16b";
+}
+.fa-stack-overflow:before {
+  content: "\f16c";
+}
+.fa-instagram:before {
+  content: "\f16d";
+}
+.fa-flickr:before {
+  content: "\f16e";
+}
+.fa-adn:before {
+  content: "\f170";
+}
+.fa-bitbucket:before {
+  content: "\f171";
+}
+.fa-bitbucket-square:before {
+  content: "\f172";
+}
+.fa-tumblr:before {
+  content: "\f173";
+}
+.fa-tumblr-square:before {
+  content: "\f174";
+}
+.fa-long-arrow-down:before {
+  content: "\f175";
+}
+.fa-long-arrow-up:before {
+  content: "\f176";
+}
+.fa-long-arrow-left:before {
+  content: "\f177";
+}
+.fa-long-arrow-right:before {
+  content: "\f178";
+}
+.fa-apple:before {
+  content: "\f179";
+}
+.fa-windows:before {
+  content: "\f17a";
+}
+.fa-android:before {
+  content: "\f17b";
+}
+.fa-linux:before {
+  content: "\f17c";
+}
+.fa-dribbble:before {
+  content: "\f17d";
+}
+.fa-skype:before {
+  content: "\f17e";
+}
+.fa-foursquare:before {
+  content: "\f180";
+}
+.fa-trello:before {
+  content: "\f181";
+}
+.fa-female:before {
+  content: "\f182";
+}
+.fa-male:before {
+  content: "\f183";
+}
+.fa-gittip:before {
+  content: "\f184";
+}
+.fa-sun-o:before {
+  content: "\f185";
+}
+.fa-moon-o:before {
+  content: "\f186";
+}
+.fa-archive:before {
+  content: "\f187";
+}
+.fa-bug:before {
+  content: "\f188";
+}
+.fa-vk:before {
+  content: "\f189";
+}
+.fa-weibo:before {
+  content: "\f18a";
+}
+.fa-renren:before {
+  content: "\f18b";
+}
+.fa-pagelines:before {
+  content: "\f18c";
+}
+.fa-stack-exchange:before {
+  content: "\f18d";
+}
+.fa-arrow-circle-o-right:before {
+  content: "\f18e";
+}
+.fa-arrow-circle-o-left:before {
+  content: "\f190";
+}
+.fa-toggle-left:before,
+.fa-caret-square-o-left:before {
+  content: "\f191";
+}
+.fa-dot-circle-o:before {
+  content: "\f192";
+}
+.fa-wheelchair:before {
+  content: "\f193";
+}
+.fa-vimeo-square:before {
+  content: "\f194";
+}
+.fa-turkish-lira:before,
+.fa-try:before {
+  content: "\f195";
+}
+.fa-plus-square-o:before {
+  content: "\f196";
+}
+.fa-space-shuttle:before {
+  content: "\f197";
+}
+.fa-slack:before {
+  content: "\f198";
+}
+.fa-envelope-square:before {
+  content: "\f199";
+}
+.fa-wordpress:before {
+  content: "\f19a";
+}
+.fa-openid:before {
+  content: "\f19b";
+}
+.fa-institution:before,
+.fa-bank:before,
+.fa-university:before {
+  content: "\f19c";
+}
+.fa-mortar-board:before,
+.fa-graduation-cap:before {
+  content: "\f19d";
+}
+.fa-yahoo:before {
+  content: "\f19e";
+}
+.fa-google:before {
+  content: "\f1a0";
+}
+.fa-reddit:before {
+  content: "\f1a1";
+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+[dir="rtl"] #ipython_notebook {
+  float: right !important;
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+[dir="rtl"] #tabs li {
+  float: right;
+}
+ul#tabs {
+  margin-bottom: 4px;
+}
+[dir="rtl"] ul#tabs {
+  margin-right: 0px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+[dir="rtl"] .list_toolbar .tree-buttons {
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-right {
+  padding-top: 1px;
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-left {
+  float: right !important;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+[dir="rtl"] #tree-selector a {
+  float: right;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+[dir="rtl"] #new-menu {
+  text-align: right;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+[dir="rtl"] #running .col-sm-8 {
+  float: right !important;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  min-width: 0;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 21ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
+    display: none;
+  }
+  span.autosave_status {
+    font-size: x-small;
+  }
+}
+.toolbar {
+  padding: 0px;
+  margin-left: -5px;
+  margin-top: 2px;
+  margin-bottom: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+  width: auto;
+  vertical-align: middle;
+  margin-right: 2px;
+  margin-bottom: 0px;
+  display: inline;
+  font-size: 92%;
+  margin-left: 0.3em;
+  margin-right: 0.3em;
+  padding: 0px;
+  padding-top: 3px;
+}
+.toolbar .btn {
+  padding: 2px 8px;
+}
+.toolbar .btn-group {
+  margin-top: 0px;
+  margin-left: 5px;
+}
+#maintoolbar {
+  margin-bottom: -3px;
+  margin-top: -8px;
+  border: 0px;
+  min-height: 27px;
+  margin-left: 0px;
+  padding-top: 11px;
+  padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+  float: none;
+  vertical-align: middle;
+  text-align: right;
+  margin-left: 5px;
+  margin-right: 0px;
+  margin-top: 0px;
+}
+.select-xs {
+  height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+  background-color: #F37626;
+  color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-webkit-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-moz-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+@-webkit-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+  overflow: auto;
+  height: 200px;
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+  text-overflow: ellipsis;
+  overflow: hidden;
+  height: 80px;
+}
+.tooltipbuttons {
+  position: absolute;
+  padding-right: 15px;
+  top: 0px;
+  right: 0px;
+}
+.tooltiptext {
+  /*avoid the button to overlap on some docstring*/
+  padding-right: 30px;
+}
+.ipython_tooltip {
+  max-width: 700px;
+  /*fade-in animation when inserted*/
+  -webkit-animation: fadeOut 400ms;
+  -moz-animation: fadeOut 400ms;
+  animation: fadeOut 400ms;
+  -webkit-animation: fadeIn 400ms;
+  -moz-animation: fadeIn 400ms;
+  animation: fadeIn 400ms;
+  vertical-align: middle;
+  background-color: #f7f7f7;
+  overflow: visible;
+  border: #ababab 1px solid;
+  outline: none;
+  padding: 3px;
+  margin: 0px;
+  padding-left: 7px;
+  font-family: monospace;
+  min-height: 50px;
+  -moz-box-shadow: 0px 6px 10px -1px #adadad;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  border-radius: 2px;
+  position: absolute;
+  z-index: 1000;
+}
+.ipython_tooltip a {
+  float: right;
+}
+.ipython_tooltip .tooltiptext pre {
+  border: 0;
+  border-radius: 0;
+  font-size: 100%;
+  background-color: #f7f7f7;
+}
+.pretooltiparrow {
+  left: 0px;
+  margin: 0px;
+  top: -16px;
+  width: 40px;
+  height: 16px;
+  overflow: hidden;
+  position: absolute;
+}
+.pretooltiparrow:before {
+  background-color: #f7f7f7;
+  border: 1px #ababab solid;
+  z-index: 11;
+  content: "";
+  position: absolute;
+  left: 15px;
+  top: 10px;
+  width: 25px;
+  height: 25px;
+  -webkit-transform: rotate(45deg);
+  -moz-transform: rotate(45deg);
+  -ms-transform: rotate(45deg);
+  -o-transform: rotate(45deg);
+}
+ul.typeahead-list i {
+  margin-left: -10px;
+  width: 18px;
+}
+ul.typeahead-list {
+  max-height: 80vh;
+  overflow: auto;
+}
+ul.typeahead-list > li > a {
+  /** Firefox bug **/
+  /* see https://github.com/jupyter/notebook/issues/559 */
+  white-space: normal;
+}
+.cmd-palette .modal-body {
+  padding: 7px;
+}
+.cmd-palette form {
+  background: white;
+}
+.cmd-palette input {
+  outline: none;
+}
+.no-shortcut {
+  display: none;
+}
+.command-shortcut:before {
+  content: "(command)";
+  padding-right: 3px;
+  color: #777777;
+}
+.edit-shortcut:before {
+  content: "(edit)";
+  padding-right: 3px;
+  color: #777777;
+}
+#find-and-replace #replace-preview .match,
+#find-and-replace #replace-preview .insert {
+  background-color: #BBDEFB;
+  border-color: #90CAF9;
+  border-style: solid;
+  border-width: 1px;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .match {
+  background-color: #FFCDD2;
+  border-color: #EF9A9A;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .insert {
+  background-color: #C8E6C9;
+  border-color: #A5D6A7;
+  border-radius: 0px;
+}
+#find-and-replace #replace-preview {
+  max-height: 60vh;
+  overflow: auto;
+}
+#find-and-replace #replace-preview pre {
+  padding: 5px 10px;
+}
+.terminal-app {
+  background: #EEE;
+}
+.terminal-app #header {
+  background: #fff;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.terminal-app .terminal {
+  width: 100%;
+  float: left;
+  font-family: monospace;
+  color: white;
+  background: black;
+  padding: 0.4em;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+}
+.terminal-app .terminal,
+.terminal-app .terminal dummy-screen {
+  line-height: 1em;
+  font-size: 14px;
+}
+.terminal-app .terminal .xterm-rows {
+  padding: 10px;
+}
+.terminal-app .terminal-cursor {
+  color: black;
+  background: white;
+}
+.terminal-app #terminado-container {
+  margin-top: 20px;
+}
+/*# sourceMappingURL=style.min.css.map */
+    </style>
+<style type="text/css">
+    .highlight .hll { background-color: #ffffcc }
+.highlight  { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #7D9029 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #A0A000 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #0000FF } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+    </style>
+<style type="text/css">
+    
+/* Temporary definitions which will become obsolete with Notebook release 5.0 */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-bold { font-weight: bold; }
+
+    </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+  overflow: visible;
+  padding: 8px;
+}
+
+div#notebook {
+  overflow: visible;
+  border-top: none;
+}@media print {
+  div.cell {
+    display: block;
+    page-break-inside: avoid;
+  } 
+  div.output_wrapper { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+  div.output { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS_HTML"></script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    MathJax.Hub.Config({
+        tex2jax: {
+            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+            processEscapes: true,
+            processEnvironments: true
+        },
+        // Center justify equations in code and markdown cells. Elsewhere
+        // we use CSS to left justify single line equations in code cells.
+        displayAlign: 'center',
+        "HTML-CSS": {
+            styles: {'.MathJax_Display': {"margin": 0}},
+            linebreaks: { automatic: true }
+        }
+    });
+    </script>
+    <!-- End of mathjax configuration --></head>
+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="&#20154;&#33080;&#29983;&#25104;&#65288;Face-Generation&#65289;">&#20154;&#33080;&#29983;&#25104;&#65288;Face Generation&#65289;<a class="anchor-link" href="#&#20154;&#33080;&#29983;&#25104;&#65288;Face-Generation&#65289;">&#182;</a></h1><p>在该项目中，你将使用生成式对抗网络（Generative Adversarial Nets）来生成新的人脸图像。</p>
+<h3 id="&#33719;&#21462;&#25968;&#25454;">&#33719;&#21462;&#25968;&#25454;<a class="anchor-link" href="#&#33719;&#21462;&#25968;&#25454;">&#182;</a></h3><p>该项目将使用以下数据集：</p>
+<ul>
+<li>MNIST</li>
+<li>CelebA</li>
+</ul>
+<p>由于 CelebA 数据集比较复杂，而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型，以让你更快的评估所建立模型的性能。</p>
+<p>如果你在使用 <a href="https://www.floydhub.com/">FloydHub</a>, 请将 <code>data_dir</code> 设置为 "/input" 并使用 <a href="http://docs.floydhub.com/home/using_datasets/">FloydHub data ID</a> "R5KrjnANiKVhLWAkpXhNBe".</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#data_dir = &#39;./data&#39;</span>
+
+<span class="c1"># FloydHub - Use with data ID &quot;R5KrjnANiKVhLWAkpXhNBe&quot;</span>
+<span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;/input&#39;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">helper</span>
+
+<span class="n">helper</span><span class="o">.</span><span class="n">download_extract</span><span class="p">(</span><span class="s1">&#39;mnist&#39;</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">)</span>
+<span class="n">helper</span><span class="o">.</span><span class="n">download_extract</span><span class="p">(</span><span class="s1">&#39;celeba&#39;</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Found mnist Data
+Found celeba Data
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#25506;&#32034;&#25968;&#25454;&#65288;Explore-the-Data&#65289;">&#25506;&#32034;&#25968;&#25454;&#65288;Explore the Data&#65289;<a class="anchor-link" href="#&#25506;&#32034;&#25968;&#25454;&#65288;Explore-the-Data&#65289;">&#182;</a></h2><h3 id="MNIST">MNIST<a class="anchor-link" href="#MNIST">&#182;</a></h3><p><a href="http://yann.lecun.com/exdb/mnist/">MNIST</a> 是一个手写数字的图像数据集。你可以更改 <code>show_n_images</code> 探索此数据集。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_n_images</span> <span class="o">=</span> <span class="mi">25</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">glob</span> <span class="k">import</span> <span class="n">glob</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">pyplot</span>
+
+<span class="n">mnist_images</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">get_batch</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;mnist/*.jpg&#39;</span><span class="p">))[:</span><span class="n">show_n_images</span><span class="p">],</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="s1">&#39;L&#39;</span><span class="p">)</span>
+<span class="n">pyplot</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">helper</span><span class="o">.</span><span class="n">images_square_grid</span><span class="p">(</span><span class="n">mnist_images</span><span class="p">,</span> <span class="s1">&#39;L&#39;</span><span class="p">),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt output_prompt">Out[2]:</div>
+
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>&lt;matplotlib.image.AxesImage at 0x7f56d19e85f8&gt;</pre>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsfXd4VGX2/+ed3hKS0IIQZCkLKmtXsIJlVZQVFERZ+Yrd
+tYFdsCvi2lARRUWXRd21Y8PdVQQUy+6KKKAioogigtSQnsm09/fH5Zyce3MnM0kmkuzvfp5nnkzu
+3PK2e97Tj9Jaw4EDBw4Irl3dAAcOHLQtOETBgQMHJjhEwYEDByY4RMGBAwcmOETBgQMHJjhEwYED
+Bya0GlFQSp2glFqtlFqjlJrUWs9x4MBBbqFaw09BKeUG8C2A3wP4GcCnAMZqrb/O+cMcOHCQU7QW
+p3AwgDVa67Va6xiAFwCMaKVnOXDgIIfwtNJ9uwNYL/7/GcCgdCcrpbRSCtlwLZnOy/Y+8nwATbqm
+JUj3POtxaz+yvW5Xw+Uy9plUKmX6TthV42195q4aN7v16XK5TGNkN27W8xv7vRFs01p3znTSLlM0
+KqUuVEotVUotBQCfz2f63eVy8UfC42mcjnk8HiileNLlPdxuN9xuN//udrsRCAQQCATgdrtz1rfG
+QM+jtlJ/vF4vvF6vqR8S8lyCUsr2+K6CUgqRSASRSAQAEAwGEQwG+TelFPx+P/x+f6P3aAwul4v7
+7PF4eE4bWy8ej6fB+pLzIO/d2rC2AwBCoVCD/63HJGhc5VgppWz7b8G6bNrYWqtpA4AS8X+PnccY
+WutZAGYBBqdQV1cHoH7QXC4X6JhEPB7nyYxGo3ycFprW2kSJ5c5r3R2SySRqa2v5XHp2MplEMpls
+Wo+zQDgcRnV1Nf+fSCQAGAs3FosBgG3fqP30V/YpHo/nvJ3NhcvlQkVFBf9PfQ0Gg9w/a7+skHPn
+crn4f/qbSqVsd0jrbktzSWNsHSea92AwaFoDrY26ujoUFRUBAMrKygAAVVVVTARisRiqqqoaXCfn
+Xa4h2szcbjePcUvRWkThUwD9lFK/gUEMzgDwx3QnK6Xg9XqRSCRsO2bdRWmCaUC01iYCQtQyGAzy
+Ikwmk7xA5A5Bz/N6vXxdrgbX2h45mQUFBbwYZdtp4uUiV0oxkdJamziDZrCQOQfNQzKZ5O+BQIBf
+zB07djTrvpKQZyLS4XAYlZWV/D+tEUlkunTpAsB4GeW80zzYsfBy7HOF8vJy0zNSqRRqamq4H7TB
+xWIx7kckEuGxoHUkN8BcrtlWIQpa64RS6jIA7wBwA5ittV7ZGs9y4MBBbtEqJskmN0IpDZhlulQq
+xbuOUop3eTuQbAkYuy7tUEop3oXt2ES/328rorQW5A4v++P1ern9xNn4fD7TDiW/0zh5vd5ftf3p
+IEWedOKPlZ1Px+G43e6MOzP1X46n3CmJ8wTqx03es2PHjti+fXujz6DrE4lETpWRUoQkjiAcDrPY
+1dg6t8LtdvM4aq2Rl5cHACaOyYLPtNYHZrpv29BQ7UQqleLFo7U2TaTUNdCEUecTiYRJmSgXCC1S
+O7lREh66lpBrEQIw6wCkgjAajfLx/Px8ADDJ5lblkVwIbQFEAGbOnIlTTz0VAPDaa6/h6quvBgDU
+1NRwX7N54e3OoeulTiEej5sIQ0FBAQCDPbeKD8XFxUwIrAShsLAQgFnMaS1dTXV1dYOXVxJ2uVFJ
+paxcD4RkMslK3aqqKpN42hI4bs4OHDgwoU1wCkopBAIB1NbWpt2h5e5BO5NUcNkpDOPxuIkK045A
+Sh35m7QA5Bpklksmkxg7diwAYM6cOax9Vkph5syZAIAHHnigwfWpVIq5I7mDNYXVbE2cc845AIDz
+zjuPObqBAwfyOOfn59vudHawcgnUb5pT2WetNY9Hx44dWWs/a9YsrFixAgDw4IMPAgB++ukn3qE7
+d+6MhQsXAgAmTZqEpUuX8j3D4bDp+bnafQmRSKSBaBUMBnkt19TUMHdQV1dnWqOdOnUCUG+1sM5/
+zpTOpMHclR+llA4EAhoAf7xer/b7/drv92sAeqeDkw6Hw3xOOBw2/U/H6Fy6j9fr1QC0z+fTPp/P
+dH5hYaEuLCzUoVCIj8nvufiEQiEdCoX0pEmTdDwe1/F4XFdXV+uHH35YP/zww3rNmjWaMH/+fD1/
+/nzdo0cP7XK5tMvlMvXD4/HktG25+EybNk1PmzZNl5eX62g0qqPRqP744495HtxuN5+bl5en8/Ly
+0t6L5k2uA6/XaxoL+p6fn6+7d++uu3fvridPnqxXr16tV69erbXWuq6uTtfV1emamhpdU1OjJeLx
+uK6oqNAVFRX6/fff5zVg155IJJLz8QoGgzoYDPL/LpdLFxcX6+LiYj1jxgxdVlamy8rK9F133aVL
+Skp0SUmJ6Xo5Fm63m8dXrvU0n6XZvI+O+ODAgQMT2pT1AbBXKPXt2xcXXXQRAINFJTFg7ty5AIBn
+n32WWf8uXbpg3TrDcWuPPfbAlClTABjsJUEqckgBecstt+D+++9vlf6RaHDOOedw/4YMGcJsq1IK
+Dz/8MADgwgsvBAAcffTR+PLLLwEA27ZtM3loSha7Lbg5f//99wCA3r1787GZM2di4sSJAAw2V7LE
+jcHj8WQUi0ih2KNHD8yZMwcAcMABB/B15LVKzwaAd999l/0DzjjjDL5XPB43eRmSExGJPk11m8+E
+UCjE96Y1OXfuXAwZMgSAoTAk5WFNTQ2+++47AMDo0aOxZs0aAPXviFSwpxMxLWhf1gfrJLpcLuy7
+774AjJe+V69eAAyZjIjFyJEjAQAjRtTHWjXm6kmynHSzpXPvuOMOJiz0gqaD1XvODsFgkOXTwYMH
+AwC+/fZbjB8/HgCwfPly0yTOmDEDgEH0AGPhb9u2DQBw4IEHYujQoQCAt956i1/CeDzeKkSBXMCB
+hqZT64K7/fbbUVxcDMDQB5SWlgIAHnvsMZPHJhGDTAs3kUiwDkZajKRVhvp69NFH44ADDgBgvED0
+Qj/wwAN4++23AQBfffUVAOCXX37hPnXp0gWHHHIIAGOejjzySADABx98YHIiAnKvU5DtnDBhAgDg
+iCOOwKZNmwAAX3/9NTp06ADAIHQDBgwAYLaGyTmR6zBXFhNHfHDgwIEJbYZTsO504XAYffv2BWBo
+sgmS0soYBtrxJZcQi8WYwiaTSabARF2TySTvtMFgEL/73e8AAH369OHd2A5Wd1j63+VyMWtbWlqK
+119/HQCw3377ATBEg+XLl/O10mfh0EMPBWDsaADwww8/4PDDDwcA/PWvf0XPnj0BAKtXr8Y333zT
+4Nm5hHSlTiaTPM4UQAbU29gvvfRSk4/Iiy++CADYuHEj30+yuc3dzeR1JAa89tprzIG4XC5s2GCE
+1yxYsMDE/hOo7Z06dTKJYLvtthsAwxeG1gtxCHl5eY05AzUZgUCA23b22Wdz2++++24AwOOPP87r
+vqCggNfT+vX1QcfSN0FCihUtgcMpOHDgwIQ2oWh0uVyaAqLkzrfnnnsCAJ566inss88+AMxhprR7
+yEAcr9fLu0B1dTVT5Yceeoh1CuR1d8QRR7DcWltby4qfkSNH4o033siq7VY5m2Rmv9/POwzZlY8/
+/njmFBKJBHMuZWVltjoBUog9//zzfOz000/HSy+9xM+gZ+eaY6Ad1uv18u4pXZBJx7NixQrmAiKR
+CA466CAAMNn+6Teg4e5mBzu9A+2CVk9X+btUNFrPKSoqwiWXXAIAuPLKKzlSETB0EwCwePFiUxAa
+PS/XIIUsKZKrqqpYLyOf179/f5SU1AcbL1iwoMG9pKIxC06h/Sgatda88EjJ5PV68fXXRva2MWPG
+8ALJy8vjTssBJKLg8Xh4YqXzx/bt23mhzJo1C4DBilPkXMeOHfHzzz8DACt90kFqpKUIIp1pJkyY
+wH165ZVXAADLli0zLXS7SD5ysCkqKmKiEI1G+WWSrth1dXUZ8w80B5JllgquZDKJrl27AgBmz54N
+oF4BCBgOQkQAQ6EQE+FUKsXEN5uXjeZXElx5jKwF0WiUX4R4PM5rJ5FI8FxT+zp16sSKPUkQFi5c
+iP/85z/cTgJdR6JKruB2u5lwEvF79tlneTxknoQBAwawCFpZWYnOnc35UVKpFJ8bi8Vy5szmiA8O
+HDgwoU1wCm63G3l5eSgrKzOx84QNGzYwJd2xY0ez3JH9fj/vBGTGktGJQD17RmxdOkhFnLTBx2Ix
+Zg0vu+wybue7774LwBzAk0qlePeXnAft0NOnT2dTaywWY4WT1dzaGuytVKxRZiPA6Ou4ceMAAEcd
+dRQAg3sg7uDuu+/Gli1bABjzJxWUtHPTLp9uDuW5MmCNjkkzrMvl4l138uTJuO+++wAYyjwSzchE
++tBDD/EYAsB7770HwFD+EkcjFbfEIdiZYVsCpRSbqGUkJvVTrvva2lpuTzAYtPXxkJyi1ceiuWgT
+RCGZTDbKpgUCAV5EsViMB1BaHGjRSBbK7XbzwEejUZaD33nnHQDGpEj289tvvwWQ3aCSJruqqsoU
+tXjwwQcDALp3787P3rp1KwAjUs9ONFFK8YSSRpr8GajPJJPTCwgAHTp0yDl7S6D+ybiSoqIi/OEP
+fwBQz2pXV1fj1VdfBWD4JtghEolwOzPpPjwejync2WpVkqImAOy+++4AgGHDhmH48OEADLHKLuUb
+XZdIJNi6sHbtWpNjlVX34fP5ckoUEokETjnlFP4OGHNKfZZh3d26dTNZtshKQoQuGo3yuCSTyRYT
+A4IjPjhw4MCENsEpAPVssF2m2mg0yh5mVVVVtokz7GDNtXjVVVcBqHe1raurY5fpzZs3sztyJiil
+TO2T3AntOtFolN2tP/zwwwbnFRUV8Q5UWVnJPhL33nsvAGNXo+uIVQdgSkuXjSa/OQgEAsyWyl1y
+xIgR7HNB85Sfn48lS5bwMckSExeWzhvPDlYWmcZZ5tOQVqfu3bsDMPuv+P1+HhuZ45GUh4lEAgce
+aCjhzzvvPPztb3/j58nxBXLv0ehyuVg8o/HZvn0794N8LQBgr7324t1/yZIlzHHKOaH+SR+LlqJN
+EAUZOk2LwOv1mpyMSNayS+YpIf3egfpFddlll3GIL4kfUk574IEHTLnzGmNztdY8WVLG93g8zPrl
+5eXh8ccfB2B+EWhxEwsIABMnTsRdd90FoF4uvO2229jX/dhjj+XQ486dO5tYRjKjZsok1BTI0F63
+283OY3/+859N1gbAWKzksJRKpXg8ZdYriSz8802iC52XbsGTZeeAAw5gUeKQQw7hF4h0HEuWLGHL
+ztlnn80mwGuvvRZ/+ctfABhzSYlW0qWGbylSqRSPV//+/QEYc02EdcOGDbxGhgwZwuO9cuXKBmMm
+CUEikcior8kWjvjgwIEDE9oEpwA0FAWklln+buUgADPFTCaTJsXRPffcAwC47rrrmG0je/amTZtY
+C03Kx6ZCFj2Jx+Mmawb5PRACgQDvwh6PB5dffjkA4NZbb+XdkSwg999/P4477jgAZuuEVGx6vd6c
+cggEl8vF/YjH4xy5STstAA7WGjNmjK0bcLrdNZPSzuv18hiFQiE+X1ocpGL3xx9/BACceeaZfM7A
+gQPxww8/AKhPYxYIBJgLO/roozlhSZ8+fXDLLbcAMILiCKRwzKWLM+G5554DAAwaZNRHOuGEE9ip
+7e2332Z/hO7du9vmtLTLxejz+TKmz88WbYIokNOPjM6Tky/ZbyuxAAx2SUbREdt6xRVX8IKuqKjg
+c+j36upq9nIrKytrUmQcER6Xy2UyI8nJO/HEEwHUhxZ/+umnvACvv/56k0nyzTffBADWTEsTqozb
+sDo/5crfXULK7SNGjMCYMWMAGC8pEWJakM1N354O1sxKBGkWlZYIq8kSMLwsJREBzCLRlClT8OST
+T/Lxiy++GIAhHtHzqX+5Dp0GDIsHANx4440AgFdffZU3iOOOO86UZYrmV65JWr/SizOVSqVNmttU
+OOKDAwcOTGgTnALQkN2Mx+NpKTQdJ8XSxo0bmU0MhUK46aabABgOLSRWyB2clFDkU0AgapzJYcXv
+9/N9ZRuVUrzDlJeX4/jjjzc9p7CwkCl7IpHgdtx///2s7CJFXCwWY25EiiXbt2+3TayRS04hkUiw
++/e4ceNM8SYkNlC8QLa5F5uDeDzO/ZPzR8fq6uqYQ+jZsyePwcaNG3leqO3V1dU8ZyeddJIptwZx
+kIcccgg++OADUxtIAZ4rSOesVatWAQC++OILPP300wAMkeGwww4DAEybNo3dyiORSKPFinKZX7TZ
+nIJSqkQp9Z5S6mul1Eql1MSdx4uUUu8qpb7b+bcwZ6114MBBq6MlnEICwNVa68+VUnkAPlNKvQvg
+bAALtdZ3K6UmAZgE4Pqsb5pmx5O7I1F/8gMA6nfjiy66CKNHjwZg7OIy1RaltqKMTdFolE2LRUVF
+zClkShkmf+/QoQNT6draWlZc3nPPPbjiiisA1JcrKy0t5Z110aJFuO222wDYx8rLNGFS+VZbW8tc
+QyqVyplyyQqKSj344INNY0jBQ6Tgy3W+AamotaYbI0gzMCmNf/rpJ9N9SL6Wsvhll10GoH7+CbSe
+fvvb37JpkI5J03EuIAu/EDfqcrl4jNevX49//OMfAIBLLrmETacHH3wwB3KRHieZTPJ1qVQqZ9xi
+s4mC1voXAL/s/F6plFoFowT9CABDd572NID3kQVRcLvd0Frbli2XFgdZd48WUHFxMc4880wAwMkn
+n8zsoGQ/v/32WxxzzDEADEclwLy4SktL+dxMVYqKiop4sUg3486dO7NI8Oc//5lzSNI5NTU1vFjr
+6upMrLddaDEtaKmAJa05oTViHwYPHozp06cDACd3AQxl7LXXXgugfuxzrZ2X1iMZSi+Vy9b4AMAc
+Mu/z+RrEiCilWKuvteaxjUQirIx+6qmn+DmyUlcuWfOKigqT4xf1QcZf0JguXLgQRxxxBAAjYpKO
+2xFI6erf0jWRE52CUqoXgP0AfAKg606CAQCbAHRNc82FAC7MxfMdOHCQO7SYKCilIgDmArhCa11h
+8QSjPP4NoC2l6K07s9vtNtn/7agf2fEHDRrErKHL5WKqGwgEmN295JJLeFchts3r9Zoiy7KtcFxa
+Wsq7mc/nYwpOSjgCBVgRF1BdXW3aWYkz8fl8DUQWpRT3Ix6P8/Py8vJMO0VrmCSDwSD22GMPAOZo
+x4cffpjNq7Kydy4VcVZukWCXHq53796sbJ46dSoHDD344IPM6ZCZ+dxzz2UuR2vN7V+xYgWeffZZ
+AMaao0hK8v/Idfk46SIvg9sIhYWFzC2SIhIwuFPiLIgbBVpufrRDi4iCUsoLgyD8XWv96s7Dm5VS
+3bTWvyilugHY0sx727JDHTp04NDThx56CICRBYgmWabI/uabb9j+K3MjSucPWWGoOaywZPelfz1Q
+rxGmc2QEoEzIIhceuS3v2LGDZUitNRONtWvXtno69xNPPJFf9GAwyHb1W2+9tYGYk0uCQKBxk74q
+RLAlwRg/fjy/6KRbAIA777wT5557LoB6t/KuXbuaIidJv3TAAQeYNgHppg7kXjxyu91MwGlD8ng8
+LEpKZ7T33nuP29mvXz+cfvrpAOoT3ESjUb5HVVVV1mn0M6El1gcF4C8AVmmtZa2zNwFQ3O94ANnl
+NXPgwEGbQEs4hcMA/B+AL5VStA3fAOBuAC8ppc4DsA7AmEw3UkrB7/ebWCFrJCJFM/bu3ZvTqUkl
+GO22kUgEn3/+OQDg4osvxmeffQbAHEglqT9RV0mhZbnwdJDUWAaiNGa3TyQSJuWp1D7TcWqHUor7
+53a72UKxdOlSk9KtNepJynkoLy/nTMNAQ84gm+ItTYG8n+SI5DOIKzjzzDNNUY2kgI5EIhxsRGPl
+8/n496+++oojT30+n4kror7TGsmkdG4qZD+sCnNqL63Jbdu24a9//SsA4IYbbsDvf/97AMAjjzxi
+Op+Qq3a2icStpHeIRCImdpwmPxaLcYcfe+yxBqwhUG+efP7553kgv/3225xFju0KUFTnrFmz8PLL
+LwMALrjgAhPBas/9s4M1QtUuqpIcegYMGMAsdV5eHr/cvXv35rgCEnc2btzICW8nT55sCr9vjTT5
+uQKJMXPnzmWiQEVz58yZgy+++AKAMW5k2Wok2UpWiVsdN2cHDhyY0KY4hcaCTyiV2ttvv83OQCRS
+bNmyBddfb7hCUG1BIPes368JyTUVFhaaAo8oOKq6urrNlKPPFaycQmPWlYKCAtbg+/1+Uy4H4jCI
+vQ4EAuwv4vP5TH4vbZlTkD4bVJrgmWeeAWBwSsQVSItEI8iKU9jlZeh3EoEGZbOp1DZ9CgoKdEFB
+gf7zn/+sCS+88IJ+4YUX9OGHH649Hg+XaacS9hnKcrfpj8/n4+8ej0eHw2EdDoe1y+XiEu+wlCL/
+X/zIsut2H5p3mnu7tWO9l8fj4e80juk+mX7fFf2PRCI6EomkfUcauZ9Tit6BAwdNR5sRH+xYfWL9
+PB4PK5rSiQSkcLPmZWyt6sG/BlrDr72tI1MaNJluL5lMmqJKCX6/n5XUZE2QBWLS3duuHVYr2K5C
+IBBg8UhGgMqCO1nAUTQ6cOCg6Wgz+RTsdn+iiNIclUwmGxS9kJmC6H/AoJ52pjoZLEMU1u12886c
+yUtPKkStdnppIrSaC61KNLt6jXb9l7untGknk8mM2XakSU9yTbSTyszIMqM2tdPqs2H1mvN4PKYS
+eqQYdLvdJl8Ou2hHO8jIVrvkr6lUyrTLy3VD18lygbJMnV0eCq11o1xYU7kEqdiUKQIbO9fn89nW
+LZGggEEJaXp0u92mNHUtQZsRH3J1L6/Xy5MvF5TH4zEVKQXMkyUnoykOOU0hCvLlTseWkmWhvLzc
+1kbvdrtNrHFTxAr5glhrOrpcLlNJeblIG3uBMmW+JmQbo5EpKlE6K6VSKdMc2hFPep4kbumsXHl5
+eQ1Sw2cD6pt8XjpIBym7dHNybtxut+1Y0EYAmDeDLHxWHPHBgQMHTUebER+aA2Jlk8kkU+h0UW0y
+Nl/ubESh5e7h9XpzotijXcwuuajH42ExSO74ZEuXdnegnoOoq6szeX1mCoKhZ8jkLID9jiZTxdm5
+UmutG6RHk8q+dGMv3bibCtlmel66zMZSAUesNXFVUgSS2arD4TCPuV3wUzZRoNZShfQMKQbR8+W9
+pBhrJ2K4XC7b+pA0116v17bieUvRLsUHK5tktUjQQErWt7q6midBssNSlGiOltnj8ZiIjcw0LFlz
++mtXeUn2SWYfTieH04vgcrmaVUtSLiYaw/z8fF64gUDAlCVYttVqzZH3sqa4t3tZmuJMJgsAWwvU
+2N2LCGQ4HOYkOHI+CI2teUrEQv1rrD4jhVnLEOhMBNLn89mmbZcEgtpnbaedeGC3MTYCR3xw4MBB
+09EuOQUrGssTSJQ7Eok0qFEQiUR4R7BqpzO018QF2CVnkQozu91D7hjpnknXBQKBtP0j9jgTi2sV
+R2SeR6Dx3ZN2xJqamqwDr2RJvqZyYFYLi4TL5TJZTmRFbJlPkdLWUUDUAw88gF9+MRKCFRcXs1vw
+O++8w8lwVq5c2aAtjWX2tnIs6XwsXC4Xc1hyHu2sYNa+yuM0DzTXdol5rM+2ICtOoV0SBVoUNKiH
+Hnoo57IbOXIkF2sF6l/EZ555huMiPvroI7s2mOTlxlgxq/ba7qWXZeLt5MJAIMDna60b6AbkYpCa
+82AwyAts27ZttrkdJei+oVCI75Huxe7RowcA4LDDDuOiNKlUCsOGDQNgJHh59913AQCffPIJAOC1
+117jqEXKfQkYRI8WaVOTftixyY0RCgIlpfF6vZy8lxLxSGuPLDmfSqWYQDz66KOYP3++qX9WYmoH
+mWaf1kUgEGCrhJybQCDAzx4wYAAAYPjw4dzePn368LmlpaUYP348AOCf//ynyXxO4yC/Z2HhccQH
+Bw4cNB3tklOgPAOUwXnQoEFMfWOxGCul8vPzmWp6PB58+eWXAIxMy4CRe4GQTWKVdJA7G+2Ou+22
+G5cVl7Z1+i65hr322ot3CkqscsMNN5h2XsKVV17JCUKuvPJKzpnYVFA75O5y7733AjDyWdLvy5cv
+N1W27tatG4B6haPH4+F8iHPnzsXSpUv5Gc3N9WBnUZH+G4SuXbuyku+CCy7g+pGPPvoo526kcV60
+aBFmzJgBwNiB9913XwBGWTwq7+dyubhK9bRp0wAATzzxREZlrh23ZhU7iFu85557cNBBBwGoL0sg
+xY6qqiqek0AggFtvvZXbY12f1hwKuRIf2qVJkkJIKWW7XHRaa5Ydt27dir///e8AgN/97nfMElNi
+jrfffpv1DFYHpKYsZDunmWAwaLu45UI58sgjARgJUanOAmVuWrNmDROvbt26MQG8+eabsWzZMgBG
+jcpMHo0y4amME7Dmh1RKYciQIQCAl156iWtSrF27lvsRjUa5LD3hwQcfxKRJkwAYGnuSzysqKmzZ
+2Gz0NlYzJGCeHwqZ/+Mf/4gHHniAf6e+yo3hoosuAgC88cYbpgLDRLyee+45FjumT5/Oa4SKzZaU
+lHCez3Sw0znINbH33ntzoeMTTjihwXWyfgMRGPr9008/BWA2qUqRoTViYhzxwYEDBya0S/GBysaT
+QrGqqopZ7c8//xwTJ04EYLZKdOrUCV999RWAeta5Y8eOJmcTu9yAuYTkQLp06YLFixcDMBROlCqM
+WNlIJIJDDjkEAHDNNddg1KhRAIzy9hMmTABgKPmytf9buR8ra9+hQwdMnjwZgJGo5ptvvgFg7Hhy
+16MdXjoNUabs8vJy5jas5xBkdXA7WN3GibMg7mD79u1ceevee+/luZRVxVetWoUzzjgDADhdWdeu
+XU1FgKgf8nskEmGu4K677uK2//a3vwUAbNq0ydQuK7cjoxbdbjeXHbj22mvZ/4H6CADvv/8+AEOk
+oPH4+OOP+R5bt27F3nvvDcDsC2GX7l4pxVxjI5YoR9HowIGDpqNd6hSomvNee+0FwEjQSqYbqnEI
+GDIw2Xal6YmuA8yRmLKOY0tz59shFouxgmvu3LmsPPv973+PBQsWAACefPJJAMCoUaO4kE1tbS0r
+zN59910bo0WAAAAgAElEQVS89tprfM9sPQStbsa0q1D/y8vLcd999wEwdl2pq5BmNtIvkPLt/PPP
+Z33INddcY3qGLIUmd/TGkEgkWImZSqV416Ms1926dWOuyev1spJz4sSJrEu68MILmUMgbN682Vb/
+EgwGeQyrqqq4MAzpTkaPHo0RI0YAAF544QXudyqVanA/qTzu1KkT+vbtC8DgSOU6u+GGGwAYSkzA
+0BdIpTdxfwsXLmQOQZrBiZNSSvFYxWKxnNXgaJdEgdhSYnGTySRrm59//nmTKy4N6i233MK5HSld
+ut/vN9UOlAk5cglaPPF4HGPGGBnve/fujRdeeAEAsGDBAmZ9L7jgAgDA4sWLcdNNNwEAdt99d36Z
+KJM13ZfY/6Y6CJGmXFpG6MXz+Xw8FuFwmO8tax6S4vPee+/FzJkzARhZp2V9RKmAk2OQqb30cmmt
++QUh7f3ZZ5+Nww8/HADw8ssvsygxe/Zs/PzzzwAM64JU2FF/qe1SRKmurjYViaF7vPXWWwCAsWPH
+sj+B1fHIqtyNRCJ8ry1btuDKK68EYBBy2simT5/O65aw7777ckUu6SND1wNmMU7OWWNh7c1Fi8UH
+pZRbKbVMKfXWzv9/o5T6RCm1Rin1olLKl+keDhw4aDvIBacwEcAqAFQv7R4AD2qtX1BKPQ7gPACP
+5eA5DKLYMo5948aNAAzKSR52gUAAl156KQDDlEc7EJUakxQ1EAi0Wll3um/nzp3ZJLVw4UKMHTsW
+gLk8OVH79957j3fjPn36sNlPnhONRnkMMnEKdXV1pgzHxGHQGLhcLt5dJYsfi8VMnnTEupOYs3jx
+YkydOhWAMS+0o8fjcRN7TWx3pvbK6tFS5KE5p3qRgCHC0PO++OIL5ioCgYCpVB9gjnaU0Z5SVHS7
+3dxm8lcAwL4Zss2BQIDbSdxPVVWVyVeB2v/666/j9ddf5+OkNCXO9e9//zv23HNPAMZ4/+EPfwCA
+BgpX+t+uEJHWOmeZy1taS7IHgJMATAVw1c5SckcD+OPOU54GcBtyTBSsmvMzzjgDf/rTnwAYrHbv
+3r0BAEOHDmWXaK01uzdTimwpp1kzIrWGBWKPPfbg6laPP/64qT9EOGjCp06dygRk3rx5XAwGqO93
+x44dTZWt7CBfTFlFi/orI/aIGCilWBezY8cOZtfPPfdcdhwjFnjYsGG8+P1+v21mKYlMrrhWhx9r
+lOLuu+/OBGLr1q2m7FsywpQgLUrSyUhG0spIWSIcdA+lFFuBpMtzNBrlvtK9ZIg0YCa4RMg9Hg+3
+g6xopFsCDMsJibxFRUUcw1FaWmqKYgUMIpZJR9MctFR8eAjAdQBoJXQEUKa1phn/GUB3uwuVUhcq
+pZYqpZba/e7AgYNdhBbUahgOYObO70MBvAWgE4A14pwSAF81p+5DNp9AIKADgYBeuXKlrqmp0TU1
+NVpi+/btpv/nzZun582bZ7pHhw4ddIcOHdLm2M/Fh54h752uJsWcOXP0nDlztNZaV1RU6IqKCt2n
+Tx/+fWfma671kJ+fr/Pz89M+21oTge5hPU8ec7lcfN/JkydzO2pqavSMGTP0jBkzdK9evXSvXr20
+3++3fW6641SzIJtxk23q1KmT7tSpk66trdXr1q3T69at0/3797cd20gkort27aq7du1qqpFhvTcd
+93q92uv1mu4xYsQIPWLECK211jt27NA7duzgdUIfn89nqs+RTf8B6O+++05/9913OhPKy8v5+4wZ
+M3Tfvn113759Tfeitufl5aXtq/hkVfeh2c5LSqk/wygwmwAQgKFTeA3A8QCKtdYJpdQhAG7TWh+f
+4V4t4tXj8TizpbW1taZwYmK1qqurmf0i09ujjz5qYjVJcx6Px1ulxLqElHGVUnj11VcBgKM9/X4/
+1w786quvWGSQDkjWpKmNQWqs7dxyfT6fif0mK8mLL77ILLhk0QkLFixgV/KvvvoKq1atAmCwznRd
+fn4+s/yZ1ps0X9J9JMrKylgs2W+//bB27VrTtYC9vqKgoIBZbSmuAWY9B7H5pMO55ZZb8N577wEw
+TMc0ztKCIaNr5TyQ7qCuro7FnFAoxLoEMqcOGTKERWKv14tt27YBMEK8SUcRiUR47qdMmQIAuPPO
+O039kw5OadC6zkta68la6x5a614AzgCwSGt9JoD3AIzeedp4OKXoHThoV2gNP4XrAbyglLoTwDIA
+f8n1A0jLTBR6woQJOP/88wEA+++/P/773/8CMHYusj6cdtppTFkp2Gf9+vV44w2DZmUqI98S0O4T
+i8UaOKAAwKWXXopjjz0WQH1AzAUXXMD9AMwJOWhXkXknM0FyB0qpBsVSrC7QixYtAmBEEQ4fPhyA
+oXQkpVq/fv0AGC7aVOU7Fovxdbfffju3v6qqivudKSmMNa27NTqysrKS8z507tyZIyO11rZKTvor
+d1FrMhz6X15PjkcAOMhLcjkej4fnQSpDpSJVJvWhfiul2MGOnKJksp+OHTtyUFbPnj3ZUrZ161Z2
+laa1/u2337J7fFlZWc78FHJCFLTW7wN4f+f3tQAOzsV9HThwsAvQXEVjLj/YqQgJh8NplSSkXHO7
+3Q2UZ263WxcVFemioiIdCARY4SIVR0opXVJSoktKSvTKlSv1ypUrdXl5ue7Ro4fu0aOH6VmNtcP6
+SadoAuoVoenOPfDAA/WBBx6otda6urpaV1dX6wkTJugJEyZk/fzW+FgL1tJ4SiUWzUEgENBnnXWW
+Puuss/TLL7+sU6mUTqVSevny5awclPfIVBDXqiizPvepp55i5dvhhx/O9yNFoVVRTNdbla30KSws
+NP3fpUsX3aVLF11XV6fr6up0ZWWlHjVqlB41apQuLi7O+djSWNopLQOBgB46dKgeOnSo3rBhQwNF
+5Keffsr9k+uskU9WisY24eZMqdCkjdfj8dgqjiTbLXMjyvx80q9futWSTZ9i2+fMmcOpxubMmcPn
+Zkq2IiMOZWIVl8tlYk2JNZe+AnRuSUkJXnzxRT730UcfBWDkVtjVSCaTJlbULomITAdPjjnPPPMM
+br/9dgCGgo7iUWbMmJF1fgq3222aY6ti0qrIo/9lajKfz9cgI7I1rT85uJHTG2A4RtE8kGj60Ucf
+cYq2dHkym4JkMsnr2i5vo9/vZxFtx44dHEm5aNEijBs3DkC9KHXggQdyBOfq1avbjpuzAwcO/rfQ
+JjgFCekdJ8t8SeUaUVqpLLPboSVkbgXaNWQBEb/fb9qVmhIUJRN3EqTJSnIMtEPNnTuXPS+XLl2K
+m2++Oevn/RqQO6vcyUiRRn+j0ahJQUuRqFVVVazci8VibJ6TLsF2kIVoZLJV2lW9Xi/vhJFIhLmY
+RCJha7aVkK7N0o2ZcOyxx2LkyJGmY4899liDnZye1xy3YulOb2cOr6iosN3pFy9ezDkipOmc1tDq
+1aub3JZ0aBNEQWuNWCxmimWQGl2Xy2VyGaWXV7L55A4ajUZNC46IiZxYSr/l8Xjwz3/+E0D6RWoH
+66KzqzwVDAa5fXR84MCBHBnZp08fjsgbOXIk989abGVXgSJRq6qq2OV5y5YtJrGBIMWnQw89FIDx
+wtKYKqUapNdvDCRu2VlXYrEYv5jHHnssi2DWwiqySApgvEBEkILBILc/FAqxNYNEBwD4y18Mo9nC
+hQtNG1W2fiHpIAkB5eV85513WPx1u90m93CZYk7mGwUMAkLj6vP5chbu74gPDhw4MGNXWx6k9SEU
+CjWwAoTDYdPxAw88UPfp08fk+mv9+P1+7ff7defOnfmY1+vVhx56qD700EP13Xffre+++26ttdah
+UEiHQiEdDAabpEXO5BIttd3k2vvcc8+x5njlypWsWVZKsbtqU9rQWh+Px5PWStCYa29+fj67my9d
+upTHSPZLupXbfdxud6PPGD9+vN6yZYvesmWL1lrrwYMH68GDB+uSkpK0faH+SEsGzcnll1+uS0tL
+dWlpqU4mk+xmvvvuu+vdd9/ddI9cjG3Xrl3ZKlNZWakrKyv1K6+8YnqG1WoFQM+YMYPXTjKZ1Mlk
+kl2vGxtPy6f9WB8I1qg6YtVqampYHrz55pv5OyWhWL16NcuWsqhLv379OMLvxBNPxEknnQSgPhT2
+rbfeMmUOTmdFsMJau5JgrX1IbN71118PADjppJPYEWbSpEmsWZYZdLJwVW11aF0fhisjA71er8kR
+i45RVuo77riD+z9r1qwGxUsAZEyXbnUVtjo7vfrqq+jfvz8Aw4mHslNt3LiRMx9/9913phqZgOEE
+JdPakzt5MBjkc7744gt89tlnAIB169YBSJ/Zu7HKUY1h8+bN2H///QHUR1cOHz4cs2fPBmBkY/r4
+448BGCIYFYehJC1AvSPbE0880SajJB04cPA/hjaVzVnGz1t3Y3J3nT9/PhfRIBvzv//9b64D2K1b
+N86I3KNHD1NACVF2uu7EE0/E119/3eT2yt3D+l3mKaD6EqRcrKqq4hRrM2fOtHWvzaL016+KYcOG
+4cMPPwRgVsaScu78889ny4nL5WLt/RtvvGHa5YnDyCYdm4RdRuiSkhIAhqKRgtsKCgqYI4nFYiZO
+h9pG8+RyuXicf/rpJ04YM2PGjIycDF0n79dUkOKWuINTTjmF130ymeSAqKeffprzX55yyimcjZqK
+Gp188smmfBhZ1AVtf7UkJUsmTXqSWPzf//0fa4lpwdTV1ZkSiBBk2u8ffviBo/loERcUFPB9Y7FY
+1pp/md5b1oyUyM/PZ804sX6vvPIKzj77bACGSCRrYspakUDmgrGtDSLCr7/+OltwOnfuzISB/h5x
+xBEcGXn11Vdj4cKFAJofzWmtekSQREUSFEqEO2jQIP4eCoUaaOq11qz5f/HFF3l8v/zyS1MOSmvM
+RCqVyjqNfrawJgm68cYbccstt3B7ZZyLjFClmAkSg625HnNVIcoRHxw4cGBCm+IUsgWJD1Rnj8QF
+wCjYQeLB9u3buXpwLp2D0imZfD4f70ydOnXCd999x8cBIzqT2N2amppftRBNUyGdhYjjOe644/Cv
+f/0LQL0i7rXXXmM3YAeZEQ6HmUuR6dwoR+NRRx2Fo48+GgCw5557Mhf61FNPcfp56Ztg5+bdCEfj
+cAoOHDhoOtolp0Cw0wHI/Pj5+flssmlqXYRMINnf6/XamoV23313vP322wDAMvecOXPYgzKdIrGt
+KBplcA2ZztxuNyv87IKkHOQGxFlKZbvU0ZBpVWvd1HXS/hSN2cKu0g8RAo/H0yz7cVNhF8EZDod5
+EqPRKPv70zFJPILBILN71dXVbUZsINDCC4fDjfpOtFbm6/9VeDwenncpPtI6ku7MEkop3jDs1rd0
+j26EUDjigwMHDpqONuXRmC2IQyAW1+/3824sTXl2lYFzBel9SVxDMpk0KXnsgoCkqcvO7Jhr81dz
+4Ha7eTeyVjumMSeRzeESmoZEIpGR5af1JE25UhlNkGn1ZMBXS9EuiQKBBkyGQAP1MlmmF0u68AL2
+jjLpIO9NhCebClPUznQRbXRf6gOQPhRYug/nkohY3bwlWyrt5oDRd/mdzpXysJwbGb6cCzRFByNd
+tGU7W2r5of7LTSjdZiQ3E7vnyXBxOe+RSKTBmunUqROHqgNtqJakAwcO/rfQLhWNNtebvLnSUWDJ
+atFf8tzLy8vj/AZtDR6Ph3dEr9fLO9uv4fUoS7XLnYssEi6Xiy0Q6epxpgsgyzXkTkm7Nymla2tr
+03ICxJVJV2FCuvUENKyLab1WBtjZpYWT97FTErrdbuYG6+rqOJCPEsTIMc2yFur/rvXBTvNvdR21
+Qib9oIXi8XhsTWrdunXDL7/80pQm5RzBYJD7EggE0rpeN0XkyRbSxVy+0OlcuiXkCyYXOJmPpXUm
+FyDiJF2i01lE6AXLz89nfY/L5WJXeKk/sbuvFZlEITu3Y7u2eTweW1FL6hQGDhyIBQsWAADH68ha
+qY0RPYHWtz4opQqUUq8opb5RSq1SSh2ilCpSSr2rlPpu59/CljzDgQMHvzJamBzlaQDn7/zuA1AA
+4F4Ak3YemwTgnmyTrGT7sUtIYldHz+/3m45TQhX63eVycUIWu9/b4idDrcCcPUMmWSksLORU6NaE
+I8XFxaYxlLUi5T2CwWCTE9k05RMIBLgddslvgsGgqT12cx2JRBr0r7EU/nYfSiyTLo29x+PJumap
+y+Ximpg7duzg5Co//vij/vHHH/Vvf/tbPtftduu8vDydl5fX2D1bvZZkBwDLAfTW4iZKqdUAhmqt
+f1FKdQPwvta6f4Z7NasRdgk+AXNknGR9KQ6C/Pevvvpqvk6G0wLpxZBfCzJaLhaL8XfZ14KCgibl
+PswWdiXgGzsu2yxFBgoRrqurazX9h51Xq8vlYvY/nccliRJFRUXcNnmunYNcJiil+L5SP+F2u5nN
+r6qqalCPU65d0osAxrgNGDAAAPDxxx9zblF63fbbbz+e/40bN2ZjgWl18eE3ALYC+KtSaplS6iml
+VBhAV601CeSbAHS1u9gpRe/AQdtES/wUPAD2B3C51voTpdR0GOICQ2ut03EBWutZAGYBzecUJEcg
+qbKd0ubhhx/mNF6/+c1vABhUm6InlVK7nDuQkFR/t912w9VXXw0AGDduHLtPP/PMM1xXMJeoqakx
+Fa356aef+DilB3vooYcAgOtMAsCaNWvw0UcfATDqeE6bNo1/o52X/uYq7RxxCD6fjxV+fr+/AYdw
+2mmncWq+CRMmsMt5LBbjGJXbb78da9asAYAGtTazgeRM5doLhULo2LEjAHNdTbl+6Zg1XwSt1aKi
+Il6f77zzDgBjvFsj63dLOIWfAfystf5k5/+vwCASm3eKDdj5t2GCfQcOHLRZtMgkqZT6EIaicbVS
+6jYA4Z0/bdda362UmgSgSGt9XYb7NKsRdnKfVe4dOHAgAODzzz/n88iMl0qluOzW999/byo3l6sc
++i0B1bKYPn06Ro0aBcAsc06fPp05iFz7AVCF461bt/J4hUIhPP/88wCMkmWAMZa0QyeTSdMYnnvu
+uQAMHQ6lEmsthMNh3jW7du2KsWPHAjAydQFGNXLiHqLRKI+t3N09Hg9X0J48eTIANNpuO5d0OzNk
+IBBg/cqmTZsanJsuiK+oqIiT0Xbp0oUjUy+//HIAwCOPPMJcY21tbc78FFrq5nw5gL8rpXwA1gI4
+Bwb38ZJS6jwA6wCMaeEz0sKqsLEeCwQCOOiggwAYBIAWNyWx2LhxI77//nsA5kXVVkAKsH79+jEx
+qKqq4sVRXl7eIAN2LuDxeEzus+QDce+99+Koo44yte3CCy/kQiYXXHABhg4dCsBQktELNnHiRDzy
+yCMA6olarhSPRISqq6uZ5X/44YcxZoyx7GiDSKVSPG7RaJSJxpdffski2HnnnYdzzjkHQP1LfcUV
+V6T1AbHbUKVIICuEETGQrvUyrkGCqojtv//+6NKlCwDDz4aS9rzyyiv8LFI0ks9ELtAioqC1Xg7A
+jvIc05L7OnDgYBeiJX4KufqgiTZpq5+CLIbh9XrZN6GgoEB/+eWX+ssvv9Raa71o0SK9aNEiLlsv
+7xkKhTKWSZcf6f9AxUtkm+zs0FbfCln0g+z41AZZIGbu3Lk6FovpWCymU6mU/uyzz/Rnn32mw+Fw
+q9n9ZZup+E4ikdCEE044QZ9wwglseyfb/oIFC/SCBQt0KpXic5ctW2brR2L3sfoF0L3t/AqsRWto
+vFasWMHjRaDiMVpr/fnnn5v8Kehzyy238Dlbt27VW7du1WeffXZOx9NufclxiUQiesSIEXrEiBFa
+a80FY7TW+sgjj9RHHnkkrxurH04WRWvaXzGYbGFNNlFeXs6uqlK7O3DgQC7AmUwmOZU3sbter5fd
+cmWR2mxCriXrSOxfNBplNk5eTyz+IYccwnLtbrvtxrkku3btyhpl6TdB/QuFQnzfsrIyTJkypcG9
+W6IbsoOsxfinP/2Jj5PcSmnfZZRhKBTi3I2JRILbPH/+/KzbF4vFWCSQz5PZmWVmZ2mlofEqKiri
+62i8SUcCAO+//z4fl2XrH330UdaDUBr5vffeO6t2ZwuZS5Hmzuv1cnv79u2L119/nc8lkeeSSy5h
+Pxup75Lu2LkSJZ0oSQcOHJjQLjkFUlLJWHqitKlUiiMfzz33XBMl/cc//mG6jyxrLzXIbre7UU5B
+eu6l86asra3l3WbWrFkADM6FiqgAZq9J2h2p7TU1NXjuuecAGEVPKDLuo48+4jyPreVXUVhYaPKU
+pFoOV199NT9zjz32AAAuswYYY0hVp6PRKO/cTzzxRMaANYLcNYGGpQRTqZStQtjj8XAUYSKRYKUy
+oba2FtOnTwdgri6dTCZZQbnHHnuwNp/a3hpWKGtEZDKZZP8PaiNg9HXq1KkADAsOtUVWwaaxkpxl
+S9EuiQKBBlVGQALA7373OwBGVR3Cv/71L3ZYkdFtctKzTQAik4kAZoIiv1988cUAgBNOOIGP0Uu0
+du1aDB48GIDB4tJLQ/24+OKLucJUZWUlVw0aP348E5BYLJZVNF9TsWPHDu5fx44dTe7kJKZR4ZWl
+S5dye44//nh2ywWAG264AYCRDl6KZkD6JCRWAktzQhuB1prFh/z8fGzfvh2A8YJQJa5evXrxeNC4
+3njjjfz7L7/8YtoMSHy4+uqruX+Er776KtNwNQl+v5/7RHOttcYDDzwAADjyyCN5Xb/77rtcxkAm
+1KH2SuJZWFjI/7fUsuOIDw4cODChXXIKxGJTbH9VVRVT0mAwiGOOMSyiBQUF7E4rlXMEaTP2eDxZ
+53O0piAjSGreqVMnXHvttQDAuRmmT5+OZ555httM9u+ePXvyTvqXv/wFgMERUP+8Xi8uvPBCvk6y
+xq3B3kpF67Zt29h1ee3atez0s9tuuwEwxvDMM88EYHAGtEt9/PHHePnllwEYuzEF85CSNx2SyaQp
++Mu66/l8Pu7z1q1bUVxcDAB4+eWXWXTZsWMHiwFPPfUUAODBBx/ke8iycrFYjDlL8sEA6n0zKHgu
+V5AJYAjXX389/vCHP/Dv5DB1ySWXmPJlEFdIxzZv3sxcXC4D49olUbDz3CIWNhwOs+MKUC8Pr1ix
+gieDxAOSJQFzrIE1d6MdpDZd5lWUMjN9f+uttwAA9913H7OOdXV1HOH3008/cZ3LP/7xjwDMhO74
+449nzbPP5+MFK4leLiH75vf7+XllZWVszTnjjDMAAOvXr+cXj9oNAGPGjOHvxcXF7LyTSUST+hop
+ptELIfUJnTt3ZqtNv379mIgWFhayqHDBBRc0eEZNTY3J2YfiCzp06MDzTusmExFrKvx+PxM6KjYs
+N6xPP/2UvSnJkgOY9TwkGgUCAVPOz1wl/XXEBwcOHJjQLjkFouZkw62treUdZN999+W6fEC9SyhQ
+H3tPO186hYxkUdP9TlxAuio+ZWVluOOOOwDU70SzZ8/mqtN5eXn8/CeeeII5BEq5tWzZMlx3XaMh
+IygrK2uVdGxaa2a/JSv+6quvYv/99wdQb3144oknWNSIx+M4+eSTARiFb2hcZPxAJiVuIpEw7Xi0
+o0sO4dhjjwUAPPfcc6wY9Pv9zPmdc845mDNnDh8HjDUjrVXENXbo0IGVo1VVVXw+RUtaub+WQmvN
+XA/Nb11dHecHvfPOO1lcGzFiBI/ncccdhxkzZgAw3M3pXnKdUttbqnR2OAUHDhyY0C45BYL0SqNd
+6dBDD2WdQyAQ4CSXgDn2HjDn/49EIrzbZtp1paJIJt0EzLUFHn74YQBGhCYA9OnTB+PHjwcAPP30
+08zpjBo1CsOGDQNQn2fgv//9L99TZnCWEXWytmMukZeXx/JrKBTiIKeLLrqIdyYaA6/Xy34Tjzzy
+CN57770G95Nej9n4KxB3kEwm+Ty6zuVyoVevXgDMXooVFRUcMfr88883mjnJ7XZzP2688Ubesf1+
+P8votJvn2hfE7/ezvwxxNm63m70YV6xYgcWLFwMwzJOEVCqFa665BgBY/7RhwwZTTc+cmaV3ddxD
+c2IfrB+Px8O+7DU1Ney/Xl1drffee2+999575zwmgPLsWf35yRdd+rhTnr17771X2yGVSrGvPl0v
+YwCsuSTpk03+QLvr5IdiB2R7Q6GQvv766/X111+vtdamuItoNKqj0aip/TIOgmI45LjIOJBMMRDp
+4keoH7169dKJRILjMOLxuI7H4/ruu++2nQf635q7MD8/X+fn5+vFixeb4iS2b9+ut2/fro855hh9
+zDHHZJVLsSmf008/XVdXV+vq6moevzfeeEMPHDhQDxw4UH/77bemMaa+1tXV8fmTJk3SkyZN0kop
+U+5Lu9yllk9WsQ+O+ODAgQMT2rX4QCxeNBrFAQccAMBsZpw9ezZWr17dKs+WgUuyPiSxn1KkIEVb
+fn4+m81KS0vZ36JDhw7MXp966qkAgLlz5/L1VoUSQSZ0TedjIa+Tnol27SSWe8iQIbjzzjv5ehK7
+PvroIyxatAiAkboMMNh6ykHw4Ycf8rlKKdu2kWiQji2Px+OmWgbWkvdXXXUVKyKj0SiWLFkCALj1
+1lt5PCsqKvgcel5lZaVJ7CMfkgEDBpjadNZZZwEAVq5c2aDtucBNN93E65Y8bEtKSvDmm28CMJTS
+JBL6/f4GFaoBw7QNGOtCKstzlltjV4sOzREfiHWnTzgcNoXsbtu2TW/btk0XFxfnXGyw+1DIqpUt
+DofDOhwO6xtvvFHfeOONev78+XrDhg16w4YN+pRTTuHzpk2bxiGyK1as0CtWrDCFg1tZ2CaGyzJr
+Ka+zigwej0f3799f9+/fn0N1tdb6+++/53MozBuAnjdvnp43b57WWuuysjJdVlame/XqZTtHdmPV
+WFtlmLT1/E2bNnHbli1bpnv37q179+5teqbdPfPy8rj/gwYNMok/NTU1uqamRk+ePNk2pDqXnxUr
+VjQQH3/55RedSqV0KpXS8Xjc1C7C9u3b9X333afvu+8+bqNsZ5alCRzxwYEDB01HuxQfiA0klrtz
+58tWmJ0AACAASURBVM7s2lxeXs5pwDKVOGsurPUN7DL4hsNh1o6TVvz777/HPvvsAwAc4AQATz75
+JAdPkc183LhxeOyxx/gcuyrPyWQyq2rL1GbpI2CNDg0EAuy6LOsSrl27ltnSaDTKtv53330XAHDE
+EUewZUDmj1RKmYKgrHPWWDulFp3OpyjCwsJCHrunnnoKP/74IwBDTLBj9WUZO/IgvPrqqzm1Wb9+
+/TgaddasWQ00+B07duSgq1xgy5YtPA8kthQXF/NYJRIJHmMpChcVFbHPDYlSXq/Xtu5FS9EuiYJ1
+YZ100kk8UB06dGAZfvDgwWzeybVs2BSQ84/Wmhe0lOu/+eYbfqHo5bnooovwxBNPADD6K51w6CXN
+pqhoOp0Dyaiy2C7JsrIozvfff88OUhUVFTz2tKA7dOjA/ciFTGutP0kvC8Ul+Hw+fkllCHSXLl1M
+SVGpf2S+HDlyJG677Tb+nXQVF1xwAetvqqureZzo91wSBMDIV/n+++8DqDepygjfSCTC+pZYLIbl
+y5cDAK688krOJyohiUGuImYd8cGBAwcmtEtOgUAurtdddx3vmJFIhDXjBx98cKtkO7ZyKtItl3ZN
+ScHnzZsHwHBGGTduHADgb3/7G+9G1dXV7OhE0ZB77LGHbaqxpkK6IBOUUg3Gpa6ujneixx57jMWZ
+oUOH4rDDDgMALFq0iK0ZQ4YMAWCIaKT1tz5XBj9RO6SrsR1k2faamhrmdGg3TSQSvCP6fD7+LrmE
+SCSCq666CgBw2WWXATB2ZXpmaWkpWxneeecddO/eHYDh0m3NdZBNar6m4Ouvv+Y8EzTXlHEcMNzc
+yeEuPz+fA7oSiQSLCgSXy2Wy1OTKealdl6KnBSG9+mpra3lCu3btaptZKZeQZq50i4cSkkyYMIHZ
+0RNPPBHffPMNAMMTkJJs0Lk//PADRyRay5fLl605kXGyxLnddW63m0WwDh06cB9vuukmFn8om5TW
+mhPHDB8+3BTnYOe9KMUgO/j9fiZgcjxJ3/Hmm29ybMtDDz2EVatWcZ9okxgzZgyHUdN4d+zYkWMK
+Hn30Ubz44osAzJmzZJwDpVanjFe5goxsTVeqXppIrWkCADAR27BhAx+zZstKg1+lFP2VSqmVSqmv
+lFLPK6UCSqnfKKU+UUqtUUq9uLMmhAMHDtoLWuBb0B3ADwCCO/9/CcDZO/+esfPY4wAuzrWfAqVU
+JxfPxYsXm+zqJSUluqSkRHfr1q3V7M1NcXOW/gHkrivTkP/www/cfnKBnTdvnsnGLvsu3VntUp/b
+tbOxNgPm1OPBYFB369ZNd+vWTX/++efsSixB7sBaa33FFVfoK664Iq0fQrqxsPvIc/Pz8xv8/vjj
+j3MbZBr52tpa/i79LAgjRozQHTp0YP8Paqd0AXe73VmXiW/Jh55N65jGRCll8k/JlMJfKcW+MFk+
++1fxU/AACCqlPABCAH4BcDSMupIA8DSAkS18hgMHDn5FtLSW5EQAUwHUApgPYCKA/2qt++78vQTA
+v7TWA22uvRDAhTv/PaDZjWin6Nq1K2d7PuusszhbFNnPjzjiiF3WNqBe3nW73awcHTRoENfepBwR
+M2fOxP33398qbbD6gwCGiXHixIkAgEsvvZT1AdJHYvHixWzOpYjEioqKRiMn/z9BVjqFlogPhQAW
+AegMwAvgdQDjAKwR55QA+CrX4kN7+2QRvaY7d+6sO3fuvMvbSh/J5stIPPpQ1ahcP9fKulM1JGK1
+Zdskq229h90nW5fw/+FPq4sPxwL4QWu9VWsdB/AqgMMAFOwUJwCgB4AN6W7gwIGDtoeW+Cn8BGCw
+UioEQ3w4BsBSAO8BGA3gBQDjAbzR0ka2d9i5F+fl5bFdWXoTtgUEAgE2dcZiMVtT6/r161vl2dZC
+PHasPom85eXlbLuPxWImU6aMCKVju9KrtT2hpTqF2wGcDiABYBmA82FYJV4AULTz2DitdaOpkZvq
+p9DeQIRAiEtpkauMvK2BUCjEL1kufe2tkFmd7HwWyP/B5/OxT4pSisfOjgC43e6c5TBsx8hKp9DS
+UvS3ArjVcngtgINbcl8HDhzsOrRrN2eC9ETzer28I8jsvLFYrEG1agANkngADT0IW4p0bCt5q0Wj
+Ufbwy8TiyuzRuc40TJCBVi6Xi7X2srKx5GhoXGXuQ+s5ssALHbOD9DCUGZjlvWTAEEFrzefYuVDL
+6EvJgdD/QENXcGt7MkGWLwwGg7yGrCJYY9ygHHtZoChdG2hu7ArnNBft0s2ZIKPCaGK11qZFQYvR
+5/M1kE+lyas1WWO6bzgc5oUcj8dNhMdKnCRhsi5iQjAYzNlCsIJchmUWo2QymdGsl209znSwxhrI
+jEPUBvkbiRLRaNSWkNP1MnQ8lUrxdYlEwhQZSfegcW2MINiNRbrYDhmtao1mzM/P59/Ly8u5DXJ9
+ut1uk34kU9vSoPXdnB04cPC/h3YtPsiS9LRDSSWSDD6JRqMNgnEkNZfXderUyZQEpaUgyi9FFMAc
+EEO/EccQjUZN5dOof7LkuMx7kEtYxSe5O9txCLJOJPVJ7vjhcJivs5Zht8K6+9H/6ThaGgv5uxwX
+We5dQu7cN954IwAj2vaLL74AAIwdOxYAuEiLHeyCuqQIQxyBTMXvcrkaKDrr6upM9+rYsSMAcy4H
+mf9T9sFOAdtSkbJdEgW7l5sWW0FBAZ9nrbNIYaaydgFNXF1dnamoai5Bz41Go8yWV1VV8ULt1q0b
+F6El4uD3+3lBy3BiGYZcWVnZKl56WmtexG63m9vv8/m4tiJltxo3bhwuv/xyAEYtC5lViMa5rq6O
++2oVBxqDx+NpkAxGvgRKKVvikk5fYWfV6N69O3ts5ufnc/9ktGc6WMPPtdamF5LaUVNTw9GvH374
+IRP+mTNnAjCqmH366acAjPmXEZHUf7kZWH8DjHHJNgtXJjjigwMHDsxorptzLj9optsmZbSVLrjS
+TXbMmDF606ZNetOmTXrfffc1ucrSh1xm9957bz116lQ9derURqP4mvORkXcHHHCAPuCAA/R//vMf
+U/RhbW2tKdLvxx9/1DNnztQzZ87UgwcP5nu53W4uLpPLNsqPHE+re3PPnj11z5499bp16/S6deu0
+1lqPHTtWjx07Nu39pIs3uS039nzKNB0KhbhoC0WDUpQnFcyhsbW2k86Rmbbls4866ih91FFH6TVr
+1nC06pQpUxo8rzH3dDv3dXIJt7pTv//++/r999/X8XhcJ5NJnUwmOYOz1lpfc801+pprrtFut9t2
+nbrdbn4e9bkZUZ1ONmcHDhw0He3aJCnNPyRf+Xw+9OvXD4CRPoyUNsOHD+eIOZLDx48fj0mTJgEw
+lGUk75966ql47bXXmtudBiBZ9rvvvjPZsQkVFRWsrCOZ1O/3c9LRqqoqPPTQQwCAm2++2XTvrl27
+AshOBm4pwuEwXnjhBQDGeAJGBqYrrrgCQPoq3tIUS2PRWFVvgtfrNdWVtF4nZXq/32/Sq8g0ZVb0
+6dOHa16WlJRwItWjjjqqRVW8pQkxLy/PlAiXUvIdd9xxPAakv6C5B4DVq1ezvmbOnDmmeaX+2vnb
+SP+VRpCVSbJdEgWrssrv95s0upTKe8qUKeyjf9VVV6F///4AwGXfBwwYwAM9depUvP322wCAjz/+
+uDndSAtSZt5xxx1c3Wfz5s2sLbamNQcMC8jkyZO57URMevbsyYstV4qlxtoslYR9+vTBv//9bwD1
+fgynnHIKj5vf7+c+yczILYFdyjI7SE289F8gohCJRHD44YcDMAq00ov44osv4owzzgBgdhySodiZ
+CJgsRptuToh4H3zwwVi6dCmA+tyMDz74IKfeSyQSnFZt+/btnIF6/vz5fNzOFyQYDGbjAOf4KThw
+4KAZ2NVKxuYoGhvLTzBq1ChW1m3btk1LkIKHMGXKFN2rVy/dq1cvk7ImkyKsqR875VqnTp1sn0eK
+I6/Xq0eOHKlHjhypS0tLuc0yjZfP52uVHAHWsaU0ZldeeSW3Y8mSJXrJkiUmZZedcozSq9H3TO1N
+V0lb5neQSkQ5Xo316bTTTtNr167Va9euZaXilClTGpSIa046Nrv1KFPJSaWwXeq0gQMHciXp8vJy
+HuPy8nK9atUqvWrVKj1s2DA+3y6/RS7LxrVL8cHOaYdkwbVr17Kvwp133oni4mIABvtIFXbmz58P
+wCw3SnkwmyIrzW2vHXtpF2sxatQoPPvsswAMdvjBBx8EAEyaNIlZ40AgwEVK0yFT9uR0kDI5jeeG
+DRtYHzNq1CgAwOuvv25bcCYcDpvcxaXbdGsgFAqZRDDq94EHGtzy008/zVWm5s2bh5NPPhmAOboy
+EAiwmNYU/w+7vsk5lTEqcm2RaJSXl8fXHnfccbjmmmsAAMcccwyLbvF4HKNHjwYA/Otf/+LnUDu9
+Xm82ehBHfHDgwEHT0S49Gmm3lW6dVHcxLy+PvcOsmnqyRNgFsEjkmnuS3AHtwAUFBezFqLXmtpEy
+dM6cOXzNBx98wMpToN5CkUgkMgYg2XEIHo+nQbSi1tpUI0NeRx5/oVCIa0i+/vrrAMwcgcvl4vGs
+rq42lcKjnbC18kVYE6sQd0O1HrTWePnllwEA55xzDo9bQUEBtm7dCsDsht4UTlFyRNR/r9dra/mw
+c7eXHM78+fOxevVqAAZXSAVjtNYYPHgwgHpFeEVFBbczl5xtuxQfrBrpqVOn4rrrruPfSWO7adMm
+HsDy8nI278hqRenCbHOp2Zfh23K8SeQZPHgwzj77bAD1lpGqqiouuEIFagmyqGhT/N1pwWbKQiSj
+L/fZZx8sWbIEgGExoYSy69ata3Bd586dmahprfllWb9+PfvxyziJXMDOOrHnnnuy+blbt24ADPM0
+iTzxeJyvk8S0qKiI70OafqtYImFH4KQrfbqCt/QM6RIurTaEzp07cz8OOuggtqSRtWTVqlUmS1QW
+a8ERHxw4cNB0tEtOoVOnTgDA7NQbb7zBrO/27duZFQfqlT8bN27kUmHE+lJFX0JLHFeyBe1cJ598
+Ms4991wAxi5A80DPHjduHLPqHo/HNr6/qKgo6x03m4QdEsR+f/LJJ5zW/fe//z3b2CloJxQK4ckn
+nwQAHHLIIZy23u128441Y8YM5t6skaIthVQIUjm1N998E/vvvz8AsMgwbtw40w4t82/QPayJduj3
+dLCe4/F4mBuT7LzMhSA50HQ5QGRaudNOOw0A8MwzzzDHOXKkUUrljTfq05/6fD7biFELWj8d264C
+veg0KGVlZcyWejwe/O1vfwMAbN26lQd73333xbXXXgvA0OoCRvFUmphoNNpqCUvIuWXixIm46KKL
+ABjRecR2yoKv9Hfs2LF46623AJgjI0tKSpiNlKHKmYh7OnGIFrHMMBQKhbgAa3FxMf773/8CMORz
+q+z65JNP4tRTTwVgiElkMZk7dy6mT58OwBB/yIOQRBGS41sKak/v3r3x+OOPAwD2339/1iuROGZ9
+GaXYYDfvtLGUlpamJaLWMVdKmXQxxM5bn289prU26YZoXQSDQfz0008AzA570huWICNpWwpHfHDg
+wIEJ7ZJTICpO1ZTKy8sxbNgwAAZ1JsouKwaXlJTwznXKKacAAI499lhm0YF6hWCu3YepotENN9zA
+sfSVlZW8y7355pu8c9K5w4cP52rOgwYN4j6tX7/elLqN2twU7bNMo27X13322YfHKh6Pc2xDNBpl
+Vps4gtGjR3Pp9JNPPpnL2RcVFeHLL78EYHAbpLiz5rhoKUjMefnll1lkoHYDwOmnnw4AOO+88/Dj
+jz8CMBRx5Jr9wQcf2Ipg2eTUsPpnSO7D5/MxFyYVlZFIhOeMYlu8Xi+Pq9vtZqXs8uXLMX78eAAG
+R0ftlCkE5b1zZdlxOAUHDhyYkYUL8mwAWyDKv8Go6fAugO92/i3ceVwBeBjAGgBfANi/Nd2cZUkw
++s3tdptcYqUL6uGHH64PP/xwdiN9/PHHTffNy8szVXjO1ad37966d+/e+vPPP9ezZ8/Ws2fPNlVX
+9vv93M4RI0boESNGmNyzBw0aZOs+m407rl2V53QuwR07dtQdO3bUS5Ys0XV1dbqurk5/+OGHJnfd
+m266Sd900006Go3qaDSqV6xYweMmq3wXFBTobdu26W3btul4PM73zvXYUvm66upqHq+tW7dqKzZv
+3mz6v7KyUldWVuqvv/5aX3rppfrSSy81uRDbuRJbP+SybecabXVr32uvvfRee+2l33zzTb1s2TK9
+bNkybks8HtebN2/Wmzdv1tXV1ZzfYd68eXrDhg16w4YNWmutly9frpcvX6779u2r+/bt2xz39qzc
+nLMRH+YAeATAM+LYJAALtdZ3K6Um7fz/egDDAPTb+RkE4LGdf3MKa45CrbWtjVam3I5EIqach4DB
+vkmRQebRy2U1obVr1wIwFGDE4smw37q6OmZFia2dO3cuBg0axMd69uwJwFCyUmr48vJyWxfj5mC3
+3XbDHnvsAcCwhpA486c//YnHeciQIZgyZQqAeivJkCFD+HtlZSX366677mIxb9q0aa1WPIbclb1e
+Lx555BEAwPPPP8+sNrHliUSCi+KedtppvIbOOecc3H777QCMiMmmpOKT6fKoDXRfqRgsKChg34IT
+TjiBlYrkvLZp0ybst99+AMz+HcOHD2dRIJlM8vc1a9bwvaXI8KuJD1rrDwBYha4RMMrMA+Zy8yMA
+PLOTAP4XRl3Jbi1qoQMHDn5dZMne94JZfCgT3xX9D+AtAIeL3xYCODDNPS+EUXtyKXayuVYxAIL1
+sYtEk2mrKH2WvE5Gjrndbj1w4EA9cOBATn326aefmn73+/3a7/dnZMNkO6nddEyKINTmgoICXVBQ
+YLpHYWGh7f2IJT3ppJM4qnP9+vW6S5cuukuXLnw+XSMjETN9aIzkc2Rqs4svvlhffPHFurq6Ws+f
+P1/Pnz/f9LxEIsEsr0zBRlGgXbp00XPmzNFz5szR0WhUr1y5Uq9cubLBszN9ZJ/s5oTafthhh+nS
+0lJdWlqqa2tr9YABA/SAAQNM58r5kNGqlMpv2rRp3KcRI0Y0eEY2qfkyrZszzzyTU+/V1dWx6DJ5
+8mQ9efJkDUAfc8wx+phjjtH3338//y5RU1PDx0ePHq1Hjx5tGispxsjIT8s7kzPxoVForXVTnY92
+XjcLwCzAcF7aSShMPvmSHSJWrbi4GJs2bQJQH522bds21sLKZBOySEw8HueMusQOz5s3z+T8QmxX
+JvFBsv5aaxZBtK7Pgiw1w6Rx79KlC1tEduzYwSJPfn4+s6107LjjjmON/YYNG0yWFDonm6pAUqyS
+bKXV6iDFrlAohJUrV/IzKEFILBZj1vaTTz4BYLDGAwYMAAC89NJL6NGjB7d5r7324ns2pX6jrL0p
+i6HQX1or559/PgoLCwEAkydPxjfffMPXywK5BGLti4uLeSwGDx7MosaSJUvYhZx8AbIRJ+zWitfr
+Zf8UaiNgzAdZSejekUgEGzYYxdn9fj9bl+rq6nj+4vE4r3eK84nH4/jnP//J32mtSyeseDyetkBN
+OjTX+rCZxIKdf2nFbgBQIs5zStE7cNDO0FxO4U0YZebvhrnc/JsALlNKvQBDwViutf4lmxva7c7S
+Hk+UvXv37pgwYQKA+khCt9vNirHa2lp2g962bRvvDn379mX3UHqO1+s1pfCS5cMai/tPJpMmZZ/0
+EejSpQsAw0eCUmyRgqu2tpbPrays5F1M7ka0ow4cOJD7TynQqJ12KcOyAY2FzCEgc0jIfIgU8KSF
+t93333/PCk8qljJmzBjsvvvuAIzamJTDcerUqbzju1wunr9s/Cokx2ZV5rndbuaORo8ejY0bNwIA
+7r//fr4+HA43cFXfbbfd2KfhlFNOYbfr7du348477wRQr/gD6gOTsinNR5wLAFOgFd3D7/ebonFp
+/dL8p1Ip9golTouuI2/SWCzGbv20vh955BGcd955AIzoSlljlDx8N23a1GS/m4xEQSn1PIChADop
+pX6GUWX6bgAvKaXOA7AOwJidp/8TwIkwTJI1AM7JtiFUBUkWh/1/7X19dFTltffvIZOZSTKQhISl
+EVDJqsVCfK8fWRSxLK4ICkrtoiBC1RpsC7ZQ7WtXLUitX1hrpRahcr2uBls/qtQPWtD2VeQi0Fa5
+CLYEAblSCuGCLwQhHySZZGb2/ePM3tlnciaZJDOEeJ/fWrMyOXPOeT7Oc55nP3v/9t46KcbEiRMB
+OI3nQJtMmnn++eddHoD6JeOXd+zYseIlp8Ed5vP5pFNTCQTCA8UYIy9NJBIR4sn5558vATg5kOzX
+vvY1ubfP53MNJhY12QJQVlYmffHKK69IGdnZ2TJxhMPhToOoeA0IImqXyKRfv35Cn25qapKXJisr
+S2IJlpWVyflMGc/PzxeLyfz584W8BHhnLOoKyUrXnSdsfX1DQ4Oc87nPfU7Ea/0cxowZA8ChWnPf
+BoNBbNy4EQDwrW99S6jEmj7Mde5oQvByW9eTHtetqqpK3KFLS0vlRdYWCr5HQ0OD3Hfx4sXip1Nd
+XS1+J/zCDxkyRJ5DMBiU96Kurk622EB7klVn6HRSIKJZSX66yuNcAjAvpZItLCzOTKSijcz0Bx7W
+BgCitc/JyXERj7Zt20bbtm0TQoi+Jisri4qLi10xEAHQxIkTKRKJUCQSoa1bt9LWrVspGAy6yD1a
+k5tYl1Q+Wru+fft2l1Zba7a92lteXk7l5eUuYsuGDRtow4YN7crRcQ5TjSnI13TUNrZwnDp1Svqq
+ublZEuoQEdXV1VFdXR2NGTOGxowZk/Qeun36WXiRqRL7RVuaOkrg84tf/EL6eM+ePXT48GE6fPgw
+1dTUSJ25vkQkvy9YsKDTMvjTEZnN6zq2ROix4Pf7ady4cTRu3Dh64YUXpM779u2jffv2UXV1NTU2
+NlJjYyMtXbqUpk6dSlOnTnU917y8PKkzx5cMh8NUW1srcR3ZqjZt2jRXfyoLTN+K0cg6hWQEjMsu
+uwyAE56b3XNZBH7vvfckrt3evXtd2tfly5cDcJNwfvzjHwOA7CWBNhHLq2yP+rbbkzN43/raa69J
+nbme8+bNw969e6UMjrl3++23i+jKZb/++uuSk+Kf//ynS/RjEVV7T3YG7ROi6+ylmc7Pz5f8kPn5
++Zg0aRIAZ/vA2x/u47feekt+/+IXvyiieCJSdUtP9GBMTNuu76XzN+gcokBbkBTO37Fq1SoRr1ta
+WsTKEA6HXRYOLq8r7vPJXKBThTFGyk0keent4aBBgwC0eZhOmDBByGSjR4+WZ7Ju3TrR+bS2tsrY
+OnXqlA2yYmFh0XWcEZJCPER3uzBSXoqcwsJCUYKxFvfkyZOyUhw+fFgUMlOmTJEVpqGhQSIi899A
+INDlwBqJ8Pl8QufV2XwCgYDEQ5gwYQIAt5dhY2OjtMvv98sKy0FBnnnmGaFHBwIBqVti8I5UKa2J
+57KEwBIPUVvG5MTwYGxFmTFjhoRbYy35X//6V/GYPH78uCiHddZprcHvLEpyYj29nokOR8dIlDC8
+JCCt+NRjS/NMuA9YGmloaOh0PPB9tWVEIzs7W9oUDAZlTLLC8NSpU9JXOlCLVn43NTW1UxgWFRVJ
+3ebOnYv77rsPgGNd0RGfVbutpGBhYdF1nBGSAjMiddgqwO1wwrO5nol5lbvttttw0003AXD2vbxy
+79y5E7/73e8AACtXrhTWmIbes3qtQF2FzgHA9mTWHVRUVMi9Dxw4IEy3P/zhD2KPZlYeAOEEHDx4
+UPpFS1Qd6TYY2gErZUab4ozk5+e7TMO80vO40aw7Lelp3oceY52Zx3SOBB0pWq/8WtrQz09LFYm5
+JPV9db+EQiFX+xKv6yiIr5c0onOacnla8tERknS/8L1CoZBnzInCwkLRk3jlydQm7mg0miw3yGc3
+l6SFhUW3YLcPFhYWXYedFCwsLFywk4KFhYULdlKwsLBwwU4KFhYWLvTpSSErK0tMQLm5uWKe8kJu
+bq4r6QrgmI3y8vLEFBkKhRAKhVzea+lAMBgUMxqDy+S6MbKzs4VY4/W7MUbqCTgmrn79+nlex+bR
+rkJ7C4ZCIRehxu/3u5KcZGVlucoOBAJiPtbncj35k0mwqZZN3F4eofrjdU6mkMrY4rrrMZOdne1Z
+T/0OpKsdfXpSsLCwSD/6NE8hGWXWi7BjjBFyh9d1RUVF4oOfKeTm5kodNPEkNzdX6qmpxryi6lgJ
+yULF8bl5eXlCo+1JVN9kGYyZGMaEGE0tLikpcQUqYWgCUHeo5B1BR/T2IksxsrKy5NxksSc0dICY
+dCcH4nvruus6d1YeS3LGGBnDsVgslb61PAULC4uuo09LCjokGs+eLS0tnkE7gTYJgVe33Nxcl4us
+DoiaymrSE+Tk5MjeO1kqNe20w3qFaDQqdcvNzZU2ejn+tLa2dmtFZn0F4DgE6fB2DF7t/H6/S+LS
+NGbt9qufVSagg7V25sLs8/lce2+djTuxv7SEma56soSQLEu0XvE7i6zFYJ0D0H7cK3x2ac6J3O9g
+MCiDIHEw6GQvienA8/LyPFO8ZwpnnXWWy5OSEQwGpW5cH600bG1tlRc9EolIXfWA5diQtbW1aZ3Q
+BgwYILEtN23aJPkY9RZMf+dJoaioSHw79u/fL/VMtyiuwX3IykOg7YXzeuG9kKgETRTt0wkdd1JH
+sOZtnx6T2nOVJ+FIJCLjxBjT0WTAsNsHCwuLrqNPSgoMXonYewxwVn9ejd588028/fbbAJzVdsWK
+FQDaxKtwOCwzrd/vF4+8IUOG4NChQ92pUsooKCiQbYNe8b2UpImeevoc/q6lAy8vuq5A56zIzs6W
+iD+HDx+WFVjnYeBjX/nKVyST8s033ywRso4cOYIvf/nLANqiBqVrG9FZToPEuBGAu28T+zVRmkw3
+gsFgO8W4jireVeUwj1+fzydjoAOp97O7fUhEouWAowtfc801coxUBGPeG8+YMUPCefn9fnkxkptM
+VQAAF1hJREFUtUY9HWCN/fHjxyU4yezZs132fv0yAcCKFSvk4V599dX4/Oc/L+fywLn//vvx5JNP
+Amgb/MeOHZNJIRaLdXsr4TWxeLkyX3fddbj33nsBABdddJErTbrGd77zHQBtiUzSBX7ZU90e9Da8
+dBRaHxKLxaRvg8GgPOtIJCKTiXbP1s8nmcVIwW4fLCwsuo5OJQVjzEoAUwAcJaKy+LHHAHwZQAuA
+fQBmE9HJ+G8LAXwDQBTAHUT0ZqeV6KakoG3JPLs2NzeLDb25uVm2GKRSunEYLMAJsAoA99xzj8Tm
+Twwski6cd955WLVqFQBIRmnAkUx4RdehwbQSifHJJ59IGjctBr/xxhsAnBWZE6R0F8mCt4RCIVmZ
+OCTeggULXCI8f29qapIVMScnRxSsHDgm3dIY4FbOJorQmnVZX18v7QsEArIaa6mK+zwnJ6dLKe+6
+Us+uKF+9ktJoK4oOK9eBdJiSpNDdVPTrACwkoogx5lEACwH80BgzAsBMACMBnAPgbWPM54moZ7mx
+k0BHY+KOnTZtmuzVhw4dKh2/Y8cOVFZWAoDk8quoqJDMPM3NzbjlllsAZM5sFgqFJLGKJtucPHlS
+aM86ZiQP1vr6ejETZmdnyzk1NTV49tlnXdfpCSGV7EZe0ISdrKwsV4IenpC4r/ReVse81GbNWCwm
+MTQzMRkwvO7NVpkLL7xQMlkRkStFPeuPdu3aJf3nRTJLF/SLzOAxOWfOHBmTgwYNknoYY7By5UoA
+wKJFiwA4C4Q2s6fL6tStVPRE9BYR8fT2HpyckYCTiv4lIgoT0X44maJGpaWmFhYWpwU9zjoN4DYA
+q+LfB8OZJBiH4scyAi1Ws9j6zW9+U7Tex44dw/r16wE4MzCveM8//zwAJx7iI488AsBZlXnl7igu
+X0/w3e9+10U24hVq2rRpkgJP571gZV9zc7OsGKFQyCVNdBRX0kvcBNy2cIYxRhRV4XDYxT3Q4jP3
+bUlJidyLsXHjRpdCVDttpbqKJWrnWWGp+423hBdddBEuv/xyAM62hKWRq6++WghXLPUxeYrvpZW8
+vGLX1tZizpw5ANryRfTv31+2o6FQyBVduTvQMRqvvPJKAA5lnJXjxcXFojTfvn27PNexY8eKEnrK
+lCkAnCzZHPH7vPPOw/e+9z0AEC5Jd9GjScEYswhABMAL3bh2DoA5PSlfv7icvGTSpEliXaivr5fj
+fr+/XVrxN954Az/96U8BOGJ5OgK3eoHv29ra6vJ84wQ2u3btkslAJ3nVA4+v0xOCTm2fDJoV2dFE
+R0SuF1e/mPyynTp1SsrjVPWXXHKJTKyLFy/G5MmTAQBr166V8pqbm/GjH/0IQOfm0sQJIZGxefbZ
+Z+P73/8+ACc/JPfHyZMn8atf/QoAsGbNGlkA/v73vwNwXni28DQ0NMhYiEajIrpfccUVoit56qmn
+ADg6E35hvfw6uopYLIZrr70WgBOwF3Ano501a5akBmhoaJAJcPTo0VIn1stMnjxZxs2xY8dksvjl
+L3/Zozp2e1IwxlTAUUBeRW3LRcqp6InoaQBPx+915tuSLCz+l6Bbk4IxZhKAuwGMIyKtml0D4LfG
+mMfhKBovAPCfPa5lEmjRlTkJJ06cENGxsrJSLA56FeRVpKWlRcg02g6cmFikp+BVIBqNihI0FAqJ
+CKvt/yye6gzcgFtC6MxnQsOrHT6fr13shURJQm9juOxIJCJJa770pS8BcKdzu/DCC/H1r39dztVe
+iZwFuTOxOzc3V56Vl9b/k08+wbJlywAAf/zjH+X57ty5U55fYjh3wOljLVbzClxfX4+1a9cCcFbu
+u+++G4CTyg8AFi5ciBkznKTqs2bNSqnPO0J+fj7uuusuAG3Ws0AgIGWsWbNGzvX5fELM+9Of/oTH
+HnsMAKT9xhj8+c9/BgBs3ry5xxKClNvZCUlS0S8EEACwLr6vf4+IbieiD40xvwOwC862Yl6mLA8W
+FhYZQipZaDP9QRezO+fn51N+fr4rC/GyZcto2bJlpFFaWurKvqvPN8bQyJEjqb6+nurr62n16tVd
+qkN3Pr/+9a8pFotRLBajlpYW2rJlC23ZsoVmz55NgwcPpsGDB3teFwwGXf/n5eVRXl4eAaCBAwfS
+wIEDk5bJbdXZnFPJUs3Zk3WGaL/fT8FgkILBIA0YMIAGDBjgyq5cWVkp7autraXjx4/T8ePHafr0
+6XJdV/qrX79+cl1ubi7l5uZSTk6O1E1nDU/MZJ2Tk0M5OTmuY8kyb3eUCfuhhx6SzM6BQMB1r+6M
+gYsvvljGXDQapWg0Sps2bZLnmFhHzoR+xRVXuDJoExE9+eSTknVc162DT0pZp9NhfTjtYI0yK8Bq
+a2tFCx2JRLBt2zYAwD/+8Q8XZZTFYBbbSktLRUOuxfN0g7cJ+/btc7npjhgxAgDw6KOPiuj31ltv
+AQBeffVVEWtra2tdxBQWwbOysvDppy5rcTuwhSYWi3XIq/f7/bKlaG5ulnP1Fk3nSmRRPRQK4Ykn
+ngDgULS5fQMGDJDsXGvXrpUtAT+zjsRw7TGYLOdkZ/Dapmhugpf3pFewm5ycHNli6i1od71q77zz
+ThlzvDW44447PJ+j3ga99NJL8nxY2fvwww+7fCZSoDmnBEtztrCwcKFPSgoMXm3uv/9+yYzs8/nw
+k5/8BIDbE1FLAkxz/sEPfiDHdLBSrfhLB3i1fuihh4Tuu2LFCpcikcGxCyZMmICJEycCAObNmyf1
+13UbMGCAzhPoiWSh23hV4VUyHA67VhjOulxfXy8BYM855xxR4vKqO2zYMMyePRsAXLb/Z599Vhyl
+dOAbHdTGC8kyaXN5Pp9PpIBoNOqZmXzQoEGyurOkOGDAANdqrIO+sGTS2toq9ykrKwPgcCH+8pe/
+SPt6yl/RAXuZjfq3v/3NRRXX0uuNN94o7eA+YH6DZq+mk1fTJyeFRFv3VVddJS96OBwWS0RTU5N4
+QUYiEXkJBw92+FRjx46Ve2pxNt08BRY7s7Oz8dvf/haAs7UZP348AGD8+PE4++yzAUCouKFQCDNn
+zgTgaNnZZ0I//Lq6OtfL2xF04JFoNOoplvPAHDJkCO68804AwIgRI8SzMycnR9yoeXBrAtmRI0eE
+1PTiiy+KpQJos5h05kcQi8VkAunXr59MELq+XGYwGJS+1Z6yPCEAbf316aefuu7LW1Dtodja2irW
+kyVLlgBw+Bic4l1PmtrduStYv349vvCFLwBo40JoV3Wgbfz1798fDz74IACHn7F48WIAwJYtWwA4
+z4AnMb/fn7YtsN0+WFhYuNCn4ynwlmHz5s0iKZBKQw5AJAVelQHggQceAOCI5WyvHj58uIhj4XC4
+R5GQE+EVZTcxoAez6piqOnPmTBGNDx48iIqKCmmPZl5qPkFHSPSoYzA7bvz48Rg1ynFTGTt2rIjP
+J06ckD7SbdAhw/QKzL9feuml4nVKKmZiZ/VMjDegQ6x1dL0uu6SkRFZblmymTp0qUkU0GhWpSHtw
+XnnllcLIZCbk7NmzUV1dDcA7QEpXYYwR6raXRBoIBKSea9euxXXXXQfAkVJGjhwJwAlvB7TfGqYr
+mnOf3D4w5s6dC8DZ63IHHTx4UAZOTk6OSxzn/fD1118PwBE5b731Vrmus4GnwQ9AE52IyOXO3VHI
+cV2GMQYffPABgDa69iWXXCJ+Bueee65LTOzK9sYrMlFWVpZsndhztLS01FO7XlhY6Bp8Oq4gt42/
+19bWyl79q1/9qojd2hXbSwegoftK+3lwfxUWFsqxgoICcUHPzc1Febkz3ufPny8Lg/Yy3LlzJwBg
+7969MgEWFxe7qOfLly8H0EZB11uGnk4IXB/eKrCupq6uzkWoY3LSpEmTZJzdcsst2LdvH4C27bMm
+nKWTcGe3DxYWFi70SUmBFYXTpk2TY+vWrQMATJ8+XVaMwsJCUdREo1EsXboUAHDxxRcDAF5++WXx
+otQrghdNViMUCslqnXie5iEweCUKh8Oycmklkb7HBRdcAMARe3nV3bJli4iMnKYNcFaHzhR3OgkL
+r0w33nijOBUNGzYMAFzhxHX9w+Gwy6rAqyWvYIFAQJS0BQUFIm3cddddsqL98Ic/7FJAES5PK860
+bZ8dicaNGyd1fv/994UDcd9990l5zJWoq6uTNmVnZ4vH4dKlS0U6WLRokStVAJCahaer4DrzfbUX
+6ZAhQySepc/nE4Xn9u3b5XqvWIzp3O72OpuxO4zGUaNG0ahRo1zsxcrKSqqsrCQANHz4cBo+fDiF
+w2FqamqipqYmWrVqlZxbVVVFVVVVNHToULlnUVFRtxhqxhjq378/9e/fn0KhkDDQsrKy5Dife+65
+5yZl7jFLb/Xq1bR69Wo6ceKE1PeGG25wMQ27Ur9QKEShUMh1zAtNTU3U2NhIjY2NVFNTQ62trdTa
+2kpEROFwmMLhMMViMTmf2XVVVVVUVlZGZWVl9N577wmjkYho+/bttH37dgLQri+SfRLPSaz/unXr
+pA6PPfYYlZaWUmlpKeXn5wvrUbMTi4uLqbi4mAoKCqi8vJzKy8vp3XffperqaqqurqbJkye7yuJn
+152xkMpHMyG5bVlZWcJifO6556Tv9+3bRyUlJVRSUpKUcclszBQZlikxGu32wcLCwoU+uX1gjSyj
+trYWv//97wE4MQb27NkDwBHPWGSeMWMG3nnnHQDAb37zGwAQrTLgRFpOFiU3EYFAQET/WCyWlCPA
+ojZvA7QyU4t+sVjMRRACHFGcy6iqqvK0MqQSGpxF8MLCQhGJjxw5IhYFro9OqMPRpwFHmcXnGGMk
+DgGHy3/iiSeEFFRZWSmKv9raWpcCj/uI+RjsNZmIxL5MtL1PmDBBYh6w3T6x/VqxOXz4cADA448/
+Llr/p556SqJgDxw4ULYMDQ0N7WJqpMPioBGJRFzRvbm+bF264YYbRDn8yCOPSAwHr8xSGtoK1NOt
+hJUULCwsXOiTkgIr4xj5+fliV//Zz34mYc7OOeccUTht3rxZ6Ljar56VU/X19e1StyWDTt0GtCnd
+QqGQzNaNjY2yMjN7Tiut+vfvL6vi0KFDJRybVvaxnV+zMRsbG11U2VTNkydOnJCVJj8/X1Zxvaro
+cG2szNKr/f79+yWU3XPPPQfAYQqyYnDDhg2iEB08eLCs0nPmzMHTTz8NILmEkFgHwOkLfhasaHzn
+nXeE01FUVCR9G41GxY5fUVGB0aNHA2hjXh44cEBo1/r5a+pzIBBo15+Z4PEkZjc/66yz8PDDD0t5
+rDRPjK3gpaTl+mnTcE/RJycFpvYyIpGIhPvKzc0V8apfv37yYs6YMUNIKjzQ+/Xr5+mtl0g7TYRO
++61JM4miL084TBk+cOCA1O3FF18U34YHH3xQBj2/pIcOHRKt+Mcff+xKrtIVnoJODqstEUxBZi6E
+toz4fD550WtqarBp0yYAwMqVKyWUPEOL159++qnQse+44w6xktx8881i5WFbezJEIhEpW7eT7zV3
+7lzpzz179mDHjh0AnO0RLxbLly+XyMdsfUjsMy4jGo26tkeJk2W6Ew3rl5sjY69atUrId0ePHpWA
+K9oqlbgQAe39RNI1gdntg4WFhQt9kubMbDRefbKzs0XsjMVisqrcdtttePnllwE4SiQ+zjO1brv2
+qOwKtKRARDLjf/vb35YVjUX/7OxsUWpVV1fLSqFDnjEt99577xVJoba2VhSR0WjUtep55dP0QiL3
+gsOpXXbZZQAc6YhF9YKCAqnPhx9+KF6C2l7PbWptbfV0DOKoyIn36Mzn3xgjK7fP55N7J2PrTZ8+
+HYDzTN99910AcGX25rwPR48eFYVvJBKRPkzmlcmSoDEm7RnJuQ+Y8XnPPffIb+Xl5bKVbGpqclGX
+Eynfum56LHfwTqdEc+51jkJ3eAp+v5/8fj9VVFRQRUUFHT9+nHbv3k27d++mBx54wBWNSEfV4Sg1
++j78Xdt5OapRR+Xr/9k+DkCiGy1YsEDs6dp2f+LECeEgtLS0UEtLC0WjUaqpqaGamhoaOXIkjRw5
+0mUrT1af7OzslPtK103Xv6CggAoKClLqd7aH+3w+T3t+UVGRq485apLuW+ZjdFSOjjzE3zVfIRXb
+fGJEqsToVJo3wW1JjNTEbejq+Ez1mTCPg4hoyZIltGTJElebEvtXR85KjJ6VIrfC8hQsLCy6jj65
+fTgT4JW/QIui06ZNwzPPPAOgTTFKRKJ5/uCDDyT02oYNG7B7924AnccbsOj7mD9/PgDg5z//OQBn
+O8HU+6qqKlc6xDTjs+8l2Zvwmkw16enVV1/Fxo0bAcCVhEYnkPXaV3fmRWjR9zFhwgQAbWNo69at
+sljEYjEZQ8XFxe0SGJ0O2O2DhYWFC1ZS6CZ0GDJGc3Oza1vBszwTaCKRiIse7QUrIXz2wV6+LDXe
+fvvtQrgD3NyS3oDVKfQQxhiXeY51Cpp/n8xdmH/PyspymTU7usbiswkdwl+b19NsDk1Jp2C3DxYW
+Fi6cKZLCMQCnAPSOvAQU27Jt2f8Lyj6PiAZ1dtIZMSkAgDHm/ZTYVrZsW7YtO6Ow2wcLCwsX7KRg
+YWHhwpk0KTxty7Zl27J7H2eMTsHCwuLMwJkkKVhYWJwB6PVJwRgzyRjzkTHmY2PMggyXNdQYs8EY
+s8sY86Ex5s748YHGmHXGmP+K/y3s7F49qEOWMeYDY8zr8f+HGWO2xNu/yhjj7+wePSi7wBjzijFm
+jzFmtzHm8tPVdmPM/433+U5jzIvGmGCm2m6MWWmMOWqM2amOebbTOFgWr8MOY8ylGSj7sXif7zDG
+rDbGFKjfFsbL/sgYc01Pyk4XenVSMMZkAXgSwGQAIwDMMsaMyGCREQDfJ6IRAEYDmBcvbwGA9UR0
+AYD18f8zhTsB7Fb/PwrgF0T0OQAnAHwjg2U/AeD/EdGFAP4lXo+Mt90YMxjAHQDKiagMQBaAmchc
+238NYFLCsWTtnAzggvhnDoB/y0DZ6wCUEdH/AbAXwEIAiI+9mQBGxq9ZEX8nehe9HFzlcgBvqv8X
+Alh4Gsv/A4CJAD4CUBI/VgLgowyVNwTOgBwP4HUABg6RxefVH2kuOx/AfsT1SOp4xtsOYDCAagAD
+4fjbvA7gmky2HcD5AHZ21k4A/w5gltd56So74bepAF6If3eNdwBvArg8E8+/K5/e3j7wYGEcih/L
+OIwx5wO4BMAWAGcR0ZH4T58AOCtDxS4FcDcAJrQXAThJROzokMn2DwNwDMAz8e3Lr4wxeTgNbSei
+/wawBMBBAEcA1ALYhtPXdiB5O0/3GLwNwJ96qeyU0NuTQq/AGBMC8CqA7xGRK1EgOVN22k0yxpgp
+AI4S0bZ03ztF+ABcCuDfiOgSOLRy11Yhg20vBPAVOBPTOQDy0F7EPm3IVDs7gzFmEZwt7Aunu+yu
+oLcnhf8GMFT9PyR+LGMwxmTDmRBeIKLX4of/vzGmJP57CYCjGSj6CgDXG2P+CeAlOFuIJwAUGGPY
+hT2T7T8E4BARbYn//wqcSeJ0tH0CgP1EdIyIWgG8Bqc/TlfbgeTtPC1j0BhTAWAKgJvik9JpK7ur
+6O1JYSuAC+JaaD8cpcuaTq7pNowTBKESwG4ielz9tAbArfHvt8LRNaQVRLSQiIYQ0flw2vkfRHQT
+gA0Apmey7Hj5nwCoNsYMjx+6CsAunIa2w9k2jDbG5MafAZd9WtoeR7J2rgHw9bgVYjSAWrXNSAuM
+MZPgbBuvJyIdb28NgJnGmIAxZhgcZed/prPsbqG3lRoAroWjkd0HYFGGy/oSHLFxB4C/xT/Xwtnb
+rwfwXwDeBjAww/X4VwCvx7+XwhkIHwN4GUAgg+VeDOD9ePt/D6DwdLUdwAMA9gDYCeA5AIFMtR3A
+i3B0F61wJKRvJGsnHGXvk/HxVwXHQpLusj+GozvgMfeUOn9RvOyPAEzO5LhL9WMZjRYWFi709vbB
+wsLiDIOdFCwsLFywk4KFhYULdlKwsLBwwU4KFhYWLthJwcLCwgU7KVhYWLhgJwULCwsX/geTBSdf
+FmBaZwAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="CelebA">CelebA<a class="anchor-link" href="#CelebA">&#182;</a></h3><p><a href="http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html">CelebFaces Attributes Dataset (CelebA)</a> 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸，不会用不到相关说明。你可以更改 <code>show_n_images</code> 探索此数据集。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_n_images</span> <span class="o">=</span> <span class="mi">25</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">mnist_images</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">get_batch</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;img_align_celeba/*.jpg&#39;</span><span class="p">))[:</span><span class="n">show_n_images</span><span class="p">],</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="s1">&#39;RGB&#39;</span><span class="p">)</span>
+<span class="n">pyplot</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">helper</span><span class="o">.</span><span class="n">images_square_grid</span><span class="p">(</span><span class="n">mnist_images</span><span class="p">,</span> <span class="s1">&#39;RGB&#39;</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt output_prompt">Out[3]:</div>
+
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>&lt;matplotlib.image.AxesImage at 0x7f56d19266d8&gt;</pre>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsZUl2HbZiOOOd333zy3w51tBV1erq7mo2LZo0bZqE
+4QGSP2zQhmkRksEvGzLgDxP+sH9sWPKH/SFANgjYgG1YlgVIsjxIoptEUyRbze4u1lxdQ1ZWVVYO
+L/PNdz5TRPhj74j7khDVWewuMT/uBhLv5h3OiRMnzo49rL22cM5hJStZyUq8yD/tAaxkJSt5umSl
+FFaykpU8JiulsJKVrOQxWSmFlaxkJY/JSimsZCUreUxWSmElK1nJY/KFKQUhxL8ihPhACPGREOLX
+v6jzrGQlK/nJivgicApCCAXgQwC/COAegB8A+Heccz/8iZ9sJStZyU9UvihL4acAfOSc+9g5VwH4
+mwD+3Bd0rpWsZCU/QdFf0HH3ANy98P97AL75x31ZSeGUlBBCwHrDxTlIIei1EPAGjXMWDv5L9Lnj
+fwAghYC6+CkfwgKwfHB34Xe4aCldeCkE/SfPEgzX+gCANI4AAOViHo4Bt/yZcw5a05RKKRHx95Wi
+ERXzBRpj+Ph0vXx5QayzAABjLEzT0HumCeO0xvozQ0UKNX0d00XFv3/c8tvfWQcA6DhCnKY0Tmvh
+DB17MhkDAM5OJ5BC8hwCsdJhPo2lMTfGwtjHxwkhICSNSGsNdeGaGkNjMdZASvnYtQoh0DQX5oI/
+X9/ZQ1EsAACnJydo/BxYOp+AgJ9xKWV4X8rlMZq6huF5cH5BCf/bx+dbhHcBJ5afKT6WtQ6G75m9
+ML8CAjevXqXf+XM5d+G1gTV0T5SWyPI2z5tAU9P7kudYSQVjagDAfLEI87JYFGi1WmG+BK8jv25M
+04QHuN3KoDR9LoWE4HsJIQC+ltfefOfYObeBHyFflFL4kSKE+DUAvwYASghsd3IInaCiuYE1NdKE
+Hyqtw0QVdYnK0kKBpOHXxoVnO4sUupImJ5IOluemdBbTBf2uFvS7xkoIR++ZxtBdBwCnkcZ04776
+pev41V/+8wCAZ67uAAA+eut1WMeLxgANn7usGmxubgIA0jTFzuVdAMBgrQcAeO+Nd3Bycg4A0HGC
+OInpMuRSCRUVnXc0nuP86BG9Nz2Hq0sAwHwyR8TnHmx3cDCjBfKP3r5D321MeFAA4K/9538RALB+
+aRdXn3+Wrnsxx2J8DAD43d/6bQDA3/lbv4NE0QJsx8BedwAA6CQJTicjAMDRbIHJlOZrxuNUaQSd
+0n1aXxugkyUAgFgJnIxpzOPpGHmL3ud1DRUnOOa5iOIWooTO/R/8F/8lbr1HXubf+F//F5wcHgEA
+FvM5/U7KoNxbrQyLBb2fpRnSjJTew6MjjBcFAKDicUopoPkhTLQOSjaSCpLVghUWcaT52DnN52KB
+0WRG19w0KFlBaKnx1//qf0Pz2dA56rpB3dACNvU5pud0TwZrHbz49Z8BAJSVwPHBfQBA3lmjc3X7
+mJwfAADefPMtHJ9MAABvv3sLX3/lazSfWYq0Q4olSjIAwNnRMdZZQf7sK19Gb4PmMIlb0DGNH1EC
+yd+PN2/SgH6EfFFK4T6Ayxf+f4nfC+Kc+w0AvwEASaRdlCQoyhJ+F8iyDGVFC3AxL+FYATgoSF5Z
+mp/iTqrA+gPreYTdlBZgvx3BSbqJBhL3z+nm3TqkmzyqBAD6oREOTe13aWDe0AP7xg/v4O/+398C
+APzKv/WvAwCKYoGS9ZITGvM5Lf7NrW041uIWAmlMx7j11jsAgGY6x86AHrZ2b4CioN+dn5+E3VEo
+fuDbGWRNyuQcFuenJ3Q+61Dz7yanFVotOl4rpwdiMZo+diPGR/Tw71+7huKcHrBiOsHbP3gXAPA7
+/+9rAABpNLpdmoudbht7A16waYxOj47dPp9iktIcHo5IUZwtJpjxAzsdTbG5QWPeWe9irU8Ls3El
+FgU9pDHbcdq5MObxZAFj6Lrrusbbb79N748nmJc0LyXvronS0Kz0m7JCxPM1Gp0Bjs691u2G78x4
+HpxzyFlp7G4OEfHnsdaYTelb1jQY9DoAgMu7tAGYusIHdx4CAD68dx+OrR8tFQyvQ8ubjI4lOo7W
+24dvv4P1Ia3Dva0NRKx4zo7vYfzgNs1XQud65sWv4PZ7rwMADm7fgk66AICNXhvS0Lw9vHMQrDSv
+TGeLEnlG556WY/z5X/oFAEAkHKxii0XhMQv4SeSLUgo/APCMEOIaSBn8MoB/94/9tnVoFhWkUuGh
+Gk/nqAx/LBTSiG5+KiP0NH1nf0B/r2y1sDkgbTjMNNZYyyeRCFrcNgazho7xvds0qd//eIzDkh4w
+JQQ0m1yLysJZOnZRKvzBH34IAOi2vk3n61jEHXIpnIix1qPF2EkTTM7pYdm7uYHDO5/Rd/gca2t9
+pCmPLUvDQ6GlxdkJ3fGzM3r4hbRY69DicMbg7OQMAFAuSoiCd6aqQcvRg9wj/YNTADUrOuUMdETn
+63bbuMM7MIzEb3/r9wEABw/pfP1Wit0uXceVzS0MOn5sArlXsq0W6gGde3dCx314nuLojB7ck2mJ
+0THtcmmksL1DO9fGWgf37tN3pqxAVVWh06ZjaFujKUiZHR48whtvvEHHOzmBlN7/o/nJshgJWyZx
+rBCxaZxGEsbQsY2R0GwJtGNa4v1OG1f3LwEAnrtyCdeuXgFAVtrJMSnLpizR7dCD2mnT2Ju6ws4G
+Kd5ZOcfHh3QfBATSFu3cpaDzJqjw/ne+AwB49NlnyNRuuKZ33yBFV05O0e3T2ulv0tgO799Ch6/p
+yv4O4pTGcPWaxvkZja2tFrj/gKyJs4ek6CdG4oDv9Se3P8W1PdqHv/G1l8nyBQBhoBS/fkL5QpSC
+c64RQvyHAH4TgALwPznn3v0izrWSlazkJytfWEzBOff3Afz9J/quEHCRRlk7zEofWJIh6NOJJdYz
+2im+tJXg5WsUK/nSVTJxdzdbiHyQRSWI2WwHEIJ15WyGkk3Rdps0fKwe4Q/ukNY9HM1RsWkrYCBE
+zWOLMC1p1/n2d8jU/tVf+hqqgj6P4gjrA9L8o9NjpDHtqhoWDe9W/S7t+CpKoNhnhUDYBbMsg1hb
+49/Re5PzYxTntCsN8xTTNh13er9GXRY8xwZW0Hd6MV2TdjbEOADghZe/AgB474MfQrLV9O3f/AN8
+dIvcyzQhi2B/ew/PXLsJABh0Wshimm/lHAybzNYa2DbNZ6dLu3yaKrQ1WV55HOFwRmM7Phmjwz5w
+t9dGl19PJ2SZVLWD5GClEsDp+SkA4PXXX8Pdz8jCEgIhyNfN6XxffuYKhgM6VitL0GvTrqq1xmLB
+AcqzEc7G83BsALh+7Qpeev55AMBGp+tjbzC2xpXNIQCgrMsQKPYBw/l8hobjOTf2d/GQjztfFNi9
+RPGjSU3n/fD1H+AH3/0+AODqbh8P79wDALzx6qtwlubl2v4etp69DgDY2d/nMQAoWzzfBiIEKysM
+t8l6MxtdxA25OfWc5vDs7hEqSxYyZIS/9y2yZJ994QXkimM40iH6fIbCn16g8aIYZ3G2KFDVgHU0
+JCFIGQDAXkfja5fo5v/Mi7t4/voWAKA3pIcxijWsTwYoDa3IpLJOhuBh2ukCFU1qntONdWUVotuv
+1Qb3Z/Rl5wz8D50wMKwsJiWdRIoIHE9Ct5+gKel4s+kYm1eu0jVVFWL2OX0gSzjA8iJvXAVvGQvn
+EPOYB2zC96IE8zk9bItihK0OKbpJp4UJm4aT2Qx1zRH8mszvXFg07Nc6OJydHAIA7n76CRJNC+X7
+338bhoOu+5dpDq9euoz1ISlb6UpI9kmFdYgE+85Ww7D/nLGrNehU0JYmI9EKKqLv3juf4Oghmb6R
+VsgTDkDy59PZAvM5Kam8lYcMzUe3PsJ8Tg9ZK0ug+ane5GDt1a0hLm2Qku22cww4IJrnOSJWuE4o
+GD/n/PRnWYZY0xyLxoVovxUKWtD7OtZhPfi/SiqkPPbttT667I6UBbA2pAdZzmmu3nn9dXTaNLY7
+d+6hG9O6KBfn2LlEimd/fxNr3TbfM46T6ARDfq+tEdaTVnKZMYLCsEvnu3GNlMnVDz/GWx98AgC4
+e7zA998gY/yTu4e4dp03CeFQi88XVFjBnFeykpU8Jk+FpeCsQFnRfqqkTxU59FMa3uV+iuc2SPNd
+HnbQGVLuPRmQ+RZnGSwHmZy0UIJ2VWsVnGNMgquDpdCNaVe9UVocndHr0aKDk3uUs59UBo4DeE4I
+cAYQbBygqR1UTDtbK44wPiMXRAkRrAPbVFDC5+bprzMGgsdjYOFCEM0t8/vefI0TtEC7TqwUFEfn
+93cbfLqga60agwm/9oGxYZagWNAcVtbi/dfJ5dm7uoNv/ebv0vWNZtjmYNfekM4x6GSwTckXWKD2
+KUdnISyNycoYgndKFdF4lUwQRXTuXgIUEVlYU51gzGM7PzlDq7M0+QFgPitCvn4yHqPNgdu6qYMJ
+LyCQsGWxxcG+TqsNrb3VkSBJ2HLJYuiIA6xCQnB2QUf0XSi9hKQIC6F8WtpBGW8JLXdUj6tw1obx
+ZEqgzfd9rDWyjNbZCbsU64MEt18n076cl5AZXf+V3TV86blnAACb65sQbFmJmi1WU4EPi6SVo+HA
+rrMWMWdXjBBQCc8BZ7D6gyE0B+Cbdz/Fq+9RluR7r76BjW2yJpRtEBm/cp9Mng6lAIohKGHhowGJ
+BAYcNX1umGF/k9yHja01dLq0gLIu+eFJ3kJZkcnZoIFkc9c1EoIv0Ygals1Apeks/bLB9Us0wccT
+hw9O6RjH0wJOeKBIgCkBoPEkrRQpm6LVYor5jJTJxsYWnPW4hxqCtYnidCq0BFuq0FEUYibOWVjG
+S3j0lhQyLH44oNPzoKgIBZvXxf0DnJ/SwtP84LYih7SmY0mkqCv63J5Pcf8TWjRCxtgd0txtMDBL
+KYFqxn54U4TxGOEgWVGrNAbcEtTkr82P0+oKLTbhO5HChNNH0/EUjk3YiJWKk0DlL7lp0Ewoa7H/
+8hYiftCdNegz6GejxwCyJIbiuY+SFFLFPEUKuABbU8IrBcaCKL2MjUgD64PztoFiBWKcCUAfD2mK
+ogiaFZOSDp0W+fBZZeD4WqsJzdtHP/wQ735Mmff1lsRL12ltPfvC5RBTaaoCpRiH66YxJoi8QtMp
+bOP9Sr0EYQkZ1rVkJbW1s4OvvPxVAMBkbvDBJxSX+c4PXsUr//zP0X3oKUTWz8uTycp9WMlKVvKY
+PBWWAkBoZCkFYt6BBqnGly/RbvbKzR3cvE4uQ95thQi+5d2ndg6Cd38JhN3aCQfpgSUihuON17L5
+lmYpdjfoHC+cC7x1TO7FZ8dTFB4+CwEt/Y5Pv0+yCJlHSk4nEBxFb+cZLG9BzpgQYPTgESGXQUcl
+FRwHM4WQ9CEAewFq7HctqRQk56PTdhvbl/YAAOezOSacrZnMyHpwOoblLIOBwPomzduDuwc4OaUd
+bW04wM4avd/KGH5rAOvnrTHBYrEKkMJjm7GEb3rUpHNhZ3HC+ctArAU0/25RlBCMLdEcOEvTFEVJ
+42wMsBiRpfDcjRv4/e/wjmgkei2yEFPt7yOQsMmshYBkd0xY+kfzLKB9UNFDg6WE8t6BFeHLCipA
+65VEgHobtgqNkAEX0W63kKa0znQkIXgcC3Yf7tx+gMMxZRl2Bgmef5YRrf1OWNfOlGgYL+Pvv3UN
+hGjxdApY/lxH8RKuDHIn6C+tXx0nuHz5KgDgqy8s8HuvfQoA+PTeQxRsFfWTCElrmY17EnkqlIIA
+maPSAYJv1qW2xtdvUMzg5pUNtBlM44RF6Rc9/5XIgo9rrFj6gxeQ7dZJwLsVARcu0Wb/bW8zx1d2
+ydx74+4jFOXyZnhsv9SsHLSE4/RDNZuiw/65gHisDgL8fQ/IEkpeqAEQBLYHZQncBUy9/9wvCPJp
+2YzUEdoccd/Z3cWIze6SYwDGVCENV9saqaL5vPfoGJqBUztrPXQ5BpGyeR5FMRRfk1QC0QX/Gz7d
+qxXM43oOMtLQNUfvlYTm+5DEEimb3YuyQOlrM/geRFqH2bJAyBaMDx9BMXQvb7XR5XRuxGlmpXTI
+VAj5R+aTxToE9J+fYyn10iwWTUCv4uLci2W2InxVCGhJ506iBDkrtVaaQDiO7TDw6mw8x5zTxVmW
+B3BarCLE7KJwNCGM0/8NsS/rlsrCNpAc74DUnBUDvBpW1oUU9nDQxYCzE2d1g/19AmftXlq6Y08q
+K/dhJStZyWPyVFgKAGsnCyjWklfWWri2RbuEUAY179ZxlEFp0sBKU9BHqjSYrVIIwAV8NMChy4vV
+flL5vHQKzVDifFHgao920vW8jWOOxEsplpWWvKNoLVBNyWRsygKtjMbRNDVySdo6iqMQEPPnE0It
+dzYsdyVrXXjtC4YkRCiSMkZAKW/9RIhYl69vbmPnlAuGKm+5NHhun+Cur9+5GyyF0ayE4qKjdqaw
+vk2YhOEeQX/n8zmSms1SGCi7xGx4DIiMIgiOlDrjzfYEyu+0rkQDrlexDboV3afpfI4ZQ72t9laF
+DZiFsjEhsHvwycdIeAeVUv6RelhAKXmhElVcqDQVoRBMawXp5967EVIHUJAQNoCihHNLy1Isqz9t
+uH6HiC2FWMfI2GLp9zowNVlpZ8eP+B40kHyOjV4LQwa1SaXguDJPQUPJlC+Kx+bi4OZKqeAxMgJ2
+GRB1gj9DcDXRuODaxRJQPPfOlejyWh6sDSDV59v7V5bCSlayksfkqbAUBAAJBy0l9rqkRV/a30In
+8ekvCRXRbqzjHtKUfOqIq8m0ziB08HKXfrmSkJxCs6jD7tBYtjTaaz7LiPJshIwhv3mcQMo6DM67
+qx5WoKXAgn34OIqQcDppsSjQGSx9QB9TEBwYjUUciq7oA596WvrovkqS9ki2JLBMKSkpEHOKs91u
+YcDxjIgLmyLZ4Ke/Tmmqg8kU5+e8m81m8BtNK8uwzWXda7sUtzl8dATH8QnbWFjjEY02xFQMGrho
+OWYA0EqFOn6XpUjZSmsZh05Jr/P5ApMxHduDBcazGVo53dN5USBm7onRySHAsaKqqjCd+SpJRiAC
+aDgcUBkXsCOxVIi8VSBkKKv3sQUVKRgOpAqhQlq6LucBvWisCRab8dwbVgRLb2d7G8MHlNY9KUsY
+hlXPp5RiVFrh+qVtAMC1S1uIc4pXtde2UDHqEY2A5XiGD2pa52A5fStjB+c5NQD4SIhQIoy58WX7
+jnA5ABArYGednot37n+K+ZwK85RUwbp7UnkqlAIACDhstjT+ja+Q6fvybht+/R2cVrj94QcAgMrd
+QislM7jV4aq+6/u4fJWgz3kngeD6AwEN67iGwRmcHlEe99YtgobeuXuIIy4tHo/OMVAcGU8UFEfL
+pVKI2JzzQR1ZGfinQicZpOOnzRhMzgmaPD46geWHvmHgUaoydPlBGPRaaHGVINX0+0XIWso1AINO
+TFlhyuXJZ+Mp5swVYJzBxNAtPBrR53VTY32NIvZff+E5jBY0hhICWtCx13ubAex0dPdjAMCH79/C
+OVd4NsYiYyW83sox4JLjJEvhfPDUZyfKCpMFPfCnxRwTLpGejKc44erJ8cKgrFnJMCy7bBrE/PB3
+sgjThb9uC60t/26Bok3vj/n6b33yCUp+GPOshY01ehCu7V/BpV3KyuR5CggPgFoGIid8jGpR4OSQ
+TP7R2TFs7cN/FtpvDIyFSfNWUNStThsdvn/FYoHRAd3ru3cJSp53urANjW1/fxsdLj9f37uJw0ek
+OF579U08uEPVjms92tR2NgYY5JxdakUQ7EoZI1CUTL5SOBwc0f2ZzCmwubXZw5CVekc6XLlEil69
+dRcN43aWW+STy8p9WMlKVvKYPBWWghACUaTw8uU2Xr7EhRzS4IgJQ+6cFdi+TLvA2mCA6YK05/kJ
+ac6D99+CGtOOsXfjOhKGgTqdhLyPmY5x9933AABn98k66KkEe9euAQAqZ/HRO2SNpFkGgDR7U9eh
+0CYEn4wJOWMpL8Bn6waO6c16rS7ylK5lzsVHxyenOD7i1Fy/DbFNRB7dbicE67yLA9vAFLSzHd79
+DA/uE1LOGoHBFllFKs9wzrRqdb10nxreEXcGXSj2j9KkC8Wm/d7+5VCYdecjIvwYn5xhNKFd/mxa
+YDaj82XS4gbvRtd395BxUNVf9GQ8xXvMG/Hp8RFKny7TMXJmlup0uiFl2szonkZa4ZTv3/pwiDSi
+ezrc3MRnR1RdOD2fYdyiHe/hMd2zLiwuXyZrMo1TlFxUdevD2zCMoXjm2WcQeSQgF4+Nzs7x8IB2
+6Mn5CKd8vHK+QMbjVFrCzcnqecT8FYPdbWwwpgOQaLMFEScZpKXfGcaQHByfIZc0nkF/DVlK373/
+8R28z3wKn905xOs/fAAAODmmtXLzUh8//Q2qnLy8t4U8pjlezCrc+oTm9ruv3sZiTnP04rO0bva6
+15EUtMaqRKHVJQux3++ix2QxEBfJCp9MnhqlkMQxntnIMczY5E5aSLnm86vrA9S82D65cw+PJlzB
+xmHx5zZzDBlUUo1OoLm6Mso7WPBDunh4F47N7llBk3RWjCGOyPTrdtrYHJI70h9fmEixVAae5UdY
+hEi2FhKCHzZhCkQg12Uxr3B6QibqbEEPQllW2OnQDW9JB/ADbapqmWf3MGlTBUajo/EMh2M2B51D
+Lcmv3dhYw4ABWYweRgOBe7z4j6YFnv8GUbA9//wLqDgDcPPFm/jkg/cBALfu0AL95OAURwy8aRqL
+hMeznmmcjQnUNVkvkLNS8EZpXTfwnHdKpih96XvjMOIqzyQSEOz7OoZdZ3kb5yNa5KPxDD3mIrx0
+9SrevkVkMKYpgvnsGjrvV158CZublDlZCBvo60xZYcIw7ZPjUwx67KNzpmJRFiEzkEcRaq5GjVpt
+OI4jdDst9GJflchZm2aBimMOkVBIMhpnnOXI+1T5uL1HcYRBHuGlm7R5feUrXwLYdf34/T/EZ+/T
+hvTR3RkmXIrfyhlUVC9wwtR7w0ELGWMh6nKK2UNSzg+Pz1Gym7rPc3h2dIy9AY1B9tpo8+F2Nofo
+9UkpELZhlX1YyUpW8mPIU2IpUES/G2vETDIZ9bsYCNLQhydT/H9vkSa9PTIYM5HHOptyQsaQEQUR
+b94Yht0ozjuYcvHQ6XmJDx6QufrdD8k6ODESOZuG5z+8hV+6QTvQXksFOq/SEG8gAKSMfryINhRw
+kJwfrqs5HnEQ6M3338b9U3o9591/e2MDL+6Q6V8JjR0O9uk0QsLFWmBswmw+xZ1HZOJ+952PcDQi
+i2d7/xqO3vsUAHB9NMELNwi5xhsirBV4831ygx7OavzUM2T6X7m8i719Mjt3r1zCP/wH/wAA8Pr7
+FGj8bNRgzgeJY4kdRhIWyqFkC6lyDglTkAkOglauwZzv00xa3OfisEokWHBQ0VQL5Dy+Hl/nphWh
+GOv0bIyYrztp95BxoDjRKgQ0+y3GrNQW779Du+4oljiekxWzN1jDlTWyXu7d+QyKsRqDIe2kiuil
+6X6MxpgxevO4bvDDT2k+17Icl/mePLvv3dUejMeZOATsyWK+gLG0zn7uZXJBD37+m7i0T7v8cKOH
+jz4gi+f86B6OjslqejTRuDehNfnL/+q/CAA4+eAHABO5tFs5Bms05sVkitNzWkOTRuKUC5taV56j
+z4vDwN3ZH0ZYZyKezDV48D5xK7Rf7kBmNM9PKk+FUpAAcuWQigyaGWN0WyGNyQTKqgR7fZqc/ctD
+3Dmgh/rSDi3yV158Bkf3iEfR1IAtPHuThqm8+SnxJWa80S2apPGswuY6RYhv5xWubXLJ9aMCmk21
+ebksd+b1ACFFgC47YbAsDYiRckR583KMqM8EsVPyU69evY7NTTpfZRZYsFsSWxPiFg3n284WJSpF
+CvLqzeeh7lHGZHt9gI0OzdGljQHyFqdX/RiVxqKiYzw6HSOXNOh+K8P1XVKAkZRYa9M4bl6mOYk7
+M8zYDcoiiZt7FMlOTIU1NnNj2UKcMzdlSQrLVQXW+ZpNnKJhFuHDaYGtlN4/OLgLV9L9a3GVYaJS
+5Ckpm3YsccyMye3hFjqsFIbtCL7Ab5PTbSrRKDgRORxeRtRihbs5wDoTlZjJBKcnHIPg6kRX17Ds
+PrWzFGmLlYW1+PiYNpStzW3oBcUSGs6oRO0ONGeinJQh8xVJCdyne1K+/ncBANf7AjuX+H5EEdp8
+7q2NdXS+ygxX5w0eTWgcw4Y2ulZPIWbEVqQ1YkW/SyKJy9v0u58VEjIjl+elNa6S7F1HzOQ7aZyj
+FdNxe9MTfO83/hrdy79YYutn/iV8Hlm5DytZyUoek6fGUkghURuHmivnotoiatMut39tG2vbbFLN
+a/z0C0QgkTBrc54qXNn6EgDA1eNQzejgQiHN9s4QMe9ou/u0280mU2BOrshX9l9CvCBT7N7JfRjj
+gS4CyqOW4CngTaCZp6gjFwFpjY0B7SS7G+swvM3VvIuXpgm1+Xk+DGCbSirUvKMbxgEg7WBjm3eE
+rQ08f2XIF+UQM1hKSonZbBauFQCSNILxPJCw6HMhWVTPIBnQIoXCz/7ZnwYArOe0+zw6PcaYg7LS
+OmQZnaPVGoRxatWF5Dx+xcGyOFbY4WseIsIGB0RnVYNFRYG/q/kGUNFOn3kAjhCBQNHpFDlH8vub
+W4FEJD8/Q9LLeL7omoZrLQyGRFiSttvIGDegtF5yXLQizEY0L56X0xiDlK2UNF9bFi5JYPMbL9F3
+6gaZJOs0D+QsUSDZMQAyLnIatNrQjN+wjo7VmT9AJ30FACBUjPUdssL2n3O4f5ssWR2fYi32OAS2
+GPY3kbVpbJ1WBzricww3cfNZutb+YBwqbDcimtd2fxMJBxSjJEXEvUq++cIlTO9SL6bJ+x9g+2sv
+4/PIU6EU0kjhhY0+YA1mHEFOxxkcm4YqExjkvPC2IsCTj8S+wYgILDZmNA+LTUmJhG9uVS6Qc71z
+Z8jpmt0u6jm9VyxqFEd0vvP6AA0/ZEqJC+bUssmKT0lCLUlYBCxMRQskkhoxjy+/QCpr+aEQUfJY
+tyQTWhOGMS3TAAAgAElEQVSxX5/m0L4TkjHIBI+5qQNZiHE2JEl8ExMVR2h8CtUZCFYy1XSExSmZ
+ybFKMOyTgtzbZtCXMihZCTe1gWKEoU4TNDwDOt+Alb7XAZ233cogGXmadwbYYLr7eVHAWHogtd0I
+KD6fLTgez/HwnB7cuZFwTAzTWVvHcJdcl5NH99Fhkl3t0aES2OGof5xGYQOQSocmQePpGBnHLnzR
+o5QKjfVkvBYdfrhdU6MfXciocCypYSgkVXD61y4cL9caYpPG2XAtw87sBB2m1Ddu2VNkfbiLYkoK
+YJZ1kLVnfA7OakgLBvIijlMIJo5JepsY3KDX2XAEy2sr99WXWRta0fkkJCImxtnvW3SvvQgA2Opn
+KO7ewueRlfuwkpWs5DF5KiyFVhzhletbiOwMYyasGOQ5XJ/hp30FwWarjmMIbylI+hsJBEhwKV3Q
+wEqp0CpsUTeBF0Ty751WiNhsd5iicQywKYuwYzhnQ0bA07cnUQTfh8kYC+s58GwDr2dV3ELMMOyA
+TxcCkncSKxQM037rSEPyObyJmEYSJR83jtNwza5uUPGOYcoFtbMCELFbkuYZGp4XrRXOObi2nqYY
+n1HwrDMYoq68mULjzbN2yI83xob6fkgJzTTqsjWEZPisZRem1W4te1A6F/gpWlkCaelalTFwfC2T
+wsNvHebM4TiaTaF5l1dJgi5zcEo4cJEnMp43hQgJc3DGKg8FKUoA85K5JYoCPXZp/L1zQHADIwe0
+mbylsQ4VZ5dUvbQEYWktONcE18w4izlnl0yxQN6lQLdbu0rH3b6PqEcZrOmsxPkjCojPHx4h5nsm
+sxgZQ5oVZ2eq8/OlRSNUyHAIlyDle5KtidAZLFyzkxAMoYd16DAIa2EnyHbIxRapwPmH7+HzyJ/Y
+UhBCXBZCfFsI8UMhxLtCiL/M768JIb4lhLjFfwd/0nOsZCUr+WcvP46l0AD4T5xzrwkhOgD+UAjx
+LQC/CuC3nXN/RQjx6wB+HcB/+k87UBRL7OxlOB9L7icJVIsFGm4eausmdHCOWhkEowad5MaZwqAu
+uZ1XY+E8uahS0AkTZtoq+N+RZL8vShHiBGKOmrX5yWjZ21AphZSJS1u8O3aTFCPeoSu3bCrqmgY6
+kLXGIWCkmM5LCrmMP1gTauid1qF4ylP0Cx2F10roYAlYVwc6LtOIQP+W8Q5tIADOq0dKYcbBvmG/
+h4rTnUJnYCqKkG6rmjpwOehIhWmZXUAKdtsDBBIfzXOYFTCN5yBwSH0H6iiC1kwxVs7RcM+MlKtZ
+VWQRpdzp0ZbotXwMwAakYAMdipUUw7gzKGiugEziOFDzVU2DBaMfY5Wixa3XPJlrWVVLBjljQ6FU
+nKRQzEDtygUst+Tz7FyNjNBwztlUBjOG3m+v9SCZym7zG/8aAKC4fAWOsQuL2uCcWb4XZ3cheRxZ
+3gnWT8NWR1lNkWaEJ3FOoDFk8cgmgZ3Qd5wEYkbterwFUKEqPDuXBLjgbXj9ecQpWTG1BcT08f6i
+P0r+xErBOXcA4IBfT4QQ74Fa0P85AD/PX/ufAfwOfoRSSOIIN/a38P7tg9BUdjyt0JrxDSqrQN0l
+oxRSsCvBJq5dzFDNKLLuqhrwUNU0QgSuRISDYYUTqLqgA6WWLSscnDGO/uHpYy3MfaenGS86FUdB
+SY3Hs9A81VVVgNJq4RBxCWwUaJvtsiO0tah9i3OBJa154IPUcJ623tWQnjjG1NDc4lxbE0xbHxis
+TQ2A89WtNhxH3yezM7SYUMY6g/ExzdcpQ6Kr8/NAeiK0DiXLp+UCm9du0Nw3VShrVvCkNwkmBQcM
+57OAs+h2OhC+VH1ewjA4x+MpkjgKiiyN08AIraTG3mUCZPWG6yiYu7Fm0pso1lAMY5eRDpTzZVVg
+XngqtAw5Z6a8e+ScCzUos7JAi11MjRIJm+suiVHzA+ezTw7BQ4OzDoLvycsv3EDDmlV3CeY8euM3
+YWeEPdh59qsw7GoBLrAyL6bj0Py24Ma2k9EC77xP2Ym9K2P0mXb/0f05LjGOPenmcKH36AV2Zm6g
+LIQLGbHe1ZfwaMTzZRUirsF5UvmJBBqFEFcBfBXA9wBsscIAgIcAtv6Y3/yaEOJVIcSr54xQXMlK
+VvKnLz92oFEI0QbwtwH8x8658UUCTeecE+Kf3LPqYiv6F/e3XN7pYW04x6MpNwgxDj2ugOstFsCM
+d/SsgdSeeoybadRzcEYLdQM4ht028xksK5yybCDZNASTiajGQbLmL0YjfHCPNPeHx5Plji4l5oyK
+9FWGiBRS7m14fnaCGe+qiXDBBZGmAninCMVA1qHhlFzZNHCModA6C4FNP1taqXBzzGKGkPg0NRS3
+FZP1sq+k9hDsxSKkprbW16G73JMhbeC6nH61BUanZNpORwS/dfMZCnZFZkWF8zFhFkyaIO2f8Dw3
+yNn68a3iiqJCzdbdZDzDyTFXF/b66DNkXbkKFe/0dSAWEcsCM9RhXoRQGG7QPnL5+g28+wPqzViy
+pQFFKUUAaGwDbvmJyfkINUPaTVGiWaeAX833rq5rJGyNVXUVzudUDcPsyVJGj9GwAUS8YozHpzhs
+bDFUfKMbCFQ9yjPt7eD4hAqY6qYJx6jrGp5K2joTugr5XhiNE5iNaN4e3S3RLMj1mZ06zDQhT7N+
+O8DtLbsJSikIdk1roSE1jWMRDdHdomstZrNAhvyk8mMpBUGULn8bwP/mnPs7/PYjIcSOc+5ACLED
+4PBHHac2Dg/HDRqhcco3NmulGPPrzskpFDMRQyWoPeiFgU5xliEf0M2qojZKNsvOPvkEhl+LpAXl
+MQsMO5bFsunJ5OQUt+6TSX1SWVi/EFwTHtQFs+MUVRVKiCMdY86kHyJS4SE11QKWF2Ht2aeNoapC
+EN+htRxrgAv049ozTsNC+AValaH0WKIJPQjrRYkpX0vE2RlZzBD7PpCVwU6fMiCjkwMYjok0vQoF
+uyBF4xmJZyh57KPxLIyjpds4vUeG37E4QI8j3B5DMS/mKPnBy7IslJE/fHSIhusksligZiVSs3tU
+QyFngNTVq5soS+/3inAtvbW1gD045YdmURVoPBV9VaDhe2IWBWqGSjfW4IMPPuRzs1siBCKORbTy
+HA1XzNpEhnNEMoXv2u4zWI1pwHoQxgkccEex7731Nr7xK/8RHZvXVffaNxAxmMooB8ExpaZpYENv
+0iawOKfc9n6n3QmdoOqyDl2xrrx4FQnjSRBJVHx/vP8rBLjqBog6a0g6FJfQeTsQ6qSxDoQrTyo/
+TvZBAPgfAbznnPtvL3z0fwH4C/z6LwD4e3/Sc6xkJSv5Zy8/jqXwMwB+BcDbQog3+L3/DMBfAfC3
+hBB/CcAdAP/2jzrQ4ekUf/1//300cYW8IW3YvrGLYZ80cFkWKJn8Qk6i0MI9YiSa1BEE79xJlkEN
+eFeazSG63uRqYBmGV/PuUpYVBKPuJosCd7kirTE6NAOBE0uqNJ+vroGEkYmttIXZhHaxeVlhxsQo
+3dkEdU3X4isqJTRiDvbJNEbD1F3F6WxZ0MTITeEc7IytADhUXPlZFlNIjnCPygqnbKU0zDhsrINk
+S6qo5hhwFH5S3Mf9T+4AADbXbwQORsPWyPmswJh3WusM1gYML24naHGR07SqMeHApc8KxFqj22dM
+QGRh2FWajuc4mBKCstvOIWPmydC8t8VxaFU/MVPMah88s0suzCQOGNLTMfdWmFXo95mopikwZ7fS
+FCW63KIeQkIxxDxhq6PT7QbXQAmFivtQCKeAjIvfFnPEAdLM/TgdcVQAQONqfHxA7sFvffc1/NeM
+vahL3xPSIMspc1JXD0IQu24a8OaPxgGlvyguukLTIEnoCx2dB6i7SS0KNlNjmGWTo+CQy8AFodt9
+yD7jO/TSkhNKQXGR4ZPKj5N9+H3gj6V/+4XPc6xFWeH1Tx9A5Q5b7CPdPy+xv03+lIziZY8/RQQk
+ADA5pIWSTirEXL1mlILg1E3TODQccS4mY8w5Sh6z2drpdWC4GnJUC9xhJhwhdWj4CikDe5E3yaQU
+sGxedtptlHP6nbMmcClO5lPEje9dyfx7WRzgzM66kL60jUHBaaPilMcQKajME3Ua1H6mtYLhBiHH
+43HIYPhHKlUxKrZ3x5MxbMIP9HwByVj9yWQCYT0ZCsdnmjp0bHJKYsFLQ4kodGdKIiDhB8inRZW1
+oezZCBE6MlUSmFYMJpqW6PmSax6pyiM0bDIXwqF/6RK8CDbzB8NNpJxJKjgedDwa4+olZp6CxHzM
+MZFFhT6DrPK8jQ7XubgLrLuNz4aUNSp+XTRzKK67yOM0dMkyAbAkQkyhMQ1GXB/ihCSQG4DZlMaA
+psKYyVI6rQUS5anaHezMZ19kgFuf8z0/fzTF7BFnrSqHjAlf1/YGGO7QdSxSBbALbTjVCx3B+rR3
+1oaOPWgNkM4rQAknVs1gVrKSlfwY8lTAnI0QGEuBjbyDBWvqNw9GEBwwGwyeRerxGogAxikcPCD+
+uoPbHyNWXDB16TJ6e0xkYhvMmUrs3e+9BpmQ9vzmv/A1AIBAjIKDMPfGNR7OePfDshW5ERcYgeF5
+GYGaA1V5K0XOO/BsWqHinbcWLuThz3kMxfkUawO2MHp9SLYp4yhGkrKLUvtIl8X5KQU+56ZEj1uC
+xSrBdEHjOBxNA4Cmx+3fTDEKOIxMakzZZBZpih5X7T0a12ix++NbwvV7bURstp5P5rjN3ZM39zV2
+dhjmXFg4f3B2xYxzqCRd03xa4lPmFJQa2OpzMU+SQDPIyDdn0dJgcka7aqQaPP88EZUAIrSWy3tr
+yDPaHQvOGB2Px1gwF+Nmv4d2m3bgjz5+FycFcTtudgeY+MCrx38oGVrNl2WFKbtgyVoLl29epblN
+EhgGSdUMwW6cDHTvVVWhYCzEfD7H+OEdvlV0f4/ufIyPXv0OAOCVV/YABo7FEVAafzzAZ+Bvf0i/
+e/X7nyDvcIfqa/swbBU9+L33kWe0hv7MV7Zx7TplVHqcRbI6h2xRIDnpDqHZqhLSBlcDQuKfnP/7
+4+WpUAoODpWkEmRvft4pG8zvEBfh8ze20WrzohqdQXP8YGufoq2iVigntFjTdh5AHK6u4Jhfr9fP
+sXWFvp9xSdqiWWDBnIKvfnAHJ3OuI9AqxBSsE6EVu29mOh6fo8M+IJxFi4Eyi/k08PmNZlOk3JOh
+s83jHM1w/1NauLF5EDpLtbMcwmc7GCBVmwZT7j2xtr+NFnMDzicl7h1SirB0Ai1Oaw652nE6OSYY
+G4AXr96E9QjDdhtXv/zP0TiydYxuf0TX9Ak9/Lmpkcd0nd12hq0NYi6qnA59ELdefDb0R1Q++zAd
+44RJZcflMVq7tHBbXe31DZRVcOwq1VyLMjMxbh/Q/X3pz34NpnUBzsIxmKzTR4/Zte4cUhJrPF/g
+ATNStdMEit2Z7Ss7WNyj78RwyLnScNmgVaDiTFPc6aC/Ttmq9vYAEbubRgClz+xwetZChEyEMQY1
+Z4Gsc6jP6Lonc7ofb37n25g++BQAcLRZYjohpV5VZVBIUkpoQVphs0sK8uagFbIhOH6InN2ETZVD
+c5l1O1WwnMsssXRjuwOKI6isAyV8OX/tW2gCwsKpz5eSXLkPK1nJSh6Tp8JSAIirpFgUWO9zQxah
+cJ9x5q/dOsJzm2RezcUZNAOEEjadh3ttjA/ou8l8gjYzA6eNg2TQy+ZOG4M9ClyW3nSsanz2iMzS
+7773IAQz0yTCgn/nrIVlzeyr7B4+OkHDrMzr/TYSNvHSPMN0QSbheDpBzhHz/haZ372dTUQcCS4e
+TSkaB8C5JODWHWcOYhmj22O6sm4OU5Obc3Q+w++9RwQal7dayLg/ZMpmpIoi7Kxzl6IvvQLPFbKx
+fx2dzW2er2dwxq7LwQ/fonkt56HNeiwcMnBmp05Qn1NwrXjjLT9kKJ6rYr6AYuxFPxbQ2zwObQJU
+vKo0Gs5WgN0B1+mjz7iJn/83/z384x+8DgDoOgcfv06zNhLeCRvzDgDg+OQIB2dkVXRaESLflr6V
+occBSFk2kBx0i9m1E0oi8WCpdg7d8gQGAgXjSOq6Dp2oarZsitpgwvc0SSMkDCff2rmE1373HwIA
+7h/RGpqezcAJB5wdHGAyesCTlTO/Aqiyl12U4ZDuwYvPd7G4zXR0RiDxdTc7a8ifIUtJrasl1wOj
+E+JYIduge4okguT+ngo2rCcLARdxYPIJZWUprGQlK3lMhHOfMwrxBYhW0vWzCJFKsMZtwKxtMOWU
+TRItKwaLug5pJs2xg6ZeFkxpKSCkh5dWITctkwQpMxSXTOxqygaWYwC5aLC/wVRqOx0c1OTv9q7n
+uPF1SpelHTrff/Vrb4agXKYUrj9HzL+/+AvfwOhT2sXtSEM0tPO2Fe1ENzdyXNqgnXR3cxMdTtNF
+cQTNSEHD45mPRqgqLqIpF5gXtFvDOZR8TaUTKNhdnDsOatYGt++Tb310NMLw5/59AMD3/tH/iU9v
+vUZz5Gq0uU7fX0esJMrGo/hqdHiHlbCh+/W0aHBecFqPU3q5FqGtXlWbgLBzQgWWJiEkYo59+PtR
+VyVqTi1LqaAjmot/OY/wtXW6Tx1bo+bAXsRxm3lRQvlzZzlO2Nh9Y1zgvjdjdAylfL9GWiv9tAXL
+8YC7Jw8w4gpcqXXoITrMcnQ8SStbPzrOYdlKg7OPQZdf+abv2E2xg9JWoc+lgcacsReHZwuUvItb
+KREp3yOVvttKY6x3aO11WwmGTEDbbeUhJRtHOrQy9GESrSMsmJ9iMV8EWsCiNBjPyWQ5mc5xzvGK
+v/Fbr/6hc+4V/Ah5KtwHAUALgW43RcZcfEXlQq653+1hdEoAoaKsoVgZSLNs7tFh7EE711iWQ1ss
+OIo8r2UonS1KrxRMCMgUVuLhCZlwaarh24Qf3Rmhs0U36dIzy2CYDaacC81eJukcV37xJgDg4Luf
+wd6lhXeJQTWXBxkubxC4pd3vI2JMRhRHgQzG4/AjtFEyK7MqgAa8MGsHy5V8Qkn4XuWJ863qHYY9
+Ot+DB4e49e4f0uvP3oPkLEGv1UHO5rWHZU8nE7Aljs08xXrmWYJlgESPpEISjkHjvbrRgWI34WRa
+4C7T2p8XNWacJRBSLhvqsMugpIasmSquLCG52u+ZdgddBkBN5kVoxX42Lfi4JTpcGXlSGHxQkLn/
+w8kEbSa1aUkDy2ukYHfgztEBEp7jKM3QDxT9QJ83i721HiKPPeDaj1k9x5Tfk1IEAFtjGjSOrpVj
+msihYdn4roxE6a/fuoBH1kqCcVzotLwL2sEmr5G1Xhs7vEaGvW4IMCstgnK2XIMzXyxQFOwfDvso
+mIRlMi7Q9k1r8gwJ1wg9qazch5WsZCWPyVNhKUgpkKUJkXTyDtzK0tBKLUkUZORpzhRy3j6GTDS6
+u7OBDW7iYV0BxbtArFUgbRmXFR6d0+tPpwwvdiZoRSME5hzgeXRWotXi6rMTgUfvkwWxvsk+jHOQ
+bBorrTE7pc8/ePMj7O1+EwDQG3aQcYBqlwlENtcGaHfIDE5bMVTYNWXARThGWNo0QxQzjVsMpFwO
+2Izm0IFI1CBhk1IxsYoRGp2IzXIl8MM3KW9u6llAFeZxGlK1Pu+epzH2uXb/yzsD7DC7sIPBhNOk
+s0pgyhWRl9nVuraewTA243Ba4vYpBVXfvX+OW0dcoSpiaDbnvbMaJUloDR8nMoxjPU0wZ9P+vDbI
+uK+iL7pScQrL7x1agdvnhzyHgOZrKq1Bab1FQ/O9szFA5sltpQz9I1t5Gtr+9ZIUNSMWx3y+u/MF
+Jj4NqWS4AlcbxIGVxuNJXVhPs8rBckBbSiBnzIpWAj3uNr41pCDipZ0NbPT4vfU1DHgtt5IkuMVV
+VYciPe8SqSRBJ1k2KCpTOt9Gd4CSLazOZIaU+2I+qTwVSoGAoALzeRF8z6qaBwWRxHEADvUyheeH
+lEV48Qb5dFkWIfFsNs0CsfbMQxo1ZxHmVYm9HpvjfBPfuvMAFZvoUsWBin2yqCE4Oq+VwOg+mYkH
+nxwvh+w5WIRAw77/8cdnOHiDctfbiNHncfib3+v1gyKIYw3tmZ2FCOZ1zOkCCYuGzfYkisPDUcR1
+qLir6zKUH4PjKGmk0eZ4wP7+JXz79g8AAHmig7JsmibUc2z0SVFstCRe2CUz+oWdLtaSKFzqlJWC
+jjI4Vob9nGMqbgFraBn1sxhrXPnXTRNMqiOat4WEVvzAsuvjmhoX6KwR8flkWcB3pS+tRqzoWhL2
+syFVWPCj2RyWIcN7rRYyH0uCQZfjEjs7xLi8tbODbcZytNIYjucWrsYpQ5MXx8ehz2jNDr+1Fg2v
+i+miCBgYDYE2r1Ufw5JawjK82EgDsMunpEbMIKpWLHBpg9bDZe4WtjnsocfZkE4cIfYT39SwPpjm
+LNSFGA0AtLxSApV6J55v1AlUnpPHCjy8d4DPIyv3YSUrWclj8lRYCs46VFWDOHZYcNDOmhJp7l0C
+iQG32V5TNX7uRQrm7TJtFbRE3HiN2gRtXTc1Go5IN5VGm5tllI4CUtP5FLcfUjGL1QKWd4R5U0Mz
+p2A7k3Ac4n/08emFQXu2ZxegtLrR6JTM4+iAnQutwQGg1Woh5qi/VvEyYCrlHz0stNAoC08NRrgN
+ADBJDPYwUDQVDO+8SYuhrwLodckUvZomaHPLN2GLZZMcW6PHO+8OI0WvDyLcZJdgrRUj99RsUiPh
+HbHf7VJwE4B0vtuzhbWeJ7BGpDk/3qT4cJ3GMTqsUbNp77MrIhJLIhNjAh0dmhpg5uZEa5ScaSgZ
+8n44XwT6u1ld4gZD2p/b3sCQ8SlRpJFzsLW7TdbkxtVn0OEejTEqVNwRW9oyVOPeNTPMzsgdmXEF
+Y7EoAsy5ccu+orFU0AE/zJaCcCGlIJQM16eFRovnbafXwj5bjpvsorWVRAw/ryJUGTqIwBWpYw3B
+hU3eUtBSBRfbWhvWkYAGd6FD2Vj02HJ8Unk6lAIoiD6dTIPPqaUL0NgsayPjJ+FL2x1c4f6PXabK
+tkojMZ4HsUHDSqGoK5ScDjS6guWFtVHQIr8yGOARt1+fWxXIMor5FFNOW7ZSGVreTw6XZBU+Cm2N
+QcLQ381+HxvcCLVTzTDg8uMWm4Y60ogiOreSyxJwrfXy5nrdpiW0r840FRSnt1QcwTHYRl5wO8Bm
+dFU2qKslk1DGGQ5TNog5/Ri5Gp2Yrm+Tayr2BjmGPM4k0og5Mq51HgBXrTyG5sXdeEr9soAwvuKy
+hOVKy7RssMvX/+lM4WzK16I9RFfCM8WWZblkFdIxNGed0sbi+AHBwk/4Ubk7r+B4XnpZipefpQ3i
+S89cQ86NY7J2F5pZnzrr5D5ka1tUkAEA1cj3C4ItRxhwO3f14pehYpqP0SmBqfR0ESDK1snQJKg2
+Fs4fz7OsSwHwg6sjQClSLGkMrHGnq81hF93cV2VyzCySiDjGEaUxVLp0Kz0fo9ZxiBuJ0JBHBBeM
+3DEfS0tDZecgUthbfD7i1pX7sJKVrOQxeSosBTgHaw2cE8FSkHaZj7//2T3sd2ioe8/toc0R5S6b
+wDJOIbkIyDUVMxoDqiqgODdd1hFy3h16Ke1sw3aMDTa1Pz2ZQnN0PrYxKiZLKRqF3KvOajnkQCTh
+XAAA7W5sIGUTLkk0Uk/ZFurco2XVnlahYlBGUTA7pbcUIAOnYGMMNM9FZJpQtal1BPCuYXhEKtJI
++L1UZpgxACyLo4BpaCcSu10a2zARPCdJCKLFaYrM58ejFDEX6KgoCe6DZ6qWSQHHZDFNEyONaO77
+jcROj4Fhjyqc+gY+XAHYVHWonIzjGCVniWQao+bMwaKqUPKYPTHJtCoD7LzbSrCxSbt8f2OIhIun
+VN5GwgzGOmOqd6kvFEfJEMFvaoOKYccibWHvWWrzDkHjPfndf4zDexRgHhsTgtHSWl93hrryTVrM
+kivUJeFedvIEA4bF99s5WgyLD2zWaYqITfwoSyB8lkQrJMoHphMo+TjjdyOW+A/Uy/1dRFGg94u1
+whrD259Ung6lIOifjwUAgJUaMwZjNOUc/SFV3211ciR+MvMlqYYn4qzmM0izbP7qnOezE3DGd3pi
+dqdUYsg362C0QMm+aitLA6inrCyy2JtoSx9SBKLVZQ+B9cEgAKqyLEPCD1OgL48iqGiZxpLsrjil
+Qv9L7z4oyACsUjoOyi1Ls7AQGmtR88NUe2WiFGLOVGibBMp1lUWQjIdfa7fQT2kcG5zWzWLC9gNA
+miahc5ZOIiRM+hGn7UCA4jirY6SB5VWkmhiOmay6aY1hmxc6mmXlY0pjm0xHIRbR6XSC/y2TFKfM
+q3l/NIPh+zrjOV44wLDSmzuJFgOWsjyDjjziz0L5WhLDZK4l4A1jVy9gmHDH1NWyQbCK0LBi3OVy
+6i+fnOCU0YHTw1OUzbItfQTPAOYfYo2SQVjTakmi04rTkA7utdrIeQ5izj5ppUN8SQgFIZbxHO/y
+OCFgPZuS3zrdso+pvpCJEAJBwSuIAKh6Ulm5DytZyUoek6fCUnCOoqdCiKAxG6GWoJIM+PINChj1
+8yTk270JCOsuEEm4sOsopUI1nxIyQIk99jyNYgw4uNaKdeD7c4kO2rquSzjn+00u2ecUm9FKa6Rs
++impgmWioyX4yv9KCrFMzctldJquw393CWV1dlk37y1fBxewDkmWhaCqj5ALnUIn3J68Xo7XuWV0
+OtEOHbYU8tTvdgYJW0SRVAGOrOMcipueSJXA+V1MeX6AJECRnV3m0uNIotfirEUWI54yWOpCn8/G
+g43KMpyvulAd+6hcoMu7v8+7V0qG2pckz9FhXEQUKXg6AYhlvYbgnpC2NMFasXbZlEdKiaVJJgHn
+OSTpuzdvXEbEazJ54x28eodwKGdWQHo31Vt51ZL5++KajHUUsDPWAQ27gv6vMs4XNUJZ6iBN87m0
+DhrY0G/Sw5ydEMuMgxDL9SYQOocZ02DBVtGTyspSWMlKVvKYPBWWAkD5fq116KdQzAvkmrT5l69c
+xkA8h20AACAASURBVPOXKd+cRxEiDwlmTWtls2ymIpb5b+dcsDwSLWAbH1MgH7mdpRjktDO0kxhH
+Y98DoQ4w5qZ2cJySlAHOWsPv7FII5Mz1Xzc1jPG5ZAHj28K5ZYMRnyqSdRNar7nGBqJQH3B0GqHV
+Wt2USwtDqRAwjJIYmiHG0kNxhYaVHmlnljuJlMEKSaRCyrtm6oOLSkH76lMpQ59LIWNYLg4zVkJ5
+FB/PhK0qiBC3MYBYQqy9pbDWSqEeES4g5SCbsU2AWldVFe7T+WyMghmq+0oCbNWNubDNWqCd0ntf
+/fJzGKxx/CSSEL4yKW5BcLzGF64puICFMMaFgilEOvR1aIyFr5rzXbLTWOLSFqVIX3n2Mg7PCDI8
+OZtDKA83X8aDXMgQN+FYCgIN0+zVlYHLPJ7A41RUsFacE2HuldJhHUopgyXg77+90IaQPmPrzS6t
+ImJ98rWrTyZPhVKQQiDTEbqdLqZMlx3B4Pldipr+zMt/JjRrnVsNzTffNzdRaQLnewNaHSZBWh2w
+AE4YWA66xVwqa5McC9/MU4jgHtRNjZgVQFUtTbgLGCMY63v4KSSsZJqmgfRt0nUcwFBML4JakGkO
+AAp6+QFccHMER5sL16BhZVTUJvTYrJs6dBbiC6P3ufLTtTUkzxWqRXBn6rqB5AdaQYYFaR2XNLsI
+hs/XQCDxJbvtNWjORDhrAQ5A+nx81JKIMlZIVQV7zg+IKRBJen+j00EkSClEDEhbLIrQ0FYIGyjs
+T84PsctELFZovDemsuQpB3A7WYpf/NlvAAB+6usvIUq98lpS+SkdLzEE/NBYZ4OCEFhyNzqnl9Rl
+tgpPtS+dNk0NzZvT5a1h2Jw+HX2MinkXvcbWSi/LoksDF3wCAc01Cq1OB622r3+hv3HWguA1lKRp
+aD+PC5sIudfMG8quorMNhPabkwybnrHLEu/5bBaUyJPKj+0+CCGUEOJ1IcT/w/+/JoT4nhDiIyHE
+/yGEiH/UMVaykpU8PfKTsBT+MoD3ADDmGH8VwH/nnPubQoj/AcBfAvDf/9MOoJREv9dFWSxQMz7g
+8uY6XmKG36qc4sE5565rCfeAdp31baLqunopRo9TfZmMEGnfuxFoWO/NIHAypzTjO0z8+dade4EU
+Q1gTXispvNUKC4na07H5AV/YlWItseDWbYv5HGJIqadF49Di6W14929khJTThcKpkG+HRMhNF6zt
+H52d4+SQCnVGZyeUOgP+f/beLNay7DwP+9a0pzOfO9dcXVU9sLs5qUlF1hBKlAOBMeQEMAIjfnAS
+A86TAyQPsZM8JA8J4IcgiYAACYIEgREEcRQbCJARsiSLMg2RFClKJJs9VndNt6rufM+4pzXkYf1r
+ndsBEVarFKoMnAU07+WtM+y99t5r/f/3f//3oWnraLc2GgzAyPSEU4ivjYvddFo7iODK3Dao2tBR
+maKk0P3ukQf1imUHeYfmcL7AeOl/v5X2MAiGI0kWzzvkDwxJnLfTcol3H3ia8On5eYzMWitiCdO2
+VKMvCiyo9Mg5Q5oGG74G22Rg88PjKR5TBKgoyvn5n/kc/sIXXwcAjIfDCII6Z1Yuz0xApp+UILPG
+RGYisysmIBdJbJCzwkFQxMVCpGhdPA/OJboEbCapwlkb7gHihagEzFGKIhmcJDvBREbfjhYWPLhm
+F0GIIYOi8rVSKkYC1bJEGezt7CoVDJEph4FTAdhWcG1IQVy8PtViEb0xnnU8r5fkFQD/IoD/BMC/
+Q1ZyvwLgX6WX/D0A/xF+wqLg4FCjxbxZ4PamVz5+65Ub2CaE/MbmFjZId7B2Encfeu27d9//AADw
+g3fewR4pNn322jVcpl4D6Wz0bnxwMsPvv3cXAHAy8RN9ZfMqxht+gp+OD/Hw/F0AgJESPMpla4jQ
+4hvDTxbrwlmaRjqyNTWeHvsFZzaZ4MmR/4zrU39Oe9vbsGN/EQuVRsMZ3ViUC9+yu3/glYHvPdpH
+SS3EaSqRUhchSxIckyksOEdBlYZgOMNsi6bxD9v5YhHl3qWQOD3z75tt5SipNXhrRB2VV3fRHZLq
+lbOoyZ3q8OGHEMTVT4o+UqJxh8qQqZZRyObp4QFk4v9+8+UrENSKnj4+wzv3/U3/wdSfk2Wr2vxF
+9a9+kkCRsU8rSwh6ADbo+ibW4OH7/joedrvokobjzqiPDs2RbVw0lg1VDWsdDHUtOu0wJ8WixWwO
+TW5RjSmhSOCFk8dmtVhGFW/DBDRd6zTh+K3v+fuwIN3GQTdDn+ZHSgXOaAFhSUwbF43GEQnGGLrH
+clGDUXoBzWDo2Ku6ih6iRmtk9D2GFgeeFxC0AVqjoeleZ1jxc7JERkr3s47nTR/+CwD/LqLUETYA
+nDsX6jp4BODyj3vjRSv6sDKux3qsx5//+FNHCoyxvwTg0Dn3XcbYVz7t+y9a0aeCOzOb4vNXdvHV
+n/ksAGCvn2NMLDABhUf7PpSumIg05n6QDJMJemkA5eZwwq/WmilMyUTk8ZNj9HKSQqPutHsfvI9H
+Tz2QNeoV6NHnnWkLTeF6oSS2uv7vtKmCubNYdy7SDJb4rrpqMXeEWvd6SKkrL1iwHU1maKjxZ1QU
+2CAtA+sMFku/u+cEnL32yh3MCFwsIXBO58GWZ8jJj1KkeUyPEgJUE6MD8xmJEGgo9GVKoGlXlOHR
+wO9iL5HS9Dg3cNZ/h0k6KLZ8dKNMjRlFDWlbx/p/oHYvF6eYVv7fe90Uw8QzT+vGwMFHP5cHDDe2
+/bm+c+LTi7Jtojr2xYoRqzimILZkliMlsG57y39uaSy+c9fv0KeLZaSK746HeO2q93K4fXUPI4pu
+XBr8SGssSJbsydMzvEP304PDk5gW9pIEY5r/MfEjOsxiRGkqBENOaczlIscDEn44KUlk5qwGZ/6c
+JQc+Q6zI8dYORhSFVcbi4Ny//mzho8LtjRbXKK1UhVqpYC8rnM/IQ3MyQ033WQBJ+4MuLpFuxHjQ
+QUJNVU05j5WmRAmMBiGzf7bxvAazv84Y+xqADB5T+A0AQ8aYpGjhCoD9n/RB3SzBz925jq989mXs
+blFulSYwRCl99/4+fnTgL+Loyi08JAOT2bG/Od64fgk7r14DAIyHKRIyCKkrh0Pi/reK484Nz2v/
+4V3v7PPe04eRDptIiSEZnh49PYQkm/ieUriUU+szC8IcWBGowCCpBKhkgu2RD32HeRoR8GO6aYwQ
+yCmoenL4BP3uDQAAk25VLqQQcTZbYkadmvuzBu/d885L13scl0n8Nc9z5OQVKYKJzGSKsg1l1nTl
+FekQS3KDjKNHZbH9c/+gfPOjc8yoEvHBg8d47bbHc375zWvYJFNYXWp0BgGE8XM8ny/QEO/+3Ap8
+87s/AgC8ffcBdnf9oridi1UNkyzn62ZFVEuSJJZvGw7UVLVYaoOCKh+3rnlzmulsincf+oWlv72D
+x9RFea4dCvKSbM1DvEFKXcWWf6Cr5QL7+/5+eXQ4xTkJ4xxphwf7fm5H400ktV+cX9v0c/wzt24g
+7/vfTd1gp+8f7i/eeQX1XS87X1HVprEiSqtzZmJ5+WS+wPnMpyvn0yoa1gYtzfn5FFsD/x1bgxFY
+qDSVFWYTv8gIlWP/qT/vQ3IOu7o7QDv388Mv72C44ee7yLNYjWtb7Vu6P8X4U6cPzrl/zzl3xTl3
+A8BfBfC7zrm/BuAfA/gr9LK/jrUV/Xqsxz9T4/8PnsLfBvD3GWP/MYDvAfjvftIbenmKX/78bez1
+EuRBBk3yqLMnuMHGwIeDd27dxJRs0+TS7+BFmsEGolBrvCoJADiDakH28lUNXfswd5f6/G9uDcGZ
+3/n2NkcoKTz7YN+gpS6fpqlQ7/sdlJN0m2MrmW1nGZKACncUNmiHqc/m6JG/45wqCk4qtEELwTSR
+ustbg+WEkHiKFCQkBgSSFVsd3Nz2O17KHRSBYIK3UKSmUZFBDnMmEl62xxsQxv+9NRpj0gHcGA3Q
+p1r5lM4pK1KMyFynnC9w5+YNAJ7TYQJEpBtoQudl4Ee0bdQg0G2K8dgf58bjJ7hOIji6bZFQOJ5T
+FHe8rCPNWQgRI4W5vABiWo2MorSrGz4K6NzcwxlFUDzJwEp/X2xubGD3kj9mYVssKcoM1R6pFsgp
+hO+2Dlfq4HOZgy1DNesSlide2v8ScWRu3LmBgq7vyf5TZESKunR9hN7+e/6zg/+kW1U1AA5Qmjtb
+zNGh5j2VcZD0JjKqDJV1icnE35vNdhNTqSxRUKEJigEvXfbHdPWSh+l2Nobocx+BpACEW1kfBG5N
+0zSeX/Ipxp/JouCc+z0Av0e/fwTgy38Wn7se67EeP/3xYjAaOZDngFAWCTG0ciXQI5Wiz790Fbtk
+1VsvDvH5W37FrIJ8WG+Vywtr0VD92zr//wGgJxSSikBF8vb6yuduoaadNFUpZlRa44yjoZ3LCAdN
+OZ67wBY1kQugURMwdD5bIk/8DnzzxhhFn1hqxKAUSYKSLNicLGITTL2so+WXpIwu63bQRYhAelGl
+yNQ1mopwktZFirGmVu+2LIGcOAHLWWRKtgYoCmrlThU6VO4N83ZjJ41ScJ+78xZSYh4WikFpcn4+
+b1HPaG6DBZtxSAmgvdTtoEdg15t7A3DnX9tq4PGB/767ZNO3f/IYhr7QXmDg5ZwBWRBHdQiGCSSy
+hasbI/zqz34BAPDBOx/g2lWPJfXGQ1we+6ii380BS14HFHklRY5N4hPs7FzC3Q89vsDKEm9due7n
+1ja4+cU3AACffeNlfzwJx+LYR6Zl1cRrnWYKfWrhT1Uw7dFo487O/LkA6CUCg8zP18bONjiFmfWS
+VMVbDUU4UDOZxfLzsMhQ0om3zmCLSu0F4RpcSnQk+TtwE7kujJkoE8AYW0ndPeN4IRYFBwdrWxgr
+wejhUBDoUN95N+NRd7C0DnZED4j2wIpSDkF8mDcaIMqsYBJdQp/LWmMkAtHDn3abdFBbMkXRFr0g
+w805SkLqDV9p5oVeBThAE4BnjcGS6sp3HzyGe+M1AEBfCSjtb0zpqC69YGgnVDnpjcFokTGljr31
+0TnYNLG2rZrFygJea3TI8boEx5IWQE1aELpcYHniVZRn1dNIeFFcIaUHFowjpUVmizo8084gOhAZ
+rACzpi4xJ2MU0xhUQZ+Aqg9NVYNN/GI7GCUY9KijtJtFCjZzEhnZsr+646/ZN997gtKsQtzA6x/z
+DJyqQyNusT8lrUQKr9Mdh88S6Hh7exNVYBJ3MqRED15MzqBL6h6leROMIaHOzsHeJjrEe5ieL2Ho
+gcxToEdds4IuTr2YoglmP4slkq5PVy7fuAr7T8gpOugrcgeJ0D/CkNN8DjMR9SuGqYgSa6czoknD
+RnEep1tomvs0U7hCCtStsZECL2XQnuCQtNAJsZIINMbG1MwY8wkN0GcZ6y7J9ViP9fjEeCEiBTjA
+agdt3Iq2CwVGh1ckKsp/JY2OCrcihuUCXIQa/AKsDf4OFkU0ADEoEv/ZHQKLmtbTfwFg2syjko6U
+HGhCI81KnyEw28AYQvzAGEMbmo4WNaql/zxXiMimZNEPMIUKmv1ZJ36eYCKy0YJVXlqomBKYxSyy
+H1Mu0IRQsyqj8lKQroMzsET35Rf6YBgDalKyapsWjIDUjGjCWdYHC14HuoamHnxXV+ioUFqtIUOw
+QSCoMRacJNZkWyJo1jGVQGbEp9BAj1Sddroh5BaoL3SPhvLkTm+IRShVWg1OaOz83B/P/HACQVJp
+w0EBTikRTxOUBCrPZ5PYSWsp9UvAo7KWns4wHHrx32HRjxaBglk0U98FWZ+TyvfkPEYprXV46YZP
+V/qDHII4DSboslkbAdhEiOhynQAxErBti5oaqaYUYfW6vRjlyEQiaKlZ5i7Yy6xUv4N4bqI4xAXp
+Nk0gdlPraK5jjImiuM86XoxFAQAcg9E6qgSbtl09/EwhpXq8yr3UNrCyhpdSwpEgXsVqWCLhKGtB
+lofInImU1z7VnVFbzOhBaXQdhVO4zJCIkB6UMPh/o7cu5t8XJd9022JO4a7ppxiPyBil50PVg5Mz
+pJQSpXkKRb83rYWmduH5OdFvpzOMxz5UzXKFpqaw3Zp4wa3WYNSkESS3ZJ5DUepTMQOQmYq0BtXS
+PzRl1WJOC0tN5qlp20AQ0s+kg8gIAe8M8GjfI/IwAh3KZwvCHIxlWMx8zj1SWZQac8ZBCGqDr8q4
+QAZTWVwQCBEXpMRmvTGOaS4eHOyjpNc8PfLy+tf7Z0gJtxFJiowWeq4y6Ma/RpsSivvji7J5cNC0
+eNWzGbo90nOUEpz6FXS1RE1emOFnOV9gdurvkVo7nC98apYuOXLqRk0pLVWsjmke44gVFeM4LFG+
+Teti6UrQQpB2+8h7fpFKixytoQXZtrBEu2ZCQCg/n4L6RLjksdphjIWhdKzV+hPpw2KxFllZj/VY
+j+cYL0ak4Lw6rmktGgrF6sYiDZ1lgkESktjt9qMMQUO73LJZRGGO5WyBNCDAUqLToWaebIFD8tTb
+GHqwK80lckKTBVdgFEZKxmM455ZllP+6yAsLwJhUMobl0BoVNdoopdDr+EiBFnu41sRqhxcy8Wty
+tViioW44RzX6bj9DRUh/lkikVIlZ1OUqNGSrbrgoPpqmWDQrl+FQcWirEopAqdZaLAgcDdRZOBcB
+WLAcQSrg+HQSbdry3ig2II2IjwCmMJn5MPjw8BR7BIzleQ5OO6WryqhsXAZZZrAYYVlrY7TwYF7i
+iFD5s8aiJcDv3Y896/BafxM9Umgu8gyGaM5G66iBoLiMNHQZrNSMgQtWalWDeuojAc41JKWsZr6A
+OaVIjxiIi/M55sHxuqzxh//X7wAA/uVf/0XM64DwE+hqBVwAVy3gqJq1KGuMyfUuTdNoCLSge6Xo
+dFCQuBADB4hjobVFHVJQwZDkIZIlUJLZSIk2xqIOFai2/USkYP88eArPOxyAyvqD0UEUAw4mhOVW
+x9yfmzY+kCXdPIu6ikayaZKCEenJyhySUgbIU2haRBZE9AGXaIgOWjkejVSZ5FEsw9Qi0lVtICw5
+F1MNXHANSi+QcJwDJJXDND1VSZbhmIhX9x7cj8SauqqQRLVmqoAkKhqEaKsj/mCtjdqUjTGeZI+V
+XiVXK2OZi7kkYyz6dDKlsAyYCWELbduCBYVj08YuSaPb2BGqtcbTpz6VOKfS6nwyiUrTCgyaOjun
+ukRK2E1bzaM0/DktCknegaAwWWsdj/nRbIoH1AW6BItt4E+I7vvek6cYkelL1lEgNjqcTmIKyZiM
+qWWQx25Kg3Lu7yGhMrg2mNnUaMNCNV1ELGFOxzCbz2PPfGU0DolkVvMcHz3xHbFhXvMsj9WOJBVR
+SbtxLgr7FEURN5nw4GZJAt0EM58WmshpUl0wKGoacLpvJeEzsDY6bjVNE6932zYX7kP3ifTsWcY6
+fViP9ViPT4wXIlIwDpgYIOUMjNyJrauhEcLdGi7Ywi01XPAXpD74PO9Fw5X5/Czq4R3Oj7GgXbo1
+QJeo0vOAQjufpgDAedPgaOl3uZY5cNrZnBIrGS+2WnED0Ki18dp+ADpJsiI1mZV93ZJYTw1zSMiK
+voGJbKjB9kZMD2xAkJmBQHCiFjBRydfG6IdZjYZ2mKjfh5U+Qds0aIlP4evZFG0wDmIKx/ShbRu0
+Mw/U1YsZBDXRSC7QITouFynawF9Y+J20SAQUgcC5MTCl39GrxqCU5PvALJYEMB4QCU0mCRjz8y3E
+yhPxeD7BGTUlaesiN6SmEH0pBZ4SLblfdmBK0uDMB+B0D0DKyN94eO4bppbTORx1sOYXPBTqpkRL
+6tHsdI4FRSRzAmWdFODUEHZwdoRzukcmVY0lBWIN5SWLZoll6b+3U6ToheoEWIz60jRFGxStKZ17
++vRpTGGGo0HUsUxUioz4DXYxRx0ihZrEcJSAps/SZnWt27aNKcNFrYpnHS/EoqCNxclkga1iA0wE
+45R0RbpgDpYeoGWjYcIJE/KsEo6Ebqok30JJZSx9doqe8hOZVAYzCo8r8locJDk0eTCWpsGMVJ+M
+NRCg9yUCzgZiiT8cjhXFvdQGJV2YMcujidQCDUpKzENOXjV1DGu7RY6CFIaYSpGRd2NdBvlvhqYN
+su8WjOqLmtmYVimu0FT00NOCNTMNBAvEnRaGUimVZVhSCvLBo1Pc2vRVhJIelFkzh6XP4tZ6dSIA
+Umjk1F+hlEJC1YfgVqvrEi1hH6ZpsKQbt1EMhuTVc2lQUp4cHqRlVceFoNvtoiRM6HRZRZclawXA
+qWoRW4E50pQYgcohpXlR0oGR4EjaSdGSCmA5pyrKYIyUziPpJtHCXjKHio7ToELF/DVjRMKSCqgI
+X3j34Agbu76Fe2tnBEWS+OE2VUqAwd+/o9EQVgfDHI6WHs75YoGKyHVh02DgMQ2YzWYwdE00DJSl
+vhvG4n0/n1HnZK8T+xqsc5G8pJ1FS7qMkvNYwnzWsU4f1mM91uMT48WIFKzB6WyOemsYQRgmREwJ
+AESePByHC8AW/XtVlmgpOrBJjpJi43aq0RDxZHJ2giOiHWfEH8i2k6iyu9RznBGIxJz1Et3wYbcg
+MM5SWuLg/R0BoHUyruBSyJWCs3MRHA1mMpN5ienC/63XdegX/n15B6hib4ffBRqjkVHIubnRi8rH
+QsmI6jMuwCh8DmFk0zSQBGo1TEWQSQgBSfM1WVYodejsow5O7uK8np4v0c7IdVskqCmEVR2DYTCJ
+oc9dHp+jIhLO1JRYkK9klmfIB5R2KImGdq6g59g0TQxxrbWRQ9I2VXR21swhSulTCed4Ngd7yWs9
+iKwHS5yF09M5RqS9kKkUgtD8lIR6XNtAOX9swtaYk2jNfDaLLK9knIJZfw90iVI8q2p8cOTP7+B8
+goIo9qNCIA+Eo+D5qXWMIJeLOdKg86lrlJRWCJ3ESC/koFJKWOIYaG1QVSFycQCpm7emifZ9kZtw
+ITpwjMVnRBsd1bFVUUTZ+Wcd60hhPdZjPT4xXohIoWkNHh6d4ZUrW2gJtLHMRdNVBg5GpTfLOara
+r6SHxLQ7fXIEpYnCnAx9Lgqgmc3gCEeYVFOcwf/+0hXfZZn3OpjQzr1/MsXZjHAELlYRywXlZhME
+EBiLpUxUNtq7ZcnK0FVbDhnwEeIGK6FwcnJAn6vQH3lmpZUCCwKw6gB21gvYnMqFA4mO9K91HNBU
+ilVSRc+CUP9vLZCSb4IYbse8PUkSLOmzmTM4XQauA3VDdjpQtONxl+JtUrX6oC5xnai9404fU5qD
+AEQi78GQPNzjR/cwPfQg4Bsv7WE88Lu1ZgZnBEwuqJZ+0QCmaS6U0C6UnC1cNLrdHvg5PJqf4/HM
+X7NRYzGiBqX67AzvfecdPy/LFtnAMwS7W1fo2jUwM89Tcc5iwqkxbXeEMeErppmAyI2Run44L/Ht
+D7xSl3QOr1CH7pP338WcwCIRnKbb5gIYzUHiT5hMz7Es/XeM+/0IgpZzfz2cMFEZuugVaLQ/Z6Ul
+BHVgcsXjfZ13yIeDuWhI7Ji74E/CkVAELIVciX8843ghFgXjHCZVg8oYlG0QBcljOMQvlFm1dWiJ
+5z8c+IlWmqM58xOccwVBctlL5mDoweJTgx5NVNC4m5dLPCVO/Q8+vBcBwSxT0XCFWRNNVNgFkZVw
+AVpjIMPzkWZoCc0vaxN9FwORZtwrcJlalcdFggE9YEXOoYb+oW8IRHMuB7UlQCQ2ysFbtgKoTNti
+SfyMOTkjd0ZdVBQAjq+8hLL83wH4EDXSo53DfVpQq5tDmleDIg0IeIrrO56cJJ+cISMRkfFoiN6W
+fyhCRMpNi/KRX0Da6RJb5OK9sZ1HQHC+qDCnhXxKD4RzFhmpNjdNg4ZUi/sqQUMoumHAiBbUN696
+zcgTw/Cd97zq9qVRisHIH3+v24UgHUc8PYuOz0tSsIYx6IQFuyyxc8UvZL2tPjhdwOOnM0iqeB0v
+/ML7T979EIdnPqV7aXMDv/rzXirkG//r/4GHlFYEh2cpBFLqOjXlEps9f+ynsyWOideRpxkSuqFH
+JME26o7Qy/15yFQhpy7RvJNCpHTdYbAIQDkB4lzI+N3GmAhgWuei0xqzLi4QzzrW6cN6rMd6fGK8
+EJGCsw5VpaGcQ0XU0GXaokgIqONpZKZpa5CSZFuwDOuoDWgKxevarSzWnAORFCHSDnq0+wd7rZN5
+hR/cewIA+HD/EIwagkSSewoxvOFGG7wDQnH/AsDDHJAFNmLaQa39Ont2XmJJ1GXZod0fFl3aSYbD
+Aikx07hyUBTRMGpgclUTgShuGRBq0LqNdFbDEIE9TuXZohhgTqKkNz7zJpaUJmhtYs1aCY57Tzyz
+8v6Z//fdoUKfdjYrObo9f8z9Y4vqga/1z5cO+R2/6wT7N31+guk7b/tjm5xieHOTjk2jNIHGrXFG
+2gGHBPwa6yKj7yKjcTNjmM5X/ombZJjy+LHflSfO4Wziz/mjw8sY0m6bXd6CJNEd7hwUMb37XUq7
+bIuq9DwMudkH3/Tn4ViFZkElwGoRrQPvf+yjn4cHJyiIQ/L61gb4U/8Zo24PmfRCqhRIwTqGmiIi
+YzVmJYnjnsyQZ/59Is9wdcPP0ZiEZgd5B0ngnggRacxCqcjobGwTweRQvhVpGuetbpoIbKdJiiTw
+TBQDD6zeZxzsT0Nu+LMejDEHLiCYwOs/9+sAgFe/9DUsKNydL6ZoiHbbzEswotLqs4cAALk8xkaX
+QuYux4ByrtmyXuXaTYMZcdWXlrrM0i1sX/88AGDz0nVcue3Vnh8fn+GPfvRd//vTx7EtOczVb/8P
+/2Y8dm9rTlTaqkFDaPHTx4/xJ3/0hwCA7//x9wAAdd3EjslGNyipxbltdQyfecyVGAx9r5Qi/t02
+OubiSqloXpvSwtMfFpGLP500+A//g7/t36cNvvH13wcA/Gf/5X+Dsxl5dtL7r4w6uLyxkgKfUJi/
+rBuErmwlFSQlDsMOeSOmCThxIQb9DP0+VXayXkx5qsUM9+lhejj1rz2bz7BJepa/9rWvYmvTw8og
+QwAAIABJREFU96P0vsDxvW/5Poff+9++j2ZJVR5aFJMkifwVo20kb1lrIy6RZSl293ZoPolefX6O
+01PqotQ6vpZzjvGI8v3RKFKWL1265M+v08XkzN97B0cHMbSXSiF95I/z4LHvnDSsQJ96Qz738svo
+EF9kOj2DpIWlyDuoaeM7OPD377KcICfXryuX97AxpoUuU5HgND2f4ejgjD7DXycrOCZTfw/l3RFA
+6uHLusLRkV+w5otJVHL6hz/8wXedc2/hJ4x1+rAe67EenxgvRPrghwMXCoORX+HLZY2GAJXlbIJy
+6lf5op5jw/hQsp/7n5tjhcvbfqeRTMce+mUmcbbwO8LZRIC1nroqyGzEmnMsH3vt/pNqEq3Rt66+
+jF/9uV8BAPzuN76OBwcEpAVRkAuOVs662O0nJIMi+LpulqiIrsvFqklGEKiVCIUFhYHWAglxCALj
+UViLpCBbsTyL0cjp+SSKabhGg4nACvTn3x90MDkn4xjIFStUOJyc+JShqRtImqM+qSy/dmmE61uE
+5DcNzsj4ZloK1BTCZipBTse/0fMv2NsYYdAlr4vtEXau+EpFZ7AV5+XoySN867t/4j/7A+/ZYViB
+GRmdHB2eYJvUqlu9xGPydVjMKugm6C+stBdCVAXn7dkAD7RFenfboiYANvAGGGMYUURQliWmJDEn
+pYyVn3K5RI+8IufkFyKFjNem1iVa2pkZT9A89g1RAHEiVIGMFLF122BOWpp13UQdita4aAUXrvXu
+uIfb1zyQ+sUvfSnS8bM8i76obTPFwWOf6r79g/cBAB88OEWWb9G/t0DwUHU2Xve2XXEnnnW8MIsC
+g0OS9qBILnxydogpLQTzusSo9Q/YdRzjet9P6mVybhrnDIM+VRlYjobclmpp0BCR5wAW+9xf3AWV
+dhh3yKS/cNadYX7ow8HzpIPxjl+cvvZLv4x//O1vAwDe2felKWNdDD/btoWhh1RKHkVijg8PcUat
+2kHqfdztI0mClTtgidAysy0yClsLwhl6qcQGuTh1u11UVNX4mFtMA61YOyQkuNEhwQ/XIJqcgq9y
+dc54JFkBFqHR7vqW/47PXN3EgDCaRSOQEKGnoxhaWhRSITCgMHdvx9+4e5e3MdjyN/xweweDbS8/
+LrM+FPU+XLpyBaDja6yfS/noFHcf+Zv85OQsKlm1dY1zMjtpahOf6kHfh8zDXi+K1fbzNOJLp5M5
+zoNepbGYnZMNAFWahBAY08PW2dmK12+5XGJGRCaVKnTIHj60nC8WS5wc+0UKYtX5ymULS9TtNJg1
+Ogcg+ECWMJSibW9uQYTQfjmDo3Rri67vzjjD5970QrEvvXQd3U1/76V5Dkv3vdVT7Oz611+/4RfQ
+b/3BO/iDP/zIf3W2jSVtaolcEeosGBb1qlv2WcZzpQ+MsSFj7B8wxt5ljL3DGPs5xtiYMfaPGGMf
+0M/R83zHeqzHevx0x/NGCr8B4P92zv0VxlgCoADw7wP4Hefc32WM/R0AfwfeIOYnjiTrY0Kr67Qp
+wSnc3+swvNbxK9+1eoa9wq+I1zd8VLEzLlDkZP6hAN0S+YN14mp+PjM4mdGucu4BTH6Bb3BuFrjX
++tr9/v4SE+tBx8s3XsNXf+GXAADz3/tHAHx4HULVxXIJhKYjJTClPvwnj/YBChNv7vra/kanGz0Y
+ZSIgyB364ePD6CF5bc+nAbubAwy71GWoEpyR0Ifi2zgnOnZZG1iqq4eEZnY2j7ZxZVnFSMEBOD/z
+kYvkDh2qVty55neljUEPjJqEuDbIAhqerxpx8kRiRFTbfkgZxiNsUHSQ9YYQ0u+wSdqBpO7JrOjh
+zS94fGtOdO4PHvxW9O501kSUnRlELcU0S2OIff2KJyH18hw7G56Y1EkkHEVp80WJCaUjZV1hQYBv
+oGNvbG5ie3OLziONepRt06Kiao6TIgJ7IcSfTCaoqFHsbDJFQ6irkDraBwSn6sY0qEk27+TUYpMM
+YLhMoiRa05ToEX9jl8R+djYK7Fzy10F2OkgJrFTdPkzjoyazEEjoewZ0v/1zX3oZmrgz3/jufbjc
+z0trzQV6u8SStCufdTyPwewAwC8B+NcAwDnXAGgYY38ZwFfoZX8P3iTmJy8KTECkfUyJNGJEjmuF
+n7yf2wGulh7hfetqhqtbPmTud4NoJWBJ7FJ0ckBS9yE4QsvdeFbj+oLyrA0qETYMU7pJz2qH/sLn
+ocPmHG3izUpPD+6iuOxDu6+89UX//raNi0Jd15BBVogZHB/6haVezLFBIegeIfI7gyGG9PvJ2RH2
++v7B6vCdKCobfCI3Bhl6HeqQSxJkksL5fBcntCicTheYLIL6lD8EJSSmxJSzeiXvXesaR0dHdJgG
+1/b8A3Lrql+wkkyhJb10DkBQtSNVPD5AUjgIoulJFRYjBx47W3NwTgtZ0oEiAo01Bv2Rv2Fv3L4N
+ANgc/lM46t5rqwUspV25yuIxN22Jy9v++F65dcvP4cYoCqSYpoXifj5HfYvLW+TPwRw0VUlSEq0p
+8iJ+rm6a2OKOLIVM/LHxoohkoCU9xKdFBwdEUlqUFarW3yNO61j5aaiKpKyB8FAFisEIir67Nhrz
+OvRdNNgm0djNMWl4Dgt0qaWeKQlB1TPe7cE1Qf+zhKhpbikNzjOOL7zuF8v9/WP8iMq2WaeHhK5T
+Ijnkp/Sif5704SaAIwD/PWPse4yx/5Yx1gGw45x7Qq95CmDnx735ohX9cxzDeqzHevwZj+dJHySA
+LwL4W865bzHGfgM+VYjDOecY+/GWtxet6BkXjvMUaXeMoCE+zix+6aYH6F61D3G98LvDjT5HgbAy
+k4iFk3DSr546yWFJww/ORU6DSGtICikd9c/XrYtSYpmwGMIDTjzT0M5XHBKu8OTIk3defvVNAECn
+M43SV56AQ+BSuQKRbF2tgC1S4b20tRvdgHOVYnvD79Z7WxxDIukUJFUvbAvehl4LDU6hauoEeqGj
+MM9Wkl8EMknGIWlHZBc4KFVVRkCtUAqvXPch/1Y/qC+3URE6EcrrkgNomY2RAhfGy9/Dg7QAYJ1G
+S2lHzhGly5hkwEXSDF3XrR2/82+Negi6OTOD6LothIodk1mWYHvLh9hjAuWyi25HnK3s7IWKtvSM
+89VxXoDeQ1cmg4jzCSB+n1BJfL2ifg7BBXYJdD44PsaSdBXhHDRd65Cuoaog6G9yYzPStau2iRFG
+TxqMerTTU3QoE+V7fQAkCQPaIK/PV6mp5HB0rjr0hrAEXZLMv3plgG++44FwLmWU7uTMQf60XKcB
+PALwyDn3Lfr//wB+kThgjO0BAP08fI7vWI/1WI+f8vhTRwrOuaeMsYeMsVecc+8B+CqAH9F/fx3A
+38UzWtEzxiGTAvloO/oivLqp8fqI6rizBbYE1XYNIIWPICztPjYv4KiUKZIETgUZaONJAABgRAQd
+WQDnnInOz9oIcJJWKliN8tyzzfo727DE3qMKKLI0jTtbLThK6n9fLpexy9G2OpYiA5utalpoalpx
+ELEhSHGAFM+g21CXF1H9yFQWhtiYrmnJYxHIlcSC+v4Ju0IjJFJB3xfMKuFLfcGBe2dUYG/bRzGc
+dhEDgEVqLIejW8Pb+QX2H4vNZGE/0Y3BfOpz2bw/QkY5OWM8lg7hXGww61ED08svv4T27bfp4MTK
+71BaqHSl2ZCTNBmnyMTpJro5MxhwivRkkq4cu0UaS78hOvAYgv+dCw4TPoN5+jngdQ8CGzREHd1u
+F5f2fHTzwccfY0bX15gaS4oWg1yfYgwybNHOoayDp0gbd+vxMMWQIoVA8+bSoSQF7g1pwbSP6Ny8
+iveZrUtokrQLWAbjEoqO8/LeONrUnZ2dYkCmQg42dhg/63je6sPfAvA/UuXhIwD/Ovzd8puMsb8B
+4D6Af+UnfQjnAkV3iKIzQIeIPm/upthinqcwEHU0Z5EiASfgxNBPkaRw9HCgbeONjlaDB4lsx2Ep
+Jtaa1JwZA6dWbWcdMuomM8Z4kBJAp9sFiNoaW4HbWVRPTpRCVQelZRe57xvjDXQIMLIU9h1PzsEv
+PGAh9Le2hiGEO9iwJ0pCIViyO5SkR2mMA5ehTTxDQwIomrpLdeuQ0gIqeRvDSK3b2D+x0c+RJUHq
+Lk5FlBVzgoEFNRRmEB4mo2MLClLiR8AygEA77licN2sd3AWSTpgDSXyM2y/fwfmRT9Hq0xYs0LtF
+jZwAVmcMHALFPPSdWDD6G2M2/jucBg8Pi9PQJrr10HyvzFngGgTjWmt1BI2t4XGRifMiFQpK7brd
+Dk5Jzt4uq2hWFEL8TMq4CZ2cneCMqj3D/gCKSF9bwz4yknE7pc7WZTkDYz6VNM6gDV6oaKJsnLUO
+hibfxlqTBSf+w+bGEJeoBfz7Hx+i36NNkvMIuj7reK5FwTn3xwB+HJf6q8/zueuxHuvx5zdeCEaj
+EBK9wRY451Ctr+kMZReq9pFCZhegKAkiTaP4Cr/wM1CQuWNB6xNoHVxNZaqqhaHmGSGDyq6LFvBC
+MQgKu81So5E+9L9y5w6q3g0AQC1WNNqw6zDGMOj5MqLkHJYihYHM0MxXNFcAWBodQ3/uXNxVODTg
+PhkpcFZCsRAmcixJ7IFZgBOdNSkKDEgSzJJQqZMKE0pn9Pk0MgLbto2/9zophAsRVNhJORSlOa2t
+V+K4DtG0hsPFUCEKhrYaNkRbMPGaOGcAFqzRL5TEaFcejsYYUGlOLSbR/o5xix4JqnCh4SiqcxQJ
+QaZYWXpyhDTGWICZIGKqgx1GFI5xcPH6N227CsvdyvYPjEXvCEuRhDMa1pEzDmthTOiCXNGqw2Cc
+oaLo6GwyQUFR0XjQhSSG6Oa4G9OVJJR12waK/B2cY1Ep3FgHkCaHhV35StLfHOMwoXRcZLj9ki+j
+v33/cVSuTpVahXfPOF6IRYFxAZUPcb5oIUhNt6wy1JSzKcGRBLpq2gEoxwehtwYSlmi0vDOAC6Yu
+QoLlNIEWYITmK5pUpp2XWgcgoeEqMlnRAgtH2o2MIaioyFD7TZLIrbfWom0D5dmgT8IvdlGhovxT
+0MNmrEVdBfNXAxUj3JVqTnjYmGvig8u5iAIwkrOIDnPjIn1WqaB2bFAUPrQvijymD2maRk3BIuHI
+VKA/B40/CRdTTxffJwRHmoT6OEORB4MbSlEEB0IIDxvDfAe7CssvOhQFdD/NUNbBnCX9hM36aEw1
+e2HQIT62iGmCAeNhseHgdC39PITuyZUte+hTYYzDGEpRbBIfTEDEOWArFfx4HsYZ1E2QrZ/BIVyT
+VedmQVqacIgqyqxxGFDaKZhBRgv53u4Imjp3U+IgKGmR0KLgry5VJeTKbBbGRpMj14aW+wSa2tMl
+Grx001dJht9J4QgzUSrz1aRPMdZdkuuxHuvxifFCRAqOcTiZotIGU1qpn0w0bhF6zjoMKjjtFhkQ
+QkJquGH5CLLwoahVCgbkE+g0lAjhl0FNQh/B9r3WGrPghtxYmDZ4E1hMaWO6xBI0JNpSUtfbzi5Q
+UAcjYFFRhMEY0FZ+VX6wWGJOWoo9ElZprY2RHGcshsEAi9p+NgJ1NuogGoco7WY5iwh5ay0kgVYp
+8TQGUqGlD9YG0dvQcAZLu+N4OEROCD+7YC0XqMbO2tVOCwtFEVInSX3aA+rKA5B10wiGGV2jIcpw
+nnVjCMu4jNBY6OqUSYKaGJT90TD6SAgB9PuBsZpjl8CzwL2wTELwIKvH4+cJqeIcMjA48pNzmu4V
+DjhqhJM8iTwLxxAt7ZxrYChlCwAmsxopzXGnSIELqUbAs8NPbTUUhRoqEehS5SQTDNsbPh3d3R1j
+SjR7mCCWwpCR76g/j3BsLkZWMkljasNZEB9yYIK8RZYlBpR2DbopplOKFGQWVcGfdbwQiwLAfG5k
+K1QUIj06rVCO6Aa0AjMK5x8dV1HFJzgl7V5S2NomsVI1h6RQrZq1OCf58Y/3J7j/yGMUJ2fU0ixF
+bF/e7ozRp7C7ZkBLxKhy2sCIGLsDAKazKbI0hK0u5tSCGzTExT89PcbDex/7t9FZpmmCMVUk+kkS
+CSvW2kgr5hTqMckgWAj7GBzF9gpuFa0bRPfa0JGY5gm6JGa6MdiEozRncXoMSSSrokgR4uSAaE/n
+czQ6LAQu5q8OFk2gIEPCNKFNfIWvTI79vDKWYEDpd74o4YJRS1ZgTNqOLOA2dnUeW5t9sGC5DocO
+lWr7RR85+W1qmsXaKdgyVIyWCMWCXhco0rBQr0JgHnANcSGNsQaWgKfGAi1Vj5izYIQDhDKyMy16
+dM0u7+zg3n1PY7eNWRkQ0XcxAIrC/X6eYkgGPwksLu35fL/T30DdkNjL2YTmp79SR3ImCvUwwSP+
+xWW+KlGTsOtiKXBy6MuXtjZoWv/aQbeDyREpXMkaRf7pFoV1+rAe67EenxgvRKTgzVU8qMNoNT9b
+1FiO/Eq7P5vjwQe+b/zxdIE6hOBEGb65/QBffuMOAGBnO4WkbrG7783x/ke+DeOdB4dYZn6lvbLn
+BS02VYGEjN4eLDV6tCvtdTaw2yG7elji7wKK3I6fPvljdIPibirACHyqlkucHfk+/mE/wzGt0NMp
+ybcbDQTJsPEGKCqFsTYq8c5Jdq7VbWzasUCs4/fzHNsjf2x53onGL5ak0MElcqJVD/pjLEiq/cHX
+v46EQE5tWhAehsNznxJ9/PgEZ9RI1bQagsCuXpJga+h3ympQI6eIpUPNPtWsxfmRB+Lu3jvBsvIy
+dp1Ojuu3vGnL7pXrkHSrDTf93M8nE2xt+KrNZ27fxnukqm0sgyRSGucSoGsyIdD2/v0PUZU+cnGw
+cJRiDvs9jIc+QtoaDdAjElxCZjG2sZiXfm5niyVmlNpNlnUkarnGxgaqnV0/x6NhAUmy2klaBBY3
+xlkKJBQW0TVNOMMWRRXb/T6EDZGuw/2HPjr47W+8jTwj0h1FBHvdPHJITOMVwgGf8gaC09HxAkeP
+Pe9hQveYtjYC09Ozc5SkI5IlOVrjv487IGWfTs35xVgUHNBoTyQJKPxiXkbNubPzSVTN+dwX3gSj
+qsODB175Zn5wjI/evQsA2Oy/Ck3trY+fLHB46i/+a9e2sHXdt6SOBsQ0LC3mZ4ToHpeYBEvyjkBK
+zEJplugOw41JRqLLBRSh96nKwegiltMZ6qDpP51hn6TPZ8GjUQCc2ndrbZCGcqLVqKnktiTcYlG2
+mAShWJEgJYZe3QBa+5tjVGuMSXEqmJIyLqKkfJKmaBnpQJ6d4EqXNBjtqpTHKFcfDHsw9AA9OVlJ
+3/ekhlBU3pIsuhQllGrJNIde+nl7fDbDgrCGV8ab2B77Y7uytxN1AiMRqq7w6p3rAIDqyT4cCx4Z
+FgVVl1TCY8omqbqyt9XFfE6kn9pG2fqTo2OcUofqZNTD1V2PxG9u+bDdcYcnj3069/joCHM65kVl
+YuheCIUQaevS+3O4a1dweY+8IyyLi+GXbt/G7z7ygjEh3E44i2a8gjnv8ARA5TlExy9Y3/nhA0xm
+vr1+qPw99otf/gxmZ/4hTvM8Svgv5kt89zteGezbf/I+uPILzmuvvurn9fJlcMJzOlmK4yde1aqp
+c8g0SL9XWCVVzzbW6cN6rMd6fGK8IJGCg7YO3DJkhAS/tj3EZu53q8EwR4cEKx4fnuLDE7ILJy3C
+WznHTthdmgXI7BddvsBbb/odwzXHMK0P137rn/pU5HCpsKH87qlUht0hKRTb4yhSkUgXgZq337nn
+/9YVkRQjBKed10vHK+5f28v7uHndh8/3D72OgUxE1Bg4PjtDjxSDhRKQdlV7BoByWuGQLNItU9ga
++0ipyBJw4g0wqSLxRhOgyCWDo12VgUOSpkNn2MdwSrJjTEQeQpCcb9sWjw/8DnZ0MsfhxM/VmQQk
+od17/R3kBJ7dft3vVpNFg69/1/cwfHQ4Ja9uH0mM7vv+kd7WJnYp0gsVF8Es+mQcc+/JEVzPRxVw
+DTqkLbCx2YGiO7RD2hNdDuiKwNO5RrskEpYFOgHtVxIFRWEJzadlDmOyAZjPppC0H0oBtASk9jsK
+uxv+uzdJ16Iz6kbPUuYctof+fvnsy7fxB7/tVboDdb1IEshA55YMc+rQPa8bzAkkPJvMolFLn0Ro
+3n/vHna2fBR77da16AP54ON9nD7x90BebOKIotBv/uBH/ni+9z5S56/Nq1c38eXP+WtyOl3gw3s+
+0jk4n0ZOyrOOdaSwHuuxHp8YL0SkAMbgmAB3Ej3tyzQ37QKbVN4Zb27g47uk8AuO3V2fn41e9zvt
+baHRRwB9ajS0e+RsietX/Q6UdK/BggRUd3yeuX+wxOSEZLsShztbVG5zKQ5Ck09aREXdknLBYsBW
+smqCx8Ymxh0x/Lzy8SXCDzRtj0trYKivnguHMQGpugLOg5cilV5nlYkOzZw7jAb+PKqmxAPKnau6
+A+v88ffIC6Do8Kj8bJ2IVnkbOzv4+EOvAjwU3eh/6agMyQ3DjV1/vL3eCI/Jp2FZLdEtQrOSAaet
+e+vKVX/+ZYmrNMcbgx42d/3cvvULP4vtSz4XzzoKKTlCBwYiaxeYkh2b2NpA0xKYax0kYRiv3rmB
+hIXGK8IiuIMiILXfy5CnXfpcjYTUqfJkFb0Z8mh0ksE5quPn/ahqNdAMc8J88pRBEPU4IVVmlXVi
+k9Swo7B51Z9fT8nYuZmIgCMAOXECpKmxQY1U6cZlvPz6GwCAjz/6ANcuey2LDpW6D+6+hwXJ7XWG
+PSR1iDYL7Oz579gd7GJC5kLvfP+PAADXX76BfsdHmK/cuoQrex5gl49OsElR2OMjC8b+WeQpOM/m
+ZLbCnCilU5sgJdBmZzPFYHADACBzBUHGL5KIN2yywJIetqwYQBOKzuoGjlDmzpXteINcNf5zd6WE
+vkSS3LZFlzjuJ5MWLU1kknagqCsxyMwzziOBSAgRxULSNI1+kxXn6JKn5dVNX6M/ns6grV9YbmyO
+8IVb/sF6+vgAFRFautRzMcg7MWzlUkJShUPkChnV+h1azEjZOSUF5yLrRfktLhgYVSUWVYuGeiJy
+KX0fA1Yy490iR4+qOuPEYFv6B302T+CI39BVKWSgGNOx7e3u4Vf+4j/vv2MyR5fESbb2xuhu+9RN
+KAUeeP5EfsrSDI8nvjJktwYRqdfMQhE/Y9wdQFfU2UopjOooXL3q57WuGzgCNk3boiXaNHMWlmr5
+1tADwRgAkmbrjSIpaJSlEATipkohy4OcHD3cSYaE5uja3iWkKQn4zBukdF8EtbMkVVGQJpMMn33V
+y91fv/MKOn2fjnz59s8jDXJrzF/T/bHC3hW/2PR39mJFIe8McJnu5WkJaFL0fvOqT8XyIsWQ0plO
+J4n9M52uQK/v56hpDXQdmoGebazTh/VYj/X4xHghIgXmDFQ7Ba8rVLUHw+5PEkiSd8yERdKlhhkn
+o5eiIHBNaAHJiNGYdZGQVsCgm0KTGUwzb5CSdJWVPnpIM4uWB9quQz3xK+15kyAlheKGpR5MBDA/
+9eAN5zvgPGgo2OhZUHQKgMqaQohYRjTEjtzsZ8gG/jhfvzpAl757lGcoKB3p0g61MxxhQi7DSZZA
+kc6EyjLkQZGl5kgpYslCt5wQSMIOJhks8Ri2XrqN9Hvexs6ZFox4eAEYc5xFz4pGl5EG3O1lyDK/
+O3aSBLFjCFTG62/h8uu+e35ydLDqPky7K4quTKM4S6PJHbzbxYDSi/1WIPpYOwttA2O1QBsaFHkA
+dlm0ZFdyJZgDmcGpYJxSQwRpNgRxX4UsDeI8CoqujRBsJccmRPSnCE1sgjsYoiNXTQNGPI1ESVTB
+JCe4OmdZFG/ZHPXxxiseaH7ljdtQ0T6eQUc6dijPbkYhXCELGAKPs75DTpTvYtlAxmu2S/PKAjsc
+zjSoKCqWrkVKAHPFUzwmod9nHS/MoiCqM6CuYWjSD8sMsxmp+3RKgGi8PEl9+zQARmSkejJFIklp
+xmZRGbiuGiwPfDrS2RwgH1CFYhC+eQkxI7rr1MFM/IWZnTKUYz813bSIAifBqEUIEZWBrbXICE12
+UsERn//0Qh9eECTJOxm6wv++OerAEWElTRV6tFjMjX9oNtUQsqRas2uRqdBa7CApZE65jCSdPuXs
+eZoEMyXUTYWWqijXP/sG7v6JV0TW5TGcCdTs1aJgKuL9Vw6MWiISlaBDaZCDjlRbSfRhyVRUmNLZ
+KWzATEwLGPrdrbgcoTTkJMeSHrxpbeBoPp1u0Rp/E4teF6Kl6gFJIzLH4twrVQCxG9LAsaDmjFg9
+CtotXDIUJDzS1iKmQRwcjBZtLgQYC3wP/z6jFzAm6Hwm6A9IDMUuMdz1PIvpxJOJZpphRA/jzuYG
+Ll32D2/W7yIhFe+WWTDCcQxVGVQiYCgVFlyipfbrtjXIUkpHMhVVs4N0vhTSA1IA9LyGJnJaM1+g
+ou94MFlA8U+XEKzTh/VYj/X4xHghIgUHhVbsoSlSuA2/m803B3hQ+fr3a9NTiNDBt82geh5lNeR5
+UNYOfTLWcEkfNdmzWyGg4VfVe+8f45WhB2gSsphDw1ATS7FaMiyI5VY2Lnor6ONT9Pb8Ku9YsKYT
+K1t3JZATb0CjgaY6vsoTVAQCBnSa6wp5XPml57QCYE4hI2t3NfUrf2Ur/xoAcCICcdz5/wBfFy/o
+uyWxEV2Sow5IvWuQEArKhn3svubr2A++9Q04QueDMAnXFhxBcIbHYzMOUdciZQ4iqEcHyjS3aCls
+ta6NLYNcyChEo9sKLGBdlBpoZ3FC3aeaqciFMDZFUMlxgxKsIvGRYGwBCU7RpOAi6hswNLBBWUWk
+ELSrRmUIziNDlhtAECjnDIOLTt8cnOr+wVfUooGm+UyEgxpRGtvj2L7lKwqT7/8BAKA/3sGIqNuD
+QYIk6EtmCimlLikAzfw1npIIDwOihqVhPKZdrm2g6fqoJIueFKHzF1JFdXBrbOSqVIsFlhSFzIxG
+E5i6zzheiEVB9DYx+oV/AwtToNelsli6wOnCo9PT5j42KF90iwqcSk+zj31qcO9hDUO03cO9AAAg
+AElEQVQ3Zus4PrzvL+zBaR3FR64kDfYeeBro5hW/qMAW8eEu6wpLqhxYmYARpfT0tER/EnoCyNfP
+juOxJ2pljS6UQkKkmf5wgOkhGbpW/jhda5D2/fk5x2EoRzSwUW2nooetqkoYzuJ3qKAIai141O9e
+yX4HYxUmRUz7pZQINCUmEtx8+TUAwMM//GZsfeYRI1iF3FJJOCIIleUSOYXdeSePxxkWBWU0LM39
+2eN99MhGXXV68YZt6yrqP4I6J2dlgya4QgGxtZrzFEz4h4apBrJDno/HJMgC5plKIFFSHpSqDAzR
+25vWW74DgBJB+FXFMkGrFzHVEMIhTiiL/xNFViwzaLmfi3xgYIaeiIZiiS6VnLskrLNz+Toq6mdA
+2ok6kbZtAeqe5ZxHM5uKyEimMeiRWQ7jfHU4zqJe0lw4FjcGQReBKQNL+JquV9WltjWwlP/UDKg+
+HXdpnT6sx3qsxyfHCxEp9Pt9/PKv/UUczcoYnqZVBTz01YeqlKiXQV7KoF76CsX00DcGNVD4zd/x
+RlNH4jqe1j6ELxcVrmR+F/trn98EBRiYH3qCVJLmaBriIFRARSCaS1OExrLa1pgtyDZs6RuwhLwR
+JckZX4mlMClizXu0sYGjjo90Zieem8DbFjPaHe7va6QyCHKkKDoroBDwxiOCuukkW4W+jLGVZRtn
+aAipVhR+CuYiaKeEjKrTYBIZ2aFDqmjnHlIb5twFh+rV1tLqFlPyaGyaBlskHT4793P/8P33Ieiz
+TNPEBqXMuZWqtjWRjm0IaJw3K80GDhfFYKQs4ILZCQCWUUWB006rY70BnAGSKk22aWDpM9I0haTX
+Bwk6KIOGooMiS9GSLZzjGgnZEzJlohBNqIY4Z9ESMW60aWE6ZGvILTp7nwEA9Ehrsq0rOOePbtow
+TKg7tpjNkISKgxCoiIRUU/oruERBOp+Msch7ELCxY3J2fu71FgGYNNgJ5j4KAVAvl6gpuqvbFkHM
+2joGSZySVj8bX+GFWBS6HeAX35J4+CTFoyd+8hZPHHIqhZkFR0U6iEWr4oPV2fIP0pubIzwilts/
+/PYjcO0n4S/c2sRf+txL/jWX+kipxDelFlpnFmgqCrNaBk25qmYcmgg0TZugqchGvSG8QKqYMngt
+U0LAnYsqPkW3g/6G57MfH/sHSIHhjHLj48kcgqokzun4AJVkT88cYIIqFJpotiQEhyU8QHMLTjev
+ppC6tSZ6D3DOY7kUjiEJhqdJinLqUxoeWozFCidxWFm4Z2kWpcrzTgc5LXqnJ36B3LvxEmQv9AkU
+KBvqfrACIqRVjEXFKU1py6K5IETq7EpSHRKW+vocOByxSQ090MYK8OBnwQFLC7l1Ji6cxi5RNf4a
+JwmJ2FqHc7ruCTKooOrFABtk99GsekkoFdHOgNEx8ERDU6u9bRL0d31JNXgvKGaQjT0JqbIlDp76
+OcpTDpBuqJM8PrxTElk5ODxGTvbzTVWjofb5B3fvoiG25dUrl2MnZXD+UkU3yusvZlMcU0v1Yllh
+sQyelw7Sfbr84Xmt6P9txtjbjLEfMsb+J8ZYxhi7yRj7FmPsQ8bY/8w+LcdyPdZjPf5cx/O4Tl8G
+8G8B+IxzrmSM/SaAvwrgawD+c+fc32eM/dcA/gaA/+r/67OUBC5vAuezDK0OyGyFB8L3Nrwmhxhp
+svIWRQSfik3SLXQlvvaWjwhev3MbMwJnrl7ahiJ5N710WBIC3Ak985MamhH/QQigDXLoAtoEgkmC
+qvQr85u/+mv+eMUhRNTLY1FyG1iF4FAc4x0PRH38ke/K1I7BEO3YKYnjme/naOZzGFvRcfj39/s9
+lAQiWWtgguKwc1GIhlse9QENwo7pIg/IGLNy48ZKMbi7uYOjM7+rZEHDUbDY/8+khKTUhVsDSdEU
+1w697oCuj08pDDMAaStwlqGI4bdDE0xSBI/ahiVFCmUDWIrM2AV7E4cyEoQk47BpICiQ7mRjAB7M
+fNIomdhoF6sEjbEwpE9R1h4YbOFWaYlg0FErkkPRh6TQkV2idYgwLJIsUK157BmBM9BPH/jPIG/S
+7PIb6N3wFQk8/j6OD/bpmjGcZOT4LTnGe753J+/6e2/24QK//3/+NgDgxksfQpNbVFUtcOv1VwB4
+TYZATnrw0X0/9xpIyQyoLJcxqmBQuEedxHAGzPx0ac4SQM4YkwAKAE8A/Aq8ryTgrej/pef8jvVY
+j/X4KY7n8ZLcZ4z9pwAeACgB/BaA7wI4d9HjC48AXP5x72eM/U0AfxMAti9fw7IGZnODwyMCsI4O
+IYhv8HmT4yViAoJppMT+O134FfrouIVtvMpRmqbIjN8R33/wACUBPyXn6JLi8auX/GmPcsSSXVqk
+qGh3KBuNkqKDOTuFmfq1c/yZLwAAOD+OYBxnq+YoC7uqMcOh1/M5/HjsgaiDe/uo9MrZmZFmfybT
+qNbM1er9PFqmOZRBxx8MygUxUh7LaYGBJxiLu7I2LS6mk4wAymt37uD+uz8AAEiqbRciiXRuxwAb
+5iXvoAkdX5YjYFWSqMb1ZIFujxSXUxU9EfM8D9VHaNOiIS2LWUkRhv3xtXPGSiBGECyeV1L419fn
+DFYHT0iLhpp9ZrMaivv7IoGCcMQypfPI+Kp71GgGHYgTjseOV6M1TIgUCOBrjUE3Cb6MJYwOGI3F
+We3PZeNn/wUAwODWZ7EkcFGML6NqPMB8XrdQXaIxC2C28J+xMfSR6+uvvYK7d732xL33P8SVqz5C
+futnvoyCaM6mqVA1BPhSpNTqFiCx1iQRUTZu/3SBDw88Jd9yBhaim2ccz5M+jAD8ZQA3AZwD+F8A
+/Nqzvv+iFf2tN99y0yXDZGbx5KkPaz/66F1Mmw8AAF8eNHhjSC2t3EATweQHP/T/fv/+Cc5P/cXI
+8g4MgWQVHBZ000uZR4mu3aEPgbf2xmANpQzWoQ4tzoahpFBMqxpLUnze/8AvPNsvrxx3HHCh0s9i
++sA4Bwtdnpc8+PTooweYnFMlQgMtIfWmXEKSOnJG3W0sF/HBmc3nkUr9/7D3pjGSJfl92C/i3fny
+rKqsq4/q7pmea1d7abm8RJoWRdMELPOLIFiQYesABAOS/cEGvCIMWF8sgIYFA7YlwzAgwRZgm5IN
+w6Rs0SZNr0GK13LJXc5ydnZ2eo6ePuvOrMx8Z7wIf4hfRHYPyZ2eXZlsARXAbtdkZWW+F++9iP/x
+O6IgXPfVpfCFTcd61J2CEOuevjseI+HZnNPrV2FYwV46cZY4QEbOhDKdN2qpW4MVodtNXaG9876d
+o4spX9PYO7BpUDbIUHNhfeWTryEkIKvRDQouFnOmdsYIPNHkWM+nrtevG+MBO8OpPc9Hxwohu0vG
+dP4hnl0s0J7buZ32NtAf2gc57BMSHAbeLWy+uMDhub2W06ubGI4J/BIaqnEFzfWC7JSyYVrAOECZ
+xvCzP2Ln3C3o6LBJRmkxDzGgBF0/rDGdWnBdkCT45tdtWvH4HUuBH8cxJsLO1f7BjjfhPX33Ao/I
+JF0tzxDxGTi4bTkVSR6iZmF6cb7Aam7n/r37j3E8X/C8Y6Qs+M6LtZz/txvfTfrwZwC8Z4w5Nlbg
+/38F8IMAxkwnAOAqgAffxXdcjstxOf6Ix3fTkvwAwPcJIXqw6cOPAvgKgC8B+HMAfgbPaEXfaYOL
+lcay0GiIKmzOH+Do3AptvrU6xmlOJqJq0SPn/ZO3bUvokze20bHN0x9swbjeexhhxoJLU8cYblNz
+oM8ikopQOJhzLVE5Pf02QMmCZ1dnSFioeesb/wwA8KdvT/0urrVeI+zM2m5NCF+3REqU38buNpYU
+L0l0gAsKazSryn/fxcIer8ik1yBQde09A0U/9mKrQsp1G9HjABqnR4KqrNevQ/jUJhkOvTL1kgVD
+veg8flq1rU8fwjBBRrHSLDKYupRoy+58t15+GaM9wsfjyNvSF8s5YoboZXOB2cJGCqvKFUTXlupa
+a+/JEBqxRj8Kg4B+EMmW/buLSYTykSvsakSEEl+5eg0lKJhTSmhJOTaHzdAGxdKmplJGuH7TwumH
+Oz0gpnqy6qB4rd2/UZwgo+BKGzaQhEfDaGBoW84BC426Vaic4M70Bk5ntiC40ZWYzex7NqYhbl23
+f/fwG0RHFhEi7s9t2WHOlruQEkngtB4S7N20EWc0tZFSW1WolbMsNDg7tdHB8YMTRHy0u1B61iyn
+5yPHd1NT+E0hxP8C4Hdgfb2/CpsO/B8AfkYI8Z/wtb//UZ+ltUBZBDBKeypsmPRhAhtS/e6ZxPss
+OG8u5kjIGNzeYgjcKnQFw+5ogLJxph8JDJv2cdegx1J9SLGVomy9u1PVCByRkXdsEi8z3hTn2O/b
+B0Av7EXWZuqBMp1eW6MDwt/cuuuwpCx5SWXozYN9LJb2wqlKIyCVuyeGaGti6lPnmSmgXDcgijyd
+VmmN0KUEQviKetc6d6cKiqw+1ao1O1FIbyijRYKMC+sZOQ6tiRERQxECSBn6hnHq5eBN3aFlHn0+
+sw/NyekQYZ908c0JkgHnTZXomHadz5eYF8zbjWNcNt6FyqZDDl4s1iE6lKdRy8h+1uTqLhaHzihW
+eKvFJImQ7tiH15wrhMT7C8qiG62RkCcRboRINu29pVOgcUItXYua89UR/dNPcySJTUUCU6MSpCGL
+yOMF3GOkwxgYUpU6SjE7tOW0B8dz5Nx84kXtcQ2jfbtgqbnA1o7tnk02dzwPolqe4dHDNwEA6bUE
+emRfr4m3MbVCU9pzuphVeHCfHQ4dYH9or++pCBAxNcHsDM8yvlsr+r8F4G996OV3AXzhu/ncy3E5
+Lscf3xAfttP+4xj9/sB85lOfQRIF2N60lfpXbu5jkxz0KEphiFYrGoXDM9q/reyu9eu//pueS745
+HuCTn/gEAODFF17EBlWEk1AgYWV/wR28C2JISpvNLi58dRpBCBPb1VWJCMrZvPO9f+8//hsY9imG
+sjlCw4q6qgokznOhN8LhuX398MwWwIw2Xq4rkNJXuKUAYhYlaxY4OwMM2YM+2BnhYMfOy/7eFO8/
+YKX64QnK5glVaQBKAwtGSqUJEWmHmlxjKOp2DUd2jMtIBl6zAabz7Ly2Mw40hziJEPN7XEhqAG83
+p4RBQRhzB4trsOetPULS2eM1jfKpzxe+8AV88YtfBAB874/9pMdeSMBHE65XIcSTBUrjcQVSA2/8
+hjWi+W/+zt/FzV2rZfCv//gPAQC2hoEP/YU2tskP6z/ROh2CpkFJUtExod1v3r+P19+zZbF7R0dY
+ME1VEPjyf2+jnp//BYtX+K/+3hIFkbBRHPhr0nYGZ2cX/rwDCuZsDWxkk6UxHvD7isYg5BwN0hDM
+FLCdRxhmdg6dX8ij8wrzJY+9rHBlyz4vg9hgyEhvMCzwgz9m09d/+T/44LeNMZ/HR4znAuYcSoHN
+foIbV67gGj33xqPMg3RUB8x54Y6WK9x5YC/SG79njTL0qsKElOWXbt3Ai9et9uFWP8eASkZ51kPI
+/HRIuC/iDBUXgjSOUdBhZz6fI2FdIurFWLBy3rJFKozCoGdDyq6pvLDIsN9DSkGSs0WNc1aAndNT
+HEe+HtC2T7YLjfM8QUSr96KoUBPmPFsW6BO/HokUeWovchxcYMmH0LU6szjCiJ8RdxJn5/YYgidg
+zEEg0SdceUgQz5WNITJ+x8VyhZqwcm0AyZt72M/Qd8a6zCnapvUgpFlV46yy87ZsNUoqtVRVhbZ1
+vcz1ObtU6/Hjx3jzTRsmf/7P/KSvfguBJ0BN7l/jodtiTYyGbjXu3bX3xfb2Nv6VH//TAICdLdrB
+6xKqc+1e5VM+I5WHMYedWEOzpRPmDRDACbSuQVhGCKwYxn/9d+y5FUrB0IQljmJoR2s2ARzxW2aR
+t7A/IbV+P5S4sWEXiEezCiUt0M7nBerKic9oL8ozYMc/DEIMqIlZyhjzBXUs+wFaap3WdYIv/7/n
++DjjkiV5OS7H5XhqPBeRQj/v4Qc+92lMhkOv5Qch0HA3OitXuHPf7gK/9+abuPuBDZ9jypztbU7w
+8oFVzn31xgvYY2h/dZJ76exAdAgTx613PoOZdXGGnYiYu0M/TVCWdgcWpsKqsBV1xwwM0EFwh26a
+Cr3EHvPVvW0cndGvcLFCx53Chbut6rzFuf2F/ScMIsSB01i0L+ZhjpYRSGACRImNDh6enqPh63Gc
+IefGfXZBb8Sy8orESZIhouSZEMbrSgZhhC1CbL/nwHo73tzd8mFrEiboiFJqm8YLpyRpjJgpVJ+R
+2c50w//d3QeH+NLX7I7/+sNjPOL510J40pg76SiK0FGu7fHjx/ilX/olAMC/+e9+0TP8DOD1ItZT
+pr0OpHmCXdlUDU4I2PlTX/gM9rdZdOzYRVLKR56ma6Dc610D7eT1tYHgLhw53EgUeJ/HUAgvcGI6
+g7OH9u/uP2AXSRuMnAaGFJgRF5DFIYaRvbdWxqBxboAsqq9WNbYG9ueR1N6ar40MLiiW8uC8hO5o
+GMTiuRYS5ws6V4exx6o8PFthwBtjdlFj5cKUZxzPxaIQBgGmkwkCSDS8I0rT4WxhH7DZcomvfNW6
+8dx/cA8ROwM589rdQQ8HGzYluDbOsEXas1yeIHV4jQ6Q2iHemJaclz7H7UmJlLlapUJfIW61Ro/A
+oTFTjSROfG4dJhGu7llQVFnVWDA3TNMYGQFAjqWmVOsfjlAGCOFuoAB90iA3Rva78jTy9uxN3aFc
+2bqE0sp/R9NpDEZ2sdiZ2jlZrEpUtQMbKc8A1FqtH24YTAmQ+cJLtjU3zTOkbD0O8x4SCr72ehk0
+/64FEDBnHvYpU44OioCkvUEfV8j3+N9//Sv4f96w/p5tXaPgdXWpVJKk65QCAp/61KfgxrqO8PvR
+TcLAn4cVZ7HvmZ3P0ZGh+cKNlyC44DifjaBtIcjF0KpFy5RQqxba3SNK+LQiYrGlF4cIeb+0deU7
+XxIGb/y2vT/nM6YlHZAnrO10Cu70ytUCWyOKvlQlBKnmPX5XqrWXDBj2IlxcOB+KEE4Ttio1Lvh0
+J7wv9rYyjEm5f3Q8w5DpH8IQS3Yl0ijCo9Nn7EVyXKYPl+NyXI6nxnMRKQCAMUCtNEqGrY9Xc0x2
+bGj7u998C48eWkiogfSAnJg99qnscIvY8pFeoE8DENkJxM4zUCYQNHtxZipaSGt3DqBsNeYr+90S
+gXd6apRG6GzeuUvGUYKa/Pg4CrDiqnz46GgtDQ6FvW3bMVisyHorlVfWDQH0KScuAAyJWdjesNHM
+dCOH0k4nUQBkcxaFwrK0u/y7HzzCGTsbw7E9tvEoQ92wmKkkHp2xT28MAicjHzbYdt2Tvo1MJkmK
+mLtgHocYbZLJNxhBspLdAl5tO+IOLVXjuRERNG6yJ/5jr93EKY9tVVao6aDtip1VVfmfu07h9PT0
+qd+7IT7079ov275qGHkcPX6EIc8lS0NIsmq1U49WNTTTgLopoMiiNF2LzqHM9LqQGDHny6MIfSfU
+ohqAYN1enOH3fpcYgQs7P2mooBjuizBBxvlcVjVK4lO2xz3s0cU8oMN4ZjqkxFDUSqNmhKkajXGP
+jlx5gq51zFx7ON2qwCYFXupBjjn1PQe9AB2pAFWjIaVTL3i2POIyUrgcl+NyPDWei0jBGLsjz6sK
+p1zBr7z0Atz+8O4H9+D6d7ITPscbsbe/N4gxCmiZhQYpEXFJkiIjnDdJBxAsYoZ0kUYYQGmHTDTo
+sXd/dHqGamW/Y36+QG/TRiyJEwoynWcLqjbC/ftWYSd8QpEpDDR2qez7kO3UURZjxAJQLA1yRhVK
+d+gTCdgnG3Cax8jYbpoXFc5mNtqQpkJA1aDt7SEeU9WpZeQiRYCIbNA4lj6Hl4FARNj0IA5xa89C
+bfvZmmWXUhk6SzNEbK3KfADJXDUOAoS5rd0ocvtlsYSrDFZoEDpLvskE3/PSDQDAB7MC5495nL6o
+pz0uJAgE7ty5A8DiEJ6MFhyOYh0piPVrQnjF6McP72O6ZaObAMr7S0jtsAktwMgLuoNwrUXdeRCE
+0mtiWcC6RS+KPF4mlhJLyrhFQYT5zEYIZWvPLYngGZ5dozDgvTCZJCj4d+ViYS3eAOxt0OjFdD7i
+uViUGBhXEJb+/ARqDENX07JzbIxBcWHvvf3pxBY1ABTLyjuld6ZD+2w8KD+ei0WhVQqPzs7QZSlu
+/AkrUpH1B7jzpjVEbbtuDYmF8V2CEcPaXig8/jzolKckh4gQMT2ANF4sI6Z5SZz1sGyouddU6DtD
+13GOiCCiqm1RsYq+v2eLaHVbQ/CBXhXNurf9hPZhv5dAsPCVkvY86sXY3ybGQGgPFuqMRtnS85Id
+laaqPDAnThKMCHTpVOcl14d5gDTd4nG6nvgaott1ytO60zjGkMzIV6/v4IAFwcBRwMMA5gnhmMDT
+s1MI4jqEEDChE4khyCoIAS42Ik4QsmAWBhc42LR/d3tnhPdObJX8zMOu1wxOrTW+9a1v8buBJ+mT
+7kcvBm3Euvxo1vJuFyenuHHdYlxMW6MjWzNgcRFawfBnadSaum3WACjzpLa1k1YPDLbItNwcDDGj
+zNliOcfKUNE6JPchSCFJ8Z+dnWOXKe3eMEbJRfZ8pdBwAV8t7fFMRqlf/fYHkceArDTQOPNaCA++
+IpIaurXpBgAUF3NssphpjPTds+EwRdF9vFXhMn24HJfjcjw1notIQUQhoukWrt+8AcFWWNtqXwTs
+tIHDtMlAePu2TRaWhmmCmKlBLCUi93MUeqSglB2Eh7k6oU7bOgSAtqs9AUk1rZfz6g+GCCK7SxfU
+9Ne6BRG8aLXwHgOtamFc2zKIsCQcO+NBTAY5djYt7LpezjEZuNQmxQnJKk3jxFo1igWlyKoOEedl
+MBx5W/eiqrFkGO92OyEDdA523GkE3GniIPK8+q1BD31GWd5GXcMj94xRazMUrT0Ks+s6BJyXyLXp
+oD0pK5ChLYrChuFj7mi3d0b48h37GWcUBbGtUuN/XjG8lh8SWfhwoREG6wjDaJQUOTVViQFRmlAr
+GLYkHQZBqxYdozjTtj7lMZ3xxcVAGAjn1cD0Q+gam0z5rk6nOCQK8WR5gSULgimvbwvj8R0wQI9s
+rRS1F9EZZQnOeVmdZkeVdBiRCNfLBbLQfsZ5rXwRe9lKaO7hpfMr1evrW5fK29sN0hBdbb9vfrpA
+SnHeZx3PxaIQJgm2XngRnZSQzgkJwpuTNBQCAYBQAJsEd9zctezF6cYYIxpy9Ho9xLzhpTQQFL0I
+O+H1Gg1NbFu0CDhhQQDo2BUNlAfFKKUR5Y46bI8nSwLE1Isf5n0IR3ttGiQ8Zt0qX/vYGNswems8
+QsqLGCapd2HKwh6ub9tw3mkfLtvOs/BqpbCsiZ1vGyxWNADpAEhX+V4bqbbMPYuy9PG3NtoaogDQ
+jYIm5daH0dr4B0V1Gg3Tscy0EKR1R2Hg82/JsDXo1gAjIYRP8sJA+vO/vr2BfXYz3mMODKw7DUII
+X1/4WKGrEJjPbYcjCICusQ9sVZ0hoOal4XmaroNxx2wEhKOiG+3vOd0pv3A4Lk2nWkhS4zdHKbYG
+9n6ZF0vMVvR/dB2nrsOci3QkBMYsQg0irOnsbYVYOIq6/Xe2UEhY28rCDv2YaaM0aDiHpwY44WMw
+cwzOnvRaodACgtD0MAK2R/aeK5rQLyzPOi7Th8txOS7HU+O5iBQMBJSIaIflqr/rQlTXdX7HS6MA
+G4wUpqzijnsZ+rndiYb9kdfsL8uSzsSAjEJIroEuAjFGI+BurVUN0zrpNmP9tgCkaQ9nK+fvyEJP
+miPvOXKNwAXNPZTp0I/tTqLbGkNGHjd3bHFxkMXIuOvIXrK2LtMtEmInQqIKR4MQDXewi6JAwYKa
+SnNoV+1v4FFzGaFvUZqgMQ4RqJ+o5Auf2rSqRcmoqXDkGhMgpP6gUNqnWKZYeWkyHUpIdigcCaoz
+xtu1KaW8Y7LSGu0T+IUXKcTytQ8s53/RrUvHQkjEDP3dxufGhy0LhIUx8hg0Lpa2gNmpCqdH9wEA
+uSkQCQfTdrJq0v9dEEpP8jJCwPu5d8Jb2bkCXt0qKOIbslBjg3iSB2cRVudESzL0T4VBS8m3UGhQ
+FwjDOPIkrl4gIajj2PA+rJTAY7qfD3sCGY8nDSKYyM5nHmov4DJvGAm2QNpzxkCRDREAGBmgLIh7
+aGqU1ceLFJ6LRQEQEFrACLOWGRdAQyCIahrvmpOnMTbYIgoIJFGthnBKMyZAyBu3aRWkZDstGXgQ
+jmDVXxuNjhOtqgq1ExVVCobV91VRQbOiXPLhByRa5nVl3XijkziMbY4KoKlaXDmw6c2rV21qEMkA
+K+akYZJ6g5MoECCrGyOq/CCSaHhseRTgaG5v/sfnc8QMfS2YiIfE95bLpQeAyTD0xjBKad+y2pwM
+kJK70AbrfNjVDuIwRsAFVNfK5+dKCoQ5FwPegDKKIAUXXqUQuHpOnCLkuXSrChusn0wZfhez5bqf
+ZDT6hE1/GNi8Fn/ne8WT7zE4PbHpyL1796AP7Xnf3hnBefOekrJ8Pl+goGR+KwxCsmfDIMCQUOHN
+wQARhX4b3k+tbuEMNGMJz8adjoagQr9v+alQgBkBemLt2hVL6VPeThlf2zki2GjRGO9OhtKgY41C
+hQKB2ywSjSFb0TF9VVsF1FyE53WFwvFAghCdpmJTqNFzosco8SzjMn24HJfjcjw1no9IwRho3UEI
+7SW9FQwe3LdsyK5p0GN43M9z1DRquXvKYpAOkU0IJW4U9qme3C46LEu7jJ9XpyDJDCHBH+PBAL0e
+WYRG+9C1qVpoTo3qFGbUTayYajRti5bQWGlCv8sLZSBZoNvbGOITL1jm5pQhZ97rQ1H0pVEtOuoN
+RNJ4Tb0+dRqyQc9HHY9Pz9ZEHKXwgIAl0wKCO4mku3IeSoQs/JVaIGS60mmBKd2jb2xNkBD6qqhL
+2cYhWoKUVJIhSUgeyweAY1rKtdiJxw0IAU0tB2lidEwZOhFAEB7cQfro5gqxC4hYsyQAACAASURB
+VI8uVn7HVzCe5KSfCBXMEzqOPtkQa61JtBoLCtksispb22/2EvRj+33v3LOqzQ+Pjr3yc9iLkQ9t
+SjcZbEAWjLKKU+RkmPac4IwIPNYjkiEGTO92Bjk0u2ArRo1dB18wzQODkNEU4j5SXh/EGqxboxdT
+I2S+8IVtoRSkdFgP4btnJg6QM7oZO4dxKbw/5KxQOGLh8+6yQ8X0LpYdQvwLmT64SrTxeZ9SCo8f
+WnlraO0txVdV7TXtj9nye/+khyNWfW9M+thlV6Kqljg6tTnsWw+PcHJh8fUxc/3rV3Y9P+G1m1d9
+zSEUERqmCqHsQ9X2swti1Y0xnhYdh9IbuupWIafIxvd98gauMY/uO/PYXh/aG7tKb9Ueo0PC84v4
+MOpwffNfTRNEBB4Ne30MhzZk/p137mLG8255I42DFBkZl6Y2EMy7gs4g5+uJjFCVNhQ9oefity7m
+SHgHjoYTjEa2RjPOM2yzjToYD9DjwtE5AM5qhVN6GD46PcNpZRfnk8UCZxd8YM9OIfiA7GzZzxqe
+LDFbkr0ngNh9Lp4ca2r0OtVY61Lq1qCgIOy7dx9CsHazvQGkFJrRXCBNGK3FaOMUGZGZaZah79Cb
+wnimZcUFJhbSd22EbH0Ld9jLcNGzf1ezM1S160UBYYRTphXH5zWaas1UlGxFZNyFNnoxMjjmpPAL
+YSUkzvgZs65D5ajffC0MEkxyewz9sEZApa6jpoaiUMtLwxCvvWRTt5//xQs8y7hMHy7H5bgcT43n
+IlIwWEcKrkKu6hozRgIBBBpq4w3SPq5csfp7it2Ck9kc1duWu5+/fAMB+/RX9rfwzV/+NQDA4cND
+KGcTv21VdqP+BpbcMb/51tu4vmc/VxqJJV2Z2yTEzraNPJrHa7xEQGBKFAsPDjk7PsZ4YrUVtGnw
+/mN6RUqq6QYz9Dnjm3mAQUqZsyDAkn31hloJlRY4ptT38Xzl+9xdvUSfxbqNjS28dWgVpheUst/d
+GGCbnIm66pAzwihKjTB0lusSD45ttPXNd+9xLpXHG3RB6rH4V/oxvuelAwDAi/v72Nm25+eYqifH
+J/jGe+8BAL569x7endsdsQgiaIbVYVMh4GdndOLu9XIfKQRSona+mb9PM9RJrz0BY3JzoQ1WBACd
+zQv0GEG8euMGermdgxH/PZtXOJ3Z+bx/coityn7I9obGMLXfPRpk6LEj4EVm9FphPBBrHctRL8Nd
+XpSE91tZAiHfG4UJLmg1P182WDb29aUOvNv2mHHRjbzFK9v29+M0gOIcLAuN09K+ftZFOGcnpWC0
+mZgVNi7sOW31AoyHNiLYyhWcAdYVofD513jT/SKeaVxGCpfjclyOp8ZHRgpCiH8A4F8DcGSM+SRf
+2wDwjwDcAPA+gD9vjDkXFljwX8A6TxcA/pIx5nee6UgEAEg4G8qyKLG6sPkudOd9CvIowvzY1gmC
+yK3KIUKuruONMTIWmeIwwMGB9eU7nc3wgEKqr79hyTff+NY9/NCftIo/o1GC0zObG/fHY6Rc/c8X
+55hu2ghij/oOQgI588nxMEfLPj0kMGAh8aJR+IAkoA/ObW1Ea4FrYxs17AwFtjfsOV2fjDDMbT58
+/9Ce26OzJR4RifboooZhBW47B6bcEcfDMVpld/qTBWHOqLA5tHWSze0xrp/a6OY9NUPKCKOTBppb
+yd6OjYJkEOOcvg8P5gWmW/Z4XjrYxpDEnvPVDJuB3elDRiBnH8zRpyHLKzdfxOLuI37GCruUwLoy
+2Yei98Uh6wz9LETC9qUJJC7Ycn06UnB79HoYY1yHEK3WOGe00e9P8P1fsM4CL93aRaZs5DTK7e65
+OVE4YUf5YraEuHC+oWeQbLnuTwa4QeHgfuBcxwPfqg3DGFlMshkCnJ/bXXqc2wg0bzVi3gtX4gaD
+ob1OhYjx/rGNaLSAbxkPByzmRg2Uk7AOAs9s7YyAoDK3UK1Xx05YRNxOAxxwjvNAIUupVj5KcIW2
+eS9vtTi49c8f5vzfAfi7AP7hE6/9TQC/ZIz5aSHE3+R/fxHATwC4zf99L6wF/fd+9FcYuiutQ6um
+rtdahBHQoyx2rFtMN+2N7GTS1LJARmDRwc4UCavFQivcum5dpNqqxXRmb8iobx+aTgSoF3YhQH+K
+iJV86A5XKIt2fDHH6YkVeIl4g40Ga9ek8XiEe/ft73tJiM2BfdA3JyPIwP48ok73qJ/hGn0sRVth
+UdgFIE4j5Oxj77DAtzvdw0FleAyV5wSoZoUxjT6CsMONfTsX7xzZh6CQCkvqUm5sDPBnv8cqev/P
+v/nrmO6weJjl2M7sDVkxxH00W2B79yYA4OU0x/bEnuvVnsCUTf/z83Pvf2kcakobbO/bOZ5mOXZf
+vA0AeHByhsWJPb/q/AgHZJj2CTg71RKLc7sQiCjDg1Obamm9lqKX0kMEnug++EYFWtVhwYV+nA3x
+2q0bAIC9zRCLI1uM7fHB25Qh9l1HSQjsbtvNIo0DFEubpvYDAQc0iNk5ydPUF4RV1XlKtTYCAfOY
+HgvXfdNBObp0rjFi1yk1KYbX7WZQS4mKC6Rh6hN0re80RWHqsRldW3isw34ucZMcjIjdM9F1GJAn
+kScxwA5PGgKvjOxCtf3CEG26TnufZXxk+mCM+WUAH7aW+UlYm3ngabv5nwTwD40dvwHrK7n3sY7o
+clyOy/HHOr7TQuOOMYb9QjwGsMOfrwC498T7nBX9I3xoPGlFP9ncgjACWhq4KtLh0X1UtCgbJAn2
+2WO/Nd3EiKgyZ5Um+z1M6HB8ML2OEDRyaQts0e8vvZ0gu2NdqjXxAaNxjuTA7o5luQI7doBW2Egc
+Aq+HQyIBW6Y2qimQjuzvz87OoMmefHF7jOsMtfcHIfZ7Y54rPSTiCClVfUUyxHLlWqQRIorEZBmF
+QAJgk62wzSRB5PC+0QBhRGHWZoZXr9rI41esszyarvOouvl8iU++bKOiK5NN7G44ZKVAnzvaBu3Q
+r+xuIxjYz+pPNpGzZSeqFYpzu+tWVYMqpHiJtMfbNspDlLe2JrhKMd39foCLEZGXFxnc/tPSbGWY
+JEg54cum9QKsTwqsaL2GJOgn6oxGO0HbGopSdy/f2McVpjxdcehJTgOeRxB3kGQqDqMYaWwjiDyN
+0duzc9RPU0TEADhiWxwGaEobjZRdAaGdPkWAHeKYD6Z0175Y+MhmIw/RI7s2CgR6oY2wYgGwxonZ
+BaHmZYcUFLgJ+j4CyUUNE7MI2g+wMXKiv0yxu8QfTxBGqDnHkyTCFiO9s4sKp18+wscZ33X3wRhj
+hBAfLhk/y995K/prt16wj53RiIgzx2KGnA/jTr+Pl3fsw31re+JFKELYm6rTAlf3LVBob+saJJWH
+gAAh5ddH2318gtXZVWGZdV3XeGzCcJxDU9euLpaeZZfH0ncEHHBnnA+hmOvdv3+MEXEIiQww4kM/
+CCUiUpyd8rOBAlG0WLQKTeUYeUDDRcHhXZAFSMnb6MkYktyHRsErNHdFi8TxOfhQ1V3n7d4HWYKv
+vm0XwqjTyIRT7JFeK9L1ueM8R0yocS9LfD9+oQzaxknVB4DL8h3YRkhUpD3rqsKAgiRXBgNsOlnz
+OMTZzD5YTmZddi0SHsOsrRFG7pp9SKfR/fiEQ4zrROi6QcYuyBc+9Rpiip6Uq4UnhSROhSpaq39P
+kgTuQmRxiJw1gTRNEDozVrcRtA26xoGJDHjLQUPihRfIA2GHQKkOEWtfcRph2KMIkBSQTEGMMki0
+o9IzvRwH6HG++8M1d2e77ZA3XFikRuBxLeTlSAO3k7Vh4kFUiHI8ZFp4tDiHPPqjEVk5dGkB/3VL
+0QMA155436UV/eW4HP+Cje80Uvg5WJv5n8bTdvM/B+BvCCF+BrbAOH8izfhDhwGghYCERsKwbzsI
+MWXhZKfXw8HUFtSubI8d9weC7tFniwIj9r+DqAfNgmFdrVCzNz0/OcfOvu0iuMq6ltoz5Gq1RLWw
+hbHIxJ5oIoRBS9Sg39kb4IxQ46YRWJF1dFF0KEqnWhxAcJfS7EcL3UFRm6AoWpQO0acjJJHdpefU
+G4g6BRGzYh0AIKtRCXgfyPmiwYxdh9bhO2AwI+lqM89x/+hdAMBnb96AdLblRkKT8GUISw5FgJQM
+T1NrFMRpCNUiZoQRhglCpx3AaxMGkde7qMsKGRGZQIwodnDcFWpnDOM1EzvcumHxDydvvOP1IKSU
+T2s0uh9cnVELLwZTzhe4ecUWDG/ub0JUhH83lddFAO+FOJJIeDy96dgXldMoRETbOGMUOsLUW0qY
+aV3CEN4uTGc1He1v8G/9+7bA+vpv2ULzr/18gnfft78f3w6xndjPSNGDodu2ltrLwmVMUVQkkBD7
+HGaRlxMctyli6msEEPbiAwhddCgNBO89FQYoCdd+/6zCG0v72KW9GLF5Gif6UeNZWpL/E4AfAbAl
+hLgP6zL90wD+sRDirwK4C+DP8+3/FLYdeQe2JfmXn/1QDAS0zy13h31c50RdHfYwTB3rK0LOh+3w
+sc3JW9WiIlWt6loIrhrtqvCmHvfv3cfJka2XvvpZqwOZbuUQTpWmEuiYO6qmWHPzdAW9tIFQIOz3
+nq9W0A6TDoWisBdu1etjxbbRcnHhTUzz1D7woZHQTJIXsxnq0qkDaUTUZnQ019WywNh2lZD0BygI
+3iqKFbp23bY9XtIzkItCFMTQvHmqpsaIrdMf/+HP4/4jex41DFxA2TYOVy58BVzGkYczm67GRUF1
+I0TQ0nFFHK1dYsUHaRcSCY16GmOgKPohtfQpiAtxkyhBnzWO7ht3cGXXPtziQ8pL0mWmwqVg8IWG
+pirwyRfsg5mUZ9DsIkDVENo9yO4hBwQfwiySiJkeouygnEmMadEZ1w50kOIWwpn+Ntob0FaqxfSq
+naMfntpWdTVb4eFDu7GUbQ3T2dA/CCKn0A+0Ap1LTajuFMgQMZm9Ik4QadcBabCkv6nUHRJeE8Fr
+EEXSGwYJbdDwWgahwJjmSIlosDflzfoWnml85KJgjPkLf8ivfvQPeK8B8Nef7asvx+W4HM/jeC5g
+zjAW3mwEoBgabY5HuLVrU4JhnGDI7kKc9nDMHvSCodW8rPC1160NeXlxigll3Y/u3cXm2IKJrh3c
+xltvvGG/7iv2vS9/8gVkuwQkofXyZ52Gl35PpMDtPXYRGIHEUeAdoa25mbMc78DNEUVZeo9Fp/uQ
+BgEkC5+rZeGLg3mS+uJRxp12WbUwbCOYRqFjCqPKEjWLdbUATleOVORMbwQSgrrKYoWW0crRvPLM
+T5jOF02NK6wJYWl+AGJhEDDlOZ/PUVVu112nDUNCqaPDh77wOT8/w7hHeTsZQJTcpVWDJcP5hXJp
+UIjHjyzUOs1zfOoznwbw+9WcfUHTMRyNQeRMUVbniFt7L5ydH8FQDyONEkxY/C2Zap2dnXnBlqDf
+g+B5d01tNRth5fEd2UoZp1nQeuOfsixRMlUq6gqGDNSYUeyLrw6xf80WsZuqRcN7KA5LRMLJwilI
+kp+ceF0UZcgY0UXBWiIvTVNEJLSFWnltDDc/RoYWzAErBlMyzXlwtsSVHRt5DPsGN1/4MATs24/n
+Y1EAfQAg0BCthWyI4dR2OuOqguSNfnhyhiMuCmM+8Ct0uChtmPVbb3wVr9x6EQDw8iuveAed/nAD
+GzSIffvN1/mdFa6RAZkPU9RsLTZKeQHVQAi8dN1yJQR1+6QAQodyEwIJgUB1o9A4CXOZehUfwYcq
+TDNoLiwnF0u0TpBFRGhIB1eaWotRD2ALzagOHQVnmrZBw/y7gkDBdCOUTqxWYDyyx5MKjdc/sACh
+f/Czv4Af+BOv2nnZHUE5K3nnE9kpxJWztW+heOzLRQWh3XG03j7eXQ+lgdUF06BGY8gFOY9jOHXb
+qq2xJGBHs3txXq7wxjtWKWn7+k3I0C2gTzeyvO28cO24Dk7V5u7dB5jdsx3wpF2gPLF5dNC1mJAa
+nVHwdnY+8/wLtT1Gn+AzqBaic0K+kUdUOn+KpmtRsyPWNI1XYWpVCy0dgIvt3T2NFz/BlvodiYIy
+80kILwgr0QGh876gI1c+RMLdJG4qKOWUs0L0eG8p3Xi+DQJXG+rQKfsZhQJKLjYGEZbn9l6ehi1y
+gu6edVxyHy7H5bgcT43nJlIQlFPouDsu4gGWrMhHqxUen9si0uHhMTYGNkIICZrZ3d/FFneuZVkD
+qV0Z+zt7GG7YSEEGBlsvWizDXWr5ff2d96G42u9f20YnHT5defx5FETIaR6T9W3xRsIg5vclcYjQ
+oZ60huvjx1nPy4s3rPrXbQsE9r2NEfhgZouEv/nmHdwgByFmYfTalV2E3vFn7qv3bQB07P+3KwNX
+MczYFcjSdXcl6jQWH9jveP3+I2zSafr2zggtI4Wqc/gHhdipZiuATQk0dY3joznnEJizK/H+vQ8A
+AGfncxh6bFa6RUL/yJ3h0BcJK936OQh4fVdl4zUo4zT1xb7g6TrjWrORr3dCoeG8fOt4iaxnr2k/
+D3D3oT3O6vwxPjixx9knFD5PMgyJoTg+X2KV2HONxPohCLB2BXfFQKU1GqYXTdv40N6mu/yZqdt0
+P8SP/IQtnn7lSyWO3rKpS9YICLC7IOHP27BTc7gsIVlQvDrJYRgJPjq7QMNIMJGxd8R2IjoGGg3v
+i0oJSPIyXnohwJUxjX9uZfjc97tI4cPA5D94XEYKl+NyXI6nxvMTKfD/XO5YhhGW3IHEssKKCjsm
+ChFM6GfIAk+W9zAiM/D2cIrEqf12wIq5eCAaSPajb3ziZQDA622NN+6RwRgLjAdczZMAhsXBWBgE
+DmXJVmiWD6Ccx9+qRMNaQxoCijURpQOPCmyYT5/WKwxYULr94gvQd60WwrJdoers7n5wxe58SRKj
+bu05L7sOFNJBF8i1H4SWPoKYkCQl4wrbdDUul4VXfpaQOCMhTGnhFZk6SnVVbW19HQC0QYjZkloA
+qwJhj7JqwkCEDtdBX4jJCA2t1FTX4uHJGd8rvPJxq/RamZvszDAyeOUlCzFPB3384J/6Efv6h4Ra
+tXT2boE/D6dEXQYxPv+9lvB1YzdHp+1xrB4+Qj2313VINaLxcICIUdrF6RlOSMYKpUbgCoaBXJOx
+fG3OeDUoLYTXtTBaInB9aXfMMbB9y0a3L60CHH3AuW0qJGxnF7rDWWt/vqCWx7xe+Pm8c77yhjrn
+FyViQlz7aYCd1H75DmsLWvawhI0wVipDTFTsZz/Tx60D+54XXhbIxh8PcPxcLAoCgHQhkXHFtwAB
+6Z81OkyGZPUp4O6xLRhGhKduFAm2FoTRXiwxINT0ZLWEJGClEQrG2PeHIeHDvREaQ9afEL74ZhQQ
+MESXwmCxsDf6aGRD/DCKnpICdwVDYYAFF4BGV4j5kNWuV94AKfUMd8fb2KYsfZYl3ia9Y8FpKVsv
+prHqNCRDykR0qBmCny4qLwXn/DWDQPjCWdsq1Fyw+knsFZOLqgFYnXfSX6VpETk79EWBGRfhCLG3
+Yg/iGIIMPSd6EyiN2tj3Hs5OsVD2+6q2REm8RNE0a+/N1qYXqlMIyOE4enwfP/uzFv/2F/7tv4In
+h2OHrrMK6aHpO3s7uHHbFoH3hxFu37Bg2pNWoYntZ/dYlMuzxDssqUGCsrY/F0XjAUlx0CGOHVDL
+MTWFr/BDrqXgbAfg6VzHCHhXs5uvJvjmgZ23428YGALRThuFxwveD0wphBHg7YaqqfyGlPVSVEy7
+HtcFllwsnLrlXi9G2bFb1bXYspANvPaaxGBApuzOGF3w8RKCy/ThclyOy/HUeC4iBcBGCE+h2aTA
+aMOG1ItegoMbdhmcLRq899Du3G981UK0vvfTr2DKIpoahYi27c9b/StwNKYHJ6f4tX9m9V7ufWBD
+yx/4wp/EdNe2PYeTPgS9+Ky3ANdLoZGP6BTNnbaua5wy/Cwa7Vf2clnj3SO7Ex5s99DLKNRBCa+6
+7XByYn8fqRliRhtFGCAhfqFwbbNQoAKLYbHEmGmHVsDx0kYmb7x/jIhIuDnbrdPNFOeHxHEo7V2l
+X7hxBRMqKb99/z6mY6t7kFFottIKESHIVbPCikjJ4niJnMSs8XSKlGxVVwAsyhr3L+z1uHvvXdyi
+qM04iyH42SvVQBFNeDK34W5re5kAgFsHL+CN92wq9WErekc79PbzUviQ+sXbL2JAe7Q47rzOwN33
+70LNLQZiEFOQZpAjIoaiMq33AAmN8Z5u0lTQwomo8OuxZmVqs7bIc//+vsFUJExb3P6cPbYHdzpI
+uIJu4LhW6DO93Epz5K4TH0kvMFsrgZW0UcWJ0igZORZMY1daYk417nl9jE/uUCBmFGI4tfeFigSE
++HiEqOdmURBPOEIB1jVpOrHpw70khQtqtra3kEX2Id0QdtKNAd58aPHn4ugE5u07AIBBmnj2WbWo
+IBlq/cSPWjDmzetbaJc27YijDppVZiEARQn389ML5BMLcAoTezxFUWJBJmKlgH5mP7dc1Xj9jpUU
+3+xHSF+0D4iDsEaZ9KzGTAsMEvuADeRaJt6xKC/KC6/KnGQRBMP8R3WI33jL9uYfLzvPich4k+9P
+tyEdq9MA167aY9jf2URBcM9stcD0of3u/Lrlg+RJ4OPGwXiI3S37+gNzFxV73pGGl2p34B/ddsjJ
+BT7Y3Ma1AxvOh3noFYYqo3FMluQFMRud1uAzjLZYYTj49r30NXNaIOYiuzvdheaEdVIiop/o1sFN
+YGnvkfOHdrF5/+gUtdNdNDUcZmt7a4KU0ulohVeIMk5MBcZ3JLQRHv6s/kA+gQEEOzgmx/VX7FEP
+r1aYfcCFBRrgQ9p3wkFlAa2I+5DwGJG6MwiY/o76AUzzdG1gJQY45P2rRYDTu/b77mxfwfe/8Al7
+SvJ9hN39P3Re/6BxmT5cjstxOZ4az02k4MY6WhDoyGRTcYrjhQ3Xr46GyPrcFa/YjsP54ak329i7
+to+YFefRYIRE2l16dbzAxtTuJFcO7M6/LE9RExFm6g5ZYHcr1bVYSFsw+9U37uJI2te/MLGdgSAI
+0TkrcyE9KtcYjfnS7gJfvfMI0y1qQFCOTbRLKO60hQmhWcxrICCdLbvzaUhjBIFzhm5suA3grQ+W
+eON9SwS7aLXXENgkAWZ7awcPl64b0OH7fvCHAQBhcYxqZglRrdB4+75ltA+op3B7e4CMBcow7SHm
+7jm9uotVZOd+GKYY9uyc14xQ4kTgIiVBR2qvlVZ1GitGU6fLGt96wEjOX1+FmH36pqnx2U98hvP5
+pMfDOqXsXP5gAkjqZ2Z5iIp28CoOMBzZ+d7YvYZRahmYV6/YyOXs5BAnDuvy+D7OTu3xjPopJpmN
+KurO+GspXQqjjS8utrpFw1RCwUATn6AlPUh1B8HukzY9RCO7W1//bIs3vvlP+Xmd12+YUG9hZGLE
+lZ3DXAXQhETPTQ3NAvtRtEJAsxfV2O+4v1SYU4hodxgjIyr2+K7AvbnVHj3Y+2G05Zc5o+7fbz+E
++X2S2n/048XbO+bv/Jd/Eb/1q4/x3tv2JAUS638IYLkskJBpOBqNkLC74CCwSinU1HM8m81wdmbz
+6/l8gc5Zp0cxlPNYdJV6KT2sNgxjSNYGgiDE4AnsfMW2ptPte/Orv+zFOF65dQ2ffvUGAGBnewM5
+ATK7uzt46WWbtx89tjdgIgKsGEbfv/vIi7wW9RyLmWXXTXKn1tODZAvt8PSxrwdUVYs5BW2v3zjA
+9Ru2rZew3nF8coqarL7lssCXvvS/AQC++eZ9zC7oSBVn65CY1yBOEwzY7ZmMB5CsL5yczXE6s4tX
+12rvWJRzIehnMUa5M7KJcU4B1tniAi3p2QhTxFzgd5gSJqlEyPZkEgsUrV1A/smv3INbFuwhMt/n
+dZICHsSTxBGG1LbczxN89roFDr203cP1bTuPg4HjzIQ+BUOrsWDK87V7C/yfX7Hp5rvHZ6jYiXC1
+mlgGngKexamHns+LAv/1P/khAEBKhqvWls4OAO+8fYrf+RWbSr7/tRIBxVLG45FXkSrIy6ibGrpx
+6UhnUwxY1zLDZyCQEsORnbsV+S6rqlpfR63X93UQ+Z+frNG8/s57v22M+Tw+YlymD5fjclyOp8Zz
+kj5IQMdoG4mKsM6u7bws1dl8gSQjU61pPEPPyb7PZjPPO1+WBSrqFIRx4nUBIKy0FmAZjIBlxWn2
+fovVCimjEUCgJCsxiEIfSkcs/AVhgJCN5clk4F2LsyjxXpD7+wde+t2ZsEgtUVFp+dbBTURUNn58
+XCELbei7OSCBqzfGiISv6I5ElHL3HEkfjaRphprQ5P7YdlwGgyGa2obJSZLi4SMbVSwK7WXZZSA9
+MctV1uumQY/z0xUFKhLMVFEi8UVQiZzy+VsjOjUPc2yNyd1PU6wYCTw4PMTjuY36LpoGNQtzKxZ7
+0URw3Dfd6bU+hXna99CH847hKNdaDkkQYMiI7cWdDdzatse0v51jtGExJRnZtUmSrpWh2xJpZI/t
+k9dS3D20P5+uCmjiOhyILs9zGOJMokAiJsGqUg3OWju3gXZaFyVWc3tsJycrSE3GqNAYEW5vhEbF
+e6sunMhO5y3kZABELKRmWbYugponFK2Vs7aTHkpvpe9dhKV9lA0AQfDxWJKXkcLluByX46nxXEQK
+QgBhGMKYzudLVWWgHAlIK7SkNZtAIFPs2bOglvb6MCQohUmKM22xAEVRYekkz0yHhn1et+Nr1UGR
+nNJ1BiFrDmVZoabL9Wg8RkJSjdu2QqExJXZhZzJBjzBgIwJs05JuMBxjNreFvRFVkldnC+yz8NUf
+DjCj9NrOdAcxlZsjab93urXrCTfD8QR9wphboxHFdmfL+yMY1h0qKhr1+wM/h11X4d4jW5TUJkLI
+mkmrFBrmsJJIw36aeAg2WoXMCdqO+ug7Q5WNTcScuykxJNd3t9BnBKJhMCey9Hh3E/doPPv2g0d4
+5CDWhPNWRmJJq7i8FyGKiAqFBJ6COlOpySE3swQNSVx5HOL2no0IXt3bDVoXtQAAIABJREFUxPUt
+KnBPJ+gPbZTVc5FCmPrds+sqGEZ6O0mHF7bteT8838Ab9yy+AdS16MUhJIvDT5bf8qyH86bkEdr7
+cLkyOHxgsRfNMsHqwvAYxn6ey2IBoez1ubVvo4ftzRGmGzbCGk+GGG/YY9/c2vSt5rIocX5q6zWv
+v251Qb789bdxcmE/qzXw/hRar+tmxhh/3z/reC4WBRgB6BBRlEIQJtrpxnPaW9WiYnpQFguUF3Zy
+IsJEgzDw1elKdShZ9W4a5VMQQPvioAtJgyCCuwG7Tnsn6SxLEYTOir71vokdjyeNQlynucnWZOB7
+2r1BH9s79vW6KiBZCA3InEyiBJub9vdN1yJjbz6NUv/ApsQu9PtDlHS5HozHGFJSvqgqBIa6g2m2
+tnt3NbRWoUeQTttodIzRoyjxUPKmVR6661KjNAoQdnbe9iZj7NO0JosDHBA+vDndxoBYgI0hUyZp
+IPmwFWXp07i9rTE26IbVSyN037R4gYYcgDBOUTF8LqsSAQuRtni8xquwwO8Xo04pX2jcHWZ49apd
+FG5d3cDmhNiL0QgZF7KERcAoShDzOpgug6KbUrdc4sYV+xmPVy0OuXjVlDzLs9gLoCxXJcLYKTjX
+OOd95oR1VssAp7Sabg4FLs7J89AZNFOzfgx87tO2M3D7Bdsh2Z5uYYdq5ZPNDaT8viAKIJlOtbVC
+QT7KztS+tzUBfuW3ftd+X9t5QRpjNLonVrCP20y4TB8ux+W4HE+N5yJSUErj9KRAsRBefLJu67XK
+jxEQnesPC5Ts3wuyxZoWXh2n60oE3F6SMEDCoo0RZv15nnylfB88CCTSlAW8JPNFnaZpvJpSzRbS
+ZDzE/g51GkINxRV6a3sKQ/Wbplwz3JxvwObGFAm9ILSqvalNL+2hXNnP7jHV0IFEw7ZZr9/zUU4c
+RAjoVp1kOWq3C5D0onWLkN8rhEAUsV3WAZrYgk5rvwVrtnKzQYzr2zYKeOXaHvr8jK2NCa7Tjm1j
+Zwc9mqskJAzpcgVTEglqOr+Lp2kAQwxF3bY4PrM/v/OA6Uwce2i37koPD/7wWF8r+99d1/n+/svb
+Y9xgyjAepejT2KaX95HxWjo/iSCKEYSOgAQI9vqjTmBny879jfMC93ftdT0qmY4NBt5LcjAIPamq
+KGs8fEi1KKZ8TSXRzO1cHH+wQD23v+9FGgHhyp96+Rpee9FGXgfXLSx5Z/8KhhP7vUmW+mspAE/W
+krL1aczBDZuCfu7Tn8Ab37Jq3UUzWxdSjYFmWiyl9KnEs47nYlGo6xbvv/sY9+8tETn/xVHgK+uL
+2RxDXuQ8C5ESHiokQR6V9jh7gdTLWSkor+BrhPGdBqczGIShxy6EQQiXv2qt/F3YlIU/Dpeb7e70
+0aO/Yl0rZAzt82Efy5WtZyQysJhVwFvV570+WrcIJRkS9vqV0hiMKbLi0gHRQdFfN81zCKc4rAyS
+yPkc5l6mrea/Go0PvqMoQsUHVsoAgqAYbdYgHWfesjnqY5/grs1RHxnD+en2tpe962d9SNY+DBdh
+AUA5sZFQQPI6oO2QshYzHA6wsWnn6M4DC5pqW+npyQJAFK0r5E/C3QU/26WBQgjsMbW5tjnCBr0i
+szRFygUwC3t+jkIeg4xjBCEllU0Awy5KZgw2uNhf28rx0oGFd4tjskvHG17n8fHRsV8U0yzD29+w
++JPA0HBGpmge23mZHy4RVnQqMyVGW3YO93e2sLNtw/89ytMPJxsIXZdBig+1XMjWDIHQeVf27Tnd
+vLqHG9fsAnFyUXrOhBQCNUFdUkqE4cd7zC/Th8txOS7HU+M7taL/zwD8WQANgHcA/GVjzIy/+ykA
+fxWWaPbvGWP+r4/8DliJs6YpUDkBy1ii4Y4fRhEONuxq/LlrfUzp4Vcy1XgwUzgsySxDiK51KYhC
+QZJT3RlIJ2bMSLXTEobphdadhxLXXYeOYbVNI5yyhj226WTkV9/WSAydMGbbQpFp2ctyCBYuR4Qg
+d8YA/L4kibwIaBwlSHv2Pa4hr9qlF2eJpUBDReEgiBA5TwatvQp05URgtUFAplGSpSi4Y2RZ5q3i
+gs4gpZSdQwRujDLsTm0FfGOj73hWSHoZUlbwbYfIPDkVUJ0VEAUACO2lByCNRwUOsgw5U4X+wO6I
+J6sK0ilQBwEaRjrOd9NOhfbsSMXvHfdy7LE6vzEZIuLxyzj0xcooTBA5bAnnKkgz763RKeMt3hFn
+0JTb296aYINpzibNW7Z293B2anEfjw5P4PbRSABjwtd1a7+rq+B1KNpl56HrMmixz0hpc9zH5rYt
+NsfEt5gwQMditdDKM3SFkd5hW0oBQXKfi8Am0zFevX0LAHDn/YcoGQmHYQgRFPw7icx5mzzj+E6t
+6H8RwE8ZY5QQ4j8F8FMAviiEeA3AvwHgEwD2AfzfQoiXjPn2FjUyEMjzEJvTHg7PbRtvWSjUXCAm
+qcDnrtkJ/L5bOaZUXlqt7A1/PFc4Iy5cG+Nv2FYbzCgGsqwBwfZOxbB+URkrkw1LyZ3T3enBeYFD
+gox0p9Dyho24EORxYrUgYR19UlIci4u5h/y2ZY0+L7pTfl42NSKqRWltvDloL8s9N7gjvHgxnyFl
+q880DVq2EHvpwD9MnVK+1hK4ha7V/qqGYeAfJo11mypLYmww5QlY3R5kMTYJQsrSiLZUQBQGCF0K
+BrNeUXm3GkgI5gGRDCF4IEoH6BLX1gsw5sPbZ2fkeDHz7eBACLSto/dKD1+XwnitTBdSTwYJ9sZO
+vjxEzNpGFBiE5ETI0EBy8XVheRinCNhmhOhgKABjghgNnbF6+QDSmchIdi1cWA+7IDs9ztEgx/Vr
+9pjP5wTFzRIYPpi60aCmD7I0xi5bjqN+D3HiAHWuXtI90YQVHo5tu14UmQkCCKZujpeRj4e4fYse
+qpsT3D2b89iF37SEEIjiCB9nfEdW9MaYXzBr6NlvwHpGAtaK/meMMbUx5j1Yp6gvfKwjuhyX43L8
+sY5/HoXGvwLgH/HnK7CLhBvOiv7bDiEkkiRDnJRwofqyLP2qOxjFuEpb880sQD+zK+2QhbpJsnIO
+bRBSrnv2nYGQNgwsigIis1DgLrXhZ751BSayu+P5bIZHx7ZI+NVvvY9fpYXc0dkKbrUOWMlWGt4S
+rZ9qaO4uVWWwSc+FclFjb4uiLK2zLusQtGvJt42xDSNhFDTTjiXFTVTbIGUHoKgrZDH77nHid9VO
+KRgHvlKuEKkB/j4UkY8q4jDEqGfnaxDBKxtXhbPKaxGzEJeFOYQDCKgOT4JkXfrQ+cKfRBh4K2Zv
+DKP1mmwlA4NB3362t6PTZg2wwROe80J4fIcMAnT0RmD2gb2NDHubvP55gNRBguOe96MMJCAZavuc
+UQS+c6CNXv8M4V3Ks16OEVmjiwcWWFYrYEaSVwfzlFnMS8PX7Ht7tqNyGnU4FvR+NIBh1CeDAIpR
+oQgShExzQuF0JwH5ZEDkrfIEtIMoy2CdukmXRuSYTm3X4uDqDh5QSVup1pvnqLZFU/8Rdh+EEP8R
+AAXgf/gO/vavAfhrADAe91GuDN65cw/aOTMlPW/ymiYxElKKhaqxnFsWpLM1N/UCsdMGTwboyDUY
+9HvrfD8IEA9tCwi5rfQn4w0goWJTucTRY8tqi1DgAZFiRd1iWZCey0Wq7Tr/cx/GPuGwobRzU5Iy
+9DfQjCg/BCEEeRcbW9uecdi0DRYre05VxZpEr4+uJmNPxh7vX5bVGkzVtv7hdAYpQhvUpU19pNEI
+GHKmcYycYWQedpgyfThqaMB7PsMRvTbHvZEPOS+KpV/UkijyHAQH9AqDAHAW9+j89YOQ8EFxoJFl
+TL2Y31qJdON/flLHSLoHwXS+Uzlg2L496iMnuKcTEUrYzxtFG+gI/JJpiogchSByaEvhu04isOhT
+wFLfA352FCXYZr1ifvKm/fdojpLIyzgf4owKV2ko8Lm9PwcAWLQWBXksHuOd0M7nSTeDEXbelssl
+vvFNy8TsZRk2tuxmkDGlCgLhxXUAASGdPmTovUe16jzCV/tcI/Ddoyt7W4i/bpXIqqpCzNStKRsU
+3dN8ko8a33H3QQjxl2ALkH/RrCFTz2xFb4z5b40xnzfGfL6ff7xCyOW4HJfj/7/xHUUKQoh/FcB/
+COBfMoZSvnb8HID/UQjxn8MWGm/jGZQdqqrG22/fxYMHJ+gPrWZiEkdonO9g08DBkbU2yBmu9sjI
+a+seWur9QRok3Eny/ggZq/rB+BqCnu0Pa+4eLRTgJNCjAKO+ff3l/QmGiTU7sbuWfU9B5mCjYggW
+tSIhsKT8WRyN16FmDfjp5XaX5X0PmTVt4wUyqmWBquFuxJ10OS/w8K6NXC7OL3x1PgoMxuTV9/O+
+Tw889MdoXMxtGGkGud/ZEwmM3G4tgA1XEC1yzmGJt+68xw8R2Nmy0ZQxGkenVuth2Ha+a+G2q1Vd
++11et43vjyttvN17G8ZerbhPZquU0vf/+3nkQ2MhJDq/FRovZ58wHYjDGPOl43a00Ec2CrvzYI6X
+X7L6FTevpohdwMI2ipCh5wYYrbyvZKUlSkqhLSp4GX/TOT2JpZdx65vQd4S2NzcgFdMxbffBMN1E
+ht/k9BgvlZeEEpvEVhSLBb7+NQtNFsba+F0/uIYwWXeUKkZ6i8USJ6f2vi6LChXxMs5iT4YxUuYd
+/SxFj9fm5OICPUoEJqHEOdmqzzq+Uyv6nwKQAPhFAk1+wxjz7xhj3hBC/GMA34BNK/76R3UeLsfl
+uBzP1/hOrej//rd5/98G8Lc/zkHIQCLNM7Q6wIIMvygyqNhLrmsNV+7q4g3Ivt3xwwF9EzYDa9wJ
+WEMZIttkGHnT2Kiee38G11VTbYWW8mK9ySYi9qs3RikOBnZr+zXVIevbnTkobBTQaoE+t+B+EqFg
+a3Q0BJaVjSaOD8+8/NfeVRv9LMqVX+0HsxOv+hQj9PDnx/et0vS3vvktNFTx2d6e4spVKyGXSImz
+U5vXzpo5xoTHllQuKoolyoXdXUJpILkmZ6HBtjOMqReY9LjzEsX48JHC+bndUX739W8g584VpqE3
+eDm4cg1X921dpkeD1qPjI6yo9HR+do4VCVF5mmBCwpcJtXd2durRMBqtg6abEJErDKJ+oj0nva+F
+cNBfpbBFKPjtq3tIqT9xWissV/b63P1gBaPtfLljiJMcjbC756IocXhi5/D+4zPPMJW6hGEheHNi
+/+4bjy9QsX3ZnJ55qHwv7aElS7Ij83NVLNfnFEj8f+2daYxk13Xff/dt9Wqv6n2Zpac5Qw453EUp
+kqxYymJIMgQZCfxBhoE4iIF8CRAnCBBY0Kd8DLI6geMkyAYEgp1E1gYFiWBTsiAjsRZaIjkkh5qF
+PWtP77W+qrfefLjnvZ5RJGgYs2cayDtAY6qrpuu+e999557lf/7Hk9jI2vIiF84bhqy5drdIjY77
+xsoJhiNqVl65G9LfN5bZzvYWvV7OueDgCtq1JgF2ZXtMR8YqDMNpEWhNU2nwC9QtRf9gzLuRYwFz
+dl2X1ROLOPaVwxqGLCIW/sRR4nC9J9VnjFF7UtbbNf+eWWpwWmjdK9U6Yd8s6mQ8YWNbHqDUIsvM
+psk3aLOa4dcNLmI1vcBMR8pX5zo8t2YUz1e+e4O+cN91pRzXcZ2CCCPLdIHk0Wlc1FqcWl0pAqXT
+oTRIGYUkEsD0l5fwZXPbtkUgbsfkwESyF9s+tpicjWYTL+8jWHGZWzCmfThJsCSwl4iyGQ/7pDJu
+FE4KoheAhTlRIPtR0Y8wf7hnum1SwSZU/AYTwWGMR1OsuyYSb2eKrrguFTFVR6Mx79w0bs7u3j6R
+AMDarSYTUciJTrEzszFtKZFOkriAmGdZipOb7T9G9Z8bmkK7Sce1aWLGDg8CXMso0xNLiyDNbKJx
+n4Mtc011Je6oBdNQqOL2ekTCpTlb85g/YwJ/Nc9mJK7StetGOb/82o0ic5AmSeHmjMdjIm2UqGPn
+Fac2FT9vOGThSZYgilLeeNu4ZkvzAWdOm+pIWyDoOzsHrPiyFzwbT9y8mU6NrgDHgsQikkPNlqB6
+Z7ZObVnwD80Gr79pxnjjxu2izf3c7Aw3N3d5N1LCnEsppZT75FhYChXP4vRqkyfPtbj0jjFV45gC
+SedZmjkJAtYrDrfH5vR47ZapELt6JeT9a0ZjPnn+HNW2ORGD/T30yHxff3PEBAm+SI/DTme+6GBt
+J2FRXek2T/D0C2Zpnr/Y408vbciVSnqvUsHNI2foArE4DcaMBR7t2T4Iw/TOjjlRak4dLSSg4Sig
+3TQnfrPbIRB8wowQfA6ylFu75mTb2Dug1zPzaFR91lZNYKtZrR62i8v7FCQxmQTJ4nBaoAbD6ZRM
+zNzu7CxxnjeXoOvKyTlqLXNazS+fZn/PmLaj3hYqlYDodFKkQHN052g0IozMeDPzSzS7xsLCUrjS
+3+CgNyCQQrG8Q7NtH+bdFQo757rgUJRSRRfqBUEEohTvbBur49VpD7dlXp9LK5xaNNZiu9YhneQp
+XuNWdWZmSGWN3SygJg1utFfHkpTkeBoyEpiykrbbaZoVgU/PcYqiuH5/wB+9+d/N+1rSn7HPQNri
+2bbCES6Lg/1eEYw+dfpJNoTIpVETmj5LsbBsiqNqnSaTwFxPHMTcumks2ev7I/ripu5tmb9fX1vl
+2acMzLni+cwK0tdzFKkERDutVoEifVA5FkqhWq3w/NNn0H814/P/9Y8BePtaH1vMsvmGw0pTzHU9
+KXjrxmMB3ng2g54xkQ4OutROG1BJuzlHbd+8PxNOaKw+DcBr14yJ+Nr1hJcEi2/bKaEgWivNkyzO
+GPjoxz7SZyqKZUs6M7meU2ATUFaxkdMwYjwSkg4nobpoHrInf/5DAFx+4wpv3DEm3jPVE7TFfHYr
+FbJRbhoKPFVXOHnWzKNz6hRf+OLvA3D9ravEgXnYnr9wjlrVbITMzrn8UgJZl0zH2G4Od4XbN01T
+kPXH1lh//DwAw6F5aC5feYe9q1fNtX/vFZoC8Dox02SmIW5FtV7kyAv0seXQbpuH0fbrXBdXYnNr
+mzl5kG0s2kLnnufmT59eY3zlqnzJYYm0URF5nt6mI+bzusRllA0/uCEsyTsDArmej1bq9ITf8+xs
+g7aYz0MpSW8GAX25N7Fd41s/MKQvb93YY17iNU07ozIRH94z1zvTbnB3XzJbuEX5cpom9CrmgVWx
+uAxpHVU3C7O8MEu4Z8ZuNdtFReTP/fxH+fY3vgHAjRtmL6yfWSkqfr2KhycKaxpq9g6M6/ns83+O
+K9dNdv/aZXMfb1y5Q12U6ZNPnuXUSbNGvucwkQyG61jUhRnsQaV0H0oppZT75FhYCpay8CtVPvDi
+E6RTo6f+yb/8KtuSx2/6c9g5WQgx51eMRvREpzVUSMMXshDXhYkx4Ry/Sa1t6uO9YURNaIlfeNz8
+vW0rTp45Kd+lcJU0bfFcXDnZnlw7wd1T5sSfpuZEuBPH1Ny8R4RLTkvsaEW3Zk7N3fGExZZxY1aW
+zCmxu9VDj00gsVlvMBHm4HgyLaLeTcmAEIDbMFaAoyxOLxm0+JJf5cySOcW7nQaO4H+TQGi7okNr
+RVkw2zHzOLk8x/KsmZ9f94gNkIKDnlmr6XREtyGReq/OirSNq3k2riX9IhxFEgtRi1SwVv0acXpI
+87Y6b4J2vluhJ926x71tmqsmazG7aNbi5Cjj5l1jBlsqxs7LK/VhPw+dZrhyEi61jSX0+Mo8a4+Z
+e7a9u08gFbHr51c4tWxKcNTggFhQqEivxfFgwETch37ocfmWab13a3eEVRX0JhH7d83pXxc371Mf
+fprvXDRoxI1be4UlN7cwT71h7nUgbpmONevnzTxfeOkZ/vBL3wTg/Nl1moILGWxt8PwzBk9hPW8y
+Ep2ZBhUx8S0NvgR/V9dPg8r7TyScWzbjdT/+EXMNtTqrpxblO9qEYry2KlWGgqyN4xjPvdcp+9ly
+LJQCAErjVz3WheXn7Ppp7vzwdQDatRUW2lLJ5vm4tvEjTz5lNlgwGtCwBQhjaZRsXLtRY2XNAET8
+RodMzPWaRIgbjRq2mG1pqkmFE9GatrAlRXhq7SQvfcBw6uXw2i/9ySWakhaaDIOinbvteqwJMetp
+t4ojD3i8ayLZz55ZwTptFEUSjAjG0skpTonjvMGJAIyqFWKh8mY65MMvPgNAFI5peQLoUVkBq55O
+8wyIi2PnHI2w3BTSmorF0pzZVH7FBoFVr0nTlLX5p1EC/W3WO9TEfFZZhO2a+U3DgKbEUuqy0Waa
+NdycntxyySRWcWK2RX9qlFeWrGBLKnIopKXXr17C9w7rD1ThPugC+58T+gK0pFPSfNfndN538mQH
+LW6TXa/SkjYAUaoYSpoxzwxkcVKYxb4T80sffZ/5xW7QbYkLFveKNGEqYDK/ovjIBeO3f/e1q3zr
+1csAbAYD/vzaLwCwsWPgxeNgm/knzP1/oX2Om69cBODptRVOCMGsU3NwxSXyJE4W65RaVWIcWVZQ
+ss/NztESGP5wNEBLduXsmnF36vUWVXENlOWwsmRc3m67w2hiXGSyDIt3pxRK96GUUkq5T46JpaDR
+pGit2dw0QRTH87ClSrDqV2jIyexXdYERiKUJR6ProGKJ6sdxAYnVtoMvefV5v0oyMqa7JacVaUIo
+VYJUKtjSVVo5Lpkr2nz2BE+8ZMy1mpySJ2+P2d82VFyu6zIJpMuz8gr6+M78Aq5AlqtSnON4HmTm
+81u9flGspLUqAFWOnBje3DyhnMDadfBzevmsih0JzHcSFNRzeau4NMuYmzMnSTAdMyuBPU+popBq
+aW6Rua45gXLroFGbIUnyVmNu0ULPcWuMp8bstp06VYGY+zLPar1WVIy2mk3SKC/a0cx2cvBSylAK
+r3Y3TIAvCQe06mZdBuMJlvAimCYsOaQ9oe4IXVwjb7gDTcEs+J5TZA4ylZKK9UMcYufUc2LaW3hU
+BTRUsTM6Rd1WjEqMCxVbGb5gVWzbrI/OQnzbfO/7H18uuk5/+X+/ytmO6RW5VDXrvbN/FTc0611J
+dUEbWPNtTp40boXyHTIl2YUccJfERQFamsQFTsPxHKpCztLsNIqskpXzMLgeTl4oqBVdwbU06w2U
+NpZCFqd49rt7zI+JUgCFRuuMjhB9bN7dRIoEGYwDqvW804+NlYoJKxs+jAOGgiR0ul1sMc+0gjQV
+31LZeMLzqAXrHmcaJea62+7gNI0/rN0apMbfH+xug5jo4cC812w22N8Sl8G2iPOW62FMJA+FZzsF
+Y1GuxGxU0b0qmiTUxb1QWIWCyI23Wr2Nk3MfAq6Vp8gSQkF9xiEEkp4NJnl1pU9NypT90GVe+lWm
+0YQwzensD/12VbB9UtC343hFlL0/OmAoc9JRRLOdV53K5m+0Gcq6BOMJXene5ToOmSicxEsLv9vJ
+40AVl2leJanTe3KR2WF5dhZTy4ltZP6OBs86JGyxCoYln5z0cePmBokgPC0lTYOjmKyI29RoyHVG
+qWYi7mbTdUglPpIT94aJJsx9ckvTEtfz8eVFfOGB9IQdoDPXoSdxEt+q0JSHNLEBKfd3G3XIhJtz
+bPasTg4JhNMsLbp2ZWlKWpGMl+tjadnXee9b28aSfpxZPMURN29uto37Tk7kmxUcow8qpftQSiml
+3CfHxFIw7oOyXB5bM2bWh15a520hunAsG1tM6UZnHkcisjkRyOTgLvWuicI2ltZxWkZzR9GUbGi+
+I0kyPDHN7ZrR4K5jo3L+wUqNVIradRIXUOHe7jYdYc+qCU33dDREJ9I/cDItKgOv7fZYnDFR+8VJ
+SM0/5FUEY/ZNhR5OaWhIhsPFLoBTsXwXWVq4IkmUFe6FjkMyCSomkSYSyPZE4MCteoOOdFq26xXW
+540ZfH1zRJpDotOMWMyweqUpc1bFdzUbraJ2/+7mXbK8SY5fIczrRsS9sByfVI75vc27BD3jaswu
+zFKX2pQ0g8kkb4Zi7t18u82u5NLRuqha1EoXvRvREZG4iHnQ1dMenrhxnl/Fl2yP6/vsCcnIZBwx
+O2OsvoawZFtJymRkXMWDNMIXgFujO4crLpgVTckkwDieDmXcyWFXqywt6P5//sWn6cc35VYJdiFK
+0VIZmXg+J8+ZLEOwv0smmTSreUjhnogbmCXBYV9NpQvgURIlIJ27napbNPApaCrUYR1PFEYk0syn
+u1ArnpdJmhLnhJsPKKWlUEoppdwnx8JS0AjhqqbwZZ98fJ2TCyb9E2eKkfj1La3Ico6EPPBiWTj5
+e7MrZILyu3x1g1kBc9Ucm9HAnCSdeRMYsl2/6MWYJppEcvfKyhgH5vWkv09VKgqvD0xwsbd/UHQM
+DqchfeFy2N3e4eZtg7Y7vXiiSJEJixvaVkWgyqp4+MKaU2nUUJIODUWrj6cTPMnRO76LLRDCaJoW
+WIFJMCGQ68iJe2zbIpMTpdcbcXLFpG3HwQgnt0YSzVQCk9WcB8zKUMLZ0L9zk50dsbBGYzLx/a/f
+uU1f4g6qkqfTNBs3zYkZ9geEEhDsT8bMdY3PnFqaSOaXybXPzs+zL2ullDqMbSgKp1mTMRBrYl+Q
+l8lcq8A02JZGloj+wR6bd0zqV6cZjZpgOeaM1RiOhlTFt+4P97klOIWFTDG/YnAPaaQIBEGo82Kt
+SUwi8OI4mJJGxoKoVVocTAwvwiAQaHOyxKq3Jmsc0ZSg5ZU33qQvcRevnWJ7ebxCyIZRJLL2xEnB
+wzCJElx5HhzHOaTnkPuoE0ikUjOcBIWF0aj5BYv1aDIt0rIPKsdCKYAxg5JME4uZ2e8NCrqyK1t9
+tkdmoVamfWyJ1E6F2mx3Z48Q82CutFaxpfHsYPMGjgTd5h47V5SZZkkexbXI9GE3npwlN4um9IXW
+2yPl+nUTPPq9L3/FjHF2nUjcAN91CwKRWq3Cjdsme3LnxBnD0gxFX0rlOhzsm++dTMKiN+V4PGac
+B53ENqw1QuIsrzOwccRED8Npgecfj8ckYvLb4j4Mhn1+cNHgOxoTmolYAAAPoElEQVRzXdpCttFs
+tQp6tEmq6eVs1JYoQttjOjDfO+j3icKcS1JzbWPDvB9PefOKeX1jRyjxNNy8ZuDKpxcW6QgzcjAd
+sSfXXK1VmYjyGgvdnO25JBJQU/fwMmZZVvBOWpbNUGj8b0gdyPmlDl15gOLQMbhnIA5D6gJC2rrT
+40ei1Hel9DiKJlScPEKnmRUI9nQ0YLhjlH3Ft4tGxvlDGk3DojQ+CEJy231hcRZXKjs9x8zDt2wq
+qVnvMBjTnTWKaWu/z40bBhTVnJuj6sl9zXtUHuyxsykKLQqLOphWp42TdxTTSYEBKQz8JCUKzTMQ
+hRNiWVtH6YJPdBIlNAWU9qBSug+llFLKfXI8LAVpJ6CzQwQbtksq0acbe1M2dsxJ+uxqu7AQLm4Y
+7fonr99he99oyZXL+9ICDqLJqOhvEMeKE1KglJ/yNMApUoEaJSmr6XiEJad0tVLhzo45pV69bcbt
+rkxoSNDSsm18QaBlvs/2rskPv3X9HWqS9iLPwaOKMUY7B1wNDBLOca0CztuWFvduprDkxAx6B+i8
+A3ecFqduzkcAhxwRw8mYodDGNdUcb2wYiG4axlSEC3OCJpE1cKRpCI5d0KMNx2N8CUAORmMCSdMl
+VgUkDXzxisEbTIbDwj3ypwGZuFJexWWwb3Ah7bhBIjn5kZi4o+khEhRtCGfBIPpy98GyFBPpd3Fr
+29yDg/6ImZb0ZLAyrDBnytZF38XVlUW0nKqZYAUWluZJo9wKsAsocRwlBANjvSVTi1AstrGs8TCY
+MBarYTiNcKrmnl6/vccT7qfNXF1D3ecnVdxIMDRAR7AgtWab1y+ae7165gSWWAg1QVIG0wnbm6bY
+qVXxqUh/EmxVtBa0bItU3Aqdp5bjpLAU0igijnOEbFyQ32ZkzMyY63hQORZKQWMi4jYaR6Km8+0q
+WrIB09RlczdnmJlnJGbS118xG/7S7QAkArwX3kXJ5ohTjWObzXTr7ogLTxq46gsvmOrDuTgiqx/m
+6acS4e4NDugJCUcyHDHtGfdhTqC2tUaTpvTzmwY9skyi6FnMVKCoO/0+u7vmofDEh6xXa0XXpNXl
+VbwchFOvUXNz2u88DJ2g8vRykjLIQVaZVSgZp+qY7rqASnOfPKMlNPN1v8JlYfFxUs1yLYcu20VG
+YSCb/yAI2JI1TjU0G9KjMhgzSsyDkmTQlxLgnH8xdV1SYYi8Ox2zv2+up1Wt4sg1deKYhijnRGI4
+YRgVD79S3FNGnZGb6BXfxxLFcftASF9ixWRkrsf1XBQSa8gUI8FvoKHZkgdS7pPn+kX3qUjHjIRF
+y7IUqSieMHKYyncHY6lLiaKiXLw31eCbefzg2ja/XDWxiK6Al+L+gFhaAyjl4MkB9+RTj/Ptl78N
+wMbVW1jS4akh7E7NVo2qK3zHaYwrLp/bqGH7ec0PROL25i5jkoRkSc4bGhJIamhnd0QYmve7s3PU
+8u5jDyil+1BKKaXcJ8fCUjDhNU2aptgSR19dXqAunZ230glXpIX5drBKLBgBS1q1v+/MCnVBfi12
+mjTFTN4fBPSl6MhCcfeOiXbvnTYR6e78IpZY4HEYEgs91mQYMJXqQSsK8VKjdWfrwmtY8Q6blwCO
+nPLTaMJB3m7u1h0+/Nz7AUhic51b+5t0JCperzbw8lOs6hc8BZG8iIIxE+E7jOIQ288Lg9qEUl2Z
+6aQwwdPc3fF9amKiZ1nGvtC8tSqVAnmZWYcM1UPJywdhzJsbJkgaRVlR8FX1PCxx4zzbZbkrHI1S
+tZrECZsHxpIa9gOmwl/Qy/aLE+fc+hn8vCeindPYZaRFoDHvumgCvjkfo2VZRXfrPaGxG8UGiQqQ
+OV7RSfzGxm12Ns0pfeLEqUOuxL4EjL0QJejWabiNFniw51q05OS2HZdULK9M3DVLqaLHxXZvyB2h
++utFmkrFnPSubVw+J6sRSZA7IStcpmeeO8+1t4z7cOn1N6lKxeTCZK4Yy7ZkvX2PZJJ3VU+I9ETW
+6BBHkjNKp2lUNP6J04ye0P5t3Nw0ndOBdqvBNMpb8j2YHBOlYNpvaw7NyHarylzH3KxrNw549ZpJ
+kb21NeJ010Rkl9s5ln2MLTe868+gtFmEbqtKtZG3J3fYlcj//rbZxOmZdWyJwoejIeOBeYAG2zuM
++iZaPFOrsiwNYjt18/e24xQ9DtM0LRqPTKIBd4Rl6dZ2wI1ts4FWT5gqy3g85dqlt+U628xKVLhW
+rRLnMYO85bxOCGVT1btN5jsmtYjjMJFVy4CpgKxyQIxrqyJeUFuqk1MXVapVhqJk2llGUx6Ecb7B
+sAjFTO602lQEJhz1hzgCK656Nm35u4qUkafaYizbKAximsJ+FFmau9LbENszddxQkJ3GcVx0tSrQ
+OPI6Z9xK07RQvvuBuZ6t4ZT1BXM97dRCCQ9ipTtLpW6AY40Lz3FaCEc8AXIpyyWVkvLenbtF6vjK
+Oz+iWzMK59RsoyBbzSsVVaIY5XGNg4DLd8w9naaH6fNE1iLOXLRAnzPXZSxNZOYW5njqwhMAfOvl
+P+LuTeNujHbMQZeOp6TF4aVN91qgtTSLJS5rvd1kYVmaGeUeZhoXRLJRCleuG6V+d+8AxzlM2/ZG
+747ivXQfSimllPvkmFgK2lgIWhear17z+fD7nwPg2q2AzR2job//5i7z7zPaeP20RPeTBCsxp3nN
+tU0jP6DdbOIK1NZzKpw6ZYI5dwRLEPV3sSSwOTzYYyd3L7ZuU5FuIkuLp8gbVfo1Y60opYjCsHid
+MyZv7w/YH0oOPU3541f+FIATp4y7cnJ+nv1bJic+PujRkG7FluvhCegntXNwE3jCf1BfnkE1haIr
+Soo2bqNgzEQi4w2pBh329nHkBPP8CqfW1sz/3d1nb99gC1qNWdq+sBxL6mAcRCxJy7QLj61xpmOu
+52Cnz/aWAHacOvNSgZnl1ZnRlHbXjPeEWqG9YuDFl3fuMBCr1alUC/dmb8+sYRRFpmEK99sJruse
+tsVLk6Kj9ViKsq7c2uJ5ue+TKKLbNJbLYrNGJTQndjOzGYjFpuR+pFmCLdbYJAjxpTfnY6fX8S2B
+IGcTdJp3fBY3Lkm4LViHq9t9DoS8pdWooHVuuh+2B7SERh63wUiK1GbbLhdeeB6AwXjInVsm07C+
+aEhhXCzmF6WVoeugxFLIKg6T/HnwfZRgUbICE36I6RgGE1594xJgOK2rUuindVIEhx9UjoVS0BqS
+NEFplXdkx3YyPvgBU5r6yhtX+ZZwLX7/8jYXzphN8fxZ45OpYIROjaJozs6hhB/fb1bxxNz1vDau
+mISdlpiUOuVgy6Q1b9/dYXvLuAzD/X0unDe49db8EtsHedcnAd7YFIonTjTiRrI3nhbIvCyNee1H
+ph/hM5fOArDw4rMsr5mNMNrsgTT+jJVNjoByJErvLdTwhDXJb9eIZQNOxuMiJTscjfEF3FJvSW/I
+3U2qomBcxyv6CN7Y2ceV6rzaVo920yjRimRGpmlEIlkEZSv8plEWy/4CVaGiD4KYkeDrA4lF2DbU
+pUS6WrPw5ZqzHZu8Q+Q0Ghd+8LbcR6/iFJ8rbRXrpizLlHFiuB3z9Fsm7ser1+/wkQtmDec6MYGg
+Wm0F2ZZBKeo4prZmMk1+VSpfLdDiglSDgKy/JWsf0GpL9WHsEqYmpRqKLz8cR/zwunEb7+7v40oq
+M8s0V773QwDOnTN8l76bYflmXa00IplIGnYa01mSmp6/8DH+17cMI1NP3IfZRp2BPPxVt4En/U+r
+rSZz4v5U/MPsiZ23qs8yIjmQrlx7hxvXDedjs96mIlWl4WRSVkmWUkopfzZRh71hH+FFKLUDjIF3
+17XivZO5cuxy7P8Pxj6ttZ7/Wf/pWCgFAKXU97XWL5Vjl2OXYz9aKd2HUkop5T4plUIppZRynxwn
+pfBvy7HLscuxH70cm5hCKaWUcjzkOFkKpZRSyjGQR64UlFKfUEq9rZS6opT6zSMe66RS6ptKqTeV
+Um8opX5D3p9RSv2BUuqy/Ns9wmuwlVI/UEp9TX4/o5T6jsz/vyiVQ+KOZOyOUuoLSqlLSqm3lFIf
+elhzV0r9XVnzi0qp31VK+Uc1d6XUf1BKbSulLt7z3k+cpzLyL+QaXlNKvXgEY/8jWfPXlFJfUkp1
+7vnsszL220qpj/9Zxn6v5JEqBWU6kfw28EngKeBXlFJPHeGQCfD3tNZPAR8E/paM95vAy1rrc8DL
+8vtRyW8Ab93z+z8E/pnW+ixwAPz6EY79W8D/1FqfB56T6zjyuSulVoG/DbyktX4aQyn5GY5u7v8J
++MSPvffT5vlJ4Jz8/E3gd45g7D8AntZaPwv8CPgsgOy9zwAX5G/+lcq78zxK0Vo/sh/gQ8DX7/n9
+s8BnH+L4XwF+AXgbWJb3loG3j2i8E5gN+ReBr2EqhXcB5yetx3s8dht4B4kj3fP+kc8dWAVuAjMY
+aP3XgI8f5dyBNeDiz5on8G+AX/lJ/++9GvvHPvsrwOfl9X37Hfg68KGjuP/v5udRuw/5Zsnllrx3
+5KKUWgNeAL4DLGqtN+Wju8DiEQ37z4G/D1JkALNAT2ud1xAf5fzPADvAfxT35d8ppeo8hLlrrW8D
+/xi4AWwCfeAVHt7c4afP82Hvwb8B/I9HNPYDyaNWCo9ElFIN4PeBv6O1Htz7mTYq+z1PySilPgVs
+a61fea+/+wHFAV4Efkdr/QIGVn6fq3CEc+8Cv4RRTCtAnf/bxH5oclTz/FmilPocxoX9/MMe+93I
+o1YKt4GT9/x+Qt47MlFKuRiF8Hmt9Rfl7S2l1LJ8vgxsH8HQPwd8Wim1AfwexoX4LaCj8vK3o53/
+LeCW1vo78vsXMEriYcz9LwPvaK13tKk3/iJmPR7W3OGnz/Oh7EGl1F8HPgX8qiilhzb2u5VHrRS+
+B5yTKLSHCbp89agGU6aL578H3tJa/9N7Pvoq8Gvy+tcwsYb3VLTWn9Van9Bar2Hm+Q2t9a8C3wR+
++SjHlvHvAjeVUk/IW38JeJOHMHeM2/BBpVRN7kE+9kOZu8hPm+dXgb8mWYgPAv173Iz3RJRSn8C4
+jZ/WWgc/dk2fUUpVlFJnMMHO776XY/8/yaMOagC/iInIXgU+d8RjfQRjNr4G/FB+fhHj278MXAb+
+EJg54uv4GPA1eb2O2QhXgP8GVI5w3OeB78v8vwx0H9bcgX8AXAIuAv8ZqBzV3IHfxcQuYoyF9Os/
+bZ6YYO9vy/57HZMhea/HvoKJHeR77l/f8/8/J2O/DXzyKPfdg/6UiMZSSinlPnnU7kMppZRyzKRU
+CqWUUsp9UiqFUkop5T4plUIppZRyn5RKoZRSSrlPSqVQSiml3CelUiillFLuk1IplFJKKffJ/wGA
+P7E6eb8E6wAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#39044;&#22788;&#29702;&#25968;&#25454;&#65288;Preprocess-the-Data&#65289;">&#39044;&#22788;&#29702;&#25968;&#25454;&#65288;Preprocess the Data&#65289;<a class="anchor-link" href="#&#39044;&#22788;&#29702;&#25968;&#25454;&#65288;Preprocess-the-Data&#65289;">&#182;</a></h2><p>由于该项目的重点是建立 GANs 模型，我们将为你预处理数据。</p>
+<p>经过数据预处理，MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分，然后调整到 28x28 维度。</p>
+<p>MNIST 数据集中的图像是单<a href="https://en.wikipedia.org/wiki/Channel_(digital_image%29">通道</a>的黑白图像，CelebA 数据集中的图像是 <a href="https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images">三通道的 RGB 彩色图像</a>。</p>
+<h2 id="&#24314;&#31435;&#31070;&#32463;&#32593;&#32476;&#65288;Build-the-Neural-Network&#65289;">&#24314;&#31435;&#31070;&#32463;&#32593;&#32476;&#65288;Build the Neural Network&#65289;<a class="anchor-link" href="#&#24314;&#31435;&#31070;&#32463;&#32593;&#32476;&#65288;Build-the-Neural-Network&#65289;">&#182;</a></h2><p>你将通过部署以下函数来建立 GANs 的主要组成部分:</p>
+<ul>
+<li><code>model_inputs</code></li>
+<li><code>discriminator</code></li>
+<li><code>generator</code></li>
+<li><code>model_loss</code></li>
+<li><code>model_opt</code></li>
+<li><code>train</code></li>
+</ul>
+<h3 id="&#26816;&#26597;-TensorFlow-&#29256;&#26412;&#24182;&#33719;&#21462;-GPU-&#22411;&#21495;">&#26816;&#26597; TensorFlow &#29256;&#26412;&#24182;&#33719;&#21462; GPU &#22411;&#21495;<a class="anchor-link" href="#&#26816;&#26597;-TensorFlow-&#29256;&#26412;&#24182;&#33719;&#21462;-GPU-&#22411;&#21495;">&#182;</a></h3><p>检查你是否使用正确的 TensorFlow 版本，并获取 GPU 型号</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">distutils.version</span> <span class="k">import</span> <span class="n">LooseVersion</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+
+<span class="c1"># Check TensorFlow Version</span>
+<span class="k">assert</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="s1">&#39;1.0&#39;</span><span class="p">),</span> <span class="s1">&#39;Please use TensorFlow version 1.0 or newer.  You are using </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;TensorFlow Version: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">))</span>
+
+<span class="c1"># Check for a GPU</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">gpu_device_name</span><span class="p">():</span>
+    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;No GPU found. Please use a GPU to train your neural network.&#39;</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Default GPU Device: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">gpu_device_name</span><span class="p">()))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>TensorFlow Version: 1.0.1
+Default GPU Device: /gpu:0
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#36755;&#20837;&#65288;Input&#65289;">&#36755;&#20837;&#65288;Input&#65289;<a class="anchor-link" href="#&#36755;&#20837;&#65288;Input&#65289;">&#182;</a></h3><p>部署 <code>model_inputs</code> 函数以创建用于神经网络的 <a href="https://www.tensorflow.org/versions/r0.11/api_docs/python/io_ops/placeholders">占位符 (TF Placeholders)</a>。请创建以下占位符：</p>
+<ul>
+<li>输入图像占位符: 使用 <code>image_width</code>，<code>image_height</code> 和 <code>image_channels</code> 设置为 rank 4。</li>
+<li>输入 Z 占位符: 设置为 rank 2，并命名为 <code>z_dim</code>。</li>
+<li>学习速率占位符: 设置为 rank 0。</li>
+</ul>
+<p>返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
+
+<span class="k">def</span> <span class="nf">model_inputs</span><span class="p">(</span><span class="n">image_width</span><span class="p">,</span> <span class="n">image_height</span><span class="p">,</span> <span class="n">image_channels</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create the model inputs</span>
+<span class="sd">    :param image_width: The input image width</span>
+<span class="sd">    :param image_height: The input image height</span>
+<span class="sd">    :param image_channels: The number of image channels</span>
+<span class="sd">    :param z_dim: The dimension of Z</span>
+<span class="sd">    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="n">input_real</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">image_width</span><span class="p">,</span> <span class="n">image_height</span><span class="p">,</span> <span class="n">image_channels</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;input_real&#39;</span><span class="p">)</span>
+    <span class="n">input_z</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;input_z&#39;</span><span class="p">)</span>
+    <span class="n">learning_rate</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;learning_rate&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">input_real</span><span class="p">,</span> <span class="n">input_z</span><span class="p">,</span> <span class="n">learning_rate</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_model_inputs</span><span class="p">(</span><span class="n">model_inputs</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#36776;&#21035;&#22120;&#65288;Discriminator&#65289;">&#36776;&#21035;&#22120;&#65288;Discriminator&#65289;<a class="anchor-link" href="#&#36776;&#21035;&#22120;&#65288;Discriminator&#65289;">&#182;</a></h3><p>部署 <code>discriminator</code> 函数创建辨别器神经网络以辨别 <code>images</code>。该函数应能够重复使用神经网络中的各种变量。 在 <a href="https://www.tensorflow.org/api_docs/python/tf/variable_scope"><code>tf.variable_scope</code></a> 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。</p>
+<p>该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">discriminator</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">reuse</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create the discriminator network</span>
+<span class="sd">    :param image: Tensor of input image(s)</span>
+<span class="sd">    :param reuse: Boolean if the weights should be reused</span>
+<span class="sd">    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">variable_scope</span><span class="p">(</span><span class="s1">&#39;discriminator&#39;</span><span class="p">,</span> <span class="n">reuse</span><span class="o">=</span><span class="n">reuse</span><span class="p">):</span>
+        <span class="c1"># alpha is the param for leaky relu</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.2</span>
+        
+        <span class="c1"># Input layer is 28x28x3</span>
+        <span class="n">x1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
+        <span class="n">relu1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">x1</span><span class="p">,</span> <span class="n">x1</span><span class="p">)</span>
+        <span class="c1"># 14x14x64 now</span>
+        
+        <span class="n">x2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">relu1</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
+        <span class="n">bn2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">relu2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">bn2</span><span class="p">,</span> <span class="n">bn2</span><span class="p">)</span>
+        <span class="c1"># 7x7x128 now</span>
+        
+        <span class="n">x3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">relu2</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
+        <span class="n">bn3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">x3</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">relu3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">bn3</span><span class="p">,</span> <span class="n">bn3</span><span class="p">)</span>
+        <span class="c1"># 4x4x256 now</span>
+
+        <span class="c1"># Flatten it</span>
+        <span class="n">flat</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">relu3</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="o">*</span><span class="mi">4</span><span class="o">*</span><span class="mi">256</span><span class="p">))</span>
+        <span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">flat</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
+        
+        <span class="k">return</span> <span class="n">out</span><span class="p">,</span> <span class="n">logits</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_discriminator</span><span class="p">(</span><span class="n">discriminator</span><span class="p">,</span> <span class="n">tf</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#29983;&#25104;&#22120;&#65288;Generator&#65289;">&#29983;&#25104;&#22120;&#65288;Generator&#65289;<a class="anchor-link" href="#&#29983;&#25104;&#22120;&#65288;Generator&#65289;">&#182;</a></h3><p>部署 <code>generator</code> 函数以使用 <code>z</code> 生成图像。该函数应能够重复使用神经网络中的各种变量。
+在 <a href="https://www.tensorflow.org/api_docs/python/tf/variable_scope"><code>tf.variable_scope</code></a> 中使用 "generator" 的变量空间名来重复使用该函数中的变量。</p>
+<p>该函数应返回所生成的 28 x 28 x <code>out_channel_dim</code> 维度图像。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">generator</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create the generator network</span>
+<span class="sd">    :param z: Input z</span>
+<span class="sd">    :param out_channel_dim: The number of channels in the output image</span>
+<span class="sd">    :param is_train: Boolean if generator is being used for training</span>
+<span class="sd">    :return: The tensor output of the generator</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">variable_scope</span><span class="p">(</span><span class="s1">&#39;generator&#39;</span><span class="p">,</span> <span class="n">reuse</span><span class="o">=</span><span class="ow">not</span> <span class="n">is_train</span><span class="p">):</span>
+        <span class="c1"># alpha is the param for leaky relu</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.2</span>
+        
+        <span class="c1"># First fully connected layer</span>
+        <span class="n">x1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="mi">7</span> <span class="o">*</span> <span class="mi">7</span> <span class="o">*</span> <span class="mi">512</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+        <span class="n">x1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">512</span><span class="p">))</span> <span class="c1"># Reshape it to start the convolutional stack</span>
+        <span class="n">x1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="n">is_train</span><span class="p">)</span>
+        <span class="n">x1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">x1</span><span class="p">,</span> <span class="n">x1</span><span class="p">)</span>
+        <span class="c1"># 7x7x512 now</span>
+        
+        <span class="n">x2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d_transpose</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
+        <span class="n">x2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="n">is_train</span><span class="p">)</span>
+        <span class="n">x2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">x2</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
+        <span class="c1"># 14x14x256 now</span>
+        
+        <span class="n">x3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d_transpose</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
+        <span class="n">x3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">x3</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="n">is_train</span><span class="p">)</span>
+        <span class="n">x3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">x3</span><span class="p">,</span> <span class="n">x3</span><span class="p">)</span>
+        <span class="c1"># 28x28x128 now</span>
+        
+        <span class="c1"># Output layer</span>
+        <span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d_transpose</span><span class="p">(</span><span class="n">x3</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
+        <span class="c1"># 28x28x3 now</span>
+        
+        <span class="k">return</span> <span class="n">out</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_generator</span><span class="p">(</span><span class="n">generator</span><span class="p">,</span> <span class="n">tf</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#25439;&#22833;&#20989;&#25968;&#65288;Loss&#65289;">&#25439;&#22833;&#20989;&#25968;&#65288;Loss&#65289;<a class="anchor-link" href="#&#25439;&#22833;&#20989;&#25968;&#65288;Loss&#65289;">&#182;</a></h3><p>部署 <code>model_loss</code> 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。</p>
+<p>使用你已实现的函数：</p>
+<ul>
+<li><code>discriminator(images, reuse=False)</code></li>
+<li><code>generator(z, out_channel_dim, is_train=True)</code></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">model_loss</span><span class="p">(</span><span class="n">input_real</span><span class="p">,</span> <span class="n">input_z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get the loss for the discriminator and generator</span>
+<span class="sd">    :param input_real: Images from the real dataset</span>
+<span class="sd">    :param input_z: Z input</span>
+<span class="sd">    :param out_channel_dim: The number of channels in the output image</span>
+<span class="sd">    :return: A tuple of (discriminator loss, generator loss)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="c1"># Generator network here</span>
+    <span class="n">g_model</span> <span class="o">=</span> <span class="n">generator</span><span class="p">(</span><span class="n">input_z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">)</span>
+
+    <span class="c1"># Disriminator network here</span>
+    <span class="n">d_model_real</span><span class="p">,</span> <span class="n">d_logits_real</span> <span class="o">=</span> <span class="n">discriminator</span><span class="p">(</span><span class="n">input_real</span><span class="p">)</span>
+    <span class="n">d_model_fake</span><span class="p">,</span> <span class="n">d_logits_fake</span> <span class="o">=</span> <span class="n">discriminator</span><span class="p">(</span><span class="n">g_model</span><span class="p">,</span> <span class="n">reuse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    
+    <span class="c1"># Calculate losses</span>
+    <span class="n">smooth</span> <span class="o">=</span> <span class="mf">0.1</span>
+    <span class="n">d_loss_real</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">sigmoid_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">d_logits_real</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">d_logits_real</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">smooth</span><span class="p">)))</span>
+
+    <span class="n">d_loss_fake</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">sigmoid_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">d_logits_fake</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">d_logits_fake</span><span class="p">)))</span>
+
+    <span class="n">d_loss</span> <span class="o">=</span> <span class="n">d_loss_real</span> <span class="o">+</span> <span class="n">d_loss_fake</span>
+
+    <span class="n">g_loss</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">sigmoid_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">d_logits_fake</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">d_logits_fake</span><span class="p">)))</span>
+
+    <span class="k">return</span> <span class="n">d_loss</span><span class="p">,</span> <span class="n">g_loss</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_model_loss</span><span class="p">(</span><span class="n">model_loss</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#20248;&#21270;&#65288;Optimization&#65289;">&#20248;&#21270;&#65288;Optimization&#65289;<a class="anchor-link" href="#&#20248;&#21270;&#65288;Optimization&#65289;">&#182;</a></h3><p>部署 <code>model_opt</code> 函数实现对 GANs 的优化。使用 <a href="https://www.tensorflow.org/api_docs/python/tf/trainable_variables"><code>tf.trainable_variables</code></a> 获取可训练的所有变量。通过变量空间名 <code>discriminator</code> 和 <code>generator</code> 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">model_opt</span><span class="p">(</span><span class="n">d_loss</span><span class="p">,</span> <span class="n">g_loss</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get optimization operations</span>
+<span class="sd">    :param d_loss: Discriminator loss Tensor</span>
+<span class="sd">    :param g_loss: Generator loss Tensor</span>
+<span class="sd">    :param learning_rate: Learning Rate Placeholder</span>
+<span class="sd">    :param beta1: The exponential decay rate for the 1st moment in the optimizer</span>
+<span class="sd">    :return: A tuple of (discriminator training operation, generator training operation)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Implement Function</span>
+    <span class="c1"># Get weights and bias to update</span>
+    <span class="n">t_vars</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">trainable_variables</span><span class="p">()</span>
+    <span class="n">d_vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">t_vars</span> <span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;discriminator&#39;</span><span class="p">)]</span>
+    <span class="n">g_vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">t_vars</span> <span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;generator&#39;</span><span class="p">)]</span>
+
+    <span class="c1"># Optimize</span>
+    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">control_dependencies</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">get_collection</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">GraphKeys</span><span class="o">.</span><span class="n">UPDATE_OPS</span><span class="p">)):</span>
+        <span class="n">d_train_opt</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="n">beta1</span><span class="p">)</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">d_loss</span><span class="p">,</span> <span class="n">var_list</span><span class="o">=</span><span class="n">d_vars</span><span class="p">)</span>
+        <span class="n">g_train_opt</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="n">beta1</span><span class="p">)</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">g_loss</span><span class="p">,</span> <span class="n">var_list</span><span class="o">=</span><span class="n">g_vars</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">d_train_opt</span><span class="p">,</span> <span class="n">g_train_opt</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">tests</span><span class="o">.</span><span class="n">test_model_opt</span><span class="p">(</span><span class="n">model_opt</span><span class="p">,</span> <span class="n">tf</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Tests Passed
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;&#65288;Neural-Network-Training&#65289;">&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;&#65288;Neural Network Training&#65289;<a class="anchor-link" href="#&#35757;&#32451;&#31070;&#32463;&#32593;&#32476;&#65288;Neural-Network-Training&#65289;">&#182;</a></h2><h3 id="&#36755;&#20986;&#26174;&#31034;">&#36755;&#20986;&#26174;&#31034;<a class="anchor-link" href="#&#36755;&#20986;&#26174;&#31034;">&#182;</a></h3><p>使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出，这会帮你评估 GANs 模型的训练程度。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="k">def</span> <span class="nf">show_generator_output</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">n_images</span><span class="p">,</span> <span class="n">input_z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">,</span> <span class="n">image_mode</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Show example output for the generator</span>
+<span class="sd">    :param sess: TensorFlow session</span>
+<span class="sd">    :param n_images: Number of Images to display</span>
+<span class="sd">    :param input_z: Input Z Tensor</span>
+<span class="sd">    :param out_channel_dim: The number of channels in the output image</span>
+<span class="sd">    :param image_mode: The mode to use for images (&quot;RGB&quot; or &quot;L&quot;)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">cmap</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">image_mode</span> <span class="o">==</span> <span class="s1">&#39;RGB&#39;</span> <span class="k">else</span> <span class="s1">&#39;gray&#39;</span>
+    <span class="n">z_dim</span> <span class="o">=</span> <span class="n">input_z</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">example_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="n">n_images</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">])</span>
+
+    <span class="n">samples</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+        <span class="n">generator</span><span class="p">(</span><span class="n">input_z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">,</span> <span class="kc">False</span><span class="p">),</span>
+        <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">input_z</span><span class="p">:</span> <span class="n">example_z</span><span class="p">})</span>
+
+    <span class="n">images_grid</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">images_square_grid</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">image_mode</span><span class="p">)</span>
+    <span class="n">pyplot</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">images_grid</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">)</span>
+    <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#35757;&#32451;">&#35757;&#32451;<a class="anchor-link" href="#&#35757;&#32451;">&#182;</a></h3><p>部署 <code>train</code> 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数：</p>
+<ul>
+<li><code>model_inputs(image_width, image_height, image_channels, z_dim)</code></li>
+<li><code>model_loss(input_real, input_z, out_channel_dim)</code></li>
+<li><code>model_opt(d_loss, g_loss, learning_rate, beta1)</code></li>
+</ul>
+<p>使用 <code>show_generator_output</code> 函数显示 <code>generator</code> 在训练过程中的输出。</p>
+<p><strong>注意</strong>：在每个批次 (batch) 中运行 <code>show_generator_output</code> 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 <code>generator</code> 的输出。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">epoch_count</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="p">,</span> <span class="n">get_batches</span><span class="p">,</span> <span class="n">data_shape</span><span class="p">,</span> <span class="n">data_image_mode</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Train the GAN</span>
+<span class="sd">    :param epoch_count: Number of epochs</span>
+<span class="sd">    :param batch_size: Batch Size</span>
+<span class="sd">    :param z_dim: Z dimension</span>
+<span class="sd">    :param learning_rate: Learning Rate</span>
+<span class="sd">    :param beta1: The exponential decay rate for the 1st moment in the optimizer</span>
+<span class="sd">    :param get_batches: Function to get batches</span>
+<span class="sd">    :param data_shape: Shape of the data</span>
+<span class="sd">    :param data_image_mode: The image mode to use for images (&quot;RGB&quot; or &quot;L&quot;)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Build Model</span>
+    <span class="n">_</span><span class="p">,</span> <span class="n">image_width</span><span class="p">,</span> <span class="n">image_height</span><span class="p">,</span> <span class="n">image_channels</span> <span class="o">=</span> <span class="n">data_shape</span>
+    <span class="n">input_real</span><span class="p">,</span> <span class="n">input_z</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="n">model_inputs</span><span class="p">(</span><span class="n">image_width</span><span class="p">,</span> <span class="n">image_height</span><span class="p">,</span> <span class="n">image_channels</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">)</span>
+    <span class="n">d_loss</span><span class="p">,</span> <span class="n">g_loss</span> <span class="o">=</span> <span class="n">model_loss</span><span class="p">(</span><span class="n">input_real</span><span class="p">,</span> <span class="n">input_z</span><span class="p">,</span> <span class="n">image_channels</span><span class="p">)</span>
+    <span class="n">d_opt</span><span class="p">,</span> <span class="n">g_opt</span> <span class="o">=</span> <span class="n">model_opt</span><span class="p">(</span><span class="n">d_loss</span><span class="p">,</span> <span class="n">g_loss</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">beta1</span><span class="p">)</span>
+    
+    <span class="n">steps</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
+        <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
+        <span class="k">for</span> <span class="n">epoch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epoch_count</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">batch_images</span> <span class="ow">in</span> <span class="n">get_batches</span><span class="p">(</span><span class="n">batch_size</span><span class="p">):</span>
+                <span class="c1"># TODO: Train Model</span>
+                <span class="n">steps</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">batch_images</span> <span class="o">*=</span> <span class="mi">2</span>
+                
+                <span class="c1"># Sample random noise for G</span>
+                <span class="n">batch_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">))</span>
+
+                <span class="c1"># Run optimizers</span>
+                <span class="n">_</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">d_opt</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">input_z</span><span class="p">:</span> <span class="n">batch_z</span><span class="p">,</span> <span class="n">input_real</span><span class="p">:</span> <span class="n">batch_images</span><span class="p">,</span> <span class="n">lr</span><span class="p">:</span> <span class="n">learning_rate</span><span class="p">})</span>
+                <span class="n">_</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">g_opt</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">input_z</span><span class="p">:</span> <span class="n">batch_z</span><span class="p">,</span> <span class="n">input_real</span><span class="p">:</span> <span class="n">batch_images</span><span class="p">,</span> <span class="n">lr</span><span class="p">:</span> <span class="n">learning_rate</span><span class="p">})</span>
+
+                <span class="k">if</span> <span class="n">steps</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="c1"># At the end of each epoch, get the losses and print them out</span>
+                    <span class="n">train_loss_d</span> <span class="o">=</span> <span class="n">d_loss</span><span class="o">.</span><span class="n">eval</span><span class="p">({</span><span class="n">input_z</span><span class="p">:</span> <span class="n">batch_z</span><span class="p">,</span> <span class="n">input_real</span><span class="p">:</span> <span class="n">batch_images</span><span class="p">})</span>
+                    <span class="n">train_loss_g</span> <span class="o">=</span> <span class="n">g_loss</span><span class="o">.</span><span class="n">eval</span><span class="p">({</span><span class="n">input_z</span><span class="p">:</span> <span class="n">batch_z</span><span class="p">})</span>
+
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Epoch </span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch_i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">epoch_count</span><span class="p">),</span> 
+                          <span class="s2">&quot;Discriminator Loss: </span><span class="si">{:.4f}</span><span class="s2">...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">train_loss_d</span><span class="p">),</span>
+                          <span class="s2">&quot;Generator Loss: </span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">train_loss_g</span><span class="p">))</span>
+                
+                <span class="k">if</span> <span class="n">steps</span> <span class="o">%</span> <span class="mi">100</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">gen_samples</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">generator</span><span class="p">(</span><span class="n">input_z</span><span class="p">,</span> <span class="n">image_channels</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">input_z</span><span class="p">:</span> <span class="n">batch_z</span><span class="p">})</span>
+                    <span class="n">_</span> <span class="o">=</span> <span class="n">show_generator_output</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="n">input_z</span><span class="p">,</span> <span class="n">image_channels</span><span class="p">,</span> <span class="n">data_image_mode</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="MNIST">MNIST<a class="anchor-link" href="#MNIST">&#182;</a></h3><p>在 MNIST 上测试你的 GANs 模型。经过 2 次迭代，GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失，或接近 0。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
+<span class="n">z_dim</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.0002</span>
+<span class="n">beta1</span> <span class="o">=</span> <span class="mf">0.5</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">mnist_dataset</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="s1">&#39;mnist&#39;</span><span class="p">,</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;mnist/*.jpg&#39;</span><span class="p">)))</span>
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span><span class="o">.</span><span class="n">as_default</span><span class="p">():</span>
+    <span class="n">train</span><span class="p">(</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="p">,</span> <span class="n">mnist_dataset</span><span class="o">.</span><span class="n">get_batches</span><span class="p">,</span>
+          <span class="n">mnist_dataset</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">mnist_dataset</span><span class="o">.</span><span class="n">image_mode</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/2... Discriminator Loss: 0.4204... Generator Loss: 3.1223
+Epoch 1/2... Discriminator Loss: 0.4827... Generator Loss: 2.2592
+Epoch 1/2... Discriminator Loss: 0.4310... Generator Loss: 4.2485
+Epoch 1/2... Discriminator Loss: 1.2898... Generator Loss: 2.9953
+Epoch 1/2... Discriminator Loss: 1.3641... Generator Loss: 1.3200
+Epoch 1/2... Discriminator Loss: 1.3577... Generator Loss: 0.6340
+Epoch 1/2... Discriminator Loss: 1.5244... Generator Loss: 0.5115
+Epoch 1/2... Discriminator Loss: 1.2379... Generator Loss: 0.8513
+Epoch 1/2... Discriminator Loss: 1.3270... Generator Loss: 0.8379
+Epoch 1/2... Discriminator Loss: 1.6648... Generator Loss: 0.4169
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJztvXuQZUd5J/jLe8991K17611d6uqW1BItBBJYEigQCgN2
+wLDGLEasAYcY5IFBoNiAZTyzG2HDOPCMiTGIlxkmgsWrwJ71bniNGcZjCHvXjLAkg7ARD72Q1Ja6
+pRZ6oO6u7q6qW6/7zv3j1vf1d7Iyz8lzb1X3xc5fREefOjdPfl/myZP5vfJLpbVGQEBAACF3oRkI
+CAgYLYRJISAgIIYwKQQEBMQQJoWAgIAYwqQQEBAQQ5gUAgICYtizSUEp9Sal1ONKqWNKqY/sFZ2A
+gIDdhdqLOAWlVB7AEwDeCOA5AD8A8C6t9WO7TiwgIGBXsVeSwqsAHNNaP6W1bgH4CoCb9ohWQEDA
+LiLao3oPAHhW/P0cgBtchWdmZvSBAwfwk5/8hO9tbW0hivrsdbtdvu50OsjlcnwfAKIoQqfTAQAU
+CgW+zufzsWtZnurK5/Ncl+v6kksuiT1XLpfx9NNPAwC01mg2mwCAXC4Xo0HXVJekZ9KWPJnPR1GE
+drvN171ebwc92ScXX3wxAKBYLKJcLgMAjh8/DqUUAGBzczNG2+wr2Z/yOp/PM+20dnS7Xeap1+tZ
+6dH1gQMHmM/x8XEcPXoUhK2trUR6RENrzffb7XaMj7R2JI0RSaPb7eLgwYP8+9jYGADwWOj1ejwW
+XDTMvjX57fV6MXrUJvO+yW+xWESr1eJ66f4ll1yCYrEIADhy5MhprfU8UrBXk0IqlFK3AbgNABYX
+F/H1r38d73//+/n3Rx99FDMzMwCA1dVVLCwsAABOnz6N8fFxAMDy8jIAYGZmhq8XFhawtLQEAJic
+nMTZs2cBALVaDevr6wCAubk5AMDJkycxNTUFAFhbW8Pk5CQAoF6vM42NjQ184hOf4LoB4KUvfSne
++973AgCazSaefPJJAEC1WkW9XmeeVldXAQDT09PMe7VaZd5rtRoAYH19nfkgfmdmZpjfmZkZnDx5
+kuuiD6VWq3H5SqXCdX384x8HABw6dAgvfelLAQC33HILCoUCAOD+++9n2hsbGwD6H+PKygoAYGpq
+ivtzfn6e+7NWq3F5ejdnzpzhvlpdXcW+ffv4mtq6trbG7ZM06Pp3f/d3ceWVVwIAbrjhBrz5zW8G
+0B/8jz3W1zgnJia4f6je1dVVbsfW1ha/v6WlJX7HRGN6eprbVKvVsLm5yfepbycnJ3eMkeXlZaZX
+r9e5b6enp3HttdcCAN73vvdxX9JYmJycxNraGl/L90o8zc7OAgBOnDjBY2Rzc5Pprays8P2NjQ3u
+5zNnzvDzNMYWFxfx3HPPAQD27dvHND772c/yInH99defW3UTsFc2hRsB/Hut9S9t//1RANBaf9JW
+vlar6euvvx4PPvggD7put8sr23Yd/D/NwPJ32Q55TWVs7ZTPm6DySil+eVdffTWA/gz9wAMPAOgP
+eOLHVTfVZfIoebPxkva7LCN5oAF91VVX8Qr00EMP8YTVbrd31Gf2j2x/Gmz9nfasbNPMzAz3bblc
+xv333w+g/1HQimfr4yQktc98N7Z35eKdJsOXvvSlPMk++OCDAPpjgaQAkxfbOJTjQo7vLKA6SIow
+61tcXOSF4Zvf/OaPtNbXp9W5V5LCDwBcoZS6DMDzAG4G8M9dhdvtNn76059iY2ODO9V8WXLwmx2s
+lHJeywnEfOFmXa7rRqMBAHjmmWcA9FdVWgW63W5MnJf05H3Jbxo9IC4aKqViIqONhuwrWu2OHTvG
+q+fKyoq1b336ynWd1g7X+zP53draYpVBSimdTmdHeR96rvYNMkZcY+Ho0aM8QdBkK8dCWr9JyHEh
+1YSk8WnWK/tWjsP19XUcO3ZsB80k7MmkoLXuKKX+FwDfBJAH8Eda60f3glZAQMDuYk/Uh6yIokhP
+TEygXq/zijhKIBGcdD2lVGx1GDUQv5VKhQ1Vo9q3QF/6IUPj2NgYSwqjyC/159jYGBvwSK8fRX6L
+xSLbY06fPn1B1YdMUEqhVCrxNbBTx02yDSTVm2RL8KVBA5Y6V2vN6kMul3Pqhr56sFJqh4rhEnFN
+2NpH/E5NTbE1PKl9Jj1fnl11ucq6ypdKJVZzzD7crUVrt9pHE8H09DT/RpOCFNsHpZ1kf8jy/qhs
+pVKRk4IXDyHMOSAgIIaRkBS01tja2oLW2jmjDrJiZK3LdZ9cgOQKKpVKXNZcGWyeBh8+zXqkkSmr
+5Z34PXXqFEtgaTxl7d+s7UtCo9Hgvh0bGxuoD4flwbc8xYucOXOGpQZCFmkuie4g34DLe+TyiCRh
+JCaFXC6H8fFxrK+ve4mlSVZY0wPguk/P+6gSpEeSq4983CaN3RTBpVqS5j406yJX2cGDB5lXs293
+qw9NvmS9UiVK6pcoitgfn8vlWDUbRn0w+TPrGrRe6tvFxUVWzcjb4+u1cb1v4ittfGYZb/l8nr0k
+xGcagvoQEBAQw0hICr1ej4OWfGZwm6iUFMdA923P+4hqJH6RoYYkB9+6fNphSgVZVQZZF4m4J0+e
+9Arwor9dtG33XauVj3HURKfT4VUsiqKBjMombPEG5m+DwBwLsj4XDR/arr5NCozzaUe322XVzBcj
+MSnkcjlUq1VsbGxkUh8IMqBHit2mNdh8Li04hGB6H7rdLovlw6gMSYEsPmK7LXhJa80x+RMTE1yH
+2be2PpS06fdcLheLqTfbKvuQyps00j6KQqHA4crDqA82PiQPWTw7aWNhbm6O+ZSeKNd4s6mCtoXM
+pC37UwY1Eb20dpRKJQ4xp4U3DUF9CAgIiGEkJIVer5fqfZAwZ10ZNJIkstrq9hHtKLSV6MkVczdU
+hrRypmiZJM4DYAPYysoKG8ZctGUf2iSJTqfDZTudTqok5+IpCa1Wi339Ln59IMvLEPGkurLeJ8/O
+6dOnrR6jtDrS1ApzxU9qk4+HqtFocDCYL4KkEBAQEMNISAr5fB4U5uzjIjSj/1wuNNvzVB7YOdO6
+aNPqRbslO50O62c+eq+Nd3k/zYAnr037ibnpqtfrcWzCRRddxHyura2l9iFdFwoFtiOYtg15TfRs
+em+S4dKEtCkopawuPhdcdhJfl6vrOm0szMzMsNHRNhaSxqHNvkBIGvdmeZk3wfUNlEoldqXThr40
+jMSk0O12WXz0EefMF+cTc+4Sx33EXaqf9qiboc1ZaMsBIcVBmzfAdi3b6moTqTtLS0vO9pl9KMvJ
+0GgfD07W/jTR6XTYcJvL5TJZ1wfxfPi0w0VbBi8lqQs+4zhtAkkqDyAWlOSi3Wq1OJeDL4L6EBAQ
+EMNISAq5XA7lcnmHiCthEw3l3z4+celmA5L3rkuQyEibdnq9Hou4PptgpHtMrsxSlTDdTa72Jblc
+CeQ2m56e5lV/a2vL2lZJPy0XAP1t8ivrkmnTfFf8YrHIUXftdnsg1Uxe20Rtsw9lu13Slw00Fsy+
+TeLXpTL4uMNl2UFckmNjY7jooosAwDuvwkhMCr1eD41GI9H7kCRG+YqpVM41CFz1kH5NASvlcpnL
++sS7p2VmyqI+yLpcapO0kNMg1loniseuulx8UV3m7zKRiy+kiOu7V8MsI/vQNoklqWNZeJXqg6l6
+ZRm7SeVdddi8D2l1NRoNTjfni6A+BAQExDASkgJFNJreB9e1zHBLzw+SSkxeJ6kfRI/Uh1wux0lW
+fLwdBLMd0idPoqFcaW2ioUtklPXSakuRbAAS+5bqHdSDY8uunGWXZxRFnBS31WqxQdf2zny8UpI/
+m+fEZeT1UVeob/ft28cSWZq3JMnzAfilBTTLA3FPlK1vgLhqRpGXaRiJSaHb7fJA8BG1TDEKwI4U
+4oQk8dHXFkEfKnlIXHsfJHzuU722j9vHy+DqH9J119bWvFUlH1HUxYe0gmfRz+UzlDE6q6jt8qgk
+BfpIlSGrOC8Dw0iVSJv8snpqfMeqj9et2WwG70NAQMBwGAlJIZ/Po1qtYnNzM9FCTqD79L95oIeE
+TdSyeQCSVgzKeUhiWKvVSt0Q5SOKyrKm+uCj+piWc4LctEOibaPRSOxbU1rxoW2ry1dlkCiVSpz/
+stvtZvI+mG0A7N4H02sjJYUkVcmsi/g8cOAASzc+Y0Fem14pX/UhKSDLJfWWSiXeyOerPoxM4tZq
+tYr19fVMyS/NyQHY2ak2S7SET/vp5dHHls/neeBm3eJ8PkAT4/j4OLc5a98OgqwfMSGKItbV8/k8
+T2S72bfmGBkUlG2pUqlwnVmT+CZ5zXwnFno+rS3k7geAzc1Nr8StQX0ICAiIYSTUB1c6Nh+Rip53
+WXfpWp7h50NDgtQH2vugtU6Nd88SeGOjneQtcRmi6G/yalQqFebd1bey3qzeHEKa9yGtH6T3YX19
+PdPeBxds/Ukwg77S3pl8liSFhYUF5tPmiTLVBNv4tJ0T6brWWu84H1MeGORqh/Q+yDSCSRhYUlBK
+XayUulsp9ZhS6lGl1G9s359RSt2plDq6/f/0oDQCAgLOPwa2KSil9gPYr7W+XylVA/AjAG8D8F4A
+Z7XWtyulPgJgWmv9W0l15fN5PT4+jo2NjYH0SNuqB+w0Hg5rU5CZl863TSGLTkz81mo1Lru+vp7K
+q+/K7uJLrlBZ6igUCty3zWaTV7RRtCnIg4FolSZ+k2wKSVKaL09mNqler+clhZFNYX19fW8Pg9Fa
+vwDghe3rNaXUEfSPoL8JwC9uF/tjAPcASJwUyBiyubmZaWBSWVe6MjNgxXwZvoYhEhlp4LbbbafF
+OY0n+dHYAm9szySdH+mKdwf6hkaqd3Nz0/rB2viVQUCSJwnbdmmJLB/e2NgYb53O5/MDp7qTMN91
+0jbjLOnYqG9nZmZYbcjifZBp79KyXbtS3dnGiI1XoO99oEzZvtmcd8WmoJQ6BOA6APcBWNieMADg
+BIAFxzN8FL1tUAUEBFwYDD0pKKWqAP4rgH+tta4bq6JWSlmneq31HQDuAPrqA+UAyLIyUFnfKLpB
+xGPAP5uz/NtlyLKVNcvTb1TetWnH1Q7Zl9KYlVSHT3SdhGulHWRl39ra4ozDWSMMXTCflVGXWaM3
+JaTaSH2bpjLJ+zLtXZqR0PZsEh0bms0mRwv7YqhJQSlVQH9C+BOt9Z9v3z6plNqvtX5h2+5wKq2e
+fD6PWq3mnc3ZwofTUu/acgz466ykR+7btw9AP3jJ5X2QtOlv8ga0221WRdrtdixgybYt1iYmmuqD
+zftANObn5zksNy2bs2ktT/NEuLIMD/L+CoUC5ufnAfRFcZs47qrX977ZJpcHJ42ePEuSJhpfsVzW
+S3wA8QnGN5DJ5N2kIb0P5NnxDV4axvugAPwhgCNa698XP30DwHu2r98D4OuD0ggICDj/GMb78BoA
+3wHwYwC05P5b9O0KXwVwCYCfAPg1rXXijox8Pq8rlQq2trZG8jhvkhTIGCYNjUnShi2eQv5ma+sw
+IrONX9q0s7m5uSeekmGMgYQoitiI22q1ePfhbo8Fn5iUNNCKXqlU+NlhvSW70YcuyL49e/bsnnsf
+7gVg35wAvCFLXblcDmNjY5m9D2lwifYEXxoyGAjw2/tgs+abXogswUtpYqKN3/HxcVYf0vrWN/OS
+pG27lu3zRaFQwPj4OID+R+eTychGI6k/XWdzurJXuUDqQ7Va5efS+HXBpcb6qkRJ74nKyqPofXdL
+BrN/QEBADCMR5qy1RrvddvqMh6nXdp0VtNpSPgWZcdjFr23ffBI/SX9ntZDL/A9yJRkkw3FW2oOg
+1WrxgSW7lavCLOOT68CXV6BvtPP1PriQlacsY0SqNln5GolJoVqt4jWveQ3uuusuHtAy7rvb7cbi
+62VyUCAe3BNFEevRMlDEtr1a1tVut2MWYFlmcXERAPDrv/7rAIBHH30U3/rWt/h3qteMa6drEufl
+3gCTtrxP7bDFuBcKBSs96eEgft/xjnfgxz/+MQDg3nvvjfWtmZRG9o/cJyK3Z7vaRCJ1q9WKvRvZ
+JlP0lXVdeumleOtb3woAeOSRR/C9732P6zD3qySNCymOm/sECoUCj4tiscjXsn1y7EgaVFev18OB
+Awe4b5944gkAwN133819L8eYLVhKvj/ZV7axoHV8vwNd0/OyTXK8yPd3+eWX421vexsA4NOf/jR8
+ENSHgICAGEZCUigUClhYWMDll1/Op9h0u12eBQuFAs+Sudy5U5Apppvu03PyvpyN6T7N2qVSKbZ6
+0GokPQPy1F4KCiJeAeDZZ5/l+hqNBq+gxC/xRLzQ7/l8nuuTuRopjFauunJPfKfT4fKyX8jYVS6X
+OaxV+qivuuoqPPXUU1y3bDfRkKsS3VdK8XW322Xa8neqa3x8PLZySWmD+pZotNttrqtWq/H14cOH
+OXnJs88+y3VQX0VRxO/aXB2ld8eUaKIo4r5tNpvcn/JddzodbhehWCzG6qLTlsbHx3Hw4EEAwItf
+/GIAwJNPPsn8ttttzhEh2y/v071SqcQqqqQvpQqlFI8H+T5kjg/6XbavWq1yYhhfBEkhICAghpHI
+vPRzP/dz+q/+6q/wgQ98gI15x48fZzfV2toar34rKys8W5OLZW5uDqdO9QMnJyYmuI75+Xm+Pzs7
+yxFdFG+wsrLCq0e73eZszadOneLoxeXlZXzxi18EAD5UY9++fXj/+98PoG/IOX78OIB+tmeKdJR8
+UL1nz57l69OnTzMfGxsbvOedQlKnpqa4rvn5eZw4cYLbSmX27dvHOf2pr+r1Oj7+8Y8D6EsHV199
+NQDgXe96F6+2x44dY/cqSRiTk5Ns7JudnWXex8bGmN7ExATzRLwvLy8z7WazGWsrucK2tra4PNU7
+Pj7O/PzO7/wO3vSmNwHor44f+MAHuF8ef/xxAOfSoK2urvL7P3PmDN/f3NzkNq2trfF9eucTExMc
+eVipVPh6amqKw9fn5+e5PL3rEydO8NhbXl7Gpz71KQDAoUOH8MpXvhIA8M53vhNAX5ohficnJzl0
+e25ujqWfqakp7gNqx+nTp1kaXVtb4+uzZ89yv62trfEYoXopWxnx+/zzz3NbqW8///nP48orrwQA
+XHPNNV5xCiMxKUxMTOjrr78eDz30EL8UV1ZmF9L2F6Q9L8uYVl1KrnLVVVcB6IuZjzzyCADEtnub
+fmOzPp++dvFp4821VZn4ve6665i3Bx54wNm3hLR8lS7+0/rW5r+XsQRzc3O44YYbAPT788EHHwSw
+Mxt1Em2TT1d/2dqRxL/JOy0W1157Ld/7wQ9+wPzazndMi/tIo2/j2bfs3NwcrrvuOgDAX//1X4d0
+bAEBAdkxEobGVquF559/HvV6nVc2n40haTNx0malLDQoepEMdZQQBojvYDQ3DJlnDyTRMOMesh5e
+Iusi3h599FEW5+Uq5srmLN1xtvgGFz3X8fO2iD0b71tbW3j44Ye5b0mi6XQ6iSukT3/6tMO18QzY
+6eokcf2RRx7hvqW8Ci5+tdaJ78x3HNryKSSNN+rbo0ePOvvQhpFQH4bNvLTXoJdBOmsURami+IWE
+jM+nax9R/EIhn89z3xaLRda5pSg+KpBhzgTidxT7t1AoMK/Ly8tBfQgICMiOkVAf5GEwWffN029p
+912ir8/OOfIry5RhJCn4HEXv0w6buCuRRaIjfqvVKtdjZnNO4teHtk1lkM+aKkMS7UqlwsZRikEx
+eTJ5tdWVpX1Jhj9XXUoplmikp0KGv2cZCy5kUYkkbPfHx8fZa0HepTSMxKRAiVCl7mUiTbdMu5/1
+WoICS8gFSm5MYPj4ddf9YdQ6ckflcjmvvQTmbz60ffZ8uOo3sbm5yfaMsbGxVG/NsGPEp60u2wDZ
+a1544YUdv+/W/gpfXn1439jYyKyGBfUhICAghpGQFEh98E3HluZ9kJDW2TT1QT4j71No7v79+wH0
+JQd5YImNXlZ1xeTDlTLMp08oxHVxcZEt42nqg1mXy6Ni5ogwreIuT0TSO4uiiIOFOp0Oi+XDiN22
+9Ha2dqR5Ikz1iMKm9+3bx2PAdniNj0rr4t2mjrnGSJpnJ5/Pxw7a8cFITAq9Xg/NZtN7ECSJUaYO
+KGnYPgofkY+syhSVVigUMom4Wa5tOrkPZF0kip86dSp1kGbh3bYdXLpLk55NemfyKPooilL71ofv
+pES4rm3iPtvHqW9XVlZYrUyr18Wj675rXJjPurwdJr/Ut74I6kNAQEAMIyEp0C7Aer2eKaxTPu9S
+H1zlAXuabrovQSIjGRijKEo97zBL4JSNb7njT2vtfboTgJj6QHH9g2bKNtUYk54p7tpyKEjY6ioW
+i5zNudfr7cpR9GYKPDOfhuTNFhhm41cpxWNgcnKS942kHQzkaoctuMsl3QLYkSPCpfpIlEolHg+k
+SqYhSAoBAQExjISkQKtDkksy7XmCz/NSF0taJQjkO6cVrFwu77lNIauLUJahFezEiROxA0SG7VsX
+PUKSfSEJzWaTd7P69K0LNmlP9if9bssE5UNPa83u3rNnz1pp2Pgx75s2CF8bGdkzsvDeaDQy9+NI
+TAr5fB5TU1PeFnKzjHnWok2ksqkYSTHnEiS20ZbWYrFotZBnDZayWfClZZnQ6/Ws7XOB1J1qtcoD
+abcP2iFoHT+FynUwTJL3oVgssveh3W5zMNCg6gOw8yhCuTfA5X2Q40i2T/JOYc6zs7OsNqR5H0wP
+jvmOfcehTcVMGxdRFHHwEm2tTsPQ6oNSKq+UekAp9Zfbf1+mlLpPKXVMKfVnSqliWh0BAQGjg92Q
+FH4DwBEAE9t/fwrA57XWX1FK/QGAWwF8KamCbrfLIZiDuCTlTJt0bXveRwQjAxVJBy6XZFY1Icm4
+2el0BlYlSN1ZXl62is9ZkEZ7N1ySzWaTDaJK7cxDMQivpmhP2cIB90YrHxcfuSFXVlZYlbCVc13L
+9yxVVx8XtK+aIdHpdLwNjIRhz5I8COB/BPB7AP5X1e/11wP459tF/hjAv0fKpJDL5fgsySznPNJL
+jqIols15EMtyEkgcp62yvV4vNcAm6SxIW1mqw/ZM1gNLyNo8NTXFH8DW1pZTh80CaVGnutKSs5jl
+TYyNjXHfAhjY+2BTx4iuubXYNTmn8Uv7Sqampth2Q5PDoJ4o04NDMD0ZSYuEi3apVIptn/fBsOrD
+fwTwmzh3bNwsgBWtNU3FzwE4YHtQKXWbUuqHSqkfjuJ26YCAf6oYWFJQSr0FwCmt9Y+UUr+Y9Xkt
+jqKPokjToRVZJgiaGU1xUK4Yg1qyJchYR7nxZPZlV71ZotvSRPusvNPKtby8zCtbr2c/ij4rbNLW
+ICqfRKPRYPWB+E17Jo1GkvfBhy/XfTkWZEIg37qyqoK+z7nqarVa3sfFEYZRH34ewFuVUm8GUEbf
+pvAFAFNKqWhbWjgIINXkmc/nOdFpmuU1zZItw2TNg0xMndGsK837QAE28oDZJNHQtk/AJnan7Q3w
+EXclyEJeq9X4emVlZWBdXbaJYDuEJUuSEfOdUe7Ddrs99N4HpVQslTwQHws+PLnGhdz7QHxm8T64
+aLhUMBlcZ/azHGOuvioUCjxuf/KTnyS2nzCw+qC1/qjW+qDW+hCAmwHcpbV+N4C7Abxju9h7EI6i
+Dwj4mcJexCn8FoCvKKX+A4AHAPxh2gOdTodF86wiGBAX1V2WZdf9LN4HSnUuyyaJhsN4PkzjU5ZV
+nrwPzWYz9XTsLLCtaD4rsKsuQqfT4diErIFotjq11rFkLVRvWh/6vBtSzU6dOrVjTPk87+OVIEhJ
+wKb++PRVu932Tq5C2JVJQWt9D4B7tq+fAvCq3ag3ICDg/GNkIhopHZvLJelrU5Aw3YKm28e1sie5
+oQC3TcFsU5JL0myfuQJLfs3VOK3d5JKcmJjg59IiGpMi8AhRFLGuTv9vbm7uOKzWBl+XZLvdHsgl
+aYv2k/SkTcll+3Hp+xLUt/v372fphlyTvuMwyW7hui9/d9Xl6luKFvWNVxiJbM5RFOlarTayGYdp
+0NPkUCwWuYN3y53qil8AsntRiN+xsTE2NK6urmY2BJrXSqnYadNAehp2H0RRFDsTkQx4u9G3Pobr
+LKDJcGxsjOuQ6f5HDVEUcV7Jer0esjkHBARkx8ioD3TWn0tN8HEdJsG1EcV1YImkQastnf3nkzLM
+R2UwoxZlmwqFQmwDjzx9OImedJtNT09zHaa7N0kFk30lQ7olT3ITFP2etLIn0SsUCrxpp9vt7ko6
+Nt/2mWpeUjo2rc+l5puZmYmpZiaNrC5J17Wv29rsB7ovc1X4qg8jMSl0u13U6/UdFlbb9aBWdNc+
+CN+UVoD90I9BvCW2+3ICALDDep7WZtOaD/T957azDW31md4SupbHszcajR2TqM0qnsaf+Xer1YoN
+WNckmoVGEj3XWPDxEtF7qtfr1riX3b5O8jD4jKtWq8WePV8E9SEgICCGkZAUcrkcxsfHnfkUlFLO
+jMGENDEql8vtMOa5UqKZ6bHICEbqw9bWVqrF2QWXxTnpnMCkutK8D/S7y7MjeZDSmNxQJFUGKXYD
+O3dzDmLYK5VKfLSZ1tpbNctCI2ksJIn2pthOfTs5OcnSXJonylaviwbxR/clj77tk6hUKpiY6G9g
+lnE2SRiJSYEO7nSJolrb90SkiZnmpJEmfrloU8AKZcW1DShfpImlWep1lSF+Sd0h2PrJ1Wab+86H
+dtb7QHzrNH10u03DpT64nnO9J1oMTp06xbzS74OqkrbFylbWt30StKcoC4L6EBAQEMNISAq5XM7b
++6CUYsMXGQDNTL3ymmZdV6otn2ApuQkG6K8WadmcfZAUxuwKqnFBtoMyDs/NzXEda2triZZsky/b
+6uaT18HXS2RayKlvtdZsdPRRH1xqpfleZfCSbSy46Jl1UazK/Pz8jvB3H0+U7EOZnZmupTqWRX1w
+taNYLPI5nb75FEZiUuh2uyzqpllhc7kci8e23IAyKafLy+Cy7krYvA8UQy49A4NOCOazSaKiDw3Z
+DhJxV1ZWYt4H330FPpbwrM8mlWs2m7H4/Cxqoc/BLvQ31evyOLnoyfvyXFHbIpPGrxyfkg8aY0l8
++I410/sBOB/wAAAgAElEQVRgqpFpCOpDQEBADCMxKeRyOVQqFRaRbMYtuk8zPolIUryjcrJe130z
+26+kYaJQKKBQKKBcLqNcLjOvpgchKyQ9kyebamF73gbis1qtolQqscjral8aj1nLZ32mUqlgamoK
+U1NTsRO9k9pt/kb3zP6ke7JvzWv5Ls1/Nl4rlQpqtVqsb9P4tfEpx3Fa+5LuJ9GOoghjY2Oxfk3D
+SEwKAQEBo4ORsCn0ej1sbm7GjGsmbPpimo7ok2XYRUNCnp1A5QaNunPRS9JPs/JLNpfTp0+zASup
+vC+Pe1Ee6PNLKcPkkW5Z2i1dzjK2wjZGXPYFH97lGCAM6xr3xSAuSRmS74uRmBRqtRpe97rX4e67
+72YjnmkwlNZZ8j6QES2KIr4uFouxFFz0ogqFQuw+PS+DcGTqK2kZvvTSSwEAH/rQhwAAd911F+66
+6y4AfUOONIJKC7f0glBdNhpm+6hN0rsiLdbUR6ZFnWgsLi4CAN797nfjwQcfBADce++9XJ/Zbhvv
+tmtbH0reZfukRb3dbu/wBsgM3IcOHcJtt93GfFLfdjqdWB9QvTZvQKvVYi9Rp9Ph+/S85J0C5cz3
+VCqV2Egr6VG97XYbhw4dAgD8yq/8Ch577DEAwHe/+12mK9+fbSzI+9I4Lmm4aJtb1M13I8cb3T94
+8CDe9a53AQA++clPwgdBfQgICIhhJCSFYrGISy+9FC95yUvw5JNPAujPjOT+KRQKPKvKnXo0i+bz
+eb5uNpt8ncvlYiswGVtohSqVSrzqFotFnonlKl0sFjm5CvmjDx48iMOHDwMAnn766dgKTFKMDIkl
+fqMo4naYs7yZs6HZbDK/0lDWarV4f3y3292xIhaLRZYUcrkcr2ynTp3C8ePHuQ7Zt0RDrrSyb4kP
+KWEQ7+Pj46yuyHbI91QsFlnqo9/b7TbzPjk5yfxcdtlluPrqqwEAR48e5ftyxZfGRKJRKpViY0SW
+p/8plHpzc5PfqYwL2Nra4vdHdckxIg2Lk5OTuOKKKwCcixx94oknmJ9msxmrS8bWyGzg1D80JmVf
+VatVbr+UrKguKZnJnBSNRmNHaH4WjESSlWuvvVbfdddduOWWW9hfffz4cYyPjwPov0TKzLO6usrB
+GPSRTk1NcZjs/Pw866e1Wo3rm5qa4msKlFlaWuK48Hq9ztt3z5w5w/TW19dx++23A+hPBgDw2te+
+FjfddBOAfkAIfWyVSoUHyOzsLPNE9dbrdR6YZ8+ejd2nl0eh1JOTkyzizs7O4uTJkwD6+xlIR5yc
+nIy1D+jrvF/84hcBAFdddRXT++AHP8hljx07tuN48rm5OT7kdWZmhmlMT0/zLrtarcZx/tRva2tr
+/J7W19eZj9OnT3OZ9fV1fmfUJ9PT06yff+pTn8LLX/5yAMDll1+Om2++mes+evQoAPDEtL6+zu9m
+ZWWF+21tbS02edPESWNkbm4uNi6I9szMDPfL/Pw880fPb2xscDuWl5fxuc99jut41av6WQdvvfVW
+AP1x88wzzwDof7jUV3IszM/Pc5/T+19aWuL+WV5ejt2n9q2urmJ6ehoA+D1RYiKg//6pfdVqldWg
+T37yk7jmmmsAAK985StDkpWAgIDsGAlJYWJiQt9www340Y9+xKuj6VEYJh4gCbYdcibdhYUFAGCx
+Nooi3HfffQD6K1daGi5XH8vQbZuv2pbjEYhHd9pAktA111zDz/7gBz/gFarb7Q4ViWnymdY+IN7P
+9DddLyws4LrrrgPQF33JOFqv11MPWhl0XNj4sNExw4tpRb/yyitZNX3ooYcA+I0FF8/yXcoNVr6e
+GLMN9PfMzAyuvPJKAMC9997rJSmMhE2h3W7jxIkTWFtbi526k7YPQsL2cSddm3Ul0aOJ6siRIwDi
+Iny3201NpJnWDlusvs8x4zLrkewT0oGfeOIJa47GQQ+fsbXJrMuV1cp028n2bW1tsS0piqLY5JWW
+Ccnn2uTd7FvpDUibFEglkOqtzCnpMxbMSV2+R3NM2g4SShsjksbW1pb3ITCEodQHpdSUUuprSql/
+UEodUUrdqJSaUUrdqZQ6uv3/9DA0AgICzi+GUh+UUn8M4Dta6y8rpYoAKgD+LYCzWuvblVIfATCt
+tf6tpHqiKNKTk5OZMw6fL5CYSEa7XC5nTc02KiB+x8fH2To9qn0L9I1ytVoNQHyXpOsAnwsJWrkr
+lQp7EWwH2QyDJIknK/L5PEs0vtmchzlgdhLA6wC8FwC01i0ALaXUTQB+cbvYH6N/SEzipJDL5WLx
++dv1ZeHFKka7tvq6ItBctIk3skib0WwuW4RvG2RMe9bdkTYQv9PT0+zSkm5N323DaTwTj2Zsv3nf
+ph5J1Go17N+/H0DfK5Nl67R5z0bbrMvk12W7MWkA51ycExMTO57LOhZctgXXjt+kyEkXjXK5zBOu
+b+LWYdSHywAsAfjPSqkHlFJfVkqNA1jQWr+wXeYEgAXbwyocRR8QMJIYWH1QSl0P4HsAfl5rfZ9S
+6gsA6gA+rLWeEuWWtdaJdoXdPgzGZ8bPAjOwqFAoOL0kg2JYMVFCBt6QodHXMj4s0rwBttW9WCxy
+3EQul4sZGkcNMjiLxsNujAVXvw07HvL5PI+Bra2tPY9TeA7Ac1rr+7b//hqAVwA4qZTaDwDb/58a
+gkZAQMB5xsA2Ba31CaXUs0qpK7XWjwN4A4DHtv+9B8Dt8DyKPpfLoVqtOrM5m7DpiwSZcVjaFGy7
+05IMOfK+mY5Nuil9dHEXDVecAT2TJR2bBBkaFxYWOATZt2+T6Ln0clsGLGlHsGXDknUVi0UcOHAA
+QN+9Z7Mp+Lwn231Xm3xclVROXlPfHjx4kKMGbWPB51r2ldwo52tH8EEURTxufV2Tw3ofrgXwZQBF
+AE8B+JfoSx9fBXAJgJ8A+DWt9dmkevL5vB4fH8fm5ubAIlgWsTUr6IWRFVcpNdLqgzxLkq53W30w
+DXt0z3bfLGP+ViwWuW+11rEYkFGD9ETRhzroWZKuPpQYdjzQggucB+8DAGitHwRgI/KGYeoNCAi4
+cBiJiEY6DIYSrQDJYqIpdrtEbZ8jwF00JMgIRhtuut1uqvqQ5CYlGjYR1dWOLKsQGZaIXyD9KHoX
+TD6k+w2I5zeQ7ZMqgyxvoz02NsabjprNplff2upKGyMy8YpLtDfbZyaGpbFQq9V2JN/JOhZc/Jo7
+UW1tTaIhUS6XeSPVz9RZkpR5yab3E+T9JP1KlvM5J9LnPoUN26ziPnXZrrW2H3Ajy8kPLwuI3zNn
+zngdrpIEs99t4q3r3fjS29jYiCUhkX2UxlMSr4D743Zdp/FMNprl5eVYVitfvtKulVKxzM6+SBq7
+tEPTF2GXZEBAQAwjISnk83ne455FNJSzq89GFJv64HNN/mjaIbe+vs4bY5JUFNPKLKMKXedTEFzH
+pdv6xtafQF99oOu1tbWB1QdZr/msbIdvf5ooFAqs6rg8Oz7jwqamyc1Dad4HV3yLzfuwb98+Vhts
+Z1/6qBKuw2uk+jCsAbpYLPIu36eeesrrmZGYFLrdLnfsIOpDFvFskGt5AAjxaxPtXc/LScP343ad
+4uQzOORR9LKOQQdWlrb6XJswj6LP8rzrQ/c9mzMrDRLtz549u2NvRta6bDs15f2kenwR1IeAgICh
+MRKSQj6fR7Va9fY+mBZiIH2TiO0o+iTRV4I2QpEYVq/XreqDi7Yvv+bzJEbmcjlelUxpwwbpfaAV
+KKv3gcq61Ji095Ql2KZSqXCAzdraWurR7mk8m6pEWl22+7ZcEMC5tHCzs7Os5lAQkw+/LhXHFbyU
+xK8PvbGxMU5f94/a+2CW8X3GVs5H5KNB+sIL/X1e8kPxzfWfRsMG+qCznk0gDz61ZfTxga19aXz4
+TFg2bG1tcW5KV/CTDwYZIy64RHiaAF544YVYkl5fGrKM7M80D5NtAvCh12g0vCcDQlAfAgICYhgJ
+SYGCl3wt5KZoKMValzfAJ9WW61ruoae6fPb8S7EbcKcos4m75uEeUn1IWyFoBVtYWGAjaZr64Oor
+V9itKyBLeld8US6XWX3IGrxk80pJcdy1f8RXzHd5ohYXF1lqSPM+uPrWla7O1bdmUJOrTfK+PIre
+9/TpICkEBATEMBKSgo9LUsIsY54NaFsRbfeTVg95bUY00rNJ/Np0Rx+XJNUro9o6nU4mnZj4JRdq
+Ej3bb1l258k2uYyuaWg2m3y2xG5Ei5ohzUnPuJDmnj5x4kRiRGOaexrI5s6W176GSOLXHAdpGIlJ
+IZfLYWxszNtCblrz5WCUJ+nIelyx5a5suZIPEscpZLhUKmWKd5e05YlVtj0D8hnpB88yKdCOw+np
+aZ5cVldXE/vWVK9kH7uCqwjSS0ID3Se/ItGoVqvs2VlZWUn1PtjembxvbuGm32xp18wJxFSPTPo0
+FiYmJphGmrqT1LeAXwCYmaZNtpdge09jY2Ocjo0OxklDUB8CAgJiGAlJQWvNG00GUR/k4SZJrkdb
+ZJ7PRhkSGQmtVivVDWUTAV2Ra7bVWKoPWd1pcn8/reJp9SRFidogf88SQ2GrY3Nzk12SPuqDKy5E
+rvhJR9G72uGjYkjVjGIW0saCT9+m8SHPwMjCL7n6s2AkJoVcLodKpZKYHUjeNy3LNs+CjYbpDZDH
+pcuDRk31gbwPdMbfbnkfqF7zyHGgL6bSAPSNqTf7Z3p6mvsoLfOSKVKnDTgpnttUIh9bBNVbKBRi
+Zyb6ZnM2+UjyPsj3Lw+/Bew2H1df0Xuanp5meq5MUT7XRMPmRTGvTe+Dz4FBcl8JqTlpCOpDQEBA
+DCMhKVDSEpfoD7gjwWx/u2jYREmXmG4Tj8lDIkOms3ofJG2SBPL5/I499PRbEl8uepJf3801Wa3z
+sl7Jqy8kjUajwSc/k+8/iY+kseC6L6W0Vqu1wxBJZdJo03taX1+P7X41nxnm2qVWSjXN1kYbWq0W
+960vRmJSIPUh694H+VvaQFZK7bDWJnkfJMjrQBlsms2mNWAliTZBirXSskwiuE2szerqI113fHyc
+B1JS35rtSFIlJM9mO5ImFlsZujc2NsYibj6f9/LsUF3mWHDdl+qDdCUqdS6pSRbvw+TkJPdtFk+U
+Tztc+y5o7JiTg2y3iVKpxOOW+ExDUB8CAgJiGAlJQWvNIqiPyGiW8fVY2FZbH7GZPCNkIS8Wi6kW
+ZxcNWv1NcdFUH7KK8xIkgvd6vdimnUHUB9MIZrbDl78ki3uj0eC+9QmcyqpOmXUl5UFIq5fe08rK
+CkscWTxRrvtpfauUYr6zjIdms5k5n8JITAq5XA7lchn1ej1T8FJWSy9d2/RJH28HxZB3Oh2OI8+i
+PiTRMK32PvW6QBbyubk5HmBZMi+51AcZGGYTfV2qhA1SJSqXy7y910c1c1ntfTGMmE99u7CwkLj3
+wYe26/2nBdS56rLRjqII8/PzAIBnn302kTfCsEfR/xul1KNKqUeUUn+qlCorpS5TSt2nlDqmlPoz
+1T+NOiAg4GcEw5w6fQDAvwJwldZ6Syn1VQA3A3gzgM9rrb+ilPoDALcC+FJSXd1ul40gg4iig1p3
+fWmQ2EZWXKVUbAbPUpcvT4NKCcA5EXdzc9PpxUiCS33Iui8hCbK9jUaDfejNZnNX1IdBnvG5T/15
+6tTO0xCzjl3X+/cJqEv7jdDtdr13RxKGNTRGAMaUUhGACoAXALwe/XMlgf5R9G8bkkZAQMB5xDBn
+ST6vlPosgGcAbAH47wB+BGBFa02WnOcAHLA9r5S6DcBtwLkNUWluMxd2wxWUpJ+STYFckz5uswsJ
+OiasXC4z71nyKdDfNtD9JF3XRcNVV7lcjvVt2oYoHxuN7Zk0W5NPXZRPoVqt8m9Z08cN2oe+tjSJ
+QqHALuo9T8emlJoGcBOAywCsAPgvAN7k+7zW+g4AdwD9o+gbjUaqhTyhroHv+4pgwLmXL/3cozYh
+APFwVjnYfL0PaWXN3wc9/JTq2Nra2mHJT+JhENUl6zt33ad9MPV63fswGFfdWdWgQdSHTqfD3jNf
+DKM+/DMAx7XWS1rrNoA/B/DzAKa21QkAOAjg+SFoBAQEnGcM45J8BsCrlVIV9NWHNwD4IYC7AbwD
+wFfgeRT9+Pg4brzxRvzd3/0dz8Rm4hS5l9yM/ouiiI2BhUKBDW35fJ7LyPRm9Hy73bZu5jFpXHTR
+RQCAX/3VXwUAHD16FH//938PoL9yUL0yn4C8tuVv6PV6iRuJZJtMt6At0lHye+mllwIAbr75Zjz8
+8MMAgLvvvpv7pdPp7IiOkyntJG15v1gsWttEbrpGoxHrQ0nDfGdyU9Ill1yCW2+9FQDwve99D/fc
+cw+AvlFP9i31j6RBtFutVixaNGmDWbFY5PvyPcmxI/mlujqdDi6++GIAwE033YRjx44BAL797W8D
+iI8FGdIu35OkLfmVY8HWb51OJ8YH8StD5eW7IXoHDx7EzTffDAC4/fbb4YNhbAr3KaW+BuB+AB0A
+D6CvDvwVgK8opf7D9r0/TKurVCrhRS96EU6dOsWn2HQ6He68UqkUE4PND0EpFRsEdJ3P51mfajab
+fF/+T51aKpX4wyuXyzw5lUoljk8gei960Ys42Obpp5/mOprNJgcLKaX4Wg5MGtC5XC7WPqItBwTx
+aXo7ZPtMz0CxWIzp5/v37wcAHD58mPtW5nwkHnO5XOwDI91Z7vPo9Xp8n+5VKhX215fLZW6HHPzm
+feKX3s2BAwf49xe96EU4efIkAODxxx/ndtH7kGdjyhBleT+Koh33yW4F9CcvuqbfzHbTu5Y2jmKx
+yM/lcjkcOnQIwLks38eOHWN+G40Gt5XqAfrvVY4R+k3yK8O/5UdPPFGbZDt6vR7fb7Va1kOGfTHs
+UfT/DsC/M24/BeBVw9QbEBBw4aBGwVD28pe/XP/FX/wFPvzhD3N02NGjRzmN1NraGl/X63XOa0Bl
+p6amOJRzbm6O4wlmZmY499/8/DxfU13Ly8scSbeyssIbR86cOcPpwc6cOYNPfOITAICXvexlTIPE
+3a2tLRYjS6USG3VqtRpbeylXwNraGksdS0tLzMfGxkaMJ+Kd0mfNzs7G2kd+51qtxmUo0/TKygq+
+9KV+WMjY2Bhe/OIXAwA+/OEP82p05MgRbivVNTs7yyv0vn37+HphYYF98pOTk1ye6G1sbHD6t+Xl
+ZSwuLgLor560iq2vr3O7id+pqSnunzvuuAOXXHIJAODyyy/HO9/5Tu7bJ598EsA5j8ry8nIsvRit
+hPJ6dXWV+SMaMzMz3IeTk5O8+k9OTnIOw1qtxmOKogDPnj3LtOv1Oj72sY8B6I+Fa665BgBYPF9f
+X8fx48e57+W7ke+SPFfE4/LycmxM03iRY2R9fT32jqkuGtNTU1N8v1qtcjs+/elPs0TzC7/wCz/S
+Wl+PFIzEpDAxMaGvv/56PPjgg2w5dyVgdbnKCD4JVwi+lmd6YS9/+cv594ceeghA/2XZtrCm8ekq
+b7Mo28Jb6Xcz0EcpxR/84cOHWRx++OGHYxmZbO4tCVs+w2HaYYaWy3uzs7O47rrrAPTF6/vvvx+A
+u29lvWk7Js3yWdshn5N9SwsEgNhYoH5zeQpsPEr1MCvPSfUC/b69+uqrAQB3332316QQdkkGBATE
+MBIbotrtNl544YVYUhDXYSlmSjMgbt2VsQ5KxdOPm7Oqi4Z5TUauI0eOAOiLfSSe9Xq91KPo02iY
+PJn8mm1KokFH8AF9wxepQRsbG2xctGU7ln0l65V9a8uILFf8pP40ffPSOt9oNLhvpQgvc28Skt5T
+mtTg04dp74zUw6effprPGJXSbdpYMPkw+828n5S3Io136ltSb30xEupDFEWadPBBA2H2EmT1JR25
+UCiwbp3lJKTzBRpI5XKZ1Yd6vT6SvAJ9yzpZzovFItsBRpFfGgvVapU/vlHmN5/Ps82nXq8H9SEg
+ICA7RkJ9UEql5lOwiXPyf9dBHy6RGNi5G81FW64O9HxaPoUs+y7kfR/xMw3Eb6VS4RU4LZ+CS/yU
+5V0iuc/+kSTalUqFvUC9Xs87m3MW9cE1FnzUBwladWdnZ1lNy5LZO60dZkwKIalvk1AoFGKeOx8E
+SSEgICCGkZAUer0eG2t83IRJM6WcXW1RkIDfwSASZKCjmVZGqg2zacd3hcpq9yF+pUtPGvt8+Uoq
+s1vPAv2NZjIic1B6SXxkfcZFm6SD06dP70gEvBv9liQRDGL/a7VaHMvgi5GYFPL5PKampryyOVN5
+s4xUDWydJ+PB5TM+IhmJ4wcO9HeBb25uDpTN2RRrbfH8rgNu0sRdG7/z8/PcV6ZqlhQDYnpqkrwP
+Nk+Gee3qEyoTRREHN7XbbefhKlRv0pmRZltcYrttwfAR82nSWlhYiO2YtPHrq6Zpfe5AHXOM2lSJ
+LMjn8xxn89Of/tTrmaA+BAQExDASkoJMGZUmaimldmTjlbOv6/msKoME0aMwWXk0vM/zrhXfdUQ9
+MFwqMqq3Xq9nFo9N2nJVlZGQrvRhvpD0u90ub6pypWOT5ZPO4Ey6L+uyGU19+pxCxc+ePbvjEJys
+apRtTJuSxLAu+l6vx1KtL0ZiUsjn86jVaonqg+t8PSCbqCavfTuc4hMoPn9packrO5BJ2xRZzROG
+JE9ShE86M9BGm2ITJicneeD6ZF4i5PN5VjuKxSK3NZ/PWz04tvZl8dlXKhXu4yiKEvtWKWU9UEd+
+6D6BPlJVMg+jlddmYBHxOTs7y/tUXPymecdcalBa+7J6H8hr5js5BPUhICAghpGQFHq9HouNrlkw
+6Zy/YazUPpChrfR8mvrgsixLJBm4TPE6i3Qjj0v3PYperkRyld/Y2Ehsq4/kkgZaaQHELPq2urTW
+O9RHW/k07wP9nVVNI14ph4L83UXTVZdLBRtG1TUhDbe+GIlJgQ6DWV1dzRSYktZJ5vmB5hl8vuoD
+WZxJDFNKWb0PrmvXcem2o9Ndx4wTZIYdlyWb1IcDBw6wqzcteEn2VbFYdGaTsvFEyVlch/imoVgs
+Yt++fQD626WJZ1f7koKUbGVsddnUStuZnWZd5CXYt28f82lL4pumusrrpD0OSR4qH89XoVDgrdjh
+LMmAgICBMBKSQq/Xsx4GM6z4L402WT0GEqQ+yACrNAu5j5hoExnT6nUdyCKvSX04c+aM19HuRI9o
+uw6QsV3LtHI+K5cNrVaLE53IuocZC2nqg+3aR2yX3oekMynNa5vhMsvYsRkrffq63W7/bB9Fv76+
+PpCF1WVZzufzLHLJhKfUub4irsz1T8/TBOE6Jt4lPtpSmUu4XFO2umQ98j5t6Z2amoolVfVVH1zB
+SxIygIxo2D4eH1QqFU6OKycHn2Ai132zn5ImLFff2mjQWJiYmODJk+wMrrFg9q3Jb5LKID0YNvUh
+iVfil/aVhL0PAQEBA2EkJIVer4fdOgzGZYiUInHWgBB6lgKsisViqkHNJQ7aJIEsz/uIy6SKmT72
+NPWB4EqFZyuvtc4kFdjQaDQ4D2Sz2czk2XHd9/W0ZFVLKbR5aWmJpQaCa1z57HZ0qZuDqryS36x7
+H4KkEBAQEEPqpKCU+iOl1Cml1CPi3oxS6k6l1NHt/6e37yul1H9S/WPoH1ZKvcKLiVyO96lLm4Av
+SN+iswsoIs/8J8tnoVEqlVAqlbCwsICFhQU+x8DlErPxJ3VDkhIkXyZPrms6h8FWnv4uFosoFouY
+n59HrVbj/fQ2Pmw0ZF/R2RJJ/1ztSOpneb9YLGJmZgYzMzOc0cj3HbnK0XhIa19Sn9vaQX27f/9+
+TExMcIZloplUF/WVjUe6lv2plEIURYiiaMd9+puec9GOogjT09OccNYHqenYlFKvA7AO4P/SWr9s
++96nAZzVWt+ulPoIgGmt9W8ppd4M4MPoH0d/A4AvaK1vSGMiiiJdrVZjeQSzwvxwzPuD1guc88PT
+x9Vut9m4dD5ScGW16tPAq9Vq/FzWvs2y83EQ47BEFEVsxJVnHw7zzmywGSKz8iwT2FA/U8zKMGNX
+8uEyePoYQk3sSTo2rfW3AZw1bt+E/jHzQPy4+ZvQnzy01vp76J8ruT+NRkBAwOhgUEPjgtaa4jxP
+AFjYvj4A4FlRjo6ifwEGlDiKnmazQV2SpstHin1p0X8+IBcf7Uvf2NjIfPw4wced5nrGdt9m3JIu
+Sd8NUWYfys1aLjcp1WUTwbNs5imXy+ySrNfrLCkkuXtt9SbRs2XzNmn4tI9W3fn5eR4DJCm4+HXx
+b4tWlL/7uCRtrmPZx2NjY1JSSOUN2AXvg9ZaK6Uyy43aOIqeOngQEdQnbtygnal+4o2SVPhkB8pK
+29cz4POM7EuZvCOL9yFLAJGrHl/LfrPZ5L0EcqBn3ZeQRG+36qK+PXXq1I7MS75eLarPtnVett/0
+xpnvRE5WLtpbW1uZvW2Deh9Oklqw/f+p7fvPA7hYlAtH0QcE/Ixh0EnhG+gfMw/Ej5v/BoB/se2F
+eDWAVaFmOEHp2EhUy+p98IHLspxUnsqQBZgs5NLi7LJeu+oahN8kq7aNBlnI5+bmMD4+jvHx8VSL
+vss6L2lLz4fNWk5lpOrhQ69QKGBychKTk5OYmpqK0bC1T7bFRc+kbfanjUYaPdm3pmfHpOEDWa/N
+gyPbZ3qd0niXfTs3N8ebonyQqj4opf4UwC8CmFNKPYf+KdO3A/iqUupWAD8B8Gvbxf9f9D0PxwBs
+AviXPkxQ2LBLR94NZA1SsQU+2ZJUZA0s8kVaMEvSNfFrJhf1DZbKEpBl2hyStri76mo2m9y3w7Q7
+6V246iW+fenJvpWTho1mGlx9brMTSDrymTTe2+12LHTcB6mTgtb6XY6f3mApqwF8KBMHAQEBI4WR
+CHPO5XKoVqtYXV21WlB3A8NYyCkFF20saTQa1j3/Ej6WaJuK4JN2TT5no02GUFLJgHTvQxKPLqs9
+8WtL5OJajW3tGB8fZ5Ws2+169S3VZUtplybpuFLduTKBy/aTZ2dhYYG9JBRW7ut9ILg8HGlp2mR+
+C2B//u8AABmaSURBVFmXjXa5XGavGfVrGkZiUuh2u3y47G5PBgTZ8Vkt5LT9mMSwtI/SpJEG2+BP
+SzyaBBmfT+4on+fSaNnEXeVw+/rwTPdk5iXT+m6Dbfu5KVonuZ9d3qq0/tFa8wRw8uTJHbtDs6q+
+aX1rtsOc7HzoNRqNsPchICBgOIyEpED5FFZWVrxEXDMEV6YMk2nXZA4FeW3br54069KKQPHj7Xbb
+KwWX5I/4ldcUQ9Dtdvl+2i7KpNWYQPVOT0+zyGmmuhsEkrZUGQimNJZET9YlRdxGo5F60I5pXSd6
+1O5Op8NlZFo5m2pmBjXZDJSyHaSaTU9P83haWVlJpOFqt0t1tam6cuwQDTmmqYyJKIpCOraAgIDh
+kLoh6nwgn8/r8fHx2JmCPpAuIdMIY5bJGhUoQTM0GZmUUgNtiDJ95y7X07AgfovFIu/5l+dK7gZk
+32d195qIooj9/a1Wi204WQ21NslxGL5soHFWrVZjRlxgbzfH2WIgfNqUz+d53K6trXltiBop9cHn
+LEkpXsngIhpA5oElNssyPWcmE3HRJpGROldrnekwGFs7iCfAnqnXBy4aMvs0ideDeh9cvMt3kFUd
+M+sql8u8S3Jzc5MnhbQ+lCK1tMTbPs4k1S5tf4UE9W25XOZ7NlXSh7ZLZSAkqRJJ9UoUCgX2noXD
+YAICAgbCSEgKWms2Dvm4tMwyZjowKQnYkNUlKXcayvqTnnG54Hwj/nyQxu/m5mYsUexe0Bu0XvlM
+o9HgVHdp9Mz7trgWH9ei+bwvbRndap5+nnXs+vDsq2K6npe5P3wxEpNCtVrFjTfeiL/9279lH7sU
+RbvdbsxSLw8fAfoiHXVYqVTiOqIo4gljbGwsJvIDfcu0tPrLM/xkang6qOTtb387AOCxxx7D97//
+feaBaEhRVB7aIvklHb/dbscyLRMfVFc+n4/VRdfyoJZCocDlqa5ms4kDBw4wvw8//DAA4L777uOy
+sj8l7zavjaRdKpW4DImkrVaLRen19fVYO2R/JlnODxw4gF/7tX6k/JEjR3DPPfdwHTbbgKyXaLfb
+bW5Ts9nk/qCPWH5ULq9UPp/n9tnGQrfbxcGDBwEAb3zjG3nX7He+8x0A/fdoi9kwx4JJQ45pOS46
+nQ6rK7J91KZCocDXroCsAwcO4I1vfCMA4Mtf/jJ8ENSHgICAGEZCUiiXy7jyyiuxtLSEo0ePAogf
+T14oFGJGRZoRaRbN5XJ83e12efWQYakbGxtcxrYCSwNluVxmaYNyMxJtAHjxi1/MUWJPP/00z/hy
+lqdn6T7xK9N5keFHto+eabVaXDaXy1nPWaB6qH1AfwWneIpSqcQnZZ85cwbPPfccgP6KJlcb6ivq
+n1arFetbKaVR3xI/U1NTHD5bLBat/n/KMSjpNZtNbuvCwgJfLy4u4vDhw9y3xKcpEVH7pARFfBYK
+BQ5Bln0opUJpKJSrMfEhDZjEQ7FYZIMo7eYE+uMBAJ544gmWCFqtFvMqx1an0+H7RKNQKHA7yuVy
+zHskJTMqQ56aZrPJvFNuUqAfgUv3Z2dnsX9/tuRnI+GSvPbaa/Wdd96JW265hV/+kSNHeK/B5uYm
+d0S9Xuf7FHY8OTnJH+nk5CTrp+Y1fehU19LSEte1sbHBH9PZs2cxNTXF15/5zGe4DgB47Wtfi1tu
+uQVAPyjo+PHjAPovjj6QWq3GmW6o3tXVVa5jeXmZQ5DX1tYwPz8PAJzqfGZmhuuam5vD6dOnAfQH
+I534Mzs7y/dpctjc3MTHPvYxAMDhw4fxilf0c+fefPPN3N9Hjx7lvQY0Mc3MzDDtiYkJ7rdqtcrB
+OXICoP5ZWVnh69OnT3MGpaWlJWd/Av39DjTpf/KTn8Rll10GALj22mvxgQ98AEA/lPipp54CcM7a
+v7a2xurcyZMnOeipXq9zm1ZXVzE7O8v9DPQnnqWlJa6L2jE5Ocntm5mZ4XZTXbJ99Xodn//85wEA
+V199NS6+uJ865B3veAeA/oR95MgRpkHqarVa5euJiQmmRzwuLS1xOyS9lZUVnoTM9hHvtBjIb6BW
+q/H93/u938Pll18OAHj961+/OzkaAwIC/mlhJCSFiYkJ/apXvQoPPPAAr1wuS6srFDXpd7pvlnft
+ADTLUJjoS17yEgB9sfX+++8H0J/BbV4Okyfzng/MeAy6tvWN5JdWmiuuuILFyIcfftgacGULQpIe
+BZv/3AWXv13yZys7PT2NK6+8EkB/Vf3hD38IIJ6BOu2d+vJne841NuhveY8kuuuuu47F/Pvuuw8A
+Yrt8B+HD9zkb32Y5+m12dhbXXHMNAODOO+8MkkJAQEB2jIShsd1u4/nnn8fa2hqvYq5NIrYjzXw2
+JdmkAjMCz0ZPHiZLRtCZmRm2F3Q6HWd2Xd+NL7ZoSvN5155/W5vIyHb8+HGWGjY2NmIH7NryN/i0
+Y9A22dpH9BqNBtsOZmdnWVrsdrvWk5bT3plPf6ZJiy4aJG0dOXKEbUKk45v8DtKHJm1btGiWNjWb
+TR63vhiZSeHEiROJwT1JgR5Zg0OyXtNHJv8mPncrZVhSmyR8gm1kinS67vV61jwEu837IO2jc0SB
+/ljY7b7dTd7JOLq2tsYLw16OhaRgN5/nNzY2zls254CAgH+kGAlJgTZEycNggOHDgJPCprNAbjAC
++r7vtANAhqWtVDybsE+aNpPfyclJNtSZB+0kiaK+/NnoZ70P9F255CaOoohddrv1/pLgI9rLsuT6
+nZycZKnBVpfPfbOMeZ3lQB0XisUiu8F9N0SNxKTQ6/WwtbUVs/TuBnarLopvIN2xUqlw3cNsyfaF
+KfanDQ6K9VheXo4ldRlEXXHBVT7rfSB+YMmw6eOywlf9oHtkU5Ah8knPJN3Pyscg/dFsNjmWxRdB
+fQgICIhh0KPoP6OU+gfVP27+vymlpsRvH1X9o+gfV0r9khcTuRzGxsZYZM7qvzX4jdVru84KOgxm
+dnYWs7OzGB8f55XXJvZl4VWqCPKf5DeKIg7TLRQKsQNCzH9AP+S1UChg//79fBiMSW8QuJ61tSON
+nrxfKpVw8OBBHDx4MCYpmH1rq8t133WAilLxI9xl38pj3l3toENb6FCgQQ4Gct2n3yTv5jHzPnVJ
+RFGEhYUFDtX3waBH0f8PAO7SWneUUp8CAN0/iv4qAH8K4FUAFgF8C8CLtdaJKWmiKNITExNYW1vb
+lePH5ct0iYFZQC9FngZEbsrdyLbj83LTdEt5TfH+MzMzLO5ubm7u2UE7wyKfz3PfKqV25Wh3gs3d
+KCE/OJ9s4rS/oFKpcJ27mXnJZeMYFLJvV1ZW9u4oeq31f9da0xv7HvpnRgL9o+i/orVuaq2Po39S
+1Kv8mxAQEHChsRuGxvcB+LPt6wPoTxIEOoo+EeR9qNfrmSysUtyi8qa4ZfPz0nO+Of9pMw7lEFDq
+XI7GYc6qkBmRbTzJcnKfPsHMHkwgEbxWq/HvW1tb3n3romE7LMWUXKTl3Fany/tAQVbtdjvRsyNV
+K9MzYMswLenKTNmEYrFozWHheh+0i7JWq3EZGgtJx9rbJBZbkBLxQbTTPEYStr4tl8vsfSCvThqG
+mhSUUr8NoAPgTwZ49jYAt21fY319PbP3QXoAqFO73W4mMc6HHnkfaLDKLcLDiHcy5XxSMJEUo33o
+ycNr5N4BX15dH7qtDnlPuk5tdSbxSzsYC4VComdHa22dIAH3ATW2uug+vVuTR1c7aBv1yspK4mEw
+gwYpKaWcatMgHqNms8l964uBJwWl1HsBvAXAG/Q57ryPotda3wHgDqBvUxiUj4CAgN3FQJOCUupN
+AH4TwC9orWUCuG8A+H+UUr+PvqHxCgDfT6svl8uhVqthbW3NS8Q1M/hK8dpcdaVa4WgLgOTswxQM
+RLkCtra2Ug8s8YFMnGLuRUgSRSXv5v1er8f87tu3j0NxkzJlm3WRFR6IH65jS51unnfoCrxJQqFQ
+4HwCdISgrQ8kr1SvTHVno512AIyZDEamp7O1g97Zvn37WGqwnX3pc207JChtv4OEax+M7Ldyuczj
+9tixYzvqsGHQo+g/CqAE4M5tpr+ntf6ftdaPKqW+CuAx9NWKD6V5HgICAkYLI5FPIYoiXa1WvQ8s
+kS5H816W5wE/vYxWB+mSJDeUmUnaF67VPwtf8llZnlZPiqcAsh8GI/3yhDTDbNZ+JdCp4wRfF99u
+uO+k4TIpTwWB+mNsbIyvid/dcKEm0U561lU2n8+z4bler//sHAajlEK5XE48sMS0iMv/lVL8cSYN
+DnMyMYOPXM+R94HSgG1sbAx9/LjkX95PO0XZbIdtYiF+a7Ua/552GEySB8eWodimMsj73W7X+yOV
+eSUbjYbVmi/bbhPns6grVA8Q9zhQwJCEOTHR5FWpVLitaWPBNSZt7z8prNnkTfaFNK7KZ8fGxtj7
+QKpkGkKYc0BAQAwjISlorXlPvWuWt7m9pFvQZ3WwlfF5jnISPP9835HisyEqjQfZDrmSZGmHqyyt
+tO12m6WGtD6S7ZDJQlx0XAYumSTHF41GgxO6ypXbJc7Lum1SjA+orEslctVFRkWZETptLLjqkuV9
+JFbf9yCxtbWVOdJyJCYFUh+StvfKTpMiI2C3ipuwBYfInW4+3gfKz6e15izBWQejS/Q1A3J8JjpX
+IAzxSxZ9ADs8O7ZAGJtF3qRnqitaaw6rpmeBuK0lrR1RFLHeWy6XM3kfZKBPlixFkl+bx8dVl/RE
+UYzDoFu9XW3y+QaAeOp4l/qQz+c5x+izzz7rxVdQHwICAmIYCUmh1+thc3Nzx+pou5YRXz4xBvL5
+pLyESSB/NEkHks+sFm+X6JsU0ZhUl60P5HmHaf1p+9tHDLaVl0fPZemXTqfDKo9MXJKmwtCzQLon
+Io13wE+VoL6lMxZcNHzgo64klfEJ0+90Oqya+WIkJoW0o+il28gmRvl83MVikbPm0ECSuReTnicX
+Hx3eIS3kWUVGm8oAnBNXs7i1pJgv+4RE8ampKRZxswQv+bj6JD154hGVbTab3v1SKpVYNdvc3LRa
+86WVXXo4ZACYTZWQz9n61hUk5lIraQzVajX+KG3BSxJJfUu8J6l2sh7X3y4VulAo7DhFLA1BfQgI
+CIhhJCSFNO+D1to6C/r4pamMPJKbZn/Tyu4CrTAyrdWg6oP0nNg28AxSlwl56ItcSZN49VUzbJAx
+IoO0o91u4+TJkwDcG6Jkf9tEflMSSGtrWrCbq29Jvel2u2x0TBsLPn2bVZVIKifRbrdZkvHFSEwK
+FNGW5H2Q6oMUEwE/EZe2vMr7vt4Hsq5TRGOv12NRzEd9cFmQXfHuvm1y6c7ycFH6UNKCl7JCiudE
+r9lsevWniSiK2ELebrc5yMZH1LaNC5etheoyj5+37ZtJozc3N8fP2fh1Iav6MOy5D1EUcWCYmWjW
+haA+BAQExDASkkK32+XZ1sdKnMUYR7Nrs9ncIWGY9bpA4rFcEWw8upDV2p1Ur9k/ttWfjIum1Xk3
+JASzLqUUr0CD1t9ut1nyksZfH/FaruxyxbclVCF0Oh2rJOGTD4Fo2M6NzDoW0lQG147fLHV1Oh3v
+5CpMN1PpgICAf/QYCUkhl8uhXC4nus2SIrvMkGGC1B2jKNrhbvKVOEhnppRhPjYFX5uAiax12SQd
+4nd6eprbmCUdmwtyA47MYyBdhIOgWq1yVuR8Pp/atzb3tOTNFiFq2nKyGlMJ1LeUfRxAqns6bSxI
+2wEQd1ub0bsufl00ZDo24jMNIzEp9HrnDkUdJIjDJVLLnXpZwm5NyMNVgHMfhC+/SbwC6epIVhoy
+wEamDBtWfZDPy/4cNks0peIjpAWlpW1xdvWhrHfQCVKqkjK8O6murGPatX/E13sk0Ww2+RAjXwT1
+ISAgIIaRkBTSIhpNJLlmzIy8ssygKxqJcBTRqLV2btqx8WRzPbpcaLbQZ9cGHrMP6G9yv1500UUs
+MvqeI5gEWwTmoKuu7J9SqcQpw+r1ujXVXZobUvJhbo6SzxPvtveUxCvRI+lgcXGRpVubupMlKjTJ
+VU0YVMqLoojzgDz99NN+zwxEaZdBex+A7BZc82+p1/qmcE8D1UODtd1uc30+FmubhTst0MdVr+sZ
+m/fh1KlTu+pxMD88G21fyGcajQZOnDjBfyfFGMj7sg9NHmyegSx7ZVy8ko1mZWWF1TRbOR+bhc8Y
+Gfb9dTqdzNmcg/oQEBAQw0hIClnVBxNZrb5ZQRtKKD/B2tpaJouzTzSeuYqZXoosab6k98G2+9AG
+n3bYNmD5rrpJ71VuVut2uxyWm5TejOpyeR9sHipbO1w8uUCbzebm5tj/nyW6Na0dErsxdkulEkc0
++m6IGolJwbV12hdZrb5ZQR8UxedLq3NWy7JLZPQNTHGVkSCx9uzZs87U9r51+fI7aP1An19SzegA
+XcCdFNenbwfpTx+QHeHMmTM7+BumT3ZTzZNotVoheCkgIGA4jISkUK1WceONN+Lee+9lw1G73baK
+2t1ul33vZPSRB5bk8/lYiCutbnLjC630MnVZq9WKhUHT/Xa7jcsuuwwA8MEPfhAA8K1vfQv33HMP
+/+46wIXoSRpEu9vt8nWn0+E20SpfLpdjB5OktVX2yaFDhwAAN910Ex599FEAwLe//W0ua7POy/4p
+FAoxelTGPHeR+JW8y3dmyx0h3wHRWFxcxFve8hYAwDPPPIPvfve7APqrsvnO2u12LPhMvif5/ih/
+IhldZTsKhQL3s9xd6RojMlDriiuuAAC8973vxXe+8x0AwN13383td409+Z4kPZNGq9Wy5ouwBYnJ
+4DxZr7w+ePAg3v72twMAPve5z8EHIzEpFAoFLC4u4oorrsBTTz0FoP8h0ACUwULFYpE7m1xvZJMA
++lFbNFDkRy+jw+iF5/N5HhxjY2MxFxl1aqVS4TpIJ1tcXMRVV10FADh69Ci/mGazyTwpde7AUxqY
+5XKZ7+Xz+dh9euF0iG2j0eCBLdvabrf5Op/Pc7tJxSkWi5yG/PDhw7xf4/LLL8czzzwDoP8xUd/K
+vqB2ykkxl8vFBqZMqELPy/dEZeXHVigUYh8k8UDtm56e5mhRAGwtf/LJJ7lual+5XI69J+rDUqnE
+tOUkKnfHUl81m80dY4p4onbR74VCgXkolUoceZnL5XDJJZdw3wJ9lx+1s9FoxPpKLgayb+l/Oaap
+fcViMXY6F024csu2rIva2mq1Ynk6yZXui6A+BAQExDASJ0QppZYAbAA4nVZ2jzAXaAfa/wRoX6q1
+nk8rNBKTAgAopX6oPY60CrQD7UB7bxHUh4CAgBjCpBAQEBDDKE0KdwTagXagfeExMjaFgICA0cAo
+SQoBAQEjgAs+KSil3qSUelwpdUwp9ZE9pnWxUupupdRjSqlHlVK/sX1/Ril1p1Lq6Pb/03vIQ14p
+9YBS6i+3/75MKXXfdvv/TClV3EPaU0qpryml/kEpdUQpdeP5artS6t9s9/kjSqk/VUqV96rtSqk/
+UkqdUko9Iu5Z26n6+E/bPDyslHrFHtD+zHafP6yU+m9KqSnx20e3aT+ulPqlYWjvFi7opKCUygP4
+IoBfBnAVgHcppa7aQ5IdAP+b1voqAK8G8KFteh8B8Dda6ysA/M3233uF3wBwRPz9KQCf11ofBrAM
+4NY9pP0FAH+ttX4JgGu2+djztiulDgD4VwCu11q/DEAewM3Yu7b/nwDeZNxztfOXAVyx/e82AF/a
+A9p3AniZ1vrnADwB4KMAsD32bgZw9fYz//v2N3FhQTsTL8Q/ADcC+Kb4+6MAPnoe6X8dwBsBPA5g
+//a9/QAe3yN6B9EfkK8H8JcAFPqBLJGtP3aZ9iSA49i2I4n7e952AAcAPAtgBv3Q+r8E8Et72XYA
+hwA8ktZOAP8HgHfZyu0WbeO3/wnAn2xfx8Y7gG8CuHEv3n+WfxdafaDBQnhu+96eQyl1CMB1AO4D
+sKC1fmH7pxMAFvaI7H8E8JsAaA/yLIAVrTWlld7L9l8GYAnAf95WX76slBrHeWi71vp5AJ8F8AyA
+FwCsAvgRzl/bAXc7z/cYfB+A/+8C0fbChZ4ULgiUUlUA/xXAv9Za1+Vvuj9l77pLRin1FgCntNY/
+2u26PREBeAWAL2mtr0M/rDymKuxh26cB3IT+xLQIYBw7Rezzhr1qZxqUUr+Nvgr7J+ebdhZc6Enh
+eQAXi78Pbt/bMyilCuhPCH+itf7z7dsnlVL7t3/fD+DUHpD+eQBvVUo9DeAr6KsQXwAwpZSi3ap7
+2f7nADyntb5v+++voT9JnI+2/zMAx7XWS1rrNoA/R78/zlfbAXc7z8sYVEq9F8BbALx7e1I6b7Sz
+4kJPCj8AcMW2FbqIvtHlG3tFTPX3nv4hgCNa698XP30DwHu2r9+Dvq1hV6G1/qjW+qDW+hD67bxL
+a/1uAHcDeMde0t6mfwLAs0qpK7dvvQHAYzgPbUdfbXi1Uqqy/Q6I9nlp+zZc7fwGgH+x7YV4NYBV
+oWbsCpRSb0JfbXyr1lqeyPINADcrpUpKqcvQN3Z+fzdpD4QLbdQA8Gb0LbJPAvjtPab1GvTFxocB
+PLj9783o6/Z/A+AogG8BmNljPn4RwF9uX1+O/kA4BuC/ACjtId1rAfxwu/1/AWD6fLUdwO8C+AcA
+jwD4vwGU9qrtAP4UfdtFG30J6VZXO9E39n5xe/z9GH0PyW7TPoa+7YDG3B+I8r+9TftxAL+8l+PO
+91+IaAwICIjhQqsPAQEBI4YwKQQEBMQQJoWAgIAYwqQQEBAQQ5gUAgICYgiTQkBAQAxhUggICIgh
+TAoBAQEx/P/QKIxOKpbFdgAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/2... Discriminator Loss: 1.2459... Generator Loss: 0.9999
+Epoch 1/2... Discriminator Loss: 1.4056... Generator Loss: 1.7060
+Epoch 1/2... Discriminator Loss: 1.3388... Generator Loss: 0.7322
+Epoch 1/2... Discriminator Loss: 1.1195... Generator Loss: 1.1036
+Epoch 1/2... Discriminator Loss: 1.1910... Generator Loss: 0.7477
+Epoch 1/2... Discriminator Loss: 1.2023... Generator Loss: 1.2572
+Epoch 1/2... Discriminator Loss: 1.2410... Generator Loss: 1.6392
+Epoch 1/2... Discriminator Loss: 1.2184... Generator Loss: 0.7372
+Epoch 1/2... Discriminator Loss: 1.2382... Generator Loss: 0.6883
+Epoch 1/2... Discriminator Loss: 1.3832... Generator Loss: 0.4813
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJztnX2sXVd14H/rfr0vf70XJya242CIw0cju4BFA1RDRAqE
+FCUdFKEA0oQWFI2gA6UjQUL+GI34B+hAS1GbTgiUzCgiZGjaRKgtk3FBw4BwkxgSkjjGJh+1jR07
+dt5zbL93/d59a/44dx/vc32v7+e5Z5/r9ZOu3n3nfux19j1n7bXW3nstUVUMwzAchawFMAwjLEwp
+GIaRwJSCYRgJTCkYhpHAlIJhGAlMKRiGkSA1pSAi14nIHhHZJyK3pdWOYRiDRdJYpyAiReBXwLuB
+A8AjwIdU9emBN2YYxkBJy1J4K7BPVZ9V1TPAfcCNKbVlGMYAKaX0vRuA/d7/B4DfafVmEbFllUau
+EBFyuBr4JVW9uN2b0lIKbRGRW4Fb3f/FYpHl5eUgO7pQiAwqEYmP1Wq1rMRpi5MXzsocsryl0tnL
+UFWDlrVYLAJRHy8vLwPEf9O+dt1v2U07/jWrqi908pm0lMJB4DLv/431YzGqehdwF0SWQsgXgvvR
+e/lRssDJC8mLIlSWlpZyISecVa6qmujnYdDLddfLZ9KKKTwCbBGRzSJSAW4GHkqpraGhqsErhEby
+IrOTMw+yAkNXCMMkFUtBVZdE5I+BHwBF4Fuq+lQabRmGMVhSmZLsWggLNBrGMHhMVbe3e9MFs6Ix
+Lz6rYWTNBaMUDMPojJFXCoVCITFFZ6SHWWOjQWbrFIZFCDGTUaZYLLJx40YAjh07xsmTJzOWyOgX
+G0INw0hglkJglMtlIFok41bPLS4uZinSeSkUCuzfH61oz1tfG80ZeaWQB4rFYnxDbdiwAYC5uTkm
+JiYAOHr0aLCKYXFxMTcrPV1sqVgsxjJXKpVgXZ5SqRSvoCwUCucs/05r/4W5D4ZhJDBLIQD85b2H
+Dh0Cov0ACwsLQNibmSB8C8Hh5FxaWoqthlOnTmUp0nlZXl6ON4uJyDkbr9Lqd7MUDMNIcMFYCiHu
+f/d9cSdbtVqNX3eWQujkJabg5BOR4K0vIJFKoFAoDK1/R14puGDd/Px8xpIk8ZVUu0U/Id1sbnbE
+mbITExNx34Z+o+VFefk4WYfZt+Y+GIaRYCQtBTciFAoF1q5dC8CLL77ImTNnshQrgW8ptBq5QhnR
+nHWwZcsWbrjhBgBe+9rXArB161Y+9alPAbBz585sBDwP7lrwA3XG+RlJpeCnTzt4MEr4FNoFEZo8
+52N6ehqAm266iXe/+93A2aj9jh072L17d2aytcIpA+c++rGarHGL0gbtEgzKPTL3wTCMBLm2FFpp
+Rvd/aIlgQ5wB6QQn8wMPPBBbCA8++CAAJ0+eDHKWxFmLLggayk7ZcrmcSs7PsbGxgVlDI5l5KY9R
+ZiMd/Gshy+vCKSVVjZ83cx+6HTj8+FkHWaUt85JhGN2Ta/ehFWYhGA5/LUiW14XfdrsAowtE+p9p
+FZj2LYVBBS5HUikYRiNZDxSdtt+4crHTWapBzmSY+2AYRgKzFAJARM4bfDJGH+cGXHzxxVx++eUA
+PPnkk/HCsdnZ2aafCyqfgohcJiI/FJGnReQpEfl0/fiMiDwsInvrf6cHJ65hGGnT85SkiFwKXKqq
+u0RkJfAY8AfAR4HjqvpFEbkNmFbVz7X5rgsyMuhGh5UrV8bz/2YpXHhUKpX4+czMTHwNnDp1Kl57
+MKDrIt0pSVU9pKq76s9fAXYTlaC/Ebin/rZ7iBSF0QQRQUQolUq5qqNoDAb3+0O0+GhsbIxjx45R
+qVSoVCrxfo1hL4kfSExBRF4NvAnYCaxT1UP1lw4D61p8JlGK3jCMMOhbKYjICuDvgD9R1RN+bgBV
+1VauQWMp+gHI4b63368aCv4o4S99NS4c3LV65syZeAevv4kvq7UVfSkFESkTKYR7VfWB+uEXReRS
+VT1Ujzsc6VfIDuSIb7BisRj7XyErCH/Z7fHjxzOWpnOaJYTJsp99efylxI68KVtf9qz6tZ/ZBwG+
+CexW1a96Lz0E3FJ/fgvwYO/iGYYxbPqZffhd4MfALwGnjj9PFFe4H9gEvAB8UFXPOxQOwn1wo0Re
+RobGETdkq8YnazetVeq6crnM0tISgAVtW9PR7MNI7pI0Rg9/P4C/n6FxG7J7j9EU2yVpGEb32DJn
+Ixc0W7wTQlBuFDFLYYRplzo+JPwZpDwQSianTui2b/NzZoZhDIVg3Iesk2B0QtaR927JY/AtjzKH
+TrfXrVkKhmEkCMZS8BNPwvm12rBHbL+OhGOQuxkHPTr68jbL0xDaCOzLqapBrzXxYwmuInQIfdvK
+0nZTud0QjFLoJh37sDu+2Vz4IBn0+fg3VR5cHlXNzZZx/1oIaTl9KxlqtVrX1625D4ZhJAjGUghB
+27YjDzI2kkeZ80Ce+rVbWc1SMAwjgSkFwzASmFIwDCNBMDEFwzCSNCaQ6aBW5EAwS8EwjARmKRgd
+IyLnLIZxiU2MwVAoFOICMBMTE/Hxq6++mp/+9KcAvPLKK0B6FoMphcBwN12Ii3n8RUbNLsg87F8J
+HVVNJHF1qyaff/55Tp8+Hb8nTcx9MAwjgaVjCwRnIUxPR1X2Zmdn4xEhRKshj7jAXeNekxDugXYU
+i8VYzj72hlg6NsMwusdiCgEgInFQyQWZ/PoV5qt3hm8BuOd+lu/x8XEAFhcXY4tsbm4urtcYMt1s
+GOyXXCuFPOwA7BSnAF588UUg2pZrbkN3+FvG3bXhAnULCwvMz88DUd1GdzwvKeCGeY337T6ISFFE
+fi4i36//v1lEdorIPhH5rohU2n2HYRjhMIiYwqeJKk47vgT8uapeAbwMfGwAbTQlb8k+W6GqzM/P
+Mz8/H1cZXlxcTLxutKdWq8X5AwqFAoVCgWq1SrVajQOKqkq1WmV2dpbZ2VkWFhayFjs4+lIKIrIR
++H3g7vr/ArwL+F79LamWou9EKbj39JKBJkv8izhr8qJ8nZxLS0uxMmhWyl1EWFxcTCjevJLGb9Ov
+pfAXwGc5WzbuImBWVd0ytwPAhmYfFJFbReRREXm0TxkMwxgg/RSYfT9wRFUf6+XzqnqXqm7vZN60
+Fc5cbMSZjk6L+qmz8oQ7jywREVatWsWqVavi/0O1GtpZVyH0Zx7oZ/bhHcANInI9MA6sAr4GrBGR
+Ut1a2Agc7F/MzikUCkxOTgLRuny3ZNSf4ssDpVIpvrjL5XJs6jYmuE0bVWVubg6ASqUSKwS352GY
+U2XtOF+RYV+RlUqlTF2HQc6apdH3PatNVb1dVTeq6quBm4F/UdWPAD8Ebqq/zUrRG0bOSMOW+hzw
+pyKyjyjG8M0U2jgH36z1I8+++1AqleL56WHL1C21Wo1yuUy5XI4j6n6V5WHi2j5z5kzch6GZ4n4/
+VypnZ8GdvP6I6qzHrAglgNyKgdwhqvoj4Ef1588Cbx3E9xqGMXxyvaKxGaoa7zeHsxuNyuUy27Zt
+A2DXrl0AqfiVbsSqVCpxDKOX+gBu2syR5cjit904rx/KiKeqiSXi7rmjXC7HsoY+UmfNyCiFdj9y
+rVbjiSeeAPraZdYW36T2L8JuCbVK0rBSgnVLuVyOFfLy8nIcCG2WnyI02UMjHKfQMIwgGBlLoRV+
+Fhs3WqQ1CotIHOSanJzk2LFjqbSTJaGOssVikbGxMYA4Q5FPuVzOLMDY6y5X/3N+FiZnBaX1W1ww
+loIrXJqmWa6qcbT7+PHjqbVjnMsVV1zB0tISS0tLFIvFeHbEzeAsLy9nNmOiqom2zzcr5S+48xfg
+lUql+PzS5oJRCoZhdMbIuw/DxM3lQ5SJt5kZ2893Q7jme6ekdR5HjhzhoosuAuDo0aOsWbMGgM2b
+NwPwxBNPJNoeRj/6OR2c6e/vgPVl8GetnEVRLBbjHBDDDJSaUhggzn0AmJ+fP++y216+O3TK5XJb
+fzet8zh16hSnTp0CYN26dfGN95vf/CZuN21fvBX+7+/cGTgb+/BdiVqtlvkScnMfDMNIMNLZnIe9
+eagT8uwGlEqluD9dpN9fE5DlhjM/Z8by8nJssfkzTln1uS/bhg0bYovGWQrz8/OJNRYp0lE255FU
+Cs48czMOEHV2nm/IELAKUbnHUrwbhtE9IxNodFbA9PR0HHkGOHz4MBCZamYh9IcfrDNGF7MUDMNI
+MDKWgrMC5ubm4p181Wo1V9mWDCMERkYpuGjz+Ph4rAhcghLDMDrH3AfDMBIEYyn0Wy/RWQQnT548
+ZzfZoMjblGZjdeU8kJe6mXmRE7q/boNQCv4uMEe3He6/v1dl0KrzmhWSCTXZCCTlbcy+HCL+zsVC
+odDTOv/z7Trs5nsa6eRaGKSLOmhF7uck7fQaMPfBMIwEQVgKbv4761G3Vft+Sfh27w2BZvKGjL/a
+tFar9dS37T6jqgNx//y+TeMaGPR3Li0tdX0dBKEUIOybzJEHGX3yJG9asvrfO8g2Rrlv+y0wu0ZE
+viciz4jIbhF5m4jMiMjDIrK3/ne6nzYMwxgu/cYUvgb8s6q+HthGVJL+NmCHqm4BdtT/NwwjJ/S8
+S1JEVgO/AF6j3peIyB7gGlU9JCKXAj9S1de1+a782GKGkSKN/r+bmRnQDEfquyQ3A0eBvxWRn4vI
+3SIyBaxT1UP19xwG1jX7sJWiN4ww6cdS2A78DHiHqu4Uka8BJ4D/pKprvPe9rKrnjSukaSlcfPHF
+QLRL0mnbxipHRue4kculsp+YmEhU5Ap5PUTe8K2GAdURTd1SOAAcUNWd9f+/B7wZeLHuNlD/e6SP
+NgzDGDI9T0mq6mER2S8ir1PVPcC1wNP1xy3AF8m4FP2qVat4+9vfDsDBgwfZs2cPcHY14uLiYq6m
+lkLA5apw/bpmzRqeeeYZIMplsXv3biBKmJplKjw3yg47g/MgSWs6tR19pWMTkd8G7gYqwLPAHxJZ
+H/cDm4AXgA+q6nkrowzafXBLO8fGxmL3Yf/+/bHp6zL95mn9eig0FlOZmpqK3bHp6WmOHj0avzbs
+vnUKYPXq1bFM7re23bJAh+5DX4uXVPUXQLNGru3new3DyI6RTNzqRrNSqdS08IYxeqxfv57XvOY1
+ALzzne/kK1/5CmBB5QYsm7NTCMZoIiJxjOPDH/4wO3bsAGDv3r3mLjTHsjkbhtE9wWyIGhQiEs+V
+WyAxfQqFQpxjYNg7XYvFIm94wxsA+MlPfhLPLvWx9iZ1+V0laTgrZ6ezNM4tdp9LS9aRUwr+Flkg
+tSxM3eDkaUwgkkfcbI67QKenp7niiisAeOyxxzh06FDLzw4K1/brX//6uG8ff/zxnm8Sp9RqtdpA
+63/6+Iqg1y3tTqa0t8Sb+2AYRoKRsxQguXAlhAU0biQSkVykR2tFpVLh5ptvBuA973kPADMzM+zd
+uxeAH//4x0ORw43mzz///EDS4vmZwNOerRgbG+u59majhZCWuzOSSsH3ubKMKbi23Y85OTmZ2Cfg
+aGYOhhgLKZfLrF27FoCtW7cCcNttt/Hgg9Gi1Wq1OhQ53M1UrVYHMsN05syZ+Pua5WAcBO57FxYW
+ejb/G6+ntK4Rcx8Mw0gw0pZCKDkKt2zZAsDx48dbWgpuBH7ppZeAyEQOxeJxLC8vMzs7C8Bb3vIW
+IDqntKPhjfiBwUGsSfHlTsPdLBaLcRszMzO8/PLLXX/HMPODmqVgGEaCkVzRGAougLVy5UogqnPZ
+bCQqFouxn+xGQVWNR8Fh+ep5o1AotB3ZW8Vr0pp6bNa2iLBuXZRr6MSJE/HxkydPdvVdA7hX098Q
+ZZwfF2V2Jne7FPJw9iItl8txAMxI4m4qPzW8qsZKWERiRbxixQogci/m5+eBKNg3jL51v/f09DQ3
+3HADAIcOHYpnaZopJv/mz8r9NffBMIwEZikMgW7MPjd6mJXQmmbJR3xXQkTiYJ4L7KpqbDUUi8Wh
+jsInTpzgkUceASJLwQVF27ku/nn6Lmba5DKmkLdCr+1wtTR9hr3Dsx+fdRj+eSvarfFwr4+NjcV5
+JQuFAnNzc+e8N00ZnRylUqmpwnd9uHr16qazE8OMKZj7YBhGgty5D4VCITYDnTl16tSpLEXqm6mp
+qXj0yMpt8CPynayLcCPfunXrOHbsWMefGzSdtre0tMTk5CQQuRTDtDb9fmn1+zorxlkwWZIbpeB+
+xLVr1/LlL38ZgOeeew6Ar3/967HJ5f/InUxZhcCZM2cy2wvhm7WbN28Goqkyt9ux8aZx73/d66L6
+Pp///Of56le/CsBTTz0VXGIbJ3+lUmFsbAyIZoNCuC6auT6t5BqmsjX3wTCMBLmxFPwos1vMs23b
+NgCuuuqqOLX4Sy+9FC8ACmE06IQsZxr8XA++6TozMwNEo6o/St1yyy0AbN++Pf68sypC7G9nHVSr
+VY4fj5KKhyZnoVAIKpdkbpSCY3Z2Nja1X3jhBQCuv/76WClMTU3FkfxqtRovWDGa426QarUap2ff
+tGlTvOBqamoq9ndvuukm7rzzToD4Bvv4xz8ex3RCTCDjBhB/MAkFfxFWSPRbiv4zIvKUiDwpIt8R
+kXER2SwiO0Vkn4h8V0QqgxLWMIz06aeW5Abg/wFvVNV5Ebkf+EfgeuABVb1PRP4GeFxV72zzXT0J
+4SwCP7rbuPTV6B/Xz1deeSVf+MIXALjjjjsA2LdvXy4TxlygDGWdQgmYEJESMAkcAt5FVFcS4B7g
+D/pswzCMYeJG2V4ewKeBk0Ql6e8F1gL7vNcvA55s8dlbgUfrD7VH+I9CoZC5DPbo6/FoJ/d1z5aC
+iEwDNwKbgfXAFHBdp59X1btUdXsn5owRBqEFxIx06Md9+D3gOVU9qqqLwAPAO4A1dXcCYCNwsE8Z
+DcMYIv0ohX8DrhaRSYkie64U/Q+Bm+rvuYUMS9EbhtE9PSsFVd1JFFDcBfyy/l13AZ8D/lRE9gEX
+Ad8cgJxB4Ha7+Y9uP9/N8X4JJUdlJ/Tap1mRN3m7IZdbp7Oi31Tsrba/ppW/P09l84aZmHQQ5E3e
+OvlKxzbMJBLd4hdzgegi6CXo1uriGfRF5XY7+hduqH0LyfqKIhLLGsrN5vejn0PT4V8Locjs8OtY
+dHoNBKMUQo5su87My+jgZyAKWU6Hv+AMwutbXx63UCsvO3BrtVrXLo7tkjQMI0EwlkJoo0Mz8iCj
+T57kzZOsELZl20i3fWuWgmEYCUwpGIaRwJSCYRgJTCkYhpHAlIIxMNz6CCPfBDP7YJxLHpLFlEql
+OAnL8vJynM05ZJmLxWI8exCinFn/7qbaDcNIYJZCTvBLs2U9kvgUi8V4KW2e0rKNj48DBJfYN4RV
+qCOtFAqFQmza5rlgq4gwMTEBRMtWQ0oHLiIdF0zNGnctrFq1inK5DERl6bO+CSF7l8HH3AfDMBKM
+pKXgTO1SqZSLiHgrk9E/Nj09DcDBg2ElsgrJammHs2ReeeWVuG9DGJnhrBzmPqTMunXrOHz4cNZi
+9E2hUIgLtRjtabXj0imFxl2ZIZG1QgBzHwzDaGAklcLk5CSTk5OsXr06MY8eKu1GBzf/v7i4GMRI
+EiouPVqlUkkUCGpGu9cvZEZSKSwvL7O8vMzll1+eC6XQCe6c8oKIJKYrh8HY2FhcUHZU8ycOg5FU
+CoZh9E7+h9AmbNq0CYBf//rXwUbHQ4gyp4mfa3EY+OnRzM3qD7MUDMNI0FYpiMi3ROSIiDzpHZsR
+kYdFZG/973T9uIjIX9bL0D8hIm9OU/hmFItFTpw4wYkTJ9i2bVtwvrjzs1WVQqGQi3UUvTDsPi+V
+StRqNWq12shYCZnFRDooIvvvgDfjFYoFvgzcVn9+G/Cl+vPrgX8CBLga2Nlhodq+i2eKiNbrR2i5
+XNZyuRz/H8qjVCrFj8nJyfh4q8Kt/jnZo/3v3+49xWJRi8Vi5rJ2+iiVSoP+zsEUmFXV/wscbzh8
+I1GZeUiWm78R+B8a8TOiupKXtmvDMIxw6DXQuE5VD9WfHwbW1Z9vAPZ77ztQP3aIBkTkVqJy9APB
+NxndBp3Q8HPwLywsxFOlqhpv0HFTav7uveXl5ZExidOik/4JyY08H24aNyt5+559UFVnunX7ubuI
+ak/mpmzcIPB/aPe8VCrFz50yqNVqCaURAv7cf2h7B1rhyxu6rHBuNaosFEOvUa4XnVtQ/3ukfvwg
+cJn3PitFbxg5o1dL4SGiMvNfJFlu/iHgj0XkPuB3gDnPzbjgaRyp/FHBWQXVajU+FkLSkkKhwMqV
+KwG4+OKL49mS/fsjLzGUfASt6CQBTEi5DF71qlfF1sGRI0favDsd2ioFEfkOcA2wVkQOAP+FSBnc
+LyIfA14APlh/+z8SzUDsA04Df5iCzCODu8FWrFjB7OxsxtIkbw5fYa1duxaIthw79ya0jEWNOPk7
+WUAVgjJwMaUNGzawa9cuIDu52ioFVf1Qi5eubfJeBT7Zr1CGYWTHSC5zDhV/9C2Xy/EIrKrMzc3F
+z7OQyQ8iFgqF2Nz2NzSdOnWK06dPD1W+XvCT6zhLoZXFEEL16PHx8Tjd3sGDB/M/+2B0jts5CJHL
+EEJk3MnjuwxLS0uJ588++2z8npBxMheLxVgJdLItPWsWFhaC2qMzmmtsDcPomZG0FFql48oKJ49v
+1p4+fToepU6cOJGZbE6GQqEQyzk+Ph7PgpRKpWAXgzXifutqtRrUjELeMEvBMIwEI2kphDY6OHka
+R9wQ/Mhm5dNqtRqrVq0CCGKqtBdCuwbyxEgqhVAJ+UL1ZVPV3CoDo3/MfTAMI4EpBcMwEphSMAwj
+gSkFwzASmFLoArcUOC/1BPIip0+eZM7LtdCtnMHMPrjEm44sIvWtdgm6pcDuWGMy2NBmFfz9Cv5y
+5UGQxqIgf+GUnxo+tH6F5ntFhpnKvhWtSgb410KncpqlYBhGgmAshRBSczfO1Tuchg1hA1Mn+PIO
+Ws40zruxCnTIfRtqGrpW8tRqta7LCJilYBhGgmAshdA0r0+oo0M78iRvnmTNG91uDzdLwTCMBKYU
+DMNIEIz7YBhp4me9CiFLdsiYUjD6Io0ZjkHiZjUqlYophQ4x98EwjAS9lqL/MxF5pl5u/u9FZI33
+2u31UvR7ROS9aQneRubzPpq91+gOvz+LxWJi5VxIFAqFOGNztVpNFNsxmtOJpfBt4LqGYw8DV6nq
+VuBXwO0AIvJG4Gbgt+qf+WsRGcrV4l+kK1asYMWKFZTLZcbHxxkfH0+U2vZxx4alGJyMlUplJBSS
+u+EaFyCFQq1Wo1arsbi4GD8PnVKpRKlUYmxsLJNrpKdS9Kr6v1XVOWY/I6oZCVEp+vtUtaqqzxFV
+inrrAOU1DCNlBhFo/CPgu/XnG4iUhMOVoh8afnGPWq3GmTNnOvrcMIJlxWKR1atXAzA1NcXBg1Ht
+3UKh0HQpdR5oDNo1G9Xyci4hUCwW48IwvmXr1+JI29rpSymIyB3AEnBvD5+9Fbi1n/Z9XOfVarW4
+zmEIhT58JiYmuOSSSwB44YUXmsqXlxuolZzu+MqVK3nllVeGKVJT3Lr/0PerOFSVkydPAucWIO50
+gOuXnpWCiHwUeD9wrZ7t7Y5L0avqXcBd9e8K/9cyjAuEnqYkReQ64LPADarqFxd8CLhZRMZEZDOw
+BfjX/sU8P/4usGKxeE6+g6yZmppiamqKa665hsOHD3P48OHgqza3ol3gy72+sLAQxKyEsxAmJiZy
+EdhdXl6OZXbBW7cWxAUg06bXUvS3A2PAw/VO/pmq/kdVfUpE7geeJnIrPqmq4Yd7DcOIkRD8rF7d
+B79K8sqVKwGYm5sLxnccHx8HYNOmTQB84hOf4DOf+QzQX+wgy2CkG/lbBbucbBMTE0FUqHYjqxuB
+IT9xG0gWzR3ASszHVHV7uzflepnzihUrANi6dWvceUeOHGHfvn1A9oFGN9PwgQ98AIC777675VLb
+SqUCJKtItbp4s1IKY2Nj560rKSLxTRhKPcc0E84MA78O6bCWZ9syZ8MwEuTaUnCj7vj4OFdeeSUA
+3/jGN4IZEVww8Z577gEiy6WZtheRc46f7xyysoCaTYn5I7CIBFuhOmursVMa+9PJPcx+zbVSmJyc
+BKIiqN/+9reBMHI9AnEE3sfNPzeSpzl0f97f/wth3njt+nVycjK+4VQ1sx2UfpZon3YxnDQw98Ew
+jAS5nn1wwTlVjTVpSKOVC7qN0v79EIKHg8TtooQwrEx/mf7ExERsbQ5IrtGffRjWss9eGaQyCCUF
+etY3TSP99os/oISAqsZu8SWXXMLc3BwAL7/88tBkMPfBMIwEF7RSCH3Jq48zc0MbqbOm3yCtn2cj
+hL6dmZlh/fr1rF+/nu3btzM3NxdbC8Mi1+5Dr+RJGcBwF67klRBu6H5wcY3LLrssjkXt2rUrPj7M
+WNkFbSkYhnEuF6Sl4BjQevLUGHTFaCNMRCS2CEqlEo8//jgQ/e5ZWEBmKRiGkSB36xQGsbEl5Ll2
+P97hng/Ln2xVnTiktR+9MjY2Fv/ei4uLQf32vmyqmuaS5tFcp+DfNG5r8sLCQlc/ckgXhMPfDQfZ
+XLiuvXK5HOcJnJ+fj+fxa7Va0Aq1Gb5iDVX20NLOm/tgGEaC3FkKy8vLTE1NAWeXOV9++eUcOHAA
+iIIzbqVjKHPPrfCtA2e6Z7nLsFwuA7B58+b4+dLSUpx1ulqtBrsLshV+2bi8psAbNrmOKbgdZP45
++GvHQ1cKjkqlEs8wZJkdyN+pd9FFFwFw+vTpYLNjd0KoLkNGdBRTMPfBMIwEuXMffI3fbCNLHkez
+UDZ2+VbK0aNHM5ZmMJiF0D1mKRiGkcCUgmEYCUwpjDB52/iVJ/LUt90WwTGl0AV5qDCUV0axb/N6
+TqYUDMNIEMrsw0vAqfrfLFjbSdspRbI7arsXOpA3tbY7INH2kGcJ+j7vTuRt8Z6h97knx+WdvD+I
+xUsAIvJoJwsrrG1r29pOF3MfDMNIYErBMIwEISmFu6xta9vazp5gYgqGYYRBSJaCYRgBkLlSEJHr
+RGSPiOwTkdtSbusyEfmhiDwtIk+JyKfrx2dE5GER2Vv/O52iDEUR+bmIfL/+/2YR2Vk//++KSCXF
+tteIyPeaYRhLAAADl0lEQVRE5BkR2S0ibxvWuYvIZ+p9/qSIfEdExtM6dxH5logcEZEnvWNNz1Mi
+/rIuwxMi8uYU2v6zep8/ISJ/LyJrvNdur7e9R0Te20/bgyJTpSAiReCvgPcBbwQ+JCJvTLHJJeA/
+q+obgauBT9bbuw3YoapbgB31/9Pi08Bu7/8vAX+uqlcALwMfS7HtrwH/rKqvB7bV5Uj93EVkA/Ap
+YLuqXgUUgZtJ79y/DVzXcKzVeb4P2FJ/3ArcmULbDwNXqepW4FfA7QD1a+9m4Lfqn/nr+j2RLY0V
+cob5AN4G/MD7/3bg9iG2/yDwbmAPcGn92KXAnpTa20h0Qb4L+D4gRAtZSs36Y8Btrwaeox5H8o6n
+fu7ABmA/MEO0YO77wHvTPHfg1cCT7c4T+O/Ah5q9b1BtN7z274F7688T1zvwA+Btafz+3Tyydh/c
+xeI4UD+WOiLyauBNwE5gnaoeqr90GFiXUrN/AXwWcEkfLgJmVdUVdkjz/DcDR4G/rbsvd4vIFEM4
+d1U9CPw34N+AQ8Ac8BjDO3dofZ7Dvgb/CPinjNruiKyVQiaIyArg74A/UdUT/msaqeyBT8mIyPuB
+I6r62KC/u0NKwJuBO1X1TUTLyhOuQornPg3cSKSY1gNTnGtiD420zrMdInIHkQt777Db7oaslcJB
+4DLv/431Y6khImUihXCvqj5QP/yiiFxaf/1S4EgKTb8DuEFEngfuI3IhvgasERG3ByXN8z8AHFDV
+nfX/v0ekJIZx7r8HPKeqR1V1EXiAqD+Gde7Q+jyHcg2KyEeB9wMfqSulobXdLVkrhUeALfUodIUo
+6PJQWo1JtI/1m8BuVf2q99JDwC3157cQxRoGiqrerqobVfXVROf5L6r6EeCHwE1ptl1v/zCwX0Re
+Vz90LfA0Qzh3IrfhahGZrP8Gru2hnHudVuf5EPAf6rMQVwNznpsxEETkOiK38QZVPd0g080iMiYi
+m4mCnf86yLZ7IuugBnA9UUT218AdKbf1u0Rm4xPAL+qP64l8+x3AXuD/ADMpy3EN8P3689cQXQj7
+gP8FjKXY7m8Dj9bP/x+A6WGdO/BfgWeAJ4H/CYylde7Ad4hiF4tEFtLHWp0nUbD3r+rX3y+JZkgG
+3fY+otiBu+b+xnv/HfW29wDvS/O66/RhKxoNw0iQtftgGEZgmFIwDCOBKQXDMBKYUjAMI4EpBcMw
+EphSMAwjgSkFwzASmFIwDCPB/wfZOLg8L5jSKgAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/2... Discriminator Loss: 1.0094... Generator Loss: 0.9240
+Epoch 1/2... Discriminator Loss: 1.1167... Generator Loss: 1.6473
+Epoch 1/2... Discriminator Loss: 1.4142... Generator Loss: 0.5206
+Epoch 1/2... Discriminator Loss: 0.9967... Generator Loss: 1.5393
+Epoch 1/2... Discriminator Loss: 1.3248... Generator Loss: 0.5312
+Epoch 1/2... Discriminator Loss: 1.0156... Generator Loss: 1.1135
+Epoch 1/2... Discriminator Loss: 0.9628... Generator Loss: 1.9009
+Epoch 1/2... Discriminator Loss: 1.0585... Generator Loss: 1.5717
+Epoch 1/2... Discriminator Loss: 1.0929... Generator Loss: 0.7814
+Epoch 1/2... Discriminator Loss: 1.0840... Generator Loss: 1.0624
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsfXmYE0X6/6eSTDIXwzAMA8PIKcghgiCC6Cqo67qKLF6A
+CiqCiq54oOu5i/ft1wMUUVZdxeXSxQtRRBCRVQFBEJFDkOG+GYZj7iT1+6Pnfae66STdnQTD/vrz
+PDxkku6q6uqqt977FVJKuHDhwgXB83sPwIULF6kFlyi4cOFCB5couHDhQgeXKLhw4UIHlyi4cOFC
+B5couHDhQoekEQUhxJ+FEGuFEOuFEPclqx8XLlwkFiIZfgpCCC+AXwGcB2ArgB8AXCmlXJXwzly4
+cJFQJItT6AFgvZRyg5SyGsBUAP2T1JcLFy4SCF+S2i0CsEX5eyuAnpEuFkKktFulEAIAQFyVEML0
+s114PB5du8bPTmEcb6ojnjlUnzXSZ7ttqTDeT+8sHA4nbZ4jtev0mZTr90opG8W6J1lEISaEEDcC
+uJH+9nq9CIVCMSdE/Rxpk4bDYTvj4LbMNikA+Hw+3fderxfBYBAAkJaWhurqagDagqHvVajtqv2l
+p6cDAGpqavhaGns4HHa8oAOBALdL3weDwahESJ1Dj8fD4xBCcBtmmyYUCuk2SiQY+1bHEAgEdHMQ
+CoUsPasQgp+1urpaN460tDQAQGVlpen96nMT0tPT+f3R96FQSDfmrKwsAEBFRQX3R2M3EjezPiLB
+6/Ue8XzqfgiHw7wO1fkxW/fqOPx+Pz9TKBTaFHMgSB5R2AagmfL3cbXfMaSUEwBMAPScgtlGML5E
+ehn0fzAYdEy1YxETIUTUjW5lMavtquOrqqoCoBEdIixm11mB+hzUrjrOWKexkWDRIjUuTNpsZoQs
+GiLNLQDds1t5bvVZ6d2kp6ejoqKC+4pGWCJxZpWVlabPp15TVlbG39H4aY7VuVKfz8oz0fz4fD4e
+u3rICCF0xMc4tkjPVF1dbXstJUvR6IOmaDwXGjH4AcBVUspfIlwv7bCQ0U6dowGPx5PQvuNhn2O1
+q27oeKFyEImGU6L+e4lKiZ5bY9tAbPHBwbpZKqXsHuuipHAKUsqgEGIkgC8AeAG8FYkguHDhIrWQ
+FE7B9iAscAokT/l8Ph2rVXu/jnWMYxwAjmSl69WrB6COdVSvjSX/2u1bhdN3k5GRAUATT1SOJtap
+Sr/7/X5mVVWuSL2mYcOGAIA9e/bYmnOVy6N2VZY51TmF7OxsAEB5efkRfSZzDB6Ph3VQpCdR5zDS
+/jFwd78fp5AMkGwVDAZ50fv9fgD6xa8SDbsEItJLpX4OHTpkf+AWoCrM6NkqKipYN2B3sdWvXx8A
+sHPnTt33sdqJtMDoM8nbAHiBqvNtpnsxQlWGEQKBgI7gWoXKwqvjPBpIlhgVrT+jEtSOrsIOXDdn
+Fy5c6JAy4oOZEsuMnVe/p9M1GAyisLAQgHaaDR48GADw8ssvo6CgAABQXFyMhx9+GEAdy19eXo73
+3nsPALB582adxpn6UFlbGp/X6+XPiVI05uXlAQBeeOEFAEDz5s3x+OOPAwDmzp1ruS2jEjSRNm9q
+X73P5/Px5wEDBuCTTz4BoHFvxpNNvd/v9/Mc1tTUxDzRqI20tDRuo0GDBvy5Xr16/K5PPfVUTJ48
+GYAm3gCRuZicnBwef69evfDzzz8DAEpLSwGYmzTp+c3m0ArnEm3u8/LyeL1VV1dH7D9WHxFw7IsP
+sZx61E2cn58PALj99tvRq1cvANrkT58+HQDQrVs3Nn1dc801ALSF++WXXwIAfvvtN13b9OLMCEWk
+8TiFlJLHtmbNGgDAOeecgxtv1Nw4vvnmG904osG4ucwWrCqL2oWxfdUX4vDhwzyf77zzDg4fPgwA
+mDlzJosHdH9ubi4OHDgAQHsPVolUOBxG27ZtAWgi1qhRowAAJ598Msv7HTp0wLPPPgugztwZDodx
+5513AgB2797NJtfbbrsNH374IQBg69atGDJkCADgtdde4/FYHZsVa0Cka/7whz8AANq1a8ei2aJF
+i1BcXAwAKCkpOWrikSs+uHDhQoeU4RSs2sCFEDjuuOMA1HmB5ebmomnTpgCAxYsXo1OnTgA0yk/s
+1+rVq/HQQw8BAFq0aAFAO0XOOOMMAMCSJUt0JwJZNoLBICvYaHwej4cVgtXV1Uc4HjkBiQ+5ubkA
+gAULFuCEE04AAPzpT3/CzJkzLbcVyWEpWcox1dOzW7duADQWfsOGDQCA1q1bY968eQDAHoFpaWlY
+sGABAE2jT0rVSBwRiYr169fHRRddBADo378/unTpAkAvxgB14gLdFw6H8eqrrwIA9u/fz9aqgoIC
+NGnSBABw4MAB3HPPPQCAnj01r/y5c+dGPKFpnml9pKWl6RSmZvOtrnNVBDv++OMBAHfffTev5XA4
+zCLkyy+/rHNKSyZShihYNe2pXmy0GQ8ePIhTTjkFgMZ+/fnPfwagvXzV1DV+/HgA2iajtt544w1u
+2+gJBmha9n79+gEA/vOf/wCoc8m2M+5Yz0Qs45YtWsjI+++/j4MHDwKoM4NZBbVVXl4e99jsYOnS
+pUxMa2pqeA7z8vIwYsQIAHWy+ksvvYSWLVsCAAoLC1nsiATaEIcOHeLPxcXFaNy4MQBtjohQL1q0
+iAn8tm2aI+2sWbNwxx13ANCIFJmZhRAsphUXF2Pz5s26fmN5gQJ1mz8WQaB7jG16vV5cfPHFAICm
+TZvy+66pqcFll10GAHjxxRcjjiManDjGueKDCxcudEgZTsHj8Vg6dcPhMPbt2wegTnxo1KgRpk6d
+CgCYOnUqs4xqMIvH48EVV1wBQBMrAOD666+PaR+vrKzE+++/r/sukgLUqbuyEILHsWPHDgDArl27
+cOKJJwKo4x6sQuUQjqZzT9OmTVkkmDVrFp5//nkAmshAIh+JcyUlJcwJrV+/PmbbNP7y8nKMGTOG
+2yVRsUGDBli6dCkATQwgMUQ9sYkbGTp0KB555BEAwIYNG7B//34AmgXKzjyp8SHGviIhUgzIpEmT
+AAAnnHACmjVrxs9MilTVCmYHTt67yym4cOFCh5ThFOwowYyKvc2bN/OJqMr7qseb1+tlmzXpBmLJ
+sdFgRrWdnsZSSjRv3hwA8NVXXwHQZGhSgP33v/91OErnYyIlWiAQYBk+1jtq06YNz+2rr77KfR84
+cIDNj3S6qqY+p56nhw8fZhNubm4u9u7dG7U94qD+9re/sXKwcePGWLt2ra5dq6B+1PXmZL6rqqow
+a9YsAECXLl1w/fXXA9D8PohLlFJaClFPBFKGKAD2o/BokjIzM3HhhRcCAP7xj39g+fLlALRNv3r1
+agCa7Zo2nB1noFh9J+IFpaenY9y4cQDALHBmZiY70pxzzjnsFHS0kJmZCQA4/vjjdSHOxOqTsldK
+yYrdhg0bMhts3ByqNQfQNPW0mQwx/zHHpoYsk1hy8skn49NPPwWgKZ6N7tSFhYWsaGzUqC7PSFZW
+liX3bDPQM5IbvJSS27IqDgMaoRwwYAAAoG3btkzcCgsLWdRt1aoVXnrpJQDAwIEDASTP7d4VH1y4
+cKFDynAK8XjZrV69mlltj8fDfgjnn38+26ZnzpzJHITTk4FgNytSLHi9XuTk5AAAOnfuDADYtGkT
+28pJYXe0IITgKMi//OUvbDdfuHAhunbtCgCsUPT7/Tj99NMBaD4ipLQzgk5NMhv6fD4WS+rXr8+i
+nR00aNAAEyZMAKAp6O69914AwIcffsgiF5mq77jjDg4UE0LweNavXx/3eqC2fD4fc5B2UFBQwCbS
+8vJynqPvvvsOu3fvBgC89dZb/E7ofyucgpMcGClDFJwMnthA1R8+FAqxnf7jjz/GO++8A0DbZIna
+yKqjTLwLCtDGTL765II9a9YsrFixAsCROpRkgeTs+vXro2PHjgA0eZds6KeccgrHmFCMRpcuXXiR
+FhYWYsaMGVH7UMUOet979uyx9W7UaE6V2NB7HzhwIH9/1113AdA2G1l4Dh8+zOIMObQ5Ac2XGpdB
+zmfV1dURCSRBDb8na8mhQ4dw7bXX8jiJQEyaNImtZ3bgWh9cuHARN1KGU3ACYq3GjBmDxx57DIA+
+aKmqqort/olk99UkJIlIpVZRUYFp06YBqGOvp06dilWrjl6ZDK/Xy6x2IBDgALNu3bqhQYMGADRN
+PZ3uTzzxBADtZCNF2yeffBLRL8KYvk7NHeh0/vbs2cPiw+jRo5lTKCsrw2233Qagzm36mWeeweef
+fw4AuPzyy9mtnLgcJzCOPxwOsxhkheslZW5JSQmP/amnnsKuXbt07QLA119/zZwHWXLsjNEOUjp0
+2ipOO+00PPfccwC0hUuuqiUlJSxbjhs3Lm6XZFrw6enpHKa7fft2yxGMkZCWloaxY8cCAC699FIA
+mrY5XjfltLQ0y2O7+eabOULwwIEDTHD79+/P8q4alk1iwLZt23hjzZ8/H5dffjmAI0UeY8i1qp2P
+J/qUXIJbtmzJB8Pdd9+NDh06AKhzBtu4cSPHO6Snp7OVpFevXuwqHStM2Qgi4HT/jh07bMUnqNYz
+aqukpIR/V82Qubm57Op80003AdAOE5t7xlLotCs+uHDhQoeU4RTiYcM9Hg+fGDfddBN++OEHAMDI
+kSPZznv//fc7PnmNLLGagiweqwmhYcOGHFFIJ2xBQYHjdsnN28qpRc9WUVHB/R08eBAPPvggAM1P
+4ZJLLgGgncbkLETIzs5mu/rixYvZ2rNq1SrL48/JyWGX53hgx6X77LPPBgDMnj0bf//73wGAczBY
+BTliRUrh7xRmCYXatm3LrtnkyzJ58mQO+LIIS5xCyhCFeMQHtRYEUGciUiMct2zZgmXLlgGwtlmM
+7QN6opAIqwON+YILLsDTTz8NoI6FPfXUU+Nu18p8tm/fHgCwYsUKdkzq2bMnJ0hRWdi0tDQ2/ZLc
+27JlSzannnHGGSzjmyU2NULNbhWvCGYXdIjs2rWLD4727dvb2tSkE6B3lghHNtU8nZOTw3k2s7Ky
+OBHLaaedBgB4/PHH7R50rvjgwoUL+0gZTiFZBVEI9erVY4oe76mU6LFmZ2ejT58+/BkAR30mG+R3
+sGzZMowcORIAMH369IjPRwoxlRshcSUvL4999WNVafq9QVzK3r172WHMJiueFPHB5/Ox63Z2dja2
+b98OAGjSpAlbKCgnBYmcNpBcTkEI0UwIMU8IsUoI8YsQ4vba7/OEEF8KIdbV/t/AaR8uXLg4+ojH
+TyEI4C4p5Y9CiHoAlgohvgQwFMBcKeXTQoj7ANwH4N5YjcVDYa0mzCQZkFBWVqazbUdqg04EOl3i
+qV1phrKyMixcuBAA2HswHqgBQ7GyThPXlJ6ezubEjz76SKczIa4gPT2dvUhJt7B161bOZDV79mx2
+JaacF1bHe7Q5VnqnVVVV7NVIGZisQo0kJcRrRg6Hw7wmPR4Pt7dr1y72T0iEPisaEiY+CCE+BvBK
+7b8+UsodQohCAF9LKdvFuNfRIIjNKisri+hSShukUaNGfD1p0xcuXMhZnDds2KBzwVXzDtJG/eCD
+D/j3RMNY4MaOg4oR1EakIqlA3bwQofz111+Z3R8/fjxmz54NAPjll1/YJbioqIgtDWRP93q97MdQ
+UVHBORMXLVpkebz169eP63mdoHt3jYv+/vvvccEFFwAA5syZE/M+szye6qFBc+5UdEpPT9c5xqlp
+8hOw7o5einchREsAXQEsAtBYSrmj9qedABpHuEdXit6FCxepgbg5BSFENoD5AJ6QUn4ghCiVUuYq
+v++XUkbVK9jlFIhFpUy3n332GebPnw9AYwfp9549e7IXW4sWLdjzjE78yspKPvHKy8v5FFCTs6il
+0khRmQx2l8xQt9xyCwDN3dUp1NPMzDzp8Xj4GuJQNm/ezGyrEAKLFy8GAKxdu5ZNuV999RXWrVtn
+2h715yRxitP5bNSoEWdBvvjii3HfffdZvpe4gpNPPhnDhg0DAMyYMcPWOGhtqMmB6Ts7QWzE2QGa
+QpzMwWpp+/r16zNHQtyDg1oQyecUhBBpAKYDmCSl/KD2611CiEJFfNgdTx9mOOmkkwDURRQOHjyY
+F2FGRoZuc7/yyisAtNBaymhDLHA0/3xqQ61TSUg0UfB4PLyxaJNmZmY6lk+j5ZAEtA1rZH1feukl
+LrJy7rnn4uSTTwagiWiUoWr79u2mmz1e7Xs8sQ/kZDV06FDWCTRr1ow3pZmlKT8/n60uCxYsYAJh
+972q4iaB+rXSFoWkz507l5PoTJ48GRs3bgSgiRLUxhlnnMEu2zT27du3M8FOZCRtPNYHAeBNAKul
+lC8oP30C4Nraz9cC+Nj58Fy4cHG0EQ+ncAaAqwH8LIRYXvvdAwCeBvCeEGI4gE0ABsY3RD3UoBzK
+sty7d2/OgKsGAVVXV7NNd/jw4ZyUwsppYCXfv50xE1QWUK2JSMEuJOLEY+M3CzAynlzUN83JmDFj
+mI3t2bMn97927Vq23ycrr4MqroXDYcuWHb/fzwFvQ4cOZRFsz549fNpeeeWVADTFLT1rkyZNeO0E
+g0EObjt8+DBzkfEi0tjVBDbnn38+AI0bve666wAAPXr04GQxrVq1YsVtSUkJc7rkEh4MBjF8+PCE
+jFc3xlR2XlLlYbUwaZs2bQCAX3xubi4nt2jUqBG+++47vp/Y5GTls4sE9XlU+Z3k9pycHHZMSU9P
+x6BBgwCAn+3+++937DZLcq0dJ63fwywYb99er5czQe3YsYMraq1fv543OkXMZmZmokePHgA0/RFl
+tVq6dCnn7iwrK0tKUlSjjorYf1qzPp/PNPW9Gkmak5PDJmMSn1955RW2nsUqVVAL183ZhQsX9pEy
+nIJZQBS5/EZzLPo9EOlkM5aBN7vPjDVWrQSqeJGIZz5aacGPJfj9fl5bubm5HCBXUlLC1qpEQxUh
+1TRsdqDm8wA0DoM44Vip32pxbJWiN9PwH+1aiFYRiSikpaVFjcBUnaIiFX+NNxuRWZ/HEo6GGKPm
+RNy4caMuv6cd2PFqpffrtNKT2g8RrpycHKtigy244oMLFy50SBlOwev1QgjBFFVNo55qp53H4zF1
+bbWjnTf6DyTqWY0nrRrJl6rzqeJocAqqdUa1VtlNC2dMMWcl4U4iI0fVPJeJhMspuHDhQoeU4RQA
+zYRm5jJrRDTlmarME0KwIiYcDh8RXab2oXoVqu6loVCI7fdqf1ayOVMbdL/KSQghdEpFGqdqeqU+
+/H4/27bD4bDOI9PoVRcOh9krUj1JVPt/pLGqc2FWH9FYp1OdR7PPxnmI9J3qSh4KhXieVcWtmR8D
+XUPf0xyq74x0PMbxkixOHCp9pjlXa14S1D7UtRBLiavObXp6Oo+Jxi6E4HdWVlam4ybMnk/9jd6/
+WnpP5VjUGiVWuZSUsT54vd6EVV4i7WwwGOTJJn9ywHzhqgvQeI2RCKWlpZm6uBphjJ8Ih8Omocxm
+rshAXf1FI3GjyMbS0tIjyqEb4URkMG4gs6pHaq1GMwJhJOrRiIVx85s9SyylnnEcZuM1I0TqQSGE
+OCJ2IdqGt6NoVA8qlWgDmpWNlIfBYFAXS6HebySQgD4i1sLcu34KLly4sI+U4RQSpWBST4wGDRow
+Na6oqIg7OQW1azfRqBoFR/fZPbnpdCksLMSmTZsAaNyBUdkV7bOd/uLhLJzc5+R+p1A5n1j9JWM8
+qrhi/M6uFyohkjewYS0cW34K8UJ90S1btgQA3HvvvXj33XcBAN9++63ltoybgjY1EZVImZfU+3Jy
+cpgg0f1CCBZt7KQ09/l8vIAKCws5q9Gpp56Kn376CUBdpqNwOMzVnYzZj6wucDtEQdWNRBJhIl1D
+fWRlZbGcnaysQkIIrnSVl5eH4uJiAJrrObHuqqgQ6ZnsiAyRxkHroVOnTgC0AslOwvLVNR+p8E8g
+EOB1aFWn4IoPLly40CGlOAWnIoTH4+GUYN27d+dkG3369OEkKnY4hUiwo2Vu3rw511GgGg5er5eT
+l2RmZvIJFct6IYRgRenKlSuZ8u/Zs4dPGFXbriolrbo5O/WVUC0gdq9R2ehku2Efd9xxHBDVpUsX
+PPnkkwC0+TLrO1mZqD0eDyszqd5EIBDQWT2IS4lVFi4zM5Pn1ePx8LpQFY25ubmch8EqUkanQO6f
+djS5aul0qh1YWFiIMWPGANAmm3L/tWvXLmYRmEghx2bfmY0zEAjwC/V6vWwloKi2++67DxMnTgSg
+Re9RJauTTjqJo90oa9SQIUOQlZUFQCNoM2fOBKCxvuTnrmqcVaiEwMx5SZVl402Q4pSQq5p+j8fD
+7ybR65EKyY4cORJXXXUVAOD111/HggULAGhRkvGIAoA9R6fs7GzOCUlmUTUyctq0abxufD4f/vGP
+f/A4qTAMFTjq2bMnHzwTJkzQiaSu9cGFCxcJQ8qID2QPV51tIsHIEpeUlGDJkiUAtBOWEm90796d
+2fW2bdvil19+4b4IsQp6mJ0CkU4GIydCJwFR8IYNG3KK9KKiIrRq1QoA0LdvX5xzzjkA6jgF1V7f
+p08ffPyxlsBq9+7dMU8oM4WZKkrYeSYriKVoNIOUUmeJUUWJRLDulK35iy++AKCd0LNmzQKgpWCj
+NGbxPLede1X3fVoXxBGMGDGCRZv8/HzdKf/Pf/4TgJYdW83FAWicad++fQEcmVo+nudKGaIARHZc
+od8I9MCUst3r9XKGmvPPP5+Tnl544YX4+eefAWjZdnr16gUATBxWrlzJGumqqiqcccYZAIDPP/88
+aoSmXZb5lFNOAaBVYaIy4ps2beK+O3TowC9cBW229u3bc22BmpoaW5uGZE6j1yDpHezW1SRQUdbT
+TjuNdTg0r3aheuo5Xcw+n4+1+VOmTOF8kyohpLT1paWlLNL9+uuvSY/GVU3KTZo04cxKNAYhBIdy
+G8209P5OPvlkU89aEiVKS0tZd2A8UO1aTFzxwYULFzqkDKcQy8WZflPTkxMVPeGEE3DnnXcCAG69
+9VbW6v7f//0fs6h5eXksSpCW1uv1okWLFgC0E4/iC9auXasTNZzapoliU2GUIUOGsHhw8OBBrFy5
+EgDQunVrU06B4PV6+aQna4NV0Jirq6t1p5BTDoEUd1QWffv27XEXLGnYsCFXsY7mqmyE3+9njmDm
+zJmcp1Ntg8ZWVlbGFbFDoRCXon/rrbdYcXvnnXfGTLLiZC1IWVc45uKLL+ZM2SQ+hMNhXVwMcYWH
+Dh1isbG8vBznnXceAHCqOSklBgwYAEBLDf/YY48B0K8RJ+KYyym4cOFCh5ThFOxQXpK/iOK2b9+e
+lYsrV640tYnTSaTC6/WywmnAgAHs07Bu3bqoyka7J+KqVasAaHLf7bffDgBYvHgxmxy3bt3KSUcH
+Dx7MY6PTvG/fvrpoPzt9qzK1Kms6OfGEEPj8888B1GX/6devH9auXWt6rVUXYtXz0sp9dOq2b9+e
+T/ymTZvqOARq89FHHwUArv8BaHNCY66oqMBFF10EQOPo3nnnHd3YIo3ZLki5uGPHDub6iENZunQp
+xo4dCwAoLi7m7NnGUnr0Lv/4xz8CALp164azzjoLgKaAbt68OQAtca2q2LSLlCEKViGlZMUQbegv
+v/ySCYGdSVD9IsaNG8elvWMlS7E70ZQ5WE3IsnnzZm5n+/btrESiYqfV1dXsrrxz504mhES47MI4
+ZieLu3///syik+hDbtZG2GlfJeJW7iN2eNOmTWjXrh23QYrZyZMnMyEmYqC2GwqFeOM98cQT6NOn
+DwBNjEuW3w7N/8svv8xEjYrQXHXVVZbWFF1DCtNvvvmG98LPP/9s6q7tyBnQ9h0GCCG8QohlQohP
+a/9uJYRYJIRYL4SYJoTwx2rDhQsXqYNEcAq3A1gNIKf272cAvCilnCqEeA3AcADj4+2EWMPLL7+c
+T9B//etfACJ79llps3379gA0ts4sb4AZzDJPR7oOAKZOncrjpFJhKoLBILOXpIisrq5mD7YmTZqw
++JOdnc2ng5UxqMlCnLoSUy2K0aNH45tvvgFgv2x7NKju4VYUu6SUbdSoEc+n6qW5d+9ePPPMM1Hb
+IJx44ol8cg8aNIgVqIl2c77rrrsA1NV6AOrEAKfIzc1lP4WRI0eyb0a8iLeW5HEA+gJ4AsCdQnub
+5wC4qvaSdwA8jAQQBbISvPXWW0wM7GrijfB6vfj111+5LasRelY3Fy1UclgSQuDhhx8GALz99ttc
+oObgwYPcpqr9pvvuv/9+nHjiiQCAd999l12ei4uLTeMV1E3lJFRbRU5ODldTEkLw4o4kYjnRVeTm
+5rJ8bZXgApqjj5qliUCuv9FA17/66qscV9K0adOkxDw0atSI3ZVVkJXB6bvx+/3o2LEjAI2Ime0H
+JxaheMWHlwDcA4DeYkMApVJK2l1bARSZ3SiEuFEIsUQIsSTOMbhw4SKBcMwpCCEuArBbSrlUCNHH
+7v1SygkAJtS2FZWUCSE4kMjv9+PHH3+0P2AFdILXq1ePXZDtpIKzepoR56H6RVAF565du3JAlFCy
+WKv+A6RUnDZtGv79738D0BSRxIK+8MILLEpECtaKN/4/Pz+fuakJEyawLwdp9wsKCnjsfr+fv7fj
+B6EWMomU30AF/b5kyRLmmtT6lzNnzoz5vBRAt3LlSnYxTqRIpEJKyVG89DcA9jtw+m7q1avH4uYT
+TzxhyuU4aTveArN/EUJcCCAdmk5hDIBcIYSvlls4DsC2OPoAoG1CYp+FEEeYaqxCdfUFLFfVOQJW
+J5qumz9/PgCgV69eHO+wd+9eltV79+7NRI82Uzgc5vEuWLAADzzwAACgVatWKCrSmC8zHYiZ+TQe
+HDp0CEOHDgWgbUZqj1x058yZg++//x6AZhZ76aWXADh3eQ4EApbdjoPBIN58800A2lyQ7kYVwegA
+6NWrFye89Xg8uPnmmwFosj7pa/bs2eNozLFwww036P6m/hYuXBhXu+vWrWN9yJQpUxIm+jgWH6SU
+90spj5NStgRwBYCvpJSDAcwDcHntZdfCLUXvwsUxhWT4KdwLYKoQ4nEAywC8GW+DaWlpuuhBOwEs
+dLK1bt0xavcaAAAgAElEQVSa07RRcMqCBQtYfLDrtmqn76+//hqAZkUgJVibNm04iOvQoUM488wz
+AYBdsWtqaphryMvL41MlLy+PTxqV1SYYWW41jbiTVGehUIjdcYPBIEfokRUiEAiwBWDGjBm6HAlO
+rB0VFRVsXYiUAMUM//znP/nU9Hq9/I5IJHj++eeZaygrK8OkSZMAaO+EfC6ShQceeEAnxlGAXLyY
+O3cu5+FIpII0IURBSvk1gK9rP28A0CMR7bpw4eLoI2UyL0X7/ZJLLsH06dMBaJR2xYoVAMDBIJs3
+bzaNzb/ttttw6aWXAtBk8dLSUgB1cubu3btx2WWXAdBOQTIRGueE3JHtFvOkfsicWlFRwcrD6upq
+0wxJF198MQAt/wNxDQUFBVi3bh0ArX4FuUJPmTKFOQ9qq6qqijmh8vJyR7kOVAghdEVyCgsLAQBb
+tmzh7+655x4AwPjx41kJunfvXmcutg6Kl5iNmdogN+i5c+fqrqEgtTPPPDNpyWKJq6J1B2ieh127
+dgXgvBI4hYir3qQzZ85E//79AUTlZP93sjmrDj9qxBkpi0aPHs1sa0VFBU4//XQAmn2fovrWrFnD
+bDAt7LZt22LUqFEANBafFILGRWIkBlZYYzUX3/bt2wFohEC9T1301OdXX33Fv1HilUAggOuuuw6A
+xraT08trr73G96t+A6p4Re7TlZWVjjTRUkoep9/vx8CBA3W/P/nkk+xKHAqFsHv3btt9qFBzDTqF
++pz0Tjdu3MjEORwOcwxHsggCoIkm1B8dVD/99JNjawMR5+XLlwPQE7+BAwcmzEXbjZJ04cKFHlLK
+3/0fABntX+/evWVVVZWsqqqSlZWV/HnAgAFywIAB0ufzyezsbJmdnS0bNmwoN27cKDdu3CiDwaAs
+Li6WxcXFctq0abJRo0ayUaNGcsaMGXLGjBkyFArJQ4cOyUOHDsnTTz9d1haliToWANLj8cS8pjYZ
+rfT5fDItLU2mpaXFvEf9d8opp8iMjAyZkZEhb731Vrl9+3a5fft2WV5eLmfNmiVnzZols7KyYo45
+KyuLr7PTv9k/r9crly1bJpctWyYrKytlZWWlbNeuXdzt2p1bJ/+uv/56WVpaKktLS2VxcbGjd2L3
+X8OGDWXDhg3l4sWLZVlZmSwrK5Nffvml9Hg8jp6zffv2sn379jIcDvM/eiaL7S2xsh+PCfHh2muv
+ZZFBZZEo+nDevHnMct533326FGsUiXbLLbcwK/38888D0GQz8lXw+/2OsvMqhA1AnR7hlltuYZmS
+bPdWqv9Quxs2bEDjxo0BaGnnyPnl0KFDeP3117k91WmJxqPqKoj1z83NZf8Op7JsQUEBR2vSs5iF
+pNtFWlqaLnGO1bT0dvDiiy+ybmjo0KG2KjGpULN1xxJzKKz55JNP5mf67bffWKyM5OBllnqwQYMG
+R/g1VFdXsyUjkXPlig8uXLjQ4ZjgFG677TZcffXVADTKSdSavmvUqBFn6t2+fTunpRo3bpzOjk9K
+JVI07tmzh0+PVq1asbY/lh+EkTsgqIqfq6++GmvWrAFQZ9MfNWoU1xswUnYaB3EETZo0QefOnQEA
+119/PStJMzIy2GejdevW3Ic6HvUkIk4hEQo1ta4FWUOcepcC+oAxUsYmqqYogbhGml/gSEuEVQQC
+Adx9990ANLfiaPB4PGyhUTFs2DCcdtppAMBK7v/+97/MuQghWDl+/PHHMycwevRo5jwJW7Zs4bwQ
+kdKuOfEXOSaIQnl5OWdovvnmm3lT0CRs27aNqzBNnDgRGzduBICIjj0ffPABAK1yEy3AL774gtnB
+WBPp9XpNN5lKLAYMGMDJO8gxacyYMbjjjjsAaBpk6q+wsJC1+pSTz+/3m7oxV1RUcPyBWTSgEELH
+lqrmRGJL7S4UIkhnn302uwJTBudIc2EH5H4MaATNScKcSCBiA9StFzI924XP5+MYFMA8tb06x/Rc
+W7ZsYVOt1+vl9UvOdOFwmEWDE088kX8fPHgwH2B5eXncD63vjz/+GN26dQOgxXCY1Sd1Moeu+ODC
+hQsdjgnnJRWdO3fGgw8+CACcl/HHH39kV9UDBw5Ydnrxer1Mwf1+P5+CVsvXWQWxi127dsXll2th
+IX//+985X+Ppp5/OJzoFSTVp0gTXXHMNAO1UVkUC4nSGDRsW81kpZj8cDvOJf/DgQT7lrJzy5Hac
+n5/PpxudbImeq/T09ISWkFNPSnp+u3k4aA6DwaBuvmMpRIlrKCgoYLFw3rx53N6nn34KQMvp8PTT
+TwPQ8mVQebv//Oc/nD/yD3/4g44LBYCOHTtyjdT169ezP83/F85LKlasWMGTRhslGAw6SlkeDof5
+hZaWlrKFw7hR4g0/pg20fPlyTlhyxRVXcOJZj8fDMjp54LVq1YqdX1SRoKamhvUI2dnZR8j0xjqR
+at1N9bMdb0HVW5K86GjeEq0DUAvVOLUQAOCMWomAKs7Q2KwQB5qXXbt2scjSu3dvDtsmPUezZs34
+MLj22mvZA7KmpobzkL799tvsOUoWoKZNm/LnYDDoOi+5cOEiOTjmxIckjoE/R8pJkMi5EkLg+OOP
+B6DVlaQTn/IQ5OXlsbv2xRdfzErJcePGcfowVbutliEndr+yslL3XKpijL63UqJevS8Z60XlNpxG
+VxpBSma13B59djI+QG/5SnTaNmpXLZ2ozrXf72dLCokJFRUVOu7WwpjcqtMuXLiwD5dTsIBkcArG
+9qNlTPL5fLqTnT7b8ZA0FoIxnnjG/um+Bg0acD+HDh1KOLdESCSnUFRUhE2bNunaXbx4MRcQ/l9B
+NO42Ao4tRWOi2MZkINmE06x9NQQ8HoVbpLZpriM9G/W9b9++pBNF9VkTgYyMDK4n+uGHHwIAOx39
+LyFZ78MVH1y4cKFDynAKQgj4fL6I7OzvMZ5I43DAtjlCrFPcTuCWsZRYNK6M3gXdH29V6UgwBnNR
+f2ofZgFf0cYNaLU5b7rpJgB13pLJqOegBsXFGtuxhJTRKZDvtpmGXGUvhRAc7ai6JdNLT0tL4++N
+mnf1GkBff1BN3mJMhkLX0HdqW+pnVQRSqx6prsbUt7GqlToO4/2qRlpNB69+NmqqjXOozm0wGLRM
+9IxjMtukkdoyEwnM2vL7/Ux4VLfpQCCgK6wL6HUqwJEb0zgmK9aVaHOg/iaEMHWAUt+NWVtGd3Xj
+HAohdLktyXpUVlamqyFqXIfqujeOk/7OzMzkOQyFQq71wYULF/aRMpyCmaLRjE2PZHe38hzRTkc1
+ECdWW16v15STAKA7uY0npc/n053WZi7GKttOp5KaF0HlGtTTSh2Penqqz2z1XSdaPLIjdvh8viMs
+Jeo41OdQRQ11/SRzTUfKW2H1PrPvPB4Pr4usrCzdWiefFHUNqPerXKiF5z62rA+RtOTRvrMbnRdt
+0uxo+I3VpGgcRsJm7C8cDkeU8WmBkOtr165dme376aef+LNaYNbn8+lCbtV+jLCzURK9qey0F2lx
+mxF0VZSwU+ErHpjpO+zcpxJI+t/obq++S1oPZpGdUsqk5JiMS3wQQuQKIf4jhFgjhFgthOglhMgT
+QnwphFhX+3+DRA3WhQsXyUe8OoUxAGZJKdsD6AKtJP19AOZKKdsCmFv79/8cSBGo/ot1cng8Hv6n
+ngakPPX7/SgqKkJRUREuuugirFu3DuvWrUNNTQ2fJsROAkC3bt1Qr169I+oU+nw+FkOOBdB8qIo3
+9e9E9WE29/HAjkim3mMGer/ETXo8Hvj9fmRnZyM7OxsZGRlo2rQpmjZtCq/Xy/9onsxybwDmpQVj
+wbFOQQhRH8ByAK2l0ogQYi2APlLKHUKIQgBfSynbxWhL2pE7rZrk1I1hxpZGWhyx2k20aU6FnRyA
+eXl5XEfCOD4gtU1k6twHAgHb4cxWoWrsI1l+UhUqAQuFQrzBaV1nZWVxPEdZWZmV5DFJtz60ArAH
+wL+EEMuEEG8IIbIANJZS7qi9ZieAxmY3C7cUvQsXKYl4OIXuABYCOENKuUgIMQbAQQC3Silzlev2
+Symj6hVUPwUrIIpJVLKoqAgdO3YEoGnkKbXZqaeeyjb7JUuWsIKOKhVv376dqe6mTZssnx5OXbKt
+cBhOtdtW2vs92yDQfKtJXyoqKmLa+q2OkUAcAtn5KysrOc1ZIjJQq30mOh6E1nV+fj67a5vB5/Pp
+clBSmrYoSDqnsBXAVinlotq//wOgG4BdtWIDav+Pr2SQCxcujioca6OklDuFEFuEEO2klGsBnAtg
+Ve2/awE8DRul6K2ehkIIpv6UwWbQoEEYPnw4AOiUbiratWvH5ps//elPAIALLriAk5/aofY+n0/n
+02D1XivXGU1u8SIRpxmd7mqmJ7tjU2VjalPxtIubK1I5DJ/PxzUzyKRXUlLC6fZSGY0bN8aYMWMA
+AG+88QZ27NAkcTMuWq2RQdclAvGqqG8FMEkI4QewAcB10LiP94QQwwFsAjAwyv0Mq2y11+vltNcv
+vPACAC1vo5m9WmXz161bh7Vr1wIA1zvs2rUrNmzYYO1JFXg8HmbxrCjIVOcmQvfu3Xnhjho1ijce
+1Yc877zz+L41a9ZwDj+7Pvx2lLcqASFX20AgwPN92WWX8TiPO+44AMAdd9zBc6hubrWWpppVW3Xe
+oj4yMjI4cYgx9sF4n+q6DdTlj+zUqRPOP/98AFqNUMq9SXNcXV3NYuM777yTEJY/UWKDEIKLzk6e
+PJlTs3Xs2JELDlPJeSNIFDJbY04RF1GQUi4HYCajnBtPuy5cuPj9cMwYs4kqB4NBfP/99wDqlEj0
+PaCVa5syZQoA7ZSjsnBCCD5VevXqBUBLvOEkVwH5DajjItAppp6w9N24ceO48rGxsAe1Q3n+W7Vq
+pTsR//znPwPQErsm0ouN+jjxxBM5vdvBgwf5lL/11ls5geyll16K1atXA6irZfHee+9xTY758+fz
+CX3ttddi3LhxAMAFSwC9FyqJD40aNWJzWqT5JA4kNzeX+7700ktZediqVSuue+D3+5mjoZR3qmv6
+tGnTjqgJYhWJVC6SEvzNN9/EFVdcwe3Ts44YMQKbN2+21FYkDtKJ4jZlYh+c3NeyNt342LFjOVRW
+Lf5h0g8Avf+CE6IQyfrg9/uZ4Dz77LNo2rQpAPDC9fl8TJiAug2yZs0aPPLIIwDAC/upp55C69at
+AWhxGbSBevfujV9//dXy2GNFMFIxESklfv75Z25X/Z1Ehd9++43nnMZTUVHBWY4yMjJw7rkak3j1
+1VdzKvIdO3aYzhf1oUa2quKD6v5NVZP69OnDmZqLior4YCgsLOS5/fHHH3ljtW3bFgDQpUsXDqO+
++eabuTzA7wEaG83r+vXreT5//PFHJhClpaWOiVcEuFGSLly4sI9jmlOg0yMrK4vtufFGS8YL0nb3
+69cPN9xwA4A6jfqZZ57JbPnXX3/NbDUVfTGCOJoRI0awwikcDrMoEc+zqtmD6fdYSkyhJF+h+zIy
+MtjfYPjw4bjxxhsBAO+//z6nQLNjUYiV38Dn8zHb7fV6uZ5o8+bNMWHCBB4TcWl33nknAI3Dmjdv
+HgCNiyFP0N9j/dMcUq2Phg0bMtfXv39/fPfddwASW0m6FsdWlKQTEGtVUFDAZekXLVpk+qLz8/M5
+boDMlok2UQkh+OXm5uZyfkAq8HL48GEWJR566KGYLCyJF4sWLcLYsWMBaIs4IyMDQOxCuMaxqfNC
+G4vYVisLUEp5RFSmx+PhKkbXXHMNa/s//fTTpOTcrKmp4Vqa+/bt4+I6Ho9HZ34k0MGhJik5ePCg
+Ls5CjWC0kpQFcE5MPB4PXn31VQAaIaO2RowYAUCrCUkiUU1NDa9xiocA6t5V8+bNj0hQmwi44oML
+Fy50SBlOwYmWlFw8Bw4cyM5LK1asYGXj1KlTsWTJEm7/rrvuAqA5hdgdGxCbGqss+OHDh5n6k7Wk
+c+fOTOXtKLoWLVqkO9nslMiLFpWn/m8Xai4AUvzt27cPf/3rXwGAWXWn7Ub7nmzzUkpWHgoh2P+k
+UaNG7D8ybdo0AMCjjz6Kffv2AdCXWIuVh8KIeDmFrKwsdsMnXH/99Zg0aRIAjYN7+OGHAWgWJ/Kt
+yczMxEknnQQAzCn+6U9/suLabBspQxTsgF4MmbEmT56MUaNGAQCXfwc01pFYxpdffpkLvZKzDZku
+Y8HOAqCFtW/fPiYGtEA3bdqEW265xXJbzZo1A6BfuCUlJbbGY7aIo4Xa2oXqNDNnzhzMmTMnrvas
+xJXE8vrcvXs3Ewuy1GzZsgUvvfTSEffb3dxOiSiJBDNmzODPVBP0s88+05ncyaNx1apVfP8VV1yh
+K4YMaHqSH3/80dF4osEVH1y4cKFDynAKahbfWCCqSopDr9fLbsAjR45kB6DevXvjm2++AQC88sor
+rARLBnU1jm3OnDlYuXIlAK2MOKCxtdGi3ozo3bv3Ed9t2LAhbgWelNJRlW4zZGdn49RTTwWguUGn
+AlQxbvny5QA0d2haL4nKh2FHlKDxnH766cylkfWBxB5A4xRojbz99tt8bVZWFivTaU0TJ5pouJyC
+CxcudEgZTgGwT8FJnm3SpAlHPk6fPp29Cjt27Mgedqo5kPQPb7zxhq5ycyLh9XrZxbZRo0YAtBOB
+uJXi4uKoz+r1ejFkyBD+m64lRZ5VqPZ9Oq3URKFOQWbWTz/9FDt37gRgz0QaCcZsQ4lCs2bN+GTt
+0qWLYwWdyh2oyl8gur6BTKNCCA7++vzzz6P2pXI8U6ZMwb333gtAc+kGIgdJxYuUIQpO/PnpZbz4
+4ouYPXs2AGDZsmXsH3DRRRdx/EHXrl11RUYAzbGFJjpW6jO7CIVCrOwiR5oRI0awa+vIkSN1LqzE
+JpIy9F//+hcTFaCu0lHDhg1thRkTMYmnHqUK6pvmWwjBittEsOSJzk7cvbvmq6OG3F922WV4/vnn
+HbWn+ojYIawUcr5mzRp2sqKoTSsoKytjonvppZcCQMJEQCNc8cGFCxc6pAyn4ET5Q6aZDz/8EP/+
+978BaNSTWK758+fzyTZo0CBWRlLUX25uruPUX7HuCwaDHDxE/hQ7duzA66+/zr8TfD4fK+meeOIJ
+ABp3QZxQVVUVK5e+/vrruE2S8eDyyy/X/b1x48aEKhgTVX2c3vt1110HQC8yLV261HGbKndnZ27J
+LN2vXz8WWa2IR9TH/PnzUVpaCgC2AuIcgTIH/Z7/hBAyEAhIALb+paWlybS0NEvXNmjQQM6bN0/O
+mzdPbty4UW7cuFG2bNlS+nw+6fP5bPXr9XotXUdtz5o1S86aNUsGg0G5atUquWrVKun3+/n3Bx98
+UJaVlcmysjIZCoVkKBSS4XBYHjp0SB46dEi+8847sk2bNrJNmzaWnzdZ/3Jzc2Vubq4cPHiwHDx4
+sExPT09o+1bn1uq/0aNHy9GjR8tgMCgPHDggDxw4IFu3bp2Qtu2sP/rn8XhsXd+9e3fZvXt3uXHj
+RhkIBBztE+XfEiv70RUfXLhwoUPKiA9SSl0iDCssmR326cYbb2SlI0WpPffcc7j++usB6MtyxWJf
+w+EwtxFNMUasH/lFnH766Zxk5fXXX2fLSOPGjY+IPjx48CA6d+4MANi/f7+VnP5Rx5Ao8YFYXtLk
+p6enJ6Rmg5lWP54xk/hw3nnn8Xc0x1YTl5hBfU+0TqysBXVcsdYXKcI7dOiAV155BYAmPiQjwMwM
+KUEUpJRcHj3Roay0wG6++WYO8aXvSktLeUGriT6sjNeKPEhE6/HHHwcAtGnThkOgBw0axElB1I1A
+UW8nnnhiQkx8iZ5PIk5OiZQZ2rVrx3IysbDxgkLUO3XqBEB754899hiAyJs3Vtg2APTt2xcA8Mkn
+n5j+Hgu0zgFwWYL9+/ezZaRz587417/+BUAzo5O147777ktoQqCo99juxYULF//TSAlOgRCpFH2s
+as7RQLb+goICvo9O4wceeIDbjZRF2Ax2ORo68QcOHIh27bQKevPnz+c2QqEQ+vfvD0CzLgCJO+ET
+UVDGrD1icQOBADvjqNGHdkDuvkBiXJA9Hg8neyFubNmyZezrEQkkwtJnGg9xhbm5uZg5c+YR19qd
+W2qboj0feughzhHRr18/5nIqKyvZsrNw4UJbfRj7soOUIQo0eFp0Ukr2QCwrK9NVECJTJBENNZGq
+ysq9+OKLHGlWU1PDxIBkdaeLOB5QKGybNm3Y+YTEp1SHEIJFMHpPmZmZnAnqk08+cSTyUFFV+hwv
+wuEwiyMUov7ZZ59xRqNI1chUAmDmpUgmQSOcriEiUuPGjeMsVcFgkEWTW265JWKfVuFkbPGWoh8l
+hPhFCLFSCDFFCJEuhGglhFgkhFgvhJhWWxPChQsXxwgccwpCiCIAtwHoKKWsEEK8B+AKABcCeFFK
+OVUI8RqA4QDGW2kzLS1NR5kpbVp1dTUrWQKBAAYPHgwAzHJlZGRwEZWDBw9yFuGbbrqJFWLPPfcc
+u7YmzenDBg4fPpxw1p6gckt0UqhVrey2RZzZ2WefzVwWZYEuLS3lDNTLly/nE9rO6Z+ZmckndCJc
+d4UQfArTSVtTU8PPEUlJHE+KNcD5e9yzZw+7t7/44ot49tlnAVgrNGRlbPRcVp8vXkWjD0CGEMIH
+IBPADgDnQKsrCQDvALg4zj5cuHBxFBFXNmchxO0AngBQAWA2gNsBLJRStqn9vRmAz6WUnUzuvRHA
+jbV/nkLfm2UEMpqpyC5MSqSqqirdiUgRaZ9++ilnTB4wYABzCImMvktFkBKwqqrKlGuwC3onBQUF
+nEqMckQcOnSIA3uWLFnCZl07CsO0tLSEcm9CCNZHqYlPaWyJev/x+lPQ/VlZWcz9TpkyhTmvJMBS
+Nud4StE3ADAdwCAApQDeh8YhPGyFKBjakmaLKN5Jr1+/PrOjKuH4vaCy4n6/n7X2ibLNE1RnGlVx
+qypzaRwkUoRCoZhjUOtNqiniVbHkaDnYmIHGlp6erisATEi0Mletb+kENN4GDRrwe9q/f38yD62k
+F4P5I4BiKeUeKWUNgA8AnAEgt1acAIDjAGyL1IALFy5SD/GYJDcDOE0IkQlNfDgXwBIA8wBcDmAq
+bJSiN0O8pyfFsCcaTm3pHo+H2WT1/kRzMHQiqglLwuGwqShBJ5SVU1TlNlQTcCIVpvH4KdB9CS61
+ZgtmvjWRnoe+r6ysTLhoEw/i1Sk8Ak18CAJYBuB6AEXQCEJe7XdDpJRRVcpCCKnGPQCJ2yjkCAJA
+l3koHth1HVUXivpcKmufjMWgbli1upOqa1CJhtM5VzM6x/scxjmKdx1EckFOxPoyOjoBMPWXidSX
+ui4ISSYKydUpJBIuUXCJAsElCr8/UUgJj0YhBLxery4wSGV3I0XOqa6oalv0t6rVTk9PZxaNNkek
+aEe1j1AoxISFXpjf7+e21M9er1dXVs1oHw6FQrrniGY/VgmP1+vlBahWoFbvVdl6CqJRA7yCwWDE
+ZzTrm35Xvf/8fj+3QXOiJi8Jh8OswAyHw2wdqq6uNiXIqsVIfQ6jaANEzo1I86KKZsa1Y+xPPYDU
+eTYTXYxrjwKXysvLj/B6VNee2o6RMJmtCxXqYWGmzFTnJ9J+oM+ZmZm8XqwSnJQgCupJafaC1DBV
+4/d0v9qWeiLSS6yoqOAFS04h6iJPT09nWdR4ahrNZVJKfnFVVVXcrvp9JE4i1kskhMNhU996dSNU
+V1ebbm7V6cVJ0ZdIfv2hUEi36el/encqUfT5fPx+8vLyjog7iGQVMOpBjNerz6POd5MmTUyT8Jqt
+kVAoFPE9mc2n+p1qMTJuduN7itZmNKjtRNP1ROKo1M8q8bIKN0rShQsXOqQEpwDUsY1mLLWVzypU
+7TsF6KSlpfEJSqdETU0NU9GysjJbFF2l4PG65kbqV9VeqyezmQ0+UntOZOdIhXlUjo24A/U69XMo
+FGIubdeuXZZdba0kuFFl8S5dugDQ8i4ag5mitWf2vZW5MtMfxOIukgWrfdgdS0oRBY/HkxBFCy2K
+zMxMnH766QC00FlKuEGL9eeff+Zy9BUVFSx2GMUFo1ynJspI5stXxQt1oZOsHg6HWW+gjoPk/Zqa
+GkfjM7LBRjNkNKisP4kMUkrWcxBhNuoOYhENdb5V5TF9btq0KSfkXbVqFRN+in1RxcNoY48mmhph
+59pkI9IcBgIB0zUSta3ED8+FCxfHMlKGUwCO1M47gcfj4Zj/O+64g0+rjIwMnYIR0ArE/PzzzwCi
+12ikU4c4haPlYEL++wcPHmTuIC0tjecoPz8fxcXFAPSsLMU+OOUUVERS4kaK4DNTIKp5ClQuJtJ9
+sdqlturVq4f27dsDAFasWIETTjiB+6WiO8QpWIk4dCp2pYJZH6jjWLxeL3/2+Xy2Cx25nIILFy50
+SBnnpWjurepvquyknjqUdu2yyy7j5KgHDhzAxx9rXtY//PAD+vXrBwBc+3DgwIEYPXo0AC0jErVb
+UlKis2MTotmzEwXVBk+nYOvWrTF8+HAAmkKNqg9XV1fj1ltvBVBXudjn8+miQRMh7xLnAYBzVbz/
+/vsAoitZ1exM1AYpfisqKkzNdsbvjOY0Vb/SrFkzXYZmMxfyZEL1JzAbb7LGQXNZXV2tU7rm5OQA
+0PYD5SIpKSlRM3wdO85LQOwJpInIyclBQUEBAK3iEqBNzrBhwwBoKaxIfJBS4qWXXgKgFXl99913
+AdS9zObNm6N169YAtKQnlLrN6CBzNMQF6o8Ucvn5+ZzDLyMjgz9LKVn8Wb58ObOG9BybNm1K+Hiz
+srIAaASJ5o7S1q9evTqm9SQ/P5/Lq8dyoFKJvqp4VokbjWHTpk2mjlpWoHp6JjKUOpkESRUJAL2f
+ihCCCW779u05jX1VVRUryq1GibrigwsXLnRIGfHBLJ6AqHlWVhazzz169GDlUdeuXQFoisOioiIA
+mnKOnqm0tBSrV68GAPz973/HmjVrAIBLeR8+fJiThnz//fc48cQTAWjVgOkE2r17d9JMTsTiXXDB
+BfeA6S0AACAASURBVJyCi1jg/Px8Pv1Wr17NotJ3333HKdF27tyJOXPmANBMroBWZ5DGq7KXdmM1
+aGwZGRmcbTk9PR0lJSUA6iIR+/bty3NsnB/qu379+vwsdJ/RDZj+zs7OZiVhSUkJK4rV/A3qMzmJ
+Z/F4PLjnnnsAAMOGDWMFpV2oMR+A3p8kHq5BFQnIfH7DDTdwMaMXX3wRgMat0e+BQADNmjUDoK2r
+X375hb8nLrO6uvrYEh/MXip9V1lZiY0bNwLQcgNSbkbSKLdo0YK18wcPHsT8+fMBAB999BEWL14M
+QNvcZ511FgDw5E2fPh3PPPMMAG0zLlq0CIAmAxMr5vV62QpAGyIeEKGrV68e3nrrLQAaoaNy9arG
+nlBYWMjfd+rUidPAA8CCBQsAaMk56DnMAnXsQErJcui3337LcwsAjzzyCIC6uSeRywz0DGeffTZa
+1hbbpQWtQtWD1NTUMOurEic1G1G8m+2nn35iHRRVurLbhuqabwanY/R6vSzynnTSSejRowcAYOvW
+rfxOaOy7d+/m9ADZ2dksNofDYfzlL3/hNidOnGhrDK744MKFCx1SRnyIdQ0p1zIyMnDBBRcAABdW
+mT59OivahBB8epaUlOi833r16gWgrviIqpk9GhBC4KSTTgKgz9q7d+9eFmnWr18PQOMOSJHaqlUr
+5mjGjx/PdQE2btwYM5GH0wQoqtcncTFdunRhEcwK6J1Nnz4dDz/8MIA6MSdStmc1mtVs/PGEvdNa
++OGHH3i9HHfccY5zIkYTK+2ID6pI1L59e7aStW7dGg0aNACgrftrr70WQF3yICHq6nAAdcr4wsJC
+tkRs2rQJ27dvBwCEQqFjS3yIBTUzzTfffAOgrtDH7t27uchKKBTSLRzVSYYWNIkGR5MgAJqegNjo
+gQMH8mKsqanB0qVLddcuX76cP2/duhUfffQRAGDq1KksI1rZIE43EbHGGzZswAMPPACgrpCNVZBp
+eNWqVVi5cuUR4zEbm+pwFSkGwyloLfz0008s9iQrSardwzY3NxeAZl2jKlQlJSVMFHfv3m1qYTEb
+/4EDB3QmYLtjccUHFy5c6HDMiA+qJpu0xfTdzp07md1dt26dKfuZlpbGCji7SSeMWma7WYpoDEuX
+LuUxd+vWzfIpfv/99+Ovf/0rAI29pJj+ZEEIwWXMHn30URbTqOxeNNCz9u/fH2+//TYATQlKFpPD
+hw8D0HMJquUpLS2NLT+HDh1KqMWHTs/Nmzfj0UcfBaCVbHMK1SIC6BPOWBEfSJwpKChgRXEoFDJd
+l045poyMDOaQgsHgsZWOLdY1ZHFo27Yty1lkQuzXrx/LiM888wybvd59912dTEbEgiIjjf73kbzR
+jP76ds1NVCNh3rx5LMKQbiEaaIOtXbuWF1uHDh2Snka9qKgIv/32GwD9Jo3lIOTxeJgtb9myJY/z
+D3/4A7+fGTNmANCLboFAgP9OpgPQ3/72NwBaQVcya5O+xAnMEtHYGTt5rALQ6WoiZW9yMi9qZjAc
+hRTvLly4+B/EMaNoJOWgz+djJRxR/mbNmqFx48YAgGeffZYVcd26deNTbseOHawoI4efPXv28O+t
+WrVizb+UkvtTE4sQrFJtUh59/vnnALSTdMKECZafmTTL5eXlbIlIJmdH9u9Fixax5UBNaWfmp+Hx
+eNjt/Mknn+Tx/fDDD5g6dSoAzZIxa9YsAObchso1qCdbIp9VCIGHHnoIgMaZqDkvnVanimZ9iFTZ
+Wr2PxBlyAaf7qL2MjAyd0pVELzuw6/4NuJyCCxcuDIjJKQgh3gJwEYDdsrb8mxAiD8A0AC0BbAQw
+UEq5X2gkcAy0ytPlAIZKKX+0MpBYpy950H333Xd8ctEJU1RUxIoYv9+P5s2bAwCuueYaTJs2DYBm
+AiTdAFVGLi8vR35+PgCgTZs2HGiyfv16nX3cLNuuFbmeFIKql+Jzzz0HAHj11Vd5PJFi/ck3IRAI
+4MorrwRg//S06qIthOBTJSsrS5dJmriprKwsdO+uiaR9+/YFoOlJ6H1MnDgRN910EwBnJxSQ+Irg
+9B7JJR7QFJiUhyInJwf79u1z1HY0RbUVJTYpbsvLy01L/QUCAfamnThxIq8Bqo+aLMRUNAohzgJw
+GMBEhSg8C6BESvm0EOI+AA2klPcKIS4EcCs0otATwBgpZc+Yg7CgaCT4fD6O2qOw5xtuuIEn0ufz
+se2WwokBYMKECejYsSOAukjE3r17Y+7cuQC0KEryFx87dixvot27d0f06Qdip0oHNAIAaASLXvKa
+NWvw3nvvAdBYWEr2QptxwYIF7OL7t7/9jUUQu7BKFHw+H4/nj3/8IxPcbdu2sWtymzZtcMcddwCo
+m8NQKIQPP/wQAHDllVfGzfL7fD52q3bCLhNo7ikdX+vWrXHeeecBAEaOHMniYTxEKFLqdqsgn5XO
+nTvjhx9+AKAXbZo1a8ZjHjVqFIu/KoGzicQoGqWU3wAwCpP9oZWZB/Tl5vtDIx5SSrkQWl3JQutj
+duHCxe8Np4rGxlLKHbWfdwJoXPu5CICafH9r7Xc7YIDQl6KPCVLKZGRk8GlKdvDVq1czpV2zZo1O
+UUPKvi5dunDmX2LDfD4fLr30UgDAtGnTmN2vqKjgz0IIPrnI1GmmfDQDXUMsdTTQyXbuuecCABo3
+bozLL78cQF3uAiewE+FJ0Y5nn302K7/WrVvHSsdvv/2WowvVBCPTp0+33EcsSCnj4hDUdgDN5RcA
+/vKXv7CbcKL8POJ9XooA/f7771FaWgpAW5OPP/44AM1jl0SM/fv3c12LZCeKjdv6IKWUdth/5b4J
+ACYAkcUHWnh+v58z9e7cuZPtu7RZJ06caMoG1tTU6KwIV1xxha5dIerKqgHgWATVVVoIcUQWYFVD
+nKgXQ/2RPX/v3r06a4hTqAk5oiEYDOLBBx8EoFkOFi5cCECzOJCvx4gRI44YixrDkAgkKqN37969
+AQCTJ08GUBdFmkjEuzmJ+KlEMBQK4bHHHgOgiQkUMfrQQw9h3rx5cfVnFU7f5i4SC2r/J+F9G4Bm
+ynVuKXoXLo4xOOUUPoFWZv5p6MvNfwJgpBBiKjRF4wFFzLANNTstRXoFAgE+QUkTHu2kIs1/aWkp
+GjVqpGs3FArh22+/BaApcsy4DTOqnAhPs0gglnLmzJkcDRcP7JQMoxP6o48+Yg4jOztbl6ORlLfk
+CRgOhzFkyBAAYEtPPEiEt6bH48GgQYP4M6AFDiXaPTwZbLxq7frpp59Yab5ixYqEW2YiwYpJcgqA
+PgDyhRBbATwEjRi8J4QYDmATgIG1l38GzfKwHppJ8jo7gzFuMHqhubm5bGmYPn06unXrBgCcoeeU
+U07hRBLkwmxs4/jjj2fTGWHhwoXMXtoxofn9fg5N3bt3b0Ii+cii8uabbwIA+vTp47gtdbGq5lA7
+i5fue/3119GmTRsAwJYtW7htEqnS09M5Ea5TqOKYURxxQiRuv/12jBgxQnc/5eeMF2ZE1ml4eiSQ
+g9sjjzzCYk88hMcu8YpJFKSUV0b46VyTayWAWyz17MKFi5RESrk5GykZKbj27t3L1oU9e/bgsssu
+AwA+werXr89Kx3feeYe5BTWicvTo0azEIzbsrrvuYv8AO5S4pqaGXX4jlT23C9Iyk6afxuUEavGa
+eBWiX3/9NQd0zZ49GytWrAAA3HvvvQC0uXjjjTe4X7u5EgHgtNNOw3fffcfjjJcd37ZtG7dBPiKU
+Ri5eqBGRZmnpEwHyBTHzkbELu+8ESDGioEI1+2VlZXEuvfLycnzwwQcAwN6Ibdu2ZVk8Pz8ft9yi
+MSs7duxg78ZNmzbxNXl5eQD09R3sTH4gEDAt8upUv5Cfn89WEsrPF48G3mnUnhnGjx+P1157DYD2
+fERkL7nkEgCa0xfpe2wn86glCsuWLUuIXE6bs3nz5qznmDJlStztqlDn1pgLMxHPUFBQwE52FMMT
+D5yING7sgwsXLnRImXwKdk5Z0ozTSUMut4CWp4BO3auvvhqTJk0CoLnuUm5G8h1Xi6nYYVvVSr50
+bzwYMmQIuzSPHz8+rraMSLSGnOacTrNmzZpxBKSxDzsKuESMk2JJzjnnHHzxxReO27EKWocqV+dk
+/EIIdOjQAYBWzIjiYyiDeQLh5lNw4cKFfaSUTiFaDLoKowlQPbW//fZb1jV88803nJPgq6++Yhs7
+ZWwaPnw4x/zbiZQLhUJHVKK2ApUbEkKwC/bmzZvZXyIRMNY4NPYdD2g+b7xR81An/Y4ZVK/QaPOV
+CEVdVlYWK5spAjKZUOeT+q2urra1HojDfeihh3DbbbcBAObOnZsQXUI8SBmiEKu4hp12yPrw4Ycf
+6lg82oTkFOT1ejktlx049UuQSh3Ejh07si1969atHGuwYcMGR20ThBCcus7os5EIkKKR0rUtXbo0
+JrGJ9W7tujarFZTUZCn0XkhMTAbMlNQU+m6X6FK0Y48ePfg5li1bxvEadteCmQjmxPrgig8uXLjQ
+IWU4hWQl7FQ9+ohDoFJyCxcudHTqxzNW1XOPgrw2bNjAHItdhZvxepVTitSeWracXH+N9TIi9UWn
+FwXqOOG0jLDLIdKzqJ6PNTU1fIonq5YDAI5mVPu3O366jziaDz74gD1yp02bFjHpjhM4UnymivXB
+CZvzeyERBCwnJ4cX8a5du47IS5iouVD1C6o+Q60PCUAXCRrt+dQMQcmA3bkl9/DKysqElZS3Ap/P
+57hqlTELtGo9q6ioSGYUpGt9cOHChX2klPiQzJz/yWzbCcrLy/lkU5VkieaWSHxQPUSlUlU6VhSo
+CpWbS1aiDyPHGKt9ldVOdvIRYx/q3NrpVy18A2iiD62FRK1TM4tOwgKijgbIZBUKhSJOsKpxNrLY
+agUnIQRPsLrA1PvUyj7EDldVVZlW95FS8stTF6uVl6iGftP9xLaHQiHu+/Dhw6aOP2btqnK0WjxX
+FT/oezUJaE1NjakoYSafq9YANYIxEAjwJqRnCoVCuudTE9io7ykasfN4PLr3q/atfqa2CGphVlV0
+EEJEvN74/CpUwhmp0Iv6vfEa4zsz68Pr9fL1qqu8Kpap7tPq99E2upqqXn2+tLQ07s+qeOWKDy5c
+uNAhpRSNVlyN1dNBhXryRXKvNVL2zMxM3WmrapaNfap9WFWKGjmFQCCgc9FWNf+Wqbhim1dPYLM8
+FMbTzOjMFG3cdF8khaKqtFTnReXG6FkBRBVXVKgnqZW1qc6x2VjVuVDfo9McCOq8Gd2c1fmWUkZs
+20zsMONi1LWuck3q72bcX5RnchWNLly4sI+U4RSOpiKQqK/f72cZv7y83DQcOtL9dsZK1Fw9SX0+
+n6kMaAdmp70x07QT5VsizMNCCDYXlpWV2fK7SIU1aQWJzrhkBrMaI3EoES1xCimhaDxaoMkktq+g
+oICLhaxZs4bzLezYsUP3oo22ebuLVm2rbdu2AIDt27fr2EHjC7ciThQUFPDCpLiOcDjMaeec5iSM
+Z5GTzT0cDnMui/Xr11tOd+eUIMQq0GOMO3HaXyRnMOBIpWa8xM3r9bIvS3l5OSfgoSjgYDCos2So
+4pMqxtl2ropr1C5cuPifQ8pwCkeDZVSDZwCgRYsW+Otf/wpAC+zp1KkTAODCCy/UUf1Eee+Fw2Gu
+Y5mens4njKpcI5FCPWmMpjk6rQ4cOKBz4wb0wUe/BxtOCsWzzjqLOYXi4mKdCTMa4jlhzfpQuUNq
+NxgMmipK7cLo5mw0ZScClEl72LBh+OmnnwDUValeu3Yt9x0p07OTCNSUIQrGl6NqXp3K3CoCgQBH
+n5FvfIsWLXgRd+zYEbNnzwaQPBnR4/HwS87Ozmb2PhAIcFwGLbR69erxeKuqqrhgSIMGDXDmmWcC
+0DIUmxEA1WXZTDsNJN/RJzs7m/vo0KEDL+hYMNOyA5FZflU737ixVqgsPz+fRcGLLroIgCYSEkFW
+xUO71ajM+lbzNqq/OZlbn8/H4mr37t1ZFBwwYACHrffsqZVnHThwIN8XiSg4OdBc8cGFCxc6WKk6
+bVaK/jkA/QBUA/gNwHVSytLa3+4HMBxACMBtUsqYebFU60Mk261y7REsUaST/bTTTkNRUREAzeOP
+KkzTSZqZmckZoTMyMjiDcnl5eVJO0KysLLRr1w6Axq2Qwmj37t06L0z6X61TQVzFxIkTOf3Z9OnT
+Y0YpmiVcieWBGe13UnZRXcYpU6bw2NT3kJaWxs+6efNmy5GLqijl9/uZk4sEVQSj8Xfs2BE333wz
+gDo36ObNm+PTTz8FADz88MNcB+KBBx6wNK5IYwXiT5QrhMDQoUMBaPNKlclHjRrFmcszMzM5OdD8
++fMBaKn7bKZss2R9cFqK/k8AvpJSBoUQzwCA1ErRdwQwBUAPAE0BzAFwgpQyqiAZySSpyoiq+6yR
+XVNfisfjwTPPPANAm1Rin/bt24fOnTsDAKdnN/qvW9k4TkCy9aRJk7gO5gsvvIC33nqLx6O6pQIa
+G0lE46STTuIcfm+88QaHLV9wwQUxF4U6b4RwOMzmQhJn6tWrx9mnVPFDdZs+44wzOGU6EdPDhw/z
+8zmtaKXOfVpaGpuJi4qKuPw6jcGYtp5CwCsqKphg5efn8zgpPN3j8fBmy8/PZ8I6YMAAx2M2ElzV
+xT7StUBdEaOTTjoJADB06FBcfPHFfC2t2fnz5/NBdsIJJ3BG87POOgsAMGPGDMycORNAnUUiBpJX
+il5KOVtKScLKQmg1IwGtFP1UKWWVlLIYWqWoHlZG68KFi9RAIhSNwwBQEcEiaESCQKXoY8LsZDbL
+kuvz+dh2S8lEVNff8ePH46qrruLv6b6xY8fySWbGEUTjDOgEslNaTsXJJ58MQGNrSST47LPP2LdA
+7VtVDNEzbdu2jStCZ2RkcEq0m2++mYuyRILq+qpyC8QN0Bynp6fz2EKhkC7FGLHw+/btw6pVqwDU
++VsEg8GEJjUJh8Msjqxbt45Ty6nvjnwhysrKuG8pJb+fsrIyPnnVYCBqa9KkSejSpUtc41TdmFXO
+VeUe6Pvs7GwubDR27FhWul5zzTUAgMLCQp0lg0TCQYMG8XPn5OSgRYsWAMCK5jfffBMvvPACAE1p
+Pnz4cN0zO0VcREEI8XcAQQCTHNx7I4AbrV5PizsUCrFmmSa6tLQU//jHPwBodSfpZWzbtg1z5swB
+ALz22muOJksI4biwJ2mRR40aBUAjLsOGDQMAS9p42rBlZWXo27fvEb/TBrUyBtUMB9QtHNpIO3fu
+jGkuXL58OS+8U045BQAwZ86cuEUt4/0qIaMNQoS5srJS55Rl1rdKIFQQcWvatKltq4MZoplAhRAs
+8pSVlfE1zzzzDK67TiuxSsl6Tz31VC6afMUVV5gS2dLSUiYQY8eOBQB06tSJRYmamhq2TsT7bI6J
+ghBiKDQF5Lmy7s1YLkUvpZwAYEJtW8eGX6sLF/8fwBFREEL8GcA9AHpLKVUNxycAJgshXoCmaGwL
+YHHco1SgRqJRVKPf72eRokePHsxJTJ06lescJsuiEA3UX/fu3flvSilvBx6Ph8UKNarzyy+/jHlv
+JEcmlfOyAzqt/vnPf1oegx0YazQSl0b/x8Mak/a+Q4cOrKCLB2bRnKojkxoZSorAdu3aHfEsQ4YM
+YeVxNFGM+vnxxx8BaLkdyVfh8OHDrDROOqcgzEvR3w8gAODLWnZpoZTyJinlL0KI9wCsgiZW3BLL
+8uDChYvUgtNS9G9Guf4JAE/YHUgsDzBSxKSnp6N3794AwB5sTzzxBMvOZWVl+O9//wsA+OWXX3Dh
+hRcCAF5//fWY7r+qIk41IZEpi05Jq6frU089BaCugMq8efMcJRe9++67+RRQ0bNnT3z44YdR76Vn
+SktL08X9OwVxaVQM5sYbb0SzZs2i3WIJxsxPgF4up//jUWr++9//5vaffPJJx+0QaB1F8hpVf6f1
+2aJFC+ZYqPRey5Yt0b9/f8v90jtYtWoVc8hpaWmsY9u/f39c7zhl3JythCsDWu3Ce+65B0DdpObl
+5XFxkvvvv59t2x6PB8cffzwAjf009qFuFKBustWw5nA4fESmZSto0aIF7rzzTt3YiYhZBRGTu+++
+W7dp6IVfeumlMYmC6suh5gR0KkrRvNB4srOz487wrKYSU0O/PR4PW0FU3wknKCwsRMuWLQFoyspF
+ixY5asfM9T5S9CX9nZeXx67Jd955JytN6f/27duz30d1dTXWrFnDbajrUE1aA9TVRKVrr7xSO7/p
+MAI0PxS7c+e6Obtw4UKHlOEUYiVAJdv0ddddh1atWgGoY+c///xzvPbaawA02zbZ2+vVq4elS5cC
+ODIdGaBPkpmdnc1cgzGBLCmJ7LBkTz31lO50B8AngFWQouq5555jd1z19CRTZzSoKcOcmlZVtG7d
+GgC4BJ9T3w0V6nyrSVwjJSu1A3q/n332GX93ySWXOG7PTMSMJJbStWVlZRg8eDAATfw1S3VHIkWv
+Xr34/c6ePZu5O1UxSu02bNiQlYpbtmzRJTCmz07eecoQhVjsLP1eXFzMC5I2/Pjx49nuX11dzdr+
+G264AY8//jgALTLOrE01tTpNZFVVlW4ynbDaVCcQqHuJX331le12AODJJ5/kBXjTTTdxlab9+/fH
+vDeSHd8pyBGLFuvq1avjtuoYowsTme6ciOL333+PH374AQDYddgJVHfsWOKSGg7/xRdaCNCAAQN4
+bdGhdsIJJ7AT0siRI5nQ+v1+nd+DmqkLABYsWMDxHOvW/b/2zj+2qvKM45+nvb23UupKxw9Blukc
+gTgyfmSiZvtjsmFvDWEZmQlGnQvo/hmZw+m0IZgQ/3Fs2RDsHMsGi0vDCI4xJNmMMBv9q0zdworQ
+KSk/tSgJeAPS0sK7P855n77nctve255ze43vN7lp77n3nud93vOe5zy/33c1KjFt2jRN7HO7eBf7
+UPPmg4eHRwQVoykMBxHhrrvuAoIsrpaWFiDw5kOQfmsl6pw5c9QzvmzZMnXw3HvvvapZuFqAlaKN
+jY2RhiVjVYut4wgGJXtHR8eQlYiFOj9bB5FbUNPd3a2FVMVIfuvMunr16ogVhyNBRLTQyD6Bu7u7
+x7xdm1sZWV1drZGWIot8hsXixYsBWLVqFW+88QYweodoVVWVakonTpzQwrqRUFNTo6ajmzlq82nq
+6+tZt24dEDhB7drI38rPrgE7P1u3blVT44YbblAT4+zZs3qOTCZT8lr+VAiFVCrFlClTgMCW3b59
+O1C4JXtXVxft7e1AIBQmT54MwJNPPskTTzwBBAsZoh7dXC6nF2ssAiH/5oZBm3PWrFm6OC5duqQ8
+Xbp0SfPy7UJbt24da9euBQIhtmHDBiCwOW2VXVNTk1b7DQXLSxzdo2pqarRRiZ3X1atXj/m8rpkw
+MDAQi5/CCuXNmzcDgUn4/PPPA6PrWwhB6fsDDzwAwNNPP120Ot7b28v7778PwIYNG9S8tdGQ8+fP
+j9j/05jBtvRWMC9fvlwF5+nTp7XK1RX+c+fOpbOzs2gewZsPHh4eeagYTaGQU8lKxlQqxdSpU4Eg
+zjtc3f6VK1fYsmULANlsVlua7d27N9JJGYKnqJX27jnHog7bMff19en/jz/+OBDUv1vJXl1drerj
+9ddfrz0EbOHTggULNDGlp6dHC4Mee+wxTd6yzqtiEIfTrq6uTnND7NNotB2jXbhaTH6OyGjQ2Nio
+jmdrPh04cEAdjKM9b21trZqupXj1jTHq6O7o6CCbzQKoczGVShWVmmznyUYyqqqqdN3Mnz8/omFZ
+jfXQoUNFj9OiYoRCIbiVfHYCi7kYdnLWr1+v5cn9/f1641tbLb+jUxy1EVagrF69Wu1PK4xctc5d
+mBcuXNCIgl3EO3fupKenR7/jVg7u2LHjmvEPhVJ2WxoJuVyOl19+GRhsHBOHqu/CbVs+WjQ0NGhz
+0xdffBGA1tbWMTe0dW31/HDzSLC/O3z4sB6z66Kzs1OF/nAPJJvM5o7f3vSXL1+OrUmQNx88PDwi
+qChNwW2KYszgjrtu84pizwNBaqtNc+7u7tbYrY2xX758ObGuxrfffjtLliwB0AYpbiu5dDqtNBsb
+G/UJsXDhQgDmzZunadH9/f3aduzChQtD7nlZCIVanI/WPEqn01rNl5+4Mxa40Yf8Lt6lwP5u9uzZ
+PPLII8DgkzmXy+kcjjaqUVVVpc7hc+fOlRTNsWPLZrOqXdl1kclkePbZZ0c8h63jcfNpmpqagHjX
+r9cUPDw8IqgYTaHQ08F1QA23P2L+xik2jjt58mT1QUydOlVTdK292d/ff03v/nxaQ41tJD6y2aw2
+R7Vhu6VLl6rD6ZlnntHvNjQ00NzcDKDt1bq6ulRrWLNmjTqUHnzwwZKeUPl+Ewi0htHY1729vRoL
+t/spjDa852LmzJkcP34ciDZmLRU2TPr2229z5swZHR9EMyVTqVTBEO1IfiW36Gjjxo1FjyuTyWgW
+bl9fn3atstdx37591+xgXWhsra2twGDYur29XX1mcaIihIKIRFTIYmEv+KRJk/SGP3r0qLZ1nzFj
+hl6AXbt2RbZ+t3TdMRQSMoVy74dbPDbfIJVKaWKJrdVIpVLKY1tbm8bQFy1apILKpkJv27ZNW23V
+1dVpbPrKlStayl2MGeGWIVvaEydOVGFZiiptjNHKPDvO/Bu6FNixnTx5sqTfDQV7s7jryI6ppqaG
+u+++W4/bxLdcLqfjKLQXY/54ba6DWwXpftedb7sWFi1apCnIuVxOHch2S4G+vr7IQ61Qy/gpU6Zo
+9aO9/nb9xA1vPnh4eERQMVvRDyUhi0FtbW2kGUd9fT0QFJrYeLWIaNjHDUm6DsxiQzrDbdVuQ4r7
+9+9Xx5ZVHVOplNJ7+OGHNcRUW1urbc3c7sS2acbAwIBqBa56XYxmZc2OgYEBNWc++eQT3WSk1PZw
+1jSzuRInTpzQeXXr+ysNVVVVmvK8adMm3e/hyJEjkX06S9F2Cm0GY1FXV6d7Ttxyyy163nnzwP4C
+gQAABgdJREFU5mkLtd27dwNBA9e2tqD3cXNzs2qYbuVjdXW1ri0bWh9Fluqnayv6scTTe3t7dSLT
+6bRegI6ODlUZX3/99YJmQzFVhIV8GG7DEouamhr97qlTpzR6YJOU2tvbWb58ORAkJFlzpre3t6Bt
+aO3iTCajdFx7uJjcCvvddDodSZCxvoFSKuhERAWuHU9zczMHDx5UnsbqX4grXyQft912m3r9V65c
+qXObXyZfzC5Z9nM7d1adz+VyOo/19fW6Q9aKFSs08jVx4kT1R1ghNWPGDBXemzdvVjPh+PHjOubq
+6mo19ZJ+kHvzwcPDI4KKMR+SOG9dXZ2mOR87dqysW7S7qqh9utTX1+uT5OLFi0W3oBtTdlqBzUni
+2FXbmj59fX2xZk0mBRHRVPlz585d05tgNMjXstxKxuuuu07PnU6nI1Wgtg9GIQele77heCl27Hkm
+UTx7SZYDQ+0lGcN5I+/jOn9SKm4+DYux0Cq0YYl7/jj4iPtcSc2tnQtjTCyCcTjTa8KECar6X716
+NRZ6FoUiYkXOWTx7SXp4eHy2UDGOxiSeDq7K/FnFcPX5SdOoNBRyNCeF/J2748RwOTRxoGKEQlxq
+41Bbyrt1FXHQGC4DMi4aSamclXwTl2Nu40J+PY6bNQnJz3NS5/fmg4eHRwSV4mj8CLgInB2nIUz2
+tD3tzwDtLxpjpoz0pYoQCgAi8mYxnlFP29P2tJOFNx88PDwi8ELBw8MjgkoSCr/ztD1tT3v8UTE+
+BQ8Pj8pAJWkKHh4eFYBxFwoikhWRLhF5T0SeSpjWF0TkNRF5R0QOicij4fFGEXlVRN4N/05KcAzV
+IvJvEdkbvr9ZRDpC/neISDpB2g0i8pKIHBGRwyJyZ7l4F5E14Zx3ish2EalNincR2SoiH4pIp3Os
+IJ8SYFM4hoMisjAB2r8I5/ygiPxVRBqcz1pC2l0i0jQW2nFhXIWCiFQDrUAzcCtwn4jcmiDJAeCn
+xphbgTuAH4X0ngL2G2NmAfvD90nhUeCw8/7nwK+NMV8GzgGrEqT9HPAPY8wcYF44jsR5F5EbgR8D
+XzPGzAWqgRUkx/sfgWzesaH4bAZmha8fAi8kQPtVYK4x5qvA/4AWgHDtrQC+Ev7mN+E9Mb4wxozb
+C7gTeMV53wK0lJH+34AlQBcwPTw2HehKiN5MggW5GNgLCEEiS6rQfMRM+3NAN6EfyTmeOO/AjcBJ
+oJEgtX4v0JQk78BNQOdIfAJbgPsKfS8u2nmffRdoC/+PrHfgFeDOJK5/Ka/xNh/sYrE4FR5LHCJy
+E7AA6ACmGWM+CD/qAaYlRHYj8DPAJvR/HjhvjLF9tZLk/2bgI2BbaL78XkTqKAPvxpjTwC+BE8AH
+wMfAW5SPdxiaz3KvwZXA38eJdlEYb6EwLhCRicBfgJ8YY3LuZyYQ2bGHZERkKfChMeatuM9dJFLA
+QuAFY8wCgrTyiKmQIO+TgO8QCKYZQB3XqthlQ1J8jgQRWUtgwraVm3YpGG+hcBr4gvN+ZngsMYhI
+DYFAaDPG7AoPnxGR6eHn04H4m+nD14FlInIM+DOBCfEc0CAitlo1Sf5PAaeMMR3h+5cIhEQ5eP82
+0G2M+cgY0w/sIpiPcvEOQ/NZljUoIj8AlgL3h0KpbLRLxXgLhX8Bs0IvdJrA6bInKWIS1OT+AThs
+jPmV89Ee4KHw/4cIfA2xwhjTYoyZaYy5iYDPfxpj7gdeA76XJO2Qfg9wUkRmh4e+BbxDGXgnMBvu
+EJEJ4TWwtMvCe4ih+NwDfD+MQtwBfOyYGbFARLIEZuMyY4y70cYeYIWIZETkZgJn54E4aY8K4+3U
+AO4h8MgeBdYmTOsbBGrjQeA/4eseAtt+P/AusA9oTHgc3wT2hv9/iWAhvAfsBDIJ0p0PvBnyvxuY
+VC7egfXAEaAT+BOQSYp3YDuB76KfQENaNRSfBM7e1nD9/ZcgQhI37fcIfAd2zf3W+f7akHYX0Jzk
+uiv25TMaPTw8Ihhv88HDw6PC4IWCh4dHBF4oeHh4ROCFgoeHRwReKHh4eETghYKHh0cEXih4eHhE
+4IWCh4dHBP8HGZVdkTHSHmEAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/2... Discriminator Loss: 1.0431... Generator Loss: 1.1230
+Epoch 1/2... Discriminator Loss: 1.0236... Generator Loss: 1.0033
+Epoch 1/2... Discriminator Loss: 1.1009... Generator Loss: 0.8963
+Epoch 1/2... Discriminator Loss: 1.0246... Generator Loss: 1.5232
+Epoch 1/2... Discriminator Loss: 1.2980... Generator Loss: 0.6295
+Epoch 1/2... Discriminator Loss: 1.0272... Generator Loss: 1.3369
+Epoch 1/2... Discriminator Loss: 1.1724... Generator Loss: 0.7514
+Epoch 1/2... Discriminator Loss: 1.0532... Generator Loss: 1.2344
+Epoch 1/2... Discriminator Loss: 1.1428... Generator Loss: 0.8638
+Epoch 1/2... Discriminator Loss: 1.1750... Generator Loss: 1.1432
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsXXl8VNX1/97ZsyeEsAcCsomyWBQE2srihrivqIXaYtW6
+FNvaFlErlrbS1lq1WqkL4oqKK6hQUdSCLAqK7PseEiABsk1mMjN5vz8e58x5L2+SWRIafp/3/Xz8
+ML7M3Hffvffde5bvOUdpmgYbNmzYIDj+1x2wYcNG64K9KdiwYcMAe1OwYcOGAfamYMOGDQPsTcGG
+DRsG2JuCDRs2DGixTUEpdaFSaotSartSakpL3ceGDRvNC9USPAWllBPAVgDnAdgP4GsA12uatrHZ
+b2bDho1mRUtJCkMAbNc0baemaXUAXgdwWQvdy4YNG80IVwu12xnAPvH/+wEMjfVlpZQmPvN1kmKU
+UjE/0/fk7+R1KQnRdYcjuhfW19cDADweD0KhEH8vEok06A99pt+YrzudTr5ffX09XC5Xg+/Ldpt6
+Jgnqs6ZpSE9P57boHrW1tZbPTDCPkbnvEubnSwayXZ/Ph7q6ugb3MD9zonA4HIY2aGwdDgc/Q6x1
+4Xa7AehjSGNbX18Pp9MJAAiHw4a+NvaMVs+RrATudDr5fk6n03IdynvIvtNn+o1Fn8o0TStoqg8t
+tSk0CaXULQBu4Y64XIaHlJPscrl4kuRn+m44HIbH4+G2vV4vACAYDHIbciHQ310uF6qrqwEAhYWF
+OHDgAADA7XajqqqKP8uNAwACgQBPusPh4HYzMzN5YwkEAsjLywMA+P1+fqbKykq+N/XN7XbzS0N9
+ky+RUoo3gkAggP79+wMAqqurkZubCwBYt24dACAUChleCBoruUnJjYPuR98H9A2G/m5+acyw2tBo
+rOh6nz59sGvXLh4veg4aK4fDwS9jJBIxjC2BrsnNLSsry9AGzZnX6+Xxk5spPWs4HEb79u0BABUV
+FcjKygIAVFZW8pyVlZXx72mNKKVibjKA8YWWm575u+bDRbabmZmJYDDIz3fs2LEG4ykPAup7bW0t
+P19VVZXhu/Q5FArtQRxoqU2hGECh+P8ux68xNE17BsAzgFFSkLv9oEGDAOgLffv27Tj+XX4R6GWr
+qanhxS9P60gkwovN5XLxANbU1ADQT7DLL78cALBlyxbeFPx+PzIzMwHoi1e+LARabA6Hg7/70EMP
+4bvvvgMAfPHFF+jSpQuA6ALbt2+fnCBulxYB3U+MET8zLczMzExs2LCBn2nv3r0Aoi+3w+HgNgKB
+AD+/+STt1q0bjwEAHD58GGlpaQD0RXXkyBHuR2OSg1z48nNdXR26du3KfaYNlZ61vr7e8ALR/Lnd
+bl70Sil07twZAHgj3LVrF4+n3+9Hhw4d+B47d+7k+xcW6stv9+7d3C6Nd9++fbm9rVu34uDBgwCA
+Rx99FO+88w4A4NtvvwUAHD16lPtOGxCgry26Ts8RiUT4c8eOHXkM5QbvdDr5WeVGJzdLOgA8Hg/6
+9OnDv6NnofnNzs42bAr0PshDRvYpXrTUpvA1gF5Kqe7QN4PxAG5o7AdygOjfiooKAPqCpQfzer3I
+yMjg64Bx0Sql+OWtr6/nFyESifBA5eTk8HfbtWsHAHjvvfd4ssLhMJ/oSimeMCmB0MsGAL179wag
+LyRaYL1798bWrVsBAKWlpQD0RSxPiXhFTHmy02kIGE8BemapUpjFa4KUGuiZA4EAn0pyk0oFJEGF
+w2E+gffs2WO4L4H65/F4+LuhUIg3cNpsq6qqeD5qamr4BZHPHQqF+AUipKWl8Xf37t3Ln4cMGYIe
+PXoAAHr16sUv+qmnngoAWLt2LY4ePQpAHzfqp1KqwRiGw2H++4EDByzVTSnaS9B3wuEwRo4cCQDY
+tm0bb9oulwu9evUCEN1kdu/ejeLiYv47jUswGExJNWuRTUHTtLBS6k4A/wHgBDBL07QNLXEvGzZs
+NC9axCWZcCeU0kgfk7ql3OWkEcXKGCS/J6UKEvnkc55//vkAdEmDJILdu3fzySbVBAlpnKK+FRUV
+8W7tdrtZtVFKsToibSB0CgaDQVYJWgpmHbhNmzYAdFuEtCUA+glNkldzwOPxoF+/fny/iRMnAgCe
+euopALpKJQ1qUrohNaZDhw7Ytm0bABhUDRrj6upqnld5wpaUlLBaRSdzt27deD6UUvz8Dz74IJYt
+WwZAlyBJfaCxKi8v5zVilhCpz9I2FGvtxAun02lo48svvwSgqzHDhw8HAMyePRsA8Oc//5nXUHp6
+OqsPZslZSCyrNU07s6k+/M8MjRIkomuaZhCv5MPJz/SSWVnqpX4WiUQMRiTS/desWQMA6NevH9av
+X8+/a2pCpd5L9z506BDrzjt27GDRLhwONxAV27Vrh2uuuQYAMHjwYPzqV78CoIu21Dd6MaVOH+tZ
+m4KmaQb9nFSPgoICfrHIVkOqWqqgfo4bNw7XXnstAOCbb77B66+/DiCqgsmN1el08py1a9eOx2D7
+9u0N9G9prJV2CYfDwXOZn5/Ph0F2djYAXdWgzw6Hg9WSKVOm8L2rq6u5TzRWdXV1Ta5JqaKmiptu
+uslgHP38888BABs2bGCVh9ap3+/nPtTU1MT0OiV68Ns0Zxs2bBjQatQHuTvKf+OBy+VCx44dAQA3
+3ngjSwKFhYVYuXIlAH1XpZPiJz/5CQDgr3/9K59K0jjTFHw+n0GMlB4OOvGPHTvGVuQ77rgDAPCz
+n/2MxVI6wWNB0zQWB/ft24cLL7wQAFgsjhdkqPJ4PGxg9Xq9bJ0n0bm5jIs///nPAQD33nsvP/+r
+r76KadOmAQCLu5qmsRTncrn45HY4HGzsO3bsmOWcSAmDJB6Px8Mn6WmnncYSG3mAdu/ezWPetWtX
+7N+/H4DudaLTtqqqylIllbDiBSSzZs2gtVlWVsbtbtq0CWeeqUv7wWCwgWtYuoPjxMmjPiQCqSOT
+7llYWIiXX34ZADBo0CCUlJQAMIrwX375Jbuy6AUYMGAAPvzww4T7ILkAaWlp3KcRI0aw2Pr888/z
+RJPILDeSWM8lxWTysvTt2xebNm0CoOu9TS1cc18Bo62lT58+7AUgO0lzbApjx47FE088AcBIvMnI
+yDDwRQCjG1Ipxf2MRCK8QdDfZD89Hg9vBMOGDcN1110HQFfjRowYAQBo27Yti/+kagDgTejf//43
+36+8vJw3yy1btvDvrNx4sUhRzQGyE7hcLrajkNuUYO5TSx3otvpgw4YNA1qNpKCUMviBJfNL0zTD
+KUsieKdOnQAAf/zjH3HKKacAMJI1cnJy+LS96KKL8MwzzwCIitSffPJJwn0E9BOHTta6ujo+8TZs
+2MDiHqkzEmvXrsUXX3wBQLfCU3vFxcW44QadxkE+8/HjxzPrzuv1slQ0Z84cNlbGA2l0JfGa1BIg
+arSSnId4DGZEfho/fjx7Frp06WKYJ3q+Dz/80EDDpn9JxA8Gg6wySDalZDS2bdsWgH6yf+973wMA
+3Hzzzdi3T2fTb926lceuW7duPF5kQD311FNZCpg6dSo2b94MQDe6fvDBB3y9MaKPVBkcDgc/nyQ1
+JQOv14vzzjsPgK7CnH766Sm1J5EM9bpVbAqSICSvyYeQHHBaYEQqWbVqFRYuXAhAJ6MQaei7777D
+nXfeCQA488wzWbefMGECgMRFZupPMBhk16J0Kx47doxFPrmAaNM444wzYk4MbViEKVOm4MUXXwSg
+20loci+55BLcdtttAICZM2c22WdpuyCxvLi4mIlKw4YNAwBs3ryZxzUcDqN79+4AgNzcXCZfVVZW
+4m9/+xsA4KqrrgKgb5CSpUnPGgqF+CU8duyY5YtD4y/VB5MLrQGff+TIkUxk2r59O4+3z+fjTb5L
+ly5sSzjttNMAAPPnz8c555zDbQ0cOBCArsMTq3XatGkGEpEV6O+SNp8qZsyYwfaX7du3p7zJmNUc
+8zps8vcp3d2GDRv/79AqvA8Oh0Pzer0GsbWx3VJSTYGoOgAYjYsej4dP4Ouuu47bpFOEJI1E4Xa7
+2Xgo4xKcTifv+GeffTa2bNkCIErtTRSkiixdupRPJaUUG0wLCwublHZIfUpLSzMY88gIeujQIQD6
+6ULj5nQ6WUXr3bs3fvaznwEARo8ezbEG8sSkdlevXs1zMmDAAB6j6667DvPmzePvNwZ5yskAo4IC
+Pbjv2LFjrKKkp6dj9OjRAHQ1gSTBUaNG8XOTofH+++/n5+vcuTNLGEeOHMH8+fMBAIsWLWLVKpZH
+Qaq0ZppzoqDn2Lp1K3Ndtm7dytJNKiDpICsri9dnJBKJy/tgSwo2bNgwoFXYFDRNQzgcRlpamiHM
+ONapYt7FpeFMIhQKYciQIQB03ZpOVdKnk4WURqRBNBwOsx798ccfp3SP9u3b49JLL21wDyBqdHvp
+pZfYyBdLYiBjXl1dnSHElyi/VohEIixFHThwgCWWw4cPs/GT7A/BYBC/+MUv+LePP/44ADBfA9Al
+skQkUun/p9OU+CSynWAwyOP8+OOPG9ibxAgl+9HGjRs5WK2uro6/63a7eS7r6up4bEkyO3TokEES
+kPdPNe8E/X7fvn0cDfrVV18lxV6VtoNIJML9j/VuNIZWsSkA+gDU1NQ0q9985MiRLJYFg0H89a9/
+BZAcZdj8OxmvLv3tqapjJHJPnjwZY8eOBaBvbjJWge5x5ZVXsmry+9//HoBx3GSUpIzgi0fcpXZ2
+7NjBxKmZM2eyV4VE0gMHDhi4HpMmTQKgi/Dvv/8+AN0rk8iYW+WDsOIFOJ1O3HTTTQB0gyFZ8Hfv
+3o2HHnoIAPCf//zH8r6k8sgQ9vT0dPaqzJkzB4CuUtCmJ6nZydCHrfpP/f3qq68A6Bt+ImMlEwdJ
+tZqez+fzMT+DNsWmYKsPNmzYMKBVSApKKXg8HtTX1zeLhEA75m233cYn7PLly/HHP/4RQOpMMPMJ
+bI6WSwb33Xef4V+Hw8Ei8GeffcaGsW7duhloviTa5+fnA9BPAytxN1kpRtM0Ft3btWvHkhz5+R9+
++GHmi9xwww0YPHgwAF3svvfeewHoxr5EckfECnQzQymFBx98EICRZTpt2jSOdoznvpKfsXTpUgDR
+LFvhcBhDh+qZBL/99lvmerRt25YNvqmiTZs2zD156623mGMhmZ1AdF2TIXLWrFl4+umnAQCvvPKK
+wTgv3eeJrstWsSkAMHgMADTI6ZcISGW48sor+dqVV16Z9GYgw3qBhvkFydLdq1cvrF69GkBiop/L
+5WKdksg2W7duxfXXXw9ADwWme8yaNYttDQ6HgzdRqXOTWGomgyVrLafvS9IUvYAVFRW8KYwdO5bn
+b8uWLQ3CnuNBrHGzyqN4xhlncNvBYJB5HUuWLElqrvv06cMbMdlO8vLy+JnkXCey0cUCtTVq1Cie
+P9qU6H6E3Nxc3H333QCisTS1tbVM+Y41p9I7FC9s9cGGDRsGtBqeAtFsk/X5SlAOw1NPPdVgcU8G
+UuyW2ZlJjA6FQsz+KykpYfEykdNR3qMpScntdmPjRr18hs/n41yCxDCUiU9le5L+bZUn0KpPgH5C
+U2qyzMxMzr8gE9BSmrAlS5bw6Td+/HjMnTs37jGwurfMjWGV5HTBggU499xzuT89e/YEoFOhE7kX
+jVHnzp2ZJUqMR7/fz8liwuGwgR2YaoASqba/+tWv8NJLLwEA7rzzTvTt2xeAbsx99dVXAeiUbpIQ
+pbpKEibxTZrAyRMlqZSC2+02JC1NFk6nk+MgAHAW4WQhxXEiJsnEG263mz0ABQUFTLLx+Xz8AjWl
+05kTnjaG/v37s/0gOzubx4yeec+ePYas03KTJV01EAg0UCXMqcWpT3fccQf3qaioCA8//DAAMD+/
+uLgYU6dOBWCkVC9ZsqTR52gMMvbB6sW74IILAADf//73eYOoq6tjLwm9SNQGoMe+0ItTVlbG9oD+
+/fszAWrSpEk8njt27AAA/Pe//zWoeaRWlJeXG3KBmvseD2js//GPf7AtRobX19fX83rKyclhGwNF
+ddbV1cUVrZmot81WH2zYsGFAq5AUgKi0kKr3YeTIkQZVwRxolQzMO20kEuEMzjt37uSd+8ILL+Rg
+pWPHjrEv/8Ybb+TfJRvsQrTkL774giWW+vp6PtnGjRsHAHj77bfZOr106VI+zTIyMvik6d69O4v/
+dFpVVVVxZmuXy8Wn8fnnn8/Gt8GDBzPNmfoTiUQMhk+6RyLqmrk4jwyIMo99Tk4Oq2uSu5GdnY03
+3ngDgJH2TirMiBEjWK1cu3Yt++w7dOiA8ePH8xiRNPHee+8B0BPxkKTkcrlYRQGiWbppPqqqqhKa
+X0mAo3mktUTPv3z5cgBAz549OTHMqFGjABjT3TeGRNWaVrEpkIU0lU2BROa5c+fyIisuLuYkrZJk
+lCjod9Q3p9PJKoPP58MZZ5wBQPcAkCW+e/fueOuttwDo9QQAnfxD3oWysrIGLqdY8Pl8+MMf/gAg
+arEGdHsGZZailOb19fUcTlxdXW3IP0hibllZGasSFDm4bNkyg95OhKQvvviCN5lvvvmGXwAiKZH1
+G9AXOXkcDh8+HLfYap4b6dkxi8eRSISJWnIzCQQCrFaFQiFcdplepZAYrQ6Hg926u3fv5shImWwX
+iHodKOKypqaG1aLMzExOdnP48GHuJ81jLHdqLFD/3W43LrroIgC6N4cicMPhMG9I77//PoeGkx3h
+b3/7W4skWrHVBxs2bBjQKiQFwChCJQOZVGPRokUA9B1YirmpQp5mdLoEg0FO8V5VVcV5HUaPHs3R
+hSS2DhkyhNOVLVq0iJ9XpqInI9OhQ4f41MnPz8ctt3CFPY7duP3227F48WIAUZ6Cy+Xiz/KZfT4f
+S1MyynPKlCkA9NwTJAXcddddrDJUVFSwhFRcXMzPR235fD7+7gMPPMCxCPHwTOj5zB6TWIl2ACNp
+iIxwBMoY3bdv3wZl49LT0/nzoEGDDKexvDepFSR1AFFKdGVlpSGxioxcpXtQrEFjUil9n9ZsQUEB
+z6lSiucvEAiwwXP58uX8vNOnTwege19aAklLCkqpQqXUZ0qpjUqpDUqpycevt1FKLVJKbTv+b17z
+ddeGDRstjVQkhTCAX2ua9o1SKgvAaqXUIgA3AfhU07QZSqkpAKYA+F1jDZGRMZVCGqQPP//883wK
+BgIB1tX27NnDuzEZweJlTVqVjaPdvFevXny6lJeX83eKiooMuQWojzfffDMAXQ8lf/qpp56Kxx57
+DEDUrbdo0SIO5pk4caIhUIxcbnPnzm0gAYVCIYNLT9YoJOmmrq6ObQJkMB04cCCP/8CBAzkh6sGD
+B9lOQslFgahtIxgMcnbsRE8uKz6FjOaURXhpXMvLyw0MQzqZ09LS2D4CGI141BbhtNNOYyNwWVkZ
+p8ibN28eGxhJipNrRBo+ZQCSzCBFa0vWshg5ciS7aEOhEPeFajp06NCBozPlnK1fv96QvYraIIp5
+SyHpTUHTtBIAJcc/VymlNkEvQX8ZgJHHv/YigM/RxKagHS8CIxN9JAr63fXXX88i8aOPPsoW28su
+u4xjHz799FMA8Yu40hcO6CIzWbi3bdvG8QdOp5NfJiv/scPh4Ii1QCDAqejff/99nnxSHwCw//yU
+U04xkGYoXXqssZKp6yTHgjayDh06sAFOGi4lXZsqE91+++1MF5chxxSF+PXXX6csxtL802f5rLJw
+LqCHi1NYd2lpKb9McrwlDV1W57KKcLzvvvvw9ddfA9A3CNosGqMNE2gNUN+DwSCrYG3btmWSWVVV
+FacIvPvuuxvk2Gzbtq3Bk0IH3KJFi1gVVkpxe6mGbDeFZrEpKKWKAJwBYCWA9sc3DAAoBdA+xm8M
+peht2LDROpDypqCUygTwNoC7NU2rNGWP1ZQoMy+hiVL0DodDk5GHyYDEvRdffJHLsRUWFrLIvGLF
+Cvax04kfT+SgpmkNduZwOMwibHp6OvvNs7KyuG0pPkpQKriuXbuyW9Pr9Tag85599tkGlxWdXEeO
+HOETKlb/ZRIa+p2kOVdUVHA0Jqklw4YNM5zKJDU888wznJCloqIC//znPwGAE7imWjuR+inHWCZZ
+oX7QCXzLLbdwefbTTjsNF198MQDgrLPOYgli/fr1+Ne//gUgOp7V1dV8Wnfu3JkNeKkkSZVqA4Hm
+/5JLLuH6I126dMHZZ58NQM97Qeof8UIoOzX1h9ZTY4lwWhIpxT4opdwAPgDwH03THj1+bQuAkZqm
+lSilOgL4XNO0Po2143K5tKysLITDYYPPNxlkZWVhxowZAKKVoABdZbj99tsBROs0Zmdn88TFgpxw
+qU+SdTotLY1F2HXr1uG1114DoIc7P/LIIwCi+jIQXaRVVVVsR5AcAso+PWTIEPaZV1RU8Oa2d+9e
+HiO3282LsCnrfSyQOjBq1Ci2GbRv394yNfjGjRs5JiBZEVaGfUuVQUZ20mepSkjimOybDHGm70j1
+oaVgxaGQNge3280xE1OmTOFxe+edd7hwEdm4xo4dy5vzs88+2+x9F+uhZXM0Kv1OzwPYRBvCccwD
+8OPjn38M4P1k72HDho0Tj6QlBaXU9wEsAbAOAB0bU6HbFd4E0BXAHgDXapp2xLKR43A4HJrP54Om
+aSySJdsvn8/Hotq8efPYgFdVVcVGQLpWWlpqqFMQKzpPnkaAzmwj/sOQIUPYGnz06FH2KNx5553c
+HkUydu/enS3kM2bM4IApacBLRJw104MBY64+pZRlLsmmIiMHDhzIno+CggL+/uTJk/Hkk0/G3T8r
+SMMnqR5KKfbZy5yCubm5htqTgDEIqKUNbo0h1thbJbVxuVz83Lm5uYbS9uY2WgJ073izObeK0Gmn
+06mlpaUhGAw2S+g06cbdu3dnsXvKlCmcpYYs6+asNFauPFnunJCens5RiQ6Hg9WEGTNmcFr34uJi
+ftFTSRjTGBwOh2VOS7qvtOonmnmJ+PUfffQRxwMUFRUlvVnLUvOAbgMyp2EHdBuFTEAqCU6EE7lm
+pZogVRsJme4+nvYILagmGK6JA8dO8W7Dho3E0SpozhTt11wiFJ2a27ZtY0v1jBkzmCMgi6JY3VNa
+w61KjtfW1rJEc9ppp3FQ0tlnn42dO3cCMGbUbSlomtZACpHchPr6+gYknnhB2YVvv/12Vn9SOdlk
+nwB9Dsjg63a7WeKRQWIOh6OB5GiVeKUlYVYJ6Dms5jdeb5b8vtX1ZCGTr1B7breb1d9412OrUB+U
+Uhol+WgswUYyIBFVuhFlRSe56KR3QY4L9UPqkcSd/+lPf8qbzezZsw21BVp6U5BZg6yiS2O5eZVS
+TY4tjYXP5+O2U/E4mJmF4XCYXzDZT5nVyuFwsKtZZtCympuWgkw+I2HllZKkqP/FeyUPL5kljPoX
+Dodt9cGGDRuJo1WoD4DRyEf/n2gaKQL9ToliIoCRtAQYDUdS5CLKdax+OJ1OlgLmz5/PZBOlFEsN
+cebMSwmSumuVZt7cdyl202lMv5OShjzZg8GgJbcg3v4RzAY6+f+yTUnUkgZfq75ZeQCaC3Jcaa7N
+zy/Xjrk/ZiOvPLnN0kQqa132V657gtfrTTj62JYUbNiwYUCrsylYMfDkDi1zD8iTvZG2Aei7OPnC
+6XeFhYUcMOX1etnIlZGRwVyGWOND19PS0ix908FgkKUG8sd7PB5uNzMzk0+3du3acSQiMSxlpWpN
+05ja7Pf7DYYjOvFlyjDziUVjJE8j6VsnkCtX0zSmFctiN/LEkzU/qY28vDym5srgNp/Px3NGVPOS
+khLLPkiJzcom4vP5+FmlVCFdx1Y2E/NpLE9VGk+Hw8HzQGNYW1vL9On6+noeo/r6ep5X6qMMagKi
++RIikQjX1iwpKWkQpSv5JNKWFiuVoJR05fqm77tcLsPz0eeqqqqTJ5szEO28XBBE8z169CgPeFZW
+FqcUX7FiBYDGaa3yOonI0vhItQg//vhjQwFaGX5ttnY7HA6D2E159c455xx+ST/99FNOvUaLo76+
+nn8XCATwwx/+EIBeqIYiN2WyDWl8o5ewTZs2vIAikYhhM6BrkghFC8Lj8RgMn7RIJVmInv+MM85g
+g6nf7+d79OzZk9O+0XMGAgEOw/7ss88MFGVKClJRUcHfJyNvdna24WWTG6vkJtDYFxYWcr9p4wmF
+QkwxP3jwII+LWS0kyLUgRXd67tzcXD4YZA5L2tyrq6sbpOaTY69pGq+bs846i9fF9u3beV49Hk8D
+j5F8TrmZxWMMlmuEkJaWxuMcTztm2OqDDRs2DGg16oNV0Q9pjKLTVvqu4018agbt5pmZmYbkHjJv
+Au20UkST/ZHuNJJoTjnlFBb7y8rKDAlAAP1kI3p0RkYGZ3l2u938LFQZO9buHotKK/8u+2ilMkij
+o5XqFcvfbuWGS09P55qR+/fvxwcffAAAnKIO0E9SUhsIBw8eNKRMk8xMKR6bmZCRSIRPafndYDCY
+shvQ7XZzG6SuuVwuVhOkKuX1enmNSHWOpNBBgwaxpNi3b19OhFtbW9ssqQHNkPT29PR0w9iSKlhe
+Xn5yqQ+k58mKRnLw6CGbw/dPYm12djYnHqmpqeGX2+l0GsQvKR7Sv7QxZWVlsahdWVnJ/cvJyWlA
+GsnPz+dEL3369MHjjz8OQF+A9NI0FdVoJQLH+rsUS+WiMY9tY200dX3o0KGszoVCIdbJq6urWWSu
+q6vjMZIivuRCWBVHlZswEZ3MWZ/NxLJUINUtsthLu4XkIUgVgPreq1cvPhS2bt1qsC9Z2b9S9ThI
+SG5CIBCw5E7EC1t9sGHDhgGtQlJQx3M0SoOTZJK53W5Lum6iOy2dKpdccgkAPYkFJd6Q9RKoLwQz
+1VbyA2pqarhvgUCAVZODBw+yMY+iNn/yk5+gqKgIALBmzRr+bqdOnfg7jYn1icIsNTSnqkhtBQIB
+NgJ++OGHXBehqqrK0sgrIY2j9LxmxqJg4/F9zc/VnJCp4AA9hyUFuUnauKwrSf/u2bOH143kqRw5
+csQy0K+5VXfplaOxdblcLLHFi1axKZC+m5mZyYsnJyeH3XdmfZHEcsmjtxK7peupXbt2XC2IXsDt
+27ezrifchnomAAAgAElEQVQ3gVgFaSRVV6oSUsenyc/Ly+NS8lT89bvvvuOcey6XizPunHPOOWxF
+J122uSjS0jXVnIuQ+jlu3DgunAIgoSQ5UpWQXifpOTC7LRtrl37n8/kMojS1Jf9O16VadeWVV2LV
+qlUAotWfZMJbshFQn819Ms+ZXCPURk5ODrvBrSj2qcyRleciGbubrT7YsGHDgFYhKZBvOhgMsgGn
+urqaT5L09HTObej3+9lqT+nbJ02ahGeeeQaAHhk5ZswYAMCf/vQnPoHz8vI4gzGdIrm5uXHlbzDv
+3rFEcZmQRXolZMQlJS8pLy/HRx99BECPtJSl7oBorcZUYE6y0hy44oorAERLl+3bt4/Ti0njbFOQ
+XI9wOGzwOFh5XuQ1Ky9Ily5duLzd1KlTuX+rV68GACxevJj5FIcOHeKTu0ePHuwx+Pzzz/n6N998
+A0BPz0+Rr2vXrmWDp6QxSzVCSo2E3NxcTvBTV1eHd955BwA4IQ+11xicTqeBG9IUrAya8aJVbAqE
+SCTCluzy8nKDhZfqMu7fvx+nnnoqAHDxzblz57LLqm3btpwmferUqVylqba2Fj/60Y8ARFl1JMYl
+Chn7ABgnlCYsFApxzUO54OlFyMrKwtKlSwEYk7TSAm0Ocd/tdjdrpObcuXPZHkPtdu7cOaHNgCBV
+MBk67ff7LRmNcgzJxauUQseOHQEAP/jBD3D//fcD0NVLsm3QZjJq1ChO5b548WJWJWbPnm1QK4gQ
+94Mf/ICvUT7Odu3acXuVlZUNNoWcnBwmZEkW7oQJEziZ8KxZswybQby4/fbb8Ze//AVAlMiVTDvx
+wFYfbNiwYUCrkRSUUkhPT2c/74033si79h/+8Ac+QUeMGME7MNVlXLZsGasXst7fsWPHDKetrBIE
+gE/qRJGZmcnEpFgpzmORgkj6CYfD6Nq1KwDrsu0dO3ZMOcW3y+VqllRw5DGhojAAOB2dTKUWD+SJ
+H8vjQFKDpBiTwUzOYWFhIaeq37FjB9eSXLBgAdavXw/AyG+hUz4SibChtLS0lHNj/PznP+fU72Rw
+9Hg8rGJKMby+vp4lUqIzh0IhrkC+adMmLuYzcOBAlmSJJp4oKKU+ENsQHgsJe+kSat2GDRv/79Fq
+aM5EhSV93+v1smEwOzsbw4cPB6CfFCRNUCXmeJO9yvh2QC+AQqnUEkF6ejq3JWsfxgO6d1ZWFksI
+a9asYaMknZITJ07koiGJgu6RmZnJbt1UIIOuXnzxRQDGmhrJ9M2c0Uima6MxABoGsdXU1PApn52d
+zRJLt27duLr30aNHuTK1OXMTgX63YcMGlkLatWuXEO9BlogDjFG3wWCQ1/JVV12FvXv3AtBrnSYS
+oCQNusS+pbUSL4SkcHLRnOvr63H48GH2Bbdp04ZrLfp8PiaDrF69mn3IiUxgly5dGlhhaaISRV1d
+XdJWffq+FLt79uzJGx1RsGmBJwO6R3NUb1q3bp0hQQhZ8BMFvfTknQkEAgY+glU0Y11dHRvxpDFT
+Wt9JxdqxYweP3SmnnMJrREao0uFRVFSEzz77DIC+CVOq/USJUPR9ellLS0tZ3amqqmKj4/Tp05NW
+4+QapUjaRHHCac5KKadS6lul1AfH/7+7UmqlUmq7UuoNpZSnqTZs2LDRetAcksJkAJsAkGP9LwD+
+oWna60qpmQAmAXi6qUYcDgdCoRDv5n6/n8XII0eO4NlnnwWguxNJ3CPRqri4uMndkPImSJDPOVE0
+d1mySCTCJxqdnp9++imLpcmmo4tFD48HZHwj3z8AvPnmmwmJvlIyI5GXyqctXryY59rj8RhyREhD
+mtXpLSnWZPCNRCJcaGfgwIFs/CMR/tJLL8XDDz8MQHcL0omuaRrefffduJ9JQhou6TlkGT9zMqBE
+0bVrV4NhdsKECUm1k6ihMaVNQSnVBcA4AH8C8Cul3300gBuOf+VFANMQx6ZAdGFJa6XFIbMuHzhw
+gC3D5K+96qqr2KorxS2Hw8FU41//+tfc9vTp0wEkbsWVfW1O/OMf/+BFTMjJyWHdOVE1QEbkyaQn
+TYnHpCOPHj0ab7zxBl+n3/3ud78zEI4AfW7oWkFBAYYOHQpAp5CTCtSlSxdue+3atQB0m4pMskJ/
+r66ujnt8NU1jXn/v3r2Zxu5wONgrQQWANm7cyCSrDh06cJ8rKio4XD1ZyJcuFn8lGdDzADonJ9k4
+jxOtPjwG4LeIlo3LB3BM0zSy/O0H0Nnqh0qpW5RSq5RSq1Lsgw0bNpoRSUsKSqmLARzSNG21Umpk
+or/XRCl6dbxcvWTxaVq0ErE5cIQiG4n9+OCDD7LPWKb+6tOnD1+XUsjMmTMT7a657yn9nkAnDPmz
+JWbNmpW0oZCMc2Zx1qq8GX13+PDhTL/NyckxjP2f//xnALoHgMaW6MCdOnXC22+/DUCXCKTKR7kj
+Xn/9dTbGmbNGA7pR2VwzMl7Qib99+3aceaZuWB8zZgzTxc877zy+B6lE4XCYvQQ33nhj0pmgqa8U
+DdupUyc2iCfK35Agg+n3vvc9vgcxc5NBoupDKgVmHwYwAUAYgA+6TeFdABcA6KBpWlgpNQzANE3T
+LmiiLY0ILPHkpSNR+6yzzgIA3HbbbUy/1bRo1aS0tDRD8kuyWhNB5ayzzko4/XVLIBQKGcR8wJrQ
+lChkYVO5sUoxlMbH7XYbxH0i/5SXl+Ouu+4CoFOFqX/9+/cHAIwcOdKQP1FmryI1b8KECfwSShuH
+TDQqQ6MJiaxNn8+Hxx57DIBuP6K2yZslYyqOHj2Kiy++GIAe7pysWE6h73LuaLOkBLzJYOzYsQCA
+999/35AJqhkOo5YtBqNp2r2apnXRNK0IwHgAizVNuxHAZwCuPv61H8MuRW/DxkmFluAp/A7A60qp
+PwL4FsDz8fyILPpNiTrSuES5CQ4ePMhSQ6dOndgXLiUPmRq9b9++APRy8VQx+n8BOmGkVPDee++l
+3C6J1NnZ2eypkSneJWQEJ52e11xzDRYsWABANy7Onj0bgK5WUFq155/Xp/W5555Dv379AAA33HAD
+z4PP52Pjb9u2bVlSsOqDx+NhT0tJSUlCJ6JMSkPeB5lrkaRKqZoeOHAAAwYMAKBHTCYT0AVEpS+S
+TJ1OZ9IeLRoXr9fL9H3qn7n/ybZ9QrwPBE3TPgfw+fHPOwEMaY52bdiwceLRamjOqeyEUh/u1q0b
+p8964IEH2B/90EMPcWCPTM5J9NkTPQ5KKWYH/vvf/zZkiALANN1kIA2NssJ2Ir+nfA7z58/HsGHD
++DqdqlSzYu3atYZTjj4PHDiQjWPLli3DwoULAUQZiOY6BXS/gwcPJjUX7du351wPY8eOZWmQUuzl
+5eXx2Kanp7NBdNasWbj77rsTvh8QlfCoLRnWLbM0xQMat6eeeor5CF6vlw2WF1xwAeeGSBQiU3pc
+NoVWtykkk5bK6XSyTz8/P59FVZkqzeVysWhH1uL/xaZAEzRu3DgmzchqSrRwyYefDGgMYxnw4gHx
+Bi677DJMnToVgG7BpzGkRDebN2+OSeGlXAdAtMiNjEWwIlklavSThlKrNuRz0wHx5JNPcm6Nw4cP
+Mwku2XtLSO9ZvPB6vVi8eDEAPTU8bZjbtm3DrFmzAOjqWqreqHg3BTtK0oYNGwa0Gknh+L98TbIY
+mwvnn38+AOC1114DAHzyyScYP358s96jKZARsLi4mDP6yLwPlK1p5syZKRuWZMXkZOF2uznvQ35+
+Pie9JR7AwoULky7KQ5D1E5P5LZBYabSCggJD1i1SjygFW7yQ40x9SCRlGuHaa6/lfAkdOnTgeX/q
+qaeYmu33+w0l/hJBopJCq4mSJPVBDrCs3tQcINIT6a+DBg1qlnbjhVKK77l161a2jB8+fJgt9V9+
++SUA46aY6EtDY5ifn8/RgskiFApxjsKqqioOOb7zzjsB6Knkfvvb3wJIXgWTnAVzLkarNum7Xq/X
+YIuIF+Xl5Wyz6dChAx8WyW4KMglLU9R5mYaO5mn48OG8mWzatIn5HRkZGazqVlRUsJqTSJSwUspQ
+nDge2OqDDRs2DGhVkoJMy+X1eg1Gt1Roo4Qf//jHAKIn2ptvvplym4niu+++AwB89dVXHKz10Ucf
+MWuQUopR4lEg8arB9HypGCut2svOzmajI12rqKhgia45AszMVHdZt4JAkkKPHj2YulxWVha3oVDT
+NFbd4jnd44V5nmSf6cR/9tlnOXKTgsPGjx/Pat7y5cvx97//HYAupZFxsaCgAL179wYQ5V6UlJRw
+Ep3GVO1EWbutalOQKoPX60WPHj0A6GL+Sy+9BEDXv5OhpZ5//vl44IEHAEQHMJmsS6lA0zT2dng8
+HlYZLr/8cl5A9Pdk7SmyuElzZXIm8XP+/PkswtKG88EHHxgyWqWiQlAbViDxWqqSXq8XEydOBADs
+2rXLUNS2MTzxxBMGNZUiaxPtf2PflSqfUopjcPr378+bwuDBgwHoY0mZqDds2MDqQUZGBr8PHo+H
+Y33oWnZ2NrvfJTlNZtuS8S/xwlYfbNiwYUCrkRSo7LjMdvz5558D0KmetCuff/75TG+WFF0iksjS
+bUopzvNIhVeA6A5PabgSBRWvkX2IB5L8891337FIOXToUD7VSWVK5cSVz98c5ciI3NOjRw8WtSlP
+gdfrTUiqiYdDYaUqyNOOfrdmzRr8/Oc/BwDcddddWLJkCQBdrDZnwpY5FiZNmsTz8PXXX3P9ELMa
+Y3WtqYAtq3WhaRp7O+69916OiqUENpFIhCWv2tpaliSOHTtmyPtAagPVkHjqqacMRksypMt6GbFK
+KjaGVuWSjAWn08m6I5U6B6KEmEmTJuGOO+6gtrBr1y7+Tp8+fbgNKp5B3PK///3vlvqrRf8aXJOi
+YVNjSNF0dXV1LEZmZmby5JaUlLAFn9hsVOMyGcgNMtUKUXl5ebx5ZmRkMGmJwqwTbVumQyd9OdbL
+Jm1MNAexVMf09HT2HvTs2dOQaAbQXxpqIxgMYvLkyQCAd99917JSk2QpyntLL0ljzy2LwQDRzS0z
+M5NVxEsvvRSAHmY9atQoAMCSJUu46MuqVavY8/Pwww+je/fuAKLq3KZNm5hM5vF4LL0LLpeLiWgV
+FRU2ecmGDRuJ46SRFKQxi3ZdSvF+7NgxTrG2du1aLvlVXl7OVuaysjIWL6me43vvvRcz8Qhdl8VH
+zGXCgMTyNcoTUSnFBiM6OYCoAS/ReZHirqxtmCoBLCMjgyM3N2/ezF6SZCENfFaql7lOJFGTSdyP
+Nd5yXZSUlPCY0qkKRAvK3HLLLUwxj5WiX0auxirsY5ZiGqPpS7XJ/Dev18un+aWXXop77rmHn5li
+KG6//XZOVEN9O3r0qOX6beQ5bEnBhg0biaNVSwpWO7DcoWXdADoZZIFSr9fLRpvc3FzWjemEPnr0
+KH/X4XA0qS/K/shTIpkxNJ+IydJ8rU4mmdEoWTYotZuXl4dp06YBAH75y18mnaWIYE782hhkZWqy
+yyRa3EZKemTYraqqalAYCNDXk3k8Y0k08rdW68LskqQ5kZKcLIAjx4PWbPv27dlgeuTIkQYMX9mv
+WH1MRlJoNZuC+eWS/2+uPkwDYE5hBjSkycrwVpmv0NwWAIM4T2K8jGCU7VohlViFVOMcrDbQVNQH
+aiM9Pd2wiaaKprwhZrGcDH40B82VXt/8YtJnq0001gtnjpKU61Rm0pYcArfb3aA988YjN2Qypsu/
+S/XQau3LjcdkwLXVBxs2bCSOVsNToN2RTqVQKGQwSknRnoJgpPHJSr2Qp6bP5+Odm06DSCTCacBk
+tepQKGRwI1qdxgRpBDUHLjUHR6AxSImFTlTJYpTjkkgfpMitaRrTZM0G1mRgpXZZGe3oftKlCOhz
+JvkpycDr9bIqIUsAFhQUcPozKwlLjqe5r4AxiE2mhJO5HjIzM9ngacU8lZIQSQl0Lyk5A/qapvuF
+w2GDBCFVl0THqVWoDw6HQ3O5XIYS4bJalHxIj8fDEyqjDMnLcOTIESbY1NXVGbIZm18Qs9guF70U
+n82DGgqFLH3Qsp+apnEWHtqEHA4H902WOJcgPkZtbS333e/3sxXe7/cbsvuY6b9SZZKLVvrYaTzM
+z0wbsgwBrqurY8u4rP8oC/VYLf6srCy2nEs1Rm76BFlQVtaVlH2T1+T8yeuxStvTM0l1U4rX8rvS
+W2Fuq66ujuensrKSr1vNqTnRi5XKKYlH1DeXy2XIEk0xP06nk+8jN0haF7W1tZaEJfms4XDYVh9s
+2LCROFqFpKCOl6KX4pemacz+q62tNWTMpUQfVDRj27Zt7H1QSnGgUzgctrQGxwPajelko/aob1YG
+pU6dOjGrrLa2lk+LLl26ANBj/mXRE2rbSmLIzc3lHb6mpoZP0/79+3Mg1aFDh/iZxGnQwKJOkM8v
+mYX0O3rmAQMGMJOuoKCAT6uMjAwOHpIqGOW+PHLkiKGuIj1fRUUFqyDSSGxFKzZ7geg69dfv93Mb
+UpqSagVg5EOYn93pdFoa5bxer4HJSNfoHtXV1Yb2pIhObUl+i9V6kZKO9KKR9CclnuzsbJYaDh8+
+3CDaUdbglO1RXVYzTrocjUTqkJMncynSxKWnp3PuP6Kn9unTh5Ns7Nu3z0D3TFavp0nKy8vjgaeB
+lvUO5cuUkZHBIp4U4ejviebYowWakZFhWPzyBbMqYipfMCv6sLwu3WKkJnTt2pWJMkA09DYcDhtE
+dPqdrM4lr9NYmERYbpfGRSnFdqIjR44Y2qBnofmorq5ukvKcKGicc3Jy2MNC95CbQjAYNPTZ7Bq0
+UlEThcPhYDrzwYMH+R5WMQyxSH2kigNGtTEUCrW8+qCUylVKvaWU2qyU2qSUGqaUaqOUWqSU2nb8
+37xU7mHDho0Ti1S9D48DWKhp2tVKKQ+AdABTAXyqadoMpdQUAFOgF4hpFJFIpAFNlE7WSCSCESNG
+AABWr17N4jPtxldccQW++uorALqYRaeRz+fjkzQQCDS5e8t70ylXWlrKxicrUdXr9fJuLlWGzMxM
+NrQlm9eAfufz+QzGU/qcn5/faCp4c6Se7L/5Ow6Hg4lBW7Zs4e+kp6fzPMg2KNIvEAhw4hgATDGX
+peaBKPlIlm+nccvOzjZkeabvdOrUiaMLqW/NLdnm5uZytOKWLVtYaiDpp6qqyqDyWakjUupKVXrR
+NI1zRFRXV+PJJ58EoEshlGRlz549fM2KpyDVDLfbbemZagyp1JLMAbAGQA9NNKKU2gJgpKZpJUqp
+jgA+1zStTxNtNVn3wYqcQzrryy+/zJtGcXExqx0FBQW8GN9++22uQxAvmw5oaLU2w7yRJRNS3RSk
+7hhvXj7qTzKJTRsD2Q8oWnL8+PFcYPaVV15hN1p5eXkDFxoQjfOoqakxvEyyz7JyFm2ATXH8EwXd
+r7CwkMPVS0tL2V0o55zUVCVC0a1sA2ZPRjLw+XwcmzNo0CAukLt79+4GRXjly69pmuHlp346nU4Z
+ft7i6kN3AIcBvKCU+lYp9ZxSKgNAe03TqLpmKYD2Vj9Wdil6GzZaJVKRFM4EsALACE3TViqlHgdQ
+CeAuTdNyxfeOaprWqF0hHkkhxu8AABMnTsS///1vALq4RIZGWSQGAFeRIu9FsqnJPR4P31uSRrxe
+r8G4lmqEIrXbv39/rgKdbMGS5pAUhgwZgpEjRwKI5n0oKipiaWzv3r1cfv61115jcVUpxUZMkuJk
+Wj2ZC0CKu5FIhNtINcWcUoqNhAUFBYZEJ1QhOiMjg9cEndZ79+7le5op1tLoCKSWdZzauueee/Cn
+P/2Jr5N0cP/993O0Kqml0htiflZp5KZ1eCKKwewHsF/TNEp0+BaA7wE4eFxtwPF/D6VwDxs2bJxg
+JG1o1DStVCm1TynVR9O0LQDGANh4/L8fA5iBBErRp6KL3XzzzXxCf/vtt5zA89VXX+UiG506deKk
+o1QZeenSpUmd5ubfyMrGPXv2BKAz3qgfyWQLbtOmDUs/qRSGaQ4JgU6d119/nceQ6MBA9BQvKiri
+lGeZmZl45pln+O9kuKMK1evWreNTULqcJZPV4/FYGijNury5n1IqoLmZNGkSF9rxeDz82w8++IB5
+Lbt27eK8HKTLz58/H2+88QYAvVo5SZu1tbUGaTFV/PSnPwUAvj+gczIo9eD+/fvRrVs3AHr0JKBn
+55KSqZVty8xbiQcp8RSUUoMAPAfAA2AngJ9Alz7eBNAVwB4A12qa1mh9btVEkpWmsGPHDqafXnrp
+pVi2bBkA3apNi6N9+/b4zW9+A0AvQAro2ZwpJVYikEYmj8fDRqkzzzyTaadTpkxhsZRE6p07dxoM
+VTK+gjYTSis3YcIEFq8XLFiAK6+8EkDzGjDjgVKKy9Kfd955/AKQ8S0YDBp4DFRNyu1249NPPwUA
+TJs2Dddccw2AqAq3bds2DmWX/HwZzp6ens7qH6kdpaWlBuq6NFZS+vmPPvqINxwyfK5Zs4Y3JiCq
+hsyYMYM3uKVLl/KcUFn7iy++mDe3lStXMo8hFAqxqpGqx8HtdmPHjh0A9DVLnIxbbrmFq5nV19fz
+2iIyXGlpKadji/UeOxwOGfPT8hWiNE1bA8DqJmNSadeGDRv/O7QaRmMyv6MdcPXq1YZScDJ6UoJE
+1y+++IL/TqJYIpCGxkgkwnRrWSxl//79vItffvnlAPSyZOvWrQOgi6qPPvooAF3EpZOCsk/LFG2X
+X3455s2bl3A/U8nTQFi+fDmGDh3K/79mzRoA0WzOc+fOZQNbz549MWaMfh7cf//93Oe1a9dyxmR6
+rs8//5ylOJ/PZ4gcpNO6traW55WMj2eeeSaXVevQoQOuvvpqAHp2bFID8vLyWCIhSeOVV17BxRdf
+DEBfN+TrP/300w3BZHQKU3+ysrIMqpKkLkuqdyp48MEHuSYJAJYw+/TpY1nIhaQmt9vNaz0UCjWZ
+DCbefAqtJnQ6GdDLSBmbAZ2MQi9CbW2tId051T8kVaOioiIpWqqMTktPT2dR2u/3MxfC7XazGEiL
++JxzzmEd8eqrr8bMmTMB6DwLWmDvv6+bYM4//3zu+wcffBB33+hZAesEMfGCNoJBgwbx2Lz88su4
+6aabYv5mw4YN7JXw+/146623AOibMInrMtGNpAcTXC4XX8/JyWFbAy3+hQsXMj+ltraWs17v2LGD
+Rfvi4mJD5CagzzmRsAKBAKZMmQJAf5moby6Xi2M+aAxljUrJt7CKxEwUtPn99re/5bYjkQj+9a9/
+AdBVM8l7oTVH697r9fIzNeb5SLSfdpSkDRs2DDipJQWqgCypxuPGjcO3334LQPcAUFSf2+3GuHHj
+DL/fsWNH0rs9+dVlTgPyHwP6zk0i6NKlSwHo2ZDpfitXruTiHZLdRz7q4cOHJ51MpDmSu3zyyScA
+dFVp48aNANColADoJylRcQ8fPoyFCxcCiC1ey0hTQiQSYbVC5tuMleiF1IBgMGh5n9NPPx2AXqKN
+1M1NmzYxC1NKKfKz1djFUxE7EVjV57j44ou5CJLkRcicE+eeey4AfSyobFxz4qTeFB5//HH+TAvp
+l7/8JSe+dLvdHCfh9/sbJPgwbxKJQIprTZFWaGLLysp4UX/55ZeWi4qs5WYxWm44TYHa9Xq9CRUX
+pUU6e/ZsJhvV1NRwzcOm0LFjR1x44YUAdPJPMp4SKRLn5eXxc0sxmp5JKWUgFkkbBamU5HHyer1M
+APvRj36UshuxOWxx5E48cOAAq6AjRozA6tWrAeiHGiUPcrlcHBNB6uSmTZuaLVJUwlYfbNiwYcBJ
+KSlQXsXvfe97fI12zPz8fDbwAWBxFoju7rRDJ3L6SiR6SshIxKZousRXkKm9KMlJvJC5JhPBmWfq
+hunx48fztQ0bNjQqCSmluAzfTTfdxPOwatWqpE7TcDiM3FydJZ+VldWA+CWLt8jUcxdddBFHa06f
+Pp35J9KAN3/+fADJ566UaA5JgcbqoosuYnXm+uuv53whMj1hfn4+q5ZUwr6lPIcn5aYwadIkw//X
+1NRwKe+SkhIm+vTr14/dNx07dmT1gcTPgoKCuMuXS7QEu5AW73XXXcfXKGw4Ucjcf/GKl0opFBYW
+AtBVLZnJiqIIycUKRD0+//znPzkeQtM0Hk8i/CTTd9qEXC5Xoxt3fX09P19dXR3bMLp3794gMc7K
+lStZV+/fvz9vtDU1NUmpOc35Qh46dMjgJbnhhhsAGHNbPvfcc7zptTSNwFYfbNiwYcBJKSmYK+Xc
+e++97PMPh8OYO3cuAN1wRlWqn3rqKVYlyBD54osv4oILLgCQGAGlJXZqSsFFhrq6ujomNyV6Pyur
+fjy/IY5Ev379cP311wPQowg3b94MwBjBSJA5LIPBIHsDZGyAVWr4WM8kU65v27atyX6TerFo0SKD
+h4IkhOeeew4AsGLFCmzatAmAzr2gEvaXXHIJ81dkspgTicrKSn7WgoICQ4p9km6mTJmSVAxNMrAl
+BRs2bBhwUkoKRAWmk+Htt9+29DGHw2HmKVx99dVMc/373/8OANi+fTvTaJujJFo8oFPT7Xaz3v7C
+Cy/g/PPPBxCl106dOpWln2SRaHQcndAzZ85knfuRRx5h5p3sP0HTNO7zI488wlRjsuUAul5Phr+P
+P/4YgG63kCefzL6cTF6Curo6jBo1CoBOxab0bcSE3LdvH1OY+/bty1GJHTt25MjEW2+9lSnGTaE5
+eAqEtm3bshQbCoV4jAOBANOfU5ESEs2pcVJuCqeeeiqAqF9dZh6OBY/HwwNPg1RbW8ti+4naFGhi
+CgoKMHv2bAB6XkMSy4kotHTp0mZVU8yLWKbrAowhtm3atGHxWqajA6Ibh4yWJB/7WWedxVF9R44c
+4U07KyuLVRDJoZAp5shj5Pf7k/YMUD/GjBmDxx57DEA0ziUQCHA+y2HDhnE0p9frxQ9/+EMAejTn
+Qw89BCAaf9CSRj0KhV6/fj0fEObs27S5Nucm1BRs9cGGDRsGnJSSwr59+wBEk000JhbRqXPllVey
+yF07qlYAACAASURBVPjll18C0MXklqCJxoOSkhIWu4uLi/mkpNOOqNqpItapS5IJXe/WrRuz5/r0
+6cNBN0eOHGF2o1KKqdkkih85coQDzGpqalhsl0zK6upqNvJZJUuh9uXfU8GuXbs4fwOpIl27dmVa
+fHl5ucGVSVJPXV0dR9KS5BirVkcqpzZJCMRcTEtLM5R8o3uvW7eOuTirV69OuG4IkJyEcVJuCuQ3
+J3HQ4/E0KMxBIPHrnnvuYU/E8OHDAUT1zf8FIpEIrrjiCgC65+HZZ58FECVbpfJyWG0E0ksgi8jQ
+5qCUwsCBAwHo6dt37doFQM92TC/4jh07WBy/7LLLAOieHCIbzZo1izeW8vJyQ5hxYzqxrIMYq/+J
+QEY+Ult79uzh9gYNGsTPVFtby7amrVu38ndSybfYGPLy8jhqlg6CyspK9pg9/vjjTGDr3bs3qxUy
+1X4iSGYMbfXBhg0bBpyUkgIFDVG+wIsvvpgprPJEys/P5105NzeXT0uZJZkMSic6zRlgzTyUyVUS
+gax8TSdQeno6G2FDoZAh+zAZD0lSqKmp4e+WlZVx0M3dd9/NrMI2bdpg0KBBAIBevXoB0MebIkYX
+LVrE/vZEA46khTzVuZAis+RskKepsLCQn3vFihUcNBcMBuM+WROtTUr3mzNnDqtLdN/p06fjnXfe
+AaCP24EDBwDoOS9JGs7MzOR1nwj/xOFwJFxm76TcFIjgQ7rXnDlzWBe/4IILeGLvu+8+pu7KhUJJ
+LJYvX56UeGUuUJos6LcdOnRgqjAhUV2QFjwQTd5RUVFhSRySFZko3qG4uJhtNf369eMEKc8//zxz
+8c8991weT2o3LS2NN9vNmzcnlYJdFlFp6pnN9THlRkeLPysrq4E3yeFw4N577wWgjze9IDNmzEjI
+3Wd2ycYLsiP079+fN2RSGTZu3MibqMPh4E14zpw57GYdO3Ysk8vo2RrbxGQSW1mHMx7Y6oMNGzYM
+OCklBbIiSxotRU6uXLmSd3PpXy8uLsZf//pXADrlGYj/lDcbvtxud8wCIYmA+nf99dfzPWg3d7vd
+CRm7yOoPRC3m5vRhMrUZZTama+PHj8cll1wCQFcf6DRavXo1W/JzcnIalNALBAIcwCMDphJBrFTt
+8jr1U9M0Pvlyc3OZjn3KKadwurJevXrhkUceARCtCdm9e3dDjkaSFPLy8gwp4hrrg/z/RIlhMvKT
+xnDYsGEAdCMo8Sa2bdvGbY8cORK//vWvAehzSiobke+OHTtmqMEp+yYlhURVuZNyU6DkoST2+Xw+
+XjQej4evl5eX86CNHz8eX3/9NQDjhFqxvWRVHasX3ufzsV6YCumJ7t2rVy9emKWlpQASt37TIne7
+3dx3ufhlPEAkEuHcg2QP8Hg8bC+orq5ml2Rubi4vXpkhiMZr4sSJLNamAqtaklIHlgueNj2lFIcc
+v/766+y+czqdXKuB3L5KKQMzk2xJu3fvNqwHsxojbQeynkSidg9asw888ABXKCMv2hNPPGG4r3TR
+0hopKyvjqFmZl9FqIzMfWon2NdVS9L9USm1QSq1XSs1RSvmUUt2VUiuVUtuVUm8ovRq1DRs2ThKk
+UkuyM4ClAPppmlarlHoTwEcALgLwjqZpryulZgL4TtO0p5toK6FO0O5J8QIffPABX6upqeF8CwsW
+LGhQqdd0X8P/S0JPY5JCRkYG79ZOpzMp45rT6cRdd90FQC9gQ54U8v8TLTheyJOWTjNpEJWVnevq
+6hqcHkOHDmWD4p133onFixcDAEaNGmWQFJYsWQIATAdesWJFs3hupKon+9xU22TkzMjI4IpaF1xw
+Ac8PkaZ69OjBnp2SkhKOxaisrGTDXigUMhSXAYzVliKRiKEidjJcBiltUFRmr169eP2Gw2FDJCo9
+/09+8hMmtNHacDgchvT01K7b7WYJOTMzk5+pqqqqxWtJArr6kaaUcgFIB1ACYDT0upIA8CKAy1O8
+hw0bNk4gUqklWayUegTAXgC1AD4GsBrAMU3TyLKxH0Bnq98rpW4BcEsy96bdmujKt956Kwfw/OUv
+f2Gfb1N+WbMUQLuu2e1lhmTgJWLEke22b9+e2Ysvv/wyn2hUFCZZKKUMVZvlc0h7gPn5VqxYwTr3
+f/7zH1x77bUA9DEi/sK2bdvY6Jhoijgr0Dya9V6ZQo1OP3mNnkMyJQOBAPdNBlvJZyc7SWVlpYFu
+bTWH9HtZ0k5+ljkPEoGmaSxZnnbaaQB0ty7VvJw6dSpnryosLGQa/rvvvsvPatVfOZ+yNmcgEGiQ
+A6MppKI+5AF4G8B1AI4BmAtdQpimaVrP498pBLBA07TTm2iryU5IIwrlraOJ8/l8nHF4+fLlBkt8
+SyERKq5UU+hzUVERW9HbtWvHaeDNhrymIK3yBFoE8mWTRkcpEssFL41rhPz8fE5vt2DBAjZ2Jasy
+WI2bvHcsqz6pCbJ+pLS4J3JvMxprw8wXkZ6tZNTGloJSit8Ll8tlMMaKA6zF1YdzAezSNO2wpmkh
+AO8AGAEg97g6AQBdACSeBNGGDRv/M6QiKQwFMAvAWdDVh9kAVgH4IYC3haFxraZp/2qirUY7IXc7
+j8fT4DR1Op18OgYCgRMWd54opP/Y4/Gw4cvpdBr4CUD8Je0a+45UV+QYNuVylUhLS2MXWWVlZYud
+jlbSlKZpDRiEUkJJZZ6lhCXvZ8WZiGWkNo+9ua0TCSkpyLqSpvwMcUkKqZaifwi6+hAG8C2Am6Hb
+EF4H0Ob4tR9pmtYoj1QppdHgWy0I82eKxCO9NhKJ8MvkcDjY934iJiYeOrK0rEtSCT2TrHBF0DTN
+0gbgdDoN9TFjieN0Tb5sUocnWjSpWuZ70KJyu91MCnI4HMzFbw6Pg0z0YvWyy76TDm/OsJVUFKCI
+B5Bt+Hw+g3+f+ibVLit1Tfa3uWNorDgUBLN3icZI2i3k+qyvrz8hpegfBPCg6fJOAENSadeGDRv/
+O7QaRqO5iq/cdd1utyGNF0WLSV8y7ebBYJClhnA4zBb1cDhsKIUG6CI8+XMlC4zuT/cwG6jk/WRu
+/liSDn3X5XJZnvjUP3lf6ROXHgWllCFngdlgKK3i8pkikYjBSEbSlITMkk3jFgqFWJqQ4ykrNct7
+S/4GPbfH42mQ30AWZHE4HIYoT9m2VR/pfhkZGax2STqvz+fj71A+DSkdaZrG41laWspit6wLSvNU
+UFDAEqmcU1m+jp5TruFIJGJYC2RUdjqdrDZS3zMyMnhNer1e5k2kpaXxeKanp7P3gZ6joqKCPSrV
+1dX8HIcPH+Z+Sv5CvPkYUlIfmgtKKY1eGCmKUUYfSbaJRCJciIQowTKTTlpaGi+qcDjMEzBy5EhO
+HCLDpceMGQNAd8lRe5JAomkavwgypkAublkBysoNKtuS+idNolR5aCPw+XyW0XsXXnghvvrqKwA6
+952+I1UUgsyvaBbFaWyJph0KhQzh25JTT7/Nz8/n70j6MEVOHj16lJ8jFArxuMm07fTiytgOuYG4
+3W5Lfb9Hjx4A9BeBImLT0tL4paioqOCXLBwOszeK5mbnzp28Lrp168ZtHD58mO+dnZ3Nc0y/C4fD
+PIZer5efqaqqyuC5oe9S3/Pz8w25Q2neBw0axDEihw4d4naJov3ll18axoJiN2pqagwbIKBXSKMw
+69raWp6T3Nxcy3mN16ZgR0nasGHDgFYjKVgFJkn1wCooRRrimrL6ZmdnN9jZI5EIFwJ56qmn+CQx
+qxHUN1I/qqurDUE7yRiXpFXdymDmcrm4n7m5uXzCTpgwgYOAKioqGhhVYxkwpSphRWiS18xSh6T2
+Ul4AIth4PB6mRx86dIjnprq6mgk5hw8f5ut0mpnvKVUJq4A1KuBz9OhRjjisqqoypDSjeejdu3cD
+rkpaWhonNTFXrrbyHlipOV6vl58jIyOD1VipMjRlMJVzIteAVDUlX0SqUGbPT2FhIQe2SU9TdnY2
+G4Tl9VAo1PLeh+aCdEkm49KR4meikC+ArDUp708LT+qeBFk6PRE05uoiSDsK3VtanCORCG8KkoFH
+z5SZmckiZ319vUGXjcfNCeibk5W+T5vD3r17WaSuqanhF7ampoZVlPLycmbvrVu3jvvT1HNLW4Q8
+IChM/ujRo/xMLpcLv//97wHokY/vvfcegGj6/8GDB3MWLrlhNDUP5r/JA4Lm3cpOFM/6JbXS5XIZ
+qluRzahLly6G+pG0BkhllHYU+fKbo2Opz/FuCrb6YMOGDQNanaTwv0Ss04FOQiGGGf5+ovgQ5nt5
+vV4+KaSkJE9Vq3Rsiao7VvTgWM9M35UEKUmsoXvLFPAS5vwFVmQi+UxkiLv55puxbNkyALqqQTkm
+SVQvKChgkTqVTM3Sqi/VDvlvopBei8LCQi5QFA6H2fOxfv365uBA2JKCDRs2Eker4SkkgkRr48UL
+q8AXaeiRNNITTWe1uk9dXV0DwxhglBqo/z6fz+BGNHMoJKQdQf49ntRz9HdZK1MmGJUSgpUdoSmq
+sPx7WloaFwTavXs3Z5LWNA1XX301gGhRncrKSmZmaprGLrtE50+yQptrDSiluHTdr371K3ZPZmZm
+8j2WLVuG6667DoDRptUSaFWbglys0ucvX37pfZgyZQoA4A9/+EOL9AUwvhSyUpL5e0Dii0O+0GYR
+PR7DqfRayI1StkVj5/f7WQ0aMGAA/47E7/79+7NRq2fPnszZ2LBhA3sXNm3axP79WAY1unfXrl3Z
+Mu5yuVhkl3Rlq8jHpjZ6pRS/3DfddBP/rry8nA2IBw8eZK/SH//4RwC6t4Ce3+FwcOHhAQMGGPgg
+5tT3koQkiw5J0lqyoDkbMGAAxo0bB0DnHtAYeDwevveYMWM4QvWss84CkHginrj71SKt2rBh46RF
+q5IUHA4HZ6zdsWMH0zlra2t5x+zZsycn5Zw1axYAfaddu3Zts/bFii9BoqpMMJKRkcFuQemyk24h
+GahC7rTc3FwWpS+99FKMGDECANiNN2fOHBZ9NU3j5zP79q1OWOmaktRmOvHD4TCfpFQ7ccCAAdxG
+eXk5sybPPPNM5ggcOnSIT6dPP/0UgO56pFPujTfeYCNZVlYWZ1T2+/0G/gKgn4LEhCwtLeV7ywQ2
+TeVZ2LlzJyelOXDgAJ/i/fr1w3nnncfPDejqk1QFaZ3t37+fy7RJrgOV0Bs6dCiPxZYtWxowM+V4
+JwqSRkaPHo2hQ4cC0NWE0aNHc9+l5Ezq2Pr16wHoaec+//xzAPq6seImOBwOlqyIV9EUWoX3weFw
+aC6XC4WFhfyCdenShV+aiRMn8iKcN28eTy4tgo4dOxr46VbEk8GDB/NmQov1q6++4iKfEjJGQdKc
+acHv2rWLF1sgEEBRUREAPc04ce03bNiAc845BwDw/e9/HwBw9tlnG6pUkT86HA4zF4L04pycHF6g
+JSUlPPn333+/JQnJSn1wOp3cn6ysLP7dZZddxnU1x48fD0CnEZPffdWqVQZqN23OHTt2ZIo43SMQ
+CLA4a66aRO09+OCDnFafkJeXx3q9w+Hg8QyHwwZCldThzUhLS2P9e8WKFbwuBg8ejF/+8pcAomnU
+N27cyIVviFQF6KoGjYXVu5CWlmYgOsWy3SQC6idlCzv77LNZ1Tr33HOZY3HuuecyXdsqb6Pf78fG
+jRsB6GSyV155BYBuRyH1z+v1on///gCApUuX2t4HGzZsJI5WoT4QjbmmpoZPz0AgwKLhiy++iJkz
+ZwIwRkySOFRbW8uiVVZWFu+qhw8fxp///GcAwF133cW7/GOPPQYAmD17Nu6++26+H8EsqtLpTmXY
+nU4nqxJHjx5lsS0UCnGAzuTJk3HBBRcAiDIiZUCRFGWdTifThuk6GQABoG3btnx927ZteO211wDo
+J5VVtW0pNdDfq6ureWwXLFjABXWo3Y4dO/K4TZ8+HTt37uQ2SFLo27cvqzE0Xu3bt+fnbN++PX7z
+m98A0CUBkkzovtRnAGywpP5Klp4cl8ZOY7/fz6K9x+PheXK73ZzRW1rq6VmrqqoMpfUak5YlW9WK
+lp0oMjIyOAv59OnTAeinOQU27d69Gy+++CIAYO3atTy255xzDrZv3w4gyibNyspi9a93797Ys2cP
+AH3t0Jqsra1N2CDZKjYFQF985eXlPAm5ubm8UHbv3s3uNCkeT5gwAYDu8qLv+v1+FgdPP/103Hjj
+jQD0CaWFLMkrMlLPitwjE1aQKBsKhXgS6+vr+aU/ePAgf7eoqIhFOFJRunbtyptJ//79WS8tLi7m
+NqRHgii6+fn5rAMfPHgQ5557LgCdMkwiOC1+qU9qmsbjGQwG+TtOp5Mj9GgzGj58eINahQTafFes
+WAEzSkpKMHv2bAD64qYCJ5MnT+YXr7S0tMHLLV+wWGSweMRzmr9QKMTzu2LFCktyFI330aNH2U7y
+z3/+s8l7WCFRtVsW/nniiScARIsJOxwOjpwcN24cj8fevXvx7rvvAjCGjN9+++0AdMIWlQYAwOvi
+kUce4Y1n1apVCRcsstUHGzZsGNBqJAVKqkIi3r59+wxEmL59+/J3accna7o5AISMlcOHD+eTpLi4
+mAuckLX5vPPOYyv8s88+y6d1RUWFIXEI7eikGmzatIl3c5n3IDs7m+P+X3nlFRbhSMopLS3lAjBV
+VVUson/22Wec7p0kgjVr1nCpsXvuuYf79sILL/DpFolEsHLlSsM4aprG4n5NTQ2PlZSwwuEwS1Nk
+iCwpKUk5bXswGOTSfFIloKjOxpCsOE6QJ3csCjWRz0hKAKJlAloaNH+fffYZewPomf1+P3ufpBQr
+JSXpgaN56tu3r4FA9eSTTwLQa3q++eabAIBLLrmEJcSFCxfG1ddWsSkQISk7O5tFVcmIGz9+PItJ
+mhatgPSPf/wDgO6Ooui7srIyFvNfeOEFXvS33norF/Gk73bu3JlZYoFAgL0TMuuT0+lkXZVeNhnS
+6vV6WT+///77eVLffPNNXgjLly8HoFvFqSZFeno6L96PP/6Y7y0rHpFlvaqqiheI3+/HT3/6UwDA
+008/bUiGQn2jvvt8PnYBSn69z+djtxf9e9dddyVtTSe4XC784he/4P+nZ7nqqqvw9NONFglrFmZo
+U21YMQFp421JOBwOPnyys7MbEONuvvnmhOIxaIzNnpAPP/yQP9NhsHLlyoST7drqgw0bNgxoFZIC
+EI3oIypxdnY2G1/eeustXH65Xn3uhz/8oSHRBaCL1xQVt2bNGvb5Hjp0iH23b731FifZ+MEPfgAA
+GDFiBFdarq2tZe/C6tWrWdro1KkT5wgg66+MUR80aBCuuuoqALroRz7mjIwMfPLJJwDAfuK//OUv
+Bglk8uTJAIwWcGk4++KLLwDodFaqVJyWloaPP/4YgK4ekGoijXPSIyGjFsnw1759ex4LknJk0pBk
+0adPH/arA1GiEvnSTyTouUlS3Lx5s8HXT7kXmgOSYESQOTEHDx7MtSslIYvGJR71CojOMdH6SUUA
+gMWLFxu8YPT8svJ63M+T0Ldt2LDx/x5NSgpKqVkALgZwSDte/k0p1QbAGwCKAOwGcK2maUeVvj09
+Dr3ytB/ATZqmfdPUPSiwp6KigqWAUChk0LPIfrBmzRr2hcvq08Rcu+OOOwxpssgos2/fPj4piCa6
+bNky1jM/+eQTrufYtm1bTvK6a9cuQ3AMYMzwvH//fjz//PMAdH2fGH+hUIj7R5JGx44dmeYcDofZ
+0Hh8TC3HBAAWLVrEkoLf78czzzwDQJcUzHq0dEMC0VwQSinuf2VlJdOpaayKioq4LXK3xgOXy8Wc
+itWrVxtOYxp7ynh0opCWloZ7770XgF6bETCe4o888ggHSiULyW6k+fV6vczJ2LFjB0499VQAuqRA
+n4HoiU/jds0112DOnDlN3pOkV+Iu1NXVMQP20UcfZZtXeXk538Pv9yccTRyP+jAbwJMAXhLXpgD4
+VNO0GUqpKcf//3cAxgLodfy/oQCePv5vk6CsyGScCwaDPJF1dXVsJR4xYgS/yDJqkYwpubm5HC3X
+rVs3Fqk6duzIn8l6X1dXx5FnTz/9tCEtuAwdprZllmGyrJeUlPDmJcOT3W43t0f3mDNnDm677Tbu
+82effQZAn2R6ln379gHQFxhN8rRp07gPCxcu5GQiTRnWpMG0vr7ekDaNVCUSLefNm8dl5idPnsyq
+24UXXsiqmaw89PDDDwPQjcDkyZCp7YLBIL+QtbW1TYa7y5yXyeL00/WSpX/605849kFuBuSv/+1v
+f5v0PQiSfEZr9oEHHmDPxquvvsrrYurUqTyXsk9kPH/hhRd4k/rFL36BW2+9FYDuqSBvzo4dOzBt
+2jQAwJAhelkVv9/PKuaaNWsMyWnofjJ9fLzqYZPqg6Zp/wVwxHT5Muhl5gFjufnLALyk6VgBva5k
+x7h6YsOGjVaBZA2N7TVNKzn+uRRA++OfOwPYJ75HpehLYIIylaKnlF3EMcjJyTEEydCJdvDgQTYw
+kkSwYsUKFtslhXfTpk245Rb9FqeccgrvsGRk69GjB5/MRUVFrD7s2rWrQXIRwDo7r9/v5/+vrq42
+qD/0mVyg9913H7P/CgoKmK46fPhwpv2OHDkSgB5ERLu9NBju378/qdM0KyuLxflBgwax9EJ9dLvd
+LAaPHj2ajVlPPvkkqxqHDh3CZZddBgAYO3YsAP20kxIUjdG+fftYJI5Vk4KglGL3ZTJJcAGdc0Jq
+VV5eHnMSZDTjFVdckVTbBCn9yahL4hjs2bOH1YcBAwZwwBPQOA/D4/FwsN38+fP5+rhx4wyJeWne
+6V2Q9UjHjBnDPITTTz+dpen8/HyWEEnCbAopex80TdOSybGoadozAJ4B9ChJSlpBC6isrIzF0pKS
+Eh7UOXPm4PrrrwcQDWV++OGHeTEOHz6cw1AvuugibkNmBiZdbufOndxG9+7dmdcfS8SNFcYrk2LI
+vIRm33N9fX1MS3yXLl0AgD0Zubm5rJ/X1tayD3rDhg1xvziaFq2EFAgE+OXdvXs3R25Sxh8gGo+w
+ZMkSXHPNNQD0+BEiiXXp0oWt6ES2MheApb4dOHCAIxWXLFnCi5ue3+v18nzk5eWxpyJR0Ev4/PPP
+80YgMy3TfEyZMoVjA2SylHggeQWyZim1QWrgxo0bOc5g2LBh/GLSGgSMGzyNiSxaZI73oI0nLS2N
+70cbaHV1Nc9fTU0NezG2b99uKJIj40ziQbLeh4OkFhz/99Dx68UACsX37FL0NmycZEhWUpgH4McA
+Zhz/931x/U6l1OvQDYwVQs2ICRI76+vrDamxKFgHiPrvy8vLeXekk7t79+4YNWoUAH3XJe/CRRdd
+ZKiRQCcT3aNHjx5sfLzmmmswb968uB5eFuyQ+RsSSStmBonzFOsvGYjBYBD//e9/Aei+6UTUB3Mw
+F/WT1C1Swdq3b88i7O7du9kD0a9fP2bjDR48mPtJv8vKyuK2ZdGXgv9r7+ujo6rOvX87M5lMBhIS
+CARCQGAV4gf4FrAU21ps9VVxKbbWtkbx9YNWbqnleluXirhcC5d0tWBtvfV6EWtLqQpaUSxZUl7E
+2yotil4uAqIhQsJXEj4STMIkmSQz+/5x5nnynJMzmY/MhLm9+7cWi8nJ5OyPs8/ez8fveZ6RIznP
+xOzZszlQiub7oYce4hMsEAikJCkopXDNNdfwmGjOQ6EQq2wy2QrltxgzZgyf6JFIhNt2S28nvTay
+kEt3dzdLDWTAa25uZm/Apk2bmFa/YsUKnov8/HxWWUkybW1t5fn50pe+xJGfo0ePxsqVKwEA06dP
+Z+MvSRWTJk3itTd37lxW1/bt28dzW1pamnROx0RckusAXA6gRCl1DFaV6Z8BeFkptQDAYQDfiX79
+DVjuyE9huSTvTLQjWuuEc945XwoZbkr3AqyQa1nklB7oww8/DAC46aabmPpcV1fHD4ZeGIJ0swGx
+y7Z7PJ6kKaUEWoS/+MUvAFgvIJGwOjo6OEIxkQ3BLUcj0CuKtre3s1uL7tfd3c2c/Ouvv57tHYsX
+L2b1oauri9WYFStWALA2BVrcx44d48358OHDHOfh8Xgwffp0AL0EsMcffxxbt24FYFnWyW5BeSIT
+gc/nY7uLjKptbW3FHXfcwWOlOSHvSmVlJatp3/zmN5lE9tlnn2HXrl18b/o7GV1L+nl+fj5vjNI7
+JaNu6eV+7bXXOJGs3+/njFpUx/TNN99klbe1tZVf/vvvv5/TB7z00ks2GxtgbbzXXXcd95MOy9ra
+Wu6zjCdKVGWKuylorStj/OoKl+9qAD9MqGUDA4OsRFbRnFOFtMxKSMlDUj+ffPJJAJbxiaSAr3zl
+K+y1OHHihGuORllOXVrciUorPRGpZnYmA9GpU6f4VKquruYo0Y8//jiuiiI5ATQ+WdIsJyeHcxCS
+x0Fasu+77z6u7N3R0cGn++7du9lXHos6S6e/UoqNuF/+8pe5r6Q+7N69m709ra2tLHbHKuPmhu7u
+biYFhcNhPuWrq6uZfCbVPPLX+/1+Ts123nnnYf369QAsYx2pI9Q3oFfdlHUuZU1Mt1R4SinO9/H6
+66+zcfurX/0q57QkaYQo/NTWLbfcAsCujspAN2qvpaWFn8OBAwdYKsrLy+u3oE48ZM2mAKQ2gGRA
+916+fDkAexanxsZGXkiSraaU4peT4KzGRJbu4uLipC29BPI+3HjjjQCsOAIi20ydOpVF35EjR7K+
+H8uKLueQ+lpeXs6Le9iwYeyW3bJlCwDLRkBW8ilTpuA3v/kNAODFF19kG00ydhKtNZYtWwYAuPPO
+O/nlJbfYunXr2EXa3t7uWug2HjweD6t6Z86c4TnctWsXb9Q0h4FAgDf1tWvXMhnutttuY0/Kxo0b
+2Y4lX0A3d2J3d7etRD1dIw/IhAkTODZhxIgRTJgbM2YM2xdoU/B4PPxs9u3b56qCyrmnz8FgkIll
+9fX1nMzns88+61PAOBmY2AcDAwMbsiKbs1JKD0bFJaKjkiEnLy+PT6hLL72UDUCSruz3+/uUJJDU
+hAAAH1BJREFUDpdZhuUp0t3dHbeuIH1fioMej4cTv9DJPWzYMFRXVwOwSEMU70H0ZMA6MfrLdgz0
+GknLyspshB4S4+lEjUQibFCsq6uz8UVSrcRF1O277rqL2yNL+NatW22FYejZtLW12ebZjR4t+SI0
+pvnz5zOhqra2lvNwUrvnnXee7ZmQRPe5z32OVannnnuuz3Nzkqxkpmz6HY1T9k1WkgbA0aMHDx5k
+LxcZXyORCJPCNm/e7DqXsSDzb5Dnqq6uzhYdS33q6ekx2ZwNDAySR9bYFOjklKdHukEGHVlFmlhg
+pDcDlhGNdmB5WtGp293dzRKG9GNLzkI8+4jMyRCJRNjgR/UdSktL2SUJ9KaQq6mpsaWekycTYM2b
+lLrkOCiJbV1dHSdppTZ8Pp9NeqCksQOR3EjCWrt2Lbt+pbGTdGev18uSiYxALSoqYslC1qGgzx6P
+h12LW7ZsYSPhnDlzODKTJK+lS5eyEfSyyy5ju1JnZye7WSkoD3Cv8i2fqbRH0fMYPnw4J8T1er1s
+J/D5fNwfrTVT2QmXX355wqnSnJC5M2RRIimxJvtOZY36QItBLpp0FpCdMmUKG34uuugiAJYRihKu
+HDhwoA9xBbC/6PSChcNhXsTS0CfJL8lAcv9vv/12AMCoUaOYVLNz5062rDut/m5GMDfxurS0lDfF
+6upqTtQiPRnUh4GUanfrm4wZkL52mueSkhJ+0evr6/lzJBLhnJVEj45EIvwSOu9L9+vs7OxDSS8q
+KmIuhM/n41qSHR0dffz//Y1Hqg/O5CoyWlfWoJTrJZEivalAktOkIVwpJcdn1AcDA4PkkTWSQqbu
+Tbt4QUEBJ2IhHD16lCmq9fX1tnJzElK0BWKfKDJ/QdK+4aiIR2rC2LFj2W149uxZdt91d3cnpVqR
+yjNs2DB22dXU1PCJli6pwA3SZUdzS+3J4DfJRgwGgyyxjB07lg2Q9JwaGxttYns6DNTJ3EOqkm61
+Quna0KFD+ZkNhOmaKKQU4ywMJCSWhCSFf/hNQUa1kVhKIrjf72eOgVPkcv5M9wDsJBWps4XDYf4s
+9VM3Ikms4qlEcuns7LRFSRKxKBgM2rwh/dGe5ULxer1MY9Za80uWThXNrX3qp9Mr4/TgyM2UrhcW
+FvJ8EIdEvmCDsXZj2YakiintSJJS7LZeBpoxOxZkf5zrU/TDqA8GBgbJI2skhUyxGUkUlT59KX6T
+IU6mGpMGw1inhDz9pTGLJA83Blo4HO7j96b7UTvEG/B4PGxN9vl8fN8hQ4awqN3V1dVHZHT2V0oh
+dIpJwyZJTZIGnq7nIE8vmnuSfqT6ICMcZX+lhZ+K7Jw5c4Yt/LKf6ZB43Nag05ArpQLn3Hs8Hqap
+BwIBlm5kX51SaDr6DPT1RNHn/Px85tz8j1IfqBS9dEk6J09mvHFCZjmSuQg7OztZJz179qwt/6Pz
+vrKcvdfrlRPJi5cWueSk5+fn8++dFZmc1mlpIfZ6vTY1gP6OcvydPHmSxyot5JFIhEXqnp4eHots
+l77rWBC2ezg3ESdRSH6W/abPUq+XC94tQtPr9fZJuS5rPzrds24JcmXciXwJ3VSz4uLiPnECANgt
+Wl1dzVTjpqYmbqOxsbFP0hjpWpTjcLqi6fdy7JKERc9Sqhj0bHw+HxOP5DMFepPfOjdLwJ44R3qP
+5NzLOejs7DTqg4GBQfLICkmBeArSUqq15lRboVCIffYylyCdNLKqLkkc0fv2MRICvRZwKUbHInx0
+dnb2ESG11mz4o+QwgCXFEH1Wzqv0Y8vUbXTSyJM7HnJzc11FRopqlOQmp7Qir9Np4+RZAH1TgtF1
+n8/XJ0IzEomwAROwRw9Kr43keACWN4TmSqpVSinuW25uLktAlOvi+PHjNi4LSYVSEgqHwxwFSW2c
+Pn2an+nYsWNtORAo1f7QoUP5+7I/NIdOD4JzbUmJLxAI2KJr6X5Tp07lIC76u46ODs6tsH37ds7T
+EAwGeXw9PT196PYyQ7nkZni9XlvuTaEiGknBwMAgeWSNpOD1em2nWU5ODtsAZEELeYrFCz5yc4W5
+tG37PtA3CIZ2fNL7WlpabK5D2omlLjgYUErxHNFJXF5ebpNGZF4AaWCNdfql2g8CBQc53aj0mdoN
+hUK2tiUVV9pwpHQD9OUmSB1f2oykbQqwuBLUB6/Xy/RpWXRIzg/9vaRjy+uSfSvXmJS2pDucns/5
+55/PRW3JzpCbm2uTcqi91tbWftdvLOO8lMz8fj/PXUtLS0KSQtbEPhD9kxZVe3s7qwxOo41zouSi
+jGU9jrUpuOUecIImmERjpRSLbePGjeMswYMNrXurQUlRlcbU1dVliy+Qc5vO2BJnfABgLUaZH5JU
+vsbGRgB2Y61MACONmoFAgFVD2YbbZzkXMoUaYebMmZz05S9/+Qt/1+/382buNg6nF8lNrZJGTWmA
+lXRsGt/+/ft5XqR6LNVOOnDikZ5irelIJMLjz83N7WM8jQejPhgYGNiQNZIC7cwyq6+bHzqen9/r
+9bKY397ezidUQ0NDSv53rXtLxFHbQ4YM4Z3/6NGjg8Ks669/QK8Uc+TIEa47uXPnTp4jyQVINXAr
+FqR0QIlUy8rKmLK9Y8cOPqVJUnBKaJI+TKf4mTNnkuqnm7GWJI8zZ87YygxSn52Zjp2SpZRonG1J
+N3is9gG7iia5JZRBSxqgvV4vGxpTfUZSrWpvb09aUsiKTYH0djfRzO27ZJElne3iiy/G/PnzAVgZ
+cmlCampqODtwe3s7p/WWpdzjidHS2k/9kw/W4/G4iu7xUFhYiJ/85CcArDLpVLbeaf1OFFLnpuzK
+kkMA2HkWAwW9EB6Ph1+2rq4uvndFRQUnhtm7dy/ee+89AL3Rjs78g/Q5FAq5iu6x1MBY9gWqdUlR
+sMePH+fN8oUXXuCs2aFQyFUdiSW2ux1UyZK+3L4nc2USUYvSq6UC6RFxi6Tt929TbtXAwOAfEnG9
+D8q9FP1KANcD6AJwEMCdWuvPor9bAmABgDCAxVrrLfE6QYzGWKKaoz9cu4/ErBUrVnChD6UU04Pb
+2tr4RCsoKGDjC/1/zz33cPor5ykvsyA7/cPhcJgtx11dXSwyFxcX83caGhr6eCOWLFmCGTNmALBq
+K/Qn1oVCIZYeFixYkHDSk1gnQzzqdqIgqYgKxyxcuBBPPfUUAEtEpzFfe+21+PrXvw4AuPLKK9kz
+8MYbbwAA3nrrLU48Ul9fz6euTBIDuAejUf8DgQB/vuGGGzh93euvv86p5eRYSVU4c+YM16eg4jaJ
+QHIW0kmx9ng8XLPh8ssvZykmGAwmRYl2o7TLviVKc061FP1WAEu01j1KqZ8DWALgAaXUhQBuBnAR
+gDIAbyqlpmit445MipD9QWvNCUdowv785z8zhbW4uJgjAPfu3cs1E9vb29mSTQt0zpw5fG3Hjh1s
+iygsLOR7FBYWck5HgnxYBQUFrM+PGzeO8/397ne/4++TjhwIBBIW5fLy8jgVeGNjI2cfvvfeezmX
+pKRKE5yEJWkhT1aMdANlfKYKTFprVle2bt3KG3ZzczOnTj927BjuuusuAMD3vvc9ANamSAVufvnL
+X3Ia+VOnTtm8EpI4BVhzSfNyySWXcL3Knp4eTqIC9JKyaCM4dOgQXnrpJQBAVVWVTZ9PRezPzc1N
+W9i5JIXJalFEqkoU0htHm0FeXh5v5HSIxkNKpei11v9fa02K6buwakYCVin69VrrkNa6FlalqFkJ
+9cTAwCArkA5D410AXop+HgtrkyBQKfp+obVmsTGRXVuWAgOAX//611zm7Fvf+hYHkSxdupTLj//t
+b3/jJCt/+MMfAFinifQlU7DOzJkzOePu0KFDWRKQqghZ1o8dO8aGtnnz5uH+++8HYKdVu1GiN27c
+yIVVJkyYwAlVfvvb3/Lfk6oBWDUGAWDNmjW4/vrruW03wpU0gNEJGwqF0iLmfvTRR7a5CIfD3M9t
+27Zh+/btAOzGzGPHjvEzIzH5gw8+YMOvpGZLz0A4HGYDXElJCc8L/d2zzz5ro1iTWrhy5UquW0F1
+G9MFt0CwgSI/Px8LFy4EYGWdJgmBpCcnEuHe0LOOVbSnPwxoU1BKLQXQA+CFFP72bgB3y2vJTrJ0
+Q1IuvxEjRnAy0u9///vsaaivr2f7AXkv2tvbbeXQaSE3NDTYbAMEya4jC3FFRQVbiV9++WV897vf
+5evyxQGsoqqkL8oXVCaNnTXLEqxycnKwevVqAFbBEupPMBjkzSsnJ8eWiAWw65WSHSet86ku5l/9
+6le8wRE+/fRTLqTa1tbG7cnFePjwYbY7UFEU2Ve5EWiteUwybTltDpFIhGs/BgIBfmZHjhzB1KlT
++7SdbmQiKc2oUaOYyFRcXMwh1zKOJxKJsNpLkPEOTkj7UbJqY8qbglLqDlgGyCt0b88SLkWvtV4N
+YHX0Xueea21gYAAgxU1BKXUNgPsBzNFat4tf/QnAi0qpJ2AZGicD2JnIPQciioVCIbz//vsArMgx
+2nWbm5s5Wq6+vp4t5mRkuuyyy9ga3tTUZKt3KOsHOmPzOzs7sW3bNu73uHHWPnjo0CHcfPPNAID1
+69ezYWfRokUAwJz3RKC1Zgu6pAEvWLCAreyJZB+ORQ9OBZWVvbWGqe09e/awyhAMBmOepG5tx/KG
+kLTR1tbWp7ZjYWEhn6R1dXU891/84heTkhDc8iomg3SSvyZOnGgraUdzW1xczEbuUaNGYd68eQDA
+dUWJB5LuvqZain4JgDwAW6Oiybta63/SWn+klHoZwH5YasUPE/E8GBgYZA9SLUX/XD/fXw5gebId
+GejOS3aE8vJy3mkXLVrETLpZs2axBEHVpXNycthguHz5cj4xJJtNRuWRLaKrq8uWKo0MQx0dHWzb
+eOqpp9gFRu7NZOD3+/kU1Fpj3bp1AKz6FP1JCFKHjBXskywoV0M4HOZUaD/96U8BAE8//XRG0osB
+1nxTMRcKEjpx4gS+/e1v87WVK1cCsOwP8Yr70r3LysrYFd3T05Nw/yVPQeYpSBVkl1q7di0bFZub
+m/HEE08AsCRPWouXXHIJ25uodOCDDz6YkaJJWUFzHii01vxyFxQUsMW9ubmZIxj37t3LngEiAvX0
+9PDidhKn+jPOOBO2UF6+hx56iKm0VVVVKW0G5FN+++23uRpyS0sL7rnnHgCJhTrTODo6Omxiciob
+g8fjwapVqwBY4ixxJJ5//nkAmclOTHMfDAZttTABa45pc963bx8bHSsrK5mT4paJOz8/H3feeScA
+S90k3seHH35oKz7jRgWXyWLo3rNmzcLbb7+d0vjIULxp0yYAlpr0yiuvALBo+lRj8uKLL2bPVnd3
+N28GtNEns5kBiR8MhuZsYGBgwz+EpCCNgT6fjz+/8847WLJkCQBrl1y71iJl0snQn+9e7qoyAQb9
+TLt9V1cXLrzwQgCWe452eWLrJQviUkybNo3bXb16tS3NWTJI9SQn6vaBAweYYae15hObVDGZCi/d
+8Hg8bKwll+SGDRuwePFiAJZaRi7LESNGMMuyqKiIx03SWn5+Prv06urqWP0rLS1l3sPkyZO5nByN
+s7W1lanQzc3N7PYkNSpZ5ObmsqGYqPJVVVXM0m1ubmb1dvTo0bjlllsAWGuLJFwZSZwJZE3mJaK0
+puoHJg7C1VdfzfeYO3cuFxUdCEikpM0kJyfHlpWYdMNrr72WF+lVV13FG0QiXgISE2kBFhQUcCKX
+ioqKPiG+iUJyJZJ51kRIevfdd21ZimksRBCbNWsWe3N6enoGrOM6OQtONc7j8fBLU1JSwraWQCBg
+8+NTBOp1110HwPKS0AtdUFDAhXzz8vK4tmhJSQlvCn//+98BWBG1JLbLwyfVqk8FBQV8YNC9du3a
+xVyVRYsW8Sa8atUqPPDAAwDcs0KlMNcmR6OBgUHyyCpJIdXagB6Phz0A5eXlLDLKzLkJ9gOAJWrK
++H4KtqJajB9++CHv0oWFhbjhhhsAWFLD3XdbJM28vDwsXboUQK8U4wTt+DNnzmRfv0xHRgE3q1ev
+ZpE5WQyUxThv3jw895zlbPrkk09YrCZvQFNTEx5//HEAwLp161i0TVVtkbUc3CqPO43B1I+WlhaW
+2I4ePcoMT/ldMlpOnz6deQ/bt2/ne06aNImlQeK9LFy4kKndWmtb9u9UvA85OTlYtmwZAMt7AFjz
+StLo6NGjmTU6Z84c7Ny5s8+45ZgSiSomKTQYDP7PKQZDiVtjlemOVzOxqKiIOe5Dhgzhh3X++eez
+OJgIaIEBvVZ+GSZNXoYjR45wJqGuri4mlTQ0NPALkpOTg4MHDwIAfvzjH/PvyW2mlOLN5v3332cd
+Xhb0oLk4dOgQW85p80gUdD/pXUn2mbuF5JJ7bM2aNbygDxw4gLlz5yZ17/7aGoy1OWTIEI66fOaZ
+Z1gtpHVTUVHhKqbn5+f32XgSBXlwSJ2R67unp4ef2ebNmznOZSAQ6oZRHwwMDJJH1ngfZCw4/SzT
+aZMRSVrh6ffvvPMOi0jhcJityWSoSxRXXnklfyYfdCgU4jZl7jyinwYCARbxKioqmAsxf/58Nh6R
+SrF+/XpWcy666CLu3w9+8ANOSEIi7hVXXMGSSUdHB58qgUCAjWRa636NXbFqXiYr2ssTm9ojQ1xn
+ZyeXYCN1aaAYTOk1GAzyM/H7/dw28TACgQAbVYHeNTeQ1Pi0dkg9lOXxZIn7Tz75xFZWIBUkmqdE
+ImvUB1q0bv3xer3sAmtra+OFfvvttwOwQmVp03j++ef5xWxubu5TIyIWfD4f2wza29vZ5SRr/7nV
+idRa23IUUj/mz5/fx301Y8YMPPPMMwAs8XPDhg18PzdQ2Pfs2bOZ715cXMwxFBdccAHuu+8+AOBE
+JxKyRmNOTg4vwnQ+8w8++IATy3zta19LWr3JBlAGqBkzZvABQPN94sQJm/owUBsNAFaxKAq2qKiI
+19jp06c55iUSiXDIuNyYBgCjPhgYGCSPrJEU3CzNEmSRraysxCOPPAKgt76gx+Nh3+9tt93G1GaZ
+lsrRHn8ma3N+fj4bD48ePcrkFhnfT54BmQFYUp6lqDZu3DjOKEyRmsFgkLNO19bWxiUk0b1ramrY
++NTQ0MAxESNHjuR7/OxnPwNgGcvIaOn3+9nYKXMfpjMnQF1dHas5xcXFaUtRlmnQs1y8eDE/p927
+d3MaPaIdd3R0sLQpM03HM34n0jbxWKZNm8Yes0ceeYTJcGfPnuW5HcgzE4l4jKRgYGCQPLLG0Aj0
+73eVQSC0W9Pp2dnZyS47SmsGxNb7aKcuLS1lWnFTUxPXJGhvb7cZPun75JrsrzYB6YZNTU0c4feF
+L3wBADB+/Hhmz9XW1sbVT4lVSHkgAIt1J2tXUp8oO/GmTZvYFvH000/zvXNzc9k4NhB3GoEyTI0f
+P76PkTgVuLEYtdZ9sic754pciJFIxOZ+pfFJzgPZezZv3ozPf/7zAHprXwJW9OzGjRsB9D5rrbUr
+lXsggWA0BqI2nzx5kt3TVVVVLCkcP+6anyhpDFrmpXQjXhQfPYQ33niDjXX0/fr6eluik1iiFi2a
+Cy64AIBFiSajzqpVq1yzCMsy8UTMkcVDZVvl5eWsunR0dLAIStTliooKNhh5vd5+x6uU4tBrOdbD
+hw9zgZsNGzaw+kALrLOzk1PQFRQUsNU6HA7zhpWOQri0mQK9VvRUVQfpGYllLZfxKgSte+tHDh8+
+nFXMZ599ljdACkkuKSlh7xIZ7wBrXl588UUAluGPng8ZF2MVrUkmC7QTzvV5/Phxrpx1xx13cNtT
+pkxh7kyynjSCXMuJwqgPBgYGNmSVpNDf7kvXT58+jd///vcAgFtvvRWAlQ351VdfBWDlSiDD0Z49
+e1gsa2ho4JOeTopx48axQfH06dMsjcjahn6/3zU5KiUeKSwsZJdjbW2tzQVK7VFRl7feeovF0kcf
+fZTF2aVLl/J3qd2rr76aDalAb6m7b3zjG3wie71ePmHpmt/vZ55Gd3e3LVs1nZ7J1hZ0A6lBAPDY
+Y4/xmJOBM/qUIJPZuHErZDt0/dSpUzyH+/fv5+hCYl76fD6WJCKRCLuDKysrXSVLmiMpeksVZSA8
+BSdkheo1a9awMTo3N5fL3r322msJ308WBEql3kfWeR/i6dmyluTYsVb2+Icffhg33XQTAHvsgMfj
+YVrxwYMHsWDBAgC9+mJ9fT0/jLa2Nv5b5+ZEi0mmIadIvXvvvZfVhLq6OhbtY4EW1fTp0/HHP/4R
+AGxFTGhhh0Ih/u5HH33EC4UyTDkhX3TSs8ePH2/j7afDx04gfsTy5cvZFkPW9P4g7UFS3aD+y3yU
+VDVM/r6/yED57K+66ioAvYSzZcuW8Qbxne98J2Y8irOfct5kynwlyt2nYz5pfCNHjuQclDk5OZz9
++oEHHojrgYi12Ytap8b7YGBgkDyyRlKg3UzWcqBTQRoh4xl4cnNz2bc7efJkPrlbWloS3tG9Xq9N
+KpASBP2e1JIJEyZwgZPHHnssqRJdJPEcOHCgjyFt5MiRnDH6Rz/6EecskKerFAvJit7R0cHeivLy
+cvz1r39NaMzJgiSlHTt2sKRw44039imx54Tss6zNKcV1WcWbMNB1mpeXxyrf3r17U76fPI3Tyfeg
++5aUlNgqc0+bNg1AbwrBZCGNuInyFLJuU6D/e3p60lo6PRk4CUmkPtAL2d3dzS9xQUEBT/rEiRNt
+dS6T0TvpHuSGzM/PR01NDd+LbB8dHR22RLLUBqkMeXl5LOLm5eXFrDI0UBBB6tFHH+UN8ODBg+wx
+cQtdBmDTyWXCEreNAEgvJVvaKga6KaSa8zIeCgoKWA0bMWIEqqqqACRmw3Drm7QvJFpg1qgPBgYG
+NmSNpEDBO1K8TKeFNxk4feXOUy4UCvHJ5vP5+JQeNWoUi3lNTU0JB2M52ybI3Z6u5+fns4GrrKyM
++0HtdnZ22iIjU5lDp4rmZnQjzkNZWRlLCoWFhUy4GTFiBPdJnlxukqDWmq/HyqmRDetUIlN5H3Jz
+c9kQLklYiUDWFY1BBktbKfpBAS1EuTjciqdmug+ANbly8TrDZWWchqznN2nSJPYOOO0gdC0enC8P
+YCceFRYWcj9OnTrF5Ba5gVDfpK7unEM3d6Cb60pec+inPH658VCob1dXF38OBoPcjkw5T3DGvbgx
+JNOxFlJ5kZP5m4EQmmToNM2n3DhjxfDI9uizVCvlAZdo34z6YGBgYEO2qA+nAAQBpMblHDhKTNum
+7f8FbZ+ntR4Z70tZsSkAgFLqg0T0HdO2adu0nVkY9cHAwMAGsykYGBjYkE2bwmrTtmnbtH3ukTU2
+BQMDg+xANkkKBgYGWYBzvikopa5RSlUrpT5VSj2Y4bbGKaX+Qym1Xyn1kVLqn6PXhyultiqlaqL/
+F8e71wD64FFK/ZdSqir680Sl1HvR8b+klPLFu8cA2i5SSr2ilPpEKfWxUurSwRq7UupfonO+Tym1
+Tinlz9TYlVK/VUqdVErtE9dcx6ks/Gu0D3uUUjMy0PbK6JzvUUq9ppQqEr9bEm27Wil19UDaThfO
+6aaglPIA+DcAcwFcCKBSKXVhBpvsAfATrfWFAGYD+GG0vQcBbNNaTwawLfpzpvDPAD4WP/8cwC+1
+1p8DcAbAggy2/SSAP2utzwfwf6L9yPjYlVJjASwGcInWeioAD4CbkbmxrwFwjeNarHHOBTA5+u9u
+AP+egba3Apiqtb4YwAEASwAguvZuBnBR9G+ejr4T5xZExz0X/wBcCmCL+HkJgCWD2P7rAP4vgGoA
+Y6LXxgCozlB75bAW5NcBVAFQsIgsXrf5SHPbwwDUImpHEtczPnYAYwEcBTAcFrW+CsDVmRw7gAkA
+9sUbJ4BnAFS6fS9dbTt+900AL0Q/29Y7gC0ALs3E80/m37lWH2ixEI5Fr2UcSqkJAKYDeA9Aqdaa
+ylM3AijNULO/AnA/ACKyjwDwmdaaYsMzOf6JAE4B+F1UffmNUmoIBmHsWuvjAB4HcARAA4AWAP+J
+wRs7EHucg70G7wJAaZ/O2frvD+d6UzgnUEoNBbABwL1aa1s9Lm1t2Wl3ySilrgNwUmv9n+m+d4Lw
+ApgB4N+11tNh0cptqkIGx14M4AZYG1MZgCHoK2IPGjI1znhQSi2FpcK+MNhtJ4NzvSkcBzBO/Fwe
+vZYxKKVyYW0IL2itX41ePqGUGhP9/RgAJzPQ9JcBzFNK1QFYD0uFeBJAkVKKolUzOf5jAI5prd+L
+/vwKrE1iMMZ+JYBarfUprXU3gFdhzcdgjR2IPc5BWYNKqTsAXAfg1uimNGhtJ4tzvSm8D2By1Art
+g2V0+VOmGlNWfOpzAD7WWj8hfvUnALdHP98Oy9aQVmitl2ity7XWE2CN8y2t9a0A/gPATZlsO9p+
+I4CjSqmK6KUrAOzHIIwdltowWykViD4DantQxh5FrHH+CcD/i3ohZgNoEWpGWqCUugaW2jhPa93u
+6NPNSqk8pdREWMbOnelsOyWca6MGgGthWWQPAlia4ba+Akts3ANgd/TftbB0+20AagC8CWB4hvtx
+OYCq6OdJsBbCpwD+CCAvg+1+HsAH0fFvBFA8WGMHsAzAJwD2AfgDgLxMjR3AOli2i25YEtKCWOOE
+Zez9t+j62wvLQ5Lutj+FZTugNbdKfH9ptO1qAHMzue4S/WcYjQYGBjaca/XBwMAgy2A2BQMDAxvM
+pmBgYGCD2RQMDAxsMJuCgYGBDWZTMDAwsMFsCgYGBjaYTcHAwMCG/wYzcYtN28YfwgAAAABJRU5E
+rkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/2... Discriminator Loss: 1.2411... Generator Loss: 1.8333
+Epoch 1/2... Discriminator Loss: 1.0503... Generator Loss: 1.0242
+Epoch 1/2... Discriminator Loss: 1.1395... Generator Loss: 1.0762
+Epoch 1/2... Discriminator Loss: 1.3215... Generator Loss: 0.5722
+Epoch 1/2... Discriminator Loss: 1.1445... Generator Loss: 0.7874
+Epoch 1/2... Discriminator Loss: 1.2529... Generator Loss: 0.6156
+Epoch 1/2... Discriminator Loss: 1.0842... Generator Loss: 0.9782
+Epoch 1/2... Discriminator Loss: 1.1459... Generator Loss: 0.7799
+Epoch 1/2... Discriminator Loss: 1.1442... Generator Loss: 1.4652
+Epoch 1/2... Discriminator Loss: 1.0300... Generator Loss: 1.2469
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsfXmYFNX19nur91lwZhgYFBEwCKIGNYKoGBWDKyJuwbgS
+xaCJS2LijomixiWYuCdqcI/iEjXuooIKKi4IRnaiLMoOIzMwMz293u+P4pw5VVM93dXdg+3vq/d5
+5pmemupbt27duvcs7zlHaa3hwYMHDwTj++6ABw8eSgveouDBgwcLvEXBgwcPFniLggcPHizwFgUP
+HjxY4C0KHjx4sKDTFgWl1NFKqSVKqa+UUld11nU8ePBQXKjO4CkopXwAlgI4AsAqAJ8BOE1rvbDo
+F/PgwUNR0VmSwv4AvtJaL9NaxwE8DWB0J13LgwcPRYS/k9rtCeBb8fcqAEMznayUciWuKKUAAMWU
+cpRS3G46nXb1vUL7oZRCVVUVACAWiwEAEokEEokEAMDn8yGVSmX8roS9L50xVpn60NE17OcYhuE4
+zsUYz1xA/ZH9UEq5evbfN/IYq01a627ZTuqsRSErlFLjAYynvzNNEif4/Wa35YtCgyMHSr5MhmHw
+Z5/PB8B8+emz3+9HIBAAADQ1NcEwDD6HPktQX/1+P5LJJF/bzaSiaweDQRx11FEAgP/9738AgA0b
+NmDt2rUAgB122AENDQ3tvi8XMmormUxaJjn1PZFI5LVAyPG0jy21FQqFAJgLmlPbWmseW+pbMBhE
+a2srAOuzDwQCiMfjfA37YpjvgiEXT8MweFzKy8vR0tICAAiHw2hqarKcn0qlLOMm71ueY78PN33S
+WnN/5P1lWuDpt9/v541Dzj15r3KOpFKplbn0q7MWhdUAeom/d952jKG1fhDAg4ApKdCAZ9oRCUop
+noQ0eeilJNAAp1Ipfog+n48Hma5hnyg0OeTLlE6nHQdb/l8+LKcXT/6fjsv/Dxo0iB/u7373OwDA
+1VdfjYqKCgBAQ0ODpQ3ZH1ognRbCdDpteaFlf5wkDKdz5eRXSvHLTf0F2qQb2YYdUuqxj4t8folE
+AmVlZQCAaDSaVXrLtNDZXyD7/8PhMACgpaWFn6XP50N5eTkA8OIgv6uUssxP+4Kcy4JgH1tCLt91
+ugf6ntMGSZ/tzzobOmtR+AzAbkqpvjAXg18AOD3bl7ItCASaYPQ70+oqJyAtIIBVdJQv0I477ggA
+qK+v50nT2NhomTTUFr0ccrVOJBIW8dg+aVKplOWlqqysBABUV1fj4YcfBgC89tprAMxJuXXrVgDg
+79D9BYNBAMBBBx2E+vp6AOAFbcWKFZYFy2kRyiTR2BcOuj86Vy6Q8j6crmEHnU8LQDgc5mvEYjGL
+tEHHQ6GQ5bk5IdM17X2SC7K8j4EDB7LE0trayosCjWt9fb3jQiBBbeUqxRSqHtH3W1tbHe9PQinF
+82zLli05td8pi4LWOqmUugjAVAA+AA9rrRd0xrU8ePBQXHSKS9J1J1waGl22DSD76iz1TPuuInde
++r88lk3CkXqo3MGojbq6OgwaNAgAcPjhhwMA/vznP7OkIHeniooKi/pE6gOdm0qlHHe2TPefTfyW
+KOZcCYVClh1W6udSsuosw5+U/g4++GAAQG1tLZYuXQoAWL58OQCgubk5oyRaqH2j0PH0+XwsgcXj
+8YxSoVDBPtdaD87W7vdmaLSjs6zOUteWLzpdkyZHMBhkNSASifD3pW5JYrtSCtFolD9nQ7ZFI5lM
+4sILLwRgLgaAVdST4yKPF3PMZFsdeQby8dA4QT4PwHqP0l7RWaBnPHDgQOy0004AgPvvv5/VsClT
+pvCxFStWADBfPLnAu1UbAPO+7YtCrmqzHVprVq8CgQD3xz5+bueIR3P24MGDBSUjKXSGlBAIBFg0
+PPfcc7HbbrsBaNttm5ubcdtttwEA1q1bxyJ4Op1mgxiJ53ScfktVI5+V3u/345prrgEAjBo1Crvv
+vjsAYOFCk/Q5d+7cvI1sbs6h/wcCAd5hpBTg9/uxzz77AAAuvPBC7LzzzgCAPffcEwBwzTXX4N//
+/jcAq1SVCTRu8Xicjblaax5vqVZ1lupQUVGBiy66CAAwYcIElhrS6TSam5sBAOPGjQMAfPbZZ/jm
+m28AmGMk+2n31uTyPLp06cLeGvpea2trXnNIer4ikQi3W15eznOZznOD/3M2hWAwiFNPPRUA8OCD
+D7L+7QR577Nnz+YBfuqpp7BypenSfe2113jS0ISRVn3pwXCDwYMH49NPP213nCbdIYccgo8//th1
+u/lC2lTS6TS7BZcsWYLu3bsDsIr8TmpTc3Mzi9/nnXceZs+eDQDYvHkz+vXrBwDo1q0bHyNOhlwI
+WlpaOmUx6NevH2bNmgUAqKqqsizqhHQ6zZsIYfPmzfx8v/32W/6cTCZdLQY0D6+99lrMnDkTALBp
+0yYAwIIFC1g1lS+z9DpFIhH89Kc/BdDmocoVop852RQ89cGDBw8WlIz6UKjRrLa2FgDw+OOPMzsw
+mxFQXnPw4MH8uXv37njppZf4vEWLFgFokxRWr17dzmiZK6qrqwEAb7/9tuP/SV0577zztqukkE6n
++dp77703Xn/9dQBtOzuhozEtLy9nP/9LL73E40WGOsDcbQHghhtu4HOVUo6MzWLgnHPOAQA89NBD
+luPSUr9q1SoAwGWXXcbSmxNhTUpKPp+v3bPPNIcNw8Dw4cMBAGPHjsWRRx4JAPj973/P/6frDB48
+GD169ABgkrcOO+wwAMDIkSOxYcMGAMAbb7xh6WOxUTKLQiELgmEYGDt2LADwgAOm3vrCCy8AMGnD
+9957L4A2fbiurg77778/AOCss85ica1Hjx4444wzAACPPfYYGhsbAYBJLpKK67bfJB5u3LiRSSWp
+VKodzXXfffe1uOa2B8imcNhhh+G7774DYOrfN998MwDgxRdf5Bdo5MiRAICDDz4Yv/jFLwCYdGx5
+H3QvTz31FC+CP/7xjwEAn3/+uUWEd3oJC8HixYsBAP379+djpHPff//9bM+RFnynl0w+XztzNlvc
+iTxOc6579+5MX6d5lUwmmb06Z84cbreqqorHebfddsN//vOfjP3sCJ73wYMHD4WBVvTv8weALuRn
+xowZOpFI6EQiodPptP7uu+/0d999p0OhUNbv1tTU6JqaGr1+/XqdTqd1Op3W0WhUT548WU+ePFkH
+AgHt8/m0z+fTwWBQB4NB7fP5tN/v136/X28zkub8E4lEdCQS0XPnztVr1qzRa9as0UuXLtVbt27V
+W7du1alUSqdSKZ1MJvWJJ56oTzzxxILGxs2PYRjaMAz92GOP6blz5+q5c+fqM844I+fvffrpp/wc
+4vG4XrJkiV6yZImura3VSqkOx4racDueTj+TJk3iZ0k/sVhMDxs2TA8bNkwHAoGijBfdE80FOVcM
+w+Dzdt55Z71w4UK9cOFCnUgk9IoVK/SKFSu4P+Fw2HF8gsGgXr9+vV6/fr1OJpN63Lhxety4cYX0
+eXYu76MnKXjw4MGCkrEp5APSSaX+nU6n2YVm1wGdsMsuuwBoi5oDTJsB+eYNw2B3kVM0oFsDKTEh
+99tvPza0TZgwgaMjZXTfueeeC8DU5fOFG7cZ6ap/+tOf2EhItoWOQG0PGDCAn0NTUxPbd8j1lsu1
+DcPI275ERtE//OEP7fq2xx574Ouvv86r3Wyw04rtGDlyJBsPlVJsMDz66KMBALNmzXK853322cdi
+6CUXb2fjB70oTJw4EQD45QJMv3IuiwGBFgU7NfSLL74AYE5SJwNToYaxdDrNRsepU6fi8ssvb3fO
+1VdfnVfbhWLlypWuwoHJu0CGU8A0oq1ZsybnaxaDsOT00tOxb7/9tugJZ+ztlZWVWchNdC8vv/wy
+dthhBwAmUW39+vUAgH/+858d9ofIdoQFC7ZPTKGnPnjw4MGCkpEU3Ijhd9xxBwBwEBHQttredNNN
+OV8zGAxyGrS5c+eyT3j16tWYO3cuAGveA8k2IxTDV3zttde2kzji8Tjmz59fULuFuPdydYNWVFSg
+V6+2fDrksuzdu7erHVlKXvns5IZhsAoon8mTTz4JwHTvkSpUXl6Ovn37AjAzXZGqlM81gbaxisVi
+lkxIhPXr1zMVfO7cuaxWZaOFjxo1ij83NDS4koALQcksCtkmApF+Vq5cyZRRmUSFvn/aaafhnnvu
+AZBbtp7Bg03W584778znz5gxA6+88gqfTw9Ppt0qBg466CAA4MVI4rLLLsu7Xdm/ziK40BiSKEzX
+Il5AviK6a5/6tnutra3lmBYZik1jXFtby4lThg0bxpThYDCICRMmAABuueUWV9emsZXz0Sk1n9Ya
+y5YtA2CqVUSuI0r17NmzsXHjRgDmJkQ2rIEDB/KcI0r49oCnPnjw4MGCkpEU7LRRrduSZD788MM4
+88wzAVjFS1qVA4EA7xhDhgzB6NFmNvmZM2dyG5s2bWLG4q9//WsAwIknnogDDjgAQFv6LcAU54l1
+lkmMLtRY9aMf/QgzZszge7KDDJ25QjIJ6bPboJ1sUEqhpqYGAPDMM88AsOaeKC8vZw+N23YJbvpp
+GAbT2w888ECeD5WVlaxKkKQwYsQI3q2rqqosUiblsLj66qvRpUuXnK9P84nmSDwezyqZbd68mSnd
+9913H7czZ84cAMDZZ5/NLMZAIMBjI6ninY2SiZK0R61prXHyyScDAJ544gkWqYC2h3D33XcDAE4+
++WR2Q8bjcR70hoYG1NXVATAnL7VNlFIpZm7YsAFfffUVAOCoo45i12EmLnu+YjmFRvfv399RDaHr
+7bnnnliyZAkfc6IPBwKBdouWz+ezJALJl45tbxMwLfjkWpOgcZOUYjdQSlmyUbv5HonuP/nJTzBt
+2jQAsETG0n0nEgmmCd9yyy2sEk6ePBmHHHIIADNCc8yYMQDa4gs6AvVZJp7JZV5Q/ygku6qqir+3
+dOlStiUdd9xxPFebm5t57CkSNVd4UZIePHgoCCWhPvh8PlRUVKC5uZklgu7du+PGG28EAIuUEI1G
+OfPxddddBwB4/fXXce211wIwV+vevXsDMHcuSUrqKBfAxRdfjFdffRVAW+BTJuS768qdLZOxkkTc
+8vJyvo+xY8fiwAMPBGASWn7yk58AMJPF2Psq8xrKXStfER0ALrjgAgBmoJh9DNeuXYu99trLVXt2
+aK3zCvrSWjNHZc2aNY5jSp6FUCjEKqiURoYPH84eqP/97398L7lICvbUdLmOK6lYlLCmZ8+ezFP5
+/e9/z+09/PDDHJhXVlaWtzfJ7fMuiUUhnU4jGo2ipqaGLcg777wz66+JRIIf5B/+8Ae8/PLLfBww
+7QG33347AFNnI9JPJBLhNtLpdLuU8H/84x85VNYp4UmxYRgGk6XsoJeCovtGjhyJjz76CICZBYgW
+k0suuYT1aJkAxOnB2ydRPiL66NGjceedd7brJ1nLJ0yYUJScivkutBTuPXr0aEs4O9mIiD34xRdf
+cHYru6uX1M199tmHSUa5kKlkYRsg+2ZiB43bihUrLO51wgcffIDTTz+d+7O9qld56oMHDx4sKAlJ
+AWhLtEFEoWXLlnG8eSQS4dVca82rP+1avXr1YgrogQceyCJj9+7dLcVe6Hu0i7z00ktYvdpSuKpT
+UVFRkTF1Ou1yffr0AWCSXMgDsmHDBkyaNAkAMG/ePDZABoPBDndYaUiVWYRl5upM3991110BAM8+
++6zFyEnGPCIpZeLtZ4Pc+WS8g5u2lFJMBa6rq+PvNjU1YfLkyQDa+C01NTUYMGAAAJMu7HQd6T1x
+Q7vOlkszXzz00EP4xz/+AcAcF/KMkBG8s5C3pKCU6qWUelcptVAptUAp9dttx2uUUm8rpf637Xd1
+8brrwYOHzkYhkkISwB+01nOUUpUAPldKvQ3glwCmaa1vVUpdBeAqAFdma4ySodIuH4vF2NXV2trK
+O9f8+fPb+d7ffPNNpq0effTR6NmzJ3+PdM54PI6pU6cCAEsHmzZt2q5VhhsbGx0lhXg8zvooUXH3
+3ntvlmxGjx7Nn6X+nsuOIcu1yWvTuJB9Yccdd2Spql+/fuy+k7t4Q0MDzjvvPEv7uWRwpnYAcEJY
+v9/P9qNwOMy7bSqVylla0Fpz5OCXX37JdOvKyko22FK7hx9+OBsRKRuXHcuWLWNJlWw4mewvnVWn
+RCKZTFqyilN2sb/85S+det28FwWt9VoAa7d93qqUWgSzBP1oAIdtO+0xAO8hh0UBsFY3isVinObr
+sssuw7HHHgsAuPLKKzF+vFmset26dQDMwaOKPk888QT/H7BWdqb8gA888ACA/MWwQiYD8RQoPRf1
+g7wklK4sEAgwuSkSieTNe5diuZ0cRm0DwP77728RucmQ+Pnnn7PhdtasWe0KjsgCMZnGRaaJp8XP
+7/czISeZTPJL6DbduawDefbZZwMwQ7hpPGlRGDNmDN555x0AwEUXXcRzQFYNHzRoEL94RCwiwy9B
+1iHNR+VxCxoLWdOzs1EUm4JSqg+AfQF8AqBu24IBAOsA1GX4jqUUvQcPHkoDBS8KSqkKAM8D+J3W
+eovNH04pptpB20rRU/JS6fOlqMSbbroJRxxxBACTrkqZeU877TQAJsOLVtSjjz4ahx56KABTBKT+
+zJs3jzM05yrydgacJIXKykrLDgSY/aV7WrlyZd67kQzQIUjDJhnWTjvtNHbfAm077IQJE1hikTsV
+qSWSnh4IBNjnr5TioLJ4PM7GXXJvNjc38y4fDAYt0oYTe9MNli1bhn333RcAmBU7ZMgQ7ufatWvx
+6KOPAjAlJKoHEYvFWFrKlERHchOcck4UW2qQz82JTdoZKGhRUEoFYC4IT2qtSVFbr5TaUWu9Vim1
+I4ANubZnFxtpsGOxGL788ksApmh4zDHHAACHN2/evJktszvvvDNPWCni+Xw+Lh6abyReoeG9QBsX
+X3oGqE2gjWwze/ZsDrd1qzo4tQvAkkGK+k+2hYaGBos3gOjhzz33HEdxrlmzhjkSJ554IgDzBaN2
+P/zwQ/a377HHHqyaJJNJtg/RM5OxJqFQKOeYCaUU91mmpZcl7CORCEcgnnXWWQBMT9Rxxx0HwJxn
+zz33HABzvhAZLBQK4YknngDQFiYviXOylmQymeTxyof/kSvkAkl2tc5GId4HBeAhAIu01n8T/3oZ
+wNhtn8cCeMn+XQ8ePJQuCpEUhgE4C8A8pRSF9F0D4FYAzyqlxgFYCWBMLo1l23kpsvHJJ5/EW2+9
+BQDscaDfBFrBW1pacOuttwIAbr311px3dzt7zP49aZxy61cn49qmTZvwox/9CIDplaCALjKQzZgx
+g419+UolUrQ3DIMlqEQiYWH/AaZXQOaLoHOrqqo4YjOZTLbz4y9btgzvvfceADNykujmzc3NfI7f
+72ePkKxXSX1oaWmxBBfRcTuXgUBi9MCBAznK9V//+hcbjocMGcJ8j65duwIwx5Ce2eOPP841QK67
+7jo2QNfV1fH1SMrp3r27hcvSkaEx3xKCmSDre/p8Pu6TUz0Qe1CZzPX5/0UtSXqBnnrqKQCmu0ni
+3XffBWCK4FTIhIhQefYPABwnq1uQuCsLwBS72It8geTLRgtOJBLhBYf6M3DgQKb4vvTSS5aIUrIv
+zJ49m1U3aqupqSkn0V/eN0HOvWx2BKkGDRo0CIDpnqYkOdFolKMOW1pa+AUi8tI111zDBWBySSQr
+Xyr6bH8J7c+vWG5KemaHHHIIU/ojkQirv1TASNrGDMNgm5BUzaSa6kVJevDgIS/8ICUFOwYPHsyW
+7G+++YZX0OrqaiYDFXKfxc4CvD0hac4kSm/ZssVC/wZMwhKJydFo1LL7FcM/XswSeG6eB527ww47
+MFlqexLWcoW8JzKaK6Vw0kknATAlLfJYXXmlSfuJx+M8BuXl5fwO2MfYraTwf2JRCIfDjhFqoVDI
+osMWilJbHJzEVfsxqUqQeNnY2OgYXu3keivWvW4P950T6P4jkQgnJ3FrB8q3n04qkc/n43Bvenn7
+9u3L3hlpO9i8eTOfW11dzbYUUiNkUd6O7Fye+uDBg4eC8H9CUnBojz+TFb2QSLZSkxAImRKnSF6F
+NJiRcTCRSGTkhHQ2nMYy025cyC4t71v+Bqyej2IiU3+ltCYrjhHRq76+3sJxoOckafihUIjbIcOu
+PYmO3aPkAE9S8ODBg3uURD4FYqklEgmLHuZEfQWskX+AdUfUIgt0ZWUlM9PKysrYviBZcLJduZvQ
+ai3dbXIFpp02GAyyFCJXa/tn+/ft17PvoB3tZNJXTm1Klp/MVCxZjB0lo812LbqePZBKRvLJ52B/
+Jvb7kxwK6faTvv6OxifX+3A6X/r0a2pq2IWXiwTRUaSpve/yWcrPZNuQCVilRCOlBjou56rb5LaE
+XJ97SSwKeluOPjnppB/YfjMywy1gVQ3kA29ubmbjmmxb+qmdXkJJ8/3222/bJSSRVFsZymwYBr8s
+sVgsayZltxPcfm4qlXJcTGS8A32WhUryRSgU4kWGJrRUQzLxN+TY0zOVi4Ld+Oi0sEiVyI3XwYlj
+IBPOtLa2coRmPB7vML2d/biMYATav6xOi3dHqhKd60Qii8Vijl6FbGpXPmqSpz548ODBgpKQFIA2
+FUHufE6rts/nY/ejdMHIXZxE/yOPPJKTbYTDYf5MWZKlFOD3+9nwIxN9dOnShcVu6ls0GuUVPBgM
+8i4Qj8cttNTONlBm23UikQirP/n2QX4vFotxUA75zKdNm8YqWjgcZimtpaWFvxsIBDhpKjEpt27d
+is2bN7frMwBHFTIXKacjUVnuun6/nyW6fffdl0uyUfo7AFklBqfr2qUfJ/XXiach1Sf7M5OSlZQQ
+6XtO/evAJZn1XoASWhQAqz6cieSSSqU6DH1WSrHY/s477zAl2jAMpjpT/UM56VKplCUPpOQ32DkQ
+ktDj9/stfaXPyWSy070VTqKsfDGk9boYfUmlUlxensZnzz33xMqVKwGYiyK96DvttBNnMQqHw5xX
+kvrX2NiYUd/PRIXOhmwvCF374YcfZrXyzTff5EUtGo3yhjFv3jy+z2wLkgz3d7qnbPcgN8CmpiZ+
+B/x+P2eqknEnbuH22XvqgwcPHiwoGUmBdt9CjWHpdJqNYK2trbz7d+nShXc3opFu2bLFsprT/wOB
+gKM0QqJcdXU174hKKUtqsmLUQCgETmIzUDweAt0f5Svo0aMHG+o++ugj/j+lx6M+2K+fafeyR0YW
+K1gsHA7z7t+3b1+WopLJJP76178CMHdmkiL79esHAPj6668ztukUEOV0H26gtWbpYPjw4fjvf/8L
+wDmIq9PU0lIg5CilNOlVhU5euxtLusDISkzZeBoaGvD5558DMPVlWXI+U3UlwJrRSWttEeu+r/Hc
+XgQrSpM+c+ZMAKYO/OSTTwIAfvOb3xT8/AzDcEzuWmi7wWAQr7/+OgBg6NChbHc69NBDueiOW5CY
+L5P6kKqZKdozFxC1ee3atZxo5/zzzy/GhuORlzx48OAeJaM+APnvBoZhcF0+rbXFikyrdXV1NXbc
+cUcA4FJcgwcPZvHwlFNO4etHo1EL98FOQpLSQSAQ6JTdWXpiamtrWayVoB0KyGwtL6YEEQqFuK6k
+LEEvS/MVCsMweGxJYnALpRTnUaBd94QTTrBIgqQ+kKSYD2gMaK6UlZXlRRCzg+57xYoVPBbbUy0t
+qUVBumZyEb+ISDNt2jQetJ///OcWhhm1EQwGOTRYlv0m28Cdd96Ju+66CwDw2WeftesX9cnez0wP
+q9Dafz179sTEiRMBmAlQKOns7bffzu1WVFSweN2ZagPpzs8++yz23ntvALBE+lFylmIgnU7z9TZu
+3Mifsy1uSik88sgjAEz1kOwcUrUjcV9GzxbysknVFDAXh3wYhHZQ8tgBAwZYEuQWOxlPJnjqgwcP
+HiwoGUmBiBqSt59pFScj0fTp0wGY1YIffPBBAG0VlgCrOCvz7FHiiv79+7ORaf78+VwR+pNPPmnX
+N6f+Ov3PKW+AG5D0M2PGDFZ3EokEZyWWu0VrayuPBVG+JeySV74gWvlhhx3W7r7i8Tg/h2LA7n0g
+NUCWlCfpSCnFauPUqVO5rqTcrf/85z8DMFWwX/3qVwBMjwPtwIVIc/Qs5DwthF5MIM+H3+/nz2PG
+jMGUKVPybtMNPEnBgwcPFpSMpEC7moxglNmHafUvKyvD008/DQCs34ZCIXaR5eLbJptDdXU177S1
+tbVMg7a7IJ2+Tzt6Mpnk6/l8PoveSpABM3RPkiMhz6EaBDvuuCP346GHHuK+SRZmIpFw3OmK7Z6k
+QjwtLS3M/pP2Fao7WQxIVqtSiutk/O1vZhWBiooKNuZ99NFHTJ/u27cv81NmzpyJO+64AwC4kM3l
+l1/OYyyNmYWAJASaQ62trdx3yVJ0C5KKJLuTrrE9UDKLAk12afWnCV9dXc3FQz/55BN+IcmaHI/H
+WT3I5UEQ/XbKlCn8kq5evdqShKQjBAIBxwjIdDrN7Y0aNYo9G0cddRQAc6E45ZRTAJiVrIgyHAgE
+cMYZZwAwq2EB5iJB9/Tuu+86pjHLRPTKN/oyE0glk+oIjWFraysXnb388ssLvpbkk+y0007Mi6Co
+1VAoZKEB33jjjQDMhbOjZ3/bbbfh+uuv57+LaZil61ZUVPCczLd9pRTX3QTa1MnTTjuNN8OSLUVP
+UEr5lFJzlVKvbvu7r1LqE6XUV0qpZ5RSwWxtePDgoXRQDEnhtwAWAeiy7e/bANyhtX5aKXU/gHEA
+/pFLQ8lk0pL0hIq8/PjHP+ZVMhAI8MpMOfH/+te/clm5XPD+++8DMN1+tAOPHz+eRfRssBdZoT5L
+sfTZZ5/l47TTNjc3s6hdX1/P/99vv/24TDq5+urr6zFnzhwAwKuvvprzvRUbMup0xYoVvHMTcy8c
+DuPjjz/mcwvdgYPBoMVdOHXqVABgo+vxxx/PLtCDDjqIC7lkQyKRYJrzoEGDLOpdIX0F2qSCsrIy
+lt6KvZsPHDiQ+SDSaN4pID5APj8AdgYwDcDhAF4FoABsAuDf9v8DAUzNoR1tGIam3/TTo0cP3aNH
+Dz19+nQY63SxAAAgAElEQVQdjUb55+6779Z333233kaP1gBy+olEIjoSiejm5mbd3NysU6mUjsfj
+Oh6P8/Vz+QkGg3xtv9+f9Xw6V/bV7/frcDisw+GwHjlypF6zZo1es2aNXrx4sV68eLH+97//rbt2
+7aq7du2ac78640cppWtqanRNTY3+8ssv+RkkEgmdSCR0fX29rqur03V1dUW5XnV1tQ4EAjoQCOhu
+3brxXOjVq5fu1auXDgaD/H+3bVNbqVRKp9NpnU6nC+orzSe/36/9fr+uqKhwPSftP5WVlTy26XRa
+J5NJnUwm9T333KN79uype/bsWUifZ+fyXheqPtwJ4AoApPB2BdCgtSZldxWAnk5fVEqNV0rNVkrN
+LrAPHjx4KCLyVh+UUscB2KC1/lwpdZjb72tbKXoAFo6CYRgYMWIEANPIRAacUCiEF154gdrI+Xo+
+n49z60tLLtVJlCpBNsiMvLkYNjMlQjn++OMBADfffDOrDYSpU6dakpB8X5Bp8Si6FGij9i5YsKCg
+knx2tLS0cI3NzZs3s2Fz1apVANw9cztk3gNq97HHHsPYsWML6TKPy9atWwtWnw444ADLe0BqSDqd
+3m7zodACs8crpY4FEIZpU7gLQJVSyr9NWtgZQFYFSCmFQCBgcfWlUinst99+AIC33nqLLdxffvkl
+05AlBZYeuCzK6ff7MXr0aADAxIkT2YJNE2L16tUWPny2XHv0wMvLy/kBuSW/kHfi5JNPxkMPPQTA
+1Hfpvumexo8fz/8vBMVwT1KfNmzYwBOWslRVVlYWtQS71pprQhbi1usI0uV81llnManJbRkAGhci
+UxWjr5J8J9ucMGFC3m5Ut3Mgb/VBa3211npnrXUfAL8AMF1rfQaAdwGcsu20sfBK0Xvw8INCZ/AU
+rgTwtFLqJgBzAWTd7mhHkDyFUCiEBx54AAAwadIk9unvsssu7NMnieCoo45iEovP5+PyWpWVlRbC
+CoFW3AsvvBBvvvlmu/9nsqLTOc3NzXnvvNTnhoYGtobPmDGDcx4SrXXZsmWWTL75olBJIZ1Oc4KP
+8ePH44033gDQFsknLfnFkBhSqZQlG3Ux8cQTTwCwqg+AqQIBwO677+5qt5ceKKA4929PpkIeKFnf
+0w18Ph9Lobl6RIqyKGit3wPw3rbPywDsX4x2PXjwsP1REpmXfD6fLisrQywWswT4kP7q8/lw3XXX
+AQDOPPNM3kHJOCdrGsg4f8l78Pl87WLoDz/88LwCYsLhcNGzLRFDkKSgiRMnYtCgQQDgmEshV8jC
+OcXo50UXXQQAHGaulMKQIUMAFJabgCBZk7k8GzLy9erVi4Onli5dymHnFDB1xBFHsNQIWGs20Pg2
+NDTgxz/+seX/HfXTHp5djPEdOnQoZs2axX/fcsstAEybQj4wDIPvOx6P55R5qWRozkopDB06FCtW
+rABgUpjpRU8kEvjHP0z+07BhwziakUTxiooKfkBffvklE2u6devGL9u6devwzDPPAADuvvtuAPlH
+yGmteUGSqcwLAS1YNEEvu+yygtsE2kRwMuQChUUG0hjeeeed3P6RRx4JoDiLAmCt/5ltbMnIN2LE
+CNx+++0ArF4sCWqrqamJxfLBgwczGaq+vp4jQrPlqdBaWwq8dHSuG0iimtaaYzjcwJ7Lw63a4UVJ
+evDgwYKSkBQovdncuXMtxySoiMjzzz/P/v0+ffoAMEVI2gUHDhyIDRs2ADBpqKSOlJWVcftSjMy3
+v9S/QCBQcMRdOBzmHZhW9XfffZdrHBYCmXnKqUxbvpCG2UsuuQRAm6hbCKRL2c3O+8QTT+CGG24A
+YEqOTkZKciM/8sgjzK045JBD2Ljds2fPdjU+OkIxx5Nc5127duVjF1xwgWMW50wgCWn48OHMv6mo
+qGCOR64oCZuCYRg6HA4jnU5birA4we/3MwmJRPiKigpeFILBIPu5u3fvbrEM/+tf/wIA5tPnG1MQ
+CAT4AUQikbz91KQD9+rVi+MxSI3YddddXU3QTCBxOBaL8ZgW4k+nSUuLNNDGxadI1nwg1Zx8FgWg
+baG64oorWCWgZzNo0CC89957AMz09JRoJxKJ8Ny57rrrcOutt+Z0bZnqrxjvEC1SpO4C1qpQuYDu
+v1u3bpas0vQ5V5uCpz548ODBgpKRFGSOgmygaLGhQ4cCMJOtUDKNY445Bs899xwAM4krrZ7nnXce
+qw0UUTlmzBjLjpcrQqEQr+DpdLpdJeZcQd6FX/3qVxwlSV6WmTNnFrwDKaV452lubrbQfPMFqWPy
+XoshKRBkGb5izk1izQLAhx9+yGxZoM1g3b9/fy6Btz0hE/ACbUlWpNTgBsFg0JKIR0g0PyzvA+Vo
+zEU/IyooEWnoNwDMmjWLB2HZsmXsotxzzz1x6KGHAmgTKZPJZF6WY5lBqba2lm0KSinHXIlOCIVC
+GDZsGABgzZo1uOaaawC0TYhivBBKKX55c8mOnQuowKwEeUyKETptL8xaLGitedN55ZVXOMOVUopt
+UKR2bm8sXbrU8ve+++5bUHupVMqiIrolgXnqgwcPHiwoCUmBdrFiWHHl7tLU1MQBT7/85S/ZMEk0
+aLmrd7TLydLodC4Zp+LxOHMk9tlnH7z77rsAMhtKiWxz6qmn4tRTTwVgZhymhDK0a8RisYJSetlR
+rFqSe+yxR7tjNBZu+msfbykdFJvebAcZeAlE2a6qqso5ErEYUhEZbWk+EijYzA1kJnR75e4fdCl6
+t2w2N2hoaGhXij4SiViSrsqX3ykHo1PfUqkU66RLly7N+GDoGFmCV65cyW4jpRS6d+9uucby5cvb
+JUntCJleJLmQFUMcHzhwYLtjmchCbiAL4WZaLIqF66+/npmZPp+PdfdRo0bhrbfeslxv48aNFpIS
+9cfp+boFbSASqVSKiyK7QTgc5jlrj8Fw7cVxfXUPHjz8n0ZJSQpA8SUECVoxaSXdunWrZfcn74RM
+M27/TL8lZVZG9dFxp91TSiBz5sxhX3o6nWZjl7R+u1nhpUQgUQyPg8R9990HABxbEI1GOUO1G0ij
+sn2s5L10hqSQSqVYYisvL8e4ceMAmAZo6QUArDkjpRFU5grNF1St+4orrmDD9amnnspZwN0gkUg4
+Rpfmo4p5koIHDx4sKBlJYXsVzwScV09p6JQFZaQE4cRgcyoZRp+lnmxHU1MTXnvtNQBWYyW5N/Md
+D3ndYrkhJcj2Qfe0cuVKV648KZnJ8czEDuwMyfHiiy+2FPOhDNtNTU0WWjhgLRor+1ZI7gRq78or
+rwRgcjOI0TpjxgzmgrjJCC37Y5+HblEyi8L3BacUbHLCKqXaFYfJxWgnX2q5ONCEiMViFlFUGrCA
+9gZO+TLJc+0vTbEMiplAHJFFixYBMAliuSDTfdExJ4NoptyWhd5fKBRig2JFRYWFEkygZy43EJ/P
+x38XsonRgnPMMccAMKMzKc4lGo3ynHRzr8V85p764MGDBwtKguZM2Zy3F2TCVyd/rn0Htu9ycgXP
+tJp3dJz6QNfw+/3triGlE7u6k+3amRKrFMNoRz598q1/8803FlpyRypTpj7kQm2Wz0zu1m65EYDJ
+DyCOQHl5ObuGnZ65hORQFINDIitf03FpdE0mkzmrKbJdp2e+7XhONOeSWBQMw9B+vx/JZDJjRmUa
+LMrlCDjrTnZfMk0mp+/loq/K9kR/HT0OmT7LCS2vLXX/bOJ1JnS08MjPbujDfr/fEhpOHhO/399O
+rJbtZuqH02IhIwBlxCHQJprLxTkbD6Oja9M13NgnyMaTTqdRW1sLwOQsSNXGnptRXiPbpmC/B5r3
+lZWVOProowGYdVMpN2m2iFn7GEovmPBAeVGSHjx4cI+SMDRqbWZzlrkWAVhEKifDjtOqS3kZqF36
+HIlE2Jpr5x04QUob9hU/lUq1oz4TZPIRAl3PMAzedQ3DYPFRRrLJ6zut/LnseE47lJudUkZ+0nMB
+rMlppHojx1P2k6zoPp+vXaCXlJTsnojOYLW6bYuek9xpJQ2a5iyAjCpTJsnM6fnQGO63335Yt24d
+AOtczmZ0lO9CKBRiL5adIZoLSmJRANoSYUoePU08n8/HE0zm7ZP/p0kMtLnNvvvuO6awSpE/E6Tq
+4mQBd3L1SRVFPhj5QKndaDTKUZtyUslQbOqjFNXlCy1fJrmIOvVTHnc7MWQ/iItfX1/fjnBUU1PD
+9PFgMMiFYFtbWy1xBIcddhgAcDHaWCzGi2I4HObnSy+EHcVQq9yArlFbW8vJX1etWsX3bZ8DQDtR
+vV1bHYHmSGtrKx599FEAwL333ovFixfndD9+v5/bKCsr42vG43HX7tOC1AelVJVS6t9KqcVKqUVK
+qQOVUjVKqbeVUv/b9rs6e0sePHgoFRQqKdwF4E2t9SlKqSCAMgDXAJimtb5VKXUVgKtgFojJCNpt
+ZWSgJA0lk0nLrunkQ6Zdt2vXrqwmHH/88dzexx9/zKt8pmQo2VZUJ6OX3DGkmiOzPMtS9bQ7VldX
+8/01NDRw2jTyVweDQd6B7d4Qu1Qh+yRVpnyhtba0Tf0IhUKWgjh0HyeeeCIAM0kJ5Zpcv34939NO
+O+3EFn5ZHo9E9Gg0aolYzbYrbg/jOI3hd999x9G1W7ZssUhm1I9Mqq18JmS4TCQSjpJOz55mHean
+nnqKx61v374532ssFuPxbGlpKUgFy9v7oJTaAcAXAHbVohGl1BIAh2mt1yqldgTwntZ6QJa22Psg
+B0yKZbJaUq432rt3bzzyyCMAzDoFxCAstJJPvgQapRS79I499liuCvXcc89ZrN0AsGLFCgvn3g27
+rZgwDAPdunUDAJx++un4+9//DqDNNVlWVsaxD9FoFN9++y0A4Ouvv8bIkSMBmGI3jb1MSOPkiUin
+053CYszksssGWTdBvvxOi7SEvCelFKuNffr04dgGOnbcccfhn//8J3+PFt5oNMph3m7rXGZAp3sf
++gLYCOARpdRcpdRkpVQ5gDqt9dpt56wDUOf0ZeWVovfgoSRRiKQwGMDHAIZprT9RSt0FYAuAi7XW
+VeK8zVrrDu0KSiktDXZAe0t2vnUVSbS94YYbeDWmYjCFIB8Si2EYOOiggwAA//jHP/DKK68AMGtl
+0m5EorrWmu+5rKyMo/eKtGPkjPLycjZ2hUIh7j/ltpTl1+Uz3HHHHVm6aWpq4p2VJAytNeeLsEci
+ymhVN3Ri+l6XLl1wzjnnADBVF8BMskO5JF944QVMnz4dgBmtSqqLUorVG2kcpucAwDHPhkSuvBHq
+JwDcf//9GDFiBADTwyEN2occcggAM69kEdDpksIqAKu01p9s+/vfAH4CYP02tQHbfm8o4BoePHjY
+ziiI0aiUmgngPK31EqXU9QDKt/2rXhgaa7TWV2RpRxuGAaUUZ2pubGzkXULyAtzqm+SS3LRpE7t6
+zj//fFdtOPQ3b5vCCSecAAD49a9/zXULe/Xq1a6CcSAQYCbdyJEjWaqg3W574dlnn8XJJ5/M/ac+
+SX+9BI13z549uQSgZOPRrrvjjjtyBix7hF9Hu21Hz58yYp9xxhlcdo9cnFVVVdyPcDjMx8vKyrgu
+5p577smVqceMGQMAePzxxzF7tqnhyiA2Jw6JW1Bb5eXlPFZlZWXsngXaSvFRvc4C0fk0Z6XUPgAm
+AwgCWAbgHJjSx7MAdgGwEsAYrfV3WdrRFIFG4qVUE3KpKZgJ5HFIJBK8yEhxMB+QUTQfDB5sPpPx
+48fjxRdfBAC8//77fL9kWBo5ciSOOuooAKYK9Lvf/Q4A8MADD7i6XqFcfUkPTyQSOOKIIwCAU+rb
+F0gSiWOxWIeqXiQSYeNqa2urJfahoxe/o//RglpZWYnTTz8dALhwUM+ePXkhmDRpEvr378/9p35G
+o1E8/fTTANrUhwULFvCYp1IpS51LO4Et3zE2DIPrcd54442W9PNyXhShOFDnp3jXWn8BwOkiPyuk
+XQ8ePHyPIHHt+/xRSulgMKiVUjoQCOhAIKBDoZDeZoDUAPL+oTbS6TT/RCIRHYlEXLfl9/u13+/X
+hmHk3R/DMLRhGLq8vFz36NFD9+jRw9JP+v+IESP0hg0b9IYNG3QsFtPhcFiHw+GCxiKfn0QiweOW
+TCZ1MBjUwWAw4/n0fzfPbVtAnPb7/Zbv0ZgUOgfoJxQK6VAopBcsWKBbW1v559JLL9WXXnqpDofD
+PP5u5lYx+kZzctq0aZa5Sj977bVXMa4zO5f3sSRozkSWkTTRZDLJluBCCriSjYKuA2SPOMsEGRmZ
+r1hO99fa2tohYaempobDe1tbW4tSVzIfyFDeRCLBxBpKtgK0ie1dunThcck1VTpg5SZID0axSUpk
+7+jfvz/fV1NTEx588EEA7udFMftHXo2PP/4Yw4cPb/f/0047DRMmTCja9TqCFyXpwYMHC0pCUgCc
+mWzF8Mk///zz/Jl2t2Ks8IUYlYD2+Q3oM+1md911Fx8jg1wpgCQA6pthGDjzzDMBmOnFxo8fX/A1
+JJOVpKVMgVK5wjAM9txItuHBBx+cc6m/TP0sxnwiQ2sm5ioxSbcHSmZRyNfNlw1E/gDcibQS9ii3
+QvqZyXpO4iylGydqMQDOJ7g9IaNOCa+++iqPBf2/pqYGv/3tbwEA99xzj+siu3bISkfdu3fnCMVN
+mzYByJ+i/vzzz1u8TpdeeimAtmLDpYIf/ehHjsepBMD2gKc+ePDgwYKSkRScdt9CJQcisxCkccwN
+qB8yMKbYUg21R+nSpeR03nnnFfVauUBWl6Z+XHDBBRZDKWCSzIjwY6+JmA9k7oiNGzc6EpzcgKSD
+0aNH87GvvvqqKFT3zpBsJUdBXsOeUbwzUTKLQmfg7LPPtvz90EMPFaXdzpgMVC2Kis5u2bKF1R2K
+EdiemDNnDn8mlcBJ/VJK4eWXXwYAjB07lslXFBvhFjLmAyjM85SpHwMGdBi0+72AbE0UOUv46KOP
+tn9ftvsVPXjwUNIoiWzOymWKd5nZ2QlkwaeIQ8DcgWTewXyQ7bpuIOP06+rq8NRTTwFoK0O+dOlS
+vPnmmwCAmTNnYvny5QAKo3y7gcyo3NTUBMCk2soq3YBpGOvbty8A4JprruFzJ0yYgHnz5lnaygVl
+ZWXsdSpETaOUfJQN2TAMLFiwAAA45qSUQGns7LEtJDUVqYKal83ZgwcP7vGDsylUVFRY4uYBM7nm
+qlWrAJg7GxmRpP//iSeeKHi1lTUk8oFSCsceeywAU4oh/fGuu+6yFHABgDvvvJMjI2U+Cb/fn7Nf
+vZBoTumGpQxBRx99NEsCZMSVdRl32mkntgEMGTKEJQU3SCQSRamUTRm3ZI4OstcUA27rSGSDrMcp
++Q/FkBDsdUqz4Qe3KGituQbfO++8A8BM/UXGMGn4Gj58OBu+Dj/88LzIJvLlkCnZ8+37G2+8AcDM
+JUlid2trK79YlEzl/fffZyOZ7G8umYELxd/+9jfL3+RpuPTSS7HbbrtZ+tmnTx/+v2EYWLhwIQBg
+ypQpeb002aIkcwUVVCEkk0ksW7as4HY7A2VlZRY6OT3vlStXFqV9tyRAT33w4MGDBSUlKdiTa0pK
+MKkMu+++O4uw9Lu1tdWSUXnq1KkATLGXVsmqqipuI5ednkSueDxeVAOj9MFPmTIFgCl2//nPf+Z+
+AiZjsFDuRr7id01NjeW7V1xh5shZvHgx71577703AFP1oUzESinmKsjM3G5QDCnhggsuaCcVnnnm
+mQW7NyWKYeytqzPTl0rVQeKiiy4q+Br5oGQWBSexWA48TZYvvviCLcr0kO3i9U9/+lMAZoYd0tFj
+sVi7GIJMi0MoFGKyy7vvvsuLSb6+90yge+rbt2+7ylLFEh3zQTweZ9Vm3bp1juInieINDQ2YNm2a
+5btAYSpWvqAEPZKYRPPKvtDlC0nzzneRoWdNqpbkZWjdVgRpwYIFRY2vyLl/2+1KHjx4+EGgpCQF
+6S0A2nzhyWTSIlbad2xZcbd379648847+ThZwC+55BK22jvVe6Q+ECg3XiwWKzjIJxOoz+Sjliim
+qOsW48ePz3ln+uijjyzZnKksXL4oZEd88sknAVhrXlIg1b/+9a+i7LqUPzEYDGbN7OwEpRQHeRGL
+Veal3Lp1K/fZHpSWr6Hc7fdKZlGwv6ihUChjGCnp9jRoPXv25O+/+OKLrKsB4DyHkqKbyTZAgxaL
+xbioiSTQdFa8A1n0gTbxu9CCNYXAzX1Sbk0CEYS+D8jkJDR3yO1L9qdCEAgELEVz84HP50OfPn0s
+fdq8eTPWrjVLpTz22GN8fPny5UVJEOv2e5764MGDBwtKRlJIp9Pw+Xxcty8Wi/GqbBffaeWTZd0n
+TpwIwNx1yXCzcuXKvHMokLoisxkXW1Kg+5Mx9HPnzi3qNToblPIdMMeM0qVvbyilOFWc1hrPPfcc
+gDb1wS0kvZgkz40bN7KnZfXq1XnNh3A4zG2QOtu1a1f22vj9fs7yXQxvl10lzwUlsygQe4ssr7Ku
+ZDZs3ryZ+ezpdJrZjXfccUfe/ZE5A4uZZEVCJlIh/Oc//ylK29sLVC8SMOM1isTRdw17bUqqVUFk
+sRNOOIHDsE844QQsWbIEgPkSUpKYZDLJsQfkfUmn05bcnHZ3uFvst99+nJ2KFlTDMFj9ra6uLmqG
+sEAgwG3naqcqtBT9pUqpBUqp+UqpKUqpsFKqr1LqE6XUV0qpZ5RZjdqDBw8/EOQtKSilegK4BMAe
+WuuoUupZAL8AcCyAO7TWTyul7gcwDsA/srVHXHKyHEuLbCbQ/+vr63HSSScBMKWDhoYGAGARMo97
+c6xIlG/lYif4fD4cf/zxAMzditqbOXNmzm0Um3+fD0488UQ26q1du9YiYXVEz7b/r9Dx1FrzTlhW
+VsacFIo+tV+fakxu2rSJOSFaa1ZJKa5m3rx5lnwWpFZIsdxN32fNmsUVpw499FA+ThJWTU0NG54D
+gUDBeUopS7obFGpo9AOIKKX8AMoArAVwOMy6kgDwGIATCryGBw8etiPylhS01quVUrcD+AZAFMBb
+AD4H0KC1Jn/aKgA9nb6vlBoPgFP/0mpLu46dm5ANVB5s1KhR7m7EATI6TUoNxTQ4ptNpNi5t2rSJ
+jU6ffvqpqza+b8RiMeaCbNy40VIdXNLUJQ0dsBrRipVdmzI/V1ZW4rTTTgMA5k08+OCD7J6MxWJc
+pu3TTz91dDnLKs/yuNTL8wlOi8fjuPzyywG0BfTttttu7JKcOnVqUdynBJm5OlcUUoq+GsDzAE4F
+0ADgOZgSwvVa637bzukF4A2t9V4ZG0JbKfpSSPiyvaCUYq/DKaecwgQmqhn5QxmLPn364J577gFg
+Er5eeOEFAGaWZLkA0AJBL1JnGCSzLdoyLb2EU6h2LhGx2aj5+SLfYspOkMV1UqlUpydZGQFgudZ6
+o9Y6AeAFAMMAVG1TJwBgZwDbt0yyBw8eCkIhLslvAByglCqDqT78DMBsAO8COAXA0wDGAngpl8ay
+rbBShO8ssTnTTtNZPAUqP/7UU0+x2vRDkRBoTNavX4/HH38cgDXZbCAQ4F1WKcVSA/2WzzBfox3Q
+tquGw2HO65CpDTqeTqf5e4ZhWKQXSdnOhs5yVRdLQqDfbtsrtBT9RJjqQxLAXADnwbQhPA2gZtux
+M7XWHTpIc8nRaNfrgeLr1PksCvl6AKStIhgM8iSliV0K9oKOQH0PBAIc7t2rVy9eCBYtWmQpL09E
+LbrPTIS0XNRIKV5LtUSGxnc0fj6fzxJXI9Ucu32ho/7Ya14ahlEUtUgunAWo9wBMzxbxLLTW26UU
+/XUArrMdXgZg/0La9eDBw/eHksnm7Pf7kUqlLKuklA5oF5DZjIvZd6WUJZed3D1ol5M+Y9qJqN/0
+WYrMdlHULuVIEVb69wEzQQyt8FVVVVw2TCnFx6XoK3kVUrKReSWl8cyeE9Ln8/FneU9S/JTXk8a5
+TKK40zlOPAb5We5suUgNTmPrRO2VY+zz+Rzvz+l69r7LdmlOOo1hpn4GAgFLP+ga1B+7WpUrZN9l
+1LDtXjpfUigmyEItU4hTHERlZSUXGC0mgcgOWYpcioF2emimiSJfCqfottraWta5DcPghKitra3t
+FgXi8QNmIpNMZConHT2TNV2OrT3aLxaL8f+TyaTlhaXJr5TiRU9OQCnCy4UzV1VP9lN6KnJRoeg+
+kskkL3TyutRf2ZZMapJMJi39p/GQXodMqgSNl5Onwn6u3Mjo2dbU1PBvisptbGy0zEM3thY6NxQK
+8ZzNRw31oiQ9ePBgQclICnYYhsEW+Wg0atn9OkPlyWRcDIVC6N27N4C2DMZr167l3UWm5UokEhaS
+DkkCtHv26dMHt956KwDglVdewXvvvQfA3GloN6bdA2grv96tWzcO5pFqxy9+8QsuEjN//nwAZuSd
+3NnoPsrLyzmpR01NDZYuXQqgTQQOBAKO1nuprjiNu+yPXXR2Oj+TVT9fA7Is/ENt+Hw+pikTF2TF
+ihVcHCgSiTAVXhodY7EYX3uXXXYBAHz77bcZiWx2voNUSzLN0WQyybRpUlftc0hKntnGgs7t0qUL
+E+Cqqqp47shzcn1vSsamYJ8s8m+l1HaNvvP5fFxhqGvXrqzP0wOqr6+3DLRcCKifslYD/b+srIw/
+77nnnjjrrLMAmC898eFJvejfvz8eeOABAGaVI7LW+3w+roUYDoeZ9ETJagFYRHx66cPhMPr37w/A
+tPzTAicXXpnz0skuYbdX0H1KK3yhz8ktic1pwkuuPxVsXbx4MXtJTj/9dI6LiUaj/DJFo9F2C1xH
+yW7s3od83yVZy6RPnz6WIsOyKHJH7WeyKdi+41WI8uDBg3uUjKRAolJnGhKz9IGvV1NTw9WEPvzw
+Q+y+++78GTDF+lx2RNpJyGAaj8cxcOBAAMCwYcM4TfrZZ5/dLh8f0LaLT5w4EX/5y1+4n9ReOBxm
+kTgTHVdGncod1G4wlLB7BuSOSCoR5Rm85557cOCBBwIwcyRecMEFHY5JNkhpqxggaa1Xr148t373
+u76AnZYAACAASURBVN/hscceA2COMalSUirIZe4VK/W/UgpPP/00AKBfv34s0c2fP5/T2/3mN78p
+OGISnqTgwYOHfFBSkoLdjiB97J1lU/jJT34CADjmmGM4l8FXX33FhqiysjJOn0W1DmQUmxtXUXl5
+OUfvRSIRjuO/5ZZb2E7gZIibP38+Bg0a5P7m0LZTVlRUsHQQi8UcJQtpqCPJJR6PW9iIf/jDHwCY
+FabpmOQmHHfccQCsNg436EAfzgt0H4MGDWIJq6amBrNmzQJg1dvdZtAuJv2d7Ag9e/a0jOf06dMB
+AMcdd1zetTQEflg8BTLYSYuszLknre+FYsKECQDM6EQS4VtbW9mgOHToUIsBjgxR2VJ6Z7IW08Qs
+KyvjtHGvvvoqP+QDDjiAFySymu+yyy48Ofr379+OKJMrqK/Nzc0ZvQT2c/1+vyX/JX0Oh8O47bbb
+ALSlpe/Rowdb6rt27Yp//9tMpdGrVy9eWN2iGC/Zo48+CgBs6f/ggw/w9ttvAzDHWBKIyLv0zTff
+WDgC2wtdunRhdUzrtlqaCxcuxKRJkwDkX1wnH3jqgwcPHiwoGUmBfMC0KymlWPTt2rUrf161alXe
+AUi0C1NFYkpyApi7ICVS3bBhg2W3svvppSHO5/Nl3YFlZujXX38dADB9+nSLT5skFrrP+fPnc1qu
+QnZOklJksI8tSKbdd5LJpCPVWBq6zj//fP4+qReLFy9mEX3dunV83A2KISXU1tbijDPOANDm4h00
+aBDefPNNAKb0QOramDFjODUbfWd7449//CN/lu9AKBTilG3r1q3D4YcfDgC4//77AbivJp0rSsam
+4PP5LC/7gAEDeGLW1NSwX33p0qV5qxCSkASYYjvpbBKRSMQxw45TJiG7Tu2EfKLehg0bhhkzZgAw
+RX9a0NyC1I50Os1EH6UUi9WSFCaJOfJzrirLI488grFjx3K7slJTrnDLU5Cg5xONRnlxXbRoEQDg
+4IMPtqT7JyLX1KlTOWPT7rvv/r1ko968ebOlWhR5Q5YvX46DDjoIgGkTsvMihg0bllNFLmH78LwP
+Hjx4cI+SUR/sO8uaNWvY4i6z6VZXV7MBS9JPc9ld6BzytZOl3I6zzjoLkydPbvc9uYvInTTT7iJZ
+aoDpvch1F6RdHWhfU9ANSBIIh8OWcnukmpBxNZFIsDgaCAS4zwMHDuRaFNmMXWREpXNzSWlmRyGS
+K7EUA4EAtzNkyBAA7fM30PPbZ599uH8VFRXM+9geICmApAQCMS+HDx/erlI60Nb3Dz74gKWcTP3O
+FDHZEUpmUSCRlV6kbt26MX+7vr6e1YcMIaE5XeOUU04BAEyZMgVA+1x91M6IESPYUp2pJDyd25Eq
+QzogTUhKLpsLBg8ezC8picCFIBgMYtdddwVgxgPQS0+JYr/99lsmHjU2NuKqq64CYLpRqR+rV6/G
+lVdeyf0DgKeffpq9OdJtGgqFWOWpr68vuP/ZEA6HccIJbYnDzzvvPADtFwMCuYYzFRveHpg2bVq7
+YyeddBK/9DNmzODiNMFg0NFdTVT5f/7zn44u1XzIgJ764MGDBwtKxtBIEgKV0qqtrWWp4b///W+7
+yD3Anah55plnsu9a7g7kl/71r3/N9NIDDjgAr7zyCgCTxuuUn0D2xel4XV0dxowZAwDsu6c03h2B
++nbffffhzDPPBGDutHvssQeAzDtfJpAIHwqFmK49btw4NgiSJfv6669nK/yAAQN4rCorKy1JROw7
+ayKRYLWE1DLAlKCIZ+LG95+vofG9997DIYccwn/LBCZ2VFVV4csvvwRg0rWJyHTwwQdvF2q9k7eK
+5phUfeS5gUAAo0ePBgDcfvvtAEy1g/7fu3dvNqRKo3EoFJLBXTkZGktmUSDdh25SZrEphLBEL0VT
+U5MluQYAPPPMMyxG2vrDn90sBEDbQ6ypqeH8/mQDueWWW7L2lxaCRx99lK/R1NSEE088EYBZYcgN
+866srAyAydajReHcc8/lF5XIMXaikUysQi/7+eefj3HjxgEAqyIrV67E119/DcDU36urqwGYiwWF
+LVNtz1zgdlEgnXvRokWsEi1YsMBi37DjjTfewGGHHQbAnGe0GVAiXTd9BdzbQWi8+m6rV6m1triO
+s4H6K9XKu+++m9mmMoYoFArx52g06nkfPHjw4B4lY2gErHkSk8kkr575Uk8jkQgnH5FSwvPPPw8A
+jlICkH3lz8TPl+m8RowYwcQTKpaSKQLQMAwuQb/XXu3r5lRWVnJ58sceewy///3v+dpOfZU5E2U+
+Bfq8bNky3qUycRDkcbLOT5o0iSULJ+ywww6cVszv91tSyuUKt7vuFVdcAcBUA2hsjzjiCMdz+/Xr
+B8Cs4Uhza+vWra5Vsnz7CpgGUBp7whVXXOGKH7Fx40a+Pj3rk046CTfeeCMAk/cgiWPkzSDqfjZ4
+koIHDx4syCopKKUeBnAcgA16W/k3pVQNgGcA9AGwAsAYrfVmZSovd8GsPN0C4Jda6zm5dIR2WTI0
+bt26ld2QblBbW8uRjzfddJNlVSaG4M9//nPX7UpIFqNhGJY0ZnS9m266iQ13d9xxBwDTYEo7aSqV
+4p27R48erKPTjiEzW/v9fvZlX3LJJdi0aRMA4O2338bnn3/O16bfJBWlUine5VOpFF/7nXfeYYmk
+mHULo9Eo2zAMw8DLL78MoE0H7gyQcTEajfJYyFRkEiRhKaU44vVPf/oTG+gk/TsXuLEpkGRy6623
+WmxFAHDvvfe6uh7NJ2n0jUQiHJgGtEl3vXv3zuhWz4Rc1IdHAdwL4HFx7CoA07TWtyqlrtr295UA
+jgGw27afoTBL0A/NdgEKk06lUiwaAe4MjPSCrV692mIkI2it2XpbKOwkJmkhvvDCCwGYUY50nIhI
+U6ZM4diA8vJyprPusssuLO5RtOTmzZvZQn7xxRdbcjdeffXVAID999+fuRcyUzPFdHTr1g1z5szh
+vtFkTCQSjj7yQiHzNQJtYqsbuDU0Usq6N998E6+++mrG8y655BL86le/AmDm2iSvy8svv5xXBKI9
+tD8bSJVasWIFJ+sh9TUXw7FSiklkUj2ihWXRokWc7Gb16tWWeBWKaJUkwI6QVX3QWs8A8J3t8GiY
+ZeYBa7n50QAe1yY+hllXcseceuLBg4eSQL6GxjqtNTnd1wGo2/a5J4BvxXlUir6dg17ZStGTEU4m
+zHSzY1D6NGlQ1FqzofHhhx/OSx1xgsybIIOcunXrhlGjRvE59kCqgQMHWkROWtmBNprqpZdeCgAs
+JQCmeEmGyJ122oldWrI2Bl1j69atrIJJQ1Q8HuexWblyZacE/lDFabr2RRdd5LoNt8a7hQsXAjCl
+KXIplpeX830/++yzAExDJD2ziRMnFhxpKNPbdcRjIZA0cP755/OzzEUSJul28uTJ7O6VKiapBvff
+fz+329DQYMlQ7ZZRWrD3QWutVQ61IB2+9yCABwGTp5BIJCyxD0opFqmzWU27detmiVUgbN26Ffvv
+b1awc5tVpyOk02mL3k79vuiiiyz3QFZt0lmrq6v5ntLpNB+fNWsWW/XnzZsHwDrBGhsb8cEHHwAw
+qdo0KZ566ql2+ukuu+zC+nLv3r0t1aRkYpFcJnKuoIVw5MiRfOzFF1/kF7IzsWTJEgDAZ599xnkO
+ZS5KCbrX448/HnfffXfB13YzdnTukiVLclaLx44di0ceeaTdcXr+jY2NPO9fe+01TgYkF6y1a9e6
+5vnk631YT2rBtt8bth1fDaCXOM8rRe/Bww8N5Ovu6Aeml2G++HsSgKu2fb4KwF+2fR4J4A0ACsAB
+AD7NsX0NQCuldCgUavcTCAR0XV2drqur036/XxuGoQ3D0JFIREciEf3kk0/qdDrNPy0tLbqlpUWH
+QiFNbRfzxzAMHQwGdTAY1H369NH9+vXT/fr10x999JFetGiRXrRokW5tbdWbNm3SmzZt0p999pn+
+7LPP9KZNm3QqldKpVErHYjH+/8yZM/XChQv1woULde/evXXv3r1J+tIA9Lhx43Rra6tubW3V6XRa
+z5s3T8+bN08PGjRIy7FTSulAIKC7deumu3XrpgOBgKXfgUBABwIBbRgGn1/oWAwbNozHPZVK8dj3
+6dOnU8Y+04/P5+NrJxIJ7lM0GtXRaFQnk0k+lkwmdffu3XX37t0LuibNz2Lfy4gRI/SIESMsczqZ
+TOqtW7fqrVu36gULFugFCxboI488UldUVOiKigrt9/st7wu9I8FgkD8DmJ3L+5iV5qyUmgLgMAC1
+ANbDrDL9HwDPAtgFwEqYLsnvtrkk7wVwNEyX5Dla69kdXsC8BneCXDd+v5+tpuvXr8eee+4JANh7
+772Z8jt8+HAAph2BdPV0Os1tFKucuwyTpr4RdtttNxbdf/7zn3PU2saNG9nau2bNGgBmklhyl9qz
+H1EbpOZ8+OGHOPLIIwGgXYIVEpPPOussR9sAWbpbWlra1Z4ETOoruQ4lX94NKEvRE088wcc2bNjA
+4ehU3GZ7gu4vEAjw/dEcmTBhAvbZZx8+l2w4tbW1eatQxUzcSrj55ps5QlVi1KhReOuttwC4C0W3
+eeCKk7hVa+1M+wN+5nCuBnBhtjY9ePBQuiiZgCgn/zSVbjMMgyMOr776atTV1bVrg3bdQhKSZIKd
+9yATq9TV1fG1DzroIM6ZQNIMAE75tnr1ag6KGjp0KKeO9/v9GWss2pFKpTiXZLZsyfbs0mSR9/v9
+7LWgJB0yg/M333yTkcRDJCuZG8JefbkQFJKOrSNUVFTgv//9LwAz6Q2N94EHHohPPvkkrzY7Q1JI
+pVKWuUBZnknaLBA/vBTv9tyAJOLJiU0vhB2UxaYQkJje2trKC0FraysvNFLcJxG9oaGBRdX333+f
+F6z+/fuzJ0Fai3/5y18CMPMBXnvttQBM0dC+mCWTSUt+RXKnDho0KOfU6fbJKjNIkReErjtt2jQm
+G61fv56ZgmvWrGFL9vjx43HXXXe1u47TIp0NMg6ksxYCiebmZvz9738HANx22208zyZPnszJYb7P
+DVLWoSA0NzcXazFwBS/2wYMHDxaUjPoAmLsH5Q5cuXKlpd4h7eJffvkli90SxEfI18Dl9/txww03
+ADCJIJS7MJlMcj4Bklii0Sjv4j6fzxLFSePZu3dvXvVpte+IKEO7saQJkwTS2trK104mk1nptfas
+v/TZqXo0qQOzZ89mlUKqEo2NjUyaccrO3L17d47FyAXUxq677soqiGEYjinliw0az/r6eo7GTafT
+HDPwfezKgCmtOUUC9+rVi9OxuUEHFai9fAoePHhwj5KxKfj9fkQiEUtkIO2E8Xicg4o++eQTNuJJ
+/WvixIkArKy6XCBdmRSJGA6H2TWolOJVXO7itPrS7gNY8z64jUyT1FWCU4ZewzDQq5fJD1uzZk27
+7FRyTOxRf9I2Q/1fvnw5AHNXGjrUjF275ppruDp2165dHSUEkqpylRLkcwVM96W9dmhngximTz75
+JNt2AHAegksvvZTdyDQ+HUkuxTI0Umo8O/KVXOwSoluUzKKQTqcRi8XYiGanNlP05Jo1a/jB/e9/
+/wNg3jhl73ULmqxVVVVMy/3mm28socj2F04WSInFYjknrygG0um0ZcGx50yUGbFzEcXp3pqamjhy
+ctq0adzu6NGjmRcRCAT4+Vx//fWu+k2LHV0vkUhYxlW+hJ2Nm2++mQ23Xbt25bR5TklrOhq/Yqk5
+u+22m+X+KY6nEP6EPfmPm/Y89cGDBw8WlJSkkEqlWBy1M/FkOXhKq0W7lttKzBL03cbGRmb3ZQqe
+kist7aT5pvIqBHJXsRsb5W5XyE5G4z916lQsXrwYgBnlSfkLSMJyG20p3aL2Y4X2OVcEg0GWirp3
+785qn+QIyEI2hey6HYEMu+eeey4fi0aj+OlPfwqgrbiNWxTat5JZFABzIjpF74VCIV4UXnnlFUvB
+1kIhHzKFmGYSvySyFZXdHpC+fmltlmHdhSIYDHKY8c0338yJYfJdFJxgH9/OXhi++uorDj/v2rUr
+30Mm+wuh2J4RUjsvv/xyXHfddQDM7FzFiOAk5NNfT33w4MGDBSXFU7AzGgmBQIApz4lEgjkEhEJ2
+K3k9YvfFYrGskkIpjFsgEGhnwJOGxlQqVbC0YBgGj/1ee+3FTM7XX38dQHHKoWfKju0WHYn2cm7J
+fBiGYTAfxu/3tws2UkpZxla2UagqQdJmeXk55/bs1asXqzb2eZjvNcQc+GEVgyEXJImlyWTSMuhO
+7iv6HYlELAlKnYgwMhMS6XKxWIxJLC0tLY7p5Zubm9s9/FzINnKiS7KR0z0ppSxFWwDrBA0EApbi
+r3SOjDWgl7WxsdFyPSeiU6aXhiDdwfaxd/qurMZERK+WlhbLcWrDKcJPjpX9JbR7JeyLtaSCyz7K
+lzfbfTu5HyW1XS5Ysh90bae5kIlAJD/T+EhvVqZraK0t7wa1Jc+VtHGnMfTISx48eMgLJWNopF1N
+rnaZdgfajWgVraqqYiruhg0bOMW3fZeQtGHA3A2o7XA4zCtwOBy2RBTKVZz6ms3Q5mRRzyRVKKUc
+uQ50/9IbEo/HLbugE6XZyWAmxzYbgsEg9ycTh0AeIwq61ppTgvl8Pt5ttdYdpsOzPycnOrYcb3mv
+9F0ZaSrv30lisO2e7c7N9TiNp+yvhF0FBkyjuf1Z26VieQ0KXJPp+ul6MoguGo06zi0n7kU2eJKC
+Bw8eLCgZSQFovzrLlZdWTCUSulIugHPOOYd96c8884xj24Zh8G5DpcYeeeQR9O7dG4C5kp999tkA
+TKYkFU75z3/+025HsO9WmUCrfyb3JbUhs0XROa2trZZiu9INS+3usMMOXOOBOBayuIvMNJ2tv1KX
+bW1tdfxetp0olUqxFCfHKBAI8C7n5HK2S4VS57b3Q/bTnqB1wIABAIDFixe3u569zxJOhmSSPCWN
+W35PPpNs1wgGg5xFLJ1Os31I3qddGgWsRtBQKMTPmhi94XAY3333HfeXWJr2nAxu6lMAJWRotIvW
+0iADwCKiUll2ehFaWlpYbG1oaHB8SIZh8GJC7Xbr1o0NdKNGjWKvhmEYePPNNwGY1YbsEzmb4a0Y
+8Pl8lshJEr/lSxEMBtk4SqpUY2NjTi9CMSGNpDQBJbFKLk6Z4DSJJej+EomERWWiMZLFblKpVDuv
+SKaFwH5d+zPOxENxw1mQfRs0aBCn77dzTDJdBzCfNb0DZChPJpN87Ouvv+a+2osVifY9Q6MHDx7c
+o2QkBXvqMDtk4Q17PQgpfkrDoD0VmV1MtLsIpYoi60M69LdTJQQAGDJkCIuu8XjcEjEnRXTqZyYG
+HuH7eM75+PEzuScztSs/0w4aDAY5qpakP8lDqa6u5gC7WCyW0aWYre/53F8kEskrgE72Xwa8UVs5
+qgY/rHRsQMeTWN60fVC11hYyColUu+66K0cUduvWjcOBKRHLmjVrLO26KXmfyeKcL6j/e++9NwAz
++zAtUtOnT8ff/vY3Pk+KxrlevzMXskyQPAU3xWfy8ewAYBVSKcXJY3bffXcAZjg4qZ1btmzhgjln
+n302e6uy2U866keucLsgSFWK5i/ZGZYvX94plb489cGDBw8W5FuKfhKAUQDiAL6GWd+hYdv/rgYw
+DkAKwCVa66m5dCQff6rEMcccA8AsN04ehZ49e3KbiUSCdxISv/v164f169e7vlYmsbYQUB2F++67
+D4DZR0qAMmnSJN4damtrLaniOoKdjVhMZBOdlVIswjc1NbHIa7e826G1LjjIrLm5mXd/krYGDhzI
+SXRqamqYun3HHXew18lt9eliRkwS/H4/J5IdOHAgG8Lj8TguvvhiAMCqVasAmIFUVDavmMi3FP3b
+AK7WWieVUrcBuBrAlUqpPQD8AsCeAHYC8I5Sqr/WOquMU8jA+v1+LgF/8MEHO74Ifr/fYjMAzPTZ
++SwKQPHVh9/85jcArIQUyrD08ccf83Xc5uwrxoSlez3ooINYBKcF9uOPP2Z7R2trq2Xsx4836wfv
+vvvuePxxc/rMmTMHgCnCO5G6itHfYDCImpoaAMBRRx0FwFxkKcxdujrLy8s5ApcK1OaKQvtK83HA
+gAEc71BdXW0ZQ1IPtmzZwosaqZh777035zTtKAbF7eKVVyl6rfVbWmtazj+GWTMSMEvRP621jmmt
+lwP4CsD+OfXEgwcPJYFiGBrPBUCMoZ4wFwkClaLvVGitMWTIEADtiSiSNk1GHvLt52ukKVTVsUMp
+xbwIKisHtBmliKySb9tAYam9aCd9/vnnWSwnEX/69OnYaaedAJhl1olQBrTlcQSAM888E0Cb9PDp
+p5+ysU/yMIqRn8Lv93NNDTJaNjY2ctstLS3c5xEjRjjyIjoTZFSm3JA/+9nPWLKRSKVSTEiKx+Pt
+PCPdu3dnaaMY0aqEghYFpdQEAEkAT+bx3fEAxhdyfcIJJ5zAohVgTcVOpbpnz57N5d5pMSgkt6Ik
+0BS6QBiGgWOPPRaA9SVesGBBQe1SO4XgrrvuYp07HA5zaXQStVevXs3szx122AEHHnggAODkk0+2
+JKMlpiUtelu3buWFoL6+nsezkAWQ8Kc//YmzF0kbBqk5lZWVbFeKRqOsptEi1ZkIBAJ45513AAAH
+HHAAAPOZ08s/ffp07se9996L2tpaAMBNN92En/3MWqmxsbExJ5uR2zmQ96Kg/l975x9bVZnm8c/T
+0tb+EKG6qTBFyuKPDcIubIwRd4MbZ+noiKwrYsAJDjphqU50uiGOECK6iRqW3aBMMju7ZnFrDLKr
+OKzYhB1ZS9SgUodIsAOD4zKrY2Vsq/JDEUrpu3+c87x9z+297T333nN7zb7fpOm959xznvd9z3ue
+93me9/khspzAAPltM0Q161L0xpingKfCe429s4SHhweQI1MQkRuAHwPXGWPcJIU7gOdEZCOBofEy
+oDPvVo6C5557LvJ9w4YNQGAEU5fSOXPm2JVLnVza29u5/PLLgfgONq7bbb6r8fnnn09TU9Ow448/
+/njsthUKqmK1tLRY+i0tLVZScFcoFWE3bNhgU+xPnjzZjtGBAwfsmGtsysDAgJUwRCS25T8ddAdn
+2bJl1vV6//79QOBerLUkb7nlFntNX1+f3YkoBvr6+oY5IS1atIjdu3cD0bT+ImINy8ePHx8W67Fv
+3z4rYRQS2WxJ2lL0IvIxQSn6NUAVsCucHG8bY1qMMb8SkeeBgwRqxQ+z2Xnw8PAoHZSMm3Mu12m5
+L3cr6csvv7R62Lhx4+yKt2bNGlpaWoAhSeHUqVNWUjh69OhI7QOGVmw390JZWVneRp6Ghga7aurq
+0d3dbaP+8skYnYuhsaGhgbffDuzFU6dOtfaACy64IO191Li4Z88epk+fDgQSlOrwCxcutIWB1b34
+008/tauim+shHw89fQ5u5ih9rjU1NZbepEmT7Pn333/flhxMwjtQocZFNyOV0jvvvPPS0hYRWltb
+AVi/fr29h9rMGhsbreE3E1K8hL9Zbs65uOGqaAhDk/7qq6+2k6O/v9+Ktp988ol1+tEw1tOnT/PQ
+Qw8BgRtsNqXd9b5KrxCW6+rq6mFVmCorKwuSPj4OU9C+tLW12UrSAwMD1jKe6R5qHKyvr7cT/quv
+vrLjfeTIERvarhPbTbpSW1ubt79HbW1tRKXbs2cPALfffjsQvHiaMXnWrFmWkdXV1UVyWsaBmwZ+
+NKiLsqsCqEExNapRF6133nnH+iG486yzM9DIs6k+7kaSZts/7+bs4eERQclICnGkBPWqmzBhgj2m
+BizN5w9R0b6trc26DT/99NNAsHosXrwYCGpUvvzyy0DAgUdK6jE4OFjQugdaV8GF66+QD+KswGrY
+bG5utv3fvHnzqL4DGnzk+ihUVFSwfft2AC6++GKrNrhBZzqGZ86cyXscXU/P48ePs3PnTiC6mq5c
+uRIInvtrr70GwLRp01i7di0QlMKLM16p5QRHmsPpyhqqgXP+/Pl2S3rJkiXWSKpu4qn01q9fD0Qr
+kI+0NR5XCisZm0Ic9UHFalUNAKsGrF+/3k6wqqoqm0Hn2LFjNnPNe++9BwzZFiAQ5bSmYEdHh52w
+x44dG+bS7IpyFRUVedeS7O/vtyKsiuKu30Ux0NzcbBnr+PHjrf591VVXjbp//+677wKBhV+fYUdH
+B8888wwQFHR1/QJSkU+Kd30WZ8+etS/n559/bl2BM7mF67Pv6emxdqfe3l67SKj6MVJ7dP65CXDS
+4cILL7SqrjqC5QK9v1aOWrVqlbXbpEtPDz7JioeHRwHwjVMf3B0F91oNKEk9riuvrlTAsOv1vJZE
+O3nyZGTl0s+uMVClhnx2HtSjzb2v1mosNi655BI7RsYMldC75557WLduHRD01VWhAFasWMGsWbPs
+fTRQ6v7777fGRRjZezQfI6MaRF1cf/31owaOuS7DKmF0dnbagK1soCtzusAuFxMnTrRzq6GhwUo3
+6YzUxhibXKe6utqqEm70qM4RVzXKJvdEtigZ9SHb31ZWVkbSiEOgW6mVtqenx4pzjY2NdlDGjx9P
+Y2MQt/XSSy8Bwcuo5++8804rPmdK1a0vb2VlZURkzFUfdrc1FcX2w3f96F955RUgsJRr286ePWsj
+ST/88EP7Eup1M2bMsJ/7+/utnSdOwpp8oIx1z5499iV11cpM0GfppkZ/8sknbVLfbJCqVqa6HLvM
+QttUX19v3aqvu+46IDpuW7dutblH9+7da+00lZWV1g6yYMECe11MePXBw8MjPkpGfcgWbjoyVQPO
+nDljfQ9OnDhh3UgbGxutA1B9fT233XYbEN1X1h2JgwcPZgwuUY6vK4JrLc9V0qqrq4tIBcVaWVOh
+7e/t7WX+/PlAIDWtWLECCHI66MrmJi+ZN28eEF0dd+3aVfR+qNHNGGOjDrPBE088AUQrd+vuS7ZI
+JyG4af3dtO2qPnV3d1vVRv0N3DlUVlbG5s2bgcCfxi1Np6pNIVzCR0LJqA/Z7j6Ul5dbXcpN3ctF
+HwAACdRJREFU0PrII48AgeVZVYnW1lark5WVlVmLuuZwPHfunLU5zJgxw+rRmdSHTFV8csHp06cj
+CVVmz54NYGM18kG+CVvdZ1FXV2d3e4yT3FYjON2YjcrKyoKEPseBPtMvvviCSy+9FBg9/Lqurs4+
+64qKikh8TJzxSl1E3OTDccdd71VfX289dN3dsZ07d7Jq1SqAiK0mJrz64OHhER/fOPXh3LlzViXY
+sWMHEIhZM2fOBAIpQFdgEYlkFFa3VDVU9vf3097eDgSrnCuquaKhqikjVR3KFhob4FaM7uvro6ur
+K+d7psKVbHIxgrqrnFtxCoZ2EVzHMUWxpQQRsbENvb29WT+XN954I+LarAa/XH0k3ApiuRqdNa/o
+FVdcYaVbt00bN260OxhJw0sKHh4eEXzjJAUYqu+nKdguuugiG+24bt26YVmbIVjdt2wJEkQ9//zz
+QOASvXDhQiBaLCa1IElqsFKuq0FZWVkk+66ubM3NzQXNDu3ug+eq444GV9IZKxhjrNG5qanJ2jsy
+BZKpwdT1vLz33nsjOQziQOeFjnE+BkCdUw888EDEVqGG29dffz3nORI3UrZkmEKcSZsaoTh79mwO
+HToEBAxDC4C4L8Wbb75prbqaIruxsdEWizlx4kTEcFnI1Ojazk2bNkXarhP65MmTBU0XrvdIMhTY
+FXEVo1X5SgJ33303ANu3b+ejjz4CAgbhFobR3+mOAwwZKDUOJhek+pkYp+hNXKjxW9VgCOah7qrl
+ynByiT726oOHh0cEJSMpjMbR3GrGqSW5Z86caYtmDAwMMG3atMg1qb9ZvXq1vV6PVVVVWY++/v7+
+iNEsNeAlLve96667AFi+fHnE8Kmeaz09PbHLhafCNS4mUaQkE71C3y9um7UEYGdnp02z1tPTw4sv
+vggM5TGYM2dO5DpNG5ePZKNtdvMq5Hq/W2+9FcAmCNK2jZZTQ+eTW4F8cHDQvhu5zIGSYwrpJkdZ
+WZkVoyZMmGBDjfVhXHnlldYKPWXKlMj+v2JgYMCGqu7duxeAQ4cOWTrV1dU2A/D+/futa2+6QY3L
+FDQ34LZt21i2bBkQRGVq2nN3f19ViqwTYjiiq8JNa18MBpFv5ik3U1KuWLx4sWWytbW1LF26FCBi
+U1Hm/sILL9hI2UzIFJLsujbr2Kpo79pZzp07l/WY19TUWGcxdyEzxti5nG5nx7V3rVy5kmeffRaI
+VuRy25Fte7z64OHhEUHJeDSOZqRy1QetQ6Cr7ty5c62hsampyXLJ2tpa6/14880389Zbbw27p7uq
+ursMmTgzxPdozFQ6PSm4uyhJGRt1dSwvL7e5B+bNm5d1v1wpxkU+4ryK0o8++ih33HEHgK2/uGjR
+Itra2oBA1VBDZDbtTJd6b3BwcMTSge4Olvscxo0bZ3+viVUefPBBrr32WiBY2fW+Dz/8MI899tiI
+7dPfTp482bqgj5AdOyuPxpJhCnFE8tTJNHXqVKs+VFVV2UGfPn26dcfNxrEmk8iY6uZcbAt7XKRO
+3kIiNcfi4OCgTXCzYcOGUZmQO5Zu2wrBLFWnHhgYGFZNqbq62jpe5bNLkG0RoFRm4tLTe2hE5n33
+3WfbfuTIEVtg9qabbqKjo8PSGwk1NTWR/mWAd3P28PCIj5IxNEL2BrzUVaC7u9tyX1cs7OrqiiU+
+u9mAR6JbKsg0XumknEK1Xe+jOzWnTp2yht/YuQALGGDmSkc1NTXW+q4rcENDgzVEpibRiYNsr3PP
+p0oJqRWxW1tbbTDXrl27rMFbDeLZoJBqYkmpD+FnIN5EqaiosJPCjVvI1ZfdFW2NkyI7Ke/AXFFW
+VhbptyJJG4beT2tufPbZZ2n181R6qclvCxUnka5SlZuCX+lVVFREYl/ccPjRxsYdw5HsCOXl5ZE8
+nplqOSi0FsbXX38dyUzl7jhku7OT5YLq1QcPD4/4KBVJoRf4CugboyZc5Gl72v8PaE81xvzBaD8q
+CaYAICK/zEa08bQ9bU87WXj1wcPDIwLPFDw8PCIoJabwlKftaXvaY4+SsSl4eHiUBkpJUvDw8CgB
+jDlTEJEbROSwiHwgIqsTpjVFRHaLyEER+ZWI/Cg8Xi8iu0TkN+H/iQm2oVxE3hWR9vD7NBHZG/b/
+P0RkeNx34WhPEJFtIvJrETkkInOL1XcR+dtwzLtEZKuInJdU30XkaRHpEZEu51jafkqAn4RtOCAi
+eZX7zkD7H8IxPyAi20VkgnNuTUj7sIh8Jx/ahcKYMgURKQd+CtwIzACWisiMBEkOAKuMMTOAa4Af
+hvRWA68aYy4DXg2/J4UfAYec738PPGGMuRT4AvhBgrQ3Af9ljPkj4E/CdiTedxH5FnA/cJUxZiZQ
+Diwhub63ATekHMvUzxuBy8K/vwF+lgDtXcBMY8wfA+8DawDCubcEuDK85p/Cd2Jsoe7EY/EHzAV+
+4XxfA6wpIv2XgPnAYWBSeGwScDgheo0EE/J6oB0QAkeWcenGo8C0LwB+S2hHco4n3nfgW8DvgHqC
+eJt24DtJ9h1oArpG6yfwL8DSdL8rFO2Uc38NbAk/R+Y78AtgbhLPP87fWKsPOlkUH4fHEoeINAFz
+gL1AgzHmaHjq98DwUsaFwZPAjwF1nL8QOGaM0UCAJPs/DegF/i1UX/5VRGopQt+NMd3APwIfAUeB
+48A+itd3yNzPYs/Bu4GdY0Q7K4w1UxgTiEgd8CLQaow54Z4zAcsu+JaMiCwAeowx+wp97ywxDvhT
+4GfGmDkEbuURVSHBvk8E/oqAMU0GahkuYhcNSfVzNIjIWgIVdkuxacfBWDOFbmCK870xPJYYRKSC
+gCFsMcb8PDz8qYhMCs9PAnoSIP1nwEIR+V/g3wlUiE3ABBHREPYk+/8x8LExRuNxtxEwiWL0/S+B
+3xpjeo0xZ4GfE4xHsfoOmftZlDkoIsuBBcD3QqZUNNpxMdZM4R3gstAKXUlgdNmRFDEJ4lY3A4eM
+MRudUzuA74efv09gaygojDFrjDGNxpgmgn52GGO+B+wGbkuSdkj/98DvROSK8NC3gYMUoe8EasM1
+IlITPgOlXZS+h8jUzx3AneEuxDXAcUfNKAhE5AYCtXGhMcZNz7wDWCIiVSIyjcDY2VlI2jlhrI0a
+wHcJLLL/A6xNmNafE4iNB4D94d93CXT7V4HfAP8N1Cfcjr8A2sPPf0gwET4AXgCqEqQ7G/hl2P//
+BCYWq+/A3wG/BrqAZ4GqpPoObCWwXZwlkJB+kKmfBMben4bz7z2CHZJC0/6AwHagc+6fnd+vDWkf
+Bm5Mct5l++c9Gj08PCIYa/XBw8OjxOCZgoeHRwSeKXh4eETgmYKHh0cEnil4eHhE4JmCh4dHBJ4p
+eHh4ROCZgoeHRwT/B7t3mfbAx/b3AAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/2... Discriminator Loss: 1.2653... Generator Loss: 0.6031
+Epoch 1/2... Discriminator Loss: 1.0865... Generator Loss: 1.2273
+Epoch 1/2... Discriminator Loss: 1.2002... Generator Loss: 0.6592
+Epoch 1/2... Discriminator Loss: 1.2886... Generator Loss: 0.6356
+Epoch 1/2... Discriminator Loss: 1.4369... Generator Loss: 0.4941
+Epoch 1/2... Discriminator Loss: 1.1444... Generator Loss: 0.9019
+Epoch 1/2... Discriminator Loss: 1.1936... Generator Loss: 0.6823
+Epoch 1/2... Discriminator Loss: 1.1784... Generator Loss: 1.4153
+Epoch 1/2... Discriminator Loss: 1.5582... Generator Loss: 0.4257
+Epoch 1/2... Discriminator Loss: 1.0860... Generator Loss: 1.3010
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsXWeYFFXWfm+nyQMMSM4ZyUFEEUHUBREQE7jiyi7msO6q
+iCK6JlTcNSsGXEVEgoqrIgoKSjASJCswgKQhZxiY1N33+1GcM7dqqruruquHwa/e5+Ghp7vq1q1b
+t+494T3nCCklXLhw4YLgOdUdcOHCRcWCuyi4cOFCB3dRcOHChQ7uouDChQsd3EXBhQsXOriLggsX
+LnRI2qIghOgrhFgvhNgohHggWddx4cKFsxDJ4CkIIbwAcgFcDCAPwBIAf5ZS/ub4xVy4cOEokiUp
+dAWwUUr5u5SyGMA0AJcl6VouXLhwEL4ktVsHwHbl7zwAZ0c6WAjh0ir/IBBC8GePR9tzpJTw+/0A
+gOLiYv6uIkO9D8Kp6LPaD59Pe12DwWCZ/gghrPRvv5TyjFgHJWtRiAkhxM0Abqa/PR4PwuGw1XPL
+fEcDov5mHCSz88wgpeRjpZQ8ual/Pp+PH4yd/hrbVT/bgdl56guo3ofX6wUAhEIhy9eLNME8Ho/p
+OKvnUT8AICsrCwBQWFiIWrVqAQDy8vK4P2Zj6PV6EQqFyvTD+Awi9cF4f3bGWAjBx6ekpJS5XlFR
+UZm+0r0Y+2sXaj/p5QfAi2k4HEbVqlUBAPv27eNj6Xo+n48XXPVdUvskpdxqpS/JWhR2AKin/F33
+5HcMKeV4AOMBTVKglyTRQVXb8Hq9upfFuOh4vV5+AMXFxbrfjS+XCjsvWLS24r1Xs0lP/xvv0Wxy
+2Glfhcfj4fGk6xUVFfF3gUCAr+f1enH8+HEAQE5ODg4cOAAAqFKlCgBg7969ptcIhUK6hYVAL2Ck
+Z2T3XmKB5kX16tWxe/duANocUdujvpgtyHahLra0yHg8HpSUlPAxgUAAAFCvnvZq7dy5kxeCkpIS
+3bNRFgLLmyEhWYvCEgDNhBCNoC0G1wC4NtoJdjpvtluZiVmRXkBafVVEu7bxYVck0Zf6QhMp1nGJ
+IBgMmu7Y9LmwsBBnnKFJp9nZ2ahevToAYNmyZTzmtDhEkgqNizpBfTmSBVWyqlGjBgAgPz+fpZxN
+mzZFPM/JPhDC4bBOGjl48CAA4MSJE/yd+i5EGlO7/UvKoiClDAoh7gTwFQAvgHeklL8m41ouXLhw
+FklxSdruhGJoNNuJYsHv97P+evToUd41U1JSWA80u0+/368TuUgUs3PtePtcXojXbhEPvF4vLrnk
+EgDAq6++in79+gEA1q9fz2Njpx+qjm9nbOvXr48dO3bozrN6XdqZMzMzAWjqUWFhYcT+GZHMcaZ5
+RpJwSUmJ3ev9IqXsEuugU2ZoNILERjsPX9Wn6YUOBAIsXkV6mIRgMGhqiLOif6svW0VcDAjlsSjQ
+ZP3pp5/QoUMHAMChQ4ewf/9+ALFVG7UNQP8im/U7IyODf6fzGjZsiB9++AFAqYEzGsjecfbZZ7Na
+EAqFuK/0ezSDcjwLViKgsaC5niy4NGcXLlzoUGEkBbtiJVBqMPR4PGjYsCEAYOtWS14XXTuAJmGQ
+Mcvr9fIOYSY1qGKt3X6T6NekSRO2aleqVImt8STdRDMc2UF5qA033ngjAKBz5848LmeccQaGDh0K
+AHjllVdMd1xVMjNa8iMhJSUFb7/9NgBg4MCBLOkZjcexDMckbSxatAgNGjQAoEk3NF5WXM5Oji3N
+i1GjRmHZsmUAgP3797O3Zt26dTp3rtPXV1HhbApWQBOHLN3XXnstbr31VgBAjx49Irq7jOjWrRt2
+7twJQO+SLCwsRH5+Ph9n5n2IxT1o3bo1/vGPfwAAWrRoAQA499xzY056wowZMzB48GAAetXGDj+i
+PODz+Vj8JlcZgcZt/fr1WLt2LQCwBf2ee+5hq/727dsjqnrGsU9LS8P27RovLicnJ+H+B4NBLF++
+HABw3nnnlYuXg54lLWhLly5FmzZtAJRdFGkByM/Px/fffw8AGDRokO43G7BkU3DVBxcuXOhQYdQH
+q/B4POjYsSMA4PzzzwcA3HnnnbzrdO/eHTNnzgSg920LIXhlJv/5iy++iKeeegqARgTZtWsXAKCg
+oEBHwiGo39FnVWxNTU3Fa6+9BkCTXuySRlRceumluO222wBoqkafPn0AlEodFQWdO3dG7dq1+W8y
+0Pn9fmbe1alTB3Xr1gVQKiZXq1YNY8aMAaCNm8oWVFmMRu9BUVER3nvvPQDAP//5T911Sa14+umn
++dpm5K2MjAyWEIUQ6Ny5M583cuRI3XnRYFWFbN26Nd//zTffzF4ZYk1GA82/7OxsdO/eXfddHJKC
+JZx2i0K7du1w//33Ayh9uatVq8a/X3311Vi9ejUATSwlsaxZs2aoX78+AKBTp04ANDWhUaNGAIDZ
+s2fzIJstBEDpJAgEAjwRwuEw6309e/ZkMbikpITbKSgoAKCJgGlpaQCATz/9lC31s2bNYhGWVIYn
+n3wSS5Ys4f7E8qSUN+jlnj17Nt+nlBJffvklAOCGG25ApUqVAGi6enZ2NoDSRXTHjh06NqnqGqYx
+N5v04XCYF/LCwkIcOXIEgOYCpQXJDOqLe/z4cSxYsAAA0K9fP772tddei/vuu8/eQERAZmYmE6De
+f/99tGzZEoCeRWtGrguFQnxPlSpV4vESQrAdJJHNxgpc9cGFCxc6nDaGRlrN27Zty5LCnj17AGji
+Ge1E27dvx7p16wAAX331FWrWrAkAGD16NKsHjRs3BgBMnToVkyZN4rai7TQq0tPT+bPP52NJZefO
+nSwmAmA/PfEm7PiXq1Wrxt4Jj8fDuwdJJacaTZs2BQDk5ubyd0VFRcwRiNcgaiUwrk6dOgCA5s2b
+4+effwZQKo1ZvQapD2eccQbvvBs2bECrVq0AJK4+CCF4Tg4YMIA9MY0aNWK16YMPPgCgjZVZPIvH
+42Ejbv369blPNN8OHz5s+Z5PwjU0unDhwj5OG5sCraS5ubl49NFHAYCNb1dddRXrr3v37mWpYvfu
+3di8eTMA4OGHH8bAgQMBAO3btweguQgnTpwIQFutjfHqgDknQdXvU1NTWWIpLCzExo0bHbnfAwcO
+6HTH66+/3pF2EwXptWQDAUrH5ZxzznHUZWrGEfF4PGxrKC4u1j0LNVrReF4gEGBDZI8ePdgepSI9
+Pd0xdqKUkqW7KVOmYPLkyfy9lXMBTVr55ZdfAGiSAt1fHBKCLVSYRSEWtVh9IUms/vrrrwFo5BkK
+K501axY+/vhjAJr4Thbew4cP44orrgBQKoJXq1aNLdg//PADk0by8vJ0RCZjuHBhYaFuoUhNTQUA
+y+pHNKjELFI70tPTsWjRooTbdgIvvvgigNLFAQDGjRsHAFi5cmXC7RtzRJBhlp5Bfn4+jh49CkCz
+yJO61rhxYzz22GMAtMWJ2qF5YQWHDx9OimU/3oUmLS0Nffv25b/Li6Piqg8uXLjQ4bSRFAhSSmYb
+0q66dOlSNtr89NNPOjYi+fW/+eabMlTYcDiMIUOGAABq1aqFtm3bAgBef/11ZkWqLEWzDDuFhYWW
+jVyqUatHjx64+WYt8dS//vUvLF68GAB4Z1QlhYyMDM5DcCpRpUqVMmpMUVERMzedMFqrbYRCIX6W
+aiQq7ZirV6/GgAEDAACPP/44KleuDEDvUjZrOxwOsxF4y5Yt7KKuVq0a/v3vfwMA7r333oTvJVG0
+atUKa9as4c+k6iYbFWZRsDOhKKyVaK5ff/01+/TD4TCLjH6/HwsXLuTPBHq5J0+ezOJwnTp18OOP
+PwLQXnQS+VJSUky9BnZ8xXTse++9hwsvvBCAfuJ27NgRXbt2BVCakejRRx/l9FslJSUVIhIzLS2t
+jH1lx44dyePgm9hzhBCs/g0ZMgS9e/cGoNkXZs+eDUDzRtHceOGFFwBoi0akfv75z38GoJGXunSJ
+aZxnOHXf6lzq2bMnWrduDQB49tlnmRb+3HPPYezYsY5cLxZc9cGFCxc6VBhJIZb6QLtDVlYWevTo
+AUAT/QDNSEjswE2bNuGmm24CAPz9739nqQIoNfg8/fTTALTdmFZpEtOMMGMS2kkyC5TuKLfccgsb
+DIntBmgGMxJ9J0yYAEAznJHIXFBQUCFSwNWsWbNMP5YuXZq060XKp3Ds2DEA2px5+eWXAWjPnRKr
+FBYW2hqv6dOnA9Du75577gFQakh1wngcCy1atGAph1i3BJonH330UbkZGivMomDMy6dmBk5NTeVB
+a9GiBb+Q5OYh2iugqRSk9xoXBFos3n33Xd014+2rXWzbto1jNLKzs5nQcuedd+Lqq68GUEoKUuMJ
+aPE7VaDox08++aSMXSaZsRiRMkbTc6xVqxb+97//AdAyHNshMKkgT9O6deuY7EZxNbNmzYqrzWhQ
+adUAOJYjGn766Se2eW3bts3xPqlw1QcXLlzoUGFozmZpsomQlJ6ezqSgQCBQxvj0zDPPsNi1e/du
+XHaZVoyqTp06zFPo0KFDRBXBLvx+f9Li7onCu3HjRu77li1bmJpdXlDzQvz0008A9NwEQjAYxMUX
+XwwAWLJkCUc7BgIBWzu3WSo8M+pvWloai/hz585ldcyJeVy3bl2sX78eAJg+fdFFF5m2rdansAuS
+ts4+W6uPNGfOHCbO7d+/n42jR44cYW9IZmYmS4xNmjSJ67qwSHOuUIuCUYdUI8ReeuklAMCwYcP4
+hSQvw/79+3nCejweJhPRuYDm0vn9998d6a8aOp2s8atfvz5PgkOHDrEnojzg9/s5vuT+++/XxXoQ
+1ExJ5N7Ly8vjxTk1NRUrVqwAADzwwAPs2SFESk4TK+OU3+9nJuvSpUuZyOYEGjZsyHOE7BZ169bl
+z+QuBrT7NxaHsQpjwZmioqKI80iNJCWQ2jFt2jS7l3ZjH1y4cGEfFUpSiLZDkLFr4sSJbISjvIwe
+j0e3oqpGSzJcBoNB9OzZEwCwatUqAPoyYOFw2HJ2Xq/XqyPCJAvUP7/fj1GjRgHQVKV4oJZ88/l8
+puoPjVWbNm3wzTffAAAqV66siyn49VetfAed37ZtW6aHqypOXl4exo8fD0AzklE0pVkafSO12Ux9
+UH+n2JeqVavirrvuAuAMLXn9+vVo1qwZAHDcQrNmzTjWQK3eZLwHq/B6vWU8G1b6rhbiIQmMSFc2
+kFxJQQhRTwgxTwjxmxDiVyHEP05+nyOEmCOE2HDy/4oR6+vChQtLiFtSEELUAlBLSrlMCJEF4BcA
+gwD8FcBBKeVYIcQDAKpIKe+P0Za0WrTV4/FwwZG//vWvAIBevXqxzl1UVIRDhw7xZ9JxpZS8Ms+f
+Px+AxhKjnS8/P59X4lAoFDX3gc/n063uyZK2KPAnMzOTr2dW8s4qSJpKT0/ndqjdOnXqcCDZvn37
+mNknhGCp4O6772YeBUUZfvnll2zDyczMxIgRIwBoBl9KNBoOh8uU+jPLkA3ooyDNIITA1KlTAWhZ
+qoge3q1bN7vDwSCOy6JFi3hcSFJo2rQpswqN9Ol4sir7fD7Lpf5UqJICzVlyUdpAcovBSCl3Adh1
+8vMxIcRaaCXoLwPQ6+RhEwHMBxB1UbCDcDiML774AgDYeNWhQwdcc801ALTBI9+11+tFr15aV0aO
+HMliG6Vgq1KlCscXqIajWBRmu0VO44W6AMSbgkvNDkyTMBgMonnz5gCAK6+8EoDGlaBJ37BhQw4H
+X7BgATZs2ABAU93ofikm4emnn+YoSb/fz3klL7/8ctNJH2m8oqkMxuPIGCilxFlnnQVAi3Ak/slD
+Dz1UJj7G6/Wy2vnuu++yKhkMBnX9pLYpLZvRg0L9y8nJYQOrHUgpbS0G7dq1090HAPaQJAuOkJeE
+EA0BdASwCECNkwsGAOwGUCPCObpS9C5cuKgYSNjQKITIBLAAwJNSyv8JIQ5LKSsrvx+SUka1Kwgh
+pNUoyUgg96Tq1gyHw8xMmzRpEteJIAbZvffey+4mO1BX7WRJCj6fj3cpr9fLSU0o87BVmJUnB8Dq
+FkWXduvWjSWFnTt3srvsP//5D9PKJ06cyBGRJF537NiR20pJScGrr74KQGOZxrOTRiq0o34mNWjT
+pk28+0eKjLQC2rn79evHgVSU3KRz587MajzzzDO5foXP52M11aqUEw8otWDz5s1ZpaXxjoOCnfxa
+kkIIP4CPAUyWUv7v5Nd7hBC1pJS7TtodLFVmSXRASe81tkNRkk2bNuXU2qR22B3UZGfRVa9RvXp1
+nY3lv//9b0Ltqno9UJo/8s033wSghXKTyH3ixAnMnTsXgMYxIJ5CnTp1OIchvZjbtm3D448/DkDL
+RThv3jwA+gI2dp9tpMWAQOPStGlTVhvffPNNHT+FYGUBf/DBBwFoZCjVtgEAu3bt4nmybNky3e/J
+9tydf/75XL0qGAziT3/6EwBr8zaRGqKJeB8EgLcBrJVSPq/8NAPAsJOfhwH4LN5ruHDhovyRiPfh
+PADfAVgNgCxvD0KzK3wIoD6ArQAGSykPxmgrYfXBSah9MZMOVE6F+rvKdVC/p8Cs5s2bc0bpBg0a
+4LfffgOgRedR5t85c+YA0HI0qmXYSIw8//zzWWxVC9xE8tzQjq5KCuo4k9rVq1cvDsp68sknOTq0
+Zs2anHBkypQpvBsT72DdunU6/gO1V1hYaCuqzyy/op1oVCEEPvtM23/69+8f9dh58+Zh9OjRADQ1
+IRpl3TgX6LNawMbs91h9BUoNyeo9Z2ZmMpv0rrvu4vvfsWMH06JJdTOC5kIUQ/jpRXM+1X0wIlbN
+RzUbEx3r8/n4pcnIyNCF+AKamEoRfsY6iDTBSJeXUvJkLSkp4QI3V1xxBXsG1LYjvTyJiJHlifKw
+0ziJtLQ0Xjjt9tf4TGjhBoBHHnmEXe2VK1dmu9LIkSPx4YcfAigtGWCESpuOAJfm7MKFC/twJYXS
+PpiqDGYibKQxU3NApKWl8WfaUbxeL4vXb775JvMmMjIyWCUgSu3cuXOZQ1C1alXOs3DRRRdxPH1B
+QYFlScCoBlWQ5w6gbDRkRehbLFgl29lFSkqKTgUllaCkpKQMWcqorpKkUFJSEkklOv3Uh1M5ISJd
+28x+YEdvVI9X8z1mZGToirEaoy69Xi+HjterV4/dqT///DOrJXa495F04/KGWWRkRZiDkaCOlepx
+iBXNmcj1VNVVnUdGVVH18AghdHVLIvTNVR9cuHBhHxVGUjD6fcujX9HUhGjeB6O4a2bsE0KwdZlE
+OXXX8fv9USPu1OtmZ2czh0BKaboLmI2XUTqwGl/gxNgbxy2aVBBNfShvacLoPTKSqdRnbfbMzLxP
+VmjNZufRNY1tm1VC93q9/FmtTWmQyFxJwYULF/ZRYRK3AtZ3A5XSDGgro9mOnpaWpqMKk2HIzF8f
+aTU327VUI5PqYzaeZ4y0VI8z/mZMR6fu5idOnDCVDmLptVJK0/qY5ZEVOFbgk2pfMbp/7UiM8doo
+IrlAY1GW1WuYlZiLZWdQ79VMGjVeN1Z/VKNjJNiVsirMohBrMFUxicqd00M5evQoTzCfz8cvgt/v
+1yVfMbPImk0k9cGZqRR2KbyxjvV4PGXEx3A4zP0oKSnRLRp2XgSasHbT0jsJM8+O+iyMfVerQUVq
+z/g50uIcCfGqI2aGRrW/kZ6HqrrReaRepqenM79l//79pguBlWetzqFEnrWrPrhw4UKHCiMpxIIq
+JtHKTMwuNQVbMBhk/3+/fv3w5ZdfAojM8orkhiQRO16XZKxrGH8nlYiupZaKU3da424UK0grlvhZ
+HrAinqtqjpXxMvtcnjDbuaM9C3V+ElT1SWW0qozFeO41UYnwtFkUCOFwGPv27Yt6zKWXXgpAi/yj
+DLx2ogzVB1deCVVoQlE2nRUrVuiurfLarUzC0wlWYjgqMuKJ1VChViFTVUljLk07i2UicNUHFy5c
+6FBheApOsezq1q3LmYgbNWrE0YdmNSErClJSUnDOOecAANcx2Lx5M4vUkcZGrY4diacQK2CqIkCt
+1REp2KeiIZm1P0g9DgQCPH+PHDmScJ0JqzyF0059iIVHHnmE03QXFhbqItCcglMLGInM77zzDrtO
+N23aBEB7iQ8cOABAK3pCi4aUkifKK6+8ErUKkxWbQ3lBjf6kv0lVqMgLVjQka0Ol8fB6vUxpL0+1
+ylUfXLhwoUOFkRScWnWpjiSg7UbxilzR4FRfKVXc2WefzQYlShQSCATw1ltvAQBXqga0lGgUMRkp
+50MypQNKCUZ9N6plJP14vV7uX6VKlZhbQgVjvvnmGx0NnCzxp5rmXBFAY9i2bVvs3LkTALB9+/Zy
+u36FWRQSBbkhSbQG9KyzZBWEjRf169fnTDpAaQJVSny6evVqXcQk3V/nzp05urJFixacyYn+VxEr
+UYxdtGzZkhctmqTnn38+10WoXLkyLr/8cgBa6DfVwtyzZw8nhqFCwYCeCWhGzlLra5gtFGqRV9us
+vZNqpcfjQbVq1QDox7C8LP0ENeS+Zs2aAICXXnoJY8eOBaDZWqIlwrXCirTcF1tHu3Dh4g+PP4yk
+cMEFFwDQxG4y1DhVZToZyM3N5d1hzJgxXB/RLAIuFAqxBJSTk4Pp06cD0PIE0i6twoyOnQjatGkD
+QKusRYYvyoh98OBBlsL27t3L2aHtwixnQSgUMjVCEtGrVatWLHlQFGkk+Hw+7NixAwBQrVo1nVpC
+iW327duHK664AkBpDspQKBQzsjNeEIemSpUqLNF2796d0/i3bNkSkydPBqBJju+//z4AYMaMGQC0
+rM70PEpKSlyeggsXLpKDCsNTSOR8r9fLrjmPx8M6d/fu3bl4hxNViZ0AVY9+8skn2c2UmpoaMQoS
+AGrXro1nn30WgFYx+7XXXgOg7RR0ntNuPbp2WloaV5UuKSnBiy++CKC0oI5Ttho1mCcatVcIgSZN
+mgAA7rnnHowZM4a/px0/EAjgjjvuAAA8/PDDADRJIVJuDJIyxo0bx4VW6DkdOnTIcfsB2QyozF2T
+Jk04s5bX62WJpmHDhrrSgWSMpSzgr7zyCksNR48eteK2/P/DU/jtt9/YcCSl5DqBBw8eZFJMvXr1
+2JBEhVsTedjxWMUDgQBPYgBcvSrSC01i8sCBA9GiRQsA2iS30/94ojk9Hg97PPr06cO/X3bZZUyu
+cnqRpf4Zqb1mfaR6lcOGDePisE2aNOH0dT6fL6qRVUrJ9Rjr1avHqtCHH37IYrxKIFLD3NWFxc5C
+XKOGVj2xTp06nMKd6mBWrswF1VBYWMjPd8yYMRg+fDgATcWgOU7z4txzz8XWrVsBAMuXL2dVItFn
+k7D6IITwCiGWCyFmnvy7kRBikRBioxDiAyFEINFruHDhovzghKTwDwBrAZAv8BkAL0gppwkh3gBw
+A4DXHbhOGZC/nhiMADBhwgQWBwsLCzF48GAAmij2+utaNyiv/u+//86/z549m2srrF27VpdCzSh2
+GkuwWQXVJwQ0w9HPP/8c9Xjqw9dff41PPvkEgObes3NtNZBKNVyaQW2Xzhs6dCjvcpmZmUmnTce6
+t3A4jK+++goAcNttt6Fr164AYrtfP/74Y9518/PzedcdPHgwcy/27dvHY04MzNTUVJ10pBpEraJu
+3bq6GpTGNqSUrP7Onz+f5+fhw4fxzDPPANAkDJKQqJTcjBkz+Dk1btyYn+uqVassJXCJhIRsCkKI
+utDKzT8J4B4AAwDsA1BTShkUQpwD4FEpZZ8ozcRtU5g6dSoAYMiQIfxdlSpVeFGoXr06W+2DwSCr
+D/QwbrnlFiY7LViwgHXnn376if3q6kOM1ydOD2b37t0sKtKks4JEEqSQqKlO6FgJSTweD2644QYA
+2hiR7rtjxw6OUP3pp5+4XSehcg8igcazX79+eOqppwCURpcSyK5EIvr69et190zeqk8//ZTF9dtv
+vx0zZ87UXQOAaQi7nZetXbt2XCAYKI3voHmam5vLY/z++++bqk9+v59tKfRMDx06xH3Pyspi9Sk3
+NzehFO+Jqg8vAhiJ0rJxVQEcllKSxSMPQB2zE4UQNwshlgohlibYBxcuXDiIuNUHIUR/AHullL8I
+IXrZPV9KOR7A+JNtxSUpDBo0iD+rNRTI/1upUiVdKXpjma8XXngBL730EgBNTCSDkrry+3w+pkrH
+G/NPFN/09HQOcrJDwU5EVDfbda2w9T7++GP++9xzzwUAtG/fHsOGDQMA5kpcf/31jgbrWLlX6v8X
+X3zBZeJr1KihK74TrUx8amoqZs+eDUCbL8QWLSkp0RVUof5EMtZalRhnzZqlkyxUtRDQaozSdVu0
+aMHeBeNY0Pwklen777/n84LBoGnOyHiQSIHZpwH8BUAQQCo0m8InAPqgnNQHeskDgQAX3WzatCkP
+yk033cR2gq+++irqQxRKEQ7VAq6KjPFmMaKHtWvXLlZnOnXqxA8/FuxGZaqT2Kxwqx1UrVoVdepo
+wl67du1YzFVdiO3atQMArFmzxnb7RiQzlySNxcSJEzF06FD+nkT7a6+9lglvam5L9SUzo1ub/QaU
+PvctW7bwGAKlagPNMbUYkNfr5Xk9YcIEdkVLKZnURIStvLw8VKlShftCLtko4efJVR+klKOklHWl
+lA0BXAPgWynlUADzAFx18rBhcEvRu3BxWsER8tJJ9WGElLK/EKIxgGkAcgAsB3CdlDKqnByvpKBm
+cKbVtUqVKrwbP/TQQ+jRowcAoEuXLnHtQGbGpXglhfnz53MQVFFREf71r38B0PIpNGrUCEBpROR3
+333HO1RxcbGpSKhGIqo5Jem7UCjERik1j2W8z9zv92PJkiUAwNKBikGDBjGZJl4kU1IgYtLOnTuZ
+FLRjxw40bNgQgH2xO9Z8IJLS5s2bWczftWsX1wIltaVVq1Y8f+k7apeea25uLlPIKe+omojHaEiO
+gPIjL0kp5wOYf/Lz7wC6OtGuCxcuyh+nNc2ZdMH27duza6ZGjRq8urZv3x6dOnUCoNFyja5FtUAM
+YH0HjXc3y8rK4pBjIQT3Q6Ur0+7yySefMKX4l19+KVPIhvphDA1XeRVSyRKt7iROuBE///xzAKVJ
+cgHNz1+joCG8AAAgAElEQVSvXj0AZYvdWEWyqjkDpUFOTZs2Zb1eDT+3i2iSglqf5Ouvv8Zzzz3H
+n4llS3aGvn37svGwY8eO7LZOS0vjtouLi9mYPnHiRADAAw88YPdZ/vFpzhRfHg6HefI3adJE9z0N
+cJs2bXiikq/96quvZsNZUVGRZR6/GvNvNDxFy+wrhOCs0gMHDuQIx0qVKrFoSy/Eli1b0LJlSwAa
+jbt69eoANHG3du3aALSoRBKDydpO9238nJWVxZMqXvKVigEDBgDQaNDkicjJyWGS1dVXXx1XvsVk
+qQ5du3ZlshsAphrHuyBEgmqAzcnJAQCMHj2aN61gMMgeqL179wLQDLTkicrIyGB15rrrrtMtFjQ/
+yRukzjEnN3c3StKFCxc6nNbqA4lnW7du5c8333wzu/quu+46XHWV5ggZMGAAqxIkRu7atQubN28G
+oO1QyU6OWbt2bVx//fUAgNatW3O6tW3btvEupoqcRN9u164dU3QPHz7MbqqlS5fyDhPpOapuVhJb
+8/PzHd2RW7duDQBYuXIlf/fBBx/guuuui9o3MziVFJdAu/WePXtY1VqzZo2podQuYhka6Xq1a9dm
+hmxJSQkbwkn0DwaDLMVWr16djY19+/ZlN+Mrr7zCUi+pPnXr1mUJxCL++OoDRYU98cQTTHe99tpr
++WF06dKF1Yq+ffsyj9xMP7eDeCduSUkJqwFz585lm0hBQQFHuxFSU1PRpYv2/J577jmkp6cDAN54
+4w0sWrSI24sGNWGJeqzTFn5aWEOhEI/9RRddxItQtIzTyQaFIav2IycWBCugl3779u28gNSsWRNX
+X301ALAaOGjQIF5QmzdvzhuEz+djdXP69Om8wW3YsAFA8soWuOqDCxcudDit1QcVFFd/ySWXcGKN
+QCDAu0MyGYRW2gP0wVVGY5+RC3HGGWdg3bp1ADRDJElFHTp04ISoVq5bHsVgSIrZunUrM+z279/P
+OzIZ1KzAKSmGDK/kx+/fvz8WLFiQcLsqYlXHNkNaWhrX9qDzc3JyeJ4a0wmS1+KXX37hpL4k8X79
+9dd2pbA/vvqgYtWqVQA0qzcRRYQQbJ0/FZWHjOXl1eKpas1Ar9fLemZmZiYALQKQ4g9atmzJxBWr
+C0KkvgDOZyKmSVxcXMyTe9++fZZjO4wFdBPF4sWLOWKQwtOdXhCA+MaxoKCAQ58HDhwIQEucQ3M2
+PT2do37XrVvH9P1KlSrxAnHjjTcC0O7JGM/jBFz1wYULFzpUGEkhUZGddpjRo0fjb3/7G4BSIhBQ
+ugPH6oP6P+DMCqymOSORMSUlhb/v3LkzR3xS9N748eNZLM/MzGT1IZFxSpaqSHH+fr+fuRDvvPMO
+f1ZhFqzllGrz5z//GQDYQAuAE5MkA/GOJxl9idOxadMmNoieOHHCdNwOHz7MyWUoCUuXLl3YQHno
+0CFWlRI1QFYYm4KTFnHKpDN79mx+AM2bNy9j4S8vkAckKysLr7zyCgBtISDd8vPPP8d3330HAKZh
+s0IIdlWWlJTwPanPTmW2JfPFMyIQCLBbzO/349dffwWgkYXMJmcsN14sAlg00MKpxg8kWhDHaftS
+IiDPDtkWdu7cyQvF5s2b2b5w/PhxnS3JboFZV31w4cKFDhVGfXByNZ43bx4ALZszWcCpJt+pABFT
+Dhw4wCSkTp06cYbmCRMmRD1fSsm7sdGjECltudnnZKBVq1a8G+fl5fHOFSn2wU5WaTtSgsfj0UkI
+33//veVzo8HY31NZ25KkQUo/n5qaymnlQqEQG9vV3BkqV8UqXEnBhQsXOlQYm4KT7REdtGbNmkxp
+TqafviLgVO5gDzzwAABNMiPj2cGDB8ulAA/d986dOznrNFBaU8MpiaEiokaNGrjmmmsAAHPmzOFA
+QAr4M8KqTaHCLApOGnTUcOGKUhmqvGBlHJ02npEBLD09nXkKJSUlSY8lAUrJSXl5ecxN2LFjB8dj
+WOVKnI7w+/18n9u2beOYiGAwqEu0ozxr19DowoUL+/hDSgq0e4TD4ZhqgxVOQixj3qmE0eUWLUFr
+tArUkXJEWIHKzCRmnpr+LZ6M0tRurGS7lIeiY8eO7JLbsGEDi9KR0pWZpS6LND5mxyQyXomCrpua
+mmrqclVdwYZ7Pb3UB8q4Y2Wgk/UwKKqvqKgoqgXcSsESFZEmklr/0nhPgUBAl2bcrES9lftXeQpW
+OAL0f6SXRq2xCOi9DGof48k+Tf2NR+VTrezqeKpxJ2YZmtXPkRaISKnTjXPEmM8zUntmFcfoGkKI
+iGqX8Z6MfTTjrxjgqg8uXLiwjwrDU6BYfDvFQJyEukLH2q3i9WSoDD3jzkHfUwBXUVFRxOy8du4/
+Hnag3++PmKuB+k2SQnZ2NkckhkIhnThrJm2ZqWvq/ViRaMz6o4rJkXZlNfN1rAAsddzoXgsKCqJG
+tqqfjXPITPKg+aZKFdHmnXG8VM4KUKoqWU0rGAkVZlEATq2eZpyYVo+107ZxQpmJiU5ay41iaizK
+s5qQxeoCUlRUxO3SZKdrmI1TLNVFnQNWEOka1Ib6ksWbz9CsclgkRBpjs7yZxj7bhZX5FA8SUh+E
+EJWFENOFEOuEEGuFEOcIIXKEEHOEEBtO/l/FkZ66cOGiXJCoTeElALOllC0BtIdWkv4BAN9IKZsB
++Obk35ZAu8upMn56vV54vV5bO1VFhlH6sTq20aQE4n6kp6cjPT0dAwcOhM/n4wQyVjw+kfpq9A7E
++xz8fj+ysrKQlZWF7OxsZGdno1q1asjIyNBRoa2Acnda2YV9Ph/fv505TGJ/PKCxUv8likQKzFYC
+cD6Av57sXDGAYiHEZQB6nTxsIrQiMffHao90sFPpDfkjEl3MdG4nMG7cOAAaYYkiO5cudbaAeLz9
+rVSpEm666SYA4LyG7du355obzZs3T1jvBsqqCqqtwgrIVkHRnUCpTYlA7W3evBlt27YFUKrOqKqR
+k0SxRCSFRgD2AZgghFguhPivECIDQA0pJSXT3w2ghtnJwi1F78JFhUQihkYfgE4A/i6lXCSEeAkG
+VUFKKSPFNUgHStHHghCC07p/+umnvDJfccUVALS0XU7sGHb7BGi7GYmN7733Hnbv3g0AeOmllwBo
+6eWc2NmdlrworoRqdFauXJkL6jgtKdgFGTqbNm2Kjh07AihNPw8A9evXB6Cl9qf0+WoRHbuIZ2yF
+EJxmjxLrGH83Q+PGjTn5CqXka9asWVJo/IlICnkA8qSUi07+PR3aIrFHCFELAE7+bz1rpwsXLk45
+EmI0CiG+A3CjlHK9EOJRAGTFOSClHCuEeABAjpRyZIx2HKU5U7TcjTfeiMceewyAng5M18nLy8Pz
+zz8PAHj55Zcd31Vp1aesSStWrOBai6r7zgzhcJh1zRMnTnD+/wkTJrBuHGuXSEayVqpj+Je//IW/
+o5qI8ZZgU9PUxasbq4lwL7jgAvTu3RsAcPHFFwPQbAo05uFwmEv21axZM+l2LCEE5syZAwDo1atX
+wtmgCM2bN8fGjRvtnGKJ0WhqvbT6D0AHAEsBrALwKYAqAKpC8zpsADAX2qIQqx2Z6L+TC4s877zz
+ZEFBgSwoKJChUEiGw2EZDoflsWPH5KFDh+ShQ4fk9u3b5fbt22VJSYkMhUIyFArJCy64IOE+GP91
+69ZNduvWTZ44cUKeOHGC+5Lov2PHjsljx47JLl26yO7du8vu3bvz/Xs8Hun3+6Xf75cpKSn8PQDp
+9Xql1+tN6J42btwoN27cyH0JBoO6a8Tzj/rl9XoTaiczM1NmZmbK66+/Xl588cXy4osvlnXr1pV1
+69aV999/v1yyZIlcsmSJLCws5Od+4MABmZqaKlNTU+Oec7GOmzJlStTnOXbsWOnz+fifWbtPPPFE
+mfNWr15tt89LrbzXCZGXpJQrAJitPBcm0q4LFy5OHSpMQFSibXz00UcANOONSiOlwjATJkxgkbFn
+z54AgEmTJnHG58mTJ3OdR6fGhNKsDRs2LOpxkQJnYiEUCvHxpF489dRTnGRDCMERc04lmVm2bBmA
+0uI7x44dK1Mx2y5Uum4irlMSy7Oysli1Ijezev9PPPEEV50WQrBKRFnA7fSb+mwGci8eO3aMjdxA
+afEcO1mXhRCm6qJNVeT/TzGYli1bsu7o8Xi4KOeQIUMwd+5cAPoHRwVCdu3axQVIs7KyynDSE8Ul
+l1yi+zscDvPLe9ttt+n6RNcePHgwAOCFF17gupPqcXv37sUZZ5wBQB+12L17dwBaLc2XX34ZgD58
+2QlkZGRwIhOq+nTo0CEOl4533CLFLdgFvfhFRUXsVaIXU+3bt99+i5EjS81cVJvTaVx77bW6PgCa
+/SWeFOyBQKDMGCUriY0bJenChQsdTmtJgar2fvTRR0xfXb9+Pe677z4AwMKFC3U7JXkBqEBIy5Yt
+eUe55ZZbHPX5CiE4PwN5EUaOHMnl34yga3/55ZcAgBkzZrBE8/3333OW5OXLl6N9+/YAgAsvvJDv
+e/Xq1QA0lcgsACsR7w7dx8svv8zl96jMXV5eHv7+978D0ArYkMRz+PBhy9W9nfaSFBUVsfRCklRq
+air3rVGjRpyQJSMjAx988EFc14nV17vuuos/f/rppwDi99Ds37+/jBR15MiRpAQQntaLwoEDBwAA
+//znP/Haa68BAHJzc9G8eXMAWgFOKnialZXFSUWJ0CKEQKtWrQAAe/bscbRvrVu35kWISCdW6hmS
+eF69enVOOvvSSy8xyWbPnj1c1Obzzz8vQ7VVw7AjJfqwM4HOOOMMrF+/HoC2ANC5VNGoU6dOaNmy
+JQCgbdu2TBb75JNPmGTz0Ucfcf5AVfQl8pYqBjtF2KLFntTDoqIiHfWZxm3FihWsbjoBNQGKmifx
+qaeest2Wz+djOnlGRgaPDY3X0KFDeVM4fvy4YwuDqz64cOFCh9NaUiDL8jfffIM2bdoA0ERYKpYx
+fPhwLs12/vnncz1JWmlvu+02/P7770npW+/evXl3JMNS9erV2TNw8OBBFnHVxCJvv/02AG2HS0tL
+AwDUrVuX70OFlLKMymOMsbdj1aedrVKlSpg6dSoAzYBJATj//e9/WbIiw+7cuXN1CWLIsBsIBJjU
+lJKSwsVsqL+qNT0rK4ulKacCt6jtWrVqAQDOOussneoza9YsAJramKi3wxjdCWhjQbUthw4dapkC
+7vP5uNT83XffbXrMv//9bwDaPZIad+LECTZoRirEYxWn9aKggsTFxYsXs2uxcuXKLNqqL8gLL7wA
+QIs5cBp0jVWrVvFLT2Ky+gLt3r2bJ8qSJUu4sCwVa83JyeH6kvPnz0/Ism/WR+P3Ho+HX+JffvmF
+1a6SkhJmUObm5nKZdDUykMZz8+bN/PLHihhUX6Djx4/HnQAlFmg8r7vuOlYbCwoKuJoSqaDxwJgr
+UV2gQ6EQ8vLyAGhuYpoLaiQuqZc9e/bEZ599pmvLDDQ2dF7VqlU5FuXgwYOORfm66oMLFy50+MNI
+CrTC3n777boMvoRwOIyffvoJADgeIpnErUcffZSt3SpxhVC/fn2O2rv88svZEKXuOkQUcirpi5mh
+Uc25OGXKFABAWloai8ZCCPZsHD58mA1bjRs3BqBJBBThefDgQVtjGskg6iRIOjjnnHN4Phw8eBDf
+fvttwm2bEcLUvJTk4bjyyiuZs/Lss89ixowZunMaNWoU81rBYJDJd0SGW7JkCRtSc3NzXUOjCxcu
+koM/jKRAvvSMjAw2tFB6NUDTjR999FEAiRtiooF2o6pVq8aMhCSYSQJer5f9/ykpKXjuuecAaAxC
+J+0L9F1OTg4aNmxY5vtjx47hnHPOAQA0bNiQbQ2kv6amprK7NN6aDSpd2ympgdoj+5IqrX322Wcs
+NToB6q+ajTwtLY2vOWDAADRo0ACAxlWhSFmzNGxSSnZFd+/ence5qKgIl112GQDgjTfeAAA8/PDD
+bMyePHkyf08SSrz4QywKaWlpeOihhwBoRjkydrVq1YoLjQKlJeoTRSSyjRCCDUpqHUuaKEeOHOE4
+ASsqARmRRo4ciREjRgAAjh49iv79+wMAVq5cyQsPTQQ1pZ36gsV60Xw+H3MPqlatyn2uXLkyi6h5
+eXnMtaAS6FJKTmiyYsUKtvCr913eEEKwVZ44Fk2aNMHixYsBaCqmE0Q1o/dBbbOgoIDHYvbs2di8
+eTMAoFq1ahgwYACAUmp7RkYGexRGjx4d8VlRchZS55577jlWj3w+n2Mh2a764MKFCx1Oa0mBRNn7
+7rsPQ4cOBQB89dVXLGq/9tprvHru2bMnZt0Dq4gmKVx++eXcN+InbNu2DQDw7rvvsuv0nHPOQa9e
+vQCAA5yiQY0AJMps79692fC3bt06vi65ED0ej2VVw+/367gEakk7ouZ+9dVX7OJTg3xISps6dSp+
++eUXAJraQZKHlYS8TgREEYQQHIz0pz/9CYBmqKNAMadS8JkV9iGoLvBvv/2Wg9tq1KiBX3/9FUAp
+03X27Nmchs+K6rRz504AGveCJCInJYXTclEgq/306dMBaNZbEtUee+wxJgitWbMG/fr1A6CJaE7G
+NkSyPJ933nkANLGbbBckGi5atIgt9VWrVuXF6/vvv+eXkM7x+XymNQM9Hg+rFcOHD8eKFSsAgMlN
+NGEAay8jYcuWLSzWpqSkMNErEAiwzSA/P58zCpNa1qBBA752dnY23/9bb70V13g7YU/weDys0lB7
++/btY0JWvIhUwSzSJkPPbfjw4WzPUhdZsjkMHjzY1n1feKGWrkTdTP7xj3/g/fffB6CR5dS4E7v0
+dld9cOHChQ6nnaRQp04dlhCIrbh06VIWW48cOcKi7TXXXMOr5Ny5cx3z45qxAQFNtKede/DgwSym
+fv/99wA0FYaMS1u3bmWjY58+fdCuXTsA4B26QYMGLBH16NGDvSu//fYbi/nPPPMMW6cpqMdYs9Cq
+qqQaRouKivgaRqxcuZKPoeuRpDBlypS4GYJORvt5vV4eT2qPImPjgVoWj9qz4mmhZ/3YY4/pRPtq
+1arx94BGf4803mpbxGT88MMPAejH6vjx45wd+s033yxjBDV+jobTblEQQrDFlSb8Dz/8gLFjxwLQ
+xG+i6+bk5PBAPP744471QS1FL6Xkfhw+fJjdQk2bNuV+UmKVadOmMfV1yJAhHNk5bNgwjnkgL0LP
+nj05zuD48eNsnygsLETfvn35e+NLrz74ZFj/qX2K5lTtD4kuCE6hcuXKrD4QUYhiHezC4/GYJt9R
+3aiRXjbaFDZu3MiRu4A2X4HSkPrHH3+cPUqBQIAXiB9//JFjeurUqcObHV1v586dbM/58ccfmdyk
+ep08Hg97xMiGEfOeLR3lwoWL/zc47SSFUCjEkY2Uyn3FihUszkop8fnnnwPQVkkid5DF1ynQ7mGM
+VKTd47fffsPtt98OoHTH6N69Ox8bDAZ598jOzubiJCT2NW/enHfgwsJCNiC+//77ZfgPkZCMmpjU
+J/L8BAIBpjzbJR6ZibiJgJ6Jmmimc+fOAGKnw48EIYRpvgc79zp27Fi89dZbADSDLhkaSTro06eP
+qXoxcOBA0/Zox+/WrRv279/P/aH34fjx46xuFhcX2773025ROHDgAFvtH3nkEf6fXJIvvfQSu3+K
+i4uZBeYEVF57JFIQ/b106VIsX74cANCli5YrkwqxApqFXy12SuKuCnrp09PT2aMyceJEHUEoGpIR
+T1C3bl0A0PWd7Bl+vz8mW5QmaygU0kVGqglJ4kFqaionk+3VqxeP85o1awDEz5Q0hnLHs9AuXryY
+VaudO3fyIkN5Na26EmlzocTDqqcJKHV9A6WLt9frtc3gTbQU/d1CiF+FEGuEEFOFEKlCiEZCiEVC
+iI1CiA+EEGWjgVy4cFFhkUjV6ToA7gJwppSyQAjxIYBrAPQD8IKUcpoQ4g0ANwB43ZHeQqM0U75C
+Igr17t2bjXodOnTA5MmTAWgcceKROwEroiN9v2bNGrZ4Uxr5Dz74gMkmxnPMdiAyRE2bNg2vvvoq
+AMS0Upv1xUnQLqfu8qSiRSNKGXMOlJSUxJ2b0czAFwqF2G9fUlKCJUuWAABLkHbHQvUo0ZjHyjod
+6X6Ki4s5N8bbb7/NPANV2ooEOu+OO+7g6t6R1EaVnEfHqFKtVSRqaPQBSBNC+ACkA9gFoDe0upKA
+Voq+bBVNFy5cVFgkWkvyHwCeBFAA4GsA/wDws5Sy6cnf6wGYJaVsY3LuzQBuPvln52juHY/Hw6nJ
+CgsL+RjSmx555BEMHz4cgGbg69OnDwDn8uKbJUdNdBdWo/ZCoRDr6uR6POuss/Djjz8C0OL/46Fo
+JyNPARnzKPpw6tSpOiNvvEiUp6AGQal1OBOFE2Po8XhMnxu5ClNSUliaPHDgADNI/X6/I9mUlLG1
+VAwm7kVBCFEFwMcAhgA4DOAjaBLCo1YWBUNbUu38yRvgvz0ej2kePCP9F4jfUOUiNrxeLxtxKfu1
+XS6E+vLHihmwMjfp2KysLE75n5qayiSyZCIZ6dWTDEuLQiLqw0UANksp90kpSwD8D0B3AJVPqhMA
+UBfAjgSu4cKFi3JGIi7JbQC6CSHSoakPF0KrQD0PwFUApgEYBuAzO42a0TJjscdUVuH/N5A4Xx4S
+UigUYvejE+Mdj8+fjieQhKgmmE0GP4PajYc2XF6I9I7YlWgStSk8Bk19CAJYDuBGAHWgLQg5J7+7
+TkoZVTESWult+szfm008j8fDtgTVAk4vh9/v5++Li4t1Vu9E9FW6zqmGUcUyy+6kjpvliaBMeONL
+pz4b1aptbF8tgBKpP2RLCQaDESNNzfphlqpera8YCAR4LFJSUlgXJ05HvOHSRntAIjEFyYbaN7M5
+a9WmkGgp+kcAPGL4+ncAXRNp14ULF6cOFaYUvc/nQzAYTDgRirpb+Xw+08Al4/H0OyGS+EVQI+Ss
+iL6RJA1V4rGaNs3Yj1jnxaNeWGlXNQKrAUNmwUNmRkUrXA+rfTW2pxbXMTNsRtpJIxmuyfOlMgND
+oVBcxu1EvBnGPqtRm2qQXpTkOqdXKXqKtktkMQCgE6eFEByKLKXUkVAIZpPCKKIbH2I4HLb1spld
+w+v1MuW3oKAgrvsOh8Mx4wecWLzUBYBAn/1+Py9uRUVFukXB7HoqzZlEeuP4xHphCdFsSWaLWiyR
+3+z31NRUDmHfunWrzkVI13Yio5cdFZWuZ3weFEWZqBvTjZJ04cKFDhVGUiDEK1rRearYlJ2dzTvT
+/v37Y7ZNv6vicyTDV7zWfvUaqmoTL6zuTlauQcf4/X7dOKqBYLQbmZWZLykpMd39Vai7qhXVzam8
+mvFC9WqoxlF1jtjpWyIqk1H68Xg8OmmBnkminqgKtSg47Vbr378/88xvvPHGuNow06+dsMOo9+rz
++ZJai8IujJZ6ddKb9VP1KNDEVHVcoHRhsZrog6DaDKgv5WEHU93h5MEwqjanyh6netpo06tVqxYT
+yhKpjwm46oMLFy4MqBCSghACPp/PsdTbKvWVqjnbWdVVAx5QujLTjmh3tzNDKBTiKLlQKFShJAU7
+8Pv9nG5uwoQJ+PrrrwFElzaiwWgQdULFSgSJEoGSATLs1qpViz0jeXl5HDeTKFxJwYULFzpUCEnB
+aVBGm61bt3LEmd2ISTUSk86l3S9S1JtdULtnnHEG64aJ6oORkIyISUCTACiV3JNPPhl3gtRIMOuz
+KrnR7piSksJJTidOnMhFaygR7qBBg7Bw4cKIbVpBamoqX6+8JYXMzEyWmtTcIVRwx8l0gxViUZBS
+Ohrm/PDDDwPQMvhSVaBYEELwZFONZBkZGWUs7U6pOaSODB8+HFu2bAEALnyam5sbceGhfqoqTqw+
+GQlETk3qjh07cvbsuXPnOuolMFt8q1SpwmnHrCQpoTGeN28eq2g5OTmWU9qZWfcBffr8ZC0QXq+X
+Iz+vuuoqrulJxYCOHz+Od9991/HruuqDCxcudKgQkgIhEZcP7VbvvfceOnXqBEBLZUW7aqQdjDLn
+tmzZkj/fd999uP/++wFoO7Za0Zn6lijq1q3Lonbz5s1551IptRs2bODvSL349ddf8c477wDQKmwf
+OXIk6nVisQPjvRfaQa+44gp88cUXAMBjlgjUvqmJbolVOG/ePEsSghmorUmTJuGaa64BoElYlOyk
+qKiozHip81CdQyrN2WlJgfrTu3dvTr6Tk5ODc889F0Cpevzll18mhb9RYRYFWhDMCETGG6eHQanF
+R40ahSuvvJJ/mzt3LgBNPyd9t2PHjvj73/8OAJwifvXq1TjzzDMBaAVfaVEoLCzkuooPPvigjl/u
+FCZNmsT9Ly4uRnp6uu5eMzMzOcOzev85OTmYM2cOAK22o50070742KkNekm3b9+OF154AUBp4pVE
+oI6xx+NhtYhe4o0bN3Iqc6D0hTx48CDnZRwzZgwXRqFM2rt378Znn2lR/P3792d17dZbb+XqYrt3
+745aXCeZoDk9btw4XmQ9Hg9nZJJSss2ExqR9+/ZJWRRc9cGFCxc6VBhJAdCrD16v1zSwo0qVKly2
++6qrrgKgiVtkBDp27BjzCLZv346ZM2cC0HYSqi9I5djatGmDxYsXA9DEQTJKeTwezqLr8/m4H06s
+yhdccAEA4Nxzz+XdIT8/n1UC+r1Ro0YsPVD/AGDZsmVYvXo1AGsGz1iBP3ZBfaIciD179sTrrzuW
+rFtXfEWtDUF1F5944glb9OJ169YB0J7pzz//DECrC1GrVi0AWs1PMu7afb6JShH0/Js1a8blBqtW
+rYqPP/4YgFa3k4yqrVq14nEhNWjKlCkJXT8SKsyiYIx4y8jI4Env9XpZzxo0aBCndqcFpKSkBJs3
+bwYA/OUvf8HatWu5HRK5qlevznaHIUOGANBKwy9atAiANgGvu+46ABohh5K/fvHFF44RVnJycli1
+UROWTJ8+HfPnzwcAfPTRRwCAK6+8kpPRpqencx8aNGgQ9+LkxCSmBZVeUlXNcwKRkq+otON4EA6H
+WZOV1lQAACAASURBVBdXx/68887jKkt2YYwetdo3qsNJafsHDRrE6syqVav4pW/fvj2nzx81ahSn
+sKex+Pbbb+Pqdyy46oMLFy50qDCSAlGdybKsWoUDgQD69esHQFtdaYWmkmCjR4/mVTMS32HLli28
+GlPqdNUPPmLECLRt2xaAZpyiwhvGhCqEeHbH+fPn69ogY9iyZcswb948AKU+6OXLl2PixIkAgL/9
+7W9sgJsyZQpWrVpl+ZpqIFG8oMjIrKwsLllGNRqjqTB0bTpfPd6JBCtWQGnfp0+fzuphKBTibM/n
+n39+3G0bx9YKqS0QCLARm8rc7d+/Hxs3bgQALFmyhEsEvv7665wDpGbNmjy36ViSLp1GhVgUhBAI
+BAL8D4Aub7/P52PPgdfr5UGjB0ouQ6swE0WPHTvGngqv18tRlW+++WaZ8+wSV8jD0bJlSybQDBw4
+EHv37gWgLQS0GJCK89e//pUXKaBUj8zJyYnphjS710SgJk4hy/ill14KAFiwYIHO7Uvj4vV6eTyb
+Nm2KvLw8AKX1Do8eParL9+hEslVq49Zbb8WIESMAaLYZI3bv3o1bb70VABKKFzCGdUdbEOj5tWvX
+judr7969AWhzj36/5ZZbuDCx3+/nQr75+fk8dn379gWQPM+Iqz64cOFChwohKXg8HqSkpKC4uJjF
+S5Uc8uKLL7K46vf7Wfy3KyHEArUnhOAdW5UKCGpMf7SYgm7dugEAc+6FEGypX7RoEYuDffv2ZQMe
+7R6NGjVCVlYWAI3HQNz2ESNGlDvvXlUfaIw+/fRTAMCGDRvQoEEDANr9kRGtXbt23P9Dhw5xhWRj
+LUqCWSIbuyBD4osvvqhTWQgkGebm5qJq1aq668YDOyqZOobr168HUKo+Sil1noirr74agD62Y/Pm
+zbjiiisAIG7DqFW4koILFy50iJnNWQjxDoD+APbKk+XfhBA5AD4A0BDAFgCDpZSHhLbMvwSt8vQJ
+AH+VUi6L2QkhpFnaLdo1tm7dynTPI0eO8CrvJJurcuXKHKEohGBjzplnnsm7G/XH5/OxHl1SUmJq
+XwgEAlw4hQymUkp2e9apU4eZdFOnTuV7onZ3797N3wkhWIefN29e0gNxVAghWBI4fvw4czYuueQS
+AJrb7KKLLgIA1KtXj20mixcvZuZl27Zt2VY0btw4ANrOR3wS9T5Uw64d+Hw+joxctWoVj6OZ5HHi
+xAnemS+77DJ2E9uFneS9ZDNo06YN3+/WrVsBaO53cjcuWbKE25VSsm3toosuwtKlS+PqpwLHsjm/
+C+BVAO8p3z0A4Bsp5VghxAMn/74fwCUAmp38dza0EvRnW+2x8SWnB6rSWpcsWeJ4JB4AfP755zoR
+9pFHtHIWPp+vDFkmNTW1TApzI15++WWeCGpBkssuuwyAFi599tna0BQXF/PDpwlRrVo1/lxQUMDU
+7PKulSml5LiLhg0b4u677wYAjthr3749h+8uXLiQF9Mvv/ySF5CFCxeW8VLk5+c7WskpGAyyWJ6W
+lsbPlQx1EydORI8ePQBoizQ9m/Hjx/NiYneRtXM8zZe1a9fiP//5D4BSY3qPHj2YIl5SUqLjPZCq
+vGnTJlt9SwQx1Qcp5UIABw1fXwatzDygLzd/GYD3pIafodWVrOVUZ124cJF8xGtorCGl3HXy824A
+tJXXAbBdOS7v5He7YIDQl6I33TXMmITkz3cCVatWxb333gsAaNKkCYv7a9euZd5DSUkJ94PUgGAw
+yGJyJOnms88+4yCtV155BQDw9ttvs5EoHA7zrq+moiOD41dffcUipdfr5d34VOCOO+4AoJWfpzGi
+++jQoQNHdqpMUiD6TprMxKeqa5Tc15deeinT3L/77juuB9KwYUM2+JFUYRXxSDrBYBDPPfccgNL5
+NGfOHFbRateujbPOOguAXs2hZEHlgYS9D1JKKU6Wkrd53ngA44HSUvRG8gd9JlEPAK6++mo8/fTT
+lq6hxlIMGzaMvQHE3+/Tpw9by4uLi9mi/ssvv7Bol5WVxZ/pxVU9DsaJTX8vWbKEYzNiZfxR8zPS
+izV+/Hg89NBDAICnnnoq4QIfiYDiB9LT05lcNnr0aAAaKSzRAj6xirQ4BVpY165dy94sj8fDc+D5
+55/Hgw8+CMAafyGevoZCIeYb0Pm5ubk8Fps3b2a7S/369VndLE/E633YQ2rByf/3nvx+B4B6ynFu
+KXoXLk4zxCspzIBWZn4s9OXmZwC4UwgxDZqB8YiiZkRFNFbb4cOH2bDVoUMHtGzZEkCpCHvw4EEW
+B4UQHEVYtWpVljJUg6HZzqSKnEeOHGHRvrCwkC3ntHsYC8So7ZE/+sSJE1iwYIGVW9eBjIv33Xcf
+f542bZrtdpwEFUNRPQPEbEzE6GsWSJQsSSErK4slxH//+9/sMbnhhht4jgwdOpSD7Yg1ePjwYVYv
+gLI1MuNBtMjVrl27sgq5adOmUyIhWnFJTgXQC0A1AHugVZn+FMCHAOoD2ArNJXnwpEvyVQB9obkk
+/yaljOlHIfXBWBCWFombb77ZNDyXJqSqJhhB7aml6ImAEw6HmT+emZmJMWPGANBiKsyoxGbirqra
+qBGD9evX58hNOx4DsmX06tWLJwTp7KcK5BrduXMnjx2FHsdLIBNCsFejuLjYdKH2er2s59PLeOjQ
+IV2Vqljzl9Sd22+/nZ/VxIkT2TZSv359prK3a9eONxdSGVNTUzF+/HgAWswMpbA/evSoo8WB6J5y
+c3PZvjBx4kT87W9/S7htBc64JKWUf47w04Umx0oAd8TumwsXLioqKgTNGSiNcTfbMd5880107doV
+AHQrp5pp13gOoBGAyANwxx13MNW2evXqADSLNKU8Gz58OKfziiSymUVJqv53VWK56aabOFkGkU6i
+idqUCk6N2iO/+qmEEAIXX3wxAG28X3vtNQCJSQiANn5q8hq14rcafaiqbIAW9Th27FgAWhISUgOu
+v/56bo8C7ADtOQAaUYgS6hw/fpyNuxs3bmRV4bvvvkOLFi0AlFbHDgQCTDtevXq1LpO2k5wRotXn
+5+ezp4H6Xt6oMIsCFcuM9OLceeedALSEp99//z2A0kELh8MsZr755pucxUa1jC9duhTt27cHoHki
+AM3SS6xCNR9gJKjRgmZQVZ7jx4/jm2++AVDqUXjsscdYF1dRo0YNdvvRYnPs2DHW5U8FyF121lln
+4a233gIAzJ49mz0i8SJSqXozG40QokydhZSUFLYZ3X333fzyHj16lBOS5Obm8nn16ml27xkzZjCx
+yufzce7D7du3czTqqlWr0Lx5cwDgDaRatWp8/59//rmOhZlonktVTRo4cCAALaKUNj6nyh7YhRv7
+4MKFCx1iGhrLpRNCyGRVMFJBuxSJ5W3atGGL9GeffcaSwo4dO3QqhJG8FM2HTcdOnTqVeQqqaEyr
+f35+PhuX0tPTddWnAC2L9LPPPhvznhJNFUf31Lp1azaMqpwNNV1ZIqngCHTPkaQyY/ZpY/Sk3+/H
+n/70JwBaijLa5WOlfQ+Hw2w83rRpE7fbsmVLbjs3N5fpxJMmTQKgPSfK7VhcXOxo9Wvq88qVK9m4
+KKXUJRpyAsocsWRodCUFFy5c6PD/SlJQrgdA0xfJIFW3bl3W4Y8fP85psFQ7Ae1yVipEZ2VlcRJW
+MiK1aNFCF71H7WzevJmzOdPOd/vtt7OOHAk+ny9m1p9YBUuoP927d2f/eJcuXTB48GAAmsGNjJ+0
+YyaCWJKNOg/8fj9/JresanysW7cuB5sNHjyYbUxnnnmmLtKQ/lclNjWFGklv7733Hl588UUA4ExR
+J06cMN2xnZivZPDOy8vj/vTu3ZuDoJyCXUmhwiwKp7oPPp9P99KrlmV1MgH2yqoDpS9mnTp1OC/f
+Cy+8gFtuuQUA2CAJlE7+goKCmJNOCBEzk7DqoYnmV09JSWERdty4cUwQ+/DDDzk+pDwQKQ+mWiSG
++AZGfoMZSD3817/+heuvvx6Axrug8VqzZg17nSZNmsQeCic5CJFAqtvhw4c5CpaKxzoFw3i66oML
+Fy7iALlWTuU/APJU/0tNTZU+n0/6fD7d9yelGP6s/h3PP2ojNTWVP3u9Xv4cCARkIBCQfr/fkftS
+r+HxeKTH4zG9B6/XKzMzM2VmZqYcN26cPHbsmDx27Ji84IILTvmzUf8FAgFZqVIlWalSJVmzZk2+
+p3jHhcbG6/VKn8/H418e95KSkiJTUlLk6tWrZYcOHWSHDh3iuher9wpgqZX3scLwFABn9LR4EYmw
+5GQEn3p/qp/eWAQH0HRZsndYsWHYgdl9hEIhHoNJkyZxrAnlhqwoKCkp0RGLVLXCjmdEHQOVLq/a
+Gqy2Ga8HiJ5r7969baWJt4N4wrtd9cGFCxc6VBhJwUxKMBr4AGfzMqpQrdOpqak6yqzR6GT0pdOu
+kpqaygYsY4lzoGw0oEqZJZBlXU28EggEdPUUokkOkXYrlUqs7q7qvZFkIqXE22+/DUCTaBLlQsRC
+JAnRbJfzeDx8/4WFhSxZqRW4Y7Vn3I3VcSFjMz0zr9erKwYULQeEXUmXjj148CAaNmwIQGNgkufr
+wIEDOi9YPIhH+q4w3gc7YpPxwRhJLpGyONHLR5Nf5e+rUXuq5d9sfIwTxc6kUPtu5iJTj6OXNyMj
+g18EKSVf2+PxxAytJUt9MBjUicSxxtDpeWGMU/F6vTrCVrzis9mzjrXAqNGske7TzGtjDO83en6s
+Pn+zhYW+8/l8rLqpxY2PHj3Kx1hRdSPA9T64cOHCPiqM+hAOh23vEpGMQaoYqYJIKmaJMtTgG7Od
+W4VaqEbd8aMRcggkjaj3akabVvtQUFCgux7tTFYCZuhYr9cbVRJw0qBKUCUh6j+pVKqUpo61lfE0
+u4YKM0lAjWqkuRbtGvS9qsYZj1cltlj9VVVT43nFxcWstng8HpxzzjkAgP79+zNhbMKECWXUV6MK
+pEqFicCVFFy4cKFDhZEUAOu7RDR9MNKOF2vXkVLackPZbRvQdh0K8W7Tpg1Xtt65c2eZXUCVFEKh
+UMJuyVAoFJd7KhGoz4nsI8ZQ6Ejn2IU6Xuo8MrOjqNeJZAegXTctLc00Ya+ZAToa6JiioiKWFsk2
+IKXka1SuXJmNvD6fD5999hn3P9q8FEKUkUCM9211bCvcouA0okXlGdUVVdSOlUDDTl/VF53SfA0d
+OhRPPfUUAC1nAcX3d+rUCYAWu08cgUQqIydDLbALn8/HVbWpD1SNK1FEelHMFll6GdUXJRgM6lQa
+YwbxjIwM9gCo7cU7ruFwmOM1zPpbv359ZGdnA9Dm58qVK6Pep9qu2paZ584qXPXBhQsXOlQYl2Si
+bVBpuSNHjrCh5r333kPt2rX5GNqdqPTZpk2bsHz5cgDWgmuU/ia861auXBm33norAC1zs1khEhIv
+hwwZgs8//xyA/d3ejkrkNEj6GTVqFEegTpgwAYD2LMzce07kKTCD1+vlPBrZ2dns9svKyuKoxG3b
+tnFmJdXobBYcp0qZTvSX2s3Ly+OkuMFgkAO6EjEenrZRkvG8aKSnXnrppfjwww8BlHIQokG13r//
+/vsAtIzR5bkoAJpdAQCWL1+uIxQZEQqFuNJRvXr1bOUGJDG4PNKoA6Uvd9euXTn6s6SkhIvN/uUv
+fwGgLXixCrQ62c9AIIBrr70WgKa60cs/e/Zs9OzZE4CWAo/yaVIou6rLG20KTvaT2lNrSUop+fk5
+NBYuT8GFCxf2EdPQKMxL0f8HwAAAxQA2QavvcPjkb6MA3AAgBOAuKeVXVjoSj+GOEmLceuutUXda
+I+hYr9fLqb28Xm+5Jso888wzcdttt+n6o0I1Fnm9XhbF8/PzuTaBFYnBrI6Gk0hLS+McADfccANn
+z87KymIjr8/nYzXOjG+RTMmF1MqRI0dyVuZFixZxqrvff/+dxfUBAwZwPgsyBs6cOZNrRCRrDAHo
+eAqEVatWnRLjcLyl6OcAGCWlDAohngEwCsD9QogzAVwDoDWA2gDmCiGaSykdrZ9OD4YyGhktwerf
+ZEeYNm0aunTRJCf63+/3c2p1u4tSvA+L0qXPmjXLNKaDMj8//PDDaNq0KQDgmWee4WNTUlLQqFEj
+AODsxNFgRqVNZKLRpKXit0uXLmXPAum/dC0S0efMmYPnn38egHnchQqnVDOqD0mFXLp06cKL0Btv
+vIFly5YB0BbN3bt3A9DUOcr4TYvJqlWrOLejXTekHVApehUXXHCBo9ewirhK0Uspv5ZS0rb6M7Sa
+kYBWin6alLJISrkZwEYAXR3srwsXLpIMJ3gKwwF8cPJzHWiLBIFK0ScFlHH4hx9+YBHv119/xapV
+qwCUDXi65557AABnn3227nvAXmm3eHHjjTfi1VdfBaAXEwsKCnDJJZcAANe0UL0Fa9aswZdffsl/
+P/nkkwA0r0QsqNKBEx4IylBN0YmBQEAXXUnX27NnD7Zs2QJAy58YK5ckwYkiKz6fj+t4NmvWDIBW
+BfyKK64AoKU/UwOwbr/9dj6PSGLEJ1mzZk3SpAPCt99+y5INANx1113cTydg1wOV0KIghBgNIAhg
+chzn3gzg5niuSyIoic8//PADu+/279+vY6ARmjdvjvvuuw+A/oXctctS/Vsd7L5cVE786aefZmty
+OBzGu+++C0DLH0jFR8ywcuVKXuBSU1PRr18/2312InlHRkYGk6vISj927Fhe0Nq3b88vzuzZszFi
+xAgAsdl4Kpx48erXr8/5Jkn079u3r44ERnPj6aef1uVFpHyNZBtJJsgt2qtXL/5u//79vHE4BbvP
+Pe5FQQjxV2gGyAtl6ZO0XIpeSjkewPiTbZ16v6gLFy4AxLkoCCH6AhgJoKeUUuVszgAwRQjxPDRD
+YzMAixPupQG0BpGxq7i4mD8vXLiQJQk1zXqrVq14ZVaRaBm0SPB4PJw9mLwkavKWmTNnsvcBMM8Y
+rYriJNFUr16d6botW7bEunXrovbDiZ2XPB+NGzdmtYGqLz/44IMs5XTs2JHT0r/77rtcwr28LOgk
+AX733Xd8zRtuuAFA2RwE1P8LLyytk1xQUFAuxr0bb7wRALjaNVA6RuQJiQfG5D/Gth2LfRBKKXoh
+RB60UvSjAKQAmHPy4j9LKW+VUv4qhPgQwG/Q1Io7nPY8uHDhIrmItxT921GOfxLAk4l0KhZoFSR9
+8fLLL2dqc8OGDbl4Se3atfl7Y8AIoO3AtGPE6wpTM+lkZ2dzMMu4cePY/Ui67CuvvMI7+7Rp07hP
+6enpOu4EoPERsrKyAACHDh1i5uW9997LdokvvvgCTZo0idk/umfaSamYL1AaKKYWlpFSsoFu/vz5
+LIUVFRUxl4Pu87zzzmO33//+9z8u2KtGftpBIlIFSYK///47G2wXLlwIQLs/MjAPHz5cJyEQsrKy
+kk4HF0IwR0LdzWnORjOyxsoyRXaUvXv38ljs3buX27RqwK0wNGc7xxPRo3HjxgA0oxaJXYFAIKJv
+nsRZesEeeeQRNuCFw2Hrgyb05dJVEHdiw4YNLOavWLECgEb9NfN2tGvXDrm5uQBKX9JQKGSaR3Dl
+ypVMj5ZScin2efPmReyrET6fjwlQdI0RI0Zg5MiRALSFyZg+zQiV7ksU7GHDhuHbb78F4HwGaisg
+nkTlypU5spHuv2fPnvjgA81JRgQlQKt6de6558Z1PWMVKisLSv/+/dlrRmO8Zs0a5keoHpwLL7yQ
+1ds2bdqwt2no0KEAtEraGzZsAAB06NCB73/KlCm8EakJZUKhkEtzduHChX2cdpKCx+NB1apVAYBj
+zatXr25pZyP+Qp8+fQBoolWc/dW1q+Ljjz8GoKk0BLrOLbfcwtGOqrpiR2QNBAJMwfV4POzLpjEx
+9kc1YFLegNTUVGYbktS1bt06lnLiRX5+PqszJD2UJ9QIRlKxiJvywAMPsDoGgO9f/c4uYgV0qaBd
+fPfu3WWktBtuuAGTJ2te/WAwyH2//PLLubbo0KFDubQcjXFRURFLujNnzsTWrVsBaHU7SOI0JC46
+vaIkY/zOE/bjjz9GvXqa15NeBJrYsUBUUrIw79y5k0kqdB0gsTDj6667DoAWtm1Es2bNuNx7Iteg
+iZCSkoIdOzSPL00Uo9huFp7s8Xh0mZQB4IknnsCoUaMA6KniqqpkBXRfDz/8MP7zn/8AsBf26wTN
+OS0tjWnK06dPB6BxKNRIRFI3EyEI2aGNk22je/fuZcYzOzs7ZjFhdVzOPPNMAFqBXfJgCCHQvXt3
+AFHtOa764MKFC/uoMOnY1Ay8QNlAnkWLFgHQfObGlVbd2YqLi1lMNh5HVvT58+cD0MTd119/HQCw
+YMECHDp0CACwY8cO0xRoVuiiZmnG6Phdu3YlbN2uVq0a3x9Q6tkwSzcH6MfALNsv9efZZ5/FrFmz
+AJRSrc1gzM+QmZmJgQMHAtDEVhqjMWPGsFeCDLyA85wFolinp6ezxPj444+zBNW8eXMAmthOInyL
+Fi0SphDbkZ48Hg/zO9TzqI+xpARAP26kRsycOZMl6FWrVrEBNZIKaTlfSEVRH7xeL4QQuglLN1Op
+UiUW/VVVgXIYfvDBB3jjjTcAaOIgqRUNGjRg8kpOTg769+8PoHRiqxF7O3bs4JLrX3zxBevtqk4W
+q+y72rYqxtMENCNP2cWqVavY+/B/7Z1/bF1VHcA/376169zI6OaA2o0OGelCySzgHxD3h5kKgxCM
+iX9QyNzWJVvII2IjMYyFLfsPUVRMJlV0mixkM0zUZcQRREgWMlAWpa2DSs2adcCEJStNFLru9fjH
+vd/Tc+/ea+97ffe9Oz2f5KV99/34nu+9533v+X7P93wPTK8GdN0gJV72u5hRq+b1HxgYsCnDxhib
+KqzR9iSU4z4sWrTIXovHHnuMjo4OIKh5qcZi//79QBBD0D4yNDSUuD2lKDdtvKurC8CuzoTp2TNd
+I5IUjVcdPHjQ9rOtW7faWbUZjIJ3HzweT/lkbqRQrPDGVVddZQN0TU1NNmlJg0mlhs5xNEA5MDAA
+TCfgQBC807ni8fFxzp49CwSLgHTUoHfdmbZqK5aHoO9358fLoaGhgV27dgFBPUfNfzh79qy92xQb
+goqzCUupu0c1V4fedtttHDlyxD7XehA63E1CkpGC6tTY2BhJosrn80Awgnj00UeB6aSga6+91o4Q
+du/enbg91UKvmVvJWdPHk86Cqa56PltaWiIJcO7vwHUZyh0pZCamoEt7i3WIsbExTp8+DQQZi1ps
+NakxUNQo6AVSuQDHjh2z/zc2NtqhfqFQsG5GkoQc/Q73cxoDSLJWQenq6rLZge73Tk5ORmIUumrx
+1VdfBaJrJty9EPQ5BJ1LDWs10T0KlFOnTs3p+1yjVqzG5OTkpDXe/f39dvqxra3NJoNpEZ1Vq1Yl
+8t3LaVs5N9RiO4qpkVqyZEnR72poaOCKK64A4OTJk9YlcmfJ9DouWLCganU4vfvg8XgiZGqkUMq6
+TUxM2CSTXC5nh8xJkkfUunZ3d9s5XT1WKBR45JFHAHj66adnjUiXE3H++OOPbZKK8uabb9ociWPH
+jkW+V0ufawprPBlL7/j333+/XZfQ2dlpc/h1peLo6Gik+rC7nkG/s7m52boyOnSdixtx9dVXA0Rm
+Rc6fP19RzUv3DlwqmOeu+tNrMjIyYq/r8PAwa9asAbAJPe+99x7PP/982e1J0s4kqAs5NTVl+60G
+iQuFgk3L3rVrly2ysjLcnl7lKSr3lVdesXUt3PMUHx0mCZC7+JGCx+OJkJlA42zv0QzBe++996Ig
+2YkTJ9izZw8QTCeuW7cOCIp2uvGDOBs2bLDppQnbCSTz1/r6+ti6debCUm5JsJno7+9n586dQDA3
+re1YunSpzczTmMu5c+ds+xYsWGCzH902NzQ02Dl7nb6dP3++9cPL4ejRo3ZBkTsy6erqYnBwsOzv
+mylfZSbi+Rga39Dck0KhYK/HxMRExT73XPd76O3t5YknnqhItvZ3zSfJ5/N2hOjGkuKjq//JNGeY
+DhgNDw9bV6LSctu6avHmm2+uqLR8ks+0t7df9KPQBJZi6HfqRV6/fr0N1BXLQYDgB6RuQLEdjebN
+mxcZwrupy2qItIN1dHTYpK58Pl9yNmPbtm0APP7441YnN+CrP8YtW7ZUFNjL5XJz3nlp8eLF7Nu3
+D8DuCtXb22u3dR8ZGYmsRq2USnffWr16NYANmLtuVxx32bNuR6BB5fjOVcUK9MTOoc9T8Hg85XPJ
+jBSUyy67jAceeAAIUmnDz896F5+amqKnpwcovlgpYTtnlBF/r9Y60NWZra2t9k66YsUKexcfHBy0
+dwGdYktK/G4VL8nlTrO6BW015Vu3c+vs7LSfdUcXExMTdjRS7DxPTU2xadMmIFgZqSnShUIhks9R
+rNxcKX1mcx+KBZjdwjEPPfQQmzdvBqaL++7du9dmVlYrN6Pc9OFS9PX12QBzc3Nz5Nxoev/OnTtt
+hepS8kq1x+kjl5b7oB24khPd3t5uEzoKhYLt/Pl83g7hjx8/XhU/spy2leq8SlpVfty0bFeGRufd
+VXlaaXrbtm02FXdsbIzly5dHdFA0V2Pt2rVAUEymWM5DXHZ8T8RSP0xXXrHl5aXOfS6XszK6u7tt
+VWZ1x8bHxysyBjOlM1fLKLgsXLjQGuG2tjZ7Q5lL33UMsXcfPB5P+WRupJBl0rgzpIl7l2hsbLTt
+vnDhQiQXAII0WTede8eOHQBs3rzZZtUdOHCAjRs3VtSOcmYR3PNcSdagu6lLNYnPPGS5P2jb3Nqb
+ScuxZcoouGShXXGy3AlKocNxdybCHQ4Xc2caGhpoaWkBgki57rWYZO/KueLOjJRaC1NrXHesFjuJ
+VYO5rH3w7oPH44mQqTTnS8GFuJRw77TxYFncfTDG2FoVhULB1kUYGRlhdHS0pIxcLhfJf5jrndSY
+6S3qs9IfiqVdZ6FdSajkHGbGKMSnrCA7Jz7u2mSls5YiPi2pqCvR1NRkO7fOSHzyySd2rcayyOtL
+VAAABF5JREFUZctsMtSZM2dsVuj58+cvMiY9PT12td9rr7025wIu7rl14wuzLfWOf65a18d1rwqF
+QmQdQdbcyWK/IdflSdpO7z54PJ4IWQk0fgj8Gygvc6d6fNrL9rL/D2S3G2OWzfamTBgFABF5I0lk
+1Mv2sr3sdPHug8fjieCNgsfjiZAlo/AzL9vL9rLrT2ZiCh6PJxtkaaTg8XgyQN2NgoisF5EhERkW
+kYdTlrVCRF4WkRMi8ncReTA8vkREXhSRd8K/LSm2IScifxWRw+Hza0Tk9VD/X4tIU4qyLxeRgyLy
+toi8JSK31kp3EekNz/mgiOwXkea0dBeRvSLygYgMOseK6ikBPw7b0C8iN6Ug+3vhOe8Xkd+KyOXO
+a9tD2UMicvtcZFeLuhoFEckBe4A7gOuBbhG5PkWRF4BvG2OuB24B8qG8h4GXjDHXAS+Fz9PiQeAt
+5/l3gR8aY1YB54AtKcp+EjhijFkNfC5sR+q6i0gb8E3g88aYG4AccA/p6f4rYH3sWCk97wCuCx9b
+gadSkP0icIMxZg3wD2A7QNj37gE6w8/8JPxN1BddnlqPB3Ar8ILzfDuwvYbyfw98BRgCWsNjrcBQ
+SvKWE3TIdcBhQAgSWeYVOx9Vlr0YOEkYR3KOp6470AaMAksIUusPA7enqTuwEhicTU/gp0B3sfdV
+S3bsta8Bz4T/R/o78AJwaxrXv5xHvd0H7SzK6fBY6ojISuBG4HXgSmPM++FLZ4ArUxL7I+A7gC4Q
+WAqMGWO0/lma+l8DfAj8MnRffi4iC6mB7saYd4HvA6eA94GPgOPUTncorWet+2AP8Ic6yU5EvY1C
+XRCRRcBvgG8ZY8bd10xgsqs+JSMidwEfGGOOz/rmdJgH3AQ8ZYy5kSCtPOIqpKh7C/BVAsP0GWAh
+Fw+xa0Zaes6GiOwgcGGT7ytQB+ptFN4FVjjPl4fHUkNEGgkMwjPGmOfCw/8Skdbw9VYg2Y6f5fEF
+4G4RGQEOELgQTwKXi4iuVk1T/9PAaWPM6+HzgwRGoha6fxk4aYz50BgzCTxHcD5qpTuU1rMmfVBE
+NgF3AfeFRqlmssul3kbhL8B1YRS6iSDocigtYRKsKf0F8JYx5gfOS4cArTO2kSDWUFWMMduNMcuN
+MSsJ9PyTMeY+4GXg62nKDuWfAUZFpCM89CXgBDXQncBtuEVEPhVeA5VdE91DSul5CPhGOAtxC/CR
+42ZUBRFZT+A23m2M+Y/z0iHgHhGZLyLXEAQ7/1xN2RVR76AGcCdBRPafwI6UZa0lGDb2A38LH3cS
++PYvAe8AfwSWpNyOLwKHw/8/S9ARhoFngfkpyu0C3gj1/x3QUivdgd3A28AgsA+Yn5buwH6C2MUk
+wQhpSyk9CYK9e8L+N0AwQ1Jt2cMEsQPtc33O+3eEsoeAO9Lsd0kfPqPR4/FEqLf74PF4MoY3Ch6P
+J4I3Ch6PJ4I3Ch6PJ4I3Ch6PJ4I3Ch6PJ4I3Ch6PJ4I3Ch6PJ8J/AQcmWZxM5zUAAAAAAElFTkSu
+QmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/2... Discriminator Loss: 1.0424... Generator Loss: 1.1105
+Epoch 1/2... Discriminator Loss: 1.0549... Generator Loss: 0.9376
+Epoch 1/2... Discriminator Loss: 1.1714... Generator Loss: 1.1036
+Epoch 1/2... Discriminator Loss: 1.1158... Generator Loss: 1.0531
+Epoch 1/2... Discriminator Loss: 1.0498... Generator Loss: 0.9878
+Epoch 1/2... Discriminator Loss: 1.2111... Generator Loss: 0.8901
+Epoch 1/2... Discriminator Loss: 1.4104... Generator Loss: 0.4942
+Epoch 1/2... Discriminator Loss: 1.0527... Generator Loss: 1.3645
+Epoch 1/2... Discriminator Loss: 1.0832... Generator Loss: 0.8888
+Epoch 1/2... Discriminator Loss: 1.1123... Generator Loss: 0.8392
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsXXd4FFX3fu/2JKTQpUovEQGlKJ8NUEBFQVSwN0BFUcBP
+LB+KHRtYALEgggoIqIgoiiIKWEFRQDpIEzB0SEjP7t7fH5Nzcmczm22zYfE37/PkyWYzc+fOnTv3
+nvKec4SUEhYsWLBAsJ3oDliwYCGxYC0KFixY0MFaFCxYsKCDtShYsGBBB2tRsGDBgg7WomDBggUd
+4rYoCCEuFkJsFkL8JYR4OF7XsWDBgrkQ8eApCCHsALYA6AFgD4DfAFwnpdxg+sUsWLBgKuIlKXQG
+8JeUcruUshjAbAB943QtCxYsmAhHnNqtB2C38vceAGcFO1gIEZW4YrNpa5rf7w9sDwBgt9vh8/kA
+AKEkIiFEyGNOFIQQfE9SyoTuZ2X3jcZFCKGbB+r3AAz/BwSfF+oxJ3Ls7XY7f6a5HAMOSSlrhjoo
+XotCSAgh7gBwh/J32ANOi4HH4wEAFBYW6h6iw6HdVmpqKo4fPw4A8Hq9hosHXdPpdPL//X4/fy+l
+1LVNiGRyhJqEwe6dznO5XDw5/H4/iouLua1Q/Qi2cIbbZ/X+hRDcnjo+9J0QAl6vl9tQ+xY4hhW9
+jJGMrdvtBqA9v4KCgnJ9SkpKAgDk5+fzGAQ+a3VsaJxpDkkp+RoFBQWGm0wk/Q32kgcuYvQ5NTWV
+r5Gbm8t9DnXdIHNuVzh9jNeisBdAA+Xv+qXfMaSUkwFMBjRJIdyBFUIgPT0dAHgCSil5oIQQ/NLk
+5OTweeqLrvRBt5OoEz6ahx7q5Q7WTqjvS0pK+F6dTmdE/Yp0MTAC9d/hcMDlcgHQFmL1f3StcHbe
+QASOdajxUtusWrUqAG1c6CUrLi7mftJLqLZbv3597Nq1y7DP9Fm9Z3UBNDo22Lwxgs1mMzxG/Y76
+bLPZcN111wEAFixYgLy8vHLHBo6HGYjXovAbgOZCiMbQFoNrAVxvVuO1a9cGAOzYsQNA8FXb5/OF
+FLnoeHWHixbBHlasL6Z6fmWIsMEml8fj4Z0rPz+f+0OTuKKxDvUiRAMpJerWrQsAyMrK4oXK7/dz
+X9R+0oKalJSke9GN+lRUVARAezHp/tQ5EqjShXs/Pp+vwvkgpeTrpKSk4IUXXgAAdOzYEXfffXe5
+fhj1PVY1Li6LgpTSK4S4B8DXAOwApkop18fjWhYsWDAXcXFJRtyJCA2N4YqXkR5rwRikXwNl40k7
+sRnqSTCoO54Zz5Huo1GjRtzvf/75hyUM9TqEYNez2WxxvXe6BknDO3bswCWXXAIAbDtRoRrVK8Dv
+UsqOoQ46YYbGWBCrjn+ywel0slE1Pz8/Iiu0GS8Tic+pqalsdCMDbm5ublxfDiODYTT30rZtWyxa
+tAgAULNmTW4jNzcXmzdvBgDcf//9WL9+PV8HKG+gJhWkSpUqOHLkSDS3FDYyMzNRp04dAEBeXh6a
+NGkCANiwQaP7hKu6RToHLJqzBQsWdDgpJYWKIIRgMbFu3brYs2cPAFN8vJUKp9OJ3r17AwBmzpyJ
+Tz75BABw99138y4dDmKVlJxOJ+9WXbt25e9bt24NAJg1axaaNWsGAFi2bBkb6FTPT7Sw2+1sVCPL
+eyTnAppUAADz58/nz6qRMC0tDZ06dQIALFq0CBs3bgQAfPnllwCAb7/9lufQwYMHuX2SJOIJt9uN
+kpISAJpk0qVLFwBlkgIQ3ryOdA4kjE0hVlGfHvLtt9/ObpysrCzceeedABDRi1S1alX06tULADBv
+3jye6KHEsEjvgUTjyZMn47fffgMANGzYEICmq99www0ANB2YcPz4cWRkZFTYj8A+hXus2i8SVW+5
+5RZeZC+77DLUqlULAPDzzz8DAGrVqoX27dsD0PgUJGqPGTMGY8eOBaBZ8o2ub+TKU8dQXRQigdvt
+xo033ggAePrppwFoHqtgXhW6nqomUN9KSkp47rz88sv4/PPPAWgvZkVeh2htDm3btsXkyZMBaAsv
+eXt8Ph8WL14MAOjfvz8AMG8hAoRlU7DUBwsWLOiQMOpDrFICGcDq16+PGjVqANCszZGs1iRyXnHF
+Ffjvf/8LANi5cydWr14NACzKBSPpqASZlJQUFjHVHYpINfXq1UP16tUBAD169ECbNm0AAH369AGg
+iaoTJ04EoFnIqQ2PxxN3j4qUEvv27eN+tGzZEgAwd+5crFmzBgDw008/AdDUhLfffhsAcN1117Eh
+rk+fPpg2bRoAYM+ePbzzqtcI9CwEfh+typeSkoJTTjkFQNl4qxKI1+tlq/7BgwdZZXA4HKxKkHqk
+9nv9+vX4559/uL2K5paqotB9GR0DaCpa586dAQBffPEFqlSpwufQebt372b1hSTHrVu3shRrJhJm
+UYgFderUwfPPPw8A6NatG1uqv/zyS2Y/hqOT0gRo164dfzd06FCMGTMGAHDo0CEAwJEjR0KyEJOS
+knDeeecBAK6++mpuf+bMmQCAyy+/HE888QQAbQIa6ag0CX755ReerAUFBahWrZquP+HcUyQvmEqp
+nTx5Mi+Gwdq46aabAAD/+c9/0LhxYwBA48aNceDAAW6PXiDVi0CLsPqCqS9TtNRsh8PBHoWUlBTu
+A43x1q1bMXr0aADA4sWLdRTyN954A0AZY9Pj8WDYsGEAtIWQxiIckNqlkqnUftJ1R48ezWpulSpV
+cPjwYQDAM888w9fLyMjA2rVrAZRtTklJSXFZFCz1wYIFCzqc1JICrbi0KwDaLk50UDVOwOFwhDRa
+UXsLFizA0qVLAQBNmzZFWloaAGD//v0AwtvBcnJy+Hrdu3cHoBncSCy98sorcfTo0QrbIF5/p06d
+2NiVnp6OW2+9FYC2i4ey8sfKIVCJPcFA0ggZSQFg27Ztut2RJCiVeBNMBYtWLaLn16FDB7z11lsA
+9MSrb7/9FoCmPpD6U1xczPMkPT0dy5YtA6AZqQHg+eef5z5HIiWoFHtVjXC73WjVqhUA4L333gOg
+zTGSaLKzs9lwm5WVxZJVx44d8cADDwAoeyYPPPAAjh07FnafwoUlKViwYEGHk1pSILdhSkoKR0YO
+Hz4cn332GR9DRpvatWuzkchoJ3I6nejZsycA4OjRo/j1118BAL/++ivr86tWrQIQnuuxqKiIpQ3S
+ZevVq8e7FRnygqFevXpYvnw5AG23I0Nqbm4u2zy6d++O+fPnB72nir43E1OnTgWgDwt+6qmnDCUz
+o+C1QFdgtH2mdvr3789uW5KkcnJyWII8cuSIoS7esmVL7Ny5EwDw8ccfA9BsUaGiEkP1N/CeGzTQ
+AojJ/pKcnMzHDBgwgOcpUDZ3UlJSWPIgo2Tz5s25v2bipF4UXn31Vf68d68Wmb1gwQLdJFNDaCt6
+eC6Xi0khf//9N0/onJwcLFiwAEB4cewqSNzs2FFzDffv35/F0mBiPU209evXs48aKDMqLlmyRGc5
+P9GxHU6nEzfffDP/TS8bjVlFMOImxHIf9Kz79+/P7dAi/PTTT7P6p6o1drudKeQbN27k/6m5GUL1
+PxJIKXmTUXMlzJ49GwB4IyHQZtCpUyd069YNAPDuu+8CAPMWzIalPliwYEGHk1pSINYdoPETAI22
+SoEqbreb/f8qNdQIxcXFvAMHIlaKNO0606dPD7m7vP766wDAxk1AE2HPPvtsAJpqQwa9HTt2mJZY
+I1oQ+46QnJwccRvqmNhstqjHm3Z8l8vF40Ku6L/++suQsehwONhtffz4cdSrVw+A5rYEzKfHCyHQ
+o0ePct+TZKVybpxOJ6uKl156Kf766y8AYP5KJIGBkRx/Ui8KDz30EADgpZdeYivzrl278MsvvwDQ
+KKM0INdffz2LkkaIxLIcLSp6KO+88w4A4LbbbuPvSIU577zz2OcvhMD27dv584mK6SAx99xzz+Xv
+5syZE5TyS6I9qRcq0Yv+BmLzlpD96NixYxzncOaZZwLQxpDmRceOHZkA1KpVK3z11VcA9OqYGUl3
+jHDhhReiRYsWAMruOT8/Hy+99BL3k2xbBQUFaNq0KQBtDGmBoyRDtEhUhGhsNJb6YMGCBR0SJiAq
+lvO/+OILTkAhpWQ/rtvt5pX/66+/xqWXXhpjT80FBRdNmTIFl112Wbn/Z2ZmAgA2bdpUqf0KBI1h
+8+bN8cEHHwAo24GLiopw0UUXASijPgeiWbNmrJqRaJyfn18uCSxdK1ppYejQoQA0JmAgt0RKyRTt
+Vq1asWTo8Xg4GhXQjMwAwuImRJPsZ86cObjgggsAgJmpAJizkpqaylKvzWYzVA8parNfv37s8dm2
+bRtLEj6fj6WJAwcO8D1IKcMKiPpXLApCCIwbNw6AJtbu3q1ll1+8eDEP/MaNG3HaaafF2FPzYLfb
+2V3mdrvLvSDffvsti5EnArRgzZ07l63kLVq0YL2dcN9992H8+PEh26voBVKpzdGqRO3bt2eVRkrJ
+3hoKge7bty+7Kd1uN7KzswFotqZHH30UALB27VpeDNT+mqFKUHudO3fmzenKK68EoNmGyM2YkpLC
+RKZgoDEsKSnhhSA/Px8zZswAoBGuaCFQI1T9fr8VJWnBgoXI8a+QFIIhJyeHjU95eXk6v/+JAu0Y
+Cxcu1EkC9BzI5z9r1qy45wCsCESvVespjB49GiNHjgRQpgZ4PB5TjbQul0sVd0MeT4bOL7/8ko+/
+/vrrmfpOBs7bb78dfftqRcrS09OZ2rx161b2+MyfP58lCLV8gBGizf/hcDh4TpLRNSMjA7fffjsA
+YOTIkSwpqKqDSpsmavPhw4fZYLpt2zZWQUkFUvtaei//3hyN4YImBACOMDvRIDsB6eGANgEnTZoE
+QFsMgPgmRA0GWgj8fj/bZdRYhZdffhnXXHMNAHA2JrO9H5G0Z7fbMWXKFADaC0ZehGXLlvHLQq7c
+c845h6NO09LSWBfPzMxkL1bVqlU5jyOFUwdb8KLdTG02G7MU1UI9lDglOTlZtxhQtGr79u1ZLaY+
+paenc3xMcnIy2xpi7aulPliwYEGHhJEUzMy8TOKXKimQj/pEom3btrr4CcKUKVM4vdmJVBloB6pf
+v74hp/6iiy7inYkMjmb1Vx0Po+IrKkh1eeSRR9jK/sMPP+CRRx4BoEk31MYZZ5wBAKhRowZLIS6X
+i9uQUnISnb1792LLli0VXtuoz5HM25KSEvYukApz/fXXcyIbNalLQUEBR0zu2LGj3HWys7NZ3TET
+UUsKQogGQoglQogNQoj1Qojhpd9XE0J8I4TYWvq7qnndtWDBQrwRi6TgBXC/lPIPIUQqgN+FEN8A
+uBXAt1LK54UQDwN4GMBDsXc1fFDCU6BsJ6NY9BMBip//4YcfytVeBIAJEyYwS/FEgnZSr9fLKe06
+derE9o9hw4axPky6vBlQ60BWVI+SQMbFQYMG8RiedtppuP56rTJhy5YtOfM00Zb9fj9LD/n5+bzD
+Tps2DU899RQATX83IzCrIqSkpHBi4cceewyAZp8x4iN8/PHHhqUR4w5KahHrD4D5AHoA2AygTul3
+dQBsDuNcWeqBMOVnxYoVcsWKFdLv9/Nns9qO5sfr9Uqv1yv9fj//5OTkyFmzZslZs2bJ2rVrx+W6
+kY6py+WSLpdLXn311TIrK0tmZWXJnTt36vpfWFgoCwsLZVJSkkxKSjKtnzabjX9CHV+tWjVZrVo1
+uXLlSl3ffD6f9Pl85caafvLy8mReXp7cuXOnnD59upw+fbp0u90x9TvSMa5Tpw6PoVEf/X6/PHDg
+gDxw4IB0OBxmz4mV4bzLptgUhBCNAJwBYAWA2lLKrNJ/7QNQO8g5ulL0FixYSAzEvCgIIaoAmAtg
+hJQyJyDIhVbScpABpehj7QdhzJgxTMGVUnIqtBMBSqahGo9ITG7WrBlTcSlBjFlQXV3hGsNsNhtH
+X06ePJkjB1UDsM/nw7p16wCEl6YtEkRisCQ//bXXXsuuZjUyMtQ13n33XQ5Ai4WtGI1IX7t2bV0y
+GmqH+rZ169aQ1aXjjZgWBSGEE9qCMFNK+Unp1/uFEHWklFlCiDoADsTayXBAL8KDDz7Ig753795K
+qeQTDEYLEvXn0KFDTMU10+OgLkA2m43/DjXBXC4XOnToAEAf/iyl5AzUY8aM4cg8M3XcSNui8dq2
+bRtHQzZt2pTJO7fffjuXqKdjt2/fzraRgwcPmkK4isb7sGbNGrz55psAwBmcly9fziUF/vjjj6jm
+g0oVjyXPJRCb90EAeAfARinly8q/PgNwS+nnW6DZGixYsHCSIGqasxDiXAA/AFgLgJa2UdDsCh8C
+aAhgF4ABUsoKy/PGWjbOZrNx3YeRI0cyc61Nmza6+n+VCZvNxnkeSZ1RceTIEQ7Qoki+ePUDCC2N
+1KtXj63wl19+OfMQVq1ahQsvvBCAOeJsOFWSzcCJTlNXEdSCQUBU5d/KITk5maUfr9cb7L7//0RJ
+1qpVCytXrgSgEW8omYpRhpvKQlpaGqZPnw4ALNYG6rw09oMHD+ZqSmYj3JfD7XYz2atXr15Mphk/
+frwpk9bCiUOksQ8WzdmCBQs6/CskhczMTDz77LMAgJ49e3JpdDVVdmWBdtv09HTOFUm7bl5eHq/a
+6enpOP/88wEA77//PudWOFHPQ1Xf3G43fzbbM/L/HaHqS8YZ/3/UB6fTiVNPPRWAxnFfsWIFgMof
+dIfDwTpzSUlJSD1ejR+gCMV4cNktJA7UnImJuihY6oMFCxZ0+FdICi6Xi63ZKhEkXlBFbZW3ryIc
+XzGlB0tLS+MMv1Q5auvWrSy6n8hnZGb0aiJer7IRGOdRybAkBQsWLESOhJEUbDZbhbRc1UBD8eik
+vxcVFfF3Pp+PP6tJQNVVmT6ru1IkBiDqa2AbZkNNZmr0fShpREoZNk8hnH4Eth3ueUZjG+x8dWwj
+6VvgNehvldGpHkv2HJvNxhKZOnfI519RXyIZW7q2w+Hg84xYlS6XSzcniRsSTvSo2i8lWevJm47N
+7/frbibwZVMfcvPmzQGAw0rV8xwOh6EqYfTyqn9H8mKrky6ei6r6AqmTSr2/YOcE/h3L4lWZlOZg
+G0Ow8TYaI/V7I8JVeno6v5D5+fk8RxwOh26+0O9gfabvw1kc6Fi73c7Pj87z+Xz82W63c9i6lJIN
+z7m5udy+UWp8FeoYRlNBzFIfLFiwoEPCSAqAvviFKu6pO77NZmPqMkXqhbOTmb2jV7baFWrHD7WT
+nghE43oLdn9mPGP6/9GjRw3HK1IadzT35/F4mJNipNIWFxdzP9q2bYu77roLgBbkRbwXkjRUqVFV
+lWMpqAMk2KLg9Xp1YaVGg+31ek9YPAPhRL5o6sN2Op1h6b4nEyprbMO9jtk2Iwr7DtafkpIS7N27
+F4BGIqO4mM6dO+Prr7/WHat6vdTnH2t/LfXBggULOiSUpADEP3ouHITaHSK1kEcLMihlZmayhfzw
+4cNci8Hr9ZazlgPGRrJE8DKFA7PHlmjnycnJnBOxS5cumD9fi+h/4YUXQnpwThTS0tI47+TSpUvL
+Pcu4eb0SYbLEq0JUwDX4JaP03mr2oEgSU1QGwaZv37548sknAWhJSYcMGQIAWL16NVc/KioqMqxk
+pFqcVQt3vKGO8YlY3Om+k5KSOGnJoEGDAAANGzYMaolv27YtAHBWqRMNUqEXLVrE8TETJkzg6lwx
+zD2LvGTBgoXIkTDqg9HuawbxhqjEY8aM4dp9W7duBaClxqKUWA8//DD7hAsKCipVbExJSUGfPn0A
+AK+88goAoGbNmnzffr8fBw5oWe28Xi8Tb/Ly8kJa6sPxt4cC7VyDBw/m9Pn169cHANStW1dXOp2M
+ZO3ateOKyDTu4SDafgohOMfkokWLuGo25ZqsCH/++ScALZiOLPzhXhMwX4wfMGAAAG0M6R2YOHFi
+pc3JhFkUjBDt5KDKO4sWLeKw5ZKSEq56ROSQM888kydPSkoKv3iVMfgOhwNDhw4FoJUOJ5WGFoLC
+wkJWb44ePcph4Hv27GGXllqMNdjCSd9He08pKSlc35KSxVQEqrOwa9cuvPyylqXvqaeeqlCdMMP2
+IYTAhAkTAGjjRVb7//znPwD0uSuD4eDBg+WSqlY2kpKSuNhsUlISx8IYVeyKFyz1wYIFCzokhKQg
+hIDD4YDX69UZqkLtGiS+ud1uLFiwAACwYcMG9OvXDwBQrVo1Pnbfvn344osvAJTV8OvWrRtTpb1e
+b9jGMZU0EolqY7PZeOdauHAhW8aFELwbU7KYI0eOcMXkLl26cL6F1NRUpKamAtAyQqtVj4xA9+rz
++SIi59D1fvvtNzRu3DjocYFUcvKS7Nmzh9Wcdu3aceZqGre8vDz+XFhYWCEtORzUqlWLr1etWjUe
+23AkBIIQAmvWrOE+h4KZRlVSwZo2bcpZtY8fP45du3bF3HaksCQFCxYs6JAQkgLtNoFGpmCZf2mF
+pizJgwYNYt/9qlWr8OGHHwIAevfujWXLlgHQdi7S0amOQaNGjdiOQDtZOFB3MzUYi+4lEFRH4c47
+72Q3o8PhwMaNGwFo9SECDVw2mw2ZmZkAgLvuugvnnXceAG33+OyzzwAAf//9N7777rsK+6qOXbiG
+24YNG7JU1bRpU77frVu34pxzzgEA7q/aVoMGDVgf7t+/PxvMevXqhSVLlgAoy1w8f/58zma8du1a
+HD9+vMI+BQMZaF944QVOw+f1etlGEwwqK5Duz+Px4PTTTwdQloauZs2aQbNhmcWn8Hg8mDt3LgAg
+JyeHx6igoAAvvviiKdeIBAmxKADGfO1gYhkdRxmc6bfaFqAV2SAxslatWujbty+AsvLkqiU/0ozF
+wXze9L3dbudKP/fddx8AbXGgCfbiiy/i9ddfD3qfVatWxeDBg/m8KlWqANDu/ZNPtLo7R48eDWnZ
+V6NHQ6ljNBaTJk1CkyZN+D5ogRgwYECF4v3+/fuxaNEiAFqRXyrIMmPGDPz4448AtGcCaIubGmWo
+8vbDCV0HgI8++ohVRRXhGAtpgxg1ahSGDx8OQNskCCTOb9y4kb0a6r1HE30YCGrjvffeQ8+ePQEA
+L7/8MqcTLCwsrFQDIyFm9UEIYRdCrBJCLCj9u7EQYoUQ4i8hxBwhhCv2blqwYKGyYIakMBzARgBp
+pX+/AOAVKeVsIcSbAAYBeCNUI8QoNGMFVndH8qePGzeORV/aSQ4ePIhrrrkmqvaNohbVzzVr1mQJ
+gdx0RUVFvJNOnTrVUEKgXXDy5MnsOvX7/axq3HXXXRxUE0ySUseQpKqSkhLe/YwSdtSoUYP72bp1
+a5ZA8vLy0L9//wqvR0hKSsJVV10FQBt7MtpNnDixwhJ5arvhuCRJlaLdNVJMmzaNn01RURFmzJgB
+QHNht2/fHoC+YMspp5wCQFNBae64XC5WWaPFTz/9BECrK3r48GEAwFdffYXq1avz9UhCrEzEWkuy
+PoDeAMYA+G9pKbnuAK4vPeQ9AE8gjEUhMFOOGRbdWrVqMXe8V69efA1qu3HjxlGlMA/mV1c/+3w+
+fqlVenWdOnUAaFFvP//8MwBtYpLoTjpyjx492ANQUFCAO+7QCnQHi7JTofZDTchhJPrTQuHxePiF
+SEtL40WhWbNmumdBx9Nk7du3L/7++28Aml5PagcAVp/IbhMOwlEfiJtRUlJimDnLCD6fj/s5cuRI
+XTUlUumuv/56Ft3J3pGdnc1jqNZrjISQFQiaA2QT2759O8/TzZs38/WuvPJK7NmzJ+rrRItY1YdX
+ATyIsrJx1QEck1LS7NsDoJ7RiUKIO4QQK4UQK43+b8GChRODqCUFIcRlAA5IKX8XQnSN9HwZpBS9
+GRZd2s3efPNN9vk6nU7eEQItzNFA3YGNdrb8/HzcfPPNAMAcBLvdjq5duwIAzj//fN5t1q1bx5wK
+EkmTkpJ4Z4/FOk8gLghBzW8JaDsm0b/T0tLYENe9e3cuw9e7d2888cQTAMqMcrt27eJK1B6PB2lp
+aXz/kXAEIrkPkqCysrJYYlGvpXJO7r//fgCawZACyQoLC3V5Genc2rVrsyRG11iyZIlOOou1nqbN
+ZsPDDz/M9wJoka/Ei9i2bRuqVq0KAJg+fXrMdUajoY3Hoj6cA6CPEOJSAB5oNoXxADKEEI5SaaE+
+gL2hGrLb7UhJSUFOTo4pFGN6cJ9//jmLaCUlJbxAxCqSuVyukOnXc3NzOTyX0rdfddVVPCGqVavG
+rsp27dphy5YtAMCWbrvdzpNm7NixUatTNOFtNptuQqsLBP1fpVUT/fujjz7i74kIBZSRrKZOnYqj
+R48CAL7//nvdRN+9e3dUfTaC6tUhW0X//v3x/vvvA9BUQVIJxo8fzwsxqS6qqqGS5KhNAHj11Vc5
+VobIYD/99BMvnIHPOlht0IpwySWXcLwNPYMOHTqgVatWADT1gebFtm3bQqoppOYUFBTwfQT2Q42h
+CQdRL+VSyv9JKetLKRsBuBbAd1LKGwAsAXB16WG3wCpFb8HCSQVT8imUqg8jpZSXCSGaAJgNoBqA
+VQBulFJWuNyJGEvRB4JWzKVLl6JjRy18fMaMGUysiRWxJAIhsfTss8/mHdbr9bLVnuL/69evz1GG
+NWvW1OV+iLSvQHmDaGCEn91uR+vWrQEAb7/9NhOniFINaJ4IUn/++OOPcu1mZGQgKysLALB7926W
+kMwASSlutxu33XYbAE3t6t27NwCgSZMmzKd4//33y6VPDza/nE4nqzwbN25kdWTbtm0AgH79+rF6
+FIhIJAUax/3797NRWQXNp3Xr1mH16tXcn8WLFwPQ5g2R7mguNGnSRJfTlNS/nj17ombNmgA09ZCC
+6Y4fP155Kd6llEsBLC39vB1AZzPatWDBQuUjYTIvqfnwY4HL5WJ67ZQpU3jHGDBgABYuXBhz+4A+
+d79ZoBWfdteNGzdy3z0eT0whxYSK2rDb7bjwwgv5nIEDBwLQdrbff/8dgLYDV9RGo0aN2JjXqFEj
+lhrMAHEonnvuOXTr1g0AsHjxYg4w27t3Ly6++GIAevtBKDidTkyaNAmAJqWRrejxxx8HgAppxuHm
+U6hTpw6MqVAMAAAgAElEQVQ2bdrE55CthaQDn8/HxsxzzjmHbT9btmxhybJBgwZ8HaJwl5SUMGNX
+SsnzZezYscy9IDcsABQUFJx8xWBiAakMderUYVFcCMEP9ZRTTgkaSxEpKitzsplJPMJJf06iqsvl
+wjfffAMgsnt9+umn+TqRcBPCAaVMa9WqFZOJmjZtigYNGgAA/vnnH0NVKVhhGUq+8vnnn+Oss87i
+70llGz9+fMx9ppd37dq1bLhcuXIlrr5aM7mpRkRSLw4ePMiqksvl4v4fOXKEFw6KO5k6dSp70jIy
+MrBhwwYAWmwPkaFsNpthJaqKYEVJWrBgQYeEkRTsdntMPmDa0UaPHs002IKCAnY/fvDBBwlXG6Fp
+06YANPclGZ9oNwgs/RVvNU8dm0iNmtT3yy67jN1sas4JM0D++hYtWvC4dOjQgaWD22+/3bDfRuzO
+jh074vvvvwdQlu2ZQDkswmEshpvvY9CgQZzvI1hNSArSU92+Q4YMwQ8//FDuesGuqyaupfvyer0R
+z/uEWRRinUA0UG+++Sb7+jt06MAxA8XFxabo5WaCOO6PPfYY67JfffUVAE2MJHE+lgWhMmxGFLKs
+8vTNtrmsWrUKgLa4k5/f7XZzYSBKlhMMtWrVwi+//AJA44IYRVKuXLkyqKchGtAiRXyVivDMM8/w
+Z1IPKOw/XNCzzs/P5/tLTk4OyakJhKU+WLBgQYeEkRTMEpF3797NolhhYSFbfU3iYwAwx9DodDrZ
+2t+nTx/exShRZ3Z2NrPrEh233norAG18aMc2W1Kg53fw4EGdWkW7qpqJuaSkhFmBxGj97rvvyrE4
+CZT45qmnnjK1z+EiNTWVK6lLKVmFiQU0Xrm5uZVKczYVZom5/fr1Yx03LS2NacWzZ89mUsiJ6hv1
+CdDyQ1LFIofDgYsuuggAmF7s8XjYvkD5K+MJu90edeZniuDMzs7G1KlTw2pDLRwDhF5E6Nhzzz1X
+FxlJ2br/+usv1seFECw+B4u/oKQ6nTp1YjdqZUCNQalduzYALRSc5uzq1aujTqxC9+xwOCL2OKiw
+1AcLFizokDCSglnqw8KFC9kP3LFjR44+a9GiRblkIWp8vIqMjAyOqDTawcIlBAWiTp06+PLLLwFA
+l3fg+++/Z+s0BfKo16lISgj0wwdSsEP1lXbSTp06cZz/jz/+WK7seTCoxKp//vmHaz0E6ycF8OTm
+5vK1w5GCSMLavHkzU63V/lO7FYH6OWbMGDz99NMAENOOGg3U7NckKXz66acsPcycOTNq9ZTGKLAc
+YqRIGEajWYuCEIJtCnPnzuUU2Y899hi7d4xsA0IIHlS/388kFqMXLJAIE27uwzlz5nBBlcLCQjz6
+6KMAgNdff53bMArnttvt/P/A/pj1/AYMGIB7770XANC8eXNeDA4ePIjt27cD0EJ5Kc8lxUY4nU72
+PixduhS33HILAJRLREuh1lRBqqKX0WghI9G4bt26LO4bxRAEQ1FREasaKsvvRMLsClNhkPOsWpIW
+LFiIHP86SQEoi1NPTU1lemkkPAUz+qKqJuS/37FjB3sU9u7dy1ZmimKLB8LdjWw2Gxd9+fLLL3n3
+9/l8LJ0UFRXx2JI/v0GDBnx/QghcfvnlAIBvv/3WVNGcdkGPx8NxEPfddx/nfnQ4HOzXX7JkCZOP
+zKK2B6IyCGVxgCUpWLBgIXIkjKHRzFWXDFc5OTlRudnUXT5YurVwQNcmo6LqWrz66qvjKiEQwu27
+3+/nHAJt2rThLFUbNmzgFHFer7dcolQhBNauXQtA243JWGlWXQQ1MzfdD7EY77nnnpDP12wJgXAS
+SglhI2EWhXiIY8F45uGcFyvU6xIh6e233+aMyZSkJBFRUlLCtGKfz8cvllHmaiklp0vfs2ePjnMf
+KwKzY1PfyLqeaLEs/xZY6oMFCxZ0+FcYGoMVZIlHMpRoQLun3W7nGHtVtUk0RDqG5BpUsyibNa/U
+hK2AJjGoqgRdJ7CPZrv7TlYEvFcnV5KVWBYFNecgoC+kqk4OoyQcahvhItIcjWSFF0LoipVS31QC
+kBo6S2Jy4KJHx6jZdoyqLKn3HEl/1fH0+Xy6saUXkvpQUlISNNuxeo7aZ2rXiHsR+LeagZmgejUC
+iwjR+YG2j8C8lEYFXtRFJth4GbUR6+IebO6rfbPb7bwwGj3zYIjmnbLUBwsWLOiQMJKC3++PKUuy
+kRFMhTCoah34fyMVJFhfIwHtkoG7Oe0CxcXFTNMl9aJq1arMJAysdqzSg+mzKl6rfQ9nx6vo/+ox
+drtdJ7obXc8IaqIPNVDJSPQP3NFDwSjgKZyyfmp/IrleOGMbKYJdN3AsgqlKZiNhFgUgvvpfuFly
+4gFaFPLz8w0nqc1m40K4RHOuUqVK0ImtcttD9TvYhA81HsGuHZiRKNwXw6ggiarORZtlKrAyVGCf
+Q0H1rkSCyiIvqeMW70hZQkzqgxAiQwjxsRBikxBioxCiixCimhDiGyHE1tLfVc3qrAULFuKPmLwP
+Qoj3APwgpZwihHABSAYwCsARKeXzQoiHAVSVUj4Uop2EMBEb7VaBwU+B35kFsuBTVt/s7OyYal0a
+wSzDWDwQbd9Ug2EoI6EZCFVDNMERlvch6kVBCJEOYDWAJlJpRAixGUBXKWWWEKIOgKVSypYh2jI1
+9iFWqPquagGmCRGPcFuyJZCIGKhzRxUCq/Q90BMTDex2O7tXieVoBoQQ3G6kJd7pnpo0acIRmNFW
+0woHZBOx2+08DxJl3oaBuMc+NAZwEMA0IcQqIcQUIUQKgNpSSqoCsg9AbaOThVWK3oKFhEQskkJH
+AMsBnCOlXCGEGA8gB8C9UsoM5bijUsoK7Qpmqg/RJkAJp00zczQCZYlBpJTo3FmrtEdRne+++y5H
+H2ZlZXFVpIKCAp3HIdCPH3jPwfz04UoNTqeTU8QtWbKEC7FQvok2bdpw4ZHA6xmB+u50Og37HOnY
+0vWSkpJYeonnzh2P+RUOVMkxhuvGXVLYA2CPlHJF6d8fAzgTwP5StQGlv80tFWTBgoW4ImqXpJRy
+nxBitxCipZRyM4ALAWwo/bkFwPOIoBS9WTaFoUOHYt68eQDKsvzEChNW6HKoVq0a11osKioqRw+u
+UqWKLtsQZXv+6KOPuL7g33//jZUrNe2LshEVFhaW4zUEfg5kgALaTqTmLKB2mzRpYsgFIClmyZIl
+eOihh7gNKrKSkpLC97J48WKOFFX78OGHHwIAhg8fzolUI50HdKzb7WbDbDxdd/EyPJPU+Oabb3IS
+X3UOuFwurmFCJfTiJanEylO4F8DMUs/DdgC3QZM+PhRCDAKwC8CAcBoy6wYbNWrERVr/+eefhDMC
+kUFx9uzZTATyer2sNlDuwH379nGZdYfDgfvvvx8AkJ6ezkVV77nnHk4999///heAZpQMVn7eSEyn
+iX3qqadyOrqPPvqIq1cFIpA+3Lx5c0ybNo3/TwbD3NxcPqZGjRp834SSkhJeQNLS0gzT30UCn8+H
+Sy65BICWpzOahaGyVYOqVTWteu/evWGlljvttNMAlM2heBlUY1oUpJSrARjpKBfG0q4FCxZOHBKG
+0Rir+kCrZ9euXdGlSxcAQO/evXUBSEagayYnJ1eKoap79+4AgC5duvDO5HK5eLd96623AGjSw9y5
+c8udf/DgQZYqXnzxRRbjAwOCAG1MaQcqLCyscBfOzc3FjTfeCEAflEXnAsCECRM43VqNGjUAaAY+
+9XqUlbpHjx6YOXMmAGDw4ME8tiQJvfLKK6aySOvWrcuJZ0eMGIEbbrgBANgI6nK5uA9+v5/F8qSk
+JFZttm3bxmMbDgL7H+68oUQ04ai3gZIZAFxwwQUAgK+//jqs60WKhFkUYnkRXS4Xp3U/44wzeBKv
+WbOGs/Q0b96ck53QCzR37lwsWbIEgFYunPT6SH3l4UIIwS8KVTACgHnz5mHs2LEAQuvDVapUwZw5
+cwBodonArMl0HQKNRTCvBC2mDoeD+9asWTNWA1588UUuouN0OjmF+4ABmlbYpk0bftkWL17ML+G8
+efM4Ucvjjz/O6oEKMxZfuo/bbruNa4ieeuqp+PXXXwEAN998MwCgcePG+OmnnwBohX1p4xg8eDD+
+/PNPAFqRWqN+BkM0qs6zzz7LBYoIUkp8+umnALRxVWnXdH9LlizBOeecAwA4evRoxNeNBFaUpAUL
+FnRIGEkhGtAqOnv2bK5HoIrMNWvWZAt/YWFhOQv4oEGDeGf77bff4iYhEGrXrs1WZlVdGjFihOGO
+r4L4Abt27WJJx2azsSeC6ips2bKFd5pQUaFAmSSRlZXF/Rk6dCirJfv379cZROnapCbMnDkTOTk5
+fD2SPDZt2sSqW2UY7SZOnIghQ4YA0IyY1A+q17lw4UKddNSxY0fu25QpUwBEvgNH4n0YNGgQAOik
+BJpvqampQSVEavu0005jlSfegVEn5aJAD/zNN98EAFxxxRW6/5N765133sFLL70EQBOPhw4dCqCs
+9qHD4eCCqFS30QhmuZ4yMjJ0+jq5EYMlcCUR/pdffsHpp58OQE9YKi4u5mSrBLfbzS9xbm5uUFJT
+4IKh/v/48eOsEtSpU4ddYO3bt+dCqNWrVwegkawWLlwIQBsnWpBycnIq1fNzzTXX8IK1Y8cOTJgw
+AQDY7amOS+vWrdkmkpeXh1NPPRUAIrInAOFH3i5btgydOnXi73/77TcAwPnnnw+g4pec3JPVqlXj
+65FrMl6w1AcLFizocNJJCsnJyVyIhERqoGzVfvTRR/HGG28AKL/7jxo1CgAwf77Gp3r22WdRs2ZN
+3fkVIdpIPtox/ve//+m+J0u8EZKTk1mctdvthtF506ZNY2/Fpk2buI+q+hDtbk1Gu+nTp7OkYFSv
+8fLLL+dr5Ofnc2k+Ko8Xb1BUaZs2bXDggEaeXbFiBWbMmAGg7Fnl5+ezWrlkyRJcf/31ADRJiCTN
+YHUwg6Gi+WC32/H6668DAM4++2zdc1ixQiMBh1JXb7rpJrz77rsA9Kn8zAxGM8JJsyi0adMGgCZK
+B05Ov9+PBg0aAADbEIxAYtqGDRsAaA+1Z8+eAICPP/6Y7Qsq48/lcvF5scY8/Pjjj+z2Ky4u5kVh
+7ty5PGkoXfpzzz2n8yJQRF5hYSEmT54MQFvUAvVgIYQu5DqaeAe3280u0rPOOitoOffAa6SkpLDL
+tXr16uz5iRfsdjt7YqSUGDlyJADtpSf9m5LXnHHGGcwKrV+/PjM269Wrx4uo0+k0LVzd4XDwJtCw
+YUOuBpaWlsaqi5oti8YwMzOTbSODBw/WzYF+/fqZ0rdQsNQHCxYs6HBSSAoOh4PFZCMR9q233qpQ
+QlDbAcrEvuTkZJx77rkANDG0bt26ALSKybRC79+/n8+PNX/ktGnTeLVv2rQpqz+TJk3CtddeC6As
+2YqaU3Lnzp248847AWjSBhkryaBqdK1AREIOKy4uxldffQVA22FJbLXb7Vi+fDmAMtWtevXq7NWx
+2Ww8tp988glLDZHmnzCK16DfPp+Pd9gzzjiDDZ7PPPMMe0SAMiMtqQQtWrRgw25RURGrR263mw29
+6enpEUk3FY1nUVERqwdXXXUVz7PPP/8cl156KYAyrkpeXh6rM6NGjWLDp0oNLywsxNKlS8PuWyyw
+JAULFizocFJICk6nk/MNqPjss88AlOnhRufRalutWjUO8iGWW5MmTbBo0SIAQK1atbgitBACW7Zs
+AaCvuhwrvF4v7r77bgCaEWn48OEAgBtvvJH7Sddas2YNu6zy8vJ0fYiETxFNDggpJZ5//nkAmlGW
+skoHC8AhCWzs2LG46667AGjGtcceewwAMHr06LCvrcLpdJbLbiSE4KrTc+bMYYmgXbt2XHVaSsnB
+ZGSLSktLYwP0ihUrmCqcmZnJtGM1L0Q4CFfyys/P17mdiQOyc+dOAMD48ePRv39/ABrzku5JxXnn
+nRdR32JBwlSIquj/L730ku7FJ8PQJ598AkDzUaugQb/hhhvw7LPPAtCnI1PFsvXr1wPQyq8Tnbdh
+w4bMgYg2FVoouFwuprb27NmTxW7ymJxyyimmGL1I1JZSVkpuRjKSjR8/nklNLVu2DEnOIoSTTITE
+7nvuuYdf/v379+PFF18EoKkMtHDSQvbAAw9w2XqXy8X09kaNGuGWW24BoJHg4gVaOFesWMGRrWTA
+3rdvH8dB7Nu3jw2R//nPf3jOdu7cmY2jMcAqRW/BgoXIkdDqA1F4VdVB9dNTIEubNm3Y2PXf//6X
+2WNutxvHjx8HoE8iQuLZhg0b2HD2/vvvs4inFi+JF/x+Pxsx1Xt65ZVXAMA015i664brkowlYpV4
+Cn6/n/MFjBkzhtWmUO2G00+SFN944w1mLObn5/NzffLJJ3lXpQAnr9fL7T700ENsaPT5fKx2xBMk
+FQwYMAAff/wxgDJW6KBBg9iI6PP5uG8rV65EtWrVAGiBaWS4jTcSWn1QM/WSaH/s2DGmiZJOtnHj
+Rrz66qsA9GG/W7duxZ49ewBok7RHjx66dgcNGsQkl3ipCcGQmZnJC5KUkkViit4zQVQEUKY+qBTk
+cHgK0caBkBq0fPlyji84dOgQJ4Yh4lkwxDOrN/Vt7969/ELm5eXxi1dZz584NaReBaOEJyUl6aI2
+Q/FFwoClPliwYCFyJLT6QKvntm3b2DgzatQoNhKRyrB//37edadOncp5Ew4dOsQr7fDhw5GZmQkA
+nHbsk08+Mb10eigQZfi2227jXbywsJD7GRjgFCtUNYiu5/f7y6lHDoeDKcMpKSls+Ip0XEiEb9++
+PX+3YcMG/P3332GdH8/nQAbmtLQ0loTeeeedSk/ZR9JrRVXQgbLnRaD5Tt6geCGhFwUVJHYOHjyY
+B5OITDk5Ofj8888BlE+sSXaJdevWccQZxT6o9NLKmBhCCHZDnnPOOTp3IdkzzE6goYqcdI81atRg
+LwepW8nJyay6XHfddRgzZgwAbdwiAR1P1nZAo3GHspEYZY4yG+Rydrlc7JWg+6xMqEV+KkJgZifa
+zCJBNEWbLfXBggULOpw0kgJBFX3Dyb9IEWVer5eJMJSQ5Oyzz8bu3bsBaOnSSaSMl9TQtWtXtG7d
+GoAW808kHI/HE9IAFy3UXYL4G2lpaUxOmjVrFgAtMIiyYNeuXRvfffcdAE3UJSPuBx98YBj7TyrD
+zJkzWcSVUrLK8M4774TsJ425CcY0QwghcNZZZwHQi+UV5dE40SBjKKBRxcmwHg6CpfYP69xE9j6Y
+ibZt26JXr14AwDzzH3/8kZlil19+OU+WQ4cOcVyBkfgVqYWc1Jxt27axCLtu3Tp+WPfeey/bROLp
+CqXr2e125t/Td7///juL/E2bNmX2X+PGjcvptuGgsLCQ2YTBdGCj0ONglaNiRdWqVTkO4pZbbsGk
+SZMAgJO9JiK6deuGb7/9FoBmN6NNJNgcMYqIVWNoUBneByHEfUKI9UKIdUKIWUIIjxCisRBihRDi
+LyHEnNKaEBYsWDhJELX6IISoB2AYgEwpZYEQ4kMA1wK4FMArUsrZQog3AQwC8IYpvY0BRUVFTHYi
+g9qXX37JO3R2djarGmpUn9GqHOkORjvw3r17mcJ6+umnY+rUqQA0n35lUJDVupMUUaimZ6f/79y5
+U6fmUE6CcEAqWP369UNSmwML0lDf4iG9NmjQgLMh5+Xl4bnnnou5zXgYqYUQTLG/6aab+Hu3282k
+pmBqRGDxn2gRqwLnAJAkhHAASAaQBaA7tLqSAPAegCuCnGvBgoUEREw2BSHEcABjABQAWARgOIDl
+Uspmpf9vAGChlLKNwbl3ALij9M8OUXciTNhsNjakkc66e/duXRRevO0rSUlJ+OOPPwBoujpdm/gB
+JwOaNWum+3vfvn18H8nJyWy4i3Qs4+UapnaHDRvGmZkAcALaWEqvmdlnauvw4cNsd1JzR+Tl5bGk
+EIPbOiybQizqQ1UAfQE0BnAMwEcALg73fCnlZACTS9uqFGsneRpoIoTyE5uNgoICJlDZbDadL99M
+xJN7UZGXJFpqtJmVoghkxCRVcciQISx2O51OU7wcZo4v8UWOHTvG4zFq1CiuyHXXXXfxfI03tyaW
+kbkIwA4p5UEpZQmATwCcAyCjVJ0AgPoAzCn9bMGChUpB1OqDEOIsAFMBdIKmPrwLYCWA8wHMVQyN
+f0opXw/RVqVICpXJXgyFymDw/X+Fy+Xi8SVprEmTJvxdixYtOFIxUcae2Io+n4+D4w4fPsz9Lykp
+YT4IsUOjmMdhqQ+x2hSeBHANAC+AVQAGA6gHYDaAaqXf3SilrFCurKxFIci1AVTeQqFyBciWcOzY
+MdMt2EBiLH6VCXqxPB4Pe5IopiQlJYXVRo/HwzyUAD9+XBDIa1HnQGB/g6m0av7LGOJ14mtTAAAp
+5eMAHg/4ejuA8rnTLFiwcFIgYRiNkQRuBBqJ1FRrpe0BCO63Nfp/RSzFQENYZewu0ULtaySJVSpq
+R20vEtBOWLt2bd7FKQo0WO7LSOaBEILFa4/Hw2K1er6qRtCxeXl5LIoXFRXpokcpMQzRtXft2sXS
+Rn5+ftS8FRpPNW8otauOtZoMSOXLqPOTfvv9/pAFigLmQ/wlBTNBEzjc+nx0TuDncF5Y9Vh1QoTT
+R/odbbWoeCNw/KJd9M3YLKiNo0ePlltkgo1bpAlmA1PAA9qCQwsAPV+Hw8Fit8PhYE+J3+/XWfXJ
+XU0p7FNTU7msvVpxK9pkMElJSXwNNXSaFk21nqXT6WT35KFDh3SLAZ0fTiarSGFFSVqwYEGHhJIU
+Ij3O6JxIVkaHw8E7SXFxcUTnJoLaleigHa2goIDzRcTg7TI8XyX3qEY6tewfoNGEKaq2qKgo6Nyh
+YyjlX35+PhsoY3nmdK7D4WBpgCQGIQRHsAohWG3w+/2cgdooOjVeUmrC2BQq83pEFHG73fyw7HY7
+J3lNhDH5tyFWj0gwtTGUGkdqhJQyIrKakVophNDZBqJhQ6o2E+q7ahtJT09nz0hBQYHZc9HK0WjB
+goXIkTDqQ2WA8hpQvsfTTjsN77//PoD4l/c+UahMzsLOnTuZn+/3+9m63qBBA85cHKyPwSpZ0Y5N
+GZcLCgpYvC4qKjKUENxuN6e9mz59OgBw3s5woRofVSmF1JGkpKSoJAW/389SAeXyyM7OxsSJEwFo
+1cvMUFdigSUpWLBgQYd/vU2BVvkOHTpwhB8F9axevVpn7EmEsTAb8ZIUyIW2fv16zggUDPfddx/G
+jx8f8TVUlxu55vx+P0t1xcXFOh1/0KBBALTMWh06aIG39KzXrVvHJQRvuOEGrt3YoEEDjux8/vnn
+OWjq999/B6BV9qY5kp+fr3NLR2voI2lq4cKFAIA6depwguFnnnmGcz3EwZAYlk2BQ4ZP5A8AWbow
+mP5To0YNWaNGDTl//nzZsGFD2bBhQ+l2u6Xb7Y66zXj1NR4/QoiY++t0OqXT6ZQXXnih9Pv9Uf14
+PB7p8XgiGlv1c1JSkkxKSpJ2u53vyW63S5fLJV0ul3z88cel1+uVXq836j4a/Rw/flzOmzdPzps3
+T9arVy/m8XS5XHL16tVy9erV3F+v1yuLi4tlcXGx7Ny5czznw8pw3kdLfbBgwYIO/3pDY+3atQEA
+M2bM4CQrBw8eBKB3Saq02xOhRhC9lph2AwcOxNChQwFo4jcVwCkpKYmY9RcL7r//fha7g+V/oMIx
+b7/9NhsBr732Wpx++ul8zJw5cwAAffv2Dbu/6mfVEKwaJemYAwcOhHdDESIlJYVzMgQyKKMR7zMy
+MsplIS8pKeHUdfHK6h0J/tWLgsPhwLnnngsA6NGjB9NVN2zYAEBPeCkqKgrbj22W/YEs2QsWLOD0
+4+QhUf3Zc+fOZRJLRkYGk1969+6NNWvWANBby0PFeYSDm2++GYCm46r1OQPx/fffo2vXruW+nzlz
+pi6XINWSNMPGYbRwkA0B0MfCGMVwFBQU4JdffgEAPPHEE/xC1q9fH1988QUAPU/h/PPPB6BPIhNY
+5zFcJCcno1atWgDKYh/U6lx169YNmdsyElTk2QkGS32wYMGCDgkjKUSzcxjtOi6XC7179wYAPP74
+42jatCkAbYWmug+PPvooAE1UI9F3/vz5+PHHHwEAmzdvrrA/kYqNVH153LhxLLkAxoVP1KAX8u0L
+IVCnTh0A2q5CO/ezzz6LO+7Q0lySH16NrFPbiwQXXXQRnnrqKQDajkkMuzVr1nBhF/L/q9KV3W7n
+AiZXXnmlrk1Sj4hvcPjw4Yj7ZQTa0c844wxD6YDG46233sJ9991Xrs8qNmzYwGNrVOdR/Rw4zuHi
++PHjfC6pY/n5+azSkkQYK1RJJ9I5kDCLgt1ur1B8pwedlpbGpbypUMbEiRP5pRg+fDi7HlWxVyh1
+JWmQ0tLSeNFo0qQJk0a2bdtW4UMPJ7zXbrejT58+AMr06WA6uZSSVQIS2//88088+OCDALT6mSpW
+r14NQMs1STkfKbZARaRqDk2k1157jdO6FxcXc+GURx55xPC+aZx///13bqNx48Z8rN/vZzVHjQI0
+E7Vq1dItBrt27QIAHp9IyWmhnq9RLEI4cDqdPM9prPbt28eLgsfjiapdFarKEI3tw1IfLFiwoEPC
+SAqhVjNKTEFiNACsXbsWADBixAg2ar377rucvrtjx46846WlpbFlt1WrVgC06su0ky5evBgrV64E
+EFo0DGflFUKwYXP9+vUAgFNPPdWQhLNixQr069cPADgoSwjBtRjVHf+nn35Ct27dAOjFYFUiiCRH
+hHpO3bp1AWgSCEUJnnLKKbp8ArQD0Y72xhtv4KqrrgKgGUnpmrt27cIPP/wAQPP2EEEomhJ0FYHG
+YNasWZzCPTs7Gy1btgSAkNWuI0WsWaBdLhePnZrBeciQIQCArKwslih9Pl/Ykp6aG0QtqBNN1emE
+WRRCgUR7Vd+/7LLLAOjddOogJiUl4eKLtazzTzzxBKsPhOTkZGRlZQEAXn75ZRY5QyEcsdzr9bJI
+T4/WSAoAACAASURBVLEW1apVY3eU+qDcbjf3rWbNmgCAL774QmdR/+abbwCA76ciRJMIpEqVKmjb
+ti0ATQ34+uuvAWiTimpvXnnllTzm6enpFba3YcMGPPzwwwC0xYLGOV4xJr/88gsv5iNGjDB9MSDQ
+oqiyLSNR0S655BJWb6mtmjVrsvpQs2ZNtrf07duX5/1ZZ52F1157DYAWswNoLE1Sx4qLiw0rm9ls
+tog9Ppb6YMGCBR1OGkmBoK52oaLUSkpKmEDTsmVLXlVpt1q5ciXuv/9+AGWFYsJBuDtw4DHB/M/F
+xcUs+l5zzTUAynYDQJM6wpEQCEY1GkNhyJAhuOGGGwBokgtVjG7dujUnADFC4FiQJLR+/XqcffbZ
+ALTxVnMExAONGjVi6aBDhw7YtGkTgDLj7muvvcZ5F30+H7766isAmhH4s88+AxB8JyWVQd11VVUq
+EknhzjvvLKeC2O127s93333H4927d2+Ok7DZbHjkkUd052VnZ7M0NmfOHPZWSSl1apolKViwYCEm
+hIySFEJMBXAZgAOytCakEKIagDkAGgHYCWCAlPKo0Jak8dAqT+cDuFVK+UfITmjBLaaXcfN4PLxj
+NGzYkFdK2lEaN27M9NhIjDFOp5NdUmZRosmQSrp3RkaGLoWX2WnMCGT0Gjt2LC699FK+NjErnU5n
+SImDntv27dv5u6KiIjaq3n777ZzFOV403kmTJuGuu+4CAF2WJdoxg2W59vv9bDOZO3duOTuBlGVJ
+eqtUqcLzpKSkRFeHVP1dETZv3syGcLU/JGFt3ryZJcqLLrqIJR0hBN+Tmm3q+++/BwDccccd2LFj
+R7nrKUGHgIl1H94F8BqA95XvHgbwrZTyeSHEw6V/PwTgEgDNS3/OglaC/qwwrhEXfPHFF7oy6qRu
+kKU33MQbgS9FJFbhcEELlcqtuOCCCwCYRwlWQROLXtwOHTqweuV0OpmC7XQ6efI/9dRTmDVrFoAy
+HkBgXkTyVJCHB9C8LnR/oRaFSLkVVE2JOCHUhtFiQC90UVGRzsJ/++23A9BTjGlR/Omnn9iz5fF4
+2CC8b9++qAyN48aN44Qq1Aev18tz0ePxMCXcZrPxuC1duhSNGjUCUDZH6tSpwzybjh07sgqshpSr
+C6Bp6oOU8nsAgcpwX2hl5gF9ufm+AN6XGpZDqytZBxYsWDhpEK2hsbaUMqv08z4AtUs/1wOgWuz2
+lH6XhQAIfSn6kIzGSEC73Pnnn89t+nw+rh84c+bMmNoPTNFlhtRAOxvtUACYdm0G1B1YCMF1DV5+
++WUAWol52q127drFx3bp0oVVsFAsPiklnnzySQCaOkJGsszMTHZxhkIkYymEwPLlywFo9RkowCg3
+N5cNzJ988gkA4MMPP+Tnv3//fn5+6enpTBV//PHHefyJL3Leeefxbl1YWMjuwnBqLhjhnXfeYXfu
+qFGjAGgRnhMmTACgjRtJP6rKMG7cOJ4jNH/dbjfzdnr16sX3BwSnZ4eDmL0PUkpKOhHpebpS9NHS
+Ro1AWXNsNhtblidMmIBly5ZF1V7goKp6WrSTIxCjR4/W/W1W1p3A6kOAtgC9+uqrAMClzl0uF1uv
+7777bl4IIuXik+h76qmn8oSeMWOGqRwQEuGvuOIKpjEfOHCAPTZHjx7lYymCM3A86RrZ2dms0qSl
+pfF40TWaN2+OLVu28DkqdTsa+P1+XqjIw/Pss89i3LhxADR1iPrg8/lYJWjdujXbPsj+kJ6ejj17
+9gDQ7DZmbarReh/2k1pQ+puC2fcCaKAcZ5Wit2DhJEO0ksJnAG4B8Hzp7/nK9/cIIWZDMzBmK2pG
+pWDEiBG8Y0gpWTQ0KyoP0HYz1XdNCLD0RgSKpKTzV6xYEWMv9VAlGp/Pxww61bBJoujff/8dVbRe
+amoqB3FJKfkaRB8PB8HGj7wznTp1wnXXXQdAC2IjI2jbtm11EgIh1I5ep04dTJs2DYBeqiJvyYIF
+C4LuwNHmhiAvza233gpAMzgaGWDtdjsbykePHs2eIrrunDlzeH6b6rkLI3/iLGg2gRJoNoJBAKoD
++BbAVgCLAVQrPVYAmARgG4C1ADqGkxMOMC/voZqjr6CgIC657mw2m2GewJSUFD7G4XBIu90u7Xa7
+rF+/vqxfv77MyMjg89Q2nE6nzMrKkllZWbKkpESWlJTIqVOnxqXv9EM5E4uKimRRUZH0+/3yyJEj
+8siRI1G3+ffff/PYFxUVybS0NJmWlmZKfylP5Ntvv835DA8ePCjfe+89+d5770XUlhBC3nvvvfLe
+e++VxcXFupyM48aNk+PGjQuZi9HsPJ0Oh0M6HA5ZWFio68/u3bvl7t275aeffir37t0r9+7dK6+7
+7jp53XXXSZvNFul1wsrRGFJSkFJeF+RfFxocKwEMDdWmBQsWEhcnHc05GNRAFQLlZ4zXtQBNrCUR
+trCwkEW8oqIiFnnJqt+sWTOm+6plxBs0aIDNmzcDKLN6v/XWW1H1TaW3+ny+oGXVAisfA2WchUgi
+65o0acL5HVRyT4cOHSosABMpaMyrVKnC/v3q1atzsFk45w8bNgwAMGzYMDRu3LjcMTt37sT//vc/
+AAipEpjNU6HnMW/ePKa6A2UGz3nz5mHEiBEAyjgiZveB8K+p+7Bx40YAWowDJV/p0aNHrM0aQtXP
+3W43u6xSU1PLJeUMPE+1RZA1uXPnzuwFoMmRnJzMOn44LygtQFJKbkOtRhRIuKKXjBY0dXEoKSnh
+9gLx6aefAijzWqgL5LFjxzif4bp160L2ORKQm/nbb78tl++R+kwLanJyMuvoFJEYLHmJz+fj+IGJ
+EyfGLboyXKSlpXGYOVCWx7FmzZpRVaQKgFVL0oIFC5EjYSSFWDIkz507l5OUAGUEoHhF5KnidSAF
+OtQ90I6cmZmJP/74g88h6/HPP/8MQMu9QJJORZ4To7gEo4Qrqo8dKNs5KZFLjRo1Kux3RSAp7fTT
+T4+ZXxFsHtC49erVC1OnTgUQm3pIc6Nq1apRSwfxqL6lEpYAsLpJFblihCUpWLBgIXIkjKExltVW
+lRIWLFgQtYQQ7sqv7oZqwEk40g6dm5GRwf3Mzs5mltsLL7wAQDNaqrpluH0OvL5R9WQpJdsSKG3Z
+P//8Y2hHCHZPdB9NmzYNm60YC6gPX331FQdbTZkyhSM7A7NqGYFSzHXv3p2DvxJBUlYR2B+yn1R6
+J070D6L07bZr1062a9dO+v1+9l2npqaa6j+O188dd9zB3IS77rqLOQ0nsk/p6ekyPT1dvvXWW1zj
+0OfzyQMHDsgDBw7IVq1aSZvNFo1//F/5Y0adTqOf7du3y+3bt8vCwkKz27ZqSVqwYCFyJIyhMZLj
+KcpMpbUuXboUgJaYIg4lvE2BzWZj49hzzz2HK67QIs6PHDmCM888EwDCUhn+vyAeiXcSHTabjYOg
+li9fjquvvhoAzFJzTEuyUimIxPtAPnLi1P/555/MAU/UBQHQ+kaZnsaOHcv1DDdu3Ggq0effgkRf
+EOLhfZBS4sILNbLwX3/9dUJsHpb6YMGCBR0SRn2IRFIILL+m+vlVqCy9YBJELGmrogH1PSkpibkC
+x44dKxennwjPJRBGiWUCadVmXy8Rx4EQr/mipmkzGf9e9SEwKWdgum2j7+12Ow+2Ucpuj8fDLsLC
+wsJybsdo+6pez+VycTnz3NxcJqao4cvUd7vdrgt1pu99Pp8uCUeofkQzcQPLlxslajG6RmpqKt+f
+Gnfh8/nKkb0qSqSqfh+qn0b0bpvNxp9VmjdRpYEyspbD4WAqsc1mY5o6nR849tSnwJD5SBA4hoH0
+c3Whpc9GhV7iBUt9sGDBgg4JIylQ1KC6o4RagYOJV8FqLNL36s5MpdFtNhvTo48dO8bkFqPdOJyd
+ITCSkr6jXUL97Pf7OS8f3VNaWpqO3qyShagcnd/v552Z+hQs7Vgk/bXZbDrSk9EYGp2bl5fHY5ue
+ns679OHDh8uVdg+shhyNCC6EYG/Onj17dONJfVLnCAUUpaam8udLL72Ux37Dhg08tpQu3W63G6p0
+CscmImks8LkHQlWF/X6/7tqVpUpZkoIFCxZ0OCkNjSZfG4Bm+FNDjlVEa/Chtrt06QIA+OOPP3Q7
+u9EOQ9/VqFFDZ3+g7/v06cNFaL/++muWJsgeYoaxL1p+gBCCJQW73Y5OnToB0OjFqo4OBLcDRXJd
+h8PBaex+++23sM91OBx48803AQDnnnsuB1ideeaZnEB1714ttWhxcTHbHIqLi3luxMKhqAyDdhCE
+ZWhMmEXhRPchGIzEvWgNS5EYiFwuFx+vqjB2u51VnpYtW3KhEqos5ff7T6jFXlVD1MkfbvRoZfFM
+zjnnHABa5fJnn30WgN6IW716dQDaYkt9onwN1F8T8htUNqwoSQsWLESOhDE0JqpPWt3lVBdbJIhm
+96tbty7H+e/fv19X1IayJB89elTntgTK79SVPabqjh/ufdvtdlY7Kmv3JZVg/fr1/FntL30nhGBX
+ptfrZaNxUlLSySgphIWEWRQSAcFE2Mp8sWjSTZkyhe0FAwcO5EmqwsjWEWghj7f+KoTgEu8pKSlM
+4w6nTif1zeFwGN5LvBa1nj17Yvr06QA0uwzVxzSClJJtO0AZX8DstG009xwOB4+Let3KhKU+WLBg
+QYeQkoIwLkU/FsDlAIqh1Xi4TUp5rPR//4NWG8IHYJiUMrwigibCaMcP5m8mZGRk4OmnnwYArF27
+ltOi5ebmcsoy1S8dr513+PDhALRyZ6QStG3blj0Yx48fD9vgaVZJOyOQuH/HHXfgueeeAwB88MEH
++PLLLwGAy/UFg9vtZn5AXl4e35Pa51j6Trst9XP48OEYOXIkAH3quW7duiE1NRWAcYRq4LNWJRqz
+pDCHw4GXXnoJAJCTk8PJb2fOnMk1OPfs2VNpAWLRlqL/BsD/pJReIcQLAP4H4CEhRCaAawGcBqAu
+gMVCiBZSykoNd6NU5aNGjeLMNdWrV8epp54KoEwEPHbsGE8Ql8ulo9pu2LABAHDTTTdxVh8S5+LA
+SefMzdRHv9/PImpJSQluuOEGAMBHH33EIeNer9dwQlI+v/z8fNMnEi24VIXLbrezVf61117jfI2h
+UFJSoosMNdProBLRqIDuwIED+bl5vV4eQzXuxAjxWFRpDCnN/IQJE9i1mpSUhCpVqgDQ3KU0J3fs
+2IHu3bsDQNwzXUVVil5KuUhKSW/Gcmg1IwGtFP1sKWWRlHIHgL8AdDaxvxYsWIgzzDA0DgQwp/Rz
+PWiLBIFK0YeEWSK5x+PBV199BQBo3759uYhKoKyGgFr2XYXNZmMJon379kxkof6ZLSnY7Xb2mw8Y
+MAAAdLvod999x5KL1+tlaSInJ4fFYzW4ingM//zzD39vFkgKo1L2rVq14t1406ZNYUsm8eAj0K76
+9ddfo23btgDAKkp2djbvtFu3bmUCWFFRkeEcCQc09sFIb0bweDysHpCxMz09nZ+TEIK9Gi6Xi+dc
+48aNcc899wAAHnjggaj6Gy5iWhSEEI8A8AKYGcW5dwC4g/6OlvsOaANNL8Jvv/3GD1yNNASg01vV
+8wnECnznnXcwYcIEABopiOIgjNKpm4Hu3bvzgyb9trCwEF988QUAYP78+Vi+XFtri4qK+MVLSkpi
+1iBF99WrV48XRbPL2Q8cOJCZh6NHjwag6cM0PrQ4nChQtaiuXbvyM16wYAEA4Nprr9Ul9CXV7NZb
+b2Ux/sknn4zK4q9G4hrZe9QNLzMzE7NnzwZQ9qwPHDjAbuarr74aHTp0AAD8/vvvWLx4MQCgfv36
+GDJkCIAEXhSEELdCM0BeKMtGIOxS9FLKyQAml7aVeAQFCxb+nyKqRUEIcTGABwFcIKXMV/71GYAP
+hBAvQzM0Ngfwa5htVigt0MpfpUoVDB2q1bClsmvNmjXjHczhcHA7xcXFeO211wBoKc9atGgBAJy6
+LTU1VRe9Rwachx56yDBNvNlGp65duwIA3n33XRZhaaf5+eefcf/99wPQ1AS1P7SbjRgxAu3atQOg
+EZwAYPz48ZzD8vDhw1HFFASCDK0NGzYsR6s+/fTTWdUxY3xiUSOppJ2aj+DKK68EUP7+SXVs2bIl
+/69du3b49dewpiuAsmcVyhukfp+fn89qB32/cuVKDBw4EICWr1MtS0/5Ghs2bMjnxZt7Eo5LchaA
+rgBqCCH2AHgcmrfBDeCb0g4ul1IOkVKuF0J8CGADNLViaGV7HixYsBAboi1F/04Fx48BMCbSjoSS
+ElTXErlsgrVDMfH9+vXDqlWrAGirOelwpJupGXDy8/PRp08fAPErN6fC4/Hw7pCUlMQGMdoZBg4c
+yOxAh8OhkySoGErXrl1Rv77m+Nm6dSsAzedPOn4k1aODwel04qqrrgIANGrUCPPmzQMAZjEWFRVh
+ypQpMV1DRSySAo0FUFa120hCstvtuPPOOwFoFajJBkPG3HARjcF5+/btzPZs2LAhAODee+81LA1o
+s9l0xWBoPsSbYZswNGc1sUcgnE4newDUlFrBQIPWpEkTbNmyBYD20tMAG1VCuvfee3ViWzzg8XiQ
+mZkJAOjbty8buIqKivj+Jk2aBAA4dOiQLq6BjGgPP/wwi/P16tXD5MmTdee1aNGCReOcnJyYF4W0
+tDROP79x40bUq6c5k8iwu2zZMowfPz6ma6hwOBxRUYgnTpyo+5tIYEZo3LgxnnnmGQDaXGndujUA
+8GIaLqIR44uLi7F69WoAQK1atfg3EeRUPPLIIzrjNlXzijcsmrMFCxZ0SBhJASifzJPEe7vdjvnz
+5wPQmF20O9KO6HA4dO7GunXrAtCCipYtWwZAizok8VKVNshoFy1LTBV3Az8TyMU0aNAg3HjjjQA0
+ujLF7B8/fpyNgxT4dMEFF3C252HDhuHcc8/lvlPbxcXFfK8qX4F2nWCMx0hw+PBhzjfw4Ycf4okn
+ngBQtjuOHDnS1OAgNUFpJLj77rv5s5SSnzH1rW7duqzmXHTRRTyGR48e5erekUpV0Y7tk08+CQDM
+pVCjL4uLi1mCfOyxx/ickpKSSlFrgQRJsmKz2aTT6Sw3uYxy7qmRf7QoNGvWjCfFxRdfzMSaoqIi
+FrWNshAfPHgQp59+OoAyfS0chJN1x+l0srhHvub09HRdGC5NwmPHjvFLTS9FlSpV+Ds1O7HNZmPr
++po1a3iikw4dmPvQTGzatAnNmjXjfgCaB8cogjNaRGsHCcz3SDYYmt/Tp0/HtddeC0Bf7v3o0aP8
+Ekb60sXqBaDnWLt2bf6cm5vLmx7ZcgBtPkSq3hjASrJiwYKFyJEQkoIQQppVN9DlcrERbPbs2Sx2
+q5IC7ca1a9eOW+1GtSZBt27dAAC9evViGnNJSQmv/KtWrULPnj0BlNGI1RoEahTd7NmzcejQIQCa
+aF+Zz++bb75hqrDK4juRc4ieq+oJ2LJlCxt0qZ9Hjhxh75Pf72dex7Fjx9iIa3aOhHAhhGDvk8Ph
+YFU2LS2NJaBoqdgBOLmKwQDli5BEg+LiYn7gK1asYJ45UKavDx48GEB8i7lKKZnDvnDhQv593333
+AdBUCYpm/P7771G1alUAZZmHvF4vi+rHjh1LiLqKLVu2LEf1rlq1Ko4cORLkjPhDJQ5R35o0acJq
+I3mc1KjT7OxsVj03b94cl6jXSCCl5LmZkZHBixdwYhKtWOqDBQsWdEgYScEsEdRms6F58+YANBqw
+2u6wYcMAAB9//HFM14iFYEPn5eTkMKW2QYOycJEHH3wQgBaUlWg5APfs2cPeDtqho/UWmA232827
+qs1mY+Pu+vXrAQDbtv1fe+caW1VWBeBvpdwCViKghPCYUIiNEyQKE39AtMGIxnagYwxNYNIwY4T4
+RyqKiaHhkfgP0QQxmY4aX4mBURzHsSHByYhDCYWgM1geAnUoEIfJ4GCCl4S06W27/LHP3pxT+riP
+c+45xP0lN7333Nuzztpn33X3Wnvttftd4Zfm5mbndvT29mZqp/J8Ph8pIx+T21ASmYkp2GnFSqvu
+9Pb2On+ypqbGddpr1665oWSlUdw44h/hCDg89Getb5mljmqH5ffv33dRcut6dXZ2ukSgtLEzO/l8
+3s3c2EzP/fv3u3Ub27ZtczNDGzZsKGnmqRqMt3Q6vJ6jAvzsg8fjKZ3MuA92lFBuXQWbp7BixYpI
+erCt1Hv69OnY6iHE8Ss+Nm/i4MGDsZ07buyv1ODgoPsVsym67e3tLrkp7Wu3o60zZ87Q2NgIPBw9
+jIyMuJJnLS0tbpXneGsO0iQcbId02tSPFDweT4RMjBRExPnppezobBfnbNy40c3jhxdWNTY2ukDT
+0NAQ69atA3Cr4srd/i2cYVgu4WknVaWjo6Oi8yWJ1XXr1q2u0tPOnTsBM5du04Rt5eG0aW1tdQvM
+bDtfuXLFFcetq6ujvr4eSH90M5ZwrCmXy6WSA5IJoxBmoqIg1hCIiEvw2bJlCwB79uxxnx8YGKCp
+qQkwpdksmzZtor29HcB17M7OThfdzefzLno9tqNM5naUOxNh05IBTp48WfL/p0F3d7dro127dgHG
+QNq0XFs7Mm3u3bvnVsfaZfZtbW2uj4TLpmWNuro65/KM3YimWnj3wePxRMjUSCGXy7mRQm1trRu2
+Dg4ORoq02kw/WxRldHSUI0eOACZl+OzZs+64Pd/Nmzfd/gR22q+7uzuyacmBAwcAM0oJ7zA8dpp0
+1qxZLqg1PDxc0lz9+vXrgYdlwsCkPz8O5PN55yKcP38eMKOu+fPnxyYjrqretjq2zQG5desWK1eu
+BMzKTtsvKpEXR6m7sYQL8+ZyOeeyzpw5s2qrJDNlFEZHR9388dDQkFta2tXV5Yb5IyMjrvKQvSkP
+HjxwRTXCpdHtOcHkw9sUYxuLOHTokIs+53I5t1ZhYGBg0o4yODgYuXHWeE3VuUSEo0ePAmbYferU
+KSCZzWXCMou5tlK5dOkSYIzCeCtQ08amDd+4ccMdC7sMts0rcSWSMArz5s2LJCzZa8vlcmUZhXKM
+XvbupsfjSZVMjBRU1c082F+gadOmuWG5qrrnDQ0NtLS0AA8DWzt27HhkhBA+N5jgk/1MeGsz+36h
+UCi6LsDIyEgkaGUt+1RuxMKFC10dwH379rmFUo8jdl/G5uZmLly4ACS7x2a5WDevtrbW9RtVdStQ
+K0nTtn0gjtGYPcfx48cj57OLzcpNdy7nmjJhFODRiH+hUKC/vx8wDWYrE61atcrFEmzj2Y00JkNV
+Jx2ml9J44anTQqEw5fDTVljavn27mw61f+Nm7BczqS+pTXNetmyZW3o8WZ3N8PWBGQ6Hv5BxbCo7
+mbzR0VEOHzZ7FrW2tsaycU2xG/0Wg3VdbcEXMIV/1q5dC+D2voRHK5RBNBmu0vvv3QePxxMhMwui
+pvqMDerMmDGDnp4eALe3Xk9PT1WHrRPVZZwIu7Z/eHi46KBkHNdWbUppl/F+7Yr5v0qwfWjRokWu
+5kYaeQDjYetxLlmyxCWG7d27142Cw21i9QgvIAynR0+yXKCoBVGPjVGw1NTUuClFuynnRDrEUbQl
+Tqr1hU16B6FiZY/33DLZPav0usc7R1h+uFpU+ItV7Lkh3rYVkcjsmp3ivXPnzpT7oZTYr/0qSY/H
+UzpZGSncBR4A/0npEj7iZXvZ/weyl6jqvKk+lAmjACAibxYztPGyvWwvO1m8++DxeCJ4o+DxeCJk
+ySj81Mv2sr3s9MlMTMHj8WSDLI0UPB5PBkjdKIhIk4j0ich1EdmVsKwnROQNEbkiIv8QkR3B8bki
+8rqIvB38nZPgNdSIyN9F5FjweqmInAv0/62I1E51jgpkzxaRl0XkmohcFZE11dJdRL4VtPllEXlJ
+RGYkpbuI/EJE3heRy6Fj4+ophh8F13BRRJ5KQPb3gza/KCJ/EJHZofc6Atl9IpKJwhqpGgURqQFe
+AJqB5cCzIrI8QZHDwLdVdTmwGvh6IG8XcEJVG4ATweuk2AFcDb3+HnBQVT8K3AO2Jij7EPAnVX0S
++GRwHYnrLiKLgG8An1LVFUANsJnkdP8V0DTm2ER6NgMNweNrwIsJyH4dWKGqnwD+CXQABH1vM/Dx
+4H86g+9Eutg86TQewBrgtdDrDqCjivL/CHwB6AMWBMcWAH0JyVuM6ZCfA44BgklkmTZee8Qs+0PA
+TYI4Uuh44roDi4B3gLmYlbnHgC8mqTtQD1yeSk/gJ8Cz430uLtlj3vsycDh4HunvwGvAmiTufymP
+tN0H21kst4NjiSMi9cAq4BwwX1XfC966A8RXXyzKD4HvADZh/cPAf1XVrulOUv+lwF3gl4H78jMR
+qaMKuqvqu8APgH8B7wF54C2qpztMrGe1++BXAVtII7X+PxlpG4VUEJEPAr8Hvqmqkeosakx27FMy
+IrIBeF9V34r73EUyDXgKeFFVV2HSyiOuQoK6zwG+hDFMC4E6Hh1iV42k9JwKEdmNcWEPV1t2KaRt
+FN4Fngi9XhwcSwwRyWEMwmFVfSU4/G8RWRC8vwBIYnPBTwPPiMgt4DcYF+IQMFtEbLGbJPW/DdxW
+1XPB65cxRqIaun8euKmqd1W1ALyCaY9q6Q4T61mVPigiXwE2AG2BUaqa7FJJ2yj8DWgIotC1mKBL
+1xT/UzZi1r3+HLiqquHSO13A88Hz5zGxhlhR1Q5VXayq9Rg9/6KqbcAbQGuSsgP5d4B3RORjwaF1
+wBWqoDvGbVgtIh8I7oGVXRXdAybSswt4LpiFWA3kQ25GLIhIE8ZtfEZVw7sbdwGbRWS6iCzFBDv/
+Gqfsskg7qAE8jYnI9gO7E5b1Gcyw8SLQGzyexvj2J4C3gT8DcxO+js8Cx4LnyzAd4TrwO2B6gnJX
+Am8G+r8KzKmW7sB3gWvAZeDXwPSkdAdewsQuCpgR0taJ9MQEe18I+t8lzAxJ3LKvY2IHts/9OPT5
+3YHsPqA5yX5X7MNnNHo8nghpuw8ejydjeKPg8XgieKPg8XgieKPg8XgieKPg8XgieKPg8XgisIq9
+LgAAABJJREFUeKPg8XgieKPg8Xgi/A+7RzdpakO0cAAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/2... Discriminator Loss: 0.9938... Generator Loss: 1.1336
+Epoch 1/2... Discriminator Loss: 1.2817... Generator Loss: 1.7325
+Epoch 1/2... Discriminator Loss: 1.0349... Generator Loss: 0.8968
+Epoch 1/2... Discriminator Loss: 1.0611... Generator Loss: 1.3221
+Epoch 1/2... Discriminator Loss: 1.0041... Generator Loss: 1.2203
+Epoch 1/2... Discriminator Loss: 1.0210... Generator Loss: 1.1737
+Epoch 1/2... Discriminator Loss: 1.0761... Generator Loss: 1.2211
+Epoch 1/2... Discriminator Loss: 0.9741... Generator Loss: 1.1375
+Epoch 1/2... Discriminator Loss: 1.0377... Generator Loss: 0.8915
+Epoch 1/2... Discriminator Loss: 1.0852... Generator Loss: 0.9029
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJztnXd4VFXawH9nJpNJQiC0EKpEBCxYsCxFENtiZ1F07a66
+9ooNFixYdte6n6vrLmLDCqIiKChWFEUpAitgQQGpIi10kdQ53x83582dkJAp904G9vyeZ54kk5l7
+zz333HPe9z1vUVprLBaLxRCo7wZYLJb0wk4KFoslCjspWCyWKOykYLFYorCTgsViicJOChaLJQrf
+JgWl1ElKqR+VUouVUkP8Oo/FYvEW5YefglIqCCwE+gI/A7OA87TW33t+MovF4il+SQrdgMVa6yVa
+61JgDNDfp3NZLBYPyfDpuG2Ala6/fwa61/ZhpZR1q4wRpVTU70bSs56pNWP6y/YPAEVa6/y6PuTX
+pFAnSqkrgSuTOUYgUCXo1HTT6xoIgUCASCQS93ndD2Mi342lbbWRkVF1y7KzsykuLgagoqIi6mcy
+xHt95prcE1YkEon6u/pntda+PqhmbJj+ikQiUfc61sk0mXudhiyP5UN+TQqrgHauv9tWvidorZ8G
+nobEJYVEJgI3iUwI8Z7Dy+8ClJeXEw6HAWcCKC8vBxK/lpqIt43mwcvOzmbbtm27PI5fD1j1h9dM
+Cg0aNABg27ZtCfVRqieEdJiE/LIpzAI6KaX2VkplAucCE3w6l6w69d2ZqUBrLatefn4+DRo0kIFf
+X4RCIUKhEA8//DCdO3emc+fONUoJflL93ps25ebmkpubK5NnuhPvGFZKed7XvkgKWutypdT1wAdA
+EBiptf7Oj3NZLBZv8WVLMu5GJKg+/K8akXJzcwG4/fbbKSkpAeDee++tt/Y0bNgQgFNOOYWLL74Y
+gPPPP5/NmzfXW5vM2DBqhJGw/seZo7U+oq4P1ZuhMVGUUmRmZgJQVlYG/G9NCoWFhXz55ZcAfP/9
+96xZs6Ze25Odnc2FF14IwNChQ2XC6tatGx9++GG9tat58+YAXHmlY8v+6quvmDVrFkC9TlZekJ2d
+DUBxcbEvY9+6OVsslijSTlKoSSVwG1ICgYD8r1OnTgCUlJRQWloKwC+//CIio/t7XmzVpQNTp06l
+VatWABQUFJCfX+e2s+ClumWktSOPPJK//vWvgKNGmHP8/ve/5+OPPwa83RmJhUAgwG233QbAzTff
+LO8XFRUBcN555/H9945z7YYNG3YbtcL0uRn3q1atYuPGjYC30nLaTQq1XZx50IPBIDfddBNQpUeH
+w2H53q+//kpOTo58xwzSSCTC9OnTAWfAAqKP7060adNGft++fXuUr0ZNVHd2guQGUCgUAqBPnz4A
+jB49WrYhv/76a5o2bQrAyJEjpW2pfuhycnJo37494PQROGPBqDbXXXcd1113HbD7qJ6ZmZn83//9
+HwCnn346ABs3buSQQw7x/FxWfbBYLFGknaRQE1prEZOHDRvGRRddBCCiU+PGjZk4cSIAy5YtY999
+9wUc0dZ8LxAI0LNnTwC++OILAHr37r3bSAsHH3zwTu8tX76cDRs2xPR9L/azlVI888wzAJx77rkA
+LFq0iKVLlwLw2WefSX//9ttv4j+xZcuWpM4bK0YyKS0t5amnnpJ2APTo0UPudXZ2No0aNQKqVAov
+8NrxSClFMBgEnJ2d/v2d8KEdO3YAiLTjNbvFpABVE8C0adNkAli4cCHgPBy1OaeYB2Hs2LHSqa1b
+twYctSOdJwXT9muvvZYnnnhC3jf2k5omiurU5uabCIcccojsNJiB2a9fP3mwsrKyaNu2LeDovVOm
+TEnqfPFirq+iooJFixYBsNdeewHQsmXLqM+uW7cOSE598MNBKxgMiv0rGAxSWFgIwFVXXUWzZs0A
++PzzzwHnWfADqz5YLJYodhtJwczoY8eOFd9/Y+Da1Qpovnfttddy0kknAVViphEt05FQKMTs2bMB
+OOigg6L+Z5yFYsGsOl4Y+84880xZHcePHw/AihUr5Ni//fab+Ad07txZJAW36pIKo2NFRYWI3fvv
+vz/gSDHG6Lh69WqRdJLBy2syBtwmTZqI9FpSUsKll14KOOrP8uVOPNN5553n2XlrwkoKFoslit1G
+UjCzYiQSkZk0Hn2wa9eusnoY+0OivgteGZTMSpOZmSltM27C9913n+iQbvLy8sSTMxbc3m/J+mq4
+bTfPP/+8vO8OnTZ76b1792b06NFA1bag+7O1tSXRcPbqtGvnBOm6Dc2m7YMHD/YkQMqs7l7Ypcw9
+dftNdOnShQsuuABw7EjDhg0D/PfI3G0mBXf8ezwPpLGA33nnnSIyjhs3LuqYibYlEcxDkZGRQbdu
+3aSNjz/+OAAdO3YEkEnCnK+goAAgKjQ5Fsw1e/Ggbdq0iRdffBFAjLabNm0Sv48LLrhADJsTJ06M
+6qdYJySvROITTzwx6u/t27czatQoANavX+/JOYzB10vc/eS+70VFRbz55puen68mrPpgsVii2C0k
+hURFyoyMDB577DEAunfvLgbGrKyspNqTqPqglBLJpXnz5lx99dUA7LfffiLmuiUEw4ABAxLeT/dy
+33z27Nk899xzQFU7r7nmGvn/6aefzldffQUQ5T8RT395oZoFAgHxejX3fMKECQwePBhwvF53By65
+5BJRx4YPH54y78u0nhSMPlyXpVgpJTc/FArRtWtXwNGjje57/vnnSwcbd9dEB2CiN8ecFxxL/ZAh
+Tub7ESNGsN9+++30+RkzZgDw9ttvJ3Q+8HZS2Lp1q9wTo09rrZk8eTIA7733XtJt8KK9WmtJU2cm
+4datW3uy41D9PH5gJtz169eLOpHMGIgXqz5YLJYo0kZSqGnVNlZdt+dYRkaG7NOb1SA3N1ei3jZu
+3Mhnn30GwPXXXy/75pFIRKSJc845R94zLtPJtnVXmJk/EAiItNK8eXNxx27Xrp28b4hEIvTu3Tvu
+ttXUVvBmVQuFQjupNxs3bpQAnXRBa73T7sIxxxwjrs+XX355fTQrZg499FAA/vKXv8j9a926tYxx
+v0mbSaEm3HYEYwdo3ry5+NIbp57hw4eLWNu4cWOefPJJwBkca9euBZxtSHM8M7B79OghbrArVqyI
+uV3xPmBGBNy6das4XgEySFu1aiXiuPnsrFmzPLHEeyninnnmmTtFZT700ENp6QT20ksvATBo0CDA
+mRzNAjBmzBgJ60438vLy+OSTTwBn/BsHtvnz58u49TsNgFUfLBZLNO5MyPX1AnRlnsY6X8FgUCul
+tFJKh0IhHQqFdEZGhg4EAjoQCOiMjAwdDAblcx07dtQdO3bU69ev1+Xl5bq8vFxXVFToiooKvXLl
+Sp2dna2zs7NjOrd5xdrWXb3OPPNMXVxcrIuLi3UkEpE2lZWV6bKyMl1YWJj0OQDpl2SOEQ6HdTgc
+1lu2bNGRSCTq9fLLL3vSTq9fM2fO1DNnztypvZFIRE+bNk3GSH23E9CNGjXSU6ZM0VOmTNHbt2/X
+69ev1+vXr9fTpk3TGRkZMqbNuE/iXLNjeR7TWn2oCbfoVJNnX3WR26gdgUBAvmucTi677LKELNLJ
+iOTGueeqq66KsuAbEfzrr78G2MnGkCjJqiChUEgy/ZSWlsq1G13Xr0i9ZAgEArIDZe651lrGy5o1
+ayQRz6+//lpviVZM+Pbnn39Oly5dAGenberUqQBceumlUfevelSmX+226oPFYolit5MU4uW4444D
+nOgzM7OaVfmjjz5KeXtMmrDjjjtOZv6ioiLmz58PwLvvvgtU7azUF8ao1b59e8mR8Morr0hiD7fE
+Ywy76cKcOXOkb43k9fnnn8uu09q1a2WV3r59e8olBSOlmAjITp06sXr1asBJMWh8EoqLi+slf2TC
+koJSqp1S6lOl1PdKqe+UUgMr32+qlPpIKbWo8mcT75prsVj8JhlJoRy4VWv9X6VUQ2COUuoj4BJg
+stb6QaXUEGAI8Jfkm5oYHTp0kN/N3vVbb70F+KeT1UYoFJIcA24bxzfffCN70Oa9dClz1rx5cz79
+9FMAFi9eLLH8LVq0AJDts3TAbJfuv//+fPPNNwCcdtppgJMSziS9PeWUU5gwwaliuHbt2pSuxu3b
+txcJwUhdixcvFmlr9OjRnntexkvCk4LWejWwuvL3bUqpBTgl6PsDx1R+7EVgCvU4KQwYMEB+NyKl
+iTlINXvvvTeNGzeWv00I7NixY8UIts8++wDQs2fPlEXF1YR5gObMmSOTZ1FR0U7Vmo2jWDpgUt9D
+Ve5FE+dQWloqYvlxxx0nqezef//9lLTN9Oe0adPE3f2XX34BnHHw7bffAnW79KcCT2wKSqlC4FBg
+JlBQOWEArAEKavlO0qXoLRaL9yQ9KSilcoE3gZu01lvd2yZaa7OvuhO6Wil6v0R5k/C1bdu2YmBM
+dbJWI9ZecMEFYsBbtWqVRBgWFRXJSmKScta3WG7K0Wmtpf39+/eX9pu+rC0Iqj4w0sHcuXNFDTN9
+/P3330vRmoyMDFGJgsGgb6qaeRYaNWrEAQccADhBfkYqNH34wgsvSPBbOpDUpKCUCuFMCKO01uMq
+316rlGqltV6tlGoFrIvlWF5l3KnWPg488ED52+z/+klNsQbmoTrppJMkpDgnJ4eTTz4ZcCYCY/sw
+6kN97Iy4cScQMSrPihUrxG3cnU49XTCT/aRJkyRj0dFHHw04/hrGFX779u2SzdlP243po379+knS
+l1WrVolKM2LECMDZ1UmnCmbJ7D4o4Dlggdb6Ude/JgAXV/5+MZC6mE+LxZI0yUgKvYCLgG+UUnMr
+37sdeBB4XSl1GbAcODuWg/lhAd5nn33kuIFAIKquoF+4Pf6M1GC861atWiX74+FwWETKiooK2af+
+5z//CaTP7gNUGUQ///xz7r//fgCpq5CO3HfffSxbtgyoKi3YqlUrMeLddddd8n8/MV6p77//vhgS
+w+Gw3NsFCxYA6VfnVKVDLb3a7A7J0qxZM3nYAoGAhFynysJrrMwmfBuqRPEmTZpIgZL33ntPnIG8
+rFhkqXLCCofD4hBWnwVlva4iFSdztNZH1PUh6+ZssVii2KPdnHfs2CGZe3/77TcJQEqVpGAMSrtL
+TsA9ESOa10fOh5qMzr4FMbnyXEQikaSS6+zxk4Kpo7B48WK2bt1azy2y/C+RSjWhukqUzLmt+mCx
+WKLYow2NsHNRFahfQ1NNuHcqqrsRW/Ysqjn3JX0s9zFiOLY1NFoslvhJG5uCH1s1gUBAjhkMBsXo
+lOxsnUwxmOqZi0zb3PUYITqrlNY6YcORl9mcvaCuvk/Fll31DEbGSBevv0A8fVv9/sb6verf31Ub
+vOq3tJkUEn3IdvVdd0d5aQFO9PvV21DTDa1tYCZ7znreH485lVgq2lj9HIk6D8XT1voac4lg1QeL
+xRJF2kgKEP9qVtdnq/+/vkXo6tdnxFatte/Gz/q+dkO6tKO+2B2uP60mhWR059owDkv1GUvgrnO5
+9957A048gSkrn47FVLzG7AKlU0yHpWas+mCxWKJIG0nBiNZeSAhmZX777bclZ8E999zD3/72t6SP
+HQ9GSjnyyCMBJ+FHr1695P+jR48GYOjQobuFWJkMqYwEDIfDsntjAs0OPPBAHn74YcCJVv3zn/+c
+8nbtLqSd81Ky6kMgEGD48OEAXHHFFXK8zZs307Rp02SbGjPhcFhCpq+66irASbZhoiQjkYgk+rjg
+ggv48ssvgT1nkGZmZsoD2bBhQ0mAsn37dqDmQj7J0KZNG8mmZBLVQO1beUZ1a9++vYSG/w9gnZcs
+FksC+FkjMtYXHtblU0rpwsJCXVhYqFeuXCn1AysqKryoxRfXq0uXLrpLly56xowZesaMGXrjxo26
+pKREl5SUSO3IiooKvWHDBn3sscfqY489NmVt8+N1xhlnRF1faWmpLi0t1VOnTtW9evXSvXr1krqU
+Xp3T3OtFixbVWDeyrteWLVs8qbe5m7x2r1qSXjnXaK0lq851113H2LFjAUe3NGLl4sWLkz5PLJh8
+jMbyvnTpUsm81KBBA0my0qRJEyZNmgQgeQTTHaMadO3alddeew1wRHF3CK/5fcqUKbWK8cm24ZJL
+LgFqr6hl1JVNmzZJ4tYzzzyTHj16AE6t0XA4DKQmpF4pJX3XrFkzwNl9Mu2MRCIJPQehUEh2dpLd
+xbPqg8ViiSJtDI2BQCBqNUl0xnQTDodlBg4EAvz9738HnBx9qcBcz1577QU4q9amTZsAJ0XbrFmz
+AKRWI8CSJUsA6NixY0raGAtG0ikoKODFF18Eqox54XBYDLi//vqrXN/gwYPl+kzRE6jqEy+ctTIz
+MyVL8m233SZVqz7++GPuu+8+AEmyUx2Tmu/bb7+VjN/G+Ognubm53HHHHQB0794dgAceeIA5c+YA
+TiWrmmJ0MjIyaNCgAVBlpA0EAlJV3fwNsG7dutoc46yh0WKxxE/a2BQgeV2oOsFgMOo4qS5xZs69
+fPnynf63du1abr/9dgBeeukleb+gwCmo5Y7qrA/MfcjPz+fwww8HHB04Pz8fqLq2l19+mXvuuQdw
+akDUJQF4KZlWVFQwffp0wLEf/fjjj0Bs251GgnzkkUdSWrsiLy+PCy+8EKjKkr1o0SKRsILBoEhm
+FRUV4uvy5JNP0rdvX8CpvwFO3QiTJXrx4sViV3E/R4lIZGk3KXg5aI488sioJCum+lK68OqrrwLw
+n//8R8RZIyKeeeaZvP766/XWNjOoOnfuzE033QQ4Vav+8Y9/ALBw4UIA5s2bl/KKW4ZIJCIPU7xZ
+sM04mzt3bh2f9JZhw4bRunVroCrLd25urrSnuhu4MUZefPHFohKYsRIMBqUwcfWJMBl38qTVB6VU
+UCn1tVLqncq/91ZKzVRKLVZKvaaUykz2HBaLJXV4ISkMBBYAjSr/fgj4p9Z6jFJqBHAZ8GRdB/Fa
+SgA49NBDo/5Ot2Acox5ccsklsq1nJJt//vOfvPHGG4C3IncsKKXo3LkzAKNGjZKALaWUiKumRqdX
+nomJqI1a66RzISxYsCBKVarNMJks5hznnnuunNv03U8//VTjd5o3b87KlSuBaHWyZ8+egCOt+TE2
+kq0l2RY4Ffg7cEtlKbnjgPMrP/IicA8xTAp+JAG58cYb5fdIJJJ2uRkNkyZNYv78+UDVRNaoUSPR
+J1Ol8xrxtGXLltJ3X331lVSy6tq1K0uXLgVg9uzZ0s50dhM2PgEVFRXyeyQSEav93XffLf4i5eXl
+vPzyywDMnDkTcPR+L8aNURmys7NlcTL1Lt0+FoFAgE6dOgEwffr0KPvCZZddBiC2E79IVn14DBgM
+mF5rBmzWWpsl+WegTU1fVEpdqZSarZSanWQbLBaLhyQsKSilTgPWaa3nKKWOiff72sdS9GaFNeXd
+IfU7D/FQVlYmK4JZlbZv3y7750uXLhWDml9kZ2eLRNCuXTvxk2jSpAnfffcd4Oz5m1qS5n4NGTKE
+d955B6jdqzAWkr3/Sqkacy26VUa3qmPaOmrUKPFkPPjgg/nXv/4FIOUGf//733siqRkfCLdR1hQJ
+ys3N5ayzzgKcOpjGAJmZmSnXcu2114oU4zcJOy8ppR7AKTBbDmTh2BTGAycCLbXW5UqpnsA9WusT
+6ziW9lJ9+OabbwDo0qWLvNehQ4eUFBVNhKysLHHwMVGUO3bs4NlnnwXg4YcfFh3ea1dco+vm5uZy
+zDHHAHDCCSdIO7TW4ly1fPly2TLt0KED4KgPxhr+wgsvMGjQIMAZ/O78kAYjwnttizBtTQa3Dm+K
+ww4YMIC3306+cLpZqNavX09OTg5Q1V7zv+qUl5czZswYwLE7eaDG+Ou8pLUeqrVuq7UuBM4FPtFa
+XwB8CpxV+bGLsaXoLZbdCk/cnCvVh9u01qcppToAY4CmwNfAhVrrXW5ke1kMJjs7W8QypZTsX7ds
+2TJtDY2FhYUiopsVNBQK8cwzzwAwfPhwKf3ulTRlVlgjcufk5IioqrUWMdctlmutZQXNy8sDHB+L
+U089VY5lJJnnnntOdk+WLVsm51u7di3g3U6QMRjG4jgVC8apyQSmffXVVxI8lQzm+r/88ktxBqtJ
+QtBay/j9+eefueWWWwD46KOPasxMHicxSQqeOC9pracAUyp/XwJ08+K4Fosl9aSNR6NXNoV169ZF
+FVQx+//pKCWYdrZv315SxZ1/vrOb27FjR7GBLF++3DdfBRM2XFxcXKueX5PhzvguDBgwQFbSF198
+UbbeOnfuLF6PmzZtkv738jqUUtx7770A3HHHHUnf42AwuFPousnmlCzmuu+44w5xa3e7jJtw/nPP
+PVfsGn/6059EUnjooYckaMq4OT/44INyz7wc32kzKSQ7WIxYGwgEpKMmTpwoImwgEEi7icE8kNu2
+bZPdBWNQDAQCks7NTz8F85AnY/gzg/Xuu+9m2LBhgJOP0lT59iLitSaysrJE7fLi3l5xxRU7vWei
+Lb1iypQp4oticne+9957ch/cVc0mTpxI//79AScq1ewOLViwQD47ZcoUwHGAMpOJzadgsVg8JW0k
+hWTVB+OeGolE+PDDDwG4/fbbKSwsBBzRMN0kBWPMa9iwofhU9O7dG3D6w2Ru8mOVNcf0Qgox25cP
+P/ywGCB79OghWa/8Un3uuusu6bdk9vBNNiyT8Beq+iUZ34ua0FqL8XvixIlAdGV0qJJ6li9fzgkn
+nAA4Rsk+ffoA8OabbwLOtrDJWWHGivs8iZI2k0IyF3HwwQdLdOG2bdt46qmnACdhhXnwGjVqJKJ5
+OiSWgap2fP7557K7MHjwYMAZBF4PSD/Iz8/nrbfeApz+Npbzd99917dJ2DxEhx56aNIW+ZYtW4qO
+DlVORn/605+SOm4sGBG/oKBAXK0XLFgQVRzIPXkbV3jjWHX44YeLO7rZNfECqz5YLJYo0kZSSIYN
+GzbIXnpeXh6PPvooAAMHDhSxqmXLlmJMM7NqutRY0FrL7G+MRR06dBDXV5O+Kx1wRxSCE8x1yCGH
+AE5uBWM889M4aiSQ3NxcWWHjSUpTWFgoae/crFu3TlLnpSIIzfhYnHLKKbzyyivArj1WjZpj1LXn
+n3/eF/f3tMnRmOwxTOfk5eWJyLVs2TKefvppwIksM5F9xtXWz62+RDE7DnPmzBGLtNuxqD5p2rQp
+7777LoBE8q1evVq2fR999NGU1sW84447uPnmmwHHpvD4448Djh3AuBIPGTIEgD//+c9RmaYNWmt5
+2LwUwXeFcVoydpB77rlHtm9rU7kyMjIkcvPnn38G4Oyzz443wY3N0WixWBIg1YVfanoBSRfj6NGj
+h+7Ro4cuKyuTQh/FxcV6xYoVesWKFfqEE07QOTk5OicnJ62Lf3Tt2lV37do1qlhM8+bNPT1HbQVx
+TL/k5OToDh066A4dOuirr75aL126VC9dulRv27ZNb9++XW/fvl3Pnz9fz58/X/fr16/e+ioQCOjy
+8nJdXl4edxGY0aNH69GjR9dLu/Py8nReXp4uKirSRUVF+pxzzqm1UJF5v0+fPnrq1Kl66tSpun37
+9rp9+/aJnHv3KgaTrBg/Y8YMwEng+X//93+A45hz3HHHAd4WgPEjIYzB7Yxjdh+MY5aXVA8vP+mk
+k+SaHn74YbEdZGZmyu+lpaUcffTRAPz3v/+VdtYXkUiEM844A4A33ngjqp/MtRiVYPXq1aKapaLo
+y64w6oqxI3Xs2JHc3FzAaZtRc5o1aybRqEceeaQ4V9WUCNhLrPpgsVii2GMMjYZQKCQW5CVLlqSd
+ITFWzjvvPNktefPNN327DrMqud1rmzdvLu8XFxeLpLBjx456X2VrQymV8vR1iaCUkvgQ42Mwfvx4
+MdzeddddYkj84YcfJFvz8OHD0z+fgsVi2TPZ4yQFqIr137Jli5eHTSktWrSQcmw//PBDPbfG4hU5
+OTkiKRjX/C1btkSVGDTjdtu2bV5vRcckKexxk0IoFBI/hHQVdS3/u7hjcOrh2bPqg8ViiZ+02ZL0
+ioYNG4onoJUUvMHL+p7/69SkDlT3tKzvaN60mRTM3n88A7CmTL6bNm3aIwavUqrG64v32rx4oNOt
+P73M4Ozu54YNG8rOhVFBi4uLZZGpfi7zMMfzENfk4xLL993X7H7PnT/THDfZScWqDxaLJYq0kRS8
+WsnSbVVLFKWU5A0oLy9P+Lr2lP5w4+U1uVdYd/4K815FRUWt5zMrcjzSmJf3sbrE5FW/pJWkYESr
+WC/Oy45IN0y9w6ysrD3iGqurQ+lISUkJoVCIUCgkfZ/ObdZaS41UkwfTi7GS1KSglGqslBqrlPpB
+KbVAKdVTKdVUKfWRUmpR5c8mSbfSYrGkjGQlhceB97XW+wGH4JSkHwJM1lp3AiZX/h0T7lkunWfo
+VGHcikOhUFqvtKZtgUCgxnYqpcjNzSU3NzetrwOq+rxr16507dp1p5TvNWFWaL+vqybjsx/V1JMp
+MJsH9AEuAdBalwKlSqn+wDGVH3sRp0jMX5JpZG2YaLMbb7xRUnGXlZWxzz77AFXJKHYnjDX5sMMO
+k9qOI0aM8Kyikte0b99eis7+8ssvPPfcc4BTm8DEblRUVEjuw3QmIyODww47DKjKitSiRQvJO1md
+6rsPfqt55viJ7HrEQzKSwt7AeuB5pdTXSqlnlVINgAKt9erKz6wBCmr6si1Fb7GkJ8nsPmQAhwE3
+aK1nKqUep5qqoLU2iSN2QlcrRW/EIrfFvSaUUlL52FRQcsfRh0KhqOy8xo+8oKCgxug593nNueNM
+ceUJJn3YNddcA8ANN9xAs2bNACd+3qT1rm+jo+kv479/6aWX8rvf/Q5wch/+8Y9/BOCpp57i3//+
+N0CtK2260b17d0k3Z4oImb93Raqcu9w+Cb6eJ4nv/gz8rLWeWfn3WJxJYq1SqhVA5c91yTXRYrGk
+koQlBa31GqXUSqXUvlrrH4Hjge8rXxcDDxJHKXqzHVlXVJhSSnSpf/3rXwDceuutUQYY9+8mYrK4
+uFhm2E8++QSAuXPncsoppwBO7oWrrroKcHTjVBIKhUSXNbYRpZRk+J06dWrC+qPX+ueZZ54JwGmn
+nQY4GYExKhFNAAAgAElEQVTM/v7WrVsl8m/lypUJeWH6mdWqNowxcfLkyeLJOG7cOGDXba9eudsr
+u4/JC5Gbmyu2mL59+4p0ZrJn33zzzb4k9E0qSlIp1RV4FsgElgCX4kgfrwN7AcuBs7XWG+s4jq5u
+uY23XQ0bNgScdNn/+c9/ACT0uDaKi4v57LPPACeNW01pv1NB9+7dee+994CqAbpw4UJJNVZf7arO
+FVdcwU033QQgxXdee+01WrRoAcCoUaNkcnvhhRdYty79hcScnBxRzU488USJlzFjJ1WqpJkIJkyY
+IFWhoGqiKS8vF+OnUcc6d+7M2rVr4zmN/6XotdZzgZpOcnwyx7VYLPVHWuRTCAQCOhwOR83KXrRL
+KSUSxKxZs2jXrh1QZczcvHmzJPM0STRjPa4X7TOrw6JFi6SoiREHL7zwQsaPH5/0Obwwgp133nkA
+vPTSS3K8Bx54AHASzRrVpGXLlnIPn3vuuYTE6VSoD0opqeb87LPPRkmUpkz8JZdcUudxzDhKVIQ3
+Vce3bt0qYyEWTP8cfvjhzJ07N55T7l5JVvwqFW9uXH5+Ptdeey0AV155JeDow/Upmhs/i4ULF4ra
+YEqL/+lPf0qLzFF9+/YVdaxVq1Z8/vnngFNcBRxnnyeeeAKAo446ij/84Q8AfPvtt763zR1y7B4/
+7nFk+jUSicju00EHHST9nJeXJxPd5s2bZeHwqzBMZmYmP/30E1CVSbs65pmcNm2a5G7Mz8+XdhqV
+oXXr1vFOoDbJisViiZ+0iZL0Q1Jo3769lBJbtmyZ7ERMmDABgKVLlyZ0XK9EXGOsa9q0qVjwTT2F
++vYANG0bMGCAiLkjRozgoYceApxVFaBbt24ceuihANxyyy2yCvqJEbVzcnKkTF31+2FWVTOmGjVq
+xOGHHw7A448/LlKaezfrlltuiUtCMFKo26PR7F64A9nOPvts/va3vwFOpuzqqoI7kGnq1KmcffbZ
+gGM8Hzp0KOD4g5hrMjUi/JLy02ZS8GpCUEqJvjt48GA57rJlyzjnnHMAJG12fYcjG0efsrIyGSjG
+slzfap0Z3B9//DEffvgh4GzlmgnCbN/ed999MhF88803vme7CofD9OvXD3DuqSnPHg6H5SFt3Lix
+PNxmwrrxxhvp1q0b4DyYbszOj6ntGCvV3ZvdaeabNGkiaqrZsQFn8TNqjJnQxo4dyz/+8Q/A2co9
+/fTTAbjooouk+I6bBQsWxNXOeLHqg8ViiSJtJAWvWL58OW3btpW/jTGqsLBQdhjSJXejCRgKh8Oy
+evTt2xdASt/VBw0aNJDV9MMPP6R9+/aAIwabKs/mvaysLClLnwqjrdZadjgKCwvFxb1fv37idHbP
+PfeIqrhhwwbAUTXcadXMjkFpaSnDhw8H4t9FMGPLfE9rLWrgmjVrGDVqFOC4Shu1q7y8nL/8xYkP
+NFJhMBiUY3344Yci0bjVjNLSUrlWE/AXDAZ9cV5Km0nBK3F5/PjxsstgxElwbuAHH3wAONbndMA4
+qQQCARHXn3/++Xprj9FZmzZtSp8+fQDo2rUrF110ERBtqXezcaPjm5aRkSETnV8Eg0EWLVoEwMCB
+A+Xh/+Mf/xjVNiPam8ntt99+EzvIihUrpL83btzIRx99lFBbahqz5r2ysjKp16GU4sQTTwQcO8G+
+++4LOKoCOP1mdj322WefqMnAPPSRSER+nz17tvSFH5OCVR8sFksUaeOnkJGR4XnOgJNPPpl33nnH
+nEPeN7sORgyrL4waEw6H5dpNlewvvvgipW1RSkmk5v3331+ndGBwW863bt3KyJEjARg0aFBC0l9d
+OzuBQECs72+99RYHHngg4Bj2jAqWmZkpqqKJNIUq34MGDRrINY0fP16uNV5jdzyOYcZF+frrrxdJ
+wBhwlVJiwC0pKeHiiy+Wa23cuDEAv//97+V3c52tW7eOd5fK+ilYLJb4SRtJwS/3VuM+vGrVqp1W
+vOOPP55PP/3U83PGQpMmTcQwlpmZKdtTpr1+edRVx/RJfn6+rFYffvihSA1NmjQRyWrMmDFiADX3
+Kjs7W1yDTzvtNDneUUcdxbRp0xJqj3uLz2BsABUVFVFeqsZFuVWrVhxxhLMIjh8/njVr1gBVxrqh
+Q4dy8MEHA3D00UdTVFQEONuoRt/3s6aG8fsw9i5wXO/BMUouXLhQjuU+nrnubt26iTep+X/Xrl35
+7rvv4mmy/wFRXuLXpGAGR+fOnaP2tAHeeecdMUSlekfil19+kXYUFxdz1113AambDAzmASsrK5P9
+/3feeUcS1YwcOXKXbSorK5N99SeeeEL8F/r375/QpBAIBGo0nrkNmEbM/+WXX2Ri/e6773ZpMBwy
+ZIj4p0DVw/b000+nxCfETCB9+vSRfjGu4Js3b641zNyolfvuu+9OlaTGjRsnRksvseqDxWKJIm0k
+Bb9n659++klWNGN8LCsrE4+3RFa1RDB7zZmZmbIiHnXUUSLFpAqz6pifwWCQyZMnS9tee+01oMrr
+LhaGDRsmXporVqyIMp7Fita6xlJosXyvJoxEcMQRR/DMM88AjnfjsGHDAHj77ZhyAMV1zpowgVlL
+liyRrUojBcSyrXjUUUftdN4LLrgg5vPHQ9pMCnXpZ2agZGVlxTVQ3d831ltzrIYNG3LbbbcBTkah
+VIiRxg9BKSUZoL7++mu5fjOIvd6Jqa6emd+NJbuoqEge3g0bNrDffvsBMGPGjJj7ZdOmTZJkZdKk
+STLhPvjgg+IKXdexlFKe7L2be3zDDTcAjruzydQ1depU5s2bByTXz7FmtVJKMWDAAMBxACssLATg
+yy+/lM/UNe7PP//8nT5r4mS8xqoPFoslirSRFKrPtjk5OSJqjxw5UkR/QLy/Vq1aVedxzUx72WWX
+0b1796hz7dixQ2oWpGoXxqRYgyqxulOnTkyaNAlApJni4mJeffVVwLFc33LLLdLmRKh+fTVdrzHm
+lZaWSl/17NkzLpdr4x+waNEi2f8fNWqU1OBw12uMpZ2JEAwG6dWrF4DkdygqKhJDY0VFhUgjtRk2
+YyFWvwa3GvTrr79KjoQuXboAVTk0arqOOXPmANEZy40a5FvJetPg+nwBuvpLKaXD4bAOh8P6sssu
+05FIRF5lZWW6rKxMV25l7vRd96tPnz66T58+uqSkRF7FxcW6uLhYb9y4UQeDQR0MBnd5jJraFs/n
+zSscDuuioiJdVFSkKyoqpD07duzQpaWlurS0VG/atElv2rRJr1y5Ul933XX6uuuu0507d9aBQEAH
+AoGEzhvvq3Xr1vqJJ57QTzzxhH7//fd1ixYtdIsWLeI6xrp163RFRYWuqKjQf/3rX2O6V4AOhUJJ
+t//yyy/XK1eu1CtXrtRLly7VS5cu1Xl5efL/QCAQc3u8epk+/PTTT6VNq1at0qtWrdJHH320jPVg
+MKgzMjJ0RkaGXrlypfRhJBLRv/zyi/7ll190QUGBLigoSKQds2N5Hq36YLFYokgb56W6PmOcad5/
+/33Jq2gsuu7Ap59//lkCX/Ly8qRwjKnHCFVi11133SW5F1JBIBAQleD444+PSj8/c6ZTPuOkk04C
+vDc0xotJQjJgwAApujN9+nRRx2pTY4yBctq0aeLau9dee0nQlFvkrcm4nKi/ilJKcg/cfPPNsn9v
+1LJbb721xlqlqRr/pt9OO+00XnjhBaBKVdy2bZv4LEQiETHWhkIhad/SpUu5/PLLgaRc4HevHI3x
+fN5ko/n73/8e13mMzmz02r59+4pXWar6wUxkl1xyiVQfWrVqlS/Rbl6glJIEINu2bWPgwIEAkr59
+/PjxjBgxAnDCqceMGQM4FbnMNeXn5++Ub9Lr/t5rr73kYWnRooXo6caGU9skZnZ7wLE1VN/9adSo
+EVu3bo273e5MYm7PzNatW8u4NTU0srOz5bjBYFC+V1paKhGh/fr1E3tNEpGo/sc+KKVuVkp9p5T6
+Vin1qlIqSym1t1JqplJqsVLqNaVUZt1Hslgs6ULCkoJSqg3wBXCA1nqHUup1YBJwCjBOaz1GKTUC
+mKe1frKOY8XVCKMumIQf9913n1hnq7uCGgv/ggULxI3X+L1Xd6pJNmV3PNRHJSQv6Ny5MwCnnnoq
+4PjvG7/97Oxspk6dCjjqntkdOuqoo6SvzSrnteV85MiRXHjhhYDjNty7d28AaVt13H4vpk2BQEDu
+iVGfysrKRFJI4lmJiueoXo/zpptukuzY4XBYdkluvvlmFi9eDMD69eu9qG6dkijJDCBbKZUB5ACr
+geNw6kqCU4r+9Fq+a7FY0pBky8YNBP4O7AA+BAYCM7TWHSv/3w54T2t9YA3fvRK4svLPwxNuxC7I
+yMiISpVl8R+zAjdp0kSiEmfOnJmQF2osmKjS6dOni+G2R48etUoIEK3jQ1UkZYsWLcRWYmwKXkg0
+dZVEbNGiRZQb88cffwyQkC2jDvyNklRKNQH6A3sDm4E3gJNi/b6uVoo+0Xbsivq24P8vYh6iDRs2
+iCrhpzp21llnAU7a88ceewxAjHO1Uf0BM67ea9eu9W1XoqZwcMPGjRtFdd17770l+YrWepcJbvwi
+GfXh98BSrfV6rXUZMA7oBTSuVCcA2gJ1ux1aLJa0IRk35xVAD6VUDo76cDwwG/gUOAsYQxyl6OvC
+HTDkd3JQizekQlIzPim9e/eWZDCJrvIVFRW+uA7X5YfRoEEDyYT97rvviuRSH1ICJG9TuBc4BygH
+vgYuB9rgTAhNK9+7UGu9y9jZWNQHI165O8qqBxaz66S1rnOxcI+dmsZ9Xf9PBnPs7OzsnXZgcnJy
+JLP3J598IhMd7JxGPklSUor+buDuam8vAbolc1yLxVJ/pI1Ho5f79mZWDgaDUWmuYhUNvWqLaYc7
+j39tq1kye+C7+r7f7rzuffddJUXxqx3uhCyGdBjTflDdByeeJDSV7F45Gr28kWYAukWueI7vVVvM
+ccxklJmZKfqiV9TVVrfV248+dv++q+P79aD6Fj6chsQS/u4FNkrSYrFEkTaSglfEKs56cR5zjrow
+BtHaDKOBQECOZ366qxOnI6ad++23nwTquA1kFu+pydfBD7Vsj5MU3MkiTOJQP8/jBQ0bNqS4uDjq
+9d1339GkSROaNGkSFRqeKF5PjqFQiFAoxA033EDDhg1p2LBhverySiny8/PJz8+PinzcE5FkKJWL
+iVJqJ3tDMuxxk4LFYkmOPWZKNWJUr169xPgUCASYMWMGULPo7lY1Um2w+uMf/yh5F3/3u9/tNNPv
+s88+rF+/HoB///vfUr48nnTpfmKyJJ9//vniK3DNNdfI7orXO0k1HS8QCEhVqE8//VRyVUBVP/Xo
+0QOAefPmpTyxiteY9pvkNY0bN5Yx4qXPjpUULBZLFHucpHDGGWdwzTXXyHsmpv+GG26QDMW33nor
+4PgxmLJyL774omR29suN2u3O2rx58xo96ExRmsLCQqmT6EdpsEQxq5SRcjIyMiR9WJ8+fSQYaeXK
+lUlLX27pyUgj119/PR06dACcGgqmj6q7BBt70tdffy3vmYxbq1atkj5N9y1NY0/KzMykYcOGgBOB
+Ck5/v/feewCSLdsL9phJwRiXrrzyShm4Wmvy8/MBeOihh+R9k5pryZIl0pmTJ0/2LZrPDO77779f
+ale6/Qa6dOkiVYPcmAfhuOOOY5999gGcpCHxiIqxFiyJFTOhmodRa83rr78OOOndzSD24nxuY655
+6FeuXCnp/hs2bCjvb9iwQa71+++/57LLLotqR9++fXniiScA6NChg5Rwz83NTVqd8FItcU9uDRo0
+kByj999/v+xGmSQsmzdv9iU+wqoPFoslij1GUjCprUzJb3CML1dffTXgpGMzIqUpt5WqaMu99toL
+QIqAgLOCXX/99QA1SglQdS0jR46U6sqvvPIKjz/+OBDfyuROJJooBQUFsgIbiWDLli1RZfjqymUQ
+C9V9NqBKbSkpKaFFixaAI/Hde++9AHVm5V68eDFPPulkBVy0aJFIXhs2bBCpJ9W4r9OoBqWlpVFp
+1373u98BTmby4447DoDly5cDTvDU6NGjPW/XHjEpZGVlSS47qLLEvv3227zxxhuA4/LspUU8ll0L
+I/6bSkA5OTlSbWnLli1SV7I2Nm3aBDhpyi+++GIAmjVrJmJwLOpOTS7fiXL77bfTpk0boKrw7MKF
+C1m7di3gTLbJOlwppaTfwuGwTORGDXzvvfckM5HWOqGMTp06dZLdicaNG0s+x3hTp1cX3eN1JXfv
+Juy9996AU5jXOIGVlZXx1ltvAU5JAPN5Y1PJysqS7NpeYtUHi8USxW4tKZiZc8KECSIdhEIhfv31
+VwDuvfdeX3IuxCKKu12XzQo0Y8YMeW/Tpk0xr9433ngjffr02ekYseCl8XTdunXStxMmTACc3IhG
+nPUCd0ZlqJKWzH484In7d6tWreS4Rx55JJB4kZV4JVATNWvUwx07dojB2y1tuCN7a3Ih3759uy8+
+F7v1pGDsCKtWrRJrclZWlqRwb9asmS8OK7G4OEciERFRzSCuqUJRLGRkZMggPvvssxk0aBDgPKR1
+tcOL6zc2g3333Vd2eYxOHg6HJUmqFzYarbVM5Nu2bfPN0Wj//fcHnP4xOyoPP/xwQseKp48DgYBM
+BqZfQ6GQqEG1jZEDDjhgp52kuXPn+hIfY9UHi8USxW4tKZiZtKCgQKzhOTk54gswbtw4CgsLAUTs
+9YJ4rfju2d+0M56AqgcffFBWid9++01Sma9fvz7mfArJYJxlDj/8cNkjN5JLWVmZpFn34lx+Rra6
+2b59u/w+fvz4hI5h2mnujVKqzrERiUREUjAqQUlJSY25P4LBoPTz/vvvL+PdOGHdd999Vn0wmJtg
+/NqVUlKAViklD82GDRukKOfzzz8vKoZbZ0s1ZjvNWOx3hdmm6t69u1jkN27cKF6RqfLGM+J1IBAQ
+ZxozQDt16uRpyHQgEEhJha6nn35afv/xxx8TOkZNmZDqQikl/WWc6DIyMiRuw20nUEqJt2ibNm1E
+PZs7dy6AFP71Gqs+WCyWKHZLScEYGM0K1qpVK6kyPGfOHF588UXAcVgxK1peXh6XXnopAD/99BMA
+n332WUqlhczMTJo1awYgMRe10bRpU/FjOOiggyR9ebdu3VKaxTorK0vaOm/ePA444AAA2rZtCzi7
+A2bl8oJUST/G0Ki1FmeweKk+dmLxU9Bak5ubCzgJagAOPvhgict54YUXWLlyJeAYyv/1r38Bjiph
+7rsZx35V3bKSgsViiaJOSUEpNRI4DVhnakIqpZoCrwGFwDLgbK31JuUsy4/jVJ7+DbhEa/1frxtt
+ohmNC+i6desYMGAAUPvsuXHjRtmHNh5h+fn5UjswFQSDQQ499FAAvvvuu6j/GZuBWZUrKirEIKWU
+YsGCBUB0fcFUkJWVJTrw1KlTxTvTVGUOBoOeboulqhq32x3eq2jOXRkajYE5KytLrs+04ZZbbhHJ
+64QTTpCxfN9994lUrJRiw4YNQJWk6xexqA8vAP8GXnK9NwSYrLV+UCk1pPLvvwAnA50qX92BJyt/
+esqxxx4LVDnmPPfcczGJUsagZOIIMjMzxY22pKTE9yQc5eXlks8wLy+PLVu2AE5592+//RaI3p0w
+A6ysrEwmrw4dOkS5dPtNeXm5hHMPHDhQdnPcfWUm2Y0bNyZ9vlRMCHfeeaf87t6FiJfq2bqzs7Nr
+LWhs/t6xY4eMVTP2zjvvPAlFHz16tESB9unTR/q5tLRU+tnvPqpTfdBafw5Uv9v9ccrMQ3S5+f7A
+S9phBk5dyVZeNdZisfhPoobGAq316srf1wAFlb+3AVa6Pvdz5XurqUa1UvQxk5OTIyKVe782HszW
+5JFHHilSx/Dhw8Wl1i/y8vIksOe///2vXEcoFKqxXLlZgUKhEBdccAEA55xzjhgr/YzyNCJxWVmZ
+BOscdNBB4tFo2lZUVCQSQjgcTjpdXCrUh7vvripqloz66JbqINozsTo1qRXGoFhWVsYLL7wAOP1p
+jI5GRQMnqC5VqfiS3n3QWutESsnrBEvRZ2VlSQe/++67QPwPh/E9b9SokYTkBgIBzxOSGMzgGTZs
+GCeddBLg7KAYO4IbM8C2bdsmDldZWVnie6GUksi4f/zjH562040ZkBkZGaLLZmVlyQRgXMlHjx4t
+fdi2bVvZe1+zZk1C/ejX7kMgEJDJze0gNXv27ISP6c4FConvBmzdupXDDz8cgAsvvJCDDz5Y2ml8
+UszPVJDo7sNaoxZU/jTT7SqgnetzthS9xbKbkaikMAGnzPyDRJebnwBcr5Qag2Ng3OJSMzyhf//+
+sl974403JnQMk5/vjTfeEEvu/fff79sqZc7Xvn17Wa2q13Iw5zaGqsGDB8uK0aZNG/r37y/fu/nm
+mwF45513xHjqpcidmZkpuyRbt26VAKy8vDxeffVVAEmm8t1330kE4z777CPST69evSQXQDySXLzq
+g5Fofv311yhPVbN6n3jiiYCTdbpnz57yPWPYHTNmTJSxORHMeRP1H8nOzpYdh/bt28v7Gzdu5Mwz
+z4w6RyqIZUvyVeAYoLlS6mecKtMPAq8rpS4DlgNnV358Es525GKcLclLvWqou1jruHHjgOhw2lgw
+A2j69OmAM/gnTpwI+Js6vV+/fgAcccQRNRZ2KSsrk209s2368ssvywPWunVrcYPt3bu3hCo/88wz
+Mmi83FqtqKgQN+yhQ4dy1FFHAU7Y9tixY4GqkOaGDRtKRqmePXtKpquRI0dKm+OZFOLNJmXiMsLh
+sKhbrVq1kmxXpn8KCwvFqW3r1q189NFHgOMkZvo50TGQ7GKitRY7l1JKVLQLL7xwp63rVFDnpKC1
+Pq+Wfx1fw2c1cF2yjbJYLPXHbuPm3K6dY6p44IEHZFU10XkmjXtNmPx7I0aM4A9/+ANQtXKtXLmS
+v/3tb4C/7rUmN+Cdd94ZtRKa63jjjTd49NFHAZg/f760x+yubN26lVNOOQVw+uGuu+4CnB0Msxp7
+6WPhlhT22WcfCeLq3LmzSDpmVW3Xrh3nnnsu4KzWRgpbuHCh9HM8KkE8n41EIqxYsQJw1CqzK3PV
+VVeJG7PxBbnooovkuBs2bJD2J1NyrXqfJ7pzkp+fT9++fQHHWGmC+CZPnlwvKeh3m0nBPBTBYFDU
+gK+++gpwBofpvJycHBo3biyfNUQiEbGMv/POOwBcfPHFKSniahxkDj30UBHFX3nlFWlzJBKJOQR6
+xYoVXHedI4w1btxYRGavS86bCWnDhg3SztatW8uOh4l3aNu2rfT3t99+K1trs2bNSigaNd62u89x
+zDHHAM5EZXZE5s2bBzgLgbn/WVlZYq+JRCKS9Wj16tVRSVPjbWu8bTeTyl577SUT08aNG8UWU1+V
+rGzsg8ViiUKlQ129WPwUzEx66qmnMmbMGICo2oG1YcTEzz//XCz4ZhWs7Rx1iWyp8s+PBb9ds5s1
+ayYp6H/++WdxXjKGxlatWnHHHXcATt7G2vrWb5RS0o5bbrlFdhSMYXTu3LkSN9K6dWs+/fRTwIm0
+/fOf/ww4qkY80k31wjeJ3oN169ZJYqAFCxZIfIkPzNFaH1HXh6ykYLFYothtbApmNp44caJk/3nk
+kUcAp1Sc+f+mTZu49tprAZgyZYp43tVFIBAQPdSvOHU/8Fti2bBhgwTihMNhcc02OQG+//57MebV
+p/SktRbPy48++oiTTz4ZcPJPgJO7wBhMf/jhB0nB9uSTT0oUaLztTzZDlKmhkZmZKee+6KKLkjqm
+F+w26oMlfmoqYLsnY9S/cDgs2aaNurNmzRqZ7Hfs2JGy6mC7wkys06dPl0zSL7/8sp+ntOqDxWKJ
+HyspWHZrTMLeRo0aiTS0bt26nXwPYtn2TTUmYragoEA8F31uY0ySwm5jU7BYqqOUEnfmVatWRSVM
+SXZHIBUYe5c7biMdsOqDxWKJwkoKlt0as/OxY8cO8RsIBAJiYDR+E6moJREv7hRtfvubxEPaTArx
++sdDfA4mNeHObhQPiTov1VY/MpmBUFPGppr+n6gLrleDtK52GmIp3us+hsl5Wf34ZnfBz4fMjwc5
+GAxK1GZpaWm9qEFWfbBYLFGkjaTgV9CMH2JjorO21lpWF69cpf2oJRlPRexYiSfyMdnjVy/n7hfu
+IDSvzmVSBUL97ZhYScFisUSRNpICpFegkV8YA5g7ZVgqwrfjwS3R7K6kchx5KSkEAgEJ4quvZyGt
+JoU9fUKAKmebvLw8SWSilBKDaCrrRO6K3fFemIfTXXcxFXiZCKVRo0ZiJDWxHKnGqg8WiyWKtJEU
+/FAdlFJiuEkHEd0diTlkyBDJwrRs2TIpZWci+TZv3ixp11atWrXbrdwZGRm1llDzC3Ov6yOFWTIo
+paQUYFlZGb169ZL36+O+p03sg5cdYMTIJk2aiFgeb+ZnLzG2g1AoJHvQ4XCYs846C4BrrrlGBrTJ
+M5idnS0TyLZt26Qk+WuvvSZipTtVWqow12JsI5mZmSLuup2GlFISlZgqUd6EdW/fvl2cmnYH7rrr
+Lu69917AmUDff/99wKl8NmfOHMCzPrRRkhaLJX7qlBRqKUX/CNAPKAV+Ai7VWm+u/N9Q4DKgArhR
+a/1BnY3wOErSrLAdO3aUoh9e4rVYFw6HJRmtyeR76623ipQTDodFkqioqOD1118H4NJLL/XNfdft
+T2ESf3Ts2JErrrgCqJJofvjhB77//nvASW7y5ptvAtGJalJRAzEjI0P65ZZbbmHZsmW+nzNZTMq4
+zZs3y++rV68W9SEYDEqZwbffflsSzCaBZ1GSL7BzKfqPgKFa63Kl1EPAUOAvSqkDgHOBLkBr4GOl
+VGetdVIjVylFgwYNAEeMMvYBI8pqrenYsSPgFEFt1copdD1r1qxkTlsrXqtcZWVlUtTkwAMPBJz0
+7aZaUIsWLWRSyMzMpHfv3oCjjvgxKeTk5Ej+y8GDB0sOw7KyMpm83EVVTR3EjIwM2V2ZMGGCFK1J
+BcEenq8AABCzSURBVAceeKBkaK4tgUq6JZ154IEHAEe1NerOqaeeKg+/Ukp2qJ544gleesl5BE3W
+KL9IqBS91vpDrbVRcmbg1IwEpxT9GK11idZ6KU6lqG4ettdisfiMF7sPfwZeq/y9Dc4kYTCl6BPC
+SALjx4+XnHslJSWiHhiKiopkpc3IyJD8/jNnzpQS7hs2bPDM4OW1+lBQUMBjjz0GVBWDqaio4J57
+7gEcMdPUqsjOzhZDo9diuSlvd//994u0NWXKFEaOHAk4RWtMnQlTH/P++++X1Ge5ubmi8rz88ssp
+WY2N2P3cc89JzsPLL7+chx56CKiSCAYOHMjQoUMBRxJ65ZVXALjiiitSbqw1Uq+R/u68804mT54M
+7FzYyJQ13LFjh0R8NmzYEEBKzXlNUpOCUuoOoBwYlcB3rwSu3NVnzM3q0qWLDDZ3WncjDubm5koH
+hcNh+cyhhx7KOeecAzjVoCZNmgSkRseNBWOpf+qpp0SPNA/8wIEDZRI76KCDJD15ZmYmw4YNA7wV
+gTMyMsSese+++8q5P/zwQ5577jkgOo7EqAaHH344Q4YMAZyH7bPPPgO8GbDxTL7Z2dmiVh544IFi
+zTdFhEzFKHPcCy+8EHDUnLfffptUcvbZTunVadOmAU4f1+ao5K5mZibtTz75xNf2JTwpKKUuwTFA
+Hq+r7lzMpei11k8DT1ceq/4VPIvFAiQ4KSilTgIGA0drrd350CcAo5VSj+IYGjsBXyXbyHPPPZd3
+330XiM7FZ2Lpr7zySsmMO3ToULGMFxQUiBgJ8PHHHwOI9JBoKncv1IeMjAwRDY8//niReozzUpMm
+TUQsf/LJJ+X98vJyKZfnJQ0aNBAVDWD48OFAdHk7qMpPYUT1QYMGicRTUlLCzJkzpZ2JEAgE4upb
+Ix00a9ZMxPJjjz1WCgZNmTIFcMbQaaedBjh9a0T3Z555JqWSQjgcZsGCBUCVFGBSzNeEUaFXr17N
+fvvtB/hfACjRUvRDgTDwUWUDZ2itr9Zaf6eUeh34HketuC7ZnQeLxZJa0sajcVf/DwaDoreuW7eO
+G2+8EYDbbrsNcIxzZusmFArJDPrFF19w2GGHAc6M65YsACZNmpTyrSmz0j7yyCPccMMNgLNimJXL
+lGP77LPP6Ny5MwAHHHCArBhr166VCtxebke+9tprnHnmmYBT5LRnz56AY/gyXpi//fYb5513HuDY
+QaDK0AfOqnvzzTcDiLE3XuLJvOSmQ4cOfP3114BjaDT+EuZYoVBIKpCvWLEiyiXaSDqpICcnh0sv
+vRSoMiKWlZWxbt06IDp6tnpuBXO/kzCY7znZnCsqKqRYxujRo+VGG9+EefPmiUhVUlIiv0+bNo0j
+jqjqA1Ovz1QNmjJlSlQGYL9RSskD3b17d7nJbn8Ds4vSr18/ES/dg6Bv376eTgZmAB577LHSb0VF
+RSKWH3/88WJozMvLiwr9Ntdkfl+2bFnSRtxEJ+klS5ZIJauajHZlZWWycLgngVSHiJeWloox1vh3
+vPLKK3IfysvL+fLLLwF48MEHpWq2WSxSgXVztlgsUewWkgJUrZbXXHONGMHOOOMMAObMmSPiF1St
+Nrm5uSJVuIN1jIfefffdl5rGV5KZmckf/vAHAMaNGye+ADk5OTz//POAU0IMnKCe008/HYBevXrJ
+dcRaGzNW8vLyAMc13PRxaWkpU6dOBaBdu3Y1rqamj90pw6644goRiU1xk3hJRp2LNf/A5s2bRSJL
+tfqotRaD6L///W8geps9HA5zwgknANEu5qlkt7ApuAmHwxxzzDFAVVRcWVkZCxcuBBz7gnHuePbZ
+Z8V33K2fmcF/8MEHS5n1eIhX7zWT0XvvvSc6eLdu3WSv//bbb2fcuHFA9CA1bZ40aRJ9+vQBHEcu
+o9d7ce+M3eKDDz6QHQX3g+62GUCVj4dRuxo3bhxVjcm836ZNG/GtiIdUhAsXFxeLnaS8vFx+TxWm
+v4zvxFdffSUOebVlvf7qq6/ETyGJhcFGSVoslvjZbdQHQ0lJCR984ARemtm1f//+4pvQokULmV3n
+zZvHwIEDAfjxxx9l5TUrd+PGjRNqQ6xSgpn1Td6EAw44QOoHuo/xySef1Lg6ms/06dNH2nzMMceI
+T8aOHTsStkSbvjMW+Xnz5okUkJWVJVLM3nvvzYwZjuf6wIEDZZUybWvTpg1LliwBnBXQiMZHHnmk
+5AWIh1TUaXBLjcZ1OJWYvjMqlukzcNpodscee+wxkWK6d+8uu2eFhYXAzi7RXrHbqQ+1YaLzIpGI
+WO2ru+Uay78hPz8/oTx4sagPSikRzY17datWraK2FkeMGAFURctV59lnnwWqbCAADz30EHfffTeQ
+eDapjIwMmUTN/W/atKn020EHHSQW8KKiojof1CeeeAJw7D3utl999dUJtc8vunTpAsA333wj7z3y
+yCP85S9/qa8m1cmpp54KOBOEGeNmITjkkEN26fhUA1Z9sFgsCaC1rvcXoP1+lZSU6EgkoiORiC4v
+L9fl5eW6UkKJ+7Wr7wWDQR0MBnWXLl30q6++ql999VW9adMmvWnTJv3bb7/pb775Rn/zzTf67LPP
+1s2aNdPNmjXTgUBAZ2dn6+zsbN2uXTvdrl07PXHiRF1RUaErKip0JBLRa9as0WvWrNGNGzfWgUBA
+BwKBuNtt2hYMBnXLli11y5YtdSgU0qFQSGdlZUkbcnNz4zpHgwYNdIMGDfSqVaukbydOnCjnSsX9
+rekeKaWirjkvL0/n5eXpsrIyefXo0aNe2hfvKxAI6EGDBulBgwbpBQsW6AULFugHHnhAZ2Zm6szM
+zFiPMzuW53GPUR9qw3iPGQccQJJkGvE+XnZlITc64E8//SRbjkaX/eSTTyQE+v3334/Ky2h2H8xW
+mRuttdglEk2w4d4h0FrXmFLetD03N1ecZZRSdapK5jqnT59O27ZOao2SkhJJevLTTz/F3E4/dx/M
+Torb2/KBBx7gjjvu8OV8XmN2h5YuXSrvmR24GHckrPpgsVjiZ7fbfYgHpZS4R0OV4dHkI0iUXa1k
+ZmcgPz8/Kl0cOHkKjJFr7NixFBQUANFZp90YI+jJJ5/M7Nmzk2pz9fZX37VQSslKqrXmkEMOAZxI
+U5P4Ze3atTtJDUop8QXJyckRqaisrEzcjuORFPyUXM3uSnl5udybRHegUk0wGJR0bG5Xcj+yVltJ
+wWKxRLFHSwpQtQ8PVanOXnvttdo+njRm28i98pvVs3Xr1pLdyL2q1uRGXFpaKinPNm7cuNP/46Uu
+u4DWWrwR+/fvz/nnnw84K+moUU5irS+++EJ0V5NZ6ZBDDhFfkMzMTPFvmDx5skQtxkOiUZKxYBLh
+ZmRkiERi7Ete4XWuAxNJe88998jYMrafzz77zJeaGnv0pJCZmSmJVMLhsIRa+ymimkpP//3vf8WQ
+6XZOMeoFVPkZbN++XR42E8+xZMkSzx1r6hqw5mF86623xFHm9NNPl8n03nvvpUePHkCVv/6OHTvE
+Icstlj/44IMJTWZ+3hu3y7a5VpO8xwuCwWCtEaxuVdL4iBjVZd26dTJGGjRoQN++fQEngY2ZyLTW
+Ml4effRRwInd8aO/rPpgsVii2KMlhYyMjKgVzUQg+onbNfnOO+8EkDRgpaWlUtdBKSWpwq666iqR
+FBJNTlIbbukg1lVFay2uzbNmzZKVbf78+ZKyzWynNm/ePMpL06gSy5cvT7uajtdee638bvqlVatW
+EkwXL9XVvtqkBLd36+233y7JbNxBULXdG/P+lClTZHt9zZo1QO31LZJlj/ZTGDRoEA8++CDgiPUm
+52Ft7sFGV/OiGG0gEKBly5ZAlV1j4cKFMnD8quzkJ6FQSCJQjet1ly5dxB/h1ltvlYk30WQrfvop
+mHqc1113nbgHH3/88cydO9eX89VEZmYmnTp1ApxM0uDE65iI0unTp8v4GzRokPiLJJpPtBrWT8Fi
+scTPHi0pFBUVySpdUVEh3nbufHh+Ut33YHeUDuoiHA5LXoBvv/02ZRWmE2HFihWA4xloMjhffvnl
+nuzu7CbsOTka48WoAevWrROLc2lpqTjkfPrpp4B/OplhT5wEqlNSUiJFfNNhgamNUCgk26WNGjUS
+e0d9hE6nO1Z9sFgsUeyR6oOxhrdr104svFprSQZi8hLm5uaKSJkO/WDxF5NPQyklCUqCwaBIjP8D
+YyAm9WGPnBQM7mpD7noQxjOstLS0xjTqXuN3RZ/6Zne7PvdWYkZGRtS9312uIUHs7oPFYomfdJEU
+1gPbAW/zl8dOc3tue+7/gXO311rn1/WhtJgUAJRSs2MRbey57bntuf3Fqg8WiyUKOylYLJYo0mlS
+eNqe257bnrv+SRubgsViSQ/SSVKwWCxpQL1PCkqpk5RSPyqlFiulhvh8rnZKqU+VUt8rpb5TSg2s
+fL+pUuojpdSiyp8751n3rg1BpdTXSql3Kv/eWyk1s/L6X1NK+VbtVCnVWCk1Vin1g1JqgVKqZ6qu
+XSl1c2Wff6uUelUpleXXtSulRiql1imlvnW9V+N1Kod/VbZhvlLqMB/O/Uhln89XSo1XSjV2/W9o
+5bl/VEqdmMy5vaJeJwWlVBD4D3AycABwnlLqAB9PWQ7cqrU+AOgBXFd5viHAZK11J2By5d9+MRBY
+4Pr7IeCfWuuOwCbgMh/P/TjwvtZ6P+CQynb4fu1KqTbAjcARWusDgSBwLv5d+wvASdXeq+06TwY6
+Vb6uBJ704dwfAQdqrQ8GFgJDASrH3rlAl8rvDK98JuqXeq4M1RP4wPX3UGBoCs//NtAX+BFoVfle
+K+BHn87XFmdAHge8AygcR5aMmvrD43PnAUuptCO53vf92oE2wEqgKU5k7jvAiX5eO1AIfFvXdQJP
+AefV9Dmvzl3tf2cAoyp/jxrvwAdATz/ufzyv+lYfzGAx/Fz5nu8opQqBQ4GZQIHWenXlv9YABT6d
+9jFgMGDylDUDNmutjfO9n9e/N7AeeL5SfXlWKdWAFFy71noV8A9gBbAa2ALMIXXXDrVfZ6rH4J+B
+9+rp3DFR35NCvaCUygXeBG7SWm91/087U7bnWzJKqdOAdVrrOV4fO0YygMOAJ7XWh+K4lUepCj5e
+exOgP87E1BpowM4idsrw6zrrQil1B44KOyrV546H+p4UVgHu+vBtK9/zDaVUCGdCGKW1Hlf59lql
+VKvK/7cC1vlw6l7AH5RSy4AxOCrE40BjpZRJduPn9f8M/Ky1nln591icSSIV1/57YKnWer3WugwY
+h9Mfqbp2qP06UzIGlVKXAKcBF1ROSik7d7zU96QwC+hUaYXOxDG6TPDrZMqJmX0OWKC1ftT1rwnA
+xZW/X4xja/AUrfVQrXVbrXUhznV+orW+APgUOMvPc1eefw2wUim1b+VbxwPfk4Jrx1Ebeiilcirv
+gTl3Sq69ktqucwLwp8pdiB7AFpea4QlKqZNw1MY/aK3dGVgnAOcqpcJKqb1xjJ1feXnuhKhvowZw
+Co5F9ifgDp/P1RtHbJwPzK18nYKj208GFgEfA019bscxwDuVv3fAGQiLgTeAsI/n7QrMrrz+t4Am
+qbp24F7gB+Bb4GUg7Ne1A6/i2C7KcCSky2q7Thxj738qx983ODskXp97MY7twIy5Ea7P31F57h+B
+k/0cd7G+rEejxWKJor7VB4vFkmbYScFisURhJwWLxRKFnRQsFksUdlKwWCxR2EnBYrFEYScFi8US
+hZ0ULBZLFP8PkM5IigprKEEAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/2... Discriminator Loss: 1.3093... Generator Loss: 2.5877
+Epoch 1/2... Discriminator Loss: 0.9853... Generator Loss: 1.1730
+Epoch 1/2... Discriminator Loss: 1.0564... Generator Loss: 0.8760
+Epoch 1/2... Discriminator Loss: 1.0396... Generator Loss: 1.4255
+Epoch 1/2... Discriminator Loss: 1.0599... Generator Loss: 0.9275
+Epoch 1/2... Discriminator Loss: 0.9641... Generator Loss: 1.3584
+Epoch 1/2... Discriminator Loss: 1.1231... Generator Loss: 0.8175
+Epoch 1/2... Discriminator Loss: 1.0010... Generator Loss: 1.1018
+Epoch 1/2... Discriminator Loss: 1.3737... Generator Loss: 0.5330
+Epoch 1/2... Discriminator Loss: 1.0614... Generator Loss: 0.9055
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXeYVEXWh9/q6Z7EMMKQk4CCqCigiAQTKhhwFbOCiBjW
+1TXgiqx5MSu6a/wwYHYNgIo5oKAsyiJiVjKSo0hmgAnd9f1x55y53fTMdBwa9/6eZ56ZuX27qm7d
+qlMnH2OtxYMHDx4Evl09AA8ePGQWPKLgwYOHMHhEwYMHD2HwiIIHDx7C4BEFDx48hMEjCh48eAhD
+2oiCMeYEY8xcY8wCY8wN6erHgwcPqYVJh5+CMSYLmAf0BZYDM4AB1tpZKe/MgwcPKUW6OIVDgQXW
+2oXW2lJgDNA/TX158OAhhfCnqd0WwDLX/8uB7lXdbIzx3Co9eEg/frfWNqrppnQRhRphjLkUuNT1
+P57LdXT4fD6dm9qYI7/fTygUAtDfAmNM2P/WWnw+X9SxJTLWeNeBjMc9Lvf35W+fz6fP4u4j2vPE
+2/euWreRzxHDOJbE0m66iMIKoJXr/5YV1xTW2tHAaHA4BY8g7IysrCzAWXS1MT+yyEOhUJX9Rbse
+STiSQaLP6fP5CAaDO113P1O0Ptx/RxKImhBJWGp7DVf1HMkiXURhBtDeGNMWhxicCwxMU19/SBhj
+9AQuKyurlT5rkxtJFWSs5eXlKWsrVkQSkT8Kt5sWomCtLTfGXAlMALKA56y1M9PRlwcPHlKLtJgk
+4x7E/6iisTq2Mzs7m+bNmwOwePHi2hyWhxgRTdzIhP0UCdc6+9Zae0hN9+8yRWMk/iisV6wwxlC3
+bl39X2TefffdF4ALLriABx54IOl+RASJV+6vimDtauVaJiGaviKV7fbu3Ztzzz0XgH/84x+sWbMm
+7rZ8Ph+BQACAkpKS2L4Tdy8ePHj4Q2O3FB/8fr98D3BOrWuuuQaAyZMn8/PPPwOxU8Z0Q8YZCAT0
+VGnUqBEnnXQSAHXr1tUTffDgwYBD4c844wwA5s2bV6vjbNiwIXXq1AFg+fLlYWMeMWIEAPfffz8A
+q1evZseOHbUyvkxDKrmmnJwcAB599FGOOOIIAPbZZx9dF2vWrGHIkCEAfPLJJ1H7lXsDgYByB9u2
+bXMrkGMSHzKGKIgtPhaWTO4Rk12vXr24+eabAdhzzz1p1qwZQBh7XlZWxttvvw3A8OHDAVi2bFnY
+hpW/ayImyYg68rKysrLIzs4GYL/99tOxNWzYEHC06a1aOVbd33//PaG+ZKxA2NxWNXYhtu3atdNF
+2rdvX72/Q4cOHH744QC0bNkSgL/97W9MnjwZgKVLl9aapUQgz9SxY0c2b94MhPskyEapV68ec+fO
+BWD79u0p7TvZtXDQQQdxwgknADBgwADatWsHOGOXPrZs2aJ/33jjjYBzAP76668A1K9fXz/fc889
++eabbwAIBoPu8cVEFDzxwYMHD2HIGEWjtRa/3x+TvTnSNl1SUkLTpk0B55ST0wEqqXl2djZnn302
+AKeddhoA/fv3V4qam5vL8uXLYxprVd5zsUBO0rKysrDxC9fjdlgSriEZTkHgPj2rgny+Y8cOund3
+vNIvueQS9t57b21DUFpaCsCoUaP02owZMzjmmGOAxH0r4vXebNKkCQB33nknS5Y4DnuFhYX06NED
+QMe+YcMGfvvtNwB69uxJcXFxQuNzI1ku2z1Hwt02atSIdevWAc7pL9xbWVkZ06ZNA1Ax4vbbb1dR
+edWqVdSrVw+Ahx9+WB25YuEQI5ERRMEYQ05ODmVlZQlN9C+//MIpp5wCOOzXE088oZ/Jyz/iiCO4
+++67gcoXsGjRIrZs2QKgLyIW5OTkpESOFqKwYMECcnNzwz7btm0b8+fPT7oPITLRvP0iIQvwwAMP
+5MEHHwSgTp062oa7HXn+/Px8JRbr1q1Lifjgdl2uiUCIRv6yyy7j6KOPBuCMM87QQ0LGW1hYqCJR
+s2bNWLBgQdLjTBUWLlzI0qVLAYewPvPMMwD8+uuvXH755YAzz/Ksjz/+OACPPPIIBxxwgLYhYsXS
+pUuT8nb0xAcPHjyEISM4BWstpaWlYYE4sVA3OQ02bNjAsmVOUOZ9990X9d7PP/+c3r17Aw43AdC8
+eXNlOeOhpsI6pwotW7akoKAg7FpWVlZcp3xVkO/GIvLccIOTC+eaa65hjz320OvC0RhjlAWXa/n5
++dp2vXr1kh6zO8AqLy+Pbdu2VTtmub5mzRref/99/V7r1q0BR+kGDqcgiucvvviCgw8+GHDY7upQ
+G/4zbdq0oUOHDoCjPPz0008BR4F+4IEHAs4aWblyJVCpjG7atKmuxU8++YQVK5zwomTH63EKHjx4
+CENGcArGGLKzs/H7/Wqm27p1a5X3y0kip1asHmVyij311FMATJo0iSuuuAKIz5W4qoi8RCFmPjdC
+oVBKOBJ5Zp/PpybO5cuX72R2zc/PZ86cOfr/hg0bAFi7di0jR44EHIVo3759AfjTn/4EOHoI4RS2
+bdsWd6RhJNwnc0lJSVzegqI/Gjt2LO+++y6APvPEiRNVb9OkSRNefPFFAE488cSwdynjj8UTNFmT
+pPTRtWtXunTpAjicrvgptGrVim7dugHOfjjkEMea2LFjR8B5t19//TXgKKOTnXtBRhAFt/gQS3x4
+otF8svlk8o455piEXmgwGAzLIZAsuybWEDeWLl2asIuyG/KseXl5rF27tsr2tm3bxrhx4wB48803
+lZi4CVOrVq3YuHEj4Gwmad8t8iUbreiey3iVlm5iIuOWsW3atImioiK9d/369UDV+RRiIfrJvvcG
+DRoAcPfdd6vCe8iQIeq0ttdee+m9eXl56l8hFocpU6aoK/yWLVtSdlB54oMHDx7CkBGcgqC4uDgm
+FihRCi1ej3JKfP7556qgjAfGmJSKD999952evILLL788JYE2YobbunVrzGMOBoNR7129erWayEQx
+aq1VUUP8QJJBVlZW0tyGm5MTcbRp06Zh60ZEyGT6SlR8EI5l7NixgKOgFR+D4cOHR90DpaWl6jkq
+Hrm//vprSvJIRCJjiEIoFErKKagmnHjiiapxlliCe+65J6GN5x5rMuOUNiRuAyrZ1rZt2/Lll18m
+3LZAWM5UzGdeXh5//etfAUcHAc4ziKhRVFSUtFNQKoitMUZdiGUjyf/gvL8ffvgh6X4STTcn+qtI
+i1N1CAaD6kciVoZAIBCXuBMrPPHBgwcPYcgYTkEUi3J65ubm1ug1KLb0tm3b8uqrrwKOckpOx8aN
+Gyslbdy4sSqu7r33XsDxhEwE2dnZ2lYyJ/All1wCOCewQDwr33vvvYTbdSMev4+acP3116s44ubq
+Nm3aBDhznywHlQq/gEaNGqn1QfwU/H6/Wlz69u2rIk8yiEcRLPOycOFCjUCNhnvvvVcjZe+44w5O
+PfVUwAmaEz+b7777DoBbb71V14n4c6QCGRMlWfFbrxUWFqpG1j3pPp9P/d0nTZoEONF7Veki3JYK
+iSg76qijAEdGTnC8O7UfL3w+n+o2CgsLtR3RJt94440Zk8hEnve///0vhx56aNg1qIwqHTdunJr9
+srOzlU3+17/+pU5CbpOfsLzuRLFZWVlJs8J77723muqiOWEdddRRzJgxQ/tOFPEQwMLCQgCefvpp
+FV3efPNNILbQ+DPPPJN///vfQKWeqLi4WLNzyV6pAV6UpAcPHuJHxnAKEsUnLJlbfHD7Avh8PmW/
+5MT/4Ycf1AU0EAjo59dff726Qh9zzDHqPnrZZZcBibsrxxJxWBP23nvvsIAnCYK59NJLq/rKLoOc
+iIsWLVJ2XN7Hjh07lGPbb7/9VLNer149/V5xcTFt27YFUD+HqrTmqXIrHjRoEIAGx5WXl/Pyyy8D
+cNVVVyXdPsTHKci69vv9Ca07YwyzZ88GnOQr4DyTcA0xztnulaPRrU8AZ/Lk5U2aNEknxOfzqfwk
+vu5ulJSUKDt7/fXX6/UuXboo+yXhvRMmTEhoASYjL9evXx/YWZ8xZcqUhNqsDchGF4IAlXMwbdo0
+9T796quvNGS5sLBQrRKBQEA3Qk0mtFQdUhMnTgQqTZJ5eXkce+yxgBOSnAqdQnWoirglehAdd9xx
+mnxFkKpkMZHwxAcPHjyEIWM4BXBOCVFUHXfccdxzzz2Ak29AfAwSjdfv2bOnZkoWzXTz5s0TSmCS
+KIubnZ2trsRuuzmgz/fKK68AqTsxU+FP0a9fv52uyYn/zDPPhIkGoiTctGmTpmybN29ezH75qRIf
+JDWbwB37IdxDqiDPVlhYqM9fUFCgfghr1qyJVRG4E0TsePnll8OS3IDzjOkQ/xPmFIwxrYwxnxtj
+ZhljZhpjhlZcLzLGfGqMmV/xu37qhuvBg4d0IxlOoRwYZq39zhhTF/jWGPMpMASYZK29zxhzA3AD
+cH017YRB5KTNmzerEuWAAw7Q0yjRjDnXXHPNTrH+AwcO5NFHH427rUSVjOXl5VVyOhKt2atXL8CJ
+AL3wwgsBx3chFcFRiUICcNx6H8kU1KNHDzp37gzA119/zYQJEwBHAen2wBOlcW3Vjdh///3D+oNK
+U151fgKJwB0lKt6pw4YN037eeust/vznPwPxZxiX/CDiBu2GRKqmGgkTBWvtKmBVxd9bjDGzcUrQ
+9wd6V9z2IjCZGIhCZDjy5MmT1Z2zRYsWDBs2DEB972OFLMypU6dqvj5hLXNycmq1uIm1VsWV7du3
+hy1OGaeEx/p8vqhRjRs2bFAR5KabbtLrsulKS0tT/izutkXskUQfZ511ljpfrVq1Sq0LU6dOVYvK
+6tWrVcFWW9auH3/8EUAV1Pvuu6/OcbQNlgjkkJH3k5OTo89fWFioYsrAgQN1Di+77LKYCXv9+vU1
+dNodb/Pcc88Blc+YaqREp2CMaQMcBEwHmlQQDIDVQJMqvhNWit6DBw+ZgaSJgjGmAHgTuMZauznC
+289WVejFRpSiD4VCYdmcS0tLeeGFFwDHhCiJWS+88EI+/PBDoNJNeMOGDXqChUIhZROPPfZYjSg7
++OCD9ZQSDmTp0qV6byRbl44Tze/3qyLxyCOPDPssMv5/jz320L/diUyKioo0WWfbtm1ZtGiR3gOO
+iJLK4BjpUyDjEE/BwsJCHeeBBx6oXqLz589XRduGDRtq3TtTxDQpnCJpzcBxD46WwyJeyDwLx1Be
+Xq4uyOXl5RocVr9+fV2rgwYN0vmM5sbv8/k04crbb79NixYt9DNxgXcnJk4HknJeMsYEgPeBCdba
+ByuuzQV6W2tXGWOaAZOttR1qaMf6/f6wxewelzFGi6W45SghIG6HkDVr1ujfe+21V5jvv+RjFFfi
+1157TYnJgQceqO6mderUUZfodMKdtTiyCI77+QsKCtQJ66STTuKrr74CHP2KsKvRvpeqqFPxrVi6
+dGlUeVzew8iRI/n2228BJ3YjkbiLVLg5uyFxLm6flc2bN6dEhBA9j8zzqaeeqq7rTZs2ZdasWYDj
+y+HO1u1O7Q+OqCw6kMaNG0ed4/nz5+shKc+UANLr5mycmXgWmC0EoQLvAhdU/H0B8E6ifXjw4KH2
+kTCnYIw5HPgC+BkQzclNOHqFccCewBLgbGvt+hra0rJxNY2nT58+jBkzBqhUGLnTgPn9fmXbcnNz
+VVn32GOP8dhjjwGVFNrn84UpH93pvMRLL5q1YFdXyBZRIRgMKusqSq3t27eHpbQTJDNe6ePNN99U
+Ts2djk7mu2PHjgklrXEj1Tk1RHErAVLgzJvMVyryYbgTurhFAvd7ECuXWJmqg3xv27Ztym0cccQR
+qcjZuXvVkkzlRpOINHf9vXjCW915B6u6LxPmDarOMRj5WSrGa4zhnHPOASrrGQ4ePFhrNKaiQE4q
+4krckDlYtWoVjRo1AhzLSKROJxEkks7e5/Opm72M55NPPlFTvJuopwFelKQHDx4SgLDsu/IHsN5P
+Yj8VXJb+pLu/rKwsm5WVZXNzc21ubm6t9JmKnw4dOthFixbZRYsW2SuuuKLW5ivDfr6JZT9mjPiQ
+pnbTwoplkviwq5Aup690za3f79dy71988YVmi/ofgyc+ePDgIX78ITkFt2Zc/BASrWidyTDG6LOm
+2mHJjVQWvokF6eBCfD6fKqCLi4tVmZnOectAeJyCBw8e4kfG5FNIpSnK3U6qK0RD7ekUouUgcPdr
+rU34pKvpNK7KnJnuALJ0za21VgPh3PUSxD9G7okHicxFqj0204GMIQq7IiQ4UaTC4cUdhhytPfem
+TMdGqUnscG8ad3xIIu8pnvGnQ3EJTvEacR9u1KiRhuAHg8GE+0zke26X9kwVZz3xwYMHD2HIGE7h
+fwWR7H9VMMaoO3MoFEp5zcCaWFi3d6c7q3YiqE3xIxJS2fnRRx/VeiFr1qzRBDa1fVqno/ZjquER
+hQyDEII999xT4y9+++23Wh1DgwYNNJ6hpKQk5VYACC8Akw6IqPD5558D0KZNG7VEDR06NO4MSJkE
+ifmpV6+eukdv27ZNRbtk63l64oMHDx7CkDGcwv+6l6Cw1eeffz4A5513HhdffHFa+qpKAy5jyMvL
+U4+/WN6JRByWl5fXqIisLc37/fffDzglBcEZmyTckTRxyaI2RaFmzZppEJoEc61evZrrrrsOcGpU
+pqoORMYQBUiOMAjb7U7WEks6+F2ZENU9Bsk9KQlgAoGAEoV//OMfKesHnGeNXNBuHcbWrVtr3LzN
+mjUDnKI2koXpww8/pH///mHt7go0atRIdQbyTGvWrFGLQ6zp5mtC5DOm42CTimGjRo3SccvmnzNn
+DgsXLgRSW2DWEx88ePAQhoziFNy2ewjXgEshj27duik1lgQpw4cPV+VLZMEM4RYWLFigZcNEcRcK
+hTLCP6J58+aayltOtrKyMq0ZmEyZMzdHUJ2TjjFGNeObNm2Keo/P59NSfsKeZ2Vl6b333HPPLuUQ
+8vPzAafMoDv9GTjcg9Qe7du3r7LiySgcI7mtVD97VlYWN998s/4t7Ut6/VdffTWlHIIgY4hCNHZW
+qiYdddRRjBw5Eth501eHUCikGuf99ttPczRKbQXJJ7irIM/38ccf63PLxrTWcvzxxwNOss/XX38d
+cNjgeBZfrCbQSOLozj8oiUQ//vhjnU/RcC9atIiBAwcC4SXV3U46OTk5+lyJVviKBVLrUhLbujFy
+5EgaN24MOLkUzzrrLMCxSiSq54jMcJVqotC4cWOtsgWVBOy9994D0lcPxBMfPHjwEIaM4BR8Ph8F
+BQVs3749jGoLmzR27FjatGkDOOneRdEihT6uvPJK9Wv3+/1hp5EUKpk3b56yl2vWrEnvA8WANm3a
+8MEHHwCOX4Ck7/7yyy8BR5kkcRsLFy4MswzEwzImepJIqrGCggJuueUWwBFjxCpx0kknAWhm6Ui0
+atVKxbtFixaxePHiuPqPFz6fT1ltN2Rd3HnnnToXH3/8sWrtA4FAQlmnIf3Wh6uvvlr3w7Zt23jr
+rbcAuP3224HUp64TeJyCBw8ewpARnEIoFKK4uJisrCylvsFgUE/PYDCoCq6aECmzCldx8MEH62kl
+NQCGDh26yxSNxx9/vMq4gCY/lfoWQ4YM0bHdcMMN/Pvf/wYc5VI8SPT5hMMaNGiQKmgXLFig2ZHF
+2zIS4lb8008/Kbdx1113qU4o1ZD1MnLkSAYMGLDT50OHDgXC3YsnTJigZe/22msv1TXF6wmYDg7B
+5/Nx1113AXDdddcpp7Bq1Sp99+4aq9OmTQNS6/+REUQB0DTt7hLtqdywvXr1Uicb8Qm4+uqrU9Z+
+rBDrwqOPPqoLury8XJWeUrK8UaNG1K1bF3CyJE+ZMgWoejOmGiK6tGrVSgnV1VdfXWP///d//wdU
+ZtQGp45jutC6dWvAIaJuJbQQAanI5UZJSYkWzV23bl3CGvxUig9iPZs9e7bGaLgtRnfccQfTp08H
+KrNmT58+PS3OYEmLD8aYLGPM98aY9yv+b2uMmW6MWWCMGWuMyU5+mB48eKgtpIJTGArMBuRoGAk8
+ZK0dY4x5ErgYqLH4nbWWnJwcZY38fr+WREuGGopy8ZFHHlHOQwqD7Aqbunj/CccAsHjxYlUeiZkS
+KtnE77//nlWrVpEIEq39IGJA3bp1VaEr76O6vmbOnKl9Sd/idZdqGGOYNGkSEF7vEtipnJ4bPp+P
+008/HYDx48drqbd4zKXu5CyJQoK2Jk+eTNeuXaPeIyLvrFmzwkRrqFQGpxpJEQVjTEvgJOBu4NqK
+UnLHAAMrbnkRuI0YiILP56OsrEzZ1sjS9IlC2Mh+/fqpdlriC3YFRG7dsWOHbvrrr79exYYffvgB
+cNhaWRDXXHNNwtmH4ymG44a4MT/00EPqHhxLX+4anLJpxKKSDojeSaxT4Ig+4iIeLWy7efPmWrD4
+888/V9Y9JydH34/Md1Xz5vbDiHedivgqDmDRakeCY30TXdPBBx+sfiDpzi+ZrPjwMPB3KsvGNQA2
+WmtFq7McaBHti8aYS40x3xhjvklyDB48eEghEuYUjDF/An6z1n5rjOkd7/dtRCl6cCi1UPaSkpKE
+bexu92hh01evXq0a8F3pihuN5Rs4cKDWDJQIvgMOOEA5hURdnCExZa3f71c34Z9++inm+dprr720
+IrK79J6IFKlGQUEBnTt31v4EJSUlDBkyBEA5l759+yqn2KBBA73/3Xff1Xfi5k7l+6+//nrUPJ+J
+Zrbu1KmT1pWMthZCoRATJkwA4Pfff9daFT179uSnn34C0u+Jm0yB2XuB84FyIBdHp/AWcDzQ1Fpb
+bozpCdxmrT2+urZ8Pp+VzSvjiSV3nhCNHj16qGz5wQcf8NprrwHO4pZFsWPHDhUbpNDs8uXL43rm
+VEBYxXXr1qk1ZMuWLarVF2ebJk2aqCb/jjvuSDhjTyIacmMMBQUFgCNni6ztTowic+/z+XRxf/bZ
+Z/Ts2VPbESex5s2bp4UQd+3aVZ2n3Bvs999/Vw296I9OOeWUMD2OG253ZflbxIennnpK18vatWvD
+3NDjObTEkrRixQrVc8m7WbZsmZpO33mnskh7kyZN1FyanZ2tYlgSdTDTm+LdWnujtbaltbYNcC7w
+mbX2POBz4MyK2y7AK0XvwcNuhXT4KVwPjDHG3AV8Dzxb0xckVXm86bbFp+H444/nm28c1UTnzp31
+hJ0/fz4PPvggAP3799f7r7zySsAJtJozZw7gKABrQ6yQE2zTpk3qQJOXl6cBTx07dgQqE5eAY8P+
+/fffE+ovnmeSPnv06KHJSWbMmMHRRx8NOFyDpDeTE7Ndu3Yq8ridsYqLi/VZ0jWv8u4Ebq28cIG9
+e/eO+l0Rze688049edu1a6dWDDnZr732WlUMDh48mHfffVfbiDUgyufz8dlnn+k948ePB9BAsqq4
+wN9++y2Mu2nXrp22B+nLAZISomCtnQxMrvh7IXBoKtr14MFD7SNjysYJl5Dq8YipasaMGWp7Fxn5
+nXfe0cw2mzdvrhWXZzlVhg4dqhmV1q5dy0UXXQRUnhr77LMPLVo4hpspU6bw3XffAZWcRrz9VXeK
+gaN/OeiggwBo3769clXz589n2bJlAKxfv17nSHQOw4YNY/DgwYATMDV58mQAzjrrLNWPpBNiLt1r
+r72UU1i8eDErV64EKv0XmjdvrgrDefPmqQJvx44dYXMjuolrr70WcDgJudatWzc1GQNh2bbdvyNR
+p04d/vvf/wIOByIenjUVKjLGqLdlTk4Oq1evBtA8Gwl4t8akU8gYN2djDNnZ2XEv+pogbOKZZ57J
+888/D6Cs3AMPPKAEoraIo/RzwAEHaHyBW3MudvdDDjlE8wL4/X51sIlkmWPtLxKyoIVo/vnPf1YC
+uXz5cs3lsGrVqrA2xCpxxx13AE7Uqmy833//Xa+nKl9gTRBCtn79et1kRUVF6v4sc7tkyRLd0NnZ
+2VW+d7l/v/32AxxLhliGxDVaEKl0hUrCkJWVpX8PGTJECcG2bdtiVhrffPPNYWKkEIF0OS0JvChJ
+Dx48hCEjOAWfz0dubi6lpaVpi1FfuHAh8+fPB1Dbdnl5uVLd3NxcDURJZ+y/mKNOPPFEvfbdd98p
+V9CoUSPAUYzKCTVjxoy4OYSaIPMrWaiuvfZaPT3Hjx+v7HckunfvDsAFF1wAOApFGdu2bdt2cjdO
+N8QT9NFHH+Vvf/sbUBnhCZUnd35+vj6TO3uVO82Z3++nffv2QOX7CYVCvPzyy8DOHoSRa9V9gltX
+xfM77rhDuYmDDjqoRjFV1sKIESPC+hBOJVHv1liREUTBWpu2NF3CJnft2lWTmghx6Nq1K3369AEc
+NvGpp54CwpOepBoiixcVFekiveuuu9RXXxxsAoGAsrvizJIq+Hw+DTN++OGHAYc1lTH8+uuvusCD
+waCysMOHD1d7uszP6aefrv4IPp9Pn0liWcCRndMtng0bNoxDD3X024cddphel82Yn5/PaaedBjgb
+/T//+Q/g6HNE5AkEAqqjEIL87rvvVpkSPlIMcBMNn8+nfS9atEhT2p144ok8+eSTQPjBJ3N88803
+awIYN8H69NNPOeOMM2KdjqTgiQ8ePHgIQ0ZwCsYYcnNzU5YrQFiuoqIiPTWaNWvGJ598AlSmeeve
+vbtyCgUFBeoVuWLFipSMIxpGjRoFOKeMKPbmzp2rJ7PkIVi7di0jRoxIur9oVa6NMcpii6j08ccf
+q7XgoIMOUmvH/Pnz1WpTXFysEZMScLR48eIqFWfRricatRkLjjjiCACmTZumYo6goKBAlaTBYFCz
+OU+fPl3f+9y5c5VDEE6oOla/OlE3FAqp4nPZsmV06tQJcALM/vSnPwEwceJEALp06aJcTvv27bW9
+hQsXcskllwCoVac2kBFEwVrL9u3bw2QyY0yNJhtBbm6uLoj77rtPw5NzcnJ0ky1YsIBFixYBlU4g
+Q4cO1Wy51lpNAf7RRx+lxUEkPz9f+/D7/box8/LyGDNmDIB+vmnTppT2HQgEdooPcPcXDAbp1q0b
+ALfddhuPP/44AO+//74SgjZt2qgzmBDWeCP1asPKc+SRR6r1w+2OLespEAioNScQCOhcLFmyhLVr
+18bcT6zG4Dr5AAAgAElEQVTPMmHCBM1pmZWVRd++fQHHcgPhWcdLSkrUOjZy5Mi0RphWBU988ODB
+QxgyglMAND+jUM+mTZtqYFNV6bIkX+D48ePVZTgYDKqvQ926dZUlNsZQv359oDI9WHFxsTqEGGNU
+seeO8EslrLU6Bp/PxyOPPAI4/hIyJjnZJk6cmNJ4+WAwqHO0bt06XnrppZ3uEeVb//792X///QFH
+xJo6dSrgFFmR0zaTS6qXlZVpPog33ngDcJzTxOln5cqVykFOmTJFT+Z457smS5n0cfnll6siPTc3
+V+dOFLHFxcW6Tm+66SYds3AztY2M8miESnNSx44dNarxlltuUX1DMBhUMcPtey6mvsMPP1wXdJcu
+XZSNzM3NVc2+OLb07NlTX9DWrVtV57B8+fK0JLAoKCjQMGh3kde1a9eqaU3CY4cPHx6XabQmccet
+R9i+fXuNrG86Zf+a+k1lf26PTdEvvfzyy5rz8rzzzkvoAHCPM5reJjs7W4nwXXfdpQdYQUGBil4S
+7/Lyyy/r9zZv3pzOIrzpjZL04MHDHxMZwylEOyHSXWzDGKMKHr/fr1xFuvrLzs7W+P99991XOZ6v
+v/6aa665Btj1pex2NdJRuVkgvgD77LMPv/zyS1JtpXOcaYTHKXjw4CF+ZAynkAFjqFXKHwgEwrL4
+ePBQC9i9oiR3NWp7Y6az+vLujN2ULf9DwRMfPHjwEIbdhlNwu+hGKiCtterCWlpamnAV4dpEvKnn
+UoF0K25TgdoY267kRvx+f0b7eEAGEYWaXlS0DeRe5LWV1CMRuP0q3OMXy0c0O3l2drZeLy8vr5LQ
+xZrx2p2JOVEks5kiCVJVbdXGho2MThRRzu20JvPmvjcV46rKByGTCLYnPnjw4CEMGcMpiDdYLJTS
+7aUG4Uo7t1dZJlBdqDwFIusPunMWyDPJvbm5uXqquPMRuNN8uZ9VEPnMcm8qTuBUzmdVbdX2O3MH
+3UXrOxAIpFQp7PP5duIMd5X3aFXIKJNkKhbu7q69lgXi9/v1OTJdBv0jItMOFkjJmNLvvGSMqWeM
+ecMYM8cYM9sY09MYU2SM+dQYM7/id/1k+vDgwUPtIlmdwiPAx9bafYHOOCXpbwAmWWvbA5Mq/o8J
+yVBld/qr3RnyHMFgkPLyco9L2EVIR7mBZBAIBGjQoAENGjSgWbNmNGvWLG3rPZlaknsAPwB7WVcj
+xpi5QG9r7SpjTDNgsrW2Qw1tRY19yFSkY6zCGkoOx2AwWGXIeCLtZsrcSrTgnDlztOy7e2zRZO7/
+ZYip/YcfftBwcMkxumTJEs455xwgZhEz7eJDW2At8Lwx5ntjzDPGmDpAE2vtqop7VgNNon3ZK0Xv
+wUNmIhnrgx84GLjKWjvdGPMIEaKCtdZWFddgI0rRJ3KSRYtjz5QTMR7Ur19fKf6FF14IOCnLJdmG
+pAvbXdGkSROtpty1a1cANm7cqHkSX3jhhf+pOJB41qrU41y2bJlWLBer1dFHH621Pfv06VPlOol3
+byTDKSwHlltrp1f8/wYOkVhTITZQ8fu3JPrw4MFDLSNhTsFau9oYs8wY08FaOxc4FphV8XMBcB+1
+VIre7/dH9RTz+XxhVLK23YprgvhZXHbZZVp7QE7MfffdV58pU/QtIt+Cky4PnOzDkjS3U6dOWsZt
++/btmgX6gw8+oFWrVkClX8CcOXM0/Z3b9TcVz5qdna1Vo6W+Rf/+/VVHs379es2k7e6rXr16WvtC
+5r66sSTCnbr9TGL5/vfffw9Av379dEziCduvXz/NRr5q1SqtW3LLLbeE7Yd45zMpPwVjTBfgGSAb
+WAhciMN9jAP2BJYAZ1trq002F2/otCxOqaRz8skna1bigw8+WCdh5cqVWsJ9n3320eunnHIK4CQ0
+SWPqqxohrPTUqVOVQMhCeeCBB7jzzjsBZ4NJpuU2bdpoGq/aUshJJuLhw4fr5pZ5a9SokW7AUCik
+afNyc3N17k8++WStblTTmKPVZawOMl/du3fn008/BVA2uzoIEQqFQjr31lpeeOEFgKhEIxVwZyxP
+du3l5OToYTJs2DC9PmjQIMaOHQvsNP70h05ba38AonVybDLtevDgYdchY9yc42EbRSkn5bUKCgpY
+tWqVtiMs1YwZM6p0XQUn27OwlG5315rGkirPy8svv1zHIyeenI7HHXccH3/8MeBUO5as08ccc4xm
+CRZWN52oU6cOb731FuCccnLCSumz559/Xk/m/Px8ra2xbNkyfZZ43ITz8/M1G7dbgRyNa+jatStX
+Xnkl4IgJ0ez2MkfFxcXcf//9AAwePJgOHRwreV5eHsuXLwccLkXS4sXjbh8Px5bKKMmSkhJN9BsM
+BpXj2XPPPZNanxlDFGJ9iK5du/LEE08AlZv7zjvv5OmnnwZg9erVNbYli3THjh00btwYCC862qBB
+A2WDS0tLw2R7SI7tkza+//57rRoU7fPWrVszcOBAAF577TUVla677jr23HNPAK644oq0ixCjRo3S
+xRYMBjn55JMBlFW31ipL3KhRI62slOjCLykp0fbcZdjd+TNlM7pl6l9++UU39P777681LaV+qHtN
+PPvss0ybNg1wxMply5YBjuVHfCdiQby6BHDmMDJeBaLHx9TkQOXz+VQUFoc3SN5atfu7AHrw4CGl
+yBhOoSYIJb3//vu1iIYosh599NGEWOkdO3aoB2F2djY33XQT4JxKd911V1gfkBql08yZM4HKgjQC
+OVl//PFHwGG/Ren1l7/8RbkGd8XokSNH1lgbIlH/DVHmDh48WL978cUXa22MaH3UqVNHWf9ERZvc
+3Fzl5Nq1a6fP5x6/ePYtXbpUuZjGjRurcm3w4MHKCbjzNwiHceutt2ptkKysLP773/8CTrXteCCn
+fyxckZu7dAe9yRjr1asHOGKO3FuV2CXf79mzJ8cee6yORb4XuSbiXQO7DVGQF3D44YfrQwrbl+gC
+3GeffbjqqqsAp6iqFO8oLCxU9vPJJ5/U6j3JEoUBAwbsRAzAEVHee+89AHXyOfvss1Uk2nfffcNY
+TtH2N2zYMK6CMfGgR48e+rfoXV5++eWd7svNzdVnatmyJR9++GFS/ZaUlOizzp07dyfRzVqr72b9
++vVK1PPy8rSgzr333qtFY6XK2FlnncXgwYMBaNGihbZXXl6ucx/v+41XlyC/5VBzFyoSUWnjxo1V
+iqdC1G6//XYAzj//fH1+a60S5FAolJQznyc+ePDgIQwZwynEqtF3a1nffPPNuPoQbkMUlBs2bFCW
+c+HChcopLF26VNnP4uLiqEkx4qHAcgpE1m+UE2HFihV6z/HHH6+/ZZyRkO+JY0s6IKXcg8Ggul67
+n/nwww8HHFZ8v/32AxwFX7KKz5ycHFXy1pSI5bPPPlOrUX5+vlplunbtqj4gQ4cOBcLrhjZs2FDH
+OXr0aK2VGS/isT7IO8vLy1OlsayxLVu2KDdaHaQWpnw/NzeXTZs2AfDWW2+xZs0awFkXyaSRyxii
+UNPARW4rKSlR9uuSSy4B4F//+leNZeuPP/54brnlFgBlP1esWKGs4zPPPKPsZ25urhaCzc7O3qnt
+aNrj6iCsrNtxxc3itW7dmjZt2lTZtnvRubXTbjmyKiQq8gwaNAhwRBuxOLRv356//OUvAGoBcY/v
+ggsuYNSoUQn1JygvL4+Z9c3JydF3FgqF2GOPPYBwByg5QBYsWKCWBdlc4Mjtcn+8VqV4LCzyTIcf
+fjhdunQBYPr06TH3u//++2ucg3iKQqWlYePGjXzxxReAU5s0GXjigwcPHsKQMZxCrBg6dCgPPfQQ
+gLL7jz/+OCNGjAB29jeYO3cu4Jz+cl2uXXfddVFZv23btmmF4qVLl2r8eqTPOlR/molGuVevXjt9
+FkuCjBUrVgAOyymssc/n09Pv2WefZciQIWHjqCr/X7wcw3fffQc4UXrnnXeethHNGiAYNGiQikhy
+gscLv9+vCrOqIM+Un5+v8RNbtmyhRYsWABQVFenf4l693377Kffnzrv49ttvJ+x3kojz0hdffMHX
+X38NEOb/EQ1+v1/X+plnnqnr3f1O58yZAzjva/LkyUDy6fs8TsGDBw9h2O04hZdeeklPIZF7X3/9
+dY4++mjAUSjdcIOT1qFly5bk5eUBDnWVU1XMVNVBbOFQyVkIYk3V9cgjj2jf7u8K3BRfqLvIvaFQ
+SBVRPXv21FPO/b1DDjlEIxTlRNywYUNKvByF8+rfv7+avZYsWaKmyg0bNgBOLgjxMD3yyCMZP348
+AKeeempc3oGC8vLyGmV8mcMdO3ao/4bb8xQq50jmorCwUBWN7s/FPT4RRJtnaTcvLy+MY5W1WlhY
+qC7W4j4+Z84cNasvXLhQdWZTpkzhwAMPBGDTpk07cYOhUEi5g48//jhlwX27HVGAysl8//33gXB2
+qaioSAnE4sWLNX5gzJgxcRWMSVZZA5XhxQL35i8tLdVNs2LFClUYiQa5U6dO9O3bF3DYZLfoIt8b
+MWKEEg531F9kn4lAxISzzz6bxx9/HIBZs2axfv36sHbXrVun4sWoUaOUCLdv315Z+3gQCoViXtxu
+x7JIRD735s2bdd0MGDBA50niHhKB23VZIEThqKOO0liLuXPnqgi6aNGinSJirbX6/hs0aEDPnj0B
+xxnMvQ6FEIvIsGXLFu699159vlTBEx88ePAQht2SUxC43UCF6g4dOlTFhCeeeEJ9GeLhEpo2bapu
+sBMnTtzp81hO35ycHI488sidvien/C+//KL25jZt2uipI/kKRFEGzkkkp9GOHTs0z8Lrr7+e9uQr
+U6ZMUX+Jhg0bqj+FO+hGgsrOOuss8vPzAed0FJEmHrbWGKMnaaozWbtFMJnvPn36qKnP3Wc8vgcC
+Y4wqA8eNG6dc0x577KEiQfv27VUZ+9VXX+l3xUx66qmnqutyixYt9Hs+n0/XsCiw69Wrl5b3v1sT
+BYExhtatWwNOFiN5sYsWLYpLCy7sfuPGjXnuueeAxF2ohwwZogvPLQOKi3KXLl10A7mL5oqVwQ2f
+z6ds5LBhwxgzZgxQOxmkysvLdQ7Wrl0b1R9EfCwKCwv1OcaMGZOQjJuTk6PfS2WYcXZ2tjo0QSVR
+uPvuu/UQWb58ub4rWTfxzHEgEODPf/4z4OgUpK3mzZurk9GOHTs0svP666/XvkTkrcphDSrd28U6
+8+GHH2pUairhiQ8ePHgIwx+CU8jJyVElUr169dRj8ZVXXqnxu8IOG2P0NHb7OsQLOSmXLFmip42w
+re7cA6LRrwrWWn77zcl5O378eLWobNu2rVbzNVprue+++wD46aefovYtz+l2/3bnQogH27dv1zlK
+RRZrYeFPP/30MPFBxjljxgxV6M6ZM0dZ9ClTpgDhlajdiObqXlZWpkFjt912m4pBUJmVGQjjWGLF
+mjVrVKko0bOJWHdiwR+CKDRt2lQX4eLFizXsubi4uMY08KKXSPVGa926tbLBbsuBLHS/3x/m9iz4
+xz/+ATiafNE2J4NkU9/Xr19fnagWLFgQ9R4hpu5IvXnz5iXUX25ublyL3Z2cROY7KytLZfGbb74Z
+cNyE3clLxDz55ptvqtvw+eefr6LSjBkzAKp1pIqcW2ttmKgY7f1WBWljxYoVXHHFFQDqgl/b8MQH
+Dx48hGG35hSEEl955ZVK7T/55BNNwQXhWuRop2WiRWiq+p67/JsoDUVZ5vf79fOVK1eqYrO8vFzd
+qhOx7acDotQ6+OCDNfjJrS13Y9y4cUC4D0W8SkaZl9LS0jD34eo4nUAgoJaPbdu2haVs22uvvQDU
+Uahu3bpRT/GRI0fSrl07wPEHkXGL5WT16tXKCU2bNi2MK3BzKfJb+nWLDtbasPkQ5eBpp50GwDff
+fJNRNUMzjijEw+6KyHDkkUdyzz33AE4obE2+87WBevXqaVLNoqIiwGGpZbPdf//9qvUeMGBAjVGe
+tQ3RE5SVlamIVVhYuJO2u2XLlnTu3Fn/X7p0KRA/sXVnSJINlJ2dHTWmo0kTpxJhMBjUz7ds2aL6
+gwYNGmhyXzH15eTkKCHIyspS/UKjRo3CiMUvv/wCoE5awWBQHY/c44xGFAYNGqRiy6uvvqqmw88+
++4wXX3wRgK1bt2bcu45EsqXo/2aMmWmM+cUY85oxJtcY09YYM90Ys8AYM9YYk5jGyYMHD7sEyVSd
+bgF8Cexvrd1ujBkHfAj0A8Zba8cYY54EfrTWPlFDWxYclst9MtTEoktyjw4dOmgasNqouxhLkpX8
+/HyN75f4+dzcXGbNmgU48QxyKn3zTfpq7CaraDzttNM0b8X69es1h+Srr74KOCnnJfYhLy9P80dK
+wZp4xih/u8cqp7BwhXvssYfGexx44IGa6+G2227TXIuXXHKJ5tt0O/9Ei0zdsWOHckUvv/yy5jiQ
+97RhwwblMIQLihy35LPs0KGD+mx8//33LFmyJOY5qCWkveo0OOJHnjHGD+QDq4BjcOpKArwInJpk
+Hx48eKhFJFs2bihwN7Ad+AQYCnxlrW1X8Xkr4CNr7QFRvnspcGnFv10jMw7FUsNPTuLaKIqSCrg9
+F2ur5FuynEIgEFBZ/aSTTmLvvfcG0KSyeXl5PPPMM4BT6m7ChAlJj7emd+9W7Ikr+eLFi9XM2KhR
+I40ufPbZZ/W78hz5+fmaubl79+4Jm34jdQrZ2dlhhWwyEDFxCsmID/WBN4FzgI3A6zgcwm2xEIWI
+tuIahLwEt9LHw65D8+bNAcfBZlfW5owGYe07deqklawuvfRSDWVORWEfWY916tRJyD063XAdDGkX
+H/oAi6y1a621ZcB44DCgXoU4AdASWJFEHx48eKhlJMMpdAeeA7rhiA8vAN8ARwJvuhSNP1lrH6+h
+rcwhqx7iRrIiSm3AXVuhpKQkpQppUUTWrVtX8xrUlngYC+LlFJLVKdyOIz6UA98DlwAtgDFAUcW1
+Qdbaat9AokRhd1iM/wuQ91BVnEAmICsrKywnYiqdhUR8cDssZYovQkTWr/QThVTBIwq7Nzyi8Mci
+Chnj0RhPgZVY6y5E2r9TtWDjLQYTqRiN/DtR+Hw+faaqMgunmnC6cxCC46ItbZeXlysr7Q4OSheM
+MZqTYvv27WGK58g5D4VCulFzc3M1SjUUCmlZvEh35Fj6lzYgOUKQ7Huqak0m0l7GEIV0nPap3oSp
+aCuV43Bvuqo2YKrnVTa9OAW5N1K8myrVcG+s6ixTdevW1XgOd57H8vLysPiJmpBo3Ey077vdvBNp
+N5Xv2YuS9ODBQxgyhlOIB7ubDiFT5ex44T7lJP3dL7/8ssu4A3fOy2ifVYXu3btr5eZ169ZpvclV
+q1apKJEu1LR2M2Ft75ZEwcOuQaNGjbQehBS3nTdvXto3UqogHrC33HKLJs198803NTRa8ij+r8MT
+Hzx48BCG3dok6aF2IPkgpk6dqrkVJM+gO81+JsMYo1GpEl0LTjzHRx99tKuGpRAF7r333qs5I9u1
+a6ei2Ysvvsg111wDJCVi1EqUpAcPHv5g+MNwCuI40qJFCy29lgkZmNKBRKodxwtJb/fzzz9rgRNr
+rWY5PvVUJyI+XRmFUwUxnS5ZskRTt7lRr169lJZciwfidv36669z0kknAVVXI9++fbsmzr3wwgsB
+R58j7toxcmy7l/NSInBrw8WZpri4OCw3YiblvosFslC6dOmi4b9//vOfdWM2b96cZcuWAU7+wBNP
+PBGoTC4TaQlwF6SJNWw7OztbK2s1bdpU2dVZs2ZpduRYHcjihdshKxWQNH2RBEHWRVWbMF0IBAKc
+e+65QGWqdvdcBoNB/X/atGk6/s8//1zL0k+aNAlwDkKpJjVjxoyUWS488cGDBw9h2O3EB3daLXfC
+TbnWt29f+vfvD8CyZct4+OGHgfhqSdYW5ESoX78+F110EQA33ngjEF4+rqr6AdZaZRsluei5556r
+VYmDwWDYd2vyJ5CSZdOnT9csyIFAQBV0AwcO1HwBksC1YcOGSVVuThckYevs2bOBnTkb8WTs0KFD
+0klWBNXtJVmfd999NxdffDFQaSINBoOanObss8+uURSQmhrt2rXTPdC5c2e9HgqFVJwuLS3VNVBe
+Xr57iQ+xune6s+i6X4qw3T169GDgwIGA4+N+9dVXA7DXXntlFGFo1aqVavAff/xxzfIsG3ft2rWq
+9XcXkcnNzdV5KikpUZlZUoufcMIJml8QKuclFplTWOoff/xRE6cMGDCA//znP0A48ZWMyhJ7kEkw
+xmiOxmhizu+//64ZpLZs2ZJw3EE8hXCl7REjRqjjlLyTeJ2/3JnG5H1ceOGFWrTn4IMP5uuvvwac
+vJPx1FMFT3zw4MFDBDKGU4gGYXvclDQrK0tZa4lKy8rKYubMmUBl5WhwTglRMD388MP85S9/qZVx
+VwcZz+jRozW/YCAQUA241CJ85pln9ET47bffokbg+f1+LWQi7U6dOjVqWTyoORJPrv/2228qSpxw
+wgnKKbi5OVGWXXTRRRx00EFA6kvHJ4pBgwYxePDgna4Lp3jWWWep1SQrK0tP23h9LtzlAKF6TkM+
+SyaSUkRE4XKgcs5nzpwZVqtCxJFEkslkjE4hmtZZXlZWVpaayPbee28lElu3bgWcdOPiotqrVy9N
+xS3++eBMVM+ePYH0plSvDkVFRSxatAhwCIE8x5w5czSXYPfu3QFidh2OFKUi51BYXDfrX9PmLSgo
+0FqK69evV9fmqVOnqq5BiqmUlJRoavVdDRHBNmzYsJNVIRgMqrg2c+ZMnSe/36/Xq6qAVRXc+Rkg
+vXELbquEwFpLs2bNALQYsYzL/Y5rM0ejBw8e/oDIGE4hmqLRTe1FsfXWW2+pZlkKk5xzzjlhlY/d
+3xctc2FhIV988QUARx99dJqeJDqEY/nyyy+Vsm/ZskV9AW677bakNfjip2GtDWMZo+UYiAXCfp9+
++ukaPLRq1SoWLlwIOFp0cMQW4RR2dTSocIvCMbhxwAEHhClgZV722WcfXnnlFQCef/55Ro0aFXN/
+6c785U4ZHw0FBQXxBqPtXtaHaHAvMsnpf/vtt6s2ddq0aUDV2ttQKKQVe9asWaMsb21DNliLFi2U
+rbvpppu0vmAyUYbuwqyw81wk6v3473//G4Cvv/5aS6I3bdpU9Ryiu7njjjuUaAQCAdUvLFu2rFbj
+It57772oxEBEAjdBgEqrTL9+/VQvc9555/Hcc88BsZmwZW7TFTouTmoCeYei70kXMfLEBw8ePIQh
+Y8SHVLu3CqQAyObNm1WUEFEk3RAriTjHGGNUzDnjjDO0StHvv/+etlM12TiJnJwc1Xo3adJERZN3
+3nkHgDPPPDMsNZuIF++99x7Dhg3T6+lCTcrTaIq4OnXqqNt4QUGBWnz2228/5ZCkfmZ1SIf4kJeX
+p8pot2u2tVbXchIxPZ6i0YMHD/GjRp2CMeY54E/Ab1L+zRhTBIwF2gCLgbOttRuMQzofwak8vQ0Y
+Yq39LoY+yMrKSgun4M4uLN53tZEa3hjD+PHjd+pPglmmT5+uHEvDhg1VhhV/hXgVg+lCMBhUXUwg
+EFBb+JgxYwC44oorOOywwwBHkSrcz1lnncUNN9wApDfd+T/+8Y+o16Xis3CH7pqYt912m9rxFy1a
+pJxQp06dNALxyiuvBKo/lVOpU5A1MmXKlKjRnA0bNqy1qN9YFI0vAP8HvOS6dgMwyVp7nzHmhor/
+rwdOBNpX/HQHnqj4XS2stWlzfNl33321DyEKc+fOBaBjx476QlNNkHJzc9UvQrB48WJ1oMrJydHx
+dO/enZ9//jlsvG+88UZK5iRZwuLz+cJcmYUAiJgAhCUpEQVkaWmpRlSKn0M6cOutt+50raysTGNJ
+RLTJz89XpXMwGNT3vWrVKlWUQuXmFFfk66+/vsq+3RmY3f/HC5/Pp5YTEREEMs+JxmckNJ6abrDW
+TgHWR1zuj1NmHsLLzfcHXrIOvsKpK9ksVYP14MFD+pGoSbKJtXZVxd+rAdHctQDcdpTlFddWEYGI
+UvT4fL60mHYksMetyJTfZ5xxBmPHjo27zViCtw477DA1HUl/F110kZofg8Ggsq3z589Xl+fRo0cD
+zgkhQS27EoWFhcq2/vrrr6oEqwryzHJ/uhEt4On888/nu+8cqVW4nKOOOios8lRMp/369Ysa1PXZ
+Z5/FPIZEOQTxYl2/fr3+DZXm0LfffpvzzjsvobaTQdJ+CtZam0jmJGvtaGA0ONYHSQKSSjna7/cz
+fPhwwEm+ImG0r732GuC4F7vCSuMZe5WfSXvjxo3byQ22d+/e6kAV2U6nTp2AykV8yy23aAj4riw+
+497kbdq0UUesxYsX63V3Naa//vWvgPPM6XZ/rirRS6NGjXj8caemscjnkydP5o033gDgwAMP1OjZ
+nJycMP8VWQeff/55jf0nWgzm3XffBdBsS26UlpZyxRVXAI4oKXonySYWC5K15CVqfVgjYkHFb7H3
+rABaue7zStF78LCbIVFO4V3gAuC+it/vuK5faYwZg6Ng3OQSM6qEMYbs7OyUlgcHhy2XU2D8+PHc
+eeedQOVp7vf7NdHFxo0bUyK+iGJL2oXKE2XUqFFRT5dAIMDIkSPDrv3zn//MCOvD22+/rdzCsmXL
+lLWVALUtW7bw2GOPAQ7bK6fc9u3bNWCtttGuXTt69eoFVHJpwWBQvTGPOeYY5SDcSWistfTr1w+I
+zWISqWD0+/36d+RaknufeOIJTjjhhCrbHDlyJN26dQOc/JGnn346AE8++WSN68Gdei8ZxGKSfA3o
+DTQ0xiwHRuAQg3HGmIuBJcDZFbd/iGOOXIBjkrwwlkGI9SErKyslG1NSYRcWFvLJJ58AjhZZHFiE
+3R08eDC9e/cG4MEHH9RYimQgbbtZWzd77X6xQjiefvpp3XiiZe7YsSNffvklsGtjCp588kllxevW
+ravPIpmXfD6f5g7Mzs6mXr16gGOS/Ne//gWQNrnYWqsxIy1bttR5OvXUU9UKImx3UVGRWioaNmwY
+FijOXIoAABnSSURBVFcj7+See+5h8uTJcfUPle86GAyqyOjz+VQUyc3N1axIzZs3jyr2yL1lZWV0
+7NgRcMyqffr0AcIjgatCqnRyNRIFa+2AKj46Nsq9Frgi2UF58OBh1yEj3Jx9Pp8NBAIpc3IRzTOg
+iq9osfJZWVkMGODQvOXLl8d1StSENWvW0KhRo7Br/fv31+AiY4x+PmfOHD11LrjgAgD+85//xJ1G
+Kx1o2LCh2srLysrU1h9tbD6fT0/KN954Q0+uWPIOJgpxNnr22Wf1tF20aJGOQ0rOB4NBFXn8fn+Y
+n4KIDJKLMlZElrv3+Xxh4oiM4YILLuCRRx4Ju+b+3ocffqjrtEGDBira/PDDD9re6tWrUyFO7j5R
+ktZaUmV9aNeuHe3btwccU487lXe0JC6i9T/88MO1pkEq2PXTTz89zNIATmiu21tN2NmsrCzVjEvS
+1Uypp1BWVqaiTXFxsepMohEFa61uwhNOOEE3zTnnnKMWn1SbnWWOrbXaX7NmzdTEF03GLy0tVVGx
+X79+STsGSb8+n0/nZ/DgwepNee2114YRA8G4ceMAuPzyy/V7oVBI63Ru3LhR129tHt5e7IMHDx7C
+kBGcgjFGWbpEKaKwWa+//rpeW716tbKEVWn95Xrz5s1p2bIl4HAYyWrOp06dqv73YtsvKipSF+se
+PXqoUu7pp5/WVPSSkTfWeRBlpYy3Kq13ovO6adMmBg0aBDhp7NwRn5HIycnR6EmoPEH//ve/qzPQ
+ypUrExpHVXAXwZHTOD8/X59XuL7s7GwVT0eMGME///lPILkTODIV3nXXXaecy7Zt29QhTTJtC+T0
+F06iuLhYx7F48WLlwnaVaO9xCh48eAhDRnAKkkIsGcooCUVbt26tp+WTTz6pp0O0tg899FANQOrR
+o4fWR3zqqacSHocbbdu2BSpPs0AgoKfGG2+8oW63L774YtwcgkBOKckklI7aFpI2ribs2LFDU939
+9a9/1ZRtjzzyiGbOSjUkO9HatWvVDGmM0XmR0/g///kPzzzzDODoIeKZ56pyUsj/8vkzzzyjqQIH
+DRqkeg13fY5QKKTu32+//TbgcDmiQ8qEjNgZYX1IZYHZZs2aKfu1adOmal9+dna2btyysjL1Y0iX
+080XX3yh7PdVV12lY5Ps0380iHVl3bp1teJrISKkW7RJ5yaLtD644xcOPPBAVq1y/PY2bNiwyxy5
+IuAlWfHgwUP82C05BTkJ3GYeYc+2bdumlDuWZ5PvBQIBjWDc1VmJ/yiojWQ2mQC3F6usnaysrIwQ
+BSKw+/gpxAuxm7szGSdqzxWdQzqzA7lR2xsl2RyNyeCPTgwE7sIyggwkCDHDEx88ePAQht2SU4jM
+U1CVJ2Rubq7eU5s1CKqDe5z/K+z1roRb6ZiueXYHRP0R3mnGEIV4XJzFxOdmiaN9N5ZEl+5INyE2
+NRGQVCWDqa4Ndx9uf3q3g1cs46hKbKhu8UarWVgd3G0luynindtYi+a6nyne54un71jnKt7+Jbkx
+sFMGsVTDEx88ePAQhozhFOKh1tGSSdR0Wvl8vhqtErGewLXBGrr7qI4tjUeR6D6lqktDF+/zxXNS
+xtNWPKjqnVU1b7H2U91aSISjESczqFRux8od1Zby0uMUPHjwEIbdkiiUlpZSWlqqmYzcPxC9kIrI
+4rFQfXdWnkxBtLHn5OSEufTG01Z5eflubTYTRL77qj5Ppv1UQN5Ts2bNdpr7WPrwQqd3EWQB1atX
+j0AgQCAQ2ElBFe8GBIdldLONqUJpaSk+ny9mIpbsBqkNyBwnMs+xYlfMgbynJk2akJ+fHzWtfHVI
+53xEwiMKHjx4CEPGKBozAeLTUJWHZKIRjBJ0NX/+/JSYkURJGEv9TTGzxiMqGGO46aabAKf2wMCB
+AwHHhVxqFgwZMkTbTeXJmy734NrwV4il/0AgoHMrQVKhUEgjdDdv3qw5KVavXk3dunUBp3iNJCRO
+t3fqbhn7ECtycnI0PdjYsWNZsGABgJYe/+qrr3SCA4FAGCGoaeJjscfLPe4S4tJHdna2hvoWFRVp
+HkTpV/QmkfD7/fpMW7ZsqTG9mYgtZWVlYWOVmA/JDThgwAAOOSS6W3x1z1pWVqaL+Oyzz97p83iR
+6oJAEhp//PHHawKerVu3aiHfWMcE8Vsb3AVoJaHOAw88wMknnwyg17KyssJc9iWMet68eVrJKicn
+h1mzZgFOBGaC8KIkPXjwED9q5BSqKEX/AHAyUAr8Clxord1Y8dmNwMVAELjaWjuhxkEYY1N5Qkhy
+jxtuuCEq2yjXtm/fzqeffgrAY489xrRp0wA0WrKa8cY1Vomzz8vL45577gGcJBxSGr0qxdpLLzmF
+vq+55hr1smzVqpXmX6hpnFApagSDQe0P4NtvvwUqT9JYEZlYxH0tLy8vaXfyVHMKTzzxBACXXHKJ
+1qqYO3euikEffPCBzqdbESs1SLdv306LFi0A+Omnn/T5avIEzc/PV67QGKNroEuXLjRs2BCARx99
+FIBXXnlF38fYsWOVE+zcubPe06VLF21bxAj5LA7ExCnEQhSOBLbiVJMWonAc8Jm1ttwYMxLAWnu9
+MWZ/4DXgUKA5MBHYx1pbLY8rRKGinZrGHBXyEn/66SeKioqi3hPpvLR9+3Z9WTt27NDS6Y899lhK
+FqZsyGOOOQZwCtqK7OhO/15WVhZWREQgi2rChAla3WrevHlaFCSWMboTgcgc77PPPvz8889hY3Sj
+vLxcxYua+lizZo2ywY8//jh/+9vfahxTbUBkcSEE1tqwJCwyF26RbuXKlVpDUrJvtW7dWus5fvDB
+B5o4xU1A3KHT0q7P5wsT/+T9ut3UY3l/UvNScngCWuCoukpTVSA14oONUoreWvuJtVa0QV/h1IwE
+pxT9GGttibV2EU6lqEPjGrYHDx52KVJhfbgIkHruLXCIhEBK0VcLCUZKlPUsKCjQvHdywgmEWj/3
+3HPccsstQGWgVE5OjpYVq1OnDs899xyQOu201LG88cYbASfzsqSKGz9+vPYnrCOgrOWkSZN0nJMn
+T9ayY5s3b45rfCIyuJPPrFy5UnNCSu2B0aNHqyI2HrRo0YLx48cDsQWg1YRkKyYLpAajmyOQKtjt
+27fXsX755Ze6Rr755htmzJgBOJYicArEuJW5bhFB8nqIMtfv96tFIVJJnKhF5f/+7/8AeOihh7Tv
+SZMmJdRWrEiKKBhjbgbKgVcS+O6lwKXyfyJFQkTOevrpp3ciBgDvvPMOgwcPBhyNc6ROITc3V9k6
+a23KE62I2e7www8HnAV/1113AU7tymga8LVr1wJOstP7778fgOOOO07Z2Xg3jCx+d1agzZs3qy4h
+2Y381Vdf6Xvo3bs3H3zwAeCkuE+EuKaCIOTk5PDss88C4ay9EAV3otySkhI9GFavXq0RuDV5vrqJ
+l2z4YDCY8mI355xzjo5fIAWM0oWEiYIxZgiOAvJYWzmDMZeit9aOBkZXtLXr7aIePHgAEiQKxpgT
+gL8DR1lr3Srwd4FXjTEP4iga2wNfx9hmzP0L2yYncefOnfWzsrIyhg0bBjiKL6HmbruxaJO//jp8
+aLGOIVYNudSmdCuWpPbhfffdF/U7onm+9tprlS1t3rx5wtmA5fkjx5sohyC1JMWvwo2CggJ95iFD
+hqhfgJy+tYUff/xxJ9fv3NxcFU87deqkJQHmzJmjZdri5RSlj3SVdissLOTf//73TtdfeSVuxjwu
+JFqK/kYgB/i0YiN9Za29zFo70xgzDpiFI1ZcUZPlwYMHD5mFREvRP1vN/XcDd8c7kFhlSWMMffr0
+AeDiiy/Wa/L9v//971Hl70MPPZQTTzwRqLTzSjEWqNqDMBpiPRF++OEHAFUS7rvvvsrlTJo0ibfe
+ekv7PvbYYwFHfwCO8kpOoPPPPz+m/pIZa3UQDqq8vLxabqqsrEwLt+bm5qpZsLY4BTm53eZeWQML
+FixQJW/jxo21nOCUKVMSUgL6fD7VH4ipMxUp//Lz87WgzLfffhvVz2bx4sVJ91MdMib2IVaW3FrL
+7NmzgUrbrzFGtehPPfVU1JczZ84crR/oduKRRXPPPfekJPGGG7LYxJ48c+ZMtS40adKEHj166DMJ
+QRK2Pjs7W+tOCnFJBKnIGShKuWgEIRQKaRWmxYsXq4Jv8uTJej0eJOO8JAT3n//8J2eccQaArpVR
+o0apZcFdtCWZ6t4yH6kgBuJb884776i7uXu+Q6GQ1g1Nd0Vyz83ZgwcPYcgYTiEeU5QoEt1ed2IW
+O/nkk9WFtaSkRFnK0047TU9mtxefnObiUhwL4j3J5KTt3r27eiYec8wxGihVXFysdQUlWAYSM9Mm
+O9ZI+P1+dae96qqrVMEofgA9e/ZUW3qnTp14/PHHAYdrEDY4njEkM155lwsXLlQvy6OOOgpwak4K
+l7Zx40ZVLiaKZCqkRyIQCKiPiHADkejVq1faOQRBxhCFeNhGsTa4/Q5Gjx4NwKeffqoybFZWlkaU
+DRs2bCeXXmstH374IYCWjU8n5s2bx4MPPgjAr7/+qsVgn3/+ed1A8mwNGzYMY3MTRaLig3yvXr16
+SpyOP/54jdST9j755BOdw0svVbcT2rZtm9CmScZ5STb98OHDtZCvtDV8+HAWLVoEwCmnnJLwBktH
+CvfXX39diZgbW7duZdCgQcDOlrJ0whMfPHjwEI5oeQ5r+wewsf4ccsghtqyszJaVldlQKGRDoZDd
+vHmz7dy5s+3cubOtcITSn3Hjxtlx48bZ0tJSvV9+5s+fb30+n/X5fDH3D9iKAK6d+qrpx+fz2caN
+G9vGjRvbXr162eOOO84ed9xxNisry+bl5dm8vDy7ceNGu3HjRhsKheymTZvspk2b4uojWp/xPp/7
+GVu3bm2LiopsUVGR3WOPPaLem52dbbOzs+2WLVvC5lfeSbz9JvKcubm5duzYsXbs2LFha0N+gsGg
+LSkpsSUlJfbmm29Oak6TWQPR5i0YDIaNdeXKlXblypX27LPPTnqcET/fxLIfdzvx4aSTTlIxQO6/
+88471RTmRn5+vsqU69atUw2vmCRHjx6dMKuaCPvYqVMnTjvtNMCReyXmwc0yu3P3iSkzGYiFJl7H
+HHm+WMQqafuwww4Ls5TceuutAJx55pkx9+s29cWCZs2aAY51oVu3boDjYi1u3OIAVlBQoKHm06dP
+DxMDYk2Tn+qw7ttuu03bFVhrVeSVCM/ahic+ePDgIQwZwynURIHF8WjEiBF6TSj76tWr9USsX7++
+asBPOeWUsErAK1euBBzFHiSu3XfnJojl5BAnqdNOO43GjRsDjuVk48aNgOObIHHzbvdcsasnA7eb
+d6rdcCMxe/ZsNmzYADjvoX///oCjUZccEDUhnjE2b96cn376CXC4FXF7//zzz3WehZMYPnw4+++/
+PwBHHnmkcmmBQEB9GoqLixk7dmzYONxp7Nxjc6+BeLHnnnsCTuKXSBQXF6tVLTs7W/92B/SlGxlD
+FKqDMUbzALoxZ84cAPbff3+NKXjsscfC0qnLRP7yyy+aqCLZaMh4NeRy76+//sqRRx4JOARKnHsK
+CwvVa9G90Lp27ZrUOGWskBrzZk0oLy9nypQpgEOQRcybPXs2LVs6KTdSEQUpptwJEyqTet15550a
+dWqMUeuCHAo9e/ZUEXPFihVqodq6dWu1Eag5OTlVemQmOrfyjsVa4kafPn1Yv95JX1K3bl09wIwx
+nHXWWUBqQtSrgyc+ePDgIQy7BacgUY0COf3lxOjQoQNDhw4Fwmv1BYNBzWUwf/58PT1ScVrFIz60
+a9cOcNhWofzdunVTkWHFihWa1MOdUVnSbiWDWF1w/X6/Ok4VFhYyZswYIL64BWOMKhf79Omj76dJ
+kyZ6yok7elXzVtO78fl8mgV77ty5qphdu3atvvvnn39ecw6ImFBeXq7JSb7//nv1Eanp/VX3/Imu
+o9tvv32nayIGffPNN2GRreKI161bN31W4XQj+3fn40wGHqfgwYOHMOwWdR/q1aunSsKcnByl7mKy
+CYVCmoegvLxcvb/OPPNMjYxLJbKzs+PSS8jYTjvtNE3RNmXKFC0SU1paqma78847D3BOCcn5L16E
+iSBWjsbn82nWpKOPPlrzN/Tp00dl8arakECkwsJCleE/+ugjPa2NMXz55ZcAqtTbunVrwrKxcCDn
+n3++Rsx+++23aspr0aIFM2fOBCpdzH/88UeNLpwzZ05KCs4k6t0oHKsE5m3atEkVou458fl8qpRs
+37696mUkUO63335TM/vPP/+sc1/Nmo8pcWtGiw+ieW3YsKGycbm5ufoyJER2w4YNmoX3+eef5/33
+3wdSE70WDfEuKCEge+65pxKy+vXr68Lt0aOHLm5hAaVOJIRbJNxKTjf7mKz7bSgU0s0dCATU7Xbi
+xIlqOSgoKFDbeuvWrQEnhmO//fbT78mc5+bmhmWSfuGFFwBUGRjJ+sYzftlUb7/9tm6sSy65RNvY
+tGmTplgTZe6f/vQnjS/YvHmzpnVPBonM9eWXX76T+3peXp7G5UybNk3Fo7Zt2+rB0atXL/W9kMzl
+JSUlDBjgZDYQcSgV8MQHDx48hCFjxAexo8tpFQwGlfIXFRUxceJEAA444ICd7MNbt27VNGEbNmxI
+uz030aAdY4zWEOjYsaNyP88++6wqI+XZQqEQH330EeDkU5Dn//HHH9W/IdaxSns1QZSyEydOjFqH
+Ih6438G3336rtS9qSivnntuqfCtkjowxGvhUp04dLrjgAsBJzCrPIsrF8vJy5Q6SMUm7uR9BLOtN
+xty7d29N3itipc/n0yS9RxxxBOeeey7gcBCiNNyxY4cqUqUW5fPPPx+T/4eLC0tNMZjagDHGymS7
+C6K66yBKFp+XXnpJk5YIq/3BBx+oo0y8jkUJjjfptgOBgFaLOv/881UUcmc5mjp1KgDvv/++ssFv
+v/122EKIJJCR44pnLtwFcR955BEAjj322GqjNd3zXVxcrJt+1qxZvPHGG4BD9KqrrBQ5BjdhrAlu
+ouVuW/QO7rElSsijZT9yI9EIVPFDeeqpp+jYsSPgrGlpb8eOHZqj8dZbb1XHsCTWnldL0oMHD/Ej
+YziFrKysmO2rwiFIibKHHnqoVjz2BKl2GX766afV+iBBULNnz9YiMlKqLFPhZucTtd3XdBpnAiLf
+eyo5UnHBnjdvXkosI1Vg9xIf4rwfqF0X3nTCGKPikbDfgUCg1lOj70qkW+TzAHjigwcPHhJBRvsp
+VIV4FFG7A6y1yiHIM/0vcQngcQiZhN2SKLjLx0PVC8oYo044W7ZsUctGslFm6QhDjnRUSqNcmZGI
+Jj5kgp6hNkLO0414RTNPfPDgwUMYMkXRuBYoBuKvHpIaNPT69vr+H+i7tbW2UU03ZQRRADDGfBOL
+ZtTr2+vb6zu98MQHDx48hMEjCh48eAhDJhGF0V7fXt9e37seGaNT8ODBQ2YgkzgFDx48ZAB2OVEw
+xpxgjJlrjFlgjLkhzX21MsZ8boyZZYyZaYwZWnG9yBjzqTFmfsXv+mkcQ5Yx5ntjzPsV/7c1xkyv
+eP6xxpjsmtpIou96xpg3jDFzjDGzjTE9a+vZjTF/q5jzX4wxrxljctP17MaY54wxvxljfnFdi/qc
+xsGjFWP4yRhzcBr6fqBizn8yxrxljKnn+uzGir7nGmOOT6bvVGGXEgVjTBYwCjgR2B8YYIzZP41d
+lgPDrLX7Az2AKyr6uwGYZK1tD0yq+D9dGArMdv0/EnjIWtsO2ABcnMa+HwE+ttbuC3SuGEfan90Y
+0wK4GjjEWnsAkAWcS/qe/QXghIhrVT3niUD7ip9LgSfS0PenwAHW2k7APOBGgIq1dy7QseI7j1fs
+iV2LdBePre4H6AlMcP1/I3BjLfb/DtAXmAs0q7jWDJibpv5a4izIY4D3AYPjyOKPNh8p7nsPYBEV
+eiTX9bQ/O9ACWAb8fzvn7xpFFMTxz4B6YCxMLEIkQiKIrbGKaCEoqCFEBAslYET/AStBUtmLaCEq
+KBZyKKhBjoAI/qijBkSDPzCiaIIxaYyFTYqxmLdwGz1yJ/veWswHFnbfO+6739th2Jnddx3Yq/Xj
+wL6Y3oEeYGoln8BV4OjfPleU9rK5Q0A17OfiHXgI7Ihx/VvZyi4fsmDJmAlj0RGRHqAPmAA6VTX7
+04I5oDOS7AXgNJCt5NoA/FDVbKFDTP+9wAJwI5Qv10SkjQTeVXUWOAd8Ab4Bi8Ak6bxDY5+pY/AE
+8KAk7aYoOymUgoisA+4Bp1T1Z/2cWsou/JGMiAwC86o6WfR3N8kqYDtwWVX7sNfKc6VCRO/twEEs
+MW0E2vjzFjsZsXyuhIiMYiVsNbV2K5SdFGaBTXXH3WEsGiKyGksIVVUdC8PfRaQrzHcB8xGkdwJD
+IvIZuI2VEBeB9SKSrVaN6X8GmFHViXB8F0sSKbzvBT6p6oKqLgFj2O+Ryjs09pkkBkXkODAIDIek
+lEy7VcpOCs+BLaELvQZrutRiiYmtIb0OvFXV83VTNWAk7I9gvYZCUdUzqtqtqj2YzyeqOgw8BQ7H
+1A76c8BXEdkahvYAb0jgHSsb+kVkbbgGmXYS74FGPmvAsfAUoh9YrCszCkFE9mNl45Cq/lp2TkdE
+pCIivViz81mR2v9E2U0NYADryH4ERiNr7cJuG18BL8M2gNX2j4EPwCOgI/J57AbGw/5mLBCmgTtA
+JaLuNuBF8H8faE/lHTgLvAOmgJtAJZZ34BbWu1jC7pBONvKJNXsvhfh7jT0hKVp7GusdZDF3pe7z
+o0H7PXAgZtw1u/kbjY7j5Ci7fHAc5z/Dk4LjODk8KTiOk8OTguM4OTwpOI6Tw5OC4zg5PCk4jpPD
+k4LjODl+Ay5UJ4xtuL2QAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/2... Discriminator Loss: 1.0209... Generator Loss: 1.0810
+Epoch 1/2... Discriminator Loss: 0.9956... Generator Loss: 0.9358
+Epoch 1/2... Discriminator Loss: 1.2294... Generator Loss: 0.6367
+Epoch 2/2... Discriminator Loss: 1.0491... Generator Loss: 0.8156
+Epoch 2/2... Discriminator Loss: 1.9660... Generator Loss: 0.3475
+Epoch 2/2... Discriminator Loss: 1.0618... Generator Loss: 1.2255
+Epoch 2/2... Discriminator Loss: 0.9658... Generator Loss: 1.0057
+Epoch 2/2... Discriminator Loss: 0.8519... Generator Loss: 1.6786
+Epoch 2/2... Discriminator Loss: 1.0465... Generator Loss: 0.9361
+Epoch 2/2... Discriminator Loss: 1.0060... Generator Loss: 1.0238
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXeYFNXSxn9nZnaXDeQMkgQVhU+SiIJiQMGEoIJZRFHM
+oPeaMWBAxZwRjOgVELOXKyiiYkIERBAQBUFUUHJc2Djn+6Op2p5lw8TdWez3efZhme3pPn36dJ0K
+b1UZay0ePHjwIPBV9gA8ePCQXPCEggcPHkLgCQUPHjyEwBMKHjx4CIEnFDx48BACTyh48OAhBAkT
+CsaYE4wxPxtjlhtjbk7UdTx48BBfmETwFIwxfuAX4HjgT2AOcI61dkncL+bBg4e4IlGawqHAcmvt
+CmttHjAJ6Jega3nw4CGOCCTovE2BP1z//xPoVtrBxpgKoVUaY0r9m8fsjB3F57ey5tQY8494nu75
+Tk1NpaCgAIBgMFja/W+w1tYv77yJEgrlwhgzFBgazXd9vlAFJxgMhvUdv98PoP8CFBYWAlBQUBD2
+QtrbF50xRhec+z5Lu2d5Hn6/P+TZyCK11ob1jOTa0c6tjDktLY28vLyQsclzlvFUFtzzI+OIdjwp
+KSl6z82aNWPz5s0AZGdnk5ubW9K5V4Vz3kQJhdVAM9f/99n9mcJaOw4YB5FrCuEusOLfkQeSmZkJ
+OItnw4YNMp6wz7U3CwRw7i/aeyzp2fh8vrCfWSxzK9+VFwKKhEGyPLNY5rY48vLySE1NBWD79u0q
+CAsLC2O6RqJ8CnOA/YwxrYwxqcDZwAcJulbYSE1NJTU1lfbt29O+fXsmTJgQsit6iA7BYFBf+vz8
+fP0pLCyksLBQNYZwEeszkRfP/ZMsiPdY8vLyyMvLIxAIUKdOHerUqUN+fn5M50yIpmCtLTDGXA18
+BPiBl6y1ixNxLQ8ePMQXCfMpWGs/BD5M1PmjQc+ePQG45pprABg7dmzEu5iH0hHrDiVIpp29qmCf
+ffZh/vz5cTlXpTkaKxo9e/bkzTffBODXX38F4KuvvgpxQCUDxO/htsOTTQX2kDyoXr06AKeccgrz
+5s2Lyzk9mrMHDx5CsFdrCj6fj5NOOgmAyZMn6y78+OOPA2gIJ1LEKyRZp04dAB599FG6dXNoHPvt
+t59eY+fOnQDccccdzJgxA4BffvmFnJycmK8dT7Rs2RKAbdu2sWnTppjOFcncGmPU+56Xl7dXalMp
+KSmAEzHbtm2b/g7w8ccfc8ABBwCwbNky7rnnnrhcMyE054gHESV5ye2hTk9PBxxB0KhRIwAGDhzI
+nXfeCTiTu3btWgA6deoEwLp166JaSPEQCgMGDOCRRx4BoF69eqxYsQJABcHIkSPZvn07AI888oiG
+m3r37s2uXbvCvo4IQmNMiDkSK+S8zz//POeccw7gRHfEHMvJyVEz7YMPPuDdd98F4McffwRKDyv7
+fD59ruWZdn6/X8/jfiY+ny/pzMKy4Pf7lTszf/58nn32WQA6d+7M6NGjAdiwYQPZ2dkA9OvnkIMn
+Tpyo58jNzVVToox7n2etPaS88XjmgwcPHkJQpc0H2VFq1qzJCSecADhsro4dOwJw9tlnqwTOy8vj
+tddeA2Djxo1A9DtmtN9LT0/n448/BqBDhw6sWuUQzN5++22uv/56oOQd9OOPP2bQoEEAEZsO7p00
+Hjj11FMBx+QBx3SQ+TDGqLqbkpJCu3btANixYwdvvfVWWOOw1hIIBPT3skhPwWCQrKwsAPbdd199
+7vXr1+fWW28F0L8HAgHdQWfMmMFHH30EwPLly/nwQydIVhHahTFGNdU33nhDxyYkuiZNmvC///0P
+gGeeeSbke4LFixfreGWutm3bFrfxV2mhUK1aNcBZqBdccAEArVu31gls0KCBLqq8vDxdyLGGIaM1
+Hzp06EBGRgYAa9eu5YUXXgCch1/W4v/555/1ZYv02iIUI6EaF4csvJUrV9K4ceOQv23cuJETTzwR
+gDVr1ujinjJlippxaWlptG/fXu8FSn8B09LSqFmzJuCw9ISdWNLxgUCA008/HYCbbrqJNm3aAM49
+lyR8ZC569+7N8ccfD8CqVauYO3cuAH/99Vc5MxE7srKy+O9//wtAjRo1AMfskvG+8sor/Pbbb3t8
+z/3Mu3btChQ9F6DE70QLz3zw4MFDCKqkpiC75u233w7AiSeeqB7w9PT0kKiCOO4efPBB1q1bF5fr
+R6oliFNu8+bN3HTTTfp7uHHlQYMGqdMuUkRrPrgTbb7++msAGjduzI4dOwAYMWIE4DgaxQnqxskn
+n6wm0VVXXcUZZ5wBFEV8pk+fXqLmkp+fr9eoU6cODRo0AOCHH37Q+5Ed8uSTT+bJJ5/U8bqp1uKU
+e+eddwA44YQT2GeffYBQZ2aTJk3U1PjXv/4VNwJWabjjjjuoXbs2ULSOc3Jy1KSdOnVqyPEyzmrV
+qnH55ZcDcO+99+5x3unTp8dtjJ6m4MGDhxBUSU1BpGe9evUAaNWqlYYkc3NzdSefMWOGStXZs2dX
+wkgdyA62bNkytanDgdicvXr10t0zUogd7d5Jw8FRRx0FwIQJE9Q3sGPHDg499FDA4UuUh0suuQSA
+Ro0aqSNYnL2ljcVaqztomzZtOOQQJ4K2ZIlTtCstLU21v+7duzNnzhzAcS4OGTIEgAULFpS443/3
+3XcAek5w5uerr74CYvc1lQXRFk844QTS0tKAIj9JMBhU38jvv/+u30lJSeHCCy8E4LHHHlN/lFvr
+27p1KwBjxoyJ21irpFAQ0s8RRxwBQEZGhqqwf//9Nw8++CAAkyZN0lh/MiCSl9Lv9/P000/r98SB
+F6mzMJqFboxRlbphw4YqZM8444xyhYGo9rNnz1aTzufz6Ti+/fbbMr8fDAZ1oc+ZM4e///4bKEqH
+zsvLo27duoAjsJ544gn9XlmOwkAgQJcuXfb4/M8//9SIUCIhz23hwoUcdNBBQJGgSElJUedqkyZN
+1Mx56aWX2H///YHQGiCCb7/9lvHjxwPxdZJ65oMHDx5CUCU1BVFnRaLm5eWxfv16AG688UZ1upSm
+couEDgaDJVYYqkzIeDp06ECTJk0AZ5yTJk2K6nxyr5EkVfn9fg17AeqgnTlzZpnfa9CggZppjRo1
+UvV4w4YNHHfccSHnKg1u1XjXrl0sX75cxy/3I3Tf/Px81QRLC3EKDXrDhg0h55bzLV26VE3PrVu3
+RrQOIlk7cqxoT3IvMsYvv/wSgPXr16tW0Lx58xIdxMJoPf7449WhGk9UOaEQCAQ021EmdePGjaqW
+9ujRgwULFgBOTFhMjOHDh9OhQwegSJX74YcfePvttwGHMioqemVAFoIIguHDh+uC2LlzZ0TUZjei
+5SaIkPX7/UpN3n///TUKIpTa4cOHc+mllwIOiUyeSX5+vkYGbr/99hIjFOGMvfgL564qVFBQUKow
+6Nu3LwDvvfceUHr9yCOOOIIbb7wRgCeffFLj/eHMWzQCxM2dcWfEynzKvyVB5l7IVokQCOCZDx48
+eCiGKpcQ1aZNG77//nugyKn1/vvvc/PNTr+Zxo0bc9999wGO1iCebCgqAiK7y6+//qpJUu549Ycf
+fhjVzhYJqlWrpg4zN7VXqLoDBw6kR48egONYlWw4GW+4EA0kJSUlIoq0RA6efvrpkNqHMocSGQkE
+AroLup2ECxcu5Nhjj9X7CxcpKSnKVI3ESdy2bVuuvfZaHXvx4r4FBQUsXLgQgFmzZmnM3+fzqYbY
+q1cvpRBHYhJEcuwll1yi9G+hjLtNitKwfft2dVCKUzEKLTCshKgqJxT2339/PvnkEwAN0Rx33HGq
+4sr/Aa677jr16p511ln8+eefQOhD7Ny5M+CQXOSl6dixY9TqelnIyspS8lLnzp356aefAMfDX6tW
+LQC++eYbAPr06UOzZk7t2w8//JBhw4btMfZwUJIdHQ6EYPPrr7/qHJZ0rtzcXFavdmrybty4UX0K
+jz76qOZ2RAI3saiMUuV74KGHHlKh4Pf7VajfcsstgGMayGZQr169EDNI/BxNmzatkPwH8XMIPfyJ
+J55Qs9FNXXZj2rRpahLFMEYvS9KDBw+Ro8o5GrOzs7XIxJYtW4DQHSUYDGoGnPxbFvbdd1/A8ZaL
+pzveRUxElb311ls58sgjAUdNPvfccwH44osvGDt2LICSVVq3bq159ffff39cxxMOhI7cu3dvPvvs
+M8ChkIuX/OGHHwYcjeaiiy4CnLmXyM8ff/xR/JRhoaS+COFg5MiRmvC2detWJTiVhC1btmgEIysr
+SzWdSNXxaCNXYoIJP2LBggVqSubk5KijvHv37nqN6dOnV1iNiConFAoLC/WBr1mzBoDDDz9cGW+R
+PNiUlBSee+45wAlrCk8+3iZVw4YNAYfNJsSrn3/+Wfn8zZs3V9VQwqzffvstDz30UMzXjvVe5s6d
+q9mHdevWVbVbzJ3HHntMhXT9+vX1nqKFu1x8JMjOzg7bG5+SksKsWbMAOO200zS/IjU1NaRnRKIh
+L/mHH37Iv/71L8CJ+tSv7zRx+vHHH9W/IsK4IuCZDx48eAhB0mgK0gCkvF1i06ZN3HDDDQD8+9//
+BhwCSjROtC+++EJ//+uvv3jggQeiGXqpENVPPPUffPCBquUHHnigag0HHnig0oA///xzwKkjGQ8u
+fjzIWRLxWLt2rUYzWrRoARSZX+Ds1pLtFy0/oiKQn5+v4/T7/ZpDE6l6Lru4mAPhPi/RrISTkJ2d
+rTkP+fn56uR2R86OPPJIrfuQaEStKRhjmhljPjPGLDHGLDbGDN/9eR1jzHRjzLLd/9aO33A9ePCQ
+aMSiKRQA/7bWfm+MqQ7MM8ZMBwYDM6y1DxhjbgZuBm4q72TW2rBy/vPy8jQWLvHl+vXrhyXlxbaX
++gC1a9dWZ89DDz0Ud4aYOM0kqScYDGqR07/++kvLctWoUUNj6DK2SPkIpSHe/hHREIQJWlBQoH6G
+gQMHxhzKrQgNo7CwMGStiWM5Uk0hWk1OriNa1uTJk0OyOoWP4EarVq2iulY0iFooWGv/Av7a/ft2
+Y8xPOC3o+wFH7z5sPPA5YQgFYwyBQCAs0pB0wpk2bRrglP5yq8midgWDQeW1N2nSROnR4iSbP38+
+L730EoB62BOBU045BXAKlgghx+fzhZCXXn75ZYC4Z+y58zxiRcOGDbXUvJRlW716tUZUYi3vDo46
+XxFedqmeXVBQoA7rSCFmoUBMkrLgrjQtTtm8vDxdv8YYTjvtND02knPHC3HxKRhjWgKdgNlAw90C
+A+BvoGEp34m6Fb0HDx4Sh5iFgjEmC3gbuNZau60Y682WxlYsqRV9uOqYhMiEEVa9enX97pAhQ5Qd
+5vP5VH3Oz8/X36XYxtVXX63chESorcJOO//88wHHJBDm5QUXXKDXnDJlimoI8aZXx8N8kKzUd999
+V+dWHKYXXnhhXDQEQUV0APf5fJoF6q5AHSmKl7pzsxHdazkQCKhT8oQTTuCLL74AipLOjDHKzn3g
+gQc0wcw9F/EsolIeYhIKxpgUHIHwurX2nd0frzXGNLbW/mWMaQyEXRgxXPNBch/k5e7Ro4eqszt3
+7lQeQ0FBgZb4XrduHYsWLQKKeP2bNm1KaDMYWWwyzv79++viyMrKUkrw5ZdfXibZJhYIpdYYozH4
+SO45PT2dyZMnAw41W6IP0ogk3vHzjIwM9e3E24xwFzVp3bo14MxLWZmJZUGemUQTIHRu5aVu3bq1
+Zjb6fL6Qyk/yHTFzzzzzzBDhIoInXvVFw0Es0QcDvAj8ZK191PWnD4ALd/9+IfB+9MPz4MFDRSMW
+TaEHcAHwozFGaGy3Ag8Ak40xQ4BVwJnhnMxaG3YlXZHQo0aNAhyJ3Lx5cwC6dOmiKu7ixYtVXV+z
+Zk1Iok0sCHenlZ2padOmgMNWlEjErFmz1AGZKC0BiswRtylVHvx+P//3f/8HOBEF6W+5efNmddAJ
+Ay/eu3l2dnbCHI3y/I844ghNNoOiCsqRao2ieZXGghTtderUqbo+g8EgK1euBIqo4Lm5uRpxSE1N
+DaHsi1O8IlHlsiSrItx9LkVQ1KpVS23KZGkY6y4EImnPLVq0UC7+5s2bNfKTKDpwvJr3lgSZ+/bt
+22u0KSUlRQWEhLrjBTFLTj31VM3Qff7559UnVhrEfBoxYoQWqokTvCxJDx48RA5PU6hExJNDUBFI
+5C5eERBN6MADD+S8884DHF6IlHivqCxEcTYLb6ZHjx5a9OW9997TcbjrQsQJe2eRFQ8eooW7ca1E
+Zay1UUVl4oHyCuCkpKTEu2OVZz548OAhciRNluQ/GcYYjf+L6pgMGtzeBGNMyNy6nbuVNdelXdfd
+P1JMzMLCwoR2sHLD0xQ8ePAQgqTRFCLJ+y9eqTfaMlpQVO3YfV1rbVydf+XZjtbaMncBv9+v9xwI
+BCLK34+mnkIgEAhpIuO+jvtz+be8c0utjOLfiycicYJmZGTose52bHl5eXqviXL++ny+cs8tYyuv
+mrV7XbnPG+vcJo1QiORG4kk+cp8rUQsh1ofk9orn5+dH5CWPlsZd2ssRzcKLpDtVtCjv/MXpx3If
+bjMi2lJwkSCe53ffUzwjJ5754MGDhxAkjaZQWagqHAEJp8U5RFUiyrrG3uAA3blzp5Zg27RpU9zU
+7spGvPqi/uOFgiCZiTl+v1+FQkV6ofdWWGs1S3Ljxo1J+9wjRbzuwzMfPHjwEIJ/vFDw+/34/X56
+9epFIBAotW1XZUC89r179+bQQw/l0EMPjVhLcCdheXDw/PPPM3PmTGbOnMn+++9f2cOJC+JZnCZ5
+3oBKQK1atbShaI0aNbRxZ/v27RPeYBaKwnvVqlXTYjCySPfbbz8GDRoEOI1jXn311YSPxw23fSqU
+4K5duzJv3jwgNEtS7qNVq1ZceeWVgFNZSioM3XHHHSxbtgyIf2WpSCDNewcNGqShyClTpnD44YcD
+RZWQkhElmbc+n0+rYjVp0kSLD8XqJ/O2EA8ePIQgaRKiEu3o8/l82vr7jDPOAJw8d3cpLpGwPXv2
+1O7PicI555yjFZyLF9YA2LFjB2lpaYCzS0i78khLv4s55Pf7I6qBIDUDA4GANt+58sordYeVGhHh
+1DfcsmWL1gUYOXJk2GOIJ6pXr65zJ/MKztiGDnXqB7/zzjtJ4XQULa1mzZr07NkTcFoLvv++U8RM
+okMdO3bk8ccfB2D27NlcccUVQJlFe8JKiEoa8yERD6N9+/ZcffXVABxzzDHap1FevGAwqOQVn8/H
+fffdB5AwgZCenq4vxXXXXacPv7CwUAtrvPjii4CzWKW1ekZGRtS+DrnXSMgtgUBAW7gPHjxYey1G
+W+C0Vq1aei8VLRRkjseOHRsiDOTF+fnnn9XMqehekm6ICbb//vtz5plOsbJ27dpx3HHHAY4gkIbE
+UjOza9eu2mVsxowZcavg5ZkPHjx4CEHSmA/xOldKSgqffPIJ4KhXIoHT0tLUcy/NP9wdoS688MKY
+OyaXh0MOOUSbqWRkZKjT7ZVXXtGdVHb01NRUVXezsrKUbCOl1cOFaBiRRC3at2+vJeelqxaE5+GW
+8RljtFmKO/ohWod090o0xNzZvHmzVl0uLCzUsnJnnnmm1s10twGoSLJYIBDQvqiDBw/Wa2dlZenY
+rr76ah2zaAyPPvqo3l/Tpk3ZsmVLeZfy6il48OAhciSNTyEWNGzYUB1Zp512mu5M27dv11Beu3bt
+9HPZ/Ro1asR///tfgIRqCbI7vvPOO2rXZmdn89BDDwFOA5DiNn9qaqqW7TLGULNmTSByTSESDUF2
+qBdffFF3VbffJS8vT3cuaXmXn5/Pe++9B8Bjjz2mlYqttXTp0gVw+mNKWFP+Hm2vhUhx4IEHAk7Y
+190Y6M477wScuZWq2osXL1btsX379oBjq8c7jCq7u2hSZ555Jjfd5HRWrFmzps73d999x9133w3A
+t99+q8/niCOOABwflbSTi2fR2SopFESNlbhyrVq1QlJ6xXHUp08fnchmzZrxzjtOvxpZ0CtXrlTP
+cyJx8803A44wkoW5YsUK7r//fqDkuHJ2dnaIui603N9++y2ia0fCh5eIw3fffafNT7dt28YzzzwD
+wMMPP6wvswib9PT0UhuVCKehc+fOWmpfhE0gEKgQura8bIFAQOfgt99+07qMdevW1ZYA7sYw//vf
+/4D4mw8+n4+jjz4aKNos7rrrrhAhLLUbL7nkEt0s0tLStAq0RM98Ph+jR48G4uuoj9l8MMb4jTHz
+jTFTdv+/lTFmtjFmuTHmDWNMauzD9ODBQ0UhHprCcOAnQFrwjgYes9ZOMsY8BwwB4tYIz+/3s3r1
+agANx0CR0/Dwww9XlqI7j/+FF14I2REAnn322YRmSUoI7/jjj9fPlixZAjhaTHnXdv89DCdSiYhk
+BxF1tkWLFnq9yy+/nM8//xzYs7gNhMcC/Omnn3Qc7hh8Ijspy1j79eunn0nPyzFjxtC/f3/AceDJ
+eho7dqxqmYlq05aamqrPVe5/4sSJqtEuW7aMp556CnCendzHpZdeyj333AMU8SxWrFjBc889F/cx
+xtpLch/gZGAU8K/dreSOBc7dfch4YCRhCIXyyEsyEZ988omqXYLff/+d7t27A+zRVlxKZx9++OF6
+funMM2HChPKGFRNq164NFL1smzZt0qYgoqqXhnr16oVkRootHincZlV5AkKu9/PPP6vv4JdfflF/
+QH5+vvpjfv/9dz1vebDWamcpMUt69eql8fZEYPjw4QAharn4aB588EFdA7JRACxatCjhVOeUlBRt
+riNCSqJlMk43jjrqKMAx3WSTkQ1w4MCB7Nq1K+5jjNV8eBy4EZA7qQtssdaKsfgn0LSkLxpjhhpj
+5hpj5sY4Bg8ePMQRUWsKxphTgHXW2nnGmKMj/X5JrehLuIYzyECAYcOGAY5XWFhn4qUdPHiwqmJp
+aWm6Ixx99NHKUvT5fLoLiHoWLcKhZFerVk2Tmx591Om/u3btWo1HT5kyRX+fMWOGOpRkZ7vzzjv1
+PlevXs2OHTuiGmskNRFl1xkwYAAffPAB4Ki74myz1vLLL78AkSXdpKamqnNXxuHeoRMBd59Rua5o
+PIWFhSGammhI69atSxjN2d3HtLgpGAwGS+SAZGZmKrXZ7Sh99tlngcRFzGJtMHuqMeYkoBqOT+EJ
+oJYxJrBbW9gHWF3eiYwxmrZcWulteaDbt2/XhyhU3D///JNWrVo5g+rRQ+3FOnXqhOQUiEosfQSr
+V6+uKlxKSkpIWLC8RV+eVz8/P19VfvHC5+fnM3PmTMBpOjp+/HjACfWJ4JDF2qlTJ108CxYsiNpT
+X17RWIHP59P7HzZsGLNnzwYcn0GsL0pKSoqaUoJvv/02pnOWhRdeeEE3BsGuXbu44447AEfI9urV
+C3CIQDK2RHaIkjnctWtXiWurpDn+/PPPNXsWUNNzxIgRCRqlg6jNB2vtLdbafay1LYGzgU+ttecB
+nwEDdh/mtaL34KGKIRE8hZuAScaYe4H5wIvlfUHa0BcUFJQoMfPz83n66acBR2M48sgjAbj++usB
+6N+/v0r5Ll26qGPPGKNqt5tWK63VxeEj1wi3t2M4O2dhYaF6td2Qmg1HHHGEZj7ef//9Gh8XLsXC
+hQtVBf7++++j7jsZrvng9/vVmfvhhx/GddeURDQoGn8iVF8Z/+DBg/Uzee7t2rVT89Hv96uzulq1
+akr8qQjKfzjPT7pgH3zwwTqmnTt3qpM20fyOuAgFa+3nwOe7f18BHBqP83rw4KHikTSMRik9Vpq0
+Fml+zz33aJixcePGgLMbCP33s88+09BN9+7dtapOnTp1lFYsjLHiqMjKzjt27GDRokUA9O3bVz8X
+CuykSZPUUbl+/fqodwc5X3k9DVq3bq07aTxLewG0bdtWfxc/i9t3FC+If8jn8+k6Eiaou7GKz+fj
+/PPP19+vu+66uI8lWgQCAR555BEgtP9G8+bNK6xgb9IIhXBVt4KCAvW+irqYl5enuQHZ2dnqZNq0
+aZPyxN9//32tVZDMEDNBOPvg8B2iLd/t7kVYFt58803N4LzlllvisgDdBCKJYIwdOzbm85aEmjVr
+auEXgLlznUh3SV2WzjzzTC3NBmgZs8pCZmYmJ554IuDwEaTEWm5uruZlRJrzEgu8LEkPHjyEQphu
+lfkDWJ/PZ4G4/DRr1sw2a9bM/vHHHzYnJ8fm5OTY1NTUuJ0/kT/du3e33bt3t8Fg0BYUFNiCggI7
+dOjQqM+XlZVls7KybHp6eol/z8zMtJmZmXbr1q36EwgEYr6P1NRU26dPH9unTx/7xx9/2CVLltgl
+S5ZYv99v/X5/md/dXZ4vouutXbvWBoNBGwwGbV5eXonHtGzZ0rZs2dJu3LjR5uXl2by8PLthw4ao
+rhfPnzZt2tgXXnjBvvDCC3bXrl363NesWRPva80N531MGvMhXvZ8SkqKkpOaNGmiZJvKrCIcCSSy
+UlBQoOW1RBWOBnKO0ub3gAMOABzOhtBno408GGNUhb/55puViLVmzRp69+4d9rkjMZGEs1KtWjU1
+eST65Ma+++4bUmZPIkP9+vWr9LqMwWCQY445BnDuR0xFIS5VNDzzwYMHDyFIGk0hXmjRooXutoAm
+SlUVCHPT5/NpjP2nn36K+nyy65QW2WnatCg1RXbxcHZOcfLm5uZqtKdevXpaaPTkk0/m4IMPBpy+
+D/EsAuKGjH/Lli2627qLrwpbcfbs2ZpVW1hYyFVXXQWgdR4qEzt37lRugrul/I033lgp49lrhIKo
+qmPGFCVkrlq1KmGLMd6QAhpCvNm1a5eGUGMJEbprNJakuruJRUKpPfjgg1m4cKF+ftlllwFO5mGt
+WrUAlBS2ZMkSPcfvv/+uL+a2bduUyixlyOMN6Z4FDm36mmuuAZy5FIKaCAK/369jfvrpp5UkVtmm
+A8DQoUM1UpOXl6eFdNw1RCsSnvngwYOHECSNphBNMxiRrn6/X02GFi1a6OdbtmxJCCEp3o1rUlJS
+1Akm6u7SpUuZOnUq4Owe0fIU3HX9hIfgxvTp0/UaYrp8//33vP7664BT9ENqE9SpU0e1Daku3aJF
+Cx3TwQdaPDgcAAAgAElEQVQfrKr7F198oT03IiEqRTK3GRkZdOrUCXDo7ZJ56a7HKDyF33//Xcug
+CcmpsiHPdPDgwbpmc3Nzo6a0xwtJIxSieclkUtPS0lRd/Ouvv/Tzs88+O34DdCHeKue///1vVcvl
+pXv00Ue1xHssRCJJh5bM0OL49ddfAXj33Xc566yzAEfYXnDBBSUeX7wpTTAY1AhHbm6uNjKZOXNm
+RGnbgkiOzc7OVoF1ySWXKEGqYcOGvPLKK0BR0Z1kMBOKQ0h2u3bt0ufu9/v1nioLnvngwYOHEFTp
+ZjBuz7qojv/5z39YtWoV4KhlyXB/pUE8519++aV6n6Uk/dChQxOa318SJE/i9NNPZ9KkSQAhZdy+
++uor/u///g9wSpqBk/M/Z84cwOE6bNu2rULHXJUhmZpz5szR5//nn39qefkEzKXXDMaDBw+RI2l8
+CtHAbbNKVaWXXnpJW54ls5YAaBy/sLBQqwhLn4KK1hLc13zzzTdLzSQtC56WEB7EoSuOZHfFsTFj
+xpSYxFWRqNJCwQ1Z0O+++27cuu8mEunp6boQnn32WW0+4r1YVQfRRoQkwiTl9jIyMnRTSETJ9kjh
+mQ8ePHgIQZV2NJaGyo7zlgYpJANFTj1weAoSTpPwYzI8l8pAvDkgyQgJ60qR3tzcXE3Yc6+RBJiQ
+YTkaq6T5UJz2W3wRJVoYhFshuTjcnnz3GCui3XkkcC/M4vcXyf26yWVyPrfQq4iXv6RnVfz+3OMU
+uMvah3NuObY8k8Ln8+kx7rL90ayp4puMCBFjjK6vaObYMx88ePAQgiqpKVS2elnZ13fvRtE6u8pC
+vHbxknar8s4b77kt7XxuTc2dHRoJC7OkY8r7Xnm1MiNBac8pGAzGNI9VUij8EyEvv8/no0WLFoDT
+Uj3Z/CYlobKFaHGEIygiQTL4sILBYEiR3lgQk/lgjKlljHnLGLPUGPOTMeZwY0wdY8x0Y8yy3f/W
+Lv9MHjx4SBbE6lN4AphmrW0LdMBpSX8zMMNaux8wY/f/PcQIURWbNWvGgAEDGDBgQImt4T1UPOJp
+EkSLQCCA3+8PcZZGfa5ov2iMqQn0BAYDWGvzgDxjTD/g6N2HjcdpEnNTLIP0UIQNGzZo3clkg/QE
+heSLqECRCVazZk0mTpwIwKeffsoLL7wARF5GXe41kopV8YZsDBdccIFmhH788ccxjSWWraYVsB54
+2Rgz3xjzgjEmE2horf1r9zF/Aw1L+rLXit6Dh+RE1OQlY8whwLdAD2vtbGPME8A24BprbS3XcZut
+tWX6FeJNXooWonpVRt5BcQQCAS0xl52drWPLzMxUGndhYWGl78g+n4/FixcDTo9OWU85OTla/3DE
+iBEsXboUKCoxlpOTE8IFiHcURc4nNQvuvfdezjjjDMDJTnS3pZe+ln369NmjTXxZcJe6SxTkPlJT
+U7VcntTHvP3227WJTF5eHo899hgQWpKwGBKeJfkn8Ke1dvbu/78FdAbWGmMaA+z+d10M1/DgwUMF
+IyaaszHmS+ASa+3PxpiRQObuP2201j5gjLkZqGOtLbMsbWVqCrID165dWxOU3EyzkpBIKq70LJg1
+a5a2D1uzZg1TpkwBnIpMosm4beDSxpMIHgOgOf8//PBDqQ5PN3vTnbAGMHnyZE0CK67txDrWjIwM
+LTwrdQqk4jQ4z9etScj4X3rpJa644gogvN0/UXMrcPcwOfvss1VTELgrPxcWFtKlSxcA7VFaAiqE
+5nwN8LoxJhVYAVyEo31MNsYMAVYBZ8Z4jbggEAhoHceTTz5Z/5XFs337dq0pWB4SsQikmeyHH34I
+EOJJrlWrli7S7du3ax3EaAk20cIYw3vvvQeENsUVBINBLUs/b948Dj3UaT5eu3ZtVdf79esHwF13
+3RX3l0oaDn/xxRe0bNkSIERgSQOYgQMHahm6SZMmab/RQYMGaQMWEcJlIRHroG7dutxwww0AnHba
+aTRs6LjkMjMzQwQAOGtENrK33347plYAbsQkFKy1PwAlSZ5esZzXgwcPlYe9ktEojpjbbrtNd6Z2
+7dqFJIyAs7NJz4Jjjjkm7KrD8TYf2rZtqxqLaAETJ07U0mfXXHONFpGJtIV7PHZj0VheffVV7YIs
+KCgoYPTo0QCMGjVKx+f3+7VE3pw5c7T0mLtRS0lO0mjnNisri3/961+A07a9uEnz0Ucf6dgLCwt1
+Xs455xzVGjIzM7VHxUcffVTqGBMB0aqmTJmi852amqoa4uLFi7Xbumg27du3VyfvvffeGzcH+V4j
+FGQRnHXWWVrJ121HBoNBvvrqK6Co49Lxxx+vNRFlcsNBvASC2Nd9+/bVc8p4Jk6cqLZhdnZ21B7u
+GKJLgFN3cciQIQD0799fPxd/Rvv27fnrr7/2+H5hYSEbN24EnEpOl156KYAWE/njjz9KJPxEOl5R
+rz/77DM1GdxZgmLO9OvXL+Slkes0aNAghPAjJshtt90GwJ133hnReKJBeno69957L+DM959//gmE
+1m586aWXNJIi99mwYUPdLKpXrx43c8yjxHnw4CEEe42mcN555wGOd97dm0B6J5xzzjnacEUk7rHH
+Hqvlz9xqbXnw+/0x5asDHHbYYWraQNEO+u9//xtw4s5ShCMWFTbaZB0ZW9u2bRk5ciTg3LfE8cU0
+KG3efD6f7mi9e/fWXUzmu7SWaJGYD2lpaZx++umAE2WQ5+73+7XOYa9ejnureNdxWQOdO3fWv6Wm
+purnPXv2LHc88dqZU1NTmT3biex//PHHTJs2DXDqdUo06thjj1UTTGp7BoNBZs2aBcDy5cvjpsHu
+FULhmGOO4cknnwQctVVU2xEjRvD8888DoS9F8+bNAUd1FLs9kpcmltRUMWm+/PJL/ey3337jpJNO
+AooW74033qj02/JCpOWNNVK0b99e+0e2a9cuZJxiBpQmDEQVb9SokTZIlfkGeOSRR8q8diTz2rRp
+UzUfAoGACkBrrfZjXLZs2R7fM8ZoSPWII47QOdq5c6cKBTHjKoK6vGvXLg09dunSRX0G3bt3102t
+a9euOk5ZOz/++CNjx44F4tt30jMfPHjwEIIqrSkIDfj6669XCb9p0ya+//57AMaPHx+yU8pOIjtY
+9erVQ3bsSBCt6ig7gs/nUwddu3bttL2bqNy9evVS2mosiGScErW54oordBxZWVmsXLlSPxcTzH1+
+0X7y8/NVhb/22ms5//zz9biZM2cChHSzjhbuyIJ0nS4oKAgxG8Vh6C5zJr936NCBzp07A46zTrSb
+jIwMNW/EWR3OOGI1JQOBgHITrrzySr0Pn8+nBLYJEybsYf7OnDlTzbl4ajRVUijIRIntJSaA/E34
+4Pfddx+jRo0CHLVsyZIlAEpi2rZtm9bejwTRPgB3j8adO3fy8MMPA86Cloat0r593LhxEfk5Yh2r
+z+fjkEMcysmSJUu0CevAgQOV9PPLL7/oC9S/f3/AEbCvvfYa4KjcYuNfeeWV+hJu3bqV0047DYhv
+IZJ9992X6tWrA45AkpfF5/Op2p2eng44PhzxL9StW5euXbsCztoRYlUwGFT/STjzFq0wkJde+ofO
+mjWLfffdF9iz7qNg3rx5/PHHHwAacVizZs0evpJ4wDMfPHjwEIIqpykYY7TPocRw3RV5CwoK9Pdh
+w4YxePBgAF5++WVVtUTSLly4UGPpkcDn86m0j0RS9+nTR3ezdevW8Z///AdwVFhxGIm3OVqzpjhk
+Zw8EAjrW0nY20Qg2btyo4zj66KN56623AIdi/eijjwJw8cUXAw7fYMGCBQCcf/75XHjhhYCzQ8t1
+Bg0aFHaTG2OM7tylaUqyQ3/33XfK9Tj//PNVa7DWqqYjHvvOnTszcOBAAOrXr0+dOnX2OG9+fj7P
+PPNMWOOE6MyH6tWr63wJcc5t9hQWFrJ161bA6aAuTuZzzz1XIxTiPI8kozMSeJqCBw8eQlDlmsH4
+/X6V8rL7FBQUhFCYxS7r1KkT9evXB5w4rtiR3bp1A+C1115j3rx5EY/XzYCLhFr6xRdf0KlTJ8Dp
+7Cwx5jvuuIPrr78eKNp19tlnH9Vo4oHy+gqkpaXpznfppZcyfPhwwImhi+N2y5YtnHvuuSHj7NCh
+AytWrAAcbezss8/W60msX5ikiYA4Rx9++GHVClNSUtR/VLduXcBxQIvm0aVLlxKfYYMGDSKuvhQu
+xLcxa9Ys5RkIgsGgOqCvv/76kLUsofYhQ4bo8znooIMAdN4jwN7ZDCYYDLJ+/fpS/+4ue+1+4evW
+ratedFnQosZFCrf3OxyhIL0D99lnH4YNGwbAJ598ouMcN25cCGlJjo2HUCgt+uAu3gHOyyUO3Ntv
+v11V8W3btqkDslatWponIN7yn3/+Wc/ldvh+/vnnUQkDt1APxykpL/rw4cOVFv7EE0/QsWNHACUx
+bdiwgbZt2wKhz89aqxyRSAVCSU7BkgSuz+fjuOOO0+/IM5ayeueee25Z6c76PYlWlbX+4wHPfPDg
+wUMIqpymEK2507NnT3WCiaoebZJRpGG1Aw44AHBi39Li3X0ff//9N+PHjwechC6A+++/X2PwsZh4
+pe1cYoJJWPSoo46iadOmgOP4kh02NzdXtYmtW7cqy1IKpICj9YBDiZYsyUGDBkU93mjuNxgMqgbg
+ToQTqnj79u3VoWeMUer4ZZddpuOPFampqao5unkTjRs31rU2ZMiQPUrTlXa/w4YN03mcMmUKQ4cO
+BUh4q/oqJxQihZu2+t133wGO5z8WRCoUhHswZ86cEs2N7OxsjYKIjZyWlhaiwscKd5Uea62Sk8RH
+YIwJ8aaLvbp06VIlVt133317mGB169ZVs8Hn82kEQzL9IkUsaeluW13MCnnxMjMzQ0wpIVONHz8+
+ZuJPaQ1hhVy3evVq5RiEcy55+e+//35dF+ecc05Ca0G64ZkPHjx4CMFerSn4fD718Ofl5WlufKw7
+b7Q7S2lOSWutxqNlV27WrFmp7LZoUFy7kV1HvPDGGB3fsGHDdBxfffWV1iTYtm2bnkdU49NPP13j
+6tnZ2comjRbRzm1GRgYDBgwAHAeeJELJPZ166ql67Pz58znnnHNiup77uyV1EjfGqIM5HHVfnsPI
+kSO56SanTcrmzZtp3bo1kNiK0cWRNEIhnvX65Fzjx49XglONGjWU/JGMkKo6srB9Ph8HHnggUGR+
+RANZbD6fL2RhiRASASmmCsBFF12kvg8oioj4/X4tHnr00UcDjgCR3pYLFiyI2myIB77++msA6tSp
+Q6tWrQBC6MNy/4888khciD/lrVmhrp966qlKOCooKNgjFT4zM1Pp9l27dlWTp1u3bgmhMZcHz3zw
+4MFDCJJGU4CytYRwNYmUlBRVv8444wyVur179670xillQXZuqQPQunVrddrFAnEepqenh5hNcp1j
+jjkGcHZZIdh07txZ6w0MHjxY5z4nJ0cjFOIQzcjIULV5zJgxlTbHPp+Pt99+G3CyOYWTIJqStVYz
+H+fMmaPrKBbHZnHzzn0ea61yDx588EHuv/9+wHHAipYlZLoxY8aoVrFx40blTcgzqmgkjVCQPoTu
+vnzuBycc9saNGyvpqKSHWb9+fS0/vmDBAj799FPAqeGXzBAbXVTxuXPnxsWUkvmUsKJAVGnpjtSl
+Sxf9PSUlRV/6Aw88MGQcJUVeRKBNmjSpwhutyouZlZWlZdlPOeUU7YEgQiE3N1eFxooVK/bwB8QC
+iXCJH0Yg4dnjjz+ecePGAY7ZJTVCxddQp04dJXqdf/75Jda8rEjE2or+OmPMYmPMImPMRGNMNWNM
+K2PMbGPMcmPMG7t7Qnjw4KGKIJZekk2Br4CDrLW7jDGTgQ+Bk4B3rLWTjDHPAQustaU2t9t9rnIH
+IVl71157rZYI22+//QCncIk4lGrWrEmHDh0Ap5mGZMslO2THkxj1YYcdpvUi/ve//8VUki3SMRxw
+wAGqjblzBHJzc3V3k6jOpk2btIPz119/vcduWVFISUkJyWGYPn26/g5OPocQrtzaTEXl/ogZ98wz
+z3DJJZcARbU333rrLSXWJXg8Ce8lCY75kW6MCQAZwF/AsTh9JcFpRd8/xmt48OChAhFrL8nhwChg
+F/AxMBz41lrbZvffmwFTrbXtS/juUGDo7v92ieCaukuJxpCWlqYMtmAwyJVXXgnA66+/Hs1tVSpk
+t/b7/bq75OfnV9iOVhLE3+HuDi27bWZmZogWkyRZt+qbEaxatSopxjZgwABefPFFwEkaA6eKVQWN
+LSxNIRbzoTbwNnAWsAV4E0dDGBmOUCh2rqgG4U4lFcfS22+/XSGqtociJLrR6t6EjIwMXbeVsE4T
+bj4cB6y01q631uYD7wA9gFq7zQmAfYDY42oePHioMMQSkvwdOMwYk4FjPvQC5gKfAQOAScCFwPux
+DrI0iAq7dOlSli9fDkTW1MVDfOBpCOGjoKCgUliKkSBWn8JdOOZDATAfuARoiiMQ6uz+7HxrbZlv
+arTmg+v7e/XC3Nvv75+GSjS3EutTiCc8oVA29vb7+6ch2YVC0jAaI4E7dg7h1Tco3ppcUFKzEHdb
+uHgmaBWvk+hmbLoLgIDDRHSzO2O9diTnKF4SLdpzuNu4lVZzoKTvRXO/7u/Fg7rsvudkEcgyJjd1
+XdZ+QUGBslRjZZVWSaFQPCwWSeOO0uDz+SIqwhoJyhMw1lp9oCIc4kUXjmZBF38Roj2H3IPf79e5
+lfsrLRU42hcw1vEW/24iBUGs9yjzmpOTE/JZvMbsZUl68OAhBFVSU0hEwYmKTuQpjmi0n2SH3ENG
+RoZSet3qrofoIPOaqDmskkIhWsiLJ515tm3bplWDKtuZV1L1nr0FwWBQw3CJMtE8xA+e+eDBg4cQ
+/GOEwiOPPKIe/VWrVrFq1SrWrVvHkiVLWLJkCXfffTctW7akZcuWca2NGA769u1LgwYNNKNvb0NO
+Tk5INMVD+PD7/fj9flq2bEkgEAjpO5ko/GOEggcPHsLDXkFeKg3p6elaoNPdIESwffv2ED+ClL/q
+27dv2HX6o4XP52POnDmAU91IOjtXUF59hWBvSJRKT0/XCkoVcR/GGA499FAA/vOf/2gB2vz8fO3p
++cYbbwAwbdo0XafWWqX4lzHOfw6jsTikLPbSpUtDiE5SIKRhw4aAM3mHHXYYAFOnTtUSZKeffjrv
+vx//lI02bdroQz711FO17Hj9+vV1bFIDMd6e5YpwpO67775ceumlADRq1EgLtYwbN46dO3cm9Nrh
+QBrgjBgxQpvdPPHEEyWOTTpo9evXj1dffVU/d5tAZdVojBRSRGj+/Pk0b95cz+92PBcn7a1du5Yn
+nngCcNrTSy/MWIWCZz548OAhBHtdSDIQCGgbcr/fr1Lz1FNPDel/KOjTpw/gFN+UY6WzcryxcuVK
+LQ+3ceNG3nnnHcBpRd+5c2egqGV5vPsFxltLkEYno0aN4qKLLgKKKjyDE4aUCsVvvPFGhWoKqamp
+dO/eHUALmjRp0kS1r7y8PM2q9fl8WuV58+bNWtbvsssuA+Cbb74pleUYrzmtXr26aiNNmjTRHhE3
+3XRTSBh9woQJQFFv0jVr1qgpsXnz5riNJ2mEQqzqbc2aNQFYtGiR+g8KCgqoX78+gPIR3PD5fNoC
+HtA+ieW1BY8WhYWFKhRycnI48sgjAcecEWFw1FFHAWhl4ljhzvmIlQORlZWlL5PMa2kqdF5envpo
+5N4SDTEFX375ZTUVZDyPPfYYt99+e8hn4MyPNBy+7rrrdI7++9//AvDmm2+WOm/u3I7i5w0HUmF7
+0qRJHHTQQXpdqRzmxtKlS7nuuuv0eHAqf0u/yngKfc988ODBQwiSRlOIRdKlpKTwwAMPAI76JZL9
+mGOOKVFDkN3tr7/+0rhvdna2toFPJESLOfzww7n44osBRxWXXee+++4D4qcpuOc1mmhAamoqd911
+FwA33HDDHtmmhYWFrF+/HoDRo0erJuRuHxdtJMcYo7tpacVzGjduDDie+sMPPxxwypyJ+i8mWmml
+z1JTU9X8qVmzptZNlO+XNVexal7Dhw8HHDP3k08+AZxGNiUhLS2NwYMHA0VOybVr1ybE1E0aoRAL
+fD6fPlhAm4tKgVeBCABZuNIXEZwy8fPmzUv0UNXuPuGEE3jqqacAaN++vdrf8W4EIoIgJSVF8w/C
+EQo9evQA4Omnn9aS+dZaPYcs6AkTJmhZ91q1aoWUWRcbPpKuUe6Uazc9ujR069YNcMK6Mp+vvfaa
++pVKenGNMSpMRo8erbR3ay2DBg3S38MZa7jHuiFFZTt27Ag461JMg+JRJ1mzzZo103uVEOmIESP0
+93jCMx88ePAQgr1CU8jLy2PhwoWAU/b9kUceAUIl+CGHHKKtuUQlDQaDnHDCCUDFtZUTp9uoUaNU
+yi9evFgb20jvy3hBdspwysRLHPzqq69m5MiRgOMZl51+8uTJ3HDDDQD8/fffQOiu27t3b+VhvPji
+i1E1hrHWhk2HNsaow/Puu+9m8uTJQKgn3m3uZGRkAFC3bl1uvPFGAHr16qX3d9xxx7Fu3bqIxhoN
+xBSSyMGcOXP0ubsL7jRo0EDn88EHH1StSZrviMMx3tgrhIK1Vu3JMWPG6MNPT09n2LBhgPMSCmQR
+9O7dm5kzZ1boWDds2LDHZ9ZaZV5+8cUXcb2euxV9WWp8amqqqt8XXXSRvkyrV69m9uzZANx2221q
+m4tvpEuXLowePRpwBPJHH30EOOqumHGRIJIoVGpqqoZuFy1apOSdQCCgBCBplLtgwQLt+Thq1Cg1
+j9LT03nssccAIjYfozUfxM8l/gtrrUYfxo0bp2ZFSkpKyDVk7ufPnw8kLuPUMx88ePAQgr1CU4Ai
+qfnll19qJOL2229XuqoxRnexAQMGAPFX1SOBMUadjt27dw/pDBVPuOs0yO7v8/nUoSVjWLBgAc2a
+NQNg586d2otx1KhR+vnUqVOVhCTfb9asmZpjO3bs0Pto0KABixcvjnq84SA1NZWPP/4YcFRyuXaL
+Fi1UtW7SpAngOHZlnMuWLdP72LhxI2PHjo342tEcD1CvXj11cItGMHbsWNVuMzIySqyPWVhYqM9K
+elG++OKLCam/4WkKHjx4CEG5moIx5iXgFGCdtH8zxtQB3gBaAr8BZ1prNxtHrD2B03l6JzDYWvt9
+YoZeMqZOncoLL7wAOLalSNr169fTt29foHJLgcnu4vf7Q9hzAwcOBIpYefG+XrVq1fS+fT6fzku/
+fv10POLIWrt2rVJt69WrxymnnAI4cXxJ2BK4qc05OTnq8P3mm2/Ytm1bxOONxKeQk5Oju3+NGjV0
+Bz755JN1br/55hvASZITpmDz5s3VWffUU0+xcePGiMcJe1YIL2vXlrFdddVVtGnTBiia+4yMDGV/
+ZmVlKcPy9ddf17nIzMzULEmhxN92223cfffdUY29LIRjPrwCPA286vrsZmCGtfYBY8zNu/9/E3Ai
+sN/un27AmN3/Vhhuu+22kDRpWZht2rRJqrqABQUFavJMmzaNIUOGAEU053hlNco5du3apfPSunXr
+PQhFEydOZPXq1XqsvPzdunVTj/x9992nzsN77rkHKHLkgZNZKCrx1q1bo1JtI7lnd1nza6+9VoV+
+7dq19aVfu3Yt4KSkCx+hevXqISX5okW499e6dWuNiG3btk1zFySC8/LLL/Poo48CjhO8pPNmZ2er
+M1J4NkceeWRC0tPLNR+stV8Am4p93A+nzTyEtpvvB7xqHXyL01eycbwG68GDh8QjWkdjQ2utUO/+
+Bhru/r0p4N6C/tz92R40vWKt6GOGSH4JQQqEBhrvrMN4QKT7tGnTlMlYt25dAFq1aqU5//G6luyq
+a9eu1fDc119/DTh0YFGHGzZsqKy71NRUVW2//vprDWtKZuHo0aM1KWfbtm089NBDABXSL9FdWKRJ
+kyYa7r3tttuUNix/f+qpp/jwww8B2H///Vm6dCkQGdsyUsh8Dh06VAunXHDBBWoGSAg1XIhmKdmS
+F198sa6XkkLd0SLm6IO11kZTJMVaOw4YB7EXWRkzZgxDhxbJF4nn5uTkaAw6mZGXl6ccASENHX74
+4XEVCm5s375dF6y8vMFgUBfdunXrWLNmDeBkjp577rn6XckPETqwz+dT4fbpp5/Stm1bwJl7UY+3
+bNmSsJi6jH/o0KFllscvKCjQKEowGIyKQxEpxPa/5ppr1FybO3duifk4kUDMCDd3IZ6INvqwVsyC
+3f8KDWw10Mx1nNeK3oOHKoZoNYUPcNrMP0Bou/kPgKuNMZNwHIxbXWZG3CFOJKG6grMrde3aFXCo
+r1JnIdlx9NFHA0Xe/KZNmypnIZZd1t1/UHZSd8ZhSY4q99/XrFmjjrF77rlnj1qX2dnZPP7444AT
+85fErgMPPFB340jGH6mDNVxnn9/vV5PIWqv1MWNBWeXYjDFMnToVcKJgYrrE4tg85BCnklqnTp30
+eonQwMIJSU4EjgbqGWP+BO7EEQaTjTFDgFXAmbsP/xAnHLkcJyR50R4njANuueUWoKiOHhQVXe3d
+u7dOfMeOHbVYRrJDbHx5Ib/66qu4PHBZqKWdq7wX0Fqr4cn+/furkP3uu+8AJ/wn6nCrVq20ktVn
+n30WlV8h3hWixEwSwQUwffp03VBiQVlj9fl8KuALCwu59dZby/1OeXj22WeBIj/IkUcemRCfSLlC
+wVp7Til/6lXCsRa4KtZBefDgofKQNDTncNXGI444gjvvvBMo2gW++OILzj77bMCJsQvNORgMJiT7
+sXhL+XhAPPgi+aUScqxwlwyLdqw//PAD4MT35Xzjxo0DQsvkr1+/nnfffRcgakJQvCDjlB36vPPO
+Uy3s/vvvT3hjmszMTI0ozZ49u9QiMeEiKytLtRspxCJEsXgjaYRCuGm9t99+u9q1MtEbN27k6aef
+BuDEE09UXvstt9wSFzUx0rFGA+Hoy8sUr0Ubj7GKmRYIBDSsKf4Ca60+j7y8vKjSpeONQCCgJuY1
+10NcrrYAABcuSURBVFwDOGtFXtJ4CdyyiEPVq1fXZ9mzZ0+OPfZYwMmMDJdE5/f7qVWrFuBshpIa
+LuHURJXs93IfPHjwEIKk0RTKMx+kLl3Pnj1DSoyB4+wSTSI7O1urJItETXYEAgEaNWoEwC+//AIQ
+tzJb7tJm0ULy/qGIFyCagjuPojSKbkVB1sC8efO0aI1oXAsXLuS5554DSq/XGC2kcI5bS9q0aZNq
+rHXr1uWDDz4AnLUqRDqZwyOPPFJNtMsuu0yjaSeddJKOdcKECUrIKp5zEW94moIHDx5CkDSaQrg+
+hQ0bNoRUVgJHy5BejOeee26V613o7nQtGZ7xQqy+ibS0NI3vFxQU8N577wFFSTluJmRlznsgENDs
+wpYtWyoPQUqbvfbaa1rZOV7jdCebFceuXbu02tfAgQPp3bs34IRGpcSahCxTUlL0XMYY9Tl88skn
+Wv4uOztbfSKxOi3LhXilK/MHsOX9+P1+/fH5fNbn84X8P5xzJOtPWlqaHTFihB0xYkSlj6X4z9ln
+n21XrFhhV6xYYZ966ilrjBFau/6U9FlF/VSvXt1Wr17djhs3zv7000/2p59+sjfffLPNzMy0mZmZ
+Ni0tzaalpdn09HQbCARsIBCo8DEaY/TagUDAduzY0Xbs2NFmZWXZrKwse/DBB9vU1FSbmppa6jnS
+09PjMZa54byPnvngwYOHEOyVXaerIuJBaU4Evv32W01sOuOMM/YYn5s+XRmQkN348eOVvfryyy8n
+3TwmCcLqOp00PoV/Moq3HE8GSGRn2bJlytsv6UVzV5CqDIh9fc8992gPUE8gxAbPfPDgwUMIqoT5
+EK/SZMmKRNCmY4V47Rs2bKis0JKYeG7zoTKfU0pKio4vWeYwCVG1zIeyFlTxlNSS/p6IWnXxhnvs
+bpMhGccsL5jUbSwNbtOhvKxB97+FhYVxvW93OXR34ZfS1oWEAwsKCvSYgoICPS5Rz8Tv96vAFTMn
+GAxGZYK5SUzuXqGxmnOe+eDBg4cQJI2mEK5kDkebSFa4x1gVxhtPJForcmeBhrNTCo3crWVWlPkj
+VPFYr+Vu8BPPSuWepuDBg4cQJI2m4OGfgUTtxJHa0SVpbRWhJcQ7XOq+75L8bdHAEwoePOwFiKfp
+45kPHjx4CEHSaAp7OxchnkhGXkO4kCpOubm56nBzO8kq6n7czsVkY5OWBHfdCvd4pZdmMBiMWxHX
+pBEKVW1xRwLxENeoUUNpuYFAQLv7rFmzJqLKx1Vxrq699lrAaQkPcO+99zJr1iyg4u5H8ksaNWrE
+/vvvD0CXLl2YMmUKEHlRnoT0cfT5tBntTTfdpNWxCwoKtAGPFGmpX78+DRs6zdkyMjK0pHys3bk8
+88GDBw8hKJfmXEor+oeAvkAe8CtwkbV2y+6/3QIMAQqBYdbaj8odRBXMkoxkl5Bju3Tpot2Hu3Xr
+pp7owsJCbZku6uAvv/yiBVcWLVoUc3m2yjTP2rVrp/e3ePFiwGl+E89+k8aYEJNAErpESzvyyCO1
+/+Xnn38eUq7tzz//BJws0Eiv6Ua08xsIBLj00ksBeOCBB7SIkPucwWBQ+6LKHK5YsYKLL74YcLSg
+Hj16AGVqPHGjOb/Cnq3opwO3WGsLjDGjgVuAm4wxBwFnA+2AJsAnxpj9rbX/6LQ1ebD16tXTFu/W
+WtavXw84D1eaja5atQpwVMQ2bdoA8Pvvv6vZEe3Ci/R78jI1aNBAqyxF+hKLuj548GBtdrNlyxYg
+vmQbcF5QEag1a9ZUGrO0AzjssMPU5u7WrZsKjffeey/qhkHRPgsRJgcffDAAb775Ji1btgQcASE+
+g507d6qpkJGRoc2AhSZ98sknU716dcBZNyI0YkVUreittR9ba+WpfovTMxKcVvSTrLW51tqVOJ2i
+Do3LSD148FAhiIej8WLgjd2/N8UREgJpRV/+QAKBmHrjyc42bNgwrr76agAaN26sO5JI1OIQaT97
+9mythycSuaxribQPZ7yyY9aqVUubvkybNo3zzjsPQKv+lva9lJQUGjduDMDff/+dcG95jRo1OPnk
+kwFo0aIF48ePB9AageFC5rZ58+Z6L9JrsjSijbs+QyQ7sZvy26lTJ+0nWrt2bQD+85//0K1bN6DI
+2QmOqv3pp5+GfR03onU0inPw0UcfBZw5lvkpKChg9uzZADz00EPMnDkTcO5PKjuLtjlx4kRdO/37
+90+O6IMxZgRQALwexXeHAto/3lpLRkYG7dq1A2DAgAHcfPPNQGhGXdu2bXUy5SFXr15dVaooxgE4
+6uXHH38MOAu3rKYmkb6Uck89evRg/vz5AFxwwQWlCgOBCJwDDzxQ7/m8885TsyPekIW5cuVK7Q85
+Y8aMEBs3XBhj2HfffQFn/DKfL7/8MlD6HEa7KRhjeO211wDHXyHjl6YwAMOHDwccv41sFj169FBf
+Q7xNmuLjAydiIOPs3r074Gwy8kIvW7aMgQMHAmjFq+KQzNXLLrtM2xmI2RkPRC0UjDGDcRyQvWyR
+qAy7Fb21dhwwbve5qpyj0YOHvRVRCQVjzAnAjcBR1lr3dvcBMMEY8yiOo3E/4Lvyzufz+ahWrRo3
+3nijemGttTz55JPOIAMBDjroIMBpiiEquGs8YY1bHGaiBm/atEmlNRTl2L/wwguq2peGcFRHcWaJ
+xnPMMcfw8MMPA5E1JGnZsmVI+zApsx6J2upu+VYajj76aMDpgyjnvvnmmyPqCykaW79+/bj77rsB
+xxz5+eefAbS0W2ljjzZKUrduXfr27avnGDBgAIBqZtWqVVPTBYrMzYULF5KdnR3x9YAQlb88iMny
+3HPP0aBBg5C/bd26lf/9738APPXUU6VqCAJZe+vWrePLL78EYucmuBFtK/pbgDRg+u4Bfmutvdxa
+u9gYMxlYgmNWXPVPjzx48FDVEG0r+hfLOH4UMCqSQQSDQXJycnj66ad1R+natau2UnvllVc44IAD
+9PjiTr7Nmzdz7733AjB58mTdrXJycnRHLi3OL8d++eWXdO7cGShyBJWFcHYzsROleWy1atWYMGFC
+ud8TyH1edNFFap9nZGSoRpOXlxe2f6O83cwYw+DBg3XckXSPlnG2aNGCZ555BnA0mg0bNgDw888/
+K6OxPNs32jDfXXfdpTv34sWLdQeVz2bPnh1SqUiezeOPPx7V9SB8/0ezZs146qmnAMenIM/s999/
+B5zO2OJzEQ5CWXCv/5UrV0Y87vKQFDRnofz6/X7eeMMJZLz77rv6EKdOnaoe18zMTDUDRo4cCaAL
+MdprgyMI5HrSsy9eEPrpQQcdFBEJ6dBDnWjuySefrAvh4YcfVu/06tWrQ4qFxAJrLT179gQcwbN8
++fIyj5eXrU6dOvTq1QtwIj/NmjkuJZ/PpxGTt956SystJwo5OTls27YNgDvuuEO7h3Xo0AFAuzIJ
+pINzLKSwcKMP27ZtU5M1IyNDu1fdc889gONcFKEQjqCRdZqVlaWbXjxTsj2aswcPHkKQFJqCtZac
+nBwCgUBI8Ulhwf33v//l1FNPBRwaqEh5YXvFgrZt2wJOGFKkbbw1henTpwNw8cUX89hjjwEO1VYS
+oubNm6fxaME+++zD+eefD8A333yjjtbVq1fr7rd9+3adI2FEuhGJ0y41NVXV2sLCQnX4vvHGG9x+
+++0AnHnmmRqelOe0cePGEBamoLCwUB1m999/f1hjiAW//fYbEydOBBwV/fTTTweKzAN3yLqwsFCd
+tVlZWcoWjTTOH+7c5ubmcvbZZwMOd0Y6TEfag1PuQcy8L7/8MiE9LpKixLvP57MpKSn4fL4QdU7U
+s3r16umCy83NjSuHf926dYDDdZBFUa9evbh6c0XVHjp0qGa6/fHHH/rSdOrUiWuuuQZw+AsAy5cv
+1/i5WxXv27eveqrr1aunXIcff/wRCFUj3UKhvE5O6enpzJgxA3D4HyVFdEoTMiKkH3zwQeUCHHXU
+Ueo/ufPOO+NGrCkNNWrU0DkaNGgQw4YNA1Ah5h77smXL1GdSs2ZNpV6PHDlSxxnOGhM1Phwim/vY
+cF/katWqhfBzHnzwQaCIkDVw4MBI+Qlh5T545oMHDx5CkBSagjHGSq3+ihxPSkqKOu323Xdfbrzx
+RgDGjRuXkOsdc8wx6lCaP3++qq0pKSmaoXf55ZcDcNJJJ4UwHmU3mjBhgrY19/v92l79yiuvBKLX
+pIwxuqsed9xxvP3220Bob4HSII7fr7/+Ws2c3377jX79+gGl07jjiUAgwPHHHw84z08c026IFjp7
+9myN4MybN09b2Ce8xXsYqF27tjIaW7durVGj66+/XjVOWUMDBw7UmhS5ubnhJM1VnWYwxhjlvFdk
+H8Bu3bppptpXX32VMGEgWLlypVJU3ep0fn4+c+fOBVA/w6mnnsqkSZP0GFkQRx99tKqP1lp9ISNR
+e0uCtVZJPO+//77ar+405IKCAg2vysIdNWqUEqv69OmjZsx5551XJlU83rDWaii5fv36IZ8DZGdn
+6z21bNmSr776CnCoz9HmksRaZEUiJBkZGVxxxRUAnH/++RpFmTVrlmZ+5uXl6fhFyE6aNEkp72ed
+dRYLFiwAYo9EeOaDBw8eQpAUmoK1lmAwmHDTQSS7JFK9+eab6mQ655ySOFrxvW5qaqru+G5NITU1
+Va8vu8Tnn3+uqru1VunYBQUFuhMsWrRIY90l7Q7xKKxirQ1xukpBEomifP/990ybNg1wzCDRdNLT
+0yvUFLTW0rx5cwBmzpyp9QnEHOvSpQsPPPAA4GRtioM5lozTaO9PTBcxGR955BElMs2aNYvbbrtN
+jxXz4bvvvqNGjRqAY4ZCaKGadevWxU3LTgqhICjPQx4rWrRoATgvnFxP6vNFkhYs5g6Ex3t3Fyzp
+2LEjAHPnztWF2ahRI8aOHQs46bLg2IgiTDIzMzXbr3r16lrFqE+fPmWSb4r34EzESzpz5ky++85J
+b+nRo4eq8CI8KgrWWg19tmvXTslSModnnnlmiJB99dVXSz5RBIi28tI++zjlR6QW49KlSzVVfdeu
+Xfpyp6enq5/nyy+/VGEiOR75+fkqkOM535754MGDhxAkjaaQaAdj/fr11aMujrONGzdy1llnRXwu
+a21Eufdyb5s2bdJCLjfccIMShDZt2qRagxtSGOayyy5TLefdd99lzJgxQGQU3USq8tdddx3gaGDC
+J6noqJa7QI843KCoqMvAgQNVC920aVNcaNfR3mPxLN++ffuGZM2KA7J///76eb9+/dR8ED7GK6+8
+ouXm4glPU/DgwUMIkkZTkJBkvHcYscOWL1+u9pnEc6+44oqEM+3cWLJkiTL+atWqpTblTz/9pMeI
+3ZuamspVV10FOGEqCftNmjRJbfhw4LajE7F7BwIB5UpkZGRoqC+WMF+043RfU7TBI444AnDWgYRI
+77zzzrhoptGEJNPS0vYoYlurVi0de2ZmpmqTQ4YMUd/V8uXL+egjpzC6rJd33303IRyQpBAKxhgS
+QV46+OCDlbqbmZnJ+++/DzhZdBB584944I8//gCcl/WQQxweSUpKisbWpQhL7dq1dS7y8/OVYCN5
+FOEiUc1TZWF//vnnShTKzs7Wua1syAt70kknAY6QFU/9nDlzEtLIJRx07dqV1193qhdKt6wdO3aw
+du1aHWfr1q0BZ41INOqpp57SzUCcilJyLt7wzAcPHjyEIGk0hfT0dPx+v0q/aNVPn8+ndNfXXntN
+pfGmTZs0Zp2owqfhQNTW1atXq7Py1Vdf1fi+mDvu5h/PP/+8shsrqu+hmB3FryeOu8suuwyAQw45
+RHfdqVOnavmzaOH3+2MuoGqMUQ1BMk0DgYDOvdtciwci0Tq++eYbpdb3798fcByL4kTMy8tT56LP
+51P+yty5czUTNtHaTVIIhWAwyPbt28u8WfciLf4QfD6f2o4PPvggXbp0AZyHJSpj165dtRJQMiA3
+N1fVyMaNGyvxRvwdd911l3ryY3lJYl1AtWvXVnV2v/3249ZbbwXQRjXgdLMCxyyL1VaPh61vrdWc
+AInQ7Nixg9GjRwMOFyCMzmj6b2mCOJq5DQaDGvG68MILAcc0kOceDAa1juXzzz+vnJSKNHM888GD
+Bw8hSJosyUiOF61BOgd/8MEHWsPQndWXk5OjDMFExHP/CUhPT2fIkCGAQwXv1KkTEMr1EC1txYoV
+SdfWXep8pqWlaTJaIvs7RIOMjAyNLgUCAR1nArg7YWVJVkmhIBASz9ChQ0Mop+KxPe6447TKTbIt
+hKoCd5bk/vvvrxRbqTu5aNGiiCsIeag0eEVWPHjwEDmSwtEYKUQrcJfDll3q119/1SxIIYd4iB6S
+wQoO+UqciuXVb0hUApaHxKNKCgVZbNL4dMuWLVpsdd26dSXatcYYLVIhpoS3aMuH++UunkbtPgYc
+4o2YEhWRCl9VUbyaVXp6etRdqhIBz3zw4MFDCJLF0bgeyAYqi0hQz7u2d+1/wLVbWGvrl3dQUggF
+AGPM3HA8o961vWt7104sPPPBgwcPIfCEggcPHkKQTEIhsfXVvWt71/auHRaSxqfgwYOH5EAyaQoe
+PHhIAlS6UDDGnGCM+dkYs9wYc3OCr9XMGPOZMWaJMWaxMWb47s/rGGOmG2OW7f63dgLH4DfGzDfG
+TNn9/1bGmNm77/8NY0xqAq9dyxjzljFmqTHmJ2PM4RV178aY63bP+SJjzERjTLVE3bsx5iVjzDpj
+zCLXZyXep3Hw5O4xLDTGdE7AtR/aPecLjTHvGmNquf52y+5r/2yM6RPLteOFShUKxhg/8AxwInAQ
+cI4x5qAEXrIA+Le19iDgMOCq3de7GZhhrd0PmLH7/4nCcMBd5WM08Ji1tg2wGRiSwGs/AUyz1rYF
+OuweR8Lv3RjTFBgGHGKtbQ/4gbNJ3L2/ApxQ7LPS7vNEYL/dP0OBMQm49nSgvbX2YOAX4Jb/b+9u
+XuQowjiOfx6ILhhBEw9hdYWNIB68GE8JehBfUEOICB4iASP6D3gSlj15FzEH30DxIIuCGnQJSMCX
+c9CAaFAXIxGzwWguxmsOj4eqgWmTJbs43e2hvlDQXTUzz/y6n3no5+muKai+dwh31/e8Xn8T4zL5
+X8QxGvbhxNT+EpYGtP8pHsEa5mvfPNZ6sregOOSDOI5QHmTZdrXjMWPbN+GsWkea6u9dO27DOexU
+Hq0/jkf71I5FnL6WTryFp6/2ulnZ/tfYk1ip2x1/xwns6+P8b6WNnT5MnGXCeu3rnYhYxB6cxK7M
+nCwRdQG7ejL7Kl7EZHLGLfgrMyfzuvvUvxsX8W5NX96OiO0G0J6Z5/EyfsPvuIRThtPOxjqH9sHn
+8NlItjfF2EFhFCLiRnyMFzLz7+mxLCF75rdkIuIA/szMU7P+7E2yDffijczcozxW3kkVetS+A08o
+gelWbHflJfZg9KXzWkTEspLCrgxteyuMHRTO4/ap/YXa1xsRcZ0SEFYy81jt/iMi5uv4PK5crum/
+cx8ORsSv+EBJIY7i5oiYzFbtU/861jPzZN3/SAkSQ2h/GGcz82JmXsYx5XgMpZ2NdQ7igxHxLA7g
+cA1Kg9neKmMHha9xZ61CX68UXVb7MhZlju87+DEzX5kaWsWRun1EqTXMlMxcysyFzFxUdH6ZmYfx
+FZ7q03a1fwHnIuKu2vUQfjCAdiVt2BsRN9RzMLE9iPbKRjpX8Uy9C7EXl6bSjJkQEY8paePBzJxe
+vWUVhyJiLiJ2K8XOza/00xdjFzWwX6nI/oLlnm3dr1w2fodva9uv5PZf4Gd8jp09f48HcLxu36E4
+whl8iLke7d6Db6r+T7BjKO14CT/hNN7DXF/a8b5Su7isXCE9v5FOpdj7WvW/75U7JLO2fUapHUx8
+7s2p1y9X22t4vE+/22xrTzQ2Go0OY6cPjUbjf0YLCo1Go0MLCo1Go0MLCo1Go0MLCo1Go0MLCo1G
+o0MLCo1Go0MLCo1Go8M/s2DwvwtwYfMAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 2/2... Discriminator Loss: 1.2068... Generator Loss: 0.6641
+Epoch 2/2... Discriminator Loss: 0.8739... Generator Loss: 1.2788
+Epoch 2/2... Discriminator Loss: 1.2420... Generator Loss: 0.5994
+Epoch 2/2... Discriminator Loss: 1.0455... Generator Loss: 1.3188
+Epoch 2/2... Discriminator Loss: 1.5271... Generator Loss: 0.4801
+Epoch 2/2... Discriminator Loss: 0.9442... Generator Loss: 0.9758
+Epoch 2/2... Discriminator Loss: 1.1125... Generator Loss: 0.8582
+Epoch 2/2... Discriminator Loss: 0.9858... Generator Loss: 0.8610
+Epoch 2/2... Discriminator Loss: 1.7708... Generator Loss: 0.3445
+Epoch 2/2... Discriminator Loss: 1.0218... Generator Loss: 0.9825
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsXXd4FNX6fs/sbpJNIaQAghAhIAhypSoKCoiAgnIR61Wx
+oKKiAlK8gop6FbiIXBUrig2sgCKCghRFQKR3MURaIGAIEkJ62XJ+f0y+LzNhN9meDb95n2cfls3u
+zJkzZ77z1fcTUkoYMGDAAEGp7QEYMGAgvGAIBQMGDOhgCAUDBgzoYAgFAwYM6GAIBQMGDOhgCAUD
+BgzoEDShIIS4TgiRLoQ4IISYEKzzGDBgILAQwchTEEKYAPwJoB+AYwC2ALhDSvlHwE9mwICBgCJY
+msJlAA5IKQ9JKcsBfAVgcJDOZcCAgQDCHKTjng8gU/P/YwC6ufuyEMIvdUUIwe+NDE3/oSgKz2O4
+zyfd+6prIFzHLYTweGyKovB1ORyOQJz+lJSyQU1fCpZQqBFCiIcAPOTD72AymXSfmUwmOJ1OAOqC
+oPcAdO/dHa8qAr2gAiG0XI1Tu8ACMWY6R2xsLEpLSwEANpuN/649h6uHsaa51n4/UOO1WCwAALPZ
+zOuirKyMHyJvHyZ38wx4dn01HTciIuKssVW9j4qiKvCxsbH8u6KiorOupeoc0u+qGesRT8YaLKFw
+HEAzzf+bVnzGkFK+D+B9wDtNQUp51uQ4HA6XC9aThRfKHUUI4dUCo++aTCa+4SaTicesFZCFhYUB
+G2d5eTkiIyMBAHa73eUceSuM6FroIbbb7bpj+XIfFEVBREQEH5eO6XA4fN5ZtXMLAFFRUSwgAwFX
+973qZzSGqKgofl9aWsrXpJ0rWhdSyoBpFcESClsAXCiEaAFVGPwLwJ2BOnhNCyjcVEdfdnMhBP75
+z38CAN5880089dRTAICTJ0/i999/B6AKiOzs7ICMUVEUXpxaIeTtuN2BhJc7zcMbaB+E5s2bAwAy
+MzP5HAFStQGoD2Mgrp+Oob1+d0hNTQUAHDt2DOXl5fw5CcCysjIAeg3DbDZ7dGxPEBShIKW0CyEe
+B7AcgAnAR1LKvcE4lwEDBgKLoIQkvR6En47GcxEJCQk4dOgQACAmJgbLly8HANxxxx1sKoSDQ9Ab
+x1kwYLVaAejNHK1pUhdB19SkSRMcP65a3WVlZbjssssAAPfccw8AYP/+/fjxxx8BAA0bNsSWLVv4
+u1qTRGNOb5NSdq3p/IZQCDPQDTx8+DDOO+88AEBeXh6rySUlJbU1NB169OgBABg/fjwv0oKCglob
+j6IoiImJCek4Auk0JSiKggceeAAA8MMPP7D50KVLF3z33XcAKs0IAMjNzQWgmg90T9LS0nRmBY2z
+rKzMI6FgpDkbMGBAh1oLSQYLta3O+ovk5GQAQGRkJNLS0gCoJkO4aAiESy+9FADQu3dvNGnSBACQ
+np5ea+O59tpr8c477wAALr/8ckRFRQGo3M3JFCP07NkTALBlyxZ23HmLQGsIANC2bVvWvKKionj3
+T0xM1GkIgOq0nD59OgBg0aJFOHDgAP+NrlvrqPQU55xQqKuIi4sDoKqJALB48WK88MILAICsrKza
+GpZbJCUlAVAXZnFxca2NgyIOb731Fs4//3wAQK9evdjcmjRpksvfrVixAgCwe/duXHHFFQBqN2pF
+EYfnnnsO//jHP/jznTt3AgAGDBiAoqIiAMBrr70GAHjppZdgNquPcFlZmS764irfwlMY5oMBAwZ0
+OGc0BVK/Xn31VYwbNw6Aqoo1a6bmUC1cuBB//KHWY02ePBkA8O2339bCSM/GY489htGjRwOo3K1e
+eumlsNQQaDfq1k3NWnc4HIiPjweg5goEAt448Gg8sbGx/FlBQQFHa3744QcAajSHduDi4mKe7yNH
+jnBClbeqdqAcjYmJifjiiy8AAPXr1+fIwdKlS/Hee+8BUB26rswcyk0wm8146CE1QXjBggX4+++/
+fR7POSEUGjRowIu0rKwMc+fOBQDccsstfMMBoFOnTgCAb775BoCaBdinTx8AwNatW2s8Dwkef9Jd
+tcd6+eWXAQB333036tevDwC48cYbAYBDTeGG6OhoAOB5czgcAc2m1GZsevKwUfixvLyc3//999/Y
+tWvXWd/99ddf+X3btm0BAGPGjPFZ1fZVmBBIoGlNhjNnzmDNmjUAgDfeeINNhpqQkpKCwYPVmsOP
+P/7Y5wxfwDAfDBgwUAV1WlMgCdi9e3ccOaLWeqxfvx67d+8GoCaBkJc5PT2dd/qOHTsCUJOCFi5c
+CED1WNMueOTIEU6VNZvNXAdAqlogcuEbN26MoUOHAlDV2f/9738AwldDILRs2RJA5dzv27ePE2wC
+gaoFbTWBvpueno6rr74agJrUUx2sVitGjRoFQE0b9zX64GuSFK3D66+/HgAwdOhQjixkZGTg+eef
+BwCPtQRANZ9GjBgB4OxcFm/NG0NTMGDAgA51UlMgSUt22KRJk9C3b18AwK5du1gy3nTTTS5/RyGo
+WbNmsRYgpeTsMCEES+6YmBjWEAJRcEJjuOeeeziUN2DAgBp3t3AB7WKkKbz00ksBK8QBfPfXaAu4
+tH4kVzh16hTf39mzZ/t0PsA3B6PFYsHXX38NoFJT0JZ6f/7559izZ4/Hx2vVqhUA1UG5b98+l+Py
+1qdQJ4UCebs//vhjAMDx48dZBazuwmnBkVf/vPPOw/r16wGoaqSr6jqtqeBP7Jfw6KOPAgAmTJjA
+FY779++vMwlXJFBJdV66dGltDocxf/589OrVCwDwyiuvYNiwYWd9h5yPVquV7ysl//gDTx46+s7L
+L7/MJq22NJ7m84ILLnB7DNrAunfvDkDd1Bo2bAgAmDhxIgsTm82mKwE3HI0GDBjwC3WuIEoIwQ66
+l156CQBw11138Y5f028BsJqVlJSE3r17AwBzFAQTZrOZd6j8/Hw2f7SOOtoNmjVrhnbt2gEA+vbt
+y2HU9evX11oVoBDiLCeW1WoNqJbja5q6oig6M6ZBA5V17PTp0wDU2D2F7EwmE2cQkoPaF1A2oSvy
+k6ogDeCLL77grFUyebKzszmj9dSpU2xW7t+/n7WCyMhIDlsTpJScj7Fz5042f2fOnMlrRMtK5nA4
+PCqIqnPmg5QSL774IgCgUaNGAIDhw4fXKBQURcFbb70FAGjRogUA9QaQgAgF+vTpwze8f//++Ouv
+v/hvJAw++eQTAMDgwYPZ7lUUhT3Lhw8fRps2bQCEPi1XSskPGS3iYJzDFzidTjzxxBMA1IeCTLOM
+jAwAahkyJQJNmjQJZ86c8XusWgrA6iCEYJM3OTmZ8xqoVuTQoUPo168fAHVt0ubVvn17Pkd5eTkL
+Pfr9Lbfcgg0bNvBYKIFLW0bui7/HMB8MGDCgB/Hj1eYLgPT0JYSQmZmZMjMzU5aVlcmysjJ59913
+V/t9IYRctmyZtNls0mazSbvdLu12u0xKSvL4vP68aAzZ2dkyIyNDZmRkyAqTiV/R0dEyOjpadu3a
+VXbt2lWuWLFCpqWlybS0NGm322VpaSm/JkyYICdMmCAtFktIxk+vmJgYnnOaw8aNG4d0DJ68iouL
+pdPplE6nUzocDulwOGRpaamMjY2VsbGxAb+viqJIRVGqnbchQ4bIIUOGyPLycpmTkyNzcnLk3Llz
+5dy5c2WrVq1kbm6uzM3N5XHTq7i4WBYXF8upU6fKlJQUmZKSwtehXUPVnV/z2urJ81jnzAeTyYSc
+nBwAYBWpsLBQ52Glstnvv/8e+fn5AICrr76abbgpU6YAAB8n2CAPcVxcHMaOHasbO4HMCkq37t+/
+P/9NCMHhq0mTJuHhhx8GoNZ5hBL16tXjOaR/33//fQwZMgRA+DAetWvXjsvOSf3+448/Am5ukU+B
+jusunOpwOPDss88C0Ie7t2/fDkC997NmzQKghqrJLFYURXcOqmegSFtERISOrDZQMMwHAwYM6FAn
+ow9UI09e3NatW6Np06YA1IIdbT4BOcYsFgunjZInOJBJN9WBdv9OnToxQzNV73kK2jE2b97Mu+Dw
+4cNDymXQrFkzTrLSEn6Q5uWOuyDUiI6OxpdffgkAnLsQHR3NBUilpaVM3eYPaA5q6jHRuHFjjm4l
+JCTwups6dSoAlSDl5MmTANQUZcq/GTRoEGtk27dvx3333Qeg0nlqtVq9rYY8d6MPNJlUw7B+/XrO
+YpNScths+vTp+OCDDwCoau6cOXMA+C8MvA2bUU2F3W6vMdvOHeh3MTExrD5GR0eHVCjk5+fjuuuu
+AwCsWrUKgDoXRGgSLrj00ktx5ZVXAqgkQSWhSp9RJIJMMV+gCfVV+73c3Fz85z//AaCafNq+DgCw
+d+9e3TGotH/AgAEseNq3b8/mIoXkacMLNAzzwYABAzrUOU0BqHRoUdpqly5d2GlYlZiE1K+rrrqK
+8xT8hbcmF5krJpPJ57Rg4oJISEjAtm3bAITOUUpzmJeXx7X+mzdvBqDuymS61TY/JlHEUUIPUJmc
+9PHHH7O5edNNN2H48OEAgI8++gibNm3y6XykKdSURlxaWsr1DtOmTWMzpnXr1gBUkpVTp07xsciU
+2LRpk44qjhzW5DwP1lz7rCkIIZoJIVYLIf4QQuwVQoyu+DxRCLFSCLG/4t+EwA3XgAEDwYY/moId
+wDgp5XYhRByAbUKIlQDuA/CTlHKaEGICgAkAnvJ/qGeDfAPVpSiTTVlQUICUlJRgDKNGjBkzBoCa
+aUeMP66YgaqCxr5x40bOwszNzeWU52DuyqQdREZG6voZVmUUdjqd6NpV9V1pC3tCjfj4eNaghBDs
+d6FU8qqcCWSP//bbbzzP3jIoVW10XJ2vir5bUFDAGg05nTt27Iinn34agDrfq1evBgAcPXqUNUST
+ycSO8kAygLmCz0JBSpkFIKvifYEQIg1qC/rBAHpXfG0OgF8QJKHgCchB17hxYyQk1I7S8tlnnwFQ
+WXiJEqxDhw7sRQYqnZHESDxw4EBObW7VqhV7mQcOHIgTJ04EfcyUZ//FF1/gwgsvBKDSqNP4tHUb
+lDJcG6YDPWzjxo3jse3bt48p6N0RqFC3pfT0dHaa9urVy6tr8LjqUFHY8SmEYKcirc0WLVowR2NO
+Tg6nvP/111/83fLycs5f8Pb83iIgPgUhRHMAnQBsAtCoQmAAwAkAjdz8xqdW9AYMGAgu/BYKQohY
+AN8AeEJKma/NEZBSSnc5CNLHVvTegpxgUspaq/2nsGGPHj3YQZeens4OquzsbHYikQrqcDiwbt06
+AGo+wsaNGwG43/mChauvvprDecuXL2cV9tixYwBUx9kvv/zCYw416tWrBwB44IEHuAK1W7duNVLm
+UWjV6XTynCYlJbHDzxPQTl3TdUspmWbv7rvvZiq4AQMG8HdoLdSvX58diU6nkzWhffv2cW5LsBsD
++SUUhBAWqALhcynlwoqPs4UQjaWUWUKIxgBO+jtIf0BqJABO+qkt7Ny5k1XAbt26cbLP9u3b2Rv+
+zDPPAFATY2bMmAGgdh42MgmKi4vZj9C4cWMcPXoUAPDnn38CAL766quQCyotiEm6sLCQqwRr8g28
+/PLLrM4XFRVx3ktZWRmr7jTn1flIPL0vWlavn3/+mdObqdFLXFycrqMVVVQWFRXxtWzdupUraINt
+pvkTfRAAPgSQJqXUJuEvBnBvxft7AXzn+/AMGDAQavic5iyEuBLAOgB7AJAb9GmofoX5AFIAHAFw
+m5Sy2tSrYJkPiqJwnLpRo0ac2urOS6w1fei90+k8i4YtGJI6GB2MA3Fci8XCeRYnT57k3Zj6R27b
+tq1WcxPIE3/rrbeyQ/f48eM4fPgwgMp2bE2bNtXdR/Lcb9iwgclXpJRsHtEaCca10ZjJNCDtBFB7
+c1LX6U2bNrEJuX379kCYDR6lOdd62bS3pdOevKikdcaMGVwuXVRUVOPvIiIiZEREhIyKiuJjuDpu
+oMdrvAIztxMnTpQTJ06UNpvtrBJkKSW/t9vtMjU1VaampgZlHGH88qh02khzNmDAgB61rSUEQ1OI
+ioqSUVFRctiwYbKoqEgWFRXJXbt2efx7DwkrjFcdeMXHx8v4+HgZERFR62MJg9e5SbLiCbSZZlS2
+TA1FPUGwMsUMhB55eXm1PYQ6B8N8MGDAgA51jmSlJphMJk62iYiI4DivEKLGhBZXzV5czU9tVwMa
+MOAjPIo+GJqCAQMGdKiTPgXa0bW5BORHcDgcuiISgtls5viw9vfaRh6e7v6hqE6sCro+Lad/OCOc
+tamqY9OuC1f+pEBeh6IoZ+WOmM1ml7kzQggdYxStVRpvsNZCnRYK2glxl3JK3zWbzdzAhJKYtOmn
+xcXFYbGItcQdNE6TycSmTziM0RNIKXXCN5yct9qxad+bTCauVqX5djqdHjd9qQ50DkVROFmJqiQL
+Cwt1x9auWSqzLikp4dTzYKe9G+aDAQMGdKiTmoKnu47WJCgrK2OpS5L6xIkTbtX12oYQgklhOnXq
+xPX2dQna3Y/U4HDpDUHmWMOGDVlbTEhI4N2Y0rnPnDkTEO2MjuF0OtmsJU1QOyeKoqBz584AgAMH
+DvDvampzp3Wq+4tzLvoQjtAy5dBilFJWK9ySkpJYlXU6nbomtKEGlSfHxcVxj0YaeziZBTWB1PKW
+LVtyY+H7778fy5YtAwDMmTOH55mEWKgrQJOTk3HvvfcCAPr164d//etfAGoWCh7CiD4YMGDAe9RJ
+86GuwmKx4LnnngOgMvJS74HCwkKu5qMd6tFHH8XEiRMBIKS9HarCYrEgOTkZgMoPGS7qv7cQQnAz
+lbfeeos1HKvVyt2/s7Oza2zsEiwQZ8U777zDbfjKy8sxbtw4AKFttHPOmQ/hHAoDKrkPW7dujR49
+egAATp06xY1tiPuwZcuWTAFeGyQrpGo3atSIqeTrSjhUC7qOTZs2sY8mKSmJowsmk4lt+H379tXK
+GBVFYValK664gs21srIyZoK69tprWSAfOHAAgE+mm2E+GDBgwHucM+YDxXz79u3LfHjhuKuRw2j7
+9u1Mu/Xyyy8zaQmhfv36TAdfHYV9sEBjS05O5qKiUPXeDASI3oyo9Fu1asUEKp9++il27NgBQO2P
+SeZRqLVM0mJuvfVW1la0lPoAcN555wFQNR2a/zvuuAMAsGLFiuAQ/oTDg+Ov+WA2m7FwoUoR2a9f
+P36Yjh075tJLThN98uTJWvOeK4rCZK3jx4/HTTfdBACs4gohuD352LFjQ+4Fp/6QTz/9NDP+vPvu
+u7WmYnuDZ555BhMmTABQuTEIIbj/xieffMIm2aOPPsqm2/bt2/Huu++GbJzffvstAOCGG27gCNUr
+r7zCHavOP/98/Pe//wWgtgCgtUp0/0uWLMEjjzwCwOMN0DAfDBgw4ANqm2AlECQrEydOlBkZGTIj
+I0OuXLlSWiwWabFYpNlslnFxcTIuLk4ClVRbRLwxZswYaTKZpMlk8up8gaDrat68uVy4cKFcuHCh
+/Pvvv2VxcbEsLi5m+jin0ynT0tJkWlqaTEpKCjkhR1JSkkxKSpLl5eXS4XBIh8Mh09LSapyXJk2a
+8CvU1GatWrWSrVq1kvn5+bK8vFyWl5fLzMxMmZmZKdu3b+/yNxEREXL37t1y9+7d8sMPPwzJOIkK
+jsZos9nk4sWL5eLFi89ai2azWZrNZrlv3z5pt9ul3W6XZWVlsqysTGZlZXl7boOOzYABA96jTjsa
+KTvQYrFgz549AFR7UesQKygo4Pdkd9HvCgsLuRMxNWnxBIHww4wYMQJ9+vQBoPoXyKaklNvExERm
+UR41ahSef/55v8/pDag/hclk4usl+7UqyGEWHR2NBQsWAFD7X77wwgsA9PcgWDCZTLjrrrsAAJmZ
+mRz3v/766wFU9qmoCkVRsH79egDwqhGMP7j44osBVM7bt99+i9tvvx3A2WuLwpA33HADhy0pXZ98
+C4FGnRYK5CEfMWIE5s2bBwBMiV0d6MH77bffQrYQCFR3ccstt3DVps1mYwceLd7Bgwez8KIGqKEE
+OersdjvTpbsSnIqi8DifffZZdtrt2rWLnaNCCBZ6wcq5GDRoELdtX7FiBcf63QkDQr169bB3714A
+Kt07zXWwujCZzWa88cYbACoFwLBhw2rcaPLz85Geng5A7doFqJGTYLQG8Nt8EEKYhBA7hBDfV/y/
+hRBikxDigBBinhAioqZjGDBgIHwQCE1hNIA0APUq/v8ygNeklF8JIWYBeABAjXEeX2LElAacmJiI
+G2+8EQA8UrNJLb/xxhv5/SOPPOI2PFm1ktLXMKYQgvtZUsgPUM2YQ4cO6Y5tNpt1FZ6hRLNmzbjX
+ohCCcysiIiLO2kGllGjWrBkA4KGHKvsF79u3j9XcU6dOsRoc6J2NzJwnn3ySs0UTEhK4zbs7kEYw
+adIkDmGPHDkSDz/8MABwf0x38DWnoU2bNpyTQvNaE00gfZfmk8YuhMCdd94JAPj888+9Hos7+NtL
+simA6wFMATC2opVcHwB3VnxlDoAX4IFQUBTFY9WSkk2GDRtG4+BklOrYe2lB0uLdsmULT2ZVkgvt
+/+l89ED4aiP3798fvXr14v/Tg7JixQosWbIEgJqTAKhqNpk2M2fO9Ol83qJv374AgB9++EH38GrN
+tKpjsdlsuOaaawCoeSE0/++++y5fX7ByYYQQ3BWqc+fOLFAHDx7M1ZyuEB0djccffxyAmjhE3923
+bx/3eazunIBqdvjCFH3dddfxJkPryJN1X15ezg1pf/31VwBA+/btMXfuXADqmqbKT3/n21/z4XUA
+/0Zl27gkAGeklFQ1cwzA+a5+KIR4SAixVQix1c8xGDBgIIDwWVMQQtwA4KSUcpsQore3v5dVWtF7
+I91I/aIog9aDbDKZXEpeIQSbCkOHDgWgOhypLwR9pyosFgtLdF8rBEnFveeee/gYTqcTu3fv5vNS
+3TztyqdPn8Zvv/0GQPVWr127lo8XlNRWIdgDXpUb8MILLwQATJ48mbNBqdrTZrPxmBMTE7mTcigy
+ZRVFwUUXXcT/p/lctWqVy++TmbNo0SJOg46Li2Pz7Pjx43zdWg4MghCCoxrU7dpbFBQU8DFpx/cU
+1KL+kksuAaBeJ0WwrrrqKtYyX3vttbPGDXh+T/wxH3oA+KcQYiCAKKg+hZkA6gshzBXaQlMAHrGD
+UCPXmgauKAqXwBI7jsPh4ImyWCxsc2mbwy5dupQrEMnrv3//fn7gtTnnQghmsXE4HD6rwbSAnn32
+WQDAgAEDuEbD4XBwvvull1561o2TUrI637lzZ7zzzjsAVEH2008/AQAyMjK8Gk91kFJi1KhRAICe
+PXvyAxQVFaXzqVB0oUGDBgDUB4kWY3x8PC6//HIA6hxqORq11xUoWK1WHkdZWRluu+02t9/dtGkT
+unatzPDVtpqn+ztw4EB+8F5//XUAaiq8q3Xh63W0a9eOBQ9tUr5i8ODBbMIoisINj7799luOGHlD
+SEzw2XyQUk6UUjaVUjYH8C8AP0sp7wKwGsAtFV+7F0YregMG6hSCkafwFICvhBCTAewA8KE3P65J
+1VEUhb21pPYpioI2bdoAAIYMGYItW7YAUFW1q666CoC629IuTaQaO3bscOksCsRuFhkZySQq1157
+LQB1dyK1TtvaTmu2aHdXLZszRVWKioqwYcMGv8fnCuRIbdOmDWs51157LTvzZs+ejeXLlwOoLNz6
+7bffeLcuLCzkOb7yyis5RyAuLo6dYFTBGggtZ8CAAazFlJWV6SIjNI8HDx4EoI/2SClZO3A4HLz7
+l5aWchIcJQZpd9pArIvu3bvz2Ei79TWSERMTo6N9b9y4MQA12Y20Ny1CYT5oT/YLgF8q3h8CcFkg
+jmvAgIHQI6wyGj2RZHa7HZMnTwZQmR344IMPsp22e/dunD59GoC6O1C82mq1MoPQ008/DUANoQXL
+ITZ9+nQ+N+1W48eP51h6p06d8OWXXwIAHn74YfTr1w+AanMCagiKHGYXXHABszDFxMTg2LFjQRmz
+FrR7LlmyhJ2LFosFTz75JIDKHBGtv+G7777jubXb7RxSveSSS3DppZfqjv/ee+/5PPe005JzFlC1
+kQ8++IDf9+zZ86zx0XjmzJnDfimLxcJhyE8++YTnPFjromnTprox+3IuurZ58+axZgZU5jt89913
+Oj8IPSeeZmmGlVDwFMRZOH78eABqYgepg0eOHOGLT09PZ5XK6XTi7bffBgDmXghGyi3d9JtuuolV
+7SlTpgDQk6V8912lq2X8+PH80NMD2LBhQ1bhn3jiCZ2aGKwUXHeg85lMJjz66KM8DkAVHvRwLlq0
+iH+jVYmXLVvGztFA0ZADwBdffMFmicViYTNNURQ20zIzMwGoZiWZBvfeey+vl4iICM4RWb16ddCj
+JitXruTol7dzQWt59uzZAFQiHu14qQ3AunXrdCaPt8lvRpWkAQMGdKiTmgKBpOFLL72kc9DRewpZ
+0ueUvRjMYhdSS/Pz8zFt2jQA4IIbdzCZTLxzkalx5swZzths0qQJOyZPnjwZjKHXOD5A1bDI/CHN
+pWvXri7p4qruuIHUEOj+Ll++HPPnzweg5oDQOEtKSrBmzRoAwPDhwwEAOTk5/LuBAwciISEBgKq9
+keYYCqZqbUoz5dmYzWZdGjjl4WRnZ7PG06BBA2zbtg0AmLFLCMEh9TVr1mDkyJEur8PbtPw6LRS0
+0KpLdPObN2+OK6+8EoDaDYpit4EGLcbk5GS+0Q6HA+3btwdQ6WnPz8/XPSx08ydOnMhJPxQZGT58
+OKvG8fHx/FARVVco0alTJwDA5ZdfzjyHZA7UBn8kwel08vlLS0vZ3HI4HHxP7r//fgDqHBK34Xnn
+nccPyuuvvx5S7sklS5bggQceAFC5abVu3ZojOOPGjeOH/sSJE5yclZKSwr4BWt9lZWX49NNPAQAv
+vvhiwASvYT4YMGBAh3NGU9CCduPRo0ezqbBp06agkbTS+crLy3mnLy8vx/Tp0wGAsxF3797NXA57
+9uzBwIEDAQCHDh3i3UObXqz1nG/cuBGASjQayoYs9erVw9dffw1AdeZR9SSp57UBrWed5kXb4j0+
+Pp7HSUXayXCtAAAgAElEQVRE2t85nU68//77AICvv/46pAzO2ntK412yZAlnUqampnJUQguHw8Hf
+J9PtoosuwtGjRwEEOFpS2/yMgeBodPcaNWqUXLNmjVyzZo385z//GVTePXoRP+Ts2bOZS494F202
+G/MdOhwOmZ+fL/Pz82V2dvZZf7fb7fL06dPy9OnT8rXXXpNWq1VardaQXIP21b9/f+YSzMrK8onT
+Mlgvk8kkO3bsKDt27CiXLVvG82mz2ZjPUDufJ06ckCdOnJCPP/4436dQjzk5OZnvNY0xNzdXx81J
+L4fDIUtLS2VpaakcPXq07Nmzp+zZs6c/a8HgaDRgwID3OCfNB8Ltt9/OqnYgvd/VgZxWc+bM4ZwD
+cnY6nU6mCVu0aBFXF5pMJlx2mZoESubOtm3b8OCDDwJArXScpnyLRYsWsWo6ffr0WmlhVx0okeu+
+++7jiEJJSQmnr5Mz9/jx4xyvLy8v91jdDnSDmPj4eE6hpurZmJgYXd4HzXGfPn04ZT+U/UnOiWYw
+7jB+/HjOvNu5cyeTgfgKb0pQmzdvzuE7svtKS0tZSNlsNp1tXLVUtzbvixCCIzUpKSkcRouPj69T
+XaLCEQkJCZz1SSxPmZmZTDq7d+/eYJLTGM1gDBgw4D3OaU0hJSWFE5Y+/fRT9jjXBG0Fo6IoXK1I
+6dWhMkVqC61atWJ2aSEEEhMTAVRPdecvgsFKbECFRgs1NAUDBgx4j3Pa0Zifn8+UVxRr9wTaGnqn
+08msu+c6aLfesGED7y4nTpwIqoZAMDSE4MFbJ+U5bT6YzWb2SJ86dcpYeDWAhEJubi4nWV1yySUh
+6fBkICQwzAcDBgx4j7AxHwIdDwZUtYkIV7w9fiAdX66YgcMZl19+OYchDS0hsNCmY4dbzgchbIRC
+MB5YVzTX1UHLYhPI+gKqZ9DmJoQjaGwZGRk6+nztmKuOv+q8anMvtO/dnctTaO85vY+MjOQEMKfT
+yclA9evX51oCIlnJz8/X3QdtDQJVKxYXF/ODqj1fIHJH6HgRERF8DqrqjI2N5aQ1bfKSVoBo7wOR
+rRQXF/OmV9UP5g8M88GAAQM6hI2m4A18kdie8j8C+vbrtQUtWUyozA46n8Vi0e3yWgKQqrumu7FV
+p134Au0xaJx2u513zfz8fHTo0AGAmp9CvIvEy5iXl6fr2UC5KHa7XRddIQ0i0JWoWk2Bzk2azenT
+p3ketYzfWvPCZDLx55Ta7XQ6+ViBNEXO6eiDtwikH0GrPntz42gMPXv25ASi/Pz8kPIyWiyWoPeB
+DBRI9U9ISOAks8zMTDZ/6N9wuQ6TycRmA403JyfH7/F56DMLfvRBCFFfCPG1EGKfECJNCHGFECJR
+CLFSCLG/4t8Ef85hwICB0MIvTUEIMQfAOinlB0KICADRAJ4GcFpKOU0IMQFAgpTyqRqOExZinOiu
+bDab3yq7xWLhXYzmOC8vz600Jw3hiSeeAKAyQFPS1IABA7hPYrjseN5CSx4DBJcPUetErCsRH38R
+SE3BZ6EghIgHsBNAqtQcRAiRDqC3lDJLCNEYwC9SyjY1HKvWVrrWZAikfVavXj1uzJqUlARALePd
+sWMHALViMj09nb9PfSWJ+zA+Ph779+8HoPaaJG96XYQQQmf7A743aPXl3IBqy9My1UaB3DUkDkeQ
+2aGtRfHSrAy6+dACwN8APhZC7BBCfCCEiAHQSEqZVfGdEwAaufqx0YregIHwhD/RBzOAzgBGSik3
+CSFmApig/YKUUrrTAmSVVvR+jMMjJCUl4ZFHHgEA7q783HPPITo6GgDw1ltvBZQrID8/nxmaqc/l
+0aNHmWCD0ogBPZ/CoUOHAADbt2/HQw89BKDumgy0SyclJXHH5++//z7o5zOZTBg8eDAAlSYdANau
+XavTCH744QceG3VcCvU8u8q9kFJyar7ZbMbFF18MQNWsqLkQ5V6sW7eOad0DCX80hWMAjkkpiXP8
+a6hCIrvCbEDFv6FvVGDAgAHf4Sfh6joAbSrevwDglYrXhIrPJgCYHgriVkVRpKIoUgjBpJzTpk1z
+SYhJr+3btzOZZ8+ePUNO4imEkEII+cknnzBB5y233CJvueWWkI8lGK+WLVvKli1byq1bt8pPP/1U
+fvrpp3yfgjGXKSkpMiUlRd5xxx18X4m41dX9J3LUxMREmZiYGPL5iYmJkTExMdJqtcrY2FgZGxsr
+b7/9diZ0dTdmIgTet2+fjIyMlJGRkZ6e0yPiVn+jDx0BfAAgAsAhAMOgah/zAaQAOALgNinl6RqO
+4/sgoObq33777QBUCnSiEqP+jARKZKG/79mzBzfffDMANYHkH//4B4BKMpVggxyQJ0+eZNOlRYsW
+AICsrCy3v6tNkJpL8/3666+zSTBy5EiumYiNjWVuyZiYGI6kEPejOweZtzUq1HCna9eu3GC2vLwc
+UVFR/B6odNK5wo8//ggAmDx5Mn777TePz+0LFEVB27ZtAQCvvvoqANXcoYY79erV05H8uII2Ffv5
+558HAEydOjVg0Qe/MhqllDsBuDqJf2SIBgwYqD34Yz4E6gUf1e4uXbrILl26yKKiIpmXlyfz8vLk
+mTNnZE5OjszJyZE2m02WlJTIkpIS+eCDD3LPArPZLM1msxwzZgz//dSpU7Jz586yc+fOIVMfly9f
+LpcvXy4dDodcuXKlXLlyZchVWE9epPL37duX50urzg4ZMkQOGTJEWq1WmZqaKlNTU+XGjRtd9lxw
+12+B7mlERITH47r55pvlli1b5JYtW2RGRgafz2azyaKiIllUVCTXr18v169fL2+++WZeF0eOHNGZ
+FWRi7tixw6d58ea7t956qzx69Kg8evRotaYtvTIzM2VmZqY8c+YMr3Ht2E+ePClPnjwpW7duHR7m
+Q6DgjfkghOCGoBT7jo6O5p6Bf/75J8fATSZTtRGFZs2aMW9jcnIytzKnyrNgwmw2s3odGxuLHj16
+AABHLMIJffv2BQDMmzePozU0x+3ateOIitVqxccffwwAGDx4MCcs7du3D1OmTAEAfPXVV36Ph+L0
+R44c4crW//3vf2x6TZ06lVPE6e8lJSU8HqvVyiZkYmKiLrmMjh1oUERhzZo1aNmyJQCwiaNFYWEh
+rrjiCgDAH3/8wZ8PGDCA2Z+p+5W24nLz5s28hqqBQbJiwIABH1DbpoO35kP37t1ZjRo9erQcPXq0
+jI+P90rtI1VVURTZv39/2b9/f3no0CE5YMAAOWDAgJCo5ImJibK4uFgWFxfLtLQ0HlMozu3tONPS
+0mRaWposKipik8fVeJcsWcJqb3l5uZw1a5acNWuWrFevHptsgRgTmVrl5eVyz549cs+ePTIpKUnG
+x8d7vBZ27dold+3aJcvKynjMp0+fDtp9aN26tWzdurU8deoUt+HTmjvp6ekyPT1dNmzY0OVa/fnn
+n2V2drbMzs7m6EPVFoQemDMemQ91pnSauiz98MMP7OF+8803AXif3641maghbHJyMoYNGwYAWLly
+ZdBy88mzvGrVKlZnN2/eHHYJStTkdPPmzWjevDkAVZ2lhq3a8Xbv3h0AcP311/NnNpsNY8eOBeBd
+NEcIwXMkZSXBCc3Vddddh6uvvpq/T412SkpKvDpPnz59AAC//fYbWrVqBUBV58l8yMnJ8fhYNcFk
+MvEcpaWlcdeqZs2aAVAjEmReaIlVGjRogM2bN/N3XRHa0H3YtGkTAgXDfDBgwIAOdUJTEEJg9erV
+ANQdoWPHjgD8r4CTUnLuwqlTp3DkyBEAqiMqGNyEQgjWeFq1asXaCDmQ3EFLsBEqjYLSkpOTkznW
+371797PmvGXLlli7du1Zv2/durVP+R7R0dHMM5Cfn8/vSQuZMWMGp4SfOXMGixcvBuB9bglpm0uW
+LMGYMWMAqFWy5LB+5ZVXajyGN7SAixYtAgD8/vvvaNeuHQBw+7jU1FTuMbpz505uN1hdbgWgVppS
+GvcjjzziEeWgJ6gTQiE1NZWTX1544YWAJvbQDTh16hRXLQYreSkiIgK9e/fm87388ssAKheoFiaT
+ifkHr7nmGk6w0fIIBgsWi4UF7+zZszliUFRUxN+hB3PRokX8vqysDAMHDgTge1NcKSVXUpaXl7NA
+0tY10EO4Z88en6tHqUy+ffv2LOiEEPjnP/8JAHjjjTcAgJvSuhurJ1AUhaM1f/75J99vipCkpKTw
+HDZo0MDlw+10Ovm+03nLy8uxYcMGAGrkJFDrwjAfDBgwoENYawokMadMmcKS/d133w3o8fv37w9A
+lbQkuQO9E5PjLC4ujh1Kd999N++8TZo0wbhx4wBUpmb37dtXRyn24osvAgBmzZoVdE3BZrNh5syZ
+APRMzFon4IgRIwCoDjAygx588EE283yFNp9ACMHXSpriqlWrcMsttwAALrvsMiQnJwMAvvzyS6/O
+c+uttwIAevTooWNPvvzyywGA0443btzoz+UwaHdPSUnB3XffDaAyRdtsNrOmUFVLIP5IKSU7P887
+7zz+G40zkOauoSkYMGBAh7DOaNT2NqRstWbNmvnc9ZmOR/9eeumlePvttwGo/AVUm16dHekNaFel
+HWHs2LE89n379mHo0KEAgLZt27p0KpGm4HQ62Sb9/vvv8cADD/DnoYCWvWju3LkAgEGDBgFQd1fq
+19mvXz+/HaFabQSovEb6rE2bNti1a5duXICamehpz8+EhARmRI6KiuIxK4rC94c0t3fffdflPHtT
+uBUfH8/zNWbMGFxwwQUA9H0ftKDjlpWV8TVqtQn6u9Pp5GzSFi1aePJcBL8gKtighdC2bVuXKaE1
+oVGjRnjrrbcAqA8htaWnNNJBgwZhxYoVAIBp06YFTBgQ7rnnHgDA448/DkBdSH/++ScAoEuXLnxD
+L774Yl54pIpv2LCBHVK9evVih+i1117rlZc5EAzVFAF49tlnOeWZ7sfbb78dUKIPLT2adsw0P3/8
+8Qc7gmlcgCqQyMPvKrU9Li6OyXW6devGJoqUlQQnNpuNqzcffPBBAGpatqucBZPJVGMuC63fAQMG
+oF+/fgDUylg6BzmStSgrK9M5PkkQaPMXCE6nE1988YXba/YVhvlgwIABHcJaUyBJnJeXxypWRESE
+SzWJVLEhQ4bgvffeAwCO/QKqpkAhx4YNG/LxP/zwQwAIODGqoig4ePAggEpzZNGiRRzeO3bsGO+2
+iqLwtZJ2IIRgh1qPHj24sOebb77xymwIhDr/0UcfAVB3PNrFKacj0HRgTqez2jE7nU6Ozbdo0ULn
+5CQH3pYtW/jeU/FQ06ZNWTsoLi5mx1xcXBzfH5vNxloBXWdVFmqCJ85euk87duxgc2Xjxo0clibK
+OG0eSkZGBq/15ORk1jacTidrCjTeI0eO8FoPpBsgrIUCIT4+nifknXfewb333gugslruiy++4Lj6
+X3/9xRVwycnJrMJnZWVxOuuTTz4JQK1Yo+8GGk6nkz3Xd955JwB1YWq7+1SXD5GUlMR2bUlJCXuZ
+jx07FpIEJprvX375hc0tRVG4gpRIVmoDZMLs2rWLH9pevXrxvAwYMOCsZjZms5mjPdpKSG2PRofD
+AavVCqDywXNHBuPJPaDvZGRk8Drr1KkTk/nQRial5HldvHgx+4wsFovOD0aCiFLzH3rooaCsX8N8
+MGDAgA51QlOgHAUAaN68OUtaivlv3ryZaal27drlUroLITB79mwAYI1B23chGCAzhxyK7lTOyMhI
+NiXmz58PQE0pJm/6Y489xqaIr/DGW64oCms5Xbp04d8VFBTwPFOhTqCg7a5ck3lE3vvIyEhdp3Ca
+7xMnTuDf//43gEqH6LBhwzgHQevAAyo5F6SUzLK9bNkyAIFx4NlsNnY0PvbYY8ynQPNaWlrK892s
+WTNe19p7pnWCEo3bhg0bgpKzEtZCgVS506dPczPO9evX88NMKuK6detqXEht27blhiukti1evNin
+sJ63PIJ0MwsLCzk8abVaOZ//6quv5s+JxERKyaQvWrINb+FLH8vJkyfjkksu4c/IzFm2bFlAk8d8
+BT00JpNJ14SX7qXT6eR0a6LM79ixI8+Fu+jNiRMnOM2Z7kPDhg25ElN7z7Vmhye1D7T2UlNTdQKJ
+/k6NgxITE3Xjo/u2du1a3hiIizJYSWyG+WDAgAEdwlpTINXtp59+4nTkH374gSWpJ5KSduAffviB
+j0e7B6nq3sJbRx/tYEVFRcxHMGHCBDaLCgoKeDemvIpZs2b5XFTk6tyegHIhRowYwQ683NxcXYp1
+sODNnFLqr91u13nn6f7u3LmT55OcvFFRUW41BMpVGTp0KDv86PrdRUO81TBJO3U4HGclIeXm5nLO
+gtYJ6nA4kJGRAUDNEaF+oq5MGqvVqjNH/EFYCwUyD+655x4O02izzrSeWe2NI4KQOXPmMCFHXFwc
+q4HTp08HENiED0+gVSOPHj3K13TzzTezSRTolvPe+BGICCQ+Pp5/Z7FYmHcx0MldruAJuQ11SDp6
+9Cir3dpoTlFREe644w4AZ2cLVkV+fj6HfrVVoLQ2AlWO/N133wEAJk2adFZmbXx8PJvK2irQ33//
+HTfeeCMA1YSubv7tdrvO9+YP/G1FP0YIsVcI8bsQ4kshRJQQooUQYpMQ4oAQYl5FN2oDBgzUEfis
+KQghzgcwCkA7KWWJEGI+gH8BGAjgNSnlV0KIWQAeAOCXd0pKWW0VmJSSHZHjx4/H6NGjAUCXQ69V
+gykdtuouGsiu066gKAqTujz22GPceCRc2qUTsYiW5uuuu+4KScdrrUpdk3ZDO2ZhYSHv6GazmdfA
+v/71rxrPR+vpP//5T438GYFIFT///PMB6BvVEOLi4nSq/8mTaqfFIUOG6PJaqju/1pTyF/46Gs0A
+rEIIM4BoAFkA+kDtKwkAcwDc6Oc5DBgwEEr4ycI8GkAh1Jb0nwNIBnBA8/dmAH5389uHAGytePnN
+ltukSRPZpEkT+d///lcuXLhQLly4UP79998yKytLZmVlyZYtW3rcvCMcWZWD/YqIiGCWbLvdLteu
+XSvXrl3rsnFLoF/ErO1tj8lu3bpxYxUtK3N1fSMdDodctmwZN60J1fzWq1dP1qtXT06dOlUWFBTI
+goIClw1pMjIyZLdu3WS3bt0COr8Vazq4zWCEEAkAvgFwO4AzABZA1RBekFK2qvhOMwDLpJTtaziW
+b4MwEDDExsZi7969AFQnG6WHB7u3YqAQERHBTsXU1FROBf7ss88AAMuXL+f28wcPHgy4Q9cbVI0+
+AJUmSv369YPZjCjozWD6AjgspfxbSmkDsBBADwD1K8wJAGgKwP+4mgEDBkIGfzSFbgA+AnApgBIA
+n0A1BXoC+EbjaNwtpXynhmMZmkItw2w2o0GDBgD0lYgGzil4pCn424r+P1DNBzuAHQAeBHA+gK8A
+JFZ8NlRKWW2AO5hCgVS1Fi1acF67v+qZt2nOgfBeBwuuuAGFEEFrhhNoaGP+2ihOdXNeG5T5nsKT
+2g8/EJJW9M8DeL7Kx4cAXObPcQ0YMFB7COuMRneomhFmsViYVCMvL+8sajNArUGnVFPaHd1RalXd
+darCT+3K72PUdHxvju2Of1CrOWhTibV8hoA691ouSe0xPN3xtOfydl7IuVhcXOxx9mHVPBTtOqK/
+aQutPC188hTailBArfakdWi3232aD23lZ005DTWhTgqFqiqv1WrVJW64mpDk5GRepJSMo01z1nLg
+ebOgPYE2KSrc1FVX0AqFqvOgFRCAmoyjvSZPHyBXfRH9HbOvx9OWJ2trFAI1tqqgOXRFZe9qXJ5A
+KxT8Tb4zqiQNGDCgQ1hTvHsKq9XKlWFadl76f8U5PN69Aj0nWrXOQPjCarWy9khFdeScDndERUVx
++nc167fuU7x7iqqJKK4mpTaFX7DObbFYuDKOhKKnUYNwjoiEGlQyft9992HJkiUAKtm5Vq9e7XOf
+kVAiKiqKzQatWVydKegOhvlgwIABHc4JTcEd3HlxiZPPHZ+CO1PD193V391YG7vWdlD69ddf8eab
+bwIAN7rRjtPduRVF4TmgXfD/q8awdetW7t9ZWFiIefPmAagkO3HntAtyPoFL0H1v1qwZALXD2fLl
+ywGoDYU2bdp01m8SEhL4d8QVWhMMTcGAAQM6nDOaAu2OkZGRTG2VmZnJ0txms+HAgQMAKinaRowY
+wd2Ktaw77nbNQITNpk2bBgBo164dj/Obb75hLgPyj2RnZ6NXr14AgN69e7PDq3v37swHsWfPHt4p
+XIUF3cHpdHLnYqrdr80CoVBACMHaUbNmzdC1q+pvI2ZwQG3UQs1gSGNwpw1om7MEU8sih6fD4WDG
+Z/osIiICkydPBgAu9gLUUCeFVp1Op9d8GOdE9CExMZEX+eOPP46HHnoIAM5iza0Kp9PJVOXXXXdd
+0MlEJkyYgClTpgBQF5Kr8Wlj5sQh2bx5c+5H2aFDB+zYsYO/S0zP3t7HNm3aAKgUCmfOnDknTYjG
+jRsDUKslKS+gUaNG/HlcXBxvCO+99x4WLFgAAEhLSwOgbhbBIt3RggSMyWTiDmZWq5U5OxMSEtCh
+QwcAYN7G5ORkZnjOy8vDo48+CkC/GVbJjQl6laQBAwbOQdRpTYFSXD/66CP07NkTgCo9ifLMYrEw
+c/OKFStY4hPDb0pKCofyRo0ahU8//RRA8OjYTCYTNyl59NFHubfAkSNHOE2belseOHCAiWZjY2OZ
+yVcIwdfnq6PLbDZzSzrqoXD99ddzO7YGDRogOTkZgFpIduLECQBqQxXSyEjt/vvvv9kMuuiii7iB
+TW2sKzIP4uPjceuttwKo7PzdoUMHXSahNl2b5rGgoIC1RVpb48aNw9y5cwEEPs+ENMXU1FQ899xz
+ANQ1SS0CLRYLazclJSV8fUQfZ7fbUVhYyMeiXhf79u1zd8rgV0kGChXMMF4vJLLDH3vsMe6pN2PG
+DPbE1xRfnjlzJh577DEAKnNujx49AOj9C66gbZceCGjNCOokZDabmWuwW7du6NOnDwAwZ5+3x9Yu
+aEVR+KGeOXMmAODKK6/kv1etmPQUJ0+exBNPPAEA3Eg3VFAUhYXTww8/zN2gaD6joqJckppIKTm3
+Y+PGjXwM+uzzzz9nFV6bA+JNMxh3oLYFn3/+OT/wVquVBYHNZuN1vXjxYtxwww0AKjubrVmzBp98
+8gkANXXfg3EY5oMBAwa8R9hEH7yRtsSGS/Han376ibn7bTabx2reM888w86Z1q1bM8lITZpCoNVI
+7fEoQrJgwQKOkmRmZrJD0Fu4mlen08kqJpktWm2l6m/o/8XFxdi5cyeASgfeH3/8wXF+q9XKGsj8
++fNDHsenNN/Vq1dzp2zagavmbpCmd+zYMW4Vl56ezmNOSko667ta+HNt2qpMQI0i0dhbt27NJu87
+77zDjuTU1FRuDvT9998DUB2OwZjjsBEK3uCyy1S6BlL3p06d6lOjEm1lZGRkJLezJ++uOwTT5KJr
+6tChA59n/fr1Pqfa1hRepTRpp9PJvoqlS5fi/fffB6A+9ESH7i6ha+nSpQBUEyQlJYU/DyWEECzU
+U1JS0Lt3bwCVCT9aMyE7Oxu//PILAOD+++93mRruqxD2BDSP27dvBwC88MILbOaYTCZ8++23ANT0
+6xEjRgBQr4nC2WRSBGsdGuaDAQMGdKiTmgI1dSHtYNu2bV79nlp0LViwgNVmu93OqnFtgNJqyfEH
+VCYUPf300wE/HyVG0c7udDpx3XXXAYDX80Ct26xWK2JiYgCoiTWhTIgym8247bbbAABTpkzh+0pa
+zrx587B//34AwPvvv6/z4NcWUlNTAQBz587VtZ+newNUmrJ79uxx2f06GKhzQkEIwSEbergffvhh
+TkKqDrRQKCnoyiuvZJustLTUbZm11sSg7wYScXFxWLNmDYBK4o2ysjJWgb2NOGjhqg4iMjKSqwD3
+7NnD39u1a5fXx4+Pj2fB4nA4OKTqrblD4dmaujW5Q8OGDXH77bcDUFXwdevWAQBHbcKxbP2aa64B
+ADRt2pQ/q8p6RT6fkpKSkJVxG+aDAQMGdKhzmoI2PZi8wg0bNnTJI3j33XdzckdaWhpuvvlmAOAk
+HYvFwsk2b731Fi655BIAKtszqZ1amqtAqppJSUnsML311lt5h6TkmY8++shrs6g6aDWfsrIyLFy4
+EEBldaWvvH7R0dFcbbphwwadGuxpbYA7OjJPQNri9OnT0bZtWwCqk5C6jXtyTaHmlqAxX3rppQBU
+E2flypUAgEOHDvHfi4uLMWTIEADqPIdK2zE0BQMGDOhQo6YghPgIwA0ATlL7NyFEIoB5AJoDyABw
+m5QyV6gidybUztPFAO6TUm4P5ICFEMjKygIAdmqVlZWhRYsWANTCEKoc7Nixo0vfACE/Px9jx44F
+oGodtDOXl5frdo9A9gggv8Tq1avRrl07AKpmQr4EikVv2bLF73MB7sccqGYvOTk5rG01adKEc0eS
+kpLY0ehwOFiboH+rjsuXkDIADB8+HABw44038hxedNFFHt8rbcpzKAqfAHWsQKWzd8OGDZwvk5ub
+qxsHreUvv/wSF1xwAYDKXJZgwRPz4RMAbwGYq/lsAoCfpJTThBATKv7/FIABAC6seHWD2oK+WyAH
+nJyczJNGpsGVV17JSR4mk6nGNF1arEOHDmUBExUVxQs6Pz9ft0gDqVZSSetFF13EwiY7O5spwSg+
+TtcT7njllVe4qi8uLg7Dhg0DoC54quZs3749J5xNnDgRALB7926XAsKbdPfY2Fg8++yzAFRhS/eV
+1kV1IEfrp59+ysfYsGGDV45OX8wOIQSnYJMQ69atG5o0aQJArVbVrtnXX38dgLru//vf/wIA13UE
+CzWaD1LKtQCqtlQaDLXNPKBvNz8YwFypYiPUvpKNAzVYAwYMBB++OhobSSmzKt6fANCo4v35ADI1
+3ztW8VkWqkAI8RDUdvQegRyJr7zyClewUeZhw4YNWepq4XA4OOb+4YcfsoZBJCX16tVjqdyyZUvu
+VEeOvO4AABvMSURBVOyOpi0QoF0lNzeXSUKtVitLf8rK69q1a9DVRH9Aps/w4cN5Dk+cOIGnnnoK
+AJCVlcV5AXfeeSc7VSkNetu2bT5rYHSvmzdvzvfU4XBwJamrHInLLrsM77yjtjRdtmwZHnnkEQBq
+fsiMGTMAAIMGDfI5JOoppJSYOnUqgErzoXXr1nj44YcBACtXruTKzi5dunC4UkqJzp07Awi+Y9Tv
+6IOUUvpS+iylfB/A+4BnpdMkFDZt2sTpyBS/T0xM5MjA1q1buSw4PT1dZ59V7SyVn5+Pl156CYAq
+FCi2ftddd3l7OR6Dzj169GhOtdUmJ5HqSwIqXDF69GgA6n2hsXbq1Mll3cibb77J5gPdJ3/Yrcjs
+OnDgAD9go0aN4gfo6aef5oebvPcdOnTQ1TvQeMrKylh4hYp7k/w55Ft4+OGH2Zc0c+ZMXd4CoaCg
+AK+99ppP5/MWvkYfssksqPiXEsWPA2im+Z7Rit6AgToGXzWFxQDuBTCt4t/vNJ8/LoT4CqqDMU9j
+ZlSLmlQiin9feOGF/F2SuEuXLsV7770HoHrPLB2bnH2XX345mjdvDkBVh6dPnw6g5uw3X7gfADUv
+gmLQGzdu5J2LstaAyuYjnmRo1gbIbLj//vsBqE5byhqsrro0kFmg2pZ1v//+OwDVhKTPBw4cyFrY
+2rVrAagOvAceeAAAcMMNNzB/hJSSKdgKCwtDmrNAmu6MGTPY+diwYUMeg8Ph4PXw+eefM9lLrac5
+CyG+BNAbQLIQ4hjULtPTAMwXQjwA4AiA2yq+vhRqOPIA1JDkME8GoSgK5827YhVSFIUf+ssvv5xt
+fkoqev3112ukr1YUhUuRBwwYAAB47rnn+Ab88ccf+PHHHz0Zrq6VuZa5p169evygFxcXnxXiiomJ
+4eSkhIQEtr/79OnD46DoQ6tWrXjBhxOIPIXGe/z4ceYJDASEEBy21YYyXSE+Ph6jRo0CoCb3kCC9
+6aabWECROSaEYJPhqquuYr+Uw+Fgk6agoMDrSALgnkafNpwuXbrgm2++4e+2bNkSQKUp9cQTT3BI
+UtsT1W6388b33nvvhawpTY1CQUp5h5s/XePiuxLAY/4OyoABA7WHsElzNplMKC0tZdVe2/Tk4osv
+xsUXXwxA9dSTY4vi4C1bttTxMpLkbt++PXuqGzRowA5IKjQqLy/nxJuxY8d6XNVnsVhYamtbh48a
+NYrpyGJjYzFo0CAA4GInbQw6Pz8fe/fuBaAm+tD1jRkzBkDwE1R8gclkYkIVuo5hw4YFVJ2NjY3V
+zVF1GDlyJM8xoOeGIHOFjtWlSxembddGLQoLC/Hbb7/x7zxFTV2eL7roIqxatQqAahIQcUp6ejrn
+pBDNvMVi4WMVFRXxml2wYAHzKZSUlOgaAgFhHH0IBJxOJ4qKiuB0OnlyTCYTM9Ps3r2bH96EhAT2
+zlKIZvTo0RxmzM7O1nXQoQq+nJwc/pwSk44cOYLZs2cDUM0HV2FNV9BWVAKVN+d///sfnnzySR4/
+kWWQzb1x40b+7unTp5lBJyoqCuvXrwdQSbrpa4ZfMGEymXiOaIESUUigUFpa6nFmYW5uLo/D6XSy
+SdCkSRO20SkUOnbsWDYfhBBsbg4bNsyn7E4pZbUPZU5ODvvBFEXhZKkLLriAx0zrG6gUgH379mUO
+Rqq/CTWM2gcDBgzoEDZszhX/8mdaqjS73c5aweTJk7nKkWA2m3Web9I2oqKiOF4dERHBKr+WnIVU
+x02bNuH0aTVxc8uWLT6rZuTE1HbsIYcSEakAqiOSog4tW7ZkTkBSM7OzsznX/e6772an45AhQ7Bs
+2TIAanrspEmTAIDTtYOJ+vXr8xzRbhcZGRnQmgFvIjtCCOTl5QFQ1wCZD0VFRRzlIZhMJl2NA2lv
+v/76a6CGrkNcXBz3drzooot0fyPKfFoP69at41TrLVu2BLMa0mBzNmDAgPcIC58CcHbhUtXdh9KV
+R44cyTFoSgkGKismgUobXwjBjD42m43DffS7vLw8TimdN29eQPgSiL0oNzdXxy0AALNnz2bKs337
+9nEbMKAyFEWx69jYWPajpKamsnO1Xr16vNMcO3asxtZ4gURZWRnPLe1mZrM5oJqCNxqalJL7NGzZ
+soUZuWJjY/k4Wt/Mzz//DEDtExKIMCrNvav+D4WFhcyX8Oqrr6J79+4A1NaBNA5ah5mZmT5pplpn
+vNPpDJiGETbmgy8JQTQhbdu25Zi/2Wxm55KiKLpyWlo0W7duBaBWntHDpnUcuWszXpPXV1EUjp5M
+nTqVTQJyKCUkJDAFl6Io/DBJKTkeT+r5rFmz0KVLFwCqIKTFTX/3dEyBxGWXXcbmFj0Q7dq1q64j
+UUhBD9nChQt5fBSdKCkpYfMxEEJMm6sSDs+QhzDMBwMGDHiPsNEUansMBmqGxWLhVu1krnXu3Nkn
+wlcDtQKPNIWw8SkYCH84nU7uxEXNYoh23MC5A8N8MGDAgA6GpmDAYzidTqxevRoA8O9//xuAZ9Rn
+3sDXClRvz0EIB/M53FDnfAomk+kswg4tzXhxcbGugtHVAqCoRVRUFHuktdV4rlKYgw2KUGhbx2sJ
+SQIdZah6PC0luzYyUjWJzN1xCDS3JpOJ5zZYNOuKonDqeklJCYefCwsLOW2YxlxduK5qw1ft+5KS
+Er9bzlcds7sGvtWNreoxALVKlNZt48aNOZJWVFTk7phG9MGAAQPeo86YDyQxtRKfpGFBQYGuMIbg
+bnfQFqRod79A7gjeQFtxRzutw+EIqsZS3W6l3c1qahJT9W80n94mgnmrIQB65m6r1coa5KlTp9xS
+yVd3bofDwbkOpCnZ7faAFqd5m2DkqpUhjS0/P58T5AoLC73SzKqDoSkYMGBAhzqjKWilHxU5+SsR
+g83c6ymsVismT54MABg/fjyA2mmISvNps9nC2gGnHSeVPWt3c3/mjvIviCnpmmuuCYsydlf3w+l0
+8hpu0aJFwMh+66SmUFMte11Djx49cPDgQRw8ePCsrsO1gXCfW+39LykpYQISf/P/nU4n8vLykJeX
+h+LiYhQXF2PcuHFhcU9cgUw7KSXX9QQCdVIoGDBgIHiocyHJcwnk1Dp48CBzAZCzLBzuy/8XUNGc
+tm9ot25qt8OMjAyuSg0XEFdEp06d2Om6d+9eZn6uBkaac7iDaNJjYmLYoxzqtuiEUCQNBQP0UCiK
+osv1IDq2I0eOAAD69evHlH15eXnMx2m1WpGcnAxA9eBTFSoxQ9eGb8cdKDLVqJHakO2NN95gmrcR
+I0bgyy+/DMh5DPPBgAEDOvjaiv4VAIMAlAM4CGCYlPJMxd8mAngAgAPAKCnl8iCNvc6D2t8JIViF
+JfMhVJGR2tJMAgEhBHdi/sc//sFM0zabjXktKI5fXl6OgQMHAgDGjRvHVGgzZ87EunXrAADPP/88
+H7u2NASTycTaj91u190X0iYpynD++eczpd/gwYMDpinU6FMQQvQEUAi1mzQJhf4AfpZS2oUQLwOA
+lPIpIUQ7AF8CuAxAEwCrALSWUlbLaiGEkK6ITUgddDqdulRbsqm0Y6ewkdVq5QeqXr16zPD70ksv
+4e233wZQ2ea9rKyMeRCpWW2ooCgK5s+fD6Cy3yEAtGnTBkDwKd6rMldPnTqVexVGRkZyqC8hIYHV
+6wULFjBJDKnXOTk5TFmekpISUuFyxx136LomaSnXyTz4+uuvAagqN62F06dPMxFPo0aN+GHbs2cP
+brrpJgDgRi4bN24MCCNXTaDOW4qiIDNT7dFM/JNVoaXApxBqQUEBC8BqBFpg0pyli1b0UsoVUkqa
+qY1Qe0YCaiv6r6SUZVLKw1A7RV1W0zkMGDAQPgiEo/F+APMq3p8PVUgQqBW9R9A6u0wmE1q0aAFA
+bZBCXIo2m40LX0iNItozQFW/tYU9WhCfPqmRu3fvZoq2//znP54OMyCIiIhgJ5h2l5s2bRoA4NZb
+bw1YAQ6g3z20MXfSTEaMGMEt2EwmE+9WKSkpOi5C4hc877zzdP8Cas8G+m5KSkrQGKZp/NRjA9AX
+bimKwjwP27ZtA6Dec2IEP3XqFHM7mkwm1hQaNGjAJgQxME+bNk3HzB0M9OjRA1OmTAGgal633XZb
+td8nDVqr7ZnNZh1foxau1kB18EsoCCGeAWAH8LkPv30IwEP0/6oJSU6nE7169QKgNuwgG9FqtfL3
+SG0tLy/X9WB0ZYedOHGCG4DQgujduzd/Fmrve3l5uc5vQDeM1PaIiIiAZNK5WgjauabOVKdPn2az
+bMuWLfzADR8+nBvvamsKKJy6atUqNGnSBIA61zS3e/fuRc+ePQEg4D0xaezdu3fnh3fu3LnsUwAq
+CXSJ57J79+78MEVEROh8KTTPOTk5/B2i2l++PHguMRpD//79meS1e/fuNXJIUvRB63P4/fffWShU
+7b/prX/EZ6EghLgPqgPyGln5NHncil5K+T6A9yuOVfe8XAYMnKPwSSgIIa4D8G8AvaSUWjf5YgBf
+CCFehepovBCARz3VtUzK9O+ECRMAVLaOp++R5KN24llZWeyUURSFnWExMTFMH9a/f382QcgJ9eab
+b7J6WRt5AXFxcQDUHZZ2DWogEmznFiVLUfOaoqIiXH311QCAw4cP83xQb8uqIA2rQ4cOvEMlJSXx
+zhodHY22bdsCCLymQCgtLWXq/6uuugqfffYZAFWLoTFRElJUVJROUyQTFKhsH/Dvf/+bSWPI8RzM
++0B5KoMGDeK+otXxXZJTnBzCBw8eZIf0559/HrCu1L62op8IIBLAyorFvFFK+YiUcq8QYj6AP6Ca
+FY/VFHkwYMBAeMHXVvQfVvP9KQCm+DIYLa9Aq1at2IGoZR4aO3Ysk4a66hKtKAp+/PFHfk/MPHFx
+cbjiiisAVIb7HA6Hx01lA424uDi2uc1mMzd+oR2ud+/e7NTzR4uheRNCcEguOjoaM2fOBFDptPrm
+m2/YjvYWZAPn5ORwl+Tly5fz+UKBvLw83HzzzQDU/hQ0nw8++CAA4LPPPmMNslevXjqfAjUX2r59
+e0jCjxTufPHFFwGoYd/Ro0e7/C75a9LS0lizJBQVFXET4xUrVrj1HXibixI2ac5VIwWtWrXiRXre
+eeexevX2229XSwumnRiHw4GVK1cCACZNmsQPQPv27QEAf/75p1flpt56cV2BxtmvXz9ceOGFANR8
+CVrEhD179gQ0+hAdHc0OtcjISLz88ssAKh2NgUiWklLy8b7++muee2/gj8OX1Of169fzdb/xxhsA
+1I5dEydOBKCnXSsqKuLvhEIgmEwm5rls3LgxALVD1Mcff3zWd/fv34+WLVtWe7wNGzYAUK/d1frU
+RmU8bYJjpDkbMGBAh7DQFKgnnt1uZ2n9/fffs7OnoKCAdzl30pzCaaWlpSwpTSYTO9QKCgrYuUQm
+g6eOmUCmAtOxDh8+rGNCrhoCI6dYoFBYWMjj//vvv3Hq1CkAwI4dOwCoKbP+olGjRqyur1mzBgUF
+BV4fIxBzLKXkXZFyJVJSUnQOa8KcOXPOasUXTDRu3JgzREtLSwGoKcradU27v1ZLyMrKwjvvvAOg
+MiM3MjISx4+rwb2YmBgmq9VC65j3FGEhFLS9FLUgk6GmhaJN3Khfvz4//E2bNmW/g8ViOass+a67
+7sKrr74KoHrVKpCRCRIK6enpLJxiYmI43k4dmAJ1TnfXVfX4lB/hC+ia7r//fs4VGDFihMtFGipo
+WaUB9cHTCgUqM37iiSdCOq5169bxfaeozJ49e/jvqampnLPgdDo5R+SZZ57R8TUC6lqnZLfDhw+z
+0NDOuy/ryDAfDBgwoENYaAqAaweTp1KuKoMzVR/OmDGDHWiRkZGsolERydNPP83vp0yZouuzEAxo
+d6r69euzydChQwfOECQPc6h5Ae12u8+OVMqwGzp0KGeWZmdn10ruB6Cq6DSPjz/+OABVc9E6s9eu
+XQtAn+YcTJCWqh3DqlWrAKhO4EsuuQSAmm9A63DRokWsCbg61jXXXMMmRrNmzTBnzhwA8FtDCxuh
+4A+cTqfOfiWvd9++ffHoo48CUHPZiXiDHk6TyYTU1FQAqqCgYwR6kdBivfnmm9nG3b17N3dZmj9/
+PpfyUi2GL557f6B9gD2JANA1RURE8Jjr16/PQljr4fcGrqplPQXN4YYNG9jHRGFtbb3HsWPH8NRT
+TwFQTU96UG02G5/bVTRLC1+jJFpa9pSUFABqaJJ8MQDYxzFjxgz2O0RGRvJYKMKzevVqJo4B3K9b
+b31ihvlgwIABHcJGUwikqkmq97Fjx/DMM88AUJ2O5LQhkyErK4s9vXa7PWhqJF3biRMnOBqSm5uL
+3bt3A1A7N1NxV4cOHQCo6m2oTQhv7oGWZp0qKgsKCthp5iuvoa/rICIiAsuWLQOg3muCVkOg+ezW
+rRubOdodWEt2Q+aHu8iEt+Okc7/yyivs3KZqXaCyiGnbtm346quvAKjrokePHgBUpySZBd6m5ns7
+1rARCsECTUhRURGHcqi+4K+//mLzoWvXrszAEyzh8OeffzK5R5s2bTBs2DAAqopLajepi+Q1D3c4
+nU5cf/31ANQMUxK8gcrDrwmUNbl+/XpWq13RsTudTvTt2xeAKrBoXbhL2gp0mJLO17hxY64kJWFU
+WFiIxYsXA1CFBl1TSkoK1/QUFhaGbJMwzAcDBgzocM5rCoTIyEhWzSkH3uFwoGtXlZ1q/vz57JEO
+Fo4fP84q4M6dO3XOLNqZSBWPiYnR1Xa4UgHDoaV6cnIyawoNGzZkL/rmzZt1Y6pKAOLOsemNA69R
+o0ZMgEJaAp2D5obSx7du3cqmjbartq/w1dG4e/fusyqC9+7diw8++ACAqqFQnk1iYiKn4ZMGGQoY
+moIBAwZ0+H+jKTRq1IjJRkeOHAlAtXtJKn/44Ych2W1/+uknAMDs2bO5otBisTCtGTHyHj58GFdd
+dRUAtVKOxpaZmelxxloo2KSef/55rj7NyclxuxsTAxLR3t12220oKioCoN/ZPQlJ0g67dOlSHSM2
+oaSkhDND6V4fP36cfxcVFcXnDjVWrFjBa4B8C40bN+YeolJKpiF88cUXWdMJpSb4/0Yo2O12zjmn
+eHZBQQHnjh8/fjykEz9y5Mj/a+/8Y7M6qzj++bblrXGC0AoLrotAAAWHusEfEIUYlbQszQyJf0Ca
+WGMT/zE4DYms4S//ITEadcIcGH8lTVOJowgh0WXiEkJI2DqkiGV1mBnH4mxtdCGEP2Y5/nHv83jf
+8Vba8d5735XzSW763ud9+5577n16es55nuc87N27F4CjR4/Gsf6wVLa9vT3Osdi0aRPDw8NAUlgj
+cKfrzS45r7duIRnW29sbM+d79uxhZGQEuD1ZGwxyKOpS61pr/V4tQiiSLZQS9oEEOHLkCBs2bAD+
+V+J927ZtHDx4ECDWb7wb3un9nJ6eZvfupBpBSMp2d3fHAjetra3xmXV2dsYyAEXi4YPjOFXcM57C
+0NBQnN0W3O8zZ87ELcfzqjz8/wjDdjt37oyl5QYHkxq4HR0dsXjqzZs36evrA+aecKq3hxBmKoak
+bEtLS3TFT5w4cdc1CWZzvcFDGBsbi9Wob926FduXLl0aPa4w/+PYsWNV1bPLJIQEYTHW+Ph4LLhS
+qVSit9Te3h5Xr+a9D0iWeW8UQmWadevWxc67fft2IHkYYZw6G9eW0WnOnj0LECtYHz9+PBaDyY5a
+lElLS0usUhRWdV6/fj1WkCpqbkIICcwsypQUDVZXV1dcgRg2g5mamqr7ate7/b4wujQwMBBHGQ4d
+OhTfr1QqVSFSUXj44DhOFfN6K/rR0dGYcAJiDb/gPTQ6YXOWc+fOceHCBaCcPQ7DrM+LFy/e9p9r
+1apVdUnczcVLy45UhBGFnp6euKHK6OgoBw4cAJKZjrP93rlQqVTq5hktWLAgXt/ChQtjCHbjxo16
+P+9ZbRs3L41CmL8+MTERY8qpqSmWLVsGlB9Tvptobm6Ow4yrV6+O9zaMgqxdu9bv57uH+uwl6TjO
+vcW8TDQGl2tycjKOXW/dutX/o82B4KIvWrSIgYEBAPbt2xdrThw+fBhwr2s+Mi/DhzC5ZePGjTEW
+h/qV8C5638la8iG/P8impqa4xDs7U7CtrS3OFAzU2nujSLJrKrL3I+Qaau2v2NTUNOO+i2URRk6m
+p6dr5hHq1Oc8fHAcZ+40iqcwCdwA/lnSJXzAZbvse0D2h8xs6Z0+1BBGAUDSyGxcG5ftsl12vnj4
+4DhOFW4UHMepopGMwo9dtst22eXTMDkFx3Eag0byFBzHaQBKNwqSuiSNS7oq6YmcZT0o6XlJY5L+
+JOnxtL1N0nOSXkl/LsnxGpol/UHSqfR8paTzqf5HJd2+NXL9ZC+W9IyklyVdkbSlKN0lfSO955cl
+DUl6T166S/qZpAlJlzNtNfVUwg/Ta7gk6ZEcZH8nveeXJB2XtDjzXn8qe1xS593IrhelGgVJzcBT
+wA5gPbBb0vocRf4H2Gtm64HNwFdTeU8Ap81sDXA6Pc+Lx4ErmfNvA983s9XAv4C+HGU/CfzWzD4C
+fDy9jtx1l/QA8DVgk5k9BDQDu8hP918AXW9rm0nPHcCa9PgK8HQOsp8DHjKzjwF/BvoB0r63C/ho
++js/Sv8mysXMSjuALcCzmfN+oL9A+SeA7cA4sDxtWw6M5ySvg6RDfgY4BYhkIktLrftRZ9nvB14l
+zSNl2nPXHXgAeA1oI1lvcwrozFN3YAVw+U56AkeA3bU+Vy/Zb3tvJzCYvq7q78CzwJY8nv9cjrLD
+h9BZAtfSttyRtAJ4GDgP3G9moR7bG8D9OYn9AfBNIExubwf+bWZhUUae+q8EJoGfp+HLTyTdRwG6
+m9nrwHeBvwF/B94EXqI43WFmPYvug18GflOS7FlRtlEoBUnvA44BXzezqjpnlpjsug/JSOoGJszs
+pXp/9yxpAR4Bnjazh0mmlVeFCjnqvgT4PIlh+iBwH7e72IWRl553QtJ+khB2sGjZc6Fso/A68GDm
+vCNtyw1JC0gMwqCZDafN/5C0PH1/OTCRg+hPAo9J+ivwS5IQ4klgsaSwhD1P/a8B18zsfHr+DImR
+KEL3zwGvmtmkmb0FDJPcj6J0h5n1LKQPSvoS0A30pEapMNlzpWyj8CKwJs1CV0iSLifzEqZkHfBP
+gStm9r3MWyeB3vR1L0muoa6YWb+ZdZjZChI9f29mPcDzwBfylJ3KfwN4TdKH06bPAmMUoDtJ2LBZ
+0nvTZxBkF6J7ykx6ngS+mI5CbAbezIQZdUFSF0nY+JiZZXe0PQnsktQqaSVJsvOFesp+R5Sd1AAe
+JcnI/gXYn7OsT5G4jZeAi+nxKElsfxp4Bfgd0JbzdXwaOJW+XkXSEa4CvwJac5T7CWAk1f/XwJKi
+dAe+BbwMXAYGgNa8dAeGSHIXb5F4SH0z6UmS7H0q7X9/JBkhqbfsqyS5g9DnDmc+vz+VPQ7syLPf
+zfbwGY2O41RRdvjgOE6D4UbBcZwq3Cg4jlOFGwXHcapwo+A4ThVuFBzHqcKNguM4VbhRcByniv8C
+SGUYY7XTZEwAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 2/2... Discriminator Loss: 0.9482... Generator Loss: 1.3030
+Epoch 2/2... Discriminator Loss: 1.3452... Generator Loss: 1.9974
+Epoch 2/2... Discriminator Loss: 1.0928... Generator Loss: 0.7811
+Epoch 2/2... Discriminator Loss: 0.8769... Generator Loss: 1.3478
+Epoch 2/2... Discriminator Loss: 1.2244... Generator Loss: 0.6451
+Epoch 2/2... Discriminator Loss: 1.0969... Generator Loss: 2.4311
+Epoch 2/2... Discriminator Loss: 1.0368... Generator Loss: 0.8528
+Epoch 2/2... Discriminator Loss: 0.9872... Generator Loss: 1.3598
+Epoch 2/2... Discriminator Loss: 1.1426... Generator Loss: 1.1906
+Epoch 2/2... Discriminator Loss: 0.9564... Generator Loss: 1.2773
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsXXd4FNX6fs+WJKSQkAABQhUEpAkiIBaqooiCBb0iV1FR
+RAQR9FK8iqCiKFf0qqAiChaqEMUfIogCiiJFqkgLSAm9h/RsOb8/hu/LmWU32zeLd97n8SHu7syc
+OXPmO199PyGlhAEDBgwQTOU9AAMGDEQXDKFgwIABHQyhYMCAAR0MoWDAgAEdDKFgwIABHQyhYMCA
+AR3CJhSEELcIIXYJIfYIIUaF6zoGDBgILUQ48hSEEGYAuwHcBOAQgPUA+kgpt4f8YgYMGAgpwqUp
+tAWwR0r5l5SyBMAcAL3CdC0DBgyEEJYwnTcDQLby/4cAtPP0YyGEkVZpoFwghECks3qFEAAAq9UK
+ADCbzfyd3W7n8UgpdWOjv+l4p9MJk8nEnzmdTt3v3OCUlLKKt/GFSyh4hRBiAIABAR7r8Tt/HnCk
+FgSN12w2e3xw7sZBx0V7KjqN03U+XRex+lmoQC+FpzGpL5i740wmE3/ncDjCPtdCCBYGNWrUAACk
+pKTwdU+fPg2bzQYAsNlsKCkpAaAJANf5LCkpQWxsLABNwBQUFAC4WLAoOODLGMMlFA4DqKX8f80L
+nzGklFMBTAVCoykE8jCllBF58ejcJBDoM3VBq9If0B4y/e1wOPh3FouFdxabzcbHqb8JFsG8xO5+
+H865VefU3bP09HzV/6e5E0K4FSahXCNSStjtdgDA8ePHAQAnTpxApUqVAADNmjXDxo0bAQD5+fks
+IIQQbp+xxaK9wvHx8XyOM2fO8G8DGXO4hMJ6AJcLIepBEwb3Abg/VCcP1SITQvALRg8qnFAXMAC3
+uyoJiieeeALVqlUDAHz22WeoU6cOAGDo0KH46KOPAADffvstKlasCAC8SxQWFoZs8fr72/LWaPwR
+SPQs1GdStWpVVKhQAYC2YwNAWloacnJyAADnzp0LyT3SNQsLCwFo67BmzZoAgMOHD/O1XdeLOxQV
+FQHQ1i+thYoVK/I5AkFYhIKU0i6EGAxgKQAzgE+klH+G41oGDBgILcLmU5BSLgawOFznDwWklKhe
+vToA4NChQxHf6dxdj+zN119/HTExMQCAwYMH49ixYwCAvLw83mGqV6/Oqm1xcTGA0PhJynvHLy8M
+GjQIbdq0AQCsW7cOALBhwwZs2rQJgKYphANSSlb3U1NTy/SZeYLJZGItp6CggDVOX7QNV4QlT8Hv
+QUQ4+qDaiDSR9KJFEmS6CCFQu3ZtAJo9CADLly/HH3/8wd+PHj0agOZHcKdequZHIAshGNB9mEwm
+XtyRHkMwILv8yJEj+P333wEADz74IADg1KlTHo8Lla8hMTERWVlZAICTJ0/i3XffBQDMmTMHeXl5
+Pl0jJSWF57y4uFjnPFV8ERuklFd7G4+R5mzAgAEd/paaAklwi8WCRo0a8efdu3cHAMybNw8AkJ2d
+HRbvfVnjohBSs2bN8NhjjwEA6tevj9TUVABAnz59AAC7d++OSjWe1NKEhAQAQO3atdG/f38AQN++
+fXnXPXv2LMaPHw8A+OKLL9iLHi2gcWZkZOC7774DANSqVQs33XQTAGDNmjVezxGspkDHL1myBNde
+ey0AoEKFCrqIw6pVqwAAw4YNw/bt23XXs1qtSExMBKBpB/n5+XxuD+aDT5rC304omM1mtG/fHgDw
+1FNPoV07LWcqISGBTQRaxN9//32oLusTUlJS+KV//fXXERcXB0CzW2lM9OCjEZUqVcKbb74JAOjZ
+sycAzZxJS0sDoC1EdTHS3/n5+Zg+fToAYMyYMQCA8+fPR9zEoJewYcOGGDx4MACgbdu2uOyyywBo
+LyeZDb68F3R/gUZfrrzySgDAokWLONJkMpnc+hSKi4tx+LAW1Sd/x59//olp06YBKA1veoFhPhgw
+YMB/lFtGY6hBavn27dtRq5aWNyWEYMedmihEzptIZTSSqrpgwQI0btyYx5ubmwsA6NChQ9Sp1ypo
+F9u3bx9HRGje1Oy5HTt2YPPmzQCAa6+9FnXr1gWgJdbcdtttAErj/xMnTuTcinCA1gBpNEIIjBgx
+AoBmMtCYS0pKcOCAlui3fft2v9YDrSl6vpQz4A333nsvAC0XBQDS09N1qc7uEBsbi3r16gEovbfG
+jRuz5hZKRI35EMwLWq1aNcyaNQsAcMMNN7Ba+vHHH7OaaDabkZ2tlWMcOnQIANCmTZuArulvxh+9
+VFlZWfxSrVu3Dvffr+Vz0bjCgUCzE8m06devH4/zmmuu4bRbMnMeeOABVmsLCgp016BzdO7cmT87
+ePAgAGDXrl1BZd2VhYSEBKxfvx4AOCkoJiaGX171eiUlJfjll18AAHfccYdfUShKFiJ/0P79+70e
+U6lSJZ4vWgueUrW9wWazsQmyc+dOXw4xzAcDBgz4j6gxHwLZLUhSDx06lFWqdevWYcAArc7qzz9L
+kyidTifnJBw9ejTY4fo13nvuuQeAtnOS+vzkk0/yjhFOqEU0vnrL4+Pj8euvvwLQUn9J81q8eDEe
+f/xxAFq+vjeQOk3efQD8DMJRfET3l5mZiYYNG+q+y87OxpAhQwAAv/76K+677z4AwI033sjj7Nix
+IzuffXGCkreffms2m71Gsbp06cLaFpm8rlCvTen3lNQG6KssKUrSpEkTHDlyxOuYfYGhKRgwYECH
+qNEUAkHz5s0B6GvJ+/fv79a+MpvNLHW//vprAIHbsr4eR86joUOHAtB2gJ9++gmApsXQ95EIzalz
+5A3Lly/nnXbx4sWYOXMmAODHH39k52igCGfmKPkMWrduzc/o/PnzAMBOOsL7778PQPM7UTapmhXo
+D2hdlaUl0NieeeYZ1pZULY7+zs/PZ2ftlVdeySnWaWlpyMjI4HESkpKSAADt2rULel3zWIM6upxA
+Dpq3334bAJCbm8uJMp4cLs899xwft3DhwqCu76tTlIRWeno6AO2FoMWZkpLCL9vixYsxZ84cAOCK
+vPT0dP47Nzc3aMHh7XghBOdKtGnTBmfPngUAvPDCC9izZw+A0JdnqzkNwS7k+Ph43HrrrQA0s5IE
+7l133eX1WLpXSsjyFaTGkxlrMpk8RlSoyrVp06YXbQbqejp+/DgaNGjA35OTev/+/SzgKA9HHcP7
+77/P97Fhw4aLTBt/YJgPBgwY0CFqQpL+/D4+Ph5AaRbXrl27cP311wPwHCv+448/WO2i3TpQDoWy
+wnz0XVxcHF544QUAwPDhwwFoOwlVO2ZmZmLQoEH8ucpe5HpuIQQX5litVt49nE4n5zeQeqqaSb7s
+wLRbX3XVVWzaxMXFsSOxffv2HDINdSo4PUe73c73Eeh67N69O2bMmAFAU7VpzJSt6Ok5JSQk8Bzk
+5eX5tbOSo/Caa64BAPz222/sRFRhtVrx7LPPAgBGjx7N9+1uPGomqPq5w+FgLZief6VKlXTaFmkp
+c+fOxccffwwA2Lhxozomn0KSl6T5UKWKnmbO4XB4FAb026pVq2LLli0AEDSxikrh5YnRx26388NT
+X25arOfPn9dRgrmDKiAqV67Mf9PDP3LkyEXC8OzZs369vGSTXn755ZzUBejTrVV1193LFagZEMrk
+pS5dujDzUGFhIXr37g3As5Ch+7ZarZzg5q9AovVD5lVsbKxOKLijfMvNzWWfAqGwsJCfdWFhId+H
+w+HAyZMnAWhz1bdvX931OnbsiG+++QaA9oyoDqJTp0545plnAMCtkPIGw3wwYMCADpekpkCqJkni
+3bt3e/xtv379AGiZdJmZmQCCV4N9Od5ms3FaLcXEVZ69pUuXsvmQkJDAOwkVu7Rp04YzL+12O2s8
+lStX5t9aLBbOeyCSFX/TpSmaMGfOHJ6fK664gq+dk5PD16tUqRKr16SZWK1WjqHTjhtJ0A57zTXX
+8HrYs2cPduzY4fGY5ORkfPbZZwCAkSNHBkxdRnNBppY6965m4F9//QVAmy8yBSlPxWKxsJOwV69e
+rLGppoyrFgJoaeX0WVxcHD+nzZs3s1MyEFySQuGKK64AUPoQqlSporPJydbr0aMHV/ClpKSwdzlS
+1Xn0oNV0VrInExMT8dprrwHQOBjpIZKqXqlSJX7Ix44d41Taffv2sf9g5MiROo6+QKDOBQmWLVu2
+8HzGxsaidevWALQSaGIgpuvl5uZymvC9994bkRJ0FTRfJSUlHO40mUxo2bIlgNIS6JSUFE5e+ve/
+/81z2KVLFzzyyCMAgPXr1/uUquwKmgs1QQwoTfNOTk5GkyZNAGgCYv78+QBKBfLy5cvx888/8324
+Az0boDTaMWnSJF7r6rX9jaK4wjAfDBgwoMMlqSlQCi6ldTZv3hy//fYbAPCuBmhSmXbrM2fO8G8i
+pSnQrjl37lwAmhnRrFkzAFrqMxFo1KpVC9u2beMxA5oWpMaaaRc8d+4c70DZ2dk+awj+0rSRs2vB
+ggXsXSeNByiNdsTExOCOO+4AoJHX3H333T5fIxSg+3/ggQewZMkSAFqOx9KlSwGURqhU00d17MbH
+xzM7dmZmJsaOHQug1CSw2+0e55h2ZpqX4uJinpfk5GQ2XVu3bs1a7Z49e7B69WoAwIoVK/havjwb
+GneLFi0AgPkkCXSODz/80Ou5ysIlKRRIxaKEn5tuuolrHxwOB0+e6ukVQmDDhg3lMNpSvr+ePXuy
+uteiRQvOqnM6nTpvOKCZHqQGWiwWvPzyywA0NZiEydq1a30egy+edTUZh1Ttpk2b8kLPzc1lDkNS
+gd944w0e55133skCxBfmolDiyJEj7KP57rvveJ4pC3DNmjX8/Nu1a4fLL78cgBaGpPW0e/duzm4k
+u542FXegdUbPzmw26wh0Bw4cCEBvCq5fv56fHwksX6MeNM8UZs3NzWXiYaA0W5SETqAwzAcDBgzo
+cElqCqQmffLJJwCAV155haVthQoVeLdt1KgRq1LVq1ePSMMXdyAz4s0332RexoSEBE7TnjVrFjp2
+7Aig1KHUvn175h6wWq1c+VlYWIjnnnsOgH9xfn/oxa644grelRYuXMj59x9//PFF+SAzZ87U7aY3
+3ngjAE2Lce16FW6Qw3P+/Pmc3kxrZMKECbyjOxwOZsdet24drwuz2exX9Ibmi9LRVSfhuXPn+Hpq
+lOjTTz/lyI6/oLVB/BTEFQFoc0zJWzSeQBGwpiCEqCWEWCGE2C6E+FMIMfTC56lCiGVCiKwL/1YK
+aoQGDBiIKILRFOwAnpFSbhRCJAHYIIRYBuAhAD9KKScIIUYBGAVgZPBDvRjkaFR3InX33LRpE/bu
+3QtAs8WrVq0KIDR8CoFgypQp3Mvh3XffRf369XlslLlGti6FIAFNU6CdPicnx1eWHb9BGs25c+dY
+I5g4cSLPl7twWUpKCtvfiYmJ7FybPXs2ny+QMF8goJ174cKFHPqdOHEiAE3DItLc3r17s/PXbre7
+bSHnD9xpoA6Hg53HrVq1Ytv/gw8+4HwQd/OpFkdZrVbOcbnttts4H4eyNdX8lqKiIixbtoyvHQwC
+FgpSyqMAjl74O1cIsQNaC/peADpd+NmnAFYiTELBXXPR5ORk/rx58+b8d2JiInuWn3/+eQDgFzFS
+OHXqFMf5k5KS2GvdpEmTMglQiouLsXLlSgCal12NWXtDIDTke/fu5bh6ZmYmC1O1RkFNtVavQbyM
+H330Ef7xj3/4fM1QQCUtITq2Bx54AIDW/YmqVW02GycOBWPa0H2rad5qrgA936SkJM6niI2NZSGq
+9v+k5LTCwkI2f3NzczkKpAoLNXpCAmDVqlXsBAZKHdZqQ1tfERKfghCiLoBWANYCSL8gMADgGIB0
+D8cE3IregAED4UPQQkEIkQhgAYCnpZTnXdI7pacKyFC3or/uuusAaOEmql3fs2cPWrVqBUBTcx9+
++GEApbvD4MGDdaqWpwrFUFWSOp1OViktFovuGupvAE0dpIrK22+/nduK+asaBjJ2m83GBTVz587l
+EJjVauWx0i63YsUKTm/u1q0bf1+hQgXOJo2URkb5GwkJCUhOTgYAJu5NTEzkHbN3796+9kkoE655
+D2azmXdos9mMzz//HICmsVIWIlCa46F2iSao2Yg0fwRXra+kpIRNs759++rStWlsARETB7PghRBW
+AIsALJVSTrrw2S4AnaSUR4UQ1QGslFI28nKeoN86IqZYv369Lu2U1LaKFSuy2k0qpUq2opYcSynD
+5jGnRbNx40aOiR88eBB33nkngNKkGbUUtrz7MtKi79KlC9u1qj+H7PcHHniAx6x2K6KchnBCTW+/
++uqr+YWkPIWioiLccMMNALQy+lDMqWt1q9PpZJPB6XTyNSwWC3ee+uqrr1goEKSUuhfeXTMYp9PJ
+UR7Kc3juueewa9cuPs4HhJfNWWgj/xjADhIIF/ANgH4X/u4HIDiaIwMGDEQUAWsKQojrAawC8AcA
+ErvPQfMrzANQG8ABAPdKKcssnwuFpkDSdfr06dxsAyhVt2NiYvDBBx8AAF566SUAmpe9W7duALQs
+MNrdvDlmAjUphBCsKj788MPo0qULAK3fQHlrA6GAyq1gsVg4EjFjxgyf6/oDnVur1crPrWnTppy9
+SJri9OnTmYk6VHPtT7t3Wp89e/bknBPi26hTpw5Hm1wrLWn8K1euZJOOIlgBRBn+N3tJqhVySUlJ
+2LdvHwDNrlXDNwSa9KSkJFZ9AyGm8AVCCPYyl5SU8Dh87Sx0qYFehCpVquDcuXMAwje3KiwWC0eY
+iHeydevWPtHSE9yp8EDoG9e4wmKxsHBT61VC5NsymsEYMGDAf/ztNIVohsVi4dTUI0eORGTXjAaY
+zeaL+i4WFxcH3K3ZF7hyC6hUc//D+N80H6IZgfZ1NGAgRDDMBwMGDPiPS7JKsrzhr9OHnJlqLoQK
+Q2sIPfyJDBjQw9AUDBgwoMMlpykIITidlXjuCwoK2IHVtGlTduCdP3+ew32nTp1im14N+ag7t0qr
+5S4GHKhjzBuvgCvhJ6AVzhBrVE5OjtvxhDIF2/W8Kvztnel6HtcGJ4AWY3eX3u3vOOm5W61Wt4Vi
+0aYxuOv+Xdb8+lrQFkp/VdQJBXfxYdebpBdETWcmOrbi4mJunLJt2zZOBnE6nbyA1HRmup7ZbGY6
+q1AvIPWF9vbA6Huz2cwCzVOSSrjMDte0W1/hqRmMSm2vfh/s+KWUHklR3KUgRwv8GQttVDab7aLj
+zGazTrCEaj0Y5oMBAwZ0iNqQpCoFVQlpNpu5+Imq8/Ly8nSqK6UrR8O9GYgMVFPq7xL6tVgsXCh3
+8OBBbihDa71y5cr4888/AYS2ICpqzQeVsEJVBRMSEpgApHHjxgCAadOmsZp19OjRS3ohuEOo27Z7
+gyeBHI3wxdwkqOsomu+L7qlu3brMMRkbG8tp+IsXLwagMWmHA4b5YMCAAR2iRlMg9c+dI8pqtXIb
++YEDB3KPRvLOjxs3jiW/SrU1efJk5uI7e/as330WyxNNmjTBhAkTAGjsvaQJCSG4CnTNmjVMYELz
+c+7cuYAJNmgnbdSoEcaPHw9Aa1FPFXzx8fHs4Sdn7ZEjR5iz4Pbbb2dG5UgxZxNVXKVKlZhLsri4
+GFdfrWnJ5IB+/vnnuU1bgwYNmMsilJ2vQwUa29VXX82RtqKiIl4DxDt64403cs9Mk8nEjulgNcmo
+8Sl4C6/Rgu3Vqxc3KiFa9AvncHscnVMl0qSmIRs2bCg3QSGE0HH43X777QC0PocAUK9ePY/3RC/9
+4sWLmU2KqhCDIYih6zVv3hzLly8HoIV9KWrjLnSqwuFwsJ+nR48eXL4crjVmNptZKNxyyy0sIMeO
+HcvEL0SEq5pgDoeDyWLuueceJsJVoyTlhbFjxzI9vcViwVdffQVAe77ffvstgFKfWVxcHNO5nzp1
+SieIicxHJXuRUhppzgYMGPAfUWM+eNtNSNotXLgQY8aMAVDatZeabrgD7WwWi4V5Fkj6rl69mhmH
+I90oJjY2FrNnzwagOUyJBZkkvKo5qTtwpUqVeDf7448/+PNQ7MZ0jj///JPV7pKSEp77tLQ03Hzz
+zQCAYcOG8WdEXy6lZFNj/PjxTKkerhb1agKUzWbj5jrp6emsKdBcFRUVMYdh1apV+Z5SU1N5zk0m
+E+eqRBrUA/Uf//gH8z7cfffdPGZ3z/fKK6/0SAwUjAYcNeaDH7/lLkuvv/46AM23oDY/9QbVs052
++5gxYyIatahduzbbhjQWoLTE1+l0spk0f/58Vn179uzJvP8DBw5kwRgptZf8OPRv3759uduS+rJJ
+KXlM6enpPM5Qg4TC2LFjeb6Sk5P5c8puXb9+PXMcdu7cmQXVM888g40bNwLQwn7l0UVMCMFjE0Iw
+n+O6des8/h7QTE1avz6O2zAfDBgw4D+ixnzwNY/fZDJxv0K1K7O/16JzXXXVVQCAGjVqcNQiVGN1
+B9phv/rqK53ji3Y0Yhw+ePAge5PtdjuPefHixahXrx4AjWqMdhXq9zh58mT2wocjp4F2JNICMjIy
+dM5HSqyRUvI9PfHEE/j6668BlHZxLioqYs/6uXPnAlZ36f5uvvlmNgOOHz/O5yMNcufOndwR+/Tp
+09iyZQsArfkKzZe/WkIgqeDuUKVKFa7j2b9/v9fu6HTdN954IyyajaEpGDBgQIeo0RR8lbYOh4Pj
+9OScq1mzJr777jsAWt8E2km9QQjBTjLawUM5VldYLBY0bdoUAFC/fn0+z65du9gpt3379jKvWVBQ
+gPfffx+Axv9P56PxT5kyJay+EdqByUewatUq7neoXtfpdOL8+fMAgJYtW2Lp0qUAtAY99H0o/Qwd
+OnTg66u7J2mRsbGx3O+jT58+3GKtQYMGPDZ/Eap5fvPNN1nzGj58uFf/EPltwkXnFzVCIRBQd5z9
++/dzz72WLVvixx9/BOA5KqFW7BFddiSaoLZu3RoPPfQQAD3hSufOnXHq1Cmfz0MJN3369OHY9bRp
+0wBoyUSRSOEltfzFF1/k5CUALAjWr1/PUaLNmzf71f8yEHh6QUiI2Ww27uPZvHlzFk4DBgwoF+ci
+UGoGZGRksKm4ZMkSn49X2Z5DiaDNByGEWQixSQix6ML/1xNCrBVC7BFCzBVC+B4WMGDAQLkjFJrC
+UAA7AFBDvNcBvCWlnCOE+ABAfwDvh+A6ZYKk/ZYtW3T9+NyBpGtxcTH3GvRnt/BXQpNjdP78+Xzc
+uXPnOAPPHy0BKN1hEhMT8euvvwIAc0iEG6SRkYOzVatW7Fy02+348ssvAQCff/45Z5BGA2u1yWTi
+rNGkpCTUr18fQHD5KYESuJCWRY2B4uLi2BTxxeFKJtHIkSNZK16zZo1fYyjz/MEcLISoCaAHgPEA
+hl9oJdcFwP0XfvIpgLGIgFBQxnQRwYYr6PuJEycGlPvuqy1J3vCZM2cC0KIl9NCnTp0aUJWbEILN
+optuuomby9CLabVag2ou6u3aL774IgCwGaTOdVZWFsaNGwcAOHbsWFgp3H0FNRseOXIk+1+EECHp
+b0n3TpuQrz4SSvCaNEnrtnj48GHO9ShrrkggU57OmDFj+Hm88847GDVqFIDgE/GCNR/eBjACpW3j
+0gCck1LSqA4ByHB3oBBigBDidyHE70GOwYABAyFEwJqCEOI2ACeklBuEEJ38PT7UregJZaU8kxT+
+v//7PwBaRmS4MgGFEPjoo48AQNcnkK49efJkLmYBSncdaj8+evRotGvXDoCWuUbx/WuvvRaPPvoo
+AG2Hotp6ikiEswVdxYoV8eCDDwIo5ccESgt03n33XY75l3emLM0nOTu7devGnxUXF3NeRzAgjcyf
+1Gir1coaYvv27QFoBXpkBpYFWgMTJ04EoOWsrF+/HoCmuVH6/tatW4OK7ARjPlwHoKcQ4lYAcdB8
+Cv8FkCKEsFzQFmoC8C0jKET4+eefPX63aNEiAOA04XB66atVq4a7774bQOkCXbx4MVdonj59mlX+
+Dh064LXXXgMAtGjRAoD2wtP4li1bpuspePDgQQCaYJkyZQoAhN27D2h5+WQHk1/j9OnTeOeddwCA
+G/hGA6h6kmo4EhMTdb1EV69eHbJr+SMAe/fujc6dOwMobRS7fPlyv1R+ut62bdt4PY0bN459DcFu
+DAGbD1LK0VLKmlLKugDuA7BcStkXwAoAvS/8zGhFb8DApQYiNgnmPwCdACy68PdlANYB2APgSwCx
+Phwvg/2vbt26sm7dutLpdLr9z+FwyLS0NJmWliYtFou0WCzSZDIFdC2z2ezxu5iYGBkTEyPnz58v
+S0pKdP/16dNHxsfHy/j4eJmQkCCPHTsmjx07xuNzOBzy+PHj8vjx4zI7O1tu3bpVbt26Vf7xxx/S
+ZrNJm80mHQ6HPHDggDxw4ICsVq1a0PPmy38pKSkyJSVF2mw2nk8az5AhQ3g+IzGWsv67wMshq1ev
+Lk+fPi1Pnz7N4ywpKZFFRUWyqKhInj17NiRjNpvNZa4Fd2MrLCzkcbRr1062a9cuqDHQGjl9+rSs
+U6eOrFOnTlm//92X9zkkyUtSypUAVl74+y8AbUNxXgMGDEQel3RGowoqcPGEgwcPckiHbDJy3PiL
+spyTxJzUvXt39hnQ7++//35O0W7YsCHbvUCp8/OOO+646JzJyclYuXIlAM3nQKy+5OALB8gPUqlS
+Jbz33nsA9M1eyEn60UcflVtGoIrExET069cPgOZcJIcz+T6Kioo4FdxkMjG70f/93/8FzaEQGxsL
+oGy/jtq/gQrB6DgaE+Cfn8tisXB+yrFjx0K2Hv4WQiEjI0PnDVdBCzYlJYV5B8mpFw7yj2+++QaA
+loJMMXJKTmrZsiW/WIcPH8aBAwcAaAKEKMHcQQihE2iUku3LIgg0waZhw4YAgGbNmqF79+4AtLkk
+J+f992upKOGMdvgCetFfe+01JiqpWLEiV7wS7dqZM2dwyy23ANDug6pRt27dWubc+wJfHI2Un5KT
+k8NCgf4VQrAD9/z58z4/q759+/J9fvTRR34nwXmCUSVpwIABHaJGUwiEo4C4EAYNGuSR/58kdGJi
+IlfGkYZOO3LGAAAgAElEQVRgNptZQicnJ/MuCJTusP6qxlQL36hRI96NiB3n8OHD/PfmzZu9Zh7S
+PbVt25aLeQCwOu/LjkJhKpvN5vP8pqWloUePHgCATp06cWbmuXPn8M9//hMAeC7LE2azmefirrvu
+4uf666+/4vPPPwcAzlxMT09nbfLKK69kolvS1gKBu6pMT6Bndf78eaSnpwMoZeCWUvJ4fAGZqPfd
+dx/nYVCOQigQFUKBmI19jbUTF9+AAQMAXNw3kODCZIvrr78eADBr1iwAmiAgOu077rgD33//PQCt
+nJYq/yZNmhRQDoCUkhNSaPEcO3aMVX9vSVMWi4VLfUeMGMECQmWl9ge+dE2izls//fQTcwNu376d
+axfsdjtatWoFAJw0ozZ5DQUztj+bw9ixYzndWkqJRx55BADwww8/6JoIA1r9RfPmzQFohDo0t++8
+8w7/tlatWixY3HEfehqXP6bZ999/zwzT//nPfwAACxYs8Nh8h0yMatWqcZ0D+aKsVitTyYUShvlg
+wIABHaJCUwA0KWyxWLyqYomJiZxqS1LUE8xms84MoB2NGoWsWLGCd48hQ4Zg4MCBADTnFKlzHTp0
+YM8/SXFfU6NJ+n/22WcANPo0InU5c+ZMmTti/fr18eyzzwIAqlevzh7yGTNm+KW50Fjj4+P5/jt2
+7MhVdZSFWFxcjG7dugHQUsVJO7j88sv5eqmpqexgpB31wIEDnIW5ZcsW1oROnjwZtoxR0mhGjhyp
+Y3Pet28fAI1vgqILNN6bb75ZR39H0YBBgwZhwYIFfK/E+E0p6seOHdNpHeo9BXJ/zzzzDKep16xZ
+EwB0psOwYcPYYdixY0c215KTk/l6ZPL06dNHZ/KGClEjFMgr7433LiUlhW1cb3A6nRw6O3jwIKcE
+//DDDwC0F+GLL74AoKVH04tXo0YNZGVlAdCEiaug8lXFpd+Qqn3HHXfgsssuA6AJHjpvWloa3z8x
+MA0aNIhTdAsKClhNVJmdfVmUanp0165dAWgq89q1awGAPfImk0nHdUkLNi0tTUeT36ZNGwDg6rzX
+XnuNIxBdunTBihUrAGjpz+r5fC2fllKWaepYLBYugVZf0qysLH65mjdvziZmtWrV+DwkIEtKSrgx
+644dO/hZ7969m01Id5GdUAg5p9PJAmfo0KEAoCv1nzp1Kv+tdkz766+/eG3Q2P3xE/kDw3wwYMCA
+DlGjKTgcDtjtdq+S7+abb+ZGHwTX3YV2roMHD+KFF14AoLEdEwWX67GA3gt94sSJkDD10rHU2i0t
+LY0dmMnJyWzG3HrrraxBdOjQAYCW2ELJVW+//TY+/fRTAFo7OdIq/Nm58vLykJmZCUBzJBK3AJkz
+8fHxnJsQGxvL9y+E4B3YbDbj5ZdfBgA+1+HDhzkJJy8vjzUCdd7KajXnDt7aB27evBmAlv9BfBJp
+aWmch9KoUSO+LzJ99u3bh48//pjHTOzSrnkWkSCEGT58OIDSCs6aNWti1apVPE6KkgwbNow1tunT
+p0esU3bUCAXXF9sVKi27qhLTv7SQhBAcppo2bRoOHToEwP9GomUJBX/Dp6Tup6en4+mnnwYA/Otf
+/+LkJvV8qqlCiTezZs1ilqaePXty9MFut190X57GpX5+6tQp/PbbbwC0xQZo5hOFUJ966ikOOS5a
+tIhfnF69ejExiOrXoAY2nuDvXJW1+EtKStgfsmzZMu7wlZCQgJ49e150DjJ39uzZw/cRDEV/KEHz
+tnPnThZu0QDDfDBgwIAOUdM2jjoae1OR1IgCkZekp6cz597KlStZAgfVT8+HuL6vIHX/66+/ZvMg
+MTFRp42QQ3TEiBEAgE8//ZSPU1Vck8mExo0bA9BSfMk5umvXLr6WqgJ7M4PIGRgN9Qv+Qs2RqF69
+Oj/vM2fORIRfIprh4bkbbeMMGDDgP6JOU4gGss9oB+0CZrNZ18PCgAEv8ElTiCpHo7GwfYM/OfcG
+DPgLw3wwYMCADlEjFPyJZZvNZh3hRzhA5owBA/9riCrzwVeQQCDPs5SSVWlPZogvL7h6XChNGdUH
+oHZT8lQZ5wqTyaSrBFUrP9X8jFCN3WQyBTQXnsbgLi/A9Xn4cw11LihxqmLFipyzUVJSwvNMiUC1
+atVCdnY2AC0Fm3wxkfZjWSwWvo6ahKaydKm1NfTbmJgYtzU3dFxsbCxHX4qLi4O6l6jRFAwYMBAd
+iBpNwR+QRFRjsZ7Sal2lMhB5Bx2NweFw8C7vT8qqygthsVh09x0OEyfQ3dOf8QS6k6mFTUBppmph
+YaHunKRBEI3d/v37ubJTnftIO7ftdruuWpPGo2qN7sbkrvhJzdOQUnJuRrD3FDUhyTCdlyetdevW
+XBkYDfdsILygtGESBOHqBHaJIfzJS0KIFCHEfCHETiHEDiFEeyFEqhBimRAi68K/lYK5hgEDBiKL
+oDQFIcSnAFZJKacJIWIAxAN4DsAZKeUEIcQoAJWklCO9nCekWzepjklJSZg8eTIArfqQ2pu9/vrr
+AMLbNs5A+cFisTCJSiBdxf/G8ElTCFgoCCGSAWwGcJlUTiKE2AWgk5TyqBCiOoCVUspGXs4VMqHQ
+rFkzbrbaunVrNhUqVKjAZBrUynvz5s38vcr972q3Gogc/K1gVKMMHTt2BKCRkBD1eXlT0LuDSloD
+aGuT2MRatGjBrFHJycnsf8jJyWFylcGDBwPQSq6//fZbfy4ddvOhHoCTAKYLITYJIaYJIRIApEsp
+j174zTEA6e4ONlrRGzAQnQgm+mABcBWAIVLKtUKI/wIYpf5ASik9aQEyxK3oiaq9X79+aNtW61pn
+NptZKufk5PBviPfOarWy9BVCcHVlqM0KNR79v+jk9Gf39/Y7q9WKRo00xfPyyy9nRu9OnTrp+Bpf
+euklAGD+h2jR/IQQvNOTxtqkSRO3jOTqvKWkpKBdu3YAwFwYAHDttdcC0JrahGrdBqMpHAJwSEq5
+9sL/z4cmJI5fMBtw4d8TwQ3RgAEDkUSwjsZVAB6VUu4SQowFQAyUpxVHY6qUcoSX8wS9fXbq1AkA
+MGfOHA5HORwOpu46c+YMhySXL18OQJO41JAjJycn5DX4JP2JIXjnzp0+O768MRBFC4QQumw8NcNS
+7Z8Yqntp2bIlU8mNHDmSuSXUPJTi4mJ2NhO3xLJly3DPPffw9+WF+vXrc6Ma6qFhsVi8pu2rVHc0
+/ipVqjCNHzGMeUFEqiSHAJh5IfLwF4CHoWkf84QQ/QEcAHBvkNfwCHLUJCQk4JNPPgEAXdPW4uJi
+fPfddwA0pmGiMaMmLQ6Hg5uehFq9NJlM3PyTOBo3btzIFOjPPvssU6Nv27YNS5cuBQB2MjVt2pSZ
+pocNGxY1AoJo9emexo8fz+za69atYz7HrKwsXryvvvoq/02C0mw261LTCZ5MDXrWvXr1YpW5cePG
+/LnT6cSRI0cAaAlLZEqQcLjtttuYofnDDz/Ek08+GeRMBIaDBw8yIQ71waxduzabtjabjSneV65c
+yev3hRdeYI5J6hBlMpm8tjkIBEEJBSnlZgDuJE/XYM5rwICB8kPUZDRSoYgvOzappW+++SYArack
+MSNbrVbeVZctW8Zc+bVr18bx48cBgP8NYrweHWLUJ7Bx48YYMmQIgFLzITY2lntCuqYD05gplFaz
+Zk2eiyuuuAJ79+4NasyhQGxsLOd69O/fH4C+mlQt8tqxYwdrAj/99BP69u0LQAuzAdpzIo2tX79+
+OHr0KJ/D3Rqg3ghr1qzhtmsq9dyCBQvYudi5c2c88cQTALS+kTROgpQSEydOBAC89NJLHI72910I
+lPH7X//6FwBwH46kpCT+rmXLlrjjjjsAaM2K6NxZWVk8B0RDWFRUxKayj6HXS49kxReoTU2oo1HV
+qlV11YfkR/jnP//J6qw/DTx9GaunikASOBkZGbjpppsAlKqwZTWxoe5GKpMS3RPl75cXaNzbt2/n
+BjUEm82ma+xKguD555/nPo+PPfaYzqwDtPsjYdmnTx9u1HP+/Hm3YyCh+f3337Oq7XA4mMJ/3rx5
+XBOzZ88ebrjSrFkzAJoJc9tttwHQ1tCgQYMAaLY45bVEKkJBPSQpMmK1WrmaMzY2ljcGGiugmcKu
+jM+nT58OSf9OVxhVkgYMGNAhaswHk8kEIYRXaZ2SkoLFixcD0OK7gL55SWFhoY7rPxDExcWxWurO
+wedr3J16KpDqLKVkk2LJkiW4++67AQCrV6/m1nJkXuzbt493gcqVK5drui61guvQoQNrAvfddx+A
+slugU8u2a665BidOaJFp+jcxMRF//fUXACA3N1d3HD1Lp9PJjkSVk4J21ZKSEnYeelo3dFzlypXZ
+yRsXF8caSadOnbBlyxYvM+AeoWgYRFAjOASTyYQZM2YAKJ1vAGxqdezYke/JR1w65gOFteLi4tg2
+8qQWDRkyBK1btwZQ6sk+duwYjh07BkCzrejYmJgY7iaVlJTEv6GFbTab2T9ht9vZZuvUqRMvvJkz
+Zwbs+afEGvJ3VK9enX0Dhw4d4roMu93O1yBhlJeXx2NPSEiIuFCguR0zZgw3iQFKbfSdO3eWeXx8
+fDx3k3r22WeZwt4bYmJikJaWBkAT9hS1IGFis9l8sp/phaXn26RJExYgcXFxbNKlpKSU2ZvTarXy
+OQoLC3W/CaVQUIUeYfv27dxMFyg1rWhe/RQIPsMwHwwYMKBDVJgPJpNJxsTE4N5779X1+KMdPzEx
+kZNUli1bxvFdwtGjR7Fo0SIAmopLO2xGRgY7ydwlh9hsNt6Zly9fjrp16wIAGjZsyL0l27Rp41a9
+9WfeqLvwm2++yTvNe++9x7vtokWLOMZOcedt27ahXr16ALQCGHKGRQo//vgjAKB9+/a8kw4cOJDV
+WU+gHo5NmjRhzeubb77x+bqVK1dmR/LOnTt5d/dkCpInftiwYZzjkZCQwFEgQklJCe/4MTExvDOX
+lJRwMlvVqlVZo6G1l5eXx+bFL7/8gp9//hmApua7U/l9AV37iiuu4H/J1JRSshZD4wU0LYZa+VHh
+VwBJWJeO+UAci0uXLuUXOj8/X5eAQiq4OlH04LKysrBu3ToAmh1LHmfXjlOugkEIgU2bNgEAbrjh
+BjYZ1JoJd6qqv4L0v//9LwDNE05Zdffffz/mzp0LAPjss8/4t/SgnU4nj4G8+5HCk08+ifbt2wPQ
+7pX6WM6cOdPt72mcvXv35hDwrFmzuAmtP8jPz+fknrp162LDhg26a7jOPSUhjRgx4iL/gwqbzcbC
+vWLFijoBccsttwDQ16aQwN63bx//9uTJk0GzNlksFn7uFA1R2bQ8weFwcJVvuJvgGuaDAQMGdIgK
+80EIIYnp2J0UNJvNrCauXr2a1WqqanzggQd4x+/atSt/vnTpUv5bCMFdnmvXrs3HUwSgc+fOnFOf
+l5fHEQ6qZAsVaNepV68ebr75ZgCaKeHq5Dpz5gwn+qSmpvrsqAsGFPlYtWoVm2gWi4WTjLZv387t
+3kmDUhOa6taty5/369cPZ8+eBeB/12mVH9KbM49ate/evbtMUzE/P5/XVlJSEmsVrqB5pm7WDocD
+t99+OwBg9OjROt4NfxyNZEotWbKEq3jd5a144rksLi7m/Buq8wmX+WBoCgYMGNAhKnwKAMrMUVAl
+cVFRkS6ODWi2HoVrvvnmG85edO1dQCEc+tdkMnFxTatWrdihdvTo0bA59mi32rVrF3bv3n3ROGkH
+S0xMdNvTwV+UFW5ToVY72mw2rFmzBoCm2ZDt26tXL4wePVp3nOr72L9/P2cKxsXFccbewoULOS2a
+QqtZWVnso6C4O3AxM7e3XZiKh55++mne3Tt06MD3QseXlJSwv0rVElx3ZvIrzZ8/n39L51q6dCkX
+KAG4iJXZFXTeGjVqsJOwSpUqujEBmlZIWtXSpUsxbdo0AMD777/PTseWLVuyhkHrZt68eZwyHUpE
+hflgMplkXFwckpOTOZfA5Xv2yq9du5a9tuQ4qlevHqt9vrSzJwgh8OmnnwLQHH+EZ599lp2DkZof
+WmCUuv3tt99yBWdGRkbA6ay+qrhCCDafevTogZUrVwLQTIJHH30UQGmuvor8/Hx+yebPn89e/5yc
+HBa4JSUlrObTi2Cz2fhZjx49Gj/88AMAzXTzZ84p2lGvXj1+7tdffz2uu+46AGDVPxQdvw4fPsxr
+r7CwUEeeA2j5JjQXaWlpGDduHADNlCLExMTwcyVn7r59+7yu2cmTJ+Pxxx8HUPosnU4nE6+QaeEF
+hvlgwIAB/xE1moLFYoHJZOKdxFObsaeffprZmCkz8dZbb+WdzR9UrFiRC5FSUlI4V6B27doRp00j
+NfH7778HoFV+UgrxQw89FHBWpT/OMG+t5xITEzlPgUKF06ZNw1VXXQUAaN68OTsrp06dioMHDwLQ
+tAPaQVUOBdIq8vPzdVR4gcy9p9wRyv4bMWKEW8ee2mSF1qArKFdi2LBhrFnabDY2R+ie69evz6nd
+TqcT7777LgAtu5PmduvWrZz27i0rVIXVamWzgsy55ORk1jqaN2/OWZ9l4NLLUyhrMdB306ZNwyuv
+vKL7zkfWmYswYcIELlvNzc3lFN5ICwQ1MkJef4fDgQkTJgAIjjPS3w5PNB53yMvLwwMPPACg1PNt
+Npuxfft2AMDPP/+s886rcNeVi4RwKODpPolty3UOKUqyd+9eToxTx07q+M8//4ypU6cCACe0Eahq
+kUyj48ePc85DYmIiP0vX7k2Uh+EPbDYbR8K+/PJLANpmSIKJNpVQwDAfDBgwoENUaAr+oKioiJ2O
+tKONGTOG6cEA995gi8XCLMBUu3/ttdeys2jnzp2swkYaSUlJrILT7mM2m3kX8DetOlD44ohz7dFY
+pUoV9thTV+fyhtls5t2fHMZ2u11nPpC3v379+rooAhWpEQnL+fPnPc49rbMdO3YA0DSNV199FYDG
+NN27d28AmhOZ8hSuvPJK1iB8KXKjsTVu3Jid6ddffz3fp+rYDNX8X3JCISUlhf0OtED79u3LnvGK
+FSviww8/BKDZsjRRw4cP56QnNUxHocwxY8ZEvPEsoWfPnhg2bBiAUqr63NxcJtuIhEBQ7V4ppdsF
+2717d4wapbH4Uz3AqFGjQlrB6Y8AFEJw9KGoqIg5MYcOHcpmDm0gqq9ACMG2v5SSE98GDx7MPJ6+
+bBBkgpBpoNbInD17ln0qzZo10wncP/74AwC4Xmf69Okcifjxxx91Ido777wTgMbWRD4YMnmllGw6
+++BP8BmG+WDAgAEdoiL6IISQ/uwQFIP+6aef6Hivqq+aFEMOpe+//56TPw4dOqRzRkVSaxg7dizz
+8pHJcPbsWa6Gi0Trs4SEBN51gVL6upycHFZXp0+fznNE+RSBEtl4QqCawvz589kUvPLKK3mcaqIS
+aQtqLsuCBQs4R8Vfhy6lrJMZ4Wq20rU/+eQTbkCkgu7T4XDw+lXHVlBQwJ9XqFCBx09r0+FwcDRo
+8ODBvszbpRN9APxTkcmjTBlzgwcP5geSkJCg86JT0o/FYmG+ferQU1BQUK4dm+gh5+fncyiSkmNW
+rlwZFv49T8jPz2cmpE8++US3wKnq9PTp0yyQwyWo/I2W0Bzt3LlTZ8PTC0kqfVFRkc5HQ5mZ5EMI
+BHRtT2Oml/fBBx/EiBFa65P9+/e7DY2qHKO0LhITE3WmLj0Tqlo9f/48076HEsG2oh8mhPhTCLFN
+CDFbCBEnhKgnhFgrhNgjhJh7oSeEAQMGLhEErCkIITIAPAWgiZSyUAgxD8B9AG4F8JaUco4Q4gMA
+/QGEtJCAJKbqKSaT4IcffmAHzsmTJzlPPC4ujhuuREtjFWIz7t69O+8UlChTq1atiI+TxtCrVy+O
+KOTm5rIT64knngi7KeNvpIWeu9Pp1FWSkqZAWqUQAsuWLQOg5R4Q/0Yw8GeclNL9+uuvc6SMnMqk
+wajjdj2/zWbjhkeUQFWnTh2/EqB8RbCORguACkIIC4B4AEcBdIHWVxIAPgVwR5DXMGDAQAQRbC/J
+oQDGAygE8D2AoQDWSCkbXPi+FoDvpJTN3Bw7AMCAC//bOuBBXKIQQmDevHkANHotyu774IMPAGi8
+EVu3bo3omGiXOnLkCPNX5OTkMO8DVfpFIxo3bsxOxz///JO1Bsr7KCgoiDiDlSeQFka5C6+99hqu
+ueYa/o40gr/++otZmlJTU3WhSoJaDUpOyTLo4XxyNAYsFIQQlQAsAPAPAOcAfAlNQxjri1BwOVf5
+h0AijIyMDFZna9WqpWtgA2gvprtei5GA1WrlcXz11VchbaQTLnir2zAAIAJVkjcC2CelPCmltAHI
+BHAdgJQL5gQA1ARw2NMJDBgwEH0IRlNoB+ATAG2gmQ8zAPwOoAOABYqjcauUcoqXc/3Pifa4uDjO
+YsvNzeWqw2jZ5ULZ08BA1CC85gMACCHGQTMf7AA2AXgUQAaAOQBSL3z2TyllmWRy/4tCwWQycQ48
+RRyiBZGqtQgFAqVZDwUuQcEZfqEQKhhCwRAKgcIQCn7h0spo9AbVkUQLgYpdgNLsMZUz0Gw267LO
+XFOh1ZRSIQSnrZaUlOjSnIMwsXTjdDqdunFSIZG3l1BN465QoYKuM7V6PkATNvR3oOOWUupSgt2d
+xx3jsrtxA/pafxqbOhfBvFTlIQwIgfZ9cB1zWedRC7kilbcSdUJBXWAqQas6ca59F9VejK4EpOrn
+rpBSchhOTY212+26HSjQHYF+r7646svkz3nU8dM43aVpB8pc5ApVoNE8V6xYkasHVXpxT9ejz9Xf
+hmthXyrajd1u92s9qenPkYJRJWnAgAEdok5T8IXe21VTcD3enVT1VrRit9sDaa7hE9TqtkB3MzJB
+evTowfTj4VQnScVV/R1SSk648WeuIqH2us4rzXkgmlm44c9Y/DGxQuXjiDqh8HeEN/vcl+OJQOTW
+W2/lJqfHjx+P6GIvKioKex/DUIEqEb1VMl7KUE1e1UcT9HlDchYDBgz8bWBoCmEEOYmIGuzMmTNY
+sWIFAK2mgHgEX3nlFWaSpqrOatWqYezYsQC0hiwdOnQAoFG3US3Cp59+yrwAkdgJY2NjOYxK1OK+
+QAjB5o+6c6teeF87WfmKWrVqASjlHsjNzY2a6lh/4O65khbUsWNHrF69GoBvfI++wtAUDBgwoMPf
+LnkpWkJTai8HYlWqU6cOE8mmpKRwV2m1SzLtnqdOnWLG4dTUVPYpuMat169fDwDo0qULAM3uD9f9
+p6enc9OdN998EwCwceNGrkgsKiri8TudTiajHTp0qC70C2ia0ksvvQRAqwwNhZOXHG3Jycn497//
+DaDUQTtu3DjmgrDb7Tz3J0+ejCoNokGDBlyAdvbsWZ636tWrM+vV1Vdr+Uf79u3jjt8+4u+VvOQO
+NGEOh4O94gMHDmSm4VWrVvkUTw8HpJRc6kp06EBpKe/Zs2eRkpICQFukxPBLDMDXXXcdMw5bLBaO
+AiQlJfF92Gw2vlcqxw0nCcp9993H9zJ06FAAGk+hKrDoBTt+/DhT6gOlZgE9j5iYGG6c8t577wU9
+NtUZ27VrV9x3330ANA5GQBNC5IhLTEzk52C327mkujw3E1q//fr1w/LlywEA69at488HDBjAgv/u
+u+8GALel1KGAYT4YMGBAh0tSUyA1kdTz4uJidrhkZGTwjjB79mxuMZeWlsakJZ5am4UaFL4j86Ft
+27a8C6xatYpJS7Kysljtph1s79697NQ7efIktxr7/fffua/F0aNHdVmdQHh2O5rvXr16IS0tDUCp
+w1A1GZYtW8b9E1u0aIEhQ4YA0JqlEGs2aQx33nkn80mEQn1v06YNa1bNmjXjzMuFCxfqxgto9Gf/
++c9/AACZmZlMZFKezkjS+NLS0rgtXH5+Pq/VKVOm4LXXXuNxhhNRIxT88QVQjcIbb7wBQOv0RC+T
+Ws9w8OBBtsv79+/PjWlHjhwJoJS/L1yg+3nxxRcBaE1Xn3rqKQCek386d+6sOxYAPvvsM4wZMwaA
++4StcIOEQoMGDdgnQC/gxo0bkZmZCUBjgSYuwjVr1nAPRiHERTH0t956K6Rj7Nu3L79YGRkZzGTl
+zrSpUKECR35eeOEF7N27F4DWiKU8UKFCBW5UlJ+fr2PcojG3atUKrVq1AgD2xYQLhvlgwIABHaJG
+U/CnAQix4FJsPzk5mXczm83GrbRmzZrF8equXbvy78lj26lTp4iYErQT3XbbbR41BIpAkDZTXFyM
+X3/9FYCmaZRnJiE5MdPS0vg5Ubu9OXPmcIv0goKCoCs0/QXN2xVXXIGqVasCAD7//HOOypCqrVbP
+1qtXj82gs2fP4vjx4xEZqye88cYbPDbSrlzRtm3bkOYilIWoEQq+wmw2c/NPosYuLCxkr3tOTg7z
+C+7evZsjFPXr1+cXi1TXcNU6eEJZD7VTp04AwP0C9+7di169egFARJvCuMM999wDQFPB6QWikN+c
+OXN4XsvDHiczpmXLlkx4+uOPP+pavxNovTz++OPc2eqFF14ImCY9VLUGd911F5+DfGCuaNiwIZtp
+4YZhPhgwYECHqNEUfHU0ms1m3HDDDQDANORms5k1BZPJhP379/M5W7RoAUBLFiLJfuuttwIAvvnm
+G95RwknWobaxU++RknsGDRrE90LjmDJlSrl1wVYRGxuLxx57DIB+bmnXslgsnCBUHqzPv/zyCwDN
+hCRNwF2uhhACw4cPB6BVmtI8Hzt2LGANR21hHwjI2ZmYmMhjcJ3DjIwMAMAtt9zCjvVwI2qEgtls
+hsPh8CoYatWqxWq42pOPFqbdbufeh+fOnePQk0pSQskfmZmZHLIKJ2icffr0QevWWouLu+66ix+4
+Crr/t956C4MGDQIAtG7dutx8Cq1atULz5s0BaHNIoT4ind20aRPb9cEIhUBUcSEE+xEsFgvWrFnj
+8bdt27blEGlcXBzXmJw6dSpg9T9QYUIJYOPGjQOgrY99+/YBKH0PCHRPdrudI2meoNaPBGPaGOaD
+AYKFAHgAAB77SURBVAMGdIgaTcEbSPKlp6dzemfDhg0BaLsEqYxbtmxBgwYNAGi7gGoe0I5GeQ4P
+P/wwq8PUjCUcIE1h0qRJvEu40sOpJgaNsWnTpgA0dZi86aNGjeI+iCaTKeyJLIcPH+Yx5eXlsabT
+t29fAFpiEuUmBINAdrTY2FgdmYq7DlYdO3YEoHVhonoHp9PJ5k92dnZQXJaBgBzHFNVxOBxsKvbs
+2ZMrO6+77jo2iQ4cOMAdsKg7uEo9qFLllZSUBOX8NDQFAwYM6OBVUxBCfALgNgAnqP2bECIVwFwA
+dQHsB3CvlPKs0LaU/0LrPF0A4CEp5UZfBuKNcJS+u+yyy9C2bVsApQ6e4uJi/rtNmzasKQwYMIDT
+a9etW8e7CknqU6dORSTcR2MvLCxEUlISAE27od1h+PDh3EmY/B3vvvsu7yQAeIdesmQJfvjhBwBa
+aC3cmkJ2djY7a5cvX8529OHDWuOvQ4cOlVtqcGxsrM6vRH+bTCbWBp999lkAWm4CaTxnz57FjBkz
+AATnYA7UbqexvfzyywC0Z06dqGfOnMmcG9OnT2cN9siRIxexY1esWJGLuXJyckL2HHwxH2YAeA/A
+Z8pnowD8KKWcIIQYdeH/RwLoDuDyC/+1g9aCvp0vA/FEE+6KxMRErglQHyi9QBUrVuTElPbt27NZ
+oS4eMj9+/vlnrkILJyiFdf/+/TyeyZMn44svvgCgCScCCYcFCxaw8yk1NZXnx2q1cupupJJuiFBF
+9fA/+uijAICxY8eyEzRULNK+nsdqtbLKbLVaWcjGxsZylWTdunUBaOq32vJdnfNgxgr4d98Wi4Uj
+TdQVbO/evejWrRsAbdOjdO1z587xBlhUVMQmDyXvVapUie8jlMliXs0HKeXPAFzb9faC1mYe0Leb
+7wXgM6lhDbS+ktVDNVgDBgyEH4E6GtOllFTMfQxA+oW/MwBkK787dOGziwq/XVrRe1V9KDOxcuXK
+7Dwkya82hVH//7nnntOFyegaZDL89ttvnIIcDpATiHaUNWvWcGGTNzKUvLw8LtDp0aOH7h6pkCuc
+eQw03/Hx8bzrJiQk8L3Qbrd7927cfvvtAKAr5AkU/ux4p06dYk3glltu4TCj1WplZxxxVtStW5fv
+ad68eZymHQwCUdftdjvv7vT8Tp8+zZWR99xzD5sMffv2ZVNj9erVOHDgAIBSLYcckqFG0NEHKaUM
+hDlJSjkVwFTAN+YlMhVmzJjBKqNaLUYvjZSSF1ZmZiarZSUlJUxUQvbknj17Anqwvqq4ZJpQfcU7
+77zD5oMvx1PKs8Vi0TVWoehDuOoLKlSogIceegiAZvLUrFkTgGbyEMvSE088AQCoUaMGszE1a9aM
+qxMjBRL6c+bM4c9sNhubiGRqxsbG8ss0evTociVUcZfPQfk08+bN4zX57rvvsuCYN28eNm70yT0X
+NAKNPhwns+DCvycufH4YQC3ld0YregMGLjEEqil8A6AfgAkX/l2ofD5YCDEHmoMxRzEzyoQ3yU3f
+Z2dn4+233wYAJvRQ1WghBGcKXn755Uz5FRMTwxRiVNUXTq+51WrlNGZScZ9//nmfnVPt2rXDVVdd
+BUDzptM9njt3DkuXLg3TqDX069ePHVw7duzAlClTAGgecBo3ZeO9/fbbuO222wAAjRo1irim4A5O
+p5O1SRpPUlISvvvuOwBgj320QErJGmRWVpbOKU7rhbTbSMCXkORsAJ0AVBZCHALwIjRhME8I0R/A
+AQD3Xvj5YmjhyD3QQpIPh2HMF/VodP2OvPIq4amUkr26wQoDk8nkNZRVoUIFPPfccwBKIx/du3dH
+mzZtAGiLlNTy3bt386K44w7NZ/vee+9xCLW4uJhVzn379vkVRvXHQ04+mkmTJjHB7IgRI9zarnT/
+q1ev5kSmHj164KeffgJQPhWTKuj6lPDjdDpZ/bZYLFHb1EZKyUlWFouFIz+RbKTrVShIKft4+Kqr
+m99KAE8GOygDBgyUHy6ZNGd/QLtEo0aNdLvqn3/+GZLzq12gXXdg+rx///7s7SZUrlwZWVlZADQz
+gCjk1PPQ8a55G3Tc7Nmz2XmWk5Pjs9nlC1599VUAWtSEHLee4vl0b0VFRfx3q1atWNsg9b28QNER
+8t5bLBbWeMqbn8IbqKAvLi4Os2bNivj1/9ZCIT4+nl+KEydOhJX+nEDX+/LLL/HCCy8AKE02Ubsi
+kVkDaALAXS0E/TY3NxfffPMNAC3hiu7DbDaHNCy5atUqAFqmJKnXrtfo0aMHAHBdxoABA/jFy87O
+jsgc+wKqx1CzWCn7M1p6g3gC8XQC2iYQaRi1DwYMGNDhb6kp0C7Qvn17TiCaO3duyHZVNRfCEw4d
+OoTLL78cgJ4lmHarunXrokaNGgD0dHLEFZGQkMA1Ds8//zzXGjgcjrDRny1evBiAtrsST0Hnzp05
+Kemnn35itZzu3+FwsKkwevToqCCGUaGOc8eOHQDK3wnqDRQ9O3ToEDZt2hTx6xuaggEDBnT4W2oK
+ZON269aN7XKqnAwFfLVJKR7ev39/ANruT+E7m83GRVxvvfUWh54o9HjmzBkO7xUWFgZsA/vTzZky
+Lzdt2sT9Cr/99lv2d6g9Lyk79Ouvv2ZmpmiK/1MoUk01p/H561MIFUGrL4iPj0fXrlpg77fffrso
+KzYS+FsKBXoRjh8/zg+S+PlCAV/VT7o2VcMBmqMwkghEVb722muZJh8A59w3btwY999/P4DSrlfe
+St7LC8TQTCnYS5YsCajeQXUAq+Xu4UKVKlU4TyE7OzsgijuVfMUXikNXGOaDAQMGdPhbagqUQVil
+ShV20JG6a8A7nE4nO0EvVZDZ+MwzzwAAc1P4C3WXjURWYYsWLXDihFZKtHDhQr+uqZp5wTTlEdGg
++gVSZVkWSCgkJydzVV+0J6wYMABoOS0UiQpDKvYGKeXV3n5kmA8GDBjQ4W9nPqgdjs+ePcvqUySc
+RAYMBApyDObm5gbsuFWdosFYAH87oeDJBizPhBU1lOcuvKV6i1UhpqqPNH71PsKVrhsXF8fjKSkp
+0Xmyafzq3NJnTqfT4zyHK6wX7Hk9Ue2HC1artczOUiaTSXdP/vgGQjV2w3wwYMCADn87TcFqteok
+JpkMJpOJP4+0c5UouUtKSnh3cNUUXHcN1THqabz+3oe6K6q7u+tn6enpXB2pFkSpY6QEMZPJxOzZ
+x48f96gphGvOgz2v2qYtEoVSycnJXOVK85qXl6fTFClS5m+0wx0jutE2zoABA0HjktQU3HEOUB3/
+kCFDdASt9Hd5sO3Q+GhnoGInV0RKc1Gv4+6a9NmZM2e85nUQo3ZMTAyzSe3atQvbt28P1XAjArvd
+jlq1NFrRzp074/PPPwcQvmfy0ksvYcKECQDgtt2eECJg/xdpbzExMUHxWVySQoFIPehhUkUfAJw/
+fx7z5s0DoOWL0wSXh1BQX7JLCf50nSopKWE1eM6cOZwGnZ2dzQszklRi/sJkMjF35+DBg1GtWjUA
+wPvvvw/Av7nwBS+//DIzTbtDoCahyWTi9W30kjRgwEBIcUlmNFJIZ/DgwQA0NqCZM2cC0LMUqTuU
+xWLh48jxl5eXxypXsNL1fxUVK1ZkQtyMjAxduO3s2bMAtN4Q0cphULduXW6+UrFiRfzxxx8ASnuK
+ZGZm/p3WhU8ZjZek+UAPadCgQQA0W51YkKdPn870Z7Qo6ZjffvsNANhbXlBQwDTx06dPZzU4XElO
+agRECMFlscOHD0ft2rUBlNrqWVlZmD9/PgCtmSu9VGrOQ3mo5fTSU6PcuXPnMo16tWrV+Hur1cpm
+XXZ2NjNXR8sLRmN7+eWXuYRdSsl9P2mzKI9IFZkEVatWZaKerVu3XsSqHa5oiWE+GDBgQAev5oOH
+VvQTAdwOoATAXgAPSynPXfhuNID+ABwAnpJSeu1cEmhBFLEzV6tWDb///jv/TY1MaNcl1K9fHwDY
+vNi9ezd+/fVXAMCbb74Ztl2hZ8+eADSN4ODBgwA0joJ//etfAPSmjZpXQcQax44dw549ewBo7MpU
++UfVdJGC2WzGO++8AwDcAKZmzZrMU7BkyRKMHTsWgNZSnZrZAECfPlqngLlz50ZwxO7RpEkTTJo0
+CYBGQEvznJqaiuuvvx5AKR9DpEDPf/z48Ty3SUlJrNWuXr0a996rtVehyFAAGq1P5oMvQqEDgDxo
+3aRJKHQDsFxKaRdCvA4AUsqRQogmAGYDaAugBoAfADSUUpap5/orFEi9IrV1//79WL16NQAtLDZ1
+6lSfjk9KSmLVMRydjSgUSWaM1WrlSERMTAxfGwBXc9IxVquVzYPi4mL+3Ol04rHHHgMAfPHFFxGx
+1alJ619//cWmGS1iAFyePmXKFO6J2Lx5c6xfvx6ANt/E89ixY0cApfcbKcTGxmL8+PEAwJ27aBzU
+ZPjVV1/FV199FdFxEZo0aQJAY71SQ+7097Zt21hgkVAIYBMLTZWku1b0UsrvpZQkptZA6xkJaK3o
+50gpi6WU+6B1imrr17ANGDBQrgiFo/ERAKQTZkATEgRqRe8V/jhNyNlG5kG1atWY+ZjanZUF1UPu
+LoEkmLGqEY4rr7wSQGnKcklJCe+q69ev5zj+7NmzedzXXHMNAK11G/EMWq1WVtGFENxP4tixY0w3
+RhpDqJ2kDRs25B2fnIsqCgsL+fvY2FjWJNq0acMmT4MGDdhhduuttwKIXD+Dhx/WOhdS3gGgzZXK
+hUmaJXFilgduvvlmANqaJD6QU6dOMZvz8OHDg9EQ/EJQQkEI8W8AdgAzAzh2AIAB9P/+3Cj9lhJL
+7HY7d1BSuQU9gV6gcLAx0bltNhur/MuXLwegPWSyqVetWsVCTUrJwuSyyy4DAEydOpVfoB9++AGN
+GzcGoPWjpBb1PXr0YM7HUCdmEdHt7NmzdWYOzT3NXb9+/dhHk5qaypmlv/zyC3vyn3jiCV7oo0aN
+AqA1ywlnKTuZCBMnTuTPaO5HjRrFRLmVKlXiKFB5JVkJIbgJcWFhIZubL774Ir744gsA2gYYqUhI
+wEJBCPEQNAdkV1k6Wp9b0UsppwKYeuFc0RGnMmDAQGBCQQhxC4ARADpKKdWE/m8AzBJCTILmaLwc
+wLqgR+kCMh8olrxmzRreKX1pWxYJietwOPD4448DAHvhx40bxzu73W7nJCqn08nqNd2T6lD866+/
+cO211wLQmJYp6jB79uywtGlLSEjgHaphw4b8uZSS6zeIqv6XX37RjZnyLR555BHe/Xbv3s3PjMyk
+6tWr+2TqBYK6detyfQE56jp37uyWSbugoIDNvFq1akXcAQpoJieZCa1ateLcma+++sptVW24EWgr
++tEAYgEsuzDpa6SUA6WUfwoh5gHYDs2seNJb5MGAAQPRhUBb0X9cxu/HAxjv70BMJpPP4TVqVEKO
+uLvvvpt3Bk8QQnDMl+zJYNh6fDmWmrGSb6FevXq8c2VkZOCVV14BoGWukc399ttvAwBWrFihK+Ya
+MEBzv6Snp3MMfffu3V6Zh/xhJqIdX80xUEOPUkrs378fgKa90D2RNnHXXXehVatWAIDExESdQ5f+
+Jo3o1VdfxQMPPOB1TP6AOmVv2bKF55Och2X126ACuxtuuAHbtm3z+XqhYpMymUycm1C9enXmskhI
+SOA1oBb3hTsMHTVpzv5MLCXCkIOooKCAk4I84ZFHHkGHDh0AlHqiN2/ezOru0aNHOR5/+vRpVsvV
+UlYaoy8PpUePHux0U9vTk/OtVq1aXHLscDjYS05OO0DrzQho6dx0rhMnTuDll1/m+y6L2ku9trf5
+tVqtnCretWtXflHU+1UdYtQM5syZM6hTpw4A7Xmo5C2uRDIqGjRooKN5CxZWqxUfffQRAO0lo+fn
+LWclLS2Nx/bBBx/4dc1QqfROp5OdixkZGfjuu+8AAEuXLmUH84gRI9hh7Uvlpvrc/RVeRpqzAQMG
+dIgaTcEfkPlA2Llzp67QiHag++67j+O/Xbt2ZfWyV69eADRiFrXXIIXI5s6di6effhqAFuoLpKa+
+d+/eF0nm1NRUdOrUCYDWiXrGjBkAgEWLFuH48eMXnYPCdwkJCTyGkSNHYs0aLRXEl5Zg7ii6VFBe
+QevWrfHGG28A0MwId5R22dnZPIcU569SpQrWrdN8ye3atdMdQ3OrahB0rszMTK9aji+g8w4aNIhV
+cIfDwc1fyClbo0YNdOvWDYAWIiYujq5du2Ly5Mm6sUUaDoeD82weeeQRno+bbrqJxzRhwgR2RlJv
+0piYGLdh7aKioqCqf6NGKPgzcCLCoKSgSZMmsYrbs2dPftmSk5M5KmG1Wjm2Tp8VFBRwDP7EiRNc
+dblo0aKAm8fQg3njjTd44bVo0QIAsGPHDvbqZ2ZmckKSuxwDq9XKY7Db7fj4Y82N88svv/i1eL35
+GqiZ6Z133skt0FX/Tk5ODveSzMvL40W4YsUKAJqgoMVat25dThl+7733cMMNN/D16B6fffZZAKHL
+U3jwwQcBaC8NnW/27Nlo1qwZAHAtRsuWLdkEKyoq0tXFvPbaa0GPI1isXbsWgJaPQAJt27ZtHBn5
+4osvODJCL/yHH37IfUoPHTrEiXjx8fHsQ3Gt//EFhvlgwIABHaJGU/A1+mA2m9mbT86wpKQk3vEy
+MzN1jhVSc1NTU1kFJ/OhWrVq6Ny5MwAte8xXXruyog80psOHD3M6sirt1SKnstTm66+/ntOKt2/f
+jh9//BEA/Obe82Q+kGpPJkO1atV0XA30LCZOnMiaQqdOnXjuaVdaunQpz2t2djauuOIKvmeKAACl
+zrE5c+YAAHvYXeFPFOqyyy7DtGnTeLxffvklAGDo0KE851REVL16dd5hi4uL2Tman5/PWaR79uwp
+t6xGcprb7XbWNmNiYnicBQUFzK9B87Np0yaOllitVv67evXq/JwCMX2jRij4uhAsFguXTFM0YejQ
+oUhNTQWgvQRkJrRu3Zpt9UWLFrF6TAt6w4YN7On1ZzGUZerQi3D+/Hmu3CQfgLtIBqC9oGoTGEAr
+SabP6tSpw+NTiWP8Gavry0bhWTLFaFESKGy7fft2bNy4EYBWt0C/I5Ph1KlTnIQ1ceJEVufpBaQx
+DB8+nH/vbbw0B57MCxJemzZt4hfo0KFDbP4VFRXxnJNaTtWbdDyV2jdt2jQquCTVRDZqRX/jjTfy
+M1u7di2Pk9b3xIkTWejHxsayiXb69GmDo9GAAQOhQ9RoCr6iuLgYN910EwDgH//4BwDNS0taw7Jl
+y5hRmHY7QCMA+fe//w0AuO666wAACxYsCPnu4K7IypsWpI6Bdken08k7osViYR7BUIHGSWqm63eU
+IJWSksJe+2bNmvFuRdRgVqsVTzzxBADNK047t5SS7+v06dPIzMz0aVxqqzRPoPyOChUq8Hiuuuoq
+tynf7nZMp9PJDuYzZ86ww7c8QRWlkydP5mK0rl27sqablZV1UZWkem+e2gcEgktOKAClLxmV3y5c
+uJC94XXq1HH7Yq5cuZIZi2gRr127ltVZf2z1cHYSovMOGDCA/16zZo1XtdvX8wLa+CkBSv2eBFJs
+bCybY0899RSrs7Vr1+YXiMKl999/PypVqsTnIUGQm5vLWaZvvfWWX9GcsuY2NjaWTZ/i4mL2tfhj
+VjVq1AhVqlQBoEUtQk3jHghozb799tsYOHAgAK0iljJHgzUJ/IFhPhgwYECHS1JTcEWtWrU4RblO
+nTqsfql57Pv27eO8fdr5HnvsMU6N9UdTCKfEpnqOtm3bshNpwYIFAee7u+sZaTab2dwidV/NrY+P
+j0fTpk0BaOYMOf7MZjPPMyWFUewf0BKayKT79ddfmfvQ3/kq6/fJyclMXZafn68bmzdTkGo0tm7d
+ypoLkdQEgkBrH2jO1WdCY7/xxhs5CUuNHEWSIt/QFAwYMKDD30JTyMnJYdsrMTGR28a1bNmSJWyz
+Zv/f3vmHyHVVcfzznaxZs11wkxnTJJviRnZRYqJu4x8bFJJopElpKkIDKYVUbPCfEKsIoZv8Efwn
+IBq1QlMFf7GyVLEaDQm41LgQAmFrGyXGZtdNWHF3aZNAYiEbwnYy1z/eu7dvtvtz9r2ZsTkfGHbe
+e7PvvPPmvjP33HvOuRtChKEP7YVovYeFstAxheTgW/L//K9coVAI6xD46EevFyyuArKfIl26dGkY
+iHPO0dPTAxCyGpctWzZtqPjUX2D/GR9D0dPTE5K4zp49m4p/Ptsv8JEjR9i0aRMQRUn6uIi9e/eG
+EOyBgYFQpNWXimtpaWHfvn3h/H6/n7KshEp7jD4uwrfZ5KBye3s7GzduDNfmr7mavC+MwrVr10Ka
+cj6fDyGujY2NwSgk59N9I7969eqM8/izMR+j0NTUFBqpf9ALhUJ4gCQFY7F///4w8OUDq3K5XIgP
+WMzIso8raG5uDhWrS6USp0+fBt4tV9bQ0FD2MHr9isViGMS7efNmuP5jx44Bc8cdVMJs9/bEiRNh
+xqS9vZ1CoQBE2ZDJ2Qf/GT9zUiqVwvHjx49z6NChOWVlhXfBvMG+d+9e+J4OHDgQDERvb+97FoCp
+BuY+GIZRxv/lWpKVkMvlwgCVT9Q5f/58KPiaVhHXZDERP/XpF4O5cOFCSAhqamoKobhbt24NxUd8
+NeeJiYmQDZessbBQfG+lVCqVJV5518WHObe1tYXc/ba2Ns6dOwdEVYT9L+zo6GhFCTZpklwLobOz
+MywFmMvlwrWNjY2FOhl+/r+/vz/USxgeHq444S0NfI/VP3srV67k6NGjQJSY5tfI2LZtW9oFed+/
+a0lWQqlUCuHRvsz63bt3wxz7QozCbO6D7/pNTEyE6kW+i7t9+/bgy+fz+VBwBd7N+PRBQwMDAyEE
+O8maNWtCl71YLM47MGrqyLzfPnjwIBA1VN+t7erqCkVU7ty5s6DiMlmTdG2GhobC/WptbQ0ZkSMj
+I+zatQsgxGOMj4/XzTqW/t5792HLli3s3r07HPczYpUahGS1rEq+M3MfDMMo475xH6Yjl8uFrmha
+4c7Joi1+lNnntufz+TIr7mVPTk6GOgv+l9svLzcVSSGJ6fbt2yEWYKbkoenmxOuFtGoc+nPVQ1ue
+D/478TEefX19wW0sFouhWM1CQ9uT5/Vl8wYHB5NtI521JKvB/bLuQ3Nzcwig6ejoCAVJbt26Fd6n
+TZoPnpENq1atCq7t5ORkSJdOe4Ef0lpL0jCM+wvrKRhGHZAs+pJF7EeMzT7UO9Xo2pv7UP9ICtmn
+vlJYLTH3wTCMMurFfbgBTACZ9ZvmoGCyTfZ9IPsjzrkPz/WhujAKAJJem4+/Y7JNtsnOFnMfDMMo
+w4yCYRhl1JNRmH0lUJNtsk12VaibMQXDMOqDeuopGIZRB9TcKEjaIWlI0hVJz2Us6yFJ/ZLekPRP
+Sc/G+1dIekXScPx3+VznWsQ1LJH0N0mn4u11kgZi/X8jaelc51iE7BZJL0salHRZ0uZq6S7pm/E9
+vyTpJUkfzEp3ST+XdF3SpcS+afVUxI/ia7go6eEMZH83vucXJZ2Q1JI41h3LHpL0yGJkp0VNjYKk
+JcALwE5gPfCkpPUZiiwC33LOrQe6gP2xvOeAM865DuBMvJ0VzwKXE9vfAX7gnGsHbgHPZCj7eeBP
+zrmPA5+KryNz3SW1Al8HPuOc2wAsAfaQne6/BHZM2TeTnjuBjvj1NeDFDGS/Amxwzn0S+BfQDRC3
+vT3AJ+L/OR4/E7XFF62oxQvYDPQltruB7irK/yPwRWAIWB3vWw0MZSRvLVGD/DxwChBRIEvDdPcj
+ZdkfAkaIx5ES+zPXHWgFRoEVRKH1p4BHstQdaAMuzaUn8BPgyek+l5bsKce+DPTG78vaO9AHbM7i
++1/Iq9bug28snrF4X+ZIagM6gQHgQefcm/Ght4AHMxL7Q+Ag4Isb5IH/Oud8wnuW+q8DbgC/iN2X
+n0p6gCro7pwbB74H/Ad4E3gbeJ3q6Q4z61ntNvhVwJfUqln7n41aG4WaIKkZ+B3wDedc2UKCLjLZ
+qU/JSHoMuO6cez3tc8+TBuBh4EXnXCdRWHmZq5Ch7suBLxEZpjXAA7y3i101stJzLiQdJnJhe6st
+eyHU2iiMAw8lttfG+zJD0geIDEKvc86venpN0ur4+GrgegaiPws8LunfwK+JXIjngRZJPls1S/3H
+gDHnnF/o4GUiI1EN3bcDI865G865d4DfE92PaukOM+tZlTYo6SvAY8BTsVGqmuyFUmuj8FegIx6F
+Xko06FJ56eI5UJRH/DPgsnPu+4lDJ4Gn4/dPE401pIpzrts5t9Y510ak51+cc08B/cATWcqO5b8F
+jEr6WLzrC8AbVEF3IrehS1JT/B142VXRPWYmPU8Ce+NZiC7g7YSbkQqSdhC5jY8755KLeJwE9khq
+lLSOaLDz1TRlV0StBzWAR4lGZK8ChzOW9TmibuNF4O/x61Ei3/4MMAz8GViR8XVsBU7F7z9K1BCu
+AL8FGjOU+2ngtVj/PwDLq6U78G1gELgE/ApozEp34CWisYt3iHpIz8ykJ9Fg7wtx+/sH0QxJ2rKv
+EI0d+Db348TnD8eyh4CdWba7+b4sotEwjDJq7T4YhlFnmFEwDKMMMwqGYZRhRsEwjDLMKBiGUYYZ
+BcMwyjCjYBhGGWYUDMMo43+f1ceyZSl1bgAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 2/2... Discriminator Loss: 0.9182... Generator Loss: 1.0495
+Epoch 2/2... Discriminator Loss: 1.1554... Generator Loss: 0.6664
+Epoch 2/2... Discriminator Loss: 1.0098... Generator Loss: 0.9703
+Epoch 2/2... Discriminator Loss: 1.0875... Generator Loss: 0.7549
+Epoch 2/2... Discriminator Loss: 0.8910... Generator Loss: 1.1223
+Epoch 2/2... Discriminator Loss: 0.9773... Generator Loss: 2.6783
+Epoch 2/2... Discriminator Loss: 1.0080... Generator Loss: 0.8345
+Epoch 2/2... Discriminator Loss: 0.9571... Generator Loss: 1.3360
+Epoch 2/2... Discriminator Loss: 0.9197... Generator Loss: 1.3654
+Epoch 2/2... Discriminator Loss: 0.8306... Generator Loss: 1.2653
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsXXd4FFX3fu/sbrIpkJAECaEFVEREQKVXERUjICL8KKIg
+HygioqioKCoCFkQ/BRFsCCKCoIgUUZEPUUREAWkiAgGC9FBCEtK23d8fk3Myk2zIbrZkg/M+zz5J
+Nrszd+7cOffU9wgpJQwYMGCAoFT0AAwYMBBaMISCAQMGdDCEggEDBnQwhIIBAwZ0MISCAQMGdDCE
+ggEDBnQImFAQQtwmhNgrhEgVQowL1HkMGDDgX4hA5CkIIUwA9gG4BcBRAJsBDJRS/uX3kxkwYMCv
+CJSm0ApAqpTyoJTSBmARgF4BOpcBAwb8CHOAjlsLwBHN30cBtC7tw0III63SQIXDYrHA4XDo3gvl
+jF9FUXh8Ho7zjJSyelkfCpRQKBNCiAcAPFBR569MEEJU2OI0mUwQQgDQL7zSFqOiqMqny+Uq8dlQ
+hKIoPObExEScPn0aQNGYbTZbUMZPc0w/3f1Oc0rvR0REoKCgAADgcDg8GedhT8YSKKFwDEAdzd+1
+C99jSCk/APABEDqagrvFX5EIhfE4nU7d32WNqbIIA4KUkq/RZDLhiiuu4PcBYN++fbDb7UEZB8Fk
+MgHQz31YWBgiIiIAqIIKAPLy8gKyRgIlFDYDuFIIUR+qMBgA4O4Ancsv0O6ITqczJBY07WDFH8yK
+RFnzEgrz5g20483IyMDAgQMBALt27QIA7NmzJ+jjKW7CAEBBQQFrBYFGQISClNIhhHgYwGoAJgBz
+pJS7A3EuAwYM+BcB8ylIKb8B8E2gju8vNGnSBADw0ksv4emnnwYA7N27t8LGQ6pjlSpVcP78+Qob
+R2WD1v4ur7Zy9dVXo02bNgCAWbNmAdCbQ/8WVJijMdiwWCwYOXIkAMBqtQIAevbsiUaNGgEADh48
+iFOnTlXI2IQQMJvVW0GmgiEQvIPFYuE5tNvtXvkByFb/8ccfceLECQBwq8IHC2Q2JicnIy4uDgAw
+evRozJkzBwCwZcsWAEBubm5AzDUjzdmAAQM6XNKaQlhYGFq0aAEAmDBhAqpUqQIA/LNhw4bIy8sD
+ALz77rvIzMwM6vjIVIiNjeVzh7q6Smo6jf3RRx9F8+bNAQDZ2dmYOnUqAODw4cNBdTrabDb2ynuL
+F154AYC6XtLT0wEAOTk5fhubN6hevTqbsQ8//DAsFgsAdd7JCXro0CEAwKhRo7B582YA8OvavSSF
+Qt26dQEAjRs3RkZGBgBg6NChuOyyywAADz74IH+W1LNDhw65De/4O0eAzlG7dm2Eh4cDAE6dOhVS
+EYbo6Gj07NkTALB06VLEx8cDUMe8atUqAEC1atUAFKm6gDpvffv2BQDUqVMnaN5yX6AoCu68807+
++9dff62wcQBAjx49cP/99wMACwQCmUcNGjQAAHz++ec83jvuuMNva8gwHwwYMKDDJacpNGjQAB9+
++CEA4I8//sD48eMBqGp5fn4+ALBJERYWxpL20KFDblV3X7QE0gpuu+02AMCLL76IevXqAVDjzrQj
+nD17tsR5zGYzq+UHDhxgx6O349FmypVlmtSpo+ab7d27F9nZ2QCA06dPIzU1FQDQunVr1rwOHjwI
+ALjssstYk4iIiODdrU6dOvy9UMaLL76Ihg0bAlDXyDXXXAMg+IljpHk9/vjjbN5KKfmeuVwunlut
+2Ulr64477sCyZcv8M2YpZYW/AEhfX2azWZrNZrllyxaZmZkpMzMzZb169aQQQhZmTMrevXvL3r17
+yyNHjsgjR47I1atXy6SkJJmUlOT1+bTHdfdSFEU2a9ZMNmvWTGZkZMiMjAxpt9tlfn6+zM/Pl5Mn
+T77oMZo1aybPnTsnz507J7OysuTSpUvl0qVLvR6nxWKRFotFRkREXPRzcXFxfL7c3Fw5YsQIOWLE
+CGk2m3XjLD7mqlWryhYtWsgWLVrIvLw8fs2YMUMqiiIVRfH53gbiVaNGDVmjRg1ps9mk0+mUTqdT
+Zmdny6ZNm8qmTZsGdewmk0nOnj1bzp49WzqdTulyuaTL5ZIOh0OuWrVKrlq1Sp4/f17a7XZpt9v5
+//QZh8Mhp06dyvf6Iufa4snzaJgPBgwY0OGSMR8GDBgAQHUukkf22LFjOlVq7dq1us9u3Lix3KoW
+fU/riIyIiGA1sE6dOpg8eTK/DwDnzp1Dy5YtAQD//PPPRY8/YcIEVhcjIyM5qSYsLMwrLzuNrTQn
+FDm4duzYgZiYGACquUImWHGTo/h8ZWVlYciQIfx3WFgYAKBv376YNGkSAHCRUUWDrnX8+PEYN07l
+/TGbzTh8WK0TuuGGG3Du3LmgjysuLg6DBw8GoK4nulfjx4/nfIvmzZvrzAr6LI03KipKl8DlCwxN
+wYABAzqElKZQ3vCfxWLBmDFjAKi73HvvvQegZFZaVlYWAOCXX37xcaTuK9ny8/PZIZiXl4dWrVoB
+KAolDR48uEwNgdC3b1/cfvvtAIDJkyfjqquuAqBqO506dQJQctd2BxqfNnSoRceOHQEASUlJvNOk
+pqZ6nC9hMplYE9KeIy4uDl27dgUALFq0yKNjBQLkPO3cuTPnIyQnJ/NYDx48iMaNGwNAufMcfEXn
+zp15jQBFmtW2bdt4DqtVq8aOcvpsTk4Oa79jxozxXzVnRTsZtY5Gk8lULifazz//zM7DOXPmyMTE
+RJmYmFghDixyUL3++uvsBCIHUVnOvuIvcuo9+eST7KB0Op1y9OjRcvTo0Rd1dBY/RvHPkmNW67wi
+h9sjjzxS5vEiIiJkRESE7jqLO8BSUlJkSkpKhdyH1atXy9WrV8uCggJZUFBQwoH3448/yh9//FEm
+JCRUyPgAyOjoaBkdHS2zs7N1c0eO6Y0bN7Lj1uFw8O9paWkyLS1Nbtu2TcbGxsrY2FhPz2k4Gg0Y
+MOA9Qsp8UBTFq6ys1q1Vhrcrr7ySvzd9+vQKLSYis6JHjx6sol64cAEAWP3zFGQezJs3T5eKO3bs
+WADA4sWLOS23rGMUxy233KIbL1AUm9+5c6eOQYl+j46OxhdffAGgyFSoX78+q91Wq1XnBPvrr+Dy
+9NKYevXqhZtvvpnHURxZWVnsaAx2arsWlC8TGRmpe5/+vvbaa9lUKCgowIYNGwCoDnQAGDlyZECy
+RkNKKHhqE9Gk0YNStWpVLF++HADQtGlTXgg7duzQfY/eD2R9Ad1EYvABUK4bZzabdZGD48ePA1CT
+s6ZMmQKg/F59RVEwceLEEu/T+a677jr8/vvvAFSKsn79+gFQ/RzXXnstAHBiktPp1NnDNMfeVir6
+ilq1auHAgQMA1Llzl3xE47Hb7bj66qsBqBvKN9+oFf5nz54Nylgp3f6rr75y+3+KOmk5I/fs2cO+
+mU8//RSA58+LtzDMBwMGDOgQUpqCp4iKigIAtG3bFoCqAq5fvx4A0K9fP7z55psAgLS0NN7xNmzY
+gOjoaADAkiVLAKjqPKm+/iomISmfl5fH56P4ctWqVd2qqyaTCUlJSQCKVOATJ07wTpCRkcHfUxSF
+3/cmUqON7AghMGPGDADA+++/D0A1S2h3HTNmDO644w4AqulDURCtKk47rda80EJRFL4m0nICAUoV
+nzlzJo8jLS2NzaKhQ4cCUDkILr/8cgCqJvTaa68BAMLDwzmaM2LEiIBXqQoh0KuX2u3AkzVHpuev
+v/6KuXPnBnRshEopFD755BPd37m5uaw6zps3D7Vq1QIA/P7770hJSQEA9O/fn1UxWqz/+9//mLDC
+X0KBVOnDhw9zqIvee+KJJzBhwgT+LC3cX375BSdPngRQxPo0efJkHpPFYuGkIABe1RTQg6woCh/D
+4XAw9yCdr1mzZvydWrVqcfWoVhV3h+JVklpBReoxhQX9BbqORo0a4fnnnwcAbN++Hc8++ywA9b66
+A5mTaWlpeP311wGofhKqkhw7dmzAfQy1atXiKl1KFisNNpuNS7jJjxQMGOaDAQMGdKh0moIQgh01
+5HCsWrUqmwnnz59n80AIwfRa1apVw+7dKnfsb7/9BgDYvXu33723pO717NkTW7duBVC0I4wfPx7/
+93//B0A1L6hCMz4+nslJyLN84cIF3S5Mqq/D4eAKRU9AO7eiKEwoY7VasX//fgBFhB3XXHMNazSK
+orCJZrPZOJXWbDazKeSOE/HAgQO8i9epU4d/VxTFb2q5EILNpz179jB3hjfIy8vDzp07AaimBDld
+KbnNU9D9oev3xJx79NFH+V5qzTlCcS4PSn8OJjdFpRMKgJqRBhSp/D/99JPbMKSUEtWrqw1xhBBY
+sGABgCL1MhA8fOSjSEtLQ40aNQComWkAUK9ePY5K5OXl8Tjef/99Ji/Rhi3pQdJGMvLz89nU8AZO
+p5MXn9aXQnUgTz31FB5++GEAKvsPnfvChQtsbmm93e4eACEEPvroIwBq9mZsbCy/7yu0ApLG5q33
+ncYxbtw45ObmAgAGDRqENWvWAPDOR6P9vCffo7no3Lkzm43aSA2ZCRaLhTc7IQQL72DCMB8MGDCg
+Q0C6Tns9CC87RHXr1g0A2FnUrVs3NhO0UBSFtQGXy8UJO+vWrfNpvOVFs2bN8OWXXwJQvcnkMF27
+du1F1evq1auzB//s2bPsuAtUnLphw4a8kxavNC0LCQkJAICTJ0/y+Nq2bYvt27eXayxkKl555ZUA
+VOIcMoO8QVhYGNcRTJkyBX///TcAYODAgeU2bdy1yHMHIQTX5tx///1sTj733HMAgOXLl7N2sH//
+fqbpczgc/L6ftNqtUsoWZX2o3JqCEKKOEGKdEOIvIcRuIcSjhe/HCSHWCCH2F/6sVt5zGDBgIPjw
+xafgAPCElPIPIUQVAFuFEGsA3AdgrZRyihBiHIBxAJ72fahFoNgzhR47duyIzz//vMTnhg8fzr87
+nU52LlUUduzYwanZ+fn57D8obachm/O1117jz+Tn5+v6GwQC+/btY7vXW02SxialZEfjFVdcUS5N
+QeuLIMq0ffv2lakpCCGY3JTSmTt37sw7u8ViwX//+18AvmW3euorqV69Os6cOQNAzan5+OOPAQDf
+fvstAJVng0LnxclaK4LQt9xCQUp5AsCJwt+zhRB7oLag7wXgxsKPzQPwI/woFMxmM6uS5AlPSUnh
+iIPL5eKFOWjQIF7UqampzL/3888/0zX4a1gew5tUWipJbtmyJS/oVatWBYUGvryLka5Pyy9Y3mNJ
+Kbm2g0ytOnXqcDRESsnRh1GjRrFTuXr16uzYIydtamoqe/0HDx5cIgW+vOMD3JfRA/qmLoR33nmH
+SYDo2rRkKVpUVE9Tv0QfhBDJAK4D8BuAGoUCAwBOAqhRyneMVvQGDIQgfBYKQohoAF8CGCOlzCoW
+c5WlORFlOVvRSylZ+pOE7tu3L77++msAwNdff40ePXoAUIk+yGHmcrlYhQsF52pZEEJwKnHNmjXZ
+0ZSdnR3UQiNvQWPOyMhgVZjyQ3wB7cKXX345k+QkJibqitzS0tIAANOmTWNtkLStY8eO8bylp6f7
+Rdsqi/GZWK779u2LP//8k8dJuSE0BqvVitmzZwNQtQs6HmXbBhs+CQUhhAWqQFggpVxa+PYpIURN
+KeUJIURNABev7fUSTqeTc9Wp3sFut+PGG28EoNpklCB07tw59l6fP38eR48e9edQAg5KNbZYLLwA
+W7du7bHXO9gQQrCJRlyVgJpWvm/fPr+cY/bs2ahZs2aJ93NyctC5c2cAauSD5obmKhC2OZ2jtJwN
+WpNNmzZlX9L69ev5vtL3R48ezaYwUFT9SqXSwYYv0QcB4CMAe6SUb2r+tQLAkMLfhwBYXv7hGTBg
+INjwRVNoD+BeALuEEORafhbAFACfCyGGATgMoJ9vQywJiiIQi/Bvv/3G6cURERGsqvXp04edOadP
+n+bPVAYoioLevXsDUNO5aVeZNWtWSLWY0yIuLg7vvvsuAHX8tOORY80XkPP4r7/+4q7hhw8f5r6L
+69evd7tjB3KuLmaGWiwWJnpp1aoV5xt06NCBC7coq1QbqXE4HFzkRV2mgw1fog8bAJQWk+la3uN6
+eG4A4I44WmRnZ7Mg+PPPP3X56ZXBl0BwuVy8+LXsSMePHw/Z67BYLDo1mPw8/mjWSj6VHj16hOz1
+a2Gz2bg8fevWrdxjs2vXrmwKakPL5Hd57rnneN4q6jqNNGcDBgzoUCnTnP8N0JJxfPHFF9ywpGnT
+phxRCTUoisIFPGlpady5OlTH+y+ER2nOlbJK8t8AKSWXg7/yyiv8e3ny/oOJ6667DoBa6lsZWtEb
+KAnDfDBgwIAOhqYQolAUhZOtFi5cyBqClrAkFEw/LaSU7EQLBFdFZUCgWtiXt3taeWBoCgYMGNDh
+knA0CiFYQoeHh+tCPdqinOKhoKSkJC6cSUtL45h2VlZWSO3CpdF1BQqxsbHsHNSmVGv7O9C8ulyu
+Cp0rSqXW5iOEWqanL/Cz5vHvcjTSgnU4HExSYbPZdEKBchZItT179iwn2NSrV48ZoUMNxRdEoFRU
+gpbRWHsOl8vF3I2UPn7s2DEuAa8I4RAoM4VyQypa6GnXtWE+GDBgoEJwSZgPhccAEHrOt4pEIJ1T
+lG1ZvXp1ZGdnAwDuuece5lOoUaMGE9NmZGQwS3VlAe3QWvboYKNRo0a4++67AQBvvfUWMjIyfD2k
+R+bDJSEUrFYrx8RD4Xr+TVAUBS1btgSgNrXRppUT6/Tq1asxatQoAKGdZ2EymbiCkao89+/fH/Q1
+Vbt2bQBqlSSVX19zzTX4559/fD10YDkaDRgwcGmiUjsaiYqrdu3aLEVPnTrF1YXr16/nnoi33HIL
+71xUhZaVlcVqcHh4uD/Us6Cjos0mRVGwcuVK/p1gs9nwww8/AABefvnlkMpuVBQFrVq1AqC2eycO
+iM2bNzPbtpZWLtig6t9atWpxPxM/aAkeo1IKBarEI8ru66+/HomJiQDU3oBdunQBoCcn0WLEiBEA
+gKNHj7LtmJmZye9v3LgxsBfgR9D1UemtzWYL6kKOi4vTNdWl5r4vvPBCSJlyiqJw+PnNN9/kBjcN
+Gzbkpj0NGjRgVidicQo2hBDclMfpdHIT5WDCMB8MGDCgQ6V0NJKG8OmnnwJQVTzaBcxmsy7Jpixo
++/l9//33AIBevXqFlLpbGqpUqcKOKFJ3L1y4ENQdesSIEXjyyScBqO3xiAovVEAmjbbrdtWqVbnL
+9vvvv8/NdYQQnPfQrl07AMHnSUxISMCRI0cAqLkJ5HT0UzfsSzN5yWq1cotzYrOx2+18M7VJSoA+
+uUXbzp2gNS+og9Rdd92Fzz77zP+D9wCUNBMbG8tJWNnZ2cwaFRMTwyr6gAEDuIkttSrfuXNnQD38
+NLczZ84EoPbgoDHfc889ATuvt6B7TT9jYmKYxLV+/fp44YUXAKh2u3YN0IbyzTffAADuvPNOrlAN
+BuX63LlzeczZ2dl+6cPpLQzzwYABAzqEjKZQVqINScx9+/aV6K+3Zs0a9hpv2bKFqa/effddjjvn
+5+dj165dAIq48bSMw9pzNG7c2C/XVBxms5l3q969e6NPnz4AVFWVcvdpJ164cCHWrl0LQI3/U8ej
+7777jn8HipKIqLM1XVsgIIRgxuSBAwcCUJ25ixYtAgBs2rQpYOf2FsXns3Hjxpg8eTIANSmIduOT
+J0+yuh4REcGNWyj9PT09PSgRHnLWNm/enFPzV61a5S+zwSsYmoIBAwZ0CBlNoSwpvGLFCgCqDUgV
+fHfddRcAtYu01nfw119/8TFJ4nft2pW/R8650kB9JfwFbdgwNTUVgJoeTNecm5vL46Rw6tGjR1nL
+admyJXfYbtCgAROhHjp0iOPtgdzFaPxvvPEG+vVTybkpLJybm4vBgwcH7NzlgRCC7zFpNv/973+5
+96iUkvs4Dho0CFlZWQBUfw51hCaNQds9O5CgtngxMTHMVj5ixIjK2zYu0GjUqBF7g3NycrjJxh9/
+/OH28+4mMjY2lm+41hHp7nv+bkRLx7Xb7Zg2bRoAtfHp3r17AQDdu3cv0b1KCMEVia+//jo3WHW5
+XNxYpWXLlgFfNIqi8Lz169ePHywSwm3btg05ynlFUbhh64cffghANd3ItOrfvz+WLy/ZjsTpdPJ1
+kcMxOTmZmZYDNddRUVFo0qQJAPX+/uc//wGgNwVjY2N5nsmRHKgKUZ/NByGESQixTQjxdeHf9YUQ
+vwkhUoUQi4UQJV3+BgwYCFn4Q1N4FMAeAFUL/34NwFtSykVCiPcADAPwbrkGVyitJ0yYwLvm559/
+7lVbc3JKzp8/v0Sb7+IgNblevXrlGW6ZcDqdPIaOHTuymeNO4teqVQurVq0CAO6yDahmBeVpBMPx
+9dlnn+HWW28FoGaLEigMuWbNGvz9998A1EzBiuxZQF2lb7zxRsyaNQtA0RqSUrIzmkzR4rBarbj/
+/vsBFKUaP/PMM9xcyN+g+Rw7dixrYG+99ZbufORIfvDBBzF8+HAAwIIFCwAAkydPDoi24GsvydoA
+ugN4GcDjha3kbgJwd+FH5gF4ER4IBXfRB7rgn376CS1aqDkXU6dO9YpZhxaKlnyFoOUU1IKayfgb
+LpcLr7zyCgD1oSJ10GQy8bVTdGLOnDksDIQQXJ7cv3//gHukrVYr+zhIGBcHzVuNGjU4xbxz586s
+8lapUiWoJcdVqlTBSy+9BECN7GiFAaAmdY0ZM0b3HoEE3J133smJTBRJCQTxDgmA8ePH83nJZ/T6
+66/r1kXVqupem52dzev+gQfUZu1bt27lfAp/CgdfzYdpAJ4CQE9pPIDzUkoa4VEAtdx9UQjxgBBi
+ixCiYlrrGjBgwC3KrSkIIXoASJdSbhVC3Ojt971pRW+327ki8sUXX8R9990HAGX2hlQUhXMOrFYr
+S2Dy3qenp3PMX+t87N+/P9f/+zvdmdTBqKgo3tliY2NZ4lO6dtu2bVn65+bmctfiYFDGff/992x2
+FYfWEVr8PaAog7BHjx6ceRpIEC3ckiVL0L59ex4PzR1pfc2aNeOdtkOHDqyFDRgwAFdccQUAYMeO
+HUwGs27dOgBg08hfqFmzJqfTk+Y6depUzJs3D4C645Pm0qlTJwwdOhSA2jfzrbfeAgDuUdmrVy92
+VtNPf8DXBrN3CCFuB2CF6lOYDiBWCGEu1BZqA/CIcudiNujYsWPZxu3Vqxen2D7zzDMA1P6K7lCj
+Rg0mALHb7SwMZs+eze+RDUmVcoCqwpOKd/DgQU+G7zFI+DRp0gRNmzYFoO+1+NBDD/HvJMR69Ojh
+93G4Q6NGjQCoDVHpoXe5XDzm/Px8FkqUsNSsWTP29GtNjSVLlrBPZMCAASxc/R2peOONNwAU1SoA
+aoIbCaRJkyYBUIU7XVNqairefvttAGpSkzYxjuY5UAKtc+fOvLbIZPj444+Z57Jr167o0KEDAJVY
+hXw069at402wZs2aANQEsj179gBQyWD8RVhbbvNBSvmMlLK2lDIZwAAAP0gpBwFYB6Bv4ceMVvQG
+DFQyBCJP4WkAi4QQLwHYBuCj8h6IVNGrrrqK3zOZTFx4Q86kYcOG6WK6pA6OHDmSk3vCwsJYLaP0
+4l27duk0BIKiKBzHbtq0qd886YqisBlUtWpV3kn//vtvrjQkp114eDjvEn/88Ue5x0C7oBDC7U5C
+/3/22We5jl9bOKatHDSZTJxCTrkS2nGlp6cjISGBv9ejRw8AKkcjFSDR7uiPXa1Zs2YYNGgQ/03J
+aS1btnTb6ZrGmpGRwQ5di8XCY0lOTsaaNWsAgHduf4Hmefz48brKTUCNQhBRTbt27bBjxw7+Xduu
+nkyl5s2bA1AdlmQykcbkD/hFKEgpfwTwY+HvBwG08sdxDRgwEHyEdEajNi6uBUldqt2/4YYbuJzY
+arWyc/Hqq6/m3+12O2sWlNZ6/fXXl8hgI5ANbLVa/VaKLKXkHVZRFGzevBmASidHuxztYP/88w/7
+F/xR5NS2bVs+t1Y7ogzLVq1a8bmdTiefMycnh+PmM2bMcKshEM6dO8eaghYWi4V5AfyhIZB/6Ycf
+ftC1qSNb3Z2WoIXNZmO7HCi6lq+//ppzR/wN0r6ioqLY2Txy5EgAwFNPPcVzn56ezuF37VyZTCY8
+9dRTAMA+h7Vr1+KRRx7x+1hDWiiQY6W0fAJSv+rWrcupz/Pnz8eJEycAAEuXLmXn18MPP8wmAS3s
+o0ePcg58zZo1dRGI+vXrA4Db85YXWq/433//zZGPhx56iB1llI9w9913M6ekL6AHKDExEU888QQA
+NfWXaL5I/QwLC9NVA5KQjIqK4sWZkJDgVhhQNIhSsYsjPz8fL7/8ss/XAqj3gzaA2NhYHs8nn3zC
+wr4s9OvXjx9CIQTzIL788ssB6y5F0Zzq1auzs5XqR6SUbCo+99xzfB9MJhMLr19++YUFK20gU6ZM
+4QpPf8KokjRgwIAOIa0pkETNzc3VSfbiOHPmDDtqDh06xJl0hw8f5p3ZXYjJarUyUWdxGjFyNPk7
+T4F2IiEEcxL07t2bTZSpU6cC8B83Ac3hpk2bsHr1an6PnLE0P8Wdi1pzihy9N910E4cfaY7vuusu
+duBqYbfb+dxbt271i9YDqKYPFQ8BRbsmMXRfDDTO4cOH8/XZbDZmrQpkpmj16tUBqCYWOQwp/Pnt
+t99yRexHH33EFZqRkZG6dU/3ipy1gSIYDmmhQKqTtjmsVn3V9oSkiYyNjeXc9tI89mQmDBs2jIWB
+VticPHmS6x/8HVenc48cORLPPvssAFUAkX9hxowZfj0fzUFmZib7CUwmE6udlM5cq1YtXW0IzYfF
+YuH70L9/f4/Pm5OTww8hUcb5A3379kXHjh0BqNdG1OfFU9iLw2q1coJYx44deV727NlT5nrxFeHh
+4Ww+RERE8LqlB/7uu+/mqFNx3xatvzNnzrD/4IsvvgjoeA3zwYABAzqEtKZAqrvD4WAHXFRUFEtI
+kqpxcXHs0a1RowbX/+fn5+uo1yhHgBxjgwcP1mkg69evB6CqyYGSwrQ7PPTQQ6yu2mw2LnIpK3Xb
+W9B1SClItrSYAAAgAElEQVR5565ZsyZf67BhwwCouxVVBppMJp3mVBr/RHGcPHmS1fhTp06xw9ef
+cxkdHa0bDzkXnU4nawvaa73tttsAqJwVlN0KFDl0BwwYEDCzgebQZrPh0KFDANSKXzIRyZkNQGeC
+kYn55Zdf4tFHHwWgasPBKjALaYp3CjFt2LBBx7WovfmAmrZKOeqnT59m9VIrCMxms676TPt9QE0j
+JTbnQM2JEIJ9BU2aNOFxLF26lJNw/H1urRlAdRX5+flsi5MqqygKj6dDhw4YMGAAADWhh8J04eHh
+rGrTPVi+fHmpHIYktF0uF/ssfE0KqlOnDg4fPsx/U23D7bffzp2eevTowfed3ouMjOTxZWVlcdRl
+7969QS3zjoiI4DD5hAkTAKhRHTLR/u///o+pAQJQFm30kjRgwID3CGlNgXaa/v37MyVW7dq1ce21
+1wIoiosvXryYd8F33nmHzQd3XnEtXC4Xe/3r1auHc+fO+XopF0VsbCz3k+jatSs7yQYMGBDwpiMm
+k4nV0lC45+WFEAL79+8HoPJVavkSSHspHj0BVFOUUpjvueceNh/+Zaj8rejdhR9LGy+pp3369GEi
+0Q4dOvDnIyMj2f4koXH+/Hn+f1JSUsDo0Uk4LVq0CL169eLrICHUsGHDgC3SsmofKiMo6efDDz/E
+TTfdBKCo5BxQ/RlETkK07rNmzWKT6V8Mw3wwYMCA9whpTcHfKG5OaDWRQDHjAuBdKzU1ldOurVYr
+98Ikr38gUNGt6g2EFAxNwYABA94jpPMU/I2K6k+gpY+jVnUOh4MpvwwYCCX8q4RCRYG6/2RkZHC+
++5QpU5hpOiwsLGBOTsNsMOAtDPPBgAEDOvyrHI0VBW1YkIqOIiIiOEfC6XSW2NH9ZeoYjkYDGnjk
+aKzU5oM3C76sVveBhLb+gOo5/F2SXda5PQFFZ+Lj4zkd2eVy8e8mk6mEmePp8YMhnOgc7mo1tMlb
+iqKUGm0KdC5HWFgYJ1aR4Lfb7bp5qWgBbpgPBgwY0OGS0BQA99K1IrWDygjaJc+ePctmjtaM8UW7
+CeZ9cGd6aTUArZYQ7DVis9l4fP7WDvyljVVqoVCaqkdqsHZxhLJwCAsL47JYehi1acnBKpmlOXI6
+nSHXXt4TXOwel/a/ilgXgZpbv7Ui8OXLQohYIcQSIcTfQog9Qoi2Qog4IcQaIcT+wp/V/DJSAwYM
+BAU+RR+EEPMA/CylnC2ECAMQCeBZAOeklFOEEOMAVJNSPl3GcQIirkO5MtBkMqFNmzYAgIULFzI1
+OhF+KIrCLcFuvfXWoHZwNhBYkCNU64AOEgJbJSmEiAGwHUADqTmIEGIvgBullCeEEDUB/CilvKq0
+4xR+x6+zQtTpq1evZsLQTp06hZxgILaoP//8k+sj4uPjAej9JZmZmcwZ6SmNub8ghGDCkpo1a3Ik
+groYBWJOKzKMqg0fA75FI6iU2+Fw8LUkJiZyhzJqL3D48OFgCf2A1z7UB3AawFwhxDYhxGwhRBSA
+GlLKE4WfOQmgZF82GK3oDRgIVfiiKbQAsAlAeynlb0KI6QCyAIyWUsZqPpchpbyoX8HfmsLSpUsB
+qB2q6fqSk5Nx9OhRf57GryAHI9GHzZgxg/tgms1m/PTTTwBUcpZg7KAUS7/xxhu5UYmWBp5YoDt1
+6sSdqN0lYXmLiowYVa9enbtp/+9//wMAvPbaa+XSFq644gqmYM/Pz+eaFyklaxBnz54FoHJb0nyG
+h4cz50YAuCMDrikcBXBUSvlb4d9LAFwP4FSh2YDCn+k+nMOAAQNBRrlDklLKk0KII0KIq6SUewF0
+BfBX4WsIgCmogFb0QghudOJyuTg8+e677+Luu+8GgJCk4iKbkkg7O3bsiD/++AOA2vmaSGwVRQl4
+uDAqKoqJZAcNGsSMRWFhYbyLkz/E323L/BWvp+NoOTSImHfcuHHcuXn79u3csfmVV15Bo0aNAIC1
+H2/HQ76I6dOnu/UPaUE9PannAyEjIwMAcP/99+Ojj8rdtL3c8DX60BzAbABhAA4CGApV+/gcQF0A
+hwH0k1JelPzQn+ZDfHw8N0TVNjs9f/48mjVrBgA4fvy4T+coK2nKHxBCYO3atQCAzp0785jr1Knj
+87HNZjNuuOEGAOBuS02bNuVjt2/fnuduyZIl+PnnnwGo6i45GMmDfubMGRayDodDlwZdnrkp79zG
+x8eja9euAFQORuqVSWq7EMLtw+lyuVgga/tpEmO0lgeyrDFLKXle9u/fr6NwLy9oHFdeeaU/iIAC
+X/sgpdwOwN1JuvpyXAMGDFQcKnVGoxbUZGXSpEmstrlcLnaYCSG4H8TixYsrZpBeQErJeQxCCL8y
+TQshEB0dDaCod2V0dLSubRzh22+/xccff1zifVLLu3fvzi375syZ4zMvhLYRTVlhOkVR+J5+9913
+JUwbLaSUbHZpNQaXy+W25VxaWhp/ryxoP0NOydtvvx3btm0DoNdAgKKMRup2ffLkSSaeJTORxkmh
+6Oeff577RAQal4xQoMkbNmyY7gZQvn5OTg7TxPsKKaVfW9S7Q82aNXXVdKNGjfLbse12O3dvovyI
+0ujw09LSdOoxmRVkomlbq+/evZvZpMob33e5XGXOLT1A06ZN4/6WJpOJS9EPHDjA56c+ljNnzsSZ
+M2cAqJT6L774IoCSPSipuczo0aPLNX7Cvn37cOWVVwJQO5GRKXHbbbexz4AiHLNmzWLT7e6772Y2
+cm20p2bNmrzx0XUGCkaVpAEDBnS4ZDSFp556CoDe87xhwwY2Jcxms87x6AuEEOxQClQkYMCAAbx7
+nz9/nrtS+wvPPfccAL2GQPOmncPvvvuOP1OaNkEazZdffsme9Pz8/HJlJpamKdB7zZo1w1tvvQVA
+n6V69uxZXH311QCKvPdAkSbkcrnw448/8jHccS44nU7897//BVCkCZUXUkocO3YMAPDqq6/y+Zo2
+bcoOUXIcfvDBB9zLonnz5m6v/z//+Q9eeeUVvhYAOHr0KM+91tTSarLlcvaGQuqvr9GHNm3asKfe
+arVyCO3QoUO47LLLAKg+B4o+kL1YXmg7EPkrPZValY8fPx6Aqr6Surhv3z6OEvjjfkVHR3M0g9Tn
+adOmsaCIiorC559/DgC4+eabdd+l89N17927l8cGFJlrv//+O/sonnzySfz6668+jZ8S0u68807d
+WOg6evbsyeFcLW699VYAqnArDfSQHTp0iAVLoCj/FUXhjWrhwoUAgBYtWrDw0grk4r4P8iuRifb1
+11/zRvfOO+/wmC8yxwbFuwEDBsoBKWWFvwDI8ryEEFIIIR977DHpdDql0+mUNptNZmRkyIyMDJma
+miqPHTsmjx07JocPHy6rVasmq1WrVubxCjWXi77MZrM0m83lGje9aDxbtmyRZ8+elWfPnpVZWVky
+KytL5uXlyfz8fJmfny87d+7s03mKvywWi0xKSpJJSUnSYrFIi8Xi0/E6dOggO3ToIF0ul+7lcDik
+w+GQO3fulJGRkTIyMtLrY8fExMiYmBi+v9rj5+bmyujoaBkdHV3ie3S+4mNy9yooKJAFBQUyMTHR
+r/Ps6atevXpy7969cu/evdLhcMjs7GyZnZ0tHQ6H2/Gmp6fL9PR0efnll8u4uDgZFxfn6bm2ePI8
+VmqfAtlpDz/8sM7bTV2YwsLCcOHCBQBqEgiFy8g2VhSF1WBtcosnnnPyJURFRbE3uKzvacOiCxYs
+0IWfCFSFaLFYeGxJSUlljscb2O12nxO4tNiwYQMA4OWXX8bTT6tV8lqSmPr162Pu3LkA1MQiGkNp
+oPsQHh7O4VB3drbNZiv1OFQ/oAWNJycnh0OyQghP1O6A4vDhw1zNW7du3RLNcUvDiRMnAtIf0zAf
+DBgwoEOl1hRIsvfp04c9y9HR0TrPMu3oSUlJuPfeewEAK1asAKA6Hw8dOgRA1R68cRrSuXNzcz2O
+q2/fvp0dn9oEG5fLhdmzZwMAazMPPfQQx6lbt26NlStX8vVoU3fJwx0KeP755/H8888DUCMSFDFp
+2rQpevfuDQB45JFHAIC9/O5Aml5cXBxSU1MBQMfETLBarfjqq68AqNEnijrcddddeOaZZ3TfS09P
+Z8deq1at0KFDBz4O7cylRVeCAcqhcLlcrHlWq1ZNd930OzUUCpRmY2gKBgwY0OGSCEledtll+Ouv
+vwCo0pV2bqfTybv/8ePH2d5dvnw5/98f9f/a32kn00r2m266CQAwZswY/uxjjz3GWYVaNiXKbKNC
+GBrvO++8A0DVhH7//XcA4O+HKgYMGABA9Z8QKMOwdevWbudeURSO2ScnJ+O6664DAK5kjIuLc3su
+KSXfaykl7/50D1avXs1aGvFUENasWQNAzTasiOfBarVi586dAPSZi4D7HhakTc6fPx+PP/44ALVP
+KV1zeHg4+9KKIbB0bP6EP6okSUV//vnn8fDDDwPQq4O7d+9mldHfpdNUbhsWFsa/L1myxKtUXxIu
+5ACsUaMGmwyDBw/mBV9QUFBpmJZpkWZlZbEpRILsYtWe2sQbOgYJg82bN7ODtrTKRyklz/2XX34J
+AHj99dexfv16ANA9dC6XiytGqQI02EhISMDw4cMBqOQ1RKgzatQoDBs2DADYMQoUCYVBgwahevXq
+AIAtW7agdevWAICPP/4YOTk57k5l5CkYMGDAe1RqR6MWVHE2efJkdOnSBYDq4CKcOnUqIOGbiIgI
+Tlvt1q0bnn32WQDeFQSFhYUxHRepztnZ2Zy9FwhtTtsiDigqBPIniEA3JyeHsxupcvBi0F4vhQtp
+ftq3b4/GjRsDUMOab7/9NgBV86DvZWZmYuLEiQDAzs5BgwbpCowIBw4cYNW9oiCE4LDuxo0bee2M
+HTuWCYO6d++u+zyghl1/+eUXAMC9997L9G0U1i4vLhmhQLDZbKxqaUkvtDa+P5Gfn88CqWrVqrj2
+2msBeJc7v2TJEh4npbK2b9/eL+OjBWQ2m91GV2icn332GR577DEA/u+nGBsbywL5gw8+KNcx6IE/
+evSojmuT5rs0kCDYuXOnW/u8Y8eOFc7yHRUVhU2bNgHQz722FiMlJYXHTwK9Xbt2bDJs3LiRBaGv
+5qVhPhgwYECHS05TiIiIYFUzOTmZd4E9e/YEbEcg7/Ubb7zBO2GdOnUwffp0AO57MHbu3BnLli0D
+oBZD0S4wZ84cAKpHmoqk8vPzfZb+xXd/+puo1kaOHMkqaqdOnXzOeIyLi+PCHYfDwUVoq1ev9um4
+3oLm7eabb9YVGtH1V61aNSCmkyeIiYkBoHKA0Br6+eefeZwWi4UJgdzR1F122WXYv38/AGDixIl+
+I+K55IRCRkYGe29btWrFDxvZ6v6GlJKF0JgxY/Dpp58CUMtlX3rpJQBqJSGgmjZ0c6+++modKxQl
+UZHpk5CQoKMA92V8QEmVkt4fMmQIAOCXX35hVTw1NZUf3r59+3oskIQQ7A3/5JNPdLTmRBIT7E5X
+2oQfrflQkQ1nio9hzJgxnJY9dOhQDkdPnjy5RJKbFunp6bjxxhsB+DeiZpgPBgwY0OGS0xSEEOz4
+0+4MpD0EArTbLFu2jB13zZo1Y02AGrxo4XK5OC136tSp+P777wEUaRUFBQVB2cUoyeXWW29lbSUs
+LIx5FA4ePMhj2rp1KzeGIXMgPT2d8wYmTZqEnj17AlDvA0UcvvvuO26MEmzQGO6//37d+9pIRUWB
+8lAiIiI4orJ48WJOc65bt66ucIs0CzJ3OnfuHJB2BZdM8pIWVNvQvXt3Vrnatm2LLVsC36GOSEs6
+derEfQaoMjAmJoa98EOHDmWOvmAJgLLQrl07AKrdTwk+WvtbURTOviSTYv369bjtttsAqMKEFnRB
+QQFzYvbp04dz+4MNij6sXLmS7wdQZMZER0dXWPNe8hmdOXOGzYTi60DrSyBBTJtMObgaA5+8JIR4
+TAixWwjxpxDiMyGEVQhRXwjxmxAiVQixuLAbtQEDBioJfOklWQvABgCNpZR5QojPAXwD4HYAS6WU
+i4QQ7wHYIaV8t4xj+W2btFqtrMLXr1+fVd9mzZoZ7dw9RHx8PFq2bAlArdGgHe2GG27gnYt24EOH
+DjHFe25uLvbt2wdA9ahTSvPp06crTBMijefQoUPsBBVCcBrwVVddxeq4lh8ymKnkQ4YMYb6J4qB5
+++effzgd24coQ1DSnM0AIoQQZgCRAE4AuAlqX0kAmAfgzlK+a8CAgVCEjzRqjwK4ALUl/QIACQBS
+Nf+vA+DPUr77AIAthS+/0lsRPVhWVpZ88cUX5YsvvlghNFuX4qtu3bqybt26snbt2rJ27dqybt26
+MioqSkZFRcmYmBiP6eyC9aLxDB06VEfpZrPZpM1mkxMnTmTqNrPZLMPCwmRYWFjQx/nYY4/Jxx57
+TNpsNmm32/m1adMmuWnTpovSCHrx8oiOzReBUA3ADwCqA7AAWAbgHngoFPzB0VjW68knn5Th4eEy
+PDy8when8arY17Fjx3RCYdmyZXLZsmXSZDK5/XxFCjcSst5whnr48kgo+GI+3AzgkJTytJTSDmAp
+gPYAYgvNCQCoDSB0qIEMGDBQNnzQFFoD2A3VlyCg+g9GA/gCwIDCz7wH4KGK0hQURQk5ddZ4BfdF
+rNvdunWTR44ckUeOHJHPPPOMjIiIkBEREaV+7xJdNx5pCr62op8IoD8AB4BtAIYDqAVgEYC4wvfu
+kVKWTP7XH6f8g7gILBYL8/3l5uayR7mykJRUFILRASvQoCgCXcd1113H3vsFCxaUxkzEFYiUm6HN
+0/DXuKjHJKWx5+XllVrWr03A88M4gtKKfgKACcXePgiglZuPGzBgoBKgUmY0kvSksV/sGmjH8LfE
+9wahUHxzqUJLnEL3V5tvQO9dbO5JOwgLC+OKVk/Wlj+hzVzU9n2g30vLevVyfIHXFCoKnj7c2nzx
+QLeOvxj+rcJAm+hEvIL+ngsyb0wmExOzZmVllShXL80MslqtzO5UUFDAG06gekmWBu28UL2GzWbj
+VGZFUdzOXSA2HKNK0oABAzpUSvPBQOhDCMEM23FxcThw4EDAz1e8S7OBErh0zYdAwx3LTSBAHat6
+9+7Npb3kkQ51aL3iRBDSv39/JmbdsWMHEhMTAYDb2gcSmvC2V9C2fg8PD0fNmjUBFFUk/hthmA8G
+DBjQ4ZI3H9w1FtE6kWjHa968Odepx8fHM2fe/v372Unmj7HQGJYuXco8BCaTiR1hVAHXvXt3/PHH
+HwBCSxWmqkNyho0dOxanTp0CoFb7ESlI7dq12dl31VVX+Y0/0FsQv4XNZuNqT5fLhc6dOwNQ55n4
+KJ9++mn+DJGXbN68GePGjQMA/PXXX35bC55Aa4JNmjSJmZ190GKMZjAGDBjwHpekT4F2/wYNGuDV
+V18FoPYeoFZlRNfWtGlTjlErisIEqQsXLuQdURtO8xa0mxKj0aOPPsocA3Xr1uXdKCYmhsdBNf8r
+VqzA5ZdfDqBcDDsBgaIoTEZL3Z6nTZvGbEwzZ87ka96/fz+qVq0KAG6bsAQSFouFNZb//Oc/AIB5
+8+Zx5+s+ffrw3NatW5fHrNWaKRNWCMFaTjCyO7U0dgMHDmR2cLPZjAceeABAUe+MRx55JCA+r0vO
+fAgPD8ekSZMAAMOHD+fFoU0IKa3/ID389957L6u+P/30k64BrKewWq3coWrGjBkAVKIXMgVWrFjB
+LcXHjh2rGx9h5syZAIDRo0d7fX5/goTs8uXLmT24Xr16AEon/EhJSWE+x+HDh5dKIuJP0MPUuXNn
+ZpKeMmUKAJURm5rVCiFYUGkdjQUFBfjtt98AgHs4pqWlBcx8o/Fefvnl7Ghu0qQJc3e2b98eCQkJ
+/Flat2T+3nPPPfjiiy+8OaVhPhgwYMB7XDKaAu1mixYtYgeexWLhXV5Kyeqstt8C7QJ5eXksufPy
+8vh7TzzxBDdt8WSuaBzR0dHc7IN6KzzzzDM6ui+ih6tSpYquQzaBzIZrrrmmQkNk1MLuhx9+4N6N
+Tz755EW/06ZNG2Zw3rRpE5tQnqA8WXpVq1bF+PHjAaikuVdddRWAIjPAbDa7zWNwOBzcpXz+/PkB
+y7zUgvp37tq1CwBQrVo1Xjcul0uXVUlkww0aNEBSUhKAovnJy8tjKjzqPVIG/l2t6AcNGgQAmD59
+Or799lsAwIMPPqhryU0PKT2MSUlJTGseGRnJHuk6deqwULDb7Vi6dKnuXE6ns1yLpkePHhg8eDAA
+4Morr+Sbm5yczOqstvkH4eTJk9ys1dfmod5CCMHnNJlMrJaXRS2+fPly9OjRA4AqVKhXoifnK163
+4AkGDhyIu+++GwDQtWtXFvBaYUv37MyZM0ypP2rUqHKZh76AHvqGDRsCAHr27KlrTEy9Ms+ePcv9
+IUeOHInrr78eQFHj3szMTF5Pq1at8uTUhvlgwIAB73FJaAoRERHcTtxqtbIk9Qd7c2lOyfLAZDKx
+gy47O5tr6BMSEli9plg0ZQMSiHH4qquu0plEgcbNN9+M7777DgCwbt06Xe8Ed6D52r59O6vw8fHx
+Oo3Nn6Aowpw5c3gOtXwQBJfLhTfffBOAeh3Uwj0nJyckOCO0lb90X7V5CitWrEDdunUBQBcZo1b1
+HvbV+HelOdPkbd682a9U7v588JxOJ0cctMjJyeH+gdRQ9LHHHsPUqVMBqIuDvNBffPEFe8aPHj0a
+cMFw5swZns+FCxeW+Xmiho+NjcWOHTsAoFQCEX+ATMJrrrmmBLEKABZG1113HVJTU0v8P1QSw9yN
+Q0rJY42NjWVhQNGHbdu2sU/h7NmzflsLhvlgwIABHS4JTeGDDz7g2G5MTAx7dwNZXBQo4hTaMSjd
+tvj5GjZsyB71YGDQoEGcs1FWEpXVasW776p9f1JTU7k4SlGUgKno8+fPB6B68LX3hM5HOSuHDx/m
+/990002sbu/YsSNk+S4URcHLL78MQL3v5DSlBkeTJk3C1q1bAfh3HVZqoVCjRg0AwKlTp/j3Bg0a
+cJnuZ599hvfeew+AmlVIiR4kLHyZyEAtJIo+PPDAA7pFTir8xIkTOWISjMV87bXXshAiv01p6Nix
+I5s8kZGRHEZVFKWEeuyPsYeHh+t6Xro7PvXxbNOmDVq1UlkCY2Nj8dZbbwEAmzihBLqWLl264NZb
+bwWghlQpXEqRBvKL+BuG+WDAgAEdKrWmQIy8+/fvZ3VRURROUhoxYgRGjBgBQN05qA6ie/fuAAIn
+aT0FpWDn5+fzbkypwdTeHVB3Dtql582bF1TnGDEgA+BqwuKgiMojjzzCc3zgwAHOZXA4HAHRamrV
+qsUJPVpIKTm3gub4lltu0c03mZuhhuTkZO5GnpSUpKvy/OmnnwAATz31VEDHYGgKBgwY0KFMTUEI
+MQdADwDpUsomhe/FAVgMIBlAGoB+UsoMoRpD06F2ns4FcJ+U8o/ADL0o3PT+++9zt+MVK1awnVk8
+Vk1Sl7SHjRs3+szW4w1MJhNnLt54443o1q0bAODYsWO8I7du3brE97Kzszl1O1haAtm1X375JTu4
+qLpUi+joaDz//PMA1JAr2ej5+fkBj/9fuHBB50ug850+fZq1FBpz9erV+Z6ZzWYuQHr//fd1qc30
+mWCHKmmtLlmyhLUfq9XK4zh//jyvgUDDE/PhYwDvAPhE8944AGullFOEEOMK/34aQAqAKwtfrQG8
+W/gz4Fi3bh0AtY6AJjgxMREbNmwAoJJ+0ANJKdF2ux3Dhw8H4J3jy1uBMHLkSADAq6++ymaCoihu
+ad/cvXfu3Lmgp+KSCZaSkoJPPvmEx0bjo9qHiRMn8nyfOnUK77zzDgDg+++/Z1OorJTo8qJdu3ac
+zgwUPVgbN25kZmeKRDkcDn74LRYLN2R56qmnMGvWLABqOjaR65TWLCZQIJPh2muv5WtyOp1chdqo
+UaOgjaVM80FKuR5A8frYXlDbxAH6dvO9AHwiVWyC2leypr8Ga8CAgcCjvI7GGlLKE4W/nwRQo/D3
+WgCOaD53tPC9EygGIcQDUNvRa9/zi0OKVK7jx49zocl7772HKlWq8HkA4K677sLjjz8OQC0u8Sei
+oqKwZ88eAKqWUhaKh9ScTidnAo4ePTqofQgUReHQopSSd64bbriBi80ok9DpdOLTTz8FoDrz+vXr
+BwAYPHgwq+4JCQke31dvSHNfeOEF3d+UT7F27Vq0adMGAJgf4eeff2ZOh9jYWNYqqlSpwlrBL7/8
+ElS6NUL79u3RoUMHAKqJqa3gJA3B3+vzovCwmWwyNC3lAZwv9v+Mwp9fA+igeX8tgBYV1WAWUJvM
+KooiH3jgAZmTkyNzcnKkw+GQDodD7ty50++t6ul8J0+elC6Xq9wvp9Mpz507J8+dOydXrlwpq1at
+KqtWrSoVRQnYXJlMJmkymeT27dvlyZMn5cmTJ+W5c+fkli1b5JYtW+Tp06dldna2zM7OlmlpaTIt
+LU22aNFCNmjQQDZo0KBEu3ea58WLF/OxPZk/T8eZl5enm6/MzEyZmZkpU1JSZExMjIyJiZEWi0Va
+LBYZFhYm165dK9euXaub53Pnzsnq1avL6tWry/vvv1/WqVNH1qlTJ2BzrH317dtX9u3bV2ZlZenG
+RPM2cuRIf58zoK3oT5FZUPgzvfD9YwDqaD5ntKI3YKCSobzmwwoAQwBMKfy5XPP+w0KIRVAdjJka
+M6NCQKrYgQMHdIQqADB37twS7cV8BXnqy0pF1pJpuIO2Qu72229nxmSHw8FOx4YNG/qt+lAIgbFj
+xwIALrvsMqxduxaA6qijopvo6Ggexx133AEA2LNnD8/rXXfdxWZFfn4+O/OaNGnCJkhZvR09ae+n
+Va8pogSonntArSh1p26npKQAUKNW2vv0ww8/8O9du3YFoPIzXGycvoCukSoci6e0Ew8FVacGHR6o
+9p9B9QnYofoIhgGIh2oa7AfwPwBxhZ8VAGYCOABgFzwwHXwxH4QQUghxUZXTarVKq9Uqt2zZwqov
+vW0aXEAAABoZSURBVIYOHRow1XDcuHE6Vbr4y+Fw8P8LCgqkzWaTNputVHOCVEqbzSbz8/Nlfn6+
+nDFjBpsrvs5hnz59ZPv27WX79u3l6NGjZZcuXWSXLl3ktm3b5LJly+SyZcvkoEGDpNlslmaz2atz
+mEwmVuPL+qwnx6ZjZWRk6MyHrVu3yq1bt8qEhAS33+vevbvs3r27tNvtpZps58+fl+fPn5c9evSQ
+PXr0kFar1e9rg0xBu91eYixnzpzhexKAdemR+VCmpiClHFjKv7q6+awEMKqsYxowYCB0UelIVpo3
+b87pqn/99RcA1TPrLlHGYrFwFd0tt9zCsfdvvvkGgJqWSzwGgUCLFiqfBamnNG4CeboPHDiARx55
+BIBawQeoxV6kRq5fvx7PPfccf69jx44AVD4FUpN///13AN4n3WjpzUkVF0Iwg3S7du04ryMYuRLu
+iqeKg9TvY8eOcSEcUJQPkZWVxdWDxD59/PhxVtdL42v8888/sXLlSgBggphu3bpxYtyTTz7JqcYA
+ym1aUITms88+4+shM7Z169ZlFp75gEuLo5HYlDZu3MgZi8TJ99133+lqHyhT8NNPP2XbMTIykif7
+9ttvB1DEZhRo0Bi+/fZbtlmllLwY+/Tpw3UYlJNfrVo19hdIDf28FrGxsRyqpPClNiuvvGjUqBGT
+2+7fv9/vfpeLwZOwND3QkZGR+OeffwCoAldLyHsxaOfowoULHPZLT09nPw8lbPXr14+rUp999lnu
+d1HeEHFERARzMBLfJQD8+uuvAFSBH8BsSoOj0YABA+WAJ46HQL/ghUNs7NixsqCgQBYUFMjdu3fL
+3bt3y+nTp7NjKDU1lR14DodDZmRkyIyMDLlgwQIZEREhIyIiAuZc9ObVoEEDuXLlSrly5UqZkpLi
+No7vqxOxvC+TyRRIZ5dfX4mJiTIxMVHu37+/zNwPctaeOXNGNm/eXDZv3rzU45Izs0mTJnwf/DEf
+iqJwPoXWwdmuXTvZrl27QM9XQPMUDBgwcImi0vgUNJ9lpxq1AVMURWeHkg08f/58PP300wBUJ1Qo
+sPYShBA8tk8//ZSdePRT24n63wRvU93JB9CtWzdusxcbG8t5IkS7duLECfbnZGdnVxhha3JycgkC
+2SNHjnAPiAD7by4tR6MWVO04ffp0AECHDh24Iu+1115jj3woXJsBA1qEhYVxTQit4+PHjzN9e4Bh
+OBoNGDDgPSolHRuF54inwICByoKUlBQOSdasqbIKEDlrqKBSmg8GDFRWWCwWbuxDeTJB9B0Z5oMB
+Awa8R6U0HwwYqKyQUuLkyZO69/xFLuQvXBJCQZvWqp1gbaPR8PBwVtOoeavT6eQy6oyMjICpceRl
+djqdiIuLA6CWFlOqrJRFzV60i4OuSzsud41T/Q0t+0/xMYUaiE2roKCA05y1ZenaMmuCNoRtNpv5
+f0IIt/fBnwgmg1Z5YZgPBgwY0OGScDR6qn5ptQZAZXOm9xRF4cSRQM4JFUdpd3+TycTsw8Te63A4
+SiUkoYKwgoKCkOmaXFEgbUrLNO1uToonuNFnq1WrxnkD2v+HYuKYH/qXGo5GAwYMeI9LQlPwNzyp
+6TfgOYz59B3x8fFcHk9ajN1uv6h25AYeaQqXhKPR35BSBqzVfHlQmiM1FMYGFBG1JCYm4sQJlZJT
+61ALlXFWRlx++eUAgPvuuw8tW7YEACbcqVmzJvf3JFIZf8AwHwwYMKCDYT6UgWCovoqicDiN6Ng6
+duyIcePGAdBrB3a7nftN7ty5M+i7MFHakVO2bdu2XJ2YnJzMjNDvv//+JWMyUNgzLy8v6E15Jk6c
+CEDVFKxWK4AizatKlSr44w+1VWv79u09OeSlaz6QOk2Rg0B6igO5sInau3Xr1pg0aRIA9SEDSuYj
+0DVbLBbu15iSkhKUnod07qZNm7LQOnZMbecRGRnJFX5ms5nHEwqbDVDEixkWFsb5IkARn2NZFPmx
+sbH48ccfAQAPPfQQNm7cGJiBukHjxo0xePBgAEBcXBwLJCoLt9vtmD17tt/Pa5gPBgwY0KG8rehf
+B9ATgA1qj4ehUsrzhf97BmpvCCeAR6SUq/09aCK8pGYpBw8evNj4dT+B4LcZd4eoqCjWCpYuXcpa
+A2kIWmenFkIIbls/bdo0PPHEEwCKiFspI89fUBSFTQar1Yr69esDABYsWABA7ZJMxDBxcXEV0oux
+OBRF4bwP6h9Zs2ZNdto5HA6cPXsWgEpwM2fOHAB64lbKPJ0/fz47UtPS0oIyfjITVqxYwW3pTSYT
+awq7d+8GAMyZMwfz5s1zfxAfUKZPQQjRCcAFqN2kSSjcCuAHKaVDCPEaAEgpnxZCNIbaPKYVgCSo
+jWIaSikvqt8X8gF6pHJWqVKFOwGRXbt582YuR928eTNTZ3/zzTdMz/30009zW/IDBw4AKEnZHQyQ
+fTpkyBBMnjwZgJqMpE2FBlSmIOrGVKVKFaxfvx6A6nOgjk3aOaPrSElJ8euDGR8fj6ZNmwJQFysJ
+COoEdd9997GPAyiizx88eDAzUwcLNLevvPIKM33TOIsLWG10yZ0g1ba4//vvvwGo7QXcfdbfkaqh
+Q4cCAGbOnKnzIxB5EG0m+/bt8/ac/klekm5a0Uspv5dSksdlE9SekYDain6RlLJASnkIQCpUAWHA
+gIFKAn84Gv8DYHHh77WgCgkCtaIvE5SmWppqT+nBs2bN4p2JpGizZs14B5NSslmRnJzstqcjNWlZ
+t24d8vPzAQCjRo1irv+yHJflrWoLCwvDNddcAwCYMGGCrocgpdquW7cOgKrZEL9gZmYmq7VhYWHc
+XKZVq1b8/vXXXw8ASEpKYjXXH9V32dnZ7FS84YYbcO+99wIoasRz7bXX6j6v7Y8YTE2hS5cu3O/j
+wQcfLOGMLg1CCJ0DsjiklBg1Sm16VpppRmvTH5GJRo0a4fHHHwegOm6JcyEiIgLffvstgCIzJlDO
+XJ+EghBiPAAHgAXl+O4DAB6gv8tqYkIqU7du3dijTHUCGzZswKxZswCoJJjUFWjHjh0YMWIEAGDY
+sGFum4WQYPnwww+5S9P1119/0bF4ezNIFb3vvvuYV1K7WP/880888IA6FRRiKn4OElR5eXkYMmQI
+f48WJP0sKCjQ+VHKu3DoGElJSUx4WqNGDX7Qa9dWlcN9+/axeWGz2fjeVKtWjYVJIEECfubMmdzV
+Sbu50IPscrn4PpQlKICi+f/iiy/YdCsN/vBR0dgXL17M0Zxff/1VFwKm6l4yk2w2W0AEQ7mFghDi
+PqgOyK6yaGQet6KXUn4A4IPCY4VG/MqAAQPlEwpCiNsAPAWgs5QyV/OvFQAWCiHehOpovBLA754c
+82IST1EUdO/eHQCYygooktAjR47U9TkkMwBQY8sA8Oijj/IxPvjggxLHUhSFW9Ndd911vGP7A1df
+fTUA4OWXX+YdXQjBMfI+ffp45dk+cuQIAGDXrl1o3rw5gKLKyTvvvBPvv/8+AN/US20qNbXZS0xM
+ZLPh7bffBqBqQZS8VK9ePXb40nUGEkIIPPnkkwDUnVbr8KM8hNdeew2AuuNTxKFKlSp49tlnAaj3
+plUr1e0VFhbGWgSlDw8bNqxMTaA4d0N5QPfx2LFjmDFjBgCV42PChAkAgFq1amH79u0AitoAVJj5
+IIT4DMCNABKEEEcBTADwDIBwAGsKb8QmKeWDUsrdQojPAfwF1awYVVbkwYABA6GF8rai/+gin38Z
+wMu+DKo47r33Xna+FJ4DgBrfBzzrhmy327FixQoAQKdOnQCoXae19iUdt0+fPhfVFDy11WnnWrVq
+FYCi2DegOhYpxuxt/Jt2pGuuuYbPQTuztguzLzsJOUE/+eQT3sWcTif7Nui86enp7NiNjIxkR2pZ
+mYL+wIMPPoi+ffvqxgOorFavvvoqAGDq1Kklvnf+/HlmAr/nnns4bNmsWTP+DF2/J+FdX30KiYmJ
+eOGFFwCovoPevXsDUNcp+bzOnDnD2Y2BzgUJ6TRnemBfeuklXdyY4vekAnoKunmU8PPggw/ypANF
+eQ//+9//yjyWJ7FpGjM5hrTCZN++fXjmmWe8Gj+BHsLs7GzOvaBrO3jwoF8cX+SgrVWrFkdwcnNz
+WeiQEEpOTubzmUwmnkNtJyRvoKWCK21uKU9j6tSpOmFAn3/77bfdCgMt6Hvbt2/nhCzt+ajq0JMU
+em/nm4RQmzZtAKgJUvSey+VC48aNAajmDDl2W7duHbRojpHmbMCAAR1CWlOgMCTtjARyGFFqr7cg
+SZyXl8eagsvlYrWsb9++nC/gDp6q5dQfkHZabfbcJ598Uu6U5Ntuuw2A3hyhGPmiRYvKdUwtkpOT
+WYuJj4/XEeFS5uS2bdsAqA4y7f2h+SSHpLcoKzQdHx+PzZs3A0CJHBQqxpoyZcpFzyGEYCfzRx99
+xHPncrnY7CHKPn9VyVI4fMSIERgzZgyAorBuWFiY25R2l8uFnj17AgD++ecfn8fgKUJKKBS31ffv
+3w9A9cKSd11RFFYfKUbtaeUaTTypatHR0brz9enTBwDYy+vpOEsDPbwEu93O3vL58+eX2+Yns0Sr
+2pIw8EftQ1xcnC4ZjITvsGHD2D9C723btg3ff/89AFWYkn+nbt267MH3BkIIfti1fgkyV7p3715q
+ngGlv1PkoTRERkay4KhatSrGjx/P56O6EjJRExISSlCyuxszUHKzoPcTExOxY8cOAKpQcycA3KGg
+oIBTrIMJw3wwYMCADiGjKWjZeAFV6pIKt2/fPq4Wk1LyTkLe+7Zt27JJUby6kHYVq9WKjh07AgB7
+erXnPHDgAKu8ZXl3TSYTq4OUJl0c7dq14xgz7XLaXdzpdPK5rVYrH4d2m4EDB7KK++GHH7IDr1On
+TnjjjTcAqCqptoDKXyieT0FRnq+++qpEKm9+fj5rbECRSZOQkFCuQiGTycRpx7m5ufxdOu5LL71U
+wpwkdOjQAQBQv3591jJIq9yzZw8f9/vvv0ejRo0AqLvxpk1qZv7111/Pjtt+/foBUJ3RvXr1AgDe
+7T2BEAL16tUDoBZUaeeAfqd7/vPPP3OOjNPpZLMiJyeHcyhWr14dNI6KkBEK7ngHKUknJSWFbdWB
+AwdycgdN5IkTJ7Bnzx4AajkpqY9ff/01P1gZGRlcfUZltWazmdXd0aNHl/qAuxsrqZyvvvqqzrdB
+45w8eTKbPHRNWVlZrGo7HA5OW/32229ZRaXFSiYOoKqfJKjGjh3Lx7Xb7VwRSuPxZeFow5skTHNy
+cjgBzF1uvxCCKyNHjhzJand6enq5xuJyufj6tN52SmlPT0/nxqzFE6SoNHrNmjW8BmjMs2bNws03
+3wxALaOm67Pb7TzXJ0+e5NoNKs93Op0skMuClkFLURQsXLgQgCqEyEc1f/589n24W28mk4krPOfM
+mYPPP/8cgJpuHgxCHcAwHwwYMFAMIcXR6G1SEHn3ly5dyl7xF154gSW7zWZjE6RXr16cn0AFPFJK
+jqW3aNGiTAcVwWw2cwGLxWLRqZXkLf7yyy9LVM89/PDDTJ+l9WhXq1aNNRYyB6Kjo3HLLbcAUIt9
+SLvResO/+uor9O/fv8Txygsab3p6Ou+eGzZs4AIzdzCbzbyjh4eH8xw2aNCA6/+9QUREBGsKpB1o
+sXz5cvTo0QNASY4EgsPhYCd1rVpqkW5mZiY7T6Ojo3XpzOQ8HTJkCK8Xgs1mY/7DshiTTSYTr9/S
+zKCyoNW8tC3q4+PjuZLWB3jEp8AhoIp8AZCBflksFrl9+3a5fft26XA4pMPhkC6XS86dO1fOnTtX
+ms1mr45nMpmkyWQqcY6CggJZUFAgXS4Xv1auXClXrlzp9TnCw8NleHi4PHjwoHQ6nfzatWuX3LVr
+l9fHK+tVSHYjt27dKrOzs2V2drZs06bNRb9Tr149mZ+fL/Pz86XT6ZRvvPGGfOONN8o9BkVReBzu
+/p+cnCzXrFkj16xZI51Op26e6eVwOHiutO9r36Pfs7KypM1mkzabTfd5+r/dbpcTJkyQEyZMkFar
+NSBrUwgh69atK+vWrSsXLFggjx8/Lo8fPy7tdrvMy8uTeXl5MjIy0h/n2uLJ82iYDwYMGNAhZByN
+wYA21wFQ1UyqcfCWIMNd+mtCQoIuHZs6Wv/f//1fuc5BzMm1a9dmVTk/P5+rFv1NN07n0NZU1K1b
+l73zQJGJQdGAX3/9ldVkh8OBadOm+XVMxXHkyBGOEnXq1Ek331pntbtcBq0Kr+1GTfPocDjYUUg/
+TSYTO1qnT5/usTO6NCQnJ3OFqdY8oHnVJridP3+e+Ud9Pa83MDQFAwYM6PCv0hS0WXqAutsT268/
+QE4tAjnaPA1paVGvXj0OR5lMJtZMunTpwqFaf4N2z8zMTA7ltm/fHl9//TUAdd7I+XnPPfcAUMO7
+NJ+//fZbubIYtSgrzdnpdHI2Yv/+/XmciqJwaFhRFNZetIxUdB1Wq5Wd1K+88gpWr1YJx7Ozszms
+SRrPlVdeyXkKnlTjlgbKlHznnXd0eR3aMRK0zFm//vorADXV353jNRD41wiFpk2bMlUYTfrBgwf9
+WuJ74cIFVvOsVivHuolqbe7cuayqahd+WFgYe703bNgAQK3P0HrXR48eDQCc9x9I5OTksAC9/fbb
+WQhdfvnlbNJQYo4Qgq/5/vvv9zkK4omXnrgr27Rpw3yVmZmZqFNHJf3SCgXKm+jYsSOXqHfo0IHn
+dtOmTWzmASpxDQDccccdANS58EeEjiIXHTp04HtNOSnPPfccJ9bFxMSw6eJyuThRK5htCQzzwYAB
+AzqEVJ7C/7d3fqFxVXkc/3wTtaaxk0nWRbKO2IqpUsWo8SFlt7BxXdKKKEIfGiwqSvsiaMtCMfSh
+9HFZ8R+ou8vuKkjJ2vXP7lBQ0Sr0rSaixGqNVrTaoLYLqw9NS0f4+XDPvTu3bZqmzrkztr8PHDL3
+3Jl8z+/m5Dfnd+7vnhOTpUuXZptopDZXq9VsEnC+16E+hTd9vWjRomwptIGBgew5/fT8+Ph4Nizd
+tWtXtiDJ9u3bs2/m+qFjmsU4PDxc6P4Ug4ODWehSKpWyMKinp+eE/Qzh/ytQr1+/nv379xfWTsgv
+LpMukjIyMpKlB6epz+noIv1M+m18JqFdDNJFXSYnJ7NU66NHj2Yp5ps2bWrEqNbzFOrL2NiY1Wo1
+q9Vq2X3ptWvXnvHvS/MU2trasrqFCxdmZcOGDTY9PW3T09NZ7kJ9rsHhw4dPei89zaE4duyYDQ0N
+2dDQUMPzEeYqW7ZsyeUe1Od1pGVmZsZmZmZs9erVVi6XrVwuF9rG40tbW5tVKhWrVCo2MDBg/f39
+1t/ff8qch1YqaTs7OjqsVCpZqVSy9vZ26+zstM7OzkbpeJ6C4zjz56wPH9Ih5fj4eDbUTIdhvb29
+hawlmOZHVKvVbL3Drq6ubAh+5MiRbP3I9B72xMRE9HbNRldXV7bhzooVK1i3bh2QDHHT9OGRkWTp
+znRbPudnwWmFD2e1U+jo6MgWTOnr68s9MwDJ47GtYH8rU/+sRaNWIXKaRmP2knQc59zirM5TqNVq
+2UyumWXPo2/cuDGrc05N/cjARwnnBme1U+ju7mbr1q1A8vxBmkSUrlLkw2HHOREPHxzHydEqE42H
+gMPAf+d6byQudm3XPge0LzezX871ppZwCgCSJk4r28q1Xdu1o+Lhg+M4OdwpOI6To5Wcwl9d27Vd
+u/m0zJyC4zitQSuNFBzHaQGa7hQkrZQ0JWmfpIcja10m6W1JH0n6UNJDob5H0huSPg0/uyO2oV3S
+e5J2hOMlknYH+1+QdEFE7bKkFyV9LGmvpOVF2S5pY7jmeySNSbowlu2S/iHpoKQ9dXUntVMJT4Y2
+TEq6MYL2n8I1n5T0iqRy3bnRoD0lafinaDeKpjoFSe3AU8AqYBkwImlZRMkfgD+Y2TJgEHgg6D0M
+7DSzPmBnOI7FQ8DeuuM/Ao+Z2ZXA/4D7I2o/AbxmZlcD/aEd0W2XdCnwIHCTmV0LtANriGf7c8DK
+4+pms3MV0BfKeuCZCNpvANea2XXAJ8AoQOh7a4BrwmeeDv8TzaXJi6ssB16vOx4FRgvU/w/we2AK
+6A11vcBUJL0KSYe8GdgBiCSR5byTXY8Ga3cBnxPmkerqo9sOXAp8BfSQpNbvAIZj2g4sBvbMZSfw
+F2DkZO9rlPZx5+4EtoXXuf4OvA4sj/H3n09pdviQdpaUA6EuOpIWAzcAu4FLzCzdtvkb4JJIso8D
+m4D0gYtfAN+ZWbqBQ0z7lwCHgGdD+PI3SZ0UYLuZTQOPAF8CXwPfA+9SnO0wu51F98H7gFebpH1a
+NNspNAVJFwEvARvMLLdutyUuu+G3ZCTdBhw0s1NvSBiP84AbgWfM7AaStPJcqBDR9m7gDhLH9Cug
+kxOH2IURy865kLSZJITdVrT2fGi2U5gGLqs7roS6aEg6n8QhbDOzl0P1t5J6w/le4GAE6V8Dt0v6
+AvgnSQjxBFCWlD6tGtP+A8ABM9sdjl8kcRJF2H4L8LmZHTKzGvAyyfUoynaY3c5C+qCke4HbgLuC
+UypMe7402ymMA31hFvoCkkmXaiwxJUsl/x3Ya2aP1p2qAveE1/eQzDU0FDMbNbOKmS0msfMtM7sL
+eBtYHVM76H8DfCXpqlD1O+AjCrCdJGwYlLQw/A1S7UJsD8xmZxW4O9yFGAS+rwszGoKklSRh4+1m
+NnNcm9ZIWiBpCclk5zuN1D4jmj2pAdxKMiP7GbA5stZvSIaNk8D7odxKEtvvBD4F3gR6Irfjt8CO
+8PoKko6wD/gXsCCi7vXARLD/30B3UbYDW4GPgT3A88CCWLYDYyRzFzWSEdL9s9lJMtn7VOh/H5Dc
+IWm09j6SuYO0z/257v2bg/YUsCpmvzvd4hmNjuPkaHb44DhOi+FOwXGcHO4UHMfJ4U7BcZwc7hQc
+x8nhTsFxnBzuFBzHyeFOwXGcHD8CPaLG+4G8bqUAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 2/2... Discriminator Loss: 0.9596... Generator Loss: 1.1294
+Epoch 2/2... Discriminator Loss: 0.9953... Generator Loss: 0.8804
+Epoch 2/2... Discriminator Loss: 0.9510... Generator Loss: 1.1515
+Epoch 2/2... Discriminator Loss: 1.3993... Generator Loss: 2.3715
+Epoch 2/2... Discriminator Loss: 0.9080... Generator Loss: 1.4359
+Epoch 2/2... Discriminator Loss: 0.8572... Generator Loss: 1.2590
+Epoch 2/2... Discriminator Loss: 0.8892... Generator Loss: 1.4629
+Epoch 2/2... Discriminator Loss: 1.5488... Generator Loss: 2.5772
+Epoch 2/2... Discriminator Loss: 0.9225... Generator Loss: 1.1753
+Epoch 2/2... Discriminator Loss: 1.0175... Generator Loss: 1.2177
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXeYFFXWxn+3p6cnAAMMg2QQRUBBkgiiZBAQQUUwJ/hQ
+dF1dc9o1oatrwIAYEHNWDKCLAVEEFREBBUkKuIhKBonDhJ7p+/1RnDPVMLnDzGC9zzMPQ0911a1b
+t8498T3GWosHDx48CHwVPQAPHjxULnhCwYMHD2HwhIIHDx7C4AkFDx48hMETCh48eAiDJxQ8ePAQ
+hpgJBWPMIGPMz8aY1caYm2N1HQ8ePEQXJhZ5CsaYBGAlcCLwBzAfOMdauzzqF/PgwUNUEStNoQuw
+2lr7P2ttLvAmcGqMruXBg4cowh+j8zYCfnf9/w+ga1EHG2Oipq4YYxDtJxAIkJ+fD0BycjJZWVkA
+pKSkAJCTk6PH5ufnk5CQAEAoFKKyZnr6fD5CoRAQfq+VBcYY/b2yja0wuMcLBWOWz621Yb9XcWy1
+1tYt6aBYCYUSYYwZA4yJ0rn0wQUCAfLy8gBo2LAhu3fvBqBVq1YsWbIEgKOOOgqAX375RYXG7t27
+qVGjBgBZWVnk5uYC6AsYbbjH7F5shS0898JNTU0lOzsbcASE3Kt8LxoL1+fzhb0cJc2BjM/n85GY
+mAg48yZzW9j3o/WCFfXC7v+5z+fD5/PpZzKmpKQk/U4oFNL5dG8Q8r1gMBiVMVcg1pbmoFj5FLoB
+d1prB+77/y0A1tr/FHF8VAchD9QYow997969pKamAuGLNCcnB4DExESqVasGOAto586dQHQWgixQ
+985emnmX++jTpw/dunUDoHr16qxYsQKA119/XY8RLSga8Pv9tGrVCoCNGzeyfft2oGgBmZycDIQL
+ZL/fz969ewGoV6+ejq99+/YAzJo1S79f3jVojNH7t9aqECor5KUPhUK6XuQ+6taty5YtW/Qasdok
+ikL16tUBZx3KWi0K7vsoAguttZ1LumashIIfx9HYD1iH42g811q7rIjjK4VelpGRATgPYu1aR6hW
+pMp42WWXATBhwgQ2btwIwNKlSxkyZAhAuV+CktCiRQu9/9IIxUAgADgvkryk7rHVqFGDc845B4DP
+PvsMcLS0aGk1EDuNriKRkpLCHXfcAcCcOXP473//W+Sxxhjq168PwPbt21WA7DfHpRIKMTEfrLV5
+xpgrgOlAAvB8UQLBgwcPlQsx0RTKPIhKoimIpO3Rowdvv/12hY4lJSWF559/HoAVK1bw73//G6j4
+HXF/Wz01NVU/c5sw7nEmJiaqWi7+EFHPPRwImc/DDz9cNaHff/+9RBPxuuuuA+Cdd97hjz/+AA7Q
+JitOU6iq6NOnDwC1atWKicc5ISEhLHJQ2Asu1x00aBB/+9vfANixY0fUxhApZHxiJtSuXVvH5/f7
+1UHrRjAYPBicdHGB2++0evXqMn33jDPOAKBbt26MHDkSgD179pR5DF6aswcPHsLgaQr70KRJE3Xs
+3XfffVHVFGRXbdu2re7+HTt2JD09HYB7772X119/XY8BePDBB9VTX1lgjKFOnToADBgwAICHH35Y
+nZLTp0/nkUceARztRuYwVg7RgwmHHnoo4Dz/adOmlfn7CQkJNGzYEIBHHnmkXBqCwBMK+9CwYUP1
+8GdkZETVdpeXYsmSJVxzzTUA3HTTTfz666+AE54bPHgwAJMnTwYcm1yESWVBnTp1+Ne//gXA6NGj
+AahWrRp16zr5MJ06dVK/zE033aShTA/Fo169ejzzzDMAqvaXFc2bN2fu3LkAvPXWWxGNxzMfPHjw
+EIYqpymccMIJbNu2DYCffvop4vNJ4k23bt045JBDAHj11VcjPm9hCIVC6kG+88479XNjDO+++y5Q
+YGr4fD5Vxe+55x7+97//xWRMpYF4wMePH6/OWIkmBIPBsIw/SbbZuXPnwZAWHFOIeXXFFVfwww8/
+ALBu3boynUMySMeOHcvll18elXFVGqFQkg3v9ztD7dmzJ+vXrwfQf3ft2lXu68rDaNCgAUcccUSx
+Y4g25MV66aWX6NixIxAeyjvvvPMAR6XMzMwEoF27dmp2xAvHH388AMOHD1eT4B//+AcATz/9tPoZ
+7rjjDj799FMgen4EETiFZYW6r+Hz+fQYd+2KfP+oo47S9XL88cfr3E+dOrXCfB4iQAFefvnlMn8/
+PT1dk5vuuusu3SwjhWc+ePDgIQyVTlMQ7L9bi6f+2muvVa2hRYsWALzwwgv8/rtTlJmcnKxVkO74
+fmHx89TUVFq2bAk4atvWrVujcStFwu04bNCgAfPmzdPf5X4vvvhiAL788kuGDRsGOLuA7Co//fQT
+xx13HACLFi2K6XjBmU+JjBhjGDp0KAALFizQY2TennjiCX1O0UBqaqom5EhkqG7duupZD4VCWs+S
+mJgYVgchcK8ryZXYs2ePnqNz586MHTsWKEisihfatGkDONpfWUxW0X4WLVrEypUrAfj555+jNi5P
+U/DgwUMYKo2mAMWX6UqxUu3atfUzkfx5eXn06tULgO7du9OvXz8Axo0bxzHHHAPA3XfffUBW3YwZ
+M3Qneeedd6LuS5Bzi/1qrSUtLQ2AuXPn0qBBA/28b9++AMyePVu//9BDD+m/F154IeBoRdOnTwec
+MGqs7eF7771Xw4yzZs0K0xD2x+rVq6lXrx4QzvtQVsi81a5dm7///e8AGvY0xmiJu/veV69ezapV
+qwAnRfzoo48G4NtvvwUcLebrr78GnDCqZAuOGjWKp556CnBSiePpHJV5HThwIHfddVeJx4uG/NVX
+XwGOhnb66adHf2DW2gr/Aey++ocif2bPnm1nz55t8/Pz7cKFC+3ChQutMcYaY6zf77eJiYk2MTHR
+1q9f31avXt1Wr17dtm3b1n7yySf2k08+sXXq1NHjR40aZUeNGmWDwaANhUI2FArZnj17Fnv98vwk
+JCTYhIQEm5GRYTMyMmxaWpp95JFH7COPPGKzs7Ntfn6+zc/Pt126dCn1Offu3Wuzs7Ntdna2TU5O
+jvqY5cfn81mfz2d37txp3333Xfvuu+/ahg0bFntsixYt7Pz58+38+fPt5MmT7UknnWRPOukk6/P5
+yj1/Q4YMsUOGDLE7d+60O3futBs3brTLly+3y5cvt/3797epqak2NTW1zPe2efNmu3nzZrty5Uob
+CARsIBCI2VwW9bN79267e/dum5uba6+//np7/fXXF3lsrVq17NatW+3WrVttVlaWzcrKsvfcc09Z
+r7mgNO+jZz548OAhDJXGfChObTPGcOyxxwJOGLJ79+5h33FX3ElWIjjVhRLrr1mzpqpaN910ExDu
+WBI1M5oQdU/Cd/3796dJkyYAbN68mV9++QWA7777rtTndNODxbJiUtip8vLyuPLKK4GCELAbffr0
+UTMnKSmJpk2b6u/33nsv4Dhxly1zKufLYu7k5+fz0UcfAah5+Ouvv+pz37VrV7nU/bFjx6oJsnjx
+4kKd0LGEPD8Jpft8PuWZcCMhIUHJaI477jj93sMPPwzA7bffHpPxVRqhUBzf4M03FzDEX3/99SUy
+0AhCoRAbNmwA4JZbbmH48OEA/PnnnwB8+OGHvP/++0DxQqm8EJ+BeND9fj9r1qwB4KqrrlIBVhqu
+RSEySUpK0sUUy/Lj//zHIckKBoNs3rxZPxfP9/LlDjF3kyZN9Hns3LmT++67D3DmXnIrVq9eXW7f
+hwi+xYsXA5E9J0n0ufbaa1UQnHnmmeU+X6SQhLRDDz2Uiy66CHCE7LXXXgs4PiN39OS9994D4NZb
+bwVityl45oMHDx7CUGk0hcJ2ANmVRo4cGcb9V1oJaYxRtbNXr15aMPLggw8CsGHDBq0sa9mypXqD
+582bV2yVWWkZixs1agQU7PLBYFDV4VatWtG5s8N30aNHD01vldyEt956S73l2dnZWpW4adMmrZ6M
+pfkgORBdunTRvI/s7GyNnhx22GGA8zwkH6Rt27bKy5iQkKCcl/JZeRBNurXevXsDTnRCoiiRZMOW
+F7JmhGuzQ4cOmoeRm5sblukoGk3fvn2VeFhyQVJTU1VLO+WUU6hVqxYAjz/+uK7f8mhWlUYoFAax
+ydPS0vTF2rt3b6E8gG7I39955x1OOukkALZs2aJ2m9i3J5xwAhMmTNDviZ3Zvn37EoWCWzAUNQ55
+6WVB7969m8MPPxxwFvvZZ58NOA9eQnmi4p533nkaQl21apWW1n7zzTdRS2ctDpJKnZaWpkJhz549
+SmgrNRw1atRQVigxF8AxbSIp3wVKVSVaWHq8O+VZno0xRm3w/Px8xoxxiMQrgslKBJ2sD0AT2c4/
+/3ydz2AwGLaBickmG1leXp6aoFlZWeq72rNnT0Rmlmc+ePDgIQyVWlMQKR4MBrUQ54cfftCdS1Sr
+YDConx199NFKUlGnTh3drb7++mtlFF64cCHgSGUxGYLBIDNmzABKrlRzcx24m8i4NQZjDCNGjAAK
+drxatWrRtavTE+fuu+9W6vALL7xQdw/390U7at26tX5+6KGH6rGxTFw666yzdByisVlr1YyTQrIe
+PXqoQ3V/RLoLl3R/Pp9P+R2uuuqqMGp/iYhMnDgRcHbV3377DYAjjjhCnacVUck5cOBAAE2w2rp1
+qzrTN2/ezLPPPgs4a+S1114DnHvdf56zs7NVq3j11VejRvNfqYWCLMCXXnqJnj17Ao4nXyazXbt2
+gDNh7p4Nojru2bOHSy+9FIBPP/1Us+JEELjLe9evX68EF6Xx6suCLypzLzk5mSOPPFKPAWexSq76
+jz/+qCr6wIEDadasGVDwIrgbvcydO5cePXoA0LhxYzU1NmzYEJNF3bBhw7AQsERw3BDPedeuXXVs
+AwYM0PsD1NSIFdmKtVbDc6FQSO3rwYMHq9ASZGdnK5lpIBAIa3ZT2O+xxIsvvggUrIvdu3dr1mvd
+unWVaKdOnTq68a1evZpHH30UQGs1pNFRtOGZDx48eAhDpdYUBLfffrvWMLz33nuqRrmTeNwq/Acf
+fADAiBEjwiS/eMFFwm7btk35Be+7774yOcZK6vSUm5uru6l4hYPBoHZe+vjjj5XU5cgjjzzAMbZz
+507tCvXnn39qclXjxo01pv3xxx+zdOlSILo5C88++6yeb8iQIYXeoyRcDR06VB1mH374YZjmdckl
+lwBOPYc4TaO5E1tr1bl5zz336Ofjxo07oOo2JSVFK2KttRox2X884mx2a4LR3pHd7evAqfGoWbMm
+4JiSojUYY9QkGDBggJqbsUa5NQVjTBNjzBfGmOXGmGXGmKv2fZ5ujJlhjFm179/aJZ3LgwcPlQeR
+aAp5wHXW2u+NMTWAhcaYGcBI4HNr7X3GmJuBm4GbSjpZcfFoa63uiF999ZVWiUmI8dBDD9WdtKjd
+vl69espuJKnPK1eu1F18/vz5pbjl0iM/P19tbWnzNmHCBPVnCK0Z4C4M0x1s4sSJagPXrl1bY9QN
+GjSgU6dOALz99ttR1RBkd+3evTsff/wxUBBLl78LC9Mpp5wCOBqK7GzuxrQ33XSTPp+KcObtf82k
+pCTVaIwxhWbFWmtjZqe7IdWcXbp00bGJD2fx4sXqD1myZIk6IEubxRsNlFsoWGs3ABv2/b7bGLMC
+pwX9qUDvfYe9BMyilEKhOG+z1CkIRZkbxfUllESQ2bNna6xfHGfff/+9OsPWrl0b9bRheZCSnpqS
+kqKe5YSEhLA4vFxbSsOvueYaTjvtNAAmTZqkAiIzM1PPF21aNsmRMMYoaQ0UCOzrr79eS3wlIpGV
+laWmweLFi7UhSbwp40pCz549dcOpSL5LcMq1wXE2g7MuxMn9yy+/aOr9vHnz4l6XAVHyKRhjDgU6
+AvOAevsEBsBGoF4R34laK3oPHjxEEVHgQqgOLARO3/f/Hfv9fXtp+BQSEhLKXZdev359rYlPSEhQ
+boXWrVvbCRMm2AkTJthgMGjz8vJsXl6e/fbbb+23335rU1NTbXp6uk1PT497Lb3f77cXXnihvfDC
+C+369eu1Rj4YDNpgMGizs7O13n7ixIm2Xbt2tl27dvbUU08tF4dAaX6EF2HLli32119/tb/++qtt
+27at3bBhg92wYYPNz89X/onMzEybmZlpn3rqKdusWTPbrFmzuM9haX6EQ2P+/PnKQ5GTkxOzOSzN
+j/BsCNdHTk6O3b59u92+fbt95ZVXbKdOnWynTp2s3++P9rVLxacQUYNZY0wiMA2Ybq19eN9nPwO9
+rbUbjDENgFnW2lYlnMdGEiP2+XxqJmRkZKgq/txzz6kNnJycrPFyKQt2e3Mrwu4VuO9dKOcTEhLC
+8i1i0duysHEATJkyResratasqWMKBAJaSyI+nMre/UlMtB9++EHzRvbs2aN1KZHUZUQL1atX1zUb
+Y57IUjWYjST6YIDngBUiEPbhA+Cifb9fBLxf3mt48OChAhCB2dAdRyX5EVi072cwUAf4HFgFfAak
+l8Z8iPRHTIb+/fsrXVt2draqu5s2bbJNmjSxTZo0UZUyGtc9WH9q1qxpa9asaevVq6dmRUWPqTw/
+jRs3to0bN7aZmZk2NzfX5ubm2m3bttmGDRsWSS93EP+UynyI2KcQjZ9o3LC86D/99JP6DkKhkC6E
+kSNHVvQD8X4q4CctLc2mpaXZr776SjkOd+7cadu3b2/bt2//V9scPI5GDx48lB1VIs25NBAHXI8e
+PTQRqW7dulpF9tJLL1XY2DxUHCRNOCsrSwliMjMzNZEtHk2AqhoOGqEg2LJliyYppaenqze3IqML
+HioO0kFq0aJFmmH63XffsWnTJsDJJpRErFhyXlYleOaDBw8ewhBRnkLUBuE4e0oFn8+nabclSXZ3
+rD8UClUI9ZaHioU8/8MPP1yp8L744gtNzS6uK9lBiNjmKXjw4OHgRJXTFApDYmKiZta5wpz4/X79
+3Vp7wI7grrk3xmj2WygUimqmXmnZn8tz3ng+v/05CiD8ftxzaK3V3909PKMxXvc4hIYvJydHK15z
+c3OVyk6K0pKTk9XpGAgE9PeaNWvqOdatWxemQURrzPuvM4FwKASDQf08NzdXNeGEhAStQA0Gg1qw
+Jr1Sg8GgZuU2adJEK1t37dpVlPZTKk2hSjoaZdJkkoLBYJEl18XBGKPnql69ulakGWPCmIkjhYyj
+sJeqPHCnPMcq/bmw87pf9MKEppsKLxAIKBX57t27tRo1GuN1z6c8s7S0NDUn09LSlINRaO5+//13
+dSiGQiHlvezevTsnnHACABdffLGOT86VlJRUbu7DouZw/88PP/xwLdkOBAJ67UaNGimZTXp6ugoA
+mct169YxdOhQwKmklXFGag555oMHDx7CUCXNh0gbhMSjuCiaiLeZEC24d+bK6sxLT09XEyM3NzfM
+1Ik12rZtCzg8FdLfdNu2bWGmcJRx8JoP5Vlgfr//gHh0VYlL161bV6s9p06dWsGjKT2qwvxKX9F4
+QtbhhRdeCISzS0PFb1qe+eDBg4cwVElNoSS4HXrCBXDllVeqg+bDDz8EUIqzygrxoL/yyit6H598
+8kmsa+4PKlT0rrs/MjIyGDduHFAQlZk0aZKaMC1atNAU7P79+/PPf/4TcHpnxIuarUoKBfEpXHnl
+lYDT/UmIL40x3HLLLYDzACRCYa1VQo2MjAwAHnjgAVVxK3rRyOKVDlLDhw9XIeb3+1XNbdu2rRJ7
+VkaCE/dLWFLPz1iOQQhyZV1Ya5WE1v2CxYOo1RjDueeeCzgCQIS9dCpr3LixtjD4+9//HtYYRkwM
+n8/H6aefDqAcjm4U1ZSoPPDMBw8ePIShSkYfJNnkgQceAKBNmzYadxYtQCCMuTk5OcriK1WU27Zt
+49133wViToNVLFJTU3UnefzxxwEnrn7ccccBzjhF46lVqxZ16tQBnB2vInsi7o9q1app5+Nly5Zp
+U55Ywe/3c8MNNwBOboL0jfzb3/6mu6poDElJSaph+nw+Zas+99xzmTt3LhC7OaxRo4aaqjVq1NB8
+gltvvRUIp79LT09XZ/KUKVO0IZDf79du40L3f/LJJ6vZUcqxlyr6UCWFwv5ISUmhb9++gDNh8qLk
+5OSETZZ4fe+77z7AESCPPfYY4NC9xxvSUrx58+ZMmTIFKOCNHDBgQKGNbm+55RatAl23bh133303
+ELsFXVRmn2QPduvWjfXr1wNOn09p4jtr1ixOPPHEmIxJ0LZtW+69917AEaZt2rQBnJdNWrTLBtKo
+USN9/kBY2E8yBL/++uuYjPOyyy5jwoQJgBM5a968OVDQ46M4nkgRZE8++SQXXHABgCbWXXrppbpu
+Sgmv9sGDBw9lR5V0NO6PnJwcjSgUB3HESNfq7du3a+XckiVLdPeIR6JN7dq1efrppwFo2rSp/n77
+7bcDB+784pwaM2aMjjPW6jk4HbGlo9Fhhx2mHYtE3Z09ezbffPMNACeddJJ+74knnojZmER7Oeqo
+o/T3M888UztxBQIBfa5iFubk5HD22WcDcNppp9G9e3fAmXsx3aKtKcjYHnjgAf199erVSupSmmiC
+rMVrrrlGU5qFLOaCCy5QUyOamqKnKXjw4CEMB4WmUNqd/YgjjgDQbs8ffvgh06ZNA4hbeqs4DB97
+7DH69+8PwDfffKPt2AqT+D6fjzfffBNwHGey0zz55JNl2iHKEiIUTeCJJ57Q7y1ZskR9CbLT/vTT
+T7oLLliwgLfffls/jxXEF3PdddfpM01NTdU+ouvXr9ew3po1awA4//zz1UczdepUxo4dCzhhbdGE
+qlWrVqZCOLlvN2eHG40bNwYczUX+lpycXK4QbSgUUv/Jgw8+CDj+HHke0tMkGjgohEJpYIzhzjvv
+BNBGIDNnzix3BVx5IUJh0KBB+kKPGDGiUKEkL+OyZcs47LDDAKdCrkOHDgDqjS4O7oVb0mIUp9bA
+gQP1pdm1axfVqlUDHOG1f+INFAiy+fPn6/2dffbZOt/RhDsHoW7duppn8PPPPzN69GjASUoT1byw
+lHZrLTNnzgTg6quvDov/l6fPZFHVr5Jbkp+fr/OyY8eOMgkFd/m5VHzKZ/Xq1ePqq68G4I477ijz
+uItCxOaDMSbBGPODMWbavv83N8bMM8asNsa8ZYwJRD5MDx48xAvR0BSuAlYAafv+fz/wiLX2TWPM
+RGA08FQUrhMRmjdvTsuWLYGCsN+yZcviPo4RI0YAjqoqqm1hqp8xhk8++QRwCDQkxDZ48OBSaQgC
+N8lMSRg2bBjgOMYefthp+vXhhx9qjL2kAqcRI0YoUaoQfkQb1lrNPfm///s/HVtxncf3R3JysoYv
+U1JS1BlZXgdzUTu/aKTJyck6d7Nnzy71eQcOHKhr5I8//tDxyfUSExM1FH///fdHrQVeRELBGNMY
+OBm4B7h2Xyu5vsC5+w55CbiTSiAUDjnkECZPngwUJD3FO0fDGKNx/Pz8fN55551CjwFYunQpLVq0
+ABz/g+Qj7Nq1KyZj8/l8mghUo0YNXn31VaCA0KM4iF179tlnq9ouizkWENNl1qxZ5T7HVVddBTiC
+7rbbbgPQhKbSoqT188wzz+jvkjsjkZri4GaQEiGbkpKififZIB566CGNsjRo0IBffvmlTOMvCpGa
+D48CNwIiYuvgdJ2WLeUPoFFhXzTGjDHGLDDGLIhwDB48eIgiyq0pGGOGAJuttQuNMb3L+n1r7SRg
+0r5zRbRl+3w+atSoAThOGFHH3ZJ83bp1qhJXVBZnQkKCUn8lJiYyY8aMA44Ryd+8eXOWL18OwI03
+3hjzMScnJ2sl5lVXXVUqDQGce5Ici6OPPlrNBsnWk2PEWSnZmD/++GOF9VsIBAIaqZg2bRqLFi0C
+olu4lZKSQpcuXQBH+5PrNW/eXDukS4pyfn6+mgZ+v18dqXPmzAkr2JM1IFm4t912m/I8vvzyywwc
+OBBAr1VeRGI+nACcYowZDCTj+BTGA7WMMf592kJj4MBc3SijXbt2LFjgKBz5+fnUrVsXcFRtUcc3
+bNhQ4aQf1lpdCKmpqfz888/6NxmnVH5u3rxZF1U0BILP59MXMyUlRf0qcu5AIKBCYX+7VzzngUBA
+7WRRYYcOHcqgQYMAxyYXGz8lJUVfsho1ami14rPPPgtAz549C03jjgfS09M544wzAMe/VFzdS0JC
+QrmERbVq1cJ8FBI96tOnjybayRxOmTJF6zamTp2q66Ko5y7nHTVqlJqVxx57rG4ibdq0iaj6s9zm
+g7X2FmttY2vtocDZwExr7XnAF8CIfYddhNeK3oOHKoVY5CncBLxpjPk38APwXFm+XB5SjIceekh3
+4AcffFCLYPx+v/Lg/e9//6twUpXU1FQ1czZt2hQmzUWV/vbbbwFHNYxGQpXMp8/no3NnpxbmlFNO
+4cYbbwQKdp1BgwZRu3ZtwInXS4HYsGHDtCoT0OInyWPo1KmTjnPatGlawVe3bt2wPo6SICRe/0hV
+3PJANK/TTjtNHX4bNmwodM2JWh4Jq7fkE0DBPE+cOFF5HeQaAwcOLFeVblJSkp7D7/drUtcDDzzA
+3/72t3KPOypCwVo7C5i17/f/AV2icV4PHjzEH5Uqo9Hn82mxUl5eXpF5BCLZRTK2b9+e1157DXB2
+PNkFk5OTdRf7/vvvlfw03o5GyRTs1auXOkHfe+89zUKbPn26Zr9JmDJaaddyr3l5eRrC69Chg9r4
+kpYszi1wdnlhAtqfSVpCZJJGvHfvXt31u3TpwltvvQU4GoTYzE2bNtXdsaKcvIsXL9a15R7Hpk2b
+lMnoiSeeUE1Idu7y+qHq1asX9n8pcZ4zZ45+5nbGlgczZ87kq6++AuCcc87Re6pfv74W0JWHwq1S
+CQVrrXpQ969YE2fXscceq44aUcXd3YHatGmjDjNrraroe/fupVOnTgDqyCmNChsJvboIg1NPPRVw
+zBzBRRddpA/u6quvVgEh6ncsMXv2bJ56ykkdadCgARDu3c7Ly1NzJj8/XxuS/Pnnn3qM1G20a9dO
+m75kZ2frczr66KOVROTpp59W8yHaNSYyx02bNlWH4ObNm1WtFkKdJk2ahH1PNpb69etz8cUXA47Q
+lg1FHNfhDcLlAAAgAElEQVRZWVlFPn+5dmEJYpmZmWE1ESJ8mzRpUmh0rDyoUaOGpj6709jXrFkT
+USTFq5L04MFDGCqdpiDZhgMHDmTUqFGAQzt15JFHAk6cV1RXN6/APffcAziVg6tXrwac3ASR1l26
+dOFf//oXgDrcjjjiCHVQQsHuMW7cOM0e2717d7klupgrUt22detW3bESExNVtRsxYoRSgsWSfVjO
+uWzZMo11y/WmTJmiKrM7DOcu5unWrZtqBW6HoTjjrrzyStUEZs6cqXX/Z599tsbmH330UYCweS8L
+jDG6FtycDTNnztQciSFDhii7kWiQa9asURLUBQsWqFN16NChXHbZZYCTSixOajFH16xZo9mPZXE6
+/vbbb3qv55xzjmqvK1asiLyt2z4NZciQIfouuPHYY49FpClUWjq2QCCgOf7umG9+fr4Kjv/85z+A
+YzeJ7VeUun/rrbceULXXpk0bVq5cCTgTLXavz+fTF1rSU8sD8XmIuhgIBFRVvfXWW7niiisAeOON
+N7TMVoTGggULyv3ixAru3puCUChUpAATP8X8+fPVTFm8eDHg1HBs2rSpXOP46KOPAIeyTuZ29OjR
+/PbbbwB07NhR8xBEcEyfPr1I/4C7Xb2YVX369AEcQSACZv/GMSUlX4ngnD17Nvfffz8AL774opa+
+l/fdk3U1b948jXDUqVNHNz2pZC0EHh2bBw8eyo5KZT64EQwGtUpw2LBhukPl5OTwwgsvAGgcfP+u
+voVh1apVYamk4EhwyQirVq2aOvvefffdqDTeEE+2ICsri5dffhlwGIfPO+88wHGennbaaUBBH4LX
+X39dNaHKAmttmdRS0QTmz58fli8CjoYhKnVZY/SS9fniiy8qi/f48eNVU6hfv77upkJZV1wUQdbM
+li1b1EEpTujRo0cXSWBS3FwYY5RZfM2aNbzxxhv6uWgeZeVuENPmrLPOApwiPzFpzjvvvEIL7MqD
+Sms+QIHt9P7779O7d2/9TNQ4SdD44YcfdIG1bdtWE2/+/PNPLSdNSUlR+nSZ1KSkJFXlzjnnHL74
+4ouo31tR9/TRRx/Ro0cPIDx0KoLk6aefVpWzsqC8kZhAIKBpviJs169fr/dcVvtX1P309HRtjCPm
+iUA2AAnZnXjiiSWOPS0tTYWzMEdJ5KUwFBd9SEhI0HT7rKws3YhmzZqlyWAixCZOnKjRtqysLDVZ
+ExIS1CfSokULrVyVsHD16tW1dFqS3kqAZz548OCh7KjUmkJh6N69u6rVRx11FOCYGqIpzJ8/X9vG
+/fjjj6o2hkIh3WFatWoFwAsvvKCOxv/7v/+La3uz1NRU3nvvPcBJdJGdQticd+3aVWnawkUjIiLn
+EIfq+vXrI74/YwwXXXQR4NCRCfdmUlKSXk9yUvr378+GDRuAou/D5/Pp7i/fz8vLK/L4ssyLRHBu
+ueWWAxi7ExIS9Pe9e/eqBuX3+9Vk2Lt3r0YapJCsW7duZXWEH/zNYCRxo169eqouLlq0qMKrIUuL
+ohqtHGzw+XzqJZeXI1osQRJaPOKII7QK9Pzzz1ceS7GzP//8c82QLS606O4iBc5mEk3Kf5/Pp81n
+JLQ6duxY3dQSEhLU1HAnlM2bN0/N4muvvRYoVyKYZz548OCh7KjSmoKHqgP3zhstJCQkaDLVjh07
+Ck3TluumpKQolV1lWPNQkFgFBd2xe/TowciRIwEnp0PqVSZNmhSNDtmepuDBg4eyw9MUPFRZ+Hw+
+DfuVNzvyL4ZSaQqVNnnJg4eSEAqFIkpD91A4PPPBgwcPYfCEgoeIUFTLNA9VF1XafCgszu/+zJ3E
+4vP5NDbu9oS7E1CE9CQYDJaps1JJcKfDuscniSnWWm2UKrZxMBjU8WZkZKjXfufOnfq53+/X8Uki
+UDS8++5kmqSkJB2/3+/XNGV3Uo38PRgMaol0bm6uzufOnTsLncfCnlk0EqQqg5+sKCQkJOgzKqox
+rRvuXqDRXJPFwdMUPHjwEIYqqSlIdpxksLnjt24pmpOTU6h6606v3f/4WCIlJSUs21Ji6U2aNNGW
+ZVIxGAwG9T6TkpL089zc3LA+DMIFKBV3Pp+v3NqCzFXNmjWVkOT7779XZ95vv/2mzWqEXi0/P181
+herVq2uxT15enmbcJScna46AfH/JkiVRbQbj9/sLPU9R2kNh60LmFQrvqh0NuFOXC2u4s3/RWWkq
+gAXRygWpkiFJ6bEoavT+JcqVGdFUB6tXr64LTAhpIlkQsqjOOussZTEqSvUvDMYYJRZJS0tT4bti
+xQoV4HL/lfWZ7Z+OXadOHR1rNN6V+vXrK6FtBSD2yUvGmFrGmHeMMT8ZY1YYY7oZY9KNMTOMMav2
+/Vs7kmt48OAhvohIUzDGvAR8Za191hgTAFKBfwJ/WmvvM8bcDNS21t5UwnnKNIiq4FCKJeT+U1NT
+VQUvbe/H4iCOweTkZCUZKavmIbX+bvXbrdZXlWcmGkNqamo00osrC2JbJWmMqQksAg6zrpMYY34G
+eltrNxhjGgCzrLWtSjhX1VgplQSxrq50czG6iVvdjVBLGltVefn/Yoi5+dAc2AK8YIz5wRjzrDGm
+GlDPWrth3zEbgXqFfdlrRe/BQ+VEJJpCZ+Bb4ARr7TxjzHhgF3CltbaW67jt1tpi/QrR0BRkhwoE
+ApWOBbkoyG582GGHaRMcqfX/+uuv6dq1K+DQl0s1YIsWLbQPonSOjjZOPvlk7SSdmpqqVGobNmxQ
+dmQZg7VWnZ1169ZVp1xl57QQ+vlDDz2UpUuXAuH8BfGGmF0nnngiZ555JuC0l5cqySit6ZhrCn8A
+f1hr5+37/ztAJ2DTPrOBff96yekePFQhlDtPwVq70RjzuzGmlbX2Z6AfsHzfz0XAfcS4Fb1oBx07
+dlTiSmMM119/PeA0xagMtq048Nw9Eo499lhl0Dn11FPVsVVYyDIvL081iOTkZKXzevjhh6PKTyDM
+yI899pjmG8yaNUu7NYdCIaXAk54VXbt2Vfq7HTt2qMbzyy+/RG1c0YKwNN18880MHToUcHJATj/9
+dKCgJ0W8YYzRPpf9+vVj+PDhgNNERzJcjznmGKWTizUiTV66EnhtX+Thf8AoHO1jsjFmNLAWODPC
+axQJUb9fffVV9cID2nBlzpw5SsEldPAVAUkNTkxM1K5XN998s6rgt99+u6rrsjh27Nihi2D69Omc
+e+65gJPoJIlMtWvXVmbrSIWfz+fTJKXjjz9e8wquueYaNVNGjRql1xPK9rvuuksF2erVq5VduLII
+BVHLL7zwQmXHdidWZWVlaSJWvCHrt1GjRso7evTRR6tj1xijvJOnnXYazzzzDBB70ywioWCtXQQU
+ZqP0i+S8Hjx4qDhUyYxGgbDb/vnnn2HFNyeffDLgEFxKC/CdO3cyb948Pb44+P1+VTWzsrJK1Z26
+OIjkf/jhh7n00ksBp53ZDTfcABTd96CwgqFjjjlGd7bdu3dHnVRUxist31q2bKmOuN27d6vDc/To
+0YCj0dx0k5OG8u9//5vnnnsOqFjNzBij7eU7duwIOPckqeT169fXpi/Dhw9nxowZ5bpOcX0fioPM
+rTz/AQMGcOihhwKO81ho2kKhkDpE58yZo1pYBO9s1WJzLk+jEbGBFy1apC/8WWedpWpw06ZNGTBg
+AOCoxNIncPny5YCzyOWFbd68ub6wgwcPVq/v5MmTI24SI4vg999/V4HUsGHDSkPhXhgKyzcwxtCy
+ZUugwD5fsWKFCucdO3ZEpbNWpHjppZfUg//Pf/4TcISwqN3PPvss7du3BxyfSHnVcfEDSfQlOzu7
+xHP16dOHF198EUA7SLkreCdNmqTNbU899VTtD/n555+rryGCOfY4Gj148FB2VJoqyfJoCu6W9OI4
+2rJli6rUv/76K5MmTQIcCSzHC3fBoEGDOOWUUwCn4lJUykAgoJ5o4dqXMULZ1TdJQV6xYoXusE2a
+NOHXX38t03niif01BHDyKcQh+scffwCOlhaNFGs3yjvPosWcd955fPbZZwA88sgjBxzXtGlTbTcX
+ibYmWqZEZJYvX16kqSmO2zfeeENNMDE//vzzTzUNli1bRo0aNQCnZ6Zc45tvvilRCxGNJVLTrdII
+hbIsAIk0iLqfnJxcKi+8+B0+//xzwKmAk4meNWuWJuksXbpUbeNolNDKQ+ratatGFFauXKnnu+ii
+i/jwww8BCs2z9/l8Yf6FaPoRSkJycrJ2Ybr77ru1BHr+/PkATJs2LerXLM88JyQkaDQnMzOTU089
+9YBjxIzr27cvd955Z7mvJRA1XjYQMSf2h8/nU79FRkaGXlPWRefOnbWvJKD9LA855BA9dsmSJSWy
+XEXLj+OZDx48eAhDldQUBOJZDoVCqjoWJU0bNWqkXntp0ZWfn6/S/sgjj2TNmjWAE48vR0uuEpGV
+laXRENl9wWmpLh20ZefdsWOH7jy5ublxo+ISyPjGjRunJo/P5wtrYAKOw7EC+QEU4lAGhwymsOf3
+/PPPA84cTp48OeJryrMQFb8o0yEjI0O1VDEZAL788ksdrxti0roRDAbjxodZaYRCWSAqv3iYly9f
+row+7du3V3XOGMMll1wCwOOPP66TKnbkiy++yKeffgo4tpy0H4+lei7JS61ateK4444DnEX19NNP
+AwU57mvWrNGxScgvXvD7/YwfPx6AGjVqhHEJyqIWxqdPPvmEu+++G4ApU6bE1bRxY/v27eozaty4
+sUaaJNsyNzdXN45gMBiVhCU3A1ZJY3ObtyJMpk6desCxNWvW1KxQN9y+sljDMx88ePAQhiqpKQgk
+GaVWrVoa2z3ttNMYPHgw4OSR9+zZE3C0BtEwJN778ccfV1g1X69evZg+fTrgOMZk523Xrh3gRCeE
+Es3n88U1pyEvL0/j+AMGDKBBgwaAoxVIIpPsysuXL+fNN98EYNeuXTz22GOAk/4czxyYefPmqVb4
+zDPPcP755wPOLg2OA7dx48aAE5WKxnyKibJjx44SjzvvvPMAJwIl9QxSUZqenq4RjP/85z9q3kKB
+Vus2IWMNT1Pw4MFDGCpNRmOk5xC77oILLuDyyy8HnLi1OxVadorKUOvvDjNaazXM2rmzk3B2zDHH
+qM9h06ZNTJw4EXB2ucowfoHf71f7vGnTpmr3vvTSS7pzx2uNyRoYPny4Oh4l3fmf//ynEv527txZ
+2a/jDZ/PpzkLMlf/+Mc/tNN0w4YNNd/A5/OpI7xOnTpaKRsBqlaac7TOFQgE1JQYNGgQM2fOBGDE
+iBExiSiUFZLaumfPHn3ghTmQfD6fOvtOOeUUVqxYATjOUVnoZY1Lu8uzoylY5LzTpk3TlzEvL4+m
+TZsC8W/+6vP51Bzr3bs34ORYyLq48847K0VJvThwmzdvzieffAI4Dly3UBDHc61ataKxfr00Zw8e
+PJQdB52mkJGRoQVP+fn5qo6vX7++wneHk046SfkSHnnkkRIlvzhJr7vuOt35gsGgEoSU5ODaH1Jx
+t2fPnpiEtzIyMrjjjjsAuOSSSzRu379/fw0Tx+sZNGvWDIAJEyYATmq7hIO/++67CgudFgafz0ef
+Pn0Ap5JWCv2MMbpG3H00IsBfsxV9//79Vf2aO3euqq4VIRBEPRTmossvv5yHHnoIKJ1fY86cOYCj
+Oj777LOA4xuRVOOywv09GZv4MiJRTcWW3759u+YFHH744UrE8vLLL2ukRar+Yl1N2bx5cwA1uxo3
+bqz1GpVJIIAzHklkGj58uKbhN2rUSMcazzF75oMHDx7CcNBpCtdff716lidMmFChnnrRWK655hrA
+SWeVopzSaC6i7o8fP16jKB988EFUdg25vnjCU1NTlXatKK3BGKNZfGIGjRgxgnXr1gEO94Tc8733
+3qsUY0cddZRGIsSMePXVVyO+h6KQmpqqeQpSwRkKhWLGfh0NyDrdvHmzUshBgYYQT033oBMKNWrU
+0EpDKZ+tKAhxxldffQXAsGHDlGevJKSkpCinYPXq1TUlduzYsVEdo/A9Pvroo5osNWXKlDD1XgRH
+jx49GDFiBFCQ5tyrVy/1HfTr10/Nhz/++EMrA2fMmKGL++23347q+AtD8+bNOemkk4CCHpt33313
+pab+d7/0UkuSlpampllRDXRjAc988ODBQxgOmuiDqNd79uxRuvfevXtXCsozqTKcP3++7lbdunXT
+3bh+/fp069YNKHAGjh07Vjs4L1myRCMO0Y75i6Pxkksu0chBMBjUyMavv/6qnvwGDRqoSSOFUdnZ
+2ZpUk5iYqLkVr7/+uqYYZ2ZmHrBLx2Ldidr92muvKRmMaGa33npruR208USTJk20mrNnz546T/Xr
+1y9ztKkQ/LWiD7JwfT6fJoJUBoEABfn3Dz30kEYftm/frhEA9wuzdetWwIk4iLd82LBhMUsAEpX0
+mWee0SjJ6aefriXSjRo1Uh9DQkICa9euBQqEU0pKShif5YIFThfAovwesSz/FX9Nr1699Pri16gK
+AgEc00GyV7t3767mQ58+fbTsPtaItBX9NcaYZcaYpcaYN4wxycaY5saYecaY1caYt/b1hPDgwUMV
+Qbk1BWNMI+AfwFHW2ixjzGTgbGAw8Ii19k1jzERgNPBUVEZbDET1BdR8KC8SExNjkhL9zDPPaDci
+SVYBR7sRDUFSl1988UV1NEZKMV8a5OXlcc899wAOsYrsrHv37lUPfn5+/gG08wkJCWVygMXSXB03
+bhzgPD+hvasMBDBlQTAY5LvvvgMcDgXRfho1ahS3jt6ROhr9QIoxxg+kAhuAvjh9JQFeAk6L8Boe
+PHiIIyLpJbnOGDMO+A3IAj4FFgI7rLWydfwBNCrs+8aYMcCY8l5/f7z00kuAUzz0888/R3SuWBVO
+5eXlqQMsPz9fnXXGmLBGLDKGeBZw+Xw+3VUzMzNL3I3cfS4rAwKBgOZL7NixQ+3yyuBILyuEJ6Rj
+x47K+H377bcrhZz0NYkVIjEfagOnAs2BHcDbwKDSft9aOwmYtO9cET85IaxYsWKF/l4Z4XZ+up1x
+8nlFVXKGQqG4mCnRhqjUSUlJym85b948pXCvyti2bRvvvvsu4OSASIr41VdfDcRO4EViPvQH1lhr
+t1hrg8B7wAlArX3mBEBjYF2EY/TgwUMcUe48BWNMV+B54Fgc8+FFYAHQE3jX5Wj80Vr7ZAnniljk
+Sby9du3alTqd1UN0UViI0xijHA+VgUMjEohZWbt2bc13EfOhHGHW2JOsGGPGAmcBecAPwMU4PoQ3
+gfR9n51vrS02vzSapdPl6TRVGVGR9+Hz+SpdJaEHwnxQgsKeUzHr5q/JvOQJhcjhCYXKiXgJhSqZ
+0ShZXuL5TklJ0ZTh1NRUjfWnpqaq+uhmw5VJTUxM1HO4Y/Bubv5owN0ARMycvLy8A/pQuFHU9d0L
+IlZCwxgTl+tEA+5xul+a4qoLK+P9yH0YY7QALTMzU+8pLy+vUAFQmPkUqVCvckLB/cCFCjsYDGoo
+LzMzkzp16gAFFXLgTKoc4xYEgsTERBUs0d6lZbxuqnb3fRR1vcJeTHf5cqyq/tzh0rIuLneCjQhA
+a23MUs7d81bYNeKV8FNauJ+fuxJVxpeUlKTrszTzJr6T/Px8/T3SMLFXJenBg4cwHHQ+hYMRVclP
+IrtVx44dadWqFeCwPEe7XX1VhZvvozAEAgHlgpg2bVq0NSyPzbmqQ2z7WrVqHWDnV1b4fD58Ph/H
+HnssW7duZevWrcW+BH81lDQXhx12GIMGDWLQoEEV5uz1hIIHDx7CUOUcjX8VGGOUZOXaa6/lgQce
+AGDVqlVhKmV5HYKxgozn+OOP17z9yjK2ygwxu77//nut3amwkHSFXNWDBw+VFgedplC/fn3lVjj2
+2GOVI2D37t3MnTsXIBo9+WKOpKQkJVJt2LAhZ599NgBjxozRtnEJCQlKQyeprxXpkDTG0L9/fwD6
+9u2rfSs8lIyaNWsCznOXkHpF4aCJPlSvXh1wuA3HjHEqspOTk8Nit5LU9PLLLwNOctO1114LEBfv
+eFJSUqlzC1q2bKlkMbVq1dKX/bPPPlOilqysLKVUj5egcyfZQHiiV2JiIt9//z3gCGdpyBKv6kuJ
+/x9++OEVroKXFenp6YAj3KWmQT6LIrzogwcPHsqOKmk+yO4v/zZv3lwdce5srv3Ti0Wb+Nvf/gY4
+O62o4h988EHMxiskqKmpqUriWlLH6Ndee03Zia21qooPHjw4zNEYT1MoNTVV2Zz//PNPIDwrz+/3
+c8QRRwCwcOHCuGgIkjWZlJSkdGzdu3fn5ptvBgp6bhx22GE0aNAAcBrSiLnVsmVLVq9efcC9xBvC
+1Gyt1TTnikKVEwopKSl8+OGHALRv3x4oeOmgeLbg/VXfQCCg55g2bVrMvORLliwBHL7DY445pthj
+xZ486qijVOht27ZNzZyKYKgWtXzcuHHazOWLL7444LixY8dqrYk0jSkrSpOoJfZ3Tk6O+lQuvvhi
+7QqVmZnJE088ARTUydSvX1/Pm5OTo2sgFAopaUlpG/XEAjIeyfOoSHjmgwcPHsJQ5TSFunXrcuih
+hwIFTVastUVqCO4qSXePPnB27tGjRwPw5JNPqkpc0k5V2rRj0WBq1KgBOCZKUeq+7Hj//e9/AUcj
+EqfklClT1IEXb/h8Ph5++GHAiSiIWl4YevXqpSzK5eURdFf4uedYtKann36aNWvWAE47OmHFvumm
+m9Tpum3bNhYuXKhjAqeNnUSiDjvsMAYPHgw4LeqjRcoTSWWpu+CtovM6qpxQ2Lx5s6rjDRs2BArU
+W4FMcCgU0qYl1apVU1VSFlhSUpLa9sFgMOqe6hNOOAEo8HM88sgjYX9306RL2/bOnQucw9JG/eqr
+ry73QikvFbugXbt2XHTRRYAT+SiO7ScQCPDRRx8B0U9Y6tq1K+AIjSZNmgCOgJAN4s8//9QI0nPP
+PceDDz4IFPgJ9n+20jBo+vTp3HnnnUB0mvdGwGQGOPMmv8ezf6QbnvngwYOHMFQ5TSE7O5thw4YB
+BbvqZ599plpATk6OqoOJiYmquteoUeMA5po9e/awcuVKoGxe/NLuBu3atQNQlTo7O1vH475eIBDQ
+nUu0mFAopHTwJUUqSjPWsu44MkdffPEFs2fPBuCcc84p9Fgx48BpYuO+blkRCoXCvivjEEdrw4YN
+1dHYoEEDfab9+vVTrbCk+fL5fNpOLiEhQddApJpiJN+X7xaWwh5vVDmhAAWq6dFHHw04fRnls6+/
+/lprBmbPns0FF1wAOBO8P+HG7t27eeONN4DYePXvu+8+oIDs5fXXX9feBLfeeqsKiMsuu0wXuoxt
+6tSpFUpAK63m/X4/t912G1A0qYvQqW/atInFixdHdN39XywxG8QUy8rKUv+Lz+fjm2++AZxeCaV9
+hoMHD9beowBPPfVUodeOJ2RtZmVlqe8rOTm5QsKknvngwYOHMFRJTUFUbEn33bVrF++99x7gqJRj
+x44FnH6NbvozgWgVS5Ys4csvvyzz9UsbfZBjRK09/vjjdTydO3cmIyMDcFK05Z6k0/Rdd91VoTtX
+t27d9HdxeBaG1q1ba7/DG2+8MSpjlmeVmprK7bffDhRoKbm5uZr+m5KSonkdgUCgRLOhRYsWANx9
+992ahLVr1y6WLVsW8ZgjhaxJNxXe0UcfXSH1I56m4MGDhzCUqCkYY54HhgCbrbVt932WDrwFHAr8
+Cpxprd1uHBE/Hqfz9F5gpLU26gF2SW0V+3XVqlWaqjpp0iS1xRcuXMgZZ5wBOI42kcB79+4FHJ9C
+eUJQZd0NW7ZsCYTbi82bN9fxWGt1HC+88AJQkAVZUZDQ4u+//64+GneuhGg2c+fO1VCgaGvRQk5O
+jjouxRezadMmrrrqKgAuv/xyOnXqBMDq1as15NurVy9NJxdfxLx58zjyyCMBR7sRx+vixYtp2rQp
+4PimCnPIxkNjk2tMmjSJG264AYAePXpUTKWpVLkV9YPT8akTsNT12QPAzft+vxm4f9/vg4GPAQMc
+B8wr6fz7vmfL8hMIBGwgELBdu3a1Xbt2tZdccomtU6eOrVOnjk1ISNDjWrRoYZctW2aXLVtm8/Ly
+bDAYtMFg0GZlZdmsrCx76aWXWp/PZ30+X5muX96fWrVq2VdeecW+8sorNjs724ZCIRsKhWxeXp6O
+KSMjw2ZkZNh9laMV/rNu3Tq7d+9eu3fvXlu3bl2dr1GjRtlRo0bZzMxM26FDB9uhQ4eoX9sYoz/u
+zxs1amQbNWpkd+3aZTMzM21mZqbNy8vT+XT/5Ofn2/z8fLt37167ceNGu3HjRrt79267Zs0au2bN
+Gvvpp5/qOmrUqJHeX2Jiok1MTIz7fE+aNEnXaevWraN9/gWleR9LNB+stV8Cf+738ak4beYhvN38
+qcDL1sG3OH0lG5R0DQ8ePFQelNfRWM9au2Hf7xuBevt+bwT87jpOWtFvYD9E0ope+j2Igys3N1fV
+2Zo1a2p13sCBA9XJ17JlS3VgCXnm+++/H9eU0h07dnDZZZfp2Pv16wc4oTV3IxpwQnFiBuXm5qqp
+sWHDBk3djodaO3LkSC1A+/TTT7WqVNLDf//9d3788ceYXLuo+5Ou4s2aNeO005z96OGHH9awrhvy
+zDMzM5kxYwbghCQl5PrRRx+pGZqTkxOWDRtPyDjXrFmjppKkc8cbEUcfrLWi3pX1e2Gt6MtCYy6J
+P5Lr3rlzZ02/Xbt2rS6an376ibp16wKODSwPWv5eEbTj8kJLqaxAvOtDhw4FnEiFCL3vv/9e/965
+c2etK/j444+ZOXMmUDAn0RYUM2bM4JZbbgGcVHHxL8h9vPnmm1G9Xmkg97h9+3b1wci/4AhZSaiS
+aM+ePXu0dPr000/Xc3zwwQdkZ2fHbexFQXJWLr74Yo2i7F/PI740QaxSoMsbfdgkZsG+f6X6ZR3Q
+xHWc14reg4cqhvJqCh8AFwH37fv3fdfnVxhj3gS6AjtdZkbUIOaDVMj17NlTvdD333+/agItW7ak
+da9FPV0AABi5SURBVOvW+j2RrOLRrohiE6EoE7UXHC1ByECkwOeII47Q+7zqqqs0rg4Fu1/Pnj15
+6623ACevIVYQr37NmjV1/GJSHHbYYZWO8iwUCoW1DBRInkJycjJLly4F0BTuioYUZTVs2FBZsPdf
+n6LpSvWtz+fTSFpWVpZqlhs3bozomZQmJPkG0BvIMMb8AdyBIwwmG2NGA2uBM/cd/hFOBGI1Tkhy
+VLlHVgwOP/xwwFmQEJ6w9PPPP6spccMNN6ivAQqIQb7++msg/kKhWrVqvPPOO0C4H2HOnDn88ssv
+QIHK2KpVK01u2l9tFCQlJfH666/Heti6GHfv3q2+DbHfJfRXFfDcc88BzhwL01as+nGWFaeeeirg
+rEkRWIFAQDcAa61uEmIq1q5dm1GjnFesX79+GnLt0KFDRPdVolCw1hZeBQP9CjnWAn8v92g8ePBQ
+4ag0ac6lVXeMMbrjC+fi5s2b1bPcrFkzTf6QuntwdjsxG8TbHC+1V4g+nnrqKXV2ub3bJ5xwAr17
+9waKroxzO0kvvPBCwNF44qnt5OfnK++DcFh8/vnnlc58KAypqamqXgP873//A+K3BkrCv/71LwAm
+TpyoJtpjjz2mfI1XXnmlRs1EC9i4cSOvvfYa4JhBohVHqv1UGqFQWqSlpR3AoPTf//5XveE333wz
+J598MuBEHOShb968Wdl44vEiJSUlKUuReO/9fr+OMy8vT18sqforCr/++qs2a5XvVxR69OgBFJg5
+o0ePZtq0aUDl7gR14YUXank9VPw87o93330XgCeeeIIOHToAqJ8M4KSTTgorURdIFGv/aFYk8Gof
+PHjwEIZKoymUNk+hbt26qiaJE2bQoEFKvJKRkRFGbSX16DNmzNBkkFiqjOIU7N27t1LJy3i3b9+u
+Xvs6deqoxtO5c2fduX7/3cn9evXVV3nssceAyrOrWWu56aabADj33HMBx6kltHhS4VkcRCsKBoNx
+1Sz69u0bZppJ4tiUKVMqRccwecZNmzZl0aJFQIFDHRyq+niZOp6m4MGDhzBUGk2htFKwevXquuNL
+dVv9+vU1XGOtVftq27Ztult//vnn0R5yoRB/xcyZM5VObdWqVYBTnVlZHFvlhey2ohHVrFlTQ6el
+0RTkOcVL+xGtsVu3bmFzP2XKFP17ZercnZmZqc7c999/X52jwiAWD1S5XpLuFFZJ+BgzZow67ay1
+mtBzwQUXVEjzlIMZ8pIJTdpvv/2mHJSVYS3tDzHd9uzZE+ZoHDlyJOAkYQm1/18AXi9JDx48lB1V
+TlMoDCkpKeo4Gjt2LCeeeCLAX2kH8FAExLRZuXKlFtDt3LlTSX+3bdtWKcyGOKFUmsJBIRTccHcY
+8uBBBMEZZ5yhlYjPP/98RLT5VRie+eDBg4eyw9MUPByUcBfCgcNXIGnCRTmfC+sFWRaejyqAUmkK
+lSYkWRa4CU8FFcWYUxq4F5uE5HJycjSsl5eXp55xSbZy9xEMBAJaLpuTk6OLunr16prnLtWK0bj/
+6tWrK/FIKBSKuI+luwFwtF8w93ndocX9X/zSpAEXNrZ4CISiBI87XOq+PzGJsrOzw1L2ozVWz3zw
+4MFDGKqkplDenUtUSpG4sZCyxaFGjRq6sxljVFPw+Xy648tn7qYgwWBQeSdzcnK0GcrevXvDUrqj
+haysrDD1uTy7vM/nU1KTtWvXxqz9mYwpKSmpTNeIleYSDcjYEhMTw3qLSsKXaHF5eXkxGb+nKXjw
+4CEMB52jsTiI3S4NQtauXat19bGE7PjJyckq+ZOSktTOrQhauNLCGKNjTkxM1F2qsHXj9/uVFu6C
+Cy7Qlm7NmzdXstnKsN7+wjh4HY3lhaiXwuHYtm3buAgFUe2zs7PDOl5XBfj9fn25N2/efECFqt/v
+1yrJ5557TpOC6tSpo8Q3e/bs8YRBFYJnPnjw4CEMfylNQSBq+w8//BDX67ra5GGMUXMmPT1dWXmF
+YWnVqlVhjqV4mxji7AoGg2zcuFE/39+hGQwGtXnJrbfeqgSk3bp145NPPgGKJp6tKBhjlE17yZIl
+qi3GO5wtdILW2jBOB7czuiJC7JXracUJQv8t/Hexhju+LLyRI0eO5LzzzgOgXr16ekxhORjLli3j
+kksuARwSFllAmZmZMRMWZVH3ZQzz589XkpjNmzergKsMzVbcmDZtGgMGDACcxrXHHHMMQKG08LGA
+CE7hZczOzua2224DnEY1PXv2BOCQQw7hp59+ApxOVkK1H2tB4ZkPHjx4CEOJ0YciWtE/CAwFcoFf
+gFHW2h37/nYLMBrIB/5hrZ1e4iDiFH3YHykpKcV606OFzp0dh++wYcM4//zzgXBaubVr16pJI7H9
+6tWrq9awZ88ezVh8/PHHmThxIkCloBFzw+fzKdno888/r2Q4p512WtwdjW5+DXAcoxJ1+uyzz/Tv
+CxYs0Kpa6RcSS9SqVUtJd9zmg7u/g2iw7kzYUCikhMTiwC2HxhC16MOLwOPAy67PZgC3WGvzjDH3
+A7cANxljjgLOBtoADYHPjDEtrbVxYzpJTU3VySyJ6ahNmzYqFKQBRzQhrDlPPvkk4Njf8qI/99xz
+PProowDaCGZ/SJLSU089pY1vPv/880onDMRnkJaWxvPPPw9A48aNeeKJJ4D4hyETEhIO6MOYlJSk
+1P+JiYlq8mRkZKg/Jx647bbb9GWWtbBy5UruvfdeAObNm6dm1/jx47VjWEJCgvJ7tmzZEnAa0FZI
+8lJhreittZ9aa8WY/RanZyQ4rejftNbmWGvX4HSK6hLF8Xrw4CHGiIaj8f+At/b93ghHSAikFX1M
+4ff7dScdNmyY0rFJT76iYK1VFS4WEEeS8Pj/8ssv6vVetGhRiVRxYjKkp6ezcuVKgLjkVZQGshP7
+/X7VFNq2bav5F5MmTdLWbPGCOyVY4HbcutVyOfabb76JS2RH2uyNHj1a15yktj///PO8/fbbgOO0
+lWf88ccfa4QqMzNTx/zwww8DMHz48JjQDUYkFIwx/wLygNfK8d0xwJhIri/2d9++fZXY8u2339YQ
+WTHXBqB169bagPbbb78t7itlRqtWrRg9ejRQsDCnTJnCjz/+CBRdvuvGpZdeCjhNQR544AEg3JPv
+bnYTS4+0zFezZs20d6EkKY0fP14JS7p06cKcOXMAmDp1algoM9YwxoTNqfwu/yYkJGiSFRRETKIh
+ZEtTRyENgWrUqKHPatasWQA8+uijRX5XwtLBYFCFgqyBunXrxmSOyy0UjDEjcRyQ/WzBHZW6Fb21
+dhIwad+5vHQ3Dx4qCcolFIwxg4AbgV7WWreX5gPgdWPMwziOxiOA70p5zlI7TUQNFK/xkCFD1Pn2
+4YcflkgfLpL9tttu47333gOiR84i5/7HP/6h9yMVjrm5udpSPjMzU3exwsY7fPhwVRN9Pl+YmSPX
+SE9P58EHHwQK2sUvXbo0qiqlMYbx48cDjuorXvtvvvkGcLSYyZMnA05Xb6G1b9WqVdS1r+JQ0tqx
+1nLEEUfo/0W7mTVrVrkrJkUDlO8XNe8+n4+//72g77KwX5955pklXlfMjs8++0yjDyeddBIAt99+
+e8VoCkW0or8FSAJm7JuQb621l1lrlxljJgPLccyKv8cz8uDBg4fIUd5W9M8Vc/w9wD1lHUhZpLSE
++DZt2gTATz/9xK233gpQKkLOoUOHAk71njRMDQQC6viJJMwjTreXXnqJhx56CCjQFKpXr65hpU6d
+OumxoVBIw2I///wz4ISdJFT53//+lzvuuEOPFWzZskX7F8QKjz/+uF4jKSlJU8OvvvpqAH788Ucd
+044dO7TpjoQjKws6dOgQ1jZO5rtly5bMnj0bKPtzl+NL+l4gENB2eaFQSFvvlSa03KiR46evVq2a
+aiQ7d+4ECjSOaKPKpTnXrFmTM844A3Bi/QA33HBDqVJp5cFIS/rdu3ergIHoxNPFgfXddwdaTVu2
+bFFB8cYbb+jnPp9PTSLpH7ho0SKNXnz99dcRj6usOOSQQwC4+OKL9WXKz8/X8Uu/Qzf8fr/OYZ8+
+fdTUqMhemDL2CRMmhH0uY0pKSlIPf1nTseVe5T7z8/P1XO528GlpabqB+Xw+rQkpDWSTa9iwoQpf
+uV5ycnJMqm29NGcPHjyEocppCjNmzFDp7y4oKQmtW7fW3Vs0hm+++YYVK1YAHJABV14Up21YazUe
+PXXqVF5//XXAKZCR74n5cMkll2g6bLyRlpbG/PnzAWde1q5dC8DixYu1B2NhyMnJ0Tk+7rjjmDlz
+JoCaaPGEmGZSaCRZggIp3HrnnXfCdvWywJ2rAdCzZ0/tAzl58mTNSO3YsaP+vnnzZuWnkNT24taM
+aAruFGw518svv6xOx2iiUgmF4iIAIgiOOeYYFQIl5ar7fD6mTp0KOBEKmXyxyUaOHKlNUePdczIY
+DHLFFVcAcP/996uNK/0xe/XqpQlLkaAw2vKiIGrpsmXLqFu3LgBffvkl99zjuIjmzp1bLA9iXl6e
+MiylpKTQrl07wMkRiNX8uu9Pfm/ZsqWmW0tValpaWliUQHwjW7ZsidhslLW5ZMkSFRCXXXaZRsfa
+t2+vLN4NGjTg/vvvB9DK14SEBLZs2VLouSX6kJmZGcbdCDBgwACNZkWzbsMzHzx48BCGSqUpFJcn
+4PYcl6TuybFDhw6lT58++rmoa126OOUYohZXFMT5tGnTJjVpjj32WABOPvlkXnnlFSA6fAQ+n093
+mNzc3APmOiEhQa9Xt25d3n33XcCJhUu1o9/vD+s5AE7Rl0RXjjnmGK1EBDTNOZZamGT5paWlceSR
+RwJOjohwJMh9urWVLVu2cN111wHRcYKKlrd3716tYPzhhx+46qqrgII+DeDMoRwvput3333HsGHD
+gHC+zuTkZNUqOnToEEbQA+Gp29HUFCqVUCgO8qJv375dXyCZkMzMTG2W0qZNGw1PrlmzhiVLlgBO
+WEjs+aKqEisSshikKW6zZs30ZWzSpEnE+fnJycn6ghx//PHMnTtXPwcYM2YM/fv3BxyhIYlH69at
+0++1bNlSQ5G33347AFdccYWaY5dffrnau/Pnz+fKK6+MaMwlISkpSV+8OnXqaBqzW+hJNCAvL0/n
+tm/fvlERBoW9pG7GKvFbtG7dOiwdXUhWRFi0bt2am2++GXAiarVr1waccLAIWbdQk5qYQCCgZt7G
+jRu9ZjAePHiIDaqMpiAOrvvuu49rr70WQKmqNm/erF5fd7u1c889l7FjxwKOySBaQ7zi5+IEkh3s
+jz/+0PvIy8sLU+Fld5AiL0B3gQULFmilZVkhO1dOTo7uTN9+++0BO+mMGTO45pprgHBOCrkHcFKv
+p093OHNk1/3qq680Hbthw4Zqoo0ZMybmjNWhUEiTpKpVq6amxIABAzjnHCfnTu4jNzdXtZxoRXXc
+XIrgaAruRC6JunTs2FFza84991xdn7L+Vq9ereZO586dNVclIyMjrCfm1q1bgYIIVefOnfX5pKWl
+qcYW8X1VBurtshZECfGE2JAvv/yymg8JCQmaDz5s2DC6du0KOCq4lFQvXLgQiC35R0JCwgEvxYwZ
+M7TWYunSpXz//feAs6ikTXrz5s0BJ4QmZtDhhx+u6vq4ceNiNmYhhZk3b576CX777TcyMjJ0zNdf
+fz1QYKuvWrVKTYb8/Hzef/99wInsyDnijVGjRvHss8+GjXPq1KmcddZZYZ/FE2KmNW3aVMOM8sL3
+6NFDS+q//vprNYt3797NZZddBjhjdp8DnCia/J6bm1saE9NrRe/Bg4eyo0pqCoVBnI9+v193tnbt
+2mkFXygUUmn82WefAbHXFMT7vnr1asBRr/v16wc46qU4PCdPnqzHSrzaWqucBd99952aHY0bN1ZP
+c+PGjWnTpg2A1hxE4pAUNfjTTz+lb9++YZ+Bo5EJr8ONN94IwPnnn6/HzJs3TzkoK4IMRsaxfv16
+6tWrBxTQ+A8ZMiRmtQJlgTsKJBrB3r17VWvIycnR3wOBgM5ny5YtNa9F/p6Zmalmp5vnsRh4moIH
+Dx7KjirjaCwJkruQk5Oj1Wd//PGH2pH33HOP2vjx6Dicn5+v9e9yvccee0wz1NLS0mjWrBng2JRS
+JOO2d8WRunv3bg3Jjhs3Tpmlrr32Wr744guggDkqEmeTzMfEiRPp3r07UKCBgcPKLPF09+eSFdq/
+f/+4kqDuD8kQrFOnju6aTz31FFD6isJYr41QKHQAcWtubq46fP1+f5gzetmyZYAT7t0/FX/Pnj16
+rmj6SQ4a86Ek1KpV64AU3Wgs4LKQw9SqVUvTb/v376/RAGMMxx9/PIA6H621SljywAMPqAMSChJV
+kpOTNY032p5+iXycc845YbRyEh0R4fPtt99y9tlnAxXb9KVr1658+eWXgDNOeYHEKS2CqyTIixoP
+ejv3uhGTIDU1VR2RycnJSn9311136YYi+O2333RdlHINeuaDBw8eyo6/jKYABbvA8OHDAYd8VNJI
+491LITU1VXkd2rdvr447URc7d+6sKvr48eOVji0YDGqexqRJk+JayJWWlqYtzWS+5syZU2yRVKwh
+ave6des0NGqM0d3W3XClJJRF64sV3EzTtWrV4oUXXgCccLHkMghyc3P1/vLz80sz9lJpCn8poSBq
+m+QxpKenc9xxxwEFuQsVBRFYjRs7LTTS0tK0DuK2227Tl79Pnz6lVoVjAakCFFU2Xv0Xi8Knn34K
+QO/evXVs2dnZKrwWLFhQYWOLFG4G6jFjxihjkwiN/2/v7EKsqqI4/vuj3Ym8kM4UMqmMRlKYNCk9
+KPUQfeAHYgQ9KEJGQi9BFkE4+NTTEEVl4FjRF4RYZFbDQImZz5ZRY5M6aRgzaqYD5UNPNqwe9j7D
+PeYwd8a7z7k06webOWefM/z3OmffxV5r73PO0NDQ2DqckZGRekIeDx8cx5k802qkkNHd3Q2E7G22
+XcYqt4mYzLsQpiOtra309/cDYXidrY3o7Oxsui9dXyuVSmVsRWO2FqWlpWWs39Y561TXSAEzK70A
+VmSpVCpWqVRM0lgpug1epl6ye9bT02Ojo6M2Ojpqw8PDVq1WrVqtlt6+Ji5H6vk9evjgOE6O/83i
+pclQZrbcaRwdHR2cO3cOCE+XTvVdi06eaZlTuEIbwGP2OmjUV7SuleyetbW1jU2N1vO9D8dnHxzH
+mQLNMlK4CPwNjJTUhJtc27WngXaHmd080UlN4RQAJB2pa7rEtV3btZPi4YPjODncKTiOk6OZnMLb
+ru3arl0+TZNTcBynOWimkYLjOE1A6U5B0mpJg5JOSdqWWGuBpEOSjkn6WdLWWN8q6YCkk/HvnIRt
+mCHpB0l9cX+RpMPR/o8lVRJqz5a0V9IJScclrSzKdknPxWs+IGmPpOtT2S7pPUkXJA3U1F3VTgXe
+iG04Kml5Au2X4zU/KukzSbNrjnVF7UFJq65Fu1GU6hQkzQB2AmuAJcBGSUsSSv4DPG9mS4AVwNNR
+bxtw0MwWAwfjfiq2Asdr9l8CXjOz24A/gS0JtXcAX5nZHUBnbEdy2yXNA54B7jGzpcAMYAPpbP8A
+WH1F3Xh2rgEWx/IUsCuB9gFgqZndBfwCdAHEvrcBuDP+T0/8TZRLyU9HrgT21+x3AV0F6n8BPAwM
+Au2xrh0YTKQ3n9AhHwD6ABEWssy82vVosPaNwGliHqmmPrntwDxgGGglPG/TB6xKaTuwEBiYyE7g
+LWDj1c5rlPYVxx4FdsftXH8H9gMrU9z/yZSyw4ess2SciXXJkbQQWAYcBuaaWfa63/PA3ESyrwMv
+ANkDBG3AX2aWfawhpf2LgIvA+zF8eUfSLAqw3czOAq8AQ8DvwCXge4qzHca3s+g++CTwZUnadVG2
+UygFSVXgU+BZM8t9w9uCy274lIykdcAFMyvrvW8zgeXALjNbRlhWngsVEto+B3iE4JhuAWbx3yF2
+YaSycyIkbSeEsLuL1p4MZTuFs8CCmv35sS4Zkq4jOITdZrYvVv8hqT0ebwcuJJC+F1gv6TfgI0II
+sQOYLSl7hD2l/WeAM2Z2OO7vJTiJImx/CDhtZhfN7DKwj3A9irIdxrezkD4o6QlgHbApOqXCtCdL
+2U7hO2BxzEJXCEmX3lRiCs/cvgscN7NXaw71Apvj9mZCrqGhmFmXmc03s4UEO78xs03AIeCxlNpR
+/zwwLOn2WPUgcIwCbCeEDSsk3RDvQaZdiO2R8ezsBR6PsxArgEs1YUZDkLSaEDauN7Paj430Ahsk
+tUhaREh2fttI7SlRdlIDWEvIyP4KbE+sdR9h2HgU+DGWtYTY/iBwEvgaaE3cjvuBvrh9K6EjnAI+
+AVoS6t4NHIn2fw7MKcp24EXgBDAAfAi0pLId2EPIXVwmjJC2jGcnIdm7M/a/nwgzJI3WPkXIHWR9
+7s2a87dH7UFgTcp+V2/xFY2O4+QoO3xwHKfJcKfgOE4OdwqO4+Rwp+A4Tg53Co7j5HCn4DhODncK
+juPkcKfgOE6OfwEnoesT8vs/JQAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 2/2... Discriminator Loss: 1.1387... Generator Loss: 2.3912
+Epoch 2/2... Discriminator Loss: 1.1287... Generator Loss: 0.7842
+Epoch 2/2... Discriminator Loss: 1.0247... Generator Loss: 1.9585
+Epoch 2/2... Discriminator Loss: 0.9418... Generator Loss: 1.0972
+Epoch 2/2... Discriminator Loss: 0.8407... Generator Loss: 1.2381
+Epoch 2/2... Discriminator Loss: 0.8819... Generator Loss: 1.0197
+Epoch 2/2... Discriminator Loss: 1.0199... Generator Loss: 0.8055
+Epoch 2/2... Discriminator Loss: 0.9692... Generator Loss: 1.6409
+Epoch 2/2... Discriminator Loss: 1.2472... Generator Loss: 0.7956
+Epoch 2/2... Discriminator Loss: 0.9436... Generator Loss: 0.9713
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl4VNXZwH9nJslkIQlJ2BcTAdlkEUVAsIriQt1KQVHq
+UlEUrUJBrQrYz7q1tFQRN1wqVSsVkKoIsoiCa5GKsq8iIFvYFzFknTnfHzfnzZ2QZSaZLXh/zzNP
+ksmde88998w573beV2mtcXBwcDC4ot0ABweH2MKZFBwcHPxwJgUHBwc/nEnBwcHBD2dScHBw8MOZ
+FBwcHPwI26SglOqvlNqolNqslHowXNdxcHAILSoccQpKKTewCbgY2Al8DQzRWq8L+cUcHBxCSrgk
+hR7AZq31Fq11ETAN+FWYruXg4BBC4sJ03ubADtvfO4GelR2slHLCKh0ihlKKn2kk7wGtdcPqDgrX
+pFAtSqnbgdtDcS6Xy+X3u9frld/Nw/f5fKG4VNhQSp3w3s904Naair709vcSExMpLi4GrD6O5Bix
+tyMUz9x+jgA++0Mg5wzXpLALaGn7u0Xpe4LW+mXgZQheUnC73X5/ezwe6ZDWrVuTkJAAwKFDh9i1
+q+yy5phY+bKZB1pRe6LZxvKD1d4W8z/7RGwm4ViifN9qreW9Vq1akZeXB8COHTuitmB4PB4AmaAg
++L6sagzVlHAZGuOwDI39sCaDr4HfaK3XVnJ8UI2oatBC2YC1P+wgZ9SIY9pnJrSSkpKY/LIZTB+f
+euqpbN26FYh9acxglyCrGwsulyvm7suMFY/HQ2FhIRDwmP5Ga929uoPCIilorUuUUncDCwA3MKWy
+CcHBwSG2CJtNQWs9F5gbpnNX+f+KZnalVMzN+BXRqVMnAPbs2eOn+sQa5hm0aNGCAwcOAHD06NFo
+NilgghkHsTRmjITQv39/AFasWEFubm7IrxM1Q2OkMGJu7969+fLLL4HYUR/MQ1ZK0aFDBwD++9//
+AlBYWEiLFi0A+PHHH6PTwCo49dRTAXj33Xf56quvALjiiiti6kt0MuHxePjuu+8AaNKkCQBr167l
+zDPPBEI7pp0wZwcHBz9OekmhVatWAJx22mkcP34cgJUrVwLRtZq73W5SUlIAqFevHjk5OfI+WNJD
+27ZtAVi2bFlU2lgRcXHWkPnwww8Bq+2ffPJJFFsUOHaPSXUSjXkOiYmJMm6iIWG2adMGgMWLF4uE
+YNqWk5NDZmYmAAcPHgzZNU+aScGI4omJidJp7du359ZbbwWgS5cuJCUlAfDYY48BlugbLZRSHDt2
+DLDUA6MbfvrppwD06NGDn376KWrtq4wxY8YA0LRpUwC2bt3K9OnTgdjRv1u1akXnzp0BuOeeewA4
+99xz5f8lJSV88803AEyfPp2XX34ZgKKiIpKTkwF4/PHHActm8tprrwGwfv16vv/+eyAyE8Tpp5/O
+JZdcAsB7771HgwYNAPjVr6zg4JSUFF544QUArr322pBd11EfHBwc/AhLnELQjahlmHPLli0ZNGgQ
+AH369BFjXW5uLn/7298AyMjIEAnijTfeAOB3v/tdRFc3t9st16us3wcMGADAjBkzZJWIFfG8e/fu
+LFmyBCgLuOnVqxerV68Gom/A/eUvfwlYq6rBqDv2OBWfz0d+fj5gSQ1GrcjNzaVhQysKOD09/YTz
+5+bmigSSl5fnF3QUKpRS9O3bF4BbbrmFVatWAfDkk0/KMX/9618BSwoyHh+jRlRDQHEKdXpSCCQg
+ad68eQD069dP3jMW2zVr1tTkskFjBl2DBg3Yv38/UHl7v/76awC6du3KKaecAljuyWhi+nnLli3S
+pr179wKWeB0LakP37t3FC+Lz+fi///s/AN5//30ANm3aJDacn376iXr16gHWhNuxY0fAUh/MOerX
+rw9YurxRNfbt2yfPJz4+nsOHD4f8Ptq0acPDDz8MWF/+isboggULALjooovYscPaYmRsUtUQ0KTg
+qA8ODg5+1GlDYyBSjlkpLr74YgkJNbNrpDChy8aIVRFpaWmAZRAF2LBhg6zG0caI1A0aNJA+nzx5
+MhA7xsVZs2ZRVFQEwG233cbUqVNPOMYeXGV+Hz16NK+//rp8znhVIo1Rc0477TTmzrVi/tat808/
+YsbROeecA1jjf9KkSSFviyMpODg4+FGnJYVA6N7dUqGUUrLymh1ykcIYpBITE8W+YI+RSEhIYMKE
+CX6fGTduXNQNdwazMnk8HnGjzpgxI5pNEowuXa9ePV599VWACqWEqj5vbAPRNOjGx8cDli1j48aN
+gGWLMvac1NRU3nrrLaBM4ty0aRP//Oc/Q96Wk35SuPLKKwFrUjAGmpKSkoi2wUwAP/zwgxi7MjIy
+JHz5jDPOEAPeokWLAESEjAVuvPFGwBJXTfsirYJVhonr2L17NyNHjgz4c0YlGjt2LDNnzgQQ9SPS
+KKV49NFHAThw4ACfffYZYC0WRq286667JNbCqGwHDx4MSwi8oz44ODj4cVJLCrfffjtZWVmAtQoY
+N1W0yM/Pl1l+woQJnH766YC/3/ymm24CYidxicvlkngJpRTz58+PcovKSEpKkh2a//jHPwJWt1JT
+U9m2bRtgjYvnnnsuXE0MiOzsbAYPHgxYhvHU1FQA2rZtK9LBpZdeypEjRwArshJg4MCBYTH01slJ
+ISMjA4CCggLAerD2zunduzdghaqagdK/f3+JEYgmxgMyefJk3n77bcDSI802abP7cOPGjTFhU0hL
+S5Pw8A0bNjBnzhygrO+jSWFhocQQ9OvXj3feeQewYijKZ4hq1qwZ7dq1A6zxY8bLwIED2b17d6Sb
+7se4ceMkbuK5556TMfKb3/yG8847D7BUTzMZ/O53vwPCZxtz1AcHBwc/6qSk0Lx5cwDZiNOyZUvZ
+Jfbyyy9z5513ApZ4aYw2xiAVKyxevJgnnngCsCQaszqYjS35+fnS9mjGAvTt21dW3ZkzZ4oHJxbi
+E3w+H08//TQAy5cvZ+DAgSccY5e2jHRz++23i9Xe9HE0MEbnQYMGsW/fPnl/xIgRAPz2t7+V/h42
+bJhsmgtHeLWdOjkpmNDPPn36APDMM89Ih40cOVIsy7m5uaKrxYIoXh4zUf3000+yp8Do75dffrno
+70OHDo24x8QwduxYmRSWLFkSc/1obAper1fcenbs1nnjXXjwwQdlf0Q0bTcmZDo9PV3ac/DgQdn/
+4nK5uP766wHYvn17xNrlqA8ODg5+1OkNURXh8XjEcLRnzx66du0KRD42IRDMzrbx48fLbs7hw4cD
+cMcdd0gK8AMHDkgQVqSNYk888YT4/1esWMGmTZsAmDhxIlD5prJoZkGuqO6Dx+MRQ112draEEHfp
+0kWOtSdhMcbV48ePhzbVmcslfWgSABUXF0uA2KpVqzjjjDMA61mH+HkHtCFKimFE8wXoUL2UUjo/
+P1/n5+frzZs364SEBJ2QkBCy84fylZycrJOTk3WbNm1O+F9mZqbOy8vTeXl5uqSkRO7pzjvv1PHx
+8fJyu93a7XaHvG1KKa2U0g0bNtSHDh3Shw4d0l6vV/t8Pu3z+bTX69Ver1d37dq1wnuKi4uLev/a
+Xy6XS2/fvl1v375dFxcX6wMHDugDBw7olJQUnZ6ertPT0/WoUaP0yJEj9ciRI/XMmTP1zJkzddOm
+Tavto0Cv73K59DPPPCN9Z57pgw8+6Hcuc2z5c2RlZemsrCwdFxdX0/5dFsj30VEfHBwc/Djp1If0
+9HSJR8jNzZXY+Fi4z/IY9aFRo0Zs2LDhhP8bcfb555/n17/+tbxvUqItXLhQxMtQi+omyYjb7RaP
+yKRJk2Q3n+GDDz7gqaeeAqxYfBNgE+n9JdURHx/P/fffD1jGaBPfMHDgQPFK2PNzmB2JXq+3UtUz
+mOpMRmU45ZRTZHyaHbGV5WVQSomH4rbbbpO+Nbs6a/DMw5tPQSnVUim1WCm1Tim1Vin1+9L3M5VS
+C5VS35X+zKjpNRwcHCJPbVySJcC9WutvlVKpwDdKqYXAzcDHWuvxSqkHgQeBB2rf1MCYOXOmzODB
+hL5Gg1GjRgFW/v6KJAWzEowYMUJqQPTr10+MkZdffjn/+c9/gLIUZCZcuja4XC4Jr/3kk0+YPXs2
+AGeffbYUIjEraf/+/SUN2qeffspFF11U6+uHEmOsPeecc2jfvj1guSaNpGDfBGUfKyZuxL5TsbLy
+hNW5NZs2bSob3g4fPiyRlSZLdHnOOusswMqwZDJAbdmyhV69egHhjxEJmfqglJoFPFf66qu1zlVK
+NQU+0Vq3q+aztW6EEal+85vfSKe1a9dOYtxjDbfbLcU9Vq9eLRl6K+P8888HrOAsExJbUlIioq2p
+53jVVVfJxFDTqlhKKclg3Lx5c4n7KCwslHRll112GeCfBqxVq1YR9adXham0ZULJGzduLF/uadOm
+8Yc//AGg2ozZ9erVky9/sDsSzfWWL18uqsKsWbMkn6h5NgkJCZLO/8Ybb5TgpYSEBIm/Of3000Ox
+IzJytSSVUjlAN2Ap0FhrbWpZ7QEaV/KZkJWid3BwCB21nhSUUvWA/wCjtNY/lkumqiuTAmpTir48
+LVu25IorrjDnldUq2glPq8NE4KWlpXHdddcBiDrg9Xr9VnlTEGb27NmShDYpKYnWrVsDZcaqli1b
+ilGrpmKm1povvvgCgKefflpWymPHjkk4+c6dOwFrc9G4ceP83os2aWlpIjmedtppgCWqv/TSS4Bl
+qA0mbqVRo0bAiZJCoIZGU8QFrD40Kk12djZgRVh269YNsFSSzZs3A/DrX/9a6kxEklqpD0qpeGAO
+sEBr/VTpexuJkPpgBuv8+fO58MILAavT77vvPgDJxBPrXHzxxbKt21ibCwsLRYxcu3atFF+ZNGmS
+BLfceuutMgGabD2htqHUr19fVIT09HQJzbXvkoyFfRBKKdHb16xZI8FHxmbw5z//WfaaBNNHbrdb
+PC7GzhAs8+fPl/D177//nvHjxwNli1ZCQgLLly8HrN2QNXmGgWQ2JwLeBwW8Cqw3E0Ip7wO/Lf39
+t8Csml7DwcEh8tRYUlBKnQt8DqwGzFIxFsuuMAM4BfgBGKy1PlTNuYJqhJkVjei8bNkyWRn27Nkj
+4aPBbnYxFvWSkpKIr34mDsFIB3369JHVf9y4cVJT8M4775S6ANOmTYuod6Wi8OFoopSS4ixxcXG8
++eabgFU7wWRrNv+vqSqZkpIiY6Gmnp3evXtLKsBVq1Zx+eWXA0i+y1Bsygrw2Zy8xWCMODtrliWE
+ZGdnSwe3adOmRmKey+USEX337t1RH/ymmhVY4mBiYiJgxcmHe+tsXUEpxcUXXwxY9UHN/pDCwkLO
+PvtswFK9akN8fLzYH2qpatf6HCHAKQbj4OAQPHUun4Ld8GPEuRkzZojfuabGIK21iJjRlhLgRJGy
+skCXnzNaaxYuXAhYgWDGMzJu3LhaSwiGUEllsTCmAqVOqg92SyvUrQ53CA8ul0u8UbG4TT5GcNQH
+BweH4Klz6gM4koHDifh8vpiIlzgZcCQFBwcHP+qkpGB0RyMxuN1uCRkuKCioUJKINR97KAgwiq1G
+5zXnq+z3WMT0R3UbwVwul9xHLNxPVf1qv6fyhEsyqpOSghEVTfqokpIS8vPzyc/Pr7RzyycHqYqK
+HkCoUEqhlBLDmH1rbrCEa0Dbz1vRdmH7K5Yw46GiL4vpd6UUZ555JgkJCRKsVlew3184VaXYeqoO
+Dg5Rp06qDzUhEH+zWbHj4+PDXoHY5XJJLEJtVvxIir+2RLsiJcTFxcW0C9A806ysLP71r38B0L17
+d1q2bBnNZvlR1TOMhprzs5kUgiGcD8Cc2+fziR0kWiXQa4P5siUlJUmIeSxi+js7O1uKB2mtY6IW
+ZqziqA8ODg5+1MmIxiDOG9Sqb0Titm3bSqq0cJUVc7vdkn+vqKjIr7owwLp162La7/7b31q740eN
+GiU5BWOpvUaSManr/vznP8tYePjhhyvNoHySE7l0bLGKKUQL1j4JExtfGY0bW5njZs2aJTkIw5X5
+Ji4ujnvvvRewtkubrd9mYlq0aBFDhgwBkJTgsYBReSZMmAAQkxZ8pRRXX301gNRiPOWUUyRBTGFh
+ofRzLE1kYC0WJiHLLbfcIgV0jxw5IlWtwr2QO+qDg4ODHyelpHD66acD8Mgjj0hRkilTpkjZ8Ypm
+2gYNGvDf//4XgNTUVKlmHGrMSnvhhRdy5ZVXynvlff4XXHABW7ZsAayU3ybvYrQxae+M6rNs2bKY
+WW2NynDttddy5513Aoh0uGTJEsl4ffXVV8sO23nz5lWb0TmUuFwuSb1nxsK2bdt4/PHHAWsc2nMv
+mJT627Ztk7qo4ZYUTrpJoV27dixevBiw9ElTQWnLli3ypTd2gqSkJHr06AHA5MmTJcFmuKzTCQkJ
+9O7dG7ByLaampgLw/vvv8/777wPw6KOPAlYiGZOv8eOPP5bMSzXdGh4KlFKSENVMYibBa6ivY64R
+qE3H5XLx2GOPAXDJJZfIZHDrrbcCVtJVc97hw4eLS9Ik6okUK1eulEXLeJ1+/PFH2Rr/wQcf8PHH
+HwMwdOhQyRzVuHHjiE2+jvrg4ODgx0kjKZj0ZXPmzCErKwuwApaMpDB16lQxiplV96mnnpJcfnaj
+5KZNm6pckYONnTeGo2effZZdu3YB8MYbb0hWX3vwz9SpUwGrzqApZJKVlcVf//pXAEaPHh21eP0r
+rriCjAyrCmD5IjShIj4+XvJVbt68mW+//bbK483qv2TJEjp27AjA//73PxG7K+L555+XsPe0tDQO
+HaoyhWhIMKqCaSMgY6Fbt24VFnp5/vnnpRhMfn5+xIyjjqTg4ODgx0kjKZiVtHnz5qKrLVy4UN53
+u91SH9EkaPV6vTRr1gywVn+z+s2bN6/KRJv2cN9AuO222wCrbsI///lPAJESKjo3WLqnkTBWrFjB
+0KFDAXjxxRcrrDsZTswKNWXKFPnd+Pl37doV8qSkxsAayIponmXr1q3lcwMGDKj2c+ZZp6SkRERS
+MC5Sn88nRk4jNVQmlZaUlIiE0aRJE6kEHu4Yi5NiUmjcuLF8aX766SfJz/fQQw9JUFCrVq1ITk4G
+kJLkXq+XDh06ANChQwcZ8Pb6iBUR7OD/4IMPAKuC0t///veAP2cqLh05ckTqOZqBEUn+8pe/yLWN
+umX8/0uWLBHVLVR7IMy9fvjhh1Uel52dzYwZMwArnsS0KZiw66KioohkWjbjE5C8koEYjY3BtEWL
+FhGbFGqtPiil3Eqp5UqpOaV/n6qUWqqU2qyUmq6Uir3oFgcHh0oJhaTwe2A9kFb691+BiVrraUqp
+F4FbgcnVncQY74KZrY3BcOnSpRLOunnzZh566CEAevToIatcfHy81BI07p/Zs2dzzz33AJCYmMi+
+ffsAy8ATylXDrFwPPPBAUKupqTkIZSK8vS5hJIiPj+d3v/sdYElWprbCjh07AEt6MK7TvXv31vp6
+WmtxKVfnjnz77belhN6//vUvqcEYCEa6ady4sYyHcG7sMtdQSkld0EAwqk0w+UBqS62upJRqAVwO
+PAHcU1pK7kLgN6WHvA78iQAmBa11wH5pE/5rtsIqpSRIacuWLRKifMkll8iAzcvLE1HTfNk++ugj
+2rdvD1i63pIlSwBrkgklc+bMAYLbDZmWlsYf//hHwAoUMtbpRYsWhbRt1bFr1y4pRNOtWze2bdvm
+9//BgwfLZPDuu+/W+nper7faMZCZmSntMfEkJkahKox+ftZZZ0n8Qt++ffnqq68Aqwx8oLhcrqC8
+ACYYzufzyXWmTJkCVD6Zejwe/ve//wFwzjnncOTIkYCvVxtqqz48DdxPWdm4LOCI1toshzuB5hV9
+UCl1u1JqmVIq8GnTwcEh7NRYUlBKXQHs01p/o5TqG+zny5eiD3RHY/v27Xn55Zf93lu+fDlz584F
+rFp9RtQaMGCArDq5ublSUvyXv/wlAGeccYbM9hs3bmTmzJlA6AqAGOwGJSO22lUl+30bNeGSSy5h
+8ODBgLXCvfHGGwARC8k1xtbMzEyJ9VizZs0Jx11++eW88847IbtuIOPAPEeXy8Xq1auBymtFGiNi
+cnKyGCIHDRrE+eefD1h9a2qPJicni2hv1NFVq1ZJvIhdMgg2VsCoqddffz3t2llF2E2/btq0iT//
++c+AZbg1pfA6duxIly5dAMsQvmLFCgCR1iZMmCCqVijLCdZGfegDXKWUugxIxLIpTALqK6XiSqWF
+FsCuQE9YVa5C05ErV66U40yH/PrXv5b3CgsL5YH36dNH1Iddu3aJ2mDcQ1A2Abzwwgu89957gTa1
+RrhcLrEJJCUlyRf82LFjYlkeOXIkYO3fMOLi4sWLue+++4DIZeB59tlnAWvwGw+OHdPfO3fuZP36
+9SG9tpkYK1IjXC6XfLmhbMJNTEwUVUIpJV/0F198EbAWE6Of/+EPfxB7VHZ2ttzLFVdcIYvLpEmT
+gNBV5jLqX5s2bWTnrQmma9eunYSP2/H5fGLniIuLo0WLFkCZG3bEiBHMmzdPjg0VNVYftNZjtNYt
+tNY5wHXAIq319cBiwHzrnFL0Dg51jHCYNB8ApimlHgeWA68G8qGqjIwJCQl89NFHgDVjmo1NxqBY
+UlLit4IaPzDgZ5wxUoE9r4JZXd59992wVXM2Bq7GjRtzww03AFaSEpO/wePxiBHS9MGmTZvE0Lhw
+4cKIp2wzbdNaV+gXN6trhw4dalzmvSLcbneV0pBSSoK6lFKyc7Bbt25iEB04cCC33HKLtN/8NMFN
+Xbp0kX5eunSpeDAOHjwowWXhCiXOzc2VHZFml2zHjh0lhgbK4j2OHDki9+T1eiUBj4lfufHGG8PS
+zpBMClrrT4BPSn/fAvQIxXkdHBwiT8ykY6vKwPTBBx/IBpfjx4/LXv5gU6W1bt0aQGwH7dq1E7fm
+8OHDw5aVeNy4cQD86U9/OqGQDVj3Yd43P3ft2sXZZ58NhMb/Hyymj6644grZlNSzZ09pd69evQCY
+P38+jzzyCABPP/10rW0epjYDVLxaK6XE0Lhhwwaxyxw5ckR0beOyhDJJcOnSpRJNunbtWp555hnA
+ihQ0YcdXXHFFTKRpM2OgTZs2Ik18+eWX8v7GjRsBqs0kVgF1Px2bSVFmsvACjBkzpkZ5E10uF59/
+/jmA7KLcv38/o0ePBsJbqdgY6txut6goS5YsEe/C4cOHmT9/PmD5zcFKtmHuPxqsXLkSsERck4Px
+0ksvFRHcGPCSkpK46KKLAMs4GYp+rEok1lrLJJmRkSHGt5KSEiZOnAhYOzcnT7ZCY8xORHvVqPT0
+dMlr4fP5JMYhFiYEKLv/AwcOyO7dPn36yGJoguwOHToUFsOzs0vSwcHBH+Mvj+YL0KUZnf1eCQkJ
+OiEhQRcUFMgrISHhhOOqerlcLu1yufS3336ri4qKdFFRkd6/f7/ev3+/btOmTVDnqulr06ZNetOm
+Tdrr9eqHH35YP/zww5UeO23aND1t2jRdXFwsv0eijeVfDRs21A0bNtSHDh2Svh89erROTEzUiYmJ
+OicnR+fk5Ojc3Fy9d+9evXfv3gqfYSy+XC6X3rNnj96zZ48uKCiQe4l2u8q/Ro8erXfs2KF37Nih
+jx49qjds2KA3bNigu3fvrrt3716Tcy4L5PsYM+qDqa9oVw1MEFJBQYFYYVNSUgK2xLvdbl591XJ+
+dOnSRXzaRscPV6bm8mRnZwOW96O6cFy7X/zSSy8Na7uqwmSQbtOmDcOHDwesBDCmD7dv3w6A1pq0
+NGvbS1xcXNg8OKEkLi5O7A4lJSWSyCQWUErJdv67775bdoxqrWXLvNl3Ei4c9cHBwcGfaKsORn1w
+uVyVip/vvvuuiLCHDx/WXbp00V26dNHx8fE6Pj7+hOOzs7N1dna2/uijj7TX69Ver1cfPXpUjxgx
+Qo8YMSJi4l/Tpk1106ZNRW05cOCAdrvd2u12n3Csed+oGiUlJfrIkSP6yJEjFR4f7Zdp78GDB3Vh
+YaEuLCzULpcr6u0K5NWoUSNdUlIifWxUzGi2yePxaI/Ho19//XV97NgxfezYMe31eqWde/fu1e3b
+t9ft27evdAwF8Kpb6kNV26YHDRokATJZWVkSn/7JJ58A8Mwzz0hc/gMPPMDNN98MWGKiEXe7dOki
+wR+RwuxzMKSmpsqOvBdffFGqUP3xj3+UzM72wqcmRLtZs2ZhFxlrSnJysoQPx4J7OxCMugNEJOtS
+ddiDsAYOHCjPHcr2ujzwwAOyNTxcVcsMjvrg4ODgR0xJCpXh8/nEH71w4UJ69uwJIP7zyZMny642
+l8slYcX79++XoKdISwlQFnRkJJpzzjmHbt26AfDyyy/7bQAzs79JdwZlK9q5557LW2+9FYkmB4yR
+ggoKCiQ2IdjandEiLy/Pr0R9VRvxIkFiYiLXXHON/G7iFL777jvZPfn222+HNZbGTsxMCtVhPA59
++/aVZB5mD8Tvfvc78VQkJCRIjsbevXtL8pVoYCzxRp1p1KiRbDNu3LixeFSOHz/O8uXLAUtVApg5
+c6bsqLSLu7GCiSpMTk6W3ad1YUIAyxtk2lq+MpfBTHpKqbB/GRs3bsyoUaMAa5I1yYafeOKJkOe/
+DARHfXBwcPCjzkgKBq21Xz4EsEJyTTKKV199NSaMR3ZMMo3du3fLPv/qOP/88/3Kp8UaZqfepEmT
+mD59OlB3JIWlS5dK6r369euLlGk34BkRPpyqhT2Pg9nNe+ONN7JgwQLA6s9ISgiG2BttDg4OUSVm
+dknW5vMej0dm+WjMrA51D+P2a9q0aVBZoEOJcUNPnTpVJJOBAweGsyxc3d8lGShFRUV1RnR1iA2M
+ATpaEwKUGaI///xzyTsaqcrSVeGoDw4ODn7USUnBGN5M4sufm6QQiTJnNbl2XYlTMHEscGIqv3Cj
+lBLVxbhyN27c6Ocijba0cFLYFFwul5+V2HSqPd+f3d9sPzbS92+ubU/6EcsE0ld2L0ll91R+Mgl3
+v5e/nqlAZv5nb0ckJ1l7OypCa12ht8nn853gCbG33fxdDQHZFBz1wcHBwY86rT7YZ3u7X9n83+12
++71f0UwUXdjbAAAgAElEQVQbLewrQixLDIH0kenXhIQE2YBW/p6i1dd2ycz+nokU9Hq9fmOkfDtD
+rRIF8qwrO6Y6CSNU1MlJobocfsY9Ge7dZDXB/vDM7/bioabNsaDWBYp5HiYBaixQvv/Kj4WKxob9
+M9G02wRDONpXK/VBKVVfKTVTKbVBKbVeKXWOUipTKbVQKfVd6c+MUDXWwcEh/NTWpjAJmK+1bg90
+xSpJ/yDwsdb6NODj0r8dqsDr9Yp6k56eTnp6Og0bNqxQ5XGIDFXl9zjZqbH3QSmVDqwAWmnbSZRS
+G4G+WutcpVRT4BOtdbtqzvXz7P1S7Hn5TCHZHj168P777wNWTkmTZCUWVSKj/thT0hcVFfkV1q0L
+eDweuQdTL8L8rMvYbFdh9z6cCuwH/qmUWq6U+odSKgVorLU2mTD3AI0r+rBTit7BITapjaTQHfgK
+6KO1XqqUmgT8CIzQWte3HXdYa12lXSEWJQUT4BKJ4JZ69epJleObbroJ8N8ZuWbNGs4991wAqUIc
+KRITEyWfQ8uWLaVqlalc1LdvX+mruLg4PyPwlClTAHjnnXekfLrJSBxLnHLKKQB8+umnEhBnan6a
+yuZ1CbfbLePHvhdIax12SWEnsFNrvbT075nAmcDeUrWB0p/7anENBweHCFNjl6TWeo9SaodSqp3W
+eiPQD1hX+votMJ46Woo+KytL6hKuW7cubJKCmc1vuOEGKVn2448/Ala2JeNLb9asWUSrTmdkZHDa
+aacBMGTIEKn74PF4qjV8mnvyer1SLi8pKYnf//73YWxx7TDZsFq2bCm2m6+//jqaTaoVHo9HJLaa
+SLq1jVMYAUxVSiUAW4ChWNLHDKXUrcAPwOBaXiMgTGqzAQMGSMKVfv36yeCOj4+XRBbmi9eoUSOp
+jfjcc8/Rr18/wMqlOGPGDAD+7//+LyztVUpx/fXXA1bRFzMwn3rqKQC/ojG7du2KaIDT7bffLuXZ
+e/ToIYZE+4RgxNL169fLe++8844kCImLi5M0dC+88ELM1Gk0mHtp166d5M0EmDt3LlCWRTnamHba
+VbP09HTAPz8mQOfOnQEr/d/UqVMB+N///hfZSUFrvQKoSEfpV5vzOjg4RI+YiGhUSpGQkIDP5wuq
+7JiZRR944AFJfFm/fn2/XXD21c1eR8FgZt1XXnlFru31euUc9lJ2oYxya926Nb/4xS8AePLJJ6WW
+RadOnQB49NFH5XpHjhyJSEo2c73NmzeLwW3r1q0ibT377LOS6m7dunXyucpKxps0dK1atYp4AlIj
+3Ziya/v37/eLFjX/f+mll+S+8/PzGTNmTMjbkpGRwdatW6UdDz30EIC4nH0+n190q6mI/cYbb0iF
+dJ/PJ5XATdJcu0HxyJEjNGjQALDyNBiJxxwHgbuzY2JSAOumPR6PdE5Vg8cMWFMDMDk5WR7sd999
+x4QJEwBLZTBf7sTERD766CMAEduvvfZauU5SUpLokXPnzhUreaNGjaQQTSgmBfMQ77rrLvEojBo1
+Snz6Ji08lPXBPffcExGfv7mvgoICET/T0tJYtWoVENweDa01Y8eOBayanSbzdqQzY5n+tLfd5XIx
+YMAAALp3LxN033nnnZB6d9q3bw/4T6BpaWmSrt++a9NQfuejwev1Sil68/969epJ9qakpCRJHDNq
+1CgWLVoEVLy7sjqcXZIODg5+xISkYHY52me7yoiPj2f+/PlAmcQwbdo0brvtNiCwlcisYJ999pnM
+vuvXr2fFihVyjJldTSIMICS7Go0o16lTJzF42svLmTgFn88noroRGyPFihUrJLLv2LFjIm0FI60k
+JiZKgZ7ExMSIek+g6nHg8XgYNmwYYI0h07a5c+eGzNOUnZ0t1c1LSkqk7w4cOCBGcYPWWsaCz+eT
+8bBq1SpeeeUVwMpAbY655JJLAPw8OiNGjJBYkPJqQqS9DyHD5/Nx4MCBahN5zJ8/X1xdkydPBqwO
+CQYzyC+++GJeeOEF4MRcfXZR2hAKvd5YiHv27CmTS2FhoQyEyy67TI596aWXgMiHNmdnZ4suu3//
+fvHmzJkzp1qbjwl0+vjjj6Wfx48fH1Ph2WPHjhW9HRB9/5tvvqnVeV0ulxTwufvuu7n99tsBGD58
+OMePHwcsm0F1Ozirw7jLN2/ezH333QcQ0knXUR8cHBz8iBlJoToRxxiGzj//fPbv3w/AyJEjg7rG
+6aefDpSVm1NK8fDDDwf8+doayVJSUqSuZEpKCq+99hrgP8ubwiqnn366nzoTCYw0NnDgQLnXm266
+iYwMK0o9NzeXc845Byir1pyRkcF7770HWCqRvXyfUXvM56ONMewOGTKE5ORkwJJQzb0aw3VN0VpL
+LMz9999f4ZiuzRj6y1/+AiCq8kUXXRQWtaxO5GhUSkmQTJs2baRClBmMdlq3bi3qxYIFC8RzEB8f
+L5OJ0Ze/+uorcQtGguHDh/P8888D1gAy7lAjWgIyWDdv3ixtv+WWWyI6QTRp0kRco88884xY0b1e
+r7TJTAodO3b0s4nYOXjwIGAFZBmL+/bt24HQZ5tyuVwyIdkt7kacv/nmmxk9ejRg1Vsw/z9+/LiM
+p48++qhW7Qpn4tozzzyTL7/8EkAmHqNGBIGTo9HBwSF4YkZ9qAqllNT+S01NZdOmTQBi3Xa73Vxw
+wQWAZSR78cUXAcuAZ1aPgQMHSvCS8XD07ds3Iu03bXjkkUfkvUmTJvlJCAZj2Pzyyy9F3Zk3b560
+dePGjWFuLezZs0ckgi5dukjo8nnnnUfz5s0B5Gd57CulMcz27t1bjMKhRCkloeljx46VOp3NmjWT
+a9tjS8x79vDgt956S1Q6u4RRkxU/HFKCGTtffvmlnD/QeqQ1xZEUHBwc/KgTNgWwdrAB/OpXv6Jx
+YytvizEYdu7cmU8//RSAZcuW+bl4TJhrbm6uzLSnnnoqUFY5OVjcbndQbqRLL70UgA8++EA+16RJ
+kwo3CZnYi0GDBolxNTU1VWwRH3zwQY3aXBvsYcCmfQav1yuRonfeeSfPPvssAGeffbZIQp07d642
+/iQYMjMzAWsT23XXXVflsfbnZJ6/z+eTjWdTpkwJaem4UIbCK6VkY5bH45HxMGfOnJqeMiCbQp2Z
+FAwul0sMXyZ444wzzmD16tWAtcPPnrLbTBZ9+vQRP3SPHj1q296gHrrx3W/fvl2Mcpdffjmff/65
+nM8cYwbVsWPHJMhl2LBhLF++HEDuJ5KYieD48eMnxGpMnDiRe++994TPXHDBBfJMDhw4ELK2KKXE
+MPjqq6+K0bi4uFhClH/44QcJHNq3z0rn8dxzz0mCmEOHDklilVCHXYdiUjBj5NChQ6Ii79u3ryaG
+xfI4hkYHB4fgqXOSgh2zK2zYsGEiUj722GMiMnbo0EFW4/z8fMkRYBJpRAqzaWXOnDl+UopZQV0u
+l2xgGTp0KGCJuG3btgWsKE4jIQwdOjTixWM+/PBDwPKLG0yNh/T09ApXW3vkXqijGe21RI2fvrI+
+MavrmjVrRHLp169fTEVYQpl0cO+994pB2uPxyCa98847LxSb4k7+UvTmS/W3v/3NT2wzHdy5c2d5
++PPnzxeLeqQxYu3u3bv9tmSbAbtp0yZJ6tKmTRvA2jp91VVXAZZIauLaIz0hpKWliYUfynzkZsIq
+PyGYvrfX7gw1pg+qyrRsxoOJZXG5XFx88cVA7GTENm1s2LChxFDcc889MumtX79eYm4imRnbUR8c
+HBz8qNOSgqH86mmiAgcMGCBGstWrV4ds5app5NqUKVNkw1Nqaqq02+VyMXHiRMCKswD/nZNHjhzh
+9ddfr22zg8KsYjt27JDfvV6veIGMN8Hlcok3KDU1VVbvjh07ilci0jkUAIllMQl1zjvvvKAS+ISL
+1NTUE3I2JCcnc8cddwCWpGt2Ad90001RqZ1xUkwK5enTpw8A/fv3lwH5yiuvhCy4pKbnWbx4seSE
+7Ny5s3zxTWYj8Ldev/nmm4ClStTUfVpTvvjiC8AaxOZ+r7rqKnEzmsn2jTfekCCrd955h3nz5snn
+ozEZAGzZskUm11DtgAwVFSVxGTdunCxk69evF1Xi6NGjEW2bwVEfHBwc/DgpJQWTREUpxdq1a4Ey
+A1k0KSkpYciQIQA8//zz4olITEyU1fjVV18F4L777hMRXSklxie32+0XhxEOw2O9evWkbVprFi5c
+CFhJP0yehX/+85+AJZWZTNTjx4+vtBR9JDA5Epo3by4btkz4eyxiguiuv/56kSAHDBggRuVoUadd
+kpWcS0TtzMxMseCbgR1LGFUhJSVFJoBgnkdSUlJIv4RGndm9e7dEgubl5cmW4927d4vL1LhZ3W43
+TzzxBGC5KaMxGYDlAjUqQqdOnSQ5qtluHIsYF2mrVq3Ew/PVV1+F85LhD15SSo1WSq1VSq1RSr2l
+lEpUSp2qlFqqlNqslJpeWhPCwcGhrmBKbgf7ApoDW4Gk0r9nADeX/ryu9L0XgTsDOJcO1at9+/Y6
+Pz9f5+fn64MHD+qEhASdkJAQsvPH0ishIUFnZGTojIwMrZTSpRJXjV6ZmZl68eLFevHixdrr9eqS
+khJdUlKi77rrLu12u7Xb7dZJSUlyHZfLpV0uV0z0QUJCgv7jH/+oCwoKdEFBgT506JBOS0vTaWlp
+tT6/y+XSiYmJOjExMWRtzsnJ0Tk5OdLe3NxcHR8fr+Pj48PdX8sC+W7X1tAYByQppeKAZCAXuBCr
+riTA68CAWl7DwcEhgtSmluQupdTfge1APvAh8A1wRGttfFE7sSSKE1BK3Q7cXtPrV8Y999wj7rID
+Bw5EPItwJCkqKhK3X21tQ/Xr1ycnJwewNl2Z2hj2FGUmtDkU1wsFbrebW2+9FbDcesZG89JLL0nm
+41BcI5SuVaUU//vf/4Ayt+6WLVtiIoZCqIX6kAEsAhoC8cB7wA3AZtsxLYE1kVQfvv/+e+3z+bTP
+59OrV6+OunhbV15xcXG6UaNGulGjRjGhFlT2crlcun79+rp+/fq6R48eeuPGjXrjxo3a6/XqY8eO
+6WPHjoVU1A/1q02bNtrr9fqpaI8++mit1b8AX2FXHy4Ctmqt92uti4F3gD5A/VJ1AqAFsKsW13Bw
+cIgwtYlT2A70UkolY6kP/YBlwGLgamAaESxFb/z4X3zxheQmePLJJ0Oa9OJkpqSkRHIPxDI+n09i
+Tg4cOCARlK1bt+Yf//gHUPVGqWjTuHFjvv/+e6CsXsnixYtjapzWKk5BKfUIcC1QAiwHhmHZEKYB
+maXv3aC1rjKAOxRxCqZT+/TpI4k3/vvf/0pYbix0tkNoUUpJeHBRUVFI9PJQVAGriiZNmoidy2Te
+so/NcGaEJhJbp7XWDwMPl3t7C1C71EYODg5Ro05GNJafzV0ul7zn9XpPmHnBPzzYHKu1jsjeerto
+aLLz2kOU7atSLDyPcFC+8nGo7tNEYfp8PklfZ6/HaFQJ+wocrahLsMZeRfceoed+8iZZKf9QfT5f
+tQ86IyNDds6ZnWrfffddeBpYDvsDN5NQMIPA4/HIl0op5ecarCuEa9DbJ3V7v5QvENysWTMpThOt
+3YcQ3QkpUJxdkg4ODn7USfXh54gJdGnRooUkOlm9erVY4sO5AhlRPCUl5QRJx+fzVZsnMRZIS0uT
+NleU0+BnwsmrPvwcMV+87t27M2jQIAD++te/Sur3cGKS4s6fP1/EclPfYc6cOVKjM5bJy8urkQoT
+Zm9ATOKoDw4ODn44kkId4+OPP5ZU9enp6WFfxZRSTJ8+HYCuXbuKMc9IKHVBSgBLtenQoQMAGzZs
+CFjVibakYLxVkUxt50gKDg4OfpzUkoJSSnTgpKQkmXVNVqH09PSYSegZKBdccIEkpj3jjDP49ttv
+AWRXYKhXtUaNGtG1a1fAsmusX78eKMuWXFeIi4uTRLirVq2S7MnVhURHynhaUXXshg0bSsGjdevW
+RUxiOem8D/Hx8bRu3RqA+++/XzINt2rVivr16wNlwUslJSWSTvuZZ55h8eLFQOxY0c0kVq9ePUmj
+/tprr0nBmLy8PJkMTPHR3bt3hyT23wzSdu3aSSq7oqIiSc1m31JdF8jMzJTMzvHx8XTs2BEg6vkQ
+wb8wUFJSEt27Ww6CRx55RPajXHrppRKyXwucWpIODg7Bc1KoD0opkQjmzZsnLrTykYAGIx3FxcVJ
+cZb+/ftLwZW77747oslZXC4X11xzDVCWJdmE7IIVqWdiBebNm8eUKVMAuPrqq2nSpAlgRWxCWZ2D
+2pKSkgLA448/LhLWJ598UmHpvVja4Vce07Zhw4ZJBefCwsKYknROO+00STTbsGFDyaRdv359qQnS
+pUuXcCd1LaOmSVZC+QKCSjBhElK0b99et2/fXr/99tv6+PHj+vjx47qkpESSWBQUFEi+xsWLF+t6
+9erpevXqSX7BHj166K1bt+qtW7fqkpISOcdVV10VVFsCPbb8q1evXrpXr1762LFjknDDvLxery4s
+LNSFhYW6pKRE5+Xl6by8PN2wYcOwJwJxuVz63//+t/73v//t16b+/ftXenysJmZp1aqVbtWqlT50
+6JDcx+jRo6PeLvs4HjJkiN61a5fetWuXLigokPFrkgX5fD49ZsyYUFwzIjkaHRwcTjJiRn0IRvQ0
+FY9vueUWAC6//HIxHh49epTHHnsMsMJZjcW5opp8K1eulM0xLVu2lFBis0e/Ksyxbdu2Zc2aNQG3
+3WzQWbt2rYQru1wuqfuwYMECAN566y15b9iwYVKXMRJxAfHx8VJERSklBq7qamdE26dfEf/6178A
+q99N/oJXXnklmk0SzJjds2cPs2fPBqx8IKbgjlENAUaOHCn1RsOdRCZmJoVgMHqtsbzfcccdkmBj
+xowZAW+HbtCggeycVEqJHcF4JKqiRYsWAJx11lkBTwoul0uKkzRr1kwe7osvvsi4ceMA/LZTG314
+1apVssMvEpSUlPgFy5hApcq+8PaKVbGCSbRjfh48eFD2PNx44428+OKLQHCLUagx3qX27duLl+GC
+Cy6Qhequu+7ib3/7G2DZjPr27QsENj5rg6M+ODg4+BEzkkIwoqcRu8aPHw+cmFilOswMbc/nqLUW
+i/RPP/1U7TmMlX/79u3VWt+NOHjTTTeJJf/JJ58Ub8fmzZsr/Jw5X1paWkTDiZOTk6UsnM/nk9Dm
+1NTUCnMRmP6MVNKa6nC73fz5z38Gysrb7d+/X1LYr1+/PqoSghkvb731FmCFjxuV5sCBA3Lciy++
+KBWomzdvzuOPPw7Ahx9+CIQvniZmJoVgMF/aYBOWtG/fHijTjZs3by6fzc/PZ8KECUBgnW0+V1VM
+urE7tGvXDoAePXrwn//8B4BFixZJEdTKMG7IESNGcO+99wJE5EuXn58vW7JTU1Pp0qULULkuO2zY
+MMCqX2BsH9EIADNftq+//lomYlNguHPnzrKYmLoL0eL9998HEHe4z+djw4YNgP/imJaWxsaNGwEr
+4WujRo2AsnEVLtuCoz44ODj4UeckBft+BhPgU1RUVO0KetZZZ/H2228DloRgMBbpAQMGsHLlSiC4
+Vc6ec698bsj+/fsDSErvMWPGBBVW26tXLwA6duwoUkMk8Hq9EgwFSNBPVlYWu3fv9js2PT1d1LjU
+1FS+/PJLAAYOHOgnCtvzYtp/hpKXXnoJsAJ9brjhBgAxAk+cOFE8PyEIF64x2dnZ9O7dGyjrg337
+9omhsVWrVmJAz8jI4NNPPwXgvPPOkzEe7rHgSAoODg5+VCspKKWmAFcA+7TWnUrfywSmAznANmCw
+1vqwspbwScBlwHHgZq31t4E0pLqVw0gHcXFxEgb6xhtvAJZrcsWKFQA8+uij7NixA7BCRs0mp8aN
+G4tR0XDs2DEuvPBCwDL21XQFqajtCQkJ/PGPfwSsDEngn7lIKSWr8Y033ijtv+SSSwB4/vnnmTx5
+srTdSDeVGSVDicfjEVee1lp86D/++KNfajaw4gCMJKG1Funmww8/lGInS5YsCbuN4f7772fo0KGA
+JekZY5y5j/j4eEaOHBnSa9YkvPuyyy4Tw+zevXsBy11qwtx/8YtfSPzNzp072blzJwDFxcVyLxXF
+3ISSQNSH14DngDds7z0IfKy1Hq+UerD07weAXwKnlb56ApNLf9Yae07Aq6++GijL3puVlUXPntZl
+/v73v0vVoD/96U+yu9CIr1CWzbdNmzY18v+X98fbB4f5ffHixZx11llAWQDN559/LhbwU045RQxG
+FTFq1CgZPOvXr5edkdu3bw/7voyGDRvKl7+kpIR169ZJm43R1HhOkpKSRKw9duyYPKcOHTrIl9Co
+FIEQbACUmbD69+8vfW8v2NqqVSvAMtpVZ9gNJ2b8XXXVVRKTYsZvWlqaPNOxY8f6paU3BuaEhAS2
+b98OhD+2olr1QWv9GVC+N3+FVWYe/MvN/wp4Q1t8hVVXsmmoGuvg4BB+ampobKy1NtvM9gCNS39v
+DuywHWdK0Z+wJa2mpei9Xi+jRo3yey8+Pl72+Y8aNYpHH30UsFY8u4Fr0aJFAAwePBgoMzIGi32m
+drlc/Pa3vwUsyePBBx8E/KUJY5w7++yzxW9ul1zsJc+MdOD1euX3LVu2iMsqEq6+HTt2iJEwOTmZ
++++/H4AHHnhAjLtGkjh48KDkJkhLSxPfe4cOHSQhS6gx177ttttYvXo1YPXRk08+CVj9bZ7RE088
+AVguSZNYZezYsSFpRzArtumjN998U3Y7GsPn999/X6FK0K5dO0khB1bODwi/W7rW3gettdkpGOzn
+XgZehtonWSkuLpYMSsnJyeKjdrlc0oF33HGHbEsO5Rerfv36XHzxxYC1ldlMBj/++KOIrmbyiYuL
+EzWnpKSEZcuWye92mwlYyVRM1ubPPvtMdMtI5err168fYKlBZiu6z+eT6xvbxumnny79mZSUJGrO
+kSNHJONzMATyRTPHvPzyy9V6M4y65nK55D4ijVKKTp06AdaEZWxepi/Lj0cz6b399tuyeOzYsUPi
+G8JNTb0Pe41aUPrTlCveBbS0HeeUondwqGPUVFJ4H6vM/Hj8y82/D9ytlJqGZWA8alMzworJW9iz
+Z0+/dGvGAj5lypSwGGgOHTrEI488AlgGQVMOvaIkHsXFxXzxxRcVnqd8ncNly5aJVPH2229HNJsv
+lPn3zz77bFHHunXrxnnnnQf4q15GDJ44caJIOjNmzAibYS8YSc9Ih48//rgYoCNNXFycGLfT0tLE
+wFxRlWy3283dd98NWBG4xujYt2/fiCWGCcQl+RbQF2iglNqJVWV6PDBDKXUr8AMwuPTwuVjuyM1Y
+LsmhYWjzCXg8HvlipqSkyBeoZ8+e4uoLJyY4afz48TUuh24msnfeeQeACy+8UM4bzSxB27Zt4847
+7wSgadOmsp3btDc9PZ1rr70WsNypRs35y1/+EhPbqI2u7vP5qvT2hBOv1yvb/a+//nrmzJkD+Ks8
+xl0+depU2Q1ZVFQkQViRzCVZ7aSgtR5Syb/6VXCsBu6qbaMcHByiSLRTsZl0bDV5ZWRk6IyMDL1y
+5UpJtXX48GE9ZMgQPWTIkKin26rulZKSolNSUvT06dP1nj179J49e+Q+Nm7cqDt16qQ7deoU9Xaa
+l9vt1pmZmTozM1M3b95cN2/eXH/zzTeS8q6oqEhfc801+pprrqlVmrpQvEyKuB07dugdO3bokpIS
+3bNnT92zZ8+otCc+Pl7Hx8frlJSUE/5Xr149vWzZMr1s2TJdXFysDx8+rA8fPqwHDRoU6nYElI6t
+zu19gDJ331NPPQVYiS+NnnnRRRdJLYRgzmUnUmKvCazq3bu3eEmMunPZZZfFXPUlr9crtgSTsOSn
+n34SVUIpJbsSo606GM+P6WOv1yvqWDQwamVF6uXAgQP9XI833ngjgKgZkcbZ++Dg4OBPtFWHmqgP
+TZs21U2bNtVHjhzRR44c0QUFBXrWrFl61qxZQWcVNhl13W53xEVKoz7ccMMN+owzztBnnHGGTk9P
+1+np6TouLi4qYm6wL5fLpVevXq1Xr16tfT6fnj17tp49e3ZU25SSkqLnzp2r586dqwsKCnRBQYGe
+Pn161Puq/Mvj8WiPx6O///57fezYMX3s2DF93333hfOaTjZnBweH4KlzZeMSEhIkK5BxMW3cuJFu
+3boBBL1ZyESPaa0jni2oqorCbrc7JlKbBcKqVasA6NSpk4RHN2nSJKL9qZTi7LPPBuCaa66RkGaT
+5Pess86SPAWxgomEnT17tthg6tWrF87nHlDZuDpjaDQGwenTpxMfHw+UxYDfeeedNd45GM0vXlUB
+SXVlQgAkyGrfvn0yKXg8HtkFGAm01mLkPPPMM7nrLsszPmuWFVcXSxOCGctXXnklYE2qJvguFp67
+oz44ODj4UWckBZNk5KKLLpL3TALO//73v1Fpk4OFkQguvvhica2FOxFIRZhoS1PTIVYxY9kUAxo4
+cGCNI2HDQZ2xKZgtu99++62oCiYOP5ZEw58zSUlJYkeIxqRQF/B4PFJ9y2ypX79+faQmBacUvYOD
+Q/DEjPpQURouez5DM5Pas9qGQkKwR+OF2shjPBtmF6G9ZqQ914M9qtIYUUOVci0SZeLtCWPsJeSi
+KYWaNkWj/kRFmLFw0003iafM5PCMNeqM+lDu+BPei4X7CIRA215ZHshIDHK32y1tSkxMlAGdn59/
+QgEee8p9+/vRfB4VtcfeTqWU9KPH45FsWPZ8neZYt9stC1K47sk+qbpcLnFVQ9niYL+23ZVt3ne5
+XNLmKhY3R31wcHAInphRH4Ih0BnbLsLGinQR6DXtx9lXOZfLFXZpwX5+ezVun8/ntzKBtZIaK3pu
+bq4YGKMpKVR0bVtIvR+FhYUVrsZ2qSLc2Ptb67J6nAkJCRU+64qMkj6f74T4nZriSAoODg5+1Emb
+QjAY/SszM1NcQaZo5+rVq2MigqwuU5H+7hCznFxhzjXFhBLn5OTQtKlVguKHH34A6v4gjoQqUR11
+vXGw+dcAABMRSURBVA8dTsRRHxwcHPw46SUFQ15enhRUMZt2yq+yplbfmjVr+OmnnwDo3r27GHYi
+4fMPBNPO4uLiqEsKDoFhr+thnpkxDBYXF8eUGnvSTwrGpjBo0CDZWrtrl1WK4q677vJ7GFOmTAEs
+VcNYpP/9738zZIiVuzaaD84Mqp49e0pNwfJl4SOJKY573333SaHfnJwcKXSilOK7774DQheIVZdI
+TU2VokRjxoyRQsZFRUWi0po+zMrKkvR28fHxPPTQQ4BVg9QsTpHEUR8cHBz8qNb7UEkp+gnAlUAR
+8D0wVGt9pPR/Y4BbAS8wUmu9oNpGhMn7oJSSXZXPPfecJPH84IMPABg6dKisYm63W/ICnHbaaXzy
+yScA9OrVi/feew8o8w9HWmR3uVwMHz4csFYSU1Mw0iQlJTFx4kQAbr31VqAsfLc8a9eulZoFq1ev
+/tmoOUaiy8rK4rPPPgOQSt0Geyg4+Ec02tFai3F83759FR4TJCHzPrzGiaXoFwJjtNYlSqm/AmOA
+B5RSHYHrgNOBZsBHSqm2WuuoyN3x8fFSj/HUU08Vsc1stbYnOfF6vSxdulT+bx6YEdUhMoEs9muZ
+naEffvghZ5xxBoCoMpEkPT0dgG+++UayJBu01n62FqNi1atXTwZyOMODK5tsygcfdenSRRKu5Ofn
+s3LlSgA++eQTGjRoAMBjjz1W6zaZe83Ly+M3v/kNAB999JF88Q8ePCiLkin6kpeXx1lnnQVYY9be
+dpM5qmHDhn5h2OGkRqXotdYfaq3NN+orrJqRYJWin6a1LtRab8WqFNUjhO11cHAIM6EwNN4CTC/9
+vTnWJGEwpeirxYihoTDmmXM1a9ZMcuhDmddh5syZwIlqgP3a4fYwJCQkyKrfqVMnevXqBVhl7wC+
++OILScbRs2dPqfJsVplgrgOWwfX48eNBt7NTp04iBtvzB957772ApZaZvmratKnUbmzTpg3NmjUD
+yvIkBkIguyvvu+8+wNplaOpVHjhwQOourlmzRmphDh1qVS7MyMigRQtr7SoqKpJn37hxY5EYJ02a
+JAa/2j7//Px8qeFhJJFAMcbxrVu3yhhYu3YtTZo0qVWbAr5+bT6slBoHlABTa/DZ24Hb7e8FqndW
+5RpUSslW5SFDhvjtOHvggQcAalxkpbYDJT4+XmoKLlu2TL6wWmsZmK+88gpglVk3BVF9Ph+jRo2q
+URtMX2VmZsq+hEAmXiPaTp8+XeocHjp0iCuuuAIoy3plJzc3l2nTpgHWF9ZY3IMpzlPZ/Rnxu0GD
+BvIcU1NTZYdjdna2eJXefPNNBg4cCCD2oJ07d8qikJWVRaNGjQBrsjC2j5YtW7J+/foq2xEJzFjI
+zs6WJMUNGzakXr16AGH3SNR4UlBK3YxlgOyny3ow4FL0WuuXgZdLz+WExTk4xAg1mhSUUv2B+4Hz
+tdZ2mfR94N9KqaewDI2nAScuKRVg34FXHVUdZzc+5ebmykpZUFDgt/JGgzvuuEOs9lprWQWee+45
+KfduVvHk5GS/1GaVlbCvDnP/ffr0EbWpOhISEvj9738PWKuxadO6dev4+uuvK/2c1lokhZycHJYt
+Wybv15SKpEIjpVxwwQXStttuu02uDfDSSy9V+nkoS+/3+uuvSzzBeeedJ5JCLODz+STu49RTTyUz
+MxOIAUmhklL0YwAPsLC007/SWt+htV6rlJoBrMNSK+6KlufBwcGhZtS0FP2rVRz/BPBEsA2prQ5n
+jItmBQDo2LGj/J6QkCCp0CLNzTffDFgur+TkZAAGDx7MggVWCEdF9REyMzNp2LAhYNUFMEa0YDHS
+xuzZswM24o4ePVrsAUePHpVCvk8//XTAzyk5OTkoW0JlmOdq/PW5ubmMGzcOgAcffFCe8dtvv13h
+5ytrb/369QFo37699Mu2bdtiKp4iMzNTolbdbndQBtvacFKEOScmJkpm5127drF3714ALrnkkgpT
+iUWaX/ziF4D1YG+/3bKtGgNYZTz77LNiiNy5c2eNr20CrqoqPGMw1xs6dKh4QQoLC0UNqCi5R0JC
+guzFOPfcc7nkkksAuPDCC8UgGAqMGA1WMBTA0qVLJbakTZs24qGBqo2pLpdLPCqtWrUSA6wpJhMN
+zj33XAC6du0qlaMuvPBCeSZvvfVWxCYsJ8zZwcHBjzotKRgj0nnnnceIESMAePzxx0WFyMzMlNl1
+yZIlUWufWcGys7PFr14ZxoXaoUMHCcGeM2dOrVeJQD5vktAY3zhY7kSzu7Sidr7++usSxtuhQweR
+zPbs2SOu4dqUj6tIwjFSwMSJE+nfvz8Af/vb32SFLS4u5ssvvwTgnnvuASxDs5G43G43p5xyCmA9
+I5Nfo6au6trSsWNH3n//fcAy7FYUOn7DDTdInMmDDz4IWAbHcLhO6/SkYNiyZQvdu1sh3TfffLOE
+165YsULCWW+66aao6YtPPvkkENhuwS5dugDWF9Pok9OnT6/qI1US6KBxu93icUhMTJS+SklJkTgF
+sEKdAb9AGnOsPYY/KytLgnbson+oMXaZzp07y6Tg8XgkzsLEVUyePFnak5WVJduWfT6fqHQ1tdvU
+lvHjx8sEas807fP5ZEJNTU2VorlGRbvuuuvkeYQSR31wcHDwx2S5jeYL0LV5paam6r179+q9e/fq
+PXv26JUrV+qVK1fqoUOH6pYtW+qWLVv6Ha+U0kopnZKSot1ut3a73bW6fmUvc534+HgdHx9f5bEu
+l0u7XC49depUPXXqVO31evXw4cP18OHDQ9KG6o7LycnR+/bt0/v27dNer1eXlJTokpISvWfPHn3w
+4EF98OBB7fP55GWwv+fz+XRBQYEuKCjQP/74o77qqqv0VVddFZa+raj/EhMTdWJiona73bpt27a6
+bdu2Oi4uTsfFxWm32y2/7927V9r75ZdfSt9Hop32V3p6uk5PT9c7duzQx48f18ePH9evvfaa3Acg
+43Py5Mm6uLhYFxcXS9uPHTsWbNuXBfJ9PCkSt8bFxbFo0SIAunXrJuLXgAED2LRpEwB79+6V4A/z
+c8KECRIOG+pafkopcT+ec845AHz88ceVhmYbvdyIgwUFBSLu1uYZ2VOxV3SPRmxt3ry5qFrx8fGi
+t+/du1dSuNt1XdMme2BVnz595JgffvhBdv5FI1FIRRg7yPHjx6WdXbt2lX0SkcKoLmPHjpX3TPBa
+Vc86JycHKLNR2ceN3fNSBU4xGAcHh+A5KQyNPp9PwlN79Oghq+NTTz0luQDMLGtHay2r45NPPinW
+6fT0dPFZb9u2TYKegsnRqLWWz3300UdVHtu4cWNmz54NlEksTZo0CallubI4BeOjP3r0qIQwd+3a
+lY8//hiAUaNGcfToUcCy+pvjjf+8uLhYvBZz5syR8y5evDhqwWKVYXZXaq1Felm3bl1E29C8eXMu
+v/xyoCwuItDw823btgFI3Ei3bt348MMPASsIqzJDdrC5RU+KScHlconVPi4uTiy2SUlJfiJ4RfUZ
+27dvD1i7E+2dZn7fv3+/HGMGUqiDoNq2bSuT17Bhw4DQqTOBDoSDBw9KwdNhw4bJ/oLK8kDaS833
+7t0bsMRic73XXnst6gluy2O2chcVFckCECmPVHZ2NmC5xo0a88QTQQf+AnD++ecDsGjRIkm+s379
+eq688koANmzYUKv7ctQHBwcHP+q0pGBW/hYtWtC6dWvASm1lgjtWr14tolaLFi0kkYkJv83JyZFw
+Xvv57L9nZWVJGK9JwBFqZs6cKauHCWIJFcFUcPrqKys/zqxZs0RlqA6XyyUxIkopUeNCse8hlHg8
+Hgl0crvdzJ8/v9bnDEYsN3s3GjRoIFKgUWemTJkS1NgyuzpbtWolgXo5OTkSuj1gwAAJ3qqshmZV
+OJKCg4ODH3VaUjBupezsbNk//+yzz0q4qn2G3Lx5s7ht3nzzTcCa6U102EMPPSSRe+3atRNj5aBB
+gyRhpnEl2fXp2mBWmtTUVNHdA9m4FAymDwJZLYwLNSMjQ8LGd+3axYQJEyr9THp6urhclVJSpzPU
+Lt7acvXVV0vS2YMHD4qBuTYEswKbjVuXXnqp7Drt3LkzYIWE//3vfwesMH0zBuLi4uQaOTk5/PKX
+vwSQZ5Oeni72reLiYpHOli9fXmEF7YDbG+3ApVAELzVs2LDWAShKKZ2cnKyTk5P1WWedpT0ej/Z4
+PCccE0ggUKCvhIQEnZCQoIuLi/V3332nv/vuu5BfoyYve4BMcXGxHjx4sB48eLBu1KiR9It5PfTQ
+QxJUU1JSoocOHaqHDh0a1fZX9Mzmz5+vvV6v9nq9etu2bTopKUknJSVFpU1ZWVk6KytL5+fn6/z8
+fO3z+aRtx44d0/v379f79+/XBw8e1Hl5eTovL0+XlJTIMSawLC8vTy9YsEAvWLBA5+TkBBIkF1Dw
+kqM+ODg4+FGn1QdDKHa3aa1lF1plm0xC7WIz6ojWWmpRxIIb75ZbbuH1118HLBXN/P7jjz+Sm5sL
+lBld7XEhxcXFITeU1hbTx02bNhU33aRJk0KupgWDUUeNujp//nz69OkDWBvQ7MZvO0ZtNVG6119/
+PVu2bAGoUabuyjgpJoW6ivGSADEV6DNjxgw6dOgAWFuPTaBSw4YNJRuUmbzs/vA333yz2q3hkcbY
+EZo1aya7IOvVqxcTNg/Thn79+kkq+meeeUY8Cvv27ZPdqLNnz5bdpqNHjwaseJlwLCKO+uDg4ODH
+SbEhqq5iqjK3atVKLNHBht2a+AZjhQ7V8zQbpR566CHuuusuwPKSGFXNhEHPnTtXEtgcPXqUw4cP
+h+T6tcVY3E3xnH79+sl7vXv3lviVnxkBbYhyJoUoYpKnDBo0SGpemnqHgeByuULuJj1ZMMFCxg3t
+8XhE1WnWrJkUhvmZ4eySdHBwCB7H0BgD+Hw+KRJjrPeBSHA+n8+RECrA7XZL6ncjHeTl5YnUECsq
+TqziqA81IJAiqIFgLMtXXnmlxOKb3Xux8FyCJVT9Eop2ZGRkAEiCmNTUVCn4Gu2kL8aFG4WcoY76
+4ODgEDyxIinsB/KAaFl/GjjXdq79M7h2tta6YXUHxcSkAKCUWhaIaONc27m2c+3w4qgPDg4OfjiT
+goODgx+xNCm87FzbubZz7egTMzYFBweH2CCWJAUHB4cYIOqTglKqv1Jqo1Jqs1Lqweo/UatrtVRK
+LVZKrVNKrVVK/b70/Uyl1EKl1HelPzPC2Aa3Umq5UmpO6d+nKqWWlt7/dKVUQhivXV8pNVMptUEp
+tV4pdU6k7l0pNbq0z9copd5SSiWG696VUlOUUvuUUmts71V4n8rimdI2rFJKnRmGa08o7fNVSql3
+lVL1bf8bU3rtjUqpS2tz7VAR1UlBKeUGngd+CXQEhiilOobxkiXAvVrrjkAv4K7S6z0IfKy1Pg34
+uPTvcPF7YL3t778CE7XWbYDDwK1hvPYkYL7Wuj3QtbQdYb93pVRzYCTQXWvdCXAD1xG+e38N6F/u
+vcru85fAaaWv24HJYbj2QqCT1roLsAkYA1A69q4DTi/9zAul34noEuXcjOcAC2x/jwHGRPD6s4CL
+gY1A09L3mgIbw3S9FlgD8kJgDqCwAlniKuqPEF87HdhKqR3J9n7Y7x1oDuwAMrH228wBLg3nvQM5
+wJrq7hN4CRhS0XGhuna5//0amFr6u994BxYA54Tj+Qfzirb6YAaLYWfpe2FHKZUDdAOWAo211rml
+/9oDNA7TZZ8G7gdM0HsWcERrbXKDhfP+TwX2A/8sVV/+oZRKIQL3rrXeBfwd2A7kAkeBb4jcvUPl
+9xnpMXgLMC9K1w6IaE8KUUEpVQ/4DzBKa/2j/X/amrJD7pJRSl0B7NNaV5wAMvzE/X87Z88aRRSF
+4ecUuqCNsYtEUEFs1SqghaCFBomNhRAwgr9CtvIPCBaijZUEBSXIYulHraYIKn5gRMEIflTWKY7F
+OQtzkUUje+9avA8MzNxZeOfdvbzcc2Z2gMPAdXc/RDxWXpQKFb1PAWeIYNoFbOf3JXYzavn8E2bW
+J0rYpdbam2HSofAF2N05nsmxapjZFiIQltx9OYe/mdl0np8GvleQPgLMm9kn4A5RQlwFdpjZ8C/s
+Nf2vA+vu/jSP7xEh0cL7CeCju/9w9w1gmfg+WnmH0T6bzEEzuwCcBhYylJppb5ZJh8JzYH92obcS
+TZdqrwO2eB/XTeCNu1/pnBoAi7m/SPQaxoq7X3L3GXffQ/h87O4LwBPgbE3t1P8KfDazAzl0HHhN
+A+9E2TBrZtvyNxhqN/GejPI5AM7nXYhZ4GenzBgLZnaSKBvn3b372uUBcM7Mema2l2h2Phun9j8x
+6aYGMEd0ZD8A/cpaR4ll4wtgNbc5orZ/BLwHHgI7K1/HMeBB7u8jJsIacBfoVdQ9CKyk//vAVCvv
+wGXgLfAKuAX0ankHbhO9iw1ihXRxlE+i2Xst599L4g7JuLXXiN7BcM7d6Hy+n9rvgFM1593fbnqi
+UQhRMOnyQQjxn6FQEEIUKBSEEAUKBSFEgUJBCFGgUBBCFCgUhBAFCgUhRMEv6Zbtfu1PCEYAAAAA
+SUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 2/2... Discriminator Loss: 0.9250... Generator Loss: 1.0925
+Epoch 2/2... Discriminator Loss: 0.9684... Generator Loss: 0.9028
+Epoch 2/2... Discriminator Loss: 0.9477... Generator Loss: 0.9569
+Epoch 2/2... Discriminator Loss: 1.2281... Generator Loss: 0.6713
+Epoch 2/2... Discriminator Loss: 0.8281... Generator Loss: 1.1973
+Epoch 2/2... Discriminator Loss: 1.0442... Generator Loss: 0.7927
+Epoch 2/2... Discriminator Loss: 0.9155... Generator Loss: 1.0041
+Epoch 2/2... Discriminator Loss: 0.8651... Generator Loss: 1.6100
+Epoch 2/2... Discriminator Loss: 1.1178... Generator Loss: 0.7747
+Epoch 2/2... Discriminator Loss: 1.6798... Generator Loss: 3.0678
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8VFX2wL93ZtJDCL1FelVRRFEQpVgRFdC1gRULujZ2
+XQvYhbViWRuIDUX5YcUVV0WxY0NRURBEilQB6XEpqff3x8s5eRMSMpPMm0zY9/188kkymbx333t3
+zj3n3FOMtRYfHx8fIVDTA/Dx8UksfKHg4+MThi8UfHx8wvCFgo+PTxi+UPDx8QnDFwo+Pj5heCYU
+jDEDjDGLjDFLjDGjvDqPj49PbDFexCkYY4LAr8CxwGrgW2CotXZBzE/m4+MTU7zSFA4Fllhrl1lr
+84GXgMEencvHxyeGhDw6bgtglev31cBhFb3ZGOOHVfpUCWMMANbasJ9jcTwvCAaDu71mrdXzeRxh
+vNFa26iyN3klFCrFGDMCGFHF/wU8v4ExQ8br/rmySRwMBikqKtLf43mt7olbXFxc7piqOp5YP7tQ
+KKTHk2MXFRWFjTuaYwUCjvJcUFAQ03HK2LKzsyksLAx7rbCwUO9rXl5emIDY0/1yz6sIx7kikjd5
+JRTWAPu4fs8peU2x1j4JPAnRawq1RRgI7vFW9HPZh+/+YLqFQzxwr1wQ/sGr7r2P9bOTD28sKC4u
+1g9srJHr3rJli/7sfuYV3Zc93S+vPgdeCYVvgQ7GmDY4wuAsYJhH54oIY0xCC5OyY8vLy9OfA4FA
+XMdeXFysK2YwGGTIkCEAfPPNN6xYEdFiU6uQD2dVtItocZ8jXs80Wu3ME6FgrS00xlwJvAcEgWet
+tT97cS4fH5/Y4plPwVr7DvCOV8ePhFAoRHJyMgBNmzZl+fLlQHxWhFggEr5t27aqwm/atIkNGzZ4
+fm73PZLz3X///Zx//vkA7Nixw/MxxAO3Xb63Eq1G4kmcQrTEcvchPT2dHj16ADBkyBC+/PJLAD7+
++GM2bdoE1B6fxKGHHgrASy+9RMuWLQFnEr/zjiNrhwwZEhd/Q/PmzQH45Zdf9N4deOCBACpoayuh
+UCjM4Vd2biS62elGriMjI4P//ve/+rqYgsXFxd9Zaw+p7Dh+mLOPj08YNbYlWR1EIqanpwOwzz77
+8NJLLwHQsWNHlZK33HKLevEzMzPJzc0FID8/P95DjprGjRtz0kknAVC/fn29ZmMMAwcOBOCaa67h
+/vvvB7zVftauXQvAH3/8QU5ODgBLliwB4IUXXuCiiy4Cao9ZFgwG2WcfZ3Ps/PPPp3379gA8+uij
+el3bt28HnJ0fr3YkYklqairPPvssAD/++CMTJ04EYOvWrVEfq1aaD3379gXQC2/VqlXYgxs+fDgA
+c+fO5bLLLgPgyCOP5Ouvvwbg73//O5A4kzgpKUkn6YQJEwA45JBDdHz5+flkZWUBjiAUAVFQUECH
+Dh0AWLlypefj/Mtf/qLCV4Ttrl27qFevHhC+Y5IoyL0KBoNccsklAIwcOZIWLVoAjvmwa9cuAHbu
+3KnzaPz48QDcd999Fc4TL+JlUlNTdTyVmS6BQEDnzejRo1U4L1u2jLPPPhuA7777Ts2HoqIi33zw
+8fGJnlpnPtSrV49XX31VfwZYs2aNOuU2btyokt0Yw7Rp0wA48cQTVcNo2LAh4KjDNYms/qeeeir3
+3HMP4JgKAOvXr2fVKidSfOXKlbqLMmjQoLDVr1u3bvoer/n444/VNKtbty4AKSkpXHDBBUCp5pYI
+yG7NYYc50fXPPvusOkx///133n33XQC+/PJLtm3bBsDtt99Oo0bhUcDxCB5KSkriyiuvBCAnJ4d5
+8+YB8P7776vp5iY1NRWAli1b8vjjjwOw//77s3PnTsDRbr777jsdY7TO6IQRCpWpSvJBuOmmm8jM
+zARg8+bNgGNOlPcBt9Yye/ZsACZNmqQfIBEgb7/9do15luvWrUufPn0AZzKKf0RUx+XLl3P77bcD
+sHTpUh544AHA8ZCLgAC47rrrAJg+fbrnY968efNu/hhjDMuWLfP83NEQCoX0WctOTVpaGr///jvg
+CNalS5cCjs+gTZs2APz0008cc8wxAMyZMwfY8we/uuaDqPVvv/02nTp1AqBJkyY6Zz///POwwCoR
+BmISDx8+nC5dugDOvJDozsmTJ1drXvvmg4+PTxi1RlPIzs4G4OKLL1Yn19SpUwFUzS4PUZ0mTpzI
+QQcdBMCDDz4IOObDN998U/3BR0hSUpKuDhkZGcydOxeAO+64g1mzZgHoDsm2bdto3LgxAP379+fE
+E0/UY7i55ZZb4jJ2QdRZUbOttey///4AzJw5M65jKYvc24EDB3LuuecCqBYzZMiQCkO0RXPs2bOn
+mp5ian700Ucxd0jLin/HHXcAcNRRR+k58vLyuPTSS3Xs7nP36tULgDvvvBNwTDfRJAKBAE888QRQ
+/XwQX1Pw8fEJI2E0hcpsoEGDBgHOCit295tvvglEtrX4559/csABBwDQrl07AJ566ikOOcTZoYll
+tl1FNGrUSMODjTGsWeMkjr7wwgvubSPAcSJee+21AFx66aWqHbnv09q1a/n88889H7ebr776CkDv
+JcBbb70Vs+MbY3a7F5EQDAb1WT7yyCP8+eefAPz1r38F2E1LkBW2U6dO/O1vfwOgTp06ep/PO+88
+AB544IEK9/rL1kaINJ6hX79+AIwYMULHIuPZunWrajfueX3ooYfyn//8ByjVNNxs2LCBG2+8MaLz
+V0atEArGGFWdAoGA7od37NgRcLzilVG3bl318KakpADQokULRo8eDcDYsWM9dzpu2bJFhc+2bdv0
+fIFAYDfB1r17d53QZesbiJd58uTJcQ+skfHLBzYQCKiTNFZUJXS7a9euXH/99QA0aNCAb7/9FnAC
+ecoSDAY1PLtZs2blpjDLztZ5553HpEmTAFTQlCWaZ5Cdnc0jjzwCoA5zgHXr1gFw6623hjlzJQ7h
+888/L7cQiwRZ3X777TovqotvPvj4+ISRMJpCZRx++OGAozXUqVMHQPdiI6G4uJi7774bQDP9Dj/8
+cC688ELAcUSuX78+lkPeDTF7IFzaJyUlqSNVEo1uvfVW1RDc9Q3WrVunJsNjjz3m6XjLY+HChUD4
+dlwsYySi1dYyMjIA+Mc//qFqeSAQUFXbnRgkzseHH35Y4yystfz2228AfPLJJ2qCyGp95pln8tNP
+P+nf3USjIYiD+KKLLlLz1Y1snT7//PP6WigUUudtKBTS88k9Kigo4KOPPgJQbSYW1AqhYK1l0aJF
+gBOkIYEpRx55JODYU7IHXVxcrDctLS0trGrQF198AaCCAEozAI844ghef/31iMZTnrof6XW4Edvw
+qKOO4rjjjgPglFNO0XG5g5TEZDrxxBPV5nRP+Hgh4xQhBaiQrkqcfXWRHaXevXurOm6MYfHixUDp
+PZ86dSpnnHGG/l2e3zXXXKPqvDFGd1XEk9+tWzc9VllEaFdm7oRCIQYMGACgJg6U+gz+/PNPrrrq
+Kn1Nnvvrr7+uJrL7fLK4XH/99Tz55JNAbH1ivvng4+MTRq3QFMBRD8FZKcVROG7cOABGjRqlXv3X
+XntNnUtz5swJWzEkErB169aAI3nFeVSeQ6oiYlEtuHnz5jRr1gyAMWPG6Iog6rCb/Px8VQ/nzZtX
+o/n9EoUpWGtVS4s3gUBAYyQaNWqkK+mvv/6q8SeiCQ4ZMiQsOvC2224DUC1BGDlyJFDq4Pvwww8r
+LWpTUWSjaFP7778/Dz/8MOA4Qd31G8CZs7LSp6Sk6LyWWAlBNAuZq88995wnu2a1RiisXr0acLYm
+xe6S3IHs7GydEMcff7ymE7t9BIFAgOOPPx4o3X2AUlVMQqYjIdrCGzIJLrjgAlV3DzjgAE1DbtOm
+TbkVgMSu/e6773TnpCYFgjEmLMQaws21eI4DnGfav39/AJKTk/V+jRgxgqZNmwLoopCUlKRp0Qcd
+dJB67d1MmzZNjyd+hDvuuKPCVPu0tDSgYjNOhP6NN96oWZlus0tM2wEDBmgBoLp16+qH3/1ea63O
+0WHDnHKn5V1DLPDNBx8fnzBqjaYg0vODDz5QqSu4Jar7vWURD7+o6G7PeTQ1ByN1MspK8MYbbwBO
+0Io78ETGXZ6WsGnTJg1tnjNnTkLUfrDW7haws2PHjriPTTSTlJQU1Qbnzp2r8SonnngiV199tb4H
+HEec7Oy49/OTk5P58MMPAcepeO+99wLw0EMPAXtejSuLC5Dsyzp16ug9KttHA1CNUXAX1JFrXbFi
+hTrIvS6BV2uEwp6IZFKmpaWp2iUTpbi4WPMPYh0E1K5dO91OErs2EAiEPfDymnmIaXTxxRcnhCBw
+EwwGyxUKNUVGRgYNGjQAHDNw6NChgHO/y45z7dq1Yc9YBPadd96pz+f666/XXYdITKLKir6KafrV
+V1/RuXNnwAlYkl0SMcXcxyk7D8Xn9fjjj/Ppp59WOqZY4JsPPj4+YewVmkIkDBs2TJ1Pbu/ve++9
+B8SuNNsJJ5wAlJoMUFqmLDc3V51PZVcZeb+U1EqEMnllqVOnzm5ZmmV/jwdy7y677DKOOuoowCmc
+I6vsli1b1AkoBINBrT2wePFiNSVmz56tQW3ROJuh8vgEGc+YMWMYM2YM4Nyv0047DSgNyHv//fc1
+9uSf//yn1nRIS0vT2I/x48fHbU5UWVMwxuxjjPnYGLPAGPOzMWZkyev1jTEzjTGLS77Xi91wfXx8
+vKY6mkIh8A9r7ffGmDrAd8aYmcAFwIfW2nuMMaOAUcAN1R9q1RBnXqdOncrt+CureCykcCAQ4N//
+/jfgrEyyzfTZZ58BMHjw4N2couCUkzv99NNjNg6vkPoOblJSUiKO7IsVshV94YUXagRiIBDQmAPJ
+LoVSf84333zDfffdB8DTTz+tIctSzDdeFBQUaB0Q+V727+5tX4m3iEcWr1BloWCtXQusLfn5T2PM
+QpwW9IOBfiVvex74hBoUCrLTMHjw4N3Sj/Py8jT0ORYfxhYtWmhuQPPmzdVzLaXaxbklPPfcc4Dj
+VExkYSCUJ9DS09M1jyBa9bs8IokB6dmzJ+B47WVMixcv1gA3N1IOH0prSE6fPj0mAsyLZ9arVy81
+yQoLC3n66af153gRE5+CMaY1cBAwG2hSIjAA1gFNKvifKrei9/Hx8Y5qCwVjTCbwOvA3a21umW02
+W1FPh+q0oo8GcSg1b95cVxWR8MuXL49JVJioe+3atVOToV27dlqZ2b3Cihr4zTffJLRTsTzc0Yvu
+rVUpMCuxAdUhEk3hzDPPBMK7cYuDVxBnnUS33nTTTVrcNl5mTjRIhXH5Ds68kC3zeFItoWCMScIR
+CFOstdNKXl5vjGlmrV1rjGkG1GgddXegk7uJCji1GmOhlsnOxS+//EL37t2B8KYtMnG3b9+uFX1q
+spJ0VVmyZIkKUXeBEFHnu3fvzs8/O83Fq9oYJpJ7ImXNzznnHA0xbt++vYbCn3XWWRp8JFmE48eP
+T7i4Dzddu3YFdt/NkUUmnlRn98EAzwALrbUPuv40HTi/5OfzgTerPjwfH594Ux1NoTdwLjDPGCM6
+zo3APcArxpiLgBXAGdUbYvWQXH+3R1fCT19//fWYrB6ibSQnJ2sYs7u0l6xWzz77rPYTqI0UFxdr
+pWFxkubn52vsxapVq6rdOi4S80GK62RmZqoj7qmnnmLLli2AE70ofR/2VOk7kZBdEjd//PFHjWiT
+tbKXZDRI+un06dN3y2pr1qxZTPsftmrVSjtStWrVSrsTSeORvZGabtUuJlqjRo3CgpcSYV5Hg/gS
+Vq1apQtYbm6u1oqMEX4vSR8fn+jZ68OcRRNYuHChxgl8//33QOy90H/88Yd64r/66is2btwY0+Mn
+IjW9Isv5a7ovaHUR02flypW0bNkSoNw+kvFgrzYf3DX38vLyNKNOBMXvv/8e00ltjFHBE88INJ/a
+jzufQ3Z4pk2bFus6nL754OPjEz17pabgDqyR0OZgMKhagTikvAhiqW4nYp//TUTDzMrK0vJvHlTr
+9jUFHx+f6NkrHY3u9loSh1BUVFRu2y2vzl0eZSstucOuy/s/d/ahvDcpKSksYaZsG7eaoGyBUXBW
+PhlnQUGBvi7jTERNqmw1LKj42cQa0V7LJpXtSfMMBoNhcTaxGudeKRTKI14Pt7IxCO4JWFEAVXn1
+/AoKCmIaW1Fd3B8idyerUCik4y8oKAgz4yC+WX+RkghzpCx7Go9XC4FvPvj4+ITxP6UpJBqVSXr3
+mBN1i7PsfRXtwF3p2BijzVUkwaeiDs7/iwSDQdWwCgsLa3yu/s8IhUTD7VPIysrSGAoJYmnfvj25
+ubkA/Pbbb6puBwKBhBMQ7lL17lLmElZet25drWwcL2EgOS+SzVlUVKT2enFxcY1mTEoYs1Sy6tmz
+p4bEP/roozFt2FsVfPPBx8cnjL1GUxCHVygU0hJsxcXFusK6W38lgpMrFAqRnp4OwN13360l22QF
+y87O1hV469at2n4+Pz9fMxXdre1rEjGDkpOTdRW8/PLLGTt2LOBkpUrX5XXr1nkyBmOM9vNo0aKF
+dpi+4QanEmBaWlqYU1RC0IcMGaJh7/GYF8YY7QHRu3dvAFq2bKl1KCSDtyap1cFLMgmSkpL0Q9W7
+d2+1Xzt16qTq47Jly7R+onQSeu+991RY7Ny503NbLikpSb3vvXr14qabbgKcKk1SDEbGkJ+fr1t6
+wWBQA1kCgYD2lZw6dWpCCDg30rz3l19+UQGRl5enVagq66pUVbKystRcmTFjBk2aOFUAJcvQbbe7
+n/PWrVu1vP7UqVO1yK5Xnn1jjNa0FDMwLS1Nn69b0BtjNHsyLy9Pzclq4Acv+fj4RE+tNB+kkIm0
+9x44cKCuDDt27Ahr5y4raYMGDejVqxfgVE+Wv4lkXrFiBUcccQTgnTPs4IMP1n6Af/nLX9QZ5q5J
+IN75yZMna93BNm3aaHu29PR0LrjgAsDReKQEWSJgjOH6668HHK1Irunrr7/2zNQRbXHkyJFcccUV
+AOq0hVInaFFRkT7X5ORk1Srq16/PKaecAsCGDRv48ssv9f1eYK3VBi/Czp07y3V8BgIBZs2aBTjm
+ZJ8+fQD49ddfPRmbUCuFgkywBQsWAHDUUUepWlpUVKQ/z5s3TwtfHnrooWrLidq2cuVKFSDvvfee
+Z629RaV+/fXX9XxpaWk68X777Tet3ejubXnjjTcCTjEY6SfRtWtXvY6cnJy4CoXs7OzdJrSbzMxM
+jj32WMARECKQ7777bs9MM/ETdOjQIcw8kHPLs160aJHe70MOCdeg5fWNGzfWiDlW0U5IUVGRplGn
+pKRw0EEHAd4LBd988PHxCaNWagrChAkTAPjiiy9UBZw7d646FJcuXaqrR926dVWDEKeOtVarL+fl
+5alKGWuNYfjw4YCz0srKlZeXx1133QXAvffeW+5KKivIli1baN++PeCo5VIjok+fPp53ODLGqJmT
+lZW1R02hXbt2WmuwuLhYd0zEsesFYj7Mnj1bn68752XKlCmAYxJKPceyuRoSNj516tSEKv/ujkkJ
+hULMnz8/PueNy1l8fHxqDbVaU5DtxO+//16Lo+bm5pYr7ctb/UOhkG6VyeoL8OOPP8ZkfOL8vPzy
+ywFnlZdxnnLKKREXdG3ZsqVqMcYYXdmkqrGXWGt1K6yyLbEXXnhBt1Hz8/MZN24c4G2ItjzXp556
+Sv0B7sQm2QIeNGiQVnh2xysUFRXxyiuvALB+/XrPxhkNosl07949LOZGNDavqdVCQW7evvvuqw80
+EvVPClrcddddXHLJJYDjlBw6dGhMxyetz+V8eXl5vPPOO0B0FZ47dOgQlgIuKvxPP/0Uy+FWGVHh
+c3Jy9AO3evVq5s2b5/m5K3MMyn275ZZbduvlCY5glSCrmorZMcZwzz33AHD++efz4YcfAk65QFkA
+UlNTVcjKrpQHRViAGJgPxpigMeYHY8x/Sn5vY4yZbYxZYox52RiTXNkxfHx8EodYaAojgYVAVsnv
+9wIPWWtfMsY8AVwETIjBeXZDIuYuvvhinnjiCcDZa5bV6uCDD9aIsNWrV+t2mcQKtGrVStXLqVOn
+smbNmpiNzRjDoEGDgNK4ip07d2o79EgQk2H06NE6Tvf2VaIkRnXo0AEI3wrcvn07v//+e00OC0Cj
+W/fdd9+w1+U+vvDCC57FpZRXIEWeI5S2iGvevDn9+vUDnKrjor3u2LFDQ7Cff/55dYqL0/mnn37y
+JLGrur0kc4ATgTuBa0payR0FDCt5y/PA7XgkFES16tq1q/YOXLJkCccffzzgeMPLQ3wRK1as4Icf
+fgBKuzjFimAwyMEHH6w/g/NBiWYCtmrVCnAClsRUKigoUF9CoqQfy/55RkaG3ttFixYlRO/G6667
+brfXrLU6zqlTp3oeQ5Gamqqhzenp6SosO3bsCDgxNLJDJbE3wssvvww4vTAlZF+a5soiF2uqaz78
+C7gekKffANhqrRVDbzXQorx/NMaMMMbMMcbU3j5qPj57IVXWFIwxJwF/WGu/M8b0i/b/Y9GKXiT8
+eeedx8yZMwHo16+fmhVl6+2V/b+ZM2eq1I31/nSjRo3Yf//9gVKH6Lx58yKOmAsEArrKdenSJUxT
++OKLLzwZc1VISkri1ltv1d/dLc/cNSbjjayqYiq6e4kCGgm6p7iL6iLmQYMGDdQ5+O9//zssyhIc
+7aCieSHPfcGCBVpzITs7W4/vzv6NFdVtMDvIGDMQSMXxKTwMZBtjQiXaQg4QO0O9AtasWcMBBxwA
+OCrs+ec7Ta+zsrI0l6B79+6ce+65QOluQK9evTyLyXcXK5UH99Zbb0X8/w0bNtT031AopMfavHkz
+bdq00ddrOkvymGOOUbs9FAqpX0YCl8CZ2PE2JUQdF7+Mm8LCQk499VTPx+AOVJMgqvz8fBWSkQhL
+Sa9PS0vTZy1p3+4grFhS5aNaa0dba3Osta2Bs4CPrLVnAx8Dp5W8zW9F7+NTy/AiTuEG4CVjzD+B
+H4BnPDjHbogU3bZtG4888shufw8Gg+q469GjB+BIX/GcxzpwxV1OTbSVH374IWKnVqNGjTQzcv36
+9bqPfd555+kKk5KS4pmm4A4PF4dm06ZNOe00R95LxumRRx6parK1VouESDwGVJzw4xWhUEgzJsvj
+3XffjUvIsFz3hg0bqnwMcaB37txZNY+pU6cC3mkKMREK1tpPgE9Kfl4GHBqL4/r4+MSfWh3RGA2Z
+mZlqi4lPITs7OxbVbCpE7GuR8Lm5ueXuXRtjVOrL9w0bNnDkkUcC4YldM2fOZODAgQCe9X8IhUJa
+q6KwsJCsLCcEZejQoaoVuJFr+fXXXzn55JMBaqTjttyjxx57TDVAN3K/hgwZEtdxRYs4aDt06MDp
+p5+ur4lj8r333gNQTTLW7NVCIRAI0LVrV8DZ2z3wwAOBUqGwY8eOqMKNo2Hjxo1aYk0+SH379lXH
+5rp16/TDVFBQoCaBW+V0t1eX93bp0kU96U2bNo1ZPYVAIKB74oMGDdJx7Nq1S4VaQUGBOk0lIMtd
+N2H69OmsWLEiJuOJloyMDG655RYALrroonJ3nrzOKI0FwWBQqzy/8cYb6mgsLi7W8GcxR73Cz5L0
+8fEJY6/TFIwx6lA855xz+Nvf/gY425Oyevzyyy+AExFWldoJ7vJpFdGuXTvNvJT3jho1Sh10mzZt
+0pXriy++UNVWVODt27drbYKGDRtqBef8/Hx9zwsvvBD12Msix+rZs6dGgrpfX758uW7lLl26VFcu
+CcUdO3as3tdHHnkkrklFgUBAq1BNmjRJw4DdocSCtZYHHnggbmOLFndbPcmGbNGihT6HlStXcu+9
+9wLeh7fXaqEgH5qcnBw1E0aMGKGv161bN8yGl2YgJ554IlD1cuPBYLDSYJOrr746LN0ZnN2O/v37
+A479LWmx48aNC3sPOFWI3WN3F1x56qmngOoF3khm4znnnKNjvPrqqwFHVZ02bRqweyaeFDKR9HJ3
+R6OyAUJeIeXUBg8ezGWXXQY4tRYrClYD5x6KkI5EqMcbt/kowi0lJUUXi1deecVT/5cb33zw8fEJ
+I2E1hYqkeTAY1NXtr3/9K+Ds6cuq6+52/Pvvv6uz7vnnn1dHzapVq6o1tj3tu4t63bFjx912Gnbs
+2KHxEG3bttUVLz09Xd9b9rv8LBpInTp1dJWOdrWTY2ZlZakZIyHTixcv1veFQqEKdzbEaTp48GDA
+ScqSOAavHWDgXMPo0aMBGDBgQJjDc0/s3LlTI0Tfe+893R2pLEw43lqFMUa1xby8PD33qlWr4jaO
+hBEKZW9+dna23pzjjz9eg2Z69OihNpXEgK9evVor3H766af6wKdMmaLvjWXnnbJhu+4JmZOTAzhb
+oHI98j0UCmnaa0pKiu6CBIPB3d7r9vQHg0Edv/SalHFEk1cgx3aXO3dfhwiePQk9eSaiyr744oua
+redFHH5ZevXqxTHHHBM2lj0h17x161Y1+S688EINKNq0aZOmJ2/btk3fL0LRqwrfFVFcXMyMGTMA
+JwBMciYuv/xy3dl5//33Ae/ut28++Pj4hJEwbePKS5oRx9WiRYs06cZaq3v94jicMmUKzzzjRFOv
+WrXK8xVrTyqlmDFXXXWVNm0Rk0JamEHpPj84sQDShESu46uvvlKP9JYtW9QxuGPHDm12smnTpiqX
+5BItRXpSrFu3TlfFPWkg4qyTAKpDDz1UzZkZM2Z43jG5d+/emlzUokUL1dLKhvzKXBLtYNWqVXz6
+6aeAM1+kzeDUqVM15NmtricCDRs21CS6Hj166NjEUS4aQxRE1DYuoYWCkJSUxJ133gk49ekkD0Ds
+4G+//TauDzMYDEaltosd3r9/fxVuhYWFzJ49G3CuIxHSoCNBhIlsmxUWFmp5/dzcXP7zn//o61Uh
+Ghs+EAioUNhnn320CG+DBg3UvyHCYvHixSpA3fMsEeb/nnjzTSef8OSTT9axSkHgKkSN+r0kfXx8
+oidhNIWaHoNP9THG6MqdCKXY9ibeeecdzYWR4j1VCCn3NQUfH5/oSZgtSZ/aj7sJi09sufTSS9W/
+IPEtq1Zr1G2tAAAgAElEQVSt8kQj880HH59aQHp6unaglh2eKqRO++aDj49P9Piagk9UlFckxsd7
+gsGgmgrVuPcRaQq12qfgDi+W/Wh3NaXi4uI92lyJPLEDgcBuoc/xwh0rUDZuYE9jKfvesvkb7v/3
+8pqMMRr4JvEfbn9HNL6PWOc+uD/c7rByif+oKL6jvLD6aJ5NNPjmg4+PTxi1WlMoT0rm5uZWWoRC
+JG1SUlJYN+dEwr2ixHtsVV19yr7Xi1WsLLLauld/Y0zM6lfGWlNwP0v3z1UpnFJWK5NQ+Pz8/GqN
+uVYLBTfu7MJYvremSOSxJRLSGOe3337T5xrLbbraEoRljNEOUl9++WW1Sv9Xy3wwxmQbY14zxvxi
+jFlojOlljKlvjJlpjFlc8r1e5Ufy8fFJFKq1+2CMeR6YZa192hiTDKQDNwKbrbX3GGNGAfWstTdU
+cpzE9fjVIJIlaYwJ8zwnsoM03khNBSkT57NHvM2SNMbUBeYCba3rIMaYRUA/a+1aY0wz4BNrbadK
+juXP8jIYY7RSUP/+/Wnbti3g9Czwqt5/LAkGg+q7qel+lz6K58FLbYANwCRjzA/GmKeNMRlAE2vt
+2pL3rAOalPfPfit6H5/EpDqawiHA10Bva+1sY8zDQC5wlbU22/W+LdbaPfoVfE2hdEdEPMi9e/fm
+iSeeAJxCKOJlX7FihWoNiYjUNLjjjju0PubYsWPjPo7yajb6Zpf3msJqYLW1dnbJ768B3YH1JWYD
+Jd//qOD/fXx8EpAqb0laa9cZY1YZYzpZaxcBRwMLSr7OB+4hTq3oU1NTtefeK6+8ojZsolfYcVcN
+kpZ2Y8aMAZyWcFKY1l1hqHXr1poQ079/f8/a3lWFQCDA+PHjAaf1nFTI8ork5GTVmlasWKFl+lJT
+U3VL110ZOR6FZfcGqrv70A14GkgGlgHDcbSPV4CWwArgDGvt5kqOU6VBiHf+sssu4+677wacGony
+Adq+fbs2X5kzJ/FcF3Xr1gWcdu7PPfccUFrPcePGjVpTcL/99tPS8FLJGODzzz/nqKOOAhJD6CUn
+J2tvy/r16+vYPvvss5ifB5x7JVW809LSVBB8++23Wv1ayvqvWbNGaxoWFBQkxP0SAoGApkN3796d
+iy++GIAbbriBuXPnAs7uiiwS7grlUQo673MfrLVzgfJOcnR1juvj41Nz1MqIRnG6HXfccYDT+kz6
+IRQVFakkTU5OVgmcaJpCIBDQIq7PPPOMtnuX9vX33Xcfzz77LBDecj47O1v7FOTk5KhjUlRnL3A7
+7fa0wroT0Ky1nrQ5c1eaTk1NVfMgNTVV78XRRx+t45T+mIWFhVoFesyYMVoJvCYiFkXD7dKlCwCP
+PvqoztNdu3Zppe833nhDNYGdO3fqdUvPjhNPPNGT6tkJkzodaYy5MUZLirdr1w6A66+/nieffBJw
+Hr5knE2bNo3evXsD0KxZs5jFw1cHEWgnnHACN910E+CYPP/85z8BtIfjnvIdpHryLbfcok1yli1b
+FrMxDh06VHtzPvfccyxZsgSo/ANkjNEqyllZWaoGi2kUa9yxHHXr1lW/UteuXXcr/Z6Zmakl7OfP
+n8/QoUMBWLt2bdnDeoII/aefflrnrbScB7QK9ldffaVl97/99lu95wMGDNCy9LJwHHfccZ6YD36W
+pI+PTxgJZT5EUkOgS5cu2n5cVObXX389LIFIfn7nnXf0tfLak8ebQCDAoYceCsCDDz6oOwfXXHMN
+33zzDRCZOiut51q1asXBBx8MVF9TMMZof8mHH35YVfHp06dHVXtAelwUFRVpkxWv+jFaa3n55ZcB
+594+/fTTgGNKyDhuvfVWAC655BJdrVu3bk2zZs0A7zSFYDCoTuNTTz2Va6+9FnBMvk8++QRAtcNZ
+s2apOeOOBHVrAaeccoq2JPzggw8A75LmEkYoVBbTLzcqMzNTPzjyWkU3Z9q0aUyaNAmIbahtpDa2
+IALpwQcf5NRTTwUcG/GBBx4AYPbs2VF9aM4880zAMTvOOussAF599dWI/788rLX89NNPAEyYMIGZ
+M2cC8PXXX0d8jKSkJPWWd+/enR49egDw/fffe+7tdwtTdxj4zTffDDiqupgMTZo0CVPdvRqPmKtz
+5sxRAfnWW2+p8Prtt9+A8DlU0aLQp08f9SVIExjP0tE9OaqPj0+tJWE0hcoQqbh582ZdqWW/etiw
+YTz11FNAuBde9vaFSLoqRzoWOVZqamqlGXonn3wyAKeffrqq5ddff73uoUeDMUY1o6KiIjp27Bj1
+MSpCVraPPvpIe1tGQ/369VUr2rVrF7/88gvgaDSyysUbuabPPvtMtapAIFDlHpyRYq1V9X/hwoVM
+nz4dgKuvvlp/jsTkE6djhw4d1Pn8+eefezDiUmqNUBBWr17N1q1bgdKGrbfccos24ly+fHnY+2WS
+9ujRQ7cA+/TpAzjNRcWWj8a8cKcyVyQQ3P6D++67D3B6HMruwiuvvBLx+dy0atWKbt266ThEzY8F
+Mul++OGHqARnZmYm4KjGsmuxfv16VeNrsmCMXMfGjRvDaiN26uQk7n7xxRdxMW3E9wGlC5zkiWzd
+urXc+x0IBPj5558BZx6LkPE6itU3H3x8fMJIGE0hUg/1rl27mDhxIgB///vf9X9F8rv762VkZNC8
+eXMAevXqpY4dWRG3b99eJVNiT2MV06ZOnToahyAayrZt2/jrX/8aNoZoef7551X7Wb9+PQ8//HCV
+jlMeci+iCTpKSUnR+1q3bl29rlmzZunriVDS7NNPP1XTMiMjg379+gFO4Fg8kHvw0ksv0b17dwDN
+ExkzZgwLFy4Ewp2HV199tQYyFRQU6LyX++oVvqbg4+MTRq3TFKA0k3Dw4MEAdOzYUW30lJQUtWF3
+7Nihttztt9+uzi6JXygsLKRFixaAk2gkCTMSMVYRkYzzjDPO4NhjjwVKHZzvvvuu+kOiRbSNQw45
+RLWRF198sSqdhyOisqYv4kf46quv1LdTVFSkWsa4ceM09DwRKi/l5+freOrUqaORrsFgMK7Vsq21
+GqYu47n33ntV03377bc5/PDDASceRe7dvHnzeP755wHvq3snjFCIRsWUGzVgwADA+ZAfcMABQHg7
+9F27dmmgU9OmTdVRI99TUlJUfczKylJz5IgjjihXhY6kO5K8p2HDhhpAI8JIzIloCQaDvP322wCE
+QiGNJ5DgFy+o6BolIOfSSy8FoG3btupsnTp1Kg8++CAAixYtSqhMxF27dmmYs7VW9/prArkvYkp2
+796d66+/HnBSzuXvgUBAs06vuOIK5s2bF5fx+eaDj49PGAmjKVQFCVHt1auXSt369etrxFiHDh3o
+2bMnALfddhutWrUCSveHf//9d5o2bQrAY489xoIFC4CKt9AiWfkka0+0DkDNknXr1ulroVAorKVZ
+eYjJcN1112k8wqJFizTzT1a+eJGSkqKOzUGDBgHOFvGFF14IOFuZiVpUNhgMapgzlJqIieAE/f77
+7zXasmHDhhr1OmrUKDUx3NvgXlOrhYKwa9cuHnrood1eX7lypXpqr776arV999tvPwDOPvts/vWv
+fwHOJImFuivp3NnZ2Xo88TK7g56aNm2qKmxKSooG08jOwj/+8Q9GjhwJON5y8UWcdNJJNab6Hnjg
+gSpkZ8yYAThpv9999x2QGIVeKiInJ4c6dero72+88QaQOGOWcWzdulUXALfJK+Hj8cA3H3x8fMLY
+KzSFinA7lFJTU3UVFvNhxowZMa/bJ3UJA4GAqntNmjhV7rt06cLAgQMBJ3lInKM5OTnqoJTxHnbY
+YerU+/nnn7nooosAWLVqVUzHGwkSQfniiy+qefTiiy8CjsmQKKtteYhmduKJJ5KRkQE43ntx1iYa
+ffr04YgjjgAcE1OyJ+MZFbpXCwUo3b5JSUnRD6lUYYq1QDDG6DGttfpBf+yxxwBHMIkZsHbtWho2
+bAg4KqP4OyQturi4WLeuLr30UhYtWqTHjSeBQEArQDVs2FBDbKV+ZKI15i2L5JoMHz5cBcT69es1
+fDhRkLGdccYZWpFp165dXHbZZUB877NvPvj4+ISx12sKUpgiFAqpZ/zyyy/35FzBYFBNk44dO2pZ
+OEl8gdKVq379+qoatm7dWh2isqMwadIkTZ7Kzc2tMRX9wgsv1FgPY4yOr7aUSxfH6L777quvzZ8/
+PyECqtxI6POZZ56pZuOWLVtUQ4wne71QOOGEEwBH7Z46dSqAZ7UaCwsLOf/88wHHhu3bty9QWvTj
+lFNO0V2GRo0aqcq4fPlyTaeV7dR169bVqGruDlKSFPUtW7bwl7/8pVrHFb9OvK5N6lkGg0EVrJdc
+cknC+UFk3mRkZOi9efPNNz0tyFsR1W1F/3djzM/GmPnGmKnGmFRjTBtjzGxjzBJjzMsl3ah9fHxq
+C1IGLdovoAXwG5BW8vsrwAUl388qee0J4K8RHMt69fXxxx/bjz/+2BYUFNjWrVvb1q1bV/uYJc1r
+Iv4KBAI2EAjYjIwMm5KSYlNSUmzjxo1tZmamzczMtPXq1fPs+qv6NXToUDt06FCbm5trCwoKbEFB
+gR05cqQtqbwd9fFSU1NtamqqDYVCNhQKeT5+GefSpUvt0qVLbVFRkd25c6fduXOnzcrKqvH7W3Z+
+PPTQQ/ahhx6yeXl5dv369Xb9+vW2U6dOsT7XnEg+29V1NIaANGNMCEgH1gJH4fSVBHgeGFLNc/j4
++MSR6vSSXGOMuR9YCewE3ge+A7Zaa8WLsxpHo9gNY8wIYERVzx8pkj25fv363aoyVZVo7VHZCnWH
+JUtH5kRFEp7c4diPP/54lW3xRo0aAZVnoMYK8dds2LABcCpWiS+pJuz0PdGoUSOGDRumv8uclZZ4
+8abKQsEYUw8YDLQBtgKvAgMi/X9r7ZPAkyXHqtpMiwDZV5ewVp/KSU5O1uIeSUlJKsyq4xz8/fff
+gfJbxHuBCGLpKWmMUedpIpT7dzNw4EAVtqFQKKzLVk1QHfPhGOA3a+0Ga20BMA3oDWSXmBMAOUB8
+lgYfH5+YUJ0tyZVAT2NMOo75cDQwB/gYOA14iTi1ot8T0vE40VTGRCY9PV0jLAsKCjQDtTorV01t
+r0o7th49euief6LFWLz22muqmd18880xLbFXFarbiv4O4EygEPgBuBjHh/ASUL/ktXOstXsMDPDK
+fAiFQtq+e+fOnXFPNa6tJCcn07ZtW8BR+71oFOsl7kI7ku/QpEkTTV3fvn17wsUpSEGeUCikQssD
+QRqXVvS3AbeVeXkZcGh1juvj41NzJEzX6WjeL5l6IkmNMRq2GggEqpxR5i63FknptWiPW/Z41T2H
+e0WMRwEOdx1N9zVJOHdRUVGlvUC9GFN55xNnovu+JMJcr4iKrkNedxdZKe+9EdY49V5TqClEvSrv
+w1Ydlcs94b1qiCpjDoVC1U6Hraz/Zqxxn8v9s9zzmqhiVN71G2N04XDXZUxkKhtfRfc+0v+PBj9L
+0sfHJ4xaqSnIyiRqa3FxcUzV6FivKuVJ+USvQxANiVDnEEoDlsaNG6eJZ2PHjgUSo8w8lK+FVqSZ
+upO44nmPa6VQENxBHokyMSPFPV6vzJX/BUQQhEIhjcI844wzWLJkCVDahLgmhYIxRrcczz77bE2J
+d2+Tu7dJZaw1tXD45oOPj08YtVpTkFXC7YV3h+UmMqmpqdql6KijjtKAFYnV9zWHyJACNllZWVo7
+Y926ddx5550ACVNyXoq8NGrUiKuvvhpAO3S3bNlSuz9NmDChZgbowtcUfHx8wqjVmoLsRdepU0er
+KL/55pu89dZbNTmsiDj22GO5//77Aaccm6weV111FeB0l67N2kJqaiqNGzcG4L///a/2XJBq1bHQ
+5oLBoPYCPeecc7TobTAY1KK3iYC1Vn0Kxx13nI5TCvcmJSWp7+O1114LaxpUE9TK4CVBmpw+/vjj
+nHbaaYAT4y6dntwEg0FtBiNdd2rCkSM7JpMmTdIxS91GKP3Q7LvvvjXa77AimjVrBpT2sZwxY4b2
+uWzatClTpkwB4KCDDlLzbtGiRdx3330AvPDCCzEbS/v27fn4448B54MlWZAzZ87kzDPPBBJj16Fu
+3bq8++67gFMuXyp6y3hXrVqloeT77LOP1u687rrrmDlzZiyHElHwkm8++Pj4hFGrzQfZxjnssMPU
+0Sgdnsty2GGHqTPn6KOPBpy2cvFGIu0aNmyoK6kbcZzNmjVLC78mSkGW9PR07Y588sknA3DooYfq
+infFFVdozwJjDCtWrACc4rnSPTkWiLb1zTffqFlSWFjI5MmTAbjhhhsSQkOQJKc2bdpoRWx31/Br
+r70WcJLOxOk4c+ZMNYneffdd1RZbtmwZt+zOvUIoXHfddToh0tLSwnYlpMR7RkaGVsyt7gStTlyB
+dItq1apVWL6Ge8zgeKnFY10TQkGEV0FBgZpZ+fn53HLLLQA899xzgNPcV+5FYWGhvn7rrbeqmhxr
+E/XYY48FnB0HOfaCBQu4/fbbAfS8NY0Iry5duqiPY+PGjdrty+1XkeY66enp6h8bOXKk+mW+/fZb
+DjzwwLiM2zcffHx8wqjVmoKwcuVKDWt98cUXtQ7ggAED1GkzaNAgFi9eDFS/JFggEKiSkzIpKYm7
+7roLcDSF8rILRWMoKirSiLd4Rzx27tyZZ555BnBUf1nRCgsL9T6LCrx48WIOOcTxXb311luMGzdO
+3+sFxhht226tVQfdhRdeyNq1ayM6RnJysnr7d+zY4Vk0bLt27fQcUhbwoYce2uPOS2FhIddddx3g
+NIgRE7Jz585ceeWVQGkbQq/YK4RC2aIZxxxzDOB49cXmPProo9Vz/uGHHwJO4ZWqUFWh0qpVKw2w
+SU1NDYtrl4xJmaBff/21+jziJRDEBv766681iEqKlEC4uitjKigo0PsxcOBAxo8fD5TWRow1xhiO
+PPJI/VnGVFFRXrmm7OxsbbTTtWtXHedDDz3kyTgDgYAK/T59+ug8lE5ge0Lu5+LFi/Vak5KSuOCC
+CwB07F4JM9988PHxCWOv0BQKCgo0PHTWrFlaTvzuu+/m66+/Bpw2biKtP/nkk2qdr6oS+rbbbtMg
+FncthKKiIlW3Fy5cCDil0LOysoDSSshec/fddwOOdrB+/XrASdqR1daNe+wXX3wx4PTPlOvzSlNo
+2bIlTZs2BZwVVcbWrFkzfS47d+7U+9imTRtgd5NPnM7z58/ngw8+AOCkk07Sv3/55ZdA1Z28xcXF
+av6tXbtW2wJWtoNgjNEdqBdeeIFzzz1Xx9+yZUuA3WpFxJpaLRTE/t62bRtvvunUh125cqW+fvbZ
+Z2t+waBBg9T+rK69G606L5GXxx57bJjpIZO4oKBAx9ypU6dqja2qpKamMnLkSMAZr2wzduzYkYMO
+Oghw+lyWvXfGGP79738DTr8CESZe0bx5c72f1loVPhs3blRzsEmTJur7kMCqDh06aPDarl27aN68
+OQCvvvqqBo8Fg0ENvnr//ferPVYxabZs2cI+++wT0f+451bbtm01dyMUCun4pZCuRMTGGt988PHx
+CaNWawoi4dPS0tTznJSUpOHPQ4cOpX///oCjEstORHXLoEW7GyAq7o4dO8I0BVH/Zs2apRqNVJ8+
+8MADGTx4MAArVqyoslM0UoqKilS9DgaDem8ff/xxPbfsSLhxV36eNGmSOte82n0YNGhQWEEdcRRu
+2rRJn8maNWtUuymP5ORkDcK64YYbVPMoKChg9OjRQNWd0G7EfHj//fc1zHvLli2qvVSEzNNVq1bx
+xRdfAE7YuGg3otGNHz/ekyxQX1Pw8fEJo9KEKGPMs8BJwB/W2v1LXqsPvAy0BpYDZ1hrtxhHhD8M
+DAR2ABdYaytNV6tqQpTYvbt27QqrwHPKKacATlSdhIwWFRUxZIjT63bWrFlA1R2G0WoK4hhauXIl
+DRo00Nelt+Hzzz+vIcESMZiSkqIr98iRI3nyySf1OrxCwr+nTJmiq2dmZqZqVmeffbY64KRZzP77
+76+OxnfffZcffvgBcLbTxLG7dOlSdbCV3XqNFNEOfv75Zzp27Ag42ZcdOnQASutQREtaWhr77bcf
+AC1atNAYF3m+P/30U0w0S6kKdc0116jf5YwzzgAcZ6a7Mrl7bDLHW7duHZb8BY7vJEqHbkQJUZG0
+nO8DdAfmu167DxhV8vMo4N6SnwcC7wIG6AnMjrCtfZVaa7vbogeDQRsMBm3z5s3t2LFj7dixY21u
+bq7Nz8+3+fn59tdff7XZ2dk2Ozu72i29A4FAlca5du1aW1xcvNvXlVdeaTt37mw7d+5s58yZY+fM
+mRP29127dtnjjjvOHnfccVGfuypfoVDITpgwwU6YMMH++eeftrCw0BYWFtr8/Hxt5759+3a7fft2
+u2PHDv1atGiR7datm+3WrZs1xmjb+aq0rq/oa9myZfpMP/30U33usTh2586d7YYNG+yGDRvsn3/+
+af/88087ZsyYsHlW0fyLdA4sX77c5uXl2by8PJubm2tzc3Pt0KFDbePGjW3jxo1tIBDQa3IfOykp
+yS5YsMAuWLBAr//ggw+O9hpj04reWvsZsLnMy4Nx2sxDeLv5wcBk6/A1Tl/JZpWdw8fHJ3GoqqOx
+ibVWYkrXAU1Kfm4BrHK9T1rR7xZ/GotW9G4VXtTRwsJCdXJJKCvAo48+GrNEmWhVXxnnaaedpjES
+7s7HF1xwgb4umZwHHHCAOu2Sk5O1cEzfvn019sIrCgsLddvryiuvpEePHjpOcYhKDMX27dv1Pp93
+3nnMnTs37DixQtTq8ePHc8cddwBObQIZh9TIqM6xjzjiCHVSy9g//vjjCk3FaExIee++++6rW4mH
+H344AIMHD1YHsyT2QXgpuWAwqMlRglfFWKq9+2CttVXxCcS6Fb3c9OTkZLWNk5KS1B4UP0JN8sUX
+X2j8+qOPPqp+kJ07d2r4qwTdvP322yooWrRooe8dNWoUf/nLX4D4FIkpKipSITRnzhxNmRbvfuvW
+rTUmQPwJXiDP99FHH9UqVU2aNNGgoL59+0YlrEXgglO1CeC+++7T1Hv54IodHyt27Nih1bWk8lKd
+OnXU57Jjx45yhY0IPyj1y5SXeh8LqnrU9WIWlHyXsK81gDtKw29F7+NTy6iqpjAdp838PYS3m58O
+XGmMeQk4DNjmMjPiQkZGhoa2QmlufSSJKPFg4sSJgLMCS7Zb586dNTRbkqBWr17NU089BTjtycXc
+6NKli9ZkiFf4s2Ct5fLLLwfQ2ITZs2fz2WefAdWP/4iEvLw83S34+eefNUNzzZo1ugsyf/58Va3d
+Xn25hzk5OZx99tmAY9JJtKExRuMUnn32Wc+uQcYkuxCRRIE2atRIk9PEtKnqjktlVCoUjDFTgX5A
+Q2PMapwu0/cArxhjLgJWAGeUvP0dnB2IJThbksM9GPMeGTZsmNpnRUVFGoTSpk0bfvzxRyA6W9Ar
+nnnmGU4//XQAlixZosLA7Rt55513ACesVQRB8+bNNYgl3kKhf//+msor93X8+PFqXsTrvoqK3759
+e93KbdiwIW+88QbghDyLAJDXfvzxR/0Q3X///ZoxGwwG1Xa/4YYb1KcTDwEXDX369NFrcqeye0Gl
+QsFaO7SCPx1dznstcEV1B+Xj41Nz1Oow5/KYMmWKes6DwaBm1I0aNYqzzjqrJocWhrWWCy+8EHAc
+omVX2ZSUFC3SkZ2dHRbUEm8NQQKuXn31VR3HRx99BDi1F2qqHmJ+fr4Gp7Vt25Z//OMfgJMRK068
+4cMdZTUQCIR1FZcxb968WTMmP/zww4Tr8eneGZGxifZTY5pCbSMrK0v9B/vuu69GEx533HGqfiXK
+g5esTSiNUhPT4KqrrtKiGikpKTqhP/jgA88zEd0Eg0G1r93RjZdddhlQ8yXUxdxasmSJ+jsOPPBA
+FRBScMcdHfjHH3/oNd19990J00WqPMQU7tOnj84B6SbmFX7ug4+PTxh7nabwww8/aHx+UlKSVkS2
+1qqXuaLSXfFG9vcPPvhgrd0oDrDOnTuH7UNLtd/zzjsvrppOjx49OO644wBHlZVxeOX5rg6yks6d
+O1eLk0i2Z506dVQV37x5c1jH8kRGYm6ys7N1rF6bj76m4OPjE8ZepykUFRWpzZWfn682WU5OjmZP
+elWsM1pE8i9dulS3mSQDMBAIhNnL4iSVHH2vEf/LAw88oGHM27ZtUz+HV0VDY41kosr32oa7roNo
+juLH8arK914nFKDUPLjhhhu0d+Grr76qqnmisWXLFk2jlYYf5557rqYnz5gxQyd1vD6Mcp6ff/5Z
+Pfw9e/aMq5PTB9577z3AMYm6desGOA143H+LNb754OPjE0at7jodDTk5OfTs2RNAi7wmWtRaRbjb
+yrkbx8SDunXrqpYSL9PFZ3dCoZBur0ojnio4HCMqsvI/IxSMMRxwwAEA/PLLL0DttTN9/reRXZQq
+fHb9VvQ+Pj7Rs1c6GitCIh0TwWwIhUI1Hg1YEe4mKwUFBQm/l783I2aju3mQ18+jVgqF8opLuOPa
+y27dCBLOKp71QCCgqpj7mEVFRTH18kvKa3FxsQYeFRYW6jnLO5c7Vr/sJKiG+hgRqampYedw38dE
+ExDu7lUytqoK22AwGFatCxwTM5ZzISsrK2wOyHfZAi4sLAw7X3nnlmcTDAbDgrBi9Wx888HHxyeM
+WqkplJWexpiwcu/uMGCR/Pn5+eWuIPJe9/+46yfGAjl2fn5+VCpgLGoDVgX3LkOiaQZliaUpWFxc
+rM5nr67bnQTnJpLQ9bJarTt4KZbj9TUFHx+fMGqlplCW5ORkbb65du3aMKm5Jwlcka0Y64Sjquzv
+lx2baDzJyckaEm2t9cS/sKdjiRZVWUKRu/5DomsbQizt8uogmoBbY83MzNztWW/bts0Pcy6L3LRD
+DjlEawYOGzZMBcTChQu58847Aaf2odxAmdBFRUUJV2PBjbR1f+SRR7Sv5FtvvRVWyyCekzg9PZ1j
+j5idb0kAABPVSURBVD0WcLpBgaO+u3s7Conw4YoEY4xmUg4fPlwD26Q3abyuQ6pLd+zYUcPK3U7e
+jIwMPvjgA8D7+BrffPDx8QkjYSIao8n4kpXp1ltvBZy6/VINGUpVbXcsgLur8ksvvQTACy+8oAVT
+V61y97CpeZKSkrR60I033qjbmvPmzaN79+5A/DMVr7rqKkaNGgU4FYsAnnrqqVoZGSr38/DDD9cK
+zocddhiLFy8G0G7l1WkyEynGGC3Se8QRR/Dhhx8CTm0Q6VA9d+7cWMS1RBTRWCvNBynPLbUYk5KS
+VAUsLCzUD38oFFLzIDk5WYWJ1EZs1KiRPozJkycnRFBTy5YtAaf7UevWrQGnfLmUafv73/8eZq/H
+AzFjbr75Zk1FP/PMMwH49ttvmT17dlzHEw1uO1xiGurXr6/p9b1799bOS6FQiC5dugCljW2OPPJI
+zxYM8R289dZbHH/88TpO2aH4/vvv+f57pz9zPBcA33zw8fEJI5K+D+W1oh8HnAzkA0uB4dbarSV/
+Gw1cBBQBV1trI0r6jtR0aNCggfYSFMn/3//+V5t/bNq0SVe21atXa/mwH3/8UZ1I0i/whBNO0JoF
+b7zxBps3l+2jGx/S09N1hZoyZQrgtCsTx9KPP/7I/vvvDzhVi+WavERWsbS0NB588EHAKQkmmlen
+Tp0AqFevnucRllVBximZhenp6eqgPeSQQ6hbty7grMBSFs8Yw6ZNmwB0LnixQotT8ZZbbgFgwIAB
+YX+XcPwvvviiRorZRGI+PAc8Bkx2vTYTGG2tLTTG3AuMBm4wxuwLnAXsBzQHPjDGdLTWVuraj9Sn
+cMYZZ2hfPQlb/te//sWTTz4JOJVqygvoMMboVp4EOqWmptK1a1fAKVwh9lu8ufvuu7VHo1SfvvHG
+G9m2bRvgpH3LtmadOnWi+vBV5QMbDAa1kEffvn21AEwwGGTNGqcLoHSvWrdunZZTLyoqqjHB6sYY
+o3UlpSdk48aN1fQpLi7W3o0ffvih+keWLl2qHcW89JNccsklAFxzzTU6XqGoqIhHHnkEoMbuZZVa
+0Vtr37fWitfja5yekeC0on/JWptnrf0Np1PUoTEcr4+Pj8fEwtF4IfByyc8tcISEIK3oq42o+Vde
+eSXz588H4JVXXgGc/oyVqVnBYJA6deoA4clP8n9Lly6NxTCjQhrVXHbZZToOcdq5vd7//e9/Oeqo
+owAYO3ZslVqgR4usYn379lVza+PGjdqWXsbXuHFjbr75ZsDxkEvbtZqs4diuXTutji1mjvs+fP/9
+99rM5rHHHlOtYMuWLZ6bQKmpqdqCvjzWrFlT49pWtYSCMeYmoBCYUoX/HQGMiPC9qgbm5OQwbdo0
+ADUZIpmAN910027mg3sCxDuNuV69etqYNRQK8dZbbwFom3k3RUVF/Pvf/wbiU56+efPmWho/KytL
+be799ttPbW5h06ZNWjq/RYsWvPbaa0Bpv8d44u4f2blzZ6B0AcjLy9P6kieddJIKteLi4rj6RFq2
+bKnboW5kDj/44IM17pupslAwxlyA44A82pZeRcSt6K21TwJPlhwrcTxUPj7/41RJKBhjBgDXA32t
+te6eW9OB/zPGPIjjaOwAfBPJMfckHbOyslTlstbqLkIkocmy1z9ixIjd6hcEAgGNTZCwVq+RVWny
+5MkacLVjxw71jLuvSd67c+dODSsWB5mXZGRkaKhtMBjUVb+slgDOCiyq+F133cVJJ50EwMsvv1yu
+Bid9KXfs2KH33n3N1VklJQTb3UhHjv3ss8+qSbF58+aw88RzZd66datqNGK2pKenayn3eDzfyqhq
+K/rRQAows2Tifm2tvcxa+7Mx5hVgAY5ZcUUkOw8+Pj6JQ1Vb0T+zh/ffCdwZ7UD2tCV5wgknqLMr
+NzdX93mbNGkCOA1Dy24/grO9J9FogwYNYvz48QC6pRkIBNS/cO2112r8g1cYY+jbty9Q2g4MnNgE
+2QoTQqEQ1113HeBUn164cKGnY3Nzxhln6NYoONujFWGt5dJLLwWc+z1ihOMmmjZtGm3atAEcZ1/9
++vWB0kjB+fPn6yoei5V6//3354knngAc7UaOLXv+Dz30kFY/jpVmIPMwGn9Ueno6Bx10EFCqKRQW
+FmpovvR2KEsgEFA/j2wL5+bmepLIlzBhznt6UG3atGHBggUAtG/fXsOc5X9uv/32cmPU3VmEy5cv
+Z/Xq1QB06NABCG/xfuqpp3ouFFq1aqVjN8boXr+YBoDmOxx//PEa0BQMBtVJOnHiRM+7JLdu3Vrv
+S2FhIZ9//nmF7w0EAmHl5iREd7/99lOh3bBhQ+bOnQvAr7/+qseNBe48GMmOdQckiRCSD5IX7Kms
+Xtlx3nHHHTpOMSNCoZD+rwjPsmRmZupum+zE/frrr5qLEsvGMH6Ys4+PTxgJoym4Vb6ytGvXTreY
+0tLSNMJOtudOP/10nn32WSC8PJfbabNp0ybOOeccAO1KXbduXZXg++yzj0bmbdy4MVaXBZSqmQ8+
++CD9+vUDnNiDl192wjtWrlyp/S1PPfVUwHFISRj3zTffrMe45ppraN++vV6fF06yTz75hKFDS63G
+3r17A+HbobI69u3bl5ycHH1NwrG7dOmi4diffPKJRmTGerziuOzcuXPYVp9ojl9/7YTN1KtXT7ck
+Y61yR7Il3q5dO8DRACUM311ERe7nqlWr9Lm7C7oaY1SLEFOja9euGhfSrVs3DfWvLgkhFKQCs7U2
+7AbLB7Zdu3b6oQgGg+oHkAf+0ksvRaSOiodXQmBXr16tD6BOnTpq77/++uuVjre8wiLlvQ9K4ykG
+DhyogurOO+/UDLj69etrn8ubbroJgPfff1+FFKA9MXNycrj44osBx5TwIr7igw8+0N2YLVu26CQM
+BAL6HMQMuuOOO8IqEYuZs3r1as1czcvL88zDL7tS7du3323HAdBgq6VLl2pcyPbt2zXrND8/XwVI
+UVFRVJmykd57d9h4gwYNyq0BKvdnyJAh6htxC4jU1FQ1ofv06QM480vmyKhRo7jhhhuA6odo++aD
+j49PGAmjKaSkpOzmQBPp2bp16zBvuDizZsyYAaCJQ5GyYcMGwCl9tWzZMsBZBa+99lrA8ZzvaWUL
+hUIRrSiSIy9t5I0xaiZMnDhRVep169ZxyimnAOHqtRSAAfTv3333nXqhvYrC/P3337XvZlJSkq6k
+SUlJ6h2XwiQtWrRQ7eDtt99W7e3HH3/U5xKNlhAIBKIKkZa6DikpKWG1C6dPnw7AihUrAGellWjR
+e+65R02Nq666Ss04r2IEAoGAlgt0Jz+5IylFy2ncuDEDBw4E4PHHH1et4oknntBnIv9njNF5WFbL
+rg4JIRSstRQUFIQ1t3Dz7rvvcsEFFwDOwxV1VjzZVWX16tUqiDIyMnSrqHfv3nv0uEdikwYCATVD
+RI1+/fXXue2223Z7b3FxcaUfHNmpSElJ0ZBoL/njjz/0Z5mwoVBI/RkSFAalAmLixInV3maM9v9k
+h0bmEMD//d//MWbMGKB0wQiFQhoeX79+fT1Pw4YNVRh4la9RVFSkOwZuyrvWlJQUnS/BYJDhw4cD
+cPLJJ4cJA3D8ZxJY9vjjj8dskfDNBx8fnzASQlOQve6yATzCvffeqznoUBp8VN2kG2OMemzbtGlT
+6V6x+/8qo2PHjuplltVTgnwEKQO2a9euCpuEgJNEIzsAW7Zs0Rp+8UJWtMzMTIYNGwYQ5lyUJKhY
+rFTR1OpMTk5mv/32A5xVXkzB0aNHq0kjFBUV6e6D+/l169at3DaEsaS4uFjLvx188ME65orOKxpw
+v379GDduXIXv3bVrlzqrly1bFrugrJgcJQbIDkR5Ktzq1avV/s7IyNCLlw+VW9WNhjp16oTZkXJc
+8fJWNl73/5Rly5YtemyZrLL7AY46K0IiGAxqKXpRgY0xmg8xduxYvS9nnXVWjdWSbNq0KQcffLCO
+D5xrki3JP//8U59TPFKnW7duHdYtaebMmQC7CQRh0aJFQHi/jJ49e8YlQ/bHH38EHD/ThAkTAKf+
+I4RXr0pKSuLVV18FnGsqb6dCdhfeeecdnn76aSB6X8ye8M0HHx+fMBJCUwgEAqSmplZYXKKoqEjL
+ud9///26dysOt8svvzwsVDhShgwZotpGUVGR7kpIvPyexluZs3Hz5s0aWiv1Bq699lrdfXCXDFu5
+cqV6w8UxduONN+o1FxYW8sADDwDE3XSQsQKMGzcuLJQYnNVXTLu5c+cyebJTtc/rUGxwqnG7V9I9
+lWNPT08P298Xvvzyy7jW0nDvNAnDhw/X2ISkpKSwgCU3S5YsARxzGuC1117TUHK/l6SPj49nJISm
+UFBQwLp16/Yo7Z55xknMHD16tDoCJSrtrrvu0ijH6dOnq+QvezyRwPLeESNG6PZPcXGx7rFXpgVE
+YrsVFhaqQ1S0gLPPPlsrSOXn56umUK9ePd2D/r//+z/A8XfIanvVVVdpleeaQO6jO7JU7kF+fr5u
+9X322WfVjqaLZsUrW8TWvb0s/hypWXHJJZdwxBFH6HvFv3PllVdWa7yxYNKkSVqab8yYMeX6ET7/
+/HPOO+88oDT2wlqrmmUgEIgoMSsSak2HKFGl9t13X626LELBfRNzc3OZOHEi4IRBS8ef3Nxcrrji
+CgCtnNy2bVsVIMuWLdOuQJXVyIvUQy6Za1Jfcc6cOVqEZNu2bSp8MjMzNR9DApPmzJmjwU8V7crE
+C7n3O3bs0BgR+QAOGzZMG+rEIqcgmt2HtLQ03VFIS0tTQbBgwQKt3CwLSGZmps6TgoICjWO45557
+arz8GZTmx3z00UdhwktiXc4666xK7688m4KCgoquKaIOUb754OPjE0bCaArRvF+iw1588UXA2VYq
+2yIdHJVKVo+kpCT9m6x8a9euZc6cOQA8/PDDmtUXq3si55FmH6mpqbz//vtAePKNu6WZvFaT1ZAr
+Yu7cudonQ8yExo0ba+2CmuD2228HnESyPcUbFBYW8vPPP+v/iHYT74K9ldG8eXPdOi0uLtb77Q55
+rwYRaQoJIxSqss8qkyAjI0Oz9vr376+x41u3blX1MRAIaDyDpEZfccUVWtEo2vyJqpCZman7+PEO
+9IkFv/76q8bwy05N8+bNYzqGql5Tv379dLemYcOGKqgkr2HChAkJUf8wEqTi1uTJk9U8ihG++eDj
+4xM9CaMpxOM8ZU2MRLj22kZZz7gXNQJ9PGPvbUVfVfwJXH38e7j345sPPj4+YfhCwcfHJ4xaLRTc
+tRJ99g5q0/P0Yv4lwvXXaqHg4+MTexLF0bgR2F7yPWJiuHvQMNpzxxD/3C7itCMUk+uu4lj3eG6P
+r3/3mnDlkBBbkgDGmDmRbJf45/bP7Z/bW3zzwcfHJwxfKPj4+ISRSELhSf/c/rn9c9c8CeNT8PHx
+SQwSSVPw8fFJAGpcKBhjBhhjFhljlhhjRnl8rn2MMR8bYxYYY342xowseb2+MWamMWZxyfd6Ho4h
+aIz5wRjzn5Lf2xhjZpdc/8vGmGQPz51tjHnNGPOLMWahMaZXvK7dGPP3kns+3xgz1RiT6tW1G2Oe
+Ncb8YYyZ73qt3Os0Do+UjOEnY0x3D849ruSe/2SMecMYk+362+iScy8yxhxfnXPHihoVCsaYIPA4
+cAKwLzDUGLOvh6csBP5hrd0X6AlcUXK+UcCH1toOwIclv3vFSGCh6/d7gYeste2BLcBFHp77YWCG
+tbYzcGDJODy/dmNMC+Bq4BBr7f5AEDgL7679OWBAmdcqus4TgA4lXyOACR6ceyawv7X2AOBXYDRA
+ydw7C9iv5H/Gl3wmahZrbY19Ab2A91y/jwZGx/H8bwLHAouAZiWvNQMWeXS+HJwJeRTwH8DgBLKE
+yrsfMT53XeA3SvxIrtc9v3agBbAKqI8TMPcf4Hgvrx1oDcyv7DqBicDQ8t4Xq3OX+dspwJSSn8Pm
+O/Ae0MuL5x/NV02bDzJZhNUlr3mOMaY1cBAwG2hirV1b8qd1QBOPTvsv4HpASkw1ALZaa6UMk5fX
+3wbYAEwqMV+eNsZkEIdrt9auAe4HVgJrgW3Ad8Tv2qHi64z3HLwQkCYlNTb/90RNC4UawRiTCbwO
+/M1aG9bE0ToiO+ZbMsaYk4A/rLXfxfrYERICugMTrLUH4YSVh5kKHl57PWAwjmBqDmSwu4odN7y6
+zsowxtyEY8LWXL3+CKhpobAG2Mf1e07Ja55hjEnCEQhTrLXTSl5eb4xpVvL3ZkDVmlPumd7AIGPM
+cuAlHBPiYSDbGCM5KF5e/2pgtbV2dsnvr+EIiXhc+zHAb9baDdbaAmAazv2I17VDxdcZlzlojLkA
+OAk4u0Qoxe3c0VLTQuFboEOJFzoZx+ky3auTGScv9RlgobX2QdefpgPnl/x8Po6vIaZYa0dba3Os
+ta1xrvMja+3ZwMfAaV6eu+T864BVxphOJS8dDSwgDteOYzb0NMaklzwDOXdcrr2Eiq5zOnBeyS5E
+T2Cby8yICcaYAThm4yBrrbuf3nTgLGNMijGmDY6z85tYnrtK/H+7doxDQBAFYPjv1BzBCRxAoeUa
+jrGVQ0j0CoVGoeQGCkFBrJNoFG8kppAojFX8XzLJFpO8fcnLy8zbbXqoAYyIiewNqArH6hPHxgOw
+T2tE3O23wBXYAJ3C7zEA1um5SxRCDSyBVsG4PWCX8l8B7V/lDkyAM3AC5kCrVO7Agphd3IkT0vhd
+nsSwd5rq70h8Ifl27JqYHTxrbvayv0qxL8CwZN19uvyjUVKm6euDpD9jU5CUsSlIytgUJGVsCpIy
+NgVJGZuCpIxNQVLmAYKx84FNOBtDAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 2/2... Discriminator Loss: 0.8941... Generator Loss: 1.0698
+Epoch 2/2... Discriminator Loss: 1.0571... Generator Loss: 0.8698
+Epoch 2/2... Discriminator Loss: 0.8889... Generator Loss: 1.0698
+Epoch 2/2... Discriminator Loss: 1.2752... Generator Loss: 2.7747
+Epoch 2/2... Discriminator Loss: 1.1641... Generator Loss: 0.7411
+Epoch 2/2... Discriminator Loss: 0.8820... Generator Loss: 1.0839
+Epoch 2/2... Discriminator Loss: 0.8707... Generator Loss: 1.0716
+Epoch 2/2... Discriminator Loss: 0.8608... Generator Loss: 1.2287
+Epoch 2/2... Discriminator Loss: 0.9702... Generator Loss: 1.0217
+Epoch 2/2... Discriminator Loss: 1.1194... Generator Loss: 0.8550
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4VVXWxn/7lnQCATRgkCIiDqAiCDZAsHcdZeyKgL2h
+Y0FHUew4FtSxfGNHZQRGbCgqiDgiKg5jQaVIEUTpHQmpd39/nKyVc8NNcpNbcoPnfZ48hJt7ztln
+n33WXuVdaxlrLR48ePAg8DX0ADx48JBa8ISCBw8ewuAJBQ8ePITBEwoePHgIgycUPHjwEAZPKHjw
+4CEMCRMKxphjjTELjDGLjDE3J+o6Hjx4iC9MIngKxhg/8BNwFPAr8F/gbGvt3LhfzIMHD3FFojSF
+3sAia+0Sa20JMA44JUHX8uDBQxwRSNB5C4Dlrv//ChxY3ZeNMXVSV4wxYf+GQqE6D7AxIxgMAlBe
+Xv6Hu/e6QtZIqjN3AwHnVTTGUFZWBiRkzOustbvUOpZ4XzVaGGMuAS6pz7HyUshEbt++PWwCfT5H
+AUqVF8YtxGSc1tpqP696nPszv99Pfn4+AFu2bGHr1q07fCde463red33Ec3n8YCM1efzhT1vv98f
+9vfy8vKIY0gFYWGMoWXLloBzH2vXrgVQ4SCoaazGmGg2yWXRjCdRQuE3YHfX/9tUfKaw1j4DPAN1
+1xRycnIAyMjIAByh4EaqCIOq8Pl8YS+/e5zyuVsQRFoEfr9fj/v9998TMs76vijVHZfIF0/OHQqF
+dphDgBYtWgCwYcMGSktLEzaOWBAMBtlzzz0BWLp0ab3GGc85TpRQ+C/QyRjTAUcYnAWcE6+Tb9u2
+DYhuIlJBfZRrl5eXqxYT6e9Vf4+E8vJytmzZAkDz5s31++vXr4/XcBslqs6h7LKrVq1qqCHVClmb
+U6ZMoW3btgDss88+9T5fvNZ4QoSCtbbMGHMV8CHgB16w1v6YiGt58OAhvkiYT8FaOxmYnIhzFxcX
+A1BSUqKfRfIjZGRkcMwxxwDw0Ucf6XHl5eXucerxYocmyoFnrdVru21AY0zU1+vcuTN77LEHAAce
+eCBvvvkmkNqaQiJ9Co0ZmZmZAPTu3ZulS5cCUFRU1IAjcpAQnkKdB1FHn0IkyASXlJTQrFkzAN5/
+/3169uwJOC/khAkTALjpppsAx84sLCyUMYTZoon2S+y222506NABgOXLl9O1a1cAVq5cCcDixYt1
+bIFAIKIwcQuvhnqOxhjS0tIAxzYWQe0WsqFQSNX5VLXrkwl5fosXLwagTZs2auZ07tx5Bx9ZHPE/
+a+0BtX3Jozl78OAhDA0Wkow3xDRo0qQJs2fPBqBdu3ZhjsYmTZoAsHHjRgDdieXvydht09PTATj0
+0EO5/fbbAWjatKmGGd3xarmnSZMmceGFF+4w5oZEx44dAZg1axbNmzff4e+lpaX07t0bgPnz56ds
+RKgh0KVLF8DRFsGZqxUrVgCwxx570KNHDwBWr17NlClTkj6+nUYoyMs0cuRI9eRCpRlw880389BD
+DwENG4nYfXcnUnv66adrbHrcuHFs2rQJgBNPPBFwFo7cU15eXkqo3T6fjwEDBgDw+OOPA5Cbm1tt
+/P/KK68E4LLLLvOEQgWMMbzxxhtApU/s3HPP5cMPPwTgggsu4IEHHgAcoSACJJnwzAcPHjyEYadx
+NIppMHv2bCWCrFixQlVYceA1JHw+H8888wwARx99NPfffz8AL7zwgpoK7u9OmzYNcHaXO++8E4Dp
+06cnccTh6Ny5MzNmzAAqiVNdu3ZVx5gxRlXiN954g9zcXMC5199+c7hrydAYcnNzdT5lDN26deO0
+004DHO7Chg0bAHj55ZfVJEvGuzBq1ChuuOEGoHJNtm/fXh3J7dq1Y968eYBjamZnZwNxi0pE5Wjc
+acyHE044AXDsNFHFjzjiiJQQBoLbb7+dQYMGAeEU1qoCAZyX5+uvvwbg6quvVvZmQ0CiOV9++aWa
+NBItcXvKrbX6grVv317No6lTp6pZtGjRooSPNysri/feew9AN4VgMKgv3rx58/jqq68ARyAnAxIy
+F4EAcNdddwHhIfLVq1fr2khLS6Nbt24A6idLBjzzwYMHD2HYaTSFiy++GHCkqxBBlixZ0oAjqoTE
+8a+66ipVA59++mmefvrpGo87/vjjAWf3kCSZhsBf//pXwOGCPPjggwARx3PggQfy7rvvApXaBcDm
+zZtVe0sGVq9ereaKaDZQuVsHAgG+/PJLwNFoRHtx79jxhlx727Zt/PrrrwC8+OKLO3zP7/eH8WXW
+rFmTsDFVB09T8ODBQxh2Gk1BHDLBYJA//elPgONf+OWXXxpyWADsv//+gMNHEBt8+PDhNR7j8/k0
+GxTgxx8bLnVEOBI+n48PPvgg7G+BQEDHJvRrcHbEfffdFyDpz8BaywUXXADAbbfdBsAjjzzCscce
+CziZk6IdLF68OKEagkAcrO+99x7//e9/AcJYqsL+XLRoEVlZWYDDSRGtIpnYaYTCvffeCzheb1HV
+mjVrlhJCoU2bNjt8lpaWFtHBKGMfMGCA5jM8/PDDSVm4kZCRkUHTpk0BxyH6888/A5VU3b59+yoh
+a9WqVTz33HMA3HPPPQ02ZqjkAMjL/+OPP6qzs7CwkDlz5gA71ixIFEQoXHjhhRp1EtJXWVkZeXl5
+gDPfEgWZNWtWg/A7PPPBgwcPYdhpNAUJQW3YsEGdXGPHjlXVPVk7QiS4HUcSWjzppJNUUzj11FNZ
+uHAhUKmCr1y5ku+//x6Ad999NywjNJk44IADwjJJr7vuOgBGjx4NOKaBsO6qVsBKBYiW5g4FvvPO
+O8pTSDZKSkq44447gMpQdHp6uo5nzpw5HHLIIUBlwlS0iFfFsZ1GKMhEnHLKKUye7GRsd+rUSSd7
+zJgxXHPNNUDyac4iFAoLC9X+7tWrF/379wfgiy++UJ+IYO7cuaqKS65GQ6B///6agRoMBjUScf75
+5wNw3XXXMW7cOCA1SpuBM98yZuEj+P1+jfzceOONDTY22LFilpvCPnz4cD7++GPAIYsJKU/K7tUE
+IWrFGunxzAcPHjyEYaehObshKvpRRx3FxIkTAcdLLmXcpOSV8BkSBeEnPP/88wD069ePa6+9FoAP
+PvhA1b3CwkJ11p133nmAQ8sVTeGHH35I6DgjwZ0FKbuVFMx1w1qrDrwPPvigQbUF0cj2228/RowY
+AThmGjiawr/+9S+gUstJRaSnp2ty1J/+9CfVdE4++eR4zO0fi+bshqiJkyZN0sKdv/76qy5usdUO
+O+wwPvvss4SNQ669yy5OVe3t27ern6BqIQ0RHKeeeirgqItiq7v9D3VFfWpU9u/fn+uvv16Pu/rq
+qwF49dVXdQ6/+OILwCH/CGHphhtuUF9DQ+Dwww8HnDmUuXfXxJQXLJVRUlLCSy+9BMDQoUN1zPvs
+s49GTBINz3zw4MFDGHZK86E6SMmrXXfdFXA8/OLtr+9OXBMkDv3TTz8Bjmq49957A/Dbb7/pLnbF
+FVfw2GOPAYSVbxfn47///W81OxJJd5brffLJJ0qmGTJkCN9++221x5x77rlK1928eXNY4ZBkIjMz
+k1NOcZqQFRUVKb15/PjxgKPxyLNOBe5KTZC5P/XUUxk2bBjgEPEOPvhgIKY18Mc1H6qDZChOmjQJ
+gJYtW9K5c2eAOqtm0YR/hLwj5B+fz6decWOMnqNbt26MHDkSgKeeekrPK/z8AQMGaMj1tNNOqxPL
+TfwroVCoxrCmz+fjnHOcKvxr1qxRRmBtBWHHjh3LVVddBUD37t1VhRe7ONEQP8duu+3G559/Djim
+l0QYxHwqKSlpkDyC+kDWzbJly9Qvtfvuu+vGcOuttyb0+p754MGDhzD8oTQFceKJelZWVlbvTMpo
+zC7RBNzfPfvsswGHBiyEqssuuyzi8X369AGc6MN+++0HOJ5zocnWBndpdbnn6tC7d2/uuecewCkJ
+Fm3JeGNMWFTCnR2ZDMgcL1++XDWhQCCglbIFmzdvTony6dFAntXixYuVn+D3+7n88suBFNYUjDG7
+G2OmG2PmGmN+NMYMq/i8uTFmqjFmYcW/efEbrgcPHhKNWDSFMuB6a+3XxpgmwP+MMVOBC4Fp1tpR
+xpibgZuBmlMC6wixE6vrJRnpuwUFBepTkM+2bt1a7+rI0WgK4myTHSo7O1ubt0RzvLSHmzVrlja1
+admype4kTZs21Z0kkmPP3XymtuSkSZMm6XwKnyManHfeeeqX8fl8bN68Oepj4wHRDtzz6XY6CiRb
+MtXh8/l0fRYXFzNq1CjAqSIm2ZMJb4Uopc1j/QHeBo4CFgCtKz5rDSyI4lhbl59gMGiDwaAdNGiQ
+HTRokB0wYIANBAI2EAhYwBpjrDHGZmVl2bvvvtvefffddvPmzTYUCtlQKGSLiopsUVGRPeCAA+p0
+3br+5OXl2by8PLt27Vq7du1aW1RUZEePHm1Hjx5tKyIuNf60a9fOtmvXzm7ZssUWFxfb4uJie+CB
+B9ZpDDIXtc3l5s2bbUlJiS0pKbFjxoyp9XytW7e2rVu3ths2bNDj5s+fb30+n/X5fAmd19p+3nzz
+TVteXm7Ly8ttYWGhLSwstNnZ2Q06pmh//H5/2P932WUXu8suu9jy8nJdv9nZ2fW9n9nRvMtx8SkY
+Y9oD+wOzgHxrrRRGXAXkV3NMvVvRe/DgIXGIWSgYY3KAicC11tot7oxAa63sUjsgllb0ouaeeeaZ
+gJNcJByEDz74QCnDQ4cODSt4KlwEyZycP39+XS5bZ4j6L87M/fffX9mBgwcPZuzYsYDDQ5g5cyZQ
+mc0ZCAQ0xp6VlaVJNMJ5iBbu0Gl1/RkAJkyYwFlnnQVAjx491CRIT0/XMXXt2lVZlpdeeikAOTk5
+arrceuutDdrfoXXr1gAceeSR+tl3330H1M0kakhUNfPcIWzBoYceCpCwRjExkZeMMUHgXeBDa+0j
+FZ8tAPpba1caY1oDn1hrO9dynjoNQkg2jzzyCOC8/K5z6cJ0/z5//nz15svLmiyIYDruuON47bXX
+gMq8CHAWgmRCvvzyy4BDNZYKUmlpacydOxdwIhLRZMwJZDGlpaXpy928eXO1/SVyMHjwYCXKtGnT
+Rn0taWlp6hPJyMjYgZ9RXFzMySefDKA8gWRCxnPMMcdobkNubq7eX/fu3YHUJyxVB1nra9as0XX0
+6aefAvDnP/+5rhmRie0laZzV9jwwTwRCBd4BBlX8PgjH1+DBg4dGgnprCsaYPsAM4HtAdMa/4fgV
+JgBtgWXAGdbaGita1JfmLN7Yd955R39v166dVvItLy+nX79+QPxpt/X1AIu07927t1Zz7ty58w6c
+huLiYv1s8eLFnH766UDdTR6h+7Zo0UJb1uXk5GhPCVGrL7/8cmVeXnbZZapB5ObmqmnWqlUrrQch
+u9WNN96YdJNB5j4QCGji1siRI3XM1lrat28PoGshFej89YHc6/PPP681JoWtKeZnHRCVpvCHyn1I
+ZUg+hjS1adq0qTYAmTlzZoMVhkmF9eGG27aGyo3hrbfeoqCgAHCa1lx00UVAcjpSNSJ4reg9ePBQ
+d3iagodGBTd120Od4WkKHnY+eAIh8fCEggcPHsLwh8qS9NAwqEJoq9NxEj1JdtGWPzI8TcGDBw9h
+aJSagmQJRgo3VbcTGWN0x6otTBVvZ1Y8w3vp6elKhU1Ugxt3pl519Oi6oL7HW2uj1hDi8cz8fr9y
+Q2SOkxXSrK82JfD5fHpcrPPQ6ISCMUYr9QoVd/v27VG96FKJuLb03ng7s2J+SIGANvrYfffdlfKc
+SAgRKBQK6YtZ1/uIdaHXBfE4fzAYVHOlasOWRMLn89Vb+IgQc1etjnWz8MwHDx48hKFR8hTEfGjI
+rsbJRIsWLbQ46qOPPpr0Qia1QbJSMzIylDbt8/mUNr1hw4Y/zLNKcey81Zz/iAtMqkslO8OzNgwa
+NEjL0xtjtIpzcXGxft5QzVzdMMZoZmogENCKTV5UY0d45oMHDx7C0CjNhz8KxHkUCATUkVZaWqrO
+MEhcBKI6iPPw8ccfB5zsShlnaWkpK1asAJxWd5JR2RBrTMYpRUpuvfVWBg8erJ/tu+++gFMF+g+E
+ndd8qC9k8R522GGAU2Zd+iCOGjUqpSi0wWCQTp06AbBgwYIwk0nSgkeNGqUVkKItyR4LfD4fN910
+E4CWG/f5fCqYli1bxkMPPQTAokWLGmw+jTGaMv/GG28AkJcXXlRcqjNJdysPlfDMBw8ePIRhp9cU
+RNUOBAIccICjOUlX39atW2u5rilTpmjhkYbUGESbOfnkk1UFro6XcNRRR/HXv/4VSGyDEHfJM7mO
+jG3ZsmVaMzAQCGi/xvT09KQ3X5HaCueeey533303ELk5TSgUCmshuG7duriNwV0sJ5U0z7ogpXwK
+8WKltWzZEoDhw4drcctp06YpIeeJJ54A4MILL1TSyFNPPaWqcUN0EpLFO3DgQAAWLlyoNQ+resil
+wOrYsWO1XmNtnZkCgUC9/Q9irnz++edaDEZ6b/bs2VOfmTFGa0xOnTpVf08GmjZtqj02s7KydM4W
+LlwIwC233KIFdK+99lrOP/98wHnWe+21F1D/xq0iILOysrRe5ZAhQ7jyyisBp3KWbE5XXXUVZ5xx
+BgDz5s0D4L777tOxb9++PZHCxEud9uDBQ92RMuZD1TJbdYXsZpMmTdJS3zfeeKPGza21ulMKqcat
+4n388ccJaUcfDXw+Hw888ADg1G4ER2OoLobu7iUp3nW5p+qITfXldvh8Pl555RUA8vPz9fy9evUC
+wk0tay0TJ04E0OrUiYao66+99ppWPg6FQqpNvfXWWzscc+mll+punZmZqeZPpO9GA5mDUCikFbF7
+9+6tmsCvv/6qGunuu++u3xcNpXPnznz11VeAU/Fb1u/tt9+udRg3btyYNE6Fpyl48OAhDCmlKfj9
+/jpJQ9EuWrRowX/+8x/AqT588MEHAztWPpadRCQ0oJWKp0yZ0mDFUbt3766VliWEtmbNmrAeC/Ld
+U089lTZt2ujx8nm7du2ASls/Xmjbtq06aK213HXXXUD1mse0adMAOP7447VydTJ8NNOmTVPtZd68
+ebz9dvWdBay1PPzwwwBceeWVqiHG4ncBxx/g1jbdXAnRWA877DD1afXs2RNwnq90I2/WrJked8cd
+d3DOOecA8OqrrzJgwAAAXevPPvtsQhK3UkYoQHimVzRo1aoVAJ999plW8v3Pf/5TbRn0bt26AWiT
+FYBvvvkGaBjqtIzjn//8J7vssgsAl1zidNJzP2yfz6edmYYMGaKfh0IhZsyYATjt6mtCfTMc//a3
+v6mDdvv27bWWFZdx5+fnJ0XIilr+8MMP64uel5dXY7q6MUYde2+99ZZ23YrHGhCh3LdvXx3bmWee
+ySeffKLjkbHJen/kkUc0ArJ9+3ZycnIAh5gmJKt77rlHc36ke9ehhx7KeeedB8RX8MZsPhhj/MaY
+b4wx71b8v4MxZpYxZpExZrwxJq22c3jw4CF1EHNI0hjzV+AAINdae6IxZgLwhrV2nDHm/4DvrLVP
+13IOa4whLy9PHVQ1jUsk7fTp0wHo16+fhubE4VYVfr+fxYsXA6iqDmic/9lnn613W/q6wB3zl103
+KyuL4447Dqi8JzcyMjL44IMPADjooIN05y4pKVEH6+rVq+M6TnFgLlmyRMOQn3/+ubJBq8v/l+O+
++uorNeOSWZsAnFZ3Mk4Zw7Rp09RJesghh3DDDTcAsHTpUi6++GLA4VzEClHxP/roI77//nvA6c1Z
+U72E9PR0nn32WcAxhYWN6e6D6vP51LR2axoSRh04cGA0bfsST3M2xrQBTgDuBf5a0UrucOCciq+M
+AUYCNQoFcITAxo0bo1I5Jebbt29f/ey0006r8Zj9999fhYFManFxsfbikyzERMLdNPaUU07Rcdxy
+yy0RhYHguOOO45BDDtFzyOI47bTT4i4MBPn5TrPwnJwcfSbr1q2rcXEbY7jtttsApzNTsvMyBKWl
+pZqhudtuuwFwww036Evm9/t17lu2bKn5GvHAokWLACczVEy72gqoFBcXc8EFFwCOT+zcc88FYM89
+91QyWI8ePZSSLQ2SBwwYoKZGnz594tbLM1bz4VHgJirbxrUANllrZTX8ChREOtAYc4kxZrYxZnaM
+Y/DgwUMcUW9NwRhzIrDGWvs/Y0z/uh5ftRV9XXgK4mgRbN++PeJOa4zR3eGWW27Zwfn0008/8frr
+r4d9lgiIZvPAAw9w9NFH6/VGjBgBwN///veIx11zzTWA44hy02dXrlwJoDtRIiCRGnd37I8//niH
+7wWDQVXFr7rqKtUwQqGQanJTp05N2DgjwVqrDtl7770XcEwGd3ap+7viwKsrDyCSM1NMLZ/Px5gx
+Y+o89i1btmiPUfc18vLydJxXXHEF4HAhxERZsGCB3l+sGlos5sOhwMnGmOOBDCAXeAxoZowJVGgL
+bYDfojlZXV5K8cRL+GfJkiW6eNPS0sK4+KeccgpQ2aMRKm3cYcOGaaWgRED8G0I2Gjx4sPoDnnzy
+yTASkkCE2JgxY7SprDsq8/vvv3PLLbcAiTV5xK71+/16nenTp+vCvO+++4DKZqcQTgbz+XxaS7Mh
+IGQgtx3uji6ISv/LL7/oM6mrBz/SmpVoQElJib6wsUCusWHDBqWyi1DYtm2bCuqZM2fGLYJWb/PB
+WnuLtbaNtbY9cBbwsbX2XGA6MLDia14reg8eGhkSwVMYDowzxtwDfAM8H8+T+3w+TWaRXaBly5b8
+/PPPADRv3lxLls2bN0+zIN2FSt555x0Avvjii3gOLQwZGRmajXn88ccDzq4r17z22mv1u8YYPvro
+IwD69++vn7kh9/r2229rG/hEmTx+v1+Tw6AyqWj9+vXqzZfxW2tVY3ObRwceeGBCxhYtZG723HNP
+/X/Hjh0BR8UXk+eggw6iR48eQCUpqL7Iz89XEtKKFSu05Fu8IBEVqQ3hrmzepUuXuJmTcREK1tpP
+gE8qfl8C9I7HeT148JB8pBSjMRo0a9ZMd3+xvyWPHhzpKX6C5cuXh+1YwmUQmzyRCVDXXnstJ554
+IlDpE1i/fr2m1rp3+WeeeUZ3AbeGIDbizz//zGeffQbAddddp5pQojSFYDCobFFjjPpg+vXrx8iR
+IwH47rvvAIdVJ1qMz+fT0JoxRud+3LhxCRlnTRC+hPgODj74YH755RfAWRfCgM3Pz9fkqBkzZtSr
+/4I8s8LCQvW1fPzxx3F/PsJJkfNmZGQo4/Hmm2+O2/UanVCAypdZFmtGRoZ+9v7773PZZZcBDqFD
+Hjg4zhhAKa71RTAYrNZTLd7nIUOG6GIR0+bQQw9VcpYxRh1GgwcP1u/KeX/++WdV4f/73/9qubV4
+q6SR4BZMoVCIDh06AI6Z9uijjwIo2cb9EvXq1UtzMNzfaQjIxiG8ELfTLy8vT3kf7tJt9W3KIs7X
+4uJinnvuOSD+TmC/3685KLLJZGRkKOEunp2svCxJDx48hKHRaQobNmzQgqaiIrolvJuqvHTp0rC+
+k+L4ixXVaQl+v58777wTgD322EPVf1Gp165dq+N59tlnNXzlprBOnjwZgHPOOUdDZMnO3szPz9dx
+unsUvvHGG2q6uHcmCb1OnjxZd7ElS5ZoPYFkIy8vTx1+UpvgjDPOUKfjiBEjVBsqLy9Xnkhd4/uR
+nMGJ0uTatGmjTmhBWVmZshzjuUYanVCAygmoLVdBOArgPPxE5zb4fL6wByc27Lfffgs46t5vvzm0
+jaZNm+p9LFq0SFXDVGj2snXr1jA/gZhp5eXlaqZJX87HHntMKeY5OTn69z59+iRVmPl8Po0i9O3b
+l/322w+ozIx1p3JDZfXr6667jn/9618xXxsSU25fBM/TTz+tpqlg48aNMRcnigTPfPDgwUMYGqWm
+EC3EgSQQLkCiUFpaqglWUCnlpQJy27Zt1aRYt26d0rU3bdqUUpV/N27cyIIFCwDYd9991dE4c+ZM
+1RCaN28OVDrZwHGCSj0IKV6TaMj1+/btq07FzMzMMNMSwhmNr7zyitZekIY1sSCRzl/RePr376/3
+KhrvypUrtW9JPLFTCgVZCBJWA6cKUzKy9sS+NsZo4ZS2bdsCTv1IoQWvW7euwbIIo8Hs2U6eWpcu
+XZRCLuQfN0KhkL5Yp59+uqan1xfualK1edSNMRppuueee8jNzdXPBSIIlixZoubkggUL4iKEEy3I
+fT6f5kEEg0G9l1mzZgGO6ZOI4kCe+eDBg4cw7JSagsSgW7RoodJ81qxZcY3lRoLf79f6iVAZpfj3
+v/8NwHvvvdcoOmaHQiFtR3fZZZfpfB5xxBF8+eWXQCX1efny5XGtMuwuV1YbzjzzTDXNcnNz9VmX
+lZVpp2tx4K5YsSKlTLRo0L17dzUfysvLtV6C9AaprnJ3rNjphIIxhgsvvBBwXlJ3Db9E49BDD1WT
+oKioiFNPPRWoJE015KKsa6Md+a61VtmU8m+iIUKhujGLeTh//nzefPNNwNkIpOntyy+/3CiEb3WQ
+ZkbPPvuspkNv375d17AwcxMFz3zw4MFDGHY6TcHv92uhj5KSEpWq+fn5WrU33pCd7bTTTlN667XX
+Xpu0nTUaNCbVubZdXu5lzpw52pqtsUO0nz322IPnn3cSi92m6F133aVl4hNtBnuaggcPHsKQUg1m
+44FgMKiFL9euXatU261bt9a7gWhtkNJlb731lrZNe+GFF5KSvORh50CfPn0AJ7Qq2ZD5+fmqhXbp
+0kUb5MaAqKo573RCoSEgcfz27dsraScV6MoeGg+EDHbUUUdp1fEBAwYo4e7RRx+NhwnodZ324MFD
+3bHTaQp1Cb3VN38+0nnASQiSmH0y+kh4aFjU1JquLscbY5SWXV5erpqnz+eLmJUaAxLfDCYVUd0D
+cguAaGm00ULOU53JkJ6eDjikGvc1ZazVCTI3iSdFhLf+7i4573453OOU7/j9fo23i58lXhTv6uZI
+Ppd8AXdEw1qrGZPu6lvWWs1EFKG+bdu2sHXjvkZ9nkkkYlYoFArL4HXPjYyzrKxshw5R7vL07rHF
+Oree+eD1MpikAAAgAElEQVTBg4cwNEpNwa12wY4SO5IEj7RDJwtSr7+wsFB3oKo7VyQ0lHbQpEkT
+HafP5wvbjWTcsmsFg8GwKIt7zPLdYDCY8Ni6e6d0a4UyBmttWLMU0RCqzrFEqCLNfTyex6677qrF
+c6SWqHvszZs3V6djq1attHRgdna2RtLk3kKhkLaN27Ztmz6nWDWFRu1TkKy433//PYyWm2pwV39K
+xfEJRMjm5+fry7Rx48awF8tDbIiXH6ueSHz0wRjTzBjzujFmvjFmnjHmYGNMc2PMVGPMwop/82K5
+hgcPHpKLmDQFY8wYYIa19jljTBqQBfwN2GCtHWWMuRnIs9YOr+U8dRqEeGqlIcmDDz6Y9NoEsXqe
+UxnuRLJk358xRh2UjTmpKUWR2OiDMaYp0A+4EMBaWwKUGGNOAfpXfG0MTpOYGoVCXSFlxMU2a4jF
+k6iXJRWETUO+jFV7Ptb1WAgvwR9ruHBnFPq1IRbzoQOwFnjRGPONMeY5Y0w2kG+tXVnxnVVAfqSD
+vVb0HjykJuptPhhjDgC+BA611s4yxjwGbAGuttY2c31vo7W2Rr9CXc0H6Qgl8X9psNJYEQgEOOig
+gwCnnTvAN998w6JFiwCn27P0aPT7/WE1+r755hsgsd2ukom61n0QZGRkaOmyli1bqjkpEYd99tmH
+3XbbTb8vRVjKy8s1d2Xq1Kk88cQTADr3a9asiWsRmQZGwh2NvwK/WmtnVfz/daAHsNoY0xqg4t81
+MVzDgwcPSUa9fQrW2lXGmOXGmM7W2gXAEcDcip9BwCgS0IreGKMhvsZOJZaOweeddx7Dhg0DKp2o
+J510kt6n0F4FsnOtXr2arl27Ag2nKVQNsbmZjvXZ8euruXbv3p0jjzwScHgWUnVa/E4yrwIpqutm
+GJ588smqhUqB3TVrUmdPk7kVJCq0GSt56WpgbEXkYQkwGEf7mGCMGQosA86o4fgwuCvWVnfDrVq1
+4plnngEqG3pcc801jS4rsaCggKlTpwJOdqW8+CLoQqGQmkdV4aZHx6uNejQIBoP6EskCbdasmTZr
+PfLII7n99tsBR5AJOefyyy/XexXh5ff7tQCO27Ho7kgVjYCQ0nv333+/kn6++eYbFZZSqr5du3aM
+GDECcASFzO2oUaNUgLj7SkoP0nvvvTequakJfr8/jFgka1vmsG3btjqHCxYs4IYbbgCcFgXS4Qoq
++5TK2NevX0/v3k6D92XLlsU8TkFMQsFa+y0QyUY5IpbzevDgoeGQMoxGYwxpaWm6E4naVxUDBw7k
+1VdfBSp3ksMOO4z//e9/QGzhtFhV32gg1YWnTJmiPSKstbqDSn/JDz74QFu5L1q0SOm3Pp9Pd4pu
+3bppdeVE8TT8fr+O48Ybb9QWcmL65OXl6a4cCAQ0gafq/Mn4pC/EXXfdxYQJE4BwrbBFixbaUKe2
+Z9m2bVt1tDZp0kTnaNKkSTovQjF/7rnnVHNxJ3F16tRJ28ZJ5WSA1157DYBBgwbFvBYKCgo466yz
+AKe4r3Qhl4rYRx99tK6LFi1a6ByWlpZqYdrs7GxtOfiXv/wFcLJy5Z579uwZjanT+LIky8vLa13c
+W7ZsCePigyMopCHJ7rvvrhOVkZGhvQTbtm3L8uXLgcpquD/99FNYmWwRCtUJpFjRrVs33n//fcBp
+GiOLdNCgQfrw3fjkk08inkdMjETWgJSX/tFHH9Wq1JmZmfqiup+BvNQvvfSSvujTp0/XGoMzZ85U
+1Vds+y+++CKiidi0aVONrlTnMzruuOMAmDhxIsFgEIC5c+dy2GGHAc4aqc3elhd97dq1WtPTGKP+
+GjE74rE5rFq1SoXURRddpA2Su3TpAjjCQQTEvffeq3PrjqoZY8jLc4J4RxzhKOI5OTmaG9GyZcu4
++T+8LEkPHjyEIWU0BWttVCrwwIEDdUeXWPMzzzyju8v8+fN19wgEArzzzjuAI2mrZsaVl5fz6aef
+Ao4ET1QNR6m598knn6gzrLy8nCeffBKAt9+Oa4AmZuTm5jJlyhTAUalFIystLWXy5MkAfP3114DD
+lRBzrmoEZOnSpQD84x//4K677gLQOoOitVXFxo0bd/CyQ6VW2KJFC5577jnAeb7yeVpamq6faLzy
+shtfcsklYY5Gubao6vGAu5FL79691Ykp1Zk3bNhQa0f0YDDIDz/8AFT2hSguLtb2ftXNZ32QMkIh
+WojqCJUt3oVoInCH7OSB77XXXuyzzz4APPTQQ4CTDSgqZ+fOneMuFGTBXn755QCq/oGT6ioCqbaO
+SJmZmZqebK1NWChKxjFx4kS1r40xvPzyywAMHTq0TtcW2/icc87RcwtJKxgMRgyjpqWl6TOZPXu2
+CnDZAAoLC7WX4iGHHKLEo1122YVBgwYBThOVmmjOe+21F4MHDwYcn4NbmMjLOW7cuKjvMxrIOH7/
+/XfGjBkD1M0PtGLFCl0/Mpfjx4/nn//8J0CtQqUu8MwHDx48hCFlog+1fUd2nU2bNql5ILTV1atX
+R3Ud8dpLTLdZs2bMmTMHgGOPPVZ3o3hB6j2Ih7x9+/a6086bN0/VwO3bt6vDSCIS33//ve4kEyZM
+UHU+UclKxhhOPPFEAF5//XWd41WrVmkCWjR0X1G/r776ajp37gw4JojscqKNXXrppcyfP3+H47Oy
+spR78dNPP2m0Q5yOaWlpaoJ99tlndOjQYYdzuE1Rcb7l5OToDpuZmRnW1l2o0Onp6cqBkSYzDVj7
+AKjsMN2rVy/9TO7ttNNOU3MuynE2vuhDTZDQUjAYVK9sXdV9IabIy7pq1SrNNYh3/oQxRr3L4nnf
+tGmTCohXXnmFRx55BHBsRPE7yMLdd999NTohHYPqOw6o3YvetGlTbrnlFsCx1SUCI2SkaK/18ccf
+A445JtGThx56SEk2Er4Utb8qmjdvrmPdvHnzDnU1S0pKNGI0YcIEhg/fMQHXGKNCTaIoZWVlylZ0
+v0CZmZlhNTsfe+yxHb4TTxhj9N5F4FWFCKzDDz9cBSRUbgivv/46AO+++25CxuiZDx48eAhDo9AU
+jDG89dZb+n9p7V4XaZ6dna2qoewi69atU9JTIswo0W7kGl9++SUjR44EHC6EOJx8Pp/W2hMP8667
+7qr316RJE/17XduPR3tfubm5ynuYPXs2999/PxBezbh58+bK8YhkSuy66648+OCDgMNTEG0jIyND
+u3ZJjD6S6QCOui8NdapzxIlT7e9//zt77rkn4JDC5PMVK1aEcUCqnisnJ0e99q1bt1aTp6ysTDWa
+6sYXK4wxagqLZrZy5Up1ui5cuJDu3bsDTvEg0RoA7YUqBLdEwdMUPHjwEIZG4WjMzs5Wh1FGRgaX
+XXYZUGlrh0KhsKo7bg1C7MglS5aog0ruecKECVrSbcuWLepQy8nJ0XBneXl5vbSIYDCoTDmxo7/9
+9tuonaK77bYbc+fOBRwH2Lp16wBnp40n49JdrFWSykpLSyNqAh07duTRRx8FUCpyeno6Dz/8MODc
+X6Qw4wEHHKBaiPAbDjnkkIjjSUtLU9s5kRWgZLeeN2+ePndrrdrrotkkgj4ucy7+kNNPP12dnVDJ
+dAwGg/qs//e//6lPLIZ3dudxNPp8Pr744gvAmbBzzjkHgL333huAIUOG6Mu/detWJc3MmDFD1cGm
+TZvqIpNiHI8//riq+EOGDOHggw8GHO78999/D9R/YRpj1GMuY5g5c2bUx69YsYI77rgDgNGjR2u2
+3JNPPslFF10ExMfkkezMbdu21RrrXrJkiQpc4d+Xlpbqiy5mkkCiPddddx3fffcd4PRHrAllZWVJ
+KYEmL9srr7zCrbfeCjjPTHgUiYw6yP2JMJ0/f77mlwwbNkwFRHl5uTp6hVuTDHjmgwcPHsLQKMwH
+n8+nO+X111+vrEYJ9eXm5oaFlUaPHg04TqRTTjkFcHYGiT1/8MEHgBP7Fq5AUVGR7uzRtJCvLdS3
+6667ctRRRwFoWGnUqFF1CqMKD2PZsmXqDJszZw49e/bUe60Nclx1361LT4r09HTVwmTe/H4/K1c6
+JTm7du2qZgWgCVHbt29XDkht18jIyNDvJKNwzJ577qnJT8YYXRsnnHACkNjCrRKafPHFF3VNZ2dn
+q8ly5513xqWegwtRmQ+aJtyQP4CN9sfv99tAIGADgYANBoM2GAxan8+nP+7vtmzZ0hYVFdmioiK7
+atUqm5OTY3NycqK+VnU/xpiI13P/7LfffnblypV25cqVdtGiRXbRokW2VatWdbpOQUGBLSgosCUl
+Jba0tNSWlpbaq666ylakmkc91pq+G825unTpYrt06WLHjBljP/30U/vpp5/aNWvW2DVr1thQKGSL
+i4ttcXGx/fOf/6zzYoyxfr/f+v3+Ot1zVlaWbdGihW3RokW145LzytijnYtIP7fffrstLy+35eXl
+NhQK2VmzZtlZs2bZ7Oxsm52dHfNaieb5rly5Up9veXm5feWVV+wrr7ySiGvOjuZ99MwHDx48hCGl
+HI3RdFmui+OvqKhInTbbt2+Pa03Hql2Uq8IYo9EOYVCeddZZ6r2vDX6/n/feew9wTABRyz/66KMw
+lb82EyLaztUZGRl6L6FQSM2OtLQ0Dj30UADeeOMNJk2aBFRyMH7++Wd1KF5++eV89NFHgOPwrY+T
+tnnz5urse/fdd3eItBhjlIXao0cP7rvvPsCJ4ddF1Zd5Ef4HhN93bUlqsUDOPX36dMAxNWXsRUVF
+WmKuoZBSQiHe9tu2bdtUEDRv3jyunYdqC1UtW7ZMXzJ5ac444wwef/xxoHobX4qQ/Otf/9JQZklJ
+iVYCWrFiRViD0WhR3dzKnNx6661K2Hn99dfD5uiFF14AwudNfAS//vqrRoHS09NjztZLS0vTeo6R
+Qq+HH364pmG76djDhg2rU6hW6M+S7wHOHAlBSMhPiUD//v0BJ8QLjpCQ9XTWWWc1eGcsz3zw4MFD
+GFJKU4g3rLWadNK8eXMt671ixYqYzusuM1/dbl1YWKiFOsTLXFRUpISmVatW6e7t9/s17i/58VlZ
+Wbp7TJw4UdXkwsLCOmlUwkOobhcVVfb0009n9913B5wd+JVXXqnxOKEXi5YAMHny5Jjj+8XFxRGv
+KePs3bu3ajfBYFDj+y1bttRM0+og5wgEAlqzIT8/X8dcVlam9RoTBWOMmpDu5yjcm0QlOdUFjSIk
+GQuEodalSxe1VeNRDl6EQnWqXmZmpnYbEvKS+7tffPGFhha3bNmixCkxNVavXs3NN9+s9yDhubq+
+dLWFJAVjx45VwfTDDz/oXLnJRD6fj+uuuw5AVfj09HTNRTn77LNjNgGDwaAKw0jn8vl8WgeyX79+
+6tt49dVX1cT6z3/+o6abCILjjz9ev3vxxRdruLegoEC/O2fOHP785z8DlYzNeJOYmjZtqpuSsCrX
+rFmjWbIJDsMmpRX9dcaYH40xPxhjXjPGZBhjOhhjZhljFhljxlf0hPDgwUMjQSxdpwuAa4Au1trt
+xpgJwFnA8cBoa+04Y8z/AUOBp+My2joiEAhoTcD33nuv2vz1usIYU6t3um3btlqhV7j1PXv2VFOi
+a9euYYQryT4UGvD//ve/uLaDr61H40033cTJJ58MOFpVpGzM0tJSdYRKLsrZZ5+tZeXiAXceS6Tx
+hkIhzXwcOHCgVjbec889eeONNwD45ZdflOwmuSadO3cO0x6kfsbcuXP529/+BjgFTUSLjHf0Qc53
+xRVXqIYgGtGNN96YUr1AY3U0BoBMY0wAyAJWAofj9JUEpxX9qTFew4MHD0lETD4FY8ww4F5gOzAF
+GAZ8aa3ds+LvuwPvW2u7RTj2EuCSiv/2rPcgaoDf79fdJtlltdw7jewM+++/vzrGxo4dqz6G4uLi
+sEYlDQXpbP3aa6+p/e3u62CtVXtY+mmIhtNQEJ9J69atNeGsuLhYfQbiaPX7/eonGD58uGobDz/8
+sFb8TuTcS1u4CRMmqL9G5rJjx45RUevjgKh8CrG0os8DJgJnApuAf+NoCCOjEQpVzpWQpxEtccdD
+OA466CDld8ybN69OpdM9RIZktv7f//2fCrILLrgAQEvkJwEJdzQeCfxsrV1rrS0F3gAOBZpVmBMA
+bYD4FdD34MFDwhELT+EX4CBjTBaO+XAEMBuYDgwExpGAVvR1gacd1A/S4sxDfGCM4fDDDwccZ630
+kpRweaohVp/CnTjmQxnwDXARUIAjEJpXfHaetbZG12o8zQc3sSgjI0Pj/hs2bIibkKjNk/9Hgs/n
+04hKYWFhg1N0UxHBYFB9HJmZmUqljtUcq8c6THzlJWvtHcAdVT5eAvSO5bwePHhoODRqRqO7dbzr
+XPp7IlvKNxT8fn9c+QuREAgEdMevOrepPJ+R+A2RPvP5fPr/VLkfd/9M+d3n82n0xN0LVZ5NKBQK
+K90WxT0lntGYaNRGEnK/9PJdSSeOppJQddeL1OA0muPcxTfjATmvmER+v5999tkHn89X5zHWBZmZ
+meTk5JCTk0MwGCQrK4usrKyoCD3uMScT7utVNzcyb2lpadWO0T3+WO6juuPS0tJ2eH4+ny/seuXl
+5ZSXl7PrrrvqZwUFBXqcfCadsMrKyuIaGUppoeDBg4fkI6WzJOuy08dDUtZXpZTvJ6IcODiJP9Ji
+7tVXX9Xy84lCSUlJWJfraHpIglN4RbIne/TowUsvvQQQ950sEtzPrLrfZQw11V1wU8/B2dnrQyyq
+bg1FOld1c+PO+qyuOU119xoLUsqn4PP56tXqXFSonQ2iYn7xxRcccIBjCvbs2TPhQqGukI5bvXr1
+4tRTHVZ7cXGxZv5t376dBx54AKjs2RnvZr4eokLj9yl48OAh+Ugp8yFaLUF2Jqkd+Pnnn++UmoJk
+JEpvQahsVZ8q8Pl8Wrxm0KBBmn8wbdo0LQO/atUq5TLIdz1NIXXhaQoePHgIQ8poCnWJgbdt2xaA
+Sy5xkiw///zzhI2rIXHkkUcChIU6ays5lmykp6drA5W0tDT1HaxYsUKzAK21YaXQkgG5nsT5y8vL
+lelaUlKSMvyEukA0ZNGoE8UeTRmhUJeHJAU0pANRY3zAtcHn8zFixAggPH1ZHHUNDXnZZs2apVWn
+ly9fzi+//ALs+Ezk/9FGMuoDEQR/+ctfaNKkCeAUVwEYP348vXr1AmDBggU6jpkzZ6bs+mnSpImm
+Wd95551avk+KBV166aW8+eabQHwFhGc+ePDgIQwpoylEaz4Eg0EtrinVh3fbbTctu7azIDMzU3c5
+qNxhmzdvrsVChP2WTIgKK6ZB8+bNVYt54IEHGszhm5mZyfXXXw84beQvv/xyAN566y0gfCe98MIL
+tZbB1Vdfzcsvv1zteWsKk4tm4mYn1ud55OXlaeOg8vJy/vOf/wDO3Aq1OTs7W02vvLw8wOmYvWjR
+Ir2nql2/64tGIxTEe/3xxx+z3377AZUVlR944AHOPfdcoObmsFXt2uzs7LCGqPEaayyQMY4bN46s
+rCzAsSHvuecewKl0JPed7BfwgAMO0DqIEgUpLS3l+eefB5wCIg2FjIwMnY8rrrhCXyz3c5KXZt68
+efrZDz/8oPfSoUMH7c4kJde//fZb3YRKS0vD1ldVGz89PV0raFW3RowxKuyluEqnTp3UHNu6dauu
+z6eeekrLwXft2pVWrVoBlc197733Xrp06QLAjBkz9PNYG/J45oMHDx7CkDKaQk1o1qwZt956K+DU
+BhSpKpK4c+fOyllo06aN7gQlJSXqBHvggQc07i87Q3FxsbbwipfqJZAxQqXqH4120aFDBwBtYw+w
+fv167WmwZcuWiBmMiUKTJk34+OOPAafKs2gpUhPg//7v/1RDaEiH3datW5k9ezbgVHOuaSxFRUUa
+MTn99NPp06cPAH379tXvyPEtWrRQFb7qOav2/nC3mjv22GO13d5PP/2kGuABBxygGo18tmrVKtW2
+/vGPfygN222KrFy5Un+X43Jzc7njDqdyQSAQ4E9/+hMAX3/9dUzPImWEQiS7TWy1Jk2a0KlTJ6CS
+2gywbt06AG644QYNSx522GEMHz4cgJNPPllVPGutFhmVlzQ7O5tPPvkEcARFtOSpaCbcrUpG830R
+ItIhyOfzqRr48MMPq1c/WSaDqKJLlixRM2bDhg06t+PHjwecuUxkRCFapKenc+ONNwLQvn17fUEi
+PdOCggJVy3fZZRctxFNWVqYv3zXXXAPAp59+Wu2cRxIWst7GjBmj/Sp33XVXfdH9fj/ffPMNAA89
+9BAAH374YZ1UfhEKmZmZas6UlJRoOftY4ZkPHjx4CEPKaAqRIFJ+1apV6p11F5s4/fTTAfjss8/0
+mClTpmgk4qCDDtLsspdffplly5YBaHu0K6+8UnfBQYMG8eKLL8Zt7HXdPcWkWbx4MeBI/nHjxgHw
+/PPPJ9WpaIxhxowZgKNNSaShQ4cOO5guya6bUB2Kiop0Dlu1ahVxXOKsnjNnjj73xYsXc8UVVwDw
+/fffKw8kGq0xUlat/D5//nxtBWiM4auvvgKcaMeCBQuAuq8RcUDKuu/Tp4+aytu2bVP+QqxmXEoL
+BUEoFFJ1Vv4P1TMZJUwjIUuBhHLkZYPKRR2rx7YqiouL6/RwZBwi0Jo2baq9FzIyMmrsmhRv5Ofn
+q7lWWlqqPR4iCaZUIf6Ul5crA/Tss88mNzcXqOwb2rFjxzDmpXSFeuSRR+p9D5GOkzk64YQTWLt2
+rV5PIgeLFy+uVRiI2dyhQwf23XdfwDFnJC1diFn33XefRoNWr16tazjW6JhnPnjw4CEMKaEp+P1+
+cnNzCYVCEXsYQmWHHah0MNZ1JxbvsqiOUOnhFYnbEDDGKPfivPPOAxxN4ZBDDgGcmLl4mUtKSlSd
+f/LJJ7VFfTwbitxxxx2qmdx0003VPhMZu+xsDV3JWRy7c+fO5ZlnngGcrlzgrB9xAr7++us8/PDD
+MV9v1113BSrNADcFfa+99gpzcovWUFBQoJqswBijTsmXX36ZY489Vv/mXuNiNk+bNg2AyZMn89tv
+TluVQCCg5tOWLVtiMjc9TcGDBw9hqFVTMMa8AJwIrJH2b8aY5sB4oD2wFDjDWrvRONvLYzidpwuB
+C621X9d2jVAoVGNHaGut5t9nZ2erTyEnJwfYsZ+hm3Yq0jw/P3+H/oKAStpEFEKtrpKw/Cuf9+3b
+V/sZSnjMWqvhppYtW+rO52YQvvPOOwnRcNq0aaNzLDtc1XsS+P1+TUxbtWpVjaXOEg2Zz++++44p
+U6YATik7cMYpO/oTTzwRl+utX79ezy0Q7WDAgAFq42/fvl1p1a1atWLw4MEAXHfddUBlMVf3PQjc
+/gcJl4ozul27durv6d69u2p0EuqsL6IxH14CngDcBPGbgWnW2lHGmJsr/j8cOA7oVPFzIE4L+gNr
+u4C1NqxEdVWEQiF9EYYNG6bEmRNPPBGobPUOjkotE5mZmalp1lu2bOGUU07R6wmk6Effvn2VGlub
+IygalfmCCy5g4MCBAOr0KigoULUuLS1NzZiWLVvqyyYEnAsuuEAF5Q033KD0W7/fr6bCwoULo3aQ
+1qWv5pQpU7TZ7IgRIzSunp6ermQvEV7dunXT706aNEnT2WNxdMm8lJSU1EsNLiwsZOrUqQAaWdiw
+YYPWjFy3bt0OxKNY4CYjyTyXlJRw8sknAw7XQa6zdu1aHnvsMaByDktLS1XAdO3aVanLc+fOVZP3
+iSee0IxIiV5cfPHF2nnK7/drhOPBBx+M6X5q3R6ttZ8CVcvknILTZh7C282fArxsHXyJ01eydUwj
+9ODBQ1IRVeFWY0x74F2X+bDJWtus4ncDbLTWNjPGvAuMstZ+VvG3acBwa+3sCOesUyt6dzKTOB1F
++h555JFKxRXmnxzjbiEnPRIl3BYIBFTKX3zxxZotF48w2+LFi7VwaaRdurS0VFX0YDCoXIsBAwbs
+cM9NmjRRGvdhhx2mTkeodD7FE8FgkDlz5gBOKE+0oo0bNyqjVMy53Nxc3fGKior02UjX6vrArUrX
+51n06dOH0aNHA5Xmz2mnnaamTU5Ojo4vFk2hpjBxvJLmxOT99ttvuf/++wFU4znmmGP45z//CThz
+du+99wIwatSo6k6X+LZxANZaW58OT9baZ4BnILoOUe6XaenSpVQcBziTFOkBWWvDXjzxL4igCIVC
+3H333YDj9Y32Idb0wGVBL1++XE0TybnYtm1bWOxaPv/kk080K0+Ov/TSSzWWXl5erj6HwYMHa15E
+kyZNtHry3Llzoxp7NCgtLVW69fnnn6+e8R9++EFVX+GNnHDCCXTs2BFwclQkAzCWitP1LQcv5J5J
+kyZpLP/9998Hwsu6FxYWhnVWSgTiIRCMMWp6rly5UqMWwr1Yvny5mro+n095GLGivt611WIWVPy7
+puLz3wA3Y8hrRe/BQyNDfTWFd3DazI8ivN38O8BVxphxOA7GzdbalZFPETuiaYQh3uDXXntNqdKC
+8ePHq6ZQ3+tWhexyRx11lGbfSRShT58+WhLsiCOOUK0gLy+PHj16APDRRx8BlZEVOecZZ5wBOOaC
+RFseeuihhBWXufnmmwG46667dDeKFFmYOHGiaitDhgxJyFiihTjdZHcFp4xZVYRCoXo1eKmKRNfU
+SE9PVwfktddeq5WyxYTr16+fOmXdXIhYEU1I8jWgP9DSGPMrTpfpUcAEY8xQYBlwRsXXJ+OEIxfh
+hCQHx2WUMWCPPfYAwm11IbkMHTo0YdctLS1l+vTpQKVguvrqqznuuOMA58F/9913gEM8kpde/B77
+7LOPHl9YWMhtt90GOJ7zZORBiFpdNdwrEIFWUFCg9+QmmCUbPp+PiRMnAs7cShTnxx9/TNg1q1Ze
+ipcpIpTotWvXqu/ms88+07T6s88+G3CiUuLP+f333+MmpGoVCtbas6v50xERvmuBK2MdlAcPHhoO
+KUFzThQCgYAmP2VnZ6unXlqwJYtoI+r38uXLNcowYsQILezS0PTgumDvvfcGKglAu+yyizpMR44c
+qZfMxTMAABZGSURBVNpPsnH00UerKr1161blU8QSBakN9S2LV1vUwl1QReDuafnXv/4VCCfy/e1v
+f4t4XH2wUwuF1q1ba7ENQMNUCxcubJDxSIESiE9D3GRj7733VkEmpKn169drmvX48eMbLGvy6quv
+1jkdMmRI3LNe44ma5qjq38Q0ad26NWPGONQg8SlYa5Wk9dRTT8Vt7r3cBw8ePIRhp9YUrr76alXV
+tm3bxsiRI4GGqwHQGLUDN4qKijRGLhTsuXPnaqQiEUSqaLH//vvrriqaS6IRL7PP5/OpKeIu25+W
+lqY1Qfr168d7770HVM7zkiVLuPTSS4H4rmlPU/DgwUMYdkpNQRxOV155pUrgDz74ICUKjDZmLFu2
+THtFiiPvxRdfjFt8vD6Q59usWTN1+DWW5yyajTuRqlOnTppIdemllyrdfNKkScpYlLKBa9euTYjW
+u1MKBYn5p6enq8oeazqpB0dFffbZZwG0KMyHH37YkENSVbtNmzZa5CRZ5mGs13Gbk+6msVKVOhgM
+Km38vPPOU9Mt0fDMBw8ePIRhp9MUWrRowUknnaT/F7VMmGEeYoNwO6SNXTzowvFAY3++onX88ssv
+yrht2bKllh6sjlmaCOw0QkEqNXfs2FFV3N69e2uvyMZiZ6Y6RF3f2Rr6pgoKCws16/SMM87QbNVk
+Rsw888GDBw9hiKrISsIHUY96DBXHAQ7VVuoEzp07V1XaffbZRx01U6dODau2u7Mg0f0gjDGadOMu
+WiMoLCxs9PyLVIM7S1LWcpzmOKoiK41aKNRwPqD2hqBuSCYjOFz2eM5LpIIeMTUArTifMUbDcPEc
+rztE5hYC2dnZmksg5lgoFNLvuovPJHtdVS18U58MxnhVS0phRCUUPPPBgwcPYWjUjkZ3Lb9oqhVH
++lx2wvz8fPX0+ny+uFJ2pTRYWVmZjnn79u11alHvHq+UQisqKlKvdDwzLY0x2ncRKouWlJeX65jd
+89NQ2oEbNVUCjxZpaWmqeSUqczVSj0ufz6dzvHXr1ojVoetbr7I+8DQFDx48hKHR+RR8Pp+2ApNC
+rN9++60m6NS3Ao0xhgMPdFpUFBYWKr00HhAfQCgUitlhZIzR86WlpanWIB21EwHRbkKhUNjvOxuS
+4VPIzc2lZ0+neLmkoWdkZLBmzZqaDosXklPNOVmQF6Fjx47acOT7778HnJx+yZ9fvny5EmzqYgJY
+a7VtvXT6jRfqWz7N3bVKXkJrbZiTT4guiUQkOm4qw92b8fjjjwecLMrXXnsNQJumVEUiBYKs3+nT
+p+tmJrU+kiQQooZnPnjw4CEMjdJ8EKfMwQcfDMDAgQPp2rUr4FBDpWrzypUrtYmKMBtTHdLnsn//
+/srMnDdvniZ5VU2KkR0oGcVcUz1kJ0zAJ598ksMOOwyobM3mxm+//cbYsWMBuO2225Iyd6L1vfHG
+G6xatQqAyy+/HEiqg3bnMh8EbpVZWm+XlZVpBdxmzZqp57xp06aa1vvEE09obbtUXNgSBbnmmmsA
+uP322/WzQCAQ0Wudlpam/IpkLOxgMJgyuQ4CEZZ33XWXVpPOyMiI2LBV5rCgoEDn+Z577qmxuXG8
+IGbXRRddpGZtKq5D8MwHDx48VEGt5kM1regfBE4CSoDFwGBr7aaKv90CDAXKgWustbUm3NeX0ehu
+6y7FLMvLy2nWrBngVL2Vdt/GGG644QagsoBrKuGss84C4L777gOcnV96A44bNy6scrConW3bttV+
+EMmoWpyVlaW7airscrm5udqWPS8vLyymL07o888/H4Cff/5Z29NLlAlgwoQJ2rIvWajKcI1mLt21
+QcTErIeTOT40Z2NMP+B3nG7SIhSOBj621pYZYx4AsNYON8Z0AV4DegO7AR8Be1lra2SCxJvm7IbU
+tTv22GPVay9hvGSojdEgNzeXn376CUD9CCNGjAj7jiz4Cy64gMcffxxwMhWlEYvYqfFGfn4+n3/+
+uV5DrvPCCy8AMG3atHjz82uFzMUjjzyiZoAbI0eOjNj5S0rRb968WV/MNWvW0K5dO6D+NSbdL3lt
+71NGRoam9kuv0A8//FAbJBcXFzNw4EDA6SgmLQp69uyp/iTJ5znttNPqWvUqPjTnSK3orbVTrLVi
+xH6J0zMSnFb046y1xdban3E6RfWuy6g9ePDQsIiHo3EIML7i9wIcISH4teKzBsNdd90FOG27xSnX
+tm1bIL6dmusD2fE+/fRTrSsZqfchVHrR77jjDv3up59+Wq8Yt5s+W93u3q1bNwC+/vprdWJmZ2er
+yipzGAgE1OG77777ah+CRKJ169aAYxq4HbCSBSsFYKpCam64k7zmzZtXbw1HzFcxVwGlyldF+/bt
+AfjHP/6hPBg5vrS0VOtTDBw4UOfwoYceUlJadnY2nTp1AuBf//oX4PTZOOGEE4D4mo8xCQVjzK1A
+GTC2HsdeAlwSy/WjwZFHHqm/y8NPlSo98mK1bdtWH3ikKEKTJk20J2JBQYF+59///ne9FnRubq72
+WhTz6vrrr9eXe8mSJdrg1t2W/vHHH1fVdcGCBQDstttujB/v7An77LMPn3zyCRA+7/GGFDZdu3at
+5pVs3ryZ22+/Hai+85J053KXUX/sscfqnecg1xG1vrrITGZmppoBf/rTn/j555+ByjlyC5JRo0ZF
+PMe2bdt0E1u9ejUAvXr10nu+++6749YAp95CwRhzIY4D8ghb+RSibkVvrX0GeKbiXA3vtfLgwQNQ
+T6FgjDkWuAk4zFrrFk/vAP8yxjyC42jsBETmlMYRgUAg4g4bDAa19bsxRnfVZPWQrAnGGFVz33zz
+zYhmwJlnngnAc889p6bPli1bNKLyxRdf1OvaoVBIHVTSefv8889XTcHn8zFv3jwAunfvHlbKTvj6
+4pRLT09XraNbt25KL/b7/QnLNBRn7GuvvaaRgxkzZrB06dJqj7n44ou1EA9UqtuxmA+yF9bG3ejZ
+s6dWvzbG6JxXZ2pUB3Foirny448/8vbbbwPhvSZj5azUtxX9LUA6MLXCpvvSWnuZtfZHY8wEYC6O
+WXFlbZEHDx48pBbq24r++Rq+fy9wbyyDqit69+4dtoOJI+7444/XjLSKsQHhTTgaKt7eunVrDYWN
+Hj1axyRszF9++UXp3NZapWkfeuihas/Xd+y///47Rx99NFCpKVx00UVqn2/atIm+ffsCOxa8rerQ
+2r59u2oKZ511FjfddBOQ2E7acu7Nmzfz9NNP1/hdWQtDhw7VnRYqae8yl4nEQQcdpNyCyZMn16sz
+d3p6umpFoumeeeaZ/PrrrwB06dJFGycnXFNoDFi4cKH23MvJyeEf//gH4KhZkgdhjFH1KhVopmee
+eaZ68ktKSjR2LZl8bs7+jBkzOPbYY4H4mD7WWuVoPPXUU4Cjkgv/Yb/99tP53Lx5c40vuDFGnV3B
+YLBB+0lGwsSJEwHo0aOHflZWVqYdrhIJEfT333+/fnbnnXfW6aWV6Mo555yjRDXplbl8+XJdwz/8
+8ENcxgwezdmDBw9VsFNoCps2bdIMub///e/ssccegFN8RUJ91lp1niWjBkF1EMm/fv16DfX9+OOP
+TJ48GajUEEpKSlQVlx08FgSDwYi9L+Sz0tJShg8fDsC7777LrFmzAMcp+eijjwJORqE45eQ+Tj75
+ZK0PsGHDhqR1fK4Np5xyCuB0awZn15Zd9f77749L34qqSVdVNc8OHTro76Jtff3117WeV+b24IMP
+Vkr2TTfdpFqvsFj9fn9Y6TYxj2LtcbJTCIXS0lKN/X722WesWLECcOLx7irNQittSOTn5wNOTH/v
+vfcGHJtTFpC8dP369au2GEh9EM1CEf7G7Nmz6d69O+DQcqW34cEHH6yFaCQy4vf79WVIhagOOF3C
+XnrpJSDcDJN18eCDD8blOvKsImVkgvNc5e+bN28OO6Y6GGPUFOjUqRPPPfcc4Pg+ZI3IGurWrZt+
+t3379px66qmAw72IRTB45oMHDx7CsFNoClBJcX3wwQdVpRo/fryqYuXl5eolbygYYxg8eDAAQ4YM
+4b///S8AhxxyiFJvhbUWTy0hWshOd/311+vvQ4cO1bEdeOCBqo4LZs6cyS677AI46rJEVGriDCQa
+8+bN08iNm849YMAAILpEuGia7FTlDfj9ftavX7/DcWVlZZrF26xZM418yDWCwaCyW0eMGMFee+0F
+OMVghMtirVWtR7S0jh07anTt0UcfVY3lyy+/VPZmfbBTCAW/36+hp02bNukD+fLLL9VrX1pamrRW
+3tWhf//+mttQXl6uD65jx45aqv2qq65qsPEJSkpKlCA1bNiwsMYqMs8yXmutEnO++eYbjZ5IVaxk
+QijbkgULler6SSedpGnWUNn8Jz8/X++pVatWmqG4cuVKwKGYS0Rl9OjRYfct566O5jxz5kzAmStZ
+kwMHDuTbb78F4PTTTwfg7LPP1o2ssLCQW2+9FXBedDmn3+/X6z300EM6XokeZWVl6d/PP/983VTq
+UxTHMx88ePAQhkatKYj69pe//EUjCyKFAU3qAYdkE6/y5D6fL8wsqQ1CCpowYYLuGDfeeKPWJOjV
+q5eWrReHVEMjUou7UCgUUdtavny5/i5U4kTSnCPB7/cr4Qoqxzxy5EgA3n//ff1bTk6OmpKBQIDn
+n3e4eIMHD1Y1X5x5bkf18OHD1SwpKyvTNVCdU09MqI4dOzJ9+nQArrzySm0fIFps06ZN1ewYOnSo
+mpXuXT43N1cTnqQuxNSpU9V0Kysr05ocTz/9dEyOxkYtFCQd9bbbbuPcc8/Vz+XlHzJkiH4Wz0ao
+btUxGkj6digU0jGNHTtWF1X//v1VhU0Gwy7ekMVrrdWwWWZmZlKL2LRo0SIs0lCVRBUMBlU4L1y4
+UJmjgUCAO+64A3CKyNQUQQkEAiokQqFQ1EJv8+bNWvRl33331axSqRlaUlKiYfJAIKDzGQgE1J9z
+++236yYnIfcpU6Zwyy236P3Gq1OXZz548OAhDI1SUxBNQGoYNmvWLGxXkt1KVD1wyn5HqohcH0Qr
+ieV6YhoEAgFef/11/bt4rXNyclR9TJVYf10gO2YoFNKdtHPnzpqPkgwMGTJEd9VQKKQOQSlo8tJL
+L2kcPzMzUzUJY4zmJUhFcKgsxFJaWqom6csvv6waontHjwZvvvkmAG+//XaNO7r7nO7eo/vtt59S
+tSX/5IQTTkjIevE0BQ8ePIShUWoKUt9fHDWBQEA/W7ZsGRdffDEQzrYrLi5OquNLrg9OFic4kr/q
+TgAO7VqSuBojZAd2z2+yHabfffed7v4ZGRnqgJMQoxuhUEifjVt7DAaDOu6//OUvgFOY1g35fqQm
+M9Ggrn4tWS9t27ZVzVc0hUQlnzVKoSAPRLyw5eXlYaqVO9YvauQ///nPJI+ycgHIg636oojjaN26
+dbzyyivJHVyc4Pf7NaV3+/btvPrqq0BiG95GwrRp09Sr36tXr4imoltdlyhKXl6eeupXrFhBnz59
+9PdIcG8yyYREQyDcsZsIeOaDBw8ewtAoNQXZhSQO3KFDB/7+978D4e3Si4uLNZknXkUt6wLRFKTP
+Za9evdS5mJWVpTtbMBhUR2NjQ7NmzVRLe/XVV5WxGWumXl1RUlLC4YcfDjjchGOOOQaoTPJav369
+ZijOnz9fd9633npLS+HVRbVPdi2OSCXxEoVGKRRkgiSF9Kuvvgrjnwu+/vprJaY0JH77zaldu2nT
+Js19eOSRR8JKfKdaj8ZoMWHCBDXjNm3apGp1WlqavmTJ6HMJlbb28OHDNQ28sUPWiLtDlKSnJ6py
+mGc+ePDgIQyNUlMQSIJLixYttBLxCy+8oDvTeeedl7RWZtFg27ZtPPHEEwC8+OKLmrW3devWlCtj
+Fi0KCgrC6krKzhZNCzUPtUM4FL/++qsWu6mtLmXMsNY2+A9g4/VT0UPC+0nSz80332yXLl1qly5d
+as8++2ybnZ1ts7OzG3xcO8tPfn6+zc/Pt5dcckk8zjc7mvfRMx88ePAQhlq7TidlEF6HqEaL3Nxc
+rZ0wa9Ys5WKkwrraGeDmW8RhTuPTij4ZqKtQqEpMcacyJ8vT/UeHzLfYvOBEUeKVqRcLGrKfR7wh
+81tWVhYP/1h8WtF78ODhj4VU0RTWAtuAujXXix9aetf2rv0HuHY7a+0utX0pJYQCgDFmdjSqjXdt
+79retRMLz3zw4MFDGDyh4MGDhzCkklB4xru2d23v2g2PlPEpePDgITWQSpqCBw8eUgANLhSMMcca
+YxYYYxYZY25O8LV2N8ZMN8bMNcb8aIwZVvF5c2PMVGPMwop/8xI4Br8x5htjzLsV/+9gjJlVcf/j
+jTFptZ0jhms3M8a8boyZb4yZZ4w5OFn3boy5rmLOfzDGvGaMyUjUvRtjXjDGrDHG/OD6LOJ9GgeP
+V4xhjjGmRwKu/WDFnM8xxrxpjGnm+tstFddeYIw5JpZrxwsNKhSMMX7gSeA4oAtwtjGmSwIvWQZc
+b63tAhwEXFlxvZuBadbaTsC0iv8nCsOAea7/PwCMttbuCWwEhibw2o8BH1hr9wb2qxhHwu/dGFMA
+XAMcYK3tBviBs0jcvb8EHFvls+ru8zigU8XPJUCsKYiRrj0V6Gat3Rf4CbgFoGLtnQV0rTjmqYp3
+omHRwNmRBwMfuv5/C3BLEq//NnAUsABoXfFZa2BBgq7XBmdBHg68+//t3b2rHkUUx/HPgegFI2hi
+EaIRbgSxNVYRLQIKaggROyVgRP8BK0FuZS+ihaigWISLghrkkkbwpY4vIBp8wUjE3JCYNKZOcSxm
+Fp7VPMarz+xjMV9Y2J3Zh/P8dg+HOWdndxDKRJZtV7seC7Z9E86odaSZ9ubacRvOYqfyuv4JPNRS
+O1Zx6lo68QaeuNp5i7L9p77HsF73R/6Oj3Bvi/u/lW3Z6cPgLAObta05EbGKfTiJXZl5vnZdwK45
+P/uvvIznMExivwW/Z+bwwkZL/XtxCW/X9OXNiNhuAu2ZeQ4v4lecx2V8ZTrtzNc5tQ8+jWENu6X5
+/9+x7KCwFCLiRnyAZzNztDhilpC98EcyEXEIFzNzuhVSxmzDPXgtM/cp08pHqUJD7TvwqBKYbsV2
+fx1iT0YrndciItaUFHZ9attbYdlB4RxunzneU9uaERHXKQFhPTOP1+bfImJ37d+Niw1M34fDEfEL
+3lVSiFdwc0QMX8BqqX8Tm5l5sh6/rwSJKbQ/iDOZeSkzr+C4cj2m0s58nZP4YEQ8hUM4UoPSZLa3
+yrKDwhe4s1ahr1eKLhutjEV53/ctfJ+ZL810beBo3T+q1BoWSmY+n5l7MnNV0flpZh7BZxhWLGli
+u9q/gLMRcVdtegDfmUC7kjbsj4gb6j0YbE+ivTJP5waerE8h9uPyTJqxECLiYSVtPJyZs58V38Dj
+EbESEXuVYufni7T9r1h2UQMHlYrsz1hrbOt+Zdj4Db6u20Elt/8EP+Fj7Gz8Pw7gRN2/Q3GE03gP
+Kw3t3o0vq/4PsWMq7XgBP+AUjmGllXa8o9QurigjpGfm6VSKva9W//tWeUKyaNunldrB4HOvz5y/
+Vm3/iEda+t0/3fqMxk6nM2LZ6UOn0/mf0YNCp9MZ0YNCp9MZ0YNCp9MZ0YNCp9MZ0YNCp9MZ0YNC
+p9MZ0YNCp9MZ8QdFKCLkC5rYQwAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 2/2... Discriminator Loss: 1.1537... Generator Loss: 0.7651
+Epoch 2/2... Discriminator Loss: 1.0387... Generator Loss: 1.4510
+Epoch 2/2... Discriminator Loss: 0.8105... Generator Loss: 1.4478
+Epoch 2/2... Discriminator Loss: 0.8504... Generator Loss: 1.3711
+Epoch 2/2... Discriminator Loss: 0.8881... Generator Loss: 1.4892
+Epoch 2/2... Discriminator Loss: 1.0879... Generator Loss: 0.7779
+Epoch 2/2... Discriminator Loss: 0.8741... Generator Loss: 1.2485
+Epoch 2/2... Discriminator Loss: 0.9110... Generator Loss: 1.7616
+Epoch 2/2... Discriminator Loss: 0.8936... Generator Loss: 1.5694
+Epoch 2/2... Discriminator Loss: 0.8861... Generator Loss: 1.0268
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8VFX6/99nZjIJSSgBYqgigoIgioKoiL2urFhB7L1i
+Q11E9rt2d0Vd/Pm1rO7aXZRiRdcGKoj4BUURFEVFei+BkJA6M+f3x+V5ciekTTIzKXs/r9e8GCYz
+95577rnP+TzdWGvx4MGDB4GvoQfgwYOHxgVPKHjw4CEKnlDw4MFDFDyh4MGDhyh4QsGDBw9R8ISC
+Bw8eopAwoWCMOcUY84sxZqkxZmyizuPBg4f4wiQiTsEY4wd+BU4E1gDfAOdZa3+K+8k8ePAQVySK
+KQwCllprl1lrS4FJwOkJOpcHDx7iiECCjtsZWO36/xrg0Kq+bIz5rwyrNMYA0NiiSmVcAH6/n1Ao
+pJ/LWGMde2O91v8ybLHWZtf0pUQJhRphjLkauLqOv93t33A4DDiL2L1w5XOfr5wURSKRSo/p9/sB
+9CFIBNwPRyAQ0PNVfGjcD2ZVY04UAoGAzkVmZibbt28HnDmUuZG/l5WV6ftIJFLlw5+SkqLfr+zv
+HpKClbX5UqKEwlqgq+v/XXZ9prDW/hP4J8TOFCouKPcDE4lEdJGmpqZSWFiov6luIbqFRjJgjNFz
+BgIBfdgqG6PP59Pv+v1+fbASibS0NADC4bCOyT23ImyhfMwV59gt2GTM7s88wVAzqpuvrKwsFdhx
+nUu5kfF84QibZUB3IAgsBPpW833b0C9jjL4aeizVjS87O7vBzt2qVSsbCARsIBBo8PmQV3Z2ts3O
+zratWrWywWDQBoNB26JFC9utWzfbrVu3Rn1Pa/Py+XzW5/PZlJQUvRa5B2PGjNG/1/J482vz/CaE
+KVhrQ8aYG4CPAT/wgrV2cSLO5cGDh/giIS7JmAeRBEOj3++PorxVfQeo8XsNCZ/Pl1T7ghstWrTQ
+c5eUlDTIGCri5ptvBmDUqFFqt8jMzOTbb78FYNiwYQCUlpY2zADriYyMDMBR3YqLiwHqoyp8a60d
+WNOXGszQmEiIHtarVy8uuOACAA477DD+9Kc/AfDbb78BUFRUpIvc7/dH2RUam2CQa2oIgSD2hZNO
+OomvvvoKgM2bNyd9HJXhm2++AeCdd97hyCOPBGDgwIEcc8wxABx33HEAfPTRRw0yvvpCbE1lZWVJ
+s8F4Yc4ePHiIQrNQH/x+P61btwbgrrvu4oorrgAgPT09ykUmu+zPP/8MwJAhQ8jPz5cx6PFq8lTE
+CmEgtdnl3WxF1JlevXqxbNkywKHtyWQxPp+Pxx9/HIBLL72Ur7/+GoBzzjkHgLy8vAZTZypC7uHF
+F1/Mww8/DJTf6+OOO67RjLMmyBro3Lkzhx12GAAzZ85k27ZtQL1c5rVSH5q0UJCH5uGHH6Z3794A
+HHXUUUp3fT6f6r55eXkUFRUBjs4JcMkll/Dxxx8DiaHlMg5RW77//nu+++47wFFdOnXqBMCVV17J
+XnvtBUBubi4A7du3p0OHDgCceeaZrF0b5dFNGnr37q36eSAQ4Jdffon6+/fff6+L9LrrrmsUtga/
+36/zlZWVBcCIESN49913G3JYQHQAWGU46qijePLJJwFn/ciafe655/jXv/4FlNtz6vDs1kooeOqD
+Bw8eotCkDY3dunUDHForu//KlSuVft13331MnDgRcKTqAQccAMAHH3wAwFlnncWnn34KxN86PXjw
+YA4//HAALr/8csDZ/cVC7g5Icp//+++/ByA7O1tZg/ybTAgVnz17tjKeBQsW8O9//xuA1audKPbz
+zz9fDXxFRUWMHeskxIpa1hCIRCKqTsp8H3vssQ3KFGQ+3RG3qampgMNcH3nkEQAuuugi/bsE3gHc
+fffdbNy4EYA333wTSJwxvNEIhZpolRvt27cH4KWXXgIct43oW3PnzmXcuHEArFmzJup3Elbctm1b
+wHkwg8EgEH+h8PXXX+sx77vvPsC5RhmDtVYXSigUYvny5QDsvffeALRp04b//Oc/AOqKqg9imV+A
+s88+G3DotyzGY489loKCAh0/OAtUhO2sWbP0vQiKuoxTUFfVNhgM6jwL6jqeeCAtLU3V227duqng
+7NmzJ1Cu4oCzFtavXw/AK6+8wg033ABA69at+dvf/gbAhx9+CCRO8HrqgwcPHqLQaJiCBOXUZncQ
+a7jQ882bN/PEE08A8Pe//73SXT8nJ4e3334bKN+NDjzwQNLT0wF0B4wXQqEQ8+fPB+D4448HHIv9
+pEmTANixY4dK+oKCAmUsM2fOBBw2lJ2dreOt667pVlFqe4z999+fl19+GXAo6lFHHQVUvTP99JNT
+JiM9PZ1DD3WSYTMzM+s0p/UxfMt9FWbmPl4wGEx6pqabCQobGD9+PD169Ij6O8DixU7A7xVXXKHG
+aChXIe666y5lyNOmTQNg6NChUSpGvOAxBQ8ePESh0TCF2rIEQHcuwbp163j00UcBJ/LLLYF79eoF
+wMsvv6wuPtnBZs6cyemnO7Vf3nrrLf083m61uXPnRv1bGWTMixYtAqBHjx58+eWXQP3cpe5U7Jp2
+StHDX3rpJf3uyy+/zNKlS2t1rk2bNrHHHnsA8Oyzz3LxxRcDyYkOTU9PVxZ2wAEHKEOS62jRooVe
+X12zTGNlbPLdcDisdqxAIKD2L4kOve222zQOpeLxH3vsMcBhaffccw/gMDlw7E8//vhjna6lOjQa
+oRDLZIulVv7NycnRBz43N5dBgwYBjt98yZIlgDOB69atAxwqBo6lf+VKJ8W8tLS0QePjxUret29f
+wFlIkhZbH/XBndZcHQKBAKeddhrg0F0J+vnTn/6kD1YwGGT48OEAqhqtWLGC/v37AzBhwgTOPPNM
+wLkPck3JEAqHHHII7dq1AxwqLsJJPCetW7fW97URCnLNPp9Px1+f+zB48GDAEV6yOVx44YUA7Ny5
+s8rjipH5hRdeYPTo0QCqVq5atapOY6kJnvrgwYOHKDTJiMYWLVoAqKssMzNTJe0nn3yiLptwOMz5
+558POEzg2WefBeCLL77Q3ws1b+h5EFfeJ598Ajgqw6mnngqUxy4kArJ7Hnnkkbz++uuAwxSuu+46
+wEk4kvc33XST7v6iYi1ZskQp7pdffqmM7aqrrlImV1v1oy6QqNYBAwbQuXNnwFHB3njjDQD69eun
+3/38888BJ0J0586dwO73XdQOyU4sKCio99owxjBnzhzAUQsvueQSILYkrdTUVF3vcg/69OmjTLeW
+aL5ZkhL6+c477wBwwQUXKN076aST2HfffQFYvnw5U6dOBZwY+A0bNgDlwgQaXhiAsxD/53/+Byj3
+WX/11Ve7xVnEEzJfbdq0AeD++++nZcuWACxcuFCpaSAQUNtARkaGzpc7n2OfffYBYPr06Ro0NGTI
+EPUIJRIyjnXr1qnwjEQiXHXVVQAabNW1a1fNIxg1apRuEAUFBVHqjWwS8YwBMMboPAeDQVW3JMS+
+ujUo92nw4MEaoCfqj9yveMNTHzx48BCFJskUBLKDzZo1iwceeABw/PtCI/fYYw/N+z/22GO58sor
+gcZXK2G//fbjlFNOAcp3qq+//potW7Yk7JyyO8lcde7cOSqMVt4bY9TomJOTo793J5rJfWjRogUD
+BgwAnF1s69atMY8rVmOeJGPt2LEj6jNJ4pLxzJo1S1WJK6+8kqeffjrq94mE2+OQk5PDscceCziG
+Wag+mlZU5euuu07nRVhMopLkmqRNoZLf06pVK8B5wD777DPA0b1EAOzcuZM77rgDgBdffBFoPMLh
+3XffVfuBWJs7d+4ctdDjgcpckuLevf/++5Webt68mT333BNwFrHowzt37uTggw8GykPF8/LyNPCq
+pKREg8FWrlzJ0UcfDdTdBRjLNbVq1Yq8vLwqv3fwwQcza9YswLE5iKdl27ZtCVchs7Ky1AvWqlUr
+FUS///47AIMGDdLP2rVrpy7HlStX6n147733VECI3WnYsGGxCjUvS9KDBw+xo1kwhbS0NJWYaWlp
+aqzr3Lkz5513HgDDhw9Xv7/4jOX/DQUxPi1evFh9zxKwdPzxx8d9B5NdNS0tTem/jKFly5bst99+
+gBObILEeaWlp/PrrrwCMGzeO2bNnA6iXYf369XTs2BGALl266A58zDHHaCm0RJZuk3krKiqqNqw6
+LS1N41RSUlI0e3bs2LHKMBL1LFx22WWMHz8ecJiCBFG5w6Dd4ejyvrCwUD03/fr1U2Y7YsQIwMn2
+jZGFNV/vg0Biwa+77joWLFgAOG4eSetdvXq1Bor861//0np+f/nLXwBn8TdUNR6fz6cPXlZWlgo1
+iYFPpLAuLS3V47vTs8XjUFBQwN133w04rlKZz/Xr1+sDJDqytVbf//TTT0ptFy1apEVTX3jhhYRc
+jzGG1157DXC8DJKvURn22GMPVc3S09M1C3TBggWqTsY7eE2K6OTk5OjDfeCBB6pLUYSC/F8gc1VW
+Vkb37t31802bNgFooZtErV1PffDgwUMUmiRTEIOLZEsedthhKj2rMrwsWbJErbZiLX/ssccSGgtQ
+Hfx+v+b4BwIBNSpKznwi4I7Fr+7vc+fO1cIwN910k8aDrFu3TqlvTfkhK1euVEb2yiuvJMTYaK1l
+ypQpAOy1116afbh8+XLdRYWKb968WYuTXHnllWqY7tmzZ0LC2935NzNnzmT69OmAE7wk2b0SxLR1
+61Y15j733HMakHTDDTdw/fXXAw4zk8rkstYTxSbrzBSMMV2NMZ8bY34yxiw2xty86/O2xpjpxpjf
+dv2bVdOxPHjw0HhQH6YQAm6z1n5njGkJfGuMmQ5cCnxqrX3IGDMWGAvcUf+hluOQQw4B4OSTTwYc
+XVAkcVWIRCLKIkSHa9WqVYN1Qw4EAro7GGN0Z4tHlaX6IhQKqR3BnRAVyxxlZ2drnEUiXb/PP/88
+AN27d1c20r17d91tZV5DoZBmIvp8Pg2PrliItj5wx1hYazVOY8OGDfr5t99+q1G2kuBUFcSACw7T
+FZtYotdqnYWCtXY9sH7X+3xjzM84LehPB47Z9bWXgZnEUSgYY7jooouAcgr717/+VY1dVWG//fZT
+r4TEvYuvvaEglnqfz6f1+mIRCu4mtYl88OqyCBcuXKgxDYnsaiXHXbFihRqefT6fxkvI/LRr146T
+TjpJPxNPhaRbxwMV56kqFau28xkIBHQ9TJ8+PWmVsuNiUzDG7AUcBMwDcnYJDIANQE4Vv6lzK3oP
+HjwkDvUWCsaYTOBN4BZr7Y4KhTdtVTEIto6t6K216uqRnPlx48Zpiaq+fftq4YmRI0dqLMLYsWN1
+15gxYwbgFHaVpJL8/PykqhBlZWVKZwEt1un2V9cEa22jicqsiP79++v9SUlJSXg4cTgcjgrNlnUo
+CVrZ2dlq4HPXSEhEObP6Qoy5hx12mEaZHnLIIUlbn/USCsaYFByBMNFa+9aujzcaYzpaa9cbYzoC
+m+o7yIoQq608/Dk5ORr006JFC7Usl5SUqB8+LS1NqxpJSu/GjRujSq4n8wELh8NRKcVdu3ZN2rkT
+CdHV27dvrz07U1JSNLM1GXB3+BK1cunSpZqVeMYZZ+hm0ZANe6uCCIKsrCydT/EAVYV4VMEW1Mf7
+YIDngZ+ttRNcf5oGXLLr/SVAw7fl8eDBQ61RH6ZwBHAR8IMxRqqAjAMeAqYYY64AVgIj6jfE3SFW
+bamJ/+KLL2oGnzFGd/9AIKDvf/zxRy0iIsabkpKSpO5gblhrtdhLY1UB6gJRx/bYYw/NVITY+mkm
+Alu2bNEyfL169dI1EIu6lixIFmVaWpoy3enTp1fLANLT0+OmCtXH+/AlYKr48/F1PW4skGCUli1b
+ajz4fvvtp+rD2rVrtfDGqFGjtMqQxOI3dN6HUNtnn31W8wSkQMx3333XYO7S+kAWZm5urlr4e/bs
+qfkT8S6lHwvE/di/f/9GO7c+n4/LLrsMcFyZUiympnqM4lGLyxjidiQPHjw0CzSLLMmmjoyMDPWX
+S2hwIusyJgPXXnut+tXfeecd3ckasmJ2U0DPnj156y3HZv/1118rU4jTc9r8W9E3J0gKsETBNTaL
+eKxw6+pN/VqSAVFn+vbtq0FfEydOjLe9ySuy4sGDh9jRJLMkKyKePtqGgDFGjY5NfVd13wu5lvo0
+UaktAoGAniMQCGjYuJQzq9hJqbEZGoVZ5eTksHDhQqDhxuYxBQ8ePEThv8am4PP5qm2hZozRAqRl
+ZWVx3bHruitVxoB8Pl9UxZ5EZFW6E7TKysqi2qZVvBb3PLkTtKy1Ucla4g4W46N7LuqzBiUOJRKJ
+6P0rLi6O+hycSEsxcrrn1Z2h6h6HhBqHw+FGwybigOZfji0W1PSQW2s19dZaG1d66T5WLMd1N4cV
++P1+XfCJSrOORCKVHjslJWW3rEy3N8Gdi+EWIEDCg8TatGmjAscdsi4PdyQSiWo6K0KvpKREx5mR
+kRFVN1F+J4jnWojX8RIBT33w4MFDFJoNU5AdoV27dlrgMlZJnOicf6jb7lCRaicjs6+ycfbs2VMN
+d1LVuaq4AzfzSiRkR6+q8UxVLr3Ksjb79u2rUY/CbOK9Jqy1muTUWMPbm41NQTodHX744bz7rpOD
+lYxF+d+E1NRUrSG5fPlyAJ555hlvnpsOvDgFDx48xI5moT646x1u3bpVm35s3LhR++01htqHscJt
+GGsMVLO0tJQTTzwRcDINwWmsc/755wON03DW0NmZlaGxxUhURLMQCsFgUDsdpaenc+aZZwJO2e8/
+/vGPgJNxJhWXbr/9dsARFO6y543hwZMMT3fXqMLCQv785z8DTkZlQy5wKUUvNpxzzjlHy6xPmzat
+weYwEAhojcb27dtrcV9pprJ+/XptFlNUVNSgD6Tb3QmNS2CBpz548OChApoFUxgzZoxK26+++op7
+7rkHcHz60kxj1KhRUW3DwNmBa2qQkiwIpRw5ciTgMAa5pjZt2mjjm3HjxukuuGHDhqSO0W05F/h8
+Pi0KIgbeZEKCovr3788+++wDOK3ZhCFKBe9WrVppNedp06bx/vvvA07xlWSzBhmz3F/3OqwP4qWW
+NAvvQ2lpqU7wHnvsUWkL9wMPPFD7IIrNoTGl8UrFIhFo3bp148ADDwQcNciddSgFSjt37pzUBe3z
++TRHQ8YbiURUYN11113qLk3WuGReWrZsyZNPPgk4HigpGpuRkaHfFcFfVlama+Ccc87R2p3JGLPP
+59MsyCeeeAJw0uU/++wzIHZVQlSRzMxM7ZwmRYArged98ODBQ+xo0uqDqAEA//d//wewG0uQneTe
+e+/l/vvvB2oubdUQOOeccwCnrDc4xrKnn34acAypp59+OgADBw7UEN2srCyt4ZcM+P1+ZVvCFIwx
+Wgrv9ddfj6rLmAzIzpqZmcnAgc4m2KZNG91BhQUUFBToTrrnnnvSrVs3wImzkNLvsULoupvFVaWG
+itp14IEHahEVKT//6KOPqtq4YcMGneOqjith7hkZGTz11FOAw36lW1Y1TKFW8JiCBw8eotCkmcJZ
+Z52l73/++edKv/Ppp58CcOSRR+qu8d133yV+cDGgV69eqpeLDpyfn699Ez766CMee+wxwGlgI+7X
+N954QxmEdNROJEKhkO5SAmOMugK7deuWdKYgOOKII5Q5FhUVMX78eABlW6Wlpbrb9u7dW9dA3759
+tc9CrEVlK8ZAVLRJuFvWHXPMMQDcf//92iPSXW1J6j1UrD0h76dPn67VyOWe5+TkcOihhwJO4VZZ
+F1LCra5o0kLhP//5D+BkusmEuLPQLrnkEqXjhYWFSrUag3EVysd65513qjCQz0pKSpg3bx4Q/TAW
+FxdrWHGiUqergrVWG9iIIc8Yo9R448aNDTa36enpGqh20003qXCqbDy///675jakpaWx9957A+Wq
+Rm1RW49VVlYW1157LeAIThmT5F8UFBTosdLT01W4ubNqu3fvzp133gmUq8i9e/fWv69bt4577703
+pvFXhXqrD8YYvzFmgTHm/V3/726MmWeMWWqMmWyMCdZ/mB48eEgW4sEUbgZ+Blrt+v944DFr7SRj
+zDPAFcA/4nCe3SASMz8/nz/84Q+A0zNyyJAhgNNUQ9qDvf766/q+sUB86G41SKoejx07Nsqod+WV
+VwLQoUMHZQpPPPFEwns0VoQwBPcOLLuuNORpCPz000+qgi1cuLBaxtK6dWs1RJaUlCTMNS1j6Nu3
+L/369dPzCbOaPn064MTZiPE7MzOTAQMGAPDrr7/qeujQoYOGlku38h49eug5ysrKNDu4vqhvL8ku
+wFDgQeDWXa3kjgPO3/WVl4F7SJBQEMo1Z84cTj75ZMCx7u6///6AY/EVWu7uL9gYkJmZyYMPPgg4
+/S/l4RbbyIoVK1RoZGRkMGbMGMCh6y+88AIAH374YdKvSewHbjVNvCFC3xsCixcv1oeiKl+/eB+m
+TJmiY96xY0fCvFFyb9w0v7CwkDPOOAOA+fPn7zbe3NxcFRZubNmyRVU32URatmypx92wYUPchFt9
+1Yf/B4wB5KraAduttbJ9rQE6V/ZDY8zVxpj5xpj59RyDBw8e4og6MwVjzB+BTdbab40xx8T6+7q2
+oq8Mw4cPV+o6ZMgQVSWg3Cq/aNEiNdY1ZCSjSPYXX3xRrch+v18puBQxuf322/nqq68AxyAlNHL1
+6tU89NBDQHI8Dm60bNkyKj5BIDuieEsaAh06dNCWe126dFFrvtvnL7R7v/32099NnTp1t9Dt2qKm
+DEyZqxtuuEEN4Vu3bo0qERcLhBm3bdsWcNaNjL1FixZxywitb4PZYcaYU4E0HJvC40AbY0xgF1vo
+AiSFU0rob1lZmRbw9Pv9Sml//vnnBmsm64aoBIMHD9YbWlZWpvUFZYwHHHCAuqDmzp3Lc889Bzg2
+h4ZKA3/ppZd2e4DC4bDS2mQGUlXEsmXL9KFfsGCBugClGEwgENC/p6enq7CYPHlynfsw1vTw9e3b
+F3AeYvnu9OnTVW2IFSKI3ZXFRO0sLS1VYVPfTa/O6oO19k5rbRdr7V7ASOAza+0FwOfAObu+5rWi
+9+ChiSERcQp3AJOMMQ8AC4DnE3COKtG9e/co45vQtiVLljR43rrP51Pq37FjRx3P9u3btQGIfPb8
+88/z9ttvAw1fZlxUmoEDB0apDeCoNoMHDwYaNv7DWsv/+3//T/8vIcRHHXUUACNGjFCmYK3lo48+
+AhxWkahxSy2HUCikhs2vvvqqzh6jTp06AQ6LFMj9aNmyZZ3VoIqIi1Cw1s4EZu56vwwYFI/jevDg
+Iflo0hGNleGll15SiXrjjTdqSGnbtm0rTamuC2JtgybSfMiQIVx88cWAs1tJGvI+++yjNgWxh6Sn
+p1fbvCZZ8Pv9/PDDDwAaDgzlY8rNzW3wWhSVQXbmYcOGAQ5jkLnduXMnL730EuBEEyZqfmW9+Xw+
+jS3p06ePji0WG1fLli256667AKeqthxX5n7y5Mlxs5k1aaHgjsMXSpaXl6fZkNdff736pt0Lur5I
+S0uL6QZIBuQLL7ygFuLCwkItCuIWVmIkOu+889R//sknnyRd9RFB9tBDD+0Wgg3RQqGx1b8MBAKq
+Nsh8r1q1SsvbpaWl6ZgTKdDmzp0LODkqEkdz4YUXcsIJJwBOHQWAzz//PGr9uiFr/IYbbuDcc8/V
+8Qtk/K+88krcxu1lSXrw4CEKTZIpiMSXZKevvvoqqhmI7GjiogGHolfsPFxX1NblI3RVCsWmpqYq
+jZw8efJuuwKUj/2II45QNeiTTz6p95hjhdQYuP7666Py+gXCXKRIbmUQw9dee+2lO7Pk+icywrRH
+jx5adfof/3CCaS+77DItY1dWVqa7eCIha/LSSy9Vo+Pjjz+uLkUpGbd8+XLN0MzPz4/q1i2hzddf
+f70yBGEHJSUlWlausrVUVzRJoSA6mUxkxY5Jsoh9Pp9O8OrVq+N2/tpSTqldKKHBJSUlzJw5E3By
+G9wPhQgD+e6QIUPUzpBs1SEjI4M33ngDKJ/ripCxrVu3rtKW8+5ekoFAICkh0JLtmJ+fzx133AGU
+BxD98Y9/1HUxadKkpNpprLUsW7YMcGwcslmJylDVWDIzM7npppsAJ6+kYp/LtWvXsnLlyqjP4gFP
+ffDgwUMUmhxTMMZobwShgBUNXZKPXlJSogbB+paoihVpaWlamFMyC2fOnMnll18OOAkubhx00EEA
+TJgwAXAKczz88MPJGi5QzlY+/PBD2rVrF/WZwG1ghOgdyp3/D+WMSvozJhrCWPLy8pQVXHjhhYAT
+Bi1h2HfddVeDxazUpsemzOHVV1/NJZdcAjiqmPxOwt+nTZvGxIkT4z7GJicUOnfurJVvpcnp3Llz
+efHFFwGHqo4bNw5wHkxZmG6LbX1RG5dkSkqK5mMIXfT5fFFdn4Tajh8/niuuuCLq92vXrk16Cfcj
+jjgCcIKUKlZYqggJwqo4Dw3lPu3SpYu+T09P58YbbwTQwiTGGE2t3rJlS6Pu0iQBd3fccUeUXUy6
+oLmbGSWiHICnPnjw4CEKTY4pdOrUSSWpWL5PP/10brnlFsDZlSWLDMp7POzcubPS3SFRvQYDgYDG
+H0gAVYcOHbj55psBh91IK7gDDzxwN6p9/vnnx1wzsD7w+Xzq1akOYtgSZtbQHaf32msvwCk4Iztp
+v379tGaB3N8FCxZowFJjDLYSGGPo378/4Bh5Za26E74kgSsQCCREDWpyQuHrr79WHV06ErVv314F
+RUFBgQbbfP/991qxaNOmTZUKhbpMam0o544dO9SVeMEFFwBOFyO54eFwWC34+fn5Wl9PCqhU9Kgk
+GpFIROezY8eOGgDWv39/ffDWr1/P2WefDTSOhr1t2rRh8uTJgGOdlzGddNJJavMQ/fvCCy9scAFW
+W0ip+oyMDN0Ybrvttt2yORNVdctTHzx48BCFZtE2rrFCmInU3Pvkk0803Do/P18ty7NmzUp6wZTm
+gP/93/9VFpaZmRnFBMXLIzUpGjpDtrbw+/0a09C5c2c+/vhjwKnLGIdr8NrGefDgIXZ4TMFDk0XL
+li1V/z4urTNIAAAgAElEQVTjjDPU8FxYWKi2m8Zg+4gFWVlZWkXM7/dr3MqYMWPi4T6tFVPwhIKH
+ZgFjjHpPNm/e3CjjD2qDtLQ0DQlfvny5Vm6OU8VpT33w4MFD7Gh2TCEzM1OTilasWBGvwyYEEjXY
+rVs3DcMWt5Pf749qWOJGQ0XjGWPU3ZvMGIraoLJaD00RrVu35rjjjgOcCEbJtHQXaqkHmr/6IAuh
+RYsWmuPQqlWrqAXrttjK9+VhrBi3766unCi4g6UktRrKG8tKwJM7tTglJSVqTHIdqampqjPH+z5W
+zHkQuLMgE3HeWOFuB19Vo9fGBJ/Pp2OWf8PhsL6PRCJRc1vV+zrCUx88ePAQO5o0U5Cd3Rijoat+
+v193jIosoaKENsZooZYFCxZoFGGidl33bib/BycyryJ72bp1axSrqKz2QiLGWh26dOkS1V+jqSIl
+JaXGWgaJQnp6urJCKYxSm6I9FddOHdH81Yd4QPIkEtnIpLFQ7bpCipfMnTuXiy66CCivBtVUrqlT
+p0489thjgJMvcf311wNoy/pkwe/365w1QEBV4tUHY0wbY8wbxpglxpifjTGHG2PaGmOmG2N+2/Vv
+Vn3O4cGDh+SivglRjwMfWWvPMcYEgXRgHPCptfYhY8xYYCxOg5hGiWS0Omsqu2llSEtLY9KkSYBj
+BBWPTlO5JlEx7777bs2c3LZtW1SdgmSiMWdoKsTKHesLaA0sZ5cK4vr8F6DjrvcdgV9qcSxb35ff
+77d+v99mZ2fbiy++2F588cU2KytLP4/HOer78vl8NjMz02ZmZtoWLVro2HapT3F/1ee4KSkpNiUl
+xS5dutQWFxfb4uJiO2LEiAafw1hfwWDQBoNBO3/+fFtWVmbLysrs3Llz9foaenxJfs2vzbNdH/Wh
+O7AZeNEYs8AY85wxJgPIsdZK7bMNQE5lP/Za0Xvw0DhRHw4VAA4GbrTWzjPGPI6jKiistbYqI2I8
+W9FnZmby3XffAY4RSay648eP53//938Bp+V4xcq3yabAgwYN4vzzzwccGtm5c2cAPvvsMy0AIoFK
+8RhbXY/h8/mYMWMG4MznTz/9BKAVnhsSFStGS7Xp0tLSqKrSoh5IXYiePXvq78rKyqqMw0gG5Nyi
+2rgNjlV5zCr+DeLmkdgN9WEKa4A11tp5u/7/Bo6Q2GiM6Qiw699N9RuiBw8ekol6uSSNMbOBK621
+vxhj7gEydv1pq8vQ2NZaO6aG49RrWwwGg7pjnHPOOVx77bWAUzVIDDu//fab9mFIhnHRDamh8M47
+72hzj5SUFO2MvH37dm699VYA3nrrLWD30OaaEM/YhdGjR2th1ry8PGU08Y5NcMdhyK4ZqyGuMndv
+y5YttU2bVMRu3749v/76K+A0Z4lXY6BYYYxRN7gU7t1///21eU1mZibz5zsa9QEHHKDrpV27dtop
+Wyph/fjjj7z88stArROmauWSrK8J9kZg4i7PwzLgMhz2McUYcwWwEhhRz3PUiNLSUg0AefHFF7V+
+4IABA7Q9eU5Ojt4MqYCbLD/x8OHDAefmS3n6QCCgD0JOTg6vvvoqUN416IUXXmDIkCGAI8RE5Zk1
+axb9+vUDYMmSJdqkVlSmgoKCOlu4RXjdeuut+rD95S9/SUqgUl3vRWVBXampqboxiEArLS3VcugN
+JRAguuvTbbfdBjgtACQWJDs7m5EjRwLRc5KWlqaNZcUD9NFHH+n9jyfqJRSstd8DlUme4+tzXA8e
+PDQcmlzh1liwbNkyNTi1bdtWm8TUl17XthW97FLjx48HnB1MxuM2IllrlUqfd955+q/boCaJUu72
+a6tWrdImMqNGjQLK1Y+64LXXXtNxyw7073//u87HqwzuEPN4V9KWub3ooos44IADgPJ7/dNPPylr
+jBdiiVSV7/bv31/XgxSC8fl8qv76fD5lkBCdtCfzJL0oJ02aFLf28240a6GQk5OjtCwlJUUXYTK8
+DsYYDavNyioP6pSbHAqFdBEXFhYqda9MaEA5td933311cYjQAbRHZV2vLRgMqs0FyrM1K1YQri/c
+2YBuYRAPQS21MK+88srd5vP777+P+32XLNea7D/GGF0Df/rTn3Sc8vtQKKQqmrVWH/SUlBQVFu6x
+SxZwRkZGQoSClyXpwYOHKDRLptCiRQvA6bcgRrcXXnhB/e31RW13HGldtmjRIgBOPvlk5s1zPLgt
+W7bkkUceAZw6grKzXXXVVYCTwCOG0WOOOSbKty27ytKlSzXLs749AIYNG6Y7Vzgc1hZy8UaijLvB
+YFBLl7Vp00YpuBhgpZVgPFGb7EZw7pm0tRs0aJDOs9zTVatWafXpuXPn6vpq1aoVzz//POB0MJeC
+K2+++SZQ3jM17qhrmHM8X8Q5nPOUU06xp5xyil20aJHdvn273b59uz344IOtz+ezPp8v6eGlgUDA
+BgIB27Jly5jCrnv37m179+5tt23bZkOhkA2FQra4uNguWrTILlq0yAaDwXqPzRhjjTH22GOPtSUl
+JbakpMTOmTMnLnMlx2jdurXOQaLmODs7286bN8/OmzfPFhYW2tzcXJubm2snTZpkJ02alPR7XnEe
++vbta/v27Wvnz59vw+GwDYfDGnZ97rnn6n0AbGpqqk1NTbUXXXSRLSwstIWFhTYSidiioiJbVFSk
+9/+VV16xXbt2tV27dq3tWBIe5uzBg4dmiGanPvh8PrXId+jQQWsfLlu2rMEaggi1j7XhS/fu3QHH
+Ry1Uc9u2bRx99NFA7elrdXA3rBGj1UMPPVTnuRJj7gknnKCei7KyMm3vdtdddwHxq/EoasKoUaM4
+8MAD9TMxkD7wwAP1Or7b4FsxBLmiGlmdV0quNz09XdeDFPX55ZdftO3hGWecwZNPPglE33coN0z2
+7t0bcNaHqJ3nnHNO/Lw4cTlKI4LbvZORkaFFNOLxACUTPXv25B//+AfguDLFNvLee+/Ftf24LKSZ
+M2eqDaR9+/ZVNuOVhSkBNm+99ZZ6LS6++GIVWG3atFEBAeX2FbGBnHjiiXHxbOy3336AY4sRT4O1
+VitZPf3004BjU5Dcl9dee41p06YBTqCWVEJasmQJp59+OlAuwLt06aJz9Pvvv0cFhlX0ZlUlECKR
+CKtXr9ZjSPCSBLLNnj1b12xtUrrdtUnF9rPHHnuwYcOGGn9bG3jqgwcPHqLQ7JiC3+9n8ODBgOPn
+XbhwIdA4OwWJl6S4uFh3CAm6eeedd+jUqRPg7EAffPAB4NDkRMRZLF++XFnVAQccQFpaGuDscrKL
+DRo0SJnANddcA8AjjzyiXpKq8i/C4bDuYtLbsUuXLvzyyy+7jaO2gWGC4493gmfbtm2r5y8rK1NG
+I6rkrFmzosYnO6z7fP369dPxSZh0KBTSIKMtW7ZEhRXHQtflu6NHj9a8DLnnwlQqYufOnfz222+A
+0wZA2I9cWyAQUBVk33339ZiCBw8eEoNmxxTC4bAmDFlradeuHdA4ug77/X6NQuzVq5eG3W7cuFFb
+nskOLQ1twLGHyA6WqOSkHTt2sOeeewJOJOgLL7wAQFFRkfrHr7jiCtWDJYovNzc3agcWnXvu3LnK
+zrKysjQ+Q5KSJGKyImLJ9jTGkJPj1PAJBAL6/e3bt6sxr0OHDvpdWQM+n0//7vf7+f777wFYuXIl
+p556KgCPP/444NgfFixYAEQbiuvK1pYuXcp9990HwL333lvpd2S+r7nmGrW7vPPOO7o2ZF4XL16s
+LO6oo47SOY41w7Yiml015+zsbA1SatmypRq25MY3JLKzs/Wh6N+/vwqsygp+WGt1ERcUFKgqkYiw
+VnCMXrNmzQKc8GkxZhYUFOgiTE1NVeoqxUs+/PBDBg0aBDgdjURopaSk0LdvX8AJzqltOXN3TkRN
+2Z69e/dm6tSpgJNOLPM1btw4DW+XojZffPGF5oekpqZq+nzr1q01hf2NN95gn332AcqF1kEHHcS6
+deuAuguCqiCqz3PPPacCORwOq8qwdu1andv09HS9vrvvvhtw1vSSJUsAZ21JynVFuHJMvGYwHjx4
+iB3NTn3o1auXSv7c3FylVw0J8UH36NGD5cuXAw5TqK4kmJspfPnll3qMRDGFoqIi/vrXvwLw4IMP
+Km2dP3+++sK7d++uBkbZoYqLi5WZuQ2mRx55pNaIWLlyJWeffTYAa9asqXYcgUAgqqxaZbuznOPM
+M8+ka9eu+rkUGlm8eDH7778/gKpoc+bMUbZyyCGHaP+K/Px8Lr30UgD22WcfNeZJXMG2bdsSlkD3
+6aefAnDJJZdoHMcvv/zCzz//DDjqmlxrcXGxhje//fbbgMNmxPAp7tbKEKvq3GyEgjxgQ4YMUbrr
+7hzVkBDr8uGHHx7Vr9KdGi2QzyKRiNLBZcuWsXnz5oSOMRKJ8PXXX+t7oc/Dhg1TAdCnTx9OO+00
+oNyqP3DgQH0wn3/+eaXB11xzjQrnfffdV6sF1SQUQqFQjQ+hZByuWLFC59OtlmRnZ7NlyxYA9UQN
+GjRIi5T07t1bf1dQUKCpyNu2bdMHSPR+se4nEvPmzdN59fv9atdwx3kUFxcze/ZsoHwOi4uLG12N
+Rg8ePDRDNDumkJ+frypDKBRKqtehojogRrmOHTsCcOGFF2qIqnsXcEPGu3nzZt0Rvvzyy4RfhzFG
+DWqPP/44f/nLXwCnNqCED/t8Ps02lJ3WWqu79IABA3TuZ8yYoQVfCgsLlW3UhNpQdTES/vDDD8oK
+27Rpo56Rv/3tb8omRB3IyMjQY7vnPjMzUw17H3/8Md98842OX+Yl0cb4kpISjREJBAJar7GkpETV
+h82bN2vYe7yL01REsxEK8uCNHTtWJzIlJaVaXSseCAQC+iCcffbZGs5aWlqqQSoXXHAB4AgJWaTu
+cFZrrao5ixcvBpyGtxMmTAAcfTHRQsGdev3MM8/wzDPPAM5D4w4EOuqoowBHDwbnQRJbRH5+vgqI
+nTt3RqlHct3xgBx31apVUYJYHhZRVdzfrVi0RlSG999/X4vUbtu2bbf6ncnyzrlDpWXNulXfrVu3
+qnoX78I3FeGpDx48eIhCs2EKt9xyC+BIV5Gwubm5GkqcKITDYTWo3XbbbaoeQHRpteoQiUT4+OOP
+AZg+fToAv/76qxoXY82urC/cu6P73DNmzFBaLdmOtT2eBNTEowO37OL5+fkMHToUcHz3YvzMysrS
+uXczLAkDXr9+PSeddBLgFGFpDLE6gjZt2jBwoBNKYIxRBmeM0bWR6PE2C6FgjFHaPXToUJ3IvLw8
+9thjD6Bmq3ddYa1VXXzgwIFaK/HQQw/dTRi43YzuunwzZszgf/7nfwBUvw0GgwmniQ2BeC5oa61G
+8Q0dOlTVnNLS0kozGBvTw18V/H4/hxxyiP5fvB9PPfVU0tZDfVvRjzbGLDbG/GiMed0Yk2aM6W6M
+mWeMWWqMmbyrJ4QHDx6aCOrMFIwxnYGbgD7W2iJjzBRgJHAq8Ji1dpIx5hngCuAfcRltFbDWarDJ
+qlWr1ErbunXrpPYMLCsr0wYub775pjZ2EXZQUlKi9Rp//vlnLQCyadOm3bI4k9GApTGirtZ+t4oC
+TaTleyUoKSlRNadfv34899xzAPznP/9JGtOpr6ExALQwxgSAdGA9cBxOX0mAl4Ez6nkODx48JBF1
+ZgrW2rXGmEeBVUAR8AnwLbDdWiv+rTVA58p+b4y5Gri6ruevCKmkM3/+fN577z0Avvrqq6QnQok0
+P++887Tj9aRJkwBH15WQ4RkzZjTZ3SyRaAp6fyKRkpJCjx49AMeeIOHPiWgPVxXqoz5kAacD3YHt
+wFTglNr+3saxFT2Uh7muWLFCg20aMl26rKyMe+65BygPc165cuV/rVrgoXbYtm2bGq5LSkoapDhQ
+fdSHE4Dl1trN1toy4C3gCKDNLnUCoAuwtqoDePDgofGhPi7JVcBhxph0HPXheGA+8DlwDjAJuAR4
+t76DjBWNoaBKJBJRg1Ft6wN48BAKhbjuuusAp/hOfQum1AV1ZgrW2nk4BsXvgB92HeufwB3ArcaY
+pUA74Pk4jDMuCAaDBIPB3UJeEwXxjVfsm1gRyfSQeGickDXZunVrDcCTeAufzxfXMPGaUN9W9HcD
+d1f4eBkwqD7H9eDBQ8OhWUQ0uuHz+SqtGQgN1/uhJot6Y7O4JyMzMF6QbM3U1FQ1yjUG9bEqpKam
+agKdFMxx19bIy8vTWha1qVHpzvz0msFUgUgkEpf4+kTBnfbaWMfZ2MZTHWSs8fDqBIPBhG8cpaWl
+1Z7D7/erqlBcXFzrNRJPQehlSXrw4CEKzVIoVJb8UpUxL1lGR0FNRseqIOPMysrC7/fj9/s9AyXl
+81lWVhY1t3W5r8mIIami67qqFSeeeCKLFy9m8eLFHH300WporOpYiUCzUx+qQnp6utbNlzwJt73B
+GJP0wKLqbmrXrl21TPnXX3+trqmdO3cqvSwrK/PcnC5IunRWVpYWS4nlnjak2iQC7G9/+5vWvDzv
+vPO07H4y0SyZggcPHuqOJs0URLoOGzZMm3i8+uqrGifeokULbUgycOBAvvjiCwCt9Nu2bVstC15U
+VJRUw19GRoaOecCAAfz9738H0NwIQPtgnnnmmVFl3mScKSkpyn6aeu2F+s69z+fTsnePPPKIFqi5
+8847Afjoo4+0zkZj8074fD4tIdelSxdlhZIhmfTxNMhZPXjw0GjR5JiCMUY7Bsvu2r9/f/VXP/TQ
+Q1FVd2RXyMvL06pGklEJ8K9//UuPmwzIOJ955hlGjhwZ9ZkbZWVlyhrWrVsXtYPK+5rcW8mGMUar
+H/Xo0YODDz4YcGw30sxGktUefvhhbYxjra03U8jMzNTu123bttVenNJWbuPGjTq233//XQvPrlix
+Qu0yDWVT6NOnj2b2tmnTRpu9SJOdZKPJCAV50Pv06cO77zrpFOnp6UB0ZWT3w+22QLs/X7vWydH6
+7rvvNODFHUCSSIwZMwaAESNG6Gd5eXmcddZZOiZwuhVJmfKqrM+NAcYYunXrBsCECRO0kUlKSkql
+QWQixHr06KFNbN9++229xro2XykpKdFGNKFQSI2x8m/nzp1VfTjggAO0xuS4ceN0PTQU7rvvPhVo
+mzdv1p6XyWhEUxka72rz4MFDg6BRMAVjDIFAoNqWYUIHzz77bK1P4E4ScRuP3OqDuKRWrFihtf6l
+mOuvv/6aVPodDAZ1hwoEAlqu7cMPP9ztuwsXLtRirn6/n48++ihp46wOYths2bIl4LRj+/e//w2g
+Va0rg9wT+X1paanem0AgUG93cKtWrdSAvHHjRrp06bLbd6QydUpKipbsa9++fcKK+tYEYbjZ2dlR
+rEpa0TcUGoVQCAQCZGdnk5ubW2lRiWAwyG233QbA9ddfr5MpAiQcDmuHnffee0+bh2ZnZ0c1Hf3y
+yy8BtB18svXx7t2769iXLFlSqTAQDBs2TNWH+lSPikc3IRG+hx56qKb1HnPMMQB06NChSnuMzG9R
+UVFUj0xwhJ5cf2lpaZQKWBfk5ubyz3/+E3BsTNIcyH1c2UyCwaB6fsTO0BCQeevQoYPO8Y8//tjg
+3hFPffDgwUMUGgVTMMaQlpZWZUGJ8ePHc/311wPRkl+MV4899ph6IkKhEP/5z38Ah1XIjtG7d2/t
+g9hQPv333ntPd4eXX3650u+IajNixAgee+wxoLyJSV0gno1Ydx9hGAcffLD6/y+99NJqVQT3edye
+H3eDHok0nDx5ctR9cLetqwustaoezJw5U/sxSn9JiGYFsjOLR6ohIEzQzRTk/tcF8tstW7bUi200
+CqFQWlrKihUrqsxXOPvss1UYuO0OU6ZMARw3pCyIHj16qCX/s88+0yCWM844Q7s3ibU5WS4oWYwb
+NmxQITV37lxNoe3bty9XX+3UsJWw1ieeeEKbncYDsaRD+/1+Dfq68847Ofroo4HoB0xgrdWHe+vW
+reo9OeGEE1SwpKSk6LXK33/44YfdjlMfRCIRFSyvvPIK5557LoC6r93Xb4xh2bJl9TpfPCAP7pYt
+W9QGIkV+Y0WrVq30/gQCAa3zWBd46oMHDx6i0CiYAlS+U8hn6enp+r6wsJBPPvkEKKfgHTp04LLL
+LgPgr3/9q0rg7du3q2W5X79+SoP79+8PsJt/OlHMQSzrCxcuZMCAAYDTifrII48EnNgF2Uk//fRT
+wAnRFgu/UO66wJ01WNP1yc5+0kkn8eijjwJOvERlRkC5ppkzZ6pfvaCgQL1Er7zyCmec4bT8OO20
+0zQQS4x9iTCmucOYq7Pgl5WV8Ze//AVIfp9ON3JzcwF4/vnnufnmmwFnvkXlq02ym6hJy5cv1+PV
+Z71AIxcKbqu2LMItW7aw3377AY6OC3DyySfTrl273X6TlpamdB3K9cgnnngCcNqQL1iwAHBUGGkB
+Hu+FIurDGWecoWNIT09Xt1hZWZl+LvHuaWlpSsvHjx/Pww8/DMQuuGSBtW/fXm02lT0w2dnZfPDB
+B4CjgolA8vl8UV4eof2nnXYawG40VewfixcvZs6cOYDjqRBbhLgkU1NTE1aUtG/fvtqpq2IwGzhz
+0rlzZ31fX3tGXSHz+u2332og3tlnn62bxZYtW3Sca9as0fV52GGHAU4RFhn7o48+qq7h+gpcT33w
+4MFDFExjKL1VVTMYkZ4bN25Uep2Xl0deXh5QHizTqlWrSqvdumPq5f8QbSGXY+3YsUNp8pFHHqnx
+DfGA7Fqff/65nu/oo4+mU6dOgLMzC2MRynjCCSeowfSggw7S3w0YMEB3jNqgZ8+egLNDi6W9tLRU
+GYSoM++++y7Z2dlA9O4aCoV0N8rPz+fNN98EUG9QdejQoQPgsAa5V1KXsG/fvnGdYyAqt0EYYmUh
+4qFQSFXJt956q8FqUsg8v/nmm6pqWWt1vkOhEC1atKj2GDL2iRMncvvttwPlWcCV4Ftr7cCaxuUx
+BQ8ePEShRpuCMeYF4I/AJmvt/rs+awtMBvYCVgAjrLXbjCP6HsfpPF0IXGqt/a6ug5NdZcWKFepO
+TE1NVX1XdO7Vq1drtRprrbKKrVu3qv7cq1cv3R1Frw2Hw+rGadu2rdotBg8eHNddbNiwYYBjO/j1
+118BZzeT91988cVu+vX8+fN56KGHACfH/scffwSciDdxXwl7qA5iaHXv+G68+OKLgGNzcGcqSjLO
+9u3bNZN00aJFms1XG4gRrLCwUA2MixcvBqrdzeoM2W3btWtXbdbrtm3bNLq1IZmyuH1PPvlk/WzH
+jh16X4PBYKVMwW0zkAjgs846SyM6t2/fXi87SW0MjS8BTwKvuD4bC3xqrX3IGDN21//vAP4A7LPr
+dShOC/pD6zo4oX4dOnTQ96Wlpbqgvv76a8B5qESAbNmyhYMOOghwKNX69ev1eGKM7NOnD+BYwk86
+6SQARo0aperD0KFD6+wvrgxS9CUYDPL555/rddTWILRmzRpNPR46dKgalC644AItB14V5BwVF4l8
+LsU93MFGubm5ajA8//zzVSDFgnbt2nHeeecBzjyLwH3ttdeA+GcAGmN0s8jPz1dVojL1cd68eTqe
+hggplnspXrRgMKgb3JtvvsmhhzqPTNeuXaO8bpJKLSHmnTt3ZsKECYBzHyWsvL6G0xrVB2vtF0Bu
+hY9Px2kzD9Ht5k8HXrEO5uL0leyIBw8emgzq6pLMsdbKFrwByNn1vjOw2vU9aUW/ngqorBW9z+eL
+8tGKobG0tDSq6q2oBC+99BIAX375ZZTxcMaMGZUOWhiGlGXz+/3qUhsxYoQa2naNT49XXwhziUQi
+TJ8+Xd/XBZ9++qm6VEePHs29995b7feryj6U65O5zM7O1jEVFxdz+eWXA+V0v7YQ5jFr1iw19oXD
+YTWOvvrqqzEdLxbMnDkTcCh4ZbUo5F7++OOPNTKsWBDLWgkEAqqCCXNdtmwZ11xzDQDr16/nySef
+BBwmIFGthx9++G7HX7ZsGbfeeivglOyTpMD6ot5xCtZaW5dW8hVb0QcCAQKBgNK+4uJiXdAlJSU6
+8ampqWoxF3/u7Nmz6/Twur0TGRkZ+lAcd9xx6s2IRyalxFX4/f5KU3pjQUZGhgqvww47LCr8uzKI
+/aS4uDhqjkTHF1tGJBLR1PJXX31VBWgs89qtWzf1orRq1SqqwM3HH38M1M4OUleIqrhkyRKdZ3fg
+lVxLx44d46I21GXjOPXUUzV4Tn4/ceJEVqxYAcAtt9yicQjfffed2hvc55BnZN68eXrNTz31VNzs
+I3X1PmwUtWDXv5t2fb4W6Or6nteK3oOHJoa6MoVpOG3mHyK63fw04AZjzCQcA2OeS82oFpFIhNLS
+0qh6CiJJf/jhBy35FYlE1DcriTpPPPFEnXaglJQULXSSkpKiVPOLL76Iaw+IJUuWAE4I6+jRowHU
+WFhbyFzceOONqmLVJsPPnQTk3kkkalOiQt9++22l9gsXLowpM3PgQMf1PXXqVDX2hUIh3Y3HjRun
+lDhRhj1rrbKeO++8U3djiQiUMYFj7GyoKMbjjz9e16+st+uuu47zzz8fgJycHB3bmDFj9LvBYFDn
+9umnnwYcj9Hjjz8OoNGocUFlHWsqdK95HccmUIZjI7gCp8X8p8BvwAyg7a7vGuAp4Hec9vQDazr+
+rt+JClLpq1OnTvbDDz+0H374oV26dKndvHmz3bx5sy0qKrJFRUX2ueees6mpqTY1NbXKY7hfxhhr
+jLF9+vSx8+bNs/PmzbNlZWW2sLDQFhYW2tNOO61Wx6ntKyUlxaakpNjVq1fb0tJSW1paavfcc08b
+CARsIBCwfr+/2t+npqbac88915577rl2w4YNNjc31+bm5trRo0frMar6rc/nsz6fr8r5DQaDNhgM
+2ssuu8wOHDjQDhw40A4ZMsS2bt3atm7dusrjZmZm2szMTLv33nvbVatW2VWrVtlQKGTD4bANh8M2
+Ly/P/v3vf7d///vfbdeuXeM6nzW9gsGgnTp1qp06daqNRCL62rFjh92xY4cNBoNJHU/FtTdlyhQ7
+ZRNkYIgAABgXSURBVMoUGwqFbCgUspFIRN8XFxfbsrIyW1ZWZr/55hu7bt06u27dOpuXl2eLi4tt
+cXGx/fbbb+23335rzzzzzJjWPTC/Ns9jjUzBWnteFX86vpLvWmBUTcf04MFD40WjDnN2Q7LvLr30
+Ug3qEYTDYebNmwc4dRelfNnGjRvZf//9ASdrT+IFpOrv+PHjOeqoowDHCCjlwU4//fR6X1NluO66
+69RzUFRUpBb+RYsW8fvvvwPlSVuhUIgDDjgAgPvvv19DpQsLC9U3PWHChBppcG2NYVlZWVrGbtSo
+URpPceedd6qqEQgEtOrwPffcA8DIkSPVSxQKhbQl3wUXXBB1H5KNY489FkC9PT6fj6VLlwJONefK
+yv4lCzKHYlxMS0vT+xQOh6OKwbiT0SS2Q2pF/N///V+sXpRahTk3GaEgyMrK0mhDdxpuZa4nd0z7
+li1bNIpNvAH77ruv6ud5eXn6ECaq5HdWVhZPPfUU4GQZuscvura4CH0+n0azlZaWapTmtddey+uv
+v66/qwm1FQrGGM3QvOiii1TYhMNhXbzdunXTOXWX13f31rjxxhsBJ5eioUqUQ7lr9JdffgGccYpb
+9LjjjtM1VB8bR13d1rJWRTicdtppGo3p8/miiqXIZnjmmWeqUKtHpysv98GDBw+xo8kxBSin/1Io
+QzotgcMe3BZ3d1CTSFg3VRPa/sADD2iTmUTl+UO5j3no0KGadyAsAMrrEbRv3147KE2cOJFXXnGi
+zN1h27VBLLuZZDJu2rQpqmuVzKHf79+NhRUVFTF79mzACYkW9aGh15UELwlTaNeuncZhHHvssZp3
+0tCVk5MMjyl48OAhdjRJpuD6HeDsCpLYdPrpp2sUmDFGd+HCwkJlClLM9a233uLBBx8EnHBgOV6y
+d49u3bppuTJpJycGMoA5c+Ykdef95ptv1M/vrrwUiUSURYlf/MUXX9Sw8nA43OAMQSB6uxhM+/fv
+r0zwqquuSigbbMRonobG2kCor8/ni8qurNhEpqECWBo7rrrqKm3hnp2draHL1113nXpwRLDGM8gr
+kfD7/VGq5H8pPPXBgwcPsaNZMgUP9UNqaqq6Q3fu3Nlk2ICHGlErptBoqjl7aDwoKSn5b9W5PeCp
+Dx48eKiAZscUKiu/1Vjg7sPYsWNHjWj8+eefGzT6r7YIBoPqzXH3xqhpzmNpWeeh4dGkbQriWXBl
+W1Z3jmr/bq3VgJe8vLykuyUrjs/9IPn9fg0WSvb98vl8UV2mZJwShFVSUqKCIhwO6/uSkhIdcyQS
+0c+laE28rqMqgeQu8FLZdbjvbzLnNCUlZbcAsHicv5aC1/M+ePDgIXY0afVBmIIxpkYLeVW0Vih8
+WVmZficlJUWPVxsWUl9UJuV9Pp/uru5rS01NTUol4soCudxjlBiPFi1a6H3w+XxaVbtFixaa2JOb
+mxt3hlATqmtDWDEgK5lwB8nJvEUikahw9IZm7x5T8ODBQxSaNFNw64iCWIxa1lotbFpYWKjHSbY7
+rrLxhsNh3XUrfi4MIhQKJSyGoKo5rBgNGgqFonY8QUFBgfbXkOjHZI6z4t/D4bBGtIbD4QZNhKos
+tb8mm1dlSJQBt0kbGgXu2gO9evXSTMOKHZE9/PfCGBPXCt2JRF0M6LV8jj1DowcPHmJHk1YfBMFg
+UCsmu6vhXn311dqm7L8sbz6uaMyxH7XFueeeq2rXyy+/XMO3GxZSpfyNN95Q9fbjjz/W5kdz5swB
+HAOuIJ6qRLNRH6Qv36GHHqqqRCgUYuzYsQA8++yzlero8Ya7OInQVSmplZ+fr0FKjT2fQBrOzJ49
+W+ezdevWukhXr3YagY0cOZKFCxcCjTPrVLpULVmyRB+a/v37a4m5xgafz6dl8aT8vkD6TY4ZMwZw
+2gTIenLbJ6qBpz548OAhdtS1Ff0jwGlAKU6Ph8ustdt3/e1OnN4QYeAma+3HCRq7IhKJaCXiV199
+VelXMBjktttuA2Dy5MkJZwqXXXaZ1hvIyMjQ3n7XX389AN27d9cyYB988IEWEl2zZo16PIRp+P1+
+LrzwQgDeeecdPUd+fn6VnaTjhQMPPFALkuTk5KjhS5gPQI8ePQCHScj4pKFJY4CoPOeccw7gFJqV
+3TQnJ0fnvjEwZTdat26tbePcBX3z8/N1rIcffrj+/fnnn4/7GOrain46cKe1NmSMGQ/cCdxhjOkD
+jAT6Ap2AGcaYfa21teI2dYXP59PKuO4ehlBuaXbrX/GAOwBF3q9du1a79xQVFWlz1n79+gHOgyRV
+oW644QZ9qP1+v3pMpFT9cccdp2HXTz75JD/88AMAt956K3vvvTfg0MeabCWxWKclkOvdd9+lQ4cO
+en2i6hQXF6sqISXSW7Rowdlnnw3AKaecompcY7HhHHHEEYBz/UK1N2zYoAIuNTU1qmOTNH0VoZEs
+yHi++OILevXqBTgq5iOPPAI4LvO7774bKG9BMG/evITMc51a0VtrP7HWyjY1F6dnJDit6CdZa0us
+tcuBpcCgOI7XgwcPCUY8vA+XA5N3ve+MIyQE0oo+ofD5fNrPUAxLAolVyMzMjFtQktvgtv/++6s1
+eM8999Tdf++996Z3795AtPFRYK3V3SESiWiFaul96PP59FgpKSnazv7jjz/mu+++AxzrdE3ZldJ/
+MBAIaGZjZUbOYDCoVZk7d+6sO1BBQYHWsZw+fbqeTyojL126VHfXqVOnMnToUMDZ8RoSovK4M1PX
+rFkDOMFpUhMzPT1d53nbtm3auEYYUTKM0z6fj9tvvx2Anj176txPnz6dRx99VMd8/PFOUzZhnmIM
+jjfqJRSMMX8GQsDEOvz2auDq+pxfEIlEtNBoSkpKFGWWBdu+fXtttFJfZGVlaUPbL774Qqn57Nmz
+tZFqMBjUz93UXW54WVlZlCdi+/btQPliLigo4NNPPwXgxBNP1IWQlpbGoYceCjhWaHFTVWVNF8/H
+5s2blSZXjAAFx4sg53CXcn/qqae0FH1lKthNN93Es88+Czhz/49//ANw9N4YuxclBNLgx+fz0alT
+J8BpBiTdq0RQVIVkpH3vs88+3HHHHYCjzoiQKigoUPW3rKyMSy65BIBJkyYBjor5t7/9DYhvFG6d
+hYIx5lIcA+TxtnzWat2K3lr7T+Cfu47VuKw9Hjz8F6NOQsEYcwowBjjaWuvmr9OA14wxE3AMjfsA
+X9d7lDUgEokwcaJDVq6++uqonVCMZy1atFAaX0ufbpVYt26dHiMcDuv5cnJyos4t3xEpvmDBAg2c
+mTZtWq1zAnr37s2UKVMAZ5cTNnHRRRdpC7mqIC3wSktLq80cvOqqq6KYhLCCZcuWKYtxf1/w9ttv
+c/DBBwNwzTXX6Njat2+vjWEawugo913UJyg3Ov/yyy/qJaoJyfBOjBw5UtdmUVGRzldqaqr2jZwx
+Y4Yaf0V9/PXXXxMSsl0bl+TrwDFAe2PMGuBuHG9DKjB91yKZa6291lq72BgzBfgJR60YlWjPgwcP
+HuKLZhHRCOUunWXLlqmxzlqrrqVx48bx0UcfAag9oK7XHgwGo+otCFq2bKmRfq1atdIItKlTpwKO
+zUFsALGc2+fzaezFlClTaNOmjf7tscceA8qj3Cr7rZyvunN26dKF++67D3Bci3KOQCDAzz//DDhG
+O2ksK81Ob731Vv71r38BTjKa2BFuvfVWZs6cCThspb7sLFZ069YNQONCUlJS9H2fPn0ahctU2MHG
+jRuVTW7dulXd0lOmTOG4444DHKYr7EfcvtOmTYu1TsV/ZzXnNWvWqEEJ0PcTJkzgkEMOAZwW9OAY
+3+oiGCKRSKW/y8/P57fffgNgwIAB6ssXYSQqQKyw1qogW7VqlcZkWGt1AdWEmgxma9eu5eGHHwbg
+yCOPJCcnR38nBkg5J5R3TJ4zZ05UkxVJT37qqafU+HnVVVfpOJOxCfn9fo0HcccgPPTQQ0kbQ21w
+8cUX6/tFixYBTkyKdN+y1mr3rSOOOEI3FPE6fPDBBwlJ8/fCnD148BCFZsMUxO8sxhiIzqFv27Yt
+Z555JgBffvkl4NAwcQvGsnu0bt1aDXHu3/n9fo1Gs9YqU5BoxVghRr2OHTsyevRowImFcJdKk1iI
+qlDbGgLWWu3QPHz4cGbNmgU4BjtRQeR77rEZY/Tv4XBY3weDQXr27AnA6NGjufzyy4HypJ5EIiMj
+Q9mNjKesrIz9998fcO5TQyZv7bvvvkC56hcMBnnzzTcBZ/d3ryl5/+WXX6pr+NprrwWcNZ2I+Ww2
+QkFu/qJFi6ICgNy6rOhkp5xyCrD7DagtcnNzK/1dJBLRcOQBAwZw7733AuXW4ljgtiO8/vrr+nC7
+7QluelkVYtE55Tvff/+9qgfdu3fXgKRQKMTIkSMB9IEPBAJRZd/F2h+JRFSVGDx4sL5PBowx7Lff
+fkB0Hc/62pLihb/+9a9A+Xq85557NIehurFJzIV4lBLVFsBTHzx48BCFZsEUjDGceuqpAKxfv16j
+6j744AN+/PFHAO644w6uuuoqAP3uLbfcUufzCSpSvTfeeANwrN5vv/02UDvKXDET8fDDD+f9998H
+0J1YzuE+/xlnnAFQpcGxrruiMKylS5fy+OOP6+eS8CVo06aNemI6deqk1Pb0009XVS4zM1PHmYwC
+J927d1dWIEZQn8+nhuaUlJSke0MEfr+fP/zhD0B5UtnkyZNrdZ+k5qUYF2sbaxErmoVQ8Pv9mqY8
+bdo0XRBuvP/++1x9tRNVLRS8rnql+8GseDOfeOIJwLE71DasOhAI6EMjemN6enrUwy/HKi4u1tDt
+1NRUVYX+/Oc/A4kPFKp4ve6F+euvv6pnZ9OmTToma21SbAkyX5s2bdIcBoHf71c3ZWpqqj6Q9YG4
+FEVlcgd5VYQI/TFjxqjg/+qrr4By9251SE1NZZ999gHK11hdir3WBp764MGDhyg0aaYgkjI7O1vD
+Qe+5555Kv9O/f399L+G3bdq0qVP58YoGTDeEfdSGJcgOc9NNN6kqI8YnN/Ly8vjjH/8IOMlYN998
+M+AkxIhFXRKfJHuxoSDzOWjQIFUr/H6/vheDYyKs/8JiNm/erCXi3BmqktnasWPHStlkrBCPV20y
+KWUcw4cPV9bgVstqwsSJE/V3MseJYgpNWijIIhg+fLhSt4pBOqKPn3XWWfqZfLc6NaA6xIuiixoz
+dOjQKK+CQB6kiy66iAULFgDO9SxbtgyA1157Ta8vXhmg9YU8eNu2bdMHwVqr7uBp06YlfAxlZWXq
+dXG7b6WaVE2ZkbWFqES1EXCyvtLT0zWFXTw41QWWiapx1FFHqUoknopEqYqe+uDBg4coNGmmIFi3
+bp0GgsyZM0dz5X0+H3vttRfg0FbZNSQG3t0/MhbEw88dCAQYPHgw4MQ0VCzEUlpaqsFEK1asiOpA
+LT74du3a8eqrrwL1z/yMB4wxauEfOnRoVMCU0PlkxAj4fD4N2RbKXVJSoga6eBk9Y1GB3NRfQu/l
+nlfHFCTWpWXLluphEqbhMQUPHjwkBc2CKWzdulWNPlOnTtXGMKFQiCOPPBJwdHGRsFIMUwyOyYQY
+EqdMmcKxxx4LEBXt567MJBV2Vq1aFaWfCyv47LPPtDpTQ0IY2L777qsuyTZt2ui1LF26VDNFE4WU
+lBQtdPvhhx/qe5mrjz76SCMBGyKiUeaiW7duei+lqKzP54va9WU93HLLLdxwww2AYwe78cYbgcT3
+DGkWQmH27Nma3tu/f3+t01hWVhYV+CPGOFEvGmJxiEA68cQTK6WPQrlXrFjBe++9B0SX9w6FQixf
+vhyA5cuX6/tEQuocGmNUsIogcFdzHjt2LN27dwechS3XsmDBAn0g4w2ppTh58mQVsunp6TpfkqNy
+++23N2i6tDzowWBQ16TUXLzrrrtUlVy9erWWpU9LS1PPxuOPP86qVauAmPtHxgxPffDgwUMUmk2R
+FclOfPrppzULraSkRHeSrKwsDRu+5pprgMSFiVaFzMxMNXK2b9++UpVgwoQJgBMOLIbGivfIvUvL
+TpKo+5ienq478IMPPqgJX6KuWWsZMGAA4JSjE2pbWlrKTz/9BMCwYcOi3MDxhBjwVqxYQZcuTqeB
+cDisvvzLLrsMcMqZNQZj7PPPP8+IESMAtGCNO7sUylWN5cuXa1Tom2++GY+5++8qsiIP0AknnKC+
+8kgkogtl+PDhanVORtnuynDEEUdoiLLb2xCJRLRKkagX1TWvkcWRqCw5N4qKilTtys/P1yxJd06B
+PGzWWtavXw84Xa0eeOABwLHduNOr4wl3ByU59o8//qhh4xKT0BgEAjgbkoRYS+yGu1R7JBJh+vTp
+AIwYMSIp4eEV4akPHjx4iIbU7mvIF2AT/fL7/TYQCNhAIGCNMXaXypLU16OPPmrD4bANh8M2EonY
+0tJSW1paaqdOnWpzcnJsTk5O0sdU0ysYDNrRo0fb0aNH223bttlIJBL1CoVCdu3atXbt2rX2n//8
+p+3fv7/t37+/9fv9SR1nSkqKzcrKsllZWQ0+Z7V9yTp0vxJ8zvm1eR6bjU2hMUNSiH/55ZeoIiS/
+//47AKeddprWdmwM98MNn8+nuu/w4cO1aYl4Fu666y6tKZiIcuMe4gqvFb0HDx5ih8cUkoALLrgA
+cGoliIHx6aef1uq833//faMxhFVETZl4jWH9eKg1mr/3QYJA3DkM1S3SRAd9VAWp0z958mSN+Pvp
+p590HG53VGOAu8Zh27ZtNfBm6dKlWoTWLcRkXqtLKW8oyNxWDFyKV7ewWJGRkaHRllu2bAGc9SsF
+eLds2aLu1Ejk/7d3fqFxVFEY/32kNmIFk1QosSkmYlDSorb4kKAP4h+alFIRfEgpWLHgi2AVQbrk
+yUdR1Aq1Kv4DCVWsVUNAS419jrYoMTZdG6nYlNZG0Ao+VTw+3LswU7Mk0b0zC54fDJl77ybffLNn
+D3Pu3M38lbttXdTiq+aKRsdxSqdZyod54A/gl5IO4VrXdu3/gfb1Zrbo8+ubIikASDq2lHrHtV3b
+tdPi5YPjODk8KTiOk6OZksLrru3arl0+TTOn4DhOc9BMVwqO4zQBpScFSYOSqpJmJe1JrLVO0lFJ
+JyR9J2l37O+QdETSqfizPeExtEj6WtJ4bPdImoz+35e0MqF2m6SDkk5KmpE0UJR3SU/Gcz4t6YCk
+K1N5l/SWpAuSpjN9C/pU4OV4DFOSNiXQfi6e8ylJH0lqy4xVonZV0ub/ot0oSk0KklqAfcAQ0Ads
+l9SXUPJP4Ckz6wP6gcei3h5gwsx6gYnYTsVuYCbTfhZ40cxuBH4FdiXU3gt8ZmY3A7fG40juXdJa
+4HHgdjPbALQAw6Tz/g4weFlfPZ9DQG/cHgX2J9A+Amwws1uA74EKQIy9YWB9/J1X4meiXEr+yvQA
+cDjTrgCVAvU/Ae4DqkBn7OsEqon0uggBeTcwDoiwkGXFQuejwdrXAKeJ80iZ/uTegbXAGaCDsLR+
+HNic0jvQDUwv5hN4Ddi+0OsapX3Z2APAaNzPxTtwGBhI8f4vZyu7fKgFS4252JccSd3ARmASWGNm
+5+LQeWBNItmXgKeB2iL21cBvZlZ7gEBK/z3APPB2LF/ekLSKAryb2VngeeAn4BxwEThOcd6hvs+i
+Y/AR4NOStJdE2UmhFCRdDXwIPGFmv2fHLKTsht+SkbQVuGBmxxv9t5fICmATsN/MNhKWledKhYTe
+24H7CYnpOmAV/7zELoxUPhdD0gihhB0tWns5lJ0UzgLrMu2u2JcMSVcQEsKomR2K3T9L6ozjnUCK
+p7TeAWyT9CPwHqGE2Au0Sap9WzWl/zlgzswmY/sgIUkU4f1e4LSZzZvZJeAQ4XwU5R3q+ywkBiU9
+DGwFdsSkVJj2cik7KXwF9MZZ6JWESZdkTyBV+I7vm8CMmb2QGRoDdsb9nYS5hoZiZhUz6zKzboLP
+L8xsB3AUeDCldtQ/D5yRdFPsugc4QQHeCWVDv6Sr4ntQ0y7Ee6SezzHgoXgXoh+4mCkzGoKkQULZ
+uM3Msv9tdwwYltQqqYcw2fllI7X/FWVPagBbCDOyPwAjibXuJFw2TgHfxG0LobafAE4BnwMdiY/j
+LmA87t9ACIRZ4AOgNaHubcCx6P9joL0o78AzwElgGngXaE3lHThAmLu4RLhC2lXPJ2Gyd1+Mv28J
+d0garT1LmDuoxdyrmdePRO0qMJQy7pa6+YpGx3FylF0+OI7TZHhScBwnhycFx3FyeFJwHCeHJwXH
+cXJ4UnAcJ4cnBcdxcnhScBwnx98T/qNalT9MKAAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 2/2... Discriminator Loss: 0.9276... Generator Loss: 0.9687
+Epoch 2/2... Discriminator Loss: 0.9666... Generator Loss: 1.0320
+Epoch 2/2... Discriminator Loss: 0.9749... Generator Loss: 0.8926
+Epoch 2/2... Discriminator Loss: 0.7613... Generator Loss: 1.6372
+Epoch 2/2... Discriminator Loss: 1.5822... Generator Loss: 0.4344
+Epoch 2/2... Discriminator Loss: 1.1882... Generator Loss: 0.8438
+Epoch 2/2... Discriminator Loss: 0.9924... Generator Loss: 1.1633
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="CelebA">CelebA<a class="anchor-link" href="#CelebA">&#182;</a></h3><p>在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代，或者当 GANs 开始产生真实人脸图像时停止它。</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[13]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
+<span class="n">z_dim</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.001</span>
+<span class="n">beta1</span> <span class="o">=</span> <span class="mf">0.5</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">1</span>
+
+<span class="n">celeba_dataset</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="s1">&#39;celeba&#39;</span><span class="p">,</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;img_align_celeba/*.jpg&#39;</span><span class="p">)))</span>
+<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span><span class="o">.</span><span class="n">as_default</span><span class="p">():</span>
+    <span class="n">train</span><span class="p">(</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="p">,</span> <span class="n">celeba_dataset</span><span class="o">.</span><span class="n">get_batches</span><span class="p">,</span>
+          <span class="n">celeba_dataset</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">celeba_dataset</span><span class="o">.</span><span class="n">image_mode</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 0.6635... Generator Loss: 2.6208
+Epoch 1/1... Discriminator Loss: 0.5171... Generator Loss: 5.9576
+Epoch 1/1... Discriminator Loss: 0.9434... Generator Loss: 4.5532
+Epoch 1/1... Discriminator Loss: 0.9883... Generator Loss: 1.3031
+Epoch 1/1... Discriminator Loss: 0.4266... Generator Loss: 3.6278
+Epoch 1/1... Discriminator Loss: 0.6535... Generator Loss: 1.7355
+Epoch 1/1... Discriminator Loss: 0.6394... Generator Loss: 3.2757
+Epoch 1/1... Discriminator Loss: 0.5272... Generator Loss: 2.5773
+Epoch 1/1... Discriminator Loss: 0.4938... Generator Loss: 2.2639
+Epoch 1/1... Discriminator Loss: 0.6107... Generator Loss: 2.0199
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmcZUlV57/xXq5VWVtXdVX1Xt3QG9B0I6u0IrKoIMqi
+gCgO4IwgLjAuCCoD7oOOIow6OIgoKrK67zgoKAg9LN3SQHfTG71Ud1V17VVZWbm8F/NHxMnzu1H3
+vveyqhqy5nPP55OfezLevRFxI+JGnP2EGCMttNBCCwadr3YHWmihhdUF7abQQgstVKDdFFpooYUK
+tJtCCy20UIF2U2ihhRYq0G4KLbTQQgUetE0hhPAtIYRbQgi3hRBe92C100ILLZxeCA+GnUIIoQt8
+CXg6cC/wKeBFMcYvnvbGWmihhdMKDxal8DjgthjjHTHGBeC9wLMfpLZaaKGF0whjD1K95wH3yP/3
+Ao9vunk8hDhJYJGI0S1doJ/xPhDk/lhcg/wei3KFkiYKNXWVz2nbXesvsCjP1bUNvuPGmnub2hj2
+HsPoOm2jk/tK7q/W12cw1I2t9VX71DSGHbm3fkw69PMdHcbohIRPxMiCPBGKOvpSR1fKl6i+N1Ju
+9XSlvNfwfnVrRH/T99axrXt2UD1a1rQWOlTf28DeIxbloaZc328J9sYYzx7QTeDB2xSGQgjh5cDL
+ASaAq8YmuG9pfnmy1gNzGZ/DJ3oMn4Tj+Topv/fk9y7VD9PKbZDG8MUxL+WTUj4HTORhmsmzcS5w
+f29puT1bHAv5z9qezLhN0LzcOy7lC/hEdOUZ7a/1rcfgxbcEjOW7pzpwXkgjsKvXY17ee0HutzZ0
+sS3KvZNyr9Vh907h87Ag/ZwBjuXRn6dPJz8xlUe2xxqOMQvAJJtZM5bwS+I8dy8tLr/rhLw3wDEp
+2yTvsQ8f27Vy/0F5jxkcDkv5tJTbmrP36+Bzo/M30Qmck9/2vn6aSRtLOPFjHZdy+83W5pSUzUnb
+66TOo3L/+nxdzOWQ5kw3KZ3ryZD6uSvGuxgBHqxNYSdwgfx/fi5bhhjj24G3A4QQ4qd783SpftD3
+ZXwT8EDGz8In2hbHYdLAQhoMG9QlKT+Ov6y1MYEvqg6+kBaK8sU8NdvyDZ+T91gC1mR8Tvq0AGyU
+/kH6uA5lfD0+oZP4h2V16eLXxbhYvN+Y4OTfQt42JvtwY34r/dAn8UWj9WrfrY2e9Ok4vjCX5F7r
+e8DHcBFYlE8j5l98Izy6vIEc4wF2LKZfrsc3lkX8A7CxmqA6bgcy3sc/6DF8nHVDtvc7Km3MCT5L
+dQOwNmwtjEsbZ/UjN+Zxtk16ER/PJXwznac6hromAc7G1/QUvl5mSJudvbe967p83SP1HhB8lipV
+NL5CueGDJWgcIwkan0raDD4FfHeM8Qt190+HEC/uwO2yverpqCdpT57THTcKrqSTlivJSP5NKQwl
+4wyvkKu5cBuwK3od+oy1oaSt1qWncV259l1Pm9iAD+pvB9iSf9gX69mxkh0o21BSvItvBkqBlXWV
+9dWxKjo33RDobEj/nXMI7pMXLPuhm5RSN7P4ib8kz+kHrWDzpOulrq8le2jtBXzD0g1I51xZYcOV
+OrXNoUd1A9kguL3THNWP3tqwTaon9Sr70KGykX8mxvgYhsCDQinEGJdCCD8M/CNpTN7ZtCG00EIL
+qwseFEphxZ0IIdqOZieRkvZd6nf2uhO/P6BccXu+6dStK9+Ur4ekjd6ANkrSXvuuwsoxuUfJTz0d
+m96vDuz3aZxEtWdPBgaNofZdx61pDJvgIfl6B/VUj7WnAuiNONk9jZ+a24Dd0j9IY70947upzo2y
+aYPWiFKv2h7yTN3ai0V7dvrb3GzBWYNtJLYA4DE4q3oxcGvGL8/Xm3EK4wjV9WIQqu83EqWwKjaF
+iRDidhKvd1TK7SUXpayOLC31qk2S81JKXkrIm6T9JWm9FSfhjlNP2mvbOlkqwNL368hzVo9OrvZ9
+0IyVEnLbyI5SLyUftilqP+u0FqWWSElmJWcHg8/EZpI8BfxQsPqsDSOjN+Mfxb34x7ZJ2jQV2CTp
+44P0gVr5OP5+C7gsQaFO89HB2Ycj+Vp+jAZjVOfENoi10l99zjbIHfj73YPLEuwjvw0ww59D+Hgt
+Up0nWX8jbQqtmXMLLbRQgVWxKSySdvojJPJKJdsLpN1cT0gTqthpXAofrdx2dhMyGm512T11wiVt
+z/Dp/LeHRDrOUVVDaZ+MfTAV6iJOahvJOo9rFqx8HGctuviJq/XW9bOOghgD9uc/Oz3qKKMStFzH
+0ygdE5yZ4M3mQ8d2WBtV6DOJaxNs3nXObHzWkSiFNcD3Sg2PJp3c64G35bomgYfnv2ngf+e/DcAL
+8l8X+I78Ny512DsZJVTaykznvh6Qvpm62Kg867vNvamCbR3ZGvoOfIy/VZ57Nb4efiz3ewPwmvy3
+BvjB/NcjUR5rOXE9W9ujwqpgH9aEEC8NsD86+6DGKH3qSVHdDOylO0X5eM29VscEztcpud5kOGU7
+6I4Ae3Mlat+goLpplT8YGbxG2jteU8cCvuHox6Uket3MlcZLZqlii9fq0Pu1zyV0cTK5tK2AqooY
+fDzX4ao1vV/ZAJUTxDy4l0XYE70uk9Dbe1xAUmkBfFMHurmS2S4cyvil07Ar8yAHMk+xZQFmMr1+
+/2HYlyfn8CLszC/+9xF25bqNJVjC2a6SzTOVc6n+hLQe7d4JnOSfkOd25Ouz8LG/GGdzHtKFg3mR
+rBuDYxkfyzf/Wx/uz/e+BzAjhH34eBc2Li370EILLawcVgWlYNoH1YOrVFsNSFRDUQpzoGo0skBV
+t1uapY4X9TZJ560dE/SoddkS1dNP7SnU0tH6aG2sw08jNV6qk4SfrPbB2BKDk9U+6HiWkvMZ/D10
+zkq7kGGrzE7H/dLecZxC2J+v34QL1/4eeG7G3xvgB3Ij/wg8Mpe/Lw/cdwPX5QF4BPDm/PuPA/8j
+4z+AC/lMfz6JaxkCPg9r8fc2KO1NdNzMmvIYcFHGTcvwBuBdGf9vwDsy/gfStx8Cfinj5nL8Upxq
+equ0rRRaB19Tc2eS9mE8hLiFNMj2AalBxwJV9U6pelF+SQ2ZoGosVEc+DzMfrtM+nI2zOQtSnxqm
+dOQ5XeSGQ2WyTjBe6lPPPtCA60ahkm4jVU1eU8IgQyh7D/X5qDMAs/FXTYyWq/aoTvOjcD6+4czh
+G+tj83Uv/uF+K/CUjP8dcKW08dCM/06+XgncnfEnAW/M+BOBz2d8B/CBoj9HcI2EGtEF3Miojn3o
+Un9odeU5O2QeBlwmfTB27SJ8re+T+m/O1x7wtxl/QN5PzZyVFWjZhxZaaOGk4KvmEKWwRDIo0dNj
+jKrDkwqqSvZhERdImbaixJV9UAGXnWJKYTSRu8aKPECVWtETsc4oxmwaxqn6OByWciNR1dxV6xrG
+PtT1t4MbxazEmKjJBFtPbmXz7P1KwzK1MyihpBLsvXcWbVyc8evy9Vm4YdIjgF/O+E8An8n4twC/
+mPHvyte/AF4mZa/K+LuAH834TwLfnvH35utm3KZBT9Czcd8cfac6vxsVRh/Dx878Gq4E3pnxlwO3
+Z/xVwE9J396T8W/N19eSxgPg3dLeLFUqRctHgZZSaKGFFiqwKmQKkyHEc0g7p/L9yhvr6W6gKkR1
+kjFYoCq4s/vVpLgvZXUnr/bDTu5zca/N0tpS2y9Vklr/Onm25MXJfW2SKTSZ4Nq9Om4qwBtmYdik
+klxT/A9OBZi9hfVZ5TwqHymtT9VpRwV4VwfYHb2Na3L5ufn6UuCB3NFnAPP53pkxOJ4rWdODBVPf
+5QVwMMB07vTOCDHXcTNwZa7jNuCW3M6/5usBXNW3Bx+jLi48NGpM1d5qCQq+Djv4eJpM4dvk3ieR
+bHYAvhGXd2wHbsi4uR9/Argx4/fhlMchXAiqY7twJgkaQwjRBDO6qOp8Bpo+BBt0dSdWvbJ6QTbV
+NexjUb20ah9U41D3cdaZT5f+HIrX1bUS0E1MhYsnO9NN9vynWq/C5nw9GGBC7BQuyeVGql+Nax++
+CfhYxn+UpHUA+F3ghRl/e75+T4CfyfV+P4nFAPhn4MkZ/yfcr8DaWI9/bOppuZUiFgBVM2gdq5Ll
+G5NySLYeRto/hOT/AUkAaf24At+wTHtxB74m9+IsmLIPAd+EjrSCxhZaaOFkYFVQChMhxK0kyzgj
+S3WnVetGBVVvKVlnUFr/9YvnoP40Lk/5UNy7hapK0qC0oSif05NE+1pHxVi5Pb+Sk1nb1SAlozso
+1dfX5KFa1/fynro66yioHfiYHsTVdyZwPICzK9vwU/Nu3MHoIPAzGf/DfN0IfCjjj8epigl8Tq7E
+yXGbm2M4haD2KYGq1yycuE6b4kuUZcpSqNpXBegaDMbqXY9bk6rQPAqu7Y6qklwVm0Kd67Tq/Jvc
+cw0iVYm14nULto5lGMRKWB22QA8X9Wp7yrroR1/WO0G967TWW4ePMlunm32oq/t0rxqV1Cs7ZrIE
+49svw6Xz23ESXrVV23Fz5Sfl6534ZnM/PjeLOG9/jBRcFHl+Gv/wlD/fipsYq62LsgaqiRikMVKz
++kmqbLNG1OrJPXCiMZlGjdKDwe452rIPLbTQwsnAqrBTmCCdCHupagjUrkAt/kphVxcXNE5SfSnT
+/5faBYNQg+tOqae0nUQX4yaqPXyXn6LeylJPPnsPDSrbp34i6sjrldgb9EhCLEinXR37NAqUbJDi
+KxU46tjq/XYiPi7AvdHvNbLaBGoPoXoC2+8qEHwmTkF8c77+Gx6nQR3sNL7nVqoOXZBYEuuzmjUf
+xqmKvVKv2tmoxqVpLu33unWhlEn5rPVN440q66xasboYEYNgVWwKCySesGQfmrQPpaltad9fZwBU
+R4I3fWD9hnJbKHdSXZhqxqzlpTm29n2e6qIZFhvwZMBcp7XOk4FBXpkrhSb/CyNxP41/pMdw9sFU
+wH2cfdiIv5+aI78Hl/mYxP7LuFfi/bi2YxGfk9uo8vDkOs3ITD/eGVwjouuwyeDMoG5eSy9YVbXr
+IVMeOEeprrfSt8f6PFlTPgha9qGFFlqowKoQNE6EELdRNbBp2nXryFXVPijZpibR6hBVRw6XobRU
+eFSGSjsHPz009JXmU9C69RTQcF51MfXqolUrNGkimrQlJiE/THUsTkZw2WQjon1p0hIN0h7pb5fh
+0vw5/HQzg517cYGvzvVh/ETs4Se+UQxrBN+CCxLX4xTgNImigHo7FD2ZwWM+GrWirKRShQqlIZqB
+joFSGLrurR/LuSfwd1KqQp3pirV8Zmof7MU0sOkUThpq8hGV7ps6ak7wWXwB7aea1wASCaj24HXB
+SMEH1T6wg0Xbtqhmi7YNN15UJ26NvMe09EO1F03ah2EftP2uYxioLthBC7P8rckgZ1C9oxicabs2
+N6X/iHlEmpXfFTjZ/lPAr2dcfRQeDfzfjJssYheeouwTJG9MSHPzExn/U3wz+XS+XoQHTNX32IRv
+BrqBGBukeSbmqbKY5RjqWOka0flTVaXJMh7A19ihAtdNRtixVvvQQgstrBxWBaUwFUK8kLSb17EP
+PaqpvQzqov2qQGZCfu9RPZkhndZGgcxS3aGNUtAgKp28/e4ghY6zem0ElZzV5zRGhO3gE9Jer8Ch
+PnYkVE9mGMw+dEisDqRTResuny/bqCPt9f3q2tP3U4l7nRGPktFK4n5LB27t+z120m/L1zUk7QLA
+FR24M997KMCNucKNuBGSaYnm8LFYwo26vhm4PC+0B5bg87kj/5jrOoB7ZaoB0RhObagmqs7GoNRm
+1cW1qDNw6+DfQJQ+m+n3OG4rcQTXrijbpRqMxQebUgghXBBC+JcQwhdDCF8IIbw6l58VQvinEMKt
++bppWF0ttNDC6oGTphRCCOcA58QYPxtCWEdyZ38OyZFtf4zxTSGE1wGbYoyvHVJXNP2sJoPR3Id2
+2k7h6iKNm6BWXsrXq4qpTD2nwUVVKKmJaPQUM/XY/dL2PNXoRhr+rVRvqaxC29CkuaPIEVYiU1CB
+U1OqtPK5JplCnY1I6cxVxxuPIlNYTmIbYFP+cR/JAQrcMehr8NPxj4H/kvEfIYUyg3T6fzDjdqre
+QXIwguR5aPgu4C8z/kKcCvmDfFWZguZvOAeXc9i6WqA5rkedk57KFOqCBpfyBcOvyNfbcNXqbqqR
+vFQ4LvEUvrKCxhDCXwK/lf+eHGO8P28cH4kxXj7o2ckQ4rl4KHIDFTip5NXuqXN7LnM4qrRYbSAg
+kWM2cbupTpJKnEty6kJcQr4o9aqJqpLHdfpoFdr1au4dZKQ0qvYBnOw+SHXc6nToBk1t64Ktk5Yr
++1BqcOr6W1fXNVJ+H75ZXJivB/BQa8/Ghbz/grs478TnwUh/De+3DTebfhQpExPApaQQ8ODaiaPS
+n11UyXnzuzCjKY3AXdrZ6PyWPihNm36nwK1ti1u5BbfZWKD67ZxKNOfTYrwUQthBGt/rgG0xRtvM
+d+HrsnxmORV9t+6GFlpo4asCp0wphBBmgI8CvxRj/LMQwsEY40b5/UCMcaBcwdgH9fSaoRqOzfBt
++C5u5NJxqo4hqnqsS0VvO//DSKmxyc8bK7EJ3/2hnn1QNZSmGa+zpqzLiaknSUlqWx9Pln0wUMEg
+DFcRNtWr5SWF0ZTnc5BFatlGoMraWfCSBeDrMn59vl6KmxW/BfjpjL8WD8Z6IfDxjNvcHMXV00fw
+03Yaj5j8K6RYCwCvz9fHAP8g72rvsRmnQtS5alhYwAWq6myoUk0lC1YXXOjifL2XamwFa+OY3Kus
+2ajRnE+JUgghjJPUu++OMf5ZLt4dQjhH2Ic9zTUkmCLpo3dTJb/s4+7iNvxdPJrQopQZTMv/k/gC
+UxNk5d8sAvABXLp7Kb5Z7EGSeObR2rHknnPqAVcXaUdx/ajUGKWJVx9kL2/4oI2hR9XAZpDxkpKZ
+Zb1176fRievsGJR103IFbcMW//PH4O7c0Xn8A7D5fwxu93Ep8AsZv5y0MUDiqY09sA9lJx7F6RAu
+qxjHSdnXST9fmq+TuEHTXfjaOYrbUNgCV/aw1IKp1qn80NW3JwquQV3Ut8FO2GlpYxP+PWgCGxjd
+vNngVLQPAfg94KYY45vlp78CXpLxl+BynBZaaOEMgFPRPnwdyfnsRnzz+2mSXOH9JCruLuAFMcb9
+tZV4XSewDxtxE84pnCRSSb1K91X7oCzDtNxjWgmrazNOim7A2YdH4ZGBdde0k+YGKV+kmppuUPi3
+UlA3SOh2OsS/pUfiydY5iH1oslxcaf3LYxhgMlcyh1NyJlC7AD+ZH4rnQHgB6TQCeBwpzBq4Zmgf
+zjLsJVEZkKwgX5zxd+Nh3Cxy8lNIOSWgapq+GWdjlZUcZS0MirNRsmBKbRilp/kmbH1rMBj1iuxU
+y88cM+fJEOJ5pI9SMzbZxz1GNUimfbwq3dYPTI0/kHvKyZjCB1UDZKzDye5PANO5pWO5lW1U2Yc6
+G3dtp8nuvymGXx2c7Memvg8q9a5jH4bZ5De1UffcsP6Wm6K1swNfAwek3Hwf5nCNwxF8oz8g5eZR
+CdX1ZB+bkuKT+DrT+Igmizgg995DVbW4NeO2FtT3odSCDQp2U/qrKG6gLLKxo5oaoNRq1GVR67dm
+zi200MLJwKqgFJR9sB1uC9UQXEaqbcL10bYLlk40U4JriKpSmKdtLKtLSKnEjGScwXdpE3p9Ru5V
+MrGOvNb2FEqSse70OFXQU6fs06nUCaPZU4wKpceoCt3K1GyX4afjBbhR0zTuoDSJU5N24qtAeF7a
+6OLGQLfic2w69e3ATXKvPaceigbadw2cU2enoXjTWig1O9Z/NZYzSkntaUr2YaVmzqsiyMp0gIeO
+wZ5Fn3BwEn4R2JFHZDK6etJIw6M4GxBwsu6o4Htw2/c9ua4nRtiX8cdF+FCmy563AI/Ls3tbB27d
+mm46eChdL5zrs7fnfahjH5psL/Sj0sVf5yfQ9BGPQprbdXteEfv61cCeg54r71HtwyAX9jqDrUF9
+rGvj6inYnyd4r7Rn87gOmMmFj+s7v7wZ+ESu5PLo6kIzeropwGNzg1/C5UM3deF5eQI/H+DsfM/f
+5cnZ0PM69gG95QEIXNBJN9tamJd30pwi48W71s2rsrw2FtNUWR77YX2ubC2+AeiHrNbA8tjI0LIP
+LbTQQgVWBfvQHQtx7UwgHIHZ3J/JCEt5y1obPfvPth48kHfxmbyNHgmwJuNHO7BRys2O/nDXyw/n
+rfP8PtyTqYP1PbdD2LgQ2D2dHjx7vsNcrvyqY4lwu27qEPFY+v14bDYxLklt1T40GS8ZlNGc66Bp
+5myn74bAeOallo5HFodMddN71LFHSu7WGeEM6l/ZRifAREg1hy3Q3ZuenI2RsVxJP8/5ZB9i7sRF
+Pbg/kwoXLMF9ufysPuzM+Jbcof3A1lzXgXHYlsv3ddKzAHdOwMZ8xO7OnRvvwWxuu9uDsYyvjV1m
+p1Il/blU8XwcHiBlKUAn+j2Q10VurxurGodeLp8C5jO+TtZ6J+MLQnrpWhoLMB7Sg7P92AoaW2ih
+hZXDqqAU1oZOvHJsgnuW5pdPzxQVKe1wc8RKwEzjlzSbswoXTeYwVpQb/2V1TUtdyoNprIMUQSf1
+YzLv1Bd3utzTSz0tzVatjQ5Vq0brbxlSC6phvszeIuD2FF0pR95PBYm2u2v+zIkQeEhOpvjlRR/b
+KVweoxGfNB/nISk3eU0fF67Ze2i/jkk/tLwumnCgmvdiMp9mF3W63JvHtkdVUAhVb9ctUscBKd8o
+5SZ81NyPgWo0LHuuLpye5u9QgXanA5d10lveszS/3EdTAR+lOrZWroFg7d3WUrWUtLWhalSN2GSg
+qk6lTNSisUNgKv9w/4iUwqoQNB4j8h9LaTrsJReA2UwPqVaigy9MG4Tj+OCp26gGm1DtgwZy0dBu
+6u2oH/dC7kfmKLi+t7S8aEq3bY2uqyHGIE2iun2r7toWY7/4H6o66AWqk1/aXnSkvW6MfGbRa7L2
+1lLdcKAaQOSY4HNUN0g7Qmzcloq+9mvKy/mz/uqH18mH0+d7PiqL8i7W30n8g96ICxQncE3SBtzA
+ydrTDfmI4IeoJqLRSNLWd/VXsfqm+nBdnF8ut+dNG3A/biy1C/ebuRc32TYNx+W4G/ZmPNzcNnmP
+GdweQkMDqG+H+gItb17EZdZ6VGjZhxZaaKECq4J9mAwhntuBe/r1wik1DS3LoVkfP6icAh8kzCtJ
+9G0B9kT/vS6Iqe622kZT26XaqM70WftUh5f1BuDi3JEvywvWqcd0fENRrvcrRVJCk6flsP5qfecE
+T0VfN/YapFfndx5nc+riCpSh5BSvS0lo9ZapB/W9vyb/c2Pf21ABrCaWUUcpo0JMzTqHswxqmn+Q
++lRw9k4aW8T6am2r8FcoyzPHzDmEEG2C62zqFeryLlakuyOWQ73UfxAomTlsMyn7V/dbXXld0tkm
+o6hhoFLtEIf7KDS1PWgMdWE2RV4ata9Wr7Whpuc25xqBaBvOPqzFeXjNJakRlc0MukyiUpdKoMkv
+wWAc1wyYhuQ47mmrUbiO4569e+Ue6+/lpChK9k7GVqhvzgwui1DfHt1M6tZkoOIf0WofWmihhZXD
+qqAUxkKIG0lklUqq68hrtfhSPb/qhHWnVLPT8pTVU2AURyRrdxMu7OoxnCQ2KN/DoIzybG3NC66U
+gr3HKFaDRqIewslOPY2VMlM/ftXgKGtRanBU+KbCXCVhR8l8bf3ZipPXGgNDT3DTImyTNh7AzX+3
+40JHTfpiGoA5/DRWK80+Lggu15i1rWNhMQZtLRyQujSXx1pc6HgWLlR8RE3fvoBb3h6W97iPKisB
+aT6PCq6aiDoW+oxyiOqRVEeWX9FkCBb63PjaiKtveqTFayakM/nPyF1VmU3i/GBH6grSXpBy/QMf
+WKv3gPShyVjHSH4jqcs21gmu7/eo/NcjLZb1uXxH/oOqDf4gCCTp9R7SorF+lH2yfl2Q//qkRWoL
+9dH5zzxXp6S/jxd8o+AalHOC6ng29TWQPu75/LdEmu+10t8L8bn+QdLHNw18G+mDOwv41dz+5cD3
+5L9LgP+Z/64Gnpr/lvJvl5A2oXX5z/oQ8z3GOlk/xkhu2zeT1sOB/PzW/HdIxm0CeH7+21Pge/J7
+7M5/P0jasO4nBSOxur9B7rkq/x0hJYY5j6psoTz8JjhRPT4IVsWm0EILLaweWBXsw9oQ4pVduKtX
+NUhSAxhNSW7xE/tyvSTju3By6TAeAnsXfrpqG0pSqz+6shUlyX8u9anolR1Rox/Vxxu+Xu49jscL
+eFhuZH/fzbE3Rhcy3YyT1wtFP0sIwLVjqZLPLcWKQ5SyVZBOYEtusgEf47UkjQAkrYAK9iA5qlli
+nPkAE9HbttRth3CSt6m/Ni6PCXCfaB8sVJqR1JfhZPeV0vZYgPGMn9OFI3kyD2WScc0SrM/4Z5bc
+jHln9AAuN+FGW7Yu1Daj1BI9O8/V3jyIi7gQcaP0eXPuNyRy39ayCT4vourhacLDOVI8D3KZJbgx
+luFm6edunPUpY3wI63UmGS/BZ/vVqEpdPFjGDL5Ie6R4e+BS2EV8kd5NNQiHLcZS4gxOUsNoLsA2
+uPdS5XFVeq0GS8bPW381d+U34O7ZF5F4SYBX5w69HHha7tC/AK/Iv3+SqoXhIAjAx3upEt0A1+F8
+sLkm304iwSEFRL024x8F/iD348V4XoR/zNdXRvhPGX9ahA9n/L8Af5txY9+G9RXgs7EaTcjyJv5r
+vj4SeFvGfyvCGzL+5ghvzfjbe/DqjL81fx2/Brw5T/zrgJfld3ob8KR87x5OzNWhshGVZ40Df5Pr
+sI3gEL5hfYLEngD8NfCMjP8lPob/lq+vB343468Bfifj/xWPZfgcfMy/Pl9344ZQNp9wYtxQjQY1
+CrTsQwsttFCBVcE+TIQQt5NOBt3xjKS+m2oSDtP5mmR5DX7Kb8RJMajqtzVWAzQnRSmNcErjoU34
+ia/SaY2j18UlzvYet+M7+1bg4Rn/G5z9sbZeCPx8xi/FIwpvImU4MigTi5Rwcb4epJph28hZi1p9
+Fi7IfDklQ73zAAAgAElEQVTwSxl/Ln5yPwqwCL0WmARSTESA38bJ/X3St5uoSsbr+mvvfTnOHh3D
+KRk7ae/Ax+rxeETl6/EIzWvx09uyO12EU5MdnErbjAftuQOnbmy97cXXjfpBBHwMTVOj6+up+Jzt
+wKnXC/GgLU+S+42qPIInuzmKmzZ/Sfp0v5RZ3w/jlPU8VdsR0aScOdqHFlpoYfXAqqAUzKJRreDW
+4QKV9fgpp5GdjX/v4zvxYVxAqRZfc5zoPAQrCyWm4bVUz69yBBXSGCVgwUAfg+/ybwZ+KONfiwuU
+jC9+GfAdGf8zUn5EgA/g+nhNqNFkoai5CO203kHVAQfSmP52xt8CvDLjHwV+POOvIclCIFE3AD+H
+J1B5JvD3Gf8uUv5AqOrmm/qrFo0qK3p6xi0ZzI/jEZx/APijjL8MpxyfgFMCT8nXvbia9DqSbAIS
+z25tfB9ObRhFNIPbCugJOo2vQ1t788CLMv5x0nhBErg+P+PvxuU1n8vXV+ByhO/Ek8+8Ak9K8/14
+jgsr+0lcbvG7OCWkYx1wweaRM8nMeW0I8YoOPNB3Kft6fBLOx8nc83EJuGZgMnJ2F06i7qUq4bcP
+pM62YJTgIMu+DziLsoRvQhP4R7ZI0jmDL/InkgJjAFwV4WhuaF0X9lhAmfzl3hphNu88HwO25Xs/
+mf8gLVYTpNaxEQH4lvFUyc2LcXljvQrPLPSw3EYfeI7sdGvy4B6NcG6udE74rTvzvef0fG4OAZ/N
++AU4KX4Dbq6r2p46w7Cv6yTNC6SPzha9sV0vxOf/8Ti7uSn42tkG9EwrkXfsfoCxfEIsHYWYv6D5
+Od8sP9VPGzB47Mfb8MNJPUkBviWPxcHc37WkBLiQDKgsu9VR4BLR4KwVHBKLZ+80ha/FLVIecM2I
+sVT/If28FddO7KGaJco23IWvFPsQQuiGEK4PIfxN/v/iEMJ1IYTbQgjvCyGsxG6ihRZa+CrD6cgl
++WMkynh9jPFZIYT3A38WY3xvCOF3gP+IMb5tSB2xk/XMdpJochZ1dlG1pZo2q7OSsg/qXDPIQWkU
+WAn7YNZ44AKoS3CS/x3k7LqkFOpvyBU+I3fuT3GqYz8u7JvlRNVqE4zjgUan+t6Pp+HJUkxQdyfw
+2Ix/hkTVQBKO/veM/xjwoxm3/IvfCfx+xs/BSfiz8bgAE7gwrgmWLTTF1mEeTxlvArWHkgTPkE5l
+E9o9HSebf47EAgH8ah7X/w28Jtf7qgCvz/jrgFfle78fF/KZunACX4dqRTpJDoEGbM51HcBTo32E
+pJYF+GMp/2NcJflP+foS4A8y/q14IppXA7+R8ecB78u4sSVvwYXDH8PX4WF8fXZxFvorkoo+hHA+
+8C6SsPrHSKruB4DtMcalEMLXAj8bY/zmAdUsp6I/ipODAf8odhf3ayQcqJI7E9QH/VAPzMo7FPcN
+AvV9MPJsUdrXKLpqIGSbgwZkWQP8VMbfJnXcIm3YB6Z9n6L6gQ2bPWOrjuMb0lqcx/1jacM2tMfi
+Mg7ToECyU/jrot27ccn5nVKuPgpG9kKzXYiN7YU4S3QYN7W2j/V++X2LtL0P/yguIMk0tN4pql6U
+78642m+cQ0ptBj4W91G1dbF5CnhEaF1Dphn5Btw+xczDIa0P28im5GpyoqNU16+tJ01qbNq3Tbhc
+wkyjIR0WamchdX1F2Ie3kOQdNtebgYMxRjvE7sXtTyoQQnh5COHTIYRPr8TFtoUWWnhw4VRyST4L
+eGaM8QdDCE8GfoKUrPeTMcaH5nsuAP4+xviIxoqo1z5soD6XpIb2UlB7g7q4AGqiqo5OdXgTlKbB
+Vldd9uAySYxdrQ9q3XghforbqTSINRiVDdLYExpk5Crcf9/Yhy/hIcMO4SfUAtXcGXaP6cR7VGNG
+1lFm2o8m0LFSTYRpDExffxVJwAZJWGrv0cXXyMNJVAt4nsjjuBDw7SQhJSTK57kZ/xNSTkpI5C8k
+zYJRquZMB0kQbhSQCUHnSKwLJCtUY0veS2ILAH4Tz2diAuOX5bYhWSt+LOPX4Gze5ThV8N35+qf5
+WUjUprGYe6gG2jHtw+GvgJnztcC3hxCeSfpu15M0ahtDCGOZWjgfp6IaYS1wVQfu7fum0MNJxz5V
+zUH5sQ16EbtHJ1QlyHV1NYEt8q1U8wdaHervoPIM+8A0Dfk1uKrrG3ETVluAasSlm1sT2V3X/z7w
+tOz7sH8pLpPXmwJ8TX7gqlzBR6JrCHo4aXuEJDeAtChN7mBsxHFcdhCpxrZsCtpSB1Z+VYBdpjnA
+Sekd1nfcp0DjLmqQ3ufgZvHPy9fb8Q3meTi5fh++mTxD3sVUlgelDyZbgHRgPb14qXPxd342bkD1
+THxcHi73mLnyI3D186VUs5VdLLixJmaQ9RiS7IL8biZrWUM1x2Rd4NxBcNLsQ4zxp2KM58cYd5BY
+uH+OMX4PaZO0dfQS2lT0LbRwRsFpsVMw9iFrHy4hUUxnkWxOXhxjHLhZhRBiN6QEMLbDqfZhEidR
+1UFFoY59UJZhGPswCjTVqydinSZCnbys77qbb8ZPNg0fVmdqvZL+qvZhfd/J3Zfigrbvzdd3k0hz
+SLYGGjF4Od0eflqZQdZanGoqQ+WtxNlMWQYdAxPmmS3Elbjz2Cac7RrHT0TNCm7m0R/FT6r34yzD
+P+BGZh/HSXvTZGylahdj87AG92K1hEMHcOOl63GNw+/ilMe/4N6oxuI8A/hUxr8ZZyUuwgXPW3FK
+ztiV3bjNwn6qjln2PWh6gK8E+7AMMcaPkCmZGOMduDl8Cy20cIbBqrBonA4hXkI6LTVWgJmPHuBE
+pySojyw86FQqoyOH4rdhI2H3b8Z5/iWpV1WSGqZMLS+N71XT7P1SbsLHLs7LasgwqEbZGQZmtvuA
+PDdPciUG13lfLu1dDfxVxi/DrTe34YI966+qyjSbs8JKwrFdgAuYVX5k/PcNuKp6Gqd+9lCNuaHu
+85D4/XsyvkXw83A50BTw6YwbdbQXn9/dVE9Ro6xs3Nbg4/IU3NpQQ8wdxCmPHfnaw6mHndLe7bhA
+9yCu4jWZwzTV+TBqcxynsnXtzJ9JZs4hhNih+lGphHIGn1zFNaaBkZxLuLHGAlUSviTBlZxvsmNQ
+aGIfNBmMtR2BJ2f84/k6iX8g15Js8CGRfSbgMvPj26WuJdxeQE23h/VXhasafvxtpLBk4JvDdXgM
+gvfjY/xx3Fz79/AxN9+CHbg2YAIn4WeoJu1ZifZBPWLNwOsj+fpGJJYDLu0fw818X4oLQo1s/yhu
+TPRruE/EF3Hfjj/B9efmw3E1bsik/iMbcdbFSPhF3Gfivbih1weAH8n4L+As2PVS9ucZ/zbgf2X8
+mcDPZHw7zm4YW/IPOHv1D/hGqPE+wDeTA62XZAsttHAysCoohU0hxCd34Qu96uloPTuAl/fx00it
+BzU3o6ov1UZAqQKonqTKBgwjcS8LcE/0Nmxn3YwLD6/GVVZ9IU0WcyO9DtyWO3RLgENFoxqpdyN+
+gt2Nk58aKqypv6/OCTCvm49cm/vz0LXw7bnyTpaynXU79LNSv/cOWHxCwo98CrpZgnXnAc/m/Bf5
+PR7AhY4HcarJgp9COlENHxZP4doO3J9vmiAFPgV4Vr7uCM4+jI/D7Zl2P9CFe/N4XhPg8+Zslive
+Gd3G4nocXw88Wu6x099sCPbh4dp2UqXSnpXHYjb392F45KXNwHnm5NaHyUxm3taDDbl8X37uIeOw
+kBfz1CQczoN1PMCf5wYXcEtGs+7cjbMu/wf3Hj2Arx2opFc8c9iHTghxLMCG6PzrJfhi24CTs+up
+SruhGgZtHt8gNCiGJvowUD+KUdgHfV5NrY08m8X5vTng1zNu5syvJBmvQIow/HsZvxgnDS1Iyf04
+rzuPmyvfxOi+Dx0g5Jsf24dPR6/DfBuuzzTnr8zBb+QK39CBH88v+4Ye/ER+7lvwsHBGtl+CG9VM
+41qi83G+XUPGN2lRlsc2wEz+8ai0Z+bHb8YNi/4QN+R5FT7Ov0iyuQd3Af+fOCvyFpJ7NSSd/9sz
+/pO4tuKd+fpInP0zeQGkOd+TX+aC3N+duE/I+4Afzb+/K6a6IbE/ZnBk8//juK/G/8DZtRfj4/xU
+fJzNVf0tuCv7B/BNYyf+bXSl3/ta9qGFFlo4GVgVlMK6EOKjqEpm+ziZqOHYJvCT0iTPqkVQfbWy
+D2UATgM98ZXVqANr5yJcS3AQp0y2SJ+24E5HV+bre3FK6B5cMHQPJ5LV0zgJqHH7t+M6+zImQR28
+JKd43xrjsiDuCvwEshPxm3CJ+zR+Om7AT6g+fmKb880ufFyPUT2h7P324RTZKOHYDA7iAlYLSHM9
+zlI8Up77d1zivhufE5PeHyj6YJL6S3Hh7o5cD/j83oezhHdSFTY/OTc+lV9mHKfArpT+LMp73SJ1
+GMWrsQX6VBPZ2D334taNZ8v1o/K7eZL2cJsGFTCfcdqHLmkB2gQ8Hbf73oi/5BW4QYfxr0eoRgBW
+7YNqKAZpHyxBysB+Cq4GS7Z5qdpoHnhTxn8lX59Pcpm2d1LXYuMN64ytwE2+dRMaZr6qz1+IfxSf
+xaMHG6n6cdyl95O4Qc/PA9+e8f8q5WamGvAPSNXBmu5+DVUed1hfbV77JJNlcPXeL+BS+5eRHG4g
+RWH6w4x/D/DTGTeDpXfjMRU/jGsAZvN7QXKvtsjUFoXqMjyS9FqcDdqIh4nfkV+6g2tGPoFvEH+H
+G0v9LG56bW28EY9S9WKqbulvzPgj8APlZ+V5q+sXcDnJ/8U35z6uqj3Ysg8ttNDCycCqoBTO7oT4
+3PHAzoW4fNKswwVV4CfNJtx+wU72g/jJfQw/rdQ2QVmJOkMoJcWHsQ9f0/HU7sdx4WIH36234oZB
+Rtk8hHQaQSJfzVBGT1vTeS9QZW2MtD+Ok77HGa4xed+FyWD54D2zXGsmz1tgbebT1uVQ0/EAdE3S
+PQvdfFzvXvQkK++JfooY2xFxsjXgFI+GFVuU8qbo0za2TwhwJPo935TLzVDoG/GxugCnINeFpD2A
+RFab9sAorH/AhbV/jgt0D+Hekwdxj1CT5PdxVmoXVcH0q3Kou7VLqeGHRU/mM9eH8zN+tO9akE/H
+E2N3nhfgltz3h5KC3Bj+F3YPvh4szNutOFX15zhL90mcdYmcwcZLYx3Y2PcF/yjcKEYjL62l6lIN
+iYxWIyUbhLrU6afUT8GVLVHtg03SIm5Hb8FSHotvBGupRvRRF2eopkhX46zSJ2JYfzuZ17i6B/+R
+v9L7cEvHT+SV9grgHfmLfSoeCeiHcHv+X8flI/bRTVE1Jus14KOyZiFUIy99bS63jfWVuGHR9+GB
+bt+Ay0l+Bmfdnp2vb8c/xhtwVuLLuEzhVnxcbsjXS/GgKGodOAkcy/9cmV/uS3jQ1bfgbMlrgf+c
+8d8gRXsCZxNejxss/QhuTPWf8YCu34T7R5j24p24gdwf4QeHWTlan20dtanoW2ihhZOCVUEprAkh
+XkYSMmpCCyOlD1L1PrSdzEjSMvCKsgHDWII6GMY+bMKFfBo3YaOUn01VewBVHw6NDFznwdl0uo5i
+MqzwXbnmjcRl78K9+Ilm8RdfiFNmW/AT+GG48GwS167YHMxKP0tjsTqKZtg8XICT6BqR2ISdR/Ek
+KnfilNmd0o87cTbOpPddqgFZNLeljef5uORf/W7sXfdRFTCbcdo2oWyMLfl6/F234hqjG6Tc1veX
+cGpzRtq+EafC+jgrZFdNLDOLe48eoaqZEwO9llJooYUWVg6rglIwhyhNRLoVV9ltohpnYX/18RNi
+Fyi/bzAK/z2ql2R5CioVs2yCiwu2TIUG1YhKg+IN6P+BwarVYf2FJMMwGcxrcEcoywx9Ix6a7U24
+ue8N0vZBqqpfewf12qyLVbGSsUXai/ipaFTXK/FYB4/FZR9b8VNzGx6FyByG7qTqqahm8xaH4CjO
+l9sam6EaT8H61gHmcqfPFpWkyQZ+DY9Z8e+4oPRHcArB7Ea+k2SRCMla0dbLGjwPhVEP4GrTD5Eo
+PEjyBZvfG6lXn49q5rwqsk6v78DXzgTmD0duzW/Tje5auhV4IJefF53EMxJwL05GzuEf2wLV8G51
+H17Th16H2/XicdiXv4RZfIFtwgVYmyfgkfke+5AORSfxNHDMGL5ZaMYmCc1d2XjsI6xjI8rN5Icu
+TyPzsFvm+fusJlk6DH+VV8qmfL3mEliXadxX9GA+r/43AQ/Ng3gjvmBMcj6FbyDaN2V9Rkm0Y3Dl
+OBzMlRzEF/q5efAXIzwsD8wTlvzj3RHhg7nP1/TdruFR+ToZYH1u/HO47v4QLhDWKMqqJbGgJoel
+/33geZtSg2cdSG/4wBjckPv+PWMpuRHAOdMwk3ekFwU4J1fykfweT16AHbnvj4ywaKbpSzCR3/tK
+4Ev5/kfmjq4LMJvruiB4GLuNVCNor/TYb9mHFlpooQKrglI42oWPbwjMHOvyQI4Ov74Hxyx1WQ+O
+5+1rrAf35Z10Y96JlwJMmN2AqLRKJ6iS7G4ia0OBd4x6ySbDO2dYlmD1iBzPlcyNJZ00pJPkjlx+
+b5YiXTzrdY31nfwcj1WPUHsnsw9YCmBc3kLR6dr+W3/p8I716czbvmYrd/fTmX4rXd5yXir/r7vS
+YH7kSI/v7aUH335+5Kn3J3xvJ9Ixb86On7bLJrrR36MTU3o269igPBva7w7QyZ2+ax10cuXHicsp
+5B7Ic/5A35Ow/HuAWzMZduXxwG2dVOvhCHfnid+bn/8yIjzswLrcgcMBFjJ+KKSQgOCsxniEeXm/
+MVuHocOHNqUfNs8lauye/jw3dVPZ/qnIE3JHv0BcNjf/4MbI1+fcgf/aTZ073oWPZirgSUuBD+fR
++UyAGzOJeGsv8OVcflvuw81jsN3SDOLr/khIfQXoBujmRTffG41mWBUyhZlON141NsVdi3PL5Oc0
+kaN5CR0nLvNFkzhpZ+TeONUYf6qV0NThZfKYQLMGQG0djIwfz4N7cQjs6vWXn6uTuKueXiXWan6q
+3o6lQVVf3qPsj8ZBnF6+P9VwlMhUbmVmDM7O83tfr1eJCmV9U2OcujHsFOXlGKqpuMoWJoHF3KdF
+ImP5ibV5q4hMcyhLiibZzoapJDW4YGmBu5cWlt/V2CaTKanp+ji+8aitSpeKvf8J79Gnynqp272O
+B6TxtbrmgKlcy5ouXICNbepFj3pjsq7UO86J60KD86gcTNeFrrO6wECB6glvrOkEgem8bu/r91vt
+QwsttLByWBWUQgghjoVAiHF5B19HvaXcNNXdH06Moqy7qwZtKbUS3RFxe86kxurcM0/VslKtG02Y
+ZaSohihbL++3ARcM2fNHqObEVItGpTA06jJUT4tH41mgVR8/hY+h1qth5VSbM2gMmyI46xgqGGUz
+h2oZOjw9n4P/EiIhetv2fnXJgKaoOpJZexrWT9dCU5Kgphyh1oaNVVd+vxLPv2Awj4/nAj7/c1TH
+uVwX49JfTQuo8T70nrqEQ/oeOk8BD9Bz15lk5rwmhHhZjrxkUEqvm/wV4EQZwMm80SDtg6qhIBn3
+HJTf+/K74TqJulB0A5mScrvHFoQGbtWPTTdIZX/UT8I+7k4X1uWb91HP469UbThIBlPWXyc/0P7a
+x722E4jnp7u23eNRreqMz3Q+FAbNXx00vUdZXrKYtkGOhRPlK33q1cylN64aqMGJH3ed+rlOg1Oq
+fbXPujlbfV+RXJIhhI0hhA+GEG4OIdwUQvjaEMJZIYR/CiHcmq+bhtfUQgstrBY41azT7wL+Lcb4
+jhDCBIky+mlgf4zxTSGE1wGbYoyvHVJP7AQIcbgTzbDTqm6nrytfCVgdGjHaoNzlldQeK+5XUlRx
+dfJSlqGOHCyNl5SULPt7OW5ea/2z30/XGK6UMmuqy3I+vpvq6XgybZ8stbgSOAe3zzAo14LOjeK6
+RqC61ssQgSuh5JrAWNKjDzb7EELYQDJ4uyRKJSGEW4AnxxjvDyGcA3wkxnh5Uz2QUtGfQyLDNOa/
+wbBFqiSePluyHE311P1Wd59dz8W9Ocv2dAMpecCI89T6QSsPr5oVNQSqIyN14alkWsk/lYMMM3aq
+Ky/Htm4MY83vwxaxfggdJoiZWdpCNWLRIIOzJtC2695D2RIYvvHUtR1wo6f98nvdulWNg7J/6lGr
+7J8eAGosVdY7ChTsz4POPlxM8l/6/RDC9SGEd4QQ1gLbYowarWtb3cNtKvoWWlidcCqUwmNIbvXX
+xhivCyG8lbTJ/0iMcaPcdyDGOFCuYKnoSyHaMNKwThjYZG8wjBQd2L981VPZysq4B5p1upQya4iy
+8/GgGNtw+3oL8rFLnj+KCyXnqCdLlQqwsvPwQDWlTnslY9iUBAeqJ/6pkLgmIb+vaGPQnDW1XSb5
+sedPJyupuUCtTOejZB/q4mTYnGk4Pk2oc7rGVtbhg04p3AvcG2O0REcfJAWx2Z3ZBvK1ZL1aaKGF
+VQwnbeYcY9wVQrgnhHB5jPEWUsCeL+a/l5B8aV7CCKno1wJXdeHunvPqpXWYWgoi5XbVVGPlKWHP
+1alvyroGgfVnO9VIvNbeuNSzDio2F9aG5QxcQ4odYGAJPmblGdVda8Qf5TmNJLOTZg4fiwPBPfgO
+Um83UDcmahGoKi21hNSckU2qx7ox1VyaNj5buoEDWb934UHYLXYKdfIhwzVScRm520DfTykh/X0Q
+NaHrRn9fxPliM00fo0pB2XMTUq7yBaMqe7h8YYmqrEE9RoeByju0Hwv1tzfXc4rah2tIAYonSI5z
+L8v9eD8pBsRdwAtijKW3c1lPDCF5RqohzEoky18J9qHOyKU0XbYJVTsEsz1Yi5Oc2/EN8GLc69D4
+rAdwKfVxwVUopUJF/Vitb1fgXpknyz5Aldwt2ZWSVD+Z1RRIiWYgBX8ZlX1oalsNqpq0AWX7TW3U
+9RWqrKCB2hiU7dXZIajGQY3B1JR8WBi7Ufpra2r/V8J1OsZ4A1DXyFNPpd4WWmjhqwerwqJxPIS4
+kWTaq05ATTt4ueOXVmJ1J+Iguwc48VSqw+16Ph5FuCTVVCWpakK7VyM/qwlr2Z/D8ntJ/tWppzRE
+nVI0xrrMSj+axlZZND3F1KlsqXhOnx8lHJtB9TTv0M8tbsBZKFXJNVExTeXD1IzDLASbQNswlaSx
+D/r+Wm/JgihFU7ZbrkMNdVfWO6hvdarYUVWSq8J1eon0kTXpvEuokyzrb3Ufel3EoiaSuqltG6yd
+UpfygOr7cBAn29THwbQMVwOfz/g2XBprqdN34pmAduObyQGqmoi6xDDW98vwUOVaXr63/mbvpF6Z
+9n6z1EvO1TdgVH26akv69JcjHt3f0KemeuvYh0D9GqmLyFXeMwzsnkl8Luu0D8pWqmensg82bpri
+XtkHZStGYSWa+m9BYvY2/F5C6yXZQgstVGDVsA+bA+yLw+MqlhZ2VqYkrmothp3+w4RadZoPtWgs
+yUGVOJf2DYs4Ob+FakThZUebfD2ECygXqeYbNMqji+ugbdw0/uB4gH70Z4YZiSmbpH23Pvekz3pq
+9aSsTvqu41mnJRoHOvmHtUtVB6O6PtaNdykYLdsuhbIrEeA1CTkty3MT+zCsvrrxbrJebaq3jk3Q
+b6BLCrQCMB/PIC9JM16CkzfQGCYPOJW3tMVU556tZKLii8X9UCWvN+Af9yZ8YRmbsJ+qH4Tmx1QS
+XsvJv1nfHkvKaWhwsmMwyHjpVIxqFCzpyyel7GTnr5QrrfT5UUDdtg1KzcKoa7JJ9nW6xtZU3/e0
+Id5baKGFk4FVQSl0Q4gzVCXkK4FSGt2kj68zhBm2s+vub2Xn4k4wKlxSaTGcKJQrSdwxwVWgZPXO
+C66SatUAKLsC1SjQfZy9aBrbQSyV4UqWlmT5IPZsmAagrnwj7hRX5/k5SswGqFI3Tf07VTDjJWN3
+tL+D2JM69qH8rQStr+n+/6+0D33cM+5kYBTtQ5MKqk6626SyMqm/2udrHEHFVRugpL190NP4O2/C
+DWGMfz9KVash+QAr0YisPVPjIX2/Gk9Wqu8xDMoPvSkYyErqGfabqfd2N9wzjKcu4cF2shvDs5kZ
+lOxDE16uyU4DXh5qdRuxQl15xLUP+2p+r4OWfWihhRYqsCoohS5J8HaQ4bthE9SRYnUCpxLUtrwO
+1xPRTv9zcPZBIwpPFbiarlq9qmWwWAdr8dNfWZG6k3Kt1DuB20KYMPOo1HF3Bzb0vbwpDXwJ5ViV
+4ej0npWS57rglEo7mH/YslQlx1dCbWjfyncox/NUoceJvg+q1VChcpPfja5ZHRddhyv5HnR+dN0f
+WaG0taUUWmihhQqsCkqhh++2J7ub15nXDlIRwWBz1zrBjvHvanWn4bVUdahRl9W6zmQKB6SNHlXv
+SEjyBrOO1GjOR6kKMPfLPVCVWzyqDx+reb9hUN6nFMZKZAp1UBf9qQdcmX/4AoNtR0ZR0+kJ+2Cp
+JDucGBNA11uTbKuuH6WH58mqJOvmJAIzuZKBXokCq0r7cJSTZx9UQ1C3gOq0D+oi3LTI67QP5+NC
+m3JTqet3nY9GSTKWNg1qPjxB1VVZg3uoIQ+kMbS6ejhbckT6OuxjqyNxrf/lODUJA4dJ1JvMdrfg
+4e6Vfaj7wLR8mOZjVBP6lUCZjLZPs4BzWHt1AXxWYrw0CGQsWjuFFlpoYeWwKtiHPm7dN4oQbJBu
+vfRjV4u/Unc9RtWZp8mP3dqzuu6larpstgCaAGQWT3FuJ5/mOpjB1ZAbcN28mc7uxjNQHwQelvFb
+cBXTHjx8207pr/X9GuBTNe9R4vZ/k52GxlAoIxGXrBZDyss+GljAkX1F2yW7ovOvjkRQpZrq7D5O
+lfVRCNQ7RDVZLg5iYzUMob5Tp8BVcAmjv0dp9ToMVgX7YK7Tymc3LdJA/UKxsi7NPFlJamtAkgV8
+AsoNofR9uIj6BCCau1Lt723j0Q9lHJ+sjuC2kVifIGkYzGOyh28Ah6XcNtWdpBTlAHEM5hb9Xh1b
+CvkdmNQAACAASURBVLxkE+pYAV28dRGBdMF3i3LdiO09tO1OrnBHP2265T1qPm72GxoVaq7oi3p5
+WhtlX+tgJV+D2VYouzOMfahj0wLVXJJ1hle6+elcDXOHh8r30rIPLbTQwsphVVAKK3GIKlNwwYks
+Q1MU3fJ007x9StqXp4r1Tc2HJRXX8uk+R1V7YCSxaRbGqWbKtvYswAy4xuEw1ZwNl2T8dinfTzX6
+s72T9f05wPvkPQaxXU2U2SDcnl+JILEJjG3aT/WU1zB0UGUNNN+ozl/TGjnd7ENpG1Oe8k1WjIPG
+sGR9hqWbGwVWGs15VWwKYyFEm+Bh7EOH5lh79ntdeemJZmUaCKNO81GyLgA7qHeXLd2lbcNR9sH6
+pvIOiuesLn2nTfK7yR+O4KbXtlHcTtXs2japQ9SzDyuFOnajDkpDmjqSue7jvAgf22NSj+bjtDpm
+8I3gOPUkdtNaGMWXYBiU2odIM+s6zBeiLtgs1LNpWjaqQVqGln1ooYUWVg6rQvvQw4U1TRJyPWlU
+SwBV9mGR4WSbnSjjVNmHYVGJrd67qAq+7BQ/SjUegrEV5vik7INSEtq2UQ+zOBWgLMpdpDDZkLQP
+pqGwtOhKfXwj8CH5/3TShKMYEJ2M9uFuqpSglWsqepv/NbjQVcOoq9S+ji2t07ysFJq0D02RtocZ
+0TWFlRsU3Vy1HYOg9NYd9f6vKnRJi14l5CXYy6vLcR2JWOYjGER+BVySXbpAU4Nb3x6Ck4wBX7hq
+ZLQdJ/On5F4Dba8r76Tx/83wSHMqbpV7zgW+TsoB7gSmckO3jMOGvAspa6YwCjsw6KNpek41Mco+
+qJRd+eXFXNEjgLvzzapp0Pm3ujbim0WpMRkmPxl03ygQcc2PHWh1viFwYj6Iuv7UGWSFhnJdv8O0
+KMNUw3VwSuxDCOFHQwhfCCF8PoTwnhDCVAjh4hDCdSGE20II78vZqFtooYUzBE4ll+R5JNP6h8UY
+50II7wf+Dngm8GcxxveGEH4H+I8Y49uG1BVHtfOuExgO83EYpa5R2q/LiqQk7iJVs+KSkpmkaiyl
+3o6aS9Dq0nBshquB1ByeccpyRmpo+eeTUrsbrGSmRx3DlUKTBsDYINU+9HAqq27cVONQGv0Mivh9
+OqA8xWG0GArDNDij4vr8MJC4HF8RQeMYMB1CGCOt1fuBp5DySgK8i6QZa6GFFs4QOJVckjtDCL9G
+kg3NkWRanwEOxhht074XTyhcgRDCy4GXQ9r51pBOxCbdq+7KpcXXIH/1QbxlEz7I5BeSoE+dYKx8
+Ru7dxIm8pqZdKwWfKiCEqrNW6Ti1HK0ZF7pdka+345TE+3BVZanuLaHJ+nNUCq6uvrIuxZU66hDY
+n3/Zijt8HaOaX8KeV1NrtZBsSgxj/TndglazrbB5VtmJUpVqe9C09mgoHyTrGfWd5offUoGT3hRC
+CJuAZ5NSIR4EPoCnBBwKMca3A2/PdcVjDH7BOjvxJoOQYeTVKAM5SPugfgZqb3AI90s4yImksgp9
+yo2sDMiiJr4RJwE1gewSvgHckq8T+Ad0NfBZqWOU8R21fBgMe07t8ANx+T32UJ0/DUkH6X1tI5zB
+NTs6tiU+Sn9OBsrwZuU6VJ+RlfRj1L6Oet8gu546OBX24WnAnTHGB2KMi8CfAdcCGzM7AYnl3dlU
+QQsttLD64FRUkncDTwghrCEdYE8FPg38C/CdwHsZMRX9GLCpCwd6zbpUdXiqUx3Wkauj6KNL1sDu
+G7SzX9iFfXnbnZd7pnCT5vVUU4FZG0odGIUxKe3p6aKUhgkz1+AswYEAT88V7s83LwK7s1HDwnyH
+TfPpzQ71BzvPNLFddQKuEurGXtkf1berM9OyYHAcQq7l8k5kbyYLVFhrAtwpYCYXnhWdUjqOj32d
+ibVSXjoPcHJURAC258mytaDh2JQSVDWqtqdzMOx0HmaBWao1tY1O/qE34oueair6nwNeSJrf64H/
+QpIhvJdESV8PvDjGOJCt6XRCnJgK9ObiUAOLDiy/fTd3PQbHy4+picVgAF7yajao4yHVPD4d6R9L
+dxyPIieQfvQDTGf8eH5+Iro+fjp6ue2qAOvyM7MdWJPxIwFm8qqY7cJ4xhc6MJ0X5LHMM2xYCsTc
+8DXz6/hYN1lLLM3HgYYwVr78zjImgzbIQRtIv4anCzI+ho+NwVn9pGc4vHEBDtrYRh/P3KGxmOYb
+YGMfDufBn+47izEmYzsmNg8GTcFLRtV+AXRDYCKrRnrH05MLsZmNrV0jVhdVjUpT/shRDrmyvNuB
+qdz6bL/3FUlF/0bgjUXxHcDjTqXeFlpo4asHq8Ihak3oxMvGJtm5dLySnt12/kWqjkK2ex6Xe9WD
+Uc2HTc+tyVU6eQ+fIbKYz8Rj9BnL+Br6hLxfHmKJqVzLTD6iHzq2hTvmkl9iZJwLstjsEGu4NxOx
+04xxaT6bj+fe30aP9bntc4kcy/g9xGX2wHz0l6ia8BrL0MdjOWiaeIPjOladwPZM3dzX61X0+DbO
+qpFQElY9DrUNExCqdZ2dbJpOT088FbTZPEUC8/lMm6TLdG78ogA7e73l50pWUc3Yp6X/x/ATzlgK
+qAoiNe+mUWYay6Lu/cblPeaB8fzLmk7g7JwA896lheX+2jwelzY0cndf+mQCYY3QfVjK1xTvZ2Nh
+bSxRNZvXb8AsPScIrMnJJO/q9c+cZDBzRD6/dLxiL6/qNiWjNBiKTdw8J0YEgrSQFmvL0xLrAQtC
+oM1nfAI4mJ9IXolpm7kqV/axpT108r1LzHNlfv56Zpddnx9giVdl/P15ymeA3bntFwN/l/E1uIrT
+1DfX4RvE7cClGf8izl/vpKpytL7bG23uR/5DZM62gNSISqX7iqvLshoQqc8HVAPFltqVOo9B60OP
+KCrZHpvzzTcW904Vz2nf11Hvlh7x8bD+Hpf+zOIbywI+nnP4B2kHy1ppI6lRU+829SOf61djGalp
++py0MYd/yAekT+YNOoarNQP+QYfiPY5LOfkZzQxma0E3lnkiW0YVJmRovSRbaKGFCqwK9mE8hLgl
+wO64MmFPk1S8SaJe6q4VV0GkmrCqGXM3H6XPmOjyV0fSCbwJmMvb8swC7M74sxfhbnOfzGTAHVPw
+gnzMHV4Le7PH1J1j8KR83N6Tn59d8LRkG0ip6iCdkiq11VgNUBW0Btx8+AGqrAIFPop5bXn6N9W1
+Ugg4abwZ1/+rbYW2N1aDqwGYmhjXCe3UixLqBX51AkMN2tMFzso/7I5+r4JSvQZqk2KUmbah76Es
+nbJNKpSso6a1fFzqPn4mBVkJIZxyJ5ok5KNKzhWUJFb4mny9oeOx9A/H5KIMSRe7I8/czggfz/dc
+m3//iS68Nc/cF6PX9zMBfj7jf5+feS5e71+RgrBCCsRqLMohqv4R1neD9TiJqouxTs04SMsw6J7T
+ZSloG69ueE1BTvVjU7Wv4es5kX1QXxLNydFkFVmnDdDD4ixONF4aNIb2fnOcyOpuwudJ2aMNOFuh
+FqDGUqn8SL1uVYYTqETnaoOstNBCCyuHVUEpdEOIpqs/2WzBqlevGG7UlCO/1xk6deS5Ogn4tSHQ
+zeN2Ky4YejzwyYw/A7At2U78XwS+O+NX4Lv5B4BHZtyEiwdJJqKQKAOjBO4Gbsh4F5dkq0ZGqQWN
+GWkkZd1YDII6oxiGlJ0sbObEd9I+RKq+H9Z2D6cE1km5hte30/oo1cjdyh4g5dauGj3ZuujgQkWb
+mzKEuhovWZ8nEJuUfN2MUwcqPFyDCzlnpT4NKFQXvrBPlW0S1qSlFFpooYWVw6qgFE6HTEH5NN0x
+NT5BadKs/KJCaUlmO6ed4rtwx6dD+Cn/BeCX5Z6fzfiH8/XJpMhIAA/H7RC2Anfmzl2aR+JWPKvx
+75NiIwC8Grgs478tfborXyfknTbgwkqoP/1Wi0xB+Xqdp5JKU7uBC0m++pCoAKOEvp9Efdk9ADeR
+qDeAv8VP+V14OL0DnKiSHJN6lfJcj9uL1PVXhZLgKuUvkyJmgVMBPwz8ScYfijuxPR/4w4xvA27L
+uD1/L257cZT6pC8dPETgvjNJ0Gjah33RybqmXpUsAfl/i2G4RJWkMqHcLNUBhES+GR7wyd+GswTH
+gck860u54ddPddk6m5bmXuDh+fcLI5yXZ2bNFHQsXtcduY1vxIMpPhfib+XyH8Y9RHbktv4VQnY6
+n9sFi7ntzywmuwVIvuq2ML+Ur4epjp2RqPupH9ths6/CNdXg1D1/KtoHa2Mr1TydpdGTalQuxj+A
+tfgGeRm++ark3z7+j8lzu/H1dBD/qI2c1zieeuB0gPPzP3uiP2PtTeECwe3AE6QOiwJt6/FxAW7L
+dWwOcFPGzwX+VPrxxYzbxmVh/SGtV2tvjqoR1pjf07IPLbTQwsphVVAKxj6MQoo2kb5qzaWqJwuE
+sZdq7gA4kTRUQU6ZTAQ8n+PNY/DIfOzeCPz3XP5zwBfzAx8EXpWP1Xfksv88BTflih++ANfnLfya
+Kbgt1/fQLIX6YAe+Pdf7lggvzp17PskdFeA38XBsRlqukffYgIdpqzvlR4VRWIxTBQ03p+S4shWQ
+KAI7ub8ZZ82eSVIJA/wx8AMZ/758/V3gv2X8p0hkOqT5uyDjX6YatwJOzOdo62EtLhBVVaC+h1E0
+R4BnZfxvcVbw3/P1D0gsD7mPr8v464HXZPxKknch+FrfR1UlrepXFYga+7D3TGIfuiHEtSQSSHn8
+ugWo5JAGJlG7d9NRq2xgHU5qG/SpklzanpuJnhjp6Sk4T3onvjj+Abc3+AGcpbkqX29jmTtgXuo4
+ILiZO28g+aFDIj+vy/hawAJeTuJsgy3cB6iGhtcU9XWJQ+pmvwwAo9qHYUlNThXOwnlt1fzYO6kb
++Xk4f70XeHTGz8bZRmMj1uLsw+24BmcRtxFYkLbV10I1HHpI2Ec/V1zJbdm6WIvPw7XA5zNuXoOb
+cbnUDTgLshM/wK7HDzD151C7DrWnUDsL6X/LPrTQQgsrh1VBKZys9kHNee2knaWaNs3KVf+rLMOw
+BDDajp0+R/FT4hgu1f4o8BPS9osybqf8t+F6843Av2b8CST7A3Bp+W+RnKYghch+UsZ/BviGjP86
+TvremK8aT/9snK2Ak7NNUFPbYXkQTwWatEQmKLUTcSN+Il9LsvCEZB1q/Xgj8IaMG/vwIeBHM/4i
+0lxAinZtp/THcOs/y3zd4URpPiRqZbYo61Fde2sE1zB9D6EKPwj874y/AGcrnk1idQAehdvA2Drc
+Q5V9qLO36OJjePBMYh80l2RT7r/le+X/Cfl9R8Z7+ATci5OXd+KTZ9LtCXyxRerlCEoyGgn47V2Y
+zDffg3/8G4Cn5c7dE+DKPLTmpDY5DsdzZVOL0Mu0eGccjuXy6bxjHe1DN9PO+/u+wG6OaTFAWuhG
++trHbzIEeyeDw9SzD3VQ+obUGYCdqhFTKRtSte/ejCurYAv7Clwz9Ep8M70WZ70eg2dvMpnSfQG2
+5QY/gUvub5A2bsTXhpHox/FNoXQNPy//sz/6vcbmdPC1dwh4YsaPAl+fcZvH7xX8HJy12UCSQUBi
+C2/KuLFw+6hqRgwiVTmIrduvVIj3Flpo4f8zWBWUwkqSwTSBCgPVkKkpx+SK+pevZky0r8Oy//9e
+kuQbEjvwnzL+QRL5B/DX+foqXEL+KuBXM/7zuIDytfn6/bgU+vdJce4gmUqbg9Vf4+yGUQplmHkz
+i4DTrzE4naAJbpR9MMm52pbYKb4Vpw4eiZP8r8LJcZPqvwX4joz/ET5ud+Isw/04OV6Xu1JNhtUE
+uc6LUlkJdcbShEFGuT0BZ/++Hvg/GX84zh6psZR6TtZ5+ZYm+5Kg6MxhH8z34RjN9u4MKFdyVy3e
+VA2nEYJKbUZdG4NyCFyFk2e7can2c4G/ybguCvuIP4GTs4dJ0W0hyRyMPDbS8THAxzN+Nr6oHgX8
+s/TdrCKNbN1FNWOVvccsJ5+Kvnz/k6lj1DbOwj9I/Zjsw92Pv8cYvuCP4uM9hnug2qaxIHgXZy90
+vc3gH55qHNStWUlrY1Ntc1BNwCT1sgj117DDqwtclPGD+Drdj2+WBziRdWta0/q/ao/6LfvQQgst
+nAysCkrhdPg+VOqrKTuVBqw+jb+n2aFNP34b6SSH5AdxccbNlmAMJ4O34afVxbiPgp0eu/HT8SAu
+nPoszq78BX5amcBNtQ9bcT09nJ7T/cEyXtKQb9rGZimHJLwzYeo6XNAKVZ+PI3I/pDG0sSl9HDQ+
+gWqY4MS09roWNGwaVH1tNE5DiZegVMUM9UF0KiHxpb1hLHGgYovTUgottNDCymFo4NYQwjtJVpp7
+YoyPyGVnkVIV7iBZh74gxngghBCAt5IOs2PAS2OMn62rV6FLEqQcYrDKaxB0avCmuAErUcnpvbab
+P60L+/OxNIM74lyIx1Do4eoi26n1FLgST7K5Tdox6mASPyWNQoFko2AnxiOkPjsl91FVWZmgzuwj
+yncaBeoor5OlGOrGVp3RdmTHOEjzaIJiEww+BKemjuBjq5GJHo3LBq7O1xvwMVBeXxO4rJXflOJS
+QaLhi8DF+Z9dub+aV1TDoI1RTWZUjt0GfP7USW+OqmyjPMGbclRq3XBinIdhMJR9CCE8KffzD2VT
++FVgf4zxTSGE1wGbYoyvDSE8E/gR0qbweOCtMcbHD+1E1j7oy5yKNmIl5NVKwIQ+iyFlJ4KkfXhK
+Lv848NiMfxK3UTcBly7cjbgU/Rw8BqNtBHtxoeQR/KP4MlVbffO4Uxdi+5BmaLZbWG2gmZ7UBdh8
+FGx8vhb4t4xvlfIp3KhJbR0uydc7cLPz/VT9YOp8BtSEXtkHNV4y9kED8tRpvnoFbmDzMYavT00M
+NIYm4R3soToI5F1PD/sQY/xXfF0bPJuUZh6q6eafTdo8Yozxk6S8kufQQgstnDFwsnkftsUY7XDa
+havwz6N6OFkq+vspoExFv4ETnXZ0d1Wf/jJYSpPPvzqDqJNPHYyy6y6b10a3fJvGbQSeA/xjxs/C
+B9fMWjWgR5d6SsCoh834SbQB3+2vwU/Hy/F+GPVwH35aHcTZh8PUh+46FSvEQXU1sWBNYGN7CT4G
+4Ce+2X/8NYkEJd9nXqK342xcwAPRmOXidlwouR5XI07i62xS+qGRofX0t3eZw4WYVq+qCKeoeuOO
+yT1NqnTyMyqsVFuHTs1zo1ipLg74rQ5OORlMjDGejPagTEVv/F6dDraLL2iN1KsJS9T4RclBxUsS
+ro4kGwT2/L/jpOgiSYgC8L9w+/r3k1xfAX4lXx8N3Jzx7yJFaYZkx/CxjNuC/2dcs3AEd7f9EPD0
+jP81ruEw+4YpnNxt0j40sWaj2IWshM1byaKwebqT6gf5xozbZvtm3F36J3F36EfgRj8/QDJQAvdE
+/CSuwfkwviHvoxpRqwzEoyS8fowT+OasLIdpL45RdeG3eDtqIGUbkK5plSnM4HIQTXaj9Y6yuatr
+9yhwstqH3cYW5KuZbu/EDy1oU9G30MIZByPZKYQQdgB/I4LG/wHsE0HjWTHGnwwhfCsp5JwJGv9n
+jHFostmpEOJFpDiDSg4p6WQ77QS+8+2Xe5UK8JyR1d2xlNTqKaepypvAdtA3TAduy5mGvyHCdO7o
+pgU4no+8TUuwkPG7cycWgycOeVRIzk2Qdn4LoGFtfA63ctwOPC3jmjfgwzhfZjYPD8j7dfCTbw+D
+U9FDM8lfp32og9K81kBTvw+zIH1pF27JHX0U8Mg8sc/IZfEsOCsfnwsXw6HMux3ZAHfl8skxuDeP
++e7c4P3RT/ZbqeYbNZbuID6Odlpr39WisQNcleu+K/rvam2peR3U/sHu0ZBvBmUymHkp17rteU/D
+N9zLd1SLxlG0D+8hxRzdQhqzN5LsZt5PYoXvIqkk92eV5G+RUiIeA14WY/x0Xb1FG9Gi2thAai5J
+lc4q6WEk/CF80I9QJa/qJMt1LMooYHVNjMGL8oN/EuH/5vKnAr+ZF8ovxswbkdxhIUUKMpbhe4Hf
+y/hTSGwBVHNGmiziAO778Ie48dJ7YDmPpbEP0/iHt56koYATTb7r2KZTZR+a8FHAxjYEuCI/eAvu
+K/JD+fqX4/D2PPb/LcIrc4d+Fvie/NyvRme3zJX95/BIVu+lam5+RcY/h3+QqlnQw8TWkGqS7Jl5
+fCM/iptgz+Pk8924VsrUpjM4+7ARZxm24TIR1UqIK/QJ8rU6kAPz9CSYjTG+qOGnp5YFMe0wP1Rz
+bwsttHCGwKowc54IIW4l7a6zUq6mr0YGb8N3YDPt1UzU4/iOWpLAdeyDwSjsg93/hhDYnsftS7jq
+5Wm4d942XLBlffsATsXcI+3twk8mTROm1JEZNV2Ev3cPj+xsp9UdVMfNxuog1WQwdSfMMI3BSgSU
+pZZo1LH9Ttw56A4SdQXucXonTiGei/f/Jvy0PYSzlhbIZhb3ouziFNQaqW8cF8wa2a6eiKVDlFFy
+duKr9mwdPqebcAO0Ps6aLMlVza4n5V6by1n8xFcvSWtj0PqV+TlzvCTNeEkDpir7MI5P/mOBz2Tc
+DH32UPWWq9M46ITW8X2jaCKW+ckAz8zDNkMKCgrJXdoW8e+ShCvguSC+Ho8IdC2em+DKmne6D1cn
+LuHk8IdJrAfAO3GLPZO8z+Bk7VnUsw/le8OJOQuGqRyHwUpZCVW3mZqxi2tlTPvwXOAzuUNPimnM
+IbFUFkD3FbgLuhnQ/CKej/MvqFp6mu/Kp6mOv/VBtQ82FnXswyK+wRzBWdp5aeMzuMGZbVLbqRqf
+mbxjI765zeAbgB1Cqn5Wf4kSVipTaH0fWmihhQqsCkphTQjx0pAkxEZaraGastt23Sfi5LNREmot
+dRA/EZVSUAMOFTSqQc8wvws7Ja6/ZIbOPamnF0aIWTXSvx8mckdn55z0uyu/yFSAv8mVr8VPuSk8
+yIadknfjwqkuno+yJ/d8GjecsWAqO6ma5W7Ind4T6/XUdVqGUcZiFKijMIaN7W9MBdZkzc4FwDV5
+Ai383dp1EDN/ODYOixnvTsGxPMljHbg54yaUey9Owv8lfhofx7UPu3H21dg4zddYGtFdnf+5L7/U
+nLShFNZWfM6OSrkJEbtUA6jYWlVz7ciJ7O8R6rUPAwTFLaXQQgstrBxWBaVgKkkVBirfq+bKystp
+AhhVSTY5uJyq09VyIpBJ+OXcuTf2XU/9ROBduZEfBl6Ty1+Rn/tmPBDnFXjOwLNw6y872Y7I+x2j
+anVnastbcWGXmVp3qGY4tpMGTo+z2YMVT8Eoum4XnpgXwXV4ODJTab0zwJtyJ17f97H9X8EjH/9S
+TEFdAV6Yr6/FVY+fwgXXs1RDsKlzE5w4VhrEtwz0u0i9Q1SU9o5RtaaENP8mO5jCZQczOOWs6177
+OMp8nJGCxrEQooXuPi7lXcGNFFeBoU7K/2vvzIM0u6rD/jvf0vvsMxqNNBIjWZuFJIMCjoTAOJEN
+ErFEnDgEhxiEiWWXKYMTqmJNyXZMElKmoGLM6tjGOIllgRGLtYRFJkixKxFBxCC0S2iEpNFIs289
+Pb18380f95w+571+X/fXM9MzrfI7Va+++9333t3fvWc/C7ltjyhxFcwnmy/nfUiEc3XcngS+p/m/
+BfyBprfgQT9sw7oDZ0Q9SfXkxo3Q8qJyy2qcqTRKcdFARoEj4ytyryP5UGU/0o8adJXevsGJIDWu
+w3UvHsE3w1/V3wO4zcEk/jF9DzdFvxffXE2HZD/u7Ab8w4vtjo5MrD3lgLex39Y2IzmOhLJaIR2d
+qMQ1XUVWdXvcjxKcOAe9no9Qkw811FDDccGywBRMJNlL6y46qVjF3BDgZdv1Kh8KxxPIZI5eg8DF
+gWFoRjnvIou7IEcLNj0FE0O2yVp6aBvHQ37EdKwfEf00ZthhXCS1CxetGfkxGMposrBzlaUiBxYL
+s6eTwOnamBW41ufv6O/n8T6dRtbqhCxKNA3SK3CDKIu3cA+OrkddGJjfbVpck1EkGcXnEZOIWGxU
+V64KQ2h1tErpqI4e6y5Dv+LixWIKx20leSJgANjcgt0zRbfZs3QmMKw9OyM5amiwG0fLYzjwQ6GM
+SXywq7wa99JRqCIffu/SIS75fiZ07l0N23WX+uMmrNEXrl4JmzT/Rs17VhzdX52KlmKmeBOdfwyG
++0Ph1xbQCE5K2JgcwfsswCYLKNNxFLZqMUW0NubHZ+PCrFqM/YxhfD9uzlbeL29qcumOPFPfHYbD
+Sk9+TAdmoAWrlKnSfAzeqJ0aasAOnfjLj7jeinH9nwK2aIUP4wpucfNtUyTfrO0xfEAcgy369ezR
+l8YpbhoGA/j8RImYtSF6fBqmqMBmz1SRD3HTiPm9xrbfjb8mH2qooYYCLAtMYboBO0aEdCjR1e1M
+APPR1k3ZwhDguQTj2urVAX9LumWOCwxrGdMCRh3NCJjXh7iLLmQQVcBYGrkRvz3W4ZKxrPv27ek9
+XCJ5b31mTZc37M/P3N9J/IRW9FVFY847DPu0H1Md2Kdb8mAXjmq+hZibEm/nJDClf45IEZsY0Hx7
+v5mgqeUONBrs1Qc6E4lIKZZPjYj6RuwAiuRYmTSLZF6ZHOuXsdsAWjq2t6yHzQcya/aJdJiHG7nG
+XWtzLe8bb/HNQ/lofs9kg09ckvP/2RMtvt7O+TsEvqRr5NV6iv8g+TweDmthJqyR6ZBf1acG0NSB
+aYvwoioldA/rGKdwKgs0Qh1WyHQDhrshH5hMMBHm37CGGaojSZeZxJTyy2Nr63aqO9+MhHKWA09h
+rNFMl7VH+cHUYbratdV0OaJLcB9d2pq/iUTS9DPa/VGENZruIDyv6QGEFZpu4Jxq2wmHcVLjIMXY
+lLaAjoZ8W7gbGkNsn8mIXdE7jnBU62viYiibzCj+irYNUa89Ogw1SUyDohl5pyI/hXcGtbTRJmwZ
+yFYTT028SFd7vp5pDmrP9+oSbCGMaSldnJwZwscomqXbGEYHOFEEPIjzSaIfRCPzOgj7tb5Bqju4
+owAAHJlJREFUWqzQB9bKANtVIylLT/IzEzrnk6RCeHqrO5qUr8DRbiNHW7gUaBphfHaehHWanqbJ
+QR3dtrZ4NTMkHeWdTDGshMDY4DRrNRjoczN5DGfw+Z2iOH9Wdxf3LGXtHcbnL5K8kXyMZKGRknE+
+hOq1M0CDYV23u7qdWvpQQw01LB6WBfkwnrp8e3ocSHTCyb5Hz4EmzJ7Am2EWE7DddR+JLZp+gjR7
+kh4mzXLt9+GnijH72ria8ACOSUS5crS6XKXo14NdyymGh5sKp1h0HW67tuA7+zDVQT+M0RXVsrvM
+deUOuf/TpecFmNanzuzAfUd3aX6XjvZqNfC8pu00OkiadWTzTOjTXvyUmyCg4Po7SHXAkphfliRB
+njMra4oZXq4v/u+gsNshBmpxDCxy520mBB/nhOsh2BqZCm0fJwXSLM2e2IfD6B7RnmwAtulYNYGD
+2vMrJtOsDwyDcvzIiOZHUtAgMn5j26fCe9MV+dbKsuq+jfEUUQemy9pFujKvMYUaaqihAMuCpzDa
+kHRRW3hgKhXkvFHjz2jRJDCkTd6rD5zbhQN65M1MwkHdotclGA9Hd/TkBHlHjZpkVZprkblmv+sb
+8ELYfXuJgiilY1nl/Co5dC8ZdZWYNN6zLrcErh7Lvf3aoRn3EhwZX5oXtSZjUJOyqLLcpsiULPej
+Kj8GRbGTbwRoaANWd2B718suQ5kJGvOj+XXU97B643tV+bFNs56gwv0pcT6RrIBhZbzs7vEJVYkF
+ocikhbntiuLQ+E6/WqTldRj0Il46as4ikkQy59zQz6jrHVHGVxPCc+uoHkpuZ/AhYJXmH0ju9+Ar
+FPXdoRhApJeab5zQ6IV3kUojc6CX/4YTGbxmHb5xtrs+nispulE3iFKN2Pao0NOv9KFX/6o8EUPR
+34DBQiuzl/JZlTJYomh/EPPn845s0dDL8A9wV3HHuxbi+u4nf7FrJPS7ZjTWUEMNi4dlgSkMiaSz
+yUwtO8ESzmQ6gpMPW4DLNP2X+nsJHv9gC/BRTZ+F+yRYC9ypaUOVI3PKWYdzZe1ljb7oHWc+X/pV
+qH1EoyOKW0avyxpsi9FKK5zAqkOxL3VnvQnFeBhRbGYQYwy0S22qko1X+aToFXynFX4nSnmQtfss
+P5J3VRCxuOg5KoryIrMz+joYr8iP8T/jGEbvyU29k0iz75WZxPZ+WV/A8sv6BnGMJ0M6knQRi4n6
+JP04Hg5r5yVGPlBE+6L32jU4avt54COatqAoX9ALcgy7yzX9GwL/Q7v3YTwwiHlG/mVycBHIC2pf
+SFcF7oz66/1AGc2LC2WE4gdp9Zl6blSZrbLbmA/i5mWo7xBFleiyrHyGoil6lZ/HKCu3eRqg+OHF
+AD5V1qpVIefbZHfukIO29NvXMrli6bNwd2r24U7gwX//H25LspscaQuyXUrkN0HeYOJGYW37Edyx
+TT8Q9RfKcB7uNi9a/F6Bk8qt0A4zva4yi6+CEx5LsoYaavi7Bf3EffgT5oai/xDZ9H2K7FD4nSml
+/XpvK9lgsAO8J6X0tcqCA6xvSLq+LTw9ldileSP46fEqgWv0iFq7GVpP53RHH2g9D+kD+vAvCdMf
+UY3GT0FHvWwceT/MKN2xXx0dPNCAj5oaLK6n0KFoPFUmHzYK7DV15DhWIR2dnUTdBRvt9fiJEAN9
+2Mm2P7QhoqVlA5jyrh4Zgw3gN9fns/nB3UdnMaGnQ1sPhGcjZmN6CEO4UdE+HO2OWE70nt0N70Vp
+j5Vtjk4GyerGAGsFnlcU4uhEdh0X6yiDtX0g9LWLM9T+MT4vhplsB16v6UdhVq9lGzmSNWRLSmu/
+Oa1Zjbv/2wWMakcmWtDWDpr0IfrFiFKbQdwhbNShMAzswqASvVN8vM9uwMO6CI6Iq8Ab1hGD2kTM
+MurICAXLzb4wBVJK817AT5Ax8gdD3huAlqY/CHxQ0xeT/V0Mksn8HwDNPupIjQZpY5Y4JoH0zyG1
+9bqnSfoFydeMkG7Sa3pjvv5ojNSVfP1am9S5IF+/vZHUzWYR6Z+0SIea+Xq1Xk9AGtHr7FB308nj
+RMi39sQ8ejwLpEG97F4LUkOvzeHZsZC/US+0Hc0F6qu6rKw1kBqtfK0M9zeE9IBeAmlIr1hGE9Iq
+vZphvOz+YGhbo9QGSzfDM6fr1YB0vl4rIb1fL6H/vpbrs/cuDe07T682pDfo1YB0mV4DkF6ll4Tx
+t7LWlMaqpdfP9ZhzqzfOn0C6UC9K49nUftt7V4b8z4X8i0L6J/WiNGe9xm1YL+D+hb7FlNLC5ENV
+KPqU0tdTSnaA3Ydvbm8GPptSmkwpbSNvuAuGjauhhhqWDxxTLMnSvTuAz6WU/kxEPg7cl1L6M733
+aeArKaXbyu9FGBVJF5NRICMfWjjz8GPADZq+AA8WYt5w11N0UbZN06fhjjXOxCMYW2yGu8ihzwFu
+glnU/giOflUp3kQmaCQJomw+ShdMcnIQR3FbOGNvJ8Xw45DRTzNKItQRuf0RInMukjE/pv/24qTZ
+UTz2QDlyMhQZlMOhvsgINii7x6viuNu74Gj1JXiouw040ywy1GJdVcxHoci4tHQbd+lmTm1aoU9/
+n3ySQe6fzeVVZBICitGnzf3bsxTnyXwk2PuxvUMU41Xae68F/lrT54b7N2r6P5HHBjIJ+XZNb6Xo
+pxMys9O8gAu9/UoGWHonKyJys7bllmN490Z8LPgOeZCNFrwBeKemP4nTuKfhDk9NyjAlMKaj8BQ5
+wCXAo+Iekm7B4wp+Rn9/FY88dD3wOU1HHf4INliHqF6kZU9P9nwMJ26b1zrcyUqURJj9wfbwflmP
+vgqqFsEw8F29Ez36tJi7GURty6jcE+NxRulD3LyiFmDcFKPWYAxQAzmgrvFPBnHHrB+muKnN17+E
+f5CxjkGy+3soBgmytt+DbyAH8A/2r0KbonWlhRCIH9sr8HUY14KN4QTFzTIGBbYxsE36X5FjXULm
+ezyk6d8FflHTV+D+Jm3TiA5oo+v48lhFn539wDFvCiJyA5kBeXVydKPvUPQppT9EPWiJyMLoSg01
+1HBS4FhD0V9DFvG/PqW0Kzz3cuDPyXyEM8gb4/kppXl1LNaJpGuacKTjocBvxsmEC5se1n0EfMs7
+T39PB1GaIW0n44eQj13F1Tr7IOkWfVCPl8YkbNOj7Tbgi/radorKSWXyYQ3uJzIqHsVnEk42WN4q
+/DR7DY5BCC6nNpLiKfy0imrHvZSa4v1oX3ClHo/Pdjw0WVSgsWenKPoUjHVYP6LTj+kezxoMhLLb
+OMf8X+jvGcBEw8t9RI/or4/PjbXYC3p5vL4EL8Nc4P8t7u15O44RvIAzxKLLPsNANof0i/gpOtSE
+rq4dk0RFrKqB+88cJ4vqLH2N9U8b/+YUdBAa8KLO2ZoZeNzKFg8PYC7/7sdJ5Wcp+q+IH9xibR8W
+xBRiKHoReY5Mmm8lz/PdOfo896WUfiWl9JCI/AXZDd4M8O6FNoQaaqhhmUE/IoqlvoDUbJDWBZHP
+fwxioy9Dep3k6y5I/0jytbOZr7saWVQ5I6RbW6QZvX53mHSYfJ3TJH1Z8vUyvd4XRDpbgsinVRLp
+mKgniu+i+GcxIjS7ohhylKKIb3CR5faqZzUkkXwNhPuxf/30I4rWopiU0vO90q3wnokhByBdp9cF
+5Hm96xj7W75aoS82vzav1vde/Y4iRSiKlNvhurG0Duz9XmLkWK7Nsa2nG8nrYQzSvyOvh1FI7w3l
+XRjS9mxck/PNn5VHnyLJZaHmPCKSLiKjP+aqfDOugvxZXAV5BR715yv6+0qyQgpksuNeTZ8OvE/T
+78YDihh6NI4zbYyJA70jS9lvZAzOp+9efi8yoqLO/WR4L3KQq1CsXpzlyDCM9Zo7+EMUPQNHJmZV
+e8vmvZYukwqRhCn3PaKhhsIaY+w1eETpDfi8z1CU7CwGYvvLasVxPOezPqwyVzfG4B6cKdnBmZhV
+0oeypMbKiwzfdvh9h6a/ijObH8KjgX0P5jCbR3EJVXnOqvqRajXnGmqo4VhgWWAKZhA1iO+i15HD
+hkEWTf5XTY+SmXDgu+EkLvJ4FvgVTT8cyngOP63s9BjAd/PoWwGqT+PFinbmg3jqRuenVUy7xULE
+aI6EvPlOknjil+uPzNMqW357VnqkYwg1Y1qO41jDBuBaTX+M4+u7tTeOrbX3WMa2PFaG/Zjqbhmq
+xnOh+tr4uj+dop5G9OXQr4Obcn2LNYhaFj4aVwGva8DzXee+N3Au7UVk5yqQOcAmYzf07QVczr03
+vPdVisottkBMwhE9/I5T1KNPIW1gE7cGR9t6obhVZESEkVD2IL4hGflQ9sXYq6wyqhcXxzTwo/pn
+Ryqi5fFDL9cR60kUPQGV6+/1XvyA4iKziE0NHMXfKfB9bdCKjksOomSjH4goetn8OEpOIsm30DyN
+4e2PPip3iEeyijYzVl60JSnPZbmeM8Iz1+IKV7twiVH2D+ll228kN6MznLLOzGKgJh9qqKGGAiwb
+8qEpsDK57P564H9p+kbg45reCPxQ0zGisqnt7gKu1vQ9OKPmIeaGAB/E0bPIfIooI8yVhZe96PaL
+JkaIKG50PdcLHexVR1W+7fSjFDXzItlUrq+fOsrts/9V/Y75sa+mGhxjYk4Cb9H0fwllHOvKjPXF
+OYvamP3OWZl8sDJeDjwQ8mFusJx+SYm49jbgmMdGXKU5qoIbg/pwqb4q0g4cGz78UnKyMiaSLiV/
+rIYOHSbbIwB8Ave21MU3A9MhX4sPxAY85Pha3Ox1Da5WaoPaxid5F0VHsQZVptMrcRS3l+2DUI2O
+RsWi6LDEno2oZlVZvaQSEVVthGdNYWcfTqtGtdsq0+/If4h9aFbkl3kLsYyqBWv1rsE35+gApo1v
+ZJGMq4I4LpHf0cRJE+tzVMEWiht75DtUeZaK6yKi1qacdCjcj5uRjW20g6mS7AhF1/DGdzmMK1Y9
+i5PWcYN9LpQx0yMd5qqWPtRQQw2Lh2WBKYhIMrm97aK3Av9N07+FG5+8BbhD03bS3AG8R9OfwtHS
+r+C6CR/AHXwY+fEmMjMS8mmxO6SrZMy9yIeqEzHhGEl0SGKjfRque7EOZ34aRz6GD+uUyopYRRXK
+aPfjCRyNvKIFX2ROxf7FE9ieKQZn8T7NhHS0qKwKrx4tRq3cQVwdOXL0FyJLBqg+Ec/C14a5XduJ
+Wzv+ED+Zj4Y2xTG3OS4bx0WVdZu/OFZxDOO4GVZxgCJmgt4zsnkIxzDeBLMBZy7DjYjMneBnQh1P
+46SZtcvaZmunX2/Oy2JTOKsh6dfbwt9Me1DWDUNwta68xgWwdps+/Esw88mcnFAzyb3fhQGd2cf2
+wws66o91/KP4IU5b2eL4cYGHtL67ccu6CeY3nd6EkyKRfIiipSEchY2LIKLPNvnRVNsW4CRFHkB0
+LhptIgyqlImawMX658U0N8IQ+OIv08DxGet3/Ljjs1VjJRQ5/5ZvikAx8lYD0DiyHO36B1K10dm7
+kMd4NDxr6bPCu9F7k43tzlD3EMUN18bIOP0RFY/taZM9JgE8mbx8m4e4IY8R41gW+w15U4h2C9aP
+v4eTf5sEztbCX6YL6vEO/I224RthfndT5BnZ2PVr+1CTDzXUUEMBlgWm0BBJAw24LsEd2pyHyHbZ
+APeMwdd1y39bF27TnfJn9Pet0/BxPcKuwP0m/A7wC5r+I7LCCThH9ybc8coZOIMyosT2H6rR6w5F
+FDzqOphfPkNlV+AMytNDOzbiar52SkT0OqKf+ykyvnqpGENRHTvqQqzCT8IYCCV6Mq5i4FUpL5X9
+JkSFpSqyIuqFRJfrr9D0/wn9qOLax1NsLY4qR+ztx/GgMuap+RE8DMDjFJWozP/G08z1pxBJych0
+XMPcKOZTFAPOxPkzkvZFih6mIY9JjPJtp/zrcWb6T5MtPQH+u/7+HE4S34+TRDFYkVDwuVBjCjXU
+UMPiYVlgCptE0g0Cm5PLfi/DT/nv4af8SnwXtBP4IK4Fthv4i5COLttMOywGiLlA01/CT52401YZ
+RJ2G7+yR8RcZXy2c8WP3o3ecKFqNno4iP8BO+TZF4xnz5VAlsivT9XYKlg2iRkM+4XmDKlXuKnVs
+yy/XHTX6Yr5hJmvw+Yi8ihF6i3utjXYijuInfoy0PIRjVlFkF8fF9FNG8FN8Asc2bFziGEcMSvB1
+ZM8eCPfHKMbWMHHiMK6RaxAZlFM4hjGCOzjdhrsPMTdu55DVwiGv3agSXdZD0XpeOoxGU156XfLF
+cTvuR+81ZFVYgB9JRYUOyJGirtL0m8h+8CDbSxjqtAtHXSN6dq+mf5Qi5zsy2uKHaveiXkCUMbdD
+fkQZoegPcBXOUIth2yMqGtWLbYM5FJ6J0YSqmHLRtiMqyERut43PBEXUuZeEo1xfLyWlaCUYP3rz
+M7gL3yAawBZNm89BqCYfmiG9meKmbx/hVfjasfqexRXcnsGlEoeBn9L0N/DDx/xHrsTJhLixrMDH
+MCpFxXiccS2YvkF0sxejcFV9xMP4ujgdH89/qr9/Dlyq6ftCO2KwosjwrYPB1FBDDccEy8Ig6vym
+8MnVbWTfFOfoMdBYDa9XPHngUjjXcK7TYOPDmn5Z/rluB3R1i/33k74zPoKjkffjjL8YfdkMdHbj
+qOgRqg2iLH02jmbGE72FD2gbR90NVV9B0VmrQTz9DJspi9Ls/jDFE7gMsb0zuNhsdyrqJqwqvlYI
+XjJANZkQT5CIRkuPZyImYKSSndDrcHItAS/qi2u6fuLHvjTDr2E0p+HY4tmhvPNxVNqwgzaO7s/g
+4z+Gr4cLcfJvY3jPxi2KgKeAy7Xj23SQDlN0RmswjKvbR0/ge8L96BfBsKoz8TF4RajfxJSvpegI
+2NJdivobi/ZLsRzIh4ZIGmjB2zpwqzZnB/B2HfQ/HYQ7Nf8tk7BVv4YPaN6vAf9Be34tHmPyE/iH
+vpu5ykSRI1+OGViFjlfRyGWUOnLqy8pOMXjqCEW7hPKHXg77vlibCLT+KCWwOgbxBRZJol7kUb/1
+RVKiF1lhm/RB/MNJOG/HeEr91BH1Qjbgm8LLcUmSfYyP4BvICxRJpSiBKccLjSb1UfqwEuft9GP7
+EOc1Wu5aPyL5YP2L8zSGb3S2pg+Essreow1q8qGGGmo4blgWmMIGkfSzZImDyWJHyIExIMuPjVET
+OdKmgbgCj+XQwd18HcVRNJiLEke3ZPPFaywbNq2k6P8gMsEM4ildZezUj4OMXr4O5oNye+10PEIR
+vYxYART1CsoGUVVt6geqJBF2sq3HmYTRWnWQokFUVZkRG4tkRcw38iB6nbb+lfU75utTrKPM/DVS
+b7L0a+2JbteipqP1tezTAoprwcqx/DKTN2KYMb+XQdtLTvogAqclpzk/BvwbTf81MKG9W5sylxh8
+wW/FlVQ+jyt0fJ/53YXPZy5dpQzU68ONkxXp/LKlXq+PrUrUV2WtV667F8SPMaLzVmbU57f2RhS3
+HHxmvg2rvGlQkV9GuyHPi6VHcL6LiZbL9cVyDcpWqVU2EXFzSCEd+zSfSXVUzhKK/KM4r/ZOVTr6
+4yzbrlTVESFV5Mf2zucJq9zmWs25hhpqOCZYFtKH0weFd25ucf5T09ylR8ahcfjCcN4fBwcSbfWx
+1rwLrlLui+iW9vYWnKvs2xGc6x+5xU/gu21E82yHLaNh88n/NzVhT2duWdEN2ACuW2Aos+Co8Sh+
+OoziHHfb1Y/iO37ZfViV8RMVeQKco1ym3dNet7UvQhNnSEVfmWUFqTIm0+uEij4EIlllBlGbQx0T
+bUgqPlqbEge00F5ccysvouXRKnN9eMbWwjP4ab2L4loIBkNz3OG1KKm368MCnKWV7NGXIrOvgFUI
+bNSGHg35ZpW7lqIylTFM11PUJzFsuZW8PmtvxH5i+8vSoX5gWWwKO5uJj69NbHlhFY+mPAw/bLTY
+tTF3+Tf2DnD34/kT/82JFp/+6Zz/L+/NS/v29tSsNeCXW3CejtSTQEMH8Ij4YE7ps43kaUnVKLEA
+TX2mqStizxB0juQnIvXVEeiaxVxIT+sqGOn4oiL5JKdUVGSxshqh7IjOz8d3ELyOljTYMaztnElu
+i5AghTGALNK14H3TYaw6Au0wbk17XsuKpEgct4SLiVOo74UwJmNaSEPgTMlzOd6egqOp0KcIUcQm
+QFe/wkbYySe6cETTM1rIPmDE1kLD+zdJsd9S6t906IckH9sBmuwcyU91D/lamCUDBTrJyzD+yUwL
+RnXArKzxBOOankp+QO0BprUfKc01d58M7e11WDQFBvT0nO5WcWnmQk0+1FBDDQVYLozGXWTsdvdC
+zy4RrK/rruv+O1D3y1JKGxZ6aFlsCgAicn8/nNG67rruuu6lhZp8qKGGGgpQbwo11FBDAZbTpvCH
+dd113XXdpx6WDU+hhhpqWB6wnDCFGmqoYRnAKd8UROQaEXlMRJ4UkZsWfuO46jpLRL4pIg+LyEMi
+8l7NXysid4vIE/q7ZqGyjqMNTRH5WxG5U/+fIyLf0v5/TkTKyoYnsu7VInKbiDwqIo+IyJUnq+8i
+8q91zB8UkVtFZGip+i4ifyIiO0XkwZBX2U/J8FFtwwMicvkS1P0hHfMHRORLIrI63NuqdT8mIm88
+nrpPFJzSTUFEmmS3B9eSPWH9vIhcPP9bxwUzwPtSSheTHT+/W+u7CfhGSul8sr3VUm5O7yWb9xt8
+EPi9lNJ5ZMW7dy1h3b8PfDWldBHwY9qOJe+7iJxJjtfzqpTSJWRFyLeydH3/Uzz4uEGvfl5Ldrtw
+Pjls6aeWoO67gUtSSpeRXT1sBdC191ayC4hrgE/qN3FqIaV0yi7gSuBr4f9WYOtJrP8vyT5ZHgM2
+ad4m4LElqm8zeUH+Q+BOsjbqbqBVNR4nuO5VZP+fUspf8r6TnQU9S1bzb2nf37iUfSe7fXxwoX6S
+Y9r+fNVzJ6ru0r2fBW7RdGG9A18DrlyK+V/MdarJB1ssBs/h3qaWFERkC/BK4FvAxpSSORd+AffG
+daLhI8C/xVXk1wH7U0pmPrCU/T+HbAv0GSVf/lhERjkJfU8pbQc+TLZL2kG28/kOJ6/v0LufJ3sN
+/iI5ouGpqLsvONWbwikBERkDvgD8ekrpYLyX8pZ9wkUyIvIzwM6U0ncWfHhpoAVcDnwqpfRKslp5
+gVRYwr6vAd5M3pjOIBuGllHskwZL1c+FQERuJpOwt5zsuhcDp3pT2I7704SMXm/v8ewJARFpkzeE
+W1JKX9TsF0Vkk97fhAdsOpFwFXC9iDwNfJZMQvw+sFpEzPBtKfv/HPBcSulb+v828iZxMvr+U8C2
+lNKulNI08EXyeJysvkPvfp6UNSgiN5CDnr1NN6WTVvdi4VRvCt8Gzlcu9ACZ6XL7UlUmIgJ8Gngk
+pfSfw63bgXdo+h1kXsMJhZTS1pTS5pTSFnI//2dK6W3AN8kRwJasbq3/BeBZETEnVVcDD3MS+k4m
+G64QkRGdA6v7pPRdoVc/bwferlKIK4ADgcw4ISAi15DJxutTSjFmy+3AW0VkUETOITM7/++JrPuY
+4FQzNcjxWx4HfgDcvMR1vZaMNj5AjvfxXa1/HZkB+ATwV8DaJW7HTwJ3avpc8kJ4kuxNbnAJ630F
+2bXlA8CXyd7vTkrfgfcDjwIPksMhDi5V34FbybyLaTKG9K5e/SQzez+h6+/7ZAnJia77STLvwNbc
+H4Tnb9a6HwOuXcp11+9VazTWUEMNBTjV5EMNNdSwzKDeFGqooYYC1JtCDTXUUIB6U6ihhhoKUG8K
+NdRQQwHqTaGGGmooQL0p1FBDDQWoN4UaaqihAP8f2c71bCpcCXwAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 0.4906... Generator Loss: 4.7177
+Epoch 1/1... Discriminator Loss: 6.6824... Generator Loss: 12.1958
+Epoch 1/1... Discriminator Loss: 1.2869... Generator Loss: 1.1015
+Epoch 1/1... Discriminator Loss: 0.8712... Generator Loss: 1.2599
+Epoch 1/1... Discriminator Loss: 0.9868... Generator Loss: 2.0520
+Epoch 1/1... Discriminator Loss: 1.0734... Generator Loss: 1.9953
+Epoch 1/1... Discriminator Loss: 1.7425... Generator Loss: 0.3826
+Epoch 1/1... Discriminator Loss: 1.1829... Generator Loss: 1.2268
+Epoch 1/1... Discriminator Loss: 1.4086... Generator Loss: 0.8518
+Epoch 1/1... Discriminator Loss: 1.2163... Generator Loss: 0.7233
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvdnrZdt23/eZc/Vr7f7XnzpVp7m6uro3EpHBKODkIUEI
+QjAYHAiOQ6KAQU+BBPwQkb/AT3mPIYE8BELAAefBEESICcZKIllYtvrbnKbqVPPrdr/6NWcexti/
+c8rWvVXnNlIR9nipXeu391pzzWbMMcf4ju8w3nuOcpSjHOUg9i+7AUc5ylHeLTkqhaMc5SivyVEp
+HOUoR3lNjkrhKEc5ymtyVApHOcpRXpOjUjjKUY7ymvzMlIIx5t83xvypMeZ7xpjf/Fk95yhHOcpP
+V8zPAqdgjAmAPwN+DXgG/A7wH3vv/+in/rCjHOUoP1X5WVkKvwJ8z3v/A+99C/zPwN/4GT3rKEc5
+yk9Rwp/RfR8BT7/y/2fAv/XDvpwliZ8UBd6D9wMA1nu8NwB4PINTi8Y70M89DgA3OJyXz8ZDq9aP
+wXOwgwIPvZH7Wb60jgKM3guM/m7gS23pzJffCWLprtloQmDlG947Am2H9wYbyO8GZ7H0el2+G4QG
+5+SzMQNW389EAW7Q19N72cHhgkDfz+N9rzf2DPoezh1aC36QtnsDg943jCzVcgfAjgF+TKvQ6L+R
+MTirfXH4o5exAmjwHLrW/VhPgsuTBcZ8pW8PY6I3NMbR62eLe7hucfROx9c6Bndop/7eGwL7lc8/
+5PrhXbQ7CezDdMOYL6+HUYhvOgD22nbTNXT6+8hDqx0XYB4mVGYsLpR5ZB/u67H6kM6YLx+If/hO
+b76cn07/td7THMbU+4e2Gb7sfwOgY+adv/Xen/2rff6vys9KKbxRjDG/AfwGwDjP+Vu/+qv0PqCp
+VwAUrqerpXlt0LErZVGYbsdQymAshxaA/a5k18rQxM3AM11h4TDQaPdMPdwbmcojo8rEGGa6gK6N
+IRrkGTvvyLVX93HARH83ef8EgL/57/wak2wibev3TL0svL5NyU9kZJablCK4k+vNSH5/FlKVBQBR
+sibRWRNfLGhW8rljK9d2JfVMnlEtO+p+KQ3a9KyjSK5vB7zZyO820ic+tKx7mYGLqxF/8A/+CQD/
+F2uomjcNi/QxD2ub0JoHBfgoDNgnqbyLUSXkHHknnfWDvsP10ve19w/KZHirp4r8+l//D8gS6aO2
+2TF28l67UpV+0LHUVZiaLetSnpKZmttK2jlJKtZVDMDIyphuOss0k8/bNmJk5b7bLmCSOr0ekvlG
+vy/3HWdQNgclCzudF7OLU9z3XgLw2zrH0hff43knz7gYLE91dc1MiEmkbb8UJ1QnpwAPcywMetJK
+eulVEOIbHSffU/TSzzc2IOpqAGrVhGnT8t3usC46troxhoNHu4UIg810vuybz97Q/fL7t/nSjyFf
+AI+/8v/39dqDeO//PvD3AS4Wc29CR9E64kgGc+g9NpaOTH2FV+26HTxet6m0E6VQOkc/yN93riHQ
+idl4R6sz8i7wBDoIdaCWxmDp9TOdZ69WSttDrYrDlI5NJBPIPpeFORrFnOdyLQvPIFBNXJ9wOnsO
+gDs9YejkfkM5A2BxtYHwEoBwmNBspf0XTyKqjy4ACHYlADf3a+YzecbtRUF/Le/3Z1HHuRXFsQoi
+6t0agOfab4QNqpco6op/OtzLf9q3X5pftScG7x+skJvIcdJJm/pIxqbsDff+S2ulPuxmb/201yVO
+LYtQ+z6ZgJd3DQKZ2CEvSPoMgF3XcRbl8ryg5azOtM2OS1WckakASF2KH6SdV2GGQxZeYgJq3Qzm
+QUo3yPWxvlPZOFISAJrBEHdyv7ja8vvmFQDdU3nn521NL93DxnjiQSbqKuoZads+z3p+wckm4kJR
++m5I2Vi5Nu0bttp7zRCwNtKe3HlqHZldJ//eugGnc2w/ODr3pcV2MCAaPLZs367zVX5WSuF3gG8a
+Yz5ClMHfAv72D/uysZY4LkhCA71ow2Q/4k61fBZBqrv/JIaXoQzSB3NZCObFlkkov3sKpKHuLkNN
+pgPTGU+iO16oCz70EWoo4O3ARjWxt55WZ7UzDqOm6N7JRDNxziScAjCeBtB9C4D7ScQsk0naby3E
+HwNwPR0DcHGyp6vldza6YjOT+04edRR3Mmn66TkAZdySzW8AWHzfsSvk2XbfEhXPAMhLMLEoE2u1
+f8wrKiPf9UFAopOmf22pv70YILAyTVIbkWdi9cwiuV/QW+5qUWT3Q8XgDwqZH+uJcTgmisUaSXMD
+G+mvqpCJnTYGUhmnwi64VquiaAeiqbx37A2rQPo8KGX8x0VIitx3HyXYnVxvU0uKzKPaRMQ7GT+b
+ytwLIrBq+wd9x66XvmiIOFvLd26MLGi6L482Bmi0LzIMfaxKyhXYSxnjD70ohY6E9UYWfxZXbHaq
+NLqQlc77UTzg1Rqeq5X2ojL4QK71ztP+kB53X3MgfiZKwXvfG2P+C+B/R46f/4P3/g9/Fs86ylGO
+8tOVn5lPwXv/j4B/9DbfNcYQJyGjJiCfqqnGnsedaPamXRJkos3LbuBSNgfyXszyD9+PKb8QTTuM
+l8Q3osHvxiHBUrT8dua43Mvr9hN11Kwt7Vx26+S+55la4MtNRx/IzmR7Q6fHlaxVUzTNoZAvR6OE
+0skXnvTg1NIJQ0etu/vjgxM0hXAsPoDEj8hSeUYSTHCXcm+/k3c+z7dElezK9mRD90p2jPenKwbd
+5ey4wTl577NYnlsVGXYtO7exOdVPGHIOA8MCeY/TccY3I2lTsdC+vIGykLadPHds1bozg/+xLIUw
+gkhN/6wIWTrpo1PpNpZhydjIM+7zkifquN2f5VyuZSyrwPNITfNdoNZhmeKsHLWuggWrufiugk3M
+3soR69TOqTLxA/lSLLfSbvBOPu9Dg2nk2WFX8dkgY7bbH8z2L60jDwS6RfvIkOm8GM0KfjEQn8Li
+scz1zcucNH0BQFxPycfy/v1LiOeNvn+Mm4sVs1LLbJd09OprW9sOo74G96/1/Ncbib80R+NXxXhD
+MIT0JgZ0oTQtXp1LVewwajJlxlAG0plhoSaeuWCRyMBWuwmLUz3Y9QPZuXzOHZxdyWK6DaXT548M
+21rukS56zFImzWm0pNqICWeso1XPsY1EG9m6YfCikFy6Iv70YMJZ6lOZKEXRE30ig9dPxFz0JiM7
+kfcLqxsGr4onAhvL575UM7N9CbG8p3N7BnU4uWVFG6ovBUet5+Sy1fNmtUf9s8xNe3A8f20T8hBz
+8GGCncylTZMJ/UTM8mWqyuusY/5KJmvy/jXXLwdtf8Nabemv9ejOUTr1zm+38FIWU7uTPt64HUmg
+/pGyY7DSF3HnCUbSpmzIObhYxrX0YTwB38mY+QDGpX5hGhG34kDurMFXco9uLH9PnKXv5XO322Mi
+mUPeBEw01PQqlIVr29ff1WuUaCBgyKQdxZBwncj1spKxLiZ7wrUoMWaGXo+p8aOWcSMKyV2GlFbG
+If95mVfjlxH2RI6Yr5YNtfpD3I/r0FE5wpyPcpSjvCbvhKWABZt5oqZmFIqeWs1bEq9Ou3QgXelO
+GNScp/J5rA7Di1FCn4hZe/nehOlKduuPFhH957L7r+cplysNN50uAAjub1l9IJo4f7Hlcir3WD0N
+GcWqze8G9urBzh9M8Z4+ERjGsJpym2sYdTghzSTIEq4+ZBuL9TJK9LgSvCB0vyi/GzfYWC0PtySI
+3pPrCz1GbHLCSqIdWTKiiMXT7RaevJT3COqQQUOStpD3SIkIb8U6apIGc4gLfk1LYaxYgauzJ/yS
+RmjCRwuuOnl2OJWxqdo7fu4D3cE+sVSP5bvlpyW/76Xv669xlPBBR4f8zlUNS3urf5C+cpOK7oXs
+umW24SSR3d/MWuJWrBh7vmbuxUSvZzIGYTOhW8iumlYzgkzmTrfL6ObyvEU3ZhkqXqCSpVFGnrzW
+EGloaL6Q6FLmPC7R8Ks6961HkSliZx0W1yROeKLWTz6KuGhlzIpI2ltWLelMrZxmYDZXh+hdRzCV
+3013Pf1Mj9a3OjYfzul20oabSUN3c3Aqdz+mW1nk3VAK3jP0jrCPCRIZrKwcU6uZmDcJ9NLzsy5h
+o61eHHwDbU9mpSN3WMypxtK7kGAuE7YNrYAVgFTDammW0fSiCEZzQ7XXZ78/cHcj5txF9JI/2WuU
+Q8/Tlevwau75uCW+k4l5G2z4jpOBdt2SvJOFswllqlzlEcPmGoDAn9NfHuLfBf1Oz6r38ntcRRJr
+5ORlRVbKO0V3DfOpTIq7riVvpU2DHjsm4Zr7QfEPnfkSefQ1xGBgIkrm/XnMZSCfu64gyRRYVEkf
+Tk3ORk10czZlcXeYsPf84At5J7cvafzb2bR95xkp2MuECW4t0YeuFZM5vg1wRubFxWbKWsOXp+uO
+YiptCsuMdibHjvhOxi6YOGL10TTRgNcQr4l7Aj1v7WloN7qw1Dfi6NgpPKm6e8m+FaVYJQNVo7gI
+HafrtvtS+RqD16NGaD1hLs/GncC5tGm8VTxCG3EXyw+LCYR7DXe7DD3FYkeefKtHXT1W3lYJiyv5
++/zpmjKTDWVfDgyv9ffX2xmOx4ejHOUor8m7YSkYi7UJvR0I1fPszy1Xd6Jp79OQiSv0s+NS/UxR
+JhowD3N8pqCTdkQQqPmZxgwj0a4f7yLKXH6YKvKtHzsuKjX4cs9VKDv7fdvzHXXcLcMLPhgEuZYW
+6rRKRrhEdk8bG4KpqPNv3M+oFB03a1K6R3L9UjV0m3gyddSFk4DYyrsOfiDQtg0KiipWMGzkeekY
+qrG088R19FbaOZ9ZbjM5YszXspvdpQmBlc+7qMC7r39+SCLDXxvJDv0rl4+IRrLLudUNbqx93qqH
+PAzIO41ExHuexPLd9H7gm3N5/8/LjmeKFHxTK3pj2emZJ056ikvFDdzLtV3Sk99KX1WzipmXZ/uz
+DKsOuvZqYL6X+VJdiYUR7S2bExmbfJ2zPlNn9MrRTBSQtJ3DVB2MiprdRgNxqY7t2Rn+XkCBpunZ
+VIqEVFSh58s92RpPplGpk3jM6VQs1kezHfNBAGyjSarfDolKaafFE2dyPUgarnqFa0eOJBaLlEGO
+H4+jCBS9+v7iis1G2hHZGq/WosNjtNffdga8G0rBOYZ9Td0k7BXZFa93BNq8uAcbyPVJBdkBptzL
+AsuyHqPhxiDa0yML1gwNg8JVDRWmlcH1imDr9wGxhoraYcDqfYPAQiyLAvMCiwyozeX3QdVgOpl0
+w2hF8KcSxnKhZTuVwQ3TO3LNCW1PZeDicE6nfgb7uWG4ks9mcoKtJSTl7iVS0fgbfKKT/H5HvZSF
+YLY97Vw+7zqo1X/S6sS8We0YjBy1pn1Apj6abfdmRKNRw3EcZmyv5Ey+DkrGajJPvOFeTeZQPe7G
+hXgNzfU2J9O1Fiwu+biXNnVlw/V9q+/1o6dm0HX0B0RZv8PdioKbqO6+2e8Z4gNs0DLE8nd3m+Kv
+5EvxbYJRsFO01uND2jHaqyeflmB7iIw4zEaVHjVs5HdeocEMY0yi82b5lESv2yTkJNRoliJa7cGh
+AETG0mfyd2MGXCMLObub8XxxyLfR8HRnyA/Ro3wEvfptnCNRNGkX5QwKlzRGlEbcN9QzWQPTIeT9
+D2SDePG9La6RfumcwWimykP+zBvkeHw4ylGO8pq8E5aC857atfTxnulO9NSy6AgVQOLm0D8Tbb4Z
+r7GBfB5P1VzqR3SnshMGTYCNRNOmu4KbqWbPLMMHXEN0LZp6XbR4NdH9ZCC5ld2jzRw+Fe2aFHPm
+e9HAsXrkW9fSBHKtKTtuz6QbV7uAYKGJWZ+dcDsS3P7ca0w8XZLu35dnP0pJNI5vzZJIHYb7TCyX
+cmuJat0RTUCtfqrlLoJUnjcMjiaSPrpVaPdQhLjPNIcjcRDqrtu9aRQgVhh4PAl5rI7dSZ2wOpd2
+9n1LEGmeg47NdjqQqJk8mA6n8Oe9SelmmoD2/ZwokF2x6X+0pdANFYGRe1d9yS5UHIbiVIIJ3L2Q
+v7fxGpOLRTe5AleK89ecN5hed+mxvIepE9pCrM1gZXG5vOuw6nGxWDrRPiVZaP6E5gtkiWeqUPnN
+6Xvcr38g72QNN5qjMBxMfGDQA4TznunhXcOMxso8+2TU8vNe+sJ0EgW7z2LSSMY9dzVjPcZu4gHX
+qPM7NWSN5lKkaqX5iEyTAt+fjmjWYtGeTdeYO/ndzteH1BzKt0x/OVoKRznKUV6Td8JSwHt819Pt
+I/bqUEvWMWWq2YnrmFxDNrZJsXN1/GiYLphG+NUBwhZinTpwbEuxEs2/jgLiQRGCgewM2TZjCDXc
+uIrQDGnyKsIqPLavaxJNZlGfFoPtqWvR5kPoCXe6s/kVJytJVw+oSTdyw10hWvtqU+BGmlx0A34h
+94jChGGlZ/XdIZF/TFTI7/f7DcFOz5PdhJmeLQPbMe2k/bGXd5t3Fa22nTZ82CWMMfwolq3AGCLt
+i8VwwijVUG0A/trpMwoC7dq5JvpHu4BaHZ+D7clL6Vtn16RraUeVhKSay7v7oS0QSa0Bo+PeOLpW
+fDde076HwPCBQtBdnVEritE/7/Bz8ecMz2Oqse7iuu9ZO+DupI864+jUp9A0Hd1e+rk2PdVanp2k
+h98bag1JNjfXGD3vr7AkB54NzRitO8MDMMQYmlT6c+o6Us3cHZVzCisOw1wT19zOE2kCV3ESoQYN
+fuNZKxIyjz2x02xORWMuvSe06j+j5vJCxuykvGKovytt2gegCV8Mb+dTeDeUgjGYIGIYdURWXqBZ
+9Fxs5IXviz1tJZ+Xc8up4gmuZ+qx3UOXSwefNxl1KmCiKEhZZwq8ua75NBGzbaYRh5ssplAz2SUd
+RvPmm8RhO7lfMJ7SrQRbkIQyAa2PidQe93lCmMtEeXx9ThlqqnJe8FQjAx9tZNLczNecx/Ie4Txj
+NMi79mWJVa93l8gEjb5wLNcSRbF2z0sF28SbFZtrnUyTjkHvMSsPabVztomAdOI8fvBC/zDXs9WJ
+nQYRY32/eVqxt7K4axdQTmQSp2VJpArHaLYkFsKNfHdla5pAM1Trnu0gx6e2271RGTy0x1jCAx/G
+6YiFxtvjQKNP7gX7Rl7metKw2EofvZr1BCslPVmkPC5lkXW6sYQuZqn4hvCu4dWgx7x1yVKGhHSI
+6aIDJ4EewYIeq5mtZnxG+1zmlktgc4Ce67/GQKiKN48DLp0s6PF4SheL4iTecltJny9mYu4HxZjp
+IMeHoGmwuTTIRyG5AhWWvmGm2ap3qYxBtvfsVWGZPH3grZhbxy6Wo0leXWN1Z9i/ZQb18fhwlKMc
+5TV5JywF72BoHENl2Y1l10leVmwVRrq+6/CaMNS9qHmmEOROUXxNccP0Xna5myykVUfVNl3zQuHR
+7Cqe3cq9NxqGdKWjDDWxqbQYxSGULaSdWCHb7Q6nOrhXZ15XtayMhOwwd/BK7hs6WDXSzl1cYj6R
+nfLpSHbz7LMJbSNWx9nLPe1Ej0H5CUkocN5nX6iltH7J81qthuqaxkl7WmakUw1PVgVlJ78zygWx
+Baw6+4ZwYKphzbIbHkg44CtAR/ulCWx0NtQm5+VOr3db5podWvqUeyMWQp3LDrauImjl2XdlS6RO
+2cgOZMoR0PuOiWbp1PxoMTbExNK3dv+UtNIQ4Vba/hkNI02aq56VrPQ4M9xZ7jUcfLEOeZopG1Yg
+u/xosuNOyauqfc2LjViNqW9INczaRgVWk/DauVgmuzriTMd/v7wGRdwShJwp8U8dK/qxswR6fBjb
+GH+iOJQ4ZOp0R+8XrBp1OmqDzu2WXZRo38/Jevnuy63DKunOKxcy9dJ7mVozO5PijMwXawZqdWaO
+kpRIHZdRlGIO2WH7t7PX3gmlgHEMtqZLOtpWz9FTT7hRT27h2d5JU19gmHvND8hlQQzllO+PpPM+
+cCPysSxGtwn4zCkhx7pjUyio6ZVMsM3YMo81QjBNMI16ugNDq0QuZjamUSxAJI+gdg2Rl2NC3w5c
+F2q2rjI6xdovnhV8qpRfp+r2rc4/53IjOQ5/MjNc6oQ/nW0xSif1eaeAny+gupD8itmN4UY94Em+
+5YOZKLJkBJHCqquFrI7LfsedHq9MZwgVbGQ3llC95V89WUaqHuIgY6xOk31QU9TSb7s65V79IOcJ
+XFm9hzIJvaxror2euZsda4WQm9Zyo5O4r3vWb4mhSk4cuUKli5OUV+qXqSJlYPKO9Ss9PvYdhZMx
+zUcd3Ur64s8WAx8okGmqR6lmlfFUPfXJqmajFHrVekCHifPEEiqTk6nlnXzuYRAlFU8uKG+kHZNk
+YJdoo+8PcGaH1+cSwAd6lp/FGU6Pv+uw5qqXiRQ4UVif5o4TKwr0IliRKM7mbujplKaty5dEt/LA
+61w6cREZ5iMF5JmCl3pfP8qZdoJ/Xm2fEn7NTIjj8eEoRznKa/JOWArea6y3jOgV7mtXsE7UCbi1
+DwitdCh5rnvd6YG+1vbMFM5bTSLO9b4mifiGshl/kQyMNVqxVI063FZcKxGKc3sulCNgGo9oD7Dc
+/YpCiUxa3VHaoSXWrLfSDQwr+byk5OxGtHy9WjHqxCn1fUXd/fI2ox7kJqNuYDiT541z6D4Tc/V0
+K+19FpZcNeKIur17TlPL9btRyvkjucfsJuRcIwadRiqCaEt8Lbp+FyUEauJOc8O97n7GQ6hmbqRo
+vZSBVBN7fBlhD4lpnWWsFpLxIempvF/Syn0vqpqlQm2tj6A8OChb+r2M5dY64gfG5x8tKSlzgXIQ
+riJ6dQRvD9yQ3tKu5RljX/LKK69DB2ksbV6soFfLK1AHdBAnnNzJizynI66lzbddS3ynz0hgEUkf
+FSPpl2zI6J0gVnf7O2IlOFllEakiLzOF25eNpVMGbmzEKpM+utxWNAp3LPcjzLmOeyVWSVp3YGX8
+T2YxfinX329qnirsP3dzKkWOxndigb0sQkKjDkob8CiS53URrJ3MvSwIaaMH7u23kndCKQBgwecB
+ieK+7TRmXsvnuljjFQCUupQzzRIrEg1fJguSTDMq45xBPcuzuCBRJXPKjrtC7heqV7jtHFYXQsg9
+Va8hK9vz4jC5hxH3lWY2Knjpg87QKoQ3WUSMMlko87ZDx4WwMxSpmIdnGgH5+UfnBKEQeoynBRyU
+3tKTnMuzL2o5GnxnUUKpDD2/kNGoH6QKey57TeU+K8gGhTHrgt9tTzAnynNpC9C/921Lom1znSHR
+iXKm0Z5FPmWqqdzxqOXxSDkjiTnw1odFSKB4/1jHIIw9mYYn/d5RZ3KPfGt5lqg2KR3S4jdLh8Vp
+intyOeJqr0QkuuDTa89esyF3vSdRn8mTUYDRNOpxAsVUgEyJhvGKdMZuKvc4IWGTKDFKXBNV2s48
+xw6ymFI1uTvf8OqQRRmcsW7kyNrvZEMAaDVq5QJPopGI6TTiY688njPDQqMnF7OObyYSto5OZD46
+F2H0SNwsHWdKwnI+PcOoz6g3IZ3GgwMli+kCCI0cV5MoptboStA19JHkV3j7fSLNsXlbOR4fjnKU
+o7wm74SlYDBELsR0higVbTfetIzGskOlfUxyoYy6FfTxYTsWzZmPAnKlvXahJ9aEkSHpSRMF+pgU
+p6aW87ILRrV7oMtumbA/cP/t1lT3svN+b7OiVx7+cDjEsAMGhRfbrmG0FtP+vZMx0UiptE7PGDbi
+lBqN5D2Kk1OSXBO39gbXyA6VnKQYJTKx+vcn7Yj+XHf5IKGrNSux3xK9JzuNH2JWmv9v1tLG2jTs
+DmEE03Kl5CvD2LNSPr8gNMz1qBRN5d8kzMnV2TUJRzT6d49lHsnvhi6h1uNIdSAYaTqUg4Y28HiN
+MqTpQLxUp3FgULQyb4LPpF2AizQZbblmpFbfSKHUm3NHamSnjOueQOdAkqZESlRyEni8Wje5mt9D
+7vBatGGUhTilTp8EYyaFRFTWdsBqYlKrllA1eALliby//ZSNHoN863iSipn/A4WmZ12Idg+PmxD/
+RB2K9pRIHd4nfkSr0SiroKes3uIVKt3lCVul2+tsz1wBTvUoI4n1aN0eyF1KerWOgiDCKWYF09Co
+RdPZiCj+epbCO6EUMJ4hbImNx+qCrqYBI6XkTqc7sljO18NJwqJXD3GkPIo+QkGFxGVMP5JZGgzQ
+TvXc18VcqbnXKKlnWVSMlMVotwupWyXBXHe8KGVBb0pLqYs37Q6AloZkkI5OnKObac0Jm/KhknEW
++elDpaNMAUSjRyVOPR5uCOhuFRU4HmPUNFycS9vXTcjJTDH+51cMSu+9vbkmL6Q9ZR1xrxyFgyI+
+x517eN4uaHFzaVvydMxHel5+1SdEemw6ELEOszkTr5ySo4RznZitHdPYA+nJQKdn41iLreyTkGgs
+zzjZNbS5jM16E9EV8rxh/2ZlcBCXO/JO+nBSBNz2YnbXyjD1QTBnc0gl2fcPeTBFsgNNr+/GLbmS
+/g76zvGQYHUujOKEsfqjmnREaOS9p61hrWjBnUYqtrYhUEUXmUs27Z8CkPmBtdZaCp9r0SJ6Go0+
+fOZr/rqSxfBowXipinVhuYjkXSJNhd74jEY3pFHvQBUZcYidyL2vLFglDGo6mZvRJsRZabur7UMh
+pSGB0UiiD7fL75N1b1cE6CDH48NRjnKU1+SdsBS8h66ztJUn0Tz9oo5pFKSTtAULxcOHPkGZ14hT
+JZ0oDWGrO+YoYaxYfF+uOa3EFB0YyLR00sFzvg1ibpQgxESe1a2YXEFT41di7jXtDqdOxZ3mpbcO
+UjUjK9fgKnEeVmfZg6UzqwJizXX3Y9G9+flj/Ev5e+u3OOWKTE5P6a8lnu4PxWIaQ3zIuPw0p9Pd
+apg/JlVq+LbaMj44uzrZtk5HL7lXWHUaGCIFcn38Xsiznewk+TCm0ONBfSE747fXA9PiUOjkhBPl
+bEg3hkpZp1fhlDNF6x4sF0pDo/F4O4Urddp1acVWiU5WYYM5hG7eEDM3xhJo7N0Njlp37lo99dF8
+w2M9liTxjJWSuozSnAPt9lBDN1GG6U5p83zJxU7L8PUd0UjaE3mDVQDQxgA7Lc2mlmLsB9Y6Fzbb
+GwJ9j40tLhJ6AAAgAElEQVSNuGz1iDXS48XO0KhTr60jfk+trf9w2RCp4zYJppwpPX5UKTjN1+yU
+e+HRScGgeS5usKjvkPNLyNREqgOZN7cTBw/VskqeJGJJ7DcNcScFg0bG0pqvF334sS0FY8xjY8z/
+aYz5I2PMHxpj/ku9vjDG/JYx5rv67/zHfcZRjnKUv3j5SSyFHvi73vvfM8aMgX9mjPkt4D8H/g/v
+/d8zxvwm8JvAf/2jbuQBh8OHjlozGZOxI9dzdjHaM4Tq2Csail40fh0Kmi1MMjYarx+3IfUhCSbp
+cZqHn5QQaMw7UGLXoQ+xSsW7pKTW8Nyq6nnRy05RtoZWT8Sh7kS+g0HLtJXeEM7kGbPckGpoqT2z
+pHN1aObKMjx43KWyL28XpMom5bcp9sDOpHDm8W5FcCf61J9vsYpyy+9r2q3uYuOebXWofShsz/dt
+RDFRx+jonFRDkk3nmSpr0KI3XJ7LLj5Hy65dpKBhv3O7IzwwOI/HD8VuMhp6relYKOvxLgsItN5A
+aB2pMhEv7z3JSM/OW//WtSVdH9Jp/ctuHnCgcpqpk7fftjjNYCyLnoXiEZrCkylG4NpnjNUicRqq
+jusaci1DWDq8tj+ZhpSKSTGbiDLTWhzdoTaD47o7BFQnNFoA2LUBd5VYEHsl3d3iMHp+DxYxlzvl
+AJlbHulcOE1KwkSswSjVQkTFnESDtkk6JlKEbDi2mEo5IEpPOHmocQjAeOv5QpGZUWK4VcYt35X4
+SMoJDmaN+ZqWwo+tFLz3L4AX+nlrjPljpAT93wD+Xf3a/wj8Y96gFHDgWkPXGgatCWlvoT2XF75f
+B4SFRg5aGDQLst5pqSh/x1oXrBsMfaPpxK6mVFNsEuS8XIrCmYVaq89ZrHZ6Vw9sG3nGbbDGqbOu
+8fVDcQ13iBAYy8aJeXrp7hm/1MxILHeqvJIUevVk+zPBOZjwIwhlobR3LaSfy7NHlzgF5GyVQMTV
+NU6px4Oup10rVLp5QeNFyWyXa+71d0sljllNlngtlhPUS2aZfPf+3PBEK1O7fOBU6fNvNXaf7T1j
+Tcmuk4haM1R90rBTaHNRDlRa1HfdHujVBgKj7MJDSKiUX/nUEK1lMvahRZMn31iB2veeVShOsvTu
+M9KtOtLuZbF9Pyo5OfjNqoFKwxrVdoTRmo6bMH4ALw16NDD9npVGkaI+ZK3e/stgQ3iIJA0lsZYD
+W/fq9U8MY41EPNu9YNAj5BCEzDTU8CpQZdNamkNh4m3L75ZyJDx9AV5h48E8JVJHeRhLe8ugZXQA
+yGUxrQK16rUj0LyTfRqRHyqPK61gXW+ptS+WVc+Ngqm2fcWLtUDkN94/FFJ6W/mpOBqNMR8CfwX4
+f4ALVRgAL4GLH/Kb3zDG/K4x5nfr9ut5R49ylKP87OQndjQaY0bAPwD+K+/9xjxUHwHvvTc/RE19
+tRT96XTqe0pc4BlqdeCNAkr9PF3U9IMcGe58yWyrRTESKbwSLC3PNCtsfucIc62luI/4VE2ti1FG
+qqXd9/fy92cY7reyc79Y9XyyFgtkuapZ6o7YDRanxwerEOXO7qgV5rytKrpBTOa+6fi2hud6MpZK
+p5atlezz7AWREsB0Z3u6T/W+4acP1ZX3CFtwGQWca5jRBDEbLSCyX4eUVmC3tt6z24k5+9KI4y+3
+GdEB0xAkNKH8fbhN6SL53f1mQX0q1y+doCY3w8BKjfxJ1zDWGgLpPuNpqrDwrX2of+m0fuLn3nOi
+1GbTYsBpgtVt6dlrHL5p3FtXvXb5mlITqWh2aF0bbq1e855Xylq89StO1dlsFy/IbxQ3sLin38jR
+K8vF4cY656VmPs2jGbkS33T7nlt1CAZ7qNSLvW/EShuCmk0rO/rAKbte7pf4mlc611utL9m4gUF3
+60+6iidK6vIvQkelqM9NuONDDTOeaZLU9ryCpR5nulu8WqHXZsAq5qSIKkJ1kN9r6LHd96z0SBzt
+G7atXL8fAkwmjufm9jMS//U23Z9IKRhjIkQh/E/e+/9VL78yxlx5718YY66A67e4E36I6KuOrUKX
+o2XKbq6L5taSJ5oKWlYsZ1rieyumnEs8Uy0kVPmYWCdmdrcitYdCH/UD2cSgvws3W7obGfBXuxXd
+RibCsm3xyp/ovlJU41C4M/YhYaTZgPuK261Cs92G3YmyDa3uyPS8Xn8s7Z2NTun1jB/XAf5Qo7GI
+cc+lm/KdmM5tskXTEqg/8dgvpB137Fgotud+ZRlrfkGi5vD5fE+w1KInQ8pM6eyTmWOndR5fUdG+
+VI+5vsd3tjEHPpYqLzhVk3nsLd/YaeyeFKv08cFelUqSEWn1psgaRhwAUC3X6l0ncA9e9DfJZZAR
+aR3H8bOORh6D18Iy25OayUr6Ytp7bidy4ye1IVY4+cfLAqvVlzgwUrkSo36nOoqxqrCrwRBdKz6l
+bLm7F8XZdjIXbtsetGDvfXmNV9bsDZ6PFAuxUuVd1V85HjWOT1cKXmt/wOc6Qc3+hCuNRBTq1zm7
+nzDSbNbpyZjgC+nj2gW0ShkwjmK6tULrNRpy1xoSxTfUneGs1apdTYlbi/IyrmPffr1l/pNEHwzw
+3wN/7L3/b7/yp/8N+HX9/OvAP/xxn3GUoxzlL15+Ekvh3wb+U+BfGmP+uV77b4C/B/wvxpi/A3wG
+/EdvupHH0/mePvT06unu855AE3SIGgbd8esJTBHtv5/LjjH3BfmJaMnFbs9Tq3z6UU6i8d+wWfHy
+TrT/fiPf/WS34k93sjM8W264Vc9x0zn6P6/MmWIT7u0er+XMylnMYy2MMvtWwShVvsYUkrEm9qRa
+us4a4rnyF7RjksO20o8J35N3HS0UY/ByxfBKqdvyNb0yKs9XDbXu/kVa007kWHX1SpxQV/4S975c
+a03K03+ulYhvKlYKIS9eNuwvpD8Xz6R/Psk65gvlDqSnSTVLsEvYK6x4xIa17raxetDTNCOJFDfB
+jlZj6J9UDd9TOra6H97oYDzIbRzxDd3dh59b8I2X8t7PfkGp1G4MnZX7bnPPRCHI12PLN3VHXM3O
+udJoxY3WWAiqBHMotLPp+ExRhcU+4oVCqXeN52UjluP1VnbjZd2zVWf0tpPCPQCmgS+Uz7GstZCP
+hwMOYzAQKBz5E9PQ6NHFpbecttKfj5RMJZw+ItUCL00Vkiu+ZRRnuPKltq0nVqdqowWqo3XLSo9a
+aTTiXp28t1XDvhFrsew3h2DFW8tPEn34J/zwSoW/+vVuZrB9iKtrjOK7q7WnU1aTfRNQqJnYtQF7
+zS5sNf21SXumhYTkgjBkpuHC7qyh+1zhqjF8V2sJJt2nANzc7OiUB/GuaRmGw+D+6PPvqZ0+sNws
+li3BRgYu+70x97+ixWeGgtAdzvZyNLDDJU7BL6ay+FSjFnaJ2cqivv9TrVdpr+mUvtxvP2P5QinO
++zVcyXXXWbYaGjkfFPr9QUSogB8zi/lAsfGfhgOztfTRS1ei9Wr5ffW/PB4y9jrhvz2fcqugr3DS
+gTqCg9Ci5QwpY5mMfudINYP1tpqTa0HYXVeSari0ffA0vLlK0aU5JRtL7vTC3nMXi49l9gdyrz+8
+fE6ooeNt50HrdN4tDVYrdS2aiiYTX4nXo8b9rGP1SuZLOdR8rkCgUXBLoMcct1tyq0cFV8mCXXct
+KOlq74Yv228800BBT7rgzfCVUpIe2oMq7ODZXrMZKfi/tXhQWGrOzMXAoGHr2oSMG/EN7G9DevUV
+vQqmJL1c3+3lnc1mzVqzRCvXslIlZIaOpWb2dgP4v4zow1GOcpT//8g7AXMGh7MlbvAMnWjGvc25
+1aSUyTxmpXBklyUcoDC9luLa1QH36k0eNw2NRhdeBSUvNUqwCZesNnJ9rdwEvoYX6gHrBmjfsube
+drZh8Wey933xwT35UzGZ/+yv3PFvKsdfevYxgWIE9q1YEnX5fU5fyY7+bARxK7tg3FbErdzv95SS
+fpM/J13L38cYGuXwG6KETE3mKIRAeRFeSfo8/8ZuzF5rrI3imPtz2V3iP2r4rmZBXg8hg+6wkbJA
+P58FzFt59neDhiuNalAGvNBMxDM2hzKG3Cmnw33XMy2UKm1/x7V633cbw1P16jv39nUM6yd3TNcS
+0b4J/oRVKjv+b31HMB3984GtclBmYcZ+UO7KfUN/J+3/kxPPtzT5Ce3XZ4HnuSa0Zaan3UtfPKVl
+qliGMHHESin/1IsFWbeeXueb9+bB2Wy9p5ko0Olern016eur79sbR9fLMz4tOpprsab+WKne/3C9
+41vvyzjNXizJJjKm31+GMFXcTrUmfWDu1/qZSUKpmIVN29LqMebOt4RG2azNHUn/tuloIu+EUrDG
+MAoSvN3QtocaAy2H00nXQqbeWd/3jJVU0znJOJyEPTSy2CZ9S/ieHLpm3/XEilBb3kd8pIw397cH
+ctWKldJtd9bRH0BK/LBJrICXrmD7RCbVWb3ns0qUwvl3O8x3NCR5N4AqofiRtCeKU5ZaPzB+6sg1
+ZTc9OYWXogy/rQw85baleKITsA1olE1pPQ7JdTKaXUCnRIGTc12AY8MiVyDTi5iplomPL2GifIfL
+0mAPRXoVmMSuJdF8gGI9Jr9U4pimJ9f8CtMGlJqXMLsRs3UcACvNDJ1YTg6K12z4VBdkbDxvKAz1
+INHK8EJT3K9Mw+9+pqHmnYLMDGRbDYFOGpzyHG5Dx0KPNKsqItQsyFgrPj1e9RT60ttlS3FIT24c
+obIUfbJrSdRcTzTK0AUDCljEGP9gWhsTEmmdzlCZl/qaP3/iOAhD6efTXcbFXOZLrCnN4yFgttTC
+tlenTDVz9zExVgl0w8uAWaNHhb0C1pIAtAbG7X6HbUU53zYdqZHPuQkwCqg7EOy+SY7Hh6Mc5Siv
+ybthKQQByXRE7Bz37uD1rogKMYHiYkPldbfNDZXSXMVqWtX2lJk/MApPMQcmwEcTHrtrvUeGVY67
+27kAO8bNDwiUdo3acLAEDP7PVfgPu0RyyuNKYKSv+id8/JFyH5pLIqVa70YX1GPFECSyM9RRSR4/
+AaBIBwYvDrXGDuRa5XmuxBz5+n0aBblEbU7/gRay+aKk1qy8KDgnfKLAm41mA7obqk7OEu2kY5E9
+AuC6/wGpWiGzpGLfK8+C4uLHo3MKdVqNzieMCt1ppx89eMbLxpFpFqtVZ99JsKbUe/l+yyoRtuqh
+6RgUTm4wvO0BIu+u6LbCWfBF+Qh/8QkA/XPlGDi3nD75SPozviFXSygaNSRbefYHZofTyknu4LGf
+z5gp6KsjpdGddFWlZN1z6a897L1GTDSPYujdAw1+NJgHT35k4NRrLUg9HkotyS/lMF8SGzFC2j+f
+Z3w4lf567+OfB+B0HxKqFZtGEE3EAv7QtpTKJ9ElHTaRbFx7IceP8AcNd17avo9HrBXGbZOQmcLY
+bdg+VBPXAt5vlKOlcJSjHOU1MT+qvuBflLyXjf3f+fiv8t89ajC//TsA3JYDkfuSAfirIS3ltaRX
+ZqM8MIShaPZLG7A7oPGMQ/1ejDvHc/1PeGDVGRyFxrNfOI/1B2o2j+Y+0cNDPUarjDj/2a/+Gk6L
+dGz7mrlSqHVDzPiQPdjDREu97RXHMIn6Bx/GNK5Zq2MvtzUrLfwy0czJdRMTKmXYpjFMlWFpXxmu
+NZR1v1ryxSGDrz0wJ3viw3uOU/7u3/5P5P1PTnClMhX3FXtNsGpjecb90hG0ksCzqyNSPXPflgG5
+ZqOWTfqQPbnVEmRpsKfvDrUOLK0iK/OooVci3DSG+lCHITsg8GImGn7euIzT92Rne/WyptnJO+3a
+PdPDYAfy91FsqbUGxjSuaAbZSaepZ6M1Q2bJlrbX2owKwd40ISOtHdHWIUaZlvcVpMq2TZ0wTBSG
+ruOUpCWBErdGJ2MaxUJkpwWf/Pa/BOBZJfftt2viTCyo3AQYJbE1XUusVcxNC1YZpjstfxcHDoYD
+G3dAow7RUeCw6ujaemiW4vt4Xonllm0a/kD9JEPdU2oyVuZgr8+YE9ArlmXz/NU/897/Vd4g78Tx
+YRW0/MPZJ/T/tGOldR69e50O/KuqqznUYNWrrYNBuRZvE8dUs/36APYKhtq7ASX2ZauOs33v2RzK
+iTv/WrX2r/prD0zyB4xBU/d0ejHwhj6Qe5xFCVazPJMmJlB+yBMFYYXBmommQPehY6JAoNTvmXh1
+IikM+iIJCZzmH/QhS8UjZElCrFyLjXc06pit9e9ugEEnR7CpmCyUvCNLsep0C+oVt7qOUyOAhSkF
+lcbSp0RYzUSdMKNVnsAxIYkyNDfqvCqbkFbj9fI3vd71hLqQE7uhcFqJS2smzqKCUMvav+8G7CHr
+clezbbTYT9ui65EzpV0bJRVTZTP2EVwk8rw0u+EyFrO7T3sS5NknWsmqY0atc2RIQzKt/1mOCyqF
+indxSqhj1seykLquY5joUSkbCBXUZQLL/6vKeXlQsH2F1fFbpJ6RKkA8OM2YjYFENyVrDkcUjzLe
+MQpaUk1Pdz0EWg3t1HW8UCBeosmSf+Q7nBaL2XvHoQDYDkCzg+9tSzR8vY3/eHw4ylGO8pq8E5ZC
+1g388rMdL4o1GhF6S7eUOoAC81AROg8sY60dMQ09Xh2Xm6HGaZ56qaa2855mOJQc542ou8P10NuH
+PPYgikgHPT4UAVEpu1g2N8R6lIgKtQjKjGKk4ashZ68hrahJOFWTEXUQ3QQZ2V4Zl+uGU42l3/YT
+vq2W0H63505zjlrdJhwPaGwGZ0iVGTnNM3It/FDWE7zWWjxUmqbaMxrEUff9KOUjNT93SYrRNj0f
+RizUghi0jmffTniulG9jt6FVtF3UxNyppWBdgNFq2rnXWpRJTKp4iy5OKdQpO5gApRagcYZU4/Db
+XM3yPmc+1RBokNPN5P0e2QlOMxwDztmk8pyricb5u5hhkGSz5yZhobwW9bJir2Xgv+9jJrFsw1Wp
+YxOcslUHbRbvMQdy2Cg5cNJgFSbd1ANprNXIk4hIneNnY0dv1GpKIjI96gaHenVdQhXJvJjHYNSS
+TV3ISsOWH4cV+WeC1ci12vrLVxXXmhD1r7PY+IfrXf22FDci74RSaJ3ns6aiuX07hp7D4i3Up7Ao
+IhadTMx4EfLRwfN6YgnVTv5i5khfysT6Qtl4yqajUvCS4WuwAwUWpWIkCcEoO9BJnpGeK+FGl2EK
+ufdlLF7j4arGaNp3G/XkvdykjO4x93rO1CzEhZvSKKMTS1hq4daimNIr8GY6LSl2srA2XznwfAm1
+HR4qRIUewpkSLPYdVzr0h0U8H3XcKQbhF/d7zELyNeZjT9NqURsGbCgedz9VxqPtwEwJTarB4/Xo
+ssruKfScXIaeyV5BZJm831U0o1EFmW869gcOygE6deK4AQYFXJ1rBazFtOPUKPi/MHw01nP7rCc9
+ZGXaKY+Tg0JW/woZnfofMt/gNWrRjC2tEwX5Yd3QqqIK1S+1MhWnqYyNCQZCLSLTBzH3gxaW1TNO
+GzkyxTeE45D3DhWrzlP8PtGmpWTKKBZmB7hyjFX/U+J6nJYwsLUn1zIH2doyfizv/UoLGnfBHYcs
+8x85d/1b1qBXOR4fjnKUo7wm74Sl4I2nsZ4+dPAWSs0EmiWoOAYWp0zV4RRGMYkiHlsbUGgJtZNl
+QfCRfH61lt+f2IpKC7aUvsPpbvWmo0vQGaxWqO6jFDiUVUtBC44Ui4hIodnpXCyUoY8Za60AvwSj
+SaDpMCF6T3aNfqUZjs6x0egE555YsQf7XcDmQ92503N+Tjn6bp5pNuTQPzicjAnolAasDM4wr7RS
+9mdbQvXw94r1yEzHRFGDiZ9gKoUSjyK8YhP6NoRErY3xobR8SLQXczaylk6zEvtlQaWJVFln0KQ9
+kk5euukd/a1iCArP+MAJicEpLZ41llC5EqPDO9UJxZUWtVlDmutWWcVMCt3lN/cEifR5FCg9XlbR
+rTXTtrqnfai7WD3wObZVRR9Im9ZqoUTbirudXJuUjlbfbxzfk6gltNLaErQRVq2KcZjjRmJt9UFM
+qDB8a8Grg9WrhZWENYN+7oMQq32R5Z6o0zl5GuKv5V2+dSl//5NlxFqp4N46DfUt5J1QCoGBWezp
+WoPly7PxnycRhqkWHPn3cplp0c99iwtNeR1OUs4Vl9qf5ezuNHX2kSf5QguhKkHcXZlSn6m5+6Ji
+iUywwf3oo4SLWtDKSqG1zDQd+lE8Ypgo8apbYC9E4cwCrf0X74mtLOhmsiLfigKIw1uM07BRKMCU
+vJoQKtiqLnPWev20eI/prZzro4tHjO9kQn4+1XyPe0OjdPgWcBqyatprJso/+DK+eyCTnWlmYbY5
+oRsdipIWZCNRIFH8mGqsUZB6RmjkO4cQ4a27Jwqk7SPu6TX7sCmWhFpopgorwntNKZ/Ie8z9GVsl
+KE3uQno1cb2vBMEDuK570EELNedH5y1ZL0Cv+mpFpHT+QXpDaAQY1p61jENZkGms5L7pCWUn79qb
+glCzCFNS7tS/YMcQa6GVy3tRXi/smizW3IigoljJWO/HAU7zR3jgXFxR6OF2ngW8P1VSlKyg1YhD
+FIWMFJpsIiUgHlrCUN45SxqySO7XNzVGj3+2XPGhboJ3g1bgnd1QKAx6+0MAdz+OHI8PRznKUV6T
+d8JS6Lzh+WA5i2Jedn9+0obVI8M4HDObiZfcfkt2nyeRY6JZhDuXkZyp83AHk1jj5nVEfqVm2a3s
+mI8vI5ZaRn46uaNUC6Pph4eaiK9rX3VxNiFO6ciGUcSwlLbdpEvsnZKQXNVEK837v1RK9m1MUKhZ
+2oxoR7K721WMDbVEfS27x9pWhPdadi5siDWKsuv2FKGSngwl3/yG7Ij/otFkpu4z/kxNf0NKpdDX
+rhuzSeWzv1tQ6o74nvarbXecKHtHfeoZq9ltqIlKLeU3qhlpvUL1pzHvF3Sh1ntsLZWiPcI646l2
+XlGFtEqnN9akrE1RMlwr5iFKKRSo1buQQROCvOvpter0F5189+p5xHtPZMefbFPKM3nIyTAhUq9b
+0IW4MyUfCcVacfuekfJp1P2STLkP/arkRI98dxXMDjUk1XKZ1DFLTeyaLgxDrceZ3DH0B+p0PYpV
+nr0W/rlvQ54pHd+3jTg6AfogYNDdP1dHpRkymlTeIyUm84cIVcL2cJQoxqxvpA80/47z+wvikfTV
+q23zU7MU3gml4JwwzpR9yaGbv3pEikzIJJEB/aXJKU/ek2Z/ZyYL4mS+oFebZ7FvHuoXBPOCSutD
+ZrnDVRoy0DJHu+WKD6wMaNWNWOmEvl1XbHSSuq8gPg9mVR92OPUKh7ZiGCn+npD0Sq57bxgO0YNO
+FlI/Gqg1bOFnjkwVwDDe4xVg0uuRY3o/Y/2eZlmuctqxoA2nu4KukEjEuTnjqWbM/bJ64f+oOuW+
+l+9aa+n1WFIFDbkWXU3OHO9Vh4KMeva2Ed2l3GuBodb3K4IxVj3/4zrEKNIx0QXRjztOrrV+ZFQR
+q9LYLmpmz3WBnefMXsnnl+e1jlPG7Zk8r7j23B7CZsY9FNkd6oFAKejnqvSzacVeiWmjK8dZoN8d
+G5zWCE2mEYk7sHYpp2QQMYyUmPYup621b2cxbiFtOn8eUOkimzsx1XfhmvdqraY16tDIKXdhTKmK
+o9eQtIscsR7XRmPLYyUD8nFCqEeiGLAHB4mGUJO8Z6r3sOlApO9KWjNXNOzAmnghbcqdzJFi4omf
+yzhYw4Mv6SeV4/HhKEc5ymvyTlgKgTVM04hXcYBRYFHgIVDTNh5nfJiJyfz+YsyVOu5axZZ31BTq
+pQ0J2Csgx3etBOiBYTdQKd7d7/W+s4KJOqqycMO57maBfUm70Z277x7oya05aPsUX6rjaJSRNFrV
+6tTgd/Kd8KIhuD1YBbIT9U3AeCZa3jwNsFod2/uAKJLP4fJQr3JNWGlsO+4J91plihqvcXEbNYwm
+0gfp+ccAXLHhuRX70tqEbi+Otm03xkTiPBw+X9OoB283l/Yks4FMOF3op4bEamRnuiG5VhM8rLAj
+xQVoJatkF9O5Q/QhoteqL+E6JjxULNo6FEZCUqoTjQFzK1ZcG3lO1Ip5HufUigxLYsNCc0Jy5X0I
+mjmB9mH6RQzTQybmmGAuVkH0NMNPpT9dLu21wQZzowVu+hVOt8O+q0Fhw4VzDIp1aGL5/Wzd0isn
+aLP3bMfS5tQMjDUTs1QK/FGTMY7EUpr4gLrTLF/b4RWQlDhHq0CsSOkGTQNGqfOHOsVpnc7YWTrF
+UHgizEa+k6tj969lU/7xQhym0Uvz1jT6b5KjpXCUoxzlNXknLIUB2FgPfchEz/JbAmJFtv1CmjGd
+SZx+OjLsBSDISSbfHTVjNhOlYwsGssO516XcRKJpwyFhrDHduR6+blrPWJ026XZMqHnzn3zesVWU
+YlXah1qDh3oSBC1WaxO4GFJFzU1thl8ozLeb4U9091d2YjvpsJq95y96yeQC4qwnLkX7lwtpr13m
+hIXsJMFtQJdL2MxsC5gpmOPaUCmG4GQmFsrWPeL9lWz5WZzQ7NVKCUpSdeDdFR21OtpOT9XZ9+qU
+9Uh24JN4hNFnZP059blaSLuOQOtyWCfWw35S0SmeIopaAsWAmHxJp+AEfzYQfKIh3MlG+ztl9Vjr
+ZXxiaNRJaGzLSHfSwfUE2replqaLFjVFKYlPq3lJqjUn4klF4OX67jIl07oOYax96ArKhbxTeR1j
+9D1Mb9kr41R3m9Eqa7b9nrTtemRJlSC4G0F0o+xVJ+AUb1Bo5ec62xA7bS81I2WdDinY6jj5ICZV
+d0euWaTl1NGr7yvOahJlEVtFNUGr/qiwpdC+2GpJv++N50xKXcKWnxpW4Z1QCtYY4iAgzWN29QG4
+EpJqNZTd+YRfVqdMBgROVvJIU2WjICBTnjyTjcgUxpx2lrmag5soJgm1Bt9eJsF06Km0oG1Y9IwV
+jPLk/Y7lMxmklblhUMeWPzgJu4i6VLLCLGC/0sKmo5p4LwM6m9Q0WkXJnYg5mL1M0eQ7+l3C/8fe
+m2aKpOkAACAASURBVMTaluXpXb+1++60t3tdRmRkpLPKLpXStgrbINwIzwzCEwshGADy2EJiAIYR
+AyOZEZQYABYIPEAyyCPEwBLCtrCMKJCrbFdVNpHRZLz34t1329Pus/u9GKzv3Kq0KyszKtLFs3SX
+lIqb9517drf2Wv/ma9L5US7cA4XP3q2bMftsxAp/X5sKf+UmQuMZxmtxIrqOhWzgOz3KzK+Yqp/d
+JSOt0GBemVMKmONVEX0oANBW0mDeQLCVO9LEshCugHlEstEsLgbEHmdUkyjeJex9GcyOI2UkfMPq
+CaXYo/atxQjSG2zdd22ijva18BS+JVWqMZopZe1eptBYmspd140KeNObjPnCHTxfeWwFZJpsMpA7
+V7Ly8SSVHwVahLcdyV50aRMQmSOI6pasdA/lrbGEKpr6EmfJd9EDUzOqAmJtVNMREOCo1gtf1ZaD
+OAx3hxBP7MoPhp5CzmDDxCeWhJ4v0cVoB4lS4SDwCEWXzvYpe0nrxXGK2aijkrj5dGYKeOZwL/5H
+a356JczffXzl9MEY4xtjfs0Y87/p/39gjPkVY8zHxpj/2RgxeR7H43gc/1yMn0Wk8O8D3wW0B/Kf
+A/+FtfZvGGP+G+AvAv/1T/wWawg6S66VNhwNT9WC+ZY3Z/lUVmi+ZepLk98ci1A+1albfWeHkVEK
+v35S0ErtOLnruBERXU7mrGPDVA330T9QzNzu2Hk5JwrXh3rOwXcyXuGxOOXX2FxhMi2dwrqh7SFz
+hb2mjLnJ3LHPbhWhTMsHcRZmU+JSRa2kxVOBbi1iDNeWK4mbZIcDN1pa501CJVGQ3DOsFTMm8qtk
+sSB643aUKIpA/hRhZkmkgZAuLU+3rlhbyQu4bjMuVZx7vmr5QmzI885ilm4HWlQhjXGf8RI3dfal
+h72SN+e4Idm7SOGTCIqVu5Z9WKEInZWgyOdlxmbmLsr7YscX0ogwY0cgYRVjR+xCfqLy64zzFc3e
+taLfTgeevXX/fvfk/oEdmkxPWUr4pauEaMwDarVRg1XP25Vr28bdmo+FBZjuR1axu75CMOhmEjKt
+3C5e5WCle9FEI8N4ZLa6uWBjg692eJZGzHqnVTHE55TyOk13IWulaUiItYsnLORTarsO5IJdJz2e
+7uFNuCGVr9+b/ihD2NGvlB6+K+mDMeYF8K8C/xnwH8hK7l8B/i195K8D/yk/YVEIjMdFnDHMQxqJ
+rBQm4FyAj3oBaykMJdsDW1XOjU6/S+4527sHsyamkjFKGe64PYbwVc3brXgJgg+H3kAt8Y6AhDhU
+fnpiOVu7iX6o92ytm5AmVA5sE6xUjPrUB72QdTJQ3UgN6vRA/8Y96KuJQuNNRKlFI3+7YZgd88WM
+uUQ/vhD4aTiseL099sxLrCC1bTLl6BdqJzHVcEyhpGh0v8dImMNLDcoS6IaYnZSkk7cV28Ddg+uD
+O8ddcsn4qfvi7+cQ7dSdOL/k6Y00GsMYI3h0LHzHp5chmyunUfj9qmFauZetGhvu5BY1DAOHEz1L
+CZKs+i2HG70cQcdMqcbOjzB6fnHQkmjh7GPxCw4Z+4Xy86uKy1yqSC8nlKXTK3xqtnSqsfiJC6/T
+cMVLuWG1qys+Fb6jqDc0owxXvFNaSfTbQunopqbXy19Zj1J+lcZYMr28peDaeeMxE1gqxqPELV5j
+0MNa99tP6a/cd6wCt/BexBveirsz1lOS2C2sr1cWb+vO7WYcKWR/EKuelRLSCG6/uDG4p/DVk4iv
+mj78l8B/yG9RFU6AtbX2yON9DTz/nf7wt1vRd8PPkM3xOB7H4/hK4/ccKRhj/jXg2lr7D4wxf+bL
+/v1vt6KfTRLrnUec3Pr0mVs9uyrGaFWeDMMDUWo1WiLJlC2MWH2HGT9UNfksDsl8FQHblDcSVgma
+gSpWBV8V8iqFqVyXlyH40u+vK0MkP79F9Zz7+hMAQkGfvaBFSmp0oU+t7TisenrBmO0qZScBjGIj
+YlN+8xDmX0cDT1S1n2QdtfQGXom0Y24btpnbMdo3I6VYhk/bhkIErLGFteDRc8mPebM5zaW6Mq2l
+VzfHeAPoXrydDJQr6VQKQWm/SHgpROdyuyN+4nb8xd0Z/2jmFu1naUshERLxe/iNzR2HW/kvFjfc
+X7lr3scjF7HgyKcRgUxnuuQoYzfDf092a5/3HGQuEyYjcgjEjxJHPgP88dgBWBOuXfvppfFYbF34
+XJ7cMNfvvztreHaQP0WoZzb6/KB1z//ubcNO/f3py5FrYSAu0gPT5+44fj3Rc2o5EfnLZgcCqdps
+FhYvE0Jyq0ixGAilhRAHLbngzGkZ8FLbZNjtMIGLQgM9/88ieK60ZBptQTqQb5saX5HcPlnjqVC6
+qaTvMB3JJLvmRQZldl85UviqBrP/ujHmzwEJrqbwy8DcGBMoWngBfPGTvsgzPpN4ytmHI2+lQ73x
+C6xCsT6fEV0pKbUN160LmaaJDGIwZLoTjR/wRBXb4JBycXAP4JKeXGCgfS9QzarmTvLkuQ+FQuK5
+H7ESFLVKXlFIg7BSB6TufMbGnacdwKptdOW3ZEdFonFL1bsX8q11k7GowWhyj4Flq9v/JA/xxW04
+vXeffW1KrLgP11XNKMPTwwReKH0Iipzz8NihUEtve0Mmtmc9+HTC18ejAcmWB9uAgxaLXI5UQ7Wj
+kADpJ4HHN9RmWAc9Rk5c67OC01xpQCmzmNWOa4Ftio3P3ZFrsS35VIv6c+/AeezOs1Bef5fWdLfq
+MjAyt9IUJHiQ6Peth1e7e7sSW/JsO6Gu3fnMmi2fqGvzfhSyk4oWhwPbc/d3z0+Ujt0mnN27DefK
+7pjuBMfebx6Uk152Hu/rOFPj5tB89DmozpM0Fl8ak3njYftjW+LoP+lxUHfirk8YGjdf2nKDEYjs
+1WA5nbr7kqkMNzuM2ExpwCTE37q58PXuwKeeOBFdxr3MXCxuQWuaE9JjmnRjMD+tYedPGL/n9MFa
++x9ba19Ya78O/JvA37bW/tvA3wH+gj72aEX/OB7HP2fjnwVO4T8C/oYx5q8Avwb89z/pDzxjyEKf
+8RDw5MyFbS+2MacfOkWSaZ/jCwjUdSFLWaqfLsVj9+fM9LsgSEAahnmWMTuVUu+upBbkOQmP1nQd
+VoSg2GwJUsF5m5ZUuyrBBbEAUNFRydzvGSTiEaeGSPDpeVGQSYrdHnJ8RSbnIsPEEfRHqfLQkk1V
+5Gxqion77HwhU5fKZ6M4ehrnWLFH/SgnxWkS+J7HqALjfnuUPjtj3bl0x+99PMkE2y7Ak4TaYpJT
+NEfNhaMmQMBS3Z6TCJ5L+dlET5lLVi5IUnq5MRdzd74vTmYMkigb/BmL2O3GpjxQR5ISizYsVNAd
+clXhu5CbiYqA2zV3jTvPBMugyn+chsTCHlxIYyAI9sSSiw+TJ8RKG5/MphC5wuY89fDVweglV5fP
+I57WksVLG3rjui+Hfs2oSK41Ian0FxL9u40rh1ADtl5N/KCYHDAqCmtEWuqynlDksdkkYqnozXYB
+voqOedBzMlHhOnbXf5JAJE1Jv2yYqJC+mMz5uqKQnRlZSH+B2j2DIfHxPjvqXHoPvplfNVT4mSwK
+1tq/C/xd/fwp8Md+Ft/7OB7H4/j9H+8EojHwfU5nc1piwiNk1it5oZ12fV6TNULVVSsXDQBxdBTA
+NGRHNeO2J1KbakhGArWYnsbBA2ahP6LSGNiPKkSmJxwdYBoToO4djV/SibYdK6qIvBSLW8FzL2au
+vPYkT8hVMDKnAeOdizBq5f3WWo49wvQwMChHHMOATirAgaKYJQGZ9P8HPyBVfrofOizufEwU0LdH
+jwC3w13f37DrlJP3hswKC5BmTIUKnK13nM4lX6dcP1icUUuOzZqB4EQWZdFIKJhv05QIZPrQIjWx
+5RtyTzY59HMHNfZ2e0pFbN00IJJY6U4aE5XtGNSmO8RQaIc1+4xa9ZVFZDkXTPv8QhJ8YUH0DeFJ
+yi3omUbLOZHuc7g3jNJfiGdHZSZLKGzFN/wJvSzyhnRKcxAOod1zUHTjCwnarz3q1t3b2335sAkX
+9YFctaZ97u7FvEtYCo34xPNJpeURTj3s1n0HRBjR3ONEdQTTPcjAhWHAqDkQRHChKKWILUYF0dm5
+izqqjcfwwilU/+G3K/6+3ubtlzOZ/qfGO7EoGB/8mWX6ds6ThQs/L/0Je70oz4scT92AvrFkEq8o
+CjeJfRuxOYqFlAEiPhLiEU9l3pGlLGMVojqFsn5FVrrfdd4eIwGNu7EhlLLxYjznIJfq9NhpjTqy
+o77eJCBUwTMLgoeXJs6m9EobFtJTaOtrWvHmd0mHL9BLkw5k4iJ4c32vyZir8Dk2c2qx5dL1yE74
++rEy7OShuZUJyTguWdeua5HbAavwegFMJPnFk4JR8O7ZC6VB2YKuF0O1bokFXuqHCXsxA7ubhmNt
+LVMhcnIaglKDk7nH2Et6jjW9OhF9vuVw5c6vTAWD7jK8J8KbfB7QqZhp0oGpIMaTKKPVscdjyrAc
+mc/dgtX5F6SSxk+SPZ1x59GmUN9L18ITa7VIyE4V5vceS6VrrfXZ15K3u0yIxSTdql1wZ/Y0Kg6X
+QY8nLczmxDAk6i6txL4tKkZJ2bd0nEk9u0gLKi3CeePjh26B8D0BpDwPT2xQm44EYuMGS8ORXvo0
+bPBad29LFcrT0FDdCrqdJQT7o8DpV1sVHlmSj+NxPI4fGe9EpAAGb4iZvgelhCgP/hRPLLI2sXxr
+I8JQlNNIsHU60U7TxASNWx2racSptuuhP5AKEmu8nvlM0lZ7F7Y1fUq50KraTrlTCD6t4eaoBNRc
+Usi8wyq0tE1AJcahHxwYWxfFtJOOQCo9F34L6lNb+TUO4ZJ6LThr7+FN3I5wQoo+womKYTZtyVKF
+9jak1TG2EwvSg9iZPadKHzZbiY/uXhMqLVmTMMgbY7QWlD7Yck6/lAaE8BYnFaB24xhnRIUr2rXr
+AbvR+RctS6E6+4PDK0zaE47Vt9lkIN4KT2Bn7J/oFtYJ3VSYhUu3233CSKb0qg1GpkJhftF7BP0R
+/t2Qi0DVPBHZyY+Yqx/vzwOMTGvi5YL+iCEoW3r32JnO3HWMh5KwltCv55MLFTm7TZkKeXl30lCV
+Rxizw2mc1iEfiVQXlVs6QYzHOmTUvW8k/DoeetbaZzdNxFRGNh/4I77IeMPUEhcX+rz7+2050M/c
+3y1MgBVmY1ovaY27t6fTkFh4iEat3lWfUj532MDoo08ZgqPSF19pvBOLwjhaDoeWdlMhiTzycOTJ
+hboMNmKUpLifxpwKEttFYgB6lr0gp2k7oCyA2GuRMQ9x04BAH57w9Kb3SHbuYVwOVw9Osrd+Q668
+fJMXxOIajLn7ssHrsaoNtINPn7uHaDilmOqz6SmhhDrSzk26rb0jVQvjOjAsBDjq/YFUXYsmkVZj
+7zNockSZT606SXzX8UWkUHsceSmex0f3DuT6poy4b1x3gsEQqKYQmJFOZqtB2FLoPEJBZ4flQKDr
+T8OY0arjMG/pRYHOhj2DsBoq9FPuOhLhMcwhwTuRpyUpyZ27ls1wINS1vE3lUbmGt0v3wp+82XI1
+6vq8EEJJ4RHT6Fll4nAYNnTzI7N1JI30gOsefynUU5ui9fYBSxCcWQLxD4JDjSdtTu+kw4ptO+k8
+7gSBjwWyuuo6EgG9tmmEt3L/3pqAWnWLoRGlOR45URcsm8acJW7hbOKnxHquk2BCbSSuIor41npM
+VUc5BC2ZAHVMLVOBvsahwRcmodO8idYDzcZBu+9D82Du+1XHY/rwOB7H4/iR8U5ECgZDSMBtFPGH
+VOnu+xui107f/9WzDqvKcXafMKpg2FWuz9t6a/aKmYb9gK9dvhtLWhl5NMbnXkYkC6ER/T6jLt2u
+u2p9Ool5rrsGaoVo3ZpGacBUDDiv8zjUR/k3S7N2K3g7P7CWBFswbxjkeN0mrni13nvUvdvFNzvD
+IOTevoat3I5HEbiaNKMt3cp/kq4wVkjC/YFGMO37+y2f7xyDcyeCz6vNPaPcla2tMerKVOGSuHSf
+9bcHmsJFOrfaoUc6EvlF1OmOYXChQNdcs9q6+xyVW7y5dAH28sjYN/SqwrfGMht2ukeWZueQd4fe
+Yy/v+r3Sw3Hfs9q4+7IPYaIuQlVDPwgjYVrSOxU5T1yqsVknJJFg5f3AToVLwyn9xoX8q/uCcHTX
+2pw4rIt5s6UTQjTwKg7yG036kf6ge7u6Yid49N3KHWNbDw/6G2/3d/S6n7MOYhWb7yJFB72PJ3GX
+sC652bgcJgxqjNKqVbyikphuZ3V/Sv9BINiLQzZCm3abA70ipL6P6RKJthxh16t77oWmXR5qUAfj
+K9YZ341FYaBjba7Zvhn4PPwYgP3+hI+fuJv2vvcMI5ORzOseJLDDwIVOxTbnZeRetvzWI585ZLU9
+5Fzq5Z1Mp5ynbgHopZpxWd3haRFacSA9CCA1lhzNIrP5FLs+TnR3vg17OuXnddfTacGiHBjnR0l1
+n7Uq/5IUdCxFN2+5TNfMRKmO7R35W5drv1b69DWTM5m78622Ple+WKKrkcud+8Lr62sub10d5Adr
+99+y9RikgOKPI0N8BCndI6Eg9l7HIPnrQKHs2J/xppYRTR9jrfge+4DP9bN/H7Gwr9zPqnFcHgbm
+S9cWOw8qWomirKprTKvaR7mmF2W8PTg6sQmWpFO9/LeWWi/TGHQP6UPSRlSp4N1SV06S3UMN561p
+mYxH/e9XD+pMt/GK8UoM0+j7ABTekqv6mBpMONPzC3ufq9LNs7q03NWyrlcLcdusadRmtklGq/td
+B4bdsUajtncVldRS2brrS+ZS+e6bKVdSA8sqnzpw8zMTnPmVd89Urdp4uiWTyvfLtuSicPNwHtd0
+nbufH7fuOpqd5QdfuMXv+0NL1/9swEuP6cPjeByP40fGOxEpeD2k64Eivmf/yu0o3+sCntxJsCTq
++NONC2cbfGr1fF90quiGA+d30lAwkEYuTfDfbtmochkOhkDqu91Barp3B24lJRZ3B1otsNMqoBS5
+JtjuyY42X5I+s21AU2lHYWD3Vqy2aEv+hTvPcFFiVWi7lzzcuIFRBcri3hBoRe/rJaOAPKnguU24
+R7wuDkOML179trN0ilz67YarvfAE7dGDoH8QGxkZCRRVBaFPJ4b7YR3QCT57VFG+v7vF28lC772G
+J0cnaXqe3rtC485CHLjKeXDpzmcaRLSNK6jZQ8pG4ib97chW97araiphK04EFLq2HbEMdfowYiHA
+zmXvCpYA+6J/2E33ogBO7mCfueN5rWH7gXsmpzanlMrI9DY+1pSZpW7X9d/ULCvpH4SWQLv8+gqM
+8BTXbUtg3XEEX+GsD3mrn9NyxyDiVl8HmEapHm4ujPuOO/375rbnVaCOQl5i9+5erJchE9n3VSpm
+Tq5DGrGZ4mFGIObnnPAB1BbbkHHvrjUXuWqzWlENR0Zwx/BT+6b/7uOdWBSqYeQ79wfsF7CS3l14
+v+f6A/dkT18NfDyTL+OTC2a6+JUw+e+ZCSZySLqz+2t28jC8mOW8yIW2G1eUwxHn7x7Atugwop7e
+RT3TRjWAaUQky8Q4n9HrxQsDt/Ds6obDUQJiqKhnChMHCKbus7Wd0clLcKbJuDnvWO7cuc0XF+T6
+kjf5hvjeffdRnLPo4EZahae9z1Wq1KayvDy433+vHFiVmty6tuG3RY5mHBnVLWD0MQLTzEZIwmMH
+xuXkfhhSL2UIazM2kp+fekvy1N3b+e6atf4un8k8NfdBHZC7Q0kuOfQqs4xKUfZJ92BLvz13/316
+H3M/de3XaGUfjFLtENClx2vJGE/l1KSF3os2jMeW86Qn6V29YxeXFLE7p5k55cxqocLNoeI8eBDm
+nazv2K7ddUc2YpWJSVpVfKHFJ9WiUocppnJzxGdO77kUazQBK20MO0EI27gmUzenmSyYizp+GAva
+hWjWvc9W6vmLYaZzuyBTSlAWPZkWDeKOSOCsbb1jGrm5c71xx/v+ruaVmKZvh/HRS/JxPI7H8c9m
+vBORQozH+7bg+3REa7eiboY9xWduxfz7T3t+0Tj/yCf+JZNChaaN+++YronEHGy9kGrvVutLU2Pe
+uJW0S0eupVQyDV2BsrztMIKM7gIPTzvJ2A+EquTW+5oxUq9Y//VbGCv1qMeRcquq/WlPtXOr/+Tc
+YxB3o5N4y9D4jBP3HUV1RyLuRnqoKCVkcrhR+DnNWK1cuFgXV1jdl3214Vbpg9mvOciI5XfaJixO
+6xLA+HPGg8Kf+wFPFva3smXrxx6jFGTsXBcAwE7XBJ1w+ybG1/F2SonKL0Y2qmBuvZQk+NRd661P
+6KtIGFrMUdtQsvax7Wi2gnEbn4ns3w6thUpiN/mO0ElIUgt45k0CUl/mMy3sBvfcwzqExIXS/mFD
+/0BpFUS5b2neuGOshxvWuvemuuT6VmFfdeBeGIFUaVBjeiKV8++bFa2i1NBYYmkmjhxxGgMHCQDZ
+yzWrxEVCk+lALWVu39tTC5Dl5dKT6O+JhLMZbc8w0zO7GigFDNseTjh4riDaqiA+3dxyOR61J+xX
+xSw9jMdI4XE8jsfxI+OdiBT6YODudMPwnQNvtOpuB59BhiyLTcjLp+738/Gc35TufXit1gwpN8oz
+0+AW/6X73uoUmlJ1hLSm2zjU372KOr4X4Gklrq1P0LhdoAlBUADCyYLdvWv7ROr/tzTQySPBu6cS
+Cm4Yagax8voxYZhLn0FeFuPYUm7csa8Dy1w2YKuqYt3oZyEJk7amFwpwexcQC847dj7N3kVQP2Sk
+127141Quu0AqTPWWQK2+fRbwVvvKXEg7U6V4KspuhwPdxl1ze3XKnQxfJ1GNbVzUsNf3X7beQzGs
+3V8+9O6nk4BUGIg6NBhhBBoh/voxwUpPorr1GD3l5bZ88Kfw9wO9UJhWU/VsapioTnBIQ4LWXdNh
+/wWnvduZLz2PTJiSmaTwTBrxUkHVLtxR3rsQJKostZCc1sBODNxq7c6n9Ab2gYqA/YK1kU1b19Gq
+zRh0x+usqGXqculXLI/mxVHIIXcFz77ccOhdxOa9dRHDqjCcCJvg1zUbFX+vARMd8Td3D7D5Xsf7
+9XRkFJqy/ZlVFN6RRcGzhnwMWD5JuLnT22jMsV1N5PX41wJuXOw437niYXjqwE3T/S2+WH9nlWX8
+0D2A5KbhRuD+fmsxMzcJC/lVVlXFvbgBBRVWtNe0g+oIIx8r3tdDMp5k5mOfPnYLzFh7hAI6lfc9
+qb6Dk4pIYhiB4KxZl5FKyj2ufAqF60WS8kKr0A8VvgZVTyCOR+55jFp43gAvpB78RTnSie7dHdXG
+f3uhEY+JdxSLsQ+FtnHvcSKGZizZ95nvg6rpUZEzJu7exuGeoXT3dtl0tAIDPb+2+veebuuKdraY
+MI3FjdhbGqkOJ01FKmp0VLlFcwwsvUBBkzh8gCMfwoyxkemJB548PTM5VvnXKdGZFJd3B1IpYsf+
+N/FViZ9sE85HAZxeuOJjcQ2+9B6rtseeyNhnZ9hu3GpxnxpSsU6Ho3T+OHCrzWDDhueaL9ab0mXu
+M22u1LXxCeSb3t4b6qnOwR84DSTUk0UkR5bvRC985XOibGeYLllIdTzxe4zaIMFiji+l813u7vfV
+dcJGOpE3+0fX6cfxOB7HP6PxTkQKgQlZBs9pq49ZqIXWRi1W5lIT4/F09i0AwgTiMxcmzuUwUpx+
+iwsV/lpjMFqtvQ9ivmVdhHHXZBwUSntCjIXnt2Rv3Op7iG/prSS42DHgziOPKgh/a3cDyL2ERLoB
+Nj2QC+pokgQj45h99Qyj4mGvVMMreirrjr2IDnSxPBTCkVF986/LAKZuE9pEhbhqjgleu+Ptelrt
+8lX4W+xQX+HjiOXB8hKJxwDpWDEG7tinL0r8yuEN+twdr+9P6M4lstLmDKlESpqnnIhROPY+1ghi
+fuqmzrN4w37j6JDVdMQIbcjTDclrd54305JQlnRVLqGadUC0cM9p2GVsJOmWBtEDc7NrUrKpWtSe
+oMHTDetWQi6TA71xkYu/qEisYwxeFCNGGIFez6w9T5jKOyK8j7g7tiHDFPvctS+zjw9soqOGhYuU
+2v4OZXQUYU8THG9uD/Xx+Sk9DAyF0rnZbE6sdm/ZnxEUR7fqKZFs78bBXcdp1mCFoTFJR6OfF+mW
+duN+buOSHa7YHog52fq3eINIfqbFU3TzT6IVvizO0Vj7s8tFfq9jHif2T754wXf6mlgU4DgPWWqB
+CBdToqNunx9ilLcuNXl2fcRSoiibJmKhnHTXe8wUXm3rkInUgrbSspuklsPobvo0spT6/XJqqQSM
+ml7kVI2kupeus3D3xTVHL/Oq2zOTIk5vUjLl32ULc2k79r0LF5cLn11zXOjW1I07dpaPbGQUGqBF
+pYzwtNhsqogsdYvboU45VeV/O6ScyWhmK2DL3C+5rt0idv6Bzx/9F/48AE/++C+R3zrI793VJ6z/
+lvu7T87d7y6/G9AFDsIcNwGt6gvlwRD57tht45PoJdsK05H5O/a6V2EwspM8eR7XVI37fRRbGuXB
+hYRJmj5kLpetakiZPxWo6bM3D+E6mxv0eHgqtmd1viTaHzUjG86lTFQvZg5dBVR285Ai3skzst1s
+Wa8cxDpZ7fn7qvM0Vc1aZi/eaFXR+imGMfyvf+2/BWD2S3/cXdvNr3Lz2T92p/63A3594e7t/ece
+jUTNp03CTinW0Ws0iHb4Ymp6afIAZ/b9A6Mov51vaISvSVTLYIiIYxkiETBVvabpAxaCp2z6hJNn
+bj78lb/2N/6BtfaXftKlPaYPj+NxPI4fGe9E+tDYgU+6Lftby0q97XBjaRWinowNo3YE34wsQxXu
+VJSLrEcgjYX384zYutB35kf0qlYugpSxl6SZdp3Wa3kmnTwz7shz7SqmZ1FIE7AdmUjUIpZ35b2x
++OpHT8MAqx298GMmuTvGaXQGYs9NBhdmXpyu8b0XAPShxyBo9mJ5T9u4MLg8tDrGhCBzbZTtzjOA
+swAAIABJREFU7YTSyJwlWTKduSLn2WaOlefl89CF8EO45/xalfPOcPaLbrf+8CSjmbhjnGSG//FP
+fAeAf0kdkP/3F2dMXrpw9jJueRZJXzJZ0PbufnZeQiGZtmPqc2gNmdI839swEYqxbBsmxm3z1tsS
+GikRWxGDoow0ll4G5oHNeWkbxp3cnMMB7yCdAZHLnvst49w9p8RMSPV9H85Gyok7p6HK2KxdxPW1
+3H3vqygnOrii429GJTORmD6z4wPz8UvFzNZy+kfc3Pn5c/cct5Nzzu3PA/Bf/dIP+NMSp/k/hzlP
+BH+/zwaexopSQjlGdz2t/B1O8pbWSlbukLARgrQwB3q9A606LjZKyAIxflkSSD26SCZkSj3no/mn
+84mfMN6JRcG3llk9cOcdQOCWwRvpS/fAD+EJF2o5JnFCIjns0EiIdBoTqwVl45BgL8GVPMQ/5lmJ
+oTvSmpVqJOR4uQQ7ypSjP/Y0ivATCW8khtoqRFO7yvcSQl9gk6AhGt3D7eKMqfL2KPLwjWMPdicu
+P302f4onPUOve8q91IFeTM7p1H3o925if7cIKcTg9KqKufLoH44Z8b1k4Gew7NzPW1XC823O9sRd
+U36y51nxTXc+RUzSuO/eNCf8yyv30tzEjgE52bX0mas/+IcJ86XLs+l8EO/kcmG4kMz4IKapbRe8
+FqM0rhNKdRSSzucKV3eJe0OvMDcQiGmIM0LZz3t5wVG9PDAZLybqtKxGGgmKdAq5bZGyUD3HN6dk
+hfu++TctJ1dHk92Um5lb1E7ec3n92W/2lKm7/u57IV8YF9pfv60fUoYva174teIb7lpn7rgpMXed
+WxT+5A9f87GgzbOyppvKV/NwwnLhFng7HOfCkrXYlefzCiMdz3bvMQpcF/cbGkHa28pN1HWQE0uc
+JcgikiNArEhJj4pbQU4w+XKrwldKH4wxc2PM3zTGfM8Y811jzL9ojFkaY/53Y8wP9N/FVznG43gc
+j+P3d3zVSOGXgb9lrf0LxpgIyID/BPg/rLV/1Rjzl4G/jDOI+bGjB1YMRK1BEnnkbUckF+j3vJEX
+kuAax4YXC+1A5zJQaVN6Va+n/gn1hXrCbUGrMDgZZ2xFVjL3gruGO2a+26La9AbaI2y6JZXe2MiO
+SFLrR7t4LzLEWom9tKBX3Pl+EhAKWDXxIgaZy7yn9MNbBOSqHBMv+MC68wnSKZlSjX3uoo5vbe74
+QuIfp+uBTyYuhH9vP3ItVeKv3cH1h+6cfkEWe6v3R55/5sLIyyAgPOopeCE2dtcaDL9KpbDza6/d
+NTdRTCfF6NnpikyagckIjQqX06GiLxSFiWm67zv+wMFFVTdpQqZq+A+9hjPpF6zjBtVDOSiimcUZ
+4dQdIxoto3Qw+8Qj2YjZeFoTXckpXOnc2d4n/5rAZw0UMgla1gn2iZSf1wNm5q7vtHTPcfX1iugT
+93enf/CS2d9xkdf3ogPm+AC/JAAoVjQZHIVQJucEzd8DYHNi+PZL9yx/0yvYDy5i+fppx/nCpXFR
+qjRgc8IzzZsiXoDvvq/etISVouXaPPiC3gXHTgaMSsFmo09buH8/D0LMXJiT0qeKvtze/1UMZmfA
+nwL+XQBrbQu0xpg/D/wZfeyv40xiftdFwRgIA58+NRhVtU0ckakC3mQLOnMU2TAMAuHEAraEs5BE
+6j9RODJqEmcZxBLn8IOBdu++zxO70gwJQeAmXb1L8VSj8MuObe8q7oPtaLTgeJ0ANINPq9w5NCOR
+aM1jbx9YivFphyfdxMC69CHIZiRn7oEG2zcPdz9MUgK5QY2d1J3qiom6LPWkJ90cLeobMuXD3dIy
+37lJ36vLEv4QEgnMPvegP7iW47BqsfzQfd9H38X8Pffz//3kB+7v2w8xUp6axDGpwER2GhFJycrU
+A4E4CmEi56XtQDse6ecDdXIEZ5kHVmpfDWwztRb3spzvqwdtRO+iRyxiMpPjqQ3pNxMudF/m1j3T
+ZpGRjPJLaNZ4eyFSjXPMAhyfRfTjupCylPXwLrTJfH5C84fFYP21gm3n6jKb7sslEFYajeORZ92v
+QejXk//rc37lvc8AKJv3qbVALocAo7qMp9ZjFqwfcpc8ibH+EWXqMTsK/3gNnTaOQO/I0LUuvQPq
+WUssVasmqQhXSh8mlsj8/qUPH+B0hP4HY8yvGWP+O2NMDlxYa0Vj4S1w8Tv98W+3oh9+VlCsx/E4
+HsdXHl8lfQiAPwr8JWvtrxhjfhmXKjwMa601xvyOb/xvt6IvktBOspDVyjCL3A4djx7PEhdGfvMs
+J0zcjh/Or3miCn68dKt9Pk7gqdutT5oLDgvJp9UB3VSOz9slY+J2zdYt5oSzAzOlDHZZ4d270Hdf
+VMylfXdnWiYb931rAW+sbx50IrPGYyOtg5mZk104yGuwfZ/DwhXrTlL5IMaXeP0fAqCeVSTqSoR+
+iYkFyFm46KIpl+QHhxuoXhUksWMflrMnmMLtUNXbOYtfcBeTxT/nzu3FG/jcXV+dhYQignTtgvDX
+v+uuKXjJ35WG4QcyEDmcJExH7WBJQpy6+5mQsdY9TMIFUaifBdXdZvegaAx/i7dyO1cXHkj0+0PU
+EW7EGM3c96aRRzgVnLf0sEc5+2QgXrsd8UXeM+z1e7FLz6hZJi46qruGXK5doV2RBa6we+ttGT33
+e7N3z/zkYsGNRF3SDxdkv+bmzh/75pbZP3Tn8SvDluFLdCKi2KUEo1ID/5M3NKeuiPid+YETcS3G
+LON56/bJtojIpTE5S1wa0UU10SgIftzipe7e9uMeZHOQVwc8Sfs3srJv/IwhdHMvKFP6ubu3uT/B
+CArvVx51/uVUnr9KpPAaeG2t/RX9/7+JWySujHFld/33+isc43E8jsfx+zx+z5GCtfatMeaVMebn
+rLXfB/4s8B39798B/io/pRW9tR6DzTjPKq4GtQ2zJaXafp+28EdSt6OdbCeMZyKSyCAmPA0ISrET
+8z3cKFedhfjSUGiiA/WbI/9due4ONvJQqK8DPM/lsvV2pNMK3NYdOxWiatUW2iYhkkGIzRoi+TyW
+WcVc2IPA25Ov3flvde5n7RQrRaDw/oT2ybE4GjI2bneMdy7b8ocvyIRsi5OG6do1cV5XHaGW2eh0
+xfDaRTcvT90vs08rzJkYlWHJau2Kh0+m0CTuMy//8RLvXvnuH3Tne/Jxw0Q1nJUXMJeNnakbzlqx
++SYt86M9nbaT5X7CjWoKi/3IRoWvi23EayHw0j6kC48qx1Jj8ntS9ebL6UgoItkwRA8KUX1nqeW1
+MdWOGZ16xGt3vKAOqOSHkSxTxlt3rcU+4FZ1o3zh/r26bknU7rVdx/m33PNd/Oo57z9zx/6Ny56m
+cb+vx5+ch2/XLio8E/Gpj295+9HRAeclhxeu0Lj4QUOqVnS9nzDXc0+P7NpDzCDId5ZkjGKXTts5
+O8/NlyiIsZGrkywkaffWi5mIjNcnPaPs6zbTgULRVj8ZoftykcJX7T78JeB/UufhU+Dfw0Uf/4sx
+5i/iAtl/4yd+izHgeVgbPmgUPskML566G3l6lhM3bqJXS8tEvATz5MiotHSulse0mjI+UUGm7alV
+gMu3GVaVanN/ND5tyeTIVJ8fiFaioQY1Ra8XK2+Ib1UlVkpRBv6D4IoxIYGMUZ54U/yJm5hDP8Us
+3XFOVKXuZiNRJLn0k5hEE30MwFMf32gyF3XItTQhoxh235Ac3Uchd+dusUmuDB996MLjP1h/DYDV
+2cBMys6fmY6fF8ag7D8l8dxkerHsqJ/LXevWhdzTZzGe5OOeHmr28lhZ+gWD4MiLraXVteTybayK
+PRPhA27Oa+I7mbbMGp5cCyqdDLDWz1qEiy6kDyQLUrccVEQjsPgq4p7MMkKJj1xkgqNXA8gZLDtt
+mSk79bqRRNheM4dzSaiNKphOpwlGRdCv7zpaFeV+/oMlqcxsvnG44fbG3fM3AgINv0seMdRu0lW5
+wzzEXsT83B3jw7nPof4AgOTCspdx0QfjAeQxGmbuOryzlFwdHML+gc1qznom4lLceT6ZtCvXL9z5
+Tq4HDhMpm28jNqdil9aWvfAbfj1SJ79P3QcAa+0/BH4nLPWf/Srf+zgex+P4/2+8E4hGz3hk0YQu
+35AdxSzDlInYjuPBwxerbSwjrIQ9R63q/ose714MkGLAbNSjTQfSUmG+6fAl0urLt9DbptijRdl9
+w3jsi+0NvUhOzarE+kpH7NHiPadrjiSojuKtK/CYhU8rO7J+8pbsE7X1zo5u1aewdEUpvt8zzLQb
+LZdYaQGMQiuOfY13XODDmvZKvXlTM1y5471NEvjM3YPLmStwtZ+X7J6465y2A+UbV2j0X36b/CAh
+k48/IrpzEcQnX3fFzPdvC/JbMQfjAC9zWJA22eK/FivJ60HQ5d6o4FjDUQgs6UNQES2v4geEaDB4
+1HLmRunDZuhAMnb38z3TXg7NJgfdb7qQTNJ6R/GWVRAzrdy1+teGQJFl92LK0Ks1vDaMEmAtly5l
+qHdrOs2FumvZFGpxXhuSZy5a/Eb5nEEGLfe6F4eh/7FFx7uPXeG2/+wXAUjZsvkHLi2zt1M+WzgS
+1OmVebAhJC1oz3RvhaXM3gaMRjoO+ZRenqa2ivBLKUUPPbU08tqNxF1aQ7tWaktNJ9Gh0Y60jRCy
+s45YCMifdrwTi4IxFhP2xGNILHpz4IGnyTY97SmPunaTkr5wL052IsUcOyGSUWw4hozqc9vBg9zd
+wGhnSBR21huBmJIBZDIznkWUl+7YXeSR6aU3ixReue/rpdDTdRWDmGrjoeNW+XLRjUSiIo/3p9zM
+9PCkwhsVe6aD65wcnheEenB+UBL2MqmdHoVAQjw5CY1eT3oU1jgk+E/kaPRRind6hMy+5373jQpz
+6RbIOh+pK7kXhSv8145+fTk55bOF+ukSp9n3U+rCnXtsEgqpXqX1nO1TLRbrCF+Am0hQ8n1+YBBL
+0hQGXzluuGiw9xJAWYB5I3l1gbCyncdW8NtsY6lx190RkAhWHngHrgP5J4rjMPNKbOmu7zoaKWR4
+O/cPD/Dnq6WH2UicpFZO3k64kyBN2VrsXoa3U8vqtWpNZwnv3ctsNlGFvxzpfwx5YCcOQiBeSvbq
+Na+l/fjZ5IZIRjXrLmGvGlWT+TyLpTHZubmwmWVkUu4mqIkEXd7FllJU7TEIMNpbKvEhut1IPVW3
+5M2IJEHphg6rRTi4N/TnR8Ocn248siQfx+N4HD8y3o1IAY+YjCYaqLRzdV7EtnO7S3UT8PPyao82
+MYOKNv3n7rP2oqFVx6HJGiShT2os41GQpLeUBxeidWtpEAQDjVSNbz4fmaJqsB2QlCLlTUmgKvlR
+2cyOKVXpbp1PS9a5HXETdeRrFxUY0z7Yspdz92UXq4LxRMXDKx+zdNcXBTGDmIH+/ugybIjlheDH
+A+lORKKhYrzULpgcGNTh+Dx0O6J/tafKZZXXVPi5Uq10wX4hD8LbA4UusE/c7npW57Q7d/37ZcoT
+8aH8Iia7UwEvMcTabT2hJpO7gNWRRdrFjLGcsss5ApYSHkJSSY/1Oxf9rApD3Oh3GGKZ79TWMCgl
+GBufUYxWKy1CgiXTTtBta2kz6V50Hp7gxpNtR61qf6Finr/ryaWxsLVLCjEt95crQqWmJzfPmDxz
+EdnnlYuk7oKO/sd4MzaeNC4k+nCdwEGkpf4e+qV7Zk86H6OCJ0VBoTTGK9SduQNPCuVJEDAquks2
+A7V1kUsRNpRiis42x1Sqwd8cC7R7apn5DBH0KprHUUO8+3Kv+buxKBiDH3jEQYCvCTGNBgrp+sXB
+hmrvJuZm4XEiOq1duJc7rVaMR6Zav2CQ2OU4Gta1PrNtWVdaLSo57KThA7PMTy21RF4Hf6AYXAuw
+nI2gmkGttNB6Hl4mhl+XgoAiS5sz4N6msp5zeeHCxA/lFHVdrDhTqy9+es60lZvS/oA3dZP/yNjz
+vtjzVtBYM1zyaqLuxGaLZ46Y+4HtVOK2gnAfTmd4b1w4+2k08qeMa5Hl+Wvy990L+XUbEkgCvbt2
+KZNNal4q7ZqtS76vMsKHQUc5czn3xQ5azy2ivgoGdwnY1+47rqItael+fpWGTLXQVVFF4cmEV+db
+rHL6VEzTpqYUaGj0eupeC8404kJCO3NxO2gPlGLM3iV7TnfunC/TinNhqOqTlMnOvSxH02B/miLp
+R+Y3e15L83MaDWzlN3qS7jjs9KHcnVu4L6l/DH+y3jrwUR672kI4S5mrvelvWt5uVCeIO14rzH9R
+rXilQzxPVTObTDnTZliXGyK1hjvjY8Vm/Xys8Cu3YL2WMc643nCQhP1gGxpJVpVNQy490VVbMY+z
+3/H8f9x4TB8ex+N4HD8y3pFIwSMJc4asxxvdjpIFCaNCynIFnTj2/tVIe1TwVaW/vfgta7ab4o6u
+1q5Z1NzL/6Tf1nx+hLmKADNWPmPhwrO27Ikm7u/aqqOWEEZ932AVHkeKYqo99OoZV9EecycPxnTL
+rpcUXHGF/cjtYh8rZZh+MudQOrjrU+/AQaYfYX5K5Ltd4JPPtdpvXvEd2crNdjfUvRyoh3P2kpvL
+lgvaO3dNjYxe1ld7hlwCMP74ABYy5bcZ8u+5m1F+Rvja7VK/8Z4kyzcd95+7c7gO5lgBslbTK967
+csd4FQV0OxdBZYmLiL7/tme8d9HDJ03Die5tmFg6MU0bGzAKL1KpCh8eGkrpZbSjIdGOl3gFra+i
+crdm6BSZSMjmauLR9O7Y9csDV4nrVvVvE1ZbJ7c2Hzw6dTD2idKH4YbXmgvBquRTFQ+Tao0nwtCu
+m2AX7v5/U1HafX7PQXaN/yRmoVq5+3Xv/wF3neYj7j9xv2vuKr6buwOmZcX4Vq7g+QVJ6yK2m7U6
+P8mELlHUVCxJPNe1+O5bj+7KRX3/T9kwk5xcIxKfbzK2ApN5xnKjrkVWdbwRHiQNDDb8ckSvd2NR
+8CCcWKaHlLo4Oh6NZJJAr8IWuaSzD1uetqJDi4ZbV1OauUBKwwwjt6jDJuC1/BRmm4ZOf1drMpKE
+5L4oq2lEKxak8Tsy303+/Kmh/0JuUEI84rdY5fvtMFIXUhMqQ9ZLSdG/TXgr5ubZxj2sLrvn6dqB
+hX593vFcL+wiucYKvfhR5c7n+nXL5sJNiPVVx853L+kkrXh+4cLWYfTp5FfYWSfq6V28wnwmX4Rm
+T/BUC0hWEu30ov/ccz576TgWvWb6dpvyRvqKY7tlkkicZLvkH03cAvnCz1iKm9LW7vpfNy2DFIbK
+yYr+UhX+HM5i99nzsxha93JuJIwzIWc8ipneGDp1Lfx5RXMlsdLlPZvevUCVJvYQlJh7qSlVllQL
+ROp3LGpXH/nNxGOp+k8eu3ufdB6fKrUZyo5N4ebIbONTqvVtpnvOhV7NnklL803OW3PMG390Veil
+iWgabTbATryF67hlyNRy/eHIrQpSi8OGReGe6/LW8V1+9WzgQ3Unlv0VVvPiu6uB7sbNhzV37Fai
+oqsjM0v2nOp4W9MwCgv21vgP5sWm6gg2X8476jF9eByP43H8yHg3IgVr8NoAP67pVHwzQcC9op62
+MgxyWg6wfLJzu8O54JtPpj7TgwvF75c+haStYGC6divtXbnDDG5Hv1Jxyu5K1ge3imaJx1yruZ9E
+rFRQqw97Yqn9poLUXvUGXzuKh8WoMn4b9/iC/F7vtrTaQf5R4s7n58m5nThobLrfUb9wf5ctTqlW
+7u9ObtwO/slwz3Trfne5XtHJeeg2jQmXLnJ52p4xU5X9pfwVua1gdMed2Z4jld4vYjzBh5tPGlLf
+hbYzSaY1tzcUrfu736gMvyDnrDayTLW7jx4s4uMzcX/3/PCa70pXM9n5XLUCEO0q7ifu3PZ9xdck
+tzZVwbFLPKJWYDIzko5Hm+cR70Tdk1XHahQeRDJ94TQjvHPPf1rv+KEgwd/IoRW2ZNa3tNpB38/1
+vXcZX2/cuX1u4GLUdTd7QsSCvZ/w9Il2dBepczLx+Ex4kpHhR4BM3jEFWUgG7S6kVgi/rUvMkT+y
+3zAR5+UlObnm2TH6SdlQW5cGpacF7c5d07Pyhu8Jn3FiIj6rBaEfVFw9hIwn+o7ut7Qyu9HD6u8i
+ax+e+087HiOFx/E4HsePjHciUsDgILFtSKHawMQEhFIMtm1JfdTmp2OayUdBmgbFNCURUy2fTAh7
+MfK8Can6iCddzHZ0O9oTsc3MoScp3Hd13BGqJ957HUGrPSHx8KTicy9mpMdIIwWiJIo4dnwuxpTR
+cztw28KJeujPzt2O+XOzlPzU1RSeziOCVGjKXUU2d7vN86WrZXx7diDxnLnJ2bdPCWt3TevY8kRt
+z+n5KZ2IOwivcBt/jXb3Q/dz0+ILltxWbwhn7uf5ac7TM3fSE8Gq0ye/JQ6bBjXfOpNkWHTGxN0i
+PD/CCCMwE+bhYn9GLY/DEktgXMTilS1tIeafvSeWxJg4XtguYCu5umRfUiuCWPV3+J+o5fZBx7Nb
+eXR8Q6hJEkIVmhfTc4pQUdNiQREKcxEFdJ3wGYJSTxYxF5Frz5rmjl5is71dYKSy1caGaet2+vuF
++2z8esOPk0MedTHj1tUn8vOY97bu+cVdx62EgtdFy0H1iCT3+MbCXXeWuXs8n3jkwqywteRz90ye
+5D5be/SnmNE/k3itcDZ12pNUrqBo8wzTunt0HZfktVrcfomvWtlPO96JRcEzHoWX0cU+qV62pT+S
+SkrrsPfIz93NtocDQy56teS8bB4QWve70ffwe8281CdW9dmcG1rBTvPShWpx0R3rX7S1hVBmrQeP
+XmHpYdVRCTKa69+TJsUavRyBYbF1Yd3zp1NicTfszxnYuQcWTd0knz49oZCl+qTtGVqF3bmPJ+hq
+NnHn+O3gjFAs0fEuA0mwVe0dwRO9NMmUy8/cBZzqZbzerWhlynpW+HRXKnKun9PMXExsX/6QpVIT
+Lhz02YxTPMnKXQwp0dwtyPk8JDkWr5ueSXbUm5P0+NTnm9K5rFOoBcLJqobbRnJlxRMiYStKmXSa
+oaXcuX+vjSWXQ9aijng1cefx7LIlkmLM6UsHCW7fb4nmMgnabR+8K02RkkuL0GtbQj2fMZP2o+lA
+RednXcIhES9lYfHEu6g6yM4l7COMQfVBQLHWgjW2D7VGgyGp9Pokktff35JWblP42hBQnUtQZpxy
+kKxaHE2YFkoVTmTGGxgCGeAE0wTUXTCnPe/5bqFePTlQXLt5XcyVPrQQSYjovh45dJpPdzFrAfHG
+zkdQj596PKYPj+NxPI4fGe9EpGA8i5db5jZEqmsEAWRixiXzkLp2y12XdixTFyaOxoV9oc1A6MZi
+iLCF27k9k+A9deveaVUwU/jfqPhk7ZqlQoV17lGqsrlJdpzKb6A6ncBbtwL3mWDQYXOMgokDH7uU
+IGyYUjx33z1NTvGkEZAhBeRiTeS5c69nFm+l0D+NiATHPX1PBirJnMWJ2yWGDwpQO7W6SQkkZFLu
+c7rCsfJ2YsM8nc64/Z4invGGMnH36MzviRSKd88WbBTdLE/dLjc/y7jYuiJoXbVMT0TsGhY0Yj7a
+G4t3lPlSi7Q4qwlP3bk/C0f6c6Vj7Y7lnbu+ndlQqqV8rYimHluCo0P1pqGXavFdvKH53N3Dz/K3
+GOsmxFYErW/PlgSRXMf3M3K5S0/nFYGe2V2UUctJOjZS4gaOJpVjHHE+kcXafkqjFMyUDamkzgoh
+TIv9KX7qoqlg59M9oBstVjv2REKyszCiOnXYjBsq3i/c89snI/VWczmdcDrTvBUeo8tjAhWrjTcQ
+xFK5nmUMivo+7EL2maKivTvHdGzx99Jm8DpeXYtJO2mZHbE67Y5YEdtPO96JRQFrsI2h7geM+tX5
+GFEpVB1rw6lkrdMwxorWbOXFSNLg6YW2y5ZQEuFBUDNVWuHZhkDU6EWttCPI6BVGeusAFHIuOthJ
+ncmUa46KfamOe9+aB3z6zjQYGdbui5FZ5x7SWTISyZjWmwqCnX8TewyfW4/21J1/MZljRZFNvDPd
+koHY/Yi5mzDKFcueTfFHaU1WJXns6g6VlIWz+iWRTHWzcKTTS9MFI4fQQWY33484qMuzENBp/jog
+VMpQ5x7ZiVOAGsv4wbVqv+goUPdAUPG8fUKkvHdxWhPei8EZD6y1QAzbiDJ1dZBjKL7uI8LOrRSe
+GR5MYuzKspS2pbmq+K7nXrIoccd4mlb8ia3UjZKEUYCyxfMY71ZK02PDjRb+cwHH+jd75sdz7+FE
+EuhpGbJJJQAT+IQSlBk/cQvB7MRj9lpYAG+NGjFY4xEKhxLP3LwYDyVV4773sO8pZDD8zSohUB1h
+Hy1YLlz6EGr+llj6ufuuZZhT710KMu8i4tadz/JFz7NLN58OUpNaDR515p579PINMwmrvL0vIJAQ
+aR8xBo8w58fxOB7HVxjvRKRgraW3HfgD2oypsv6BVx9NOzxp3bdZy6ls2tZL7a7rkEuZhcQVjKnS
+h3qgFR+96EJUcCZKBY/2IjL5KdxmKxIx3G5Dj6JSNLLIiO7dTrnRGjoOlkFh5L43xFP5JfhLpnKH
+7icp4UzFpUAmM2YDOnY5FGTS4mvrFv9C/X3p6aVjiD1qSJzfwOBC5mQLrQhWcbynlobfKW53+TzP
+Oc3cz6/ChK5xO37jfU6gar953vL0WgIfN253Cc4PGO1WUyyoG9BHPZ3vdpq0rTlaI3jSQuiGHaHY
+lV3pE51Ix3IMiF+5D997dwzSPPxC5jTNXclBJDazaxg9d4zP1nvG17pHSc1074qjxS+6h/d+NafT
+c8+yObkKnj4eRpJ8rApmjWieCu2DpwdSFVeTZvMQdptT8EUeKuqGXSOvTLmHvzwYdvLSHEYPq+du
+rAXZCI6jhHOmAZm8K188L/CE+mQ5YjP3XM+jAauUwChyrceUo+BybSzmmELXHYWQjmEzIf6a/B4a
+d76L65JPccXsPPf4YeW+t+Me+lTnfI8XfjmTtndiUcACnaEZfJKj4coWPAmerm7tg7joATwpAAAg
+AElEQVSHOYT4oXvx7i4lxhHs2OzdC5TGFSo10IQNhyPtsEgpb9xNe565D9ghYvBVJ2ijh6rv2I8M
+gQspq22NLBjJQk9/51MKeLMIemKp9FDsWK9UP4gHfFnYd6kLk0c7Z/DcJL/ftpQSFonnBanAWcbN
+d9ol9DL6SPuOceu+t+s/ph4URl5tuV6pviAx1xt7SSv/wcS3HKTXeH33LU5nDlPf/8Y1ncBemw/U
+cSgTYtnF11GFCdwi1Pdv2ctlifIaAncejXgk21VHqJB5aDyWmbuAwKQcRBpo9h5riaquB+W3u4aX
+O3duofGYHLSoW5+PxBW92PlsZORbf8+Fw7/29HP+WKP0IfIwC7Wq2ydY417OdTMj2LpFoXH7B7Y5
+sL4XnXjo2coLc5YdsOLYtH3FnURqb1f/H3tvFqtdlp93/dae937nM37fV3NVV3fbbSc2mDgoICHs
+KCSAkosQQJFQUKJcAEICCRG4yQ0XFkIKICQGMSlCih2iAIkTkQgIZHBsJ93toavb1V1VX33zmd9x
+z8PiYj3v6argdlWloVNB5y+VvlPvec8e1l57rf/w/J/HPbMq2RGyZ+9qbrnfrTEk2px6z+lHJuEH
+HIhSP7/yeSqpgYMiwuyUz0gKbO1WrzZ017sz0KrUG4zGdKpU7EqI9EzWfs+81NjiFoW2ySlalT3z
+LZvePUtbGp4ODkqdWKj1d5/W7sKHO7uzO/uYfS48hcEMVCantwGlePbWUQKqFgTJwFoEEuvRijdK
+5w7tpBMZXHuc33cr4+zFlHrsVtr1TcBu7Fbol6qMUI1LSt6yS0sOC7fq2tDQNfLhopZUOIX5ZEJ9
+IwpveS6D3WDF2dA3Hq3APUXVMz125/a6OS9EfT5q9pWTDxiJF+L97hmpdq55+5BD3O73QrRcx94B
+r4+VpS5jVq1zBdqVoW0l2tK2VJvnAGxUvZiPYnbvCs68rag9EcskZ7AUlgE4E+jnDcGSq+iQjYRh
+omQgzJycXLidcB64pFt/bYkT9/Nw7abON5drJpnzJE5mFTu56Fd+g3/j7v+irtnduF3xelCDT9HQ
+CENys1nRCxL8qF2yFdFMYUs6hZBF5byOP7Zr2B5KDGZnmW4FToqeEBtVH7wb7M49s7nvulK7KuDZ
+VpR9Scph4J5TWfmcbd14+p7PcufurxYkPn/asLnNLnrwkfBh/MNKXIuoJrucUR857olvtBsOt86r
+Wocp3Y26UbsUb+S8iXTrXNAPRy+YikQnXj4mkkTe4x2MpnqubYd65nghD8tUDRtVFtq2YNi4e7ro
+N/TyUm+6NXX3g6V4/3/FjIWo9WlMTaMXs2tjtmqXrdY1h4Gyxbueq4VAMW4doB9ZoifqDfBLauUf
+0rqkkGt4zgVR4Qb4QKpCQz6wFF9ecVOTWukWNiEmdH8X1Q2hRqkN3FNpbMRQuvPlQUMqZqLztCWW
+Gz9aLBl2ylGcuAl9uo3ZitZ8et2SC7CThgd0D91LE+Fc0ny8ZBA4Zntt4My9YEv/holAVLubntPS
+3dNzkcnM64JKpdBxUHCojrr5ZEK3Ubycd/iXbmIF0k2on11ghNxbn0S8rF6DamhZ3LhQajU0BJ6q
+Ejfq9OsSdiKvsbmhqCW2ulpyw/4616zVd1BuxJpV7LiWix8MlkvNW99CKM7Lqvwu6cz6ylUf/tfH
+z/nRxl1DGGdUkXuWD7wRRoK8rzcBWxGzLh7omX31nM0e3VlbRioj27OBUe5OfjH0jNWnUlxK9Sns
+SHo3nwq+iwy0xvDmoVPlGqlSEyY3XL4nUNtXL9m9rArNWYPRy3tZw6valNrM3dP03GAlW+CNMuyN
+G5fExgxC1pqRR6UF16hdfmkG4npPJlPTq2wd5A2lejBMN7AbPhui8fuVov+3jDHvGGO+YYz5M8aY
+xBjzhjHml4wx7xljfk6aEHd2Z3f2D4l9P6rTLwH/JvDD1trSGPNngX8J+H3An7LW/qwx5r8A/ijw
+n/9WxxqwbP2ObhveZpAX7UAkPcAmKKnWotye1pxoR7iauzXt1SZmM5GmYA0bpXIX65BQ0NfRkLAU
+U7QvfsUmapnq5zrJSSrxIKaQSnymGfvESn4WcmWHYYf1dIwBghO3yo+mCal66AlT7Ik73zgQjmHU
+kQjIdJKeEONWcz8ZE7/h7m8RC9rsrWlWbreOM5/uVPdRJbSllKXG3i1c9y25iOfpEfe0K7+fhdSx
+i20Cf2D0uoMKf6FfsZWcu1m683qxIRflfNJ2XMsbmZmEyHP9GgfeGdfylibifpwYy3jnknJnRceB
+SGYuRobN+b6bL+dC4izXwmmcFRXdXtnaL8h6eUVdQ67O1R6LZxW6CfL+j1yP2LzlttqjxSGZPMid
+77E4dN9Ji1OS4kMAysrxTMSvBdxbuefUdGfU6jqNxj4odEnbirx1u3QhvcpNv6EQUcFgDVaZRs9a
+7MwxaGczFwb04y/x2hM3Dy/iF5hfdCHhRbSl0HglbcOlcCv3jQuDh8MpB7lk5uOOg8B5HuNRRNQ6
+j2W5LBnpdV0r8d1tG1bCp6wHw1oAv4vmjFKcDH3YMN7zCH5K+34TjQGQGtcjmgEvgH8apysJTor+
+D3yf57izO7uzH6B9P1qSz4wx/xHwGCiBvwp8FVhZa/f8t0+Bl36zvzfG/HHgjwNkcUxYdwy+xRc0
+tvYDIlyMtOs7BnEnbLqQRLXpXvXGs3HIVPDULFsQKoYK7yX4S7fbbrKBjcp6JlNn2SrA1+5Y7CJC
+1Y3zwicQtHdb9MSqPQcqWcb4VCpJJl6Hf+nOPRoleNKkiEKwaszyhfizfkyo447Dp4Rq7PJnNV4h
+j+WRmKajFc3M7VaBeUz5WJiF4Rw72+ddfMqNkJza+eoup1UkfuQNjMVcNPZfJwpcHsC7eYZ5ph37
+TbcTxUvDIBJX3w8YpCpdjK4Zco1X3bHae03iYzh/VFKaPU9FynXkKMbaVUOukuSzTUm1dj9f7fMP
+TUOx73ztDAhqnPoBOyFLHReEdCRW7rO/fHDF7KlL5jXJC17t3TPdXiU0Kv32FGDd95eNKwFn2xtu
+zlUijioeS48xtme3zMde27BTy2vei78juSZWbiQ3A+a2JAkTQU49oS4DP8Pec3/36n96wF9//RcA
+WJ+NKQQPn7eW8rm8m6njzgg550aM0GXd81yiQ92jgU2qvIxNSUOXVO7kNft9Q6152G4KdnafzBzY
+iCXMNNBGn0ZD+7v2/YQPC+D3A28AK+B/BP6ZT/v3H5WiP5iNbZ+1mEcB61clI7+KaFWDzoeIciOK
+90lAI+2/PZVYue7o9jh0v2GQTuDVoiYUUMQ3ln5P564E4ChpGAvc0qUxG31eZw1Dr8EeRXTiSvQV
+itSmxhPte+kNFOrEu/QMi6noydOERkkws3H31FbXoJr/zSJmrmReutwyyD2+mLpE143dEYl3cWoT
+UEt5WxlisRn75UATupfpTGFEN4rIbzT5+xoWSpixwlMdv39rwS8FDrMQCq7cFnNmkRis7VPMe+7c
+WZxw4Qs4FRe0e0owVS3OO0Mdu0XBlkvOBKCZeCGVMPxdNeaFQDZ7Tzanxw7q0fB7wv3kPgAj1aOB
+7zYtL1UBWFzlPP2d7rPp7j6rhVsIoicfsBZ+4zd8w5EWiGyrDSRqeH/rFoL6OGdYuhcytpZBbd1e
+MNDvVLmS8E+5mlH67mUzjYfVFRkLvl50TxD8ocnx7rtn/h//8+/x1tfdgvTtV2Le2rmxfdTfwNKN
+xfqFu56bucdBqF6TXUGnebMqw1tm8ja/YiKcTCI2Z2sNK0H2V1FAJN7Qm2ANkq1vg464/cHRsf00
+8NBae2mtbYE/D/wuYG72lDPwMvDs+zjHnd3Znf2A7fspST4GfqcxJsOFDz8F/F3grwF/EPhZPqUU
+fYBhQUQ7K0mXItGsG9qrfSNSjxoDiYqaNHQu8b58PAs6dtLLM3WJlULxK0vDpQQ0wk3NSD3rsUg9
+m6Jlo4Ya2xaE7d4t7/AFYTVNxUyIts5zxz1MR6wbScpjiAt37rQP6dduFY9Ghli0Wv7MfdY0C6I9
+CWg5YzJWOHKUEq7FBSBaskO/I1wIDmtChp27hm1SkSk86iLod+4RHkk7cV14LBKVS3sfJOm2iQZO
+Uved6/cHviiy1V0qnjQTYOSBZd4J0akIboY17Upy8FVHd+ju5U3pSz5OKwrpJoTz2S00vc13PJWq
+tjdeUirRaFXqbVqPwdt3HHpEgoL7QUmgkmrf9Rh5E77+bQnoP3DYA14/4F4lFe/5hG7rdvTXdjCu
+3PUFL4l74UWNGiYpryyhpPdGa8il0XhlYkba/c1Tt3O/Ntrx/kYJZr9Hzh3GhJQSjPHFJxH6Fzz7
+ZfdsfvrbH/BMJcTZriY6dp/f6+4xKBEeJe4cXRNyKvx4fzhneuWu4Vk8YITZ8A5ixsLpl9KmqIOI
+A4W/z0uPSF7q85uIYi+YZDxCJTRpNG6fYN9PTuGXjDF/Dvga0AFfx4UDfwn4WWPMf6DP/ptPOpbn
++UxHC8z1kqciNImCHZHvYkcbb0kUGzexoRNIZaYJ5vspD0Qq0QYzYrn29TRkLhWfql0QT/WdUjXh
+eAMCmLTpigp3js7f3QqpBmlN20oQNBS70zRiIkzDjfVIwr2gyoTec27ravcGVnXotNVDmRYYKf5k
+445BTEFN3bCnNxrNRXtendKJWcr4I+yJ8haPApZqhx75HvlCmoHvu1P0xzN8KVbdTLbY3lUOhvwx
+G8nSj75yTfW3XZknCNx9BA9O6WMXXgR1SiP8xpQvMD2QDmIdUyq+Dl5z9/S6v2R7JWxFWlGrR8Of
+PyV8qHHxlpiR+idqdbtGA+EekxL7zMSOHYcLCsXG1ngMepZxqMXSWN4I/1EAkmiCeeCOO8Q944Mf
+AmBelNTGXdNg3MKavTniC55z26+3W0RsTRyMaTPhCR4X5NbF7VvfjVVvN6Sheg6aAE8LWWQM3Ubt
+9ebb7npXM7avfwOA1ddh95I7xlv3vszqUOzRZUKr+ZJJqn7aV/SiareBJQzdYvrFaMO6FoFN3IBg
+1aOZW4y6qwBEqd+M5xxJ+GY6yhHOjia1TIWveOEivk+071eK/k8Cf/Lv+fgD4Hd8P8e9szu7s39w
+Zqz9bJnJ/y/sR77yFfvn/8zPcfj2WwQSeNjkj7F/0e0ev7D4P3j4192u8huP/yJfWrlV9VcTt3sM
+yx7rudU+bUJWgiiXlSUTzJc2pNp7EPIBR36HeofIbU/bqSHKHzBCiq3NcEvBNRK89mf/6/+Be7/d
+odn8ckm3ddDY6z+34qu+i5a++bc6XjTvAjBxl8aLvmclWG7oF/TNPjwYUGmeieKkpg+JtGTXg8dc
+HlTVh2SqguS9x0I99LX4ESKvYS087NGDGUjB2G5LCjUjzROfpnffH8tT2DQek8RdxLYKmUhVe1n5
+zLSzrdqYkaeqjHgvJl3DTrjxh9sVKzVgPWpr2Iv58ClMz+zf/z0/zbPM7Yjjh7/CmTAXsQhivhG3
+BGrAsvHAa0IC7hZzYmEkomAgkxeylASbLQpyHWtcNXwgD2IoKraqdrza+zxWuCH5DrYT+AMixvn1
+P/zj/N4LEfwcznjn538ZgG8KNTo6e4cb8Ve82sVcHDnv79jGhOoqPWkTSpGlNCJWCb0KTzgNP4mo
+W3GP+jWx5mQejzAa28ut0K/rkq/tY+hdzbWKfuPO3uJMpsbHE+fjxfPLr1prf+KTHsXnAuYcxD6H
+b45ZxAFdKPfag//wH/86AP9aC3/q2mVy/2D3Y/xv4TsA/D7BYX95mNMIFFP2DS/5rsqQBylbvSC+
+55FaNyl2g4Rfuw5fdNtB290uCpuux5O2YdMZut65z4EUgQ5+aMR0D5WOFvtIg6/9npJ/7Ffcy/To
+Swlvf8tNoIdqp56WG1oRfeRtR6D4O6XCiGSkFI9g6gVEAs2EfUhR7yXlIzyFLtPeUCmzfKD25sIM
+TDu125Y5hdirOno8K3fd65iLGj4K3ViFNqT33bFeylJQ7iMafHrfXdtpGJFoFR2JxCSvOmKBVkc7
+eKqaXdNbPlPOW+O9nRfcv3HX/CQs2Elc5lsSGD5oDZfqP/DLiIup+/mNoaAK3T117UCp0OtU+u3b
+2KNR7ufMr5jtxWy8nr5w5/imX/PyM3e8S/19uon57xYupPj3fi3h/CsOsPQHHzzgL3nuc/PYjcV7
+7RoKUfxPO74iyvnqIGLSuWsr456ZqmClcmNt7xFIuHfqb7EiLDZ9RqxW81PPcDN3C1y/dcf69TBn
+tnH38WjoaJo9pH/A9O5Zr7ye7GbzqR7B3u66JO/szu7sY/a58BR84zMODzAGVIqlDRb84XfdTvt8
+/pB/8qED3iyHH+WV4U0Ayred2/p67VOK4urplc9i7BJVi63FU5LlYR8zUsXAiF7stGt5KLbFbCjB
+E1ff4FOIIry1NZ3k7HO5eBP/iD0bjA8MyoD/yAt41Ehu7Pya5cjtBPXKJUwPxgGeADvHxuNKHZpT
+b8dIK3us467DjKjdw1MHjuVeb8OEoN4zGFsSJcy0meOtDLmScjZ0jWYATePj60vJEILwEJ6qNtO5
+Tyja3yGJ8aXsPRqBkYtahgGZ+AZG8jpeNh0X4mZ4BUuRO3zDsoFu31zIp7fX2inze24Mji4b/qZg
+33MlKKuoQ6r2xPGCTPwO6cmYl+QBmmDKbq/sPHNfXt8saZWY/nCdcTAT0/blihcqR9kWNtLptLn7
+7jU9yuvxV9oL/h1hQB73P8Lx2n3nue9C3mDZcaPnmK3hoRKUbxUtTFXl8aeM5N2kGh+/H1hrHt4/
+aPHFLp1GKZU82TeOKy7ecxdSCcK8etayGjkPOl1bij2yanCtAwD+AEX7D2GXJBh8z8PgYRXreeV7
+bBbupTj5xZanvQuFoou/Q6ZS3pebtwAoflvFTpjz1+YrvNjh3fuyoV9rAfANO70gpUqFze6KPhSy
+beNTDW4CbndbrqM9t6N/SxAbqBXa9zrMfqqbgCH4AID86ZbkO9KurKdsCkdP/tbhnkFqRqQKyG4T
+8pIw8EPk42kx6dVNd5JOqNVmHNQBde9Cl8l4yiD3M+5iKuk1Hipbfp6tMWI8Mm3DXhzITwyhaNZJ
+W45Sd3/Rwl3PqA3plF+Yp3P6E3evXuHT496w0JtjrTot1S/ed2uMKOyD6w3J1C2QwfbqlqXoM1nS
+EIiAtH99zPixCHVUxvtilVEJ+2+anoWIct9OJ8yO3P0tbxImc3dfs1ZEuA9aimfqiZnnvPqBe0mf
+LwounorgxGsQTwm13PmurSgivVR/o+DxH3EL/Bvv/M9cte7adtduwVr6lkHdql0c8UrujvHKqylz
+IUGzk46pZO6lZ0y1DngQSe9kFN8iXeMqhbE4S+uA+Zui+d9p0bx/SfRtd6z3/Zp9E6fluwtxb8Eb
+frC9D3d2Z3f2/zP7nHgKAww11iagPgFz/h2a/+q/BODXXnnIL6/d50HcErZuRz+rVFc3Y9LEuYNZ
+lzKIvjuaj7BjMebmHSu5YvVU//YTPAFBzMjnXOQWU1sjHhMG8x6bQgInqh9X1+/g82O68qd0X3N1
+6u3f/AV+efQtd23Xp2y0wwbSCZyyoVNIkU0K2l4MzX5APBEnYi8XvvUIG3cfXRri966FxHpgBGft
+52NSXXMQSXL98oZAu2oYeJTSQayLDl/nMG2AUSWm37pr8GaQqFcjCMBT92icepjKHduGllaubaJ+
+EN8cMBKk1vy2U377IC6AZstfvRABCJ/ebBbTqHtye5YyO3LnOdAueHNiqMQ5cTj26awbz1UScqNK
+y2FaUqrjdSuuxYsu4/i+O8hkfUD/lvgyLmt+7NB5Xr+w6wmFQ9hIK3TmwUZJ0MvQ8HPvumf9L94/
+wFeY1gvm7RcGb++M9Zb1WMCxNsVXD0Owbek19rmSw5NoS+85byv0I4zvDhIGLbUqLe3EJ8/dc08e
+OIxI+uEB/itKpD8pSSQG03b9d2njwMm6A9hP57ndeQp3dmd39jH7XHgKhgFjCiCFnavF9vNLXgiq
+Ov92yatCxFG9T3vPrbqn6v5Kx8fUI0FO2yOSSDwF0YRV5qCdcXnKqFWiRmy4hXnB0UwkmlvDvFZZ
+j45XRi7J+SKKaB79BgDHYoa2w0Pa0nkP4buPuYn+LgDfXF1RSd4t8DvGjUtAzYWnWKTtbalos2g4
+6ly/vTXXGOlTLIXmy+J7lIIzN/WIduy8oql/ctv81TcRnnIC9xq3ezZpSP6uy2UkpmGn5OkwCRj2
+eoaTnpmSiv2p87DC3QEcOPdo4s2wc/d50M9oFm48x9UhbeTGMOzVRdmviGJJ+m1yfuiHvgDA4dOQ
+vxH+CuBKhJ822ThedDQP3bT8YQb+L2GTT+T9zDyf6sDlT+LtJUYaokfDksC6Z7npcvyxu6Z9w9eb
+0zG7tRTGXxvx4Npd0XzxEttviZMgfo/2zO2mrwfigugNqbyHe/nAv/BIGhHeNZE80t1D4UIYuFYJ
+OAt8viRG7OP5CfNaRKpxwiSQKvjg5tsu6ZgE+4T3hpFx3Zcrcoz4MOx6yf2ZWMyVU3jwxSPMO25c
+fvK04Z0P3bN5z5R0etYGMU9/BvtcLArWdnTNOZE5wKolN3/nHsFIrcc/8jbZ15XAi1/i+Y174CcP
+3HdNvoPavcTrycA0kg83eKS5G+CL0JCUUtNRhvmgnnGmjLXXJkyUXFrEAZfqhhvaF4TiYxNRM8vL
+M443jsG391ac/Z/uF+f5Db16A5JHlhPffX4hd/iNeUOgcx/nE1ZTUbWXIezJYArnDi/9Hl8Z8CSs
+WYhiLvcqBhFy2GlAoL6Dre/c9vWT5zSdWwiu/QbbuonUFdtbTcFuFXEVuYya/8SNSXZcE+7UvntS
+EAvvH497wn1ScVoSrkRIuaer6zMKYRqsl95OQPvllEWuxXe9Yd9L/0lWNXPe+IJ76Z+8WBGICm1T
+ifvyZTh0WDGyNGG5dS/W+OUac1PqKAPnjYhoFoIor3oiYUGqXUxwKg7Gy4T2vtx4m/L2F9y4/Pw7
+7iRHhc+F7777O37sNYz6WCaTN+k/cKHEGzN3ju/kBVaDbMOIswdu7F/pWqYi1yn7gKnAZ3v6v7QI
+2QnenoQhnrAq4zpkpUV9EgYU1+6egkaiN/aAkx9y5/v6r/YsZm6Ug9WAL47Jxlp8I4q8fj8+v7Xd
+hQ93dmd39jH7XHgKDD62OWCIL29JLJIHJW9Jertc/ThfeXlP8/WIH9q6XdGOnAs/nkyohfY6XtYM
+E7cqR03CRpIFoxcdS9WsAzWyrO+tSS+U2HsJJoJPX3sdC2ESxvfuM7ly3Uat6vWrq7cIp841HpUG
+o3O/Pfd4ogakg9OaldiK31bCsBrDPSWUirnlvkhXN9OOZN8LP3W7wWIZcC7ZuHhtyefSRqwmtCpp
+RW3LKnKr/6RwpbLg3gnJ+XvuOjcNRrt4GxkQR0Q1MYRybeMH7tqGOqBdiBKuiehnKq0Nhk6yaPN6
+TDUX34X6+MspZNqtq/CGQOW06TTgvq6z2ORcfsokV9EkLJUwOzo64QuiW0MozSo0LO658a79gFNB
+e3vbMZKacx+FvC7PKtYzMwcZncKA4yEj6dUFeX9CqfAoKweOhTz8oz8qbo1VwhMxab88JLy20EOZ
+v04hicBCjUij0DBTePjq8Zgfkebl67PJLQDn1IAn7oyR5gKZZbLvKI13tx2jTEsOhSPBu2Z64I6X
+i+X75TwkEJT8d732gK8Lvfus3qFLIqdl5LnPzz5lhfhzsSj0Tc720S8xyX4C1Mm2+0vvsn3hMu7P
+f/Jdxu8oJHi6xpP8dvtAAp2THcHzPXCjY0jl7tot/pUb7LYpscrs7+vu3npCJ7ERbwlWMFl/gDYV
+Gmi1Ix25Y+jXPPrWL7DKvwLAJH7E06+5RaNcjXi+7zRsa9KNziOsu60X2FO1J68TfEF343aEH7nv
+jmrBp8Oe5HZC+ASVe+l9O9CJ7n44mBKKWKTZk6x8uEGlebI4pFUW3qtKAuVg/LXBn4iufu3uzRx2
++EtVKiYlg+j1JwcV/p41e5xDuSetkchpDo11uZOuTxgpp9ItXuKnxEI0bFZci8BmT5ryvcw0HReV
+m/zjrsaKvWksLPkyKxirn2W0Kwm08A52ghUaaJr3oAx+I8hwYDsywYAbv6TUw+xL8AQ9PwpPCA/U
+4i3yndFwwT2Jt1x+6ZCx4M82eMYkEjRd/SemSslUfrg/jLnR5/fbDaNci3qYOEQY0EvJLL4J8UI9
+jzBjkEqVX/v0qsZ1ccIgUWAjOH5cV3RaV6JozD1hNt7a3ONZ5kBN3S4g8gScqj9dm+Rd+HBnd3Zn
+H7PPhacwDAN5WRL6z0jfVZ3/dMw3M0falBQ99M41rOc5uZiUTyfSf9i9xkb12uS8x1cTCZsZhdzd
+uo4xUi7mQjRho82t98BJii9yCz9rmahCMX37PtGltBqU9Hm6hLxyGf7XN4/5tcwt136xxJu43f/q
+w4h67HaNB5nbjafHhqGQ3Pv9jqkadMIwJ1q6HS2fum05uB4RHAhye2EwgSoqzZzkRF5B2RFncvkL
+l3ydv/GAm6+7e05sSTnongOPWsg2Gxh8JVjjA3kz7QH1WF2E3pzoxHlQplswHOoYpU+sMfd3+9Bn
+i1mKHi7aEclLWeeW6p6rrnzpvXN+1bjmofoTMuH50DGqhKAMVzxPxZmoMCgJe1q5ycvMsFAraZR1
++Lk7+erIY1qJ+CYRkrCccqnQbChCInV+TlufZ8ICNKYjlRd5IHKepb3HM/NNAO5dNNSh1MZPLY2v
+zkZVyU68LYUk3Zqk53V1lPbtmKVwCl4YMx6LP7F0ocj6uGek6/X8klSVpKtJSS+4uRdVxEow5nIg
+694n1JycjWPGOt70fon3QvyR4TWxEu+P17/l0N/a52JR6G3Lpn5Kar9Ir+67+v0lsYRLPf8eo6Ub
+yND3KA4kCCr9wSgOGV0I7594REpMWN8QX7hB3doav3EDWHrS1jsfsdkXKh7nhA75P4IAACAASURB
+VOLGi4qIXsw11eMXxKKGbxWT1m1O2bpBfy+b4j11L4opIZEC1D0G2lyApJE7yegyJFqIBPYqwZd6
+VdQZAuHvp2uV1bIQX6CgcigJpJW5Gwa6tUQ/5tEtLPfauDzL9XuPaSSIar2WTrmRqtiCOgZ3JuFM
+DQQTgZEmWU0o3cLisGHyQuHTtMNKbWjIfOjVXakxTvIJa4Ultg3oxUfZjyumnkpoLx0y+o5zZ5vO
+XcP3Whr6wdIq/l56GXtZJKNrG80jTvb8mUPIJlbZr49IVL6M8p4hFrOSFuykNfu0BMsoJlNlqCu2
+hL27piZMmWTuGKlUwfJ4zVca96xPfnTC4rmbF98uB/zQPb/ZgUSK2zmxxGr96IgycPf/hTZgkCxB
+fxwhZDqpcM7+usObKU/kj7BiA0s2AYVa5kN/jN0T2tbavHxLJACUR8V47u71teJltq8+dGP05BCk
+i/pp7S58uLM7u7OP2efCU7BDRL97lX7x6ySCIB9+8R5cuR3v5oXFUw32UeZxb+P8oCdqE39wNCM/
+csmUg3VHWcnVNjErrfzDwxWPQgfumK7czv9hZMlErd749Xf5FuhY7NyOHS+O4ZE7XqUFt6tnDNqZ
+p6OAV19VU1Xgc9G5z/3M5+nIbU2nvfMwnnk7Egl25Mcxp43qx8mGSFTlN0ocBU9rXhiXcBrttrxQ
+w0zmjZzCBuD3BaUo4iaNo/aK7x0yeub+7jLuHcc3QOQxDHu2mJZEfH+JeAKbasp67K7z5CZjPXFj
+4ZUlzdhd1EudTyN3tZcozLY1xGoIetyfE6rKcLWtSTz3/BZYfO3ogVon2+/hK3RNwKDkbzqNmOv5
+jQb3fG2wIz5wodbag7kSn1dezrHwJOU45ljiOG2n5NxoQi1Sm9EKrgQsG3two+z8kakIlcTthGOY
+e3CpMR7erShfd7v02x94xHoQVjiGkyzlfuKe49svHTKSorkNYzYTN6+PO9iqKS7rnfdbHAQcyoup
+gvVtYjAfQXStqltyzUEnnc5Yitmlpaid2xFPRkS1O97ct+xE3ZZlZ0zTzybS9rlYFIa2YXfxlGz9
+Y7RTpxtw8+uX5OfuYbx/co2p3AudP7nh4fgEAP+Zu/Ft85zDZ25wnoeGJnIuXOhd8/hcnP3XK76T
+u4k+GdxL05mBa6uYrfWZLdxkK1KfwIoI9fk1nYA6e7WprixZiwzT2g3tCzf5Z0PHjbLyVdjASroU
+6kvgIoEDV504LkdcKcdRmJSRMtHnK3e9dVXzwdrdU9ZsCEP3Ei0jj0itj+2DAyp1fk7Ecbh5vKXI
+xIqUBZSlgnzb3LZGR1V8u1hsNpow85L+wn32eGTwV3rBDhpmhWjkRzG9746Rpe7luLo21ApnXuSF
+a0EHvJHFes7tvnd0yD9x7cb8b4kD8bLof9NlwessjcA2edniVe6liKX1cW16PMndx6uGWnF9vhlh
+JDg8Wfvk0uxsvD23Z86lyG+97YozhSBTWxLKLR+SUwbpLwTKT8RHl5yI4n/9hs9CIr3r6RkTkd42
+0tnwuxQpChC0PUvlO0yY00v89cofbpWxmtTljyYbnwupoXX5hDxyc+RibemlBnbd9FxIB8VT6JYH
+cwIthHFUUKkq4Q0tvXIwAQl14v8mI/297S58uLM7u7OP2efDU/CgSjyGaE0gWrXrw5QPHzrX9ira
+YQS7fdGNCEu34x9aFz8crR7wjnbHe95AJE9gqOBbOyW+NiUb9UcUTwQNTnqO5Doeji3IFcv9jvng
+MBLJyycMV6JjU/a+8loGYepTr+ZmLgKQC0stPHx3GfIMt6scSPEniUp6JRLfO2h5S8kuL9jQrJ1r
++H7rjjtbQxtKXfjKMmTC33sdMyXPwr4AJfnmOG6JxVseu6+Kjs3uaCQmMqoCWv08eB1+KQEXZcKH
+m5TnvfMIFrl3uxvb4oAnmfvuK0NA4rldrFVC9VGTE8jlXpsNtbQp67XPRKCgSTRw+prbCb/wjpKP
+/s0tCctHrYla0p07X5DUrPdQd/EWDnFPdS2QTm8ZiROxGW0Y5W5c3k867su7G8fuPqI24GLvxVSW
+curmSHLms/T2iemckcZ8pkRl3Mz59gMXarzx8JpKMvDnc4MduesY74TdmPYgfo5y6G5FYup1xLk2
+69lgCXw3D4fc4TE+GLXck1RK4C/xVZX6cGgIpAa1zJaMztx5dpnzJg+SDYcKS8wwYiPXqx9lLCTE
+U2+fY5tPQod83O48hTu7szv7mH2ip2CM+W+Bfw64sNb+iD47AH4OeB34EPhD1tqlMcYA/wlOeboA
+/oi19mufdA5rB7puC0lAMezlunZYKe7ONxm7G9ftOG6XPNyq63Ci1TCqGElsJJ/FLFKx5uxG3NfO
+9a1mS6ba/I0kysJtzkPtKHXeMxZMNhk8VrHzRqqz58TSlGj6PRtPTYZINAfwJGDTUBCvhG4rtpyo
+y/GsdPd00kMn9eHFsiEXeeqpCW8l5l65ctdzRkmSu787azsiNcO8tzNspYV51B/d8jBcJy4+XT96
+Cp3bzQwNbaeGoL7aU1XQ+QEXey3MaxG7Dluixnk2H1BxX6XOIqtJVcfPD3sOYqEzBct9kOc8GaTj
+2Xnscnfc0l9xI7LZN8eGMHffn+3Vs1+EVGqT6gaL1U7pNQNCYNMPBtvs+ebc/afNlEiUb0dNy2OV
+WU988PeM2MNAoRLvSypvet2I16Vj+Ti0TEoRqTY7TOdu9irJuO+L5VkZ3+h+zhc2LtcUvdkRviN9
+zEen+FN9d5/4tRmII6OOIiLVu02RM6slxBMPHGovtntl76KnUdl64cW3Y3FalFx7apQqUnKxcTfX
+7ljPRz1GkndRHbHPJ65NiCe2sNQEDMFnCwg+zbf/e+A/A/70Rz77E8D/bq39GWPMn9D//7vA7wXe
+1n8/iZOg/8lPPIP16bsF9fac+NTd5NHJIayUXNxljE71MgUZU9Ggv3ngEj1x+hozKeW0NsDqAWSL
+lFNNzDLx2PeITQVQ6Yo1Uw2Y8W4YawJVdDSC5ebhgrx0k7De04V7Aa0y+cE442Df9VaG7JT1NlXA
+XO2wJxItHc8MSeAAPfGkIJPcedlvmUqIZjzfs/eOWQorkcYtVEoyxRmD3M8RA40y3KZ0xzJHC9KH
+bjG92oGXCPsfJihHSJYFHIvTMhQBe7AeMdJY3Asjsn0zZHpEMlaFIzmiFYnKeH9P05x7muTXNiYS
+7LqqYzzBgI29oVIt//CRXjZ/TdXsIdM9e8azZgBTSyzlIOJA4cNMydrOlgQT93NkQ97WwhvMI3xV
+c5LMIwr2K4sbz3GSMT9wUPGu3lEaYdaHBCutzMkkYq4K1PCKxHYHy1XhxvvZr7Yciebt3vqMh9NX
+3P3tuSEXhpl0Pk+SEdZ3L2ZoJwQT3X8Uk6jdO7MuEdslHZFg1fWwY5EIODfxiEXgsjM1deKemZGi
+lZ9GeArn4tH4lkSlrQfORH1PtCPUQv5p7RPDB2vtXwdu/p6Pfz9OZh4+Ljf/+4E/bZ39Ik5X8v5n
+uqI7u7M7+wdqf7+JxlNr7V6Y7gw41c8vAU8+8r29FP3/Q8Tuo1L0R/MpzXLNLjwmUENU/+icA+kg
+BicrZnK1q13Lq3INkweuNJkmFlOLoLMq8BVW2N4SCRH2WpOwk5aWUcnSq2Mu1E42eAdY6UdWxAzS
+XLi+OGenElCkjjy/9ukEtR6qlvGNSpIjH1+cDPFLCaWSppFq9AQR2ULUXTbGV5IsJsRLJFCjBFF2
+OGYkOfEo8QlKVwJdDwWt7372xwt6aUf0iRur3fsXlCIkGSURCPFGYMlU9jq2PqeCXkfiK/Bf9emu
+hINNPazc0nRmmUpYpe8LvNtGI9XujSERa/FkGPDkUod9QmmF2LT36FfuuZ6pyfBgFdOoCazvfXxd
+W9gOtyXJbtni45J8iVCshT8QzUVksi4J5YI3Xsi+8hvR4al70FfX5pB1GIVjpwTkYsdm5hEO4icI
+AtKRm1PhtRKt04oXDxV2+D1XIn/tbEkyuOto1SSVNAETK1LVrKfVGMbHBsl0EoU+rUrD+2eeNT2d
+J7Ri71OrUS4wFdEexu3Ht1RvYgWk9AaInffgeR2B9CjxPDyVJ9tkij/6bCXJ77v6YK21xpjfGtD+
+m//drRT9G6/ctzsGjs2GsBKE9TC7ddEPT1L8Y3X7dS9hRZM+l2hnPxyjcj3lhSHYk6w0A+NjdbVl
+Hq+mbuGoKuHT7YrRUmxL1Zq14uiLfk0oduUwPmFdui7IA18hSlCTCXYb9hapjOM3IfEDFwaMqhgv
+UIdfJ/HUcY6RNmA9KQmVf2iyDrt/8dSmPPMzDsT01A1HWN85a0frlLVYj8qiIw/cgnRYuePOXn6d
+9dJN6NTsGPTypm3IeOxe2GAEsWaY5CyZ+lPyubunrIrpjHsZwyGlmOh4pUet+n/Q7bP+PYHEYA5M
+QL9XbPJ8pqrHD9tLEmH/60t3wj66IKrUO0BDo56IKmoIlwrBgpazdM9ZKVzBNGIY3PmawBDrHKNJ
+fquhuYt8Ii0AkXH/DoOhEzOyF46YSUjWbg6pBMiK2ojBc6HCbY/GkzGP1Xfy4bcCfqJzIcHfTj1e
+U3gX5sI/+DWFFtudB7OFm29Zk7FdqL26j+kV/vlCw90kA5Fa0W1Q0Wlu9SOItbCM/RZf86iQPmqW
+hzSB+Odzn7WwCWEYMN2zlBc7mv4HU30434cF+vdCnz8DXvnI9+6k6O/szv4hs79fT+Ev4GTmf4aP
+y83/BeDfMMb8LC7BuP5ImPE9zWJpTUPf+qx8t6LWlwG93Mg42bIQGYp3kNKr8WUkPQW7TClEYdWl
+ASMlmar6Bl8UbJPaZ6pe+VPJvbfDAUvxCgSDpSqcq3DcxZyJv7+6eYzXOPdxpZ1t6KCRF1Pa7hab
+3GQ+I8mRnZiQYHDJPzuRnLo/Abm7dRlSzYRG28wZROQxEfdjlHakSs5NBsOgRGSetrRrdephCbRL
+nQnfsHvx4a0ScWs7UDKvbRr8UnXzOqUQ9mAqPoXJvGOuUGKY9hg1fNEbPEk016llqt2qz8X/UKS0
+SubNDnyCvW5C6LNROFb7p9QfOCRjkIoUZhgxpO5Zd62PDdUwVBki343Rrm3xVCXYifIsTH3uqxoy
+DWI8ATbT8RGRJNtWTUcvGrbDVLwJTcm0FQ/HsLvlzUwqQ82e5TsjUTJveemSy31wTrJzY/H6wYb3
+1pJjsxmvqmtxr/1I0xHeOE+oDRPKrbAzYc+0FnJ2ajG9i7aHVH9XeFSqgsXdBGvFJVkkBMqHjqch
+gkXQdfLG5j6xeDZyv2IiT+liCImNmwPW94g+I6Lx05Qk/wzwTwFHxpinOJXpnwH+rDHmjwKPgD+k
+r/9lXDnyPVxJ8l/9VFdhfWw7p403xHIzuddztFX2/SZjeCAo6cRjpDLbnstumHbUa8WvbUshNSET
+rqiVfQ+amr5S++pek69tUfjGJmtIcsnLDw07cfz18RHd4AZ46L6bq0BZ78oGWPVlHFuPWFDUNuoR
+YpapZNGLrL4NSy5HIZNSk2piCfWiq2ObURfhK84MMkMumLN/k7KZyn28KXki5qVMi0N87x4Hl+4a
+lk3ASJURz+8ZRCbSzRrCfc5gKqLc4BQz1cvWHpLH4r9sRmz1kmZNSKlFeySuScaQ7TSunSU4dMct
+rU92I5HXakmuDtNzLTA3Q85u34vgwUJCNXbwqds97Dhgun/pE/fdlJJCeYIohYnClT7oYSH4cBmR
+iUC1VX0zmtUE0sFM8u4W0GOn5jbGT8uBrRbn3cKtbt/+ds4vnztn98VyYCoR3umQU6is6+2ZtcKO
+TFUw401IMrXt24BAArOZnZGP3OdRqc0kCkgl2NulEGtO9klHLG5H39viK29US2QnzRPOQ/ecwjBk
+pUpSlltWB9IpLVuqRKWkT2mfuChYa//l7/Grn/pNvmuBf/0zXcGd3dmdfa7s8wFzHnrqcse6fx3f
+uL57+6ihUJ97/SDnaCkprSKljNzq6Hmu5m/ac653kggvLigD0WJvK9YbhQeF5UJEFkfiePTDEU3u
+EnjLFVSiILuxEb0AOZebx8gLxBOwqmlhpR1qUa2JBEWtkpDHoiMb5iVprTAlFMXcckIYup157adY
+YSjq3uBJu7KUzuVq5HP53O2Orx6sserTH/IlvVieyz6jFE1Zovr45YsPGVR9SWMfuw8JBn8vf4m/
+9emn7v52V26s0uMaBGTaJmvyrTvewDW98LPFAL2gu7meTZuXWAEgdkXENBZuou0ZlOBa1z65mqZu
+7Jl+b2jkCeF7tzNxaCGX1zApG3rJ1Ok2eNb7DJk7x7T28VXHb/sDCNxzzesYq4a2SvwPdl1QqrAe
+2I5GtHejpCJQt2JTN9wIc3D1bZfA+0b+IeeaQ1ddz1ZScBdeyf3K/d1NI5GZtqGWvHwbW56dKWQY
+b4kLgcgyy06kNEmixrzG4mtudQRYAZb6zUAXu+e06gNM6ubOIP7JZlgjSA6bMqdTGNe0sLp0v/CD
+kDT8TPrfn49FASydqanSDXsWv00Iz+VyPrAVo8Y93PPtmmyk+CtzRBJJHfKsc6mLZlkSaXJ4m5bH
+S7ngaciJssw7ubvn5MR648u2pVy7vyu9DQJI4vmnbOsPAUiN0I9ssIMAS7sVG83YvBg4PHYvy3o5
+5qm63e5lrjzRj56Q3LgJ8XR6yfEz90LWozX90j2KKxGV3mfMVIxHNxufMnKTY5wHt112bX5DqPhn
+HrzmxuqNN3j0jujX/d3tixd6llQxqZkMoMlrDxUmlTE3yshPtgm72FUfomXIs8iNS3YWMozcz+HG
+LUZnQcepeBBH04Zy6+7/w8ESCwB2vVnxtHIvwPrSXcRFX9MpDDR+x0YMUTtvQ71WVSK6ofEUo5fu
+vEfzOYXIdVa+RW0nJMkl451bhM+ynOi5xmuqTH8d8Sh3L9soPmA+FcX9Ds5EH5+VPk80j863bnN6
++m7PC02GroNec8D6DavOLfZ7bcc63GFVQjwsY04eqNN07XM9dvd3XHnU6uZEY3hzkDO7EcAoWGNE
+6LuLWjKBmqJJTa4QcSmOyjgPWQm85q0bloGqRHZ0G2oEy4E2/LQE+87ueh/u7M7u7GP2ufAUfM8w
+S0IO0hBffAL9rsWow9GkHtulW/HbneHmgfvO63P3b9EZRtf6edsQSuewPi+I1I9ftB07AVYGZYKz
+cmCjHb/fVrQS0Eg2HvVOO9v66hYqu1E4EHcGX8QaVWfZrlVLNx2BWALGdkco9/LqvlvBj9YBrdi7
+Z09iWrnX5TYlu9G9Cg5bxEvMpcDsSYhEr1h1LSupaFnPEKqSkqteXT89x+u1g9meQHRsTd6Ra1ym
+KyhOtfu9UD1/UuKv3K6zOWzoz/Qc0o7kyt2f8SHCdXlmEhY5CjL8Pcx7GGGUfJvku1tgWNNWNOIh
+uBF+P2gtvTggAsstJoW8u82+r9Ytxt8DgyQpn5ac1mJlbqE4VIUmn93yDMxeeAxW9GaJ8zSyqzUz
+K2bnAVL1rpgCoqX6EqKBQNqbF+fus/f7lZNuBjD2lo3aDvZWrr6U+vSQN0TifFslGa1CgklS4p8p
+7DqCSB2m4YE712Q9JREgz4ZjIoWQXZXQyyvutgmVsCH7eHbl1bdak5G1xOU+uWqJxMMQBAGe+Wx7
+/52ncGd3dmcfs8+Fp4AHXuqRxR6jN1wN97XumkJQYnOdw54KLW5IVFpbagGfpiNmanYJR4ZN41bU
+ZDImFCPyutmxERotaF3Mtkp6CsWDVxQ02nUvbc2TnYsBN01Kod1tj9tceT1IUCY7TJiok286GUhC
+993+pmWjnflEO83FuOVlKSN7s5eJN6JNm8FUfArHUyEpjeFGCEqvhBupaodDyk6x/7TqaZWUasSg
+1E0OyJ66XMYqhPlYO03Y0apZq1qA2T96oRXbNmQ9kS5jEd3Ssc3rEd5MDTrFwHqkclinxG4ykKqE
+dlFtyHTcs6HnxcpdZ3Gx41vCeojGgR0Dvkq8dWA5Fiz3qmrI1SnaTjsW2vF2gjZP24bHer7RZMJU
+nuCVLTjxpZkRv0xcu5zArnMew3jkc6wmqKC9Yr3RfXiGtfQZtusN5XOXU/h65e4/rw3tRxiotfkT
+2p6NiIBzzaEmbpi07j7b1GM8VoK5iKnVHXtYGXbSAt3nC7zZnEhSgBsfQo3hMDXEQvgu/ZxMZcsb
+4RuiraHcpyKqACOVa2/n3WqfBCX0wWdriPpcLAq+DZgMR0zHb5CoC28Yrqk+VOvx6QZu3OD0ZUOs
+NuPYuBezTTd4eoheU5MK2ltPW3ZKOK2bHZcCNV2rFaO78ukat1Cc1y2esAlXRYERDqFsNwx7n1Fk
+HGnr04mcJVwXBNU+8RNwqcyXTQJqHe9aibFd7RMeiqvQe4hRK+K4KqgPlfAUoUs+CVgJBluOzwkU
+oiT+ikovwjDOWEn5JRQcePn0Q7pI0OYgwPbCVhTQC7PRrzyK0J0nluBMOm3pd246rOKC1Uq4iLTC
+r/RMwgwr7MhSjMPtVcda4JirXUxsXAt3fb0lUnLx3aYkEC6gE/VZMIBVn4jnGUrRqqVtz5VerOFs
+xUZEOovajdU3j2teF/DIdiW1ekWKF4fUwgV4/RJPu8iVQiLb76guBXaLDE96d4ypd0GhMff6gofC
+siRKSvZ+jUSmsFoQAAbjEatS0Rr1pBcDre7PrresRUeXTqEQF2iQtlQrEcco2Rl3BYUId4q1oRO3
+Zf4UtuLmvOkDMnVd5qIDSOioVNUIfEshnE1sBwoledsoJPbuEo13dmd39n3Y58JTIOzxj9ek/RJf
+JS/vJOMbvdom8h2dkICJN+ZCrRaTx+671fSIWj3o/qRntHIr4yre3kp3Pa9r1iu30jZKWmVeRKvd
+s80zVqo3txXcIG59YiqRsYbiD2umBf63JDzy6hYjaOuLezWJ0H/VzuILL7EVzRn5lvc38mLmhnv6
+brOruYjF8aDdZ+iglOu86Q1HiRCdQUQTiRdgs8QI6Ydq7Yt7D3j4vrveI79lYN8F2lO1csUzy7TT
+/c2VLOt8ciU57a4m78XfcB5zJSq4aTPQiKllL0C2KwdidM9VSyu8xdDBlXb/tui4EYy3LVSPN5Y9
+KHLwLIMIV4qwxqqsuU5LltJLyIW8PNqFPBLDc1i8xJnvvL7gomLwVZI0C44Sh4eI1Wh2HsLTG033
+aY3RWIStj69w9NpLaFZuTp3t5eEq810PwQK33sJArdAtUAJ6FxXk0mTYBiXjVGS6dUApDoihLehF
+ZnP+wt3bbhpyILaPqgGrgblpzS1eZrAhgTp3rXg6kgGq2P1crbvbuZx3LUbcH13HLQbi09rnYlEw
+g0fQRqxNzWkgl/tpx2vi6tuaMYNeINv3zD3X3prdF42119LWDgsw2lWYexLzvJhzkQmW23bURwoJ
+Nu6zdlPxVEy9h+0N3c7F4qFtkRYInqluJ72n2Oyki2iO3QIzKWquSrU9v2hJBPOdtxYjTsdx5iZE
+Pz5kIoKXVZsyFzx2M8v4ypV7FM+EMKrXOVN1Mka+IVbMubI9RjX7evBAakprhRG7bs09VTVC6xOE
+LuNuvRuM8BThZqBRt6N34e5tPutIVm5coihg1O7JUCpEW8jcaynUS/BS7gboLBgIS6mZpgHevick
+aWAr1qODlqfiF9yTAG2sJVJWfJJZjIR5F0GGXSj/sDMgSK9ROJPNC+ILVRzi50zUBxOPDvGUMzpo
+Bo7UB2BP1D16XeAHwkVse+Jj5Rpq6Fs3L268Ec2b7po++A2FKFGH0PTub/dtBHbOXOpVVnPMNgGJ
+qO93N4ZaQK/j1DBq3TMLkoi6cX8XKNTwVz2eYpQ0S0kLKVY1hkHhn5/0zPaLbL3HRUQkuvbEH6j2
+UGkCGm1gU+vhV5+N4v0ufLizO7uzj9nnw1MwIb7/MjTPWS6dFxC/ec3u16S0HDeMRq7jsIlXJL3q
+zYmUj8N7TJTsGaoRjVbgbpERn4ih+NGMayHBStTplz4neuR2/22wYyeKsXK7QTR5jLrgdnfbU4Z5
+ySnHnUP8ncWnHB0JRBDeI5YmWJyc0I/2TrbbrYK0JTIu+fRGl1NJcyK11hE4AveVaF0XB9QKGeou
+ppg5HIJ9EZGLaZnKo7Tq6Zes+eHBHDkg7Pya+VzdgPGYdanmsKDC7mXT1Km4qcc0mZK5/QQjwRLa
+Ix4o/LFhRircAEpsvsqGXek8tjIuqAJ3r1nzApSgHPcxoVzpfZa9x6frhJvwEkwo1OToNUZCnm7H
+HZE4Aow8lLhtiOduDH3TMizcHBl5LenhF9w1bWsa8RrsodbRq/f54l7sp57TCh4dtCdMJau9uS7x
+r1Xmkop3Vhi24mRoLcTenkJux2LkGLQTwaD7tGCmSsVoOiWQYFBtX7ptjgsZ44+F37DOu130O/rA
+haDh0NKKK/Q0XVO0QnTGOaW6bgOFUv42pBfkvzZzjNmrUqeEmnt9P6NPPxvdibGfIPj5g7AHhwf2
+j/2+n6ZfHBGefwhAEDYE187teX4Ag/IE9VCQCqRR6MFVuw7PVya3D0B9AGVrSEVCEvQRnuKvPQtO
+am6Y7tyLcHOUUn7HDeo795e89cRNhF/8V97mn5Ur+UTiH7/ys3+FR/uDWHsrauLzXan10DOgCXQi
+ZqJoNuFU17CYTgi0JidxTKuy5VgrT9N57Amb8hrSQPmMPmTPaZPvcnJPvQYqoa7GMeoGZ/7mfd44
+dspR0RBSSnhyNjTkavsNQ4UlhUci1qttFZEqvFhXPtPILXRVlzFR1SFv1b4ebKikRxmHA2WjrH6U
+U0j1ahL37MSfOFXL9roISY279osm5uQlCdZuPDrBistmx1iw704LaBwONIJHZ35BpTbiSTywrffC
+ODmdri8M9xD0hq52L2m82vIdhQHV5Yb3pL35Wg2/MtdLv1RfSmaZq5tz9TNf4se/5sKcR/XAj/9l
+J4b819RFa/IdWufw2Qc+Lg2xnyOZgUqLTKJ/8eF1tW+/CD0SzYXa9Lyq3I5YuAAAIABJREFUHNOT
+0CdV2bZUqPHlweOdyX7x6tmnTN7uDN/5stv4vrxLKWdu8Xn37/zaV621P8En2F34cGd3dmcfs89F
++OD5lmzaMm4G2iOp/u6mvJi5HerLSc3zqXOP+60lF3jjSLtckWX0+6y373O4FzjpZwzqRPTigImE
+WgbhG+L1jI2y/r+73fDzR+73b70f8L/kzqV860+/z/8ksNDv/uIPA/Ckq/nNPKyPapu0g8WIr28p
+WrWTqqGVUnFZ9mRiKg5shy+gVi93d+QF+BKRMca/7UrM/JBBEOM0NKzUJLPT74MrjyJzfzd5cU1w
+z7m4qd8wjkTPblpmgnrHuF0ubUcUut40CDCxqOSGKb3/XfrxmejlrbyAcvDxQ/ERhNd03p7ncmAu
+FuFpcE0r3cVC1z6KpxTW7dAPmhqj5FrfxrSqpAQWOlUlUoU7WVCBOkbBMN1n4qM1k8i53UOfYIRf
+mAqzUo5TzLl7Qk8OCn6bugj/ZjAQqQv06/QE7rFT7ff2NSz3T/bffpevjX8DgHtv/Ch/u3ch5CB8
+wEdnxEfnwkc/Lyz4+qSVoxB0Hk/lCR53sJEn6DUeH6oScdzCRs1YUqnjV/yeBxLieYpFQ8s3zEDy
+Dff5t9I1X/xsMIU7T+HO7uzOPm6fD0/BhEyil3hw5FMo0bPeTBkpJl9NSw5UxtnNjuiF9DueuSRL
+u7H0SuBtbMbJQizIvQeKnddJwivHir+X7rjRSU1eu0TVyw++zus/74io/+685EgY6pvwiq/o+3/t
+ay5O86z92E7wvcyYPVuQ4r50waszEbuOFsRCAiZDRCD5t1CxZZv4eMIptEPDRLXmyo84lPrI5XbL
+WCXAZ4Jat3F9y5uQ2/F3xUL6AF/sP3E+YZXt6b/kSQ2WpHfXdp7NuS/mojoJCQTHLdMDFkokRqGI
+dMuD/7u9N425LcvPu35rz/vsM73znWrqrrbdg2nbMpEtIoHAKHYIjhB8cGSJhFiykCwlIKSQkj/x
+IR+ioECQQiAiEAmZJBACWFbA2MYIkLATOzFNu7ura647vvOZ97wXH9az33tvd92uW+W6Vdfi/KWr
++77nnPesvfaw1n94/s/DA7XpTpsE9TIRFBHH2rnT1jJI3HfsSuvg1Axg5byOy9BjoOOxZYRRTF0F
+hsyqvbzXQiAmHojGzsYUqbsme/4Qi5CCWFaR8yxeORT25DxgfNOdz/jOdb740rcBWPwfb3Ouc+8V
+UPX7+gel2toaaazw4Os+ez22ot/BnzI9Z1XiztSgF3sJPWPa9WHAnvAyxBGn8gBfTTvmGzGMi5j4
+su0o5EkEhe0dCOigEA4lWRneLP8Q4hSsZ6nTmmQZ0xxKLOWiwE918WdDzK4k5Zcdw9TdTINIfIaD
+krZ0D8rNpGSo/v4uaPEEftlLO8YivOtuSUdxEbOciDfgjYYZznd8707FmTrS6rcNM3Esjje/DsDT
+eGO+gUQXf6JM+BeThBdvuWOICp9w4m54n4ZItPNN04cGQwqN660TLnOXBM3CAQPVpic7GbmyS+mO
+eunPfC7Vn3BQzol6hSTfZySZ9CIpeXHtHqCFYLR71udcYcKrWBi74zkoLLWy5NNBRJSJ3kzp9KaF
+fS0gM9MwkObncrzgc6JtX3owUW/GRebmd2Pl8+418UO+nbPqM3TG4IuTIAm8q/r9zcy9No0MceDG
+Nl7NMOq7IS2ekpHGhtyQXNJQwILilmFffJ0vDdYc/Zbr9swm71Oeq+qC5cPy7rbPJHe/w1IP9UfN
+1ff3xeFQAjFFTCfW8R8uphwfCWT3ICHccz+/Okt4V7Do9Zkg6EFH16t+PXLsFq4y3oVnCcs7H+n4
+tuHD1ra2tcfsufAUPAtxY8mb5EonL6oXdGq+OT/oYKNOr6KltL1egGqxsUcqxJg1wZVQSxQ+7Mf3
+i5auT7TJxc0yOJq78cq0ptGuexRv+JpAetazNBJ1aUQCaiif6Cn2jpoJfILI1Zj9kfNcquk1jpVw
+Gw89YuvW5LiJ8CUMEgg16UUdgdy+JjIMPLcLVp3PRgIvDTDddefovRM3VjpdURqxKId7GDUitZ1H
+q07F4eUaX2OHOj9RBCPhGCKvxuTu/Unm44mZKDPg9zoEY7nlFz5G77eRJRbDkL8KaTfOo6uMjxFU
+OlWHakVDeV/n3m/oS+mh9SnkCQWeT6xutEhutG0C0ok4BJYQiGosakIiieAkeXlVGoxTd7y+tQQD
+lYPzc9IvC9E4m/AnJdDzty/aK72Hp9n8/Y/mlQNgDES9NsTIeWOvXNvDDBXGjXaocZ7ZS7stgbpq
+d67F3N24cPmrglf/3+sVgdjJTgt7lcB81JO1HbTmD2P4YCxdVFM2a3aNm/A7Qx81spGWHYGo0dfl
+OY0eMt0DJN6UZdRTbaWEkTLn3i6LqAcQDRgYB/qIfQcCmZcNa4Uo2esLDhJ3Or9xz8OT4EwNREog
+VOpeM48Wnh8xA6RyV/ene7wqd3bvJVfbfinwiLKe9jxnrCy68XJiCapYsSWP2gHrxB27XaXMPBce
+jFP/isV6bIesFRJ836EbNz8NGFYO6FS0lWtHBFpvQVCoRXi4Iiyd+xyotTw5H7BRq+/AZITicEy7
+PdprbuykO2AgefnICEy2f0m3drmDzD/Gnit0G56AwruYNcx1fKJHG5bXsftaIL65ps5UaQkaPLWc
+p54lUs/EOHPnKttbMq3cAljsnjPs26HTM4bi7CwGSzIJ5iTieIxHu6xXSs9fP6B93x3PH/3qTe6J
+ROYfRm9zLKiwvPLvafYjPGs9OjqNQ35aPRqHL30FgKNuRvWyy9W8srT88M1bALS3ZxxO3fX9yrnl
+Sy+4fMz822rJTt8ivivVMr/jHd2nLY9jZ+xVzPN0tg0ftra1rT1mH1eK/q8A/ypQAW8B/5a1bkk2
+xrwG/ByuVPvnrLW/+qFHYQ1dFeBFGRtBlGkD6j5Lu9MQHOuj7ZDNSkmkGz2Pf0WknWGRGK4nwiFU
+LZ4SkMsEQiWoOgmZmLIhVxLNvPASX/t/XNLxPKqx6qQMjKG9yuBILt3UD197/GS5mATYzzJujtxu
+PBaENRi2eFqHJ0XAJnE7V7hoCXekCyD5sE1akasCEts1A1UymromUJLPq1oG4pYoPDfPwwPD6z0X
+QpxRC/McVhHdVJnq+ZCVvKKJpOtMBOONq8TUE8NU5LDDtKCVGIU57Ei7HmLu3p8srzMXAcxgmdAI
+zmzOR6jXiriIaGNRpS3dd53Zhu6eqkfUhDrOpoNOKG47aAjp5yfvaBYQjdz1izcp9ch5GOMmJVAz
+3WAT0R4oxOicF9OsWnzRoJE1eGqUOp1fZ/k5HeftOUcPXBj6XvXh6eSnzS8az5AJhj6aHFJ/0R3H
+3tS9dmTh1Lhzn/zACu9U1GyTETuZYNwvFFy8q0a3W+76H9QJ33fLeW7/9GvvcSgOhfeb+iqUaK0l
+MH3o+TT+z8eXov814DVrbWOM+cvAa8C/b4z5EvAzwJeBG8CvG2O+z1r7PSt4HYa8SyjaFbvqMtwP
+Db5aUk0D4ZFah+uARA91qGpCOh0RDNxDM6zBVzuxF8f4u6oAbGqsGJS8zN0oZAX7C5GznF2yI+7H
+/PyhA9V09ip2DOxDUVXDw9iz9yKT0OOriXtovvjii9xUF53pOxJTiFVCrMc+O42ba71fkarbr48t
+R1VKc02ApHMf9OAN24h64F73ViNWkiIfKm5c+AEj3AMUR2N8LRqdb/F1uTcHHYeicK+G7rODPKA+
+dN877Dzsbi9Mu0MktaVhM8D2D71EWe2tluGxu8ln4xXhQuHRK+cMxFZ9OV4ynbnP3xsqB/B+yHLH
+PZjt8RlLcSPGiSEcSFErDq/KqLuh5OfTBa0aU+r9gpFgzkW2JBLdvRl3V/qdJpZUezICHfvOZkAt
+Fa2jz49JBNT6oU1C88Bdh7uKzL/X0tB9iFfu9VWGMOEHh871f/VzCX/kpmMXuzV18+/Ge9xcqFxq
+BnTX3LGXARwKfDewHq9+3t3vuRifolnBzvJlAH6BlG98262m/3D2Tt88C17LQIxU71RPtyh8LCl6
+a+3/aq3tz9dv4TQjwUnR/11rbWmtfQenFPVHnupItra1rT0X9kkkGv8s8Pf0803cItFbL0X/va2z
+kJcUdpfVmTq9FmsyNbPMhg125VZSb1OS9oIbquPXaYkvjb8otPiiTLasCFUfr+2aWrDbbq0GlrrB
+05L6HiHxHWW6wxLbI0HMwyYnKxc+C70rybDOWny9vjvOmL7oGpAOsphGScdM9GllYOjEmRgWrkce
+wCs9Uu3+SSPoc1oyXqr/P4O5AFuGgnrRdzsuMNKu7Hv+13bFUhWMwrTsiuOvq0MqeUrxgxVNJBZo
+ue1xvMacuXMYxD6+NBXjnQ3xifAU2RLfyvvxnduaXCTk2nW91lKK3q68AxQuHGtWFXNhIBpJyW0o
+Wd7R+35O2PWQ75C2VtgUQaYGVC90Hl3XBDBx8xicttjAhSBBE9ON3HHEdwNaidJYZfrb8gKrJqmi
+K6jlmXWnhixxe9q//mrN/7UWHuQNEdzY7olhQuwrpOu+G+ZscJ4OwMF0zOHInc+XmdAOnYdgQ411
+nhDqHirjXXxVc4ZNdNU8VWUT6qWuu7yjvfBFvO93139KykvG4RF+4N0bvIVEhxYNQSvPuFo8YSaP
+2x9oUTDG/CLOw/qlj/G3Pw/8PMBkmFHUDYm3IFDcczZIqAXkCcKCVuKu56EhUpw5GbuT5G+mrPbc
+TZcuc4ePB8JmwEztwN0mwlN7tQoZ3O8Syr7PeLHkG6pgnG9C2g+IeHqCby8ICa84B5srspBrgc9X
+E3EephNK5Qw6SYtHg5BMFYB2t7vSDPQyj1RdhJVaHNOlDyIC6R6sqUfu7/xzqCbu2PL7DxmNSvUR
+LE1KkyvjHvqUqr/6SUGtxWmZtNS1kJMSfF1dZJSKz8MoId3TsTVj1gof0qIlkJBJoAdsOSipajf/
+Ng7pztQFGVVYVRyKg4TyjjuO09S9762h3HGL9+lxwW4savhxSyyOyZGNUQoDT6Xc8OaS9NJVPi52
+L9nJ3QNmDleEK/f6ar8m0ibhq5144I04k3BOkXv4bQ8Gg7O5O8/fDI642bgHayxO/VVpaJ+wLLRX
+1YdHqZl0vAZuiCTn1uAF9nbce6tDjxdTdx8mjQsjLg9bpiJC8aIlw8bN4zSydIUo7ruCiVCh93Xt
+CtsyER38/s2EBxu3uGUvzvmi8g9Lv8IbuJP4xuwDp/Fd9rGrD8aYP4NLQP6sfdgd9NRS9Nbav2mt
+/VFr7Y9myUdjm93a1rb27OxjeQrGmJ8E/gLwz1urLcrZLwP/jTHmr+ISjV8A/tGHfV+Hpeoq8nrI
+QozLXQWVFdVWOGRie2ERjzJyu8aO2sySOMBKdi0JPYYCqcQXHW3pFpy535AKTGTnwitUFbkqCnG6
+h7dwvQ+X/uYhg+8jx9lj1mMvpJKQCZ2Hrz6AKBpxsuN2nX/WdhRy/23acx96KBFMd9ZdSYxV1hBI
+3s2I6beMfdq5Qg1qvLU8jLiiOhNxit3gSV7el8sdtcurykjbtFd7WLmMyLVL++sEFTnw56IGC4HC
+/VxMY6x0JRm2eGcuweXtdATG7cyoK9WbezTisDRdRal5+KewkLdlTls8gYwGCucW3Zq16NT9pmKm
+1w8PfVrxlpdDQ7YWvZmSoPFlRifsxWiZUe6qf2Q2wsaqDp1BN+n7I7Th2I5k5b6r8hqCgSDfFxsW
+0mvc84Z0P+ii3ejYcTx6TUH7gWlyQ6jwoTIPM/1GZ9z3fYp9t+N/ITEcCIcyqCaM5Vml6ij1zku8
+feEqgoyefDlbt1wOBePOhtTH7o1wo56YaEAoHEP3Hkx33Xn5gfoLRD/iynXffMteySNw+4Pm8d32
+caXoX8MN9WvGxTy/Za39t621v2+M+W+Bb+DCil/4sMrD1ra2tefLPq4U/d/6Hp//S8Bf+khHYQ22
+C+lY0io5M8xC9lSSrNYrupE6w5qa4dols+6IPHQ/i2nHzgsYtw2mrzHvRCDR0XTeciI9gUzIxAd+
+ja9EY/vuCW8rB9DM7Hd4CM489Ua2WIyagDyvI1VjzGRvwAudQ+wZc8BaeYBISdJFYhguRUo6SIl0
+nGHcUKs8eRkJJnxac6zoLtnMuVQ3YNZZKnUJ1nVLrZzCbNUjFxMWhWvsGsQBnt/rDTRXEOpiP2dy
+Luq5gTvePA85G7pk3rWTmOPrysWsRzRqsHohTylxgalVonJlAtq7rjj1ZndJqM7Ht+KUeOHmUpua
+TLDQczVoXa6hrV3i4rR8n1h13zT2sKFo2uIAm7nP7zfOW2nTcxoxQp8OOw5UF3swXrOzceNtRglH
+Io3Ney2IMGEjjoXo3OO0dOco9g0bbc3BpsY/dccfCEqd5Ib6A3IKPhYjSGMvEuSZhw/UtUHCDyXO
+q7px8wbdxJ2v/XbFpVyPsRHb90HEochY62SJN3CJwVUSMDh3r5/X52TilFjIWRvlLY30UPzDjJ1c
+fmEz4kTPxuevrwmGDovDP/6uaXygPRcwZ6ylrRs2dowviu1JsUEeM6exxe/cw7Q8W7EQ52FPv3XC
+A67HkixPIzzrEjiT+JIHp3IfVxe8Lxo3v7ivgTtC9QAc03Bj4U7qnaCDD1DvtnpIx2FCJXfYtD5J
+qNbormMVKdmVWjr1VaxrqRgtGy4bKS+tV1yqRyNfj9kZuJDmgUg+gvWS95Rk2m3XBErDh+mIulYP
+hzVcSv/SK13i7G6xplUSdFPlfF4/11XCIlWS8KJg2Sd0z4XdCDva99SFNwBz6dzd6eiEF4xbkPK9
+DBu6+Y1Sdw7feQDVqXsy/8l6zXDhKgpt3BHpXHQ2phqLC7MSTLo651yU+11VsuqzvzbCVyLNDqor
+uv61rm9VQ6sHvblbcNt3C25+6bGJBaE+9TgW+Uq7Ejw6ueRkLX3Miw0n6oIdVnPaoRKpdofo0AHO
+fuIt537/5uCMbwlM1ZmHC4A1HmNR6/VUek0LAyUXj7KM7gW3UV34C5ITMUbHA4oziRhl9wDYWWVc
+apEt2ympdTfBydxg+3bpmc+odfNT5zQzfwQ7Yt0uVlQKIf2mohmI0q1MadNH6KifwrYw561tbWuP
+2XPhKXS2o2py2qYmESbqdhggTxsbl8RSo35/aa5Ycr3M7RjDTcLvDd369nLYcdNzu1HTpLyduy+p
+LmtmsSs3ZXLJTmuuXNVXZjMeRBLbWHs8RCc8tL4kWfkNvshD26C5Ehj0A8tU+AVTJ1eK1tb2XYY5
+a4UMd6zlusqXo3DGeu0uxR2FAeEsJxdY4rio6SRq8nIHWarSYlOxVpmxJ+DY1CGLlTs/SdvRBeKO
+qHOQluRsAJn0GtcKmbjfcaI9YlRdEGTyKs6G/NZE53aVc6CefqPE7j+57LBnCse8Y6Iz54GsEp/D
+gfMgbk3HdF1PPKvGNn/MQFve+fo+RihVL+4IEyH2/AGziTv+PYUDzXhGceF22tttw1DhXzeekwtN
+eSeqEVUDIzV5VeuA10XSmyxr5pnzaPJLy8XC7bY7kyWfL9y9szxy1yn7VoJn1JhmHzY2ddZiFMZF
+okQLvZZQaMtB4HGr7/bshtzumaS7NRlieZ45r+Rro5qXJd4yDC8INI832xpfwjj1+D7hzIUPtxUS
+DpOcPRHhDqMxZ9KcaEYJscK0vfMll9VHa4h6LhYFcHF6YKDpWx8qn1oUQtM2o5EU/XCz5O3Snajr
+wtM3G59p7W7MTTglE+NwMI85lFDJe13NbqWwY+luCI8VC2W930oaOsWq1rd8MLWSAFKdz0YZcK/1
+aBQ+bMqQU9wFfXlekQhWen/V5w5KAnEjjtqWpe8+e5CFRIozXyzcw/hObQmVoz3Pa4LKzfWtNuSa
+uicPu5oDfabRw3FSPgQ0LbyHYiKR9fA1qWTpsRC/ZSctwsVyQU/y97b1OdoX92ELiUKTTZWQ7boH
+iKVzd1+Y3+f1xj1ImfG4pxvQywveVcu5Hy65vunJUgS5paEVqU3ctGx6d78z+AqPzNQwUsv8UjiG
+JE+oS9X5y4J7rXt9r26otPCERc1SWfsjLWhBHXJj5c7baVswWInhK89ZWac4lre7DMfumM2FAFuj
+nGD+SO5AMHVjfFK1kXtD99qiagki93dhOmGRuVj+hZOca+r+PS8CJkMtHAo7DlYNjaToR16MrxLV
+C+sz7vqq2nS7LIq+Z8ed77PxiMG+4OarGh06F0HMqHNzLVsY1x8t178NH7a2ta09Zs+Fp2CBurG0
+tU+yqwRXNGZHjT2+rUmUUHl5OOZA8NBdNRzlOwkTcdXVmQ9i7U2nIdfUh98NS+pcKs+33O6TrTfE
+G9Gxvd8yEzihbZ8EbHWvb5qKRjtw6zdEPePwfsZ1oeaMDUl9N/aLYQ9dnFCJGZq6xhuq/l9VDDOX
+zDsXrHd3aCiqHnac4GnXzYYxNM79nLUJCyVP31i7xNhFDo3CjmAJvmrpeBAPtcsPKrKZOwcq3ROM
+OspC52Ua8krcq+Hsc6SGrmB/Qh9tTFSDf3njk8v13ZQVwZGbh7+uacZigW7nhFM1rymEKbo15dpd
+v9JbY5QwjSIfIaKJ0iFM3evXdKCFvyHom8eqhGuCkndJTVf3jM81WSz1FaFiB6PsSqbPxDEL6TLG
+XcZYOhPpyDDUeVyrGW367eDqIalaSy/dGVhLKDe+FIdlOmi5qQ7dz3/+OjdE8JLuJhglx4+Cmmjk
+vsTgzmE29BgrlKyLgkT39WjnGi+1LvwpIoPvCa7cuLDDCyGwLoQOJiNSeRXZecdthbSeV1OIc+Np
+7blYFIy1RLajDj2MyErT1X2mE7W9xobkhrgNlxtu4F5vRdIxrnyi3LlLVdtipoIMRxVt6C7utTpm
+rSxzJSGXg0FIcey+4539AdcuJfXtuRbeDzhSN14YkQduvLDzCPTgDdYlKx1TkkUM1u4ztSDBpWfx
+BKYZDAMa3JzCYUehbsZEsedws4sn0dEw97ilG2kTxVCJsKSac2Fd3O5L2DRvqysXtw5bQrVAW8+/
+Yi+abjbEmR5kibUW6TXatVsgbTRkOHTHGaQp+2pnD+OQ3XHPidizGCX80MB1AJ4NNtx8V8S05Mzk
+Bo8mB3hq7a4UH+5WLSeZqyiYNiVWuc2vwWvV59HkjC7V8SmhYN9Auue+a7BaXREkVmFMN+zDB4uv
+/I+vMmw78ogkBz+hQ6kk6hHEKrm2TUq264havvxAYeewwVepLwweRpUhPntjlfoErLNdxq3MzeNG
+OKDuCWZjj3Dh7q00jilFsmK1QaRFR+v3rF4xtQhu4nlB0i96aYTvK0dR9ZWKkqaXPvAKFi7CJl/l
+nFbqFSkLBsUHlNK+h23Dh61tbWuP2XPhKXTGsjYFdhZSipX4xB9jpJN4fZKRhG4FDibjvi2ewUhy
+ZmXISuCedrm5ovqOfJ9gR+FD5XMUu3W+SJz7dT63XKom/tJ7byNdFWz1BL61PnygwujvmqAmF+R3
+Qc1QY8RJQCVOx7EaorpqyUrZ95VXMxC/nkd51XGXi7V4vJPhz9148zjGqrHpyIONix64MJa5qjIP
+lHCrG+j6xq2qxqq5KChrErErV1OfSKrMe5nb2f04pskFefYykkCdpOEApNbs1z5eKkCWvLFBuEsk
+0NBuPWQt8FlTLzhSQjihZiNP4P6l2wUPdn3K3OmGms37WAFDOq8j0Nh+M2a1Ixi3SHSykWUQOE/R
+H3ukov5v7TFV4a71eZoTq5MQkckEvsHuuGNOsgFZ6+K0fDlg0yl06TzMSnRsSlDa4x0C4/AENHBT
+QIULz2KEBRgf694cLWkTd5y513AktXGvich1HYZmxGjgPKSwctfgJLZMeo7KtCaW0M5m3DAUR8Rk
+pySp3flahuLAOC/oYnmjc5jpHJ5TsxYJjne3o/E+WqLxuVgUbGdpy5YgLCklE7/2dtkLVVeKEm7q
+Jm7DkE4sn+NDh4hr3m85xS0QZuhxdE2gmVlOIpx5VUZMXxJjz+t68PbWBDrRi1eP8I+d+2z86gnV
+BzlWlaERE1DXWDbSpzifN7wrQNVPhAZPN3Jp1CeQDYmlRuTVll4sMmlCAomU7ql6UeSQ7eg4z0Iq
+xYuLNiMU6tHJ00srs+lJX9qr9aw1UOl1r4nJ5br7q4BSN3QfI98oA0wvyhr6pCPnRnsryJU/8fZC
+ptLT9Eo37nSdkKtQNziKOLwnKfp0h5V6VwK/pj11D8LByF3Hu8tDBkN3vpPLhFr6n03TXpH01gc1
+yYV7KNqJ2tpLw44WSBukdKnic7tPmbvz0pQRheKDrHXjmUXHuOxzAA1DSQZM85Cl1JnWdUQYuXk3
+b7hFo53eIc7dd4RJR6FqT+BntCKGsQpbqsZQatHfrANWGu/lssWITLeNO4ahCzv6VFNcWUoRy0zM
+BF/oxmk1Qhy97E8TBguFy9Ydz2p/QGfd9ZhtVgzVK/Ngbdht3fOw7GqC5qMRt27Dh61tbWuP2XPh
+KTSd5XzTECwqTqXn+FJcsqOa+D4d5Z4UjUKfTAAnq6Qk+w12KQ48DEbJtWAf4pn+Ll5fgT/MTbcr
+m1lK6ju47uqkIo7dZ7vvQIVe0bbT16OLK0hp53X4tduhgpHhZdXTi4Eh0+6+J2zCeWpJlXQ8xjDU
+it+lBaEo1avQzf+w8q52wWA3Yib14WhtOJNK8u0OjgupaKlG39pHejW67ooTsjEFjS53lVn2lcEf
+SPKongxIJM8+NFMasUrbLGUlVupdL6DUuQ/llhfxmlA9/d2iw78mbHoTMVRoM2vOSaSO/Pa56Oa6
+GYvUwYD30jVtX4nxQ8epB1gSNmM3zl7PpxBX2LHUqaKWYeN2ypxTupGo76OKYeWudSlZ+9QraaJe
+ISukP0v+fkgv8DS+8DndOIBbNXLzPzn3aeTpFRUcKSp50cJK959gHNRZw1g8kdFOwKHmUacTYpFn
+HNiMauzOuR8o5O1apkrEdiHEAtH5tU/mqQOzqEHUgr2WZrKoOJsY+Eq+AAARi0lEQVSf6npYNqmq
+D6biWF2gXlhS9B73U9rWU9ja1rb2mD0XnoKxFr/MOW5Lbt5zK3Q7uk0duZXxrtnlpnbChuaqGQlP
+xJfVnEWuqZgN3VieQlUxn7u/y+qKY8W4AzUDNa0lU4fcm/sJO2+52Dj0G4pHGDsfphxVmrKwEabB
+dNAqkVOdn/PupWO/OYgHNOJqaD2XOJuf+tQqXxV1Q2AfeibVlSagRFYGhqXIVUemINJ30Va0vZBL
+WVKb/LFz+Wh6tANqOVO2iUjl0aTzlmIovIBEZkKvxZPwSOutydfCNNhLVvIOPNOxUQefN3dzqhcG
+X65V3nhMJADjm4RGicZNl1Gps7UTsq9sU5ZK9q33p0TazZqiAuVlVnZFdq5kq3bJomyvavOZ75Mn
+YkgqEqra5SjKhUenuXoDfRc1Xq/R6EM+7+HIawJd16IpWFfyWJTMtXGOp/NtIstaCeEkihkpf3Ae
+uGs6WXfUkTu2eL7gNHD5quFkhb/svbQNpWDVhfA2Veld5SoaEjaR+z5/XrBSrqU0Kb2QpafzWm7O
+KAS3X81KLkXyuirgeC4otW2ZeH8IxWDqruFBfsHiQUQ7csXWVXVItO9+/kqcsqndAnB/vWGsxcJP
+HKnTsAq4r+y7t8i54TkXMGTAuaDL8yDmSB11rR7G984aSlHA29MFp3IvTf69qw+rtqbr7NVrViKt
+769LvqyKyXnb4imBVQuE1KZrRKPH2XiFvVDdebDCE7HIfOw++2I3IBLte3G/5r2k78soeXDhMs6/
+Xxasei3BJ+CtOrnPbdzgK3Rpk4pCnIiZp3NSJdyvpEfpRxSxg/6OVj73Ykc4cjkPmWRuAqkwFncX
+DRMpHU2SirZQp2l+Saos+ircEKtCk5daxDLDgfAmwfGSUNe0tjVdIn+8CJklwimILMff3VAp4TZP
+CyaE+rsL7MI9IPe9cwa5qOgVHo7WI86UHL42nBArPDCXLe/0wKlVwMnCzXtm3TEUpxHLzj2MbeMx
+FRz9jJpN6TAiK9GcFdGKSovJ69cu+MGJw2/M5iG1oOlVmeHtur/LZm5xOB3O2TlXt2NyTHLmEpF3
+u4rd0M17P15RqdJyWWiB3XjMlPC1xZpCC+/KN3gT8di9P6NNH9nhnsK24cPWtra1x+y58BRsa2lm
+FXm3ojl2K988XXHwvju8+zcO2C9VN1/6zPfcan1LPfjVBvaFOiuCkOGOW13jN2o2CjFavyNTWMG3
+RKtWFMyW6qg8aEnfFKOuZx9rknzofKk010H/AYtL7gFczkv+d/Xhf7+10MlNnAihN/cIxOC8c+HT
+CJlYeyl7qt83ppeUtyQiPo3biNGF22Hni5xjNU2xya8Sf99NHeqs0yX2co8ilV7lKqCRtqNdCNPg
+rWnVcDMfhexJ12GVdvBAqDo66oEr96ZnbueLO4+1iHLT0HA+U5luXnBfY2RBTaXynaQvOWkM46mI
+dOchjbgJ2gY6EeOsxw1TCdtsJkrOPQhYZm7sYFmx2hVqsA4wCsEOFiNq8RoEuN3aXxdkajpadQVR
+n9itfLyF88IuyjVN587n7MKdl/dZIR4b/AhpBkLX+XQiMqnE9VGtGgrhBoZvX1BEzuv4V9IE78yN
+Nz+wHIqZW3lGjs7Sq/L0ILrOQGRAuzbCWp2jbsDmUjwTl27cpWmJlSVdNj57aiQrm4q9xt0jF16H
+V3+0vf+5WBTqtuXecslmYWmVAb+2XlDuuRtwrzRUqgWbMGAgcpJCN26257NXOWIVz5xiBGIJb3Rc
+qzONcUx9rG431YSbs4bbpQtRxm81vKv695NEPsK+ddp+58MnqK3pGJ27xevNg5CB+ANfkHLPajfg
+mtStTJYxVthxNy2JjfoABHdtZmvelsu5v055Sy29d2Yb7p65n99vG5oPEUQ1aqH1w5hAnYjZsGCo
+Ft9GSYeuDVkO3HiDJr0KpQ5MymjitDBHzCj1fYFATzuRxcqFPZ9fklXufM8Tj1RHldMwFSzaH7u/
+O5i3ELrrtNpv6MS7eb+qUJsAnjeg08JxELpKRRtdgHVP03LYsK+Q6CK+ZF95EC+bsKek0P2kZ20e
+0GmRGm/gQSdVqyLhWNfhclFSSSHqm3qoihrWmkdQWZbCyHyuafi2WuJ7RvDWtETCSlz4A75/7d5/
+x6+ZqPfjoIi5vO7ObSZwm7+7z676Z4osYLAjZbGoI9A8VpsNA90jy0w36MZw0eqaeT6V2MnipaGI
+1CdhTmlV2Xha24YPW9va1h6z58JT8DAMO5+lXYP67otgg3nH1WDv3DogLSXrXm3wfLdazyq3YzR0
+xLhkmDU1rWC5XVmQP3BuZ+4XnAXqkZ+/A8Dp2YobG7fr/p9mw0goxuWjfAqPJG5b1fY9W38ABQt0
+Vcdbudthji4TBvJkBhO3S8wvUrqp2IfbgsD2NX2fYkeQZ/Eano2HPDhxntK5d4eNkovh6pJTZdZN
+92Shkt76hqiwDbCqodtLSydxFSGwqf2SUtiLqssRSTRN17KD21W7KMNTxj3vXfHjmrXnvuu4Cxla
+dx3aZUubCB69k9GK8TqNxDo98JiLCyAdhBSBIMHv1ViFgt24RjozLCc6hsonFAFMe9lxKh2K03lN
+oUpEtTylk6Rgce5268uopb7v5n87TriUNODAP6Y4VTjWFrydC8OiE5DbUuEitKEFVSLOAo9ESM+N
+4MWms7Ta8fOTOb9nXJIz8W6yKdW5OrE0D9Tcd127fF3gCaYf2pZcUn71vZp5p3B6k5FG7+r6KYxo
+S5biDinxmMlLMabivmgN67pmQK9s9HT2XCwKHZZFUGFqQ6mM/WUd8i2VZvbPN6w+5x6QTbdDaFz2
+Npyf6O/HHIuwZCddM9QD1KUNbyoLvRmfUN3Tk66MfF7GfE1glOCi4DyQK/7oOXxM9kegqScUJzos
+rVzRY79lqhjvXX1hVHcYSYefTQwviIOxydurxaQWnDsO75K79YyVKWnW7ntnQYMIfaieQuK0CZTt
+b1uM7m5vGlCKHh/1WpilT5f2JDSXbKSU1M6GnMotHbUVyaUqGKqMXNiIwpegTDtjpWOOMx8rha8k
+aEhFUmoVoFeHHqPKubhdssRv3ANWmxydAvLG0imUSLR6teOSy4UexpElXbuFYNatWD1wnzkd1Oyp
+I7QvAd/uYFMJAEbJJu+Feixo8T7HZy369DMB2ZqloVbc7lfQCly2Citq5TD6HaIz0EugLE1z1ZL9
+IKrYUcjTdS1+LcDRO7qn9wJeasXitKkwalt/u7Y0vshSlidXHZOxelj80LJR23aS1+Ra1C9tRxi7
+TXS+ekBYbqsPW9va1v4A9lx4Cr5nmEQRXVLTyj0NPR+xcNNcbwnvuxV/NIgYShPSn7p6bpznHCmj
+Oyw8gutaSY8XvKJs/upsRPKC8AIzgYn2ary5a4BZfu4Bv/u23LIONq0wC3AlpmGrGwBE/j266rsT
+fNZCoyzl4v6Ca4fqJIwciMWPE8a9t7FwXYwAYdhwUw1Rp6HbwYq7HVMBjA42DUvBjufLlixQ8ql+
+ROfyCed2ouRU7XeM9CGztCjZj6/afhZbwsKNMYwT1sidrXNWqo/vdx3eDUGTVRW4FzbUco2LOGCy
+q27PdUs4ct5B6vvsRYIdSwfUT8EbCTK9tJytRZDiZZTiu8g8S6zKQDAQ3uISBqLCs/OcWKHisBwx
+CEXxvvEYikSlEtz3Zt5xYoTJKBp2xV8Rm4ZNz1OQQ3YgDs07ArKFllrXNPA9AjWSee0tholTV2mU
+DMxbMH11ogKr6+s9WDIRedBeM8KEriIyih9Wg0Y9eG1nh4nCp9Zv6fQddidBzZGsdK+UgUcqr7ep
+GvZ1HbqiZuO7ezy03lUV7GntOVkUfHYHO6R+y235LmMsO1OXhTWbBVXwJQCiMVgx03gi+PTHL3LY
+SmPAxjRSKYqPDjk6UpvqYu8q+4znRGCz6V3uffsN9x0nMalKa23ZXaHcmu4hW5AN3SqVmohWcVp9
+pRsFnucxNL10+gGBdRfDiPFokDR0vquSHFQrNurgCykpjtwCd60Wq9I4YiDX955/jeHKgYa6sML2
+egPGw9iHpdHvNAPEKocONyW1CEfC/TXeSnLn0n3wqimFxG9NOyYbuvn57HOkHIaNh5heol1EJzve
+nM3cucFmVEOuePnakkCIzHa4YKWSQjRWXqPOKDtpSMQjwj13o2edT6K2367zQDycbSkykemKXC3e
+zaDDE++m2S+wGzenm3aDtWIbUstyE6ccRW68PA/YSBWqzHcYNy72P242ZGpnDtW3kMxbSlVqitoy
+FS8j4TFhIi0K5RQa3xKI8SiJIqbKfU2SI/xExCqjCSOdZzy3IR35Da30Hm1Qk0uAdne6Js9d5Scf
+VFdVtejAITe513Epsh8vHFEpPAxqQ9bXUXf9q5DtaW0bPmxta1t7zMxDbdjP8CCMOQXWwNlndAj7
+27G3Y///YOyXrLUHH/ah52JRADDG/I619ke3Y2/H3o792do2fNja1rb2mG0Xha1tbWuP2fO0KPzN
+7djbsbdjf/b23OQUtra1rT0f9jx5Clvb2taeA/vMFwVjzE8aY143xrxpjPmLz3isF4wxv2mM+YYx
+5veNMX9er+8aY37NGPOG/t95hsfgG2P+qTHmV/T7K8aY39b8/54xJnqGY0+NMX/fGPMtY8w3jTE/
+/mnN3Rjz7+qcf90Y83eMMcmzmrsx5r80xpwYY77+yGsfOE/j7D/RMXzNGPMjz2Dsv6Jz/jVjzP9g
+jJk+8t5rGvt1Y8wf+4OM/UnZZ7ooGGN84K8DPwV8CfhTxpgvPcMhG+Dfs9Z+Cfgx4Bc03l8EfsNa
++wXgN/T7s7I/D3zzkd//MvAfWWtfBS6Bn3uGY/814H+x1v4A8FUdxzOfuzHmJvDngB+11n4Fp+j+
+Mzy7uf9t4Ce/47UnzfOngC/o388Df+MZjP1rwFestf8M8G3gNQDdez8DfFl/85/qmfhszVr7mf0D
+fhz41Ud+fw147VMc/38C/mXgdeC6XrsOvP6MxruFuyH/ReBXcEjkMyD4oPPxCY89Ad5BeaRHXn/m
+cwduAreBXRy0/leAP/Ys5w68DHz9w+YJ/OfAn/qgz31SY3/He/8a8Ev6+bH7HfhV4MefxfX/KP8+
+6/Chv1l6u6PXnrkZY14Gfhj4beDIWntfbz0Ajp7RsP8x8Bd42Me0B8ystX1v67Oc/yvAKfBfKXz5
+L4wxGZ/C3K21d4H/EHgfuA/Mgd/l05s7PHmen/Y9+GeB//kzGvup7LNeFD4TM8YMgf8e+HestYtH
+37Nuyf7ESzLGmD8BnFhrf/eT/u6ntAD4EeBvWGt/GAcrfyxUeIZz3wH+JG5hugFkfLeL/anZs5rn
+h5kx5hdxIewvfdpjfxT7rBeFu8ALj/x+S689MzPGhLgF4Zestf9ALx8bY67r/evAyTMY+p8DftoY
+8y7wd3EhxF8DpsaYvlv1Wc7/DnDHWvvb+v3v4xaJT2PuPwG8Y609tU4p9x/gzsenNXd48jw/lXvQ
+GPNngD8B/KwWpU9t7I9qn/Wi8I+BLygLHeGSLr/8rAYzrtn9bwHftNb+1Ufe+mXgT+vnP43LNXyi
+Zq19zVp7y1r7Mm6e/5u19meB3wT+jWc5tsZ/ANw2xny/XvqXgG/wKcwdFzb8mDFmoGvQj/2pzF32
+pHn+MvBvqgrxY8D8kTDjEzFjzE/iwsaftlb98A/H/hljTGyMeQWX7PxHn+TYH8s+66QG8MdxGdm3
+gF98xmP9UZzb+DXg9/Tvj+Ni+98A3gB+Hdh9xsfxLwC/op8/h7sR3gT+OyB+huP+EPA7mv//COx8
+WnMH/gPgW8DXgf8aR13zTOYO/B1c7qLGeUg/96R54pK9f1333/+Lq5B80mO/icsd9Pfcf/bI539R
+Y78O/NSzvO+e9t8W0bi1rW3tMfusw4etbW1rz5ltF4WtbW1rj9l2Udja1rb2mG0Xha1tbWuP2XZR
+2NrWtvaYbReFrW1ta4/ZdlHY2ta29phtF4WtbW1rj9n/B1FnQTciSctAAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.2762... Generator Loss: 0.8202
+Epoch 1/1... Discriminator Loss: 1.0826... Generator Loss: 1.4488
+Epoch 1/1... Discriminator Loss: 1.5480... Generator Loss: 1.4554
+Epoch 1/1... Discriminator Loss: 1.1184... Generator Loss: 0.7442
+Epoch 1/1... Discriminator Loss: 1.3038... Generator Loss: 0.8651
+Epoch 1/1... Discriminator Loss: 1.2226... Generator Loss: 1.0445
+Epoch 1/1... Discriminator Loss: 1.2978... Generator Loss: 0.7549
+Epoch 1/1... Discriminator Loss: 1.2956... Generator Loss: 0.8022
+Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.6897
+Epoch 1/1... Discriminator Loss: 1.0502... Generator Loss: 1.4712
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsZel13/f79rzPPNyxbs3VE9kcRFokTUkeJFuS4QGO
+DcVDJhmI4Tw4gYPkwYJfAgRIYOQhT0GAOImDxHYiG4gDO4YTepAl2YooUiIlkWz23FVdw607nnuG
+PQ9fHtY6xW6RXV1Ndltl4KyXu+85++z9jetb438Zay0b2tCGNrQm53e7ARva0IaeLtowhQ1taEPv
+og1T2NCGNvQu2jCFDW1oQ++iDVPY0IY29C7aMIUNbWhD76KPjCkYY/6IMeYVY8zrxpif+6jes6EN
+bejDJfNRxCkYY1zgVeAngXvAV4E/b6196UN/2YY2tKEPlT4qSeHzwOvW2jettSXw88Cf/IjetaEN
+behDJO8jeu4BcPcd/98DvvBeN7tuYD2vQ21zbL2WXKpHzQudBmMCAEaxpSYEwPMbANrKB1sAkDUO
+tCUAdeuArQEoWrBW77dG3/GkUpIPQDdoARiMJxjHlbYD7Vraai2VPtNtLNaR+41+bRxwWvmdcRuM
+tsO6BrcV/vyd3xgaR773cGj1IY6FWj93G2iUrbeN9K1qLTT6Pq+l0+kCELgejl2fARVoO9bvc1pD
+a2p9mMHq+0xraHQMm6qlsnK/DiUtDW0jnzXWYlrz6LmtXjdNTV3rdSvzVDSAPgvAcaRtdV191+h/
+UHIAaxy9lne01gG9/rBk49HwJgAp5wCUixxs/j3vNY8uDL5r9DOZg9B3aPQOx3EeNdA4Brv+pQGj
+18aVvvmuixvIHolwcXz5PjAOdv2O1qFB5u/12/dPrbXb79evj4opvC8ZY/4S8JcAXDdm/+DHmJWv
+UZzJonHahzR2C4Ab3TlecBmAf+sTDac8C8Du7hyA1dE2tG8C8I2LGJM9AOBsGdKWJwC8mbkUldyf
+V7ohKLHvxyAMGFfG8YcuZwD8+J/6GeLOAIAehryRQW+XBcc6Ad1FRduVvvjK6LyuoZvKJvUGS/xK
+GF098OjnHbmOE/m+8Fn1hfmN6og8ls0S5g6zWNo/nBtmHXl2Nl8B8LAoMBfKNLYyfvgzvweAK6Mp
+YS3vMO0xJusDUHSkjZ2lR+7L4nZyQxlKP9wkZN4cA3BxmHBcyBg0c3nHihmrC9kI87rGz4WBNkFC
+ksvyWi1POTmWz2fFWwDcOW9p6kwH2aXTiwA4eXj/e8/DB6DIcbBeLP1C+rdqArDyvqI1rBnED0I/
+8fv/GwB+w/wdAO596du05SsAWOvwiDtj8Ixues9nfyjz7nsyBze3+yxdPXiimFab5gcutSNjaD2X
+QAV7fyRrb7/fY3B9AsBzzYj4QJ57xYtpR/I8J4tZWJnXP/GzP3fnSfr1UTGF+8CVd/x/WT97RNba
+vwH8DQDXc+zp/J+RzhoI9cQsS0x4BMDLC8tzL1wAkMfbfPxHPglAenEGwN4Nj/5DGZDFMmX1bbk2
+NxYMXxWp4cFoSX6oJ6UjC15Ou/c5NyzYQNrxK2/JJP8Zx2ADGfRh02XRzPWxLlUuExAGAcTyngO7
+C0C923LTyEIwOzmT2YG0eXvOKJOJTvozAOIHE463lwCMijEnHelr936PaiDv61URS0faFjSXAFj5
+R/R8uZeVZT+TDevtZmzFwtxuY7lkhDnVQxmf3dGIdlcls7sHLA7kuZ3jHulINlj+WyPejm8D4D+U
+RZ7HM7JXZEPfjc4JZ7IJay/j9FjG++XGozOU+WteE4ZWOwllKozO6RUkZ2sG8QOQ8ncfl3YiDHUw
+k79VN8dZSnsK03wo4sJX3vqPADh8ZQEoC3hko2vecael0s+DkUOaCnMeXxlKGzOXZl8OwF7uUvZU
+gkgqlpFKlklDHcq6ZiFz1tiQUpYIw0kPbyTf77XXyC/JF72ky8wrP1C/Piqm8FXgWWPMDYQZ/Dng
+33mvm21rKBMX3Ak2P/7O54UMbM932bqQzfQnnr3CWNUH92OfByC5fYzzuRcAuPTLr3LyBVnED996
+hd3PyWA7X7vNN4ayKF5dyIDVpqJ5EkNr2rzrX9PpQyMbwe9WDIxcp945nUJWpt8p6OnwDrdlYrt0
+2B2ouDfaozuVPk2LCd5A2jR296RvL/h05rJoTHyC2+qJMSkY656PozMGlYzFrCcfhncyyko2W91J
+KPflJNkqbtDEsnhjf4sslvdtLeR9ZmrpevI+57Jlq7wlLxmeEEbC38vLFe3849K+fTl0SteheUFV
+qYsJ3Y7w/mUbMHVko+femONSFmZ7aSzPfe0CHFXnEkvj/GC71ACuiuN/ZrTFFYQBBp8QCfP/vvtt
+frgv7/gfHtwlp/mez/kgFEXy7OjH/zMAkn/yF9/z3rXiFs0sP3JT1qcXyPzfuroNrTCK5TQgUg3k
+hIKRJ2LDYZURqNqY6tNMr4aZqkc7M4beNQDc3YpppePcdenlgw/Ur4+EKVhra2PMfwx8CVG7/6a1
+9lsfxbs2tKENfbj0kdkUrLX/GPjHT3ZvS1tkWNJ3f6HGrhSDdyCi+HF8ne5QJYgdEUk7SxcbyakU
+3pww6qs+nI5JXTk9ru2MmZ0KV72/lFPy4vt0x9ZVSW3l5Dv0JyyX0o6SitoRqSFxa64Hcor3EH1/
+ONkh6vcA6AYBoeq1ZqfEzUSisUZUA782hBM5GYrVDrUR24jJEmqVTGa5AU/6t0jVYBjsknkyjmO6
++A9OATi5nNJzRZo6TRc8n4qYb6+IGuQHfbxW2unZgnZX7Q+LPZxGbDRex2fQ0zYfi4SR1oePTuiD
+qw72ZEeeW9/FLuUZV4YR1bGKvI2oR63j0DbyO9wMu77+Pska6PtqrLw+5FZH2nHxU/Lcn/lbl3gj
+eQ2AAe6HIinYr70MQBL87Pve2651m9DDH4lE1vdEopv291jp+o2cCUkgEl1/2WXh63UVcG6kzYEa
+bU8XKdtG1tjdImY7V1WjbnAP5HNyDzcYf6B+/a4ZGt9FxtB6HjQNj6wsAEY6eWWvy60/+KJ8dKnH
++Lroz7luquHAIxnKZurvD2nUKLf1GcudoQzIi5HLTI1rD35Z5LN0MSOfF/qyJ2cQZdbiD+X+enXM
+xUKNgAuP2hMG4VxYbrui421Hor+3fkKUySY0PY9ALceuG+L6sml8Xdhe5uOoPhmXGcbKdRv12O4J
+U6sSl9dUfYgceW6eLzGqwiziai1dstVcIrs40mdPsEN5d1/l2tDJcR01go4DUMMYfQevlUVsY4+t
+RBhHW8oC7dgxpi9jGFQNTrF+bp9FJe3MmoTJUJ53pzuV350NcX0ZqyqPsKo+NOX3533wIo8fv34D
+gOlnp3zq2Y8B8PV9YcKf/fMnpLXYdr74D36V/+e+MMvy4vv3dsz+6E8BYLgNgP1H3+S91pHnyUBf
+ubRFfSBz9YJ9DoB595RuKox+1T9msJI5LXsn7CQynmdegVVvzplOTbxyaXflADg9W/HWVO69PPWx
+yiCCICZolh+oX5sw5w1taEPvoqdDUrBgGrCt5ZEJ2VgmoRhkDvZ+lH+vI0adS8/t062l2WYk3DWu
+ZjShcN944NH05HpkurgDkQqKK0MueiIG778m7/jazn2+9PV7AORt8sTNLUxFtpYOfBGFAZxBRXQh
+J9PlTklHT/qeuoeCqkPUEdXGmbg469PY1LiqatiBnMCuB47GHnheRNiRvpr2giiT06hXeuzq6X54
+IM/qH4Jx3wags6ipVExezpfshmJwKoYpfU+e53T1RLEGM5F7TRNCJO00WY3t6Gm67NJ0RMz1UzV8
+dh3MqUoY3gw/1OfFIwapGtTKnN1Tuf/grozVTm+f81Ykt7y5h6mkT3M+mBeip+ra3k+8wF+upR2D
+L/4YO+4+AJ+7JeOyb3f5t3dEUvrxl6/xyjN/H4Djf3iPc/v9qRIHX5U5O9/59mPvM45h2JGx+Gxw
+ifDyZwHoPxBj7a6/w2xL3dplQK8v0ljQTFipGzV1fPxKr1OVJOKc+yfq4Rom5Go8xo9wdBxdm9Hp
+fjDV7OlgCiDBQBYMa93YYbwtG+xzL04oI9WRzlvYloXVKeR7S0Psi6huRymjUha/F5/TXXshpxlX
+TkV83P6cLKR53eXjpzJ4v/mgpq3WqsTjKVm2XFSiG2/1OqS1MB6nXOBZGdIkcAg02IlWJi6eRNSZ
+LNLIjUH1aNeNH1niPRURcV18Izp5210SOaJblq6L40i/m2GGm0ibvYVY/auOxZlL/5peziyVcUmS
+DC9Qcf5ShzyS343n+o7AxbVDHawK04hOjl2CI/dYm+Ahm63xZYO5FFSuqD5RdYk2VlemW1K60u++
+mWJGIuaOPGHuO0VF4ci42TYia7930M/jyfCTv1ee9/tufZF4JJM9Kiqcm8KIxwsZK7PVMO3J9eTP
+zvirX/sZAH7jW1/if3p4G4B8tfpAb5/8URkv/7f/RwAq5z+B9rufYQ3Uqm692T7kyonM5TnCKHpB
+yDSWdRNMt9layPrNvIa0lDan5yVBIPO6OJPfux6cVdLPVZvRm8mhN59YBttio6qLENUwn5g26sOG
+NrShd9FTISkYwLENDRajxjffGfLMs9cB+JF4l3FfRM2g5+BoxIaZqtHORHitBnb0DesgxSYb4OYq
+ig0jdvXgPi/kdx877rLcGQFw/yzntBFff9O+lzgpPHRFgqPSyPmgoZuqcdDUTI0YNpuxw7Tq6O80
+eGSR0RxMtXEeVg2i1qawDlJphK2bKMeqhNFEIUbjNJx+Qk+NjmeNQ7fQoBgrz+2tKo4GcmI0c0Ng
+NYilG9Psyem/XYT0Y/FmeGN5nxPFmKbU8VxiIznFcGNo5BQ3nYA2kxPf9HTplH3cWAyGdV3TdFUN
+WtU4obw7CgKSWNq3W8rv9/t92iPp00vxCaFKTavf4YB6HPlewBd9mb8fDRz6ugS6O9cJWlVDtuS9
+TtshsKIituOCLz4vcQzx8y/yysUhAL+wSmg+gMH51v/7/wHwL6//PADGrd4Zuc1aFTYYvFbW3Lyt
+efG2vO/hDWnPtWyfZipzPcbBDmVd+Ksuo6ncs19GzDR0u6Pz0cQtQ4mV477TMjyUd9SXLlipmjfw
+JrTZ7In7BE8LU3A9ouE2QZswUfXn1s0Bf2D6PADdZxraUESnfF4z2JKNZ5bSfKcOsUN1Q2YxgboL
+aQOCoTywyPq0AxnszlI2tNP3sa0GuXS+zt/6yqsAJOUpTbPW9xtMIM/wNea8PmtoPQmy8u96JCvR
+Zbf8HlUsVm3vvMvdobR5Uqk46Fu6c2lb4eZ42dqjkGNqDUiJ5HvPjXAiVSnmDdZI290mpBnJPU7h
+cXuudoBCfn8WnZKfy8qsqOlWIn7ueB5hKovDbbdxHPHgmFp2oW0D2krdjd09TKlLw3wnP4Kqg1GG
+bBN9r5+Bho2bsMQca86EW+PqRuh0Zngqwi49+X7c75BekoW7fW+LeSzeIzRK9XHkqCW/3wkZ9mTT
+z8hxl7Iu/OSMLqr+KJcxgQNdDf3NJ4x3pP0/9IWb/NjH/igAL/8vv8y92ds6Hu/PHB7+uf9S2jH/
+EgCLr/9tSdmRN+I8YjAOlZE5s8kADuQ6XIlb93T7lOk9Ucuy/TO2FrKe7EGJqxGZu7sVXbUfrDTi
+sbY9hnvCNE4WW8zVzsPgOj0rHDLue4Tu8H378k7aqA8b2tCG3kVPhaTgGOh4UGYjdjsqfrZDboZy
+Ws/aiFsLtdrvhNilcsotNXA10KpRy2kcSpXa/bKh6ag6Ere4uYjEbSwcfKc3oF7KZ/8qGbLdl5O9
+ODM064QZC6aU+ys9MXOTYlU6CH2HUD8vm4rmTNq2O2jIuiLmrfTeepExHUg7qxZKtXqXS5eokXY2
+rqoPeU6tCUyuNaBtdpIGF3luVFrGXTkRLkbyjuE3fbxKZfCyIAr0fTEc5HKq5jdaHHW2lKpeeKc1
+dk+P84uWtqNSQ22xoZ62WZdGxX9TamKUk0sKKlCnNVYDtawf4oaad5F0SQNRMVTCZzzucn4sfe6F
+Hbxcvn/I+1OoyU6f2BtRehKbcLFtuKKSl9+vwZexazR5zMs9WnedfljS1iI1hMMxP70trXrtY0f8
+718Wg+1adXscLX/9bwKQnn0NAKdq3xESZR8FLLm0eCp5+Ls1F6lcX7ukktSqwF6S+SuSEq8v49J6
+Bl8t5X4DU41ButJI/49zy6un0s7nxhfUV+TtzskL1KqamDTBBvP37cs7aSMpbGhDG3oXPRWSguu6
+DPpjcq9ibyyGo2svXKazJfpwGPZZ82135ZF25VTx6+sANHmDUb228TJCq/qku8TTE7isj3DU2OWe
+i+KX50s8R/TsftonWscbbEVwvPaXG4z6wiN1izY5ZK38Llj18dWd5CcrvEmsbbZ05CDAGmmvG1yl
+WmjMQmxpNOfdi3zqperosUg/ldPg+tJem5b4qouXZUOjGAmmdTCZPnspJ5yzbXAfaGquX1L7ij2R
+GGpPTowg71B5IiGEjbhpq4XFaMJYW83xfEmCshcVjDSte3mKQdpU52I7sW1BuZLnmnCHRlO4bb4g
+O5fTaoUl0Ai7Vo2kUX1GpyvjuuVHHAZP5kt3HZeexqGUiYe9Ju3Iyps46uIj8Wm2RKLxvGf1lzmo
+naguGloN/W3LknPEjnF5GhB0NSN0/v5RgHONjfGq/0B+Y/97HhmVZRTkHa0hVckzuXPM8vnr8vtT
+GZ+dS0N6c7EYurtjolylCkKMinQLP8KzMl5hR8Y4ci7oTWV+l7OC3Z64meer21yLBL7Eccf4mmT3
+pPRUMAUPw9RzIJwyuiSLdOq8QDDSkNmqZRVqNliSUWhsv9EN39nr01N/dBQusBoy3NoBtfq/04Fl
+8bIs3sNaBjVtKuaZpr3O7uF6urkfVuSaF2ERQBSASoOJXLdguFRVYycAFeGKjkNwJG17bdsw1CCr
+06n8bvpWzfxZ2dCXF5fpT6U93spQh6oqaFyBNzTYuxLc4uz4NHP1ErhnrBbybjohKtkTxTJu/XLG
+IpKxSrOAnsbOD3oBZigbvSXE7Yn3wZYyFtXQp1EsCz88IUnkd/6lCd6ZtM3xj6lmEodQdXST5yFF
+T5hs8dbJo7j9tmlZKQ7D7GGE9aWhsY5xYzsMI7l+MHSJ7NpT814kG7rjRXTHkvkZj54jq2SuRw9L
+vvWibORn5zeIJ6f6K9nkTW9KU6kq1TNcfEM8NHeDFauHwlipznDLdazK+wPxPP/ybwHwbf4ZAK6x
+vFvp+A5AiqPMcOVY/JkwgNd2NFZksUutboT9tqaMxCtTej51R/pX3i65p+roUrOH50lG9EDG/mzn
+gvZrsrayW8fcjQX58OPBTRL3rffsw/eijfqwoQ1t6F30VEgKQeBz5dJldvo+t6biKtu6VhMZFf1t
+QRsKFywbqFeqHqgUkJ8uMbtXAfD8Cb6V08G1KaWr4uxRhzIUlcDTw8BzR/S6wpU/92LMNxSEpdc7
+ZbVSQ5uTEQ1FCnlOAx2qPIapXI9SQ93R7LUkoOrIqVN4O9TqZuyuNKquycl/S/t8aYmvYblBv8XR
+0OU80PiBsx1qdT25Rymup4bN6irxRETKJslJG/k8WvuunZS6VDXArQituEM7zgjP1d/lC+yFjFGl
+BsWq11IVasAt9mgzdY0mr9OJJWrQy7cwa+i1XETuvL1HPZP+rXxDVmn4c+tiM5FMgvEpxX1p01z7
+OeyGpCu53vY60H982J1RH/2nIx9vLNF6V4cr3KVkfq6cU86/opLlM0tGuUhOQSnqqBMU1GpUbR6G
+1NrXMl+wCgWHYPDCJT6p0Y1f/toa1+O9Q6AffurPArBfiiv78PyXMJmsMWtdPKPSrevgeWpUDHep
+NIbA3pW5e+PaCc+eC+BOPHTZbRS9qj2jPlJJp7skUlCaUjMjx/2bTJ8TxLE3bl8n0/iGxXOf55r2
+z44ChvW/geqDA/Q9Q2iHTMYSN+DFhtDIgJxEGRrbQb4Mmfsidj+4L99fjlLa+nUAOkMXuy0AIUE8
+pzhRzMD2Acv7skCCPVnEHTNh5opt4OtnfW5dlYGsFoaZ6vB5aQlzeflrD0Vs/8yNgmYhzxpcDmnU
+kl8NWnKFIBv6MY4jeutcUTPmhx0OYmFS5/27jDV82NQHOEN5SHIuEx6Hb3Dx6nUAJrd8FoV8Pwjv
+kdzXTMMej0KpzTDW78dcUdtI3vhEujCbQcWgFGaa9s9o5yLOp7pYnZOcRSLjkp29wZ1TufeK/4D+
+WN49HbdUVpi2E4h4vjhz8I0szNnJM3R3Nd7CHZF1RP3JjkYknjIk9de3hQuqv48XhrZ+/FLULnHk
+d/jDN8Tj8GIZkiqa1GvH52wrluTD+SvcuicMoOjIPIWTlvxQxfnqLU6PZW6C4YgbiDpi+i9xoh4R
+V/NSmseEK1xdfh2Arx/K2tvD566qDJ4DreJgRp7H1NG+DsZYDV0uXZmD/vIcfyDt9JorhGtGnXUo
+FUUrOQ4Jp/K7XV/amzQ5L92TGISbO8dEN+V3k7TlWLv6zPmcpPf+sR/vpI36sKENbehd9HRICq5L
+OOhTeR6RyvbduI+jmXM263C8VLCQumFxJKf7shLxqzuNMQooOin6jNQAWZ/FNK48b3lsSdTAPX9F
+RPR5+C06j2xMLq1KBN0vPEP8z8U40/Y7+JGI4FcnGv1XQu2uwVpLjKoP/nnOQ42xnt0+wWnlpExy
+jafo9Bh35VmzSUauRrAgCgkPVafROIbyQUiuhs1XXr5HpuCwu2VDOxGjVNHp0wzUIp2KBNIJHUqN
+zQixuKr6RM02BjmtXccnU0CbJJH2pEfH3G9kMN766pskaiTMt122NMqyqH2iWER37qj00ylBcRkT
+u2T1uoB4L9pvsHog73g9N0wbEW3PXcXAiFx8RFIK4ojGPN77EOtS3Z8MuXhdJJNvDvpMSz1J7YB9
+fV7atMxVsgwbOVXdo1Oarhpg70YsFfn4+Bu3uTeSOIP+WzmFft4+wXkZjUSqey76czI+Z/+IroLk
+NGX7SLxprGWhSNnm7G2Knqg2B4q3cZGX3FCDqeuEBLkmhzk1zrnsgYu2xRzJGGbhGwCMWpdAw9HN
+wuPmJz4BQPWNM67wRbl2xvj1Bzv7nwqmABbHVnQKQ6ax+GHVxemJJTsoYhIN5OkkLWGoi17xDgnn
+sJTJtwOHLJAF3ekb2ldloduOwJEDLFRsby/G5Ioj2AZz9geygbZuzzhTK/lrQctzaos4jUX3tKbF
+TWQSl1seVoNRgrAlUpdjamviVsXSQL0WvkPjyKJp722TP6vZk/45wZamHN9WT0cUPgKYdYJ9bCq/
+s82clTKIjk3wPNnUtVWd3Kvo9eT6bBUTVgr6MjA4rojddTKjr8FOqWLEuxcD7D1JIw/iIZkGkRXe
+AmehYbI7lkJxHt3LwoTMm60A5ABuNyCpFKT2zJKpHaBYzTmLFHi20LDk1mAdYRrusKDO38emoO6/
+rWnIZFdE7dH4KjulMMXtXo8oEyZcPxxRb2meSvsNAPzJNu3rCjIz7DFQvMZFeYWdVMFswnMuqwqy
+tig8DvL0mbclh+EXD2ST/ug05nV1B/c7FleRu/Mw4yCWMOZuJ+T6SOxfrSu/6+Xb2EDa03dTiKVP
+9Twj7Uk/gnPDxVDhASrNzmRBJ5Vwr1EVcPxLvw7Ap9JneOCL+nS1+Wle1sCwJ6WN+rChDW3oXfRU
+SAqOcYi8DtN9S1/DmbsHJWEmJ3d/2LDjy/V5nGGG4jGIVQTusIPnK5xXOKTWOgRlGNFqoEscjNnV
+E3t3Lr9vRi61Jpzsb58xvyMSwVvPDNj7TVFX3P2UF26JhLBdidh68UpMvSdi5qg01KGIdf1iwOiy
+vKNrbqI5SpRrQ2SQMlQpZbATC6YCYBuDc19DYrfllOiHvUeiYS9NSDsidUTjHZpd4fyeMyQvZLyC
+qdzbXXgUqUgP/emSeCSW576Z4ipwyqRyCZBTbKzxA4NLEQb5XRh1SLV2Rjg9oOOL2B10hzQav2AK
+udedrJh01ejqG7qVjO1qbDhFPh/2VmTHonYca6Eez4+oc4WJTyL8wXukR6rBbB0e/ZnJNp954dMA
+FL2SHUcMoid1STcTA3NjUlwFlKlONVCtnuEqZF08GnD5XKHtr26x0LDpfOcGPx1Iv3/znuIi1O8t
+K8x+WkT0PxysJdev8fvuyPy+8dYWl0ai5prhFbqNfH5puIUZybPjuXhnvO2KvnocaruEVBHBQ5dx
+qV63rYLLisVRj+Wzfj3i6nVR3fLDIbMrIgnNDn4SdyFrOTmYcCv/NzBL0mJpTc3qtE/9MWnSw7Jk
+T8Ul48G2ug7Htzr498Ti/LAn4m5vPuTM1YiwoiKNZRFHqaFUK3K/gK6iFPW2VTQOXTytf7AqCq6t
+5N1p94jPX5YF/Wsh/PBbMvBva9ZmGy4JV9Keaq9HmohoP+lDFMmm/sRkgu3pRl2JpftN+zb7Gree
+1MUjtKiibWg1xl0lWUauT78v9/ajKWEkEYvhhU+qmYFh0afsKGPUAKvAaemEwkCrBALdhLZX4qey
+MRfBOZGmaNfqZRnWHaJPyga79PKQu4qgNM72WKqnIWhDzmMFMil1gboBUaHZidsdRo5s365r6OmC
+Lm5vc6gcMntLC4cNI5a6Vv3IxVm8B+KSWv8dzVtYTYcc7Gua+DWP3pkwvU7ngv65vO9edptWK1Et
+1AU6dbZIurIZe7lLqLkG4xQU1Io6CXk9l/EMVNV8HPSLvS3j8kYka+8PV32+pBiVv/9yy4NTURl2
++iG3RtLmfrTH26Ew9R3Nc8mzBdmOjE+5rLD9tQpZ46t9LF512NlVyP+BjNU42eJltS89m845viqq
+xNVmxS8a8Yh8/qU+dyab3IcNbWhDPwA9JZKCocCjrhKyU/EMdC7vkioUe6cbQaBYCKUP6gUYl3JK
+WP8MT+Pa88UKVysyJe2SelvEyPnqHF9zBrxCw6RDF6tl2rr1GK8rXHc4uIGrlZN+73iHYk/es78U
+CeTs9GVSPZnH5RLUwu8sE1ott+a3GV31w8daoeeyt0W8kj6lvqGn2YV+Y/A0EGs4UKNem9Mr1KDU
+KXAdxYBcHrNDAAAgAElEQVQYQaxFQdqyxFdf9/xITsae37IcaEhwHZB3RGztrfaxrRj7nP6E8kxh
+0zyJO2ir+hEQiA1XjDQewXXOcHSZFKfHuGrYTFM1xfVj5oVIbNvlc7h66jrtkNaca/v7dNQoV2vM
+RvWgYqS5KI0XgK95C+9Bvko2A38HP5WTMi6mBJdlvKerfcxA+2d6uEfy7oGGjxdJQbirYcDnM6KF
+qISmnhFo5aW2t2Sk+Sa1wtZjeM9I55OufBGeSlGi0/Qu2x/7IfnuW29ieor43bisSul3v5/R82R9
+jh1p48kQ4lzWdzy9RajyiYkmJOeyH+Jwm7Gzxq0QKbU7stxsRAWJds+4eetz8rzfGvFM9qMAvN2b
+4MwfVbJ8Ivq+JQVjzBVjzL8wxrxkjPmWMeav6OcTY8w/Nca8pn8/GOj8hja0od9V+kEkhRr4z621
+XzPG9IHfMMb8U+AvAP/cWvvXjTE/B/wc8Fcf9yCLpaSmO/e4f1242q2kglBOv3jL4E6Ey7vuCBMq
+xJqe+Ek3oj3SRJy+j6N+9daJCWZ63Y0JFBnXjjUbsunQ+KLYetWK5ZqhNvkjSWFrErG1rsA8Eh2x
+dr71SHee930iDdctSdgpFTBzaAnVkIgmDG3nLScaQ+CkkOmJ3qsi6rG6NTWYoum6tCoFMJxgajW1
+1QtYYy/0oLlQZCn1RecXDo0iNs17MW6q9Q0mGW5XXJLNcYan+Ueto/7xDtAqlsP4mP6ZtDlxLa2e
+zHQsbqKGO/Xn+3PvEWZBYzOMJrHZzMdsy/XoxONtnZOduZwRr9sEV2sduOGMRfD4pbhS52CT3edc
+k+bGcRdUCgu9Uy58kXTcKqXe0lN8jWi1V8GRjn3Ph0gkJeP6oNgKpTtn9qYUjKkb7fNjIhrP3pBi
+sm/W8jd/dsydX5X1dG1oOJiJpJc907KDtLkZLPn4kUhb3+4rSteyw0Ot37CfnZEoWpj3sGWp0kjn
+fEZxS2xpvUL2AvUFVSj2gnI+IXn1lwHYyz/Nr4xlnnZXV/mX1b+mWpLW2kPgUK+XxphvIyXo/yTw
+B/W2/xX4Rd6HKdCCm4DXW+CeqkGq3cIdaLUhB8h1U/QSXB2UVhGQ/XRG7K5LoHdpLmSgomFME2rs
+euGQq8gfLRS4YlhhNK7AtimpMpnZssH1ZPE6jgM9LVibiMgc2IB2LGLrVm3JFdAjLndwlJnUiUut
+2YpDrQN54aZ4M1k0oe/hXKg4208I1bLcqvHRuDmtEbWkvljgdTV/Ig1p1RtgFzm5ptkaBQfMti3Z
+GuZt6ZApTmTvYpeqXqecW2wtsfYKDI11WhyFuXecfdbgf+HiGF/Bbup8QKWo0q2WtW+iGq+rOSqm
+oT5TWLy+wWhB17lxqRTH8szq3Pg+mRpJfT/CaeS570Wpiteduyn1SzKPzQtn2FLE/aKxdC5kkw2s
+R6nVqbJIPvOTkFYxKLtOAJmuJ9eFVoPBqiGLRAFz7PuL3HdbMUa3Cy3I8gu3iSoR/V87G7LfvQ2A
+ub3FmeJm7uSXOAsUeVucWczDQ4avCAO5v/MyB4mobuFohFW07oVTEr2iNUmvSRvdurOux0zY8aka
+YfpvH1xicE9rUF7t8MLyd6EYjDHmOvAZ4NeAXWUYIEA6u+/xm79kjPl1Y8yv5+WTQatvaEMb+ujp
+BzY0GmN6wP8J/KfW2oUx3+Gw1lprjPmeAtg7S9FvjcY2MxX1ymNbC1fkdvaoQEhVdnGtlhjr9Wk1
+IzDR76M2wkZ60pwVLNSdFM0ADW315jllRw1Kmsef1xGOK79bpC3lQq5TJ6WjUYOrw4i+cutA3aJF
+c47V5KFXOxXumaIZW4s9FBFvb3ifSuHdZkaToLKQTFGGl0clCcLB3SSir1GIg76M36BcERVab2Fi
+SbXPYbtkeaH9SBMWmfqxVUyuHpYYhT7rZC61ZmslzTmDU1FnZn7JnsKmFRcqSbgVlZZ2M/k5ebou
+EjMSXQcBh50rWEpHYwycuMGcybPqbkyluBfl0lD6YiSrZzPmChGnGCz4vR6eqiv9Zk7jPH4prmt3
+/ne3X+K/ek6knGfGN6jWakXrYWKZnzR5E6OJcHOtOxpnA9jX+plvn1NdvS5j7yW0rozBye17/Pbd
+tcTy/iL3w9uCWTA/l+e2vqFZqeG6M+drWrRl37WP1JzZqEt7Ies20TmPHxjuXNZCLy9D/Qn5/upF
+RmHVjUoPZ0/WZLqWeOKIWqN7veYCZyjzvmuu82r+mwB89te63DcfbJv/QEzBGOMjDOHvWGv/vn58
+ZIzZt9YeGmP2+U7E6HuStYa2ccjKFe16sRFgtAhqUcfkes3YxVcRz/W0GEzj47kiclW8TWjEW1Bj
+8HVynSgBV1QCeyGLtY0WLNUecJLm1JVM6LysWFllPLvX6GqR2lD16awTks1lE45XDeeBLqC7NfeG
+oh5MVy7zh9L1PQ2sOusNmGpgUdrL6WhIrRfvEayz8jxFcDbdda0YquOCSutHlmcNCwWRYbUi82TR
+ZFrqfdmPsbmqRPaEo6ViOxZdHFdrUPYdsgu1V0xlI2UXS+q1wX01o3Fl49XWoVUk6XJ5HzeXMczX
+CYfFKR0Ny21qqB7K4jZByfzNNdLwDKtja7VyUVhkuLtrD0APz7wPtrseLUlhefMlEb+/8MWrhNu6
+icfXcDN5Rzy6jt+KuB4gIcW2U+MooyeKsaWI6/nJKVkggu29r77BifvklvqZMriqUlUkW4Bmpa5W
+1SPYfmdV8lpX1u+N2X0WvWcA2FLmsNqOCR/Ihp53bpKeagGfFwy7C2Eci+4YN1dVuJZ+5itDXUs/
+Ci/C60v9zNO3GzJPAqu+ms2x36NAzePoB/E+GOB/Br5trf1v3/HVPwTWZXh/FvgH3+87NrShDf3r
+px9EUvhR4N8HvmGM+U397K8Bfx34e8aY/xC4A/yZ93uQpaWgxJQxD7TqsjsPmWliU++iJB2I6Ouc
+umjEKCZVeK3+AFetGHXXI0jllDDjy1hHTjw7fo3mnpxG+Vjz1dOERS4nTZ0FvK1qQl64FH05HaN0
+ydm2vPCqEQ5enix5uBKf+NFoxFuvSa2At/sDBm+rdNN3edbVAi5XFAvhPGB1U9WA355gnpVTqbtY
+UV3Rys6lgn9MF9gzrYO5V1E91HoLLFktxABbewWt1ghIcs0GPXdINfYC43Aj0RMo8AmmYpRbnXqM
+RuuybyK5lJHzKLzW+h7OUgyC+V5IdF/uaYdLplo+PVkD2YxvYj2x2OenPq4iFDfpBafmVMez5UQN
+iSNXjHMravyFlggcpazy9wYzQXsOcLY65quReHh+/+qUm5cUh8A7plG8ws6rAU5XxrM70qxTr0ut
+WBet+5Aqk+g/f9CS3ZMoy189fJ1y+XiD5zupWYlU2NbadsN3qqY7llQraF+YJelM1Mo7gxXPawGf
+9BlZQ3uvbPNNlQRH2QJvoh4T59PUz4tU1D128HZVVVoogMrgHEezZJ1lD3sm473jhNgd8Yh0khFn
+Jx+ssvYP4n34V3wHyO530h/6IM8y1uDnDo5zQbuQjTSLfVq02lKnh1X9M29rSGQg1sFNTlpg9Hsa
+h1KLvXjOCfm+5lLcc1i2ijt4KJutdjJQ5KLQXeIrKMZZmdFdan5Ez5DrZjnS9N+jZcCeVmQ6PVmy
+o6Cj7UWX7evyDq/ZpdIKSUczLXyav039ZWEse94Z3iuyuZ2BS/d13SAH8t72FZe0o0G2dxJyDc4a
+Nz1spPDdCTxMNNOwku/Ltmalm62sDYkGrvQMnJXCOZ1wSJvJGPnq/nKamtzRyls2JC1k07i3A1oN
+Be/4JYUiGY0jtRG0FfVbwgjORxc0CkTTGEuA5qhMW6aa+/BQPRmdsIsZqpsZh7UE/n7UMw6H6kJ0
+qi50JF24feMUVxlWeFDgFmr/yTQvocootXhvsxizrDTr9ARu3xeGere9gqfn25OUnG3WGRmOqrZr
+hgDQGgJPsTvzLg8UX/Fyu8ORnmDBq9rG3inbM62AdmC4Vmim8PaK4aHkvJz4KcUDzZrtyfx73oAo
+EdWgLR3mc2lH6fTxDsV9nu5UBM4GZGVDG9rQD0BPSZizpXYqirJHTzk77QVL9QO7ucFdqxWdHi3C
+5WcKuVVhHllkB03J6aEmkUzg8FUR7W/4PvcUa/d5BbeIP3UNVzmtdxZxZSyf10VDclmrK58XTAdy
+Mg0USuzG1gUvvy3c/JmrLb/wDTkFnv0MvPlt+fzHvniFfY1lSCfy2ckvpNzXgKTD+wt2bqgR9Nsx
+4fNykp6/JeAul7sT5gq1dm23T3BJvq/ajEY9A441xFpD86Iv74pOLONY1YC8pqpEcrmbxNxUj8n8
+IOOg1GSsQjM8aQjXoCDFKQ+/IaJo7TiYVgNhhl1WqYrXgZxWL79UMtRiOQ8o+bTC8vc+9jylK2Pv
+vT14lBloNRy7urpHXMvp6E5gevJkvvRl6fF7Pydhxf7HXfxIPN6n0zeI1btycXyPHa0xOVcV060M
+33xZVKbdzHCkCV/XRgEv3BIYeO+1r1B9gKr0DtKXZi0hvCMk2viQa5DZeNxjEGui2PVLlPdE7Ug8
+hdK7SCk9mbPs5YqXXlCj86+/zvO+GNCPwyNeNCKdete39R1dFIeHYVtSagh534f6/rcBuHXc47B5
+j2Sz96Cngim4xmUU9HGGR2x5IlJPgoKO1jP0bUxtNKg+9IguRKQa3BC9sUrLR/kA/lHL9IZa9cOW
+nup7QZvyKVVH/H2ZoKguudA17sY57UJdmaMuvRPRxeO9Cb6RxRtqZN/ZYYdtdSd5r+a82BMmFf6r
+GZ+8JhMzOi0YXpZ2Xj1X1+If7zH/qjwj/1jKzTMFJPlMSqh5Dsmz4jkJSourCyXYneCvRdFVH7+v
+lvpkiN8VT0pf62p6w5Is13h/p2Ku5bI6tmA1kk0TlTW55jn0phqNWI1oW1VFbJeDL8g8FIuKxqhb
+Lw+JDjRqMNfisb2cTLNELxsXRzNQo2bG7LVG23GGq+hMgerOw6MTphK2j5OEFOj8vg/5bcav/F//
+FIC/2P3z+J+SjT4eTMkSGdsomtKq4WmggLBlmXLVl3cEs2O21D2djrZYJLKTX7h9zpcUs7Ks3587
+GA3gYu2Gt+2jS1MZxhpkFi0KRgO5/sRRyviWrMPqgbSn89wVeq8I43344jmf0kC1Zr/LljL9a9zA
+O1CbSaxz0PgMNMWzDkJ85UgXZy7X7U0AFm4fZ7EOG3oy2qgPG9rQht5FT4WkEHhwbezSFNvEmlNg
+FgPySINNZnPCK5rnfj7BUc9ArCdiZ9xQrxT6bHiGp+HBYX9Fs9IqS+V9OpHGKWiQh2kDHMUljAqX
+VI1nvTyiUUyGoGhxe2J0S1UE3IoLvp5ruXfnkNfP5ZSPR8cMliLmZ/svca0Q0I9apYrRyT6dLS0N
+H13HHyhMlnOZzlj997VmLcYnmJ70yd9OaXI5Ebx6Tpuq+2V0hnMup3+nlecmHsSRhopb2NUTKqgj
+Ojsybs7FDdoDMT6VhVqvd89wEmm73cnoqYcjnpzRJuLB8ZK7FGtcyb7MzSDs46kM60c9iqEGRTUJ
+dSVicnVRk2vwmaPBT2mb4s2kDaEbsjBP5ksvreXOiWRl/h+/+Y/4C89rMZs7A+K+qDTp2QvQ13dr
+UFe0XbN9JH3KBws8xZwIO4cEKzXWORf4evo/SbaAp7iStZaaNrTYdXi001KisO5uxsBVCH53yVBr
+mja6DvvtDr6joD7elLir4eb2Y9TIPb29AX5fnuGg9VbDlCpZ189M8BuRaIeDilIlL7/7Knea/Sfo
+zXdoIylsaEMbehcZax+TBvavia5d3rN/7S//LJ3RlIlW+12ZmvZUOOM9W8CRnKTH7oyLudZbVJTk
+JgGrFY6r3GLW3Lq1j6Lx2sbhd/bVehajwpJxW6wazJrAw1PQUdwAWrn2pvL38PUa02iJNVsyUskl
+b4dc39awwKQlVgNlrsVZop4h0NBlp5uAGvvqEDx1Q+W+nFrlyiEJpT1xEdFMRErpuX0cvb5a7rP/
+oiIKX7ku3+8NCDSeoljN+bn/4lcAOHG/yctf1XDc+S9xohF9gZVYkLKNiRTEtjARgZF767ZDpNGG
+q9bHWH2GnqgODc36bLENVr3UBvu4BMPvQRqz0fmDpNWX5XFVyfd2DmptBWNwFb3o8tDFBCJZPTP1
+OUckgYGieWdVh1Aj+44qFwqRJNLKxynEsHRcB5SFJEeV6uL9nYXgUJwF3/NoFTG5qX9Jv/ugPZZ3
+RI6hUai4yG0prRrKbU2i5eZoS6p23aYPYA19N/2GtfaH3++mp0J98HDZMkPCoc+OVn9K4kOipaaI
+lvdwNOXY5iEjX8TcC08W8cBL8Ur1HPRGmEYNcU6DWQmzyPwurtECISpSNnVDqaqG7yxZaYyHTRua
+oYhcsV+CL+9xZ5W29w7H96VtW7+ny0uvaQ3DFy8xOxWj29XL14k1Xfiq5oSVw5xrrvZptKA/k77O
+RitiVQ+WWjSlOHJ5Q0OCh45LoqHU3STmNQ1+uRIWrIayoOPyUwC0cULXkfFpg4ckyd8F4PVvFyRT
+WVQXhxlGn5cq0IsTpGTaf9NryFbKCLslmjyKjRradTSyWs6bGuy6xHtjQFNdPvhZowE9xS9iH1eB
+5R331hacidZoTPvsXJd+HzQW/5IYbIdHmj26A9saL1KELc4djafYyh9lFD4IllSrdQ3R99p4CpVW
+N0gC8PdPVo3j1jo4ChcfzR3WZTUnqUsWS3u8lUul6N4aSf0BWdCT00Z92NCGNvQueiokBcc1REOX
+cRUymMjpeKUes9TKyOPXLIlmM8b16lGRjfFMTtVxEGM1FmBsI1ZaC2CIJdGowI4bcjRXF6eqFGdF
+QqQJSEd5y46y4CO3ZaJQYlkFvpb5qmI1KK0ixrtyKu29fMreDfl8emdFvCM+5EthxGAkhp/toYi1
+oe8wWYOTDLcJ97R4SX4JUN+1Rg9WPtQX6/DUGUazo5xBQaxoMKfDGbuV+PXua2mwm0mfXKUNx0wY
+RWIkfPGP/QVe+vn/WtofOswVh8HT08rULvt6Kq0yl/117YjK45NbIoJ/9aymViCTTF12Xa8lqRWJ
+2jFU6yhfmu8SvJ+EbAPfWZaPf4JvLMMLefeffr6Po7EJP/T8czSpZiW+KMZldzEjuqo1It465s6z
+soa25g+Ir8g8zO4U3PHU0Fivz+EP/zxen8SuqmAfCwNirRGxdynmoWJPHAw89ucieZ4Mam6vFMtC
+DZsV74ig/BDpqWAKWDCtIbUubiNqwKktUNwQks6UpRY2veFFnGuWoDOWDdELS/qqh4XdPqHiM4RO
+g19o0IgTUg1kI7u5MBuzCmgUAGVYjZjHol93E59U61h6jU86lIkxi0J/n3Oq3ge/ZygUTaicFlz3
+VJ/vG8Y9YSZdLcwxvLRDX3MNnK7F15LidlDTqrW/bBUJKkjYVeTk2WqCo6Gq2XFKtZLNsnT7nHji
+p79yXXMDDi6xq8VZZrWL+TWJgf+F/b/CjuZVzJoWV5nBettFrmGh1vSe7zCXZnJzWfGq6tGD0HCm
+MQu9Vv5muITemik4uLWWpW8fA274WLK8HzNYU4ODGajaOHHY6cq6CCaGoitMbahBXT4hqFdmsD3i
+OfUkHZ80JCeaB9KJ8TRXpPzIhHOwGsygWc/4gyHBQLxkl65sM34oto9JZ4yrQDRbeUlSSkDSuVYy
+qz6iJm7Uhw1taEPvoqdDUmgsdlbgxQ35SsT2RZoT35cTKuOI+ExO27eigG1NXGqm8tlWFj/CtZtW
+BUkgJ/qwyjmP5UQb1QW51hqM1HOQ9Fv6C/Xvxz5TxSR4vRfR07oBqTV0tSzcuVFvwX6Xgy1RDQYn
+DZVWsd7OtvEmGkGZrkhiFQk7muRkckKFATO9lliLvbhhgFNpMo+e8rYe0vZFlRqdL3ByLcE26lEN
+FIimrTg6E5H54YH07drMYTbVGo2rU45+RkKCL3cisvk/l3fg0SoUXF9hdbvTziOgl2v9CcVVxco8
+9tkKRR1Z3oWBYkBEmpV55izoLDQ6MIRG6zecJDn5uahubftBxNwnkDDW0atxyMdvSlzHxUGHH9ay
+eIugZtcXUedhX8Ztp+lxMZK27ToO9/QV21mXs21R70bpkhONrUjPNdP0Q5YYjGMINZz+kwfS9vFe
+ly+OLgPw9mjKjxzI/L4+LPncQ1lzX/ZWfOorIk38imJkmLPkuzxqHwY9FUyhdSEbWGyV0FXrbmP7
+FFsSnrlzb4dQ8RWvuh5mrPq1Bt5s7+VMag2T7VX0zrXo6rDCFx5DFoKrenSrYJj+sUve00y92pAq
+4J370Ce/uU7VDllqkFGgFaR8YoIL2ehblwJyRT+6NrakgZZwj0r8iYKHrovROl1cX/XCoAMKJGts
+jKfgp36s760Lelo1qF9O0VKaLOySw1MRL3e9Hnag8e5qmW6Wx8SVPLfpdrnysjCsb7/wBn92T8A9
+/rfmdf50R8Jg71wWm8MfmywIXxQGs/PqlJMflU318V895K2bsgnjIua3PyXPfvFlYWJ39wui35B2
+/tpnS679qqg535yc8ZVflA12WB1jmw9L/zXEkWyqz9+4xk+MhBGkHz/giqImOVsd/JWqR4p5ubfd
+MtDfmaZDqUFBIR6mFDtQuGO5MxdVwpmJ2N5+WJtOVYY9f8KVPyKM+t89lDZMfuyHGMprmbzQ5xkt
+JLvTgTqXtXXWX2IuS1uKRubpy195k6R+8lTvJ6WN+rChDW3oXfR0SAoNpAtIg+IRBNlydURTSehn
+U54y1oId7pZBbUcYLbXVXY2xz6rIfJTi7chD3GUHZ09OK2flCDIzAOLD3ru8ZKlAJqt4RaiVq51p
+SqDFO+rY0tyXYUpUWukEfZJSjYSJpRxpCGsaUBkxQC5NzFQz9Dyt8+iZAKuhr34QPfIoBM74UZn4
+SDMxWw8CIyeCGc6JFGuxbg02kXZi73Cm2BL3DuUkzp0d0CQgL3e48XkROT89v0m8J1WOP/3pKfED
+OY0+bQSubNLN8GPp59aPDDGF1jm8NWB3oFiCfyDlY5q5uvNxTRjrN3gjaVvQRARfkOt+e8bWTAxj
+f/eNiuRcAUmeSJVYw3R89ynteB5d9doEeUuronYwW1B6MhZdPDwF5ek4GpbdpLSadUpoiS60bmbX
+Z9sTQ3Ldh2FH+nfqSf/a6nGF456QDPiKQXn9s1Ne0AI1kxtryHaH+LJIntM2Ju6q1GfAagLatWxM
+8IKG6au06Z+n/JPXtWp6/cGAVB5HTwVTwLTYMMfxRqw60klnDt16jdfYx9mW663aB41SqzWYKNsP
+CC80unHQw52rBXyc0ZwqjmO3olHAEWeo2YCHDo4G9Hh3pjg9UVf6iUvtq0dhBnlX0G+WD2WB2ElA
+UMkCvNj22S5lQuksuK5eCxP6OFYh2nP5nVsmFH2tJpW11B2FancWoKm8baK1F3oNQSnfF26fWFMD
+bFSy48hmOTTbeJksoH4tCztw9nB6ivOYQXRbcMT7n7ecPSu1MBe/fcq1bcFHHAQahOX38TUgyU+X
+DD2xtTRDn5FG07Thil31mLAt/RxVPpXmHNy88FkMFUz3oot3IPf8xNEu/0IxIZcKXPteQrlYFL77
+W6OekaE75FIsKtrNK7tsraTfQ6+DUeBZx+nga0WxgdazzLodmnWkaz+jl0g/Ws/gq1uzsxvy/Cty
+fVe9WR/KVjMO/a6sp+tlyO9brxG1fU22nsG3stEvtQGpekk8N6BR4JgD65Kq3ezWUtaevTbgW3dl
+Pd2tL/iw7B8b9WFDG9rQu+ipkBTaCvL7LUHnDDeWEz+ZNwSuxOcvp3N2FV5rHhgmWsE414oSdu6Q
+DfSEurvkqFUj0ryi7Mvn/eOA1VROwq4CfSwDcA419qBbEirsWhrFeKkczSuvYl2oyQkVJqvp0NFs
+wK2LGE9h6afOFnEsMRAh4Xcy7Vo5GUrjEygKdDZ16CoGQhWUeFqt2HQ147CJaDp63RpspLEA+ZDr
+mmlpjyvur7QQiVaULpM5ca44E/mK4U/9cQD+JCmXKsEi/EPPTEgXYnR8JpI4hlXwLLSirrGzS3Qh
+Y1W3PZZTNbSmQ5xqXTlJ+lTHKb7OTRtVDHM5uZJ9uNJI6PVPffYOr2ciLd2+EOCVpGj4Xieb8TpY
+RdI2bbt2NOBp/cRPTWJ2p9JXP6voD/d03Dp4Ctqy8guimeIiDOQE7s861EM5dYOzBjfQQjSpoaPz
+t3XR4TP78oxfeFskDNOs3jtk21HjdruuW/LuG9cnrheFjCNpz+70Cka9PFueeByysCI4V/i//oqu
+QuUV0wrvVDEvvSWDUn7n7KmaN3+WP/CcrMOf/62Euv1glaDeizaSwoY2tKF30VMhKVjTUoYpOV12
+1T3c8yI6Rg14hz7jkXD2C+uRRlqH4IHq3PvzR6jGF1HOQp3QvptSnGj+fifFuy+n2OlQXlLdD0iH
+WkNi2acZiDFs+yxiNhUOPVq5pLEYzGKNJLRRRpGooWrsPtJfQ2tYqe9w37as1CaQK7BrmaVEvj4j
+9/EVOyIjoKcuu0ZDrd2qwvFVeggtcU9OElMVJHM5/cYYlpooFqgBc3VnhlknKw22SN/4bQAO9xyS
+Q7nn7730q/wp1Wf/biR2lJ8MV/if1HoDxwOOFhpi7nWJx4r009ac6mk4qrQozMTHaFj5skqwCsw6
+2oZCo0kPe3M+d0cSzLK5vO/N94haNFSg2YCOcR6dvQdqBM3dA3pao3PSb5kpOGxQN4xG6gJuGsqB
+zE+tBjgnDAm1IEsbQ72S59UuxGP193oDTh+K5BQr0lViH6Op2/XJ/L3vaPXMDYCbKo043Sknaku6
+4avkYnPsQMTeAgi8dZZvRaFxNsXKkOi6j3WtDHd6mIVITcOXH3KWfTiSwtPBFCxUlUNTrijWRULj
+GptpluDVPp6K2oMhVIc60SrqOfc8UKbhndWEml/QLGICTak2WUk91Xj9hSyCelBSHWl48PYKbyYL
+KSAW+WYAACAASURBVBstH1VASodgjrQik6tBD+2IrBUDV5NEDCfSti4Wd6S4kkVOrLEHmaMAI/Yy
+teYtGLel0mzOsNOl1cApR/MPmjbBWVfcKzIcTSmv2xzrKIPo5kSpPPvB/HVpw/6U+YUsxp5nuRvI
+hj199RMc/9pX5Z5P7PBPfknEeP/TIsJ++f4SXz0uSXEHN5CS6pedJXvaj2p5hN+VgiNVKWNhlj6t
+1nB0wmvE54rt6Dp4ip49DnbYC4TJjA5U3H/zDtZ+tyfCc71Hhka3tcQ9NTAHMgc7u4Aa305PLdUN
+eW7A/8/em8XYlp33fb+1573PfKpOTbfu1LdvN7tJkZJIU5IlT3EEO5HhBIgn2EHs2AECBDAC5CF2
+8pSHBHDyEitBECOxIRhwDDuRncSxHTmyI9OWQ0omRTbZ7Pn2nWquOnXmc/a88vB9p7qb6m421bJy
+HdQCyD5dvc8+e6+99vqm//f/32RLaR7b7Tbo3CaKG/HqJbZWWrziCCeSzH88nbBULchsPCOtlApN
++xKs63JFhPjrxrpKsna4P3g/a2q2oqo5mMsmfHPxGs38dwIwmcnxg7hBFch8+s2beLkarTzGzXTt
+JD1aQ5nPSnEvQZXRasimsB93mahITFl8uvTopw4fjDGuMeabxpi/q/9+1xjzK8aYd4wxf9MY8wnJ
+u6/H9bgez8L4zfAU/kPgddBOH/gvgf/aWvs3jDF/CfgzwH//cScwjsULCrplDIkmVha5EJwAzlHG
+gZa97EHFqatdgK+qK37HsvlEE1+9GncuHsbctawV1latkPqxWMKlwqP9s5ppJCHD6myLqCfWf3AW
+skzEwixXJV4kx68mKh8XpYRLsSjT5x0aqq483iponMv5DnyXopTPZVcsQnIJT5+XKb/9tElnR84b
+pktQSLRZyP17nYz6XFztuuOynMr3lvkF1YVYrnejFZ4yBueu3HMyhJUmVGNyil/6hwD8P+H/wv5U
+sAdf+duv8txt+dx+R9zlfOBTCrcJ+fMlxbkkJR/9TMVPfvO3yXW+/JSdQKjpcm388rKSTEtl9eMR
+0w31GlY9ckfu384cBhsyX194R57Bt42h/BCvO8BSaWYvCjxc1fPY3hcE5kvugEN9qHO34nQlv+Gf
+ugy/LB7U5544dHfF+re0I3ZGG0/JZlcNn9kb8r2L+IRiJL83cYccHDyWC1HvwFTVx4QPa8/gw49Y
+k/04gYtVFOJRuWT7QObw659RdOjRcyT3FHuwyjhUWrV0VjBvqJ7H60NGXbn+3pnMd+afEadyDZuD
+Lbanct7DT1lI/bRakvvAzwD/BfAfqZTcvwL8cT3krwL/Gd9nU7CVoZ66LMOKVqGVhTzHncmDmTYz
+vFomqqxWLCbiBlbqzpdvelitj7cuGyhRMWHqMLZyTHXQZOJpu/BQz+snJCs5VxovQM87irso9oUq
+dXDVBY+0I4/cJ97SvoRJg2pbodmnhkVb21qbLTIFL9Vz2S8ru8T9jmxoFxsNGrnE2X5ocLXVNw9V
+pORpn0LBNsVoSYluGsUmQU83k2HOcKxAF+2vyN0z7EwuPlvAd3r/GgC7XPB69hYAG7Xl/G25pudi
+UXd6Jb3NlvIkngx3uFk/krn4SsL4viy2e8EtAoWCrxmF02qCM5IFv/TOmS20K7WREDnCUdnwR2TK
+UXjvJa0c/POUKtV7fV8Y0exs0lPtzduOz6bsBewaqajUwQiLgLfS8pLZiZLkuBesfllc9Pi5Fp9L
+BbTVH2gbvY0YqVhrcdhlEQiueJmGOJHMhSl3eF7FfH7lgcYixnw0Y4xiJ7Aflh8xhJofsibAaD4g
+TO+xSAUK7r8i8zZ7wRIt5EYbTXBV7KcoU5YPZE2u7Dkrxd+YREIwt25xZ1cwNON3E/JbMkdHj0ps
+/RtpXJfxacOHvwj8x7wXTG0AY2uvZukAuPFhX3y/FP1i9YPx0l+P63E9/sWN37CnYIz5A8CZtfYb
+xpjf/YN+//1S9Ld3tmwQF+AkKC0hLgkLV3vbRyF2W6nQ8g2GkeyO7kPZGU18esXBXyQ5TSs7qRMf
+0VSm5Vk4xR2KRQj21dM4azDsaoJn2Kbuyw7enRlqTzvnmg55JMms5dma1H/O4lwskfv5kEr72xdx
+QFyqhYoDjJVE4blKw18+aNL3xW3NwqfkhcKR9+5jQlWPHq3FcB5xeSIufmvPpfLk2CI4JRuJJ9Cy
+FtsXS7ksZV+ezTZpaqdpq7tD/4GED/+gGpGoluJZmtFJlGdBvZmbWwc4iv4cbC5ZHopF79s53qF4
+GI1uiE0kQddQr6seudSZzNtkFrCzrYjNIKAKxDs4HLZo3RHLu9QQrOc5HH+IAe75DhPlfbi918OU
+ct8vPSdW9Y1hh4a61PH5DuO+zNvyURdPdSOj0QEqWYltSsNUsFkSK/qviC5Ij8XzGmw3aRt51ofJ
+Ba8fyxppaTA8O/ve9OH7hv04rkRLpq/XRuKwrXL3/u4eK9V2jLuyZsPlMZFC08t8QKhyg/ZpSNuX
+MG5y1GVrT3knNU036+WMdY3c+Nwc59viLX7dGbOq19f2g6McP63A7B80xvzrQITkFH4W6BpjPPUW
+9oHD738qA26MweLpzbh+QanqTeOyQfZUXM26OOfsUBZhHcmD3UsdRjsSy94qN0iaKuJaNnFVdy+/
+ABPJ7R49lJd0kR1i35EHtOwW7BbyQvtbE/b0WNdJWJzIQ2gFsuiOFj6V8ieao5zgsyrqcZFy6arO
+5eGEldLHT08kN7ByOrhK0FnGY7Z7Gj6kFk8h2GgYtLh0Gemxl+8eMvKe6G8seGcpKz5xxky1DFWW
+mi9pDpjNNPeRLBjdlGM38j/B6slfASDwXRZLcbU9FT85ebpipaQu9etTeg2VVK9iFkpIsnIviUoB
+PRVn4mZbP2K+XHNfNnjzbQlHyo2U9ok8y1k/wj3Vjfi+aD/efO2M85XyJFYptYYQtze3yT3Z9L64
+vYn3OTlmcyX34eclByfSw5GXJaO3ZC1UTsCG5nnmgcGmklMojLxs9dmMlZYA09OUofJ0PvnuQ2ah
+ApzGFQeXcr6Fsh9Z34Pvm83/8F6NNZHNcpZyohT+0Su/hrsv/Sa9pbzEtqwJU+1sjQMClX40bUv2
+XZnDeVRz/kSo7UneAKD9BswVFNU+b+B98Q4Az19OeWekoVKRUuncftLt4TccPlhr/xNr7b619g7w
+x4D/21r7J4BfAv6QHnYtRX89rse/ZONfBE7hzwF/wxjznwPfBP7K9/uCMZYgyAgcn9oTdz8dnaKK
+WdilxQSyzzWXMZOWuLaxdk52vCntuVjd6GaCmyiIpXZYam96nkQsUqVK21Np9YceTls8jLB0iRV7
+0Fk2ryDNVVngJXKOqVrzolxhFAtw8tkFvQOxguFOjVOua/05rlK9RbFq/yU+pdXk2rBPqRBs2xrh
+KUeEf6zVkE4Dt9aseKeDlVwfdWBorFTD0EKiDU2VZldN4NMOVUnbCel9Q1z/o9Y/p6H8DFle4ise
+JF83Dno1nlLcJ519Xr5xB4B2ckinlDCos2jiRSqeo15Terq6WkXRwKdaqd5hmVOrIEtYuASbEtLs
+K2V2w2/iI9bcOiuolCrNqZmo5Z31XYKpPKubW+I9bM8cZk3xGkrHpazFkzAmohUqXf1lh2xTzpGu
+w7WWhQN51qvY0FSw1yzawr9UHEY8pZNqJyzyHGdl+TGE6h9new1o9cHzHfqRzMugv8ntbXH5N7xH
+AFTFfVyFwoe7C0IFrXmHMxjIvTbzPvOO4hOeqsJ6kBMrIKu7dcH2pdzTq16LtiMPduHn5OVakfyT
+UcP/pmwK1tp/jPJdW2vfBb78m3He63E9rsdv/XgmEI0ODkkZ4/UCuqpwnIc+tSvtps7zBc1MPIFF
+ryKeqWio6jHcqHdIevK9zdQwd7RBZVSDog37pY9Wqfj8Qs57+VKHUvn/R4MJA1W8Hvc92kq2Wsyb
+GGV46vZlNx+OSqItyVUk77psvCCJqhurJmZXYb7lHstSIdZ92bXH1YiWsvxsbK9oaxnRsSmeJiij
+PRV9YZtQLenNcUipSaZqNWDrtl7bOOR4KokoY+We5l2PrRuK4rM56Z/+MwC8MH7K8d/7JQDC8xFl
+Lde8Gci53MEmPRVgvbV/m75Svt3rf5nGQCnmtvpUir2wWiKm4dAJVd7O1ISF3F8d56zGck/z8ozG
+VCHmAwmYzYMu26Xc3+loRaH2uL23w86meDrbnQBvUzwIM5draw+WfCmRtu/ZfEiSiJc2cybseqIe
+HW5UROpNeJWiUIddnESe317QpNGQ9XJvNmCxJ+cYP/FJfPEQjobawl/OIP2I8t5aMOhDkJnGOAy0
+iW1vf497m+Lx/PD+HZbK97FZ/RAAvR3Y6GkTmzW4mlPZHGQ46tFFLHlxJc84+4I833paY11RKU9O
+N6mel9zV/mgDT5XHD08LzhXDkq9+Cz2FTzuMAT+2eF5EqOIsRZIRa19CNHe4u6fQ3sTDnMukPQok
+8XInbZGropEXQqjdYsvWiAulSXeCgoZqMLa21NVeTai0EhHlPtGedi2exdibSuk2NRSq3+KpMlXF
+ipn2UQy/1GSlXISDu70rt/T2oEO11nEcykN8033MTe3eO7XH4MrnOjR4mfIuauIvWPl46kb2uk2W
+sSTDoklyRd0WhxHBrkrJD+UFPGxdsu3JBZeJw6ayOf+TW19n05OFeQG0Y1kol9p59ztahvsDeaF/
+2+bnOX1eXO2X7S5eXzaOpAy5aGrospT5yc2STQ1LqsThpvZolBsppSb5HpZzIlXZClNlKm6FvK5/
+8425oj2zjqW8lI1x3G5QZ7K4tzpyva0yZF+fR/XCNl94IqHNK/EB93OZg7GZ0dCXIqu0u9QPqJTA
+pmUd0LBrqx2Qa1K58GN+4Ylu8B0JRQ6n8JE0Kx9D1eY4lqWVObq/2+UnXhSj9nx8l8d7Mp/PT3Sj
+30nxHPmcJUOahazJ3K3ZVu4E3Ipt1UJdthWPkUQ8VcPRCy3nEw0rkhUXC+3+9Q0m/cGo8K67JK/H
+9bgeHxjPhKeA62CbTQKnSeTILtrqdOHiEQBh4l6RrraiAq+ppaz2HQA2szlZQ3bXeGwZlWK5lsMM
+fNlJj59cEKg7t2bTDf0Y1E0sOinhWHZav7mkpZRnbgMupwqhVth1VseU6n6GxzPiL4iF9UcZNlGU
+2yonTuT4qCtW/rl6D08bXFr+BqFyNjSyXUyhegpdcY2L8YiOK3gLLyhJjFgJk+Qklfye66/oJ5s6
+hzI/W83tqySik7m4f0Qs6f13/htW7n8r5+s+IlUPyteQ4fULhx1HruH43mM2kManpFpSqIJx6q7w
+Urmmleov5mHIhWolNN1dHHVVg2V+RTEX1DF2NNFzyOX2y4huS8Vw8oS1Hp3xIgqVjXOzGuPJMQuF
+rju1h1ZIadAh3xcreL/eo6+l0ZWN8bXLs3bl/iu7wu3Kcp9eDIljmee4XRFqWS9rrtjyxYOICnnm
+deLB9KPCBy1FXjkM9qo6aazBD+T3ZssVY228q74wYcuRtZVoAruqE4x2XCbpTexCvJR29wbjUsLD
+ZrhBW5O7kXqQ1lnSUG4Re1zAjkxMf9pgV5PtT/IaR5ORFJ8M5fhMbAoGS+hUNLoplU6YczakcFWA
+89JncX+tBrVFqpj6gbIoV60ExyoF242IRKnhL8IGyaEstgmGm+r+T2NxrXqTJgdbcq7qAi5CcZnj
+aoOFtu9Wq4A4kJf3MF2rIy3oaFb8ycs1n19rFPZX7GUy8fl+SezJi1xq70TrcsJZpNqGsxlDzVsw
+dEn3lLvxgeokuhNCI+dyWgmFak06ZkGFLPg8Tihz3egcubcwCfF1seYudP4rET89/Mm/RuVL1ru8
+nOJrdaVQrkminEYilOOdhst9rURk2ysipZur2xmdNWu0rypds5A0UlyIV1D3dGOtW2Q9KZnE50sW
+iQrIqrDMwq3pLGRDOwpPCFaK37A5De1mHA8C+toLQiybSjQz5JvywrZjl0g3k91RyZlu+sX5BVMN
+ETe0KzWLDOZQq0iBT2LluedbTaKFuO5pdMFGJc8kUIocd/kxL9JV+PC+MGJN+eY5RGuR2iChvyXr
+2rqXbC7k77qEMPMKp6OCvdMLzJrm/0FFpQasMc3IbuuGdSn/nHdPiU/k+qbdFfZU7unr9SWXF6qQ
+5Y0oPqzJ5GPGdfhwPa7H9fjAeDY8BePgBzGh06ahFr9jfOZWEnSz22NKrQxM7ZKWJrnSTbHg7nAJ
+muF3TlvMlGMgHM8pjLi5ZrrgcVc8hOQd2WmdvqFxJud9HM+x52JdFhuGG+rOX0Y1XiXWr69uZl17
+zJXma/AgZHZPrEs680hVCzO/aJH2xPNojcUSTYIclMS1LJYMRxIGlO0LGkNlHQ5UVs7rg8qQ28sU
+X6nZrG2g/CawWGGUwdiJxSvxHIvbVrbj0Zh3/p1/H4Ctwzd5vPjbcgwOxUquqafZdqdOmFnxQNo8
+z1Bh1/Glz6Sh3ASLJmUl1j/QpGVhVxhN5i2DKfFKnpnbmuGdi80p5xlWLa5dqAQfHnsDqQw8PQ6Y
+uGuCkADTkc/Nizleot6gsi9XjRXLqXoxQUpcybzZuKBxrPqfTkH5UI6ZesIzEW30Sdc2cJkyUp6G
+6KGDtyu/580ifCPrYVgr4a9nPlLFzrhq0df1f2uv6ONs7bKhZCrtec1oKHM7cPfJYrHokSZU/X6N
+l4sXZ7JLknWCeiunPpRzXwQLmg/k84aSDZupS5qKBz19nHKCMIlPDpc4RtmKFpb8Y+HYv348E5uC
+A4RUmLCiWslDTplTKJho+m5KO5FJnXX7ZNoaOj6Tl3Sz7RLOVdswtSyVtv3M1rQXUurx3Uu6Q8X+
+a09FdQ4jhUGHY4ehoxvIRURrV9y9qIqwawIMBYHMVufUY21lvlkzeENgp60b2/S0N6DdPiNXuvBZ
+IS9Y6rbIHbl2N4u4KKVN98mbMbe3VGo+lrJSw2YUG9JL1twoWWpGOuSCY+Xw23BTZurO72q5KfVq
+vHLdYBZz+r/9NQDeOP0Ohaoe1WUJRkqDY83utyeW+89pP8fcZdGTv/fKEC+W+fTmFac6BzvaEeBu
+VLhjbfeuQ2LNqXjtLirEhSm6mBO5V0/5F7265GSi11zmoMQitV2wOJdwZdSNyC9lDfiaR/DHJY5S
+yqdRSRWoIlXm09hQcd6DEaXS509y+eJenNDQlus0rWmq4pY3qKgrFQRKCsYagiQqbuycfEzmfl2K
+fF8VYl1F8Sg4nsmzvpPmBJmsgVkxItVS9bbSBFCnjB3t+D2BHRU2quo5jrJfpycFVVdf7qmydVvD
+pJR7Os4e8Ogt3WSnUzLlxMRYqh9Ioes6fLge1+N6fM94JjwFXAcaCXXRAN1Rs6AiWmhzUBsWqp9X
+hBc0lxoqbElzTtu28QbahXbyiLohnXH98xOmO4JluPmkxQN1u7ZW8s/j5RHpobqZSUoTdUXblyjt
+Iu7gJsw0waMJzuXKUGh9uHoC9Y42VT2tePuGhBq9I5eV8vd3NQl4WfaJ1ziF1RmX3xUMwVvlTc7U
+Iu18Rizb7aqmbohLOXg8pWrKb7u1z6FiIYrihGVfrEoQK/SXzSuxgqKoeKcjYUX16h+mXv3ce3O+
+tnLKCXCcWv7ZY/Gg2P4GPfN7AHiuTlkpRVldnxPUkrWvNOyopyWh8h8YtinHSoBjl9QXmkhNM4y6
+2LXSzS2rSybagFWdGVJN289TyCq5gcHBhNW+PJOGzs/FhsN8IZ+n0ynNdeKyuUVXxXOaOy0YSdbe
+U7AYnk+hXlXXDVgoz2M9LsiRbL9ZWAql4bvQEGXV8GD2Ee63+V6bWl+1RlEbVq7890fTCf1z8Tye
+dwty1dxoacRUThvED6Th7ajcZDQSj9XtLAlm4ilM2w36WjFarlTjI3fJMgnnzg/h1U1Ncj/Nr6DZ
+trZ8nLjOh41nY1Ooa8xqSbnnUofiwuZPFqTbKiP+0HCqbllrClNXFttA49P6MwVGewOKTnyFuss9
+h5VmuLM7c5IDZbRpr1FuLhe+nMNNfZYN+V5/usuZZsv9ixFuIi/AcKFddqsZpaLc8lbK6lTc+W/d
+DfjiubxY325V7I6UwGVPXpRmNmS8oXqU37DYXL43CCZY5ZWcHEnIUH4OgpF8b9mfUl9oIiEa460l
+5T3DRqHIQiUx8eMc6nWlwif/P/++nK+av5cZf99YC5RWNmeSy/UaJ+Ou7hmz/Sn9hZQhJ62Q/krL
+ftr2G6z2yFzpWgyKOVlHe0bMnFTzEstsSKUb8QKZ45NJxuGxzP3cm1NrE8Y4nVIM5W25uGFxR/L8
+zrflWC9zONUKQXvcByWcaeab1M/J3G6+3aTYknW0VcuGXnciskzOFfTewg71BjseqSvXWZsZi5WI
+5zgLdb/nH90hud7o3i9es/5UGUumJcBRueJChYWjcsznS60C7chaSWYDJkrC4pYzMq0IOf4tVjuS
+E3GPHDLtBC71Oc28OaMT3RTmM07fkc2kqEo+6QbwYeM6fLge1+N6fGA8G56CcTB+i1a1ga+OT68B
+2SNJ9i13n7JURuVLf4ln5e+ekdCg9a1btG5I4iy8aBBkqjQ8ralayrX3cIdjX3bVaimWtjQpTVdp
+zpoVzlys7qzlk2jGNq/cq/NZT93oysMooy4Li02UZGWVsKeUbf1kh2jNGIMwJuflivhcpnxrPyLZ
+EOx7PM/w1DPxPqOgoMd7jLTDbzkKKVS8ZLvo0VDewdXU8Fjp5l7cE9e5OlniqaRbUc6YN0WQheWb
+sO4DkDvW/19fY0DZFgs9uXuP+VDl5oreFZFJlLZYjDRBN1cNzsGK7aVUHLIBeEr24oQRDRVL2YmW
+nHaUF6EWuO+l4xOqhZ4tzZUTU44tYVdBPYcVnReUu3CmWqK9Fd5QLOVJtSReaW/E4gG735RQIWm0
+aWmIUSmNfmdcsezK5+S0zVlTOzSXOVWs3Ydejxta7Thn3YlohHf9Q4Z19ZhK4wBrubLQ1qHpyjXk
+dciFwvA37RZHlczd7qF4iu6WS6uWvg2/5bJRytqb9A3Bu8K9cNQ9Jp/J+nNDxaR4PXpaAfm1CVy4
+vznK3teewvW4HtfjA+OZ8BQcLIlJSd0FTatQ4jgn2NTPT3u0I9kd+/0WjxQJePBV+e/TzVPaytTc
+HrSYHilT0D4cvSZxWF12KbVE5KtV3m1vc9IWa5UcBCwUx1BdwuyG7OLNeY+qLxY2yLSm72YUiiR0
+Iocik+/5+zGPp2Kxb770HH3tQMxiFQE9PCTRhiePlK52w51cXvJCV+Ldr39XYuc9BrxRiSf0hdbz
+tO9IwrBbdxhNJRbdx2O5qbiOtnhP83pKrLDj1IuIi9cAWGRTPhhnagOS2oWw5fOZz8j1fPbll5j8
+qsSys/MJ7iOZo+72FqdPJG73FHI7frIij+Xa61nEzq7iJTY3qJG5L55GNDZVs+Cbcn87e2Mevq6q
+3B7kiqAMminzE/HYopc8Tk7ktwcvCAmsGY1oByrB1i5ZqrjO8GHGPFIMRPaYuwr/LnWtTLsNjs/l
+XLdnIUfaYPYCMcFnJWdCtuLBhVzHi5oFvDix78n/fc9wFcBwBRg0XOVtHB9myu50s9uk25K5bd65
+w0x5HXxPuTeY4GizUzWOSJWabfrkkoaCUk5nBTeUAq+xK9ebxiuOXpO5aLQXmIPfOAXb+8czsSmU
+1nCRhWwuYgqzdjN9KlVUfn7DpVD5eNMzvPxAOwp/m/ZCX3jEz8vk9B5aLp7Xl3RWsLEnEzQ/z3ix
+Iccv1I1skOM/lc/z7YLgXFzH2Y0Z25rpbfSG2IluMpVWOKxPrcrXTmZpah9E75sj7v6IAmve6VK+
+JBiJ3Ut5WP17ffK5/K3d36X5itKy33ZIdFP4oS3ZVGJgy/ssAEF7By/Ubs/KZ98TPH/kOrgK9io1
+YRg3trAKr63TkjRbg/GboGzGvI825EqDKXPoHUoC7OavPKW6Lfd6+djH7cqG07y0mE0lNdHE5mbd
+pK9dm/2NHujc+mnJ8kDcWVPVXBzK3B52xcW/+Ltv42n4UKXgKTQ7WDQZKCN2PLLcvCUb3M7rihW5
+YfAcVXSKcnaXuineb+ErwK0Kd9lSpS6j1Y5Ww2dTn1/crdhSRmivU2MUcbQaVbywI4neX91QoJsJ
+mB/K5mypeS+TX+MpXV7tvXefxlXDU7psaYNjME3pdOVZbz89J9mU60gUQ+HFMW3F52S9kiSXdT/w
+PdD+mJcaEXbNTaks2ObRAi+U9ZKnyVU/DrPvNQA/2LgOH67H9bgeHxjPhKfgOJakmbGsM9pd7aY7
+36bclGQXxzFZV2rJm67HQrUgY4VBe1vnmEIwC2V5gqM16GY3w6ZigVf1JQ0lMHFrtZh+ge/LZ28a
+kNfyG+6iSW1lZx83KnqZWIKREQsbOy6l7qe1U5FqifBR45Q3lKbt3sarRENJEkUq9NKY9vGaYnW8
+2Sa1ouboh4QNbR4qfgoA0/wWW6HgLcrNKY6SsNj8kvZCrIPZnOOtxGvwGmJRS1zKSF31kUegeISV
+XfAeL7HhypIYRdUlS+K2oCnncZubK2mO6u2eMU4lWRn1xjR8hVCH6m1U+/i5Km17EVbLtxk1Cw0f
+0osFb11I2TI7UZm7dEqaaULRvOe57LddllMlp+EpT4/lOtIN8RQ2lx38hnABb5URtWpMdvIITxW7
+vbxPQ13zNW9C3C3wF3IusiP8SIU92tMrQGJlp3iqSN7KZB1uXF5wfhVq8d4cGoemds1equq0Y2oq
+faVqr6ZG58pb4ipWpTZTGoqmDJtKXlO2saq5QepQKGdFWLSYKedEK/KZV0qWq8ncSTalfizXucjm
+eEuFNn/K8MHYjyGK+K0anfaW/ckv/VsczL7L47dkgRXLB6RK3uGwwupkR06N0Vj8hj7X0u1zK9EW
+aduhVcjCO88bOJUszLPcp65UZENVlYzJKe16XyywynPo+AWuKy/hiz/T5odu/wkA/oMXBBDjEBmY
+RAAAIABJREFUvPjHuHVDFphTFdijVwD41a/GzKL/C4DL10LKtlzTdCIPsY4g0FCkTiZkK8UI+BZX
+mY0zTzEWK5eF8uxFVUIVykIprU+q7mojC6lbmqNQRaPU1HhaLYkHlpNHqqxV1+vuapIkpKnAmlZH
+uzNXTUIFSNULB5Q/Mli1QAVq0pkl1R4Fq4Q1hR2ymMk5RpMnPHqiQrfVmzxWcZ1VPuQ9pO3Hr7c/
+9W//8Su2oWbl4qv4621tQ493euwWKgCTLNjoKqX+jou3eAGAcOcd3L5UdhyFQTvzfZzyqwAcFpv4
+vmxS4fBLNAbfAmDS+jGasayX/O0fBaD78teo3Z+UY+MaR0MQWx/w07/v35Njx9p+76bEulFYp6bt
+aOiaG3xPnl9WhoTKBD5XYp3QSVkotiRyCpZWiXZMQR3KIo98l9CRXEOkosJx1CDQHo5u3aJu6Vq2
+NTPdcEldor6EI//j//Dz37DWfuljHwDX4cP1uB7X43vGMxE+FOWYk/Hf4fGbSxaa1a7KArQeX1lA
+IappZQgkV8c0VfGS2zG3UtlRF7sJ3Yeyo2a7Kb2nksk+b66wytqLr0i0DKwmhuRH1MUrwGp35Xf/
+zpibPyfW9k///W8A8Isv/jSmo5Daepf/4/Kb8vnl+/yd//0RAF/s3yVX8zjYFBd/WqTcVq5F2i7J
+RLyNSXNKu1BiGEebeo66HClzclz4ZMpHOT0NOHTl2hwT4Ky7DxW7UTZWhK54KM7MBUXH2TTG3VLZ
++tBlW3kVA72ce1sNPOVGbJ1tMdmVe+6kMWZTrFzxKOZSwxR/KVbQ97Z5eiDz+s5ZwLIljU+Db0Y8
+9OQ66/STe6NmkZMqjVujG1xl/ntW4cqx4eamPFP/VsTGVBKD2e6UrlYDvM0mnqJFFy3xPJN2ccUd
+sZf2+Woh+hRf6hSkCkRoGZf/df5dAH5aE6qX5h/Trn8cgHc54HlPyGfScsZyLs/XS8V7yDqQ5OqF
+NjYZeFrZMpAo9mDR3mGzEG/kUr2xaJoz6co99S+XjLUZLZnVTJsK885KiFVfRN9av2xiNNHcNDGp
+cnnU1YAMCbdCxh+Ep3yC8UxsCliXKmsT7v9BFu9IV5/hgxR4Zo1K9WF3JYvm992XMk7k9/ntPyWu
+4+iNOeOfkAX01pNvs6/YHe/Vhxw2xbU7V+GNIMgZ6kIpsdg1oMdYrMJum7ttHv+s5Br+zfsqHb93
+j04ov2eWc35PX4Anr/z9Gb8rlg0gTzM8faC1lql2vCZbmp1v9/cIBsq5l20Sa4dmttaMbPrEl6po
+5Z4zzlX0YxuGQ3kJW2bKVDvugkRe1tm0wLKmLM8IfJmrFIcNbQuO+l22wo6eW3aFm9sOjS357eR2
+i1Q1NuNwiklkMVq/4KJUclTt8KwbIfvaB3H79i53vqvlwt91g4d/958A8IYprroHv+8IYioVuDEG
+vECVk3blnjayBmzIudrBNsEt5d20+1cVES/5Ekbp/Nu1iOPa+YSwJ0I09TuWn2xJZacezYhe1k37
+9SV/oCUVmOOJPOv7t/84C5Uzeq5x70oUKqSDX8gaSdWA9FYdQuV73PMtkbI4PRcm5Jo/2PAc5qVU
+0l4u18LEhp5qqJ5HlrulvOjznZSXrNzfU9fQaWoJVMl3+qHPqeaMNqKc4VrAiDlDlTaoS5dcxW4+
+6fhU4YMxpmuM+XljzBvGmNeNMT9hjOkbY37RGPO2/rP3aX7jelyP6/FbOz6tp/CzwC9Ya/+QMSYA
+EuA/Bf6RtfYvGGP+PPDnEYGYjxxVljN/9ykX9i+zzkj9Orui5eGCgKijzSUviJW7U3YoVMOwXYVU
+98RavZDdYqG8AZ/r9vCMeAj1mViiYelgzJq85L1ftRaMuqLzyYiXfWkq+ou3pLvtz73+lPENJSQp
+bvHfpVJRePHuOd96R6xqv1+xd6nJow1J1G1u7dNRIFO3HeLlch3RVoA7ETfYVSVtt8gY7GpSa7hL
+2hZPIJtNiJVefVxaHO1yvFjTkNdtFgrM6eeGVamsvsuI8Y5877nUJ1TPpHdL/hY3BzTWzGdFif+c
+nC8cD6g8AQA5kUNPqfSdI7F2OZegnIH93gzzVIljHvxTNjRT/4Mks2fLOcVEMQ0bCfsrJZoZSnUm
+vL+HEyuRS27xUdm4vUv8yR35PP0W/kSsf9kX4hHn9CYTfhGA1uJ5LjclfPBOd3iz9W0Ann/1Vb6y
+L6HgrV8Wj+jn/Xf5g0//FADfutXmR0pJYP5T12GlXZJOJc8u70+5oRR8UdNnd1M7Zt2ChsrL+2GL
+7USDokzmrZuVuJrwjoOCXDtpW26O0erDjS2fcCVzP/EktDNeTdtVmrfaoa2e4HFa4ij8ec6YoPj1
+jXAfNz6NwGwH+J3AnwKw1uZAboz5N4DfrYf9VUQk5uM3hcBntH8DZzWnPtJ2RwtX+HwDnkqfd7fa
+/PiPvAyA+xkpN95rPMe5ZmH3ei5zFWN96csebyv/f2srwdWe4ntPZNF9rVhx9h2J70bVkkyJWvBr
+XHWrvd/v8tU/LAnbW29+B4DhrYjGTEqgx7O3+dwjWfzf+spLhH1ph05ncyZGFkKnljDAxmMa2i1n
+o/fk3F2/JI7FXY204uBvxleqWJ4PVgV0W2nEULsE5yWcLDRPoC/gIluS6GO1rQpHZbac/ozOXKnM
+m3u0OhoKBUruwhxPS2jxICBQ4fAwKqgCXfzNCL+QTcZsysu6cmNQefrCKWn0ZW6/8/t/mGSm9/Q0
+ZzX5KFzgB4dTesRdicWdccUyuiO/x7ra4eKv5KUoww5OV0FtmcHRhlGyDey2vJzuXDfWWy6t/IcB
+qO5s0MpViezFiluZPJ/8h1t8wYok/Du/Q051r/4yr/7oFwF4sYSzQM73o8UQv5BwpdAScGveYrYv
+hmDbW7AoxAj15g7Djsz9XW/KUyWAuZfLehy1Pbb1TTxoJOyHMvdPbcVNT449MKCETBwrMXEvqznQ
+/FESxRwrC1WyyjlTFbEitZT+99PC/OD4NOHDXeAc+DljzDeNMX/ZGNMAtq21x3rMCbD9YV9+vxS9
+rX4wuqjrcT2ux7+48WnCBw/4UeDPWmt/xRjzs0iocDWstdYY86G+4/ul6D3Pt5xPqdLL9zjuLDhr
+Cm0TcKcrFuh3f/az/HTnRQBmL4gbuTWqaA20V+EyoOiJVdoNtmj1lBF5scvWDXHj255CirsrHp+I
+1f3bG0/Y+pZklB5uZdwZiwV6tDPkL/0t8SZ+7ktSu87Ohkx74pa2zj2++q70CXyh/ZR/9q5YkluN
+iq3bsrVv6U7t2w1avoQB7s4NwmrdfekQRTpNfbkP/7KkoSFM2G6yqbXryXJEayJ/b9Y1PuIJjbqq
+Pr0oaKimYrKyoLVyWzToafiw2c3ZjcTNdzTT3XE8jELC/bqBUcERd1FBW37PnTcpI+W4ULZr2/RA
+k50xIc62qkyFfX6sL/P1tctjMvUUvl8f36rOqJfi3dy4EdPUDsatrnh/LSehp55JtOfirjsVmyXO
+TNsZN1YYhchbZZp2xgFoCOYutinaSkN3aAi2FG78jsdwW9bUj78mc7i6/yadxWcAWFbvMJiKJ5Em
+CSvN/LtaBVvtFbS1e7S+0SRSYEi7kzJXns+yGbJZK2Wd0so5RU2gmJvNOEKXMmXh01OgUxUFDCrt
+ulzKb7SKikzp/fqlQ6mewnnDJVLOz9p7hDv9ZF7aenwaT+EAOLDW/or++88jm8SpMWYXQP959il+
+43pcj+vxWzx+w56CtfbEGPPUGPOitfZN4PcCr+n//iTwF/ikUvTGYH2DWW1h1/LKxl712PtBQNAV
+C9wPKsw91RwoxOIbd0acK/lms6ZbihUM4zm+JsG4U3A7lzgzflmTPknFvT8q8aA33eXmbfEI3nou
+pXsk2/Wj21/kuy8rjdtQ4rdhWbL6NSlJXj54hcOZREiX77zNSuvpYyrcU7mX/k05V3fVpQzFkvpe
+AIFaWH8bo81BkaeJz8SQVMpW6i3pKjtvPS9oKPXcsjqGE7GO9Uq5FyKHpiIai05FPdfyZDbDZjpH
+jQq/pyVXJLa2BgJf59MLCFiLzKxwVX/DmoLACh+CVayEZy2FCqv5i94V8rLZ8PkdPyHH/MPyJb55
+8SoAq/wjyAl01LXLYinfW4wdwg31pjKxxrHTpdIOVVuEWFetbb6D1XwHeYJVslxTCNS8NmNsplRy
+9hI7U44L75x6TXrbWWJKOb6SlBGm/jxThRfXky9xrkrglBankLWVOvKcu4tbRMoWtuFAonR8Yb5N
+eFee5WYRUCqatKOyccELln4pz6GThLQ05xC2cuJUdTSqiDAQT/feWnV8nrLQhjc/yQlH8r327AxX
+u3GruUfh/GCewqetPvxZ4H/SysO7wL+LeB//szHmzwCPgT/y/U5iq5JqOsKWBayjjdqCo5RflSH0
+xU1uNG/izuQFaCqVe9EoaSgduhfVNNW9NLaBrw6rSQraibpwgbYhFwuyDXnRfyK4wVJr9593h9xS
+SvUbd8dsa3E6OJbfe/TqU75zU8Ra84bL1/6awGTvdhtsKovuUeLxGQUU5UrvHQcO6Y66wXl85dri
+jKkUQ+GoGhPJ8krRpyp93KXCkbsRg6kshEdpi44n158qCqk/i6i7ilbJa1wjC6lh2pQbckynbuOV
++lKrKEzd7kGpFQyzoFZ6MNNM1pguTFxQZcpN2dCwJPOxTe3aK6DQWrkzqwg9uc6f6gQ8NuLOHilQ
+6KPqEWU9J9ZM/bwf86LOYVM3xTCqcBRk5dr6isrc+ieY3pZeU4mrYjW1r45qFb63KYY1dq096sSQ
+6eYbjylUEMiqLqWzGNFUXMDEeYjnyrUtlwY87eZMZaP0N1M2AzEQQeAQpzJXdSejPJH1u9qOMFZ+
+e6phi710KXbX5DQ5BYpPqTycngrM2hqnlHP7qbbybzbYLnVjyjP6kfxemT5HR+f+IDygWgvqfMLx
+qTYFa+23gA/DUv/eT3Pe63E9rsf/d+OZQDRaJ6Bs3MJZHmJz1SwwBpRwZeAXaA8Q9XxM3ReYotHu
+xbruk6luQuA08JQMxTgBjvbTuyYh0sSQddYWIydyJKHY8lZMjaDZqrZP+wVBvHVaI9o3lBehJ4y7
+f+Wvz3jybfVSnvwK/UuxuofDLi92pdGmnO5xqd1snY7AmeukIDtXReU4pV+qRmVZ4K31RNra6VeE
+mFDuw124ZGqtIlzam/L3W8OQtzRB5WkDTBZd0KgUgRhVtFy59sFWxp5a/EYjwlfLXSs0tpxnaH8O
+jmnCTP8lz1g7L1Qu9UqeT5Vq2BJXWD3WFgUmUw8iiQl2xRX/0R8e84vfEG9pNJXE7jL98IpTYtu0
+BmLxmosW/rZ4Cq2ueDlhlGAUm1HXgFpmk7lXXqZxHaw2tJmlNnB56XuaiqaHl0t4Yd0QX5umaN+g
+PZO5zRsKlZ+VpDti5d2zEfVNRYVOZoSa/HW7Svg7HFB8Xo4NpgtOVTCIsz5syT21hyGLvjyrmyoG
+Y29YHO2uLTcz/HUzU7ci0VDC6Vq8sSZ5NTns5TmRhoGUEQcaJuTLHFTPpLuKmCXrB/jJxjOxKVAX
+VKsD6jzjPY47i6Mu3mEW8Jlab7K3ItP48kA7IO/54ZW7aIoW6iXSKgwKFcAZ+lS+YtUdOW/QDzHK
+mXjWXRGqIEmwNcUfybmP6m/QfE0W5pP8EQBf+/abPLpQV7uYMzkXd67bnvL3nsiD/gm3Jt2XTPVU
++xq4WNHbVLe2HJM78sDzDFrKDFxohtkpSupCNzSvwtX0tJ87+AoD7jYc7m3IS3+0BqucZ/R106io
+qFXcxNoeN9sqfe7McCv53srIsd2loerIb5fzilLdTz8vrzpJ/cxhoTvH+qUqrYOrlOxFajCaDQ/C
+Nr7W2B/P52zoi2fzjwcyOXFFqriBl3fbxNr96i7lGWQmx1/quTYNdqTZ/naGp/V7m9Q4c1WUipQZ
+eQo2EaCTmZdUyr7lFDVGhWdNWlKqzmigc18PLvC0w/20OCL5NZn7ZSfC0fbxcN3Kfq/CHqk4zZ5P
+tFCsRDelnCm5zG5BY6FiLy3l41zFLNvyPVNFV6QttV9TFMolWfq4gbb+K24kXDWp15WhWcVAwVLM
+HQYnunHEIcP5DwZeuu6SvB7X43p8YDwTnoIFausCAaisGECtmV7jW966ECt+90HBtPnLALRbPwLA
+7mSB2xZLOjdnhJG4rabKQXflrLzEuJJRt0pxVYeGtBB3tuHvYtbJt87LTB6JeWiGP0WqpC2+1vYf
+PX2F2VITdIs5VlGIo8sZcSi7+XcvHDoNJS1pye/d7P4Iz410Z+9bCu0i9Ps7FIUm9tSTLVZTTEu5
+DxcFnqLf0nlGpYhN33VoKhmMV0myL7wZ4Whtu5mkzJTtuPTLK6vTD1pXibbEEWtWLMqr314tVpiW
+/L2emqvnUCzmGF/c8lL1PCsnpZhqBSDqgzYikaVkY7m/BJ9zna+irWHJ+MMRC0UNaS3fm80jOqpZ
+WTXl4lrugHKpjNJZSFbL83PzAaV2LZrQo17/fV19yKbQEjhyER7gGUHFVtMhKCdinU2wvoR6xVSu
+oUy6HBxLSOjFMcNa7r+Y+FitPsxUG3J7chP3trKRzy2ZomzzoY/dVuThKsGowrR3oR7G/opQIe9x
+r4O3kOuxfoira9J1+lceWcya06HARTkxXYdA1dHj6Jjels7bImAW/mCIxmdiU8DWUKyQFrRf715W
+lSVX1//BqCAfqhjImZSC0h9z+dwjeci7d8c4Wta0foNaXyYbp8wPpEchaylU9zihdk8BWFyckUSy
+YKOvnuMprv2N7iv8UCB499MjebCL0THFOlMP2nYNGIuG2pxXS946lmvudVRQ9M2n/PKPycv05adt
+zHOSGW/kUwi1b6xQgdlOTfFErrcYhFQKUU69E5ZPZNGsNipiT+PMQME4M4OvTQyFb4l9ueZuEbNM
+5NytNKZsSMhTzbUfxI/IdON1kxmrQ7lmb69BU7Ud/dYIowKRZUvj4TSiasoc54eXZB2lxp9Zpsjc
+fvXr3+FyqcI96ccvULdc0lDw0igMGaxjwVzJZ4qclad09qOURqwtydmUZl827fiiRdjUnoj1Ttfq
+Uyq3oRM6pEcCWS+aOfWp3F8QnDI9k2v2B1p6PalpbmuvST0l0jyCZwPcQJ5vUkhoQz8l1j6DrJ3D
+UK75uKyZH0nodn7DkBzKNT3pyvdvvN2AF9UYTlLKRPIk1UVFOlDw0ukDHAVwVSeyKUT9EflC5ids
+NXDPtGW+GdFoyiZzNOhebRyfdFyHD9fjelyPD4xnw1MwLtZvQTmHam2BLes9q+1U1Mpa62+E5MEd
+ACZjsbTVV5tsvyBWoG8HV+6VcQ2FVTdwtMFyJdZjqZ2Dkd/GWEGpxMmQy4fq2leQtuXv7qMmw5uy
++98fKFVXHXIF2H2/zLcFT6sE6QLGasUD7dg76I4YfU3xFs9d8CMqieaaAF9pzkr3kVz7eZuZWp38
+/AInX1vMbfyu3NPyYslEAStOKdY1dS+pVIQlpKRVS+izsRWwqclYzy5x5gpuyTRx1odcQUHkWxQz
+lawbH1NoM08n6xJoY1ZVi1XKnBHVqYZuec7qQp5DWSesCnlOjW2XM63E1N9H1jBxO2wIxohB2CCK
+xTp6SkvmVC1sR55vTclULeVytWB1KZyeG50buD0BJ/naEEbgU1XSrJaf3ac8X3seC1Bhn5r7+Cq0
+Uitk2qZQrploxhmrPbkB9+yERPktnb5Y+c5ih1JlCaKRZaTeWxD1SNbw/UmTzBMvzZzIXC67DnUq
+GAs2Xdb9S0WwIHtHhXaSKQxVgduRJHE8u0PYV5zG0YJCKxVB1aSlHmK/FeKqSvknHc/EpmCoceoV
+VfV+Cm2Lo7j9GSEv92VRvez0yTXGf3ohL1srWZIOpRWWRkURSewYBlzRs7N6yvlY3Khed03VnlMV
+slE8SiPitriOzvwIxnId/+j1N/mjN+Qle0UlwqkXH2SAuRqWTGGYTbekYzX231Ce/vOajgKWzOwp
+q5HGiNEtXJV7X00V/FOeMFXWqGDXkipe3tZPGZ/LxuJEBUYz0es9yrctto22Oocu+TrL7rXoe/IC
+lUl1dfxK3eE4NVTKIJTmpzzRHo5mlGKWD+U6QsMslrn1kJzLbBrhGZn7k/EebW3l85IGx5l87ytf
+n19tIt+PUzR0MpZLWfQ3tkOiQEu06z04ca9AUbXpks0kVHz9JGZ/qYSnN9+hnUkfS31HwkqnvqA8
+lmsw59/i6bm4/ANbkCl5b7I4ZuRLWbqv7nfWLGiqwPClXdJ4ohUDr4ESRJFMtQv2bkmtMvHjXkHr
+UinCBg7+UH7vcnNFqaHEIBKjZhsV1VQ3zflLWC0nzocNwkA2sosHN9i9q9IGaJ6h8YTsiWwarXB6
+RVVvVsEV3+iths+F91vXJXk9rsf1+P/heCY8BYzB8T1M3YRK5dBNjVGrG3gOj1QpaCtZkL4ipBiV
+I7vk0oeR1s9H1RxX8eLu/JKFL9Z9eVYxUlDI8KF0OFbdkg0FhIwYUFyotb7X5eAfCFffYvs2vxaJ
+txEkCnh5H9fDR5m+dJlz5Ii16r+h8NP2LhuKBDqdT9gfiYUJO4ZAmZ1Rl7McOyxcOfbiZMgsewRA
+soBHtbiRW0FFrpWPUvENZd3FqglzvRKUzszzfUJnqfcR4gzl93LEG5mOjhkq4czwtWOONLM+CBxm
+SmFet/pS0QFaEyX8CFaUQ0mcndolkwux3Ee8zcHrKq+eppSfUOZwFYQE9ZowJqWpUZNpyFwUaYvJ
+RCzszCsYPRaPZTmLyBWTMR5EdFRZqZWKZ2Nnl5ieeAGXwxGXCuoafuN1FgPFfcwzFqF6U2utyUGE
+XYi7P3ViinKtUVlgFFy3DGTdhMs2/q7MazgKeKziO8HTlLmnidY3l1hH4eZakRn357L2AfwQZ6re
+ZBPyd8Ruz8qc4Sty35NQunLvZCVOR7zC0ibU95UAZ7kkcoUDokqn9PrxJ5r79bj2FK7H9bgeHxjP
+hKdgaoubFdh6iWGNwANnjVJ0YhrKp28qn92WJJF6fYnV/fotfGXMCVIf05Nd3ss8gnPJGRSNFhsb
+Ym0W51K6smVK6KvlswtcX5NLXzsmr+XvT+ev8WNKifXwQllgP4HYhjUW1xcr3FadwLv9HULlnykv
+N8m3xDLXjRmOUvQGI7X4/RaJNsOQ36CaKKtvPWRzufZSKkKNHUtNGAZAtM4jOBHGEyseOi3KnlyP
+U4Jpruv48k+b+fgaD5uoR6QdnFUwwllJQtRJPJxQzlfqtfNkxiLUBJdTs9iQuXVfG3Og8mf5aMn7
+peo+dt6WcwpFrB43fCLt+PSbKpay0aRWrcnapHS6uzpFl5Rvq/fzYEmwK9/L+78m1xaHVG9Jl383
+eJnBc3IfC+dlrJFnsm1Kxm2ZjzDU5JyJsUqYGvo1sZK1VnmTWpPKXq7rqlui8g4smwvCsZZvnYqW
+dujmrQFm3blrJIdlDhLqz0sOx+lcYNR7axycs1Aekch6jBXpaZSuLWdJoSxUzWSI94askVnsMdz6
+VQA2/Saz0x/sNX82NoUgJNi/Q315Qb3UWmudYpR/7vM9uP9jQq3dDyO2m7IZzLWFdCP6HC3NALdv
+9kkyebCRv4Gj8urtsHtFgOEPFIDiLqgKmchuds5lJec95pw3SnE12/Exx7sibvp5bZH9gMLS9wzt
+MiYOExqhTO9+V7v3kkt6VsAxydaKBMUTZBdY/T1H9QBbbkCtL9KNy4rphpzLC3bpqstohw2O5uKi
+l4VqOMYLHA2r4kZJrZtMs1UTKjzaiz0atSy2VGngem7CMNS6ux/TrtT1tbvEsdxUI/IpUwnvKgXY
+FGZMEMiL2Zw+wZ3rSxrBP5koX6FzxTT/fYdXR3S3lbtyNqepEOu2bjZe1CZ4Xl6gPJ/CHVkjt859
++O1KUDN7RD0Qda3wTF54J26S7InhqNy73L1QtaUbSwqlyzP3ttmsJVSoXfm91eSCVMWHGpMFzoYk
+ee14ekU042zIi9kahjRvaFv0IibdlptuZQPqSHUnq4hcRW/jmZyrv1XTaurmVznErlaUBi3aem0B
+M24rd2WxoQK8VUIVi0BRMLlNGgjDdlpvc0uFY7budrl948Enm3wd1+HD9bge1+MD45nwFNwaegt4
+iqETies4ygte1G6wU6/LH0tkV73x8g7tsfD3f/uOlMJennVhUyxeI29Sqp5hlDWo17v4pA1bYgm7
+qstY9E+xR/I5c+fsKJx31jDsaufj107GbH9FZOHe7SpE92PCh7XGQ6fh8/lb4nZ/cUvo4w62T3l5
+Ijv4whi8hiaXXA9XrUOpXZL+Irqy0K12E1c9ofiog9fW5GHdJFM9gYZ2AE6GGW2VXTNuepWItFVC
+oDoEq2aG0hrgaq295bcI72oo8rBFGGmT1yogVeIU37osNXyItPGpicfkSK7hKM+5vy/PoWUL3L7g
+OhZvfsIsI+D6FaOhWMLn9zqMagn/yMWLcxoQaZdrfDOEyUD/8xDnVJKHedvDW0libxWIZ9N62iDz
+JFEXe3eo1POKVn38vmAWnNOQ5aZ4BeGFNqvtBjQeytweOgt6Z4ppiDxMoHRsl3K96UsJLFRJfGDp
+ZXI9d260IBaLnxRbPHYl0b3bEI9n7J3hBRraJYZAsRWLVkl7rHDyjoeio8laMq/BWcywu6/3lFFr
+A9rG6Yzvqld7axwxzNcak59sPBubQsOj++VtFm/9q2xf/GMAnD3/qiX35Z07jKw8rO0Dh2pfXMJ9
+dbljd36VRZ+bIb2FuI5VnRNsCRImSy/oVHcA8JStx3W6VB15iFurFxnmcl5/5z7LX5ANJ2q8yDuq
+xbc118I0r/KRG4OGD7NVwVTZod3PymK8Hd6g39YehXZMoG9mxxlc6Tw2PVlIdZ3SqlRyvrUiVFYk
+u+PSKbUnIhnRUexEdqqtuVsJqDR8UCSstJrRtw4oG1Ez6GNXykBsZWNyqIiVMzJNVmwKYPx2AAAg
+AElEQVTUKhZTjKkCuc50PiNWLMPibXnRi/sV7z4W93Tj1g7eoeZGnruHfSxsS7VjhDTnE4yl54Fi
+GorxMeHz0sK+KrWderaJ1XZ4p9wClWcPqh+mduX5meCzLB+KbmRUCUNWYXPsD92Rc43OCCYSjuIP
+cR2ZZ7uZEdRyfG2Uaj+3XKi6U1W0mfpKfLN0yTRkq7QD1D2LCO5qZeEsp1Y920bhEu7L2mmuSmpP
+Qqzemqo9HtBQWH2S9zFKZNzp9SnPZb200k187co0Dc3bbFb0jIoPLRY0PiMbz+rRiOduC0V90Qjx
+Zx/KnfyR4zp8uB7X43p8YDwTnkKwKtl7dcjprb9FHon1n0yOubuviEXTYNCQHTpp9wi05rujYhqX
+zTEqu8hqsKK5FK8i3Y8IL7SvPApx9CAnUU4DYjzd+U12gB2JG/lw/BYnpXw+rAzJsezos9G63vsx
+Vm8ta+469ALNYDuSWPpsUXHak8+dImChPfS96ZL0pioKH4uVzzoOSSnWquwFoLJxlbOk0srIwnfJ
+pmJBJ7XKhC1c6pZCrVsh9UjOYeucVLUQknmNUVi1qxl0Io9SNSmq1iX1QznvaWBYDCVLPglLzGvi
+YTwOdcL/+oxfDeXYu995i8GPi7ZC9pWMi0SbkepPHj6Ql3SVeu5k4LE9UVhxR7ytwokIFIpMmVyh
+Ci0PIBFXenn6HZKBelwa7pi6glM5OPBijErbm8gFo8zW/oRKsQVWOxyLmQfIMzNbCVa7Q1t+xDCT
+OWgsxUt9Y3vK3SP5751uyXMapqabMxorSf7mwQQtjnGuOqaLecpIDsWdOjg3lIfh6Yp63VXKKfWW
+egjKHm3cIbWzht6nLB/LcyrmYx5tyOegvMvhv4wkK85Gl+af/Bl+V55SfVdc0fujMx4NBYBx96Ul
+dSITsWRCUsoEz1byYPOx5UKJU5KgR3Uqcei+n2DiNalJgFms+wTkd91mTa3QX6oVY18ehj0peRpK
+HqBnH+Pdk4e+9daadPSjqw9G/26yihMlaikO5WG+2z6Gc+3q61zSGctKON04pVvqBqJEJ/XUYe7L
+JmTPKqKWXnsaUCihijNdUmk5zNFHWXVW1C2Ni/OaWkt6tltiT5UYJUr+X/beNMaSLLvv+91YX8Tb
+873cs9auqu6uXmbrWTgkbYqkqaEgYURJEGTABiRQpg3LoGHog0RDgPzBH2jYgGVAlikD3gQDomSK
+BmiNRInbkNRw9uE0p9fq2iuzKvd8+4sX2/WHc15W1Ux3T9Y0OawB8gAz9fplvIgbN26ce9b/H6PF
+O1bZipyxA6EUArm7TQptw6VXkCTyguxlZ3AeCB7lnR353VHR5zUlmM2XCvi6KOyz7QlbvXnlkXmP
+svDvFN/xcBY0+3CQkkeaoq1LBogiplA6eFMmkLf18yWMkuTUi9pxp6G3rdmHcz5FcV/v/xzBVBWV
+0wK9V+s0QYlzS8XHdKYhZVOeXzMZY5d1Y9jLyBXVqYzknhdvRhSX5nGGALOobe3DBaxmeaqDGr26
+kuTsKoV9mZM+UAj/7jbe/VRvdZFSC+58r0K+J0poDmKM1yHPNF4QejixjL3/0kuc1bVTvwzP3++d
+YOYfyqn7cCqnciqPydNhKcxmVK/d4Sv1+0RHsiu9/vYeV+M/AOBzf+jxXxrZKbpXr+Booc79WHai
+Vj4kTETbb98YsK2w5t64Rrws3y/jMTujqLsKhJKbGmYiu+N27xYHEykfff3WTfY3JdA4Gu9Re1O0
++I3y0Q7Od5eS+U4zoz4HC9EiLNcv5twsLIxyDpQNeHLXp1xUvMaxEpYEGW5dxhsuWkZjrUPwMmZ7
+akH4CYniFTbVIjo4TKlHcs9etUauwCq9XkxLG2bGwZgoVVTmmZZEOymJdmUW3CXRgqV4JcHflmPu
+cofRrlw7qMjuuHrPECj/5cHtkPXzkjc/HFUxNcmY2P2Tc0mms4SjRJZlWDVs52IGJxrAHLZXCaYS
+WS/jLo7iNBSVTdxMXM+s+k3czwuBS+/C1wFo/eYS6SXBp4jvDMg+LQHoIM8oNfJvxtskU7UEFCk8
+KXKivliIN6c9Fodyr1G1yX3t0jX35f5urkXEX5TS9vsbS/yQuiVhe4q3oNkM22M8EqvXauDw/uYU
+KuKulvsuLytkX9TJCTK1RtbP4MRaDKVemcuQ/nhOPXiPbQWW6WR13sykkWrpm0uMGz+A2YeyGjH5
+5POsfOkvk9z/VQDCqMb9bW1JvbjK549kJl7cvEZL69PvmPOAAFVuagtzunuH0hW/dn2vwsq6vExp
+5FDxlAh0V8x6jympYiM6wSqv3XkTgFdXOoy+IAqidGsMFKhlzn9wEknygreG2s59IL0a+/FHuKo9
+GjumoDaWl7fffIaGgq0aRXDtzixJVY4dbY1A0X2c1GdLfXS/7DGaoyKNZPEkzQqH2gLZmvU40u7K
+YJxSVBWKPW9QqOLM68oh0Zsy0peY7SFjhcEf3ixJPf3+92/x5qF2qI5FmfrhgNqezE+vHnOvr+5K
+I4Xdoc7Ge7tb3y6TMMIOZV5WBvv0FTlqmkooP5zNjl0mqB/zORr/R0G7Q932Z5g9+3sAxAfCMOBE
+BUHjC3KNj5wnUOSl2eYdzIpWvU4zRoqslO/LC39QdznaV9zFbhOj7ExeYZhov0qh2Qnv9oA0lvu/
+f/2QP9R4QDd3QMFbqoHDHUWTOq9Q7hNvSrEpz8atLLCnfJWdxRA9HbmtUGwr61VHwWVHOUNlyPIH
+FbwXZI5Gd2OC+McBGFYgPfo+ug/GmP/KGPO6MeY1Y8w/NcZUjDEXjDFfNsZcN8b8M+WEOJVTOZUf
+EPkgrNPrwM8DV621U2PMPwf+GvDngP/RWvvLxphfAn4W+F/e71zOXo/KP/6XfO3CL+FoD8PRdo9E
+izuS3pR7mm+/sniWPBTNfl47+Y7W73L5j6Tc9S3XZ90RrRxePUM9kICht7Z1nHt2tAhpVk4wGuG/
+s/0F7n5VPj8YvkGRa4Q7s8e1B/kTtKVbCo40QHeoPRWfdkMOz4p5ufrgMnkkoCDnq5ZQLE1qntx/
+vnQHX3fJsmUoxmqK5z08ZRgqTEwrl91q4spudSYMaGnPhV0sGb8uwbUjHDa1MKw+dKhq9iDSm0uC
+9JhwZhIHtBU2rb+S495V9ut4iZVl7fW/K2MY2CajpgbAkgwHsWjuPYgZm3lt88ndhywdM1Gym2ur
+JRtTGfPwwRsAOGeXaZZq+u+U+HVlxPb/V6h/Rk7yL36N2iWZ0Cz4RzJXH/qLTB+IpVg7t0OqOI8s
+R5QK/W/yPgSydlLlfjRHeziO7OL9hmWIMpobS5rIXBRaV5A5MB1qb0gtJbujhWUbBZ++L8V3jasJ
+zX1ZW7eekd83blQYaRn0WhDgr2phlb9BuXBbrnGwRy7DZ3qoGRUSJhMdw9YRs6p8v3a4QvW8WFvF
+ned40Ll9gpl/KB800OgBkTHGA2LgAfDjCK8kCBX9X/yA1ziVUzmV76N8EC7JLWPM/wDcBabAvwW+
+DvSstfOI3Caw/m6/N8b8HPBzAH7c4ptXf4au84DN8e8D4NuSfCS7/2o0oFQf97A1wm3Lblq9Lcg+
+4zcMB85tAPrTEE+Djhde3Wb445K+bBxGTLSSMe9req82JvAF7bd59C1+fyK73DjNwc7zwwXHpoKZ
+w/+cYH5wmc65GErZtXZ7O3R6Yv3knV0WJxLMGxcZrV253uSiWA/t7ZL9QPz2+tGYqRLDtN0KFf3s
+7BgeOLI7dLW8Oq1kmJrGGSZT8pF2mi7kTHeV12KhhpnJmJJAjq2NY4ZVCeB5eYWhO6fmC3A2ZMwf
+jc5xeSqfX1mWefsD5zrXX5OA6P3OFv1QrJQsnlIk8swcM6U8YUrSmbpUA7HYpjs+RwoQm58T66C1
+VMcmWrEav0FeFd/ZfH5K5sku75ptbv+OWIjx1xRly/uXFD8v6yn7dy9gF+a0cfvYBYWv85cID5RQ
+RXkgR6MJ27Hyam5N8RfEUjhIpscl7fPlYUuLUab0ycRlXZm7bVanUJDW2WGLPBNrMXpN5qQSV2k4
+MrZqs8ZKobUZCyVeT74/sGNmWjtCW9ZKlSWauqa3Ky1wNHbww8/xnJoV9tmIhaPRCWb+oXwQ96EN
+fBa4APSA/wf4zEl//ygVfadZsy8NPsevXD+iptHy/bykuiQ3+c5hxA99VB7ch86H1NuyIF/flOj9
+IBmQ7cmLkhUFt/VzPC1I+DwAf2b5BcbL8gK9oA/ZXVoCZZN68/aIyw3Jae/0C8mtgyBNHz/17wIw
++Oj8eA6xBgrbn/y4jPP2bYqeBg/vb1K68uBGk32cloxpcF3ueTVZYr8pY3vO+lRX5cUL8oBSCV07
+EeRKqV6byQs4M4csl3Kfw6iCpxHu/qBKW7M1B0WfZYW087RMuh6lRIEswJyEgwei1+v1iMCVMXX9
+Fmjh0GpFAnI3NnvkdVEgvb7PhUjO11hf5a4jJv/41ZMHGgt3Rn+sdSGeS91INqOYSN3BcGhoWble
+1FmHgfz95o3/l+Fvysv2z167w/OVfw3A/31P5uKHI5ff/rrM/d+++kmOzshc/PSHzxL/yIdkLlKH
+Qai9MrqtLbegPpZ7fevON/j4WHEuVzMMGvh7ROFZqzDztZxvKn7kz76wRLUjyi1sr3DnG1KLY1N5
+BtHwHguR1I2MjgrmSa7r977IGS0oux6N+XhLxhGvaNdtadjRUutuOCGrybWvLK2x1xAXe23c4k74
+ZA7BB3EffhK4Za3ds9ZmwK8CPwy01J0A2AC2PsA1TuVUTuX7LB8kJXkX+JQxJkbch58Avgb8DvBX
+gF/mhFT0k8Tlq9eruKbLOJVUoOtaij3RfM/XZ+wMNP+/tUZzImbuh54VjbnttgkvS4rJuzZmdkZ2
+tsULTapV0cCdxoyquh1OW03KzCM/kJ10Y+MqRxXRrk4thYF25+E8dCXmJcEn2PRMblnQqrgXvyr3
+tPhKi9KRXTBud3mmkCDabCliqaaBLa1pCMi4rIkbr+ke4zRYBxbVvAzbPksazPM9Rb6OFyk1ZWlH
+hkDp7EtnFzUwqKQewUQefRxpdaDpYBqKBeDMeF6dPt+vUSivRWQtgQK85FOx6F7xDUtH1wBYqpUc
+NcTiCfOc5TsSwNtxnBOXOrf8BmFddv9K1iRS7srsrsxl9VyNaVs6JsPRAs59+X7tlc+yPRG347Pr
+v035JXlmf++MrJXdKxu8rIAz5z+8yKfa0jDER1dxlWsj9RMCI7vxWDsVZ5MK16+LRfpC9SzbM60U
+vJZg5nvqsVtp0VIQvIHlhY5WSr7Zo9aROpvzfUP8kpRjW3UZR+4y5wfyHEbLBR1dA9XKKtFQ1uEZ
+ZwF/Q9ZOpKjNxdjSnePcrTSJGnJP966PWVSMi6OsyfRgjgNyMvkgMYUvG2N+BfgGkAN/iLgDnwN+
+2Rjz3+p3/9t3O5dvcladQ96Z7NFVv2inbOFVZeGNuIhdk7xxc6GP0TLPlZ6+8JemOIkWoKwNmVWl
+iGX1TEaeC1qSX7+G35CH73YkmkxWp8hvy8ftHudyqV+4kQyZPvrmz5WBcj9SvDtpzWNiCjLFRBwq
+KcrlPQe/pYjDlSvEytLUiEJqTSWtyUVxGX8TNxC/33T72NmcFegQfyQL112cEk5lgTnhA/17jVxr
+GvIipdOWR2ynEVYRiu14gaF+dpM5r+aQWEuGnVpGTT97rRlFrm3LwweksdZIBPKidKcN9tZlXl7s
+neVtZD6jWUau/RW+NSfEXYKLTYe00FiLcTGKgnywKIri2reGXPi4gIkU15fpKODIJFhh49Py/Pbc
+X+CFpX8KwOHozwHw8Veusbf9HwPQfP438GtSy+Je6EEi7qhXvEU+EJM/3pAXKd3u8OxFAbK5u7VE
+9Zx0X/YOLuCoUijsXOHZ4zhD6qZc05qUZvYOZ4ay9hZWPZa1A7XoyNy3zSpNI+u+VqlSU6Sn2qQD
+rtRe+I0FaM2zDtrwkeyjrRrEjQl+TxRW8/IOW4U895XoLd5QJKuTygelov/7wN//tq9vAp/4IOc9
+lVM5lT89MfaEUeE/SalWm/bFF36YPJnSUGCBen3MOJUd8XxnDErbXglTthVnwXEUfXkc4RqJ1O+P
+fGyueeWZT5CKpt3NDVZpw0ZKelLmE6bHfAqzR3p2Hp8To+QqnZqYlPuDMSfBHDyuf9RS5K5v8ALJ
+V//IYsmRcl6ue7vcO1TqsVSyD69OImaZ7B7TzGOOilJYh2PShvesFJTrRYHDrFDK+WL4Hsc+XfLM
+pf+IBwOpRpzuD7CKXP0w/FUwvz/XlJQavgpMQaGYG4GTkKJl4UYssLysEOp6SaxHwJwAJqRqxKIZ
+4YJ2m+ZKeuNSkM2nzdrjALTvBZw/+5cB2C3+EIDxgxGONl1lRUzkzsuL67yolPHTeJkPn5W1k4+k
+AnF1acymWrrubJ99BeIh3+VmImsvCMZEiq/xisStufoX/xP+wo/Kd/H+f0D3RQnfOe6Vh4FyDHNz
+wnGbX7fWvvJecz+Xp6LM2dqELH+TgwcR7qfFdLx+bcL5D0vMYO+25dIVMaUbTkpzHqkfyc2Giznh
+UCb1+oKluKcAIc0xC5sywf32DGdTHsawIma933eYOGpSv49ynJOx7g/nXZIne7nmR83jBKPC48yq
+LNxzlQZnnxOl52zVWLsgYxp8RczMm+0po/uaTvRyinnhlCkfufx7Ab3IfU7TAsyTEYH8acve0a8w
+PVBXyil5eI+PKmEFPbEInRcwyy2OPz3+bJVmKUnlZXSiGdNElWlYMJ3Jef1ahtb/YGugjZZYV7tV
+c4ud6yNrj8eTZzO2e1KSPzrU8foOds4x6g4Za2GZH0+4oz0VFz7VZrYra/Lsi+KWPe9NqPqyKUyu
+x7hrWvL8jSa7Skx7/xosXZX7e/WmxlFuvMU/MF8D4L/++CXGSilQt88er1lD9ND9PaGcdkmeyqmc
+ymPyVFgKlA7FoELQWqHzTdn9n3thivO2aLvW2jobnsJcLdYIM4XUXpIQeTztS/ITWL/Z5+6z8v25
+/W2yK6LFvQd7bK8pU7Jy8s0qJYOp5prfb3wafT95lYLIXOPWc/n0s2ttFpSW7Gf+/I/ibGk64BNd
+7r8m3Xy1nxL3YvFzt/hSU+7zi8MBvitzcVCU3/369j0+/wBIOvHAU1cqf/C+xxoA3Y1D1yHPxa1Y
+qsC+WgWup4HWmUHZ/9iaGeJAMSkSj5casga+NTQYhasfapW741gKbTqzx/8n/+SawUGBgez05sPB
+FY9kIlKHH1+XY1/ervPSp4WnNEnkWmevbHDlbcmcjF8+y7U3BbOClyPsNyVIfWEhxx5IKfSlz4hV
+vPJOi59ekZsaTgu6Rv2KUi2EY/k+Bhr/2KQscSYJu/YGjZrczO2DRS6clxd6xQmJlyQqvFytYLTn
+ob0kkxpuL5IrH6C76uLo69tLZ2RGbMNxanjWzisMJYXWS0bAyau9nvT9KnUctUiuO3k+5BM1Bets
+pSxNFXDlR26wdFfu71YksZHVZo3IFXPxmbHlmlqAJy8D+sGUIplg7cmeieWh65yWJYGRmTlMzXEK
+N9XYQYThns5h0/eYqkvXKQte17hEHBusRvbzUtZNWrjAd5rfFsi196G01951fPNMhAlK7lclfvDS
+lZBwdV5FOucDmOF21c1Z6tO4LWO4mfdpVzTe4VSPx/H2a5L5OTf8df5RKMrkb9+e8Orf+iQAH6n/
+NLYu68nYyhyj98Ry6j6cyqmcymPyVFgK1jOkyyHdRkis5CUrGzHLqfoEFyMqWghU5AVd1YJjtZCW
+miFDze0GxjDVrrVLeZXrGshdDeD2WII2q9p51/MaOLdU26fJiSHDTiphTab3Z56TLENxeYPnVgVF
+2Jzv0DwnrkLZgNZflRx7aaTo5NzqEa52HN7+3bdhJGbkO28MGWeKx5g+AfbhD4gUUQUURIfi/QNk
+xjF4gWYZQgg102KqCShkWxjIuWpOhK80AM1sAdNUkpm4Al3Fy7gbgrJ49/cl4rg/HvHgvvI1Zulj
+S6SI5lkCDYKm6aOjI1BXpHZxgfo5CSBfuXSFwZKc5NmRspE3chob4vKaqOTKp+Se3HiVfkfWwEI0
+I1NAmcG69IH0RjHP/ZBYk799uM5nptoT0q3i2jnyuCF8F0vn/eTUUjiVUzmVx+SpsBQc61LJa7iD
+gKVV2cXdosNLS6KzJpUWG6Hy+W1UWNCYQlAXbdj2CwIlTknzKkOrfepBlZmCtQ7HDoNINHqgrNRF
+fo5dV/yz3CZ/rPcUhgHPnT8PwKdXpaoy+eg5GtuyK2UhOIq3UK0Z4rFYRUsrsjP4vTM452Rn+LHD
+BZ5fuQ3At7Zn/OP6W3Ifbx+xpw2p715F/ChS1PtbQcaY96zT+O5y8ut8N3FTn1Ibut7rWr7GA2pR
+zLIj1la/FfDJQp5h3u2yfChp6a9qN+TfNCG3L8gkffLBM3z543LsD+/ASPkSTCXiD2WzpfkFgW67
+XvT5/JF83i2OKMrk+DadVCsaC03/GYPV+694PpVIdv8fW1ngx18WAqP1Sp2kLZZuLVW26lqEpzRv
+ZVwQoxWWlQHZhlSWbi0kRNdlLR/UlIjnKOe2khl9/J0b5H9FqjCd7AXQGBu2dZyePKk8HUrBdanX
+GwwSiBUgxKzWj8FEKun4ODBUmUwxde3EMw39/Ziq/i6sJSyOxIx0zN5xHblbCYiOtJ7dSHQ7cW8d
+L7AnQRx+PzGOnG/1mRofqkltxfonzwOQpzFFXSHnsyrIuiXorVDUtNxYi7TMhRELWtxifmidjzqi
+NJ75uS3G96Rg5bByjc8fSG56d1uUW5YbjgNjpTlGl7aPhs4dcxyMnRdWGRes1tFbLMeVuzzMzesP
+js8h/zjHMHWl9cARxWuzktKevFdkLn4IWTJH2J487CuYX93xcCNltaq0WF+Rl6nrn6Om0P0byy2W
+VAGcC2Su2ssul5o65lcyPjGSl63aGFNX8pz8guUFT9aWfU7Xxajg9Tnc3J2UmSI4F0WJ6ysMPBrU
+K2fgyXWjasDyhqzPcKFJcSjKvtjoEGv/hPUUX7Go4GhdhJ+2KbXoKShquEtKNDMLscvyjI+scoIu
+eAwO5dqTSzn+8M8CkLhTKtGSzpiDVd7Qk8qp+3Aqp3Iqj8lTYSlASWFnNIoGoyXR/BdMi1K7Fjec
+Cq5StYdOHaMIt0ZJOrJGiNF8dVGFWIOSk6KCM7/DWcLCocKNVUUrL+z6OK6SwVj7x5LqixRI83y8
+xsXnpbkpDGSX6JoQ0xT3KHBrOFasA7c1A80ru1pmS+DituYNTC6VqbIdL0z5jBHN/85uycSR3eN3
+d2RH6VsoldjiO4x6zd851sEPdZfKZF/ITXFs+ubfMROaWjOA1d+pdRCYCrE+Gz+rkmhDW1kWjHST
+L07SQKbi5zkGnQPPxR73oimtGhFVT+Z4zY1paA1J25mwrkCpK9WzrITKl9CRgONCM8MJlGYwHNPQ
+Mbvk5No8VARTNrQDcai4EMbk+MGcB7JLouAzTuHg5g/XDoATOUK3DVSbDT7ui6XXjJssLUlQ0R8e
+UlFylpnycwQFlHPoOmdCEWgZ9yzCtGWNrE2q7Ose3hwpA3k4oPJAG8VqTZZeFc7TF6/mFJ2PyjG2
+Q87kRHM/l6dCKTjGpe63qK2mrGgrc3U1ozERM9/UeuQKvDF0BtSq8rlQczfoQaHErO6hpVAuwmpi
+KTx5aZJRSc1RevWxTHRqJ5SlvoxOj/yEfIfveR+Oy4tVcVGWwgp+qiaj+reJb3Fm8mBtNCUOtKgk
+yXA0V15qF6JDk1CxvHPXIdTz+v4ZrrQ07jCo4XQEfepr29pR2XubqbZL40CuWtEWMypKnd6IDEMr
+c9jUOo6dwsPMy2FLB0fN9tIajOb/MT4VbdGuNOX3i/UmUSEvTcMZ8cAqFX0yJB0o8lDaJ/kumYS5
+xBsXKYeSiWE4oNRy5UKRj1cCi6nLfGZ+SVE7D0AQuaS5jGk/HrGyoBkfJQly6otELY1LOUv4iayL
+Il2n8JWWvqhiNfMRrCt3Z29IvSl+e6e+xeBI41FOjtsUE71i5fdhllNdlRd+uXaG8+dk3hphjWyi
+CqSxRpIqxLvGlLKFgmoxLzbK8FItrGrluENRak6UHbuWVns4bLHC5XPKara3xO0FJUte+TjVUrtq
+HYOPf5KpP5ZT9+FUTuVUHpOnwlIwlDjOiEmvgXtReRryGU4ou9VR7nJJuReKumWqJq+XaLTZKY6r
+B73I4k5FmzvelCCW3aPSGBIOtWOwKrvH1WiBP1Bk6OIDWgkAnmPYVpr0Z23lGH5hpNmOmZtS084m
+NyxAA05ZzcfR3vuyqviLQ4fUFQ3vFFOchoKh7IzoqcXjeffpHMrnTEltDicjXJ2LwpZUKhpEKw1G
+zdJRaqlECtk21vt2C+x8Dow9/iyYg4oZ6IJV6vpOVXbJc02PsCIW3bDvE+qKapqCkS/nGG47cMIi
+cTs5IldLzjUOyUzWQByIRbM/DekqR2XsVTlSS2A6yHheq15bjsX0dSC6nuJimbApcxXfN/Q0WBlg
+SJry2T90MBqsDNUK7SzUOKMZsTtvtDG+4lhOS2wh2aFSKxP92CPpy+9eXoVxInN0cWZAvD9GE49M
+cSY686ar1JAXym/h+NS1C9bxwVeyzLzp4mn9zdk1sQjS+x6/sS+ZkXz4NmurEnQ+uPMyPCfrqM5L
+pN73CWTlj1MshqwISRmRHon5tbjYOo4NVKMmbj5P+1SYumJexo6kecw4wa+oT5aMiUIN6xcFePIi
+JH6foC4mdmtfXsw3eICdaofbE9UPP37wHKwzNB5VZe+Y2ZQDXSCDy+Jnt+OXcdSMdJKIQlOkHgvY
+eRukKpCkOMC6knHIjxJSLcdOxwdUQlkUN2/2+eJzckzvXykNuwkoj5msDIEqltJ3CEtFZ2q7TEdy
+7YmnsZg0P2a3EoXwEJfSVeXmYqjVRQEsKHjoevs8NpzpuVpU9tQsN5ZgrOPwHe14lw0AACAASURB
+VMhPphRMEFAaWQNluo/rKxy/FmoZL2dnqKS6yYRY2+GNdwZPfee83mBh6TYAq96n5arBDG8q6yJv
+HxKj3JRH2wTaij+djKEuc2uUQSsv60SOgLoQWbwjGX9iIJwXCHnaP5O6LK2JAsmzAl/jAYnnMhso
+ipZ3myKXa4dDuScPHzdUMJX6Mo4GUty8CrF8HzgLOArYOyefmcZ9Or7kULez17gRSVHTnf/jK/yn
+/81PyO/CCl555n3n/Nvl1H04lVM5lcfkKbEUCnL6BMMGQ80PT448Rue1aWV8xEGoxUkPckpFZc4O
+pIin83JE95aWta6O8ZQKrvRDZoUce1Ax9Pdlt72u0FfT3pgk00KZD1KjMLe6Aw/lfWE3SDF9cQ+W
+xrKbpXdvMHpBdquNA4+l1Tl9XYapSYAuS2U8ZTVjfENo7A7rBcW2kp7kD3j7DUED3t9y2fpNAfjI
+9D7K8lEkaosp5jBvOU4oO3BvmFOLFV24J7vS1JQPsVseFafEakTei12W1ULYWJBoemMlJtEO1vb9
+EbuxWCxlzydXrAonLx4d0vuK0z+k0DlwcMmUddnVnL6fQ6lWYT6t0FfcxcpgwLVLWsZ84yOMVuVB
+dCLZ5cvoAqVmXGxQZXJHwFD6lQHJgQZ/2Weo2AptX46NshFWsx1R2iJTSHw3dzClQtqpkWe6HoVi
+LXqXWywN5Bx7zZxSTf9hmmK2xOTfPytzee5wEbMqFkFkXGahzKeTJThVxYXYO2JaV8vjSMabjPfZ
+uv4aANNBlWu/9DkALhwu8g8LoSr8hfIX2c7n9H0nk6dCKWAd3Dymup6yokA79WdK6jsakW86zJQ6
+neqIQU8W5HgkPlTvdyzFuvQUrI66VFqy4B1bMiq0suugjl+Z06vLyzGptnHVJDPJ7fcFWnlMHil0
+Mhg0W0aeOZxVHoZZf5loUSLDR9ti4rnOId6uLNzgfExjQUz/ZtbC6OLONF5Q7C0wmwgu5SAfkufi
+n9ZqH2NxTY79rf6I1zWwXDBn50seKikDKA9DPcyIMjl4qT7hnhYIrfgK7pIFGPXPc+tg5hrCGpS+
+gtjU+eg5GceVKzL2tDqmvC3XqLUPcfviPDvtlGAo1+jYazw4OllabOgt4gWyCPJkctztWGr7ed3N
+GBZyPfyUdCbXCJwdBq/K52vLb/JSX4B8ybRIqe0yU24Qf6dLT5mcjqYzskxTg+YcYSjjNLqxZN4C
+9bb2oyze5/5AXtjEm+ApMW2k6c92GdA9pynbsUNxXu4jKc/gKNfpIFmh1OdqXlMEsZUqVY0/RM0I
+V+M2SXiAsylZl6mdkGrvTlaVOTbhFS6/LOf4F79VcuOBYoye+Rk+eVNiZW++nPFC8WQpyVP34VRO
+5VQek6fCUphnH7IHNaofUe25P8RqwUfpdVnQ0ubscIk9R0y/O/ckKLQQHVHndQAa2QKOI+xAfniI
+e6AMQ8EdDvsanV4Tk/mcqfOHiXQfjp+AmPdhhbuUBBea//fCgi3ddZ5bWmZdcfdKJV65temyVJcd
+eKvxGhulFrSYF6l2FYNRTVl3eou9fdkRqp0qTqYZlXKbL12TnaRWu0N4SxF+lQ/x0RIsayE08vfB
+uKDdlXHezwLWzsjn0S3Z+eJ4CkPZzUaOxSqMvHFAYTNZCCx+Kcef1Yh9OmzTbchO9M0HbZ5d0mcy
+NXzhBQEd2fvt6BgkJs0fhVX7TgmLfUZTzcA49rh2xAvkd73CpV6Vsa2M6txvyXylB84xqInd3yJR
+d3L0nACaLJ6Z4WkQO5jucKQttnG7ycioa2Z2mPQlkNpenEPgl1zSmoV7K2ep7wrBTeVgQqbuUTLU
+368HZBq0jNdC9qcyF6srVSoTGc9+VLJ5T55rLRCrcDi6TaKuSN66gG2KD2MmFQrEqtjttag1dPzq
+zpTFgFf3ZC2fD95hdFbmezj8Mr+hbuVfu32JX33lyUBWTi2FUzmVU3lMvqulYIz534E/D+xaa1/U
+7xaAfwacB24Df9Vae2QkN/c/IczTE+CvW2u/8d2uUZaG6dhjGpb4B6L5Vl7p0lW4qgdBhaObEjAa
+OgHXvyUBxj1FWfaGHkfK87VcxNTmWKUHltQXrTvcmrCllXn3vnkbgC13TLm5P7/RJwg2msf+y/O0
+5DdzYUGDS7duk3XFH+xeE3ScXbd5nMaqDqcMIxlzJa+QH2jAsyq/H993OCj0XN+8ybQrgaPVnZye
+NrhcH4Qcritn4Fu3Hhnbw/soNEjohDHVoTzuqy8EJJs69ob43AfDKUlFS39nKVadedd4tLXY7nK9
+xpllKd11FYosimCogS9bqbO78y0AbpshxRsyt2ObUh4T6rx/xDErHZxA04KzHr6njNBquUR+SKYM
+3Iehgx3IvFm3QqAl2F4twPpikYXaEOUdjPEWZLeeFg44kp6+8/Y32FdavHg3JV+Uc7cUGbo4e4Zm
+phWkQYUFrTx9EHg4pca8NAjuThyWLss1lguXnbrc8/17u8z33+ntIX3EgvQTef55IyFvafflLMIO
+dW4Cj8lYrtGbuRzdlHHYVbEeOklAzYi1udXd5KWP/Q0AfuMfbNF95ucB+Fx0hc/uH77rXL+XnMR9
++D+Bfwj8k0e++7vAb1lrf9EY83f1v/8O8NPAZf3fJxEK+k9+twtYW5DNhrj9KrtXxBRt9wrsGTH9
+a2nCTPP/7qSg3ZASVlc58tx8k8FAHnLaduk7shibtRl774hJteVkzBTOfdhVN+JmH6t5/CfpfbCP
+oPoCFFp4UtalgAmATkDckmBcw5OFEtfrLJXirphBB6OR/Ly+gx8r4Mq+PPCw1WK5rsU/5QplKvl/
+18sJh6Igz/dSrmnxynvm/zMNHuYzem2Zz+ntmAsXRBm0NxUIpFKjroVCA1vg6uI37oSWJ8rtmYVV
+VlZlnEstuc+j/gDUnO8uVdl1RGkEO7skSnDT7cGuBjE13vaeAPnOLKHIJFrumYf4JVEs4/RNE0+L
+z864bYYV7ZJ0q0wdyXzUshbdhqyHwhVlaRa7lA8O9FxdOpf0hfSeYTqROWzNZuQKjBPpfDbLGQOF
+8esEAVbBWaI8ZlIoDqSWrk+WYTKRuRpeDKiPlcfTywhcWQPV6gKOoyXruayFZNjEaSkDWmOAr8Hh
+cviAimJJxosZUyPBdld7ZipZhPUk+/Ajm+f5licuw5/3a/yblV8H4JM7Mf948AS+MSdwH6y1vwd8
+u6r5LEIzD4/TzX8W+CdW5EsIr+TqE43oVE7lVP5U5XsNNC5ba+dQu9vAsn5eB+49ctyciv47YHkf
+paIPfR/HCWhtGC5ORWOunoUOShG+7LNcES25Pz1isSHDPppJoKbu1bGx7FbtRhOTisswmnokihfg
+RR0uKtX8aiHpn9GHu2x+Q7R2lgyZzk4GRmEc5yG7sOW44s8vPC4vixXywuJlzIZ83qiIXtwLh6wM
+xHoI6gOiddkRgmyCk8uxlZZ2g7LE+QMZ78raMmNFJQ7cNhc1B/q58bNEN6RmgXc07VQmPFpSbLXM
+u+lkdNSVOH8lgKSpn+W860mLkRETttfzyCoKmovBajb4YucZipqW9ub6pe+z0tLUXN1yNj4v59jo
+cO1tsRr2V97CuybX3hqLJTSZPgpd9lDKdoNQ3QOSBFfdAF/Lxz+05NO8IN2ncWEIdG4raY+4lLmd
+VG7g+rIzT0YyP7N7AVFTSXKaNc7q7n72rMdUzZFswyWryv21XVkjUzchFKOKjeU6F+/IPd0pimM4
+vFqsrmtuaCkQtbPvE19UC2raZRor6rStM8ll3UaFMom7CZVlsTD8Ykqg9+o269TqEuT0hyPcdW3e
+q+i54jarnjyH+60z2AUZ6Ds/+Wf5zDnZx7sfu8QvDH4fEHLXk8gHzj5Ya605bqN7ot8dU9HXotji
+FIy2XYbn5GG+sTNhfUNNTt+n25EJaXcc/Jty83db4ibU+y16RkufZ5Y9fUmjCPy5D1/OONxXSO0P
+aXfaJOLq5fMAbG69zolB3K19hOkF5hyfQQWswpOvnV1i5bJM79JITNnrCwHnDkXR7ZsZ7lh+mGLx
+lSty2hA3oTqyJKsy3lbUJtE2av/eCq93pJb9J1p7fHFFznHznXn24fHxz7MS40FJuSKu0ht7AX/u
+OTXHHVmYL56rELb12rdWuXVOXszzB022a0pr7sbsqyntOtqLEU/pzEFDwpwzbbnXQyLWqxL1/uLd
+I6pH8nmqZckTsnedZ5MUZFM1j31DovUJGzVl8mqs81MXpZz33As1KneFVPVa6wbLezKmOztT/FRM
+5r19iU9cKCrsa5v5xaLAVf7LRrVBHMkm4my16C2Igog0W5BWExaVpSmPt2nEch9LE5fDmbogQwXv
+uVBneyjzvboxwZmoEl5wiLoyR43xIm9rDGpVdd9kNsPpaeanmbCsQC2zcEZrqDAA1YyOp52bK1r6
+3Ktz2JLPVyubRJ+UYrd/b/PP8EtViUFdeafGlzrfHyr6nblboP/u6vdbwKOF1qdU9KdyKj9g8r1a
+Cr+G0Mz/Io/Tzf8a8F8YY34ZCTD2H3Ez3lMshlnhU4TgDFTTfmQZf6ZBllmVUKPVbloDCbiypGzI
+buWARBtuiv4DSk92q527h9zpyu7wjX99Gz8S06/6mgaLXl5jZSZmlvF9mL0fNuAjYpyHETNr8BRj
+oJ4FxNqAFc5SPEWNrqzLseeKVaqBaPZRvE4+E+umlV+gtLIzRR2pzEztAdWJBlS9IZEyUNszI55x
+BEDjxsqrxGeVAOT3f+94NnmkiqKcc2X6LqOBWE2LV7owUcp7DXbWsioRYoEtXY1xFWJsozY5RkRr
+GoOvFoItFADG8TgcSMDMrZzHNxoobri4CpbSaHaZKUxdsCiBSncypSjfBWOhUsFRq8mUQwJ1p8qK
+WDkr7RaEco7mdJ3GVbknb/ZxvJlkpWbNq8S3ZS9yc9ldsyI9xmE4nG7SOBTWaapTwlRM97KeUlO+
+xuN659ySHckza84C2m2lITw0uO7cmtC1MC4IuvJ3d+gy87SJzzd4iiEat1LWMlm33liZrZt1JmNx
+FZenZyDQOpRLl0m2ZH3Wi4sYzZJ4Rtw1u5Cymsl6KS8dcK7znwOw2VvkLzl/CYCd1ZCzgyd7zU+S
+kvynwI8BXWPMJsIy/YvAPzfG/CxwB/irevi/QtKR15GU5N84ySBKW5BkIxozl7cWZbG19xyKC7Ko
+oiCh1BZgjypuRR5S3JcFmHgx+UC+K1fbVLbl85RNgt8TczWLPaqJGDTluiz4+oPzlNoq66ZPAG5p
+H3YRWsce9waUoUtHO/xmKxFxVV6y0sh4KsMR2zV5w5K7exwpKUi8fwv3RQX3uCsL3guB6B25XrSA
+yRT228mYjb8MgP+FnMPdL+mQ5j764yb5MajozDBc1LjLXUP5EXlB1rWwKrjgsRrLy1EzExoK9DGt
+Vom1qGlcn9Le10i7dgbGI48dBQZdyu9hl2XB19I1xhfkvj9yzedaR44fFXLdu+4ulPMcxMMx5+MB
+VqHSZ1UD2l1oqzKvUV7Fb2tmoFunon0scaXkaFnOvbQ/pnxG3LiGe17mbfEQf1/b6NtgavISuq2Y
+ciaKw60cHHfN5vMXPbVM03lnq0dfX3RaTZKhvNThRNbT0RloDGScSWfCxkziEr1WSdNTFyM4ottX
+blJNo3t39xlpiGZx+i3S8y/I9/dS4kj+YKMdvFCUUKlxsqAcM1BXI0wrjLbEZXhu8mn+qCrKZH28
+yr9yT7jZqXxXpWCt/Q/f408/8S7HWuBvPdEITuVUTuWpkqeizBkccALK5ZTulkRm/fWErCdasuh4
+mFzrFNp93HTeEKJuwOSQdkW076i/hB1IKWqUtSgXxbTdv1USdpWh+L7siMWzB4y2ZUfxPODdA+Lf
+IcZxjnc5Yw2OauthkuGre8D9GaNArl0dyk40dqZ4d+TvR2XGzi2xJFKzx+UFvXgoEfTCaeLNOQDL
+DEch7k2lQ30kO9Cvb/hkR/NGqHkp67d1xGlzjVdJCZQdmbMDNu/K3F24Kr+r7lYpz8iOEpQLjBpq
+uezvos2FOHtVBo4W8mhz1WyWMIdU7NVcmlogFS8cEW/Jznbg7eAqhF63dlt+7wagGIe5fVi1kLmV
+h22H4wyj6NC7B0oAtHyb/vaceu+Q1YY8y6QsqSjOwigaY6fyfWqlJD6eQa6Z9dRv4mpzUeGHuJF8
+X4wdirG6RX1Fbc5SXK1lqS9Ad1ssgalbgMKcJZFYK627LsOuzM/+ocP6sriH+V04WJR7qmRdJqkA
+4ozvSKRxNN7E86RpbvxgD6cr1q2zBNOJYnWkUFTVNakr4JDXIUjEasoil0p2HoC9Sy3WNbs0iS2f
+eLJ+qKdDKZQ2Z5QckR3V+apyST741tt0zkrqiWiB2JMJTojxtZ12VsoDiqMFPMXQd/ePSKbysBqN
+Cmc0APGJhbtMRmL6ZkuKlOOC01ZXY5ZzUpQVa8vjjkrrGLJ5SstmbCqAS7d5xFoi49hvytinaRWr
+NfXm/oSDnox5dz+kpkVP1SUptlmq+BSr5wGoNCyZ+tnBaJdbIwnTtPki5facO/3dKedz5SkopiU9
+eU+Ybe6xcVE7RXfl783l5JhGfRK4TDTmEDVbx6hOkZNzMJBxLsUynlYrwR5q+zIRUUMWrol9wkVN
+2yYesaJotbRR1XFLynepasynR+TpPNZgjwlki0IU7L99fYpVvMeXL3rcG0hFZ9pJqHtyL362DbsS
+G3igKcJws3b8srk3Dilfkp4I4+dkCuuOM2WihMOOtrvPjM+chmKYZBRW3rBolDEeiTuaaRys34qI
+bkkPTra8SKxuycbClG5dwXC8PmPV43kgc7lclPzRW+Iqbg0zwooU3HlrM84Fcr3xMzEN7f8oNN7j
+jUdMdN3EB4dMOnJsd7zKO7GQ1K6/s8Bmdv875vn95LT34VRO5VQek6fCUihKGCaGQd6nfk0DXK9M
+MD0NBrkr2KHsAlnbwVO05qgqmtjJA2KFYMujrxIlErWf7R2QVmQXWMpiNjVQ42jPxO5On/Qd2QUz
+z7x37e13yCO9D6XFzOHOZw57NXUDBmO2DsRCWJyK7n3gLhMdyt+3iSi2dHcoL3Hrnmyh83x8xSvx
+E3GTsuEeZkmi4VlW4seyy3199w02P6bW1O+8/a4jzTUrU5oSTzHXwy7UNf+dteS7Rp7TO9IcffWA
+RIlsaoklTLTTtL+Ll0ne/0i3T2OmtNXCGJc+HMzLsR3CbXHpYpNi1bTfjtXtKspH+iAejrcoHMXG
+A0F9mVsKClIym/DV2xLgu/zNa3x0ZVOP/AQrh3LM/pWL2ESsqTj6GABeNsSrap9EtIRViLJsawej
+/Q6z7TG50gfYiRw79fpMzDxQt0hFXdZJnjOdyTjzObbGwRirAfHR/R5vXlbr7zBic6R0BdGIoCJd
+vA3FOZiuvEz15hcB6HlXsZviSvgf2+FA4ejq/mWmQ3Fz5v0gs6IgN/IOzHwPG34IgNv9lKNCslJv
+7w0YDMY8iZxaCqdyKqfymDwVloItS5LpgGxa8HZNtPLu9YgPvyzxhapzHwWbIRo3KHU3dp15QKaK
+uaGwXXFA0BYfarpYUE5kR0ifuUvjjuyKzbr4p7mpcC3Vst303X3ydx2vfbiDAZRKSNKvTNncE+vm
+6xfh7J7ssP0NsQIq04jDBbVybsRQSJDv/EaGr1BpWSAxkGAlx87vL7bMJrKbebMhO4U0vsRvDiiG
+78xH9a5jLcs5zVlBoj7pUT/izpKMozGQXf715W06Y6Uaa4xoJDK2o0YCmudOjCUyj6P/2EqHwVB8
+4GLQo1iUgOI0syRaMszh3jHytq8lzGlZUD7kpns4t/mMd8eFk++yNGFrLNbdv/xySHBV5vjDrSlb
+HQmadL7aIF3TFKaVrs3i6jnKROpU/M6E0pFYjO26zFJllTZ9zQXDRGsoSq9BL5Fr7+6N+VoiVfxZ
+DrmW2efKAo5jyDQwuFU1jDYVgHWjzuSOWF6Ee1yZ81+elWdT/a0eTqL1K5MvsHVVLMzi9o/yQz+p
+ltUswWi3aqJB7pAeua+vcOaQq+W57hVcP6upWuvwrc3b7zKf7y1Ph1KwkGcuuAnprqLlNgt6dYXn
+rl7EjGQC0zjDVZNp0hezKBoNj2Hbs/tVjhTKvDI8JF+UY+rbz3O4KialLWXhHuxsMSolA+AYQ/E9
+tk4rRgdZEbCmofoka5K0dGGGYg56ZUZ7qJHsS4aKL2akUzZYXtKioBW5Dz9fZJAoNBuWNNKahtVV
+1u9Il+Q3w+lxcRKz+ZgeLwgyCtPmeglG6yncpsdE4e0WunLdWtlgFumxdpFEg7m10hyjFbebHSaF
+zFclkhdlL6jRmkoBTf9CgqMMWaHrEGowsuL1uBfL+RZVYZc3Q47TFo+Jy6PFV98pllS7OTd7PX7d
+fgSAq7+1S96Sl23jpS12j6Q4qaEcm8GNd/A/IxBtxUGDoqrZh8LiRPIaVCoL5GrSB0tyru1ZSRTL
+XJWVgvKG3NNeaSmVWYo5iU5hj9dCmkAzlmd5MFpj0JV7OfAvs99T4JvX9O/uNr8zk3n1k30uviru
+UfTTX+Xe74orXDvnUa9qkLqpJEhem0jLqmfWI09Ea+wstahckzHfDu8Qu09GnnzqPpzKqZzKY/JU
+WAqYEuNMKKYurnY7ptsPKDui7XeLjE5Tdl23lVIciWk01qDdME840vReMBrSu63NJV7JmzdkV62l
+PlPljlhblh3zE5+6yu9+6esAlO8ep3v34VJiH7EWjolTFh3u9mUn+aGPnKU+J7LsSl7saPuAmiPa
+fBpvs9yT3eio0aemnAR3H0hKMkhc7sZiql7I1wiuKFhp3+f6SHamn1rd5a035NqH8560b9tcHUcr
+9GYubkVLwcczKuvy+YGCq648e5ZIy2hNUOWor5Wg04JSA7vL9QYTze/PDZQH9w4ocvmP/dGMM10l
+sHmpQVko1+frPpUN7Y78NWVU9hPydzEUDAXvj2xhUKgHqgsuz/+oNHR9/penrNfuAvDOrxxw5kVZ
+D7/9FbmPl1eavHFPGoY++8IrpOtyjefa63gvitvkMMYcaln4nMQy8ZkqhkI59igUVXpp2uctxYgo
+NG1qHChL5XLoFuyMxAW78mKLRbUaw4trbL8hwdEdJf7h+pjpVOb+rc2MeCRzdPufHPKfiSFEOtnk
+R88/J3PXEZfYyQJmaPPYpIer+BVReYHDo38HwLPXdvnCwc77zOd3ylOhFIx18G2F0h1jEu0jiHr4
+vyqTV/mZNqOu5KOrJiLWVVFdlWOLo4Tclwkusyr1NTW7e33WtKstnA5YU1SjqS8P6GhnxvNvywO4
+4boUJ+Q7dIxDcewP22M053Arod4VJbT7FVh+SZRB5w0Zb/uMoaadc/HCFVo1ieS3I0tlRV7OM+O5
+8TbheSukIXlYxdG3MM0KzkVirr+60cKZyf2Zb31ZR/OoP26pzJGBvRSTanlwfUL4BxLJjp6VfoFB
+OsPfkGs0A0vQFVciGmaMdA6jUUjelHNUJqJ416ohtZGYw0txhGlosc04I3lDvh9Zj/vCfcqrSrBb
+HO2/R0/qQ0aq93IfyJRUduxw8X8WJXruEzC4LWtk6aMrXLgvnZSND2nx0kqHtZqMud51WOiIWc5K
+HW+qczvwKB2ZL30vGc165DdFua1V4PxM5mXb9TBKruM4Os6yxNXP3l7OSk1e9PyN+1Q/JS/slS/f
+wyh1QeVQ1+bVD9P+d6I0L69us67oVh+vWTYuyMaxfO4CLMrG4es7kpmSUAurZvECUy3Rvnf7Hv6e
+xDC+cljlCRHeT92HUzmVU3lcngpLwTVQc11M5pE6stONJh5fbIg52LlZ5eVYVHe3v4qnnWqkCkbR
+zgj35XNWG1PViq+oktJSRN2t4T0Wa6Ll95WJutHrc5iLNnfLk5cpuMal1B3ZIhiTAGM3476yFtN7
+wK2b8nnlnFx39UEDtyEWTXRQwWkoUYm3hGmqKTmWYJjX+AZeLlV5YaeHTcV9cPJ7FPui+p8xb3Pu
+gUScb2oroyRC5jusQ00bcYq8JDeKrTB1uaF0ZF5fdpoiukt2KNH79rJPQ8uE/ZqlptcumWBypWhX
+qLiFcZPczusRQvDkvOlkxH4mZuvkRp+v7cs4R0cy95kt39VNcI1D/i5ZiUfFajD1zuCQ11fE0in3
+z/AxzajseT9J5aJ0Sa5FMp+tD/WZ7P0UANXVP8JtiMvgdXpkmayXotJnti+vxNBIRiXHo9/XzEBu
+mAf73UaAr1HF1D6swNREFDMnZTuVdRpnW7zxjjzfxgXLmQOxaNodpQ0c1KitKdt6eZ6Xz8uxY++n
+aawpxFxzDU8JYxwN9rrFDtlUK12bRxjFfQgWx2zpkD7cfJs/ur3xvvP57WJOTIDyJyitZtP++5/6
+NLF5GMlvBobeUNNp+ZSJpnoKM6KnLdVVpWOazCrE+qIPyoiqFtb0kwBnLAtzKwuOy1LHGqm3Nn0k
+NnDyefg7f+tvYhTZp02Jo/iBTeuRK6BF/SDGNsREtwpyahtQG2mbbmVAVdOQTuxRTbUQq6pgMVEH
+v6PnTc8Rn5UFX1l6iaCmv7NLmDmNOtpm52bHqC/FbJe//nN/D4B8avFU4TqhT0WViKstvbWsRlzX
+IvlZgKvltcVRB99eA+DGqEZxKIp6cCgLfjC9wdsjVTzZEYPZfJ+ZkiqwqX2PEuyHYo75OL8d//LJ
+RM5R91ycQJ5PV2NUmRvTUmVyRIir5d8JLlVV8FMvxHPkmDngbT0ylI7c68bKAn5N5vnS2jL/6P8S
+xWPLz3+P4/2+y9etta98t4NO3YdTOZVTeUyeHvfBdzD9BtWLYs5mOxOClnZJbu0d99Mz9Qk72hev
+hTDOikd7KibVdjOmeSDfD1Ys3S059tDfI9OA3/gxZOEn35XiPASlMm85VbK6ugmzFn0trDobBcwa
+EmjsHkngaG+px/oc0GOlQXVPCpVGK4e01VLIW8pbeLRKf1ki59U8xOlqX8mFSQAAIABJREFUByMB
+hScWiEsbq5aCU8i1UucBvqtAIeEYO5nbswFFVaPkeUGsXZWFYjFu1H3iJSVCGS2w25brLa4FVOwV
+ufZ+yjsHYuauPqfAKl+qUGnKPe9tGfCV1CQBa05aEGZPTtn3fjKvmi4dfK2/WBxqV2OrxpmxXCNv
+Qrwr3+/FOTWFVE8aHuGe7JODplqmhxnlGXEv0m3Dwkf1+R0Mf5AshCeSU0vhVE7lVB6Tp8NSsIaG
+9XBWYy4VslNOL4V498TXu9tZoav497OF1jGhyqwt33UdCM/Jdx8+NAyelVoAfzAg/oSCn37jLb60
+Jsd/+f5tADKTPpJaPLlEcYBjxI/uNhNShfmKu+CPZVeptjIaFUU5Pi9WxZnBOvGC+KxBtIC3Kueo
+z7rYUAKeUSAWg+kYlvoSkDKtlMCTz4VT4E0lSGa9BONL6qlU1mnfLFLOkWRthdCXeRlmKV0NOtoo
+pKH59rSQHbXe8OksivXQXeuwEGhKcjqjvSYNWNXfvMW5F+V6w9uyn/if9Ii/LGXHd9suryuMmRck
+7KfzYOz3J27l6h73UlznkpU5WnxBGsYmaUo8R9c+OGTzrBzb6PdJ2jLmytSS1GWNeFp7ETdnBH35
+buV8i3WFtq7Unnzd/KDIU6EUSiClpDUJGWkPfpw3GC1JtHWhV0XTx2xUfCaZmLAbHQVIyR1adVn8
+SbWkrsElpxbiaY376toKzyEP/+aOBIi28+TkjZGPSO4UUGo+23YJMq1xNwmOujxEMQ1HrufOMxJn
+KoRINsSzCYFCpWUrKe5QFEiuBT/hNCHtiJvgJWfIJlLIVJ0NKVa1zyO9TKEdc16mGYKKh9HahKGp
+ks5rL/KIgRZONVNLqWZ+Q0ubA79OU/EpqnZMpAU7nlsjP5JrV4IZgWISVBUye7P0aXeU1WoUsjAR
+d2Y3+SABw+9N5lD7ZbtNsK5l3C/IPS/fb+C3RSnsRQ0aGuS9u3eLYiDPZDMb0E5k7RxYUdKVtMZ+
+SxRuNYGgKm5TfbbwfbijPx05dR9O5VRO5TF5KiwFi2VWZhx1jojvy47fW/Coj2QnHbsjapqSvE8N
+raSlV5dd9dndkGF1TsgRcl9TeWfdkr2m7ITPlHUevCCm+YtD0fIHg4Ls8AmxqoB45uMvKQ+kM8Eq
+E3M49pgpjFk0zZjVlaui5ut95kSZXi/yCJSk0XMcTFXBOB05l9OKj0uUCcAZSTlv0ahixjJHRcXF
+6StobEN2QSdzKH1tukoGVJTD0UYj4kRpzJwqXbV+C20YM1OHnkKsdeOYTF2QRmFIFEotOBvSCOU6
+7Y+IFTCpdanVlK8xOGT8ddlnZrMxs3tiVaR59iduNBjH0KpKEHDa9llV3sh3lEzm0/U697QDdxl4
+q5Sq17OjiBs1sW4W9w2HFUWR0vqNXjujrghas6Uj8n1ZQzvRE5YJ/gDJU6EUSmBaOpi+pa+B84gS
+rZPBuDFeKYs3NyOIpPQzRl6O2lqGr9DqtabDukbwl9tVGgp95dg6zyvh5xUlOokTj//vK+JKpPbk
+nWSmYbCBDK5ZqTJrapGKVxKoAljISuySvPQVZIEWYYavL7ptlDhaZEVY4uoLWyhkmpvkWF/dkh7k
+ivHn5FXKiixor1dgV7SNXLMaNhriaBksfkrhquuSVYlU8VRLiEJlG1L/ac0L8GsKChKHpDr3kZfj
+15XcNWnghaLU4lTiDG5tH9sSRTZbafHcHVlSr7cO+PyOKJM3uEeZnayE/HsVJ66zqnGQlxbP4SgR
+zZlQYfY7M86GChWXZCyH8vKHU8uytokPQhe08/EgkpqWvO9xqGQx1QcedkVdqe3wT/R+/jTl1H04
+lVM5lcfke6Wi/++Bv4DgH98A/oa1EpkxxvwC8LNI1fDPW2v/zXe7hmMNYekxnAxYGsiuOquNiLVm
+dNwf4oRqCfgVutGcEVn+bRRtWJZdol0GNCPZNatZghfIOZxuyYWe1AXYj8vO8NHqMpN9IVH517e2
+HuEheH9JCHCHstMkocU4yh7tlgTa0565M3x/3lwv5/WLJjaXndYYDwrd/csl7BzeLFc4sKLEUdyI
+0h1jE/l77uxhUmmUKmyBKZSt2ogJbOwKOPP+/gijjNc2m1LxNQga+viBcjQyd0UMcVUrE8mpzOlB
+yymOEYsm8EeEpYKFKAJy6HRgQ9yEC1mT4KMynkp9n7VESpv/4dsuBw+kpHuWPQLK+scg80rIShCQ
+Ku/DZHBIsaiWk3pgsV+lohwX/kLOSNmho1afdCBzFzkVZvvisgbIscPwiEy7UgfeIfkdddMqTwZx
+9oMk3ysV/W8Av2CtzY0x/x3wC8DfMcZcBf4a8AKwBvymMeaKtfZ937bSWBInw0vrHDbENF5M6kyR
+hxX7BSOlnW+lPqki6i4gC3Cw7NJSglZnwSPQlzG3dVDkIef/b+/MYyzLr/r++d39bfWW2rt6nX3G
+4/GCMWMbAoqDbBBrlDhGjjBLQiIRQVAkgmNlkyAKSkJCxJKgABGRAQOBYEyMF4yDA4yH8TbM1jPT
+e3V1Vdfylnrv3XfXX/4459VMY49neujqbinvSKN5feu9+1vu757fOed3zvfrQ0tfliSW7rxm7DC3
+JoriT9Z3GCSvLL7ge2MqykI1qbZpKD9I4PbwmnI9r4ZURppNE0gku3ANdOTF83IfQllY1u2BUqCj
+R7Im2qPUY0/ruDiZxhrCLqWnpDbeAo4qHGdaJeoNMUYVQZkQeKqEnCoTNakb1sU3ghDVQk8tajW8
+PVW2ixllpCnm1QhXqxKZ8wkVwzDXNqJ9h0Lh6ZfikEJ/503qtNZEqX0nbf5YU9bPDCSXf1y8cqSr
+VyJz44x8qAAplWNkilrV3pM53FoJ6VjxicoophHLsxlEFr8v66XatCx3RRlMKjKf/Z7LxNGU7z3D
+fkfu61++Fmjn8MVws05zXhUVvbX2Y/ag4JxHEM5IECr6X7fWJtbacwhT1JtvYH9nMpOZHLLciEDj
+9wEf1M9riJKYypSK/iuKKQUJ2Yt2md+S3cVrGpyREpnYwQF1+X49pKNn+ugJQGVkUDYyvFFA3hIt
+X0ur5HOq97oe1UCskNq8Vvqt3E8DSUH+OxH88iek6D8vvzKFXDWt0GlL23VSjFGIMqKDNN8gC0gr
+SnXnivVgihKriMNFw8PRXdpMHKzmLFCZMtK0DjAUyiI7oK40jVXcadFR4OBosRXLJwFxxdDEJLdI
+cTWdudouaSu9W+4toFnM2Hn5MI4rRMrN0LQVSCbaZ5+8PkWrruKVWqmH9M20oBzKOLwowY4UVmyh
+QrMhLsg7TvVYrcqO/cmrshP/yef3yHKxViwlRoFqyvJ6d0MteLI5fiJW1o6/R+eKLLvomPJw7EK8
+IOOv74Qk6m7N9TOsBn/LnuFoTeb/6nBaaXuGZHPKGzqm1PyM1HcxFXVHlY6QV5EI9/LDU8wGa7H6
++SAZ7JqK2BsnfyWlYIx5PwIK+IFX8dsfAH4AoBJGTJwcf9yg1CM9M4asooxOfZ9GINf3+mOGTTFd
+fT2yXOvs4ihOXhoY7EHMwYD6zo7v4LvygpR2mrjj4C0px9/aEvNa+biVfGWl0Kg7uAodv+o3GWke
+ix8HTDRBKkxyigPEHgUrdS2BQodnXoijiyh1K4QTXZiK1uTEGUUgL5CTZpT6YtLvkwby3TCewyqC
+EokeF0QFqCuF06WhzFmjrMFcTTpapCmlr+NWgNIT+QRXuSTLxMdqtqjLWFhzADMOyavy4gWJHnXW
+U5zRNKbi4ih2o9v28HR5DeoBr79bOTaXxRS/eibluf40jjJBq89Juc5TCv3d2DHk6mJWBy7mPi1n
+HygQz4mGFGQA+/WYciB9HtcMnh451qoexVjxKAuBZG9ecnjclWeaDHOcQKsoxyWe4lHeWEfohUE5
+BoyjLmSZYzQ7yymnpDCl8preWHnVSsEY8z1IAPLt9oVqlldMRW+t/QXgFwBac81bX789k5nMBHiV
+SsEY807gR4Gvt9a+ODr3IeBXjTE/hQQa7wYefbn7OdYQWZ887BNmkqQTzr8AXRbPeXiaflpbqVBT
+zMBqW0MZe4b6ikJceQmuJ2arO0wwVQXQ8HoHUXRHqcFiE+Nq1P/UZIWO1jDsuCOK8UsTS2ZBSMXK
+1BVZn0qhAC8mJ1Kk6DwZUyqTda6Iu3lQgS3FOsiGB9Bp7lyVdF+3PGXBtkUPx9dKxt2cUhGVy3QI
+7t1yv50U54ju/vkUH6ENU2xIjuHr7m/KkFAtIK9TJ9yW4GdWV6j2ckyeiXvVs/vUJgoFN8wxmgqd
+2zGVcl7vLbtnYSsUrvJL+qv4+4IHMR5NSGKZW48KfiDjWpmXvn/1vRnOloLejPbJhrL7bfevD08Q
+dTcKIB4rxVrLp+jJ551jMj93xUPQE6xgkhFrELcW++SKLZFkMWWkCOI9mfsLKwHFszr3ppzGrcFY
+aneJ69k/p/vg5HGutRuuH6sDzAFJjuN4GFfrR0xIqUn5B1w5h4SF8mqp6N8HhMDH9UjoEWvtP7TW
+PmmM+Q3gKcSt+MGXO3mYyUxmcnvJq6Wi/8Wv8P2fAH7iejphjMXxM6p5h6It2s8d5QdEyrWNMefV
+j86eTSju1crHJ4Rd+vIDDe56Unawu18zoKJks0W0QjJSv7Vq6T0nAaGBZvkl3QSGShuXXOV4XTT/
+le5lel+2p7Lb1bwxDQV/zVvz5LoDN9wh6WTKCByS7ulurFwCO2dS8iW5x9LVGnPzsqtWhmMK3dFd
+RVf2G7uwKb9LmxXKXc3irG4yunhJP9+Ft6PBU93xCzPGNQJaWtqYlsLNOU6DcUWCuPFOj5W2wq1p
+H8/4GdFFrbRcTIg0qLW4WmPpqgQPw6h3gN+QRGKN5aP4AKchvrDNpKoo0H0XRwOJ3XRCRdGM1/oy
+vt3jJyCUAOzp/iXqekT4seu1FHTX9IuSRLMtu1sddu+QgOjaSJ7NxnyIlynhrZtjL8v19bkh/hRz
+IhwR9WRtJTXpe/NsTKH7WlGWLzDdlfDAuhyvfqau9IZplazsa7ecv2QfvLJd3WBwFAk4IsJocZ8/
+dhg5uq41nlMe0n57W6Q5Yw1OFuDXd2gMxBTz50NSfTP9Wkmg1NrVjsVVJqOwIy+0fXaCe0JRjYs1
+AoXP8k2B0fhbdj4EPUP3lWSlrC/hab7Bm9/YYnNbYMca6QL9LXm41sYYZfadojZHzDEXyOKuOjmZ
+okv7ZYvCU0afwMOm8qIOxxIwzOMrbJ6W+xaNACdY1PEFWA2C5b62uz+PURclK3ZxtPKx9O8imBPF
+M0lbGC0vr3y1mPuuNdhpf3EJWjKfd821aSuV+SjtUb+kVZ5aArxfbRHri1kWAdFA5mrY9ahNCWqC
+FQKjcxvISzfyRmQbCobjjyhzmSQ3mMPVBKC5aEKouSF5W757DxUWjyqQy06NvasaenqC65KpBV2W
+KbFCxI1X9onXZZNwHpR2m4UhddWd2wspAnmRvczBNGQ9VQuP9lGBm9t6VpTmqO1g9zXQ6hQYjYga
+x7L8DwTq7uGhbE5PfPRTuJcVg7JYpoZsQkPjYjUvw9qXyjfQDSdwqbRl3fiRxdd175ZDYjuFrFMX
+JTNQXh/RyyuRWZrzTGYyk2vk9rAUHLCRxYnbVFdFG1adKqOTSmN29i6Orqn7cLnBeEE5ES+KmTxf
+HxLnzwNQ9A1FXc6ow0qfcVf0nudeZXRZzOfGypT6rA2e7MzPj9e4/6iY81d259ja0Qq/zOBMMUt0
+uipega2pZi/qxHMSdHNGlolWGka2pEg0CDYWk+fs9jyhgvCXzvbBWX/MnfgKsjLem+rpM3SVB7PT
+KUm0gMcfXOGCclAuJ5/H+QY99uwJmEi22KOSC01d4e1xRDPzaPicasoOdPFCl8YR2Qn3FJtipRJh
+NSg7ynqc35PvHjcjRkpK00pqZG3hS/DUUuhfHuKP5Pju8u4CC3eoz2d88GVM8ahBQ6nLQqX3s2nA
+lrJxN9mlUEvoemVqxeWli9bEkXdjgjXFiejKw6uvVUhGMldp1GWoVkVrLSLQ7MxstWRzSwKTzXuV
+z/ERq8R7kJXmgOOhKOHh54Vc5mdqXwDgbWmdj6nbdbwyZD2WX4ZOTqbs3y914OpMg4dejXs0RbsT
+e6RaEnzmqktHj4bbY5n7p+zeC4HPGyi3hVKwFsrEEPtjyrGYfdUTAe5l8RHXWw7xWVlgg/oe0Z8q
+jPiSgoLszJOckMXmjscY9RnKnRFuR1GaLu6TaEnx4IKYqoPms7Q3ZQr29uuMx+JW8OAKjYuy0Pte
+eEDw0VD0JxsFVKaQ626Mq1T0aZww0lyIYW+XbU2rvvyEEJJs7Dt0NEfCq45pWlFSUc2h0ZOXc9xU
+N2HLQ9HGGV/oM2yJq7TmBMShzNH2zhxBIQpg9FFZoGvvfjO5uk9OsUCk6MPGXcYdyLxV76jDOTFz
+J1ZM/I31i6Bl5rvPbZBoUtfKoiFWfsw0NITKQclAXhrbrrC9J3MfBz6XnxHlPKrEVHqaK+C1qEwp
+6Bfk2VhnjKvw5BXjYrxXmTY8TXZyLb5a1X69pLGv7FRrosQiC4EyZHU3crr6850Lm8TheZm7Cw67
+rlamXlAGreN13D3ZIFzHxdXcEuNY0q+R5/fw6P0A1NY/zv1HZF4G50a0PJnb/ngLO9X15ZdmNRhj
+qCp2/J3zq6x0ZG01m6tsKmFxMSnIhkKOtO5q6voNThWfysx9mMlMZnKN3BaWgiS65njjiJ7yDVR2
+eyR1xQ3YTunrdTvpECsbtac72LiyzvaGIA4nYUHYkqhwFDQwIyV7WW3iaWFPzxX3IRtbYk8DY9GE
+jiM78Gv7V7lYETNyUsa0NUDZdOXvQZlialoOUjtCMpRdqV6FXHeCzLqY6Tm91vTXQh8v1ZTYQY1y
+WkXZHmOainWwpfnHjYDWlOauXMBXM9/PekyGfyI/2z7Gxkc/BMDJc+IyfZYneWv+PQBcZoC3LzvN
+aOEpslwwEPZ3N1lRhuZOqdWAgwrDvvTdd1fw2tKfSX6VWN001jLSk1L5aCKtBv3caTxX7kV1wr4v
+c2tHNQpXdtiyzMnVO4g8sRQmccp0ax/WC7yJAklcr+hxQMWJ8MJMuzGPUVyL4woVV6/PHfA12naF
+6o6C9jTr+F2xrLrBEG8kz3q3KpZi6+zogJymLIsD96G0hocfEZr7z3zdpwH49jvfzocekfbe9eAS
+/+dZWS+2nJDqWhjjHERHS6Ywfg5rFVkjb7hnjbcuyj0uxyXNFZn7djPi8Quai6JW2jNe+iKqwxuX
+s3B7KAULTmmgPqS6q0Syd9ZoKbhNdL/LyXUxZ/fmXIKxmJ29ilB2t5MTBziBlVMrBKUoDb/epKGR
+ehsfxU3kQR8PZRFnTsj03PNE3GN4TEzj+sTlmVD8+aPhZe44Id8/NpTfh36dqiOL3ytTWlo6Hfod
+bFUVQdChp2XgNQXpmB/uE1i5b+ikLC/KWD0zIXLFZCxW5CH7Xo1qKp9N6h1Q0deqd3JPKPddT+/n
+iaFEy59/7d8G4LXjXdLp8V++w+kjcnR4KlqiWpMX4a5hHV/dNKuALdTuZb8vLsrkKBRa4t2qPUSr
+oseazTXMtixI9JShs+gyykUhrRU9Kpm6F82UYqyK1e0yp2XLuRLhmiigrglpSdmBhsQtrld8LZ+v
+1hzqviZqzRmaRvqRBAqNX2QHCWALVChXRGEt9iuMjiiy1OgIti4nUM6+3Hf93gHmC6I0PCchnOZj
+OwVn/+a3AfCtGkfY9x7h71VlXVx6+s2862uFZeq5/F7OXRAXcn27hjOSdZuoy1T1Yl57SvhB19YK
+Fo7KBucXfUY9Sea7e7LHsiOEy1d8cSO6n2uxcfVxALL0K6fmX4/M3IeZzGQm18htYSmUQGxLKnsV
+upIFS9LbZ2VOTOYo9zh6XMk9GiELStt+uiI74vF+Tr+hwaDcYjWo5SUOmdKiVcYOqQbSqo7cK53f
+xd8Tze4s+XSCKX10lXdsi6n220mVxfOyy40amvBjMqziFwR2iYEmutRSH8eVPjUin0BxSpYuSD7C
+laWLNPtidYzDPSpK9ZY1UkI1n4c1GUc4qJEvKWxc11AsS2Aw3A3YKOR6Z/NPOfbWzwHQfey3APg3
+r/00Hzz3HwD48zvPEiWaJuu6VBSefNu9ykk9JBhGmn4cVMnukw776/NstWRnmx+1SRV8pTop6Wqg
+NBrK78bDCZ1UrIrY8VmeV5CcBgQ6t5vE1GNpsK9VoK5n2ddU5KAcMh5efxjdYIgCzZuoLtBy9bTK
+lgxrYiHYkZrciwXudByFod5Vl7AzR6yB4qgScUlRmhePKnP55QquK3NfTARiHyAvoPvI5wF4LBIL
+8lt2HT7aFbfyR05s8WQq1u27Tgb07hPLbPDoCT7ckKDwXdua/u52uU9dsCteiKccqZVySCsSC/hs
+GfLwA2LRpLIcsXmH3/mUcGmSZsCNqdK8LZQCFsrcMqDPwlU99jtuKfc0J33Sx6srpmAzolqVl+KY
+gpxGfsxYAUTKrEfUf0hua1yiVfGjs/0edcQUc6qyGF2vSaH8idVsmVQTh2r1e2nX5YTiDWGDeWUI
+IpeFkvghjpYI5/4AV+HCk0mPSibXizxDaRYoNB7StosY5bw0fkh6VUzx0F+iUECZsKUPvhxQSaS2
+ww+GqJWMmc9ZK98CwPbac3Tu/UcA/OpPia91z7v+Jb9XFRflzXseZ+pyGlAbdMj12DNsLjEcyuJt
+DkVhWd8QTRScpRLTTKQs2Ng+ppQXJb5ymUAr4eOuJi+trdB/8ozca/5+ykSBT2sepVEmK6dNafXY
+VrkgJv0BdS0dHwYRluvHPLRAobAeY1uQ60lE3xkRjLX0W7k3/DLA9+Q5TpzL+A15CefSAreiZfDx
+mK4az0M9+olP1ag+q2C0QQ1XaxtcCp7Xl3O0LvSMF4fnuPMNbwfgM1c3OdKSueqNEmotOalof03K
+dw3eJn1uCRrVlfIU6UTcp3ZxJzsj6c9keZH0nHynEt6Po5mz9aY8mzcunucjTVkjSfz8DSvdnrkP
+M5nJTK6R28JSsEagysK4RV8pzuf3S7K67LALbki/o2AZSUimdO61rux8e62AwaaY8NuLHt6WgKXY
+E6uY82Iye5GlNBJEMk1Jay2ti1EylazskupOM9o9R00Ti8r7Y/Ku1rSrWWeyffJIcvTt/CLZjqYa
++yWFIzulW3cxhQZNNaXW3ZswDtT83B+SaNsmMdimYids6U4UOZSOtFHMV3BycS+cYpfR5JMA1B8/
+xnr/lwA4sS5z9Zu9MafSfw7A98aX+Fd9TbVdmBCuSk5DvB0z72kSkQLKmHaOOwV9iboEWjFZeA6Z
+Bli9FZd0oIFbR6wc53QV01Jadya4Swonl6xATU6BvF7K0Ggehu7s/cIQa+R8z52Qa/D0ukWzfo5P
+Kux2pE/RqMlZdcOWnpNg3/pXHaF1Xsa31coJe2IJ7h3tUNsVS2hQ2aCu2BcjrX4pTg+ZHKRSZ+TT
+Z5a7dP+vnAJdqn8KAPfh+3j6CzKm7z0aMDUwl9fm6GiCVFBucUQDjJsN5UTdnjBeFFdjtRdRzMk4
+2ntr7CyL5bkcDqgp7p9byHfnl3yOa1r90zjXi0TxkjKzFGYyk5lcI7eFpWCsxc8LTG1Ioyeacbzk
+0FJkZNMqCLe1GGR5SKj+fKa7hH9h76BirOcs4myJlvcbXcK67PJZWccrxRcrFbHJCXOsFd/SywuK
+SHRtbbjAxl2i5l+XLVN7k+jO4KIEpyaDhEKPRdNygFtqpqNt4+ruQGZxNLW3zBTRqFrgDOSam4HV
+YqwszfHTKfGL1u7jYpTPMN+LCRoaMIzmaeQSP3n6jhbnu/cC8Pvf8C4Alkaf4s/eInGU962vk1yU
+tut2gbIrwcOqUyXbVwKXRUVQii1o6rLjtXAKsRRMXhJOq44mCzAW6yWK5Jq/tMFQA7/9KIaeBvOi
+LWyqbM7l6CDle6DIU2kSYqdYAWkVi+ZnXIcYYJoAkYdXcRSkdeL32XharLT1k2Kt1J6wFHPif09i
+yDJ5ppUzI8KOrB133KRALK7LiPWwN3eOXDMTQzfFU6yKyCv4opJ0JJsSDzr/G5+n2ZRrf5DdyXtP
+yfW02cA2pI2weYK4kOvNHbm2V9vDG8ua3p1cYG4g7e1ULjDRVOm+9QkcraRVro9geIRTa/Icn9nq
+QTaFhbvuqbxGbgulgBF4dCepM2gqcenYMmzpycF2STKnyUJ7OcW8vpz7YqrmjQrpBTEXz+w9zwWx
+dvHGJXN3ykvYarrEngK4tOUFTJOIUM39/WGVUCG4skqXO4byYB7xnmfxM/JgYqN/D9YYT2scyhpD
+TcKpMCIdy2Ksk5Lqi0MsD6s/DnELeRGGwwnZ5Jx8NjWOdRVXsSH9adiUiS7iai1FUdMIh2PO9UTp
+1Scfwbpiwi5elJf4yV/5BO/4FYlI/9v7R/yQdx8ABQGJsh7t2k3u11z8/VxM/9UoJl9VXLnhNllN
+0ZrTElsXJVqsT5goN6d/SXEunYxOV/Ib+p0GJpDFnTYcvELaGFuPINHEYl+0pue7JFqJWUsGZOmr
+S+J3leCmoEWoAd9wtEe6InO/+bw8u7XXHWVVN45hZKn0NJuq6ZPrW1T1HIaaCt9elGfWfb4kCBXi
+vRfT8hUFOisZnP8sAHFf+r4ZOJQXJG/kWGeHj2xL8PBdyd04XyNrL+tXGej9mhWZqyW6bGzItefH
+E45k0uco2GNe8TjLEw5RVcmANdGtNfLoaeVn4KVkN6gOYuY+zGQmM7lGbg9LwQK5S+r2WZzI0Q0L
+I+YTTWNuJuQ78jm7IyUaaKWapg8Xewl5KKZV9PhTrNfFTTi953HXkgKQ5gmmI99Jz4sZXXYKEt0x
+J06VWOvm7dFVNrZFc18o3ky6Lxp/kigv5YpLuKsZlNGQXIOE6bhACxiJ+xPippK9DGUXT4uQZCDt
+XUpLnEty30FwL75SnjVOKXBGkWPrCgaxGeMe0XtYFzeQ1NeNcJepUFL7AAAOHklEQVTgW/8FAHu/
+KwE1/5u/ng9+Vvt5/lGGr1dcgO02jVAsnbRt6F8Si8Q5KU0M4hhXrbGaLXD0+NY1E4qWnLFPNh8j
+GUrmnT/StPPONkld+lxWjpD1NQ21KBhOdIdNoapkNpkGNuPYkigDt+N4jMvrL4iyWIxCsPWWO8wp
+QU8cWZqXpR9d2YzZ2NwhDDUo2fO5qsVtSxMPq4FgP0+YUzDWeCS7snn9Uar/+7SMv3aEJFXimCBh
+d1/dH7X+ynGO0UrNzd0uzy7JOnzmyohoX3634I8JHZ3DKVRepUm0JsHxxf4DNPdlfdtmcEBmU2ne
+hRdrha2uf3/Spa74FLa0vFAm+lcDX7ktlII1UHg57qhFf0FLhCctdivyIgSDgkuRRGSDSwuUpzS3
+YKx4hqt13L9Qf7JWp9ZWOvsoYxyISZwv7JNpiW8wp+fA3YxQCUXHvo831mSSjR5LfU2KedMGzhfV
+5EfSS9PRGl0lbwkbbfKhKqzwqpQMA2nkgrIhZa4+xHGPwbzcq/bFCjvK3tTeW6d4nfTTjiXFNT+6
+TTEWE9Y0c5wpL61N2PIl96D5WJPl/m8A8G5H5uLn7jzD2mPvAODz3Ud5z5WH5X7zA9bnpGLw8Wc3
+qEznKBa3K7A5jVj6mbsJni9oJ3GwgnNZIvVOEOHlfyrzdURiMd1+A29J0XAujhgfl3wEnzrjQtJ5
+86FPfyTXM6t5GJOUQnM2Bm6C676a0mlDogk7q5lhHMpLs1gsMa7K2lkLREvXKwnWPwlAsjCilU/h
+94eMYlG+O9GEfSUALhXhp/m5DbKD57iNZrQzKSw2nbJEveDET8GQUi/m/JbM26MrLif2JPazvDSg
+XlcSID1RW3raUrTUZdjZp3ybPMu9c/fgH30SgGJyFbso30kRxdrP9uhekkQom6avmOHs5WTmPsxk
+JjO5Rm4LS8GUEKQObmNINBGTMltbpKY7qefs4/d0F+uUmES+M9TeV/cmrCwqBFnrBHFDNP9Kp4Vz
+RCvR9lv0rOyK8aZyLDQKfD2bj5IREyVfSbIB5ck3AHDPjkf8gOwaq18Qcz/pW9xQcxq6A7xIThEc
+OjgKhOHFJbFu78aT3WqcbZFd0nN+d0CgkXqbJbgXFAzl9WLCm26FoWawxfvZAQ5B2JnniLo5Ty7V
+Ob0kO/bp9/59AL7WfYbePxPz9Due/gLDpyQ6fbTfxrWycz9USwliuR40pY0saTCeSB+8Iys4Z2Wn
+deZCwqrsxqPyAcLhNHNUdsHafT7x4/I89qIJVinYjDOiqsjPgbcHynI90SU3144olNvSGUUMlQ7+
++sTFr6r53K5xMpHnUO1ErOlZfnyX+A9fFa0xXlUAlFGLS8qFyajAq2hw129RceXzVi7zunN8iBfL
+fHcqHi1NCw/Hu3zx9BTI/Et3aFtAQ62OS6lhW08GVlbvpjitRWi+rKG9yiXOnNEcio0dqusyt/sP
+pdx5Vt6BSbOPW8hYqjVdv7WTPFiTef1zE4GZMvy84gn8snJbKAUcKyQm4zZmXl7uMIkZ6vGOv+9S
+b8sEOzQYK4jrYFf+36o0KVx5yCcnTRIFTw1syKUNrWycNFl3tEpSqce91SaBwr73M8uClvoWiy6N
+ZUn06XqP8bqzolj+qCEmrulNuKisSPfkDr2qmIMruaVM5LutMqNRmZrKykt4tWRPk2MGlzKSWGIb
+4y3L8Z7c4ymN5N89fyfPBTKmN1QWaWsln9uvclrH8WD1KnN3iEvz3dvSt/f4v8+Pf1x+95Nrd/Id
+Dfl8uR/wGiWM+ewg5siyfD53WfzpNzY7tJYVAj2LGeoJB+NtunqEWxbneOJjcsQXNuV5PPLJhAdO
+COrTxbLgjcuKbvXAEk4mY8rHVVpaXxCo2+V32gcoUwu1GHf/+qv8jGOpnhKzfGWpRUMPOFxnxOKy
+pHrnWvviNDsUego0GhdMPAnVt21IqsQ+1JpsdmUOVo/Jc7K7Hvt3iLJ5dHvImyvykn7BVoGvADLr
+W3Ymct+3PnAPx18jG8BS4wgbVXEJsk15jmee3edKKqni559IKedkLrYufprRUZlbv+Jy6gF5xit3
+yIlRd2R5bkMU0lyQ0r0xOmHmPsxkJjO5Vm4LS8HDpUUNd6FPoyfWQXV1k9qG7DruXEq0oxBjd6cs
+xLK7LyrOYBTXyJtyreHNETamsOcFp7SYJ/f2uVPRlZ1V0fZu4pIVol7nKhWKOYkW59VjbJ6Vbeek
+fx/DrmIcXJBdN7cZlVj+PnbbBFrAklifaiRWSO7XKLR4ZlqIs/aaBpVtMZOXjhSET0qEPzk+we/I
+d1a18rPhDVkOpKDGW1omzBVfsFLyoFbyDb66Q+XkXwPgE8/L7/9p/cc5/ZD8/aGnf4etDQnQtoyl
+qIjL8MDiWfwNsYoePCZzUdabpBM1jXfHVOYlKDnaGpOH8t3889uESzK+/YnsqMeOBPiJWARv7Czi
+KUBIrTskUTyIumsoD4BKAKDYGlAPxPoZ9w2VKe3ddYhrLYuXJNfjZHOZilZiuisuR61SrLVkJ10L
++ywpMEzTc1lQXIc0NFR8JcnZGrPqyvdHWrXYePAhjv2x7Oj1k6e4QytbqTzF41+J7CUtaQTyu41P
+P0UUfqP87NQOawviVm2sy++O3DfHwp/Jc5g/vsNZxEq5w4k4nssaWm02yXS+yoFYHSuPXmFtUayY
+z+7u43h6Kpe/Mvb0l5LbQin4Hqx2PPKkQeFqLULfYd/KgvZMjtUMtOhyyKQlpl9lX8+b/JhQzXnH
+rJNrjUJWGWMH4gY4lSF+pMedHa1ILNvYQtpIRy5uR176sDdHpSbm8xNpk7XjWp68K37qUbtDlMji
+8BcLnKGYc3lnn0RfLGexizdR4JRQS6CzKp5yKZaTBZyJLMbELNPQPiW5mMOO9yxuTbMOl2McZc7y
+zSYDhWI/fmJE/y9kLu57rbxtP+Os8HeP/SYAPzx+kHfu/h4A61GDk3WBIt+++HVUjkrZb6ixBf/4
+RRyrXA4LQyaJKtzVTQaJnIi4Cz3CmoLJeoLA1B0+TGYlYafRbOPP6VFn6MNVUc7uyCPRU54yl4U7
+nljGQ3nWiTsgCa4/cu464Gr9hLfTp9QXvdELKetSXVjdlOe0v1gQTeRFj5kQ1fUFK3pYBYwZ2zFm
+OFVe0t+jlzP6LXk2XeNj24K2NPFXXob/qWSktTRb8Rke35Z+LN77WqpDUQqr01O0jRWyJTk5qcSn
+uLclbk4+PE6ZyDg6C3W8mmYsTuR3+84z5Jta2ZvvMylkLXzpTF4fU9XMfZjJTGZyjRh7SHx019UJ
+Y7YRcsKdW9SFhVnbs7b/P2j7hLV28eW+dFsoBQBjzGPW2jfN2p61PWv71srMfZjJTGZyjcyUwkxm
+MpNr5HZSCr8wa3vW9qztWy+3TUxhJjOZye0ht5OlMJOZzOQ2kFuuFIwx7zTGnDbGPG+M+bFDbuuY
+MeaPjDFPGWOeNMb8sF7vGGM+box5Tv/fPsQ+uMaYzxtjPqz/PmWM+YyO/4PGmODl7vFXaLtljPkt
+Y8wzxpinjTFvuVljN8b8iM75E8aYXzPGRIc1dmPMLxljrhpjnnjRtS87TiPyn7UPjxtj3ngIbf87
+nfPHjTG/Y4xpvehv79O2Txtj3vFXaftGyS1VCsYYF/hZ4JuAB4DvMsY8cIhN5sA/sdY+ADwM/KC2
+92PAH1pr7wb+UP99WPLDwNMv+vdPAv/RWnsX0AW+/xDb/mngD6y19wGv034c+tiNMWvADwFvstY+
+CLjAuzm8sf934J1/6dpLjfObgLv1vx8Afv4Q2v448KC19iHgWeB9ALr23g28Rn/zc/pO3Fqx1t6y
+/4C3AB990b/fB7zvJrb/u8A3AqeBVb22Cpw+pPaOIgvyrwMfRvJPdwDvy83HDW67CZxD40gvun7o
+YwfWgEtAB0mt/zDwjsMcO3ASeOLlxgn8V+C7vtz3blTbf+lv3wl8QD9fs96BjwJvOYznfz3/3Wr3
+YbpYprKu1w5djDEngTcAnwGWrbVX9E+bwPIhNfufgB/lBX6veaBnrZ1C9h/m+E8B28Avq/vy34wx
+NW7C2K21l4F/D1wErgB94LPcvLHDS4/zZq/B7wM+covafkVyq5XCLRFjTB34n8A/ttZeg+5hRWXf
+8CMZY8y3AFettZ+90fd+heIBbwR+3lr7BiSt/BpX4RDH3ga+HVFMR4AaX2pi3zQ5rHG+nBhj3o+4
+sB+42W1fj9xqpXAZOPaifx/Va4cmxhgfUQgfsNb+tl7eMsas6t9XgauH0PTbgG8zxpwHfh1xIX4a
+aBljptWqhzn+dWDdWvsZ/fdvIUriZoz9bwDnrLXb1toM+G1kPm7W2OGlx3lT1qAx5nuAbwHeo0rp
+prV9vXKrlcKfA3drFDpAgi4fOqzGjDEG+EXgaWvtT73oTx8C3quf34vEGm6oWGvfZ609aq09iYzz
+k9ba9wB/BPytw2xb298ELhlj7tVLbwee4iaMHXEbHjbGVPUZTNu+KWNXealxfgj4bj2FeBjov8jN
+uCFijHkn4jZ+m7X2xWAHHwLebYwJjTGnkGDnozey7VcltzqoAXwzEpE9A7z/kNv6WsRsfBz4gv73
+zYhv/4fAc8AngM4h9+MbgA/r5zuQhfA88JtAeIjtvh54TMf/v4D2zRo78K+BZ4AngP8BhIc1duDX
+kNhFhlhI3/9S40SCvT+r6+8vkBOSG93280jsYLrm/suLvv9+bfs08E2Hue5e6X+zjMaZzGQm18it
+dh9mMpOZ3GYyUwozmclMrpGZUpjJTGZyjcyUwkxmMpNrZKYUZjKTmVwjM6Uwk5nM5BqZKYWZzGQm
+18hMKcxkJjO5Rv4fYYSrPFJzp2sAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 0.9124... Generator Loss: 1.4509
+Epoch 1/1... Discriminator Loss: 1.0586... Generator Loss: 0.9160
+Epoch 1/1... Discriminator Loss: 1.4511... Generator Loss: 0.4657
+Epoch 1/1... Discriminator Loss: 1.2195... Generator Loss: 1.5226
+Epoch 1/1... Discriminator Loss: 1.1125... Generator Loss: 1.1325
+Epoch 1/1... Discriminator Loss: 1.2902... Generator Loss: 0.7191
+Epoch 1/1... Discriminator Loss: 1.1498... Generator Loss: 0.8416
+Epoch 1/1... Discriminator Loss: 1.2848... Generator Loss: 0.7776
+Epoch 1/1... Discriminator Loss: 1.9145... Generator Loss: 0.2802
+Epoch 1/1... Discriminator Loss: 1.1082... Generator Loss: 1.2230
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmwbOd13/f79rx77jMPd74ACIAkCI4SNZiSLEWyLEcW
+XdZgRZErqchxbMdxJSnbUVVcqXKl/BInVc5D7JRcicpRIqckW5Il2ZFsytZEiSQ4gACIC+DO98yn
+5+497y8Pa/UlIAIgaBI2Hnq93L59du9vXvP6f8Zay4pWtKIVLcn5992BFa1oRe8sWjGFFa1oRa+h
+FVNY0YpW9BpaMYUVrWhFr6EVU1jRilb0GloxhRWtaEWvobeNKRhjvs8Y86Ix5mVjzN94u9pZ0YpW
+9I0l83bkKRhjXOAG8D3AfeBTwI9Za5//hje2ohWt6BtKb5em8BHgZWvtTWttDvw/wA++TW2taEUr
++gaS9za9dx+496r/3we+6Y0eXlvr24v7+xg3ACPfGQtZVQFQ5LCYDQEYDgsoz+R76wPgkVObCIDQ
+zXCCHgCBm2Crlr5vQloHANh8DkBaORib67sM8OZak3aN3d1tsNI3x3GpqwL9D3VRyvtwKdMUgErf
+ayuLMUbfZfAD+ew7Pr7vAuAa9+EEWLOcDMNSobPGYiv9j+th61o/yrOOBRyZF1NXuKEssetYHKPj
+r6uH767rSv91SVOZi8rmmFr74wDW0TYqHO2fY6x206FG36Hjkj84uK6O1Rgcx9Nxy7scj4fvstIo
+AOP5GOPIM55xMEZ+53vL13o4zlKWObhurVMffHm+rMGYSp9ZtlFjZWmo6hRbG/1cUtX6M1t+eQvo
+uywO3nJuMVijbVcV4/lUxr1cDyx1Ie2WtqYqK32vpdJ1Agu17odlY39EWzev/rzshzG4jv7F0bXx
+HBydnygM8YIYgHbLJ/Djh+Mw2t7nn/vimbV2k69CbxdT+KpkjPkp4KcA9vf2+LVf/kX81h6uLwPw
+c5dXZiMADm5ZPvfJfwLAL/y/B1TnPwPAYbYLwKa9Rxo8DsC17k3iK38SgEvNL5EuhBcFxW/wpdkV
+AIq7vw/Ai+MGfvYAgOPaw1YZIJv79SjQf//zv/CTOJn0LYzaZPMj+V3QJDs+kb7VHY5feRmASSW7
+sZoUOIFu8trlwkV5425jj609YV7dcE0a8VNKT5fH9SlL2RC5W1LM5fDa1jqVHuTWujzbyCpM84K8
+Yj6gc132QDcuaYQXZfzJmMqXthfTMQDTtM2NGzIX4/wuYSqMtRVavDqUd6yPiZ11mYtQGJ5fNUjt
+DICkrgn08Nduk25XmJMfBDQaMi5XmXe7D41AvksxMJb3/YtP/wu8QNpbj1v4fh+AHZ2WqLlFEMuG
+d0yDTkt+12xdpHLlwNa5jxvJgfWM/D4tFxQjWdnR7AblQvo5WZwwWci7y2pAueTv2ofKRmz05BDG
+XkDhNqWN0YR//ge/CcB8Jj8yVcn8bALAMEsYncoeSauC6XSufS6pMnm+UmZaZQWe7rqqNkRGd6Bx
+8TwZUxYG9CNlrLGsQXMzph3J+j5y+Qo7V94DwB/76D4Xdp6U9kIPfybtbL77kTu8BXq7mMID4OKr
+/n9Bv3tI1tp/APwDgKef/oANOrvYhour0v+WU3GqG/4L9+5zNu/K96NfpJvKM4P8NgC9OOaBK+Pd
+3+ozaMqkv/9d38Zzc2Eye8l1Tr4om/d6U5jJcHKHWKVnVaWv4dCvR5n+6zVj3reVAPDcpOKbowYA
+nz8oePSa9O3+nYp+JBtocSK/nPUXNErpT2VLmsE+APFeQf+iLPRG3QbAxBFBLcxk3Ijo6qY6SB2c
+DVnkAzLe40g/Tgpp95FLXY4DOShr45hwQ+btatRgiDxzwV3jwUKWo9eRLfCH9ZxGPpB5OfI47twF
+oAyu4Gan8mxzB6+WwxauyRy2pgWVK3MYThfkHTmw/WJO5UrbbT/FRka/l/7W8QZtlfimE2FLOXhd
+x6cTxvp9lx3VekJ9727Dp1gTBtpaQLnWkc9eTd6U76O0Zh7J93GxXLQWbVfWf9Fo0zqTz7Q79JWR
+P1h41L7M3cSTddhL59TI581OwOAhU2/T9GXdvXUZx3BocSOZi3z6gKArn6djaMbyTJpEVH2Zi2Ai
+nTsLHTZ03w+ckp4VpnDHsVwN9d14hJ4wqtqRc5ENPObdQxnzbIvdySsAjN2P8Jgvv7O9Nk7wtfkN
+3y6m8CngUWPMVYQZ/Cjw597waWPBKXkl8/FdOUCDe5Z/cvMPAbj9XM3gzt8HYIMBo0o4cJzLRqr9
+hA+odFxLKrYC5brjM/74+mMAnDoRH23JRB3PxbJ5KmhxZGXDzwoHa5cq55vTbt5i3JbNvRmWvHgq
+nx0v59dfkoNlsoTn7h4D0KnVRDnLCENZ/CBqsLklbftn68R7Mu5uewOAMnAJliqwG2J2ZKzePcs4
+G2hPznghl2f6lfzu5mDB2pqqxjZjw/sIAGm7wNPDO/KnTKxI0Bfv3ZQ58QZ86ouflebslPNnRYO4
+duk5Op4wr6ZzyGkuB2QjEXMu93YYGDlgzTJhsRBGllSGRiBawfFiQSuWtmeJMMqWzZhkMqYor4ld
+2bhes8u0I5+7ayEp8o64K+MbZIbYkznMOwYC6U9S+iw1dBP45EuGo1I3qypKNaWSeYyPMNl5uSCN
+5H1H4ynlSNZs2pbxneUNtrTv2SSguSF9Dtwc1dwZqvlQNmsmp7LWdVBTTOXQt5s5ZSJrsh7W5EYZ
+Z1Pe9X7fI3bU7DRdYmX07y1jrCv9vGY9jgt5fjiTMdX+lPO5MDE3+iKefS8A28VLvFR9EIALVUZT
+zY23Sm8LU7DWlsaYvwz8C8So+4fW2ufejrZWtKIVfWPpbfMpWGt/Dfi1t/YslBV8wuT8qYF06W/d
+O+b77gnX/o17r/DR7MMA/NPO7/N+dR5+Um3ujz29yT8ZirXyYx/q8XML+fwTH34v/2xyDsCf2Ho3
+z9Ty+XuevALA3/zt3+O7lev+0itjvFKkdWnfyKsg5AUOW55w3y+9aPjgt4tE+J1f8Pn449Knn3um
+yQceFfXy9AVR+9Y2fZxQpEoniGkFIjXDIKCn6mNDnWE2CGhpG23XoxHrUtVT9j1Ry1867XL5MdF6
+bj4r0vrxRs5g+AgAm5cCTEMkZpyFpOKfZX235PnPiu17vScN3vx0zOaeSLDj3x8Sqv0aWY+paiz1
+vYqxFQ2ikYvkDrrHZFbG1zdzZlP9vFNw8rJIuX58wni4BUDrkkjgwRcC1jdGOqQ9krZqU55Da6oO
+2NJlY0f64ZzL2vTXYpyZfNdsGYqBOATCTot6IXPruhneRNbQqI8qKAPcUp5dG5W40hx7eYM0l++n
+pwZHJXp+Lk6M/b0TTl8UbWVze0peyNw66y7jVPrRzmUOb82h48gkDxYFu01p+2jaYPuCfC6OHdZ3
+pA23JeuYzAv2e7IXfr+M+IHrsu6/8Qw8viHzfTreoKsacjaU9RhPhvjqrB0d+xRd0Tzv/vqQ/Y89
+C0B69cPY7pvv5z9K/94cja+mJF/w3N3PsTfZ4ecLObjfdWj5py/cB+ByNGYaii37Ta0etZHD9oOx
+qI6PbLT5bx4R1Snv3OUvvFf+7gQDfnBDNvrZ+X1+4IIM16vFMfiTG2u86N4GYKfTYLoQVaxM3ry/
+ZTfktpVDtXjS5+ZLskjDywUvn0gbjz7apXxZ3md3ZFEeqzLmoW6wnkvcEz/CxT2HuC2OveaGbI7K
+VgSNpf+hj22rSn29xVTVUru2wclEmIGeUU6sR3vvio4jexiVGflj7mzKgT0/ynle57O8K5su3Q7J
+/lDmzTZqLjqiMjcXHdZ0T90vx6yHsqHHlfo+MhipQ7gKR0yUkc3vDpktxN59/uic/IKsw60/kHFk
+3ftsnsuYr39onZ2i+fB9SVvGZIMe5wvpc6cpB3de+/hqgpnCA0e+z3JDJo/iVj4jIwwpTuXLLKhp
+uNKHeTikFcm8JGOXxJX32VbNcSFjuWfkwJ7cSpiaWzKHTsjVS08A0EpbpGrGnSbS3zKvOVdXRWpy
+5tIFgthQp9J2p+ux6encqvlkG9DWEMj3rcW0EtlP336xQRHLM484hvOJ7o1AzkjqOMwz2WOLyYDn
+Pi1zcbL4Ne7ZDwDwpx95D49XYiq+VVqlOa9oRSt6Db0jNAWndolnPbKjGe9Trv3PPneTp9SJtuZ7
+tLevALDOKfOuhGF2U/n77lPvphpsA+A+cZXtqTqZNtpwInpivHOV/kA47Glbnv2Oy1/k2nu+HYBx
+MsRJxeP+hzde5tWByWVUYunDbdicTijcd/SlA/oaP24+c8L2t4lEWMwyNjelnXYtKqXbrWldEG6/
+MW3SuirStuO2aKzJ70Ij46cb01MHpR9B5MjvaFVs1pcBqPISP5C5sOcSTbhyMWBWihazttPDNNV7
+n/UIhqK6O9OK1rMHAFx4r8zVMyeWcldzPSbmYY6BsxkSI30LamhozNuppG89E2A9Ua12fEuYyRx3
+221OhyJ1W2sh94fSp05LpfWhy9r7NNfjdIJzRSRz06uJM/m88A09jdM7nox/y4lINOzZCTwmtWgb
+jaogVcnrBzUmk7G0Uo03pgFhQ/8eRvQX0rbxczqqFeThiI32JenfRP7e6occ3RGt6vLlCH8qn5sX
+woehyAiZi/NByvpMNC87SIj21HmaOzRV68sWFZlqMYs1WbuwLCgaOv7CMNuUvVVOHJy+aDqbScE8
+lHa8poaTz8cUtcxFZROKWtZh+Nwmw8dEG753Y8bO07LP3iq9I5hCURTcP35AerHDJ+/JpO/vnvPZ
+F+VgbndzaIndtvXEFoNCJmWtJRvN7u5y7ZpsntMqoqF28jTy2ZCgBKdDi9uX52s9xLsfvszZPbFV
+/9IP3+Lnj+Tv3VsR42LxsH9/NKBzkjm8bKSfs36T527Ioa+vNrk90BBos0WvuzxA4uPI67skp7LI
+wZWKYC4b12xDWkifei2NXecRiarqtd+hVhu/NJAiB28669JcSNvZphyaB+OE9r6olNn5mDKWd5x1
+FtyM5EDGwxGvPCkhxdO7ou+2r22yNZYxH3Z28EZiny6ylOaerMPpAVhfxl1YMTV6VU6xkL+PrWUR
+iS0eRDVpR8ZXVjNmS1f9RMJ/SbjG8QPp2/aT0C31oBQR8zVZhyjexLZlLctQQowDXIyG/VqUpPre
+ae0zMvKsN0l5aRnxmWmkYjOlUUl/pmVFRyNNWehi1JQY91qkavKcaTjZnB8zUyZ0+/45735MGKu3
+aKLRVYa19LcqYFDIfjJewXwi4wvbEKsPo2cM5UL9FoWMeb1lidRMCtdjxieyDu2uQ3qseRFxji2F
+WRSa55A1WwSlmDAnaQdHzZmZ+wKTX5cGv+fPnrFXapLHW6SV+bCiFa3oNfSO0BQwFs+rSB6c078h
+UvD++C5+chuAKrI0p8IxOzstZuqJjjRLrl8nNBJRxTomwY9EpWzPU3xXPdXpiCAWabM5Ekka1S7d
+QLiuX4bsu6KWZ+ErOOqpru0yURaWWQymLvFeEgmbHWeUsUrr0yaXFur42o+xVhNduqoC35vhXBJp
+3fMi6AjnD9waX5fCpKJd1L5lmX/rjixOR73puYeZiPRby0uWWbddTV5ql1MCEeZE2w0G6qiaHdaE
+p9Ln0fAu41N538QXyR0CjSekP9Mzn3Ao/ehtGdxStAI/tvQ1azBfttvwcQs1fbwpff2+5VYMG/K7
+8txlo7OMp8ssXrIRmSNzH6QpzrqsjdNwKEay1q4t8FvijK00du/GfZxMpKDpNsnHMthjA47mSAw9
+j8WprMOx/m7rfshMc0Hc+Yx5pc7MORgjErYeJhQD6VM3V23LG+LX4sVtzAaUQ2nbuW5ICs2m1HT8
+2bzGsSL9TenTcKS9zO9iY3kmGcaEnrwjXJf5IUmZN9Tc6Rja6nRdJ+fMlTZMZQhKWbMY6fsDk1PP
+l6nrc6YLnZd6RJ0/A8DP/crv0PyPrvO10DuCKVRlwWj0gMKWnOk8VQ8KQlUHJ2nNhibCvDztcyER
+2z+zEposipqBJrw08oLcykYxxudcj3LDWlIjG8VpSSPTosUmsiEmexs87Um04xOXLnB8Q76XfaZ5
+/trf+8MBD1RVG3gZY1WDHyQDzjpiGz5e5tS59PnwgfRn1wRkauumUUmg9m7u5lgrh2I01yQlf0iq
+2Y9u6JGkMtZkPmWhCTnnnoM/F7VzrC6QRr8BfWFu8yzi7EBCli8vTvmiMqz0JOTlxScBCPNvBeBb
+rmzinEnfgvnLtK8Kw52XKXVbsxEn2UP/QU+z+YYTn0DnYur4xGpfj2YW50yenVMyctRUGmm40Z+x
+oYla8cZFImXk2XzG3FmGf9Y5TWQ+eprUduZBsynbdjSy5GM5IBN/G5DxNQYO6bGo+Qfqo0k3pzTv
+yYFNpodMPZ3buuY8kfbOzg44UJ/IbfVVlC+VZJkwznLPpd9Y13f0Ka0c2KHWuNTGYkthkF6Uk6ov
+JnIsDJRbFjNCjdB4GvqOYw9Pk7d6VYXTkjkaJdBoyOfpsKRppJ8vqblmspypJurl05JKhUhdJhgr
+bSTPJrynsYo+rGhFK/o66B2hKRgMXh0QnFs2JsLhkzLF1yqzK7VLIxfJ1I0nTDt7ADyuzqnK9Ig1
+dz6LO2ypirtwSxq5SIpzf53thqiG07Fy5dih9CSpZsMbkzjifGssPk+s3u55OeeCqgh31OPoFCnB
+Qjy69fE500QkRfvcxd0WaWWiHkeaM3+tL/0spoY1dZg27TrFmrQRVOsP1RBf7YHKXycuRcPInA6O
+prgav0+vkjasX2ESkWhOLI7BHdumyGUOg9YW5JpWnHZpHaj5MDqgf0/Sv+dPSYLYZvkd/PyeSMnv
+e+oyJ1+Q6MSVyw3W1VYYXnTpD0X6+bFMRhgEJN6j0kYwIPdE+ps0p4zUZCigUWviUKA1Dpu79DQS
+YayhVodhy7QwjnxfWZemakuFOhp35oa8J3vB1DWlK07cxukIpynPFCajtSnzEt6UMcVBjR9qLQot
+mnOZizR3aOWibd1N2wRzeXdzJr9z/ZCpRrl61qVO5Hu7vovJRHvxrajwHkMClf5uAR3VsMLQJ1ft
+rldb4kjacztaMzKDQn2B7UYb09DK1tpnrBWajU7EkWSkE2l7pwuPdKpGbZZT6nkxFupS9tkLn/5V
+/tQXPsbXQitNYUUrWtFr6B2hKVDXsEhIWmPmWhbcbM2olQue5wWmI1L+YhaRBBILPlQJttZZ0FDu
+a+uA1BVpVLQCQuXyRekySSV0NNH002ZdMe3JO5LaYLZEQv25p7f4X39XnIdekXKu1YrLyarnE6a5
+2OpZbjFj0QS6QU5+pJV6V0e8pyWf6cu/3kZN4YtDdG2nJtfiIC9KWCgGhJtqMZcHC3VgmXZOMNPw
+JDVFJP2xmU+kEvg8F3vTq2siLVAqJj5+KvM2dw7IKxnrpk2ZP6K5DMfvB6C8+IC/9ED6ef/5z7PX
+lL6l5YLmtvS5OnPwOpqH3dOS7DJgfU38BUVW4K1p8dT4HCdSP48JOEnk3RtatXiejigVJGHbnRJp
+CNh3G5z7Mve7/ehhfH+icz+LDAukD/FZwue0CioKMnqx+DYeGfncHmluiFZXnvtjLmil6SA/o8zk
+jV4vwpuoTyA+o17IO5Kp7KHGeMgslfaOTjwupqKRBZMRsWJKZFYrP90IG8j+3Q1qNlhiHcR0jJaa
+Wx+vp3tSK0AvbacEyPpH/bWHmtDOmsNYi60mgzPGV+WZ+kvi45jvBRxr6e5gUWHsEqeBh3H09Ow2
+P3UmvrKf5q3RO4MpGCCoCQYRTiYbep75xOpkMV6MMxK17bnYp6EF8Fkoi/Xuez2yfVHVnTDFOqKL
+ReOMehkyyM7JFTikni4dQyn5XBY0bBTEjjCWG1WLSMtNS+PQ0byGmS5Q2I3ZH8kCpdEA29ODnIYE
+emaiVsWZI43v6yJPDwyt65rLn3p4yjNI2oSZbJBEy2IX1Yxc8/fjpAL93rQQ1BkgrCo8X3ZFL5Jn
+m+M5wUydaH2L1ehD86jDWkNi8Hf3anpncjqfbckm/2G/zf/sSsLLf7Ad8dxzYj483ewwHci7bQjO
+Ypk7oWW/5YJKVf901CKwcqi6dc5CN394y+L15Plc56QdbhKoF907zxnsyTr4UU5pxVlbTKFuiZPM
+9eQQ5ybEfSDm0b+sTkluyedPnb/C98SCIXDvsktwfhuAVwoRINtn8KIna3Z/doh3LqZZ6kRca0nb
+x+dDHC2ptrmMs+gmRJqw1S0SikTmPvdSIo1LdUJZyCqsmA212jHwmGhtjnEcXinkHWt+RHEk7TWv
+ixlQjD3qbZkXrza4+mwxWzBQB+vpIOZwKmM9V9CHaZEw1bRqa+rXhQjKqoyf/pu//jp/eWNamQ8r
+WtGKXkPvCE3B1gXV9BDPCXC6WjgS9ViMtB59w2e9KdVpqeMyuSfc38QiwYZZzi1Nid4vx7gN4dyl
+F5ChGXhpRqnQXUu1beGEdBRtiZZPT4uAOlevceX53wXgzAkkVQ2IVGMIbEXREinRymKcRDSBs/kC
+d1emdDw19LbF8fXCoUjgVnvGqVYAbjQdzGgZu3coC+nz5FSkwSSbslCJ0ent0NBQWDieE2rYNvdi
+Qo3TL45ENba9MZ6OoxjVmFo0gTpf0FRn18WR4TMnggq1U8vYXm4P+KbWDgC3nx9wYVfmaDQrMZ5I
+v+B8yLivmkciv7s7S3FeFq1iRpOtUxnfi3GLaCJSderX5KVocpmahLk3Zv2SDGQjsjRUMg+S+0Sa
+ut1s+ZRaznh6JlrOfD7BU8CS5w5ucv7JTwOQpCW/0/2STMzsEtdvf0bma//dMhcm56VaPHXlSy+T
+qLOuXQS81NIMyfGQVEN8qQLStKcl2UhRmq4aEkVeWsxd2hvSj0BRmlpJ4+HeW4znVKq9rbsNrKKj
+ZYMMpy3rnp5JGvjZTo89TdF2YvB1n94r4PxcnnkwmlAOZS2Hnsz9hg1pb0nf0/uoUfVaMnXNex6R
+/j97/DoPvA69I5iCMQ6u36abV7iazhuMN9l4XJNm5g2idcVaPK2JN2Tzno00/75haY1FJcsvbaI4
+GBQNl3CksFrNHk1VjBaBmB+9MuZsXxa/2/PQfB6uDw85WPsQAPMHn+VPKn7izyeqordiLuUaV58m
+WFV3e80aX+GzxrbgtiL69FWxqwYOwb6aAaWhdmVMPc/jJFdwDs2dndxJyfxlXL1kb02YxXTzAk+q
+7ZgYQ0M92GmqSEOOIVMAlKgbYVVlvuDEqFlLXazxnuYVAL6gFXk7o4DPviDM9qmLaxyMZItd3LF0
+jGzCohfhKRKQLeTfcjBheqwlws6YhdadOH6J0xPVv19nWC1L3+hpH8pLNBRirj7PqSL53UZ4jZky
+Kt/rkJ1panZfGEVjPiE7koHs3DqivxB/xu+OE56eCvO9n5T4uxJdSdQftO33cQ6EgbaykHM1G6kW
+ROqDGhOwoz6KhSu/29vdIFckr47nEWkJrfVzuv5S+GgS1mbE7JZ8F28YzuYa+XISHsw0Tb9bM57K
+M4+FovvXY4f8ouyhNd+nUFAUm06pxlpXMcsZz9TszdV0C2LS8yUupccbsAWe/fzJ63z/xrQyH1a0
+ohW9ht4RmoJjDZF1GPYsbcVM7F13WKjnnKbDuaa2uo7DK0OR9E0tDDrpXWevI1zZqQoKhOuWxhB2
+1MmX5MS1qGITzWNw4pKFZpqVyQK/LVrF+z/Y5zOZSLknDta4txDpsK3Rjm5Zc6xQWv1ml0ILsyYz
+C2oSHN0+xNXU1nEm3Hy/n9EsJa5+oeWSaEqz07VLCwU7lXfdnc9oKO7DbMMHBQQNhiX3tuV3/d1r
+uAoKu8zFdkIfJxbp6c1T1jTVOAsrKoUou7ITcHRTlv69Vhr+onvE9Vjm7UuHJygMBXO3xX6oVXll
+RUeLkc5UnOxYw7n24YKXkbmS69FvWeq54j60F7hWvncy0X5GTkGhDspL6x4dTSsfD2acaOMXehVa
+uMkXH8i8NkzOC1bmpZmmPDMUx+aVasC9XTGxnvYsfqkmW/caAKUz5t1NWcffvpez5ouUPjYR1UDT
+n9MxQ8WS7K1LhWtcp6Sl7i3HPFTt3emIy2o2nHTENGq1mlxUrSK5N2ErlPZun05RaEvunkxZ4uWc
+NqW/H1j3aGs0JIx3H0LadeqKW8kyg3TCsSJQOwvpb2VTjFZaVbnFFEsE6le7HC1rXY26zHhL9I5g
+Ctax2MjSSCBQz7rnBERa+ddpN2lqamftRVysZXSBK0kzfa9NK5CFK2sXT6MIfpXjzhXJ1i1prEsO
++AWtoIud7GGuehz3KUqZjnl3wYZ67W+uX+MvRaKOf3quYbNeiysKoGHdGaWCny7KNp1YFjRqr7Gj
+5dWlpgaHoU/XU3W4yEnVE+/js1bqwYs1dNfIiXxJ0lprdrjQkVNY5C6dJdL3LKPZkrGUqjr645yq
+q6jULXD6mvSVeGwFWlXq+WxfFHOjraaPTX2KvpZOV018ncOdqM2aop+a3HuIj9iv5b3HjQlbHYXM
+37zChXV5n5tlZFoFGEwsA4XSt5H8G9nsYaQpOxoy3ZckstJJODqV340bfebKILYU7LTRjHm/pref
+Xt18mCrscJ3WZVmzjYVhoJWLaPj5gu9zbyx9e9f+jI4rh36fgJZR0+zBglirVBtdGd/uBhjFdtws
+S4Il2rMpMFruviyd77ahOlMAmIt9vFzW/7HScHIk8xmNH/BAqy53Lygz8RzcpjqKypqOOo1GZkKk
+widqPMH1SObr9rH4V8LZEKsVla5fkWtk5NVkgcHB+Vd8/2a0Mh9WtKIVvYbeEZoC1kJV4LmWfJmq
+SoOGJ1w7iLt02lpxN/eIXWHXjoJONMMeaSB/b9dTcl+lphdSBsKtu4FH0NVcBr0UxgkMu75WQzYW
+ZIk6JfOavcu/BcD3Ph9ymgjvvNgVzaWJYaJqdGe7RaJVgvUgx9Xciku9TSIjzrFc8wPieI5RCXzm
++MR6mUiau/hNFf9jkYJ78QjPE4npsYufaaJWZPF8xfgLDLcUjm16S3EA3IKmagfzfI0nt9UOiCM0
+rwjPMRyOV+UEAAAgAElEQVTcFO2lasrv94sNBq7GyvMOraZoG+trHVwt8to8Pqbs6J0MmkY7T3ao
+Ffw+biW01QE795s0csVh8EckqeYTnIjUqub3aWzJe9c/9EEuaD7JjTuffIhdOd3vkCocvzeQ35tW
+h+11dTo6DdZU2/I6Hj2942GGQ/tYIg3zpvR3wx2R7WolredDQ+b+2ryHdTf0HWcEsVYlIqr9vDwk
+msv4RlFKXxPKJrOSjjpHTVvWLPBiokuiKYzPc/xLumbpBhNNsT5Jci6GYsZuOTJXnc11Wr78ru4G
+BArwU17e4LqqhWWWsziW/Jt1TYVPJ11mqvW+dHNG8hVwQJIC5Ou+zUe8JXpHMAXHuARuByd3KBW8
+IyTC68mC7sdNCk0AajYaDNZk0O2pAo+sx0QKwJq12/Q0yahotwmWwDtrO7RRjDvF6ffymLQlMxVk
+fdAqtP3zmi9EApF9d/0Wf7El4dB/PRSsvjCKuaBAq/eHGX4p/dz2SvbVk23aCZstaXyhF4/EZRs3
+lH76izNmGma84pZMFdN/fUs9zHSJFXhj3krpKjoQnQb9lh4a69JUW/VAzYC2mZNo5l633STsaNmv
+ram1z1lu2N6TjL+2ZkpmjZKrS74UrREvq0tbTRoa1h3srLGumYcTXw+Sf0zYFzOnMU5ZbGmCV9Fi
+2NYyZLvOqJB3LLEIp3WHQKHO01nKWL/fnG/wJU0sKnYXzDU7s/uIcLTLpiDVjM6LTThaFxOyu2iR
+drVM/u6Ak6vvAmBD/QVh9yLrUwmd1o9eZk2zUNPejK4KiXlrg5bWmMx8ZSZ3K+oL44djyjWJzHHm
+xH2JgrU0KS5vtfA1mtPcbbKpmXPJFqx7Wq5fRJQnsp+6+7KOvldTXRZhuJ2HZC1F5EpOyZZl9wdT
+Dje12tYVZjLpRzRuSJjSMgWFrX81GRzy2VvkBkor82FFK1rRa+gdoSlgXLywzXE7I/DF2eWHDq7e
+vDTyu/TU+RJ4Hn4mTilXocGybgtHc86t65NpAkodhHiaEupEbUqV4o62YUIHJxfVz5qUEk213e7i
+phIl+PjVV3juSDj+Uw1p16t8ZnqxTLPTIlzXC15maywUvGPNOMvrEamnIolK54ShJs1sZhV+Je0l
+LRdfa+jDSqVAw6fT0f7011lT/II8KdBgAOfOlz3OE00ZNos5Xkf6Hk0NoVYcDl2XqisStn95xJ07
+Ipmsevf9XhNfbxXKDmtChcNfNCuCUubIWZSkLE0v2ToXTMRthSDrbNaUin/XLSqqmfQ/KoccFMsq
+SJHA0zjD0RukNi9ucEXxBH4VQ6pX4BVdWNtX0BI17RatJr6q2l67oNdQE7NjqXS/RHmTTKMcoSuS
+tGy57M5Fst8zMxodGX+0u0uvkjEF6YjIqONWc1OM5xGIgkhUjyg0T8NdVETLNVGTym80WFfV9Gi0
+hQlFw+g0I9xa2pud+jT7Uouw6El/dgwE6jy1Xe9hLshWuM6Z7qHGRWgbeX5T4QZfOB2x/8hLAPz+
+qMQs7+a0X85XsFhaGoGbDXhL9G+tKRhjLhpjPmGMed4Y85wx5q/q92vGmN8wxryk/35tCA8rWtGK
+/r3S16MplMB/ba19xhjTBj5jjPkN4M8D/9Ja+3eMMX8D+BvAX3+zF1kH8shikgbmXO/JazcpjYSV
+NpstrMZu8S2xIuw4ilcQjhPqbHnFWkauziV3mlM4wq3LPKFWB5WTaJVh4OIoV62zinCufonRnHXN
+aXg2eoRtrdEbxpJGW4aGpkq+8QKshj1LN2M7DLU9h0ZHvh+pJPJpUx1qoQ0VQw0jNrsWf6yXFmi8
+PnAW+L5IF8/1CTX7r4xb1IpwHIwLykSLn9TxVxUWR+/PzDtTykD75lS4d2R8hwcFqStiY1OdU4vT
+DFcdm3XpYNTOLoYhqSt9GmbRQyQgFHR2mFmSTJGD/T5tRS6yc4dhpSjXuUutuBZTvcF6P/bxtQLS
+GWcMFesidJOHl51EN48ZRoIi1b+oa+528TTF2vgQ6C3PtopxzkUzmZ6ekCkU2kwTQOIJnA3Ep+BG
+C/JItL5wlGL1WRwfx1X0Ka1WbeRDOl3RaBozg1F8Cs+bUSmiUazr2xhUOJmiInkzAtWmmDr4qcx3
+O7TMjYRDe4mGwNOU3JffzZMJLVf26Wg6IK/EgZwtQmoNd4+Xodxsxo3nxQFbT4vXvfbQYpkN7n3F
+929G/9ZMwVp7CBzq56kx5gXkCvofBL5DH/s/gd/iqzEFA6VjKLyCWU8Ogls5dHSiznFZU1itHEOt
+6qpdzvm8Jq9UzVxM6WryUhCukZe6yMUI62kcfqmquz5FtmQw5mGZ6vl6j8VIkXbrU2xTPrfaWr23
+mDJRCPQqrMlV1fTqkBNHHEpd1+XuA3nfYi7w66d2TLguDrN0CLEj6afTtR5berNxop7wMC2oYy3J
+zZqkClhCNWeuMHV1u8UDTQA60kQh383ZWGL5pRFJJiZWkjeZKWRdx4TYl8TJeVfh6AIKXjqQNpKD
+l7jvK7ahvcBoedN0cptEoebXdPxnbkSwBLhJByT3hakPGiGJokNPkjGZMrVzZd6DyZxrenNR7lV4
+qoofnLxArDdij4p95qqOb6SyBlmzjaNmTmotpYKeLGzAKJV9MaxSRmdS2xGqc7njB9xTVbw7HbC8
+XrpTtKi2xavvV1My3VMOsg8nfoNCy9LPGFOGosLPxhkdTXTyFbW7zJsEmqbfzHKySPrctA0GhTD9
+JDlgopWPSbpMCQ/wtQIyaLgPIyPnQZOlj7CoCqpTGetEk5jmkyGDDXVMH2V85WUEQkFbGGA+vc9b
+oW+Io9EYcwV4P/AHwLYyDIAjYPsNfvNTxphPG2M+PZ1MvxHdWNGKVvQNoK/b0WiMaQG/APxX1tqJ
+MV++0N1aa40xr3sP9quvon/kkUdsw9+kUVhSDem03CYzKxz1kdmEtCEcv1fDsBCp0lLY4igrcbU4
+albX+FrMUqQPcFWFnbrrtF2RzJXCb9WtmkolmBdMCRWEpT1f0I1EOkZxxI5mkt23cj+fqUMUjYxZ
+mtNQ51vsp+zq8GdZShTr9eKnmhLd91mUWgRT1g/BYEInZ46YOV2tzrP9NrGCkKQFeKoJpfRoaPXd
+YLigrfc8umq2NAuLqynhTpQRquZR+xn+2bISM6Xekuev3BOJeVoYtvROxZcXHn2Nj/vG0NCCn8Jr
+0DTyjqW6G5s52a5mbi4yXEWBDodjBpqm7kcl81zXSquyup1t2oquTVmTq+rbmIcsy3ciOyQcyR7I
+L4hK3SpTqlDMRjctmShQjWumhHOVcacLWKg3VlPNF8kZ3kALzFpNep5iRFQGlhCAjRbr6vDMtECt
+5dUEmh7ej1ocTiSU2zEljVhRw1WLLX1LS/dF6QRsaCbr6SyloWbVYQKemnSFAr+OT+eE6jztNC0L
+X/ZCVqYkmuFrZzl2rE7zRPbp+P6C8+fkc5kWvPoCo1dTPjt83e/fiL4upmCM8RGG8H9Za39Rvz42
+xuxaaw+NMbvAVy3RMrg4ToujdkrS001Xlgz0/r0XSoeW3i9Ypw6vqEp8UcFWhi5ErkzkjYnD8fQO
+AMH6Dj09IKfBKZWiOkVyCRDbkzVOdYE2pjUTPWC2YUjnouBsrn2e+6cK5Y0wo8lowbl6vT23pKW3
+wprTNlNfqx1NQZYodHihiNEnU8oNee9OXTCvZSPMJi4bV+WQNmr19JclapWQhhW21MjAZEyl6azz
+vOJkrMAbZzInEzNgqxRG0Bl4pGPp2ythCFvy7BN+zoNndS40x+KUGV6h6b75kPBM/r52pcXlSA79
+g3bMpgKKsC4q97Y340ARld32bW7qn6v7M9JMDkUwHTJRuOlAy7fdNGNYq5d9Y5NLWl2aOh1cVZ+r
+vS7968r01G8zbYQUlfw9mOccqinZSxacKZx/1Cg4W+aOaEQpbXfY04rRoa0wjtZgRDWxHqZFMsXT
+epRK76tMPcNCTbQiXbDQW6aK0yn9qzLYmQLyEDi0Ne16er9FsRBGMCyOyc+lbvnOnTGu+sSKjvQn
+nlhe1CrSVhQQXZL5Xpu0OFdUJ4cFh76ch+SurNPNfMHCXV5F8MaXyBplWq/nc3g9+nqiDwb4GeAF
+a+3ffdWffhn4Sf38k8Av/du2saIVrejfPX09msK3Aj8BPGuM+Zx+998Bfwf4x8aY/xS4A/zwV3uR
+ccBruFR1h7MDvcSj4TNzhCNu24j0aFnk49OYKnfUNNO4ztErE7ngzdncEdXfNiBwRUU9vbfAc9XD
+f0sdlftz/IGoX0UjwNV7EcpshlWMvxenXVrKhF9pSH8em2U8UATnRmrIFd5tGpfsqTYyLGviddFe
+FrlIly0voNZ0ZafdJF5W33kdPL1JO1DzIY0crDqn/HJKQwFXzkcZtRbJ1BOLr1pIqtqDa2vGx4ra
+fA1SX/r2WVux96I64qzB7aiE1avck3FBodGeap4R6KUtdlGQqRO0WcUkmd6P6C7vlxxg9G7EO2dd
+TmeqXs9GjE5k4s78CqvyZ6rzs7sT03Tk77MpjHdE3XedgqRQvIDzEclApGZrX+bC2Bpf747Iqxx/
+JH3ObI1f65VtwG4k79jUa+xM5NFWJGmbjPFrcb456ZzQaKQiz/DWFCdDIwMt75h8efkQCxLFi3C7
+EzyNcqnmTzJKHuZQGHNCnEp7B0cOfiX9TKYVXc1DMHOZwzI0dPWehiiM6FsZc9RwGKrJc3IGUy3y
+evlYci/u3R2RzjRy8rpgbPqX+isLpd6Mvp7ow+/wZXfnH6U//rW8q3JgGlnCgSXeEHtxMVtwoKrv
+3HHY1pM5HUPLEbVs7kl6bWwNjt4kdKl9SjOS1Fe3B2asuejxARMN1Tmhht5GFkdtttrzKFVNHAZd
+iCTM5puEmV79bobyruPj5zk0Yss+ElkCTQn2p5ZxX94Ruh3OTjTdOB9ofxw2NYzVDGOcZVls3CRQ
+YI25pwxtdkCpamZZ+IzUrvXCkoWmGtdeg1sLhV+faemxzejlatc/MNx6oAhEZ5doO9KPJy8HfOoZ
+6f9wTdNoiwlHtxQQNZ0w1tuLLhqHkd4QFSxOSbVMuJ3K4b9hdplWwiyzZER194F+n1DXMn6nqulq
+qjdaDXg6P+dqINGcva02Gxp+nZVnxJ48s+73cTUK4pzLgVi0e1jN/V/UGYmVvVDbJijjnFuXlsKv
+F2oexqbmbFkRWuUsFD6/TuCBpr13KodMgXDRWpr7aZtxIs/eGcyY6P2Y5rDgqW9TU09NImfLwywU
+Faxa59jIpUWFzXnhZa3MHY+oLsrvtjeEMbWCDRzdI04/wlEfTX7eJawlDF5Pz6gOFHlJa2YWyTk4
+ygzexDIwru6p6vSNH3oVrdKcV7SiFb2G3hFpzp7jsdZY4/AspacQV4Mzy0iLU66dHDF99CoAVzYs
+5OoEfEW48vDKJmsT4eD1xY/SHap5EO0yUjUyqDzcZ24AkF2TNhprMYkv6mJzOGOoIQV7lHB8W/p2
+dGPCmqL5jhR7gbRHrapq4nR4Xu+6a6+PuDpWyd0bcFFr8o+neiHJScE8FInX9CtsKOMwVcR8LpKy
+ir+sHXRnMv7Km9HqKYx6y2NDk3cG8ymb6lBbgq2E1sVXR6zpWnqhFHbdT0/Z3/4AAP/yXk7nCQmA
+X/09GdJ9c4cnNEX51miEOxCz47i1oOuL9J+MQmINnB/GUr14tDjEKgDM+c2E4VDh5X1LEquJcVJT
+aepyjvR9+12P0cglZ8MzHY40h6QVbnE60grOKuLOPZmD9WuipT12mjHrL83GJqmmhTsnKTOF0u46
+NUkgqd5dvcbOYYuwqdiIiw0ilf65N6MTaV5L19LVVOFRpeZD7TA7lvG1CsMol7lo9QyNnjh0fXWo
+ZiYmn4tUhmP2TkXiD0fn7KwpBkSW0D3RKt6+XnO3XWE7TwDQmVWkvmgQs0aGp+aTczhmONZcFtUE
+M+NjK81feUNqYKv/TD//j1/lWaF3BFMoiorDwxmzdUt+JmrU9kbNS7fl0D/XKnjCEdWnG26zaIoK
+O1dVtNFo0tfrxOetiqKv4cRGyb5mHg7DiO53SOLJKBRdq2P9h9V5o1bIopTDa+qMtClBkw33gNG5
+MJMluKZfn7Ol9umOV/PERTmYBzMLqo73es2HgCru/hIIxMHozUU900YTKzGtGNuWjdcpZKON/Zpa
+K/LcHf9hdpw7T1moqeCGOWjFoEJY0rAFpWbodYYN7qvKefHxLhtacfiR/SZ/8ILa5dtiRoRmm411
+TRDy0och18WVdbYTvT8xPaGtiUWTtmQHvnvQ5ouZbMxL9YBwX9ZhN7Pc0GrHA8YPwVw+9KgmZ12M
+2H9SQFXdZsSOZibGTsDaVQXsjRdsbsk7xpq3f8tv0CiFMfWKmCySgbd2LIEe7n5RMXU1yzBUptH2
+2ZrKfjnZaxHp7U31+i4bupbjqiBXAVAv72XMpzQUjNc5L/CUWXhnBTPNJrWacNcoMjoKVHNntEF6
+UUzQXrhFV++RiKMmHd0XrSelWnLb22SiodC4E+O2ZX1bqcNYa3oe387I+tKnzgvCFD9harTIVayH
+17gVhMkYcwM63yn9HL81prAyH1a0ohW9ht4RmoJjoB1X+KnPJb1s5FPFIY+taw7/8AR/Jpz/dh2w
+p1JlmZgSn1te6oik3ZgUfKYpjq8PTUP+dV+eecycMNJchrbWGRw1DI46pCZ2SH0oz96fnZGfijS6
+VSVsKT73gS/i6tvbEben8rtL5ZgvTGQa9y5YFM6RKK7JNP5deMuLQCoqRRE2eQ2RaDzV6AQ0UWte
+ajSk5VAvIeUHFYUiKs/KCl+hzcrphEhv0t5tqQSzAakRaWWjijWNMvzhWcb3q2by9+1dvkPnazCU
+xJa2KcmXYCm9mLbGzavFlHohmkCcZBR6B6M70RyLe3ceOn4nnYAddS5+bpw8dJSOo5B9ddCdxBIN
++N71x6g0GtSYjbih0PfXLwW8qAjcjYMZz/uyDjuKpzFkhHeoEj9Isf4SH63AHWuatrVUest1mkh/
+/EGbgcL52zYP7670FhkLTZgrYw+jkY9qMXk4/rbiWNoY1vQmcZoVtX6f6+U7rRncmolzMY5mTA7E
+GVvNBxSaA1JHJYFGKEp1SiZVRqHRHBsmpGfSt+RwRpmIuXIYZ0Snsu6/qQl5cbPG6iVCnhNQ6r2b
+vulTVIJm/QNr38evJF+b7F9pCita0YpeQ8baN45v/ruiq1cu2v/hb/1VOkFJU51vFDluQ7hgVAyp
+XQk/pqNDknqJYqN3PdgW+UIkyjRPsBqymdqQRDP+zkoLCuk18kTrsNmCuik2aTvJoS/2rpPOWSjc
+mnMyYtER6RbX0t7//nO/RKnz9uqYrG/AaGix5VgKdVptajyeRsh1NfzWWvvs9ETCBo9/lEuu2Oht
+I9HcE/MJPnUiTqZXPvMHfE6vI6tt+iYR6a+k923LmG5MW+wbkVy128cama/clzY67pRZJRK4Z8YM
+FffAny84LdQJaMcM0DCcZhXmdYhF1sNieLN4+ZvRDyu2wrPVmBf4Fv3290BTyGEZa49BsxQxv8BG
+568AcPl/e5H/uPsKANevv4f3bWjWY6S4EVVKNlMsh+o+RSX7rCgOKSfid5jaz5NPNByYicR3Fx06
+Hdk3vgnIakX7Go74/h/5EeD1k4t9YHdbgIK/51KDj//Ifw9Ac+OU0RJ67VzWYzipyFLJwk3KLsVQ
+/Fmno3Mmmi06yCwsNR0FfnUaDq5muob9NSKFiivigMZUtJC8s0HkyDv+3v/xK5+x1n7odbr7GnpH
+mA+u69Bpt7jaCJiqGrWbVpw5srAXtkLOVGu76PqcKmR6pykTMnMaROeaw5/HGI1a3E5DLgaygdyx
+eYiqPNKkkiuh4VAXeftqwW2Nxz+9nvPCfWmjf8njjlZSxnrRaPUqRvrqI1ACnsLHN2OHheYpb6na
+fmunzXsVMuy3Ht3hO0PJ5z//1m/h6YYmHBlhfsGDj/DKC3Igsudv8xmFgvtaaTiTQ5zYM461YnBW
+LWhrGa6nFZf9dgsFbSYqm8yV3T0RR9xWh2erbpDrZSmxllYv8Rm/cja+NvrH1Vg/bSDMYEl/NPEm
+eVVzf4Yzrdoc/U8/y/1/9OMAFO6CKJJ9dFGPbC8IudGSsbrlGkcKk74X9YgceefhtEk/0xu6tK7h
+XfmUsdao7Ach58vLiureG1QaCBXAA4Wfz/7bP827rsnTR/trXHtJHbAbKniGdzgrJeIwLu7T1/Ls
+fzO+xY5Cuk2zDKPRrEhTnxMbsNnWpDY/5Fs2ZZ++cg7Xr0qfb+Uzdr/GdVmZDyta0YpeQ+8MTQGP
+vllj3ijR2g3GFTi1OOJOJgt8vUJtXI6JEClQjTTO2/IoFIKtUdSUqaizu3nJUDESWrYm0e97qlWc
+kBDrLSoni4SOLyrjDQcC1SbuzIY4mr041UrFP8p3FVqAnmP4pn2R/h++3OCHLj4FgNnS+xPrK7Su
+iybwtx77FtLqC/I+7wmWuKx+Q9Tde1sddkfPAfAPP7JF51ekFn5Svloyf3WaOeJIdDKfWh2CLb+H
+r+nKe/uiSVzYtXxbU3JBTFhy665ef+cd82NDHX9xh08cizlifcErcGqf2n5tabRvTmdf2+NDWZPy
+C7/F//Jjcrvytf/iZ/nAB0TzmLXETHgyWrCZiWR+MD5gMhBNoJt8iZ0tWZNbRy8SnGpuRVdU+Jm7
+S9MqdkZeoakXD2/7fjOyCpF37x/9LDd/+mMALO6fPsSWOFYzOFtMOKhlnY5ePueeZh7GhzkL1W77
+fsBsoCaB5nS4VYVeRUInPuSmOun7xJxqucBG1CNzvhLQ9c3oHcEUcKAKoDutGRzLwu1cGnP7Gdls
+jz2WMr13BYC1rYLRZ2VlFP2bYm5otUQlu3/XIwjl0M/nFXs9mZyTI5e+QqPfL4TzvGd3xOdui0p5
+bafks9r2t35kxG//jmym9+2d8HsKuPLeK0v47y8zBsdApUzqT3xgjYt73wzAj//1P4FzJn6CbUWk
+O/EvsveU3FiUnU3pdSSxaD5oEm/LQudTORR7vStk3yyM7k/vPMo/el7Trl++TVG9dXUwUPuzpqQZ
+CsM5yXb58FPLKIgwse/9gQ9jx7LR32srTt8v/fn4u/44/8kn5ID8zPouv/LLwqi/qy/j+Fe//yIR
+ghOYZmA03/bfnadKOXL+STbt/w3AjZe/yMdSYXC//Igctm/ZWudvu3JIf3oU82svyzz/5eY6v54I
+HPwjr1TU6Qvy1gP5fXz5Aef3RW2/tOVR5ZLg5emNZG9GdS5z/x0f/FF+cyzv/eigz8KK70MhLDk7
+HXHnrjCC305O+cBM9v1vnyZ8UKwKzrOAa31hznZZB1PWdLVCczQM+OC7ZZ8+/0yLP/Yuecfnpzkf
+fvL17ph8Y1qZDyta0YpeQ+8ITcFaS1XUHAUPOGmpHv3SmHulOH0aRxntx4Rzn5+vUzSF+0cTlRJ9
+y3ykl5CYI6ID8axXbocTTSl1mhXJqWgQbluvsD/3SNvCUe8fLUCrGr/0+YKRkfr3F24siDZFa0i+
+8JUIUU1jeGxfvMzX9yzf/2c/CkCrCGlekLRizxOH037b4ugdCo3NHWqkjeZugV2iWOtdCPlgwZYW
+6oT5Df6UI+P/zfguxeyt1cUDpJr/4BkfT++1uPZon4WmaX/0QxLPDqcx+xuienX8GT+icLubzPh7
+Fz8CwKG5yZ9/WjAT40BMn+ePrzE8FEnrlE0MOvdfgzbz9ZG247icnv6XADSf+bv86uQfA7B+8HEA
+fvUjB3xUr/H7pw9e4N1tWbPfTG/xRFeiEne9Kbar6eSamn78yhwb6p2efo9H9mT8YfnV8YiDhhyv
+fzX/PT5+KJGKV5JnmA9ESxlrQdyDo3PSu6I9rKcw1ijDvu8RJbLH37Pl4KgjOFSczAxDoXgSzmbC
+3duKG9pJOLgr87J9aZvw3sFX7eur6R3BFBzHEDcNcX2BuYZjIqa0bsrkXXh0nVSNp7jlsxgskzcU
+VadM8dSz3DiuQOsnoskZRsF9wrOKWmHio1NJTPG6TdYOFYSj5ZJO5H3tXofmHTmw9ZbH3GqF3pZC
+xBvzMALR7Ptk0XcDcO0HN7iu1X7u9jrx8nr5jScB8Ofn1A1Rv81ihu1K2rWbJtTqy3aWySiNDptG
+VNV6t8/3XnkegOHmh/k3/+YZnbmvbss3FTp8bDO6a+IPGJ6v820/JO/+bo32dK5v8Z5QS8DXH2F7
+LnM/3bjGFb2FKr761zjRi16/+2PfD8A//4MXePonHgfgEz/7WZ5a15ugbt3AVSZU1W8jg1CgVWMN
+O72/AEA5z3lkX2z4yUsyjo+ZR/mErtn7nno35nf0lqbre5i5Jk7ttPBvyj66qwhRF+KKs9uy/pfW
+A/xzBdy52njTbnmuQxDIhTQ/9PTH2VNQ2RMK2rVeQnumIDTlnPNU1ukgG7Kjl8vcnRdsK2hsMom5
+vCV7J1bkrY3pjEJvHFtMk4dXIgTHI+x1EShzO2ShqedvlVbmw4pWtKLX0DtCU6grWIwMZ2tDBnpx
+RfRgQXlZOOPdk4qtdylicg7+0gWc6t3aUY1TfBnqvNKY97w0VHqdnJ/NGSomga/SZVgXDwuNRtkC
+r5Bnbx2e4nXVYZYucB1RFX0FCHGNwdcU5M3tNf7KX5P3PVZtYfbV/Im7mLZeca74ALQNVu9JdKIa
+NNZPaKk1hOH2pD/VqKJWZOg8yPn2H/4BAP7VL/0We7uCFXlwlH9Vj15aL69/c6gb0t43f1dOcS6f
+zYf1HsxORNAXLacTtCj1vs4ybnDp3TK+B0XED/0Z6ecLCsjyM39xg799Uzrxo//hFp9X3IvHf/Ex
+Xh6JdmOy/G1yPLqgMPlubBlf/UMAvrNVcuiKlN7/pisAfL4358e1ovT/G025sqvQbOsjvrkj7/jN
+F13aDVnjsToBG6P7nOpV05++kfCtT4iDlaH7uj1aSlnfc/jB7xHcv6P4Fo929Iq88xFBLtrUXF8R
+DgAcKi0AACAASURBVFOsAqjsV5a7Wox1JSgJFXs0urgg10t5wq78MKgaVJpWXucRN0/lPDScSvYG
+0Nnfojx8I9iT16d3BFPANZiuhzlwaR3oFeKmIB8qsEhzCgph7vQcrObG56qehxstUNQkP96gUjCV
+3l6B1cyu484a0xMNI2mFXDkZcajYgMmdgsm6/H03nHBS6QGZWtyrinGnFXnGcUABaj0si2e1vPU7
+mzgaqgrmOaYrG89qFSFxgJMoOpAJcfSeBdwYV5GcUBw9f3EGijnYOXdJUxnrk92YZ5H3GjfFlm/u
+XyhUVU0XlnQqKvPNZ3I+KIVz/z97bxprWXZeh6195nvuufN983v1Xk3d1d3sJtnNQSStkY4ciYll
+GZKjyBY8RQkQwAkQJJHlXwaUBJYTIPavGLETQ7aSyIHiCHYGyTZFQ+IgikMPZM81v3rjnaczn7Pz
+41v3sZrurqpm03IFeB9A1u377j33nH322fsb1rfWWVt4Oy/hEQiUjlJYbUrY5xpOQtITY47IkLH4
+8JQ9DrmDH6Fa1njtKXyaXIx/uO6hWtwGAMxyDU39he+3KTIWleUE9Z6EfHe3LyAYy32/7Uk4+pm3
+LfyDXQnXtk8HeC2Qv39sNMNd9mOEnkLnpjxYa0QHat2HHnBc/B5mt9iW/uzuv0Ko7iogoQLYhb0W
+6qz2fCLfQ70r1YXaQRWhyTIjgUfTZgXZWMb4phHhWXKFvjgB2oHc97vDHC+wfTw5ke8vKja8MSUM
+LAXfknMbz6pwOJ/0lgFsBu9rTM/Dh3M7t3N7hz0WnkKRpRieHOIoG2BINuT2YYbekYCJUNnA81dl
+Zxqd7CA5lepD0CHnXpYBVe7Wrg13g52GoYfcE68iQIoKOyI1FXZiHSE/YaImTzEk/VtiGEjZiRk4
+VWSaij5TyUJbtgGP4UPFaKPJTkzz9FXEDQHCqEYKYywrt0lYapGY0NyVytIAMtm5SztFucQTkBa9
+WIRIPfle7hqYVGS3emqthg/9uLiw9/7PEyymDxnbpeehFEoWTy5fDrFN/sTelpxPTec4JTlNdTTA
+hJTxtquQsOvSwgSWJeOs1+TfTSuHPhFehEWeIme2PMUR8GUZrzf17TPgzXu32ry7kMmDTcMgaQts
+GzqTKkh4+BJWY0KBS4H69y9VYJ7IblwOrsOZSGWotZWhSq9vfahwj0rS4ZRAoRs2xiOZhxNUcLku
+1z2OAzgWz5l458A0sEsP6weu+rh4VTyFWm2O3pDM1UYfx0yK7zBkjDsteCSqWSs9NCl8M/dsrHFe
+WA3Aj4lVIV6hMwTG7J7VSYY5QVGnYYKcT3arqCB6fwJR557CuZ3bub3THgtPQSkDFctHfQGUI1nW
+ssECFSbDajspZhHjaDtGY1WYgMoFpeICC4plHGvNhXMsuzWCIU4IV7Yaa3AZixsnskO/NTIQmaQd
+Ux6yJaVZUcAgJLpwXJh1qVnPyJhjKIWSSUvPXaDHMuqi+xSqIXeuuQ2DWomlQU3IWYZiqYJdyZEb
+cj52XEJbZFPKHf7uKqrLrrg8RrcqrxvmDv7dnozRF0sfCzzYVVhS/adFgbQqv/H2/gx7F4WZ5xm7
+zd8N0CLNWVRsYFWLNxWWHjxDDhIFu1id8XWNSs2+ifCCJIQvHc7Rs6SO/3T9FdxeE2i2d3eIiKxP
++XtqD7wfD2HpVRgwArIyK2ANnwMA+Gjj0jXBXJhvy2ev7XYx2CdD1LUfxt23ZdfV7hOYstvWvryN
+9S8xucsmqdyqwqQAjqOB5JQktRspKvRCQ2pFPHvBwcIWr+nKMx/HZ6pS9jXMBCurcv0H0TYukS7v
+lPRW1XiE+RNCH3f41hRPkXrvt9MET18WyrrJLEC7KvPlBpm3Wt0CEXU3lTUBJqQFLKaI2ZiX4x6O
+GqvvY2wfl0Wh1LDSHHl1gqIitfROcB3RNhN8pYkaa7OdSGNKXjqHicHSssGGSeiogMHKwWJeRaPC
+7Gw5OxObHbKPwtMTRKHcZNcIEVHdqJ9naJAxeGx6MG+y1ZWFBVNbZ8K0abWGtQ05n1WdIGH7sWGa
+sCaS+MKa1PELL0Rhk1OxTFFaNZ5/joJuoknijjwIELNVe2GYMOeyIM03WhiZEs7k33RAeP17mmYi
+0VR6majHE7tT1Ae3OBYy3o2uCZDZ2mi3EC594pU2bAqNlG4NZZ0S7Q4VjawcHhs3InuKKZWgmre6
+UBT09bYq0DeZak8fHXj1gKviNZlQqdxTt9pF8Ywk8352ewdHrgDGLl+TsTpu5PhpJqb/YDiHtRRW
+yd/GUwQi/WHvNXjybGJ0IPch8DLMlFyTMbKgqrIIN2YzrNhLinaZK5uXV/GJH5AHt51qqAZh8S0F
+q8cNrlairMh9X/NJXWfUcfuWLKBXd13cTWSO/Gm9QI33YbWbo7slC9zmlH0pNYWNLRnXo5tt9Bje
+jsoUmryR84MqonTZfv5o9oHDB6WUqZR6USn1f/G/LyqlvqqUuq6U+kdKqfd3Rud2buf2b9S+H57C
+fwrgdYAyvcCvAvjvtda/oZT6OwD+MoD/4UEHUJaC1TBgH/uwXNm5jwsgZBeZPa2iOxBaqoXZhk1X
+NLxH3YTtBgoq+ea6gGayKy9TTMguPKwqvBzJrrgUBbkbaSRV2T7DUweFS9EPu0SouRPqAh1qINh0
+r7UGcrqw03SBVxnm7HariEfSPdhJHRRNuthsxMnzQziQ3dNo4SxkyB0PoA5iSpRiPJtgspCdb9YD
+yly8jtPBEV7/Z1+Vz1LV+UGmuavmGojZrPTSvSae/rclBJtTyThwhlisUz167EMr2YHrSQvFiiS2
+lNlDZsguZ/fk+ntRjgmpxr5xfAJ1Xcbzyycvon8smgXT496DMozfg7Fr0TBgkaA1LWZIqHj9radi
+XGQzUtGVsW+dZPhnJPqtHL+O3x/Ltfq31/H1F2Sc16MZvGPxoJxYxmpujLHCMGGWxUimRMBeAExy
+NqTkW8hgYHZdXPX6CzY8ajlkBwFKJa/DRRUeGcYXhYRurjFDtCbe1mnLRieXR+mf9Pu45kqo+5V+
+F59bFS/kmJR4l1amSA54Ds0C3pAI16qFcE5inFoA3a69r9H9oFqS2wA+B+C/BvCfUUruxwD8PD/y
+awD+Oh6yKKDIgUUfoW2iINimtDRu36ZO4lqG+lgGMEUN3qHE8J0VuvWjEMGq3PAsSRFRsWh4coQ3
+eyS62I8xSWUAvUS+b1VLuGO5AbY5QZIyDjNs1Nj5uN6uwvUlR7FVyrG0kUORfVefakz2xfW7O7yB
+qCHu6u5uhOwGCVxsAdVk9RI2/14bb8HRstBVOk9Bsz23PxN38e6t38dsCdnIHdxj3Ty+fgN3Vylq
+YzxC7Z/6vo4CmoWM11MrJdwTwcP3Lkrc6zo5xoTwGqNXENVlwWk2NIJcJmPLj6A9MmOVMj5vTOd4
+7foXAACvfwtQPcHwvz3uw74lr42qCYNakN+P4GEZPhimhSrFW5JKFeZMFss3bru4VJfPrNQ+JL9r
+AeWxjPf+23+AtQO5pqPAR97ZAwAsXnwb8a4slhWK7Tq5j3Iui0Zz00K1JvMwV8ZZN26NnYq+biDY
+lNeNxTFGjhyr6hY47Ms8K6NjnDB35ZAsqK9tNBgHVlsFFseyIO9VZ5geypxrRHNMvi3dqAMKHzVe
+mmIWEJw2ipE4BKJFERqEJhi6jVr87kCr97IPGj78LQD/Jb7DSNUBMNZaL3s17wHYercv3i9FP5m9
+v37vczu3c/vXZ9+zp6CU+ncAnGqtv6GU+pH3+/37peifvLKnreoaGskCoL7i7eM+Kor8iqMM996S
+bfMe7uAZdrVhQGqwxgQWOfhDE9CENh8PHbx0TOmypMQbzHzvcZfbj8szcpN0VIdN6K5VaCxdVFWY
+MOqsGzMcUABYRECBBb5GSGlza+NMlKXuaZzkkvjxIK7cyXEVOx0K0tT7aHak89GpHmPJLpOeSAjj
+Dhu4M5VjbRZDNPKS32th9YbsFGbhAXiwt2AwYZoUJVCVMdqfdPDhPybdkeshk7nNEi3iJvrzHG64
+FBQ4hlcu4dY7CJiszUjLVutPsTcR7+HLi2NcGRNWfhzD7LKT8O4UFht3svvp1N7VHsbzqGBQe8Iu
+AW9VbmA6y9AayLV0b7yNySeF53GdWh3tVh17N2Q+baYX8AdarmMtn2LxTfEEnM0OfAopdNty3HYn
+xiyQikI/TlErKO8X2/BN8U5TwpLtGhDPSRHYWcVl3tNxWqDlUQdyUKCVyXgOWCHwlcaswkY6nSOr
+kppNh6hkJIaZlehw790nhiK7UkOL3qSu1mAxSb3qB8iYNG6vDRGv/dFVHz4D4E8qpX4SgAfJKfxt
+AE2llEVvYRvAwcMOZJQFKuEImemi8MRtu1If4kuHjHfzBG+R3nqzWMXrdyQbvLcusW5lbQMVkmEa
+cYyUfkoR97A5lGPsJxGeoU91xA+Egwk0mYx0kaDkDc/zFKtyaCwyC4rwaJdQY1UATEsgr5h4YV3e
+vzDvobUj5/HSzTtwZ+IS/h5LSXurdRxtiADIhwyFOB3wPNeQs822QRn2b1sGNhoyWV+dVbDKisl0
+OEXUlddm14KSyOU9HyNr+QfTgM2KwSdeSLFzLOOs9+Tc3rp3E+5I8haHMxerpsy2jZ1PoV2SmzKe
+Iq/KwxKbcs0db4EvEir+x9bm+P2FXMdHsiq+TUBOyzdwStl5I6UIy3flGBQXYf0IpUlN6Haj2j5T
+S6o6Lk7asgg9a7hoJzJ2nUBKr5k3w6d25Bz+t4GDT3TkGLeNGtYcub/J8RiVmixerid9CzVTYVCV
+a6o4Fqy6vF7JZ/CZQzdZerZaAfbk52CPSszZ4jzNJzAoFDsbJ1hQ0LZCAJifFzAymSsqqGNQyuvn
+3RwvDeScP90tccSO1u1IFgfvVh9BQ/4epE3U57LYvJrNUKXGpJlWcEG/v96H7zl80Fr/stZ6W2u9
+B+DnAPyu1vrPAvgCgJ/hx86l6M/t3P5/Zv86cAq/BOA3lFL/FYAXAfxPD/uCNgyUQQBnNEMrlp3y
+1XEIt06xF7+DNWZQK9Ye6lOBOYNS4K6/ARtLiKeJmNiDA6RncGU4Do5bUoReCWSbH0zuouSOMgtd
+aAJWDEuhxzBAL6bYIs1x3+ZwKYWSCTw4GaYzST5ZH7mGoiq77dOdFrIpVZVNccuznWtYkw0IwcRE
+Btk9LGRwSbJSaLmmi8EQsIUI5FMbCmFvqa9oIslkjFa6F9A7IAuyfnduYcVdvCg0lCmfGRx24HxO
+sBMt1tKvTGpQu+Ie1fdn2CTc1zQr6Ngy9sM8h03PyuT5JoaHD5ECfVb9MD63ISHTvXszNG7L8V66
+N0Q2o3jMlI6j/g5FmAQM9+9mD/YWTDamxfU1rDPZl2UKrW267hUDc8Z3VYdqzm4X7HHDx3fH2HYk
+1fW0WaLGEGs6nKJhc85V5N7VuhGyU/EIwjSDP5ZdfGrO4VQJNOO0MJMQozcoKf+Ewjq9O3tRRa1B
+tvGBiaYnvzdjGOuoIY4IWqvbY7gNeiDVAOs7DIs7W/hkg6rn1+UeBOZAOPAAeBULDj2CrdDEgp7u
+5e1V+K21B47nd9v3ZVHQWv9LAP+Sr28C+MT347jndm7n9kdvjwWiURcZ8tkRrLyKaUV2xM5OHfde
+pL5DM8bluvTH22oVBVGKtRXZrRpWFYuqrKLOaYLelHJe906wX0rsW3Pq+KS71CyQBNHOE5dxRLXq
+ydEhRkSK7acaRSI72QwKg5sS+3cZ9zuOglOhwGwYYLtO5h77HrauCcuSswgA1tA1pd3MRgg9F69C
+1xP4FGCNyxTeMnnky9/X6sGy6oc4rYJ6NPCcXfhEbM7GR3jrFUqQ6XdnF646ck01BXQd2Sr3ujZW
+KT5Sr8n5dq6tg8A9xOoEVkt2RMtcxYxBpj0+xYJsV0z1Iq+tYpfXl+ce8kRKnE94U+xTeLV78RZe
+/bZ8pndbxj5JpmfegQkNZcnYZvnD2KQUbCJaq2UXHePbMhZbO3hiLMfY8C7hI6SDq29L89h6tYS3
+LondZtBASc4NO3JQOrw/rTZyV+af5QuepBeHKCIprcaFgYUlc2hq5DAt7thsjEpiF4smeQx6PSxW
+5ab5rQowY86ongKWjGHTYc5hWkWbTXOYm1hj4tLvdrBicQwbHrQituQC57cy4UA8oSDx0WvLZ/Nw
+H8/uiauwuf0E9lbXHzKm77THYlGAYaN0VtFaaJgQzHpvMMUqqajWohIgEUatWcI05YY5Sxn2agMl
+NRiHToZ0ITfrMMnRp6s9zOtY4QOS8QFzjhw0VsQVTfUOZoa4/sVpCU28xKi0USU112scLcOwkKVy
+4+ZWjusDWTTu6A6uHpEG/tk6/KWwZJ3EKuMu0jUCXY7eRt6Sa23FIYqaJPwstjIWmxdQJyw3bmYo
+B1RmqoXwjsX1f3k9RamkmnF/j+GyKl0A8EjRFWflmbs7XAAjV0KpT7BnJG/7qDblQYq7TZiRnFtc
+MwAmIMd+E5t0+zNm1pt2CJCjsj5TiBlqFX0FY5PVl5ddHGxSWQmSGXVMF0kh17dhmdgvl5WdAvqB
+aAYTTlc2iFo9hPvMhwEA0aszXH5OSCIm1h1EL3wUAHCtJ+MZrP8AbJLorD1ToH1dHqb5xhzuXBJ+
+09U5KrmMx4xKXxgOsCBNfBkvUCNXh0aKmiPfC4khMZWB2YyYhUYNz7hLsSILbVkrsYgKVBOZD4e5
+jKXnTnEUy/U/gRS9QMI1L5nCbkvlYDU3cMp521XL4xroMnQdtkw0e4K/MXZWcMGS+xNsKsT2Elf4
+aHbeJXlu53Zu77DHwlPQRYk8jHDiW0jJTrvmznFEzELSiOFXZKdcbQWY3mVDlC3ewSwvkY5kF4hm
+KSYnpLs6nCAZy25dm+1jyp3yqiMIRa/qIzmRne2iEaPHldssM5is5dXzOSqxeCQrsezHN0oDBv+e
+phqbriQrt3pPodhj+Qol3EC+VxAGa/oFZkwyOX4bZi47Rmb1oQKBYHsZ6+6lgkGJMuVVUFnyG/RL
+1PbEtdeHwDbVnPfHDB/0O/dZ31oy8yRIXMrY+XfQOhS3erYl5xAYFky6xqbRgeFQwblSQSVmd2kR
+YEa326nL7lNJPfisz1pKgVUzuDZQnBBivGcim8nYX16R3fXWyTFUKfd3gBwNntskepAYGwDDQpoJ
+GetT5g7SSO7f+uoe7rbknnzWuwpPHAHMPiwud1CZwVeyA0/mCorlaaPuwyMTVxQaMHMJJZaE2aXj
+4yCT+1tOh8C2XMfm3IHFTlmL3lFiOgg4F8yBQo+w+eqGA01PQOnaWWNeRJq39BCoc3+eQsFl9+RK
+xcWUCVM/aKAbUNO0K3Nan1po1uT3xlUD62TIqhQLeBtyvKZXR9tZBnuPZo/FolAqhYWyYC0KpNfF
+pTyYhoiIv6/ChkrZyjq4B01R1BErB06vAc2W5Tj1EXVlwjr7qzAXMrnjrMDcFFdyTlddD2boM862
+8gJFTQbVmkwR8qGPMgNBRR6soV4SiAjuHhDylrt95iKuWrjEh0llFhQrApYtD7ryFGrUMEwGCllF
+HprCduFIgQJZTOIVM0Ue8HymHnRBundkWBwQJntQ4Ii8ke8pFFxlhhsV+ImM0d07Jl5fkzDnwkJy
+C+4gANoycS2rCsOQ367HQDknsMpO4BNwY0zkvcJIUbLGXlhVkBEMaWzAIa7j6KQACCK7PV/SzpXQ
+BOOUJTCJHxHVWiYoGPJ8sRLjeYYdL1tNfK4ni8XfcV38Ykd+52sncu/+eNLBvC2VDyMaIGUlxpta
+0CQ7qZSA4nU3ZvL9SVmg48pjMrJ9lCcy9pNaD0UgY2tRyczPYxwfyXuz9RDrBNTpkxbq63J9aW7C
+IB4mHbP9PstwyF6bC40CSSzzxY8GGMey8QVeFQXnsp7IHElgQrHr1pnkKNk/o237LLzFNMHYeggT
+z3fZefhwbud2bu+wx8NTKDIspqeY5DnCumw1+ihBRsjpaTVEbSqr5DwD8oFknBVryq5VoFKThExN
+F7A82aGySg5NqTjPChAoWT1XiHKsB9WzZNdsGKJBjMSRAkDknW3m0JOMvyPna5rFWTbPCw3UPNk9
+to9eBiaiH5nXQuQMFUyc8joNhJSlT80FRsPbAIAizFAL5OAxm5ZcNcNSdF47gGYIUqgMIUk4itUM
+FZKD5mzg+W5/oVUjTqHMkDFcSZMQ1oEkKE9XJOFoV4ewY0moWeUxUk+uf5bZKBeyM4WLIRR5vsqu
+eFtjVYNRikcXGym0y5p+qTGJmPytaBSujLPvyq46m/dwNog6h0uF7iT9Dn7hXU2ZMKeMDfoxEEuT
+kL1m441Qmry8aQtfOxWvYcWV5OO+sqAsycJXo2NkJE6BUnD4UqcF6IQgrsj1j+0KBux8zZM5DE1q
+utKFJs4kY/VhNK8iKGVuOT0ThyYrAP4cd/q87sUYhwnHZUnBN0lhcLzTuYFak41SRoCAoVkST5HR
+05sTTeqXwDCRe1Nxm+gRs9+fLOBSSAhtIGBj1qPaY7EoaBgotYuudjEmf2LdbiJuy+B1YCCkK+4V
+IU5KmZBeJJOjG/voD1lKqhgYaOr9rR3ATOQmrqw6SLoyKVLG5/MJMNLyMMU1H+OZuI45cij2FMSq
+Bs3O0wFl24sox4SttUHg4KWegKmuHbaxcyLnnHYTeIwNi4y09aYJi8ddDEM4DFytRgOzubjxmhyV
+i85FrNKlRKxhUNEpSivQ12Ui3PvdEWazHsfw3c1jlUCbLjyfMXBq4ojkLE+zGW04TNAK5Nxjs4qA
+BC+eCSR0KG3Vgk+vNJ/I76KYYkRinJVghpwtyW5aQpPD3DnOEN5k/8hQFkiUNqCXlPnAIlv2cDyk
+90EXKE0JH/NRD+NtKTPm4RyXOgKPee30LtCRz0RvyEM6d4/hTeRhy9ZqZ0zaRuHDptLYXJuokT8x
+juXfFb3AWl3m29z00bBlAHScny0QS8IQv6JhhRI+JCqHRwj2cJ6h2ZLxHMCAR8h3PJXrz0tATQm3
+7wQYGwwZ3Aghw6OaVWAyk893Lbn/o9jELklvbs5H8AzC8BurMDVh1b0CaL4/UeLz8OHczu3c3mGP
+h6dQaMTzFAM7RmnTfXSnmPSYfXdCBCNZHe08QczXxbJSYRRo0UWKJhO0p8zqGyu48pxgGlynAmNF
+jpfQX0ynKSp3WHGYx8giWV2jJIRBPYWaymBmcux1Ld//Vl5CMUmWzxK0NplcDHNkJGLxomOUS9Ea
+Stmnrg2tCSbqWpgogdoa/ggJecA6TKjGpYfMooybreFHS/nyEn0qbH85P3ooCbLtiJdSGAtEhexs
+FX+KcCZjcOtYKjW7ayeY9eT3XHcTcZf7n11Dgxn3pFhBpSpjHzfEG2vNDcSpjFuBFKdL+b5xirfp
+3Qyzu3iFIYZN8Z0oWmB58rEu4RAXkOqHhA/QyCim0rUMTE7kPj3x5BXc7cogvGDbOPL2AADNHTn3
+bLuNa5rutRmioD7DzIzQJAflIAGyIWHT1JNIXQOZId+rWCeIKObTmOag04CMwjnarMLrEAg1snFK
+qHwlmaBkU13WH6K05P2I3byYjUGsHA6OCtRd+Y0Nv40KPVJTR9BLNXWyhM/GNo7YdDfQFVQL+e3Z
+JMVddtV2a3V031/x4fFYFAoAMxjw8wIHcr/RNkpoZrqblsZJn1j2xgjHLO+0bZnEh1ONzJKHqWvZ
+UEQbBteehjcUN9dyHBweEb24JQPW2j/EAZFmqZVjsjwho0BB8EdU8dBqy6IQMpZLihwFF4UjRDg+
+ks/u7gDPvSrMPWuf/jhWQjmPiKAZx8+Rs1uuOAlR5U3OawGCgNn8kdzBWGXI+JAmlo98Im7wvVsH
++K3f+QYAoPfWTej3JEIVi4kUtKwacj5wi6KChCK7EcO1ZFKgCOShmqsUFlGh0AVQsMekdgwnFbdc
+sZ06D03oTEKCKM1RhPJ+f3GC3osS43/pWzcwui006SF/T2mN5UpWKnVGrvNw02ckTqfJAhcjWdzu
+7QPPd+WhuNdqYFvJvVqJuBAuchTsKESWARnLxPEMmkhVezKGVZMHMs/lRypFjDofesv2sOBmEdcT
+hCUBWaT7zyyNiK3OlbaBnNWX0cxA4MnEHhbGWfmx5EKoPQ8W76Nj2bBduSZfAW/wt1f7GmG2JO9d
+jn15BsQr5hH6oSzYw6iN4VwWjq27+yj33l/vw3n4cG7ndm7vsMfCUyiLHItRHyOMkbP+P5qUiLgy
+DlQJh6vjfD9EhXVakIoq8KYIauJ+r7UXyHxJKGo1hVJXAQD70TE0uxUnt4lfjy04PhNqcwubXPkd
+L0BEN3+nVcEqM+Zt1tL/+f0dibmGyarGwSvfxmhXGur9mycYMdllcweLjAoKuvBxswJEsppbSRem
+K6+zmVRRInuObEyAVHKKE1dCkeN4iLcqcryCPQAPMoc4+8QGvFQqFVk0w4LVigE/t9afYWBLqNFs
+vY1pJsm10ujimIQk+b0D3PHkmtyBfPZ00kExl+x8kpmIQT4JrXGyEO/gIJpAU8bMoFuudYpl9cHQ
+BQzK0S1l7h5odMtLlSGdC5TavLwG1ZPfGxkGmkO512/uCuV8EBSYErDUMm8gypdVBIV5hRycBRDl
+hKmTln9iebBz2eUnmUZCMFg6N6C1vK/4GHXMKnxevx4vMPNk/jbjOfbHMt6BSmGSNm3Z1+AZCnSK
+UalWYCjxzG72QowpV7+YjRBSTm6Z8Pbvayyteg7CWH77eHAH1YiAMitHi6zTj2rnnsK5ndu5vcMe
+C08BAKA1mvAwmEs5pur6aBPmt2E3EDHvFfkdzJXsbzeP2SRVyXC8It+Lsm0kLAFWK0/gmCzIxckC
+A0Kej7TsLt1cY+CQViwuz/riy0oOM5NVd5H6CPbkeIcUT70/p1dAI8/kPL6uF7jyednpfvA/dHCR
+TMIZEY3OazP0GCOmcYS89REAQHOUw8iFHu2NhTQMWYs5zJsi+jJp15A3iA6cuYhf4879CGG43lBc
+XAAAIABJREFUJrLNSBU0xBtJDI0JE1F5KLvnMLexzb6ZmdlCfSC/N8QcMTkUxsMdlI7U/80DGZPj
+1T4Kvg50jkkuv1EPXNzsi9cT77+NuFgyLslYKcMGyOhkGzZifX9D1IOhzpoyb7ossPAlB9C+MUS8
+Kx2fRR7jhLiV4A7zMmoH8fMyn6xsDasUJDa6Ci1XvKK8W0HAhqcFOxjLuMSI+A5nPkbBhrWsNOE4
+hKTzPhSWh4NIPltrZGiSHu4UJWrVJSeDQp2Orm4tCWjbsChHZ7k24G7wfCaI3pZ57WsPcSy5myc3
+ZOfPbA8r9EyiuEBEIeNqYwP3FmzYG+X4va33x6T9WCwKBoDAUMjtDOstdnflOaq23KyO4WJZIK+6
+dUS+TOTPXBO3Pup0sBPIQEZODsOXunma3YbPBOTX9RD2XCa0V5OBTiIFO5LJMZxm8EiyEmQK66Tg
+6nYsgNx3z5MA5X+979z1/f8/zfCqfhMA8MT1IdLGDwEA/G254fFmCrCW3Op7mPjycBt+A8q/DQD4
+aE/cvm+qAv6aHDe4EmCXCdGb2oXH9lwjKfAwb3s1EHe3LOaICAxqFgnsjMzV5Au0gyYCQxJSbqUB
+TQIbxzGxQRDZjXaJtissMSG1O59NKniJKdpaeg9+Q1aWJ49HuOOIK16pxrDYaeqnS5BWAs1eBK3m
+cF1J5mbx3bMKjfABf3d5RZ1VfmqmBZfqTBtPtlB7Tr73M/oy3qagypO7bF++fAGXHZlPd8sIdSbl
+1IaPtiMP7GnkIFUyBzKK6Li2A5Os4W27hhFDx6AfYZUqWSaz+ytdH8805Rx6QwMB8QFBBVilupjT
+qkNRBVqRPm4NLuZc6IJGDREh2MHCg7FCrc+wj053DwCwycRoVgI526zXEo01V87zlf4QTzflGXA6
+2/iU8ZB+ku+y8/Dh3M7t3N5hj4WnYCqg4ZZw8hIhkW1mMoNa1ooxR06MgJdkSFdkLVuSpVh+hGRE
+TQa3xOktSTi5noHZbQk1NotTzAk79tjYNI/nZxJ0llJnu25TKVTYJGPrGK14SaIh52MrgGA1GMA7
+nN3rhE3rGy56G/KXRiDhip/VoSdUzz6aI3Tl9YGtsMnd8eunchJrm8foubLabx9P8C8yOYeFewv9
+hoyFceoCeHCycc2nqElswqRXMIm+o1UQkXw0GhUYVsSTqFZS+IoydlGKWZ+JrWaJciLv+yQf7Q9N
+NOtyzaVrgXKV+DpiWIHck8VaFS0SkO4zfPIjGwvu5k/5Ll6Kxe2uBg0s5kz+okBMVKcyiPgrFXxC
+vkPLwwUSreZrObbJhfCGTnCRwjYZ+TTqo3u4Y9BbWYtQUrLNTXMMGR6VpUIENqyF1KnII5gsT2oj
+gZGQ4MaJsdFcKpaTzTkd4+hEztf3MgwZutWhMSUtXqUSom6I16ASEuvMDKg60bSnM5S8N2k/gket
+0JptAVSVthIZ5InhwmJycWzbKDrkZKhX8CbYpGXP8eLhwxi032mPxaIQdLr41J/9RQS1Dmzy7yll
+w3Y5CNpGyuL0dJpjRny5lYg7WOg5hmMZ6HrzLi5CSD9KNUDel5t/OPwCopm4vlEooiHlrIMP59JH
+kWgPOUVlZ3ONpJB8wHyeokeosN+Rc8juC9HuXxCiUiNiF+Avf/k1mF+RUGKbi1Hr0irWlg+bVRXF
+UgB5GiEm6L4g516uFPKEC4xpoCDWwfRsXOPv165tomT7scHciAMXPjn+dv0Wgkt7AADPuIKP7bKi
+srWLk77AqmNNPcTJCb5+R2LW/X/8FbzOlvLi+g28SZSsn01xwsy4z8V0AQcmWZ9CZcHkyprBgqJ4
+5btFtPf3RL6cAr/KWv8vxWdoES538hAuCz6mobDBfpaf+tSP4s98TsLG+nMfgYLE+LrwYdlyTnZD
+qg9ZPINzynCz/Dz6ibBql4OvYHLWr/IFlPoH5fqM1wAAyewK5vGXAQCj7CKqWnotusYG/vO/9z/y
+Wu4Pd97dFCstLVND+zLmtYK9DHBR43jOLQtrvGbHrmGlQujyxha2AvmdjR0RuGk0Y9gtCS/qlQpU
+INe0dtHD0yuSr3JqKT5CfsxHtfPw4dzO7dzeYY+Fp2CYFiqtFXS8KmYU+qhrE7Mls26hEHInsa0S
+fbrxDmXcCreCuStJREOvolcRl/GC08QN1pKBdYxn0rjU7MrqWtVTDLi7tNMEpzXZSbrGDLdn3EEb
+GgXdR8VuyUfN5S6z6HlHzvdiu4qtdfm9BhyckAvBqfgYzeTYHll9FxEwq8j343l2hvgzSxdokMOv
+qCBc8vmVck1zH1ghL8RdFeGjEO9ouh7D2BXqspnXhFOTvXqDgjq9+iou9mSs5u6TOBzJ7nhJOzgl
+qUmgTYSFuKImd75IxzCYJCvL7D5W5vx9jBPwS+XpI3221MDmxyWs+k/+vVUYn3wBANBtXUbKNlZr
+nCP0ZDyWuIhCryDzCLVOnsV+JF7MhfwjeOVQvKZt7OA4kWrVkxWhvDtK7qCAHKs37aOzKb89Gkyw
+OLvWhyfyltQ3oaVg0+ttMsE5SFPUiG6cqgIXiUJ93UvRJTTfaNnY2RXI/nZXvF+jUT0j8tFeFU3j
+tvxY5d8CfM6nZgcLPIz38p32eCwKhoVqpYmpaSAivFhpYKBkoMZa44hU7Yd5iK+SAMM9EAiz7xRY
+UCey3phiSN2+T7aOgJm8vnv8CjCg/HjxEgCg1ryCeMa4LnegWAIdGyYKgn4WkxGynC3Q8cMFXe83
+dtTiwkKu6UObz6GyIm7tVmUFV+bMgFcUUpLEKAKh0kGEhSvn0O+nmGq5Zi80wcY5eE6AkmQwdiK3
+cm5psAkU9shF5gqxSHhSRbQtJc4OGlgQbD8uJVzTtTHWtmQxuW3Nce06Jc4nC1TLZTkxhWI+JmFo
+oPBOgpdHEXP5IFaxTfy8Kwtr91M/B6zLA2uiDpthhdnO4bADExRjzcwSgSn34chJsfWWLIpju4c9
+dsceha9hRMj6WxdeBwCoyToO5yKUe9qrw46oY+rs4dG3h/ssA7bqDEdJJPxjaR0xBWB8XcEaN8NP
+NVbRIRR+fW8bVYLh2k+IYtU8j5Ewv6B8B9lCJkZPHeOQbQEX7DFaiz9CmLNSqqmU+k2l1BtKqdeV
+Up9SSrWVUv9cKfU2/219kN84t3M7tz9a+6Cewt8G8Nta659RSjkAfAB/DcDntdZ/Qyn1VwH8VYhA
+zHuaBlAoE55h4JjJ9JajEHH3bJkG8impu6YFnK9JNj+tSpiwbjbxB1Sn+4k3XfzjQFzAj6kN/IOF
+0HP/XBrgxTu3AQCf7cjKeZzvw81JO2YVmLK5plvr4+AWqeHzAkeKJCvOo/elGwAy6id+6AqFV1wL
+Fy9JEvSCcxsnt8Vz6WyY6Beyyq8qOffbXgcVcgfe7ALNSHb5V3seLgTy/pHbQEnhm0OTlHFFhnXS
+3fcGc/wYSUNeDF2E++yM9G+gsyK7nJdJ4kzNN9Cry7jW9jW+vSlJ3KcWI/zhmLqEWoHi0bAYMqQa
+MCiMo/X7k397P7bcvXK1g5/9b34VAGCuVWGx81HZDowlA3MxgZsRCs4EbbWoIEvl+rYWLr7liqdz
+2XoGXzReBgA8mdTxCinmPzQWnob/F0e4Sj3Kb6QxPm5LOLZfPnrtXxgi5Aq2Ah8z8k/8xA8IHP+f
+3prjz0Vybn8zcvEXrsqc/J/7a/iVz0hIe5xdxYevCCBL0eOpwIZJkESSdJE6AnxT90bo+8SIpFfx
+hnXvkc8V+GACsw0APwTgLwCAFjB7qpT6KQA/wo/9GkQk5oGLglIKpm0jKjReIw27kwHfZPgQpApz
+EnuepAFukVhiI5YYa5JPcXEgk/jLyZfwoZ6gA18338Qfr8kNeOnwVey0pex14MiNr7RryE/FhZ9b
+GjEn9O2Jg1RJHN0fzBBV5cYsy1GPYloB12oygTb25AHpbK0gYCkvqV1Aa0cmbtV3UCFQy3bkxm/W
+QtjsOIRtIqUb3C4MxOyAsxDjzoCZ6lgWEyPNcEruvygy8fqRdG1eco5wRFjo7uoOju/KRNE1VkPs
+MYoTmWDTsI8fZWZ8ZCRokCGoKDOYpBdXxOGr+3QKlVJQ+M4C8f20pXbnf/ezPwXDZ7u7tQG1rGNY
+6jtSTV7wHQKXqlxHWVoIqEMxRA9XtuX9YTTEZ+Zy8Jf7ES4RZXi7IuO5OvJw41jmwqUyQI89ESuN
+zUc+dw0gIAtTs2HgmSflu5fZGv/nLjwNbyr395etTew9KdfxK82n0XBlfm6vNqDYil8h85Y1zBA6
+JIGNYqSncm8G5S0UzGG8/dEKbFLKP6p9kPDhIoAegL+vlHpRKfX3lFJVAGtaa+q64RjAuwY090vR
+D/r9D3Aa53Zu5/b9tA8SPlgAngfwV7TWX1VK/W1IqHBmWmutlHrXPeN+KfrnX/iY9gwDylQwSDah
+LY07JBb5i6bCv0hl/XpixcLdgUBir/YIOgnW4SaysFzOP4Z7U3GpXyi38dKRHHBv5zJW3pRV1e+Q
+t7GooCR9XZAb6PG3DSNBL5KVthoABmvn5rtfyrvapUub+PGKdGhufVzcxI+Gc0SGeARdI8bAWmah
+c8xM2Slc9tVbKkOFLB55OYWdLIErM9gEJJ1MYuRKdsr+XLyAiYGz/v80SVAr5RywMKFJIlO8OYKq
+yWdWKjKGo5MMNVZnFosqJlTEVu4mYlO8ii27xISgnh1yKr6RlCB+CNNEoc4tfZKkZ3Dk79VpELdb
+vLSf/ksCGf/3n/sJ+Ft7AABTl9C2JBpVOgdM/lJRQLnsJWA/AEoNc0kyE6zC4WvLniPNZCfd8RtY
+MFSahwS4ndxGyRDTUhEamnwL7yN8WKn6WDdlb/z4D+7gMvtNsCcYitVxH7XnfhgAEN6xcOWKdPxe
+v+HD/YycR2sQIGsRDJfKOYTtCKu5HNdACSsQz3MxUPBZ2Qju9JD6j3yqPNb3bvcA3NNaf5X//ZuQ
+ReJEKbUBAPz30WpN53Zu5/ZY2PfsKWitj5VS+0qpJ7XWbwL4LIDX+L8/D+Bv4H1I0RuGgYGh8SX+
+dzcF9m1Zs34nV+DCjnRhoMf+8BHZdVJjjitMrn1hcopd0nLdat3Dp6uykn6zl6G2QW3Hjhys7bs4
+HpD9CAvk3LnLuQOQCUn3U6S+LLX+YPzQ6zAJwVXNDB9/dqntJy5I301heqQHKxRca8l8rFAQ6VjY
+SwiswpRUXHlpI/KYUEoTmOkS5ZcDsyV2Qs69Vi4wWNATmi0wIfuT0Z7DJZux2+jhmEQK97TkJ8YG
+kPTEIxqOe7AL8SpulBEueSQgTTQ6PnM7LJ3WS4ViWXevWcjJSuwPLUQP1YV8sFkG8IM/Jp7Vn78s
+eaLqT64AjM9hJgATnnAq0MROKGWiJCx8yW6UewUsJuXCaQaLsOmBU8Bbl8/eGxiocg+7CxbNshlO
+pyx9AxgRA3OpfDhK0CA3iNkx8KfXhL5uajswLvH0/aXC+DoKUxKmux8vsSBT9jOrQF+xoW01RsFu
+Tqcux+1PHNjUGdG5jZw4jakJlCl1Sps56sPDh57r/fZBqw9/BcD/wsrDTQB/EeJ9/O9Kqb8M4A6A
+P/PQoyjAMDQmUIiJIT4wFV5nV9+2ZWBBnMJHWxonr8kFH9ry72U/Qj8RN3O9jHBIkMqVOxFu0Let
+5hNMCMFdD8mZmC5gLmHCGvCoSGX7p2AOCDVYmLPFN288fECWcNbLwVWwqQ2jgbzX2LSgFnI+FTtB
+kpGwpLlAmVMhCqOzIzlcINJCoT2S7PS3sgImJ/Q4tmAm8vqUPSMTrdFiEvBmEuIKORPbmYsq8R2G
+YyImt19Rld9LjusIKXI6mNg4yUg15gI9g263W2DKFuddUrbfzBw0K3LcMPVQZc/IQGWAwWaS8nsL
+IDxTITr8aQDApc8K/NiwPJjFMs6rAZwXUAYU8RQoDRgk6Fkulva8QEnH2I2nyCn+2xpOEA0YuoUR
+7FTO2e5JovGrL38bN0/ELR+rBcaRfPaLzYcnGhXh8U+ub0M/I5OnGplosvehbRGEZzpLdjhEjkKH
+hCzjuYLPPut5I0AlIcyec0znIUyKGVXTAjYXhUa6gMOEtjGIzngxH9U+0KKgtX4JwMfe5U+f/SDH
+PbdzO7d/c/ZYIBoBoIRCWAL73GleyjQydid+yTbxCW4Cv9NXaE3pEjal5Dcb3kEjpDZBOESXGu5h
+U6E0xPUzFnP0bNnxPJb6rIqFcixQ6dDTsNjzfhLWYNhcoQEUJC5NogcjGhUAj6Sxe8kIN48kdLls
+yLFOcherHdmZ+0YdDglJwsyCxWTQPJbMp3ZGmI6JeLQzvE13OJqm6NM7mKsFjsf0YphQM0uNMl5C
+n1PE7B7tVxdQllzHcd9FocVNfv2mdOEZlSn2b8oxytEhPHpHRrLAikOZutzBBTblLBNZcWbA5w48
+qZRwTXZl2iUyEqukSj9yttFQwNIx32rU8Nf/Y8F4dBKhfCutTRiGeHo6jYFMzl9bAcplIji3UJJW
+DcuwzHOQUbJPp8CiJAmv4+GAytwtHeOe9U7BlZPExJQ8HDkU0lfFY7v0dAtf4Xm+26WZJtBuyny6
+bJsoqXm5m40QEja9Rgj+yN1CeyavY6eClCHmot2ESyRrlAdosYNTWeJhNosQ6RKaYZmYLcRbtpJj
+zEy5fk9ZmCdLZYpHs8diUVAAHAXUDGCH3uB+AgwYk30qKRAyv1AEJfyrEhs3X5fA+FJ9FWUhD1Bn
+bxeDV+T9C6WHA+YM0uYVbN+7DQBwuZhkyQwLwpmb0QIJSSrW/BSnFHJpzUzk5Ch0rQfTj2ulYGcy
+EcrWZeyScCNJJDS47JvISrp7louQuY2qWSJlbOzwwTXsAI2W/H30xgwm6bsHcYSS7viov0CPSkgx
+Yc4jHWKTE+U4y/E8qxamVYFlE9DjWsCcwClTrunkwELECQizhaki44/jYWZRdGfVxzyURetKIA/N
+m7MQBjkxy9kCQSET8wA3AbroyBI86qpgGwaWxCovXHLhUDe0DKST0Y4VtLfESABlIW65EYVg+A2l
+DeRcZW1iS7QZwuGin1W6aMxkQR6FQ6zk5L8sath4Tdruv/W6gOHu3LmFjFLzJTReyWQsvv7yl/+V
+czeBM3Ffr+Ljsi95kI3Lz2DXl+9V9CXsVals5sumYSNF2BV8SnNawGS4poczFJtSPWpN+jBaH5Vr
+ZS9D1tFoTmShKIIIlVjmQN/0EVC1LI1OUXPOuyTP7dzO7QPYY+EpALLqjxTwBW4oP2qWGBzIivp7
+kYmLF2Xl+8Rd4LdelGaVJkU6RivX8UlX0I0vngzRbjDRZpVYzyR5dmNygDCkNsSqZGO3FhYGueyI
+C8eCQ1yAlVVQrTLDXQuRUM0X0YOVkZXWqDiSiLqt7iBJpN7sbsruGtdMVLdk56tnJmxCmw2rgFmX
+3cqmtFnQriMciEtpXrJg/CEFZ7wC2VheD6MIc/JDptmSKCTHiGPoKMBgh+ckz7HWEY+l2axC2XLr
+KyOGGm0bTy/kGIctEytkV57FwIfI3Zg6XbSelh24T5f0c8EpjpTsck+lAwxLuT7vzgleo/5nXmYo
+i0dDg7qmdUZwU/vwRey+IGPukHatqOkztKKa9ZERNWoZCqUtnzHzCJrJvJJTPDOss4pKbmpMtNyn
+XjXG4q6EmIflEKdEu36NAje5LlHe5+UkrLQ42Xe8gqX/qCEVEwCw8xSbGyzxbM2wASa3W3VUm6Qc
+3BYxoPa0wITnqYMYI1u8rTU9wpRHn3kWdqvs8qRHkKsF0pbcnKQo4LjiYcyaNXiU51t0AzSH74+O
+7bFZFLQC/iaADrVDf31fwVjIAztyavgoQUj/1F4gHMrgvD34PADgWn4Rb6xSf68YoGeJm9x+M8KX
+FAdtfhvHY5lsz5JMpVf34VWoPBUaKMhlt+om8BjZ1mpVTKiraK8/eLg0gBOq/mwf53h7QyZWm7KL
+bhijoJZktZUiJTlJmdtQZEBKqe04MgucUCHr1miKsC/Z8EE4QdSj3uQiwjhmSZIhRak1SKmIogQi
+ZtMd08A8l+t+9ThG5PIBCmXcpqaBY4YoqzZwiy3Z7U0XR6/LA7a5F+PUEZc3MOT735758AxpPb6z
+qKDhSThzI/FhUhOxfB9AH99yYLvMW0QmjJEAfIpVuag8H8HYJyhKZShDGZeFnsEqZGMw1h0g5ENN
+GnUjHCKjUMtgcYykLw/s7OYId8Y3AQDXbw7w9a8IuUqPKNviu859uTwkpYb5XfyRCkDOCoi2LNwk
+29TVHnCHgq+12MJ4JAuS4cp4+y5gkP1pDhs+//6HoUJz9k0AwP+NDn5hLHOyf0E2GeXOYZEC3lMK
+JUMblYWwGEvpSQmj+v6Yl87Dh3M7t3N7hz0WnoIGkGuNH48U/m4iK/PleoHr/4cke5LnLmGyLaud
+GidwXpWuNnNLwofe6DZURV6vh3fgm7I7TJ0JDvvSEFTc+DYiyPshYaYTvQ7vlmST3bUK2nS1ndUW
+fDI7l6aFWo0qx4NH6NGgtzG9dQ+/SyblTwdCy3a04+AJZsNHZRvNOeXjGw4QLsVVZGff7x3g3pF4
+BP3TO7gzJgP1OIVh03NBDnra0OQrKyGhGCA3dziT7sokNWDdXNK7raGMJKF2zF3Jy1KcjMUhNuwM
+OT2Bxb0ci0ySn6MD60xkJDXke2GaIB6IK1TEChF5EJFaMJZ8m6Y6I6p5mFWrJtYp7+YF1xCaMkbe
+UDgNsqAJyyFP4rRAMnxLrqnmwa7JbuuPV+EWpKInCC0pbPRTgWsf37yH6UyOG4Yz3I7ke+boHoxN
+zoH+w0VpzPu6QwFRnyanC1aMEhe25V6vLyboUe0lP7mFYYf4G3pjs+0tdOnZmK0mUlLr5dkC3zqW
+uVOf13C4Jl6tuS4JzJW3DqCvCl7CKgHtSlWjkZ+iRy9tUdQxHcweei3322OxKCgAFhR+3QJeqMjg
+/frfB/w/JW3G7pc1dtdloIZv9HDlCXFh57duAwCecxLkp3LzvZ0raE3koSmUhndd8geeWcOC93lB
+qnbMM4QtccW2wwUcZvvXggy9KVV8AiCPxS0rVx9e2ikKotjaNayxBHZUlQflytTCwJI8QbM6w0km
+7u5KlmAYSRzZH9GtvTPFEXs/wlmBJaQz90tY7HY0oZBxRlr3ubLLVxmALJPvZWWGPkk+Fxow6WqP
+WSozYo0hEZ0VeDhhWfOyAQz5cAdlFSUJdAOqTaVhH4pMWIM0wxoRnWVRwKzQhZ2mUGcP0IMXh7pr
+octOzE7QR9iXh7e+KvfJH4YoHImXi9EQasry3dBEc00WOsvQ0E2J123NeNSsoMLu0ZWKC3O+bIcf
+4wle363NJuzrJABedlzivStOBs+z5IJsG8CMBLxOO8AsFf5Io7uGJrsgxzpBc8SSuiEPqxfcxaIr
+uZGuCRQtQS4mbwywO5QN7PbchsMy8tHXJD9ReaaFNYaPbrcKh4tMubmOWk/G5bjsQ1vnUvTndm7n
+9gHssfAUoMXl/RMm8GvcSf7SL2j8w98U11fba/jqTCoOP+zt4PNfk/DhI9zw73VTfBSyuqaLMVZJ
+l/1mluMZqit/ZWhgh9p+Q6ord7MI0V1xs+atBFuG7J5e6qC5Iscwcg+ektfZQ6oPAEDcCQbjIQYU
+VJn02X+QpdhckzCn7JfIlbjdkddAWsgO5TOhGKZT7DA2+GIaYz4T17EIE1SJLajaJerkrgyXCTH9
+nay4AqCo4WhWgWpLvnexSJD2mcmmoMkriz5WSNGmS40d4vK14eNZ8rabKsdmS65lxM7BH1qM8Eok
+HshHqgZmTJjumCmGDDXKWoFZSE8he6+dV67DMKqYEA8Stlrw1+S3S+6ukRvi5qmEQePrN9Bjm+tO
+NYF9VaDQLdNHnIlH5mDJ4DwHCMK6lS+wSg/itSjFlOrQvYMBDGI5En4WD8jRmct/iTExFLBF8NqK
+AXz6GTnGK8c3USNb93CQYIVNktaWzLetxIYfkcXa30bJ6sqHXAO/TS6La/49/F6VGBGK79y7foDa
+mlR+1rsrqDrs1/CqqFaY3bbrsE7eXw/Kuadwbud2bu+wx8NTUIA2gL9VAj/FXN7f/UqB2sdlmV47
+jbFL5Np86w5Wu/J605fVcNveQYPJm0VPQzOZt2UaKKn3d6mRwe/KyrzHer0bphh6sjN39AIuocRB
+NcfoWLyCuqcRsAMzO9sb3ttSCpJ8+PIF7FwViO5VingYjRo212Q17yLG2yw/mqMxZmyeqUxlBzMd
+A866JJF+IGhj/5Yk1ObjGJoNSLlyEFK52l1Q7iwvQbkMRBqIzygGcoRkvz4a+vDWKGPGnXDV72LU
+E+9gzZjhoC7jdqFRIrsjW1uwmsIkvmGvJddxa7JAi7v5LNjEcxfluKd3DFw1JT8yeg0IjSWEes6R
++u4ypZx0xXTQ4W9Y4QKNSM7DWmXzULOG3QnZujs7mE3kuLXuHqzWki+hiiIVD1DR83TjHHpKpuYo
+g72xBwD4dL2F8Yp4E6+nGvu8V8FQznOx3MHfxRS9tKLgbzgKx7Gcw8cvr0Ar6Yx85rILo5TfuFrr
+waiTeq8m87ETlOgxh1PXc/jLuV6YuNyUY8fe0/hkS8bWK8Urdq0qmlREr6YeqkzQNoICiOX69nQP
+k2DZZPdo9ngsClqjKAr8tzMDv0Z3/0/+pIXP/0OKjHQtfKgmD/2dGx52QlkMKmvycK/kCUyPhBXe
+AdCRB8xNHUy3xeV62nFgr8hN7FLDsKzYmLKTz7EL1Fry/kluwqQ735sFyElZVpIg5b1MAejwhj9d
+t/HDW08CAKrkkqxfaKFqSwIsySK0xvKg78+GKBKK5jp8MKsKFwigCv0mfqAmib17gxkaNflsvD/G
+6oasoocHQpDyrYWCR6KWXCnoQn47zw3gNgEy9RhNZrtrdclYXypc5FsE/DhNPNOVz7ZmGosrcrzI
+KtFsk8eSPRDOehvNpeR6xQDlE3G3YWORiGu7uj3C4haFWZetzt+dbzRk3Gw/Q0CwVFsynkuHAAAg
+AElEQVR1oS15QAJb7rndegZVAr20AlbmZJWuZtCF3OvSjmBM5b4m1O5MswgR9SpbXaBKRuVOfRdF
+Xe7rqrOKp5+SMf9GIeP5K1+avgO8dL+5rNDkpKSzCoVLpApcCSNc3ZTfuNDowmFloGwAlVUmJnm+
+2ivRHcn4LKISFtvZ/bUVXPbkOip1DYMAvYDds5ZXR2OplhZsIA+YBA6nSFa4iOY7MPTDKyn323n4
+cG7ndm7vsMfDU4CCpUx83gH+i6ascL/4FtD6D54HADz3T+bYosv/dDbAAeT9a6y/bq934Dksve1t
+IZhQOKV7AStEq1UuNLBNNd+kKbuSn9YwIpd+LQkQVsi9MJvglGFAxy7RZ6OQ+ZCGKGUZaCgplz7/
+2T+Ba03CUVf2AABB6CJryu6Qn95F2qCa8cTCWxHJZGPBTUydHfDPSGv5GfFnp76AOZPXdz7axuhA
+tubVbflwerOPzzrizv8/8xIJodRxlGNYkXFJhz3MCznG8+RvuFttocXrU20TQY8IwraPmF2CSfci
+mkTQlVUpoc39N1GvSul4dqOP2QabuOY1FIEkR2s3azCXBBUgzRusM4EUR5lIycrs5ybMuezASTFD
+ObsNANBPPAUAsHITmt6Kyl1YhLQjcpDbPP9pDsMjV8UyjCg1KqV4R4v1C+hUOV/sBUyGTV4baNz4
+BADg7U8SKfqVfwSDHaMlsKRewQgKJr2blCS3XsXEUSnHcp+6hKtU8a7srmCNj1qysQ13Ssjzuvyb
+9U4QdsQ96pYKCzZ2tZwhvIviAbdVgawhxw4SSfKmaz7attz3ourAoC5J2d6ETwwMkjkMm5nNR7TH
+ZFEQb/IXKgq/Qfz6bz2j8fMvU01wx8O4Lu78lbCJAcVAgk12y+1WEWTiqqbWBEbOwTHGWPP5vkpg
+c9J4GxKr1wofeU+cpXrQR8ibkWkbUcaM7eQEuS9TQQ0fnFNwSo3nnpUb4DhHsFuyeBGGD8N0EZl0
+o6GRT+Umz6IhQnIfgjj1Tj5Evy4LYTvoIuBiOT9uosE266PBGD+4I79340gm/PM1ByeET3dt40yb
+UlsaMWvzgVOiPpTrDllZsYocypGFwE0qWLBL8u60gjpxCCtpDoOLc6W5BwD4U0aIb98jluApCy9z
+rFaCOb51KBN9Y2Mfr3JxcscyyGmeAmxxz80UbiD3NLdcjCpyLTFsFHU5thXK5C8bVRhLqXrXwnIK
+Kx9QhKYr30OcEH+RS20/rRhwtISHtgmUzA+ZdhvGBlWY+hYaOzJeKwdyHz5Wr+EbE8bk+jvFCFsp
+uFT76Sy1Rh0bP8MW1d2FC3tbrrXmOnA8CVEMMwcFoBBRKLiSBEiIL0rsU4Bak16jhZSYGtPNYFGW
+3jPknqnEBChSXLgFDM2FLswAQtrjio3cePBm9t12Hj6c27md2zvs8fAUFKCVxl8D8B8RgPb/sffm
+QZZld33n5+737UvuVVlVWUtXdas3LS2pJYMQkixArBOYbRz24MGW7cDhMYNjhoGIYWJmYgZHYDA2
+E2Y8G4FxsAgYC7BgAO1oaS2tXtXdtWdW7vny7e++u9/54/d72dVC3V2tJnA5Ik9ER71++d6755x7
+7jm/5fv7fr+/D25TIr8r2zklVRGe2Ndpm5o315I0f29C2JDIcjGISUuzU6nBfiYnhZnsE/piztWn
+s8h3SKUiU+DmC9QjJaZIY3ZT2V2nsUs2kl0+9Q9fcRiFZXF1S0k/4hNMYrE2vKHs8BEZlvI1Fn3Q
+Ik/MYpXaTTVzPTmLdpOEE5kSskTLFJH8vW/ZmLly9NmLDBQ7sVuVAOatuSH36yk5HYcMlM9xkCW0
+NAC5GVv0NZJdVJRkhQqe0rgNDMDS6sJRwUSpKbuVhOumuBKn9DR/YT1jqoIy/amLm8lJubUbM0xl
+vm6tT8gUeRelM7rujBcVpQ0izbrk50/haxGbN0kxFVmaz2kEszWEWAEqZnRUSEZhYYaKZD2McBLp
+dKrWgzvwsAqBzacJGKrlYWQFpqJQfcNgotwK1fqDMq/Ox/FnWJCsoKFx0r2iIFM6taFmOE45OR/b
+lVP84gM5fUWntustcuXksN06aDDZP1SCnL2CXMVbCtPDHUrfivEhhcrJFbUW7kjh2K5ao1aGYcja
+cgMwA/mskxWYmoHzRimp9doCjXfFppDlOf045EdCn3+v6Z3/ehX+9y/KpD3jN7hfH5CngjKr3S8A
+sH1BUj52MaFQfsVmbx3DE383tSOmtjzQRXhAFMpCiRQa6ywtYankUWJPcT3x8Q6mBpNMbmjggqsw
+0Wxaf8VxuHlGQ6Py8a2vcrMukuGnC3mQkkqdXMuzw2rpqKx5rlbgNGUh7IcqNmLCTk9N2WSXW0rr
+PhyapJncfKNImWYy7rqy+FzMQyLdNOZ9l0RdKTMvMAPZpBzboNqTuQ21RLruJdzUCr+52gGZAqRK
+rslEiWCdG7sc+vL5h3cFPv6pXSgpUGjkgXcgWYL1kU08kQdhYI4ptL7gCIxd5ByZ/kWGtyhu3jQL
+GCsr0iBxOdyS682fkt/NoxpGeZYNMGFGDGPYJEqYk3kBw4HS+SsIKXOaZLlsnL45JCuUWcooUygt
+fWHZGHW5D4sLsjk8et8l/uzzcrCkuYGrqdSSZeAZ8hsLSndvxg73LkrfGsGARD8bTQegtTl+tEOi
+4w7UhYmciHAi/kOpUmBYMhdpaQk3lixIEvQw1A+1fL3/GOS5Vtq6bVJNv6fDbdJIxh95NQbFa2Ne
+OnYfjttxO24vaXeFpUBhYsc+/2av4IdiOUs+NIXsYSkoedfHdvFVNq46MZioTPhZhf6W/RXiffne
+xJijpkE7Ix5DV06Y1G+SKc9CrtS5RTAmV1xy1TGIevL3ljllT09QI6xTqPpzkr8yXDQyTLoHYqqt
+Jxep6Wl1UJXg3NwoIClJIM4MLZoqI55HF2HhplwjkL/3S2NyJds4GCW4h/JbW2nKiboyJg/to+Bg
+MJVTZDDXYmUip1U5HLDVk5N0WKTMzulJWGCWVVquryZwE0ptBcpkJifUfYhzn5FW+JUSn22tAr2x
+L0Gtm4NrzGtw+ObYpaUm7sbgkKkCgTrTMbkW6xT5DIRt8mKxkU3Yl6yLWW+Sqaz21E6wlLE7Rqw/
+L9qniMSaMqyMQvU6jayHFWsGZic+Ynz2CzG1p+Yt7FjGNGksUdVxFJmDqUVeuWFSSjXarKb9ja88
+T6rWWFxELwLDsvxoHXVSueftusWVA+nv5YtnuKRUacNqlUUl3xkWVcqWrNs8lftLEGGri1IyGsSm
+uMKuYRAoaMOppDBjsQ4Ve1JZpKQ0hXERYB+Vx7rUHOURicbMvUY27btiU4izgpv9jPl5i9/WAfyE
+XfDff1LM7k9vu2xX5PVDhwm7mhZbU6668vkSTY2w7rtzNDX1mLRdziIP5Pp0RFvV7KaaVzLsjNFk
+lrpJCLRkNcxDDhQUY4WbTKsCGvEOXtk3M/KCubr0aWvyOPf2tdpNF92oVaJUUpJQJ6evi8L3HajK
+om9rNeDuZoc0kgflqtPlsKfS6cMCW6sdkxz6kYKIFEjTTA8Y6vVcs2CqC8koCuIZ3bkFqVKjn1WN
+jF6Ssqy+cTkxaDVUn6I1z/tVwzAoz/OeeXGxOqU3APBPrv4RTyp70+nDy/yJ6jJGUcRI99AiT8n+
+As9KzsxQzYuCekPmuJsXRDPAVdCHqswBgQDBknoVM5e/W4lJohTnVmoTaMQ9d0O2evL+YHxT5qJ+
+hroSn8ZJwXiimaTWgFIin+3lBnYqHf2UupWj5RrhtRdRjTdvK0ufyRG4utnGkcGl8zLosDNi4uo4
+kg1qPZnPsRURIP2Mq/Ke70UMdMPyR30sRd42KhVsjcGkcUSsGpqzhzaYBi9qgNRLR3GguKiAIRtP
+4NqEwSsTDn9te71S9D9hGMazhmE8YxjGbxiG4RuGcdYwjMcMw7hqGMZvGUeRoON23I7bfwrt9ahO
+nwT+MfCGoiimhmH8NvDDwAeAXyyK4jcNw/gV4MeAf/1Kv5VnBeNhyJc2Teb/VPapn3yTTTmXYJ9z
+pYeVKx2Z28c4UJN4QXbixtY247Ke4vspRXmGky1jaCCmwphIpeZNzWQYToapoihFxSPpqpvQ22Ov
+oyfoFFoaUAqcV65Lz02DK5tyoi/e77CnUXs3lcj6SrZAourQWWaS1xXGTQOvpH3T4GLaqrLxrIxj
+LwjxlRSkPyrI9ITpJ2XymdLRVCyQ0GnjGUoXXrNJdrU6kYJwhjBOwVag0uM35PulszVMdTUMs6AU
+qFtVL5gMxKI520hZj8W8fstDYgV94fMn6AVCBPJHX40IQ7l2dxodicC8HEx4Vv9gGA7Drlx7ZBcM
+lYH5i3s93q88Eyd8Vc4q+hhjhWu7PkZ/or9k4Ojr6WCfBaXBryo/ZmomeCU5gTuTMaZaAsXUJG9q
+DcruLTa+INW4z3xYtMouX9+guM1tvD3jH6gVNtZ3t5KCzSvK7fn2nMPHJRjbfHgB6nK9Xhax6ivP
+RCLXdTKLplobTb9E4clYK8YcqcLt/XQOSzM4jqpzWXkHY07WTT7wMHz5bLifE+uzMx5C6Lx8/cbX
+a6830GgDJcMwbERVawd4D6IrCSJF/32v8xrH7bgdt7/C9nq0JLcMw/h5YAMBev0J8GWgXxTFbEPd
+BE5+ve8bhvFB4IMA5dYcv/LJ36X04TcSDyVecPqbz9H7XaFS2zl7iROJnJRb3W1qybMAPJeIH+53
+9vm0IT7iyRvPs74s/udSMGWaXQDA7O3hqLahFcjpYy6V8DTgFBsTXKVPuzJY4mAkBJ77kwlZrKzL
+0SszEltZjq/psmh7h+uxBC8u6EnULXLaWokZNudpK8Nx5JXxtYpwXBUsRWc/BIUlFwcGB7M+lFJ8
+U/q/0KgwUGGcuqcMPIcBmQYa/Qm8pPJIXxpArtwJfkO+V98bokYAJ6o29Yq8fzZOGSuWww4PUVAo
+o0/9OgBf6lbp9OQ+xVGfUIlgi/zl7YMXm7JUkXHqje8DYHj9947SofvrE77yRTnxVn05uZ1LFzEa
+erJHAVkkcaKcNqFqWRT+In4sZLJGJsKN1XkLAhmHk6YMZpaCF4IiS6e2zfYZSQduO7Pe28DXDzDn
+s7SmfrQUZrQqiiB9bp0rjwib9/m9Q9YjZXAmYleLsdzSGgBpYdN2tHrUWcE7Yq/ymJEmZcWAJJLx
+jWfRziQlTsUKyEyXoTL2HnQ22dyXAHvfKjMavLaYwutxH1rA9wJngT7wIeDb7/T7t0vRr555Y/GQ
+/QF+Y32blXc8DMC1f2tS/+FHAXjnr/eZPirm8cnwFI4lC6/ygrIyL3n0IiUhOZjHm0hwyuqu0F8T
+c708arKiOXvDUUbl+hKGRq8rvYytvmxI1uYOt7bl9waDBOe0/F43f+VNIbJgv694eKuKqeKfYy1v
+Ld8as9mW99zpBsNFlYkfd6mZ9wNwcyDX6m0dEGxKf3rTHpmhwSnfJVaFq0lWYhBLPyeHsnoGoUuj
+rhiE+KX9vf0hnWqken0kD+bJhYKybpCuleFtyhx9dSFhPRWX4J5ejasKotpwxX14fmefdKis20F8
+xCr96huCxYympL7wt7jR/fsAtOPfJFKsSjja47H1ZwA4fY9ItS82xxhl2b1cp0au4jrm5UMyFeHN
+swaclM876l7ZLNLVDTDvFaBUfuHpjIWagsuMBSpPCrHP/raOI3v5jJOpm1qmoLDQMrisOOj4dMa9
+z8ua7a0anG/IWMNpmdahYk4ach/nmg5JQ8BSzSKiyNbk2tWCPJNAeXkAuda0lLWyNbWrlPWwmBQW
+eU8zVDf6jHdlk+mZB2S1Wd3JnbXX4z68D7hRFMVBURQJ8HvAXwOa6k4ArAJbr+Max+24Hbe/4vZ6
+UpIbwKOGYZQR9+G9wJeAjwN/A/hN7lCKPkginty5wfw/O0P1QE6d//xMi088JkScL7ypxX2moMoe
+XJrniiPBoxOnJKgzaeR8z0hMsmcWDzhV0fr2hQr35rKrXl/q4mv1pH9eAk7lSoORVpbFZp9QTbhx
+OMFIJEhUi8d0u0rimmkhCi+jH5gV1HU3n4yv0ZwT07XiauHP6SregpxmJycWg1jTbd4CUUVOxPq2
+nPLXoi7WRNKwtj+hPMvpTafEmn613ABrkOs15DRoFRkqLs1qYvDCy8z5zORtq3aiS8abXZnPqlPG
+bssJPD6Z8kOJzO3V7pAPrAhqbrcpFsp9ozZ/oErabXvCzdtYY2d2yu1zZcxo1ywoGv9M+rz8ac5+
+j6BTt3+pwDDkdA4Gffq9rwDQfUbm/or7Nk61VHbNLcibIllXXHIgE+ulEWUM59WM92QcuZOwoEjP
+HdeifK981mgkuNrBacNn67R8xl4S6LZ58+VFsx1Nfbt6ttrkLCmmpbKbUJwWKzVPDVJfGJgbzUUs
+PTObbVnH1cSm0Cq3rNzEVu4QL60QaoGjUUxw22I1uIqgjBOItfo0zHJiFT4aOX2yTCyeIDcpXXtt
+Z//riSk8ZhjG7wCPI0HZryDuwH8AftMwjP9Z3/u/Xu23ahWLb35bgw914OfVL/yx9Qnf9hYZ8NWn
+/4hvTuQmfvG+Xc6o7+siD00jtVHXmhPWhCKQm+UPczqh+KR1PzkiUDTVnw4GXUIVQc2zgMGOfDYe
+D/Gn8pkNy6CpcOSOcufZBiS35atnARTHMhmq/uXZuEJfpeYXbFVhsgyinty48dQk6sv7g6JDSyPc
+W1uyMVWdDmlNBnUqNRmrgM3AyVCkNOkko6oKo7mSxYTYLDnykPbLBfbM/Sxe5BTMAFvJTg4VNPMW
+06WHrMC5UkKqojXVyGF3U6LazXLGXiz9X1aNzscOtjnlyuvPlX0WZ/OWpliKe8gAU6+TW3J/T556
+BxvnxX36549+K/+ZCty8pWxxK1C3wiyOwFVbWj16ur/O/LashdKJgmIoJrNpuDhD1dvMYuxbcrhc
+9mQ+LyU2j2sp+6lik4kyKZdHFXpa4l0cTrneEFcpHQsOI7f34GV4Jee1xqan9Swty+RGIvd/tWXy
+5Vj6fO/IYtzVjMn8mGVP5jlUoFNkuqS5rOV8mFE6UAkCcgplXoozG3Mkcx+r5HxqOqBl3XFnRBRq
+7cM0JVR+Tys0mDZfmxjM65Wi/1ngZ7/m7evA217P7x6343bc/uM149V4+P8q2qnTJ4uf/Kf/EMcu
+qDhawJRZeGXd7SiwLAnWjfsBha36gmhFm+dASczIcydsGm0x1XAjzKGYvjvTK0eFUEEoptVkUsF2
+9ZQwKkfVOkXu4ao5bhU+4YwpWbUW/4t/8PcYK9KsTAEKA667JlWVNXd9h0QDZmVf/l6xcsoVGZ9f
+mCwp5VmjVqKlnIhWLt9PnZg0lb+Phx0ee0Hl5A5u8NWhFvkk0SsG9FoY9O5UA/5rmqnouKZjcHpZ
+eCHecSFmWlNXSIlQghgcW6sTcxNsRVsmGag0HZGNp3DdSGHsVFNc1ej0Wm1aevo9/K43U1fiyIp7
+kiyV089WjgGiHqkWNu1v7zI0ZF7soIQaaYzzFd52Vk78hbPfIb9VT6locHQcPgeZZBlGk3Vy5Zbo
+xBsEfVkP3VjmuLvfoxuKNRJs95hUZoVNE06f04I8xcI4ts/4UNzc3SSnty4u6O4oY+eWrLnxdEyk
+CMNUq1mjLMXT5zAxDOrq25mGfcS9ELsuTVfh5tqHesXBLclzYQFF+50A/M2Te5z8tv8BgNPtm7QX
+hKCm8sD5LxdF8Qiv0u4KmLNlmNQ9n0bJIzTFr13JTfqGmFcLjRIDnZB2atDT1JJfkgVjzS0yp7DO
+6sL9lFSGu6jUKfTh9icVvJ6Ywaly8pXSLiOl0PbyhImadY1pwlQXdwubXlUJOYrZgs9JVQAkNEGL
+C6m5LrUVuUnVssGClhEbmoZcuFhQ9+V6bbvMmbr6lOd93LJ8r5RIDGSQpiTq7vRuVbgWCp9j3p0j
+TTbvaF6/0Q0BXtR/PHWmxfctydybl1qUGzJHw6E8jNVpzmAq/tywbZMFstDLYUpH3aP0LEyn0pfT
+mgFaH7i48kxh2dkRbn++XeNiVe7PfgqnVJ1qV0uh5+ZdLiv2f6UTEu7L9Qb5TbLGWQC2h5/jXr5L
+xmE8BcA9c29nrLEKt7GA19Ey5GoTX7NZSVpmwZbPjFUk583JFp/dkPfqqzlXx/LQ1xMTuypzcaEs
+62J95B0R2navdmjOy/2/EQC2loAnGRNHS+p14xkZ4GucYWgVLKjpf8syOF+W35hYHl5ZPpNpJsOw
+/CMt0YZd5unuxwE49+C/YjIvqdqfOfsof1gci8Ect+N23F5HuyssBdOw8N0W/UqCp2bkYWxRFHJq
+ricT3JKYlP30kEIltw87AtCYL1s43oxWrMdY4cVlAuxQTprOcISlO/NWKlFhcoex5qGTYYjRVun3
+cRnfk92/m6aUlEOgpqQu2W1k7xUMWiqP9pZakwcfEJxF1TFZ8zULonlsZ7lOdUkyH6vzc3haHGXV
+6uR66vhqrQwOh2RKUjKcW2JJr/e7pct8/vcly5u8NoXx19RMU6sng5g33P/dAGRngiOZ+P5pGdNS
+YdHbk36mRshIY3KGFeMrg/MoHhCbsypWLQIyc4Z7ck8Xmh6WmsxLziniulghFRyGY+V0DMR6eKHb
+pV+W+/T0jevEWhy3cfMGblMyBsGg4GlDTP7O5YcA+NvvLdFeFFDbKL9FNhFKviDtYSqHQrTrYWoA
+1lfCknXXoVA3bnswwE5lDXSTgHv1/Z6ui2Y5Z3tf+hOaPZ69KZZplPQJFMtRLkJcDVxXPVUoT10a
+GigujBKXfAVWlZo4GsR12y2GysdYKov7kDkx211Z7EbJ5k19WU9V69f5zDM/DsAt45M8WBaRnDtt
+d8WmIGKSJnbXIAhl0SydDtlW7r9z8waHu/JwL7Rr7Dwni2a1KiaZl5eoKAqssjHioCKLpj48waGq
+MFUPUoKRmFQLE5m8gd8l354lzkakO5LyMdtD+j1xYxr1AlvLaQul3TEM60g/0KxaDC150M++4xz3
+rMhv1M8vcr6uC/pZRbNdqmCsCcqtkXhk6rdbpTkKVbWy1PzOPYe8EL+XuTrbF2SRXghC0rpsCkb/
+4HU4CK/c8lz6/CMf+DuYl8Q3Pu+v4ldULUsj+Y10wq4yF5mmS6iZlnp8i0/ta/TddnihJ/dqeeUa
+AH/2WYPFqjJExTnOsqbh6ga1VDbG7q2cxXvkOk89JtH51bNj9r8o8YlLCxZfEIuZll/D14rR6xWL
+ymcl3fs7S7IWfnjlDE8kcv/f6FbJU5nD+bgKnjy8i0GKFWuptQaYLiwfsvd56dtb6/A5Jd+6tJIT
+qerTsrpEz12FxZb87hcfy7mvLuP79IZNfU5LvA9tkgX5vXNz8nB//mbG29vy2Q8FNf7mo7JJ/doL
+J/ihN2gWpb/Kg+c1nTuRed2OxoRVBcb5Hv/+tHz2m3cdvhz+nrzPD/A+987czVk7dh+O23E7bi9p
+d4WlUJgmqedz6AQENdkN3YnNUPPAm1mB3ZQdvDcwMJQf0FQaLSL7KJBzaG/ha638rbaBo+7IrfAW
+nppi+8rFGIcRu7mYmdNswkhdEG/PIi7JqXLSWeH0quzojqsuilVQ1ozDWdPnDW+W6G7LK5i7IPUY
+zWqV8ryc9O4DSgG+YkAhZqsxV8dW/j3TKyhmNNwNBcSYFnlZKbomNU7XJCq3W32WC6pJc33I1+Ep
++MtpFQ32ZdFnKTUEglzxR5RmQogqSOKUz3CiolRpZsE0kPcTp8m5TN4/HG5Rn5O5u3lLMgdGbZMt
+BZOVahY9xXFZfpOxL1bB4ekShVZ57jZU77E7ID+jgcinG9gN+eJaWmBm4m4uhlO6yOn4QUcst0l1
+mzdU5X6EwSZVraSdOjGuL/fXaJiYWs1oteWzvTCkdFbW5N5ej+aMNq6/TdEQC+GpA1kro1LI1hNi
+mWxmQ0Y3xZWYcx28sYrPtCu8YUVeLypT87c8UKGsLtM3u02WL8gc//SJ8wxLMr5GWqZQvkm7qsQz
+Ow7YYpkEWcL7npE5+mTzkzz4uMCm//np3+Z7mt8KwK9wZ+3YUjhux+24vaTdFZaCaZiUHY/2YJ5Q
+Jbgsy8Hty4lx7lSFnqYDF1tlBkrX1VZY4bRcpqFBm4QWrhYMlYY5Q/X9FwwfJ5LXmVYkGrlDrn7h
+NHZBRT0qRcy0p77agkOqqEdLZdwM0yHTlF226NAZyQllzs+zrBhjp1WmUmh12rLmtuMpcV1ODzPL
+ycoSi7DjCak165NSxXk1XNVbiOwJralYFYsLDzM/L8Glg85X6I+Hr3W6X7V5LZ+3m4L7+NaLP8q9
+ZY0jLLfRgk8SDYw6GEwWFDbeNckrYnmVxjXqNbl/LbPOWJWPz1Tl38u9CWZF+RamXWjJ/a3USziJ
+WBODyR7oFDpX5DS+7y1lnlP9yIsnypR7mjp2bCbLSnrbq3DOk9DscCz/njVaZHua5z+5QF0rXrOy
+j23Lfc3bKdWJsjxr9aHhLXCQ35R5WSrxuAZV662MSSqpvpkgy43rfeqR3t/1IfZqpuODalMsqLJr
+47bksQvvEaEXugn1s5JOre1V8c5ogdatjPppiUHVtgMGSqHXUuYt80TB6bFYTRuVClsjiX09lC/w
+b7ry+r98zuRnJ5JGvdN2V2wKeQ7jicXV2gFhKINsj3uMdCJvjjLm2hrJT01yRx6yKJYAUWJHZPrw
+9+OUyizXTJ+WKxvIziShplDisSETZtcjuoH8bpQO6Kmox6TbY6zmZWc94VvukxtTPaq4M/AUN2H6
+de59u6o0hSa53vCiXcNWFwNHctdpbpJqxaTnmJDNshkusUawPYUBR4MxhdKLTYix52Rx97dj/sY7
+ZUy/MLQYXlb48F9CyHFGkbVYwPu+RcbceTDDVZfOKjfIFJCU1XQcto+ZyYOQllJypT136xGBllF7
+WYExr+Z/X74XV2yygbJc+zbTdTGJw6lPRwVgnq8sMH8gZdkHDwkkutjpUT2lVHy66v4AACAASURB
+VP2bDtOWuGPzyS6DTOZu9T6PQEVn1u6VBzNYbbFUVYUoyyf3ZL6LUglbs0RmUSUvq3hvrGQpYQyq
+R9pfPyBrqqpTd8KBSsPcVDbrKAu5saWbeymhf6jw9lLBomZz1qixmMpMRx3pw/KZBu5QA9RnDEaK
+a2mfNDnszwLaJkZJ+1TVzXQ9wlSK/mAKrZqM9SuBxYPn5P2P3hvxvTtCNfDn3Fk7dh+O23E7bi9p
+d4WlUJgFWTnh5rWcOJLAiesXZGWVoncqJFoBZs/7mMqSGyt82MvHR8oqRlYwSdR9mBQEKmvuJqFq
+DcBcLH9P0gJXmXqNaUE5V9gpPWyFREf1A4a7cnKF94pZmxkFiUqXRfGUrefl76cfsbBU1MQPFrEU
+OZmqwIhZi/CSWXDUhVk+2jVwp3orZqi7NCQ3ZJxeaJNrkZdHxpwvVszp8w+wffXz8r38RVLZI22F
+V5n3r23JrHrPXyR2ZPz3DayjCj5jGhIqItObqHVgJ1iW8gm0wBgocq/bJ9G5bWQZ/lC+t9IQU9aa
+QKY5/zQyKFryOqk5jDpKfHNwyEQJaw/6WvhU6THKlU/hXJl+T9dA4VA7q3ByFvHVsjRslW7rDyls
+YQf3rJhY2cHdcUahFl2BQRorLkDdwCQ5IBlpoDgMqSDBwdqqS7SplmMsfdvbjilX5XvToU9NIfSD
+xhxpW+Zw4p0kbymEflWrUkc2wwWZn4V6BUNvZWUaEaqQS9XxMbQate6JFVCywbOVZyR3CEoyphMh
+fEKl7ZeeuM5zqVghd9ruik0hTVMOux2mwwmHasr52xE1R9mWTl5kWfXzuoMW8VgisqnSpQdZQaqs
+SnF3QGKqFqFbIw8lJ+xmE1I10XNdKLEVU51V9TnQV3ac4LBGN5Wy7Xp631EkuqZ6h4Zp4FryyFlj
+m1Oab/c7L7CpwJKV8ABXTVivEFBNsuRiKuGFZyyTqwhoXi9BSRdxKN/Pc4NcWYadUkySyEO6bM3R
+PSu1HY8MD3mhrqzFA/F1s+Ib3xTKWl35rdWAUUMr8pKbbE8kvnDSHBFYMud5RT6b2XU8W+IdSd6i
+pFkCy/WoaG3D2APXFBP75hPyW6HZZaglnH5aot/XEuAg5IWhvP4CCXZXrtcJRCh4yKO854K8VwQ1
+6o787so95wiKmUvg4ykMO6vrBlMtk2m9ihWPSAoVFq6npFoSn477pKliWNQ1mEQ2h1pkGJo+h7a4
+VZXuDtsD2eC6Y9kUQoaMxCNiYkSUVLuynBks6uZVrkfUlZ1Lh0luFNS1JsQORxjqugahgadApxFD
+jIo89L1YDpuqdchURZFL5RaTRN2L8ZOcD6Wf81HB3Jy8//9yZ+3YfThux+24vaTdFZaCWViU8yrX
+N/dJlFJqGj1HuSnR2ftLzzPI3g2AXU1xFyXQ5O7K6eiWS2SqD2mWM2qHemLkBaky3+blC9QTgTcH
+hX4/mtIri9Uxt9Fk5Ck0dvcmeaY6BI1bbHXlRN9eE3MxiTPGCl2uFF1+57NPAvCui49wj6XBzLLL
+8mkNyhmCMahNE6JETqKinpK50o/yuCBTeC1aGGRYFygZckRFRoWsKm5J0XqOtV0xB89yCiN5Wsat
+HJVZkWntKEzuZPK1WabFaXVLFt7//Tyirk3ZfoSa9i1tLFJXKrFcJczSOCA7IaeSlw+x1USP8j5x
+U+5D7YqBrUjN0+p+PJGMmSqY1CkOsBVz4vtl5lVVu7xpsr8lXItmJKedf36LgS3Q5fBUwoohuJDk
+6oS5NfntSuySLSuKNFMkoVFgKJ117s1RGqsUoLWEEWvA12ngTGWeHUWblrIGC45YjWnqY9iyXurt
+hOBpsZBizU6N+xGFZpzc0KSvmZZzrsG2WmEPNCFXM99VuL4V28QaJCx7LabqMlhOib6e2y3Ppj/U
+alzNnh3i09A1kkyGTG+qNTVY5BMHYp2W/IhPXhGZxTttd8WmkCY5e1sh/Z0Nxko80chvEnbk9RfM
+k7z3AUlJzefL7KompFmRGz+19pnX2PkgCKCiVO1GhbKWL+dezFQfSHtJblA5jEEzEZVzFTrXlJzE
+qLLdl6h3/pkTvP+vi5l4Ppe0UZKm5FrqOg5zFlqyOLzOFYxiTQYVl+BQFpCt6aa0MsCoifnsxgmG
+AmsKNyd1pG+OxhzyaoOp0qzjGlRUuDSr1OCS+t/GKb7pkvzeHz+lWuZ5zqyg2uJF9qNXa4aZ810n
+Jc24cMKkekrTpZcq1CMlm3VMCpVP72oVqdsbsxOrElLf4FRb7OfaBPp9ecDs+SrpVIBM8Y5cI2tX
+KB1Kn/uZiaMkK06QE5aU2zJPWGvJbz+jUOLD4RpeLr9173iBrR15XSzYJLbqR84tE6grWJhKjuv6
+lAPZJg9tHy8Qt8Nt2zTG6u/7A3yNpaTRjH49oqtVhqY5ZmJofUE/JYplrFPVzMzDkEA3v2Uzp6yb
+kNuZ0liUA6kWQl7X+g91Z+pzEY7qRxaOTao1MW40AhWTHQw7MCf3OtGMhNXIiTxZQ72swF+Se7Pb
+uYFKUPJFOpx0ZI1f5c7asftw3I7bcXtJuzsshTyhN95ma/sFrFuyo3azPs05lZyvfIa37cvu+fS5
+glwj0WFHoKxNo0GuteYpQwZD5fDLB7wwUamtw4wntaLsjfNi4mZWmR3lZJg+e5WuAk+eu3aZRHUq
+p/Ymv/5Ha3Lt5nkAkryg0NN4kMb0lJrWXSsof0L+59wHnsNRSu55hcOy06es6tKWbZMvSP+T2MbM
+5P3eWBmcrQ3+vCsn6UNhlcNITMM9p0P4rFgg0/0Of66s02k+owwrmCGfXwsC2nY8rutJ9NCkyWFf
+ItyNGyOiVTVzJ1MCZU92D6RI6jPdMdm2mKr/j2PydzuCG+iuTEBFcJyDDpkyRdc8McUbhUnflvx/
+vWTgqNZiVkuJtNrvlNPj+YrMx+JE/r5TjvBVUfmTPqxWlM9xa5t7XLl2UU0pKU9Gbknfy4MpYw1E
+huNttpTmbXUwYLqgytzplKkGrGfVkEW+SUlxL348JS7J+4snC5JPKdCskH9TxyVUzsvMKLGnlHaV
+BtyainVwwSxza0/moqnyhae682SaObFSF7OvbNzTiAOlv8t7FaY9uScnNMPRzS3q62LFJm7BhxXg
+FW0fcqiBXXMc80zw2gBud8WmEAcD1p/4E8ytCQTyoNimQbIjC2GyvMpwUx6KuDlHck0q7RIFoPSm
+e0T6UK10NkgVbWdOY76wKxMV736VjY4stqghD3R3dZX5Q3moNkol+ruy2JIoIlLz0nCqlB+X93/8
+HwvS7Ldui+sXeYGlN7+/foPPnFYQypee4Le0TuDhF+RBmDQj2opu9MrnOXNVHnr3zEU8/c0/3xBT
+9coLf8inH5fFc9aYcnVekXm725x4VMqzw9EmJRWhDZCH9HY2vpcjmP16zcsSura4T3HvKdaVRr1a
+ctkvSyp2zRgTmDK+MBTz+qt7Gzx9VTQZuldqfKymbs6bHmDpWSE4Sd52hpoiCJNC4it1v4Ply8PW
+DRuY6oIE+7sYGrco8kMe0LTmlwfyoFxqT0hzeSjOmW32NuV6c6dWONSH00ljqlPpn9GUMaVWg04g
+m9St7avEgaZDW0s4A2XDutWn2pZ7ZigRSpHb5Hty7YmfM7AVvdnZxTI066KfnR8nVFUjozL1OK1u
+6qLdZE0DJT23yVxNNvVDncPNGrS6SinfyMm1zLw3GnBzUxib+qMRji19u6X2fQWbvTmZq3Qas1wS
+N7XvXqY6kg23OT1gWStbn4ruLMp07D4ct+N23F7S7gpLwcSnWtxDFH+SiqFMt1Eft6EEKTem8G1y
+wnaen3BJqa6fe16sgLPVgO5QmXEbDWyNz0Whz0Sr0xYHbT4zkJPipMJIr+3tkF6QAM/4ckCotfTB
++BpWISdlGk24ORDL5Ns//hdDNTkvUpftuTZBT3b8y6dKVDTBva6R5c1Dnws3xb2YtnYJV+XEX2n2
+8VyxQjaf1mDZZy2ePJTxLWcDbrwglkDp4glObEnWJQwqDMbPaU8c/Tc+wincmfsgn14+dZJ3KnBq
+6s1RV9BQZgdUkNNqtHyGkkbGA1vuBzspD90UO/jZ4YBVJa35/BeusPBGOYHr45zmsnzGDyQ4N5yv
+MT2Uk73WnjBWq88u1Vn2VLSnPMeuMjR/20Wp+kusOguOzFVnI2RlSVw0t28yvyIWRCNxSJtyOtaV
+FGZsJ3jKlN0MSowyHd+oj68cjOniApVMsxIanKtYMYsaGKxnTRzEvV05VRDoOor1MYqshG3lrizP
+22yoAndtDa7EspbPtQYc7ooLNn9C3eDDiGlL3mvYNlPFwOShQU35OPfHPqkn932uLOt0XCmzou7m
+Rl7maiJu3ANzH2R04/8E4FJ1gS+pXuqdtmNL4bgdt+P2kvaqloJhGP838F3AflEUD+h7beC3gDXg
+JvCDRVH0DMMwgF9ClKcD4EeLonj81a6Rp1Om/WdxsIhTCYq4JmTKntxajLm29VkA3rX2fTyxISfX
+vKp43DRgRSsc02CHsqt+r5fzrqqcl58xTd7fUiJUX06t95Qs9pVRlxMG29fl90qUmObK6YZDMpAd
++IOZvPcvvqb/ehjRznLee1by6Zu711nUXPfj+3JKVD2Hq6vinz8cDCiCmwCYRYtEA1SP1sQ6+OVW
+wTer1uQTA5t7VV+R/T36SqQ6XaxxX1VOxK+opQR3noYEMPRYOBzskbgSgN0JBixpQO2wtUJbGZWT
+ySG+ks0ODTkx32xP+VVFY76/FfLFtoz/AcsiuCbjm66tMs+MgkxiILU5DzcTK2CYVWincuJ5w0Ck
+ioFxELH2gJy2z4/kJD1hGNyYSnCtwhwv7Mvr+52Qvlov7bKLqXOXa+o0ziIcX9bCZmGzrMI/1xyP
+5VTm3r61S62peAEVasmsjFA1I714gqms29P+hIpqcWRqYZFk1FWop9afcFLTt+vPTcCUU360U8Iv
+yfuBBkYfWsqw1YplvgVqgVjVnB3FnzT9DtdTmQNbg+qVccJeReZw6eQiPxKK+NBnimepeiK5eJB8
+hBXl6th5eQW8l7Q7cR9+Ffhl4Ndue++ngI8WRfFzhmH8lP7/fwt8B3CP/vd2RIL+7a92AdOz8c4u
+kGx0qCqQZJxAtS6vh1HBfEOEYjecgjlX1WAUxLHgOJQ8uRlJ5uNrjUNlocn1gUzqxUsD9gKBB79l
+RTabjVGLReVrvPnMHqYu0kk6wihmj1ZGoDRf/+JffuLr9j/Tcu+46vJETzae9z3axBxJUO2CAlqu
+tc9ycVn6XFuvEeSqTrWzQXVNNqrDiizGh088x5NNMZm/091ma1PM5FZtyolAHrwrFYenEqVvu42q
+/7XAnAtVTTp98Q1UHpbrXcyqePOyAH2vRLWQPu8NAkquBs+0nmPkNvimUxKULfx3845lhRWHC1ha
+8dp0HOqFvD6hPJi3NkskGpSjZNHToB0NaCkuYjGBku7ZF1fluvPNJQoVZ6kWJbo9eUhb3hJ13eHq
+ts0o0FoZBUtV0hRHMworTkS7JcC4uuswr/D3ST9l3lfmbgVn1atjFp0ZY3IVSyHy1Wp4xPg90bmv
+t3KCHXXjTtToK8vzYqNBNhGXZ7WSMdLyl0pN7vlcahCpy2sXIdWyrAHP8DjfkgfariyyUpI+zekN
+HmcJmRIKDcurfGjpJgCXujFjXzahNxt1PjXDU99he1X3oSiKTwHdr3n7exGZeXip3Pz3Ar9WSPs8
+oiu58pp6dNyO23H7j9q+0UDjUlEUipliF47Ihk+CRqWkzaTod/iadrsUfa3ksFRs0szLqMVNalqg
+qskLcxnZSNJ6sXsGT+FajkJj3alPoiIsc0EHpyq7csvIyE7ISXKYL7GoKbCyErFOvRxUm8GftzB6
+go4zco5wCGBiKK/BPQ+IeXZl92tHMyuOmvLWh2Q3f7R9ktYF1ZL05RR865kq7q6YjAflXYbbMo5D
+26Y9El2H5opgId5cXOBNqo/ZO0h54wnVmWg0Mc9In8v7MR+1dHyF9P21uA4ArvJCPGAdcqEqls2a
+sU6uOp21ScRAQ5a1gwnTlhZ5Raq9cHYFf6JaDxWfaq5FY3OQFMKBUGmNKVcEAzFRIZezy4/Ti/S3
+jNqROrSTWphq6bnLbVrqruSFLKt6Y5XTSvoyHVkUy9KP5WqOq4snqhqUhlrFqRBt07BI9WQ+QU6p
+JNewnQV8U+Z5oTzCn5cj23AX9fs95uflGkmc4Vfl/mRZn9XSEzI+NQ7cgwJXDCwWcpt712QOW/4q
+DUPWxcCI8criIldVE5VqTv0oaOmhSvXYy2usacGTZQ0pdO4chTbnRumIQ6JtZzzQlbW1PvkDqn1J
+nR4YIxbbSl93IFbaq7XXnX0oiqIwDOM1M3zcLkW/tnSyeOjM9zNtP8Z3Ij7nhzd3OPuQLNj5Yo0l
+pedeNB0mDVlg3kQf8pqJrUw5wfwJlpTQwpq/RC1RWfpT9zLZlpubakn2cs+nq/lj66mAeFrTvh3y
+ohGVYynxyZWnn37FMfWxGN6QqHb6gQuc06xDekIWmLU/R7KmdOKfukanJibsxe0+VvkBAJK+ZBmq
+F9/F+AXp++qZJtNrWtHw4BjrlvT5uXiXfvfT0ufblCLNo1ev3qxCFu6b3vPdvLUlD3fcuAevL2Zr
+sjBPcUtqO/asCvcqNDlalAVYKiLKp6Tvi0lGIsPDGZQYt8TMnzMWCS152Mq7XwWg5/ssG9pDa4Id
+yvjsco2VUKPvZobf1zqAVXH97nE9aMlFmhULU+NHrW2DbF4358mU6Zw8FDUto5/a0LAVyDa3wKKW
+Tsd+TtlULEO9RBnZ1BJH+uaObKr6W97EYs+WBys3oiPxICuTa8Qt6Krm5al5sDUG4K0Z+BMZX2PB
+wtiRzdc7qfds0DmazzXbJq/I98qmidGWg6V0eMDEnvGXymcn5YRlS/p+YxQR3npM5mL0FgJLQnnf
+1Wjyi4evLZ/wjWYf9mZugf4743vaAk7d9rljKfrjdtz+E2vfqKXw+4jM/M/xUrn53wf+kWEYv4kE
+GAe3uRkv20y7oNzOeOfb38FG988AeG+5ymYglsA9lypEKs9dtxyGqg9p+LJD9xpLrAxlx1yqNjE1
+QOlXTS4siFVxdTRk5YLstFuemGGL7Yx+X8zue86nfKU/U5V2SI+ktiwyPXMvXJIT4+rey4wjzXDn
+NWB2cBlzUU433xZ3ppj36ORK0uGfZUEl0eJaH3tJTMlHGtL3rSLn/HmxMPqlFXw9xZ85XGdJuf+S
+8YQlDYxthC/aBa/FhWgpuUecPYtdf4+MY6mOW5U5HpZcSlpIFrsOI1twBnXlVKxXF/FULMc3DSzF
+lpTaFmlfLDLDCY4k03NlMB6kBXlXgmH9lQXQfLuFQ1fVtqdZRk1FYgbKtL1v+yypuW94Boat/A71
+MWFZAreVzCMN5f6FhcLY3QxfMTBYBZkWStnVeUxf3BgnLygUIzDTp88tkyCXz8bTXcJFWQPGrkFD
+3cbM0oDjBO6tyXv3jMssrMm9PGkvMD+vFIJpgaGB7u2yWDlzcYSnvKOhBWXV6yw5LpEiPd1Gk4kG
+eT114cZBgDmToh8VvOOMWBi/Y26zuC9r/LHlLd5fkTn6yLPigr9au5OU5G8A7wbmDcPYRFSmfw74
+bcMwfgxYB35QP/4RJB15FUlJ/p076oVvUlwq81x3wnu25MH9yO4iZ++XRVMtDPxDFYdtb5Iocacq
+yhMlA26EGguNM+Y0yjxnNLGUvGPq75DkYnbOpOj3oojpWMy9a+shtpqtaZHyYuw+J1fM/NUvfPIV
+hxEXBR/7qvTj0iMN3qqbmj3Whes6uMoJeXUnJarJ7tJxM8pdWXgHofibeXqDsSlmop3U2OorJLoo
+2L0uCz18PuDWWMb3jeoE97UsPEruZ6gPRK1nMzWkz87BkMvrKnHuHlJcmjEZyf2Y2CEFmn1oLuGN
+ZMymU8NXH98wqpizKshIPjvaCXFDzb5sBrhVLQG2Y0rINeq7BVONL1iFbEZGa55spBqVPrhKxptb
+CaW+kujEAZZuCoWrJdeRxyxebsdjjLqOLzOxCxlLbjgYmsGwJjLf5ijG1XVmJSVKysiVlzpEqqtZ
+dTRu4STkWsEY1WJMe3Z4+eS65rxmhVz1Qud1nSY9j1BTztU4w+sqb2g+JVEBG3IfW3Uj01jdJDdl
+qgxRlbHFH3dmqdOQ3rJsLCeMMn86K4u5w/aqm0JRFD/yMn9679f5bAH8+GvrwnE7bsftbmp3B8y5
+yChlQ77POUt4WgIr33lmh72hmFzdGqwlsnM/eeBSOngBgBeY8RYa9FTEsB4/x60FOQUeWQvZN8Xc
+q/Q6xIbCZ5UuvpNbREOtLGsGFDMmXsOmKGbbq4WVy8688t3fC8DWh5542bFEyhg9+exHubr6bgDu
+dyRAOa54BIEAoEJrj/0XJIBXtFu8TSvnnpyXINRiPCFcUj7D6ZCxIqS6wwkThQFH3haeVgPeXgh1
+J22GZXA9eeVc/hzXL4ls+6XoMoeFmKKjqMu6wmv99Wv4TRG+ydSdyxcW8fQ0y6KYQuX7CjeEWCny
+zJisLu9v7YnJPMw26OjJV67ZMNLCrsmEifK6d0pVGvqZjuIxilGMVxdLYpJBoNWsbsUjVWEf17Ax
+Ewn4Wk05lY24RGFLksye5CTKz4BbwlKYumGOKWbVlUo3l8RtJpZYKaZT0FN3xRhkrJaFc8OpKklL
+sIzbEkto3mtSLsm6cROI63JyW/mAVHkYhmrF5HYIyj0xrnqUtTJ0XDgkCjgKwi6ZcobklgazRwl9
+U8YxqPuUz0m2Z3rrOR4ZCDzo3pWnePN3fRMA/81/9znupN0lm0KVWvoOPj59njcdyg3/8o0Y+0G5
+MW/ojOn48vqEFdOJVbZ9JgqQlhn09GGLM2qFbCA37H3Sipi+40qdiqLNjBk+PQ3Y1VqFyfWERNmb
+ijyG26L5hiX+7NZnXtl9KIBhLL/xuXGJ92zKw7S3KFmGucQiLCTGMd7co9QT1qDPGgaPqrn60Uiy
+CW8++xAVrcVYjgsmPRUuJaFQl2H9KxPCKHjFPr1SXwGCsTxsT0f38IBKru8vNvE0M2CkdfIZkYk5
+j6WiqtNcXB+rZ5CvauWgFYLqVhjTjEwp80u2Rb8rD0vb0OxFN2VJRXyngy6Nsorw2mXmlK+wyHJK
+StKaKVX/UhxShAroMSxiSzav2mSfisq2F4FN7CirlfIvxqUp5lDvdaOJ58/qASwMBZEVbgPT0dhH
++iKdf9nXcnfOECgzE9kIQzfAedV96C8l1AfSX6NqUslkrvqpQXUoayExFqj6Mgd2ImMeDUIcRyUD
+MpfRSDJQZg4TTWWWvQKjJ31zFYE5zhr4GvsoBkN6T0qNTrv8nXz8lCpkPfg2/nVbwX532I5rH47b
+cTtuL2l3haWQxjkH61OK/dM8YUgG88xCj+eekp14c9VGGbowwoIDZc91bdlpTdfn4Zrs1tfDKnMN
+OVUq5y/yoNJZbWUplORE2G/J7mts7nOo+ei0MmGqwTDDsG5zHxxSdR8uXhCsxOXtT7zsWEyFbFw+
+3GJvLCfMvNqAxakq5XANgO//wRMMEwkk/sDqLunCOwH4V3vPA3Al3aUcCU7hD7cOOJnLSXvl+gAr
+EvPzitn7xqmbj/or43xq6+N83+BNABj1N5BqRmFiVFldlPk8zM+isVrKF+4FYMFYOILoGtmEsKIu
+QW7QUysk7o4ZaOBvOJYTesMwYV/GUTq9SLGtdQRRRl9P/F3bZlmBOjeUAn8SZlQ8+Y2FCK7PqP9D
+F6cknav4MVlZXk+Vm8Cem6Osp2oS90SBCEgrObbSoqXZGLSEJJ3VmlgZw6lqOBpbdJVVnO4IV4s0
+Jkdnq423KGM2h3V6SrufhZtHithhNMXWTFJSWQNgaS5iR7E1qelgKi5iceoSa/1Pbo7YUi3UWiLv
+7RUdslgs5CtxhXP3yO9t1HzOBz8AQG91wr/Uepu3cWft2FI4bsftuL2k3SWWQkLn1g5XsxtUHhe/
+6PJkit+QXfDm023WlHUmKE2ZiqHAgRJcztsRh0pztpBGZKrP4Nd9DpUy+CAfMr6qbMyOHAfTaEjY
+lVNnb2dCrkIgxUtSkskRWvDyY0+96liUJ5bI9ri1rsIiq+JPnhqaBFXhZLi12WPtgvinz5srPHBa
++vblm+qfZ1f50jXtTy9nd1v8+tHNLte3xJ8fHE6OBG6+0Ram8v3JwGdvQ9Nb7oCTTbFi/Dg60u48
+Z0ecqEv/SkpgapZXsBXlZ00M3JEyVpkZ/qFi2gybeG8WMRO48mR9QGFpnKSTYp3WUKlt0VBuiLgf
+4zky9w0tyqp5YCkyNVi08XuanrWHmPtiCY78kGRbi+KWJdAYT6qUFYFoUGAo5ZudjbBLcr2834eS
+xgRUs4J0n1zpzOLOhFADnlSHhKbMS02p5sZGgadw5ZGb4Gjl5+igwlTjFrfSlFOOBBhnzE1h5FBT
+LVQ3MSmronc+dslKMp/xoAodue+5ytRNtgp6tszxKF/gk1pg8JY1mxt/XXVE8jb/6Ahzc2ftrtgU
+QmPMc9anqT79MPPj3wdgftlnpIKgg1MG8ZY8FM9VLU6lsll4Wsa6ZlTpqmhGeWzSKcnNGGw8z7MT
+ieYHo+v0RvJ7jk7SME8Zq55h5KUYg9kDZvIiBMjHU1fiDX/7nwDw+P/x6lHcdDjhsyoM8sBQbuZe
+pcScBh//oJfx1s98AoCPFe+ED/08AL+7824AXPNzlB0BL2WTLoGa86k9Jl6WcVQ7Kd9YmPG2pu7H
+YG+P/7ApG+vfanmMarLgXXwipfwy9w4YKPy5FkgGqBct4phCw26ELrGCbYqmy8wB6zNkqvqHX74p
+gcHdZIOp5u5bQYp9WfD5eZaSqkBw4LugHJujVAOUicdiRfrZ6UwJE8kARMVJFtRc3whclkainzj2
+ZHM7WZowVuzFuDugUM7HvCjItQTBzsfkCmrK9RrBqM7hWEVfRhGbhTwyXVkwiQAAIABJREFUya0U
+ZX/jQHkdLctnf6Q1DPGYG1ozUylVWFd+xWqlQ+DIWBsKnw+NCn5fgEU99yTsytx2wjUmgZyAk86A
+YUcW1FOeXm/YZ1MV0Oykz/kFgZsXyyE/+lUJaL/tbQHfcWENgIvcWTt2H47bcTtuL2l3haXg2kus
+zf9TRpXf4/z9QmHylWc+R/WBDwFQeaZF90E5Ke4zyuSqgsy2EqYu5Oxr+q7daGIoyWfXqLGnJ0za
+NRlrldiMrs1Zskm0yMToGRTGTM8xApRqt5hSa/8MAI//f2+94zGlpkmo1GNPxlIOcs+1G3ziQKyb
+z+7tcLgjVeUf9j7BaWMNgH+XfQqAN604nNQ6+KWWgV9Rktp6i/gLM3ISC3hRQ/IbasoFkRgWc4qO
+2/bLPLguJ9uo5dBW6fNJ9W2Ux4q2u/oG+ffCCHck2IPatEtREUvBt9vkimVod336B0olprqMQTcg
+jWfovz4HSoBju2VKkZyEoetgDlQrUlOEa4OIrlpuvlumCOXakxvr7DXkdN8sBlT3BU+x0xV3rdm9
+n8Nzkt83e2VKB2K9pXNjXF9Sw7hncGb8FFo9m/X7DLdViOeWTWpLqs9rBJgTuZcVVdqeTGISdbWm
+QY+SuredcZ8TCiefRB5OR8ayryzRFceiZz4i/QymjHpCwLrlhZiaAbW3PUYD6X97SZXUnTalW1oI
+eOI8VxXT8d0XPsiHlQDmH6zdz0+kr+0xvys2hYQBO8ZHmP+m78HbFsW7c8nbGd0Uyob5tSnVQibk
+vtXTZBUFoaiASulEg3drXvr5zoATCnHfrrg8vCUL77FpF6VBZH9BFs9qPOWK5pjXyzEqO4kd+MRK
+4JJbP0tc+h0AFr777wFw8L/dwaCKlDndcDw1KaMFn8gVoM9PJTZ/qtmTn2m9g8aqdPqj+V8D4E86
+B1w8qzDok00eVbaeP3j6JsGc6jJa+0QHerk76NLXa+bsm0VEvaIS6JUW6Zo8bJVmjTMafZ96C8yt
+ymZpLWoZclan09Cy52mB0dSYiO0dlVxnwYSJZgzGsdzHUmOKO5TvGabH+UO5RjiKSNoK440yWnoA
+PB+Ir3/YhFxxx3O5wZaKDC+frbGl8ZFHOjUea0mu/3RD+nPY6nF+Kn3+aprQ0JoKd/EEhoKIEsum
+mFVValYjCHtMDJnvWmmTRIFvpS2oN+W1pQQ/Dc+kpm5n4DUo62trvsKKlvYXbglbmbKzk+Lankvb
+7CQyjnZ7EUPLt8915nhaK02r0VU4LQ7AKSs4mu+vKut4bSnjpxclyzA4PeCnF2TT7iQhP6skMuKY
+v3o7dh+O23E7bi9pd4Wl4JVqnH3gXXReGPHLE4ES/1D4Jd7/LjG7n/zikHeofuSWF7Jckt3TUZbd
+amnKYFfsrEU/IVW6rua4YKhMtovOiLAqqMhVFU7pxCmurxmChTZ1lbPfI2dpKHDrPT/kv7r3pwH4
+H78y42189dYyLW6oavTDqezmh0GbkoqC/HGQcl6JUT7Wr/G/LsvrX3paTtIf+PYuG7GYkQ8vu3y6
+pxqF81OundQCnH6NwtBUzMtkIUwlGcmL4jaGiBfhDbn+/aTrs62Q8IQxhipSWj2b0QwybNlEGzJf
+9VBO0m5Rx1uZ5fkdrEBReowp1A2YxgFRT+5PZikpimlzMMM0mAYDR/PxWYChVkFlktExxI1Zdma6
+nIfYA1m2BzWHeT2lHSfmlAZ2d6pjHj6Qz3emNwHw8hLPKyHNfH2CqSA/Nw5JQ7kncW5jKsvzdEfu
+2bRzQDWacTHm1DWgbczFLFqqXzpzd4qcYqRWkwOZWiNmmpKqunleTalpVWXYl2t1h1NsdTV6wQ6N
+rrze6MZ4DZm3sGzS1lt8VeXvJmmAsSzPxcLcEn90jwQX/64xxy9a0o9fSAz+/v6dkavMmlF8o+V1
+f4mtWvaLN146RTewOOuqEIhXwi5kslMcTDXz0wxaSsZamRFYlubQNUOjmh0FDfLMYqTalAdhj/5A
+Jmocy00eTlJyBe+kqQF6E6eTnKyQBTtJMjzdOtu+mJlPbP4F6qWXaV+LLPKBGV/eAgYKHzZOYhYC
+2sqVjsLgFgUzeGr4dX7rzto/fKuIsX5qcJ5LiRBvWCyzHUvGYE8ffi/uc6jpNDedMtHLVfKcXMlD
+KzbknqbhtDLQMsu4mk4zDAdX4dGHo4JkKpvotXFKogCul0ugzkb37371F6ioq+RZjSPh4NxTszxM
+ybT8en87oVyXBb/UvJe6CuRm3gKLizcBKNffIj9sjnACedh2Dz7FOJCUw7j/JFN9CKNig3Ek8xto
+fw86IammE5MpoOnCigGHiRw4qR48tutSTHXNOgWJCg1t9sdcviHZhfFwxFThzSgPaFYYWHpfC9Oi
+rVmNxeYq952Q31t45Du5qDUvRenbZWz2H/JvLwvgbOMj/wubGrsyiq+SK1lRmRH/U0nYon5yeu3L
+RVE88jK34Kgduw/H7bgdt5e0u8J9MHITa1plxIRJQ3biqVnllFaAVeolSnoaLSUWtqfFUfdI98uV
+MvMoIMTxKalcVzBNCfflxL+1f8A1FSoZb0gAbzgMGKq52ytbmB21DooYAo0oOxm+CqDsZa+1FvFr
+T/XbWXUPXvxrsUV+dFbeuu2bt3/+G7PoRqaAW6/UXqA+lVPlC6ON/7+9N4+19Lzv+z7Pu5/93HPP
+3ebOPsNFpEiLlGQtji3ZjmLJtuI6rQMZSRzXLoygAeIUDtIIBgq0aP4wUqRNETeNkaQtAiVOnbqO
+oMi1FVkpbDmSJVkWSZEcDsnZ7p27nnPv2d/96R+/3xlybC4zMoecoOcHDObcs7zP+yzv8/zW75eT
+kWhNEwWn6YYRZTp3rrokmh7cpqSvDtN2xSVRNbeh+JhFFBBpwD6rhLT09NsPcuyO3HOaz4G5Xl/m
+vavXa5zQNmK/TtVRarlEzIhGzXKoGt3JYQ9bivfY83cIT0n8f+ZOiZYk+mA0P6IadDEK+15pP0hD
+8c7Hy6eoNGSdbadrrOj931DVfiXb4gWFMwu7JbGmRIdTyHPp9zyt/rAIOKOgN/1JQbgpY9ubphTI
+a4qYXLXQOZ6Lxd7KirG2JFX6vsuVCasKtFM3Pp0nPwLAy4oB4mV/hm//838hP8zqWPusXs8wz9ee
+Ar8Qv/RGQ/8nZKEpLGQhC7lN7gtNwXFLovqEk5nLyY7supvBOYzuduFyjdqy7J6PBgFBRRxGVSUD
+rW8us6KQU9ZYHNUkUgO+lq+euXGN757IaXNjTez3YTxk53kJXfXLgpmnJ8asYHYodmInG+FlehJq
+Ucub4st9R3JvfDtmVcA8a889xJDfA+BEskI2Ea1neU4k608505LXx3nOBaXNa1UKTlSU0swvqZ0Q
+Z2Ss/o6lZoOJEr6aqKDUE/EHRjHfWpZT9caXPSZzO/pNpO53GNfUqK4ETOZp2JrmPpkeMq5pEVva
+p6LFWrt7Cac6Mr8b7RZW8zdGk3mZdU7NKDJy7hEoWKtjq7inFIXpMMSOxFsZKTnPbqXKBPENDPcP
+aSj3wiwt8VOFiEO0rrprGBSiVdWCggNde+1uwvpV0Uy2/ARPHStzuOOSVwr1S6CqGs1jzhIPqx95
+5YEq07Gs2/dWhANlp50SGA3VOn0oX8fvZO/OH3VfbArGuPhRg8QxeC1Rl6ZZxIMKAbvs1Gidlj8u
+LGU4h4oPuKoAGmvLNLTuPjUxngJhFFWLp1yEtlOlrMlm0liSGLzb/0O+mkps92RZY1sXt3d4jZeU
+S3DQ79KtKijGzbs1H955ObemTMQHR/yYK8lX/8dBj/edlrG70ZNF/r4HNrnSk/6/y53yrDqqPvZ4
+yIvXZCzevZqx58h4bSgTVjSc4szxDXDIz8h1r/RusqJEPMmLCZ7C2WflG9dqJLakOZSHYrzt06nL
+Azl7URO5ujOqsTgJvc4GvtZUpOsdTngKDOMlnNCU9ZeUQWu9iBgZeXgrtoavB8C6v46nMG1O4FGG
+ZwGwrpg8K509Dp6Vh+qkG3CUqaO1GbM9lDa6itNxNIX3nhWz42inxgc6crB8YdomPyGvl2c5R1rH
+0VHTqDczrHjywO5Yn++/IGv9N6rfx8+8X+bhS7Mq39t9DwC/rUQvPzRrMni/skv/3gfoHf7u64zq
+3R04C/NhIQtZyG1yX2gKloKMMZuNiFVHTp3V8xeIU8EWaKz61LRAxanXaEbq7FmX08qxbWxLtICw
+HGIUpckxVeiK86kSuqCORkcpvOIoZE2ZeqejEelAdtStwQodV5yRa8GAeDAPIc3VsP945NpIWKnf
+PTvBVlUIZ5547Dx9RcR+72nNQKxFfMiVOLd1d/iJZRnv806DM49JOnbpDmlFWoGqiMpxK2Cq1YC+
+OyBQs+uccekVMk9PLsFzinClKQ+vK9PxlAOtDIz9Cvs35Ac9hbmbJQnNrpyk3qBJuKzz1O3Q8mXe
+W+4aUyPh3ndpCnLuejQ8We4jb0hFtcw8jjHzzNiyStZSTsu6jEXvekx9TcZwNJwRViQvIE6GzAq5
+z2sT0STroWFwJPdgWzmZkmA83k1o92WtXl0e09IszGWlx3vROtQrssZOh0tUH5Rr/M3Ty9zMxfH8
+0Ys1RpmEkb8/FW1lK3mJT5QSHv9a+1k4fD3Gj/8IzQdhYaoRuIaRAqGcPTyivqz8g5FHtSYd6rgF
+ZUvMg6oSe6Z1Bx/Frat0CfThzz33lsfZjdqEMzUlNOU2HCU0Nf+hFhZs5TJ4K2bMoeYvWNOiokk2
+6fQOGTpvyZ/cRIy+Z++QKF7E4U6J5f+4PNgWfL6veUM+duEsAL/xtZQPvUserDMatYkudDmTihq9
+V3a5qCnfdm2dhiY4xWGTNa00Pa7KwuxMjonrMj7dpGTPiGp8ggGDiqi23eUJS+oxH155E4+MKVmO
+Ff6uPyVKZF6X9uRhffxklWOFLju9XmGmhDobaUC8Lg9ykEBZl3qGOTiPNT6mUOj/VpvacL52Irxc
+fVDLBZ2BPMhZJGaLG1a5oYdB2PAYF2Lkd7yMUqHwljXiMJ6NaLUUnv7QY+1BNStGLpttMW3qrYjr
+++Iru7gh7R580/AX3i8m2rMvPsKPf7eM2/PXTnDmu+R3y2MXV4GE2or8HXYe5nsekU3KP/u9/Nov
+S1lAUPZINcfnHIYrOsdwZ5T0C/NhIQtZyG3ynVLR/z3gk0AKvAT859baY/3s08DPIjrM37DW/tab
+tWGtQ1kETP0ZcSk7o38+xcwUbCIoKJQPz40iXE92vlwz7Qo/gkC74lQotFt5AL7ScOejId68ItDV
+vbDVZbwvO7+TRXhVOZWStIKJZDdup7vcVKdVmd+dpuCpT7lQEA7sqyyQ0vCm6tycJ94FoxwBd6dh
+wFQxCz740RbXrKjM7//oNa7uKkVeW68XOFQj+bw1KsjV+541I1Z1bJPDlLwlR+9YvezLVY9ySxx4
+aeQwU1DRzG9RqCnxxKkaE81UPbyh1YL5a5MRHB3AaFVU/74b4Cn03OCk4CI8N4EHT4s5Uw3AKCBL
+XvEotfAnjBx6Os6hEj1mPijoMlnuUHiytnI/w9XCtGIWkkVaCKXwb3GtxnRZnN/l3oBJW7Esbs4o
+U+nDrqJaNMIVjjOZ027DvxX5WCenviraT6+fcuEBGZdeJqbYX/m4wahj90cfdTlYEUf4e06M2Jvq
+ul7KSZQiLtpUaLeh5YknFFjmIOZjF2Rc/v1BiRlIP657ExxNzX4TH+8t+U6p6L8AfNpamxtjfgn4
+NPBfG2MeAT4FPAqcAP6dMeZBa+0bx6OMJQ9KosKjPhPVKhqtoM8+VS+imilOXq1Gpnn+JtQU3Ty4
+Bdtu3AQ02cafGFDiUi+bUGqYypvow3jUxz/WWysT0iOZ5Ea5w7F6lgc0MQrxPbgL7iXHOBSK5+fo
+opF0Vq0cxEP2VBCjQtNcdZFjcubTEziGVJGRsXemAs6lH0j/L9+ccnYk9ufoOCb15TpLHeVw7Jc0
+V2TcerlLuKb1B25EbTxHEzJUIzEb1qfy/1LgEbeUij3wyIfyIKz4O/yRbiZnmjOCXNR5V8OJ3rhP
+rjBVHtwCZIlrJf6WtNfsj6ApG3Vbw8GtUxYzr3DsrBAPxeQxjSo13ZDwqtS1/iVTCPtwCFldk92S
+glQzyN20BPX8m8IjnScnKaK0m+0QDOTgqCUxU0UCD9dK7HXdGPWhS21GVNGdZ7yC50sE46DcxPGl
+H3HUJgpkXQSZHjYjj9oJuaGo2qKlJDn+tMqKLw93xY1wNDwbumo+2xs0lPXqA91THH5E5vLg+c/z
+rafl/b/IN/nVsZaGl89xJ/IdUdFba3/bvoJs+hWEMxKEiv5XrbWJtfYKwhR1p3iRC1nIQu4DeSsc
+jT8D/Ct9vYlsEnOZU9G/qRgsNswIQjEf3MoyK67sRUOncouHYDIq8DLleGgrnh4BgdvVKx1jA8Xc
+cyugtGJpPAX1YGeqzs/cCjMFuuiPciaFaClP9zzyiXi1J0mB25fd2pndOf+WExi8SKGykjkNmINR
+TcFmr+hyFoOnzrxbFDTGgHrLwyqYWFTHIp3eQpe+E664TLUDvx8QK/BG1Ylp6am6p8kx79qIudEX
+p1VneoOpFXV2KZ8yqYqauzwpxYsH1BXvcJIUdDUylIczVppyCma9Jc4pAnXp1HhCUaCPzouJsv3i
+iN547s0sb+lg+zdepp++LNegxfGWaiGty3qBD3P2glwsTSNINbqQVLFqKtjMIUvltHXUBJtVfJif
+6BQ4mtfi+5DqNbLMgpG+DrRYbadoMFDNdDvLSTIZI2YlucIC7qs22ggNw5Got0Onz0ALrViHkZpN
+1rNY1UKtUvikWcbSUCnvagWhMmkPTcFyJglZ1nrUdHWUqj2sTAIy5RWtz0oee0ySmpqNFc7OBHsh
+bjs8oNG6Fz7HHcmfalMwxvwiso4/8x389ueAnwOoBD6VPCMdldhSJiM/CpisyAScmxaUpQy2V/Eo
+PQUAUQaiLDS4Cn+du0sECpCRZzFW/Q7WaVLRpBGbygR4FhjL583Ukm/LBIT9Pfp7ou5tpzM2FPuv
+n9wJAKYmVrUfwlPUJ3dJ+lHs71F15WE6Ko6IVLWdxTlrmu9/U6v0qidaFMopuNl5gO1DxUGsOYz7
+cl1LxptZZgeX5PNvH17j9DzJZjykuywe7gtKWjpZqrJ+QsYtqaxzypO+FmWDQO3kuBVwUv0SaSif
+N4MqE4XR70QeR0fKsFTx2HK0XiH3CIZy7aYSmmz7VVIlhllxYH/ejXhKNJO5LqZ9uoE8QI1EahlO
+pxOKQLNXPR9PSVobkym5lsYHGExFrhHMowyug6cZjbkXUZ/J/SdBODfYyAKP+kjuqalvrhcRl3Xv
+bQaGYSkhws2g4CmtYWgrF+VsdgwN2WzCSZNgQ/o8S9fpaylz1ZTgK0irq1QEpWVcF9V/w7bxInm9
+MnUpo0cB6DKBSB50N1ccyWWPNSW/dYOSJ13lLn3sUzx7RTbFv/Zol//+aWn7Be4xQ5Qx5qcRB+QP
+2lfqr++Yit5a+yvArwC0a9V3vn57IQtZCPAdbgrGmI8Dfxv4iLX21YDCnwX+hTHm7yOOxgeAP3iz
+61kLZVwyDBJirRBrnXHozJRm26/hhep9n4woGnIyacYptIJXSN4cl1LhxzOvwNdrlL5PWiqARk1P
+j6MBA3XcuVnCSB1jvdhhoNoGsyn95M7VdaOmSeJt8XAoEZNdBYUJVpuwIubRdx/CM6p4ZCak2pYe
+/AV183wji1h5TE2pU1W+97rstZ+/mWI1hDGdDihmb6wp9HuK4TeYckMdnlU/5vBIrnFtVSsgT1bZ
+0NN1uOTiKUlOtLJMN1EsA6eKr6jSRU1PuWmME83xLMdUNmTsyxcTzKq4msL6EXEpnX3wrEYhLhcc
+j3U+8hJfCwHsxDLzxHTDW8MP5eQuV2Rce47DUqGAM4XhULU4r+rgqnYTlTmJrgija2RsDfWpXGu7
+GrA0z18xU1ozGfNhPkXBkRlP1DnslBxrOWM9zxko1V10PMRXd/6RagzVqEpfTbSuO2NfYTfC9jX8
+fTE16ksdjKZb+6GM20Y7x1OzOawHOA2JdiwFAcmpE3ofe9SV/q2hzOXjASQVdVYHASuBvN40LX7k
+r8h9bE3a/GVXckN+/99wR/KdUtF/GgiBLxixhb9irf1r1tpvG2P+T+BZxKz4628aeVjIQhZyX8l3
+SkX/T9/g+38X+Lt3cxOOtVSKnOu54Uh36Gs7K5gzek0DnW3Z7YZ+BVftqNKbhxAbWEUDNsURhdp4
+3jAj1/h+kvU5OtKwUF8zFPdfoD9Q8pL9PWbKAbE97HE0ke8M05Jojm5wB4Feq07AII94uSa7/1Kh
+zMluzrryrr3QbHEROUqeqa/yYa3a+/JMwkdnTr3ATimn7rvHAV9uiF9i2b/KULElIltjcsv5+dp7
+78s9DUOmI+YKz25R0mrKOF9+SezXzqPb1DriZEu9Bj2tamwmEZ4nJ5S7XDLO1LdzrNWLwwkDBW61
+RwFjRDPJt6+yF0vhTu+pXV7UCr6vPSNoyL3RkJnaxo61zH2nYafCySN1tPkOQVvGYC1TvoWwxLki
+83RtNcBTtvGes8fKUBCG8tMVwpFqg+pHqOxN6Ku9P94fcE2Rlc6OamQbqjWMJkxzuX9/KBpkI75C
+Ux18FwJwFMrv3JmCb27LOtNlSi8taHdVwxxvMC1k7MfDGUcT0Vye6FQ47MnYLa3JHBzNQpa0EjOz
+p3EUvWmnyKiOZLwOY8PasYznrCLXKr0hM4Vm87JlrPoo8rTPFV07F4/GfOUujfP7I83ZtZhmxoVa
+k/c2zgLwxEM1ZgcSfZg0fXxd9EPGmGsyYTVJ22fmBTSNDLRb7ONUxRmWJA7DoaiMO8fX2FdvVjGU
+ib9x1OP4srg8nMqY2MqE1qIqHWWunlqLV6hjSG/3jdyN6tekOhvhteUhW82kPVv3aBsp1a4011jR
+5/mTay1Wu7J5fUAxB831hBXd9OpFj4sNpS3v+/gnJEnlaP+Q6yNN3nmt0mRjmKiH3OTm1oIOXIPV
+XI2bbS0xvrzD723IctiY3gRFKl6djdlbF9PlXXsJTldUW1fzAG7mY46fFifo1sDBO5bN+9nejLGq
+zMaN2VLH3jCf4xbObmVuO7ySEJ6N+4yCI73PE/Q18rNXCEFK1QwIlmV+neMag0tXAVg/GxGrs7Z9
+2KCSyX04kSIcW5/r2/LdZ3YuM8nE0ToKa3R8mSdubNNZFdM0mZuPlRYruTyM0wj8ilxvb5Bg9HDq
+RXL3Va9JosxSk/SQgfa1ZnJagXxn+zijFci1dzRdOev4+PuyYZWVA1CH8O4wZvCi5BbMRgFnFY8x
+XRbK+XC4zdIZMUG8iiG1yrTtbNPpi+mWrEWcD+8u6W6R5ryQhSzkNrkvNIXQCzi/fIrL+YRwTRmH
+tzdoP6IUZTsTHHXyub1DskJU3kIiXhi2KdXfWdQ2qCkiryl87Fi+5ByBO9AU3UPRAqrjIVviC+Sh
+xGVwLHvk7uCIiRJoZEV2K2R1JwFJW8q3O2vr1PRkbq/ISTQcF6wrZVhiSzoXhLnZbzZ55AF57T0r
+WsXKOYdnB6KbnGuWxH1Vh9/3KF2FihueMdzcFfIYM97V+82p6rE7xcMqqYktE/J5LkQBvpLLDCcK
+E9aI8A7lhO4bi1FNoB73qGqK8t7KKZbVqYZRsJGtA+yOHPlX45JlzQqdTSylqrOj3ozRsYLmjmQ+
+JO1knt35SsJ3xTd0PBmvMQknYjlBM+Yp6hnRsYLkjEMaM431X4uJSnkddc+BVjlWSo3zhzUq+3Jv
+Z4+qXNfTvx0fMBtWdR6WqauGUGsqh2Veh5Oipa2UG3zrQMZ5faPk95+XPm06um6yY0LN6RiNXAa+
+jJVJUg4n0o8zwYTDUtb1iSXReKMixWgMtFGJiJvKC3HlAOe6jOENk1CNxQHrHqmKtXGSUNHBTVRQ
+t5q/U3ucttFwqfHpWc2XuEO5LzYFt4RWYvCqXcoTYk+eP1VwU8tmPS/j5UNRB93BMYdjGcCHFH8v
+ec9DVBThN0+H5AqhndgUVyvDrmYTqqVc4w8nsiAOrl+l0HwEuoZC6wBWvYAjdPHyymZwJwWo1bqS
+o5Z93rMi1uZN3VbOt0t4SEBPfiCeMq3L5G9udKity+uPNmSBff2a5cm23PsVv8bZrizol29MOLeu
+xKXLAX95S9T5X/2mbHTOZIqj0NYdUyVORrfurdSH0HcdfGVneuSs9Or6uM+pithjWwdTuuq32Os0
+WdJYfxnukmnC2BxnsG5zrmvE5aFWxgtT2SzaRYwSXJG3Siax5oYo41PGK4lgrx7Pig2JA40ueC0q
+TRn9eFfadYaHfOtAFnl1P2fnSO7jkS4sXRQMyq5bUpSahKte/SSZ0VScxy+WM1ZmkiD1H7wm3X25
+Ub+/T005NE9dFBOl0qoSrWqp9hRqWjI+PXoJL5U739UOOD4MBzJW0WhEoFWUL8QJk9n8UKvQXZZN
+YeTK3F3cCIgULr5a8Uj1d6vVjC/oqjsRHbPTErPhQU0rj2djhorjWQ27NLQuYBTVCMTCZFSNeVe2
+AFlZyEIW8qeQ+0NT8B0aaxUOQshUtT8s6tQ2ZfdspxbfF9XIr3ToHoqKvbkp22FU71BT7eA4Hd8C
+v/OzFHssu+5aGuOflDTQ73PFybTrwr5mUDbcAS9oOuul3JLYeYHSKyfZm+23xhgy9ZI//OjjNE6I
+N/zDPdFGsnaTVa2hrw4mt6rWmrWErjpKK55EJ75rI2WpLZGIi0CzkP6ttQ+pacFXUg94/gNijjyp
+B++3rsNHqqIR/UF/lSQX9mWK4lYqdVp6tNblRKchavaTm0s4kWhpa84hWV1Os267wprW5o+tSxmL
+9hIo1RpFzvoJ6ZNXf4SNszLe+5evM+xKlODS0xlGaefT8o0j1JWOx0nlBZ1mHmstLY5SWj2vWyMt
+xLlWb0Zs7onWt77SpBvJvNbaDcZjda7pfNTyjELn9AlbkHdkbpZDt6i2AAAZmklEQVRDCDUKQmJo
+1qRf/lwTqlVpd0RbaVPlRCyvw1VDpJmHp3zRSvYzeGBV5nFUOUnYFHW/dgjDQ9F0LrY8yo5oCBvK
+b9glYaxe7IpbYnzpX+qPOb8m492uP0xtTd5fUvzMODAsaSGgW4KnXu6SGb0Nmdflac61bviGY/7H
+5b7YFHAtppHzY/U1Tp8S2/rMyQ7Tngx2tNSmpcCsZemwWpMwTWVDB8RpkERacXezx6Su6bqzmGFd
+HpoVv8Sv+PpaVPgHGjX6y7LBBM1dnrwiXvSnXzjipQNN+rmLbhgs7SVZKBeYcCaSSa+puu+uprSN
+EqK2IppHCueedoliWcSVrni3HywMgRKm5lkdxRWh3WxgaqIGp0Wb2ntEPQ5KWYD/ybkC05UFv1lU
++LV/8ox8N3cINAmnEVVoh7KAzm+KyXBhcxXP03COyfFXZaxa/jrWl+9GR/tYrVaMNG+/PHOSU1bu
+J2p4BBPp36mHKtw4UG+5O+al/1f8AFu3QGZee4utWo9UIzGNsImr2UdNDXVWlto06ooENWvS1U2m
+cbZFqOnWqQkJJ7J25gTCWZ6SL8laOGWnoGYeWYNIf+enM6pLNZ0H+d9GBTWlGkgaDXIFLJn1e1QC
+qYJMtOZi0yyxokC4J6vQWRWWsd5Gh4qS2gy9Gq3qnNVLxsetpHS1nN+6EWrBMFle55wS61aqI7DK
+eam33jUumSaZ4QVsByv63ZhHEzGxVk4EuKy85li/nizMh4UsZCG3yX2hKYRuhbOtx/h2mhHOnSXX
+I5yLEmvdiGcU67J9hqlLouy6VeSUzKIaxVjMgEm1QV2da3kMzVhO4+nSaU5oMtGsqVBb0TJRVwup
+Jht8ZVe24OtHz5Lf2i+L2zgY30hM6LNiRDU8/YGP8R6tnpsG0o/aOKPUEyjc3WaghVKb8ZTAVUh1
+Zbtm9SxL2nJS8fBHMlVuu0bdaoy9TGkk0pf0uwWpOdiPscq63bla4YvR5wHoJ2NOdkSN3Eov8q5P
+yjW+P5PTbPPhc3QKabu/kRMdisZjNzsUBzKGx6vrLClVGnWFARv3sauiqq4VMOmKVhFGdbK62DRX
+n2nTU75G+yZ4EPVqhRUt0hp5Pl2lsssVa3PdseTqEK5UfbKOaH1RWqHQsTWThKQq6cHRTMwL4xlC
+R07V5PR5mnriZ62UYCrXLlZzwlRel1qIZZKYyrKMS9ArGfQkX8KtQr0i33nQkWttOxO6iqeQBwFr
+dWkvWC0IbojjcmXFwTmWk7tc1QrP3b1bnJDrDZ9ZoCnkRYxrZJ7qWciwOo9WqAa51GK5KvN07Dm0
+e1pmtPJBwqbCy7eqHOR3l1R8X2wKpeMR15dJOx5xV1Z0qz5mONKsuSAkVHBNU/Vwc0XIUDu6qFic
+qYSCSjcj1tDNJHKJZpqLn41INbzlt+Q9t9uAPeWlXBrSWr4KwHe1PH73+JUt4E6xjry8ZPUBVTvN
+FSqt83p/mi9fSzlQSvKoVidQbsuBXxCp2hpW6zomDoVi+hP5uIqJ6E4DrBEfhaWGr8lENa0+DJYO
+mGhIazUZ4Cp+4BJ1vK4soB8/HbC5L9ervl+TkeoBfqYApq6Dr3UER46PUbDWMvY51NBvSxmiOq7D
+SPP9bbVFTTNL/bDKzT1ZxKcvetinZWGaNzEfCtPgUBd/sxbiatKPU8p9pv6Yij7wXrOCQkLi2Ixc
+M/28skaZyGZfWFniRdUQamiurFRvgVqF1VX8to45FjdQNV7rE4xxGMSy0JqzGbOajK3ddvB1fvo1
++bxT+hS+9H/NqZArN0Yn7+Kd1TD6NLoFHLMfyLi2VkJKxY9M8gJP62CCoo1bKPiK55M1xYRuK+DO
+rKxita4mcRr4GtYc+i6xqwBEvuVscXcGwcJ8WMhCFnKb3BeagiUnsUc836uwciTOm+3NkzRXNE/B
+r+Bpjr9jc4JcPTFaDekPM/JjcdQZBuSuVpxNXbJC1Mekl5GfUodSLJ87JicIFKtvcMwkk9PoUhHd
+omifM/PdiZSuy+6OeNzt9L2MWrLnmqFcwQtrVBQ7Lx0bploT4XcCHNVMEldbM0MCRUn2jqpzmAZ8
+U4LCvEXxBLSC0Vek6opXMtVEIZcYX1NwJzMHR1ON/8OLHh8/J1M/OJa062XPMNE6fz82TMcK+uFM
+OdZU41HUI1awG7sv3x2PUqapzJlnYzxkDJ1kyvFYvvvU8zOmmnD1ehrCXJw6LOcKy3/oE4r2TBHI
+WojaEZ7G3V07xfGbOvghoeYCFMcpvqMJbGqKRFkF9D2vSDEaafFyB+PIOAuz+ZxFSh2po0zRE8FN
+E9rztdeMabZEuzuj1YlFHrJS1X5GXZorCmvvuVQ01yNeXqWYyO86sYxhvhsy62ilZcMQKUFPVg4p
+MjGLM0KCqahFjit35PolRu+nlpbsDWQNzGoDLqlm0hrBbnB35sNCU1jIQhZym9wXmsIsj3m+9yzV
+4iLTC+JwqS85GK2YnIY1TKi2obV4mZz+VBQ9xxZMQ9mVRzs38Hx1NgxrHBVy6tScHokCYjoKnJlG
+DmUsu2hcc1lekutd7Lbp74hzLeG12BteW4IyQxUPihuX2A4fAeC0hrR2XBe3lJPkqBzh716R73pr
+0JF7niqaVJiNGCl6tFfJCBSsNC8TfC36j01A7ijitVbkDZIZWSR9PozBCeUEOtnycQJxdj120WNp
+ICG7m11xEq4MDnmxIqfk6v6LjEM5jcrxjCtaEBZevcFMeTzXlLDl6VnA6p5oCvHqOquK9jyNIgaa
+EjyuT4jcOeXeG+eFpsMZ45b4FJz6GVL1H3mKXJRmBkedaNZ6ECtwa+iRaqUpQUkSS5i0ojyYeBFW
+592xY6ziOxROCEoiY02Oo87I0pXv5vWEsULCeZ7LkXpx03wVX8FvE+1z09nA1WxDN6iQa7ZhHraZ
+NuW702RAmYrGMnIU88EUlFN1GOdtKg1X+7qBr5gTE6YUSuBTLGkYvfAZlaIp9KM2W85ZuffqgMeP
+JMeldcLwcDBHG7kzuS82BSfzqd48wQuHB6xWJMa+FZTUHhXd8WQ2xc5NhdwQZzIxVV8G3c19vKO5
+ahhRdaRbaXREcySTP1taI1A0XxQB15nNKBQCPXIKhj2tdzhMbkFlWeJbtQ/zxNzXA2fPHB/nQIE3
+3HM8oGppHMkDdjJJGYZzNqIZo0DgtVoO5EbV1alsePHyMh1Fe7YTC6ku4kpIVXPtcyfDd2XCfXXE
+LtmSA6332EgyGrE8xC+PJjQUruvgpRGXv09i/R85EnPnoDiPGcjC3Bu38BWLMXC6NI7keuNiicOZ
+XC/Wh2fvYAdnWUYoSFP8G/L+bHqDrWP53dNP926ZEm+WAuYbl7qRB9mUPtWaRh/UOVyz41fwF8sK
+ea4OWjvDdfWBHqe3VGxfS+dz9wh3Jg9/1mzgKlQcqb2F3UklwqgT12jUw5k5ROrkDaYdGmM5LAwJ
+od7bhm6gU9dQV6CaPMqoKlboXjkhmqrTfGoIFWs+1yrKSW+E15TNfTlJGU7l3tx4SKrz66aGUhOn
+7LH8blhdJpxpIpt7xOHXrst3v/dHOHpE+rEe1/m6e+fYorAwHxaykIX8MbkvNIVJWvD714/Zr+Yc
+L8lptf6uDba3ZLfrN2u4qn6Rpkys7Kpmotj+9ZCgoYCh1ZP4Vpwztv4I7al8Z0CGpyfvROv1o9hw
+MFUKuSwjqYtaFq5DqqFKh9s1BPiTZ938fZMXODXZzW9ML/HBXEwhY+VUTlfb+IqGXztVo3FOT7+O
+h3/2vQB0tfRzd5aAmhqjWoGr6u7AWhzVxHPXQdHrGGtlYTE5ZqiwcnlvihY74hMyDkUVf/jhhOlV
+ud7OGdFmSrvPWU0Jn5iQdeV3aHYf5JFzMg+XxpY/q2rTpC5a3M+Njnluqtwa6Us852ox0+42T2uh
+0XF5TFzcmbOrNykpNGS3Uq1SRFqAFYsDc1TOKDQjMEomxFbDpbOYtKlkME5OrKe3p5WTZbBEVbVN
+XEOuWH5FNSHQoPPUFlRH8p0imOeypuyqk3Q1O6KvnI/FdkGiRV43NS9kwyuIlc3aj2v0JpIvk5TX
+GB3JfRwP6mSK1WHr0qcGCf1SHaL9GY1lDYGO6hQKFpw7Uwaehk6V0nCSj5mqlvNUb53+qpgMu3HM
+jedE29p8JONj4RwJ5M7kvtgU0izm5u4lXrYjMvXU2+wUH3q/5tSPj/GO1C4ylnKoCUeqvtnpEHRh
+VoMxvqYXuxUXT/H3fMeSzPQBGGv5bzYm3xF13QlH7L8g17uyPyBRBqTSGNxX1UG8lszfLxzD9Zvy
+UJ+5YLiqsN3rVhZ5xamT5mJnH2Q9mvqgj4KIExqHn+Zn5VqVY/J9xY8c5VBTApF8Qqmx8rKwWM0n
+MGqTUmakfTU16hmpergHzoxcSV5/81un2LyoHI1f1wV41nCponiN+ZCtWHwND24M2VbbtxL1GWh0
+oaE1HJeHBmaSF/I73xwycGVsL790neO+tHGtNyIv7izbY+xYljSykSYF7gU1H6za0bWSUteI6xYw
+kzEqvQznQOfJxviZ4iDW1OcyzXErishlYxxXlr4ZlDgNBZ8ZzCDUfAqtvszzfVz1jezv7TCLtRy6
+PaU8kI2nqb6vYVGhvqGl47lPq64cnL0GS5qUtodLW/0Vc3SviU1upbm7swQSebgzz5CrXyk7DCg8
+6aupatJef8zVebRuMuA3L0mdy4q7xTf/08cBOPXb6/x3H54bwHcmC/NhIQtZyG1yf2gKRcLVwcs4
+qY+bS517OLnM4RfFDDh89wepq3NtGk0wM9kdh6o7O+4KEz3Ng+1tZktymgXZkDyRlNJ0vI9bqjdY
+gVCmoYOJZLeeeiXLa3JKLHe6HN6cx4dfZR7o/6+nMZi8IFdatIObN9hqKUjKg9LGLGhRV4fTU+ku
+p54Tavj9nUdwR18A4Lj75wAYj79NA1HRM+swWFP6s3JKpsBwRdAgU6VnPJBT5OZowkindbTrMGvJ
+ad3oN3hcqx1PtDO2rspY7J6SSEVjP+GqI578B9wp+QXF+7t2hatN0Rqqey/wkiOq7/krQhH62zcu
+YHvfkH4cJGSKc7k/y5mqw8w6JeYNtC0H8NU5vH3tJhNfNI8lU9J8WcazUlen67KPX5MoSpwcMhtL
+PxI/J/PkPotZQZTI6W47kgXodguyY3UwBzvkhZqbpoIbK3kQM6xm/6XqtDwcF3y7L1pYf2vCSE0N
+ZxuGar6+XFFOi8Cy35dT3AtTfE1X9up1JkMp4jPxHgeaKl5Ecr+zIURGIlFH7gpMxWF4PHoXs1QJ
+dUYDZorinG5qdmsRc7OqGJVXjrCaZ/PSzq8T/ZJCtn3sKX7lkz8AwEdeY+xfS+6LTcFYhzAPOY4T
+BkrY8pkvDfjEX5RJ/OGDmEw5D5eiGmOteQgVhOUgOCQfyALslRs0ddDjww2OQllg5rCko0AWmS8L
+u32qSVyVjaV2MGP7kqic+zd7JLo4SpszzydSTI3XjT7kLqiLgqmtkxSyWPYjAek4tTXj5Uyue+XF
+CkVfQpaf39rmxO6TAPzRuyV//aFah1CTm4qVnCUNPSZhh0qqHn7rM0d4T9W77/dyikwWUr2Wc6or
+NRGXj6+xf+GHAfjybp3N8/8WgIe/Lffz8skJdU106oXLrL4oY/TcxiZ7npatzzbY0RAgiZShX+pd
+I9SH5mY/xlGcy0Nmt0YpT15rtF4ZxYobMLHret0ZrpYDp1tHeMtKE68PbqsMmOaaqFVZJhvKAeFu
+FUw0TBqWJSNf6mMqY/XYe21YkrWVTdepxrJ5pS1LqBiNqVPDn8jmk8Qa4r4y48o35CE1/WNmFa0/
+6A5Aa1AqusHkh8f4HU1IGjs0WwrwUhnROiGAO9n4ZSZq/vQVmSmoQ1wK2Y1beMx68kAfBwYGktKd
+74zJmrIZrCh1QGoqLP176X9Jl50twXOsOz/PaPS70nb3v+AHX7q7kOTCfFjIQhZym9wXmgKU5GVM
+GJbYVE72J9eukH9Z9qxvv6fByXTuFe6St7WSLdS4szHU1FM/ccDVJJW8nXLmWE6Bq8szHOUuDM/P
+i0zqFGP57rjs0+jKrrseFuwqFbtXcAt+/NZ+a6B4ldYwF7eAmvL2lek12lY0AUeRk4fdGnkqDrOf
+mHg8pQ68v7X+ONULQg/206vSt2+NB7jqXHOWOlQVVmzqGQaqimdOgjdVNGZtt1otQGPzbs9iChnP
+E49/DxebAr7ynvUP8txl/f6mnK5ECe9bkdM6bi9zZk0LpTZP8KOlqOB/tDfkRxTNeEdzMD78XMA/
+/oaoxidnEy4bOWlrscOOxtVTY7Hz7O1XXlAi7blen2rtAzI3xgdPT/9Gm1QdiX5NTuX+IMVXyroy
+dzENxQpwxpRK1e6PJ+R6qmbqlHXqDvVC3pt0q9hE1oVXb+AqWEVcGkpPTvoD1SRGg+tcz4XHcnV0
+g0NNm169HBGdUCRtXSuh3+RYHZHteosj1W4ix8dZkn4vPfgY7dOi4QbKZ9m2Dv1S7qfR8bEaSTs/
+qnFZtc1aGVCcFe3s7JKs2St5RH5RzI5g6vCTuWgVny9/ncbyLwBw9fCr/FQs5sM/487kvtgUAtfh
+XCfiD49zVqw8KF+7dMTHnpBFc3X2MqsHUiI7MhEz9S+0VP3y+wm5em9NlpAqa5AZhLfsvnApx1UO
+AHMkkxWbAZOePBTTZJfL1+Qa10twrSaYmJKKZhOmyl1QdQ2TOS24gVi/G/rm1r2t5RWuDcU7vaF2
+aKWYYRMxA55lysm2tP1Ur8LHWk8B8JW+qJmbD84gk9/XKRnNZCEVro9V1MikX5Jq1aKPOhdsQqiQ
+TslSyuOPiSr6O3sj/ksryVL/zdY1fuhhMc1eeFFU/z/XrvJ8Kff2WC0jVlNiM59yrS/9X8777Ckw
+a0c3rC/t9Xh3W0yNf5v7nE7lwfvSNKOuACdl6eCrDZbqWLX9gGNNUvqptUf4h/vycJ/pJkRTjR5l
+Jbkml8W6EUbOmERBEb2Wz0yrGWuexT+WeypwyIbKozA3q45Kxk0trfbHFFqq7sQxiSIWmTRjoqZX
+oSC2R0fbnNDowsyxc35dsqUx59rK+6ll4U6eEqbqrzq2+IX4pWJToaUAuZlbUFOfkK9JWCYGXwPf
+8czFqclGcKM3xq/Lpjh0PFYSiZRd0joKp+pwdErC3Wd6G/zD89LXT+6+m88gG/VPrJ/mb11Txq07
+lIX5sJCFLOQ2MfYO+BHv+U0YcwBMgMN36Ba6i7YXbf//oO0z1to3xWa7LzYFAGPM162171u0vWh7
+0fY7KwvzYSELWchtstgUFrKQhdwm99Om8CuLthdtL9p+5+W+8SksZCELuT/kftIUFrKQhdwH8o5v
+CsaYjxtjLhljXjTG/J173NYpY8yXjDHPGmO+bYz5eX2/Y4z5gjHmsv6/dA/vwTXGfNMY8zn9+5wx
+5qva/39ljLm74ve7a7ttjPnXxpjnjTHPGWM+9Hb13RjzX+mYP2OM+ZfGmOhe9d0Y88+MMfvGmGde
+9d5r9tOI/M96D08ZY568B23/PR3zp4wx/7cxpv2qzz6tbV8yxvzQn6btt0re0U3BGOMCvwx8AngE
++EljzCP3sMkc+AVr7SPAB4G/ru39HeCL1toHgC/q3/dKfh547lV//xLwP1prLwJHwM/ew7b/AfD/
+WGsfBr5L7+Oe990Yswn8DeB91tp3Ixnjn+Le9f1/Bz7+x957vX5+AnhA//0c8I/uQdtfAN5trX0c
+eAH4NICuvU8Bj+pv/hd9Jt5Zsda+Y/+ADwG/9aq/Pw18+m1s/98AHwMuARv63gZw6R61dxJZkD8A
+fA4pnTgEvNcaj7e47RZwBfUjver9e953YBO4AXSQ1PrPAT90L/sOnAWeebN+Av8Y+MnX+t5b1fYf
+++zHgc/o69vWO/BbwIfuxfzfzb932nyYL5a5bOl791yMMWeBJ4CvAmvW2h39aBdYu0fN/k/A3+YV
+0qll4NhaO0d8u5f9PwccAP+bmi//xBhT423ou7V2G/gfgOvADjAAvsHb13d4/X6+3WvwZ4DffIfa
+viN5pzeFd0SMMXXg/wL+prVKmaxiZct+y0MyxpgfBfattd94q699h+IBTwL/yFr7BJJWfpupcA/7
+vgT8GLIxnQBq/EkV+22Te9XPNxNjzC8iJuxn3u6270be6U1hGzj1qr9P6nv3TIwxPrIhfMZa++v6
+9p4xZkM/3wD270HT3wP8eWPMVeBXERPiHwBtY8y8WvVe9n8L2LLWflX//tfIJvF29P3PAlestQfW
+2gz4dWQ83q6+w+v3821Zg8aYnwZ+FPhLuim9bW3frbzTm8LXgAfUCx0gTpfP3qvGjDEG+KfAc9ba
+v/+qjz4L/FV9/VcRX8NbKtbaT1trT1przyL9/B1r7V8CvgT8Z/eybW1/F7hhjHlI3/pB4Fnehr4j
+ZsMHjTFVnYN5229L31Ver5+fBX5KoxAfBAavMjPeEjHGfBwxG/+8tXb6qo8+C3zKGBMaY84hzs4/
+eCvb/o7knXZqAD+MeGRfAn7xHrf1ZxC18Sngj/TfDyO2/ReBy8C/Azr3+D4+CnxOX59HFsKLwK8B
+4T1s9z3A17X/vwEsvV19B/5b4HngGeCfI6SN96TvwL9EfBcZoiH97Ov1E3H2/rKuv6eRCMlb3faL
+iO9gvub+11d9/xe17UvAJ+7lurvTf4uMxoUsZCG3yTttPixkIQu5z2SxKSxkIQu5TRabwkIWspDb
+ZLEpLGQhC7lNFpvCQhaykNtksSksZCELuU0Wm8JCFrKQ22SxKSxkIQu5Tf4/PUplhpUoQL8AAAAA
+SUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.4833... Generator Loss: 2.0466
+Epoch 1/1... Discriminator Loss: 0.8034... Generator Loss: 1.5415
+Epoch 1/1... Discriminator Loss: 1.1546... Generator Loss: 0.6480
+Epoch 1/1... Discriminator Loss: 1.1812... Generator Loss: 0.8394
+Epoch 1/1... Discriminator Loss: 0.9518... Generator Loss: 1.0477
+Epoch 1/1... Discriminator Loss: 1.2184... Generator Loss: 0.9924
+Epoch 1/1... Discriminator Loss: 1.2908... Generator Loss: 0.8527
+Epoch 1/1... Discriminator Loss: 1.2357... Generator Loss: 0.9712
+Epoch 1/1... Discriminator Loss: 1.3955... Generator Loss: 0.6073
+Epoch 1/1... Discriminator Loss: 1.0635... Generator Loss: 1.1717
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVesZll2mPedfP6cb763buXqrg6TZ8ghh4TIkUawBBmG
+gk3AkAwZgh9o2IAfSDnAD3wRYEh+siEIlgQLMGwo0pZFmRpGiRI5mtTT093V3ZWrbvzv/XM4+Rw/
+rHVrqskJPaGlHuBfL/e/f9hn77XXXnmtbRRFwQpWsIIVXID573sCK1jBCj5csGIKK1jBCt4DK6aw
+ghWs4D2wYgorWMEK3gMrprCCFazgPbBiCitYwQreAx8YUzAM4wuGYbxjGMY9wzB++YN6zgpWsIIf
+LRgfRJ6CYRgW8C7weeAA+DLwnxRF8daP/GErWMEKfqTwQWkKnwLuFUXxoCiKGPi/gD/zAT1rBStY
+wY8Q7A9o3G3g6XP/HwCf/k5fdgyj8IH5D/FA47m/xXOvTf2gAPKL959Tjgz9POdbHNK0DRoVC4BO
+7xrlknximD4Ap4MTMv22Z9rgyGvfMklxZAzTBksG9x0Zy8L81kSL5yZSGBQ6uyyR97I8I8/lvTxL
+yZMIgMQwmJ08AWAeZqTFt4b7drhoNCryT1aQZal8tyjIs0zG1h9mefHc3IpniMnzAsu8eIbxDF8X
+CqZlyPsXuCx0QPmevl8Uz+aZ60yL4ltjGDxDIdvXrj4bzzYsMsUBeQhAmJoYeaxzBt0assLGNGXA
+3ABHcV8U9rOHGDp52zbJC1fnExMrzm0rI01lr0z5Q5zneFEAwIwUJ9X1eymTgzEAwbdRtp9bPp5p
+UPY9wZflgKXzlC3ANDMyRVBBQZLImk2jkH1RSBUXz++vof8Vz63PMsDQTXMsB9sTHJyeT8+Louj9
+0dm+Fz4opvA9wTCMvwL8FQAP+LgBv/s+LRndL3LFjlmAra8twyBTTuBR4JmCnMQoiJS+LEVugYGe
+VyIMfB233LH4U59pAvAL/8Xf5WO3ywD4tRsA/M9/768zymSTL1fWYUN+eavlMzC2AKiWO9AQwrvV
+k7HqlkdxwaXygkKJuEgNEiX0+VkCwHQ5ZrkQNrkcnzPtPwLgzHD4zf/pFwH4vbdHDC6YyMXhVnyC
+bO7nf+oVAIxZxmRyIriIE4KpjD1X5jBepNi2jhUXFIrQZVzQ0AHTwsLzFJ+xfLfmGSSZfNd1TKJY
+xvNtKHSDrKxgnMhpWupJSOKcSA+FB2zq6f6Vv/nXnzHcptVhEiwBMIO3AXh3UMJcCC4WocEtZd7z
+sIVXFtzFjkmvLrjPko6s30sxrRIA6x2PZXFZ1/+Qo77gvl2fc3bWAKDckMkdhgGX778JwO9yznpf
+5hxfOeP/++VfBeAN4VfvETw+kCmurlRcPn79muCr2cOoyzwXY8FPpTJjOBT8ZEbE8fECgJKTsFgo
+F8oL+gt5kKcMzyzAMWT9icmzw99xLczqxVo36e61APgbf/tfPOZ9wAfFFA6B3ef+39H3nkFRFH8L
++FsAhmEU/+r7cG1cfPVC0rhApK9LVsEil82oWgUT3aWSXTBLBZkl/f2yKKjo50FRPNMqJifwT744
+BeCf3vu7/L2f/iYA/f/y1wGYZRWWhhyqw8ET7LWbAFgHLo86Kh3jjN2KvK4N5G/QA1slQtcyWFwc
+5ALCULWNshDjYmEy00Nlxx69dhWAxr0p/7oshDJKCuJvg7fwuddWKgyr0wpZBrLdXh4Q5/raEgTU
+fIuqSpqKZ1AIH8TM4IovBywomVTLdQAGoTzllmvwKNa5uzZnSyHojpURLOX9hRnjjOU585FMuE9B
+oBvoA++Gyqj9JuFS1vc4yImyIQBHX5eD+054TnrnXQAm3YA0aQMwvTJl51SeFzTAXgojntrCVEqU
+8MoDAHL7RcaJaFvLQcFR9BoA9+5t8U5dpH/5RA5b5fJj/u0dYUijWsprbwpjrTmf50ARrcf2j2ip
+qRLl1c0a29cFoZ7T5dq1GgAzxeFuFQ6eqhZgTnlnU+bQCcecD2VPzoIl3kBorshkTUZgMqmo1hEU
+WAiO8rmF0ZL55+mMabzJ9wMfFFP4MnDdMIzLCDP4j4Ff+G4/yL/bh9/huxfqfgDPpPw0M2gZcrDO
+cNkrhCs/Tk1c5P257pz93GsDnh1SyOnPRV3nm3+Ln7+7AcC1L/53APzif3aD3a6wFnPUJ7blwFpN
+l823ZUOftEIemCKlDuoyu88cBxRb8t0NC+6oxvKJtOBpJvNkIX/fHg/56FQO2MA5pXx4BsBbs9+k
+8vYMgPB9MNJ5KKpvJSlgqmZQ7j0zTW5kQgLv5gYvlsX0Gds1XioLUc02rvOFthDV63nCCz35/tNU
+1nHTKPOVQA7bnhfweCCvN4qcO4sRAK1syJemMudKVT7/1bGBqUxhAFRUa7hUarIoyT/Z00d8YyAH
+5/zRlwBYvn6Xo6nIl8Bw+Gp9DYDdbxo8acnvzOYOE2XO1b4y6c0SdkM159aI6lTWdzDo81Ysc5sf
+nbNbERy153riP9rj8mtXAPhC66t4qqeelQb8xh/C9fPbsQRcXd+bYcqf968DUHphl9tdmXNRVfPS
+usJ6W33wuU3bewBAOS/zdukYgP1ozDcDscirmezp15KQbX09DpJnE0hTjytD2aerFR+nVuX7gQ+E
+KRRFkRqG8YvAryPa/t8piuLND+JZK1jBCn608IH5FIqi+DXg1z6Qwb9llgNgm8YzLtnquESqqrZa
+FVCOv9V0OToRiaCmHkmWP3OiZfl7fYDveVwiKuzDx6JGRpuvEBSnADgtaC0FjfOr25z4ouKNnozZ
+WIravSXaIpVbJaZqw68nFmn5mZcMVyXlrtreVCuMPZmvf17CQNTPK/YN/hc1KwyKPzLXP4KqVMar
+lieMSqphLCI6NZF4sWpPl0wLryRayk++ukbpUDSlGz9ZpVEVif+xax/HfE20l60dWVu90udS3AXA
+eveUrS0xL3btJVmwDsD5m0teuSzPufNQNKnrvsE35rJPtglLVf8WvsVkIFrRIDOITkVS3hnL3ILq
+gPRc1GS/lGMl8tqtxMQzNWPyU1JL5jQ0Zc2dEw/fEE2ocKvMxHXA1nHKaSz7d+I+wfuso3OWOdyq
+X+Hrf1Vs8he5zz/5A8HLz9Ya3wPzkClFXfvpBq09ccz0rmxjjWWey7LQSoNDrJuieeZvHtPYl88r
+1jG9m2IGxa/N+VRJ1mTY5zLHQxPUB1UyM6JCcDxJEwxfXr+djrmWDr/nXJ+Hf2+Oxh8UbMBV56Cv
+dnHq5LxQyGYmTY+qJ+pu0przYlNUtTutiMtqKN+LJwCshSmP9Fw2g5zDTImU/JlH2QAKTxC/0zgC
+YLexw8NIfmhbN6nqhl+pdUkfiKp21txjngkBlUoSAaiMoVaV302LAlMddF6a01A/yLtispJFBfZU
+tycrmB9Pda0dfsWXNf2l6SnL78UWYiG847FHlgqOGraJl8phKjIhtMDI2KsLAb5obrPYuS1rSkNc
+u6f48hnsyEHfSeXQhKd7vFCS9Z+tdbmcqq0+zbmskRjPvcy09BUAxmtCrIE5ZmzK6z4F9UDWv1Hu
+MZftYelbLOZyqNcTYUyPD2KmgfyuHcdUHVlfNjVY1uVg2c051aX4GiyVHHF5h4r6ePLmPmU9QA+9
+27iWmCadeIdvflWY3ty5BcDhIOcvbMnr3oNd/upflLXGZ8F3xztwxRN6e+F4ncqtbQB20wVpSXDY
+VAM4Sxz2AmVG5T3KhZhYwWSbV5R5P927DuOvATA9lt/fKs74pinzGWVHFIWsbz2Gs5m8vt20KIIL
+r8f7g1Wa8wpWsIL3wI+dpoAJt5sieVvqXWy2trhUF466+epHMNS5NqpcZa8pr3fdFtbhfQBuPRXO
+Wa884HggXNcK3+Ttd+X90/SMhQgo5q6JVxZH49pHXpYp9DpkfVGDHzV6XL2s2kHhE39EfncwKfG5
+tnDxUCWU55scKh/+VKXgTIW8k8NkKJLi2nVRyx88zTAb8rvw3hLzmkgd46TN2V+WlI8/9Td/l9+Y
+yDyG38FTG+cy7qVexPlCxmuUYmZLsWka6yIZe9Y2L6mTura/xRV93+5eo6thv4QK9Z5oGGkkmkSv
+tGQwF7zZk1OMXMK29Rdint4Tkd+07xAORLvZuiLmnP24xe2RmGCvzyOG56JVFb5JWOg+zEq4NzQn
+49/K+vNOgjmW+SyLmJkr80k2ctxIHLtVI6DbFq1hVr4I9ZUpOvKe02wxTUTzmne2yDpCSPHbL9DP
+JMJkPvwkAJU/t+QPTNGgtjcXlIYS4v3MxvDZ4fl20YeNbontPRl3b/cmk5bg4mBSpePKZoUzmU+n
+YrGIZR2WMyOKBVebl5ccR7J/zeCIqTordy5JZPHkaz5NW5yPUdgkMsVMOBmbWDqrx5OMbvL9aQo/
+dkzBLAxO54LAnpoPJzsjrpWuAuAZEZ0tUXe95JzFUF53N2NKlUvy/nXxXk8HL3GtK5s1HlxlrftQ
+xhu72JE8o14xsNuakKQ2aY7NflnU05fKBU4h9uW2a1OfCCGMrZTDTA54V82SYmLQq8sGvRMYJJao
+sFsDA1tt3288ksOY2yd035GDEtUfsDyRtW73jrnxQGLexksP6Gsk4hsTzTeI3+sTmS/l8+w4x51r
+hMMpUc2FWW6cC4Otd2xmFfGy78R9KnOJKFd3ZhT1jwLgm2fYvtrSGr8N5+u4czm4Xq1K4IhZkc5a
+dNY0FDu+jF19A4DSWPejd86xhsqsyWN8zV5yTYu1sszp9viErw7lcHbifwhA/9jBKYSxGKaDcxFy
+fuqRNcT2ekKT8lyYZTwVxry+E5K7e7LmSoLRl4P+0saY12xhcOvzjDT8aQDOX5Cw31vmPn/8jnzX
+KC6z8xeELpbhOqVnCTPyJzLAd2XNzQq81BGTodSd018IA3CTE/xMkh38F0WYnCzWKJc1gSoIKTeE
+PsfBJn5J6CWK6jTXxIRaTiTH4vruU0Yj2Zt5923yWCcyCHA0SmL4GfFiyfcDK/NhBStYwXvgx0ZT
+0CQuUqPgiorChSsvLp0VbJeEg9+yXIKSSvSZxbQuXLe+2KXcEY6ZzkV13quf8/hMOHi1MmKRiOd8
+MRpyYGgabG7gOCJtt6+p2uon9FU9K7cL9qaaTNQuiPQ7s0XOx9XBYyQqUloGX5mLRL8eGyw0hyIs
+wehA5u8UogK65wlHdTF3jFOHYC5OztRscfqiDFfvfpxP5DK3+qn8fWUy4X9TKT6cpTjqAW92M+xC
+nhFmETfLqt3saPLSTsr1m0IO9naHuiOqeNFyqNTE8VXU2hiaNnyRMu3nD0kLkXhJeIi7FE+9W48J
+Zxe5y0OWD2U8+7KsY353jfqWrPVm5PM0l70ZGAkjW563uNGm/6vvyFpsmXu1HhJpxMQBvGemWXaR
+hoE3nZNW6jpn0RSWXow/EK3wpN5m5otG86hfJ8xFck/2zwjXROvJH8h8W3cDxlfEGdtc2+VkKvha
+t7ZxFbdqubLtGkw0E/TTV2/wM1cEx3nJJFCHZzZwON8W08W/p1mhLY9FKrSQ5UvsU6G9xDoiGMvr
+2D7EeCjj2W3RJhduTnfjQMaKCh5d0GHLZqn4nC0Nmvb3V/T4Y8MUHP3bMxyqa2JfbiiiX/CavFjR
+jL/9q2j6Oo3hhOJAifR2TP9cRuke3wXgYBjSq4gZ8Cirk51P9Rk1Dg1B6oadkA9lwG1NEr3ibNGe
+iYr7+MEGp7fk8+tGifRcCPaTyxJ31Uv+U01B82KUsq0a3vFBjKcE1HRT4pkQxWtfE9V4s7F8pmq7
+yZxkLmNEhkl7LEzNSBxevPHHAbi5LQfpzexd/qQrBG+/XXA3lHEnfY/ZROa528zptYTYipngrTbc
+IrKV4E9axK5k+fnhT5GrDWv72+SFHKyLTMn4HFy1h4t4DcuUaMBsvKScCY6K5AV6NRkvGQlht70h
+bwdy8Nqzt7k7Vc85JcJI1n0/PKFXyHfm6g9IHrUgkbVmpoGxELy4kY1R0lBtu4aDHORYs9PycgOz
+qVmFocnJUn00kUmsYWuvv4b3mu7VwasABKbHraa8Nr5usv4L6ida+uSaGltoRufT1ODTVcFx4zhj
+2FN6y6o4GrYttSPMvhzeXKNSkZXRNSU9PjBGhJ7MJ1iC68j8x4st6IhJlExlTyuxTzQSP8MJd6mr
+MPDtJYNQmaWZEy9W0YcVrGAFPwT82GgKVS1bG/smt3clYnDDVs/6zufo3BK27V26+szzuiinWJdF
+vVr6u7RroiYOc3E4vjt6F39N1O7oyYCaFpG8naVoli8nOLQMGe98JJ5ev2ySa2K12TO43pEv962M
+rCUc+knVYE1NhfueSOCbFhxrHsmVdYt5IO9HLThbyBhXXxFJtJiCvyuOs+jrI0JLnI6j0THujswn
+6C8J6pp7EImk+Whjk0eaEp0Vfe6/KVK+vJ7TteV3lpNjlEVDKO/LfLydS3RSwUVydYYViQaVey0s
+WxyChQlGUdf3Lzz5JbJAvOWO8ZBkLpGaSmXMoC/OzLh1TDKSeY7qIvkOBg6x1sVOyiUGhWhmCQm5
+KXhxkwr2njjXJvcF38vtEHshWs4iWbBQs3HZi2gsZR5GGDEvq4qtGb7mYsYikTFmszVGhZiVJ+Mu
+07I6m1sbDFNxNucNiYzYswMeWqJVRB/ZIB+Kk/fndlPqmhvja/3IdqWgn4icvbpXY+gLLWR2hcVc
+aMe1fSwt1moUMp+m02CRy/4G1ZxsqIlM5oQzRLOKu4csRZHFqmka/yxn2JFxjZnFoqKRqNQnHGp6
+u1MnyVaOxhWsYAU/BPzYaApzQ7jxZRyenolN+ZGm2JuPfmKKn0sIrVk/x1fHV5xFlPoiuQLrnNGx
+cP837krByYODY9xj4fZ7y5ivaVVONYMjtcXdNGfUE79EvypSrm45VB2x+zphSqqSy69nqE+SShjw
+ljBumlWRZmtPHYaXtChrbOJWRJrt3bEpLJFQ997VPg6VO9j3Zc3n2X0mWpJcngc03pRtc9o5uSOa
+RVWz+cIIXrghobCDSoXkq5KaXT53CZeCl71bJo5KsYpmVSbFOSeehu8mBaVIHHzGT7xMYotEsyop
+xuyRvF8VGzldtDAqsmgn6JF68vn47hwzl/XV3y2YNgRffqCp312f03cFx8dpjhvJur0CWlrPvrs8
+48nsYwBcNyQj8p1hnZGmmOeuT6pVp5UTmzNXEV71ybVM3BnI87yeSzyWdXjR1zE1jbtcXTA35P39
+7CGXU8Hja56MNe9uUXlLJO3Whsnep+X9waKKqyFltPq2vyzR0EKsUTag60oos9MckWih22jxhM1z
+lf4/K7Q3SAu2euJfsA5Spl3RYvrjKvZMshiDE4eRJ/h8vJTnXg1zGuKLZlpKCGN5njuaYYUy9qmf
+Uo8uPHLvD34smIIJrPmiwlntDi/7wgy+8qcFOZvDFklTVOPKYpvxxQ+LKrOx5Ikb90d8/Uhqss7v
+C0G8OZnx2ZoQwVe8BY1ciHEUWVx2BZFnVkrFlbGvtGXjEicm1e+eOnAtk0N/NikItZji5CDm8bsa
+Vy7UEfUKJHeEiNuTnBtdef+dcMpsKoQ3Wd4DIB9FTH0xfaYnJqNQXneNTc7amrATZVjaqMU1hPn5
+9ZQTNbWq7ZyWo9GVLaibso6aaT9rFjI09fM7A+758vonzQnHPyUx/fWnJ5QLIbbq5oQ00vx7bYaQ
+nE3Im7KOwclTilgI/uz0HdxM9izu3CV6IgcvsqSWYRF1iFPZm4/YVZ6URIU/T6eMc/nOslJjOfo9
+waf2DVhrLyjHcvAGxHQMeYbjQhJrRCSKKOlBj9UZGCZLGIj+3XfLPMmESsLzmLwszNBobWDfljk1
+Hsp3K4sW7Ruy/q1P2kQT2aeblRolTXWfadOUy+WIYayMtbVGqpR4/LTEO6mYrtePbb7xWWFYGw9l
+HduVjGUq5t84XbJ4KMfy+OFDCq2Nf9O+R6kvDG4Uy7i+3WZZl2hOaexihuoEbpeZT4QJ1x0br37R
+YeP9wcp8WMEKVvAe+LHQFDzT5JWqSIQ/99HPsfE5CdQHd78BQJkNnH1dSrmNFiUS5Amlnqi2h+Ye
+u5GondmecOq/lG9BT7PDjgNmquJVdzc51UKcVzhj1BHN5KPajq1t1YhzcZJtzVyOtbqwblmYmv68
+sYBJQxx+nkrXvd9/wOlCxkrXh9Tm8v5GtmSsUud1rQaMWynNI3H2FcOCsqemTaWFPxI10s/B2JE5
+1XwNpSUhO+pQPAgjrIq8ngU7mBOROt6VgrW5xu+fitZh9LrsXxHMpcUnsP+NdAuYbuwQvCKxe2u6
+jqNNJ9K6mCKzZE72RCXUIGHsieSKXYPMUafjI4Mkeaw4EifvRqlEsyn7eGb8Q9aPZX+rkUNfm9L0
+w4eUZ7KuwtJ2bOctxtFFjoSLaYpGF80NQlNj/Ta0tKVZdtFayy/jeRftzCyqc5G6hlXDV4fhTnWb
+k0cisfOHomIYmwu8qjgX3d9PuPVnRBMaTE2Ote+DH8h8vzk3uNqSOTx8/THtnjjEk+IBuRZ/3ZvN
+iP9PWUu/Kvv46FMjPqctiex5QWZqNqadMj4XOqwOLEZqKvlrsk+l2gbpg4s2fgG9itBCkfqsO7IO
+Yz1l6WrHHF7n/cCHmilcqDEZ0G6IZ/mLu33+wlLSUk9v7ADQO0jJh1p9aJxS2hHCi4sFC0vbci1M
+Fi0h0ttap3wnDPiYZv4+uRKzfyq+gyPnkNaubn7isJ2J+mhWxfNuWSme9twLPbis1WkPZyloJMJ8
+OiI3xAPsPxTV//hmQv7VRwAYfkKgCVLp5Trzc/nOTMu0N+77TLeEeNqjjPu2mDzVRYZxTQl6ZGN6
+cqgfTeRgmnWD7EC96ZOMsiUEtLkTkDW0rVwx5lD16s6+MB63XuZxJgRoL99l9LIm7xwPaR0KkS6O
+Eiq3JBIR9SVtOTyFNBRcLRshnjb3KBKb8lxU8fAjSxp3u7qbokYfDzIql2Wey/slatoCa5HMiNUM
+yE8aZBVhrMFDObhG8xhzluveLPBced7InVOOtD4iCVlqirnZloNkuiYLRzY7n6fMC5lHMog4r2lS
+19kO05LMOaoIveXGfY61VV569ZNUHwgNfe4Vgw1tfDdUs6xbNziay/5vbbc51voKa2Hxju5rL7SY
+2UJ/24Hge3JnwdsHsqa1j20xUCbbnw+ZJLJPCydkI5c1nUbCTBx7Qn5J1tQ7ccmqOndsvB2hva5X
+Z2Ku8hRWsIIV/BDwodYUnrVdswz+YCke2Ze/3uLXN6SS7UZFdK75bpVJLhIPK6I3FtXerBQsBtpi
+zHyTZCCOv9+dyVhuAX8wErV2e+ZxrBlxXrXNLBDpsFG3qexIPX2mWXKeWRA48vnWWcFxRfsd1gPM
+J8KV19sB9zSFOu2JxLh5t4x9XfjwgbVJpalOrVHBQAuJwkKcmWfhA/YOZNyjOMKviBYzTSI2tKKy
+vpaT1lTSpyIZ8pFBdlEZGCdQiDRanG5yqI1Ck06ZK4Y2LTiQuX1z/oQzUYS4EzzlMx1VUV+8TJoJ
+nhe9jOpMcGhpvkG2NEFThu2szFuHokE8GC6wjkS7+cxBwkJj8pVLWmjVM3DGIo2D5A655qz6ZNQ0
+p709f8LBXIq0tgtxwJ4MHSwt+ar6dVx1lG5FBmfav8LOTALNHahqokKzXWVpikaTRQN6Gu14upjT
+vCgKazxkQyNb39BxZ1aZ8IlI4093Ha7/gjx7EZukao7sqCO5H9doNTW65ERsqyZxFM9pa/TsdJ5w
+Vbv8jLUxiJvmPIzVBL1/TKFVqWmlxPKB0IhdGHzpVPbK1ByMzE140dMchFqHuifmyiyM8TUrdNLI
+WWbfn+z/UDMFU9M2W6bNy7dFbf25j77E1nWpnPPUE3y8XuEKihxnk7QlhFCEZaqZMIByb58NTf2s
+XNMQTTbArYvtVZ6fkbiyiV7q0t0UAmqEIwJtPrLTESJeZCFo4tGp7+LFWtnolildUps7hp9eE5Vx
+oodqz415GgjhftZaks/Ew283ILPkO5dn4vcw7St0N4UwW/6E6lLLl/0alaUczLRmU1Fbe6ERGdsa
+caR2f5Rk7Fsy9xvNkEoh83QrCfm6mFg9rQ2wkxEfSUT9nu+/xL4ta02uQVeTjPKwTHDRGORc8+mX
+c9KK4CqJXHovyCEO3nmKfVNU7fzgkNKuHjy1uY9OPJpdYSCNsxqbVWFk03zBTJuMFK1N5uk/lfc1
+XNpqBuzqHIJqTksP7zKPceZqU9sxGzXhcNVNeW5zq0GvJHQxrjXI7kvz166XMtWGtW6pTbQluO0k
+F7UmBf6u4Mr8VJXHmeD7C/Umvob9IkOb8JRCElXxr+10KWlz32bJZ6mNaau9Dr4mw03X5fhthXNy
+T+e+WaGijVMuT6d0rouJfNYPud1RhqN18rVOF1N9W+56lYa6T5ZGDSdX08XpUWji2/uFlfmwghWs
+4D3w4dYUtBvu9domP1X+WQA+1fhj9F6SiEEwFw5/ZVCwzNXrXa1gaBUdcYapMeQyXUq+SE2vJgVR
+RtrAb6zrs2qYsUiByK1S0ctHglqPzapw2lirHiuUCWJR62pZhZmqvm3TomEJu27nDhNTpNTMEenR
+KQp29B6DaRRjdnIdN6Ep1ggn55qM1GjgJSKB66GP7Wl7sUryzMSoBkPm6pEua838bBHT1FbfcZ5Q
+aPRlPl/n6ECkSnUW8BlRkFi7Ki+s9DZ+SSXizho1lczWeErSFKlTLBegjr/4kd49kA9o+KLFmU6X
+tuYNbG7uk+1osVX3Y7i2tMlfLC+6QI84OpN1+OHbz9rBV/M656HmEBiHtDLRWJ6o5A7mNfrqaOsm
+PoG2OC/FDYaR4KDhVmk3xNyqd4RW1ppXwJJnOyUHR+/kyPqn7Lbl9VrN4yiSdv1TdSju7wbUAunV
+cSW2uGGLRpYlNlNP6KKikaN704R9nc/srUc0tAs2hcO2aoLpXoWuo/0ZtMmM5dcoNJ+ilBY4seZ0
+eF0qLS0gM2KOJrLu7V3RxpqdKk2thjSSMUYuuO8EY/KSOooji6mjduH7hA8NU3i+a80F7FSFMG9t
+R9z8hBA9j7/mAAAgAElEQVRs5ac9vKZgsKwJLcOGgXEs78XDDLt7UWdtUu7oZkQF/rogcG0gY+Es
+sLV6LVgLCU7kMM3cEXGmPopWmYU2YPWq2uqpyHBKYnMXmFwvqdptWJjaZMNyClraX89JtfdjCjNT
+vpvlFsy0v6BtkNhCxLWeEEGz4rKzIcymFjVxtMJvjIWvaZPRwqaIHgHw9Exz570A60gO8WlSsOfK
++ta6Kaar+fCnEY2GjHGRbelvtbE0yzGvhTiGRhHqJsZYmGjWjzG03Ntw1AcQVLGV8ZbWMqolwZu5
+4xNpFxIjGmNMxeSzEeZwfH/EoqxMMSmYa4t708yoaEvyqr/J5k0h6KNcqwjtO9SPhS5MJ8StCF6C
+IqSTqQruRJS3ZB4dNRmMeo1GVSMKZkiipcrtWhOrISaD397maia4s7uyH8PKmI2O1musteiqGdfZ
+MTDDi6Y7so5urcDRzNOKb2F1tWms06PUETx7VzbYiAS3SSi0ORndJ1NG7+UOtOTzcs2jrNWhRreg
+p231y674g1yjjqu+mjBscjaT0HFSgKmt6j0/Jru4Bel9wsp8WMEKVvAe+NBoCt+uDUQ/0ivUSiX8
+kjiG8vMXyCuSD56Gohpm8YTHWjNucET7QCRCug+Lc+GkJX9CRW8eCn29i3BRJrbEEVldBBRNUc+q
+Ths0dblqb1DXJhsn2nvAq9gYnkiMS6HJXKs1G1aCqw1VLDtjqB2KvVClRGIy0FTUo2VIMpZ6h+ai
+wmCsuQyaE2BUFxi5mDbG5QquKZKvGvdxVDIPKyNmiWyhXxFcdaZl4jWZ52QRMTyRePzWicPTN+R3
+QTMjeqTNWS6Ls6/suVD6BAD28iFFTfCWT+5z/Lamij89pjqQ9WVdmc/Er1A7vUiqmeJkF1fhvYo/
+kYiBUSoT6LUfC435R+0C676YaIfziJE2Lyn7Hq2aaGHlusnDrZ8H4NUjMVtO0n0Gcy019euU1SnX
+CEsMExlv2qpiaipwbuwqfiIi/6J2JaDQJizmdIpTFfOg5o5xZzLerC7zsTY/RUlzHj7X9mmvyeeT
+zCA3tL38Rcr7yMBQ7Wc2T9nT43XpWpP2pkSwlnafWL9/0BeTaDTKqOWCY8urUu2qI7zZxDNknwzH
+p3BFG3QzMW08+wxHazVi4wy0cY7ph3QvHJd5xBxNxnmf8ANrCoZh7BqG8duGYbxlGMabhmH8V/p+
+2zCMLxqGcVf/tn7QZ6xgBSv4dw8/jKaQAv9NURRfMwyjBnzVMIwvAn8J+M2iKP6aYRi/DPwy8Evf
+a7Bv51Ooagpry0g4WQpHbI4G5FqgMylEegyHCdFTsafMzRbnbe1MM44x9eqycq3BReaDXuGHl4+w
+L4paioRgog66YkFXOXvRgLnaz3rlARHFs9ttQ88m14rKgZNT1xh7Fud4Sxkv0DsGwmRGfyzaCIM+
+tqGxcHfx7Pbkml6r5vlN0JBVkC5poRmLtRKTkfZLSC28SFNitVFn2AkxQg3/BSkN7RB1lthc2pDv
+fvksQfvOgiEVlenQw1uItMK0SY8lFTyO6hgLyeUwK/sY6kgrV9VZ1spJFnqDdQhZLtLKMs5Bm7zm
+d+9jGfL90kwdjlOLk5KMu5OXKWmWaehkYGpvhe064VAzJzcE+dXZIYkWYBleTkMzM5fGHM1ypr4I
+ydU/Eu4JPo1Dh86W4MIoxVS1wC7fDjAdebZX7YIv0rinWkAyf8ylj30WAL/uM9FeD9uWjaEdlxaa
+E3HJzlnqHPYqPi+si8Zju12cSIiunNZZ9EXTKWnXqJgcU0PHYSmgGMscWs0YI9G5WRXiTO/R0BBj
+5tWoRJpb41ZZy2Xcmd8mMzSnwTQxo+/vPvcfmCkURXEMHOvrmWEYd5Ar6P8M8LP6tf8d+B3eB1OA
+P3pBp6GtqY/PHYzf1wP2Hy6ISxpD72tvvcQmUudbsGyxMRSExNiMNcbuk1LXVt0bliA3iOoY2pAl
+jRxKucSES40y63U5NZFXxdNLX5fakdezSmR6hx+GRaS3R5cM91mJLJnBYC7EvUg1uWm+wFVGMTwb
+YHaFSDcjG6ck6l6KJJ2UnQrZgWx4t9Il39MWbMsEWxucGNmSTC+MqaUau48SGlX1rLeaHCojuzn3
+OJjLIb22kWGqdzp8rNWAVgP7qrZfz6rP0qeNrENDr532mjco7+/LPAOZr1HcxfTFXDHPT7BK2iCk
+nGBp4pSx1iT3pPKxeFfWvGaWGQbyemj+cywlxarRINN4e4mCpiN7ctKQvZ76N8GQ1m7uJMOxNHV5
+VmKxkLWUCh80b+XwkQiFdscnqsmh2S91MNVh6GUbmNoWrYXNSaKO1Adal3LL5kqoCVAWuKb2qEwN
+5lpq6itzuJMUlPREnRyMeaMpv/sMAcZVwXfTznE8MV2iHfld7dzE1zYA2TIl1bL8eGmytiaKdnwM
+Jb0j9aJhad1yoHQREXNIE8HVhl8wncn6SvUxfvHvIXnJMIx94KPAl4B1ZRgAJ8D6d/jNs6voV7CC
+FXx44IdmCoZhVIF/BPzXRVFMDcN49llRFIVhGN+2lewfvore4L03TxdArD/th2PuqQ4RhsdYT4WL
+J5pqm/QDTK07j+YjxteE2zsDG1MF+mRus3FZJF6gqa+FlbO46GPQmFA81DZu203CVK+eKzyqZ6Jq
+di+uRs4h1tyDSQa9sqDx7RAqmsI66Cc0NCw0f6wFPj2TbCQSuLPRJhjKeLXLLk5f5n+ipe9+MsbY
+1bsPZw5uSTSIYGoRaqbn5CzhcSb8d6zVjqVdg41DkdbTCs8aglq9CrduCIZf/90piVZdTpsitfz4
+mEIzL+3zOYV2SS6sAXZZKwd9oC3vX9yL4d2pkzuC5KLqk87FtKHWpdD1xd6E6G2RxsuFmEHvjs4Z
+eSK5G2mVOSLRMwy5XBJYLMHWOqrxoV4t7/0rsoms44kxYTEXCRtZ5ySqWU7Cc1xNTT7ZkGfsDhIu
+Kw3Ni5BcW6JZcYtWVZvbVjaZJ6Kaz7VJ6sQe40/lgpt2YeDrVd+bJRNTTc9Mb/D27IJFIp+fmTFr
+avIdTkN6ur7pxKVsayh6pFpldcr8woy1p/hzWaudH7NEtYaKRXquYds1odl5UMVv6p2gAxfTk+c9
+OMlIC5l/4yCir2O8X/ihmIJhGA7CEP6Poij+sb59ahjGZlEUx4ZhbHJRFvcDwFzj+2+NFly/9zsA
+pA9/huyKEGH9gjk0baaR2ri1iOpUCLa2bbFQD36n5lOy5MQl2tAjjiOmai/b/Rmx1gz4xQY1SzsU
+RxW4LAfnwUA2/KOuha+EuxGbDNS/ULVjimNNZ80W3BloBKMkfoTG44yG5krMjqDcEz/I4pHPeSjd
+oEI1Sk+LBZeOtLrtpsMslY1N6xM8baBhNRbUQlHjHUtU9dbhx9h6WX53J6ozOtPy8lKVQOef9Bqc
+JIJDdyjfbdYdklBvWNqMcRaCq6w7Jp+KuebNv8j4t7SMeE1V/ycjKmVhoGXrkHBDDls4mz+79NM4
+CFiuq9n0rjAKO5tT7sv+faV4i/NEU41dC1yNPhDyJJb96/J1AManZc6U4OPUILbl0OcLi354cVFL
+yFzzKTSnjWLDJNEy8spGj95cE4/aFYySHkL3DO9MolGJ1s8Y3VcpbKGXSrJDu6Ut/FMwVJ0PtHvV
+PJE+lgCvTxMs9Q28vHsKytTt6hnRExFKi5pEnAbHCbVEnvvgwGRcSKQtq5T4+I5EGjYzi0gjTOfH
+gnvbSqlPtXy7V2eRiMlbK56y0EjZyDxl+e+qm7MhKsHfBu4URfE3nvvo/wH+or7+i8D//YM+YwUr
+WMG/e/hhNIXPAv8p8E3DUM8P/LfAXwP+vmEYfxl4DPz59zOY8V0+K9KUY3Wu3HzUJ9bin2lXpFLJ
+hI2acOX6ziWqA01Xdgqqpkppd4Oko9eNHWpWmrPkRBt2DLwFsWYu7gUZrIl2MMrOiFWaXlHpmBY5
+KhyIHOhqbP4wM1hqj77FYUFHTanclXGXuwvimfYJLJ1QStREsU+opiKtat43db4dzhryPCYJPS2u
+wXB5upQOvsdncx4fSZSguRTV3/3TZ/S14/DHX9njy/9KzYMdk2Kp/Sj9jL62ook0+3M6DWnq1XNB
+qU0SikaTzXv4iSh7dvoFZvlXAbBO92U+l98gnomEGpYcGjMxtcKTMzDFSTaZPcA8EifYKBDz4sEM
++mXJN7l+XOZQOxHP0pxlJhGDqVXBikSCnmuR19ztM79wrrZS/FQ0mnE5oD0V3NpZTF3b0D0xhC7a
+05xAnY/hcMzpluC7FcQ0LgqGIp/DXJ+dq0nxzgH1Sy8BMDBSCs1D2XBsXNVuhva3MlbVeqBuFmyU
+RXN5Gro0p6KZjUOLVC+fefRUnauTOXNNnz41jinpvR5mLWCgqf5ZpUEt127cgYy18A1Mra7Mpzam
+LzRyvrQ4z6THZnpQkF5Emt4n/DDRh9/jO5/ln/t+x/t2iZgXKRfzOOfBAyH+K+2XKd3WxBPtZWcb
+U+KSNkAxp/hLzdt/NAFNbqncGEAmRGqrXTic1rniaIVf6BAG2tQk26CkVYLhE4viJVHtT74kG2d9
+1H4WZQgKONGkqMyyqGrj1swtM3xdCL2srlZrXqaqh20+8/HVjFk3eoy0p3xr8XEAjLJJZSm6r1v0
+yLWVu+9a1B2Z27A8Yl3Dj+5NSUXuvvVX2P6c9u0r2owtUVF7Z9ukFWUsdgtH6w6S+zL3ZLOLoSmz
+5dwkG+ndjmdVyj056G6ly473JwCIqkJp5RIsB9JBqf70NxiEeulJt4dGcnHtFxlmwsjqJ8K8PtU4
+ZzQWeXHc+zsY5xqhMT0Mvf9xms05P5U9cXzBT9d4lbT+WwB0Fi6Gowllyx6JJdGHG601XDUJbmtN
+yRM89gz1JTk9zKdqjm1UCT29qalkYgfCqMeHQk9b14Ys7gmNeL0Y/wVtNb/IGV1cZKk8ZVl8S/V+
+sMyf+ahu5AOyPampWK8XTE6EYe2HkrxWVHo0NqVi9ubkFZ6syzraLQenJ5e91JcHHGjOVqUpD1xr
+tXC0ca9Ryom1QesLjZyHJ0JH/caXWGgU/P3CKs15BStYwXvgQ5Pm/N0gywsc7XHnm/fp3hAu2HpH
+W3jtNGgM5PNJo8BAxPW0nFN6Kl7fYJjhLTUBqCqiu7Y5pl8SVSwdHmPXJW06ut7CUNMkyqo8mes1
+dYn2eMxy9J4X7jrwghZBvVUUGE2Z03hosH1Dcwc08D5hyrlW+EWnh6RqakT+jETveZxbolpWQ4OB
+Leto1AJqDZGw0XLORB1tj/OMt9Yl+pBMRNJcvf41rt/VXoxFxiAXifeyH2Lbkpx0cDAj92UBg7ZI
+nZumi1fRWHmeMkplnnF9RFtvv65/JsXVy16Kkqiny392RsmXfo530nt09NKXRXWNeiy/Wxiw1GKe
+u2VZ0+ysT+VVadt+6Y0qZU9bicUxI21hPkgyTO14/eRU3su698mP5BnH7piudmIeW31eWlcTqpTS
+1L4O41PB1WXHwNVr7Gx/xkNk37eyJa2GaD2xVSK8rBfRqFPyYD6md3ElX9ChOxXcfrJiYmpr91Sj
+GqYBWrRKYSY8PBfN7KSXUcnEoR1TY6K9FQYtlfLRgq4WVC6bJpe0WUopAVPvP717ltPz9Ko7be7i
+deuYhdBpULYZqRb60I857IlmXX8CJ7Gqr+8TPjRM4XtdgflQ7ys8OT4neFvs0u6GbMoG27i7Qvyt
+9Spo1t02IWlDfmeW68y0niHpCEF87esHhGqfL98+ZdAQQqhuWbi+jHeenGLG8v3XZ0KYf2wZ089k
+w4Opy28uhHDvdxLc17TqsDTmtaV85wXtE+j1EyqZbFZQyYlqslnLYx/P1+SeROsyXIeS2q/19T6L
+QtRytzYi+6ZelHv369hqXx5+4/dlTf8ixHtBeljuh2U6EyHof/laxDKT7Z6c9RloBeMn1Z6++2KJ
+9kDMjktcJlf7tPx0yFFLcOH2p7x5LPPcekcI+t13H5Cvi1kWPbKwPqv+Byq4OnY4jjk+l/snzqZy
+oGdv9Ln3huDiYTIlObww6ebM9ap5c3DCE7HcqBfy3f79jLGuOQtd6qFWJRZlnurdnE65iqn+kYua
+5O4Clrt6P0eWsa7NWXrGMf2lJp/5fYavCaMKhlo7cG2LbCLPa7wyoajIur+SZs9C5upSeo8JPM5g
+rZB3fmd+hnNf+3FWYKol0LNQBNZp7lLS6MM4LtjUysiYGlu5MLJ8EWFXRaiFTclC9csZsXZ38qwT
+xlp9yXGf7FDGfmMyIZmvmqysYAUr+CHgQ6MpfLvah+ch1x53r80i/uRTuRan15bYr5F3CDTx3Tsw
+yUp66d7cJkqFY9ZGIbG2R0u/Lt7dLx8esTWWz8+8J6Cdc1+7M+Qzc3lGyypRVZXxSFX7k7sHJBWN
+RJxmvLnQ3gRHBctQLzuZOhieaDTNiah99bUz/EciwSKnT+tUKxG9x6QTrZnwNYkn3qN6cc/lsEah
+6R7pNMC17gCwNm3yO2+IGp+PRBpMwxlNra94q9Lkz7raP9Dqk6gW+fZswb5652ca+z6bzHD7grel
+PaF20bwj9HGmok0ld4eY2n48GmhkYTmgVpP17VxJKNfVKWf0SbVK1I7usKGt2u8/kPnGi4Q7kUi+
+22GdL+nujw+eUtLUZTNMMULJTyjrrdxV8+BZD8d7xYxOV1PP44BCm8tkxRxrKSbBekXrBJwqHb3O
+3qdHs651LkmJUGsRimCEl4tW0CtrH4fJjJc0R2R/7BDZgqOk6mJrCCrXub8nTb+ASP8pLSMO+7LW
+bs+kkgoO7bHgrZ+cc6ZqhlNNCMeiCZjegnipzV6sJa61oePJWIVZxnfEkRwHkB6L45IiZpKL5rGZ
+V3mg/UTfL6w0hRWsYAXvAaMovpc1/8HD/pVLxf/wK79EkTlYeofC2dGA/tvStfnuYcBItYO8KHC0
+z0KiEdE4N2lW5bVh2FQ9vRglL6hp5mEcF7h6pdtCu+74rg2aS2CZFrYWSvkNn7JeIpJRQRv6sKFh
+z7//D/45D9RMK/iWLVkFMu3E+2LV5faWeI8CVchyJ2Ssl71U3YyZBrUrrs1Iw5oN76LpKji6vmEY
+4Sj7DpIMR3n5MsspqR2dZlr/j0lx0Q25U6G5Lb0C5lGEn8kz7FZKQ285PtM2aE+O5tR7IlEaKVQS
+8WFE5yOqTbFbnyYFgdbpN9Xu33IrjDWTMDEs0A5KJd/CsUWbGh8/5pHmjky0n4IX5QzVUfcyBVe0
+29Cf+6XLfP03xWHYf+M1vqp2e3kmf99qW+ykMka92iTVMPLMSBjO5H3HKkhi3RXFRZQUlC769aq7
+AWARZ3j6HQNwTcFHrvj2fYdNLTArNRtsbu8D0N1u88W/+48AUBcPefGtMHoDCJQWLrkWu9pWbRam
+BOo0H2nmrW/lxEr3rm2SqLeyVTKxtFLY9xwKdV44qt8PopxDdaTOkuLZs7/Lif5qURSf+M4fC3w4
+zAfDwnDr5JHJ6FyvXB+/w9N3Bdsn0QnziSZpWCl7qR5YTcrYNmw2tZLPKpdwtOnJosipaPfdRegQ
+6aUYpqq1rhdhB/LdZTXB0mSUwkgxbNnEIE6p6Q1AE1Utz2K4SBw1+dYmOECkORB7GzXaGzLPKBAV
+t111Oa/JGG1gbmqbtyRl1BZCaWqJ7QKHUA+6MzdBVdgkd1jaFz0oLSxNtTULeVZuZ7jK6OZRDBe9
+D1sLpid6oa3XwOsLg7O14YxRc2kE4vVex+RAbz/aKi95dyK4+Mn9hK+MxGTbu2hY0lrgzIWMUg9M
+ddo1uwYtvVexbziM5upc0/yHgzRnqQLpdeC3B4LRL/zb27z5WEyGg/MRv6d78jNaizHuu9zSy1i9
+SkApFhzWmiZlXXfdLgj1vslU2/Yv45w9rSuZLE2muaxvGKT42o3Zik2mrjoPVePO04Slml1ONybV
+MuRhtMGBMoNACcCGi+6gWAYk+sF2vUxHmV4rLuPa8rxhWdvjZTmJMnfbckl0bvu+yRRZX7lZJtDa
+Dl8vti2GEf9ScxfGyfd21r9fWJkPK1jBCt4DHwpNwQJqJszNAm8hDpLR0wWPT+W1MR4z1ZBkBThz
+RIW9PNQQVLvMpYqo6jPHp+fprcSmRT0V7vokn7KpRTInep2bmzpMVN3tTCxM5dBO2SdXtdTIXAam
+vK5M9Trx51jy82GoIdDUMNTjLOVPaXXh2ZZ2nU7cZ52Ia1aZ05GIINMpyJeq8uvVZhExu8qycyek
+lKujzoxppqoJJSkXBfwTrenfJCVU1dl1IDnXDsBuyuWhjN3ca2NpHf6WNh9dDjLWu+LMfG3Uggdi
+rt1zb/LxRJyn//igy39QF0fbk/oNfZ7JUVlyDF4xI2bbor5d9uqkGvq99Y7J/6tXgXtqwiyfw+Hg
+ORz+j7//69w9kt+JdSVf/G1NxtywY+6r5vXzSZulhu8+6cMD7WvRLpV5S9e6o81WonaPGyWRum8E
+KXVN3X7nbMy2K9/92jRiNxPxf65S2TIg1XZsvTOLGz0Na5aq/K9/SDQ/3/TsrICqUse9ouBz2gPk
+uONxS4nmna44vF9NE95QrelqGU41T+Hjdok7vszjkmcw0ZLfS5qa/y8b9/iJR7KON/nRwYeCKeQF
+LBOTxfCIk2NJEDp+8iZLtR0DO6aiF4aWHYN97ZLcU39Bs2vT0m659a0OyZnGrh2Lkjb6qLs2iTKW
+hvoLKmbEUm1VM49IYm28USwgVRtwMaRRFiaU+N+72izSDkj7t2s0OmqmtLQKrz+ChjyvEQ7Jrsi4
+oycTujXttejIIW5lFYJQD7SVEyrB96YmZiHzWIsMtEUjagqTZukz/0Nq5qRNORTlTThPxTTr2gnu
+5yXt9vS3JC7PXsxcbdyf2Sjxe6aYF9dbCWcjIejPf/4zzL8shLn3sqjw3ekpkXZVssZjGtqj8FLD
+INb2+f8mfpdXSzL/f6MXU5l8+9R2s588qx94HmxVr0NsPqb5BpfaFt5l8cj7pwGXajLiuj/D3dSO
+3W+Kfu3vltnqaaKa0WP4NeEytzoZLW3Qc3yUMdHK76Ye0tQsyJQOM3/Jg5Ecwr3N7rMc/2+rthuQ
+KC1cu1amrjdH7d3eInhH8kFeXNdr6/MZlUvi+8nuLemsCT7X10MiQ/t0PhlR0iS5qnZ6emXjEn/9
+tyWaY1HwnKvkh4KV+bCCFazgPfCh0BSMAqyoYDRe8mQhcdfaLMLW7rzdxKKvYqWOTUez1NqWxMrb
+fhWnsS+fZw3GmrpsFlOKZVPfXzC+CNeGmo3o2NRUbR/E4GvzkjwxqGp1nRdX6GfaU6/4brWcYFrw
+ee1d+PKyTDWVtOGtRCR+P91gSzPXCqNGZSy8/TytkupFNInmD9RdCzeW38fFhLoqKUEeMog0HJIH
+GLqFVy8a0mQOy0xvc85zGmo+HZ1ZtEui8m/cvsZ1LQT7LX4SgGp0zmZJriqvlF7kP7qs1Y6tEvuf
+lJThx9sOXU/TnB29ui7dpKd9Iq04oZUIbud5QCdU1T64ia3qy7n7CIDj7xA6P3JzXF1rDHrNDhiq
+KXzSb7OjDtEdv8dWTaIkob+L5eq9i1aPylLMgAe7Uqy1X06xMvnuNT/j7r5IZuP090nOxfTcMI7o
+X0S/QlEZDAPW1cR8cGrwqqtNec4G31ZDuKAQw4Q/URZaeCXr8kJlH4C66zDf+TnFoWjFbrRP+aJa
+t3mVniHZm+F5l229ynDsNukidJRqK8267/KXy0Ij//38nNmPyNX4oWAKRVGQZglZYcJC87s7cxxt
+J15qRuwuhDx2Sxnb6n32tcddZe0qu5dEpZxRo6I3Oc1mXfymNDIZPbUotPedr6GyUqlgPpFxs/o5
+c21YYvgpUSrficoz6qnWQUTCbDzjW6mtxnMu549darK1L5u43dyjXxNreRAL82q4ISNfbO6dxozT
+qTIAf0K0lO/sVIUB9ScuZl2vnw8alJD3h0cVSr42Fknr1DQJ5+xc1hHbI7JA8DYvUiraoLS0mZJN
+ZU1bt9eYmp8CoPapX5PJD2/wUV8vL9m+xmbrMwAs6nt0tYnpXu4S7Eje8fyBRCHiWw8Z3ZdnJNUz
+tFM5USvn6ViTyF5YsqM9IX9K066jd2LeWMr6lgZopTJXrTL3bNmnqg+5RnNevibM6MaWwZ7ellV6
+xWWht1N1yibxmqR375ZzhgjTq/bleVZ7ny291OXJLGffl6rS4cZnaTfEf3L2rxt089+VdYWyplE8
+p6/xySoxTzVK0lsL8C7uHFJasGwwNKHp09c77Gjj3cu9TYorqpTXt9hrCB0FPVlTLc+Y6aU924sB
+gSXMu5kn9NXPVT6eUugFuY3bGlp/kjL8GWE8r/6zEa9rWFux/gPDynxYwQpW8B74UGgKWZ6yCEa4
+8ZRNvSa+v+jQ0f6Di8ihyIUdj9wama+prZFw12YrYVGTCsdSHpCpd9ryzgkjae7RcPuU1Ctnit8M
+K9/h8pZIEvvMItZGJrbj09RipIAms1hUyUhVvLJr0FNzZuqZbDZFSm+u2dyqyODR5pQxIm3KQ3FO
+GTODxZpIksd2naqvEmjoUmjq6thq6BwK3InG4BsJWSIJPfutEY9O9Eo3P6OknYEdvbV5kec8mMqa
+zBxm2lG6NICX9C7NhvMimqVNMBQ1ejwb8tCRvJZOmBMfybib2zmBLa/Lgwc42hMxLEnr9UXgUZRE
+sq8vmyyuyrNnAay15dn14y7xdZVfZ2KK/OfbT/lVEda8Pk5xQ8FFoxpz0xV8niRQWZd9KNvaSi1r
+4mvhWlRbp1vIM5zMoV0d6fsfp5tqe7p9aThjlTNiS2hhKx/gVqVPQcO9TxJ/FIBPXL5LfiI4mJx9
+GYAlEbEWXRmhSaot4Gdng2fJUF01ORaOybZGJ652LF5tSPTJ35oQab/OLHhKuBSN1FLncuBdomGr
+h7NaoWoJLUyXFQw1K8tunaInuF2ORVPq1gNezrQBTO2Me2rGLTJ+KKfjh4IpFBQERUxa7YPewDM8
+61v4LwwAACAASURBVFMqCyPoWNmzfn/rNYPahoRyNmpygOYp9GIhlMBzL1r8kYwb+J4QyqLhUlNf
+gu3LF6q1nJEnm9gwY0Zjvd0pz4gv7uKzIkqenKCRtmevGTY/W1P/w1qJumiBbGzc4lpNG21GJgca
+XvQdbVraPMOK5IB1mjFzTcLyagXJxT2IGi6dU6ath8qihl3RisrMYGtPbMvJuYldV6+8VkAGccKu
+Uuv9NKKqJdLlVsKiI6p/PB1SkUpmzJ+UOe7FIbva5La+8QL7G1KKPSoSuprQ0zf2sctiutSfyueV
+2n0Oltpdtf6E4kgYT7U5YTaUtS6uZaRLwXlHQ4SPRyZ/9oYQ/+7pnNlM1vf2oU1F61h+wjfpqz2/
+U5FxP73mEOntTc0wYKphxkolZqCJaNveCbnWY5QuOhdZNlUNfg6zjHJXXs9nHXwtTz5OymxflfUt
+pzL3b5zMmIUy56RcEF6EsO2Mkh6fTymDdVseuzfkve2tW1zxBKH5/8/em8Valp3nYd/a83jmc+48
+1djVze7m0E1SA0OKkiFbDmRYlo3AMZAYDvISGEHykDh5ykMMOHmJ9RAogGIkfhDgKIQND0KUUKJo
+iaLIZpM9subh1p3PPO15Wnn4/1vdzTSb1WxJKQN3AYXadeqcvdde0z9/XyURcCYrFBeKwxiTnEBW
+9xdgUCXotoKKY96GNoHCFbiiOwIiOpDP13cSeZh+hkqynXEP229w7cqywvK8hBsfv12YDxftol20
+D7RnQlOoqhJpMEd8oGD/lPMKcg/1+nmabwMqw075K9vYZq9uUJFkqHkNLLneoa62seS8fN1VkDCB
+SxNznOZM4a7RST2aqtAYtViNN2AopOa7pQ3fZUbhtIM4JnRdkZBUEp7AKROyKImNGyndozsNYRis
+apsp4hHFym2bvMzh0kVwziyVa+g55KxblhUKgxyiWk7PaGkSs5RUe885Qc6wa+0sxIMJSYxpMUKb
+E7i2OVEhz3uYSKqWswsVlkoSM53VEYVfpesvdVAvSY13H54T2ewBXTLXdos9lCX1x57toqxovHzp
+oTCoT/Him/Qep6tYcUhzCaM2WgphOA4Oa2gzbmZyUIMfkgR2+P2f0zw8ICUOK4M+kpwJeswEYUDf
+eZDlaHI69fX7jA+g9HBtSmpOYq1gyyINK05M9Ca0LhSvgYYk1T05Z3+SJSJ2gtadHOOcNJ1mc4Z4
+SnO502xCNslxqXf+TwDA3jTALQa1qeISGWdXTCYJwH07NDgaFJu4yjiRTbFExWQwjZoGMNFQcyNC
+OqX1ohs0p0o9h2+TORNXAcDrNx8CPnN6Lua9J/kpOWOG2paP9YSiR3BSaEyYM04XCJlpusR7kv9p
+uacvNIWLdtEu2gfaM6EpKABMKAgdG16N0YfzA+QRdU+9XqCdUshO8RcIfboWbFtlZQ1NltyJokBZ
+48rHSROuSY7LE6MDxyJcgDSkZ1RagDmTOM79PhL2KUgrxJCfbfhD2BXd2y0ofuwJA1vsJNpoAxVr
+NNkyxSQmu04v2ig4JyHq+vx7CdskR+R6K0SgcTaln6PByMdCsFZRbmLNYNReuQE1oXs9yDVMSvpO
+MteR1khK35+z/e5P0axIugyrJdwWcyhsp7B8kv713gZyTgZovcyQcZ3PYXdC4yMv2bAqiu8XigqX
+Ga9VN0fGUGkrz5FzLr7yEIMl+RTU7Azlm7sAgIV4AI1TunM/RDUkreGxQ7b18eMFypQ+249LzAIu
+BMs1gMNwQpQYhTSXt21Gm4r6sJvkPFyzH2Fi0vVW04WqUD/q612UvB4s1kxy4cJvkLYxj1zUXRrP
+cLYK5wqHdY/nMLdIW2o8JE3igXkXc+s83zxHo+QK1SSCKmkNtHV6xpVmgYw1k2CWIvbpvkm0gpyd
+oLG1Bk6tgMr8kV7LfeKIFNKFzs5m2xtjwaFq1RpARqxZ2MxfgiUWXdIO4jUfwiDNs/nNBCec45NV
+H9/p+GwcCrKCny2QVRILhutK1W1sNWiDFbkLtKiry3oDbkmfO49JPatemmDKFYctM4U+owWvtyTi
+hNTkTXWAgCHVY1bnx0sPNU4IsZYdHCtkujjCRJsrIs9mPh5npHeGCwZCQYkxE3vGMx+7Ji30TreC
+yuXZ8CZImBRXP6M4uDu1cO8q9XORVbjG5d76KWByBCO2aZGvhwNkHHGo3AR3D2mjry5nKA5oUTw0
+CiQx40dyms8sbWFfZep4aaIouNqx0rGxoI1uxesQNi3YTkWHxrL8Hk45yrAynQD7RCMf7t5AwAlQ
+RmnA4qIFlUFY4kkLWkHjiczBfkaOrzsPDiC57mI3zPGY5691dg61FuIWk9qM4xx03AKFzBBwYlMi
+3wPXKcf0vGlRwCuYpLZ2Db2Ysv6N4grUL9KBm9k2PK5KVCIaH90tEM5p/k29jzKhgy5y+2BKUmzZ
+wGhGYz68SodDe3INrf13uW8aTrnuWg8rZHw9ZV7KP81sXOaKS6OzxAqT+wZyiNCkteUdjlGFTHT7
+5RsAgLymo85ERNVsiZIJdOOpB4Uh2Mqli8imCMVwRP/fsSrUH3FULgfsOd1j06vww3NOWQGw7MFP
+TtKn9onNByGEKoR4Qwjxb/jfe0KI7woh7gsh/g8hhPFJn3HRLtpF+4trfxaawn8O4BaAGv/7fwDw
+P0kp/5kQ4n8B8PcA/OZHdkJT0O76ENkE4jKpQEr4EDlnAjacAospSYx0GGLB2WrMOoZ6FGGTnUj5
+qo6Kk02LuITKoPcpLMwlfWk+ojNThhMoBUnzmT9HxbBrQVhAN5g92Y/xYkx9Ou7Q8TsfO7hOH8Fq
+5XA4Uy6cdxGrdJrLQwcnzBl4qU/9efPFKWoBSXQtsZGbdJ17KbKStB5xwFqM1cLMJXVefWghikhy
+naUWhhuceTg2oKUkQRWfczNEik/HJAV/WOZYWaPr0pJYXqP+VFWMdMTo0R71ff5WjscH5KAUsYLX
+XiaJ5/zwOzBvk5by8i99CksGBFXXWWOQI0jmpDh6cBvpjGTAtB/DZjyyb/k51lh7+15GffCDAvss
+u/ZUiSF/NyiBkANpKt7LHC05tDjLCswY3DYLxjhNaNntf2GGawHN7/NKgEqQhlCcg+wkFYo58zmW
+JW5mBCSrDGoQMY1zIXwsNjmn5E3mEVFHUNt0LzWd4yrjNJQqkLCJ2eEisG49QbtNWpyCLmYMXdef
+6Vjy+7XOVOx/kcymnRPSAsyBiXyTuVKXFgSHVrPhFBGbFXemj6APaY4nIelVTq2D/RqtFzEsMOVK
+2k3dQ9OgZ4u8Qv/jscZ9Yi7JTQB/FcA/BPBfMpXcVwH8bf7KPwXw3+EnHApQVVRND3nfQwqy9XTs
+oGfR5p9WGjotroBzfWg84cmAF2jVhezSZPllHfJcX7ITTLnCLZsMMB3xgmVb1yglXJtt7lkHRUaT
+VG9qKEEb4VLZxNwhVboW0CEV+Cc4zGiziTMLOyqpj714Bp3rMmS0wLZDGz1o0d/qawFOmewz/qyK
+LY40OMo6PJVmbrZOqmXZnyE9pd9NohCVTv3UG11YDD6TRBFc9nyvcfq0X7RRtql+ZEXVsdGh06vd
+3MDKglKbM03CYg7C8hYdBErkYnObkYCUX8AvjL8GAHh85/Nwf4YRml0FK9vUj5jZtBZKhJxNkGvt
+n8XZNdpsf6e1igcF9a3dP8TxIVUGPjem/38DCwTch+9ICU6bwEIATCGKHO/VEiw4l7hKgTEf6i9d
+N+E0XgUAGN+6A+2M+jT4u2fY8ckMUFQugZ9bmElOCspsiDMyNeZRBjtjPs6NFmqH9Llcpzl/6eTz
+ODEIKTudZLhf0KFmqioSq+IxoPU4nlq42mBfkyhgOVzBqQt4mxzVMDagv0PmSMBydPDVFA4n3Bki
+R8aAOWm9hsWCnlGb6jhjMmTLo/+feDWs9el5aTVDr0VRqdfjBI0xs0VVGbZ5PB8+Zfjhk5oP/xjA
+f4X3oh1tADMp5fnZdARg48N+KIT4T4UQrwshXl8sPyaFzUW7aBftz6391JqCEOLfBzCQUn5fCPGV
+j/v791PRX7u6J1vuJvTuETQmvrPjQ4xNOhETfQJ9TFKgXx9jheP0/SadhhuxidmUHYaxgL9CGkY0
+CbEoKHPtbBBjrJEKbjCqR9KroMSkyinbgD0miZEgQNnhjDgjwO6U7JTHp/sACOl3mzMJXbfEOcXA
+WWRBMkeEBQv7AV3vsUf6viNwWdLz0oMQgUL3M29cAZacojokaTAJS8wZjq2XGzjkdGVXHyFeYdqw
+iYbSIafrsOLMvusZ1gu25IwUK206k1fXXAiXq0cdB9Mj+l3Qonc2wggPeCxWxn+C/mcpH8HPpvDn
+5CW35hLVOkdJGPBDlg1YrO4u1jKsF4TTcNK9i5em1I/ZZ0p86tukhTy+y9BtSwlOsYBVAQfnRUX4
+YBbe+fW51FnkFTo6ffp/HQ3wdwyCbrsldYALxXAvQaukZ9u75GgOwikSHuNFVCHge6iTBaZNMvnM
+uxLFNcZsu89gMPYSZYf6PFumuMbRkAd5gQ6bbDucebvZqJAanFlaecgUzrOpN3GT06OvixSH67TG
+V5ecgfhYw7JJZoC5voJ4RtdBXCJklO8TzUKNMeImLEPb2RDJFc5+VCSKitapWaRontEi9xcFbhdP
+m6FA7ZMSzP6qEOJXAFggn8JvAGgIITTWFjYBtgc+oomqhJLOIFUNS04k2VdacHIyD/RZiWHKdRB9
+A/tDPgBK5hzsnEAbUrKK1g7hPGYAz1aBkL2w5fIULbbr7k/OIb0LWMywc8nOkVi0uM3VPShztnFz
+DTe5ulKTHMpUh8g4EerBvA7XYrIQOwZbB7jdnyLl5JYBq4Dblok+J2HZuoFHTFW+c/AYikcK9IiB
+XrLRMSqu5vyDRyEUPkwGKLDHGH9HpYlOTqq75APEPDMwukxjGFabyJiQRVQCdc59DRsqdK7ANE7o
+Xv1pH48L2vD5NMMrFDDBytUO4hZFLdCeQnD9iORkq2lqwNbZFzHp4SAg0I+DfoxkQFECb7/C/JS+
+v2Ra9KO4QMEOg7F8byH+OPP3vNJakxV+d0Tr4ouWg9/hQ/+vbHYgr9BcBso1DDhMuDqhwzSvzzFh
+ZilpDAHqDr7z+j0cZQycoj3E5x5wCHCDvrvbeAnPCU7B1ob4NpdG7mY5GC8GCmNRfnveRIch9UU9
+R1tlpKfDUyyXNCdnwQRbzMQlLpGpktc2MDJo/lqzBSq2paZRjoph8B3ouH1G18sGC5CpgV0mLIau
+w3CobxsGYPvU/7uVxPWE5vi18umcCz+1+SCl/G+klJtSyl0A/wGAb0gp/0MAfwjg1/lrF1T0F+2i
+/TvW/jzyFP5rAP9MCPHfA3gDwD/5ST+QEihyiWq8gMnpup1iiKrBKaNND86EZMWe3kC4S+nDZp+k
+QNoV6Hn0O4ka8iabHbMQZsTEG60eJHPqbXMFk1iEaDMMO5ZTWPw8JzeQb9Hp7/VD7HCt+3iD1Mz1
+hw2st+jUFfkckqHhW7VVhBFpIc+ZbdS4OKpUydnXEhaEy4k54RS6Sc+oGg6kSlpBU2XtaG8XxSn9
+/pXrNjKmMoffhTnk1F/TgOR8gYTvZa8LCJv66xpzOJrH1z6yBkmatuNC234RAKBsMDzcz53hUwwy
+E1VXsFI7py7L0WVeyTToIeaEqqBPzxDxW9As0tLstofV2hY/7zHG2z9DfR7ch6vQUhsGpBr7moqS
+UQ1N8R5xyk8q4BES2Fgh0+4L6zV0f/kvAQC2tBWMd2gM10QMY0la3dinvieDCnZF2qaNPeAyvevz
+/h46t5k13OohZ9IdwVVHhf0AjW2S+FszE6tcgHVaLrGm0thucclsU09hdUgL2FrfRo2xPe1dH1sG
+w/vtXsXagrkre/S7erxAUnK+iAWobCr6YgHs0Nqp5xUa24SzYMzIkRwWHryYvluZNrhuDT3DwJcn
+9I8rDYm7xT79R/9ct/no9mdyKEgpvwngm3z9EMDn/yzue9Eu2kX7i2/PREajRIlCBDidZ3gYkI3Y
+yBI0OQQoTAVNg9GL3BY0l+C4rD2uS09jlD7nSFVAwTagoigIOLS40fIhJ6QVZDpJBrNpotYliagF
+KSyNpLTMa0gVOlVHKz1EERdEccZfUZtgkJHzbZ4C61zW7do69lxytC2sEF1G7xnvkvRYSysEjDZk
+uTXENr2TJiv4AUmdgvkNTLuFnkrSox8eAwpJjMQQkFxok8wOYHOfXI6Ze8UmWgxdNmm+AMciaSwX
+OTzBiD4u4GucmrxDv5fpFVSMRF2aDgweIxFUyDkvJEeIfEmSNJiShJodLNB8gbSDltZAWXCIzK2h
+xtpbLFsI5wSeYObkZ1jFBBxZQ1iBn/BBpOwPa0KIJ9wSz/ufxdoqM5A3arh6RlpYpcdY8A3FkEKL
+i9s+ZgGVjrc+fw3rrL0ppcAVnzTHR80cbZ1wORqSMjPn45fRi2hdRP4uziq6x3Zcx4Ax8uYg30A/
+BjaZC2JNKGh1CetgR5ioGwwk2+tAW+P09oDmJlGmyOdMHptMkLCfpypz1C4xN0i0DTujfAolp+Ip
+p2FCYb+VXyyf4DRYQsUKIz2p/iG2DHK2fqP/xkcPLrdn4lAQlQo1qKNuFnAsUtE3Kh3CZwaldQkv
+pcFpdgDZoZf3OL200LcgUlqsi2WOkuP/QitgNhizQFNg8CQaDH7i+QKaypuiOUOR00LJZYgo4GQg
+maNR0AGQgpJjasJDh9F5rbJEi6MkG7qA0aADqbXbhJnwNUPFqXOBOlc7Thcj1DgJR5MtWK1zRiM6
+8JxuHQan0frBCqqYnHlh3kCyIBMlPPFxFpJKnHHq7PpmCJeBXmCUUJmKvtZ2YO6S17W58QIUBlRQ
++EBDwyA7DkAlBQRXoGZKBcG6vZjmSPmgKhhLE24TBhO2uJs2FE6gwdYmDMZrDJ1DzCtSmU8DMjWK
+4zMIxinwc4nZT3CQvx85+avXdgEAK7/awvo1rktRHKSbnEMxO4X/mJmTOBKj1APUTKpn6CFDe4fe
+r7HTAka0HuqQiAfkF89PaQwP3ddxxgjcsZHj8jG9x9hbwlVoDDfatK6aNuAzanXDUNBgVnFtpQmP
+qe3zRglrSesh4nRmOVFQsdM5N1OoOplowmjC1LhvWxV80Prk1AuMFAllQeZYkdaQNBnmbaqjvk7r
+yD2zseB6jqdtF1WSF+2iXbQPtGdCU1DUHH7jGMdDG+YZSa4HNRWXOanJnNpY2WFKN3eXeBkAmHWS
+4FFxiCFnpZXVMbSA1V33DCUjH1fBMVSbdUr/vOJuHUaNpL+VSkiXJHCsr8NgZODJoIO5IKnIdBJI
+EGPJxS6LAGBNHBtmgKpkc6TKoRqkrhclSYxYkQgCik1PUwsiJ62opyiI61zyeV7dpxwjLzi8lUsg
+IimRqifwQkYUVnIoXFqyKUnF7ZRrsLcoN2FceshmlD3XK2w0luc4dAUUZiIWXEglZIZKcopukUMK
+Vj+rBBPm7pxGhygPSaKdxjQmp6cTrHCad2NkQe9Qf8pUQAtu0tjPNcwmFOOcnXGMWMmeFOrMATAg
+E4IfYz6cf6wIidGc1Ggj6EFKMg9g70Hk9IxkASy1fXqvJReuhTkSLg6bCR0WU93ZVY5wSGOgVwco
+FmzyFZR5mU8zyAWnZk+8J1RvxUxFyvHnWUxrbDwUyDdorOJWhIxD5jV/gUqSiaWX9yF1RsSuaL2N
+8grTmEK5ciYQcSjackawWOu1EUIXXACYOzwojwBe97l2hGjK1IH5EMUJmax6y0LEVaxP256JQwGl
+QLlQ4SQDrLRoAhaTE4SbZN9tWAZiHpxGNoHOyMBVSJNcFBJOQgMcyDbKOqc2Ry5MhjdLHAueZJy8
+jCvdvAQVpyVnWoiKJ1fGUyxyttudAcqErnst2hDBpAbLZZgvRaDkqEaQ6XA4dTfTBHSNiUgCmmR1
+liDjOH89SlC1aaFALyEi0p+tkhOMVB2S1XIlyqCUtOlds4FS0H27NpAy96S/SnbmypdqgEv3ulFE
+eOsuv3N9HREo1dgYXYZ6jp1ecbm4qQI8LpWio2Qi2DTOkJ/Q7wp4GLOvxfbOEbFvITqhw3axXcAd
+ck7HZIKSSWyX996Cy5sz5QpXaZjQeW7WtQr97OmAw1Qp0LzKh1v0GHqfKg2zyQIhb9J8MoPmUp/m
+jMTtOAPMOJqT7A8Q8Jymbh2qyUlPhYMgukVjPqf3K8LXkYa8TVpjdBhEJW2nCM/Y/OFEKLdeQGeG
+qSwGpEprK51ocBiNW+Q1JAn7D7h0XB9MYHQJhEVOM3gmjZFQFGhcjVstBDLJ+TcM529JD0FKwkvP
+VORzhoavbJRNep5edLDCJu3Ttgvz4aJdtIv2gfZMaAqVWiBxpzi5P8TNu+TdVdwZXuD0zNMix7UZ
+nbrlVQtuStItj+hE1VwTEeNi1ttNCEbX9RsxZlM6XVeVHOmUpK3J8WWkKmxOS82THDrTjsnKRZ0l
+6ey0g6qkmv35mEEz7BLTlCRROCrhcX6DktoIWdpqSgqjxl7kgkEx4iUWXG+vySVKlkCZMFCqbBJo
+dKr39C6qlP5ft3OgIlPJjULkgjoXDA3suvRsl2P+1sSByRKzWL2OaUoe5/E7Bezgr1I/NhYw5bkK
+y16rpIGSzbIiUyAqBjaNTUwCclQdzAxsssZ2fMSpzyMfOfNv5LdHkC5FVKJ6gbQkSQiriTOOWlxi
+FfhUq7Dm0fOGSYobrMW9nn20x1FVBPTb9Dv1RQ+SqyALcYCcQUjiYI44pfdz2Ts/G3fgCfqdkrqQ
+DFqjqkDFqKlxFcL1yEE3Bpkl8m4TWb5PYzGy0OB7eJqDwCDJ3I/pueNJiV6TnndDLzB5QP+vBnWI
+DXJ8KqKOkqX7jGH8FtEIsxnnqVQG5px5mVsBNtfpeUGRoXaeO5LTGktlBZUL5aJqCpUd6IhKLGKm
+WaydQlU+3jZ/Jg4FJdfh9tdhIII0mb3pMMLpF0jNquwUU50mqzCTJwg0Gpvh1RJgqkUEswn0VXot
+MYqRsgdcyyQCiw4Rl+nu7W0d6YzBO+wllCENatnIcDilg2VQTKCfcFXalH6vVj40hjUvmwkcxtQ7
+XbhodJmIZm6jqDP33wn1oXAjaExwU2gpygVNeLkeQVvSZjGY71JEM/hN8tSXCVA1GN0pcxBxumrR
+ThEk1P9DBlvZ6vaRPebS8NRFzKAmwWc7SJpk4wv9KyiZV1LRaQEWKCA42lHOE5QOm0HjJXwOh/rz
+GNU2I1Kxb2GezqFz8s/mL6xhekqfu1kTBdPOL5NjKDOaoAdcUzKfLZFzpKI7lDjO30/P+v9t59GH
+QkrMPFK1H9oBVE5pd9Yk0gk7fbQWOPADjROEikwgGtIm1IoEK5xkVWYVFIcOeL8f4awgX8nyFj3x
+B0fvYJ855+s9wDoHbampUDj61eASeeEUiLkw4a4cY6ND9y1HE3Q2VB43BQWvnTzg0LhmQ424pP5K
+BaPGPoV5CLYw0NtooeS5rmr0HtlQQyxoj8SjAscR+VqOjiUijo5lgyaM1keP7Y+2C/Phol20i/aB
+9kxoClBTVPU7GAcRjh7SiXqvzHDjDXJwlc/VsXWDEzfQglIn9cuL2VnUXqBPuSZIZ7egPWBgDSvF
+mE9zXQhsFJwSzLwBy3kTJsO4maMS6jqpeFLbgS/JUXN6rOPegvoRMM5gd0VBPyXYtHSk450m/e5X
+6hlmCZ3ibm2BUJKKXfjUOWPZRL7JRCZ9H4rLHIwPa1j0mG+AowmKqsJLSDOpmkDJJkicn8Eecgx+
+tsAqF1htlKTuHr9eg3/j1wAAM63Ad4bk1W7fPIaZfBkAEF7vw2xR39gXCEWNsUxZiutzuKds5qzm
+CB/RMnGvTaCf0HXCztUsbCE4pSjK/OsCUqHrd8wcNhOZnFQJeOjgKSQF95omDhfU92OnQD0jSXkC
+/uKPtCduSBXIckqEWi6uYtmhiIMxXYN2lcum7rrobjD4SEjSWml7UDgCUGsKiIqku6wpiBKak1Gt
+j+I+rZcx8zwWmQbMOPV50UHB5sGxCeQVaQXHc3qPo1mFE5vuVa+OEXMa/meu6JhHdX72FEZCKm79
+Cmt0gz3UtshsNtQ9RJyCHY9rcFzqj6WWKFj6Bwk7TO1TlKx5Bm6I+YDmRlueQA1Js463iUDp47Rn
+4lCQS4n8jwrcVO8iFcyUEyWoevTylXQwZlCPvQgoHXrJkM3P6dRHPucknmIHg4Q2iB040FfYOxuv
+YMz2s8qksjUkmDEQp6JPUBwzwKozxWRGi/TO8j5qM9osC49z3Kc1pC/QJOZLBV+t0+QfJR7WdrlE
+eB/Y5Oq0pMkoPq6CKmMIeLGE2f40vSoG6GrsLW9whEDaGNZ4My4KGCn5Q/Kxgof5gt81QcoqqGUz
+fH1xCOPO7wIAYv8FOEecVXhV4oHzFgDgs+kXUDHfpgQjTEmgmPB9LQ2VH/NYVbBs8i/U3F1oOdnJ
+ZpeiHc/90m2M/glFiarPfwuT36J5unb9Hr55i/p/zw2w6tCGFOwJNxpNbI/ocOsIA0tJm/R2lH4k
+cataCeTnGa1BgPCEMwW3K2BO/hXbPYHdJl5Jk9m9IttAo05rRAk2kTJJjpgDOVj9n9i4PaHs1Tu3
+abO9sTxEHtD16DN9tE5p/n65s4c/fsAl41dobvywwHZOvT8rFOw1aL0dzRJc5hLoyHMgOGwdZ+fk
+QhN4PaoMMGcxTE5qUtTHcFPKwg18HXJMn2sZzUd45mBQcmh4P8UkZtSuiQbxebpuhQpe3Nz+kBH9
+8e3CfLhoF+2ifaA9E5pCYuS4vXGGxR+c4d6MpAdkheNTOl1broVtjyRQsmWixZDjSDluC8BokiSS
+JXDVof+PiwIW19XLmgbMSMqNwNJKz9Hkmvfx1Acjs8GpNDiMPvz5+au4Hf0OAEDr03cPNg4QfJck
+xul4hNOAJNSXfQMHN0l69PQQouQYOY+yMq49SbzS3SZ8jiLsmZ/CNCKJVjdJwlmdFjBmnD1d/LFq
+bwAAIABJREFURcFF9j07RhiRo60/WaLpMfT7CXMx/ouHmP8aE6/ck1gkLEm+u4Lw6K8AAJ5/JYKv
+kbQSnNBTwAUYeyEdVCh4HuRUgzJhiLzLb8FafhYA0DiipKjqmweIj/4QAPDmt4e4f8iVq48rgKHm
+b9vAtCQT6jOrlGPQWnOwrhJvY3/7D7A9IU3n6/jwmPq59qCqAtMzRtfuv4rWJmljkx/MoPWp//3u
+EG2XABO6zLW4SM6gMziNZhaouJJWgwtw6na9GeESr6PDtd+m8X5X4KbkPn1fx/cYdzGwjjBghGZ5
+h+41jQucwxTvJnMcfJ/m7EvNGCtt0ryaHQ3hIdfmPKCxOranaM9Ii6lPLyEoeOz9HJpBfTaDAhpD
+0mUlSf7t5imCE65Kzf8EiyPS/oJoguxPKZdnvHOGweBDwc9+bLvQFC7aRbtoH2jPhKYgMxXZ4zoO
+chOFTXb7aFKhvUO22t2lwGdMOhETd4I5O6tKTsXN7AoxhxbVnoFEIWnsGB5UxmfAfIoji8I3Cmfa
+zizAZCDOuTlGwChGcztAwfAFs4051tihGUomEIlqiEs6zWM3wjsndJpffeE++uW5WtCF0+KqyyZJ
+RyWaIWTkY+XoGBV/nhg5Co3sz5TBaK30BAOOs26oXSibBo/VAPMOPdtGiZQJa2ufI9tzOL6KakTv
+dPyuhinHqNOtEOt7fwIAcItLQMJaiEZaleIpaDCaVOgC92JOCe6ouDQ8d3Zdh9PmXIY6oxx9W+AW
+E9j8w8MAn8tJWv1xDvyNNknxhzMVL+ySxnaq0mfN5zykS+ZImK/hHYc0JQUfTm/GUA8IiwoWazn3
+xbtwOe/BkhFOejQP5esBki8znFqfaOwCp4J1kytUt9rIZgxD19pC7QpnEy5TKAY5Fd85pPn4YThH
+woznUmZgMGe8fTRAw+Zsw3NEI73EAVdJtnsRMiaVNTsdWDo5ynP9BqTBBD4VzXX53cfoXyO/1boE
+ooozGk8TBD6TvdTbcG1aL7VrtKaH+zHm7OPZj6d4i1GeA12i0Oh57cQDDm5+yIj++PZMHAppleJh
+so9YDrFkHkFNSNw+JfPgU/YE+2c0OPXpCladiL/DSUpFjCOHJrmOBAnXO5jqKQYhL5p8gmzEmHk1
+qjicooP2lFTtcigQM6R6VpXwF7Qo7h8s0J/RYaAMaKOM8gWOBrTxZrGAxh7z//1+iC8zxN/osg53
+Rok825xYNFdK6AxVP3Sa6HECVJxL1BiVOfM4lq4CMqbJn9WPMTlmBqzBECc3SdWMUhPXtuh+qxY9
+y/xLIyTuXwYAFJ0I3/pNjrG/u0DtIS0qc/X38co2qZTunBao5lhIuQ9tWSHlVOl2fx/edToh/Utt
+5Kd0bdBQ4aS4ieMpe8XTHP+a57QqJX5rKJ7c79+c0cb565epP404Q2Rw+bXuo8mMVRDDD0Va4X0J
+UwCvT7la9a7AhkJmwvrGEk6f1Gr9ZQt17zka28uM0L24jtKgsu3AXIHPHnl/u0Iq6UAqxSmObpOj
+sf8ubbAiKMHpGxAAjjgxbq0qEVh0WJ4nrE0LQOFK0++PSmxq9PnX3SH0x/TdXXcHmk5rbvU8cvDp
+XbTXyGFa5BINToVPjh8iaDB8gCZRrdJBfjyn9TReHOCPX6MN//b+FAHXxESphK/R/H0rcfEF/yLN
++aJdtIv2CdozoSmUcYXZuxEWAeDwSTyRAhwBhCwkIv7HPImhcdVew2MVcVbBriiUJ2YduApJdjk3
+oWkMGLow0GanXMoSup4MkTMMWts5xpKrz8Zn+8gS5gwMD3GJK+duc+qzMy0wT0gKqBUQsXSohSnG
+XGC0sSxwJEibWPNJHVyTBaolpwErR9AG7Oxyp3AypjwTDB9XbqLnk0ZTjjwsMnIYqoMUIy4kajoa
+jlmzusGciRuj51HxWf9Y2HiRJaxIFbzhn2s8Lex8bR8AYPwy5Vvk0wySC3VU3UJDYzRgaaPPlZ3N
+r7+NKTNal/+c8h/+55tDdBgqblhI2OdqfgUOdgIjIbFX0pz96SlpJpcOFuhZ54A6TXzdIOmpyh/D
+jsz3LSWgqfRSPwwrfDWle2iZig026foixeoxSfwTLuyylRKHzAnZEnUYq2TylNEqEpO00Hx/jpOT
+1wAAKwm90+1SfoC1WfBcDyXwKbYlTliT0KVEzONtVBKLgtfycokfPCAzb6d7hBqjP6sqOQPLbIoW
+c4EmWgAvpQzSw7aEy2DBZes+ygl93l/QWghuTTCakNbkxxkOOczsAYi4wMxdznG3+njbXEj5dNVp
+f56t7tny5z99BWpcIgrJzhJKjoLzt0tDIMyYZKMDNF2yg5ucRhqFNlbILESR12HXSQ2eny2g2zRj
+YVBCM5kMZkwLQoFEnjIwRQIkvIBgqrjUoMOiarax1iM8Q6dDk/zX//Z/BpFxLYYSYnJEC/3d23+E
+N39AORKTO9/AgpGL7YgrP2WJOdvqjiVRcPapogrwfKLlMqmuUKBqZEqMogC2Tn3XjepJebZqC1QO
+LZorTHg7cxxs+gTPbq1J3PkB5VOUcQ5dMi17y8BWiyv42I8ymqTwm1xG7ki0qpdorIZjrK8y4rO2
+DsVnJOKANmBLVwCPFqtnulAYKNCqYmQaHYDjd/8t3nn3ewCA/bu0oN8eFRjy7tfxnsr6j/eA/4LO
+HaR4jxz1/SAr50u8qSsAC5FIkVC5VsZUBHR2QggO/ayoKi6v0bt6rVV4Jn2+SCssQxIoj9QUt79H
+B+M5ZuRHVWK4/PdPYi2xBPA8j7em21BMprNf0HrzdAnm7YVrKFhy/YevAwFvbl0AQcq5FTxYhQCS
+iAVjKXGezKxXeHJ9A8DVGv3ga4vq+1LKV35Cdy/Mh4t20S7aB9szYT5ACiBVsdEpccJHsy8FCgaQ
+kC0FBqNwvNCsoVMnp8zqcwyyYjrYNel6qXbh+qSWn42nqHMk4rhvofLpHuNHpC5W1X0c3yNJ+7g1
+QH7GmAZWhIgJ+Kr1GuKInE5ZTvwHiumBIfZxmJgIIxJt/Ycj3GVvd3a8RKySg2eDM+K0roIVjobU
+bAWGIE2gsCVSVv9XGWtSlD6mLBKUiQvJKM/VzMRZnbSbcirg8Ps9HDAy9NUmqgWn/hqfQxHQPZxm
+imxKGouteNAYL2DJ2BO5k0L0aQw7dRsDi95pSxvAPKHciRdf1nDI2JQ9l0yqTmOOOau4TrcFIyOn
+a7PlImRHWvuGh/t/RFJuGNL7jariiRTO8J5v8bf2AXbgf6C9X589ZyyelkBT54KnQqDNnw8r4LrJ
+1ZFszr3YMNDiQqJO24DeZOi95RjzQ7oubj3CW0+hIZy3D+vnh7WGIqBzuvLKiouuStuuz1mjKyjA
+hOZoqDpGzPLdlRIjzpBMsgIjI+N3pd55AB5xkWtUAJzGgBzvjddtAG8t/uLIYP7MWlVViMIQEBXs
+OVee+Tq4cAyfKgyEkkJuN9avo71NZsPmKqXXouvB818GAKT5CcwmHSbrkxwAk9R2R3BrNFQTex8A
+sFxcx2JMoBqvJiXenj7g/ihQGHT1UyfAtavM+tPgCrkywpRNm/Fhgm++Qxvou997Dc4+qccn4RIO
+81iOderPF+bACau1G14DM0ZW6hUCpUnoOOt1WqChMLDJoKv75QhgUJB3qgWaAy6znsRIeJMpXGZ9
+tZrj09u0+FO7wB8HdDD5yFAMOHnHa0I16N6rp1x9OYvg6HT4feNhA/Uzqi/4XvNFfFlQ6fjX0l38
+epPe9dEKefc9aeO4Rer358MM4Sor1UELeY1+tzG6h+8PyS9RMCT5uVkAfHDDv/4U1uz5ZnSqCrOS
+xrMpK5zyIbpRVXjAHvxfMmmD3VK6+FuMwXnoGviZhEylb44Erh3SO/1mGv9EiPn3t6f9br+U+FJG
+4+KsruNVlczfu1xavhcLPOA6lx1DwSEnql1WMnyfTc96NEXCpfu7HLV5uyzwApME/T/v68z7+/Xh
+lSQf3T6R+SCEaAghviaEuC2EuCWE+BkhREsI8XUhxD3+u/lJnnHRLtpF+4ttn1RT+A0Avyel/HUh
+hAHAAfDfAvgDKeU/EkL8AwD/AEQQ82ObAKAoEo1ajrBiJNtKx+YaS3kPcJ+jM29tPYXzAnnq3QX9
+na7msBSS0GK1B/2YXqvaWsBi7MJoqweVabec5+i+fqXAdUjpzG8fY3XOFGxagiXHee1mjncY/myH
+IbWmsxjLIcW240MgZCeaWE4x0qj/LeSw2f3e5Xi2ZqlYYzVyvZvA3iAgEO0ohb3CDrE1kuCL9W2U
+j0lKvOQ5CEp63oO7EcSETYJUojLYkWqTFFFXK3xbktR5ScwgmebMreWYJOzYqnLoaySNpo9JYtbd
+GCHjEXzWqnCfwVKu6zH0Go3Llz//HLRH1L9L69TfddeEXt8FACxHS6hc+ONqEQqdIhtvRa/hOrNq
+//6fhV+bVW0JgS5rhYtJihWTMS0zBS/tkVY3WZAm9eJOF36NTJ/rW1v44Yip4qwRIk6G2xEZ3v2x
+xHU/fdNrKlTGw7jx2Rso7pEWtqKSRtfwR7jKDOVxf4Edpp933RjPp0w3d1/BZ01WnRP6++dUG/+C
+k/ZsVD/R4fm07ZMQzNYB/HsA/mMAkFJmADIhxF8D8BX+2j8FkcR85KEAWQFFgrO5B5+hxXt1C9s8
+yYtgDesLymg0NuvYBl3nXPbcWHpQDLquxR5khzPwUh9FREbXhqIh79Ek+IyjOL4DfFEhP8FtW8EZ
+h8XSqMAlxms80FO8vO1xN6k/SZxgMKON9yaOUaW0YYtihjWuwBxUKjgggsscOWhaGwiZIartt7DF
+6Eejtop2gzae3SAVd8PuYrpJ98oWfWhTpnu3PDxS6IA8zmNYKt3vnG68/1jic69Qf51EgWATZjjW
+YLD6bDkaNgMG4WBujUnWwBoDq5idVdzgbMtStdFapd+JlomaRklPespmxyiDf+6XEBL5gsZilGQQ
+DVKTN89exqc42+lYJXPtsHh69ftHG9Na4NdND8kqzbUvPNxhgJ7PJSaSFm30WpNMzJUVHaZgLtBI
+YHfCoWFZw2mb+qZJC80jusf0p+zbh7VaBvxi81XqRzFDZNOaM+ZkXinSh5LQnIaZD/uchDhpoM5h
+VGntIYxpbDWWNtMc+FU2Jf5lFgAVC4hP2N9PYj7sARgC+N+EEG8IIf5XIYQLYEVKeQ40fwZg5cN+
+/H4q+qz8eI6Qi3bRLtqfX/sk5oMG4LMA/r6U8rtCiN8AmQpPmpRSCiE+VCC8n4res0wZVxp66ykq
+5kk0GxEqg055vxOitkVSdaNVQ854ChYnNMlaFxan6Fa29V6yiV2D1eBTt6ygcBKOcAh6ff1zNsL7
+pEm01Ue4NKIKwEH6JxhzwpLwUzwek4bR4lz+PE4RM76id9KGwZyRB9JCxZV4zRMB6TABCMO2xxsh
+2m169u5VHZlFuoRZhVBrdHautZlz0GrCq7gir34dm9dJIvaVDmag1NaVUQepTWrwklOi9VqJu316
+3tZaBaGxNrUZoRyTJGmtKSgVcmxqgn7v2iu4wtzOtUvPYZNrG8rWDnyGs4/bLmzO288CUsU9dYFT
+ztgpghjLCTuKtz2cMm178ooFb0Z5Dy8eklPyO68tMC8/vq6gCmBrlTSQ+lUbL7o0ht0bMT5X0Xv7
+8QzqJWJF0jnvI1fWUK/T/IULC9Eew7bHOoxXvggAePW1AebhtwEAM6aq+iTWzvk63NhZw/gzZFZ2
+xRbWOjTOcZOc5119CnXC7NHdCKVC77TbizCYk+bc9CYwFgz/TwoP7ImHoUnJW1898PD2gtb3o1Bi
+/pTcnB/V75+mHQE4klJ+l//9NdAh0RdCrAEA/z34BM+4aBftov0Ft59aU5BSngkhDoUQ16WUdwD8
+IoCb/Oc/AvCP8JRU9GVVYhkFWDxQITKSNF7TQSzJCdacGNDWKFw2cDvoJRRCqlI67fXWGLmgEJmq
+axAc71KtAFXEBUYyPWdFg+R052CQoWVypVq+g7NL9DxnuAdnRKE8NVlDa4WBNjUG37QLOByn7xUH
+uFOSQ61ePkAa0skulRwFZxs6LK3qoQ19g07zE/M5bGiMwFzVoJtMMqKQJtEWKULmLujWUowTwgX4
+9NZDZGc/DwCYNr6NY50kdj5mzki4cLdZQyk1RBmXhA4rNBgGLCs8mBa9n7eg/trlKU45Q7RWHCMZ
+kQPWMw6QdahPunEGvaT31rh4bHmkodJoPsS4RMFcF/fvZGD3CMyTFRQb5OSdP6Rn/FJriX89YnwA
++V7GosCPqZJk8bWja+hyJe1kakELeC2cqUgYqQrra3BKZgJ3zvMOJ0gK0jA0ewYnJb9LVJtiQcsB
+n3mlxL3Xab0cMCL2+0OnH7f12MG8sVqgKWncgtkR9Jwkvu2RVjlMOmgyQZG+NGEwzNsyX4fvcmXn
+yIDvUiZuzGH2tl/CYf+aWR9hySHszizBmxwyn5eAyTkwy+Lp9IZPGn34+wB+myMPDwH8XZD28TtC
+iL8H4DGAv/WTbqJAwJYCTb9AzvhzIhLo+jRx9YYLtUkKhzZfIAWXIlq0yKv+ErrFmHxKBqhcWZZm
+kDFDusFCHnPK75wmSGYHyBVG0d16gOtM3nE7SbF4mdN5wwAzJlypZ0zioVaIOaavbHRgHlIdABwV
+mxqphpEK6AptTpVTbb2agpATUNcKiYj5BWt5CjAas6MTGEmqrMCzyHxIqzraLicpbQn0GJrtM6IJ
+cZ+cT9XmOSx4gYWg/+85OTz1nKUohMNQaGrqQGnQ502yypDXK6wyonLNvISVGm2gwFzAWzIZjOEh
+ZyCWpM8l1OIxih8yHF1tgOo1uoeyusToASWZHbbfQT6gXX3o8fjAQINJEeMKCPgk0IAn/n8D78XZ
+V3jD/81VEzOP1sW4XOA5NuNKN0dnSddbRf4E21B3uRw+LlFzWW2fbcBr0jvJsY92hw7O5LGNjPF7
+fPY0zn5K+8FWBb7UoP683O0hcuhNWkEDOrOPiZKERrMeIWaJ5ZslSqYzcJwxpow55HenWPC6rdWY
+lCgW0K5zHsrAwY0FJ+LZFv5GRELmjUxiwQ7IW6dPh+r8iQ4FKeWbAD4sl/oXP8l9L9pFu2j//7Vn
+IqNRQiJDjuHIeQJ08nxNYNMm/TMONLhLAgpV1wVqCtXNFwFlIKJ8DqXG/JL5FiQjMcuyjXTGlX/q
+AuxHA3SSbMHBMYTJpCHB85hZJB5WXA1lwLwI8T7MkE7ugolc2k4XCx66e9lbEEs6oVddB/GMNAwl
+j6CY9C6NkKRWrNpoFaTFqJqOVY1Mnnk5gM7unZhToptWhGlJ1049Q8zAKe2igTyic/ihvIttZjw+
+PeVwYjCFZ5PTMh0akOK8krSOnEOgzZ6BpkEhx4i5BEQAWD0ybXqpAhkwQOt+G8oKPdutryDnMRID
+kkTzExV1FvPpWYTjI9LoHt2eo7XK/BRvGSjG9Pk1BrOdpQIFFzOVUoJ9Z0g1gFMvUAFYZ7Nhk3nu
+6qKLSwXNxyBtwVwhUarPSjgeZzeKAnaT5iFlsp/aaY6oT9ervTHGIyYEuncCgyHPVNvCFqvjDwVp
+lbos8XTyldo5G58jgFWDTLtursEd0di3eidYsJct4exWUy/RMWk+FtIEzH0ao1MPLdaVZtkmpEZB
+vYDNLidLMZsyZ2QcYLc6X4cZHjG+yHOTGVTQ9S3MnuodnolDARAQqgF3tcROSJvbbhqImTtP2XUh
+Nyg+biUZojotUpPdsEVRwFHoxYs0gmJzPDrXoHGFWD6pQbDalc2YXLTeRZDSxhU7MWpDRl0WFkLG
+TxyqgCaZTDZjQBbosCVt2HThQtVp2QxyIOjw4XQKJFzK2+Vqx/XVFK5BXv+O3UDGBRSG7SHnNGXL
+ot8vinXoNi+CwINRZ0TpqAZ1g4zgRraNpUdVmTWb+pNFC/gciZFuAN8jG77dq+AyvuDK2ipMXurK
+VWax0tbQO6/R2G3B4Uq+crMJU/Ch6Fow2OJXrjFE/JUQwQndwxM+WiX17ebbGfQmHTKlWaLBB8A7
+CwYWWVbgLG94AEZ8ENSEAFcco2MCGf/jiz16XtyroPHBOtEXuHKJNlt7msDt0oZ2jBLKKs2lxahI
++toKDK4fCUUPukFja8IFTyuyrg71efr+1uQ90privIQfH2ziR/5tiPfAYF7wHYAZwsLQxyNJhwzW
+OvAVlk4uHUwtP0HE81QWS2hTeo+Veh+TigSgr5xhNGYOTfYtHAQOlmAi5BJIOH8lXyioGKX8ROpA
+/PGSnS+qJC/aRbtoH2jPhKZQyQpxlkL2NZyUdHruxQrSdTpR11HBE+SAi9w1NCzSv5QhcxM8L5EW
+RLFmd3OImGK3iq8/0QpkZwgxo+9LjaTZOCyfeHTtgYeqy7lg+RrWrX0AQNtew9Cl+wlWcmt6gGOD
++lk+muMPDkki9OIF8iVJwroj4HVJC1FtOns7kYaKqB6w7GpoM0xWOYiR+CRipuwVd+QdMCQkyl0T
+J6wydvwCBROcnK6MMC+uAwC8E3J2lvEq0jZpNobmIsvpnc3UgFZ1+DsGqhaZVXrC9frVPpIOaUeO
+eIikz1ra3nUsOL3brDcgpjQWJecrjN+cYRATsvOtt47wvdskjd8Zp+gdk2nyklHha5x9eikn5+JZ
+VaBgsTqp3luIqgbYLG2TSsUKa1MHCt3rcuXhxKb+7Kk54hF9HoYZ9l8iSXrV8uErDBs3Z9ZxKwBi
+0tgs5xh9pqKPoeNBRGPhzh24j0nLClhDsZX3sBV+FHrkXFM4r9qsAKwanIVql6hZdK+lO4RlMW/k
+/n0CwQQQXCbtZ+rWsdWlz5JBjmyNM0TVq9hmzTEcb8Fvkeb19pT6u1OkGDxkDVlW0JhX0wvm+P6M
+q0dRIv2Ysv+ZOBQ0oaCpGlhpAblFk9zxPYA3SOL5WJwyItFZjoyjDvke6Zy1wRQul5ua0kepdvjO
+ASLOcS8tHWeHlHMuC9pUJ8f3serQYbLcSKFMGOBkeQaV1VUkj+GxpehwNGE5SxHOKEU11B/BntB9
+F7bE5ipXIs4tKJI3ZEETftyu4EU0ye5RiWyT1PJRHANLWnFHB3QSmJqGE5dJRmYZopJ13MY2MmYv
+EnMNtZAOy/Y1eqdiEkDj16/VPKBJ//CtFFWNNqRpKsglpy4rnO6cRMAZmzB3lzjdpcPNvnUXWsUJ
+UGsGGOcW5ZIW2uTxAPNHdLj99p05LE4hf5hWqLHX+19pFdY4gnObEas+rQK3eVs1dQk+g9ApdZQ6
+fWfFVhHzJltjIBdNBLA54nDg2PjZPn334ItTdGlYMPcstJtM1MJh62KqIR7RGDZs4Dgjn4nzSMVB
+Tpvs87mNpEX9fImJZf4kCaExSOOPJtw88R/wnqsZAjWOelxrryNjAt38IMcxc4yuDiq8+0X23ZzQ
+M0Kziww0T1GYo9BpvaSzAUoOqe4bt5Af0L0HzFjWGNk4tOk96mMPD5k3VZkKjC0a0JVMwv6YQEoX
+5sNFu2gX7QPtmdAUICSEUeEoacBgT7/e0nAlYmKRhwt46yS5tR0fPiiRZ/p7RI8WRS8i/MskYX33
+EnSLnC/FooHkvIjpMWCx2h0zlb3pNlE6dEJ7d5YQdTpRFbGOBhf8aItd5CyCZowfNiwH+Oe/S4mc
+b/zbf4XFklTqLb0Gt8ZYD5qONYWiADXJUusHAYwDkqrHXzmCPyV11wsVLDjSUNSZ13BUIT2h5y6g
+oOIEqoaVoWRKszibocaQZ0LhZCNVh92iz3KrB8MlLSbSV6DF1LdFGqPBWHBKQH+rbhsWw7CbeBGd
+Qxrb6fwK8hcpF6IhNTicNh2VJOU6qzU4BaUw/4+bAt+7Sff4T/QZbvsUXXlufoj9Pn2+1yYpt69q
+yLhq9bZe4NNcXXm3BTzH0YVDXeDVJi3RJjsM19CBxgAqr9gmrA1yCDbfjJ84oI0XDOQZOab9ihyR
+iZxBNNg0WFbYZgKfU7PAz3VII7VrO/jcYxq7M5c0sO3VDL83IY1vGadPAlheBTCMJ1qstqdCwSUu
+fjPHE+SsS0yCPuopSfwfCB3J75KmMDQIP3T0cgzXprlx5wAERdVmlorFY9JuJpMQ0yEnNWlcEGd6
+MI7ovlleYZWxGMdyiYpRw4/KHKvWv4tU9EKBpTu4ulMBvEihFdgf0gs3mypUDu8E6QTKmDy5t67T
+gmjfPUXvJi3ccH4TtZ/9CgCgiI+hc9ViXJhIm/R99QFt4mU1gcfoQNFLdTQmZPeZfg2SbefgUgKP
+6S1LlRbHyZ8OcOsN8l7PFksEc1oqci3DiaDrhqzhT7mc+3JOG3bkJbjG/IHFyRDDKVUMKls7mDNu
+5GRGz436yhMwEWP2CPvMGdkZVlCYFal2aEBwgpNgZCrnkou2QotcbRbo+3QwbXYkMsYEbHk58iM6
+ZGSXf1eMcDalZ/SyHCdt3myzMxS3SH3O07soNqimINepn3OhQzIfgdww8Ysmzdn3Jrfwy5wJedi1
+8KUHtNTCOkdDMh1yn+Zxt4jwnbfpbbu2j12VNtleTUPIPn+dUbj61gIe+wbe0gK8yhmrw0YddQZS
+XZ72kTKmZ75J9TPpcoqjPk2kGbmIPM421BXc4ZLxnp9j/AKN7S7XpQxQ4mf3aRMHD/YR5lyJGFZg
+VxG2zXPULAVLxn5M0xoWcxIASaXjjk5r54oG7At69h5HgCZnZzgpee1tbSA95CS5aIp9DhwMIbHJ
+mJ9TTqJbtVXMOTPVzw0cZjSGZ0WFAUfumpXEzfCCiv6iXbSL9gnaM6EpSEiUVY7ozEAgqUuFr0Ox
+SV1SZgI3j1ky2zrcManEL3OacKN3DZM6qbANDahH5BLSO+8x/Gr5PgxGvr17RqrhTJHQZiShr3Ui
+mA2mjnfaqEYkSZ2JiplPp+6cc9aXp/834tF3AACDWYKKUZJ/OIlxmSnVZ7UcW5I0hDCls/ey2kTh
+0Kl9FrbhCTrZ16YLWCxtkpAJYIbHcAxSAe8fq9C7rDKGp3iZU7cn2ITrUqVdtiTVeSP8VBUhAAAg
+AElEQVR1YFwlkyjTNiHE7wMA8sQECxgciRxuxeAqR4xlmKYYewxMspzA5lqMyOkCXdI2Zh0HLU78
+Qo895Eug6L0NAJimTdybkPQ/tiT6h2RibcwFBgrjPzK5ia4tsdum8XwncfHzE+rHt8opUq52fDSV
+cBm0JjdII2pOKxwF9FmYAN84Io3lOddH36f+twIdBww6s322T783cwQxfTYbPUJR0Bjd67vINlgT
+GvTR4ESsRCdndsv0UGzS2O5ULk44KnHtUMG7bDaCWcG+H9pYURlnwlzCYai4e8sYOge2vlvkuMQ1
+EdOc5norN3DGhDJbpxOUjOG4n2UIj8nZrDgN/GH/HEuetc3ZFD12yh4XJnKG0V/EKWLGdlwCqH9M
+0X+hKVy0i3bRPtCeCU1BhYKmZmFnDTgIOayiO9jZoNRm03IgayTmXs1UFL9GVYJXx+REWlyeYyMk
+e1G3NyENyieQWQRXJWcOeh0oj+hkX/tZshd78xEKlzgSlGWCkisfbSXBoEEOo3h8DMRkJ3dAv3/9
+D49xOKf+aCJDzCAxhl5g2yZN4dKWjlevURgtW6XCIOdEw1mPTvAreo6KoeBq3RpCRkm+0ibJcOrs
+QR8y1sNmgZLzDez2FqoJiZ3Wuoo6c1BGNXasKSpKj8lpxBK1NvWhmesYtzmsawbQXO4T5w0Ia4lV
+tmvnn95Dk/EkYrlE26Xv6EoTkKQ1KJIp5pwzhKyldSoL4Qpl4F3N5xglnNexOIPmM7QepyuXlcAe
+06fVrB0cvkhaRfvN+9jrcBZiDMQc1lzRqD/1WoVWk9N8TQtGnbP/LBNFkyTzCuaQXNxWNbgiMTfR
+8Ek7StW9Jxmbz2+1kTFKcupuI+e8hppL75fmPdSahJS02z3DD1VOsR/cwsN/+Q0AwBY7qFfMFAaj
+e62qPYzYL/WKaiG12Ddi11HjDEmVgYC3fBWlRestbSlIA/rcXAqgTetCdVX8SpO+c6/PFaCWg4D9
+CJ0sw2ucFhoVQMq+GFO8V2z2tO2ZOBQURUI3cjzo20iYpWlFV/A8O7vq2z+DhJ0zLjrQNwly3OzS
+Iu/NFRTKbQCAGCuQnTfovo+fR15SHNdwfRgN2jjaGS0UGa5CbVFZnNlSYTEuYxjoaI5IXRd5Bxqr
++UuVHGoHjddRZVzhV5ZPkljCZYkz1r1enjfRCIlExm9wYpXTxKUFmUFVkQAmLTzdyaEnXNar0UbK
+rRp0zjcIJhMYnPzi1D00asy3eRRBYbahGpcWGjsGfMb1i7QaTOscIlygmtAzHDvFDkPW+a1d6o8U
+0LhEsGH2oDOMvJyPUZW0aQwJSIWTAc6YzPUkQ7tDy0jprEPv08YrYwNuk+6XFTbskMZL1eidnZaG
+kushu+1DyIrGSHn4Q/Q5h39/NsMq84W6DfruDbWLlJ2qbecKFv8ve28ac1uWngc9a8/Tmc833O+7
+8625qmd3t4f2kNjECUYYHLCCghiUKEgEIfEr+YEUIYHkICQUKQoRCIRCSBwbGegQ2zHYMXbHE93V
+Xd3V1TXd+ZvPfM6ep8WP9zm3q2y3u8oFyY30rT/11bnn7L322mt4h+d9nj1iKCwX6lzKuZ1uDy2z
+AF5LIp7TBvspKybDEbpEJM0eZ+gSL9IZHmDTkTllJPK70OmisyvXOnvuGr4vkOeI738WP/uL/ycA
+4FEtzzRLgOcp5OL2YrzYkQ351C5wSAKyx4djDFnO7moK0JoOiDCHXZjwWecyrFrc5UF1q7qCd1mP
+EhJEt84tRJX8cFbGOOAcWOsW5pbuXQMR5+QHpaS/dB8u22W7bO9rT4WlACgYcDE40EgYRNobOXDI
+luJ2ZwiefREA4JQmMJSddHvqNKWH9k35rD2dQvmE87o5vOBz8vnQhyI9mr3DSkUAlSWugd2aQiAL
+wDE3UCSNzQcO3HMWUqWy/Ubv2lC0XCyjQU1asX6k4PuEPO/7sD5HdOMu8/8JgGtifuqLN1FRpyCM
+TdRyIMA9EFPc8AE3EfMzUSZCRywFI+qhy3ExrzfIyVZd8eTzOzegGLT0DGDkymkd7plQHenPTfcQ
+DiG4IZF/ZhTBMOUEyqBhMnjW9nuo1+KuNPMMlkWrgBWJ3c7HYe+RHXvXR3tAZhXdw9ogpdlFg3It
+/U88EsCkGghuynVPL5A0VL/WAXZp3SS7JgYM7A1MFo+NMoRDEtA6S9j78q79dQQdiTuWxm/B1WR2
+TslNcM2GPhFIeO/FAA4tvVFdwSGZTTOwYK2Iot2QSDVSiA25xqC3g5yM2FU/R8iA4HVKEz7TN2Dy
+iN4LDFwZyrM+e/MaHPoMz1zZRSNxbhARD9QTDFt5pm5rYnqb43avh+duy/u7cpE9kbp7eCL/Pmpc
+nBeSktSnNY5I4htr/YSoxgawaj8covGp2BQarbHRBTADcMEIsatRkFOx2TyARwIQmF+A2YqkuM7J
+qjOdICFUVZtHsG2JL6iDAFUhk95wVjCpFmQT/KHjATxHQE9qEKGlIGy1AeJKqg+bxQQLAmjOG+o5
+dqdPdBmT9/hrs7jFmhmDVC8AS/xBI5O4hnJzZLHcb1V04FbCtWi1O8AhIa/0oZXewOYC8iuNkJkP
+y5zAC6SfjW3CZjm028ozB7cUDEKCLy48tA6zKJ0Aw1r6bx0eoGMIiMggkY3tLFBribX4+WO0inLo
+1gPEVICyulMomuA1/dt0sEHXlvfUIoLy6POsFLz2gYynWSB3pX95Iv9ueDlUIQu9HVcoc7pEVo2a
+0rT5wkBK6uaECy9eVrh+yO+O9pBXsihK4wznR+LaTNIWA1NM7T2WtRc3fJhkfm43UxQu2bScG1C5
+3K/MU/gsiWwdGfu4beAYBMOVQwRDeU/z0kJNv93MZI69Htt4uSf9zLsWorG8f/9KD5EjG1KcnWFm
+iRsaz2ReNCpE1MoBkDVXYRom302K1UOO7UZjYsmmFyxlLSz2WlynCOWDrEJBCHmrv01UU+MPVnN+
+t3bpPly2y3bZ3teeCkvBUgYGVoBh34K3JbzIC2QkJ2nrfdSM4FnVW8COnG7thlJkVQU8ll3X7N9E
+0ciubJcz6Jyy9IhQt2TMtRn0UjFg03R8FAO0IJrpIySGnBhZu4QTs6iolfuV5wo5i0xC9aSWCY4F
+DLgvO2EIY5Wyz/L7XLWYb+RUUqsEBslQMieETRISixaB5+snPA6WESPwZe83nADWliCks4ZdM7A3
+lGuptIVmHjtUCSyavk7egz2U/jiLKco9uTbV4FEHPlQpY9V4Q5SMnKdZhPhcTuBgOEJBwg6bGRC1
+WqJtxcTNahMOKz+tooDZkxO0fvcMPkk/ylYCrUmxh9phlWjio0NLL9AeRn0Z0EKbqInGswjh9fz2
+SVGWgw1ch7wH8Qo++xblFuwOMSfsQ7fWcKkq3pgpasKcHT+HEci7VkYHacMAayWWUJtsEPuCBXH6
+GxSpPGvXbzEyxCXapQba0KjhdSh33wvh+dI3z4hgkMZOqw7qFYWLSCjRZGuojszDSXkOKDKJx1MM
+yK+YOgF2aMka5AUJ6xDpVqujKLGgG1vj29wPBj6YLuZ721OxKbRKozBKnKy6yEkL3hnWCLSYqsnJ
+CexK6h309SkUo72aMOG0vY2CcuLKvgnXEux4M7+BPJS/w6iBIgmFauUFaKeE0cgGYfQM1PTF26kP
+N6bPXN+EdUipJ5nDWF6t4Z3LsK+0flIt19YKK05enBRo5oz3dil9Ot0AW580O0Pqy+blzGNULBlP
+fYkRDEwfVi3XsrIGRkRXwgBaknnaqYuKkGCHtQPGDqBZadrMDKS5LLZpotELZaF43QID1oFU9Ekd
+5aBSJFBJMqg1CW2VA80oeZ0DXWY2Mt43r9cISEhjnuQwxrIBth0LxkZ+p7p9VBvyVbLmIvQDrNas
+IvXnMKgEkHspFo1kic4vlnB5ba5bnF00sClw014rETDWEhQKCTfD3dBGl7Lz3UhiUVWzQcua7E40
+RL6UBaZaC5pZoMAcAKwTWKdyLc+2gFA20DSx4DEuo2sFUNinYFXuNE3RIdPTZ4saRSnP3IkTtEyT
+h3UGsI4jnctBMFAZKleeQ9UhTpaSqm0tGyFTuE5pwOSh5ZB3M29LdEnQWtZAj2P03mrOFhI7Az44
+Ce2l+3DZLttle197KiwFpQGzNdDZVfBJ+mGpFI+o19i/so+2kt0zShq0j7mT7sguWs7Poddi4hXL
+ObwflkBOe5FCL8S2KyoH/nM8xWbkX+yYqAnSaf016kc8EXWOmHTYZ3trXGMxUk323SuPB3hVSd9M
+owURqvA8A1MWPM1SD6+b8p0X3xFT1N8vsZpInzfLHJ250JM7L0VQMcEmBPyURoXeTbpMqw4smsFN
+ZcAgM3BbFTB5Mq1zmtebHAZz5Vbto54xCt+zUBji/vTdFpkl1oTBgGldmDBtsQ7yU42yT3KSR6fI
+15RSmxjIPsWMykP53fnDJayFjJv9yhWglndj2YdoyK5cWBPkDCTmZDierFZIWe3Ym5pIU1ZtNj46
+ZHm+uedhciGWzpLnV9SWSLbU/o2Jihbbutx8WwTIKlFl0r/sUAhgzMUYJtmM60MFI6C1WGo0BEa1
+hUJiECpcyPhMrBgZeSb03hDR+VYX1EJECsDbe8RQKBvTnFWuDXBQyhjGiULQF/zDJvYRz5hV2kgf
+tbHGXXIlYOcEJ6fMYKQzuAHBV4cRGmqdNntyXW+q8DDdChE1WOMPbx+Wpv6p2BRqrbGoCyw3OYpj
+mXSrALjzrLyYdDHBgD71tH0RnQMBqfgJo+LXHZSLmwCAYP9duExVGs/eQaNIpOo5MLtiKlf0w/NY
+ofHlBVlnFYoB1aTOW9SVuCNhWWO+LxN6wajvD39ug3/yiyyHbltshz1NW8zJuPH1zT185kRe4nQk
+9+ueX8fckkk+r3y09L+vxxH0SzJJXfqsTj+DYzDLEFrIKrpMuoG/ksg6wj7KjKXmPmMLOkJF+vbp
+wxjvHHFRZLvYZabhFBGuXifvYswqS/8R5oWgHNPoIcJjMvdcaZBTdzIp7iL9sjyTUVCj0rQRUqC1
+W0/Q2rLAkngJHcmErScaxa78XVKj03M1WpZIV+M55poIxLbEppGN/GhdoCDBi0EQ0mms0OWGlpc2
+Ci4FvVDIWAfQNxTa5msAgPv/Gx1++wESpoC/R7+E7lDeb7t/iKKVvhVmgpSxkjX1HFfrBibBa815
+hOgW+SEzDwlTw8cLEs7kLXybYLKdAuuU2arxBcpCwFmr6tET3vp5LdcNSx/P85D5zbfHOItlTt41
+K3w+kzm+ZzbYCei/ssw+iwoMOBXyVj9xEz5q+6hS9P+JUuqbSqnXlVJ/XynlKaVuKaV+Vyn1rlLq
+H1AT4rJdtsv2L0j7KKrThwD+YwAvaa0zpdTPAfhzAP5lAP+11vpnlVJ/G8BfAPDf/FHXMluFKLER
+jlPEazmNX9ltMSW6Y+/TfcwIk+2/VSD72BX2gYExGOgfyknpvfzTsHiK6UMLBlWmoJ5BtYW85kQK
+FV9FNmHeOZ8hOBEc/Wx1jouamYjsCN5j2dcc4uhXrw6xCSVSb1QaW+EdZQAtaeHmiYXOhOzBsVxX
+vRTApIz4c7sJdg5EidjoOLBr+XyLsej6B6gdWjlI4ZEZOi4bzFfyHGat0VTy/YzKWjDfRXcuNSPa
+9lDfZ948OsVFJa/7xvgQa46tUgSAnaeoKZW02eujCCVc5ZUGhpY84JXbX0BLLgvFkPvtwQlMU6yb
+qmuhPZdzxuyeoTgVy2RdTNHQ9apo/SSxwiYUM9ia9zCOqMbdhBj15V1HpUK2kWd9OJfx8cIc8428
+D3OeoiR3xDJYIdxQAStp0duReoVv2II3+cTJHiaflixK69ZYkLSnszLhVXIyl3qI03ib0aLr1jSo
+I7GElt01gkYsusOrPXRYK3J1LHO22QD7BCkVG4XNDVoxFyYiUqVt/Ajf8Ah1n8o7mHc0XvIFun/t
+zgzf+6rM72/tvw1NK6R/x0VxIc931ZQ5ff9xi9+m4nd+pJF/BP3I97aPGmi0APhKKQtAAOAUwJ+E
+6EoCIkX/r33Ee1y2y3bZ/hm2j6IleayU+q8APAKQAfgVAF8BsNRabwFVRwAO/7DfK6X+EoC/BACB
+peAYS8wevoyzpZxQrRfh+5h71682cG4J44859NDGLFD6mnzXqXpoOyyGKX4FqhbK5PZrK7TDB/K7
+sQ+TCtQN/cYi78EvZOcf7IyQ1VvefAf+hQxNWXwa4Uuyo1cs1PnNly6Q/2N5xPI9SeC6aZBznz2e
+z/DqN+UU+/gdOc2d3/WhCjmVljsdeMw7Xzk2kNFScK8wRqBamB1Ctzce4Ek/+7aLckcsnSodICvl
+eppakrnZR0bdzezMxcVKTkp8bYx5Kyfe9wSP8fFrYr04LROq3TEK+sPqYYzFmiSvKwvDbX7feR37
+FKKZZvJM3qIHk5J1zeM18glh0LGDiIX8VfgikpVge60l03QYoJkLGtPZuYvVOVOkvQQFYxv32wkG
+HHNjm4bMA9SkP3NLH4r0R3uTPjbEtTx3YwSXnBI/dSy/f8MAPsmYStZ7Ee7p2wCA+XqAcGdbdNXD
+PnU/Hz4ifwMazBiU7FxchTOWz/MHFgJiD5a5TPF1EcPJ5b43lUKPQdBg5COoxarQGhjF0ue3GUQN
+4hJvHJKx61ULv5zK+xsuNApiat65F2NYiKUTVdQnaWt0aOnqpnySGt8uvj9u+yjuwwDATwK4BWAJ
+4OcB/OkP+vv3StGHrqXvNga+0DlCUJLCKp+iWspAlR+7gh6hvb3+CHFPJlPSk8V9MlK4+cvy3fXo
+IVxNeLTXwP0/CBT5VI7KJnw2E+5A73kbjSOQ4NrdQJsywFN/jQvi1pW/gEf+vIqTzvxGF6XOt0/y
+7WcCULKMemEAr2WyUV0jRdtOUyI/FNM3+eoRgtsUo00c1BHNePIoNrMJ/BWDj8Nr8Amf1Y2Hmrn+
+wi+wYtHEhuXNSQMEpLLPkgYzBqVGYYZnGKC7uRlDrcRVaA8kj+8ObPgrucfc9DC/y0Dc9SEGDwko
++8Qr0FMZw94NqeHwfmONeFf+ffFwAzNkCfe1AKVHEzyNYQxlnM4pTJK0Z5iyvmRn2qBqZMJ7wS76
+pEp73vUxoyaiGspnkzZDn2XR1dCEx03hXpWgd1MWy8Iqsf+jsnk1r8q8eeHH52j/sVyjP6zxlQnZ
+lcMK7YhgsJ4Nm4pa/Tvy2SKugauyoKu4xIYuwyDIkREjMSThTM9osHBloWcbAznn2ydahc5tUgHa
+BdzHlCZ4UZZfEDfo/YS4fG8vJ+gRl/zrrwF/8iW53q8/MvEjA5kjD0h0M+o02CwIkLNacO+Gwkdz
+IT6K+/BjAO5rrSda0C2/AOAHAPTpTgDAVQDHH+Eel+2yXbZ/xu2jpCQfAfhepVQAcR9+FMCXAfwT
+AP8GgJ/FB5ai11gmJX7+1SlOtjK/qsJoT8yoPz+b4dpPS5DFHe8goEbj+mUJ5F39agPjxyiAcvuT
+aFPhVvCC70XR/LY86K3PQDUP5PMbhNFOTLRdCZxlxxWmLOwp4GCXclyG04NDVNmMgi1vOGt8t714
+XrfAVLbuX/oKC632ZxibYqX4t3dg20JsGu5vEPaYitxqTAxdGOTw8q9nKFesWoymKB/LaZZ5KXIm
+dwKLfTcMpCyoeVxkuBdTdfthgjdbCQ6epjN8bCR9eyUVrERwuIM6FNP3uSxHa94EAOg3lqhelPHe
+tVssCZrtz+S7s+vnsBvpe9tZwB8xpdc4cHJ57uPzBVb8++F94h82a7ihnElvjnxYJM31PAdrBiVP
+6x0s6tc5nnJ8HlQNYnJIqKbFVYqehLUL6w25x9HLFu68KyfsW1P57630KuKrFBGqFTYrGbdhZ4Vz
+YkT6usXDR2IhLQs52Us0sFz5brdXoVWSWnzQ5LBNGee3T6Rvjyff1pkY+C6eoaXbednDeEFCGe3C
+zmiZvEEo+dUG1/4f+eyd9HVkpbjKV9x38TsT+bzTJvi1tVhZfaaIv9oALVGjx7WCRVRo+R1Eaz6o
+9fBRYgq/q5T6XwC8CnFjvgpxB/4RgJ9VSv3n/Oy//27XcrTG9arFO2kJ6q1gqTWsc5nQ95WB3fuy
+SF82LzC+vpX1pgKTrTG9kEEfvPsaYipLOclrOGe12+F6iuIqGYQSmYDL1QUCIpE31duwY+bpV8fo
+sgzXt89Q11I96JjiT06Pmj+gFvT7mwKwJBZdZbJgFzMDN5RMzGTQxeFKFqTtBfC5YLW3Zh8PYTAa
+U689FAWFcpcF8lZMbSPZRUwW6LEnk9VcPQA8WaQ1SjzPEuLztICr5BpfX9jof0MW53Mkb9ksK9iN
+TFK76+AqszZ1FOH4nrhB1e8usH6OtQGvSX8fWykO+4wNjE6ByQ3e710MyRxyXt2FeiTv6s25bPT7
+hoekkPH0sitYmfIibsCBacv9uhsTpyV9e4N1BF4Fn2XfVp5AM04wqtewXbpSj4+waX4LANDZVi0u
+z7foYtjeCM97BEvVESZnZGs2E8QlMxQLcSsfGhVeGsjz9ZM1NDVE577GTYcZH/JdDjUQt8S6bEys
+uzIPTydr1AXjWVcHaKkwe4dCPL91d4ODk98EABytNriuZWP5atHiFkEN66rETUsOjHe4EXQNhVOW
+RUcK4P4IH98mVDHeo2/5QdtHlaL/awD+2u/7+B6Az32U6162y3bZ/vk1pT+kpNT/H21vd6j/3J/9
+MdQJoBlobBoNh/TDy0phK/3rhxo+1fsUCT/yQsMn6UdTK5iGnI6bzQYWEyHxYg0ikHGRUL4cBkxW
+Q1rahgqIRxh18fwNyXy442vY35FiLMeXe6zqDRoyNJu6xnpCC+L+b+Fkzlr3yTkcY6tZQHyECRCG
+gVHHQskqucgBpqwG7DL4Fqc1yDqGs6SAR5KRrGkQEbuWGBo2iT48knuYjokeVbejwxDrXxLuif+1
++A7mI+/hGQa+8Irk9j9++4fhvCjPH+UVHBYalTsdOHOxtt69K2a9Xs+w2ZN3dv+1CEXOIrXGxLM/
+KGbw6VkXQSR9+tf/HXE7/tXvuY5R8FcAAD1rDmWKZfY3/tZ/BsVgntI50jndojPJohzNMsTnksmo
+tAYow9fARMxis35oQW+Jjzk+aVWh78jYrrMGLV2sZZ6ixxM41xoOXQKD/7UcCw05LkxHwSZaNhoH
+6JJDFLn0t8wvoEs52b2yxoaunYZCh5wcvg6gqRBeE7PiDzQUYeC1YSNPWQaqc5RLVubWwGaLPuWc
+TpMSii+wrGooFqkZukZL7hBtAP2RWGn/86+98RWttURg/4j2VMCclVawKhdWlGNBUUGFFYqJvIxN
+p0GYcdEH7hPMOYZUD2oVwlBejGn4qBoxnnzLRluIaWdnHVxombyulpdVIYGfyotZdXO4RH8Y2QLZ
+hMQbI6BDMze3BFQCSz8h4ZgcpUiXkt56dDfFRUshj0UBQuqxV8k9OnsmuhRa3e8HMEzyCJoNHIrg
+9BXNT9vAmqZo1CoYYDVnbKLySUOeKdiUNa9YXer6LZJSFkq+6uBLhNR+x62f/xAqhXsUU3llrDEc
+b6XcS4wIH66bEZasj7hCRqejxII6kjF+/V6BMd2cx7WJ9lygxnczGz0atL/7yz8BAPjru9/ExP43
+AQB/8fRF/L2/K27Ff2mFTxi1NtMKeSE+/sWJXPc0PUW6IHzaKrFP0FYdVOhuD7i8wSCS+bAF/tuG
+BZOakEHgoKICWN/0npQ1h5WByt7WucgC81WOguPiGhot/fbFxsNVxTEakSdxGsIEF3QCONSP7DUO
+OiPpT1gOoMYhh561Hx2FJqZ+pFUiXrIqt7pAWosbk7kJbMKqVSPzOCoNrDy6c7EBkzthW5moWcPh
+tgrL9FKK/rJdtsv2EdpTYSm0bYu0TGBnBZpzAotMjTKRHW6wzlFGcqqO8xBRT47gqwWr8Do2hh05
+xeMygS5ZbmGtheoMwD1jhn0+blyRw7C2MKnkxBjNG5Sky67aEJtCduBb/hS3PiOBJuXLbv9os4BJ
+yLQ/v4/zB7Jzn00mMGI5KeK6RsRAW0KY8KeUh4LUus+MrmFDmMkVW2NAjkLXkb7fVQuM2V+zaJ5Q
+qr9VlOgwYNbGJYxga4rKMx22QI8ne9YtsfxgrwCzusEuhVXu2Rk+TdO2uLODPXKEv4k59icSwPu1
+Uwk0Dh7/Br642cJ8F3hIUhCFGkwAAMi/3Y/yiwAAwe2I+f3fAcBPsLL1b/6nSGO6K7MzTB7L2J5O
+iGOYrXGUs0pQA8c0mUdr4Ji8mTdUjbt0zcbkNJj6Ee4wEDexWoxY2npRK1ynUMsDBTzLsTXpMji2
+AxpvuGYa0PRBN3aBtCEnQ0wymXmDjGJAujbwPLMP9U4fVwOZQ223gzHp4ytyUQbRPmJmvlRRYkqi
+FiMN8Yiw9zuNiTNmXTxWxN43Y+xzrs+SNUq6v2Wh0CeNXV4WCJ8oYH6w9lRsCkLc6sCs5jBIEGJl
+GVpXXnLZVlAmpebDLkZXCfrIaH6PTPQcyT7UBz3kb8pE8kPAdeXFdUwbKdnJb7MGoG0VyjVjCkrD
+3drSukRMGfm0OcO3Hsl9dinPnuUd6EqYnh4vz3C6EB830+UTXkU31/A8ufZuT37X7Zood2VCDK6t
+0d5ixuCdGZqRmI+BTfrvtg/zQszyAz9EoZiVOFcIthsSDKS2/L3EtjS5xDlNY6f+TsW0f7Apz8CA
+/fyR7/8RlGsZw7620bPl2p++8TweMPbxQ0vJnKyLO3iOxK6vzddPyFJajffkwj5I3Eq+02xcYE2u
+xfn5Ex7DiotxYWWI6AZoU2Pfko260A1ucIOojBZ7jLGsGNcZqgYV+TP3bRMbplbv2CZc+vjdqkUT
+MprPg8N1KywpSLxxEuSkVK/qGEOqmW0ZnaIoR7Hegq1C1B6Fa/dydPs0yg934c7oBtC98NoVkgER
+j49qhDwYLGeGcYc8pKc5ugHvR4DcrmXBKqjboQzUllx3UlmoeMDB0tjC7D5ou5WyhkoAACAASURB
+VHQfLttlu2zva0+JpaDR6gKrykVRyI7ZM2qYNQONmQNNXT47q9GlkrRJzUEvMVB64lL0lwFmpuzQ
+Xt7AyMXMHWUxQlsi3BMtQS3VBuix3qFsDVwUcgoGjsY+ocL37tZ4pS9w4/JCTLnabfDwsZjaj89O
+4RJq6mYFXJqtG90gquVU2SdduqoGGFjkaOyOcWMulsKZZWFIbL9hSH8P1hnOyDnoWBPYhCuPyzXO
+WMGXNAls7us+Kd7Pzw3cZCYmTT44Ct5VJn7i4M8AAF6JE7SV1Eb4uQWTlZ/WxsTzvmRiejekynBt
+b/DuIwk+Xmsu8CVCeL0qxRRbiy7Hqt2aDczE/IEeMIoer3FxIdc7n0zQJ+gpIWFJPzNxn4CDIdQT
+nsOxHWC5/dx2MGdw0GdQuipaWC45MNBiSNxDmmdPXDarKpCspWcVVZs7rY9dPn+6sGFbrEpsgYJ9
+Wxfy+3jjoefI/Q7qPoxG7jeu9mESTn/YBMh5vx4DyWXdYJeVu7Gt4Grpe5EojKlyXUQ2zIVA9itW
+DDtFg3VKIJRu0NK6OdAFTkjvV7QrVNWHo1m5tBQu22W7bO9rT4mloGDBgdeZolrwJHEz5KWcqoUT
+w6mpoeA3KE3xs6Id+nf2IW4QPbbUPvoQHzezLPT2xN+Pz22kSzmBuhQvUcYplmRPLrGAwxMdZo0Z
+YWBVXeDePdKDkaIMeQ2PGo5N6qAyWYDUAiZjCjuVjaErO7RHSi3zag1v8AwA4M6ehYWSZwqjDTaZ
+WA2HI0Hz5ZNddKngXHdvY7crNfgzs490Ljl7d96DIhQ6b2V/r8MSj6lP0ZrfnfHfYUDqYz/wQ+j9
+KRnXxN/H7jWT11XwqeWQRj6CgTx3ZyWYhlufvwm1kdPRX76NF78ucYx1+es4PhHZvGX2Rbz5JRnP
+tCvp2/wkRG1zPK3vh9GVezdtC9clmKNuAOozmIQ5136FLi0w36yxQz/bCAr0qBRtWhl6DU9esiY5
+TQDHlTliwYXBIrVht4sgkDGMzwLUijiZkvnkLnBMiLltJQAtk2VV4RqxKv0RA4Cqxl6HVqG2EVyV
+f2+dHrqscoodGz5xNCtH0ro7UYZVxmK70EKaC0Zk90aJ+8fEupgJSup4dlk9u1iHsIdi9SZxCIt0
+e9PGhib6VqUGkuaDW4wyPk9Ba9sa63gBr2ngUtCiNiOYBCyNEEDbLA3WPVTMBdczEmXcmWNtibnr
+qRXKRibYnvsYiZZqvp3OBFYqcOXVgEKj6+sYd2SjaJc9NLxH6lrgmMK1bPgM/pV0ExbGKfSSue14
+hSznomgyWDk3KmhUpkysBUVPBgsf1SHz7dXH0LPFVYrUAXyXE3MtC2lQzpGWEp0fjRQ6jTzHp3Yu
+UHPCn+YPkJNmvKJQrF5rVAw4ecv6O+Le+TN8aihR/1d2azycsQYgfBfGXFym4add5Iyod7stQlLt
+23syhslyiOduyuIu7+7j0x8Tt+rtxedxfSCD+OCtT2D0sgQMf6++KeP2yT6KZ+WZ8p3nMH70UwCA
+Ovnb8AqZxAfpGjVdxFDJ+JSlAxfkWoSLmpuhnwagFQ8nikDvAV3OhaIETNLBO41C5JM+37MQUhlr
+GG2wTORZtzgU37G2+wBUq3BWUJyl1SgzmTtYSx+jykYbyELfMwM4PLwGUYbSk+xYZGbQZAqPfFnQ
+y2QI02FgMwVUR8aqSPvoheRzXAfodB8AAJqVvLPdgzlmD+VvRy2BjtzbTko0dCXul+fQl+7DZbts
+l+2jtKfCUtBaUlhdL0XN6r06yzHaIeNu0aIdyc63G9qIuvJ5aIobUDU1OrQe1tCwOqTEyqInp/xc
+eYg8ErdSJiwcxdhSU4z8Cj3SnF2ghZZboHJbpCTx3GWQKcs1XE/SZv0dA5tjsSD6gcKYmITKVIgi
+ud6Qbk40cFARMhyYKRKandZJAcMgYpOCLNkA6LdykgRWH92rRNJ1Gtz2pKCm75o4XpM+jIHGqqyg
+aF4XTvs+zv/ty24UcOcFsaz+7c+QIORgjBWLscLjm6g+QbzFxMP1jpw0xixHMSAnBbOdvrNCuiCV
+3MCADTmN96sjbEhg03nmDMuBWGl7vyMBsCb9UXzu+34cAPB/3Qzwgzflu/Vfb9FlpeVmx8bmSCwg
+j/Dv0Gjg2/JUqdmgQ9xHYAAwmLLrAMU2XUh+hKq2Ebnb9+dgZ0yL1ArQj8Ty2MCAH7GKkwhRO2wR
+Ew8zSTP0SPAyaWsY1PgY72wrrQzs0N2xertwSGhbBC6ChjwSlQXDJGHMnD8zl8iKLXS/RBXTJWqX
+KBhU9pwLbBJaOo5YKJmq0dsXa6yY1NAUUrIdYO7IC+p4BhbqX0D3AQBUW2Mxt7FayMvaCV34JKnQ
+RgCDAxL1Bui1VNBhlVpkhyj5JD21g3Uti195Capcor47tsKpT1GW+psAgHjlwloRmZIPcV7J4u56
+BiqKzZppA3MjLyYjGMVsF4incsM3HyfoEmo70iYiCtVUloVhRFOyEl+3rSyMDOlPEri4pm8BAKbW
+DJUhk7EuOQniEg9K8eV7ukZG+LcZO8hSIUY5qr+BppBrI5W+F7kLhxQ8TaXAqm8ENVBx4/lcZOFO
+IP3QkNjAneN9vE6SmejFDoqp9GdUGMhHMhZ7t3eQZRRU2ci1LmYudnfl32duhJ0LueG7bxrYJ1z5
+/moEvM6y7LFsRvrlT2J/LBvBn7c89N6W78ZNjnzOOE/aYshsjcfNNFmNkBFKbmsHNeW5YtuA9mgm
+VzYcumYlsw91s0aylr71vQYXhcyFkZlgsxb3oVPmOE/knW1YnRnlPq5xE2oaGxtWqDaVQmpJnOfx
+CctZJx68z0gfnu/tIuV49mofSSkbRKhrbEgc07LisjBrDDxS++scNUFtxQZw+ayb/AoqJbGkZCa/
+8zYay7U8x6Y4gjenG6tzIJYDc5Me4UPuCZfuw2W7bJft/e2psBQMpeA6NjqegiIewe82yGhyBYc+
+HFuCilFkwDyUUydiPXpr7KJLiGuia5ik/grbATAQM2pV9DEIxBwvKtldk+I+5oRMa/8MLq/R2Cly
+BomSusCQqLlciWmYFSZMIiwHoQ2PVWuVNlDtyak5tjzY1D0wh9Lf7p6LsC+nyi1/ByUlyvx9R5B8
+AEahnAxn6xH2aSYmZQizy4pRYwSnlEBUP9vHshDl6prAN98tMG5IK+c2GLIC0DSA3QM5A654Y9z8
++L8kz3ogp9ZkMcB5/BYA4K7excdCBkzdGxjeIeFIUMEkHiTVUgG5e3WOU8h4dswc37oQ4pjqxgz/
+iIVLM7VGtivuQ/IJ6WdnlEBduQkAePtI4T98kdgKGDCof6YtH5qs0mdrUrv1U6iSepZZgahHjY9e
+C48Yj72eRuqIBaVqWmDZGCX5ImpjgGukr1uZPVi00pamD0AsAYeUb3YnxymtVKO7wYjFdHOdwCR1
+3m4kc8zfnWIAscDmqoVJOrpN0INjbbMWDjSDyi0Rub2Bi5zZLt0FWoq+WKM1psuIf8+xOedcdWQe
+n6kGRSDYmSTzYZKy8FHjoYjI8dDamBFl+UHbU7EpQGuYZY3A8WHR3PM8EweRdM8xBggZU9DjQziW
+mHZ6LStBPZPgnGSYoZ3BOadf2K2gK5kow26GkmZixezDvNnDNUaQy/kAbUiQiuHAPxUXpFUONl3a
+X4S7eukUAWsRTlMHGXUCD0xgRDGUIIqgb8q9TZZO24kF+1kqSEUGdgfUCTyy4Y1JAlrIotqpzzGn
+6+OGBVJIpP75joF4LcS0xe43UdRCatK5R05F7eCIuQY3MeBTPzGsLFwpBH8/+N49uHuyARSJ/P5R
+8RVMvyUbz6R6hFksWZCLfzfDZCYbwEs9H+EphW+GMoYPTvoY7HxVxvvRGOmxsF796jePcbuWsT37
+5hILj+/qTCa5apaovyK+/Bd+IMCvkgzmVhnDIXVQvyyxoesVEuadLA00rBaEa6Ox5XeddQTvpnw3
+2HWexB2KExnjpZfDrGQ8bauAriRqb/VrTM4IC19XsM9lc75wydG58dEjLL6q+rhLLklVGJheSLr7
+3YQuTnkTGzm78NyVC3Q4L9xohQ2/YxpnqMku1pDwdlVpjEfy78ZJC6sri35RdtAN5ABITiOYJjM7
+RzI399MC6Tn1T7slXEfG9goMxGSvequ4QFt8ODH6S/fhsl22y/a+9lRYCqZlorPTxwgr5D0xqSJn
+isqQ3K6BEpM1SSPKEzih7PhrAmmixxXsjCbeno81yTZM04GTbgubQkxa2dmXMzkN1uljdEnoMd9d
+oUuBFLNMEbL+fa00Oh3y+bEOfllv0B/Kyd7TNkoyp3jBEHs7MqQXGaBY5dkqOQVmBw4UTcdBbKKg
+ynWOEA3r95ulnAIrM8DEktO/SUy4EIhrrA5RXhOTOnjHwsAQ87G6xorRlcYui3Y2gY29iTyHG2js
+MbNz4FkYOcK1cT/+PenbN128yVrG6DdL/MOf/BUAwO3//SU8cyhh8ivLWzjZlTGYfpNM2m+8BvOR
+FEf9wp0zVL8o7/TLy/uYNXK/u9EKwTml18h9mFgTHH1eXIovP9zBX35OrKpEe/BdeaaFZ2K+EuvF
+YN7dsDMEPLlTs0GfyuTRYQ2HVpFR2bBtcdkaR7I5dt1BRuGcTd3BqckT/2GAOZW5e7mDeFeeqzuX
+MbStCprw+KJd4QVK3X29LLFfMIrryXveHz7AvikgNOVdwaalluSFwjyR+5lpjnko49xbyMne1y0q
+ch0XbYJsQRdtfoa4krl3Xt5DSTnpeC0Ww2liQO3JtcyND5NQedPqo6E84Q3XwWvehyuJeio2BWUq
+OF0DWXkdJYnmtGHjqkGseuqhzxJh9PowbJlA3TfEtMqjHSjyNiq9g31et6gKpB61D3ULJxVEWFML
+O5BT91EQ0Vcc+1huZFDtrgdri9vXEYKumO6GIyZnOTrB6UzMs8eLBK6WhfJy1cEqFvtxv+NA+wSs
+sAy3eCdGPpNJdfLZC7xUycR1Wxsl2UAKYueT+RQXD1njkJ/DpTnbvXqA9dsymfL1EUr6tW0mE9Cv
+DhGxtrpwLJx1ZNJcayycnsu9D5sCxtsPAACmov9qXODzFCsN+l288KvvSN8np8h/gqXB48/jSo9I
+uYfy2TdwF+9a3DT/7yO881A2oY41R2FKpiE6bZCXz0v/PBkfb/fzqH9eNmnvCybOVvJMDkqsNvJ+
+y3KBPmMDbUNwVjXERlODUfloWqpQPbbQu0IGpUMT+578rfrijpnlMcpA3t9mYSBnXCKulhj0pE9u
+bx/OhDyPrSy8Xu1i5EsflvoQiSeMU73Ex4NQ5sudTO71VrYP4yZkDI/maCgNgGEOzZRxU1RovyGr
++9zgAfBSjp4t3w2KADW1PDaNicVEMhz1wsCMJLwjXw5FFXqoj5llydaoWFEZ1RbaSmpUZs63EFC6
+/tvMjX90u3QfLttlu2zva0+FpWAZFkb+PobDAmvu8E4zxYwKO2W/QFHIzr7IJ7h9LDvlSSg77eG8
+RMUAX3etEO+TG2+eoy4JwlmZWIRyGlszni76GDbD9stOBYsYgdLYQPEENXYSXDHFeulGW3NxH4p6
+fj2zh9UDUnCNLLSMnJfwcerLLn9lRZGS3Rj7rQTfrFWLdS5mt78/QrqmiEjMLMOmgQrlu0FpIyPe
+fb6Zw75F+Pf9A1QEOy1pUu9fW2Ncyqljewbu3pWxSBvAHYj5fPraAMHz8vlxQUWusxV+LxPL61A7
+uKfFJXq2fQzvS/KsjwwH665UUp7MJevx1VffwfJdCQJ+q4wRxOLmnFcxtC/XmLcNPPNVAEBCsZRk
+M0dafkHG+H96C7/9F+Wd/mit4PC9mrYF5YoFuKHgchsk6JPeLtMZap5+8aBF1LKqVCdYr+S0dboy
+L3LDQJbKeDZOiIYu4QvuAdY96dNeVmO6K9feOxM3wLJmQtcPIBxN4TFQelEkGA/EEvTIvTHu5nBj
+4lDGFkqLAerHJd7ifAlOa0x7pFO7IGXaoykmtCQ6BwdYzuS7k/MjrIiNmQc5rtG6uVDizo2hcLov
+995djOFFMkdmuYPdZyVY6ZzYOMnfI2P2AdqlpXDZLttle1/7rpaCUup/APCvALjQWr/Cz4YA/gGA
+mwAeAPhprfVCKaUA/A2I8nQK4N/TWr/63bvRAm2MvLBQLGWfurDGiBT1DRYt5jPZ+WcxEE/kdNhh
+0Up2MEDkUubLLLDDuESmYpyQjNVL5vBT2WnvXbDAyepCrSRWcUf3MGfgT3kh6i17URKg/Zj4xi0R
+kdaDBwhYcfP6PIIiSrHVEZ5hgd+qWAnpPoDzczmhryw8dCkxFi9DUHYQ/nQDN5ATMae2Y5jM4bPw
+6Y3TOcqVPMfaq/ByKs+99GsEQwkYKmo19osRyq7cN0CDdUuYcGlgeSI3fHBwjnj56wCAybk858km
+gSY57nGVYceivoHr4zAU5OX99gAfz5he3aEl5USYOmIdBFWMM/ImOIaBVSXP5LcWUs2A2KmkQuts
+ATyS9GX7wn+AB2/LM+XZCgMyKCEpkFUyBi51N43EwCwj90Ro4ojp3oPaRki9D2V0kTLIOTplANfN
+sV5RaGcSY7wrlsBbp2s01LdcFiluD6hYziBiOHoGFk/aJh/gTH1Z/tYOrJ5857AldsF4GdmYzEtW
+Apuap6v1GjmDmcnJGrtb5i9qd7Z5H4tG4j39xRyGK5ZEqYBqJdamsw7xm8fUpCjFMnObDT7OCk+z
+Z+FgJDETY2XBJQr3XryE/pApyQ/iPvyPAP4mgL/zns/+KoBf1Vr/jFLqr/L//wqAPwMh3nsWwOch
+EvSf/243MAwTUdjFqKjgUJLc9TbQYwkZdrIS0Uge8rA2EV+TFzpilqEamNjbVr2NxvBYZtsYLvZo
+thXBDRi5LJDnWKab5Q+Q13KP4vgNeBVBStkUJSHWbs+By7z4MGItRidCXclEuhYqzAmrvnrdxzgU
+mGwXMTxOlvqOLKrd0kDWZ8muVcFihV8ROqhJNxZmci371i6KU5lgL3w6wnIupq9/7RbqQsxHO7Jh
+kPrcPiREuTxDSgizMg0EKQVZjBodJZuTWoewPZE+71uySAeHLvrE8u8fPouuIc/6lUGD7+/LeF6/
+fRv+JyXoujmXCXj16Ms4vaCupL1AxIzQ6ZlGxyd/YJYjIOt0ltKULZdojC3e/yEC9zMAACfowLFl
+DAYjH4uVPKvZl/exk2a4SvIZ022QsMJxp+fAIrW9hxopP1+NWDrdWjjkvMCNG7CZUdh/6RDFUlyw
+vH+I0eP7vJ/8vt/Z4CvnMl+u9s+AhxKsruolRj0+N5+thxxbgTO3OwK4MTm2wjPLrYDwx+BtZIPL
+ehQ1ahfwFDeegY/2QgKt/XYG90BwJPM4wcc/KWMwfSjf9Xo9GBtWxO51ELDKN65KqEjcwmFg4272
+4XDO39V90Fr/BoD57/v4JyEy88D75eZ/EsDf0dJ+B6IreeVD9eiyXbbL9s+1/XEDjXta61P+fQZg
+j38fAnj8nu9tpehP8fvae6XoR/0ItlcjWxvYMKBotxEOduQ0ino2mlxSSzYC5K7s4jbNqAA5TOr9
++S2gSfhq5iEUTwdzvAu9zZGvWWWmbyPuUPswD5HM5dS00hGyDQN/bvCEPXdLQuD1DLRExBXmBlc6
+Ym0ETYQx5eYM3EHXlNPWGEl/ewCKGQOR4QVAui6zmkLH/A7TsHAPYO/R0tDlE2Vk/7oNmzgEI30T
+R0tBHu6txaxd4gXsWHKPedfBKU+PJtc45iHdR4rTr0igUHXktPOdFa5sWGh25WVcHcjzuZMzPLNh
+EdMnriMlX0R3KaedczRAQmi2uekgZz/sykJNlKJr9LDJZVwci7oWznNwLOkDVmvcIKluqzLkCU/5
+ZQKvkucOamokeF1sMnmmjjGAb8u/92wbXVa8hs4QXYNQYlpr2RroKpkL/b2XEBIhWmGN1pQxbMYZ
+VEfQm17DornFFXysEbzIbHYLw+SfAgBOEcDYIwnQRlypc+2hY0nfrKxG6Mu/70ZD+Jb0Mx4E2CUF
+YH5MK8gbow5IEKMv4K8bPlMHQSBjseu7mJGM9eo1sR4yr8RoLLB51TxGmss7686OMC13+dz3Yecf
+LnT4kbMPWmutlPrQMlPvlaJ/5vqhHrl7UHsNdE8udRAewCZsMxpoGLYMju+aWDP6bKy3DEM2TFJ5
+GzCeVMjVUY2uJ6Z0NerCoi5fviO+dWXmmKwlR9331zgi4OWRfg0GYwaqC5SkFN8Kd3rRHXiERL88
+z2COZR/csSJEB1tSEAsBmYWMoZiDbmHBHFO0ZnaGasUou+HCJJTa7hH8Enmgqju02QeTMsiNLlRK
+Mpj2Dgr3VwEAcwrLGN05AlbfLa0OIpbenqGBzc2iqG3sfEzGdpmIS/H56889YTx67gf3YacUVTW/
+H5OpbL7PjTXyR8RIMP6ibz6DPhmQNd6A7bBqbzHF7q78PV8tYJBjcs2FYq4KVN0/K39713EuMAbs
+3e/iCmtXXCPCmACgNmP1aBc4MOQ9+UGObLvxWB68jswRJ0hRMn6gTQLL+ja8iqpXQxtRQOFdP4BH
+EZVEu2g3hL0vZNPQ7hGOGtn0BrsJfF7XM07RCW/KO6NyWKdq0XPJ19komJ0t/LmBO+L87OZwWWFq
+E2+Tb6awKdiitQenQ+buWiMYEPIdtbjBDdIgA9C6DdByAymyjyHeiCt5vyngdeV67gYozf+P3Yfv
+0M63bgH/S6wVjgFce8/3LqXoL9tl+xes/XEthS9CZOZ/Bu+Xm/8igP9IKfWzkADj6j1uxndsymhh
+hSWCnoH+XHZ7DHx0Ldm1bSdEyNxuU99Gv5ZLbnUh0uoIMVFeg/wcbSsnienUyEJqQ7QbmIp5bKpy
+GfEQXVf2rAouZiSm8I0uKi33Hhg+xrfkyG6ph+gV5/CIEjPiAnBkHzSyJSJqLYx2LLQmXR5iAWo/
+QPv4AQDgcZbBS+TzntGHTQEQPFFXjuFREdoyc5gsfLKNGJrUXU30CB41GL1IrhvkHWQ8ufOkxGKr
+m9ZqpIRSO3aF3/maWDefuCljFSdLfBJSwlievY1eK67P/XyOmDDtL31tjuuBnLwPyP94dnYG8HQd
+JileZxDwuu4iIyvxDauPNzj2ViXjU7V3YRKNaHRexorHSpklaFwJ7A2dHYQmK2I7lP/r+HA7LHbC
+Hmz+0LQt1JX8fbr2YE8lT2/XtKB2QhRduV9YTp7gSQIbMBIiL51jNCTJUX3JqBhNgYBuqn7Hxz0i
+K6cThU/cFMtjTUTr1bs1NqF811hMoDZkoN41AVZPuvUpFO+xbGTuLbIGOhP/qRN3UNKl9QIXLenf
+4I1hunI9cymugVmdoaPFclnoE5i0wpStMGBg8yhtQZW5D9w+SEry7wP4EQBjpdQRRGX6ZwD8nFLq
+LwB4COCn+fVfhKQj34WkJP/9D9QLZUBZAazSh93dEkxMn7D8OJVCuSG7T7NCRVeiScWMSgsfaCnx
+7vVhMZKLpQWrZUUlAjSKyjvkM3QxhclB3ZRT2ISI+8UKdldMu96NLuqOLM59/nflRbDI5VeMPdQT
+8Y11f4Sm5ueFgqdk81I0qduTKRpKkvvpAqpD0pduFxYXurllUzQc2JU8p7ZLmMSvV4aPYiKTqc0j
+OCmFXulb5845rJb+qa9BHllsALgU060aE6Ndeb4dUoT3wiEmZKm6vmiRMWuRbda4T9DPs7dv4uuh
+xF3UhgQ45TFubEuPfRc3uAmlAxN7XQq1RCkGdEGyNeHohQ8Vktj0Wozxbfl8XHbQYwk7hhYMLYul
+8LbgNAM2J79rrLEkyKzKVmipjBWUG6BHCDmBR0EYwja5QRoGzJT3biwYrmw4WkXQM8k+OK24lc7p
+W8hduce0u8TzE1mQF9EZ+rfFxTDJbXn0gg2XWppTs4cDX5bXtFLoM8ZhGR20S/m7yuW6XrUAOgP+
+uw3F0nc3mGJFAh+nqdByrno7nL95H+b8EcfCx6CRjdByXJyxJuYwc/FOTXDdB2zfdVPQWv9b3+Gf
+fvQP+a4G8Jc/VA8u22W7bE9VeypgzlorVI2BNC/gKDm5tF3AqWTHLNIMFkViaqMGmdORM//qGT7q
+akunPoBOJD/c+ArFVmLN8mHGpF6jxnuhHVgMIlk6wpqSe8MmRK1oKZi7uNmXrKoTstQqXsL05Vrd
+UmFDFl09T7BQLExaXwVYcefShEWyQuHLCWQXaziRXM+zFcCsS014cc810TZyujpmB61itaOTQFOL
+cOM9RutIPwNGphfxPrpjOYGGEbAhQMYvha4dADoeMCC4xb0ljNFXjC4OKXOnnBsoagEWmXWIG7Gc
+RnfXLgYkwck5WN3WQsET+pnBLaxdAVFFWRcZueDOnA4mKYlmHLE0KusTcC05obFK8MJdeZfrKkGk
+5DQO6hA1syf9lJWRVgGPGJGmaWEQHpylCTIGM02nhEcT3SIALJkGcPsyRh3HQUu6NXRHaMhkqZDC
+JOlOXspJG/VdXD2RsU+UiXQoVtNhsocr18Q9tGzyLTxcY0acSQ8ZllM5oa+5NYpgW9BloonlRN+S
+COliDYvAONcaoLLkGsl6jJFJq2JRwPE4QUndFtRzKAagVXuCJpW5sFvNcZyKFTNv78P/kMv8qdgU
+FBSMxkJtWWipmuOqAtOCi0plqGOahkMHyYq1DcSsFzMXliEvdjVP4e6TNWiaYas8lKOAO5AXXpMH
+Eo6JOJYFdrZeYH4hg/6oPYPFyrczxHCO5Xq3XmZKMgzQkH8wK3OEjNo/nttofLm2qRP0SS+uF0wL
+Oh4sql4F3g6arfBnx4Biqs9ypY9FXcMjq1CZ13AIeko2DlaJLKbzixBvp1LN+PaEwJXBGt6COg3e
+HgZcKFO08LlQ0NroPPcpufV1WSgtXsRb+2Kq700LVD26PKmJqSMbmXWygfFxiTU0U6b6mgUsJX0e
+3GlRvS1ArWXvEdY0q6dVicITRCp5cVAf30cdSiWfum/j4Rfku3/6aBejiD8tfwAAIABJREFUDsFJ
+aBG4MubLmNmZskZJklMjWCFjdWWODAaVweymRlNxPHbpP1UaPtGtWbGGJn+mzmJQsAnVpkJJpqZ4
+JfedPprhoUfeze4Gn6jk+S/aGSzGCRz6nb9Xz3GdTFe/t6lwJRQ3r54M4XUITjsyoEksrE+YtTE9
+tMw+lNfWUEtWNSZzVDbdLVNDr6ihSRRjU7ZIGnnOdXIVSSH3u2sW6JJxrKtDmDUVdD9gu6x9uGyX
+7bK9rz0VloJGiwoFPDtHh8CctHXhm6RE28RoqBh8Hu/BN4h0OWMga7DCgjwFlvkQMZmBW3eJFYNu
+h/4ODhgNdsc0VXMLFYNM7WKOk1RM33nmYrel6nJyHS/8+AsAgNyU0yWIj2GSLnxclVjEYkZ26m8g
+n8qJaAQFpoy42zuya1vJEHpMufuZB6MUUEw1MWGN5ZSrtsIrQ/fbgbF+irRm3byVIZ2ycm75Vczv
+yrgkZxItr5cDLG+624GFSWyC2wIlCWW0b+Lxu98AAPRcgTtf3fsWirlYBBfGt7BTiwlvfyFAeFdM
+0XZUYETSj/Uu1bGLAzg+iWOObZzy79nMhkuLpl47T7g3VSwnrVXdRf2mqGXZNxXuvc1sBk7hetT/
+bEeYUu1KMdCar3O4HrEEVYhCMSvR+ijIOZEXLjpUL8cF6f1u2Ei1WDTDcR+2QbVmt0S2VZKOCpT3
+ZI4kEBM/Vw7Ms63wzR5+5+VtheYQQ2YwpuT8PJxH+G+p6PTyG/dwRKj0jcM3UaQCilI7FxgtpU/p
+8/LdeuLDaAVjsHp7Dxv7AQAgzkpElXxnHJfQhJAvSKm/KTRGDMA3gwnaVNaAv1giNMXlParuI00+
+3DK/tBQu22W7bO9rT4el0AJ1CbSmRrJl2FnOsWBApdYpPIn7wIoSpD1qHWimkrIIfebPtXsLMZl5
+dN1Bv0e/rujjPJBTxz5hkE3NMX8gu/279RmqlAUsRYZ2X3badifFOZVPnjmQ03PdAMaawaXBAO3y
+LgBgEjkYUERkkWfYYWo050limA1AnQY7X8PqSH68WiVQlTjbTkBxl3qImNWCVuxCFxIY22QaD2YS
+BFyeWVht5ERLyRZshGewNH3raIQuaczOjRYmlZS7lYHBMxJTgCmn7r31Hl5aUaPQ72FN6LYyShxc
+lbHd2fksqlb6p+hbd9xvIDyXE3/xJ2aofk4spf1hi68/lvudHE6RvytIzcqVexR5Bt3/orynTYlP
+XRdIY/BWAJ9B48xcwWPQLWYcqdQbJBNqSXo51hYL4cwYFsfWVBV8xkEKBlqjtAe1zxIe3UNGPQkn
+t9BkFFRRIS7WMl/iVKyq2XyKTSQn+7q3wGfTjwMAHg5TNPxO2hPL9Y1DAz9FlP+XixR+V9LkR5Mh
+XmEQt+iMwNeDNStid4wcfu9zAID56h0ECaHLo68guZBxftzJMViT6RxrjqGHMxa55cchprU8x1mu
+cHZN5sgnZ31kO7SaJF78XdtTsSm0bYs0zlBlJTTx6UbRIqKSjrGIUBIsY8drNAF190iW0iYh3B1C
+iq8Y2PUk2DWJNzCJozfDDioydWhHJm68dtHtykTx7l7D8ZkM8MhtMZnIpDl4awf9H5K/64VMxr49
+Qsu8szJLJK1sINZmhjblC/VL5BMxZ6N9mRDtQqGNaTJaPUQEEw0dHxWDdYpioE7mo2X1ZW1EaLjo
+u50UV3qyCB+dfgVdgqgmzO13NwN416UPfWcPRy6DWSmQP1GUzHH0T4WbMb8jzNBp+BqaWNwj/5ku
+sm/IIh6kb+HYl8n7yqGBUU82MvO+VMT7sQ2jKwth8lqAbCUz79GDFCe5PMv0mw7AEudmQZewzqEf
+MdDqfBH3/5a4MRdfmMCdSj/MyEUvk3c8YM6/La6jGjNjlPrYUA6+Wg4QF7LBmS6giGGxKQBzYT/E
+gKa2c7iBqeV3eTSEbZAx2tTYZe1Dnoo5P8YAMTMft+obUNdJuIJrmBLS/Pf+iy8BAP7hL7yOOP0l
+GXv9pzC4RkGWa99Cx5BN+HZgQM+kH1dJllJ5ERwlAcUX9z6DaS4bPdo/gSUp33R6D5UmqIn0cDe9
+AkUqc+8Cp9gvJfDZ9M9QzT8r79V7A+r/be9NYyVLsvu+X9wl7819e3u92nvv6Z6tOR6KY5LmApGE
+KEMAbY1Mi6JJgLZBQLIhQNaAn/SBHwTKsmVApkyIWiDREm2aoglS3CWTlEjN3sv0UtXVtb795cs9
+8+Zdwx/OedVV5PQ27Oou2nmAQmVlZWbciBs34pwT//P/z/WI4l3aMnxY2tKWdp89FJ4CGKwxpIFP
+rscntXbOQkVW/E2Hkp6FVxclkpa4VLMjJZ24VCO4op7EE2UilZwvb3ukU03guWMOT3cPXTn9Ncvo
+ptJ51W7cZWi+MZmyvqVw1tqYr+3I7vCZTdUfLBkK1Spc4JGq/Pi8sYXXlSPCIAsoqS5hEstvDYmw
+ekRIb4I9I/1IvQb5XRIRZRHO9hhrhWepZqmtSAiSzgKmVSXyaNS5WZHXlY6i4FoRjbZqAZDRVpbg
+kUlRJ4x5DieqCH24K5oN7vYaJ5rguhA8xsZIqgRfCjNGVyWJ6db6XNBwZRdlcz65zUDd4KNbGcFQ
+xvOrRR89XWWxt8BRhuZcORusMwP1ioos5uDk56X/dhWjIioOEV5DwobJRELFvMLd8CKvz8gUhTof
+n2DaSsY6GFFeEW+jr/oPQRDAXOXgoylTJdFppRGNLQlBkmLE0IoXkigU/qZ7QN6VHThZOyFUJoAw
+nXCkSd5f/bJ4SnvRz5AdqHzf5X32X5cfudh6hkld3PlB6QKVsuz+w1g8Vntzypkn5f7Oo4xckaf5
+/IRoLGN/bdHjkblKyz2uXmVkmfpyH3pZg4OWzPvhDB59WubhYwOXxe33diT5UCwKBZaFLSgVMQ2t
+doxyh0Dj6+EwZ0NlxPt1h02rYcAZfWhOFgwuyOTZBGzplGxjRqQ32bFTXIWRp95VAK5eK5i+JrDW
+P/zyNW5PFAwVWsrq2j79ZEJVJ81rnjy4jyY5haMgIyLG6n6u1q9xdF0BUs6CmT4sj3aVcXkxwy9k
+0hwlJQIF3pj5jNqmtDcZyC2p1it4SiJT3tpjeMoEZUeMXr0JwN6rX+WwrwxDQ3k6/LhDfEb7YQqG
+vmpiGlDELwbLUU+uo+bK9b68P2dTi13rZs72ljyEteBjnH9GxrO6dZl6IJN0ZV9OJOrumK+dyDXv
+9G/w4kLGJR2OmStGxGQZmeq5GxV1oTBwWpdhq5DJAp+5LXqKQ2jM55wo5f3pdXqDmLQj3xsHdZra
+qYkTogz1JNUOnqpWeap0tRJUyBX1lpXLOFp9GayOGSm+wZ3N6O3Lg7q7u6f3yWc1lrGa5E2O6tKn
+Pg6///v/EID9135SfvfAguZtZjczAu3/F2/6bHeUqMbvUoo1bNR5vNeqkM7l2nonC0JX5uze7SG5
+skzlixLTNRmjmQoIr84TkpH81mh4TL9QtqzXh+xek3H5pVqZtcMPpkpyaUtb2v9H7aHwFDxraacp
+oZMztyrCMtihqaItuTtmpPwFRWzYVu6ArsrpFsmcoa7m/jgjW5UtIx01iPXkwEkbOEaKh6JburPt
+3ybZlwxxL57TVR6CflxwRuW/Hg18qrviilXPi3hJ3Dsi0EqjbGCZ78mZf3kwwVfRD1tusFDPI1EJ
++1YpgpGemxfHGIXPxs0p3r56E766r2OPkrqD+aiLTaW4ajofkE3ltGPTCbmjKLbuipyMnK1PqChq
+MvTnlGbKUVjknDL1WcDV0CRWV7xWinlDQ5uPx3v4Kt/3kWzALYXofqqYE41knC+OZCf6tbphfSDu
+6ZfcIaHyGQ5z8U4AciyOwr+LU2/FCLwdwLgpVslX1q1PrZDfm0UOw4Xcn1gLt6phn4WGhNGkR61V
+17Ed4alKtzE9ylqBWdaxD4p1jKMnO7MN6g2tuk0a5DPVGJ1FoJDuLopoXOxTr0nitubvUzqSsMQ3
+Eb/7P0rIkxyoxFxR3E3lYnNy7R9exO1U5uRz6YyGsi7P96WNhtmltKMwZ9sjGIlX7DknLHSun+sc
+UVO+j3ImXtzELeGr8nU7htsH0g8ntVyN5J49Mis4yt8b3YmRGqYP1x7f9O3P/EiLf/7PHV7fkw6/
+hKWqgJfjmsOGBsSPtFskd6UdxVUdThKqevSU4RCqvmCUWpoan2a4VAJx54tTqHGjRGNFy6wHIyLE
+Tb78rSnf/2dksp1b/SestG8C4KwJSeoPXNzg1zVKeCvybM/AIw0l49TgOqsZhurm1z3LXNmEXB/m
+mp3vaLWkNS6BkqfO85yqL+97OHeh0sM4JVaCkHgmg9LPhH0KoB0YvvuH/zMAkmHKoHcdgNl4zuhY
+YvyxhiX9WXy3ujRLLMoxwzQt0Ap1HGOo6NjGWkfRqjh4nB4ROuT6RdcU9BJ1iQcRM40U8rcYr1P7
+/s88ezcHUypyKpLAZ7Olx39Onb7qhq6tWuo1Wb1X/EepKjtVvVHgVuRI1cv0WNufkmtoUxhLoUpP
+rj8mU/hwFC840WNZq8d7cQGh1qOkToVGWy4o9EvYbXmQR7GM/UpRZ6B076XRF3jxSMLO+Z3XyTUn
+9pTrktYuANBdUbGgc2ucXZfS+CBcxSkpGdDUkGmF4zAuyCK5Z1OtO/HtiKO+5leKI2Z6PPuJjSbz
+spR1d6sb5ArE++xn/4svW2ufe4dbsAwflra0pd1vD0X4kI19jn59m985eY1IE0d9INDEVzKFcx1Z
+Bf26S1e5En1NHM3qMS1HPpu6DkFZWZnTjA31YScmoKQ8jmMtRApMzqaewe+SMR8qJ8OX1vnZ1yRU
+6G3+Iv/t1Z8C4J/9l0Jo/fvxW3sIp1bBMNUd9knVlyw3KyR63t4ylr72LzQuI2VzPqNFS1PrgUrI
+TeOYakmvee4xDZVibJAxVUr819WrmhSWkjp/r8aW7zMybuX2mJMjlVdPT7DK+1ByVSrOdyifMhGX
+HDL9xyKznD/lenA9uhvyG0Pd87+p7HMcKb9Bx2GqXkx9kTPuy2//4XTBV9+lCzudwXpH4bz9BWdq
+yjkwlDaK1TIVGRbMtETFFzxF1p7iqkvTzJuYQuZApFgXNzVU9Jx/nDQwp7SbkY+vWpDTfsZQE6G+
+SgjmzglOrPIBHUs4lC/mjTaOp6dHheANbu0es1CSldmXC17PJfxLr/aYthSunK6w+Ba5jrqyPYdF
+BaPUg7geiSZlm/WEXdWVXJBS5OKlxKEkx0d7ITtGEr8cxEy64t1UXl+QPS6eQjybEnRa72bo79rS
+U1ja0pZ2nz0UnsLtKOLHX32R4+j+/XdfNxefguc1bvuhrE10QZI9nyrLmnYlcnlUEzK30oKzKg93
+YCs87sv7Xy0WbOSyQl8fSzsX3JjrCiV++mTOP7giO9SOd8yLM2FTiotf4ld1Wyl/4YcAiN5Fn8bW
+8qgi4RZrkqj6jmCFg8dlR3hmVuGlSI6/tryENxLZ2Z5RItJXnIhtRTHuzuasZFqeXZvySYUaf+XE
+Yb0ku8OLehSdW9ByMYwFi4ybjTyciSQJx1PDSLkoVnRnH2UeXc3LRKU63xYqm5Db4fu3JHZ+3m3z
+rapuPVfW6qdXHuW1qfRjpWNZRJLs6iSW37gjqMdvvdPjy+9S+Djpj2ipjsLKYUJnVcZlqyxeQFJU
+uNmX9vKy4ThTCtArPaaXnpTXzYi+clmsa0nycalCQ/Mnh6Uel5Wfo98JCUfiCZ2kx6yrTFtPKdhK
+vsdUS/g3DlOCy4oHMdnd0nijbGHTqze5NZedff/oBP9AdvTeeAwjGbcb9ZCL/0YGw3tOvJzSVkBv
+Jl7Flk3JlPC1lwRkyg69GTlMfE1u55IvmXPI9lCu/ebeGyQTyZ+kzVUu3FbV6Y93sM6pwOy7s4di
+UTCWu2f2X88y4Mx5uRnttuWZT14GoHRdqhof26iyoXiE2vo22TUdkG6NRlke4adKa8yvS9LmqYbc
+lIaTgxJk/MHRLTYVCLI3lUzyXVMQVaSKR+/GHN9Q0cXpmz8hk9U/PuZCKO2ttft8TJOcg6tDHlUG
+6pU1cUXPZw2yQ5kQa62cM4FSw8+r7B3M9DcM/V1ZOC5qReXz9142kGtY4ZtjMkfP2+MhDQ1t8pJ8
+77K1lAL5rU8+VeWsciE8/Wyd7TOq8vz0M6x/RU9UPqqnAY0hG75MUvfFPmlXXgfugk9sC+fj3/q1
+V+7SzL1TojEPob4hnz0uMs7V5JrSJ2Vhza70GCtfRpgOaCjxjS1VaKgKk7fqsOZpvYnCv9tpQkX9
+4rgIOVGegvqooKlkMIOwRFaSvpaVj6EczckchbkzYHwk19ZesSyUmXui0vBRWBCNJFE+ONcgLhRn
+kuXU9Lnc3GxwUcFQ1TW5N816RqIJ07iAhSY210oumSYPZ5UYdybXsRnJe2MT0TcCnKo0K9hEw6BS
+StKVsXh9OODiSukdRv1+W4YPS1va0u6zh8JTKIzQhnkxnGKvDKCwAZ71Qj5pZSX9WPMSLSMrcLEi
+lWW5s4+biisWLFyGq3LUsxZGUEjCsOT5RGfkBzMlInXiOduR/G4tmfN5FcIy5Re4rTGCYxV8BwR6
+ZBe/03YHVKzh+xvCs3Axld0n9C/j6Jm/iTt30Zsz7xwVX5KcaU8SRGcCn30Vhllhj0Sh2esnhome
+Y6fuEcmq7A4trS6t5hlzdbo8C+iZ/3xUvStNt2pKTK18vhzLrpN6hmdq8vrT1XOU25+W9rpDwrIU
+QV0c+QwuiYdwVmnskuMGl7X69KRbZs1osdmNEtWS7Dl/vbnJfz0ShGDvHcbNTRJuyUfp2C4NhXd/
+oir9/9fJBWJVtm4WR3QUszDLLEFb7qUp+biRTO1TDl8/r5CobKDje4SqsZmbhJGiTCuJT38oNzeZ
+adVtscCfyBgfJHOamzKeURzxWFU8vVdiOU5crLZpFV8G4IlpmReG4s4fFy4bSHsX7FnWQqHA6wQX
+AAhrKzRyCYlTO8BH+jGOc4KmeAeNPCRRaP1wJtd7JthiEctEvTOeYDXpuLE34iCTzz75HQVm9t4Q
+jQ/FolB1PT7dbHFlPmKicXQyTXnskmRbP3Up5Km2VAbWnm1QTuTBCaSgjby6RVVPH6IooOtImFC4
+azQrMqj9OKOZyHntIpIbEFRLHOzIEz+s+1woyXAkiybnXpWJsEPGKQblFNATz96aM/sUsHN5a535
+pxV3r/iH7uWEqcqlt+oVpsi1bZd7LHTxagTyUB1PPepKOV/xzlJR2u/XX58zUUHXdFKhHMoCeFlD
+n+GdI3ZzhQHPLa6KxnqNCclpJaY5wSh9ndmQfl6kwVOX5NrWv+kSq6ofWbrwHG1lEU7rIdtjpTuv
+SnVlPUiYKFCmceeAeUWup/0ZGClhyegvXuAv/mNZRH7hRK7z8C2ixcQLaG9rxetRQf2samHWRZK0
+vfEHhEg49nRvRHFR6eCLHKtn81tlS6aMz6eL8GToUXZ1pZ9AKZQxWoxcqmeUgfqOQ3lTxmN2S8Zq
+4B0yOqX/8+YUmrBxnTG2Lp9xezJnp8UKq09Jx0bjdSqX5XXj5gotpagvPVmm8Yj0qTgr5eLrXY9M
+fytLQo5Vcv7sSs6RCt3mNmU2lM80VwWwdPOgDtuSt6hd22DHCojullnBN6pJ+rLLpY8uqySXtrSl
+/QnsG5Wi/yng+4EEeAP4r6wVrXFjzOeAH0VySn/VWvsb79RGpQqf/JTHU+k5bo9kR7GZR1ur3p5r
+rNBe1QtueISKeAtVtZjygtyKi9uujgGB/FqGzDNxxeqliGwqnw+aKkWfBGx15XW1t0awLmHFizd9
+zisUdZQZMuU9KBQRydsUnbW0Kq9RiRlPleCkIqt5/dU63qc0SeRUqdVlNfeOS3gKV53MxV0uBxFW
+QybvzILFSAZgZfWI5lgSrYfVA0q+vH/5vGI2KDPUSr1qBnOkH41pQVNl6o4IhdUGCI5Vtr7pkKi8
+ne/nuEY6WS3dICkLsUgYTEALkxztUxyv43pKJFpy8Fcl+Tvbq7O6JW17L30Kc05OCZ5XCb5oXjD+
+OmCPKIkpafjQzmuESCJxqyLh1YlZYXUqH7hVXqcylPkSDi2dR2Qbn1fXCZUnozxT8tzahLmqinvh
+CE9BJF53ymQs79erI9wDTWhuy/0IJmdpVZSyb9ZlWJHTnv1FcJp/puPLPO0WA3bj/xiAR91XMUMJ
+K2z1hGhVvJty0KZ6LJ5q/QlFmNaepKyszR1TpdyV0CVOqnRr0ojXq1Auy/27NZTvbxVD/PkFAAbd
+XYpMeETs7Yyuzp3VT/bI484fH+i3sW9Uiv63gM9ZazNjzN8GPgf8D8aYp4DPAk8DW8BvG2Mes9a+
+bRReuGWixpN8ZsNlrDduNL9CrSru1ZMti1HsbpGDqSqLbqrxnSlTU06+xFQJC810J20qjtzEaVql
+0hTXPFHWoGAlYjDW0urHZyyUndfcuUNftRSdIKGtENWJKvQYuItxv/d12TN8X1PJUNaqGI3bW31Z
+sKLtIdFVcWvPnk+YKVW5VyzIdk7bU/rvuEGtLa/t0Sa1TeWV9Gv0ci0ZP6iQKsvzcCgLxaOFT8nI
+A3Rtd4xRGLupjGh0tMT5qI+KLPFIR663u5rQUEhWOjH4TYUVj2Y0TkE4aYynUyYfyFjY4ohkIpM4
+cm/h7MpkzJ2MQo/sZp/coW6EZORZ1UHMb8456sn3dnhTp7PmWkptrSXwe0yPlHtyU3JGTqfHuZpq
+cB5G+FsSdrU7rbtHdpV4AbHE+74C2bw8pqH1MXFWJ68q72I/Y1OZlg+TOvac9CscyRxJSjOyhvQp
+646paCjxSDlnoqCntKpkKY/4LK7KYlmcydlWDZbQzWkhfa2er7FyVhaeQz1avtgbYlc0fGg5xDp/
+y0xIFY5tuwtmuoqe70o/dquQVuTeXAq6HPy7VwEobdXwNuWzd8aHPKu1G+/WviEpemvtb1p7uk7y
+HxDNSBAp+n9prY2ttTcQpahPvacrWtrSlvah2vuRaPwR4Of19RlkkTi1Uyn6tzVjwPXgyqKLO5CV
+r7VYp6XKxtNehXWV1ZrkZ2muymcWqqhcHTvYsqyerdoKC19Wfi9dMJso9NXPia3SjSl+YDHNaCMw
+WpNaRnuysz8zdnilkDVvPsmY6PFDNz6tM3zTLG+urE4O+6ms7E+clFlUxNMpPyK7xKJfp1HXbP+4
+oKmqzCfDBWkh1Xmzo1M255zJRDgLOvUhkxOloHPatGNx523pdZoqg24UXvv5nZB0rOzKhYOnkGhn
+cpY7YymoKaUeSrPAXl+Toe4K8RMSdq07VaKRjEWtsUWmBTVVc55MKcRsKEm9ZL+Po4lIJ1mFqor2
+jE7wrIrSzD6O23oRgO+8JAnD9eJl/mkqnlA4zVH8DyXXIenrLh1vU39WwodT8ZrOzRpZRTk46wu2
+U9mPFuk+DWWKNl7OmrJAZ65McXe/YKAaERuV6C7Tdv3khJOpXH+3mXEylulaVw+z4X2E8YoA2RbX
+qhyfSOiyV63znHqW+6oncZy5tJUqsOplHCQyZ1fyXYqaFvT11klr8tvbHbn/ec2hkcpYFdM5NdUi
+mUZQ3tQEuvXpKr3dUU95JztVOJETrpu2xfbFU8q7KWZPvMUz5S3GO0p79y7tT7QoGGN+AjlF/Llv
+4Ls/BvwYQKMS4iYh9TwjbCuF9lFMqmW6g3MFHvJAl5JjIldufqg3ALdNra0gF7eMr0dhNi9T8STm
+jNIGJc3m29PYslZipsi+tDtl0VTmpVqJhdEYPQdPaxR253/8aMfwZh1E1TMcOvIb/bDKsZ4SXM4l
+tjzTdXGV0KO+2WGsRQpFNyOwKhpalsVhbptUFJWXztepKehp5qdMtIw6yjMGSn0+1Dg8NXMSRWAe
+JhFnVPMyyIeUtTZgNEoY6nlv0JCrPxekVJT1ql/OaFeVjSiqUF2R8Y4di+PIfUiUtNStHTPVGDdt
+jlkcy29Yr8x8rgjK+gHzgZ4CPaEP/OQsFwfqX2cTTuZKRV+vUr2kqlDZPiVltVpozqj6jEtQkvh8
+686LBOdUm3NWkA70JKJWY6GgrOqKvJc5mzhndT7Fhpau5Ee2xmQhC51TaeJ1NH9wLItK2hhzqGjE
+nndAWcVexpM+Rh/0oqF/pyFnV+XhvnUY0/0u+eydnY8QRoLu7HVjLm/IQ+8sZCzalSq5p9yclRJz
+XchaXUPsSlhRCSOua12Quy6fPcnKGGUI62QOYwVvnSQpCw2JSkOfRuu9Kcx+w6cPxpgfRhKQP2jf
+rL9+11L01tqfsdY+Z619rhK8N8TV0pa2tAdn35CnYIz5HuBvAN9mrfKpi/0y8L8bY/4ukmh8FPjC
+O/1eUeQsoiE9v6C2kBXeT3ymniR9GgOLq2zNcdXFOMrae4r3f8oj0mxrtV1gRpq9Xc/IpuLieWmP
+QqXZFo4kHG/vJJRRmq/bU0ZaGWenPSYqwoGF05cKoaC454z93uP2k7ygotWAO+aQxkTcx5395wFo
+jVaYf5eqahc56+4piWGfqKHn4zPZaUr08PuSLMvXhrx+pHRetZDNHSWDyQ0d3fE3Esn6/+beiGsq
+U9fPDJVoX/vfJVE26tj4tBTG3VFOyHI147aR73Wmt6npzu5/e4dI6e2qGyWcQxl0f11u++xwA6sJ
+XO+wimmLVmRvr8p6TatAd0JOmuLKbPW1XmANnn9dPT3HUFG+jCLOWFXJtjBeIynJeGRluefZARik
+jdeynM092XPahynmo0qFVs8JT72wWAFpgUOsoiiLeEx/X0KXl169xm31btr1Iz7iSV8HlyQ8CrOU
+2Y6e5hwOeUHHbTP2aevjU6+K93fmMGXsS8L3IxtXiF+R/bHwXuCqowQw+QHmRYXLf7tshlP/DOWW
+/K43KKGlD8xmJaa+Eu3szjnalb5+VTUqH2l5bExljk1bEZ4v19zIb9OI5XXpTI+0eJ9rH95Civ5z
+QAD8lpGb+R+stf+NtfZlY8z/AbyChBU//k4nD0tb2tIeLvtGpehfPhmrAAAgAElEQVR/9m0+/5PA
+T76Xi3BwqBYh656P15VLGo+GzDWPUBRTvBNZrTOb0llI/H34mKyi3UGNih4h5l6dWOWz3Cwh31ex
+jJph/+gmAGYku8/hySGdXDyMO6VDjCbUkoFlTb2CAfd4CBptGXK+HiCv7BhK+j9FFLKrHAjnX5UY
+8g8/scf6NYkzy90yiy0VqY0h1pr96FCuN2q1uZ5KfqF6u8Yglb6udNe405Td3+557Gt1nRlIn0ZB
+n2dVVfv3ogKj1YCl9TGPntXq0Z2MiUKo21vKm7BIWRxJTHrY87n13eLRXN69wXk91iulIami7Wwi
+u1X/+m2mysZ0e/4aVr2YpL9DbUXOzWeNPpmSu6bTI+3/JT52Vtp7pjD8dq7Jt9UWlQ09kmxaanpW
+Oe8rUjC0ZG/IuB4dLgglV0f/8iZbWvDl7tUpNmU8Ug2nDRGFqo7ni4yvvKG4kH2XqwOFIE8sk0vy
++rzmFKZRTFnVnvdLDs9qSdfc+ExVEdvqcfhkrYoXS5/mU59c0ZFh3yU+EY9lZ7jK/NtkftaPxFu5
+6JbxlSthSonJTK4h2Z1xnMh4ffHgJodX5J7tx+IVumvbZM+qox4H+HOV0DvfZmUu3s+k5rNV/lOo
+Op2bgr43x89rZIfiRjlOiWokIUM+tPihDGojaDGsyGeifyXu1EGrSu0/ks9Wo21CxX0XTkiu1OfZ
+oKCm8uNTLSVtbm3gV8TFe+pKhyPlc3xi7SYva6a3UyyYOfKQXdbV4eX5/c7P6ZlEUcB2KJPxvA3Z
+7Egm21+XG+S8umCupB9Hzx2xOZPQpjlbZabcjEVHzuNH84xuT/qxNzgiEG+QXqnL2k2ZjKNZxKNK
+9Taqy/f//cspX50qMMlCryyLzPXXK+xf00XPFjiJhli3ZKKdKaWc25SeZCbj4Kdvymc7JUZ/Wfr7
+qfoajnY9Vaq4Xr3C6I6M6+xVl3GsVPWNGUfKpOy/WqOkoJ8zFenzrOwxzuV0Zad8k49rAq/aqeKX
+ZdxWF+fwNIHcVRGdILNMn5Yx2v40VIKnpI3bV4huyQrhP5XSDuXUwqnKA1bkpbvxplde4y98s2ws
+1zdmfK8vD9ad5jbbUwViFcpzaQ17Ws9xdlxjzxeMSHPos+lrjUKkFHVYjkcyb1pBxvy20shXH+Oj
+l+X+nIkfpfuKLPZZIQno6FMOayob5fkpRvkoD8N9yCQZ+WyzzbVtef1EQ6XoN8+woYUuiWsYKVVc
+ViyoTeV56AQF6XxZJbm0pS3tT2APhafgWIdqWmOjmhN7slvNRxBZcbOScEag2b1rvWOe1Z37tQ35
++2L/iI19LWCJrtJ8Us5uk/kRVo+T5qMSfYWjhifi1vWCCRdGsvLvPz6lrjWandpZPqFu4m/fsXSV
+/itXhJ4z7989hrwX0bha8pg5SmriBnw5ERf1ibn8wFEpYCtVlej9Q44HSsf1+EUWE9lJF1pROZvV
+OA6l/xuBZaHUXWvTAUeflLW8/YaP1UL9YxUemVUyNvWk7w9yaC/EcwnyEa5KcF49tDQq6s7rDPBD
+h6+ppmIxdfh8U8d23qN4XnbPc7tfoPHpbwWgr7oIs91DjvelT7udgu4t2aEG85jFDdHXiD5ep72r
+6tFN+fs4n1J7QjyzJ19ocgfxaC6119h+TLwGLy+oKOXe6Ej+tqtNmhrCDBonlKeyA/eeqRHs6JHx
+3GGqNHPNuiYqoxNiRaYWTpMslEG6uF5hpyFz4Jl5mf55ZQo/UpxDKaKrCerpc8ecvS1eykll+mYJ
+piYGx4uAtRXxaG/vFVitynWfn3IrFXGdSpRxdEHu5cax9LM+bjFWdGN7e4VprnqbSU4xkb6a9irn
+xxIqXqvI/69PFsy2Zd4HI0s9lPaOxw6DLZUsnJWwlT+FVZLW5qTJkGl/g8jKzRj6HboqvhJFCV+6
+LfFS1Co40GrHsxWVEF+tMtPcwBiHplKOB62QiRL62eE+kZ5KvHBHMOf7zpy9ROK67m3DRF21mQlx
+Qhnsj56Zc1ARd+05R3z4K/3+XRer4E13q0fOE8oA9Vp8RE0r6r6o7Ltdp8JLFZmYG6OcxUIevCde
+eY30jLh+A6NVceM+raa89+LhBKuQaTt2uVCogtLUUmkqkMXKQ3U4SbmlcyAEokgZhKYZX1Ogk++l
+jLVupKvVpdejkK1IxvOVRcFHc+lzO6ySWsEF7ARdnp3IPam35Em4dVhndEfAPWE45/dekvHMVhZ3
+F7KP5xPMWRm7cl3ARhcmGV9U3McLPhjkehbJmOmJ3LOy9eh5MkWbrsTR89tTxiNpY89pseZJ/7Zb
+dVa25P5FnUdx9KF3FXjrtqZMFjJufnGFZCz34frRbW4eynXePrlJ91jCm0pX+hd5W0SqUVkcewxq
+CgW3FXxO2bj1HtzscTWV+xd6A+xXtF4jPSBeyGJyZzzn428o/fxTCjYqn8Ffl7np5RXaSgx0Z9Kl
+KG4CMD2acdiTsOOwL9dmSld4bKo6pmsu80x+N0inVGO5f0W3z0KBUe/WluHD0pa2tPvsofAUjHUI
+8jIrlQmTXNzhljvH00KU2jShXBHXfa1SobgkCsXdoXoB5y0X9SjWNBoUetBrspjQyio/WmtTTmXl
+3r4kq3ZttEe5ogIv0xFhoNVyJ1us6M71yKDJybqsxi31HsyXvnI3ZLh3Vd2ulbm8KTtFs9Fkq63n
+4q7skrVJQk9RmFv5MVNfrjPf7hCUpO2ziojMPnEeV6G6qxfWCeeySxS1DZoz/cxKjqsotnEiO0Ng
+HbaVfflGZCnr6cOtYsZTVbnq19OCJ9bl/VXd5ToNwycfk+tML38L7QPZdcZPz3lakZCpt4XZVuTl
+SHbozebLzJ9TYovBAe1tjbX6R9iGnNnXrI+/Kb8X+uLx7AYVNtVLCctdXlIcwmZti65yYExLI1a1
+crNY/wgA66MAvyQJxa0ZjM7J+62kxKSsSb4iIk+UgVmRjfEdSzGRkKcorePXZGfeeOosjYGM5+SJ
+C9QH4nHN1UvrlBKmqWo9VPfxVAU7rMQsFOOyiMVzPXFDSomoVeemTeOj0nZ7YVjXBGZWe5xGXe7r
+TFGMF5oZgWIMbD1kpjyPneCAXcWIuA1D+ZwkT88oBD1on2WhJyCZF1JRzzrJAyqhXNPIqVEP3hsq
+4KFYFDISesUOVw+3GClx5kot4smVU4jnWSKNr89ULhKePtRKjOrtgrcpg4M/B3sqVd4kVwadilnB
+D2SSelOJ+5q1DYKOuJzl1YJCy3pP3FWsuSm/t1Flta0qRFqmjBFeSZDw4XQQk1HMqCeLwlYpoNGX
+LPrm49JGc63Lo2OtfHTW6OsJRynNKKtkeliVh6p2/hyMpJ/z+BCM/IbfaN4VIC1PjwgTeWA/6sjv
+ni/BS8p3uWHgUHH23VGZr6YyadqeR65Z8qArD1jDXGAjkcW2sfZRaquqr3gypNSUHsYbASaXB6uk
+0OZg0GS1JhO6El3iUKXoK6WLlDZkAWmW67T1dIW+js/tL/C8lnhPezdpOrK8BquwULBNJXuESiYP
+3plVWbz9ixucn4nMemGhaMg9CRjAUPo9iXs4mfx2NNWH/HB2FwZ8cfssruaH1ktbxIp5zjZbFCvS
+TlDIZ4cnc6qx/EY/atONlSHLdaloMiGKpF0ncbCZgJSaLY81DUdL7gUype7Pqh02jFybUbp4GzqU
+27qrWUugOZPpUU7ZqrRB6lBWjc2sLtfrBXUCrW0pWYeTmVLxx0MyDQXbdUuCguTepS3Dh6UtbWn3
+2UPhKZjc4IwDurUp1bbs1ucK6Ori2TpbUN2SHaHdLeGcldeu7nbp4yGMpI49TSpUlHcwq7rUHUmS
+eY5L0ZHVv7IuK7jn5aRaOVmyCyJly53UCprq8i9qHVZTWXVtWRM29yCXHN4siFovGTY0M7y6cong
+rLwuPyZtbA6rRB3BRYyj1yhrhaKbGEKVXK+0Tz2CGuXwlHexTKCewsyHrKGy7IchM+U9uPq63MpG
+K2BjLrvH89ayORFPYLWZ46xodejtGY88Im7p+YYCrB5xOfO07FDuIym1WME/z3Rx+nKKQrzA68vu
+n2zKGK8uvp3mOdlbwmTI1kh22rDkMXeVDr1s4FgLurQK/6WXpsxO5dhm8NWRJiVNi2pbd7bKlK1t
+AUCVatL/WtPFbshrx1oi5zTpWmecqoBLFjLRKsGDivI+DA8Z6MnQ2cmQ+pOCkfCMIfDELR9UAqzy
+REyUOn1WnTLZU3q7ypzWXCnmjMG1cudDDXM7gcdllaqPSg4NZeCuxTn2VG4vs3iIF5boBA9Ci8lO
+TwgCZoqRqDe7TCPl4FwxeF2l1quJtzUtGtQV9zKZFIQaJiTHAcmK6nTOU2zwp/D0Ic0zjqdHrOw1
+GExkQszPJZQVvFTOHTqo++mfp5TJ6QEVnTzjEUOVqjfT14hdJeRoNplq9t3BEBbaXX3Y7KRKuXYT
+ADcLmPriJuZ5zjyTSVWZukQteYBi1Uy8F81oeRO89OoiY0Un5qdaEdubItu3yOW3kmDGwULd2UmJ
+WMVT2+UyA6UZjwN5kJrDOzh6hGrKhmQsbqkTHhKoTkES7lP0JVZ9vCoVmb80zdhXaL0BAiWEJV9n
+fkce5LBRMNdjS6NcjM1yA7ctE7rqnGCrCgAbDZhoVald9Kh5OuaZjHHcKbBKiIqZ4DZyvQ8rVEJZ
+THzTId+S63Ouyf9fiq/xK9fl2q7vxbygWpoHtk8zU+DNvEmh41JWMhkvrOFUVBw2f4TqQgBsaVjD
+9yW02V+MGc+lPiK7Kdd2Y3HARKtA25lHWbvRfLRLUciiEE5vkCiycOHJJjM4jJlMZRGunMRMTxXH
+UkOgp18VBcNd7DtMQrlnq0Wfmr4fNCyRhitBPMBTNOm8kP4P52XiUMI8b9phEUl71w5G1AOlBFgY
+/KYe1aZy8aEZoGsNSTAl1xDUdUasF7IZpN2I/uiPl/y/nS3Dh6UtbWn32UPhKTg5hCOHyE05F8gq
+aSYWq0mfEivkSl7i9BMIVbZ9opTrxsXe0Sq68joTrWQL8xOyWLpYChwWRtw2q2rVPj1MpsmbQYp6
+Yrgnu5CrdmF5QqFn3Z36m3Xpf9RbAGgbuDCVtivlBsFEcf66sk/yKafEhLU4obSypT/gEmgQ0lSs
+v53EJEoy4/YdHCX98LwSs0LGyD0OmaWyo+0dquZi2OOTqlD9rzOLq6QtXnfOf6J8Ec9HEStd2RGb
+6v0USQb7SsA+bpDV5bPxpGB+Im2XV8oME9WmzLTmYpJimjKeSWYwqs3puVPQisNskJAOZdccHPw2
+AP/uhTm/P5Vd8FJekGt1aZDWIJVdLmsOhGkEKKxm52OfQmHuTrRH5km/k/mAeCpeiHe0Q9GQMKek
+IcPqZE44Fw9zQYVFRfEgvTkrJfWmSi7TY/FogomMYdi7Sa57Z7Yxp6zvnw0zFkqxhqps5a2AknJE
+zGsep7WJaVFQnirEvOQxV4j5QKW4k/EueSifTvwxc1W1MrMjcJQusJljFFxXVtCUqdZJFABH4VBJ
+xZsuNZoUKg5kyiGrpyzW79KWnsLSlra0++yh8BRiU3DTneKMcq7q8U64YnhcJbx65SENPQ7MOnNC
+3d2tlsAVdHGqSi7qNwkUohzlNdJCdrnAb1BSPoFccwuFm1NONb8Q5jiJ7B4V26Wt+38pM4SqQZnP
+ZOd7K4G7w8zy72eyin/LC7dZfFp21WpTvhcejoj8U4HMlIYjCaqSCUkKhQcr0m6lWyc+kEKcUqeE
+VZLaUtqiqpiEyB/SVXm3ngrk7JxkHCqUegV4OZDdo7OTMNUEpG0UnN2T9nZQXAURzRWJz8sdl1x3
+vNSt42hNvw0uU8tOoefyvUXSp6J7ojccY091CoIYfy7990oheMpJ8Irsyt7g5C7vw6sZd7U1hosZ
+x7t6rp59jMY5PToNxLPBcUCTp7kzJtM+FYsjRrrHjeZDJpqYvehoGWUSgY5tvV4lui7fa3brJCpD
+Z0yMp3oYA9XZCHo1GrqLx9MSbUSmzZo2vtXEhOpOOp5PUZXrDQufLDlFVZaZJsqLEAV4WniXKZfF
+eDYgsZoQTmYkCyUpHi/AV3XwyKWqvA1G2ahKNkNvHxXrEyktXs0ElEvKzBsWLCaKw36X9lAsCuRQ
+DA0TbwGaQT3ZN1ypKTbe9Tg+FSFZeNixZskbCoMex6CValE0xjyi1X7HMZlWTKb5CKch3yv66vaV
+MhZT+V7eWJBpJV5RSxjEMqj1jr1LzRWWh+/UDQ7m+nAmNXJXlXx6sng54YjiQMlEahG5CrOmlwKK
+Q5l4fqrZ7+MCRzUA84MZ7rqeaQ+OmGhmOTqek+iZ/u+qeOxOAYpR4oaFTx2owI1dUNUs9M2eoffE
+6TWrAGse42oVqfNGm/CMllSP5xiF7sYHEeaCLqI9uc50ViL15DeKjZiSVkzmm3XsgbJqn6lw+8ta
+AnxN7uPf6c/RglhKQHS6ViYe6bYsJnn7ABsKz2W00DLkWonU0fBvkZMeK/Aon5Pty+RfaZ8jHsn7
+7uMqAV9qMB5Kg4t8TNiRB2yeJoQqMpyMS3cJeNIdLdV2R/Riuafz5ohAk7WpGZNpmJqfgrtszkoo
+9+xmBOZU2OgkoRHq6cN+ir8q35squ3RWVChuKPX94w5mLvPFuhYbK8fmRY9Mw4pSXU8vpjlWT4+S
+PjhVaeM4deioRqo/KUBFat+tLcOHpS1taffZQ+EpZNbSy2P6sctirKQageXcHTm+y7YdvrMmSaJJ
+VIYVSa6Vp5JkcrYNo9c0rRNeZXxVXM04hbG6eBt0qSrkeaE7+DiG3JdVOTxxSaqyWvuzOo1Ydoyi
+V2XqyWduDN++Lj0BRlrN+fz0DdYSCXmOGzcB6Izq9DsKd+2HNJqaiNxvU2iyrq+0ci23RFOrFof0
+iAbqRpZcynfkdWEcMoXxrmpS0qPgtrriIbBnNck0dzgZy3/EpYyVAwkrRpsybu3g4C6WoHLhKpUT
+SfLO2xnjG7I71ldfYvryKZGqytRNO3Q68hubt46Zq65kMnAwDfEKqnsVFityXHisMvK2KMjUO7j3
+FP3G5JDmSPpdyy4SjeVeOSrtlk9TSq7cj15aYe5K1Zk5drHnlODkqke5JWM+lrwh08mQE1d29M5i
+RrKvsu6bGZOR7ujOAekdGaO+J9eQnTgw0OvpW/YvyP87vTqOwrQdFQlqZYaphqa+PyPbF6/KNz3U
+QaLazOncUPTmGfn/0W4M26rNuetTVOR1UA2pnkI2sggU7TpLdEzqc0ojJeatL4hUV3PVnVFJFEpd
+jzk++DqqO29jD8WiYC1kMQz9DKXWo5QZ8GQilL0xry9kkj4zLJgpb1+u58D50CUwcuPCtc+Q9F4H
+oErAyoZq+KVnicoyqIn6rUFyyEDPxyfuGDOWyR9zQhbIABfJHp6Sqjyx/vaLggFOOWwdW6F6rGQo
+moV3XJ9pX0lDTE69ruz36YJGplWEqxKz+sl5pvq9dDDDHSuu3e8wUEIVd9Kir0Isg0iz14WDhsgc
+ZAVGF4IbYYbSAJKnhroyXrc1gz5IfC7o0xmN1igEFkExbNN0pL2Gc55ZIQtrYOQelDdu4wVP67VZ
+rIZgJXfAQhmuXi8OeeE35eH9R0NZpHpvkZippT6uur6DzgHtWOoqYlXbMibDXajLfBwzLmuI5fRw
+5loFWR3TUkh7diIbiF/boPyYbDL+4Sazi/IbldfnrJyXuXOlZ9kZytzJx5o7ycYkp2GCO6J+ILF/
+veMzSuShLunJSe46+Ko+PHUsI1V3MgcWX/NclHxmZ+V7p1Bkz51SNgrXbicECuoqtcdUfWkv8jzQ
+MKZUUZbv2GWSyQKZLUIizbHZvMQEwYi4+2Uq4XtbFJbhw9KWtrT77KHwFHKgbyxJ8qZMo2ssf3Ak
+K+rx8y7/+VmBx45bCV3ZQJn1xBU1gyq+siGb2lVWJqciKy6q+4K76YMiJB2tq58mZZpGdtgZDeKZ
+vN/MXYpIOe5mLfqq5jv7Svi2/bC8yfx862TERLUR1l3ZdrNdQ2V6U9oOHWpNubhKr8loIeFRtS+7
+a209INfsNQufQisDvUoFE6kglzPgsqeFQhp+fLTZ51Xlm7iQO9xwVdRmatGDCqoOvLQnLugzyrzS
+TebESmRS2ZyRvya7y3zfp6NiNr4XsBlIe/2B4jGSAH9NRViIyacKPXerBDUJ41onM1YUxntOTwAO
+32IMo/mY8WvisXH8DPmW4gWClo5FTFqS8Sz8XeKR4leOLGly6uos8LoyBqtV8WJK8Q2SGyoZn+zB
+8zK2g0MfU1Ovae6ykwgkfRpJgdlKbijpHMkWjxO0RJrN3S0TonD6oXiji1rIXCsuTejSHYjvPyZh
+qDiatJPjzhUbomjb6UmO05Hwas2eYa7kQn7sUlOpv2K2AC2OWuSKQq2GeN6pR+MQa8jrFTnpSMYo
+3TwgPXyf2Zw/CLNAVsjicArIzCwsjAzwS/sFf/m8Anm8Q+YNFSTZlc6enMvZvKrMPh/pMtZMtXu+
+RNiTG5O6c8aRTATjK9y3C2kiE6LkxpiWvF9MG3ddvGhnAjpRbu2+1VR+007j5NS6jBy50bV1CQ38
+4ZRoVRaWykHO9Jw8TIsiwSkL1DYfysQ+TnsMc3kYQ79K1oj0Og2JETc4cS0jhb7uKGFJw6tz6ba4
+mbemMade6xxQUitOcnikJp+5rjH7Zy528fTosVTdYhKrqMuTFm9HTz4+45EeK6PPWelHdXaZaEXG
+ez4q4wRawh34JJpfma9HXPt/5MHpO6fO6dcv503dAHNRLrS+sk+jegGAQk9GXL+N05A+r0RrDPWB
+vnZ0nbkKAVenfWaKY85dCceiakzUkxzOgDtcn8n7l+NL+PpwJxs5+XW5x13lhhzPFgy1KjHwp6AU
+/JPsDlmiJx/6GM1dh3U9MbqV+jRU/3NnUqai+axB5DMtDfWeyPfHgz6bCmmv1Hfv8l+a6YxCqbHm
+8wV+VYlx9Ggydj0i1SM9CRccDWXOLpIMAj1S3YsJkntVGN7ZluHD0pa2tPvs4fEU9O9780+aIGbb
+ZvyqVur9wLTM5Z5W/GzJ35cGTcw3yerZql1kbGQX6OZnmbXEDcRzyU6zxR1xs6bzAtcRFy4elzEl
+5VfsepxXfMPt1pBhT1x3o7Lub2enns7L0ZyvXpe2Yy3UWfFOyBOB35qzK1xUJunpox5VJdxIJrLa
+h17KfKTBVHWPiRE3sTTrcbKj4U8esXCVHkyTmod7U3YUQFUzLvtG+2RhpoPrAF8cyT++SUlPfufW
+gD+jO5dz5Yi4KbvZ5t6THG/I71WnAakqLZ9yPOauuSvfZ506pbrsiDM2qag72361gXNTPJNYPYS3
+Sn1Fds54TzkUBm2udYS05JEtCR/c/oKKkqmMKiHrWql4Ui8RqER91irTWJf+pUdyndVhwrAsCdPJ
+bo4q2HP7YszJQObL+GjBzityzcO27Mob5Q7ZvsyRoDZnV1XFq7Z5tyrTOsoHmWf0E/Ek1pszelfk
+/q04PfZS6dP5dkKxqwA99X6SNZdKW35rMTRUlF9ynE0pZQp8c3KqbbmOWaq6ouUB8b5iJJwe0ULx
+NOkxsQL1RpUFsZ6CvFt7KBYFePNh+no2LGAeS8eu34q4vKbin6mCMpIS8x1ZNJzhiFD95KS0YFYo
+S01cx3jykBYLFasdD+kqJ2Ti3KG0EBe+N1hwVgk0nEGPVQU4XdGagnvJWt/S8oLP74vb9k3dmwDU
+mwlremx04s8xh0q+4s4IFO+/cOXG+6UOrbr0KR826Q+1yjAJyH3hKwzGZW5lkmupadY/jDIeK8QV
+veEtqCoPZN/au25hBoSaMb+jD9XWPOIrepb5WPsWJUUjhsmQseYPiteuwZrWOUw0dxD0CFKZuL53
+Qq4sRVFyAIn83u3i96gpKOt0PXf4+gHE48al5UlYuBcuaOsR5urvyljUnl4nO1A05eKAslLcny8P
+SMsyttcm1ym9Idc0c78qP/yCZXciBDBef06hkgGLXcMX9Cj2CULiVN5vDeQ04M74kCc6Ej7WiiH1
+kSzqB5WU+Y6GhwqESoc+/dP7MUrI0WPtqSFdyOumG2K0hD/fV2CZP2a1pyFB6QbOUOXnwxhH1bK8
+zQnJTD8TysLr77pkroZ805BkJIjUlq2RqV5EPS4x994edPdHbRk+LG1pS7vPzJvasB/iRRhzjBw8
+9D6kS1hZtr1s+/8HbZ+31q6+04ceikUBwBjzJWvtc8u2l20v2/5wbRk+LG1pS7vPlovC0pa2tPvs
+YVoUfmbZ9rLtZdsfvj00OYWlLW1pD4c9TJ7C0pa2tIfAPvRFwRjzPcaYK8aYa8aYv/mA2zprjPm3
+xphXjDEvG2P+mr7fMcb8ljHmdf27/QCvwTXGfNUY8yv674vGmM9r/3/eGPP29dl/srZbxphfMMa8
+Zox51RjzzR9U340x/72O+deMMf/CGBM+qL4bY/6RMebIGPO1e977uv00Yv+LXsOLxphPPIC2f0rH
+/EVjzL8yxrTu+b/PadtXjDF/9k/S9vtlH+qiYIxxgb8PfC/wFPCXjDFPPcAmM+CvW2ufAj4N/Li2
+9zeB37HWPgr8jv77QdlfA169599/G/ifrLWPAAPgRx9g238P+HVr7RPAR/U6HnjfjTFngL8KPGet
+/QjgAp/lwfX9nwDf80fee6t+fi/wqP75MeCnH0DbvwV8xFr7LHAV+ByAzr3PAk/rd/5XfSY+XLPW
+fmh/gG8GfuOef38O+NwH2P7/DXw3cAXY1Pc2gSsPqL1tZEJ+B/ArCGK6B3hfbzze57abwA00j3TP
++w+878AZ4A7QQaD1vwL82QfZd+AC8LV36ifwvwF/6et97v1q+4/8318Afk5f3zffgd8AvvlB3P/3
+8ufDDh9OJ8up7eh7D9yMMReAjwOfB9attaqNxgGw/oCa/Z+Bv8Gb9UBdYGitPWUke5D9vwgcA/9Y
+w5d/aIyp8gH03Vq7C/wd4DawD4yAL/PB9R3eup8f9Bz8EYatdUwAAAI3SURBVODXPqS235V92IvC
+h2LGmBrwfwH/nbV2fO//WVmy3/cjGWPMnwOOrLVffr9/+12aB3wC+Glr7ccRWPl9ocID7Hsb+E+R
+hWkLqPLHXewPzB5UP9/JjDE/gYSwP/dBt/1e7MNeFHaBs/f8e1vfe2BmjPGRBeHnrLW/qG8fGiPK
+oPr30QNo+luAP2+MuQn8SySE+HtAyxhzWq36IPu/A+xYaz+v//4FZJH4IPr+XcANa+2xtTYFfhEZ
+jw+q7/DW/fxA5qAx5oeBPwf8oC5KH1jb79U+7EXhi8CjmoUuIUmXX35QjRljDPCzwKvW2r97z3/9
+MvBX9PVfQXIN76tZaz9nrd221l5A+vlvrLU/CPxb4AceZNva/gFwxxjzuL71ncArfAB9R8KGTxtj
+KnoPTtv+QPqu9lb9/GXgh/QU4tPA6J4w430xY8z3IGHjn7fW3kuD9MvAZ40xgTHmIpLs/ML72fY3
+ZB92UgP4PiQj+wbwEw+4rc8gbuOLwPP65/uQ2P53gNeB3wY6D/g6vh34FX19CZkI14D/EwgeYLsf
+A76k/f8loP1B9R34W8BrwNeAfwYED6rvwL9Achcp4iH96Fv1E0n2/n2dfy8hJyTvd9vXkNzB6Zz7
+B/d8/ie07SvA9z7Iefdu/ywRjUtb2tLusw87fFja0pb2kNlyUVja0pZ2ny0XhaUtbWn32XJRWNrS
+lnafLReFpS1taffZclFY2tKWdp8tF4WlLW1p99lyUVja0pZ2n/2/mb4jAhSE70YAAAAASUVORK5C
+YII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.5523... Generator Loss: 0.5931
+Epoch 1/1... Discriminator Loss: 1.5956... Generator Loss: 0.4272
+Epoch 1/1... Discriminator Loss: 1.2407... Generator Loss: 0.6906
+Epoch 1/1... Discriminator Loss: 1.5402... Generator Loss: 0.6955
+Epoch 1/1... Discriminator Loss: 1.0282... Generator Loss: 1.7090
+Epoch 1/1... Discriminator Loss: 2.0579... Generator Loss: 0.2622
+Epoch 1/1... Discriminator Loss: 1.1844... Generator Loss: 1.0045
+Epoch 1/1... Discriminator Loss: 1.5009... Generator Loss: 0.7875
+Epoch 1/1... Discriminator Loss: 1.2890... Generator Loss: 0.6643
+Epoch 1/1... Discriminator Loss: 1.2547... Generator Loss: 1.2426
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsred5mPf887DmtfY8nLPPyEEiKYoaLKmxVdcG3KBt
+0gZN2gJBWhTwVdEUaIEavepNgFwVuWpRIy2QFkXTNk2QBnERG64t25JsiaYsiuMhz3z2vNe81j8P
+vXjffUTakklZYsKL9QIE/7P2P3zf+33fOw9GXdesYAUrWMElmP+qB7CCFazg0wUrorCCFazgQ7Ai
+CitYwQo+BCuisIIVrOBDsCIKK1jBCj4EK6KwghWs4EPwiREFwzB+xTCMdw3DeN8wjF/7pL6zghWs
+4GcLxicRp2AYhgXcAX4ZeAJ8F/gP67p+62f+sRWsYAU/U/ikJIUvAe/XdX2vrusM+IfAX/mEvrWC
+FazgZwj2J/TeXeDxB/79BPjyj7t5sLZW71+9ggFkuQHAeRKTxAkATWeKVTQA8CjJklIeNCsAyqrG
+VvJW1xaWI++oMbAs+d0obSxHpKJCHsOiIiv0VWZJWcjfDSryXL5RZAlpKb/blqArbLZxLPl7FueY
+ZAAkZYWN/J4XNdYlyTXkwqyhrPTjhiH/AUZtUlPrOOReyzQx9QU1PH1XiYmt/yhLcBz5Pc7kvSY5
+Sabzcw2KVN9b1WRFLr8bkFPpt+XevKqgqp9+rygvx15DbXxgGoZ+p9Z3mVQGT58r9YVlXvMXkUK3
+1zewFLdFWuD6Ms6qdgFwPShTmb8bQFUIAgy7pkhlMU2jIk7kuq70XXmBbgWyqsK8nHddYSvOKwMc
+xe3lMnmOiW4FDKMiyRSfrsnEjeUP5x89L8OUr9uYGMblWsvfzBpKxWdd1VSXi1JBpb9X9dPtwlO0
+GsCPuDYaJrURAPDi9Wd5/fIPr792Udf1+keN9ZMiCh8JhmH8KvCrAHv7+/zWN38P17J4eCxD+h/v
+vM6d778NwNe2fpPm6BUAblVLHr4/BKD2IwAWSUE/FAznWYfutixAXrp0O/K7sejR2ZbVHc9lDC17
+weGJXAeNOeORHBqfBUeHCwBGR+9wZyynbK0j+Hz5L/0i280ZAI9/cEbTEPr39mTBmikvPzzLGbR0
+oV1fvpHXjOepjMd1MW2Zq1l5VLp5g1AWs+OHNDotABKzouPLPOZmwKAr75tPYVOX+K0nQkC96oR3
+juRA9HZ9zu7JnMI45cnwCIC2Y3BWyjisUu49WaYYeqhiCobTy3UqqWoZp+uCZcrh9EwZb88KWTry
+XGpazG3B1ewoIy3l23Xx8YnDr/61v0GnJ7g9fW/I1c/IGkfJAQB7t2qiu4KjnRchPtuUsfVLzu/L
+vgi8JW++dQFAmU4AODse0lbifLiMcZUznMYRa4rzyDXYbgvzSSIZ8/XdJoe6Xzwj4q0jwXNrr8E/
+3fs+AMX/8BGTsgy8sA3AWuXjKKHzBJW0cphZsh5xnJM6gtsqMogvGU5WYyolK0vZ34YjxE4HB7n8
+3f9cg8T4LAC/+X98k11L1qHc8h5+xEiBT44oHAL7H/j3nv72FOq6/nXg1wFe/vzLtZcXFFhslrIJ
+/vPpjL/XvA7As9//Au9dF0SdPLBxNgURZxddeVkzY5LLwnb3DGaRLKzbgCjpATC4CsXCA6DRFwof
+X4S090YAzKc9gi2VCMZr7OzIcN+8FzKrZLMNj+X5r+1ZROcb+u0jvvu2LJx9s+Td95SLhz4XYxnn
+lefkcC+GYG3LppqN4FpHxnaSpXQashRlITult7lOVQsXbLYgK2Wug42cYib3dq/ZLEbyvY1bMo/j
+P24T5U8AOHqzpKfDPIkclkEIwOk8oTEQfB2PlJi4JcO5vGtgwaEh+O64Lo5KB71Wl0JFMkd3dF1V
+bPgyzmle0ymEbd6vC7Lih5LHx4XyuYon78j6pd4Fv/09wfmL/77g+LUnn+VgTd54OOyw84LgYn5k
+EdwQYjm9V7L5WXnHd7+n7x04DIeytzq7De7fHwNQuSF3l3LP/nqTJ6mteG4CcC/pMbgqfx/ePWZZ
+C4V4fGcJ/+xjTsow2FJxo7me0uSGfLsl47GSGYNcDu5FEeBbsibTOqepyEtrcFQkq2t5l1WYGPpb
+kdRPJYjsm3Nq59vyjddOGX5uDYDuxxzuJ0UUvgvcMgzjGkIM/gPgP/pxNw+rMf8g/0c89w9+iV87
+Eur7t5+F9j/+AwDe+fIG7XeEyy3W1ijP5bDZTdmAycxhKfsd+yLA9XWj1w3MDdmwjSzA66rIXAgi
+J4OC7ESuSyvFmAm1nnk5x6kgPnSWXIxlwVxLpIfk/gS7vC9ju3+H48kpANmbHk3lwM6hwySU5164
+J2NYbjfZvhAqf7cNWy3ZuEHhM9DrM18OwdWNAbFSfieGkUo8rcQjs2Uc/h2DowOZa/xYDoTdm2I8
+lnfQXzB8X+YRJ2NOp7IJE7NikHUAMB3ByVrkE4dCLDt1gyuB/P5yuc7RTXn3X6q+yvv+mwAclELz
+32ieszaRsd+L7tIxZDz37y5UQfnJYPK7E0pfJMQ33j9hZgvu2t/8HACNvXfZunsFgPkvtNg+EoIb
+rFV478m2v3ttSvK2ENz1tjDHeGowC2VtinED15G51qcZc1P20+R+CU3ZD9WRHP7yM1PiQ6GsrU5F
+fVfWwe7FFB9zTmZR0egKEd6oQxq7Ir3sBSI9HI07OKUwHjdY0EPW71G2ZFypilIWKjOI+idQs7xU
+Oz5AeUsA2Xrs/Nvb/MLXxh9zpAKfCFGo67owDOM/A/4FYAH/c13Xb34S31rBClbws4VPzKZQ1/Vv
+AL/xsW6+8DH//jNY34KvjEVsD8vv4n5G5LqTac49tbLsLk/wUqGkliuUNmVBv1axNmwR2sK5Mgxa
+iXBV+m1qNaQtSnnXcj4kdwQFRpkya8hz60XGRSQ6aZq5pIZQ63klIvOFYzM7F47/aLbgfKZGraKm
+cIUjuFZKjHDYWsXrbh3irMvfB0ZBqVxwze/gBzL+3VBEHsPy6aiRcBmAtVSOH7QgFo5/2p1RDuUb
+p57q3rHDWS5SzGTUJjQTnbNFrgbDooDUlTF7TREtS6Nm25GxvdLaZX1rR/4eNmhuyjdCq0s/E5Xu
+ohJJ40a2wFsTHhZFO+RqXn6+ec53Z5muw8eH2QCyR7IOR6MUW0XlN56IevTS1pc5Qwwe3iRmuitj
+82oTqy9r2V4sqfdUsqpFqqiKI/aXaoy1Z5zH8o1FWJCngpckT1nGcp0Gal088kl3BId5BRNkHGfn
+/kfO5alrzzFYXlV7QNIncAS3x3254/pOE3spxqEDt+BiJDaVn9+Y8eShSMOZVfODUiSMG7Z8+0G6
+5FQNqvOy+rFq2sb9/+ojx/pB+FdmaPwg1GVGNjpk6lxh7+QWAPfMEbO77wIwSg6x9mWo6WJK4YqS
+117KYR1s7NAK5NpIXUJLRapeQBjKgpp5TaiichrKgd7G4mIpCDaKDPPSPm2cEepBDkuDKlfdREXj
+0btvsBjeA+DsbMI0EeK1YbQJLi3gFmSpHFQ3kIN0o9nFdOVdzXZMP5DNUZUpptMHwG/KM123ieOJ
+uFtmLkEoKkpiQKHGwSwJmDnvA7A2uQbAaVTSdwcy3DBjfijvS6OUtFCB16kYuKIz7zmyGcsbHTa3
+5EB89Zkvc7MhY7M2+tiOqG5JZnJtJPg6LISA1MtzhkcypxvOIyabNwH4neGSUIlTFVcfW9Q+f+8u
+F1PBbVZnzEtZ15upEqa3K8IvyPqbazkDV9QEtyypN4Vo++2Qw2NRJVJTDpK3s4VxLgxiWG+y5zwC
+4KHnstB1WuQGy6WQsFxP9Kw5ZSNTQ13DxY9k3qaffuRcLg/py7bD35gJjt55bo2vflYI8dK5DcD1
+csjIkD3Ujk3mz8ja+NWE4fA9AIInNrfV6zZXQt8+e8LhXZnf42TG0Y+x4fzG8NsfOdYPwirMeQUr
+WMGH4NMhKVgOWXeDR194m2vPiLn4wfWIC0/UhOh+hpeJSDWJ92jdFmrebe4BkIYOobkFgLOT0c9F
+tB1bMYVa7ZNeQjMTo5TryPNTo8HSF4NSy1zHawuntBZt3OwdAMwtC+NCue1CuJXfKIhtMT75jyYY
+hkgg/p7DIBXOe5bOaRWC3nhTXUiDEF/dYoXZpFIppiwcHEs4ol0JJyr8hCpVacVbMi+F+1tOQelv
+AxAkc/qRuGoXt+X5/cUmJ7lwj+MjhzQQLmdaPtZCxlE5BvVnBBcDNa0bjZDbvuBw+7MBTVMkF7cN
+2fJAFqr/DllDJJK9Wr4xf/gK5YtvCI5PP8NmU/D59a+/xOj/Ewnje3cz6lK43GX4w4+D3k6BuSbW
++dl3DumpGJ9/Vdb0mXqXqCGc9Nq4gXFT5mfPfIpKuHeSbVBtHgPQLNQJVo2wnGcAqLcOscpdGf/s
+kNyUfVZUS1RooNR4GbNwSXdE1fBDiC6EDyfTj+anPVV5l7/oU9vPCd4OYsa2qL07V2T9g+I2m8Uf
+y9jqDQxP9meYXsXtioy12G7yOQTnE0fW7r13DfL1zwDQ+P43uH4he+D36w+beLcCkdjufrRwA3xK
+iELgWbxwq0M9LnhnKqLV/ffG9B6LGDk8TYhcGeoyvcdXn8gGyQ9kE68bFnTUUr8MyFS/bps+eSkI
+csY2sSGEpQhUzEot/JkgsjQLjKm6C8s5RiTPtWyHriEiau6JiLecJoQjET9bZcpU3Xefi1q01mWc
++w/bPFBr97ORjDPc6RKcqdV7c0ZHXSZZaNJCFtrUQxxYIYYt48ksn/alZm66FEsZxzgxOc9F5+wv
+hUhF6ZCbmRz05f4JKsFT5jNaSmTmDYfnJkIsd67L3A4qh9azQnh6XCPsyr2OCf6mXOejLRzVqVO1
+bjtbKdYT+faT9BG+oS7VnV1euiZqk3Fyh5O54OjeR/gns6WDeSrr3vRMFr4cyGcO5RDHL6S8kgiu
+lls15SO5LtpLTLUZ+YZJ51jGkVsy3qBymLkyD/O+x2wu62ecJZSRBiIUDtVS1qxS+0PizCnvqcqw
+VXN9IgRk2ayZ/Tnz6ACfsYUIf/V+gPusHOiDvEnsiqfhet5TJF7g1LKnF8mSgSXfKK2S9obMezJa
+EDhiH9npCE62ypKt8q7g5fkX+Mev/kDGuVw+VQFKDB5MfzI/0Ep9WMEKVvAh+FRICrUDyaaBtQ51
+JOKS/96IwhBu5oZzlkPhlIZXEM2FcrciMRzNm92nQU+e08OohEJHVYWlxsii2SMy1bKsgU5pdMqy
+lm/YxZgE9YlbawTVmYyj61D3hXtk4pDADsC7Itw/uYgIVNRMnSWtTaH+3dJmXa3EUU/G2cgqUl/D
+dQlIA/m7tayo28J6DTUW5ZaDqZbwqmVRlhr9SAPv0intLdkwRSQ2NMql1ZhycUNEZ/vUJUZ9865P
+rM85tg3qUbiyI5JZw1tjXUN8jWaJpUbXqm7AXAN9rA5GoZZ449JAW0BLxtlNR8xqkRoGvcf0rovk
+sX1yDfeOxAuksYznQ5FsH4SgYOOKqEf3J4f0moKvyUCe+Pn2K9ihBnXlGUVfOHAVzckqGdNiOafQ
+57y5iOpLq8JQQ3FUX3CqRtdlw8CuhfPWiyWGBmdlvopCeExq2Vv2oEF+oF6g8x8t8lyGEDiWwebz
+Yqz9Hn2+qOY/b3aTzrpINycLuXuzn1HEsg6uG5OppBAECVks+6lnWdQNkSZqR43nW2165ecBsIoT
+fuUFwfHDV2Mi3UcmNY6GrZTJjxzyn4FPBVGw65r1rMCpMu6qvtSoUkYq5rtHKbtrMtTAahCsHQBw
+dUPDgNsenVCu6yKkqWG3I6um5WsIblbTdHWjGxoG3PO4cyGYsoqcNBOxLjXmRC1ZgOZhm+1KNnoR
+yuHomDn+XJ7b77k8KITw7Pb32CzkOXdg0XFEdO3aIkYPBiXlROZRrOV0kA1LY0F2GZnWlE1nRil1
+RzZmUpgEOg+rtLH0UARFQKcj36g80S3LCWz7Quie33/A6xoAZSYZiWMpDku8VDZs3RHbge12cdZE
+LXHLAFMDekq7xrKEiDpZht2Q61wDbLzQwTBEDeqUY3x1wwZlj4MNIeTurSWPS1mf+xr9aZT1j3Sh
+1eOci1MRzI22T9KW641ztR+9EEElBNluT8mngs/cKok1rNotbObxpXFA5lGZGfZY1ncxd6liwXOd
+gGcIoVpaDnYi65pqjkNcLzGWgu/yvZr2TOYRtiSyGMDTiUSArYznxW6X3URCsLe+4nGwJfhq9bbY
+VluS15SxN/rrmA0NyKscZsrU3MAkdNWmYgZEapCxVNsJ5ga9Uuw2xQaUqRCIrcY3cBcy/3drA9O4
+zPr4eLBSH1awghV8CD4VkkJlQOJCli54MRQZ/Rtdn+kDIYmTqqJbCJfur9/CvCEk027J/7tOTKBT
+icMSQw1qHSeiqjTT0J2Sl0Ktu6759L1GSzjX0bGB6Wn2XdQh0pgGWgbmpkgC+UOloUEBTZEewlnG
+pjBPyn0IXOHSgZkyT8TybV2XORnVLsG6cAnb8sg0UcVJGjjq97Y0qKayc6q5qB2h51JoIFDR9jHj
+y6zNjDIVzpV1JaDHNSxsWzhi8b5NZF3iyEHDN+gYId7L8u2DQMVsp8CZCMcvnzmnjESKsZyUqpZ7
+UyfB0WCvhhpzy2kHsyNrY9W79C1RXeKqzXouUsxp+Aw3NRCteSb//1/OYyY/QlRwrxi0Q8Hb0cNT
+9izBR/G8zG93MSDcUwNs7VH1Za6zo4xCA6uzaso81cQzVSlMI2OhUlHZOKY3kOsmS/JU5tpaxtSa
+gzCZqFejgLly8e2DTWZqHE5nEYFKnpfa3KCseXZNxnvw7C5OX9SnlwdTurGqec9dYGrSmBuKVBGa
+LRJfvU9xQLejokDUZKmZmKYdEbRk/w018jlKS8aZqB3O2iH76u26vdfmW3diXZMKx15JCitYwQp+
+CvhUSAqWYdCxXWqzzamhhirrgsQVw0nkRAxy0YHPM/hsoTUHVJftxTa1RgoOrAzXUC5WBSxK4bbd
+yqTS0OXcF+NNmcwp1O3pJRXRSJ477OR0WyJtuFWDa9WBjKMjMbztNGSrI1yp297kjZ5w22fKW1Sa
+reKeBtgDMYK1z4XzOQNoxcI9ylZJR/XvqhVT5RLfUIWaGFM2SBGuWjh9vJbo1laVQ0MovzcbsOzL
+PWuX6Ue1QT0Tn/jR9ru0tfZA16zxTXXfDfp8efysvEPtEwOngdHS92YutkoYZuGAJ+NsREuMQO6v
+1YVWbiZYZzLPRWtOcSK4WDhzeKQRmb0F6T3B+euJuALtEFzNTvxgGPRLxg3Ykve18s8w/KxId1/v
+/uuCl22fRiFrPc1naKY61SwjjgVH8ySjUYlkYarebhQW06W8t7+IOVqIFGMmJhcakdp3vKe1Dtpq
+PE6qmmtTmfN2XPKvzWV/5vsptRob12sRFfc8B29NJMWXDjwiS9ZyLXwO76aMec/xiDI1MJ6LrWm5
+HdMzBVd14IDaX7IwYl3TpaO+jxGLXaVoiRE8fzwBdZOHwzWySKS0zk6LLx/L/N7P66fh9lPmfBz4
+VBCFyjCIXQdv6VKpUaS2MmINwmhUHksNANrfDGg3FWmxHPh30pwdDUV1tttUWgxlFsegWWanVkr+
+SO6fbsiGSccly1zuPZ8cM7QFaTvTHlkuG2HhDBm3BcFojHzRC2mqsRNzyu2hbNyqPcYsRSwt9mzC
+UqzocVfG7scOsRoS7bxNtaai+8QnUzHeV9Vm7kD6huZc7A9hLt/oNVuYsdwz6s5wh0JMihtifMM/
+IVrekffOQZ0EWE6DgS//2Oh0md2Q+XmaDbrEwNKU7LKTUup15c9wIxmb5XexMy1aoji24opEc00W
+7484sQVXD0YRmSO/E9tUz8n4/vqhENaNt2P+yY8wNVqfb9Iq5bBY9mP2myLa566sjWM3mbly+KPz
+EqOS359Mz4kjOVhm6dJtyVzXDN0rZkEzlLWh5ZCdCz6T/py5hm5bZUqigVF5R/aKubQ5u4xPabvc
+2paDXmYO7X0hEFe3JS5kdrLFCwcSN+Lc6uDdkwC4aDglk4hm6sOSI8QAOTmSLOB2OaD2hGg23ZTY
+VQ9VELAstLDFPZjuSEzCo0z3W+5wnqg647nsbosqcWW0xbkjYfFRviCzoj+D5z8PVurDClawgg/B
+p0JSMMoaZxZjlQmzTKi9l/OUYs7iEemJlEjqTw3+5ESo+MGOcIGzxRD/poht1WSXzZ5WYapy0qVQ
+zEfDCef63Ewr9Jye58xS4caN1OQwFbHspNPg5oEkZvWCgF0NlS13hAuYSclyKtwjCCz6feF4WbXJ
+ZKJiYDYl0px9L9Hw6b11zjWyr789w1gK93R7NWi2W1rLey+eLHjzkXCG8+89YaZxA1e3r7A7EE4z
+Sg4JtoSTXj1TN2XDwCxEzGw0HxO4wlWc0iTVsNtFtOTR2yLGc0tjKOI3KBLBd2nX7Dc1oShIGLii
+urXXW/QbyrlLMS6m6YKzU8HLnffucjQSHEbunGUm8+s4Bg9yWYcfnMr8vtM0cNRgehkRAHCj2GPp
+qRTz0hdxfVkr2xYOPl9E1FoIhWTG8YU83TRdhpngu+tckESq3nTUyJvU1HPhutESopFIGGmeE9fy
+XN9p0ssEH+NEY12ShPNSIzq/OeGBxgrMb1n0C40n0BBua/uCJ5bGOY5Peef7opr62VvwO/KOb5Ey
+qeU5Q13Eg4vr/PyzLwJwsLdNcS7S1KiEXFXIi7fHFK/K+N+MRTpYJjGZGjYrw6DwRY1ZuDUD9ZdW
+C5PyJ2T9nwqigG1SDppwHGOo3/VtO+bu+6IDDkczOip2P/Db7KPlhFQbLZoejy8EUeZmjbuQjRvY
+Ux4ngqiz4zFvXMjG9Baykc4ikxRBamwmmKp7nc1t1lJZjEa3hdlR/32mqdp1TG1cVh7ysNTvvExm
+LNQU7fomdSkiYbImm2AauGQLVV3uBxQ7cl0uuoR9mcs4kjHcfXSHbx1JBaXxYoydyqFIXDhVUdqd
+ZYyH6on4mmzmbbrQk0CfYtTE78u3rxk9IleJU2hQ2XKoHw9ls26WAcfHWiwm9DkbaMmw44zjplxv
+Fj2qNVmHhi9rcz4xeXAoB+9dOybRIiVlcQO/L7i1Og7XJzLmqx3B4fjBjO/wZ6H38j7PTITYPz55
+zIWmkqeJENvHo5hQD+lJnpCjaeTTmHksBGk58mjvyO/hSLNSGzlHKhjPCou0I3guK4OOBi817Cam
+qh2HkerqccbM1hgC2+F1X36PpiZLXfdDzUrdd21iLXXVfev7PB7K3ztFzDSQd/SHAWVP94uqyouz
+h9zRakyBn9BvCCE8mVQcfVfW8o4xxp4JwXkd2QvupMvAVxvI7QGxlgRo9W5z9LoQlsRfkJUflXHy
+YVipDytYwQo+BJ8KScGuC9byIQ/qkOxMjHOP3voT7KfVhS2qWMQ2Kw1pCSHlXKnkQRUy3ddEltSn
+DtX7sHQoK61gHOcEY6GB5yOhnMN8yK4jxhmjETNQCrzsxBwstOaj79CvZEzmQCh1dZxj18IdA7dJ
+sCmcJlx2KdMHABw+mnGmsQeBMFXK2xnriXxj2BnTeqIZnOszzGPhUHEmGUyNkU0rFk4yPwk50+jI
+aycLKlPm18i6eLeEOxzoe5uhQToWw9coOGVHDViOEbBuy/cW2002E1Ex0LiQo8dzDjNR0TqjDZZa
+2LTd6JFqVJ0zjjDUh56osXM+PSHUOo9rixbvPBRO+mr6Oi/mwkEP9wyuaH7kvan8f7oD2yJd86T6
+IXe6FW4TNeW5W94VGh2Rlk4eyXNH90e880CyMt8/SjBr2Qyma7GmXhKj2WNwJhJC1dcM10lMciFz
+bZQZG6Xsp8PjYx6MBbdxJ8JTo9yGVomOLYt1jXVZSy36aoD1dkrKh7K+k1hUsRcO9pnlskdanoOr
+cQNnCSy11N+wyuiMZZxf25IEpxMzZn9D1rrRyCGW31m+QaWqZ7ps8yQSqe9Uv3c7S3gg6OFG2yCd
+yV6/+WzC91VF+RJdvnFZspyPF+f8qSAKpWUzb61DecjyLREBZ+E9hlobcRCadLqSLfby/hV2t0Tf
+b/UFkbERsK7Ze+3uGm3dxLGf4pxrjca+y+DS6hvKhr/ibPGcp3Hv5BSR6JZDb0muRS+mayVhrLkS
+nmq/vgm+jKeyTbzL0Ni1AjPRjdkrOWiJqDzYEpvE7toWVaUqz/yciWZXtpZL5qFW7U21lP2uz059
+AMC8OsJzZJNvDwIItb5iy6GtwT11Ww9BK6dM5XCXxRy3Jwes39rAUlfEIM1ZO9DS6F0hIPMs4XYm
+KpHpdznoKSFs+kQzwW3etik0d2GpatJi5sC62A6CTkGQi+i/P7XxLSFCrWBOyxeidfPzgtf02xNG
+lqpMZE+rQtkbzzDIZR1mR4f01I0cJzJnzzOpAjk0vfVH1JFcB40avyU4cGrwAtkbaSSHfFhkLPSg
+h0EH41kZj12nNCz53TJSKvV2dANRx0q/SaSuzkPbYk/dge9O2vS0xUC3I+96/+3bZK9IjdFx1OT5
+rhyvh1MbWwOIBrnJ2r4GNX1F1Ydonaa6hjtxRtXRys6nAVNHiJ6zvo41l+/taCySs3C4qhW0dsJN
+6hvy3OLNIRdXhSjsXGQYWiuUj+mEWKkPK1jBCj4EnwpJYba44F98+9e5+08O+cM3hAOlwZReICJu
+c61i60DCRLcPutzS4iq9PaHqtRsQNEVGd2IfxxED3sWFRWtfuMdW8zbPrIkh7dmWcMG+dU7PlXd5
+0ZTjsYjuD94658JQCeOtCLst3K8zOJDxTn5AFAg3tvshblM4Xqvoc72lPRKeu47fFmPeda2D2HIb
+TFUs3ygM0kKNXXVEoZwy0KrNgdHgi5fW9ME6ppYP2/auY6rretEpCFRVMizhwNbcglo4V8922VsX
+HHWCDsNYxtZcn+JqyOxuV77R7OziFCp5xet0NKJo7s9paIGbomiTaZ2J3FEvkVHS0GCw9UZKtys4
++kUjJLWVo4/OOVQvUHym2YAv7nL+jrz3yXJCrPXPoskhtY5zPG7jaF0HSyWN5/cDWm15r1NVPHgs
+3w7yc2igmQjoAAAgAElEQVSIpNP1JpiVGpt1HsPDJ0RnMub97S7rfcH95LkvMduTtUyNDONYJJOR
+juH09BRDa2m605TvagLWqJewVG9GtRBJsRvamKWoVy/evkbhidfmuSaYpjx30zNIpiI5NtfUc5I0
+6bkSQxEbc8xMxrazsY6rxu+DxZKJL0bMs5lIrl4dcj4XQ+Tr8THTf/QAgOP2KeVbMqa7Vpv+ukhs
+hxcfr2vjp4IoGBHYrxq8f/QIcyY1B23ToXBEhBvUXQ625PrG3ufpt+UADLqqL68BC9koRW9ErsFE
+nbWKi0rFRCdiLRGRP9AowM3qFkFP4/qPungdQfbFZMA8kwM9WeaYW0IUZrVs4jyo8D0hNq7hY6j4
+WXCB35RF3HZzWoaI5g21Ngd1gzKTjXQ2MplY8g07LTBUlPbsy2KvS7x12QTXzSc4udQ+9Jo5SXjZ
+OSrF0r4W6HerZYqtB30+Smhq1aAtu4u/lA1bVG1KrV1oB6JS7Ycm1lxVieY5xVzm3LaWJNoMpjAT
+lpoHUcY/7MA0qrRk+WCbW03BrVPcJtVS9JHh0uhokNT0Mv33Ku6aHMC1H9TEqgMH7gbJUvBimn/E
+bK7ViyrZ2E2rzw2tWJQuP4PrC7EsZj089QaEyT6Zegwm54LXlCWRVot6NzZ4xpJx7Ny22ZnK3kln
+Dzn2tDGM2kk2xm2GqprWicWJLfNfRBW2RpGWifw2Su8TakGeN48f8nPrsvee+0KT7c4vyzrd+w5l
+oDkY1zQSNIqpFjLOoB5idGVskV9RD2U8YT8g6Mr+29sVRlYXp0weCfE7ab2KrW7iw8c5iWaHPr+e
+8bY2Gvqx6ep/ClbqwwpWsIIPwadCUpiVY35n+n9hP/a5UEOVM4u5opSx7nyRPUOoeZi36XdFTGy0
+VFJodTCEuJINc3JPA0/mNkFDOKEXP8ZU8XLDEEOP3THxNOvP612QnQpV3nZq3KlQ4HvxiJta2/B4
+T7junZOcMhAOZXrbaCc1+ssu6Le7lkvmCWceNHUMhkkjEpHS7R/T0jJZeVxQq6W6Unm3iYXr6nWy
+g9kWzuW4bZaZvNfO1zHWRX0YZJq30EiIR1qYIzhjkYiqFF5x8DRmI+rYbDjye0ML2bTNEHWy0Ix2
+qW2RaOKow1SzIBtzIL9snqNdnOyULW1o6ZLTrTTMe82hmKh/35syv6tzVZG7vd3GOZNvv3Jwhcca
+OJYZQ3xLxp8tPs+kISpduxJuXdZ9GiraO25AoyHqCI0EV8vOJ2sWS42dcLQ0vp345JHgKPQimAje
+9s3u06Y7p7YLZ3K/f6FZlmnMTlM3V7gkVWNfKzQ5PNfSeoW863rL5bXXRU36yivXKbRMfqfaxde4
+EO+XPw/3NGy8KwiP7O+RFzK/5fsmYSV1MEeDt+l1RdoqJh6NWO4vtZao63YxA+H/2bzLqydy/X4Q
+E6un4nHc5crBXwbgB+9KdfSPgr+wpGAYxr5hGL9jGMZbhmG8aRjG39bf+4Zh/JZhGO/p/3t/0W+s
+YAUr+JcPP42kUAD/ZV3XrxmG0QL+2DCM3wL+Y+C367r+u4Zh/Brwa8B//ee9yIwqvO/FPFyWmNoQ
+dNMJSLVfYW/+PlEhbq/CPcVYqI/d09JgpYOlXKDKSqKZRkJacypNlLLtDSz1yWQa7tvMoVIOy9xi
+cZlU1dvEr4Xq3his424IJ2lc9vBrQtAQw2fS8Alj4R5xw6RxIpwiD3s0m6JTWrnaNVybJBTpJ70o
+SAINUY2WJForK7BlzsvUpMpkHmbYJ6i0+k9cYidCy5fGlDV11RnKravMI1I9ez7J8Pa0n0S9xawj
+76uTEVFH7rfVRVoZHXzNqCw9m6pWo6MVYaotgq6FrU14E22MY4UOuSXzM/KKoim2liCNSVWEMiZz
+Rto0Nepo9+XSw97TMdsXFEOtNpR2iZcqNc0esxUsdXlkzf1szqItc2pkObaKiEXtEGnl42Bhkvua
+HanVs/OwhbOn9qOoYKq1LJJ+n3IhhkbjpGSuVZiObFkne9DA127dZW+PoCGh5ydzD/V8c6qZld5i
+wdpU5j+cbvNKJe3vqvU2llbBNo8s6oFKHm0NmzcOKFQyiY0InO8KulufhVgTm4wxuRqHW46sR70I
+8AKRrK0Lk22tiuWepvS03frLPYftn5fx/fPf52PBX5go1HV9DBzr9dwwjLeRFvR/Bfi63vYPgN/l
+I4hCVJR8/2KKk9jE2gP8pEi5NtR04CsNRpFs3vnpkoe2hELvaIppWriEtVqT4ymcyb3nxZDwXBC4
+NA021GBYmhor4LqU2vyDqiCoRajZNGf012UTmkc1Y21ke7wu0TanFzMe9CWte9sPmGtz2MayT6Vl
+zIaTMU0t+9bsia/ZokN0qhbraoGteRDLPOZkKpvQvFAvQjigp+O1kzHrrm6kHCw9sFadUzfkfctU
+xFN3ueTsWA8gMwpNMy52U0otINJ2Eig1z8GU32Ijw9GQ4iqKMEytcl3E+C0N8y66OOrHT7RjthtN
+mWmmaa/lk2jWpRtYlFo3rOGFNDVduHEuhMld79LVzM+TeAfXENy+/da3CRfCAB6c1ew25bCkGhfR
+6kJ8We/Qz5hqdy6nnlPNNNitSEFjCPpdjTtYjKg1vP3eNMPf0MYwd4/pbqjBMI9pxbofTjTYKI1o
+DjTfI14QqVpZ2TkjTfL0NcjusVEQar/Oh4sz3n9B63UOAzQFhWbvGONUS+gdy9ino5j4/AEA528V
+tLS1/fKtNznVM9C66uFMtdxcQ6uAZwlKw2ga2+xuyD9e8R2+GH5Vxvb83+TWrwjR5u/8PT4O/Exs
+CoZhHAAvA38EbCrBADgBjRj6s888bUVv/WSFYVawghV8gvBTEwXDMJrA/w38F3VdzwzjaUtc6rqu
+jcuqFX8KPtiK3ret2k1czqsMV3PmunWN1dcEniiltiWe83TmM0nEiLTUiruN9V3uqQtqeq9kESs3
+NmLqRCi726mxlAv76qMeTg3slrabK0oKDZl1sib1rlD8IMg5NP8EgIciHJD4GWjLeNfZxNHKwFE1
+o5pqX0Vvgamk8UJrDLTbM8qFcMrppOBkKSpDkaXM1b9dq5rgd1v0VJIwtzrMtBBnbXl4U434CyIa
+WsAl1ai1cTrjKBLj3JsXFs99RdywadbEVLHzIu1S1SIGd9SV2/QLPDWyRf4Me6Gif5FSaIm4uZlj
+XgpWmrg2HQ+YtQTf/jBmoCrfLPep8lTHBHND1iwzxFjm2CmjXa11Mb3LQ+1PUVdN3jsXVhnU32au
+zXVGqooZRYhXyt9nU5fjpYj+ZurT7er4K49Ysx2HC5Xx/RGxFputnZSzQ/n72s0FsfZywKwYlTLB
+Qy2xlxchrqpa0a7P9J6swzyynhZsvdzgARWBVldNF8c0/0Dbw/3KQ8yxcPzZa0+w17SlvCf3Phka
+TP5QVL4/GU/pRtpjtH+FmSdS09rjhNY17Ro+lueNRcI8lrHP/SGBlpgbOA2imxoB/Fcfsbbxi/wk
+8FMRBcMwHIQg/G91Xf9j/fnUMIztuq6PDcPYBs4++kU1OAX7lslI/dUTw+HnlqojbnkszuT6UfUm
+G9ogxF6TDXY4vWCmIlx58QS0SrJrVlRdWeSBAa6Kz7MTLZphzwnP1JLrLMi0fuBFfMH+c1qd6cxh
+91X53jKRU744hAtbevz1nIxUxbmNRUiqwUnDsyVJLSL9nmbkWeEuvqY19xoP6amvvDBiCv39oieH
+p1M2cXWDjh7HDPVQ9RIX2pqJeegT7Uo2nK3x9OnojKWcd6pgzvANIXTJjsHmsRKkVpdd7XCUaFFy
+p5wzbWqdwHFKoVl9RAamI+92l2Pm2jxnqYTpSfaYnUw9Ec0m+VjjAtop9oXMP6umlKIa80ADxK5d
+HZC8I/Pfu+/zrang9o2jQ/ZVrTi9PyC15Ns327KN6nCdWOtVzjITL9YOWHaGqwFARmhTRTLXfKFF
+UcYe0+IStwWRpqov7g05U8+ON81ZqMrnj7SQTT7HtuSAbRl7/Fwhc/3d4BghR6CdRkkxsLTy1ou5
+R31L7n38ako/kHwNL8oJ1T5yGfrdsGsa12VOjbN1sr7Ep8zXa57RvTwuYtKxitThmb6rZhYLgawr
+h5EGn4UvNeieaFzL9Gs8evdj1nZX+Gm8DwbwPwFv13X9333gT/8P8Lf0+m8B//Qv+o0VrGAF//Lh
+p5EUvgb8TeAHhmH8if723wB/F/g/DcP4T4GHwF//qBeZmLQMn7FbU2jRkytmxdlAS1RlNZpkhluZ
+mA2h3JbWHogCi2ktlHazu4+fagORps1A69I5psX5VETGx6WIu2GR01ALeauTMFPu0Q1CkpNLY86U
+N2NJcnnnVL7Rt2xiU8TaC3ODgY5j2rDgfS2QEefYWucxr4Xal6VDrZ2k63mPYKCW+FGP+ZZQ862J
+ljBzSu5OtI28mRBrM5jc9Bho2bFhkLBbaUs7W0TRWWZwpFWU3y9ygoZw3XTWYXQuc3qQHWNuSPTi
+rha1mbKL4wp+srCBO9b6ikGT5VLEVtO18R2J8ZgF8t0N5wmmLxw6KCoWtfAZezYn0+rZcVI+LSxC
+R8Zgz6+wvS3fe/P7J0zU61TNcx7o+p37UwKtIXDefU5xbzFbyLfN4pi6UoNnMadU74OVGNTaR6Oq
+tR5D02ZdozHz4TFnmvz1wM7Z1BDkkppKK5Lk2nBlahgcai3J4nmD9D3t51hnXDajvywS0wKeUwNZ
+fG2L8p4m2A1KMg3X3p9tEpuyH8xa1IsiXNI40PkFDmdaPXqrCZnGUEzHFYlm5nqG7MN4bjxt+jIt
+fVyNWUnnX2P5oozjPDnnov+TtY37abwPf8APG+L8afg3fqKXmVAGsBbBUIuXnJU2+49l09zfDfDH
+sjKTRotdLYASv6TLssgJtKZi4Lvc3BOFcB6n2FrFp4gtSrVBBCr6ut4YUxvFJgmUXZnOo+MpDLSM
++Hce8f13BfEz3VSNsmQyF3F3bd7GvSlBKn7mcbEhC23ePeRIRe0XctWn64BzVZ5nSUF/rkE4ZcDo
+vuiUdzRk9sAPqLSSTn6+JN+WQ+XaDdI1EbvDOGaiLeptDfoaRyW16pmdIOK73/wNAG6vfw7DPpB3
+32jjIEQt25G5BXaFoxmjdlQzy7VK1WJEoGnPRu0Qahl8P9ew7GaPutRCL65BnsnG9Ns1tdaUb6YN
+Ohsy5kqJ5l57wc2J4Ps7kxB7KWLw+Mn4ad/M2mjgDWT9MvWW2E0IfBlbqwo5Hwq+jkYRTiR605rV
+wba0MIz2XWSWUqrX5q0JeKmoFd7SZ6n9Rm82Gzx8rMRQvbDzLKFW9/Rfblr8wVALsvQXLFTO3ta8
+FQubVDM1X4o6nO/KOqydLlhoKLxXpaQaZr88lfE8zn32tD6oGzTwtKvZee2xqWN23RhTVWhTmwQZ
+pctUvTKROWeqhXjK+j4vWF8CoL91Ddf/iAaefwpWYc4rWMEKPgSfijBnwzAJnJCFkxCgzS+8krGv
+Yb7DGcZVtbi2S0pNGgpMobiH4SHpUqjk1iLhzNa6hOYJp8OBfuWMxNBuvrWGDM+3sfa1L6Hn49XC
+dfdHmyzvyvU5EWlP1I1EM0ouWiaf1bb2Xu2Tai/FKjp+yqW9LZNGJd7YI+1gPBs94UkpHGp8lrN7
+qEbJfkl6LJzy7VR+ey8xaR5oYRls3KVIDcbVirY2B6zdkroQNSAuhaMkRQ0HWup8CIfK8rqjnKYj
+1YXL+Cqd5x4A0KhFbA0GS6xIa//ZIx6riJpVF3S0oYrr2NgqHq9p7EJSNYltEanHqU1Dk9Wqqk3t
+aEh036LrirT08rn6+edjThIxRF7sPaD4gXDBpF1hqIrVHJ1jWBq01lRXjnGDPRHMWAxtUlvjApYz
+Ao25KBopa6nElpRaA6PlzXhX8TYzFox0D4Qdj/11ke5qy6G3o8FlI41/qCouDE1iOz3iyTPqaTpK
+6GjfyVJxf92ueF4d8MPuGc1IXRjPLelq45eT+xVdTVZ64kpsxqTZo3lffpsFHhueSEJx8wrnJ2JU
+PIwN1gfaF1Sl6SqMCR0ZQ2cWE0/Uq9bdYLGUee/sjllsi7r9ceFTQRRs22RtLeBKbjHW7k6TKmNT
+m32P6pLTc5mw9/aUB196DYDr1fMAPHv932VdrbCNyiDUgqmRv8vmkdqI521ydZclWvgUu8Kba3BT
+EREt5EDeyx6x1LTn5bHNZCRoCi8DcBIYjkXcv7rfZnkoG3O9sUFXrcj+ewbLWtyoa6b0WMA1eUbT
+jJdrZ/R8cSfeMCJOdMP2NLBl4IcsNUCotbXHoiXfuOG0MZRYLv0OlmZzaoAhgZewcy4EcmxE/NGZ
+bOi7ye+ztSHfe/TqKV9IvyAoeEUOhJ928EPBhRfF3Nbs0cjYxtQgpL5t4oQaydiTCD2rzKk1HyAv
+XFjXuoxGSTIVApjHcyLdpHcvBG/B9oy9b2hXr4sKN1IvSJ1QTzTnBYfzc80MVMO72xvjqJuyE5xw
+TWsmhtv9p/0NvGpCX7YRzmUk6FqH53ONlu33ONKqVldvb9DZ1H2xMPn+64LPqQY6LeY5P9DKRQ8v
+Uhpaw7GZQrsp73gl1uI17Q6Wfu8Fw2AS6j5cuFj3tD7oesBl79pbz34FAGP0gMZnxDVcFzHzNc3B
+GaXke6LetpIFsSvzzpU7FVnJ46G65/Meh7Fc7z075dkvq2u0ex0r+3j9Hi5hpT6sYAUr+BB8KiQF
+qzZoFT5RJ2Q5FooaWDYP1Oc9cw2SkXCdLDPo/KGQ2vtdEXefb+zDuoYun/ssN4Wrzs49HPMyJHgP
+Y1vrURXCHS1zSKFdkxbREbNS3mfZLWbfke+d5QmRVpKutC5h6NtUrnocqprtQOswLH0MU8bfafnY
+M+GgtYrieRnQsrWYSrdFrKXNhm5FlWi8v+Yw5KFHqeG1RjDAb8jvS9PCNoQDFXOoTBnzYioSRmok
+TLV6NPMKL9KgpqoiTkQUfbZq8Z5WMB4kMl7X7hCopdt1GjStH/IL09SuUGkLtPxXoWXn7GJGZYv3
+wfRT6vKyF2ZKnmgV68XbnB6qaK+hyPf+1wXfKAQvZ0ZOogYzr4oxtYzbxCjwtc7EQjMn+4XJItEK
+xrVLQ7uDbzYKxhrs5bubFE+0TmdDJcFhRar9SGvfIdzRish2iDp2OHv9Dm8NRbIaqieqriHW3JbE
+zGlqt+qsMnjGF9xP2pqt65Xc3hRc3Y8bOIgaMB9FnKs68sxhxPgzgrvraDxzOyE5kjVNezHVExlb
+as+oCy1t38jJU9nLQ+2MZmRjZpHWHS0SOiq5bGUNmqbk5pjxGKPzk/H+laSwghWs4ENg1PVP5q74
+JGDQb9X/5i99HmNi4S40WywoQQ04PbfkZCxUNzALzpTD5rHok1legXK52oJCYw96nkGofmrbLsnU
+gxoXqocS4DQ086y/Q6k9Gm996QW+el16F+6+sos3F85VaH0Ec32HlqdRd+/X/PFbvwnAa3/8Btup
+2DuOhiUbPdEBl9qzwG9WzLWbccNIGU7Vr24VT5OVHDU0zuKYmZb5KqscR5uzbPR6DK5KtJrf2eL2
+1w8AePnZvwrA5l4Hx1C/e1Xw3/+3f0ee6w45uSP6+aB3woOh4CIPJXb7JEq4d0ddp8OCV+8Jh3Zq
+g1j3iGNbmCqFXL8qNoVn3AGDTCQlvz0j0a7bswfH/MHoAQDHxyPqS/bzwc4vPwKSpMBSA2WaQzqX
+tfr2GyLRnDz4h/y/74ldYvh7r/HGIxn/5HCOoX0Xi9LAVCOgqdGvvZ09Niwp+Nt3MnbVbe03A662
+Zcz99hY7t4TrF662YHuuyV7vswC0d8OnpdJKYr76ZbEDjI/VRVjUTDWicWc95MqeSKGbZoCnsSVH
+8ZJUQ+HNy/B4y2ajp31Rd9bxNYpzIwhIbfndb1g81PvnD8RO9npywQPt71DaoH1jJFBAj7VleJSG
+xilU+R/Xdf2FP38FPiXqg0GNU2dMjA12e2Jt3nEWvO9q04xoSa2++dloQUerOJ+cy8I6YUSih60T
+Oiy0ycag28bUDe07DY40J6KO5LeHWcWexgI8mY0YaJGO+E+mvPKSGKVGyyts9jQwaK4eDsukQsTE
+dmvGbiSL9aoDb74pYuC4bbI4FSKyeSBEap5uYGktyeGFR6ht6Z9cJHRsbVqjfv6W5/BIG94GhUGk
+omjQ22CmQVhNZ0b9+7ITbt0S9aFZNOlq2rCNhXVdy5I96pK5YsH/1psp/vPybeOReGc69R2m7woO
+syjlsn+IWf9QnAwrn69UWjF5XULMX84tplvaaNW6Sabeh+m4ZlMNsFFtMFOi/ZFhNLaBqcTbtSum
+GkMQtYVgfeu7MSdDifU4/ME9sljwWZZgaMCSWddYOs6GpoP71ha7qko2r13FMbTgjBlxmMvv7S2L
+hxM9sB1Ny7/XZ/0XdB8WLZqhdrVKPMwLDfnWwjNN0+S6Kdd+c4Nf2RaD9hPPwNBy8EbjCQ+yS49X
+qXgt2FuTe6/s7ZFqubZOaVG3ZWymY7OWiYHx0RNN1T8bM9Fvj9P6h7j9AJ+v6xQNF+HjtoRZqQ8r
+WMEKPgSfCkkhzjPeOHtM5+iCb1dCGZ+71qY3E1Fu0TdJNE89MFIsdVu2WsIF24nHmWay7bhNso5M
+a9fb5LKu6WbVwFRpYvhAMyqrGelIo8uSjOREDD/V6ZR//k255+e+0GTthhhtHFfG9lryiN4dES9/
+884bbEQPZB5/+B3OtV9EfS9iqmGn4SNRI4K9Ib5G8S1bKTPta5EvpyRaCNbQLtkZFV6lqlRekFw2
+Fjl+wPJCONegeI+T9+T+e5+Te//a+X/Clz6npdksg+hVrbNgvcVr78o4TyYRHe0vkag00qpjTrUP
+5jmgDb9J+SHnqOqMQMvXvVKIVNV5aY22usWcIqHdlgdfizs4tnK0qv5oCUEhrWsqzcUapSWJKXO9
+93tS0Pf88X2WkagPy2jBvPwhW6xUKjQwoJS52LaI8M1RSeNr0vp5K29hbco6lYvl0/4bj548IddW
+f9biTQBOh79EfVsLlmy1CEyRFguz4l6ihs3LitEGeFoF/MUZlA359tUY0nWRUouJzzPaItBfyl44
+7azxuU1pGxf2e7Qt7RW647AbX/ZFjai0fd9FJGpgHJYsqj9fAvvJApwFPhVEwSwM/DOXTr7NIpRN
+HJs5yb6G0uYelaEpq5XHplrou6EE7oT9iDVtthFi09HajUbTxteuQUE1Y5KJyDtU78TOtEc112y6
+MmektoqiPOUb3xZRzev/Eb1SA4dua3DPb4c0juUwrh8/JDK+B8BF44i5ZgPO6pwN7Uc4cJQoLK2n
+h9sxZ+S64Q2nSaLVfZrqB/cXS1quzCNl9LSSThpHZGq1vv+DJlb4bQDu/X1JZb75hZc46H9R8LPn
+chrKnJJ7BneONSgmzjjRbE63JXOr3p+yrmnv86J+2kvIAkIV59u+zVc0Lj+6quK1s8TRUPLYdJiq
+V6Lx3Dn7Gqh0zxySPe329efDcFoy0Hsn4ynLsaTVpOo5OXUzpve0m5JrMY6Kp89W+vbQtKi0LPvn
+etof9EbAYEvmsU5OFsq8HzkeHXVKpcMZk0Rw9PCOqH5r67/Pe4HEE6x/fYOGvrfwoVGoumJcqig1
+VwLtMfpsiKnXUdCgmsjYdpxzDG3Qk26KOtf3dwg2ZDyd5gBD16RX5bga9+HlOeOxEItnvig2hfm3
+N7lnCoF8XP1Fjv+PhpX6sIIVrOBD8KmQFIzawKlNFuRY2mtw1DJYG6vKsG5ieVp1ucrobErY5i1f
+rfutDi2VJAh9GsoFy2aTRqWiFiF7S+Hc46Vw4Fl0l2Ol8iQpWSrcuqpzlk8eAPDWG11e0KYmwZpw
+4Mmj90iHMobj104Y9kS6WRymT1vUl6FFpFmHibZy72QOhrYIt8oayxJLfitfElkaKajRc7kf0G1p
+KbJ5j1kpnDJeuBQLea/hpAQap2BF8vxjHvLekeTrb9ptHrwmefzD8X1GE/XWFAU3bOFAfa33uHtl
+k7cSkUC2shhbozdLTEKtu/hLa5tEm4LzaxrmvRwFZFqJuJ+6pL72n7BusnVL1Iqvjecs1av028rQ
+fpzEcPhoRqFr8s6bY2LzjwC4eyjJTvbjY5o92QvZsEO7Elx0SofEEsmk3Q1Y034W21dEZbj52V22
+Wxr6bFe01OwWFh3SuVQ5Hp8ZxBrXsbS1vsHoPta5eB9GSckyl+c80yZUdbJdXBZpMUm3JAJxP1ln
+byDX03mNv6Zq5fazrOtaLbXZkdWuWOuIFNsMHVINY/b9BYEaT8OWg6sVtLuheH7mZcq/c1fW7Huz
+Jd/6GQkLnwqiUJsGhe+QBQuKWA63nXfJDkSMSrwmXqEhs/M1NFYGsyX6Xb9lYZoikpnmlPqyTLyf
+cVlwJTQKFpm6mTTN+Am3cCxRE1IzJTrVwp6FjdvWpiVFytiRcZgjDX0m5qIrjdRbzz3hgRbdnHcN
+SrVk13WJWwgxqHrq/hps4Wus+mzk47gaTFOZ2Br7WmoR1LZlYGrmXKtTcmeqodnunFIVfjPK8NRl
+lQQyhrPlGYkG6aRJiNcQMd8y1vDO5Ru+VcANzdEoheAlfsVf0jTsd8eP8dX1tp6P6L4kuDU6XZoD
+UdnyPSWmiwnZuYznzE5paEMSZ71Bpk16y5e+hP39PwTg39ImqP+MHw2TbEE6FyKzsT3mtQs5FAu1
+1BvrfezLxilXnqGYyvo90zjAVo/BwQu/SK7Nfdc6Mj+7ZdCxNYvQSXC1ruStbMqpK+HyVmNOVcsh
+Kx7LfhvYVyl21a2dJVh6YgzTxAkFR722vDfPa3otbVj81TXSrny7354QPxJ8W+szSk2lD7XJjuM4
+VK3LuqEDPLUrFdMO6Z4ygHwNX1se4AsBsXYfYP3ivwfA1d/+39lRF/c/+jG4/biwUh9WsIIVfAg+
+FZICVY2ZlBTxklkqFH4UR/QtoXydKy4dzXBcrlns5EKNGxpj0IlNlk3NaluGTwtkuLFJacn7XMPD
+Q7KfTJYAACAASURBVIyKO45S6lswHEkg0Dz+HjNT6zCQYGuSj708hXti2Dm1hFsFzMmHwlHee/SQ
+x09ETCZbPg1CCk2HuBaR8Zq2L3fXTNoakGUHBrH2BJySYOdqzFSD4iBoMFGjnm32MTrCuc5PW8wm
+ErAT0eFc/fS9TMu8LR7jaCXi+8kRM+Wq1ZMjzOKycjPsnqiIKjlSfD3pEd8Wlehz1TMkarhdjzze
+35F5XM8L7plqVJuJKDubTjAWYl2dJyaBBgLN/n/23jNWt+w8D3vW7u3r3+nnnnNum84ZFhVSokhV
+J5YsKQkkI0gAJ4ZS/tgw4MAljhEEQQI4yY9ESP0RI5ADQ1JcIUuRJUeiFNCySA41JIdT79xy7j39
+fL3svvfKj/f5Locq5KVGUq6As4DBnHvK/tZee+31tud9niDFXs0QxS2wfv0GAOCX3hWMgZ/kj+vm
+TN4DANwqgZ/L2r7+8F2cHsq1109IW69cNE3xBHVvC997TcKD3jMvoE9sQr7ZhlERyUMQU6181GxW
+CpISoNCO7XcQ7YsndDIZYsyQtcmmubPFEW5Q57KNCjZJTR7GCTrsxjVTWcvSrLE05Xl4Rznam+QE
+vTSALmHcyw2Y9CwCwrLjoEBzQTmAZgar5JxDDW8mn1FWl7Ag+0FRYfx5+zqiltACjm+8gL/3umhF
+rkBRwJ/i6oM2amRuCmU6KDRJLkoTpcl4ud8G+EJGAMwewTnsDCw9HxZf+KLpwSKYPa0dWOTvz7QD
+A3QfCbbZrXvwIAt53Oohj8h3mBhokFq8v34dCwJycoYitZshZlw7tEyUPLwKAFbA0AUOooh9AKG8
+KG5dIlXycwMzYKWsFPgwAnn4Hg+02nDh+jLfes3CTchL1XYe4B0lB8DYHMBiW3YUyWZOtlqII3n5
+3YYNcD399QhpTE1EZWFIqfXrWwT52G001nhQqAhtzse+1DggA1Y+Abo2F51kK+sOMKcQrlYGSpYs
+I3uGUYt08KaJKfUJ/kwoB+jsCyN8tfi9mYVYp6j4uyMzwWJK8VcCj/xtB/Yl+zZ8A1tdia+71x04
+Sl7krpogzcl2xW7JqXbgs0vS8iIEgazbxAqwV64AbilmtqzzkSFtzdadAB71I+cesICsZ+A6SHiN
+DjtHC0vDXso8G1aAiqFdGVbICMJCS6OnZP6FuRLpDQGClELdQO7L2prpELkre8CrHCREepql7Onp
+7josm30X1SZ+HNKVe/LlMYas4KQADAKqyvrJ0MtX4cPVuBpX4+vGU+Ep1LVGtsxQxgss2Z1mOhYM
+YvhDBHCb5P5za0SmJFp8R04+o9YIqLZk1C5sJm3mhQlnRUhR2UBLLFBNxl7oCTxaF686h03X3jAz
+KAJTouUY2aEkz9KG/L03vkA9ogjLYoyYwKpmEMJkTT/o9rG1J9aqSz7E3TWNObPXUd3FNLcez/mU
+FZM14vdNZSBlgnNR21BUTM4yF0YmFt9Ic4DiM6OH4nJ/8toQzYn8brmzDZ9WcH6ePE5y5qrCM025
+732qLVnNdaAjVqmnNmE0BPcwr7rwmLTztkwsqVpV1HKtrG7DdGQ+7dyEomeizRYKWijfnsNw5f6O
+zwXGvrGRYfNUPLBfqTRWDq87GsGqxJsw4glsJpiDtnhb7VkI/wbXtbWGnVCSeTcaHibsefGMNcQJ
+MQvcIw8rAz12xKa1iY4n83FtE61c9sv5EMjHdP+H4v3YvSEGD6Ty07z9PFICiOpIoyLb+IQYisLz
+MCNP5ERNsDekAlY5R01CGb94Dpr6kIocluFahYBdoK4qURD0FVgaFZtFDK1RFzInn2FpI2miqchy
+/SJg2t8DAPiL8VfxHZz/307nSD1WcwZTPMm48hSuxtW4Gl83ngpPQUMjUTkM24LD0Ms2HYRUeW61
+NsFwF8uFgWCLdGrUaFR2CZdNQKWh4TMR5YcLIKYSsbNEzXg3YBdaVtqIqXHYPT5EdCkIs2xiQ/fJ
+ADXSCHbFCp+NJEZ0TI0hCTVRpVDWStuxwn5LLPDORoDWmkBXe4KSRqP0ESpqW6YZioLJ0VkIp8GG
+p1Isd2Xa8OZiadsBsKCKsN+30FrxTJQx8pEsmM0S47vTKb69LdcolMIyZPluo4mADVpNG3Busac/
+kKTt5qaJKdfTacwQL9jY01wAnqyRziyY5QpiLvPNRoBmvf7UjXGN35/7DlrMwTyKazQNsWjdlngS
+4dDBL3hkiU70SuUNmZ9gSOGbLW3gjDHzspS/azobCDx5/s0ygrVLHokqRIOK3RoOTD5rh/mlvV4K
+sERqGAsoaiRsROqxwI3bdZGzyc7VZH8atTHdlOf7228d4Xt2BHuQmwoZuSU6EfUcVetxV257fx++
+Qdo8v4FswbxT4xShIwldg7mfyGqgbq5EiZpoFCsItoMslPWslAePjOULepu6lSKDzCcw3oPfloRp
++OkXMaDw0U/mD3HWk8bIt3/mZ/Ek46k4FIxaIcws2KjgGbJZ066H25a43Vv9AC2Sd2zdqOCBtWAm
+fcLMRuHLrvLSJZTFVl97AwhkIVXqYVFJBttkok7752jO2QOw3cUzX5YN+AVzhH1upts7LnZJ526y
+Q848O4Ii63KYOXjIvuAX/QDtTTmEnt96Ac3bktrf4kZxdYmCiUFtedg0VkCnClByTxZp10LDwcim
+MInahGrJC91RFaJ9upEPryHlPXW4yW+YNp7x5GW8oxS22aPgOkvAkZf+og8clFJ1WTCkUqM+Kn52
+lTUeZ9md2gU8JrisCVyHHYyQje33zuEYsm7BWQk3EOo5yxtAXcr9XdYTHFMBKjuS7zWfTfEXpvIC
+/pMqxVf5Yka5xXQwcFQlCDxu9ExeDtMrsaFZp1/zsZFIWBF4JgxiD2rLQ2DScLBCUCY95D6FgvMC
+NkmeddmEHcpLtns2g+7JehRjwS48apxhgyQzfcvHGj/j7rRGwL6EkIKOQaNGusWW7YHCbIfAuYcJ
+4kgOxXJ8gCqUvdOpKDwctBEy6eoFxWPMTbGbIkrlgNfuABXJgYK2PNPiPEVCDkpvfIDRuuyXD338
+JrYpgFzhJn6bTOH/4xMeCh84fFBKmUqp15RSv8h/X1dKfU4p9Z5S6ueVYlB/Na7G1fhTMf4oPIW/
+AuAtrKCDwH8D4L/XWv+cUup/A/BTAP7Xb3gFQ0N7NZpBG0vyFLR9B+U6EzVZBjTlBJ+kQCuUE7hc
+UuijymAzsWQFAeg0oFQRAsJgF36O4i05mc9uSRLNTq/BtykF51zD8PrrAID+sQ8jkM9OXQcXfUkS
+zahF6KUF5qwlnw9KKMq5T8wSz/vkG+g1UGSkLmMX3jwpsKSqcUNnqJtyXgYGkBOu3KKXE1cG6jOx
+ysPNczjEULheCdOT5FqxcQ+3ibkYMGm5WHcxIuS7GzZRbcs8I2cHbUvuI4gcLJv0PHLyH7QaKFlC
+m1c19Mqj8QoYcxLRtHsw2Zxfu2Ktmobx2PupTBuzSBKDZdlCQuTpfJpg5ksCz2C49uw7Dcw2uBZZ
+CsVux9SFiIAASIoGWkykDQyWVtFFsko6pinSjqxhNk8wof5C3xmj0ZCEJtHcSCsNO5VE26IoEJOu
+Le1kaFKTwTZNWCTWne3IPG9dWjgK5CInPRsnTPatrUXgcmFrW27Ubm+gog3M7RkWbILK/RJpJvNR
+ez1ESlz+uiHeXTlTMEn6YlUadVOepV8EyMjlYRx5WHaJaGQYpHe7UJfiEY16czQM6mE0vwf2dfE2
+qrRA1OciPOH4oFqSuwB+BMB/DeCvUkru+wH8O/yVnwHwX+CbHAoKCq6yUKcaPmu08ExMRhIvvr1I
+0KPAbFXVsB3mDNp0F5WNrb64VnXpw6YKka7HWGSy8Q4nD/Hm2w8AAKPX5FqjzMUza7J4g2mMnOQV
+i6LGLJMNNkgKJO9RDKYnG3R+cR/VPbLl5ksYWr424WPEl7p5MUKX2feTM3khen0L46m8mG63gkHX
+0AgymHwUubVik9IYV1RHemeIMXsRPOVjxnzGQivogGpJfNk6hY17EzmM3M0YB03ZgFaiEFwnzsIp
+4QdysMSaPQyXJTJHXrw8c+E1Zd1OL4dYZ/u5nvWxuc4Mvys/Nw0PMQ+Krz68j/JNgR2fmAo5Oxjn
+oyneZFt6ygPE2PHxk8RvnDQq3GHG3Z7mMAln6mwGaBL2PZ0RSrw0cTrgod4K8NYdmc+tNYUTHoZB
+UEOVq0OWfQaFBU0BmHuTKZJjORQn8zF2DckDpTp6LIjjOXItdV0jWcra7k1zHCfyHLqLDDbb/Adz
+OSg2/AXiSAzHYJFhQI5KdzpBwvChpz1ELzLXMJf16beBPBdIfNQMRFsV0tuyGMs856enOD4R0NqS
++piB9Sxc5hneS87ga1mj3YNjmI193oeDG+a39pp/0PDhfwDw1/E14FQPwERrvaowHQH4fUnnlVL/
+kVLqVaXUq0X1h8FdXY2rcTX+OMYf2lNQSv05ABda6y8qpb73W/3790vRh4Gt87DGtu1DEwVotqPH
+nZGLKkNUyOk6Mky4ZNHtMsE3L6bILuV3t9c1jAWtRKvEiO76/TeP8Llz6bQbE1dQ1wGWZGGxOwp+
+g0misQtjg2Qac41LWzyWR9Qw3FpWmBNPMU5mjxFjcRJjSrXj2HWQnMjvRCsqucRAYkti6OjSR6tN
+Qe6qg36bOgup/O7F5AJ3Sbs2KAvM5LJwHAt9up1rVY2SmgOLQFzj06/O8ex3PpDfjTswurJuu0kP
+qMTVvAhteCG5I8bijeSNLnJb7jkOU0xisX6T+hST+/L923sFnLG4wdd25Fpp1sTsbKVdOYFDzcSm
+uYOI83zdewRNWbicHJQZ1jCSplPAsWCQCi51lqj5zLpuhPO2fJ5bincwGQI2valHEwMBiWzerBVi
+6kw8Outh63nycE7Yfdpa4Ig0fCdnI7w9pOc5AQYOkbORwjUqgTc4tcX5BpQja/ulL3v4s8+LRff9
+TRTELOxvSuKz2d3DlMlaIwpgMhFu6TYCWuvsto+M3k+SMPF94cNqUUl6uAGPCcN4aeBiLJ7XG/EE
+4wGl5cgZ2rpuIKCO5+LiKzg8FnzHi5/41xCQ3Ttoh0gZjj3p+KACsz+mlPphAB4kp/DTANpKKYve
+wi6A4286CW1gIwth2xobSl68eM3FJiXO708G+NK5uE6DKkZkSqgQrckDfGF9G9UaX+hhgXyNuoQn
+Nc4u5ePnZyVMKuhkFE9N9ATXqalYqhzPegcAAHtzgk9GshmzPMIGexT+QS4PsRydYjmiMG2q8Ygl
+u/3KwWgp1747OkVqSL6ifZ9KT5sBXmhIuGJ2K7RrdkT6gElxkiqXh9lYAD5FWc2pC81uzp20h6Uv
+IUhbtWBQnWq9IS5w0LIwjmWjOCdj3LblnpxGBWe2yl5r2Ims830KiJyfnuLUYUVlaWHRZQw/ylFF
+FBw530LTJaR5IfcEc46Q9YL9qo/DI5n/l+u7uNWTzy7SMfYSWcPXCNH1o1MkrwuR6g+4e/g1V9Sr
+7MSAZo+Gmk4Q8WVao/Duop7h7B25RhpMsXdHvh7sBWgRAHa+kWAj/hoJDACUJwmmS3mpirtnKBZy
+H2fjBU582S/fFt3G/X35/R1fXrxe4MFlmdkPbXiKIdisQJtCuK2FrI8TdLFFspQoAeYL+bzDd+7g
+mF2wn8gq2B9mCMJnnrRrZPfEANS7JirCuHPMYJ8ShDXMcXhH/u60LwfaD56to9qUz7g+u42T3oDX
+HcGzPyZzMnqPw6YnHX/o8EFr/Z9qrXe11gcA/m0Av661/ncBfAbAT/DXrqTor8bV+FM2/jhwCn8D
+wM8ppf4rAK8B+Lvf9C9MAA0NvxfCt+SEtq0SDumsOpaLy4Wc0Nf9LsK2pClu9gVvEHVclCv4reui
+zsVKzGAhYaeeud5Gg25+wxZ3MTIL9NviKFo9H9tsbOlbU2xDLOHrawaCL4uX4l0jwcbrCtNgxa9Y
+o0kKbccxcIPEKSdWgIDgltAXC73T3YDryf011RylwzN5mSL2Cb4iXVsRuWjSAl/awBYp5b3Mgsds
+eTmz4HXFaxjO2HMYfBTdWFxtvROg2hKPp5HZUCBFvVsgp3cTuLKuOlDYINmgm0fYXvEk1jFySFPS
+WBnYYLViUYibnBcKoNK2W9Zw78t632is40UmhNeqjyC1X5VrvCPPJjt3cP6DYq13v3IHqkNOCqXh
+UiY+yR3AJDy4v9IPdWAoeTZr5QLdfUmYdiIfl0tZz36dI+M+qmqx3ENXYXksLrq5tYVuSpVuU6G7
+uu/gAIHLJB8rBG5YwKU3NSpuYGvOCs5GBzqk1F2PMnabNkAt0fWNHHOCuqyhi2uWeGadZ7ZxsCZN
+V1Uu3sHw8hTuptzfdlohbXLxswjWLrEVpYlgR577M12Z2+Z6Fw1C77P+OT4+kHuG9QqsHvd9XqJL
+gZonHX8kh4LW+jcA/Aa/vgfgO/4orns1rsbV+JMfTwWi0dIabV3CziuMGAPu2SZusBb2vPLx7L7E
+SP1wAlORuspYEWMCDstfWWkjJELNLGs4TTm5I38bfVrmu7RsTXOCrbac2m5hYdmiLsThAK85QtFV
+/vIE91w5gTfJ4/9bRQqbjECxNsDyOFynh52X5cTfy17EBkVq/VrKQ52oAeZLEdYpkpSSdmaCBdmg
+LE/uadNto898QXPLg002HizXYZgStz9s5NALmZtDgtaod4H5Qj6kvrjElkGL0ciREy3qqAkc5jNC
+Jnarbh9+JNfFeANVKW3kZieAS4i1pT24DiXkCrH4VtlC35e1/+RmB7c25NmE5gydNfFSFl96D585
+ZUv5QvIs52tL3PiMxMa/kvoguA/2ooBtUeynCViZeCRZJR6N708QkaF51xvBSyRHExkxsi2ZZ7+h
+4ZI7waAXkM8KRMbqurtoBDKP3q0d3CTq1Y730Gby8GEln+ssM2Qsl66b72GSiWfSGyg0iVXJStk3
+xtKH2Ze/6wXPok825xNjH0NHvK39dvQYLzEiojW0TpAnsm6ZZ4HVZdRIpK0awHXXgP0h2X/9lnjF
+u+vPwL6UffGgrrDPXJO/PYPLsiYiF2H9NX6FJxlPxaEAS8HoOTgwPJyn4qol6AKmuFcb3R66wYqK
+/CZKZoOREIdvTTAm+KX2E9RUMokaPmLWvI1ZDG9TDpl9JYmltnmA9Q5rxqmBlLwIc7gYzeTv7mYp
+rl8Tt7ucycZcmxk4ZDdjaBSPD6fW1hS36xdlHls1dk3pfYAn92HrAEZTNuZioLEk3VoeZ6h4GGhW
+VBZGjGbEsMMdQceSlLM25picUfXInuCMh+Fic0X4YWKNX8/tEHs86Jy5BY9AoLPUQUm58wZPtEYj
+RkhYub52hnJGLsbkEAmo7WgUqKnwtCTxCMzhirIBbmhgv8MOPmMPVs2+hP0SB7Gs+cXrchBOxyXe
+Yna+UinqC2JOmiXSpXztGR6mPJCzJp9p3MKWyXAtfRlbz8j9N8oGAouw8Gwb9Q5FZHjwrkVNXGwz
+JLCBXvI8AGB7rcJaJs/JeaFEdUwFab6Zw9gCqH5+eZrg5vWVHHwFk8QpO+E1fq+BNCYVXiNHg8RA
+6zDRXZIYqL+ATeEf/5LdrEugJi6kNAtY7C8pCgeVkjVUmzb61N7csCRsdqNL6Ioq2LMvo8xlrerZ
+EAWxE27PhsEejCcdV12SV+NqXI2vG0+Fp2BoDb8socs5ArKyLqY51pggsYMN9ByZqgpdWOyhN0ny
+WqIHJxD3rLyYIl25jmmwYuNC1y1QE2NlaUlUOn0XrZAsONUMiSmuaK4fIKbSsJUD82NxA298u7hv
+n/MrPJPKZ4xsE92uzPm71TYc/2tIukZXTmvLoQuf1SgpV1a6KXpkDIsTH17JxBjxCr0SAFGDUbWF
+ukuNiMJHQkyC8dDGgLoVO654QdX+LbxsiBX8oqq/1kgUjTAnStYsHDgkDc3pKXTKCIqcFWbZgdmh
+222tY+bIRLtxgZyhhEMqvKyI0QJxH4aDTk0EZWigtGl172nczoQh6Q39FQDAOz3g1hHFdQC4pKHT
+I8DxCEeexTBI2taluvJEGVhnExt6Hnpsxoo6JmwydSnPRUX8XEX17LQwYBiCMbiWjFAyNNsouqjY
+gmtNLMQumbrYuJUnGQwSre7uzBFyHlayQDVi+OcJS9N83gFYGm8PbHR3GdotXAzYKNW3t+ASNtBk
+B+jRWoY1WnxTebAIp4dbolnR+lslzFqembkh9+8bayiU7PvWbA+nOZPHTQVVUTW7slFqMp0/4Xgq
+DgXbMLDleajmIUZk8lXzBi5XUNX0GEUgL3ID3mOcvKpJgxVOUJKaOzUb0JVsqtTIYHry8lZJAIOU
+bXWP6kYoYVGLr6wqTKk1WTcdqEdsz/U24W7KtX2KdPRTGzk3YMup8f099nzttxEQdpo6baiUTMoE
+qzhmhQXFRGrlIuPfqeUCNTv4LGcl+uLA4X3GTo12JXOYlg7KTDbQaV3B8mUDpWtCQ/7sx15GEK6q
+AXOUA1nPctjCYib16uP4Aiaz4VuRhAmzysIGkaWO1UJdy9yTCLBz+X7pePBIg29mK97JEMaQqkrF
+CWqS3VgqRT2STZqUMQ4dOSA3+7IWrwwqeIF83TcnWPJZpn4NL5VrTJYxApOdm6UcNqFZPaa365sZ
+fHJJ+trEjBYgSGNkVGrKGe/MlAGrlhc91gFM9qucOwb2qSZVWi5MtqsParYnVzPAIRzd3UJJ5RjP
+acLr0OKQtRlBgrRgz0Howwnl2RiFRrtk9aRlwGZYaJeS13AfmNDE3LgGYNLgqEYTBjtljfkAqk1R
+IXZqGl4FA+SHDCzQNsGpd2BScQx1Atu4kqK/GlfjanyA8VR4CtpUyJouOtrFdEzG5PEY5yOpt2/d
+2EGVyinvaBuqkGQOyPw8HhcoZ+Ima3gISbs1M2zYA/ndSV7CI71bzIx21bCQUyZMoYI1FetozCx0
+XCIagwrOXBJwF1yuMrJwQG0J2AEueUQ3zhViJp/i8QV668xO03sLjRzpdOUJLJGy6SqbLjBaEMn4
+SCybjtrYcMmxUCyQmiu9iAnOHhLSvKgQa7m/HYgl/vi1W3CpWty2CxzeFQ1Gw8xwWcm9YjbGYEWw
+6glSMqobyLR4Qnm8hK1XmpYFPIedemYAlxYotsSyhfM5xrV4YL7nYXlJ3Y62RsxmNDM1YZ3RZyYh
+ycsbbfSX4hHMzxt43ZK1H98ZI+2x4tDJ0CFJjGbYElQVzhhSOE6OEQlJykxjWAnzc7ewMCC1njeT
+/59bJRqnMs9DpXCwLp6HvZehVoQd5wVcl8hSNrY1zBg5qdI60RiWwVaeKgaLB5hDPrc5N5FRe8LL
+nn9clTLNAHUk6+lUFcAkoKY3snPNQuhQGMjrIKZlt6slciI6Xc9BsmLSZtdtXSsYDGcqow1vk0TA
+fvnYQ9awUVIH5EnHU3Eo+GGEV77940jfWKJ5/FkAwKTwMJiKezV8CPSZtR9VGTJmtSu6hsuiwmBO
+lwopOoYset0uUNAVXSJBNmfnG9uzz5eAYsbamAHjmNUADaTcjKmqUEZyOCWpbCS/00Ftigsb+iki
+cuANunPYkmSH1ygxmlHa3pSKSstvoaBzlqcljs/l5T5ZzgG6sw5BN0GQwjyXTdxf62HM8GiRTXFG
+F/4cYyiyQS0/KdCQ0g/AhD2K2kXZkp2ZjjU8lgnem6YoI4lFzwijLexHqJcE8fRzOMsVscocy4Jd
+p+YY4Zg8l30p2U50H4ucOYDTGYKDVSzuoGBfxcl0ineYJ/A9+Xt3rQXjO+RlXHzhHLMzmUeyMYBp
+ygESFCGCkiQ4W3xOhz3YgTyn6dSHbrHCEZuIU9kD02SCy6W465p5p0k2RbngwRoV8Nl1GjZcnLF/
+xGkp5Bnl6um2x9YB1EssuSy2UG/JgTvMmkCfnamhEMvAzBCS2XqwPoFNQR3HsFE+lHmO2jWqC+5D
+VteKRQHzFkPiWYLKk0Nmma1Du7L3soUBxdJvxv1b2ikqguUS6xh1KXmb9uY+tE3tTjNFXn5rvQ9X
+4cPVuBpX4+vGU+EpWJaJfr+F8XofN9fEuvxm8gDGUE7G4fYCNenGGk4Fm+5XRU58nZWPAS9OlaJM
+eLLDxdIVCxUtC2jSZqUjsRKTcALnTM5FZY8wv5CfDy7GOKWK78l5hs0Gm38MgTu3rAZsUpt5KsQR
+mY0/9LBEsSWnf1wqxEpc83XCdudrDpokRTEbFp5VYoEPio3HGgFZixRfiQVVyn3GcYKSBCnLoxij
+Md2RyTnuL+Xa3z0QWbbD4NvxccLDfzWu0JjS45ke4959mf/56SWsgIlCNkE5SuOR5LGwNdFQTMbm
+qYJind4xYixG8nVVkG8hPkWPQJ+4clHeFSs26w5hXMicqzKGdyL3dbgQrMSz3+WgPxZKuB8b38Dr
+2btyf1+Zo7EjHmKSLzHYOgAAtCHgnkV5ifqY3Z6ZRuuMieS2iaqiW27ZsNlWWpINOSo1lpGEAQd5
+DpO0ctVogfkaOztPDZhsXHJO5J7GQQt7icyzu7GL5xia/qrKkbL6kIVCzlNXbVRkEm+cHyDryl4I
+DueYrlGf46j5GCNSnxKzEtUwz0TUZTrcRpkKcGzkTHCDVHja82GSdTqnknows5FT/7Od9ZDtyf15
+ngnDXPHNGRhOxdt40vF0HAp2E72dH4LjXuLwVdncfSR4aMgGDA9tLDYkxu+05tjssKTIjsSqasJW
+8rumW4C6rrjUAUwqBdWewog6hrNa3PlorjCnmo5RWjgl0uzcKDFeiGs4VhoF8xlFIdn7fujAZNa7
+jC3EmWzG42YXYczOt1wjJE8gInmI/VghI4IwMgyAbDvNYYA4YixLSvPSqHHKoHSRT9FiNeBU1Zgz
+Xj4NNlDGMs/PJz8CAHjxQKNYhTD9DDVLfeO5jYcTcbtPxwPkC4KlyMqTjwx0qW3Zaz2LZMReEuXB
+ZLyfu+5jcZnpjO65nWFuymkymU2BHglAUhc2s+QncY5jV14yvaIvv9hE8SF5ZodfvIsRUYEjEyhq
+ygAAIABJREFUN0Y0llzKybTEs4a43eOmlBNdbWKesIyMI8SWfL9dGcjIBtUwcjj7zOaTa3JZurCW
+ciBlVQMVn98oc9Ec8iVtt6HJnHRIId3gErBvSGXHaJs4X8gBsujksBhiaFv2Y2EVmBUSXkz0HP1Y
+7nVa+wBzXstWAxOGNqT8xOXpEoYl+7tMxygpOe8aA0wODuSeJksseuQIJfeltdlCVZK2fsdFKyQj
+k90HKByDuIL2iYZ9wnEVPlyNq3E1vm48FZ6CYSqEkQs338H17R8HACTLv4diISfmRTPB9UwsiYdN
+tAkswjUyP98rkNFNtOEhZP24uDxDSXo3pSz4lP+qp2R+DpYoyFlwulhgyTLBneEEBanT+44Fi4ka
+Tcx9tTx6rOt3MVFIeRDvzUt4L8vvtrMChisub8BOvTpUWNLVnswjdHKxzE5VIT4RS5IwQeRrGwV/
+t7HMsDTFylcXwOfviqt9oWroVOY5+KV/AAD4zuKv4+aPkrrNCjA8YSLuYoDZRCaaxjkyLRbvlBa8
+9i7heowfzBzmptTbk6MlAvZMqKJGuMMuznMmc2sTBuXPOg0fk0fi+uvmOc5HTPzNYmh6NPfGsm5H
+B3fx4m/Iz+dHBUAuhOEbxyh70hNhH/iYbsk8b61EfbSPiF6hkbrIV55AVSJl9cFJG2iRKq0J0qtl
+Y8TEYZimDcWKCsICfmtF12/ikuQyqc8u0I0TPCg+InOodtHpy7P8mBngt0h8U3QYojZ7iK5JGLhd
+3kTOa/jNEMNS3Dd34OHkkszOrHyt9zw41C5tdLdxPJT76Gx6qIhVyVoVhtQpjSLZNyoJMWcX6bhs
+oGCncKt4GZ6WbLO2bISmeCFPOq48hatxNa7G142nwlMALMDsQzWnePajbwAA3nyvg+kZeQymJRJK
+moVdD0YgJ5/JhEvR/wKmUyZWhjUuqe4RqDM8WIh1d+ZLZKFYndBlkimLUARUdnZdmItV8s3DlPJf
+3SRCQ3qRMCBNdGpptAqxXK4zQErauKo7QVgw97FtISVaMmeJVCPENBNLai5CJCOx/kluwfXklD+3
+JSaN9BxLdgBe27dB2ABGjVNsyW3j4vQc3kgsV9wRVqhX31viPyBfROAq/DMmHeNWhPWAaD3fRWGx
+/EguCN+z0VDiKVg3fCxH9LCapzhnjsZbprgcyz+cRDyQZd5FNZfy5nGtsLEu1uxR5QBMlE4bJUwm
+Jl+O5RmY92e4w5j8cmuK8l2x3OnGErc2pUHJTkJs2eTX2OZ8HzUQ7ayEejooSvm7QptISIW3Xdto
+kFqvrMWKt60J0CIJrD8FZrIvut0FQlKauc0KXizPslFLQnh+3MCruayxd6zxZ18Ryx1ZBkYtisE0
+pSQ5iSsELKcmW+/AvHxF1rOj0YeoY4+yY5wRdmyVZM0yAxhd8YqXkYGtbSZuow6SQvbLpEgR57K2
+FUuoxbUBXL4DGPwKwi98Wn7XPYW7LWvUaTjwFT2hJxxPxaFQ6hqDcgE/U4iN7wQAXOsN0HokbtRb
+SYnBGbECk3N8/3cTeEP68hudH8T1mbwURhYipZs8srsI3pZr2JGJRUS16nVKmdsJ8lPZVO+cD3DO
+OOB4OEPLZiKxaaBviLCGT3jxfV1Dp/JSmIaNdCgbdr2VwjTZcqz30Pfl2ja7+kqVot2UTLZVTuCW
+shE0DCzastm2hvRJZ8A5cfjHdQcrksZwOsXhxSWvUWPARKqx/gAA8LFPxfhFtoh/GhoVW66D7BEq
+si7nqsBuuUpsykseZimGY5KzXEZo9FjZma8hZFepZ9qwUjmoYs63MShgedREnDkoV0zE9QgG28EH
+wxQ+D9FDaipWfeDaq/K9WW1grZIXZK9uwq8lE7/V+wgMks+sLWWTx2GBnIzKOl+iQ+q2zAuw0bzJ
+56Cx0ZHD2ScoqPI/ii3eXzVdIr6+6spcgyLF3GI5xmQo83tDSz/D26/FmASiZHO+/ufw7p683J/w
+KzTYBz84/h0AQNRYw733JJT6mH4FdkOAY9G4gWVH1rk/6+C5XA5Ri12UxfwUPS0t/Ko0Yd2WfaG0
+BYNQ8aUCihX1O3tUVFLj9I7wjiIy8Mtf+GUAQPN77uDT7/wUAGD+0ouwFE/1JxxX4cPVuBpX4+vG
+U+EpVFmJybtj6KiFkSEW0ay6GLCWPhkZmJ7Jad5o2/jIq9RdXGlG9isoUAthUmDK8GFyYiKnKrPj
+9KA22PgPdkPWQyzYBDUoKhzNiHWwDOTEOuTNELNA5jRls0+QasSEn86yGBYhsQ8WGWIShlpLhcSW
+eTbZDKMDBTdaiawAo2P5WjVqzElva7CZq84bGLapTXA4xYiCLOnZFBWbo5KshE+KueqzD+RaBxE+
+fJPh076JmgnaLOhg3iDhzCRCsSJBYCl0rt3HdGzlDDADJhItCzWbv+K6Rko0oRGTNKV4iDQVV3vS
+XCAg/V3iRagHUp6c1RlqQro9dmeOfivFERGpU69AothFuGFj0xLJtjJYf9xINFl1Peopljnd4VrB
+pifgKQ9BwCY1s0a1IF0eZeOS6TEMIi8rrWFEMp9xWcAn50I2KTGi53Xn12Tu8Z1LDL7yWa7tDl7i
+GpofaqPBcDKmVoSDDI2m7IWzdAwSgkOpCh65P8rdCVosKSOXe661BcpDwgwqWFxvK8tRrlNcqDRg
+0rszuafT8hJTwtwfvlGgTW2N5f9s4sGLcu3b7Qv4m98aolFprb+lP/jjGGvtlv63Pv1JtPQE948k
+htpo1hjyzFoPC5wfyssWdUwUesU5J4u0nMwfb45E16gKYgXSxWMp+hwVHDpGMckqItdHygbHbtCA
+Qdd352ATILCo07Jwyix675p8xj/99V/C4dvESBTAqgrcaDlYf0F2wkfXbuI5xuuXiWy0k9MBMsbW
+75wniPlSXE4qrBrZqDkLA0DNR6MUoClxbzsGDPIVHnzkFTwTkgewoCiM4+Nv/Jt/CwDw9oGNya/9
+AgCg36pxecRQoTVGmcvBuCdeK6bjFrb6VNMadNDscLMdVWjxoD6+0HAD4gZ4SK13bPhkRep2DIwp
+pNrpVDgj1f5mR+H8HXkhrz8v3/vNfzWB25Mw4f/9/BDBbbm/Tz7zl7Au3jPmhyk8XzL1dx+xgcS/
+wEOK3UTTc7zBcOvkzTnmjNWXsUbN0MUhwUi/62LPkgtbXo12r8/10vBoXGpd44ujzwMA7hHUlRxO
+sfVdknO4/xmNn/5J0WP8K9VbaA/+EgBg8PdZDfvdrxK3qbHdeRz+LMsC6YL9NgZD1LKCYRFXYCj4
+WwJX7nRfwO1rIg77sR9qI7j5QwCAF8aCs/n5BzU+8+vCkTx7bhv1/y1t6e3/7Psw/l9k3X7hZ1/F
+j71GUqI/3/ui1vrb8E3GVfhwNa7G1fi68VSED7CBaqvG4F6ElOy0XxrE2P6EuGVvv1UhWJMs9L15
+hp1bdFepo9fdjzAeyMne6ru4f0aL6Lk4W4hFX2+6OB6z7k8OgkdpibWWuFb3khxbVGC+fwncfl6s
+8XRmoSKy8vW3Od27KbKcytYaYM8ROkYLf80Xqqw7+02sEYLrrzgB/Bb6jnhCo6rGkhJqEw3QI0S5
+8g7etzyGBmqaIbtU+FRK8ZJGis6/krmdNb8MAFh660gbEot8yngBv7Yr534ya8F0xfofnTaw92F2
+mE4EEdho20gncv/N/RizE7GOXn+EywfihYQbMxyeUTaOjWTD+RpefkbWajQu4VAd+3iyhfUXZW3v
+v72A+pA8q88fSVOZ8akxHv5z+fmj8RzT/0es5g/+GWA6lWdttA7xxn02bDXENX50bMNM5DOOjhS8
+lF5MXKJpEyJeFrAc2SMuv+dYa3A8mWej14fJUMP3DKSERzcK4GAm3sTpWNYzfVgjeSDPrFYK1mf/
+NgDg7177YfzVf/QHeAgcTVYXKp3DpgCRpXLoVajALkmz1lB8/i5MECmNHx0t4PwbwtXwE/5LuMNG
+t865vLbX01/Bgy+Kx/P6578ITYeg/g9/9bG5N3/6n+K1D38SAPCR33+av2c8FYdCERcYvHYOS5/j
+3ik7x1QJ8/OyGa2wRPAuW0jXS7z0jhwWd/ZlI71wCHx1Q57M/iLEzBV3vT0ysbAp257Z8Mi71yYD
+0dzMsZREMBaokY9IM76T4PBt+btmJ3jczm305XtfSWuYfLBLABHj+r28wrQj8NpXZuvAlvy+wVDk
+xhmQguWtLMUhH5xRA6tsx2pUv+vrlUtX6QpHFDl96e4E57HczOfokjrTCn/tv/3vAAD/+U/9l3Am
+cjBZ9gCjWHZNEubIz67x/sjhmHaAHrP6IxMhKyfzkxhlRKj40kFokJGKDFJFt4DSsjHb7SG8Kbsy
+/UsUPESN2Rz+L8lLffeHJCOf/JMm0Gci5UKj2GGG/MxE6kspenT0AOeJvPTZOfU8h5doubIay/EA
+pyVFazRQZfJ9t9nABmTvOGtyEDwTHaC+KQddHz5qU+5bqQIm27LjfIj3KGazdAiK0hrzVSsNNP7j
+z/1LAMDz72xh+E0i7zl/3jrLUK70P2sN0nuieF/ozkgTrgLcqVTMps9+D17+slQ+lr2Xce1SQF3v
+juV0OHztM3h3ImtYvM/nn1Z4vIF+5H/6CbjeV77xRH/X+EDhg1KqrZT6h0qpt5VSbymlPqGU6iql
+/oVS6g7/3/kgn3E1rsbV+JMdH9RT+GkA/1xr/RNKKQdAAOBvAfg1rfXfUUr9TQB/EyIQ8wcObQCJ
+W6KY2FiSibnKFc49sYih14Nms87YcLHcEBe21xUL7KJE1xaLYbdNBEtKs9kLtHJJ/EVmjYDNKjVp
+5O1CQcdMGNY1TpjtU+P0cUflEhkmdP1mubjXYQnkdPAtaByQAKV54CJoyfwfBh7WKRbSPhYXMOin
+GI/EQn1b20fJRpxTpTHgWhDVive3sGh87UFZBnCLFYwlFMwuSTburi5QoHxdrMt7J78Cf4099iMT
+86VYFa+eYOKLG99OxcpP6wptutEDhHDzlVZm8Dj8yaMeOoQ8MyrDvpnBYk+/lfYQu/zBYIwFu0uP
+cIbRp+Tah+/IDV5snCI2xAQ/zEZIH8l152ETCSHfZ6MCy6ngTy4SdpE2Q2Tsnt3oeqguyXjdW8d5
+LZ/9kes25pbsgbU+hWX8NTRYcWhBI5nJ9Y7SBHYmn5EnM5R7xLJQos0D8H6GwxUMaLf8TbyFbzw6
+jCvqhoWgZqJ4omBw72R0D0IorEjYP2raOGf373cfnMPZFL7NZfAuTi+k8arZEwzFrY0Po9n8qvx8
+8gdGMdhX/wkA4N1vMt/V+CACsy0AnwLw7wOA1joHkCulfhzA9/LXfgYiEvMND4WyKDA5uUSSLZCx
+3JbWNXYKWRx7ptAJJUZcVBmudwRMNKNg6lpPY+kJMKVVbGNrLi9FZXjIqIfQdLu4nVFLkIuOZIjz
+hTzmfJE+jtsrJGgMSZxSu/BIOOI25NE1HQOdgmAaAP66XO/jag87IVWrXIVttsGZHxZ3zztJUc4E
+nLVr3cetgbzJX7qfYUmWorsFKxZaY9XiEQPwyT+44zlII9nwP9DdxKkvG2U8elNuKdGoKBT7xc89
+wA9+mt134QwueQCd+j049GFL8hYi8pAV4qonMVCR3apoWVi3ZZ0d3YDTJXqzJejHrjNAkxyGKo4x
+glwvK7+Kd4cSi58+eIgWS2cjU1xj9VvPwqQBaGobFasF87NTjCgU+3A5gbmUA8Kt5Z5bU8Dblns6
+aDyDlxZyH892biJty+8YwQNoVqDcVbOg20SDSlejeY4uaZMWcQGfVaDJVOElljuvb8i1Xo2HSDlP
+E0DNF/odzbLVNxgJqwvfYXrwWcKuAwMbfO1GrKL1XIWQocQrjT1EL0j0767voUOq+rWlRtlb3Yw8
+R+2t4cO29NdU7gSXZCL73RruhwW+pfFBwofrAC4B/B9KqdeUUv+7UioEsKG1PuXvnAHY+P3++P1S
+9OWVFP3VuBpPzfgg4YMF4KMA/rLW+nNKqZ+GhAqPh9ZaK6V+X6/m/VL0UWBrO6xgB2uYnLEDzKhh
+flQs7Hp6C3Ym1mpDm4hfohtYiWtcqhnWY7GOqjlHwxPrqZMljIW4pc11A9VI3P+Ibmt+2kBuiocx
+O81BY4UENnzSc8MzkGNFVMLPbTawQcx5mSRY3xVLmXxnhIxF9qZ9ifqSitZarF0KHx71YayNF+B+
+Vlzq3lqJ/qXM41mDHAR3FxiRvGVWFdA9+X6qbDRJ8Z2t2XimewAAeCMjRd35EuiIpb2057AJBW+j
+ByOl3H32IWT78v1oJFZnbvvIMqpF7czgLxheVDE8LZbLWcvRM8QTKiJ5HmF5AB2tOBwb8G0CkrKP
+IBpJrTy4FcBcSEL0BpN66XfdweGY3tbUREqJemujIzyGANplgBl7Qco+5enjDSy7YsU9fwebH5PP
+W7c34PeoNK0+hmUlGIhqLn8fekM4C+6L7l1Mjqgy1aoxqMmunN0BbLm2cyxz+NG1EP/nQMKLqjLg
+sUywf7PGw2+Qv1MK2GGVS3+kgfWh7IvL+TECT77eZOfrzedeRHosydX1Zz6MsCN7q319FxOKILU8
+BbNBWxuJl5Jt1Zh934/KWnzmCC/Tu/1S/bXgUwHY3hUSmPsP/uD5vn98EE/hCMCR1vpz/Pc/hBwS
+50qJrhv/f/EBPuNqXI2r8Sc8/tCegtb6TCn1SCn1rNb6HQA/AOBN/vfvAfg7eEIpel0DRaqB+RnY
+tIjYMPDcfSl7WTdr3BzI6Xj3wML2O9KUkn1IYuBrZ5sYNCWJ5p7uIrMkpWJNGzhzqLF42sZZJiWd
+FsRKrPcK2LHEZHP7PWRULc7cBOqctFxbClul5DOKrlxre7GG0BDPJA4XqEJJWrUvLLS2xEpbsQ+z
+K9bRZRya2z7qE7EChn6Ea69IWdC6F+NaQyzwGaXkPvb8OR69yqRdZeO3NsVShktgkRKOvXBwtJQS
+3/W+zL27qPDwXObz+uDLCH9YGJkCf4qK5dnUc9Aby9qmpcwxv0wwreWz16cO2CSIsEigIupdqDU4
+rMV2nBanZgAFy5P1FBUbm/SkQLsr1r/5VoJL5kQGA8n37C8P8PZQrPn+zMa9dSaYBwkWAyGFnWUz
+zBJ57tsXcq2qqXDAvEvnmoVdR3AWnU4DliHenbKAIJXnM9Nyfzq2kdekR5sYWNqS2q3TBE2SAm9e
+NFHQyuZN8R6GoYGPOrTcySnuFXLfy4kHYBUl/96xBYVtanzsnToIyej3vY01WJF4LK2WeDFOtIs9
+6ptazT2oFr23WYYNgg+yPILRlIcyJg3cnp6g8OTrzWc+gX/xBUFjqjKFxcRmhTYeHG5zVmd/4Hzf
+Pz5o9eEvA/j7rDzcA/AXId7H/6WU+ikAhwD+/De7iIZGUVcIWj5yEotUMPAuH9wzG+twmNhxfQeX
+L8vDOKBoRtlKkNpULLo2RziiJuJ2Bp9Z9Ek6RUYVnoRdZhveJqxYEl9nyyZGWg4Zq/YRcUMHNzdh
+DynaUok7OOsu0fLlWvGyRiOhCEmjiZpwY10ZyNg6XNjEClgaKV3tRrQJUDOxc2sBh+3VN1ukR7sT
+IPooFaaOHuI2+6XfeKCRk2LtRC+ws0ao7LlsqkfNEvOZHJCJAWhK2EdqF2MlyTVvcow4IDM1qwlp
+VSDLWXHRLbgkAImLEOuOHEhFFiFQ7CgkuYurC1QGRV+KEtOlHEiX9hQe121jz8fYlmvcxPfJtYyf
+xTWCt97cyVGwpTrvlMBYnt9s+RC6lOez8OSF3/YtLCO5pxYSKB42Wb1EDrkXt4iR5rKOMSHmBVyA
+lHZF3Yav5euRCmBYsufs9QjLAasuDCU71hIOexQusQF7Jj9vPbsB9VAOr/fHx6sXyowsdF+UNX6Y
++tgiEqWJLTgHYgBsLT+/3k3gF2LoVOSiYv9I31/AN6UPxHVKzDM5FFpsB4/NZ5E2ZM8u3Vdw0/sC
+AOArSxM126VNzKDbghF5Uqb3D3QoaK2/BOD3w1L/wAe57tW4Glfj/7/xVCAaDQ04lcJimmHVAJdm
+GTxKqhdnOTIlrnbtnaBzXzgJztbEFW2XPnISfwboQoGS8o6FBpN18aKBmoQVScxSoZNCkUXZLwdg
+LhNFvXgM+d1NFW5Hkh30D8Ql++rPv4d5wvJPbWLSp8cSFvBXcl16AZ8q1kEkJTQ3SjAdkjauqdHc
+Ypl17kBR2r2ibmHvpsL4rrjzS99DTm+kXxZ4q5D72xxr3LN5bWph7KczHC9YzXE85BfUEGh40J7c
+t5EU8IinthekDPMtNImOqHwbnklJvn4EpyP3Fxg1bIOyd+w+NKopKmoYeo4Fn8Sst1WI05y0ae+G
+2N+Vz3OifwYA+Ozdl3Hv3q/Lz1MbTkt+fvjVN7C8LybtYjhFa0V+SzGLNHLQa7NU3VMw+Hxdt0BB
+2LBl2rAt8RB65HqYlBaajjybsQawYBNXncIiMUo9cRFpsaox2xZzpVF1xHLfilOkTYqs1OZjKPpK
+07kAYBDdumG1sD6Qz7CuKexQMKi1dhvPr9ObbMp9BN4N+IpydNrEjKm+oogQ17Jnm0kTC3pyBpOd
+h/crqEPhcnioKwxt8TyUnWGPIdZDbUJR5h54hCcZT8WhUBsauVdiu9HF9Ew2vGVWWF6XBd7dfA6K
+8aKrXSxvys15lN6eVwUCtpUWfgGfi2A5E5Rk+3U3H2J7JNWARihuZt3KoenWbl02cFqIyzybWyjk
+DIJet+Huibu3WLK2bcTwAkKwywgduq3ReoRqKS99o7PAhAAZvymbvHY03G35jMjdRJXLS2/lBcJ9
+kqXM+AALG71r3BA2MGBuRMUh9AWxAEjRjyVUMI0DAMDUn8G8JhuivkhReaS1txTCgWy2sREiYfKm
+pLuPYohUSeZdbTyElQjgqmWlMGO2QzcGcCmEazI/kWQOErIfmVUCzfbrARwM2fKZBwViHlRm8SGZ
+Z/+L2O3Ktd5YjJDx+SVVgoR5hLBYwmDMXO7LGrarm7jGMlGQ9eBcp1bm2IRHXMiiLrAgVZVDRulW
+L4GzlJdUhcdIS6otWQssphJ6Zf4DDCp56ZNackNJbKBNxqq3kyWWZIuqp22wrQIV43dTaeyz3b9/
+I0IeElbdukS7kOfa7SRwfbnvBkWDXTVHSrBcNQuwsS7vQDm1cUGpgTdnUzT3JSc2W1L1qjvG/KHk
+H7r9rwBthrephcNc9pltlnB6lLYnqvybjasuyatxNa7G142nwlOwtIFu4cKJF+hzSkMDuP5ITvDF
+1hjfMRfTnT2/RPdS8AmLttT212dtxKwJN5Yu0Bar0kn24W9IRXTn7ksY7AmC0KrJbttZwrPFCt6/
+oZDSmo2SU/ROZB6qb+D7t5i4fElO6DfHFTparPxWI8D8gM1YaRPhpszDjrfR2BSPpunJfA3XQk+L
+12GEFkyyIJfBEpYpIYrqyd8YawaSt8UKGNnLeKUhmeU3p0305yRvWTZR0iKsfZeECc8db2Ljvlio
+XwyWsBKyGjdr1ER3FlYXIbUqBgxFppcT3J3Jen5ksIbxrrjRGzqC7ohlblUWbBLHlEzkaUyR0/Wd
+DOfIDLHyb33+HViUSU8mXQQU5XlIdOituIffITVbf+igpFiKtQyw15e/C85vYLYtlvnbS3n+W9sG
+NtpiadfWTLj0YiyzgjkUr6cyF2gVkqQuDVlDc6mwLCUp56VTDAuZs3FhIq5l7fQ4R5YzGTuVZzrN
+5yAlA/Z8ExUrLWdmhZDaEftabKtpOgi3xdv61/vbOGfoU+s+7D1Zt/3mPhoNCRt6hGIbZoiuSah1
+mQOp3F+GFNVEusoWJwnGA/He7j8gwnb4RbybMZF+UeD4TO6vaiTYLGUPxI1byOefkBvAv8STjKfi
+UKgNjSSoEbTb8C/kZQsLH8eRADo6SQfjG/LgArePfEdi1c6lPPBxWCKv+BL3EvQgzCHOroNNuvaT
+5y9hvCehRLnBjLSxBtNhlcF8BVEq191/VKIM5XrZCz7SZ6g7uCYP07QqBA260Wu7+AjzEnkvgku+
+xqqxRBZLiFFRit4wQlQmf144UJ5saNcwAIclKV/uqRw+ghlTO3D7CzimSpG1uEDOjX4UzbDFeHjt
+KxIaOa9sYMcRyLPtOtCP5dAj5HTn9XSIhSP3p9k5Osr8x1n4y7IBj0I7o7qBPtV1EqcHl7FqurqW
+aWNGFapH9y5wxB7wr46G2IgdPocMCaHgPVZJJvYDVA26+3mKgm47/BkCWzLuzt4RXqR462RDPndz
+WuOCsvbwQpipvPzpeITJuczZajXRNuV6rbbc03zmIvDkJT0eznHvTTEWc3+JC1al+oMJDgmbTrgu
+DaWwuJDvDbDAQkl4ODJjNB35DJdqYYHq4PuuMz9xax+adPeTKWBukqVpFMBm5Wo4lLl1oxKKbGDa
+NbCYkUDYGuL0LQkZ3r6T4BFJfy9ZUXPSBHOyP81T53FlqJyHyCy5hpk9QLgm+3DJ7uBvNq7Ch6tx
+Na7G142nwlMwYaBpOKjOE5iUAs9nOaYXYkl/e/oO8tep7ddx8AyBPuFzRITMcigmD3XVQLgjbmkX
+HRTk/lsMc9y7FKuSM5mZuEd4OaJVcmvYnQMAgH82hlWJlQtenSKhV9DclkSVLoCEMmCNnXMUPYGt
+xnqOs3NmkR8eYkwMQO6Jaxju7iFmc/7GfoSuKdlipxHCsFZdK5Sid4GT+2LN4rMMX2qzJ+9YI47l
+M6YnS7xLyLZ+nnRmag99wpJffN7E9L4Auc67fVSrVplqBr8Qi+3SN76218VNYj3Mcgs7XbGCw6oG
+2K1a5xUQiFdgsOJQzkcoB9TgnCwwnIs17nkujFAqONe7O2jVEhKoAwnhTr94gOJSvlcuLWgy9Lvz
+BNqR0MVRBRJWMyYPxGKeW0B/+UCezWGAciHPsrVQeHcgptBrtXF9W/bIS7coapOe4uFUntnJyQMc
+vSt/Z1cLzB3Zc2mmYLq8r5Hc8wOjRoMYkipJcEFsxaK7xFpL9si+kv3hRxEGhEmfXl7O50cyAAAg
+AElEQVRiTNo0VWq4d+W6F7cewSak+3hELEi3gwYrKpFhQNEbWSQ1YkO8WwdD9Dqyzvtr8r2TBz3s
+EKb/ejCFWYg3WTsO/oI+AAD8XDWB4wpY6kmhxU/FoaANjdKvsbu2jfMxN1Vn8riN2i5N3DdJqZ4p
+jHbkIRSsPsAsoaYUFHWX8MYHco2gRJyI63daLHBEktY5CTydrIE7obxgtxpbuMb0bIEmHnYkjJlp
+B8dKSp9IJXOeeDU2ydxzPdwHWvJ1uqzgueSS3NiDSmR+i+vyEK1Q4+yhXGt6OsJ1ltOU5yHcJ5dk
+Lgfd9I0zHNFdPOkUiGvJXjstAw37kPdUISNR7KNCOkfLtQrOLapUnVao2IuQ5Etc3KdaVD+Gza69
+fij5lV67hMmwq25lMNiJ5zvnmI5kbmlrACslcSlbxOemhTHLpeNyCcOXdWs76zAa8oKEjo3+bVm7
+e8cS5iw7v4HGmhyyp+UFajYlV6GJgsjSaM1EzPWoqCl5MVFwazlsBpYBpxTX+M3BAKOpbHtzNkNh
+yHr15WxA26pwOpY98uDOBPfZBRqqDItM7q8d96CcVQs+Q7u4QhExzImBEfUysnmNjkv9CR5ozXUP
+dps19TjDmCS1+w2N5o68mEZvC3FJLQ4etndjYJ3qT+1uC1028S5ME4q6HdOtAmsM76YOCYRbFpQl
+eYYt5234zdXh9Bx+55S9IvUD4KboUuAJuVauwoercTWuxteNp8JTsLWB9cxHZKYo1Yp/0MBuQgXf
+aogoEasSN1N0RmKBlj264nkbMSnYrIGL0hE3spyFyAlntU/qx1TeM3InXrhneK4WF35mnKMREcew
+XuBT7HMwFwcwc1H6GVOqvlU46FYrCfcUvXWZ2/qsgwu6s/c+ew9fKMXl/fDb4ta9/eIOvr8Sa3xx
+/QStXJbf7jWAKfvzF+JSO54FVwv0tXrvAV5bCrHGx7x1nLFf4YU4wh1Clz8aiYXuGx9C13tZfrd3
+BO/RP5ZrFHvYUBJivGHO8RKt7WQuLu70ZIwj0qnvdDwsyLXYdz0siQsIZ/8fe28aY1ma5vX9zn7u
+ufsSe0RmRC5VWUt3Vy9DM/T0eGYYFiPEWNhG2PIHo7GxEBaSJVvmG5LlDyPkRciWMJaNEBJiANuS
+B2EYPAzMwvT0WvRSa+6ZkbHefTn74g/Pc7Mrx8101bQEiRSvVMqoiHvP8p73vM/2f/7/EquhAJpY
+rKeXhHRVLGZn5fG1c/FuHp4+4UBJcE46bf6ELcrN1/tyn43eFyjsXwEgyuqc1SR51gk7lL5iOSYW
+zoYSuJzLMx8vn/HhufRMDBcrbM05Oq7PpiWW8tRz2Ao0yakgZCttEo8FlpxWK4qhgnuKguFK5mDf
+C59rWt5Sla2oiugZCo/PCq4vxY7ecwra605ZrT54jkeh4K6tMuU0UUKZVcZFKMlfFjHT1yXcHKhW
+arS1JEiV1KXM8Qp5NhYZbV/mJTq3eS8W6/8w+6Zcb2Ey64intB3UuNR7un37Ic3wSwBsWH+Uv/pd
+SdZ+3HHlKVyNq3E1XhgvhadQmZDVK/L9Pvva5NQzXCJtZrGra3ja4bbZvIGzI7v47W3BD7gJDFU2
+bl4uaKVyjHGeYSrDUKNr0Uglrptqk03fapE3xaLYnQYNtQh3jioiSzUf999gxxELk3cES+A2LK7t
+yLG6B20GC0na1Taaz7UZrZ7Jp3zxEHa35HtfvPU5+h0VgIktVlMte41PyDfWXYsqEOLv0fiCWAEj
+WnBNE26DZcJnY/FuMu8Uo5RHeHpXPJfTny3YtiX5NNnLuKG8CFZScvyeJvZGI87acv3NffE6RnmL
+lpa3SuuAzvrZWDVIVU/BqGiN5ZmkLS2Xzi0sTa5ZOzFmKNav7fo0lKbNMytSSyxhX1F+lvOrLHe0
+e3Qekep9h4MAbyWWMmlWuIi3tRZ66TV2iY7FYt5qL2mG4t0N9nx81Y+8NbXYO5T73lehnkkeMl83
+xE0MjB2VxZsb1Kzvc2fXtJRZV2nBV1o1eCy5iH9hFZSmeJ4pBrkrn/F9+U6x2qFtiKc4zmq0Wmsh
+G49rG3I9/d5NDlTdu+xpkrg0iSs51pZVkiunhlWl1BT9eHAn4b0H8vM1R9ZvLwzYbEl+wdnu0e8q
+ZH1xk+UfFK+483e/Q/3HxcvkbT7WeCk2BbOCWgmdeUSnr7RbTkCh7bmzcMbClYRYZedokyN2Jgva
+8Vz2NOkzzBsUuojDcIihSSTDDLiuEFxnX4AwDX/JvlYcejWfrCaLIx+H+Jqxfet6jWLjxwDY6MmL
+sOs5hE3VMLSh6qp0eK9BrybhwUHnVVLNdrevaVZ45FGqrmTDirFUACRZrjA0YVhqa27nwOGLeqNH
+P/uH+AO+9AlsO0c8+OVfA+Bb3/Z5eygL9mlTruFzw++yqDSxuT/g3kg4/PiGzW+dyIKd22O+dEc+
+s/OmhD77nev0tIPTynt09UVZmSV9VCQmNKma8vtllOq8prR8eWHfvObjbf5+mQuGoJiL/XlO/VBh
+vGf/AoAPRgG1Z0q5H/msunKv1WhIiBzvPIMNrQxs7MiL9EYBr9yQa99tZdRX8lK0yoDLlmxYxnhK
+syGJRqMmm8rsUUFrrv0eOxu8XpOfx90dIkuNT+iwWPNR6gYT3n/GsdLR2aOYCyXRLCzIEvlerh28
+D7L3efJYXqkbzS12tB36sL3P/nXJeN7c2KQq5JqnhUCp/YsER1vmsb3nTN61KmDQkM3ky2VCvyP3
+PWjpRp/dZrKQas637ZjFPbnX7958ny//dXmuX6l/EWd8yCcZV+HD1bgaV+OF8VJ4CoUBCweCehe7
+r+wetZhyKta41azjamOIYzRpb4pX0FSuAM9JCLUpxXRW2Ik2tRQVjidWrkptXBX329ZEXdfaobOj
+SaICikLpw7yc2S0tITnX6B3JMYqGuL7OwOR6U3Zrb1ay1B78qB/SXGlIsO1jIJ+hlNAm29jERH62
+8in5udxr4h6Tueop2OJGMnqF+nWxSls7d+m74q04F4+J3xRr9hsXY8JQodlrbonsgrAu2IRs5XBL
+sRDfLM44H4slmTomx3MJj75QSrmqv1XQzOWzsbfEXKlwijtlrHTGs2qOMZNnslItSsfxWWiprOVt
+80ZXPJdmdUjpqMJ0WpBrAu68lDlM2+/htFX2rzJYadfm4qCF9Y54IePsjJkCGOKGWNXP1a4R3JLn
+fmi9hlMqp0HqsTmQ66iSPWLlQLAmypjcvqTdlfs/uaxzfUOt+A0L1xVPLlkdMzyTZzJvyrG81QDr
+WDyCMwtSJVitiopYPb1CcQeT5PI5VZpjW+xuyRra2a9zc6BMzO6MTJOKlaPCQM4KI5DnbpcxtjaB
+JY6HHUomtbG3zxdburaakrRNspDLE/EeiuUvMY5Us+LrGf9gLOQ7PxW9j+t8XBkYGS/FpmCUYC8q
+cjskULfOebVLK5WYOjSWpIbGWW7ORl1iq0CBJkXZYKnsvN2ZTaiYdJaw1JhyCzBr4pZu2gpXbtdp
+onx3WYSxkp9P45g3ffm5dvOIwJEFZCzlATkTF/dA4vMs38RThytYOjh7kufwk5KsJw/Jbt6W381X
+VLwFwDz6NZrIRrA47+BvyT3NMtmY+hsXFBcSMgXuTTxDjlW92sb+x3J/P3bi88uXGqJsiPt5//6C
+m10Jj/zmGcu6hBU38Hjfkvk8MxN8S1W2DJnD/dTH6Mli7WYBrtbj47xJ4Mu5zVFEqaImppJ4zLKc
+LX1RAiejVmis7oUYSr6S2DPmChaKnklvRy1qU/elavHluMH3dmVuveMRx5dyL2mWMlHo8qG2JE/u
+BNzWjEerbtGoS7jm5DWMuty3FaVMNce0ZikqxruMZnK+fe+CmsKqb1R3KHXDmptL5qZsPkfaM3I+
+G5EM5KX3Hxr4iv/KTZNKgVoTVQvrBm0exnIfX+o3cQK5zh2nQ7Mm89nudtCWF5aZrIt645hKW//z
+VRe3L2uorBI2tJOyMgJ8S3t2FLuQVynjXLA1zmWd7zwVLsZ3dme4Y9k4f6NeZ/i8ufvjjavw4Wpc
+javxwngpPIWKisKsqLW2KbbEmg1o4F5XVN2oJMpUp8AxqSkdmamNSHmZ4qi8d9RuYisBSloYVNq0
+kpsujbWAi4qUNKsco9T++KriqbrEXr1N5kqtv7vVwg2UekwRiM6ez5qWOeqlWKo/WN7Yx6rEvTYs
+EzfT8EGlxat+QG5KTTwYH1Bo92FzMcRTy1u9Lh2VPEkwAvms1b6BaSmy7/0TLOX3PzGWog4DBDP5
+92Y7wK1JcrFwmrxiipjI+zsLrHtipQ+dimag2ArNb60GHfqxdt85TSjWUPCEQis4abeLMRaLt6jW
+z6NgotRsdjXB6omb3MgL8kLuu7wc8yRUTQlf5iS1zjDUq4ivnZFmYs2WgU2onZb3np7htdQDupR1
+cWv3hEkiFrNTzHFU2dpwC4pCvKZkmVNqGLdwtMnLGbFzJEns4TMolfdgUQPfVDj5ImI11UYwDR/a
+16+x9513APjAMHFVQzMtK5xKE8U1pWuLUvrK35AZNfoL5Xowj3DUw3CyAEOb0Sx/qL9rkI80fPAz
+0pWcux74kIvXYGWXZFtyTzVF5hqVQ03h/ZFp86be0937BpFqWVwPj6m68qwkePrh4+XYFCqRYD8P
+l7TmsgjKHQft0qXRq5iPtSOtjEhU1KWpmWKzcthQpaR6XMNW9+q08qnF4g5mNHC1vyDK5cE2vZRU
+H2IeRpjqMjqBTb8nkx206+QaYvSUuCMbpTzcEve0Ok0Z1mVRvTLbJtBSn5lDrn0FjrZZlxOHylIe
+waZNTctUZWuHVDP4TRVCCa1THFM2m+TCweloqFQPMIeqjzl0uOPJA39zIC/Yzp1X8CMBrqT2u3wl
+knJU77xNVinduTckQzaZUDc8Oy8x23K9aTLD1opDOEspCwnNmlULe1e+l0xl7svlOROlhvd2UkLl
+pXS9AMOQ3E2rkbKnuY8ol40pzAZcKKR9OhlQ25FzfO3rv01+b82rGeFpr8GliuMOnVfwVUA4yJuk
+Tfl9I6qIlsq8lNgsY6WgVyDQVpozGUvIN5tFGB0pAdeeTOm2ZO6nywRXjY9zruedRuQDOUbn8Yqn
+a9FYw+C4kOvc1I7SyoRoKZvmRZQxUfHichbRcGRt2d4cN9Z+HO1zyeYRpRLievngOb9kORsz15Jz
+vx+Q6vlqKjKTLFc0lI2pZbR55Gm/Tt/gtgpZfjW3iafKXvUxx1X4cDWuxtV4YbwUnkJpQhxAo1sj
+64hbW1ZNko5m/ccFpibawrFDbCuBxlKsY73lYmgm2Gwk6OaJ5TxlFYnb6TgjFpFYaSMQ92uyrDAV
+NzCMKi5tsfhb2etER5IEKi2TSjkQJrprW5s5dV8aUaLFkCSUvfWCGbVL7YWv1cgvVXzlXBJcRtrG
+bqtVSZtkygOYXYSUe78un40lsxydlMSmksL0ljgjsaTFhxOmSznu282MqJR76msytNaouGj9BgDT
+pwmfrX8egG949xmotF7m1ki64kHkc2FXTrfHpGMl5sjGlCq7ViUL0kIs3srJcBbaEOVLOHAxHTBW
+ApVqVrJpatNO5uLVNSFmWCyUt8KtpCvV3rpHR3Eh9++PeKpNV0atxSKR5GiRz8m0qSjVBOziZMLo
+UDkizmImc5nPXsNGGdVZJSbxqdKpXcpni9pTFkqRbkUQKW/Hoj8m1EThajHnQpPJpTYl7Q/2sM7l
+s+2mRTWXuS/zioZWvNaUmHUrpq0kLF5/RDEWD2TmnTCcaPNbbtBoyTUlpYLCFiWpdp0mC5fimpK+
+OBssk/U74FDbkc+MYpmroppxph6fVf86bynn4+RkzndNuWavTIjSj2qY//DxUmwKVQHZCJ65T7HU
+fauCgqO5uG1ZZRJNxHUK4xWNM5UOl/nHddtE+nKbUch0od1r4wmPUlm8b7ZrxNppvSYKWTVMAtV3
+mFYRLeVMPG7E/JRuPHHYJ1Bg1K8cKy/hhY3Vlri9nLrM2rI4aqOK4pq8TOUwIjXFXfVUgcioNTAM
+CQly9wyjrei4tzPymTzQe1OJX/eCiolmkINHDRaqwRgUIx5M5fqvXbq8q9WDvZ/Q0mp4xF4leYn3
+l7/KaUdepl1/k6mS3y4cjzcvtdSnhDPuccFEQm6cYUSlaLwir5GtQ4KsZKpake5EeS6tkG3NRSS2
+QTiW57T0zxkoSUyZLSgvZHHfW34DgK63y4WCu5zjGrWmbLxns0tQfUujNEkqzQ1oXfSsiOhfSsjw
+oIzZLhVEtbFBw5frmJYhpZbnWOi8VD1sRZtuNZtMJspCNclZaHfs5GLEWD1ttRt8EA+p3daN8KFF
+q5DjTTFINNfSs5SU1nShKxWHm6sulpbXF2cxw7byioYdLASIFSsvZbrKKR3Vd0gqeCzfm3UWDBTK
+tHIzyqFyZeqGbJ0OmUQSopWXBv/8VOZz8OmYo2fyat8rDJ6tPtmm8KNK0f8XhmG8YxjG9wzD+NuG
+YfiGYRwZhvFVwzDuGYbxd1QT4mpcjavxb8j4UVSn94C/ALxeVVVkGMbfBf408MeA/7Gqql80DON/
+AX4e+Ks/7HiVUbDC48IT72C1cNgcyI5am58wXsrv215MtqbRXTMAJyWRpz34VpNcE4KrrENNBWWW
+2QaNUKG7be3Ii+HSl100vogZbgsUtZ01uB+JVb3VLHGeKbS1IRaq2Tax/UO5hs4cU7Ps4yChG8n5
+sqwuGVSg3FawyijGrrRikjewz8TK5X7ISv3VgVKIz5MaWUvuY+zcJWjI/U8eOjxy5Ljv2SlzpWIf
+P5JqyfZP9hlH0kV4XpS8ci4dnsfNMTOl6OrWlix3pAOzb3T1sy1cTc65QRdnqYlGt4UViEvm2y6D
+hfYrDOTfXv6IItK+jUnJk0Tms+9VTM+U6i5JOavkXi2tPiwnC44NOd+DZMiHpSb48jaxUs3nZcia
+R325plyfFpxuC+CqNZ+RBfL75SJkmaskQKMkMFTBS+HTYcOjg1hYoyh5rHR7GRP6NVlH09hmqJUr
+TzEWbs8mvC/X/hiThRLVpICby7OKDQ0ZzIq3GnK+/FqNREOboVVRKcCpYzXwIjl2WMr3zKrEd2Xt
+VY0ai754YT0jpnQ0PEjnXGiC3bPl2ZRuk8lYwqSV22JrVykE33V5YsvcRlkK5vd7Oz7O+FETjTZQ
+MwzDBgJER+tnEF1JECn6f+dHPMfVuBpX41/h+FG0JJ8ZhvHfAU+ACPjHwDeBaVVVqgjJMbD3g75v
+GMafBf4sgGeZ5POEMpgSObKb77+6jxdpj/nuJvsKpS3CJakmDJdKtBqmCfW+6k66Aa/cFAxBo5rj
+aFdentYpa4oOU0anIBiyOpVjfePsESPWdeMLvvddsbz/Se/zTHbk2I7qIvhLC0MTan63hhI40842
+KLQP37EMSOV8ZizTHMURkwux4k7lYnT1uF2b4gPZ/Y2B/K7pjKkUF1HcN0lbwrZkZiHBQpt2FgYj
+1TqYG4/k380vEDyQJGjXOmOVyGe3zAEXLRHz6raeYCylYSg+VMGaZkF7V5W7L22ytb5BntIw1Qvz
+XGoN8SwmY8VbxA6Xmfw9rsYYing0Yp/GkVybPdzgCDnev6gEgn1n/CpvhWL5pmGLzo78PBnOMDPx
+FCp47m2Fc1kXd6dTjj6UJGl281PYam2LjQfE5ypvN3Ko1SVXtKPQdK+KSZWCbpUOaVQyL50lRIpo
+rPv58/KdF8nvvns55wtKEbgfpjxcr2wDXMUn7GfaJVnVuVS05eGjipGWkavLBe4NJe8tDYyOeBCL
+Z3KfppljK9fFxqCOoxgD1/EplZoNz6Gbrd8BXf/VmLnOy7PpnONHcrxTO2KZyPdOKtAOgY89fpTw
+oQv8HHAETIG/B/zRj/v9j0rR+75VXdZTblU1bF0EcRniHkjyxYv22NyVCYnO9vkwl4X1bKauXlEx
+0KRk0D2kOBU3360fcxquMeX3iCLtqFOiD6d0yT1JxG0EJud3BSa69eQWF4lk/v/Ga0f85Z+TF+HT
+TZmuDwc5zaaAgszLC0JtAV5YH1IfC+2Ys+HiKM32dCkv9IOnc5KhLLogsOk/kQU06c7pevJSzELB
+slcXT/hQCT/eeKPEVums1dMxqQKkHnj5mhOETPtEeuOc5IZg6pO0xApkTx5ZI26tZN6GDZ9oU+6P
+UsKLTmuFkcj1loMRsxNZ0PNqiDuS6/R7JSvtGwl8+fdpZLFYyks6S1aUnlzQ9fw6blPupWHPMQ/k
+GHcuZWOaBffpn6qAz6tTolMFfYWNNZMkBtVzncZS6dK9x6eMviCdmHu3Kq6j9Gi5z/1TIR9Znq9I
+FC/wpC3HfbUJj2x5jvNn95mWcu7dN1r4ih0pVk16DfkeSjl/rYDTp5L4HOc5uSYVqxJsT4V19TWq
+e0saDWX23osxJ9qVuztj4YpBsXshpobIl7bwbrqjgFIT6cvOGQOlyrt0BhiqbzqjxjXdWCqtguUT
+BwLZmHaMp2xohSc+K/jwea9QxXof+7jjRwkffhZ4WFXVZVVVGfB/AV8COhpOAOwDH1OX5mpcjavx
+MowfpST5BPj9hmEESPjwB4FvAP8U+PeAX+RjStGbJXirivPkCUeaqHn8TsyXcqmxewdnoIivxtaS
+KFHFZyW8MAqb81PxKlr2BXXVRTA7Bj3FGHTKGjNFlVUNcffcoqCWyd+fPbmgUPqsXxu9S+L/LQA+
+lb/O/6B6jX9C99x20SCdi1WqWx3m6gZvj+/g70np0Fv4GF2xpo1H4pnsByVLTWol5YK29sXXqw7x
+ntz3RqiWLws4eiohRTq0iN+X4w5rE375WK14YpCqRVj1xJqRPKNn/hEA/sBgwW8+FQ/LmlVcJGK5
+j6MzPq3177kpe/bG4pC4K3iKbpRhtTVkiHfwFP5d73qgpKppUyzia8mIZU0s33YvoLMj1rjRbuJp
+cjE3E+7P5Pq+976Uco0w4r37ikY8KUC1DIpuhDn8qI6zDtWZeDK9T/VNYQvxPxwy/MM/AcCP3WzQ
+dP99AJy9d6lOFL8dq5XfS3j1iZwvano8Ug6E5tYRrVDuO9nK8c5lTT2ayxwPn8yIVYHczCscxSaU
+FQyaKkCk4YNnupxp8njPvWShb1dymTF/IKXm4ksmQV2O9/md1/UaKuqGUrAZNtnuWjDHJU80iVuz
+KTqSeLeX4t1msyckFxLGnRslzxZyz+/0CjZP5BjPKnCVFDf/mD7Dj5JT+KphGP8H8C0gR3hd/lfg
+HwC/aBjGf6u/+99/2LFMAwIbEsNirC9p0095rACam+UFvnIpppmJry5Ty5fN4SLMKRUIc/E0o7Yn
+LlcwaeOqJmBht6gUR29MZUFM7BXRWlWWnOF0LRdesvj74oIXP1nnJ9+VY+x+Wc7XbppEqm3p1Cw6
+NXHr5r6Bm6j76VjPgRTV5trHr9MYaKyelkw9BWrVR9iJbGTVSh54ktWYdCVGDC4SHms2/Mk3l4wU
+Z5FR4Wssap9rFcV2KdJHcu1Wk/RUEO+W2eVY2/Mso+CpJe7strIIn40rbFM2sabVwlFCmtRuYHYV
+ojy1MbTrNB4pqKiMSfsSdvQaJraGDJ4bkg1VOWo+5vEzua/5XO757OFT3p2pqG4DEm1xNs5Tio8k
+y9c/5pW6xnnI5f3fAuA3NnZ57dflpY+2juh2lCSntAhVN3Os0O3ZhcdYGYtWxSZhT9m6L0OeeTIH
+w7spj1XodzKRuZ9lkDySuYjK6vm1lRV42m+TdTRnlCTUlZzm+LJgqaI1qzCh0Oc0fbCgvSfz1WzJ
+vyYzShUWtn0DI9b2eWcIlsyn4WdEoeIaNNd0eWEwVQGY6UlBrrPlnIMKXGGaYBr6Px8zt/CjStH/
+JeAv/Y5fPwB+349y3KtxNa7Gv75hVNUPcNX+FY/97Z3qL/xHf4befoGdiVt6+LmIjeqnAOjupcyV
+kKQ9OMOypDLgNWVXj9MBhuoWLp6ZGEgCZ1hep18Tl7FtvIG7lu4yZIcOzEuShVr8zpIsFtfXtU44
+eSgu4WL6W3xPe/qbquH4l0//G97+K2uBFKjUimGAUZddvL/V4kttwTpEKt5SmDmWWvmaYxGuM8uG
+Rb7urlRCk0kWstQGn4qcrNSQxzc5K+RDeRPSc/E8VMeFeFLxal066z6oxfz8j0lCtJvknM5kjprA
+UJGAFPLvKCmoqxeAB+lEqxpZTKjVBcstydYM1MplEc4NMm3sWaUVTUUVVkaNoCGW7Xg5J1ZthEyh
+397AZCWXw5/73J/jr12T+/jbf/LTmIkc48NvXHA6kzCtzMRLuzGImcsjZbNV8nSiVaB0xt1L8XR6
+JkTKm7inmhzTDLY0UexbDdpacRhNpwRaJXpiVAy0Q/PhSMLRW6/tMM1l7e3dKHl2X74X3Cw4/S+/
+DsD/tH2qD81hLeYDfN/NGdT41HWxkwdTB1MxHp4qTftZxjRTBXIjJ1zzisawyFVhfFWQKf6mzL7/
+DHra8VvaFc26eH95ldPUZ7m06uzuCor2r/zi//3Nqqq+wA8ZLwfM2S3JDlcspw1aNeUDfHDI3k/L
+0w8vr1PWJYP/8NslR5+VbPezUyW/aCY0lSK91iiJT1Ve3btPqmKrxltnlKEyJzXlHGbcwR1o2bDw
+MbqyMO1wh+5NedC//H9mxKHEsMMHMune31qxfqf4yJ5qVAavJApeaTcwda+o9VQ0N2rS6itePqto
+achzlhT0NVa9SLWz0Ko4XyqHoeVQKtzVLS1uqJjIyXCFernoegEgCaWD80ZRAwX3zMqAUt38x7MY
+S2HM0VIFVRsVifqc+02HJ1rS2ncMzi7kZrdcjzNV3NpU8M/JPKFUcIxrGTTWbFhWia1dortmk+/M
+BHY8ChUI9uT70Nvy7t/kfyv+MACNwOMyEve5YT2gk8jGcqG6k0napBzoHMclG9flHBePajS25Jom
+lwbXr8saWGtUbvo25ULm7cZhwN0nMmHdLZdnY7nXwTWXe+9KuLXqyXG/9jjn0z9VjjIAACAASURB
+VP+WHCOcNki1zfrJV0u+pGtEMVG8sCEAa6/dTA3aqhxWf7XCVKKdtCnPOouHNJQpapyXGBrSLSrj
+ueDMo2JFXXeZSMvFNb+LoVySuHVybRFotwPGCm22A4NHi09WT7jqkrwaV+NqvDBeCk+BsKT8ZkLv
+lTOiSCz35bUlh4ufAcApHhHnmjlOzrj3SHbB8p64RbWd91j0xE32L44ZPVJAS/ceWaZakbMx3kQw
+BN6uZJvn9evULrTzpbmJq4QlpWeSTzQD7MI/+q4Sa0TiYfzzkBc8hO+PiqknU3r72MV9U3zmdCW7
+fauTsF1IBvmkCQ0NlXrBmNs17eZU9uIw9Nh6zvBcgCZdaxQ8XaklcXieT/5oXvmRQoatKGaqVRkz
+O+dkzYHg5WwmKueu7nV37nDRl7/faPUZKPDmKMp52xOL1ndh80I+4yq82uxW0tGGWG5PgU6559Nw
+ZQ6/cx4+bx5a29KPTt9fG835zaWwPP/xcZ/6b4pH85sffIfNunxyb6VJ2VcC2lNVXd4LsRWHsre9
+xPiezNG4H9NPJIQKDuR6dstNFgrUOujvEzQk6Wg+mzPpy3oYzFzejzXUe0fm7dHuJe4vi5t/9GN1
+OsfiQSzuZPxnv8TvOtb3aMwSVq/K9bvzknRfyW5KWQt3kw32TDlHZC54S+Xsv5vaDLQRzjw/xVhJ
+QnusTXBb+EwVJ7i1Krl05Bjnc4MqV4BUbNIdPHdlPtZ4KTaF3KkYbacMLvq8PRaAzZfjEY99mRDf
+sLj8upTOUvcJfKiErorDL59V5HdkUU1DhzQSYM501uRgR46X3D2iLITkc7YUZJvbeId0S0g7W96M
+RAEmrplTq3y9uq3nC3qh7bRG+eKiXoeOlgWfV9KT0+sOk1IeXktVr+rWDdKaLLpt36JvK2a+bGH4
+sthutDWrf3pBLdN+h+gUWxl/ojCmnq9JNr7/kv2gPjibklAVsozEIFlXDiKbnV15qf2OVHVMJ+HQ
+lXl5bfcAs61EH+GQz63E9V+ej9jQORpXskG+agekQ4mHndDikS9XdNDtUCopSM/Omd6VzyQ6c6vf
+MX+ektu6XxuQnctn6hkYylQ1HshL1ctbTFuy4DsrG0c1EvLiGmVX0KK3C/e5bsOuKigFiUdbxXgd
+y6Fryb3OOjbb2j5rZ1O6SnZzTzeK+Qje1m7X4WUXRvKCxY+3fsCMvzjW66IKYNaRCtZisU/DFV7J
+ka6FLb9OX9vTt2wTV/sWbm1E7A3ls5fdPuM1oYzGink6olWqDIKVUiuVvWq8ZBEroZC94nT6yV7z
+q/DhalyNq/HCeCk8BdKS6vGK4eFdmntiwZ7t3Kc2FiXl0anNsBQX1j1bYDTE9TUdsT5Vr0lgyM+F
+2SRXwQ5z0yKxlZwkj3GUrirqy217ZkGpHApG0cZYi8zYBqa6vrv7LdrHEnY0hgKDNkDhIOK2r3fW
+o8ol98T1fdOw2VB+wNmWWOPDDRPTkg6/ZjAmjcR93GGJ4cuO307Ewp0GAXtNsS7vhw2qUqzEw/M5
+O4lYq89YJb82Uk7BHzCtGRCp3Hm+yllmKsjiGfiZdOV9dlssv2P0aSuUfH/3dXrhmn59n/25JHnH
+13YoT8SKLdULSssV+Z642o25S0N1FWu1glkuCcqfthx+Sifp739XLPDbRfnckpbAhUKNo9kxeSi9
+G/aii6malr2ZXE/9KAbtTqx7TwncQzn3IoGahAxO7ZRbLfm52VM258Y2gaGWttHCMOV8tV6T+qUk
+ki8il031pq7pmzFyZvBEtUeTMa8W8ofxegH8LmPtTR5UFl98Vzye02s1NuaKrbAEnLc9WeJ2ZQ63
+yjrLQNb9G7UZxpvy/F6b1PggFsj6YvK+HGscPae/s2ODUIlVEgMsBW2dJzlm9nFF6GVceQpX42pc
+jRfGS+EpmH5J882QO/Wf4GQiFui6HeK/IXV+8+wZo1xJLhOXlZIkNzVeTqdjfE+6/lbBCKclt2WH
+DzA4BGDZnZHlcmxnItZq2eijXK34uYVvSxxdWDUq5RAIuybNVyTevaslTc80yTWZ51Gx6SmSbNuj
+sS+x5qjj0MrF+vfEKBO4R/T25RxVfIATSMJpNevhqkpyqX33jaVJokIurzBnkkpCI8rf5cwWlJs/
+v+A/TcRS/PdL9ZQ+Mq91E5aW6hU2PVLNqTqeh6mCKvVdubiWH9DMNXHbyJ4LndhlwtxQZcmwZLYj
+c2uW4nXYeYCnZdgsMBhor39UNqmpmnO8d0BDcQN/3pJz/FffeYdMWYxcI+fghljuKuix2JGyoJ/c
+hbp4TtGhNpI5XbquWEFn+DnsV4Rizq3v0NM5CMxt3CP5vF870Juu8EJlxK5XmLk8h1q5JDTEE+zP
+nrDxWcHAHGrOZPykwWRfG5uCGmOlWFstXiw//qCxdiZW1w1iT75n2iGrSrU3+5LvagY9WnPxCut9
+6N+W3++0GgwVmVlttBlYktuo2jKHdnmJjawLJzKfK7Pb7oxHmjAtyxWL5Sez/S/FpmAXNr3pBn79
+K9xyhRsxb5f4j+VmFstLkvdV3Sg6Yz+Tl7OmJCzu1KOo5MXttg0yRxa8m/cwA9kAmrVtDNUSzG2Z
+vHiWE6hbXpUDMtUitL0clEZ7x21zaym8ifcrCR8+3azjKCPveZGT+fLzH6razysDDa8i35CFeZAp
+Zn0zY9vSzrqOg6cZ8lFtQkuhq4kpizXI97mw5J6cwieQfBmJswmake5tbPC41FZzBeA4WcZTdcxz
+DJgrbj+b467XcWDy6lSFSrZlge2HFd5NeXF9owXaWhxOQ+pKj2YVBpHSy1sSzZE3I5oq0hunGYFy
+IsZbIbNUk5nLMfX2IQCXCjX/dw2L+Fxe6LdXLivFfdTciEo3r8uyzVR7QT4zUjbkrRW+9hq4OxH9
+RObWr9vUFOBEt8aGKRunYyktv+FAR1/T3KQsZzq3CU3ttEz7ffbP5JrSQOblYfCMzXPZnO+2HnP7
+XM5xfEexMP+SYQC2Poejc+s5Yc7rdgWB0qYpKKr7LKDYkxe+MfbwzmXTWEwMPJ3oxE5p5vLMKkOu
+d2VsMimmOm8m01S1Ke06dUsrNEuLxE9+12v9neMqfLgaV+NqvDBeCk8B18A8cNjyfoInjngEnUnA
+ovHbAIzMIWfazbdol3R9cSnzXDkW6hF15cJ3ix7pSkk66jOceC0TXmPdzTJZKNorjcnqkvhbEpKO
+tSzkg+vLbl0Gj3Fur2XL5byt3S65ah86pyv8hljjJ9sd/A0heFmGIw5T7a03JLTpmm1i1UZsOSkZ
+cr46bRK1KoWmDEPfws7lfKZdo+Wpe3nNwSvEYqRlwKuvKRxZE0v35hF+qt2JrkWuHY6V65NpSc4z
+ahQ7qu5dk/lxGn08bUZLBwmeZi5LOyDH1/vo0tImr3xTCVHdExJtB7TmPLeIrlVhGKoE7lWc53L9
+m1viSXzG3+CualUYD5/i7Oq9BlDvrBGbDl2VYcsDcf27TkCaynxbeUWoOm6NrCR3Zb47eY1Kcd9r
+76hwG5ipHssxKdM1bqJHrEnqPFzRaYvn0bopn/2c2+XJ4lsAXItcRsromuW175cc+cFjEMiaPHVd
+Ck1gbsxr1G/I84sWco2D7jnpB8qh0BnSOZFn2ekXFHXxnFsNk8iR55Co3klzcxNCmc85MbeUCf38
+9BmXqlWSBnNWPzzSeWG8FJuCmRvURibjwTPMpsJy4+8RT+VlOnnnMU/PZZXOsogt5MWatLRLbZWT
+3pAFUfMaBNqLkJsrIs0NuPMLfJW2rxz5fh6UnFxIZnY/C4h8cblatQaW8oX32rvcWchkV5+SB/v3
+eg0yhVifuSmrWK95XGC3xbXv7GzQO5QXr6uuaLsJjaY8WD8ICLQFvKo5hNqD0lAY0rgwiAPFvcc+
+vZpqP1odqpYqD9keiXZXmgP57OtxRqzVl4VjE6l6U6OKKBRYFPoxdVMZolrSFl6lFWVNzu2FLfJK
+8gFlWeHrC+b7NqlSis8Ugl1Y0OnJy504OfGlzr2XUldimCycMVF482MlGNmsb/LpQ/nd2WKbEwWO
+5ctTUOGfKA1ptGSjGmjlqBMElCq06uQLPFuu37JjzJo2gNRcbFXcQnscSgBXIV5xhRHoGogimva6
+69QmLbTPoSf3uVu/z+VvviFz/+hbDFSdzNiboY2PKO/KCwAy1zDoq+pVYxDw6oGEM27t8/yMYiiq
+A/272ya7KWFsL99jXhvpZ0vqnkLr+21SPeFOX0KKe1VJMFIw2aSg8mTt2Zt1gruyqRkzA9P5ZP1N
+V+HD1bgaV+OF8VJ4CoZT4mylNHiV8lLZl50VM82lL3MLQ+viidEmaWhlQBuKtt2SSmWy5m5OV42E
+a2YYz2TfG/ULGspVYKby/XFRJ1Sk2YPxFNuUbJ5dBewqGYpnZnQP5XgNRxKDywLOlYk4NSIMS/Uq
+/ZK6YigO6wGNpWS+G6+LFW8se9Tq2jxkemTK1efNA2rapFWs5PvWxgnBhSS4mv2IUqnk6lsZ2alS
+e9VDasgx9nbF6/itocVE1XDyoiBUFe+6VcNVd75r92l9TixiS3Pk9U5BqiFDvjUn1kRq4UW4z6HX
+OZVqJ3S35cNFtEeSK0uyW7GhjJzzpctC+Q3mk4qRoaQuGrptmHuc5HqOnRmcalXGLClUoXnXWtHW
+DKt7IHbYNEoatljBZNEmO5Bws1ZsYCpO0sJjpTwa1rpyYlZUyTqcW2Bow5cdZM8FZTK3hqEISaOU
+77nVTSa3pFOzNexxphqTi3mFp5Y7/kin8drKOraJ0REP6tY+3O4Ie7azn+BsyD11bqn3U7WZKKzc
+WBZsbug12H0ynfsqz1CqChZDedZ2d87lSBnDOyVBKB5pwQm2JtvD5oLso/DRjzFeik3BLhw6iy28
+zQe4F0pCYmbsKf3404bJzXvqwjdCXivEtes35bNHj/tEb8rk+J5Joj0D2bMGYV1yEf3IJ9KwIlAS
+C7cZ4Worr11vUS1k0YT1GUWpvQjlJqUCXYi1izJd0GIdk3qMCzlGvzBR2j76Gzt09lTBSpWsXNvG
+T5UP0DJoqL9p+gmkSheuTFHdYcG5q0Kikw6FEteWT12WjmSqr80Mjk/0Mz15UQYpHCqs+v8tcnwN
+H7puQVtLrtmBxa2ZCMb4WnHwcgf7uixMa9HF7shxrWybROPopjUh16mwDAHYFIML7FiqQctGRHQs
+Jc6k/QBzqVWgwMD/QF6XY1WTav3EhFePJJ/zyv1DvurKy93yumxtycsWFi2WO/qMA600tQ2qVBWW
+vCnBSPMddkrbURy679FRRkANwzGMFmVLNiY7c0l0My3DPoYlhsgeTon194ES0A4nGW89kbXw2+UT
+9rWv5uQL0GrIOXaXshZOyuI5mcqP1x3aTQVZ5XtkWhp+zQtoaq6hl0vo49ba7G1om6sdEOtzWqxi
+PEuASqPVFs37As1+dCJgsvNHD1hdam/LMuOilGPU7zTY6si5d888TrQSMebjJReuwoercTWuxgvj
+pfAUKtci229RZV1ypQAvMpeVLYmTnZbP+ZuSXPni9jVuac9+oYmc8dGEhmb4E7tJpVWGZVBi98Rq
+jqZL0qVmZ/sCGrKiBnZNLObpLGUZicu5jU9H6dftjRWlApUMdf2r0kHb/FkmCTXVGZ/YJp/SBN4s
+9EiVl7BUGvZFVodQ7mkrqFOqtmGtLEiUdh5TPIVLK2d+rLyFzQsm+vcLKybUenzYduk78vnhM/ns
+o2ZMU0ldSscAFc5pdevYbfGw9pwd4j159OVUricOTMIzbdzqQqwydqRDqpUyGDsb1JR3sVJQUeAP
+CLVpKb87Z7Yllu3yyTbWRGnZxxbHLeUOiOS802GD/p54dGfNCx5rB+p+7YCGgrby9pjramEj9QKc
+VU6kYiqLsmSpfBCelZE6yqnhVLhduRdPtSjLysZUXEBoLVk+VLnAg2O+cyHf21uZeJqsXCm+pZzs
+U20/lGuPXRJH7sNctdn1ZD7PU5lvI07ZVPlCf6/FQVPc+SdhjTSVOepfDkgHcr7lUK59v20RWHKs
+zM4YX8ocm37IyQdi/YeXd5nUvgPAty/k2tv3Yk4UZ7P0U94yJaxsPAiZKz5j2UiJF58s0fhSbApG
+UeJPQxIvJN6UxdGazXAP5EUP4xmW6g4eP3xG95ry+2uW+uTiHGNX3M9Pda/RPhJ3r0oSLkPF/o9h
+oZC+ciETfddtYcdK6lJWlFvaM7GdcaTusVVcY6w8jnWl1j4LxyBrhqwqiJRnfRUX3NP4+pXpGYUl
+i/DhscTfg0FAuVBQyaBDpqR5RmCyUjalciwHfnb2lIcP5YU1LEi0zDh8GnOmtN8ny4jZQymnXZZK
+Ef8s51LXQFXZeIFc8yyGg/6aRnzOxVPpGFXPF3v5jOKpzEujW8fckoU7zX22mpIb6W4W+J5WTAqZ
+t3ixJEpkXt+5f8LTX5Hn8DXrfW6n4vJn6YzzB3J/54qU/OzmE7ZH4iYbj2CxBtuYCaGyEGVBSmat
+dG7lZSzKklLnYrlMqEbyggR7A8ZKW7WXPMOai6ttd7TbswjJ9XuX4yG/9XUhkL381RFjFZB82G9y
+Z0/yRn4mHZeLZsZ797RleXzONhIqRW2LpRKw7s5UmcosWKvcLuItpi150bc2DHqbkhCYpjY3Mt0U
+Mpm3RT3GyOUcvr0kU57P0fkZ55cS/r7zQYRRidGq6nK++/MYv66b2OMLvlqTdfG5neuUM9U2WXqY
+5jpsuAofrsbVuBq/h/FyeAomOHWokh6NC7EI40ZGoRRWxaog2NcM/5MNwkSswKguVumkXTJfqcJO
+YGHPZIfe3gi5HIqf/yy75OJMFXlqSo9mFVSa3R0n5zQvZbd+767PoZ6vYYxR0mVWaz6CtCKrVAAm
+T7HV+iVlzEoxAmnNJl1q0rQnx5okBrFSl2UXFZub6ooabQKUJ1C9kscXC44juV7PtJgrVCZ0HWqq
+XZlnMdvqpQxViTquKvRyMPOCSvtA3uh06W+K57UMAgpNSY9ysfidecKpVtr9vI4fKZnIdMIqFn6K
+HW8P01Xm5i211mmHc6Uv/x5jHirWIUr6lJXKfPdLyvORXrPyTbR7PKnJ/b/DOfeeihucv/oGlkqn
+D8yK1JJ7LQw57mUYs+Frn4jXZH5dPJd60Gau3YDJmcG20qnZuXbEtgqmWnH48NtnfPPikVznbMTK
+koRfp+hxTa140BPQ0LvjIec7cn+NfMB8XeGwHVztO7irmI6CHEcrUTEhT125j5u1EjsT77XYgvvK
+z+G68qyTyyZ9tfIt08HXbsdJVHD2vnzmPBhyzdE+lkC8ma2FQV4XL7RTdpjo/V0OSvxKzjebjsgX
+n0wi6spTuBpX42q8MH6op2AYxl8H/jhwUVXVm/q7HvB3gEPgEfCnqqqaGIZhAH8FUZ4Ogf+4qqpv
+/bBzWJVJIw/oBSMm2ojTjRK2BpKoyTsxd56K1ZlfG7PtS1fbt1ZiDW7dH/HrmxJ/7xHQUV3JQXTA
+rCaxoR1WjCNtJIoVxXewYE+bo94rRtRUo9IzHrGcy2fDuP/cCzkfaUJtuSJVvoF6aTBWhtYSi0tN
+Lt6b3SNeS3d9INYu2PA4akjJbr5h0ptottKZEmlf/FxZhL3LEEPhw+dpzsVUGnU2cpeZI+e43Q14
+oGjKSJOZ9cgkVk+h49TpaTK2TowC99jsVWxo1+XDk0cA/NZv3+crqg+5E3Rp3pHr/NOHP81yX8p0
+W8sAe0vvdaGl1eVDTNVzbEwHmA9k3t6ZHhNo7f0iHPNmLJ8/V0t7a3xJcSwJxZ952uefjCV2Hpgt
+GhsyL+bMgracW7lKiaZLvv1APIK3pyO6riQ87+8ccKuhVHeNkN1ELHZ2pJMxrrEIVRF8taBxKbmG
+u+cjni4lOfqF7oxvL+R4/+Hv+3EAvvSaj/8VAV+81/0K7TNZO+/vFZxow9qaQSsqbWbKNuUZGauh
+PNNH84Rv2/8cgAPfJdmSHM1P78ixjpsrflw1KGm6oGJGxeoSL1CF9NEmb48kQf5o+RUArnkeibI3
+vUVAGMhcfa7c5aF6W39sXucXtcNsrXDyw8bHCR/+BvA/A3/zI7/7i8A/qarqFwzD+Iv6//818G8D
+t/W/LyIS9F/84VdhY/T75Jc+hYJOSB1U44fNVkn9TZn4tnubciwu06ZqNT69NWW/JRPtzHr0C1mA
+1k7JxqXSh3UvcKfa+2CI27pxbZsNrUvHpz3eG8kZ++Me8yeyeBcHJpdK7Z6tvbAE5pplTvMKU6sT
+q0XCW44sxnFep9vSLkEVXtnb2qSlsGvsHKU5xCgqojX9WyV/Lzs1fIW42ss5TUfbwZMRHnKOyi44
+6msFRpWgpoZFw5EF4dcNzK58b2tvF29P+wfmTbauyTWPXK1kvBfwmi+Lf3CwyU++JnqN27fbpKly
+Qg5MgljmKPbXUu41Ak1mNj9rUE108wpyOppIDQYDtrfle59Zygs/81Peq8uzHk+fMdfEJZvbeCt9
+JuaSdKWc921VOWq1GTbks9ebHjWtOh3tXsPtiONrLucsHdnsi5m2lDcLypXCtTsDNj4tayF4F3ZT
+MT5HmwP2O9qBq3O1u+WRHv1DAD6c5XymJffXs+s067IJXTaVrm5UgNL2P5s7vPqmsi8nhxz2labO
+fosbt+XaOn35/rZ9RuBqz0U5Z6pt6fPEAFOeSfszHsZjeVbdiWxuAxJsTcRuvNLhUNnIq8enfK2t
+rePOjLT4ZGqSPzR8qKrq1/n/E/v8HCIzDy/Kzf8c8DcrGb+N6ErufKIruhpX42r8ax2/10TjVlVV
+SnrPGbBmsdwDnn7kc2sp+lN+x/ioFP32oIuTRNitEEPps7ok1LWem140MbQbLimnNLYkibIdKmNt
+1sbXZp+91zo0dw8BcF2TgSO7tZXfJD2VMtxJIEkyI3pIqyHErX6t4ECl2O+8adPbk918y/cYnou1
+KUOxEk+THG3BJ8VACXVJcIkEpMdPtr/MZl2+VwslMdpo1si0bc/NJuSZQnuLJbnW/5uGWDNjsEdv
+JRbxvLvPGoNsFTsYao0Te0kxkjm6eaBdeGnAsbrz5d6AbiqfjeoFgRKd9F4J2VHG4P2xLIFX/+Sb
+rFbiwjcHr2Aqc7DluVihnLvKlyRr93iuSdBxg9qOuL5/ZByx9/vEMVwUH7KxISHIo3efcBnKEhh+
+IGi8D/aecfdviDX7amoQuXr9doGpScLx4hIrllBBK6EcNgwanxV+i5YX46diSVtmj2VLjlc5JUYp
+86LRIXFm4edy7XuDPgNVZts9fJ2W1vpv3HyVnaUKtXxKXO7l29/inwqNBsbjx1RbYtE7X9zjA+2e
+DIfima3KEhRBOmRBU5mmX//0HX72s/KKvFoesTEQb4QNFdnJa0RLWQtOPGL+SO5/4Fv0XhePpbHV
+5PWbn9KbuQdAzfEpjyVcS/uPePefydz+Rppw9k3FL5RQfgzquI+OH7n6UFVVZRjGJ0NH8KIU/eu3
+r1Vey8BY1RgozHcW72Cq1HyRntFWIRNr3qJQundUDr3eGD/vW1g0EgYaO9q9LqkCfVJriaNY/MpX
+9mFnD39DlYdK0KQum/U7XFP30Zkv2QkkM/zkUhaV8f09gZIKbQegMkZ8KfkyAIdHKYeWQoFtWdhW
+VCNurJWVPEJVZyqX5RpjRNwWd3A5rfBVUHTHm9Mspc5tmlPGriygobck0/r2VijZ8tubDXY8Oca0
+u8vwWCo019tbWAqxJjTJTXnRPUO+v3tkQSgqRnltSlLoTeWPyPSlyTKPXMlJbGWfLtwR2TMFFvUr
+br+2huu+AioH3/l0m/fOBJT20w80T/Iw5h3tbZmXJSqERPdwn/hCuwSTJzxUtajqXD7QvNZmtyHn
+aOf7lB3ZCOxkjoWEGuMqo1T261DJYErfJtK27qQwaercvm6O2fCEMdnvDPGQPIelrcfnrsO9pogP
+4VpMDyU/VOZLqrFc20rVvSqq533UUTnnoJCQ9otvWPz+Q3mhm80FtVyOYR/o9cyvY/gagjycUjYE
+xm7UpyxVY3Kv3uezA31mprBDGfmHFG1xxC9YsWzJxvudbw6Za+vmquLjwhOej99r9eF8HRbov2tm
+yGfAwUc+dyVFfzWuxr9h4/fqKfwSIjP/C7woN/9LwH9uGMYvIgnG2UfCjH/pMCsLL27Tap+RKgFj
+sDXGq2RHjUoHcy4/G+UxXlM+YyuBSjveZ6zc/PtOHT+Qz7pWH0exANVkk3FP+fvVsnnejP38EID0
+9S6Hc61KHDlsI5bQaFdEF8oerbz7cfXibrp2k+qGw3BbrumnjR5tRQWaqHJwmlNUYqEmpkFwJlZu
+kTuYCs1O1LPZLBJyxTS0qj6Olvwb2DQ3xXOpvudRhWIG9rT5ym7d5M2OQLcfODOSr0tmvb55gaWI
+TMf08TVJa7fl31YWkdXl/uzePsaaqCV6BaeQCk5nFFEpn0JgiNteNEd4LbmebNamVhOSGZoL8gO1
+pL8eclvVkx+OxH6cDAp81ZIcFsI/ADDodlh44gn5T9+C428A0O/LvJpVg01DQh82bYKmJmNjjyxV
+wcpyDJF4DVUlLnwSpSSKLdlwcgJL5qLmHUAgx675A0xfvJR4LHNhvj/kzvti535l9T0+P5a1F/X7
+zAvlLFgjSPn+sMuCEyWnuWN2qbflOuqtGo6yURu28jZaS5JYxY4uNohOxEs7fhLz5jUJCYy8wWYi
+IUjpagXINFgl4kFtlivePpa5LTxjne/ELcFT4Mok/XgO/ccpSf5t4KeAgWEYx4jK9C8Af9cwjJ8H
+HgN/Sj/+/yDlyHtISfLPfJyLqCyLvNemmroUlrj27qLGQklUnCgnb2nIYG+RKy4/V666uD7FsWXS
+s9jDt8XFLWYBhYpuFtUpC4kqiBry93atTxnL31t4DBWQszXdx+hrWcxwKTe1EjGXl9iqXiTUeM7A
+4/q8rqQt4U2DjjIE2QpoKguPLFBa+oVNIvsO+TgkrJRYRGGyllOAskmVo2FjCgAAIABJREFUnSa9
+NZNHMUIRvxwHU3LtCQiUB7Lx6gGJLSsiLW0ah5JfMO2bzJS23py0CTe1O9JRvHZRx9M2Y9PuPF/p
+ZeMSJ5LQLN1r4T+e6md0Bnb2sB7q87DOYE9bv6s68YlMuOnNWSp8+05PjjU/GzLRtu2WVTBRLc3G
+zk3codiRIloxU3Leh8qv+Qc6c5KmvEwbdgtTX7zKzinr2l6dVOTZQO9Lrre0PHzta8gLC9OUZ2r4
+bTxlpzJGJqUSpc5VAGcabBMfSuizd9pjuSObRiMdrNtUKH4A91Jewo1I0mvLDColsTXzgFLXZ6Z5
+lHyckU7koZ6tnvBEK2KbR/3nLdy+bVP4Ekr4WnHIJzbVXK5ztUho35KNevtX7/Jw3RJfK1h+suLD
+D98Uqqr6D/4lf/qDP+CzFfDnP9klXI2rcTVepvFSwJyroiSfLCmaEYupcvylj8iWqhhdhGTrBiQ/
+ZKAS9IUq9VZJifJ1UPlzYkO77CYj8gt1y+YFhhKOTI5lF42NJdf3NdmTdnAcsUDVzgxX6/e24WFH
+ksz5dF0sWA4v8POt7US+KnhHuyt3VhlRsE77aq08iolVV9JIEtxMKyNBh6nSGfdW8tmT1GCzKRYh
+Dh2KmnJEmBm2uvbOeUGihCO9IznvrZsH+EOxkl5nxFf+4T8C4GxrTKKAFqMYkiykMpDtKGQ4dLHW
+zMfZEEMnNLqYkkWauDV9cg1z4pVYtvyfTRn6Msd+o0M1VBVvL2UZyfWH4xRnJs/yXGdr57DJa3Nx
+8Y8jC9NSL6YscJpy/Un7gDes3wQgVWhz7luEitNYlQl+LFbVTFLSlYQ2rmHg1ZQbQkPNdGnR0ArO
+qqqoFPexKiNsS/kh/SWOJoKbWu64sZhhNCQkWniPcEwpL82timjdPYuMj/oLBRUf6HWenZ5z1pcQ
+JGmF1NWdXypBSpXAbKrdrEaPHUsrO/MF5554m23LxVppUlX1SiM7xJupzOAIGto9+amdLnN1J+/n
+JkuluiP5ePCll2JTwKqoOiVRWBEoUcRo0Wehi9E8XVHfVL2By02G53LDlqIAzfaKXMt+09wjWUoZ
+rvILJkoz7sYZp+dSvnmsx7XOjzgaygt23Tx/3p7rnfwYSVMmOKlCCt0M3ntPFyDK+ceayluG3YvZ
+eagdk7cXjJUOvMzk+2XoUipbVGQY5Np/vZyvmCDXHM/W2hNzzjV92+pnDJfruHfGqYKpjr0FkSkb
+1uc/8zkAnHafsit/v5zYOEdyf43giEp1EUYL4znZiTtVkI9Z4mp4Va5yTO0ZKMMhU+UBLLMh0zNl
+iKpLQmBVbOCt5Jo3Vo8p2hIe5MOU8RO5gWezGeOGdjNeVxWnuzb9DblnRgllojoanX3yuRz789vv
+87VzKT8mF3LeUQ4DQyxEnPhEvrjodlnD9DWcNBpUWop2Q103jRnxRJ5v6K+YajRa38lYRBJiuAa4
+et+FalkQDDBfkVL2DWvAsiPnu1x0sTVHkf2A8MGyKgah9ms4Ix6O5J4Wk4SGr23yNZm3cRYxf1fW
+5mJpQVOQkmX7CLMma3x5Xqe8KXOwUjLb9HjM2VI+OzSXZNrh6Uwe8vlbck3vX04plUXrBQHP32Vc
+9T5cjatxNV4YL4WnUGaQnFbU3VMmI4UUZ2c0lEsxsR3m72qfuvkBRwPxCsKmZOpqD7e4DGQnPuhm
+LBeScEnunvHdUPrmv9jvEDTEKm4/VLc1G7KhMFjjWpftUJOS2YhIRUbiZytO72kFQ0FMlQH+Rxh8
+ba0SfCltUXlircZ3J5RtuRdzrJBo18KYyN8v7BJrJuZqvKqYK9nJUC3mlu/gaKLVG5fPKdHi04RQ
+9S/rpxanDbFyGyiuP/kJbmno893hI4ZPlXMi/SbpuXZ2bvx/7b1nrGVZeh229snn3JxeflWvYsfp
+nuEMh8NgySQlJo1pUCBsigIsWYQJAwREB0DmgH/sH4JByJBMA7Js2qRt2LQkW6LsAWExiMkSOZxh
+Nyf0dKjuCq9eDjeHk8/Z/vGt96aL5Ki7h13dJet+QKFu3br37nj2/uJaAW4/ELV6tk6v/uwcEV3W
+lePaZbTD1G10mT5bags+8SBKPAsAOKzPsMP6ijkMGMfiGOvbryDl3A6HKbK3ZFwPUgEsuf3JCjY/
+J9Ge6zjEmxfJHihRWmRQMn8IdvX/AQBcIRBKaEeYM+Ki3DO0yMJl2E2okqaZW8LNxVRImZsejOpI
+mtKHldiH4ZLs5qSEdskxOUsB1pWkkWiFaXgO35bISbx6BP9QXs/d7JICyuTF/3Z/ngsLaUU0gelu
+jAcT0TYSy8Za5yo/T624DGETw9ItXXQtwXMMO1WsMJ/EyMeYn5HkJxMNazbdw/RYfqM4XeBIS983
+v3UD6oFEKp4f7uFVR8YyYB7HO8lSU1jKUpbyiDwZmoKpENYcGP0VnJ/JiRqHMeaBnIzNcXCJEFTd
+WkOZMQR2QoQaN0ZYiuPs9MjCjDx6D6enKFkyuOv5OJjKafyAbNXDsoqzvmgjV0LgoBCNYHvUQjRl
+hd/0AG8d7wIAXpqLNuLga04lVwFPufQN7NhAIr99PDER0sZtZax6K0okRBTu53N0CNRgxRo57dOW
+L3Z4pEwYE7nlTrsLmGT+UnGEw5G0vueliG1x4J21Pg0AeH6ngbAvv/Vge4wKHXijqIUzyI14PjKw
+zRyCeiaaRppWEJA/MVsPYCVyM5sVF8Zc5sJdUzAX1MKq0ofrmMBldWlojJDQN5IrFyesGN1NIkyJ
+puQ25Pac3K3BIv9BOtlHxJp/ZVXhVsQh5vYKrB7RP7Ii42zeDzFuEWn5WoHSlt8w+zPEDeaZBFWY
+INBrQaTmpoFKKne56Tjo9+X9vnN2qf0kmQPFe3JO9KPMr2HOEOJoZmJ//CUAwBu7VVj8bHyBoAX9
+NaejqVDwNx6WDvKQ0HtrHXhjZl56RARv27gSS39Nr4KQRW71agE3k/2yOxxiRDTyFkPLi0MbI/qX
+DjGDJjzc/KFG1hNt62z3KzhL3xuewpNxKCQZZrsnyKwpjgpCcS1SrDZEVSuvtNFcyMINcILtUiZt
+xCSO9MEUu2sywS+oJmpV2dCLL03xRkM2fXe2AUU8w+iUKaCNCfqapKTeDrZqJOm4EqMkNmASrWLI
+h3o1FhW/4Shcu8D+sxw0t+V73+FuYhjJhl1Jd7GeSppzjXDbg0Bjxtj2atNFk6nZzSsGTh6KGmjT
+s3x0lGGSSjJoNsyQWeJcy+HhfMHqwn6O7KY8eGeDL0vf3B9CY0MOuh/Cn8XPH//nMiZf42hEYtaO
+ifguuSs3emw3BFrywJqwEHJe4rOHSAhXVs1NpMQPjN4kSW/WwNYW1dK5DYfOX7udo0mINb+cYUqU
+7jsT+ezNW3V4rAytxgE8Quab0DA4X+2tb8XzW2JuHN7dAQCc+7+DaiaHlG9qOBnJULoVTBjBcfoa
+NvMw2utiai7mQ5SMrowXC9gGAXxmcyiq7na3BoP5ItaI6dijPdin8r1GmWH/lIlxawWY3waPUaRI
+68vLokw19vnwb765i+Q56b8xcYDrMhf5A/l7vKewsSaH1JV2AzFJg6FaiGMxC51ZBGt+4aCU/eb2
+EsR7RKU+tvHbDTn0f8Kv4UYq6/Ob5wGUxcSWdylL82EpS1nKI/JEaAowAdQ1Il1F05Ob9mylxAGd
+Z9uzBoIbcgZHWQsHD0WNGmZyMharEZw1spCcVtCnmlXdiVBlFlt/OsI05a3qEgYrqWLRkVN5zU4Q
+sLrSLxsweIuPG6+juilhqDFzCHoNH7d86eeqrdH6qDjtVCeBF0objW4NHrH8FXkRHHsEqxRtwzBq
+qPPEV26AG7fEoXRA9TzQX8bRW3LTWDULSEX9zNIYa+RafG04gXcm81KvSyzdhY1qXd7rlAGOG3J7
+vqAr8GJp4yhfYN6W1OUikSrRygoQRHILTu0jTFktOBoP4BMDYf5KjD1XNK+1TLSV0o0xvCfzOapp
+PP08cR+MEqM35DNRlOGgLTea49LZObNhd0lhf0VDMzoJKFxUmAVbCxTpd3IOf0vW4PUhmpvMpyjr
+SOqibYzODvDV3dek7WEPN10xMUcrso5Kn+NhKms2On0T40qV/VeoKPIvXO3BZG6B68k4i0UXp/br
+AIDWxMH5c7IHds80HGo6kSITty5BKxClVUCT3/O4EmLjqsz9zdoGTF8+/zn9BzKvxynMt+gQvmVg
+hXwmY30V+ZnsvTPHx8eeb7LPornGwzoiYjn0by7wjMExx3NUaYIdr0+RHr63lMYn4lDQpUIZGah7
+DxBVZcFXUWIlF3U2cZNL1cmbniEgOi05X6B1DYs/lMlLbvgIjmQzzuwa2g8Jo327gZw+hY83JJFk
+aCT4JkvqBPyaRmDIw1b1hjinErU4mSF8Q9oj8DO+5/Y2ng3EtLlyfRPTjjyE7WyEs4uU2dTC4o6U
+uFbEikDH9dBtf7P81vAEdZKF6LJA2ZR8gxXCRLsDoGgTidlMkBJ4wypK3D2RMXVzBw9mcnAcF2Jq
+DIpb8AwxCSo6RDKVDXF3+AZmZGc6Hp7Bc4gW1JSNuVpuwCaqUm8SoMGakRVswl9ltEPfxEcqMtaC
+f1ujMxRMlJk86COO5PCa7n4ZoxGRh04XMMhOZbK/3e+pYiuXPnzP7BaMkfQ/Kgr4nHtdVuHR+36F
+sfnxjS7KXannuD84wM01mTcVmNhsywEXhoAZMPks5Dqu17Hdlz6suR1MCXYTbNfQYLm+iTm0lrHs
+70tuyRdevYMVIkafVVKssaz5q2WMgLkz1QM5CYZ5hJyEQ3ZZIqIZsLe7B+9Ixt34YRef3pLfeHbr
+BwEAG9VT+AvWyTgRFpuyZlfOp5gRGKbh+zCrcsB5iVyGM28Og7iM0wHwYMLq0mdKnN6TPbJ9ChCk
+m7GOd5al+bCUpSzlEXkiNAVAo1QZyvImysouAMArTPTnRPjNFqixrty2bCTnoiKck4TDDw1MmAmZ
+f34Xu2us+e8byMgNOB0+RMzU1SpxFWqBj8S/CDI3MGcGmkqqiBeCQ9A3R4ivilpW3SeD8+o1VLbE
+ZEgqHfikd0unTSQzOa2L6RAGSWu6r7P68GPrSBO5iTxfY3JC82Alw+ycWAcEL1noCmYNEqhMcUlH
+psb7yBLpxzFS9EyS1nxO8jGsT30XDMbaTS+HRTizk8LE/IKK3jNxh2Ag3VhuxLVsA17I1OC2CTeW
+W8ltaygiMGungsJgFulCvl/kCyyIK3BcNRDdk/X7g2GBk75oGP00R80jLgLXoP/VMYbPiooeWwlS
+5kWMT0OYVVY7OjkszUgToxbpS0fYP5G94OoR4rtcs6cMeKQZdDeHSKZisuUbMqZi1EZky5ri6i00
+O7zRnTV4rIyMzqeYjkRTu3cka7YIXZwS7GEQZrAb1DzGBTzS4oVMWlGLCBbXKS01ciVtZ8MRXiZy
+883Pfg67zR0AwKbJcVZylHRcxx0fER2b8/IUhS85DWVrjJC4kyPmkyR7OU6J+B3lKcpMvnf/5TP8
+C4MmCIpHuC7fjSj9Hr/wOMS7Yeitn7Fw79/NvpY//HYxAKMmk/ZMYxUuQ0uKGIdlmsO1SBEeyMMO
+AHmRocIPzdIUKUtHF5lMpE5sTOn1Ph7PkRF6u4wEdh4AdAk0CMw5cWVR/s5/8lPY6MrmefiFU9Q7
+Ut66O/dw8worFI8DXP2ELLoVi4ni1hUGh2KDVJspZlPZ3I16iiHRexoEKp1OPLRZ73Ay0eitSYfO
+5hWs0SY9vJdim76WP3hDvl+tHeBLX6Vf46aDf+cv/JfyesNFMpG5sINjlFMxMYKeHGjjEx8e85zP
+HxQAac2ncwsB04qHxwrjMwENnTHkp/QQHyET0qTVQ9CV0LCuz+DnYqZtbxgg5ig2npUH6e7nC1g3
+vwgA+Of/9Bi9Z+Vw+sV/8jr2XpbDMJmkGHFDtAjGe2urgjrNjp7ysBeS3LbIEMXiPzFzhVHOWhFG
+BjKdwitIE2AWUJr+jDJDTXMjuSWqTPyaEZmkU62jxnqW7VYTxwzVtnsBJodMeiqkrcFijI0eDwrb
+xyZBV+8+jFBz5XXqOVhnaf8skffqngOTJKTNoIqQ9SUN34FL31Wz28SAiVgtJmE55TWsX+P+LrvY
+uCK/Z5cvoLZOsKIjBya5R5/+xA+8rLX+BN5BlubDUpaylEfkiTAfjAcalR/9OloCAK8E4JCkI1dw
+1+S1zkWFL90cRSYn+1rPRUKVejMIEM3l/Z5Z4s65OK16JZ0zUQQvukhAiTDkyZ1CNIRLYex9nfBh
+7vMGRvfldgwrB/i9LzM1+S/E+NL9HQDAjZU1zMdya2x+VNTa8thH44q42dMjE6uCAobRfoJqR347
+PpNbpHtNY7wrfav2NKJE9Ovt5z1M7kh7wQtDHO7LWFe+WW7543/uY5TJLX/v9yz86H/AuLt2UZB1
+eTiuwu+RGp59RCXD+Ei2Q9gb4fgBHYmNCMGh3J6x8Spe2pV2klAwBuqhj499kkVQ5RtYsPjGc0wY
+H5d1urOvYFyTG32+J3kD9rd9Gf0vyC24t/hdvPS/MAHobIARuQ8rRYFtQ+ZFu2IOfMRZwf4q4dvH
+Jdq8NcdTG3VCKISTOdp0zB7M5BZ3UIGmhtnxbJxHLNCyfMS53MydmodZSfxOOp2nqYXrHVIN2B48
+AqXfP1XwqtJGPmQUolrDAatgr35LF4OH0je/O8HuuWhvV27WMY1lzeqrMvflAtjaIAfp3ECDVHBW
+UcH6NdEm09gFoUkRR2JSdZ/xkI8Z2dq0UYSikbZulshC+bCzMsPZQyKIv0tZagpLWcpSHpEnQlOI
+NPCVfwm4ZAKgw1JXdbuCIJJTvE77vZWt4mFN3nvB2ETUk1P5GfcqTmty8vszC6YvKaq1UE7wO9Mh
+TEe0h/tHA3y9aO6Efy+IBrz4/Sk8U0J5v/fmGzhmWW/026u4QjDO6w9s5N8nJ/fmEWHl1hyou3KT
+DK4AVVKzObUYUSQ35ZQksF4YoNdhsdZQYdSWMRkPC/gm0YR+6xwPnpL3i98lQnDjBOaX5KxXmwtU
+THlflwWKjChM1eQyrRhMCZ8aGUJmk86PTnBM3wf6U0y/KuHCqXMHD7/CzEP2bRoVeCkSyLTG6y0c
+lgwNr6yh8eD3AQBp8DQ2/geBRB78xSsAAPdXFHqfEI2m+gA4elY0kJfvzGCRfOcEGmvEUXhmQgyC
+pxysjC32QcEdS0jSWQlRH8v4Djo+/IX4mDSRpdZVDadEk9r2W9gopG0v9tB3ZM57uoZRSnTkvvg1
+xpUY0zmdsisGMJW2W60Qg1dIehvIms/CEinDjeX9DhobdGa+rOA1ROO5OW7D3hFf0/ZM/t7d0bju
+SFpyWNGo0Q923vGw7rO0PUgQ0wl9VqX2M24jY1ZvZRBhvi17oRW24djEZIhqqPTebTBS5Ik4FN5J
+OgCylQt6eYVeIeqT70qlntEa4xqxENbbNbgdySFoGA34vjz0Vj1FCvHkjull/8gVG/lQNsqbp31E
+5/L4f72yc4/2zaBrAGQiPjgJETJ3fncww/qWICLPPI0Ws0vTq7L4zixDRtg4czpASAeWkTWRFdJP
+m06rwnFgEIor9kMYPDTiVgc6k4Pu8FaJ2T2Zl72OPFRlrHEvEhNleljD2GAEJBthOmc9fniOkEu/
+GMtoh4MJDFP6cPzgBCMSsiRHMTThwfRbORzi2bdHMofXfBPXvyy/NS9yxKzUG8aHONIy7rH7/+LX
+qGqr/00OmPQTt7FJh+9b+6+izOWhaE4dFIpEM0aCHY+QdDdoXhh1TNsyb44xRCeQ+fT8TZi+fGbH
+mKFmELItkkMIRoBVQx74XqWHLBKTYACNLrEW7bSP7FTmazdg5CBXGE9IS+/mGDAXoB91ELC2ReVi
+PiRWDGMg38+bCcJMEtHS6gQlIedVTaPbkPftQPq75mooT36jXSqYhOLfRg6TcHNuYSAi72mVhDxh
+TaESy/49ST2sW7K+/bqBNc7hvBxh4b83g2BpPixlKUt5RP6V0BQSw8C3M5zUnk+RPyWn/zffEvPB
+qn4SFV9ugZXaDXQvGNiCNpxUvpfkJTYIhLpHNmQ7U7hjy63z6e457tKh+BvD9JKy7u2SK/n+4LVX
+MR6SCKRIMM3lFvwWrOPGRNS9a9+5hXRdztzAkjRa2OcwmKXYtiJoYjYUwQClJ05Tm1wQXrkK5cjJ
+X9YrsEtWH2YzJKw43FAuBldFLa998UUAwGF0iLqSWzJ1UxgzmaPC8TDPCTVmv4pdQtLduycFR+dW
+BOeOpAkfT1JExwwLVutYu0AGznxsb4gDq8I08N4VAw2C444WJepnxKpIX8IDFkGdJQv0qjQJavL/
+td/3cT6Quf94r4G9bfkNr3kKfyG3eJQvYF6Rm3c1Ea2pXmaImAa9akdo9YTvYrseIKrJPNeSfYwK
+MZtuXqS22000c8LwGQHWiafw5tCCORPt5X5WolISZo5FV/NsBusic/YwxYT5HWVziHgg/5FznXLP
+wNSTybLLHdTmNOMqPcxLCbmutG5gk6aCTU2x6/XhM8Tr5TPEDEP61ggeEaQUHGSsqm1G0kdlWDB5
+r1dv+qhTK7YNDZulWUFQx6b+/6H50Ahc7D8lG72x2EF7nYdBR9z3za4DM3kOABBsDOGTOAXVAohl
+gjPjCKYlcfN1n4Al0TPYssXujT7xCaz84e8BAH40neMXCF2m8TUMvuttWYDelgVnTQg52m++hCbx
+/hrf08TTWvpRmgGaYzJcPSMqpx5aKMhOUuRNZB2CsCxasBJRUX0tfU+66pJZKsiHWKRiO+eeAXfl
+GQCAnU+w0Ze8gPT7JRYdPLyKL5SSBpwOFBJXNneSRlgh6Mv+sIGoKps7GvLBzcYwQpLAzmdoGDR5
+ijlaG2J2FTMXW56YTT3+1urHnka6IL5gWMD1ZfMfHa3Bq3NMFQ/qRA7OupYHd/1HfOys78jvvrQO
+YyRztLK1A7fCfIJ+Aacp66euSbtJXkedh3cZ3oL5EeZ3VDZQbbFuIX4WrkefQSp9d40xnIm81s0R
+zIHM53pngsXpxwAAN2qv4aEh/VshcI5zGsCnuyZPNTLSAA2GJroVRrZIbDuKDDikone+fRv1AWsR
+hja2hmLmlNdi2CTFNZhMpooeMs6ni8ZlpalCC2kgh5pntRGQdCbLuR7dHIGS/RI4GiYuzGlAkU/V
+9YDFeyuSXJoPS1nKUh6Vb5SK/m8B+LcgIf17AP59rfWY//cZAD8GoADw17XWv/qNdo5WAEwNfORc
+zi/3aoEXTfL5Mb10x7QQtQgakq7DpArnFRYyR4ao+gEWhTjjakN6ZlsWUMqNYdh3sH1lBwBwxwvx
+0XuirlvRGGOS0pg0NdKoRPdYVO1OpcSZLRrBx4+fgvNn5LdXB03Et+QWM96SqyarD6EZ+dDVHN7Z
+BfXaCHVGBnJWcFamFnLS4xXzHIuEKd1BBRG5JKMix3Qu1Y6dIzE/TuMTPD+S+dG3JjCZ0VekEc6Z
+8pyPE8QjAbOZjSRaEJ2dYnRf1M+qOUZMSLT10kN+IvN5tWWhQsTrWkNQjbOpgVZLxp+lxxjXZT57
+5xMs1mRM/sMzHCvRPE6ZRvzsr21gcEPm8IcPnsevfK9oPCuTDAs6M/uRwqQk4MoBcwIqp6i4JAba
+AFYn8tpuaPSY5qs6NoxQTMyMgDqNJEBav8DI6CDV5M2MS4DO0dCqoklnZMk8hYU7gTmT9ZsnfXgE
+sHGaC5isJA0j0dJqThUZHZztuxpBVcbROMtw0pT+e/c7mF1jlIBo5Kqp0QxlfowghENntFFTMGmm
+lcXisojNouZioYacUZRm1kBSp9YU+vDoBM1DBW2+N964b5SK/tcBfEZrnSulfgbAZwD8Z0qpZwH8
+CIDnAGwA+GdKqdta6+KdGnk7QvKFXOT7X9/qoO+JKv0sKghJ9LEK2bgzw0NAD3mmPPj0I8SmDZXT
+3nPasGKWTHu0X9UCsy7TgxfrOCTy807vDDPm3N8LXWDMBf0mUS3tpoFenaWPD15Gm2B90+6baPkf
+BQCYWxnyUEY0WxHV2Do8R0RSl2xhwa1T1VzUkZDYBkzcSWoLFCfSt6EqMWKFZxTZsAn7PVYlmt7H
+AQBlT1KtW/MWXiGLn33iY84ceMdwERnygETTY2iaDZEtc3K2mCOl93419uE16ZH3PWySgzFc2Kg2
+mODFlGhVVjBgUlCoGxgY5J30V7E9uifrsNHGPZadNwayyYfOHXzLqszbw60hmob4EZIrDjqcisNo
+DmdIkl6bxDrVDjKC7NTiEsUmyXtdAwXp5xvFHEXAWplYHtKxacMmclZiBghcMUumSQ3Nhpg8ce7A
+YnpwxMPk480Ag3OZz3lsYFaV98e5iYB9WmXacuQYcArp29Q6QqUjB6c9VQgYZgxXM6xnRFziHrOj
+DMmKXIH1UKFk9awKNUruERXXkJDf0ksIw9Wow2Mg/SB3sHoqfdctB4pJXzAWMFMmqL1L+Yao6LXW
+v6b1Bdcyfh/CGQkIFf0/0FonWusHEKaoT76nHi1lKUv5UOX9cDT+NQD/kK83IYfEhVxQ0b+jaCVM
+ZY+WZ8mZdRLneP6m3CS1a3VYHWIgUAGxjBLKlNPQhAGDrsGkNOAyPqzLFHZFbg/mIKHhe9BV0cW8
+ziFWZ3Liz6cKXltU8J34GCUr+zQ5ANuxAYcmyDMvrmJ3KDdQy76G0UgSi5peBQty6+Z3xRlU5DOU
+MU2btgsVM8EmS5G6LNJyyMt4WkFBr/E4OoeRi7fodB5fetGd5giOK5gNUfkd8v0wx1NMcR1fOQAO
+5ffCcoHjPvEfB3voh6LGFw/FFHGLGWzmP/gbT+PPPcW58lvwCW+HNECVeQMXOQGupeCSnn6hZrCZ
+JtzshHjIhDNjP0bbI2xYU5y8B4cbmBKa7dNbQ5TfSQyI3ESpeAfZOVIm4USBrFMriYCeRI96LYUG
+AUdqdReGTw2i4kFHTHBizD/PNGqevOfGNZgNws0lNjqcTytYRzqJDnUXAAAgAElEQVSR9oKA4CzT
+ryK0aWI+fA1D4noYHmDZ8r0xo09lQyPmvJ7vrqE+J99HbCNpiXbQDNswmaBmeoxO1Ty4vJ7dhg92
+HYYZI6f5ZyEVryGAgrksGJ/BYEq/37ThNeigLBJYpB/UqKL6bgkfKH+qQ0Ep9dMQZOtf/Aa+++MA
+fvzy3/qPmw+E1MMnmju48Yxs0k7+FIIN2eieSShwo4BXygIVwQw5wVEdI0We0mPrzmDQbm925Idz
+J4BLD3A2uob2mpQfl0kN3Q3ZNA+PS0wD4vkNZJe7mwnqKxICDL74ZXwzPc7urQL2UNTSSTTDGQdg
+tWRzZINA6IAAGFGKkIAeTasKk/2PMxlTrIpLYNBGWeKUCVJRx4UVkvRk7KJL2zd8UUhBnqvu4NWW
+hCmT+wZiV9qbT8ZY0fK911SM/luy2Q4vwGuiCdprEp3Z2A7Q3SQ35QbgELFoHNuwmNTkENHJTmtI
+WWWYJX3UyXPYjwosGAIuDIXGgByNAfu70kdvKn6Q/fUmBqxReL5podTyQG4OYkwWzOHvye6oJTU4
+LtGmok3UtggsYq3DtmWdVOwiJomKNxcVvVkNkcxY2dnuwwrlkNlwUuiC+8ieAvMdAED3OkOnx9+G
+e5PfBQDkrokFw7ppqlGw3Hu9Tn/OokCbZc3Zegq7Ir6BIPFglPSD9AbwGqz/yGSuKoWB3CcDVm7C
+5pqVCwuRRYzGwoE5l/djAsKWdQcWwVkSdYhJKOsUNIewaTKYVRdpfuGde3fyDR8KSqm/CnFAfrf+
+Wv31u6ai11r/HICf4299+PXbS1nKUgB8g4eCUur7APwNAH9Wa/12honPAvjflVJ/G+JovAXgC+/m
+NzX+uLOxQ69+ZcNDdSan8cqLNqxQTmCbZDBtswnlXpgPEVxbTutSByhYIOZGPgziEKhcbgz4cxhj
+Oh8rc1gncvSn6TEKnlM1L7skIjkiaEZvtoL1mtz+aLTxsCae46uT5xAGoj4Gr9i468jtvbMvnvBF
+ZYittMbveVhNL/ANUnia+RQtufmMqYWEWAHDkQUzkRtj5SiEU8qtW5vYyNakT8+xwrG1WsWNfTFX
+jq6kcFibb+kI98bye/aZjfWKaCnxivShmG1j2xA4s29b20SXcOm1RYa8Kr/tlUPEEZ1k4QXzdx+a
+N950dIp4Rvbs81OscTXPdIo2Vd83CtkuLx76iJvSnysvh3B2qNqPN2CT5dureXAcWasab9pqQ8Fn
+FMhaM9AIZV+4XgDXIOSbV4FHhGaDEYA096ACYilODKQ5c0ecAOOxfEbPZ8j4SDTmvIHzCJ0NyYfp
++A/RGUgbUxuoRXQClmIS9dwmFjSvbgx30FmXPl+braB8VtpeM2/Bb8q8OKQNNPziEmncjBOUJXMa
+WlPYIWsprBglcxL0GlG5cwslK36bZQORIfuiGWWwWVWMKIZS7zNG49ehov8MBOD615VECH5fa/0f
+aq1fVUr9HwBeg5gVP/FuIg9LWcpSnhz5Rqnof/5f8vm/CeBvvteO/FEtwQHQpSawWbcQ1eU23j/X
+2PQYWovkZj81p2gSz8tvmygZskpy+9KBEzdtlLviaAuvyt/pkQ/HkdM8jjWGNbldbqzfgHoo8f/d
+rIL7lnx+QGKReM1DEIjjsz5+Hdu03yLnVaSpBGJOmhOUJcFRHfocMhMZCWbdrArdkddBWUNZ4e1v
+EOatmyB5XRxc0ytv4exUxtcNTMRE6Zm3+9ATsYcjV+YnqJ0gvckCnvsREtLYpYsAJKvGIJ5iwvBs
+vSRJSdPA2joJcaslUsJ89q0GPFZSlmYFihl9Ientms06opFoHee7JYaezNWBrbDBOH681sVJLn2y
+U9Ew0nyOtEmOi/UJ7ETGXbRs1C0Zd2YW6EDWgaBRaKgqUku0qmikseAc2rMEitgYXS+E3RJfhEmf
+UQqNfCaayXlhID/k+Co5BgNmr8Y2UJP9EDJL9fnG0wgjQZJ+qVZF1mLIda6Q1KhNknhFuwEqvAOz
+9T24KQufrnpQzOS0dny456IhZD4rX2MXKiB9X2Ihoy/GRRsL5lYUuwYWXXHAFgMZf6NZQ8mCsFEe
+oToj/0QRwOY6GXkG/UH5FN5v0UqSlC4ZBZVCnJIT8WGOjUi87KpiYMwc8FtXOWHTCN1AHopq2kan
+Lg9NYQHzuWz+vckQp/dlw569tAsAcEoLzTVRyVprTcR92TRfjo5hBaK2NvQBDNJ9g5p/a2RgnjAt
+dbMB51jMhGJxCyGZh4LSQZ1Va4owcEHHwpSsxj3nDOkFSEyQXnpV80JU0WkY496xxPlfOXwDf/hA
+klRW6zVskoVkNOnjbFMSgJJUWKc3Rjb8fZm3RTuEPmaexnSMcCGbsJPbUD3ZpEGVh6LOkTL99jw2
+0ekQZCQeo97hgzVx4BPbsCTxShaNL9Noh94CSGVDr/R8eJaM1Q4M+KzBGBDo5fyoiclQDhvTdzBy
+6Dk/7aNklCDUCi5zAeJc+nO2CJHTTdWzYvzBl+SBvr1dQVKXOSyNKhpzeSBrPmnr5zNkY0nUOhzP
+cHpPHrBDPUJOkpy82sP1ROblAkDl0Iwx5gUQzP8QVkjmLAOwQnkIj85kzYJOhlkha91/ReON7HcA
+AFdLYMqkr4/eXsVTL0guy2IhkZ9et45qLodlq+ZD0ekc6QIL0ssPRkNM+2LyjJkLsdl+Cisr8tnc
+D6HpQJ9NZvBjOZxyN0NavDsOyQtZpjkvZSlLeUSeGE3BwKPFRzVDobYqp7K14cMkGKsqPUQNOf0n
+zPyahSEGfeLqpxnSQk77dqBBxjPs7k/xyr7c6KOxqLKm1cbzAZ1oqx4qDVb79V3MyIloOhXMGvL5
+mLdg0NVo+XJ7DO+WsKpyyp+lE6QR6//tBjyyXOt16buZBihY+HQ2MhFZ4qz0xy34hNqakJDl5OgU
+r54RWbg/g5VIPwdugIAI1ZPMwKuM7RgNuUX+fOc2Tpk3cT5IoGmuzI/naOZyqx74OXRBDYHhVBhT
+FLloCnHdwd6UXA+VCMdnNM3MMSISw9RqcrOfRBUcp6IqjBYTBBXS6aUNLLqyfrUTGzZ5E/cJUhKv
+nKNLVOawLjwZMr4F8ki0t0mSocb8k8iS9qygc+lQPM8qUCtyrx3ABcjo7Y1tJNdY7chwqqFzHMTS
+xv5piDeZQp5OhtDnomWGVokbHjMhidkwm5RIcgGRMVdXgVi0AjMBiiqrTnusdtV11C3RQEaxeYnm
+rIsSJR2J97SDPKGjkLgeJ2+FWN2U+bk2W0GLtH/zeYCjvmg3Xzm5j+FCNNm8lPam+QiZL3vWTROc
+MCRpXZ+CeDmoOBUs3uNj/kQcCgqAoQAP6hJGWbkuXmDed72oY3omExnZh1DEpXNZyVZPahhUxDsf
+HNZRuPSsJzUMUnlAyuMpbMZ0+3sk9yheRZ3lr0ZexW2SqLTrMzR7OwCAh9McT5/IYrzFxJweWqj7
+hEtf9VClH6Ayr+BzfdrA+w9w15cH6NqObKTTTh1rMQ+yGtDcEzXfuJpAzS5yK6S/fhrjBg+h3WML
+u0MxfV6AB6su/Xj+tILjLen/9/blILxhA4sTGce9rQRrDuezXcHgLXnoe75GzZZ+nJCsdPLQx7kl
+D8rqaIzpqvRtx23C7xKBOjJg8ZCJE0Z7ZufolIzpx3WcDIkDubiPGw1Zv1M1wjfVRZ2vb8na+f01
+9FcEcn3ntIVzkvS++qX7OGKq9I3Sxx5Lp2+GcjCpmsLgiKxIZoH1I+I8bqe40pQH+mErQXNIE7Il
++yaeTDAOpW/6OIQ1Eo/8/bMpDln/8ZHxGr7EPIO/aDFFudvEpkUCn16O5ECetlfrUzRnTH8nz6dp
+O0jJXfmpaB1nxOyaHqbIWF7dODSgrpKbk+XdcXsEc8ykh40cxUj2aYRDGCMxn/S5hYO3ZCz3Ddnr
+qwMX92huPn/1GTg9OdzW4x5MIpMHiYfzcskluZSlLOVPIU+EpgAAhlLQyrgEjdhwDHhbcvt1rrQx
+4Q3UKVvwK3I73FgRgI3MnyK7Ty+7raHI7bfwPJhJlb+hsULn2inDtmpeh9uVW9UtC2SkDwuwCnsk
+t9ii3kCxQh7LMbP/dtZhZ6zaKx5iymKf4ayPtMG8h3GJLd5cLh14bbcHm/wVVjhBRGozHRWIaoRh
+m8mNoesZxvRqF+1jtGy5aTe6gNcT9dru7qFBDEanJo7Iozpg3hJTIu9HUG3S3U9OsGCqca6GmNUk
+SlIJWFRWNLCayQ3sFT488iJEqg6b0Ye5UUV1Qoo4aiBxGiCnGRdsOsiPpB9+1wcvXQTOOiJS0rU3
+ZA1MdR+vKdECGuUx0pRrk8SYkhglGVZQJ1eianH8zSZeg2ggV8wYq6uCLXCt10RuUPPKJ4hteZ1H
+smZT1JCdMhN2tYvtnqzJw6KD9UBMxXZrE9foeK21qZZ3AxSRYHtO1gsE5zKHttYw6BYvVmQN1lQF
+CSt3G60UqEp+iucPUDFFg2g9exXPbbHCNJTfOh7vYe7KZBlZgajG18kG3IZoju3VHqwLROgK06Q3
+1rG5Kmas5VXQY5p3sFFDyPyFPIlg4L05Gp+MQ4FWQ1AAKc2HkXKRkPvQsjWeWpcwlecA3aosaJOo
+NMMiQiMjIahuYkRmoqANqJzIPa0OGjQr2p8Q72+mDnC1S1jsWY60FJ/D6f4D6IocFutJH+uutH3W
+lIPJTI8wmsjiT89mmI8lAhD4t/ECAWSTjz4N3ZGHpcHyj8AwMWeCSSsIYV/QnteAiElBOanVPZXi
+NlmF9M1VPD0T/8MzL1xB9WU5AMcJ0JjJpjnYkgfl+v0z1IaysZttdYntWKRVDDVz/GchTJ+btyvt
+VTbaCBgB0Mdd1FjCnXoG2gwNZ3MLFslrkxmjBW6CliEPxYtbGtUuQ7XuCA2WEfuqQLspB9W8ENPg
+ftGBx2iA7VVhEdTljfu7sJkUdGwcoMGQcduVtPKNnoenNuUA2fRGWHFl/VYDFzNiEXpZioR1KpbN
+6tmsRCuQB6VqrMEiQYr//bfRySQJdyW4ih2bIaamHEaz13exy1Cn+9o+uooXUtvF4FjAbOy+7Iuj
+mo0qk7S66y1cD+RCOrzVhWIk6sZqC3Xm+008UgfYESxGJ5TpwuK+rhQRKi2Zz2qcw/qU8LhUumI+
+XG+8iFpDPmusdhEwfJnmIfyBmDRZsIW29T5XSS5lKUv510ueDE0BAKCQGkDBOnBbJYjbBI0ImvDb
+cgOvxQ2UNXGuuGRsseIYESm6ImOKJuO40SyFZ5KGzQzQXhMV7qlcnHZWehPVDhOSzBwjQotPchc5
+6eSCVQMui2B8IvUGjRUkMTWT0SGOefs7oxwbN+TWbNgKFUvaywmpZYYBPENujGSeIOHNFi1MJAQT
+GVNTSML5JQGO1zGw6QjV/IYxR3b1gkLOwoJELReJMJ6/jeiWaASjg1OUzBVIk3PULcFjvKsruErC
+kSq91/WmiWIqaq158xTGmGZXLUdJSLuk2sdsIPNVkBSlTExoErL4zjpuWxIOaeEW6qsyR3pXwWhJ
+n4YHcsst1g9QHxMFedXC+YKFRlEBFpKiNCdoEufQIfP1du0plEwa2jJuo7JKiry0iqDOtR72UHoX
+mglNG1uh3BAtQLkp1hsCm7dpDFCPBd5OdUsYh4x/BdLfU3OE44kQ35xZi68hcDsGCJSNJvEUprYL
+w5ZxjK0UvZ60fVv5UDH5SBtTOB5NlEy0pvPUQOKTVzMPYEYyL6ZSKIjf4F9fx3ORaLJ+VdbJby5Q
+sZiyX0nhFKJtlU0fAxLUVIxDHJBj893KE3EoKAAmNHytkGtmZeXAxpmoye2Kg22f3IfrGTJCn7us
+FkyiKsZk+dkoHBSs2mtaNsDsv1bhoMKaAY8PaxkoWCanQPfh0F5ENEGLWXWJU8NTC1Kc++JnyKHR
+8uRASpu30c7uAwDW/CbGzGK8GtUw60r/Oq48eI6xgGbfh/kQNYKDWlYVurwooxYTx5zncGhKXU/q
+sJryW7XNG8inBKl9zcd11m48G4gaPY6GeGogwCs7qzE8yMZstjZwqsVsajZmWFXST49q66puIGEI
+WC9MGC3ZpBbUJaKPf1RgQWp3TdLZONfYLFgOXnPhlYTWsDNUEtmMce0YFnEca6as04uH13G3KclZ
+V/td+OREjPICTsEEKW3iNJTxrd6XwzL7WANPO4T4X2uiQcAc13RQQMaCVgz7nElLTBYr4xgRo0Rb
+RYw2fQ6BvYOSSL8qX6Coy6F1QmCV9M5DRORyj6YGvsUTUyO9VsXvvCL9Xy3JvVA6QEsOntv2FrpM
+gKuqKnLWc9QqXQSs8pzPZB9GnWNUmFlq5jYMmj4qAJo0wVQVaCm5GIqejL/i1JETVdYyXJSFtOGk
+JkKC05RvVfFFf5m8tJSlLOVPIU+EpgDI6ZQY6pL5uWYUiOl9t60CKVNfK4lGwLh4TG8znBAWwSZQ
+bSAgLqOJGhSr1qBcGDQ3FKMXTp7A5OmqJynOEnJBddaREjgkdkzEO8x3P2fdguqh4I1vtx5gdcgo
+SK+FNX4vcwzUCJDhsJ+mXSKypA9G5kDTiVYmGTSdQRm97PlsgZgsyisdD3WTJDKnYxTkazx0Z9CE
+A1+0xLHUqK9i7u8CAILYRVKlw81xkVHdtRITKdOqA2Inpg0fjVJuxNxykBPpY+gU0CNxus0tAymB
+WE6pMdQq+hJWrAoFw5Hf81WIlPUVauzgyBGzIrSJj9A+R8A4/VSNoWNxqKlCI6W2CF2iRfbns5rM
+4TenZ1jQk18vUhj5hYNOIzKk/3E0QWqR5j2XvbLwbTgM15e1HgwC2BR2BxX2M81NJExTnxzImp9U
+K5jqPc5tE4stolLbTXRa0kanJ+p8paGQK0Z7Oh3UfSbG+T4sPmq26cK0CNtOxcY8d5DYMhcVd4ac
+7NKBUwfIgm1ls0stxKZmAsO5rJhN5hqeI5GrxbyP80PRYmrju1hzSLL5LmWpKSxlKUt5RJ4ITUEp
+wLAVahlANjKEEfDgnmQHPvPiVfi5hOTMxgrqPGnBuLq5KLBCzP+gWoNtys0VwoBdimOvMAOUdALF
+zDGomTlK/pRveegu6OFa7COzJRfAyCzkfULBdeS2m4xPYM6IQzB0UKZSrGX2G9DbzAWI2rBZPJQx
+DGfkMVKm81pughqx+e1eB4Mz+V6LFGVjuw7TlLDZaGTCqsjNVqn5UDO58jYyHyXBT29e/Yj8brWH
+YCS3zm7v83CnF0wuVVjNHQBAL/88Sp84BMQb8F0bgUUnWTQFyguw1ggB0ZTM2EOd6b8+qygdjBFD
+biLlm8jodK3ZDkrmCGTJBF7KlF9mbE7SGiYz0Tpc3UO4xuQRQ6HDu2puWEjpxCtZ4hmVLkLG3bOZ
+g8ggWGmkMWb6szWPkeXyGxVqQmM7Q425v+dzGw2G8lIzuvS75PkCAX1QOpI2OveH2KWNnxYKWIjP
+ZHHbgF0TraBPZ2Y2y2DSCZ6FU5jUXrM8gEFYOKjwEobvAhWs3RCfGgBUjHXMWcRl6BlKYpobjo+I
+2qRvEnkpSQFHvpcqB/FYfF6H6RT5Lwkq4s/GETprj0CsvqM8EYeCoRSqtoWKVohZeqpVgZD4fAf9
+IerMTchOB0jpWTZYZ5AgxpxqlF1foKATTAdzJAvGnasTzJj3UG3JpE4iDU1K8rPxAR5qeXhnSQ9V
+5iSMzQzHxE28f8bFzD2EsfRBuX3EESHUvAfIR6IeB40TZAKwjFGVqcYjC5U2E6EWLjJ67Y29BSJf
+EmgGpCkPrclllpXhmzhnWayqF9CWHAq7DYXAYYLMNVnKaq2Kkftlmbc3J3j+2+WAzA5HWO+IGnxn
+v4vAkgekGNMzXc+QzuUBTMxjjFI50PwSCOkEm9szqLH0I9ckYp21YbT5kIZVeC2aXREwZwQniVMM
+TI7FlgN23B7ANS+Qlsc4ZQm46xjw6By0bY3NCh2wHTks5+cjVGsyn2O0YYTyEHq6RERo9PNEI2Od
+R5uOOjgjDIiGPPEGGA2kH81GjpB7oCwCDMYyroWW371vKvQbcmA32zaOOkzOmm/DastDelGVOwsL
+zKeypqO1M5xN5HCuNUxkh0xvb2bwE5q6JVOwYUNxT5pJgTyXNubZGjyXF0pso+Tnpxffry5gZ6zb
+MEcgADVG+7v4x3sC4R9V9vHGvdt4L7I0H5aylKU8Ik+EpmAaCs2qi3YCaHL1jVQJJ+GZNTtFeiCn
+brg1hT+gJlAX1So8nyFiavN600XB2ns/biGlv8mJiktH47xP4NNyCIuZeWESIj2U6ehnu8jrYj7M
+wwzZidyOPmvsx+MhHHJBnB2bCDO5Vdq6CnTk1ozu5Dh3RduoTOW2hl/DnBWHE3+B6li0g3DcBRK5
+SR8kkuW3aQEJb+vZ6QgTT1TAwOjCuQBIOVKIusSZIPkJekM4XxR1vmONUFfStq6sIg8lJdhwzlAl
+FX1Os6QcZ1jQqVccpDAN4hjMCoSsgsyiEBnzCZrEuRtVC2wS98L0TWAi4xjrCGpMjUynmB/K65ne
+le+3uphqmZ/N2Sry6/K7K7aFHpGIA8dGvyG3/8qQJmH3DOeET0vdBL0xTYbmGhKak+PSRGUhIcUJ
+U6LrrodRRTbDRhjB8Jjxt9+55HLIDs4Qh2KmHr4lZs7RwT6CGvENHAvPZRJmPLEBg5pASKKXxThE
+0hDtwZibSBvSXpynUARjrS8cwLgIpcvvLjDFCp3jUeSjZCh+1BziekjzqWIBdJTGLMarnMYIqxfm
+k8JwX8aUdk6wxVyIyX4Dh38UEfkd5Ik4FCzDQLNSQa/loRiIqh4VOeZE5z2MM/iFLG41C8GSAeR9
+iSmfDqeImW/QacSwWF2YVHJokqvomocpK+NCYjX6MaAIbmFkMyxasjGdfgVZQiIWJHgwkXb2S9J7
+T0yYc1KAFxkGfUYcVkz0iAA1VB5MksVmBIAxJjGSlqjiaerBqstrxywvPepVwpvnqsBkIofirjNE
+j6fbtJ/A58G5505QUURnmpMbcjbHyURy9X/zNMSfqcv4rDhG5sjm74/uQq/IAdFasLzbrMGzZdfp
+ah3FvrwewEJhyZiqbgbNFOScdQtNzJEQcjyaFLDITOQbGnOmVeexhSlZihYgvqJRYGLyYctPcbKQ
+ftZqATyiSDWzGq4yme2swjLs4QgFczLstomkRQr7NELk0stuF2gFfJ0y2aoONHPpQ8OpYVbI+Ibm
+OdYZaQmVhdMT2XP3UzmwXoLG9YIAKJubGJ3JBXGQjNBh2faMB8/cVtg7l+/3ugZWiY85RQ9rkH2t
+mhXEIZmqSC1fpA2Ac+xWcswSeR3EU2RMkTcnKcYk85kMeQgVVbhMiQ7TOqxVmXtruo6pov8reg1p
+VcbybmVpPixlKUt5RJ4ITcF2LGxtN2GfKWT0emdxgZRq/qQ+RTMW51nWvYEiE1VhQtNg+Eofh4wo
+WGGORkNO2mJ/AKPLOvW+j0qD5B0HcrOZnQQWvdv5tESVt+Bd+wA3+nJe7tglTmbMiuRJ3D86g5XI
+ab97OoNF8+H0fgOVj8hrpYtL3AObaNDTIkJC4BTfbaBCH9jqles4eEOyIutMbV6EChZZqffeLDC3
+dwEA0Y0VgGpnNbHxZSVq8o0Hos00VBfhoUxMoz1FpWAWZ70OryHsylc3z5GTmTvrkkOhmqJiSduj
+2EHM/I5F/wgOOSfMiosrF4Ayp2L6GKaN6IQ4BW0FKyMuRM9BnWA4o2ENAVPF51q+l489rDD3JMob
+6IjCgutX2tiqiBNQx6uwG+JouyiYOo1TbI1krjz/Kjw6gVW9AZxLP7a1jx5Tmte3ZJzDaAIzkv8f
+TObwQKezAzhK2ijrIWIt8zhjRuDkrI/Ekc9+8sVr6FyRYqzqRxXu/MJvS6frMj97hykM5lPEuz6M
+DdEgfGUiaZPzMvcuzc0x92xQACY1r3r3FjQ5T+1qicKS9SuKBENGa0wlYzaUDzAvIm7ksI9kLYvp
+HO2ROCsPdQvJ4l/JKkkDuV1Ha8tEO5UJGyqFzLmwvUYIdyQ/3W3YqDKMGI4ZburGiGSe0B9PMfZp
+yxcJxgSpUPkQFlGNDJdgn9Mq6qxkRMVFcFUehN7pOnKqwepMo7hObLwpEX9QwLMYpkvG6F+UGRv7
+6DHFuNYq4CZi5x+ZYqc+jGLktMk3ggx1EIx2dgfVVCru7mUXpdUHOEjl8Jq4KToheSw9F81N6ees
+PsV1mlUhcRSrtof0Finn34xQksUot0o897Rs/r28gbkS38XGXEKTzpU6nAvmrLUF9njihrME4Gac
+z0wsILZ2UcjDfTCoYMYQYdVtYuUi2BNWLgldrZqBSo2w5vEOAGCaL7A9Zxsf6WMay4HUu7aBpxqS
+Vt4rYxyzhHkQSX8XexqaiVemtwqnIeZTPz7Bm0MBWA3f7OK5OunhrxC9Kz/A/an81iTZR2VF+nH9
+6TZsJaZGZgHWtsynfUf2oX2q0CcRzWpjA9XvYjJRauKU6cObpXzHKvoYMrSoVw+RJEINH3RjeDRT
+p9lD3B/RNxXLXG6XLXQ6/P+T+7ANOejPJwGQCEPCxHXQD+WwqObiy9AbczTJNlvtzlCwTuTBF/r4
+jUwiTbY3gmksqySXspSl/CnkidAUXNvGrbUesuEEvbqcuidhhHoqN3o2nuN0LEH/4KUjLD7+bwAA
+1jbkRvzYYhPnVPvaXhu5RbwBS8EOxRzZKDwU9kWEgryFqybcgniHeYo7E7nF7w/2UbvgsTydw5nL
+FVqSM7AIh4giUc9m6QJHhDP+Dt2C6kh6aStaR0YosGcJrb7arCOvS3tKWVhZl9thbZZjekNusVt3
+ZRy5cuDk7HvVQtIRraFjrABVcUbWhj7uMFphP5Db4OH8c6i9wuhLkmCcMT8gSzGay9z2PBe1M5nP
+UU3Mq7g/upwfZz7DjTUmIY23kW5Jn9ZUBUXMqkyabv7REGpMzWWRYOdp+Y+aZ6Mk6/aZFeKcUZ77
+u7syDmXhARN6VvY6GNH5+/yNp3CFGAJrZg/dikRXBm+IlpeSsVoAAAc7SURBVPMvygWO9uQW3H/9
+BN/z3TIvK9cCbK78IACgac7QI6y+V5CO71oNHztncdTxAmlNVBq/3Ybniqkw2N3Dg9fETH39PqHP
+TiLcZmToK9nv4TuznwQA7OhjZEQKPzx/HQCQJjmOT8XBVxo58qrQEFY6m6hViJEw92Gx+nc4k/Uw
+piEiash6kiFfEw3LHqcImBavC416ZYu/LXPZiGPkNCuHIwvTN6Xvrza/AJtEM2FkonyPrNNPxKGg
+lUbhaJStCvRAVPWmbSOMZWEGroXeqTyESgW48opMdnxVNlIlcDDz5QFTRophkx7yiYLNRB/VaqDW
+Yf1EjTDeXoz4jJiC2Qx9vlaxhz22d1RkcFl+HDLnXJkxYmaaTc0KXIthOMNBwEy6JPFQY/QBTF6q
+WgVADH5DATmBWqLeDOWI5KGGbLSR42FGHope3oZRJYBGNIU1lc/EeYReLOrjyBZfxf6oQMBxZqcG
+zo7lgW5ZDvqpbKaa4WJiS83EjGHDPSTYqMnrSqjhs/qu3bZRptJGfjRGFDDrccBwqQ5hkdY9WLdg
+aVHFtRMhPCdB7mSC4wEzS5nFmaQLpLQDB+0S6QUortqA60pkJKloGIX83nxG2PrhHIehHIquZSH8
+Apm6Vm5igyxhfmcCM+UeoKmVPNyAtuQA1Ve+Be4NhiEXDmYMxSb3TrB/R+bxfp/oR6VGzEjUw/sh
+Rp8kBmdWwJzLmBbMiJyWBUxmGO4lHq6ZDBFG28hN2YdWxYJHQts6OK+WgYRl5JEfIT9niX4SImG5
+v+UWKMgi5pA/NIGDeS59mDzMMWZ49vCXJ9idybOTOjmU9d4IZpfmw1KWspRHRH2NG/ZD7IRS5wAW
+APofUhe6y7aXbf9r0PZVrXXvnT70RBwKAKCUeklr/Yll28u2l21/uLI0H5aylKU8IstDYSlLWcoj
+8iQdCj+3bHvZ9rLtD1+eGJ/CUpaylCdDniRNYSlLWcoTIB/6oaCU+j6l1B2l1F2l1E895ra2lVK/
+pZR6TSn1qlLqJ/l+Wyn160qpt/j3ewPKf299MJVSX1RK/TL/fU0p9XmO/x8qpZzH2HZTKfWPlFJv
+KKVeV0p96wc1dqXUf8w5/6pS6u8rpbzHNXal1C8opc6UUl9923t/4jiVyH/DPnxFKfVNj6Htv8U5
+/4pS6p8oRZop+b/PsO07Sqnv/dO0/X7Jh3ooKKVMAH8XwPcDeBbAX1JKPfsYm8wB/Kda62cBfArA
+T7C9nwLwG1rrWwB+g/9+XPKTAF5/279/BsDf0VrfBDAC8GOPse2fBfArWuunAbzIfjz2sSulNgH8
+dQCf0Fo/D8AE8CN4fGP/nwF83x957+uN8/sB3OKfHwfw9x5D278O4Hmt9QsA3gTwGQDg3vsRAM/x
+O/8tn4kPV7TWH9ofAN8K4Fff9u/PAPjMB9j+/w3gzwO4A2Cd760DuPOY2tuCbMjvAvDLEB6cPgDr
+T5qP97ntBoAHoB/pbe8/9rED2ASwD6ANSa3/ZQDf+zjHDmAHwFffaZwA/nsAf+lP+tz71fYf+b8f
+AvCLfP3IfgfwqwC+9XGs/3v582GbDxeb5UIO+N5jF6XUDoCPAfg8gFWtNZPocQJg9TE1+18D+BsA
+LgCyOgDGWmsWLT/W8V8DcA7gf6L58j8qpSr4AMautT4E8F8B2ANwDGAC4GV8cGMHvv44P+g9+NcA
+/NMPqe13JR/2ofChiFKqCuAfA/iPtNbTt/+fliP7fQ/JKKU+DeBMa/3y+/3b71IsAN8E4O9prT8G
+SSt/xFR4jGNvAfi3IQfTBoAK/riK/YHJ4xrnO4lS6qchJuwvftBtvxf5sA+FQwDbb/v3Ft97bKKU
+siEHwi9qrX+Jb58qJWCH/PvsMTT97QB+UCm1C+AfQEyInwXQVEpdVKs+zvEfADjQWn+e//5HkEPi
+gxj7nwPwQGt9rrXOAPwSZD4+qLEDX3+cH8geVEr9VQCfBvCXeSh9YG2/V/mwD4U/AHCLXmgH4nT5
+7ONqTCmlAPw8gNe11n/7bf/1WQB/ha//CsTX8L6K1vozWustrfUOZJy/qbX+ywB+C8APP8622f4J
+gH2l1FN867sBvIYPYOwQs+FTSqmAa3DR9gcydsrXG+dnAfx7jEJ8CsDkbWbG+yJKqe+DmI0/qLV+
+OzbaZwH8iFLKVUpdgzg7v/B+tv0NyYft1ADwAxCP7D0AP/2Y2/oOiNr4FQBf4p8fgNj2vwHgLQD/
+DED7Mffj3wTwy3x9HbIR7gL4PwG4j7HdjwJ4ieP/vwC0PqixA/gvALwB4KsA/lcA7uMaO4C/D/Fd
+ZBAN6ce+3jghzt6/y/33CiRC8n63fRfiO7jYc//d2z7/02z7DoDvf5z77t3+WWY0LmUpS3lEPmzz
+YSlLWcoTJstDYSlLWcojsjwUlrKUpTwiy0NhKUtZyiOyPBSWspSlPCLLQ2EpS1nKI7I8FJaylKU8
+IstDYSlLWcoj8v8BCWsq1ZxHLwQAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3409... Generator Loss: 0.6901
+Epoch 1/1... Discriminator Loss: 1.3804... Generator Loss: 0.5383
+Epoch 1/1... Discriminator Loss: 1.4042... Generator Loss: 0.7307
+Epoch 1/1... Discriminator Loss: 1.5044... Generator Loss: 1.7383
+Epoch 1/1... Discriminator Loss: 1.4054... Generator Loss: 0.5633
+Epoch 1/1... Discriminator Loss: 0.8011... Generator Loss: 2.2206
+Epoch 1/1... Discriminator Loss: 1.2483... Generator Loss: 0.5992
+Epoch 1/1... Discriminator Loss: 1.3313... Generator Loss: 0.5899
+Epoch 1/1... Discriminator Loss: 1.2300... Generator Loss: 0.9554
+Epoch 1/1... Discriminator Loss: 1.3818... Generator Loss: 1.0496
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmwptlZ2Pc77/7ty93X3mbr2UcaRkICIYmQuOxQBIck
+TuwEJyZkDy6TSqgkOJXKP7gqi1OVxUUFlzEmEMqQAIGEKCABQgszI2mWnpnu6e123/3eb9/e/c0f
+z3PvTGNAI5CgK/U9//TX332/857znHOefTFFUTCHOcxhDmdg/XlPYA5zmMPDBXOiMIc5zOEBmBOF
+OcxhDg/AnCjMYQ5zeADmRGEOc5jDAzAnCnOYwxwegG8ZUTDG/AVjzHVjzE1jzI99q94zhznM4ZsL
+5lsRp2CMsYEbwPcAu8DLwL9cFMVb3/SXzWEOc/imwrdKUngJuFkUxe2iKGLg54Hv+xa9aw5zmMM3
+EZxv0bgbwP33/X8X+Mgf9bAxphD6ZIDsD33Gwsiz8jwAOcV735393QKb9/290M+mwFKh6Ox3FOac
+LPpYhIUnY+Qx6fk8HCAFwMWVxW0tYtmRPuviBIl8Lsq4RsaOcwvflcHzQv61DSRZpr9LSWKdTxaR
+ZblM6Uxws2wsS9dkW/iebFVhB9i2OR/XsWRusUyHLA8Jo/xsCHZ25SV23iRjRwfPAFu+V1xhlzGZ
+DJKSK1Y5X/sZ5s5+J5/R584m7YHJFRcF6D7ZOFhODYAVW3BxHNS46JQBOFmGjbHg5Y37b2EpvnLS
+833HlvdWMOefizwlLPLz6RSKe1OY88/22Vim4P3b7pytwzY4uTwTmoySXolpXle89UlQ5GK/Dx+G
+J598AgDXkYGzxMYwA2AyGpPF8uw4ibASW98RU0xlhFD3zstsCl/mUMocQlvXEefMkLNl5Q6FLThy
+db6Fa1HWMxmaEk4sA4/yKe/tX4KthzwjPy2KYomvA98qovB1wRjzw8AP6//AVKBwgN77nwJkAz09
+CC7g22cIlgPhGoNr5DvHN9SMLGtmMopYEBLZCUGqv7ME0SQu+PLxEiXeSjZkvNkup0Vf57AIpgPA
+SrEKwH/5H/0QQf2OvC9cY+VRoX9W9hJrnlzCvVGJy2ty6KdZAEDdLjgcTuQd01MO7skmj8e3GPTk
+MOVnd63couLLOkw94JHtFQCy+hM0m/L9OPFZKMvcdm/LOvvTG7x9dwRArVHwN35U5lYbfx99/l3F
+ap/CkkNfLfRyVD9MaXILgONiAllJ5mOOMPpMwRio69bIATSFS2FkHVaxDY6sz0tTck8ObLtoEyx/
+NwA/WpNx/+4Tn+Dvtz8MwP/4Hxr+zheGAFz4W88RhIo3c0w5V0LdEKLyYu7jNFoAhJNjrqcTnY5N
+6Mq++rFDHMiFqyeC+6kb40RynkI3Z4GGjhuwMpND8LY95hkWAXhl9mn5/fRXOShu66bUgFMAHBx+
+7n/9RwBsLMm7+kc17OwaAK9+/vP0bh8D8MXjHfwDed8N5z7RV+XS36zK3m0MquSXqgA8MWhzoyn4
+9O5NeCM/BKA0bRM3BgBsTQQXyWaZ57NlAN7xnmPh3isAfDb6KqSC+8IcUSsEn32GZ1zhj4VvFVHY
+A7be9/9N/e4ciqL4SeAnQSWFYvSHDKMUmJS8LofeTz3cM4qpnN11LTLl7AtlB7ckmxzE0JnIoalk
+kHnK8RMZK3ESLCPIO66OGOy/pq99v7Syf85hdpXTVloBWfQSAB/Nc454Qd7tDohTubxr3imzoazJ
+qc50HQ5BIId/vGOBcwLApDclm8izO2PZEi9ImYZy8R6r+9wey2X0199h1hXitbHlEo0Fzb4rB3e2
+X2PRyOE5Pchh/NMA9PnpB7FqhTIPZXyF+zlm+l2eZLzHVt+TAwRO3jeI/nMugV2DXAhIRA5KqEfW
+Pr1QDvd/sy5zu9v9n/lrz8je3F/4z/jeRblA+azLlO754GOEyFrLevlHAZMLgqPwbsyoLzhyrIzw
+TFq0cxIje2xKsqbMOBSZEI3YGPKyfJ75FWxX3jGb5dwuHQAw6X1F/n3gLITnn1IS6s2Jft4GYHkx
+Zu+dNQD8Lly7cROAt28ecKTEa78XYcUyThjLfJPshCKUi75Q3WUvFkIWWydMpvL+MN8jm8m6j1Xk
+zao1phW5Vu/cfY082xW0pe/bsQIGDPlG4FtFFF4GHjXGXEKIwV8B/pU/+nEDeHAupj0IBQ7btiBt
+HKywUpNLVlIRcG25SarSQd13Cc6XVfDOkRxiU6SkhRyE5ZIQk71RguUJ0rdWFkhH+wAcDrtEeoDe
+E5MBmvLP7piVF+US7t+OiT05FGE3IW/JM9HufeKBcLSFTZnntNxgeiCS0N27ffqT6wD07mfc7N4D
+4PqxEKkk6MNIDvYX/YhmVdbRupjw4nMfBSAe12k15QCcTOXSpeaY44MjAI7Hp2D/GzLn7Kd5QCVI
+5XNGBYD1pM6pEYJmzBEUoWLQAGe4eL+qcIbjDNk7MFjYRU2xNsZVAuG4ZZ5dE45Wv/QDAHz68gv8
+5e3nAOhcX+fmULiZsf8BViaHuDAejl7KZ4ZtAHbyTb7XE7x8tVnjqorPeTyl4grh7M1i1qry7jiX
+ea61y7zVEcLreoZ2VSSWx9ba7AidYnM647lVGeN/OxGJ4GB2l7w4O5fvPwsQJPJspSJEZXx/l4Ph
+FwH4/Ftf5PfuChPZ702YjAVvpbKHpcM4lszRo2DDlvX5syZPekLorgOJoxJZNsWzRNoo6xZcyJrU
+HMHhX1jpcS1ZAOD2yT55dqqzLCh0f/6o+/UH4VtCFIqiSI0x/z7wG4gi9veLorj2rXjXHOYwh28u
+fMtsCkVR/Drw6x/waf5QKqYE7jtaFsWHLgBwIV7i6W3hwO7iGICrG5eIpip2V1vYRkTAcOSwcSyc
+2WtUWSsJBwoHwpVO9zqMXKHE8VrO3uui44WNhKPug1wB4Dlb7AzV1SqTySMA2OHvk58ItX5j+BpZ
+T0Tf2e49ak2h1m/fkLW1Jy75WLjcMM65cyAcJjqNOOwK554lIgUNOxnTXLlEnOM33pE1H1e4df3z
+ADzx1AYf+7RIJuVMbRnHJULV5Tc2tyD7H96H438St49XRAroBS61QnDVy2IsNail+XvyRaFyg3yW
+v9tAZsvntikYuIK3Vg5JVZ5erUYUfVEPrpQ+K/O89wrHWyJV7MR/mx/55F0A/gtnzKItY5xaCT98
+Scb43U+ISP2DA5uDWGw7V4IZEzkWrC2HeKlw7k0v5KQszyz5ImlMhjWeikUk6DgOj63LuMenJRaG
+gudX74fY6yJlDXMRxT1rRviH274Zqm0qnsi448E+b7wpNonf3B3THcrfB5MM11XMWSHluqzvzDb2
+8XWLzrpIAU/XZrx5R87IkyV4tyufty2Xk1TG+8iK/O503WNlU/C282bORz8sc9/5nRGlmbxjksKG
+I+dv7/024z8G/twMjQ+CBSbALmZk7zt2pZIg6t3FJt/WkcN/8eqMp1dkw198fhMAO2vSXBE7Qtwt
+EbTEwDo56uFelt+FsYNbXQfg5LaI4geBw2hHrc2DAQeLclPS5DGGEzWBZBFJLqLmTUsOj1veobZ8
+hvQqi/arAOSdXe6rlXncDzh8U4hCaU0u+klk089lfdO9KaHqsrnJGeVy8qYjeTYpEvI4178b4lO1
+S5Sm9MdC3PrBiE2lXasfEtGxvNZmdk+IXqV+DJbgwuRdinPPQXwu5t9R70xlEpPr3B1TJj971mQU
+xZn6cO6rwFhnxscc28iFHYEK8zDNUtypXrzMwQSy1i/8nuzpi4/VuD/7awBUT3N+MRR15crqZT7l
+C77ffKzFq558/1Isas7NeIbXFaK/tQmuK2rJhzerZFX5XGFGrLaihbqci97BmPFMCONobGi6apcK
+LA725dIsk9DbFTwu1ASx0XTEweTM6Pwg4woSuYR2Lrj83XGfbPc3ZKxixqnuqeUUZOoBWKl5XCzL
+/vgtMS4WtZzLnozhTBw2HCEykV8+H7ueTXGV2N/P5HZXhzHdXTmzTa/AXfk4AM8/GjI5FXvG3dMZ
+o1ztQOmUDwLzMOc5zGEOD8DDISnYBXYtpT0r6Ckv2vAcNp4WNeGKu0ylIVz+kadKPF0T0b1cEcNR
+Ja0yGwtH8e0Rjhqcao0y8UA4VMmzKCpCVf2KUGjrtM52Tf5+325y6YJwv8HxTQKVBPxRSr8QNeXj
+NaHUy0mZVL6iVqwyU7H7aJzTV/VhEIGdy/eHd9WCXFiEE+W10yF2VX3MucVyody2JNQ8GdtERua2
+lrl0VQweTgyRkbWu7ozoXVJpJBXpabu5yuZzsv6hZYElKpF4VITDWAUEbZnHJTXEHaZ1LFsMrXns
+QKhcsQBLVY/CKnDUDZxprISbZ+RqrA3ymFBlBc8kqO0X10zpJ4Jzb3wDgF//Wp31mag27oef5vsd
+4f5Pf7rGJx6XdzydLFLcUgkqkfmMjY1niyXfcmyeXhUDnbMasGSJhBFPcpaqusdL8p07a1KoirW0
+7DNtyvzbw5S3PXl34RhmqRpsA3lHMuuCulz/oAZmW3KeBmPB8QtRg1+IVfI8yshnipfCpVSRzy8+
+ucVCVfbhmQti2J2c9imXRaK1px4L6n49OOmyWRZ8nsYZfiY8fDYWya09M3h6LjxjMTuRdVx4zGfi
+qqpUirjkCC6+dO2DSQoPBVGwsalZdabZhHJVLoe/WKWeymLSZonLj8jh3yhdwG/J5jqOiMa564Fa
+kO0MrFQ2y3EDcg0KMXFMNpQD0mjJpWqMbBjJBmz7huBx2dz9ZIvLir+9maFtRAQ1qoePbKg76t6c
+HBNrcIEVWfgayOQcZaD64EAt6FaSYAV6mQKXNJFxl3yb03VZU31PDzMT3ExOYU6BPZHvfX9ClskY
+vXyMfaqxHHpgnmytEg/ldyveKqZQgb4oMGe31MoJJvLMIBCcLJQHdAeC+6qdkCPfj60ENODKtl1a
+6gaeleXfIkzwq4LD9STlUANvRv0BQf5ewFGucQN9JYqtos/oXdmH9rWUL1YE90//xW/j/rG8u1Vt
+8oorRKR0rF4kd8Tikqyp5rUJ1CZSs9cJNK4jMxW8XObhI4wlW+rRn8nFc4yh6ctaK2unbPfEDuD3
+HTxPvk83RcT/knHoRkLo8+T96kNBrgSwiezNtfv3iQ/l2dSAd0YUg5yFTPB1YcXl26tXBUe+qIHN
+tXUx3gCuH+BFcvhyqwaJ2i3iElYoZ+QwFFz68RhXA6QCy6IbCr4bTkG3ENW66XZIiphvBObqwxzm
+MIcH4KGQFExR4KQpjUrOVMXTej2n0pTAnO0LFdYaYmiyizZx+Sx8VP4tzCmlmhiwprZFdSai3zQA
+TyWFyAUnEco8OBaxopyXCZWzF40Kg/5TAFxuvM7uiXCMYb2PGQnlbpcEXU69QqHcOl4f0rsu7zia
+jPFtEd2jvMtIw1+LWDiK03bYyDS014LAl8/lUkwxFk6SNNXIZPVIVdyfpTnZvnDVSl5hqAajBi5v
+3hGDaHNdcHXPvc/yE48CkJd8ilzDZMnJ1WDoWYY8EHy2NSgsSny8Qt49LQy5hnGT5ueh29gZ5bJw
+ndSS3y2XUyYtkSQ2aik778o7KiYnVU+EUzachML9o6lw/J3UxhqJdGDZA9Z0z16of5LVdcH9+NSl
+lIhaVNPY4KTWpKoq5uLCOk5NcFhUy+QaT1G318h1LYV6Mkapw4IvRsSkPMNTSefwqMzVQI24VxNW
+6jLeVz4rv380v8+97MwT9Z7+YABTE8ljEMuarPoxb+3LPDvTiEQls1pkqC7J+8qzGv1HZV8vlMV1
+MiPEPouLClu4keC42nIYzcRrE7guJ7fFsBm1RS1tDRKyFdnH5LTMssbvJEOXvCGfvXHGmbD4QeGh
+IAqWBXXfIp7UqFc1TnvSpLgoB7NWKejlsvhFJ8HviJjkq45f0Mb1ZcMCx8FT74M1qZBolJfj2Fip
+LLfkyd+daJ9mS0T4JMkYW2IouJM3ebStuvqkSWtBDukg0BDX6RDXFn1wNm1R02DNxSzhJJZNLAcG
+K5L3rajLLli2OMsdeMKukKQyrlU6JazL94sVOZTjfkJqxOo9nNk4dRlr73hMNJPDP6nm9BINYd25
+C0B8aZMjjWi8UFo9x3GBwagaVBQGVN040rGSOCXVPIEAm8LSICQnwkrlkAZWmYon+1ByZb6TKGUp
+qimuPJoluSDDMCPQAClCH40VIhrUdEIDcg2fzk1IbyJ7+fxqk3W1yt8xhtG9r8p7NMS3aSVU6kIA
+1xZnbDZkTeWFMonmT3h2hKPE11Vm4tMmNHrBWnVsFbudBZtRojaj1gKHPZnzJ54TG9a+2eALp5+T
+uYchmeY2FOT4kVx0zxX14+Vpg6ZGoYZuSqjqX1TJsGxZ06Ibs6lzsiLZ/+UgI9bAuOq2IewIY0m8
+CUsnSvS8CXsDWWtFiWLZinE9DV6rpPSmagcrxgSqVtqxyzgUgvtBYa4+zGEOc3gAHgpJocgLwjDC
+8W1W1XOwsbLCS+ljAFTbNld7wmpuDRJKaqDzGyoa+xY5QlHTioeLUP6sYcjUS5CZGNsRKm9r8AhZ
+lUzDYb00o31JuNiaNWJ0Wyh3GoyZHYnEsqbcMUhLoBZ3P6rTL4kY3E1ivKr8rlQ4FIF4R1rqg47r
+AVs14SrdqIevasV+4XPJkbEPUhENHynXiVeEIza6M35Dw5i9bsppRbjcpTjgRNWfN45loXa8z8cq
+f0nWZ4/O8ziEn4iMauMQuMKlzkJ48ygm19yHyJQpVWPFi01VPRTtzRoXl4UD3Y+E213oTaEs3/Wd
+DrMDeV/ZjYk1OMdUc7pTVfUsDWFO3xccloHmKrFQyqAhHPi50GM/E5HfOhJpbLJaYUvDv60sJ7DF
+oOZbFapLGm7ct7BUgkBVGKuckPdkn2aRj+to9mSlcx6IhfEItlRSG4nB8Ib3KjMNGirM5AEPRKxj
+3OlKTMDl3Q5fiASfu5PkPMt1waqztCp7vXLxeaqqurWW9VyYEjVL1llkHl5bPS0nhkpJA6C8OhfX
+5fw6mjvhlXwOXJFANqox4zOPUQwj9VzViojQPYtP+WAwlxTmMIc5PAAPhaSAMRjLIc/AcYUUp7Yh
+94U7LA3bHCeiU9ZZwFL3TurIv/WkBWWhb+6gDBVdVpiRVYVjWB2w6mKMtDTtl+WM/EiljXIJ1xZu
+20hhZapxA2GOo5GMyxpGGxYWLXUPhd2EcV+4n5/ZOA3hTM1SjammAOdaOKFsXHp9MRYlRYn+RPzK
+rlsjqai+vyoutOZxSGNJOEo3MDzV04Sw/iEVVRFv5lOcqcxp2pA5bozXWVTjlE2b9+h+gaM+1Yq9
+RNMIV/F9ed+JkxJ3BG92rcrFXL9fjKlMZIy15SaP+GKPWV+V9/VKFqib9XK0wjBXY9jYMNI9uer5
+LDZk7NNQIxPzLkWqnM0YLNQdWl6kHci7R8EMrybPzDQu+4LxWLgkZ6Sx9iIlNaK5tTrmLOmoFWH1
+RbK0Xd0DJySvCDcOhhnWqvw9KHISCXXgdO8+/oas72k1YJ5uL1N2flnmkCbk55mSBam6JFecKwD8
+zOkb3J9F+tcCV+tpbK06bARio6i1Axqe2KOKvmb51nzSiUgrpuaSqL1n6Be0joTLL1/xyGyZc1GV
+Mzt6u8O62tJ6bkhdU14zB7ZdkVgP6dEYnyVEfTB4KIhCURRERcFyxdAry6YsJimxXsJXRz1MTw/h
+3pDnr8riV18R0Wl1a0L1zHhTGpCMNHPOpBQaVpxWEqKZ/C7R0F4O2lhtEcuzmWF2IH/fGlS4lcjF
+Gkd9wokgdTURQ6NplM69JI6/j9EY9ygfUAsl1JY0o6IHaKAqzO3jPbypFtDwHKZqtKo3C8qZrLWt
+Xo1J5pKeSObkwaBFpnUhypuLVG/L++ypYezKIYx7cnjevv8WT1T/ouCwGnMm7xoKMvU+1Eo98sXH
+5T1DucRZP8So9d4JXPKKrK9UtHjqgqzjyRdWWPdlnoPoouC4uU80FXwPiwOaBxpuXjJUNJ5ge9li
+NpT9ubItBHRnJwc1jMUFrCnhLdoL5GVZS5YnpLHM6UTn1qymDFLBcfPeiOgZSSMf3etjxpomX3XJ
+HFHDiplc7iydUOLsAiWUu/L38V6NQpMb3NYKti3z+8dfkzlcfvVrTOIzQ92M98OpJ/M/ikXVGI0+
+T57os1ZO05NzE2Q1PF/2NZrNeOO2BInVNA6nUo0JSprOPxqTa2xCOhvRm+ie3A0ojBDLcibXNmrX
+6J6l5w8W2VR1dTbJ2C1pIN5RnV5xljH5wWCuPsxhDnN4AB4KScEYCJyCeOYT1NQN1w94N5E6r2Y3
+5mQqHPF0ULBzRzho2xZKfGW9yYVVCX3+6PMNykuXARjPJgx3hHrm7R6n74ok0NNCIdOdGU++cBGA
+hnXIWA1YfavE+pqIewu9Gt1lkRqOToWym2FKuSWi3CjYoC3MiPpyQaguxdTyGe2LXGq1RBoZn0R0
+OrK+MIGNTeG6Dc+isiEGyFYuXDC3RpRTDX39jiWSd4WbXapaFL/0SwDca0yJO8JhbV/w4wQue0Ph
+dstW5T0cY7DUKDUu1lgZikoz0WC3IHBYc+R9S1c2aI+EQw3TKYUtx2TBtNhUv3h1JD/sZjl1LRCT
+n1bYrqnRlTZPLGklK2/Ao8uyV8Oa4PXxaIfhoeDlOEkYFLI3tazA15Dvbs3HHoq08ZhKD/FpyKQk
+3PjW8YDNpvxu1OkSTWSe1cDl0hUpduI0VHJzQkI1CDY8h+GhjDGrj7n7VTkPzefWmI1kLd/zzHcB
+0LY/jPWZ/wmAYjileJ+0sKKh23Yoaunb6Rqz9E3BfckwU6lvd9xh/y3NRh2PCRONWdCo2CWnzPMf
+EqN6Y7XNyIgUc3xjiOXJXtZGS4RPCS7WDsXVvHzJhwNRqwe1AcMjOQtXHl9idFddzb0ew8H7q5l9
+fXgoiEJRFIRJilsOKakXoeQkMBXkxV5KOZIL5Pk59/VATjWG/PDYZupIeTR2F3lWs8lGvZxhX4ua
+3LrO4USDYkbyjna5jB2LXr/s1RlbWkln1OMNTy7vdHXEgYpwzzY1cCccMkxFpE7CgpFW1RkWMaH6
+209HUzqHM/0stpG390+pRGf19QyBqhVW1GBZS7bd2Ze06P/nzgFOJHO/8Ys2H9eMyeutjPhUVQLb
+x9aKHe+cylZO3r7BdEfE02dLVc7UhwIwyDwX/SGU9MKqKLrk1Ai2xW6xuf4kaSrxHeWhzUIma71g
+WZTKWlouUm+H7ZwHWb1uTSiX5NKHxQCvIb9LvTqtmsxjdSi/69kbnBghCoNuzqOBzGOYDIgLuaRx
+74Q0lEM/1HqP5cUFpqkGgDVD7tySS3ySHxCLJsSlmkeyKPu3tal5C1OXzj0pTXfreJ/ds0olbzQJ
+VWUY3bpHb0HO2Vev/yQAV6Y3yJOznIEHsyTvZzK3NxIpg9ac7NMxGpac2ziRfB5nHrHmIsx2Unqp
+zNlXO8lmpYKzrinS5gmSgeDz5Zs3+PKBMMCS52C/KjaRTznCkGaPGqKRfK7VHDaUCN2a+PRcOYcz
+11BpfWM2hbn6MIc5zOEBeCgkBdtYNLwyTeNTU+t729/E0fBR8oCBGu2sQY5TFpHQqQg1rFoxuXLu
+/r7FicaMno5n3N4RkbEzMOe1Eu/0hAPfG47phGLYfG6toNFU0X/V48KBzGNWaVJNxVDja0RZnBq8
+oUaMpSmWRhva0wr5mU/YHrG3q9mTGjHX7c2YKmevxgHjTJ59plZBtQ6mHRENJwd3ONmR+U5dize1
+FkB3kuGr96XWTLDV+NQINBuyZ5h8RuoL3rzxAkZrFdoFlPyzKMU1vNFd+d6SF/ecDukdLRYSx5RT
++V2tNsHWWov94wPKGkHHgvxbbpQZa+TeRm7xeyPhbLv9PuNCVI2LrRKXC+Fok5LMdy2ZcFIW8bs8
+tEg0ecrMEoKxqDGWVSFuylryjqhiqVfleixSzM3fdrCags/xbMLjalzrGY9Qn/dm4hkwpZxx/jYA
+d/ehp4biG4PbHB+LBOXXZqwsijTwV5ckBLn52Pfyf++JNDl85x+SFu9lGrZzkZqeNxIe/38sP0Xk
+vqtzn3DGc61KRDBT43c1IBhp4pZ61PrRiIO+vO/qxQCtTEceuUSxGl2jgnagWZIaPj/pR7g9Gauf
+9CW7FVhrezS0CHERDMh2fb4ReCiIApYFvsf2BRenJbpgUDjU1kVcGk6HVEfqMbjgc1G/X26IHr5c
+TBncU+t1CyJNhx52RwwTOZjpwoQF1dtfUpflgJwNjYEvr05pWXJIk4HN3ZbYESwTsWDkoq6X7wKQ
+VR3iXDbZKmZ4U9lFuxXSzDXseGpYe1IOZnGqxMR14UQuerUVsKjh2GsLdbxVOWDeIzLH6a5NQ6sK
+jfZtFC1MTINnNePwfryAY0QU1dglToZ3GVyTgJ4Pu2+9V1HRKog1kKlaGmKpiyzW/INZ7OE0BVf1
+hWUWFwQv65Myti3PRG6LKJd35/fkkieJg6dp2GtbG1xOxN22fnnIlak801yeUi1kXZfUIp+HY276
+oj5UTErJlz372m7Gi9kbALx7e4SnIbrOuuxNcwHyniDm8RcttI4LpbJhcSpjtGyftCn74zqiGgzZ
+oxfJ+vebIZdb8vfO7SfY2tKgp+GQ1UuyP+ZY1vno7LPYZfEutMsTjtV71DAFuxoWPwpEXXOnt6hb
+6g0iO6tET5zV8euyvwvLNcYr6nU61kzTElSW1Pu06DFtyw/TxRrTI3lfK8jJ9cxtPSLn6V6wjJ2p
+C7jvU9d8lNSqMKkIQ7LDVXz/gZrJXxfm6sMc5jCHB+ChkBRsC9plyMYtnHWhtHZRQlPlaS622FIR
+fRI4LLeFoy3YGpgSz6jWtLx3b4ppCHVtlm1amtXXKLlUtO9DY0Ge3V6os64ejM2FNmoX4nDqsKTW
++dPpiJqWjB9l6g3oglEjZ1z42CqdtbMKMw2oWi5XsTWEeLUu6s5GEDGqiPhsNxo8tqHxD0s1FlrS
+WOSKtsyZ3Dzi7UhE8e/cLhGpcemxTpeOcr8mjxCeqtV+/DUA0ukSvVyMbF95ZxlLDXRF4WBrsY3O
+oIkdiUqKZSyXAAAgAElEQVSUO/L3eqnG5qJw8xc2L/P4ReFss3sjpmrwzMMxpyrpjAMtEGMZHq1I
+OPZ6xcHdFKkqWsi5rAlZm1sZR7dlrSdact/ePyLvaLxFXjDRgiTTmy8z2Zd5Dlc8Jjsyv6dflL1b
+arV4vC64mpopRhPoKuQEuj84BQ0tkmKrQbFVeokXXtD6kTsJJ1og5dHlJXYzkbbaSZnVZVmfvy5x
+HPWnnuZHBlJz+LcTly/fF3w3olus7cucnXvifRi215lq6T7LmtGPVU5zp6ijgWocsqUBVbO6SiVO
+mQtaYdzYFgueSA0Xl5bZKsl44SRhRYQ73hiINLY9yRnmIo2GpYK+ZnM+Y6/haw2FuBmSafGZDwoP
+BVFI84KTMMFb7LEYyaUJShF2VQ5pYz2gHcv3RaVD0xdZ2lvQhd9e5e1ACpsuWgGpLQfWT0ss2KIG
+OHYCa3KwFvqCSNuZslbTLLp6gNFAkMhOODAiEg7akGhxjac156CwZ0w1d6CReQx0x/2gQaLRfUu+
+S7Mu45lUDk3Vjwk2tEZjacjWgmyu185prGtnpbE0ZHn6n77KthYazVjBT+Udk9MVTgdiRb9WJIR7
+Itr/5oEWk8lukY0FV5Nsj1xbTqUkWLFc7nrNJtMgJF+J5hONTbafkYP5iWceZ3lBL33llC++JeJz
+dtDlLW32UlbPnNUqY6t9wV8o8fi2rG9YWeLRJdkn2w9wV4XAHYqJh6/unnI3lkH6Ycq65l387PUO
+d58Xa/7v7DX5Z1IhgNGWEJhSy6fpab5KMaTsLOv3NpZGSHaGJ7gNzS5clH001iJuQyNkrzZZ17TN
+ot/GVS/RheYF0iUNEtOxjqe/w9HWXfmufZf6F+V3f9nUuDkR9+NdxFbxkh0zLWnW4szBqP1oyaqw
+vSzn5fGty5iKqB3lUxnrXrPH2qqqsbUZQVkL0K52WdIqTPZyn0Zb5nZpQbN1nSXcqRDWldCiXtIC
+PuUhgdqBmosNlr0Hg66+HszVhznMYQ4PwEMhKVhIjvjCaUCwKhR6EFapKrVeS9o0lrQ3YL6K62kV
+3Jlm53ETTWNnVlg0tQOUY2Y0FkQqmIUej7nStKVY16rMRZ1MDWdmWibNtc4jE2rq948JcALhlB01
+KCaxRVXr/aWzAqumsmHHoaFVhI0H05lw7HQq71utLYCW2mq4a7S0Rd5ibZNypnkZdWlXtly5TdX7
+XpnDCnBL1+R+jawpgS4f9woOohcBuLr6awAcHNWYaFbfu70lbJV43MKmVhNclO2LWI4Y+epaKOT5
+j1R5cvFpAJYqLSqWxnRU7uFpKPVR2GWoY6fauamIPXpdbaiz3sJeExwG0TK5hjF7bp3MVkNbU7jk
+eggtNUQm+QlOIvi+VCr41DXxGDzxkUUOKyLpNY61TF9zi/qi1mToXSGYqvrQbFKo+NI0m7hjUaHc
+XPY8n/lEpxcBmL7RYfnbLsn8lz2uOCLRlAanmFz2Ic9EAiubJS5OZM6Tgxf5xCPCxev9JlUt1782
+EoPxjhOR18QI7uQTyho30N4seGRDzuyFJUNgiXSz50mzmOaghqOdrsoXr2C6coY2S5fZ0gI2nrNJ
++arMzevK+xa8fRztSBb7HWzNMJ5NI3xPAviy9CuE0QdrAnMGf2JJwRizZYz5rDHmLWPMNWPMj+j3
+bWPMZ4wx7+q/rT/pO+Ywhzn82cOfRlJIgR8tiuIrxpga8Kox5jPAXwd+syiKnzDG/BjwY8B/8scN
+lBUFwyhj3OxxKIyNx9IRyZb4f3tFRDDTasBpil8XPTNTO4NtV8+bylYXKzQ8ofxWKSFLhMsHKw7O
+okZ/ZcJpilmO0SSSvBgS9oUqx2HGQKP/jtMpgfq9aso9qDmMNKS2NBuCVrmJ7Bw31fr9xAQVzbTU
+xqdubKNVx3CXx8Tay2By+5RAG7d6ttYNSHZJ+9rjMLKoO/K+kTUiVAnp/kmFYiB+/DQUqaNEn0jj
+BlrbE9TmiGVlTGaazdm8jd1SXbx0Zn9ZJVVXp1uPyLXWQzGpk80En649YFGFojPOFpuCaEntJEFI
+VfFiOWWKmiy2MGOGPe2/8K787pf2egxm4v+fJQVoJOCX7u7ySEVsKZ/7/TJ/U+cRLgpHtKIxE43y
+q/gB5qyVW5aQqnRXrYYUmgWZ7d6V38celho5xwsDFiyxy3jdLWoNLUnXCDAjiY2ZjdXl3Nvh+hsS
+ezCaHjBeFtvGpY9U2M/kfEa5Rl2Gd1lWaTNzHRxthtPI69S0gGyttkii9qH6SKSKKV1SDUGfHo0w
+Gmdj90Y0tuXZhcChrS7Hxqqciz5VfI02LWZVKlrXouz7ZIVWiErL5MV7PTA/CPyJiUJRFAfAgX4e
+GWPeRlrQfx/wSX3sp4HP8XWIgm2g4hiy4zKlFRF17gw9vNuycatZQrerXoSFNuWBppxqByVnPKGV
+nwUypVQ1EGbiJaDhnnXX4CZnpbrPKiqnOPFZ05MZsRrieicTnESbpEQpro69q6pGOoDCk3l2nQTn
+LATXzwkCbR5alCh5chEa3ll59j7HejGHL8f4vryvUq2z7cj63KmIhuNwRF+lvnK7TmegIdhDmyMN
+tjlmSJGISLm9rHEH9SfpqDukfinCeVMNbbnBVhH+zqjCck0OckM7WHemIRdUFB/tFnhaeu7k9JR0
+JvuQlexznO+pQbuXzNhwhPKMpjMSI/MpJceUtcRY1Izpayj0JJFLvLHgY/XkUmSmR6Ht1Wv9hEFf
+COQnn97gy78vBO6fUmIaVsZUErmMuckpaZMZwjrughBkK04J2tqxu/q4rt8mDuXdj19cx3G0d2N7
+k2wgPT1LTptEU+0dLdc2y8p89yWZzzh6DOcl6Z59+fETjq4LQxmrsbc/GzM4a2NeMueen4EdEp4V
+0o6m+Gq4PtIguqwbcuSLmuTmEXZJ6y5WPJ5QBpd5BqP9JkeaMVqOHdSOTD91IFGvm1/D09J0JgE7
++HNoMGuMuQi8AHwZWFGCAXAIrPwRvzlvRe/Yc3vnHObwsMCfmigYY6rALwJ/syiKoTHm/G9FURTG
+nJUCfhDe34redZyiH2aUmhOyWH209owoFnpyHLi4mllGPAFthlHVUmq56zM4S1bpGG6tS3JUlrhM
+JipGtQLK2r8gK9SHPcmwNGTUT63z4hau45Nqa64RFioFYmtYteXGjF3hSs0Z9EJNDrIMjmbzFXGP
+pKcdpHNVUWzDQltYbPdkQO++it3OPh3tcu2cSp+DsG7haaGMID6AU5n7bjfk3qmsNTFTZlrebKLF
+Yf2lY565IMa1zeV1fv2sTbyVQS64WCyVqJQuAtBqCPfZrjSZ5DJumo6xVAx20vS857SXW3SbIm3U
+tG3csACvJiJ17jmEQ5Ew+mGIWRQu7vcs9o80/DkUUfzaqIfly7NxZtFWfN8Jx7QWZdKnuwn/Qlm4
+5mFdohhLFY+RK1w1HE6xFLeNxiF5IntSjiMmB/LZb4hBdRZPOb4pEkGxPaN5WWIr8t41Kn112TUu
+4DiqrjhaIyHf4x1L1mE/scfqo6LebTUXeWeikZddmcPm1MbXwrbdpE6krekezcvEIxXzd2PiJcFz
+WWNPXi9Oie+KtLUzHLKyKGM4xza1y3IfyqUKeabqj0ZuFmHAaSrvqCVTbFVH7VnIQCXdYNYnn/0Z
+FlkxxrgIQfjZoih+Sb8+MsasFUVxYIxZA46/3jgW4GOxNg0Yagjy/njGSl8OTXS9R68hYrUdepSq
+op8lWrHXsgClPSbPSMdqA4gjIjTApFvC03Lmmfp2+7MUk4k4HPgukfb+61t9plP5XPFS8lhFey0X
+Po5sXFsOzXAaM9ZAkbxrkxUSdurnVRL1UHCmotRjSoGEIF++UmG/Kc8Opi7+SHTKA209VezGrG3L
+QcoHNh21d9y9vXdOhDqZOfdHR8tyYFrDCzCRw+aMyziqh5YKF89b0PkHlEaSPzDuyO/6izlZV+Zw
+PO7jasDWjBmxpXUXA4tmIM84WhEptyqUS/KdO/EZj+XS7J2OyTUrdX11lWVH1Io7Oodn3Q6J9lS8
+H48otABOw855biSHeG3zKuOK7GtlV3AxymKqS9qlamrjhfKOsF6hpIFYN28YSsVr8vxZ5e47J7y9
+Ixdv860q7rfLeKWSoXeoORjVd4jasj+5EofTcIGKo2Xrszprr2v1rUe2mY4ktb8SClG51UsZqj0g
+nIXnRW1uVBIuqX2oX06xQiFYE2VSaVgiqasXLK8z2VN7TQTxbS1208oIS5phqnJ4FN0k0uzZmJyJ
+5mVEdkJTA6D2x6fMNKP1g8KfxvtggJ8C3i6K4r99359+BfhB/fyDwC//Sd8xhznM4c8e/jSSwseB
+fxV4wxjzNf3uPwV+AvgFY8zfAHaAf/HrDZQW0I1z7pd7nB4J5QuSMcvKPQ9rEZkmNuVjQ0lDWMmE
+Qkf7Nsrkse2IXLmHFTYouyJeu2WLma81+UNtxxblpLZQcz+Cjlat2j/1mA01QzFwKDsynqstx+22
+B5ZIK5WVCfFAXj5OD8+NedE0xNFQ2lwjHsupw0TF8tSBVqBlwLwmZlc4r6t9C5O0wApl7ieRx7FG
+3d3t5xwOZf2D1Ge1qe6FPcHPXrNDyRNRe7QFiYa+Tk3MaKBCm+2wnIhhcjqVse7fnoCqBI+vh5Qb
+gvvoYIqnnY99K8LWsNvyQKW0uktZf9evx5zsCbfudkMs3Z+glHFHqzzHHTHKhWlM3tBYgn7BRa20
+/UpoEy/LnrzTP+T7luTd6apwcMdPOO2KlGZbVY5UalqdWuxrLENz2ad3X3CQa6/FqNjgisYYTKwK
+RkPdj+7nuAPB83GesGKJunHU0wIqex2+8jWRCJoLTfpPi8SzGexyrNWau5qJij8hMmfNdwxD7c/g
+HTvsaL2I7YaFk4vU0OloclleUIRajs2KiPV7r2pIM5HYTosILejNuCtn6GhgEWoCVpHlVLTz+jCy
+OdII0X7Ppx//GRVZKYri84D5I/783d/IWMYY7MDBdAIs7cLUG1vc9FSUdhKe3JKLvFBNKbRBRldj
+8ifDgsMD2VjT9CmpdyJcShn0BGmWbZ9XS7K1kGqSHOK6gsjBtEtPkx9udfdxVWT2JhFlLY4ZloWo
+1E2ZSkuWPhqXqYRinc+ZUKjeNyvqnPkD/UIuZncy41QbgBzdH1Hqi6h9EE2ZaFHOk1PN9LMS+hNt
+ELK2wFR9mbuTGScz1Sk9i566zoKGXLbLPMVYy6fbxwnWWapeaigsWWs/SrmlFYYuaUbeepJhe+KO
+G5yuEGpdvzhyGM9k7IrlYrRm/vFZncCpzUiL1pR3U4xe0jS6R8VIgFAY54xizVAcyvu215ZJuqJK
+uOaEm9qUphE4LGl/z2c/8nHCd8R7spGoPWdhgqfNZ4p4QEWDwdJOhbLmlTijMhc2ZH1lW9SHo6t1
+xvfFBrBZjbGmWulqNeBo+AVZX1yhp9ylqJ0VKXG5pPkVrdIKZvM5wdFgQKskwUeBhkxfSxaJ1DZQ
+tnI8rQ+auAmZK/OZRBUCXy5sTwO29jpDMuesl6TNgXYOc8YFF/RcuGWf0UwI52gqaufMzhgoEymc
+KlEi96HR9nG68u538xGTwZ9R8NIc5jCH/3/CQxHmnOUZg+mIW0XCVH3lXp5xPxauYzoe7S2hgi+5
+TYZNoXzRoXDw3aMRHRWD3xzep7ms3YXHVda1FVqPQ8YjecZriahdLpfItNT5LK1wbyAccVCE3BkL
+p6zWPXyl3JfVD+zZIY4asBYbGadDETVH7oQs05rjeZlI4yEi9SxMw4SOeg5O8ykDXevEshlrp+hc
+jUW9wqZxKtJP2Wlw2xHD4M6oR6rNQOwspdkSDlM0pEZlaTtjdU2t8FsBmdY+hJRM+0oaYGxp0Zae
+eB+SFYvYlflmlYihlpWbTCJ62s3Y2CmFdmUutKtz2IGTUCxfeTXB0XdklSpxRcPQI7ilXqAjhJO+
+lgypa63FnWFKS8XuATnvroua9k7ny/zrgaxltiYejmZljbJ2l+5MDP1Y/m68MeNdDRvOBuRqYK5e
+1JD4ic1sVySho+aQINdydFaFtKWZn5ME20hQVziVs3eDO/yaI3t2pXWXUlMyJj/SXmb/LdmHoa2J
+ZmlCX4vQjIeGSKsyl5waE83yHds2E417SVSC9HyHjsZvZFZOSQ2QNStjX0vT3d/xqKiadhxo24Jp
+hOWpOud7NCpaN7RUY7Io5+h4r0luf2PVnB8KomBbNs1Si5VxwHFLplScdii0Ok4c1jjSEtneeBvr
+gpamGciBrq3foHRfdL2e67FVEf2z8mjKilbxGSc9ahXRoy1fDo9janT3ZOOOJwP6Wqq7MjAsx3L4
+l/qb8HER1ytvaDWbvIKXqrcjjvHVTVfdazAt5CI75Q6FRk7mA537zCEoywV61GnS0UzLTXeNwYJc
+kEzLzN8Y7LJghPC8U09ovrKic+6QaWFTu1JnoSH5CmvPiBvy8ZVvo3VVxlqttQg8zTqNDHFdy+CP
+QlztDzmN5YDtO6d8qiqEJalV8WPBZ2X1mO2O4NB4NrWyfM+SXO6kk5GHsr5qc4moEJdjkDSk4g3g
+Wk0utsVNbE5knh2vS0sj+mZLr2PflX1vVRwua2rw0xc+RHwoOFy8KYQivxSQNOXSNNJVStoJqTuM
+2bqgHqP+VaxMLnJQiArDxRNsRz7v7e5RCQRvbN0gO3wegIPxfUaRnK1gRfb30ewZ/tmxNOx9dPs5
+FrQik7EXWdBW8usbGgjViLBGcnEP87dQmse4PuGxQjtdNcus2XIO24+JGrDdXeBIXbIrfoNsUy5x
+ZVRlt6/ubK9CVheb0GZfu5eVYixLPm896vPkpuxfP1lj1tGsYvOT3HpZ3K/v8MFsC3P1YQ5zmMMD
+8FBICoUpSNwZSbNHrt2IFssp++lZ+HCCrf7aO1fusFoIta6p/ziMbfIF4QxL5TIrGkDkBSu0tZnI
+SusSFfVQRJ4WxTjIsXUMxkOyI+1BOR3TU5E/bd9g5ZpQ40zrQFqBQzJRY16RYvaEWzlBSjnWWoTT
+Gb7GoIaWiKeuX2AN5LtkdoSrPRo96zaW5gYYFVsdCqJcVIbuTswwEc6cmgLXPesCHbJxQbjH1UvP
+ArB58ZiSLdzYGo7Jg7NOUCFJoqXfa4a+ZnPWXO2vedzmmpYTv7jcoG5EaiomGZEWdatWDW6i/SaH
+gsMisliqyz45WQ5alj4+HRNviQibRz2mQ/WSqKoxngzxVkR6sG+H+NozsVoqky/JMydv3eJ57RzV
+3RCxfv3OITOtPVE69AmuyjrW92pk6u1p1lzyFcXtoeAzmtVx1Ai6dLVO0BEuPYpLpEey7mApw96T
+dQ868t5yb8SVp2T92/XbjDW4ru2cMqhpsRsNeksZsKnh6qOKj/HVkxaVmTY1XqTkU+TiRkjHqjJk
+R6y0BW+tYkKWiBE0ixKqFW0rkMUMNDjJUal5a3mZkqoUreYauZaKS2pdpgtiVE3erbCoDY8+KMwl
+hTnMYQ4PgCnOMir+HOGZZ54pfumXf4WVls10pBS63KGnrdDKpYgjzcpbahqm6tZy1eV368YB4Uyo
+4Z2bR+wfis1gHA5Zroou11hao6neuU4i3PHKapWoIbn7l9YNB9pL8cnNMdcPhSOkxWu8dUM54apw
+5f7vJsw8cUM2rTKrj4m+W46eYcMSHfDawOb5K+oCnIlevG4G3NAIwuVmwVjbhi2aDvu67rYrnHia
+WpSOhWtdG3e5vKoZlauPcHlduHTHLPPcZZnzrXtif+gPPsfLb8uezrZO+EtP/Li8e+WE8EhsLV71
+dfbva10Dlca+fOSyui/69E+cfInnf1WksZ975ho/dF249Mm//Qx/dVd7QX6n5OtfufUG72iFrCfD
+DfZekvk8PTwhXRDut+gmHE5Fn1/TJLD/eh++/1dlb/6D7/oqP/5zgsN/7Ttf5At35dkXVkb8x39X
+bBT/8G/Ls//496f89Kfl70/8vS/xt3bFnWgPFpg50qvBS3ISV+YRaOvBKLDxQjkjkZXh5VL3Lqvd
+51HNVgo+/d38lbZ2+v6OZwB4pPcmP3dHG+C83OO3LgsH/r47EaMn5QyUHQ1hthwatkgKgxC21c36
+mX6TyTtiJ/itwTXMRDm6UQmycEhtDbfPa5iS2BcqMdjqol/y64S+2C6irpz73XQfT0sMRsUFQKqP
+cdZF+5+EV4uiePGP+uMZPBTqg7Ey3GDAKFqmVRMZv9trgxExsnucUtZY9PC0gVvW2H9NWa42JtyW
++BKi3iG79+XiVdMp+wtyWfzShH1tNLOkfuf9cZtaXQ7prdeWePyKvHsyfom6NkqdHX6Uj0ZSIu21
+e5p555+QzaRO4HfbNfaaYji6GMVMNUfjxWqIpd1eVtVi78Y1nrgicw6HQ5bUvz1K61zSWoL9icxn
+tWLxtgbpLEUlbmlJ+c7gkDc/L4fpn//2VSYb0jcy0JiNynCB7+nfBeC34zdof4cG01jPstnQxikn
+T7A4lZJndweytv/ztXe5dk8O/GtfGvKb2tY8fcXiv1Ox1J9t8dGqHMx3Gp8EYKd+ny9VBMcsH/HO
+TC7TqtPAXxEiFKYWp6kQ6h1HLvF/v/8uvxx8CYDrr7b4hWN5X/9/qfBkIvPHN/xXGmjGc2J8/KnP
+BPzUWIyE5ugzxNrHk6LgLEkjhvN7MTvjeRGcDUUGEVoXbmK4rt873htcLz4BQHtBBojKd/nVUyFM
+Ne83uXcoe7aUGJ7NpdiNbWulasthplWka0dD+otyhl658xbdqaw/nb7vwup7E1LOMsDjIgJNk68C
+eUPeV61OmKXy/awqxK3ovBfGD2/yzYK5+jCHOczhAXg4JAVsPNOgUoKJhhKPvRnhifhoC3uANRQR
+Le51qKxqHEIhVPnwnSl7d4QS348GWI6McTib4AzUzbjq8OiSLDccnkV4HdA/EvFya21K/566oy6/
+zv2xcN6m9y4Ht2S8uKVNQe4ElD8k4756UuZSSTv/pnUWlkVVSMYZ1VikBqeq/7bqTPe0t1lksHzh
++EteQO9IIveSrva7LDpEAxETv/TWEcfHmpvfhKAs3COf7PADbVFRdn15ds0+4N2bmqe/tUPQ/+sA
+1LdPmWhE33G4z/G7Xwbg3RMNmX75iO6uFvHwV2CoYxRlCjW6fv/rT3LbvATAx5dlP35+/DQf08rX
+TW+L7xoJbj8b1/mYlNtgYfMlnnBlfoelHwLgZx5xyYeStPTLb3S5NpA9rfz49/PJf08MkL+5sUHM
+3wHgyt8Raet/v/Uhnh3+PACvjZ6F4obu5Zhz1ksBxRm/U50xtzkXJTDvPZu5Z0ya4HNlvrol6uS/
+86xIeeO1j/FvqbQ5aG/zuR3B2+3+OpfPqtBuy/7fSTz8VOYZ532qtpytq1mJd4yWBbTHFKlm7ipP
+NuTYGm1q5TmruUgeMydjzZHvk2CBj9XlfL5dFbVzFh2warSv5mDKH6M2fEPwUBCFpCg4jkPKxKRT
+OUgTp8lXjwTp7bxGkN8GYGPzSe6peOycaOn1bh+zrbH690s0Cq0ibM2oX5INrS4uEWk598qybKLd
+rjPoa9/JfZ/hguiIJa5SqL73yltP0cxFzC0nooeuLtyhH2qNv+kJ/T35frg8pKbNXYOgS+qK7j+L
+hLitnq4QatCQvdJiqmGpDNo4TbGZVDXrsTvJzzszhQzZL8vmnx5B3ZLDvTPuYL8hHoptFa9ffuN7
+ePzCZwCojTeJqtp+fWwYdeWg37q2xi//nhC6Wz1Z893rw/M08kmS0FbL+Tib8b2XhSC93jjgOxq/
+C8AXXxfC9OxSwV3N91hftBm8ogE71iu8PpQL5gNWR/BSFycJo8Bi+0XZx3S8xt/72GcB+Miv/QNu
+Xf2irPtuziekRgrXvvMFAH7g6uf4+X8kKo+dnpIWZxf9D0Ku75Z/I5LzmHzpq3n2OTsXlyfxIWb4
+CwD8X69/BwDf5r+L0Zqf2fodzKEQwE9tvYrRDlhn1bbGaYnhba3LaYW81RVCfzjoYHzBt5kmlNWO
+N9XmPNsuHFbl8z9XsvjKknz+sGPoa2j+s01DR+tHPq15FNPkkJLmUTD45hAEmKsPc5jDHP4APBSS
+gkNKmx5xtsJ1hOpunezwSEl9294y9kwSUeLRIRVbw4e13Nl4tYl3KJLEC1cW8NTwYyYFntZ9WnYt
+nIqEymqRaEwyY3tF2275C0RTiVxzOy/Tm4q1eLwyYue6cPQnl+4CMK05LF3SCLyLbZ7Y0lJaRxNM
+INzRjlp42o+x6Qm3SusugSYSOVlKrSzzyerL+BOxrtc0r34922avKipKHBa4rwsuDlZ2GGjdg+Vt
+iK6LCP5F9WHPotcY7Qin+cSHb2KNn5T51Mt8eSic6+T1z9I/ljl37gkHn6YJ6HzbJZ9VTQjKV5a5
+rfX+vsta4OZY6056amW/1uHKRYn07CQBs5Zwsze+8hYfyl4H4H67jOdodOOuRN09F50yXpFIwk8s
+FfxKTySh7Rf/Jf7NLVElfvxZh6/9v/85AM/fkBJsP/+Fn2Ey0z4UWcZ7fO1MCRAwKmWlKhMYYzhP
+pcWieN/zuaoSdpzzTk+Tjt4VacQ5+BCXHpVztjO8iGPJPnxtGLOlZdWGJa0OXvdo2iK5FcOcpoaj
+F3lBw9Mz2UhZKwnujs7iKjzD1QXBTxbBpxZlTf1ZSFWTmQbklFWaqLdkD56dthnkZ7U975FkZ2rJ
+n86j+FAQhbywCFOfbnaXq+p2OXBsSoVYd0tFTFrX2neRh9/XlGR1/zW9gkpTLkqtVqWkMfx5bJOU
+ZWMq7gKlqnottLhoFkUUqbyv7veY5TKG5VZpT7W8/KnNBc2lOAzuArB5NMNV92XDbDJVAhHVMvxI
+rMA2DnXNYOseavnuUg3biF5YdSMy5CJ7dKF0ls0oaxrGQ0p1ycXYbjWwPiRq1Ve+ElDSRrnrOw2a
+n5DPhy9rkFIl5cnHpEy8tf4GQSG4mnRTnj6Ri/6zvTK7O5paO9FirVg42rxkZaXM9obM59k1m/As
+reWbBYMAACAASURBVNvr4mshE29PXbZ5ch4eTqXP9EjE3f74kFduCYEYb7xOOxDqnMZSgSmqrhHs
+ixV9stDiihIZ+6Mvk2a/BcAzxVUat9TtF8r+rw+GvKs5A4binCaY/D2yYMP5yS7rt5PcwqjqlpNj
+dIy8MDIOkJORhYLzY/U0nW7YRDNxzx6eduj3hIkEboQ1EtzZVa2OdFplqjYlJ+pxpKpks+JiG3n2
+iQ9f4ClkjLF6pfxwQhzI51aa0/XkTFb3e4y0oliYZviaH3PYkn8vbNc46gp+3LaF1dNguT+lJjFX
+H+Ywhzk8AA+FpGAsg1V22WCZyf/H3psGS5Kd12Hn5p6VtVe9evvW6/RMzw4QKwkCIEDSokTJpm1J
+tkOyFJLlCFOyaYdFb2FHWHZIP7zQEZLFsB2maDMoSqBlK0iRFEwAA2CIbQYzg5menl6mu1/367fW
+Xlm5L/7xndfEkACmwaHIdsS7ERNdUy8r8+bNm/d+y/nOIVVaLdehmbKTNise8pi78dzBuJTV3E1k
+TetYJQJHzC8t0+DZ8jtVC5ElYrY5uoKdk/abDMAB2jBiEn3odRhK+A0sfQHzpiy3jmYiHIhrcm4q
+q/lRM3sg8+XNx3APZUc0kiqMOnf8uYZ0zmrOQI5NMwNt8vKVlQZK1rznnQoM0mqVBKOo3RJFTXad
+enMfjeklAMAzazdxjffn7B9g5W3BBRwRWPW2rmCPJHp/5mAVM0J7rTjA7dtiEh/19xHTijEImsmN
+Ah7dh7VlHR/UJZB6uBSikQtQacF1scIircOLEjzt32ijx3GtOxuYXBBL4N5oExWyJ58tzmKjJ2Pk
+5AIOiocD1NuC7/C/6WNKCPZf+dTH8dz9HwUAvK438fVtGXv3LTnvUR6jZJS9KEuccIIqaDihAy0B
+mJSWO8kAJW6KIuTYmgo6dR5To0R5wvINQGOxmSpPKnB9rG1yTg7q2J2LVeQHEWwyP5+fiRWX1eto
+EQbuqxiLFi3WMkGLGpMdlcDLxN3yWX15P3DRJVlKkTRQNcWy3NMLOJyHLgo4pHqba6SuKx2s0AI7
+6EV4gZWWmERA+U536vtpj8SiUAJIlIaiTFHU5IVXfvlAJy8pXJjE4gdaiPiYLzX5KLVYh8bUjdJS
+hDTVjNSFSS78XA8fCMimjFincYi8lJcmjGLM6zI57KMhPFYUdpMKQkNiCsGItOhnElgsHc56Y0x9
+knXmLtZI4OK7I5REL85oj1VnNUT1E2ae9gNxVG0WISfzEDK5qXghgGJ6b6SHyKlDoZYMLJPHf+xW
+8eJtQW0VJOxYLl1MV2SsRn6KtcXn5XzTIbxFGZe9UYEklkmjkUnbSYEetQovF8vofUReoI/1nsR0
+U+5/9ewFGHzJzh+S3WllgCo5MysrC7jB6Ltafw23U4mTeI2reGtH0ghbd8S9+nsTFwtDifTfbj+F
+S70vSp/v/zQ+1fk6AKBmtvCcJzGaW+uCqjSnO9BvnnAOJig5hTU9g0YGJAcFnAYrUNvMMxxXUDVl
+DAeOgzZL1HdGgOJLlikFgwQohaKYcDHA3pEszlHwGoKAxLxFhIjzaZzK3NvPe9ieMs3oGejTBa3G
+NpIGGaAGOe6RLWt2LN8VZYHRTFw710vhQxYQW1Oo8/nE7QTDfRLqECk57RnQLjJjdHcVTxoyF77x
+wCH6g7VT9+G0nbbT9o72SFgKegm0EwWjVsMgkmBfQ9tDlYE2zdYQHsnq2Qz24VZllZ+TOzFXGurk
+QdRSwGhSo3BqwWC9vco0aCyTLBmojGMfZU5lZ6cJnS6KAlAlnfvx3iHug8ew2uxipQHPJOlHasKs
+ydpq7wUPACmNZBVxLjtCeVKJWTdhaCQFKV3YjAhpFQsoKELCyHS1rCLrSlCypSyUSzRxZwNoFtWd
+piNUTbne1ViCV1dHU6Se7FBbFy8+uEZFc3HFJGS2CFHRTngl5V/dUbAdGZdPrhY4/4wACsqNi3iM
+lX86HoOqiBmfbokbEcz3UCUPpNb0UK2KBbFguFh+60W5JyicOyeQ5+kOYcJHv4PpjrgUB509bH5F
+gso/9e9Wcd75CQDAuudAI/1ntSvz4vZ0glLncyy1B1R5LWUj9Sgok+RY8+Tc5wklP9cpMPblu8WZ
+wrYrboy2MMf+dXmumipgUcylQqIaPTyHpiPm/jCo4dgU4JQWGBgRZ1AnBH3lYoTzEWUJ/GMkdEHu
+2wVWpyTf0RJYLolqIN95UQyDwex21UKhyb0e6xOUVJJe7QM+NUvPEFuz3dQRkAzn8SdNXAsEem9N
+biBJT1yp79+NOLUUTttpO23vaI+EpQClUOga/HKICiG8U5UDsazathZCI23aNNCAhMxLTOPUKwoJ
+17eKV0V+ggVoJgh82f0cw4GmCDdmpV4Z6Mgou5YYOUyyLRl6joyFTc2FGpYPJCB2jZt1o+LC7Ypv
+aSQWYkUy0ziF53H390oYjG2UCfvjudBMknLaBfJS7lXTNJCfE/lczquZNipEYBaNDOFIfjcbeMgJ
+q14eXEK8RJGVV09k5xJ0Xdl188UGTArKHEbXsUq9gCMAhwxKneTxu6mFNYqwpB/+FIpzEifpLD0N
+Q3EnTRrIS7GQHI/CI4fHsJTwN6CXILfEimvVpzjelF3s9nqM+/cEFRoNXgYA/J9vz+Bpcqx/tYpl
+6mz88vweSuczAIBQPQ33WNCN37jFmFKQg+p90FSJBVLlVbcLrNRIIFuE+IFlCcx6T4i1ef+oQPu+
+PCd38WlUnxcMxdd+o8T6nozdXSg4toxtSgq6TjLCFcaSDPcGkjH5LooYEx6zT7at4so+HluWPtzX
+LEQx516ao19nUZnlYkLrrd6V+EtrfYyVplhSetXH+Fjmy5qTI7LlHGM3hp2LBVFEMha7XhcrVLne
+yGO4bbEWX9wtAZL3/kGCC4/IoqBBc1w0DRcpo8KxNX8gT+6VDlJStddqMRJmDFxOTDvTYLqkHI8A
+ryKmXxLEAOsLMFMwTXkYORmVbSMCWdRRwsTYZ165WYGeMEiUVZAR31AdyiROkwrqFIiJjkIczuX7
+MMkQEdzTyAqEXAwSmtcVN0dCKLFpN1EE7JtRIh8yQ2FSbeowR9xkHcixBp8S8P4gxJgv9Aw3ce0t
+CYyFUzm2n6W4zizD0y834DNg2Ha3cKuUrMQoih+YiAuM0i9saNjOxfRfPWuj7vwwAGA+BKqQRbFw
+DTgWKdv4QkfmIsqApePjBZQxaepmPYRTyeZsvqYhdSULZEcfBgA8tjLEKrEOzloNzX35+798/n34
+gC/1Fc1Ix5fO3ZHPb78AADjnJbitpGoxmxXobsvG8RPaZXzLkwXy/JEN9KR/TxGaHvVex6Qv82Wx
+meGZg48BAPbaN/Amy51nRorzVMkKm9KfXrgGtS2L3uSVxzGzf0XGInVg0X2w+Zwbnot7FCLSigFi
+vpi+imEfSD8HqznOU6jFzU/mylm4hvTNnbcw12VhGneW8bgn87rereHtfYH9M1EBIwQ8l2C/TQ1q
+IsHc9bsZ7rJcIEcK7aTM4yFXiPfsPiildKXUK0qpX+P/byulvqaUuqmU+hWl1PenWXXaTttp+2Nt
+fxiWwt8AcBWgljrwdwD8D2VZ/kOl1N8H8JcB/M/f6wR5mWOSzVAkOZQnaSPPBDRTzPwkayAzKCIy
+O8SMFFTRseyqXhyj1mMhSuojK2X1NLUAWS7HTuIU8Z7squmYwhupjzmp0sbTCI2UhBVFBfVlmtKR
+jTbXznlKEtRODSnTX5naRzAU23/iT9Hiaryv5rAoF5cyeDXZyxA2ZcdvZ4DtypKfjC1YDHiWmpjc
+YzVCSaVtf2rjkPmvqWsjYjpRr+gYagyOEh6RZgDSEyVtDX1aU3VtiqNCTHEzzR6IxBSUwhsOHHjU
+MfhHb7wO7bd+TcbC38Zfowluf+LPwKalU5yg8fqHuHVFLKyF2ht447rszNvzb+HaBpmRX/oK7t4S
+N+ZjJDopdIUPbMm5PtvawM98VFB8vxINsabEovlngzfwI6/eAQB8+Smx8p7eyRG8IeO9Zs9w7oIU
+v4XWTVRuyL2uWRPoCwzirVyV714oURzL7tn3c+z2iNl43cTTVbE884UeWiw2M6lT8ZHNCb7Sphz8
+Uzdw50XiGCz/gevlMgAY6iE0ulqzIEOfDONBmsOmSrme6ThmgdUiaQFrjQI18iYU8RwNmwzNSNB5
+TMZQq2lYcwQ7YuhyDeOcgyVT3IdyWsVWQ+byy80lzGI5pt/H952gfK9akmsA/gSA/wbAz1BK7hMA
+/jwP+QcA/iu8y6KgFQqeb8BsV5HkYrbp+QyOIZMtL2L4VGE66keob4vxcf9A6hOy1EKuxFzcWm9B
+NWSgJn0Ps30xKbWKjpL06pOqmGfRvo3VCxJFV7f2EBLCG2EXrhK/vLAnOJqQdt0SXHsysAHqRBZF
+BS6FWBq5CZu49Cg0sDeSCTZn7jtPhojn4hcmrRyLLQKgmkCYktSDNQB+OMFxINftFyWOr0uf3YZC
+QZCR3ak8kJTfIRuVn6S4T9Ga4XKCj8WyVpuFAW9T4M+WvoOPkZR5wIzD2dUq+pH4tR9vPIlfe0nO
+q/WP8QuEOf/M84solkioUohfu/fKPjonmIfDNaw/LxNw79eewZ+5tAUAeONbd7H1QaoX7ciL+++s
+19Belaj+Y85z6Jvywv51awFLplxjC8/hG0//JgDg7yxJjOCry1/D9pIIxe7f1PGf/bUfBADceGEX
+e/uCgdiZ6/g3baljCUlqstR6E69/Sz5/aKuGza64BBt/ycDbn5XPHz63jJtHQuHuEG+SqRp+bEVM
++Ff3n0e3JSCxaZ5gQclzbSgyf7ccRIeEygdHSEnVbusalgo5JtMLjIk/md4nq/igig7Zq2zDwGAs
+8+z+YYlGRepVjiITo7EAue6+LovYs5MtlNvCtjTo65h48iy3ztfw+p64goF+GzGZovGQLGvv1X34
+HwH8x/hd2HkHwLgsH4gN7AJY/U4/VEr9VaXUS0qpl4ZkGDptp+20/fG3P7CloJT6CQBHZVm+rJT6
+4e/3998uRf/4ExfKfnQfTqhQJ90XigipI7tckM9wPJFVebc/x/RYVtLrE4HatvdCdLbFzEo31tEr
+ZDcahQWu35Gg1E7/HnyKrxxGYlo2VIpXjsXkiuZVtFisdGGzjlEqO3OptVGrijtyyEpGO0sQECPh
+TB3oNVnlNb0CZcuOmNt1JLFYMqORnCtLY8wCFugYESLiBraqZ5Ay6Li3JzvRrYO3cJ8s0YdRhIKB
+xqP0GBXIbvTcuYsYMw8fMnSjpwlWXAayVpu4Hci1reSf48Zd4VkokwDfMJnfD2Rcr9x3sbAlJvXt
+X81x7d4duY+4wHNy+/jm7i/jcufjMl6xjPGVK5/B77zM3bNd4uD/pgbjKMDsRVpKhY71J8Q0eY5U
+Y15DR7QhY7WwlOEcGaivakNMEgYomxGWq/L83u4QsXo0wt0jeY5P1u/hJnUe//nV13C3z+yCynE7
+kx1UUUruS/difJFya19+6QgbISny3l7DRTJNv3Xg4s4dsbKstvTzmaaPAwrEVC5ch0VL4Izdx3hI
+OTlCkZcTA3dZUXp3niKktkbHqcHclOeknHXMJuIepZnMt8N4gsOpjKFlFNi/Ifdx5+gQR/cluOjX
+F2AUci/dVbFozUsVzAYyLnolR3VGN6gB1NZl7IvrGkj+jOy7UU/8nvZeBWb/lFLqXwLgQGIKPweg
+qZQyaC2sAbj/bifSdR3Vah0VzYSd0Pyqm0h8lrTmwIC6fPdu7WPPkYm+/7YMpKbPYI3E5GxMHkez
+JhMssm7gBk3fNwYxbL4gRyT8KCY2ikV5sL0iB5bFYkndxxHPT6rWcugzeUiVSEzqkaejNie9/DRG
+XmXE2bWgM+U4uX8fN18n3Ji4/sAPH5CXxPMezEAmysieQkUygfb74orsHxSYUuJ8HCSITkp69fJB
+RuUwPsbyTHz16y5NVbNAFMnEbrxtovqU9G0h+SGcXxDX5DXtDlz6ziVTXqE2x7VvyHTQ1yfo70s/
+DSfF1blMvOu3N/D0WYlwV2ry91p5Hjda1M+8luKuLn0z+jH8kBL2aYFVW8YwarL2oRljlapJ64YF
+vSPm7rPeArxSFtkgKnD1CZno8ze/AACYHq6i6MlC8dkva7j3srgML78Q4W7K1GGs8Mwdee6fhpSO
+rz+7h+xL8mwGRgT1BoFOWzPUB9wsLB8FPzuEkiNpwduUxT0+XkKnLZ+ju2uo2rI5eXyNdsoc+xOq
+gWU+Ms7ZzIgQk9VrungAcyxzfG7JuQ6SEm8vSfxsO23iYC7zZW8aY5kl52cqS7gfyz3tUragew/Y
+YrwjCqYwSdRzabaMK4xheM0+0sGJ20BClndpf2D3oSzL/6Qsy7WyLLcA/FkAnyvL8t8A8HkAP8XD
+TqXoT9tp+/9Z+xeBU/ibAP6hUupvAXgFwP/2bj/IigyjeR95YEJfYkahaENVZPWM5wrtOiPOHzkP
+m9yFuic7SnX3AGd7sgOZSxXorqy0cVWhSt1IO9awsb4lvyOvgJtp8B1ZRWtqgCYTuqqmS/oDQOIr
+WI5c26BmZKNTw5QFUak/RjwTl6dszaGIQvL1GPYCKyJ97phdD+6EAUpTx7giK/6mVj4o5rFqEk1X
+7SnWWHFpjV2MmpR1Hx3DZCCxNDXcZQajygzHsRah47Ji1HLxC/syhn8Ov4rP/L+S63eTAEdKHv2K
+KX0L8jo2e6w+fHYRC+dlZ/N2fLxvQ6ymJ55bQW2RYK+K/O7CT1zGhc9LMOz5yyP85m3u+D2F+5GM
+/fPz63BDua+SJDLzcA3FgUTI0/NPwKE7c5wlMHEi+TbCNlXD/59EnqmVTnH9ptzfqh6iPxFXKW2O
+oO7KeG1ZJToV+b77uIzruf334YPrYrS+0M/x5Bn5PsAlfPRjYkHecdqoVCSIOab8YifTUPblnmfl
+LioTyuktHGJ8JFZWTeKzCGYmEFP9vFTIKHs4jEvotG7jzEa5LFZIt6CquBFBbxOzYafQDsnXCCC3
+Zbz8ZoHVmYTnUlqma90aQl3eF9PzYBzLPJzrE9Q35NrVuxXMrBOrBw/V/lAWhbIsvwDgC/x8C8AP
+/GGc97SdttP2R98eCUSjWeroZVXYvQ7KE7iDpsE5YdVpRcgmkqPNOzHOb0j84AeoTmwtPoHas2I1
+LHkN2NzlF8cmLl2Q0tuN7RosBuiwymBgRUdMOTaFDZjkZ/CqFqrc8fP0GLOQte4OJdZShRpRcFPH
+gM5irAWnijYFWJvmErYNwp+rslLbwwxFdAIvBkxXPjfcHMqW+z5Prjgtr+EwEV+9bRRokCEqNwNU
+FmSHWt04i/yOnONFpsfMlQLputyn+1gHrRclqPWNr5YY3ieiM9FQIZKxsGW3+lef/zhWOzJum1uP
+QzfJajWtILko9/pc7wkYLTJp8zltfvRP4788CQh3Svz43S0AwHFtjM5U7j8ounACKbC6eZXK3pPP
+IWXRUtXVUaVVVEk1mMzTXy91vMky6fy2xI++/upt9IkmHc0SPHMon7fyKroN2UE79SqeXRO8hFkR
+Gr/zZw7xJ/+UpKc/+PoeIk+sn8srLg6IJvzIVGF/XfpkUTHbnVmISf92uJsgyaQ/ZWhgjeXZtVDG
+bdIBbForuiqh5XIOp2KgCpmzSzbQpPAuWQPRzju4UJFgpjcZwnOIkM1TxBQuKg8OYTV5bcrUNWsF
+VqtSgLU3PYJOZOm2aeNbV2S+LFY1+MHJa/5wkcZHYlHIkcOHj2kcYdk70VoskLN7uRHBpjjHtvJg
+VmQgCkb9jbMREhJa2B5QmKR1r5m41JHJHbo9qUYEYOnyAJLwCJq2BQCI4xHUhKAoK0Urk+tNahrq
+XZmkwwOajqaGPYvnQhs+Tfi83QAWZKJ0CxdeIdmRJqGqRTMDpvJ5b7oPm5MGtgmHSk1w5CVfXqwj
+5bHLVQ3HDFY23RpyCo48YS/jrUtyr62X5Nie3cDHLoor1V2s4b/7soR02qMvwR+wIhI5cq642ysy
+6R7/0RV89GnWj6y+H+ackFpVQZiIcIzVrUGZtKtZl6HH11D/sAjS1PU7KC/Iteuz34HSJCjZKByU
+Azm+bctYffnn57gVSX/U4g46FgFiS4+hLGUx/GHbwNiTF2F2IOM6LUbosjLQaobYbItbMvQGuODK
+c2qs+2g8L78rusI+XalcxpMpN4XnfwqpLaAmpdpokqLegQf3UF6cpZFsFjcPTBy9LX2+PhmD/CdY
+aAEsckROoNv5tI6xRS7NqQbNkjm0aphYbIobp/XO4eK6XG/OIO8ajtBuCZQ8DAJoqTzLpjcFBbYR
+dhTyWNJAHhcHhFNMiBdJ90IoEvT0gzH8Ljt32ILpyWIBeRXetZ1WSZ6203ba3tEeCUtB001UGj0Y
+jkIZiGlf6hFin5RoZQ0u2XFyLURxUn1my2pYZhkcnczIcQyjFEtAL2cwqoJsc6cZ2g0GalhkWaZt
+hHMxS+GvwC8FVWehiokvO2iSGijoVlRqMly+7qJbkb9PsyNEJUVk/AQupb1sVUCzZPXXRrL7R26G
+yVysh8DX4JABSs8V9JBkpA75Fg6BFQaUQuVjM5MduI0J9HNisRTQcG4qVGmTVVLJOQ14uoxhPVvG
+B+pi2j926xL+j4oE9pJgCIeK19WZRMnev91GtRSWpoqfQ7OE5q3Q91DoPyZjlEZQJfUu8pOp81GA
+tfu6/oMobbJJxW2UoSDwVPYcIkc+IyAOxZzh7l3ZMW/gBVwOaGE848DQ5bmOIh1Lz8p4fPNFub9u
+WUOyIFZTY25Cs2TXfLbcQuuyuCa94izURCwau/ZpuZ42gd38CAAgHqZwTbFukuYIde7+iPbR9oST
+IPcl1dlLNXyLknfpTCFjwdo8aGGjLvOwdSjP6ahZIOO4WE6BVi5/X1g28KQn49xZdbFRE2bqIdOb
+wT0X0YHMBatw0WzKnOxEOkKKxJydN6GIXm0k5HqY6KiQhlC3+lDk4ijHFVhTVhjbU9jZ97f3PxKL
+Ql7mGOU+KlMPZZv8i1EFGSSqHY0rUCb9IbuAHsvgFBNmCNIAeoV1wTlQkD05QQbLZ368HCAM5eHp
+CYEwpoLGOIHhZwAj4GVawGQcYOJbMCpyfDUQ8FJ1qYoJBWFD+wjehBkH10SbCkGGmcJ2iAUgu3QS
+mChnxAKYU4SBfN8cOSh57cKXv0+tCLrDCrmogZKWb+F6D4hA5laGrCaAnGqNB4ybcFqitZgbFbx4
+V0qEM20Ho77Yoo2yRMxItJXLInX7qwdY/KhMftW0kDGXXlg+LJr8BVrIhzLOJSsAtWCGoiOTX1Mr
+KEuOrcpQWDRbh0PM78iLnN4USvrPXp1jronp+8qkgo9Uf0nOkfwoaoU8V82Y4EIkC+sbl1gbkHqI
+7svflxZCXCThTKIF8PbEb69tWSgJl59fvyPPw+nBijifvDlyiqdYiQGTnJ1pJUfllvRZT1hLE+7D
+IJ4EegpDF9fV6nlQYG3OkozFwSjEid/etWxoFIrtlg7azD60vVXYS+JiNCPpY9tIobp0TX0bCbMd
++9YNxFz00gUdG7Zc2yUk3l6qwzwpkc4tZH15DnlyjMCWd6DI6tCMh/Qb2E7dh9N22k7bO9ojYSkY
+pY5O1oDTbiIgXLfQ53Ao9lJ2XUSHrMBWPiybuXWyJU/jOdSbNCkXAUuJKxFEMeYz2fE110cwZzCS
+hKkIfOgkU9F0HToRhLo1e8AL4BYzBNzRS0p/2foZdBvEFSzGmLLAxTVzWCc7ZebB5HXKUPpmThNE
+zC6UoxyaSz6FvAk/kc8TcvvbZQ5/xMi7aaGWSYS85/WgE4LbsQxkhGxrtGZmmo5ZjUHLuoZNmpGz
+gQeLUbJZlsEtZIe5RXLcQXCIyZFgDDpZG7lN0lxlIRvLjli/ZABgJRUVvJPIh6NJ30pt4YGgTD67
+CZWLZZUlA2R0j16je/ipp7pYyWRnH3/gWUzW5J7OhTkqHQmYDco2yiVBDb7vjDzT4zsT3J2QgTp3
+8YwhQclmpcTxoezAN+5l2LgteIMyJaJzu4GC7sra2TrQ5q67kcMiZZ9jX0DuSbYGfSITG1W45JB5
+vGOh2pXsy+blRYx/Q2DjDlGQXQSYslzVdQp0SiJdnQiDWH7XCQ5RGYqr5Hq0aD0bmiP3pxc5Koty
+7Wbag+PK7xbqFXQVodJUXa9bLVgUojEChaDL6011aEOxDlpqH4HFoONDtkdiUSg1hcQxMC0yrBCo
+HYUKSpdJU+RzZIbcfLwTIaArEds0ScsYE0bn/bddVGoikBGrAn1GkQ3DQp2MRT5fUnuuo9RYAl0G
+0EihnQd4UOEWJiVMRTBUSvEWM8GIZbN2pYXCF5cniIF5Jua4XkZwjxiDYH+jKIfDuMTUB8JATPRD
+c/aAS280InmLnSAlfXk+CzC3aeIWMSpk+12p1AHFik9WkUZRju4SQVi6jRuh+KcNYwqiZGGhRFKc
+MG/IPd+/cxMHK2KqR/k+zC6Fc8ImdinIsrCbovakvOi5RsGS4xjYkLiGnRvII7IhH7yGjGW9ub+H
+vX2JZ3iBRP131ypYvSjjWt0aYJ1sRLkKMSej9bKVYaTEjA87Qs7SXg/wiUN5ce8UKToHMkbjzRhV
+PsuX4iPcelWeySEIerpXR3Msz+Gcu4RLhLq3UhsNkvLYdQ0B7/VE9OXOJMdswjiCbuKD52SFONtb
+xBdJk59UyJSU1ZBXyIClewg35PP2sgkcSfxgOlzC/pZcw6vTRUkAg6Q1+bKFKd3Nmt9A56xsFlnH
+RT5j7IYuwzTOkGcnxYQphmMZ72B+iIhxGcfsorvA+sS7zBy9Szt1H07baTtt72iPhKWg6SZqtUX0
+LBOK5rdRCRDT9FfjEAWLS5JihoRgIo0mqVnx4FBkJS6nKE80/EIHYO7aDFxoLqneZiQsSUIoSnkb
+gYZMJ8wXMSJy9kdqhIT5fbNTsg9Powb5bjIfYU7+SDt2cUyNxgXDw5wBwWhGfv+6DjsRc7CSPLtl
+0QAAIABJREFUlsiIhUAVyHeZiaBVkc1dpNQONPIQgzF34HSGnk4Lo/SR+mIGT8g4XdZHOL4r93zJ
+eQwLJENZndUQsWgq8gPoxEUc87w3D/p4/FtiqjvnTcAUjIEZdnFvJqAf89jC3JX780LZBfOFDipS
+XAlr0UA6k51tcGsP+YHoNxiFhSGLfA4nYgW8f7GKtVwARqP7x9gbyg7c+oSGOvsMrY7uptzX0d02
+78PAzVVaU2/M8DZhwFnsYr0m53b7bRh1ygUaYpYvQMOQRVnHOwpnzso5BtM5onsMRmtXEQzkdzcC
++e7uwMWwxuKidg9GKe5BVy0jmst8WGNw8XaaIjaIvTAibE6oB1ItkEVy7GKlj/Km9CmhJsW39gqs
+eZKdGQ57yCC7/8v7M6wdSn+WFlYwNiQDEzLIvbU9wrIhwcosi7DriyVw+94cDis056mPZfb5Ydsj
+sSgkRYl7SYoeTNRIT16WBjRbXtipXSAfy+dRaSOfycQ84bWLwwga6b3r0JBoYkZZYQmNyDSVZhjN
+mUb0qcYUp0gKcS8aFQcJkWKl1kaeywOI4joCZjtqc0bAGxkiqg25iy3opPj2hyM0yRV5PMvgEvWY
+UQkIcQVJSB9QB1qk7w6cLhTzYmXALIThI6Lk+MEQ8DO5/4MjH0Zd+t8PI4SOTJrjm2KeX+kXcD8u
+5vDYHuKYsYqF1hzhWM5tqBIZ3YcK/33pao7zvrzdsf0U6kom6bx6hAXWCcSzH0BkiulrZZKaXGxP
+gVIWulTfQzCQ8Ty4EePebclU1JIJvnJPYhGrHelPeqwwWRQS12JnEUZVPn/h4OP4UUMWAL05h1El
+QnRTFsLh+zJEf5d1HjMfKRMDVtXA3oylxRspKoYcX+cmo+8mWFsm41amISzl2SRDGxTnQn+S4vau
+9P+Nvnzp9QyM73Gskgpq/4osZNlCHS4BV9GaXMO9Y8NkWqeWO5iuyzPLbutwjmThOG61sHJWXMz9
+6wRmzX38Dsv2y8ZdJAO557Ic4nop13gbfVgVOeb5UH53Y9ZC3ZB3Yfc4xm3Gj24exNhNxH1q1baR
+Qp7Zw7ZT9+G0nbbT9o72SFgKugLqhoaKY2LK7EM996EX3I1NE8O5fB8lBzC4iw+m8l0Ze4gp3bbp
+jOC0ZPecjlPMxpQtVxFmDQKDXFlRk+wYlkEAlKEjpUJzFt1ElFNwxj9Cn2y9C2T6rWo/AqtJeu/M
+hjeUXdqPp4gZzTcyDxEj+NaUXAhZH8cjWYf98Rxt7la2t4dj2aAwm0guPS1txMS4DkYp5tQoXGoY
+SKn/WKs2EVBCL9QYJFU+bpICfBK0cHFRzPLn9izcrskxs/4EBXUX6T2gV5nj3pQch/cPUG19SMa7
+dR7jXycU+tM2Gol8j7MyJpOre6h9iLT24zNwOhKgK+bXoFijcCWIMe7I7jZksO99XQU73gIA7IZz
+vHznDgDgY8UeTFegy75aQLM4oYwXa8S51cQ9SsDfDBJ8nEbY8koTVVt+9+Wr91GS+2Kiy3gqq45X
+b4sLs1EJMSBU/HyzQHkou+29mY03Ysq7sS4lTXOsXBKL7rkPbUOvsFK24QHkcXT2ZHwsU4PDupue
+pqMYyLiMYeCAlHVekaFJTk/9svTXeyXGxgoBSVoVyXNiKahZhEUGQaPJAaaZ7P5ZKIHWBa8LRSCf
+3qpjfk8sk0nNQrjD4LZ9CId1MQ/bTi2F03baTts72iNhKURljqvpFGdVDFsTvzDAGAmhzVY4R9mS
+lTYYuXAi7iQpq9fuHEPlkpeea3U0qLTsBxqO70mOfdc/hklyzMWWrIXtiz3U6kQrJnWYrqyuE91D
+7MvvZtVFVJhmG7EIqLQipJYMXatrYHBIdGNuw06oNKxr0EqmJF3ZXfz+HIcsAjKbDna4Iy7lQNSW
+HcGZsXa/P0a/L75nOMygb1CcZXUBq0QvztMOyoogFq9dly3TzEosSRwLRV3H/hbJarsDBAdUXR4W
+OMlInhD93vJ9rFRekXPFT+FPND4PAPjalz6P9EWRbvv5FyJ85Izs2JNI7nO7tPDUt2Q8N5/6SWQd
+8Xtf9o/w4lsSuJwrHQWxJe9fkvtIV59GzoK2SL+BPuXrfnHnNZw5L7ENpf0VNIjGO7ZlXliqjakl
+cPWpcYxfH8kNPH9zhOXWDsf5Lm6Qvm9ApqtKMIFiqu+KbmHppoz3mYs13F2UnXs8juFTlMbl/S2e
+N7B2SRCi6+eXsLYlsHEt17BDhiufludoasCmBfYtW6FC7Eg/A8YV6c+1mcL2iY//Rdn5R3GI+aFc
+d2Orh5VE+uNNFXYMmQOH8xIeMSmLW2SHSgyUpAV803dxsynj/ertWyg1FoT5KZoPS6TA9kgsCpbS
+sanXUMntB7TZqd7EhPRpzkENaU3s60VXQ9hgye1YbraynaKqCe2WadRRsyXfHptDtA0Gz3ZC6FUJ
+QC5sihm52OqgoP1s2AWKXF4glSoUDNoYeQRFiGqDpdf5DLAICEnCCWCRusw3MAzFbPWqM1ix9MN1
+TlShXKAlC5llLcC0pT/rSxsIGC2fXKc7E9yE49GEbd+GW5U3veu2oTvyMjlmhmKfFZGk9jLaq6iz
+LLrjLOFvfugvAgA+Pq7jrC6T8Jf/7v+E/UiOL7g62DUHXYrBVC6/H7V7WwCADz27D+tQXuTX+hHa
+VVkYn3hKTO4Nv4mCeAqn+2HsTr4s97fbwNtkKmnGNi5/WoKHH1h9Tr7b2IRXSN+zlT4C4kz+5PN/
+DiumLABGWYHhCgaiyopTZ/1HcP5TXwMAaP/Yw6gm4zW/dRG1vyTP7N/qXMRv5DLO6zTV/e5NLDIb
+sFsorF6SgOHMvo3tO/L9q+7bsKk4VVmWV2Nx5TIuLsp4rzvbsIgL0M0luDoBZT7nRSdFk/Dw2Iuw
+kcrv3j67h/MEbb3uZdD2qPZ1VuaV8aaBAauD2zsZrj8uLtjWsIdrhSwWXliFsSrPTw8IMktMVJZl
+XqwEBzgk5ZsT5QhDWbz0FR3BSBbyhy2TPHUfTttpO23vaI+EpVAUCcLgHoyihpKBnM4oQkpCi7w5
+xrxPRmQ/hq4ksON1CYMuunApMFg4IxgD+fswyRATs7Bil2hXWMTjUWBjGqHClGWR5chjklscptBK
+FgHlKVxaCCyQQ6qnyImKzGdzOHeImnQDeNQXTEcFLBZVmSyMMR0DFT/gtfcRcZcL5lcx9ngvhOp6
+xR00dFntM28d1IhBx80ASsTdLAeYx7Jjr1ELo1LTcLYpxDLmLMRwVUzqZO11XPstcY9+vDHDL7BO
+38mIfvQDDBckgKsNXkH7otxss7eFlT8tFs9mReH827K7zzu01mYmrG3qX/q7OKD1Nrw3RmdZOn0m
+DvFkRXb/tYbsZmrVRHksjMu6YcPVZEe3K3so2afScKGRIFexcvC56lv4zSvcy8wE2ZSIzPqraL1E
+xevuNegtwW8sMsaWBU3AkDFaWqijEUq6dM+PsH8gu79Z13EukP7FdQZdWwnOrco9F5U59DnVqssA
+JYORI1P6pvo60kjmzWHuwtVFQ0LdcmCW8tztvAm3Lc8vJC1gq6OQk4F7pVJFXsp92C0DT5JarzEa
+YYUENy6rLxfXGij2xaoY7R6gHwueJMgjlBZZvPsmLIfaiA/ZVPmQAhH/IptSVgksQMifv/6djoB6
+sH4VKB8o3qgH3wEnAI0cD9gvkH6XY05+r0E4kE4+EzSD8NuO+f3tn41ifJpv6X8+yPDRvyDiqf/X
+2tcQvfhPAABH9jEK1k8YDoVekhxZcVIuXQgXJICWoUHZcn9tjQzAronaXF6wq4GPJbqFg66LSyxP
+nto2fvzD4oLMH/soAGCrE2I8lrLnau8QxzvyQrcaIwwJ61/o7SHwZbL1HJnE9/0E59bkhRiGVXRY
+4bh7P4BFf/hoYKPdkAj3LORL0y0wDLlAmEfY2yOxyFqOQSqZj5XaEa5fkxfowgUZk1vlKuqmuAQH
+g0VUWQ79X/+t38LWUBbt8fIITw3lOlN5F9GZJrhP3EgYpAgtwr8jDcqSczdSCxHjQxnrJCZaBIuR
+elXT4ISsNHV8NCP5fuqV6OgyBnaPIjmtDooz4sJsrCyg3hWRoM2mid/4J0Jg0+IYpqWBjibjM0h1
+VMZyf2+NYzgEe0VGBpMVmpMKdTxVBS7FZi+bDbyYyD1HV+f4p9F1ea7Hj+HeqrhjHyQX4/WPPIG/
+vi5l7f946QP42V8UV/nH7v02VErlKPXvwStlXs9x+HJZlu/Du7RT9+G0nbbT9o72SLgPsqPv8b/v
+1EqU1ClQxXc55MGOD7yTtvbbd/z89/ym+D2fAzxM+/PaPv4DW6yD/96K8Fn/PwIA3GvreDqUfgwM
+G9RQAYPT0E0DJWncRkGMXoWEJHUL2xVqQLDI5gnPwW4of7800PAWkZAKwGtiMeKTbRP7A4Ejr1HB
+2a28D9us4DycA2FdXKlRuIqnXHEJ3sqbWNIkUp1Gsutc7GZQhfxupWlCC6QYbXtrhr2T6kJ3hjSV
+4882SAqjWthusWp1uo6n1onS09tYq8oxR9d7eMwQt2h8LKrVNS0BTMkiPKVn6PuyQ98a7GGaC5oy
+CCysEEcStcR6yKYpilye6f14DteXeTFEiTbN6gMth8vdf0BqNzstcZTJGLpTC/2CwjExcI2VqyoE
+9nMxu58J5Om9MfWwOJSx8s94+NEPy/XSogGTGAqN9H4XFRBZMm5e0sc9kmCsaMBVFp4t5xqmRGG2
+yClZaDFW1uTLecfHlbfEwrip3sCQWIdpfgPZnjyfz/H+k/wr+I3erwIAXvjyGl44ErZupL9rS6ME
+5oTAP2x7RBYFHUAV+B6dt2j6p8qExpf+xPVRSkdZnkxSEyUZjRR0FO9YLE6G6ttdje+/fcvdQIdM
+QeXtGP8LORq3f/Pn8UomZvDjbgR/QiFRgqZqtomAfTasBDVCs1c8F02SgDYcWUEWdRPtRenfyBzj
+mFV2mmNiSOKNa/0pmr5UHR4MJHZQW7gH90Cup3VK5GRK6qzdx62hpMKivIo5iTpa5BfUQgV9Ji+g
+aTrQPZmk6a4PNZfnEmsJTrhctPSkEhMAOQ6bbRtJnwuyNwTm8qLXVo9x9ysy0fdasqINsgIX2nKs
+dq8JxdLpx+xVbLDmJW+sICclfpexijf9AjFh7mM/wcQ8qfbUMDnZOKAeUMmbnOLTMkGQkgFLxShY
+lTlVBVK6ebmegxlFvEpRFzfRcGsmfJWbxX18kxW4Hzj7UfgHrMBkCfR+aSMjV2jmTxBQkOWWP4Y/
+JoCt5aDCOh2XC55tWGg0uPBWLuFTrojdHBebmFNkGfEYGRe9gjGXpasdbPf+KgDgX19YwRc+/DcA
+AAef/wqQ/j35HUL8rjv9cCCm9+Q+KKWaSqnPKKXeUkpdVUp9SCnVVkp9Vil1g/+23ss1TttpO21/
+tO29Wgo/B+A3y7L8KaWUBWHe+E8B/HZZln9bKfWzAH4WIhDzPVqO72UluArQurLruJGCzQyFRxry
+1KphtaDsWmsZy5lElu+M68BEcrtTTccKa913WamHMECcf1d/5Lu2l/M5njLExL21M8CfvSBUYp+t
+JvjJVQkSzbQOWtuyHq63xQpIvRxOTP4G3YRNXcVer44uyTlycgnYPfVAjqy/sAv0xdSehCEuDKg1
+mPcR5iyeoabgN764jGcuyN8XjS3EJPoID1wEh9w9t1fwFt2HJ6fSN62Tod4WM9gfj+FN5HyFbqOy
+IcHK/f0j1BLJvau6jKVVa4IFecjDKgpmhqzqMgYnoj37PUwcyWxMG5IZmd7awSvEQjy5kmDNlO8r
+jS/g8mUZg/1qiuc8GYNXBtLPxUGG3REzNRpgU1E5N0tU6JrVKh48Fg9NSbNfCU1YxonVqCNjsV0W
+lpiWrGzMShi0JmOC0GD6sLlZ3/YnaDZkt93aCpCzItImZXtcVBDtyfOfRCGOWI07m2rQTJmzpaHB
+axH4RZr5RsVBs5TnGGsDpEcybs+tzjGkZOGiVeDOWCySHlUQpuoQ18O/DwC48rKFyxfFWjxAAsXK
+zRLC3g08vF38XgRmGwB+CMBfBICyLBMAiVLqJwH8MA/7BxCRmHdZFAAx6X9vt0m4YuhokVu/U3fR
+WxC/7YNrRNd1G3gfB/h41kS3KbP01Z0dBFNJN93an8N0ZSIEZNqZFepB1aK4Hw+Xibk0KJAG8qLc
+H4X4ylUSjnTGeJ2ELJuxgrUhMZJuT0g7z3RczO+xGm41QpssRgtbdWiswLRaMuGtRIOqyn0uenUs
+OeTqc+/DP5QX8+yGiat3ZMZmjozdbHGAaztyvdXlGQyqNI2MJkYtGaPHtX1sKfIHLkj6yzEtmESC
+unYNcKRv+cSGG4rpf6bhAg05n5FJdLvIZnDpriWmibxCsd0gwyYZgrJzSzAaAhZydiVtNnEHONbl
+vK/eb2JpXcbt6eYKNlpy7nbXwW6fqd+B/G4cH2OY8pllBWhRwzFN5ByDhVqKCjM755X098YgQocv
++jBJkCby92MrgZPIWAQqB9cHaCxhT2c5QlM2jkKLcXVf0qgfuuFgRnDWQUPu0/IqqLGeRU0niCka
+PElzVInYXOxUcYbCwifzzS0nqJ/Qt09SrDMmFCoXP7Yi7l+YTACmto+nshlWSoW7t5nWrNRRe+a/
+kO/vjKAPWedy/E3kxVd4vW93pb97ey/uwzaAYwD/u1LqFaXU/6qU8gAsliVpkYEDAIvf6cffLkX/
+Hvpw2k7baftDbu/FfTAAPAfgp8uy/JpS6ucgrsKDVpZlqZT6jtvvt0vRyzG/37jRKFiy4jqotATG
+vPV8iU9tfBIA4DYkyFa3n0IRiOn0gfMbyDwJZnWWPoy7t38HAPCxC5cwbcpa9bkvy47xGl5FSgBJ
+lkTIHxKysY8dzDIJOH16+RBfKmS3Ng8UXEeshse0GswGeQfJD5BbHpZb8t3QsrHgsPKvVoNDYE3I
+yrtYC6BzJ01RwHLknsrZHG5P+ryxtwjtaTn+zauys3n2Oj5wWSjEtdYQ+lVSeLWOsVTIDpzYCqEu
+VkNuilmfO3VopewkZWzCsuSedAtQPlmCq224pMsrM1LgFwoB8+CuESLyGcwyS0SktKvXj+CCwCEx
+TND5nEIZy++e2TiLoi2/q/YIWQHQVQbSY7GcpgPZ2f2RQiOSeTEwgSo1MeOixGVyReoVGxeb1FWk
+PuiHzBxX6Ep2Zhlu0a3YTDTs0A3opAamOnf0VMZ4aGaIWQcRJzm6zJL09RwWZQAcMj+3RiaOCBvv
+H6WYkDatlmog9QdarofUYF2FkmscIYTNys+ZacGl61MZTnD+DKuDUxt1jsGVHdb2JApJn/uvNUW0
+84sAgOzxM6jii3K+L+iw51Q6f8hiyfdiKewC2C3L8mv8/89AFolDpdQyAPDfo/dwjdN22k7bH3H7
+A1sKZVkeKKXuKaUulmV5DcAnAbzJ//4CgL+N70uKXsM7cQOARdaZ2KjiB7dldVxvrOPMBVnyNoxn
+AAApcsSReCleI8JCIYIe0WYfC3g/ACCoT/GRmnz//BmB9v3Sr2X4rZdEhyAd5tinb/luKM9OlkE7
+FB/w1r0jjDzZpVtHGZZ74u92z5bo1iU4WGUe33IqwCZpufIGdEu2D1Nv4kSPTCPSDkmOhLl2Pc+g
+YqmobDgLKCpiYcRbb2KBzMDPrImfubNtQY3E1+22N/CqLvfqGibaTGXqUQ81+rWuktiBGSsYHP4o
+DFGcMBahhEGkIOYaNJsBSMZlSl9HHsgP526Gk6ErMwsuA6l62kCzSj2PcAsAsLU1xSDnOeIJ1sbi
+Oxv+WwiHrPAzbByXYiENI1oueoiIvrWbKnhkzF6tWmgscAdu19FuytiuE64czkaY62I9RM4cZPRD
+asWYD07YriI4muzGJXd8J7KRMrWYKWAvkJ25eb2Ha7Fcr+0Ln0bg1KGGJHPNI1RMBkFthTop0cya
+wrnOiVaFPLNu7KKq5J4dexlpg8zkSRseyWhTexGxx6A51c/dsQ/NFGuj4ZTwKzLXL3d/EL3xfwgA
++HztZZST/1ZuFickr9+7vdfsw08D+CVmHm4B+Lchb/c/Ukr9ZQA7AP61hzvVO19EDYDipDqzXkO1
+KzalVqsjZ373dkACFeTYJHY8go55KRNoWCZot2jirSzDasnA770sD/ODeo6vXySF150E+zvEOiD/
+niHHItcQtagf6E6xo8mL1akaaLO0diXrINWkgi0qZBHLDBsmKwrdSgPgYlLGOmJOGp36gjNjCsXa
+j1Ewgc7AYFrLUeGbNz7jIiWBRoPj81y5Aq8lC0zmhzDINGylJoKmfF/H+AHbbxyzD/YUEclkkMco
+5idU7ikKj/nxIkBKGro0YyBOMxERalxGJYoTcZJKgTyXsVdpimRBgrzh2wyueQa2+7KwVrQS/lT6
+llYcVJosRS7rCObUDVVkIi4zGIysGw5QMcUFsRoWVisyBo31JVQXJBBcIXGO4bWwSbi2nxyjSRTc
+/bdnSChKg2yCgt/HcxmL+/oEHoPcaVnC9eV6V477MAlJ91gav5d0Ec+kn6nuQnFR6AWA1Sb5CrrI
+iWprEZyWmAtwuuynU0etL98/VcuQe8zyLK5Bvy4u6ydWpW9z28UR6e2uXPdw/tcFvPR18wj3SqG3
+S4M6rBMtyYfEML2nRaEsy1cBfCcs9Sffy3lP22k7bX987RFBNH57YyALgE5Sk4mt4wQot2QqeDMW
+tlRlyY0LhTKRnaa5vATGltDybcw0MfcWzUWoffm8Sgbn414b/z7r5j8zNFBtyFI6n0xRlt+dmKIW
+pTBS+fuXzSoWiQVYWSrRYsBMy4FeV3b6Ri47m1ZrQ/flWEtTsImstD0gm4tLUNqSo65GOlSFEnRZ
+FWkopqNnxVAtcUsqsyqinuwUGk3n6fwY6ZwFMLUBilvUrKiuoKqT8svWUfNlDExyUxizLkp1kuoL
+AY8M1KEFk1gAK8uhsdK0IOGHQoSSseTcyRHO5Z6MKINtsG9lE/ZYgmcn1YJRt4XhSIKc81yDXWGh
+mOajbUlANO5a2L4lJvaYhVZ5ESIhWUrTMLFcl111abmCxrJYB0+dW0bV4BitEDdwmCIjmak/aUKx
+cnDTbGCySkLegYZjMnMfkjZuftdFUsozHQcRpl7E/vsI9kmnF9IlPJshnUp/7EmGGeHTvmthm27A
+et1CfV3GIt0XC6Olz2DWKEfXieCYch+z8SEWq9J/tTCDS+361JcAdBaneGUi13h8u47gztMAgEYw
+QD0W92Fovgo1OzERjvEw7RFaFN5JBWRAoeWJD7jc2MCZqZjiw2SK1/blwZxfkX9jR0xNAHC8EBYh
+uFOVo9gnN+BkDCORARxBvrPiDOE5eYEubTbx9UPCefUUyfdI6U7zKY6Zdf2BZgyjJv5+PSthp6xK
+TKoYTOSlqNTkhfc0CxZpvUsFgLEBzTBgkT4+HpNHsqKgMxJumwpFQACUpsFypZ9G1kO9KqZvyArP
+et0DQxUoNIU8EFdKLWfQXTl3qTuYRfLoFSeuZSTIE8KEwwpskqnAA8pYji0qHRTkh1QnvB2BgqaI
+rTAVTEOeSVCWQCbf524Ok4seiF2Y9z10FUVYYGNObs6jQYibHVksvOkUgyO53v6cGJPSRJOMVpWe
+g5VlSWcsLjp4/KJ8rvWqqHZkUbBpoutLPmZHVPKycuSlLDzuuokB4cpau4uqLy/6liP9MbMD3NqV
++dLPgfe7sojW6k28+RYX8JosNridYIOLpW+aSMiKdKYw4GxwjM70kOSyKHQvyJzNjT6qhrg+sSpQ
+oZsX5R4Mzp2wbaOeCO6lQ6Cbb1Qx6Ml86r8xxn72ORnDUYlBRbAJ+dxFCSqRPWQ7rZI8bafttL2j
+PUKWgjSFE/PaRYs75bq1iLwuq3Xt1iYqZ2VVjVIxh0w0URBGat5roboiK7GuNPjrLDqZZg92Y28i
+EN9oXSE/lJ32XO8cnntezIOvvthGMqF0+ndAgXmaB9eSar/b2QjReSlgWRhNsNQmA7WdYYVcgq7B
+wh9Nh0khF1gedOpPYMGB4m6sVZgh6LtQTeIDsuTB7q+OJoDBIGAZoiSkz2KxS1LRkRxRDMew4BNP
+sBCGcCNmDlwdoIWQzWS3UiWgiJVIkxQlhWqcxIPGCH92XACkiMNcnk1aFshIhJLZOvITNWqjROaf
+SFubSFmhCPJdZn4fMWSnnc51LB2Rsq4wYB6QVXuhjcNSIvtlKtewUcClVmavqKHTlettdJagqIun
+FWvQ92j1sBgtmWsYUOCh5RvwGvIcDEcDKJFX3D+CIvHNcRnyOxMLDKRO2w6uZ2KlPXXXgkWcQXNw
+Uvi1hCPScteMEJZDyj4zxGRHHuBOb4zljlgpdwZ3AAAVTSGunRS0XUTgyrzuN1NU6PI2Fs7hmNZS
+Ecuz2c99XKaU3FebOlYjya5Nasto9gVBOrSuws4kgxNBtC/frT1yi8JJ0aepx4iZujnM38QOQTjD
+6E04ruDkt9+QF7q6/Dg+/rj4hZP2GVhz8b3MmoJNUc5JOcE0kgG+cVtMam+cYkzB0DU1weuvy7Hd
+8BbGDyopf/+iMNRSqIx1F9ouFiJyNJoGLJp2h5MURiQQjXiDlN39ABXyOXqegk39wHJqQT9RJGG2
+INUnKAKm0xILBf9uVksYrLjT6wqKprRZ5wQcOtAsxgtSPHjCZZAiYvbFTSxksZzjhKg0xhzZmDEM
+I4e6TUCSPYVNDkKV+SgI6ilTpt6UhiinzzotkGUn9PkldNamVMYuFGMlyZT1cbYDbSZ+dC0+gslr
+XI8TrFsybt+8OUa2x5hC/ruVjDEXN+VNMApljvz29Vt4uivnS3IDkSfP1UhlUUj7Q8zmMl/miUI9
+JhtR2UDG1G9UN+GPKFo8JruR7WCXIq/JbIalnNcIJhhQpDdtMR08vIezzC7dTRRylm3fPQbsQkz/
+L9zQYLxM0B0FYJ5u6vCelfvYLl5+QB3f3o2wT1GaWXAXr74qz6R6LPcxXlnA8f4dAEAooRW7AAAg
+AElEQVS2p+FpQ6o5b+gBNjqvyzHJAi5VJS39ouxd79pO3YfTdtpO2zvaI2QpiOOgFHO3pQeDEmTD
+wEJKc1cLLJi3pNv71OdbiO7g1ZlQZm22FKwqAzhFjngm5mc+GuGrL4jbcPtAcrhHNyKsnRd6rfvx
+AUpNVuWx/SEgIy1cYQN4J8ddMyhBRDCuplVMaCksViPEvuxywTTG3V2xGrY02Ukrlo3WCd37ZIbV
+FrMPbg8pc97JoewupRZiRnPRNxTUsZjda2d6MBqkajdMZLFYPbbOYGYvx+yQAT47gn8ofy9KE2ZT
+rq3lY2gMgqaUo5vHCodHBAi5OXICixZqCzAJ3um6GXRXdtA4JouyP4Kfy3PqjwLMCdc19QyLPemT
+ZU1QphIo1iwxk/OgxCCTHWw6TdFi5qcFC2BU3l1rYH5DnlmPwKuRnqO7xQC0toUmA23xfoR7t0mJ
+vzfD+z50Se6beIq741289U2xFJeXNRxZxLV070K/Lff0+LklGLSmClLeFfYEH1iV+4iujHEDDNxC
+xxO59PNpW/rTv7yA/c+TidkKcINjUfUsVMj9eKbdxC2O/f0x4crjAj+5RmZr10XGYqyjO8fotKSf
+7fkSHv+kWLJXfl3m/09+4IP4wlf+qTybjoVhX6ywT/VW8EMXhKZNCxWCV6SSEjeEtv/d2qOzKChA
+lSU0vphdt4BLdZxWJYY2kMGZtSLEofDWmQT03EsC1CKScPQT1DeZvpo7mL4hk/CNgyt47VCixAc7
+ctueU8eOIWbdLC0wGAg4pEhfB4oTbsf093V1nh1hFMjLtGL7mDjMmGhNhJyE9+5G2CG46mBHzMuV
+5RoaM+mn64YobHnIa0UDOQVtJxRiPfb30R9TozKqoUtwSzi/joYrPmk9a6GA/C5hyjLTWlAOQUN6
+FYqKR2Zdg1vIfZe2iZxIv5RMT36sY+LKtYfTMSxWHA6GU7Q8Rt/hYN0kKpCpRb/QHyyKUVZBFrIK
+dM3ApKAPnLTRsZk+JomMUUzgOhKXcWojJLbcx/V0H1EoFZHDN13YgfTpZkQQmlIo7si4dDYK7N+R
+F/Ngtw+NKMVew0FJuvPOAhmd3ujjpVsSJ4pvTVHUyMWYN7DuygLwVngVNusxqqTlrzoBXrLknmfN
+KVK6Gm1ziusUshw2ZVG8dn8O05exetHIMKPLt1gCI4LdJneGuEPCnJgaIEtWgSvU8Vx1trD/lhz7
+9bvX8PLLMp8W2gXeDuR6ixMK4h59BYkMMeBa2O5yLPQqWmeEK3RyfwVP9OQcL+Ph2qn7cNpO22l7
+R3tkLAVd6TABVLmD1dx19CgdXla7mFEMZLIPBJ6siLotJuy258FyZTU3xjXoAzGjakrH4ES9Z7KI
+riM7zE3tDgBg1x+jeSi77ma1jucprHGzeBbHt8W0LYsj/H/svVmMpel53/f79uWsdZZau6qruqdn
+emY4Qw5JUVZkmXRkCFJkQ068wAYcIIFjB0mMAAECxHcGggDRRQDFiIEkFzISXViybBiw4siSgoiW
+ZHORKJJDctbel9pPnf18+5KL56kmR6LIpgeRR0C9AMGeU+d8y/u93/ts/+f//zaNlZKUlBb9Wizf
+iVFhXJfrmD6xqLSzcxrOObonVi5Q97LIDfZU1KbMIatl508aJnkhv3t0LH9fjU1mWl++fzjljjhH
+vHAt5MVtxWRcW9FQKjiVucRvlJSJJgZzg1yp7+v5hEixDJ1+m0J5BkxfQpGLk3MevyMWbJzmNFVQ
+ZnurxnEFh2GOKmLr25T4AFEyJ5op0MnOeKrKWtm7JQOlARts1Lh9tXh9+SyurGceSBbmhNpzcBD2
+eUmTke9trvHokfAXGKrtGdfgKavLnbsZRSzh2vKkgko8ssnMYmND3PH1Slz/ZTbj3lj+XlcV1zKF
+insRxVQsurssGajn5OzJZ32zw+0Dmc8N889wfkewAMd3e7y0L8/vdSRJWO1c43e+/OsApHlCrv0T
+U39OpN6puRYzmqgauXKCzkvYO5PzfWbyMsF1CUG+8KttRolWDu6bGC35neLOWE5K2pGS8oQG86kk
+QYeDlN7bPwPA1uGC43t/FPfpdx9XnsLVuBpX4wPjI+IpGGCYtNyKUhuCtndL1lXDr46XEIqFsXbg
+usp4vdQRhqF+38ZIZft0O0Ms1R98mq5YFQoJfdlnYEgM+3GFka78ETfXVM/xdsL//A/EUjYvvs5Z
+dQkNXf6hq408h1KtVZDmGGOxwFH7Aa6hSc7CY3hdLJ63K97KYHudHWX/KeOAaCDTXzJnriWyIpLP
+HnFGR+PesNMgGIi3QccGJfk0Ao9Kz5e3taPyIsfvqVVahbS6YikNt/2MsqK8CMh9Rc2tlK6tWGAp
+2rKIC1o9Od6gG4AiQbOOQZHJfERKpJrEFiiexAldeoqaXNQmuUqeraqcWBOFJ3OxqvZmivVALNtm
+c4SrEOypc0j7Y9rQ9Tilob97pJwAflkxuWTh6q3wG9JdWfXGWPqojMDG31F16AMpB4ejbQJPrO4i
+m+MP5ZltOi8TNmW9LLOCOhPPY6YalTs9l0iTylHrDpuFXGene8Rvn4s38qcGbwPwz968T1vzKEvT
+wNV5qc3uMx4Ne3tAsC3eYPFA10Uj4nrvFgA3Pp5ycijr02mmrEaaTG+nJLmsh9cG8pzeboZ8elOe
+zReedtg2JJH628e7PHb/ewDOzc+xUz8fPuFyfCQ2BcMA14Ky9GkE4tZG8x6Wds5du9niZleqBJkd
+ETTlgb6wpeQWwRqB5gXNbIJriyu6s/MqPe9y4UE8FccoUXryrH1E25K/99bOefDZ1wH4tV/9BMx+
+Tg5Yu3w72SgPaFAFGIGc4yudFvru0jM6VJacI7x2jVOlels/kEV8DQ9XXfjCWxHm6vrVsVCgAd2O
+nOOVTp+4VNzEQYt2W16mG9dtur7iCUKb6kISjHVTJ6BhY2umu+ouaXuX7qWJrxyGflgzTOTYZlcT
+a9aASUM2ppGf0NLqQguH2roMFTJiS16gSt39qliieTiqKqHfkU1qr+XSUH3Evh1QB9oroS/uBSVZ
+RzbheWEz7Mrm/e+5TTaV6m7zFYfxHdVSrOW8SWkQWJeVjxY9JXVZM30KpZRvBN4zPIFnyot3faPH
+i0O50PM4x9G53bsJhisv9850+ixxG2nCNHWavKrQ9RN7g2JfmaQfGrRi7Td5Wyod7s2SxUQ2BXto
+U2sifLd3gFnINe84Ni6qknVbNtZm3eVzrwvwyGvc5lOvyXGdP/2jjBe/A0CnyLn5gqz3Oxdynz8W
+OMw1u/qpV15nel/mPswfweyzAMT2W5z/Ybv2PcdV+HA1rsbV+MD4SHgKdV2TFhWmk5CvxLV6temx
+u/MGALv7cFBIyDC1x+xvqiya0nYZRcDjC4Frpd86IdlTfbQHT9kdS1mwfvEG69clqWhdE5cySbcZ
+aN99Hvr8q5OfB+DJ4svwPbokCQxGKl32Sphz1xOrGRpb+JvKOhxEXC8EI7HR2Zdr6JcYJ2IFHqZL
+Ovq7Mqjxc/k81GRSYTrYplp208cLxULZlo3ZV12HyHlGyGEXSuLR7WA0tSQ7cVlT/Yas3Sdcqote
+JGTKUNpuiSVtODW28h90m31UmgB7EXCu9XQvLqiUIDdTYhXTd/GUg6AMWjiWHM8MY0JNxlqdAFcb
+hWaheE/NiwmmL5atOzSxlbwlGZ4Q35ZndjvbgU2xvE/n6vFYMYFa4OsbM+hK+Oe7C5qFWvd+wo19
++U5rTf7/Rtnnk8pDcDhfw9zS57Rp0dSEX6vT4CSWa+6PVdqtOee0pWS1GyZDR9bQfnDEL/+GhCO/
+0Pw6AI8eBezosZxum2sKG+9es9nuKtZj84BmW9z8y+aygfGE1quCsym2P0+eSUi0e/MtXv+ceDef
+2T5l1JHjva4l7vzFl7AzWYd//6tHnI6FBO38bZNRKWJFdQ3j6gfTN/lIbAoAplHjFzYbDXlh82VI
+msukb+fbbA3l8w13g44lblSgHWtJmdI/F17C866HrYKhTbvHPJfY0jv2WGtI3Ga05QVqJH1MhQw3
+nBf5Hz/+VwD4L+6s8/S9S8KoPwxz9sIWu5ZM3XkaY7ak/l0ezwi1T8Bu5pix7FqXbrtdBiws2bwc
+y8KMtCei08RUDHteKn35eUV7U/sS7BJzIvdsmCW1JVWSaDWi1rfXvFSbam1iruSeiiInt+X+225C
+HmmsnnnUisOwPV+vEVaOHLdYWBhKNT9fTYky5WOcryg1O+8p/qE0U5ZKLz9YryhN2UDMpUvma8fk
+xGamWoqmJYs4q0OG6zqf44CgJc9yz37E3lT+kA7XORlqrH0sv0sim+GuzEvD22J7Q1uy002CdTEo
+nuXSUnh7z5P+kzxYcOumMnK984C2J/PS7LqEiYQPreKIypFntqxkDQV2j16pIjnFK9AUiPLjp3vc
+asmc/4ea9f/Kpw9YPJUXMywzvELm842X+gxNeekHQ5PNDQlT066ET9GTHyKYKHFM+BJrWhkx/J/g
+U32BLh8sfoIXfkI2wKiUTaWI7uH3fhqAv/PZGb9WyQZydPcfka40t1U/oHymp/p8JKRX4cPVuBpX
+4wPjI+MpFGXNysx471hc7mZjgtuU6sN8kRJfNtcMDCxHdvFKKbOiYoalOvFW/xh/ov3/5oheR6xY
+1AsoHOUOUGvsuVB3NENsPeTnviiYr/Th/8X34sgvGw0K5Y+sg0O8XKxc0Y9JNVFqjx26hlKFqS5C
+WfmYibifzSTFVpyFlwSUKnvXUgXK3JpRN8WjaRYhRldDDTcnnkkoURkxnkrPWQphtiJARU8MQppD
+sRhZ7lAptZc1c5g1VRpdNRSC2idQVGHmdUhLrYxUJbFCnkunpqWMyUWp1qdYESvd3IVd0K8vhVoa
+ZLZ2KtY5uEpOoujBgV/RViKU7r5FpdY4yiuqUCz+vdTmmlLSPTmQ8JHlkq4j17a9kzDQRrDKd/BT
+sZRhK6fRU2bnWJ5B7UJPM8L9no05lL+vW1s0WuJVWMYmpWbltO8Lv5Vguaovsjgimcl3u50598/E
+6/uqJ17Mv/nSN/hpNbOna332DFmz0ftzOvuClm27LYqFdqjOtMuyTKmV4Tm58z6xeo3enbexVUek
+eW1C9a54i0MFpdyxbVqeeCb/8Bdd+t/6J3K+1RnUPxiHwneOj8ymYBg1ZWXg1zJRR0uXR3cFdHHj
+sxskkZQTw8zAUC29Stt7jaTCzOR3+94Wzq6QbZSGySSRTWbYd6h1MdWX7cvmEZZCR8u04I2+uJdf
+d65B+lCv7A8rSHXNNmZPNoKnhYfZ1O5D28dWQtOkzjGVUISGLMB6OWYxkSx7liZ0N8S99LwuC0tA
+P2Yq5+sOwVCX2/GbeIHcc89pYCgEtywsDCX/9DU/YVs1uao7ZeUxtr7EjeYaWa2lMKOgrXDdQAlS
+rKSDrTGraRSk6s4uLlKiC1n0lhGDkpYkscT641mOpzyY1cR6VjruNAsajh7b8VAqSDxdca1uTbul
+RC4pONqSPX9yzmNTNk7z5hSjJ/++qfDwZZYx0w35bOzQ0bnvYBCa8p1W1ceOLk+kYCkvw2yq4OtG
+h3EsxmDu5BBImDp0WlhdbQNPZD7TysYs5Tm2Mwsi2dwOkxWtTK5jqWK0/Q2fr58qdXxUcuHL+pyU
+CSdzrZKcNqn7shFHiZzr8L0pGy/29TML21LRl/4mHzOl6uYO1/BaEtqcPRaAVGvh81BzUa9s3ubN
+97QM5nwJI9NcGlO+2xr+XuMqfLgaV+NqfGB8ZDyFuq4pqJ917QVmwtGWZuLbTSpXd88kJw+VfruU
+3TC1IhYrqThE8QbW1kMA8mTK7InsypU9pHddLFOmACIntrBczaZnF/zzo38BwCJ9yPfaXWvfY6ky
+5Nf7FY+0Jo6XUCvSxUoOiVUyPszEC0htk1yVj0+XCzJDLNesvkOu9fHTR2I93D54auVInxKqJ1Q0
+HVpdDZVwCbj0dOQ+jCCgMlU4Zm6RJgoPbrg0NeGZJhUrhSublsxxmh/RHollj13I9Dpp2dRKibZc
+2cQt8UwyRX5PspxOKtfg2yazldxHaTtUjpwvdHMcxRYYlXZnxh6Fq6AnL6aKxaM5Phtj9yVUaE09
++tr8ZSuz93HDYEdhwkeGRXumYU7fAG1Aurs8I7lQluqOrIuqETF/U6XUvAULpa3vzVOKNTmfF64w
+fLkXJ9Bwx/RJlK/xJCloaudqGARcKJDrfVO+O56YvKp8ncmgz65iRDabNWUpYUA0M1lqF2Ss1340
+fcTZ70tImDtLjL7yRTxdcetzUokYrl3wcCn3crh6CMBbdxs4E3kHfv63voylYSVFTf2cEnHfbXxk
+NgUwsAFHlYvMysY7lxu7884ZL7yoIp7TBoHGaqlmWx3T5nQs7nxWP8U7kTi6+WDJLJUJbnY75Epf
+ZDvygOaZg6UkmK6/xn/kSKb7f/HukWvl47tlbD23SYC80GdFD/dAQpSzr3okK2lS6DqnZEpgYmgc
+mvRsSq0SlEmTSoFKuRNg6b1eSr3XWZfC1MpAuiRdasmusDHUhQ/6FYZyU4YKlErrgli5A1fegpEy
+JDXLmJWiPkuzSaVgmnNt1W5aHgt1W83UxVVQ09Dq4h3I+Ua5SUcl3jnRisS1GecLeXGNMsdVdFIW
+FegpKLGodfPJKu2STAqSUEOpMxdLwWD3TmcMHMm477g+CZchj8yxPY3R5kSSWUnkK72806BSHYaL
+C4NJW0LPeiXP/HplUmgMU+JQq5Zk1JwSH8tzMAc27UguutR8yLJ0qOdyr1F9SnqqxLwbBqFm9duJ
+zJUV+CxLpdw/T3iq9Ptb3ZrpsW6c0RleLvO11I3u90Ypbi4gNB+H7FDu+XxVEP+a8FX+8Ms3Wepc
+/Ot/I8/68PQpA9V92MkDjqvL9nQLtGu44pxvG7jnK01+WCn6/8YwjLcMw/iWYRi/aBiGbxjGgWEY
+XzYM465hGP9YNSGuxtW4Gn9CxodRnd4B/mvglbquY8Mwfhn4a8B/APxcXde/ZBjG/wb8TeB//f5H
+FIcnz2VXdkyD8bm4Q4textFTTdaVQ1LNhq8rMGc0KYkv3eTDGYtAQ4ZpQXBTvIpGuUkeyLEXj9Wa
+nUU4qrpjdc/5x3fU/Vqd871qulNqGvWlqEnEaiB19fb+CdmZWO75Nw1KzSjHiqe3C4/JSNxrLwhp
+aubfYB1fXe0s1JDJLTH1/h5nEb4SvSyymC1HzlFXHqYqPKWWcheM2qyUJ7HMDZSmgVW1pKMJyjoy
+8VSIplDuw4skpaGiNlYjIyzFksZ1RmbJ8bbWSmzFMjiqxXh24rLekXPPLJeLldgZJy8otNZvGR1M
+UzyM4lI1qkgpRuq51RBo+HRnktC1xYP4WjfmZ7bkO/1AEs2LrbucflOO9bSu2NEOzbqOOJlLHb+s
+F6xpEtCw5bhxDnEozyP3LLxSrPvxWUag1PYpATcFssBMjDK56VGcS/h3/6IgUHxDN5qzSBXgpgnO
+5STihj7zRdqgqZLzZ2/l1Aou62Gx29Mu1rlc+wtbQzgUDyrdqljeFQ9jWK04mcr9Pyjf4h/9thxj
+9q7gG363zOkqpd95tqCtnkBV2Zia8Kb+tuV/3nTjh0002kBgGIYNhMAx8O8jupIgUvR/8UOe42pc
+javxxzg+jJbkoWEY/xPwGIiB30DIXaZ1fUlWxlNg57v93jCMvw387e/4ROjYlDA1r6CeyW5eFRVO
+dpkQfEqhpJrvrmTHrLmNd6hx/Vvv0hjIjvmN8Ss0vyax5fjWhI0zMQN1uK83f46nreb5KqOh3AKV
+0QYt3303j6Ft2nhK0Hk/74ImOY3EIVJC1JxzVq4wCA1rSWRNJodMl2LN4sl7hLlYh84LEy4Smaay
+Fs8lmzepXpBrsC4CFqVY426YYduS2DSLkESbxiy1A8X0LY4j1SX0T7m46Og9GSjwji4+a6rxcIlz
+iJMmmTJIGUXJfC7XcTjLmByL5e4WFZ4ptflEm2zS/BGFNp11dhxQzIa5VtLOL1m1Ic2UmVqvLW5G
+VJXmC9w2tVq5ZZ1xbyLX9NLeFtdUsTvUMl46tZ81cV2MT7mrjNjhcUaqnBodLjh5JB5Ctyvz9i8W
+fZxImaGrJsGWEs+mA4YNOfat5i6FvhKuxurnswuOcy0tjk+Id+RZDsIMR0vcnhrl3LeYKNKzXTnM
+J7IW2uSUylQVJANyV3Uikn0AZmdv053LvBiU2IrfN2fWM3Tq9BsDLE0gH6muSV5VjDWvYdk+uXo/
+ltMA9UJh9qzJ63nHhwkf1oCfAQ6AKfBPgJ983t//YSn6Wpa17icFcK//EIA/V34a94Z2T05grC7j
+OJGbPU4Kbh3IRH1r4y9xEAoFWz74cTYuJPF3HG4xeSItrkPVFPQ3x2SuJDDffXrInXPBsBcqLf9H
+jfM6pa0ZZ9f1iZSh+al5Qv22/NZP+5QNnV5PGZNXPrbCeXc/voflCjir4SzwmqoQlMgivvMo5uRE
+rvf08IKd68rL2NrAbSrewq9AGYNnmjhbDNaxZhIyrXKfu+cKmtno4mo7cLtlkGjm30IFZodQKjN0
+GlWMFSPx8GyJry/It/obvKBzd7QhfSmfnr3N25UmT0+fgLIud/oNvKGEGkFlUyrHZqph15FR07Qk
+PPJIcVWjMs1zDivZsM7f+ef8ld1XALBfEwZvn33KLTnGggvuLhSENY8I1iWs+s9+coOvdwSyvjsS
+SXaz+wa95VsAvPcmnL//GzLfkY23K4m/jZdeZEsBZ7OWXmd9l68+lsTn3MpoqJ77dpaSaUgUKYV/
+Ypm0tZ9hbC9ppCq+EwbMdNN+lNe0NVnJSjap49GCsTI4F6VNVilUPJjzhUgBbA/GfEXJYHIl3ynq
++pnJqowIR8Ogyk8xzctdW+j74XlBzh8ufPhzwIO6rs/rus6Bfwb8KNDVcALgGnD4Ic5xNa7G1fhj
+Hh+mJPkY+FOGYYRI+PDjwFeAzwN/GfglfgApesO0RQo9kJ26kRrcWkhy6SgecDEVC7znr+MoCm+h
+pb5P7DpsXf/rAHzK6rA1/FsAlDsm0SMRxZgc36dWWjFvW4lfy3V+/6EkAct3hgyUrHS0cMmfRUA1
+39471dLWDg5aSy5LuCZW/uL39kkNCRmuv/weL6dihf1Q3Nr9fItyU+jFWu0fo6VK2e3BG+RKtpqo
+mEijcZ9WKn/fOLjACyW8GEQtPGVUNqgxXUXsPZbf5eaEVElczdCkSJT+7a7LZEcxCUuP7YU8+rWm
+WK12YOJd8i14AUr7wO1Ni9BXrof2Hi9pU1GhGo6Hv+cym8g9TeMOW3uCfmzaazRiTSp6JcrHi6ke
+QZhUGA1NujZzbEc8Nt9y6KlS9Csf+ynstU8BYOdLnZeU669IA9ogeYUvRGLFi+OPsbUt59uZ/cfs
+vSGhy/7efwvAeH3O+Cty7fHBb2M//AQAL3/uAdcGL8nvgjYNTcYuawmZbp522Fel6cdxh9auQr6X
+N9nbkJu6rfwGF7f6eA+UqMcPWKnlnnciXqy0dHxtjYPwMwAEnxEv740H17ijz/12/1VOtr4BQPWN
+NeqF3F/70Ys8rWWeo4Ws03MP/Fyeg3/wIq8USmPXv0XxnqyBifWboCFbUV/SCn7v8WFyCl82DOOf
+Al9FvP2vIeHA/w38kmEY/4N+9vPPdUCzwjGrZ0pCN1sGF564V28woLpQ4I2xoHlLwUszychPp3PK
+t+RFX/1ITf6WuK3TWwcMNMZLvXsEprxYlyxHs/dSvhzJAjubvkk0lwfjmjm5usGmAZW2GSvrNxfJ
+iJYt/5FXFq8dygJaDZ5g3NSY+nCAoX0Xrtauq+ibVEuN9Z7+U5JPyCYVPAqYaaxq3pNFFSQP2UmU
+KOTmNt1CNsBgM8NRNanUjCkP5cXyNXbOLpZYrvZBjHMqzZAvykNcrYxYbs3CkfO1NRyIIzD1u8F6
+SK+norjpAnMmzt44fYeLxRcAeHDZeHd2RLwUGvatag/fU37MaomrACAjDTH1pTZz2dyDRomlcGXL
+aGGMZbMw/RpPKeXdh99kY09O9J6yZe2Pz3ioXIvV/Amnpr6E3iFH/1rm5fSdr3GeS/7Iz6Vv4a31
+DuGbkkCaFg+4Wcrnm94WW6WyOnUiDK2e5CNZC8fnI8YaHhRrM8yR9kl07onGPHCtLy/gaTnlp2+K
+MfmtfpcfUiHk81UISkpjWg2wJTdQPNAQ7nSM31DQ0/irHM81t3P0TcYKdKJ8n3ymeSVNYsxCi1eV
+1Mdsp/z4jsz3k+KEYesrAHztSwmBwq2nz9kO8WGl6P8e8Pf+wMf3gc98mONejatxNf7dDaOunzf9
+8P/f2NncqP/Lv/HX2A0splpYt2ZjHp2IBYomF5wo779X11iawBuoix+7Hlsqt1a3GtxSdNy4sHDV
+5T83a/YdzbQr58EwbHJayW7easF8JVauWdTcV7Xqo7cf81YplmC/0IaaP/MZetqgFK8KWppEOryY
+UMzFfT5KTLqJnOebhlqzJx2OW2IpB9Mmc+VEfKFYZzmUMKA3EevzXnVBdyau7HlnzI2V/N3caXLd
+kfNFYcGnPyHez/Wf/MsAvHHrk5SeeAGhnXP3gVj59fU5yalktcPBI6bncm49LQ/GNTuuWMGvn32L
+9q+I1fk/N3+XV74s9xr9cJPwW3K8hYZavl1RhJKo63Y8zFC8o4PNJnNDrPv2Zsmp1ttv74q1u3fY
+JCslkfr23RyjL/P9Qu+zbLUF3zA5cgkcOd/5hfyuVy+fWdKBZ2IqX2XnScGTjszddmGRHyh/p3rM
+S7dJR2n1MqdDR3EM7xfvc6DP9fzGq+wrhP6J0rzd2PY53/gcAJtBTRRJKNjyI379XwpV2mBDxYJG
+62zvi3e7GG2yrliQx+OI3aHM98lZQcOW9fTwSH7nkvP0TObTNSfMI1PP4ZCqh9h2ckaqlbmh1HYz
+f5PtgXx3vnS5eV3Wy5lzg742443mAb2mHPvP/tTf+P26rj/N9xkfCZizQYFpXRj/iaAAACAASURB
+VJB2h88y3ZPIRhPBzA2HrlK1R3ZETwVFWl25/P1uE1tLWr21HbymTMhB4fN4Kg9pLbNJA6Xt1n4H
+w3FZ1+A5CEO2VVlqno8JJwpC8SqisSy8x6m83LvFlEkiL9gN1+a4VjhyPubcksVk5TUPlUD26EwW
+a8N8yrl237n2U1aVEr52TlkgL1asEOxpFmG15bwrbKYqqur11jhZyP33KoeHx3Lf1977FgDnuwM2
+tI2awqa9JnORF/tsawfgxfI2LeOhzPNS1Z0WD3g7fhOAn/v8e+w9EDKRz79T89kTBUNtrPNDWkYt
+O3K9q9qnqa3TUWyx25BNdlF0sJXC/fRuh/1NLaNlkiPo2RfMziWufy25zzcey7PsXE/J7X15fq0V
+DzV+7ppSUVomJoFyRj4qzvDO5dqOvQVHcw15cujNFaikRDUdSs4V+nutETFRJSj3MOYhGjZVd3n3
+6/J8tvqqCTn7FNZEdBnPN2+zs61Gq/QJQjFUcST9CZ9pwtiQd24YHlGmCrFeK8m0mjMMK0gkdLkp
++xZnjy322xqCLLvsqqDtOZ58H0hKny1HOzuLS5KZmPmx5Hs2OhnjkYab4YjzmRiG/lpBGcsze95x
+1SV5Na7G1fjA+Ih4CuDUBtFJykhBOuVoysVUrGZtJawUBtyyQvYGkszb3FL6btfEaIvrtO5bBJ5Y
+8fFkjF+pFHl0xEoz36WttGR1G6+SpE+v2KdSPUej02FdGYq31td5eNmNKflEpuMVbles47sYz+DB
+E3JC7dlvGBbNWKxpX/KJlOk5eSa7/DJa8YJqSbruGh/fkhPeW0pIdJAlNBuaWKLBXc0gb7Ucnio4
+i9EZX38qc1SefhGAPz9aZ9DQKos1xNVmnfbmkuWRVEaWdk0ylax2rnDY/M4hJ78j9/QJdlgeS5hz
+O82JlmLlX5q1eLxUnIVCxkeZw1LBOP21hJOpXLNPA28slrs3iDg/VEozFcu5yGN8X5LDTw7HpF15
+Ds7sFmFfKkKH00c8Gcvn1UTO4VowHqtnclFx/aZyTC5brK3kO0+SDps3Vb18InNh+ylhJqGW6yQM
+ekKP1m2e8NaphHze1CbvyX3ND2WN3DHfY1bJcT/jndJbiFfQat0iP1I48msC6DodzSnLfQCM1Qyr
+K2u2mpUY6sVYof8sY11fSGhg1AsSBUg126AFI5Iclkpm0+lDNtckqPI7FPOaVk/u82KcMcvkePkq
+Yact8zZ9EhIoff7zjo/EplABWV1hmTOWM6XYTqxnZCLtIKTWsKLrtxkOVdFH8whrPQdTX/6yqMmU
+qCSrUjIlZFlUDoEr31+kWllYLVkoMtF2MyzFxg+9FrEyDxUbHQZn8qJnobwoy7Sgo910S0IiXzah
+IojJlPHGbJtsbqo6lcZ0fWcfFuJSrnUi5qH0THQNH1t1M/c0xo+snNst+d106nNDKwNTY8rNuRxj
+vrgHr8j5Zl+XBfq79kM6f0nCnIONFrFmr/OpTR5J9n1mv8TvPNauw0I2vN/7wptYlsz9l45OeMXX
+akHd5Cf/tCzu97Z93pjpubVLtOeYpKX8PcpsXM2yl96KmS7oaBGTNyU3MNCqgFEU3H0ogCTHnONl
+UhZc2ZBoZ+fR2YKjqdKyT+VN2bJLHmvPwJrt8OZUrn/XNLG07+D69ZRxqu3XWuHI7Qa9UF68bDOg
+7cv1lxs7vLor1arTrGI4lucQDeVF3zxo8ujzsmn81jdTzJ+Wc9/eK8i0PXn2RHJRyXJBO5TQ4J3l
+CX1to/fNhI1N+Xw+n9NQBSzblg27tdlEdYJpGh1sU8vSbodlLucOPOcZm9Jld2acFZTaBZzlMZ5W
+u6ZHCe+eyXPv9nL2b2gc/pzjKny4GlfjanxgfCQ8BfKC8mTMzChY5mLxsmSO6niw1gxwFRew1bXZ
+assfttbEPS/MCj9QoIwbwFypyWyLzBILtbO2TqZ4gZUpnsI8G6Ms3NTmIYu5WCXXytntyPHGqxX3
+V7Ljn620V3415qi4FFzJuKHVB8eocTtiaYzU4LpKmFdaK++3G2SbyvYcxNQquDLY8Ykmck++KjeV
+sUW/obwQWcw0F0+hWzSxBmIFps0u0V2xHmMktjlyfpfHxyJOsu4GNGxJ0FneAe964pl0jr/K3kKT
+gCcSfliOw0p7Dj7d32RwIPe0bR7wTiDWarfRJVdeycaFJj6tJUule0/rnMqWeTGjnL4SsqRBH8+W
+OfDPpeJgNEucvszrw7tLtnsCx05HDVLtynxUzSlnEmL0VRPTL0LWTLnOtl1xIxAr390McFS9qeqU
+vLApWTy/MdBn2sJqyry17AbVUMIA2h7NSDyra4MFudKiZUeSBJ1N75LeFnj0W/Nj7j2WtXVzEJJH
+8vlZV70fr+LWVD4bjMeggDPPXaMai4fRCXws1UIttYehlY5otMWa552Q9uKSiXnGtVh7RoIWc2Xm
+zscSznTCgkKfR1WuU08V4+KdMHHk2I/iNbrjH0wN5spTuBpX42p8YHwkPIWaipyEpIT1keyMZ7WL
+r80umZXSCaXcUngpi1wTdJlYIivzuahll4ytgFCTWcuFh6ONSw+zGEdxCoEhFsVp5pzNxAItcodA
+YaBx0yNW6zeq21Sh5hS0gSWdxZRt2Yk3znwyFRmxbQdPcx/9zR6XWnaDpiTnWk0LT5uH5mOb7T2x
+qn4/xGyIJZmoBW5umDjKNF3MC2Yav3bDGr8nFqjxYsWJypQd3VWo9Xs+zmvyWZ2NcRVhmCTH3FQL
+9TQwSC/kOtJzmWPfbtINJecw2B2wrklQx/RoRKpQ3d161h1almJ17xdQaxKtCgrsmXguab8iW8gx
+umYOcy39NtXyxSXtSLyj17b3eKyco36yItNu1d2RxUNFmyeK5svdHD+QHE6zu8TZl2fpNgy2LfFG
+CnMKDRXMGeqzyRJybe1c9Eo8WzwWlgFGLc+1tFycUM7jrKnmpT/Dfls5K45Ldn5Uk3ZBgjuSuagb
+Cu1ONhkP5YLHUU7bUOSi18VVaH5VebSUharKlI0qMp/B1X3TwvZU5zOvQYVoAs8ky5QKTjEiedbA
+17VuVksi7Yb0qhBHmbo2Ol0If7DX/COxKVRlxWoeURspF0qgkhcptcqzD0sLy5eXfjjrEa7LorHU
+hS+imFppvoLKxujI4nfKnMlcHqJvVJiG/C5RyrDloqalHXmrtKbUTHyZZFSOuJd7a31GnlC/H102
+t3UqHFVTitsLNi3JcIehw+6OuH6e5eJb8m87lg0kbNn4tnIVrtU0VKim7QXUykxtNZWurKqw1b20
+NxzMVBNm4xV2R87d2V7jZW1J3vmEbJDfavt4R5K9dzZfJ1OQVdNYIzpX5ue4JlAATH9dFt2NkYuX
+yctm1z16pRKBNB2cpoCQjNEUoy/fT3syGcERZI5SmzkmiULCnchlXXkZrzV7OCoi01ThyWXLoKlM
+xMeTu+wmct+z5QrnMjlYjLEVoNbRjXytaRArccog6qJdz4ROg1Tr+5Z/Hf8Spq4clrVVYhqymdpz
+QHsRzE4GjggJ2ckMK5WqRN1VEV/zNV79mOJN9japJ9Im75y9yjJU+Hsg19hYjTHGsgm3rQ26StHf
+yFt4psxFndYYlkKeVWrAdmtKZZe2GwaGqmwRN6i1BdqqbQJV0VpouGqVS8xCofSuS+BKKGUXY0yl
+vrfOFwxSeX7PO67Ch6txNa7GB8ZHwlOwLJNOwydpNymUT+D+RUqvpUSbGy4tZdldmiVqgHTLh5WX
+YGqT0LRVsKVy4AvHBrX+q2bNUHkN5ol8t2v7VErYEQQ1yfuqPXCv5KIlCcrklZqosQdA5cpnBjUN
+pRXLCp+ZWqXmWpdYGZrdoKAwxKKZypycGE2oxUoEGy5GpW5ktYapBCehNrsU+QJDk0jFYxPDVNbi
+dkGQiLUeByn5UOnmFH57UPSeEYUkZyeUG3LtlDHGUNzki/s1y3P1dFxtklqOsUKZ47AOKD2Z+0G+
+RqIJ0dJvkWlP/2ou58jNJnEg8+1WDivVMmB1Qqrq2OkkpbmhoZQmOKvwAFMbkTbcDuOJfD7YdKmC
+S+4Im8lD1WPUqlruGngqllJ2MhxVbamNGk89hcC3MbQMzEQ8mrxI8AINc7wAVAu0PCuwfSkBVq0I
+c6leioYBZW3gNsQaXzvxMAN5vtHkAU5TnkOo+IBVr8SYyLFip01biXcrJ2Kl3Yx1DZfNio7S5lWG
+SW7I34Xs97JRzCJXFvOKklR5RMxCCWmMAlvX3qxcYCrGxfYhUHxK5jZYLp+vO/JyfCQ2BeoKI4/p
+ZiaGCoK+VscU2ok3nFYU2/LAt401uqq45Cu82KOBr91i/dYapmaTg2aPRBWGhm2TUOu8iU567J3h
+qohrdj7jPQULsXT45Ja4kc5sk81tedDGSF6gWVpxri9Ho51hqwT6WpYzVCYjN+kQiieJpQQabmBj
+VZq9Lz1a2g0Y9nyyseIiaqm7u5VFlagLXBWUKiTaMBKyphS1rQuexe22cclVeJcikRbhyeI+w0ji
+bMdZ40Il3jfsMZUKLr0z1nh4UGBr9n69k9IYqI7lwiJ0VbxknpMYcn2Zsm5XzPHUnTUXE2Ktm5PZ
+hIUS0Vxs4u9JzmOgbeThxgb3ngiA6ri8IFWex3ya4etL5hlwU914M5FzGJOaQqnvy9JFybHpNAxc
+U0ldLAd3oS9WT+cl2SbXTs3ASqjX5eU24yG1Un64hkndU4bwXDdLf0xrqHTw0ycsp/Lvk3hJR3Ed
+joZaldHG8uU+2quEpiHX2S57GEq+UuZz0Jc603yJmUT4StRjOlBpB6dpjnF1MymcBZa+roYpz9/L
+I1Lt4Kxzj1S1STFMziZStYmtEMO9wilcjatxNT7E+Eh4CmYFzbwmyh12FmKtRk0PlPfAbsJGqerC
+9pxKG2YueabsnT5NV0OJbogdi4mOspK1XMVJ+gGtQBGSyqxrOy2WSgl2GlY0H4plu8+cw+IeAAev
+T3nwSHbuhuohHk8tLElI01nZNBrKqbe+BUqA4rVqxqr70FKUn2c4+OrWFjXQFfezokCR0lRPlWvR
+qDFUWCStE2LtDqtMB2xNLgU5a7X21i8kTDDCJUNLjpvXLqVWO6L4kFBDm8d5wd0zCTtKbQjLU5tG
+R67TD7pY2nQT+iZnKp5TxRW5K8k6X1mEc+JnCdORlWMUipuIZjzRRrKta3N2Zx8H4IYr5z1PHUK1
+wM2kx1I1GazFnIlK2xurNiulHhsV4q3ZgcteJJNVb3lYtsKYww2ayn5telAr43WuzVNmc0UxlrmY
+JDaOVpfM+hgbTf6aPpZ6k5c0dUYBuSbqGvY57qVH5vn4mXobW8ocnXvk7iWa1sK+FPEoq2fcnZQG
+loaCliaBS8JLugWK0iNQzzOZtTANFQey++Bp17A2z7mJS2Rq9/DSYaVNgbN5SYKs9U7qkJY/mO3/
+SGwKRQ1naUUnWDAqVchkVdFTcRLXdoXJFTBtj1BvstlV8dTYoih0oiJIk0uJ+ilPz2Vh3jipMbUt
+LUZc5tx2qLV7reV1mUTSG7A6j/GW8mK56ce41vkqAIex+Nw5ySXtIkZQYCqYJrAKuq58p15E+Lr5
+WJpHqOyKUMtmVnsN+1Lo1mtRKBlMrWw+9bFDpeU/w0lpaLv3xdEpxVIWimNCoTF1x5ENq9fukiea
+Dbdb5JUcNygCRkfido4mZ6y0Rb2lG4HXbhGrfuTIWGBN5XgFJZkuzGI0Y6rVkVIrBMs8JVTcvm1X
+qBYru1WfsqUlsmyTl3yZzyyTTd+azJlN5NyraEmmGo1nVUVX3eujZME00vyQit20jAzlomVt0SNp
+Sjhj1CHVQl5Cuz3EVDl3JciiKEyWrnIbjhMszX3UzgS3kI0lOz/C35HwwVKhGivJKDT/YK8CpiuR
+ga/iFYaGMUNbew5Sk4kyEbZCh46ySZlxQaqVtHy5oFKWLStWMpxmSJnIZhnYNqZuwqUVkynwyEoi
+lppvi9XYpLMZ85VCyedTltnl5lY8kwwo7BndWhl7n3NchQ9X42pcjQ+Mj4SnYJhg+zZFJ6BWSHE0
+zbDa6kZRYSC7pDmLqVXxeXakiR47pNUUy5DaLplyKOQltGZiYVbXOpjxQzlfV6xVZ9YkVdhxen+J
+pdp+jVHKIxWJ8V/7Tbq+KP/eGgrHwBenJp6nHANug0CxDlBzojTiy/mI+DIb3BKPxn3H5KIn13Pj
+ZoSvVjdfbWKqVbU0u1qEEYkK3JwuE0b3xVqt7DFo0s2rHRzEIjg9Zf0d2WSO8jecXJC8Ism3RZpR
+xHL9k8cJy6VY2GFrX86LhaPhWDSLWSijcLosqepLmvgaR8OxWmnLbDxqX3UpjRBPm9HyVkzQFcvl
++vWzervpyrGClkMzk3/3ywRf5aiN/PhZ9cEZ56wmMoerUlmbmz4TtfLdbMqx4izmbz1hfVd+t31S
+YDe1MS1VsFSeUan3c//uMUUp81k4B2x2JCnnvtpn7cnvAdC+JlbeStpUDQ1T3TWcWrytOrUotAEp
+UT6GsFHRnSrepKpBiYEWjFioRZ8bFtMn4g33lMNxfbOHZWh3ae1hKkO3UbicjcRjqbyMC0VyJRpW
+lm5ENpF5izMDy1SZwnaAcVnhqEKs5Z/A8IG6xq4zrEnN3NZOMKvGWmp5LnZ5W1FjzmMXX/HlI+X1
+a3YrNiMJHz7x6R1M1YiYFxMWT2Vxb6+bMFZkYi3HeuvNGa97gnGfP3nASslCFpbPZz8meYJPdf4C
+RihgoDfXJW73Hj5lsZKpG4YWbZVw70cJhoKQqtSl1qqD6cvfkyKiiOUcs7MOm1qpsFudZzyQZf5Q
+f7/i4ZclK/40OuTkSNzLGx/bwU/FhV3QZ6bl101PgVzmEcuxtuxac7ZHQn5rlzGHCEqv5Y64faDC
+rUP1rxMH01EVLsDRTsR5nOIgyLx24GN58nwuNPQxlmNc1erIzJilKs9OFi57ynrldFdMlHmpbUqI
+Nqg+jtmWBR+d2njX5JrH36pQgiEWpo2jqNC9WJ6pbbfp7KlbPmmSahvx8WjCuT732Blx+6V9+c5A
+XPvCa7Gay3PcCDY51M7O9Zev01J05sFrL2Grm18VyrXZuIPRlbDTMh9wOtXyY/WEIXJPLc1LxU6D
+WkPTTp1grVS3o4DZiWpQ9k3KhWwsUaH3HFTc0C7K5tCm1LyaNXtK0JK5PZ1GxEojdRl2XD+4Tq5M
+XWdJjZmowlnfYn4uczsdu2TuVe/D1bgaV+NDjI+Ep2BUNcYyZeGBrTRaq7Jgqn31iTWlTMUSxs6S
+2aEyO8/EgtmTgJc8sZj2qKRVy05a5DX3Itkx3/nSQ/JQLMlqKsfqVTnvu5LRPX284p6KdGzZMb+n
+u3Fn8OtMLLG2g0AqHFWRM9hSC9YIqNXYmr0eVNr51nI4msi5k3Ox7KVhkygOIXYOCbqSALo+MCgK
+BZs8kN9cRCOeVuIdjCd9yp5Yj2kZE+wKBNuPPVaFWKZYvYBlFbOMpOPQMhOSVKzEKBkTKOjHbFtU
+yv3n5Fp9MBym6nOWsyVHShlfRTkzBV+FYc1Kaes9xVA8XK3w1DpOoxpPqyRG02CqibhFbTJQ6G5c
+qTdirLC0w3Hv9oSp8i4exTFzzeIWM0g1o1625HrXeiGGod2n2z5vVQrqqlbceyTPctTOWa2LB3Gj
+Ix2jptVinElo9zAecagU97vfvMNLn5R7uljcpeFIdaXd0esstkFxMYmzjd8WL7XOXGrV4SzamhAu
+LIpaIfR1QKadrdOLmEdTCUfHTyeMtRO468g6PAgyWtvadVoNcbViMr7IOZvINb/1NMe7hEVvazhz
+MSfWYzRdl0TDDiu1iRRO7rgrivKKju1qXI2r8SHG9/UUDMP4h8CfB87quv6YftYD/jGwDzwE/mpd
+1xPDMAzg7yPK0xHwn9R1/dXvd47KMIgtnwEVE092TKeyaLS0/NVoPCvTTAuLSpGOhaK5yjTjaazl
+r7OU17piSe5NT/nKHYnfUq/kusKjl1q+WyUVjikWeDv1SULxBJyZQ0uto/neG7Qt1Qt4IhalE6R0
+lIjBbFb0O/J5nsWgvA7ffPc9fuuLsssXrliUjlPxyesSy3veDUwVg8nW2pjK8bDw5P8f36+glPzE
+W4fv80iZh3bbfV5V5PKNXZeX96UEGr+vsFz7IS3VRRjMckrNcQzwOIvlPjIzxdAGHGNDYtLFaMab
+X1b15OyQucJ17SzGcjQh6GwxWFeWpVBLx6cZ6SXM2zLItI7vxW38tsxFYOaYijnZ3FICi9yi52gS
+MbYIctWyqCz6WnI+9nIiLeDbqkZ+24SVWt07jwySbfFMFicOFXJNi1ObnnoY1/pyvrBrk6SSJzqZ
+jDgeybp4995j3rkjnuCPf87glV3JH7gviNVtNVwy5THoFkviS6aukU/elfv2tIvUriBSWcB4BoUm
+o6fxgkd35f7PzIJEJfQGSvln5w72hfyHP2hQqceWmzXHyrpd13PeP1aKtTuqxL3ucast5VRn4D5T
+YT/zAhpKWZfHY5xLEMxzjucJH/4P4B8Av/Adn/1d4P+t6/pnDcP4u/rf/x3wU8At/d8PIxL0P/z9
+TmCYBnbTxBw4+PoiGCuDxrpcXmWb1ArGcMIC25MHbW+p2tBFhr8m393cGbC9I2558rjgobYWr+qY
+67dkA8hHsmkYyQUzzSzX7QWNd+UcAyfhfKZELLe/hjUV8ErgyKJrr7XZ0G7IoLFHUMhD8toFM+0T
+WHRs9n7sOgAzbQHf7zXYa8pL7LRcJrI/0D4fEelLs7rQZFIjwm/LdW6vDujYco5NXLYGck9re9tY
+2sfxRUfr+b//JmUuC/svbD7GUjr4i2nC4kwIVy6OcvJMFuZSMQ/jUcokVVnzeU6s+IZW3qHX1SRm
+LyRUTkCOFNBV5ywUsFT5kK9kYW73wQw1WVeNSHXzXWlXY6dlYWSy6Yf9GqxLA+CTKc16dpSyPNEk
+mXZGvvXkmEepvECDPCHSF90/qGkdKSO0HRN7KuyjdHOZYTFUsFhQWGxZmqyMOrzxmdcBGH7qBRr2
+ZWeq9m1kNZWrUONFhq/3YbUzRpoo1twktZWzpkzbcXsEmTxro93lpR+VeRkUCWjF6LoKwm7tbBCq
+scAwmCsZaFa7FFtyvGG1SVzJd64pTWFS2zQc2cgbW20K1bYcDHaYRQqWWq1TppfKPc83vm/4UNf1
+bwPjP/DxzyAy8/BBufmfAX6hlvElRFdy6we6oqtxNa7Gv9Pxb5to3Kjr+lj/fQJcQqZ2gCff8b1L
+Kfpj/sD4Tin6buDSxqSZVCxV4srzDHqKYrRtmzO1eBQhDWVj3i4VljsIGWgjy+sbW1wfqlx4ZbMe
+y+/OozFbawr93FaZ8XifYqRJspVHfFNJTkch+6py/cLiRWLVFXyv0jr4mkdtasOQmdFWPYVtz+P2
+gSSqXui+SqEcEJHSi/VrE3uuSVB3hp1JCckcuFiGHC9W4Znh9oBeLfe0zgt0rokF63ba9JVYI8+3
+yZTk4WX38wB88dhk+qLs4cn8VbojxVBkETNPQ5d6hquNO9mZ3NvjNCCbXyZJYRArJVirycaaeizh
+Do2mPJ96Uyzf+jTDmku4UmcmvetCTbbZu8auYghC06NWBuowFgveag/xHIVPT3uY20oxVhqYik2Y
+GiZZplJwiTIuxy3WdmU+u9EOL6qydbeuma/L75ajKd1K5nmuoi9bTo9gT67zR/yXOdUwZhBfY/9H
+JBm5s3cNp6k0e2pcy9XsmQr0xPoqiZaUUyshULi1F8qx4irAVG6JdbePO5TnN2zcYFXKWqCR4ui6
+DdTLcQbQUqyHFdqE6umtNSI+2ZJ/T0yXaw1tJtvScrhd0FL6u7HhsHqqJVDfY6mo0GkckQY/WEny
+Q1cf6rquRUr+B/7dMyn6671m3XQt8p7F+ljhsN0llis3bK9brJsqPuLNyBAXLtUe1INOk+auuEsv
+Hlyj6SmX4sELhIGKXicHsCNxpFmL6xiczqi3JWufuCecnijf3VZKtildkvlgzjtP5PYKZe7JwzaE
+4ra1zyoO17VLct+loy7ltVds8oluasoTaLkVhYqbHD1JKSaKVXcnRHqOUsVhrapNGMh1bt0IMUPZ
+CLttC3dNiUPOnnB+7WsA/O//6l/Ktbff5+4jmcO/ZT3CsEV8ZRbZbPjyokcbK+aP5N8TS+XLlzmO
+ahzmZZOGqlf1h302NmXRb+x2CRVr72jJpbNpkKorXrRKCOTF8zrHnCu0ub1l0plo/qetuY/cwAkE
+FGbtlMQj7UvpWJxotj8YmyxCrZ6MFbDTneAr0/Yb/RTz9i0Atq0aS8v0kfWElRKfxJk88zye4Wrf
+xfatLbq1PjPLIlhTm2U51LbMS60Q9cq4S3whaygfnVBo375fw7zQqoTmbVqWy7LSXgS7gRXIWug0
+Yhravuy0e9juJUmQAueyDLgE6k3IL5nF/IhCGcgHTkBbAWoo7iDwGwRKduOdZgR7svEcn60wNDdV
+e9YfW/Xh9DIs0P/Xx8EhsPsd37uSor8aV+NP2Pi39RR+BZGZ/1k+KDf/K8DfMQzjl5AE4+w7wow/
+ctQGVJbF9cjkUN2oNLLoGLKzdxZbTLXRJlvY+FrH3gjEe9i+FtDblGRg13IJ9l4DoFpkOLd+CIDT
+u+e0LEn81fr75mCd2alUCIL0BVBBkijtUp6pKM3pDYwjgb4+PtXM8y2HhiIQF9kpxlTcwHIU4K6L
+pfEJcdtixYxI69hmSJYKoq9aBkxWsoPXo4xL4v/VTFzS4qHP3osqAzYMIZV/G+MVoAmszKV5LFRi
+/7kpVuvddpOf7Ik1y/KPYSvMtx9mLI7VelQ+nikhxoapTUTdmHEmc9j0HHqb4o21r3usOeI1uasM
+W8Mc11S1a7MFypU5jFo4mlG3ljt4tdbmLwIWakGHQ3mQrYaLodWJ+rSg1mqP7XfZ0zp91pwRuqpT
+6Uvl5IW8zxtDeY5Nu80NbUAjDzkL3wPg7KxBXkjidrut3aDdCfT25dpnxn9h5AAAIABJREFUBp2+
+XKjRtXALKeeYuU2tYVMeyfMoVi7RVOY2Mro0zIVe5zr1UiteypVQrTLMUAlilhVeWzw6329QKo+E
+uSxxlKui1ORwVfXIZ/Ka5EmXSuUJ87hHvZJzbK1v4nbkudqp4FSMZEp5pnDs2WPmihrNYgtHeUJa
+zhmhcl4+73iekuQvAp8DBoZhPEVUpn8W+GXDMP4m8Aj4q/r1X0XKkXeRkuR/+lxXYRiUnsmsU5CO
+dLGdx0Rr2vVlr4iP1aWsZtQqtrpwdZGnWwQqPGJ0h1RzZTSyutiKTw96NY7y/VXaWWhkJZ5mmb1F
+TBZrh9zRA373XB/u4JR3tZuvrYSAfrODoaCZ87LGfqCCqAOPgUqY21sllhJmGppTqKKaPJdFlTXG
+RPrwu2c+C81XeMpRWRkOqa+9HZGFI+uLyl0QT+R808UFk0vI8813AdhZ5s/EWfZfn3Iq64/l2Gdu
+SbrHqywuVEzW1g6/Vp7jaNwfGA0aKozTXoQ4be3Uc20qLTmyknkLLZOqlnmZ+9Be6HEHC+auAtGK
+BZb2oGyooLHhrVGutPO1GWNq96S51aJUzccZUzraV3Cm7n7SWZLWMof7WzVeoOUMy8E9l3N3w5Su
+zoFlyGdplBOaslEUQUqSaVy/cDB6Ci4rc9BegqxSFabJObX2SXQXLkZL1ohndykKcZAz1Wp0qzn2
+UunuzZxsoZIBrHB1o67dgkoOR6EkO7WdUGqYxIlBquGIkSZUGq7l5QpH6d4NndfMWrI6V4GfZUaq
+YLfKmdOoxIg0hteYaz7mecf33RTquv7rf8Sffvy7fLcG/qsf6AquxtW4Gh+p8dGAOdfgpBbmkUvp
+iBWcRxVtTVq1ckjbmnGfgau7ahirDNzZA8aRuOq+NcXTmn7dXxJ9Q75jdrrkqhhuadIuSca4ibjP
+6WyCpZWKWbJDrVqJG2c7FJuSFpkfXdcLNljN1DUMcjINeVxjjt1QMpRsDUc5Fg21OqVZPOMTMCcn
+XGupqMn6GquH4gY+WIj16fWn5NoZWiRNTOSencyg0p79eGIQx5Kg23eEgm28tYGhUOTfHEe8HmkC
+r12Q1nL9wx50G+q6L8QDu5ekrGki0nbTZ3yAS2eBP1PosulhKTgr8ZRKzgnYbElIMF7Fz6i/TNNm
+W7sr0yxlFsjn1iXNvhfgdTTRNmtSq9p2pxxg6rH7awueuvJ8PFu8qnDRplA+w9FiwI2FQteXFySn
+CjI7i6k6koA7X0lIuDbpYaq7vkZBqfyfycrGmSkQreFRq5TbZVdnkp5Sq2WeGCucC/F0/P6UfK6c
+BcrUvMLGV0zAeiN6BmQrjB4KIcC2FuTq4aaazMwXy2/zjnYcLKXmi80IX8OR3K1JcnmWtXVJSGNC
+Jmu9XOXEyo7d6W1hK9hvde6RVX8CORqrumZVJJSdHO1MxahiTAV3XKxVtBRJtjQyigtxKS9s2UDM
+Obz/+AsAWG99mVZfFtVsc439d5Rw5JMhW8p9l2n3YpgZJKo1WeQRd5UH8X455v95rISo/heYl+IG
+/sVCW5mjLoki+9aWXZK2vNDjosUm2h9hVRTnGq4oIKbKI6pQQTrBDFtdWxou1p667koBb/c38UJZ
+BGk+JVMwle1bFKWSn9o53lKuc6LgrtqOmWss3/RCSm3JPVmU9HI5R5Tk+E05T6SxZydZED/UvIZr
+sFRFpvWkyWkheZAoWyc39J48mavFw4JlfqHHqmhoaLdatUBj57xr0FVa83Yg92yZXVLtE7HCFeVE
+s/phwplm3zfbAYd6fae6Ec5bc5Spnpd8l9OJclBS4QSqydB3CfTfeVeeTbruESloyN/u4tbKzWku
+WI208lP1MD25F0MRlu5FTqRU+2VpYRraOl23SZR4dlzKBXWzgpnKmjlOG0t1PEvnhPxM15lZYmo4
+WfrKNVlFJEutdpgVhYaNtBwKFU4ufZtc8xl1pF2pRUWiupOJndHSEr5R5FTazZqbS+rsB3vNr3of
+rsbVuBofGB8JT6GuoSwMGmcmI+XqO6dmvJCdcS2z/j/23jRWtuy67/vtM1bVqfnOw7tvfj2wSYoz
+RVG2RIU2RStyDMiBBAOxLSV2AAUeEMA2IcCAP/iD48COgzhOFDsOYii2bFmJZAaRTMmSrImkODZf
+z91vuvNQc9WpM598WKueuhVRbFJ83c9ILaDR99Wte87e++yz99pr/df/T19pe8eDgpqqJdmh7AKd
+wKFUTsGqnzA/l893/Cb3tG5h84WUtka7R46s9g/mc4oFJXdk81osbtb9U4daoepU+1WuXxEXtqeV
+iLkbsKqY/EoQ4yonok2VvtK/7UxiInUJF6zMbn0VX93TanMKp7Ljl2sWxUB2h2ZDoun1kyb48t1s
+PCUcqcvIGFs5GtPUZqgBtVTbPkpjemcKsGnkdCMN3EYzxor3t1yHLFdMRiSejZXHjJQUhCRhohkA
+9yIlTmRsi+AFCnVtq6pyNJ5ZlKnShNVc6gu49cii8GXs80lK1NRaC+QI044mOMqVFp1PyBWc5thr
+XG/Izwcjl/aWqD97FzIW7nDGWGsmXrEGPFMTLk2qa1haETqfmYdKXKECq2xrhn9ZpejPPCzlh8xj
+h6HyPgR5SEUVnSOUFCYd009kjsTpKdapfNdzDKUlWaeG4ioorIeap8xqzPsynk6tQhgpziKOset6
+lNJq3gSXaKK0coFPqPjhtKhSVc8ZxydRevyJkjbH+RCNLTJPZ/TUwVghxVFveDZJiBSy/mZt6Sks
+bWlLe4M9Fp4CtoXVquCtiTYjgBnO8JSazDQLip6e1aI5Xc27Bqo/6GV1VlZl92jOcqxNBVj2Ii4t
+gn2RS9rWyr6+rOzJGcwmstPu9894VWvsw3TCqe6w1ZWMTAlkrz8p130ptij8xe6/hqvEpaUdEyq/
+wcmsT1WJO4tcduPG1CZVzn63WWBpgUuW21RW5buurTtbNWBayt+FWcxMiT1jc8S6FtrMMESKGhyP
+Jd2YF4ZoMW6TiEjP4sPMJ4zEg6g4q0wSDewp4fDwwOFCx7UcxnhaZXg88EhU0To/nT1MjdanMq5z
+t0pelTaQJOzPNBBXnxKpVmRiZRilaauol3fRP6RwJW1mOzlGGZzDWpdSU3WJG5EqI/ICxzDwUqan
+qog9DOlX1Pvr9gjvqVcUjphrLGLYl/He867SmCq68eoKicZXomSOscW7mQ4mtBVokSkydUaXYi7B
+XzvZxvFlt7bmbeZa0TvXuFSrYZFr8Dv358zViyumEXmmqc5pj8DIczdTJZKt2qTq0czTGFdRr1YV
+Qo1HDU76uHOFhWvMJSz6VDQ2Ek0NtqeeVxqSxxrMLCCNvjnA8WOxKHiWxW61ybZV8nxd3LY4c7FV
+QSk7Sml0ZLGIIpu6Cr80NQprNTMmLyhEdzwDhdKG9gZTFQB558oa1lzps1S5aR4fk1TkAU2cjEID
+QHFWskBu1yOHmrrxhypSUq+7WMr2HHV9Kpq7j3oj6hpIS50VbA3K2XNpw/lwn8lMORWjCtuXpf1B
+r0Nak5ds+kAXjY0T7HRB9JGTJPJituoOpqpsvlOXZCLEIo22Ct3cmz4MpKbZCbX0GQASJ2Rf9QUb
+02M6qqtpz5VRGZtxqDl/K4eZLkh2BV8DrPnFHNZlYTl0BLgazE+I+zI+eSd8WCV5eHT3YTWraVVY
+W5GAYBzrC5YFBE1dpOM6XiDPJE0Nm6Fc4zP1Gicq/e4r3Vzen6PJB15MSlb1JbSnJb6KsfqjnNZC
+K9MTKHXzRv1hpiW+6BOp0lOcG9CFo9mYg2ZgUiWLycu7ZK48v8w+YD6TI2S1c0w1fgKAmgbBe1YF
+fyDzsLTAXtc2xwHxXPEp8wEzXQAyFTPK8xqbWvNTMwVj5aucXgyoLSQBphZDVSXLQs2++SVJrhSC
+M5coEbzFXqeBGcsZ5GQaEhYC0Huztjw+LG1pS3uDPRaeQmlB6uf0OzmO0oTlNdgfKrOx67IXqZtU
+z3lW2Wk3Ivl/txdxWl+4TiVzTcN55Zh2INd4vjZg7a7qSmq39304fnAHgLu9DFdRhaVfPhQvrKzU
+6OzIDrSqLv5zxyM6eyraMg/wNRV0Vqa4iZK71iZkC5VrX6nUJkNu70vb/Itznr0ju/u7blzlQpGF
+1ouyqofPj6h2JCiXNRMaSjhTttoYVduuBjPiQnbgSFmw89UtqjM5SlxYNlUNGE4puKKVf8OqzVid
+gkD3hcyvUFXcxMlpRH8mY7VZq9B4Sis4nZvUE8ELdNoqPhPe5c5L8tnZZEh1Iv3rXvNoqwbjer3L
+akvxEt3FkXCNtC3PtI5HMpVn8hvhiD1XKM9+Gwc3lUDi7UhShX44e0hesuPn3C7EU1oJAw5VH3My
+veBrz4nn8VQqGJOXhh1u1tVbaeW0mzK2bselrYG/oLGJrWSsNT0nmVqXoRbeNWo7qENHWsvpq/S7
+pczRUZphLpRYxfOoFJp+baXEyl8xL+tc6C5+caz6I0zYD+TCqxtbjDVVO7voMVOSoElePJSrX12R
++VSrd/BU4KfanmPbKl9X5BzX1BPqTegX/wGyOduWSzvYZN1vYRriUiZXZnBbSEESy2K+K5N756zL
+meLZV86k5LX6yZyPnkoNwNE7L9gaC1b/YuezVF+UM+IDK6Rdl4nwQiKYhv7ncvYzhdFOS5y2PNCt
+tMpWW17orcoOl3fFZdxbEcz53d5zNIdacbgScq8nD79bScgKxU5EPitN5VK05PceNrsNjUJHNl1/
+IQZznV0928/aCq9NHSxVAloLTqi2JdLNJAfVKHSxaOiZOj2T8/kaEbUtxTSc1yhUqn7NC/HLhdx9
+jXTqaZukPbvbY5oT6WfZSVkdy/Gguj3mqi2VlskNh6ejjwJwuCuuau+3O7xU+78BCI532XmPXO9y
+5RksPQrWGhXaXXnxNgJ5BrXRHEfZpkzQw+tK+9812GLFEpf/Y1zw5aq+kPnnZdyKGpbGj3YvOlQ2
+5Dm0H1yjuPw8AJvRJq/66qIr8IodQ1Hq8bE0tG0Zz24tpNqWvnozl3yh1KQVsWVRwqHcz2qfUGjV
+Ys2s01JGIztVlap8Sl9FWJxRwkQFZJsYqkptb7ySQI+p2YosdNasg69zoZbtktfPtQ0uhxr7SMce
+G7fk+LOxovdLDYG+/I6TYitD1PQ0pqJAJr+V4uwvORqXtrSl/SHssfAUSrsk7RZE+YxcRT865wlH
+iviy0wTuafFT2iPSZvfsrwIQ/GqDoJRjwHyvQxbKLpb0c/IThb52baqh7DaRUrT1kgljDQZW7IJu
+Kitqt5NzrSUVg9uXS7aVdTjVijXPSx4Sr+S9CtVYvJvMqmNp3n80cPFTOQpULou71zYutq86BFUf
+t6pUWukFw/kC9yC/37NTFlrBSWsFT9WOrUZKobtYkcUUmje3q+LCO/GMshRXvVImuI1F9qVGU2Xu
+rXHEmbrBpaNIukmJq5kRJy+pbsq49Y8yTvqfljE8W6Ws/ioAr76qQcRXRow0Or/hVWnasuPXWn1W
+VNm5U9QIlMasphJsjc0m1kIjw2lhFD68X94mmos7fnCnx6VIBGw+q8Ipa8GUUPt83klpnsk1troz
+HEXAutsjnlJY9TXdPa3udfaU4Xhtp01Di+LydgN3phR4zYRC6fTmmkXKh6OHRwYnDXAWCs5Dm7kj
+c8CJlHGaglWlt6NusyBBmycJrh557aBgvVA4uQY+cR1KeyGxd0LvQvoUxzGrrsz15opPUFdNS4VH
+B4GNp9kJkxYMzrVQcH7BZCRzcjDKSM1CrOjNmSnLb5of5dtu6912+UMf/yO485iepo0aZc4g19RM
+OOZcmWQud1xoygTb04E8nBQ0FfwytH3WlfxiUAa09YHNGiUrd2UwP1+Xc+gH4jb8KeHn++SNpwi3
+5Mz57qsrTOvfA0CrmjCdihtfqcjRYPsfd/n1/13cwQ8MPktj/68DEAY2G6pClQY2SahpJs12FLlN
+oT87ZRWjbfaKGrZCW1s6sUs/wVXZ+shKUbZwEq/kslbA7Tv/KR+byT1+RtOGrfgnGF/5AgB/67DG
+30wX9WweKARbTIlpVcil7rfY1gm21+ww0mh41bJxLbl5vVt9mOINtb6EXsy+I4tsu+dw35OXqTJw
+mTflOdy0uiRNeREuKaXRrza7/Hl+CIAX/4sd/qJWpZYfW2FjRSL40+M13IYs5Ocai+maPkeRLHpP
+Xd2GQJ5NNcw4KSSW4uwfENYk/uMfSkxhZLl4OreyVov6SDaAi5Uj1pVef9Dapq406Q/2tUrUP+Gr
+Ml2IJg84ONBXfc1meEfGYpGdWF2t8v4Pajn/5p/m2rYs8GG6werm4rtr1FSEOFSSFTcfMzxcEK0e
+cn6uGbh0n3FPeUr9KbGi4RyNqYz7Lu/YlPv9zZ/7CP6/+WcA/Ozs74NWl8IYoweCkuKLZVm+n29g
+y+PD0pa2tDfY4+EpdJrln/6+DxBnPmsq2X1gPIK57DrTdM5mRVb2ouWyt6KU257Sd9sWr/ZktV43
+OectuUZ+YfPiXNxqM3YYZnKsuHN/sQuWWDdkpX2/vceP/knJ6a+9+z/HUxis09qh2VjoEWpx1VMf
+hJ4UCZGxYNKCLlQUlpoFPrZ6KckCZlqUlJrTtikpFW9RsQy2YiGamoS3fQ+VVGSeWkzSRV29wa/I
+DtwbAYsKuEUbgMW5w4ofatN8Q6u9L+DJSIJv11s+ZS7eWN2HC+VLeKbjYdZUdj2UXevueY9QJfTK
+bMD+WFWzZzGpq9HyqmFuye7u+eKtfG6Qs7ujO95f+W7+6VADjX/sx7ELOa6thiH7Q/m+6f0qAKP+
+OY6S5LSvudRWhZ7dLwfcnshRw/mdB/gr0hc7Fe8ujhx6KrHXaqziaeHTMCy55IhXEXZWaF6oRJxW
+qA6jLv1Tucbh4YjkXH5+hRnHOucylb3/U+0GtT8mgdgnr63gK/26X7MpbOV3aK5Q0SNBpFB5M46Y
+aFFVfP8OPYU8X9zLiarqWQ4t/JbyLNQEvFaU4O/JO/LxH/s0+Yv/Vh7m10c1Lz2FpS1tad+8PRaB
+xiIvCccpZSPkjsJZW26OpTnjncs+rVS8g+ZOhS1XUme7V+T3/TtDtioqzDG3eKotO820DJldyHcr
+tZAjlaSLAzmzvnKaUD2TXfALWwfc+E1Z+W82nsbW3fi9zXeTnoo3Yatn4v4Pr7H5Q7cB2Oc28KMA
+WENDpCi1LaugV2rqUM/kJTlG04kWUFPEX1nYbKoydaJpuiDwUBQtq27O6aJ235Tsrcoufue+Q79U
+yGt/4Y3sQyx9pvyrwH+lo/z1XAbZF94zanHSkbSu1z6mpaxRddfHzuR+3WrBXkdJWpULwXXW6auC
+9XRexfNlPO9FY1xX22R5XNVitLlSn72zFbIXSTtv/Na72V/5pIz3CJrbErgcHHyFw7ngFCaH4gXY
+JqepFUHx3W2u/YCkOPPZDuarkmr+/M+P2Phucdm6h7JbR81Dzo7kOdbfd0FhNOYwvuAFFU7Ze2JI
+ZEubrL7s0P3qAaeKJs3aYwavym6dtkYM9OzfekbudbG+zuUdDfjmHl5VCrCKpEfiSJDQm07J60rN
+lij7dAzTnoxhOLGZqNL0i/dj5ohHuuPt0FbKPUfTqRV7zo1A6Ab/4rtW+ekzeSa9838PWtD1rdhj
+sSiUliGveDR9D7MAjTgb1DbkwbR9m11VR17b2KWqC4Bnixu1fnWI418HIB4OqZYKgHLPeIcKixx3
+JnzAr+u1FR7dmnAcyUt1Y1LlSz0FFn3Xi7hflJd09mzIRz4uE6WrAJz0X/4I/fa/lsYPSpwFHXjH
+5gPq8p/aLVZU6cdSEE+rEpGpQvETHYdJVybKVjElb2rATHkRnLUuV5UMpnff5rCvlOz2OZ3Lco3e
+wRGVLVngXu1JnwwlpdKVfXfw4/za7A8e+10NLs6/Y4P36nFnd2P9oQBMbVrwdFUCsN1qSm1Ta1Mi
+mdBuGjO6rJWTtZgHz2nQreLQV3j3dqtB1JMJG2xpNecLY6wNORId9bf4M9/50zK21T/H2UDrFo4e
+cIYc6X7h5WcBuNZMsJWW/qPvgme/+gvStijnwedVrr415PN3ZMw/qjqX0ajFWl0rIMsGqJp1NmpR
+b8jfHfZWuKk8BEe58ifau8x1jIa9a1gtqdps5R3qruAiNowCobw9NhIhuxkzYHQsL3Q6uIO1I9HK
+wXDKRkNeal/p1VLP5/4D6V+8nzO8J/icWZQwGEj7B26f3RM52qxfugdA0jX8xmu/DcCDB7/FTudX
+Aeidf3OkKr/XlseHpS1taW+wx8JTMGWOnU+Ypg2cVVVzbs1Y1/TV5uUrtLQYpNX2aShs0wu0KKmE
+pqo5W90Wbks8iOm9AK7Kd52oYKbFSnvPXAHgF4cvcWNL3NNhbvOulrLsfv5rWE+p6/vMjLunHwFg
+rSruda37E9yyPgHAl73fIEv+OQBBtsfzRlb2ehxTqUkgrb4p7uKHghrhigTU9joFsTIj77YtXCWC
+DVoSWHPCJs1NcZPPpxmbHdnBju/bhFqJuN7qMLa+AwDXF8xGns4oS7nub0V94OuQdmrA81jz+c2v
+pIw3ZSy6nWs8rcjK0l2jo0xWnUqLxoa43fNTadvGtQnZgobBmuB2pc+TcZ+6UXYqa8ZQ9597DxTF
+WfOJfGnn9TTg3/yO7Mw/+u7bOOoBvrLSYeVVCZ59UuXR/NoGjqbh7NBls6vufjVnbUdSkv2XB3yX
+trNVSP9a0yp2RfUs3VWstlwjzSP6x4InWds1eEr/tqaFa6PwkKYS144nR/RDxZZsnBOhsOqqYALc
+q7vYRlKgwfE5o5Y864uhR/maeArlxpx7CoVW4m6KL/QIK+Ldnd/vc6IB9tPn+8xU+Ig8YHhF5uS1
+I+nHTrpC05V3oN5eYXXjrwBg9v8N5fxl9A95YxT6G9tjsShQlJSzGLdbZWsibqu12qKmQJGgmuEp
+6Geel5RKBtKpiuucWSnxXN3ZwML1tER42yacKGy65eCvaDXcQK713e+POb4vT+b8SsF3vCKTMf6j
+I37rgUyqfNXmIx98WtpZ0RjAU/8NvXf/CwDs1xwqB/KwOp2ErVBpw7wqnT2ZQN97S1zK2kbGZXU1
+Ey9mM1Bq+LZFW0u4B4lMwJ1uxqQuvJP19ilf7MlL/HR3xl2Nvg/WLcpQMAmZvUh7RKAUXS0PLr7O
+8cFSV/qmVk7WW3V2r2zovbtYqt5ULxJmeu/W5pwklqOErbyU6TABFT1JS/9hDn6jErCmqY/zagtH
+X4AHyozsnficnOpzdF7hSV1A/PQ/YaL6j++x2tzXY2P0isY4wnPqqwIfr7q3MXouj0xJVYVwb6yV
+BB3FPagQcLu7Qal+sWlWSTwZI4PBasrY5Scx4aaA4OL+opR9THws7XFrLa5dlUXohARPadl36nLf
+7qwNWosw7BpCJc4pojmTmdxj+EpOvSMxk/Bl6X94/5Aznb9RHDE7UaWyZM5A1bJcb4h/X8bzVaW2
+K4IW9TM5ovhPzugcCqbDWhtilP08S3kIonqzecbl8WFpS1vaG+xblaL/u8B/jMCmXgP+fFmWQ/3d
+p4AfQ/yWv1SW5S9+o3uUQGJK6kWCva4CMF4bvxC3xy0DSkU0WnOPPFYylIVsvVUn0eSsPSowgUJb
+nQR3se71Uyxkl2uuy3eL8MNs1sStG9/0aWzIyr/mfZw/+V3iheTv/QC+6h86luz4T0fvYviyuJx+
+aVPUfl3uN4m5UBKRzc2EVUei9rt74rlsNNapdZT8olynowUzXrOOo6g4kyq5aG7j6q6bXg14WvUb
+vEmFFcXdXq4P+ZmXlXi1exeA6UmPvFQUXAIwed0o/+6e4WiF6ZkG3J6pj6iO9Miw4+F40vZyz2Gu
+pCZRYqgs4MFaORjhUKYKE84Mvi0/1xoFlipJX99yCLflGi+/qNLy+ZhjpTE7+GpId1e8txk9qnqP
+9OKcqkrEXd4R7oKdmz5GA3T26S3SWK7nUqXiaIFR5ybeXPpXvybFcXYyxKRX5LtxgTEStIuzl5n3
+NZrvOVgT8RZ9W64Vj6oEvvx+XmtRGYo7/6Szyr/T4bQVzt6sXaKaiHebDvsP9Sj7c5hp9WiSOExS
+CZSej2WspqM+5yP5fc3yGATiYYZRia3yd2FUkCurcXYu/Z9WbVq5ZDiecuBwUyAI11dOObmQa0wI
+sTO5T1a+OVq2b1WK/jPAp8qyzIwxfwf4FPDXjTFPAz8MvAPYBn7JGHOrLP/g1liWoe67NOo+ua2z
+P/VJFaQUmQJLCVeKPMPXVFcRyXmqKObY6iaWPtgLRpsyx1Ex1rhWYBLlf4xUiNOEVK9pFWHS4Pyq
+MvOcfYEVXVjS0xnWDZmwBbJQDJ+/Q60lZ3i7n9JYKARVGmxqdeUmOY2GZBdqsaRIB82AXEFBXaeF
+qSjIauYRe7Jg+Qojxk5x9Lw465U09AWbrMxBXf+jYcBH6uIyfl413g1zjM7Gtm/op693GhUnD5Ra
+7Xd9R+HH7R3clk5oJ2CeahYozmhWVbXKhVSVnqJIqdxxyTw549bqLhM9L3eyKl5XeSevdIgfaPYB
+SS3emWUMbXk5RonH+pm8WHZlg7mWSefbrxA/r8zHmlHy3Q62lsanW4ZCa0Z84+I2ZQyLTYPd1KpD
+R3U83RVKBRAl9glafEmc1HEUeh6T4ytj8kDjVtMkIVII+rpdx1+VfjvjE/JU5pOv4K1hs8VGIWMR
+eykD6R7h7JS+lrDbUYbvynPoqmjRUeiRKo49red050q+YmxqWpsT10uMMllh5LvzfsxZRWIYYdqi
+25efa9aI1o6Wau8nVPQoOHmTWcpvSYq+LMt/W5ZahwufRTQjQaTo/0VZlnFZlncRpagPvrmmLG1p
+S3sc7NsRaPxR4Kf15x1kkVjYQor+D7SyLMnzlNHIYlNBMZV6TiuU5bzerpHNtACnyB7y/Nk1XX3D
+GpZmJ6qEVJT+Pxm3MK66s3FCqqId7Ux2lNBJ8HRHyJsl12ayq9xucuupAAAgAElEQVQLU2o9uch0
+7T4mky6UFVn7rkcuTiigkaOmIRpLrnjVyjlONdrvedxUMRujvItrjYCqDnmrnuLl0ievWWE6Vhiw
+4qQ93Ic7Yuj62LbsQKvGYqb8kdNOydfOJCPyZCD3PZw7hAo8cvwJzF9/fFCIMiVBTTUhu3LfSVhQ
+awrwpp4XWKoVmUy72BU52nSqdaoK+S6UlbqILqhXVB+y7bCAzdedEV5D8STWnHhLjzx35b6j1hHh
+vrRnWI2ZqYxbZXZKDZWesz/IUzsS+KtpoV9lexszVa7CSZWipcCw5jrpUMbLIsWq6DUa6uWNqhSJ
+eCAuHWzVZbTzTbyhpk/qEyqqDeK1lDl5VLK1LUHHcjYj0iNrtOGQqMLLoTJtP2OnGHWK3QcTqg1p
+T5lVWU1km266Kbkte+x9xcs49KjMlddjHpJrBiTMU2rq6aVZiPH0uDmTNoyGZ/jKKRlvlqRGgGGX
+/ReZzeSo2wkOyeLFnr7Yx/9g+0MtCsaYn9A7/dS38Ld/AfgLAIHvUsY5zVWfNUceSr2x+lAsxao6
+2MpyM55kFIW8TIGtmYq2wdIDSl7pkrmKIOqWFD393HgYRwbFUal6Z+6SKarOupThpnJu7Ww8xwtD
+LZOeGva8hUKStOf22dewPdFu9NKYqiUPKUgycmQCXZnbjNS1zQZyfJivttlZFRcwttpUlG3HMQ7d
+dUVbqkiosXNKFRZp1YfM+rJIeTWIleJ8x0y5X7kHQBQoeMkbU6qwyDvbLi+fvf74oJPKwNqWXOM9
+OxLJv5bVOFA0pnPawGtoqtJziVX1qhbUSfVoslAuyqwmjTVVWLJKOtu60EUNHFVhSlcczJnyX2oM
+5xfGMbnWQYSJxbbySlpJwFxdYt9pMW+rWG4oRxjfG1EqJ2acvwJFR/+uwKnps0wGGD3yGFvp1IOY
+Uo+PJgFTkZfU8yNw5efh+QznqoxnOdF0iTtjpPGX+s4Odqgb0SRkOtbMgD6Pk1GPrbZuOGsNolxl
+B4IUNDsUzKsMKoKyvDJVUlYnoq4T+DS32VIhmvt2wbbGwR4EEzZUe/PlSPqxOzS8pCLMe+M5RSZz
+dpZOsHWTyOOYelOeT1+P1d/IvuVFwRjz55AA5PeVv1tV9aal6Muy/EngJwFWG9W3vypraUtbGvAt
+LgrGmE8Afw34o2VZvj588fPA/2GM+XtIoPEm8PlvdL0SSA0wD/HXZOVzi1VcjZoSBhilt3aoUCSy
+hixELtppQFHXiPTcxlroGWYWcU2hxmcuhe5uhUbng66LFcpulSdVxloZFx13qQUCNa16LbKhuht1
+cc9uXf0I8ZEGDysps6oCh/I+XfVG5hs+lZlErWPELffDlJMzOaKsrHdxkV3Qa/gPMyqFejnlZABa
+GRqmOWVdawdmY3BUWMXErHuyG71jT442BwcdvBXxoG621+FlgQFbZUHgKmei3eSTRtq26kt7NtsJ
+zkI6vazS2pAdivEcWwOp6dTQCDSYp5/NazNQpXBv7xbpmYBmsuqccF92tEbUpqoU77ds2YG/v5Py
+8gPp/6E9IVPuiLmZUIulTeG4z0zd55pK6NnW05SrKjJzcfG7AWZTpZzp8ci5STlcEKfI3xU4FJ48
+X2cWgnJS5MUDTEs9nYFDOlWvUOeeV3pUBgr+qVWxY5nu6ZlLpPj26YHswI1xQabtYTilVtejmYkZ
+LTBIcU6gR9YHkxP9LGOu2qWXsoBhfajXMMxs+bmZl+zrcXKuJCsPogxPQWQv1nLqlnhK0yRndUEj
+38goowXs+c2pT3+rUvSfAnzgM0aQcZ8ty/K/LMvyOWPMvwSeR44VP/6NMg9LW9rSHi/7VqXo/8kf
+8P2/Dfztb6YRljEEjkXNhkiZM4txTqGViqMwYzSRM9loZLC0Ht2LNGjn2tSUx79ey6iosnPVtSg1
+DpD5YxJNIcVjTfu5BW1lB7LPfGap7EBFOWAa3QMg/O0ZO9//bgCanuSY7kUTNjcUF3BwSFCouIwX
+PAyCnp5BnAvuwa0rO++rB9zYlMKt970jw9fdvQhzbPV6Mu1/NO0TKpqt6Ln4WoxlaiNyBS/GfZc9
+RQ2+vyF9vvKEzXmiyL5rPnxGeSFMyVx1Mn7wZkj2iSsAbKk3Ys4N1pnsJHE34eBFxU3EMe01reZz
+UwpNP9YjhStbFpl6ZtHsHqkjnotJtyk1xFzEp8yVYqxyqmrQzIkb0pF4BNcXQaF6ldlcdsScF7BP
+VT25KfcN2hFzRRIyO+KBJwjR1sEx6VxuuHolp9TxMBMJVFpBG9tdULA5WMpDkTUuYc21kOpd6/hz
+RQLOZOcuBzHmqngKtpNQaopwpVJH6/JYnH5nto2vaNvCz7kYSDtHp2f0T2XHv8gMmY7z8VjG2E7n
+zBR7st6cUJ+qLOAkZawB9KSckymHhVuKd+DUVvAUY26ZLr5qdVhFnal6ReHM4BdvLpawsMcC5lyU
+JeM8xYwzgjUF0Dgj1OsGN2asQZ2Dg8nDyHGZSMdXG12qmsK9teVgrelAmirpSJV6rYzjY/l+X8Ej
+8STn/e+UEIiTDTAqOHI6mdPSqPxoB3KdIKFy8n10/TLVY3kw99tr9E6/CEDHTDhVQFJpStBc/ssv
+ywKSp4bphUzAIgq5pLRyjR2XofI/zk4EApsx4OhUji0j12HnQtzSG09cw1Za8+OTOWeRTpCmvDyd
+6iWu12SxSdt9XIXEtkuXnSty/HnedfkTr4kLe16ReofD1BCqy73mj7jbU8r4cYr9krwoH/vIdeq2
+LGqpUd3GF+e4N/QF7K8wMNL+B7854NZHBKYdj+4TaWn3A1fG9WNXt+lXJXJ+3zrnjoJ3/GHOQu0l
+cd5FdUteoLaWi9vdGw9JWM7u5bQ/rNT/MwtPgVqDi5zNDWU8bsjzzeYRxaEsEFR8IgXGXQwPuP3z
+XwNg74Nb1BQWn2umZpKP2PauyHNqORRTacdoPaShUvMTDQivNy0WJbP1o5C5p1yatSartmbHriW8
+otmAe+fyEifjhCcXlOyRIVTF72w+p9uR8ao7HpMdFQw6VYXutSZVrYM5cSHSo49TzDBK45YWc9Lw
+mwMuL2HOS1va0t5gj4WnYAC/MNRbNVYtrV6rdolZqPkGJAN1/b2Ss564EG4gK+rZxYgNRc9FcZ2n
+lM04TxJyhZcO0gMeHGklmnogblAwf0EKita3r7OqxUym0+WBpn2SfpfpZfEQ2pr+OlrvY127B8Dm
+i8eEjqLRkuIhXaaX+cSe/GugGITcnZMr+eZZNOFA1ZqDcg1H9Qh7GtS8d3yf+xo4W7fXaVyXINLQ
+nWEsQUp22w84izX3HKt0nZVBR6670QowygXQrBb4a9Lx97Z2mKF5+FjGJLyf0lMNx9e+WOUCce3L
+ocOaK57S6tDnHaohOTkRj+fBwQP2vyS7a7Dt89U7ypjcO+JzlkBWvLHNeSk76NpcvJGv7tVZ1yDa
+7aLkpsLUTR4TV+RzL3BItf15XY4lJccMe9K2+9XnOPt12W1TO2MWSvD0qVqEfVWeX1dTdlnfYXwo
+wePh6Jieamx+8XZAoSnO0+Ed3FTGfE81N2prMFQ0ou9vYjfkfpUzqCls/PKW7NBeZNHXs10yO+NA
+qeRefCUiy2S8T/dTHhzJcwjVq3eyhGG5EA6y8DXO+GoxoDaSdjZ2oK3FaL53ofewuKdM1G6ZYGlw
+9O64INag+XxeUOc/wOMDQGlZFNGEcSkZzKuFj5Up660HE8WXn56NmOTyMKqaz11tlviaE7bPbSYK
+A3aKmP0zGcDzYcFEB/Cu8uzN70wYrIk76IRnNNe11LfqURTiEtbtBE8xC0ZxEX9k831UYnHRfy0+
+5NkHvwJAMb6NZWkE3E1wY3EJcyUssWMHz9OXN02Y9zWq3UzpKyX36VAZeEZQ1SzD8/3XeG0ibvl0
+uM3NNdVMtAq2KjIhj7S6spUXdFVtqdXZpaVu6So+n7glgCtTa+PeE1c6RHH0jZjxsapFbTpUNEuS
+1E4xQ5mw8axLMxRhmNgXuPLhyTE9Bfx0T9aYRdLOo96clVd0vGo+za5GwFUQ9QlsKrnEA7adffK5
+CqSYgiCThSXKj8BTKPtMFbLoEA0FI/Kb/7LPxbY8P3+YsPmkgrqOu7Aj45J1FCMyu8v9F+RFGVsN
+KoonsEdnPOjLwrPT3ma7oezWLVmcN5sBqYKUXGeOOVM+ykFJqixhYz0GnJ4es+JLPCc6HXGmuIKT
+6RmHz8oieurMYaowfaX472Q2TkcW2++pbHB2WRaW06/WyRWivNlfodTS6fGxzPVxbUCp77tlF2Bp
+GXU1pjmVfverp1SmqsTMmyNfWR4flra0pb3BHgtPwbFtVlt1au0VWr7kjw0bOGvqwk4h01Bvda+K
+U8oKvNGWXWK1cCg1Dx7VLBIVZxmmOZHKZ82DkBUl5BhpFZ7/5DW6vupRthyCPXUDS58s113z7ABr
+V2G1et2o02RzRYuOzs5ppOKNjEyOpVoVudVEkcTsXhNX1LJLNtFof8unoryFuRsSay55pnJ0p9aI
+iu7ynalHY1ci692tNTzNyjSsDimyM1di8aS8ooav3BNVx8ZTt/RD10pC1Yr80GbOEeIJdE7l789C
+mxub0rb5jg8C7qR6Z5tLq7IdbT+9irulfIX7ihHxHUaO/L79joJ3vCSSb7XLAbWOtP/K6pS+JHZw
+XpWisi8XPteuyr3rZ1V8Tzwke61LqsFTb5RiRvLcszXFwPXPOVYOw6c/2OYLWvB2db3D+kR2/8u1
+Lvk1CTQaFecJRxNsxZAkzSmdVdlV1y936D4pu3trr8amIg+3qtKP4ryP2ZE5aY0NpiHtrE0Duq6i
+PncWaMtVKqE8v2zLUL0r7TydpBRV8VLizGNrQ56fGynJzk7JrVsSwL1W9cmO5bqX1zL6igdZfTJn
+eCG7/82r4sUchN2HVHKzcYVS6f+OYp+krZmI8wq+q5VZb9Iei0XBsi2Ceo1mt4G3JRNpbwf8c2VC
+6hRUUiE6WfdHZC0Z1LqWx7pFimKbKOPsocqU73Zpamlwzd2iUJaN913R8ti1FXaUDn57p4FrS3R+
+un+CSSXCf7fuMlSsuVXKi/feqEaq8uv3kg6jREu5kwzKhfvp0FRc+pon7lur7VDP5MWzGzlcyEPM
+g5RIy4hdrTO4tNokVkDP1eYl1p6UCfHM1hbNhiyA5/cGTE9kQbJVICQNIhYiRiU9NlW7slXdZu1p
+0d6cvHzGaqbEIvp371ldw9V06M1NH1erRL21giyUa1xbe4KKTpktS7ICD9bafFArOK20xQd3ZLF4
+NtziZlPTxAzIlIWqF8g93lXL6J6IEE+1epcvFkrXnzWxA80eFT6W4v19TUObRot3Py3Q7F5nn/fu
+Ke35qIqXqAyAmxNUF2S7ElPohntwSZ+116Vsywax8pQhqcoCsX2rjXsqR5Dck3Ti9DjEqks/7EqE
+M5fFJt+ZorgofK0+bTTmrO/KM798vsbeu+RBtN01vqr1Djf8CzwlfC1v6IaU7XGzIdft+CXv3ZTF
+bccxXGhdRjMI6GzIfHj1UObCCuf0EllAk26AGcnfXXfGDFQ/8tVin1DrSt6sLY8PS1va0t5gj4en
+YARo5Ho1Lk8lwh/HDZyOFi4Zw6oWnThmgFGorbuQ4hrbnDgq+hLD3FfqrjShUZVV2ao3Qd31INaK
+xGDC9pqs7K3dJmWo9FlJxPBAVvlOYGO0YjBTvr9Te8iwJi7Zk8GcE0fz4xm4qtdYNW3e/7S075kP
+i0BIw+thLzQTiz5rK9KPzC2pz8Xdcz1Zp6tlTrYtbdtz12mv6uf1KrYCWip+n9BTGq9TBav4DdYX
+nlLiM9GylOPNAbWJ5OM3QxuUDMRT7cpbrRrJmrT98hNPYatEnj0tyGLhPvQaMwqNosc12cE26j3s
+FdmV/VsZ2Vi8sA/v32P9snh6aTbnOFeJv215Ts8OGjx9S6jEPvNiykq+YL52SZWN2q1ViFI5brjI
+WBjOMa4S8QQvgSViMOX6BKOeY8E9iBUApXgK22/ideX3VrOF3ZW+upUGjViwGk6yA1cVfPSa4jBu
+xthnyq949b04ylfJmUdX99RmV+bsdr5OMVaJtmZAqs9m85bFcSzB0eisYO1prY68Jjv++ysTVjWD
+UZgJQUu8GO/BgL26sFlXW4eEmo266atI0myTVqyFVlHJ7EgzO0cWfU8zLfOC9sOc2Juzx2JRKDEU
+loNbDOlFAvTZmBhK1dorax1qOmkmpokzF3dvgVAbx+eMVAuAKMQy4i5Vy4JCwTKVgctuUwbbWVG5
+8LxGZSruqTPYIlE2ITvv0FSceaXuPCRnyZQivmtKdpTH/2R3nbYnac2hc0qQyUv21PU6uyvyQC+p
+G931Kkya9wAIxw1mEyUTKQ3nMzlfj9UdbOQ+ewpuatcTasqwk53PCPWIMhglWNr+1MiYtP2AWqxi
+pekq7bpWWt7vcOl75dw6LY5ohPKib2jtQ+UJWK9u698l1KtyXIuD28TnkiWYzU6oqkirOVNFo6xD
+PpE+35i+j2RTxjAO1/G0bL1RbnGtLXTokVa4muEFrWfl+PD0yW/ywlhrPvDxtP4jmR0Squitq6LA
+Tq37sDyZ7BLWoi6hvks+l5c3HV8BI2NrmpKmLKMJRUVjOz2LoCrAqrTZpzyV+IGdGJJ7Mgbnh78G
+wMXpnOCK3KJSSakcahzIMsw1zRr05V794D5rXYk/2PmURibX6oWH5D1ZAPbSM9Y9KWteb6oYb9zC
+DWUhrzdv4mUaB1u7xehYq0fTSyj7P67Guzr+gCKR67rtKcd6BLs2uYc5E8apC75GmC6OD29OaHZ5
+fFja0pb2BnssPAXLsqkEbcJZ8FBVqeJWiNZVtjxyyGLZQSu1HKN471JX1CwsSFWcxDIGX8UUjXFw
+NVecVGNGhexuzZHW2HdSZqXCqo8n5DWF9mYp1DX70Msp9TuFYtW/YFW4GUo0/He+dps0vAdAWuZk
+CgCyJwVrHdm5OquymtuJRXMg3sr6fE5TtQFzUyHIZKdoaETbb3m4vsq313yqWtmZ+WNyrfmYzQti
+ZXbWTYJKDMWqMlEXGbNQ+l/bG/Hs/VcB+D6Tcropu1hL6cT90zatm6pOZcWUqmfoFW1WtJ2J3yFX
+1uHZqQTGsqOvwaaqJplTKkN1fe0ZaDQ8t+acHSt3xMuCb/i5ezlXmyJx/4WJy4qtZCmBRaYVo7hT
+aqF4FplyHliFi4mNjpFLsqDvmxekoRKrWCG5Vija5zpHMhdH2ZVTP6XI5OjiOhVMW+/XmFBMZDzm
+Qw1mt21qgWQX6pNV7E2Ze96kzqpKDOQd+X0SNchP5VnHuyNihUyHF0d0mjKe5w2fxro8k3Ck1HWd
+C0JPvIbKxYSyIRiYMrsg1MxBOEzRUyobOjeHWZfNXNpzf+DRHissfhBxoXU6WQKu9c1J0S89haUt
+bWlvsMfCU8AyGM/DeFUuMtl1NqojOnpeDro+00jO5TVnTu4rn4LSYCVZghUriWZ9zjry3dLmIUzW
+SqYkswUxqfzeDQ11RfQVlRrzRc17OmDBdzodjpkuauiR1XfQb/HKQPEESYMkVWozb4CrWpGZnXJ+
+LufvHclkUsn72KqJublR0FzsDus+xxqvaFkSiCuNwQ40ppJ2KTXP7c0sSJUcNJwzVtLQlY7sklvd
+AFsPnzNrzK2WXPfW2hPM3il0mfc//SyXV+Qaw5lc99C4WHdUA+LJCv6OEoZOG6TnsqsmwRquBkTj
+QgKAdyYF19aUCSkqcRz5/Tzq4U6lTfG8T1Xh2JOqpEVvPnHEM699t4xx8JvMzzRmkDSxPGVbyj+A
+HaqugaNiP26XQmHeWD2cRXVpllOki+rCCF+hyaWyUiepzbQnz7qx51EoS6BtqlharemYVcpUPMC2
+I20YljVWrsruH6xtUijzEpVDhjovTs8V/Xr5kEQL01bTLYJ18UzODy4zUQRl1zqhooVyOJL2/Mpn
+Lbab4qH0y02Cm+ISzO7buEreO0hKmtsyx+eqi9HwWuSOvCPTbMSBts2zS7pTiZ/MvJgyXnAYPWLm
+pW+nGcC1Cs7SkiuJdGZaBvjKwehZFnmgDL7HIVN1NXMNuOGArXUGaVhhoDRgZVLSVwrb1U59oeXC
+aSoPIzh3GXVk0gReSKTD0Tgf0bdlsPtxzEwr+Ap94X/xt09ozH8JgF5yQWsR6a6CGyjIyjcYSybY
+WINMZdOmpfTdF16FakvunTtt6pdk5chOdKFwI3JVbzLTC+YTuXfhBRRKzlJWDRXFGdhacZg2PeaR
+lj3PQ0JXJuN8LeRsJMeHK3HCqYqvXGzJ3+/tj3ktU4GU02M2tpSOzNtjbBTPP3wBWwO3h6oJOXs1
+wrsl18qDjFg5JrN4SKxZkMHsHr9zIRN9pD7wr5yX7K9/BYDnTlw+YikE17PINEjo+hZ5tui3LG5Y
+c4QTGIqDhHAmL1PmRMwX2Yc8ZnpPFuqi+py0IbIZzRVkVYRsGmW2Dl1aubzUWctiVpNFrdCK2dZG
+hqNB5dJLMJY8J+vYo1C1qDCQ7zYvNikbErjNt1fIFG5f3XmV9POSDeifz/A25KWOlar9/GjI7Rd0
+IfSeJ76npC/nGZVVJb6pgHcqbf6OdZWiX60RjRRWP51woHDrO+cDJrZybBqPVWWjvniTEpPL48PS
+lra0N9hj4SlgORCsseqlTNU1qk5rREZW4Gk2w1JF5OFsRpzLiukuJM8qVaa6q05mMRjZEWpTG6ui
+Of1slTJSpmGtkIvnLpYy1c+TCkZdsZPIcNLXIpfJAfOFUEdDdpRVoD0W93Ns36Tg30s3TEmqRVeR
+PeDwWDyZlfaz2lEfO5RV/oXzgBDZKSdxk1ooOOC+ipHsbo2o7koqMCgjYksl7UYjSoV5h/GESarF
+Olozn81KnIm0IbRcLqmk2Tv9D9FsvheAV5P/hfRExtlo317bcZg/kGtt24ZyS9J0lfUmZz3Z3Z3h
+jP0L8SYmSnrztdGQ7DNyv9YzHZK6jP3Ji2eMFDXZTmugVZAXpXiCf9yBRk+FWopD+pYyH5curn4n
+TaaUquNYDJQghRBLCWhTcnIVognz2kPMRWbch5DfVI8J3jTFCXRuDdehpnoQ1oSpjqEd32dyIe08
+8+V4FPVarCn3grFc8iPZ/ZO0z6tT8Ui3XpGA8mudITc7OrfOS5gI8tLKDScjvccwpa1FeCuavty1
+1nlhJCnpJN5kHkufL/IJhRKu1GKLZFU+f/FAnkGZT3FSmfe9WcIo1fRlVhJEyjNRhiTpN/eaPyaL
+go3xm5xGF7SQF+m8F1Gpq1gpDpmW+A5ym6SQs32jkEkw6vuMpjLo7cwmjhR74HoPS2GrnSlTrWob
+6DnckBAnct3deZPMl3tPUhjNxYWN5hZjpRT3KzIxXzg8JFAwyjQ2eIU8LDcLyKs6Ye+7DNYXjEtK
+0nIp5d6+PPzzxOYLGg0PqwPWxqpHuCZuZtS4RPdYvttp1qmqrHvcCslURSo6hVyh0BOlfQ/sEX5X
+Xe0o42deXQB9fpYvHcvk/ejsiPWOvBR3Gsof+ZLDttK2+9g4ubwU8e2MTQVUnbstgg152foTyT58
+/GpJlunEXO9jLsRtd6bQ12rAfnrBfcUhrGiJ+2zuk23IcyzvGt7XkaNGbpdgL17CGHI5emUVJUiZ
+pSTnSp1fpESK63CTOrkymdu1HE+zFVkkL55TcbGb8lm1apOrEM8sTqmcy3yY1k8ZT5Vc5VzasPP+
+FUqde0WvoFRa9mQ4o61V63OtKZmywfSuPI/BLjgjybS89tIrTBWH0i9d9rS2YzqSRd9LDrnRkazN
+ReQRKYmK3TJkjoxbahJ6qjHp6Gdjq0WrI3OzdwwXZzK2p+OUWOMHeVmlvkhbvElbHh+WtrSlvcEe
+C0/Bsi38Vo3KeYMvIav59WGPDQ32dK+vkJUqBlM5IzxVEY4jcblHkUOyLzveaZrQbStpRn2DukrE
+Ff0tvA0toFJXnBK6Ghi0VxyGM2UUnu0zUX6C+8dHzBL5u5lSjTlBA3sogb2cGcrHQm6luKoBMakn
+FANp030lZym8nIorO1fV2LzvkgqLVK8zb8rqH2gEubnm4CpEeZZDOBdEoMk7hFMJrh2M5vQUs3Ft
+XcZn1W0wz5SDIBiCFiINvnSNO++/It3+8pf45CVxqzsT8QjaruFcXfG2G+BpVVW85tL/krjza5dT
+NiaCirzxJ+R4NXrx3azKRzSKLabdlwB47mvHeKoRsT+eMTVarBSK67zRaVGotkIz7fElzfw4ONhK
+bxbbAVb2Zfl8McauR+4q2/Pxi6AybdPBgFBFfpIDn6ZCoYtSjiUTA45mC/xOG8fXIG5pSHzxQvzU
+elgJu/AkRsO7WDelnbYTE+ouHxYPGOoxZrAv/7/5CcNKS4ur5jXMhrJxr38XyWtyxPxKfs4Xbss8
+Km9+CYCjuxcUiv8wlZDTmXIyJD08lcvLqikLtPJJVY5lG5U2ZSDP7Ng2xOqxWe4QJ1QuDzdhqlXB
+bxbR+FgsCmlecDSbcV49JT2Sh9grYu7m8uLVojbOhjTVPXApVaWndyIDcuesJKjIiN1r1/nOSzKQ
+g50P8cy5uHD3jUtVxTBcLY+9vNmAVblH4dTxFKQzHFSY9OTFS9OAljLaJA8JSu+TcP9h+0utkktN
+Tq5psbzpkQTSTk8Vm06ijLVYrvuKM8QLZSHYMNt46SLdJP04eD7GVdCMF6yi0o/U8jmxpkCnmaFU
+FSZLyUimGwFzFbuxJiXD01cA+KnyiyRf/nfSjyjBaYvr/r3DK3Lhpkfpajox9xhM5aU6+e3n+Lnf
+uCd/F46wVHuypXGdclrQqInburJ1has78tK/5AaczuQFu5N7uDUZu7WudOSuZ3GsIq6H5Yy6xhwK
+Ez4UTrGsNrFS8CcKanOjKbnC2+fVJieHmuqMTugppbwduOTKprQi6zGT2Kej6cvzyQNqzx4vHh8N
+LRm3mjFeV8lX1OPubjQomgJLTms+hZ7hpy/bHMYyn65q2oH5lbEAAAirSURBVHdw16d8StWbbtWo
+KhBt/h0XhC/Lc/9XL13gKednpqnJ79rpcPgOIQfeNEfUtwVi/r7siPtDufa9z/0yeSLj3F6VeROu
+VZlZ8vuD5AGnqSw2F/M5lDJuZepQsb85QvXl8WFpS1vaG+yx8BQMhkrhsGNd5awuO/D9+1WMwjm7
+B1MuaUVkt9nBpLqDakCtnR5S0YDUO1d8nl75TgDc622Gkbi+lfDOQ4rz7SvijdQr29jKhZAXHrkl
+K7ApY8xcdrzN1pSmRpRrK5oZeO8PUv+85Mrv10Ky418GwLZTCl25/T2Xd9kSPMqV/+D6eIv4soBx
+Nl5ZZV8r3KwHE5JN2ZqCqbL6ViN2x9Kn7WtDOu0rAMyOUxTxzOo6WEqF1rJlS6zPd3BrcmyZlBU6
+GzIWm4cpvV1pz9bkNepfkb+711Jp+L3XeEIrVId2hWRf23Ej50P35edfPMjZUT4IryPHq8tOhZc0
+mHuzvseDmVQctnKX40IVuJ0qtT1p9DUkuNasF2S3Jf9/EXyWdKxucurgWRpcLMbYGqEvXpNnloUB
+RUPG3vdiKuqlXdyrUQlkbOvldVCuz10dt2zlHOdCvKmz0wPsVCUHnTt4iX7nBZc4kF28s6Z4k+Am
+zmgRUayTjeXIajYKHH0Q7l35/fkzfc7mcq3W4Qgr0PG2p1irkvn50GrKvl6v0ZDf34pvcPmWZGI+
+5P8Iybu1r180VOqSuaq89kF6e3Lc2AvFu/W2KmwgHCA7/TGlspUP3Qximb9FM6emx+Ieb45s5bFY
+FHJS+pxBGdGcS8PrToYVySSdM2AwkQdaLedYymfXtTQ7MYuxxkpVfjGnH8k5dH/Y4apG6mutkF1l
+03Fb4gK704iyJe5emSTEU5no0bCkrvGHwO3QDPSMq15Y2Ppl9j4oqlD3nyu41pGJdODZPKFusj2G
+Xlvc+KuBxhHcgsKTiVDbTHlCXxA3hmFFqMg7etRo+xkriqprNH3mClzp1HNm6tq6uf1Q5DRXsr5p
+eodoKG0oZzGxL2lPszXCHMgYfmwbfueyxBQ+MZAqQs66jLWmYGPDx02UWar6NJV3Sz9+4MaQ1Uza
+P1c5dD80bAUyxvVyRFHIwjk5OWVlVcZ5dTDFCbXEby4xjMOhy7AqYCpzL8Uo+W1mxliqnWC7Hr5S
+6UdN7fRoTLIgljmPCBQButu1SOZa71AZQEtc7YoS5GR+FVfjS5ttg7lQifvmGr7yQ9rXExqHegTx
+ZONpD0LsGzIv8tkJti/jVR+HtFVx7KImsYp33IvprsnCNF3fwzmWOELfymEmsRazGvO0zrOTCyW6
+ad7ha7/0Oblu82e4d1sWyNWLmDttuXY7y+hoNqemwsUxKxwPZN5fnE2ZKsV9ToEXyM9W7NBqyFjs
+L8FLS1va0r4VM7+rDfs2NsKYc2DG78q/vNW2urz38t7/P7j35bIs177Rlx6LRQHAGPOFsizfv7z3
+8t7Le7+9tjw+LG1pS3uDLReFpS1taW+wx2lR+MnlvZf3Xt777bfHJqawtKUt7fGwx8lTWNrSlvYY
+2Nu+KBhjPmGMeckY86ox5m884ntdMsb8ijHmeWPMc8aYv6yfd40xnzHGvKL/7zzCNtjGmC8bYz6t
+/75qjPmc9v+njVFKpEdz77Yx5meMMS8aY14wxnznW9V3Y8xf1TG/bYz558aYyqPquzHmfzXGnBlj
+br/us9+3n0bsv9c2PGuMee8juPff1TF/1hjzfxpj2q/73af03i8ZY/74H+be3y57WxcFY4wN/EPg
++4GngR8xxjz9CG+ZAf91WZZPAx8Gflzv9zeAXy7L8ibwy/rvR2V/GXjhdf/+O8DfL8vyBjAAfuwR
+3vsfAL9QluWTwLu1HY+878aYHeAvAe8vy/IZwAZ+mEfX9/8N+MTv+ezr9fP7gZv6318A/tEjuPdn
+gGfKsnwX8DLwKQCdez8MvEP/5n/Ud+LttbIs37b/gO8EfvF1//4U8Km38P4/B3wceAnY0s+2gJce
+0f12kQn5MeDTCD3lBeD8fuPxbb53C7iLxpFe9/kj7zuwA+wDXQRa/2ngjz/KvgNXgNvfqJ/A/wz8
+yO/3vW/XvX/P7/4U8FP68xvmO/CLwHc+iuf/zfz3dh8fFpNlYQf62SM3Y8wV4D3A54CNsiwXtbQn
+wMYjuu1/B/w10EJ8WAGGZamccI+2/1eBc+Cf6vHlHxtjAt6CvpdleQj8t8AD4BgYAV/kres7fP1+
+vtVz8EeB/+dtuvebsrd7UXhbzBhTB/418FfKUgvP1UpZsr/tKRljzA8AZ2VZfvHbfe03aQ7wXuAf
+lWX5HgRW/oajwiPsewf4k8jCtA0E/H9d7LfMHlU/v5EZY34COcL+1Ft972/G3u5F4RC49Lp/7+pn
+j8yMMS6yIPxUWZY/qx+fGmO29PdbwNkjuPV3AT9ojLkH/AvkCPEPgLYxZlGt+ij7fwAclGX5Of33
+zyCLxFvR9/8IuFuW5XlZlinws8h4vFV9h6/fz7dkDhpj/hzwA8Cf0UXpLbv3N2tv96LwO8BNjUJ7
+SNDl5x/VzYwxBvgnwAtlWf691/3q54E/qz//WSTW8G21siw/VZblblmWV5B+/ruyLP8M8CvADz3K
+e+v9T4B9Y8wT+tH3Ac/zFvQdOTZ82BhT02ewuPdb0ne1r9fPnwf+M81CfBgYve6Y8W0xY8wnkGPj
+D5ZlGf6eNv2wMcY3xlxFgp2f/3be+1uytzuoAXwSici+BvzEI77XRxG38VngK/rfJ5Gz/S8DrwC/
+BHQfcTu+B/i0/nwNmQivAv8K8B/hfb8D+IL2//8COm9V34G/BbwI3Ab+GeA/qr4D/xyJXaSIh/Rj
+X6+fSLD3H+r8+xqSIfl23/tVJHawmHP/0+u+/xN675eA73+U8+7N/rdENC5taUt7g73dx4elLW1p
+j5n9v+3UsQAAAADAIH/r3XMoiKQAjBSAkQIwUgBGCsBIARgpABNASbw2XodlWQAAAABJRU5ErkJg
+gg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3802... Generator Loss: 0.7881
+Epoch 1/1... Discriminator Loss: 1.3059... Generator Loss: 1.2800
+Epoch 1/1... Discriminator Loss: 1.2707... Generator Loss: 0.8262
+Epoch 1/1... Discriminator Loss: 1.3435... Generator Loss: 0.7216
+Epoch 1/1... Discriminator Loss: 1.1423... Generator Loss: 1.5386
+Epoch 1/1... Discriminator Loss: 1.3705... Generator Loss: 1.4615
+Epoch 1/1... Discriminator Loss: 1.3879... Generator Loss: 0.5866
+Epoch 1/1... Discriminator Loss: 1.2942... Generator Loss: 1.0528
+Epoch 1/1... Discriminator Loss: 1.2455... Generator Loss: 0.7173
+Epoch 1/1... Discriminator Loss: 1.4368... Generator Loss: 0.5770
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsZVmamPWtPZ95vmPcGxEZkVNkVuVQ5Sq6yz3Y7cY2
+tts8gLEtgVtC2IAssMSDLZCQABlZPAAPCCMDNhYCeZCRbHDZ3R5ol9vdNWVNmVk5xBxx5+HMZ88D
+D/9/oyuRuyurq6udoPM/xD1xzt5rr/Wvtf95MFVVsYY1rGENV2D9i57AGtawhk8WrInCGtawho/A
+miisYQ1r+AisicIa1rCGj8CaKKxhDWv4CKyJwhrWsIaPwI+MKBhjfp8x5gNjzD1jzJ/7UT1nDWtY
+w28tmB9FnIIxxgY+BH4WOAC+Bvyxqqq++1v+sDWsYQ2/pfCjkhQ+B9yrqupBVVUp8NeAP/wjetYa
+1rCG30JwfkTj7gJPv+f/B8Dnf72Lje1Uluvh2hWuLVPqBi52UgIQlRlZlgGQVhVX0k2p9zsYCv1s
+UVFaBgAXQ2n0s4Hc6EW53JkWFRW/NpatP5fYuPofY9k4tvzHsYSGBlVOpGM4bkVZysC5bePojW7p
+4BkZuyhl7pVr4VxN2lQYXUdlV5jC0mvlgrwoqSz53cbG6LMrY6gwOn+IFS9RLte6rsH4dQB2ax6J
+3QDAs0qKXLCUJxGr+QKAZZ7LuGVBqUKjMTx7hgWUzz5Xz/Bsnu2BwSgOq2f/XH34+FLo1dY8f/0G
+pREclkVBVRYfmSe+jZ/L756pyBS3xnNwiyvkVmSF3Gf7MrKdO1i6H0me8mwjkl+bZloW2K7g2Srl
+r13ZZFWieLEoFEmmKjmareQaPSuebdGu+fI5zUkqeUaWFc8+U1Xfc24FCiDQM1tY4Ol8Mts8+1wa
+A0X1bMq6TBQr2Bjyyiguv2efyopK34E0zy+qqhrxfeBHRRS+Lxhj/iTwJwGM41K/9hKbnYzd3hCA
+n3t5i86HcnDfCU85OTkB4EmakVcpAKEioW8sZorpplUR+rKhG1VArC/phmsxVQxXF6GMtczIihiA
+VeXQVrkpMQ22uvLZrw8YtjsAdBuy4a9E53x7InMYbSasVvKMi06X0U4bgJ1wg31frpmuTgHIt32G
+kYxbuTl2rASgl+GMPbk20XsmC9KGbG3LtAgCeblTxyYzcu3jmcUHp0eCo7FcuzNycG69DsB/8do1
+HrQ+B8B+I2V6NgdgfP/b/Oo//hIAv3J6BkCZTJkr1aw5kOiL2SwNK0uOiV8VrBTPRglMisHoMS+r
+klJfpqpMQV/YXyPfvz5cHcT//j/9zwltwXc4m1BEMudfOb+QC273uHkuON73Ek5jWb+3v8HmWJBr
+TMzhXO5rPSd71hkPaBh5hR6MH2MPZN+Lh2Bl8v3D1ZzejuC5sWjK/VWLk/ghAK7rMV/JGbKqBf/Z
+F78OQLspuLjRafOzr14HYO/JlHulnLOjkwlPYnleWaaE+qKPlJlM7IoXAsFAWIftRHB42rW5nso1
+q5oFE53/FY7zgrGyslZlcV7KuahRML0iimlFack1D87PHv/GuyDwoyIKh8De9/z/mn73DKqq+kvA
+XwLwhs2q8ztr/JnGQ/6K2wPA87/M11s3ATgdTxgbQcQiKel1AwA6+hJ37YBmIsjb3LBY6UvToiIo
+BCGvvLjNROnnyYMlAMuHxyyVQ4fLmKUir0hi5pEcjpGfQipoilM5dF92E3Jkk7OFxUYhz/7JJOHx
+oXCP9q7Lsi4veCdwAXDKBoMNOfD1GDovyu/LpUOGShOPxwAM92wOl8ITvMKhphz/uV2buSdzzk4M
+7dk2AI9eE8Hs94fHfO3N5+V3729w69qnAXhp1OGLYzmkh5dzHkcyzzScyt/Kwqg26TV9tpvyMpW5
+xYZyZlOvQTQD4DiSA2riiqIh+B45c+5FMoa7SJCnASnfF67Ix8ydYmcyp6fzI8L3BXebxQMAzg7b
+9F58FYAos9i5JgTCnWQMN4UJrh6m/PgrgufppRCkRi+gOpM5f+HV2zw5ECJTjQxnp4KLO7cCLk5k
+slZdnjuPMxp12d+z2YJqXpNxw4TadbnmX+/Jnv2jYZ1G8B0Avt2/weRUzsvcc1iFMsZ24NGW29hx
+BFdNKj57Q8bN2ja9sUrIwN5I3oeFlTE5lPHGgh6sKsNRgtZ0AhpGBi5p0ZupdNAOeXp58f3Q/xH4
+URGFrwHPG2NuIsTgjwJ//Ne7uJnCjx0W/E/Oc/zxL8h3+fJnOOPbAMzLgiiTl9D3U2KltG1lQC07
+x+kJgk3d53NKSRvXGwSZEJBbd67RX8jR+3ZzIjdWMV+7kMGiNGIVy4FIqopYX3THCdjYkV2YHysh
+OLjAFXrFIG+RqjTyfn+OawSl884ZrRP5/qSU++7029Tr8lK1Oj0qJRbdmc10Wza8Wch3C3K2Zioo
+7kI0ld8zt8f1XNSDzH/CVz3hiK89lTn+tdDnv9G30I7/FIMTORD3jg3dw38KwOX7E57MRfLKn3Fz
+G+PIfAO3z+264rZTh4UgetZwaI2FoF4LBK/nWUK7JQe3mUa0LTmk7ywNtVLGiz6GpHAF8f0xvrKT
+7dVDJo4QnLdcme/OKqbekc/bzoDzQn7fbrVwjEiWwzdsEku+v65nxRpA2b0SJ/u8agmRPa8uKUrB
+3aLu0Q1kjNQI7mOTskyEaDTbLvZIPk/fnbA/lrX+X4kQoH/rs028Qojw6cVd0lJ+j5MIz5Lxljns
+qRTWVW5utS0cX87NmzRw9mSv43rAxrAvcz6MeGcgY4zm+rvlYKt05zoWc5U8tq8lRD3Zk0nPpfdN
+Oe+PPyZt+JEQhaqqcmPMnwZ+AVHV/3JVVe/+KJ61hjWs4bcWfmQ2haqqvgh88eNca7eg9xOGf818
+iqQaALD41oQ9hBvZeUWvLuLsPGhz0wgnLAdC7a83KtymUNTXdh2mqgbcCobUBiJebvQ2mOfnANy5
+KwaD1csl/Qdy7d9LwUW4bppm+GqpmUYlj89V30uF+2xVFYtEnleObnARCgkeTDdJd2TsGUvmgUgI
+z7dENAx2NxiMZE2+sWmrEWPVKqhNWwDUr12ZiFbctdQWkUFhCxfwwoqwLdf4xTVevn0JQKQc/KeS
+IY/kNjaWS+xPy7WLVcSX7otA/9b0PnYha7Jc+bvp1zF7Mvff//w21kjwsmlqnKgS/JONgncS0bWH
+oYy77+zx+U3RDI/mXb56T9SYqdXlNJPP/rFhWn48o2MxW5Du7AIw773B/eYTWcvj24LjN0fceEXU
+BKvZ51W1I/i9Hv3ecwCk9Zx6LHuVq3GxVuuQOrIf7sKwGIotZf+8g/umrDU8b/LUF0mgd3YMwNmw
+xH8s0t24snAmIlWsjM/zv0/ue613S57RvEXyFcHxy4nH8VIkE+PbPKzJvr8QpyybytFrisOGy++5
+LfO86MRsqU3BDYY0unLN3XRJcShjJ2pQvWUXvKcm2sDkjLq5rsMlseS+YV7yRKXPjwv/wgyN3wuN
+1ONzT/cwUcrbdTnRR51zTr+rep+J2PLlwL7ccbhuywu5+aogepcBG6+8CICbx1hdFZ9Dl5otB8ze
+dAgey4FOOo8A2I9c4pfkhX2+Mrz7vojSdgVLWzfUK/AdIUh+Sw7MeFFQ/7Q8Y7i1SzCWedSOzqm7
+cs3mckR9Wz5vtGXc6ztNGi2ZQ4CHqeuL6WVknr70CxUz6bGr4uVxNME+kUOetVpgy+eOE+Nlsr5L
+S4hC3zOcPRZiOXE/5Pl3xHD7rfvHPDp8D4D56oJI7dZWQ5472tjlp27uAHDt5nWu9QUv1qLk9S3Z
+p/Mq5bOFEM7Vhay/61/DbcvevFG7ACVOoy3DOJIX9uuTCdPwNzYs1PRvuu1wMJKXcL9o8nohBODi
+RTH23dnuMzQbANRnKc6OvJh+p0Hgy7moWx405Hl+IHtnshq1WPahaM2oI2OkrfcYngoODwZz2gt5
+IdOu4LicZpz2ZE1eUqNREybzYmcOh4KvvVRm/3CZ86ArjOfkrRmWL0SotfRoK03MM5tbsiVcC+Xc
+3PhcD6sp497sNaieCt6c/Q1K3Yf+cQJTIUhtVz0yTZ92y9Zxa8+8ZH6wxOsqc30MJlv9hrj/f8M6
+zHkNa1jDR+ATISkUZc5iNeF+uOLgSDhRcgq7llDavFbjhY5Yvq4NfO70NwFwdoTSjqwNug31XXON
+WEVANwRTF85rzTJsIxyo7gln270eUop0yrjdATUizc8MdY1D8IzHwBFOkk1FfOtmLtsj4WC7lc1G
+RwxN2ajBmXKNHeOx0RUWu70tc3f6bdwzGdeqrfAL4Q6pO8VfapzCUI19saE0MpbzBOymfK7lHqj3
+ATuj3heu8pIt7Oebjy4w5hEAp1mFuxAu99bRAy5OhIsVaUXPl+cMu2KQ+vHf8Rwv78rnW3mD86ng
+oqwsTCpzvt3yuJjJWod94aR1Z0a/KbiYrgpubohLblau2PFlzu2jb/I3E40zUV97Cqocyme1keEf
+9RnuirEvGLXoT4X7v/mCuFnLTkbHk/3w7BW1uuCQro2NSGHGszFGOGVlyxkyJoBA4y3iHpSCIzt5
+AbZFzdmYtMlagi8zE3Uu208ZfCBzCLdsOpdydk6OTiFWr8RAzsXp+ft4j2U/bvsFK1fE9v2ey4ka
+JV+suwQjkV5eG8ncu0MXf0Okjsi2aCTyWtphgvEESxYNbt6R9aUHsh+rmsWOntPTgUM01xiJw4IL
+Ncy3ez3eOb6KwPl48IkgCnFS8P6TCSdVBJlaU4OIWC2yrw9rvPiCIG3T26XRk03ot0SHbGx6FIEQ
+ijwO8Qs5EGbHIi3lcDt5g6CpwQeuHMwwOaR9XV+EZUV2IQfia2FEoi+F7ccEHXmZZroBYWioO3Lt
+zUEPV0Xjy8kJN0bintoa7JBrHEKoATideYrZUf2uvE6uTjs7G1CpiF4tZawszwjUbrEYTDGWEsui
+RWDLeI12hwv1UExsES3tZkVYyOFYRWO+dSAHfhVOcVT/dNuw1RN8fVptLq+90GNgC45XySWbdRGv
+g9LmLJB5ZqVP36iq1JYXfuVElJkQqY6bUziyZxf+BQdvyZzc6zvcCkX8/yBSm0lZPfNUuoCv4vXB
+zZy6JwRuw2niDpUxlELQd8o2NaFLmLxGWVM7QXEdq+XpiD5XflALubjyAZ1n5axwEPdRPlxhnYp1
+vvRO6apXYtaRvbPDgkJtV/W2R7Ah++dXm3zjF98XHBVC9Ov4xOqqXlQFO0q8t2sB9Y4scNiqeEHP
+YWtbcJnW69jIuPZRSaqBaLN8SqPc0OcZ0ons68yS+faSGmONF7EfVMwcOXvNTsDuDXXVlhHW3Y/h
+E/4eWKsPa1jDGj4CnwhJoTQVKzvFTiyM+l3PEsOrKimY/gC/ISKq1/IImiKK5b7eH1XY+tkxAYWr
+XKJIcRtCla2VIXKEcxWRUHOn0aVxKdc2ugErX7lgWZKphddc5twzYvAMVOTeL0u8e8KVvjJ7lx/f
+Fzbf3u1yORWucW5dstUUblzXIJXUruGshLNX1gpbQ7pzq8JOZE52R8OSlyGJBrQGK4+Rqg/xrGS5
+lPUnq1OMBjWxEE7j5gbb1tDfKqVU41oeWc8klpqpM3IFYbVdkQ7ycpdSQ3+rPGeSybNbjoetsQB5
+aKi6wsXipvzeYYSlvv2CLl4u4nPHcihfUg/Nmc1zpeBw+o5w/KdZhUZx0zGgq2DrNKJqyP8euPe4
+cU28DhsrxaETEGTyPMeqYS8FbwxT0HnilM8CzipbYz0KA4pvEzuUnoxhogmWGg/9k5iFGgezM7nP
+qUpsEQopA5/TpxJEVSSw0xEcVupZcKOM41TO0yCNWCxkPs5WTLMte9YyDpl6z1wj0midGpUj0ljs
+xziV4KjX28eta9BeWLBxW/b4jUDwah555LFcOz1a4qpkulwm7HiC7/z2EO8HTHr8RBAFQ4Vblqzs
+kLpa+nPHoXBErysc6OtMm0UPd6qb64qnoshrlFfhp6OA4konY0n5SHX1a4ZC3Wmpqy/mHC4a8vnw
+OGehLxB5yUxFdKuK2LgKzNewVbfvEGzJBr3s98nOZPOPkzF1X0S7hrtH1ZH5o5vi5TOqXIOsXI8y
+0EOcWRTqPWCqIu52CzeXdTQaB+RP5UW58EvyXA505TikscxzHsjLuMhTqkuZcLnIWGik19KOqddk
+/f6oyeaeEKz9PVG1+hTUNVLusvCxHFmHnbewlEAmbQsvUgu3J3MPF1Cpdb66TIg03K5+2STXMO26
+FxNogkGzkvuMiZ/lHIwrWCiK007EpC6q0meXbYozeYFypRpOusIsXcWhD60ry0RClargG3vgXWUF
+aBRWGkEi11ZeSLnU6NXljOJA5jkpZ1ycClGIV7LX4wtYeIKL5MGMmuagFNmSu+cqrjdlvvXzgKMr
+9SiGWk3m7lxaOJUQ9cZOwXBPnh3P9FAbQ6FEI8jrlKpLZU4bM5T76tkdVuaufD6QuV3ejPDeFlwU
+8YpEw//rGxVhV86y//AS27pyc388WKsPa1jDGj4CnwhJwasM+7lN6FusxsLlfGpYPaHs284OfrAP
+QK9d4LsS719oaDNPIrzdq8xIC9vW7MKoQxoIla+WHsVcqKdrxLi4ahV0jiRk1k0WtDuCjs5lgyQS
+CSJMLFzlQHmkoah7fQZtCVgpM6imEggzMDlZW9Scbi1gQwOuGq6wuazvYJ+rYaiWYjQcO3McrKlw
+8aouwUhmYp5lSZp5nXlNOEl3fs7EUt97mmBplmR/ItzndNzED1S9yMtnFv6uA6Vmne5VG7y69ykA
+6ka+890e81iMko3SYuULjkrj0+rKPOvNiqzSz7GMHOUnWJeCl8lFRB7LPGb1iKASLt2oGsRqSa28
+A5l7bmHpfBxLsgMB9swWA5UWk67LnmYtdjvXZD6dCKeSubkNC9uIKF05BtTgZ/wEcuGw1VVWY9Gk
+8oWzm2UOasyzyz5pR/KE/PdmWHORemxLxkqCc4pD2bPEB/sq1iGO+Kzm2Ewawq3HtmHLks9Jq+L1
+puDouU/3cSdy3/btOlZTjJ+erbEi9op6KFJl1QKj+SNFfP4sWStZhPQagudsW57bGFdcqofO6UCe
+yPf2rMDyZB+279Twv/mDeR/WksIa1rCGj8AnQlLIgBMDpg5+U6hd0K/TtIVm7Q0C+vvCbd2gD5Fw
+/3pdXFfWazFUQn1NssRRX3jWi8hWakeIl+QaPlrWRf8+enDBOFGd/DJhqJmWTq2G21SpgYJEw5WL
+TO9zm5ShcJcgvs2lJTreYlLy4pbObfQcaPRiFmrcRLWPtaXRZd6IcimatJUZiqHm05diGC2S8Fmd
+gtT3WCTCwY6TjELnHJQeZ5bM6VS5zrBnYdQ1V09sjh6InrkyBXV1kZ02Sw7OPgDgU57YFsJZysTI
+Oi0vZKMu7t7tYVuMOkCtyqhfcV41OBb+BvlKE5GuwcVEOGwrmbI6EyPYcGfEK6Hg5WKsMQgHEZpF
+ToDhSuibDUp6Gk3aq9epNPFqlYvhbLjsYzb14tqQUo2xtrUN3lXNAp8KwbPJVQLzEyhkrNJbPpME
+crcg0OjG1faKfilrPVWDoePMuDzV6E1y6ru6p2OL/+WprOl2R+0s4ZCaft49zdm4IdLB6jDk7lyu
+vT5xeHVf9srRMOflIqK+J/h0sjpVTfZ6WlV4b4u9IswNhdp8Hsb6+3TF4lKeMT6unmXlOmWAmi04
+eLKk6f1gNoVPBFFwbYvNTp08Krmo1I2wiJlpjPhZWtE6EoyUrYrAlriBpW5y07HweyoOTqbkKj6X
+k4pEA5bGd894fCkH5VuaNmzjEcZybZbPWKgf/9peQEMP0zyr6KpxaZ6JaHi9VcNSq//J4j6lBuYE
+acXZuYiBzX7ENRUv07nMIUyP6Whehj+q4FTE2bJWwKW8kIlmXCYXp4ynMrfLwmHVkM/T2KZM1VK9
+yMkbckBcVSMqu0G7K9t6+yjjTNWDjuWwUMNeczwmrMlz7pairoSJT4zguJavGG2LRX4ZpuzvC+Fo
+rRZYLcGBVQjxqkxIrGnWUdbD3pHn1aIhVinV91aZR3NTiPatXcHJ25FDZyx4fdMrOc7k4PbjBE/x
+PF4es2ckGMpPhZgs6g0C9b54kYsby5yrrSYEamx2G5ix1i+oqUeFCjR+w0QepSNjpNmC0lZVI3Sx
+m7Lv1qWM22rVaWnhm6LeIJvLnhVxzo1NzWPx1bPgLkjVUDzxcs61RobbyPBSuebxRcSpxpa80lZv
+16AiP9H4h/wS5YWMF+fEpezDQR7T0xiJYVdUpiyqkfmSoxF0bPxKfu/YNjPkzN1qt3lOCblU0Pj+
+sFYf1rCGNXwEPhGSQlKUPJiuOAxSKpWul3kGQpT5unmfbixi1KE3oL6Uz9V1MRLu0qHTvwFAt35O
+rgVZysmS06ciVXw4OeNpJFy1EakRqXWTpfqEI8+lvLJ2hQ0KVTE8EzK7KlhRiHhW1B3mCxW1w4Iz
+9SaehRN22+Li20kXHMyEA3mHspBxf852pGLicoeuJVw6TW28uXCPcx33MhzzSBOK2laNRycyZ9su
+CDUociOrIBfR1xQiXru1JeOHWrBjUScIBKEz36KrrtrCr/hwKtfbc8FPu6xzFokEks4XtPX706Mt
+0lCMko0btWcqG4XgbTHOOA9lfW8/OmMzEM726DTFdUV6aQ4j6io+b0WyTy81QsbK/Y+KGjcc2dPl
+as7SlnnsVHCg7uc00EjPRUhfxeHIe0RPDZ9FFNJSlc+xR7ieZG5m1Q1dZ8RsqtGR7iHjY5lno3jE
+6ZFw7GV6zIHGe3QsWdODC5ujM5m7Wy1ozzQmoZ7wZXVfbgdqwE1qFDWZz2hmGO+oOhrZPFmJlHK+
+THh5IGfgn95X1ecg5w3Njq23XMKHsu+/MMuYRjKPszrshDLnE1UvHCyyusyhPnYZ7YmRe7/RxdfK
+UscHcxJz9Zp/vMjGTwRRsI1F162zihc8TGUxXTp4AxE5B/0+Xlc8DvZFiNW7AUChVtrT6DHlQg5B
+fVTHqF6XFSnxYq5jWKQzEfk3rt/Q37s8F8kz3ls9hU3ZjHTqsVWXl/vrBxkbmol2X3W6YRRAIAf+
+ySQlncgm+8Mm9kzjG6Y5uRYyWaqr/OTDjHEs4t7GvRjnJXm2VbeJl/IinNyXZIykVXJ6It8deyuy
+I8XFvkNNy8rFVkSu9WJsfQFTq8XeTSEKj1MYRiLC+jWHWA9HiU1Pz0dck7Fe6g+5bUR0rm92iVRX
+3/agk8khDIp9bC04El4IXk+fTHj8VDwKD+YrDptXQU+7DJ6TwCivekKiuSnNlhCjjZ0hL7yrAUZb
+GXGoXo3KxTLqfaBkpOpPoNmuYTllFcv6ouOcVU08ERdHF2y2BV/bzZD6hhBLHK1FOZ/znUcfCr7P
+jjh8JHuTdTt4kbxsC2eOrR6hQ19jIaqKrgY3LefJM8/WdFyw25V5vqBh4N+wl/RkWI58hz3NRL3z
+qRrdx4Lw2Z0Xn+XQ7HYEh28fF/iaRh85BaHWc5xOciZqB1otSo46Ml4vVcK11eLJRHN72jm1qRCh
+5p2cTHM3undS8kc/mEKwVh/WsIY1fAQ+EZJCkhfcncxI6xVbNzTCrtvihUI4117X4ekTEVcfTE54
+8rZYznevCfV9ee85LFfE3YbrM3A1IWp7C3cqhT2Tw4DxTK7/h98SA9iTeUizpSJnPOHlnRsAWK0G
+tY48+w23xUrLqWmKPqcmoww1nDeomIstjGrVJ4qFk/7iV97hvmYoXqphaWuQslsTI9GoNcWkws12
+gyFLNeCdaYbnwVN4qAbRB8cLLiKJIdieNthtynxudYZ4deFcDQ0N3ogNQV2kg3FrSHQuMRRWvaTn
+iMgyLR3ShdaSVKuWZeUs6yLlHJ4fsdHXxfZvEKtVe+Ec0VkIZ06UC5blnEL97oOGx+lcnvE0PWLy
+WMZ7qRczGsn6Wr7EK5h7M4568vvmrRHnZ4LPyElwNX6h0Qyo1IgXISJRK4ZTT7MS7QuODkSycu2E
+OBBp0mVF370qaKoZjm5BvpJ6EpOoR3MonPQXDhLy01+RNcUOmwOZZ3A1VsdiqQVuurWMlobWOmcl
+985kHjM15N0c+LyiMQTLs5TtUiSasy/n/JImrF28HdHd1LqKR7LOr0Yhlqqov7vXJg9F6nP6Fd5M
+K1t7LvNQxj6vZP/7iU+21FJxQYOwKzgc1TY4fSJnL/UrNt2rahWq534f+EQQBcc3jG765LZDTfXo
+7WrE9VuyQY3mBuca2tnc67BVk5ds5zkp4DnotvDUIu8sls+8D8QVwa4EGfnZmO2GHKznEXdbOYh5
+IdPU07MWTUs+39huks1lE99Z5rS0eEVDg4k2nRqnTa2OM7tkL5AXpbZR47zSasCZx6AjVvtEKy07
+zYANTUP2DYw0o9DvBBSaUt3oSpDOkCluImP5lcvjnmz+m55LeUWkcPFrKuaqlyXdGtLX+d7sVDzR
+ykpxp8NYD1gntbh2XXTjwtOgmfaAV7evqn9PiW35vdfwaKuXxw0jZp48ZzKWPZi5Hk5HDutea8Rg
+KvM5HRW8qJ4dggG+uhntuhz4OG3QQp69s3GdFkKo/STHcuQQl+ESPxAC39K8jGyrxZZ6Hv12k75m
+qy67LjuezL/ZgV6pIfKqSpaZT+6/JPO8XhKMbgDwe/ZL3l/IM5pHC0J9Tn2hZfs7Ge5S9q9IfRIt
+xpqnJXtauPXVT8m1G65h/C3B9/M3MwqthNQZjfkxR1SQSdMhaWsdT1fO2P6Zxw3NhzDNmP2XZNwf
+C3ssMjlz9/OS0UTu+4qWEfiJbslXNWt1VFbsDjXNehqy0NqdfuwxsK7i9D8erNWHNaxhDR+BT4Sk
+UGUV+VFG3JlTZELhZ05IdKFcIlixeUPEwO2wgTUQbsqGZupFOY5a1qO2h6MluLwCvJmoAf39FnYp
+Y3TU3/uFoseiruLprYcMVKQunC4X2lNia/+Sx1oS3r5KZuoEDCwNSCoslpof79qbfGZLA4f2PoWn
+QUZLxfJk+R7mUA1DBvCusnwcnEI46EZNvmubBmFbjGR7oyY/nakvvetRIYFTtdMZmcZLFKn87bs2
+2eoqEKaEE178AAAgAElEQVRFrF4Jy15yo6l4cx2S5ZW0JVzn07euMzCyjtyqM1mI2uEB3kKumXXr
+kF41JBFp5XwR4aq6YvsJ/U2VGmjT2lRJp1VQTUW9uzgRbtZY+VzbkLGePMhZaPDVa55FoQk8fb+g
+KIUzh5Z6ckyGp4VVtsIOq44aHbsZA+XuQdqi3FDrrmaoOtMxviYgZdczKg1Zb5o5r4Yi0V00a9TU
+mNdSlWk8hvlVwc7OkuVTlZDCFfcfaxCSSn8PXUNDJc/4nsNIMzjPbY9U8dXy++y+dFW+T9Y2qUOq
+dS0aA5vqoYw72ndxJ2qALUKONfv1Dc2YrWp9fnwg86wZi65W1W64DV7x5dp3ZjEXzv8Hi6wEvsML
+Nwe4dcP7mgPgeB6FJxtQazTY8WTBfn+J6wpSY0+Q4y4rnubyomzOh3g9QYJt2dgqajtJRUdj8Vt1
+zUVozWlrPsTSd+incjjSfEmlASvfPrHYqsucTmLZxD2nzVlTDvSZ65I8VV1tO2NgvwbANTK0jgnp
+Sl7Mmb2Jpy/VcXFIT90SzpZNrSmfIz08rgftzg0Aml5BUmmgU1oQa6Jx1XY5Oxcc1bvqNrV6dBsy
+VsiKpqoXreY2s1jW0UgSykozPmMhvMGlRX9TRc7CodHSlPNpzkqDxVrlFmNN8V7ONFpv7LCt+nst
+a9FpCOFttFY0VY0p84qVRt4daEHUWRwzV3Xsx246vJXIsztOA9OXOVdxDV97dWjgJnYdWp787tUd
+trReY1xP8bXSVVZe4q/kGq2rQjPYpr0rg7h5TKVZsNe7L3DR0eKw8x7zUN3dWgezaITPUrLPLzxa
+NVFRTs/H7F0XBvaF23LPl2KX+Lsa3Tr0eWVLzudg2OeVlTCR4+fadHK5r3dbcHn/bsYbouUyfH6E
+dUvT9s/q3HxD1jE9uGDvVJiEOsForwqSoRCTuuXhaRq1XRje0v4Vo4bhUbHOfVjDGtbwQ8AnQlIo
+8pLFZcxOv81nX9EMOHdI47tasKLo01LrvMsOiZGgn7bWR4iHK+qBUGXjQlPFqNT28VcasOKU9Avh
+YoWWLzdLj5URLlEfB6Rt+X6JQ2SEs+21XVapBtbkMlY4qNOxrrL35jTVQGfFPm0Nvmr7CXYiJb/K
+pXD5lmkTaXEPz+pBoBZy5zr5QDjXhma9jQlpLUQiKJ9Lqc3FOLqYHGI03mJiMkrFy0qNevWsJFOt
+ZBy7nFwK56o2z9lUi7pd65Cor7xvrjprBQQNiQUoF48Yac2zZc0wPRcOe5EtyUvtnKQGLmuwJGwJ
+Lm6NNmhtiuqWThqkmUaf5TWmWq9xVYpU9c++fZ/fuyMc+MlkyV2tEl2+USOoNLfTnoEaSkvNWq3F
+XfwNDS8OtihSPQtcI2+rQS1pEmncRi3RkvtmRUtrJJi2zVZLYiiyTsDmTNSqaXSM54i0cdaWuacm
+JM5lzRu+R3gkz5s7K8ZPZbP/QSj7+Ptf8bBeEM49exzS10CoV+sDZp7WhXiQ4HVknpGGebtOTnUp
+m1YbN6jU2B7UUmIt/d9IepRXErAWi6mCkprG9fjG5UCbC7GakeXazGae8HBxVa3i48FvWlIwxuwZ
+Y/5vY8x3jTHvGmP+Q/2+b4z5B8aYu/q395t9xhrWsIbffvhhJIUc+I+qqvqGMaYFvGWM+QfAzwP/
+qKqqv2CM+XPAnwP+7G80kFezuPa6z+ZOnflYKOrkvTF1DW2N4x6xVspx2y4Nbe92ZZDy0pKWZrfV
+FxnVnlBwK44otJ+CO1lJ4hFQS4QjBpstfITLL5IzjLohLb9icqEuySfnPM1Vi9Pouq7vstB2Zt1m
+j2PlckXkMVsJZV89sTADmVOwVM7gHFGqD9rkCyzN+S86jynUOHpV+NQ5n5BosdL2bIOiLtfaswZT
+R2IvnPmSTAt3xmrgSn0fWw1fVlAQqEQTxm3uusJVhmmMrdWnEnXdjcdLWjXhjjXfJdXitibMaagN
+J1nkRIqDNBeOHy1yelpINBoOcJYijTUbTbJLwdGCgouVjP1Pnz4C4N1sxviRjPvv/4RH74nYXbxi
+Qamc0I7rTDThqaF9JuJml7rGgphWhhep/cFbYrSwrrEKvJnMPx/IuancOYVmnTaWOfRDfR44ravq
+THWiTOwn/lUcilWy0toLi4OMTPFWZjnNO7Kvn9mWv5duQfRA9mOvW5CVV52kTygbItV2wjPONNHN
+KDf/dDOgXZPnreb3wRK8ZKaHqyUE02zFSMOpw00Zy54mzLTWhx1H+Bpt+vi04GtHWg2rVVE968b9
+8eA3TRSqqjoGjvXzwhjzHtKC/g8DP62X/VXgl/g+RCGNKg6+U3L+wSOaWi78LJlhn2qV3OE5fX3B
+8mkAGkyy0n5/+WJMOFeiMLIoFlrIJJxyoSXcfT/CVX+6p6nO5sChuPLhVs6zENZ0BufaobnfSqjQ
+Gn566BJ/i9VKqyRHIePpVc5AjFUTg+eN1H1WEbqy5OWez6aEoYhy2dInu67Gwfo1nEqs/WGkJcPc
+NqmlIucyo9ROSIlVkWjH66KYUE61DqAW1WjlMYUlor9dWBzPtV7l5SH7ueQtzCoLR1+ahSPzdU2b
+WJM4ktYCzerGshOMuk+spkOgZdgGKxF3n0wjDudaMzOd89K8rXPLnjXNPZnNePRAxO7xQ+3CdR4x
+7wuR+ov/KOc5VWOsYEDZUIJkVhglqKU2QFnlF/S141Fxckmh5fSSeUXlSHm+1ZNtgl01UMbyu7t8
+SqS0vfAfU2ViYO1aGcVY1rcsHcZain2xlIuPz0pCJUITc8Gl9hNdXRS4GvdxciH32O2CVNVO66Ii
+0cI4Vlqw0NqcKxd6mv3bUPWiciuWG9q0qF5jeSLr+DA1bEai2vi1u8y1F2R5KvuUlRHLiaqBdZuJ
+psEezWICNYSXZY3SuuoX+vHgt8TQaIy5AbwBfAXYVIIBcAJs/jr3/EljzNeNMV+Psh9s0mtYwxp+
+dPBDGxqNMU3gbwF/pqqquTG/Fj1VVVVljPnnlpL93lb0+4NWdXPTIulZnF5otlhQI+prg5ccZifa
+P7E3x9HkmUrdjXnRxmgBlNyuY12VCruIsXwVndq7z9q/VdoTMm8vKM+V/IZLbG3jtVpNYHHVcr3i
+Rls4b6CFRZw0otCiquNxSajFWeqmwlFD3OwsxtSl18FV2bHTeYxpirheS9NnRV+iw4xEE6KiUGsF
+1HKshiYG5Q6lqhLpfMpU24elYUGSqkuy1JoG9YyO1htI7eTKTQ9uj3NtSbc76rGpvRWut6TFs2d3
+KYxk85hjj6SSMdzARqVWavWAwFaXcUf2Y3tlcVWBtWu3yLUEa+CneKHMyV5W3DuT7x9MNKHKBluL
+nP7cFyre0t4ETXcbmiLCL44cPI0XcV0VmamjiYPQWWBp3MCKKZb2EzXeHEsLzGaZ/LX8bfo3BIdO
+/iKBjlvmNkVPK0KfLjChSpELrWtRJsy0f+Q4LHB9OQt5Z8oXduXZ1zTK87FxiDUy1W8GdK5qm2wF
+tB/Kuq97hrCSsetb2gzofIm5JviuVgXnqs3UT+oEn9LM3vqI3FXj51ybwbwfPqs6XqUWaM/PoVXx
+OJZzuNUKWKZX2ZEfz+D4QxEFY4yLEIT/raqq/0O/PjXGbFdVdWyM2QbOvt84SVry6ChmGDS4dUfs
+kgu7wfL/lKy2ac/QH8r388hm7l0hSg62sSu8murtVYifCVbjzQpbawb6psJtX5UwVx15umB11XLe
+q1E1VX89W3HhCCL7psK6JtccqJg5LhNC1d/yWoSaATg9C0k0v2Kj3WGsm5ckonPXPBdXM+vKs5z7
+jyW7sM8ZH2iTWhPLYFs3dthSolBVDjPtB7hIKhytWxg3DZ7GThQa3t4pW0Ra1nxWlHwwEXG920n4
+zCticW95HcxE1Aa7aOg6+sRLbSJjzvDcq5r5LRa5pi1fpFQqjk9r6tWwUxpqSh54K3xXu0VFK1Za
+rn9epowvRSR21BZRhSlLjT34h780e+YFWZgzuNBKy7slwZXH50CJX5iSaw/OaJGQqAppWVtQ06C1
+ZkWsadJNfR+OghRfG7JYpY2vXo2wzFiq/ejpZUiqm7nUzlJusHhW+n677RFrbkMfi/e/Iwt4pF6P
+xrCg35PfT95ZsqGNf+ZHAQdKAPtk3EFUl4uZ3P+NixnHX5TGMo2ywYHWlH+QPuRn5uLB8rsVWxq+
+HypxP03Cq4LYBJbNhc55Vi/pac+D/h2bp9/8wWwKP4z3wQD/M/BeVVX/9ff89HeAP6Gf/wTwt3+z
+z1jDGtbw2w8/jKTwBeDfBN42xnxLv/uPgb8A/A1jzL8NPAb+yPcbqBYY7jzvUH+1QcMSX/rdr55i
+qUX28mJKTf28g1sRwblQxEp9++3mTfwdkRr81YwkEFrXX7aZq8ehkadEYxG/bI1my7qG6qFW5w1L
+Kld82qEJqbTeX9mG6lLF445mr1U5XqDibKvB2T2twGzDZKI1E82CfKQRggu5z9tss7spBqXmTkZ6
+Idx2ko55/kTE+Ht9mWO/XZLN9P5mG0t7TFZHS85nIjUEXonV1GhCrZWQ1lZsdQQXSbyke1WtuXI5
+0lDhRrrEM3LfWA2YQXxCfSicyE+zZ94HN26wVLEzP8/JbPl8fqk1MT2LXTWohWlIogZYy4xoK5d+
+Pwr5pmYJXqpnpDRwxb8+7VTc1cSfBgVmV8RqbxJxOJe9ch35e36W4jra4KYZ4x2qdPd8QFOb79jN
+gKpSdbItXHVzMSTvxDrujEuNoIzHIan2w+g0PC4XWmFaI0yJUyyNi5nFEZ/KZW5l02f0pqiCe7dl
+br1Zg198T/t+1EruadLYtaGh3FJj7HnB+76oR9u6HzeGHlUq0sbd5JKrfL7UN0xsUU1uLg29kahH
+F5pU5p/DwVR7PVglCz2zp6sS1CtVnNVYJb993odfBn699Kuf+UHGWiQW/+RhnVeXK+xb2qHHXjA+
+k8XPa2O66vZyj0fEGmqbaWv4m2eHkInYWqUJicbR+86KUgOAIs9jcZUTUNN26udtak35PbYKqly+
+P5nbvP1IDv+1FzyuaZeiVzT/IpzAyUw2NlrGZPpiXpwlXNX2uPANbiIvzkRVg5OqpPFYxOi4sYG7
+EKKQtwNs7RDULGU+6arP9KqrEmfk2nkqHdagUl10NaHS1umPCpnv7WGPSxVVa5bN45Ucml4Ws7WS
+wJyyA0xEdcm1u1Pm1JieanAMBY6+hG37lK7aH9J2xLIua7qthO48rBFOtYFPssKuK/Ei4FDTwB8c
+nlNcucjyq+KqPIO/OS/Z0pOU17aptPlrblmYSvF8pjkO7YSjqbzwjekWUSbnYpa77DySeVjtbepa
+RSqeq72nd0Z6IC9/YkWMP9RuYe2YZCy4XdVKTnRi84Xg7XwS8+BM5jDqeTx5pN9Xc/p1ydJtH4vt
+6KuFS6Eu4PvLjA3NhoyzgnwhwWff9R7S1wpR1om6TS2XSFWb/U6b959oCHoASy1bX74y5Nv35Xxe
+ajDSLM/JdL6HpcVEP0+igkTVmPQ4Jbb/BXgf1rCGNfz/Bz4RYc6WUxEMMwbXXb6jmS+1o5QPtXhF
+Oc1pKYfdfn5ETcXEUNWIFQUmEVHNWzrMHeGwtchmUhfxi8RipoEnLRWd69sOZaGZblVEqD7x+xch
+jVjoZRIP2LohBqPt66JePLj/mFQDp2Yzi2yu9RYKl6jUpCsnp6+W+mOtWTB9VHHoiN21Vc0ZLsXg
+eTGMcS+FC4y1nbozvODCF1w4CYxG8r1bxTxjtoXLQn3lRU228uCywdZNwVsrrVFo7IVZ5Tw4/hoA
+nyk/S01b0HuqMjVrDpZyXZc6D8+1P2Rs4Wpfwo2ZT7EpEtlqIusYT1f4lcx91k3pauj5du+EUkvD
+N21YlFcirIoEtrR8BCg90CrqdEcGf+8zAMzDnNpcON5U6w0cRk94Tj0D+W2bvvcCAIuTGsfq389X
+36V9qupGSySM1tAhX2iNhfIhH8xkfZu7I7oqAabLDLTzeLiQNUdZjsZHMT9d4X5Wq3U/8vnUZ2Wv
+P39TVL93DwMe/a9y30HucqRZkp1WwSuqxrlVm2kkOKhpT9RascFIy7E9dTJeT2Qd36wchlp6jQtw
+rtrOq/Q7yldcaoh5LzW0NOGv2ag4UGP7cMPj3j2Nvf+Y8IkgCiYD78Rwz2ywvyny94VvODqT3nne
+rOQwVDGwveJnXhPx+eZNKXlknAZVKOKgV/rsGzm4eb1O04jotzpdMfDE6pv11FRfJaSq9xmnRpzI
+JvVrh3xjpbrlRcpLGxJAYibawNNyaWtm4KPQcKEEKb5MqGkgT/YTN8GXA/TKi/K8mtsh1aAfa3GJ
+05fPL/cCwk3Z/GtNLRpyOqGlXZqKay0sT0TAxWETYnWhmSHWluYEPBBbxCIN2FW35yI9I9Jek7Pz
+hIZm9d2bTXhJA7le0IYLvc42K82488cL9tStW/ouq6m89EekNMaiXzs9OXRbPY9Eo1CbTp1mR2xC
+s8Bw+E2578kH7zEIZZ4nmhnpVIarbo8dx2WosSpWfwPbEULVDs6xd6Ro7Fe/9AuC12nJ2S1NnY4M
+9r6sYz9tkdwUNad62iHWoKbr2s+z7HfIK1GZjj60udaTgitstlkamcl4umSq1a7mGkG7Wq7wVO20
+iz5DS649mYbcKl8GYNp8HoA/EH2HS627+O77/4xK3cvvJBkHfbG1PB90+TntHNUohbmdWxMabcHb
+a/mSo7bgojddMtQzYGYOYarFZzQXZ1U26eeiagRVm3ig/SiPwQ1k30MqEs2P+LiwVh/WsIY1fAQ+
+GZJCrcJ+Jafm+DyIRHQ6PJmSK1dZhjn3x+JX3809JkuhsN2BUEm77uA5IrYyyyg0dt7yM2oN4SpW
+3CbxtAmMluGOjUeqQSUzMp6MRTL51uyUmdY1sFaGb777NgB3PiWGOscNeTiWuT2KZpxpRd08jalp
+jsLxcsb2pnDTSo1zjcaAugablPMtzGORYpLKo7atNQLaVw1L2iwtEXHrnYqZxoAtVwnjqYwxNuNn
+Brv3ClE1BukZyyMZd9cxGBWKQiqm978NwMMbQ3pHIv08aWs3LW9GUysHT8oK42m4tT9nsCX4NscF
+C7Xqd/rCgdvthFDLtYWcY2yNp4gK3kpElH6Sg5YXpIVw4LBVcGsl0l/sw1W7KMdAZWTspZuQTjW3
+Q+tCPBhPsVVy+b23XyWbqxdkE7yGLNZy9nG0yU2pmahWzaLqieene1RnpV6eVSMk096c83nJVFXW
+s4lw9olrOF7IWAM/JXz3EQDFKiL5SdmHUVvUj388rjjUXqHYFaqZclReUte+ojM/5aFKp3euaX3F
+ZY0H52r4LeYsNShvu9Yi8TUXKJqSZxo7oXpXL4jJVO0qGxNSzS59WKXc12Y4xWVBXq7Lsa1hDWv4
+IcBU1T83Cvm3FfztW9W1n/8v+fk3XaaO6FZ/tHHM5Re/DsDf/fBdTh4Jt364TEGr+aalUMYNYxir
+0WrgZ8wQqtytLKaa8BQ7JRs3xF4xagrHf/3WFpuRGrCqiuRIKPcHkwn351oc1c5IjEgbP6Wlxv6r
+v/UL/9x1/EsGIq0C3QhzYs2bX6mxyLRrdC3hgqlj8HwtbWY59LV12SrTzEpmnGt2XrBacJqoXlvF
+pFpUdcu1ySx53qYWCQ1qbUYjsZ0MXJv/9p+I9FNrxHRaYmv5N968w52nwm0f1LQi0NEZR4kmXZkE
+ZeiMnCaFRjQmdkah8QvNXF25Rf6sc/W8ZlGbyTyOioqaNs9xBzu8rDYIU9NCuu0Zg5G46azmLhuW
+2IQef/2XOAq190e3xGTaeu15kVb2uhtUkeBqw2T84tED+f6mw+kT0cV39gqe08QstVNyeQrjrkgH
+T5/WefPz4mauX/8MP/UpmVOz6mF1NENRe4qUUcJdjQptdGuMx/LsUX7J1//qnwfgiVa/SjlnrMlq
+h2VCrpm0mzj42suiEeTP4iJaKzGCn0SQRPKM48ylpUbVeeGwr3E2s9omexqrM3bl/LrhKeWLvwuA
+F2olzfzzsif8ElvDVwDImyWNSCKD/50//xffqqrqs3wf+ESoD/Vuxes/V/DvXvsW/8mxLPL08V/n
+lzVj8FcefovVVGMWVhF9DRV2tOPNsvLQbGqyRp26+mUrz6F1KS9m67mb/OxIRLS31QDYWd6HkYjR
+L1YxH2iJseeSBjPNYPz045R3duVAL6bv/Ibr+LIBe6a9HR0bMxdi8MJ1uX/gt2gihqOTPKWj8RRL
+u8DK1PikhTK2u128VMTv9NJjOpYDZDkOmb68lxiMGsYcfYs3rCVmLuJs4sa4b8j3P1+74H9vy7Od
+xt/jV/ffBKA4kZdx5vqs1Bi41fVpaK3IjaDDMtKU637GpXpuBonswSKO2R01dIyKyVOZ29Mnl0zU
+81FenrHSsvudqeRX7I9jTpeiztwMLJYb2kC3jAhcVY+WJUPtMfm6dqyaFwlRXYjb01kM20oAT97m
+2zXBoXlwzHLnRQA+LVvNu6GFpf0a49dv82JbmMwXk+s8/5YGTt24+ayOY65t3+O7bRoam/fPvjXn
+VvNzMo/hnKd6/rJS1LzTzCLUuJBBmXGk+SN+exunqU2BPZdCK4/PNU/mfD57lvp+uZqyUBznRY6j
+cQq5OadmhDCuNI8ks1zuf+mLAHzw/A22Jlrb0T/iq5r9+tnWT7DbFfXn48JafVjDGtbwEfhESAqj
+tOLfe5LxPy5+mv9gcA+Ab83/Zb784H8AYHoZMl8JB4qrjHyqMQlqfGuUITOtsVDMPPbVrhJkSx5q
+Xs/tg4q/f0/ue3PzEQAPam22tdJtN+wyyrRDdRpzR42Hb6UOr7vaTq64ozN+/GuTNzwjraYJpbqE
+C8fB147Hai/FKyK2LREpy8Al1DUN3Yq2LTECsfZibNTafF4NYO97EcuWLOoiyXE0Z9+scuKrCmT5
+VR/FAcNtWcfqNOFVrQb81919/vS/IurTIP7dHL4rImWsrlW3nrNz1Y7O2GjHdYZNQ7OrhXKNw8vq
+Ni/76spc9Wn35NndRcbcFa5U81Iu1D3b8VJqT9Wd2xcx+XGSsbElYnv/ubtcfqgi+NGY2r48Yy+r
+M1cX4FN1C7oLi0ksRTI+iBzefyDxEr8UZmxoNuCD3YrXH0mCUccR6ah/MeGXR1rG7e27/FmVzL5Q
+/+/41d/1BwFo3/0uP/PSH5LrG9KGMG4ccv+7jwB4rtmmd03O58W3T8ieylqngUhSZh6x1MIpx3ZG
+4QleVt4SK9dCsIuIUvuLXNXsOEsSwkzWmaQloVooEwyhuh9bsQdacCZxtRxfnuJq7Qn7a1Mud+4D
+ME9CXv2MqA83b36dD/+xzP/jwieCKFR+SfJizB9qDDm4L4v8xt99SOdSO+zkCS0tImI7Nm0NgElK
+QV5M9Uzsfr7tYgL5fVA3bLZEvHzjdsHTuaYX35dNOU7g9Qt5wWz7lIZ6DvYbFkdqpHh1p4tdqZ6v
+HX/4STBf0cl3DZ44THjNNWQaYLJoGHpaIaeRyv2NXotSsy8dr8a2eklM4HO9oVb0a0IU3mgOeaqZ
+g5977FPTasBMYr5xplQoyVmq+pBqBZ7TWYJ9oHjNYOOPyGH8g80vsNOSgxL9csIrlhz680RLuTdb
+XNYlPuC55YzGtuitO4MGdkfwtpkmJHXB86bmAKyaIX3Vlw8ii1wP9L15wR21NcyqgLo2eKm0pqSX
+FkRL+fw4GXI50Ea3D0rwREx26wOqUL5PtdDLRZnyQU3OwtHTOUeaEVpVKUsNzbanFrml6khLztOq
+VXJbW9G/7c+xStn3o8LjwZd+FYA/9sqr8MojAILrEnuwaDpcU0v+QTmlsSWxEE/cDPdva+q7qrkL
+q86lEeLumSbdruBzf6dFpm6CIOvyaHahc5LzYSrJcgSpJeprundVlQy0p2VkO4y0bP15KWPdjkfE
+gTyjEVQc7Ml8bi8rxhPxtMzf2OaD61e55h8P1urDGtawho/AJ0JScBObnXttssu7vHsuYu070Zd4
+MJVEk5VjESi3rhc5ofpdlfnQzwMaanzcHvZ5XesfDCKH+oZwvN29TX46Ft/z3z8XQ0+Rz7nUTDV7
+YFGp/9ieGTrafXiYhNy/EC6+SqU2Yus9+FPq4fgrezfZ+1kpNPXhX48J9jS3vujgxmqMy+T+tLWg
+eiiWL6fjkGstgxffaLI1FYPn3jURd4NRh5sT4QzL8BRHw5LvthdsaY2Bx4dzskI44biQuWfRiptq
+nIqqiD82+UkAOosW72mS0OrVYx7/soZQd4UT7Te7dDSqsG3X6dyWed5ydwl8Mex59jmOtiZztILx
+ptOCtpr4H54T9cUI9vIs4j2tm9kOQspKYw+0FF5UFgy7IuVsWBWbbRn3URuaz2n9RHuX4FC4aWi0
+JuSsIH1Ps0unKyKtX+HVMkItWuNmBQdaUGcgjJTBypZGlMDvya7xdF/7M9zrEDVELH9reoltiwHy
+d05E1XLHUy5Hsu8mbOJqdODreZ1f0YSvaKSxJZc1+poEVaxKtrXuw6g5wK2rAfbpFM0Ze1bTodHz
+cT3Zs11TUGr36NRLcRxVeboW7UtZTHcoY2WdNrlGrIaTNlYpIus7RcWNtqxp5L/KH3hV6mj8HT4e
+fCKIAkVBsZjy3uqI9O3vyHcXY7bVpTOrBXS06Md0tWRXdfSTQH5/sd9mvym//6uvXWM+kmXtxBZo
+jcLrL1xnmsj1v0/7D37z4DGjQJAetBwiLQoSVku2czmMR2HGi1r6/ZtaX/B37LTJNC9hf36fN+7L
+7zdGgDZACW53QfXEINUCMYucZKDqTC9htCOhtp1tj4F6QbaHSlScBuO+vKQd5xq7uRzAW+c7VGP1
+OFyGXF61cFeVqmZqVKWM1YofkufaKah8wvipHPjm2OXzPQ240sY4HdvHVrdhK7dpO9okhhadtgZy
+5Q1sDZCxKy2aW1sQR4JPxzNs7QvefofZxrTEtrFIUxqRrkXdwf5RRGNLcga2/Tdxxr8MQLus076m
+2QM9vsUAACAASURBVJxHLpvqfiUXvH55dcKp1q6cLlOyga47tWluCi425ym1L6ixJVIXat/Ffiqf
+N+7UUbrK3qdWsBQ3+Es7d7h5UzIfa1ri3mvs0j8XleHB7gGjRH5feF+hqYWMMm06a1UFkSOqz+5e
+xWBPxrjhDZiO5YVtxCEHoaqQlezB9v/D3pvGWpad12Frn3m48/yGevVqrp7Y1c2hSUqKKDF2bElJ
+FEGSAwtRBv82AviHYxtIEAcJ4vyKBRhIEDiDbQS2AiVKIjlSLCnUYFJqspvN7mZXd9dc9ab73n13
+vvfMQ35865VEgDZLaoWqAG8DRF++uvecffbeZ+9vWN9aeh03r8gaWRXx0zT5h4sAN6pyjb1Kgc/d
+lE2haEi9R7e2gzsEOg1O9vCeLnPmf+Ah+oxs5DdaX0L2kQjrPms7dx/O23k7b9/RngtLYZ3E+OaT
+hzBNAyF3Rq+xjxrltWpaiS71CoelhhUFRV5mZeAP9BrIuhL4Uls13KCcnN4wkPWoVuwAtbVce25T
+Wrzahsv7+crGmlVrVphimIglsNuoYkz+hpDUX+9/HCCxGe03PVRI7zYuQjR2xVRrml1sEOhzl1qE
+nzUvYTCQ3XyvE6MfyenQL+robAm2IHTOMhYuBiyAyZHhmEQflp5jcyim7XDvBCA13ZqnS8tZo2JI
+cG5fL3CXhVJpzYFLfEZYjnFoSN+uEOjUqFZhnhGIFFM0yKpd7XrICBKzs+lTS8HYolU1zVDShXFy
+Aw4zGJm1Qq8uY1iuFDQGCtdrGeO1E8KiaW/0FwhLsW5G9ofokdp/cLmLfWJOjt4lQU4SwDsT+DET
+6Kdyv1IDPC7n2C/R2xfLMSUX46NFjph8nZPlGjrdv/UeoN+gvF01RV+TAJ3myXxkaYi4Kn0Y5Neg
+cT1hVceCFmTEDMBGWIVNRXRHVyRdA2pOiAXx6MtpCEVXqOeQ66Omw7CkP1uWhjrTVfW88jRwu6n7
+8Pfk80dEUvfaGa425Rrm8AWU5Cndb+/BaMn8LVoRTusv4I/TnotNwVYGdrQWxnaKq/TJDs06cocv
+glOBsuXhdfcEVxIZwDrLfl+6YaO+K+mtXs1DkZ6V6caoVGSS7VBHxmq4Gs29bWsHGk252I+Qk7wj
+tYHqRBbTyoqwfVFezitUGHrfNhD2ZWZ21xH29uV+HShUSvHRlw0X8Zraf5ysse3C2ZQFfa19Fcmp
+pAkWZRv5SqLWrZr010gSZJAFFhpTOBYBSccz1NsE4ZgmFGspDKZn56cZJhXqU1S0pz68BQsWRWwD
+GNii/GXO2Mkq0+DzBaxaPhJuGmWSQTPPyp09pBzDkilb02giI3lsoGZIWLW32e5BcfOer9Y4IGAn
+DUjAu1JI3xb34mu9N3G1Lyb8p2sKdirzmqQpLI0KWExN3p9mGPnyfO5aIfG4IaXAxX7KeQAsn8pJ
+uWz6cZnhKgls0vkKMesnRj0DxhFBVuUHmJ7IxpjqfwEAUPdduEcy/3P9APOlIChLYxt9Cg4PZerw
+KDjBBUtcn+5GHbou9x6fHmNEImDLS2DEsl00WV/Tq1aexh+CuMDjlYxRpzTQ5YZzb7zCW8yY1MgG
+kN2LobEy9L3ZHLFGN+b+HNNfEZfhf+qf4NblXfxx2rn7cN7O23n7jvZcWAqjKMHf//gxLl07gT6X
+U8Ib9LDlSdDmiTrAJjMDtWUVJfP3Fz9LQAs6cFirsDRTuASHmCpANpOTJh7UUJItQ0FMUTXSkTTJ
+Ah0qOAyYxScJxoF83ujaGD2QHboxleG6GxRwqQQ08lz0etRSVF1M+nKiXdADHAZyIigyIIeDHCvB
+vuDw7gLKZK7/xQN09li/T2GRVd8EeEqqZY6UuPeRX+LukmCqJHvKarY6E6rxU6xJELM2S1xi3cI8
+PUa9KuNpmAWUK+Nsk46ua9gwEvmbiwQ6yxbzRQqNBClFpQQo8FIYzHZMdeRNugypBUXodrxcYD/6
+Q9n2OQlHhjwFoSWYEdz06fpNrI7EqvrNeYRXqafZy3KsKZhycsIx1BOUe3ymTCGninfml9jbl+fO
+WzoWQ3GVrtFFaSyBgzMuh8zB3KULchjigSuu6fjhAiczwW38JFW+tWYXJwYtujXQmEr/l6M7iC35
+nNBdbbk+NP8Mjr+GXZAncmEiIa17MjcwYPagUUhQ1vF0LCLySWQhNnlUT0oDI459bi4RDWW8xpH0
+canHSGhtadME788pbVDcR/BYrOkfLmsY70n9y7O2c0vhvJ238/Yd7bmwFCqmiS/2t7HZX+OdUHbG
+fmSgwgrAxnqGlOhAbXaC169IoK3TkZRW27uAdkdOFF0l0KjLWB6NUSgy+D62EPG0slipqOtTlCyC
+0nIDMQOQuq1jzpTbdmxjcJMVjGQ+tmyAwtUIzRA6U5l3Bx3cWjBld6GLfkNOoF5dUn2+0YDDashL
+PqBvyWcrsZ4G3dSZirDlII9o8VRaCALGQYoRLpK/4Kjpwj2mhgUZjstAgzGQE9GDQnVFJumej9VI
+Tpqe62Fnhwyzhsf7+fCo1FyxOgA5J4wRoFFK28wMwCBzFNU8ysoSakx5PJXDI+56XibYZrziMNTR
+4dgeM0VoKx2Kuptm2odfl5O5N+hjUFI3MlhBX0hc6ZAQ5trawKTFKsnTCNm23OMVpcHekjG6PMmg
+iKfoxTKPR34J65gpwIstuDzlJzUT/kdyjXSQIXiLcPLPSJ5/q95ETZfgcRzNoBdnCMP7qOnyWbNl
+MSxKHd2UdHR1G5QKhR0DPlmi/W4DRkXWhc/fVUsdFtf6ynYAhwGfYQCLRLAncx91BjxLxjJaixwn
+jPHAiPEKdR/2Ix/3RtS31EOAGIlnbc/FprBaB/jqW2/BXye41ZMXfWV6GB+TLETLUJ7RY23o0EgT
+vrWWgWxu1wCdke5Ch0W1pLTnILVk4rLQgttl9J0CMX4S4XjNMutMw4R540Z7jWtj+W42rsAQPQ7U
+GZEvwxkOGHv7/KaBKy9Jn0+GNooOq9pWHmZKQC84kP622zkuD6RcOK8Axlnu3qoipPhrRLxFvl6j
+S6xAchrjKJB6i5puwVzKQtiODbjMwKwlbgZLzzBhgPN0vcZFl7UUdoGLO7Lz6EYNIaXrt+vye6th
+w4AExtI0RIuViNqNHEXErMT6BAah5fkFRuzXHhLSwzllirQq98iVwoI0dbY9RrKSFyGiruE4SPFq
+SxZ/UgwxYRD08cEEJ+/JtS80NrEgLFxRlNXxE7w8l2u96UZ4kVDp656N5pQHhzvFHW4AOrk0j+c5
+7iv5fPHxIarMkhwf57jLlwm3dSSl9OmWJwHfR/EajRdkTnc9C0OC6GbHc0zpxq7PnnNl4I4jm9vJ
+kxyOQ8UmowW4FKLRTNRIx7bNzFdkKCRk4Lb0ECqW+x20Yhhj0rnHSyQEZOXcvMeZQpZQTHkR4c5K
+7vEkGKEgIcuDeYgDBkKftX1i90EppSul3lFK/Sr//yWl1JtKqXtKqV9USlmf9B7n7bydt+9f+9Ow
+FP5DAB8ClGYG/isA/3VZlv9EKfXfAvgrAP6bf9kF9EqJ6g/keLVRYP/4EQBgPdpAP5ZT4ni+BoF7
+WMULfMjUTNGUANCtRor2C5LS8qYlivyAD+ciCyUQUyxzBAxgldv87uAa2g/kiC31CS4sJL0zLg04
+kO1Vv6pBd+QkaLFC0Boo1GNWBna7KD+Wz516hjmvpzVmmLKacUn9xJWt42j0AQDgpc4lbObyUJk3
+R3Isp/uikHsZywBjyPPdDfew1MVyaU9sGH3W7x+XaJAFOKZJuU4BjSefhgR15wx74WJFE3WQ5Uhy
+6dNwJqeWm5vwWP9vpQFCYhNqBmB58tkME8RnRUf7kmKM7QgZ5ZxnqwBLalysPRvjE4qoqALT7IzB
+mMG+HDjmPF62U/Qohdf4tIvOBpfS4QNMSG+matK3cAnsXJHvfsnwYDCo2tht4xYLuvK1BY2Mz9FS
+rM3GIkQ1Pks9pviQkn4rD6AxAkPPMCWN27ff/5rM6a2XsLP3gwCAsh+gWAoyUdPbcImByJkWfhSu
+4dLNs430qVr5HTVGJaDpX64QkbC1ZPCx6jWeyszP9SrCU+lQp8gxsmUuj9YpPDJTd3syHx1UEDHI
+O/+oBpMVqhuBiUckapnM52g0ZL0/a/ukWpLbAH4cwH8B4K9RSu5HAfxlfuUfAPhP8T02hWiR485v
+LpDsHmI+lYH0rABeIgOZGBE8Sm4vlwkMmmXHXIAn90vE1OW7cN2ERrhnsXeEESHBpXmKE4pubpAI
+5WDRQqPCKHtZwqLeoa3V4FITMbcsaGegJ5J/tLMjXCIwqdQHGH1ermvfXiNlVd4lb4ZXc5n8KdWB
+vE6JDqHPyd0Ai10xB1fDOYJjMTvvDCmaoitUGYZurFz0t4TVuP2Cjyfflo3lojnGCQFHS/qhllUg
+CsTvV1mKh9yQ0vYxLheCdTgqUvgL+b4i+/C1zTb2+czudI5iTRUj2PCb4lO3siVyn3UnFCQZj0Is
+ItlAj7IAfRqfo9EmCkPGJdBK1CvM2VMgZxI9hJ7KBjg6ieGHshQ3Lmxik772kdlCe4Ow8X2ZG++K
+h1PCi/OZj0tbdIPKOo5uS5+PVhEeE3CWM9ZSi4CGkr61DQ/Vbfl7OHJw/4x8plzD2GAJ91j++94v
+H8D/t34PANAbv4yS4jK1KMSC2IvHrIItUiA2yAyd5ehn8rlquEhtmetH+zNMWaF6ciyb9M5uBN1k
+fOlSiR4LNtLSRflEDrXZxgH8M7FczlPZqaPgRu5UQxQ8RB+XB1jy735RQZ5+f9mc/y6Av44/VABr
+A5iVZXnG3r0PYOu7/fCPStGn2bkU/Xk7b89L+xNbCkqpnwBwUpbl20qpL/1xf/9Hpejb7Ua5e7OG
+m9UjfI358U6YwW/JfpLNRqi2ZPesz0dotMTML5ZySmY6kNyl+nC1hsKXkysYh3hwW8LkgQrxiJV9
+Wp3svJMB4luyQ5vKgF7K53iew+0zMLS2oHlyquas5Ltec7HTkX+/X1/ghVj6ERsFFANRFbuHeC19
+avbEctFSDYrFWCrJ0IDs+EndAQPu0OdiMWzVfVRccRm07gXUpjweVwplJKdH7pgApe5qjgSnJpMU
+FySWiUdzDQ1CsGHXsDfm2MLAnNt4lRWnwdqATtIXbHaQkdRkmpzApCVQdBowaZYWCeHVowVyU/5W
+hgn2uKTWd8dYlOJWVGBgSSZtLxJLwan5KJgZ2b5so1KV597sX0OVlsJJI0TzEcVZfBkrjE6xbFBG
+vhwjmsn17j+5iyqh5wf7MQZ10pvp8t9W00KDAdprVyv4Ft0nvNDCrUNZW1dvXcLtmfy9e1H6oFyg
+cl/moXwpfspiPY2HSAOqmCuxGIJYQy+Rf+8PHNTI4Hyp0sKE2aqNlo8Fs2DbhF2vRjNodZ7P4zZA
+GrvGSYCARXHG0IbGYGV3LnPdr+oYEcmaVjM092Wur9lNfHMmlbu1mgaVExvyjO2TCsz+G0qpHwPg
+QGIKvwCgoZQyaC1sAzj4Xhdar0J843c/wLcbEa7uiCnXdOqoMiK94TsYnciCfaBFeDgUnr9dXxZS
+pjromOJKDCoWKppATdfaGAuy4tyfm5gzSvxkRMhP8AhX74tp/EIrQZUMQ5brIiJIp7OyMKuImUcs
+Dr61P0NMn3un5SDKmAJdzWD2ZPGO9qd4QorzdF9Kti92fAxIvGEba8wYg71gDJD0xNR+gTLk8WmC
+Bw9kJX20eBMBBVGr9RqaS7I0hTYMblRtiub6ZYE5MwvLIMXXUunD5mmEGxclDWlrGoqP+FIT7vvO
+JMKSQKDV/RAXueldv3gJliObUMVO0CbrT0y/P4mfYLTHlzGI8fBIXqr91RBBSM1HI8F1CvdYBIul
+Ew3bTLc1iwgJx9BKNGS5jJEZZIjrJNd5TzbFInfQ1GSTfawSuOTgzAzg7mP5zuMyxmIlL85nB0xN
+ajoOuZl8++EY305kjCp5gbRGweGxg9fqMl6PWPn6rjvC67ekLuO1pIKYZD+e0kBtICTkarRyF3GT
+WQRHR40xpY+He3g4k3U4jdbwnLM4iVzXa1ZwzFjSjhbD7VLMKC9QMt2brksckYPxY0uYpz5dacHl
+prdR1LCzS/r8wykuZXLwOZ6BGSt3n7X9id2Hsiz/ZlmW22VZ7gL4twH8P2VZ/hyArwD4aX7tXIr+
+vJ23/5+1/y9wCv8RgH+ilPrPAbwD4L//Xj/QHQ2NmzZe2ypRX5BeLPKwQXBPHFhoEAhzq7EJh+Be
+nQQhlzQT/qaYbdVcR2nLLlkaDhwKdVzqmpgTyHRrJTv03laJz/HUybwQDQJPwmKBkpV4a7eANpaT
+2WO1o2vrMOSyqFUdrM8YnD0dLis79Z6FS8xHm2Q+7vVbqBHfYOVAP2Cw0plDNZldcISFOPInMF6S
+3zcfbeJxS06jF1Zr3B+K5WE9meBGT353uicX3o9ybDflOYYYY5s8FJZmwSMf5ZZbR/IicREkgAlW
+GZyOjHeKCF5dLIGe5aNJi6ZWJlCkkCsoiWa5PdhtcRMGjT5KiqmkOwv4x3LqTpUPQ2fQlOrKY93A
+OJHTfMMvENligtvdBpqtTQ7SBHeG0n+d4LQ8muHmT8gzv3zyMoxYaNOOH74NrxCjdFWs8SWejq9e
+pDXW9fEuKfYejNfIDmSdFUaMfltO6VudAhEDnpEp1lE7NaF9XT4nP3OARSRjtFrb6BM3EJOLs8gU
+Nn1ZGFf6HWg+IfYHS7TpPnX1KpotmZ9XWMk4nk1RXYr1Wzgm0hWp25p1VKhGfkG3sLOU+wUs0LtV
+r2FJN8ice7hCTg17pDAj9DpCghorKZ+1/alsCmVZ/jaA3+bnBwA+96dx3fN23s7b9789F4jGIlFY
+PzaxcMcom4IO/Fy3jm4mJ8nYnsEZyM5vTQw4hImatBRq6wJeLKdq0urAAnPp+Qwl4bpVvwGXJJ8m
+qdt+qLIBnyXJen0CtZCgZDGzkWvUYajEWPN0qJABeG8ZYvoxfcTlFNd7cr3tfABzg360aiBhLhw1
+ORnyjoMGkWtlUmJB9J9ft3C2l1uQIGrzZgv1uZwSbuMU9prPZznwd+R0aMcahhTeTZsMdul13Dkk
+49EqwTux/PvVrECNwq2dRhsGlbCtDgVbtDbymArWGrBmQVCzyGEkRCPq6qklkFMd3M5b6PZZ068m
+qJBirB3s4kCXMuNBb432QiyyccIS+KhAyxIL458dlfgirRj7VhsF4d3BxwluE8l450CeL3IVjn9F
+/tZOZiga78j1vBQBEaKFpTCvS/wkJe9DPNaxzfUSRAp/flOeezkxoFelz4tTDflV6Uf6EclTG8Dw
+DVkj/dUWypH48/FoCrsi12vaFBUOAPiyFpLCQp1o2q0LfVQYw6hrGTSb65A5um69iYil4W6nRK04
+Y5sysF1KsL0+mAKhWG/9tqS6tUYLpivWmD5JsEfo+TcmLdhnBLLLGq5TvPb3QVaz79Gei02h1rLw
+F35uBz/1KR8fBaLcs333GCVfxlqSoJGR53Bjik1mH5ZU2W0ObaRkNfZPbSSeLDYzdqARXlt1FBTV
+m7qaXEuvLuAzcFbkNkJKlS/LJerE6GfNGnxWu0URIaWGgX6P1OPdGbb4onzQNnFzJRPX2Gyh02Eg
+rU09y6qBKhf/7PAQdZ/Ao7mGJRl6Wx5Nx8KD7coCa26YeLwS89KGjcqCwbe6j+pSFtjHhiyeO0dP
+4PoSrF2vJmgRdr3pOHh3Lte4VqsAhMq2HBmLRtlA4UjQ0VIWZrb02Tm1sSiY4Uir0Cx5yRxdxnJd
+zJBkstG1zBZcPnM1WKFjEzbtOTAsjvOQlPK7PuwDWfzdCxWU6kzJSoN1xvlYs+EsZOxVV9bCxSDC
+iG7O4DjApEGQ0v4a3V0GinMHf/mL5IkfMECrcmQUi/nixmU8Wgn/pzOOMCHOoLOV4+5aXrhP/egu
+AMBbuHijKkHSxTJGGsoz7R/v41KPgjkkCz3IC/gUr3HDAjZxMca6AGOL6NoFrJZsMlouYzyKT9Bm
+psLLN+CebcKI0SZFnldacEjuaHhyINXiMTKfm5dVh65IF2iUOFhLn94YbMG/STDYr+KZ2nmV5Hk7
+b+ftO9pzYSmYiYbeXgWPO1t445bsjIV6BXe+JrJiG5kFsObd7Qyeouq2Ndlxy+0lFiNqOvgRrFCu
+scYRbBbgaHUH27qcps412eHLeQ1lKSg4beYgrss99JWBVV12+W3Xg+bTgkjFpM6TBI9YEfWT123c
+sMU8mz8OUaFEXKcsoFhRtyA3w5bVg5uRjajh4XAqBVMt1wJCuce8Kuag3b2EVimn8czR0Sb7UZ6k
+2I7FxVoO1sjacpJePRErxjVd/LO7QtBaFCnW1CwY3tXwAz8mJ2VWqcA5lFOleUH67lgKphL2qjgb
+opOJqxHXQxgsIAtXGRrEEIQUWbG9GU5o7m/oBrS2PEfTcpHbYsKaqQeDbFAVsjG98iTDsi2n7lbV
+wuPjXQDAF10L2ULGsJaVqBEh2nxA/YaVhgHPsrntIn/CKsmGAS9kOvuWjc3LYnE2enJihouPYRNj
+kbgFLh+yGKmawoQEntU0wcvEDswIQZ9emONUp0XQcvBN4kgemlNcrHD9UfsS1gqKWg7wak8ZqnXX
+hE69DMeMUW2QmJb4hmxYRUDtSoUCCdeenlvIyCDVzfsw6sSIEE+j5RH0GQPlVgCfJMR61ccPXJZ5
+uvapNk7174of/Be252NTqJTY/GKMWze7KLJdAEA4XqNZykJfFGs0ST9ebWyjTb8chQA0VAFUWCrr
+rmNENRls08iQk3ClNp0j77JybiycdUargJrJhC+9Naw56w5UCpcoy3gRwW/KhPmUQ1cmUGW+/UGR
+InhfILiXGg4yojIONpZwO6yCHJ4J4sYIGRuYWyvE3CzGpYWmLRvSZr/O5whQNgjRNXzoJGRRywwn
+BC+liylqlI8/YLzj2w/2sWCkv8hLNFrST7PrPiUkWT0+RIP8gglpwmxbAQ0ZCy9JkRuSwYmXK4AY
+CMv0UJArMyVNWJl5cBfy/GuUaMzlu17DEVp1ANMkRspsRZxKf5b1FMZc5jeDgSqzOUVpQaP4TNjs
+I5P3BxvEltw/tqCqspHPawN8jvObZwl6rAP51I1Po7EpL0seS3VpZi6RZRJnCCdDTE9l8210M4Sb
+EifQ7rRwCALDusRp1D+Nqi0uxbwxhkYuxaP8Ml6vy++sJt2H8RrznBWj+gpRJHNSKUKQeQ2+bkIV
+4t5GMxmr2XwMbUVQW+MidIfYhEIBI9lYlwhRV7J56YxhYRhBNQiLn4ZYBnLI5MkUB2Ppf7gf4PLn
+zsqSnq2duw/n7bydt+9oz4WloEcGah+1sSqeIKnKPnW0OIJGiqqkjNBkxkG7F2LZl9NGkaDVzOfQ
+kl0AQHrVh27KseMschiRmMmr0kZJ8leXtFz61EZmcFce54gJGV5NFPSz79QrWJKs4PiIFZdJieWJ
+3PvwWwnqLcrQ7eWY9eVk2hzlmIshgwvMZw8xR3Utz/dooWNFUzQw17hcY6EQr+u+skCjLsEyp4ih
+eK1VLcN0TotFz3DyRE6/40C0EyeVABNKsqscaLA2vxYC7kxOjMOaQmUop5vRl5PITqvITxjMtAMs
+aJlF+gqzPXElrLZCNBJ3pULcyHBSQRbRqlAJmmS8Lg8dYaMBoPIMWSxow+khqdSWY7Qo6ffecIAq
+KybLygWUtH5sy0eZCmb7JP6mzIcFLEZkLzlc4Dddmb8NN8dulZWGbx3jJBa3aEHcy8m7YxwQNVit
+VEEJEJzuA7Mj+Z2lzXGgxP25tE/0q24h+Kw8c11dR0wG5qs3YnTZTzWWedyujbBiEdTRJEEJWTeG
+piGjUjhyHTkDkFEoY2ItC9Q5/37fg8XA9XIUYnVChurOKYol128kf6u2LRSUm9NagHYi7tPXD8YY
+M5tRnoS4/i7JNp6xPRebgq2AK44OfdvDvVN58RplAY0hW60AqgSF6MEC3lwW27SUgS7sLmoxXYa0
+gqJk5NzpoUEOQiOOoHEAc7oGRmOEckUcfZIBvG6mxwhPmaYyY1RZ9jo5lMWjDA0+zedZawVmyxBd
+7OKNQhZV3qygMZNJ36oyhmFFmJPURZ+eAqzaC2ID+8TRa0rySmb8eWiEyZaoo6zyWU8PETKLoC1K
+lKcCZJoeE168N4fjdDkuU7S6simoZhXDlSyOnVoToEtUtRk7WJmwlfQ3nhVYK7neMslQnsr9HgzX
+qFbk+8NQrpV7TYR8jrrpIS4J5NHmsMleVS8M5CGFfSbyfKMgxWrNOo9XLsIgb6G5LhGkcu04s2CS
+X/AK5e6/cSdAhT53cHiCHu9nmzrCnmwEVsWH1pb7LT+W/94rTeRitWOr4+GeKdfwYSObyEaVWcCU
+ac3KlmzIO8kuro3kuh/YS9Sb8rmMp2ixRH1BUJsZOU+rFutOCJMsVWVsIE1JKLOOEbEMvjDP+u6i
+ssmsld5BEci4OI5C0ZTxVrqONbNgOt3OolGFwWyWHrYxfSwEoN4CeJvVwTd/ZBfaD9N3+bt4pnbu
+Ppy383bevqM9F5bCfLnC//WVr+L11i66bYG4lm6EZEk5MrtEQpGNaKZwZIot3aX8euxl2D+Rk9u9
+M4drihkVuBE0Vg86XQuuKaeG50sAaDVTiHTyAaYBxlXu5oslzJac6K0mUNTFXFWMMpdZgTktsk2/
+RK8mJ0L1QQy/Jzt0JS4Ai8rGmpy0W7MGLlI5eKO2hdunYrrf6rSRDuS5twntruhNNL0zzYIQ0ZzB
+tVAhCXgKJlOMWLh1j7oRA1/DYyVHolIZCK2A3TBQ7zPP3R6gmDAiGkm0XK9oiGdyci20McpI7qEa
+HmzqVI5mU9hKnuWwobGf5VPeBL8OmCmrCItjaErGOSh1RCQiMUsZw+PjCS41OU/HQ0yO5BRcvhog
+DEitp2uovcDA3u/J76tZjPFQgoTLLIVVyCndiB0MH4oVcjc/wYsjwSHMD+Vaj+ZHeBDLKX6sji/k
+xwAAIABJREFUrdCk+9D3A6yocVFEKS5SvTy9L+O9+MLXMW+9CAB4xe7jUUXG1l5qCMhloagInjfu
+QhG8FSU6aoGYkHMLOF2fVU8t0XaWnB8CpcY5ZppYfNvGGHpPninPt2D05Hot14c2l/WwMGWu1VKD
+Rcq+bDXEgDyY3xqOcGFXnmMYxtAeEzb+jO252BRMAP0yw8odol4QDx6ZCImaUyugco8vb2eM5oha
+gkSiVRcOSjLzZMMEo0QWvLLrKEjy6VRNhBP62ozCG+0WNGYcFsihSD9ea9twl0wBHo3RuSaL94Ak
+F9AAeiKIYmDvMRGGtoWje3IP70WFLgVNT05JxLldwHQlBtDoVfE6J9806+j0BSBTNCk6Ol1iTRLU
+LIuxIpApHZ9AMUKehiVmpXxmQSIeTudwWmL66s4JmvTrvZYDrU7GnycjBCy5PZ7JS16zugARn0Uc
+oJBHRrd3AQ41DC+mVzHjRn2BYjkTlaBuySKdTzOkmix4vesjfyzPb5gKiSMD9oibwrIscKCk06/a
+BUqWs0fLKapXduV3nW3c9WWu2++Iafzeo2P4TB3Hxgq2IS9m5Kb4aCrXC7HCHSpKjRayeydZjoBA
+wdXkCFcIWOp/fgvGBZm/ww8MZJa8vFcd2SzrnQ106cuv6wV6nrxgo80cjaE8y4r6mfXZZUwDYcta
+5UC+lD408gJzIkRbWYiVL/d2uKnoVo5GytL34hT1RzLeoX+MZpVCO0ceLBIBWy26hE9i5LY8X9a/
+iK/8npDvPM4KjEeyDl/9lIniC+cU7+ftvJ23T9CeC0vhMMrwtz+a4cu/foRXPicn+4uhgS5lsKa1
+BBbN1vUjDY/JVpxmctpth1U4NOHDeQx1kfnhZYEylWuc3NNg8fRDStXmky5yYg/ydYKAJ2W+dpFq
+BAPV27j7B5JR+OYBTe4CKEnJMF8AJk+ggyBFV0nO/qXQxP5crIKIVsdpcw3/nUdyrTiEw4j0olbi
+1aa4RCa53osrXVgpeRCXU4zJWhw3XRxNxWQ8WH6AxZGcHh8cirmc6RoWR6z9QA7yqiCalTD35LsB
+5gADalpHTjvl5UDIjERgISbcunp6jPTseuEcC8LJ3YyncpYC1pnM/ByaQ0r5eBMVcmMkhULMTIu7
+EMtlEkXosoLzt49qGOzJief8ZA2VnvQzSW1sTQWE9M8vS43DptrCvbfFtE8yDW9TY/O666GeE1bs
+NXHCU9rYoTn/0ESNPJCThYGd12Wc375bwLlAQSBLw9XXJJBY7JMZutrGA7oM22YX90kW+mJoIaoJ
+3H5Nd+aojDFbyljFETAvxDqK9QVWtIr2jmYw13K/yxSkqdcqsMlDgcsaVCTrW0ts5E+kn3NnifVa
+7tOciFXpXjWQ2QLkeHh/D/fmcr/hyQwXX5C5/DD+Mv52KWC3/xL/A56lnVsK5+28nbfvaM+FpWBY
+Cp2LOt74lIU75N3fmTlYF9K90PDR1mQH950hnLGcYn9ABuQtw4YGOSW6RhPrqsA6K3GEJVmE4+kp
+RmT68Q/kd73NCUpaG0mew1Wyu/qWiyKTXfdgFMNh0dD+lGQIhoJDHEPkZpiszlKVCk+OKShTW+EN
+xgHyKlmKgjqWKzn9/aCKNvt8pAWYeBS+oe89WXtYHvH0tEJQ8Q41BXSJeHs3N/AHJ1KJeBbIK9MU
+Zlt+p1IgZa2/niU4mog1cllzMadKaVWXU8SepWhSA0JDG8MhGZrDGULI58bYxRNTUHM9TU7Jfazh
+k0su0AKYSu53ZUuHz+rBONExpcjNg5gpS9NAZclYxeUqZi7Zsx9liAdMC/oeuiwIerH2A/LMk19E
+WSNZ7ekIji7jfTDP8fItOUF3Kx6qxyLW8Rap+epqjJkh81i1B5gn8hyOa2CSieX1xoaNSusz0qcX
+JS5Trb2O1kqe9VtFhL5LTYZBC7VHZIquMQW8vovUECvHMSOYRJb2azuoLiTWkBYrfLgv89M6U90O
+PdxQYlXGRhtY0EJcpYhzucfQTxGRk+HCrvSnggES4js+XhT4jY/kHoUOVPdkXn/2P+lg80Vy/T1j
+ey42BWQK2omO//vrDfzUj8vE7jiv4lvfEkDOC/0MFVdALH5qQG+IWfoz5Be0q5vICInWliVakUxc
+UnNgQ/4eRwZaqbzItZsCFzU8BwWDjzUzBDyqJXkNxAv5e75Y4dvJfQBAdAZFzYEgl3tXDBsW6ysW
+aYkZVZWHdwx81JeX6ZVLEuD7i1c2kVx7Te7dPoGZCfbiyztVWJBNb6ik3iPfy2CRpGWWe0h1WRy5
+baJN7O9gPEVExuszvccMwNkOYmQ5RjRLg6VC0RT3ZxkY2KLm5YBgHPfqNRg1cQNqgwzVDVKCaQ0Y
+VM3Ocwe7zhsyBqwM3NUf4ehbstmkKoJNwZ3MBgpSrFW0HFu5LPovvCwL9NqBh0uXPw0A+JHP/jA+
+uk3Sl60lfEPGwiwNNAwZu4Lu447eQpkwsKnnyDJ5CfuRCf+h3GO/OobJKtALQ3EJs7CDVY2s21mC
+3UDm+jjpounIPN3c/CKuvyIQ+JQKQKqtYZrJIeLGDuK6jIXx0R5m5ZkAkfS9qGrIj2RTX7nA1pIS
+9VcU5k2Z64ae4sKariC5FjNY2Lekn9tLwLsgwcx6I0c8kc27aSQoN+QaJg+ZoHCQMtuz/t1DVCxx
+XUv4sDqy0f3YzVuwIgJpnrGduw/n7bydt+9oz4WlYLcUdn7OxC17F2ZfzLfR+wtUPTmNju+FyF9n
+RaGvMJ7K6dBg9RouzVBTsosGt6dYJHJy6Z6Cxeo8b6wht1g0RRMdLYVYDmDkhgadSf2ltsZDmnj3
+jg7x8UjuPSOyER0Fm/z/mpljHZ+hEf9Q/XmOY6wdMfMmhNceLicw69+Wvi+qmIzFEnLjDcRtybF7
+NcqSBSEWXQbcsjkqzEdPxzmWmQScflO/ixl1BlKa7apUyFNJ3yVliaAmfV6HC2TUThgZLhxNbNdH
+RAruxmvUTTm5cmXB8kmGkinkZ7yfwxlCumyoyf0Sb4iSGhDlpgFFujXPNzF7SEESpXBwgYQkiVg5
+2ksNvLgpp/K6q6MdiSVYMfsoSnnW43IJsy2n/6xJeLTdxuHgjvTtyEFKrYMhUrzZ/AMAwIbhIgxp
+yRG9ut5KsWuJBfJuMILJVLTWGeIlU6yRSXOIWXldno/Fc7VKE+VHMsZTJ8T4iGhDr4Y8lnE+jOS0
+PlgdY06XtxMAk6r87nSdoM2iqaRTh0c+kNwR6yfIPJAWAvN0DpeQfreeQVHsxqjo0OhKoULyofsz
+vMmq01/S3kREKTz/BYUf/9EfAQDESQqLOJtnbao8C6P/GbaLdb/8W198Ge8dx/CpfjSblNhr0Mf3
+a+iRzv2ClWORyZscsqJwF0DOzIGzyHCSEX8/iXEvO4OgpkBPrnG5LZNSG2wiIfBkqddQjMhetPIw
+Wd0GABzOCuTqjJZdXoQbdR973CAqdoo4pcHlW2iz+nBg1zFwKI1OM1OvACbrNeIsgkX3J9IKWJGY
+2mNqSlYSoHpDNrpuq4NdmrV6akK3SXcOC8lKFuGjfdlsSsPFUpNF3goP8fmf/5syLo6JmJHx1XyF
+/+4b/ycA4M2xLNyHX3kf5e2PAQD7yUfwfRnbPAhQr8k8VNcrBNw4pmPZNAsjQbiUF7PRr8G15QX5
+sc9dxQuEQj8oLAQfCTjnbnwmohNhmXDBazpc4ileuLyLoCQEO8twSualyUr85ZMogMcXfR7lMKn/
+OdOBDRbPRqaGXZeRfx4AzQB4dDZqSY6lx5Ls1IZGBayepRAxDtTW5L+Hug2zLZvJZ69fxPKaqEX9
+7GaKn/+nsi6+PDirgtXxI6ZkqvZ+7Tb+twPJCI0nQxyRecnVSyRkhN5gaX3q6rjA0upTpXBMl3aR
+hAhJIa5pgCKc3GvIpukaCrM1syurCMglE4OiAIhDgV3FP/3Pfh4A8ON//e+9XZblZ/A92rn7cN7O
+23n7jvZcuA9rlPiDIkapFoholleSEn9xLVv/x+ECvQHNyDJAlfLpfSoKF5qDy5QFj7c15Gc8BZGG
+mPnvvGIAgXw+oBtRNRIsWeg+DJYoWJQURGvgTOU6jKARbbaMJcBVqycwWcc/yTXUlQzjjl7AMCWL
+0K40YFICvMnKuTVKVDnkp5mOnhzGONEzhBHNYyIF1UUXXU2e40HgovYNWkeNOcJY6vhDQ4NxLOa4
+vnwbAPD1VYpXnNcBAGlnAYOqxLB8LBl0ffjLB3Dvi+vy+He+Is8Rd5HMCZ+9uIHXA0HHfdX0ED2W
+AOV6p4bXKAzzNapdO+MIim7OD3on+P0B8/Sjr+DDLWGmTh4u8JCCJAuiSWuuBZcFUwomlmsGTMsA
+DVK3PYwjlMRyFFS7LpIEqU5giJai4lHGzSjgETZ8WTNx8yo5KD0595JpDnNP5rq5bSMgQaKdmcjJ
+Vt1t2tAJU44TuW450VE0pA8vt3Ic7bBIzxzi9Z8Vi/OvNd8CAPzHH7yKg3vy+WtljmPqWK5KDcVZ
+ULxm4BrdgLYtz7HpWNh9SeZxleb41Qfi5jnv59gXowCDaI2hQ2rBTNzj7MU+fmhfgqS/4RQA8STQ
+SxC5jeyzAX7mf/97+OO052JTQJwBD05g7hRwaQ4vbR3v1+m/FhVkvvjc6lBhvJSBMEjAWu3kmNJc
+MmINjVN5YZ8EcwS5TGgwSrEgOUuHL+vQmmHQlYkpzQryKTcTzYKpka+wV4NWinmYHlKoZZwhq5JM
+RFVQmPJ5YikMKmdkrQsUFKEdzyWC3DJ85Jl81j0Nq0RMw8rcwnpBPD9VrJbvHuMdWoNt+yHe0T6U
+52tmeMGTdNklbYljR0z7uZLfV5q7qL8oYzV8awSDJqeZKXgn8qKn6xP8rw9/TcbzVBaYljxEyAyG
+cc/F73B9tVSIlcE6h+MU9+hC9ejLH5UF2oyy//pUx08ztrFw6vB8wqZXQME025Ry8Jphw2QtiuXm
+yMYytrPRBIEtfruVAlOqbD0OmbJMc5QkfSlVjhXhypdRoMbsQq1hoDeQzfk6SV2OrAgmhVOsug1r
+dEZFbyKayjV6XQttgt0embKB6qsS5lTG9vffb+CnXpbNdProOv6SLd/5R0NxKX4m/yreeizW+cfJ
+/4GUdRlFUcAhfLrqtfAyRYeu3ZBNpa65aO9IxmFjPkYxlM3kH9dKXEjpxsQhblZkHkrStw8SDQXL
+t2vjEIszlyFnFgrAVbPEF5c3AQD/EB/hWdonch+UUg2l1C8ppT5SSn2olPqCUqqllPoNpdRd/rf5
+va903s7beXte2ie1FH4BwK+XZfnTSikLgAfgbwH4rbIs/45S6m8A+BsQgZh/YVMoYaDESVBH35M9
+5CSZYjAX28nYrGHlsorOWGODFWC1HTG5tpQOHWR+9gMMyQtwA32khXyehzO8dRaUSuSUO8mmcCLZ
+rVWaYE4dyNDKEE7FhF0FOjq+BChXsZxmrlMg1MQ8Nfwq1plcrw8HSS7BwazQMaMqcYUn4jjX0GZA
+qaZ52KYZOdRjNDS5xxWeKFmW4wGDU8UyxzCX53jdqGLG55u16nhIXoRaIuOmZzZCwo6LS2vkp9TE
+3C2QKhFO+a2re/B+XT4/tMQcsXc19Oek7boRoR7INbwkRj6Sz//aZ5uYfEipdoKRLkVV1K+J6d9e
+mYg9SrEftPC6Jqf1/XiJmSsndp88Bk6oI2fAzcw15Ay6aXqGiiuZkSMFTKgT39QI0XZL9FkQpHkm
+Pt+l5FvTxysEUVnVLl7ZPiO2kXZpaKPekLnp1Qp8UJNT3g4yTDgnnTxDfAY40mQ95f0U0Uz66XpH
+uP378kx+pQH1hvzuc4X09613LuHOB+La9RMfHouVui0D4Zb87qcvteDclMDlNVpVutFHvyHzdNSx
+MShkXf98dxuPWhL8nX+ji4Qck8ZU/M6rXQMlmcnv3nwI56GM8cgP4Yx53h9u4ov7so7+IZ6tfRKB
+2TqAfwXAvwcAZVkmABKl1L8J4Ev82j+AiMT8SzeFXAPmfobKF4AegTnV3IczIlW7mcA9IOd9I8NZ
+ZuZalQQTXh0qIlOS08PmBnkJ0wKfYRR5T9lwLTEDbx/I5jBXGe6cUcOrDBmHY7HQ4BqsS7B6cGky
+u/YZqYsNRdSd53iw6S960OD7BEOFJtaMomexLFynsUaxlsWddTQEhWw8tcYCTsKoN0VEC6uOOrkI
+D5/4yLi5jW0Nmc+67aSKGV2pJU3H7c0IA00W65VBB+Wm3DtAjIJ1ArvDAL96gdoJPjMVp120S5Kb
+LOv4c5vy0qd+H9l1Ga/LDRvVrmzUO+9INqBRVHD1krwUPR/47dvy3Tsba9w+4Tj7FhQzHzkp0G1H
+YeHIuLm5AZ8vfxQlmJjyKltaBzbHQy9Zyt7N8LoniNXL7Qr8mvTz4o4Nh9To9Qs7MGyqh7HsO4oW
+6Djc9CoGPu1JmvnJw1M4TVLp1wbIWfOyPKBuwtzAuiJj1YkyTAkGG0Vv4lOWANGm90VP4TC6hzF5
+G08nC/B9hV1r44d7MkYXmn1c6sqm4DL74rc3kZOwZUfVYZOv0nS3UPRJuuqP8dv77BtdIr+0Ee1I
+3380mWBC6viT2j5+elMyVL84u4T7TOfjd/FM7ZO4D5cAjAD8j0qpd5RSf18p5QPol2VJ8jAMAfS/
+24//qBR9nBXf7Svn7bydtz+D9kncBwPA6wD+almWbyqlfgHiKjxtZVmWSqnvCoT4o1L0G65VXktt
+1BptbCSkpUpMqE0x7T8cLrHTlb/nSx27V4gv98mVsLIQEYpbdyzMCe64XE+xsmSXv97s4h0SaNwk
+LfqvHa5QozZTbMRIYznlHVSgkXAkrwcwLaE+D1YShR+0bDgNMQedRIeZy+fdfhUadfsqiwL6hBRx
+VBLKTQsuASb2qYJ9gUQmfhWNiFBZasTXrvbhLKU/X9hS2H0kJvxOvYl71+RUvXKoY9QV/EJzSJKZ
+PzfAxUQCX3jyyzDH0resF8JtySnf6W7gP/ihHwMA/M693wIAfOrmX8LhP5YquiuvWTAaco9/9fol
+1CB7fLN3EyfHUpt/ayBQ8eFHd3DjB6UPk+EJ/vWmPOu7ew+QkQzmcKHhQJfPNqklBpGJQ9ZrNCp1
+LBdi8Vi5gkO9ynS2RJUKV1f6Mqfblzt46YJAkJv5ClsmNRNdF3VyK9h6CJO4jwWttKprQicGpKxW
+ALqNF66ayNlPUykkAYlTamI1VVYRWkqO/IdBiEoiQdx5mOD0sQQMf3dKDMX+AYyJ9P2Kp8NiFuW1
+K13sbsi43Kj0UUa0HGP5dy07gkVLcZWkuNCWytAimkMbCJhqFj/BD3nSp49PZawq8J7qmDY3Bnhv
+KpbgzZs3oX9D1v2LzdtIrrP24ftgKewD2C/L8k3+/1+CbBLHSqkNAOB/Tz7BPc7beTtv3+f2J7YU
+yrIcKqX2lFI3yrL8GMCXAdzm//5dAH8HzyhFn5kK44EF3c+wyTzxIlVYsFJvo7JGnUUgp+UaMQue
+GqSfKmsKriOBtiI20GRlZLGVApn8vbQMvJTL7nk3OuTflohSOT1ypWCQJTc0M9gug1YbVdiKWgBH
+3HH9OooK8+2FgzW1K1dJgS0SnsIAMtbLT6fyu4GrwTgrjas0YHbkfl64BjbJ8Fuh3oTK0SMBba5i
+eJsknW0buGSSHLaiAyzMii9TkMXdhiuHONbxa5j1xKd2MwsLyKmiD6ao2GJB/DvHMpZbOwG+eV38
+0NyL8RPGNX53Ayp9BQCg7AV2dMFAzNtyOvZv3ERckdO633gJB3OJ29TNKVZt6fMNp4KSLFpjiu9E
+booLFE7xmilun8oYjtIEimzG/W4fmcr4XHJav7bRwsZVyv5Ne8gYJ2nYNThtGdu0NJCn8juTsGO1
+CQTgnGY1OKnELTK7hlOyLlcyD2WdaVnKx6WdfYxo8Xl1Az6l8PaOD/HNe2JBTZbSh3StUFIn0mo7
+eI0FZp/+zABeIZNS+Cs0qrvyHYrFxOUaJfktTMuC1qb2aGAh5fNXqltIqKBuFwKvXo9jaC1ZI5Yq
+UNsVi+e9gxU6WzIWXvQKjj6USlqAOe7v0T5p9uGvAvifmXl4AODfh1gf/4tS6q8AeAzgZ7/XRZxS
+4WamI9sz8Dbx5FvNJsqWmFdH0yayVEz3bncbg21Gg3XJv/rZ4ikLr2triClDrpIAtZY8Yjk8RsCc
+t8N6gRfrOmYnshgP8gIrkmJYWonVihLuj0MEpMEqucCu2CbCWF6EIFyjWZHJ7/UthEpeMt2OoMh/
+uL1JfkK3jRZBM0bFQsWSRarsPooTMagadblWJT3E6VQWq9mwcJnBMKdIcUKY7+N5hjWj5K+EzJbc
+OcAjRyDa3eY2aqH8TqsUUORsS1q7uBwJHPe2LqbqoLHAtZ+SRbe5nMFoCPCos7FASp1LK6oiuUwd
+Q2Yn1I1tWCyOyK0lNjd5v0YTxakYoofxKerH8twti7Dcoxg5szMjrUSFVekDU0OiM/KfAV4uL87O
+FgOwmolsSlEfPQffGUSrAiZrCbRKDyXdjpSYBs3KUHHlgMgmGRJmHNQqRM/m/ZbFU3q7yamMfTAO
+YdGtjBMgYHVp1UoxI5dkyo05T0usSGpzyS6R7xL0tqqiOpD+m6pA4cg1FIlXdMNEycrWwkug6XQx
+ax1kS9LJlTE8Au12OOfzIsIRIf+psYLLash27uIeXd3Wa1XUl9/H0umyLL8F4Lthqb/8Sa573s7b
+efuza88FolEzAbunYPZs9EfMQecmHo1l1247BRqZVNHVmhX4kQTMrDYp2o4swGHVnqqgZAGT7i6B
+hcCjs60GDDI764n8uz0tcegJem421VFCdujFKkeVVXmrRh0mIa9nOgxmx4ZeY1ooBQJaJtHahU+U
+m0p9+FW5X4valm5lAe1I7qG8AMlCTDwHIzgGU4Akesk6F1CvSz+1eA3LE3N2iTnihaQnvfUC+7ac
+zLcdeY6dqcKVnhBsaGMPBccl1YEaK+428gp05tA/9QVZAhtHBaYkWp2dLlD78wLXjd7dRmbJd8tG
+C6CVpc/EsslLC/kml5E1QHEkhU/JkyomvsB1oySHRri1TVbjrAnkTCnXlgVKFoo5lRKK+okbqCKn
+tLvSyc3gJPBMWoIzA8pjrl9fIT2rXF0GKJqMby9oKcYR8rO0aLVEPmPVoj5BMRdLcF3PEVLiryQ7
+9slqjTW1HDoN+6nG5iIzsCAp7Ekm323AAv8Zum7CIJt324hRmVIfsgZohXw/WfI5iioK6kSqpQ4w
+jZqNEyQd+ZzMMkSluGYluT6SjQzqSMaqEa/w/lSe7yNjiuu0OLPb16AM0gjiCZ6lPRebgq0MXHW7
+0LwuQsKH8/kKF8n3lzX72OnIAG7WW6jUKdBJgU9tPIXNFz23EkCJ2a4CHylFYfFgDSYisMVKv3fL
+FFYskxUGq6dRV9fwYERijreCGqpU7zmmsOdlp4miJWbtcZpgwEo2lCUKSri3DQWN9RNdVmdqG9uw
+dImT6EUE3aG/W92E2qMO4Eom2XUqiEnxXtvcRTSSBV/NCsx4u74bI+H/aVPZKBz04STiv5qNIdxU
++lw6CeKebKYbSYmKJ0xGAd2y3m4XtRZrEZLLMJhdsGtNKLXPiTJRULBWI6uSXiawnG3ew4Xlfl76
+loxQIxnK/pMMMTUh65mMt5uWKLm5D7YNPBzJC/aiWUGywUj8iY5dS+Zql2PYbNXhWxL7UP4ILmSu
+o6oGfcwNwh1DRTJ2GVmzzDGgcZMt1wUMci1W8iqmZIyuBGtkhIWvKVuvredQkTzrk8LGziVWLSYZ
+bjKTHjMLtgx8NCn04nomNnRZn5X6BdQ47xX/OnJiK/QHNOu3VzBBwhXDhk63K7RHUPycLxPoxGEs
+z3RVj2IErNc5DTQcUBfU0BXuU8zoys4+6peZ8vk6nqmdV0met/N23r6jPReWQqYDx36Odj5DSVXm
+iVohC+S03mjFaG2/DADQKR8HADr5+J0tINXlKAqOJ9ASyp81G1gEEiF2kip0RplTBglDfYhwTUmt
+AkhYlbky19jusXa9aiB0ZEfXaeJHdQWPFZpVX8OULMHZPEN1JC5NsdGB0eUpRnO4PdWhtSh3jw5S
+BtrMbIVsIEGwBQUlilkMi1Vx8X0N9w/E9DtYLBAMJUg4i4GHJOfoEfH4I7YDfbALAFivPKQ+o+kw
+EJM/UG9kKGbyTNuEa6tuD/kZdHs5gUXiEa3TQHIiJxDiNQwG1YqBoPK0qsIZVE2veshXJGdx7mL/
+TbpKhyPcJ0fA/pLS6ls1vD6Q+x0hQ0IZvmMzQZdnVf/CAHlFTvqSVaR1R4NBK2UdlohycWOa2IHW
+lXsUhY/iLKBLubZiO0UCKsBMDXiXKFQztJE5Mp5haMIgr4NJ72PdGuHrb4nr01pF+DZl2gK1wOlC
+rJ6zzFBFeXD6rM48GaPjt/msHbi0plSUwW/L/KB9RpNmISM/hVmkSK5xTS4TZI/lud2ujeWIeBei
+MY8PCuRK5sMpbWhV+TzdW8NjsDINLmMxJQT4GQuinotNwdZ0XK01kNds3H0iL1WSAhVTTK5S62B9
+Bu2MZRMBgITciOUiBVgtqKIZopxm1KkOpyaDmmszHDD7sKdJaqbraXBKWRxFOUfOKjrf1aDoZ8b+
+HBXKwBtUPNqu1ZFRz3AyOYWxJIdhvMSspJz7eo0NAq7OpmRRm6OeUAJeK+ARUpvZJSxCWL0zuO/J
+Ie7f5sS2DRhUrJqNU4SMZAePMiSkYu8x7nEwWyEZCV5+s7oJg1F93VSwQqoeLUpsL+RzkIifqrnv
+QbHEe/6Nb8C6KpueUQDmBmMxQYiSlas6WAGpbQMkw8n1IeI+S6C3PgVzLAChvYtHiH6FxCnX6Aam
+JfKajFtNa+Mlysjf6nlwN+R3pwtApzJWQh7E4UMDBlW0cqwQ35E1cupr2KDilLkNFBP65Ywv+dMY
+OXVFleZjNWf84WIVViAVisHek6fZilLxhdddpK6sp3urCJtk5/KzOS40djkWMh+jaokYvFDyAAAg
+AElEQVR0X9bvqRNhkUg/T0cL9OpctPoK5ULuZ9KN1SsutJDErkkCNZXNQjemyEj0ezwCLIoflR5r
+cSbHCPkO6JkOgxXluaXBsWU8N2/GcHSJ7TxrO3cfztt5O2/f0Z4LSyEtMpysRsDKg0USjlQpRKQ8
+q6cljEx2/OmkAcuk9Dkr9dR4hYTVJ156ilgjUUkYIWZEerwK8JB53OhjOV1/fx7jMCedtlLIWNik
+YgtVkmnY9QZi8vnZBL+MJkuAtfJ2EiECf5e4qPfkhGnV23AqFAuhirBZTrEgd6BrFshtUqrrERSr
+Cx3KlK9VB/ZL8vz+uoWyLYUxn7qhYToSMzCcjnFIvojfnEpQ79LBBD9aJ89gUUfJAqREj5Hvi52/
+9/bbSBLJEhgzuUfXOMAHb1HmrTlBvyXnRSPow1pKNqO2vURpivuWfp196Myhn+EKNkxYkPD7KtvB
+zhuCdSi/dQuNivAqfj2X+Q2VDYc8kMnEwIYjvztNATcj1VvSeJqBekIF78pSwy7FUj44KKAz2+Fc
+nePyh4+kH5sN7BADkFB9uj5KscfT36llWPXFnO8ODXx1Lqd/d5Uj4YneK+R3Fxs3cLVCQFrLQHZK
+vgvfxlcfihnvEkrdW9ewppL0tQB4py0cGLUkwDcTShBMj5G88C0AwAtkFbC8S/Dt+xyXOiwSh46P
+Qix1ef77oYHOSK79zlzmabvqo7lFHc+7K7xKGr7QGWLjqmTdLttfwlZHrg18jGdpz8Wm4GQmbp5u
+YvzGELNtMWG3VzUMqRmYpybCfXl589kdqBaJM/bkb9ZiD+VKFmv/sgmbrqNmaYjoq+VpgOWJbCzH
+9En9eYomkX2L6Riaw4o8mOhnMsBRWaJFJNkD4uzNUEPYkcmYT1doUCKq6DpISVTSsH2oKZWoKoym
+j+swKeueBDlcMhYVfRMO02xaKOa8f7WNo6FsJuskw+Y/l8/d102oXDab5JUS1u+J2blB1qHTMWCF
+jIG0V9D/X/beNFaSLL0OO7Evua8v8y1Vr9beu6enZ3oWcrhoSJtD0aAEWIIJGJAEGTIMGYINCKBA
+G5Z/0CABCzYsSLBsS4IgAyYpkTZBU6ZEjjQznLWH09PTM73VXvVevTX3zMjYI65/fOcVp2mSXWQD
+Vgl4F2hUdrzMiBs3btz7Lec7hwudoWwEJAzd6V6D7whiMbr5ZX73I6iHX5Rjz2xgcVNM3Noghc5J
+qG9vQytkEcmGEsNZf+tNTA0qGn35GDdIYtr44q9C+xzrD/Zb0He4IH9P+rPZAyJG0Z/56AWgkKm4
+vWwhH8q4fCdR6LHK9TbTdJNRhDq1DrybGfaYUcHtAlkmC6M/yuFdYz+IGpzoK8zeocvUMZGSVPZ3
+MoXsAclwNhOQ8R4J1auaQRfXPiNjsfzODFdkncO375bY5HyoNeS6B501KsxqjOwahmThGpcW1L4s
+UrfrFzH9PXnR3y0EZHYh3sPWNXElavUERcpU550RThpkyzpOcEo+VX1P7ql3vQqPKeejdgXv3pHn
+MNzycOUlWfQ+9dE6zE1J5+O/x2O1c/fhvJ238/a+9kRYCsfhGv/dt7+Bn+jpKAlrbT2rcOlUdqNV
+GuLBQ9lBbz/McD+TVd6PJPK80eij15RgSt3rodmmhmMWYUbT/e40xjFBHweENr+HEAkp2nItRUlc
+QegskNFcf2VwGTMG6Dxfvntnfox6hSCrlgeTFo2RAopqUQcHR3iwln7OSMfW1Ve43iZu36rCNWT3
+bLubMDqyRcU3qUx19x6+TkKTu5MEq7Ws3z+cbeCqTXCW6cKgqzRn5V1luUAZyrGTkwhbvIaemyj4
+u2pzgZisyr2x7Kjfu3cX/3JfIOb37ur4VEM4Al781NPYrHG8bR+OJq6JcUmusX49wNED2QW/8XqM
+7xKKi3UK57fl3B9vn+J0QY1MVkDujSP82FusEo3uwWV9yDenR3g2EYvmUn8HUxbZNu7L7hljgd9b
+y1hN9Cnih7Kjb/djHBOTsdRH2KBrskN4+GJSwdiX8bx5L8GKas6jVQ0qErdqNqlhm7UwlzuUb28G
+eIqvydeQ4a1D6c9kYuErx2L19SM5Vm0XeI7uyquRj4ErFgQWPhZNmXtvngZwfJlPmpLxWfn3UaZy
+z8/6fWSGzK2lv8Q98ko+jAscMkgNX96FC1oVA/esmy6u9OR6ZVLBM2t5d1qVAYITIqoes51bCuft
+vJ2397UnwlKwDeBy3cANN8Sza6b3KhbSJnUHa300Dsn+07PQP9NxNMTvv7Dy4Ne5Y9QMmC3Wxx+V
+MC9IPGBgTpFUzlicuZtlCvpIds/fy0xAMSVZsVCL5BxxeIKWI6u4V8jus2kYCG35u5/G6Pjyd7sB
+jHPZmYNVBr3H4BKHuVHfgk8tCCgFQ5fv5lmIgvBfbUd2O8fs4BIJUbt1FwebsjN/olqBRTbj5DRC
+n0SvKZWMp4mJNx+Ir/rRTz4H84wx2TJQo4p17u6ixXRvcPWrAIArroY//0lxmE/v1MFSf/TCBtok
+FbWKBuDeBwDoJA9t/sCPY3giQccfe9HDj67FSjvZdHFtLjvUYXwbtXcZMEvl+XpLHV/0ZMf7ybSD
+SlUsvZeMi7AoiFNVxzCVPD/jZen7/ZmJ51goZ2xtwGIh2NILsfyWBP4uuxVskQHL78iuW8YR3OiM
+q2OJKp/NzrqJ44pc75oVISEaNrNkPC883cI6kL43XB9JTDawIgIz1AjaLGBSOf4NGZWt1hiX5tS2
+rPiPxGA+0l2hqEjgdkC2sHVcx/aZRVetoc651d7ZxlAXK0bLdVwvxbI4bsn8fcUzEFbIYp5pKCns
+E8/mMBtkq3ZMqMYZjevjtSdiURjFJf7e7TUuI8IeeRL/440Mu9vCWmy2EoQNGeBWWCImt10QMGAI
+hQoVlAo3hEU8PLQCOyTIWAwt1EN5uBNy3XlHNXyLwKJuaWA9JhbC99B7TiZjrXoRJzclelvVGexK
+NTQd+V27tYl6V2C3euhhoy3uiuPGqNMM9C9L34zKMey5PMS8cLCgCW+Ya1glCUKIauhubOMlBijD
+wxKfbsmL6XoO7KZMlGC9wHaTPI+pTMaeqWCTkj58aCAPiMLRb6MYye/iVQabbFcuqfN914H3FeIw
+dlJoORfC6gSGRvOz6wCKZnUgY2IXdQw68mw6tRaih8RNJDsoGPDt9XJ09jku5Jr80q1TtCIZw195
++wA/yGzHb5UlPnksGYUt7SoGHWI9WOHYdCbwe/KCzaYVBIYsMnq5AsiOnZodqCY1KCkmk4xTVCpy
+jt2qQkGwFLwIfULEY7ePCyTguagRVh12UWFp/Ed6K/z2Owx4w0Yayr1k9+VcgV7iOkEpvmGj6sp9
+aJUK5uTbbLgVKLKJq7EEEZVysWQNi+skqCQs+7Yt9Dfl+VjLAAbp2zq5LFxhvQci8JGN9mAsZOx7
+DRvepszfYDqHtpaF7HHbuftw3s7beXtfeyIsBbtp4tKf7eFndw7wDyiGcvPNY9it7wEAhsGzsInK
+qutVDHwxmReEETfMCmJbVvAGGtA92UkMbwZYrL0/LWA2ZDUeUHFYLU+h+WJylXkGtU3dSbuKpyxZ
+obXSQNqWlE5uiwzYdsuF0xUrRk0T1EIZxtQ0UFalb1uag7MKLIvpLdW6jopDTYqTQ1SpOmx7G3Bn
+ZzwE3A2cOizqLQzjNRaQ1GNrvYEVpdBqvQGGiexWn6RVcXec46rNYiAjhsnKTs0YQjHo1jlZQ2MF
+o1GRXcvJnwM+Q44Bx0FGxWh9dgEJ0Y/GvRZwUQqorFJ2Yq+6hqtkLLLsGN0t2Y0j+zY0fme5slA+
+I30qaY29/Wdy9G5I4O/jOyWOH8jn515p4UpfxvMgd9Bn2nks3UHdaqDPVF+rOkd4ZjW6m9i9Kr9b
+5An6pfzAoXndatfRuiWWYn2rA43aCw3TxCSSOVKzfVRacu46cQVeGMJpitUULuZ4ZUOsvn8xigHq
+MAxcsSROBgoPZYrg84M18m/L37NrEbY8ztXNGoqcgUlbrvFuMEEn4t+LKkzyNJiTDNvUlWwNGvBX
+TG3TDdzwB5idyvMwlI+AmhU9R8EyZLwrhouQ5K6P256IRaGaavjUAwt/956Pv/DDMmDXtp7H0VIi
+qPWKDpu+XrXZB8hu1Dwj+hik8MgPq6sQjkZXYrcJ965EZOe1Er1IBirblt9d9TzkE8lgXBv7WBRi
+fi07KUzmh7v+MzhZiI9emvKCvX0yx+B7MpF67T7CXIbR1hTqjiwmpbeEqkif9ZWcy7WaKM+oxdGA
+QXFQX+/CvETfOSN4S5+hQZ882zJQj+Szpiu0WA4eNxaokLZ+NJIJeKleIO3RfdANFOQl1GMdKGUh
+MFs6kkReEP97lFz/WAnd35W+hw/hgFL0zVPopfStrOcwKARbuoTiHo2QcSGrpM9DPScTsLIYIFvI
+9dphDeue+PtZJKbshRspvnBD+n7xioFZLuZ6fAgc2/J5+LwHDARP0bgn8Qc7yqGozmX0hvBG4pb0
+lYmVIwv8tFBIc+lTmxF+3a9g44Jc751JjMGI4rfbHmpk9aqaCk1e22R1ZlpNUDLWdHEwxBsHEj9Z
+RTHMWBa4Y/77o08Z2HhF7n9vYSC+IvMw1xpIt5itqhjocDEIWQWcLWOsyDZ1Ie+jrHPRy0MUFOzd
+sNpQF2RxarJ+RJUL9Fy5v71kCYe8kg1zE31Psme6ayBZ/MlIVs7dh/N23s7b+9oTYSl4Gxpe+i9M
+/KXKFZyciVj8lo1rrANXNwtoQhgMqzeAtZAdISNBilqVKFkrb6T2Iz7AUi+hDSTS698dIZjKSupX
+5bbr3RSDY/n8rpZB12QF7mcu1FpW8z3zdYxYF6/XxBppVeYILObd9SWq1CkY5nWAFZOeDoAmrKKO
+YjpZoSBiMY9CmAwAackcOoNkJYNh2skE2UJMaj3vw6IMmvmgQJiJjZqUKWyDsGlWAEYtH9larICy
+3wNCMf3RvICCLkFalKjMZBcvduQatrGE5guJllmaKHPy+p1OoFNCTT8dAGcVfCZ5GpoOjLlcQ+mH
+0BlxR3Mb1KxB4o+BWIJuHVY9rj/yAM+eigm/ylcY6rKbha8o1C8IRdw6s6Bl8sw0Bnbz0SlKkLqs
+vos23aNodfJIeq7dMOHT6st8GWO9WkW1IpWdl6K3EVM8aFfPoQ1lDrTWDSTEKZSn5PHsmIgmYhXt
+PzzCjAVrngbYovaOF0mWYl2uo/y/ZWf/2HMuBsxEVLoz1E3Z5WuTCVIWZq2XRGDmCi4LnyJ7BJNu
+gFsD1Jp8IGYAh26ozUB6upfixkjcysN0hpCVgqXrIipkjnjucwA5Ph63PRGLQq308CPrF2FPfBSs
+YDz+UQfRF2SAm89paFGlSYtMKKZv0vCMG2+BYp902f0VNOpRmtoSs1vUpkxDJFRfsgk2KY9tJDo5
+B00NUSIv9Hym4c5EgDxldBVTpjDPVIpy5aLIqKw0r2CiS+zArxZwqAallx5MUpgnzAysgjnKhMxM
+hQGdlOulryMnMKoIWNXptbGmmegkAfCe3F8+VAiOSPK6nuDOnvzuXlV+1zE34ZqysLT1NgqmLAvj
+BoxMJlC01mGTxFWv8SXOulAUki2sABmFXPLwPZTRczJuT6XQPSFxRSKLRpZWoBWyKOSVNmzCoLUb
+GQqPpcwrBzqrTkO6DNuRi3xX+hkvuqhfYi1JfAm4KatJauu4O5OFww7Ir4kCFl/C+I0TnBaEsZtz
+5IGMbRl2sbpIBqs5ZdvbBZYzeU6ZsYHSZeQ/r8I6sjkEOsqVPDO9KdfLxlWsIzJ19WtIJuxnpuPi
+TbnG9ULmTdzqoC7M+Zi/liN6Qe7viqZjss8YjlWD4cjGYITSh5rmQW/LM4Vfh0F3M4x0FCxzjWyF
+WiyLfcyN7KSY4/ahfHc6W+BuJP1ptFaw57KJ9DHG4p4syI/bzt2H83beztv72hNhKejKgqcGCPsJ
+7CNZ4boHKUxCaV3TgKOzolBX0HIxQXVy4KlogCIR62F9fwWNPIeY7SMiEUZQ0WERfJTpZ5LzS2ik
+NvNjoOmKeTYZ53jwnrggUfW7qO/+iFxvV3L07TRHxEKU8XGAGvn8dq75MGhNJLkCzioUJ7JLLDIF
+i9WclaKE25NgUBn6WNO1UZnsIlpQINHPOAFs2Cl59sY+QhZ5rRIfq4WMwQ75JqZ6gLYjO1RmL1FS
+eq/MIpRzsRS8gzaspyTAqFuUoMubKJnhKG6NkURiPhvdF2BCdusis1Ge0dxz99TDGlAT6LPpO9BA
+wFn3EFhQJXkRQS+kQGfRFAvs/izFtivn/cFP1qGZcny6MDFmdH60fwIzlGeyuS3jphUuTJr+0fES
+NVaH3lc59LOsxDjEXiG7o3dHLLq4DUQPZYzaGzpyTwJ/kTJgVqlHuVhBp+S9PqPFly1gEdMRrFIM
+bQnyfttf4cdqYtF0+O/NdolOTGtys0CDEHpro4KS7lHHqj6yHCNW0TbWIWoXyAdS1mAYtLZWCVot
+ue/QUTAIdkqZDUKsMJ/KPD1MTJSBjPfsIMGdquBIpkYXM1qFj9ueiEWhyHOsxiPY8wYK+t+o+YgJ
+4jBS49FDTEoFpZ85q/SVLkfAA4prHp7AT2RCvBclUESgrVIN15py7pgVkHMnwSImktB2kMxpUpoF
+9uhTXjZewozy8fFM/LsTT0HRDUjbOhxPHsZSKShqBLgtHcdMVc5D0nvXFUAC08NkidNbLPvV6jDo
+KhQs8ax36nBZU2EWVcwzyZKkMwWsZLFYpwtYRLfZvmRqBt06/K68bMsgw5Jmvj0C4BPl9nQOxTJw
+bULE3yBFnsiCFFgJzFzSjHnNQzGV48l3DlEtRCFqaslkdO0RbJCLUBnIyTakFiFKAnb07gbyQPrR
+uCcvaWMO+KAb8PEWzMmLAIDT0xQxNTeCLEbOxdebM5ugAcqSv0/Wa7x+Ks+34eVwB7LoR2GCdCnX
+vtSXOMK8sKG3CWSqDNAm2W7dUsjm8szKioKWsHK1JfMpDEPkMVPAnRb2WBFa3I/wW2/JfPjkBtOX
+KoO5zUradzT4z8u5PKtAh7Tsp7URehQDDlx5WY/un+Ltfymfn/FuICWD0ryIcJWl6tt+HSXp4xfc
+DMeTFC2iKq0oR0auybXuw4zkD/MLBfLlGcPT47UPK0X/X2qa9ramaW9pmvZLmqa5mqZd0jTtNU3T
+bmua9ivUhDhv5+28/TvSPozq9BaAvwHgWaVUpGnaPwPwHwH4SQD/o1LqlzVN+wcA/iqA//mDzqdK
+IG+58Gi+RYch/KWs0PtYwicMtHa9AnvNHYhRWFdVkVM1yO86iIiv//gyx1jJDmsePcTBWFZ23ZTV
+3tzy4VCBelyECLhCrxYZVpqYiXv6BJ/t/PsAgG9uy648mKfQCJxCbGFB/oIUAcaKNF6nNkJX+qGd
+0bdngEXtSjNfI6de5Y3lGLs56dXJFeHUE6gj2V3MdgrVkN0huDXDhDwNtV4FOrMAZ3Ubvm1DsaIy
+9AwYFBPRLm7DJZ6i0DRoc7lOPLov18ACaU12F/NkBGxL3/32ZWgXRHSnvPc0Ysj3e6XsdvqgAYuM
+wpm6AY0ZALWxBfNQnlm8nAHbYp1N98XSODFWuE5eysF4iqRDsZi0i7AqGIlaoePWWjI74VysvMks
+gdOXv582l/DI9VBUfQyoOFUZ+NBYSxKQe+Fp3cEDBvNqVoR8Js9BXXCwZPWouZ/BEIseLsUOSy9F
+Qotv/94hgh0JzAaagQGzWKMt6ftFZeHwBt21NEUxOuOr3IAxlO+MjkLklA9wlgQVbXuoHsh93AgW
+6DJgmF0z4DC7kiRLuMzcuIRdG0mKdxhcDaFj2JL35ZK7gdKXe3q6fh3fpRv7uO3DBhpNAJ6maSaE
+ivAIwJ+B6EoCIkX/5z7kNc7beTtv/z+2D6MleaBp2t+BKExEAH4bwOsA5kqps7KshwC2/rDfa5r2
+1wD8NQAYNnzEqxyWduuRT+bWSuQZd928irCQgJJzCJjEJJSK/mu8QjlmYMgAqLUBUw+h9mXHiM0+
+Uq6qkc9imLCB2rOyLj59UsWXviM+/jS0cXspMYWPPnMdv3kkK/ozVbEeutct5FViIWYxNFoHs0RH
+hSzPczOEVrCgKZEV3GkYwJIIw6aHeMZinms5bn6VmpBkGEpWK9hE4DVP1jAIeU5dHeWwze+EmJG1
+OLTkpp9zu0gdud6G3QM0Bjvzd2ExGBuf+tBJPKsUVamjKdIHrLKbT5DnrDRdLqE0oi3LEpYjGAKL
+hWTZrInSls/hrRyWSQbnqAFFIROlbKy+I9Rky3dkC3ZXC/gfucy+b4BFicjrPaAugdnZTMN6QjEY
+EsXmtoJ7xvS08jDV5L67roGUGh6jVYSUysyqJ2MxuZVh0ZexWLo72HxeduC9m3vISzn38IILD/KM
+i4r8Piw93FpJnyddB3v73LnDNd6cyNi3viX/vlVbPopb+XPAvSTxk92kguOxWDdRWGLKc+/QUjCq
+BqoMDmxdbaNNhOTIsICWjH3eSbEYyfyNF/KO7BsBEhqsx8cp3MlZ3O0ePvqc4HPeeHuEhycy9o/b
+Poz70ALw0wAuAZgD+OcAfuJxf//9UvTP95vKGUdILtWgvScmfrPfRt4QgFASLOEUNJOdJdSEcGVW
+yKXHCwShgDXchwn2KaRZC4DDnC+ZGWDGh79F+rROwwVYWhyrBKcMaj08tpCzfNULBvBfIKa+lKhv
+f20g7Am8dPbQhnJkYsbjCAsqGV2v1KBoluYskV48NHCtJvdUtHvo5JL/j+x72GzLRD/tEpuRWbiz
+kKrG9TJDvy6T1XPWKAy6WE4FeSL9GDAoO0oK7NaY865rqAQcQ6+GfCUvm+bpKA7qHJf78tX4KtyW
+jJs27+HwHhevaQiv9hUAQPmOi1mHQiQrWTRDrQenSt3FVonWjgQo68+sUVAkZz6eY34kE/rrFGdp
+d9fYbknWwtupQ1FfcfNNB0FHgoMPFwVWhIKvSaUXmTlqhrxgg3yOZCUv0N0sxPWOjL1f2UG9Iy/F
+vY48081eEwUFhXz1NKbjN+V3N28jq8q+tVMFHJr2GnEx1XkINZfzfv3uKbpbhGPnHuBLALrFvh9U
+V3jvIdmVXROrW1wgLkzxVF+eU9u9gCCRsV1ysTHuxqiRU/LWhsKKpDxBUIV+SeZc77n+I/GjcSzv
+yPE0wd5Crh2bBjqQvlWqT+NoX/qce03cIlblcduHcR9+DMA9pdRIKZUB+D8B/ACAJt0JANgGcPBH
+neC8nbfz9uS1D5OS3APwSU3TfIj78FkA3wLwBQD/IYBfxmNK0WelwlGUY2M0QcGiFtMcwiKaazNz
+UHTleKIbqPTIe8Bgl709RPWYAjCbIZ6HFNFkloe+Kbn3468dYkh9QZMajystgnks9teGbeGpqazA
+N1shnokloLRo7uBn+mIy739bzLDk4zqaFhGIvRL5XHa2aTlBSWZgY8eCKmSX7jek772mCyuTFbyM
+TMxtOce2dg31j8mjqDVkx8iPj/EcM0lluw6d2pXlaRVtphNnKxtJjVWEpGPbztrwqRGRJUC4I/da
+9VMEfZLBLOsoHVpT+hW5yGYfFkleoQxcJQFrfmEL2j5Tkt4c2z5JSDwJPlabAXBPgoHtK1ehhrJD
+F1mB4zvC4Dz51gN8cS57wxDynD61vYvWpnzX3ryCaCHfXbkZHLp3lVYXXYrI3MhpQcYKA1/GVa+U
+0BMZ+8uOgqEEe1HbbqLliDVx8Zrg47Vpgv2JuIdBOgboXlzcHSKsy9gNdy6hsSUuaRKQKq0WY/Ta
+FwEA89DA4U0piJotjkHkNe4SQdtPXDT5Sj0MchzSUvpyEOB+IAHWn3i2jk8RI2Jckr5Nbt1EfiQW
+wdUoQ0CUaedqhE3Ol07/ZRwvLI7Ra/Lv0sCQgeue3Ye1wwpPw0TM4r+Hp/cR3Rexm8dtHyam8Jqm
+ab8K4NsAcgBvQNyBfwHglzVN+3ke+0cfdC7DApoDhcN5C3SXUWpTIGK1o7NCoWTC+kYbJYlDCkaT
+TaVQbsitGIGPLGNNgWEi98iAW2sgy8V8LskjmJsFbhNWfLrI8T3KrF9HB6iK2fnsq31MyQVZ68vD
+rFRGWOQEQiFFrPN6yoDOEuYkXKPZkheSpMVwlINIycOPRjrWrINw7SHyTZmYbfIMzhcTJMy4eG0F
+3RYT8DgNsCTz8SFO4ZG2vFjJsXFcgUO+w06vAYckLJnahO7LS1HMHZR86VcaFbR0wPHkHGXsQm/I
+/VteAlyWF0ytHeSZLABWXY4ZG01AJ5V7e4HCJqYDJd6+I+P99YPbOA3l2i8PqdG5uY2yJQtgkhw/
+Um8y+wnmkYDEAu8Q9A6gka164SnMWWl6baeGlDLxVpIjSUVFqzxY4UiXce7M5NgsnsHI5WR3RgdA
+ImO7s13Bi33WtphtZD5BTan0fRTm+HUycO9NR4gVa2yUQvmijH1nj+6aWeI0Oov0l1gSA1OUE/gL
+Me0PTo5weZMLNRWyWk91cJjKM5vHSzSIt+heu4ws53OqBJgxa7aiCPP9hgabuIqkucA1qna99uYU
+k0QWr+X4AGZChN5jtg8rRf+3AfztP3D4LoBXP8x5z9t5O2//9pqmyCX/b7NZuy+ozn/z6/j5T8R4
+izx6f8O8j5N/+DsAgH/87mu4d1eELO6vY5gO4c3UBOjAx9JklD1NEbNevZU6UHVZVYd5gjFJT8yx
+BMZOcgWTfAShbqJNUZdQcx8Fj0rNhkf025m24y/8w/8DjQaVhhfA6Fh2lS+/9hq+9K7svHdvvoP1
+CQuQYtlpNM2ARsiwV2tg4wzTcLmJy7qY9tUOK/ZcC2uSyTw8MfDOnbcBAMezMYrJfRk4VQD4o5/f
+846Gt5L/7981XcN/wB14STo2zNY4JQHMOFaIdAk3OaWJRKfgiAvEJCZ0lfRtrTehUbcwLXyUCeHa
+Sn8kmFOqKgqKqyCngnWhQ9sgHO+HfgGnf+clAMA//9/+ATRKnmWzNY4PxW249fejVBAAACAASURB
+VFCe/1snKY4JK4/DDHEsO7dWliipuaGbFkpWneoGw2ZlAZOuz9D0YDLwV1+WiPisVZTiIGQmhZDi
+Ml8A5Rm9nwIseT7/1Wefwf/6tlg6FV/m4VbNwzYl9Iw7J3iTdG3heoEJodK2VoLARFRp/Ua6jg5d
+5bFSiELpT6EKqLNrf3/Tzng+S+Ax3l/KouK0xOtKqY990PefCJhzowl87s8p/OXam/gr488AAG6/
++/P4VS4QX7z5XWSRDM46STGkwMdOh/kY5cFnxWRUy1AmMmky3cJHqjI52heuPcoGHN1kznK8gulR
+P3EdwiRdupsqwBQTztN0WCZNSjL0+NEatQ35++k8wfIunf87U2Tv/C4AIJhFKELWFTDbUSlPEbod
+XuMGyuuSffjE5RB6JDDlXkdethcu9RHQXPyGOUdrJX7m53cWKL4qE2GRqj9uTZAFgS//939PKYXf
+4JO3uaAZhgXF0ttuw4V9BngpTJR0UWqVCpoOSXMjOYGTO3A35YXuu3t46wH9/eAmEl/cgEp8Gyd0
+D7SEqUJvjeusObj5N9/Az90TOMsPqf8MtkvxnTTHnKSpUcBsSHCKlC+YkZSALzfYiELMKP6qJxFK
+LixWxjJj24SnSIF+2cMnCThaHe9CkbSnRITWiVzvmzErGU80FKyudQsgviyf//6N76KzK6LHn2uJ
+e/lNXMO2K79/49QGqF2aWfYjER23aqHOZEDN4GJrGri4I/GxeZjhy8dyvvwkQeJyTuY5IorJ6jkX
+W1cDkxZ/3DTA6R+yrvxx7bxK8rydt/P2vvZEWArDHPivRwq/Ev84/gdLIvxfS34Obx7/HABgHcVI
+M8q9GznClN0OaQVoQEhzNw9jlAzwPLvh4Hpf4LU7T/UwmMkS/dZVWVddP8e0PMtdRwh43sgsARad
+VCpV6CQz1o7OqNRSaGQJ7i8f4sGJ7EBfuf8lLBnh1oIQOU0/LWelptKwoSSgVOk1cIkByFfzAS6T
+HzJ6lizCWQkv8NmHW/hNUn1fe5jjjTOT8gMsRwtA9n3f0c7S1Sag02vIT2T3UZoOg/iHLM2gqEA9
+0DUoEscYtoXnLOn0WUHLkV/g4pZYQvWZhTbh2HecDSSspFRajDVN/uBMJTkxcGNPrv3blXdRORLK
+uxvLX8DqzMAKDhCN5H/ukRRmHQYwGcwLSsAJZGznhYYKraJY1+GQ4yDjIDWUgSr5M5+t+GjqsjPv
+bCnEdG00pSEjZd2D92RuHes5dFK7xarEy025yMvRJvYmMh++Ecgze/WqjmAhmapp9hpKAtnKModv
+srjP9HHVlnMM22LpOr6J4TWZpy9O53CnYm38KydD15C+TfMV+kR4Lfj4DU1HSHDaHzcXznb+xzUY
+nohFobRyRMMZftLs4/6JmMnf/CcneG4hEW7oGixy/ecbXTxH/ryC0txV30GFVWi7xhGWjoBRPuXa
+uPzcRwEAFzsNTJby/Wdvknq9UwEoOvuap2E6EnM9X8fiQgBYlQo+zbZxIpN/EcxhdiUGEAUGvjyV
+dFqwAMY0E0tHh0XAlTqTXIeHC32mRbMO/oLMH3RbEfyamNXVpoB/9NEhwpo8xtXYh9uRFFOaPcC1
+lXx++3QO/DGwdgdA9jfls/m/41GGpvFdHZR5xB5TIw+rGkzWc/hrQJHlx9Uz7GyI//0D9SomVItq
+MnV8YesCXtjlfbofw8dZo/CF2zm0lYzL3ZM69MlbAIA5U8Dz3UO8AHGf3rj9n+CnfpvMS2WClGxS
+dyYW7pGdKicbUagb0Dz5ew0+VJdcmkEda7IiNRYuzK64Gx6ZvLQGsKNkPr1csVFw5SkaNlybNQoq
+wuSyoCz7LEms7Ru4T+Wl1r4Nay1z66m9CONPyr3u2OL6hVYLE1ZtDgoXh+TH7CkNAYlbPzOwUL0q
+i8jLdHHyZhevbMrcejcosMUS8J85GSJ9WVK52u9W8eaZCtgeq0vbMbaP5Bw3i/KPXBjIOv/YBdTn
+7sN5O2/n7X3tibAUzERH746LZLGH744EPntv99dw46ZAl+NmBcOW7MwbRQ6D1OH9LmXUwxZ2dncB
+AOW0D/+S7PjNvIlNwmqNqotGwBI46ghetIeY0my/mFUQhJLT1iKFlZJzxG7tkfloaITcFjrisfRt
+Mn6IgDRmcy2BskkFF9kwStYXJGKqPt0sMcvkcy9Z4PNvy4r/Q58ZIbnNKsGAlFqDBjKy/TrZCOap
+7IjDooF9mqUWgOwPjKWG398wOjpw6ctyjf+23MDPXn0ZALD1uTfxhb8v5/OuUUkp9NFjZkSvVGFf
+lB3vmXkfrW0hSLH7NVyv0fWiX9KpXUW9J75I1dCxH8v5fnLTwTeOZQe9bL6F9RGrVQk46648/PVL
+PwQA+Fe/+hDN5yW7cvKNCY5JsjIN1pjHxPuf8T/oGly6fJ6y4MYSuO07BY5JrtO4MIBJuryLgo/C
+YaFh0JV5sx8rbHTlubfWOVwGPDVzAxZZd/69puzm02yAj27K8/3c1R38+rGAto4//SZ6xyT2YRXs
+cjHDmNWqszBC7kifB6jhCrVCn+418fI1GYMO60s6u1eh6FJ8Qj/FVkv64HQ/gsoLkrly1BT/yy15
+N04HtBQmAU4pbLR/MENy5klowA4nwb5mosvsSnCW9viAdm4pnLfzdt7e154IS6HIEkyPb+OdyRrq
+jW8CAPLvPcBlKvjaXgM7m6QuM2dwxoQYU4V3c6uJS125lfZTz2DFKrqe6sHtEfGW+1hVJYBT2ZCd
+r2YEqE7kHJG/QNw4SzPGSEgDpqkE9YYgxSah7J6VsYk54wzpsYEkles1VB2RYuyjWYfDFGbvqiD+
+nMoaO9wpP117AxpZllrxGsaG+Ltdl2oi+TNQM3IklD3o3HWadR/5saD1Dm0LUST3F3J5r5ZAwYDb
+wgB+5mXxkY+MCT598GUAwAXHxnM7TPFSpq/zUg9T6nh+wh/goSMn2bn0DDao6dkx6rjPmEmFfTfz
+CG1iIdZain6HTMu1Gv7sNfn8tS8sUO1I8dPnx68DALbdV3GTXBYvXvsuxp+QVO76dwwsGCScnqwQ
+M2d/xgWBIoNGxe84KlEzJQo8Viv4FbEUKn6GlzbERIhZXLTbrzwqfrvU6uJkKs/SrOiweO4dz8Cc
+c2rRknFbT8fY6Etq//SdABe7AjE2WjGow4KlEkthPF9Ai+WeB2UBRcvl6m4Vz/bE8vjczkVoRI7W
+IVaO44UwSRs4zyzsXhFyXDuYwfUkJjYb3MVPOnK+1/apFzLoIHz9vpyr6+HhWMZz1tbww2N5Zv/a
+zsEKAdx/TEvhiVgU4izDu8fHmB4WuDOjyIhVwK2IuT+sJrhymcIqo21MdgXLfaEhD/7SsA9zQ142
+ZZXYgJitdl8BZ3TgJVCD4PwTTwbPig5QI/Ckt4ywnoqpatanWDO/7fYMwJKJlTMQ+YXTt3CxIy9x
+0ncfYRb8cYkO1ZsqrfojKu+BLX37882rqA9kst1YKDRDCWBt5Q1YjuTmJ6Q5q5oOBrJ24aSwUG0L
+6Ofk3TV0lkZ3LQ0PEnl5NYqJrAEQ2Y1KAdz+ovTd8DZhDUnaYrlYNyQP3x2Ke9X12rhGCjKjpuFV
+S3LwW41N7Htn5nGIDWIWcpaD73vHsFZyn9uVbVgUJxm7BmLCsZ9/5hq+ukem6IYsDl+9+SUM2hJU
+vv9rP4jZa3LeZrrChHRrum7DZFBRsYze1jI4zDQZloE4lRekrruoUo+zYjcQW3Kvl7dkEHUX0AjO
+mkGhv02W6xMLo/qc52jAYxWkF8ixke7hbQoGvVqtIDiW53Q42cPBKd0HT87bNtuPXD7N9vCRgcyt
+V1+5is2qzEmvZaHVEPyGQRclthLkZKX2CwdGnce1FfIzkme3i25NzjfcEDd39DBAZVfqfF5Y3UGD
+i/e3xwtYvoxRqzAeMZZLNcIHt3P34bydt/P2vvZEWAqaApxEw6xcohnJanac6PAzajH2bRwyDVfp
+rGHwuNmSHcqyC3hVkrPkJUBCEqQRTF9WXaPMkdmyCxup7J5etQfthDqCfQdrBvtqSsM6ZnHQUYqM
+8cmCdvkgr0CFsmMUZoGcUnG5dROKFXzFhS461F/okD1ab/vAS4I3eGXvaYwjqZzLhyYyplz7qQTf
+Ci1GzLRYjlOAVkd7w0JOU7PMDDghST/OBrMELhCPoBSw02AhzvYKrxC6G4wMXKOLYTGtW/pVLAtC
+hqcLxKXslJml0Cb5aXC6Br0tRI5YVYhLBPclGHaveQBsyY7Y7VyHFomLkRYnuPeiWAon377HYwq/
+NhYG55+6FkI9JYHB8o0GcupvhIkORWvDJqw610uYlHYz9AAGjyvTQo128uawjl3uqlWmck3dgJWI
+BeIbEWYli5IaKUpWsxoqRkiLK2Wq1k9LeGuxft6aTtDflHNUjyIYzPybtJoiaCiobbnraHC2xeVt
+tnuob8h88R0TKXdsLxY3UNdaAJWvc1dBpyp1xRygICQ/cgw4dF+HLvtQqeCUKN2P9IewY8FY2M0W
+9kdihV7eLEFoDESZ9YPbE7EoJKWGu2sTo+AAGfHyU8NAryoDpWoZrjikFJ94mJJYJHLEjUjNDtw7
+MiGKDQAeYatphoIvUL6ZoeQkzTXCkic61jXCXY80ZMSRx+scKR/M0nRQZ0Rdke/v7379HXykKSCl
+C5NPw6duX8+/hLIqEeLNuITJSsSSQqTvdY6x+x059vB4iR7FUI4zB/Vjud6dgP5iSyGgv498DWMt
+EzdWS6yUvEBWCmjqDAcv46YA3GAUuqEDvzGX+780XUBvyeKlhj4+NZQX/bQt190yAizelh8eawk0
+l8K8xwlODuW+i2qImOAja8zFOyhwUpX6BL/no7aS/h83GshYJXpz/hbufUlegHglY5+lq0dCJ7/2
+rolXlrsyVs4Ic5ZO26UPjW4ASJaj6wY0VhSWsY4gJd6gtobJew0NHVPGF2zC3zsocLoit2EPyM4E
+gQoNOcc5cEqkZL7SeY1wEWJJyvWiARzdJcFPMEGYy73GFPLxDAN2Q+65U7Ox2ZfPPdhorTgXiinM
+KtmySM+vzVNkTfmcz3VoLsVwpgpFh2xhowyztbh861skr2mPERDIFU2XmJ/Kvd5uhRhw7LVFG+8S
++HbuPpy383be/lTtibAU7FLhQlJiadcxo2k/NKuokPO+rdZAXXY2LVlix+Vn8gxqqyrqz1POq16F
+rsuqbK6AvPH7st5ZTI0EItuiXo765CxQFaBZlfPVfR+LkazG9kpHZ1OOHxIO20CJOqXbbusnqBVk
+Pu71Ueduhd0+uivZbfpdKma719AhL0K9PYa7SxzC0oGuBCMQQlZ1T9OhRySTcXdRs8Uy2Rw6sCPp
+/9TKULHEpFxQos7KFc4KAxMNeN6Q45WOhXkpFsazLRNZIdbNJpGbfrOBqScmfi/xMSfMeRG6GFRo
+vVgVrKiPWdapqbgYw2zLc5ovC0ybYrFVbo6xdUWeSW02wKgibkNZsnP672tsutspFi+LK7X11hYy
+FjyFUQLXEGvKpMVQKU2k1ABR4RQ96iSaFfdRFeSFjTaebsoc8Wlq5+scfkqm5ZWJhJZZ6VYRWfJM
+GrpCzqrZGf2x1LDQIq9FcGjAbopVdEdlaLKAbMGqszRLcbGU677wzCaebsvn/sULqJJHw3YvoijP
+smfUiezpUHTtDMeBSQ3KyBuh3BfLKjkMHvmIp7ynxUhDMCEcO9OwZ4tV4Uce9kISx/QW0F2+5uLl
+fWB7IhaFTBU4TqbIkhwhy56ThoFxKlHfi4aHikvQzIUmTI7O7o5ElquDPiyNWpLZAiZTQdkgASg7
+kSOETQrwnAaSiTWSsxevotDlRN/YibBkHMAKQ4TOGXBGFpX7N9/GkkKwn/74M0Au543vvYHr5DDs
+BAMckFS1sZC+v1Ar0PDk/qJKE9ZaTN+218SSNQVVUyaSMUux/bS4DLPDHM9clXv+2lcPMGRl5/Mt
+A98J5V6TpUyCmVaiy2rBqlnirUNqXoYlPnNFzmc1tjF5KMe3WZ4dLTKAk+o4nmHXZZlxu4aRomnr
+JBjo8kKOSdA67rqYsqy97fZR/Bu5p6+bv4fPpJ8AAOyoMT57KOP8Sw59m6CEIsxKO44R/Z48y3tl
+hILfMfUadIKlvIQLU91GlVWnKCzopF+/4Fv42LbEM/pbAwwIw/Yo1DNRCdCT32VZBmd4RoCiY5XI
+AmEpB74n43mRfXuIDAZFet/TlgjJwekoF2NuYGeCNe2qh6Ipfy9WazRI7mubCXKKFKs8gU2qdoPA
+qjRNYHLx1i0NxSVuLCsf2lz6UX+6gvAm6zkiWUxmsxBLgqXaysIeqy73F2vUmdZ9cGRi5PxBiNsf
+387dh/N23s7b+9oTYSnA0qANPFROGyhJbZYdz5DTbN2fm8ioEtz2LTRIKb4mJZqf69ArsgsYywmK
+CoNFqwQZSTFUUmA9J88CNxqnvoGCu0TpK9im7I6Xd3tYH8k1bms5arlYCjazGmpzhm1PILOL1Rjx
+HglJCh2LA7FujpoaEoNU9KFcMG1fQQ3i2lytOGgTAAV/Dp+L+ZlUXrWqoeBulW8ZaHiykzqXDgCK
+yNhWjE9RAOSbY1ojBxqGpFKbj9Y4vES6d9OCUxMrJHk3gm5JMGtECvxIi3AEMVXthcIRzc84WqCy
+Qa3FhzqOCOk9ph6iCuaYc6zuBw8QNxjA0zS8ceO7ct8v9/GNnrhF1dnZfWqgWDf8Wg69EMvF9brI
+CSAKDQVE8twTjYVkuYOFJj+s1FxUietoDLbRvyjFZJeuDbARUwvUkbHPT9YIyOWwLBSOZ+J2zMoC
+NrElUTBGBnJQEuvR6ERIlFhYraYPh3Rz00KhYpEWkGQ5pVKwlfw90z2MT2RckuwhLMLf7WAEyydo
+iYI1/m4Nxlju3zAMIGW1WhqjoMW6HpvQu2LF6IFYHat4jolcDjM9R5RRGEcZGBFQ9vxWDas5y04f
+sz0Ri4JW6DCWHk6n72E2lwjrZBUgDuRNmVaPoVO78a1ljiHx6e7zMpCvnGyjPxRQUBtLlBZziLM5
+Jmv6lMkMh1SWajpM/+g6VFWucboooDioWrqB4SWZVKrQcfQu9SEZ6Y9PfKRDRtMPFOYryYIcn96E
+fuYnnp6gPMuSEOWn9ld4RhPzey+4CPcpgm0WziONyf0TeVktp0BATskd28L3HnIBPCkwYRnublhA
+T6TUstMVcJPrAnuRfNdRe1hRD8P3IuxTGt7cVthi2fIDVmfWxxO8PaGi1TpAQjLTa/cdrG/JJG0a
+OrS2TLZLhtxbkmnw6Q9nuxku3JeX8NCz4XV9jssMlyiKO32EFNVBaxfjpYPLFXkZj9IAJbUaahEe
+qVa5ZDFK3SXampjfam1hwayEr05wfCQv76CqISPZJ8m54PgFEi6m02WKFRehhrUD15V+GnYN5gaZ
+l96m5kY+RLNKuahmibflkUBlCWLWaGSsLdgwjEcgsuODA9jJgv3x8B7jS+0ognXpPT5Lef4bk0vY
+odaFsjdhsk7k3niJKCRpS1SiRtKqM87IfBmiqDAFmmuo5HIfJ1mEakXG8GBuoGvKGN59zDrJc/fh
+vJ238/a+9kRYCrpWomIG2LBqOCIjb0fZqLckmNfwEvQ6Ei030hk05mDVWnbBB4dzWIHs1v7mECYt
+iXC+xDEJUJbFHMuZ7AIPCH653KohJzYhjVYIddmt7ZWGIc3IyVGCHRoetxYEvDgZetSaXJtTDCti
+NZyUHiKCbdqdHiokgRleflZ+l2kIDdkR3P0Rqn3qMfZKRKSbmy7kXOl0jQkrBN9zNqCPpT/Bjovm
+nPiN4S6Y+MDGJusrbk7gXpad63vzKloj2TF2TAcLhp+fCnuoMY9fc+Xf6iUPEU11o9nBtKTepp0h
+nst9rGwFpDIGYU9+d20eY39H+pOWGupXiQ8Y23hpU8z5EY7xoCe7u3MolqDZb6GzkM4X2yamdXlO
+w4MeXOpqLuwltqqyy/kOOSy7DVzpkWcjTGGSmi+PEngN2eMWixUGFolTSNe2nkyRTGlelws4tvzu
++tVNbA6kn5pl4IiAqrwvmZgkD7GRitt1e34DNgN7i8LGNmnaUCOAytfRrtAyK3REZI8uDGBzzlft
+8ha0Ff1XU8Yi39tDvCHjmtsPUKxpNR4UOAkoEhSWeCMUizVfEeZd8eAQe5C4Cicj6U9SMWAV0qeX
+ftDGwQl9IYHQfGA7txTO23k7b+9rH2gpaJr2jwH8FIBTpdTzPNYG8CsAdgHcB/AXlVIzTdM0AP8T
+RHk6BPCXlVLffoxrwLRMlCbgXJBdPItyOOzepbaDghVi0TDEZCW76bAuTqnT38XKlF0nMEdoWpKa
+KjYawEIcsWTkYBKKX3f7UHalN/a+Ao+ByKoeoeHLzu35Co3L0o+ndYV9ItYGx/LvuwcncMm+efnj
+BtJj2Umy+VdQp9ycOtFwwBTf7G3JwV/srlGS0SionuCInDg7wSYSslHXmD66Y9qPNCvuLEJUHLnn
+Yt9GvSc7hu/WkbMOP1jLjnKl08YeC+stK0B+IjGDg4qF4bbssGl7gGgkx7dbsoNj3UDzsvR3flri
+Aul6PKOGvEIE3jzBRQb8PnVJinrisEB8X+5vGdq4NZF+GLUIOwVz9lUbYSBxjhMi96ajQ6xIK9c5
+KhEfiSX0MFugxbx/o9OBbYiV8gyrT7tbVdisRLwdLjBbiuXVrZbopfLcl1hjxFhDK5djequBBi2h
+9biCspTzvfetm/i8Seu0rqPFQOGmwfnmGfCHMp4vq6t4YyV4C2ce413yKeySh+LVzQ5Ki4VdxxO0
+dZkLk4c5DiFz7uT1Mbot6YfbGfAaAeo2BXBMD6eH4vuP1ydIUqa7nRx2LnESsyH3XOg+WhU574be
+RHaG1B3HyA2iN29rqFM7QsrlPrg9jvvwTwD8PQD/9PuO/S0A/1op9Yuapv0t/v/PAvgcgGv87xMQ
+CfpPfNAFDMtAbaOBa40qHAYX316OsbND0LZh4+y2zK06ejSr/Q0JLlYMH25GQdRFgLxKKO2yRHkG
+dKoH6JHuPZd3H1HPxybrKMIwgknzrLfZgDGSgOEqn6A8IcUaTTJlF/AqYmT19QDvncndW1VkOXkc
+Bymu98XlqbOaczgEFJWJHKXQJUairI1gKnFHjJ5kGXY6GUxWy+2e2Jj3ZNJsTyaYEYpbm4cIm3Lc
+OpAX7LvhGq9e2wUAzO/fxMGWTNLNzTautKUfbmmhM6CrQOLGyFXIib9vVU7hUbfQt1NUSBbjBmsk
+5GucGPJsLl0fYqlJEHE2yrHBYGyy08BHXZmYoW7j/haFb4gxyHIHNgVaB56Be7EsJnazCrvNnL1a
+YrmUMVhvkRQl9HB6VpfQqwJLPrNeH4p1LkWxQkFXYaHJSx4GQETKvsgpoZcy9Vd2gsqmjGEfLuyC
+SmSZ3FO/XYXGUuXl0QmUOpMSMNA03k+VPfdWePmCPPNL7a1HlHZ+lKEOGc8rHqBxM2tQyMZHDa0z
+zcwyAT06VJSPCitX690mjFPp2+tU8no2neJORNIeU8ceqd8nQQkzl5O8tDPAAWRDwdt4rPaB7oNS
+6ncBTP/A4Z+GyMwD75eb/2kA/1RJ+wZEV3L4eF05b+ftvD0J7U8baNxQSp1VWRwD2ODnLQD73/e9
+Myn6I/yB9j4p+n4fG80dzBcPMKkwn2sVWC1Y897NMdyRHWZjYQOerLoai3O8fIFkymBY3UbOFbjM
+Y+jcVaoNHzFN3+FZrbnRQUYdg6I3hj+hnH3hIqGoh4oqiKh4bDOqt1hmGK3klt46rMKJaSnoLta2
+rJ9W7qHcpyVgSd8mWYmnqEGplI6QZJ6aVYGlyzWsCXkj6grhVI65+gyd+6SUbnRQo/tQLXNkZ0hG
+ErP16wV+9837AIB7JyvoRHp6aYiUFYVu1YCWC5dBUZXgo1eEcKby3dKrw5cND8O5i5QFX/fMFLov
+O3YtP2Ny8VCnvsOV3n2s74oldHinwO2nxZKz8wWaM7EK9kd0V2YHUCQ2fX0yR527XJBmyAl/Xnk1
+PMWUWzYVKy+4eIIaocTrQEe7KhaW3i5hjZm2jCOMiWh0e3Iu3dSQLOQ5VMwajA1aHmqALUssJG9Q
+hyrEHE9ZBBblOlak/ZstLayoC+GbKW7FYi1FK7m3Z6Y6LjD93KjXYNkMDFYMzCjllxpAsWR0mCI6
+etZEOCCNX5giYbUqNA3tdp2fC8RNse6urmQulLUqniPaNp2V2GTR1Y31Etc25Fn/5l6Gj1fPqFsf
+r33o7INSSmma9ieWmfp+KfoXn3patVwbTqWH07fEfFY4wnQtg9N0K+hQUn3QWcE2xBeLCS8tThPc
+pd5jy+7DYGVcFpmwCr70ngGTpc87jrx4ST2ERVr3FAY84utLtwQeyvG9eY66KWbgmmzOTsPGtafl
+YQWXcwwnMmlurzP4rLMebg/xmT6hwpdEgzIo56iNpJ/zwyUaKc3dtETJ8lv7TLg2t1CtyUOupBsI
+2mSNskyUxFskAFz6s2ua5eOTCL2mTMxvHWXYbMtC19/pISY8fMOtwF0x00LSlLRex24s1yjsGto8
+b22gcDinlqbvYqMqfdZjAoi8BNtNuWenNDHbksUyWpbYIsBpma0xOSv9pox6uePB3jurvrQQkNHJ
+3uzgKgVV7rsFwpUsMrc9Oa8z2YHDzMmWlqDzqDQaSAmselgu4RJo5sTyzOOwQJxSe7QGVJTcR9uq
+obNF5mpDIed8CG1C5WMF76707WSyhM7fZXn1kWz9VdrC9/vBI7KUAOkjBTA3qCNj5qcKFxPStg/J
+oJLrC/QDcQNmYQqPtRbpKkKDYDaj58Kw5Z6qdFG9dg854yT7boAHBEDNfB0lF+0f/ayJBWQM8TU8
+VvvTZh9OztwC/kt0Bw4A7Hzf986l6M/beft3rP1pLYXfgMjM/yLeLzf/GwD+c03TfhkSYFx8n5vx
+RzdDh9GowV46cJ5jEVSwjfvvfAcA0NE1eBcZdMQQqSOrbk1Raqs6gk2SVhbmhgAAIABJREFUwswp
+obM+Pq1oyAhtTiwDHVKelXVKeEU1lOQm8EIPGbHGmZYDV2QV3zEvI5uLZoF1lyrJRyG+/hWxQP7S
+doL2XPLcs+l34ZLU5EW3ic26mLYmg1oveA7iuvS5WreREE3YLWs4icRSsKgQbDg2BhazBV0DFtFq
+ebCCw2Klog+omtyf9j3p2ypKcO9YMBuHywxFxgDegYdXPynnq1odeGvx8nxLdtqw1JAQBTedaSjO
+YMLdCuDKDrxKAgwnsvv7A3E/YqMApSbRblnINLEa+rUZPAYde1oVPx7K+X5jQ3a26u0ES1ZDmnEM
+HWRinpnYp+7BT3xmGxl35tm7cp9WO4bPzELp21CuXKNbdbHypP8tf4GIyuI1StKvrQw+5dYyI0a9
+lGfjtmMsFzIGuXYMi4jLdUqoeBog6JILVBtiZtI9nORQI9nFb7wnffhPP1KH0RarMD08hXFR+m5X
+LXTZjzieoU+LUyPPxnLtQGXUBrEtjAO6ZnmAeU3my0vVDtCScfFmYkEn0FEU8l5YMxM2s2TOwzVu
+ETvx8ufnmGln0j2P1x4nJflLAH4EQFfTtIcQlelfBPDPNE37qwAeAPiL/Pr/A0lH3oakJP/K43RC
+13XYjg/rQhO9qbxg/9f626iwDuLGSmF7KanF1nYVtZixBpsAm8xEi/BhOylQbMik0SYBQkWA/TRA
+Qvesw9in1bFgMnq7LBbwAvEBK40Kkpk8pAeHd3FKwZiUgiRaRaFG3/rtvYcoydhUaeno+JxgzRjO
+dbngDjUFnZoFjak1U+moGfSTewpxLmYniZTQjCOoFgVNigZ0CtpqKsachLAINXimHL/DeoD1Iget
+a9SKFC1f3ljDB2Y8ecMIkRDefTY8pW5A1elbxyukOBN5PcWahCRtW0dIMFFAkZbmbg1GIi9KbmrY
+pHtRVAB3n9yOnRzz51jZ+aY8u6/6OlzCo3MzhZ7J5K59ysBld4fjvI3idTm+3ZaXNR8XiCjOYjg1
+NNbSz6LpIieQx0wc2LqMUcDaiHJWAKxQ1dIYMaP9dlRHnRWqbuYi5OdyIs9cKxKkh/J5ND3B1OQK
+WLpwWeex0ZZ7unG6RvtY0pvDTonsmNW4jgOjRj3OlUJI+QBzRaCbXqJKKPI0XMCoy6LgKx/DlBqa
+xRxuzLoMVgyXhxH2qSb1peQA7xBKPrOBTkbS4O0KVrPHlYGR9oGLglLqZ/6IP332D/muAvDX/0Q9
+OG/n7bw9Ue2JgDkrzUBp15BGI0TECvjzBEdzWWnn7hLLgBRU3zlBukWiQFpZafgAOYuA7IaNVMmu
+YtoxSgYXlSpRbhAeSl1KJ/OgCPLQFaCR/yAJNKTcEcxGBfpUdkK7lD7kawOzEYNa+3U8Q4hu+qCF
+oEuegqyJ4FviCk0uEpa8nMAsCCaaVtEgP8Px7QM4c/m8n0oF3fW+BXSlDz3fA7jrRIaFMKGSchYj
+OpVd4wrBONrFFb51INaPa8/xnVPZlS64EZq69G3hxbgaEfJLII2FHCNG+JEBusfdbJwhp8twHCjY
+qdxrh3DuReZi2JcgsFZLES/ld6sHORIqfqdHOhqUU3v7VO7PWMbQyEoN4wdxYUMAOdvjNgaEqZ9W
+e0h74uaMDphpamWI5yzsOp2huCiZkYZ3CofB09JwAEeswc7z4s5kNw14DEaPT30UXWJZ5iaWpnyO
+XQ0RpegTEqEEAbDg31eOh5Qw55qZoUhkDIacT7XIx0Nmbex7GrSnSBiU+DCO5bvFKsS6QvEZyh96
+TgeZIdcobAtqzuDqaQGTjDlJtYJtU46vz6jw1jO8+1DueboXIpjRyklLLCvy+Re/fornzT9Z6PCJ
+WBQMaKjAgV2vwW6Qcny7h7IU/8wMTxESlZWaI9ROmZIh0q7IBzCoya3qOgpGmREZqNvyO6PWR01j
+9sAlXXg7Q0YAjVEWsM58OT9DeUMewJ3pHPlcHl5CMhWzGWLADKHy7qNP1N3pyzUMiTorvD5MSx5Y
+LSHxvlsgL1mGu5whIU5eJRUc/L/svXmsJdl9Hvad2qvuvrz9vd57eqZn5XA45FikqYWSqFhCHNgw
+JDMBFAmQHcuREwSQTRjKP4IQODKi2IAjRIkVBY5kK04sm1FiUTIlW5Go4TrkLD3T09vr9/pt9919
+q71O/vh9t8VxRLGHZnM6yD3AoO/cd6vq1KlT5/yW7/d9sUyKOk3g1a2z2ChJlsVUOfK6/N0YzQHy
+IBYThQmr8g5rTFOOKvjY83K933vzLtSx3HO7ZeLaXMDvLzXOI2IKsERR2Tx3USEgqaIMFKTGN1oV
+DJhyu9BuQVGRKGPdczE4RkAVLm2VkAXkTDxKYZhE7s1j3GP85zqFYmEV99mmLv57DTz35+RFqf7O
+BizqVfZ7LqoUkJ2Qit9LQmQsh7b0HC2yH7kzE1PqQSi7iq33iQuyU/2QjOGlI4yGkjrdru6hQ8Si
+F2rE1LiwUg2bsQTwRTfiDMUxN6R4Cp9lz9pqYafNeM2q9C0uMjQJZHJrCVYsGRfPLCFPSYqbG8hJ
+IJsxjmRaI4TUjpj1U4zIhmUmNmyWkfumiZRpZ02fL5sW0GNZePaSFBFTudpU98mNy48NcOvkAQUf
+2Ja1D8u2bMv2jvZIWApZUWAwn8IyFAIqGF96fAuT14WU2sccVkBTO9IYl8QMXOOuPPMSFAu58E6I
+ZlVW5bBmwGIe3jQLaGK/c0t26OlghjAlXlzbSCukFtcjDGpyjlWnhHRbrmePZPV172Q4ymR3+JEP
+VGG1BFth/cGrqF8hLNVW8EmAEhmye9bNTRRkjzYfK+G4Iyv45ToQXBWgT51mea1ko2Kx2tOyMOVv
+h/MIioQdKBdAJv2v9mmSFiaOMkbq4wpe7YjLcK5m4zs/JDvsuGGj/ors2PZ5ktM4LpKMxCvGGBvM
+BrgrFs5SPfvebIh15u+DTZpKRQazJGOxvllGGEpUf9J+GUNCr11VB3qSMXo8kLF/e5RAU9SlOMgx
+/9x3yXE33kDzikTqr15cwe498m1e/z/lMVUVQD6JHAauRbsAgPP9OoZj4kWSPYy+LNWOmw2xjq6c
+tzEwqZXp9VGi6W75Mfrk6jBN475gTEJXMiwmOOHz80s+dJMuZDTHzV0GQbm1NtsZOrmcdz0dwLTo
+0tUtGFSH7lgRPLq6wXlmlCYJOoTSG2kfHXIqujsT2L5YPGUzQ9GXce6Sjq2bhIgKzqdJCr1ga05z
+zEnHVno7hJsv9v4/RaL8a9rSUli2ZVu2d7RHwlIwIOzFquJgh6VPX/HX4FIf8vCNEDUKv6yfP4FD
+H2l8kbtyWkaxIquoPzOQMQ1VmVYQkprM82zEORWMmf6yAgcGOQ/CxIBFdJjtlWBM5Te9O4eYEI4c
+csU1qxorZBTudV2U96TYR5UKHFC8Q7XKKNcoZjMh3NnroJ2SC6BaYMOT7zOzjPOWxA+YWUV13MeA
+6tLx0MCUtFxIZohZBTlLExgMV3RnYjFUYGKLsYoNL8Eqc/eluoExuSO2Dk8xLDFGQRq3keugKEt/
+7NMMJ4FYSpsTC9WSfH9OO4gT+bwSyvGdokCUCHFrN9iESdJcq2KgRum9I6uH4vFnAABvH4qeZaIU
+tCV/33iqCf8TMobpaxp1CsoEzadRuv4FAMCA1GfmZA7bktiA3ZghZ3DtaH2A3a70qR9P8NZkIcoi
+lHC/8cYqnnhcnuPZS5uok9/A8qqwbMaSwvy+4M8i4AgFmKRHm07GcCCWUJ4Z2GCgtF6XcwVrDla6
+kpIcJSnu3ZMd3Z/mqDapDaEBm6xWdRZwqQ0bFXJrDGZlfJzs0rfHczzOgOfAjuBOiXYlNF+NIlxn
+AP7IyZCTgk0b92PwuOia2J89mIWwaI/EojDOgN8+zfCYdRdvkigiffU1pLdY4eYl6MwFpxC+qdGh
+tqFNHu5n+zPYZEKxMvM+pbVTGwOZBIbUzEBOV2IhmTOfluCUZaJMOhNYC1rzzMR1Zhz2Ggnmh8wL
+m3LdNXMVZ1lmvZ+uokTQ5slhD4/tEAx0dA/mQLQrx6tyvdJaAtxjVD/OoAuW02rgfEM+T3ty/Lji
+Iz0ikMlJMT0htbprIGLWYl7EiEigETAjgbKP3puyKIyGNm7OOZ77KVoEVq02TeSnYkrvbcgi1tAa
+84G80LNC3ee5rPaG6DGw6WQzJMTB7J/KtLsdAx3qUZayE8SsP0gNG3WqXp2/cgG/9tsCALs35AKZ
+pSBMA3fv/gX81RN52f76rb+O51fkWcat92MllnPPUumbWc/gMiDa7VjYp4u1Eo+xQRzCh69cgu2L
+e7P1ArNIAxeewyCh7WDGkuu0SJF0Zb6kNQsDZmVSBmL7c4W7hH+nKxbSiOQtZoFj4ixcVruqzjp6
+xDlgfoCxL3OobmVIO3IfiVbQhOdXCB/PBxXYdFcGxwbcSObCMFd4uyJjaI5y5MTR9JldG/kObEKf
+MS+gF/UoGtCG9O3lfoLyggsUD1aNsHQflm3Zlu0d7ZGwFAzkCDBFw23hlPnVV/cmuD4Ss+co6kIz
+uPKRi9tYp2ZgROLLOD9Cvic7XikuIbogq3JxMkfB1TqMZ5gv9AgpHV/Z0Ahj+ZwnIQotK6qRTqBm
+8vnWYYqty+LSOKdyvSwokJOKy9IjYCJWTJKluLknu+pGcYyzm7IzPVWTIGIWmojnYl723j5F0JPd
+6i07wl0iGu1c3IuVNY0SKeiCtgGTO5dRxMhZjGRGCiz/h9OWnbE+8tB8Vq579+Y92KT73akBe2T+
+3O54CJXsiNaY6Tg/hTGVe6opIDymDJ8qYLH6zo0y7JEINUzl2XQzjcEJLaHRDN4qyUKmCsYFMfPz
+QuGVmVgCvVAg2ACQM6i68Rf38fx3iXUQqj56exJg7oQewgNxTeYsAso7CTQRq+3MxLMbct+HkYet
+bbH6SpbCkytUds6flL4PXkFnyKrF/KuYNmWOqBQYk3rPHQ6hIrmvjJBorWKYTF9ODnK0roqFWOrO
+EAaC4A8of3eSnuCKkrEfxBYcLYFwO7TRID1ckZRxcCr9ODghEjQeoMI05S0zQkZXcbdvw2cqeqtq
+wCLpjh7JuI0nCiPiJkJD/bEdYCk4TFXnXo5p/O7qFR+JRUFrjSJJ8VXUccmUl3QfT+PGyT8HAARx
+iAHjAW+NOviODz4HANjekIk2Ta9gzui2PU5RJf8e1svQCaGvQ42ApBdxk64GfKSM2rsNF0WfroTq
+4tqRHDeeAy5NzVnIOgn3DGxXXtiSX8Vs/oKcd/x/wSRgZ/Z2gT+c7QIAtjbkRf+zO1ehV0U6vF/5
+A3RvyMv2IZwi9+We5iW5Vm1qoHZWJtjcKmGUCFFGPNH3QTNG2UODylAt3rNVqUJbsrBuzgNYMpy4
+vufiOx+XPvtGBddO5cXbrhNvMG5icF+A18TOpvRt1dvBuHLEa9fxIhdtl9gLzzrCRi6Tv9ZUGDEj
+dMft4fY1uZeuXsXsbXm5FYFJGgYyLtI/e/b7URGCY+QnJ7jGcvDauIvTU1lkp4Usmut2jm26LtN6
+iA1PXtL31zfhUH3r9RsdjHf/SK43+RwAYFArwcnJn9iYo0zMu7Vaue8q+GkZCGQ8AtarFD5gRORS
+NHwoQsX1fIDJRF62o2M5phm46FMUeDSf46wMBRx1CRFL8Vc8BxVHMgrZWdZAFAkU3aM1y8SI9Tor
+s7uY7LLOIZ3BcWShu2vKXB+MIljMuLRThQ7fZqewkVD81oMPk8QwU373jdrSfVi2ZVu2d7RHw1Jw
+MoTnTvCMv4FfJ6fgrYmLgivbIM9xK74FADjfPoseKwnXy2Iau0YJji1mZBqPEfus5HN8WCsLyHMd
+IRGGris7RmwUyMZiHcCeoBNJbvv6OMNnB/J5Oklwl6QYNk0y94wPi0zTo/QQMxK5TJTClAQvXVzD
+KmRlf21PfnvpXIIydRfLaw30TqX/YdZAe0d+0yarr3WnjqwmY5HrECEtgsnEQMbgmhPGsIitCFm0
+VPXnOKJFk1gJrEvy93ruYprK/X91eAqX8O45LaVOY4gKi45mpwrhlMVM5k1EE0KwO3PELIls12Xn
+Ljsm8obsctkqkBA3YR+4+PXTXRnD/j56huy2IPWGsjQaDNbefauL7e8jK3MzgUvxnfHRb2NE6jGT
+BDcHaQ64Ethdrddxdyrfl9ersNdlXJ4pnUO4J7w/R+ty3bONBqptyWr48wgJg7yGZ8BUdBl6Bgy6
+kPtD2Y1P5hFuUXshShyMbgvlaGAr6Kb8ll4u/P4tvApWVCYKN1gfvN0aY4tSfplZhc3cgOFIf3NT
+ITllRm0Y3S+Oy3WGlLHxnlnAtiQwOWZW6o0iQW8R1yxrNMjfMA40GmR8josE0O/OfVD6XR7wMJpp
+uzpobyLwHWySjOPJc+sIbsqDez0ZYDSSF6SbFzApCT/nBKsUJuYEwrhFiinvyctyzLGAiRaI+Llg
+SW+SZcj4oiOd4RtFZ3/2rLw0P3M3BdTCFFPYXpMF6eeNAv0rMhkP3urjVVsm9yn7Y1jAmiYFupdj
+hQxQhZmjmsn6fI9kI5VUo0ceSG+kccxajFKRw1wXt+KKV4YqC3187awct1K9iCH5Oa3sFE+ek6rT
+qFugy4X16Poxfne8CwDoM3bSjDNoHlf35wgZ9b6UJugRPmsWKWYsr14LOQHNHKt8kbDtYF3LIltZ
+qdwn3nVXylApsyecuCdhCDeQhSA2NlEnC9GP/sKnYBKchsLFx54W6vo/35fOvRaYuHcgL/quDu8v
+wpGhUMkJiVYF/JxVo7SFa9pEyBdz21VImIYMwhQni/gBciSG9LllMiNheGizJDk3bHgGMw2Whd96
+7bac3FjMGwNbZ+V5fFInsD4un+/cLCENJJayezDHhOerMz2deBnK1Ly8m85xc1/mSDS+jdFAFiSn
+DJgVWdR+6Ht/CADwibPnsfUd4opEsxUMWc3b9VawfU6e34HdwgcJN79ycfNLWusX8A3a0n1YtmVb
+tne0R8J9MFwDpYtlfNzfwx/aTwAAmv4errM2H/0YhUu+hCSFy+KYBnvvwoG1KOBxDIQhyVCSBCUW
+lNSqDkxSnS3YgI2hxkmV3AR7M8RfLw7DjfBn9vjbkoEKTcZJSaPriEXz1zYCnLkhFODIKxhwm7q8
+IidolGycteU+IqVwhTReOrBQCWnpHJGd+LyNPeImHE/dD4iaLYUO+RTe9k3U92VX3arIee+M30Iz
+lBMP0g76F2UMTauP/QMJVt64uQ+PJvGI7NlesIoJQV9lz8QmgTnNtTXofYlWbp8BTmO5F49Ap7yX
+YecMlZYrFsqp9KOS1BCclR00Sr37EfUJg4R1Z4ajvtxrzSxhTnix+pjGj5Kt+5eTAjcSCRj+13Vx
+xVrHE6RagqDzxEazJa6ga5iwSG/mpoAin4JLS9AxgDo5C86sVGBxLiTjCNNTAs6QYbwQkXTlXDY0
+LJM8l7YDW7E6dDK+//YsoAJJrcCoJHiMnz+3hY/uCv/ZKPoAZqey+2+vK6hQrF6PxVyxpfDEBbEa
+Plqa4efK8nfn8xoT7vgX54e40aD40b2/DwB4/Tt/Fudd6XsY1qBopZ2xhrCpU7kZVBDXFziFB2tL
+S2HZlm3Z3tEeCUvByTTOnIT4A7eOF5+ktoJ6ARNDgjqJqWCyCKZmaBi0GlYZLFup1WAT5TUNLJQm
+JEdFDUlO+G97DU+k4oteZ4n07SzBOnPJXzF6+LoZG24eG1xwj0OA2Si89DED+SpFP5SN/pj9H+TQ
+RF4OqBdRmWVwiG9IKjb6hazJV2MPblV26RlZpaq2iTMnZDUuKZCLFdPcxBPEGKR3Q4zbZNthUPNq
+6yrsLdlpPvfbp3DepBCsO4d9R/J+p6MZXiXBaExiWyM7BcMZOLQ8tKnZ0BuMEHnStzszBy1CbSNC
+0A3PwJxw7XNjhWKbsPFuBI/xk0aiME2lT/1ELtKLpshZWmyf62NwXY7bvubhl2fy/V/5MRcnE4mJ
+3KDk36jjwSTHQFGKURSLtF6EqkVmLNtETrLdOqnbrLKPKpnC11druESr6O2TDHEs5ztNDXhkCFcc
+jImpUFlQvjXq8KosxroTARSYXRAFvvhhE2sflrRveljg6FVJmfuVCGYu6dnd7hylESnpiExdr28i
+75zwc4of35P5+w+CGeqHpHyLTbxIrMaZTUlfl7p7CBpP81wjrNTk2fSPriM6lljMpe/wcHKPvBUP
+2B6JRcH0NCqP5zhvXoJRkkE9vmngjJaXf+il0FQGjmo+Hgv44jH3u2mbmAViOn00KLBHifpS38Sg
+Kp+fcqc4oQb9pWMxh4N2E8c1Mfc+/so6PnVLIs5fr5iM7Fvo/mMDH3lFgovm1WfwZ39KAkBbP/QV
+/N6RgJMOAwcTuj8bIadN4GNG5ed6PcD7WpxUTRf1lAsH6wzqOxqvbMticylW2HpaVoUNo4w3STKC
+OyO8SSqwNRZNjDwXvifw6sn5KdK5/P1uofBqLAvHV/O3EM+J5WDArb5Whc3J/9xWCavnJGC6UViY
+TSVXfmndxJDZgMriZYwKnKWa1iACbJLFDAOF1ZKM0dS3UD6RqebVqeQ11tCmPLPZ2EJSkuPK/8jE
+f9n7DwEAnWkVZ35GFtEPPPevAQCvz28iZbbDPr+JNqnuukUOlnmg6gW4REh6xxBMypYGKsSnXCkB
+98hbeGaSY4/PZEPNkHNzsTkJkryAomvXN3OsMBDZnztQH5HrbfK7je//GL7vvxL39/IPvoUvHkkG
+6865BFNiTk5vRph7MnZhQ8bVv5ri/Qw0qrNVhKlkV/7S6VX8i23ZGKvXXUxqcp1br0h26S9WXwWa
+svAEjz0Hh8zdxUkLs21xFadZGVmd6kcP2Jbuw7It27K9oz0SloKTWTh72kbhFOgwuIaaQv8OK+Mq
+GhccMfN31ppotWghbMruGMQuqm1ZDcM8wpojK3DWMRHUZRdz7QRbNLt7lqSSnknWoVjf//sHe6je
+EUth/DWWgg0gZXDpYl3+8J98dQ0/MP3LAIBfjC8i+h++CAD4lZ9TSLZldwymBRQZkYfUPvTXE2xA
+dozzV3ewSdhq43wFxpg8BatidRT2GN/HQv1oClxymSJbK6O0L/18c74LBLJTvk4yzyuugbYnW+bH
+njiP41cEuXhNpeiaDGDVgZTMPEFFzrsdrOPp82LRrNVdPHVWLAXfW4XLIh/LdeE6pPyakG/BsGD4
+1H5MIgyVBF31NEFek7GwwggxqeX0SK7hKA/KJVtU0ECdKd6/9+pTWDsVxq1f0RbSvyYuz+/9EyJP
+L7ax5cmOv6bKcGpy3tbKCOVTh+O8CZDpaLMq1yuPNMy6jH2xaqLSYf7VnWNrldiSaYFNFjdNifsY
+I8WcehlZSaNBfQ7fj/EMGbl+eksssy8ZfwnzX5Bn8yu/eBP154mc7fvI3hJ3LZk6sM6QFHYsrl3+
+OeC32tLfF9I52jOyUk/n+CvtxwAA1Zf6+Ke3xYqerAlF3aduaByZbwIA/kz3LDY78kyTYIpsIPfR
+jToITykb94DtkVgUsqJAL5phoiN098U0qprrWHfIklyq4+y2RNQvnSnjgiOR2pQRZsfzETB6vQIH
+k5AQ1roJMoUh0AGGLJM26jK4vcxFrSqw4w/UB7jFar/X98c4YTXgc7HG22RK7tE8vWC/hM/VxKzb
+uP4Z1IeseqtEOBrL52LFwTWW9W4FMszVNQ8vbMsEunw+QLlElaZmCM9iySBFV1VrB/O5+Ja12lkY
+ZHaGbqF+Wfq541io7RN2TPM8O+fhUkXKlIfjz2JGUZvS2Qk2iJMfJCVcukhKdQr6/uCTT2CdMZpN
+L4YdiNtlFzlMslhXy979/L5rs+iiNIXFQY6yFGUthDPT9SmSsdzTpDFAhTRnxiZLq7supjtiqleG
+KxieiNChnl/G78dC7R+c3EXck+M+ekaegYKPdlOev+WlqHAhOI48+E15yZoVA34h10noglbqFiwy
+cLtHMRRTSkW1isuEOXu2gSHHICrTVxznCEak+stNBL4slsXhAV58Sj7PL/K8d/4RnpvKwrOxMkOH
+mRijVUIaymLptR2srj8u40K27+cmb+KA8y0exIhKzJLoI4yvyFxtvVLDCxQAfvOYcgBbIZ5bELZs
+lOC1xR1rjx3cPBLklH0Y47T27haFpfuwbMu2bO9o36wU/c8D+CFI4PUWgP9Yaz3k3z4J4Mch4bqf
+0lp/+htdI8ky7HYHmDsm6j7JRtwEmgGZM9sNXL0kAZWms46Est6lqvy2anuYMe+cTlJ4NGfLNR8z
+Zi3iSQaPuXeb+oJTPcbgQMxLf+cqPtIXs+xoaqA3FlP7rlIoEQnZnchxN25MYbmyy8+8FnozyWp0
+7AQNkmlAeXiOas02i46ebW5hh+zCeWkVOWGyjmoDayTznMtvjdSGNxbryNxRKAySxvoVVGfUqqha
+KK2JKxQxEt6qbsJYp4k/2MG1zZcBAHNHwyOWo9wCdlrSjxcIk75y0YQ6kPtMvAIVhzn/egAdy+6X
+mSHciPX9OySdnTXuM2K7Ux8po7FqqqBIJlKe1jBn+sRjAC9rJqhXxMJwt3zMc9l1r1/rYERm6klc
+wZyK1mmJLNflElbX5f6rsypOWkK3tooNBCTiiSwXmSG7e33B7G2lmGmKyJQcGIUEm91T4Jhzx8hN
+uHTp7D45N3SIkEHLoDZHuSLPWh+n6N+Qcd6FVMFe/N73YbAI7FYzrDXk2pNxgMvr4h70Zxke82Uu
+V9flmf7mzENlX+7z1jTGJoVcbnllVI6ohdqoY4sUgH3qTq5nDjIlrpZnF5iyeOwk7aNMwZl+S2N0
++GAS9Iv2zUrR/w6AT2qtM6XU3wHwSQB/Uyl1FcAPA3gSwCaAf6WUekwv9Lu/TtMQF8JMTIQL1m8v
+Q+BwAno7SJUMYGTl8JjKmxB+q9IhCpepN2Sw3YVwSggnI9OwOUNCjr44pEhoUodVobJSzcS5VB7u
+cwdfwV3CcvNZjB1HrmMRzupWHGQNmRyXT6b4yok80NZwjmNCsGuWx58bAAAgAElEQVRlG711MSvP
+El6LchkW00q2mQEsgU4LE6YSE09Z1OqdDaCJccesDHtHFghjpAEyJlu6i0ou99Scy4TYu/saOj05
+V3P9LJ6eign7B6Merg3l+3q1jo1zLAdvSsovGReoUgymUTegFU30eYSUTNmmMlGQDMRgPYTptQBm
+V9I8gh5To9KrQg0p+puFyJhSVJ4sJuXQQEHNz/HpHbCwExOnQJyQnCSMMCPTE5nTYdYdTFM5R6Sm
+CMne5LoWFuRUddeFIutTSvh0HhvwHVLfBykGM+lPPwqhyGhd9HLMNFORfDY6y5ExvqAPUvTW5bgk
+MmBVWXW7KptUu9+Dm1M/NCkQk71JWy6iMrUp8xRJLPPvWMsze2LFx0lHnk2tYWLGLMMTuo2QhRUz
+V2Paln48zmre2CwwzG4AAO4e29iuiFviDhRmFD2e5hlsVgc/aPumpOi11r+tFzzTwMsQzUhApOj/
+idY61lrfgShFvfiuerRsy7Zs72n7VgQafwzAr/PzFmSRWLSFFP2f2opCY56kmLs5GkQKKc+FIr+/
+W1ZwCVd2kgyJQwoymwCcQkFFhC77OQqqHecoMKfJn1dj5FNGZyl5ZswrSCnMkfdHGBmy43npGpQt
+Zrnp2XgroglqSNR4DhcGOQojy8axQ8ivBXRN8kO6Puoj2ZmaDfmuVk8RM9hZ8kuAS7o1rWGQhEPT
++oFnAtRULJwJzA6xGWddgArFltWBGkuf/6gp8Oqa2caTZSmSsfs17Dd5TyrAi105brIaoBmJBbHN
+4pyGV79v2tt5CaZPluuZgpGTiKbswCC1V0HxEnQqwLZcQ08daJfK1ac55jbZqMMMKcFS8x6xCV4I
+RXPXNFwUC6svy9FjUK5uaaTcexTdHbNkoU3eh3RQQc74q+WNAAb20p4pZIgATO78MEPEc/luiAiD
+mZzjMC4wmCf8PMGIqLSUMvODLIZF9yhWM5RH4gbFCDEzxH3VA3JvNHxMbLqMq1uIDdmtrdkY4R2q
+g3sZ9hcFe6/J32enGkNqW6rVEOvE2fQaddhk0ek6BVyyNY/4b6Eq2J3LK1xJI5wOxHqdF8eY0Jqe
+H8QI+9/G7INS6m8DyAD86jdx7E8A+AkAsAwDDhS0NpCTPDXId7BdY8zAaCKgWKudDlAnEi62yUbU
+m8MivjswfRR8MM7MQuSJkWMMM0RkGUpz+nr2HG6f0f4oRndMny1Q8JWk0+ajMXyWuMa+/PaJJITa
+kYcVvj5FNaKpqnyYnNzOuS1cpl/3DEVCa1sVlAcLItkObEPuCZUGcEDNw5pMYtOrAURCquQMQGFW
+NbagqFTlrG9jfVci9c9NxQ14+c0Rnl8oDzUtPOGJL26VfNxdl/s/b9VwZVv6VCdRzaq5CsuUl9Qz
+faR1Ih1TA7os3xtmCYXFzE5X+qOrE6DDdKPO76dWU38Kl3qcYZYiYQ2wzijQ2s0Q80X3QwM6knjO
+ztwAqnKO6d0Uq1wsKxTCLRWAooitE0yx5jETZVpIIoKBKiMEJrkZSYJqzTxEXNRVPINPNqILay2A
+SmRZXmA+4QJI1GQVLkwuEHnoo8TqURXZOGfLirS5LWnDcdrFDkv4lTIwzpn68l1Mmiw1dy2YBJql
+TKPXB7cwaci8OTupY9aWOdKMMsQbJp+PBc0xGBzIolBfn8CYCiBt6kSoGNL3RmEjiSmwa1YRrcm1
+H7R904uCUupHIQHI79F/XH/9wFL0WutfAvBLAOBZ1ntfv71sy7ZsAL7JRUEp9XEAPw3go1rr+df8
+6VMAfk0p9d9AAo2XAXz+G52vABAWBWIjQ+DJLhEbHroTiSyfgweDAKDU0OgSFNQmOKZ8tg2DUWad
+zmCTMy9vFigoMx7lE3iM9rPUHmqSY0YzMi+7OLcqvx2eS6Belvr3BAkSCs0oyt2PdIF0wloFy0N1
+VXauz3z2DTy+Jbv4Y+46Slus8kzETNwIVuBUWbXYrkBHCxPXRHGOJZOQv+skgVUiBXgNAKXuiqwC
+06BsnpOi8pSAZab/RvLSlWEft1PBeng3bsBsUefQAqqM4PfbGXqstDxrUHZtswqDwVodzuA2ZW3X
+/hSFSTN5NIWZyQ5U7MhuplUdeiBTwDItpCROUfMJEpLdmFGImDJziSf/ns5m2GZVY7HdQnokVlXi
+G/AbrGwsXAwtcTeGcwZzy2UYfL5FEGC+oGkr+TB2xOoxQwsZ3TuLLlFaLxBTCi6LAK/B4OjcgEHS
+mqg7gEGstD2X40KVoURei1obKIJFwDTBHoFY2V0xzz/wzJOYl2WMk8MuNrblPjonCvuFPB9jb4Az
+tgDxArq8X5mfID6S+dTwKpiMpA/DpofpobgEj21tI78p8+gWRY303Rk+SPZpe+sZ9C157oWhcc4T
+r/2krqAG3+Lah68jRf9JAC6A31HiY76stf6rWus3lFL/K4BrELfiJ79R5mHZlm3ZHq32zUrR/8M/
+5fc/B+Dn3k0nFERk1k9txNwRkQyRpLJz98YG5ilzxVaEEnn/VUl26EKbKJPFKIgNJGRWyiOFjCnC
+3DUQ5bITFgwuprmLuSUFTFHYgA5lt6qumDBasuI7BwO0A8JgubKvXGrCLstq3x2d4p9/XqCmBSbY
+G8i1n7XGeG5T0ovtkfSz7zXRTmg9zDSKkXhWkW3AYBA0d6SIxilvIM0kaGc3rsBkrTy6R9CrTAdW
+VlGqSMXcs/vyKE+9CWaTtwEA45Uynp3JPY3sBAepQIbVvoecFsvwVHYip5TAg/TXSE/va2S4UQrV
+kt8a0yFypgAXMmjai5ExDjSfAYlDXYRqFTHVqrNqDD8SCyJmALNyMkKfbFLV8QhlVieutUqol2W8
+OtMMg47ssDNWLR4aOWJD5kXVteAw8DmxA1QyFml5Cg530MwjNmFiIioYU7BCRFPp50TNYGCBoQDy
+hXYClZprvodSKPec9QqUXRIBxyYaq/KbrRaRi1kM41j6W9Zj7N1eUPPVEdHfv3M4wV2PWhsspDLt
+GHN60Gl5hOhErrE36ILQCox3O5SXBY4KWiiFAe+Ue65VwpVtia80pw4ST+IrlqvQtt5dSvKRgDlr
+rRHnKULXQo1VeIlloz+RCXurW2DHl5f07fEAOb93NyVDcNldx9oZyYqe9XJkNZqw0RR7QxnAZHSK
+21oi9GupBIvujfpIma822wfYotBHud/A2kLUo17ghMEzkw8D/hZS1lecGCmyshy3ez2CR1dj9yun
+mFyTh9GEBNGMx+/haYqh6GYVq5DJMWplWJvJozggt54zew1v+dLP97Vext2hLGhPPe2igu8BANQ2
+M1iFmInGZVncrm49g/51Odf8KEJekUh+d2+EEqnYj4wYXyQ/pGbZsDWfIqLOZWkyg1G5BgCoDkpw
+DGaB9BRZyv6TU3Ca5EhSMZ+PEwMlRoc65hZKfszfhKgwNz9PyVCdDeDEzAKpHeQkHNnrniJiFiib
+mfdZvM0+8QNxgSCQ53F0HCAbCW292jzBOWJZSvU22qCAiy/jZqQhOnRzOqfHOCIGxOmXcT0nnuIk
+wSnTGT4rI3PDQZV1Jf6KhzEVqMdFCpuK5sqSPvqpjaNMFpA7J13Ut+Vc0W4X8S0Zt/VKBEUXWe2R
+6j3JkDHL8vuDDM7CdSkK2IbMIedCjPMs889tZmK0hmrIeVdhILop/Um3NPpgcLhXhnH0YCzOi7aE
+OS/bsi3bO9qjYSkoBa0sBJlCkRBtmK+h6YqZ2C5XcaYku3/D8BE2xMyd0aUYZXOUSeY5Xq0jIFFl
+d3SKKSXfJqNjpCyCuU0IrC7aCHzZ5dOohx4JPIN4itUnBcY8/9cdVFbE9GPWEDsa0ISifiGOkBzJ
+9WLDgksk3BtmjvdNZLd9Lacr8n/v4nOJ7A71aYDnrpKH4coKokRObrwmZBu37Qi/dSKBo1+3Qmwx
+5Wjri7iyIm6Av+HCVrKLt9ckLRbf2sVqW/r28miEeizj5nunoCGEjagG/3GxQoY0l/NxAfuEDMaN
+KmrXZNcJ4wHW98QKUdseFHP2eczc/EkHiSf3v3cSYcgqV/t2iPZVuhimDQ15VnFCjYXMQUKLrRQU
+SBn9vTwvYb4mHf1ColGiRuZpujDbM5wcy/WS7hSqLqZ73FG4pSUwfb4ToUXRBXthYUz7mB3Kc5zO
+p9BMkRZ1G1ci2bl31w3kpzJfLF+uV818nKNu6N1ODrcqr0zcM7FJcpmdtcvso8LshliF0+MJru/K
+84tND3lXUrHzWgX2gfTDZ1DzcJyjWhaLIHMKFETOhsMC5dUFytTEICDK9BrTm20TFS2uXU8bWHdl
+jF3Dh+PRVTariNa+9TDnh9601oiyDLarUCtzMgYpwCjriu0gJG35MEuxPxWzs9rgg6tuIHfF5IyL
+MSzWHCTaRWLIQxqkCtnCnSd34ji+AZ+KQKuNEGdXZCGwGx5eOqUI7YaFg0XdwUQm6O7BEVaZqYiL
+GBsNmXjX7iYwSdi3PWohaTLNccC4Rj1C0ZEH/gXzEMe35Xz/0dkIK+vClDO6Kvf5+ldD1HwKi+gS
+Ws+LOVh5fAWNTXEZLLcFI1lQh8s11kt1dOoUlf3yXezNdwEA0SzFAm2drkRosK4iO5G4xv8RHuDO
+XCZrpsu4ckWmxvPPPQePYKG6nSCw+XxYkTfp3sDentzTayc9fGmfEu7JPlpjGfALpQkubUk2o04i
+EG/FxrkSK/w2KxhzjP7ey7+H72nLcS1vA1FbFr3NU5kLJ2aK7kReip5h4oQQ8616hPKqPL+ek2OT
+oi71Msl55nWUbEKJjRXkJPgcjqvYG8tiMk1niAiyWmf9Beo5rPPy+UpqYkBuy+oJMCaL1P5AsgKt
+5hUUHOPPvr2PjAGBpHDu65DmvRm2CXa7TYzM1NKwExnDp+0yUm4QN+sK46E8k9eVBxiyYaBEHM4g
+xocpMKvUKnrUmtyupKjP5PmGvkYcLjkal23Zlu3foT0SloKCgmOasAwLKQuGqmYDa1RwtupVUPIR
+jXoJXotmUkXMqZptQY1lJ1XWGJOCwjCjFKkpO5PhZ6j6cj4fYqpFQYIWuQML1UBEE3Xlgo9brMev
+5WsIWrI79LsSWd66vAb3rJj+l1/cxT/9LHPsATAmotGr3kRwTkzYx1gNNzJ38FhlFwBQP87hZRSZ
+2TKx8UEJlNZGsrN9RL+Br5L977uMNuznZId+8vGzMFuyU6hkDM0qR6tDTUE/Rz6T3c7Z2MSOErxF
+1o6xclfGbWNlFc0Lcp3DPsVpTqbYqspnt1pFlezC65NV+ErGy3MiwCRCkG6evbaBBoN6zzRW0arI
+vd7ejPABIjJDO8Q2CUkW2YBAF4gIV1cnhzCJY6hUDBxwh13RM6hcjgs3CDXXFbQSGYt6u4Qr/Dua
+Ps5wmy7ZQJX4i5zFXNMiRkIhHr9h4+KGBOtOb0ZItklpNtzHSUfue5tJMH+9hfKIitBRiAqzJGZm
+YoU8DOfqcryzYqFPpnGUDEyZUSqv6fuYFLvcQI/Iy0UBV7NS4MWmzNlz1TJUmxWc+8Z9Tku7bsHj
+PR1TWv5cO0NGZuvBfII2AbKDozIsX6yi7iBHNH13lsIjsSgAGrrIAVeh4nGSuiZAfT3Xd9AsBfd/
+nbHbmiQsZpEgZxpykDkwIuZxyimSPklGaj6MVQqfZLJQrM42MFYykpX2BBdZUejO2riYy8tUXIjx
+r26RXzCSf//hGzm+1xNGo89+YQZNrH4y08j5pH/jjRku3qCSVUv+/eAHY2SkVJ/YNZBgB+PARTEn
+QchZmazbH65A778KAAjmGdycKb1TDX+DpKnJELDoBtQJJprkAMldLphtDOgju80e1rhAljZqaEZy
+nfVVMY3jp8owKEVvuEASysLi6RkKReiyBmymeAvGX6qmh2BLzlG35lgpy019sLyCDAvS1C7Kp9K/
+uSeuQTKJ4DP9nA3nsFnt+albI1zgGL7wWIA/d06+N0J5TmFrApPQ5yyxoLXEa3K/hxLTvRXdQszp
+YnKhcNCHiqklWXOBkMxTrQxXZzLn+mWg3BDfvzIhHN10YJc5tp7C7XsyLoaZoevJczjLTSNQT6Dh
+iZu0UjqA64hbMpwAc2pQTtMJTLJXmXT93MjDW7kcN3cOEX2VpLl+Ch/y3J3RKU77Mp67zM70sgaa
+WmJpF80CBTe7jcsVpCylNxKgGFOd6wHb0n1YtmVbtne0R8JSUKYBp+aj4fvQ1DOsNhsIKFHmelU0
+yMQcGH14FFSZMfqLUY5bqWAQ3MSByXr7uU5hs4rQrdkoLaodXWIhAgNbijtJqQmf8NlKGGLclmBe
+8rrC06uycn9GSx9ePF/FM0+Lefa/9JuYf5qagI4Bi5qPSUvjKuniTh4nR+OpickKadP2Izz/tJiM
+L1x6HhXyDHgQt+TCpRraGxTAORzjsMdiH7sNx2BRTnMdSslni5bSfD5DlcVjs0qIHSpNR00LaU+y
+CO2xj6xOchnK7Z1Jq9BiPMC0c4SGHOf2TIRKdrxgWgNokViEWsfmBA6p1X3LwFlaMVO1ByuW32Sl
+KhyCoQbkOTBsG4krz6NtWZhaYm05WxaevyR9m7wvhrorgdCjC9LP1XkN1VTOa9fnaFBGvlcxUB/L
+8ynSU1RT2Xn7CbkRtf5jjgUFVAmLd20DEen1K7FCHJMirsVz5RGKruy6d3oRamQHN/sOWgROlT0h
+Otk/PcCGRcKZMxovhmJh/W6awntbgoADF7Bm5GdYk7l3LvDxiU8Ihd54P8e1jDqXRQnnmVUqGbt4
+80CC0BMKBiUt//57MfI8PE7XzlQmpgxcJrMUA7JxP2h7JBYFXWik0wR5ycallqQb82YZak984LUn
+XLTIGeh6ZShqOQSshuznYxhDGZBxlKBKAYe0CFCUZEB6hkZpIeBAKvN8lmBW0O9L6nAIiom9ApWx
++HVPXNrAy71dAEC2J8PVO4rw6dtCyPKJlUP0z0ufO5+/s6BYxPZxDQcb0v8LJySY3TGRvy6TplJR
+WOlxoes6cB+TyDmrYlHkOUrhOelP9QCrFwhSymNYmnUSrgGtxHTNFiXE2QzxOsdwN0PEGIcVmmhs
+0JVwdpBTC7LSISnrag6zLBMwm91COROzNW/PYfRporsaniWmdkxymuDkFFOa0fVpE+qcfG7ONpFk
+EpUvejWkTXlm+YBAoThDhYjNuKkwm5IV6iTHp1+W5/OfXvFhX34fACDcZxypUULZkgW0utaGyRqH
+s7kPbMrgjyYtRIwleDHRqzMDhU1aEKOEBiXsdS1BNpZFJvR6iEn8QrwSDqM58pIsUpXCRzhjmjFL
+cLcvL2/wZUk9P795BSNS3384d/AFkqFciGPcpM6EN4zvK0qdn8viXd9pwLgl17icOAgPZS7vBxPU
+SKyyqi3kFXExK6yC7Z7E2CCZsDJ97C0AW3u3cXVD5kjPddFhqvJB29J9WLZlW7Z3tEfCUlAKsHwT
+hmOiM5YI/87NOuItEnJMTMzVAnBkokS5c3DlNGY5fGoG2l4Evyq7gJ3aQCQrd2XWR0YQSjmR8xqB
+jRZhu46RI6OytWnH98k2bnSOcGchHUWYc+3PT/BdZ8RS+NzNCKMTUelxrF2A4JypeQxVSOCrzf6G
+MTBsyGc/rWCFrsTEuwVD4poot8UU1b2boHgT7Kp7v17DvusBZ7jj5Sb0Ip9O9t65NUQyIXZhYwdV
+MlHr6jr8sexs+WSIgJiEeMEUN2/CrMnfLaOBrCw58WIUA9wdLbOEojTn2IlVhWoJQZ+/NQcIYhkX
+vWbA6cj9h6Ue8lPpc5FTsAQ9DEkmsqpNNEgGU39O4WqDdR7JOk4/L9dbbUl/jdsGrG2a/paCTXet
+UEDRZQbDL5ARNt2z5PhYzZBCrt1WCilrEUojB/QKUYODKen8vZAw6ChG90j6fnuYICJxjM6BmDR0
+Jz2ZQ7+7MsVTJbnGaM3FFS19eH3WR5kZkyKI4JNibuILWOw72ms4U6Zi1XEXbzkyD5tuGzmzR4Mi
+QpN1F+THgR2biClRUHX1fc6KYTzGHU3Snl4AxcDsg7alpbBsy7Zs72iPhKUADZiJQjZO4AeybE+r
+IYa3ZKvcr9yDSzJWc2qiyjr9PGFlYTxDOiUCMS3QZZFTuRThdCSrfMlTCEiU6VlkvIl9WFSdNlwL
+SAQj0b/n495UdAjujMfojIlIZH549+UtvLEnO83tN1LkjqAmdaagF1V7MwtvkxZuTBXsM+MQK3P5
+fFoAL7Oqb8/ew0cr0o9hS+5p9dIOcqa0zDCFsSsMO8WqA53IcbbTQzFlqq8tfff0FXgGLRSrhinp
+z7L+LXgTGYuTboqE1DelCyQ2NWowmI9XzhBpV+451ofIU4kj6JUCuspinEgCrWlswTAZPG3X7qcq
+1YENzQCJzg3kzLdHJAFKUILHYF5sVGCT0uylsIHHA4lhGMUquk8Rh/F5BnAfD9FyJAaQHxuwbN5/
+nkBlcvKsU0PkM23XIapwPsXhTCyMsTFHvWBa2k1gkSFLpVNMmIod92W3Pu6PcXhMLEtJQzF1WrIV
+9nryLDvedQDAUzrHl8mEZGkHU0K7g4EDZ0FumxmwmXLdYYq4fNjGawwOl7wK6pQ0D+rnUPcE3j7J
+vowBKz8PWJQ1y3OM70m6sZSPMGMxYbXlI2WB3eONNawFhPI+YHskFgXLNtFYq6BU9ZGV5MGV6i5y
+lqx6sBFDbKaS7iMnGCPTpFcLc+QDDrq20R3Kg70HB72ZLAQbwSr8MjEC5ElUbg6fuWIdh0BHjjvd
+u4dBV4bmTmeMsiOR7KOaTI6X1tdw9byYfm9Fq3BZa2HUK1BD+Q1qJoxMrtdglH2rCZxQOMXqKWxf
+IF1XGOCrqZju72cZsvm8DVUIoElbBUCGY406FEVTdXIWigQh5ak8+HnlCH5ZXBDlJPDoH0wqNQz6
+RxwjBbdCsBfkJc/6gFkheUc4w2zKQg9dhpETQn5kAn0yUHckiBjpKvwqlaAcH1mu+LmDnGObTwE7
+levMyVodTlLYrGYtmRXoprgo5yoVqBXWJaQTOIcMXK4RWGa3YWj5u1maoRgykm8UwFzGIB+NEU7k
+uHFX+j4uXLQcWSArXhOuz3PAgc0sVnKcQg/JVp3L8YNYIWc9x8lEYbsh91T3A/RJ0xaPpQ9fntzF
+BYrGnn2sjozfZ6sxkonMl6qn4LIGJyFUvN4eov1+ef7+a5uoPynX27xjYX1Txtkf+1CaAV2HWQYY
+yKeyOc0UENqURMgU2nSVV0sllL2v5UH6xm3pPizbsi3bO9ojYSnkaYHhSQinZOIClZbDyhr8Lwsp
+6azrob4hK/tkMkOXteIrZQYMqzbO073oWiHOUI7tJJoiZPDFyFpYqcjKXbbkt6dhH2BgCZkDcGcL
+XBt/0JM005Wyiy/NxYyPWOH41hd7uJ3L7thRczz7uEBjv3zt1v20ZzxIMabEWsxCKqvbwCrZ8vcA
+/JuvCmf/c4GL8aZYE0kmKtgfuR2gvUZLomrcR2zqyQDBipiU2uhBOaRH86lF6V8FqGBcjDPcu/NV
++f6CQl6W3zYCD+aCSDXi8VsRqJCO2bgHn7Rq8CIYVLk+Pe6hzCDY3oSCPHUFRfJUJ02hlRwXzUNk
+5ALQloNpTdJlzZSBv7IPjwE37QFDwrQPhwkaDDRaQQ7Pld3/dCw7ZsOaw6iQVDeykJmsODQDqHVy
+UbQKKPJSrLMKrDBncGlZqsSEqcj+nQGTRHb8qS6QMl+Y0fT3S1Mc97hz2x7aTWqOxBqHpORTDILv
+1M7gxBVrM78X4rwtc2stNzAPZOfeO56hZJJk+ET68M8md7D1d2UuzPIII7pgM/sWnh2LO3Kp7CIn
+zFlR7MiahNiixugg0hgSb2H6VZyhG752xoJJAZ8HbY/EomAaCg3PRKMaYFIT09c/6GBOGHkajXDY
+lckR2BFKnMiDRQAcTczmMuibzQwDVotdyA28Qb9+xz7FfCgDnFJQtBRYyE8IifZm6HsyqK9OTzEk
+V+JrxyfYbEhE3dxkldr7p3jhI/LdM/tr+MRYFhDD+iIGKfP+hoJZlQViryB4xAceI3nHmRUDMypP
+ndgZPuAJaMl+impFYQ8JJ6ZnPoEwkMlvd8fIOkIs4mxeRk7MhXlKgMOOAhaT3PPQpo5ltGKgZclE
+GUQaypbfzBgvse70UeywbgNzFOssyV5ZByx5CWu9CNNYFoMn4ksAgHkQo84S98nwDgoCNfJ2DbhL
+Gvw0hkmTWa3Ib72pBd+lC9MrkLNO4lLDhbNKqflagO6JjLnKSGozsKEsshaXE4DcjcoCaoWcW5Wb
+sJUsvp1U7mk9MnCH4J52MMVhj/Tztg23xQi+BcwJLHJLFT67HjyD5eUqwzpfmdOSDU8tODalvwNV
+wJ6RWMY18QYZlWeui5A1H6lroCDjlGPLcasbwP5dxtKiGeYL9idT4xrZpbNihu2GxHYcGviZBYxJ
+FmMiwGpZPq8YBYyCILJ+CNNneuUB29J9WLZlW7Z3tEfCUoizHDcHU0zuBmgTabZZ08h7Qre2v9HD
+JfLynUYR9k9ISMJswrO5RlKSHe/eZ3vY25HjjLdr97nqesEm2k9z1zlmDtr0UGc5XDFp4PaRuCvX
+jyy8siuq0parMEyeAgBsrUqE+DP6JVzZlZ32b95ScHZ3AQBhFgNUdk6SEHmHBC7UjUhTE7Ux5eVz
+HxtkLV67VEV7QyLudV8sFHdlDfkKxWDGXWQzMb+tLEIqBg9McwA1IRy3IekE03scBQt//FjjM1+U
++28at+FRb6BcCWBmLH6qyK4UmSPkx7I7jk5j2JS4L00KKMrIm6chNPUQNHdBz3CRUSotH5eRuOJe
+6OMW3EXA8NDANJJAWzKQsS9KIwRKUHezZIZoKFZFN7Nhko5s1QigRKIUYVf6WWoPoDOxXJJZEwYr
+DidBDmMsQcx4bCHvk/tiIj5RMerjlO7hXS9AoskRYZTgzFhI5QEg2/TxgVx3t2/gbodu13kPRz2C
+R0oZCloQFlGFq5U2ShSkOU1PUPHl2odHI2QTMXvn6QRr7WNs+sEAAApySURBVIXsnYzPen8N7W35
+bTMFurvUy1AaCjLvZ1WFe7QyNaHUt0/nsFssXItrcAjzblUBc4WWY5phOv//IsxZAZmtkUcd3Dn6
+FAAgOGrBK8lAuf0qkrI85FZcoCCn3o1jknPWXLTOPQ8AWKtV0fAlTdPcuITiCTlHdLuC/ckfAgDC
+A1kcVlarqMXib6XBBK9QLOaVewfISUKt5xrOh+R8hk1peP8AV88/CwBIjB7AF6EwHIAEo1AF8oKA
+K2JH/LiNaVUe7EYnQ2sq/b+eAbWbUhF5blMWoNrFMbxMXBRllZA2ZSVwe8eIJVAPt7EC1RRIsxWJ
+OZ9DwwDNxVKMs8/K/flPrGKPYrqVcQkdJZ1yPPn75jyFyRRhtwgweJsMSsk+gpZcUN0zEZ4hM9GI
+PnnTh2nIGPcwhjEkMc6lOYoJU7m5C4NVoLMKXbcuMKVJDVtjxEyTbSQoMXI+SPV9LdCUpvHEKiOd
+EJq+OgfIyzgdpJgP+KwNF+apvHBzch+mUQNTxoTiKMU0kkVW+RmecFlSXfYx4LMMqUHZnSWICacv
+DXzMnpPxavdylA0yIJmywGozgSaiuBx4iEK5D10bIxwO+NBsTIdM29blx5OTtxD35HkMYgOzQlyf
+/tRAi5kfq7SGZEeg7qNbUsOS6RRhX+asUTPxTOkiAODyhRYU+RztOTDK36H6+A3b0n1YtmVbtne0
+R8JSQKGhZzGOIwNVklgc54coGFxbKwpsbAtWoKgFWGvKar290BncL2NMcEi94uHZy98FAKi21u7r
+Eh6v3sbamJWIT0t2Imk4CBgkcg0fH7qzCwD4Iz2AsShMUkBwKHiBDVoof9nz8C/nknf/nzMLP+n/
+BelH/rOLkitA4/7nnEI2TyUFvnP7uwEAHe/zSEgH/z2VEKtnPggAaFyV/qy11xE8JdbIOAngziS4
+mHsV1DwpXFL1HZjMWWcO3QvdQE4qMjVMcf2aWCtVmAADTk50HjndkfJCqv3MGdikXz8TrAB0Ywq0
+YFF8J2lmqNYl06A9WlLGDJMj6Vt9GEA9w1JLt46MlZ0qixGRD8OhLqXjB/B9sX4yhLCVuBd3jhO0
+tfymqAewaTKfHouV1jwdYvOpqwCAk5mH7YZYGFuVNag1ub/ImMG6KDtlhQVv4TCC0xGQ0bU79xDN
+yMWx4qFL3od2mOGUFGm9Y1oupsJatuBPtFG2xTJT4yOExA1UuJvXjSpCZhnyk7vwaLBtRgFukcfS
+nM5RY/bgfcEHOIY3MSBMv+EMcLsnruQsmGCb7sOKs47OTZlH+xMZ13AWYYsZtcs778faM9I3rwJs
+N+Xic9dBOCTm5AHbI7EoaEOjCAqYqcK8LxoKsWWjQd941y5wcSBm3cbzW8hn8sAcouR2rXuIrjHi
+HuxjhRqGxmMptpUM6ujuVzBlnUOlJqbsWr2KmA9/N5/gN6sy8eBaKIiUMwoTcfhZAEC0iORfMvHd
+LTFbf80KsbL5ZwAAquQCE4qtKo0F341iCfVB1Me9SxKrONe1cSuXF7Ob+Fjflpd74+KTct3mJWSl
+hXZBBylRd6YOERMDbzsa5kJPk4tUZmokCV9Gtwr3O+nCxC4SlknfvbmHlEbi9Fj8cH8L8Ams0q4P
+p0bsfALkIcFJ8x6SubheZiAvnQqS+wQpYS1GQOYlVBzEB1RIGs+gNcltSYeeDA2EMyJByykijlZR
+0zgkB2cWDjEiYtOYyXHT1EaLRLKVUoHQkLiEacxhFsypxg7mU1moFuJI3XmMzCIwKS1glWVjKdv6
+fsXn6XGEbl9cgv1UFqlJZkGRK3MWFzh6Tdy8VKeosrR/Rn0Sw5zDdqUPByUXZZLIjIM+DGbKMm0i
+ceT76abUNayZPnbvsq4htVEQZVtx2xibMvan7gTXR6IJMuK8j60phqbcx7h6iMaqPJNZr4G3uhTF
+ne3BjhYC8Q/Wlu7Dsi3bsr2jqT/Whn0PO6HUKYS6+d3xRn3rWnt57eW1/39w7bNa65Vv9KNHYlEA
+AKXUF7XWLyyvvbz28trvbVu6D8u2bMv2jrZcFJZt2ZbtHe1RWhR+aXnt5bWX137v2yMTU1i2ZVu2
+R6M9SpbCsi3bsj0C7T1fFJRSH1dKXVdK3VRK/a2HfK0dpdTvKaWuKaXeUEr9DX7fVEr9jlLqBv9t
+PMQ+mEqpV5RSv8n/P6+U+hzv/9eVUu+OZfPdXbuulPrflFJvKaXeVEq99O26d6XUf84xf10p9Y+V
+Ut7Dunel1C8rpTpKqde/5rs/8T6VtL/PPryqlHr+IVz75znmryqlfkMpVf+av32S176ulPr+f5dr
+f6vae7ooKKVMAP8AwA8AuArgR5RSVx/iJTMA/4XW+iqADwH4SV7vbwH4jNb6MoDP8P8fVvsbAN78
+mv//OwB+QWt9CcAAwI8/xGv/PQC/pbV+HMCz7MdDv3el1BaAnwLwgtb6KQAmgB/Gw7v3XwHw8X/r
+u693nz8A4DL/+wkAv/gQrv07AJ7SWj8D4G0AnwQAzr0fBvAkj/nv+E68t01r/Z79B+AlAJ/+mv//
+JIBPfhuv/y8AfC+A6wA2+N0GgOsP6XrbkAn53QB+E4CCAFmsP2k8vsXXrgG4A8aRvub7h37vALYA
+7ANoQqD1vwng+x/mvQM4B+D1b3SfAP57AD/yJ/3uW3Xtf+tv/wGAX+Xnd8x3AJ8G8NLDeP7v5r/3
+2n1YTJZFu8fvHnpTSp0D8D4AnwOwprU+4p+OAaw9pMv+twB+GlgISaAFYKi1XoDTH+b9nwdwCuB/
+ovvyPyrRnHvo9661PgDwdyEsdEcARgC+hG/fvQNf/z6/3XPwxwD8y/fo2g/U3utF4T1pSqkygP8d
+wH+mtR5/7d+0LNnf8pSMUuoHAXS01l/6Vp/7AZsF4HkAv6i1fh8EVv4OV+Eh3nsDwL8PWZg2AZTw
+/zaxv23tYd3nN2pKqb8NcWF/9dt97XfT3utF4QDAztf8/za/e2hNKWVDFoRf1Vr/M359opTQAPHf
+/6e9M2ZpIw7D+O9dGnBqO3eoBenqGKiD0A4aSiYHIWAEP4U4+QUEB8GlU4cWFJHgqHauOpRWrGKK
+hTqUOjlneB3efyA3BKrc/+Lw/ODgcgk8ee5eHu5977j7l0H6DdA0s9/AZ6KFWAeemvWfHpvV/zVw
+7e5f0+dtIiSq8P4OuHL3G3fvATvE/qjKOwz3WUkNmtki8B5opVCqTPu+jDoUjoGJNIV+QgxdOrnE
+zMyAD8BPd18b+KoDtNN6m5g1lIq7L7v7C3d/Sfg8dPcW8AWYy6md9P8Cf8zsddr0FjijAu9E21A3
+s7F0DPralXhPDPPZARbSVYg6cDvQZpSCmc0QbWPT3QdfwtAB5s2sZmbjxLDzqEztBzHqoQbQICay
+v4CVzFpTxGnjd+BbWhpEb38AXAL7wPPM/2Ma2Evrr4hC6AJbQC2j7iRwkvzvAs+q8g6sAufAKfAR
+qOXyDnwiZhc94gxpaZhPYti7kervB3GFpGztLjE76Nfc5sDvV5L2BTCbs+7+d9EdjUKIAqNuH4QQ
+jwyFghCigEJBCFFAoSCEKKBQEEIUUCgIIQooFIQQBRQKQogCdx9w5/yaBkpKAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3614... Generator Loss: 1.5139
+Epoch 1/1... Discriminator Loss: 1.3375... Generator Loss: 1.1554
+Epoch 1/1... Discriminator Loss: 1.5048... Generator Loss: 0.8155
+Epoch 1/1... Discriminator Loss: 1.3531... Generator Loss: 0.7889
+Epoch 1/1... Discriminator Loss: 1.2752... Generator Loss: 0.8632
+Epoch 1/1... Discriminator Loss: 1.3345... Generator Loss: 0.6996
+Epoch 1/1... Discriminator Loss: 1.5458... Generator Loss: 1.1624
+Epoch 1/1... Discriminator Loss: 1.8949... Generator Loss: 2.2906
+Epoch 1/1... Discriminator Loss: 1.5799... Generator Loss: 0.3830
+Epoch 1/1... Discriminator Loss: 1.3848... Generator Loss: 0.7840
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsZdl5mPetPe995jvPdauqqwd2s8lWNynZlhQNkCU7
+jpXYhiEHCRxEjh6CBHaQAHYGJMibn2wYCBzEiA3YToIkkA1EiBxYliyHGik2RYo9V1XXXHe+Zx72
+vPLw/7fIDiKxKbLtDnD+l3vuOXvvtfZa//rnwVhrWcISlrCEK3D+VU9gCUtYwqcLlkRhCUtYwkdg
+SRSWsIQlfASWRGEJS1jCR2BJFJawhCV8BJZEYQlLWMJH4BMjCsaYnzLGfGCMuWuM+Wuf1DhLWMIS
+vrdgPok4BWOMC9wGfgJ4AnwF+AvW2ne/54MtYQlL+J7CJyUpfBG4a629Z63Ngf8V+OlPaKwlLGEJ
+30PwPqHn7gKPv+X/J8D3/34XtxpNu7qyglNXpGUKQJoV5Jl8rq3FMQYAg0MYyLQDz9HvoEYknqqo
+wZFrXcf5JtmrLXVdyzW11a9qzNUkaihsrR8NjidjRI2EbpIA4AfyN80tod6YVyVlOgVgPB2zKCoZ
+w1ZY+YhxZRKuhVqHs3VN/S1SmuXqs76nMZir2RmezdNiwX7ze8f15dmufOW4PoEnv3tuwM7mmtz3
+ZMHD6cNna5HqcIGuVe04VM7V2ODo7xXm2dystd+c09VsjcHofbauKSt9Q/vNNwKDc7Unuq6B7xMl
+EQCtKMHzA/09wEUWrihSMlvI/GZjvQ9spfOsHNBRnNDF0X3Fc6G6WhCZjy0tVr+yJVij86xqHF/2
+pypdrFE80veoDGQLwcPMWjwdwngejUDfW7afcTkjMldr5eD68q4lkD8bz2L0mlrxA0dw7v+99sYx
+XEnytTUYxd8k1PnWDhX6UtTUnn6uLdaRawLHxfNlnY9OTy+stet8G/ikiMK3BWPMzwE/B7Da6/Ff
+/yf/GWE64c75BwB8cP8pj+6+B8C8KImUEIR1k8N9QfT9VTmkgTHMakGe6cWcOhEE6/oRRi7Bzktm
++QKAyVT+Lsrs2SbXac2xEqQ5Pu3uCgA3v/gGP/3a5wDYO3wdgHefZDynSP5ges7lu78JwC//xq/w
+jaOhjFGOyCfy7Kgrm9LIXeZ1qeNlzItM5lY5lI58b3STvcAlMHLg8cCpZZMLU2ErWQvXr2i0dgBo
+teT3xsomB115/9WVff6bv/KzAFR/9S1+7jf+AxljUvKevvdBKHObxwGjpowXui6h/j61Dlkla1vU
+FrdSQuzK+/uej9eQ+VTTlP50BkCZV5RKZF3jE8fyXu1V2buD3R2ef/UzAPzYi59jdecaAL2VazTc
+AQDHJ3e4lz8BIPvyLwOws2upL+X9vHEDY2TdkptN4nku67naxUy68gKJ7HV2llO25RTWF5D72bM5
+NzbkvafDDqkrLz4ZyXuM/ZrbbwlO3q9LVgt5p3BljTcOZO3sr8lzf+n8q7wYyhzSImJlU87fmQNP
+AplHPc7xXbl+ofhBAKlyLzdwiXKZg990KLJSnxfgpnMA3rjVBKA/D5nSljnYjOlKR/ZmVlA2QgD2
+k1U2N28C8F/+jb+hXOEPhk+KKDwF9r/l/z397hlYa/8O8HcADve3LRwzcIaMj2Szju69x6Avn8PY
+patUt7vf5fpaD4DDPTm4ee0yz2SFT3OLtbLJ7SSg3WsBYBJL5ghypxPZ8KPZBD+VMe4HKeMT+TwZ
+98kmQiA67W/QuvVZABprgoztN58w7MnYF/kJi+kJANN5SLuS+xapg68UfaOQOTghVLlsbBDkVKXM
+s3YKAleJnhI/x42Jr4hbI2GSytwX84xhLfO3FWQjWVaPLZmjc4rJhFAUW9D+b/83AFq/+I8olHAa
+IFCmcqTfvby+zuaqINJqHREop0ldw9lEDuloWJIXgvRN5a691TaJG8u17ZLw9ELWZTRGzyilLZiX
+iuiXZwCclR6bniB3tPN9dF5tyGfnnCySA1Q8WfD2r/+SrMugL8/KttlZyBqWUZNGVw5pJ2rjqOTh
+NdawkUqcU3mu27zE9eT9imSKTWVtR+6EVigEqbleUlayJyue/M2nTwk78n470wlhpgSk4bP5S3cB
++Nm7gnuXjLmVyMJ+cS/gVCnrG7eusYIwC84zMpXu1j058BdlzelECMW8qlgtFQd8w6QQCelBmjK3
+8k53LmWM2tacZLJWzbBBdSS4UBUN/JlcG3facP07sxJ8UkThK8AtY8x1hBj8DPBv/34Xl3XF5XTC
+xdePeOvDdwA4mwa0EsGq3Tjm888Ltbtx7Rbbm4JMcSwbPk5HjC5kk+O1BWYhm5+sOnRbwpk838VR
+Uazqibz3fB6RJpcAvHA75auZSCYf5OtkqORxlnI0OAZgeyyH8Z3qF7jzoXCBB2/9GlEqn01vwUtK
+uZMcwlyQ6frzIq5Es4C3R/cAGJ632GnJPDqLhHBdNjouhNjE6y12kk0A9g6btGfXAfj6+Ku8+55w
+z6fpE0KzK++XqHi68EhDQVJ/Mmel/bcBKK70FkTgzlR0LY18sHuGH9oUzt1bX6WryH+a3efRXVnD
+UeMJ2bnct74lv19v7rH/snKrUcTt47cAePPdBu/PZN3SsUOpsrK1shajdMiHAyEQD0fvsXq+AUBz
+e4/sNx4B8Dtf+nnuX8qefaEn63OYZaxuqvhXOGxcE1xwki6OHmRjDCaW/RsMVRrzcupMiIlZfcrk
+sRyUqDni4Uz2b2vVZbWUZ9tQ5puW19hoy+GtBpZGLISnbHb52eABABfFNxf2di733Qkn/FcvHADw
+6sEm3bbgb9PJsZUQ8CQSxj0/6zFIP5RnPc5wm0IILo7H3LshxOR3fjnlqJJn335U6txdNip5/6pY
+EIWyT25SMpyLJHcWD8jHfb4T+ESIgrW2NMb8R8A/BVzg71lr3/kkxlrCEpbwvYVPzKZgrf0nwD/5
+ONfWRcnk/JTfO7/LmYqtDZOyuyk60meuH/KF50SEX+/tEa8JxeyfCUe0dYDNRVzqRT1SIebEQYlZ
+CMUMGyWpUtWNLeGudpYSqPHw69MnvFIKt5pnfejIPPy1Bu6Nl3QcYbdvfyUjTU/l9+glPluItHG2
+uodpiaj9/bMG40Kklx986RYAk8LjhaFwifuPz+j4IvrNVlq8XovUM2yJarR/cxW6wsFe2XuNiTAH
+fvDsM/yD9f8FgIdfr7g9FWnDCeXd5vWYsS+aW2fiUPyfv8+iqzHyf/jTfxaAF16+RtEQbtbt1vTH
+ogYczlbY9EQ8aDZ3uJgLt73ZEa6UtFbZuCbvNC0GvPRE7T1bD3nnq7IRX4qeUo5UVFYRuCgj5gNZ
+ty8fWfb78vva5iO+WvwuAO+22zSPviHPbsre5LHhvCFrtDbIwZX3ztMRTlvGDi4L6kSkxXQqc596
+LmGmhr/p87hG1m1WtbhmZZ/MvM1cBAua27Ifq6VHGot94ixc4BmR3rZzB+4KjoS+4EVWwrVIpJXd
+Fw/413/4TwOw1TO4qcyzed2hLmVdfPbkO2dIwxWpuEhG+HN519ZaSfvyEID4B+/z974sktWLqgY9
+TqF2deyFi1G1MgsifJUAz8Ib7OYqWX1M+FdmaPxWyLKcB3ePiC6muKEsatLy+b4NWbSX1p5jZV8Q
+od2E7KlaaHLZWH/hkUSCrLFpsan2ucwUBJ4cPCfv0OuKmBsk8iy3k+IMZcGeb0x5uiWi6r+138YM
+5PuTVcuWFYT1cpET33n/LvtzGSTY3OLEkU3eaYeUKj4Oe4abgYpzKqpfb65xWsrmv7LjY9TG4bVi
+VjbFDnBd/zZME66JyOzGHTYbQiDHjYjX3xVV4qT5kOsLQeiTqeq1ucN5JbplZzpjTw2QT/o1hRoP
+1wOfv/znfwSAn/j3hShk1QWhGjC9oUsnOZQlHg9Y76kBq7TPvDymrcbTJMb3ReXxs4TFgahMnzEb
+ZJUg6fFvzPmwPQLgWi5jDOuSTNW58cUT3vry7wBwsPLDvDqXax41M86vy2F6VT1AO/EeCzW+0Tuj
+PlWr/fUA51K+t94JzGT+reYqAMV8ihOokaNu09GDZUyDhRr5/HlK1FBjq65Fo9Gg8IU4rzZdFpFs
+ZreOeH1TnvHkRA7moxb82VfEPvHv/ORPs3FLbEl2URAKyuEPLfWe2rnOhCi6QZ9qop6YzpzSkTEa
+Tpe4oc6CE5c/cyDjNCZCsKzf52KonpGgJCwEX+wgZaAeh6Q4Jx/LnnxcWIY5L2EJS/gIfCokhcqW
+jPMzntY5DfUMbG2vc7gjksL2zQ6NTC3H85SLkYhRs5nIeiaqqQOVAgKD0WgAz0bU6nKb5iXJVET7
+shaumjgNiko8B1nXoZULpQ27G3jqaei1VgmntwFIKyH3w/k5U53PYT7DXxVuxKJNFsrY3cJjgXCm
+4ZmIsKerQDDWsSFQV17pVBSeXLswYhTygpryXMRWp3VKR33wfjJg7WVRDz7zZI/bxfty30Lo+2xh
+qU5E9O/v9xg7wmnDaI7a4fixz23z5158Vf5Rn/nKxRa5cqh8dBcnUQOtu8CNZB5+mOMp13R94VDW
+aYN6XNwgJqCl1x6xEghHW3khYvB1keQude0jArKF7F//w6/xtCX3YX+LfijPe81LSDui0qwcKHc1
+C5z3H8h+BKssNkRajPshXqIxFMlzVAvFAfU+rK3F1JFIPHUFRapxD9mI0UJwzqSW2Kpor5Jgc61g
+diFzPmz4jJ8KDrGzRh7Iuuw/L3u3t+7zxhdFUljbi59Zc5NJQeXI8ybBQ+IngjtXoTyZ63M6Fpz2
+vS5+S/DCmoRMvWqlt8ZLB/J+09l9GeO2y7F6MMzQMtR1y01EUcucMgrGa1t8J/CpIAq2rslmGeE8
+Z6BT2o6SZ3JM4K1Rq45Ldw6K9P62bGBYdnDVGh7lNbWKzGV/ykL96qkZYmsRo7KxBp0kGQGCmAWG
+UPXB1GlhZyLazRtDzFDiFIq+IkR/zoqKmRfpLZ5zBPE6fk3bEcRrug4dtYlwJmPEZcXlpSBrvJKx
+0KCYKnAohjI325D5LvKKi45Y7+dHXfCvXIEtVg/lwLYOdikuxDo9VH9+4oSc6Nmwi5yuI8StDONn
+gUXPNw5wnhNEcWZKCObHoBZ+ay22EnE/8puUTZ2zCbGKhH6hiN2xOKqH584CSvm9tdJlLRNCfXi2
+zrAtxO70Qr0PixSrROhRAe49sb777gs0+nJAzu0Mf0v37EJsOGM/fBZbsrJl6dwQ1YX2dYyROYVF
+m6qp+nVL1s3LmxBoYFk2p9AAFneywI0UR8ozSg12igMZJM9XCKfyXTOdk63LfYMw56bGNASBrGVk
+E7ZXhEGYVo5/FZwWQdiSTXGqbdzWVWCVzN1exmyGso92Jcf1hdjUdYJfy+IGWwF5LGPnTw8BmG/e
+pb8QPLzrLajm8txZnYK6tidZxOlMz87HhKX6sIQlLOEj8OmQFCoopzA2NUapb1TnFFtqXJueMXWE
+8gdvTzm7ONfvhaZ1Dio2p0KhbZwQToS71LlLW6lrVHpkV2G86lcuHQ+vFm7m5xl5Qw0yZc5JrOGl
+px75johwO0YlFCryjliIX7lZc+aLCLu945Gfa6Rc8hIXD4X69yuJiHv0/gW7qpZ8Nl2DLfls04oL
+VTvcuXCzWTBicleklXMumWlc8trLLxEORCXYPdhkdShxDw/U913Q5zVH1uJu0eQq6nhGQcNXLh9G
+NFdFNcs0ArGwPs6Z7kcveSZ+GlMTqafCcUOsGkptS42PWGyg71EZQg0gKsyQeCJz6u6d4h8L9zML
+2buy8p49K7Q1CyvqRZWX5KGsYea5OKlGN2qwEc6MJiIReDOXxkDmUZZTsPK8sl0TuKryaIAYYQs3
+lGcEURczU8/VzgpFfWWMzpkvRK0wDXnnxI45U6mwrCNC9Sit4xG7IiF6Xb1206FA9sZ3A8qhRq+G
+JbbU2Jp2g1Tjot2p/J7XD3A35HNRJKCSZ+2XJGpgzT3nmfdkek2+6wUpTTU0NmYFp5WsW1q7xHqO
+kkZCO1aJ9WPCp4IogKUyKSuFy5OrPYxrumeyQdmWR92QRXuSv8dQgzhCcxVPv0USye+um1CnisRO
+gNWY8shp4lcaMqr3u7YiUKQJWzDU0NesCPHvyGl62obDQpCmMZHNmPklP2QFuY/r6/zkpiB/MJjw
+XiaIeRn+Hm4hp+zNOw8AWKQwRZBq06SMTwVxN4Mmi7kQkEqDptKFS2YFccfzGluJetC4f0BzQ8N8
+m5t8ZvOLAGysyKF6+MBy70LG+/72Cv/TB3Loq7ym0oO8+cI6zlgOlq+GhpG9BE9UhvByCzVF4CYt
+rCfrZeoIJ9KDpxGW1vdw1BZjnZS5hpAbLyLyxX0XTjaYJUK8Yn1wL54xzlRlCgJWdC/TwZzdSuZ2
+15nzzrGqTedCkBtJQuuLQvTcoGaxo6J4bnELDbee5eQavORrdGvl5FiZGnhgjdgoSvcpgVLOPPAJ
+Sxl7NlX9PPaprUYYYrjwVa3qz2m25b7nrgLd9rr4ev6KYYVpCb7U6Rk2EdsV1U1MR/CsHsizvMaY
+haoztfWpzuRdg84OVnM4bDBnYdQjsq52orMObVfmm9kJjuKyYytyJdQkhp166X1YwhKW8F3Ap0JS
+qCxMFy6jaoEz1lDkQUV/IdPrnF9g7gonnF+2OBsfAXCglnAWBaYSEh16SJYcEPgNrmw9fmColGt0
+VdwrKQly+c7GJY1SKP7Jk6/xYCii++IoYXYotNM+L5blxbzkX5wL27Gt/5trjdcAWHG3OPng6/KM
+8RlWk5ju9WXuLoArY+TRNquxcKvjYkFb8xzqsXCRyG8zGWo+hDsiWwi1r86fcKmW8TIIcFQU9TTq
+Zja/h/dYjHqD51ss1PBXV9AuRRoZD0vySNYom8tamsUls7nmDqzN8VQ0doMUU2tch5dTFSpCoPkX
+BeSVrFU59jBNNebNSsJcrqk3F9gPNJioEgmryh3QgLNykTPS+IyiGPM4FK7qPvKZHImB8amqBj+Y
+hOxPZQ2T3RdoTESNs06bqCtzNl7yzDtiUo0VMAVWeWA1H+BpLkaEj+YfERuL39VYjVSzK+mTzeSC
+bFbinAp3z9tNYjVQtq4JHu63DYlGFM9vToj78k+UjPDGL8jYW6e4Y1FZWRVDo6l38SuRDpy+9+xU
+OoVHP5SQ78UpZB3Z62Ko6kqV01fDaG0dykCN5pmDpxJrMMypfuAqk/LjwVJSWMISlvAR+FRICo5j
+SZoLZnOXgabpDuqSYi5GpmKrw3Au9oWqMX7mF3/QlGujYobVVF7PWMoV9ckdz/A6wuUiJ6XSEOJA
+s/5MHGCVuqZuRaausMsnBW++JxS6SiJ2VrcByGeaUGMWHOfCweYP4BfS3wPgh8LbPDTCCc+9ObOJ
+5uR3NVV24dBTA9ZwL8PfkN+3si0cNTqZVKSAYjRlaJTC91PKWrjxRXhMqq7D4ycj2lPVF1eFi5jh
+lMG2XHunvoujqdoVDtNMeMCDyRBbyhosrrI6R3MaG6LXmrrGNEQiaHirGGVsbpZzFTFrLoTz2zAG
+xPaRdgPsXPXkaB13R95vY7jLdktSX+49kvku7AQ18VCamrOprGfL93Fn4nKdDN9F1XLWz1TvXw+f
+uRMpfEpNVfa74A3k2f56iFfJNZVKLvUgh5YmT2WWRaQ696mlCr8lRV1D4a0aOG0VYxdiD5lVHoEn
+85iVM3a17kMnlmdFmcfadfm9lRZMuxpHUxeYdZWgHEgTjb4dqeSST/D6um7dJo6GkDumxh1qSD8p
+jiY2FUciFS9aHutGpKNmPGDeVzexyQkSddE3IhKtVfFx4VNBFLAeVOts9SYUE5lSUkSc5mowHDqU
+uWzW2vY+3QNZzL0dTRfurrAdiPjphBVzzQeYd2N8VzZgWkZUI9kkz6o/e5JS+jJGMTdUWgzFa+Z4
+oSBYsL7C2vMSVtxcU1E1D+gF8nvpGtbnsjEPMkOVaTrw2OXFVSFCt1XU/vGNJqOGXPvDG68x0LiI
+2osorASyTGbqX/di7Kl87m1u02wLITiLG8Rjja9vb2Giga7BDQBu9NaJjdyX7r7InV+R8GEXS6CG
+qq10g+FU3ns8EjVovChYaMyD2zA0z9WK/pxDpBZ333EormI8VmQNq9mUVFWwh8cnTDX4zMnucaRV
+Zo5un3OuWXvW1bTozMfXLFi38Oipb972GqRqrIyftskvVaxWA2XjeoeZqgStssNQCXV2/oi9WzJG
+02nhqXeo1verkxaVFriYDUqGWsmknoXYuRDRwoupBupd0RoLbpriRJIrs5IOMaHmYJg5Zw0x/l6p
+H63mHhjBsfGpT92Qse89adKey/7+7rt95hpzMZ3IIX95L6J2xCi71dmisSrr7UR9fvMXHgDwzy9P
+mRxrLIOROZ4lMdmlqgylBUdjMnyPlU3Bzz/yxh9j5wWpAwL/kI8DS/VhCUtYwkfgUyEp+K5lo5Mz
+fuLRikT88iPDzrqG9rZd7IqG15aGdiIGGicWuTYkolQO5KUBxheO4PpT+qciNZwNp5QavZhrvMJ2
+O2HDk+eur1gcTT456I743Jo8IzxY44Wb6srrPAfA6qZlS0uCzXsOkYZVN0vDVF1d13ZjXmgLh1lt
+CYVveh69SDheGllMoq6u+pJAE412M5EkZiH419UQ5TncUENc3Egoz4XTjLoFCy2ykSiHa3Zu8KN/
+XOa7v/U8//BvaqBC7WDUAOu1JvilqkINGXdoXB5diuoTPHDwt4Xjr4zX2N4QF+CKu0m4rkEehRgl
+8zTjzplwvNsP7nDnTJKx/Dyn1AxU73TO7b6s51yj7lwvJ9a6FzQMTVeLgpQJ3bE8++14Sqbj3FoT
+/rXjrLH3BVmL1eYGqbK1weyCeq7/tD0wKt1o9PTs9JjxmUhVF4Mp5ULWKw8jVjUy1isbVJtyTZZp
+mLSTkGRqlIwblCuKQydQanxDZyQ4tgieMjoXnGy5U1xE5XVfLHA9MVLvbl/wRKtQnd2T9x8ufMIN
+jdz0csKGhNWX77c5ykWK8eYDhqqu7G3KS3WsZaoRqV8bWGxxVZ7Qoa14+OK1NT7/3It8J/CpIAqV
+dRilCU57xJ6K9m7gcdGXA+IkZwy+pnkLJqe5It8HDzSFthuzpQsdLGqqlhzGnbXgWdjqohozTmUB
+/SsRz3fZWpcDu2iuY1VUPX1wj8lAxluvD+n4mgeg+mnkrnFtU8aebLW4rkjxwb0FVgnPRqfH3kuy
+Me1C5vP48jEttbjfPz3Dd+T3sWvZ1tqFZ1MRM+8+WPB7Q5lbOM+50xG7xv5OTKbZnuXDjLHqnCv6
+nXn+Zf61G2Lpbj23Tn1V3s2paWg67cLCZCiH5qoK0Ebo4OgBnO00GI+1ulF+ilPLoe/7J6zN5V02
+bsh8a1uTazGVbD5hR+MekrUeRnX4c3NB90Suz428P7lL6guB6dmY0pO1mI2GPEXsOe79lEv13Lyj
+uRFbwYL0/gOZz+oFma7h9MSndU3tJ7ZLmWqJUM2jN/WUkapmM2eIja5qW2YUml3YimcUlTwv1LlV
+swnZpYy36GzjaxasFyWM9dpyVVXbvMF0KraBkR/RDmRs58Qw6gjBzbdaxHNRe//4G1qFK2qSDuW+
+JN5jfUtUiXl5wucjKXyzvhjzI1f0XauQhVnAV78qKszAv8NTzdEobUlfvQ9brTbJ9pXH6OPBUn1Y
+whKW8BH4VEgKngtrHYfZRZus0OIXWcYgFM6dnKR89b6IpZOFIeiJl+ClLSGdTrpFW+vhZW7B5o5G
+4Pk9GlrmeMWeMdV6hCdnaiSLDHup1n40Jc1YuMf26g53Hwn3KxlSX6qRTNWZV14ouOwI9Z2dXvDh
+hcYNmBGBJ9xhpbvBzQ1RN0orHCoct7l4KiKln9S0toWzXY9aDAOhz3PlymeXjynPhJMcZRWxI5zk
+uXiTTlu4/NGjEqOSSTST+3d7hrCjNRn8iIYWnLG5j6fejGoxfFYMJtYiHcePMi4u5PM4/2aWZOK0
+qUYaxViPKXdFlajR6Llywpau26Lb4p9/Xd7v8uiEjbasc3CxoN1RVSmX5/aLGWsaCxEFHm5PnrG6
+38M9lszIefkuX1gTFaS5rpF7oyf87Z+XOIbboxpnT97pc+s7/BtTyfy8tR/gXBdJzouEm2eXESZT
+NaGChUpsHzxccFwKh13vFOxocZYXD+T+xAuYKGdu+00cNaQuehUvbmocTaIh3/2S7dUr43HFZC7S
+wa9+6Zy3jmQNZ3bIZk/W4E/8EZECXkxKwlVZi1bb4GgdzKBOaGpm7/R8wptHWi9C41+CVoFdiDT5
++WaTaxr9+GSWs74jkqOXtPC5ysr8ePCpIAoWQ2VCTHfGai569H57jc9tii5UNqfM1eKe5jWtjmxC
+S6vj3GyvMh+piy0vaKeyeJsrBr8liNCM9ig1Xba3o6GvdYK/K+NVVYanB2GQvc0HujTrQQtzIOOg
+qc7pxXVe3n5ZnvXSjNgVAvLIzQjbcu3LN27R25WKS64WUzkdLPAGQmBG+Rx/LMRr6jscqGejtSMI
+sbZw+VIihUE3Ji572yJSHt54jXPnbQDiBB48FaTwX5LDc2Nlk9XnNJXbDXE0xDVOSrbVo9I0WwSO
+5iJ4QoRXWqsYzTnozQOI5Nr2asJUbRhnswW+Ivd2rIjZ7LAdi7rC9iZ/bkv27L3+gBfagphvPn2H
+7WOZ/zuxuCYnZ1C15P2fcw85amjocmOdVIn+TrTJWInkq+s/AMD4ep8XX5eDvj80NFflucmapdwU
+D8zErfFSwRGrIcweASa8ysB9kYEnz73ZDUjUgr+ynmCHwoiiHamv6AyHFB2xNc39nE0NONoIFvQ1
++Cx3FFd2elitoNRdXcHTA/t9399m51wYSiNoMtUMzu1VwY88Kim0QOti2CLQkgCZGbC6K/t02Oyw
+otXLJ1oFfL+T8TVVmw8Ll7sXQixnC4/Dm6JuHr7yBfyrQi0fE5bqwxKWsISPwKdCUjC1xZstcKaG
+XMUvf81nRWOUo+YenTc0gSeICH2NplHD2TTLiDRMePS4JrRC6xwHYhW1a3vA3rlQ474vYnl3pcuq
+VoT2csO+4ZIWAAAgAElEQVRxIcapizqnqwEfGaigDGEkXKkOMvYXwlHirZe4saOVna3LQAtk7O1F
+bKmVeKAJLntrXd7VIKPrzga+r8FCaw0ijZE42BGuu765TfANeda7oz7BupYWz4+o+zK3J9P8WXm0
+5J4mdv1Um0BDeMOGT0uNo34aUjdV4glyiguZR/u6jOHvbdJKRYzOzQGLuRrtoimmI/dN+xBp7ww0
+gcmPY9xAjI+HdUm2Ir9/xoRUmvm5/VaPX6v/LwBmj2Qf63JANhNuFt4w/EBXK3C3P8eLmmn51eRt
+Hg5k7BdPZM8+80f/JG/8jHqGWtcYqxoweHBMHco7RVFIfDU/DRab1Qlrdar7uE+nIZLAdr3g9UDr
+GtgUZiJx1p7gStlbRctzUKYuZaA1NXwHfyFre6BJYuMioqHxFH6+zcaBqAcrvQuMjjFJK6bqrUl6
+6llIdrl8InNzqLG63vE0wu88D8AXtnLItTdEItJDWYy4oZLXbDRkdVPw89bRGc7nRZUKrEN01V3m
+Y8KngiiUdc15Nic3FZuzq9LcDVLNgAuNy7UV0TNNskrgqqilwU0+I05yjWzbMMQdqUzkmhhXsxJN
+nNPZVj1T49P9PMLV2ufzNOPO2+IKun3nybN02u3ePp6nMfXqLj2dFry7kAN/I85JtG5fa6/NqpYA
+XynbFCq6tgPVa+ebeJFY1gk7bDwniGnKgPgq8CTUVO8o4TndzOTDR4w0b6MXtLlw5ICMJxdUgRze
+6aFsZa84JGiJ6G9CD4x8TlqWXU2dHtqawUQOVnMkiLTW7WG7sm61v6DSkuyzfpMPRqI+1PEY1Bqe
+uyICx0EPL1Aruu0Qq8XdVhFYsYPYww2674pLLkbK6Fc4UKtbNG/jrglhdfwhs31B/oQZo0xE8OmW
+Rvz1xyTPy0Fxo326M/ndm8WUpYjPReXjqXrjajhmlNSYQtLF3bUU35XxGjYCq41jiseUWkWr0pTk
+Mr/E174eqbtFpX0rbGPzWXr1zNES/+l9aiWQdiPDvcLJ7R2qQuw83eScRiR4aPS+rExxfMF1p0pw
+AsGb0hYUntjSkuCFZ8Fe9ipaMz/BOZB38k5LxpeyT6XXonMqHo5seoJfiVr8cWGpPixhCUv4CHwq
+JAXHMbQCnyL1IRQxstHpoK5iOlFComW4wzDHb2opLc0XGH34GI2JYcdJCHzhYlQxce+KAsO8VslD
+qwF7Uf0s9yHPRriXQokb7jqtSqSRRndOpR17rI7nZafczR4AMH9asacFSw4vIoq2luiKI9aNlgpb
+lWeNbjhEj7Q/ZlgQqTfEb8wJPA16idRC3gxwlXvkkylte8XxI25pRmg62OS4EBH8xhPhPtc+D5W2
+m/Osw/6a5gMUCT1VlbqxT0NzHnytgu2kPlolHmsqikorLZ8/ZHEiUkM8hdKKUdUdCeezYf6sWrXp
+zJ7lbpiGS6WVm0PHo6udnFZ7YlzbOb/D5zaEm23Yklkh+QXB+vOYkXzf3tvhx13hvDe1CU3jRwc4
+V30XvRmuFt8JnSFWC9x4az4+WthAVRiOU8wVNz4dYnSfHC9+FsBlqwwz1iIrWh3bpA0aGoRVLBYM
+hxrLMblk38p4vq+ZvfNNep8Rg3E4DTHqBXOKNZyOKqHDACcQdbI6lftdPNpD9YitPCHIxYjtbg0x
+90USMskRnhZqcXsiNVWBQz6Q+ca4XO+JyrAbDBn8oIjDR2mfRDN+Py78oSUFY8y+MeZXjTHvGmPe
+Mcb8Zf1+xRjzz4wxd/Rv7w87xhKWsIR/+fDdSAol8J9aa3/XGNMCvmqM+WfAvwf8irX2rxtj/hrw
+14C/+gc+yRpqGzKPR2gBZAbHF9zTikZxv0+9rjrZZUqllDlPtQmoWz9r+hIcrhI3RIdv+A0cTcpx
+4gQKobBuR7iy65W4M6HQ8/mIodZTGJdD4htyzWqyR7ghHL/UBown54ZNbQwTNz0qjZGwWwnuVYKV
+dVhoxaYgUR1/bvA0jDkgY6bJnJ3Qx/O0CpHWW6hcQ65tzrywi1GOaE2Aq3aHcD9k+L60HnsvEn3y
+x4YG/+Cqk7TB90RfDhoZnZYaa1PDWKtPtYbCacJuQq11A2y7IlMpLW2H2ErDeOOA4KqKzzOpIqfQ
+XpLOAnztT4EtqbVpbjqdMNdKQIXVTNQVyG5qi73YcKT9N6zjUMaaEOXUvDWXysVbiTQDWsMj0Ard
+NjdYRzl7ksJVEpObYkPttzgRLlklOdVcG+f4G1SVJmO1N3BU1LGNFsy1BoR2NHcY4K/rGo5y1mux
+uzSSEcWpXDPqyxjNtZgq10xGwL3QPeus4A6n+n4p9qpYbqC1GdIRpatdt+dNirbyaqdielXFezIj
+6IkU4qiEWZkcajnCWeAwzUSKq/e7BI4YrJPV5ynVdfpx4Q9NFKy1x8Cxfp4YY95DWtD/NPAjetnf
+B/4F34YoGAfcxNI98niCirWzC85vy0sOXYt/Vb07iQnPtKHpVAuvjEripohi7cUmjavM2kWOc9UR
+uUhAfcGZNt+0jQBXU67DQZuB5jM01poc9rT3X5KQTmWjnVxDcesFWS7i7km9xVz9E8llRLmqKb6j
+gnpb5jTQ/IvzoyNu6xxeHufMzmRzHb8gampat6s5BX3ILuT9LmazZ4i7ZTNMqQfstM3mhpzOvXPt
+wO3cpRzJ3N2kS6Mj79SbRJxr0EvHzhlrw9aedmIObETV1JDoywSrgU6zrAZtuR7mMb6GjZea4j6e
+1gTaPDXpRNQjbRaDS6aHcHKx4GKhjU+MvN9n9x1GSvTevPsm91Qsx4kJfdnfs9WE07sy9r1TIX4H
+J5sk2w9kjKRHqb0i5wuP/KrUfMNgC01F74kxry5dMlebBzkOnq63KRcY9cqYsYPX1uxKreDMuoVK
+GNL6DszaYviLzA0el9L89po2B56EPaKRfDZ5COuy3vXiAWj+SD1YkJUaej9WyjtxqJS4eUkPq2nW
+k/sFmRJkx0CWyRoYVxsW2wpf1cNgBt1Im+jMt5iEYkBeNZaVSr1OHxO+JzYFY8wh8BrwZWBTCQbA
+CbD5+9zzrBV9En4qTBtLWMIS+B4QBWNME/hHwF+x1o6N+ocBrLXWGPP/6ST91lb0a63ENvE5aqTk
+faGS59Oah+qO6Z4FeKXWv1/ZINUejOcaRprlF5R9cbsU1xaMH2sxjVYJmdZkmD1hMhADz2QkHCos
+W5SRTG9QjGlqyS+bRFxTFcNUbU7vC4UO26JGLKzDQItxNNI+w1o4ZREAJ8IRO+0p1YdazMWKSP34
+wRG+9lOoaRC2hFPEsw38FW1ddtWxhTPmQ3m/yficMNTouSTAKLf2dmu8C/n+SUfm8Nav5/DHJeLx
+wHmFoNb4jWgEc7lvMDUMIuFAq2qQCxcDHO1tZv0xeSnzaeaWJBTxOQsr5hrGPTqXMm5OHNHSuAL3
+zGK0p4FbpZRXklx1jtGIVIO8/wfvVzwdiAt4OpoQa7h2PZ2RZsI1m3EbeyFcTm2PzB6kdDbV+LZu
+KbWJij0CHJF+quyArH1Hvr8QSaFI75KpMS/aeIw3u2q0Y7ALdcW6JzDTMnQdmXtdNXBn6lotNonV
+PV02Wky158JYG8h4NmOkkpJtZDTPdF3aI+xEpbvZlFyNpjYRY2blxMwGKoEVExaZVv++94BHqiK/
+kmxRPy97VqVaUMcZY0OJ4vSuBfQ1uvXtd+5x+lhw68af+iLd/W+eyY8D3xVRMMb4CEH4n621/1i/
+PjXGbFtrj40x28DZt3uO6zu0NprsDA1PKxEyvLlhrKXTZ/mYTHvjVX5N7Y8/cn/gNvDjK13+iCh8
+AwA7fwKeBghVllLrEkbardXGNRitcecsaKgHw5ptSkXuSTEkmAgyaY9UnCInN0KYBucuPW1Ici0c
+8Li6Ep8TOhqclGr58oPdFeJz1fcTy/CREJt8ZYCvlY/Z1MCU8ZSqp3EYFzVOrHpv4BKobeDz93zu
+aHDS7FKaqfzW4n/kS78gyPb6Sz+D39POWo9bLDTMe9GrKDUIaaEdmyrTo1bVrc5drGZPBq7D6roQ
+juxyjJtqNaGxfJccNmlody5npSI/ESQujUOuGa8NxyXTLkxP9UCczx8/q2Q1qEou1PZD5DIvNY14
+VPOjr4lnZw0l5NfnFHoonLH7rKiJ07J4Y1nDYjYGVV1KrYrEqENzVUOzywW2oes5s9iuPm/iURsh
+5O5VN6kpEAqxKascF3nXqnLoaVVmtymE2TuDjVeFCMV+SI7aEWY9THnliUhxGsrglAEUpsJEMob1
+W0yOlY+6a+zbb6ox/lMN7BNthqDZJF3T+I0HlsV9taVNhuz8kKq8nTa++535E74b74MB/i7wnrX2
+b3zLT78A/EX9/BeB/+MPO8YSlrCEf/nw3UgKfwz4d4G3jDFf1+/+C+CvA/+7MeZngYfAn/92DwqN
+y02/zW+tzFmbypTmtkCrfDGbzcly4R6TfIJWo+IoVW5dZM/ammdP+0yM9FfsxSlxKJR7ZbPHQg10
+nTXNSDOGytE6/6GLu631D5wSR+0c56enZMq5XDUSpimUAxEb1psnaLcy6vVdGh8Khb4sYH4q6s/4
+UkvC+YYyFcGpdAJKLXjopHMctdq3TyQ/fpgvOB8LF1gEFl/FlFmrZKiW8/u9iq/9jojx95+I92H7
+fIORkTG+MHqKP9Z6jumUdQ0LN2lFoeHY6VzEzFm7xptrFF9QMdeeDH7TkGgQyPqL17DK3ToN7TQd
+RARX3qDM4kfCSQtT4dYy/6HrkWpfyWOtWTEYGZxIxfmsoq3t7fJJn5G2OQuSDaqu1ieYi/G0uHhE
+PZBw9OTmEVQaN1C6VJWs/Tgd051rebeWSGOs7uFrr0l/tkV5JupFlewQjbUxTFqRp1ryTAWXYjal
+1BZsTMBsqCQYuky0GMyiL9Lt9sYWhZbe88sNSm18k1UukdZ2dMLoWQEYtPu5F9Tsbor6m5Ye8U2N
+tzhxaatXps/9Z2qV44n0BD6Zdry+8GJ+9/RNAE5ihz+28qcAuLa3hwn/JbWit9b+OvD7KSs//h1N
+InBY24/5wcs1fj7R4ibG0tR04FFhuEzlYDXa67jqy/O1F+NiUqP9VXn/ZIAbCLH49Uuf5w+1kMcF
+rN8SRB5NZWM3Awd3Jgttj+bMrgq1tNqgVXzm8zHHCw1kGSji1gWJpts+mrdItXhHMasZqs5pSTm+
+KlevhWK9uqBOhPC8vnPIpKHdfbIZZS0HxNEtMWWNp6myA6dkTV2V5x+cEaqe8/hiQakFWF9uCoLW
+nfvcPZLn/vW//4/4o6+Ka+ogN3xYyDO6qwsWT2Rui0T7KKYewYoGiGUhVusHjoaQ51pwpZ+TNOWa
+2aZe21hQToRQ5GYKVwevtU0diN2lnDzg7QfCNyr1gGx0A5pa+/EynjPTzljpZEz7GxIKfrF4yEN1
+5d2oJTx69czFdoRQbI72QIOvbOrzWPuNBpOKWj2jLfXm1N6EfKL2payg0EzE/OBDqDTIrLBkI1Wh
+tPCpaSfkWmo/93OCQoh2GNzgXHua3nTlcJ801wkXssZeNCDX0PQ8H4Gqja6fY1MlnJUQ7zqDSF2P
+TuJj+6oGjM4ZvSUh1gN7wofazeuz1+WQx51XcLW/6f0vv8vtvsx9LdinNb8qGjvFy5cl3pewhCV8
+F/Cp8AUa6+GV65S7Dqu1hO3WeZ+pNjKJywEXY+HA190OoVY2TkbCaV56bouoJwVNXDekWIik8EdP
+M/JNDUVNI45HolakQ7m/HacYrXE4D6AdCMVfqQyJxjc8msDZ22LEMz3hKEUNl1qzwb18wmQo1uLw
+4IcJPC2PVU6p1VhnVAKxU8P6qjzX9SyhJhyenDrMF1+Wa5SL5HbIUFuXZX7MSVstzoVP/0Ru/ODe
+lLTS8uoa9XV5r839sfx+K5gz8UQCOe/VTE60CvB4zGxLy4OlMrc0i9lONDgmsRQT4VyXZ0+41BoK
+nSAm6KiupPUOu36TSus92srQ+z6RTLaiOag6MvaOuX1fxnusrdXXEw/PaJj63KF51TZu3Ge6Lft6
+0HoVc0c8KVMtCbCWFYyeynx2XwxprUpS2WR4iZtrHYIa4ivd00h8wPzegOmRxhO03ydRUcKYlEYg
+OGeyAl/rTFTa2trNW88Sn8rMpa5ETXO8gKFW9B73roJ2F/hajbzurhIMtSdkVOBr67m0nlEOReSf
+lKL65ePVq74wuEWOmWlBlidzjnV/37ksngXtvR5qgx9rmark9Y3zPmeXovocfuaQ9UCSxvJLg9f7
+/2GWpA0rqlt98rtdzIl2xClgeCpINaszHp09AMBYeOM1eeG1A9nM7LJia12z3pIOx1qfbuY8Js1F
+nLuo+wy1AOdV8dC184hYq+qUjYJGJZvb6oUMr3oQVk/ItXJNHmpWHzVlIc8qx/Dffe0tAP7D3R43
+NUglvZ3QbWmQipYsr1YD2nPtA3h9H1/FwdH0mOlAYvxvqbegt7LLYEvm+aU3LykqESMvzie8q+7A
+89MFGx1VoTRQ6n0/fdbR6O0i5/v25PfocgNHoyLP04w33xJ7x+WmINjNhc+aZir2WhDuy9quVy1u
+3pCDEpJQ9zTVfKDBRvEZmeaMzLczanVxLmqHi0LW8EsfvMuToagjuaZkz5KS3VjXx6/oa6HUZPc6
+FILcwZnL9q4Qk6//lqgPv9fss9OVuV17eEk7FCIaX+uwpiJ4dV7hqvchLcXWNDyp2dYIOLcO4VC9
+QLZJNtRuX+kQVmX+USxl/d1WyKwvODlLM4JUA4Hqp7iKA+l7WhsxCPF/RCMlz48ptWmFOwvwVe1y
+pynZmvaieCoEbe+LBwSa+egcbuJO5fNaccH2bwn+BskZM/VQOXN552yx4KmvWaTHPj8SSc7EK/GP
+k7yglapSh+xy2Yp+CUtYwncBnwpJoUodhu82qRZ9lHnS6UcMVQ2Y+1D1hfLfO5qz2hCuubqpHPi9
+ORePhSO0u11KLcF2cQ6rytAmiwEBagTU7tJ5acitZiq6EVbDRHF7eJk61L2AzrqIifmVFbp2cDUm
+q6wNjnbmefetmlSEGJp9n0o7H987E8NYPIVHmv8/Di35RPsdVlOiVEu/d0ViGPaatJvyfqtBgdr9
+eNofMdZ8jUWZkqUid148Eg71SpTx21rH4PpWk/Q94Sq2eMxUDbNBFnGqJfFTlX684z7bj0UlGG9G
+NAOZe3G5YKhdlcMhVP2rnAC1nDdr6lqNi9Yn1VDjwaTB7fsi0dz7IEDbShJoAZx6HJLtaz3DsqSl
+vvTi/JxFJfdRPCXXTk0HO7IHD+Y1g7lwvovZGv5jsfz7UUioY3g7AV4m65gZwZXI9LgKBQmLW5hY
+St2dn5Yo48VmPUIN8JroPoVNj8VTLfte9LnU/qZJ3KCh4z0M5Nqel/NQ1a59F+q5BlZVbdyWxnIE
+llq9QJUW/Tl//ymR5sSY92oYisRWdY44+caJzsPnXAQSRlYiuSZ3Ljk9v6op4rJ4Xi4oyqccvyeI
+70896sOPxvV8O1hKCktYwhI+Asba78wI8UlAM/bsZ292KR45nBXCEl/0wKi+v9PscU+p6xnFM996
+rZWN5hVYjUzEGGotR5ZXNapmYh1Avy80w9HFgGY++oUPvnDbmJjC0aSpmUthhat+Tj2w//Hf/O9Z
+qJvuUf8d3j4Wv/nTt9+iUhvGpKgohlotSUOYe1GE48nvi7RkpNly4JHoPmyqaOO5Ic9aAGY5vnY4
+nswKUq0L4RtwVjQRqpC5ta9v8saasMTmznV+9Rf/KQDnns+NNXlg1Q5oqguw1RQJ7GLoE+aiT+/0
+LF2tUlXnC1K1DVTzkotM2GNfjVqjrCZTg3ASOTQ1I29rY4teV20U7ZixRi/mlezpOw/vMlAj2Uo1
+J2pLqPGjd77KqS9c86WDFqYrEpSnHLwVTDnT0PXkcoOpJ2vfaG8RaEjwXq/LYCrvN5hcZZeOmGni
+mk0LrIaNb+2tsrUlNR424yaDUkOXc5EO/O4e8aVII9GtDeILscVUYcIv/q2/BcC7GjX5U8+HnGsU
+qvd+jt0Szv3emxVrrkgKh7s+r35RqlClfZECpqOa9z6Uax8ULsVUOXscE1rN9ox9dhUHHK3udXrS
+Z6r9QlYCj9WuGIojmzIL5do4rbBavu3v/uNf/aq1VsJ9/wD4VKgPTlbRvD3il4sKtOZg34XrLVng
+kzDnpnZI8ooxk3M9sFpKzYnss65PVf7NCr5Vaan1IE9TS6klz1L113uOWHABcgpqrYE3ZPKMsNTf
+Ms/f1rn9pbU+84lkSY5+fcad978i911WOFrUwy2h1vLpthBxv7VuQcNkY2fBUEuiZVlK42oTAzmY
+bhDj+9rANAq40IzCvKy41EMY4GCPRM3xNDioc3ZJpykHLN5p8kRVmOZiRDIQZLxXPCYdxbq28qxi
+1CdzNVeDLpVWJbapg6fqmO9YWpEaxGL93a1xNeU8WVtjVy314cYKm7GMMY8bdDTM+fhSgomidEGk
+RUqcictcy+efJAXtQsTu3dE6D1rizbHHMoeztiHUMPUL9ynekRzi+8cnFNrcd8t5HifQmpaOqJWz
+SU2RaYBb4VA4KouTU+rBi1qrvKth0+VDIXq1/zaHSiCvzWp2FhqEtRvy4b4Qg8OxXLtfvs6TvhhE
+p5VP/47sf5ZmPHBlvNWojat1HlNXjNKD4gl3NXP3vD+nUtxzpgWJ5pWENqPQNPjJpTYbnuakV1nF
+YUiknpGqtcpKJHtii4TMLOMUlrCEJXwX8KmQFCbAL1N+5LtZBW9fynef3Yb1PeG2r9fXSW5o38Sh
+9i5onzM/F4Pb+k6GfyEiZ7A3I7+rxTt2zrjzDaGwj1piqHr7w4K7yhlmQ4u24kMZ2O8Ldhrw9BdF
+9Pvnt7/CyUAoccNJ6anfP274ROrWvKl9Ag9bK4zUDdd/mJPGV9WoDZ0V4bBdVWd6Bw7NhUgNsdtn
+dSSi+FF0gf9U7iucOVl1VTxCJazzmssV4Vy7w4zqqbzzk0VJ2tQErHKdAy22Oj8r9feMWI19nZUx
+9SPN6a9SOmMRtQ8/u4+nKtGsq81nspgdNQK23DbJlWW3uU8UqFswqxitaWZnLvEkeXEH+1C5meOR
+V5qsdDZloEbce9EZsZHiKi+vaMfo2ZjclTm3pmNOQnEXPj3KmHgaFdmp2V4THNhQ4/IiHTIbyhiX
+9QBfi7U+ri5Ya8oY5V5M+FD24dFQMjhD95BDjRZ1bk9YdLStXNakdVcMusfaSu6Xy/uMEuH+r49P
+6GhW5n2bs6mVn7/veErrsUiLX3kkUsWHHxScF1owaGSpNQvW4D1re9hcJAyMqNah1fbzTko2kd9H
++QzHEVzYby4YDWWMti1ZaELex4VPBVHAgimuFAeB0Bj8LXmx//wn/k0aQ0GEszznYEsLkVSCoHve
+LiNt/Hn9C5uMNHx2299k/LockO7eJq/9CRFLzQP57rY95Vd/9RsA/Ob9R3xwLBszMSXZH2BqaXkX
+vOPJARt5DdxSnus2AuJYEGE/7pBo5uYb2xLXXq81OVS9/WzvghdL0f1niWXPqk+7o01RAstYzent
+PKa9KhNqpz4VQtTqic+56s51Ry3absmp1j48zEqMelpSWzPZkrG3pimDphyQJ6q/Ps1qdrXxyvn5
+JmMrIq6tPLoa97DiBpTaQ/OgKym79bRi7UXxt+fjGZHaM+qypNZck1nxkLClHadaMkaz8qiuMgfN
+Pmsq4l6sVRjt89j74nU6KsZPd2X/3/vdSzIrz+oOCjo9iVn4gRcdTjTW/c/sf455Qz6PfVmrVulT
+qVpSjjr0tfnrWrjL5bnYCW5lX2RU/o7Ms6GequNLxlL6kLt1jxde/WG57/KUaFOI2lmhmaoHHeZv
+yxhfbhouSp37sGSm1aXb04CHZ+JdefdIvqsWBUa7O3lJTaB2Dc91aGg17iKqqbUydXWldlkfo8R9
+kXmY0VU9xw6uke/Pwy0OplfGqY8HS/VhCUtYwkfgUyEpGMB3oa5BCSY/em2Tv/QXfhKAw1deYqy9
+HRN/yvZMCwRuKPepK9ZviYcg8raJt7VmIG3WI7nGZYUN7bE4jcV49dLlIY0fkd8/G/we/yIQkfF3
+nx5zP9dqxuVHJRiA8WCdV1PhiO/Gt6EplP2llsfriUTCBa8kvKL9ExulRiluHVJrZeDP+FsUWt6t
+rmqMxgsEjnCDQVnQuNSiIfmcpnIjm8Q8p3MbNUt2Ko0QnKlxtZuw0VIjaQjDc7mvGRncRq7rHRFr
+IlivkDkep3M0IpqwqvmChnQv6jafeUl4xxrXiK+LtBEEItGEL4JvteTZSkGt4d/Wc0nHmvBVBYxT
+TVZSicAYB08DBNL1jBUt3mLyJre08U1nI6aTaYftY9mba1WLe3PhiM+1ApLWN6NQzYHMac/doUi0
+b6YnXLLv9tiIxDp/5/gCV+tJnGdNDrqqMtw+YX8ouHOqksLGbknnqYZK7yWYQoyZ/o5HfiT3rWk7
+t9khrGmRllb/EY4neFr5Y55TtbHlVjxXqrFSe4y2Yh9Hu26v1CVRW/bkIFnhPSvruZhMSbUVfSNT
+qapO0TITVH6GoxmjTBfUbVGPYm/MuPyoav7t4FNBFCxQGwfPh0gP0k/86C4vaX/Fab5g0141YF0n
+iGUxk5aGpIYL6lCCVeoyJ1Dx0u8sMEYs7tYb4GuJ7PaahkcnoB4k+p+fsKsW6TmWwalmNk5SLpUq
+BFc2h/wrfGjkUO1cZDhqkX9lf5M3vl+IQtcPSBJJ963HotfHu20a6mZN7XWCSDcrCIhVZFyov8Of
+DAk1hLcenlNrwRlvZvAa8n5rpxkzDXPtqbtqalfojQRxG9ev46sLNPFavHEpSBPtWZ6qvlvrO8dF
+StLUPp4Hm2wfCFKttgJa3ddk3TZjXE8IQKT1B914E6P2IDOLQD0YtY0wobx32O4Qn2kOSiii8ypz
+NtranOb+QzgUGX27m7O1InaHzw6v4a3K9b+thXkntxekpRD1rPcaz90ST0tzr0WiNpHAXcFRHX3U
+llUES8MAACAASURBVD3fDWKSrhz4va2EDy7lvYPTAVdJKPudivc1f6Kl9Tq3tgN2VrTEf9ylcypr
+G8Uv02xojU1NrT/4rYz8muDC+vVNXv9QA9yOa3ZU7Xpa14x7ssc/NpW5vRnXfE7dmm7D50/uimpW
+rXT58UzG/s1JH/dEVJPRVLuT9Rv8P+y9WYxtWXom9K09T2eOc2K+EXfKvDfHqkxXld0eyrjpboSh
+4cFqIRA0qAXipYUEUnfDE0KN1Dwg8BM8NKAWAjWmkZBo3GBjyu7G5Skrs6qyMvNm5p1jjjhx5rPn
+vRcP/3fClabsynIa+7YUS0rdyIhz9l57rbXX+ofv/76ndFH0XKPic1S5CZPAuLpy4FDT8/O2a/fh
+ul236/aZ9kJYCgYAR2s4sNGyZUe9u30TanUaJSeoqQzTbm0iJFUWWdFhuw3UhOWa6QK1L5kBVfag
+ItlJjWUMzc8YxCM4gY3NgVgjvnEfxYmccr0S2B7Ijn/y8Bn+5yFNYmIeRqaD6gkl15EjJJjEUkBf
+U9RF23A3Ka3WJJzXt+BSrqwReiCRNAqlYZIO3azls56Zw2mJuZ+pCkVCWno3Q8mIej7IMB8y1z0X
+EE8SJMgSsY6S+QRZykh2OsN31uTkfnnWR/KUBC6kC1+zbdyn3Pubt7uI9qXa0TEchBQyUUaFwiOb
+sZZ7GGYORbxIZaUgAxmMRg2XZm4d1kjb8izzU5m7fPwQQUKJ+14Hs4C056mFfCb3OHeewFvKJB9+
+U/qu7QI3vX0AwN79PvZvCq68PVhHMZPreVGFfEleikBOzCoOEZRy7xIpLM7vb41OMIzJO5m8j6or
+43lKsh83bcEk16azniFovy39rKZXkGafHI7DOyb2pmJhrT+2cKMp4xndBrZIKPPlssYxCW7s16gY
+fdFG4Urf87UAWy+L1RuZt1GlYpmoiyUuCbM/ovDPeHp4JZiUocI8IYVgvsQ6jQNb+ait70fb/PD2
+QmwKGvLCKVthQLWe46mJHZKHjkcG/AU5Az0Am1TsybgCSwsmORwNpwNVkp+vkcPQKx4YA7oiOShl
+1pVdwegQmLNsoH9LTP+jIENPiYvhbXpo/xMpne715GW8PCowjUjOUgPdkqXFL+2j7lDnzw+QH8tC
+cBuswksKVAE3qVzBpJCoMc9RWSQAoftgNRsIGL1HZaCmFoKathCSH3JhdzHjC/SAqStvbGKbQKCb
+noZiqs8KLHRYcj4txzjhalowe7HjODADlnWjgfETeQnbWy5qluw6tg87k8+XK9O5KmEQpVinGVSb
+cRIzhaJmIiYnUETV1Z5kbU49F6AL3KpDWKxBGWuFgASy3a0mjt8ThqTnGUl3UwNf36ZOYhwjY4n0
+4YOP0bspcZ50UVzVnShS39vtAUaX4nbUykVoSD/13j7S5/LSfzhb4P5I5mH3rsxdWQ3xYCX3PjKx
+wU2mTmywNAe1w8wQUtQLZrZ2m9hl+f3dzLtyKxoFsN4W908fUjls10ajT2Sqs42gsy/XrWuUJsd+
+GiJsyAEW7kp8LZ9GADkzvdxATDcurS0YrEEJygai1en5Odu1+3Ddrtt1+0x7ISwFQwGBA6wpBQzE
+7rGCHAe/IepA52GJrZuyS7a7CjphkIz07CVMKPIW1rYFRcWiclGgtlmJmCmogp8xVnLqJlQhO7TV
+MpGTm6B50Ieu5CSpevfwb3z5XQDAG1tyQj3MctyYipl5gDOkDBJ1kjkUpdGzsI3ck4i5r8XqMD0L
+yGQfLq1LgNgLFVioCEKpyQZd6QJVKj9n2fQKUGXaJhqkl79QGQzIKZacUOimGWJJARFUIfoUeHnV
+DzAPxHxuLGwcksYLFCZpf7mHm6+TK2AHcAu5Rm43ELKi0lF9wJKfyyEtBSuBTYpzOBolFZbg4Uo6
+3WkH0BdyvdXJHhkWcprty06JPs2Gcz/AKz2xKjxrD46z4k98T8ZKp1gE1I/cyjE2hOatbtQoPpAx
+2Nm1kGYD9ol8GidHWJZi5scxENWslC1mSCZihcxOzhEz+Hl+InP9Yze6sJlFWXZcTGbydyMswOJZ
+bDHqf5jZaBNj8ZpKYGyKq1iGPtq0Fr3wFgJiCApmmuyoxCE5MbudOdJYsk6m1cecNR8IahRjGc8N
+VknutVOMzmQszs0MNtF3RVUiT+XVnjZSRHQxPm+7thSu23W7bp9pL4iloOBYJho9B9v0BR+88xwP
+G3ICB+cVNsliZHY6qEKm8licYrgxVC3pNMMoAaYegQRIybATz1AnJCv15ZQ0KgcWVZJLH2iGgmOw
+t8dYUIhlNrHw2lclRdS5J2mz4//tf8H7lgQz9TD//Xx0OUDJgKE+/AQ5tXUD+vK+soG2/N11O1DM
+oSNzYNCi0cx71pdLaMIqbS9AoXg6jhVyErcGeQeTWOjKplTSdi8LTFb3WK8Rkri06FiIKFTyeHqC
+81zG8zaVqF9pW7jzys/JdScNmB6r/cIaDrEelu2hnlN/g3BmXdWAwZxt6kMHtCAKBQNyOhoqQMNl
+BZ/5VMaiyBGvinme5PC/zFSnbSMNZTyjmYnhSE7xMzIzDZSFZCJj38RXMWdRWXkZo2iKPz9PXkNN
+qb9pSpq6skTNKspFHGPBE72xtPA0ofJ2nOJbM4FN23OxKjrVGl6+eR8A4CzHOHkkf/fv3QUIYx8T
+rh6dZzhnHGEabeA+NRs22haM3k8DAJLCRGQSX/Mai8cqEwZTripXCJmKrhoFyrn0f3ppoGC8aUk0
+ZlPZMCMZe3dmIzNlPA2oq6JA5AbsJllsP2d7ITYF29DYdjXutjzUVCMKLAcnjyUo9bgM8QYlvsM1
+Dxbp1masevTOIlh7jN4nJoqANNxnGobPyH+SI++Q9ptkIvVGG4osym5TYUCo8dJooM8NonU/QsxK
+1qAhJbZ2+EvwZqwyLBPUxNw3ggS1I99TOoMd0iVgcHHhO2guVzRmNkxiE6oIsBlItYljsJoK2YoZ
++mwOI5BNr65iGIRPd+MMfkmQ0eIpACBWBaxUzEs3UYjaZKiGjZRYj74RYFjIGEwpJrPeXcOA68ju
+T3HMuoX2RynKXUby6xAF4cPW8Yhj2ERGvEXqhKhJemJFLVgrtWDbg6K57toUTqmXaHDs7TUf9UoH
+0qiQaZKFqBx7pL6/x2Df1C0xIOfgJ/Pfg2nIc1yMAZcVk5PdJTYIvgJBVj5KdFtUumq3MOH9yvkc
+2JYNSVcZnlMg9mwhh8JHlyUuIfdYC3bQJBO453swPR5UZIm+RInmSJ6zc6hhbpPSrX+BtrsqI1co
+KFjsXsrmZ1+WiKiclW+kcJWMUTFP0WO597LpIClk8/IpsjNo53hzIcHVPDvAR8cyRnlRwCHnZVEm
+QP5Dinn+QPvC7oNSylRKvaeU+of8/5tKqd9RSj1USv1PSqkfDXh93a7bdfszbX8SlsK/B+AjrIgC
+gP8MwH+htf77Sqn/GsBfA/Bf/VEXUDBgmy4M5WElRflodI4HF2KK7vULPB7LqXNnVCCn2Tk9Jnvv
+doE+q9A8u4X4WI72slnAol5EVgDjQzFFY6oo++fPUQVyGtvP1hDsyM97RY7qnpj+3tpNRKmcMEe5
+nPKLmYdJKCdpnSu4Mc1nYwfjjHRkyxydkpRn1JCwGjOku3KqDiofBk33cpzAYiWeSZh3rdqoKU+u
+BvdhVGJt2J6HlAFKr5kjZKFNvyXfv/Bt7O+JteKsDeCactJqDwhppHzveIxTYi7uR2IdLYoBMuoS
+5lWOxlhM7WTTRKtFerAqQBUzWEd+gMX8DPWhnGZnk2M4tCSayyGMlgRYndkcNtOvvktBnq0tuOSb
+6HgDaHIvPLIeo6wEpWi3HSw9mVczoLr2ugc/kOe74dzGu59IuriTLfCUrNI3TyOcMg23wX/XLAd2
+UwYgsH2gJiN0+03sU2Fa2b+JrVgsnfdJwbbeb6OiCMvleIya5vpyWSKr5FlmTVqsiUJKAp/KbyBr
+y/o8+8DEdCCFVrOD9Cq4nXdkrBpw4bGIzU03Yd4mN0gaYy7TjsfDxZXrNp1IHxdWgClT8aEbQFvE
+ZGRAQgo9u2jAofX9edsX1ZLcAfDzAP5TAP8+peR+DsC/yo/8PQD/MX7IpmBYGsFaCQc5HrM6bzcp
+4fPlH6UGUppAT56dYDKSkbowZIHaZz28ThOwM7gL3xbTr1qWmLPy8eHxAmenMphPyb+XDJfodWUy
+/pk764D/GgCg297Hmi3MuMpWUInkt/da+3IPBewSojo2JqioO+lElwjImfgIFo7H9H1rWZiBMUf0
+SMy9aruNjZq55AZQaelTzHLx6WyJ0YwbWmmhSdGTpj1Ac2Wu97Zw/1VZpB9+csp7GGhvSt86nbuI
++rLA9hMf7+WMNZQeHJv+NRexF0aoSCkPp0ZCt8JOIpDaEYZvQrXow5MzU5/kGJIUZFFOkM3kRYm8
+AQy6eY6RoWQOveLO1A3WYHrMBvk5TFPG7fnpED99V+C8bvUldEmseHNb/u61A7x1T+bp1RtbeOmO
+uAejyQn2HklH17suKsrZO7yH260RkUk71cVV1qV2p7ixLZtX1N3Cb38k43xgyXprKg+WL79bzjOM
+F/KyXZ5eoGPIGFkUzz23auwR9/LqPQ2TMaPn7iUefUsyad96WmCwRRbrD+Uc/fGXtnHTEbDYa18K
+4AasUdE7eMI4Sf30Yzw+FNemcMgY7XrYYmzkedNFaMrGO1OTK4BeYpZolJzAz9m+qPvwXwL4G/h9
+gqIegInWelWBcQhg+wd9USn17yil3lFKvVNUf/aUcNftul03aX9sS0Ep9S8AONdaf0sp9bM/6ve/
+X4q+6VlaZz6MhgmHiLmJVihd2e3vt31sm2JyjatneJ7Ijv/hA7EInPAQBk+BjdkFfEtOj83tCOMj
+sQ4ePHmGT0+pukzevu5aG7pBaTa7gTVGb12rRmUItVU+2YTFgiZlscqyynAayOnRGmmAAcM8bmNG
+zsT18zPMGOQMaY2oQsNllsG0J8iogWAVXZiB/LyYygl8dP4U44l8Ni6W2F6XQGvQNJAHcnr6VQPq
+SAqGhlN5tpFh4vJc+l6bC2BVkdfJYTAQdZEsME/pdpAG7MmHD1BAIutq3MOTXCyC3RZwYyABs2bL
+h1Ziiq7RWTxPpkhIFffx6SXahVzveDHDFqXl4sYeHBKElMcylun4EfyenI5+aUBTsu40TvERi3nc
+y9/Fs+dipX2sZVy+bHQROBRD6UTwa3m+nupg61WepG6EKmaQ05FxawUu4lisqYPDI/z6XNyO4nSO
+FrUqLqoai3P5XsKiu6rKkZNLclElSEfMZngh4mqFORHrYVcrTA1Zs8tZF3s3JGDYcoDwq5Jx2Hrb
+QcbK1f5bLvuWwyWDdXaxjeaO9LkqCkR0Cbob6+i2qDd6Y1++FwVYkufT+/ZTTA/+EQDgZKqwZGB3
+MtU4nf5olsIXFZj9y0qpfx4CVWkC+EUAbaWURWthB8DRD+2EWaPXzhCETcxYervn27jL4oBmEODh
+kJLpIxsfnwooKOODbxtdZDLfeHZ2gNd/SjYIjTa61G685TpwCIp5v5CJa3ZC3HGkkrHVsNEO3gIA
+tPc2YXCxpd5v4fzZPwYAfJDJC//yy1uYfEfcnIf2EXaasqDX+oBPH/hXPznBdyf0NRtSl7Db3cAb
+N4WQxDirgXXyHPY18nPCh7mYfWWgcuUlHk0iPDyR3y/GI+wREr27uYHXviym9OZv/gYAYL7IkV3K
+tFbjEfY2WcqrtzE0xSzfcJqYavl5uZTxfufwHEczMdH99gJzUOR0tIEO2an8wMLamoxdRV5DUy9g
+sJzYuVjgm89lnoKDKV7pyM5x784QHVLXO5a8bE6soFryovtGHykj6+ZoCn0gk3k46eF1pqiDlDqe
+1RInT0mGo7+LNlYCPzFaifQt7JswGzKeLjNUykxgcG4q4wzGx2KWf3eYwGYJc1FniFcajA15Ydfa
+fSQbVIg69TBiZuTx+Aw3WBNSUanMsWv85S1Zb6+++SqcHfn7w1/+FO9/R17MoTtGvy/uyktvSqqz
+o2uUpfTBeuUcupRDzaiqq813OWkjoUvkErzmVhZsplnvbFv46V253uPhb2PKFGecl3jEFO7nbX9s
+90Fr/R9qrXe01vsA/hUA/7fW+l8D8A0Av8CPXUvRX7fr9k9Z+/8Dp/A3Afx9pdTfBvAegP/mh36j
+NqBjH/OwgSpZSVx52NmXbXJzvQc3o+hJXuINyqFPGOh5pRlhxCzD2WSMwZmcLnf6XSTkBtxPFzCZ
+Dbh9U647rE28+rpcN6tCtMmlWBkxkElgaBb34Bk/BQB4c0uCmSf2LrzbIhU3eBCgRzDKev8NpKGc
+QLfu3YJD+fh11vQbTRtbNylSUudYkULWh0v4V4VL8vT9cAe6lv7e6poYxvKz37aQE3ClzAgLtcIC
+yDN1WgV6oYxLYgANsjyXVg+9QE70D6oYgUsZeFbybfZa2N2iibs5wJJ091u6B5804q7bhyYFXoNZ
+G6e/jbVum30/RJ/ZlcvUwGsvy3iZ1gwd0sxlc8KLO2tXpC7Ki+AQW3I5LvFL3xOXoec+xWRTvnfj
+trgwN1s+mjfEjev5LVg22aXT51BkqK5qBbBIqyKk3UwaaPN+r269jvM35H76WY1xLq6XOengXVtg
+0yGVmmPDRGlR3zRMoHiiu8qD4ZEWsCFWwMY0AFySz3T3rtilF24DAcflrdY+3E2Rkh+8LM+0TJ7j
+jDqfwaFGm+zZ2srh9WS99KwpPIOwaSXP5jnAUSZux3q0C/+OFI+5HzfhW/JMdqqQJ9TV/JztT2RT
+0Fr/OoBf58+PAXz1T+K61+26Xbc//fZCIBorXWOWLxA9P8SUQSS/leO+loDaztZrWKN/ahg25oS+
+pqzjLwZz7Czls6dPDXTpFdUW4K0REuzcw+BCTqlgXXbim00fW5BTxw2XsGomTcwQGLLs1/hNONZT
+AEBky173yr230aWP+D/2vwl7l3wKy0Ps7Ukqs/XTXXhEzRkrBqW4QhlIMEznCtWIcmxdE5ontz3m
+ceYPsOWKX5j1ZrhvSn9KI4dLNeesOsF8Rj+ZKcT0osaEGhjx4RhgmfXL0wTv0sK60/DAeBm6hDD/
+zN3beP1VQWyaVhsLkrjGSYoGU2sBXPgrNWaWiwcdBzZpzpxGF/2WnFBhO4RH6jnfjlESpl2wOGxd
+l8hZSKUwRrWQU16HBVoLCUa+mwM9QrZDQ07g3d0vY2tTmJn8qAmsBIEKCwaDv4bjID9a5VE5B2EF
+k2jK0BzgZ3ga322eIyZq8vnZUzTekf797kKuNZxmyEtRK18kgRS1AVizAmwQer0S/XnsVhhUEmv6
+ytkThG+KJXDv7du4f0mNks012OQcVEw3NoINGFPGksIcBDHCsAG32JfxcuZQSta1HclY5MsJdgIZ
+q8fDQ1RjCWhvbX+MRw+ZfjWX8FdajJ+zvRibQgksxgrvqBLnDgEYcOB8VRaK51roRPLyVsqAZaw4
+EuTvi3qKkS0BIKMTodcU86wyNDxDNot2Z4I+FaFBiuxMObBpfjvNfegGWXJhIDEkPjp9v4NgXQKF
+rTYrGO0LHK7LZDSepJgwupsqC5jIi95xNDRkkixTJkW1I5AiArPqAgtDXjyVR7AV6/RZq6Dc0ZW5
+2LR6sJwO+zZDRf695fgc46On8j2+YFGjxvhSsg/DfIaC1Yzv6iEeXcpns2V6JTLSbMoYvrSzh+76
+DY63h8ZYXpQJjpFQRt3sGvCUuFsmo+LKCGCvyUO1Wzto+LJhl1ULrsfgsGrCnMnPHrMaS9OGT9KQ
+2fAci1z6MZ8aeM4U9Xld4pJQafMVugaxgo7lxYsTjTCQOXNmTdgM/NWVgWpDxjxNCEKrEhgEHlmd
+HK1cnjXo9LGkCvl25KBkoPHwfQlmn43OUbMqd5hM0KHP462FmJ/IOkxIkGLkFR6S5m2spvAeyxj1
+urdQrsmGpIpLWKyJKVl9Op+OEZfyva7xCupU8DemiqCUrDMTPZir2oYVHL1IYfVlTXvDU7T0x/L3
+qoHakqxbuSxxoX+00OF1leR1u27X7TPthbAUCqVxojKM8xwLouN+zY9x81QCTndu37+iLgs7JXxF
+5BZTkul7BxjTtOpZQOVIvr0ubqA2xPS1kwya0E+TO3SjXcEzyc1gXsK8YJlG7xMAclK8v/Xf46iQ
+z7c/EqvjKxs/i41cTodCfw82r9GKx3AZdDJzHzqS08pkQFQbGaoznp7LFD5PYLuYwHTEKnAqVhaO
+Z1D2SgsggEnMg6o1siXlz47HQCYW1OuhyNid+ya6hZzy8eNTmAMZi2w0wEEipr2LABH7H7Gox6p8
+tAiVth0XBpmZ5r8+Q92l7F3Yht1gMdlc+ma0TTgLl8+Xw6LL4zqAYlWqUS+gVwxBpoyh6RwgtmXO
+RgczPJhT5Xu9wIVMOwoN/J9ERQ7mUhz39uBtWKWcuk6g4RSEh0cJFPUpSseAnjKl6qxUxXswCBu3
+cg81ZfEMJ4RFnMkkuMQaBTwDwrir0kfJdbbV8rHeFFdpsN9Gj9Wx2RPpz6GeYF3L2HfPXYSvyc8G
+THgr19RwUBEtqUhmq+IUm3SVXWsC0xKYt2GaMGj613UGrJi4AumvoRQ0C7iUlaHpi1t1b3+CXWpE
+/MbHH2C0uvfnbC/EplBXQDw3YJgKoFDsa4UH0yIFuPaQ0wRylhYqjy4GiUkuI4V6yUXgbcJrUhWq
+cAFyFFauj5r5facp5pvtKYBqRGq4QO6Jabw4nmBMDsPB8C/iEcuS/9z9nwAAxEsTJ2uySIu6RpSw
+DkBto8UyXBU2YUxJwzYgG3CeQ7Wlb2UxRiOUBaa0BbdLEBXpvLCcQ7Mk12rfhpHyWqpEFsv9kjLB
+aSLkI8f0+7tOipT3PT6eIL6QF/YwrjA/Y7ymjJE1ZSFvdTkW/RZQsOS8paBmLDlve1AEJ9WOjdIV
+bIEqV6W5MapKNjSUFQyHifUyhSLtXT0ZoXYobpsKPmIOYEqKtcexwngh17UnEe425UX+OK/xV3fE
+BXspkrJ1U/VgMGNiOAZg0gHPEhRNCgiXM+Qk2jFoci/SBTx79YIZUMSqmIGBopB+LOMl0oruDWSe
+JtUhUkVxliDCy1vimjbW3kJ68B0AwMOW4ACSWQ4rIYQ+T1FeiAnv7N+DoltZ+wU0y7lzn4fafAaf
+a9JVHpRLN8hIURGHkc8nsNvUUyWJTq1T5FrGVasQ9tpD6Xuxhe6mrIH+QYSzFXx9VbX6Q9q1+3Dd
+rtt1+0x7ISwFKKB2auhcgRQCOPcLGKzESRZL5A0xmYuwAdB0rWMSUxQ1DDCYVzRRjHkK+AoFsxlh
+2YFLdJwuZYfWtQOTlGe64yNnwLCeXcK8EnV5in+5JadDy5Sdug48vKnfBAB8I3gPE55sY+Mhtmtq
+JY7PYJKTwJqTCyHqIb+QIGARjWCcye+bb4fInoll4jfkpHp0kKHXk+mJj4+giJEwCucqqHiRXmAZ
+M1vRl5Ph8GGBj45Eo9D5xMLNu9KH8ZMhstVJYVWIMprMM7mvjhcwWD2qYhtkeUNRlrDIB4GFBast
+J1pekCbNM2Dx1DW8CiWFWkzXh17yenaEjBmFtJa+p5XG9FROubNygtOZjOHX3goxpshKbeZQXWJA
+yIhd6hMkQ0qyr3WhGXXLiwwpqfCWlyMMGfAzPXl+07KRHxILYhvY3KbbMeujIDozmy1Qsjp0TAo2
+O3ex4HVzZeNiS37+klPjgMVkdydyrV+rMngkDZ4WR9ggHL2efQCwcKle1Ki5yGMGX0s3h22R5q3b
+hSaBR+lMME0pC2gWcCbiKtgFkZSFg9qgtWFcoFL7AID7/Rl++9uyXg7SfwyHgVuSEP7Q9mJsChow
+ShOBBZQUNblveBgz5fPRx0fwlxS62N6GS98wm8gEeNrEfCKm77JXwGEprKnmsOnj1n4MxDTjbQF8
+pHEIk2o81qIH5dM0LjZgs/Lxfv9fR3RLNgM48gKef/sh3vfFVKuNHAvCip3EwSKRvvnmCFYlC7qo
+SU8e26hdMZ/b+X2YL1NLsd6Gvy9mcpELWKV58imqWlyUKluDZqVbpRykGTe1UYZ8KddLjmVRHZ9c
+4NGpvJgVHIQcq8dZjIpsSxUUHrkk3ngspvOb6++gzdhBs9VAdjjlZw8RX0iEu9o6hxFLebUdsMrQ
+KFFTr7G6rABWeyr0UREerVMbjiMbR7KU2IG+vICmrqaVVvA9eVFc+6u48zV5MTeTGrsGmaPIOn3y
+aQmLsHHbMWE58vsscWCwPiIZ18jIFRlPZR7tfAjy4mCjsYE5Kyrt9QTVnK9LPgbDQ2iQ//PcjFEo
+ucc0maCyJaaSdR2krAN82hb3qp8ppNwg02mMnGlIb+ABuTxfkT6F5qa+qlQNk12YfZ6GSYSSGZW6
+iKAzuV8xWsBoybuhUrpl+RJ5ulIqasIpJQ72zuEE734k63OUlCh/xHrDa/fhul236/aZ9kJYCqYB
+tP0ahTLhcYeerFl4lRp/lZ/jlDvmmjmASTInl3JY3mSOVlt2ZV3OYBosiEoBtyMnsFPXMLijqzP5
+vtE1YLCqTXdquJTvXtpr6NNsa9zagclIrkFiErfdRvNCCqnK7B9Bs3gmcxcwm7L7GzpERbNNrfQd
+jBmCjHoDURvRFouKmndhKAKZpmJStu6MMWLkWS1r1OSlNOYpLJ6IyzJGlogLMifAyjDNK73GuVFj
+GrJ6cgGkrGg/VSZ6zMTMa7nfh5cLfIkVjmZvAEVz1v+tHeS1jJsezpF3xIJQK37FyIeRERMQxFAU
+zqnMBUihAK1i1KXczyHpixUVCJlZuNleRzyQMbzTaUFpsUzWTo6gIpmrDUv6YNtT6IQaElkK0yal
+maPgMFC8FdQYTIgXp1bC8uQcmkrbyqyhG8w+zEsUDFw6toW7fbn21pFYeePzElUuay/w2liQgTpP
+Z1jboiL0iZjqHyQnWA9YxNdzUa3Tgorzq/GylIPCFzfAiuV7rqFgch5yI4A5ZBC3AJyKFbrrrUtP
+ggAAIABJREFUHkwK44DBU5XWAN3mubHAs1KsuNHBr6HPStOjWv9/tFB/WHsxNgUoNGsb5sCGS7r0
+t9Y3kcZMvRQxjlki2h/E6A9kYU05WWlXAyQlbbe2YXPyvU4T7kriPahRkgfPalM4xouuiCCwyJAN
+KJDy/LswKHcfFvdghaQ1Jw15OU1x0BUT1o8MBNQtnJfbmCTyJnTdCKaiOzKWYV40DczekT4/e/jL
+6Oby9852HwYp6kPqT06jGv4q1fdja2jR/K6cJeYs5W2vrcG7KWg74zuyGB/WC5Q1NyPHhslFvNGo
+UbNU+55v4HIVjyGaKktynFFcx0ocDFmV+mR2gLig8E15Az6pgIJ1WdCR4cEip6C2c5hNZiJUhiqV
+8czKBRxmfsqmjLjjA+MVonO9j1caMk+z8xwJ4wA3dzrQtfxcVdywF0Pkjrxs9VhBN8S101EL9gr4
+ZY5hd2him0SxdtpIbdZdZAukE3kmw96BWTOuoh08N2SuCmp3XtQLLGlQF1YCj0jHi+MG+mReetqR
+tO9yrHDJeNXpJeCfyAtdej2EHRLzjkrkkO9NL6UPE3WI9qlsaFXjCbJa+tDwGihypkhbARRrTFay
+BYV1hpzvyMU0xEfxNwAAv/veUySJ9OOPQ1Vy7T5ct+t23T7TXgxLwQRabWCjsHG8J6djmmookqGc
+VTO4tpw0L7+8g9wguQVl2LNFDcXf6aSGXVAyzCxQuHIKYGhdacrXXdIz2+FVhLwwl1g+FrP1XE2w
+XQi3gll8gHop5rheSGbBBPB1ioX8yhJIadpfXo6xty6ZijI0UK8i7YX0Yf5gig8LyQw8zk6QM1A6
+cI7w4VPZ2X/yDfls49DHjS8JV0J5/ACa+pCu7cCiaZhZFQKfNGUNBhePOrCackLdaTewThDSxsKG
+vUkq8rwBVUnAL6dM+afzDCMG89qTIySEEh8ZJTrOyj2aIuBpvGRlIZRCSHZhyzJRrYlVVWflVZWn
+ijNUfVpntVgE9WGBZEGehn6OG7aM2//z4UfoElz2uLGNAaHLoSH38JXG5Yg1DuYc9Vye1UuWSF2x
+QrJDA1ZLfu7SKtSNDE3yFMymNspExjlTLtKurJ2WlaNP6424K+w0QpiUzeu1+ljMZFyG3/sneGlX
+vvcT5+LC/LcYYkKw1GE6x/qlnOzurQWSTH42vBjJJYOmFkltoDCPTjkWLsoTeb5Z6gApMQa2Qm1R
+8dohACzwoR4LFmJeHuDBQ3JPVDni5Pf1I1dFAZ/XaLi2FK7bdbtun2kvhKWgYMBQPsL9ECGDh3aq
+8d2VvNtwhkUh+9ydpw/R2JRdt7yQXXQSD2HNmHdunsGm9sJ8eQKnIddzMEXAAJYinNdMxigZwEoW
+Hp49egcAsBiYUFuCtS2f/CQOvvt3AQCDviAazZ2Xcf4l6VrneR8hZb5Cx4VHKTDXWoNDXgNFhGEc
+NHHPFpXkW2//OHYj2fEb4T6Wj8SCqBkPOHnvQ+RkWzo35tjoEZuRFfBd8eGbXgO9WO4RduTvUXOG
+Hsdio7eOmEjI1i3gLqs19eUUxwmnPpO/n4yO8ITK1TtqC8VcTvT+RhPBXO7X3nChCDHWF3JqJapA
+6RIXYWzC4HkUuGtwPOqVmX2ACEr9RNJm5+kHeHJOpORBjZ96W+ZmaI+gbbJFVQlaPnVA1uX75plG
+lsnpqB0XDgQ3kS0SnE/Earh4eoaWJ5ZM3RIRlpl5AHcoUPDD0TOELbG85hsLbPgyLvG8QhHI9Xo9
+GdcbpYHclbPz48kpnn8s6eyzcYy/8IZYjkdfkvjLa59olNTtiB0P1YLaE4t12G1WsdohljERiUux
+fqrmOgZbEhDtBPuISrJiZQukM6GNm5wPUYzk87ublAWMQnj3JYYTvzvHBVmY5osauv59u4CQE3xe
+sPMLsSlYpsZas4YeWwjI2mw1PTRP5KU/GeV4ynzsb33rMbqMON/YlTJXPw3Q7slkRnEBq0NsQraL
+HnEPUdBGdYO54CUVncwE85GYWcf5EaqpXOOtVwFrUzaF991/F//JN4R448Nv/O8AgN/4z38FOx0p
+kb4VvYu5L3379GiCtU0xL+2Ff1Wq6wZSkdfbPcfoU0b1HyyRBEL7/XT0bXz4nd+RflIDcNyssdWU
+F6X/8j04DHJWoULWYk3FpQWTzPpZLM+2kbrICY8uzCFeJlOxW0SYZTQkI4XugbgbJ9x3D2ONb/+W
+EMu8Gt6EuUF+wdMOfIrPqMsKaSYLNmXefXvLQRTKC6b2+6iGVJYyK2jWV2DNAVLKwAcS1f/2dxI8
+pineqBr4aFM2i9AwkR2RX7CcodkgNTyrMms/REmqPFN50GSothoevGNx6bZ9E7Mn0o9HnlDpPX6S
+Ys2QcdORA91jZmfRQX0igLLKGuFTZnbcjvT91vptXBKO/P63vofnqfT503KB5u2fBwB8iQze8B/i
+zBS3JH04x2MyEZquB5eHhR+EyAd8qb9F+PTpGMdn4j6MgxEKMnq3XQcWK4GdtgltklyFDNcaBsZK
+NrrT7x0gfS7PoXX9GVfhR6t8uHYfrtt1u25/oL0QloJRm/DjJmy/xIK52PNyhnRGFl3DgKYFcTgu
+8fRjMV1jws/6aEGzUAcAlnPm1e0KGZl6PXcLjlppHTAYVvsYXciJOV7mSMg4vDG9C/09MQk3Gl/H
+5COxLP7GW7JDzx9dYml+IH1vn6NLc93tmzhleitKWrBYrGMYlEFDAX1Bd8UsYaZiMhrWA9ThPgBg
+5yWZkt55jf4G8Q1VhMITI3AxyZEzkTo/miJfyom2ulf/VhtnR1SMTgfwl5Sj82qUJYOqqUKbIikj
+agwU8wTPR3IaP3jwGM1aTrZg4qNBERVlOHBbpIUrySuw0YftiYVlWhMsif7LK6AmY7S1rLFciMk/
+y2QspssYC1YyLkwT5om4Gt6wwOVSTP+pthEdMD3ny9gj1ViSpKQFC/M506iVBYsupo5KWEznHlcy
+p5OljbIlvwvLCD6DtfF8jFlO6rZMYz2UlHBMLos7+wOc/J6c+G39FGkpFmRoulCX4q4cUB9zpsdY
+kh3bMRXOqFAdnih41AxpVD5qzoNNCkGr1UDoCpI1VhewSDZsehkcBnYNZ446F1cqp2p1vhjjg+9I
+sPL40QkSjothWGi4Mg/zTMMk/0hVf75Qo9L6z15z4daNG/pv/wd/E2p4gu+eiKn+eDaCcSamaqdt
+oheyXLbwAUKXHcqiLy9TzFyZWB8+uhQ/9eHAajH/fTLDmKYhElmAuWmhpLKU3bDgkH/QdFo4O5XB
+PtU1HE40GrL4L5+e4MSTzeYv/MRLuPPnhEX3p9e+jt1bsslMn9lo0W8/eCqugdpaIj2RRep7PcTU
+OQz8cxx+T16aERfrw7MzOHymVqeHe7ek1uLOj/2lK5M6tXzUfLkfjZmP325ggxmFRquF3/0VcVHq
+fIoPzoTD7zSfAUdCyLHmyhh++nSB+QErNdUYDvEibp3AoIT9+k4fN9ryoufnzJVXBh6dSywiLjS0
+4ovQ6OPH7/0YAGDv1quo+gSa0e1aliYGL4nZfVOZCDlP//Y/9xO4JAfhZkuhCGVOmpy62pzj6Jw+
+z1IjJeu0ClyYK9ET10GDY9SNZK2MTxIsaEcfz5fQ5QryrVHWLEvfdgFBByMOZGNaDoGMdR5VAawM
+cx/AWzdl4xiReOW1nQDWujzH5tSB35UD4uIMCEmnFM/mSBvcnFf1FYEDn3EU2y4wZEl17ZsoCFMP
+rBLzKbNqFgWLXRs3O3JgLUoN05efTa+L1//ZHwcAfG33NpobrwAAtl55/Vtaa5mUP6Jduw/X7bpd
+t8+0F8J9AErAOMO5cYijZ2KSHR4+RMTddas/wBsNVhpurkEThwBCdS/86VVi2YMJi9BlyzTgVPIZ
+37VQrH6fyU479BTCWn4uAx9tosdiY4Lal92/l8eISxmmlEQuz8IK7URcmHvzV1EM5Pdh8QTlWIJu
+Xf8BaktO2A5dAtO9DZOciPBvwOEplxi7MLYFmZg8ewoAaJlzmKyxD9zyigvCyp5g2pQKTldnmB8w
+Uj2T78X1LdzpC0kHbAUVyXhO0jPkH8qJ/tHR72IzJ8Jwl9H2OL+iFWuZGq1NsbZ6YQ+WJWN/t92E
+Se0My5WA2ydPnsEs5bOZFSMei+U1zIbYPZBj95Negr2ZQHANJRZYZdkoHUnhvLp9HysZ4mM7R5CS
+53GxhpNKLKjJiC6MlaNMxFK4LDVcnpq9UGMrkL45nTZuNEjlRyZtfxDjkwM58du2f8VWbc1NOKSR
+6MHF6J7c5/feFXflPHsX+gfAAhMAxyv8AhGtg2oNKbk+iqWNjCzKvpHggi7r0KxgM2Pkcj2FDQ8d
+nzyfXhM9FqvNTQ+sy4PdjdBlgeCjuazpbp1jTBfTPo9xeSyZkUsM4bwr3/vJjW247VX+4fO1F2JT
+qHSNabXEk987wScPJQUz0iZur8mL+WY4wEv3xUSP2luwHfGpFhNZ2BfeY0ynnIAww5T4e1UtIUp2
+QKcxRZxRLGSdIA8dImKUOS0tOCF1B9Nd1JH4kQfHGqFNoRb69eFoiRnXyTcnF/h55+cAAHvBTYB+
+HaIWNK3ZZsFosdmGcfOe9M0KoFzpRzWq0N6TZ4ooLf+lW0eoKVVudUoYffE5w4aLguXOqgVEtkTc
+7zmS6lx8MsVyXRZPrzBgM6UX/18P8NsPvylju3Dx5r4syNsEb/m7fby+kIW5t9+Ay5qBxu4WPFc2
+N98HYlYfTlyZJ2/ewEZDNrTTZ0scNeWFPitbcCigGp750AOJkve8r8l10x6ydyTiftzcxF7K+Mls
+gRHt/Mw7h0rFRH+pzzTeRYCYrEle5eIX7ks/txvb8Pe4BuwNbO7L70vGC+rkAG+TsDfPLjA5pvhv
+UGI2lnk4tz7FYiTPNd9ghefjP9y9NqcyFkMSAH0yX2Krks3vS7s50rG4vEfpITCVz9zAEsVc+qlX
+VZt5hJ/8mhwmVdyFpVgzkW1j97Zs2gotqEj6PBvRzW0cYPY96fvx/Wf4Frkk68szWEesRxlOYM5/
+tPzDF3IflFJtpdQ/UEo9UEp9pJT6CaVUVyn1q0qpT/lv54vc47pdt+v2p9u+qKXwiwD+D631Lyil
+HAABgP8IwK9prf+OUupvAfhbEIGYP7RVZYH56QW+M36Ic2qx96oS+3tySgxubiMcyEnpZSGMhuyO
+BjkHQ38N9kRMQzvqYDaTYF/pRnAYzCkbJvJyRW8mJ992VsP0ZTc/SXK4PTnRvVmFJdWRq2oGPZMT
+u5lK3/oDhYRWzFf/yk/h/onYvuXXbaSP5NREUsE7oBTYV8TcNwoLZsScdqGBgFmQ+RwOtSLX6FKY
+4dtIKYxjdTZRU07OtmwsZpKPbu+9dgUWqljMdbmrEfRk3DLUaJI9+WjtCAbH8HbxDJt3BUIdkWps
+q9OHT3Ga3a/uQa94J7t3UK+8NScDTo45zmLxOPZTbC7lWtr/BO2pBBRvJBluOmLFfFqfY7MjJ6gf
+00p74wyXBQu+tjauiEwMo0IREGfRaiOiZZIwX39cLqAJO+/kKbb2xMJo3ungZl+sJTVTCO7KOKec
+f8v5Ghos8irMNk4fiStVqTWcsFAumkf46Lflem/PhMviGxj/ftHcH2iKdPeGI5/wNxuImA2qdjs4
+PhNX6cE4RzAh/qZlYdSU51sn52JrqdGIZI0lThMdT+bPToHWbXE3q8yH05Uxb+4Rb5Ftw7SFEs6a
+38PkhgSPy3YDOpBxnvUGyIz4D3mCH9y+iMBsC8DPAPg3AUBrnQPIlVL/EoCf5cf+HkQk5o/eFPIa
+4+MF1ibAkDUOnY6Dr2yKSfXyxm04IdmUnDE0ETcuq9Sy0oK3SbaaqokBNRAuqhwFtQBQhohslld7
+5AssKtRUZmprE4Epk+w4LuaUj1+z+zhtUEGIsYzOsInXtuRlGmx00d6hEtLwPdiUeNK5gnGPZn7M
+ku1WBK35OxPQZBuCa8IgsanTk0WgtI+oIZtJmSwBIuKQFWg2GKOIbTQHsqnVhbzwe/kYdcGKS9+B
+MmSz+Tn3ZaQ7Mm69mwZeY3R+qyMLTbc9tNeZwfB7MF3ZFGEVsByyHk1r2JQ7VxYrPLvbMCzZhL+8
+LPGYNRideI6U6d5Nt0AjlUV/+035N8A2tCfuQxbPYQZkP5prdMjSm3ccRLZsHHsc+4Ht4RnL67+y
+56BpiSG60wmvVnN414fi5mRTpwFVAZeRej0x0OnJNY4WR3BZxaqqPv5iKC/cgy2Z848nYxz+IdZ3
+HXO9EFSUr3totWQzWrPn8MjHuTzx0F2X+eu6/tXYK5b4v/q6By9nDUsvghOxZiJKoHKme30DNWUA
+THJfamWi6chmm7We4/VXZVNcO5xj0ZX7tYohdLL2gx/gD2lfxH24CeACwH+nlHpPKfV3lVIhgHWt
+9Qk/cwpg/Qd9+ful6GMyCV2363bd/uzbF3EfLABvAfjrWuvfUUr9IsRVuGpaa62U+oGRmu+Xot/o
+hjovDjB2EuyR6fZOaw9btwVIEqw3Uc1kn8nqFjKICWtVrB3ozGA2he23hoa7kJ2xVcyQkRm3iBMo
+curlqZxyRSPBiLUBURjBZTCo0SuxRoTuxFEozsgIHIpFsLWh8VpXTOYvD0107lCopf0yHPIpFPYv
+w7L/RQC4IgoxzCa0KaejzrdQawlmqkWIasWSDIEla3cEzUBVlTwBIJaA4S5hsWbCjgCTFYwW4dzt
+dIAqloyEcnaBSjIAWcfE2zMZF8e7he27rCmIxJUykzO4gQRzLd+GMqlqVY5QrrIv2QUwp8lPfIMd
+NOBCnqksIvTOxDpYOBHytnxm3I3QIJdgg4zLjaaHV2diCdXnH6HckvkrkcNlzv7eRQZvnyzVkfSn
+rSJ8dSHu02tv/yVs3d2XZwq7MCDulva3UZiSHTFiOYGVewHTluc3jedwIRZUlI7g9GWNnB5PcNKT
+iR9W8pw3Hns4JU7lD9KarTImHa6xr49MrL1BXMXgNloT6U+2eYJOV9S3NvcDrPdlnAOqYKvBSzDI
+a6GKEHaH1q1xA9qW9WQuI+iKlalgHYl5CYs0bq1jB5nFDM1OjYRZLnP8FMaNl/CjtC9iKRwCONRa
+/w7//x9ANokzpdQmAPDf8y9wj+t23a7bn3L7Y1sKWutTpdSBUuplrfXHAP48gA/5318F8HfwOaXo
+NYC6rhHEQE4qsXrLgLUqYFEuMuYAy84YdkboZ0d2ZSfYhGIgqqwW0NFKZKUFh4ZKnieoqZ6sCYgr
+7QjujMw+jSXgyL2TsgGHBVgtDSy75BAgpZajbIzPJcVW1WOYmcQJ6uMURUNOTad6BQZZnhUZh7Vr
+AbRuYGXQKyJV5wB1Lr83bFYA5lNU6qn0c/oMiihO5Bo1eXmtogtzFQYjfLhIj6E9wSmU5RJOxIDp
+xQkMCpyoNYWKZLM5A5vNIILtER3oGqhKwTegUqipiakyE2WLYjCZfD/LZ8iouVFbQOQSVddwMKUG
+41IHOJoTnTqmXuX8HKeOYD0a/ltXDMZ5WcMlj8ZJL8KXfUkpD2Ii+3oKd2+vEHoRooFYaRVyGBRU
+0aqESdRq1ZL7GqYHXbMIarQEDDmB7dLAkniBZtTAG5rxoXOZ//2XL1AfyWc/OCsx/z5roSJuIGMw
+85GX4p4hcYTGhwuUPfn7XtLF1l05uQe9Puq+/OyyYA6+B70kNN1ZAjlP+UZ9pfdR6yVA2L/aWM2/
+daU7GfU7yBj4DI+eQXOurfAlpIRjf972RbMPfx3A/8DMw2MA/xbE+vglpdRfA/AMwF/5oVepgSpT
+qFs1FIVWd+wWrDUJNCbTSyzJS4eJC8sRk7fImE0wDdQeJccNDyZpwKyGB1TyGbPVQkbKeJ+akqpO
+kGhWGVYVwKBUWSW4bNA29DK0j0mVRUh4OrHR7srkp3spkqVsENZkiWoqMOai7cKlRqF5ICaejr6L
+ypC+I0hRH5C+2xqiIoLGaVPP8jRCZbHizrqNkCZjhQDOUvppDELoFWNyveIcNJETVJMbMRYXZDWu
+jjELZZFuL2PErCq1V+pVrRCa1OG6qq4EUrQRIqtWDM0hSi7SypbnyO0JNKsW83yImCXS5TzHvJIx
+6l4WUG/LteepbARGlMEeMmB2R2NpkvItN1D48ny7bQ8hsf/hG/LZjt3DYF+g0lbUhyLAC7MjKAb8
+arOCEUnmQ1GqXTue8EYCqAwfJUVpVNBGw6aAS2ijtOV+3buyOZyEe/jautyj8e4T/OopuTIVUJO+
+xOEhtL5uIpvLczyOpsgfy5w0swWmibyYkbkJsCqz7pASrlwDWO1poAQarOGAhjJJSlOPYazo2Dhn
+hgpQM7uUh03oWNzRieECLKOeLcdoupIF+bztC20KWutvA/hBWOo//0Wue92u23X7s2svBKJRaQ07
+rbCTRJh0mf7aVPBo55uwkNU8ue1zNGwxHx3SI0DbMK7imdaVZkOWV1hRTOjMBlj4UrHm3aptNEw5
+2ex8jpgMzsrOofVKOKaJaYu4B0rZRw0T25sMCI4bWJqUCHdPUdEa0cY+lgsGRFllqM/PUUCuZYcZ
+apraXuZDG3JylZQ1N4MQZcYgWTRBsSJvMT0YaxQWSQoYxFNokoEWbRdxyXsUDVyeiztjpyYSLZbC
+YWihy+czK34/91BRJVrnPkqeXEVcAgxi1kYLhrsS4pG/u/ka6lBcCrfsAGQfzpBD5StWYoXzI+nz
+7W3S5qUZTqix0C6mVylLz6rxFaaMXXcXr+3L73cLmWxrbw32mpzi1kYAMHWsghB6Thi7A9RM261S
+slW6RJnT0lPAKlhX+edXWg1FXqAsGWEOZe667j7iGWHH/gi+LZZgWxno0aV7iZD4hjmAT0t38u0h
+Dsi9EG12YbVlTvpeCYeRvIJJN8+cAuSZMEqNeiL9rDsFDGIZVGahyhmApDVj5D5MWrTFYoqa81ub
+JVK6jb59gfKYUn6fs70Qm4IGUKgKs1aODb7bu2kblUFzqThFNhYwie3vQg1YkkxRDRUYMAyZZEPF
+QL7KsedXbLZpNoeqCAxa+WlVBSKXoToWNHntKpUioA+LWsGkIpHtkxHIKlCQXdkYL5EFNFun6/DJ
+G6mXpzAvCaZpsSpTB1cbmYcACit/0Ye2SLhRR3y2FKYhH64dA3W6eiYbOhHTXbVToCRWlpqC5sJF
+sNpM2w6W1Ka8SI4Rs8rO3+7CoTiq7fM5jBhqxRSEDLqiixUp2GTEVkaOPCMdOrtjmBbsgrEar4Yi
+JebxwQjlUO73JD5BMpIXpEPhmJ21H0dNynnkMziE8ya1gWcUnt2bHyAu35Zr70m8YOBtwGtSKLZc
+BxSl6qttoEsAFNpXwjArFujaqGBRsNcv58hB122ZIF+pV8U1VETG5xk35M0IE6pvlZ96iLgpRr6N
+gIdBqyVjeCOvcDGUTeP51IA9ITO0P8Igk6rFOr0BvTrAXHn+Gg1YMQ8T1UJNcJNdOACxCUoZqBlD
+swyufwcAKzQDq4GM68kJelgpMvvjM4S7nKzP2a6rJK/bdbtun2kvhKVgGcBaaKKeBgApw7S7gFnK
+qTJxbCwmLFbqVphpCVY5wT4AIJgVUC2asyVQE9GoLwool6q8Vola8xSmajNaLSCRgAxKA3ZGN6E0
+kK00EVWAnRZr8kkgEpkVDEb7vbiBViF/N0sf2Q4Dm08XKNqkEBtzt49aMJdyehZujIxBSavjw7uk
+SMr3Qb0SxYzD4wxYp0WAKQru5XbsA5QY0yU5CZ0KqHnKVRq2zWDWxEBIvMHWzRlckqhUAf+daFBF
+HXWtUdEMtuYGKsKO1SRH3SdMmZBvw61AqxWW04HzTIJd+90WRtS9OD7u4Hwsz/rxJ2IxnJ18A9HL
+gqaM9BYUx8VAjoRQ6ELZ6JEpu0P+g7KVoaYFogcpVnwgpZvBHtHNacRQOVWemVEyRhVAOLKR5yi7
+DIh+vMC0S7TsPINDmbquSX1Q10HrTMbiZ1oB/uG5XK/QJtoB9UWIhM0QYpNBwOk4wfeoY/ozHQdB
+k4Qz1iESXwa6ccGA4rpzJRxUNVKY5wz4tg3knrgMWaMB81Tm0toUy61Cjpp8C7bbQrNDt3GaYUG9
+SldZ0OPPqyIp7YXYFGCaMKIGrH6MrKJ9nXaRFVzci0uMlUxWa1njkulHxVRg2DbRKVYrM0I95HUD
+B1VOJqC5hTOCPyz60fX5CBmJTLLLFCmJNepFhjnJS3xXo2lL/CAkAAdxAW8grk34qgEHBPToCtlc
++hFbDZSkH5+d06XAOVxW+7lxhtkFqwgDD5ub8rPJF7deFBitTMoih1qS6ANbcEhx7tyNoFkrApaF
+10UGi8IqpQmYmSw201vCjlZ06T6UI8+UUaVp6ZVIL4Wjcf74FBOm29wtAw4FYAxzDe25uBVbO6Sc
+VwbqlUBrMgUgUf80OcOCVXtVPoTWFFG5kJqDod7G62OyN325BR2toOf2lfy6rUsYiYxL2l2RoOYY
+UajFuHiE2SNhQprHOVpbMiftpgPLlH6aLBE3+gY03ad5qTE6lk1q0etCpfJzspHjYkgXoy0+fs8Y
+Y8RK2stNH86TlUy8AZ8Q7Lwp4+33KnxyzuyD0ugSznxZdVA3hUx4UQUwuLGMO/KczdSGIk1+epxC
+UR/TSmOUp/L8h8OPYTa5QWbyzIHZhJ+TfOhWB4rvCNpdODUPIs9HTs3Kz9uu3Yfrdt2u22faC2Ep
+2IaJjaCF/mGKhyz8qeIpbBanwATsPTkJo5YLl+Cl1KHJNTeRkgLdKwIEXUp85zWykvTqixrxgoQk
+LK7Z6jlQjLJX9hxjRn2rRYEFg5GW6yPZkOtlc2YfUgP7AcE0fg+mIadYUZxfuTxJUKFcSJ/cPVoB
+8ymWq5NvXuFiKCfQRmcJ62VKzZMluSpS1LFEwCuvh7UdOWncvAtFOXRd4irQVGWr51jCIiWaZzQQ
+ajnZ3mwNcE6W5ySIsGRQyqDi8jTNMVvK818mCziOnJ4nh010CgbEshEcVjNmhAar3Lg/hLotAAAg
+AElEQVQKYA4Pj3A4XlGMFbBrMldbCsOEoB5qZuqHI4zX5fmQFvAYRW+FFW4RTOQEFuarytZa+qOs
+CDmFaKzagQ7FJSwbZzg8Y24+jtFaFxN95QaUsymyA7EsD2fA+VjWQlyPoEeEYAebCFssJIrkWt3y
+NcARXMG7wwNUJGdpKhvKF2tqn/yYMUwEtNwcv8CYXIxr2sXDj9l/dw6DlsDiUJ5zszmHT7nATjeG
+QZzJbGTg2Zm4yqPjE4wZ+94kL0TDHOJuX55fLbZhEppthRswCvneLHiA4vE/hSQrhqEQBRaONk1s
+0vQ/DSocHROY4VQYPRaTyQ4vELHabzEkOKZroC4lep2mU6iSwp1tIOdLYTVnaDoyqk0SiwS9HtZc
+2WyGF5dQHMgxZjg7oWR6p4VqhZknb6HyNGpvX/qjmygX0unzk2NcnlHQNXkEi3UJdYOp0FEKPRFT
+fJlofEhT8810B/cYkM5JcJo5GZ6OxP0I/CPUj0RjoDuoYJ9Jmq3eeAZTi7JSFR9yMLdg0Xc2TQtO
+xHRb6KFPvz1OMsz5klWMwsfJDDFVo1TbRiJWPvzlCZamXG8Wp/BMcRvWjuVFWSwLTJh6O8gS0JNC
+4QVYuyu1DZcPvoVwJNmjZMJNw1JYTOX5k/EQNdOMtdGEvSbjvFcpTOfSz6cX8pK3qgzKWFWMzrEg
+4OzRJ0M02c+q1UPAmoFhwDTlcIEH77wPAPj28+d4TlCbndTYWpO109yt0R2J+9NglWhsTnGRy7Oe
+zGKURLourRppIi/bE6JC95+NcLgkI9UM2CcIqTxeYBLKvDvo48kTbvbMVC3HGWxPyF22xjdx+54c
+AL7SWONGPm+auME0Y9SV9bgWeigZO7BGCcoJD8BwguWEsY+zJWyHYrufs127D9ftul23z7QXwlLw
+PB8v3X8NG+4ujq0PAQCNbgOOLybcdHiK//X/Ze9NYyVL0uuwE3dfcl/e/qpebV3d1d0z08MZDvdV
+1HBsmZRNm5bsH5JNmIZsQ4B+WZYNUIANgT8EGwYMS4Ytg5ANi5RpEqAtypJIcyQus3B6erbqpaqr
+upb36m25Z959Cf/4TtZMDyRNNRumSsYLoFHZ+TLzxo2IG/Et5zvnN+X9o9RD70BYiV/dlNPz+25e
+h58zIt+04PaIxYcN2xBzr9HUSEgBfv9UTuPDwwk8cur1nQANRtmbYQMp+R+dvIZLSfgGA3V9BzBy
+OcXr0wg2adxa5wkeFCJq8tU3HiFnrX/REIvnktOBVa/r3D30CJIY7gYoCtJ6UxPz/MFXcHwokfzR
+mxZ6V+QUvLp/CHUg1sH16Ca8XVbOEQWuGhNoBvugNba3JeCWPqlQ1WJNNAMbNU+0hS+n1ddfP8IX
+nlCP0hqjTSCXZXm4QVepvemhz1x5zAClEU0RxwKQWs5L3H0kVtiqXsINxTqozjL0qKRcEusxWRVY
+RDKGTx7MMOzJtT++66AfyElp2wH2Xfl815f5SMoZvva78r23JxFyX248T0o4uYznzcEKfldO1YO+
+1IGMyhVqmtdxWmOvIa+PYOGI1vXiSQxFHc7egVgmGy2N1qkESV/pznHvhNmFEvguxvV8Utk/mafY
+IGhq6SzBqcSbh1McUj/S3xghbJL/UxGHs5pheS7BU+daAZuYG68RQxPmvFrWuPNA5jodyXgfGBrt
+jvzWzmAPNLYQncXQHdYCHWlMqz9GOraLdtEu2v//2nNhKZi2hebWBnKV4Ir1SQBArU30EtlJExe4
+uSs7/oFhYeOyWAgb2xJk6V3bR0JG3SLVMKgxado2FOXWQv8SEtb397eZ/ooS0OWElWn0uvI/s/IJ
+1Jo2zameButqBpHM2kDGoiR1Q0MRKedevYSdYzkRXvvEEHVIJCAL8ZVZw7wvJ97MjXGFvmxv4wpc
+fy1lJyetZ2xClSyOuupgRXLQUdJEdV9O4GGrBT1mIVG5RnSG8MjeW+oStkNEX3uKNSdDpWsE1K0o
+KRryyos+VCC+7mTRx7wQX35XeetYJjrDLZQ8ucNMrhc5GTwtcQa9PEWLrEjZPEV8ItbNSTlFp1hD
+cMmY7fuwCumD6xUYrcSa6jm72NmWC74wGKLREUunaRBhiSV2D2Ts3W6KilDxpTlDvJ53ow3blrVT
+CKYZu52rmLcl0Pja5RyzgYzRx8IdjGMGkH0Dc5LGNpT8bmzVOF+P1e4lDO6I1eDbFnxiGpYMpGwr
+hRWDslvbPdjEJjiqQOVRZq/dRbAja+uSJ3NzNrExWpJ6L+uj3ZZ50sjRpYxgap0h6AiI5YSxjGu7
+NqqFxIHauwNUtqzvSewiBdnCohDJBmGmz9iej03BCtEefjf6yQ7ebr0OAJikKTorMZOu71zDn/sp
+Unb7DgpSnJc0Yc1mJWVrAPTRCs4au+x40FRs8txtbNM9aJDy7ZUyfFp9qScLJJT6zusNbHTE1F4l
+NmaOLFhjLAt7Z+M6eg3WVJQOapO0594CO/uiGxl2e6gz2Uwyak0mJzNEm4IF2G0NEDBI1O534Vi0
+/cjc0dzy8N2OBJRWMxcBay3q+AQZg6MFbGSsIlxS1r4MFXxyVzpOG0Ff6kS61SHOF+J2jTMPrVrc
+h82dA+lDr4Ub16T6cBydY/qA96xPYXWZOchaaFJox6kprZ4E8Ljgf7C9i9UO4cVxD/dzcfnuf0nj
+HVK29Ww+ELsWNg/koTh/UsCppJLv1WttvBzK+4d2GzFLtPuEM/f2BwgoS39TByjb8vDOziPMBlKq
+7btdbFuyibRI86bMCFduybU7T9pINd3K7RAvNWTsC7vC4kjcB5OU+stjBxVLDiYjF20yVHcSEx1P
+NoDrtLfvmxo9QrdbKxcbl2Wz3LheoEucAgIfBaUEckfWd7fTx+Cc5Cwqg7Z4GFgFbLqCw7KBRiD3
+fY3z4XoK5ljuKXFGGD+Sjszyxzg/lrXqtE+weveDCT5duA8X7aJdtPe158JSAHKY1UMsggjFUk72
+k8MzdA5IZqlLbA9l1zVbu7DIC5BpauQlEVY0tct2DsNZE2NGUDyNjSCBBTndvJ64InWhEGRiJsdb
+JfSIsubFbaQjag3qDO8SQRi68v0rWxUW5Aow5yewbFahOQXMki5NK0RMS8FZn9wND75DAphGG25A
+nYLGHpyQcGxD/m24AeyBUL5tVRYqrAlaB5jx6CrLMaqRpMtyFjDlJxo5A7TGIIBJeGfWCKBJKjt5
+PIJmNao/knFzmzZKQ/rWq4HmgZxsltlGXssJZSOHuRZcYVFPoWOonNWgzgC7bVLT1R66cxF7qW9W
+OLtDGDOl/nabH4OeUGF7/i7GsVhCrzUbSDoyZ944xooEL8uWzHkvDdEfiHkN1QRIaRbYLXgnxFBY
+NUyetoqkNsrWcHxZT/bwEN0edTTMLuAxYugYCIl2Xa1kXSzMc0wJhV+MV0/TkJ7jIGcF4ykJgcs0
+wlFEbYaWQovEQDtXPgGHlbIoViDfK+pI5vR8lUAxbR1ubEGviXMKExZRjA13Gz7TqFVJLEQ0g+qJ
+JWGd57DSrwEAJo9WmJIKbhEv0d+gFfqM7bnYFAzTg9N/CY3JBGYh7oO7sYsBhTxarRq+Kw+e0dAw
++GAZBBPF8wVsLlK7AFRIH2rpow6J21/FIHkuDNLVWIEFxYG2EgW1kIE06jYaLUbZZzXOJ7KgT1ne
+/EPqALOpJPKd0TbaL8uEBysXekP6qecaVovDS8BO4sYoprwPp4GAUGqjEcGo+NpZq1jlsNvERZQe
+1Do2EGsUBO+sHi1QKHl/tSCRxo0molp0J73ShDsQpqPqOIYFcR8GWxvYIoQ6IFhMFQEsbrKG6cCj
+4IrWPqwGawZiwKJeo+uKa2MHCjUBTaWvYGnm/4MOasiDteHY6CSykd2pZfO6EWu88BFxAz//1Ufw
+Ie7Mlcs9WIYAeZLpu+jTFRxYLHFvFLBtVkm6CjXnvfY1FnMKvLgKJnkT64GY6EYGGAU3kGWBgJuM
+Y5qwvLVgTIKU4KSa2Atn1cUuiWH+1E0Pv/5lmcv7SYWf3KSoL7U24yKHpeT+qvMltq5L36wiQatD
+rkVvA2okv3f89x4AAKaehZcuy2ZqBTFAtvJ6aMIc0021IlgEQ1kN2dwtM0RdyGf1dAZHE+atCkTM
+cEyOp3grXuP+n61duA8X7aJdtPe158JS0KigscA4exeRQSq12V2cNYT1tlmsYJN1y1lo1FtizlUl
+IbNhBb1a8wsGABmhcy+DqiXAladN2NToc+gGKF1AEf1XLVKk68QyJnB9uWDHnSCeiDl7SAIOw3Cf
+BsDKoUZtkbcw6KMiHZfyQuicpy35FqpJ9ZTSra4mqELBGxhZjSpcU3AR/2CPgYimuB9AF2SBLlJY
+U3EZsiLDYSbvH8VyYgzPhrAvkcHa0rAVqcaqYxiksWsad1Eqccdqir643SbMmZzmtduBLhgwzH2g
+lPur6hU0RU3AzE9ljRDRXbFSG24oJ2UWvYOIltzCmOMx5xXEJkzsWxhF0rdrQY2xK2Zw5ZxhPpZ7
+muan0LTSPHIXDPrb8GjFmTBheGuJwAR1Uyw2Fxp5yLGj62aqFmolQd6i00BN3IQKHCiq3WhPo1rK
+nE1j+a2T+Da+TtnAa7vXYH5dMj9eXCNPxCyPCQtZPEyxPJP7i/MSV2ZiEWxffhmtlfSnbAFJLuPy
+yJXxrjIHzvZNAIDfbKD2WCW5nEMx41UbDdgtImvVWn3aRkXrtjQ05onc35lho6IYjLYa0LSGn7U9
+J5uChazsoWu1cYdMSaMSCKM1QMhGXgrgyDzowiA7Uc0qtXhSIq9IVBm3YW6yHiBViCuCQhCjw0VT
+Lkjm6VmoIjHXqzxCspTFn+U1fPr4eboJw2e6iGxDhTHAkGXRqtlCRtYgO9FwuuK6ZOcLKOL5I8KZ
+09kKCclDA7eLkhqGVjdCTT+xpPFWnQXQnnA/6rMxDAqm1mmJJR/eJ6sYxVzu7/xY+jMNHuH6SPrr
+mruoDImfhE6GSf0AAHC2NNDOBRjVWjJNqb4JnCqnESJmdmojRs0H2pn7cIas1mRpcu62sDpj1aJ/
+jOkTwq3T6mmNSfxEwyEZTLclcxO6NhbMGD05P8JVpoDjrES/lvv77GIJl/TqNjkHg3AM31y7f0NU
+xXrcLBSGPISlDlDX8ts2qx2RKdSbZDc6qlGHco1iOYemuGtRVchYVZqTo3I6dmEzhvO1B2McL2Qu
+s5WG2aXpfkTxmrpAStBXGWvMxrLpzQ6/iuGWPPTmov9UYDZbceNVEZaPZKMPLAAJSVyLc7Q6snk3
+NkpUJMwxuiSrjZbQlczTcn6EyWhdwWnj5FDu473yDGP271nbhftw0S7aRXtfez4shTJDPr2Po/k9
+jMgcnE5MqCtiZs3KAo1zgQ/b3gsweKJVqZw6Zj2HjgnrtAqYGaXb6hUyWq3aSJE/oWTblgTnzDxD
+nsr1ltEMOYEnq3iBZCKvT3SGkzGVqUmZVhczzGhe2t4IlU23xEuBGc3g5RPUuZiXy5FYOcl5CdUm
+zXoWIKEkRjEB7IFYKTHJUvK6hvEOKx/7FsJITnzD08gpOFPNJ3hwh/wEBFMNjR5OD+Q+m8M+3CUZ
+fpePcB7LSXrycAVnVz4fM+Box3MYpYxbPH2ElK/NTcBXrOAc2rBtyUqoptxzNU0QU8hmcThDXNGV
+WKQ4sWSeotX8qbZhGtC9uDvB3Udyz6a28YJL/gYYGLvEdYQ1xiygshIZw87DCgE5MKpgiVIz2Jw0
+oGg1GDARlaKxaD5k0LV1goLBXLsaI6O1qaopNHkeC6WgKHN/asp75/oB3iDBz6MHJ1iWMvZJbaKc
+ECNBDsFNvYXFiVh3h3WEL74rgdb9UmFrKNkVb2MT8bmsgXYqv/W1UxuuIUHgB+8dw7ZY5brh4wY5
+OM1JBw4rONWCJ//qDBn7U9cG4ieyFp6MZ3h3KnPyxoMIioCxZ23PxaZQGxqZV2F6DsTHMmCbWxZ6
+DQKSgjkSshs5ZQSf0dc1Gk9NPNA1hlmmMAj6MRYddKg25DsRnC1G+Gl+a9/FU1iHkaOgrxbXHh4Z
+Ys4VIxMWdRtKagUkSFFSg7I3HaIzFHOu4VeoSYgK00WRUFSUEXm3s3oaqygSjZjReVgZ8q/KwlPU
+vfCtczgvSUzFPwvR3CPhTGABLembfqSRkq+xTOS9Mpwipz5Alemn/mk+7iA7k/sYbpnosApQ9yjx
+vnCQ8v4XhoLJmpCOvfuUHNSABljnUSyY9o1rDKmJ2bx5Dct7stFluxmcQzHB480BVo+YVWHKtvNy
+hsO5fLa1pzDblf50AgPOXLIP0I+x58nn+3QfVs4c8Uqu7Xg+TKZZTb9EayEb8un0GJrgnUXvDbnu
+xEBzU2JJfnsJtyebkzFSAKtnzbKEt0EVrbGsw3cX+yjf+SIA4GO5id9aMi1YVGjtkCPUlT6cLiKE
+zMrcDDIEHM+75QqvpbJRt8IIDsVkm8xa/NC1NgbXJLsSbN5CmcrBEIQO2h2Zdz0oYTDFWRRkDqtK
+ZIwpzdMZVmTTWoUn8FZrQh0DZ9Ufo/uglPpLSqnbSqlvKKX+jlLKU0pdUUp9QSn1rlLqV6gJcdEu
+2kX7l6R9GNXpXQB/EcAtrXWilPq7AP4MgH8FwH+jtf5lpdTfBPBzAP7GP++36qJAdPoEJ4t3kfiy
+T3UtG1YoNQ6RcQwzYb1CmaBTMjBESGlU50/1IZvBBtSa6rpjomLgR88KJKBpy6CWASDlrjxPKyxK
+2V2jZIwplZvHVgyT7Ln9FuXN6wIxsx27t2yAuImyCJAtHgAAkuUc5brsf8UMx24Peim7eeqdIRrL
+Dj42M4CArBXr4LuZAfWHYorutXqocjGFfN3E7ImcDrEZ4wl1HC3yBNZpC10qDqfKQLFgPUNyF6XH
+gKGRIrUkwFjO5QSzNVDkVLqqMpRUaE6MCvlSIu6GNpBT4AaJjMnZWYbWmVg8ZVIgpbk/3ahRMup9
++ugRmtRSPJ3K9/vGFj713Z8CALz4wk2019D1L//XOJ3KSRgm86ckKw5rHFQcoUMOCIUnKAg9n1oe
+KgY2H2GEaiKn8HwiZnlYbWG7EPO6YV5Hl8IwHecSPNLjm67GUssa+SLduC+OPo/TiWBAysJERpo6
+BUU4GXCf4KddtDDfI6Xdez5cQqnTWYHjU7GavEs2DrW8nlisytVTtNpy/+jMEdhiedhlG1aT+JRi
+BfRl/dUMUBZliTkh2HntYEE1rKM7Bb5Bla0o09D+B3vMP2yg0QLgK6UsAAGAYwA/BtGVBESK/k9/
+yGtctIt20f4Y24fRkjxSSv11AI8AJAD+IYDXAcy0XhP64xDr0rxva0qpnwfw8wCw1e8iGye4Oiqx
+oJ7hkWNjl3RXVlThjLJo/VrD0pRTk7gZHoxHKEmptb1bYY++fxmaSCLGA+Ip6lS2VasvwTfX2UdG
+dqQ8miNh0Y0OAxgrOXVc3UZ/W763O5RT7sk7Ywy6hLhGLyBknjooD2FahEfXC8xPJGiVlLJr2w93
+YOwTEdjqYcMWP3trN0B8KP1Y2sLAU7x1jIzsSL9ynOBHAjmte842qlhO6dUqxGBTWJgcslBZXQuL
+Y5nWjTDDigHT9izFEVNr81rDiuR1z2LqFQlSnuwLtYJ1KOP5IFxAsUq0YXoYtOX947GcYMXjc4zP
+H8hvPKjxO5WcVtfPO2jTojMcG1+8LSfo9e854Nw1cZU++eXmBlpMsz2upui+K9bPF0+PEM4M9o+n
+7k4L3QHTz8kAdcnXyxiLllher3pXoG5Jn5vkZliVgKlIFDupsJoy3rHzBEYkUGiroVAdySm8NZJi
+LuvhFAOHuIJJ/VRHBNA4ozjQC8QdvKFNNFkclwcRzicy/4+SMa4u5J685BA1xWXOadkcGyXsr78D
+AAimLixWZW5t96HGYkE4PRfpSCydXBGnUSUwiOJMVIRRInPi5QmMfJ0CPsarLKD6Hx7jmdqHcR+6
+AH4awBUAMwD/O4CffNbvf6sU/UtXd3RtLLC4ZCIk1XkzbcLwGXxJtjCr5AFphi7CUPaZ6Fgi0tGs
+QkTQSeMsRszyYz2uEPGhiM/uIqQWnyIUdXc7R80HwWsC1Vy+l6YJQlZUupmN0BOzbactT/+d/Bxt
+n+QXiyNYGaPznWswCaXtdE5hrWQDeO9c5OATq0ZAN8iyTBhNBiXHIXwtn40SyTLYwX18+b4sns89
+PMLVrlyjN6xB1XKE2xu4NWIW5Jp870vLR3jwtkDF+73X0KnloUk6NuyINQVLH25DxsI2yTRd+XAY
+9S+yHDNyVMazDAYDumHLQXQkgbT0VAKbiycVVseSwflaMccbxB4YfoQuszJvnVb4MmHD51+VjfVP
+f+qTaG1Jn4POAF6X2JKoj+WQtRvnPVjMw89ZJ7JtJDg/lQe3OwzgbcrcLE8yKJaXH89j9FkSH+SS
+fYgXr6O4T+i6egO1LdWsvldig3UH7iJFxID2w7ckW4D+I5y/yYPl27To3yB8+CuWjPFJ7eAGg46T
+yzXqhYzh2ajAOyO5p8F0Aj0jbyQD2G1vF+aWzE3DvgGrSVh500OSUDnsQQuRIePc6u5yjAOEG7Kh
+tfIudhp/AAB4vB2jIubGmldYfEDw0odxH/4EgPe01udaNNZ+DcD3A+jQnQCAPQBHH+IaF+2iXbQ/
+5vZhUpKPAHyPUiqAuA8/DuBLAH4HwL8J4JfxjFL0hmEhbPShUCKh5Ftjq4IiceuxSnB+RnKLIIF/
+TdyHYEdO/O1lDW2xkrH0ATL45mcGuuv3N4bIL7HwhQGgedLAJJO/jzHFOYOSRyMT87mc3JdsAwPu
+nU4tu7ZjFpi9K33I5xso9wn9DRzUm1SPRhv+ZTkVd4wD+axeYHqPcvetOVJqEqjNGt5CjrYNR04X
+dTVCWbPyMwIen8hY3LjswrxO6bZ3e2jdkJPizSMSxk5iZCdiVbkHb+NHX2F6dlJAn8q4tJoF6hXJ
+b8mP4FcOFNON7aAJn7pw80c22n0GOash0n35TDAnqct2hDmLp+aHJQxyR8xXFR6SdPRL8xSLMbEV
+EJ/vs/e/gRf/dQkkV7WBMqOk38cyHN6Ta+e3lyhJljsmXdntysQmg4+9RwUuCVIc7kYDeEfePzyc
+4MFXZa5mqaQk705q3GSwst3XuPGauCvmqg94dCETjcPF5wAAsSHBxfbZCPcbtBC+TVPlIYOHyT3p
+4xIWuhRqMdMMMTPO41WN229Jf14YPEK/LWZ+vpbxKyIUtayxyfYJ+ivpp3lSwO1S83K4hBsTOeus
+VacbKHdJynPSgBeKdXP8ztsoqY49zzIk7gcriPowMYUvKKV+FcCXAZQA3oC4A38PwC8rpf4rvve3
+vuOPVRWq+QRLfQKtKHJatFCzQm41mmJKeGlv3sH5ISvYKNveckpYuyQCKQIUtSwgr9VF7ctvWMt9
+OIY8yKOYJq5jIB7LxMwXLuZHYtRUukSbRCxmXz+F1U6pirSRaMyarK8Il5gSw9+IujAZWc4zFzal
+0deiIa6/hVUgLo+GhiJgR09OkMXygBS8j3t3hviUkr+/Xjh4eUPKqI+WbeyScTjXHqJ4/SBIHxbT
+CI8TRu/PusgIQc7KM1SlrNI8D2GzMrAoGcmGi2bFTbMATIrLuG6Ccu3DehpGLJuoawpsVw9zPPia
+bDw3bAdHNI132jv4ytfFifVjA2PWhOScg9vvfhVf/R0hgOn/iRvo75L16ssb6FEg18j3sE+3au6w
+LFpbWMZ0RZIemjTL3cSFwyrYW9d2UY2JWSiFeKU/2sEWZQLaCNBpk436bIbHJbkizzK8/aZcZ/So
+wXlqoZ5Lf5SqoL/Fg4jlOcc0kzFc1Rq3M7J7KQ9TfljnNR4RIv/Wm8cw9+Uz7jFBUe0nqFlfkybH
+WFJs1r26C9gUe3E0jLVAMrMveVQiJ7v06vEZspmMbZEqmB1KCRwr5JGND9I+rBT9LwD4hW97+z6A
+7/4wv3vRLtpF+xfXlNb6O3/q/+PWCTz9Qy9exlHtYEs8AqTaQk0W5ayIQM0S2GUDu5dlJ90byr/L
+Yoz5mZxQR+eHWHL3rJGjJMuz4Wh4xBsELHaylAuLuAgzUiibfF1YaG5LwDN70ISxksDda80DAMDn
+rbcw+ap06K06x1rlEQBeXCsRN3xURFDOEnI0FhnmGS0Xq0JNrUhDKYDYyozv1aqGVXC3LwoM1pVz
+pgVF8RmEGeqBoP82WHDzys/8LP78R68CAA76A/y1v/C98r3XC3xpLriHm0rjjFbMdSLmZr6DR1RB
+zucJ1gmkOAG0L31q2BZsi7wVlHUvbCBOWOBT1YioA1mb1lM4stcI0KCmY8hgbRFPcV7Lb/hJAuWL
+Sf2ZP/mD+Pjn5L5/+e6v4hUWXv0DgvJe9vbhvSi0cfHOLkpiRG7NDTxaEQnoLPHeOenmCvI3XGtj
+84cFIXqAy+iYkrVJ7PtobEs/Zu95MBiktm5I1qLeaiNbyTVWYYr8SCxBO2jgt37pf5LvrXExKkHG
+APXmdIndHY7LxEcjEpdoYSxhNGidsmDqbJri/lT6XpQZopiQb9NBSai47wEB1++gLUHnK9dv4doV
+CdaWfgOtpqyFr7z1FrLLMq+7J3egNmTMf/4//Euva60/ge/QnguYc25oPPJKBPM5miOZrPPwFOac
+wp+ehS6p0a2Oh72+RM6v3yDEd9bHnULMxPHCh1nLA1sVFjKa6FA2+tQrHFBVyOkGcBOW0PYtBLls
+MsN+ihk1Bf+XR19CkoudeDyVTeql+RK/+y39/1alvje4QH5gr4v+JTH9rk3lGqNZgcJkdZ5hQNV0
+HyyNYk38UolLtIprLGg6VyiwYF2GpU3M61Penw/rTDas8V1xffKpgf/or/5nMm6XLGz8I9kI/soy
+R03lqK8ZwDUK2R6SoehjBwfoFQJ0miYultGabLaCwY2u4TtoN2VcOmSIyuoCZ1O5j0kZQTNnt6w0
+1ujaRZnCYH1BXUpqtVQhOozt5CsDxGbh2n//a/jMbE3Rv0KXD+n1LRmfv2/P8OosTCIAACAASURB
+VG+89BEAwI3tAu6dAwDAb75zB28a/xgAsDfrwe6LSR+xivS98OP4k4wNXW9n+L9+TWojfq3/C/i3
+Zj8h9/dqiUnyAgDge16SAWpWOXr+WpHLxhaFVZ44BiYtMderkayP4ImHu764h0ERoiAv456XwBzI
+mm14FpptVuvOJCDy9mQM76F8b5IasDICjxwDlZa1026F2OTTGnVkU9jpmzhqyf3diD0cncv9p8YU
+5kO5Rr8Kcbx7wdF40S7aRfsQ7bmwFFDW0KMFTuYFxg65C3QPH6MrEZYemntyUuz1L+NjLx7I+0Mx
+ix6aRyiXYjoZcY5sRFZbA1gx79wMLXQJwrm0QZ3BzUtwLQKkTAXFKrpB+g38TVtO4HlegEkA3Kb8
+/O1/zq3QIsY3ggn+6ksfBwB0CS/GqYMwFL6B+dkNwJfXTtKC05d+JE/ks2f+XXzxq2IdfWW6QhrL
+ybwwU9gZVa7tFQzSmxXEAdw7/Tp++1Csg3/n5S7+C2ZfamDtoWBeAV8+F6vh5Q2KlHQydFL5LXfj
+DA5Pmq6bokEquI9c7cB3OXaGjEVRhaj3xfR960EHI09AOvemFhSp3iZzEwbpx00lZm23DUwnMt5B
+qJBTnOXfNcZPxxAApsQG/CGDcpduRU85HT566Sb+/kCSW1/7wu/g8SPpx93SgKL6d1NTPfxLX8OU
+LND/ZeMcj39L+lbPgH9k/RIAwPgnFn7s039WxoB4jM+0P4NOl6Cg+TmSvrzegoVsIYHbh18jlHwV
+wdqVNfnylRA3AhbK7Q7hs/gptBswLBmDalP6uD9JYF2l9uMxoEknOB810N1hxWQUQG0ys1PKPHU2
+SsRHMi7Z7jl6DyguU1VYvSOWx+O9AG1yjz5rez42BWgAFSq/hEVQkK9qtF8QkIafFNhlOulgYxO9
+Axn4KGWKrRWiH5Iv79Y+RqyD8AoHE3mJYQcYEUl2eV3G2mrAYyXmMtFokmP/0L6FW3fk92Z7X8cX
+7ssk+Zb8PaGf+09rf+6ymHafvvUJDK79MACgz3LifKtEq5SIu7FdoaIbM4oUBqa8PtmQDu8v2+j6
+stjc33+ARxTTXSwLnJFHsAMfY/L7a/qbYZRiaksfJrEC/wyl8DRy7hsKGMo4/5Uf+BkZQyvDGw/k
+ga6sGGlfNpNNZeIKsfjXN0Kcs3y8R/KWbBXDDuSHp0aG/pG4f40wRpNwld+2VrBqia7XlEiPXBtx
+n7UY6gCb3LH09P3juV6guiXj87f+g7+GrZcEkHQy8dF7KH2+ttPEyaFsEAduD4eZHC77NPfHRxaS
+3xNzffQkgbOS+0vrAnbOkvhlG+dfp6Drx2QeJtUhmp+WA0erNjqkn4+WS/SUbCz3BxTrbfbwAueh
+9/Gb0AtWlHohwpRZhK0QxVIedNeXTarnZ/APDgAAzc0VbEPWZ9Qv0dyjvsZkhnAg677HuhzLbuBR
+W46oVmMToHZl1qoxItvX9sEPwiyfEcrIduE+XLSLdtHe154LS6GugSyuYVcGShJWbIQOLpFAI7C2
+4fRkx2zul1BT2SlDRsiTzMB2jxBeawPXKGWeBys4WszgOi9RkPk2ZoTfNQJUyfq0MlBQPdhRNl7e
+F1yAPZnjwewBAKBLLP/bDDx+e+vvmfgLf/1fkz7X+7BpEtuHpHFrBfBiiqwkJlJi5pthgdKW3x4G
+BKhYNdqKVHFXU5xRuej3Hy5QR/IbC8vCAVmOF8wGxFWBRw/l79nlJS61ZSzmS2DGzMH3bw3wZ/5t
+yUpcvSlm8vHRObaZGWneddDoyeu9rsI18i56nottl1T05DnwbBcW6e6vh0usrsjYXpq38YTq0J94
+mD8FMtl0g2ZJDaOWvqu9Er5LJfAAoNgXEgU0yXXw7/+kZLl3PrID7UrfWov3kH9ZXm/G97BJYRh3
+nKDDAOWQvJraq+EtH0g/KyBnVP++MjEgBeA0j6FP5eQ9+XWxDqxfuAbrWCo866EJxTXXbfpIF9K3
+S6WsvdNLLWxuyVhs5Cu0HTnxc+VCd2myzQs4VK/SdI3sroZFshTf6aEkd2PTBxRrftxWFyV1UZ2Q
+Z3k+xQZBZmmxeApk0+c7WC3FXSm6I1xvErL9jO3CUrhoF+2iva89F5YCtEZdFdC1geGRHBO9T+yi
+uy3+914jhFKkGHM3kZPkNCNbcp2dwW3xNAuAkEq9lmrApExZkS+QMsVnnBN/atvIS9mVbbNAwdST
+75h4SIzAzLUwpJii02VB1eybloIG8FFKif2NH3sRQ/UDAIA2bCQMdhWQNFwjvwaDrM1pssKiFiRd
+EXfhdZieg5wYR7MjRGfyepomGLPCsdYxspDSZJEHu8N4Rym+fFFpqC/8bwCA25/6izB4en5kC1he
+k/v4qes38CILgmbMj3t3ZsgWJGA1M7y2K9V5OwMLwUBOnSSq4TDIaXlEShoW5kwtGovu0xM62ynR
+Jwmq6V9G55CUbaHk5s2ogKb1458vER5If1RpgkLKKEKNf/VAxvwnXhDL7fj2DKonFt+Xf+MN/Oq7
+cq8nIyDPiJrUJVaEQr9RyRx4hY05Lci4KpBmtFygMWd1bKUU3mP15L3z3wYAzMtPwCA/RWCaMFnE
+pE0XgSvzl1NJ+6OJxiud1wAAw919OIw5ROM34UKwI7A82NT2qAlph7WPilaxrXwoh0VqzgA6JvFw
+7CBPJX5ShzLXdlCj4cvvhlEEk5Bw1UvRMCWulL/zJuzXfgofpD0Xm4JWGpnSqJICEw5IURSoKAHu
+9LtwljS57AJWRFIPBt+a3SFMn7DcqgJY9ebnBooGq7hnBSjuA5OU3XNdoCBJBdoFohHxDVYIj/1w
+/A48j/yJhJd6BlCBICMD+JlrMkk7n/kpBOsVfTKH05XhNc9lYwo2QoDX0505/HtUiwoTOFjrA7Ki
+rauwjGTzGQcTLAmg0Q2FNqsBI1XBqeSBtao1GUmC2zPZID/yZIVPQwJjP3rz+/COL5/9rkaCkgzV
+pydyb661RLsnn7WVh40htRi3bAQezfzkGEmbtOS5bDaWrcB4KFQ/hZoQx2AHaFmywTcaBvR6Xh+S
+ZGb0BEkiG0SpA+Rk2H6lpTAgLPwfrCqkS7o3LHveC3OcfVZwGr93fBuLY3H/zGyFiGpSJYCaFZ8V
+cRNxncFjBWdpAgUf7qIGFNMdpsZTOr0vEjJ8+wtHuNWScnb/2h5qk7Uk+ltwGNFaQcxH0JQ1lB8e
+QbPy0cqaMCj6a+QpigbnXct8wKuBmALJjQIqWi/UGaqUPJ6VDzWXMTRCUrxnJeDSHW3UyBPC6I7P
+MI+lz+72dUQZOQaesV24Dxftol2097Xnw1KoFcrUhOnUaDDg1CkNVJfktEq1gtHnTju24TR5mpI+
+zWsqgGar6w5RkryibDswcvlsYQcwWawC1rwHWYkJWYar2EZAa2NirhDl8plqCLiPSP/GFJuhDNgO
+Kxx9ExvfLyZcu7+BjPJoVTTBbE7mZkNOgfj8HjKmRZMowfRUcsmp5SLYl5NQESacHjUwXcp1J/dt
+WLbc67io0WOgzeo6yEiuYFAW3VgBr3CsFm4Cn4VbT4ZLBJa4MZ897uCFrpyOhw9Yd1+Y2GuSmm1v
+F3kgr2O9A4tmcN1UyEnDZvAwq21As5IvTZbQRGbqwIAilNipYrQ0A4x9mYPZyEahZCwGgY8Oi31m
+hUJBPUqrHuGukrF7++gBAODRYYL/+76M8e0vfg5R/k0S0zW8QRkKFbPGHq2qUgOa1l1R1LD5flHX
+cMgOXiigXRL+Tdm5cXUPhyz+2pmEaOzJmqzrGslSPttkADce1sBKxnsRtFCfSipQuQodisRUdQM1
+C7pUwGKuwoMiGzfMCQoGV4OyBVQkbs0WyOmmKt5cXfioyNqMqy7qTCyo88xGQAuyDgPUBd3lZ2zP
+xaZgKCB0a9i5g4J01Iav0Y/INOzYSCm5bm8sYFNtx2EFZFH7sGieVlkFizj7JM5QFbLwijIHUlaZ
+0bdUtQXPIK15NnlKL7+YmYgp1X48cpGzVmLIaPFjz4DNUtfh9T5effGHeI0+VC6Tf7J8B8mEgis1
++Q47ClVJwZJ0hdSV3wjsEMaYmPqQtPXNHL2IrL+7PmqWcu9oDYeArNL1cWVHfHGLZd9ff6kHc1tg
+wD/iN/C/srpw443X8Y+vyfc+qQPcfktev7eUe+tfslBmspB2swVmZKS6ogu4uZjapmnBJEzboOis
+k5uwaO5XVYpqrddZ1TD5mBq2B4/gpR4Ffy83TZyRHSj2M3TIWHXaq7HHzbJqa9SpjOe7d6SU2VxG
+eBgJyDzNI9SMGWgNrFk8Cq1ZT4KnJr4RAGBNjOfop5uGsr/pYjgl0OkRps1NuvfxAG3ZS7H8xAou
+AWxG5cBkNa6viMEwm3hCYIiRJChjrr2wi8SUTbYNCyYVzLJEXOLQncHhQeXmNkriZWJ7DKxl61ON
+OGUpvZYxUdqGyWzQcrSCIuOY07MRJ8RnlCt4BWtlnrFduA8X7aJdtPe158JS0AAqbcF2a7QzBqS0
+hYo7sFuW8EvqMEy3Ua9rzOk+ON0KJqGjpuWgoMsQVCkSakXqVYV0LSGXy6m1XMVYMbgYOwtMlixA
+MmNMzuWISaIIDRKS1F3J9zqehcsNOVV/+No2GkRFlnqJxd0HAIDFl9/GOQuJolCsALNsIluIBRIU
+AbJ1xLlRw2vJjp/wVF5kOSbUDTAHDs6oVREXwHlBerAKmGmJSCtf+Bi8uwna5j8EAIwbPweXQiZ/
+oA38Hgll3p2tnhblFClh4FkPw4H8vWW7SFmMVqoVFoydGvEc0ZLmOGncGtpB3iJj9rREzgCfXVqw
+yJKsHYVuKNFwc1O+1z9tYEkXJX0So7wup6Y3U3iXoi7ZJEdKSrc37wml3Wr+JuYRtURr/dRlMPFN
+xGbHNJ9Wm16y5LdmysZWmxybaYYhg47vIkeb1sQUCiYL5FLybCw+O8f5J4VfcviWgfwFZrlaHZia
+5CzMKLUnBRS5OyelBZOBT60PUWtBU+ZuDSrawSzXZn0PGnR5YcIkaY1ZtWCwQA5OgXpNEEnotkpC
+aMrfOYUFg8IxReI91SVp3JtCvcAJfMb2XGwKhgn4DQ0sTazZ5ByUaFEE1G42UM7FLAsbJXRNMome
+gDXc2IE1lMlwCg2TlXw6B1ymJKvGFIkvm0h6KIuq3thCnIjfl9UNmBTZOB6biEi22oMLvUWAEB/c
+vfsKbrBmI2qhUTFqb89x1hPwy3gwggH5zCyUxXFiLOEQxJIeNUFlcWybCdxCNj27lGvFaokH3CBP
+jqeIIsKxiwI5XZc4mSM4k8+HvlSJzpoV7sUyLv333kJzRxbVb57leJtiMPfKCi1CxG+25PstPwDI
+3HNynmBGkZHRmxUafJCdtEDdkgU5J3mL2/Cg1qnTsolWJPfX3Blio0U4rt+EZ8iCblF56aBfYzqm
+22FFyFJqVLZKPKYCVp0Ad9nPhkUiG0cji9elygLfBqTmhF4hCltji5mfqyTHzVzgQMm9fnyY4ehc
++jbLcvhzmVfXU8hZPj9IZM4Wh5/DaFvu6fja72Nr+oMAgPbyNYSMO2Q0903Lh2baGwvgnMkA6xzQ
+3Nys7BhBKGsyJF08vBI+SVhUy0Y4YSZpX6GmOxLbLZiMSxgdakm25qjJY4pVAb1+eKw54pgZlVYX
+Olun2J6tXbgPF+2iXbT3tefCUoA2oAsPZjtDSAXghg7g92RHVCiQEEyUzUs4zBL4hzwxbpjoM4Js
+dEIw2A/ncgsmATRVx5WIJgDToyk3fYwVxOo4fGcFh9BeP8lRM4BjbAbYc6TGfmpJfw7Vl7BHS6Gz
+/1EUgfR5GQHFE7EajudbePGyRN9TCrV8ahDg8YnknXdfCHFMBuqg0cTZQnbzLBWT8uQEyJmpSMs2
+bF9O0riwUTLgZGobiUnqtR6JVYY+9m5K8NHrX0c+k7H6fkvhGyxG2istLBiJN6nEXcNBTlh11FZ4
++BbZsx2gOZbv5Y6H6UOxaB6f0sQ1CmRryvnyGAGzQDdP5ujsieWx07Fx5YpYL20W6gx2DnCJKtj6
+PQ2LOIRoYaNPP+BMleiQCm+N47jUMAFSuwW2h6grc/aDjQLvQTAE//knbyHa7bD/cgK/e3uMrYGc
+qn2/Db0j1/j8Fx5ioOXk/j/vPkJzKBbJ9JBQau8lTMYHAICNew2854sbdOugQKNBHEJLxqe/OcSw
+kH5GfgoTnDPbRjUVF9SCgUks1+gM5WjfiG1oksXkoxlyjkU1NkD0MxIdI1/D8+dr4FgNcyTXdi77
+8Fiw53Y30GowiO0PkT61v5+tPRebgqk0Wk4NI7ZhhtKlztDGcINqPMrBsS0TXlpLrM5l0OY9Mf17
+j/uwL8lD3E0CBH1GvZWCJknFcqRwnslkrBbUXUwXsFayIDa8OR6sZCEo24WixdVwdzHckYV5fSB9
++N0GMKCtpozVU/ScfbZARFTg0inx+Xfkeu2BTNys2ETNWotxXMAmlbcuF2gzZrI6loVWRSsErNVA
+UkExc9JDgimrNGc50KB/2SIJS+NgE59+Reoabu738H+8SNak2zX6VGWPTzS6pO3bYrbn46928f2v
+CtlIHBzh+GXZWJJ3zuAsJCbyMKvwFsuFHaIA0/MaM9YDWEaNyJbf8y0PNqPvdXuEOWnw+x0+3EEb
+moC0UfsUBe9psK1gPZD5mzQM2NwUfmBf5umG4+PJFfnsj5Yb0J+W+Q3nL6JLdqqDratPazNmJFL0
+nBr1uWzYgQuUM1Lct0aI78hk77tPkM75QHKzybMUl67I/NU7Na5Q/3NyZK2z4LhqyO8mVonCkr4Z
+RQpzxXXRCdBsMIc79VBwcZlMN3oNH0bFcauXKGzZTMazU2Tk4FwsaqwYrxj6VAVLAvRajCMsdwFu
+BBpDdNrMYvkOmvZay+rZ2oX7cNEu2kV7X3suLAUNhVyZCMIKZGFH7JhYUjnXCwoUY+onVimWBO+Y
+h7JzhttHKLijrool7EJq/d2tCnksJ9royVfxZEZzbiF58M3hEGiTcdkI0c4ZwDEeIuLJu8iPUVHm
+3vLkFC/hwOzI6bBTVihpeUTTGBWZeC8lDmaM0J+xxv6dByPs0DQMNwuUI0qu2xlatkS1l7b81lm6
+wrus7aiSAkWy5nDUGDE3n+oMY1ZH7pOubj/Zx3Wazl4/hKblsdevsENcQD8r8Jg5+4QR8jt/cB/2
+6n+U+YhtfOForSC1gJ3TekGMe7REN305iReNBGuu4Gld4QVGwO0uUDE3MJlk6Blfl88vhROg4dcw
+PelPz7Bx5lCFqmpjZyAXiUPgu10ZrySTuX4nzWFRSeThoMLp/yOWx357gc1NKozbBhpKTvSztZvX
+u4XZQrAObzy6jz+kK3V8Z4qcXKDnVfU0YwC6BOU0Q3Uq19i58iq8hlhQegC8zqrSfIPKUwuNhMIw
+qyyDyayMGZ8/dY/MQRNuIfe6Ya8t2jkyRp3LDGgR8hzVFoj5QjgEWgbZti0Z46ATAgT7mdlIBh2A
+GaUoLXGRA9cG/A+WfbiwFC7aRbto72vf0VJQSv3PAP4UgDOt9St8rwfgVwAcAHgA4Ge11lOllALw
+30KUp2MAf15r/eXv2AkDGAYGjMRCwVN1V9UIW/J6no/w+38g2n6vn5So7S8BAG4MBX76E/pVuEQH
+qnCJkpWDTt6BJsLQczM0Hcp7URTl8WwCIxDcQN/w0G3LSROoy1gmAmPTFlAp2ZkzniL7TYWagc00
+NREyJzwcmBi6ElxcNDqIGRxtP5DfauoKLfrIg6xARm2JLc/GLCJLD/UW5qszFBPef2HhKtGbyqlh
+8ZQ/T0r4tbxv0ydfXT9CwkBdMy9x60VSeDldeO8ImjKfuXDKNRxb7um2U2P2nlggUWog1fL3w3sF
+Wqzjn7sKr7VoTVFz44VmgCOyEr81UniLv3uiLfRciYmkRYF4ItbLj9+SUzfc72CnJmPyeYYVg2E/
+5Fh4jyxFxuMxFqzKbLOwbcuqEQ9knpYvDnCg1yelhhnJNaxoCXtPrnOZKd66b8OK5JSfzN9Gj1Jw
+dxITNy0Jgl4NNLQpp3S64HiuHiOtJYhbmQ+BRCw6ZQaw2nJ/0Zh4DPMcrQ3pW9NQWC1kXF5/8xiP
+KRdouAle3pJT/HtfkP5sqc2nqUzXd7E6pgXhKYQMQGaVwuFjWdePeZZvGY/RaMp8HHR78C7TjNMx
+LE+Csaa9CeMDErY/i/vwSwD+OwB/+1ve+8sAfltr/YtKqb/M//9PAXwGwA3+9ymIBP2nvtMFlDJg
+mz6MYYa9ngRfhp1d9CieWpyVGG4KNdsn2hUUockbVIjavLmHuSmDapYWqiOCgvo5FOnDWs19RJ6Y
+9i/S3l3Gc5SWLLbJosBGIBN3oo5hpLLABq1NHFyREm5Nrj5HNzAgLdelzT6sgFyDAwdmQtPOMbFg
+7r3vXQYAWPkJ+hSCLd0MISG8K6eBgJuCG8hive71UDJA2Z3XqLjpvdrycVat1bAMrAh6uU9cxeVR
+D8GmRP1Lx0dQSoAv2LuCj+bU6bRSvH1fFuz+ZUb37QCGlgXdb7tIYnGx6oaDmKZt27ZhhdL/Vwey
+GJMK2OjK707DY1jnFPBBhZCRzTiboOImmpNyP0MT83LKa4S4zvue5NfwyS15WNKN+wgWMj9NCs0O
+X7bxUkfu72DzB1AfyBqZ3v8tFI9l7ZQNEzZ5JZdKnoggMeGQj/LW5GM4uSVszje+aGLGbM4LZhMP
+N6iuRfWmZdzEO0cyVs5hG9vbjPb7NuxUXKhMyXub7S30XOI+tvuYZWTHTm0E5MRMqgKtjqytktSD
+i0jD5trzMhu+v84IWWgzMLt0NdqKkOYxJex7PahENiZ7v4+KcemFU8OkmlR7J4TBrNuztu/oPmit
+/wmAb6+o+GmIzDzwfrn5nwbwt7W0z0N0JT8Y7ctFu2gX7V9o+6MGGje11sd8fQKAKgLYBfCtLJFr
+KfpjfFv7Vin60DbR0RX8OIR5RXZ7q2fCYpXh5cE+jJ+Q3bNaxShDOYHsiFoQTQveGl12OEbQ4c7o
+uUAiJ7DdvoJ9wkfLvphhZr6LJQkVFidznBYSwSoTF22eND1/iE1yJHgkLOkEJj7RZUouDOAYcjqY
+cyDoc0jTBA1bhmVCufdL5zacy1Ln3mpex3wp/QwfFzBaFDWpZA+99dExvm8iwdXXT05grFNPZY2X
+YunbF+wFAtKKBZn81iLIYZcktDVMnFHbcedwCh6qcE5DbDFH7vtiwrdxgmu70s/jVOHJtlg3qnuO
+LSJH47qB1YLS70w9Xr0xRJFL3z81K/F6W8bW8wNcIcXcK04b2l9reFDzYFI8rVZthhV8isScFhNc
+nsmRd+naJXTv0uobietjPukjJL32zscrnEYSPPS9K/D3KCN/eQiDjN4BOSlKbcEL5BrdTYUfmsvr
+J+EJamInxuUJtpRYVuOhWDEbDRPdTOZ6aVbYYYrTVAEcallcIkv4zAhgNOW9jtnFkBWVg/4OsJL5
+mcUJ0mQtW0iXwalh00ozAhsFi9HMIoKC/EaggO5gbZHQcnNMuAzmRo5GRgt4PrPRGcoY6TqFFa4f
+z2drHzr7oLXWSqkPLDP1rVL03cDVEYBWw8amxSozQ6EM5eYdeLg6lMWbbnZgIGfvSTySVlhqMvq0
+5zAD+pZeBYA52nwOs6YghyNPhw49+BXBP66L9KFsEMfVHHrNsNMLEazdhg35nmo6WHnMuycPEVWy
+wNxAIYjltR02UXKiQ4e5/WIXhbM25RQcVgwmfvaUFapmVafTcNHtiBLSC64Pg+xPZ4sE455kKNpl
+iRj8HjMd1nttOIzLaF8hkY9itaPRywkLr2c42l1Xo66pxQvcz6niZPowHnJRmT7sPfnelSubGN8X
+N8WL5OG3qgE627JZXv3eAtntdwAAJ/MabcLKs6iJwF6b3TIW0TLH+DGzR70aAYFj+d0EC1fmaU8b
+WGZrMRdCf5dTdNoyNy+ZDjb60jcjWaDVI4ehcwneSxSmfY9MUfE5Kh4QxkCh33kVAPBTbQujVD4z
+TeZ4+EjWke7IWvCaHkwyLxl3QuS3WD4fRFhRSOcJq0TV6hHilcw/+l1YkPndDjYRM3NgliOUAat7
+2Z8MJQgXgWPZTxmbKreHIpBruKWPPmScC4KYbKuE9mTzS85PkLOCM52mmLSooraRQ9kfLJ/wR80+
+nK7dAv675ic7ArD/LZ+7kKK/aBftX7L2R7UUfgMiM/+LeL/c/G8A+E+UUr8MCTDOv8XN+Gc20wBa
+nglDWXDncgKHRgfNiCbnfgmnlNPa9xyhfwZQMQCW5UtYfM8o2zCJc7aqPWiDVWbFCiqgfUX1aCNQ
+0BkRaKWCS2xCHQ3RZWngR65dQdMVE86ihsAVM0fG76VpA82ApC1FinpI6qt6B05Phteay8mXNRNE
+Izlha2MIYy59bhgTVBn1LgbSt8BTmEGO+S5WsFcMEnZSmBQ1CZIMd6YUhin5/e8aYUECkWZlwCEn
+4sjx8bAU8ZmrN/exeyrGnUWV78NFBpc0d529HVhKTujZeAlNKDjmLTQ6YtoXhGi3Ag+dDbHioBWy
+I3F5ek0HDSpwF54DZ63eZ8lYFHGCyJFAaxhtPj2dXhpEiElddr81xUtbcjqGSsb1fHaMJSnm5tUJ
+NuiitNsreAykhgMXitoKDWedAXHQISLQNw8wq+Veh5tNDMcMULYVDli4dO9tVkuWNew5181Lj5FY
+5K/IamzQ5VueUGtyWmFrl/J/owwembKt2IMmTB1uhQXdkZQVs0kYwGHWSdUuKgadlVvBII+G8mzY
+hDzbDblnyyqRrYuqTlc4pbp0ns3g12KxZdmL8BI+A8/YniUl+XcA/AiAgVLqEKIy/YsA/q5S6ucA
+PATws/z4b0LSke9CUpL/3jP1QivoysKRm+FlxgOcqkbiyeD4p4DekQk38gJgpHbNQGP0DZgzAkU6
+LkwtD4jRsqHT9SIOoSsZVbshD7mJFJWzLnvOsPTIWIR7OL4qQ/O9nTa6Q9WboAAAIABJREFUL0gK
+acpNKo49kBIRtV9jRY4+7ZQwzuQPpZMj8Mj5SKHVYmlgxToBnZyhJn1RVgdr6kbkfBgT3YVBMFUj
+cmGtzetlF1aD9xSmUJSSjwfSt5udj2Frh0pBlo0xsx2NRoi4lteep/CEupin1N38yukKH6W5G7cB
+xQfvcJ7iUirjPbcsJCzhXo1lofX2U/gOSUZywPflAcvyERxF5ix7ifKcNQFU55l4BR7FpJTfamKv
+K67ZncUjbHRkk7lebUA7hEozUxFuWBifkSnp0EG2Ie6hXWuAZdK6yFE3KC5D0Fe5VCj5VJVtC9aC
+rl3aQ+LKb5c5sPLXlYuME+kMo1D6cFB30e4QK+43ALIbzQhRr6wSBTf9xeUOzMey6Tm7WzCn8iBr
+34HibGfcVEwAPtPhVVrBJDFvvExR1iyfz3M02xJv8su1qpmHeCVnbhZUwEQ2nqgP9DIeZK0WSrom
+z9q+46agtf6z/4w//fg/5bMawH/8wbpw0S7aRXue2nMBcy51jVmSolW5OExlF9yrY+Qr2XUbnRBV
+TLBNoGBMebqzVj5Z1rCYfXDzEP6QphgskMIQqSqwjk+iZO2+GYNaGygKE/OYpqYFvLxghsO0EX3l
+DflaLViJ7iUHNwc0HQP/KQTXOqtgDGj6LacwO3RHCLDJshmSMYle0ggrcoUt9AILRSpykpg0Wx4c
+hzBnO0eLBTOhkaFFYhSv8HBFDnRYufRtf+BDpWvREAu3Z2IR7L75ELOX5J7evN9CNJHTb7YirLcq
+8am+WDljNcPjOU0hM4G1Pv2qJubFnGPPIK9tAgWhzXaOhAIwQe3CIwQ7X1RQDAq3N8WKic8UNppi
+wu/sd9DcFPGVt8rfhnEmeLcnl/vYS+Q0fsr9eK7QpDiN07JQEroOc4aCdIV2tEJKncqHE3E7ktrB
+LkWEDLMHs5CA9sJboEU9UT/yENM6m5QyPp29BlxXMjGe04a1Vv9uekgb0qdtZoDecSzMU1mns6MJ
+QrklYAIoZoTySYHRmo6NCuXNXo0WBWLcvsZqNuM9zbE44cssh0EeEWPAbFhswa1JzhP5MHfFXWuX
+B2gOuTCyBUx/Dx+kPRebQlUD86xCbaforyTFNFp1EXU4WYkDz5PJtcpt5Jn4xiV56Aw/gqqINPMz
+6JQAofQUFR/0cnYMlcnijUhm6loOFGtTdRwhMVn5qA28zszq27/268j0ZwEA9lJATFv2BlYNiaf2
+3AAZy55X1QQBE7I98x6MkqkgJhyy8xUsohjzykWxRpusfChXHt7VegOZzaCoRdjrOkhYWZeNMxRk
+Z7KH1jcBK6T9zh83Ub7CNCx8jPl3Zxii54n5OU4O8aWVLKDRiVy3WVT43D15UC6pq4iW8sAa2sf9
+EUuVzVOsDilR3xSzdXY2RH8gQKCzbxwiWQpgJ1mY6KzjOZYFj6nRMicJS+jBMASROtjoIewz1jC1
+MGcpc1P34boyP7cbrPeYxbhRCwvT9GgDoU0VppEF3RAzPr7bwZgm+oPPUdC3v0DiyRq5dmWFdEQC
+XTOGoruJKkM5kfHvMaMSbHfRasuD1zMuQZFNyyxSxHShTnJ5WJ/M5jBNuUbTMNGeyYPZiHMoZgxO
+l6dYrTUmmZEJ3Q0UDVnf6qwF1AQ6TYGUmpiGqlEyBW8w8+X6BQp7nXEZIy/E5fFrD9lS7qnIMmiT
+LtEztovah4t20S7a+9pzYSloVaNwUpRpAw1G4Q1TQxFYVAY29Ep2bm3OYeh1HpfMuksHhSMnV/n/
+tvfmsZZl13nfb5/53PnN79Vc3V09sZvspiialAhbsKSYUmzZiQNDjuAhNqDYcGA7cOCYEGA4CfyH
+h9hWAg9xrMRIIMmTrISQbJqyRFsyaXHoZpM9d3V3zfXmO99zz7zzx1r3dZXULXbTrO5CfBdQqFe3
+7jvn7HP22XsN3/q+yqFSmvRqMMdqIrEuLKalYYWKbVRrYDVcqUKHjioeH2U99v/tZwHY9+c8/4bW
+5H1xA37yj3w/KG2XHRvWtsTFbaQZk0o1A+chNQq4UjGOYNvBWahbHbSoGrLKRxs9vFpTjUeKdScn
+WZGdyKmGmELG725VNNrynY/sd3lZu+EuqsfZPh0xGS6UniI2VAnp4V7MTBOsD3cK6tsyli8rq/GE
+msTIDpW1jnkkEDf61iBlrCHG4ewqc6VKi3THPJjs01UxmOJiBc/I56sbDrXuiCbI8ZVqua7O6nMa
+k/dkF6zsDkbBV6bc5QXt87CzCTsN8SY+rNyJ13yHWhO+XbuJq+rYUTdirmFMdpjj6O59+jEJq86v
+n6Wt9H44wLaqgR0nlErfF0znuOty71qazAzmIWFPkoB5fIi7oIwvPFjRTsrb4q34aUZDhYiaYYCn
+KtDGTym0M9LaOa6Cr7xKPyszpnMFZzkF2a4kKEssLVeJdtpdvKlSrEVK3RZ7WJ1Pnb7hWEFtzvoR
+ZUfeo5pHIFt6Cktb2tL+A+y+8BSc2iFMQpz1ksYZWT19GyhWD5zDIZ4mD6syRRdacl3TKuORKONy
+MUqoXYmHvaCgUFbirmlQuwutSK3tm+CEULOwKa+ekt34+q/9Cl/Yk11sNqup8kVNX7kZjgsSX9l6
+PnSKSPvVvZWzmN3L8nu2ZF4LpmuBXCwdF1c5IibxMU6l4jNHk5MuyWwocfFeadlQFirCVba0YzQ5
+TtnVjshrrSEHmWDDnlcy0HOTFM+Ra5/PungqVDJ0ujzmC9rucCfl8DXZVRMVWzmew1eHWv7bqygQ
+t8nPMhq645XzNUyoHpnCgSe15dUjHb8Zky/gvGXCVMc6o09TxWxa2n06X22yNpJdbmYyOirgc/WW
+ZaMhXsoDZx7nwuYFAFJlhj60I/qvyU752Tde4Hu39R5NAiJltp72YTyRsRhXjmuTkFuaJJ3tj8kV
+xbgRDtk5p/G+bTGy8p3cyHzq5Q1S5Ho2BlOGiFfgreQnrF2ThrhKG16XqNZGrLBDQyHhVSMC9dic
+LGfWUS9TcSo3964T7S3K3TnzVJ5JM97hzII0yWSUirOJNelc5B4zPcdh3GAylusYFYZLx5LldM+n
+TK0mlt6l3ReLgjGWyC/pVS1SV7OmaxFWM72u5zJXuKvxDVZBAvMbSqzSrE+EW7EuPe0KGyU9ylQF
+Xo7GJ/Tb+Y5myDmFVbc8u3mb9KpMqmuDKzQ0M5wUFSo1SK4/fP3Fm/wn2k58ZW2HJ85JQtEEU0ql
+FB5nCXasvJENeZGKowa5JhKrvRzX1wnoFuy9KQuZVYWiq9MJhyqs0lxLOZzLfdlolNTH8nvXjwNq
+ZWP+A9/zCQAeWd+l/+JXATj8yiZ9BVn9AGNGSgce2oCm4nFKJWzJ65qJCsq8MrvN+Q2Z/DMT8d3m
+AgD7H06o39TFWas90ajByqoCstIRL8SqH3mYMZrKwjKxDhfOyMJYNCTUOuV0qM/JhJ4Pj8msuM/7
+ScqHKpncpxtP0W4LfqGtLeCP/bsOP5dKovGNWymtn5Nn9uDHt7i6K5O/exByRQVwG6kyYntdfP25
+f/A6D+3IXPj8Efy2lkKJ/ZBSQ8sFfHi67rMxk9/bzYa4c4W/X/FJFVp/TtmXv0jOJzZl4/HKmlTZ
+vJ20YqZVibFjyBQ4lkTybDZOdZlruBKGlsO+VraqfUKthvgmwldFqbbyciapzzSVcd5KfbIz2ucy
+OUPD17hwmhG339trvgwflra0pd1l94Wn4LoO7U6L7dNtNlsK180NRFr/p4VZEIL6PRLVPFygFT1b
+Uarbtn22QaW6AGvHCaPjhX7gkMHeDT2GJoM296gX5aTZFZ79kuxAe4eWbCarbn0HEe6il+l1k9DV
+Dsez6U2eagr0Neiusz5UBmd/TqLdcMVQk2h+SEOFY8xOD5QgpU0Dc152/KtH2qmZW+YLJuqhZX1F
+rnPfuIyR3zsqU1q1agYqxuCL/7Li//qpfwiAbyOOVSvzK7MpsWobxtbypn+3JmRu4XghKT9JyRTy
+fGq9wfVFmfiVjP1dTY7Gcqxpp+KsclasRtuUI3kmt7IUT2nDWk6JMpexXcru2tzY4uCKXPu18DbN
+BwRDMawdhrV4Xtsba+yck+9M1VNqbL3ElfFXABgkQxz1GqfjnPPfLed78BMXafZld5/o81stcvZC
+2cVH8Rm+eahydKM+65ooPbexReDKK2Ha8jzOJeBoc9T+sObqs+KFuWGH5rZ4U+NNRa4ez2k6ElL4
+Gyt0lQoudCLCSHb5uXsTX5vetpriPXVWzrG9Ih7BaDDjtBK3TmY1ua9oS6Y4fVX6Vmm7tMooFcKc
+zQ9pqNf74FMP4W8LMUxQtfHD9wZpvG8WhZVOyMYspjOTRWEtcAmVf26yYvAVy+4Gc+JIatpmW1zZ
+4mpFs7UQgAmhLQ/DT3qsfFiqAXuvTPCRG+VVEg8XhctAY7J/c/AaN4fi5j8UZcw+Ijf+2VcABcUo
+PIBOPuH117UeP2tTXVKJ8LDA3ZHJ1D7aIGzLsct9Ze3dDChVV7LdbaCNhgzKGaVCsM8p1HjFLUma
+8gL2iyY99QbTsWUvUUKZWxEPKg9gXEj+4ucf/Cd8oZIXOp86fCoWZ/DZb+yD0oz/9tYpzk/k5IcL
+vUcLubrGB9enBB1dFJsR5y7KmFb2WqyF8lJfU93JB8I2v/PSxwAYn8t49l9LTsQOXQYzOd525NNz
+ZOFs6wKy9+KUg5aGcfsBBy1ZDB/aqOjri/DS9cs8ffZp+Y6+CNkjc84bedbJYMIVzUWMgys88qYs
+CqvrOzSV1aitWBZ7mNAeybU1/Smf0/r/7izlypUFPDrkoTMCVFor5IVvn4o5HCujcnaF44FC4Vem
+fHJNjrd7Tdmoxnsnz6Z5MME/L8+yVdWESsqz7Z2mqTyct1/RPNh0SKCTzMYxsfJSbjVLEu14zcoV
+klqrK5nC5tMxrxWy2ORHIZuX5JpT3yOO5TqcaI1aCXjerS3Dh6UtbWl32X3hKQTWcKoISTcL8rY2
+zCQRlSIM44MjEqW1DQKPSllw0SqCqWaYVFZr2k2iQsVL3BZ+pHXeuSFa02MrYqw+OstXrsqO8fKX
+LWNFJhZRl0xJOv7T7ZJfeEOOt96VVbvse1Tq4h04t3lJBV6eDDZJK9nlZnmFq3oIhWIQ0kGBOZbf
+6/v7ZLdkp7hRFUxV1MPVGGWjY4iaskOfGtccqD6mV6b0J7q7d2CuBC8vPaseVutxyGWcG02LkhZT
+1jWD6zKm51eOOBgqqYdSiVFXFAtIeFZzWyXVG9eH/LpCmsNJyVg9i6Zm0w+qmmNNZhaHGY1dX++x
+R9fX+v/W5klt/hj5u9+fMDiSc+RRjz19vs1inSdVcKUqn2SkPARGuQfi/imGiXgpAQVXND6oqLhy
+Q77TPPo6V7wXAHiiJaFIvmuZqVf15tERnqNJ51FGptc5HhWkZ1QGflUrRt0VisM3Adg7LsgdbX4r
+HPa/riJAmfz/JJ+QHUr4OItLjifyHArPpVsuuiArvEA83SoTj67sDwkU0dk0KUEs2Iq1tRyOxNtq
+dkpyRedOlO8xnYE7UWlC5lxSNXIvL/GV3TyjotaE5rs1YxeqnB+gffhDj9hf/Jm/h/e7/imPHvxd
+AHwLC6fnvOOwru2i5UaDWnsGVpXbJali1jaVjSZcY3tdvrsSddl5UEONwmeUyAIwOdKH1XGIh+LO
+b330UTbOXwLg1Nmnuf6MQHefX02InvlVAI40RPn9n/6LRKo7+fWb3+Sbt0WA9OD5X2Z1VVtnCYn3
+ZaGa+gph9WqKY5Wqn0KpJc62F7Gtx3bacr0NmzGYysScHV3hIVVYmh+41OvyUlCVFD15gW6/KlqS
+r6UZvlKEO3GH7/uYTKCbf/s1/kGtrElwohm0qHiFnkOlGfeksDgqZppbB1fzARjDYj0+aca1llIT
+L6Y2VKhsPZwIqVIbrD6rd5pui1P83h84T/mrEqJ8Ph/R088zXbAuOD5zLc313YrRXEdSWEo9tuuY
+k47JoJbvFk4BqsGZlxnaakJNfbIzWgORhm+OdkuGpsUkV+KVwlIgC/1ZYOejjwHQUMBdGMF3qfKU
+mTU50rs8LubUpS6AlSFUwRiUc9FzHTIFd7k2p1q83M2QOFT+z3bEqnKBuh0Fy7khTeW2nM0Lylqe
+5t74WMqgQK+e01uV6/xz/+NfesZa+7G3fwJv2TJ8WNrSlnaX3Rfhg/fKFda/98dozN6ej+U1W9Pc
+UHblsx1WtZMQVdbdWqnxtf+92XBptGT3P729QrsnnxcjOFQ9wtpIku316yMcFQ35tZtH/AFN8Hje
+EfMVCQMuZBXRVD6/trWQR9tnP5Qd/+Zzt/jqF/6JnCPJ2HxIvIPvjloYhZp6Klled1KGU/ksmKW4
+uovlYc1INQgbShYz38/50g1p5rk9OmZH2ZdXWy7FSMb0yOZpnLmqXKscXatIyRQOOw8tH/9fnwHg
+L95xP/M7flZ0NN3QI4y18ceWTBW/YEJY1c+pINAdLVc3YJwnJFr/z+5wAyre2nEqLHwLh3ThuTz0
+y9f5q3ccR5sET2jKX6DkwRXxjlarOYWyGSe2wFkwN3suVmHYSpFBWkfMas0Y1zV3An/LxQ8WEvVP
+faWWn9ojbP2bL/46sKEe56Feg92c8vTqEwCsrZ1hXclpwtBnpjcjH1UEygfSVbDIcVXiaEg4rHOK
+mdLMm5JOLAnK9kaLTQXB9ZVvYbt0GSgVXGtWcTtVObk0JVXoX5h7ZGffW/iw9BSWtrSl3WX3hafw
+dVvQqN+Ztc1auKKouR95+ElayqATafNQc+OArC+NNs2zCf5ckj3b6yGRkZ/LMyO4LCvtfqzx224b
+fMkzNF/2edX9twB4fD+nlTG6//JlRlp7bhTy+4Rr1J8TVuZnf/3nuan8DhfjigeHsgdd+PgOO44w
+CDXOynY1Gc4Z6jiPqhGjhrImjS1T7ZWfvCSfPdvq88qurPBJarmlDDze1LCWqKTdfMal9gUA2h39
+7JaLFyiZp2/49Dve1btt5OZ8ROPX4FRFqhRj/naB39dy2vkZmxP9zobsZs98LeeZRe4geet4Fqi+
+jXTVX30Hl2KxWde15SAW/+bpcpXWaTnptT3IAoVjZzXhisKKFwhfr6Y6sCfX9k62+L+8+tYUZruO
+lFEHqmVRH1o+eyRJ5z/3xGkeP/1dADSb4BrJCfnlFM9XvEFDE9HFGnUpu/zkaMa4khzUcBTjaet4
+UNeESoQ7iVTBO5qzMpbnkK3kuEN5Byp3xmFfPN2ws4Jvo285ljvtvlgUsMBvAc/+wxfX+M+fEqKn
+6GPfT3MqN3C8JRiEuChY3dR69JrHROvtDWNOXD+3bGJ6EiqYUroFH1/rQyYv7ovVEZNC6bX6Q3hE
+woPjpksnEDXmh177N3KsdI8vFV8GYLfZIs4F3NPaWmPzMVlAHnrgKRqJTJbmObkGvzVgY03q7hfi
+a4w08feaf8BgICFB2pXznt6LmK3K5Hj9dkrDU2KZwkPxSFRJyEjZr0vV18w7JY7iMDZP/OJ3tsd0
+gfyec9s89H0/DMCZYkJhlGSkKk4msdOpGStG4MKeisKYLzN/5mW5V1XJTY1NWg4o/gvfCjjqXdm3
++N4fuHSa33lJOiev97Z5oFAYexu8PblfTf80+1ae9VGmGJI3Ev5FIsnYlybpbzzsiS3Wt3dzub4K
+rpxXLk67vc1/d/ZHAHj6sYuYSMMZp8ZfUOF1G2QKZou1Zyb0C6yKyBRVjVerzIFT4Sl8fzACb0c+
+39JKVNXwyEayKJjVVTYjmfd1HJIfK2lPdIlG9P6wOS9taUv7/6ndH54CYMxvLle112XN+pM/+QeJ
+KgkD3EaF95okanoqJRcGBaFq7jkmoN1Rlp5iiqc7W1HAtrIxhxckZAiaq+zNZadpXK8Yt+W4m+fH
+sK/0WNE69ZZ8/1RbvIrxOOaTE1nZnzMFqH7gD+5s8zu2PwqAv9mltWh4Uu3KdtAiV32GqBdRKdru
+sbbhliNew6ornYztcMzmNW2+WevjJLK7HLslnu5+pi7IFdEXaNLVryLqWNwu90Rj4jebr5oaP/m/
+/x4AutkaUUe0EIrrE7yWNvPMBqBMw3k2p6uaBG4kruzFtQf4s6p/cOvqIT81Enf+QzZCiYDYPEz5
+nHYtHqv38k4d/m83DwB2Tsn5/uv/5gdxkNDuYuixfiDaGM2VDkUhXlZcrTBOtRlrKM9gvlmy+U3x
+MH/mzUPeULq5iX3LOwCIlCFp4WQZLMWi1+43XFNb51xHE9iPbjzJhSdk3nQ6LUwoY66SnEyZrb2J
+j1XIfq7yf3VegrJj10VNUikPhVdSKNFvo+lTqwr5yb2burQWKMfSod1VLESjidHy5Dx26USN33xD
+fwu7fxYF7r7pOyH89McFqrpaPkWjLwvEcFRiFD/f7QquIO6AUQEYYzMC5UzsmDGZwmrzYkyoWPSN
+VFmLwxGB9ifMTm8TqAbluu/it5Qya7RCrgIn9iF54evZF7kaSuz/dOxRrwt8+qMff4LORy7Id3Z3
+qTxx42tXwC3GrNHUyke5sU1jrMI3vTM8rBnnA+3S255MuX1WFohL18a8MpEcxt5hyZ6yWO/UHs2G
+uMKrqq85Kl2KqYwjPvX2in1bTcNP/eCjAFzo/ajck8xnVinjcPgK1Yq8eDb2mStpTW1zfOUj9D0F
+E8UP0dISxsM7m/zRgUz0i6tr9PdkoftG8xo7N7QLVCnMjt7BP3d4ax7UBp7syfn+0nddBKDtPsG6
+hkqTIKS5Ji963YvoZnK/S+thxjIHSqXDj5pTLjoSgn7P/As85sgC8rlJyRlXzvhmAYWGm7VehY/B
+3DEz77zsRKtHXXXP14KrlLUsUm6ZgYLX6mKE60lommcpkc7J0iq/ZNwmyRaLgqUoVTin1SXXKlBU
+ethMocuBhC2pmeE1FpW4CW0FXAW+S7Qq70P/9jXS3sW3v9nvYMvwYWlLW9pd9u1K0f814PcgJe83
+gP/KWmkuN8Z8BvjjSOn5T1tr/9W3vAgDq6FhVLzFWfDkepPg9z0FgLW3mSrE03Y7BLGslItEjksL
+Vym8nBycQFxDO/GplM7ZSTJqXz93NPkUrOHW4taZvKBSNNq43yF6UzLLE56h8/CHAait6hrGHTrK
+iHqbBOcBSVxW3oSxah5WwzHOhrL1xqoH2GwTREKf5tiY1NeOSFtRduXcq6nuIuUhD1QSHk1WbvLI
+m+IOPu+9xpdVO2FsSsKp7IhThd+WuDhau54VE0LllgiMS6J++R/aPs1Hft9/qWPRBC0TmkqOWmQx
+qEdQ5B61egWum2JVgMdRn3o1Dmmckyaior1DfH2kY13j9Cl5JpdWO8SZeFtlJM1Anx/ljCeqNM1b
+1vVhrhiIDMvmiux+vU9JcrHVO8RawYKsthziSEI644dY/b3SSXEUAhnoZ9a7wOlLsjOfiT9Feij4
+jvOvH3PmQObDzwcZX78pHttAx1dg8QMZR1hVjO/omo2VKs0qWU4xaYFyHqSzY2xXSV6nKc6WPD+n
+nDNXKj9v7p9cb6nkLukaOH3x2Oa2wmqIYbwAo9SBmSceg82nzJV42DgZUw0lw+kRI4Xy21aLqRnz
+XuzblaL/JeAz1trSGPNXgM8A/70x5nHgR4EPAaeAf22MedhaW/FbmOs4tBtNfDdnWMpXP/J9W+y4
+wp58NPSpFB7cNDHlhmRhFy9/GtXE6mZZfGrNLpd+iaOceqa5glWabddVEFOVUWRS3onrXbINhRUP
+jnlZX4RNIprK4eerdHw2HzPxxG1Nd0IeiPXBtHonDM12dY1YOQGjdQkDbJXhhcrbmI8xm+qq9ksC
+FSYtVVjGMxFG+RBNeZpEBUBa5SpbGvG2xxVTZfO9ZuUaeyGcacpLE4dvyZDXgUegHIyDJ9boPST5
+g2SqvQVpj8VjMlsR+Vhe3sxaPBXlMcbDzORFyHtyvX7tUS+EaooxRmHC9DK8hlZUsh5nTslimJyR
+5/HJ4YC9l+RF+MLUnrissevT0nzHwIUf+Ji4vuc35e/MbBArF6NTNQhOywZRThIWCj1+M8RRmG+1
+iNWrFSptc42n55kaFeG9tUv022RhfXxynUyTHv++WFQoDHprCb0IM5fPfcfgazwfNeQ5ts5GsqAC
+6ZolP1SuzDo7YWEyORQLLnpX4cw2JMvlGuqioFRBYi8IqbW93tYGp+frr+lcdzrYBQS73aRSIqI9
+z8PRCkXtNgjqd1GGusO+LSl6a+3nrbWLM/06ohkJIkX/j6y1mbX2CqIU9fH3dEVLW9rSPlD7TiQa
+/xjwj/Xn08gisbCFFP1vaWEccOlDp+m/MaApuUNObTzFkUqN5WbKIF4ItRyyU8pOWFTqhjnuCdjE
+L8E01I2cOdS5glDKGtUpIddMv7ElbqGdmO4mI21a8WKfjURWa9vYYKSK0N1AJdFe3uN8JtcwCWfs
+aojywHBCuOBrXF2hVlfS81VWbTOkHix4ISzVVI5RR8dkR8ol6C7Yp6EqlBKu7GMXZA51ia80b1On
+wKikW0ev0Q865EovFlOTa2rMzXJczU7/3o99lHKosmiaqBrPruBpIotrhiTS67A5eaHeT2ZB73kx
+F5c0xaHQWvm8SBkG8nMx7RFO5vr8Jtg12VceGksY0GvCT7YVLDabo9wzrKwEqHfMzsMrPPmEhJBO
+9rCMs+NSaAUnaFQYrezQMeAslLstlRKOeD15ZkX/gFQbjdLGPokCjsaPOtx+Q+bD4HZGrjDuRYeW
+U0OkGu9JbNCGSnZMQKDq110VqnG9BlY7EmcHBaW6/vMgoFkq5Llu42gH40yrF6QjaleeTZhZMp0D
+DVw8bVmrmS9oHmlr5acsfNoaCt+qB0y1KpXVFaW2kgWrOf7sTmD7t7b/oEXBGPMTSPXmp7+N3/1x
+4McBWo2Ac1urnFvbYuWCyqEHDsdKjT6Y36ChLEtVcQ6nKROvVnWccrKLi2R3vYbF5ppzyI8w6uJR
+jEDLkwtBljo3+LUSZnozokUlYpYRa+cbYUB0W2M1LTfdrvYYBpJBmR38AAAgAElEQVQPcLKKhna7
+jWuHcKGQNHwDkIlcBupGVl1qJU6x8zVMKd2VXhZROUqyoe53mdwkd5R+Ph3RV6RcMgHb0lh14uE0
+5L74pRJzuAF+oiQzO80T8FZhDejLnb55heoTcp1JXxiUysEVjg81692oqHItSTIn0pxIxQhXw5VQ
+F8LCz5iqqEmdljQDbaNO9xhqNcPEDp11uRfuWTnWcfeI6BvaDm/mGA1zOnHEudPyLC98qEM3kvth
+Oxpzj6Y09ZGa0UXqtrJv5S2sdg9ShrjuTO+zPEfj7dBUcdw8eIBTKk7z2Oo6L197HoDXXPcEcGUW
+XYuuYYouPImLUnDidGsinTvNFc0p1AOKfSU9afuUmW5I2ZgyFVRoEFXkOv9IF4pVAaGGdsbzaSiP
+Z1yG1P4idJNuWgDXU1Rpo2AUysKzctBgMJVeGac0OI5caHY4wzyoAsDv0r7t6oMx5o8iCcgfs2/1
+X79rKXpr7d+31n7MWvuxSElAl7a0pX3w9m15CsaYTwN/Hvgd1to7EO98FvgZY8zfQBKNl4CvfKvj
+9fwWP3L2E8yaq+weSpa6Mi5Xj6S+fzwY8WAsu3TrsXNkSlFV6UrsHPt457SrL3OwKqHmzeKThJP1
+J9RKieWOJGFYr2+Su+KNxPUqkQKA8nTGYCxrWZUUdM/KLhdNxM1s1Dm5CpL0Dwb4F+TzXj5nqnRr
+5aFDprXw3NfOO9vDV0BS7eYEuYYxnR6+wpi9huzgYepBIOfgaJ9HLjwEwObaLhszcSlfvrbHjcNF
+2CE7TeVDQ13KTEk+ACw1i0jqrx/f4FHzNbk2ZUZIrl4haslx7dTgrMk97kRbuJ56MfPyhH0gUPm3
+1GngDMVTmIQ+8z3t8owdwrF6MVQobozemuz8n8ot7im5zv95DGPdVx5pO6BJV3fk4hil49fQYOZO
+aNeaSN3xcZQxuYx8vJFWGqI5rnaKOpoEDAqD48vvhblLek7mxcZzx3RW5N7GjsO/qKQq4d1U0Zfa
+4miSt3aqE7XxK/2Cpx9T6v+GDK7nNE529nw8ZKTViaif4F4QL2aSjsEoocpcE9ibHrW6+EnHgV3t
+Z1g1uIpJSQOPtiqFOyqDYEyNr/TzTI6ZzuXe3rp6nVCZu1dWtjHJohf23dm3K0X/GSAEfkk18n7d
+WvsnrLUvGmP+CfASElb8qW9VeVja0pZ2f9m3K0X/U7/F9/8y8Jffy0WYyMN7eItmGnE2FpaY5978
+MuNdbWqZp6Taafh9SUbRlZV5PFZ+gwe6NBYZoKhNqZ1jtnOHGIjpkZWLMo2s2s5kyFRZiuaJh7Mm
+u0OctJiva1IxCojOqKqypyUv3zLVnvaiG7CpsV7kbZBpwu+4tNTaqdZ/UzsYy1doaNloPfZZPyuI
+zbUpNJVxKVA15CwPqWKlYFs/TaIl2cxtkSvc1To1blOZdwpF+a0bTje3dMxvQYZdx0AsO/CfvPBp
+ivSC3Nt92Rlv7NfEY2nsyqMGW6oBUe1M6SoqkMilVA2EWUcTsf0ZM63jl/OAcF1yBsxGTHcU5nur
+oNBdsTuSc7gPb9N9SopW8WuXUSoEZu4K55vyfJsrj2AC9TwWOY66S98qvDixlHPxturxiMqT+dJo
+O1Qa7xeK42DdwUvk3tdBzVzppU3vEW6eew6A598IiFV3xNU8grXFCczZGIcFyNjBnDBp7yhNX3tn
+i4Ynv58VFaWSuB57htZIxWm8AncgO7rZ0uc7DrCaUxgeQqCJRqZzcqWxOzhyaClT17qK/BarLvlI
+Q++2S1pJ3mUY5LhKQLG93iS0760keV/AnF0vYHX1HPHNI17RpFw9DbndF7fHTCvaF+UBdBsdvFxe
+wlrVdEfH4G2pEnNSYVS9qSgrZn0Fo+wlHA0FW3CoxIXBLCFX5aHVFR9ceUl7bWjqwrEeRBhlWl5k
+iKuRw4cdebCv73gEXXkR1s/06CkN2F5zxGqsdXolQBnXBRyqWErDobEmL29YjhkXknQ0N2VRODQV
+06syCW4cDbhxWyZ8VmVEWlG4WllWrIQrm6omdXrtHGd35NqyfoG3qIUbh8a6Jru21kgTaeG+MZIQ
+7YXRdWaXNUvdKLhwSsd/6PPImraOb3YJNDTx57LgpccOI53Qk/mMVkup+MsOW5mMf+Dd4OpU2K/L
+TJSLVho9XpvK/dxsQKbVgDhyUCJiOm2PgULWw0SuraoHzLQqMxrWFBO9jrWE+UGlx25gFzyHHRWF
+oaYYy/iPC3CMwMZvHNzk8ldF1cs7vEWtOAVfoc9hDZUmQWd1/VajhLG4StkW6PXGWOYKIErmE6Za
+cZj7zRO+UfbmZJpmu56qDqTvYPrKAt6e0jinHZVVj5Fez/TgKmPlxes+oJR/1Vm8joRoh3lML5Fj
+nAlnlKkCwNwZriah360tYc5LW9rS7rL7wlMQRPSYdDti57as8L86+iK3+kps6pbsaa/89Sv/hjyW
+BqSbtxVS2xrxxFgafOrWGrEru2fUqbGKNxhMr7I/1oSLumF+r0EjXshyeYSleBhB1iDQ3cjx2zRy
+WXVdXUMzJ2eoeoDbdUytjMpT18doSTIYDRgmmtDUWrkziTlzVsk2mhuESqAxGxli1XG8noqnsH80
+Zl+TgHuDMYmmZrx2k1SvZ90paSrybiGlN+kPSRSxWbkhVtVemo2QR7ekYai5kVHtyuduItf2Xa0Z
+YyWPZc2QKkuyUyVUtepI9FNyxTWYUu7rraM3efFAnsPYBHRflym1024z2VHS1Lxxoj0wVvq7w/7z
+XHtDxhpWDuWCNNZ1CNbEg3ggqk6g5bO54i2CBkEuxzpMb7CnVHeHX+3j1+I1nFkLiH1N8q5KiHK2
+eYbpWMqvv/LVr/KNoZz71msHzGYy1r6pcOvFc5dr8JyaiXoj1R2tnYGBMBSPrLEh19AtG4wymWMH
+h1OssnjPzIDGXBuifI9+X+XyDmVMx9WUiSJLz2yfY+tsT+99Cp58d1YVtLRsnWui1fEL0sU83Z8L
+GzBQjkfEPfFe/d0Uu7rCe7H7YlHw/ZCd7XNUCVweCzX3udWzXL8lru0oKfjSdRn87eyQ3qrc+EAz
+zw8W2+Q9edHH7gD3jNZxTYQTyiTshDWVxqpvKsnTqDZEKgK72o54sCnV1G7Pp1CodO3OiCvpGDRK
+F7wW+RRj7b6cT9m5KBN6veczU5DRc9+8zS9flYfkhTKpzrXW+dTjHwHg4jkHT0OiZnOT3YEQlXAs
+j6TVssRN6XJM0hUSFf2IwzmNWFzUrhOdMP9ePtBzpSVHh1oFbsW0tKrRjjtsquu+fRiz/YjG7foy
+ZvGDzNQNvnnlZQpVMcqrklKPEa6EdBW6PO2rMK9pUR3LPa68isupHG9QZZzJZZE5vzanvS33sOOq
+GMyo4OOn5Zn94sAynGunYVlTaLg23PN5YFUXX+XoLJw2g5nU4/NjGOocGY5GNJT7sJiXVKfkZeoG
+cp2lW1ErTiPb3yPX3pV+CI91ZWF80PjS3QjMU2nV7zjP8gu3lOhkzEmbZFrDqabS0ntyT5KioNJz
+5NNDrg7lvuwdJLzS0TxPK+WcLy9sEGhoVEaME7neuFWeYE6isMTd1xxTNuXmVVlkDnKBoD+2mWCV
+Gj8bDig0zBnkFYXyZjpxgVu8VYV6N7YMH5a2tKXdZfeFp2CwGLcmCDo8flrCh/HOG/Qqoai/7h6x
+2ZJd88yDD9PckFX8eCSy5itBh4VufYZHMtKkVcPHKJXY+s6DVJuyYj7UUF6/ZMqGKlGXmSXeVDe/
+NNQqcJBXOeGiS9Bb6Cm0KVTWvBGu0wpkFwjic+SF7GLR6Q4PqGS8ZyTc6W55FC1tmPEjGqW615OE
+IJCdtLkl13Zq43GOlef/k2HB15ty3K6bM9Z++1ONBtd3xe05sAsegJx2LjuXX4VYbcoytWF1Q0Kw
+WTMm1HvYsTKmwIsZTTQJGj3Mm6l4UO3ExVbyeVmWDPqy+zfUS+vFEU+flwTt9ZblCVdc1bJ2eHhD
+rn887tMINCvvy3H//WRG45Q0ij10Y07QVuhu7DOslffi1DoNhfQ2t+S75QzydTlHYwZnCkHrxb2Q
+VYWhNxoZVaQck+GmXucGZlt246fPP4h7QXbxpyanaCr7dyst+NJI9EQfPJLrfXb2KB5C4xb7c+aL
+pqooot2SawtiSXyfO3OGuXJYdgloKMP4mpsSrcp1NFrOiYewoYzTb+zvMwnEm6Ru0lDcg+M6bG3L
+vd0xhsBfhDZyf3K3wVyxIHHYxWiI6fa2aS0KRkSYE1WNd2f3xaKA8TDOKi07Z6wlnVO906xMtadg
+5TFOPSGLxeqZHXLtKJutyXfn5RSrsWB5nBIvsKiNJoGyD5lgix0tEc1imZgP2AaZQom9aUKkgrZ1
+6lDNZAHJ6gKvlMkUZcqdZ1qc1Rj5hlsyVTGU1TyhuymL10cfeYwnd1RFKpfJY0uPzqpCWLddfJ38
+XteSn9JSnio3NcwmbdVaHJ8e8kOnheClLIeku3LcW7NrbIZyDx5al8lRFzG5An2S/hxcWdA2vBnX
+lRo/nt/AHIg73u1JzG38KfGKvByTY496IJPb6R7Q9FV7M0uoi8VLIf9/xq5BTz57rFUwVAn0OGji
+ankvOmgzzqXys38oL9CpvYznFGD0sFewUmqn5XTO+UhekL0s55RWcALt8QhXmlRzyRdcXG1wW1u8
+u/N1Wmf12uIVKs3RrCptf2BqViIJDy9eLFhLj/Te9pkqXD4rKj6kC/hzY3n+q+0WP1BLQ86RvcVL
+R3KfP7XxPYTIPTivYUsjDmjEsri365ANJVYZXKhotuR+5/gUI3k+aVNbucM1Uq2uBA2XsCuLXqvV
+JGjLvLhUtdiMtX9CpQNix1AoW+6N/JBM15XQSRhMFAseWRqz34IA9W1sGT4sbWlLu8vuC0/B1iV1
+dkjqN0hyqWcPpn3sGdlpzq579DQzHtiIWDv4Il/c9mzqMFnwKXQdmrpaNyIX48hKa4KCOJadvpOo
+am/h0daKQ9kIqRZucj4gmalidOUyryTZ42oys7IT9pRyvX/7Jr4SvJw6fQZH6dRW2l3yppCPNBXm
+TLVCFGlWP2yc1PyraUirt0iILcRWhnR9cR23ttdQlXjm85o93QXL3YJdrXB4iYx/4E+Zal7JD5uU
+Wli/mTtkiSQzZ/7TOOoVhNoB5MQu+VAFVLo5W1a8m3mWUGvDEzYHq6CuSpK9k9mM1JUTnvIfp6Wk
+H6FZQRnyiG2XaizHiEdSAXijMefoddnBtuO3gFXHBRyoJ9g4fo4q+e1yjzSci4xHZ+2CjK/hEs3f
+0GfSw1WXOXUyCl+7ZnW3DtsN3GOlPV9LOL0h1arN6ZR5Kd8Z+TMqI+CqjTV5jtHxBi9uy878/eOP
+8of+ilzbQx/67Xzpf/pxuQdWQoPVLMHRbt0kymloV+qpOqJUr6k5S5krg3akzXpeNGT9onp0vbPE
+6gkEfggaKm6ur9NSUZ7KU2kDt8GkkPDRvVqTTmSeFmlFoCFvPpnjb8q5363dF4uC41iCqMaNmkz3
+ZOCtlRVqLTk6sYunbcbhlnOCH1lkesvjBGexUKQxblduTj3s4K1oKWsyodZSnavkJW7Ho5pqW3Bd
+g4q/GhPgOcolmA/paDnJDGVSrW+tsK+6CMP+LmeVq88bl5hNWWScfA0UeecqF2PpDiGVz7ytEFdR
+iHUnJbilHXD69rv9CV5HuzNrF0cnhBt4eArSYdAnGym6b0WuoRe3uPK6vHjXqyN8jTOzYsbBQM6x
+P3wOX7PrVt1a5hHhqvYc9Atc7cEoj7MTslU3KwjWVCdDCWa9VZdyrgxEzhBX8f5x7OHMtEclKDCq
+gFQHslA+fWPA9z0pb/FnvzjjxVqew/deXKM/kwVre7xJqLmGUEFhtOfooyHwDFbBWzVgB3LvypZP
+cyLPLLqU6Xc7+Bfkfq5/PSBXmGI8m9M7K2HF+jwgULao+LoyKH3oE3z8efnsU3/rUfa15LrZ7/Bz
+R5KXcIzMq3V6NM7JC7iS1zgtmbPmCIKmxvst96S0PenLmOdzn2ZfyYa3j4RNFvBO2ZOffb/C0RK9
+Xff0vqbkGh4nk0MOlFCoGKSsKOq3rGvmucYV79KW4cPSlra0u+y+8BTqsmR+dEA9iRiNBKdwsDek
+tym7QNH1ybUXPhjVmFiZlvuasGlkeIX2o/c2cLUPwmn5mGLRJ9CiUnfdayi+vcxAiS6KYZ9aE42G
+Ga4yMTt5ykxJNhZg0do26deSpR4VOUUlLvysAkfJUtyug6OKyEVPdoxq1qcOlOugyqiUerscFRTa
+w1DOZCcuqgy31tDA2wStZBgqrHYo7lUuueojGiNhUtwuiNuahc/mlAp0sjUYlZePrkdMnpKxRho+
+uZWHVyk2IwopFQyVBi6JqnV7BBTKOWE66p7GBYnqLnbKCt+VRKupI2odU1F4XNfM+VduvATAS+1D
+PhZKeHS5glxzYek4xnTVWwqgHCheQhwb3OMKqyGW49Z4azJHsnFN1dJ+heyYciEimavr7DZP4O9m
+rSbNBMthrE+USMha9yLcRBKiVy7J839h9E12d/4dAL3R32R7U7ywX509wFQ9pOsD8TDPPlBxXunZ
+86YPAwWWBS6Oclx4PZdM+0fSUEPU8ZxMad9X/VXQbl78EOtpeGstdDTcsAuejZRxIpiFQVZSKYXe
+qGNoKlZ8p7XBeMEp8i5t6SksbWlLu8vuC09hmg75tVd/kdZ1eE7LLo0i4/HXLgBQ5TDty24VP9HB
+HGj5Svvc50m9yFPhOS3cRctdbSlVz9irwbW6kyioweJiZuodBG2qZIHM8zFWdt4wSsk03p2XmgBi
+wGltmPlGbBmkyjaUTGj1lBQ2yyBUT0G1DqajFK+jMGhnBW9B49VqYbUBy04lcZTsT+lMZeUviwRX
+d8R6MMdVVijfhRVthFo0y8xnTZJadSYci6Ndhj4V2kfGl0dv8OhV4YioTsuOGMxKHI1DmZdkU4mp
+R4NjskMVtXHbNFtKLacKyPN5QqINZn5e4Pmy+3muAc13VJOaQLv9ThfyoDqxxy+9IN2JtwYl5QlV
+3gyVqWR37nKsXA4bSpjq9Rwq/dmagPJQuzltetKhalJYsK2q80PdNhTHkhwt3AKOxSPdexM6R8oQ
+1d2mCCS/0Lgsz9xJR3S/KLv138n+BIfPCA/Fo83fza6iIs8rhelxCoGWG9tzQ6SeyTxxiTf05h+b
+E7o51ONpFjDXMnJ6eEzygOBJ7GGTSj3EqsrIjNyLUEvu6Txn73V5NnvzEYlC06Nei6KS+TKoBzB6
+bw1R98WikIwLvvH5G7y4N+CNQMVD05j6Y/JgR9f32eq9BkBuHqe7Jr5koRntMpwzUSWdtLqOqzSz
+dX2MU8nkjVbAT3XSq4CKW8XYUhab0nbwVD+xyg0MBSxUli42lQMmtbh1jeOc3VRx5mbILJcqSGVz
+iqm2Wdsh9VgezPxAcBHTSYWvEzpcycl1ASjGMazK53uvy4TIDq/iIcdtn27Q1jFl44xMJ1u3EzPU
+xGStsuiXqwFTTbi1ei3MghTF87GxTKZea53+VNudr0kSqhOCYxfUZVDOZDL6URt3QVseFLSGcp8z
+50DH71DP1WWuoFKquEaRY1XdtXSn7KWCN7g5FyDQ558/YveGKiTdwbgxqUJGmuRspG9wcyCJ0DON
+BRV/F0d5EG1Skc9l4UxyD7S1PUtK5qm8LG118TN3hJ1J2HX78vPwigrVVIds93UD2Egx2jX7TCyg
+qINsyr89/lU5xt93sJrlfD74xzx5UUKlSSghVTHbZd6S51/aiMZ4wbc5IZjt6M8BmVLWhQpnPi6O
+Obglz/9g/yVe2ZMKyPm1Ft1twZG0jmsGqnxmx1oFm05IZtqXMc5oKE2Ak2aMtM/j5k2PRtjjvdgy
+fFja0pZ2l90XnkJhU/bsZV6clBwq90C1UdE+EI6BtfYGfU2kbT4wJ9fEXqLNPPkoJwmU8+D6ZRqK
+8nOmNdGG7Gz+JIa2JiOPlO25VaIVNOpwhKcNT2Qd8lA8hUE+PVnRjZKNNNcHeDdkJ2kVQ9aai8RX
+TqVlRDMPqJUdulD0o3FSrJKNHDgj3FJ20kb7HJF2vl3cVvbehsVR9GOjnFKd1oRhmWK1JFnXKdOJ
+nHvfyi7oJQ5N1Xu0PWioErFpOgS6Bxybkl5Xdph5pYzZpUtDG2pskhFuyXG7xwHHR/Lz0Y0DRtq5
+2UwkDFhph6y3lbNhO8JT7cY0Xscxsmu2GwWt2/LMrl8XT3Dan76taORWqyJWbEmcNOlo6dBTOHbW
+Uao6gDDEKtGO61c4WnLOWymJsk2XmmhO9x1CJf8lG3O4Lh5Zd5Cx72uIMp6R7cg1r+3LHLr86tco
++iqbV3LSEDXIClKNTYYKDDnsV2ytyvNbYYZysOCVPq4mx02dEChmwSgfw4PNBwgCgexXdcbegcyz
+eXeN1ZkkjVurIeMb4lmOQ7mXzrTALvQpTMGeqpzXxZhSvcXDeE6gmI13a/fFokDtYsddzvkZU9XU
+e6Reo6mQ38vzmjUrGeL41CrrGzIJK1Xm8XseQSZDaXS75MrUu7odUWlWN7INppW4vJ5qEVZ1ilPJ
+d+fTEUFbXszpdI891au8WVRsKuz0TFuZhY86+KtXAehlbYKGPP2s7FKMZIK4foRVgFOoYJO8t4bn
+qKJTckRXOwbHyS1oysM/LmVM2/OSROPF2XpFZyTw27yomShAaFh4uEpXP1ng21sZm45c70Z7k8/P
+JNtvLYTK8Pvh3g5FT6C71b4svEPfo1Jor0tJlir3YZYwd+QFCxoRaSKLiacvfJFVjAMZ/6o1pOp2
+p7ZmW6G7e70u/+KacG9+bU9BT3P7tkKycRIzUPjv2YbHRLtAB4oFaQ/BWXA4lsUJFXvuOHhNeSHz
+ucfOpt7bQqHdXZ+xgtCq/jarCk3eHc3oaZfkUT3A02t6JpUqxLPP36Z8h3fK0UpMXwWYIsenUBaq
+uRPjTOX5Z56Dq4pVtWshkJOsdCSf45qYsxcE9zFgyqWpPN+D3OWBSp5Df15iCrmm3X0JpbK4oFjI
+EjR92spZ2s8q8rZ2Ws4DTsXn9Yq/8fYD+Y3jelffWtrSlvYfjd0XnkLkOzx8qsH5aUCiCLR26bDm
+yo5h04qbikzc6h4RqV8W6K7KIAaFEtdli4aqZkxxCBtKiUVBodtApl4ADRelCsCWLhPN9M49hwNJ
+4NNPC3Z+m3y+ofRp/tHznFHylpsrLiPdSjYmM/zugml3Qq3IhnxBXjKGeaR97onLdLEzRwnX9uQ7
+qftV+eqbfayWq0fThCdXlILO6eGqlkPQNQQofFb77sczD+es7B6NzYK58vN1C8NUG8yuDPtcUm5D
+JUAmKhIKzd57+Rwj+TuGZUJnLOcOwpRTugOlym3plE2KQr7cnxrmWnt3qnNs7agYzpUreLe+poOR
+5GIIzN/GU4iCKVsqgHI1c7hktfIjmyNxWGGVhm9e5hSBeG9VMiebSDZ2nh9S5nLwnkrQHb62Qoy4
+34OkIFKswCE5U614PVK0qK7Ldw6f+SYAxfToN1+k2jiV+3Va78mujXnUV54KUzMLVVfUKzlUyrpm
+HeApK/hI6diK9oDkUOby0A65/qaMuR0U3FCOD9vp88Vrck1hW6tg44hMk8N1OqVWYphubjhc0NhF
+Hn7y3vgU7otFwZiAyD/HylMljyUKk51Mua6D8TKHfY0Nf3+8ynpDMrIzlVwvswSnkJejChxu7UrM
+Zao9VjpSqQjWG5RDCR8qZSNa3W7hKujJZhEjFeEoDq8R+uIm95wzdFK52a2eTFaveZr8YXHRt0fX
+MYWGBy5EqcKV17qE6vom2iE4SRI8FQApg3VWt2RCh26TSME900wUkQ78l/nmkZCqziuHT3xEFptB
+EWK0/uoct3DUpVzTwkqeeZRKdNIuuyw83+OqosrlhcwnU5qu6hXqmOdecOLuJl5FpcSm09JiFqKy
+rksZyX2eKSPLrXTA/kDuVWs9xtsUV3WnfZV/dyAh35Xbli9fUVUk7VGp3oFLNOt2mKprn9o500QX
+9RX5uxyu42xJVacRWgaert6VZT6VlzCtV3ADebHGer/Hs8vc0tLx1fyYyaG87DezgA8/LAvIXjzl
+yvPyjK/dkOPWteFuAfq3bKyfN7XsfTR5k0JXctNq0k5Um9QpiZTcdzjp42v79Xwo11BNL9BwlJnq
+aMBA9URZ7dLRNurXXn+Rq7dlLI8+rpD29gq55qv6VYynubZJDL6RY9fzLrmSwLxbW4YPS1va0u6y
++8JTiGKPh59cJ6lWuPSywIfTVsLL+7LyvXpYUmrN93PPXuaHPy511601UaU2ZcT6I0INH1QtShUA
+MbctlS+7RzFM6K8pU/RQBWIKS6kw36Lj4SQKf962TL4q4JZm95imK0CfQnfaaDOm3pUVfHU8IFxV
+voR5xnxTKxRFiFUOxqYqEcdbHtlN1X48v07kyTGyXkRwS7yYYk86GUfpSxxeVSDXR3r0FZhShyOm
+CugZpTAZyTH2hio4Uxk8o+Qm9TFoqJEXFeoU8I+efZ1HPyzu/FPbKiN/NMNRmHe816BSWfr6oEPU
+kmlSTyZM1OU93ldG7MLyoCZ869wnXTQ+uQNuHkm9/ad/ZY9jddc19/oOey/EzTneVJ6vO52zpwCN
+1ugqANUTIeFAEoMr603akXTEejsT3JFcx7nvPo+vbMbpmsrpXd7nyrHIwyUvVtw4kHAr763w8FR2
+dHe1z99JxEXPFEpuTH1y37iDoxGgTJU/8qbck1vmiN1HxTuKijWsqkenE8tGT35e91YZqYr3bF9+
+/7s+/Ahrp+U5ONtNvH25O2n5God9gWN/bfACXyrk2G+8KeN/5OGAnUBJbdw5qHRibnJiT8Y9IeEw
+O3iHu/32dl8sCnXlkU7WiYKcpJS4PZpbxhOJ/Xv+lGtaeor32rz0sriG4SPyckS3M4ZGaLopK5pK
+Wpm/PsKckwd2dP02bldu9lQJNclWqbQESgkTBRPN0hHNrgyQ8lAAAAYWSURBVJzDbXWxnrp7Sqoa
+jwpmlTygrGeIFBRkbAtq1ZFILe5CLFZzHMYPqacyptH1KVPVjbTxmNuvyYS2RibBS6/kbOmiMd3z
+mH5YXObx7ZI0kZ+TQR/rK1hI/57HDk29nvnYYCvtVcCikHmGxxVf+MWrABx8UibPQ26LtdWF1sGE
+6UCmht0bcKRtuNU444ZqTqQqkopvOadhFbagMZPz7c9zfu2bqkUxcLAaShktObzTopBc6TLx5B68
+Octx3pAMfdrVxX29S68hn0U9SLQMZwvDwWX5zu7BS9x6UxbUuCPjeOG5Q6aBfPbcwRGnlNFpOi+5
+ovqWv/K5MUev6gJfKbEtUOuNM+buKqqv5DnzQO7JaHrMi5cVZXauSWtRhkwsjab2leQeZSJzef95
+GeevjT4HoTzrnbPnMVcl5xBuRPzs9WfkXKMtauVavOSJolUxq8ishqBFTaZaDzadkeg19WcVafre
+AoJl+LC0pS3tLjP27YrF7/dFGHMIzIB3TvXeW1tfnnt57v8Izn3eWrvxrb50XywKAMaYr1lrP7Y8
+9/Lcy3N/sLYMH5a2tKXdZctFYWlLW9pddj8tCn9/ee7luZfn/uDtvskpLG1pS7s/7H7yFJa2tKXd
+B/aBLwrGmE8bY141xrxujPkL9/hcZ40xXzDGvGSMedEY82f081VjzC8ZYy7r3+9Npve9XYNrjPm6
+MeYX9N8XjTFf1vH/Y2NMcA/P3TPG/DNjzCvGmJeNMZ98v8ZujPlv9Z6/YIz5WWNMdK/Gboz5P4wx
+B8aYF+747G3HacT+F72GbxpjPnoPzv3X9J5/0xjz88aY3h3/9xk996vGmN/1H3Lu75R9oIuCMcYF
+/jbwQ8DjwB80xjx+D09ZAn/OWvs48AngT+n5/gLwy9baS8Av67/vlf0Z4OU7/v1XgL9prX0IGAB/
+/B6e+yeBz1lrHwU+otdxz8dujDkN/GngY9baJxCg4I9y78b+D4FP/4bP3mmcPwRc0j8/Dvzde3Du
+XwKesNZ+GHgN+AyAzr0fBT6kv/N39J34YM1a+4H9AT4J/Ks7/v0Z4DPv4/n/X+AHgVeBHf1sB3j1
+Hp3vDDIhfyfwC4BBgCze292P7/C5u8AVNI90x+f3fOzAaeAGsIpA638B+F33cuzABeCFbzVO4H8D
+/uDbfe87de7f8H//GfDT+vNd8x34V8An78Xzfy9/PujwYTFZFnZTP7vnZoy5ADwNfBnYstbu6n/t
+gTKmfuftbwF/nrdaa9aAobV20Uh8L8d/ETgE/k8NX/6BMabJ+zB2a+0t4K8D14FdYAQ8w/s3dnjn
+cb7fc/CPAf/yAzr3u7IPelH4QMwY0wJ+Dviz1iq5oZqVJfs7XpIxxvxu4MBa+8x3+tjv0jzgo8Df
+tdY+jcDK7woV7uHYV4DfiyxMp4Amv9nFft/sXo3zW5kx5ieQEPan3+9zvxf7oBeFW8DZO/59Rj+7
+Z2aM8ZEF4aettf9cP943xuzo/+8A763X9N3Z9wI/Yoy5CvwjJIT4SaBnjFl0q97L8d8Eblprv6z/
+/mfIIvF+jP0HgCvW2kNrbQH8c+R+vF9jh3ce5/syB40xfxT43cCP6aL0vp37vdoHvSh8FbikWegA
+Sbp89l6dzBhjgJ8CXrbW/o07/uuzwB/Rn/8Ikmv4jpq19jPW2jPW2gvIOH/FWvtjwBeA/+JenlvP
+vwfcMMY8oh99P/AS78PYkbDhE8aYhj6Dxbnfl7GrvdM4Pwv8Ya1CfAIY3RFmfEfMGPNpJGz8EWtt
+8huu6UeNMaEx5iKS7PzKd/Lc35Z90EkN4IeRjOwbwE/c43N9CnEbvwk8p39+GIntfxm4DPxrYPUe
+X8f3Ab+gPz+ATITXgX8KhPfwvE8BX9Px/z/Ayvs1duB/AF4BXgD+b4Sm8Z6MHfhZJHdRIB7SH3+n
+cSLJ3r+t8+95pELynT7360juYDHn/t4d3/8JPferwA/dy3n3bv8sEY1LW9rS7rIPOnxY2tKWdp/Z
+clFY2tKWdpctF4WlLW1pd9lyUVja0pZ2ly0XhaUtbWl32XJRWNrSlnaXLReFpS1taXfZclFY2tKW
+dpf9fxjdHT2o6ioaAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.1885... Generator Loss: 0.7337
+Epoch 1/1... Discriminator Loss: 1.4297... Generator Loss: 0.9953
+Epoch 1/1... Discriminator Loss: 1.1458... Generator Loss: 0.9505
+Epoch 1/1... Discriminator Loss: 1.3446... Generator Loss: 0.7885
+Epoch 1/1... Discriminator Loss: 1.2624... Generator Loss: 0.8597
+Epoch 1/1... Discriminator Loss: 1.4662... Generator Loss: 0.5536
+Epoch 1/1... Discriminator Loss: 1.1388... Generator Loss: 0.7493
+Epoch 1/1... Discriminator Loss: 1.4432... Generator Loss: 0.4454
+Epoch 1/1... Discriminator Loss: 1.4186... Generator Loss: 1.0688
+Epoch 1/1... Discriminator Loss: 1.4904... Generator Loss: 0.4468
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWeQZFl23/e7z+ZLb8qb7mo3093T49fMLtYThjAEAoRo
+RAZJKcSgImSCEqUQEYxgUMGQQvzEkAtRATqRITIIILgCAWnBxS6wBmtmd7xtb6rLV2VVpc98Xh/O
+qZ6d5ZoZ7A4xH/J86eysl+/dd+695/yPvSbPc6Y0pSlN6YSsP+4BTGlKU3p/0VQoTGlKU3obTYXC
+lKY0pbfRVChMaUpTehtNhcKUpjSlt9FUKExpSlN6G71nQsEY8yeNMdeNMbeMMb/6Xj1nSlOa0o+X
+zHuRp2CMsYEbwE8Bm8BzwH+Y5/mbP/aHTWlKU/qx0nuFFD4E3Mrz/E6e5xHwr4Bfeo+eNaUpTenH
+SM57dN9lYOM7/r8JfPj7XVwKTN6sGXb2c9L8ZGCG1BKZ1fI8MleGGvgGx7EB8G35N88dCkEAgCkU
+sZCbxOMJGPk8yVJMIp8zk8rvvSq5ryxILaLxGAC7EGBMCEDBKbKXyPflrgFgMLrO8UA+x1lG2ZJ7
+TCyo6thSA4ZMxpflOs6cRD8b89b7pxlkitick++N4QTE5eRY+gPjGqxUnpFEIb4l3/f1vmkO34n9
+nn5aeHjvBTjU8bwTsnUcWQ6OPjsh571OgH30YXjt+g+7SsbjWAZdIjiWRZjoX42F7cgfHCPrwiLC
+8eSz42d4eR2AXjIgsJSfFpTtEgBRTb6reAVcV+7lYh6sT9uAY7YAGN6SL6/19jE6tpwc+2SSLQtP
+16FlwNa3SHU6MpOfLFPS/C3eG2MefM5zyHSOkxMu5Dm5/t2xLRx9D98zBI6rnHKwbB+AO+39dp7n
+sz+Mu++VUPihZIz5a8BfA2hU4G/8RZe/+7+ldBPZsHVToFuSl/ml1dOECy0AHjvv0JptArBWLgOQ
+pi0uPPIEAMGlJymkEQA7V6+R2XK/m8MeTke+H7sd+f3KTxKflfs6/Qrrr74MQO3SE9juHQAu1h/l
+77fF6vnEvxWmf/WFP8G//prMxm405kP+HADXijk/M1cFoGNbWHkfgCyKAUjimMOejMd1kRUCdEYZ
+Y13RdV8WoO1ZhLEu1iyi4MlUuQs2xb68f2fjNmcLHgBfGorg6sSGUHeuBTz3nCzyv+ql/JNk9APn
+5GSxYkFZN0IU5zR8WWAHaUQay5gfCDHeWqTfSRZ8TxF0Ak2/n3j6nX8MZz4mn7+X/DFYGEfWRdO3
+KBVl1I2gwO1DuXvB96jPVACYsS7Ld2zSPPUoAK1zY87GvwjA7x19gytFWUd7BcPHGh8AYONnZV18
+evUiy4sFABaNQyeSkddcm7r7twB44Rdlfj/8hf8VNxNeRSam6sjcWMWAlYJcEzg5TWVCdyRjH5kY
+oxKil0BVJYHr2LQCuXgS5owmwunjTK514oxIr51reMwEsi7OnrJ5ZG4FAC9r4dfPAPDnfu1/Wf8+
+bH8bvVdCYQtY/Y7/r+h3DyjP818Dfg3A2F7+3/6fs2Tx9okSoOeN+fSMDO9PfaLBrX1h8DiJOWvL
+yzfqFwAo1g2VpRoAZTsATyZx7sIao14XgEfcGXK/CEC8KIyuLC5gq8aI1zx22k8BsPNiif/p7uMA
+7P3PXYa/JcLki8N/BsCN188y7t4AwMwazj7dk2e0yywvzANwM+kxHKqA04V7nFpMvIGM3bhYKtkb
+XsJ+Is94qCnfxamN7cj73+6MqDuyIMzEw1+Q8X/lTp3nxvJ++ffYbhlgFXUdJE2+F1X13z6Q6mfL
+GMJYnh2anJ2xPDs3hu+GCt9LILx9FO/s+xNa+1gJGH7fv/9FP+cPdANNbJ9isKrjuIRb+RYAruNS
+suTNPvCBNQCef7PIvbvC22s8xeadIwD2fn5CYMn8PXp8nid+Wa7/WEOEQm2hQFE3+jCHqgrnkZVz
+6m/8Hfn8+SdlcCYnQubRMYb/YFaE14tjm0iRy3zVJi+IoHZDEeRWd8iwrzy2wVH0ulhzqRlZyzvF
+CH8sMxRPJgAMehDIn6kXPS6fl80z65yhcyj3DpbrHJR2vi8/vxe9V0LhOeCCMeYMIgz+PPAXvu/V
+WUw2Upmha25pAguRTOyN+g6udwmAUytFkP1PoSxMqi1CsSJCwy5Z5LrByjMtAl8WWNpcIjMird2K
+CAIqJUahcNVxYk6dF3j5B/NDrv0/BwAY/y9j/vMrAMztbALwinUT0AkdWKx9ReHsh2Y4eFI2ffGO
+i6/Ytm9kMZ5pzBFM5Ltz2Qz3Mxn/Ka/MZVfG3LJkIUVBgKWmSHNnk6OuvMf17T7r18UyC82Et7by
+96HJ9xYGJ9TXf3PDA95nWU6kvDoxfeQ/P/hRPx76/gIB4ItJziiQuXYaMXdGMk9Vf8Ikls3ixwGx
+TCXPb14D4LjX5ziVuTl46VvcCnWN/N8+BZ2HZOWA438uG/ZX/vpHAeiOHJ4sNwC4GWZcsGXLWFnO
+6B+c01Htyz/5W2grwLBRl3dxF1aoe/K8I9djxZc53juUvxvLxSoLc8u5RbWuyqJcwikqakh9rL6s
+uaW+fHcvH2B0PaXVmG19+IcuuHzlpozzETcgjb7DVn0H9J4IhTzPE2PMfwF8HuHTP8nz/I334llT
+mtKUfrz0nvkU8jz/HPC5d/6LE1UlUm3DMdwVwc/Pph/jvidi0D4uYAXqrItFz/mNZ2AsqiErdzCJ
+wEvjWJiBmAxec4ixBRKahiCQNLEIAoWiI5u1OYGJ/RdD7HsvyjWTfUxPoOZX89dlQOlbKnMuLfG5
+s3K/s0eGK9cFxmzEKXEs917IBY0spxUuXHlYxjyocllhYuAavDOiodIjGUOzXGN0IMjkuLXGt29v
+A5BEr7PebgPi+IofaNYfUY3nBqP3MHynmfD9vAZ/PJQEAZkRH04wCcWkATInpLIi5t+s57NYlPXQ
+qYh5VWtV2drYAyAfHhBF8n79UoNXUnEqn+83WDwn37+xLX6G5uGY84/KnK45OQreyA4SyHr/zvhO
+cFuaG9aSswBcDxvMl3T8wyFlRbJLZfH5zZqEOJPvvMCj3hIk4XklaiPh/UoN9nriE+p3ZI0k6TpZ
+Uf0LwzJzh3Jt2nH5+EUZ6HAvId659U5Y+4D+2ByN302ObcjTnLI6Z2YrNhc+Inb91w5v84Haslw4
+PsCak43lpAqNDw9I1YGHk2Ji2YTGGZH1BF5ZzRamJDDwxGNteRa2UbOjnpHFsiD+7k+XOHvrIgD/
+9Auf5uDWa/LoUCbgcc/mIBLWfWQmgFm5x1y1QjeXyT1fCohdGdPcGTE/Ls6cpbogY2vOnyPzZaVk
+YUboCAT1l+U7uzxHNj4FwO7W85SWRbhd+0ZEcU4W/I3nr3M9loUw3JPnTshIsrc84D+MHmx985a9
+79gWnq/RlSgnU1CcJ5Dl8k7vJI7xw5yK74ZOvPqdaMJqS8bTLQzxEYdasGJTacqmafrLNNXR6PRE
+YB/2b9B0RctUbZejQK5NxlukunR2D1Nu/cN/C8DpPytr5bmFQ5wZsXzXKHH5jDzbnTH4tvA+0t/b
+WGALR8/Mtvg6xwB8cqZO3xEBUivUmV2V9fJULibafGMOx5dxloIGiSdCyoQFwrIIiPHhmJWJmNiR
++MlZcUrs9Q4BWAgM64eivL6wvsFDRTG3P/HJMofbsl/4f7/+jng9TXOe0pSm9DZ6XyAF41i4jSJJ
+e0ig4a+fu1DDU+fLvPUIo1zgc8k3TA7EbOjVRBuUOjmWJVLb3xqTuRpmrLfJqhqvHdcw5ZNA9glU
+4EEoyLFsKMjnGWMx+oRAu+C3fpq0KdLY6ggMS2aL1PdFc4V1l9OefJ7kY+rhDADlJUOzsgbAYlEi
+Es16lfqqfPYKAZajZkycEQgQwLZlvK7rkIxEWy0tPkJJ37m7EpNuyTM6p/ukh2JKXLVF81X3Ruyr
+BjNZRnSipr9PgoF1Eu6xwTuB4gYSRVAlz6ZREn4NTZm4K866YSZa1yElVUdqFiVk5iTfIkeBFyaH
+9I+Y4HDiIrM0fDtTeIpMvezLBZ/9O2I+jatPM1MRvpw5C0WdH39evjtu38PxROse2sfEQ3XQZSlo
+3kpkR3SeFnSWF3XdlPd587ckVM3PP8ZjRu6X5eA+ugBA8rJocNe2eKRygkxtLjVEW9fP2zSUR8c3
+hmQa8Vo9LSbK6YVVKjNLMobjFJqCFrsHA8qpjGOnEJF5Mk4zEIfj6kMRwwO5thinHIdiKrk7B9x2
+ZG984GMfJD29+U7ZDbxPhIKf55yOE/Zch4cVGm9PDGfbshFGs3tcaMpkJOMIP5eF4Ks9nYWQjgRT
+eV4F05TfJYcRuSYk+c0MygLdc08mwxBhObIbc8fFGGGH7cT85W1Z9C9+ImTwRfFw7yYy4aV+SFsF
+SMNOODqSDXSqFdM6I/c4d26eIBXbt9mSv1cXZimU5f1s18KyNXw16wMaW8plQ9tFByfQqMZkEZPI
+mE/N3yXT9/6FmTO89ryGuhKxl++tOAS6cbfGMQWNbU++i+fmuz5UqjaRhiKcDE4vyGJsNYqsBGIb
+r7olBnURZL0N8a9sDmHUF4FkZSEJAok3D8d0IoHBmckp66YeqT8myd6dF8RVUBsXN/npp34ZgK9+
+4Yt4XYHlLXPMky3ZCNFOjh/Ky4R18Qec8R02xzK2LIqx3BNpachCGVsQxSzr2qIk1775a9/imSfv
+yvsN/jMGQ3lGYix+bk+e/XlXNrxjcrYSudfP5A7FhvD+1HKTwYasw1IQ8ciy8HZNg/bVYvXBugj8
+MpkjY7dcD29eBNlStURdw51RIGbCIJnhglhPZNYxr//2bQCORyNWEzFdhq+/BMWVd8xnmJoPU5rS
+lL6L3hdIIbcMWcklHaUPchByP8fS5KWlqk1QV42eW4xnNLpQVjUX1LFs0UphzcUuiaT14jlCdeaM
+yi7+4J5874vjD2dIXlCn5He4w0xukz0m98h/c5eoKVI+13TnYbFEtSfaYb5gcWyJHi4WW5xeEjnb
+XK3hDAQhFE6LGVBoFLADgY5mkGEqvj4vBTR3YijjNVUfo9mdrl3C9wUa1lunmA3FKZmPymy2hAc9
+W/6+a3usH8jvrDcjCi1NwW5/VwRBWdcoaRy8XibpKVRoGVqLgkzOVK/w2Fnx6q8+6tHbl/fuP7wG
+wOwb+yysCjLp3+vQzeTvz37zFVCTrp/mlDVRK7WEz2kck38PD6QxhrcV6Z2k/FaFV5/5qTWqF2T+
+3F//AkcVucl82KOgGbxrtRFOWb4vjuT9s/oMd6vyTpGZoaue/IKVkKrzNDl9gfP+QwCUhpL0tXIB
+bixJPsLjTgShOisTj+SSLtavv5WtWKnK/OdnZvj4xZOMxjGHW2LmtYsZLc1adfJFeY+oTJ5qij0Z
+tqWJeMUMPOVFcfTg/cYzuqatKsVjeUZWTDm9Kn//vcke54eCRrIs4rTz7qJH7wuh4BhDy7XxCjbp
+RBZPLSpSrMtG8bKQvmYmVrOMgpGoQyRuBg7n9ilFsmiCYol0XyYustuYsSwOqxERpSIAMs3wsu7s
+Yz0qqa92bmHVZfNiDPMVWQh/8888zK/893I/depTzWEUCKM7KTQzYeMTZ4qsWTJm56BPpXhaxuSK
+H8E5LpIj/gniEoQap4ogs8VEIZExWlkRlWdk3hCnJdc6Oy71stifm+4WzWUZ83hPFvzFRY//47b8
+cGj3+RNqn37usMPgO/baSS1FoIkt4e6Akiufa3aTj2WSHjz7qTM8hGSOLp26RGlVFvRrW1cBWLU8
+Kkty44WHYz53/wUAnooKpNefk1ftj+l7wq+5UH7fjVMqCuHvxW+Ny3Mdco2opJaDyeX68qyUzjSG
+Pl/7h78BwCju4/mygVr3YbgmJs2o3aT5iPDlbEn4eWfJ8MwjsuEX9nZJVQm8sNmndCwb8sKpiywE
+3wbguT+UuSuRYH1MTIaXf/dZZv6SQPGJa3EukmdvF2XubuQWjVQ26QdPl0lj9b/8/n2MZi+em6ng
+3ZZr/HOiyKx4gonEP2FnJVJHfuf4ZRI1tzxj6BsxkXONuvmTAVRU8B7MUNf1ds4bspGLEOq06xTL
+P7Tc4W00NR+mNKUpvY3eF0ghxxBZHmGasaza7AOXJ0wi0aoNc57UEul53OtTHYjDJTsvErPYDzCO
+QEC775IpVHXyEbmmDeeHEXlZ7udsiGRPazn+pkQUCksL+LFoVeM42JN78rs3c8pNed7IUidZ0VA8
+EtZ5PcNkUVRdfDCgWxXnk+fnRNwEYKkvzrnYj8m0jsCYFH+gKtIpkmsarKVFYHk4fhANIYmwNbmi
+Ol/B9QR5JP2cTkveKayK1vIri6yekzEM2ja7D4sGGt/owoPkJHBOUpq1mi7JE7TYk1NuzumPCeKp
+Zqs0TgkKqWUGXysNH1kVrZSeCskPBGHYtYyfHJ2Xm3x6Ga1bI7nT4VVNNAt7ou2+No4YRiew9q1U
+ba9UIexohaqdcakhc2lGghQ37n2cg7HkxMWMmVXnYfWj86S2jNOaH1PekRd0FwQxLNYDNufEQTff
+G7GfC7+W6gMWFwTGn1qMuadFSta88GX2/BlObwhSPFpN2Ni8B0CjYriljut+IuvmsjviQ09rTsfu
+gGtqluzsdHArMs7L9x36TwtSreyqyTjboRTLeHIrx9FamTTPsdVcGVsWtqWmgh/qfLgcdJWHhRz/
+nHgul5I2m1ru8MZuAevcMe+GpkhhSlOa0tvofYEU0gx6w4zZms9Hrohma2aG9VjCQ1cHLzE/Esn9
+8p0ey6Fcs+SLFJ23SwzL8ip5vEHhYQkF+qb6oBgkOb7B5LSoLvuu2Fsbxy8yfygS2vvTl5k9/ZPy
+2WqR3BAEUf3oszz5dZH4v68hy0EnIVdVW1gA1NlzOD8kuyVSuVjtEfsirgsdkeaVhXMk6jzM7BCr
+Jlosn13E9NTpmIiWT8yQrC7f2VaLfChIyaRjrJp8LiceTi4aNNaCsLVPlPnEhmi+N1/a4dkviOMl
+J38r5g9o2JxYY/SFwMIIWGEw8shy7SdQ7UMq/PLTgMKyZMeVErFTh5v7HI7F8Rk3MlrPiC/G3trm
+I7tiw7/gPMtSR+bq9a469TKD5ysSCnlA6aSAVxRd9UtnniIdChJ62QjS2Lc+R9nR4qI0w9Y8k6g/
+5tEZueZwMGQhEX5EuYaL0xJdzT3Y6BwRFuUen75o09GcC9PepviEzHHzSMKQt1/rc3n+voy5XOeF
+L8r489oSt25oTklFC97KFcymfPfK8i6jPbnvRhiiUUaWZyckGvUcDyWM7JsS3oogKFOZxUrVqW5H
+ZLH6oKwClubwuFXNYu1BJ9EbWzaFTG5cshLKyts3tm8z+NIPKZr7LnpfCAXbsag1qlRHHqsLAj/t
+1SHp1XsAdG4N6bYkT2G81eXenG7C9gmsr1PvCaOSRpHyzolHPSC31FNbHLH5gnomR7Jxjw6HtB+R
+BVi8+iy2LV7tcnKP2wtSGv07/90NNhOFlJk68HzvgZA6b+r0ntJnhwUOA5nEtlfAuy8TdtuT73z7
+mHxXhlAKLNJzjwGQh1sUPE1I0nyD3BwzviqbrWTNExhZKFHxAOtIhFtIwtGeCIU9TYEd755ltKIe
+9OwFDjS6kE3eyk0wRhrXADiexv9LPoHW6Vcf9sg03bofB9RK8mz31CyOmlCmKM63uNAD9WPt7WyQ
+1iU9fCZcZf4ZqR9Z+4055nX+vhl/U35vxYSZpwMKHyQtmPkl2BUp8cif+Qvc7IoJ5rz4BwBM7t0j
+b8iYvdQwLAnvu6MB2+vCuyhP2dRNMbOpFYdrHslAeBVlA27eEkFHr8oHNYX+/uM1iusypoktG/ps
+3SE8Jz0WiuEmt9pS19e6epuB5hnUXxPz8MqZMhsNcShurPdp78t7dJycx9Rh2ivDaFbuHeteDb0B
+qS/OY8fJMFoTM4mGTHTTd9sbWFW5xkeURTc8oL8hSqQw72Jptaddq+PsyRqfjAcspT+4j8Z309R8
+mNKUpvQ2el8ghUbR51eeOk29DI010YL9YZnxUNIzO+Mx3duSrbXUTxmf0uqyI5G4vfIueV9bn/V8
+RvcFipU+lNDIxSFmHRj6oSCF3o5o4GBiUdgU6fry/g7b6f8OwDPJEr01kcqPXu7xDz8vGqag6dHl
+UUJfYfd+KWa+K3DdKxu6tjxj79kBjjrSTEXNiNfGvNARbVYOSnxas/wardO0i6IR9rZF49+8eovb
+1+7qfVMcS5HJ6gzzmmo7KezSHYqz67yGPWdSj93f/UMAohyW1eQ5MIZI1XFgGc5dlntcULVw16qw
+gGj/Jz/8DLU3tcvP/D28ofzu1Nw5fMW+QyOadn3vFnduSM+C3rVdblyRXg/JyLCqFX5HQcZzN18B
+oKhOtFO2w+qcvP9X1t/KbvyFp5/h1Jz8b3nuYe52tOPUQMy5Yn6V7j2Z93ScUtGyxeH1Nq86MiYr
+SrEWNPOyLeONDw2lI02b386ZSUTb0hnj5aJVf3bo86oip14m6GghOc0HPyjIa+t6xgtviJN6f7DD
+nwzl2btzoomfPRwTnmSFJimROhqtoWFbsx69rZiv/ZbMcWNGYOOl00s8YZ2EXh8mQ8yVwdGAO3cE
+Ze7cv0NtWVBTbUb2SBot4SuKq1Z9jJpHmy9us6Vh1nrZJzlb4d3Q+0IoBL7How+t0kgrVKpaDXn8
+Ot/cEfi1dSslqcmmv90Oufi6MC38jGxGa9NmKxMm3G13mFOmzXXGOCXxOO+HIXfvy2I63JD7+qWQ
+XkEmY7Tfo/3PZXH3L9zgzX8pnuH0VkRB7e+xJpLEVZuZvnw5Fy4zOStsPDoK6Lwgz9gsbGPUrn1y
+pPkIFx0mX5MFmI9iNtbFN1AqlfA06cVH/CXHx+t0XZlMM06Y14St4x0LtyrP2O5YHE/kfncPZQEu
+jGdpa2emKIqZ+UnJ5T/87Q4K1nlo0eVKTYTlQ5o+noUjCg3ZKH6S4J4Sv0zdpDRntHJz4JGpAyCe
+iKDL+wntDdn8znyFSGsKFtyI9R0RpiYu0WjJvFYSEZoHnQITlC/WjhQTAIWnznKmLGbc5fMNvED4
+/NKXRJBnoxmyXJ6dOQmTUH43LibsHGvvTSvHWhdhcPqcppWPoW3ku9cHQyKd07Tv8HxBePjC8wln
+52Xeo0CudT5TB63AnZ97GG8kAnez08V+RASx+brwzclC+tp6b9BL8TV1vVJ1mWuK7+pKvUJSlXu3
+ZWmyHUUs72okynSgoD6To4itAzEPjpszHKrfaQYRQmb3Dv2SCL/y8jya0c7dcZu9tqwR7/wc5TVN
+snqHNDUfpjSlKb2N3hdIwbUs5v0KxWaLoCyS+lpms7ElEjXJxkz62q14aPG8FsAPXxUz4amZObaP
+REz2jgYPtEDiXyTUbMOd+/dpI5qrF8p97+yN6P6haIktUpaW5Ien7zj0jXxfWIhYUlTQPxKt44UZ
+XUc093przNlDGXNSqhBfECm//9IGqNb8XUe046Jfp1ATuLfXm5BrmZJdBK8oJlESi8lQTm3cZYXA
+Wx1eXRck1LK3KCov/EqRjuY9uBXtB7l2k1P3BHK+lhqW9mWcXddhovkAo56htKIVg5rH4W7v8G3V
+rm17h5XHxWH4yfPLuH2t7+9dJatLcZSjjWNG29/mnEY+wtUix3dF/b3x3E3ujATulr2MtfOCPM7t
+CDLZKU14+rRovBe3eVDNecXxcFeEh0GzRLgpzzmt4ZLuKUP9UHjYbSdo4SDDKEM7pTEMc9KS3FCB
+BK2zi7x5TczR3UkbR0232RnDWPnpHh3xovLzzMMCxa9+c4uPz2lTmxmPug60PSpxxcj8HJVlbONu
+ytFQ/l4OcnxN+U7yiPZIvn8269OKhV/nW/Iek0FIFgvfMmeOTKMP3b09gpGsof18yO6OzOtX9wQ1
+WUmXxtwaAKeeXCY4FtulNRmx0pT3my9HBCj6fof0vhAKlutQXGpS9jz8mpQ993Y8Eu1fPezZlGva
+OMTLmK3JhD2sZbF+ocKML4xuFR3OzcrmLRWWKLliSpQejSi8JPe4q/0Vj3oR2wV9Rjdl+6S/pZNy
+2FDW3AZ7UduEe7KIh1WXxV1ZPI/0LcrnZWMVTz9O8KbY1O3mAWFFazcuiNC4/Ogs2zWBzHO9lFpZ
+xhb4I0o1ecb58xLys0/VOL0uQm/v/AHrGyKkrOEmifoz5usBrZFs2Ds3ZPB37nncndEOU/Yu6c+J
+PRw/v46vacULZzzOrXwSADeW3x0f9PmARkAqF5e4vCafC+UmgdZrJE5CPND+kD15rpdUqFxUM8f2
+KV6UBTg4KhJuyubuxMcsNOS92g+LYO4/+wbfNNp0tTyAY21YMrdCIRO+uJWUovZHLD71CAC1V4ZE
+NfUH9BJyXzsTFRx62k2pVPT4wBUxj548I9A5evhxLk7kXut7HRLlW8XzKBbkfoNikar2RHQUzV+p
+JHBuTcbZ3SbyZMOei4dEmtR16k0NIZaGWNqVO7UDZn0Zz8j4D2z/1mKZVW3KY6eiFIKwTH+kNRXj
+I9ya3LdeaxDOyhq6nBVY0LTouW3h4Wh4zJov83T68gfZcMXvNjz9LAWNuiwW63zYenchyan5MKUp
+Telt9P5ACo5FsVWhEBaItALwwodO8yvrAlXN3IQ7WgxS64ZkM+LgKatHPnR86lU96KNqkfS1Gs4Z
+khYFthZ6iyzVRdK+lAkMmyuOGQ7kWr9mMdJeBnXXwdYKzUdNykvaZKSqVXa9/oAt9Ze/nuzz6VSK
+ZM5eWiC4LM7P5tUmuR4eU1sUjViKtshm1BlWgfnLUnRUSlbxtDGK2xRIesrUqKYyznKSc2FV7tEf
+PUHUF5gfJTcZabtv35XxVCmz84o0BXFw+JkDQRjjwOGaFtdkOxazj4jmOh9IBWSpUSeIBGEUzz5K
+KxEve0SXmtHKRqeGc1J9Z0sVoe10KC0JOqiFJSoI8lj75Yt888aJg7JHqo67Jc2QeixY4XRd+Pry
+4C1N1qoHLJbk2qP9PkMtXDp9WRBPfRzx5btfA6BQqOLVhcfFgkdPO17XvBLLHxZkMtt6BoDO7BGP
+/tSfAmCGmRcAAAAgAElEQVSYhrz00qsAvHY8IIg1WlUts/yYRJ16VVljhC3GE0EbmekwnhUeXfjI
+GrWNewC8pGvzcDB+0LTmiTM+q478rlvOWa5KUpfjVchtWePDHYEj6ThmsHRSzWqRlAQJEDg0F8Tx
+7IxyAq0EXpqTcxyyQgv1i2K5VexQHNf7Lw/Z0p6PH/o49KvvrsHN+0IokOVYg5A8z3By2dy1yZjl
+BZkgRjGrS9LFxusdMnFlsQWaxXiUTqgW1beelihoxtvkZo/iOQn7LNtN/BlJFho9LQv75tUtCpo1
+uXnvFhPNz8/8hMlNmbBXjMucNupYfUI27h/sjuC2PCO5G9P4tPxuoVckduWaD11cITvS9vHqR5h0
+U2KFjMY9pDjShh21GK0oxtcszdbYpVQVgVYuFslTDa0dpHQCze7bHDLSKMAru7KI1197g4OqwlNG
+7D0jZxIM/q9XaGhD2w99zOHMVe1apVWPTz+1ijURyF1u1bAnMg+TtEuqkR3TC0gDee+Bhhuz8gRz
++6S/5AXKayLoTGrx06n4SQbHEUeeRIxuacn5emi495ys6NxyQRuIdOMBh6EIxqI15IwnQi0YyvO2
+1p7mkvZiDBvblDSUVy8YBvuymeZmHc5oY5hgUdZKwS5iFuV51uMfINPejS/cep6xI7w9BtxdGd+J
+ibo536Py6xJxaJ+LOX9ehMzZtTJfvyo+k+RVLXHvgzcv42x2C5wU3S7NLWNrY5/ZWsCgLbzd1Vbu
+UXmI1ZdxDkspWUcre62IwqG22rcrOA3thpWdVLOG2IiwMVnCkdZG3Ei60nkIaI+qTCrvrnR6aj5M
+aUpTehu9T5BCShb1wKlga/pspexxsSkatrPWpak9AydejSjTo9mKojHWGkskWn033OgwVJjca/k0
+jxSuBxmzHxfN/alt6RJ9vrrE7z37PAALZ2YoOSLNj+8fsmXk3l4hZGVFpPUjfdE+v3d3i2Qiz3jN
+G/DRb4qGWr5w+0EUoWgVSRfFgXWSXmuVPda0FmN/PcMsCwR3vCXsvl47Fi+0VZjH09+3KJF1ZWwH
+7iGlVK5pNC2Oe9rDL5MIh7UYkXdF687kLi99VkwJ3wQMq6KBNp9N2XlccjL8gkDRmaSBVVJTyk2x
+K/p5HNLf1BOJ2Mb2BD6X9kSzXb2+jalqf8g1l3KqMDlqYPSoNHs5Ju/LUivEoofCQsATZwXlXP1G
+Sn5S/3Bzm9tV5VfDZzTQLsi+3Kuy3OXJdUGQu3mF/X2J1iRNn2daMifhUUTYE/SSoi3+szK2EaRQ
+KgWc+mUxeQrfOua1l8V5etwZM3tK3u/Jz0j/hmTriBe+LoigVRlx5oPy99Xm44Tf/kcADDU/4k4W
+0tzXFOxHetRrgt6Wyh7pSbVnEjKr5aNBV+Zscz8k00OLGDqEh5raHKYsnNG6mfmz1G1Z95NYmOVY
+PkXNWcmKTdJjWRe1fvQA6URbbV79g39PjkZjzKox5kvGmDeNMW8YY/66ft80xnzBGHNT/238UZ8x
+pSlN6d8//ShIIQH+mzzPXzTGVIAXjDFfAP4j4PfzPP97xphfBX4V+Js/6Ea5bZFXA+zIwa+KNBzs
+hRQa4g8Y70SY5RM/QhFTl2HPaP8DF4vuSR//2QLxoRaijHyiGXXAnSrh9rVtWkM0xqDW5IMfFT/D
+5jVDLxIN23YTQo1JHU4MxhXtt/CkOH3ar9sQy99r7Zjg4xq/SmpgyTjsQgFSPdehqVqg3cPRVlye
+Y7C1T/8wzQjUeerGJ+dQpLh6toQz0yRK5BkzBYujW5rdZzfpH4m2LWr14e12CVvTa/OSYecxcbhN
+fvtNzmt4a+bCAmVH8hB8deq55QyvqmclVN0HDWSNXcXWRrFBoUCeihZ3ijKewswc3VD4nUcJblML
+e4ZgNFt0fGtI1ZbQ2f37XwKg397m65E4D/F3MXru5rrnULmvvTPKyYNzaOxzgsAak5CZNe2XEdkM
+9UyK3vYmqWaQznmGLBNfRG9PtPJMo4al5zD4Z3xO33T1PWocFdXxV1ugORQ/TnH+aeHbwQGTK1rZ
+GddYXvoUAAutJZ7TKlZL8xyaGZzSJM18UsBWdFPwCtiJPG9iYpyCOoe1TskfpDgNPa8yi4li8eH0
+MgtfOzf788fUZoQHlqZ+O9U6lmZQ+l4K6icZVsqcRG2jY1hMtNLyHdIfWSjkeb4D4mrO87xvjLmK
+HEH/S8Cn9LJ/BnyZHyYU8pw4jkkGPeyyTKxbyylrZdnpWpW8oM1LnBIlR8BHpPkG/WiEF8qrtKIG
+k4kwIRjvEB4pd85UcDQWPHYkiWWmUaeQ64lMl85ydVOcYStRlWheoPapnsUHPyCLd3FOBMh88ll6
+yrktN2d4VTbF5E8PcSYy/sQbYGlrubwjEDY8uE9/KF798VEHuyEbyN0v0hvI+eu5RgDmFmcJfIWc
+pkNBhWW+nVHWKMgg2mP1tCzIT6kZ8eFZl1sjMXNCE9G6L1V9t1sZ+7Hw4lP3Yqq/KoLRPdZVbPu4
+emS5VZgjVyEUD9ZxHXGMpamP0Q7Uo1B4WM/LmDUZm2/mtd8kmHKFdFfbvddctnsCwSurInjOlnzW
+KgLbf3PjpAc0PDw/S2rL93t7W3gIj85rhKNxFLPekbZrjYYFb8j4794dExiB46lXoaHXr9yRDb+9
+tI0byXdR6nF4U+oPrt2586Bk/Mmay/zD4mz1NGlosVoi/4hEaMrjmNVlTdeu1fhMJuvwhh4ke33T
+sKEC4olKBpHMSWz1KVoSoXKDiEgLJLrHsqZzF+Yq8ruiVeZY2+RP+oZI90C8F9PREnbPk+c6RUg1
+9X5/e5t0JML7rO8wsUWwPr+/wQvXeVf0Y3E0GmPWgCeBbwHzKjAAdoH57/Obv2aMed4Y8/xx792V
+dk5pSlN67+hHdjQaY8rAvwb+qzzPe8a8dcJtnue5MeZ7Bkm/8yj6y2uz+ah3iD2JcLZECsbpJiMN
+z+WRjdHUZIsqvvYIcPRR4zxkMnD1vil5RTTzME7ZcbUL8uvXMGWBsKknGiUMU5I5QQSze0XuN4Ud
+7TsTjkeiuz5aPsXjn/mEXKPukWGziDUUaX9+YpE/ImObvDLgxINSjnhwLPnwSJDL8DAmdkSzhV6f
+8U3RYslMwEDzCZqq+RvrQ5IL8gw7CjGaXmvcHr4l71cq1rEq2quhrJl7w5xz5+U967bHsx8Qvqz/
++ktUNLRY+I+LlDdEH9jabMR1fDJftXw+gvTk7xaxnt2Zbh6RaEpvtyMafzjfp3QoSMKcy+FE52cR
+tp6kbaUlPE3j7WvYN56Bo5GgKhPYWFrN41cDrIlo4+TGkEDTxtH2anbZp6btz9Z797i9Lz0b3HHG
+RDMBRy2wjmROOhc123DfZTCQClV7d4/tI9Gke4MDxkY0/RPVhMeXxVQ8fVbMiOx0CeuWZk1eeIxa
+Q/jsWA69c8KL0jXh8UU/YFjWOb+fsKdrdmF1zMCR93cmEWNdW4n2v8usAbmGHpMgJKiK87dS75Ae
+yyLvV2KiUO5RUCe3bSWEoSDL0Dli3xbeDms+sYZ+94Zj0ENi3in9SELBGOMiAuFf5Hn+Wf16zxiz
+mOf5jjFmkQfndH9/SqOYw/tbRMMY6yFJ/kmGUB0Lgw+LMZbC/Cjuku9rTYTawEXPYn8sm2pj/YBh
+TRb3TDqh/5qgkGBmQmlVIaitlXOWReckj7xYpNyRCMZO3Odxjekvf7TOqqb/Gq1ELHQNxpXxHPgp
+X/qWPKPYeo1WrNWV6QwjLdUe3ReoOllaYFb7+gXFJTqJQPBROaOlJ/oUQ7lXFm8RH+rnURX6CjWj
+CaYi93AnkKlvA1f+LdUtmmXZNLmxOfqX0p04iTM2NOHqc7++x6dXnhUenJG8AkKbTPma+9uYmpgg
+5GOyWHgbHQ3RRkZUa9ovsO9TntUelL0d4rZW5EUDEj3ncGJCRkOZywtz8p6PVR0aSwKHv34/UfEJ
+K16NeL6p81TgZi48GI+0I1XJIs81Oa07ZrAvm3viGn7xCYHo3XaRYy2P39QqUmccsTnQYwDaHRJV
+LJPOkPZY3nvHRGw9KYLj6aJcu5d5XDLCz8o8+LlumTRn5UA0wKbmsdzfP+RYj4k/v5ZgtIf9KIyp
+6jOiYIilpm6hq0lPvZCdTHhRwKaFrJtKsYil6f2TLMJoUl4nlIiLa0qUNZV+dDih3RezqzBIuHRO
+xnz8+pit5N0ZBD9K9MEA/xi4muf53/+OP/028Ff0818B/s0f9RlTmtKU/v3Tj4IUfgL4S8Brxhg9
+bI+/Bfw94DeMMf8JsA782R92o8w3ROd8si8dkWtV0qASMdJ05N4hFDWN1zYzJHo+ZLQnWsJquOR6
+IIu1UmBhKK81O7+CbYnETx2XMQKvXPXUOwsuPCSOv+1vHfK516WRx3C3Tfu0aLTLpxM81W69trRo
+i2sFCuq0NE7ORFt/7e4OKZySe1fiWfJYT26uyXjqWURlUbRgPihT0mYik4MMb04PkzzWdmz+iGNt
+/hEczuHEgm6YGWIfifPNdnKSXdGgaShIoVytYQLtzRCm3FgTjd7+WsyJJRdaOU5JIGphQbRV5i6R
+GuFVkgzwOneEn0GFKNbWa2cn2McKRW1NLy6Vcary7EneZbyrRyIXXNKyaOvRLUNpJKZLWpb5fd7Z
+YrAu4xyEb/WPHC/UmYuEF/VTZfJDjXaUtLHKwTGVpkBjP83oVjSiNID6oozzgudyUw/u2bohkHut
+WGFOG7zcm/cYaeOYxBgKWq16zw64tPcmADv9T8nY+33mH5Go03xt6cFBNVE/5fhxQWz1l7VHY9Oj
+dqzrcGjR0ahNujfgWA8psSyItcL2zlBNnMRh6QQhZutUM2lJmHs1/JpWWo5DhpYir6E6zL0SZijv
+6QV9hrua95COaaSaLTvj0zzUngy8M/pRog9f4zuOJPwu+hPv5l7JcEL7G2+yc3RM47QsFHOvjN/Q
+3PlOh1FXW3nX2gS2HimvSUrHu22StmzG87MNnIZMlt2NcRS2eStzdLWMOF2QCZgbl+BlWcRf/cMb
+PHtdFmxnAucvKyy/ZXNn/V8AED4rz/vlssdrTW2iOQ7p3NcknYcOcNpqi7YOCOryOS1J3ULW6xIf
+ydTYvSEFT8+PDELiW1rhdl/t3qVVulvaBHZlk6oeAFOfOUtaVMi8/yo7x7J5S9rvcDG0qOltB7bD
+x78pC+lzBobayOReO2E3l4NaZo40ZLkypKTJMal9mfFdgah9s83kW3Lwy7G9RKDRkw1LNls0OqKu
+Xvh2P+PhD0sPxoWV86DhvSRa5zCXe2zfkN+vvJTypbam8KZvuZ0uNZpUtavNaNfhlCvCcGgJvM69
+kNQXc6zS8imEmtRmDRltyzO6t1YonpJ3ORvI/IZJiOmLUvjkpUUO7ouf4Hfbbdo9Fah3jvla8BIA
+s49KG/nHrU/SvCLzV/OLZFq5e2SP+Mi+nje5qI1nnh/Tlj3KU3MpkR58M44jypo/FI3GRJrAhUYL
+Zp0SNe16leQukSbiuRSJu7IHOumEQ/VjnZw7WpqLsG1ZL+t3b3JfIy0z4w5z2ky4eLTPvXSa5jyl
+KU3pR6D3RZrzxKRc9YeE2xPOHOtBID/XYbwvTrByUuVQq/La3ZhIU4KLRRHL472YuzuiPXqvbDE3
+I5I0GY4IPHnFam/MwmNSkz83Iw6bMDnk7/+maNov39wjVq+ua+DZl+Tz/3jnGms3JH/Bz0WC21dm
+eeK6SN/RjstuTZDHay92MJfkd+Xza1ia6JON1XtvMrracdiOYsJN0TTWWpOjOwL9dnqSsHV09Zja
+GdHAa3aBkq3FMz1D2hL4uP6HXbb1OD17T3hy7pl5woJoNjM54oWL4vicPL/HSRwoKuRc/xcSvF77
+r7XfZWiRaTfgbLzH4LoWCW09y25XxlQY9Whf0nTsPdHgB4dH3D4U7d9phZyzJafDLTdJ1Izzrpzn
++m8KMvm958S5+vn22xHCCfXSjKp2K55tpfT1OL37X5Tf9U9lnM+1NX7tiK42FhllEW9++STx62W6
+/58gucG8dtw5jDDqaH1pZ0TzopgEeeTSU4fgQS9i56ogtie/LU67j/yXbVztkZDUHdBTzId7PTaX
+5PNATspjJi/SWZbvjnsWkZq8xxnEHZnryDX0u7JeOrGsw6pdJ9Kj3awsJwll/sZHQzpDQWy90JCf
+1nZs2u05ipocBzIPdw4H9HI1mXoWD+s4+nkFKz/6d/j8g+h9IRQ67YR/84/3yHLDy+fuAfBXrz7D
+oxdkYQ7mLAo35OVv7kccTWSTnl/VpJmLdaodPd8hC5nRLMbkco1WX0I2j6+tUP2QJvUci836G195
+jS+9LsIkztIHJyPYluFYIdeNwQ7fekEm71fG8rzFn6xhzWnnoskO1zsyAfuTA9KyLujOASuunpYU
+y4Y3xRrlZUmOGe/2sZtiHswslSg21gDIX5VnnCkkVM7L58zNSfTUpN2jO0Tq+/j2i7e5tSML+sys
+/OuXPfJMFkFvK2bjd8SetIxFpmXI4xF86Z4kNS38vvD1qV+pMpdJlmPBdeBxtY2XVih9Xlun2xat
+onw/0Tb73q7L3b7WYrQsPC1htwYpmbadP37tGl95/jUAvq7ZmHn6PU6XBdxwyKHa7VlY5PqWPO/u
+m/LO7caIhfYTAGy+OqE/0jMxXYvjhvhrlvsBjkagntLMy91ynYn6Rq7MPsK5n5fPnz2ocOfwWwCk
+WUw6kHFtDbR8+82cxad1Q4/adLdlyzx/dY9bn5f56x4I4N51HNpaXbpcyXFtgf5x0aOnkL/essgr
+WhGp1aW7nTGeLYLXjpoM9JyJ0UFMoSLzXl9oEWjZpWtkPTpeyKAvQuzGq9vc1db4lhfgWXJtMD7g
+QCNljE9iPD+YpubDlKY0pbfR+wIpRAY2bUNxBJ/Ss/Hc5SP6S6IRCuE6nbJo92TcZ1fbcC9oGnCw
+XGHhjPbGa80wo7AtnAQUIj2h+snToI0+trSe/bO/vUWoDUTyzGDZmlKKTRSLZL/b72G7onW+YYlm
++In6wzQ1OeRgzlDYEa3ZtXOGmZ4CfG9C71Fxjo0LopVmqgarITkP+VGfg4YmYe3uE1oixe0T59tP
+NCmtyjjDvQK9rmjr24UJ7Rui/e9N4gcHgDS1v2S+PCTW8xAjr8fEVa9jZj04aToGvtWX333wWLT5
+o6/7RJfFKZmfeQivL+Mpe+cYL2kcv9RjZiy/G/dkDPsh5Je0HfpKHasvzsVho0tb0darv/sGL23L
+nAx6J9rqezf+uN4fMasdo0e1hNFd4e29BeFh9GV4/oq839duewwLmjgxzrmrGds7uyHesmjHtVB+
+ZzdaRJuST/D8U2Xsvjhut2avEb+m28DEJHoy1vqxIMz1SY6vXaDnhjF72hjFeXWDb2pzlqI203Hd
+lFlFB2GYUjilkZ8+BKrxI8ejpDUT2bE8ayGoUFVU6OceBe0fmc/PE2pFpD9rg+bXTDTnY+don6v3
+Zc6uR11idWDG3oSvFLVSuOpQGckaOImG/DCaIoUpTWlKbyNzEnf946RWo5r/7KeewWxuc9jTVM3I
+JU3EiTYOYwoFdRj6VYKKHvWmQ8/DGFcbt1IK8GJtGNqbYHt6HmPFoazXJ7FoMBufPBCk4GUFIl9+
+l1tVzj8mCOMnnv4M9RWxtevNNQD+0z//U+TqcJpzXRw9cCXMInw98XrBTvjoJ6WAavVjoq2s4Rxf
+/ezvAOBmhuf1fMXTVhWjacAL2hn5KI4wkdiOk1LCJ5+RTE//1KNUfcncGz7+FK3yBQCM9kIoZQ5H
+XXXWuiXeXJfioXoQc7wumubclUdJWhri1T4Gh+GQqladvvrKNwj7Yu9WKikLZclpmAQehUTGvH8s
+DsA8Srm+dU8+j1OKdRn/mcbD9F1tMTbscPO2+DBC7fXQ7m5QzESb7w0OyUN1DI62cSI9N3NsyBLt
+qREJSjucRDiaIzFJHXJtxBBmUNYj7SzXeUvb6vo2ccZEuzSlWUyq4dk8zkjUl1TyDEZTj2f1rNDE
+dTmruQIf+/AjXPmlPwdAs36B4MoHhUe63m7dXaejyHO498aDLFTCmKYvyGN74w7JRBBkNpaxFwMX
+Z1bO5yjFhpFWSRarVfKWIJpgkjHQ/IayFkQdZRbpWLtLz1osuOI8DasxjwTiuOz0Q8p6NsSlJ9Ze
+yPNczsD7AfS+MB+MZfAqLsc46Nkf9IMhk54myAQFZlqysZbW5lgJBAYW9ezD2POwA4FIi1aZgbbT
+Ps4ijCeQqTj0iRU+D7XSr211KPVkozTmGvQjrXZMDpnckQV0p3KVRxfXAMjKMllZ1cVR+N2oOQy1
+zNoKDUe2fp447GjDDSf9GQBe+adf5F9pqe/MnQ7pSXutUY83dDG6GzK2uOWwcCQCMlmF0p44n57c
+qTHzF4QXtSinpIlRqZ53ORmnlBVS9/MRq7NSj9be7jCn0ZWu36esx6CPPXnnkm+RpvfkvsUipqPl
+yVYLtyTvWvUC9PxbakXhWyftMGPLgs8ZEBRFmHrzLstZRZ9RJl7WQ3LqYgalWzZE4oWPopBoLO9q
+rdQ4elH4fN7NWNfWc4cTXapFQzUQATjTcEDPEPWCjIIta6Bc86hoe/XxWObcuIaBJmeZowmH2vnZ
+pCl9S55Xji18FWpWoqZWOuDaoTwjf/4u9c+IuVVutmhp+D/SOa9HEXsdiZKNb0TY6gR1lzJiI0K0
+UCyTR9pNXAVIhk090YpS1+JkW3qWRUnv4ZTKOLa2B9CToCrdIkYd3q2SobIo8zA+COnoCVGm4HDg
+vrvah6n5MKUpTelt9L5ACpmBsQMbyYQg1qPFSfD0CLygVuHsRYHPj556iGZV4FOtplqpMKYaCHTy
+vTK5q8U6WUw4EGfQeBCTTkQLp3qkd3fS5UCLZAISqnOiga7eeI2uZim+fu8+i2dFu51kUtpMaOvR
+XgvVAl6omZJWREEPn6HmcLQgsfV/8NnfBaC69yxbeyLte+GITlePKs8yDhVBzOuZDhsDi+pJZWQX
+OtdEY2yuPsfCjsTQf/qvt2nUfh6AVu3TAOyOJpS1sW2ejLCqAr+3d4vMPyJaN8gjBtoApHiS8YnP
+WIvD3EFCrM61+docfvFEG7uE2hTXjcWh6GQ9nHnhWzbv0qiLtirXAmwjiK5Q6GLrITLsCO/vpT6Z
+5iCUhrMcd0WblU2Pe9rZOvcK+NrswCqHynuf4ilBKQuVGYIzcr8Fz6e2JJ/Lbp1c5yFT1FQJPNKJ
+vPNR+5iBQnfbgWws5spWb4yrKcSmKb/bvnqb9VS07rd3DrH+kZTyeE/3WfzbvyDv1BaEEqUBxx0x
+A0fpJrk6TOfCZWLNJ2kPJ5Q0pXm2rB3InQRXbWErNMzonNlOiNeSdzKOReDLHhhlMt5ePWM80azI
+c/OMYm1cW1/g5W1Z95XUZ6KFV++U3hdCIQW6WUprGJEn2iXYsWkUZXGfX5jhnJ55WC1Uma3KYqvr
+oZ2F2iJOKnA4qAU4Ch3zaspoWxifOG3ikWy4SCMZxX0Xr6q5/1FORQ8eqVdmGdkC1w+yIRsdmYT5
+G5JGG+cwryCreJixafTcPjtHzxKhmTq88GX5fjiSw1V3t/p4rlwwcrwHkHFgoKD366st7EU5/ROf
+SQwvqA3c2Uh4piGTv/7aPHVNN04vyoavFQakuTaWuXWXyUXxOVw+A0ZPNBrvGCqr8k7dI3n/YBxz
+cEfTa+2cosa2TSEn1YhD7nRAjzV31a4vDGFGS6ujxKbUkGc4Q4Ot7eydgcuMpqyPeloOX8vZUL+M
+W+xR0me0jzKWNYJDt09b/QR2SX01vs/D6rcJAodmWXsmlgrU1ByzTItSSeY9cNQsmysT9bV+ZH6X
+VLtDW9UinX3h3ZnePhNNPXa0pHyw2aViZJx7Scg33tQU66Mv8OTf+R/kWu/kTNAetIUvfmxIOmoS
++QeMBzK/eTgmSUSIeFrD45sGQSbfZUGOp1WuUX+CG2nNRzQk10NrrJG8h+/ZjHWuDQ2KmYzdNgkr
+6iw66GQMXpLU7XdKU/NhSlOa0tvofYEU7CSjfDhk3RlzSp0oTcvl/JU1AC7NPsyFK6IRgmgJp6KO
+Flc0fqFscMciXS2vBidIId0n0C7QY6uJZcQecY2mGvs2aagmQZCgh/mySoWwJA66ZivgjELJYz24
+0O4l7CmkXA2geqB9/32o1WVsq3bKGdX0H9COxN9asHBiudbzHS65IpOvjXI2QtFMnjq4Zo3NS+oM
+G04yeooqjki59prc76cvfZk7iXj1ZyuioZONJnNX9BToxQm28qpqCngjeUZWd9jZEFQwOZD3cIJD
+omPxig+bDnX1yJtkTLwv94ujCGeiekTbfZHEZAXRXIWkRKCxcM8qE2sxU32xiFpHnF2VAqxBajit
+gODe7hHjsoyzvz2gpxGOS/MBLUW+ka0FPsUil9akEUqjtUpNi9+azRmqejq2HzWw1QFr6ZHydjPF
+DGU8UTRHqnzOCh1aNe2zsT3DRiaZhfm28PjM0gRnW3IBbo8NI0WyX2nv8be1yMnKhCfHN/ZIgxPn
+6ZhCLBEA25vQieR+jl9kZkbzVua1x2jfwakKEnZyGyZazWuVsIuy7p2JT6ZH3VnqlCYoUSnKIIph
+xGAk39/1I8aR8KvaSMgvyD3eKb0vhEJsMg6ckCx2HvSkY9ZiMROToFo3kMhQx/6AmjabSAsyAXnZ
+ISvI5FtxShZpDn+YkNgnpawpuZY4p7qeszQhkD8zDnzQa/Nig1yr65KiT6z55X4oCyltGkrbAtsK
+UZODhtjis4lPLZHv40rAjkqZA0cmqB7llHQRPz5jYZ20UT/uUTuQDevoIR4F38fR8N2rnTGeQuo0
+N+zoBrt1p0+wIu9k//5XAQhXH2Im0/Ta7K2qvnSc4CqEPww7pB0xQXZvSO9E/+KYsZ41OXuc4cp6
+ZphOSHWVFHbHjDzZvLamOyfFGHcgF5iKCyce9RoEWs5t+RW0hwyWNilZLZW5o3a906qSnbTwL2Wk
+XfsBRV4AACAASURBVDUDBlXeKMk8XNQDZitVm6ymN6tZNDTaUU4sHKO9JIMYe6w+BS2hNp4hT/QI
+9yTHCk9SqVNC9dTb9ghvKHNyWJQQaGwcnIviPykdHLF5X/iWj0IiNRstNTlMEZxNESDZTkJUENPu
+sOcSqclT69sUVcn4mljmtAIKyHNzH3Lt14hbwELezyk5JGURLEZ5URpnGF/2SLHhYJ3kcbX3GWjP
+Tv+My76r/HqHNDUfpjSlKb2N3hdIwWQZZjQmdDP8lsbxi3Wqmg7arC7jKkQjjsgt0R7GFylqEZD1
+5bu0MCZXzZxODMmOSOs8jTGBaE3L0Z4NkwNyPVK9wJigIt7do6NrtE66nHk+lla1HSskKw4zUk16
+6i+Mqe6KJK7XHdbWBKo1/n/23izWliw9E/rWijliz8OZ773n5h2yMrOysmaPbdldNpbAwiC5GboR
+0OqGtxYSD9DwAhIgDDx0Wzx0C4HAQEvGMjy0jdztdpdHVVbaNWRW5Xzne8Z9ztnzEHMsHv5vn6yU
+inams2xfS2dJqbtzn9gRK1asWOsfvv/7pkBky07iEF1qBxrXduXavfYGag6Vhk2IsJBd87UhsQtJ
+Ap+CHj/X8/CHR9L3ySxHwLH45skInX8kfbvzs1Jw5LdzJCfi+iyTLrbII1jkBslILI84TlEOJGBW
+euIyhE80LCo4Ww5QEYehnQwpaepSpZASnuzGhNSmUwQ+3RVlwSUICdYFXFK8IzKwbFLYe7Lbdd0Q
+VVsGee/+Fl7n5tgKLBgG+VbNFTaWYgGqulgB/aCNFs32bmhDMbKrfR+eR5cgLaHWFM3cPU3hopyK
+iWgpB5pRe7vmo3BkDHXuoeZL8Vq4I6bSYJkgeCDPtFvL4FvS/6PSIKArmBL/cjKa4GAilsKOO4FL
+foq2jpC7BB7VNFoEqAUBra7Mhcexsmr+JcVetUzgrYVx6i2UnNcmXvMqKKAj74hjRSgL+X7H89Ag
+1+dsbCNJP95r/kwsCnkFDNICtaVGv8vyUMeBs02SzGCB8LL2wQZqaypypqs8DwYywEXoQRmaWU4N
+pUdz3lWw1zJEJct/SxsJX+4ILkATPbJCDLZlgH3Hg2aUeHcuxw5hcJP1BZ+3QpT78n2n18VtVgk2
+P53Df18WmX1fJmCj4aH9gixkVtKDblDJKTtH+4k88O67MiHmywWarETs3rBx510xcf+3wxg+SUnN
+xKC+IQvLfSo3/Vi9A4vprc2yxEks97RllbDX7tFihsxm9SQJUxv7HdQYA8iWBQpLCGdUWMHO5f5L
+14LkigCdMrNQOEjalID3k0tkZZVqZG2ZXgFcWEScWvTrrY0cNfrnfgO4lst4/sHSoEX3brNuw7GI
+LGTas9aM0GYVbOhYcElx7rgltM0FqbIAl3GlYj0X3EsmrCIqJYUEAFkBw/oCYytYC1ngPboRe+4C
+s01qZbY7WD2WmINXlpiSscle10ZEObZcMec95aNtybxoX7eBAcu9NwOEPcbCIpZLzwGHtR22awO5
+/L30E1ib4rpYnoJLHyyl7kPhunAJ2rM9F86anCaoXaJ9szLGZyjE/FHblftw1a7aVftQeyYsBV1U
+CIcJJmqJvJTVrqV8dFi1aM0LZIxq26UPl5TCaspqyGAGi7x+hU6hqKyUezFSKuikyGGRzy5xxcTL
+5jlymuKFZ8MmcKgTbaICGZzrC5RrM5FS4Nd9D5FgpeB1I9wiWGjz9gb8lpiM+vAB7rwkNQNBn/Tl
+4eeRK9lp7GoTRWutLGTDN0JjtkdadOMrBDdkdzlbePjihQChlqsDfLcSKO3DkYFNHcMgFXdgcFDi
++i2xUEqnhGeYcVilyEhHZqUZypqYpW5FF0eV0BkZh70K2YiAC2sFzZx+VWoYQpPnlG7yltllVaae
+FCgTcTXMwIZPTkT7ooTySV/nyA4c1Lpwc3lOkavgbsrza59XGGmCjJIVnisJPorJyt1eIUioxp3V
+YRlaPzFQ1tdBZQ85QXDkHQH8OVIqgKmqhFnI+cpII2WgcF5pKFbexjT3w0YTNw5lRy+iIZyF3PdS
+GXjUBTX8fRqP0GrId63GBiLOT69cwe2whLPRhp/J+VREdaeoB9uWGgetS5SkandqPhTnFrIMRUG3
+ecrx7trAnBykOr10V0wjgF1Q1cqxYG6tpXY+WruyFK7aVbtqH2rPhKVQaoO5W0IlgFnKKpi+VKAk
+zHfuxpdScCrz0CokaFURUusXDijTACvRKOkDGzcHSF3lJCUyT3ajhKmwVZDApSBJ6uSwy3WBVQkn
+Iy9CVsBZi/6t9RRaDupMm91u7qB5XXYKL/exmlKT4mSCkHoB7S1BFeq2gaIuRGXlCByJmRiVobtH
+oZoac6QTICWVXLssYN+QYz/VifH2uxSOSVKkW3J89Ib0cdQpsDFnIDVcAKQam49mKDIJuj56+h6q
+x+In5y2xGKZphmUl/r5ZOXBdBu0yINf0/eMCZp3282ULXlkpwiWLh0ILizVlcH2J9mPx5/0tB3bO
++BCl6cKswCJjMVq/j6An9z/z1Ae+/1zh6QYVv7njZ4slxjO5SBo9QWso41koBYTrwh8bFuX78ozW
+ZlTBYizKrkpkbWplxhMYoiWtuQ9m++C5tAhSjdUGYddxD6kvVhoWGbK1VTSR/qjMgQ3pQzlYIeu1
++f0HAd3a0oNpy/ElA9eZP4bDNDIqH5oQdCgHSNbxjhKKIkDWHmMERqNkUaDj5tCM2/h5iaJkStLN
+MQ/+EgYaUVQoxyuUtkZ7R27cSQMMz4iNzwAnlkk8skrcoondCUllvrBQMeij8wqmI2aUW9gwjPCb
+eQI05HhnRlBQOkTGoE3k12CNqf3nFDBLmqtbIZJUZsp8It/ZZxmI18HCPkd4IAGg6s4CoFBqVcvh
+aKparclUMgflhNo4egrdlUVNRZuw7tBEfSyTZz78LlBKOa33pTb0Q3n4jUkDPxvIC/TPpsf42oFM
+2O6mLJqdewYXfSmXdpzPYHNT/h4nCodvC9/fG4NDdDMZ2+7aBD4MMaGgju1FANWWao6HkguLWeUA
+q/IiBrKWABYX8rsis2DzvTzTEzg26cfDPaghK1656Fl+/VIT8/lHm/gnLGePkgIJJ3HaBtSCtHcu
+I/JnM4x9Ca5pu8CCJriKMrinxGx4PmxFTAK1Sa3EgaOIlsoT2MS4ZK6PfCZjofMV0rW/kQlPYuIe
+o+Aiu9Wr4BLrkmgFi2X+OedHXhkkVNZy7AUMCVdsZZBQmxJVAsMApB3KfVpVBMM0ieq2oJXcn1nO
+UaYCtKtaEXRJgFopEHwz9FFRGsAkgMWaCEe3YIck15namF38OUnRr5tSylJKfVsp9Rv8/5tKqdeU
+UveVUv+XUkSlXLWrdtX+UrQfhKXwHwF4B0CD///fAfh7xphfUUr9QwB/C8A/+BedoIJCXClsVi42
+SAjRv+5B0YQ7HY7h2bLaLZ9M8ajLIqAZBVScD27FURk0LQEnakHbLDTZm0KxKMXpy+8aRRcLMiNn
+WYVovZMsCsRNrpfLFCuKVgaUuB86FW6Tgqtf9BHeYjVjrY1qLAHBiXGRP2RV4o6YnHb+HpYDbqUD
+jcYPi1thV1PoF+UzWFyk2z1MLygDdvt5NBzmvH+shuA3hD641/t19AfSj4uJ9He61FgsxWzve6eY
+rUV02kCDRTL9kwx2c61cLd919AQx8/ilNYO2hWDWC+ZwWVVq1evQDLraxASkiY9zJdZNOYrR4I7X
+a2+gqov5vPIq1LWkYkuSh6qmDTDIaTkDPL+U/v+G61xS3b3iBTB1uV5OqTyrLDCianO+MAiNPLNt
+7cBtyzlcS8OiNaHKNR+BhyymWb7hXOqEJOUcCxLvpiaGdS6WiUvLxJlrTEPS1K002DU0DDCv8ZhQ
+nn+308RgzZWQHmF2KMHf/mYf3lSOmfYmuGAgvM30Z7+rUO6KVVEVKTRd4XSxQKbkmSweDeDZtCxp
+pcUdwJoQxh4GKMlsrbdrsOgHuV6BXknX5CO2T6oluQfgXwHw3wD4jykl91cB/HUe8ssA/kv8CYuC
+ZQxaRYEiAgKaQ/1kDw8XYtadxRNgwIrC4xmGc2oznsrDOrnhoUaGnk6jDt2W7/NGHY5m7n2V4YRx
+CZ3JJD49u0CzLrj3NLZQETegtA13KC9NrlYwVJkak6txa1lgRcKLOBijraQs2Bk7mM/FZXjnn5/i
+mBVuP87ctp/YeOtY3IdazcMPz+X++l/5cfgkg9EdwfXHv3mA3/wDYRn+zj98C7NC+vZTGw1s35LU
+x+PvznGwWLsPMpYX4xVul7JoFkGMtiXj4vkVFpXcUxhtodGUhWPFst9BOoBhHGVlKeRUG6qUjb4r
+L3Rzvw9ty0szOhW1LHu0QosS9+PMwG/KS7HTDqF2xQQvHhxg4QmcOljypas14fBcyjLo3aVE/Rsa
+Q/rUx1mOFsFQeSX9fcdUqLFCc3pRIGQmaWAdYm9f4hb92jaaHda5tOTfcuVjvtaSfHeKx4/FBF/M
+JniSLPi7LtoBcSSMZUQbIW68IUxXZ+Y+mmMZoxMNeCsCubT8PrZz1AgDPz2o4DPbkzYKuKU8h+qk
+g8P7wkx93JNr3Oluw6ZL6PlNFOQCTRcV5k/k8/HiGG6bFZqWPOx68zOwOYZ5VsLO5HNp2XAsxhQi
+BxuvfDxj/ZO6D38fwH8CYM3X3QUwMcasJWkOAex+vx9+rxT9Olhy1a7aVfuLb39qS0Ep9XMAzowx
+31RK/eTH/f33StFHoWv8fojtvI6mJXyID1sLPPy2rKTnT31kTUb10wIvMCKr78oKeHoQQ0N2ktNR
+jD1aBypLsGDc6GI8wTEDPKPvyM6dujPsUH69ea0N+4zwaFfDzJj/9eqomgxg0RVZ1i3sMnDWzvuw
+nHXI2sfsiexM03oAh/wFbiR59ePoHE8HYsrtmBxnb8s9RTtvwf8hUdrTdFUK7eOdSszFIxTYJhN1
+PezhsJSd4r3oEAnhvO/JhomVv4L/UMbtxfIQjXUhVVXBqYmFYQfnWB5TQTuSHWzl7CD35STBeQTP
+E0TjcBAh2JD77xXlpZYBtIzFUX6KMS8+ckc4o2wenBFapVgbWbhCSZbqMhSXov7gAaotcVGsXKHX
+lx16sRmidih71aap46wuD1Az2OdZGjMW+Lj9GhQtKKViTMkEnpSnqFhJ2NRiHRXWHJMLse7uHx5h
+yMKs1SyEVZe+5XMXi4AoWRa/5XNgSWj2ybCBMpTxCtICpUV6vnStc9pC5dFqgI2CO7qatPBgJePp
+zs8RN/jcY5k3ozxF75QB8W4Ip00yoNLBgHrcQ2PgzEnm0qGLeb5A1JM565gJFCkLcb5C2ROryQos
+KFYNf9T2SQVm/1Wl1L8MEbdrAPglAC2llE1rYQ/A0Z/YCa2wEflwwxDVNdYRZD4WZ6wyCxYYHjCO
+MC1wRnz5o5U8zGvtCKuZPIztoLyEe1b7fRzel9/dezzDoisP43AqD/P0fI4+sey7owQ//HnGDnSE
+GqPyVVfBo1bihCnS/irHmGQoj6szvFIQkHOzj+d+Wq49+b9P8Yjkn68OOSHKEi/syLHxYo5FjaKi
+5QJlTDEQLYZV3lniyz8tC+RXNvfQHYk5GHYU3G15QarJI/z335H+V4WMhXta4hFVjqxxB7f+NVb7
+rQzmK5qldR/bpSwQfo+1AycTHB3LeS9WI7gLmYz1qEA4lkX2urWE1ZfYR5iKST18801885Bgm3yG
+NhcQL3PQdga8XhO7FLpN+YJFTQPF2g4dAzmfyaZdg9qhf92NUM1YHbuSsUqrJSyKEFuTFgpDAtqG
+jU0ybjUXJRqFHBP15Pe228DwRPozfjrFBdOMZ9MVjh5JnzrOEB0KBdWpFboTdbFF9ibv9hJv35Pv
+345tqJHEj/IZyXZRYsXy9J7ZwoUnf//9b30D52dUk1oV2LolL+y/9OI+AGCaKozPxb2y23fhKgEy
+JcUxEqaOT7MVAtaK5GttyMMci1dE9ez2ThfFShZhz1LQJJdB1YApPp4l/qd2H4wx/5kxZs8Ysw/g
+3wLwVWPM3wDwOwB+gYddSdFftav2l6z9WeAU/lMAv6KU+q8BfBvA//In/UBpC7ZXxwvhJq5tSr7+
+7OkI9k1KsB3soE6uurRpoMl9d/0mySNsoLbWv44chA2u7PY11JtyjmwjQ4fAocUezWQTICRJxZ3d
+FmptsQhcaGTcHSLLRhHRQqAk3zSy8TJZQ7aXDojtgWp20aC/svliB3hE2rjPrmXrp2is6aqNBfvx
+Y+m+saHctcsj97nxU5/DT74nlkb9S7uoxhQLaWbIqDD94l9/gjvENRyxSOq8MjhYyGD8+PwYB3S1
+OmmCWk/2gDvTPQQ7VJhmsFZtWnAg99+uz+HQLLeSFBuhuAR+7xZcBgGdFhmVay+hfVvwDziP0Oox
+wNXZQrIp5wiTClbU5qMi63Tbh8fnWAYxuhDrYKEtPJfTMtnwLwlMDlnYtZ0ZBOQh8DsNaHJEtO0C
+oCSAmZc4IjS7S0Baq9ZHfYvs2KrELZKv4PoEAYOxKzVHNyBTdlfmYWlszFx58MtVgYRz4aZrY2bJ
+61PP5Rk07ACZL8/c/fwO9Fvy9zs3NfZblNO76eEWyVXadZr7qYIVUYav6cKqE+C13IG6KRawdzLB
+RleCkVZEN3argTYBeVZzAyUxHUWtBZtFUFopWNnH2/t/IIuCMeZ3AfwuPz8E8OUfxHmv2lW7an/+
+7ZlANIa+i8+/sI9mvY7CyGre7gR4qUZmoi+6OKskvTUerGCTXTgkxdV0OrsspFJBDfNULAI/Aqqm
+fN7KDHxXjrdK2QUq30J0Q1bfnvwYAFAWE2SMKdRyB6FPgQ/K1X25FqIsmd4KJ9AVuQKCAM4NEQi5
+cT7E/ouywxrSxiGZwxqLv+9t9xGzBNgc1YCK4irML+u0i5rL0tzxHFax5hV4HkFL7sn81mtQ7h8A
+AJqh9CevNNqkhBve2cbNcl1UNkdC5KHj76K2xTFiCm5aeGg1ZQfq+RY0YzRID7HVkAIrp7sHix5n
+rgV7UbvTwZevy3NaPh4AJZ9fu42YcGWnFsPYHAPyMKzgwJpf8pmh4lg8bwErUpqdX1RYE3FtkeG4
+sCzcvCHj2o9uwmtJn51ihPF9GbuhWUIRvTpnYZe7Z8Ml7dpuaUE5ModuWhb0nlicudtEg4zP7abM
+EZPOUb0p15hhhjuEaR/XKzAzinjNFLVh0CZxbeS3ceNHfgwAcNe8jCiR3y3DJvw17JOEsFHuoCAb
+k5/YUJxvwU4bm+Zl6X91CkVo9o4l70IabGPrNi0TRHAY/K1sDV2uWaAV0Fib0R+tPROLguXYaG40
+0U9cKPIdLqwzuHwJfdPDTl26upFqFFQeMgymjGYx3ID8iXMFbLBarHQQ1mRy90Y5jC+Te11eroM5
+NjsEhCQFEvItFMiRD8hd2PXgcbEAI/2tT9XRvE/TcdpEKe8V7JkLUN2nef0mTEXevWQd3Tao7rwi
+p7IvUN6TibeqL+G9JvFYr0dosLkBRbNd+QpgRaGzmKMg3LV+x8Zze2KWf+sdBi39CsuuVGf2ru+j
+qkhxP0sQZ+S5rMcIyA1hk66tHjagyEHZWClUjkxis3ELLjkpvFzBkAwGKV0718JWIAuMc2sLq4ZM
+3GwRI1gywJUPUc1lgctbZOA+niIhxXmpbfgErVVNG+4hWWliF5XDxY4w6LpxgdmaUv0CETM8qtCY
+VjLOhQ1YY0Klu/L3ZbzEqpC54NqAG5I/0VEobQlAtrV7yXEQkFviYrTCcClBy6PlAisCubY2gHhE
+8SDCw1sXbdR2qfq0AmwC0Tq7W7BB/E3lwSzJ1kwIfoUV3JWct3JTKAa0HRTYiij2cnMfZot4AyXf
+tbwQPrMrjq9h6JouZgUUceiBVpdgp4/arqokr9pVu2ofas+EpeBqG9ejHlQRIaEEVzoBvJx6Ch2D
+De5Gwyy6FNlAU0ynO/kUDwaM9tkV7FB2Td9sYb5kKsx20ejJ+ZqKuet5GynVlQPbICQG6/Q8RjyU
+czR7PgoWSs2IHtxP2ghZ1Xe6OUFrIihEvJBD2bILBGYTFTULMJRdx+3ZQI3FM4MJfDDwNbkA9ugS
+PJW0ktpewQol7Wc5e0BOpqRGD25DxuhG+SmckL1oRebnXVvhU601a/NtONSVnCyB2VRQiA1vC9d2
+5P48m9WLeQ15yt3cnsJtynmDqAlFZKYxMSpF1eVzPrx0jCaDi/6dDrpkeV36BU5jUr45CkGd+Aam
+FpXOoDneZh4hYaC0CQtj0psl2QJDBgxRyj15lYdug1wBqQND2LQJLIRa+jlYJHB71FYgBNmKLRim
+C7O8QMACurAoMV+QNNbKYUfSj1JTO6PIUc3EetuzMhzQZ1guSpw9kUGoTeS7U32B3UfyHOvbU1ic
+s77twrIZYFYOsC7kq4iFyDJUlB5UeSmy4ABUvkJApWlnP4RN5iVDC1pnLlyyYGvHRkZiWq+q4BCT
+UxkFVX68lOQzsSgox4G1fQ21KoFN/sV5liAmqYk3t+BwUDt2idyTQYmJ8ffsBCFfjr1+Bx3Kj/th
+HVmL0vZ6jIA08I2ePCw1W6GgaGe6Gl2axlVS4dTI5NiaJXBflAnWJmw1/0IPGyc0B502bFb7wQ6h
+1kQYnWtQY1aq1cU8xcoCCE7CsQIo7gFvBxap5qtNLnieAnxW7zmbMGR+VoGHkpWY+pqLF8p9AMA0
+EtO42wbu7kqc93O9Ph7RpAwaPmqlXKMRdODWBaeg6dfaqYUyJlWaTqAJlbbVFtYaxJWeIZuva6NJ
+17Z0GJABnGUCqy6LTGmA7ia1JOcxHEbU10WIeWijojiPKeeIOCyregORS81EHcNQK7Ik03Tg+GgV
+0s/cT1DQF3TSKUrWUmzYLp7fFVcp8hlT0AYFAU1GZ6DQFZxOBIel9EpVsJM1aQ05DtMMqSefkwKY
+kbRnY2pwxgrMYCB9aHnlJbjJNBpQJGrRjgtFk1+FGmCcx1A63swS6BorI/U5LFLTmSq79HXttITT
+4utqGF/q1KDpSivLAtL1fDOXPoA2AMqPF1O4ch+u2lW7ah9qz4Sl4Lo+9q7dhWWd4OmbUhk4jBMU
+NBnrsY2lL7lgTzUQsBDFUN9hFMdoN2Qlvr7fws46cmyNUZFodA8hcu7+5kz+dfIKNiu7iypDmq8F
+WVJomngLM8PsSJBpo6diKfyHP/tT8LZlN8vvP4BlmG82DpQraDSsRtCuRImrFTn9dY6ChUSFymCd
+y33kWYRkKvdtM4tSbhl4DEgV5RnsSIquVOnCIvZi+nsLLG1BvO0zem1lLopQ0JGzPRt9I9u4CY5h
+j+TcoduGoqyazWrPrBhAExKdDCsUNInz7bfhpWTNTkIoEoNUSiLojh3CZdYCqYWSRC5BfQNqTCET
+t0IeTznOco00teBT2xN1D8aXsdhv9DGuyzkGx3NMStmZqe2CUmUYxfL3WtWEayhjZ2eokVCnETWw
+0ZMx75DObTU7h08kYEf7cEhTZ08zVMRk+E4dKpJjilwsL1U46JENOWnlOD8Xa+S78xj/bk+ery7F
+EnS0BlHJQAGA1ZDlcgFN3RLYLYBszdmSwjO5BZwS3n5ukIWS2bF0B3pDLDqrsgByfFgOn4ftQblr
+3AtgW7QwNEADGMpVuEyTfMT2TCwK0Ap25CLRJbKJvMST/BzVKZmGuxaC1roa7AIqlZe6oplVOS0E
+lBPPj1McTCl3bsUYkD/PSlJEjEHEMQsFtEbPEFJbpRhpecGO0jHm9MvNYQMTpsue3hNzPmhG0Mm6
+JPkVpG1xY8xpDBNSq71WwShScTM2ArOCIbchwibsrkCGIyuGoSKVukkFqayNgnVl2stRrYVHkKJc
+yAu99Eps7sgidMaX49aNFhq3fhgAYC/sS1x/lUVwG5KSc7oKWMisKWL69W6FFYVT4skx5iu5dm2R
+okYocXBmoaSOZ1qRFKYTI1/j9qM5bE5Ay5pDa7o5cFBRGCZevzWHKyi+uNoNsOYbDq/XMKdrZqo5
+KtLkk1IRmVtiWVET1MwQ8j58V8GjIrF2K2AsPxhwoU+y7PJzzXGgXFnIFvEMcSr33c5K1AOq4DjS
+31WRoqyRWMXpYExlsGVeQrFMWifrdJaFuCXzLVyml5uME+XAehHOJzDMJBmLgkNxAsVpU9gGmlD3
+op9fVmKWrRKKbjNcCtykPhQzcMoFtLMm86kuuSaVUpfu30dtV+7DVbtqV+1D7ZmwFCzloO5tQgcl
+pqFAZk8P7qEVsEoyn6CZyMo+z3JYuazGQSir73ONGs4uZDU/HkxROhIMWy4zzCnDVvMsqEs8svw7
+PVgi6MkSndUjOGOu7LUSd5fMle8D7kz68f5DMfcQB7CYV4+dN2AIdNK7KUC9P5O6QEohmhnP1S+h
+SU7ihRrVy2IGGrcBRcIOjMRNQj2/JDTRXgDYMi5VooEai5KGc9yfyLl/llZAv7mJdiYWyuODc9ym
+OI3yQzgkKim1Bay4S3F3KYYGZSYmcxrPYVrrHbiHnOOC5QQVeSMLMmPH8QgGk8trNKlT4BZnAIlT
+EACGlG4xVa4xWsKJKNeWhsgmMm77nVdgtcU6eMc7w2ws21yD7sUkizElo7SbaSgG2izbQcT8flzY
+eHxB05xck4UyKI3snjO4KGmFVQoYDaRPdn0ONyLexSL2ItCoEVbd2y/wnbfkOT1NCjgVsQV8i9J0
+jnjJatD8GKAATjEpUS0kMFvBR0UtSUXtR0un0A3qN7R6AJnLy2WFbCVuZa7qqBFDoYg9KZceVPgB
+X+Oaj9PWGupyu1eXY/9R2zOxKMBWqLoerIMEW4G8SGFRwqwrPnMDQxPWgUFCclTPojJRr41NR9J3
+eX+B82PxX+v1FppKbrGzqVGQM3F0SNBJtsCRYfRa57h3X9KBUaPEq32ZeF86KHFQE798GBLlt2VD
+M03l3+wgfcyXJm0AljxEHbZQlTSPtxgDWWXQlrw0hZ/CXrsoc4VyXcnGakGzMpfoxspZoMpJj89/
+CAAAIABJREFUbBXNkF+IezRsPkGfpB7Jyy8AAPbuKsShHOv/7lOMXhSw1HYUokGx2WymUJHRqCQp
+jN+IkJBctejP4ZFEJmq2LlOVib+6NC098kumC4XZWCZpHp8jXLF+REco+utrJNBrEZmMfu9WA27I
+LBByaKZvay9tof2ePMsX2veRrORZVnzhM5Uina7TqQaLNU1RFkDRxfT9XbT6BKKxZsJrbmHJrEXg
+VCiYBhnHCzgk0RnFLuoEXHVJ/jvyJjjdlue0CQtvUSszKErkXdbYEDRl5znSifSh6nculaegW8gL
+ihV1bNhc1NYbS5YAVo2gNSuDZh1EujRYpOLSVnqJoJB5qKmTmXRKeHTzXMdFRQSp5StAreMIBurj
+UTReuQ9X7apdtQ+3Z8NSqABrYZCkYxy+J2bycjbDtW1WgC1cFClBKI4BVdIxpxBGOBmhR7M2uLaB
+jS1aDeUCEwJWmvUmTmjynk5kpz1frXA3lmvMLyo4XMG/caQxfSKr/68uC7wbfwsAsD8jx6PxL6Go
+eHoPli2sy6qmoBYEMnkxtEOpMHITVAZIj78BACjeX8GtE5MQ1IET4V3MyV1g7D4yl9LqvbeBptTN
+e+1Pw6E1VfzONzA+Flqxv/HvSM1FJ/sZ/PGbvw0AeJKf4OcpKxZYDjxWA0bdBkom6i1SaxrMUSPY
+xi19ZCtxY+JcI2B2Jas14bBmYD6WHcxPPOSWuEHjCwuTNnkCXRsqJ9hG15BzJ4y25diqcKAITkhd
+F4mW4O+u00fwaTGTv/1ajipe4wbk2ZwWwDVaB6XnruN3sDPg9o48n+52gLBGCPmUAeoghU+WZNcO
+MSc3ZXwwwpyeW23TQUwcSU4rNKg81N4Ua+Voa4XtlViL7yqDcO02ksvCjArUWK8TBAFc4kJK24Ui
+HLmEhZxzxxRigS0Pl9AEJtlYAQS1mUONc4d1GbUI4boomJgV2wthEYdiFKAdUvsrBayDiwowH081
+7spSuGpX7ap9uD0TlkKhDAZOgsOLpxieUMyzs0I+4U4ZlsgjyrvNM5wz120nLCh54mEU0Je/f4GS
+TMyLaQJNotD68yOkhv4uA5SNrQDTU9mJ4s4K/+8Ri2CmS5xpshutssugzftYV/XlqEgCay4iVF1+
+XiogJ4xXhQCZoQzoZ67mKCuSa3oFTCXBQcvvoLwtRUyrJ/8UAJD88Rj2Fvvw/BZC+plZ1kDG3P1b
+eQ/XupImPfm6bAfujy6BRAJc6cV7WKOEQ8uGR+xFqVPYc9nlYm4poe8i4f0FeYrs0g91sQzo1z/O
+MWJB0zr9lZkKo6VYHUWjREErLg9yuKy0jC3m8AH4RPxVyka+Rt3ZDkjJjWPHYEyrbzO08Bbl+aYP
+eV0rx2P+sBOkWCzl2CmWsN+TzzuzI8CjNgSDgablI3DFKgrqGVYcmNhTqFMUtmFChDWJ+ThNQtPP
+p/gmWZKXsxTnTGE73hQFhYwLBhHVPEFKvgkTb6NUROR2K1SGWJXhEtlC7sXbkUH2brThnJL3oxnA
+U2LlzP1jFO/K2A5CC7cbxJywWlLPcsSU46spSyoiAUlBcmwV8LELop6JRSGdJXj42+/ija+f4A1W
+lUaZg9YOsfhRDyyyQ7wCbF8mliYEdAkXc1Kcb/QbiFry8vvNFIY08JvRBvwtGbS1kMnvvZXi0UQu
+qMYJUmLnY69ANV9zz+Iyz+uzorA4nkFfyLHz+UPYC7Hr9N43AJ9U7fkCoDS4qgiU6cfwHZmAy+88
+weSctQhbHhY29S1fl4nk9DI0t9diMSUUhWgm51/D4FQWwN994w085eS++0MyMZ/vD1BGpC7TN+BV
+JAhZ5PC0LBamWsEnHr7wWQK+DFCmcl43KtFhjUaj2UNMujLTKeCzbD0Zyb/D0QWCJWtKdupo1qh6
+NQOKpVzbdQwsax01pgsGA7silHjqYTpkxuRbR5g+lcDlU7ODswmp7FgT48JBSVz/ycTGTkfOW8DF
+KRe4auFjb0sWhUZXXLtaawvxlG9KtoDPhe7WTgc5edsbrRKRz0rLWNycB2eneH8i13u+aV0Cj4og
+QjaURS8/k/PO4tnly7jVex9W83npzyxHsXpb+h90obYJec5Z9n1+ACoYwG33kBEDo4/PsEWG8Z1X
+nkNIWLyOGfDWGWwCpIyrsI4oKsuBKYlvgIU0vdKSvGpX7ap9gvZMWArjYoVfu/g20ncHsMhHUGtu
+YsZa8sC3kTAVVHgpMpKW9C1ZDeOyRAQWQe03UKemmxlaSC0xu5cNg5IBoYTagZiXiDU1GEfmMmW1
+WqQfBGqAy8/xWh+mfgaXOgTeMaDW6T3tQPlvAAB0+hwMzcvKXQeAYugaT7aZQoUssPJm8CqmqZ6T
+R+I9vwnnGnP65U3MkvcAAN88i/HbfyS7+1MnhiGt2OOmWAG3FLD3ggQd28PvYrDWP4kSlBwvqzBI
+6vJZkaqrsgrkK7GEZl6FHg0lKyhh99aK3jZcQu8cBsay5DrUnuxc7Y6GR72BvFwidamLYBfQ1I1U
+6yrYoAKYbkyyChUr/P7p03fQ/aN1Gu4cbaZ1Z0auoYMA05zWlKuROHK9a1sBSlLZOXUDe0esLJc0
+cJaqLtWzl9YYLgvsbNuBZrZXeQYV1sQ9ct333pnioKIat9VFui392DnWmBLToHZlzjoPCjhqTcjT
+RGVJlaif76Bgn1cbFmoOUbaZuCLLTCPdlefQM0t4DVbMtnuYvSL97Deb0CEDtwn1JDzAcaS/JTbW
+8BuImgzT3ZkNa7HGXn+0pszHxUD+GbRP3b1j/qf/8e9hI5zh6Xck6r258wqeDL4KAKjOzhBRrHVj
+9yU8ef3rAACnSSWodA63QY6/SQUrYF59UQn2G8DR0RkKtSYIkX+8oH4pRV+WKc5owp5nGSpO2Jsb
+LfQalD5v3QYAvOcniChU8/TBAqEWqPHT9CEm5/Iid/oRdhovAgDcGzLBrrk15KX0re7UcGhkUjnJ
+GIfHMoEK5smPlgfIKV6SxwvEa8pxnQIVTWMfiJg9UJ5MzFbUxj32PZj7+B/+wX8BAOhl38H7v/77
+AICxXyB/KPj6YCETdDIsMWLwJE8LnLFv7qxCk8pLNc9HPSLWYST9XIQllhzPk6LAaMgJ6Ctca8ix
+9aiOu3vrDIZMeF1WaD/H2oGNO2hRbLf1c38bXcLYf/N3X8fxhbwUf/z6L8uzef8QISnOe/rHYO3L
+9frlJjKIC2YnTdw7+47c66mMRVacIV3I7wo1g8lls+i0XNQjie1k9gKTc4oMLx8CALp+G+NMXJjR
+3McRCVW8ysWr/4/07d/8t2Ws3k9S1BgbWdoaP92XeRO3O7jelLnlBgbPkbQGjB3cvrGF5mc/K/0p
+Xdz/xrfl2bS6uJ/L53e+ofDaI/m8V8rz/64dwyM1wHg2RofxnKfVh/e072nfNMZ88fv/6YN25T5c
+tat21T7Ungn3wXVdXL92DaP7j7GTiA10XLyDJ+8TajqN8akvCPYg2htgt5JIvZcIAjHJtqAiWcHd
+oIliRVTkVh2VJZaHwnWMJ/J9ztx93gU2mEvOHAWbIh1ecY7ZIRPgVYG0xoDflvx9s3Ed939HzlVP
+czxqSMBwcGyjEUmfd7e38MoXxYLY7oroSUPZyC25duICN5gZmQ8SdPoCy82V7HbX7m9eStCdZzNc
+sPqwHKwQtcnfUH2gefhkJPexeFjD+W2SeIwG2OrLDr2676DHKsJ7B48xIXchxZdRuhEQkY9gHuAl
+ZnvOewHuSqwOntmGRx2Fs5XcR5yfITmXXb6nh/iuQ3xDscCMaswbvQIZ3bjckp17MnCxGIq11flr
++3BLudebSmNIhGBnucTvDL4m9/c6uQnMBnq7Mm57r3TRoeBKrwfk2b70uZqh+zVBeJo2cS+xi7BB
+XMDAQkArs9PvY/vFu9K3gykmu8Qh/L78/fjYwcOGYE/K+WsAsy4pUnzhP/gZeQyx4EKggAVJaHZ9
+Gz//BXG7vnNe4Yxu2otRE9f7oudR/5SMyY3eTfSaUgVbBAaP74nVN/jmCg/2fgIA8PY7X8MJ2cbt
+IWHOtouEeIu5UZj/gKz+Z2JRUFrDrUW48G4guStmtFWNUNTlpW9lA3gdMfHU3EONIisuo7GdrR5S
++gQmqmPGCrloo49iKqZdzVMoKOASkvjUr1WoSFyqVgk2X5GX99GjtxCQ6vtodA6fCjseXfzsmoW3
+18AibwZNcg+9maBdFx7IFz5zDZ9+XgBHva48cDe0YdEsLx2NMqXrUqWIU3nzsoUsDuM7CbJEFrrp
+IsfRRMbirQcHcFmpV/oW5kfSqUEhgCx/+wDjE6pXLTJ4hGjnZg9H1ExMnQQWmX4mBIUhSnHNpn/+
+0iY+sy2L8LKzQHnBOoHGNpyI921zYZpuoyAJTbxYoul+FwDw++9leMqK0OxRgi41Jh2StVY6w6Ej
+/Vm9amBVknvM/5qH08eyIH9rUuK9o8dyjC8graYb4e7tnwQAfPnOHaTeuqoxxi5Tme3Cx+CEZYd7
+smjcia9d3vOjRw9R1OTYWzs93HpJLOpyJ8GTCwGRvXVNFrfz2grloWg/Qs2wLnK1DFBccDFYG+sG
+cNcfM41vaD6Trdu4UedC3d3CzX3JStR3Zd5sejfh09UoiwS3Py1u1YPWY3zju+T8bD3GDtO6ASHx
+80mKnKCuH2T7RO6DUqqllPo1pdS7Sql3lFI/opTqKKX+mVLqHv9t/6A6e9Wu2lX7s2+f1FL4JQD/
+xBjzC0opF0AI4D8H8M+NMb+olPq7AP4uRCDm/7cZCH3Xrf0UiwMxI9//vQy727IiNlQLnYms3Lnv
+wiLphclIG+5NYS3lc+nM4ZI4BekKTr6G+U7QISS0YrDPX9hIQ+7yykWNOok3689h3BFiFa+8ZACA
+plzdfHIbL7qyCzxdDkDZSey+1MZL1Gu8s7kFQ9otxYo8O6ugyKlXnhfwI9bCKxdeV7aYLCGQpjHF
+inXznXYfbkZegOtjLM7Eirm2EeLrA9kptmk6DhYubGZZUlSwG3LP9Z1zbFM38423LRQ0O++wgi4w
+Nva5++81XkTjulhmtdstZI9ZrON0YChnn/fEIrKPQmh+l81mqG2Qlt/ZwJtvS24+iUqEpB+3Peoo
+TioccludP52gqYjDKAzcmgT2HPUt2A8Z7aeYyouNl/ATn/mC9Hm7cVmiuJjHqNaQ4AsH+9dlDsQn
+YoFtvdRAzOKh/k4P05nMp9rWHXjMGHQ+00Lyh+IqfOUzcq5f+a0/BEBX0oRY68SXSL5vNG89V/LS
+wp3ycwCA9yoHzaU8h2vtJrr2Wilc/u7GLXhrsp94gL0dsW7mDzX+Zl3G65vmDvoMsL7OotvO7AKn
+5oPCpx9U+yQCs00APwHg3wcAY0wGIFNK/TyAn+RhvwwRifkXLwrKoHBK1KomHIq4fupuB6cDMZ99
+v4vwurws1UGBgkAfRf9OeQDOadf5BayC6LfVGIY8gEVZICQIXLvk/ctLeIzqeyXg0JeFCTG2xSwN
+OzY211VrC3nJ0zjFnP3cbwbQJCvd7F7DZzYl47CzdwNuIkaSwxhGlWg4rMVwlQsVkHcxB9yMPI4k
+KVlELahCXv4wuo/NazJJZ4gxsOVFycocjZb08xokrrHRUnjjifz9pEjgkhsQjWtoLeT+bt6ysXVK
+Eo5cFtNbX/bR+fwPSd+e/yJ0V/QGoKfwN0jC2PCB1VrolfyRbR+GNRWOdrH5UF6aWusWrjEd+A7u
+o84YxoqVg3cjg22yBg2XIxwmMoav5BnMgmnZ+xY2Nhk/eF7u86+0fgifenlfnt9M4ykrZlcVsKXI
+llS34foSU9Bdsi21HRiK1J6rHcxGMt52x0OYEf1oN7D7ZZlnd0firu3d3sar/7tkPp5YBe5zkUpy
+C4pgobVouoZQawLAtc4Gvnoiz+RH956D71Ph6u4PodMQdzJYk9E269A9xguCLvRYfve5n9nBC0fy
+zH7mR34Bj++/CQD41gn1LP/oCb62ELfq5GSJlGCwTxpa+CTuw00A5wD+V6XUt5VS/7NSKgKwaYw5
+4TGnADa/34+/V4p+NBx/v0Ou2lW7an8B7ZO4DzaAzwP4O8aY15RSvwRxFS6bMcYo9f2R198rRf/y
+Ky8ZqAzFaImyTjBOPkWb5l4WP8VixeizrRDGtBCI9a4yg3RNShcrFCQnMYGNjKa7yQ0MlXfyjLTo
+lYF9TtEQFSNiMMyoCmFAzMJCo0adx4qCNFMzwnQo57DsLmYtcUE+o3uwWnLtXBUA8RLenGQchYeK
+ZCpK16DXVFuVDVMn4IoekTuYQdNqyLEBi7LndVhIfLEaSnuMtfBScp28lDULKTEE1mIJZbOiMlsi
+a0v/HzyJ4SeyS1/fk3+juy9D70u2RDt3oKZcqBsKhjTpykQAGbYt0plBWQBdEbVYwmXZormw0Pth
+wXXcfWJjlpMsZCVj8qQq4ddkDJ9OKlgTGe9BPsboAfkitgsUQ7nOi5mY9b07ISri/UfLBPGFAJ1U
+qZCwJiByA5DNH0mLOI4ghSYr87W4jgU1H2f22Rq5DD1coduRwLN3V/5tVl3gK4Ih2Hi8wL2LV+V8
+wxVUwDoOEvJopfHZGms86jFeiCTQrMIK13ckkBilGrktQVClCEaKCmF7AQDlwm5TRt54sAkGCy+2
+0OzI/rozFCh9ktxD9v5rAICvjt8CEpkjiTFSkvunbJ/EUjgEcGiMeY3//2uQRWKglNoGAP579gmu
+cdWu2lX7c25/akvBGHOqlDpQSj1vjHkPwFcAvM3//j0Av4iPKEVvQaFuLKQKWF6QaXlhUOaye9j5
+Cj4DkPZ2CS+QlRRrxl3loWDRUWkAizBSUwVATD1GGyhZm2+YmrSqJUqW9eUmhj+XlT8uYoRLWaE3
+gwXA+vc1wnBj00OyySKo4RF2PfG5g3YLtYjw2mGBqi67bclVW9Wa0OQYQL6EWevXBRYMqcvUuezs
+jqegaWSZ3KCaEYdhDFxX1lnH9UDh48u4hu8YhKwSHZwa6Ep2SjuI8HhAqwEKdbJB9Ynss3opbI8x
+jnYOxbiMqVmXfAFQK4AFUUavMd8pFJ1Y7UzghSzycTzYI0m9edkRpg1aN99mqhAxolh+10xT3BvL
+s9kqEiyohWnG40vRnVtfkt/fvHYH9lz6NpidA4Q/hzUbGznpz1QM21+zEMnV6sZCzAIty2rAIgN1
+lLTw5rFAyMskh92VmMc+JC3Yd4bYJKu2uZ3g9fepATE0aKyhy5xPfUdDWTIuW26EeV/69tm9XYQ0
+6Twzhdu8xvHk853MAE5pFTZh5WvzL4Ud0oL4tA0vk6Cp/1S+++ntx7B47Q1/A689lTjPk7MYI6a+
+v9dg+Ki2wyfNPvwdAP+ImYeHAP4mxPr4VaXU3wLwBMC/8SedJC0LPJiPsBlfIMolwLNpLbBigE6r
+HE5HTKPA24W6rAvly+9acKhLCK3gEQBlXAdOQ74vXMAdySKTrNV4CgtzEqtgpZBW5Aw0IpUOAM2F
+g0UpD7dDFt5/fDLHHiHR9brG9Sbly/e3EdXIqNu0gbUoLP/ueYBTI217rAGfM7ZcQVO4NGdQs/Iy
+FDRLq7hCGUs/YxMD56QBa9fgsLpuRLN+u3MT8zo5B41BQnSSW51jnyQcF8cVos9JYCvqUNFqpwcd
+sE7ZswCf1aPW9TXnB7DpAznNddaamCoH6oy5z0uolDUOyyEcchE6x5vw1kQzrN5z8wJnfCleSww2
+yHz81fNzNCcCMdbhAp9ti6m8uS+0cmFXo1pDsA88hD0x812rQL0h18szA1AhqkrXQi8eLEb4q4aB
+xeigQgqXrMwTy2DzjIph2ywN1zmKTfl71ruLpab7YMwl1b66kL97noW5TxezZeNfJ6FMs9OFQ3q3
+1bYBCJJTIQl54MI0uSBbGZRD4hudAAE3i9LAJhu5x8rJvR+/hRe/Ka7WRdXA6ZgbnDrC7EzGNq0q
+4GPSsX2iRcEY8zqA74el/sonOe9Vu2pX7S+uPROIRpOmKB7ex8n5GSISVthDDd1gBdg0hSlI47Us
+gLWWwbr7tgfDXLMxChURiFWWQZM3oMoNUi0rbMqCoXSeoWCwskiBMiMPgw5hU0KuxBTlgtqGKzHh
+90obc1bT7WUau5BVvh/4cGKa3V4B15adyXbkX5VpFOtMdm4j565Z5UCpZHdPqAm5mGbIFtLPVXmI
+4zMxfee6gsvU6qo0aEbki7Cl71tqBW8gLlNpgIDuSjZN8Nb7cr6ZFeNGV8bTprK1LmKo1Zq3KwNW
+TBo1E4A7F7IK4BiBaWG4BiAtnslXMIQ5G3OE4pBuUzS6lKGr9WUsum8XKEgOu3sR45S27ecPLnDy
+UCyFzqjA5jXZjdublLnLIozHtCTSQygt91df4JLsxlr6WDH1C2pZLNwpslP5Ll/F8HntwSzG4T1J
+6+ktjdqZHH/YJjNy5SHIxdzfPzpCdUrrFAo/uSnWyAPqWea2DaXl+b98rQ+PBWbBaoo+kbMt+zrU
+obhCxQ32t9y7VBVH7KAiQbCCBayRo5UCArrQfRLQHrTxXCjuVXnrFH+1JYHiz/1eipTn+MUHM+Qf
+E8PwTCwKZZVjvDhB/s5TNGy58biawT/iyx0aTEcCf160EgSxTF6LjLXKVMjXzpOyAOLsbS+FMRTh
+KHLkLGFeMfQ5tAsUK4JGrBKrEVmT2ho2JcdTNFGEhASzQi4zORKSrFz0fRxEYl9fn2WX5cL5cg6L
+HIwt5sctvw5Q2NS4NvRcJmBRaKguhUTpn3unCRaeTLb5sQZY7u2PissIf6ptaMVoPyfjPN1EXicL
+FRQQETaun2BAMd0/WpX4yrEsBp7gZ5Af1KE+fU/G6uwOlGLNiAJQybEq7QIlbVGLsYFoA2AcQWkF
+nMrgGlMCZJXWeYbgmuT/203pw+nQQUFVpCVsbBE7UmUjTHmOMw3YLClO+UIvzDkeHUtsOx+OsAmJ
+8Id3WigoDlRaMfRCxjkt1jTsJbImYyOTFIeswbg4P4ITrkVzMwT7VGTivMljDZeVn99aGFTWuj7Z
+oH1H+tZ8Kv9OTYU2qeibcYUwIHO3t8B5JnOnNpwi26NA7JLjFl7AI9GLcvxL9i7j1aEoD6AiDyC8
+2SKrVPP2NlxPdEOtyRYGU9lQ3xzG6D1kibs7R0nSlo9K9H5VJXnVrtpV+1B7JiwFFCn0xQOk8RLj
+TFa4XKWoWGWHJMP5SPLwzvIMddKx+eRadEoDQ60+R6+gWeFnaw8FZDeu5gYZsweDqZhWw3SBinx/
+eVHB3CIycWLQCuXaTtODP5QdbbJWe1ZjTBmo2pjOsTyhad89hxdRPfhgAkXtiJAQZmf3BpyVmLtl
+sYKiwrHdtABWHSYzFkQ9HWCwkJ17fJ5imMjO1qxsFFp2tjwrES/WkX8ZqudurLB9X3blc8eGtQ4y
+2T4eMIswm5RYOLL7bRyLyZn0Y3j3xeKpumfQzC4Y40Mp6ZNKE0E1AgB5KBSWMLM1+OwIJfUUyvE5
+DCHIFQbQDJiVh3If48MVTCp9ODY5LD7rzxUjOK6Y163ZAAHxIO/eewAAiGoFJl+Xc5yO7iPYI0p1
+4wbquTyTVb6EO6U7SWyGu9KYEaZ+9OAxpm15NvqiwhMWXb3UiDCdiHXjSfwVy7mHwVzmpLUzQEBz
+fmGt8P5r8v0BOUN7oYVoLuMzXK0wXIpLuOEUGMQy9tHq4Qe6oCTIqeYJtC3HWrvXoWkBm2IIRX0R
+c30DOiFeJhEIvjmfoaKYUR36MoNVq4fY+hHSuB0oPCIM/6N6Ec/EopBXFQZxhrBK4VbywEPtoqT/
+uTjXqFySWwwdlDQD64xCBzCwCPJQng+HMuJ2s46Egq1zdYrFlEw/jHr3wgZmxI7bqxI6F/+1cGKM
+mPZsZRammsQpLJF7da7QIFNQgQIB3RJ7t4Yko3swGyGbyEOsNSnikYw+GPEMl5D6YlVe2myTE3mC
+7128j0f3z/j3BQKqO838FA3CrW2/i9lU3IM1mesi30S8R1z/NxRKW9JUpTXCNVr8pg7svSixYPc5
+gdQuTQOgyV01HsIiyamV7sHiymIaFkCRVhQkIs0yVKlEwLO330PGsdfJBXKSrOAUyEkNnz6gLqed
+4P6FfB4WGr/Al3Dh+ejRVzf+NdieLBAVy6XfffsEq0wWqfNkgQPS9ZfTBuYLWdQcODAEnJVzKl11
+2ihJTnLuzPHkDXGxDo+HaDErc5Ivcbsm1aF1ygscJkOYrmwigzQAQhLjTBROAun/iJH+PUdh6wsy
+n7784m2Av8t7dSjCow/tIey5bAyWJfdmag7s5npzOoFTyqJeJCvAknvKnpwgDCXFq7khJa1jJKes
+S1n4WIzl84PZMb55jyXungVlPl764cp9uGpX7ap9qD0TloLOCrgHFxiNz9EmCMmvAuSFWAeF46O1
+DmYZoMaFrzqUHTzbcNEgTXdgtaB9YgGmj+FxY7PjAtlcjo+oRF3ba6BF9eSTZIjQXsOOY5QsOhkH
+K6yxO4YFWDvTBCcETtULDw1KvOs0hqGK82RmYRavo8vy+4vX76NxW/pmLxoItqi7qDzMjNzrg0PB
+aRwOhpjOxLKxGin6u9LPtt9CQmbnlTNF6MvJb7FC/VZdo/tA7vNIKSRPhAvgO//tV/FfDcUK+duW
+wrQl1F5OJbtW8f4hElesoygOoS7EXLduHsBrS/TddgGVU+AmENemmjooSPG+fDTEMJbrBa5GQdl2
+L9yCYuS8+VckkPf83x/g9Vj62XOBfE929s91biKm+IyZ1tDrCVQ6ZaYm6+R4nYCri6cGSklw7b1/
+fISQiZF2vYubN+R37aZUc66mY6RTcfPuv/oY7z4V8pW8nOOcgWmrtoVwLM9h/vgtAEBD1fGj+2I9
+wAxxkMgcOLc0luTqoKGIgQZ+7p7M04vPu9hiluv0qxPkM97r8y5mM6HCy3bE4tmw+lC3BY+hlItF
+JdZfebZESps/SQp0P0+MCEF4trqO5SO5xtT7KqqhHPtj7Zfh9eU+fvWNB5fiSX+p3IdEV3wrAAAg
+AElEQVTKArK6QnbhI1cyMY/1BbJzKvDUVsimdA9qMbaJQnQi+mkXdai2vKROowN/fWITY8mS1aKo
+o9oVRNjqCRV4kiVsvrFxrUBA/y1r2ADRb3nZh67xjDWm3q67UEOCorCB+x05R++kgB3IMZXWcAla
+WsVEPzYGKOkjB84I6pzItUAjyenaTOQ+/GWF7g3x96t6AIdlz/m4Qk5dzdW8QvwBpBEAMExbyJmm
+ctVj/B/vybG/+OunyPhivd9S2GadWlyTF36yuIdpKH1vzgCX1ef2gzFq1J+oVX1YrPOoSL2eZW3k
+pE63dr+AfleqRLUaoTwiw9WqgCLt/vK78jK+Wo9xQVr+T1+L8KVbcsEwcuEUsljC6mNJV2/KDWD4
+pAA9FHjXa1gu+Lv6GGOWFId2jOVQxqNOqnpbZRiOmNU4vUBB4tbcql9ySZZxE2dTpheb8jz8qESn
+lDE6rXVh+1xApgpLan0WjGXcLm10viSfm4MFRrHcx7tPXseU9SHXqzr2X2T17Nsyx+JNwDhybHt7
+A3ks1z6ZVjg8ZoytM0b023Jtv6Duw/wNhNvyu+WpjxHBd28+fIrrC5k7jchFsq4U/ojlk1fuw1W7
+alftQ+2ZsBQsKIRKQYcuXOZgs2WEg3sE9BRTFAvqPxYGAcVArndkhf/yjRb2S1aQ7cVwSLe2epAi
+OZF1L4tcKFJ8D85l9X31j0+QGDGDe16EL/6IrKit5SYCmvx2lKIsJMiVJNQqdAMsCP6ZeQUaBCfZ
+OkSpZUiHh4/wiNBdBxLU2ulH6DVll1iVBQKqFld9G3odBEzF7OtHNcTcle4/vcCrjL5byQoRuRZ3
+n2+ia+0DABTh053NDC2CpnIb+D9/S+jRNrTGkHbkzamFJyNmA+ZSo//d+w+wpLCIb9WwR3zAxp4P
+94D+U+cQsMT8N9yVzeMjlCO5T3XLRbWQfsy/foCziZjHSaixzyBZn2rQL8KF317rfGbwZ7Lj1bWL
+zGaQsJmANAtw+RzDl2u4UYpL8M58iTQjt0Rho90h+U5uwXdlQDUh2DqqIbak0/1OByYkR8YwQ7WW
+qI9nmBMjoHfEXesMAHuXmqatCbbqEoAdjC9QELTkc2u1GxrmG3KuJy/P8K4WS+CsylHNpG+lSRA3
+5RpdWkHj0wk6kYxVuLmBypH7Oz76Ov4/9t4sVrIkPQ/74ux5Tu55b96t7q2lq6p7umd6eobDmeE6
+Y1K2ZIIw9WDL9oMByxL0YsGAAQOmHwy96IGADRuGvAKWF0G2RqQo2DQpWKQILsPhrL1M79VdXevd
+b+6ZZz8nwg//l8VpQuR0s2GpCNwAGnU6b+ZZIuJE/Mv3f9+dt8UF1QMbcGXeT+/Id9P5HMMN6e+d
+2yHysdznQrnY/zGx5K5NDUYXH89/uLQULttlu2wfak+FpWAAlEqh3/AQdmSXX2UKhyEDbRMHFt3v
+G04HzPDh1hVZAVtuCLcjXzChAjLm1bUPm/5e5+YuGudy7tNSdqLhUuM4lfTdntNCMxI/rGV7yJio
+jnIgtlhsxLTnu7qBgzZTb36FISHGjdsHCChhXA6OUTFtp5vUJmgBbZKLmqSGbnAF10t4lazPvRaL
+kq624ROlqBs9LLkLjNM7aBIjcGVjH2EoMZjcZxCgChG3xCJqWg5GLnUQS4NtFuuYZwfwLWEmUqlQ
+prnRJp4Zyrm2nh0iJMWabQE+LSXTaUARI2G3ZTezwhnshviy1qyGek6KfKrnNfqJ9KF9lqNDAtU5
+uR7yG29i+pY8/5bvYysnwhA+Wi35jutEqEgaa10Vy8w8stC8wb7qXEFKQR23TKAoqIO8RJe8CKHH
+QqyqQNMSa2T3szEOWAVb7udYUqpwZebIiWXZ7khw0docoNyVZ7ZUD0umJFtwsfTkOCN+5XoZYP5v
+yHUjdQv7jySgOIdBf1f68JnPDbG9JfNsdSyWRNtvwt+VcfcGBgXh5sP2bay+IPPT0wb+FbFSLmyx
+juLlO9C0fg52DnDOeaYfvI+3PpD7ONlpw7n/8dgLnopFwTIKYeWhQA4QI9/ttPCpvrzE6W6IPjn6
+Bv0b8Mlxp5cMyuklfJc1B14LdckKt+JNrPj5ZrwN0N24QgEVN7qF7LFEfR13jDaBAyZdYMUsQjPy
+0WvIS59ZMgDP+9cxG0mU3V4ViIacpJ5C95p8VwUvYotw7IQsydb0ESyChgKlUQUkAMltZDbz7VQj
+6g77CGsZ2KEp8KNfEc7Ek9Mh+pSR7wybCCGLwclMXC2rrtHnonHXNvg7lcz4X227eI3ByJd8jeDZ
+dU2EXONZpTDck8nqBVtP5O716oMnFaiq0wdY1ms8gm0GDgJPFgiVWYAr13OeOYBXC0gHz6SwWMPQ
+osrRF8s38GgozzwxFf5wKYvsF7sDBBSoKecLBHSFUmYqupsRakrYR36IMmTJ/HIGQyhx17VQE2Jd
+Uyl3Xp4irgn0iRqgUBVyK0M0oACw3ofalnH3mJGozQrZibwmkTfBQUDw0kaEFtXMagazZwMfV09k
+IbR+6hbam4Ro33cQZNJv7e3PYosVrejKItZXTTgbXITiLkBlqe7VPXx2KHTwzjRDRsq+na/I4vZg
+ugeXQeegU+KZVMbMaii0Qplz1dfeQPkx0w+X7sNlu2yX7UPtqbAUYBlYXg47t+FCVtRlcY6anKNR
+EaJNaTZ7vkCTUNuaFZDQHiqLK/uZh8oSC2I60UgoB588mEK1JHjU6slu5XhNlM+w6m3aREFob2Iv
+EZB7YaEUWn3qLbrc7fbaSB/JfV5MDVybeImzGNQ0wbDdRkmKuJqpQBO1ULIgqnBnsJnfzyyD2ND0
+pdakPcoRtCmtfuUA/VR2pX0/QUUCFLtZY5nI71bkW5gkHvKe7NztqI3uZ8Qsjay38WNKdo/P//wm
+msR9VKfSb91rDXiblHSLNqBiSqw5O9AMcNXvO0CXBU+ktquzGqDeo213oajcbesFImpnVPYeHFaY
+5iRIefMgxOyRPF8804CS332uKuBW1GgMLGhiMlwG6izbgs/0pt3T8Axdm8hF0afl5Sxgz4mGreX3
+o2UKFwwixiWitmh85N0MuZFxaCWdJ2k7QxblZKUxOxI34LhpoaTuxeZmH/NMzpedy3y7Ufh48eeY
+fi7bmJSyyw8/3YBP7c3u1qdgFRI8LGm5+YGCH5PZu1fDVix46mvYLPb0eruoBtvsc4oSRS4WGede
+/BArLRbIw/ES7telPyu3AWBNbPPR2lOxKCgN+LmC6xu4XXIfThtwanlIr5Fho7HmZWyg7a0nG0ku
+sgkUI8Hp8ggBJ6O9qJEcEozywkNsMmtxJZC880RVOKYLYkU5NGXIp/drNJlF8CMXDv3ggi/ujZee
+h7ctE/Px4jdw8ZjXfvYMtSOmXVi2YLho2TRblW+hSez89GSBwhagkh1soUHilItT+cy5FqExlAka
+6gAx4yBJW6PWhB0HHuKFnHvMiTnsAV0K48RVDu+7YnL+eDfCN4j6Ut/LUV2VrIQdyQRr3NiHpdbs
+0jFUyJcw1qjGXOnUORRBWyj43fkIVpOxn+0Eql6jREqAZDdWtAK4IK9l66+PI0zY3//gJMWxrHn4
+t6oKWfpHjFPLXBbncM3B2W0gIjTdsh0EzNA4HQ9rlNlyUWNMU3tBkzlMbDwmQbCrbLRvSL/tezuY
+L8kFiiVAxqYWgRqT1TFOHstC6GyU2Gowi9Bp4eweCWzIflTv2bBeJpfoFw2aWxSnMfvwsFaxXcGQ
+SOeGYZ3E4xSFL3EwvdmGQ/xGFi/gkB7av9ZEM2QZdSqxnxQJ3EL6Zw5Ak2RlURzC7ZBl6ngB9VHL
+I9ku3YfLdtku24faU2EpwLFg+gGaqY2AdexpkmNur4NINvw9WQU7rS6cvqyU9Up2Ve14AGvenSsR
+ygWrFt0SM5ru0ShG0mB+mLx3rTbQ5I5gkhJVyVr4IoU7vAYA2LYjlGtOxxU5Fq5excY92T33H3pw
+aYFkZQs1rQnHeIDL7ENLTE6dJrBYlZmXKZDIKp+WBVIGGCvutIs8RndF/sFhF2Euu7wdRchYtWeU
+i4zYi1Eqv+8vtpBFsu0OPQcPf44w3396jC9fp7V1awOuRZKVQKwKp2hAcTczzRGUs9ZuHMB5TBj3
+rRbAoJpqENGpCpiK0vEl4PRI0OCsAGZa1KqEBWI9RlKg9b7K8Ktn8tnj+RK3WbQzNTUiaiSETo04
+kbF2N2kdaQ2PHBJOw8BrcPyQQNlyb3muUVBhvCZUvC4yOLQg4zLDJuXrggMfihWa43YBNxdro+mL
+K4V6jAl5NPoXCnVb+uj5qIW3unL/9Uyu9SW3j+orkn1phREaFasrlQsdiKVkN+coF0TUBvIcnY4L
+jwVj5TKDR8yJU7YBKn7bxoNDpOraIgo3NPJHFBq6KJCfyfvw3mtneOdIxm9kgGpNqP7nCeasNOBl
+gAmnKEv6QlrDmRBWbJdY5QLG6GdN+DmrzxhVTdICHsup3TCEo+Xl7Y/exaFaqyUVqEYyQdKxLBpe
+ZwP9Ccuo0zmWZ+KfLRdLdHfkuNUNUVAcdEYil+c6B5hck1RR/ls+ZqyJqIITYC4+ZT7MYdNPLMlP
+WFcaRSn+aTxaPRErDWsXOpHzaaYmrbAJvSK02Z8CzDKgsGB4vIwnQCXnJuIbyB/DmclEyeoGfoxM
+SYvnm/jtGTkfj2awfoou1lpH3j2E5TIdWjRhGqxwLP4A1jbBO94AhufThbAf2a6CyVnJOPZRa8nK
+WI4PaIkDwQlQkXuziJluvHuKg4A0+srgEdW0bKuCS21OVAbdviysLHeAO01QefJ3NfOQE65sVjVy
+8kemiwTxlJmfOV0D8wAOeRL72w20XJbiQ8NhOreoGlhSEmA0Fr+/mi+hKQkQmxz7pH733Q6236Eu
+KKtdH24AX3yNhC3/9h6cEcvP9TkcqpKZZANmKudLtUC+6zyEZrrUyUJoMPOT9+B6XAhKH6YkIIsk
+sGbiQdHliwcxjo5lLox0jYKxqyyr4Jq1VMBl9uGyXbbL9mdoT4WlAKWhnRzLcY22ZvBF38d7qayM
+14MIYCAxDiew1Fqhl6tyYKGgmIY6WiDjTloOdtFoy46fzguMKahiTWUXyMYJZuRMXCwLjBi0M46L
+IYNdaaJhCL1epevIuwumnWHXBdKhnC997zZiVqd5fYNyzRHD3xWlh4r8irm1QnVKiO7A4II19jOH
+wauHOVrPkUsyVbBr0p5nQE1LqKozjEhUMs/lOXW+h5wgJd8BGs/J7j/3K/yoGCno/AcGFpMd5oJB
+xF4FrQWODT8EDAkOEgeKAiemdIGQ0ttL2Wm1WqEmzyWcDBizYKphAaWY4Krdgl4JTHtxJjn4B26J
+kCQ53bZCTYulzlKUlLfzOyE0LSGXeGetHFjEi+hOCUWwUVqtUK7JsXUMfSLPkpKX82gewevLd915
+AGtbdvlCp6i5NzomQ4Nq4qbBQKU9x4USK+cFu4/NF8QdK+4uELzATNmxjJP51hHMfyE7uz8rURPr
+Yqk5KkfAW2U8RpzL/a9qums6Rn5H5kiy14fFjFntz+COZV54rQw2xV4UrSMrWMIhnffy0MN3vvEA
+APD9I42CwVN46gmV/J8r98FSNhp2G0WwxBH9sGyusUsARl1nyFgZFicJMJKJFbDmwLZt1OTGG40z
+uD3pvKSoUfClqaFRX4i5ahEXb8wUi2TtyxfQkbykdupiHehtNgyWI/l8wii8W4fYYLpp4R7g9Xcl
+cnyj8y6aE3kJw4aPlO5GOpUBr7dD+JzcqWqioBah61pQdBsyVlFOhxm8O+J/d7ZrmBWFYH0LBU37
+GAsEpLnvUOr8U9sxFsJDivFiAeub8hJ3ogiv3mDM5DcstP/it+RZ6cs62S4UOLmnC6gB6xmcGmbE
+FcQ5BVr07Uk4Y2UOLFKkV4tDlDT31dsJ0Gd/PnMF5K9BaklfPNOwcZUkJWWt8HVmzVS2wHTBtOwK
+iPvy3Bs509CRDaQENMULLGnmp6FChz5GoQqkRvqj4oK26ftP0tOh50MzVVlOgEVGktYyge5zcyEa
+NUsUXJrleGmBNmtlfuN3JvDGctMJUbO/b+V47r8TDYkv//X7UJ6cw826iBMyap0/Qj2UxbLDuMx0
+CixYlevEOZr+OquWI6/kd95FDpBox14yJlEtn3CCzpL7uM+Ua13nsGwibtWTLDcdih/ePqkU/X+s
+lHpLKfWmUuofKKUCpdR1pdS3lVJ3lVL/kJoQl+2yXbY/J+2TqE7vAfiPADxvjEmVUr8M4N8B8HMA
+/mtjzNeUUv8jgL8G4H/4U88VOgi+MIB/8hj2lGy/aYaE1oFXAJoA9drynsiPrxWW1PUAoSVmm9tT
+UFPZoQbXrwKxwINNNcbxsQR2zhZiGtqlxiGlxa30jzQTW40acShmYteqkNBcNaQyjyMLGXfMvWv3
+8Xgpu0u6CLFizh7Khk2KtAzyr5tqKLoijdYAPjURfbeJ6BoT9VuMUusCmU+cAyq4Fs1B30JFog8H
+NqZUwJpRPDFqXsOoK7unm6dY/AQpv/6vAT73k7IDNfoOnOZ1uc38FTlvbWAyVuS5BUD4M1QTmgwV
+druESta1BMyo1ClMRDNg2YfOKYDTzGGY88dxAbNBerAjsRTuejGuteXv718Y7FSynR1XOULujqmK
+UExkXPUt6RdracNpynjMFz4o0wmdLoFQrDTX6sG/JhaSu5LsheOlWNpiiiu3QKoIUc4BWxP41tcw
+ZEy2ihG75RgPGdjczxu4d09Mfm/6Bia35bknVPk+Pp5g8RnCjtu7qCuZb3mRYsEbnZkSbVpWjYFA
+m+19A3+L2ZwiR9SUYK6varibYm2k9hxmTt5QurZqb46UNPrp4xqHBHsoU/+RNqVlIeEc/6iiMJ80
+0OgAaCilHAAhgBMAPwPRlQREiv4vf8JrXLbLdtn+BbZPoiV5pJT6LwE8ApAC+E0ALwOYGWPW5PiH
+APb+eb9XSv0NAH8DALYHIdSjMS6sC+ilrMrLiwK7HVnBE0vDz2XlaxQZbIfpq4goscUSzbbsJP5w
+GyUr+OxsDI87aXK+iTuppDVz5rCDrEBA3zNFicaU6cu8gXJbEH/LbAN+JT6cHYrPtq0tLPqyS0zf
+A1bUb7D8FNaF7Mx4zoJhdeFyIruVOS0R9gnbdRRc5q5dq4l5Igy9PqszPR0ipuTZtBgjYFFVhBZK
+amwm1hgNsiW9dCK71cWdVzD+AyFjfbTKMfTEIih/JsViJs8UjStUtxknOOQ9fDmBarKwaTFGXUhg
+zER3YLvCWAXThabatLbekX+LLgzxBlnqQ1vSx/bcgkvOCR0C5UOxSE7vyN9vHqVPitWuzxJ8h7vY
+7P0HOD+SHdYPPbg3fkyOyX4V2A1kluyqYWpjeiGBXZ2dA7UEU+xThYYtFkZJGrjV/BxYybQMghCn
+JDwN+ymULxZEWDUBIgQXqQQXZ6MSz/gyR5bHOf7+vd8BAOSPNQ6op1AQGv0wr/Cd35Tff+U/eQ2D
+lSBnrR0HvT7TiIctVBljEcSpREsN5ck4FH6OkpB1la8AQtOzSYZ6i3Okoir1SQNJIfG1sfsGrlwh
+23hu4Yz3NK9q1B8T0fhJ3IcegF8AcB3ADMCvAPhLH/X3PyhFf/Nm3zzqVnj3JMXGAzI0fyZHPZKB
+jeoCYy4EZVygwXJhl0EmdexhFHEivXe21syAsTXynNHk4RwqIDyawbXEZEhTOa5yBwuCbfqNAvEF
+KcLtJbItyXasQIyBq5EwSLjr1Pjepgzu+Ztz7H6a5CV+AzZhvjVp5vNSwRBk1LAs2IUM+LK1QE2w
+jE3SEFTqCaDJtjUaDTEjgzpA3aa5Oz4ESYvxSkFzfvIAb50xL13XqHIKkjjfxfWaeIovNmGPWYo8
+kGCmLjdBSAe06aFmTYXyNmFYt67at54oLlnVT8jvkwvkilyaZQblEy9yZQh0hJrNRBpFKtdpthmo
+ezvFrU3p48d2A7Ul572wE1RTWTgen0fobsris3csi1vV9eG7shAunQIOS9UnyxR+Is+3yk4BuRyK
+HQYlH08Qj+W8mWkg68uCdDXqg1g4mMrAvS5z7uTXpaR8dpTiG4/X5e4x6jXcuqxwl1L0OfdAXQP1
+DQaPv7aA/QtrV6pGwczP6uwQhvgaN13jH1Yoz8SljXdcpCN5+VVZAErGNbiyiWfWlZ9MDBUXY2R9
+ef53P/AQj1kdqz0UOakF8UcUjR+1fRL34S8AuG+MuTDGlAD+MYCfANClOwEAVwAcfYJrXLbLdtn+
+BbdPkpJ8BODLSqkQ4j78LIDvAfgdAP8mgK/hI0rRj49j/P3//NtYXSxwyMqxF99u4y9/jmzAVR9V
+JbvmKPXQyphacmUlbvRdFDRh3W6IBnX54jaQPWbKbW5jm6rEK092moskhBVJKqjvlCgpxzaZVFgQ
+inq1GyKeyypesYBldpri+L7c2/fia3jvoaShep17OFt+Sa49nyMiU/K1LTHFF1iiIslpvVqiJhFs
+EHUQxnLtUSaWwmqWoSb0t9Xy4FiUlFclVC4WUjyK8S7lx197LObillUiS4gENQrnJKlt9Yew918E
+AFivfQvx0T+V/iJa0d1vAkr4D+xo9URRWWsN/a4ET8vRP4FyWF3JPtT3QtSO7P6+68O9Iu6TO9+A
+acuOh8rg/I704W/ekXH8fg78CAPGqzRDBdm5z7/+CsaH8p3D5hQvnQps+Pnn5TlXdhPeXPoqPn0T
+HY8Bww0bzYjQbb2CvSeWVYN6EUunh/vUAMFJAqcl+2cQtKALGSdLG1w8EMvk6D2531emMao1I3jL
+wMSEvBsbyQcfTvJVAL75tvz9gx/5f/A8Gba9aginlHnWa1uwSCYbdOV+83sG6ItFs9FSyKjvmR3G
+VCkF3G0PTiFzIEuIwn18jpM3hE4vm01wTLKXPDPwCc3PtQFJyj+y+PQniSl8Wyn1jwC8AumPVyHu
+wG8A+JpS6m/zs7/7w84VG41vlynceYW9UEZgu+1iyU5o7mosR9KRoVui7spE9tiRruWjTX7vxkEE
+m/6UfjRDQp7EQht4JF50yMPnRhoJv6tazSeRZ7cJTGjGN7ZitDj2z9CM/juTI/ivvgsAOK5ew7CU
++xzrGsaX3+XLDHaTIBSSxXRUEykrPxd5EzlVqCxdI6slWl4tCWF2pvBJMKIcFxbZnxK/QkZFqvOx
+j+++Li/9HUui5e/MNGoOvwLwNlmYPvNGF61/VeTc8/EUOQVcnA6Zft97HxiSseqZISyXxCqZD/Qp
+Jtt2n2RgbALB1JUKwWBdLqxha3lWEx6jolldjh7hHl2998/l9w/cCv87Ycer0EKfFM3voIDH+w+1
+hZJ1JY9sWYyG6RiGJrUTNVERhLStm3BJ4FKvdjBjRajPegjoFcpawlurnfvYigjX9jzEFLiZWxoP
+Xpa+fb1gxeGkhOFGVcUOYK/ZsgqYtaFtmLEAcM436h+/qnD13zvmONwEBgRc9bpoEk/gNKSviuMl
+4h0KHKFCMJOTFF4L5bMyt8ItoCYj03IsxvdDPcedV2Tc31waHI/J+mXpJ2A+A/Nxleg/sRT93wLw
+t/7Yx/cAfPGTnPeyXbbL9i+vKfMRueD//2zP3rph/vv/6m9j6Jzj8Luyug4OXkBiS/CwuVwhPmfQ
+rdnGg1dEdXhGnvu2qtDdZPCxuY+gxR02d+DwOI5rVMwiVNwF+tsb2Lj6IwCAyFZYEiHpBy5GjCwP
+QoXxmNWKgbgBv/if/XVUDBhu+wF8V3YxF4BDVeJmM8LBrnx/e1vMxI1O+ETXoXVlF+6m5Kl7gz4M
+c9PdnuTaH9y9h/NjKS567eWvY0ZCluJ4jEYkO0z/R2+imd8GAFieFCjd2ryKs/ckoBZXPfz6q78i
+x4sUTWYwdkMHszl5GBIGDksDl7X7O54NRaScN9ewSDJzrd3C9q5YEG3yKTgDjbO7sru+M1/h0bH0
+y6Kh8Beuy859+/oz6P+M9DNyCRh+7+XfxPmU43FawNTS99mz22iTkOX0KENJPPlhIa5Iuarht2T3
+3+p9AXpTnmN+7GA6lezDMGyh4LN4dCWz6gKKGo1Ox4FPKzToWdjdEeXqaz/+InoJcS270t87B5/H
+zWcoIx80YNEsVwr4P7/2P8s4nIql8ObpCngsIju///o38ehCrL8kTVCsMwCuxoI4E8W0wIYfICXp
+gQsHKWneHMuCR2SiawXoBPLcUYsYkhsNrN6T48NHv4ef7ooF9c1+jPxIxuc8X8HX4kLlyF82xnwB
+P6Q9FTBn13GwNxxi/MYFBoSwjpZvADMZoLzWAE1t2z+BaslLcZ3mW1IH6A/Ju2iHsF3i5C0XFRl9
+mraPwsh3FuQnL6oCbkFTbjhAZw3hdz04tLnKSYWwJxHp3JXf1aEDi2Z+p++h7NJdWTXgMDrd3+hj
+56Zc75kN4YF0PQv5gACUvgU3JflpohHuiAnucMJvDw8Qk9jT7t7A3fF35dorF7f25Dv7W7vY2ZNr
+pIeyOJw/OsKRkXt7sHoPiyfiJcD1bemXWzctvPo6adRp+oYtg15PPrsRRU/IPfK0QEhQV09todOT
+a3d6kqaMgwv0MxKoRO/i6FT6+ywv0brPGoYrRzhwpKTaCWXRP2i3cXKPwCtH4YLanJ/e3sDRd5iq
+0xXegiwGpxOWjgfAwBDQNAhgd2Sx0SffRMqy5p2BQalZlsxU5uNzQK9rX4yNRlfmjmc7yHqsPhz5
+aH9azl2TqKbZMiD1IZpbgE9vRAEYVfLcq57c7777PjKfoKm36icxoVzbUKx5KWoLhhWTIGHLra6H
+GV/cplXhEfFvTUejJN/mZjeCz0rgjSFjVKsWMJB7P33g4pcnTDNPFABmsQDkyPFx2mWV5GW7bJft
+Q+2psBSgFIxr4QQDuHtittlmiYSstlG5QEE6Mi9LYRO2SRZyXP/UHgxl1YzloSA5SdDaQJ3Jl/JQ
+w6HZ5rXE1bBUgdojX97CRniVUd/kBM2WmJRja/pEArxiJV+lUixIHX6l3cGARAuGGWoAACAASURB
+VB9VQ6HfEjfgYK+N4T65EnuMQnttkAYS2l/AkNwjrzM4/IM1JwQ2jOBty651O34GhycS2Hxn8xAP
+T+Q5bm8eoaIC9QtDidJ/42SFlrWWsz7BmPDwjaqE64mpuTw2GLQprtIRk7PZdvBiS6yVzkYLW5G4
+Niv9GIZqxkYFMOSWsFqys/WSBryr0i+9VYQtCqC8NzP4biCW1fSf1dj/8ZcBAP1MNB63wzbud2RX
+3amHaFky1qmb4w7N6q52EZfkQ3DX+AgHui0WiBdl+M7JAwDAVW+JYSH3lKQV2iRAGZNQpzfoorbk
+WUfLJaYEwz1nSjAejJPsEIszsSy+elsyMVYyx901K3dQ4daAPJAAvvmOZJ2uH0uA9jnP4O49Cfyd
+zxeYm3W1o0HDZcFeodEitaC7BmSpCBE5L3NPIWRWJnAV6D0gbzjoUt6uM5Rxeue1Cm+cfF++gDWh
+xidvT8WiYGCgdYkbeytUxyTeeBBgk5MNJwq9UlJS82mJHdZYRUSB+VqhsuTY6BJcB1AHJaxMOjIM
+fSxoojoROQB1GzGj6ZU7ggfSbOdtgEjBwC1Q8nr2BzL4ZaFwhapC/bQGuUPR6wTYJP/eRtdFSElx
+n6QYQcOBvSkTsx5FAGsfXDRhsTLOjmXy236J3Uj8wnyjgW2mr+7de4TVGnz19j6GG5JFMBTD+Pz1
+BHd+V2Ix8ZtvwKbMepAYRARkjQKFgJoS1zZlkm81ffTpEgyjJqJIJmy3/TzWOS1Tp0hYVap4vUjt
+oR1JXxV5E9+8J33UTSqsmCI7MQUef136YPisLMLG38NfvC598e6dCZwL+Xxxvo8dkqVcTGNMqO+p
++CIN/RC3Bj2Og4MrbYq8zhrYviF9mDa2sdEjyS7fqv7VEBlrZTYPD3G0kGuEvQBLh9mMjsFGLOPn
+RRIPeataYXQhsYq3VI0rHYmhBzkQn0vM54Nvihitf6uFb59IPOd4kSMgWCyMfChXNiKvBYREVr7w
+/DUAwETXGLJsf6or9JoyTpU2cAvSA4Qhnr0pG9VRLa/t8PkZ9AOwWQA+JnTxT2iX7sNlu2yX7UPt
+6bAUTIWinCKKevCpHt3zfSSZmGKmv4maRCXd/BQB6w4CUmE7WxHUmNBfr4bPnLZdpVjb68qx0ePO
+XBO7sIznyEMJ+gRJGxhJ1NvMDRYMKqIIEXo0V68xoFZqjMkf+fktGx1NAFCjib2W7KBbrU10KNvu
+0X1wrAFsgrAcr406kt85qQ+L6teKu3KmLbjMarTbOXbaYjVEpgkrlx1BjV/H198Rc91viyXVam5g
+2JLd9YPtAJ9msPJhlAOsDGz7AQ52xfK4uS+Uadu7HYQtCo8EHly6WNXsGM4NHvsFmgvyEnbX6l0K
+piN/3+u08NXf/R4A4NQqccyIelIZrC6kHuNVUGZ+C1Cx7NAvHQS4W8vn3354hMlcrMWBU2LAfnEa
+8t3dzhZ+9AVxQTZbNzFNZNdd3uwh3JXp7Di76PGeDM1Gz66gaDXONw+wtZJsjTUrUFYyB/IzG52X
+5LikiMxu0kC9kHlx70xhdEAyGwPs3hcL4TG361ffrnE/FmtU2xouGcFfvB7hc7vSt3MDKBL0tPdZ
+u6M7WE7EFVlMKsFLAygzg6a/pvqLcLArbsP1iEzb99pY/Gufl377f/8QxccFJPwJ7dJSuGyX7bJ9
+qD0VloKChucsYR/ZsDevAQBG9TnClSyp0/kjJIX4XL5jY4PgTy9iMX0eoFgLr5QuYNEH9jLodW46
+tVAxRaRtQo0jwF7Ibjx3KzRWYplYvg17KrtD4g7RbMsulStCf1s22hM59rI+TgayG90qbGgGxrIk
+h0914ZDkocbKgDZzWt0IDgOYdeDBCtcRSLkffx5D8zg6uI3m+/L8w+gDnBDld6wtnNM/HUykgOdR
+7yqKoVTnnbkODJWYd059GI/CKf0SPQ69R2EZ1+qgJm+EV4fQpGlzBy0omxwCdgStifmllJxezYGZ
+WA3uSRv7Py0+9436exg9JjS5rnH3nMFhaj1cOT/G6IAcCc4Olm15vmWRI07IGA0b59R3u0WIcm/H
+hTsU66YOA/SJUvUzFw2moqPIB7OoqCyxGPxeCTdjLKpYIXAkMHu2+hYeEdJuTwu4iQQYbzXEEgyh
+8N5SqN3ODzfw7kjmSH+VIfsq093/h5z3SK9g1pxnmUKjK+N38/ZNhLdkl99bRZgsmC5k4NOkczis
+hnStNqaBWHehCxy0iGK0B6iJQtUBNU9vt/GFpcQZXvFeRUnsjPmovGt/QnsqFgUHBj1jYNpATXhx
+z/ORj8VlKBcTdPn+uz2DxqaYmnpJmiyloWgmlnmOam3u2yHUlNWO/hKKgaaANQw2EtQlyVuMjWJO
+90FnaDvyIjRaNTQnypIs0MPAQUFsgnMF2EtZyeZ48Bmg8p0QYUNedDckvZhfw9JrUZAEymMkvwEg
+oQpVTOEYt4bXkefr6wY+dVVeoOnVHXQXFB0djRCfy4vn84UYtucwRzQ/Yxv2XCbuwtFoXpMXup+G
+iEg7394gqUvXgw9qXjYKOANZeK2mD0V+SAUDTfp1zQpPY+UwfI72zRqfe0Gg1AMnQ/t3ZKF649EF
+HrKE/cr78vv2DQ8t1nkEyRRORVWkRoKMwUMrq9Bm9WeH6k8b3S1skmbeivIn7qFuamxapGyriifR
+fs1x8nIXyVxM/xox7AYxJAfP4DSWsu6TV95H+TyfOxFsia4u8Pn3pMTxt7dLXBzJ+ZKTBp4/l6Dq
+N5ssz7/QyBK5917gYaPHa9y4ib4jQeeymqLFys1Nbl4PkxxgmXyjW2ObcO137o4wp+pT0bOxx3LO
+U7oom0GEg6/Kuf7q27fxP92XTESZf9iP+HhKkpfuw2W7bJftj7WnwlKoDbCoDPquRkQO/tWiwNQj
+m/HJDL1bYq5uhPuw6T7ULglRaw9gUMduAyS4he8FMBuyPtaWD59cADZVi1G2kWiyBRcFVEN2oHpV
+YRJSJThqoKacOzdulK6FLRKo7DlNFJHsfm6vgSbz963tEDbZXw0Vs1XoPeF0MMslVJeF/FbyxJS0
+iLDUpoZNOHbY9jDoC4qtd3UP8/epR2mlaDI9+S6JX/+VykE5lA74qdzH7zFeejV3cD2VZ6ocF94G
++RlYqekPtmCRMszuR7CJnlNhD6RLgBoGUCvpc2tAsZQqholJXlDO4VHv8NqnP4sfobu2/L+XOCHz
+dkpLY6QNbtMyGXcMbiYyfr8xB+yZPPe1wIO9Qbn6q4SB920E27TAlI2alG6echH4tF60DdujdVMy
+Zen5KLy1jkiIgBZZ0I5glTIOo90UW6x8zG+zmKnlY/Jp6YvWO0sEC6YWT8aIr5AFWhPlGdtIfBm/
+650r+AyLmbZubMFOiUnxTtCqGJhmodXA8zHj3DOWQoN6IL2WgzyT+6l1hYT8Cy1iQVo7W/AnMp/2
+n22jQ8Xrcb74kFXwcZ2Jp2JRUAB8DZi8huYMbGgXnpbJtsQMA5pExfEI6jZfXqopKaeCzbqFBjxo
+luGa3MBljZhl26jYqZqColZqwSLoI53GKBvy8jupRhXJguTPKuhKBinO5bNdx4GmRofxa/Ro4kae
+jZBwbMd48ElF76wFZhcZtE3ykkLD5Gs9dBumwUpEvjza9qEJQdblBC4Xy43eFk5bAvQ5ujfBaLT2
+8eUab88u8FxHXtjTosZV5srToMKclZb7joMha0KCgJmRZQ3blWPLt2GRflm5Copl1MYJYLWuybEW
+F8btD1GTWEVnE9RrVqzBDm6QZOaf+QYZ2ajf42dVJ4ZLBu7njIuEboA7LjAl//zSCvCcLVmSg5Ys
+Yk3YqC4od68MbMrIN2Ibekh6/LoHxfhPqOS8RVXCyskh3HDgEU4+Ti5wwPjQpHSxmorLc3QqEPsd
++zZuHYjf7rcm+DYFXQfWFL/7q7KwPLgnm82iMLhB4RXv8wl2m2S2vvMuGo7EeVL4CBKK2WzJ74Kl
+Qrsti80gr5HS1drMPVhcvF0Vwjfs51OZp2V9B5/eEaHcV67E+PnPiMvz9stv4B3iG5bafFyG90v3
+4bJdtsv24fZUWAqWpRAEFqw4AVgzfjZ9B3ffkqjvxTSBdhjV39HonDGCT74Bq/CQMknbdBTWxE+2
+s0RORRaTWCi5AznMBsQKyHLWrvsuAloSJtQIaXZXDReGkmYJ2YkLx8KA5rqjfBQdWdnbaEOThKSs
+Myy0FPM0l2JS26ENixoQgII7ZWGM14Fa01pS21LNUlQuC1yWKRSj7xFSaGoNzh3hIgAAl2I5ptvE
+ySLk883Q68jfb8ws7KyDnP0K65qckpoFdrlAzQpAP9lFHVBSPosBm6IuUDDEHhAiAqX6WNN31EEb
+dkxfw1XwPytBzN6bLTycUICH1ZeZnaBFfoq61cbFYxnrM0+hzvisnguLsHe9ks8mzhR5KjusFwXY
+XpGrYm//CUrTRCkazKroNRq1pVEQz+ycFJg4ssvnyxiLDnUrVjEUlb7Pp/L35pULVEeyi5dngBPI
++C3KJV6bcl4smAGxgGhLPvvpqYfkmlggb9w/R6nFuhsUFtyhnK9F7YlV4MJlcNjuBjB0fdyNEh7n
+tTVsItfk3LDEUohPQ/gdwVu0q1sIQ+nDu7aPlBwRyhKLCgDKjwh4fDoWBQChZcNt7WIxk7TLhVY4
+PhLf6aSYo78rL9b0sIBdrKnIpfO8qAGXmnpVbmDTNKzLCjoVkzlbJJjHEu1WjD8URkEZ8u9FTST8
+HJlBK+QEimqUC07Ic2Yqljmihpi13m4EXHCiOwFcXm+aa8QjeZYm/cLWdgMOI/l2EKNNE965cgtW
+Im+ZiflsyyU0iU2rCqhZTtzq+7hNM/L8scbrhGmHTBV2ihzVWNyu02WNPYJ/ehs+ms/LrIhGA7h8
+WTSBUFVhQdWEMOdHcFjDYSYOrL68CPZiA2pbUmvKFdcIjoaaUkjWZKhoPsMkaBH7/4VrIe6fibtx
+54S8lfEKIWscWo8q3H2D4jLagWHKuBh6mI1Z4tyVF2KVVYhyZjsGAYgLg762g6ZmWroqEdC3rymS
+o0ofxsjLP6uOYHcJZw62EI1el2tPM5wwxtI2AhWHm2L7XBbh5KUKV84ECn46ayAhQ5SiYV5DIb2Q
+57+/tcLokDGAmXlCHJN7PnbJD2lGsuAleYKIsS1Tt5/A2z+YGMQjmQ92z0VQy3cOL2R8s6nGIYV7
+D6YeFiTMaVgWSOSFRmTj5kqe9dWMm8wPaZfuw2W7bJftQ+2psBSU68HeOYCVeQht2VXjexc48WWl
+zZcOPBZ79COgXJPOSeAVoTuE61LV1/VRJWL61aXGeCK7+3y5wtGR5JUrimlEgYWqJ+bnRuagQ3iw
+zmqs+hJJHNpNFDTXWpQX8zwHPneHHSdCel1W/nhksNRy7YW7g+SRHB92KNX+2EI7lvvphSH2bkqQ
+qB1P4PnUx6TYSlGvMDolPVrqIX4oVsexfQFrJGZ52xi0mY+/iOXfvAS8odzPzbwGOnI8hI+DSHa5
+8sBAsxKvqgmkaWyjmhLC66ZQqRQBORngH68zFNefKH47NyQbopQH45Ky/NH7KB7KOHi1jZEt1kFv
+2YPP80Xst6PE4FMnZGW+4aO1Ideen7YwyGSvum478OUyKG3K7c1y2ISmu7XCMpQx65kEOc1uS1fQ
+Fl1Luhpuy0NKmjN/K0BGApTWzj6q6KvSFy/9FnrHYrElsbh+975/geLFTwEAzu+UsGwxTfpLF0yu
+gOqA8AyQdOmCmU1sGdmZj3UGn2I3vYENt5D7X/EE6SoGWd3RT23YzEpt9wLEhHfnFxN4hJvvEf/w
+qDvFdM5AeZhj+i4xLmGJmorXtYpgSFGPj2gpPBWLApQLZQ9hdI7RKQVhjxIUS3mIXBtMTqX67OjM
+oLFLEtMNps22l+iT/jqsuqg4cZenJ4gZ7bZHCWZnYoKnBIrEDR87BN54fgB3RUacLICdswPLEIYR
+bB3JC3ujGWGny3SjcRFdiK020zEaRFYqf4WIFZGgvkP27iM89uRFieMQ4TazE/djpEMxVw1FSUfz
+FI++L37oSXwPU6LgoroB5YvZejFJ18kKdGn0nccpdhjpT1wLVxZyPG+XuLDlHM3DLdRNxi46LCkd
+FUgergV2LWSHkpI7O72Pflsm9PZzM3Sf+4rcx7Pi2in7OvKFLKznvz/GREv0XrUbiJSMSblvYfk9
+ZmDoXrhG4zuM7bz3xkN8KpYXes8UyBtyz6VroZfIuXOWTtdmjsW5vLgXqzHyIzGvD1+7jxZ1Qvpe
+F7c+K5H40BW30xk8wpLxo/R4gcd07VYXR7h4h3U1sykek2p+ckJVr7pCV8vxZz77BXibMmb5vg3v
+79JdWXtMloKfyjishkucsMIzP68wKchtmeeID6XvliumP4saXdY4qKub+ExfFp7tah9H92VjeDc9
+RPItAVG9eyq/H2QV7L70xZ04x6cZj/u85+F7JNQ59iNc2ZCV9bVvM97zQ9ql+3DZLttl+1B7KiwF
+Y4CiNDDlHNVaUddLcc5qx4PIxqsXYmodXZyjNZKVe++WmOLT4ymu7soKvt+vYWyxIAqtnjD/1lsu
+ujbNS+pVrtQc2UJW6HmzQEV15bJd4GAm1479AcqQtOZGdvnWRhMDkGk62EVqi3VjV11khF6n0xjp
+TM63FYgVoPdqDBbEu9stgGZyHeZw8rXcu+yoQe2g/cyn5T71FTgTkXJP3y+REr8w8rpAg5YHZcqt
+LMcZsw8tVSIhx1yYAL24z+ezULk8HhH/URc4pyZmYzXFnHx0pzODvCLpx32N9vOMagcU/jIKTpNq
+Ui/+JOJYiGWC5dt4cEcsk6MHc7xPw+uCrtsCgEV3LJlopOSKxLUuNqg63UcTq1DmQ3okYz5bVnB9
+unl+A2Nu06dpgvyxjEM7GqHgpnhAbcewiHC6kh32zjsjTJj5SHSJjYDAsb0IN8k/8fqx9Pdpcoz8
+fbFA9vYfY/gFgXG7dQQTMWA4k/ttQeHKAbNSR0DOeoZFlcEKpA9Du4Ep4e+HE2Z4igqnc3mOTmsF
+u5Zgbt06xeGhWIvfnKdQE3mWzYGc9zEsbHFs3i01VoqBy0JhSJ+mc63Gl56V7/+6UJv+0HZpKVy2
+y3bZPtR+qKWglPpfAPw8gHNjzKf5WR/APwRwDcADAH/FGDNVQpf730CUpxMA/74x5pUfdo26LBBf
+PIZ3UuHRVFbt+3dDPGJKZ9JZwuMKfr5SGFHlOD3jytjTqO6Kn73xQoL2PhmL6haWFwxmzTKMEkqM
+EdH49pHGoS9byk4B3Hqe9fgzB5UjOxTsGBVJQ2Py6neUDZcMnpVaQq/kfI2shdO5WAWvvvIAb55w
+5+Jz7t7awucI120GDlqW7KSevYmaRU4J0Xq1AsCiJfdBil//huSjHz46Qi+Svuh0fIAYCkNNTN9R
+WCzEz17lAX6claSdQYjqpuxAjWWAhHGHRS0+6/n9OQ7JenU+miPckd9d3+8hovXS3avhrivTCsYI
+0gz53e8AAObmGO6pmATf+tYKv3tfOBQaaQxNhF2+lk0AnlSzxosSHVpNXQ3kTKeN3ArTMS0TBsnm
+foFdppFPKwc1LQ8ndBCnEhM5X5R4cVNiBmlNctVRhYdrduXlEtOWjHtkOrh/KOPgqhRXflKssy+3
+JAj8tW8cwZ/Lbj21I7ywYqWi/QJcwt6ZQYXvWTg7YgHe9gx5Q+bp9R0XExISw7Wx15dxtc/kfr+/
+Kp5Un87iHElP5tvi2MM3H0i8Y+JW6DIOdD6VubdrVagZO3j2ROG3zmmZLiocUkXmL/kRdloSKAW+
+g4/SPor78L8B+G8B/L0f+OwXAfy2MeaXlFK/yP//TwH86wBu8b8vQSTov/TDLqCNxrJM0TitsDeU
+DvvO1jYq7zUAwPiowsZVMXevbijsXpco7M421X/ONEKbnGiNAIErA1OE29gaUNFnewzvXEbRLxml
+dSJUHIxb2z30rovp649TFAz26MqgUPKSRZX8vnIbaDJ6H7khKub886CCOiG8dtjB54zc8941Odfm
+7R48woC3vA78HSoT+SuYKUlWOMPszhY2KgannvHwwgdC0jGZLRE15He39hrwQllEXrtHjkNjofCp
+FpWXsHoyOwbw0W9LtWfhVPApHNk6JabjYIhWS+joXvxJCw2Cl4KOQsgXr9n24axLv9e1d9qC9ynR
+jLz+ygDVV+S7VtnDyUr67fUzDeQM+DJfb2pAr8ubSxsXrANoGBebjH02VYmVRbeBmI2+FcILpe+v
+BAMYwso7To3HJJHpLhvo7kiAscXxrxMDn0Fld+Bh25N+abe78FiXUDU89CjOmXcfAAB6rsJRIfd+
+57sz/OiXxJXsTU9Rr2tlUtZXGAWPtRiF9rBL98KyffSYdTH9EB5dOr0v5+pXJVjygl3bQjuQcSij
+OQa7cp/L0xhDriu9gPfQclBtyvGj1RzdC+mXcVghLuUa9/wWnrvzDj5O+6HugzHm9wFM/tjHvwCR
+mQc+LDf/CwD+npH2LYiu5M7HuqPLdtku27/U9mcNNG4ZY054fAqA2WTsAXj8A99bS9Gf4I+1H5Si
+39vZQrO5B2vjBHdPxKxra41eJBbBSVJA57JLd9sbsLs0g9cErM4UC6aSsm6EqpKgo+olCIZyvJtt
+4jyXlM6Egcqbe320mWLa6bcQcScpuwUKUqzlqwIVg1mlJ1tbBzZsCgza1gSdnpyjqz1YP/uTcu7T
+Y1Rr7UKLAiNqAl0IGq0furAo8W6VGlktq7nJ5Dm9Kw00IbudZT3Ez/yU7MbdvV1YOVNZ0QKZkme6
+Nl7zQmhMF+JKPciBdoOwazSRGrm2l7bQCqgz8IJYD60yh32dPGHeJhyb4gMXHyDclHXduRLBzIXg
+A9RGhAlhsfqyuT9AlorFcu1WhB+biiZDd3QH/+gPRPOw4bAvYWBIgFPrGhZlzq5ELnJiVWYV0GJh
+VhXJmLW1QpsaGT1zAD1g1Wk6xuNC7tnySmQ7YnEWhHYv1ASFkjFrdNpPqlxrx0W4Qe2M5h58uiYn
+dygnuBLxHADYbfk4Hck45fZLaBI3XPGZlo6FAbke+rs2FK2qRuHCdeV6/bCJmnwI8blYGN3AhyLe
+5DjxcXxHaN5q5SE7pWr2fIwZY7G1kt/bqoMp9U/fOh5hRQXuL4c2wq480zxwcJc8Ex+1feLsgzHG
+KLWuw/pYv3siRf+Z526bYpJAWTW2KPaZDG0EbXmY5jxC3ZLP2wHQZpTVJsff2AKaNVmRThzEWyyR
+VTYUTTsECr225K7rFuXZ5zG6SgauzG2M1nZTlaHJMus4bKJeVw9ycriODX/ty64iFJDJ6Kl9OCTZ
+2NreQ0lpdE2mZo0I9VKO/Q0Lei0IqlNkdLaLNm3qd87hbsvze1ub6EXyor80DHBKyvFZbeGRI3iC
+BYljnLRERuirgxgu60A8O4F9SFdqcwY/E+YhU8tECvotGObx/W4TdSyfV4PrWNvz+qKNuiX+t6b/
+qpwmFJd8KxzAp4qRaoV48UdEoCb+w3PUJGXJSnnhM60Awpwt1wLfRXjDEAGJTOyVhZz16jZLRsJ2
+G7t0g9yGhw5rHEboor0li0U3NOhVspCVzACsVg0YWxbF+L0Sdl9MdGfTgk04yU5VYlFQjIg1M6l2
+MJ3L/TQelwgO5e/P9D4ACFM3rHrchIVPHTAutVQI11mNsAmf+IwtBHiYsm9Z+dqwHTSJMUgchUcv
+M5PUX8JQyMX12yjIQpW4/DdxMH9PjldJhZyb2usNhb95U679/rUQBVW7Pmr7s2YfztZuAf9dV/kc
+Adj/ge9dStFftsv256z9WS2FX4PIzP8SPiw3/2sA/qZS6muQAOP8B9yMP7VpDQRuE3ZLds/puUJC
+Vl/fmuLGQMykqGzAtyT4oim7tVX4UIzCWyGe6O+txhVsW4JdTtRBtEd4cCir6PTBDKsZg48mhlkx
+q5EXiEiG4hggpxZDzgKfRryE5zMQ6cRwqnXQbQrbp9aD14RLuKFacyQELsDAULkqUGQCpXW6ITyP
+Zu67soaWwRwehUyaagOaZrTV7zwpXKoeHiMeM4/PgOlux4J9Jn2YaPVHAjhhE4VLDoRJE1ZIvQjm
+vD23C4uCJJaq4WzJcwSphXop1ki9egOqSRcDEvjEYgRFpKe7tYCrxL3wsYWylr3imS2F/YaMySFN
+YGP0E5owaI2I8mgd4+KM1Y6ZkyFbrCsmWaC24WOP8na234fPoPJGp42IAj3j5AIJEZARd2UUNdKp
+XLG7YaGzJX3YVS2creR4nI9gR9J3++TkeMuqkTBIurAWSGPxjlf1S09wFh0GQbc3A2Aun01bGhUt
+2tu7Llq+BJ3DWuHFG3K8eU/u52IxxSEts3YKrLoSCPemATqETdeeQZjLHHjArNz34iVAmPem8nDB
+622nBfx9+e5uVOLXzj6eIf9RUpL/AMBXAWwopQ4hKtO/BOCXlVJ/DcBDAH+FX/8nkHTkXUhK8q9+
+lJvQlkISuVBnGt62vIwX3/GfyLaXhY0hJ9NWz0MRyMCtU2VeYKNZyu/6Wy0YRqzdQqEwMoGCyoZr
+SIdOYQ7jt2BvSUfmyxgoWSFXWPC35KX3jQWLsYuAen/uRhMRF5DG7gYSpiqzUqNklaMbNOGytNsM
+WcqdVlDE59fTJUAiVbXM4IUkgt0Ws3Y5P4a/kPttb+zBkN2p6dlYncjzuxqouHDa7ItVMASa/G6c
+I9iVBTAsO2i2ttifGo6S36kF7217G6ombLxfPynlNgGAdWZElahZ5YjNteS8gm4QHj2uYLN0WLtz
+OD1JAc6OWk9IVBS9OWV+kPTDQrrm4A1bGJAMps5X0OyvjKxKHW3QYel4YLsANwulS7TJK2nnDoK2
+3J9HgpuhrjCvmFlYraBryt33cnSY4hv70TrTinyHC8gHTZxT6Wo1VkgyOa/bTGE4ZhkrVbcDHw4h
+4YgthFxYmnYLBz1xZ9xNB0G2JhyVPqle9/GQqlfFEtggaU86rGHaBE7NeH0FWgAAIABJREFULFhk
+jhqxniUuNM4Yt+r3arzUJIDPJJjacs+P9QauULTmo7YfuigYY/7dP+FPP/vP+a4B8B9+rDu4bJft
+sj1V7amAOSsoNJQLuznD0T1Z1ZJ+iRZX+eM0x72JZCW2gjY6pC1vOlxdEw2Hud+g6SIgACoJR4hY
+zBSEGygTCQiuZnKuMDBQK1bFGRfKZ4BSp7AZqW6GPlzu6CWxENVZipo7X4UpbCPX8yoXNunX6zx7
+wllgZYRaZymykkEfKwBIdGI7BQrNHR8E63ge8rlcI56NYLOWPj31ENOdOTp7H2MWf/Udufcte4oT
+mqKJ3cAOzX3XzmAqcU2suPdECs4h5sHkJ7BtyZKoZRuaLli9vIAmX0JVduAQNl7l78vNz12UFNfR
+Z0vYSp5Pj3Lkd+U7iT7CxYoEN4xiuT9o0VpAROGb/Y0eCoKQinkDppJdtUUyGceyoEsZP1/3QY8O
+VZyjIMFL6gDDTCw9paWvzpYJFAFeXtBERWKU8aMMBcVXbFvBJvdFdUbXwDe41pF72+6EqGdi/Rw1
+N9BnlaPflLk3MiH2mJHYGPgISTenyyVKTQbm1lUEBM9NM3J57PdxeynnPUGBb5PZe0tV0CwqW5kV
+lgxGG8697bb1RM06HwG3yfHh77r4/h2ZD//ry99FkH680OFTsSgAGlWdIC0UGraYuGH+NpZ86S14
+0LZ0yNQeI2Sk1ngUH40rpLW8VKu3j7AkAMVtmCcS9k61RMr6gBX1HeyiCcWILlwNKyEazXJgz+Vl
+q9wGPFJra/I5JvMZAoLrvXAHRUfMfFNdAzIqGmVj1IzEG6a/SmRQFmFwbgmbqdEy8gGmX/NArpue
+GuQDTuIygZeQPSdPsEjkO6PFEvaag5aL38iUiEgW0w1cWGvdi6IAJrJIFY3HcEuJryhFGfbCQ+WQ
+lLWuYdRasBeo2Id4v0Z5nQhKlkibLqCJOqxsHzZrBorcwrzPm6tzeOTQVOwfrSyANRwN18KnKHHv
+RkCWrR8qQMr+qC+IeFQNjFgxWoUP0Dlmys42sBmX6EYNBAFJS0jK2g5qeH2ZN9WjFTJe+yKLUTM2
+0KgUTIPVry2qMA1CmJLIIsvFlGxJvXyMgGTCzqska7UMNvblHo4ubGhmV9zlChX5P73RCjVp210y
+a23pHpwvC+rwYG7DJ0eju29D3Zdnfc8qn9AApMxkHLQ89F1ZyBd7LqoteXd+5eXfw+EbMpaZrqA/
+JnXrZe3DZbtsl+1D7emwFLSClSigAVQ7FCcZXUPI4Nm5lyGntPMisbDjiYlqkUij7dpYERp7dHGB
+Tp/BIM9FVMgunlwsYWgSWzSZRydTIJHkiNt2YQ/F1DZLwKV966gJzJKBH5JjLI+WcEsG1IZjhJqQ
+4FaNEtyhco1sKcQiti/1BcZrwCH2QqOEacguYIURVE6hGrJW+24JxUCj6vvIZhLAzBdLpAvuGIsE
+vs+oNcFIKs+Ru2tosA814067YaGqCF7KAbdBRmFDEzdWQHbBe1sCHQbtBhEsUtQXgxiVEhfEJNTB
+TB7BYubHabioaWHo5WPM35TsyvnFEUK6P2v1IscyUAQs7UYWrtI1K1c1zklI4ukU6ZzBZm5fkzTB
++aFkTvKWDdWQe7M2AnS0HMd2jupMfrDkdSuzREZ+Dss6h01V7aZl4YhWYW4phGu1auJerNpGk3yc
+uSmxk8p3J6MlDmj1zIgtSX0Xj96XsWn3DWKa++OVxpAKWEU6h+/LtUNiS4JuCkIdkFQ1wn3pWyuO
+gV353f6wD4fEod+nWpi2c2xdkbm+vWziN1mjc/LBAgWtEcDAMcRHY/3Zn94uLYXLdtku24faU2Ep
+aMdCthmhsepjh0GbB4sD2Hvc8VcpbBYH9YoFauaxc6YQ7VAh4k7badpw6KuF0T5qi5oFrgO9IlUa
+STLL2GBGv78PByF3hNANYLaEjs3WBhmDVaBfj2cd2K9S23I1gg4pjOI40CX5IFBBU7TG0CW3nRLW
+/9fem8dYduX3fZ9zl3fv29+rvbqqeiebTXKGM5yN1GgZSaNlBpLsREEsQUlkS4BgQIGV2IjjgYIE
+RuA/DAfyEihSBFkxkMiSHS3ReGBJs0ijmdGs5Ihrk002e+/a377e9eSP3+8V2fQsJIdNduL3Axpd
+dd+re+5Z7jm/9futiibkZzl2Q+PtqcVodtvYEbswHA+IrDrt2kNyhZhreS321ReRmhGOkibmVflu
+//qYoSOa0H1AeF60nEo/J9BQ1qQ3xdU4vqPeTlMugZ6OlGKMsmOb0goEShzzvireTdEmrKPjE+yA
+5ilM9vbJLmqKstPh8qpoYXtfi5loRaSvmknKEX8PplEgdaQfbT+nqGnaxkSE6hyclWFlxlLQ+UsL
+I0Y1zd7ME0xZ5r3bjzlMRbPav6ZUgJOMoKhOXDsmKGqWasXBU4TqadFSzGdaj8yjH03oFeQEXi4n
+cFyxKl48oHRO+SIUBi3Mu4THpY2mV6ejzzDsT9htyLNtNOwRfV13KhrPxXZKWWHjui+NSXfED3LO
+nTCqqIblGLZ7+h1Fglr0CrjqYHePx+z9jjh2J8mYTHNnCpUi9+g4P9Z5bZrCXbEpAGAdqsvgF2QR
+P3TmXTxZlhfoSrzN9FAWypV+xKZOUllz1gNyavryNxsNMnWuWTPCqrfYLjbpqmfY82Z4jhGuBqZH
+WUZNX1J/EpLaGZuQi+/L9UJNJ/zpMXsKpVXav4K7J5No3lsn1BfSej6eok1n6hhKW0UiZKGk0xxf
+gU7i2CNVPMNUnUx5z9Aaah5+9iIhszz7jKF6qsP+mGgoC6TSk8VzfZQTLisGYG2ZUk8WY5xEhB3N
+mW8V8TyNlbsS23ZthqN1EpRDZtCNWWsPVx1cZtXHU+5JxlJzYXe65OqdH13aoT+V5J7BpREnr0l7
+n9yZcNCTOdOKbZouNDR9aWEw5ZbmDZy5ts+24lwu+pZaoBWxS5q8RpmJRiT8QURalpc3pEJvKmNb
+GZXIFY4/U5Owe30HoynRVbdCd6TOvjRhoglOZS8g6YvJN7ku87HX6rPmyHjen9fpXhPz6a9aZfoX
+pb0bM6Ii2tz7kozFse8ZYQ5kvSQHKY263K8x2iZRB+RzL0ppea1dZHlB1uFSDbJDGVuPkKVY+v/i
+YJ9EoQoHmnC31gwJlfD2wldeYKrjsukaVtbl71pbDcodSTen8ye8FpmbD3OZy1xuk7tCU5gmlgs7
+U+xSytllORFK5QHrNU1L3ixw/Ybs7AdmSuOy7GVnXeU66K/RTWQX3W2lGD2B4pJPRWvPs25EqCrv
+SDEbDrIuE1XFF1YWj1KJw6qPr5rHaJpQUEDQVJ03w2OG5JISyjzp0PyooiD3mrgzKrh7i2SHqq4r
+J+SwtYttaXZgrUaQiANyXE8pKciInZF/NOrEqZwC0dij0lANZJyyelb6dLXTJ1M4tk9c1vBsmrOq
+BVg/eHxKVlGyl8Oc5EklXznbZtqWsQ01WzOLfKxWmmJWyLSwNX6xTbatmsLFEmZLTKX88CUd1wG9
+Q/ncnlvFD6X0JVm5yq9eVL6B8ZjWq4hIDjIoqEmR9mCrKOO2W8iYzABcEg9/XbkqPDn5pnlKqmZV
+0k3QaCIjP8NbloKoXmGb4RU1O1TLi4rLDHoyVjuHOxSbcr/KmSqZgt0cjA1G8R5e1NN4mo55saXr
+Ke4y6ol2miXbPDGbV+XOKKWWD36PPPuW2eSFipgHT3S38RTLY2M7oaRO87JmObaifW6+JPc4s7iG
+rcjcXMiucevr4mw+vbpAsqKAxb6st68fhpzTDNLPXSvTGst6OpxYhpp7cX/jB3n3w6J5/MULvCa5
+KzaFdDqgffFzfHK3RufsDwFQH5WoNyR2e/rMk1y5KS/k1U7KH6n6/+N1WeTveVdG90BTPBs5dWbV
+k306uzLY66c8Fs7Ki+Un8gLtT31yTYkuGodGTT3ZSU42FVs0jQ3ZoQxTpOXSf/Abbdb05f3s0iE/
+9HnZbB78ka9g6u+Te4x7eKkyASn5h+uHTLT6rlwq4DfFfGg2apipTPhIc+snF69RTeXZl483qbhS
+UzCudohbmghEwLPXZLH19e3wDByojv7EfpUf/Jom01jLbiA1DM2LdRob0o5V1T+bjPEjLYu2z+Ck
+YnMXH76HcSYqtd0fEY60ylEJZuOrDoWJqNGlzpR2Ij6HF54b8+eXNZ9gmpO+KlRuDGihKd08p6l5
+zuuTlL6nG3LTw2/IJnNsU+ZuGBZxbqgp2WsximQjWzt2jJWCjOfXruwz2t7TdhQH0XM4GL4M7X++
+LnO94BSYamXucJTQH8wOF8WXHFsUs4ZOzWE4UHAd5zjdSxJdmUVwrjuGzz8un//kT4+oOjJG93T6
+3NS2v3x9j3eeFW7K1XV5of/g8T1ae9Lutf0+Qy0vuT40NJTm4F1Fh7ImnL0wki8c+GPGV8Sc2e/2
+aeuazHOHRImHkzgge+rHdNQ/yWuRufkwl7nM5Ta5KzSFUZryxVabzeshE3WSVW1Gdq+og2tX7uUr
+vpxAk8QeQai1Yjkl2wmcPKmRg1qZugJvsJ1SUpOgcqyEv6jZhIqdWApTfM1cq60GOBorz50xEz3a
+bDnGJKJ2eq6cLk/XPbZvyAn8/XFOuqTP0y6w/JBgKWbDJfwFaS/bOAdAOI1wd6W9pB0Rr2omXZSQ
+qIZw8LTyAXYvUM6VaTkpEFVkXPZaEV0lmXmyO+FGVzkSdCxTC44+e68/4mBdNBD/psvSrBirHBE1
+5BRzy5pDMW2SFeTky8NV/FBj+pWQwgkFLGkasql8341lvKe9fcarWkjV2uOFoVz/ra+36Kpmld1W
+/KRioaVmnpMadpTeruFAVbWQugfH1NQzy6KNle2UdlNp+qKAQS4myjIx8THRJh84e5yeVnkulBQL
+wXNoT6S9aeeQ1U2Zm27P0tO08RvJmJY6B2fOw9HE0imIdjdueYQVNW+nMZpmgVFHspfDtppjl55w
+uPcRdShWiziqzrcJ6AeynjgrffqRi1t8XW9WJqGg/Vvcg0ArgZfOWh47VNg/xaS4medE1zQD1qQk
+ySwL1WOkWCPbnWVOmMuvHv1vKcba142P8qbLya11+z/8vZ/Dvdlmuy/e5IozoT+Qzjsmoh7KC3Js
+dY1cE3VmaaKH2z08rZwMGguEiU5cfYGa2uLd3oSOkruOhspyVHPwZ+pgwSHeUKSjvYzd5/8CgPq6
+R+8peaauAqj85C/d4O/+1zKJf9rbp6k2YtfNeE9NUXyKBWqa0qycsZjcYazkqY1ayOqW2I7FSYX9
+bUHb8UvKI2gOaSlbUXvSpqnU6gd+gj/Veo1pH41IcfhNprF9Re73Ew8YvjCWfjiYo9TXcMakFAac
+UmDQzc0qD2vK7FOXp6y8QxCiPvhjH+Bs43ul7ec/B8AD7/8e/nL0WQBe+M02v/74vwJgawSXtDDh
+B+o13JNy7we2ZIy/92d/gvvWvkvmoXySYkXG7Rf/xveTa4KQE0HgiBkTeTqWuYOtaeJUUqc9kU00
+yR0qyhvZrKyzcFLT5WMZ771+5wjcduIN8QYzAtoxvi9rJHOm+FbGq7GivpNpgWsHz0t7EUw1UlEK
+l2mWRXWvR+JTWchcVtWkLRZyGp4mOl2fMC7Myi8Tnu2q/2RHNqNq1Wd1Xf4uicrsqulzdq3KgZL1
+DPabPK/RlQc1mez6yRreDRnXK48/xyN6wP1qL0E5kF69GT9urX0v30bm5sNc5jKX2+SuMB8wLnh1
+pmFMLZHTfG0twb6gXJJjQ1UTb6hDTeHRxur1nY4OcBRxNxv0yRR7oBw0cPSEqVjvqJBkf6AFPA5U
+Fa6t2myyVhTHYPkhS3Xh/dL283sM1mTvvLIt6NLv/h//NlH7V+R5PEtfMQVXApefPCen+Bf3XbpK
+PnOqqWQypUU6mty01WiwsiwncGojvrotJ1vvJfE2t5dqRJpjkQQOnlYiGuNhZ8lXGA6/jaa38OB/
+Jz+M/+HRtRx7BMb843q6Pp5CHMrznt08DosCdd7f+QKOwnk9/WwVf0GclZ96STznpV9/hi//l3L4
+XPt3v0J/WSsAxxELqvp+5IGASwpD5ylpYv7JKww+rPiY79rAUYdaYanBwdNygp4sWHYUhi6P5DRP
+Ki5N1Sqi0gj3upzojjfGc+WUL25UWK3J82kGNgvFGhPlBC22QvKqFrfZCrFyiJq9Io015YpcEdM1
+8mC5J9f2Rm2muobGZsTpJZlX87zcd2MJRka0Bs8rsa2RqIGB4pr0b62zSL8g37k1mkG7FaiuCVSg
+GbX4Qkf+7vB6RPe8aJPOQYdrmp+zPpYxrFwtUNqU+/52UuQLLY0efYcy1xTmMpe53CZ3haYQ5xnX
+Bl1W9iYUq0pWur+H683KV1OKS+I4KeLQ11LWaVvJVNKQoVWSDifASWf18TkFzaTLAgdPkW8rVU2p
+7e4yWlbosl7GxvuFAGTQ2uf05rsAeDy7zsJUTqANI6d49IV/BmjKXwqL2o9jYYGXFuV6GC5zRmHf
+gmXRAu5buZebSv+2OKnT0QKt1vYe7ancexTI3/dbOa6yFhMdca8wzXIydVq9Jhn9T9/w8gxq90kt
+8jJrNZorcgp+dZSyNH0KgBuHe9xSwpjn99t8UqHgDlJNQZ+02f0tOcVd8yzmQJm585ypojf94V/t
+cuZe0cI6bfn8eO1Jhs9K24unHyBU1CeCAbsV5XJYqLKoteH7ZS06SnJCLakf9xOyUNrwc49lLTar
+V4oEddF6EnXobBSKRJosPSjnZKpZrucuE6vs4FmLbiqn/tqSaBru1LK4JuXJN4opvjr7pnaMm8pc
+9aqa61ENWLTSp36/S1+ZwPu7Q3Yi+bm0OuU9DVkPa/fIWJaLHs9q2vmw3aY7keuDfMqFF2Sul9p9
+Is3reGpfxqQfxNx4WvqR21clgnwHcldsCsZaApvij3vUGjLot8YlPMUMbEz6BLMXPQ7pTUSt7rdk
+8LxKyolAUjmbjQWyQP6uUm/izjj1KlVGWtXmBvL3zeoKo4FENTompL8tL3E1KALynfOnm9z6unyn
+MVS4ydveSQfNbSGMDPVtQRoeFkJKmodwb00WVR3L6r2PAtC9lbKkpQbj610WF2VDGsd97ec2s+kp
+OIaJ5rtj82/gyn/9MsNHNEVRxU26zKICeqS+R18Zq4rFBZJInikdDzkcygaR5PJCdKMxs7HKMnu0
+kY+xhBoFuRY7GE04O35GXsz93hgzSynuDij0rwIw7WXco5ZiM0rYV/h0V2tinFGC0Y3FnQ7JlNRl
+0S9SVnTlqqmwUJJNZoaxEJ6ukI10I0ug1ROnHWGZjvJOdgctZmTjZqKp7XGLal3ucTpvsK1rb3qz
+BRvyHPfqW1QduDyrqn93MqLQVej3KGW1I/c4Uamw+qDkXmw+KmbXeG/M4rOylj/jdzhXkT5dNhH1
+iZhjPTsmVVCXA90AwnFKdJQAYnhTFgZz82Euc5nLq+Su0BRKjuE9gUv/tEva1zwFJkd8CUtZmWVF
+zbGjlEomKtPCinJBNIqsBJKN555YxTnUk6SRo5omk3JIrSX3U6xP2uMRE6PVl0mPrHsRgMODhOcb
+snNXCotsnJNTZ5hIJtr9f3aRPQ0trrgh3aZSz5WaPJPKsfP+s8eoaPHkmRPvACColrDDqwDc9/AW
+TkFOjNNn3kH1s/8OgOx96px6bpWXJvLdm7fatDVMZ6YZmgpAllmyb3c4zFQCe/uloiun6oFWgH5g
+1RCHohE083W8FRmkSXuPBVXLH1xe4YUD+XmnJ/1MEo+SnsadyCfXLMxebJhF4Q6GEctWTtgbL2nY
+MAhYekHZqn/kOQauaFXt7gRH09fv3aqyqGQcF3YUbWo4pqYOzCyFNUWnOra4wpkzEu6seyuUmpID
+UFUG8kI1wakqWndeJCgp0tX4FoQyFjs0KRa18nYkz9s6GDHRa6VywFZD+j+JKiTbokEeavHc+WNw
+XDWClR70NVv0XUnOmbPS12N/60N4J/6GPD9XASjfv0z9fhnPD58rU/r0xwF4ICuxFov29qmDlC+/
+KN/Z1XCjl1u0yJccO0P3O6Lje6NyV2wK+JCvuYQdcxSPNn7KosaSS2GIoyrT2LeE6YzWWzaFpZVF
+GhVZEMXyBrHi4VGKyWfU7lOD78tbGq2cBCDx9kiuy32HNuZQ6dAn0wjbugDA1cI5luqSbn3YUMLX
+j66x8nGJUQfLBc7r5pUEHltleY5wscKZTYkueKFCvEeWxXXZvJZWj1MsyndXOj7+DyticF8mfm8l
+4YErstieOvZFLl75GgDPXx5QUjVyaHNcXQFHFs2rNglHcw/yQXy0P5QccLWKcK14CgD3+DInNefr
+oD0miZWFqZFw75os6OWVVWobMicXXhTGJzvq0lbewvDAZ+qoSWfGDHWDyD3Y1nlYV9zCl3bHNDYV
+Y7N9QFFzia2f40y0WtM7xZMV8cFMd2RdjKYJNxUFeblR52RBxnB9dZn6umyyjhNSSjUyoAlpXs3B
+VZMgiCyhpkRP6gExMubH9x36mkIfl2TcvJJLYSB+hqy5wkTrErKDPo4+s38o9536ixw0hGC4f1OY
+xACW3ncfC9/1fQAUTv41TFnadnzZuGzq4S/Ihry2UeU9kgqC2b15lApf/9oY6wpB7KW2JCM9tx0R
+aPRpZEDrXokcIH/jpsTcfJjLXOZym7xRKvp/Avw4EAMvAX/LWtEPjTEfA34eObz+jrX2T79dG25u
+qcYTWo4h1dh+EkU09WSuNlYpqbrrJxOCppKk6G7erASUFOKqEKR4M8jg1CVXpmhTjHCPK2XbUDSG
+4mRMXpIdejSZEGvGY7YfEaizZ6s8JlfClaQju+9fb/f442V1YGUpVzuizz24WCRcVoCTe09RsXIS
+hKqBLJ9dpbEsJ1tQrBEUFYKt4XB/Qbzd7bGcDAvFFqVH5fMPDDf5o9+V9g62P8cgldNsI/QY60kR
+jV7WHmaSA2uaEr4HOKoqxBYaim82UTCRraUi3WuiMpuDHpvvkrZPNitkeqKddBKmZ8VpupBJ5d3z
+V0KqA8VQOOaQKSBLfcdwVePmBouvK213fzZWE7bUpEi6z+NF75YxqgWMZpWPtKl2lX59oIjRbsaq
+4j4sLtVYOS/a2EJzkcV8hrqd43sK1KKnudcrYDQyUAwqVFTjyWigfkSKoxGHVrMpdQxfGnXJJ+rY
+W4lp5MpQbYqUNK24vqp4GYWEJS2C8mptzqzKWth4/ybl94i2ZVaWMbPXTpG/TRSDapOlk3BGiWOG
+5QWSijhEHzjd4PjkhwH4xB8+BsC4/TVuTUWbbPiGTG3JKBPNYdaPmYn5Wq2KN0pF/yngY9ba1Bjz
+j4GPAf+9MeZ+4KeAB4BjwKeNMfdaa79lDC13LHFoCbMpjnrZTXWRck3V63oBp6FkIrsugSd2VlUJ
+SSrNJXydRq9ZwctkMdrMww5mJIUVPLXLC2qIxdkirqZV+9Mp5LLYBsEhlxW2/bsxDItiKpwpin36
+1CrkF/UlHEGxKD8vrCzwfQ/Lsy2dOUl2VfoSnZC/K60tUdQog2ccvJpS0UcRtRPC7Zhfl9Bk43Qd
+T5Gl/JN1Ru+WBfHMlSd4pq1EJbHBVzOg78jmFo1copkxkUJ9Wfq6O8qPFoU1AiEP8L7z8gzVrUX6
+z0o/DxpTtlQZrZVXObYiC3pxeYFI1dLkjIRsC26P0VjU51vDDmsL0v+b6SGtsmwWlb5loNycJU3+
+2fahlMuLWywuUHRkg9gbOGxqlWcxyfEVAWpaks2kmhTZOi3PfO/mFhsbYo7VmgVKGknJJjEFJYzx
+CoqDGJQwI2WyWgzwdel71foRiE6cW9iTgyGx8rzFwGNXqeo3bRl/S35+qFViohiMD8by91uNgPZV
+xdVccVg+Leuz8aFH8MqSDGfKTVAfxAye3Q5iHPVL2bxAoIS+JptgtDLSxh5LxyXB6T/9oGx+rf51
+/vApiYhNRylKi4r17BE3aeraIxj81ypviIreWvtJazUIDV9GOCNBqOh/11obWWuvIExR7399jzSX
+uczl7ZQ3w9H4c8C/0Z83kE1iJjMq+m8tWY4djOgethhkouJt1ZqsvlM0hZJbI+0IQkSlWsBfFM9x
+MFV8BNfFz5U6OPEglZM5t50jVS039gi3bpJLDN6mBrVAGBZc0okmgsQ+ue55l5I2p9py70FbHJif
+f3xMq6WFKo7hVCI7/ul3NtkqyokfXu9QKUsab7EqO3wzX8BRfkg/K2EKitrrhVhXNYQNUR3TxOIX
+5eGSwohzP/oIAI9+6QF6rSd0cMcEmiDzkJHT6h1bDr9ydXA0tFP93DcvJ0A5FjJNK67flO92PvkU
+RT2VT/tNHtTx9FYLLDpKNb+yzpqyMl9THIrTToFxKvNwpp3zZCgRnEbnFD+8K2P0fHzI9PDlkwtg
+xSlwRXkXl6ILbPrCTbkauEwbCkTjxCzqKXdMcTndmuWMVkCeqqwTlqXtIg4KYk0xreCpU9nzZxiP
+GZ5qD05QltAFYL0xYVWul0eLLCgW5r7C29UKNZYWpc/HFh06SuwzKrucUP7PQPk4vUaZB46J5lL0
+BgTvlDwEN08xiYaiHBdSXasa+TKVFIumawcOeU1MhkIxBFb070K0zo+VD8sr9eGnP88TL4hm87VR
+woywfMN1WBUrjj8ZZa87e+E72hSMMb+MYHD+9hv4218AfgFgaaFCN8s5aHdwEgU9ubeBo0CpcXNE
+1JKMxclyg/KO2Lj+ikxgEpXIi7KgC7GPyZR4pOhiUU/2eEDiaM2DEo3m2RRfSUrcQoXJVFTYoVOl
+NiND8ZYZWFl4LV/y/p8aOXQ1/tMMLe/V2oitWwGT41rWO24xUvj1MwNZKM7W8lG0gDDFqL+DzMNT
+02TGMOXsDjCBfF40DiXdLH7s7/4A4b+VF+S3/vgxYkVLuhBpee+tAKM8kdbCnqKjRkeKHRQc2Iql
+nVi96cNhn1RDgWejEeVNzSr0l6kd08rOgYenGYTHF8SkGHkDprtSyzK4AAAgAElEQVSKA3nM432H
+miB2esp+IArkjhlzSxmZTEEGbmeSUb4l99q84BCuXpW/Ww1wbinBbN/nqi/zmkXKgLW0SOrI29Eq
+DNkYaG1EpXAEL5+GBWpa2m01IcsseVjFpbTDlLSuRENZEa+snI9NQz6W8RipCVaOi6zeUNxMU8QU
+NKGsVqCkwDCuhg0db4Vx8UkAksRnWcu2470K/r0SlfCmddmh4eWNiSmMlTksiY5MOxNWsB0xeW2j
+j3tTSWI06/fB/+Ih/vNtSSa78uXrJMpqtmMdUgV18V1QaNJvH75WecPRB2PM30QckD9jX66/fs1U
+9Nba37DWvtda+95aJXyjjzGXuczlTZY3pCkYY34U+PvA91k7S9AHhIr+XxtjfgVxNN4DfPXb3S8a
+Trj8xWfobfcpb2j04VZMujVDNnbpaP1BcnWMqyQqXkFi6e5kh8BTfWk5hZpoEk7mkWkeeTpoH50k
+piqbkENGsKPxaHdK4iuPYH+KO5WTJKgV6Shl2e6+YvntRwS6nTYXXZYyNS+OWS71JKGlGaaEmWg3
+w6kmQm1EGEUGtvkA4ynfYal+RGwzUykzN8bOgM3DIkZPvlNL7+CepYv6bBE3rqmHX71MrdUER3Pj
+s8gyPngZ1nuWpxDmQrMGcB1RPxdWypTGco8kDykqpF25MSaP9HRs7BEuiklXVQCYAS79qVYfri5g
+3ylaVe+ZAx7XZJuwFFAIFdZczZmDZESlLj9/5fA6zkXJl6iXJ0TqXBtO2gxaMzi1GU5+gDGiVY0H
+Md1Uza68LjznAKUQa2T+3Jo8uz9dIs+Va7LVJdXq2EKwTlHtDtvPcbQqM9TaiHw6xNc5WawZcq3t
+eGq7y/1q6oYnVa/P9igpK7VTScgUu3IYP4d7XTAZau8p4SrnY66p+9ngWVx1xFLfxDjKPdq7SfJX
+ki8zqbZwF05Kez0ZK+9WkWMn5dkbT1muiOJM5GQo/Aa1gmHQl/F8rTWUb5SK/mNAAHzKCJz6l621
+f9ta+6wx5t8CFxCz4he/XeRhLnOZy90lb5SK/l9+i+//I+AfvZ6HSN2cw8qUUW/MGQ3H+Jt1EiOP
+N+ruc3mo+9zhmNEpsc8qY9klw4llWfEWguImziy3OSsSadx51D/ksCuniufpTp0bDlPZXr2OS2BU
+gzAjRuoEPBH59Etyj6rWs+PmZJrF9wG/SvGDYjGV3SX6ynxsyhuEXTlhOmc1yy8fUOjLcRYGLpUF
+ORHcyQRXHWbWagqvM2D7RelnPT9J1cjJPK19luENcUTl3YRUswZTDTheP/TItYiI6BsbkVMPllSz
+OqZsz+NiE1/heir3VJlqKG+ahVRC0cj8UhlNF6Gg2aSFSUq9phpG/yb1YwK861danH5UwmXpYxsc
+Kur0WCHROoCviMnpk1PONhSAtFklVO1g0YOGErgMHM1/WGjiVZQoNo7oIqdxGk0oZ3K94i+TBvLM
+VsPTXjBluC3z3+1f58LTAj23WTnF8oxVvDbGn4hjLzP6bO2ITMfei0o4Ws24MsrorCjlXk/Ru06t
+kr9P+pF0yxyqbyc1RewzMper4ZdxC/Ic6basq3y3TeW8FPQViwnecXEkToeW/ZuS0fn0V5/jTF1C
+vJsPy5hMHkqY/l/i2/HzHYy6FLPU0neVFd2a100we1ekOReinM3rQ646MQeuDNS5wwmxEmRErktZ
+X3qntk7VFWdQ/3GpsrtR7ZIua1x5dZMQiU4kcYdJX9T5mzeG3LoiCyHW1Nnqeh1jFMnXMWSqwlkn
+oanYjsMwo6Dw63lBFspZ3yNTTMG1lRIbuSYphRVKFY3jxy6FRJ2HY2mj/+mL9GvirCzFy5z+XuUU
+LDsoa/3RBO5dzvjcp6V8OTv3RY4dqlqapVxvK7NQa3KU515RL/v3blg+8dLLaSrubKG8YrxLuaGg
+8fblGbuyDSmfkec5ubBGRc2O3qRDUS3EpP4wJYWCS4yMZZYmJLmMt/WW6U4el/utLfPwRLApzTmP
+nW1Z0J1INrTTUUJbfZ+j3ZhPaa1BIU25Hskm2h47BOpAPaYYnGuLPse09sHzKiSaQzC6fo1+IM+8
+UG4RaHpzrS7PNkpuMZ3I55efavPcTTGbbqxvs+nLS7+6tUWgeTI9K5t7d/8WpXWZ33r9JGNlycnq
+MU3d4LKaXEvCPsVY6mO83h7jVbnXlT986shMrZz0SJVv8s+/IpB2yXTKhzyZs2OrdfwFYXqyvUUO
+u+LEvDLwmSzJhtO5Ihb5RjsnvEfxQb+aoVYXZz2DstXz/CjnZRfza5N5mvNc5jKX2+Su0BSygqG3
+HlK6OeVspinIm5UjskF/ukJFi47iQplcVV9Ps/GWJyG+0oplY4dcsf6tGYHmEBTqdUonBetgqg6e
+cdzGqHMqL/lMtP59mgcErqL9DnNcR07HvoJ2BsshKz2N04eLFDUEPXFLuHp9wJCikQ9GLeUzLFoy
+5WeIS0NGL8j14LyPq7hhuZIhTK/foqpkIdZrUDwhJ9t0+n466szEfYxcAVdGaj48cTW+TVm0M4/o
+JD86AVbKcM9Y+rRUE9NnAmRTVYdjSxzI2HoYIkUXzttTMsQMyLRyMM1g0tIs1GUfu6+0cnGb2BE1
++Zi/wun7JB352rMy4Nsj8McyxttJxKo69sb3NRg+J33xM1g4I09d9uS0LgQh1zVU3W+1WNA5GTpD
+HB37abKDe000BH/rYQCKVQdP08qzYukIlXlsQ0augq/suJhT0l5XeUZ6lQhPyXAm5zxiNUscr0w2
+Ix3aUQKfdo1EHbetasjBEzIWh3ZC1JQ1ee5Wh7Gi7Q50rTvEpC8ogO4JB5C+pm5GpEzapa3TdDXk
+6KlZMth3uaxjlbm+IMsC13Ooz+bS41X4H99e7opNwUY56bUxmfXpa/Qh77p4SjM+YZdnvyLe2wud
+MfFIJmlL0Zc/+OhJ0HTmfHiDTMuocycnUQr3MCuQqp11sC8L+9Klm4yGsnALZYets6KqFdMCComI
+m0b0FJ1opCQsq9YQaRmuOVukEKg/w80ZZPIck2ttnlJV+foTYuZUHlziXasScXDHQ1yk8jEdfQB3
+LIsiviZx50q8x9pmX5+3z+c/JWjP/377XxN1pUquYOwRyYqWOLDnZLeXSWt1ogO4M1fDyHI1kwUU
+txSJ2LNcG2pC1u4B6/fKZvHAag1XwWkinicpa1Wl+iKm21eJ9YWODgwDRbbee/oFRj1ZvNOTTc6f
+Ej/O3r68VDemO4SujNWNJOKCJjItP9fFKFlPqxxg97TOZVnBd3Z6TJ8RX8W1bsxYY/3VxZAPNuWw
+GKZjljwlsLlfkskKlTPEsaj5K26RW1qpmHW7vHBL5vXZzousXJXrSyd0rdxMqRZlMYSLhvAlGYvd
+/R7v14pepyjPPnLalEqyJpfTRfLTsnl/9flbvHSg/bCPEzV8HU9ZeztT+IhYWhTWlnE1dyQ52GWq
+a72wUmFJ63suPybP++UXLtFSMyfNEgaZtGE8y1DT0T1ehv9/rTI3H+Yyl7ncJneFppC5Dv1Kkeb+
+mHtD8f7WN8/i1UWNSkY9qk05xc+bJZx3iQPvlHzMQm2TYMbsbMekYzlJ8qSM74gaWTqxyIk9Vdf0
+XolZptURp2MxSllRLEUT9mlrTnAlSemPxbEXJhpFWK5xXrEeTlYajDRTzviGYqTAMMeXOZmJxuLf
+L97kpaWcRia7vY2nWC2kStnB3VOSmD05GbITqxSvyb1ObFZ4cVnadm+8yLaaRFlujpIPZg7H+NWl
+cJo9l6eWWYrZIIQFrXx87ynRXG4OHO7RE295uczxFTl1a836Ec9EbDpMtCIyQE5ENy/iNDQFeTyh
+tqB8CWfeRWUkzsh6rcCaMnof9kQb2Rt0OdQ560QJvuaFdHLIFW374SDk1GnFx6xJdqTJplyuSv/X
+jAvL8vO51QY1jQxYG+BpJWXoyBj7xZBaWaI9q4/4vOemmGbDbMTBdXFybnduUNYclsa6/L95CElJ
+nt2mGdOKPH99lBCdl7YbmkFbDytYzUhN60t4it537pHTvPMljWZ97wfor4mJ8ayyuD26uMrmO6RE
+KFgogEZzgkKTcx/+HgCOX+3Q/C4xwRZLoq0Nx0M+uSfPEwYJFWYAN/lRFmNiXj9I212xKRRdh3fU
+yhwWYlqZTNDyqE/lpFZDNt6NW9YKuDyBolQaFhMNMYYJJdWNM4rkmlIbFw+PzIBSY5NAVV7blRds
+4f0bXFd1seC0yRwZyf4oJh+IidHyIkqaODTW6soPlRo4RSVH9VPcqajEjdUSa2dOyvVSTjmQfP6s
+qeGxUYcsFnWvUV0mCZXgZBqQ+3J9GsuL5CyeZuOM9DMpjvnrH5GQVVwacWFPXpQ//dJXSXpqO+qC
+MK9iY/I1t9UCWvqBHxmiE/ISriwoM9NSnXKuoDVrx2nEM8LaEcVYwVACD9ORJROXJYqSmQLVddlM
+i9MFIi3FboYBnZF44stliCrab4Wjep8XcEu5FJ/fH9DWh35gmKBNsF/IuW8kv6zeI39XC3yavdlc
+u5RLomrX6WI1MtBMChR1s3MG8rnN2nhaklyvbxFq3UK3c50tBd1ZG5+nURTVfO9QXtxmIyXf1LL9
+vImJZe106oaTDU0+6+rmnh/ghTLnlfU6xWMflPa2n6LyIXmhx8Exioq+Ze6Xe506c57i/bIpuGkJ
+m8pBFRzzqfuyGVZZBMWHPPleudcj00fp3hAz7y8+9znsVJ69NLF01DcVJfYoae21ytx8mMtc5nKb
+3BWagvFdCus11rYHVPSYMOEIdyB7lpMNWNAs5jhdwFOVuBDqd4OAzFcwlQNDNJnBqvVxXKXv7icU
+tPhpaUlOj7hvKK/LPfpJmWGkRCxemzxVQo5RjK80dY4VNXLzhEdtV06HcLLMaEOjHfvmCBKrdv4s
+BS2kmqmyGYtkjZmp4WI1eScvDIkPJQadIo5G8zSE5+SkrawsUQmlHz8+LrP5mJwCf5E+Tz9v3TaW
+r1YV00Dh2OIIRQjHL8NDL4naWXxISV3KBUqKZ7ga+ARaoTkxLklFTuD4UkDckGeOFLU4y4f415St
+e6FOcUn+LnQc6sqrGOUVcs1BLmm68mWvyFhxGGKvi9XPk6U6nuI/nko9jp/XYqU9ZdVu5GwtaZFT
+HFAtaeFTFNJ1NELFlEIqWkFUkef0BwWCmj7D1KOoKeale3xS1baK/ZjJYEZJqP1r7tHQitj28ohW
+Jm3ntkL5jKyByr6aFwcbGMX29KZlPE3jLn3f9+EkmjYeu4x2Fb5OyWQqC1VcZSjPvRZWtTTHHxNG
+ykdaXcI0ZX78oTig73v4PNOK9P9Ln6sy6Er/9nOHLHk5O+H/k+aDk0LQynEbx4hPyyPZTgG7LCqc
+CX1C9eo63Sm+Jji5GqZ0AodYs+BSt02mmwV9iDrihWbRx6mKqlUtyATlkYufKg5ksU9Ly4KvXB/Q
+UvUrsBHujINwIJvNVvNeSqvyPNRS7K68mDZIGFflOeo2p6DRE1e9xt56AavchulkyGRXahjMioMz
+1WrGC/JCeMUBpiyLIHDOU9DtZrXgs6QRmnIlg9G3htXJ05kX+uWElnACj6npYrqyCT26ci8FfamM
+ExEuyFi5kwP2lJA38wZYJdkNZxwKBxPyBZmn3PMopoo7OLIYTTJySxOiWWl7rD6OSpnvvl+ufWl/
+h+0ZGu0gZ60oJ4BZdLlyVX0C7xBfS5MqS2q3Rx74CufvNY/TjDQc2O/jxDe0PTU7d6skPZnf0rEi
+RqcvcGuk6ncw6RjXkxeupkb5jVFEGirhbbmM/6yYTZ1Bm9Kulpc3ZYOZjm4R7CtO5OkxTl0rRr0S
+Rov+8taYYEUrMH3xOZk8I+s+Jw9kDrAzk7B3lVxJep2tHn5DTBMU5Jaxx8aGjPHZJXi+r2bV8Dur
+LJibD3OZy1xuk7tCU7CBR3zPAhsvxGwsi2e1WFrBKMaAjYc4RoEsTBlT093TKA6fV4OJVtMVLZnC
+XRXcnLgqKvykM6bky0kSrElQOAstoToP05ujowhGeJiQjaSNRrFEZOVUXdBEoMrDZ1k5VObjY2Ou
+ayHiNA3wtKLOcQLcsiak1OR/mxqsr2nHO/ugtfnR3iGOFhWYdTklR1lGZahx8K3mUf/Kpz3aX5Fo
+SBAsQt6djeI3HFujLNb5pEOgII2jkqEay/UTCmHm5SUqeuIFCyF+WROSvDrlKzLOpXVD2dPU7VD6
+GRlL2hMNKotbOAXVFIoOiotC2h0S+Mr1eSix++k45jkEJTuuXyTYEwflpFKmojD/mw0PT8EdM60j
+CWounib3FBo1QoXpC9wER2sY3JYHykI+9mR8fMfDnaFLM8U3sxyDBt5Yni0oR/R6ooX6Gs6pJiFB
+Q1+TeIqna2/F9Yjul7Go9WXcisfK5EFJx20FM4MWDIugpqepJPh91chm9S79Llkm2ub0uev4ymlq
+ii5pUQYxnPg4ivlolaPSX2vj3lT8kaBMPgO3TF+vwXC7zDWFucxlLrfJXaEpFKzl9DTHlMaQqx8h
+a+BomrPne6SKTOPkPkYdVLlVe9HZAUWBdqM6RT39J9PnKWrlXBZ62Ex26KyviL2FELM7C9i4DPoS
+DhzkHQqa8efWXJoakhxM5bvNsAJaoBX3fCqK7hOUAtxEbcckgpHuuVU9JUyG+i8x/gKZ4iyY0Zhp
+Wfrd3VPUodWUqhKhlKMOjmaoRXtDVtYlVFk2f0ZQkGeKZve1YI05+nnZlX6MXUOiZ0CYOqw/JKdt
+rDHbLB1S6GhqcH35aIzT6DL+qvTPd8v4seZTINpKIQtINGU4H1eJduXE9wo+eaqhWDegp8Q2VOXa
+g84B9bH4M4LDoSZPwyO+R0cddHt7CW5FOR/PyjwWk4DckT6VpzlBpLklNqLXlTbiToTviTPZHSjA
+bnCN2Eq+iFnPoSfaiJfuYxU7Ih0b0LnuT8SHYR2L11jWsTCMBuKM7ofQCCRb0jmuXvDOLfyuzJ85
+U8Ggtn8+5qjiLStiFeErV7q9vDdk9FXRtvrXBrAlIUkzcvHWFTMkXMBZVye04nfkB2MKCmLcmRxS
+KSmHyQAydStYviEf0LeUu2JTsORMvRHFYR/nmkxy+u4+zow+cZBgAlVRw0WsApVgFOE5K5IqwJPt
+WFxFFE7tCvGqIgNTO8IldFwlD4wbpEojPxmmTBXaK3N8SoGmRycnCGpatqw5BMnYkHUE4jzuufQU
+Jr6YnMaqumfzhLygACgKyU7uYrSSL88mmH15jkGhTLwrC2iwqeW210KiFfWAj1vkmvQ0LQwpKRxZ
+4JVxZpi6uhFgLUZLp4McCu+UZLDuZ67hKMjMxppLrSv3CzQRqDgZMFFIu2rUISko8Ijv4aKYj5MC
+qSZc2VjnqXJIrolcUZhj0CSr2MdXtOIkC8kVzl0LFRnVEqirI7bUIOhoEs6pMssXpL1CXsCXfYpI
+U5G764Z6LCp6Ukxxtf95kjNRB9s0jrG74mhMV2QDzdNFnFWZ/+KtPnZR2yuGGHUqZqMekdYMpLqB
+FmplAq3gbLtV+joPJglwFSbZiWSenewE2abCqvUD0GgHaRMcJSQeTo/Uf6tkwvlwCvvyPJOSD7c0
+x+L+mFJP1nK8PqHQk77aknzXLhiKSi/Qi1x6Y+1HbrCv2AJe76YwNx/mMpe53CZ3h6YQW+IrMbbn
+sVeRHX6lC2ZJlEpTdjBDjZv3euSxOKv8FTlGXEbEfXWy9KfkixJCKixW8NQM8HwX2xeVMMk0ffrw
+MqNbcsrHdIl9+bkS5tQaorYtbhQYDuWkv6L/Dw/3cG6pSZGYo6KV2kJKrqBX+WBEVpDrM8o7W1o6
+UiOdskukVW+5v0c6Kzq6pTwUTsBwT8Kpw0qBwkT6NB1cp5s8DUA27DCLRc5wEzBQnPELppbKtj6z
+a9AoKy9sJ9y/KZpVfSon6dlCHSfV0GvnKm54XNuYkKiJ4YwzjJpKjuJJhGkRW1ZNaBqRaUFUfNAh
+UyKTtFBmrE7h9XWtrixssnFGTsw/9odcV4Cu+xeXubimBWjDhESLtE4syalrk4BEqyGzpE3aE7PL
+WT9BJZT1kC575Ieihc3AUrwkJd6W+e0XIwqq2mfTVZjIPYYHE8ZT+Y4zK4IKINcwqpOPoCPzO4j6
+pMp6HmzM8lBuguY0OOe7R5VINiqC8m3a8QACNXU9ze9YNhTeLc/eTBKsanFuvk+woZWR9MhRM1Sd
+rnZ0gNVM2FFnwCTTdfZKLcFwxOk55bXJXbEp5KEhOhdSe7pFpjZrtB1jz4m31dgOFBV0bvQyOu3M
+I+8vr4KmuPrLU3xdmKbWJFUbkcKY6EDVOasq3jSjV5W/61938BZVpfY9DpW4tGS7XFLNr6Yx49GS
+h3lWsPMid0Js1OYsBriKwJy64GpVpqOz4sQjTFlMEadkqTwiURD/ZgFHcSc3NNlmkg/oTOTFLX+1
+SOW0Lv61J+ldkH4cRDGZ+hrsK3JZp5qQYEiJ3intjZ/bEycDEHiG875sBhsnNVffMUymmutRGOBo
+Oq9JQ3pqBpQqHdyxmlVlxSX0yjhaepxlHews3dqfoi4YRoc5YVmuHx5Txqf15xi3ZawupbkCnEP4
+vVucUdj5rtuiq1RHTqLp1W6Xof7sJ0WmCnxTHXcprGnOyXAFqwxfk0z74XgYjSgMnBRvqKZiOsWM
+pN/77iETrXkpai5LwYlJFqWNYX9CT30ppWFKtqILUfNX3JMNss5sJyhgAjkUhKVFzY6mwdXqUQpK
+3hkXCO8Rf4d7KSTZ0I0pybAFWRfT7iH5tpodq7q+T1/HfkH6sTNJsDP+yFfaCa+sfbjTaM5zmctc
+/v8pd4WmUMThAVOkneaYHTEZIreEk8rPnlkiKavzDBjd1Pp9xfOaZodUxtqV8hZoLsB07zKpYvu5
+sUf3luL+L2rVo1PEy0WNNPU2PjMSkinuJWn7YjrBLypElxYXFa9f5XJLTu6SyTBaV1869xC0lIGY
+CWFTU5or4pRjb0SsrNLxzh6RFtJksUOWyAmc9ZT+bTvnyReljf3TO9x7oNwROzkvPHZJrg/HKF4M
+gZ7KBhgrqQ0W7n1e7tsyMNAyyd4EdmtXATjbFrXVK60TLCjkXbLMSIaI2LuFqzkNZlplqt78gTIf
+O9UVQsUwHI9rJEqv7hsP4yvKdTomVbNqNRJN4d2fG/EvLija9fbkKLS+0RrxlI5362aHA+3L+ZMy
+boVOQFaUe5UrlsKqnLB+32W0I99x+hOsVjbGkTxv//AWYazcnVtlUvU1mzNtUgVq6XZTplPNll2R
+Aa06AcmhFsplLUY6f22vS3GWn6EOXO/C0zhjybMx92QwVh6kYhfjn9QpKR3lp2QtcVbnTx+QGcmU
+pODivigwbdNnhuTKr/HiC1dZ2BIYtoKC7yzfqnLzs6Kx7vejoypYj5dP+9QeKYivWe6KTcGELv65
+CrVbFVqXFOjj2CHjy6J+lcoG976TAHjtGKPq6rAjC97vpuSKNVg2U7KGQlpPl5i0pX513B7QVS/x
+opoJ1UINWxDF1fcL5BNN/U0criuI6WA6JNfwVi2UBTOsRfT78sJ2XvBY/wF55qTvit0JRPGUaCx2
+uT9jnorGjC4KhuH2c1cZX1GV8oEQ2hLZOLwp/1887HJNcSfPFtaPIjGffH7MH+i4jBKOFsIRzv6r
+FsATCuoysJ2j2od61VISzBomW/LylANL2hW1NrYuZqS2fymmGmr9RFjHKaltPxEuycTuEHdlAx30
+hrjLCv7aXMCxCsvfbEBNwV8Vpuqf3vw8X7isNSoZhPrgF0OXiz2ZV/cgovIeTfbalmcYHUuPXmIz
+6dAYin9oGkV0t+Xv+ocDsoGC8C4qFH2e4U0kpNe7ucPxeyRxasstM/EV3PewT7Qk/V7T6kvPBCRj
+6d/eeEo702S3cU6ioLCFoWwUyTNlnB9Q9rFhSU4wwLhljggdrY/VHTDdl01h9PRT2Cuazv3+Lbq7
+MmcHNy7x5GeFGa1+ZgVXE8NWC7K5ffkxh997Vtob5S9nuue8HHEwvOKXufkwl7nM5Y3IXaEp5BPD
+5IKPiRe5WRPn2vQ5j836ZwHwTn+UwkB2edcpsrCgVZIz2vqsAHuyW+fOLfxYVGLHpsTbopYloxa1
+RYlvLzTFwVNJXSaKb1DydjlU9/zFWy0uadz8vF+ipaQfnbG09/nfvMTeZfn8SdPlo1+XHfzUypfI
+m0Kpno1vUL2pjqS6QsUVyxTf8U4Alm/tMVlWh1mtR+LJSWr2RbOJhxFr4uinVpqy3ZXvfuIzL9E6
+UCox/kMnkuH2A+HgL9RphQNGQTgG8KVlGc/0mpykH6h2CQIZt/pCCIp5GR04pAfyTJNggFvTKsFU
+tJXOjT64clo5BY9V5XmseUvkjqrzUYGuFpX95Zf/PQBffqaPBl/IcojVOfr7/+tFCook3YkNxb/S
+IVRH65pdoTlSp/JBDSdQx9/SMYonBXNiZ/dpfNXYqoHqUCZnotBuGzWf5bMKDDNyiA9VUxjuExbU
+w684Du1Jj85AI0aTGIaqpWGYXJAOGC2o6h2+RO0vRd0v1H8f0xCAFBu4oKRE+BbjayXm2kNyLfo6
+g95VAJrXDslVozE7cGKWW1KJaGsC34WnZA7+5yevc9jWXAduFw1AkQNl9UJPX6OqcFdsCmkh53Ar
+omQ9Ng7kxRubkFaqzEQ8jtPRAfRzklDUq1Dx/8NpQKobBAtFHJ2k0OtSXpaNINwCr6wZfbNMyOqU
+SFMM40sDWgcy4buHUw5dZfdZXKKkSUjNkiz4L04yhgpxVCFlqqGgm8UGKw3lDDxYIGqIOj5WTsHQ
+tPAdSSYK7rmPib6YWdaCtvT14EA3oAdDVlU97fSX+fRXBfb7OTumPwPds6/eAr5B6bTaDMYaPNUL
+M8fhspYiv+9BaW9vYgjKak+vLVDVrFAih6HiX8bV5AjQ1inIRuCGU3L1xdRWqwQFmZu8Gh9Ffuzo
+MrtX5fpv/L6EnLv5+ChMBxmO6rjPpWNcxcIsFUbkqoNfUUU+vAAAAAU8SURBVLLaWnNCrJWalCcM
+1Nd0pjSgeEw2543+SSYaJq5qKHPNFOhomXWwuUx9USstxymDGfrMYpGgoWaKOmsm44xLYzlYoqFh
+kMl9635I/6R8J7mlQLNLDpGn4DSFEK/4BRmryfeDArrmbh2TyjibigL8PHISZ6K1D8kuoSaOmXpK
+VZPPvHzMsCM+il97QvwIz4zHRN+kzmFWOG0sjObmw1zmMpfvRMzL3LBv40MYcwCM4CgF/q2WpXnb
+87b/I2j7hLV2+dt96a7YFACMMY9Za987b3ve9rztt1fm5sNc5jKX22S+KcxlLnO5Te6mTeE35m3P
+2563/fbLXeNTmMtc5nJ3yN2kKcxlLnO5C+Rt3xSMMT9qjLlojLlkjPkHd7itLWPMnxtjLhhjnjXG
+/JJeXzDGfMoY86L+37yDz+AaY/7KGPMJ/f2UMeYr2v9/Y8wMt+uOtN0wxvyeMeZ5Y8xzxphH36q+
+G2P+Wx3zZ4wxv2OMCe9U340xv2WM2TfGPPOKa9+wn0bkX+gzPGWMefgOtP1PdMyfMsb8oTGm8YrP
+PqZtXzTG/Mh30vabJW/rpmCMcYFfBT4C3A/8tDHm/jvYZAr8PWvt/cAjwC9qe/8A+Iy19h7gM/r7
+nZJfAp57xe//GPin1tqzQAf4+TvY9j8H/sRaex/wkD7HHe+7MWYD+DvAe621DyJZuD/Fnev7vwJ+
+9FXXvlk/PwLco/9+Afi1O9D2p4AHrbXvBF4APgaga++ngAf0b/43fSfeXrHWvm3/gEeBP33F7x8D
+PvYWtv9HwA8BF4F1vbYOXLxD7W0iC/IHgE8gpQqHgPeNxuNNbrsOXEH9SK+4fsf7DmwAN4AFJLX+
+E8CP3Mm+AyeBZ75dP4H/Hfjpb/S9N6vtV332nwC/rT/ftt6BPwUevRPz/3r+vd3mw2yxzOSmXrvj
+Yow5Cbwb+Aqwaq1VjmB2gdU71Ow/A/4+L9evLAJda+0sVf1O9v8UcAD8H2q+/KYxpsxb0Hdr7S3g
+fwGuAztAD3ict67v8M37+VavwZ8D/vhtavs1ydu9KbwtYoypAL8P/DfW2v4rP7OyZb/pIRljzI8B
++9bax9/se79G8YCHgV+z1r4bSSu/zVS4g31vAn8N2ZiOAWX+QxX7LZM71c9vJ8aYX0ZM2N9+q9t+
+PfJ2bwq3gK1X/L6p1+6YGGN8ZEP4bWvtH+jlPWPMun6+zqyk7c2VDwI/YYy5CvwuYkL8c6BhjJlV
+q97J/t8Eblprv6K//x6ySbwVff8wcMVae2CtTYA/QMbjreo7fPN+viVr0BjzN4EfA35GN6W3rO3X
+K2/3pvA14B71QhcQp8vH71RjxhgD/EvgOWvtr7zio48DP6s//yzia3hTxVr7MWvtprX2JNLPP7PW
+/gzw58B/difb1vZ3gRvGmHN66QeBC7wFfUfMhkeMMSWdg1nbb0nfVb5ZPz8O/FcahXgE6L3CzHhT
+xBjzo4jZ+BPW2vErPvo48FPGmMAYcwpxdn71zWz7Dcnb7dQAPop4ZF8CfvkOt/XdiNr4FPCE/vso
+Ytt/BngR+DSwcIef40PAJ/Tn08hCuAT830BwB9t9F/CY9v//AZpvVd+Bfwg8DzwD/J8IWNkd6Tvw
+O4jvIkE0pJ//Zv1EnL2/quvvaSRC8ma3fQnxHczW3K+/4vu/rG1fBD5yJ9fda/03z2icy1zmcpu8
+3ebDXOYyl7tM5pvCXOYyl9tkvinMZS5zuU3mm8Jc5jKX22S+KcxlLnO5TeabwlzmMpfbZL4pzGUu
+c7lN5pvCXOYyl9vk/wXydv6uFCRQigAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3577... Generator Loss: 0.9579
+Epoch 1/1... Discriminator Loss: 1.4756... Generator Loss: 0.6143
+Epoch 1/1... Discriminator Loss: 1.2961... Generator Loss: 0.8042
+Epoch 1/1... Discriminator Loss: 0.9653... Generator Loss: 1.3168
+Epoch 1/1... Discriminator Loss: 1.4968... Generator Loss: 0.7849
+Epoch 1/1... Discriminator Loss: 1.3341... Generator Loss: 0.8891
+Epoch 1/1... Discriminator Loss: 1.3685... Generator Loss: 0.7253
+Epoch 1/1... Discriminator Loss: 1.3404... Generator Loss: 0.9025
+Epoch 1/1... Discriminator Loss: 1.2645... Generator Loss: 0.8479
+Epoch 1/1... Discriminator Loss: 1.6065... Generator Loss: 0.4065
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmvLFl2mPftmDMycs4z33vuWFPX1CObaordnDTYBDXA
+A0TDNg0bECBAgg34wYT9B/RkA34UIEN6kC3PMCWKsFoyaZOi1ezuqq6+Nd66deczn5NzZswRfljr
+3O6SyGY1yZLKQK6XkycyMmLvtdfea17L1HXNGtawhjVcgvWvewBrWMMaPluwPhTWsIY1fAzWh8Ia
+1rCGj8H6UFjDGtbwMVgfCmtYwxo+ButDYQ1rWMPH4FM7FIwxf94Y84Ex5p4x5lc/rfesYQ1r+JMF
+82nEKRhjbOAu8GeAp8C3gV+u6/rdP/GXrWENa/gThU9LUvgJ4F5d1/frus6Avw/8xU/pXWtYwxr+
+BMH5lJ67Bzz5of+fAl/9g242xtQYcBxwXblWN12shUoxNVTGyMeioqgrnn0BGAyO/tCqK2pLzjpT
+GS5vra3q8nZs/Wts+9kzKmMw5eUNFUYlKMfUpPqDVicEoBvt49hya5YuyYoSgLKMqWtFaZFRVvJy
+C/lb4uFUhXxd1Rgj13NqrEw+V+R6b42n78CqwfL1uTWrWu4pFhV5peP/fQQ+E0FYteQf38H25Rl1
+BUWZKI4CAMIgxUE+p0UClczDsnKMTI/KAtuS67bixMamUtbi2A72JZspa1Z5JuMswNSpvlt+nxcZ
+mEvyK3GM/DDJc1xbJu7bDlyueyl4q6oKjKLFQGnp/KuaWtehNmDpuleO3GyXYOtnyzJYOtC6KsHT
+ueZQ6ruNvhcsVstYnoV59m4HmC3ifxnpfwQwLgT63MIFk+oXNeQ6D6PzArCb8rePoQhl7L4T4llC
+n412C98NdPgGWwf9xhvfO6/reuMPG8+ndSj8oWCM+avAX73833KhvwE7W/J//rVNon+mGyStSZSg
+k7Mlk2wBQKEbumFsBvrDMM3Imw0AnKVLmQqhJF787FBo54KkoNN5Rqwr28eeyr1VZ4lfCBEOnZgH
+XRnH1/+NLwPwl7/+39LtyMOe3vsujy4uAJjN3qGIN+UZk8fMlJjCagnAwuzSX8q9ozTH6OofOxnR
+I7l3WR8DMCflSlsPtzDHtK7J4E8r3syOADj55zEnK/2dEpJVQ3m5Sb9oeH0hY65ubtK/dUNwm8Lp
+5EMA3OVtAL700iP61vMAfHT8ASbpA9CMjrFG8sC4Be2gB0C3Ixu+RYe4KRtpq7VBFAoOzaTiu6f3
+Za4XBit7JGu2lN8fnz/F+AMZqJnTV4L+4PCAnV4XgNudAXierN/4TNYpXT071ZuezSKUtVnOc4rz
+scwvqPB1v6Z92RztObT6Ms6o6eFHQiNFMoe9oczlqGbcawvuHN0adchb374ja2L7WI7s0n5l8Y9/
++y3+Jbg8S36UVq6HvfIEgiG8qMzw9KqF/4E8pCpKjlZy3bF+sK6dr8qHX65tTj6/DcALg9fYb38R
+gJd+/ue4ufOS/M63CREcNhqdRz9iVM/g0zoUDoCrP/T/Fb32DOq6/lvA3wJwbVP3A8Mvnde8pSN6
++n/tc3LyGIANL6TREs523Q+4a2STlVPB7q3tiqIZAWD7OaYhhLffa9IIBdtFfEK8EmQO27IaC29A
+upwCcHgKbm8GwI1qyNPuCQA/+dTh1Y6szL1vTgAIbj3h0aEgOj8smdTy/fnbK9yVbPrzi4rzmRDy
+liPviJs1uy0ZT7sFdaLc726G8YWKfsaXzXEa5Wx8JBvsbCfnwVSI+Gg2Z3RPNk3mJfy1VOb0dwKZ
+0y+u4DeUIE//GWRfkHdM3tmksZQ5edeusJ11AGi4IwB8/xvEF0IzLze7HMby3MaqTz6QB95wY0ws
+4wtLGY8dObykOF6kAR3dDXHL4oUzWZPjJOMiFwY1s+YA7EUux6cytjhNuQhlLRtJjD2T+XV7Gfo6
+HGWZruPRdeXdW9tN0kQP2bzE7co6PE0raiP0slrIvZt+xU1X3mE3IZjLoZaUS+68L2t5dlYx1t8N
+NuV3sXVAU+c0O/cpEqGBM/dy98Nt/XsPfvRhgJwZl5JXRzd5eg6Hyv2zNx0cX/Dy72SGX3fkgZ+P
+4Q3dG09+W679982K/rHg3mtP6XzxnuCovUP6p24BEG7a2EHwowf1L8CndSh8G3jOGHMDOQz+CvDv
+/UE3N9s+X/25fV45vMVvfE8Id0WDq5UsTNkIaap85dYJW45wmJvKRR5VPu2pHARXrIh2S4hxZyeg
+58s9WJu4czlMlqpTJB4khSCs657wOBdkb5ZN6vZNACZRj+/ngqbGQjZ3mha0S+FQ48KheCzXN5YX
+xEsh+mqZMhvLhgsCIca9YkEUCldqFiviROb35UZNeakqlDK3zvAqrUSkoydHcxZKKJvHbQaRXD/3
+l+wlwt1/5S8JMZ//DymTTDQ3U9YsT+8CUHRqvtj8KwB8QM7nXMHz7bZs1uTagNyWQ7E4PuelgeB7
+J9qAloql+RlRKBs2aAjeCsAuZINtRjktT6h7mlu02i2dy4SpEUnu6Vgmejqe0/FF4tvvbHKoov+i
+BNfI8xps0AtlrcKZSiZXmljIRnAbHstcPl+vUx4ZwdGWt6TliKTjZLL+oVswcOVZRbHNPJF1Ojh2
+WOQiYUxnS0Y6/kEqokZzGNFUtXIZZbywL/j+7ukxt16WJXvxHfl7jz8YLrUqx8CGHhwjFSvc0mDm
+QgsuEXsNeXfbhPz1rwi+zn9vxrdSVfl0PNbE4UJVmODLOXUu62dm7xEkPyXvc3apfkxnwqdyKNR1
+XRhj/jrwfyLC0n9X1/U7n8a71rCGNfzJwqdmU6jr+h8B/+gT3Vx4mNOrvP9kyl8eyAl/48Uh//RA
+TvCyMSNXQ5zbrbg2Er2v1RWO+fXQJ9yRk3Yyj9nYks++FVM2hQM5lkWteltLOXRRJGSBnL7tDYdw
+LM/9KJoz+0jGcWHO6J8dArDXE3vB0jM4ys2CZUE3lRP6W49duomI4GG7y/VQ7vmcSh2tax2ee044
+cz3JWTaFU8w7h5SZ3Gu7wtmaZYg1kLF/pYa2L2LrY3tF1JUxnx70+M6fFhzs/bNXAfhLWyW7M8Fb
+YQr+ntoGXnwu4HEkGtywMnR/+msAXN0TY0TT5Cyawv3PvGvsqa7uRC5eIpLC0o7oqZETtR2UscNC
+7T19k1FGytmnMdOO4LMTQLUQKe1Sv62Pr7M7FGllEcJ+IRLS8WMwqioYN6aViprjNYXXuo2ShhFp
+hShnmQgnTfYKdk7lGY3KJz8XDloqrmbzlLSWdTfjJV4kxNCIXLoTecZFUoEr97+fyjy7M5fQEQXh
+xlZBpoa9/aLD/u/IXI7QtQME23DGM9MBBrihtLcqDH9B7RVPbVn/b/QafDMV3HfaJdOp2rxe6bNX
+CM198eo5L86EBqxA3ve3Jjm3N+RZ4XbFTiTP+NAdsB+IJGTZNebHjDr412Zo/GFIc8OHpx75sP/M
+YHY4OWfmysSycYzTEOKITJf5FVmMbr+r1wK29SBwszmNSpC62dig3RdiWiUWritqRdUUIm/WI5xI
+Fn9yUjJMZOUu4ilT6xSAk8WC1UyIdNcVI+BXVydYahi78+Qd7j6QzTazj0lVx58+mPKc6vn+y3Lt
+la0XaDTkd71BwTRVm8K8z1EmurFfCAKWjT5OIWrJnTzBWqkdJCyYT1UBjWxOz0RFGe+JynC3b/PG
+PxYV5XpvhnFVpCy3uD4Ugt/ufZXdpmzYgSd6uD1JCXMVuYcOw7aKtgV4XqSfl9SKW0s3TWAickvG
+bCwXT7+3G13atjy7WOVMkENktxSSO90+ZejJJm7XCThC/HGy5MRWr0xyjbqS+dVbgjfHKqh0u/lx
+jPHk3f605Hwx1nUdU6r1PVvoAboqGZ7LZjJNn6opqo3babKTyYH1oO1RxmpsDoTGWrbNkZEx9Mua
+diKfafT5n9VTcaUQvJX1mItnHrEfmBdajsVmJPc+tRzud2X9Wks5TO9t2pQLmcfZCrKBvPu30xH3
+BjLvL7/yMh/9rhiYw5bQZtuMOQuEZl+OrnBlY0fe3eiSzdUTU4B16dL7hLAOc17DGtbwMfhMSApl
+nTJJP+KJ32WwKafkEhszFU7qFy7bkZy7W1cinovU8BXImdbttuh15Vr7oIGjfufWZgPXFY5gTxfY
+ysVKV7hcnLcZqlpStH2aXeEYk5HHlbZwBOvA48NUxLbzU3VvzsbElYinfubSU7Xi/Pwho5GqFQuX
+7lVRFV72RMppRhXWVOZR100CS7jYMjNsIdwj64mqsRmeks2uyLV9l4OVSEI7Hz5moVx+Vs0xljh5
+oudljPnjmpktXo+35xW+uhBz06ShRrfnBgGb22Lki8pQcbIk1u8Dx6LO5X0kM/K2cLk6gzRVn34s
+Y096OVkq3xeui7dQ6aft4OaXBl2HhryOrCtrNsg8SlvwaS0rmoE8t6gMjiXj2LAM9lA4vqdeosBu
+UjrqObBSNhf6uWFwPZlLxzrn0YlImZEaqJtORssRHLvuHBv1cGy1qPbk3eM3DU+nMtBKRXu7k7E/
+k9/5eU41lfHsuCmZLXN9YgutmFJiJwDcCiJf5hr1XV6+Iu97tXBxQvW61CoxbNp88bFKtLuGxyot
+h7bNjabc65WnNG6L1Od2hKZvVjlLX5571aSUA3lGmbuUisNqPsLtfDa8Dz8WVGVJNh/TiTp84cZP
+AHAxOWG0EnFwOZ3Sboio9VKrQ7MlSCk0kKZPlzCTiVtXIE3ks1P7z4KFnKZHHV+KxEI8nhmxasgm
+7cc57oVc9+sTPlTini9SrqtuHKdyaESjcxxXRN/FYsLFmYhz0+WSp3Mhxpc3G2x+STbkcz8r/mMv
+2sdcyMJW3YrWSsYZmBg/lIOFPbUsxxuUAyH4QbRBeym+8sBZcfeOHADh0zPijoiUizfl5x+eu7Qc
+wVVWxPg9tVvMLuj2hFDs+oDyoby7GsjGDZoRVSYb3Z6NSSayCU2rRXkkn3NrznwpB2rTVw/AeIWv
+VBQvwWrJu6vxKSaRe+skpqkbpMh0DdKCUa4W/miLcSnvaBQpfVt2RXi1RR/BvbWSa1m9JFIvg+nU
+hG1lEJlhsCm4ndgR24UcjLVoZQynOTRUDYo9VksZ/00/JFfx+rm9OYeJ4GA+Vav+ERS6TUqr5kvX
+ZH73R1P8VHC3HchzS8fGq2ScQWAxHMhh2bFa/NwXRLS3gx5PJkJHjSM9TFslxW3Z3FaZsjkWHE2q
+BW0NKGuYgK8OxLYxMYIrbqwYq76dzWOML/h+rp1R5uLON05Nbf2h8Uofg7X6sIY1rOFj8JmQFGog
+qy3Oi4SVBgoNkwFzjSXuDLrcuiIn7eDqDraeZY4nf73cwkMty03wVK2wqhgr1yCj+gdRtVUh7CNb
+WJhCTmXHcnDackN9EdFQCaM5WHF6qvLcZejz/g7+Un7X2Xod972HADw+npMqF/PbHXb7IvVY10UK
+qMeGVP3g7jQm3RIO5bgBVkvGbNfCdawtcGK1aocrdp68IJ+TIcsb78tcjcOTyVMAHpXCDfLmCWen
+yknymu5Kxhz4JRN9XukmzBzhhF3FW5lkVGrdPj3P8TROuDQ1Gm/E0ssIFPe2BtVY84S0rHQ9bCwN
+V47HCzK15AfGp2rLXPPTsc6jJFMPTj05wDEqojcbbOyK4a61c4VGLO85UqkwiJsEur6m4dGohEYc
+uyC2RE1rXRjm1TkAG7WoKO+UKS8gqsGi2STsy3OzxoooFy7dq7bZ3hAunqqUw6Kg0VHdZ6vFaClz
+vWo3yRwZx3kp89gawA2NxmzkBRcaNNTvRrRfEYmnW16loTEu5Y7QupXl7A3V+9Kck6uauhyNaYVC
+D06vg7vU0PKlRLc6owWpRtOOdyv4vqzl4s8u6WtkZrl8jGNd+kE+GXwmDgVMTeVktPds9i6EeFbB
+hEBF0VY5p98T5PS9bWK1ygeFiqdWhteQxcpiD6PG1mwFrhHEZ+kCS0OXU3X/WaQsfPmdvcoJ1Xez
+P8zJNAa1Ool4oSX3H3jy4E6zQaBRk6fvv8WBEa+EY1lseHIA/MTWLV7/mdcBaJayoUtnRt2QTWxt
+unAuRBNnFbZuIH9HDjErsamNis7hAZvP7wmqooLnj3YB2H2xyc5Mdqz9QMb41pOINhoS7BakvtpB
+iprGuRB8thuwZckmdOYaJlxNiBH8lHZJOlH1KU7hMu8g92jWQmCrRDZK6NqsNJimt8xZXKal+B6F
+I/OLypKylrV0enLNbKXYB3Iw3Y+ntBqywV57vstPvf4FALb9gHEqc8nURjAMa+xLr4XvY0JZh9C2
+cFS/bnbfwZnJxokiGfs3wkPMK3Jt/qhPfKGq236E0xecu2mLfi7jPEn0wPJrLBXhny8t/L48z5qC
+q+6Fhtotvli1Ge5cJu8M2awER7eu7nLFFrpoWde43pF7qq54jPz2gBpRibeXJaV6uRZRk6IUul9k
+Ea4vtOy1ZWzpuMfTUnCYL2LKV8W20Z7exj/VALYXN6jC6/w4sFYf1rCGNXwMPhOSgoWhYbn4hcPK
+PAQgLQf0VLS9EoQM1Hg24imlBi+lnnCGVjDDVis7VcFlfI3rLCn1d5QVq8vkoVIMUglNrJlcW+Ut
+Cneh7w7pXEoh2wUrjW+4mqnYdxBjnpPn2uE2m3PhXJPWKa/2rwPw+S/sENpyj2mIWO63NrE1rsAs
+fRbqBbHdimSp2W4T4ezzIMfTEGwrc3F8FSMtn+aNfQAas0dEe98AIIofADBz7zAay7OaaQUigOAV
+GWUiIrWpLaxA/djqySknhuVSsyTjU7JSuGcxOqc2Q53HgpVatcNM8Ja0InwNy12aCHcs6sGEiMiT
+63Frj6DWTEMjHHHo9XiaCV6K8xlJV+Z3O9zg9nVRH7aCPhpBzXn0EIBeK6QTizrn+pvPEpfqLGJo
+iygdfeUnGbwnQWROWzhmK4yoNfbgbmDTvao5A8WCWpOjDEu2hprQNpKxPXEXhKm8r7/YxfNFRXGC
+AxzNuszVc5L1Ito3Zez9uYelnorXuxmldakqnZEonhvqfclDG3cltFIWGbWGQtjtFvVEVcgyYOkv
+9d2qomxk7Gjo/g07Jf2W0PXJ6wd87gv/ttxbLjC5EsEnhLWksIY1rOFj8JmQFDBg2RahPaRnfwWA
+RQdeKIWzZZsBuwPhVlFjn/y6hB3TEomhVw+oAzlpm3FBqrYIL+1itTQ9d/qUhUaSxRdyFi4mS0xf
+s/7iFVmmrs56jLUhnxfziui6SCHnGna9LHPah2rDePoGcw0JvpbZlB2N7nNf4f594d5XRjqG9hzO
+5Ps8OKA+F/105YdYql/PusJF8qomMZJis9/couwJR0hyj9ASiWa1Cgia8rnUTMXG8YDNvnCg44tz
+jCfvGBUZc/lIVMT4oqpjXaZZLy38XJO5yoSFJt94tk+uGZx5URF7cn1TszktmjQ0UnDWSGkYwb07
+XmC21chZQrulxkMNE87HTb5vXRplB0w02ezKTpvBlqR4u+dtyi3Ro1+4K5zbbl9j76rYVNgYYjzB
+S+V2sVdq0C0buL8gCVhF8ksyhl//DUYqEdxo2ZyrZHk1TFm01Ii5n+Br2PB25zKX+QPaKrFOmykb
+mSSYjS6gKoQGBprT/OWbN7nqy9iC5SOS1+QZ/ewqQSUpzrU1JVHJwmf3GV4vs1WDTohViUSTuoaW
+Jv+Vsxx3KjhPnqobMnTIPRlbHW4RvSo0O7i6yRJxkzeDfShUWv6E8Bk5FAzGcbGmYxwl8t1qzmEt
+RBMsDAd92Xh7cQYttVSn8tceNrDVAJbZFp4W97C6LcjVF9zdJljJ87It2TSh06ZyZCMskhVGQ0at
+coMbpW42Up6cye8aGkC1bM5ZHKnBKdzFS78DwN0jh9sDGefv3n/M4v/RsNvNtwHYKHLaQw2x7gU0
+AjnUGsGCueYGfPimeBZ++3CFFahoGLTZ6Mmmam9E7JUyznjo4OnB0lcj4Y1dw4NHItaGlo2n4nez
+UWI15B7bcRhvaIzAVIOQ5jEHjyXm4f3TR5QayGQiD3cpJ8gy9Gk15NknmicRlEu+Vwo+7aMzPC0o
+w7KiWuiabXnQE+9RQw3Jdr/PxoZsptP5gg1LU7G329jnGjjmdXHekDF9cCiE/cLOhMdGPC075zmr
+bZ3fzAY9UJ1ZhaVBYK4ewp2f+llaroxzuhkT9IV2CuuQ7AO5J05CiguhhzzSAKrpHr7SYTpcsTyR
+jddvTrBUfQi0+knLy+nUlzk4OeUTQf6dOmZ7Ju8o2w1OYsHnrpYAaJYW1Q1ZX2+4wHa1lsXEsNID
+K4mPGavXZaH0/eDJBYu50Ni8mTJ9KmrF8KVjhp56vpYVlSXM9ZPCWn1YwxrW8DH4TEgKBoNje5RR
+wGgi4uJH2YhHT1RNiFPOHoo46DUeUWkCS3cgp/3t/Wvs7wtn8N0cT33zcZ2ySOXz6OKA5EBE0PFY
+pICRl7PXEEkiNDaWut76NwaYUDj6/rIkWYgB50hey3ya4Ku4/tZ7b/PWIzFwzfOYzkfCEWfB2+y+
+KuLhK9tfAmB69JiZJmhd2bXp9qQQhnW64N1NyRg8OUn13nPMROZ3/2bFtefkuVc6PaKVcLwiHxPk
+er9mjG487bHXUcNfPMFohKsfhbS3JCS6rF1SrfR0/0RwcnDxgPfG4gqbPD4nT7SwiFuzrZWVbCfi
+qtZIOPdkbQrfkJ8I58qSBZ6nJehaAdsrudfxntC7kPG1N0TET+OU/ZaI8A82zgnUfbmz+RypL3NN
+F2POqo8AeC8WcXj2dk70pqhV1oaPr6Lxlb0dnLaomL1ej14ldQ9czShMZhcUKo299bvv8saFjN92
+S/JCE6wCm02NeoweCV43eharSiS6zaMlq7bWnLAaeJdlljRas6paHGo49hunIxaP5X173Yz3GoJb
+/7HN2YUaY3OtJxE1+KIl0Yqf93do9GQedt1k/ljG+fjRCTM1GJZNjflYXJDq2I/vr3D7YlQd3tml
+YcnvopsGP1B16xPCZ+RQqLHrjDSZsohlcxRVwnwiSOjWEbNYFn83dDhWv/HJYyHoZJZTLGXxr+z3
+8LWKTzwxjC+rEB2e8mgmC7qayIbYGgbYtRYLiUoCLd3mFDktI4fCKDWErry7q9brd7//IVcDIdxh
+dIMv7DwE4MFkia8FXL71aMatp+o3fkm+n8SbtDX+fhn22B8KcQQ39nnlRJ4d+UIcm9Ydjs7lsHm8
+MBy/KfaJzvYZ5UCIFM9jpunX12cqym422D3W3IhFyFx1zkFzl1s7op/WGwPqBxrrocE/y4XPbiGW
+dToJZ7V8bycetaom7WGFreqY0TyD5ek5Z2NZhzgvaWqswC3XYjUU9SHNaoqlzHWmcQXBosBRS/7L
+Zu+Z6D8fTWnkgqPN4Q4vf/XPyZBm35X3uhnHT+R91fSUYw3o+TC5TziX68+/5PFSLJswvHkdgMaw
+T3lDbBW3Vy4f3lU7yKnD7rbQw2BW8CS9TDmWcbaqY3qoqrXXpR5rbElyjtHgpZYvOPEaSxa6o7bc
+Dm1NE88cQ1ML+HQDj9llvMtK1IRFOqKZyCYeHzdpbUrOi7sbsoEcbkl3g85YY1I0ruLabp/3P/oe
+ABenBY8+kmcMtt5mGAsjMqucwgjdf1JYqw9rWMMaPgafCUmhrmrKpCDNoSrUOu0bIg2DPa6OQeMJ
+uruv0d8VEfTpsYiRWW0Rx6J2xHMPz9HQ3bQgGcnpuawLXC0A4mjsghNaNNTwF8RzOl0Rd6NGTllp
+6SuvoqcJWPO5cM8HDz6ivyGnb1R32euIuDe0C/7vJ2JEun94xtuecO87ucQV+M0JP7mS9220v4hl
+a2JW9xo7hYz/pBJJKXVtfusD+Xy6eMIbiqsrGxG3lMP+zOf26WtCU6xhnKHT47Vt4VwPTp9Qq8Gs
+0TF4hXCgplOx0EKBD1eSOPPw5H3OdQxJMmOsNSSsvKCtJdg2m22aQw2l1bJzi/aK0bmIwTNTcVNj
+OSZ5gyuJcPyLi4R4R0u2aXHpoKqJc5m//dwOB5rteDY9ohuJqui2fJxAOOwLv/iKzOl7T7nzlhRM
+PZvnuJ7M4/y04BtX5eFWEjKxZByDQLhxsLlLpuHtp9MT3rojdLFIJ3yg1UPbUZPJVIxy3/BFsnE7
+Hvua4TjPHZobWn9i0cNBYiFy9T6Mxk1aWiqvb7kcIDg6Oag4VHxPghxLM2z/1LaI9T08VoWsU8et
+8FVKNV5GUsn7Pnj/A54eiuRZRpp0lZW8eyp0+LVwh2Ui6kqnMMx9kZTabo/ysjr0J4TPxKGAMdSO
+g7ta4VaCSG9qM1eXT1wnjCxZmLBV4m/JIlzryGZrjWPCSAjQBDmV1g/0ZlBqZZ5uK8dXq/2yKfeW
+SUahIlne9phpGfVlZtFUu0PhlsRa+MTrye8/eJLTiIUgXni+yeeHYs94NBmwh1SdS90h19Wt+bM/
+8SIAZ2GLDbUHtLrbVD1NQ7ZTiIR4dx/KeBqft4g19fjucZc0ESLdx8ba1/yJgUOsacS9i8uagiW/
+p+4913iUWrm6H4aYXRU/sxpvT6tVa8ru57Z6HKh+/vjYoqFVn5I8Y9jX1O8XDaHmktgI4Q4SQ9GW
+sfWSJkFDK0e1AjKtr7iz0yfRyknJhfpF2zmBitRxo+T5HZnT6cESR+0LrRtdPHUfl8/JPLfPM37i
+FwSfoxJ8PZCSvW22Nd7fW8xZZoKD2tcdEflkdwRHjlXy6g2xHcROB2sheAnaQ44uzQRjGY/XavLo
+rmy87qbH8UQOlsAupQQ9sKHFVgI7J10IjexuQao5Fbnv0taqSE3fcL4rqucrWkey7U1JngoO055L
+pUFKTLq4bZlfN7IormiQnIaxRwdNvnxL3mcyn2oq+L73JOalhTLJ9AlG1apPCmv1YQ1rWMPH4DMh
+KTiuzWAzYrN7hVwLT4zKDznTAJp+7fDq81oS7VoTk6k1/LKryzCk46gompTUB3pK1haVZgNOVgWu
+BoW4auhpNirSy1M+tXHUOt3c9rAGGgYbFzS09Nhl45Gz5RFcEZVhb1KR3ZJ3X3nhKv9h7zkA8jBn
+0NZMQleZUDTTAAAgAElEQVTujaOI0JWx3Rp2ablyb00T5+KhPO+mcJeNxj57kRgd7z/+LoX2p8jy
+hK5KFa0qo6vZnHNbs/tiQ9tWzu3XPNAyaEWS4seCw2bHJ9fq17u7wgW9lksfTbjpz7AO1aBox+w8
+Jwa6vt/D1ixId09rPbQPQWMTVknMVlukiq3dAW1b4wYaFju+rGukxXBOzudYWtosGjboRuKJmR+O
+iYYiYXnWBo3rMv6sEIPb7tf2cf/578h6MOPppnzfDrs01ZPk+R4jnZ851EIuw5juy9dlnXKHf2tP
+RHe736DpCz4nbsLsUAy6xaFIgul5xmNVMd988oTzpXDdz+236V8GYmnmK1ZIT+NlllaHL6pE234h
+wmiJtSwfEDZV8lTjuJMNGalnIf5/V6wcDWTa8qkKefatnQG72sagg6hXR69bOCuRiKqTd3lD1ZKD
+jyqKX/8tGccvvcxu5yf5ceAzcShYGEKrQb8PvroTb+DzQAmwUde0LotJvH1KagnRO5F8vz0MyTT+
+vrFMqFxB5Cpdkmt9/7ByCTtCsLYG3rhWwNBoVaFqhq05E5Zl00g1emyVUZQaDKSCVT1xiHXjXWw/
+Irorrr7u6phsU0XDq0M6fe1so0FTualxQ9HJrY3b2EoINRZ2rhtBi8k06gRrR659qfslpipS54/v
+YTQ9zy0uWGmZe++e1pdcnFLo4bc3aHFvJNeTuEE6lYMj9iIa6lFI2/I+Pw0JtXhoaJbMt7XgTOgz
+vCp4s87nWJsSLdTUADJMyFKrSc2Pjwg1WaHjWlga77/f2qCldR4pNTJzasi1XmMnC/B1TnXWIVP7
+iDVfUrdk83q1/K52XQa7nwegClbY3/s2AE5UYxwRta1eE3cu9hhrKGtn8nPqTNap5fXIdWnC5Rml
+qlW9Y0NrR+ZXaoGfQ84o7orKMJyBmpqYvzchUkZ0W+0sOwOfqfaFiKYrsp6I+3b2A7rupQVWoSn/
+Wkl4aZfM1AvmZCn2W3IwRVsr/C1Rke1Wl61M60eqWnp7vGTpbCsuMoa9hwA8ni3paE+U8qMZ2WUE
+8CeEtfqwhjWs4WPw2ZAUbIt2y+fVvevPSoB//27NVk9CXOuFzUKbZUxeqIgWckIXttZDXEW0NDbB
+5Daltmnr1B4jS8SrVenQzkQ8DrR4i99xn5UTt3Noat/FhikoInm2W0FXuymdXohlOq+WZJlw2LPT
+mqwvUspmVlOo+hPUm4TXhVP4FzKeUQ3Wh8LN3OACb0tE3HgBZS51FhZz4R4te0hTxVOvsYXX0z5o
++Q6LqYj8VlFRztQibcnc3Aj6c+E604saW+tATpInnI1EHWmaCCfXKslqOQ/9Gqsv3Hx74wZPLPmc
+3iuZaVOejrE4nGnJr1TLrgU2nS3Bm/ErypXWCvBLtmP1VBgbX3tIeslltecFsWY7ev6QVLMuA+sx
+JpP6E3TdZzESTrPS8Yb428IxV96EzjXB8Sr1aGixk6zd4NrzYnS0+6Ia2GGTSqtL+80pRsvCOW4H
+T7tBWcYl0w5R+Uqkik5a8PqW0Mv/cvcD3ljK523fYqghyBuqMnXskETxuioT+pUagfvbuJsqHSx6
+1Jqhas00w9OZcu2KfH//3cWz8mlVXVJpTYYo7NPU2pwmUlo4TamaYnTM70W0tczgz36ug4a90Ix8
+bDXSf1L4I0sKxpirxpjfNMa8a4x5xxjzn+r1vjHmm8aYD/Vv74/6jjWsYQ3/6uGPIykUwH9e1/Ub
+xpgW8F1jzDeB/wj4p3Vd/01jzK8Cvwr8Fz/yScai8EJcK2WhlZFbgwXFoeadhyuMpvMVJxNMX7hG
+LxL9vTQZiTb0cCOfQvv8ecslaam6ep6SaSfhhrqHgjqkobaBlVWz0io28dKiraXXEifhRJNkFlrJ
+f5XAk1Q7f+5d8BOuhOA+LbpcdS5dYRHmSKSGNFQ33vFHrFoaVj2JKG9oI9wEvH1tIzLSugO2g6NJ
+NM7VEOtIe2I2QgINTV6wohprAlIhHC6jwNK6c8NbPay3ZDz97U1Kzeb0/BW5+v/NSObWyRwKbZAy
+osQ6UENbWtA8kRiB8EqfrlZuzkL5vV/ZONp7oUHG1o5IEJY/xB/IddtYFJfOcu2faVPQ64k+bFs2
+WlqAVXid3lD7c3g+JtNCt666bzODpYV03btdYk9sB63YotaustajFEttA26ojXmDOWauTWbMOfaZ
+GgduRJgDDZ7wauxDGV9yTfDSvoDpW8K5p/7b1JrNehHZtDR+ZaOtzYmu7ZBoVbB4PMJWSSFPLayp
+4CUlwVHbQHZJY+9ekLZl/evbEclKbSNJRlnI+1qLU6otrVo11voPlot7Jmt2bp0RXraorl3sPbVX
+5DV9W+vpfUL4Ix8KdV0fAUf6eW6MeQ9pQf8XgZ/R2/4u8Fv8IYdCaQqm7phkc5/yfW2AkhxSaqn2
+ZFRxpMEffgauVhRuqcU9a3WptNfi6nRGoUhvOBattoYPdzsMB0Igmv2L24Ti8llpTKmWYzewmWlM
+eXpRkmo2m6XFVrIyY6xGTv9iwe9cBrxEHcZ3ROU5HzRotjT7Ti3hR995Qr6pHpOtFS3d6M7K4uxA
+gk3sqZbaGiRsahPXIlzSaIsIWJyOKWMZcz6bs9TiJbGWEYxsD7Y1LNkZstIgnLxR0NKGOq43pNb3
+2JouPa0XJHr4jfKUp2+LceqwTrmqasz4g11s/+OFRZrBgjzXkmFxQqYZfD3rgoan5eXTnEpFZo0U
+Z7aq2bxsFDw02BrItXGlid/W311MsdBOVZbiKsnJ52o4yy+wVEWJs4xEu4bH5h7pY2EY4ZEcbv4L
+NbZ2ckrnm1QzjSe5c5doQ4yZtb+F0Y7m9X1tslOd8MaJjHN8sCTVOIa25z37vNQs0cVFQlloHUW3
+iZMJjaTxObYGUZnUUCEHxMoI3cRDh6cfSt7CuVMy1pqX19wV7lQOt2Bgky0eAtCIJAgtmdsUatit
+y5hcO4FfHKW0L/ttfu0UbyiFeD4p/InYFIwx14EvAN8CtvTAADgG9Z/8y7951orea/x4HWzWsIY1
+fHrwxz4UjDER8L8C/1ld1zNjftCiu67r2pjfv5PdD7ei98NmPXrscNx5RKpdiXtZxellp+HY4Gt7
+t3SVchEox1OOH7Gg0EKci+kENHKx6Ri29oTreIFHlcvJnavBaZnYGM2YKuMAo4kjjtthutDTP4ZT
+rUa66WkvhMKAcqWptyK7I6f878YJX7smrYh3H9Wcp8Jt5tqz4Oh8wVB7SyytCruU39lBSvVY8JZc
+U3VncsTDlbYlO/EwXxSOkV7MmauqsBgVJE3B0YkaHytifO263Y4MmWaJlhcWzGT8562QhjZrKBZa
+0+A4w9a4kFWdcHgZbbiwGR2JiH64mHI+1jgLdVMOtwP8WiSM5ahkotWsZ1UDVw2JUbuHq/0ULK0h
+4bkRhVZwtqYVaUOe68S7FNq6zYl9Ei0Y05yIQa1yLMonqmK5FiqhQ9gj1rDwfANOVMXKR/+7fP0t
+n8Gr6vYr2yxb6sr9bobjao/GeE7ZFbwspxrO7EdM5/LumbUgy7V2BCnHWkLt/kxw9VJSUxqh05UN
+cSI4ikuDrY2NSjegVvdqrnSxKCta6uqNL0b0XXW/DwYMN7QtXut56rFIPZl2zJ5dXHB2KtLG6l5O
+tC/IeBj6NLSNeeQ8h5NrhOQnhD/WoWCMcZED4e/Vdf2/6eUTY8xOXddHxpgd0BIwPwqqHCs+pr/y
+OL8v4uBu80V2fPEP37Gb+IksUn1rSKRi8EpTV71VTK2NWfsbbVxteLod+s/yHBq2wTiyoCZX3cxO
+qbSOYGFSBrV6C5oBW3pAmGpB71jVFK0jaFk16mpmnhgsXfDPP6g4d1Wcd++R7UlAThmq/SEIcLVD
+UtPxsT1NB66aBF0NXdW5Bf6Q7B3N7WgU1He0HHjLodSAFXpL4pVsts6mzHM2LWkP5RmL2YxCxeGn
+86ccXciB1WvV+LFmNqqu7wUTjrUAysSNaatnx263OdLYgpOTA061X2Mykc39wrhHT4OeTpsDIi2N
+bh8vwFE1IGziaDxFrdmuQZ2x0jLynr/HXIu9DNo5hf4uNlOcREjU3tAelvEWzp4IosmowhrIPNJs
+hfecPMOdV0TfEJXg0dtakWt1THoiY3N3u9hjuR59NaTQIiqet6RIZd5+KHhJ9jyuvS90808euDyv
+Vb0WWYFR1abVkE0aDm1aTYkrqMNDjCPfmzoka2g9xmpMeixrcmkn8xtwgWzuRdfihVA8H/btbTa0
+5H+9F+CqirU8Uy/LbMmoUC/SwKZqiMr03GZEqvkTttXgTBvbfFL443gfDPC3gffquv6vf+irXwN+
+RT//CvB//FHfsYY1rOFfPfxxJIWfAv4D4I4x5nt67b8E/ibwPxlj/hPgEfDv/mEPKvKa44OEX8tP
+eaTVfv/GixHfLa4D0LwYw1K4UXU+xb4hXGPlCDcwpcNAox8rzwZtwrG0u3Q1Sm8anOKrOoIlp7Zb
+VM/qH8ypGWnV3vo8oFzKPaejnO/OhQO9dEPDnUuYFZeFXCpCFZ//fmlzWxgpUW/K7Xc1zPW2SD/N
+nQ5hTxvZBHCmIqoznVJqfEb0RIvC7NjMNMR3+jDjqkjG+J+3sdXoRjPCURVjoC3Xm9s3iC+0VyY+
+Raqh3TmMMjHQnc+bGE3GQsuu5UWf3JGXjC9G2NpKrdn3uKZRjw/SiI4mKL3UEW7++taLHGiX6Mby
+PlMNu15OZ0z02Rt+AV2RXtKpcuAALPUorC6OGD3VLMo/9wqBrb0v2n1cTUDS/ikYy2B8CRu3zi8o
+1SjpTuckHS204+VYfc1c3dPu0m6JOZB55PYGgfr884FL+W1R45wrMX6ilcK1GAyTnLYtRsLu3gnv
+nsqa9SuXqYbIn6jRdml8BqFGepaHLF0Zm5VUZGoQdeqESV/mbTQ5zsGnoclRQTehqAVXG6Ntii/L
+9YaxmOr1/EyIbBFNqbTF3GoY0fBl7M3NDh2Nz/CqnCxWovyE8MfxPvwOYP6Ar3/+x3lWRUFizpm2
+LPYnIg7+7n6HllbbefT9JXlbCK81yhlVct1Wt1jLdyk1nbgYj7jS0MKYm6ckDXEn+UXIaqWiba6q
+SGCTpJfNPldYTfld6K44UGJ89/Ex0WUadSTx95b7AOfS+1PUHKge2V+MObgvY3pwE35q+BIATk9y
+B1bjE85jJYiTD4gPtIT77B7fWcmGvLat4ulbN1lciJoweAlcVV3K5ZLLzuJ5nTBQ/bO8LD5al8Sx
+bH5/vCRFRP+zxObsQua92U9pa/ZkS59rz2fMnmjVq7MFRjfmTj7hq3tfBmDvCw6dSuLoh3taHNcp
+GCSian3zO3NOTz6Q+a0Srqttp5t0mGnmZmjLpnl0PCHUJjunbo7RGo1brT6huprT+3dBszHtnhaE
+dRrPqs3aV10WH8hzH7/nkG++B8B26zmK73xfcBTI+5LqKVEqmzSbFrgvi2rnHB/T0wCo2rxGlok4
+nnXvA9AZbDH+lgbJzQKe09yVa47D3xnJu5cLzSI1PkY9HCtcZmcP5d1ENNS1mHkV6UJwP10K3rYi
+H6Oepqu2Re1ILvfZySnhIymWsjwekWqrgLmWABi9V5JrV7NOP2ZLw6qTaoCtamHupfSakmPzSWEd
+5ryGNazhY/DZCHOupSzYy49qFjuax//Nmlmtou/eiLGWD9sMa/zLstZafzDot9k2l52mb+NpFqRL
+RFYKl09nKyoN7ihVfajyOX6qVZuDNnWlmZFhmziXuIFWo8G25vLsHGo4s23z0xo38S1qRuqwnpsK
+o9Wo4zsl4x0JXd4caK/CbZ/5bwsXWOYVlXZ2PiJmeU+efe+uXLtXHfHc88LNdpZdVptaL6FYkG5p
+sZjfzXFf0tZ6c5VA2sNnocGj8j1cS6sPW20s5ehnqzM21Rjb6kg7ur6JGfQFV9GTEfemYvgMypqP
+jsXqbfotzifalOdUpLXavs5qJeJ3eXbKySP10LQr8qVIW0VdUao3Z5nJc604Z94Qb/Xq+JjFZT/K
+5S6Wlr9zl10SWWr8c1Uf92LqU+2N6NyEhXD2anDKpi34uvLCVzC/8HUAcke4q/f4dWqt4FxNbZYL
+teR/c0X9iyIBeoWN/7oYCr23tNP4ImW7FDV1c+MCDuR62Kuo1YSeqtr58Og9hg1RO+bnOaEGZSwu
+pjRtoS0/7HPhiaF0eShS5cn5jKvalCexQpZaxg93Rv6b/yMAt376ZcYa1zFSqWsRL/E1aW4zHWCF
+GpznhNSRSMjN9Ay78f/HEu+OhTMMODZt3n0kyMn9glc1IOnwaEVDm8nOqjmZtgDfLUWXb80iHmm3
+IWPZBBptmNtgawPZOID9gSDwekfrgpcxqXY8MuUK35MNkoyWJJm84yx1OdsVysx6QsR7r03Y0EzE
+9lsnHGjAjkfFkdot3vFDXp/IZonUDdnyX2D75+Qd2aSHNZZNupmGvPYLQnjLI1FFrl/kLLSs/ZUX
+95iO3gXg5Nyl/LbMe+t6A8YaIKOBV+XJ2bOcEH+UEGovxcpbkYQqlp+nLFS87Gkb9Y1XNoibWqA2
+bjP7SA83x8LeFvXn5RcDxlqAtR9Jf45VsSQ/F7yW1YRrN0WEXUwmBA3R611/RlDIe8Zzee/TZYwd
+yOZoX+8yfqTRfYNznFAI2rrZZPK2Hk4Djcp7/zGN19UifzrH+5zgrfOdt4gfyYE77U6wL8SCn6kn
+o78zBKWFKhxRvisFYTt/5iauNqG1BhWFdriqrwvuL+5+h29fiEr0zvkRv7ClXb8Kl/lKNunTCzkd
+3nt8wsZVmd8ec+KDy0hCw6qSjT6dlBSqxm5Gsv2mF0vua2TtC9OK1p/SzMiHU1aKz6ydEGimaXpw
+B4AH54am2jVu/fzrWJ6sQ9V8iEmEVoPbu2SLH6/B7Fp9WMMa1vAx+ExICpYx+MbDzObc1gzG5q1D
+ikw4zW0ynmhPvYVl8FfyeaRGtCIZ0/SVO2YBXfVpNxtDovZl8ItDraXPV5qRVzopJlVvQAsOa41V
+n8441cIhHa/F/FxO/DhUw9/+BjfuapnunQHOSqsuzwqiy2YoecL3tW9k2xMppvvqBcu2iqfxEfVN
+kSD6dpMKqRFgBw8BCG5XlAutVr3yOZ2roep4iRtq7cppk4b2v/TUI7GaP2RRatBMb0Z4IlJOb8em
+Vgki70451poSfc2HsC2HUrtJ1UGD1/eF09w7TTHa1j01L9G5qpmGmuFnFRZnGgvRNCWx5pWEfhNb
+O3jNg5qpeh3OtOP3RQJNLb/ukHH1ioa0JzEtT5vB+JuEQ+nItNIgK7e/oE61A3d9iKVz6g83Gb0r
+4/zOb/wGlkpFgdaFuHH1S3g72r2r9vG3NFuzfxVjqYcjiCAR/Of6jrMnS9o9WdNbS8NprZmR12oa
+7wqtOhpBVZ6ectrVAjdWh0rXaTSraR5rDoO9YqFNcEYLrQkaJFgz7eq1P+FUdaYtf8zWdZWanBnL
+uYQ3zzXwKrqw2emLJDTKXAKj0nLao7elehcd3Ia6bj4hrCWFNaxhDR8DU9e/bxTyv9pB/H6h0Ab4
+fYZmgEAPQWf/slNzhy1fE2Y2a2rNRMyf1Fhqaxg5Bftan7/WEl7d21e4olV0z1cb1Bp2Gz+qOJhK
+lePpqqTI5ZQPVYr5r/7OP8RTV9CD997lAy35NSu/g3UoJ/ug9YDsVKsezcS9ZVvb+L5IHcWsYKR9
+ECdjSCfyvrlyxKxc4Fjia7bSMZOFjKEuVpSWcGPfKjH2ZUUj+d5ttPG1H0EnavHLPy9hIld/uUWz
+K7aB+emH/De/9bfl2U+lCOq/v/WA7nWpt3D83hTfyOdh44Kllg0bxyWWJqm1I23HVzgcXaixzwvY
+bWrh1maPVaL9NnOIZ+ImPZnIot55+13GypNMleHZGrFob+JdFcPsjedvYOu8nozFXmDmG3z1purk
+m/vE6p4cWpvYvkgxq9GYC1uMplWsLfbSOecnMs7zdIRtiRQz9BsUpYxpkiaUieJWMwvnic/TR7p+
+zQ6Ntoyz5zR498mbAHz5FaGbV/se863PAZC/MeejmUasniwoOiJN9fsu3YG878nbWlvjNqRPBVeP
+3ZyTb8vnNAeNqsaYH2wHc2l8TSFVWb/+URWblURY8N26rr/8I+4EPiPqwyV8FcmoAn7fA+HysnaJ
+xzkUMfnnXmiSqoW/PRtxOlMsbFa8ogR9J58xOBAMHxVabvtkxakGHhVhk01tpLoKVjQ13yHLDItM
+rqdq4IwfPOE7M1mZ5d0Lpn3ZILM7C4aXpeDOGoxWQhQ9LVuflUt6avgrrg3Z00IlJ7ZNrB6VJw0R
+ZZ0c5rGWnw9cLF8Ot3JqkWt5OCvwKVXlsXRTscooNZNxWViEG7JJP3z7p/iSJ2W+sl2PGw+Usg5F
+PD/d+o+x/onUPty/Crkj4wyLLrH6zfsNh2Kl2ZGXsRJ+wWZDxOB4VuOoV8b4KV1P8DJNXBbapDWt
+xOo/6IfkZ7Kh56uUlQaDeVslq3sy79o7wt4Q8fimNhD2KXAjCdd2V0t25AwmyVYEWsiFbotAvR1z
+zVQMiwZ2W+jCO1uw0GIxeGBrMFE/LUgUtzGaJZucMNBuYMupR6FG7Lln8eWOPPtPaxBSGi15eCgG
+zrPZ+5xrnctkvuBVrRS+52fYx3J9v6l5FIctPlzIAfHgnkH77vL5Ci6jAr0aLve9VuInAbQEJwCX
+CkPMDwKIaoAFPxas1Yc1rGENH4PPhKTghrD1Mtz69g9JCn8AWECnlrPsVy+bxVzv8LIGLQxaX8BO
+5FQ+Do8pT0Sa8M2SbC5H7APtFTCtc1yNuqubKZZGTUYdi7lKGN3IpeVK+OxE8/gfnKxIcuE0lmdY
+PRHuvu+vGHbErbd0hni1xkBMJeah2yq4taMutGiPK7vKEmo4X8qcHo7EWHZ0dEBYyPcdr80Trd1/
+Or4gPheD59w3rDQce5bKGGorw3XFSGjMisXv/V2Z08/B517+aQDu3F/x00fCjRvKU+KbNsuVqAzW
+5JShVnneqnYpXXU5zjJQ910vVBcqGY52x/biFQ2t4GynBjRjMsxzmg116zqyHrPlBU0j+L6+d50H
+Uxn/7OIhhdYFeM3+85yotLGtLtTrm0OmWkmbKVjaWnAzcPAzIeduJ8Rood9a28+vkpRZIM8a5SVj
+SyP+0oz4SDl9oyb0NYZFIy9ZlBxrjw8/rLk2FHXrokp58YoYh/f7Uj7unbcNk0jG4xyOqTRGZqOG
+V1oiyb5wa5/2RNbno6m4rD+q79OdCt5eY8m7tmbrljVflWXi7qrmX0xrUnlRP1cM9PqFDVpS5IcF
+iU8Mn4lDwVnBxndt3qV8JvaEwGXCpwEuPa3Ph9CttIpzXxD9mned618Ra3PuPYevcQODNKJeysJc
+e9yFHRGfbzyUTfq98YgHcxFbG4OYlWa1zYuS1Ig+HzVKCg0auQzRfXwItS1hySXHbBayiHFzgOXK
+vX3fZqlW7XEgy9XwCrTwMV0WpD3ZWP1yjtXXFGdtl+6ZPp6GtW61N9g6Egw8XHWZZRIUNTlNONE+
+jqsjwYXlrsg0Zbsm5M3FzwLw1760x9OVqAFb799h+rNyQHz+Vfl9O69J/qwG27y7DeoBSC5O8bUi
+dJaDo3kQpQivNCybOtDKz5sWSaK2jdJnrtWqaZSkqo4VvlJ54yrdtlr9q5SuGooORy1e1hLvh/0l
+W9rhqrv3BRnnZsxOcVlMpWY6VxuGb2FrGnkw8Sk8OTj8SNfXLeksu7o2A5KB4OLuKMEdq2fErdCM
+cmLNnGxmHsWJxLUMOzF5oLExZYClIdQLZSAv3YiI1Abya+6SlXqlqrRiVQltjZIu5b7gpf2OvCMY
++9zNL5nJilR3/4broOEyfMMUvKn2P2PJLjmsQTUbJhkMNZekyB3+piobf50azeLgPp8M1urDGtaw
+ho/BZ0JSiIE7xtDGo9aEkn+xLERbLf9f6Yeke3JCX3lNDE6vXfs64dcl6aNaNqi1nddwsoRQk0jS
+JkkqUXjmTYlm2z77kH/wRKLVFscudls53mTER1rvzzQiXG0GY2tL9vvpG/S0NJs7mVFpn4KCOacH
+wrkm6SG1FrdwXOEIm96QjmoMg1b3B5WNvRaeFiG51hVR1Gmc0/dEkui2OrSuilV7K55w8q68+151
+SrUSsfPIEz6wyixyTSlMrQVf/Asiom43/k2y/CEAOy98jb4vnHKgxTjysxhrpSG87hmW1lKMsxVB
+pXUX2w5abpJLs5dftTCBVka2fBJNpHILh0S9AXkMi1rGtKGh60etDOuy/F2cslSPifFXGLW0Xbva
+YEuT0HxXft8uLVRwo1NvMtwUVtoPW1Qabh48b0guGy1fGkSTHv6W/DNOY+KpctVozEIlxKSYgIYs
+Z1ryrrl0cJuXJc8cCo1irAOf48f6u1AiM13b4vGZqJjHi9Ezj1GIw/1ziczMGt/H0Wpy//ADURWr
+bIJqGmxRE6sz7i1T0NL4k1eigLFGQvqalerVUGk1bs+zeO+y0KWBv6Gy9SIrVKb75PCZOBQASlMy
+NcUzr4PFD8SYhmMIhzLUn/7c8+yofvqcdvzxX72J05PiolWwBCUwp7Og0i5MdvyAMpFN5n5dNtWT
+9Hm+8g/kuU8HDSYq+s/jTZI7D+XlvsXTkVq+1YaRPjjgQruJbN+06DfF4jwZv8lUC3mM4wk9W5vH
+NFTP3Ijo9kSEjZwORkO3C2PIdbaeVpUKKw+j3WkaSYdSEzDCbEprW1Slm6HPsikIu6KBXvPljEN1
++xW5If/oVQDi1wrmSsTP7/RYhnJYVAtRk9KzJyxVnzZlReFo/sg8J2uqNwAfarVxq6u36uS46hdz
+/RDPl3dkvRrvVDM7WWJr96LlZal206B0BZ/VzDC4TDvNQypXDydjs6H5CkFHbSbTgMoW1caxN2jq
+cFyvh9NSPHduYXkarqyp8Y47x3S0B+fTI+xAaGHQ8KhvyuEV3VsyUy080OzT1aahocFEjpVTqZvV
+KV+bXy8AACAASURBVEsWthwGja4813G6z1LOu3nFSjNRd4zNbW3ms5sYDozmhCw0WIwKTf3AcySY
+D8Cp62cHZGXXeIEWntU9EhRQa77D0jJYiR6sQYWtfVjJfny7wlp9WMMa1vAx+MxICnYJbdfl0qbq
+UhIFmjffcvhyX8t//Znr3H5RrOR+T9QHb+8aaG0FO2xQZ9r8wuthfOUezm3qnqoHSzH8NTmn9TWx
+IP/m+wX/H3tvGnNblp4HPWvP05nP+ebvu2MNt6bu6i73YLrtdtrBcQZHQBgUIgYH8YsoAiEI/EIC
+pPAHiASCHyAUJIRjGRIJiCAdx25s4nZXdVdV13Crbt35m4czn332vBc/3ud8dgmTrk7Z5iJ9Syrd
+U+c7e++11157rXd43ufZOJfdb9y8QMr6/f2lwhatlKML6i7qBDUhz5F5Cx4xAnUS4yAXs9ws50hd
+2SlvU3899B00qQNpNw04ttxfAgdNui4g9fpO5qIm3sJq1DCUXG/DUfCYu29nCstIvh8SxDOFA58B
+2OnwGM+/Jn/ff/MDrF0X032evYVkpb9O0txgr4lqIfdnHk1Qm/Rz+j2krLR0syXyWsYoaJLSrrIQ
+0sStdAnfkj7PkgU8hsBnSXEplaaYvSjSAgazK4PdLkZnlFvraKQ00YN2FyFhzPmB/Nb1KgRtuT9L
+eWi3VlRqPZjkV0TgwOU4lyv6t2KKgqSe/V4DKcVgdJGhmkjwsGdqzBjEM1Oyh8OBKkgco5do9Zl1
+iWO0h5Jd6tBc8QAcEDZfWzYK0tRttQxs/0UZ++fqLThvizX1p7tiaXxQG4jilaUEfIFQ+X1bYYfu
+z8BX+LmZuATvrQAJhkIe0ypMSpCAHGFlYEb3wVI1VhAWwmx+bHtmFoXKUFhUJTo0h641Q4ATMwsC
+pGTuCQYhjC2yyrDyTqcGSLwEHVhQLB3WUQFFhGDt2zDJQgSWCHvHAYKOkFi81L+LBYVE5+9NUZkk
+Z/HPcETqc4/oR6MAAkqrR4MdeIwdHJ0MMTmTzIbr+NigclDGCHjlW8jVaiGoMSbHX6tQmLMiNOIk
+Tj0LpkMqd79CuJRrp6GFkDyIy0EFZ8J+BKwGtIDpEVNatY1eLGPUfb6Gqsl9OH0TQxJ8hF1xfVRe
+QZ9R83KewGJFqWk5CKZMvzYBh2K5JsFN9lKhIDpGuSEqUPdiomBZ1H3wSph0mpMxU716ijlBVuNh
+BpvlvWFhYosLZGUASSGLxcST8xplE2FNnzqrUDbks2cDhrdKHRTQ0ap8ngtWU0Mx7Zf6FjwuCum6
+RieVMWq3W+izfuCCpvh4NIGqWbZtW4gvGCdyLASsnr1Bini/fQ2ffPe3AABxnsAggvblnT5ecL4F
+ANh8zUdBFrHRUznXjSzBuySunU0rjFjNuQMgpAs6Nw08YVxmky7KogZOyP2YluoSvBS7DirGqEoN
+VD8haPnKfbhqV+2qfao9M5aCqYDQMnCTpmiwBjhYaeAZ2N6QHU1XAdTFdQBATUKLIslhMzqtZh5W
+ZkO91JcKzSq3oWlSaUqyW30brTO5xjVrD1Mt+f/w+RAZ1aPV+GV85+5v8XrkZWwFMFxy/2UjPClJ
+fjE9Q5HKTuhbCu3nJAC125QIet+3oAjXhefDJW51WdcoqF5kd1Y1BQo22Y7zWXxZBWmYDZiGXC/U
+LrYpg/70puwT6WMHh4z6x0WG/hbp1s6vY6LEXB2e2GgRW+CT2brMalgkBSnbHdhU466VDatFSyd3
+oBiANMmL4EU26jH7ZswxWyXWKxNkukM2WWJEkFFBopupruGsxFKMFLMF6fzbDkzWczSyEDZxDdFI
+rBG/DdhUW/Ju+fCpSqgCFzohnCe0AO7+cNifOIJiFkHlbVQGsb8zAyaVwJthB+2x9DOmy9R0Khyz
+EtWrHcQMiGbpAn9qW/q2uU4Y/NE9FIZYinkG7NA1+9LLt3H7578q3cl7CH9BguI3m39PutDcx4vf
+leDjd++OYDtixW1u2bhBHMahCqAaYi3lzGZZsYHZBWs/AgWTYDHbA46oeWmgxkp14bNaDFeWwlW7
+alftU+2ZsBSUAkxTQQcKMRPhm9YAISvdIr+Jn7vJYJ27jjqXFdGgLJuhNcAKR5g1NPO1OlEAaa60
+kVzmrPWMhKnGAKUhKMei4aFB1enma7+E8h9+BADIwxKjBwJtrbqSa/7t0xiIZPc4mU+RkpUZywVs
+pkM3O228AEHCXb8uq3bb3YHHwFhUe0h9yqZpDxb9ZMW8M+wMNenhUIWoydKjFzPUqzxcvoQ9EEun
+dy5jMTU+hsHd2K4NDD+mmrP1AD7TWy23gZBRKUW17mIxRM49QhcmSkPGUy9TgBBde5nC9OUcPgNc
+vuFhYdHnrmzUFG2Z2CZSWgeq8GFR8dug7qQFGx1abrnjYUJhlFg56JFhKHQK5LSW7BbTnp4HM5B7
+ssqbMPpyDtMLLytFlb8ORYJVkCUbTgBlE4KtlsgMEqzOTZS2zJG8GkN1BrwvsTwXVgPemNWMSYUF
+g6e6KGGPKU94k6nX0Tr2cmHIOg8U3tgQePydmyHcWhCwbnsArydp8rCUYO/aool5710AwK03Enz0
+llxvvQ9kVLx+tdPDW/synjeJ8nzr7fT3GLF1CRAS7i0rVMQ8L48lAAp89rqoZ2JRkNJHjXVt4ee3
+xCXIwx3YgZhizes9rPVIUbV1HYpBoIzRe19ZqBIxv8zIhi5kEus6BRYyebWVo87l4asu9RDLDAaD
+/rUZoHtDHqKxBnzhW5Lh+ODuCZqvsdJyXwKOmW5iTtquCC4WpVxbGQUsaju64RaONWseHlHncrdG
+ayZZlHSrRn4sj6mwXfgF4cOhHD+NJ6gI195oesgTclC6KYJa7skrTDS5hkR8CZZw0ezKjJidPYXd
+l++r8whZIQug44XoMIhrzWXye40mDLoEbq+GcqSfht8EDJls3YaPNORLzeCbtRtgdyQvxSifIWCk
+PkuXqMmVWaFC3Weg9EwWQrsuMSEM2gwD1KwRNlBhSnKZWjswCfDKRoJNSPohNm7d5jO1YTBSXzsW
+TJfupsouZ7YmJF7ZGlXCClWjRM5F0Ww5mJFER7mAyWrNHkWE4noBTYzFeVVBkzU8SabI2f/+hTx/
+++ICNslN/mQKJK7cqypexvIxadxuV6gZzNQXstkkcYnseamvKD4K4fC8v363hmaF7cfqHOtUzvqQ
+WOzHboCUOpYq0djuynhPQ40WhYc7Von5ym0oP5v/8LndB6WUqZR6Wyn1v/L/byilflcpdV8p9beU
+Us6PO8dVu2pX7dlpfxiWwl8FcBdAk///nwD4z7TWv6KU+q8B/GUA/9U/8gwK0LYBndWwCMpsOTHu
+PZYV3HUbONphgcrjEWbEHnRGTNld8y/ThbbXhcWaeB1YKE8luLZUFjzCJVc7WH44vmTGjeIY5joR
+j8sMFvECL6/VsBLZgdxEdqv7D13YBoOO1hw++f2zOEV4ab3YyIeyw3x/LpwFa0kfzXXZadYOBrCY
+kmvlNVJP7umc5KNvfXQMjyburUETPk3c7oaHPZc6BT0DyYiyapX82zDGsEkP1ot8gHl31TiBS5q2
+4qLAGVNg3Yb8vaxqJBPZXQo3QXRplrdhLqmr2HPQLEh026JbNlGY06Su/BamDXEf5oclUkLWp5MS
+ixXxDYN64WKKMe1avZiLiA8AK3Ow1hI3zmhVyPZlPI6IW+4aBkr2oTgcYhFwt15GAElOVRIDHFvF
+4im4CuWJnOviLEc2lnmxcGM8eCB9ttTyknV6Tu3SuM5QxgzgoUYZi3VnGTaWrvTp7F2KyaCNIJHv
+Hi0auPmG9OGdsydojpkyniQ4eyDQ+vpUfmsENdbWJbDd8J4i3JX735ik+PUzihLpJT6gW7hLOrbt
+sAlQktB0HDzPCtVFVuMtysk9mAJyNnxmuPPn1ZLcAfBnAPzHAP4tSsn9CQB/kT/5mwD+A/yYRUEp
+wDaBtY4LfyFry4NOjKEl3XMODeRf44QNawTE+0/nYrYtWx76fXLZ5S5MTtKkmiMuxJe79+ZMRFYB
+HL4g/65dmJjlYg5v5W2sfyBY9O5gDUWXUfuugQYFXX+28yW5qXf/FgJmH8JFigu9imc4aAXCo/fy
+izfwxrdEOGVxzBz04gRGR17Mzb5GlVE3si5w91Qm7A8PhYHpbHqIthY8xn4nwuubMuEbzQCK7kOd
+eXD4siQtqjQtWsBcXBtXW+iu6PATHyPIb4tWCot1Ak+OZAyPHz/GJJNF2KkKbG7JOYLWOq5vEeyl
+mzByWVBnNsd+XOBHv/U+AODu9AJLxnt0r4FdfwV/rhBc0P/mgjxquFAc+3FWIi+ox+g1YJBvMy4c
+zHJiQO7Li9eyNpCyDiarlnAP5XpTYwa7FNeza/fgEdfiTCm8Yp7i4kDcp3c/eIjJI3mmD40JOoQ/
+9yIPvmLsJiFDd26jIjCu1i4WBGEV8xSKG0DRkONPH8WYZNK3OTIs78lczl9+jIzxLzu6jn5L+vl9
+ivUa8TGWZIJad9t4siObyfH4BAfEshxnNmyLRDSB9DEzC3gEi5nLAicUnDk2PTzmeFcVYP5kZM6f
+2334zwH8O8CKrRQ9ABOtWXcKHADY/oMOVEr960qpt5RSbz0DjHBX7apdNbZ/bEtBKfVnAZxprX+g
+lPrWT3r875eiN01DG4ZCritMSNixF+yi0ZG1ZbvbQaVkRZwfTBCTUKXi0rMxMVE6NI4ac6BHNGLs
+omxLBqDVu4sPZ98HANz7X7jb9TvY3JDrXTRmCGMyAA8MuMxQ2L02rmXXAQD5LrMWcQxDy2+j9SYq
+7n5JcYK98Jrc39p1ZLRoOk0x5dxqA25HdgRXW2h3Ut7HJhozckySa8uqDpEy0Lj/6AwRkYv2rglc
+E2skLDXKcGVqyr8jx8W1SoJaSWShWMp6nVYxuoHs+JnZw/SJWCSUfoTrtPHKtgRzF0aBaU2r4vwC
+Z4Qmb+/VsChzH2hZ67PuFP01EWFBNcMsll3zmpXCpgDKwrKw0iOpfLmnKPeQZnL/TlVDLUnI0gjQ
+smRse7VCzGKztXXZBZudAdKM1s8iQG6J6X9ax+h+LKa9sZGht2Kp7kh/1IGLOpAAZWuQ4sFEtBMe
+PpnjJVpNvpEh6YoL6TLC3/AUtiYyn+LZCBMWMS0TDWdMeXlfnu92YxO+Q2GghoML4lo+epziDola
+cge495SW45jur5nijYVYit4ru9i4zwKrrsaf+47c37utDJ/QShuQobwfK5ywwvORGaFhiaVbPZph
+jQmqRwlAShrqWv/49nkFZn9JKfWnIVmPJoC/AaCtlLJoLewAOPyxZ9IauqjQqkz4DJUuS4U3HKlE
+/KRo4J3f+UA+Rz5CT8yvsCMvwnqvf5nKKy4yOJ68eFYzh1vIAwubBcYP5HY/zP8vAMDJ0xCFI+ey
+hm08vy2T0VY+LI8046YBf42gGBK4FkaJ3JQh3r3+IppzptD8fczoU/7o3bfx8VBclxcZf5i7XXxt
+SB/3VgdzVkE2mwZudeT7x7e48Ohr+ORDyV4sZ08xog+8LDpYZ27p9Vs9NBuUJ2eazrM9dPbE7UhG
+FzAdUtynDdRTiXqjZaPF1OgJqceTdIqDcxJ9RAHOT+X+IsNE6BIgk4XwWXdQU7I9uTjCQS73+cnp
+DMOpxGjef5pjc0f86IGucbNN8dO29Leul5eVmEapkTOKbtQpJhM5X1Z9AZEv196lYGqtMjx6IiCz
+Dz5+iIr1Ma7Twje/JC9bsNxEdkGAU09eCa87Q3Qu9xRiifQx7zt+jH/wSI47ibfgPpHBfXFP3I+u
+ZeOpKab/SekhY8o8XtzHnAS6KbUkF5MMMd/ALoB7R3K93z6d4bsH8qyj/SmGIxnnn2Jq+Vq/jTSS
+ebipXoe/+zsAADXqYUgGrOOnNbKxzMl3WcrejhzMF/I8vlTW+D4zKplf4oyxkTYAZRG9VPwRZx+0
+1v+e1npHa30dwL8A4B9orf9FAL8B4C/wZ1dS9Fftqv3/rP1R4BT+XQC/opT6jwC8DeC//fGHKGhl
+IlsWuNeQ3ey1ehPB62L6f9l6HjPCVduRgagjK2yrKbiC1kYALFbBqSH8pQStdGCiPFspRld4/cti
+CaD6CgAgm01xrScQZK/pwxzILpblR5idSyBtvT2+LGhyeT0kNdqs2nQ7EfZIejEeteBPCdMOm3hu
+INvGspS+DaoQx2vyuTWN0YxkB2oUGtgRc/xVYjA2ggDdluwCw+km+uQz3N6N0I/EDTB7BmYLOUdC
+UpAu5jgnm9/aWoiahVawMrTIZlxNUyxJ+9Yk0EvtRihM2YGaSQn/BRl7Y5wiCVjZaMxEMAVAuZBd
+Mo4zrJHo5Au6gxNG2WejBRoM0PmugyCQ3+fEiuhkhiV1PO+dLpGu+Bq1hbVQzOfCSVCT31JROKdV
+r2GHQKbA9EARb4TrLazvUmJtNMEkFQM11OIO1J6FjEritgrxxleF7r49a+Lxffl+czNCk/xm62TB
+XqQpCtK+D4xT7LOas0xrVH3ye7La89rzryD3xDpw6gW21uW4Lw63sMkCsrVmA79D0NqNhYzVSy9s
+osf7s7oF7KUQ6gzmT9H8k3IN7/vH+OhIdnx3Tfq2hi0M7xCk9X4LN4mj+e58dEm0c1xr3PwJY3Z/
+KIuC1vo3AfwmPz8E8JU/jPNetat21f742zOBaFSGhhNUuNXz8BVL4gTLToiuL4GvGzeeg78hq65n
+AVUt/pfZY3GOY8Nsi0WQD3MU3DXr4RJmayWIehstLb/pXshx9d4UFSnPnKgJj76jMts4nElgCKMZ
+Wl8kx4MvKcnMrJHYYq20cxeawSl7egt3dsS/fO5OD/0N2f1PT+S8pc6x3magTYfQLECy/AAtIjLD
+PdntNm82cXsgQbbZ7DFCh+nLdgCf8RNTj1CQlfjQlnP5YQPbREeb1jrSJ+SAgImqIX12BxrVmHJy
+LBf3igpOm3wLlcYaA5QZZmiyTNz0BqiPiQY1ZcfcaEQQml3gZ+5sInYpppLUl3Rsy7ML6HMK1jLV
+9CQAnkwIbU4SFKR3s10Nsxbf2JlMEDBVZ46orvzSJjYMGYt8cxNxJlaFE7igEh6sztoly7Umw3OR
+LNFj0NnrRbgGCda+tpyj/inZxVMnEGUeAKNziWvYRYy8LfR9i7wJ2GKF1ShRzSn245I0GCm8klbO
+85v4Vi5cHX/iGy6iBhm+pjaeJxtUl/wPg0YHusE5XZrwPHlmnVcGWA7lpvpf/xAvPxTKvWaPehp6
+gDSUORZ/+wgH/5ukbe+PY3xwLvPMRI0FU6dIPxsH0zOxKHhK4SXbxTebGo2WTA6nvAFjzL8/p9Hu
+yeRQ0S50KRPM9CT4BJVC+zQT9RHUKgi2P0VA/IIVzmEP5EXY8cU8K5tNVKkExuq6QkGq5aLlQZfy
+W6NsAaeEI9P8rBZAMpMX6CAz0KX74NsVQktcjBsbHRghGYV7EhhzbB9WKROp8H04MzE1kzxGXZCe
+nGzJvpPDp0jJevAcioDEIssLOK5cezlOURAUhKdU5R5m8AmDtToWRqy+VOUCHgOeerMPtaq0M+XF
+dYY2rDmBV3WCtJLvW24Ga00Cvm5VQnMxVMxCTyvjMshbpgYc6koaUYn+jOPZ7yLhM6spXhM9sjFg
+tWphGdAMktmqB7fNIKHdhHK4aLVZ11ClcEMZC89poFGtyBgdmIH0owoHaJA+3SLlXX5gQmvZZDyM
+oC1WRtYWsCHzqIpHqNfFpM+Jtxg2G2h/IotJrp+iJHV8XRtQsTyHcSQT9Wari4tSsgW7B+eorgkV
+Xr9nwVwX7s2bVgZFghqTWpoIN1AdfCLfNdehiIvR7Qj2x3K+ut/FLVfGy/iqLATdt23Uu/JeDB8s
+EL0qi9M37g/xNUfG8DdUBSLPsY/P1q6qJK/aVbtqn2rPhKUAS0F3FOrODeRzWbXvRzl+kQiu3PBg
++rLrGh0DRiK/0RQp0UuFuiYazzIRn3CHwgEmT8UMDDdasFh956zJ7mOHNRRVmfM8h2nLrqM/eIwN
+7v73R/dgsHrSeyireRIfYT5/CwBQFs9hMiTsuJrhQEmAazxtodEn3DiR42emA5tF7RoJHAqVnB4s
+oViHXynp25oZwg+YrzYDgLBkNwbKlRBNUaEgz4BL2rJZW0HZ0p9kGCOPVozBA6Q90sbNLhDwfCbN
+b7NpgPykMCc2TCL7gqwBgyg9V2vU1OwMTNk9E3//Mqhl5wbMakVyGsNlhWqW5jDmrESkK9LaKOFM
+5beNtMaU6d5mM0VdE7/gGojPWXim6BIOFGwyMynXhLmUe60cCwaZiUwrh9liUDWny7TZRHImu64q
+FrCUWD/ern9Jfmqa/UsLoc25cvjwE2iiRg8yDXsgu7FxeACUpJCz5Tt/uQSYWo7aDbisYPW3Xwd2
+iZfwClQkcVW0PAs1hMMKIR0YMJTs/mU6Afoyf42wD29TrA3FqkyvVsh8uY/oh/tYMjX+cz+1hV/9
+nqQvz0YFZitass9I4fpMLAqGVghqG+XBGTZeF/DPrZstVASgNEILmtBWlSlo5ooN0lZVQQ39VAah
+6DqwIxkEz2yg2JdBzScVvIa4EsonLHlUXYqv2IsKKTHlaZrj4IKVbOMxikN5sfYD6k6mFvKpvITv
+xOe4Zcnn01GF7ZgiprsX8A9leGNGqcM4R+ywwnFZQ1GSvD0wMZqx2m8m7szSqNBhxqVqhHAI0a5d
+Ew6pvhchkJIqrmbWoueEmLIaMCtqJNQwbPVsaM3FMKtREnTqkMHZNJuXXIVVXcLieJeqgkdAkm44
+sAmrVRTKdY0ABV2wwkxgeStgUQvKkXEz6wroyH1bzJV3CwMuy6jHRY6ci02tPKy1VzyWTQQZKfpz
+WTR68+T3GL8LhZq0+1ZioVoJqQ4zoMV4DTG+quGjHss5lsMRbE2259461DFBSE4Bg1W1hS+L7bKY
+YbFgRsKd42zKUm7HxrIl3ze4OUVfeQnOPmNRWQprk4tXFAKnxAp4HoyMIKOI1bVPLIALneXuAm0+
+k4sGjD2C2T44hPppVq5OxA1SAwvWuWxUcf4OwPL7sPE6vrIrJdxPl2Occ3M9XlWT/5h25T5ctat2
+1T7VnglLoYTGRV3jqNOGfSSmVbQR42ZTIsSL4yncLTH9THsbKpfdr2bVWF4XKFjtV09iIKbZurSg
+DMoSBy3UDNSsWIttK0RFiGtZmkhHghqcNIaYncruf3xiYL7SSlxpevsLWGti0TifTPDoFQazphpW
+LUHFBycmGl25l+VczEtrMceYJqdru2hTL6EBjYp5+kqx2Mu1YLPysa6mcAkvXiYLZKQdm5Qpcuow
+TNUFzxUhVjJWo1hhi4QLnapCQfco0Sbc1X7AAp9lcoqKRUBlmkJTuzENHRhEHlbzEDUl1gwyIxfJ
+FAUrMa0gQEpEo+OlqAvqbxQJsKD5z9jak3mJSstxoSmmMADsx1Ns0dLZmFeoDTHBEwYil3EMb0HY
+uD0Q3DCAMq+QcvdXULAy8k8wE5WiQEIrZVbnyKdyDu+4gMuMV54YqI8Ie6d2W1ZYSCq5p7LsIVpB
+iW0Tk5HczDkFhy4+foiQBLN1ewuKget64xFMUgdWpwnqiajBFKz2NSwXJbVE4aXQASX2xiMsmO1Z
+jGYYsHgtvCYhw7xwkd6jbODjFB9OJEui3S7OHHE1Lox91D4Vtv8fapR/cHsmFoW6AhZT4G5+CvSI
+sx99DaOQEfBNfbkA1LUJo16JctM/P8+BmuCY+RkUXxDbtlHviu8bliWMS7eDx7UbUASeaLtAfE/+
+XgfXcFwI81IVaEzIvONRoNRGhHAq/Sy9p9D3mNK6OELMSPzpkx5mWyxPppR7NjaRzZk6zU6Qk+Ck
+rvsYkN0o3ZL7CNMCxCWhmmVAl7GPaY6CvnM6LS9Ljk2Sf8yTGhcnct1prqGo+jSya/QnhOW2atQp
+iUyYqYhHBTQrEtMUKJm+tBcKBasyx5NzTMb0cel7h2suImoqWqqNkkI0Sy9DEMtL6vVbqCwZLyuX
++09rE4uEmaZKI6RfP54rjGK+kMsYGd0cTZ7yGBohlZeMqILJNHJZV0j35XvTD1CEpKunq5VpYLli
+ltIG0ogu0UUOY5sLcpIiaclLff5I7iO2TSglY1HbE9TkoPQ8R1ipALxLhuc71QgL6kRuBzOUNOft
+hxOolnxfpx1oUzaJigtQZU9AjhkY2QcoP+Im4x0hfiQv+mnloPmObDje8yTkeeIgPZF5evbkAht0
+tw/0h3garchngGuenPx7+Gztyn24alftqn2qPROWgtYaVZ5D+z4aDGRZhokWFSWXuoGKydY6nwMG
+a8VT6gMEOcoh8+7+FEUuppobVbC5kzqtDjQhtkYkQTLUMUyeq0gseE2xMKaI0adJ/A+nY6y3VjX2
+8l00uIl+V4I9fvc5LJgTbk+e4CKRPl9bzKCWshtXpCubljlm1Ig4P34E/ZCigv09fHNX8A0diLvT
+CDXOyTFgOBni4UoGLcOSlXp5maJSNOcLsQ4uygw5KyensyFOCcYZ2Bo5swHFRQqXJCJ1udpdZxiu
+AD+egYp8jrbhILXkHMvARDJjRoXumm8qNKMV1thCkckOOxydwvNXO/McLglAKrJL9y/2sRbLcZPS
+QEEylLyc4Xgk45IVe8hpLrmWjFvsN5FrsYosNUIRy9hnExsVZeCzYoaSVaDpSH4Lc4GMwKmoUSCn
+22g7JZb74jYWRo4RLZ1kFQwsEpQci1Exh0uZPrMKgUSspsiRMam8HhqU2Ou3fGhiRHTbQk66PB1P
+kNElqBoyPpPjQ8zO6LrGFexzsVbMgY0OtSxmWQmTsHG1FBEkuz9FRiVxa3yIyfNiFW+32/jed84A
+AIOywqL8yQgVnolFodLAKFd4NF1AszrxG9UUR125mVvWEnlN5FqcoyLwxjTIOmSbgEnSj8yDT+pw
+o2fAXjDW0KgvTV5N4gpj4opKJwCVT2GRktv9JEadyucXbAdPEnkpmhBfPWyuo2mTMHWtAyuTPH2f
+6QAAIABJREFUCf0uykswkKqmWM6ZSSBXnzlSiJipKP0MD89Zf1DOkW+RhIQyPqNAQadyXG4WiCjl
+njdrGEpM4yDMsZyz5JjEGwNUmJKm3I1MqAGRkI0IVpdENE+HWIylb+11ifSbugXLkO8s00RiyXhP
+2wEapK3faCkEgZRJh4pkpmUPFms4El1gQiLYZB4ABX+TNTHnQjUmc9HEqTGi7zwc19CruEXhouJ4
+LjKNsqSrRMFfz6vBgkqU0wQFFwvDbcMLCeWcZZiRcGWFUjVCB02Wsi8tALa8NMsqgzNm7KZfwKT+
+o6tksZkWCfIlGZKSGaYz+X7vRoohP99mzYjZ76GhJda0dEroJhfedAm9z3nWMFGYqwVQnlPYb6Ag
+B2eshzAokLwd1ljcohbq3b+PH9JNfeP7QgFQP9fA+K5kO76vgC+RmHaK65i1hO3rk8MEd+qfzCG4
+ch+u2lW7ap9qz4SlAKUBt4RpmTBICfY4O8JLqexi6aMhsj65/Twfei67X0Xtx0U9Rk3atemDFP6J
+fF6E72D9uuxs3snXAQJy7BuyozidCJrBN6QzLBjtabRibDKq3UoGOLonwZ6KtfnOYA/GdYGwnu3H
+l3qOSxiwcgnsTQHElHrTNJPj2RRZKWZpM4jgkrm5mVnYPxTePpuR7jrbQDGk6bxRoLsuu3yyWMBi
+vUOaxYiowD0h54EftGFRVs6vGuhVc37fxNwUgFQdVnBJVFIryo7Fc9QkdTmZZUBP+u7qCRyCaZLR
+BJbF3DvBNnkyhsFxm9k2ygVp+WvAbIg5u9A1plp25oxB0sdPhhhTUj60DBAuggsUYJwRxXwJKyQl
+HyXvrHmFrCv3ZHpNVBxbjCcoYum/F5iwCBs3C+n7YvxDqED67iz6WBwQYt1aoNuRGpM8LxDzXh4e
+P+Z3Y5TkpPDyCH5IDtG929gjfd9GQ67hOBeAZrC66MJ8Qjf1uoJHKHVmRVAE1E0mMgAt6xTmQkBI
+20aA6bpYvXV5jvgtuae7b17Af06smJcZ+MW+wmQpfz/bLmEtmSW5eAtnH9PdWqYwGiuS98/WriyF
+q3bVrtqn2jNhKdiGgZ3AxZ2sAMixP36/xrsU0Pjiq9vonZOhOdqAtRLobclqWPzmU1Qh+Q2KCsOm
+7Er4XRunTx/LceEQnW/+PACgxwpB9B0YDBKVL1bA/yjHWRvPoUGhVHV9jA5VgjODQrORg4i+f+I/
+RT4Wf7maJLBrytfNHUwK8a83DKLy0gkOHso9NZSHni/nuzAMxO/J+c5JUKq7H2C9L4Vb1y0DSUvW
+bwclYoqK5scFzD4h2ORsmGiNIF9pSJgIyg4HeQc+2YgWVoVlcV/6sSr8yjQM+p6mqTHNKFKbuUAg
+xzmDPuqc1XcU683s+hIJucQQ6VyeX2fQQkXnv7XXw+SRPKvZPoVtlx7alPSrKiDsyHlHhQubyuPx
+cA7MaCH2qLnhNKEnEpQz+21YDgNxxhj1+xLzyXZvwGEc5CwRUtnk7QWKSOI9efEecsZXmpWG2lpJ
+6C1xSmHWk7FcbzFcYHRBZK2q0KT03F6WYr2S40JWlzaG1+DsSX/jKdAh4tE4GEKRn8I1m8iuSbXj
+4v/4AQAg1QZqJXNv3M+g3n8MAFCOgw8XkoZsrGk8fyzX1gkZpd+NMdiTa//zT3tYU3KN0YMnMMnq
+9QVb4dukwvs1fLb2TCwKWilkpovToIEpLa4YY2w/lkn6kXmBvWusgixT1MzH1owU+280EN9lpN6a
+XVKce/90iPpQXky/M0BBTUDj1jbPdYGEi9DB9/aRvEIdwFkFe10e6Jtvv4eceuATQmA7t3ZRky24
+fPAqnpbycO3AwGgiC0C/7iJOBYKa8mVMkhm6hPvaoxKdQBaylluieZO5a1LVN3ob8Lvirqw/twZU
+0ocyDxB/LAtLq2ehYtS+ilhFOh2jsSOman56hMYtuf/tvoEmzdzsB09Qs0Yhn0hg178RIh8zIh+H
+CIjLj7MSF6ZEuJe1h5IZikEoWYTJ6AKJubqnCbokn2nGS6AnwdjZxQVm7Oc+gUc/zKdwWbV5eyNC
+zUUmzRJMc5YvW2O4/H1osjK0zlHSlYLWsDzWXdgR8GX5jbmwYHVkkW3OpDai/ec6SIgtqfINzLXM
+l/VBE7qWe50vbNx9LIvl+VwyIKPZGI4lC8Wo1HBaQs4ybv4epZ3flMVmXixwSNq8aBFj8q68Xje+
+/Rxsqlfp4ACu3BJu/lOSwYrPPNRU+HLyM6z/JdGdVLqJbx7I5vTBm+8Br8i8XcxlYb1Ipzh9Rxbb
+4EtNHBSyofzti1N8OJQ+37YV3iquAo1X7apdtc/RnglLQRkGvMgDRkv0HAmK+OESFzUrBnsnmC6E
+Ni2cPIITkCyjSYRhtg2rJcSuo4VC0+eqG72Ios+6+r02GpvUq6EoSpUdI2ahTcMcw7FfBwBkwVOM
+mTp8RaU4Dqkh0GKAKz9Hh0GiWT3FWi5m68KoETbEKsiNOS5IBuKxSq25phFPZTfrNAN4JEFtuDGC
+luzuK4Vgqx9ia01Mf9sOUJGnIM4rOORZmKY2DK7rSSy7VZKMoIgw7G0EsCn/pjYyGAzW9W9/iOMT
+IuzIjF1oGxWVu63cgd+R7+cPSiRads867yL2ZLwmE9mBXW8Me8biqXaGmHl10ysRKLnGdDrFwUh2
+7nsLsQKcvEBJ/oOTvES7TwzIgQ2nQVTkOMciIpmsKWN1OxgjTWVcZ/MT2L70I1p3oLXcX10uUDMF
+2nSZfrYG8OiOZpGDVkoXy3KQcec9qyeoziR4uKB7OM4KxHQJu5EHM5X+57mBkII5zTE5Ijoujs7F
+XSnqEJUv998+7KCuWRzWuQG7T/h3JurT3tMfYkyWa7ecYPoJpemKu7C/KBby9WkblSGWwslCnkfm
+29h+jmn09gbuPxWG6qVtYo1WWGAoFNZKceGzNaWfAdGFQW9D//lf+JcQ/OIajKaY0aPjIf7P3/mf
+AQDZ0xfQK38bADCuDJhkGq5NyeH2cYiSVX2DyEO2qoxbJJhkYn7NdYqQoh2bEavbrr+GYCq/PUpM
+pIzUl+YLGGyR1GT7DrwWmZs9WYz+p//w34DdE/P5lZefh0uo9CdPH2NOwMpuVKC90Wc/iUEI19DX
+8vL6LuC0ZUI3+95lBV9JBqKpZWOHAjAdI0K7IZM89jx4hHTDjOGbJJppSn87QRMpKxiDPMI/+8t/
+FQCwtd7Ga2tiriZOH6f37wIAHs4ey3eTDFPqQ6aphk+NTdPSaHxVFpa1eyWOWP5RPCTVeVEgTTkB
+m0BANuPOL3Vx7XfkZXp/qlFfyGJRmYT2psD1rozL8UDjZ/7NbwAA3vu3D6FDigx3e6gJj17OSKZS
+e9hqy/PrDgxMCNoqpgVqiOvmd/qw+RxiWzqcXiQ4oxBwnC1QEjYtkprSN2Uq5MweuMzEqMhGQEBS
+NzRRt+X5fOUb/wR+9T8VKfmSTNVbm9vIXPnt7PwIigLDX9zTaKxLfOjw8UcYT8gOXgrGIKs8OB7H
+M7VQJhJfSNIaPB0cr42AICrLksWv0fNgKLJO90y0BsL09KWfehV3Xv0CAKDZs7BGhq+NVvsHWus3
+8GPalftw1a7aVftUeybchxoauZXj47//Gl7ZkMis8UIE/1h2PzV6G2P75wAA8cO/i76SVTylKrUf
+eFCEjO41bZwQaut5Np7ryqp6OBminshxgSFm3eJgjoQaMvE0gG0SYbZeopiIhaHTOZrrcu4hmZgz
+E8CQAbVaY9AXU7TMmph4Yl5urb2GW1z999YJk0Ubt5qCmyjbJvqEKKfLBmpJU2OqZbcrJnNUD2Wn
+dRox5lp+21z6yAmbzo0YC1dM4k1y/I1at2DlEjizdl20SRbTyRyEDNDCdeCQU8I6IfGMVeNPMfj4
+o84St0iFt+wB99+V7eogWyA5XDEYy7m+Wiu8yyzC9TFAJC7e/+9dnDDgWxdNRMRf/Cz5Ft7zYvxr
+B7JjPjRq/O5/IdG3VNUwhjKedjuDo8lFQZZlrWp0ezJYtywN9MR1W2Y1CgY8R6qBgs8SrCLtDQIY
+5F5IzwssU/ncaBjIWRRm1znG5E6oOW5u6UDnYqXs5hF6XXHjZj8aYWkwDXZGbIK7QGeDhDt2E3ZF
+XsrwJRhTwslrA9lM5m24KuyLFDqE4yvdQFBTzzLMEJBurio0GiGrJ5vEt1Q51Fz67sKGY0umonzo
+wiJLeavtwWJB12dtz8SiUKYznN79DpwXXXy5/2cBAH/n/VPcpsVYzircG4gflSqNpSmTtG3IxNw0
+W3D54q3HDbxIkkwVaqiM5vp6Cwlx/h8ey8PQyRhBytLbdgRY8kB7vobFSko7SmBEMgltX96UejlB
+3SCtefZP4oipylv1CNcDAcJYr++gSx+wzRTS19tthJuySCX9DP4Zqei9AEZLrndRr8hNPJyl8qI4
+VY1zqgqpRgprxTDk+jinStHFRDIEzYWL/ksSnV/UBizI/TnwUY1EIanqaDgEOL3SY9mw4+MXpuLj
+XvtpEy6BOb/yYIFMU4A1dWByIiuLUPLaxjfoMpwOFd7lM8uNAh3GMCqzhU5XFoBrlpTDv/JtG/Vb
+4jK8HxwhPyRgJ/5tVIwDbBVfRhwSUEZA03WnCYNCLYNJDxn1j7bbBvbn5LxUwIDVkSvykht9Cynr
+L7DWQkZiUwWNEyo5lQsX54yDHBOi7S0sJFgx4W7gcfgtAMCmXaJcSJ81j78d/iVMKE7Tn55js0UG
+JTvCdCq/nZ0cQhE2b1nybJ4fhOixfPt6uwudyfMb6hH0UL4vao3ckH7YBJwtlzma1Kisex3cacq8
+38xjNA2mLe0+avxkMYXP5T4opdpKqV9TSn2klLqrlPq6UqqrlPqOUuoT/tv5PNe4alftqv3xts9r
+KfwNAP+71vovKKUcCNf3vw/g17XWf10p9dcA/DWIQMz/a8srDwfzF/FzL3fxICB0NHuI89clG/CS
+FePVE1nxH/YNrGViGrlSe4Jrbh8pI8CWnyAKuDIGDWgWUm2PLBzFAiXeyeTvj5ZnGK74JxYljBah
+xJ4DxxIz326ZyG0x93pnMlxlaWOXO9Bh5wKOIbt44+YNbL4kpu+OCuGRr89aUrxm0IDXks/OWRMl
+zcEg9FG1pP/9CwkGzpoTXCOf4WFeYn0hFsTI0QjJIzE1EhQEHH3MaPoXe0dICDZqZwEy8lAk2Qyj
+mdy/WgZQzJsTBQwz7eDgS+JKzEZNNEml/bw7QsI8/Ye6gslg3oJEKIcuMB/JDrZW5bjN9MlGANwj
+Ddj1nRnOyOx8/LIcN3/vNbz8vOwXv+zeQyuQa//y33bQZ1Zp6o7RJOtyY0cedrSTo0eNSntjimYp
+u7+zFeK1gsChRYwRC+RccmiEfgmDdHThZgsJsxrL0sCgkPs7aroYsJ8GYeCz/gxn+zKGB/4Q60/k
+/oPWDeRk5h6QSu5x9QThRMZetzZRrstvO9kZakOg8hPkMEwZeyuSsWoZLvyOuMpmOENvTYLH67qN
+szXK9A1PkHliNayK2c6yAtlSLIIb9hIjm4CrYA/XWUCm0kNYlZzjs7bPIzDbAvAzAP4VANBa5wBy
+pdSfB/At/uxvQkRi/pGLgnJyuHsHCOovYoM8iJsvfw1PpgIK+jJexOlTeTFvWHfQ+7qwyrzQF7P+
+YrZEQOn007KAS7advKFgzWQSxkaOckgyTqaHkqzEpCJrUJ1hrSXn27jzAsauvC3V2MbBgUSJV6hC
+bRSoSDS6tmdj1/s6AMDUh9hltWKvaCCy5XwG4wVeN8KqxM/v1ygoRW9GNupcXoRwg2nIwEa5kAXN
+TufISZSh8iUmiSxCk2kKJ6VeQiX3n4wi5PcoZrrVw/mMqcplDTNgpkIPkXlyXMT7h1Phu4eSTjs7
+d5EljwEAx/EcilmJqtIwSBxC/lk8KQqspAqVa2Ka8+XH4rJK8v5RgIBZpfGZ3Ge8fo7/ciHlzeqe
+wpe+Ki+TMgoU1GpwegbWPUnJ9al1sBl14ZOxyvJM1A71K9rWpZLTeieEywXVspgiVRp7rriHkzRB
+v0MUIwJkXHgO5hMcnck4FqT1X44LuLa4cQejDPeO5byDvRQVqErGF9NtJAi0uDPTaojJIWMKy1No
+mv6Wb+G2J8+4w1qaO2s9pMyIDdrr2Ajk767vIqI26RhSQQoABsuwS7PEjFT71dMEr7ky9mGU4dGb
+Qr4SN17GoPXHV/twA8A5gP9OKfW2Uuq/UUqFANa11sf8zQmA9T/o4N8vRV8WP5nPc9Wu2lX7o2uf
+x32wAHwJwF/RWv+uUupvQFyFy6a11kqpPxAI8ful6IOgpauzNtKqDdeSHfhON7okN1FHDm6vRF3W
+e3iVFNdlLWZRmFqY+pIT7p0+gHJXAZ4E1Y7sMDenj7A4EfPrsCfm4nKRwRpS8WdSo99jALN3B9uM
+8J7P+tifiyk6na5IQ0yYvlyjU2i01iT33mmZCDOy8joxKnIiOqZkBnSyhFEygux3oLAK1vlAzNWc
+GoZKGXBcVjLOUxgdAqGGy0sqc2c2RbXiL6DK8LgsYT0Qa6s7P0DBnb2oKhSUfm+3PDis4Fx3ZfeZ
+201kIwkoJuk5lgs5R5GlUKQGr2oJzEkH5Z+6UtD2ih8A0MRkpKle/RK6TKF6kiUZF1+WZ6ZGKEZy
+z0fxI5TvkCoPDjxPxnDHcNHZkzmw16FLVAfwI5ZUmh20ucOitmCRv2DpheiR2t1alXMiQ2NNxnB5
+PEaLis9aNaBJqOMUOQwGDTu7ctyZUyMK5PlmdYppLuNyfpACNRXKWDlqVyXctmxwnWwCdcJnbZvw
+fOn/Gx0ToS2WwPa6WE+b19ZwzvFu1RswWRnqm110aHmhXSDlM5sRu9FOhiie0tLdnKP05b1oXKth
+0QodPf4EzcYfX6DxAMCB1vp3+f+/BlkkTpVSmwDAf88+xzWu2lW7an/M7R/bUtBanyil9pVSL2it
+PwbwbQAf8r9/GcBfx2eUorddjZ0bJWYPn2D9OUlTGdEB5m8Je0w1ucCdrwl6se4N0NkRj4RrD/Kz
+CVyH6tJdC5Mz2dlhBsgtWZMMmNghFViQyC7xnWWGgorSo6rAdCIr+MtfaCEq5Ny/oRPsbYpf++Cp
+YChsD0Amu1K01UOX7E31qQFzjVwG1/aIlgMsVkkCIQwqRmtdwuozNZpHMAldLliF6BkmEu7QbuSi
+pF9vZRXiGVNayQKGy8AeC5X243tokwko3mihIjKvUhUanvRz3W4gXlU+MtfnlAbahBcfnS2gKThj
+qQo1WZdNQ8Pm5+xSfAHwV8TQdX2JU0i1RsW6/3mVIL0v/XjpmkBxu82b+N5E8urtdIaoFKsvCmRs
+AMBa66NbkxVJDA14WzV665LWdGqNkLu8clvQOZnAGynWb7DAKiGnhZmhIhXeVs/HkhWshi6Q1eL7
+o8jRVDIeLtXDT6s5Zqf068dLBCw803UOg2nSnOc1axNhLs/0yeEYrXCFU7mGARGwvUpDuTIWuxSW
+iZwQDUKmp5mCMljY1c/QXlkhZoHaZyKPOpdHJfCJJj9HsoG2lnnfPItwbP8IANBJr+HAlXH+rO3z
+Zh/+CoD/gZmHhwD+VYj18atKqb8M4AmAf+7HncQ1LFwLenjpRQdeUx7y2f6bePMTidi+HIYYknpt
+R28gY5DPyaj403HQnMikKq430aW+YlkWmBGwZDk1elwAvBflgb+SKwyfSrBrgQo+eQuPLhT2NuXB
+9AYmPnkqk6J3Tq7F2sAeSUjsusQ0l5fe8WKY0XUAkDLkJfkf+TI6noKirLs5dgCXoqJ+ipqah+aB
+TKoiT+CQ9jw1U9RPKaaSLxCxknKc2hg+kklxNBLzc2zOcDST+/hnmn/3UiCkVg6MJkVWgiZCW8au
+ImTaT2q0CCwyjBAmwT9aKUSG9G2hKtgMTBYUSQ1MBeKAYGogpnsEBaTV73mOKRe4H5zKgub3E1Ra
+nuPpYoacGRzfbGCdcPSeVUPRVcr78oIZUQ82PVLTdJFxIQsKDc0Xy142URFHElJwprBLVBnJYrI5
+AgZB80WMmuMVORViUsR51Lw0hhpZwgBfkGA6l/7bMGDz+Xis4Cx1iZRkMoZbwvbEhN9pReizqtb2
+Crg2qQXDlQDMOmpPXtx+YCJwqci1tJCxjieoQzh9MkyPhc+zZRwisKbsQ4I5S4zPdwqA7N7pg49h
++J9NGWrVPteioLV+B8AfhKX+9uc571W7alft/7v2TCAaPc/Ciy+toR/toRzJrvzJaY3xRD57nS1E
+tMCjax4MRS1Jhyi/ooBqkNCidOFQjdosx2hvUlHYdhH2uVOSDbmzvon4N6nOu38PFtM/qjfEEPLb
+W76HH7ZImrrF4zoObDIKY+bBIYotUCVs7oh1ksDxqXVZcfeED8IDYHo1aqapVNVGPZF7qbmz53MN
+RSsntXPUlvzdWAbwc9kdHmULxIYgGRVTr5jMUHK3+7BuoCYU13EUOrZcz2uGyEcs6q9kYGPPB6hj
+ubXpICGSzkoc6Jq7dF5A0xIIVg/PNuGS7DTXgOkwAlmVlxWfWkMo9wDM23JPrf2nyLVMv7Cl4G1y
+91womCQu1YULp0ntSpLxmp6CokiOt9aBnlM2r9OEQWIV3TBgssK2Yn90XmKV5UpnQJ5SZ2KUIr7U
+oijhxGI1lIRHv7gd4iyWeTE/cKDpE6ZmDV1d5mJlKGofmngRC3M0iMwMfWCrT9M/BwK6uj6ZxF1v
+AUdJerJSBgoK55T+AiU5PIyFQkA9zZ2XpW/DxSZOScHmVwqtDbEw1r0IfVa/PrgWQP+ExK3PxKJg
+1AbCxEPYLDEgBnw9bGLNkIlybWsXjS3xxZFoKIKCNFYCs3Mom9yBWFxGvVG4qP0V4Gh4yZloEVse
+NvrYui7+2fbiFbx2Wwg0Nq1tgDqOxWkC41QyHy0WSiSlhx2WJ2/ZBSrCrW2vgqLikqpGqFaU8gSd
+VLUHtWDcNdqDKglzDl2AgiIgXbzjGMg7VK96MkEW+Lz2HBWBAVmyhD6VSfr9C5m4t40a/Vi+894+
+hcaKsclG11uV09ZICJXNmS0JrAT9DmMuzQ5MQ/qclue4RiKTD55kKDkGTziGyq8xJYW45zlIKXS7
+Y5t4f7RiVtKoV+/PKeHc7hjrrCLtlBq3mD051RFCW8agjwIucQgNyrc3zDYM0ssXVQi0qAVaN1BC
+fGozi1A6jBNQcQv5Ei5LP5N8gjRm1maqUfF6OpvjvJbNoMGMQ+ls4SUK8zrBMd79kC9ynmFBSLrP
+RSGy5lAMNDTcDANbYO6+ZaMgxL4ZhfCIX1kRr1hlhMJlHUSpYBBqXRU16lKe66RoIzmTe/L7pA7o
++PAsmZueucTRRObkCy9O0LkmWZ7BB3cxWi1en7FdVUletat21T7VnglLQZkadqtACBdNkp74aYpb
+4aqK8AheKNmHOM5QEXUGV1bcQHkomCs38gz6slgH0DO5RWOWYXwmQcUmMwAzrbHVkR1xY8fDjdvf
+BAC8eD3EcCLH/boaYf2W7H5PP2CBkldgSahqXZTwSMjhXCxQ0aWxomuoFd0DVj5qO4Ttye5YoIBH
+jIG2Faz1FYWx7GxuGqDM5XgfE1Sx7FyWp5Fk0p+b7QQ/YNB1k6rVH50X6DHA17EUzBWeoNaYX1Dl
+uu9gjfvBmMHXOmugQWur7nlobYvVcO+4jxOSr2zfnAM5UZjHjwAAIxfY4tbitjoAg7/pssZuXyyh
+o3GGhJwTNnkvHO1jNpXO7bx0CwndHP88wYrjo9VoIarl+XQW5IZct+Cakg2aTcYAA395kCEM5LmW
+QQKX1lJJ3Qc7qaGZqVFlEzMtdGtuPMecsHd3zUJjKePcow6Fb42geqyiPLLQ6svzOTjPAVLMzbib
+FwngMBVTp8ASDEram/BcukeoYJEz0VzKeKd2AbOS+ytywF5Kf4rIBUpautkxDFLyLY4FsVo5FUgT
+iTVnC84m3dgkxIIZkc3XXsCjdwQN/FnbM7EoGIaF0FtDo6uhO2Iuum+f4kFCCXALGJK+Ozm7wPJY
+zKgN5r/y3SbMMf0w14bLFwhtF9kjeTB39zVGlKWPKTOuCuCI+PO1rI1GX47TM4WQgq+7TYUPKAN/
+J5IJ8VFlgwFy6BBIqQo1RwbLpr+b5MguSNfu07zuLFGusxx8/xzpFqnmHQWEkp4y6dNWXQWfWpPq
+lddQ7MvnIj6COpGHfP4YmIzk3B+cyQTtFRofcFG4kS9Q1TID0yLBJBXXZX3iIyaZS02YLOoaCdmN
+umsuYlbW9coSza68bLvhdYQEJ0Urt6O9gzin3+5oPD6Rl61tdvHeuSwcw7ef4nsjGXuHVPQGLGw+
+J9mXNaURuHLth0aJBuNDpSqhWcJ8yFjLteUSGSHdBx/u4+mFfDbzKcItWXCbQYAgkkVt0KQmphNg
++UBYuN48HmL2RPp2NDrGGV0X676BDt2023tMPboWQOasO80Qjw6YJUG10syFwTjSuJjAYTxkUabw
+KXIbn8aYcHE6Dwq0Joz/+GThqj3MWSdiLzNYbZlcUVzhSSWxiNReoiDJ7slD2QjemY2xeCAu00X7
+FNabUjfT+zN7+FJDxmKpTWwXj/GTtCv34apdtav2qfZMWAqmoRBFNvxuB5fyu250ScVVLDzcf0c4
+GGMjhq1kJR3NqZw8a6JHReig20V/TNmxZY4ZlYE/OP0QT+7LLka5SoRrW5gfkT35Vo6I19trudin
+ZPqr/TaSYzGZh1I4ic0mEDKgOJ3MUV7Iaj1ZXiCmhLtj+Zc04SFNx+2ggwlVtZu6RnMkq/mW0Yfa
+YfFTKTv7+P4UF3d/KJ/rC5wuaQYvSizISn2/WGBEerdd4jsWY4Cqa/huYaBmQLDSFqAJajIdJMwY
+lCR66QQRVFN244tZgYpS7BdD4GImLsFwrcKdgexGXUeqFhPTxoNDYa3+6HiMNs3g98JDJAdiVp+Y
+PUwKsZpAsFHXcJDNxTqK3RI5gVxFlaHW8sxmBlCMyOwcy1hOMsD1CTs3MpCGAOUB8MONOpAhAAAg
+AElEQVRzAZf1HRc3bkr/gonsrtlaggxyjruf/AhPHpzwHCm21+Q5DOoakStjMKF259ZGCxazVpZd
+o9+Q+5gtHZQc+4QZGR0DicnrpXPkBDLN9RIlOT/tucZ5LBYLWKw16HrQjMRuOB1UDNamkYszzvu7
+Hx+jt0YIvMfg6XCKhBbtk/0TdInJGM++iIi/yfUQaoulsJ+xPROLAmoFLA3MEx/rxL3feONn8BLJ
+SI1hFwelPETDr2EoiQ0cEi9ePfGwviZv+k/f6SHfEHCHm3RRUNGnbXs4aMqgVvOVglINVwK5CJNt
+uI6YdVVRY5f+Z2n6SH2J8D4YkmYcDfTIXLTX6+LIlofRGHtYhdmPTzIgYbXfhryY99MjmG9R/NWJ
+ceOmvGzj7VtonUvlZ+7wntIFLqhHkD1cx82XGOl2gbc/Fn7Fyf0Kr3NxeszJ8RVL4e8QSXg6+z3w
+kFnV8B3GJUwHAeMd80R+c1BM0XxE8tDawxF1MCdLE64pfdrJEpxwsei+IP01PYU2+RDrsgaryxE1
+cnisV1j4S2ClxAVZKLSv0JBkDw6HwCZ1NU3lwszFXdnwGiiZysvPqEORlHgaU0h2kqDviFsyMgyE
+Y9Yi9DR2W/KM+11BP9ZWE2lDsgE/exTjY6atl7WBWwOWFucxHh0/lv4xCzbJYjzPOgq1s4H2exJX
+6hszPNSsxCQCMYoClAaRklYFayqD8agxwfNnFD3eMJAtmYqNVwS8I/gsYE3cAJuObEK52cZWSybo
+ZM+FF8rcen5X7unLr7yKuz8S1O/fe8fD8JwKYE/eRa1/EQBQTiIYJ3SnP2O7ch+u2lW7ap9qz4Sl
+oFGi0EOo+QJoEUuQprjDWoWP0yM8OBUYaNzO0KnEHLp/LmZ0v5piyyeAxuijQdBQ6SzQYK274yvE
+jMQPbsmy3Ao7eH5TMhif2BvwbNmhVBFDEXP+OJ383+y9WYxtWXom9K09D2eeYh7ukHfIyqpK21Uu
+N+Wp3RZqBmFArRb9BmrJSIBASEj0W7/wYCEEQkKCF1qohbDdRgwtaGPsxi2bsl3lyqrMyvHevEPE
+jTnixBn3PPLwf+e6summbjpb9jWKJZUqMu6JfdZee+31T9//fagGYqUOn0lyarNRwWFi6PRojNQl
+Vn+a4DoRq/oiDDFbsrsulvm4VYQ32O3YrzVEMeG8ywBVR+4vIYDqOJ9iEkh4VOQpnjyR6xqqiY+f
+ye+dLMOnJPhobIiF+u7TEnNyLNjQX4KGCgBhQFUnLFCRMn4SiEcQFjUWjngKy3KGmGGAnzgv+QPT
+soTDrsN6Kl6crQDckfvonS5x/nxVV0/RcIgnMFzs+bLmj6hklZo6Dkg317csNC3593xxCYM1e9Md
+vhSM0Wm/nkYTfHQi7ncwX2JO4JFbG3i4IRb/XmsLTV1CgqYrlYqyukaa0N3XaxxeyXfPZ6f46EzW
+sNuycPpMwoafXJPn1GmayOgd6ZGLtT3xGo/Dy5fq5Rk9s7hIsN2R/ZQnY5yXEuZ4lwEChrTJgSFx
+BoDdtnzHlt9CcSVzGzUquGSgbt52UMQkl0lyXBzKff8+aehbVoXvklzI8RWSqTyn65mJw6l4oV7L
+xzHBUq86bjyFm3EzbsZnxmvhKQAaoBqoVYKKJUJ/nsNYE2u9fhVg8A1m+Rp9hBEblCYSW5nBAgP2
+1ftrHVgtsvhAR/5EPjtou3iblF7NHTmJ2y0HkSvW5UsWAEOs8XJmwmS82Byf4sW5cMaMaFUbjo6e
+I3H0YNhCkIin4L2RwstkTsMaWMzE4hlM5gUbFnarVafmAho5G4xdHYoAvOiKjU92BI/sytWohSgh
+w+95BTjUOgBgronX9OKpXODrWYnfIaZz1yhQs86NuoKzajpamshWjT/UnYRmYYPKyJeLBLnHzyZt
+3CWa0KwzqDVZz4rJXi1oYPuueFjVdoloKDHwJ/MnWFyKFVtrxTgjf0GHiuFVWMPgPT8N59ijyIxf
+G2gReVg0YnQI0e18VX7XmO1g6xYVnGMTs2uxxmnTwg47P4dtH/pgJUpD0ZtYB07l59ZIw8N1kqMO
+DNRMzJp1gXyLMG7ec+IYmM/lnl1VIaBWZs83XmpvmoRS6wYQM5mpdxW8mChbo4H1oXguqZ3BYKPU
+ns9mND1DOCVa1pqjIvRcO3XQ3pLJ7Y2bsBwh3n0xEZzCo6cxmqTK+1CvUWpy/1f5Ekks1yjjCvUF
+E5uvOF6LQ0EzTfjDDTQbW3DYcqf6XbQcvmBfu484IYZAyzBN5MXaGBAcNDvDkF1ovr4Jg/x6UHOM
+7lA/8ekeWvfkgXVIT+VoPRwzSVa3dJipLKRRdHHAikLxaIrDiVBbqUque39niPXmQwCApxmwqVKk
+2R1sDrgRnAoVa/JxviLhWMKs5CGaxgjTE3FhjSMXxRrlzkm7FpwNsbvFF9bx8EYoh9dZL4bHA+Bk
+X8d8LNgDa0Me5R+e5wj5sl3oGrQVH4lmYNAWpuntdQeLWF7eBuXpLc+FP5J1qXwdJrUdnYGGIQFV
+hWEhYxiTdOVFMfUIBdE4mlEDhGbvzSwcsutSTYe4PZKNeWiykzHowYjkuu3iAC/25SVV76ZoUc7e
+1wxsDKixSZKSdb/ALjELKSJMFpLAM+0mPIKIGmYTmpL1qomnqBHDH1Jx7PIWBl+W6wbh5GW4cnGd
+Yi2RxKVDoVmVKswI7Z4scziFHJZ9y0WzIWvkspzVMxvQCY+fTmpsUG9z78EAu1uko4tc6E0CpMh8
+PXQtfDIhd+XTEtcd6nW2E5QZBYzWNQwlEsJtUuB/0g6RxLKeB9/+TaQmwzVziOpMDs6n7VNcVisi
+0lcbN+HDzbgZN+Mz47XwFOpKQ5k6COwaTTYt2R0fLUJ4czdFi0jB0mrBWshJa9H1rbe3UZEroJ6d
+IiODryorOIZ4Ck09gs+f11hOxHYbasLOutqBIoFnskjhULrr2eU11HN26G3K3+lZF7on1sVvNLCo
+JKmjTYHMkLn5sACL0NaEqEGt/dITyDvr0Kc06R5QsC4eUTSkjEpk9DQasykqWuZblg1jX5JZxYs5
+tuiiXhOqW2oVzslQFtbqJRrP0jQ0bQl/YoxgE46na2LNGo6CnrLkFek4I76jFWXAtlhdZxpiwXu5
+OpZk17FrY4udionVgUsqseamg/unMqdL00PzWkqLu0o+a3lL3Keoy5Owgfh9StGXNopUrJ+xDFC0
+5eeGvcJb6HCpomzU67Dasm5a2YRtsuS8tgktkuendH42dGAQ/q6HZ6hNedbDRhPFQPaFW42RKjYr
+sWHsRRggXJGszOZIiOjc3Wyi8VzWbhW29PwY5xHh6osZYFDSzWvAUPLZN10NRSF71SHPQTFYg3ct
+Hsrx4ymy5F0AwP72XfQ2ZT1bo3vwmfzub4nH124u8YLoVk97iCWEeLdRnOOcGhhXY4Vw/mfE5vzP
+cmh6BacRIy9KhC32CcQmXCo5aUYTOmG3VSNDK6cMfI8vXahjTkl50ypRBCSm6DmoUnmgTd9CQACJ
+RjFX3cwxYPx9Xs5hk7knUZc4uZLw4enlAfq78vJWLisH6xoahP5W0RImcwaOV8HUZPP73S7qLhl8
+HXkohapgGLLpamuKNvMSeV2jsSGhUko8Rpot4bniqvqZC70vczO0AXZcvvXPF5ja7MAcyctxWHVQ
+XEs7da6X0FZS9ZpCQsjsjteCRvHTpCFz71steGxfLo0QXZPu85UOI5aXKV0EsAjN3WrKGs6jOTTG
+sk0jQZsHrx0rzBxm9f0E69cy/2FH3P3tzQ08Ybfg5aMxzk05TO9pU+S1PLM0tJFds8ZO0hNneAtO
+R16wvOXCYIt34lswmMPJNA0W1aJKS+7fqg1EIas5VgbLlzl7pg2DeBDTcLEgRF7x0AzSMwRdeU3O
+5wFMXdZoEZZY68l365XkCy4DG3NWu4q0RECQ3LC7i90H+wCA9jRG3WELP0O0eRQhYr/D750u0WjI
+daezEJ1dubblmHDZ65M3JOw0VYzrWPJdenGMjHvZaNxCxMqGdX2Ga4r5vOq4CR9uxs24GZ8Zr4Wn
+AKUDVgN9sw2TtGRGfIqAyDU/q6E15TTPr2skJJBoMCNfmjq0E1ozq4FGW45oe+hBuxKXeGkewD6n
+u96V076e2cgoPNJceIhM8TaC8wjnjyUxVhVHmCZiYe/26H52HHgNUmYN1qGOiRycXUD3xT1GsQ7t
+msAIahZYpotSE0tUvchQ6qxEOLdgjORRtNmjX2YfImHCrbPmoVrQu7EBxax3sd1BRWGU9FAssGF/
+BINQ3VYJXJBT0YRCry0/twwT5jpVmRmuubkOmw1oS8+HysRyZWaN6FLWZWK6UAzNQqL4QjVBSF1J
+q67QaTLp6LfgULosmYawiOXwdslZ0d3GWl9c+PmnDaSFeGZ5aWNEFGPh1Kh0CXkCKol3kxCKVtBc
+KMSsguBijIiVBnPsoWyJJ2euks49Ew0Sq0Tta+QX4pGZt2/DWpD3YmDDp5e5dIkLmenQyRcRaxZ8
+eh6D7Ra8Q+JkbHmmbqtCHDPkVSX6NoVqagcq4npv9DFk4jlggjZ9tMB3U0lEqvUIB+zwrNxr+Pxs
+L1ii6HDvHMtziusSzpl4AZf1HBYb9tZubaBN7yZKXQxXrbKvOF6LQ0G3HLR3H8DzXBRjCo3qPlJT
+NoqvdZEU5L6rLeSE9M5OiM8vFzghWWs2DbDbFpjzlrUNncQiSeEh5MYrLgUo5A7WkYR0v4sQWSAP
+qfQqBASmuGELVo/t3HQX60ug5uGVzpcAhUQLTQFULCqKk5dhQ0mIb60BsSOxXlKXmBzKfBqjNhom
+y2wE94SVg4ziorPrMVp9cu5lPnLqXGplCxdnsi6fhjLHx5c1jsjC5PsKNbkUo7LE5aVsjsF2jCY7
++FzF/hE3R1IyL4HqJW9huoxxQq3MoxczRMxzrDGObmgKhSHfcW9jCNOSLdXtDpDUdK9nZxhQ5NTp
+kq9y4zbKhP0TzfdQWnIQRJenyEt2h9YlCuo/lnO2eGsJkoz5gqqJ8Io8j48/ROiQhKRowh2I291x
+5HDQlYM4l/W8CkrkE5Z1G8fo0l0v5wrFhGEDq12TLMaYbelbuoutfQmP2tsPoSz2T7BfJbgsseBz
+yGAiYH4hTS5RkpjWDlxkPLSzQvIIP/Cf4OOPvgUAuAyX0Nj/c7loIfTk/kc/vQWNpXi3Lfd2FQMz
+GpZu7aBYk3vNjHvQprKei/JdhO3PFxDchA8342bcjM+M18JTQFmjntfISwWT7lJmGQA75DItQ7aU
+k08ZBdKFeBNRJFb16jLHmO5uOJ9gyrpy9c4U3TfFrYaTIW2ydn0qJ3hhZqh0+Y4y6yHlEem4Qzht
+6bhDHAAxlaInrGe/WMKgBPxI30FNS5lXNTISYdTjKdwVPyBh11pXR3FNKnfDQUxKt/jiGPNL+b6K
+QjXBLENCGjdvkSAnjFtZGRYzsWLPlwFURyzoH37/hPd/hjktfjMHCj7iqqiQJuIhXUctqJzEIaRd
+c9IJWmTMDpIucuYyzY6DhNbTrW145EdsJmL59W6FrkXreq2QN+XnpK5hsbMzjSxUvri5t9pfkfsY
+znE8ExpyBAY8MkYXlQeDsnFuUWNKIR6jwQ5Pqw2L8zG1HMlc1uLJdYxyRWk3yqAdMhnXkGejv+dD
+a4jHkwVzBJpY4PToGtUew7tUITbl8+fn7FRUOY7Z+WqnCor6kaYVoCAEuySmoWk30CIfQ5DlMKlt
+OUtCbBBjbzZDKE28jSokt2O2hrU+FcrV8iVNfjWy0STr9nKioeGRdt8Tj8hb5ji5IIAqt/B1V5LV
+hrPE01yaBssXfbjG58MpvB6HghJKbN3WYTArHF7naPSpk3dpoG7KQ15cFS9JNiaKsVenwICVg1Z3
+hDOCgpbLGbxHssLujgPFl1DnRkkTwCU+P7A0mKTWrlsV3mgLUuTRZY3GaMXJTzab4AieTSJOlUNj
+e3K/pSMN5bvrdokQcvhokN+VyxA6yUrD2TGiRP796dkh2uzjsEkCO9zQoGoB9LQ6GlSHPOqVgaov
+D/nOtYGzDZl/n0p9F2EAg8xF67qJMwrMllqNS7qab7e72KKuQ0btS23pIzflgFBJjipnz0G/h4fU
+KDypDeiavCx7uxIDF1EGrVj1TGTwWXrTqxqdbbnepZHCOZWW6+7+A/l338ZkLnNrdyocTJgHQYir
+UvIIkyyHQd2KgiFFZSyhEegVBAEOpnLfs3CK/gMiVXsGTl+Q+v5Y9kqRhrBZwm73KsxJzNrQTVin
+cu1+30NEHdKUHI+p0rDTlb21tGMosnYlLyLoGefE/pOttoNZIGFes5lD77JjMm9C5wFfxQZCHiar
+g3drtIbogYRS75xc4ctN2Vulr+EbD2TdrI0BOvoqZJVnHtclxoagG0NT4WpEsJ+7hpAMUu1hgTOq
+Wr3q+KJS9P+hUupDpdQHSqlfVUo5SqlbSqlvK6WeKKV+nZoQN+Nm3Iy/IOOLqE5vAfj3AbxZ13Ws
+lPp7AP4NAP8igP+irutfU0r9NwD+JoD/+v/rWrXSkJoemroFGHKGWItLTMla1dQKaCvAUXyBhIAc
+n9RXeteHya6+aeIhp9agXmaoCXQZZ12YS4YbVFvSjzIk98W6uKVC5dIlntbQPPZdbI6wyKQSoRNM
+dWmb2FuwB2BLobwiN6B+ArVJDEVcoVyybqyt6uc6ipW0Zg0oiBupw8VMyZzWS7FmM9WCztP+UDfQ
+JQV8YWaYnYklff/9DSyWYtH+4FjmVuc12LSHfitHQSGTqqpgYEVaM4I2kD78tCQ2ofBQz+g1IURM
+b6osA1TUoNS7GkxaWDChWE4MLAhdnmslHl/IPO9UCyQkbdEmKfQhqc89CeHmtYcXy+/LeppT3GoR
+hLXwMCStOywgmDDp1iDtmr8DkyCe9GqCOJDfdwc1tt8Q76Wr+/DJcuxpYknzcIIuNUYjbQgtlyTh
+ZHGK8FSSrbrTh16xp8OUeSbhAgGBc+aggxMmwpGWiLjOFpXEM0uD25RwVeVXcLkuqV5gEshzqttN
+GExc1rwPq3yGJRXH9GyOp0fyHN5oNvHRHWqr6iEumBTX2XF5HHyK8dE518VCvyv3XMYFopDs0HaC
+jRbfnVccXzTRaABwlVIGRArgDMAvQHQlAZGi/1e/4HfcjJtxM/4MxxfRkjxRSv1nAF4AiAH8nwDe
+ATCrV2KAIkK79U/6e6XULwP4ZQBY39yE5rgofR2LS7GCueUiYnNUq24hCCVxojVNZFOegjMmetJT
+NJgPUBMDQ02QX7p+CJNWx0lKnNCqPLp6AgAYNLawFsjJ7q43kLBU5Pk91FSYblsdHLMW3GDPP7I5
+6khiPQRXqMn1X8CHETG5iBQaPYT5NWv+YYlwKJamiw3Unljgh/sPMOvJnJq8p1oPMSaz1BI6NFqd
+IG3ifUqvPX8Y4NEPviPrUq9ETF4KQuO9Wy3gkiIzcICAOZP0FJVFxe5YrpV3alxcSHz+3sUESzJI
+qRIwyGzsTUs06UHV12KhktLCbMFysEqgMyF41e0gOpdYPHYWuHuPDU8dsdbTdxMcn8m6TedXeGvw
+Jfl37xgN5j5sFIiIai0u6Gl0Eqg2qd0GBdIjmc8izFCcyDq7e210uF6NliQ249YRKvJCuGGO62Px
+SA/GM+yTQq20GkhSiecvauHOiPMUMzZa1XMNNrU+s1whieX5FkRuGqqGTXYvrx6iYuR8dTGH3pM1
+qhITDsurh5cfAQDOTs/x/rns7/51hueQvVAHTWz8o7fkXj86QO8taWKbMtf03rfniIlurXo9NNri
+ERwFJSZj8Uxm3hJf3pe1fdXxRcKHLoBfAnALwAzAbwD4q6/69z8sRf/gwZfr6KqEVygYmbhGqdOF
+xo1QDDQYP2DHYH+ATi6LdsKsav00hrEmtVuvXWGhxO12rgwUzGRnSYBwIr8PnsvLpt2foVVKRr7t
+3EdzSf3Efg82z7W5SqFfyMYLCTW+HTdR5gwJnpfQOgST6DHaTBLV10vodFdNag2OmxOUrD6UTQ9D
+4h7iSmE7FGxF1RFXNpmHIC4J3WUMa4+6g/MS67Gcsz+4SvBLZ7Khv18QngvgDnOS/9pVjf+UWX3P
+qFDpUjF5cTWCyQNrryXJqcyL0abG4Wap8AxyQFTFGlqu3Pf6KEaLn++8pKszcc2ekdn0DBRFQpVG
+0Khktda+jX5HWt/Lqfzd4uoIh0/lOQ7TDXz5TaHwf3T0DG1fNn873cR1LFWZUJcQTkX3oFE1yS+7
+2OrJsxxnOebP+NnOBspKXuSgw0rU0xOgeQ8AEMUZLsYSVhx9MkHrbeklUOECqkE28ancc66XcENi
+BTZaOHosCczJ+AglmFRliBqnGvy+PPM8CeEwvpiZ12hfEL4/aGFMSP75sazPxXiM7piHcFajHwrA
+67w4wOTs/wIAfPOtn8b4IcO078phsjj7BBP2vOztbcEuJYwNi2NMKULbaQL+HvHWrzi+SPjwiwCe
+13V9Vdd1DuB/AvBNAB2GEwCwDeDkC3zHzbgZN+PPeHyRkuQLAD+llPIg4cNfAfBdAL8L4K8B+DW8
+ohS9MipYwwRZ5cLqi7UKD2O0tiVpFx5P0fmJPylVujvi+g7HcuI639wBmAAz6hwGm1pau2vIKEKS
+BCXOTXHd9dssJ1oGGgaFYcIE7R0JJZI6wpJIyIOzEzh3xEpfUNn5g+gCI5JyqraBnPJoLQ9IqP/o
+7fuoVkIkdOs6Fza0NTbwXB/ieCb3FF1corviYYhJpmKZABNOo+EaDOpBpEmNy0hYqcP8HL8ey/3V
+lJKrc+CaZ/L/MO2hKkhdpgHvkPz0Y+Nj/EtUEXFJ3Lq1N0LeY0ItMtGbkK26vAZZzBD012AZck/6
+lqzbVZQiYcg0HS/Q7YiVby1jLFZiNypB6MjaLvgMfuvkD3H4Qu7ja18bINyQdcnsGCbLq1Wnj/yx
+fIbKbJgfXsBll6hm1ejsyrPcnM3g9Bh2rLtoKbI5M7nqbH8di6bcSPThBOFH7wEA9jfXsUVNz1p5
+0Obi5mtkYj5+/hxPVh7k0adwiEnR8xoam8aYZ8SVFsBPxCMYeQ4uStkvi7DEmKQn1sSGT0GgvTdl
+rdLvTPGCzW+N+AI5DXs1j6GT7GXaP0WfJdXvsovyW0dHL3U91h/+LC62ZG3jjy5wfHwgz/JOG2eF
+rOerji+SU/i2Uup/BPA9CAXg9yHhwP8O4NeUUv8Jf/ff/shrlSKo2hwuYXCzjpo5UpC2vduA4clK
+OfMx6D1ijfFt6fsoGRvHqoEGGZR0p4WaPIBzc4l18iqSDR6hleGK4cpGN0K9+odFAIOAnN2WQs4d
+2WLsmJvGS6y+Wz9E3hI3OExasD1CqeM2akrUg1BVe81ERlWgqjpDSYGb0hkjKsRltIjHaBQ51Abb
+qI0+xjNxW2eagTARt3M6rnDMSooii/DbJnBnxTT9z3v4z39NXlitKJAs5drVNMIHobxMDKERpg5s
+Jdf1bQPqNlmn309eZvgfxUuAvQ3uhfyhZYTQKaaiejouCNFN/QCaKa703bZCHEgO4ukRY/U4wv1t
+mfNOdwujpoRYF8pC7bD/IM6gcaNPQ7nWYBihxZ4SzThCQKHf3e0OlqzfB88OsSSXoh+w38GMELP3
+5fz6KVxT9kJjYMPqshdBq7Ew5OBcMK9RQ8Fjb8d1GIMFGnhWDpcwbq1JbdMwwaIla5U22zDIlZlU
+S1wQlGfPLFhvMs+l5O/3H/gwS1bBNhtoOnJYFMUFmqSB7/QtHFIE6fhcYN55mmPUkr+L61McXMla
+BBcXcCwC/OYFsoXsnVcdX1SK/m8D+Nv/2K+fAfjJL3Ldm3Ezbsaf31Ar3b4/z7G5uVv/27/8H6Hx
+tRY6HuXTGj4+KSXrv6ffwZuEydp5C+8+E5GUj5j0KYJ3EB+Ku9u908DahqARe64Lj1DTT7NzFI64
+nW+xC29j+8fRXxe3tqFZqCxyJqYFDgiV9q0GQlusXD+Q0/4Hv/8B2tuk6Hr8ET4lu+6nJx/CXsh3
+D9wYp3NJpzw7Ehd4OgWa9Fb8bhNGJq5oUlvIWHUIa7EupSoBKmZv6hYGO2IptzaG2HtDkn3rPQtW
+U35/QiKQLz28D3ddLFA/9XHi/5zcX1fDOanbjoMKT2e/BwBwI0EYmsH3MD2ROUyOv4WTA7F+PSxe
+1tNHrglSK6AiOc14/gIv6Br7noa1ITUN7v44vHOx/ufTGtckrQljNhR1TLTvvQ0AuPWwi4eeJCLt
+7a8g/LJ8xs8MaEz8fYekLzuOB5eYjosr4L3/+/dljb9fwmkK7iF/4mKwReGfWpKI3e4E1iXDNecS
+TV322e3tCj7ndJRfYsbChm2JR1PMXJzMJCm5OfwSXEoLDhMPe1t/WdboF6Uq1Te34NGb/J/Hn2Cj
+lnt6q/4IRSXh1sH5J0gz2Ye3unJPXtGBTotvGxriCRvsqgQXpeytRpwjXcp+MggfP7s6wfOnsq4X
+F+eIOfcgT5AQ95DaNe7uSij13//GP3ynruuv4UeM1wLmrFDAqKd4+v2HuMfsu/+VDrZJX93DOaJN
+ebh5PENzSkaiQ3mJl+MevC7BP/MY5H6F3XKQDWVxGi8SnH8gh8n7Q/Yc/FSMZvXjAIDU2IPN1ml4
+BvqX8h2R68EgOUnQkQs7kxc4GMtDbEUGCjIWpc8LFInEb/n2XXinPMiIwnIihTsDuW7tuLBMtirD
+BIsWWBRs/w0qBIQiD0wHek0h2ThB9ceyERZ7PgxTSll3bXGXl49LeCQzLdcKWJGs59PlCE5IFao2
+UH0oh2EZSMb+IN/A/NkfAQCSg2MsxxTNdUo019hXUupYJux9cOUA+aluB02KzGgnGXLO86oMYLIE
+Ol24qHjI2lQ/qooay0QO0FwZmHxFrtG9SvHx+3IIdTs5brG1Ham80H5R448SCccevxfi+n+VtU+e
+HCCG3Ku9H8O+YkmZupS9eBf5hoSVjbEFa87+Cc+H2iFxzLGGJvtKLpgnqbwJ3iAVCXYAACAASURB
+VGCVYfE0QTqW+V93DXzptqzBi0M5WPveArgtc9ueNdG5PgAAfOA9wPgdObCaKkY5Z+Xqjvx98+0B
+uuzKNWIHxkLWytjqwn/G8PD4Amdsqe5uyz1NEwNr7K5dpHM4HTlA8+sr9EgvAHOKnHT8rzpuuiRv
+xs24GZ8Zr4WnUGYh5offRr3Vxv2hgDUenWb4KtWMWz0NMfn1lvkSmIklvO3IyWntD6FtspMtWKCh
+kxuvbWEaEB68WOJTJlx0/v9ZpqM9ETfzKz/7N9DsibkOywI9AovaZgGNEtNPFjzhz4/RbYoL2Klb
+8K/F3ftK7uGaVMpnz59hSlxEyfncqwzoENd+YHbQBiHDboWBt6qLSxigJfWfUMRbJiKKl9SYwaZs
++xocTJj5ylzxStreMTb3pB5/HnfBr8O+XUHRRQ2OQnxzKH/34lz+7snJFa4/EGs2i+bw2BgEwweb
+81AUCiMyAxukwHdLDzsDoR7PZzG+R7h1EcbwCfO2BxqsBiXsU7knVcewTKmlV8UaciYiHznnmHUk
+PPq5fRe5knn+1aZ4DItK5gEAJ59MUQe/BQBIo+fojwS/sdlZxy1SJb/9QCpVA28NpSvffWkAFkEg
+o0YTNmHzecfFFYU4u6l4XrPpFEEhe6TbP8WtpoQEzxIdwVg4ENw7IqF3Z2eEZ1N5pd6eW5im4pmE
+boEXxE3sFxfo70no8uV9eU7WsI0G8Qa65aEmCVCmByhZgcudPuwJu3VNSWb2ihANepvaXokrwvjN
+eYKQzYJTZxejz2n7X4tDIStsHE3u4Bf/ko+pL5tudHmM/CvyAPq+Bt2kuk+WY3BXNo29S3adHl7q
+No6v5uiSZhtdoJPRXde+Ds+VUs4n5+y4nE/xLZYF70dzpNQX1GMXeVdeoA5qxE3ZhNsEAj3t7WOz
+J5tUL3XcHsvv/9jqASRR6VoOPLIb/Ywmrnr1ZgM7dGetdg863cSq14VH3cyyLQ9eyxR8uuIXMWCn
+co1PszG6Q3nTZ2aNnNn3EwJl9m+3EA1kbp20xGOSh+61TJQEe93yL3FAjsn9LZl7VlVoBQSixhuw
+iNLcazdRgOxOQYmMepvRkoKw202s1dysm1d4UMmheGUEmJqySS2tREn6cYe/m0Q76LLakeMAxomA
+t56Y2/jmCgxmK2xzP088+UFFFUJqgTaePUHYlhdr516OtbckHPnJjSGGe1LN2eIL3xppqNnubby5
+A5vl2aypgSBMxK0hRuwaLUOyehkepgsJXe9uFUh8uf+d1MbpQnIi/8qu7JvQNGBlsrc+HrSx70o4
+sx6c45s7JBu238ab7GbUd8m7mZiIeX9+XKPqMXSbW0g7cjiZ+RgdEr2al7L20chE1pHndLty0VrK
+uj3ONzGzpBTtRwvUzp8deOlm3Iyb8f/D8Vp4CsrMoW2cobbeQHMp7qxy1tCi+IpXKWDV9KXaMOj6
+UnYSmqFg6mTn9ZbQCCBxbANmTyxJ1Q3R1iSp2FgTC3X87Bnei8Wy/dHiHF8fS314s9FDRl4DQwcc
+Upo5rpy4o8ECLUJxL84nOC9kcufOBRqEq1bTJTZHzLTfFqv11Z0hDIseSKeJ+VLcQKtOYEHCkaYv
+liH0QpBNHGtBDiOQOZjlABkJZcqywC61Kad0je3EghcxDY278LlWXsdEwF4DU/XgOuy3d8TltvwA
+3xyJwM3luok3QlncWNfhEq78aB5Bm4jLr5rkDahqTFklKXo60pgUdMEEU/Yt2JmBTVus+CaFeOpu
+DbWU7zh7uoR3R55Z/MBGZ13Wvg8FIojxB1wLJ67xwW8KND04+xD2hlj8n7j9C/jqT4k3dafZwJIJ
+20YlLny78mF0mGBe1CgbcuGB6WBgiYW9bkfwMvl5wi7D9drEwZZk/VVmokMvVNkDFD8me87x5VpV
+nqIoyMQcv0DzVJ7v0fVjOFT7Gt110WyQw6IgzkalSEN6t5UGcynPzHBs1OSRyCca5uxHyekVTqsa
+FXll5vBREagXts6R0ns9GVdor90kGm/GzbgZX2C8Fp4CCgP1dQeLkxRbGxJz7m+34DMmM7QWVLiy
+fm207ZXFk5NfLReoGIfadQzNE6ukhQFAJKSdztDsimXyybvvjXq4fOdDAMDx776Lr3TYzej/DOqC
+ffWuB510WytSUn1moPLFInTMDE0qFGvvJyCIDW8YGjYHYh0ebohFaHZcuNRwrK0UnZBUcVYKDXJ/
+mUstsYUO3ZHvs32FvE9k21GGMWvXblIiHDIuX8p8lmEOg7Rx/WaAnB15ml2iRdLV2m2iYpk05Pft
+JTa0vng0t5tjLMlv0AwXWDTFe7mdn2OppDu0chkj6x10fXke0bSNiihFc1Zi/pQsz6mH4e0VLFxy
+GaMsQpzL85jFSyQX4grMD32c35N5fmNNYchE428VFKF5t0TxHWo5XO1iwxJKt3vfvIU7zKW0shIl
+S8Omy1pvHsLOZI0aeYHSYjcjKtRkTnKzHBGRjl0SzZ6vJei8kOvmpguXTFZd18KzM1mviB21WhWj
+r4hcnL/AybHE9dazBJ078tmRFSEjq7SXyhwNZwMhOzFLuNCocWI3W0BOacGhQutQ9nJIVu5mlGK2
+4rVYnL5E/Y7W+kim8kxOFykWY7pZrzhei0PBMHKMBmOMj2042+wBSJ9APyeXna9g9cVVy+czWJW8
+CBXFPjW3gWImbqJd1QhTuuWuhpzuozatkTEj60NeeP/g+KVo7O/kE9x6LiHB+l6Gts4W4byE2ZRl
+Ssl92LFCdOlkzTyFKWGnXpYg4PX6X9nA3ohw41tk8u3cxkuOFaOEcgkwSV0oCqNki9XcE5QahUur
+Fmpe19NKGGzJ1a0CFTk/fG6e2TzG2o4ciolzgaHPVt8kh88NH0VTVEqy/SFd3O6tFAUrNT3LRYuU
+6mbZxoqjwzK3MU2Z4WY7dVVW0FYCulaF0UIOkMdJhXAk859VBTRqIt7akAN0ee7iqSvf3Rr0EXjy
+LO+9WUKRpNCvFVYLNkjlZfw/Jhcw7/+xXPfFt7F9R75vmh7Cm8phXzgWNpWEKVeBrIvTayEijZ+X
+1ziZs5/BAFDJPaGKURFQZjEp6y109NmMXmUxvPUN3tME97bZ5XgoB8juyMM8lkpF+v67OD36FADw
+1R+7D5fMz63iFuqQrebUIC2rKxikdjsYz7F/l8Q3bgbPpL5nocHZZW8KSW8QAOFc5t43FBQFi8sL
+HZ9G8vzazRHm17I/X3XchA8342bcjM+M18JTUCVgz2tsrNXQPCpJqwVCcgS4WgP1ipB2lmJOoguH
+v8yrHCXRj2W7hhWxQcUCqhUZSBaiweRZLRUh2AsbY3obnXmA4EIam46+9wnUPycfGjkPACYaTSZy
+jLt78NmABHsfbvRtAMDFIoBFOOX2WhfDu5K4UwxFVF3IzQJQgYe6QTouPUBhMCQKZI5FUULTWCLU
+Aih2gdmWhj7RaoFWIWUYoFlitczBABnRenoye1necswCYHONXp/gmoSmFpOWtmHDBiXgSw26zgVX
+IssHAPrQRzMiSzIxFjUqhKG4vh4iRE35fb/xBjaX7wMA0jRFgx2jccLssBG/JDYdX8wwpL7ioDTx
+ZXZ8VhrAqAHffiLPd/YP3kf4mHogYYyjQ/nubXOCP6QFfcMysBzJPDuReJuhGaONFSlrgo7BdXMq
+ZNwDpZnACWWNKNGJ+miChG2iTbeNspTP2rWNZip7tb0uv1uoCtcHggr94NEjNFdCPVULw0zW5SqY
+gb1fsEnukpzOEDIh3ECBOl5RaWvITZYhdRNqIPPXDqkBYvioUjbgOToUkb61bsNtkcvhcowi/wvI
+5lyjQlxHyGJAY9tzWjlwLHYUziLkiWziSjORrcRCYjL7JFPUBPSoZQMuIbHJ7Bo1X+RlUMO8Ztsv
+239xV4N/IK790dExlv+L5Bfyf+cWOtdStVj3apQr6llDNvFGfwgT4kZePv8BrhgbxnGFt7clrvOr
+DoYUUNW5scsoR16s3O8a2Ywvm64hJ1ZdkS25UAoGRV/SqkJNHUzUFqyKh1OuQ3lsDY8kXm5PIuC+
+rEtmrEFnzGnqQDZjx2FSYpLxZWJeRpsv0WRbd5EVsClqkzcC2DbfkEKDZlCsVNH91irYfDmyvIOe
+ywP0gYegFOzB8vgJFN1jnWK8odvGbcjC5r0EDis15oaFHte7qoTOCwC+/UzyKPn3nqDEbwMA7LdH
+2LsjwKkt24ZWiut+nltonJFKn9yIyCMkbe6LVEOPWIe0ykHoBEpde8nDWfE5WbaDkgd9QysQmSvQ
+lovAlLVvsVowOT/H757J/g3NEPcNgT/b4xr+19mqnrRhMD0WEYRUjK4QXcgBEY8VagoB160OigvS
+A1wXqLc5N4ZXmV0guiRArlVi0JID5MSJ4CY8WLQI8ap094rjJny4GTfjZnxmvB6eQq1QpibKNIDD
+ZBdcB7pGlF+j9ycIs6qDWqO0HEON4CSAmVCzoDVFQu0I7WoC2LSUVYl4SFn2QKzAonkX61tS8946
+thEnYlU+/tYBvvlArE6t7UFj+KCYcIoPSlgPxVK6GOB2QzLLnzRP0KXqdMfIUaQSuhSaYAHKaYXa
+EIun9e6gLIjSsxUUXXCdVjnTEsR0Ucr5CTKXytVxiJRCJla1RMAQKqaeRD7zoabigTjbE1xfUcG5
+04Sm5L5V3EWDiscVk6HluEAarXzmGaa0/lqWwmS4Uqg/4X/UKUmvLCCn7kVQ10giPr8SSKgDWek6
+LPF4QVFmWO0MGjU3mlcu7CXDqhmwosIsPaBFEhj99wmJ9j9Fl3Jz3xg7+ObXBEPQW/sJlKlUqxpB
+A4tEPl/avM+kQpgxQatXuGaitJkCKUNBYzFFbklFqCbXRa18tMkVGqUKVsEwrW9heSLP8qCQZHZu
+jWETp9Irgd498RotL0B0SEnCxlPsrguzQBDKHP0nNtIeJeX7DRQl99nzDHlbPIG6uYb8Qubkkmyl
+WuYot1lFWUbIKZG3EdW4pqp4I9WQxp+vE/q1OBSAElBzzFMHaUwGmtCFQV2+um7AoljnLD5HNpE4
+alpKVrUMCkSZ/K6teUiWsmg9K0KHPQotu0KkyYulV7J5POMKaYvdcrfWMaKkutrsw2EeoCwzaKT4
+LunOtzohmo03AQDZ5hm8Lbnuz+9qsCgkOnx4H4YhDDt6k2pDug7LII+tVaCxYt7WPCiNfRAteSPc
+aIyMHYllPsCyJEmtZwLE58PvomR1wWen5hQTkGMG9RVQbst/lDMbbpcir60YOz6FWzN5S/2hj/Mz
+0qhbVxgzq93VPYCdlo0WoDSGR5xPFRsoeEAkyxmuSWQyDUOc072exxHWl7JhG0Ni8s/nOGQPy9Lt
+vHTbH7RrXJCj3iwUPmXVwe1I6TE4+kdobEmo8XQ2wu4P5BB+ZoWwGX8P1pvwohXHJis4uoWWQ2Oh
+KjRY2bEH/suDrtILaLEcoitIsWubsEqGmIslcl2ez/nRGJ5PuDzJY/PsE1xRp1SrQlx8IL0R4eYe
+nidCsPvA2kKg/6rcX8gczhAIn8vPM6uExxxO7GY4YW9LkV3gzlDm0Srl4LqoLlFeyDuQ2CZu+bJW
+VZCi4cv78uD+XfzRux/g84yb8OFm3Iyb8ZnxWngKWq3gVBq2hjasdbGYVlEiWKmaJAFqTnXxaIyP
+xgIKyeZiBSe2+bKhyNw0sd0gV4A/wEoZJW410SI9WNonO/OnS4xJILIzdRDcFuuZvLBwrOTadzQN
+itfQmLTMNwYvpeLczdvo/4Ykvv5gobDzhsz/NJ7BmotVNTrsfLRGsFty8rtxBZ24Ad3UodiMhYTh
+QGqhJqw12rSRPqdeJQokhMRWnQjGORus1sVCx14Lis1HlRnBoJdjeAZ0XyxN4+AKj+hN6SuKNlvH
+1ZF4CpfaHMmC4KS2jz6JQ1q5DTZaQmcSWCsyREwYxkgRUfZ8MsuRsdZvmBYUYdh6JRdI6jn0ijDo
+fIk+wUIblY4uTfdC1diay3NNH8k8bb9Ck4Itg3KB7x/JXmiZNtbZ+GOWW0gGcn99TeKWsgxwdCJk
+KWfLAvf78lmtvYlmIFY10TU0MrlvfY8UehchWk35ecdSyNjtuAwzXNMbKYJHAIBQB9apbP1ekCOj
+OvpmdInT5zLP7y4/QoOU1yUJgG7fsaGNJCm5013Hp+SJfPztD/DuWH5Weo5terrbPe5jW0PPZCet
+b2L2qfz7fqNE1xNP7+ywRM/7fCJtr8WhoOtAxzMwbFswGSaESY7ltbit4UWCoHcAALhSGcJVbEzs
++NyYwtugKKnZgG4wy+xVCCt2Nr4zxfGBuHAGAePvTp9jcSwPqD1qwvwBpdr/9Qod6kFUOZCz+lhS
+S6DVXofyJSxZfvg7+M2AkuLPr/GcxC+LZxFGAwJn2rKx/cYzNHsCLNrd3kabOHtvuUBEEtCK8eLx
+d9/DfEbpdL1Cn1L0vZ6FmmSk1UUJsyeHyQVRa3prjPpNQR16va8CLeo7+DZqSpmHjQrnhcTD9lg2
+TNh/joRchUgdVNdyH/NoAp2ls3ruwSLarqCQrJ9qOLySZ3N2NMZsTg1Gs0DB3EevWb4EOHXJqzlZ
+TJGzJX0a57DZGl01NXQopruIavzeisDGeAoAWPf/MgZ3ZF1/eu0uxilDyPD0JQvV1raN2hAgWoPg
+pxfTU3xyxnleTrE4pgJUPsHDuxIKWqUJgzka7YrVEq9CwtBub93HMXMxTtfDZSLraTYYUh1+hCc8
+NDYdE3e9r8sa92f4uV+QatYymgPfE4Nz1ZK5P7zdQUii2SIwcMQOx+HYxR2ul4sebt2XOf/029I9
+HJynmHwsDFEn8RJNVpQWlo8Ow4fWZgxrvpJhebVxEz7cjJtxMz4zXg9PQRlomR0Yxga6JmnF3BKZ
+Jidqqts4ei6WS88M1JDfX/Pv1wsPQ7rftzYAn5wE3voIFa/nP32KJUExpy/EGt8ajBC/Jdc9PteR
+UpZ99EwBXyc0VN0GSmbac9KoX0aoBmK5zMDAV9bk5A43lvD5Wafdwf5dmceYNPLhIoYdyPdNXlzC
+36fuZLcJFUtSsnbEEqntXTwthfRk8fEUPcJdU1fHwiHj88yE0SCAK5DzPZk34OyKdVRr58BMuAAK
+2DCY1UY9RCMVT2fakrV8//0aFuHhnnGMBUV0NqoGKk2+wxnGsIiv9z1Zw9hQaLHz87KRYkS+c3vg
+ok/uiGBhoD2Uz0TMp/Z8Gz65HvLlHBol3pNFgZR4ENus8ZOcx/fzHwMA3Pm5Nhq2eB0baxZ2SM+O
+dA06KeVd1UTHlvuKSKUWaPvodBiaXc3RphcTRCZmR+KlDbaBhArTXsJkrtZCj6ziRupgu5LnE7U1
+pEfiFeUzseb3Nr+EVi4eQavzEQYtwbLU9jr2t2QfNloLqLeohcmMcHUaoiCOI+orXF1IMtq97+E+
+WZ59Zxc1ARzFpXhYXSyBXfb2THWk5CK5l1QIDfGaeoWGNd7rq44bT+Fm3Iyb8ZnxIz0FpdTfAfAv
+A7is6/ot/q4H4NcB7AM4APDX67qeKqUUgP8SojwdAfg367r+3o+chFZj0CjQdyvoPJWdVon1uViB
+i1mMKhQr9r3DY5yTLyAjkeWwbeJrQ4EUv6Hdg+2KdTQTGwl1GE7tx/gwplhVKKi6ICtRa7IE7+sG
+vkadw/jOAK5LLYA6g8uadkpIsLIi6MW+XPfRB/iUVGkbVYp3F/Lzi0+W+PhQYsNyn/mJJMLP3BaU
+X8coEU7F0tieA0WE4HImHsjBxRjf/wNJjE0xw8mRxIvr1104FklT+yOkLNmZnqzJXDNQkEW4iF1o
+LcaTeQWNJVDHz/HWvpjsD5/K33XdCN/59EDuv85hUvfC7QNtXSyNv+HBJraCHKmotQg5NRUz5Gg5
+ct2u3caDLbF4y20To5as10wXD+zaNvF4Jl5Td2ChyMSbmhglnhKd+kApaIT//rX/QPQQp0cFbmdS
+03/nLMDX2fB2nLvYOSb13raOgqhWoy9x+Cg9Q74he2ut3MJyueqSjAFP9pZKbBQ55Qe3mMBtOnAI
+bU41CwrUmnxygiAlFiKQfYO9Dbx5X/bNbvBjeHEif3fw8SN897dlzqNhhjVf6Nv6Ldl7VrcLkyXe
+2mliQO2MyCjQYm7kxdESB+/L8wkNQp9DBZtiQD97r4Etm8xSmg6X3aFvbvdw+FQas151vEr48N8B
++K8A/N0f+t3fAvAP67r+FaXU3+J//8cA/gUAb/B/34BI0H/jR32BUjpMqwnL9KGIG3CnKfJtUpw7
+Pvw7dJl6XVxM5WFccvZbzQbukX6rse3BJVV7ncTIPpCfh3qOB2zVzb8kCzaZXuHjx/JS7G0PMCPt
+WP6hhsOfl4Xfqmrk7IdOF7LpHMdGdUna951b+PGjd2Q+a8bLB5MZLXSZ4e6wH2B5q4sW8fBzFcEK
+5QVp1A0YrP9nS2IJEOHBvmzoRd6DZ8umGZrAcFfc0lwzEc7kMLmYM4k4aCGAMDzbSQt298uyxlaG
+mgeuPh4jyCVZuwXiDd6+C6Ok7mYcY6HLdTc8H40OD2rbgiVRB3JCxqOLHE2K+O77A9gJhXO6NjJL
+7rvXcVAUsnbhtRwqR5mBDZtchEaBJhW5prMKPFdQmsBXmeT9bYaHdzBFQiDXLk5xxArNZu4jGcm1
+7cpDQQbqJitH9nCAmlWb9g6QskpSFx5cguBSr8Qa9T8Vr4Wl/ZL6v2FpSHVJUq91dSyeyDXWLVnL
+jcE6bq0EitYDuAHJWfwmfPInLr0lRkP2WqQEqumAwVPWMmpYTEBvmD2MiIHp+gEaU1mYE0oKGJgh
+IcFL4LtoeRL+OlkJ15f98OFlBcv9ZyxFX9f17wGY/GO//iWIzDzwWbn5XwLwd2sZfwTRldz4XDO6
+GTfjZvy5jj9tonGtruuVQN05QC4xkZ0/+qHPraTo/19idj8sRT9oeihViWxZopyJRbF1Czov27rV
+RYcovtHPrmN8KW5p3RKL302W6FPwokw91LTouTmGztJZdtjGT3xJrHFvS/6uyAx8yxXdg2dlhvNU
+oM2z5rfw4vv7AIA3VISky2Qem2j2nQ6qPnvho++ip8mJf9XfesmSvPPlDXSIC7hmQ5QWL1ErNj7N
+Q+RLOfmjx3NoG2Jh25Qyr/23sLMrzT61XaLHWvky1lHNJPwp6kuYnri7z8jDMBufYbyUBF7LbaBm
+rdyJK8Qzub9lEmCWirUadSXUGpYTWA+kM/QyjKHHpDGzFHYIvVSNLoyAuAdaR29dw7yUuW30KviU
+W7PmNSISmYQnc8wyWed5Sdn2qEJvW757pi5hDsV2HJoKt6gpkaXAIZGHTxn7bPVdpOHHAADd2oS2
+kPub1ilusTS43m6h0ReraRL+PZtN0dwVt0MlPbQm4hGUaoJWlyGD6cLT5Zk55I2wLQMRa9K+GSJj
+92hnZxfqU0LIx8QNXF1AG0rJ2Vlr4M2vC7Fr73YDMRuzFmkGmw1Yq+Ti0EqQVxImVFGJnFgJ30vR
+plp3W9+A2TgAANzZlX1TTEdQtazP0AV6DfGm0nQCg+znLf8YDfvzlSS/cPWhrutaKfX5wNX4rBT9
+rdGgzlMfizxHNCM4vtuDRaKPKo9REWKspw0MdPnMCgbcafowuLnV1RQJyQ3jC+0lw0704SlUTzaW
+1yG33psd/KVQ3Ou7iwv8+nfkIY3fu0a487vyHfUtsEETEZV50tM5jA5BLp4JEHbrXQBdHlSNqQaH
+dO1tMjehsqBTrzJqNaFRB7KsC6iFwGOLTDZlZ13B7EouwpjNoHnyoptZAqzJw4+PF9AJhromEKY6
+XyJ/JG7raf0RNtuyqeaGBzvnC1l20SaMe9lg+DCxVoTz2DWCl8xLO2YNe1NwD05dQLF7MivFeQwS
+Dcm13GcVt9AiSMna8ZBRDMatTZyQ7j7lc9SaDXQI2NlNPMwZjhw/CXHAEKS3D9wlG/OxReZjt8Di
+gjmayzmcUFxpzzeR56yI2D7azHOs+gPr0kHXkj2iJeZLVqtMS6Hbsp51HEAfsepAyvXC16BHDHPi
+DEYtP9eWgZT+83LB7sWgiznZwzezDCpjBcC7g1jJ/dvRd5BXMv+Vi5837sCfSu4rzHLkPHjrIAV6
+cpBrvS30ePhWrNpkywuYTXlOfTdFSpIV+7CN+ZWEkHrqYrNPYNwrjj9t9eFiFRbw/1fULicAdn7o
+czdS9DfjZvwFG39aT+HvQ2TmfwWflZv/+wD+PaXUr0ESjPMfCjP+qUOhgFmPkYY6FjN2sgUl7C02
+ASVb0F3WaN0KviGntUH0o9V9A6pkRiqfYXIurq/jlZg/oY5jP0dVE1ZrSvhhegqbbTldn/UidP+A
+cN2+hf1Ncf3Mjo0GLfqzc0lKbhTPoUqxwJq5QOHI920OQ7g6eQBRATy5rUhO9lrP4FGCzlABSkJR
+qxmgfLHTivyTvmfCqsWimKMG6g5FVEINkSH3kQctxEuxlGu2WLh3tSUuz8RC1XGJtbukqVsogIIx
+SbSAwRp5dsl/92oUDAma8wYsksEM/XU4nFsdhohpuR1NwqdpdoSAuJG+WcBtiFdhIIdPFuhZPYOz
+ggQzqTfodJGxYrI0TVznshWn+RgfWfJ8f0JzsGLmfHsk/354FSOPJMn7zJrjF4iJnic2hmRGKbMC
+OqUB85Sw+eoc0VhcPrNVIYpX+g4dZJasYS/OEYzpfdKDQqjB5Loox4JicjQ4G8MlnVzEhGHkBSCp
+OBwrh07EprksUVIzxG70EYeyBmYia5jWCzhkD59rJhS9ivn8EgG9pvaiB8+QvZPb8vxL30UZyM9J
+5MEt5efCTKC3ZR+26hxHrPK86niVkuSvAvh5AAOl1DFEZfpXAPw9pdTfBHAI4K/z4/8AUo58AilJ
+/luvNg0FBQPNrgOH8EzDiJGQKMJSU+jUSjSWBQKCW9ozButbGRS1DxdlE6pmD4Ovwx1Qj3LehhGt
+YMfy78Z0H9ck0LjzqIu/4/whAKAz/BK235A4s2XqmLLzsVNK2DKuQzgz+SGZzgAAIABJREFUbjCv
+h95C4sFwegKdAqSJ6cA7k0NGIyDJcG3kfNnqS0DvUL6p20ZNkdYkks9apwuYfXEpK7MHHHCT6hOY
+C2LZGzUMknA0KdUO38W7hzK328Vz7K9gu+0auiY5Gk+b4mJJJzFZ8s/aqNlSrmneS6akXJUwpzLn
+wq6hs824Zj9E0+tjciHVoAApQo9dnucthJD1UosY5xSFzQnjrvMUxqU8j3mYQ+f3FU+BwSbpyQH8
+OKsEh+QcNPUCcS3zWVfXQEcO+P1ZDrAK4qfmy8PH4cs4a5bQSGK7nGQwqWnpDBzkXIvcKtFW8j2l
+z5br+RTQ5e+cRhs5X7y6LrBM5DB4wGpI89YmegPZN5WbwSjYDm67L1XE5vVtWL4wfJWNLteqB7Uj
+e7IxvYLNalWuNlEuuF9acxgG27oXJHqpdCQ1aQCMCNmcIkB6Do3rWRcavK3PFz78yEOhruu/8U/5
+p7/yT/hsDeDf/VwzuBk342a8VuO1gDnXSiE1DBimjlrnqV24qAnMqJIUQSnWyKxMgEQkMd16bTJF
+Rurtd/63D3A1lky3t+Zil57H4TzHdpO98M/ktB++cY0rNpFcl8d4KyC3wsMu5sd01dwKMZtcVknL
+6wvA09mVWV4iMiSlMolrDKmIXKkQuiOWWVtVH1QTJcVE7D0fyOQahtNGRlit7YvlVlYTeUpJt8UB
+DHY+1vMWFDkD42WMlKHU9UKitGYEnFridVxXW7hmkrPja1CQv9O7LWhXci92IlaurmP0KCxTphUU
+1zPVpwAz3OkiwUqLXvXEQuV2Cp3JLk2vEK7Yqs0Ql1cyj8mLU0xnpHv/ivx7GVVYMGu/sI4wGFBV
+3LRQ7YnF264UYhKxzMmFALiYkd9u3RoCK3GZoMKczM1eo4JGyngifxGMA1gQLy4vbJQg9HeeoNFg
+k5NroS7le9JI1s3RPEwqUqLNAiypbl45behrUu3IB6ySVS6MesWvaEOrKJJTngBNeqdxhvIJ72WL
+DVWDGkoR3zHqY3Akz9J1ZjBYXTJSD6BX0BjIdaO5DaOUNYziJRx6N8u0gqlTcKZZoFH9BeySrKoa
+aVIiXFZYb/FFMDJUZO1MrBLqigdAs4GKIKSUpBr+6SWmJFNRuYZqpWH4vEL+i7LZHloZYrIzZZrE
+kIm3gdYLucbFEwvb6xKi7JcjVIa47vOsjSqgKCwrB+8/PkBMzcE3G31ovrh1Sf0MQSBum6lvIqCY
+as68vu5nKCl1bjt9mOzRqDULTo8vpCnXsjGFyuTfk7NTFGxDzrIYJfsgFgsbJVu8oyHRbGMHDt/M
+JRaYXsq6TVWGjRWrS2lBryU7nQ4obDoHLFYnarNCzBx0EQIRXfFFOkPA56DGKwoloAgkXHGsXUQs
+KaduhRn1JudFjYDhww5x++M7FTTqVfpooUWgk3ZUwjyR7+5+SWGNJckL9m3kjgu3EqCa63rQpvIC
+lXEBjWHeOKuRROKi95QYAt9uwhuxXLic4Zot2VGWwqjl5XaCObQWX9iSoVS3g2bAqoZpILtmZSvP
+oc7lMwYrFdaegSXLqU4CVLxGOR+iCqiPOX2MoJJ10U7lWsZmC1rGEpem0O6wOtHvoVoSvZsUMAkX
+KgvZp3WgvVTQMvQKCXNN5dxFmDJcq2v4zuerJ9z0PtyMm3EzPjNeD0+hLLCcX6Pt+UgzSdrpYQSL
+Wd8wcVETaqoSD1abFGPMgOuDLrb+n/beNEayLLvv+923xYs9IiNyz6zM2np6nY1NasYakENSFkmR
+ki3AMCgItjZAMiBDsmFA1oCf/EEwBBmyZUCWLZiyAEObLVE0MZAtUjOSrBlpFs7Wa3XXnpV7ZmTs
+y1uvP5wT2V30jKZ72NVdhuMAhYqM5d3l3XfvWf//hiYy/cEbHB7KTtrKzwjXJQGoeG2DI3XELGlq
+7HSaUdwRTWL/9ITR7ZsAdIu77KhT52IWsbIip3S3Lzv77Ud3eWlXHaI7HnFfNJe16yXoqae+FdPz
+5l5mxfA7dyhopWVU6+FO9DQKm9RV1WRZU5GNQ6qszHn1gvGBOu3iKkP1uKd1y1jBxNpD+f243mRn
+SbSm0yjg6FRqKdpunfWmzEtajqhWNO22o6QnVUOip7KdjRir2RHM4HQobR+enXGoWsGqIhib0GNt
+VU/5RoTvzrMdMjxPtKKeGaMQCWSrMt+bfpuuL1pDbWOdQl2xHAYTjuqq8+ceE9XGrxfl/PqK63Jt
+U9mijsq0WzL3p7ZA0RF13PNS2k1Rq72SJEVttoqXeJ1Db8ihws0VSy6e3p9iEdKZ5lHMqy8HFlfx
+O2ZRjAZJOD7pU3BEmzzXdRglIVbPWWMHl7ySWemIoc7nNIyYFRSyT7WKJJlCJH035QpVjfZMSymO
+pgAN9kcwhwhUc9YrukQXClXvGAqa1xP5EQFaE+M2ODrQQpX3KAtNYSELWchj8nRoCsYh8kqkmQsK
+mJnNClirjrhKATRtkygjVQi1UMOJjufAmTgi0wIsafbbuFukeEf8ByfX1sk06v12R65bi5YZbsku
+ujJ0qX5cYuw3ayuXseCC414iHbWUczBvVKi0RaMp7tyk+pZkdu95TYyGQB0zw1qt1FMOxyjw6Sth
+iT/1L+3WcTQgmzNa78upk17JMJqinPhLpIrTEFTHFIvifBq7Pv2hnHIrGxq+zVa4f6RkKaFDV8O2
+8XrCWMlg/OOYWE/Ngp1DwuXkA+nbyOagJ83Azyil8t1SsUx5og7Dsry32WpgVqTtrBxxdqhUfk6R
+ztwuP8/pxtJOcCbXHRV8msr1ENZc6nMshDOXTQUrPd62rKfqU9DQ5E7V45Vj5bjgkLuasrie9zEt
+uV7tfEg3lu+vaA5FvJRjzuUEvhjklOZwWqWcckf6ljYM5VTGZ5sK4ju1REX1u/hVYoVKy+MBNhft
+dE3DzBfVhOUzGf9sx6WkkHUuDdxzyVj1bZn6uvQtVKBV5zxmOtO5GkXETdU2zmvE6kwfezCNZCwV
+DYUGrRm5Oo+TksVONG+nkpFqFmY0ihlqxe97ladiU0iThJOjQ8oFn8RIabFfB9fIzfcKATOFeE/9
+KYlqQ0NNvHkQPiQ8lNXx9umMNYUZvz/+Hq2CqNLRv2zRUaKVSS43trj5PIU9cfAVt8v0FDp+WLJ0
+31LPcD1j5yVNXVXi2pVaQG1FFk1QhuCmqOV5t0bnoVx7Ou2xqoSnuT8nSzEMPEkganoNYn1oKsEy
+oaIrGx2z7zWYHWuVXVCguCIbwaQHM81Fyf2I7SUFETHKg7nW4FFD0HsrZy1unQtL08bFBtet9N9f
+X8K5LX02CtgyHF3g6KaRTDJMpKqoGVFQ7s1ma42Vdbk/dUXSNsZcwuJ995W7xIE8QNaBQUdBVvyM
+R1buT2VVrtuMSsRW2Zs8h+Wa5Bt42wWSLd2QIsux2h37Vu7BVtWHmlxjdrzE4EIWQ3TSoNGW9VBd
+WmGkeIyDQzks7u0a2ordmWQRNleiobMh9bZEYIxTINdKw1xRnYNqkaluhMXQMKrLBlLJbrCViWnW
+UUKdV7/xPcIrL+k4alSKmoeSn1LclvXSf1DCXMi9nmjZez0Dq/DzWQwXD8V5GoR1HM3JiQcRgSa7
+4Wsew8yAJvDZbEJVN6GLaf+ScWxGTM15fwxRC/NhIQtZyGPyVGgKWBeT1HBthfRQnFq2UsdrKGpx
+ai7DPra8jGba0vHllMjuz+hp7sJ6IaCgob7rDzz6kZy240f3sFqBeK7hrZXo21Q0DJnln6C5Lb8b
+3elyTxmKtysvEWg2nVfRk/98lRzldLiISVWdD4rLBBUFO7kfM5mfjjuK0+CMuThVdX08JmzK6dgd
+J2SncoqNqnJClV/7HqUrWg1qXYxm6xWyKZOSpsx2YjLNoKv6yi/oL9OKJGTXqftEd+UU6w8Cko6a
+WPUdwjlwjGoavDnG1uRks9kJJwpu292bcJTJOPLZCQdW3q8N5f+6rYNqG2N8IlW7i8WcqeYNnJxa
+6qGmOd+Sa93bHXPDVcq6eEqWK8HJkUP5QFONb+aUFHh3XR2ArgfNc+nzQWeFs9ekcG1oErYD0WKu
+PLvLmjoge5tyP9z+EF9ZnsfxgI6SDmUnfYp1mbu2zVGlCKscnaNZTqoZjxdBEX+gxUi1Kpv5row1
+V9PtdMjRqqzZ7fMO+ZZqP4OAzMo9KdnXGWilbPaq0sO16qTTOQr4IeN9zZFo50RajVco1jBKShRV
+FBk7TsiVEd3vQrKu6diTKpmGpYtOQKxh6/cqT8WmYJyUQuUCLwkxFY0+9Ab46p21UQlXUZGM5+Er
+NHp7Kg+x/+k1uvdEnT2YJkR7gm1INeLgtqjrD40hP5GbUL4mi//+SY+3z2VjSbfh2Su/AIBXm5Fq
+TcFJ54BRQcqyTzW2na/fxYtkATrNLaZduYn1UolHvXcSaAZ9rXDraZqp9VnVjeX8bMBAbcTSLCLT
+XH1PQV+6G5YbI/XOX11mptdNfEN2rgQwS8tkoWyGvqYGZ2nO+k/IYrw4PCZRtN9oukNaekb7UWRJ
+7fnRsWxChefLnJ8pL2GWESr4yrWPbzEZy7Wn44zo6IF8X1NxS1FGqt73WTqgqNGVO/t7PNIciX4O
+ridmXHBNNrEr7SLdqnzebu/QUTr45vMT+pqO3Ukq5JpiXFS0qbtnM5xPaRr0YEjxQub2e9/9NzSV
+Tam25rFWkHkeK2ZkpRQyUMIZp+rjKaT89s2X8LWcu1QxKBYMnm4U40FMpKXHyWRM9Yq8P+1ZNl6S
+uV8zujanDe7eVp/JNGd4Jgdc9ZkqzpGC5+QVkjuy5sJPyuYWdQyeRte82YzSivKJDi7ING/HhGOW
+dxQeoC+/H+QJ7om0sby+Qa4IUXE2YjKT9RtU67jnCzTnhSxkIb8LeSo0BReXilOhuGGI1dNN+4Iz
+RVeuVib4vpwwJDMyIztssaX5w8USjZHshhezIdGynn7xKuGmqMxLexMyVe2mmsL7wBmRKl/C9eVV
+Oh3RNsbBGc1E2t65meJooRBlOYnaZpPWtvbTBBTLEn2YVGKqerLdY8ZYgTqSh3Jd90qJMBRnX2Nn
+TFtzGvznZjSR94NNOWkblXX8pjIt52vMNCNwmtZwQ+lz33PI1Zaas0SH1SEVX+bnEysNbk/lFFu9
+VgD14FM8Iw5U5e3J57H1MArBlmQ+9bpoKW6zRrMpJ9BkUGFJU5rTfTmhh4UDPE2rm9gxd7QA67A7
+5lEq16vlAZlm7B2lWkmftvh4SebTmXQpl7T/hynPP6dwek5AU6MEjif37NOlS/JvSs+5HHc0irLj
+4Sud4O2H3+GiLfkJBeUjjdMSnqZMp+OElWVZT5VyDS/XbNEEiupInLkKlTYeMlWYumrFJVdE8FbD
+4cFIvrvRUo22X+KGEgqNgoCoJmp+OW5jXKnsjDOHhpo2qPbreQMipdXbKPVIIomCRdExeaS4D97Z
+JYSe9TXvpetjlsXsurABkWYxDsYzjoaamt8dEiqc3nsVY+37xkf5wOX7grS4oJFA/ZL851gINBO1
++glZjOV9w8t/WFCDfqq1xL0d8ROsv9VirSAe4qNVWFf18dWJpPhmVz7NT2SyOM7sGo6v4cAjQ9fX
+Cr5Zhb6aAY2Z3MRf+g//DOGaLNL7+29zqyM369v3fx3/0ScAaJrfZv8teSi6x8IgNBrWCQPFUhzO
+cDX91A/DS9s5VtDO8WxKphV7JkqxoYKnepbE11Dl6ZSulQ1noAhEbgYajcN/ziV65d2T+COKeec/
+e0m8+EO+W36n7mBaBqtZvPPlZn/Hb8wflpfXv3GNrCIP5LWVDYKmevA1MhK4LZ5flYet2r5Bp6s+
+nHFCNH4AQGeYU1LTM80USLYag0Y7CoUcT4FKGtUR8VQRokYnDIY650Y25OE0oHck4US33qSgpkQN
+n2/fkgfdr0ofP7lzjaoS6jheSsmT97eXizS0IrITDSnofXW1j0GSE2nFrJvN6Cjgyulxh2OrZeml
+ZTYacu26ZlCdOQlWN41Bb4/+qdzrvdNDPGXLCooObUfG96u//i++Za19mR8iC/NhIQtZyGPyVJgP
+c/k88C/mf/zOA06PFgNo2J/WK7Lj/sHtjKMD+cK39y6Yfk8cbWX3gt//WdEEnvXuUf6SnDBzfMKv
+f+2cf4wcYUuby1zPFdLjmk9FqdGHZzluT6ap31Iq+/Mzbg8kWjB86DOuyvVG/9rFngk7cr+8xv6d
+LwNQVWZnGzjUinJCVWtlmgrrnpgKy+r5H+sRfHHU4fhMTgnrZngaay57Dp2u0sSXJ/yRqfzunyhf
+5edPLb+p2uLxG+8c558Evsv7F4dLOk6R36HTBcA8Cu6+6/N0BOM590xkmCuDP6Hf/SbwMX1910L8
+a/J6diXDU5gzrz6iVRYNcD7+ipmS+mK69IZ9ZmMZ9/7be5fYlZNiiOa3kSmmQTLxLrEuGOUkiZz4
+06FhVtGK12lOJRU1dKDwdvHkmHpBaew6GbHmuvQ9mGWKt6kmqL/RoaZkOSW/QF2xJ8JiiVyBUxpe
+znig7OVqxrrLJXYChfgf1amrEzRolVnrCDy77RbY0II2b13+XxqO6WuUwSYthoFowMuBoXMm/ZwG
+ERe65t6rLDSFhSxkIY/JU6EplGrwwmfg87/5Lk3hB0gReEFr1v+ClhD/s3KJ9QeiBazFO+hByqev
+eAQDyVarLf8J4mdl9+z95lcAOOoe0YoVSPVkTOnHlcwzjZhqQcxKLWZJ6+WP1OE4Gp9SjmU3D9cr
+3Prnclq9NEu423sAwOnogEwLiXJFKXquGPLCpmgYwWqRSiq2XjsLcNSO7mlRz55b4tZMsvI644zY
+kdOomgasVGTcEydmM/40AD/7s5p+/JvQ63wTACeN2HxRTqCt1967pmCAOXpBxKWbAIN51ymi9jCG
+mjow08zSf7cqoQ4IBw9fU4Fv6JWfezHn7qtyUr7JOxl32aQHJbnG9aXPUWuJfyiYivqwUq1yrGm7
+ne45B2/fktfHFwS52vslj4L2ulaV8VcKyxSsgvhmXQIFufDzIoFWgeNaqiUlGNIw44NozGgmvoOy
+N+aa4j4cJhHZA7lXsfoG2v5LtAvil1riglZDcxrCEpHidkS9IXUlqlmbs3lX6jS1GC1cbRG35Xrb
+NLGnCkM4dCgp23SmGkgcxDTKMq816gRW2n5khziapr9RX2fP/iAH0PeXp2NTGDl84itlvstwDn3B
+BnBPX/uAanD83NY76nOosfZfjCo0r8hQ/tVFzlWNBrw6ndKyEpsvDENajtzw3/+SPJhXL3K+dy5q
+3cpOh75yDabjCrkveQhr5ZjuukYoeso/GGyxui59uJg4XA8EcvxrpslK+20AGpWQiT7on6iIObP5
+/A43VjS9OA8xyhBUYkKs2BHTU/lN4/kiflnU2od3HDpjhQEreIR1+e5wUufrVzXR5bvS3x9v+fx8
++j0AzmYW+7psZA/e5Rl0+P5+wvlisMAcwGtioK0RjlOgqruCooRxveCSapz/BTJe1wcTA3d0fwi8
+DIVo5F5L5rB1d5v/2pc3/3b2Fs+ryfOXU481JVTplyOaCqnu7kqP8iCmcabAOKMHnCmScpT0yLXG
+ZCkNIBY1PgtVRQ8yKp4cHJlTw1EHXl7KKWrnho5LQUlpYt00SlGJ/kAiGUuNKYlyd7biEKu4oMsK
+ABNVDVoeQ16tk5bUrLARVrE1esahUp1XxCpMny2SztmdvACjSUgrpyHTllTu+q0LEsXwcBWO7SSs
+UmrK+p2WU0qZmJKl4g3CqmxkMz9nJX5/tQ8L82EhC1nIY/JUaAoXGP4+DlvOFWa5hH8ekvPu6OqV
+mjhXrrSrnCzLXvabipa7NQl4qSpZXlvZmFRRbho3LYHu7OW1HmZXsBXG35ATuLg8xlfn4nFnTG1V
+TvFrGw36StMW+GVcrWxs1sUUqW+71BUt99GdPYbKL9hvfpuriBbiUGb3ijg5dz8hyNAf293A1dOl
+UKowizUen0+wWm9vluTEKI/PL0/Mk7Uu58eSb3Hkzsj6opZG6ZC9B6IVzVI5Gb5WCTjTkyT0U7Tg
+kIp9J8b7/bQEBwjmhajGXDJ0h6lLonNRxlDV0z/yZRynbnZZcRlXA46PdBwmJlV+xDQ1OKoev6pO
+13K1z59ZkxN2/7Uiz5Q0pd2zxK6c3OvbIZtFyXDtTeW642GPRw8lRLx/3mcWi1kRZIZEw7rDccpZ
+ItpEYyb97E8SNrUgqlcds6YFVsk4oZhr9qMzxVPg3LE6ARuzCqeqsZXcgGKizmG/TKZVrFPNzai3
+yjQDWQtZNiGfKj1cHqP0j6zWimy1Rc1vleT/ZnuNNNGcjaCIp6ZpaaVEmIzmE06q2aLuksxrNDOk
+Uy066wfcUG2ju/uISqRp7/GEWCHd3qs8FZtCbnOmdsJ9M7j0UrsuKGo5u0sh6y/IA3l9/QWqXYkh
+l9XwrfuW1VAe6EKxj7ssk+3lM5YTSQpKekf426IGlms6qfUq5luqipsJzl2x4SeV5mWegim1mSq4
+SmueLNU1xIqXV6u41DZksa3fiygrb99LwRKNZyQtdXNdcfZsgUDtZ+v4eFbj1U6BVA3bXKvequM+
+XllubCGskDXkGuVHe5zURDWurbiU31KwEIUbL5xNONRy4WiWkM1LoH/A3Hvz/A8ftgJZDlFuUHcH
+UeZQUcIZZ2ppNrWaT5XMSuyyonkTpWqNzkzV9ShlOlIkYiyZbjiZPqSF5REoR87Um3Fb3QqhX6cc
+yP1bLjUpbGoC20giC2e3MsbqqxkNptR0o0vCIrWCLIhS4GJirQ+ItNK2CH2di3aUUmlKGyGQBIqC
+3B+TKVlNQSHT4qWAskYiZnZCQTEqyxUH1/iXfQZYNgFea16jkuOMFa8z6xEoNHw5NDSLYgpVNAfD
+KTdwEjWc85RM8TpNuYLrKOhL6uNFch+yttaXnCZMFSm81IPhmty/Wloj0nqVZBThBu/Pp7AwHxay
+kIU8Jj8qFf1fAf4gEAN3gT9hre3pZ18A/hSiq/45a+0//aFtWIsTpVRdj5Ii67Y8w9VV2V3XPt6g
+pcQZ2z/WYnOoNPHncrxccwakCri58Uyd2ZF8XqnP6K9IbXp98HEmPUmxXVZn0Gh4wHNFMR9eP+/h
+31Nv8L/T4JmqkGU/KoeUtdrP6sndqmQYBfdoV+BBVzIWV5wRmRbULP2+ZwiN7OzVkjrLAhffaB1/
+MKXoy1jjpICjdGNTPdOrhZBQTYlkmjANxTyYpG3MQBs/KTFSh9nb90VTOL1wcPTEzGcJWpBHwDup
+H76BqkYMXIU5a7ouv/iMOG7fNA59BUMpZgmlVenn1ZrP9VxOuYcz6efZapmWMmWXgxWqytz9pfv7
+GE1NtqnFJnLiGeUj8Hs+vVD5LH2PkqfaW9nFKidDOWywpEPt3NLIUXR8ifJd8lJSNTJHSUzJk376
+FClrSnOkfagVlyjrWKu2QqDjb5RK9CfK+BxbLvQGOop1Qe4y7Ytqn2ZjmpuyDkf5kKAw12plksNW
+nSUF1hl1Zlhfq2SLRXzVphqlEHSOPMVhwInx5zQcs5Cgqie7F1NWYhzCJlZh6hJFpTYlCz3NwCxL
+lAdgI4N7Uy0kW9qge6rppO9RflQq+t8CvmCtTY0xfxn4AvBfGmOeB34ZeAEJIPwzY8wz1tp/a66t
+BVKgn6cU9SZ+bHeLurICTWjTWpKHqdaC0jVJJU7vyUPesEv4WprrLZdo1XRSkzGrgZgdUTukrOrc
++T1RRbMk5ItKeHd+NuFVVW3r33kL98efB2CnBl/ti1nxc4rc5H3286gJjO9uUUkkLfXW3RGffE4e
+LMddppSLyj9I5SZWkxmOhq+cWUhWlQXkEpHObaULtXVdD2LRr3MTE/bEZMgrlpIiUq1ueQQXCoai
+SNV5cEqiZcFk+WWAMALUrCdwHSoaRijWZeOpBB5Xf1ZMsOvZKrfvyxy5gyEvbojZtf1iyOxCQnJl
+5b70Gy1CT9rrJEv0taT6p29s809eewDAyKSX6c2xzttxmuMqxP14FhE6c2QlaJdlrGnRoZ/Khjw3
+53oPuiSafFbNDVONV5WrMUbVec/JmGqVZEkLJVw/x9cQab7kUlLo/8APWE5ko4tLPUq6CcUa9s6H
+M4zaObMg5+xCvlCvlCiq+bexJL8PK2ViLS3vF6cUFagm8Ax+rHDvYYLrKJPTPBR71iera3m+X2Pu
+uPESB1vV99MAq5il+YGW8NsQX9dC1HAw9+Th91yHRkNRpjo9YsUCfa/yI1HRW2t/01o7r8f8GsIZ
+CUJF//ettZG19j7CFPUTLGQhC/n/jHwQjsY/CfwDfb2JbBJzmVPR/1AxQME1vKxQU9efc9lYFWir
+2WhITeG6CnnO+kQTgG78OAB2dpfimgKB9JvvFJcE+wQ7qibfXmaoeAjxS5LwEvUfcu0NUR3finJS
+tQm+9akBv38kOATJpEGyKh7+r2pCy+fqy5fIuYPD7/CGxqDHXkojE295ttfB/4T0040Vji2doVo0
+hTDgMng/9ZjpaRvraTaOE3w9aSZRn5mCJGeJoTydewcDVtXz76uj0h1WKOgJlLgxCu0o86tth2V4
+5obS1fsypkZhjXV1uK2PV/mxT/6YzP3hIe0X5DvFtV3SbXGePqso0Wl5jD0TLW6YZWy9LOru1x98
+hZVzUeftkb1M355TsBVmEXPvYwio5o9TDYi0YCjqj0nVm3/WmTtoYxzVqophi2pbrrE9nZJp0lOR
+Mg/GSkOnORQkM2xd5yqtUlRqe88Z4GmUp55vYRLRkE4mcy0NRrm058YZA1Ul8gjqLU12a2hUyoYU
+1FHudMGvjLW9GlWtZizMSpcRqFRxEbxmBRRf0RYd8p5GiZoeRlG+jee9w7w+L7pKK7iKKWmmDsWS
+Ion3+hRVa+pkMR07z856b/K72hSMMb+CaP5/50f47Z8G/vT8b8eB3DUUGzKYnZufYlnxvYeeZdcX
+1ag4XcNRVTIoKbZeH9y+IiF5Fv/jMmnJ2zmeTnZW7VFty0OaHoi+u2kWAAAgAElEQVS6tzN6hmlF
+rnXqBWS+TPDWFw2FT0vq1M3rPwlvy8PbmfsWLgZEmrDiFupcrX0GgAf1N6lo9V0aNpkqGIpf07Bo
+sEaWianhTStkSpSeeYZEqxytGpfWy+koaEb/fMZEodOjix59JS/xwjH+uqjzzdti4/fKXYx6qQt4
+lzn+FlAXBzfCIjeGcr3nPiYPXa3t0dL58XtlqppMVWvVqFyRxKhCfxlHy4RnY+lbfavA4FweoGrZ
+YxjKAvz8tW36r4iJ8U0/5ZGdR3mkD3mWsazJWycptHTBe3lAWSnlvbTHvvbDyXXubYGyJhaVCwGb
+dc34s6uUSkrkWyuQ3BZf0ngsD57r+5SsrIVKYUKsuJJO7tBJlbRl0MEoLX1dN4cOMQUNX7qxjz8H
+07UzPEWLWqup+VBymGmVq1dNIJX++MkEo2FGv5YR1hp6Pa25KLTx3Pl9MqB+CccJsBp9yD2HfKA8
+nq6OMzslKcs4vJML8uqG3oeM0VQOMtNLyYbvD6PxR94UjDF/HHFA/qx9p/76PVPRW2v/JvA39Vof
+ff32QhayEOBH3BSMMT8P/AXgp+wch13kN4C/a4z5q4ij8SbwjR96PcBYqBqXF+qSYNQoX2FpJieN
+2XVYSsRtUdpcIQhkZ45cOa1KqzVS3VGDkiWdKlZdMuPivgKd7MfspXL6uydy3VdfT/iakr68FiWk
+qlVMGx7jt3YB+MrDA3yr0FxD0Vb+o9BQsLLzV6sxnvIEfq5dIgtkD/STNq++9joA7VUxZ8JayrKi
+BcfVHp5iP2axj40UCk3h6ePuhIu+ov4edjhUp5ZfMoQ61tVRyFhLRiNNz52dJwSh9C0zEWhSlAuX
+VXv1Gzv8+yuK//gLzwLQukhoPas4grsl4qKaDFNDWlYE4+stMiUkccdSvZfadeo7chKNA7h5WzSF
+t95YwX9Zkoz8b3QIFHbe9ZRizTr057DvjsNU1ZhWkNO3ykF5ZkGda2f78nsbRPzYTUn9dQp1ylbW
+QO4VKSj13MTktBTxeahUeG4cw0DaDgtNypr+7IwczCWTtCXIVWML9ZRPu2SK5nwQj1hSTTAbzXAD
+0QpirctglhNoAtWkP0E1eBo3dwkKmmfig9sWTa+gkRzrg7GaI5JkOKo1JaMJTj7XDgr4FU2nnqkp
+5XmXzlOvGeDVZBztvuWgq+nk9QoPTuU+vFf5Uanov4DUzPyWkZv5NWvtf2Ktfd0Y878BbyBmxZ/9
+YZGHhSxkIU+X/KhU9L/6b/n+XwL+0vvqhQHHNRSspRzI6bgZ5RxqsUsyGrBXlfjw+gPLA1fqxicH
+Ykev1nIaFTnZqtd3CDLRBFK/wuiunNYH/SInZ2Jn9dS/169M6Cl5bKPgYBXkc6lYZliUk/uGH3G8
+J9dzlNjWrxUI0nmW2A4fm/xrAP7WoeWq1uybR/vcvyO5BcmbMqbm0hrrO2JPrrWWKFfkZKt5Vfyq
+EpBeiC1868Qy6smJ+Wg4ZqY5GcW1gGKu2Y9XqnhKF9dUSDTPxkwyGWCrUWHQUxZrC79H/RX/6c0X
+CRvyHaN+kk7YJrhQGz8eU3igpDyVCZU9OXWKtWW8WGP6W3JaB7OQTHNEfAaczRGFxydsxOKL+Hhx
+Rrg2d3bJOM96oJizvHmesKwOSCd2qanXsed08E5lPsdavVixLmMNtNbslHN18hZjQ1mRrStpTKTU
+a6tKEhJQZVkRv6mETKfSnyyb0dU2uumQQqAQam0ZcxRNydRb6wKxOiBd38FVhvSaanEjL8XsyT0/
+7U2ol9UnNo5xjsVHse/32O5p0dWuaKnh1JDOi50yl1SJcWZH++wrmU8rrFK7qbwch7IWhnGHgqYg
+mM0CaBWv5/qsaoJkx42oKUHwe5WnIs3ZAAXjEIYBtiDe+/3xGbeOJIlgcj5m9wV5oB8OTil1RGW6
+6MgN+lKzxx+4IqrvVnuJUBGtD7J9sq4s+lv7Ocd95YT0lZ23bIiVqTivrZGMZBPaSwycSCr1XlZg
+TwErtpek3UrYINAHLL54xLFO47B2Tt4Tr/10Y8jzn5M+pVqAkOdTpsFcPU3wA1FFQ2CgD/K54hq+
+ffIW3/6WmDsn0x5pJH1fv6jw3IpuZN46gTJVJRqXL+JT0Jj/SrHEnnprHGPIQlFX970LKkjbX/2S
+mAFx+QFhIijY58cDEsUDvJ+N+MnVXQB+Ofhprl2ROg5vVRGMnYxHX5Gi7K9+7RVONZnqzLNEHdkM
+s9IGRmnp/YrMVb1Voncu99fmltNA5zawJLyDjj2cyriSgSIchw57Z/K7PC7R1cSj0EvZmkl7O1vb
+pPocPH9FzM4UB0cD8A/3T9h/JP3s9k/pKZx9zfOptES1N1LsSuY5jDQDzHNgrEznThKzVtFGFCqu
+MMs50sjIxXGfYkXGcbxcw1Gzqb/XpzdPSZ/IQXdj+3k8rWQsRSHnBTkY9vaPuPU9MUfLQZMVex2A
+K811bbbBWDcb25lSVjatUehQ1Lye+MIlLL0/jMZFmvNCFrKQx+Tp0BSMwQtcGqGhXlLnWrrDc1o4
+Mr1eJdBTbs2t495UN0Ugp8TN7CqhIueWwhmZ7uy1B036G7J7firI+PJAduB8Tx1S0SF1V6nnbI+x
+8iOmFY+ynjob9W1Oz0Q9no3VwTNJyLUQpeDXufL85wBY/vY3LivnxsNVblZkRy+tS7ujtE6prAU6
+vs+KovqWSkv4SnYytpJVWIsO8JS0ZjaATT2VroQpuQKI9J2MUiTaRD7UkF7RUstFd/zEtsNv31F2
+aAM3KtK3lZ3P4qkp9dmGqPi9apkdLai5v5tw+1XJ4gzudSAQ82n29teYbYiGUVcOCRNcY3lXTKLS
+yT7mvpy0O+VjroWizn/TjLiTC0JxOZX8hvBownZZTrD9iaEYSf99N6Doyxy0Cw6ZOjzXqnKt9aU2
+mZoPYzejmCnEWuwTK2nLeBZR1rRi19HU9OUm40D6Vj6M6ClwyqNhRE3BTqoFGGnMdKbxfyceUVWz
+cho5ZBrunWQJ7pz7Qs3AQuhROtd0+2qTnRUxD1qVNpW6aAVX1g3TiZzuvqJuR9MejjKJ57WAUJGd
+y7UlWhvS9r3zOzz6spg83rL49pd2EjLNTC1PPBLVwmr9EfdGsn5XeiHTUFOl36M8FZuCAxSMIU58
+9seyIF5suWzk8rozjbm/L2bAa5OH+KEswsMTee/37BqCsZa8ns8Ik3n68D5+KhPZcypMTkWFOziU
+xKTb/XNcfcC6k4Rnm4KwhNekuCrfvT2Ygi/XcJR5yvgJRm1jv5ETapLKL36qwb/5nqbjHnf54v4d
+ANY2ZCPoYvnMqmwU13bbFBRF2C+sUFZa83ZbbubuTpP7PYk7n0xPMVqp5zbbWF/Rixp1Om1FGi7K
+Qot7E4payvuyafCr+gBZx6WyJslgW5/cZflEkpAO1Yfx2jdvcWcg4/ja0REP98RjvVEvsV2dE8Eu
+4Rt5OI2mARvvnKgoD9jDgx53TwRwpnN3xOtb8kCOZlVuKKHM66GmYxdHnGk0IPAcKkaWYsXNyTV/
+I0nW2N6VB6c9UrzK5YBmS+7/o70R7pKS64wt2025P/XSChXlXWyUpe+VoIyr8O2ldsDktsx9kk3p
+RDKW1VYTm86JaeX/08ji6xyOAp9wHh0ajqhp9CHUepW4N8Xq5t1q1JgoOM04jshHMrdhqcR5JG1X
+NRIfZxnRnpgU/laFUkV9Ffkp/Tfld1/9yh49TxC13ixKfuDq8hJ1ZC5+6ifX2a2t6fWGuMi4lzYK
+3FdCoPcqC/NhIQtZyGPydGgKxqHkF9gquuzW1IPaNyw/Kydi2SyR35cTavlsQtAQh+Dyy6JmXc1X
+aG6pg2h6gquU5L7Twlcc/4+9sEzelPc3t2VHXXnzVeK+esPdU5b0VFlf3+V7R3JiJ/vf4UILcHY0
+1pwZg2/mlY8FlhUxeC8pctOXKV36mZsE6oBy9RTs47Gqr/1qgViJEbzsgiTWSrxHWuy00+T3RnLy
+Wfc5So58vrq8gatmQDws4CohyZ0jTeud5bxpZA7j9WM0ZIwP7LSU8Xq8if+SVo++Jarl+s0VHrmi
+rVy3Q0pXRaPZTepsLalJ5PRJ63IqppqvYN4aEWhx0ed+5hmuj8TMu/3gDWpHoj6fPX/C8K6YKct9
+udbX7uYsWZnj/cxSV2i2IPeoKa5D6p0RDRTUZFs0jGIWkmoOQavhM9TIwG7LZWlJvrMUFNBAESW9
+HzRc6gNZT5tba7x4/Vy/m+MVlQm7USMIZe6G6tgM7kdECrhCMGGgOTCBFxJohGPJaHFVDSonSlu/
+bUl9Gb+p1QiDedICLKtG6moUaTiYMBuKttV2tnGaMrelTsB6TTNPbzY4m8j6rGlFaX21RFXBh0zN
+ZzqW+5dnU6oKPTdLupSLc5DD9yZPxabge4bNZkgpWMJRfsjmOCLzxHO8uXaV9RW5iSZwiVNZsFms
+YbV4RLkkN3zaKZMfK219vkdL89rd8rO8eFVUtN0NWYyfbfm82pcH/uSszkZbXo/zgG++KQ/Q1K1S
+8eVBLbwo/ZnGOb56y7P+kNSX13Gxyc1nNRlo2aXYkk1kTmW+7o4JNUW3XipiNZkoG6aMh3ITvYKY
+H65Z58a21pkVIqZ9URNnscd0IIvpmFMu7knZtuPOUX5y2vpAvNos4zqidhf8kCu1n5e2W0W8ygsA
+BDdkXgtOiZsFBYrd2iJVr392vsdSQa49PS8y/hfi+S/8lDyAQdNi9+T1x7aucrMkyWe/d32Fg7E8
+CL2HMf+y/VXp/4HMa1DLOdaK0DzL2dfoyZXQZ6QU7ePRlKnayf2plslX6xRj6bOthtSNbEKrhQI1
+rbAtBjUSTRuehyGCUULQ1Acz9nh+WeZzuQpNvUbxYyGnezLW3sFvy8/TnENNtZnNHDKNDgWOw4qm
+WDfbEmVq+WXCFfnuZLDNzrocWo2WT6ggwzPTJlNo/0jZ0IqFEmMtbI/PI9yCjLW51eC5QKp125UV
+uhPZyBtr0veMAhVPy8GDlKpWjD6YvonvzUvHEyru+0sYXpgPC1nIQh6Tp0JTCByP7fIyN1ZK7D4r
+u3lWdylf6OlX6FJQAhCCIp6mgRoj2oEp5qRFLSOcdcmVenu8P2ESyCm/xhsMFXK7cazptS+v8dlX
+tMrsmZc401TcQ29IYSpaReki47mrom2sZrIT9466OFrz7yU5gaun1dI1xpqavBYZHKX0qoWq2qc+
+RT0R3eUtGAvuYjZMiPV0NMrO7Jzvg6rRH6NOck1Ux2Hf0pnKSbl/eJ8X1aEUWun7M9bhswr2EH95
+gKOJQLXAYVOdkqwWcfT0CxQuvV5ukqrTyuMOzqq0bbMGlOTs6O09xH9WnJ/ZPYl6RG6bfCKmTb5p
+qHQUgu3ju9y4pdWTrT6v/1259o7ygJ4khzRc6efDFHIFBfGtf0ng0u1PWFYPfVVVZrwySaZRBGMg
+H+p9MBSKqiLVC5Q0hdxoFGISJSgMIuHI4Gl6+HPOLvaawqp1ZgRaYPRQoehNZURDi9XOpsmlKRgW
+HKq5OjGLmkyWOxjlDy34Me4c6ARwlOynPJ2Sa+UjYxnnIKzhojymcQ+/K30IgiXaK9KPql8m1aI/
+uyQO+Hy8j6/w0WZ8TKQ4IsWDBgd7kt+QXXSJs/d39j8Vm4LnwErRUqtVqQQy8DwZ4RTlgfaKZXwN
++9iqxR3M0YvkptjeGWlXASbMlN5AfhcUUnqaFdive9TUBmy8LPataTs4qaiZs2UP70IW2+zWba6r
+ffqvZl3uxvIg9LWUuT97iDuV92qtiOlIN5DaEf1DWRRRq0lR/SMl9WTHeQV3zujT78NUNovJWYqv
+9mX3UPo7yqfUNDRX3bxKuqoq5Zs5QUmhwcenDB+If+QXf0I2o3/92x3+oYbVVlLwlG9hlPvc17Dg
+jjG4itJqFdknuLKK0fBszVRANy/HbTDoyoJdbgZMz7Qi8vNiwqWDKbmSmRZmG4TPShtmzb8ktXQn
+B9y4kI1srPNzXCjwdcU7nNlESEKBkg8DrT9wZy7pmUYitPy3zylhQ7EP8WhrspAJPRwtbS/5PhWt
+BRlpxaE1hlmkUaSKQzURe7/mVwiUI8EUfZKe+o98BYzNYnLdZLsGCpq9mDgpAzsH/dVy6uyYg4n4
+BjY9SzbnrKhv4eiG5WYuiachzJ78zna71LT8enToQFND3zahpGZHUAnIlYR3XFHa+lGBeKw+jKiG
+f6K73uARUSZ9q3g1Hu1/35rEHygL82EhC1nIY/JUaArGdXBrJTYbTWovSuV11hmT6glk1obgysns
+dA1W6wQchWePS1Nmr8tpbSsVqm1R5yeTgFxz/6fHfQKkZsJeUxj2oyo2VPW7v88wkBNxWtkjVDXy
+WnvKqlanLS3r6dFPqSrzUJyUcQNxAJ0/SLBqdqSZjzNST73G1b1+TlrVk+0oJa7Jzu+VExIjp4pz
+qtBm8RGlgXxe2K1jeloN2exRUqjvzNlgdkMAY85fkXF8MurzbXWGLTsT8lxOqLJrqTU7OuMpVgll
+jLbh+EU8ZS2OgmUKBT2hiRl8SesnyobKhtwfq2bArHvETKMzHB9Svir3yT20pEOFAXt4hztFcaBO
+n1FW7fs7bPri2BzOUubF86FXolzUOhDXpZCL5tRJ1BE7PMVfEWdm2ViMqs/VnEs4fzcqoch7lBTH
+IKrEuFpHEGU5oUKieU2DdyH3N0knuJHkZ3g6P/VpzpnWhKS2R6I5De2lFTytUFxS9vDW9jrJgZLM
+DCIcX+fFzUCjLknRYtSpnCgWo3M0I9kQB3u4YsiVV9L0O+QKghPEy6RV1RD3FWnaJpeI51FtxvSB
+zFU8KGBS6dNZd8jYf3+P+UJTWMhCFvKYPBWags1yotGQzI1IL2T3LDjgauVj1g9IPTlVPH+ZvKfV
+bmPp/jjrMlJkpuH+lPKWhPIG/cElmOfJ2MfVGPNUiVUqmyHRWPMD7p3Sff1bAHyVPg/3xP4M3RpD
+PdGDnrx3lAyppXL6B9MZmYZ/skL1Er+gd3pOuyR2t6lJH2wSk2g4yhQsqYKEWhMx0WKsVMNRWV5i
+otl/Z6MppWBecZfQP5YT4cHokNt7ov30NOvufsFyoqm4OT7zQyI3IXuHEiJ78TTHq8pJ7ygdXWb7
+ZK5c185SxqNA57DDuZ6644OAtVV18lbnqearXGhWZL2dUFOkaVPtMc1E8xq91cGbV3xqkdAkTTlR
+9KMED0d9H+Vrq1SKcnLH0YhM80gG6nOpREX6J3L/qNexvqZY5zk9BSj13XPsTAY+nSPejSNC5I/U
+9ak0xJcynUUEZRnraOgyPlVU7b6MY5gUeaCVpJMUvEDD4AHkmocyUCdxdtBheKK5ArFL+ZGSCLWO
+KHqbek8q5MpoPlK7P2/5zHH6fM8n7sj10qKDk8p9cisQDxWTQlGpTRSSTtQvM9gn92S9dLLbXD7a
+tQx3MmcDfW/yVGwKSWY57ae88bDLJxSwZLYcE6Za3ZWVcOZYdZUhVpMxrHrF8/0m8dzhVBpwdiKL
+NB3N6GjZaCmfMnBEtZ0cShulq13i+zqpj/a5dU/SQae9IZ05q6XjsqWx7tlYfje+d8FoXfMp1sqY
+SBZ3aDxO2gp3PgoZa0KSM9Eb6/i4U/XqV9o4PelnZsH05X2nJX1cPugSKfTxbDLCqSgqdZYxS2QD
+uf1wQG9fHpbv9DStN8vJFQ/RJDHWkbYrjkNbzafT8R12TqWdecq0HaU4ij6de8fkmvRkB5uUrsj4
+y1cGNFZ1wY5FTY6rIUbzOOL6deJ9mUN3dw1Hgx2M+hQ0l2ND/I183Rsz0o0lm6SX7FRtpwCBmGnN
+eoVcU8hPFKk462b054lJxQLhROZiYH2YSD9MocIsEzMgOpuvoZTVq0o2zJixI/fMdB0uGkocfPCI
+41TGdT6WeS15MZ4+/HaaUHI1RbngMh4qxFyieSg0mGkUbBh3OZ+TCB1NMW11mgcu0RzzuKPJeUUX
+f0WRqFOPQJOb8qVlfCUWtolD2tVJUkermQ3IdOImh1UudFPvxJb+RDftTk7mLfIUFrKQhfwu5KnQ
+FMgz8ukAMoeJOg8bWZm6pBVgU0tWnH85Io8k7JNNlCyl1MU6emJMzrk9kx1zs5OQt2Q33rmxQ6ZO
+qcaqOBrtKMd3JFxzkjxg/1TRdf0KaxrfPw0r1FxxAk401DfsP+DCk916ub6C68zBRXtEGp5KVlKi
+OalJJll8ubNJQU0JChZPT4zx2KNSkTEZzcFobTzHsZ7GhW5K5si8OO4UG8hYm/k5B0Vpe2kk7fYn
+ySVv4fORy21NH77Ic+4kogk8mxdQwGSMJ6enUx6TaCGS4QQ7VQdsK6aaidnhltt0FVV5Sfue2AnL
+y6J15JMcsyzvZ1ECu1oQ9EqFE+U8/I7yZ44GU8YKKOpai6vnU3N1ncaKaArtVpOTY9EQzs5E4+n2
+p/hFaaPgZgRK+pJFlkDNppE3IdIQ9UPl8rjiJcxmCrRaMmRK7+aYlPG5AqgaQ6xh4kBpzmOzTq0q
+n8/SEF8rDhNcZgNxJDuBFjPZc3xV99dqKdNMxjedphSnsrZSmzJVs3cONZc6Hq5Cpo1a27RVe3NN
+ir4kw8NrSj9GHQ2tZimRIgbFySnRRN6fdsb0lDBmfXeF/E3RSN+rPBWbQmIMZ26B1VnKmTL+lIor
+RFpGTehd2lzpOMcpq21YnCcxNTCa8FKp1mijpaLNGTuukqis7pJEYoueHcnNbA5b9AbzmoMxZ4q7
+GNcSLrR8dZrOeHv2AIDlWKMWeQHfUwRgk+Efao5B3CPUlNJyxRArKeqsoVDm+RSrefbuqMdECVj9
+UYoWNrKqT2tveQnPk+uen/bwL5Qfs2zoDsXGfXjq0xlLP95WO7Rt4VhT9W/5KUmmJDpOAqEiHE8h
+KcnD6astT1rCKhDKdFq5JPdNyuDOZHPKuhZnLPN5ZjXl9rDO8Ia8Z887zNTrvTwak7SVDIUiLSt+
+noKRytHe2GB0M41TS0mTqZxanfqSmg+by/hjeThvqcm4mUK7qVyKQYotKUej7xNq6Xd74vBgLJva
+sifjLNSbrCTy3V4zoaq5EH3fMj2XdTYMJ0Su4jVW1ESNMmquXLcbOKSK5xj6FU6Que8oQUx3s3FZ
+qt0fx+ThnNgnJVJ+yCCcEZTE9CwVlCg4njA6URMty7FXNTV7DHOKScdGEGo0SpP64gYYV9av1zQk
+D6RvpbJLKdFK0rOUDu/Pp7AwHxaykIU8Jk+FpmCMxfVnlGwTBqJGJXFArqpY0B2T62nruQUy5T1I
+zhT6a3yIo+ml5+cpjUxRb51HjKdy6uT3izw4ELzGtWd35b0cMs0e28dw6stJWXJrRGPpx0W3QlrU
+Y1wdY5XizUswlWzUI1XTxkwbLDWUBTmKWK1pjNkozj8+Q9VS3MkZ5/cUJbl3wNFItJj2lpxmdlSn
+/5a8d7rUo674DZNJzAPFprzvHXOkp0axoE6vyTs7/dvW4DuiVU2yIm99V06Sn3zhHOel5wBwPKVH
+c8fQFs0kfcXj6EDaHvlvsOqL42s2rlFQE2zqy+dRtYy9Jdc9PzwmvCFzH1BhqujYd7ouX96RPi/d
+VgTuSsyor2zW5MwU7zCqlqCtE+qk1Helbf+WUrxPRkxOxRyrrtSZKSbDUqVO7oga75RqpOqEbnui
+YQXFAWmg4CbZKp1D0bbOTUJHc0vGhwN6ik1uVdl08wZ7I8FmizKDq4AsWaFArJmsuRVsCnd0QKz8
+oJVCgXwg6yZqjjFqgmW2TN5RajlX82ziERN9r3twQVGzOFdquxS3BMzGHSTEBcW5XFEuk0GCzeVZ
+6MfnNIqyzk6CAxJP3h91jpkqlsN7ladiU/CMQ8srUvEARRCKjmb0FD2+VmhTUI5CZ20DR+Ns01Ae
+4ui1GVZDj2U8Ohp6Mre2GJTk4e4/vIsXivrffyRe+K2XP40bib+gUch5RnkHD7s+uaqJM7oYDfts
+zCsup0Xq23IDKjOHmRKFNmyXuCiL3jmeEK/Joi9q6W3mJaSvCfLQcOoyeCQPytvThwzfEt/GQ4Ue
+71S/A1sSxvLHBWrrGn49OeXehWyAg7OUZ3ULOFJT5Cex/N+6K/QjyKsSplo2LnUjfXv17l02ThUB
+qqWOm7HBj6Tar7x0xEDNg+gbDQovybjb13+M6ZKG2d74JQASe8T5I6mDKCwVaCgJr1MDtA7EWfVp
+7stCXlFH0e6jAWfq/OikEGr/B/sZfQU8XV51mbnyu2Yom/ugfszgodrUNuPKdRnsyMTkD+X1wfSc
+857M7fBCHtyWA28VZD0VR2+wvClz+6gz5FDNplrewWnKGok0SSnPewS6IY+ilKImVjnFjGCkRL4D
++f3JQZ2Z1kyk6Tk1dQgU7gfMnpHrVm3IONXN/g3p49S4l4S354MZlTfk/h4uvcbmfdmQVp//DGak
+9RxbetjMpkzLmir9ikOkodWCWSbRjS47h3G+oKJfyEIW8ruQp0JTSPKc49mU5anHbd3Z9+mzo/X4
+U9vnyid3AXByg1GK71DTfdeutzm/L5iDg8gl6UiAPFuuYU5FhfPbbZhI8ketobgIbx/woC7X+uZe
+zFd7stN2Zmco+zjjLL0sZula2dlrGxFNrd2vbLZwunLiFbyQvYeSsOOt+wzP5QQpKyiMGa1Q/WlB
+5PXvQbgq3y32BhQ+KydoV2tXit0Jp550Yq0Wcn8oJ8atwzF3VdMZTYccaE3+HMLry3lOT/Mb4gwq
+irMwCXxe0yjI0Stv8uLHleV5TeYifK6EUSep7zi0FJew+DMtZocy7vHb/4YzRWAuKRSZ6cb06jpZ
+/YBVLVJLBufsqXd+784evUROxzcPRHs4sj6nWoxmc9Bbidc4J9GqS1uqM1VH6s6OnLT3oz2Kz0ka
++zBPGUVK0+a4tDZFe7v33SEnagf0jkUzi9eWqJ7MK1QrRCX53B10GQ9l0pNCQKDtVdYkUnM86EFL
+SWQIWNkUc6a20ubB/VcAuHMi83betZyqc3gpzck14vL8MzMVLQsAAAavSURBVFs0p2LqhrGBVCHz
+N9QsfXsEqzIBy7UZ5Wel7dJpQOOTYjZ57RIFecnkSOZyZmKGr0rUqtLOObsQZO5HvQGv78t68co5
+hbMPiTbugxTXgVrBYKIR3kwe0vNoRH4qdtHq9YxiVyaqmc9wkEVhKvPqtCVc3Sjy8xkzR0yGODY4
+y7Iw13eWsLECsEby/yPvgGlXFlK56tLQpBD6CSPlEwhNiqKvE+cyuRVnheqK8gsWqhRDuUkXJqCq
+STFxkDLWbMnehby3tD7ACaV2oLj8iEgr4zZHy6RjTRzSSEe2VMLrSnuns5zze1rWfXrBaKIclIlh
+pqyxVUf6vu3PIJf3lgqGQyXCLQcZyVQeyE7L8s0HEkYs734PgJX+yxCryZDPKDXUdDEBg+hVACYX
+GU5N+ueHCvW+fcRu6WMyQaMDcq0kHfqnTBWWPpoc4Ng5ya68l7qgealEBsqqszqTBqXryvWQTKg5
+WnauD8SV5hadXPrpzmZ0z+WHS9sOPeWKXNtMMTOd84+Jf2K9vEbjWVnu5ZJHPJFxHNqQyIpJVLY+
+hYrM3UEu7/WdCbluvLX1OqXGHL2pSvdbmjw3lf4myYAxcp+SLKSog9rvnbN0Jg/sWuEqrvorUk0s
+89Yn+BVljaplVFuy8QRLM0rKFRqWweayXtAkpWkUETSlv5OgwUTL5012hBvJpjeYWqob+pi/yXuS
+hfmwkIUs5DEx73DDfoSdMOYMGAPnH1EX2ou2F23//6DtHWuVQ+DfIk/FpgBgjPlta+3Li7YXbS/a
+/mhlYT4sZCELeUwWm8JCFrKQx+Rp2hT+5qLtRduLtj96eWp8CgtZyEKeDnmaNIWFLGQhT4F85JuC
+MebnjTFvGWPuGGP+4hNua9sY88+NMW8YY143xvx5fX/JGPNbxpjb+n/zCfbBNcZ8xxjzRf37qjHm
+6zr+f2CMCZ5g2w1jzD80xtwyxrxpjPnshzV2Y8x/rnP+mjHm7xljwic1dmPM3zLGnBpjXnvXe993
+nEbkv9c+vGKM+fQTaPuv6Jy/Yoz5x8ZohZx89gVt+y1jzM/9btr+oOQj3RSMMS7w14FfAJ4H/ogx
+5vkn2GQK/BfW2ueBzwB/Vtv7i8CXrLU3gS/p309K/jyP55b9ZeC/tdbeALrAn3qCbf814P+y1j4L
+fEL78cTHbozZBP4c8LK19kXABX6ZJzf2vw38/O947weN8xeAm/rvTwN/4wm0/VvAi9bajwNvA18A
+0LX3y8AL+pv/QZ+Jj1astR/ZP+CzwD99199fAL7wIbb/fwD/LvAWsK7vrQNvPaH2tpAF+TPAFwGD
+JLJ4328+PuC268B91I/0rvef+NiBTeARsISk1n8R+LknOXZgF3jth40T+J+AP/L9vvdBtf07PvvD
+wN/R14+td+CfAp99Evf//fz7qM2H+WKZy76+98TFGLMLfAr4OrBqrZ1jVh0Dq0+o2f8O+AvAvJa1
+BfSsnSN5PtHxXwXOgP9FzZf/2RhT5kMYu7X2APhvgD3gCOgD3+LDGzv84HF+2GvwTwL/50fU9nuS
+j3pT+EjEGFMB/hHwn1mrpY8qVrbsDzwkY4z5JeDUWvutD/ra71E84NPA37DWfgpJK3/MVHiCY28C
+/x6yMW0AZf7fKvaHJk9qnD9MjDG/gpiwf+fDbvv9yEe9KRwA2+/6e0vfe2JihAjiHyEq3K/p2yfG
+mHX9fB2U7fODld8L/CFjzAPg7yMmxF8DGsaYebXqkxz/PrBvrf26/v0PkU3iwxj77wPuW2vPrLUJ
+8GvIfHxYY4cfPM4PZQ0aY/448EvAH9VN6UNr+/3KR70pfBO4qV7oAHG6/MaTaswYY4BfBd601v7V
+d330G8Af09d/DPE1fKBirf2CtXbLWruLjPPL1to/Cvxz4D94km1r+8fAI2OM1jnzs8AbfAhjR8yG
+zxhjSnoP5m1/KGNX+UHj/A3gP9YoxGeA/rvMjA9EjDE/j5iNf8hahRN7p+1fNsYUjDFXEWfnNz7I
+tn8k+aidGsAfQDyyd4FfecJtfQ5RG18Bvqv//gBi238JuA38M2DpCffj88AX9fU1ZCHcAf53oPAE
+2/0k8Ns6/l8Hmh/W2IH/CrgFvAb8r0DhSY0d+HuI7yJBNKQ/9YPGiTh7/7quv1eRCMkH3fYdxHcw
+X3P/47u+/yva9lvALzzJdfde/y0yGheykIU8Jh+1+bCQhSzkKZPFprCQhSzkMVlsCgtZyEIek8Wm
+sJCFLOQxWWwKC1nIQh6TxaawkIUs5DFZbAoLWchCHpPFprCQhSzkMfl/AFTPwNE6il00AAAAAElF
+TkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.6008... Generator Loss: 0.3544
+Epoch 1/1... Discriminator Loss: 1.2181... Generator Loss: 1.1289
+Epoch 1/1... Discriminator Loss: 1.2560... Generator Loss: 0.9682
+Epoch 1/1... Discriminator Loss: 1.3160... Generator Loss: 1.1959
+Epoch 1/1... Discriminator Loss: 1.2201... Generator Loss: 0.7981
+Epoch 1/1... Discriminator Loss: 1.2491... Generator Loss: 1.1460
+Epoch 1/1... Discriminator Loss: 1.2200... Generator Loss: 0.7130
+Epoch 1/1... Discriminator Loss: 1.4682... Generator Loss: 0.8232
+Epoch 1/1... Discriminator Loss: 1.4792... Generator Loss: 0.9793
+Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.6182
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsbWt23/X7Zj/n6tfa/T79OberW12qynYVdnDKkUOk
+oESKFBSQkCMQkUAIkHjAQogHHlCQEC8QEJGIBBIiCsTEDkoUnDh27CqX7brlqrp1++7c0+1+r37N
+fn48jLH2qRs3da+rrnON1ng5+6w115xfN8c3mv/4f8Zay0Y2spGNrMX5l92AjWxkI58s2SiFjWxk
+Ix+QjVLYyEY28gHZKIWNbGQjH5CNUtjIRjbyAdkohY1sZCMfkI9NKRhj/rwx5g1jzNvGmJ//uJ6z
+kY1s5Ecr5uPAKRhjXOBN4GeBR8DvAP+mtfbVH/nDNrKRjfxI5eOyFH4ceNta+661tgD+DvCXPqZn
+bWQjG/kRivcx3fcQePh9/38E/MQfdLHrOtZzPcz3fWYtNGsrxjY0rC0ac3Xd2sqxWNBPv/8emO+7
+2oBr9Brz9Kpa72EAN5Dh8OIQnwgA3wTY0AUg9Br513HwHKtt9/Fc+byqLNlqDkA6mzNrarmmked5
+nkMkt8LxXKhFJxd1RVNVADSV3IvEsMzQ51lc9IfWpdaxqOqaRofFmHU/DEYfErgxnXZHnmfBBKG0
+o6qY5yUAq9VSxqEqqHVcQtfHN9I2a2qqutZpsFR1o2Mu/9ZNw1Nr06A/w3U8giiQv42HE0ibrJVn
+NGWDbeTi2jP4rs5NWeC42v5Wh3Yt43K6KgBYzC+pqlLbYDFX/Xdw1mNrXIyO0bppT9ePzPV63Kyt
+sc3TLz6K4fzMnZvSP32u58d4Rtpb1g1pugAgW6wodL34BbiO9tW9ahzWcdYNulqfjbGg40XdPF23
+6+VrXGodn7oxVNqRPK+u3p3GwvrWaV6eW2u3f1C/Pi6l8APFGPPXgb8O4Lou+3tb+I1ztWjqumFV
+yUJoipxMO+8Yc9XJqpTPqubpgBkLjl7guODpjBnX0PJ9AGJ9OaxtmOVyD4Old2MLgN3P3WO/fh6A
+Hf+Q+rkRAHf78gLdbUX0W7Iw++1ddns5AOeXBa++9CsAvPzLv86vrMYAtHN5ObYGEc8OpO3twYBm
+1gLg4fSc7OIUgPllKm3/nMs33pJrn21ZevRkXIouS+R5x5M5aSEvbKAKK3AcvGelvYfDT/EzX/kq
+AEnd4B/cBWA4PedX3zmRdv7uNwCYnD5kquNyp7PHbiBta/wxZ+MZAHmaM5mtAMiQds6WKUVZ6Bj6
++LGMd7874vqzNwDoekNa17sAFLUsucWjlDoVhTXbdThoybPN2Xt0u0Np/499lZ+cyLj8zW/LHvP1
+f/53OD85kvbYCu02kR8TtWUNxFEXr5b5KfXlyKsSqy+ji6GoZNzyYkGRqtLzLaUq4g8j//1//V9K
+X/vSiOHB59gKzwCZm+9992sAvPm1l3kQyXjtPnEYJtJXvy3zWK8ayrgtN60LPF82pFVY4mS62Gc5
+rm5Oa6VSh12m40sZz8LhNJP1+d79MVmuSrRqiFvyu+++8ej9D9Ovj0spPAauf9//r+lnV2Kt/VvA
+3wIIAt8W1ARVgHVloLoYllYmtnHAU08ncmCpE6obGAaudobGcQk93ZU8Q2CkizK2co+sXu+qlnkq
+gxcbQ3omK+LJ9xa4O/Iif+ovD4jelHZ4RhbruT/hMJTvw3dXZIeifF/+7f+L/+3r8ibXDy+5vJDF
+8paRieu838Huy4TffGHOQ7VGhl5OsZJnD1Npe/pbKWcTeZEOWwXLXVEwdnfA9lJ+F7ozrK6Z04WM
+W1KFDF6VhXl/9w0mX/gZAO7udkhfEytmvOpz9PIvS18mro5rwKetKJ6jZkmzUiVczVmUUwAuVg2h
+bm6ebstBGJKqebDl+/itGIDd/jalEcXy2b17mEh+eKJ9vrRTdq/LeHZPz6mdXWlz+5L/4FlRyP3X
+Db55BoA3vvY/Sj/PFthGxtVtoFJTobY1rifP22r7rDx5zpbOuS1LEj8BYNaUeDJclNOMopB7PCpq
+RP093YzXRsTvJzNPlNqnB9Ln8GRGHR8CMHnjl/jn35Jfx3nEzqm04x1WuBNZuH1UEVrLVMfbZDHd
+HV/b4LK0cs2gFROqJeCpApmUFZ1gR/rRTIli6XMVOjRr67RoYOX+Ib34vfJxKYXfAZ4xxtxGlMFf
+Bf6tP+higyWsaqKgoMpkcC69Gt10yI2hLtREB2JdhCtdEJ7n4frSlWESQCSD47odylq0Z6euaRo1
+0XUy0mxFz5H7zouaaCHX2qyiXz+Q311cpzOUxfRmLLvWl0+eYDyZjNq/4Fe+dw7A737jFd5++Ip8
+flKyKmTlqQ6jchZklUzWo9qnfFP05DsDSz3XF13bMCs9vlDI7uJ2DItBH4DnjxzO7sjA1NMtVpUo
+gKaQMZkVBVN9xjPLGZ1cnrE6vUYzk7b944fHnOfSF9+Xtm/ZPj6ieLaqFhWiyFarmoWVfrSsJdKX
+vteTxb+koK1vTtPxGToH0tddl2cj+TtIplzf/TQAxkr/ktMJ7pYoqQfLPY5r2f1fmL7Ftd7n5B7F
+S/w3/+QlAMYL+b5pxrhrk9pYQrUKWy2XG31RaoPbbfpj+Txsy+C3wl1MX9bIAdc48uX1X/224f1U
+rJCzMidGrhd76A+XO6tvyx/RT8m/9cs8WMramuSWpPgmAPlFjyoQy+wzboft62IuJqEosUfOCe0T
+aVtaWEpH10LgstPRtez7VKUoi9LIBrJXWi5yUdjDbpdyKt8/G8248KT/R6VPHuUfojdP5WNRCtba
+yhjzHwL/GHCBv22tfeXjeNZGNrKRH618bDEFa+0/BP7hh7vYYBuHFEPhyS4QtB2cuTQvbwqiULSg
+63p0EjGfcisacz9J2L4h3sqn793hMhdtPyoazkP5e/zenAfjRwCsprJbFZFDPlNTzVnSGNnyntsJ
+2Lsu/vBuZ5f5eB0olOd1O4YqFOvh3XfOeXj82wD82ssPOdeYQG4tvu6gVvvU67ikbelTOIbjSnbK
+5bnPwbb0KRyo3ziFXikafn6tRWzl+2o7x5nLjW92Mt5KZEcorbga4/kMm8r3T5oF5lKsqcfREa++
++0SuKR4xP5Hn+GqK2mGHypWxCI2Pm0s7nWFAkImZPNiJGe3JbrzVlfiLZwwdX9yrSR3Sj8WCYitg
+FIp148U1sSd77+et7JKz57Z44og1cvakoWfEArnlbTPO5e+TkwHn7rHce6ZWXAWluoq+Y/BjNa93
+WmzvijvS2YoZdaR/68DtTi+m15H27A+HDBNpv7n2LKtv/SYAn3nrjO9OxO3OL9YBPPn395N+JONF
+pkHb822a+GUAHrxTkj2UflyYC64H8rxkN2B3ILt/4onL1A12mAQy1yUVrUDW1v5om7gra6eqHcqF
+PCct5d+zRU6Yyposswm7PZmz2bKFnUi/yzIjs3+YE/R75V9aoPH7pQGyxmJNTasri87zK9xAX9jF
+nACZnO29PvduSdT3pi66O3fusNuTQR89c8D0QgbYrVOWMzETz/bP+frrcv3FEzGpL5ZT6lgWWDqd
+kar/2cwjbh3KizCOfXq5tONnDvYByC8eU47F7H7pt4946UwWd5ZXV5kBx3kaEHI1wFn5Lp1aFuuj
+x1OMLia3B71Cg2Rq4u77AWlXFvzzZY93VxMArg88TmrpX9/CrS1ZQIWmKn5zCoW+CGlQcd+I4pk/
+qXj9vvzOtAytkcREOrGMycHNHUwh0fKirggc6X/odMg0+nbz2h7dlsxPtyWmL5Mp7o60s5/HJBoH
+Mr2EVkv64lcJdSVjsFKF1t2pKHJ5ife2CnY70rbCvcnxhcR5vnPfwz7UuNI6s9AYXI0ZRZ7L3p7c
+48XbOzz7nKyLYpITtTTu4MqmMAy79DSmkPQMsSoIz5R84fNfkvlL3mbhi5J85v99A4DfulxeRfL/
+xcREeEOfV8vLPy6OKSYyVuPgkmUs4xmHHfo7oixvPLNLvydt9pfSj4SKwYGMT50H9FTRhb5De0/+
+riqPZixjtFLXL+7W+LkomKJok2pwMRj6FKWs5U5iaGv86z4fTjYw541sZCMfkE+EpWCweNQEbsgo
+lh3I+BnWFw0XULDXFkvg5p0+X3nmRQBuXxdNvX3zBmuUQ+AGDA9EpxdZykyDOa5n+LKRneLJjnz/
+6sMIx6iZuYpZqdmNNydEIuD7o4TFgexWF5k8Y6fZ5eHXfxGAiXnI5VR2hypy8TSoGNWQabBnHfs1
+eclbJ2JVXOYNjgbMrtmSZShafl5Knwu/wqrFM03nHKsJ+OpFSabR69wzOHPZpY6X0reqsTie9G9R
+1oRn0oa3HrzNqiUmeNg47NyUYF5rIM842BswX8pY2fmYkScWwY3DFr4n2ZV226OlwTxHraBlf4ss
+F2ss6gBG2t94Dn6p0XWvwo9kjNqefL+oezjqPsTDhmPNuryY3sVpxM2ztx4xf0v6GvVkZzfZjK6a
+yf3I486utO1GP2JH10Aa+zil7KDdbflsGPWIQp2PskM1lTY3xHQUT3F5cEj3TQncVZ8Tq7D/zWMu
+NfhL89QMNxhi557cr3lHxrU15dFccQpeh6YjLt3N2OX6LbG8Dto91Dgl7MlYNkFBaORv0/cI1bIE
+i5dLm2Pfp9A1UiFzHS4MbXWlk+aSaamYk6yhrZZ1NoopmpSPIp8MpWDA9Q3DVshAF1sVxvTamoaM
+d3lmX/yvFz97i3s3ngOg0xIzLG4F+JGj94qv0jFV5RFEakaafapCIvWr4BoAz1YuC0dMPCe/w7WR
+vBTHaUwVSrR7d2uLazOZhLwjC3vyzn0eJuIjT9+v6GseOInhvUIWWGQajKwJwoWCUUJQC53EsYSK
+oCkz0FvgVupbFjWnavouFh5vzTVWUVhSTameBQ5fFUgCn1O34xfSgHGlLoxneG8hC3ZmV8Sn0v5r
+d+7ymR+TlyzJ5MEjJ2IxkoXUuuiwvS/fDzq7xENZpC03wdFceVOof9s5Z3YuHU3zJZn2qfYq0oW4
+LqFTEqhbiHod7jSj64nSaPkWH1nw7zRvQiXt7H1zwfVEzO5WrFmpk4pRKL873IroOjLeO05AqS9k
+txMTdWTOWgpIa8V9QqPuWuBRlNLOVW1IHXmZ+hTc2hElOzuRNXLzhZzOa9Kex3NzBXZzDGRWx7aU
+uWnyjHC6rfd6zH5H7rXfGnFttCfj6fWIB9I2p9QUY3cb35MxNC64ugGYVoGzDHTAXBxVnLala30Z
+4g7k+3TZJdc5KdOKJpJneIucy2KtZD6cbNyHjWxkIx+QT4SlgDVQO5Sly7wreqofhIRicTHA4/oN
+2Wlu7tyifygWguOIdrVlg7MOSjoGo1qynhrQoIwTQu9QPr85lfx5mU1IfLFAdoOQRHEPo6Cm6ig8
+eF5T90Ubx8i/uXeMPRLzcxFlHPbFhKttRTWW3arwMvq6S13blp3tyXnOUaLgrMpSqnWwVRpWCjtW
+MBvnVU1k5fdHxYpGrYOFtVeafFk0fPOx7HIPDsSqOLCGSyt/lxUslgpHPp+Tt+Qet565zgvdOwBM
+Q8mfZ1nKKJYx3r7dpduT/sdOm0DN5iSyWA3yrYFjtgqpFK3nxi0S3ZVm+Qp/JO23NZSZWAJlI7tu
+40XkqQRPlzR0ajEh8ixgPhWLbv5szq2VfD5KZQc+cg2HO/JZp7PDcO2ihRWJAqSSxKenQdpAnbck
+CXGstt3JyVZybbaawkzwGb6fca0jlsk4F0txXkTUezIWebriWC25BvfqfqGa7as8pVFU1GBkqAoF
+ch0k9F3FJvQDMHKNp2PlNg3+2sd0wJT6WqYrUMyJZXblvphSzE23HeIr6KlfLzlRWP0iyJmdazut
+xWt/tL3/E6EULJbK1pRlia84iyooWalpe+daj+6ORosj9wqoZGSd4YcOxsjb5LVCavWn/NgFhdJ2
+2zWeRvi9XCbx/WSfvUgGcufWDRL1qd+ZXTDoyeL1Bwk9TdtFsUzKGwvL6VjN0+X5VWq0f7vHaCj3
+dhcR3UxrHw6lH97S5VNtzUQ8KYglTMK3H1k+rfDn1Uza+Nnr8J5Y32zdcDn5njy77YKGFPBdyNVV
+eLYrff6NiWVX05RVCUvta2patBRF1U5Cdg/l4cFSrs2Pjuio4hkNe7iB9DlgeYWIcyOD9TWjsoaY
+W4/hUN2HxqVY6svvBgS6FnO3IpuJ0purS5EVJ9DIPMZeQmjFHXuwOiPO1oogJVf483ZPfecgIFKX
+YdC4eKqkjG8IPE2pVi65+u2RKmaWC1KFyld1Rmb171lNquZ6PS4ZJ5ICtSvp564/5PRSFGcKuIrI
+dAz4vo5zKp+1lwXvVgKEupjFRLsKpmrvEfuKuKWGStZkoxuAm4YQy7hZN8QUirXOaqyvcQTjUBuZ
+v6ZU16DJCSLN8JDQaouysHWFu/7cpvS8j4Zo3LgPG9nIRj4gnwxLwVrquqaoLDhicgXVDG8iqnTc
+Kcjnqr8Opjj5bfk7EW3v2gGu7sCuP8S4shuZOifRwp7SHlE3cs06j5/UUGuwZxBfZ9SX3Wox2GYy
+EfCS7Wxhww/Co8v7x2KbA003ojPU6sMs4lxNvNU4JYo1V/yO4gPalqkW39xtGdyLdfGGw0zrJM61
+mi6ZBDS6k3JS4OvnVQXrsFHYQK4uzzeO5F5+7mAUsT+zlkqh1rVJyTVSHZkRTS27u+/I953tfcJY
+dmDPD69wAWUdk7RlHqxxcbxSJ00zQy2LzWXcHLPC5LLDRk15FYwsnRqv1kIvte6mtYtvFEzczMg9
+ifY3qaVS1FfWjulrhkY3ScblAtfKjrj0aoon+rxtn3wmN9/bqgkUlLbqrV0pH1vL97O0YKFVl0W6
+5HIlbVvMfFa5uC49zfNXo4ZHgazJlU1BYfGOk+CWYt65LclYNOeWYqUQ+klNqRBsyyWpEYstt1uE
+kVxvtMjNjRuaWrMP/hKrBS3WS2gyxW9Ul+QX0s55LiC0fOWST9TVuhgzS7Vw7cJyqdkVGwdX8P4P
+KxtLYSMb2cgH5BNhKYhYfLchrmQXqFsBnhbihLXDQNFapnapTyV46I1EUxt3jO1Jygc3wwkjvbYh
+GMjvvJlPso5LZBLgSooV2x1JPe1c7zDY+QwA+ewU5z3R5tutBZ4j9260TOZ0ksBKC3Tygkx9eL9a
+UaailVNr8FRBL9Sq6DUBgfp3+Qze17x6uGw401TduaYvV0XDSINoJ7WDVR6DGovGXykdcHQHvTiT
+75umIYrXnAUunqIK+9WIoRaSNTsubY1huJryCmuHUqshl9OautHYSFlTbotVMeiMCI083aoPXKeX
+uBqLaLstnER+5/jga5FX4sccFzIGi9W78lkQXiFWF6tdJo816FhfkLQl+LvttKg0LetUMvajIKPU
+qkb8mkQtKG/pXeXpyyZgu6NWoVqeHb++qoit04ZyLn8/GM94kssOnEQRnqZJ5xoY3G6mPKdt+G0L
+lZX7WhPjtcT3d2odk17M4nxdPGZorcT3XxRzSoUxD3ODi6ynSovD3DjA08it68ag1ljVXNKspN/1
+vODs+AKAs0rGqpVB0JX5LYOUbCJj3LY5ib7Zs1lNZj6apfCJUQqmrsFpsFoVdmNv6yrKutcfUWtE
+tloY8h0dCIXMNpHBPJGXNOmFuIqjt2VJ1Sg2YR6QPZay5le+IxWQv/7aa3zqpkzWMy98gSbTunjX
+xQkVWJLGFFqVZudybR4WuB0Z6PfziutKkOJEJRN1CRY0aICblq8mpanpKlBm2Sso1dROnIIT5X2o
+5FLCToelmtFmEVIrfiOpIVH3Z+nbq5LxqWIz2pW94hDwPUtL4cq1m1ImsqAPBh5+oWO45jdYTJkh
+iy2qW6RjURC2cehHcu9W4sBKPi8VmLRghuco18PxKU/O5R4Xq5IbIwFRePmc1aVE+PNSxqczdNkK
+Rdmcn53yeCJAprbfouNofcggYDeQvk4n0qeL2lIs5CWeZD5JS/P4q5xcMxyFO6cVyz2imeb2XZeF
+ZgZqL2ShLshi1lzVN/S6BqdS4IduTsGoS1c1qMsEu86eNC6Okjn4RgKR9ekxRrNI1d41llNpz8nx
+5ZU74990aSpVBm3ZCYI6xSggqZ7lkMm1OZfkubR/tnjIyVSUwnkqa3pg+jRax1PNG2JHHcukgblC
+wU1D4XyAeugHysZ92MhGNvIB+URYCo7R4g9aPL+rwbBuB80gkZsFT6ayO0y/NWcRSBV2toaAOhUH
+Q/nd85+9Q++ewKDNYkI21/Tj19/g/3hNquHefEdM2LnnEwwl/724zOnvak7Y6TA6VHO3Dsi1IMrp
+rXeojMcaI8vnFSe1/Gd/B06RnSJfVFdMWtaRwGcQOiRTafNO4uDrjr7A4CjEWhnaeJQvmatpHBTN
+VUDRqQ2O5vqGnoOjpntLTdy0tvTNunLS4CTSiJyETqUQ8rlzFfE7vZTCrmZ+RNaIFTCeLTheyu7n
+mpR9xO0ada6TuTLO6QOxuh4snjB9R8b4a/ff4/xMfle0WzyniNP9u9vUE0WO3pFU6BcOO7T2BCux
+vHjEVkd2v6M6IxmJ+3C9N6ApJGB2cinBtbNyjNXcfE3J7FT5D4IcLmUergVDZksNvCpGpLUaU+sY
+v/lwzlklfY0L8LfFmuqHMfF1xTVMhNzFP/Q4tZJmdIxzBYv3rIvfF06J+aViVh7PqUvZzb1Bj7EG
+V09mZ7CSZz9OV2yNtMBsT9p42LpHEGkhWdSlmYtVVTUVK8VsPJlccnQkY7BQK7UOGqwiSDOnxlEX
+dKcb4ARi3Ty8nBFrgPXDyidCKRhrCZqalu/gK8Z92C1pLsT3Shlzf6Gm2OxtHh6JezBHcspx4XLz
+tgzqV+qcP+eIKdYdbHGuWIevP3zA0YWYqL7WQ+zd67PqfwGAbFpQqB/tR30qzZV77YLigZbvqiI4
+WTVcTORFX+Q5VDKM0+WYUheebcwVLx9r2PWqJtfPUgu+xhS82l6RehQ6yVVRXPEIVo4DoVxbYpmt
+uRQbh9CXBXKWysURhoW6FB6WfM26sxhjBwoscgNWS2Xv0Yi9nc54eywL+vV3x6ymErd5tt8mfF5M
+9CduRUsJtFIt2X289BjPtfYh9vj8XXnR84Muy4W4B++dnjLQ2McdZWTJ3c8QLLV+YvmINx6J2xEP
+25RKtJMzZ7GSe7/2RLECRYtDXfx+L+Zkon1avUutyK/p5ZxSa0iauSq9wx4nWmn67nRCmcmcffpO
+F/earCevuslNzYg4fY0/mIiH21JjY957eAVBdj2XZo3JeHJfxvJiSrkl9ypPfF57LDUc3vuLK9el
+rHKCviiZF9ridn31SyGJK+u+W8xZI5nSVcPlpbJevXdKZdekPepSnPh4mvmxdUmWiBIq513qQtZy
+7HqcrT4aycrGfdjIRjbyAflEWAqO4xAHEaMs5taa4XZ4QKyBnMuiw8OxBBLfPV9xma+huVpZNxwx
+v5BdwB1b3ES2Ev/eXVrl1wGIL1LQ6x9oXXr9ckmv+mcAfMN5QO+WMMZ1DhzWMZuwsVTXJEORKBFr
+N0uplY8gzHpkoWjrfAXuminMM3iaPehqvvqiNiQ65B3Pst+Vh2S1i6MFPA8eaZVe2bDSGJohuKIZ
+bpmGQJF0iRsy1mxHqQU+dW3ZUYKRVu7SaisByqxFRy2apNch3lWsxky28HMb8+g7Yh2cP3lErlH7
+cc/lugZEW60GrLgEs5XswGYxpb8udor7PLiU57317UfsdCSgOz3KObglVsPOpWaMbr/NdKpkOeGI
+Qe++tD+qiUsZz+XiktfGMq9OJhZDt3ZwFIL+/mVOY8QtKbMYV9fOLF9yc6hBYcUHVHQ4PVN8wOUS
+rZ3ia9+dE9wXl+bHP/UKB12Z62v3vgjAyLP8tYfyjLe/m/BYLQzX72DUelu6Epx0D1sUE+XKzBds
+BfL9/bBkqftvu28xvoxhMpTnLv0YR6tH3TjCuIpGTCeEWil8ePc2L70h7sM7x7IOb7em5BpEjAKP
+yJd5OPRDLhVn8agpSD8JdGwfVRxjaPshBzccZpEsmkOnz0j9u9ZsQKDVh73+DgsFxXja/J+606Z9
+LrN8eGdE5znxSX3Xp9OXBTS8l7Dflsk7GEiVZXT7gOc/K+burRuHTBcy+f1FgrdmvMGlXMhiqtXU
+rsKCdeFCu5Mxasvf355X62wSK7fBUdx6Ju87LcewlSh0+zBgr6uMRsYl1FjEtzQ6/93LJflCFnns
+WYwqyNyBrmYzMr8mXejnGnNoe4aFQp/dqKZjRUFmzjmXrpKZRi6RMiuNdLyn8/fwuvKS7j97l52e
+jPedwXPc2ZfFO+gbrJKo+HNpw+VuyEhfiv4iZaCZim4vpK0Y/3q3orMrz3nxzgsyH37IiQJzTh+9
+cZXu3favobqUk7RkOpcNIFeXopMELNRs7zU+M83a7AwDXKWrd9w2kaY7t65JLUM/S3io2Y6tvll7
+Y6xmsKtpvYHbooXEmKqxEuUObuHuCQv2taTHaSovpjU1dSPuVjh9E4DmQUn/roLX0hGJblRdIlo6
+tndfOGA70jqe3i35d69L3FHF6rugNSFg6O8JqMssJ3zuhlaHZgLea/cdirHEHKytcFytArUO6bqc
+/+TRR6Kth437sJGNbORfkE+EpWCMwY99mrDPLeX8D6OAkR5kcrDd4faWaMdr+Zyko1Bg5QMcZmcU
+sWjtwQ0DWqzEtQ6RUn598Se/SO9c+RkC0b7t6xGHN8VcNLnFz0Sbr5YVnVDz9J5LgkKvFe58Oa2o
+FbA0qLYJrAQd7w0sx2pNmAZiDSpaNWtD1+VzuzLkX751ndFN0fw7ZxGd28ov+IvfkmuLjK9plN3H
+MlAXZNrUuFrY1N+qmJ6phaBj2TSWkXJCepVDZ0t2oMXcsKXU8M6kInxR4c16rsDt/bu4a7r7UZcy
+EJj3ji052JZn9HZHKKk2jgby7j0oMHvy9H4a0OzIs19wUvztvj6vJHLlen+o9PrBGEerlu4MX+D5
+vuzi2e6IWS67XHo+xWqAeaz5/9H+Nvduy/yFFx6pto2pgVPZxWty9m4q3Vwk62a0F1Ip5uG9/g5x
+V74fOj3Pj5sbAAAgAElEQVRcPUdj52BAS92mYS2ZBafIiI2srdbeiK1KxjBqDCglfhmIpTTamTE7
+Uvbs7hhXq1JvHNxjRynrrt8YsNUTcpaemv7Dfgz+GsZur5i53SLB1eBhv39I2MgYHISaqdrKmZ3r
++RwnGZVmPmbhFE2u4AUxz4d/AqskjQHXWObpgtTTcuNOl1i5+7tJB68vgzpqutRKaFqtgW2dgnVF
+gCkyGmUeqmcr/C2Z3EHH44VHGql1tYbBN7KYAD/x8LcVKcmQxhGTOYwdXM1KFLog8qzk7ETrK+KM
+oZbpkg+I4zWRZokWc9LSLINfGfy2LLrUm5HYZwHY/4nnaemJAz/51U/JfX/jZY4Uy+6ahlyj3mXt
+kqr7NHlQ0VrHGtYnHvkuXY30z43FVwYlp3mI3ZKX96RqKGfSl5Yu6O6zIf2BmPjV5BGZvv293T6R
+EtU0uYObaWajJb+Pr40wWgfh2xx60j/HtDAK6vICl1pLxlOlredsSDyQ728835AuRcG7acxYgVN1
+nnO51FSdulXdske7UXKd3iltrTXAz4m1xLmuDW2NH60RilXgXlUihlMHeyGuYnQYUWvFZHZpuXlb
++RGH0obAvY315Hl/7l+9xu53XwfgO9Mp1ZqCXzeCQecGb3ZlQ/JPVmwVMt6f/eJN9q5JJi2IuhSL
+lY6hZiqModSqTFMVV6hXN3GoNUsUVQVRZ00eJM+tT9r4qkCn/pyJph7t2TZOJGu5NQioP2JKcuM+
+bGQjG/mAfDIsBSwelUTQzyS3W93pE4XqSvQDorZozLjOsZmYmqVVDVimNLkAkprKxzlWePCzFlOL
+xm+VQ4zmfzM9sSkY+ERaFWeaCM8q7XfH4roKRXVCqli0v6sVh8WyBoXGHs8Mo5sa9a1rXldYQBV4
+tNR96Ovm2HUhXsjvhm6L/oWeV/k5j2ggdRe3lVvQfRDxRKnj33VLpro72GlJpZWYcxwCBS/114Qt
+hWGhefwsd7l3TyP1R9fxpmLdeLag8mTsHDUtE79L2EiAqwkOsRokxS1x15GqeUmhQ17kmq+3EKoZ
+7B8MQbMhXlJjVlpr4Va4GrVv1hyO7gxPqwTTNCRbW30Dn34uc92chnhaw6C0Auz1LLtqgeCFOJpR
+Cba6WOWvyE2LRvEirVoJSdwOiZLybA1KglDPDZ2XpJppMs4leaoYl1I4OsNuxB09MiC6dcDltVsA
+vP3rr0Ah42lvCGmPU81IjrVW43SX/U9JO3dbu4w00+A1PrlaqlbXE4mHM1MG8q5HM1O+EN9eUdPF
+u3tYDba6HS1cmeQsdT3lD6dojBfHT2mUAKZouizWXH8fUv7IloIx5rox5p8ZY141xrxijPmP9fOh
+MeaXjTFv6b+DP+ozNrKRjfzxyw9jKVTAf2qt/ZYxpgO8ZIz5ZeCvAf/UWvs3jDE/D/w88J/9YTey
+DTR5g+cUHCk70sGswrY1xVT3QFNoblBjNT235jko3go4fyTXbt1+hmYo9yhK8HRnon0GGkhzNY0X
+zDzCrjIYhwGNBr6ixL06oRkDtZ4eXC7WsYwV85X4ulO35DDT4imvoVY924lLPKUCczQomRtLoAef
+Gizh83ogS3uIl4iFkF2Tf9PnW7gn2vazJUanyhnBMJW/C6/CUV97oNz+w8bhgabmUlujQ8Sz3oon
+a+YhW1wdYOJo0VWDhxdJnx0TUetxZNmlT1qrNWU9loqwK9bErYGLMrfh1C6BwqB902A0+lmtHLKp
+xnPW8ZV2QqostlVWYzUAU5eWYiafn09rWgorDgbr8w8iGo1ntG2J1XiH44E71P3HephEzxtVzEaZ
+Z6C7Z9CuaCmDrG1qBppHbvcSfOUAzJVnwq/bGC26aheWi98UiH0+n1JYCewleg5HNnHY9aUNzos7
+ZMrTkCQJXrk+CLi46ms+1nWY1ITherAsVlGqTdPGW8cR6jl+oWeirPRA3LDAGeu5GFlJoPETbxRx
+8b7Eo/ygpKOHz3xY+SMrBWvtEXCkf8+NMa8hR9D/JeDP6GX/K/Cr/ACl4DgOUSvCa9okShByUs0Z
+LcR9aCUZjlbO2azETzRSqwCNxaTi3QcCppkulhxpAcHdO1PsSlyFaPuQTCvxUOjssnHxlKHZ1h6R
+5vpt0eDEWrKaeXh6clLUlmsvs4bl+tSd0nByKYvi5pZPpCQcVC6uL5MX68lTF65hqSb12cmco+I+
+AGnT4u4dxVasDxS9NSJ+Wck9Hi+otEZ6Hw9nqMrE+lfch2ua8veLhnilCiSw7N+S7MrR+yvupeKa
+pacPmRWCz4jVFfHqBLQ82YsjVuoeFLOCs5VgBap5wyNXF/JEiUWSFo2+YG0/pOepIifGKENzWZfU
+rvR7qWXK9SpjXkr/3FZAz9dzFSvLRSXXBsbS0pOxVjquedvgaMCwrBpaCjgIow6N1hfUTcN4KXMc
+NPKMaAULVUJN5VDL0qJrDMtgDSIrudBai8FDee6iY1np76YLl75SqW3PnpBeSjvyhaw9NwrpaAVn
+FjtXNH6YBgpVip4LOseOckqyaCi35Htjh7iBuHGmbKjMutw7ASvYiTUlnDEFRlF2QdLF03U9rWGx
+PgC45XKoh898WPmRxBSMMbeAPwX8FrCrCgPgGNj9A35zdRR97H80n2cjG9nIxyc/tFIwxrSBvwf8
+J9bamTFPa7ettdYY8/viqb7/KPpeEtrCgNNxCLcldZPkIbWSmK7aBW4tWtDrtCnXDMVrCGszYaZm
+1rxIaM4lbdQJpsR6IAeLx9R6yIajOylRwHIqWru7H+D2NZfn9Kk12GVDi81lJ8hyTWU2lrWhYJ2G
+uRalPJy3WJ/jkaYlSy34meuYuJHlHd0pF2/BuZrEd/ZfJTiWHHuyL/1fuFNW/hql2LBM14VEhp6S
+ZmSuuSJxPVLikUu3YKnBx17iCGkHsNdpaO7dkvZ0R8zeE7PTjhTu3JvjKOIxPR0zyySlVZ7POQm0
+YnRZXNGRtWJB/tluQ14rae5igVGLLikraj3zsK4LVqkE5S7UfWKxRaMR2CoHPU6C5XnGcm0Sm4hA
+j6fLz2X+j+YzkhPZPXe9kLmmE5N2dWU5ls2SCz1DcxXLMw5wmS9lpzVZSKp0bHngs2qUzCfcxgvF
+GjThfRnD+vNMO4Jo9ByftJGTpo/9GXWu7h0SRGxFPnWspKxjS4ES8VQDcrXkytwlq8XtyKda+Ro7
+sBK3o2kmtNVzLaMCq6dn1+Eltbq9pqckOn5EqRZRU88p9KzT6WnEMlMSV8fQ+1BnaD+VH0opGGN8
+RCH879baX9CPT4wx+9baI2PMPnD6g+7jOA5Jp0WnNaDtKblFb0TpSCfryqHQBdvMnlwdSOK6smCc
+a1vsqrnnFCGuHuiRTdrUyibk5CVeJofQhrdlQU9nGZFG9Y3rYTyJIhMZyGQRNzZgobHSwtGqzEpo
+yQHSvGE6Vdh1MQWtQfBbHo4e8unry30Qh3T3FNAzTbm1r5iM/oj+jkCv07FU0C1PKjqe9Olgv2Ko
+EGy3cnD1frYLYz0kZluzCFETMu5rxB5LW9mQzWCf/nTNTORwapRQRU3ZIgsxemJTE5SYNePwrQ6D
+M1FYOwf71Mo72VW+x0lzjtGMgy0NpdaLT7LpFaahrmuWjfwuVY5Kx5kRqvtQFw5jVawnVUprpLl5
+v+FSixQWVv3zKmOsSiGLCjoa++muMlJ1bR7PCtrqgoyVJn+Z1VxoRmVUz+jVYsBOs+yK5XlYT6mU
+oMcgGJKzoqZQHk9/mjFDD/I9CxkfS8YrTmQ91YG5Ct27TkrkybpZzeaUWuEY+Qk2kLFVAnIushna
+dHzPo1aSFeqGQDNCjhlQ6GE2Va1M4+Mllb8u9w+o1EVx7JRkXUPkblGuPtoBsz9M9sEA/wvwmrX2
+v/u+r34J+Dn9++eAX/yjPmMjG9nIH7/8MJbCTwL/NvCyMebb+tl/DvwN4O8aY/5d4H3g3/hBN7IY
+6iZgu+9Q60nStnFxtPowXbpXR2yFQYNRAo1CWXEf/eoD3n5XdvFnnotJ9ACRaNChWrOW1F1C5Rq0
+RlFrLGn68r3X6WI0Om0aS6N4gSK/YHJfdtBcWaJzH3x3fd+GlSLGPBOyo8y/sZeRKcFJpT5FUcOD
+h9LmVV1y/kDu+xdDBw5lF7j/phyF/mDRpehLnyO3oqWQxbFnuKncCW+dZjTK/5gqbZc3bDPw1Sy3
+YBV1NwwLUi0eilsOgWIvGvXuVqbBWdPKBQFFKuO5euSyOJd79G5OiZSuzNUUu2P6uJlWdjo1leI3
+3NLiaDbDugvsqYxRtIZ+tyM0/U8xOyFXumZbJjjKMVkWDoniTOaKwDyZjWmrpRgtV5hYT2v2PaZz
+2UHntuR4rtWosVw7vYRVoq5Pu4OrGY46n+PoYSnFoH1lnTkKXujvO7zxumRfuvNHPPq2VNVmxSVe
+66dlzPUA0dW8ASVLsa7H5Fj5OYoluZr54a6LVTfHU9zEdnuIp8Va+AYu1O3ycmI9rTpfTcj1eDpH
+ad6sU6HscFRZgxpThMMurmaoAlNiFBfxYeWHyT78BvAHkb/92Y9yrwZLZioWmc+eq4AWb0mtKcJm
+EJHPZEIrWxIqxvvhG0pv/pu/yZsrGZ0hc7xrgnfvns1w1ISd9x+zVPJTL5OX8SwwfOaWsDQ5fgtX
+o8VNlePUGq8oS4pAorcDLW/thR6VcgA6pb0yL9O0INiWa/aDhIke4X6pfvQjfFJNa5bWkjSyUH5t
+5NJ/Vxb/TAEtKRP2hkLuEY5DUgVqlWXGK9k6vlARaLWmF8vCnK2WxKo0b+3GtBX0NesOKMbKJTjP
+aCLp3yxQDsO5Q4l8FoynvPNYXpr59x5TKttLx90l+KKMrXsmcxBYQ6nmdz2MKdflyQl0HX1Bco9C
+D6HNdMkFQU6qGY5ZVdPpyTO+9/CCZ2Jx4/Z37jG80GzNe68BsGgsF09k/jpbBv9cTepei/FCTO2h
+H9DVUu2RLy/VwacaQlUQpo55+31pvwngMWu4csNyV+Y6qmUDmL9uaU7k2leOT3lHx9bOhrgKR670
+bMduNCSv7stn9Yq6K33KH86J9O/04gynJf2zlSjesLuF19N0uTvCdWUzLPOc2iiZLhaUXWt9BikR
+5EtVsnVOo/GVRbUk1MrdqA6Im4/mEGxgzhvZyEY+IJ8ImHNjLcuq5NX5KYe6Q+20PcbK1pzbKR09
+xiy0ASs9Bfpb978DwEU34vP3JB//4u3PE+2t6+33qbZl53XSm7yrHHfn92XHvP6FEY2aia7rgVbL
+mTCi1AM5zswRgUbfO7uySzrtiEC5ADqBy1jZlfMaIs0hu92Ak7nsYk804FhSEStb9U7L5faORK1v
+RLBUqrSpQn9v+z0qhei+bzMKDSg+nK1orc9EjCyXCu6JrOyMbp1ilZbsAovNNae9nOMq6KsYxKSP
+ZSdcKj+C8QM8/d0si5g7MkbJp5/lmlLjX79+j6Cru61mBQKbk51LVP/982PsVMal57WvaOoWdsl5
+KhmcVagFXBfPcqE75dHZY/a16Oj0okuSyLUd16fuKhGJWlDniwwHscCCWYe31HpzTi641OCvuTyj
+eaDwYFfi3DeuDXEU0xBlCbNC2hx3XKxWnc6nF7SD96R9h8rH0O/wvXcF3/Hay/eZZbKG8K9hEazH
+YvGG3PeyoNoS9yo/OWOSyzNa8xRHT88+XVakZ68CXJ2OvtM/JRlLhiP13iU4lblOQwdPCXUmdYZ3
+JnO1c1OrgKcBC2U8X6WXPFpJX4vTHXauixt7mWa0yj+BFO91XTO9nBEXLt+1vwrAc/EX2NZyP38+
+wq5BRk5NomWvzym3nlPvczBUUM3tLUZbMsBBEFIpeWaVPSZeSIrooCPd3urEJKP1EfZAoCchNXOK
+QMlWZw3lQCZ/pYCQ50Yx511pz8l9B9ZgoaZmX03Jr3xph+l0DYxSIEldMBxJ5LndbvHpe7LgV8WK
+O3tiUvaOZeLveObKlbgebfH6ywqm8jwyjSY3bY9aT5Za6AJMug5xJAs6tl18BTrZwYC2nvTkOAFF
+S7MAisxc2IKgpYeVtj2Gk/V5nXPIZSyq5R5RSyLtZqSgodzBUXLca92EVblGnpb46ub4lUucrT1N
+XXLuEfmaR7A9wNcUcFW/ShIKaWq6spQjmeNnPiXVrsWrj8guXe3zlK7mhsNWzG0lpv3Ogxm5Ru19
+zTg8WUwIlYp+dwTXtyQz0I6664QRkWNxFE24FcomM7Y5kbqgs/IEo2eKOMtdFrmSq6hZX20bInUr
+t9qjp0d5pT76btOKDMVA5tpoe2I3otsXxdtztsnVVfSSmvqxlGJv94e4CmDzlVLAz2YUmnKepxZv
+IX3a2p0z2hEl6xX75A9/YALwA7JxHzaykY18QD4RloLTQCu1hC2Pse7G09kpw56YYoW3ZKXmuut0
+CNqiSbf78r19610mCiSZHN+nSRVKG9TE6yi5u6TV0lOJD+TfVusaJtbDP4IAqyAWmzcEWhnYD3au
+oKZ+Kpq9Y3PCTPLcwbWUAwXCBG7E/rbs/vPc4V5LzLxdPfwjd4cMNFLdMiHPqlWRmmuc3Z9rn6Q5
+YQqOqy6BO76qEj0ACsW4P86XtEI9AEWhuu0yoqOVhd0kodBDWIKwIdWotddqY0daaaqw3KJesVro
+uIQlozXY5uSIsbJjx/mb1BqAq5TNualrah1v17N4LeVGrCNKb30O4phSafCt0aO2s5q2BuoGbYdY
+t9KdJwXLvgab20s+pdRzUU+tqsPrHFmlaCsbdpSqvvIDom25x0/H94j05KtKXb95U1M3cq8bLTBa
+jzJzKhzFMrRaHRI9QbvsStvPzgJczZjs7DzDSrlCW90VnrpNblcsm8bvYXy1UDoOka6LhU2pLtUF
+jUMczdA0WqNitgPsGvRXFVSpBDZX8ymBpyWvzYo60joOrZwsnZLlhbha44s5qcKmt7JtQq32LNIp
+UyPt/LCysRQ2spGNfECM/aisjh9HI/4AKPTaC73luqSa867LjEyDVZqFJHQdAi1aSlyHvNGThm35
+1P/25MRmgDXEwMFBAWGEvnv1eWQgVp+5th43nhdf7W5bOA92f7zNe8fSnp+MV7hzZSVOXRal7CTT
+IsMY2VVfe03+Hc/GPFHS0WVVXp2kXBuLrwG/5UJTXh1YSBaPTgtcT/rXaxy6uqs2ns/WLT0iT9Fz
+N+MB3pakMnf3Df/Ff/W3ZSxtdbUbuZQ8el2CZ7/0W5J3/7t/83/i1Qtpe7QydCLpUxKW5LrD+r5l
+qUjPXANc02lFE2jqreCqQClxAzqKz/B9l1oZilsawJ0VBU6gsRg3ZHtHLLbsP3qZbyr0zY8g0apZ
+48nOdzNxOdZzLebLFbXz9JwNV4Ouvu9Qa6zB0YNxHNch1Am2roPVqsWmqXHUYrHW0FMEaFdTk9u9
+LaaKN8jTnIkuoqjr8OVPfxaAa9cEKfuv/ZV/j7ux7NyPypr3XvtdGaOTR2SKFRiegadnXs6NpmkX
+c3KtnGwWKZVSgmfWsj7+20kLEiXTDT2NpXW2MBN5nk0CLrSa98HDS6qJPOOyatCD1/m//8lLL1lr
+v8QPkE+E+2CAgHXs/6msJ6t908Vb6qEnlw71cl1aqi9HXZMqBZu1Poku/rqwV7EeSqn1hqt0L5XT
+aNYBGtM8fWkqB9TcWzoJrXfkhT19RnLln/0mvDT/CwA8DlP27go0+Wi1zWIpmmpyfMIbmjF470jp
+xZYpdb3GW1gaJSdpu4ZKF3FXYcLVxDJbq8oFzH2tGWi3sBqIWtWGo7cV/qsHydp7lsP7cuk871Kt
+y8EjQ3Ok5nzU4pf/nmRu/of/53fkXo+OqXRcL8hY6IGwrQj6CsiZ1g2RvqSlwpWjBhR1S9YY1qVt
+WVTQDuVFTgKPTF/klWZqFgY8rY1wa5gpxuD//Dl4Tu9RZTDV/aKbaEnzQYdQldd04VArJ1/Ld4nW
+7mEYkWv1Z08p0oOux8BIe07SmkYp/fIiu6LPn6Qlqeb9/UsZ17NmwmxNTOnmoEq7qV3OR6IM/v2v
+yGaxdzzHC+Wz5vIf8ehEXJGOc5cXlSXmySDFVV7Nnqv0cCFkRvEbrQG0lfLOuEx0vKLao6Pug69A
+pzkrvEr4Kpc2pdNTxXuZk1UyttFsjqNgvg8rG/dhIxvZyAfkE2EpWH6vlQDQVS35YO7hTUXj22p5
+ld91dfepLfhrM6CpyNSVqGqHwIpmX4JyMj91S0xjCNcYA+OgNSvUpsbXgpq7TU4VitY9fEd2q4e9
+fX7q4csAFJ/O+bZiC1on5zy8kDz2m/cnPNL69koLhqrGXmlh64C3tgqCCKtHVMcahJpb9K5gLCTK
+8Ntf5pzpTWxdXqXD1lRdZ64l0FTueJXj6nkSTWFolDz11bce8Z1X5FzNk+O/L98vC+pGUXU41OvT
+lTOXmabCTA4rHb1SrbTQ1iy1H0OANa1a4bGjlZ2F5+GpeZzmSnNXNRRrr7Gq8JSg9Us83akCuKJp
+W/M3nPVSSoUw+15JV6Hb3u4OP/uipKKjdk1roZwFNwUf0LNDLj1JzXUvGr43lXm6OC9595G4Uu2s
+pFBY+OVY3YQSkrXF5oBV1y0Ncn68EcKV5PrPSnum3+XbY0lZrlYV93hJxmj2ZVY7cjDO7hKaa0oC
+NFYm5oOYA600rXOPMFbKOr/klidrLjMF2UTPBY1lrDqLkMKTcRl4XdxS5u9uMuHEFatoMqvJkj+B
+h8H8fmIwHGiO/czt0omUjQcPZ03Rhy5WF2KNvvtxcnVIa6cyV7nwJrNE6kDkV3DXp74nZcM6tFF5
+BqNm55MApnqgaz1SXsP9HqYjvvWDlqWtlXyP5zkPjyTSezyfUyi7UaPmqWMMkZ50krRDWlppSMdj
+L5LJLy4VPnxR0FaMxWniEmtNhQmgfaEU6E2B563LaJXEpLQcqevT7l7iuGsXq+H4t2Rh/sbb/zP/
+6Nd+Re4xW1210Wg7jWvxdVwKx6XUUmzXMaiVS7slba+rgkQxCIkpKTprEz7B76xxHw6tSsauWdcq
+4FBkiuMoKwqtH2kB65q+JWA0G3VdwTgXXsLWrpr47iF/4U/LWaDJwYivfFYqG4PYZSsQlbry5eVo
+NT4rhbc3Vc6XLrQU+3vf49dGkvJ589UjjibiCqZqfvumJl3TVeKQ56rgvZo762Pr9eyWxfIZ/JYy
+M71SUZbShnH3bbaWkj2p+jM8jSW0VLGO3D2MKs3SLAk03hG3+/iaBcpLj1RZptaZpnHoY9W1K21K
+T9fQMutiFETm1ZZV9sdUJbmRjWzk/5/yibAUDE/BX66agxWWVS4R9dDNOdGDUYxriLTVtRJ6tNrh
+FWVaL+5S6M7erl2qQIIy/tLF1aIhvxaz7Xh8TKF8Chd1QaVBK8dCpTRnlyXkWoHoqIYfdFfMtYjG
+GVvO9NRlz2Yca3AxzWvWtrSjAcU4DnjxjkS1n7u9x607twCwRHTWBC5IRd7sxLBaaoCyCHntUnew
+bEUTKEFI7a1JpSnU9D9vpvih7KrXbY9an51PG17+p9L/X/vu6yxyJeGw37eLrAOtxsHXeYgil0av
+8XFA79fVMW5aCYHu/kEbULerqC2+7mhuVeI7GtVXy4ZyQe49tWJcve9fjHr8fSUICVzDXKnsxlOZ
+xzvXIjzNcPz5H3uGL/+Zn5D7lh7b1wRt6duQ+ECrDpXOzbFg9QyF0lsxPJVdftpvsTsSzMk3Oi/z
+tW/Ksx9O1/RohkZdzKVbX/EUxI5H+BXBJyzU9H9yPsMP5HdFf0ZWaJ8HI2yigds0IVLotrFPT4a2
+yim5aJ66wo5NCbVi1I1jQiVRyZQTsq5yZkrEMM8dKj0x2+t7NLX0tdud01K8zIeVT4RSsAh819Q1
+QaIw0bpFpoNQzIurhVk3YJVYY6gUNbfu7BAqS2jiOxSamur4CUv1VdN+eqVw+roY3RiOjsWkDnKD
+q6mg0kK5ZlZqnr40gb7wl5OHXD4RRXC93WXrUF70V89XrDTl2Nh67V5fpcKub7X501/5UwDc3O1w
+b0vg0+HAJdYy2tmF/Kp4PifVQ0ZO509o3tOzG0/fwNMJd45nnK7ZjbQcl6ZgpiQsx2mDqZUk9Nuv
+E9yU+MHZP5hQrXG3mk1w4CoKbx2LWfe7QYI2QGpq2rpgSzUym9yhUgKi0HHo9LRc2hpCrbUITIij
+bFGhxkbmnrmK7XiRw5rKqrVvyd9X98B3GerZV3u7Sta6NeJLWzJWu/duEupuMmg3eKXcMWi5mKUS
+8bTXcQtYd8mZWSI9dKiIodWWLw57IYfXtLxeXbfjWbHuPqZpqCJlmYpL9nlRv5AajvSwZHKsbW9f
+J1+zhTlLWkre0o5d0KrZcE1fH2Vg9eDdnofVGI7n5JSalvbblnU0zNPTwhIbUsUyrqt0QpGvtYlH
+WysmL4YRhbPko8jGfdjIRjbyAflEWAqeMQw9w8r49CrZGVx/xUI1X1o2OGraBkBfA1i3D6Ww5F//
+4k+w3RNtP7koOU31HMiyRdUVs3s1H1AHooHv3JZM+KPJ6/Rf0bzze4+YqAav0hJnbZkAtZqPa6qx
+PeuwVDM4z118Pe3XKbOr4hrXOFenJ9/dEvPtzz57j595QQptbhw+g6PD74ceji87yaEG0apLy2Rb
+LIXk3KVQIpOH8xHhNS2wSlxm70jwbM3qbIFyqhmJqUOu5w++2l/y0i/I7ufjMthSHMZyncportIy
+vrUozQJuY+mphZW5NayJPHQ+Er8h1r+7wz5tPVG5tnNctUJiP8ck4rKVY61EJaRWkNmyAEUa8+/8
+1ef4B//t92Q8bYWrkPahUvONun1aA7l4381JUJo6tlFENK5nMetotGaqjNtgWut5SvACDcQtAroD
+PT+y1+KOHtoy0UKxqJyRLZUsxYWw0F3cC3mcvCOfz8RS7F7mdFqCG6jCObFmn9p2QE8Bd+HCIVpz
+LGow08XH0yCiW7l4SqdXuwWOkv041dNT1vHWRwd4GM0SFW2fuc6701QQy3jXgVRmfhT5RCgFsFgq
+fLR6r58AACAASURBVAKW9drUca7SX64Dib6Eoyjk3nWpmHvhU18F4OBuj3iN726WLHKJJo+bU5qx
+DFpnlDHaE3PvVl98yLp4wtmuLI5na493HkvZ7GpVrtcSZk1oAaQKPCrzR0R6RmMna5g3Yvot/SMi
+dUGMAwM9n+DLX5Dj1/+VH/tpOn09F8AJSOI1sWkfVxdbpKZj3fVBo9f5ZE5Ya6blcMDtQvpXpq8z
+Ur99pUi7/4+9N4u1LDvv+35rz3uf+Zw7V9Wtoauqq7vZbJJNkaJEShQZSzAZxIbgGEEcxHGMBEkA
+I0AeEidPfkgABwiQ6CUJEgeBgQSxbDkGYjORRcmUSEkmJVLNodlDVXXNVXe+Zz5nzysP33cu1YBs
+dasluQXcBTTq9r3n7GHttdc3/b//P8tK0JfDBgWmVCz+b36Hb49/Tf4wSGmNVpK009V046nj2Gx4
+NLWNuu0LcSzAxFS4qqPR103TizzWN+Q6L169SazhXz49pdAcTZXPqbR35alWg6ZlgavZd6e2ZDq3
+s8l36SpK8VFZU2p+ZP0zgtJ8vt/hUl821s21LdrKc+g6bUrt43AcH6Nw0cpoP0tucXSjqAOHTDkh
+i7lLdiIVn3HcxKpr3lQFLeOUWFWTyucZ6P25sUc4kbyEUer/Zdey4a70MyNGmilrlVMiK5/1miW1
+xgSBhlJO1CDQ+Qw8eyYdX7qhJEPk6FBp+KCbqWn4hFqK7tiUhZafp6ZiNJLnWs2nEJ5XH87H+Tgf
+H2B8KDwFa6EsYGFz2qvuvKoEtY6e653Vx9tBgxtbYpkiX0IDZ5aTWtmVp5MZx1O1fqMHzFVZqh0k
+0gABFANh4W3vOfy4ElYsO4adprh+X3ntPmOFB6dwpqUYawKzrCxHI9mhP/6q5fBYeBXrhaWhVRDH
+h+eVGGb3hriktljiVIpHWBaEShBSsiBQi2D0Pvy2oVqKNev2C3abcs/9HZ9Q2aXXw4rjPaHHPMzV
+WuUiRw8wiAOyUq752ydtuhoq3NsfU6hqUKCWsXYsLTUoW0mAq5j7jW2XtvIrbnR8FLHN7qZc+7rt
+sXZF3Of+msHTJHARdhnXAhaqR4apViV6qvFxSkamljtwDKG6xE9+o+Z7CrHuJJaudhomh/L9qx95
+ji3lw4gGDWpNNKaLBe4KQr60ZyEdSvnmuFBpItKPCqwmPivHJU9USj7bJ9uUZ7Z/KvfUiTKOVFks
+N/YMIFQtfbJEQpfBQtZN4o4o+0paU0MYigvv2gCrYRzVHMdXSS0tCgS4+NoxW5cedabHyCucWDyr
+yoRYVqzZGj66Do6qXJfDKYlyiD5dzliquU+9kuYf3Fr0LxznnsL5OB/n413jw+EpIBDgyhrSbKUL
+UOFqYq9yLJnGUyfFkjePxCpeb0iH2P37Bs0F8viwZqaq0nZu2VWm3pbjMFwo/Pn7KqAxvI+jJa9m
+4LGucea1huG15e+7ON1oK7Vgl08q9ipFGD5pYVrSoONXJUZBFM3QYW1LEWZzsZ6L9pjpWPIBUd8h
+X0ii1O3OKFXoo1BkGwufyUwp08Yz9nNh4Mnvumwq7nYwS7msupm/d6gXaS255mL2D5ZMjmQu+g8O
+8DdXOpcb1Npi+kCTiG5laQaaD6lgY0M5FCrL567LHI33Tyj7cv0thY93L9W025LjKSrwxuJKnMxO
+0DI8MRUtxZCPNM+wWFQU6UoH0qWqVurJOUY7KS+UPh3V23xlIHM1HJ2wXUnZcNac4ivzEO4xcaH8
+E60CKrH+K0yHqc1ZWbQuLLV6Ql46BiW0nT8bYk9Uhk89gsT3Mbr2yrJkqTJtfl0SLuX5Vm0tCxqH
+Vrl6DD65Mir7zgJXPYUq9XAKWWeNWhGfQYlF4NhukuKsVKltcKbxUMxTyoksyoUS/mJjrKJC59OM
+seI7JsOSkXZM4puzcv57HR+KTcEAvuNQVJZCkyhgWHk9rjWEunizsmT/kQJ5MnkojwZLokLc2cl8
+TlGv2mYth2/Li9V/XHC1ECz6m2uyQA+rinXFQuxcNLAjCUh7e0iiC6jAnlUUCqX22itabCjV2jB+
+wMGGLMa+awm0cO4kNY9XUuM/vANAmW6TXdeFm2xirYQxSbaFo63RJwsBL9UHKWPt/Hz6nSlfu69i
+IpMln3tZr6fdpFZtyg0F9Mwzs4q6mC8tZSVhx9NL20SPhGMyyJ5iApmvXXU5/WbButK+32z02NmQ
+ZN6l52taJ1qpWKzx6jsKG9+V7x2OE4JcNoLmJZ+xrkWvEbO5gutuNki1dyXP5GV7pY75PcV0DBeW
+ShOlpd8kUg7G07okzhSmPJFkYNNZo0rl59E8oLkS+2n2CVa5U9fDKLmKr/Nqyxxayte5sMTafly1
+WyQXZB1dqBzaKkQzt/LivTOaoNALytIl147JvCipFJ/gRzJX3TrENHUjKF1srcLEx7BU8pnWbIzf
+kY11hWNw3DUCNYCm2cV1VyQqc7wDhYKXJeOpJI1nkRyrm9d4KpDcTofEWmUYuAWpt+KdhHH9/nof
+PnD4YIxxjTGvGWP+if7/VWPMt4wxd40xv2jMSvb5fJyP8/FnYfxxeAr/KfAmqJ4W/LfAf2+t/XvG
+mP8Z+OvA//SHH6YGjBZVAGvPEIiR8TGaiKq8mqUmz+yxWK3ZLMdPFFLreTQVQeg7LU4GUqqczgtu
+Lx4AcHBHXUoXOqGYgetlj7YmicotH/eZSsHNy7OGpkJbdboDh0DP8Xa3Q5Ur8+/lJg05NFuRh5co
+8m5TXN84iuis4Ni9Bt1kJShTsJzKzv5oLBb49P4+85Fc+4O9Q1otsTqdzmXmaworTkdcvSyu+2s6
+c95iBJpoXOQVthZX9dY6vL0jXoO9VBAW0vef2K/LcbehFYv1fPnlF1hradnvaptyIdf08eMlx0/u
+yjVlYrUmo4xnTTlH+dBhnqnKd8+lpXJq0UmNp41NXUXxHeMRK55kbzEnVe+u3Kp4eShW860wYrYt
+bnX/81KSLJ7CIyVO8Z4ecLUjc+yVM3LthDMdi99Qch0NA8gcqpXgDAGLqXRGPn3rHe4NxZOzJ01m
+tdyrq+XCsNuhnuv9TVM0L0tZGaJau2MrDSmCgqbCuevQJZvI77PxMwpFlpZhSDGVa2quFMqLJXYF
+DJnkZ2FXGU9YqmeyWBxweChze6D6HNveJg1NqhfDCU61QqRW1MqkHRYZnnl/tv+DakleBL4M/DfA
+f6ZScl8A/m39yN8F/hbvYVOojfQ7hIqBr4xzxprTbHhn9W/f+kTBCiqrGoAWjE7wWuKRbKtC1GlF
+qS/b3mnKWLPdjsbDzSRiV9l3C39BvJAF1gw84kQZg9Mat1rh3fW+uwMWU/mfF5qWR7phJUGHtb4c
+b2MnZFvpvjc35MWdjF12FcjUb0J/TQVds5ClxrWOZuy9rZqhK07W2vpFNpUncme9ybNHGhKFA+qe
+5FW2lcr92YF/tpCakUOZyCIdt7cZnMhLf/+0IE8eAHCowJz9tyZnIKTHbz/j2vPyMn76ZIcbL14H
+YN13mCu34+JbkuN4Nj/hB6rteJqWuCoAk3QbLPpynde2m7S11t9dl+Pubi/Y01BjOKmYGYmXf/on
+/zy/9fDbcq8bM041dps/0S7Q8hDPyFyt1zu8fl+Bar0NbihlfLIZkazwOqEK/ExHlCtBncOn3H8s
+G8HbD+9xokxIW77PdKZQd9UpbQY5/VjmdhqmLLQy4Dk1cUuxJfr3hAhP10KdZhQqqrt0a2yuc1Rm
+GC2ZZJmEivH6Gq6rrZb+JapC2ZdtSj2VUOn1d+7z6LbkrlJds8N4QqRU+15aouoCDNYijDJ67zsj
+LgV/uuHD/wD85/yo23UAjKy1q0fyBLjwB33RGPMfGmO+bYz59vtLg5yP83E+/iTHH9lTMMb868Ch
+tfY7xpjPv9/v/34pes8Y7QExZ95B6Fh87b6znk+qVmyWpfi1WLxQd0zbiQk0e900Pru5JI7i3Sb7
+t8UCpcGYKpVdvK0wU9cLOC3Ecl8fQjxYuV8Oc3X3CEsCjWlUg4V8MSXT7ks37nGttzpei5FyF87f
+eMK3HEmI+qUkFLc3E8wVCSXWej8GG5L4i9iHSizFO5qcevo7C759W2C0hyd7rPTJA9Pg+U1xWz97
+s4vTF0uYP5WEVHdtQTxSghTH8Ez1FRevH+EpQ/FsGuNMJDQplKKs5wVYrZsvcDg4lL/fMz7boeAs
+3FaL8anyECiG4vHxkmPVmlyUJYlWT6Kk4jSXObqSh7SUldhR7sv8ZI1SOwqTps9SkXnz5yKuqzeV
+Xt1i/BEJcxpWE8mzBp2Gel5YnsxljXjDR3g74pltbw1JYwl/Ag1zbJWd8VvsjzLeeSZW/Gics69o
+2eGyy7Z6ZM2BwrX78PhUcS+c/j4NUTALWYdFIp5Gbjx89QImQ3h6Il7M6dM9cu2CfLrnM80EObuu
+a+ynTUZ461MAxJM9Ug0hj8o5+/fEKzp4NKHStersqmjPqM+zA/l7b+nibmujVbEFvmJ8ApfhcpW8
+f2/jgwrM/hvGmC8hjksb+AWga4zx1Fu4CDz9ww5kUDn6Cppa/8tcF1djY9dCoTDguqhBs6+5lYXW
+ihIGGpOudfu88Jz0Nmze6jOeqJT3pOaecgke6sKu6gnrLVmYT1sJ4bYswGg257mG/P7QBky1XdaJ
+VzmFXVLVtrxgQxYDBQKlGWkmC+zAGmZDWWBtBbkczVosHZWi90NWGJYiH+DpuVtPJW7s9is2duVl
+3C+WLDK5591GhvW1fNm+SL+UhfelS/IS/9a8xWP1I+t+DirG+vBKl+5EyhY7h99kqGCnZqIgpFaP
+XFWTtjoJxxqTpjZmtpAQ5cKaYdySl2VrVyo1g9M5nb68CM9sjc3kpSlMQqurHZ9xTOeWdIQ2tCIT
++iMOe7LRpZMEo3N78Wf/A/yv/n8yB+3vkCiJSKTEtkHickfLrE++d0SlrdXGjWkPBNR1PF3SHcg1
+e7nyE/oBNhV33WRjVjxc33xnxJt7EnZcu7zHSVPCmxsvyBpqFTs835HvHfsNDnXTCFvgN7S1PdXy
+hFew1FKncSakugk9mcx5pOQ7p5Mps4nMwVUt7z5dNnj+TLG3iavhiD+v6TW0BLwV8OChbD7f+45c
+z0Z0l1RVyLZ3Aq5kWwBcWXNYWs1t+FDkq/r6ext/5PDBWvtfWmsvWmuvAP8W8M+stX8F+Brwl/Rj
+51L05+N8/BkbfxI4hf8C+HvGmP8aeA343/7QbxipBPgYUu1zJ/BwVnTbQeesw9Fvl3SUfnt3IJZ0
+a+05fFU1vtxq8dzHhJar2Wjw6k2xAp7fYHPFY/hMKciMxydekd2161XQFuuxl6XcVjd+WrpYJSRB
+M9KtNUOsEOVp0mCwuSvHIMA/kGSQG7fZ6YtV2b4p59iKNtnoiIV1nHWq6aohaoldyDVdeUky9s+9
+/JNcuSuZ/i98Fk6Gcm0fe2UdRpJ8cr2Cek+sxmGoIKXjkJNCYNdJEeJPxLo8bypuzyWMKS6OOP2B
+WjTlm8hbBVqIIdz1eNURF33DW6OhXYS5Z+lrAva6CtLkL16htPL3w9MZVrkBg06XT74olnsriOlc
+FAvcVd3G2r2PeyLW03OmJGq5c57wynUJux6PAjq70sR2c0cs9/aTJxwrFuRO0iVaW0F+I7YiWQ+B
+16PS+awS7Uic5NS6tprtHTYviIv+Ezdf4OJFIUt5/tYauYrc9PSeo0WLB21x6WzTwzuV+UynDi1P
+wiNH10Jd5HhKxGErF0dFW0ILFxoSzlzdclmkct999cYu9C7iKCiqznLquaxZWhldo99z9kiUKbrZ
+29I5XEicAjQbFdfWJTT1exFT5cLMn+1Tvc+3/I9lU7DW/jrw6/rzPeBTfxzHPR/n43z86Y8PBaIR
+AMehrA3rmjyc1A6lWvak6dBQK95uB1y+KQ0oG7HUri/3E3KNZXsbLVoN7W9PWlzri9fQu7zFbLGq
+70uc2V/zad+Qn5fHJ1QPxYrdTe4yUeGNk6zieaMeSyRWZ1RDrpDaz95aY6Y8DIP1Bs1Irq39OMfV
+klX/iiQDW1xiY9V6W0+xCsErZjVxJkmyzlTO5V7OSbfkHNEjuHxVJdYueZi2eBNx5TKaCHPUXiXX
+e3Ur5np9BYDtjnuWG8jrFhcDsbbfe/MupVK9LdXDWi4qXlyTeXv1hYuQyn1caKb0Boqa832WCjMN
+lSV5azxltC4W8dGdCbYh17l9cYvtq3KMVhbTUWGYOhfPpghL2kr+eqVjKT3N/VQRj1N51lv9OeZY
+ynPN3Zs6lxcZBBJnb1TPcLpq0RkQqOZGHFsK5eKwWn52yhireaBkw2OrJ/mVbgMKRRB2d7sUx+Jh
+OJobGh69Q6elJenAZajED4FTEqq8nU0VE1AXVEqJ5gQVHc1x3KAH8sgYtCIKZZpuLcWrvDAIcX2Z
+i+VkRJWLx+MfpJgt+WzXv06sLNYXt+S4i1ZB+kTl+MqMlnJklEmKI84iQdTnms7tex0fik3BopqE
+eFh1RQN/yerySsdy7YIssFtXt7iieo2RMvbW7QZjVUlumg5NZT5uRDEb18XV6tEGxZQnqsuYLypC
+pe3Kbc2iKRLhaQTxUo7xXFSdqQn5it+3pwccjjWhNrpO/7oKrkybpKm8ZFsXfAZ9OXezpX33eUmk
+vRil51AcauZz6aN5IeKWPHDfbbCmPf3dC1M8JWFpnES4RjvnxnvMFENhDmUuti/3aHQlvIgY8/Yz
+yXQvv/EDuo5Ck/sN6mMJY1IFwpRFSdiSiyifLPFSyQ8Pr8T4E+1mrAfE6oL7FxR+vOvS1gpGkj/G
+US1Gz22zqXwQSWApVHxlvgrFhgl9rZ/HV3LGqmO5OPwK0/tCsnLr8i2ql24BEM614lQeEXXketre
+Bpket86P8RRiXJb+WaJtxUORNHJc5Sr0bINNZYsptxtn5DJBGeFsKXbkQDavhR+QK5zedyoCf8WJ
+WGAW2tFbyLyausSq6naR+YRazgn7C2JN3wV5k2UqSdBIOUYL17IcKSvzuJKwACgiCHKZw06aMlPd
+0EqZzcOJhUjmInUtqSbEOe2TBBKCLPolabaS6Hlv47xL8nycj/PxrvGh8BQM4BiXpPLYCWVnf+KH
+2IWKjAyaXNuQZNBL1/tsK3PSLJXPppM9ltpX3wwi4q7s4D6W9rqW5+Zt3DXxEKzWrpOOoXqsyr+e
+ZaqlvAuDJk2V4Fq4BW8qD8OpCraEdHBU0+1o7x3iT4g1c90aV93LfrfH+kDc8ZZmepZxiKOJKv9S
+TK3Q5LzKWTW1uZ5YKMe49Nbl+65dJ+wo2WxqKLXjbjKERiS/396QuQpbEdOZWNX5YnzGIPTV7Bk3
+5nIdlzZ26ayJN/Fo70cErNVMy2Lz5Oy4pA5lUyylCWOMwskDhXZXWYHRkK+9vk2lSyrpebjaDYix
+eFoy9pWire84uB15HqfzktrT50CHrz6Ua15zb/OCskw5UyVM3e7gqnRQ5GfkE13CrQYLbRgiqvFU
+jXrFXWD9gGAl09YO8dqbej5wdM2xDLBGEYta6mwcP+GCleP2gIcrflzH4Gqp2dHFV6cZhXomVeFg
+VGMzKpo0VdKuGxgWkTzX4li/HzSop6sFUFErHoHZCTQ0rAotnVi+53iK0gwKlrl4Kd5ohslWbORz
+UmelQp5QrTyI9zg+FJsCSPWh6btMmqrFXi2JOzLBoR/SvSyu+PqgR6JxYqZiMMO0xlGlpEYnxFNw
+U1mmIsUE+M0cCnEZ61QWirucQygLd/FsTDaWv8d4VFcljj54vWJ/KCHBgTInf9zN2VIQzm0cNhTE
+sruzhunKQwqXPrFeX6QKU5QZuZFzO5R4kbqzdY47USqtlfKSsTiqfutkAa5m7R1TYk41s55VoO2y
+0YaEHeNhRlmswpKK5a//LQDWbnv88lQy1Y3gBZLkY3K8ltxba93H0T6Ctat9Ir12b1HR1E7DuJ/g
+KE9dqe2QC7/G19p90o4wc3l5Q8fiKjVZvSyp1M0vdPebeQvmc/m5KnJKJWF54x/8HTzFCv2/b/co
+9+Uzr9yS47qVJa7UHa4gbitmY7rAmWhFxbMkLbkXo3kg5gW1MjEbU2GVG9+t2zhW58sssKoVuWKa
+Tm3OnVxeqsdHM2ZT7YOoDEb7HDyFT+cLB7Qj0Qsj7FBxNlV6BsQziSHQis9KYNhOF7iOrlm7oNZu
+3KUNMBrmBFH7rIdhBdOvA4uv+YxlmWKs5hd6DfyhGMbEr0jfZzxwHj6cj/NxPt41PhSegsVQWQdb
+G5QljHTuY9Rtb4Q+PqL9V5cJtfbNL5ScsswXBHorVeycNa2EnFCqux5u9alURMNz1NJaQ4V2TMYF
+DSXsqFvbTLRP/8QMcULZdTcVDhsl6ywVOnpxx+AoYswNPRqVeDoNMgJ1c6261/kwwtVjuFWCo4hG
+z+vQVNowM1S+gnrOci7nbdqaulCtB39BHar3E/iEsSRgZ4qIqwIPRzPZg96C7x7IdX796IhWIq5o
++tYTNp6T5OdI1ZCDImRLOwsjCiaZWLa27xFomGM8S6nEKKuMvVfUJMrUbOvGGVOxE4c4pQrOlDm5
+urb1SuynckldseZZNKah/GHffb3iGyM5xxevHnH0PWmOmvuiExnvblOqgI8XOGeEsGlRUyse3QSt
+s+4115drMzbHKpdDOp1SrkS8zfRMR8Fr+tRKpssz5ct455gf3BbcxHFV46rX1AoMXqTWXUlYkrqi
+VlxEOiwIlQzHpv4ZtwLWB6Uc9BLFdLiWcpU8dl2MKnD74Zw0l3N4eYgXyDVV6kk4foGjtHMWn1qT
+q6XxmKv+dxVVdN8nHduHZFOwFKZmTs2ylIe4niyolGZ7YXMm2jh22j5hnsminw4lQ35yNGWui3F5
+f5NBIi/9WsM9g8H2jkrcUMMHdT+NX5KuwCgnC/xKcOS2XLI4kXM8cytOlPsPZYaw3RZrOs+XupdY
+7wrAxM9jrJWcgGctqaf8j6eywSzmnOlHukkEtXZzhjVcUT6/nsSNdVaQn0rJqqgdKiUNqZaGWiHP
+xhmT6qLIjqXmVcb75KXCwMOCVw5kM103fZqqJjTuTWho6bTWhW3tnIdHcoOLfMZWRzbc2mtglL2o
+xFAv5Ni+urJuEKB8JBjG5CuOynoBCv+1RUq6kBzGYqK9GOOa00LCmdl8wUSrNu23U1IN+Z4cRez6
+ch2LVLLwk+EDPIW3F8DyiYC2ZnaGU4pF6bQcSit9JZ7G1m4nJtecQ2XXzticSYVdCaDKpziRXt9T
++exvHx1xoJWBRV3S0ZzKRtNgFMzma8k6TAJqXSN5PafSfhbfpFRGrn86Kah1g/C0UmEii61WG8wp
+nlZwnKI8E/PJxwucYMVuLht9XllKbT+fjJ6gVJq42SUKX+5jWlegz++9jvPw4Xycj/PxrvGh8BQM
+BkdbEKul7K5ut3Umn1WOCw6UjutSltFR4NBStfrspsPTt8Qi7j/+DutKJnIh7mK0EnEpSum1xF/P
+lPCj8HKKFdQ2qMhd+XuYGNYO5Xhb6w3uH4h1XCoR5JWddaoLcu71QYtk8KMElqmVt89ZUq30Gpea
+1TcRGLE0y9NjrB7P7/Xw1Q3xFfyTTYeE6gJmdUmWK3XXLD+TzbNugrFiKVqhoFV6/XVm8ysAHNc1
+2zf/IgCvfOP/5siTez1YbHH70T+VS9IkWzno4WtisONYio7MYZE6HJ3o/Z/s0b4kYceadol6fRdT
+aNLOi1jJdqQVhMj5bFBSr5RatMqQhDnb6kl97+mURwr53nECjDY/3Zsvab0u5762+B0AXvqJGxir
+4VqWMq/EhazCLkYpv5/snRDMRf25GWpDUWOTeSlehSkOcUJlQZ5U2G25pqbtYULFvlyQ393bP6RW
+Rex+0CBuKsFP6IAyLfuVYktYnHXzOkmNp25DdrIkzRSE5fgEaosrVTZf5hYv1zCgGZM/k+NmfRdm
+SjfntnHm8j4Uii2JbEmqLAVlEWM0FPaqEVFL7i87hZPln0GORoslrwo8zzLSsliv0yQPV2IaBYGK
+iMwfWlDxDactk3rFRLReEsSbG3n4Dc0BOH0CFWSJY09UZYBypqo6oxNsV2PA+ZigXGn4+ZQ9Ocb4
+dsXRUAkxtWtz7eKAJFKiqdIlcuSzYRxRaYUjrQ1WGXaS5grQ4xBqltkxBqPchuUQalVOqjWbbtOc
+hapCVUVItBJOGSSgMbUtXQLdTHLdmLywQactG8RyVtL/aVnkf/mdT/I/PpPFW/tjnPBFAKJcwE2b
+/SbGkWtIvJrsVBZpO/SY60vR6iZ4E5kLvyGbX73gLK6dL7MzMVa7yPFUqcXJZ1gNefQ2mPouTyYS
+Guztn3BfkZUhBYHGwFPj8nisvQtXdFMxPnkurn+a7hOdMacPqRUJafwAR+mLlg3NZTwaY7a1RJg6
+pNozYU2Bp92McWII9AVKfyjG6e6DPY617Bd5MbsK3nJNk0DBc6ue+mrqUmplwIY+lTJIFUWN1VKs
+6QYYfWYrgtrIA0erJFltKTNdn5MMT9mbls6UCWIYAy0xLqMQRxWijAcdJcBJTcJIK2nLYoRGJu95
+nIcP5+N8nI93jQ+JpyDUTWXl0lX3rMxSmprMyudzvv9DIRwJb2yxs0r47Up3YvZsTOOGegfbLklX
+eiIaWQvXVSmxVsVcGZitWrZJnRNo3dy6FR6KZZ+MGE/F1Ty1KYVyQjZVfmtrvUvak98NqgGBchg6
+wQyU57CaTkBZiXN1DaNGn0o9HsIARzPkJRUoq3ShbnQRWKaaJPOqBamy/bpY6kqspmN9HKWFCwfy
+uyyqWT3WSxtb7Lz6ZQD89DJ/7WvfAOC/+62v8+CxuNehykgXeUp3TZOOZkTal7k4eTxl66Xe2XUs
+E8VDZJLkdaYxS2Wdtm6DYEU7GECsOo+VaVCtKKb1n7oY82Apz+b+ckGhuIEv7Qb8iup7XmxCsPrM
+tyWcu9Jqk5TiVcS9kGIpkOHc2yZBPIjmeoNGT5KRdUPWgueNsRpe+NspdSB/z4f7oP0ObvOIJVP6
+CQAAIABJREFUsRLG3HkqdG3j4yHLxYod3KFoyLXtrGUEXQlNSqVZD50eNhXux3I2O1NQrycRhZG5
+WM7nOKtqgIbHpghIVlWL3GJr8VLCUUWq3uTMD6lX1ZOmAqFC4eIASFoZcwUkZH7Gs7GEK8vxgo14
+RXP93sa5p3A+zsf5eNf4UHgKWDB1TWoqZloH3+otGI8VrsuUI7WUxTOHi33p9TdvirV7epCxeyKW
+8gv3X2HjM7LX2cLBrFSQ92uOlKnXUe2zeVASp5q06QdkY6lHn5ZLvnlfruPJyYw8XImWKEItvEjk
+SfmycTQlXxO0pT25T1Zr+ac2WMU9TFX4dDx5gKtYueYwoDuW2nvqvEl4JMceahIxrWqWpVxvc5nT
+u6VNVdOQUmP8Mj9l2JBrrh5KWbTRm5Bpti/2upRKhJusrbFxSUtdF3eIlJFqosm3dFryZir38bSf
+0F9K92U6LOidSHPNgA7Hmq/oavlvY7dF5Hf0fNDT/MraZoOFoxDzZsFY8yNTTYbdGzvsH4g1G7sl
+E/UU2kc5A21c+4jj84ZiOZbKFPWduw/4dEsbkcKQoyP57LR8g0qrjNeKU6KJQM/n/m8B0HpUkF+U
+dTPYrzloyH34w4ytm8IKlVUx87kkR//+W/8cgIfplEJzSXk547bVvMzBEEc1NRxHcz/5AouK/86P
+qRRZGno5rgq/TJdLTpdKRrbUXEZoMTN5FcfmCPdAvbf1grCWUvPe6JBsrJwbN1Qg+RDKpnhNs6Lg
+RKHd+3s1T0fyzKzXkJL3+xgfjk0BwBrCuqLSpM442yT2lPtvXLBQHcC99JAwkN9v6Av/4nNbbCq0
+efPyDtuRuIx+11kl+6lP57iqlDNaUV/FJU1VJW62d3ADOff+3TsMtIZuXUNb+RE9rQZ0WuMzluBg
+PcRoi/T8SYyz6gIMYKm1cE/dPjdISBS44qYVvmbifW+dhaw1qoUsMO9kxED5F7sXd4ia8r1gOsdR
+eG1eQ1SqNmUtm4ONfTYvfByA8GiKvylu8lHVZHD5pwH4xLMDpvd+IPM8k2uc1zX3K3lRrpkmifYM
+dAcJXQVObWw06TUVmq1kIssSPE0iRmt9orMqQ4tAN1ETxrihcvnmModbjUPqRMFSUUigm1f8XJ+f
+/6q8TB95MefJscztt9+ShOgLtOltSNt2u+iwrmvBidv4OkfdnS3Wb0ri2TgyscHHmqSaoHWfPiLQ
+ykg1LakUe+AYyz/91V8F4M235aUq5xUrhnTfqYkV5jysfPL6AQC1KkE5JqXShKmtAmJlqLaud9bn
+0o0MqcKtbUM2+iAviNZl/SZ1i1GwEoBJMUov3wsSHFX3bgdyT43YI19VO7yAocLf89kJoVZ2nE6H
+WDlL3+s4Dx/Ox/k4H+8aHwpPwQESx6HlhKRtcXFbRUVj1dRSOhxMZAf2Gh6OkmX4ZoVtOIW+uIvW
+WjJ1jYvc4qv8m+dH+Koe7CnCK61c/J643a7JmGdipccnObOxfG9r6lOppkSi5UQ3L6gDceuqJCJQ
+SPSy24CRIszSKflMrq+14iCIDI5aj3BtjSjWZJhrqBfixidW0W5b6wRNlTnzl1T5qve+ZLGU0tR0
+dECuSa7AiPXpcZEWiviLl5SK+2jXCXeHDwDY2Njk0q2fAWD22v8DwEFxiquMyW5t6RQrUpQSs6Hl
+0MISqyO04nGoyfBdJa6NnTOSFetZikCXl51TqpdWqWDYbJrSmyo3ATWuUp4V223+3Ktyvt/93AaD
+35CE31Kh3aWJOXTknvumS9xWUprMMDtQ1KQXskxU0k2JabP6mLpYrYWYKpe/1+sxmWpmpMdvcPee
+eEt72ohlDHhqjZtlRL7SiqxzqhPlolBv1B0X4Mk6LfOKpa6nuioptdRpHIuvwi92qp2hUUipnkQ2
+WTJ9IknVSTWjpfR28VqI29KEoXquZRBRaqi4GGfMtNGqmDlEq27WZU4Yvj8+BWPt+8NF/0mMrQsX
+7L/7H/0nZHfu8PDRbQAORhWnQ4EMZ1nKTPsWqqrCareiXcV6dYXV1lyLxWj+wcJZu5sx5gzjvnIH
+HRzKle6k4+CqTHqctFgpVzTdJrUyBHXXFNAzfEign+2ECVe3lSvyynVmTx4A8N1Hj5lph9sNfVGS
+RsJCqb77vsPVC3JcP+4y1k0oUImp8WHG4YkszHceHDNXjP90CY1YFoWJfHxfFnSx0I3HOCxUkGR9
+fZt/+I/lpaeoyJSWfThLeePbwv/4i78pvQXf//r/wv5D5bMMK1Iln/G8Elc3mW4jpFaw03Ahm818
+Up3JwVcFaGsDPu7ZHHmOIddOwlTbz60HysqPG4Nn5Bwb3RahiuCEjQ6hhianWrufzU5ZquqT9Vxi
+BaK14wDXl2N3WhH9tvaK6FpIy5JKYcLFzBBqV2bhWmaKW5nklkIh7aFiXZL1XdY8ieG3omtkF2We
+e92Az39UIOmBhhRu6FIu5CV9fHDEQ10L4/kJqXa83upvcV0ZmWIVIO76I8bP5D6//2yfU1UG45rP
+57c+CcCV3iWWDVn3o6fyjvyTr/yA33kmG2FapHh9hVsvEy7FYrSKdkK7Jw/oF3/pl79jrZUD/kvG
+efhwPs7H+XjX+FCED2Wecvz0DfbePOXwUOrOOBXjVBJ/tnTQTZ7CGpbqzgVqlmLfgFqdzFb4Wt/H
+t3iqL5gZSBRWGniKJHN99pUXwLX1j7T4iorlCkHoLAk1DKgVETm3OQ2lB4v9nAtaMThMJ+wpIjOo
+SuZDsSC3Q7EIPx9e5LlPi3Wx9irxpngNt5Kb0BcLc6qkJ6813mbQUhm04i1++FDCi2fF5Iy70gQ5
+rhErpz07uNYSaXepW5ZU6lJSWLLXlc5rq8mvfF28sAdviMU3x0sc7c2fLebUtbitvmtpdpVvoHuR
+H4vlmm/fk+89w5Ap38AYWPGV2KAiSGSOtq3LdCHnnutzzErQPiqCJaSadM39JhuOeA2NIKCrz3KU
+y3OK4yZhorgPx6HQDtRLfsJgINe83kqotLN1TVtRJ+WUI0WYPsqnBKvQZmHPBGwOJycEimUol4oa
+HKbsRYpM3VwSH2hYVblU6nmFvla7Jh65nm+aPqbvSbWjvxYzVA9k0Wiz3lPPUhufRlmfe6pA3vDb
+9FsySQ+OQh735HpuXdlhbeXFlNLMtoz3GUTyu5kdMh4ppZ1T4wTK8ny4IFJavPc6PhSbQlVYxvsF
+x6cPmStl9SSyBKpGlzkVRiHIrg9bibjMlfYydKwhV0BS18TQl9+vOdtMkMW/TcSirUKgCvg4zvbY
+eialxTv7+6QrIdmsINCwwsOj1HbZif6uW1Ukq7ZnW/J2KRvZ1jjjYCjuXLJY0tmW7/3Hn30BgJd+
+9i9yc1dyH2WjJFICT6fXxLHy2c2bUqa7st1n4kqX5A//+S4PjPQq9IYzRgqlXmYGNGvvl3LtqYF8
+RRdeLEAXfDZfnpVUv/n4DtWxhA+nCxGYzUcFbq2lzNrB0/6D3W7CzlWhDv9I50XGr4rrm/yibNif
+mDzhtxWYdBm4oPvxXRNyVasWBy0PfyEbWUuJQCbYM0XiGmhpO3CcZ3Q3VMJ+PudOJPe61tMXz3js
+KHGt6znsqPJU51KDF7ZlbtmsGCjxzUxl60tu8MMn2h/yyCd35LjTsMmRVgxuOh0eHsv6c7WDMV+M
+8VKFVR/mBJ7koJ7Mj/GslJRXOYlmdsJ95Vdci2PQnNB68zn2rRx3be7g92QuGsqktGwe8rm5lDLv
+xydsuaLdWZQZl82Klt5l0Bb2MTZk7v/q5z7GP/qGVEseHvbJZ7JxBtMUowwuhbHs1++vJPmBwgdj
+TNcY80vGmLeMMW8aYz5jjOkbY75qjLmj//Y+yDnOx/k4H3+644N6Cr8A/LK19i8ZYwIgAf4r4Nes
+tX/bGPM3gb+JCMT8C0dZlRyNhoyWc8aahY1iD38h7mDBgkSZeMM44iO7IorR3hSL//HBJt6O1K5f
+3L3OWHkD1h2fuWYMm7g8VHfPPZB/H+QjDm4r5fh3v8aRgmkqY5grtDl3PGqVOqt1x40Dj05fLFTc
+7NN9XpWd35gzP1KNRqfiL9z8HAB//ue+INd74xMEDQEIETgYhRjj+Fj1hFoK4om2M6JUrIf/nMfP
+HYj1+MpDmBwIsMguUqyGUsuVxGFtcdRrMLMp6hxQefDGm+LyPxn/GtMD+Xn2TO6/8kMqZWXeutDj
+ReWz/MyXvkQzEXf15atrHHbFKrY+/nMAPP7G/8Unvif3HKYPuPNFuc7New4XNCH6PUoaJ7LU3nnr
+NwGYPFzgVdpo5nLWVObVPgsNf9KWx9qa3N/6UlyzRifkhkK62+EaqjFDY3CRnba4604votL6/rKQ
+NTI5mfL8tnawDpccKImM61sSBbOkdY9BTysiShff9i2pgtCK3HKqwDHHKXAX6r3omj0oLe1Q6eBb
+bZrKjt0Im6xZuQ63d0BbORpRro+1ZUSpXuxHWz9BqWvgC/OSSpmrw2pAoXDrWCtO7Y0dXryhnqd9
+SKyw6XdszVKbreapJUzfH5/CBxGY7QA/Bfx7ANbaHMiNMX8B+Lx+7O8iIjH/0k2hqmsmixmZU9GO
+xakMw4BBVxbm8XBMuyMT/OIL2/zMpySBemNLJmRnsEGkuhCN7TVK7UhzrcFqjG/bGeunyu14QV6E
+q5NNRtcks9wKS775PQH03N17xjTVsqa1hFr6DLRkt9Hr0NqRTeFSY4viWI5352hOrWSdV7oNvvAx
+cTW7L72q19PE0YoEywrTlpfQliVGO9+Mo/+uxzhTWRD+9hX+3I99BoAoswx/RUKeh1lJqm5ipbWy
+EnC0KkNeUmq8O8sdnggAlLfm4Vko0fBlsaZNuNiVe3rxU1/gedVU/OSLlwmVg/Hi1S22tHO1qWK1
+1zb/CvbLusEs5/xYJo7h293XaAUrBqiA+L7M0c6rssF86utv8MsP3gTAOS2INJ+T+T7KnM5me8Du
+lhyj3Za4eJeEy9dWuYMBLe1mjcMBYaCCxAtL1VcWpon83ekHZFoCHVywTI4kZDgdH+FpTiRoRfSU
+KHbVtzJflBjVtR/PM9qr8KCKmDeVhWqkuahpRqj5gN56l6aWUV3PozpVtqgwwdHqSjnWPp+jmuCS
+0sjXMbH2ZcxnpzQ1pzAZP6NpZa3WS801bSSsD+T5NboV86GEm16QM9YKXcNmZ4xi73V8kPDhKnAE
+/O/GmNeMMX/HGNMANq21e/qZfWDzD/ry75eiL1fcWOfjfJyPf+Xjg4QPHvAJ4G9Ya79ljPkFJFQ4
+G9Zaa8wfTBD3+6XokyS2dTbBq3y21D2NOwk9tVbdZsJHPyJu6aUb27xySfQFtzVkSHqbuJHsuo6T
+EHR+JBdeq4hKWZyeQXRTlFmXU6xyDX78E7ewKtJx/+QRru6XZZVjk1Uvv1iMQSdmQysYz3ciflMa
+4xiVExIFjXzpy5/mhX9NXGzXXJFrc0dYBU6YqIFVOi9MAVqPt4rxr6cFqMiILfZwtM7v4dJT0ZPx
+sOJUI5BCXWBb5lhnhdXPSJX78Z1f2se7/B0AWr+e0bwqbM43Y7Hgg+ZzLBSwtHPhEi9dEYu/3m4T
+BipFX8KgIVbM62hoEwcschU3sQW1VkN23c/yKJXO1ou02HlFvnd9IUnCX0122PnHYgweZw8plMOx
+YQsSTZ62B12ayplx64Yk9bpOSlNhwmHL0qjVszTgVHL9RZDhVvL7uK2us8OZN9ktt9hc4apszqM9
+CRsTH/K2Cv+cKETZSSmVp8FWp+jlM817BIoNGR08kHltxDR8mbeNZh+rEGzmC46UXt4ez+ioFXd1
+7ZlmitXrdVoTjOIwzOUKtxLeizp+TDmU60w18VmW9VkYGxUT9jSpmg5zaiXlmXsO9Vxh/e9xfBBP
+4QnwxFr7Lf3/X0I2iQNjzDaA/nv4Ac5xPs7H+fhTHn9kT8Fau2+MeWyMed5a+zbwReAN/e+vAn+b
+9yFF7zkOg60OO6HsfIMr61zYFmuUjTa5sS3JmWs7W3S6qt2nXoAXGlwVLzEeoIrIxrcYVwVCFs0f
+0VUp6q7RNzieFkeSgI/FPw7Aa4+OmY2+D0DseGh+klr5D7Y6IZcuS2wcuzGuWuZLTZ9OS6KlF178
+6BmM2Zayg9c2xQlXMesYcoWtmgx71lyp0NlhgWkrQ7Vp4MSSXNzaaLG7Llbl4Sgj0vq/PcMBGFSa
+AWt8Ck20fuuTEzr/q9z4C5/s86kvS5nx+18VvoXl+B1sS25026nptsTqJs0OjZaKqHhNPEUpGiUr
+9YMRrVQ8MBOl1LnMS++521T39P42Ija2xOJ1tTHquSeG8g3Rczx5dshCkZJ1VLOmVHC71uFqT7zB
+y5p/cYuanqo8NxIf5SfFK8A4Goa2wBQy90Ek/zrOnHZfsB5rRYvKF+s53J9jFXptq4JE8xJ+V/Ek
+pmSseanIdXCO1Ip3XTwV2smUgamVLhk0ZU22mi42lbmaFjk4Cmlepvh9hTwbmZ8qsIQKA/fKBoky
+VsUFZMrVkQ1zjo+1e3IleWcXuEpCfK2zw2EkHtssnzFWLIstLKrb857HB60+/A3g/9TKwz3gryHe
+x983xvx14CHwl/+wgxjjYLyYjbhJcEEWa3f7IrsX5AXK14/Y6klmuRftEHW0A0y1020O6MPElOAq
+zLkEVEfPUGOaK+UopTA7HRIpVVpYNKn68lJsXvBx78mDWUwWOPoiOJpwbLV/JEizSBdYdS93bnT5
+qY//JACXb/wUnm44q8VqHQ9br4RhLEYTeHU1Rbu6KbSPwjZ8CmWXroqSaqSJqu0eFxX8cjk44U0F
+2ZSBOn2zEjRiq6qafCL39NHXR/yDg98F4PNf/Fl21+QlHX5CdBvvf3uKuyHfe+Fmk65WHOKkSxBo
+t2cU4WiVoM5Xc5zjKYDIhGtYpQdrZgZ2tFek1aXlyIsT1UpXtlbxk5+WkPBXfuNXmClWoNlo4xT6
+fLZjNlcqW4rfzwt3lbQnMgmeJqCrMkcbXqnmGYGGikYTqv4iwBZqOKIao9WFfuwRaGLTrcDV+zOR
+vMQDt6aslcAmm2I6cvKg6eOpkdluyAsYmx79jib+4oCRgu/y8TGeduaGa01cxcOsnr+X5niRWh43
+oRwrOUs4x9M+h6pwUeQ2k/lKDKaBVZKgwttj05O18013SqYVisJxaJr395p/oE3BWvtd4A/CUn/x
+gxz3fJyP8/GvbnwoEI04Dl4SUnc3eXFLEkoXBy6hhgGFu82q4F47FqvNLGWk2nlVRpnJrXh+xArz
+bGxFrfVaJ/HPdLqMu0r0hERK7OoEOW51BYBr/cu45vf0fFCXcrxEv99p+czUmiWnJaGRHXqwvcPu
+C5/Qz7hY7ZKz2gxTOzGOdurVpcFEWk6sDFUpCb/5XLkXKLCKpKzLilr1FHrGcvl5yXY9O6g4qR4A
+MDs+PZufVROQU9fsq1V5Pbb89JqUcLfmDoliCFoIbqK1+Q7NWunM3N0zoRJTDqk0oeZ5FaWqLptU
+CWqrnGagCj7UZwlRxzN01YUvgojI0znXRqOu1+LKJ+Xc3bUXWGTfleM5BU4s577eaNPqahekhis2
+mFOvqBlyF3fVuWYgr1TXo45wY71Ou1L5zlcOFHEyJ9HSY+ROacQyX1UW4qr8ICs9R2rcRBO+mYtd
+rPQiTgl7kqytC03KOjOcWjkbvPisQau0PrVqmDCAWrGctYoF+cQ4ys9R1U0KxRWUp4bgoqwh0+ji
+rMJYxXfEzYhCOSk6/S6PtWls7VLBg0M5d+JUhLpW3+v4cGwKxgG/wXpji+e3hBzD79bkE3Wfawer
+lNyecSlVNadU3T63bOIox52tYozGelgHN9Qara3OavN4mr2OmphVz4CbEHgPALjeCkChq7PiCE9j
+2KX2EeTpAadPZZEOkg5rXXnI1zoXGWgN2iyW1PpSW5VyN50au+qz9QNspm5gPqXKV9UTrU7UEdWK
+zfn0kCzX7slpSVM/O2hkNDQ+zTWdW9d2RS6MqUrujBX+fdvho//+FQAu3foMRsOOu3s6h/dctl4S
+BqLabVJrtrxIa3ytm5dVSOmIm2/OdDkT0nqlvzjGWbEvZwuKSKnPjcVVyHqg4qhlHdNqSEjxytYR
+dx7L8+2ylAQBkBqHSOco03d/MgNHs/qNes5yKcdI3QVG79WJcrx5W8+tMgF1itW+hNnJEam2Mi/y
+ktlcN468xtWuy1Jfbq8Z4Czl/tP6Kal26x7NBihhNHWpeSDPI1SOzmJkefxEKvPzg8dMdX2uPYJi
+QxXMdPNuJ2t42hpfeRNylainY7AK2rJ2SWmVQVtZt52TkFph4I3lhC9elHzWye8d8ZrWECIslLqb
+vMdx3iV5Ps7H+XjX+FB4Cp7vMdhc5+bLL3DxsliXWTRlhc70R3NcRbxNJw5WGZFLzXrTKTGlUmLF
+cxztzccHjGbwagezMqEKS63jCldTsyaAsKOu9EWH64pEPUl9pprJXYayQx8cznn4WKC9aW+EVTRa
+01bECoO15QIidXmVd9JLA2pNgjrlmFqh1KTpmVBJORPvyKlCXEUmDi2cTFQHs+sTNSTR2JoMcd+W
+qkSsEOzcWmqdq6JyGXTFUh79m7tcULGbRuJSK0ty546cb3YxIJsLwnBxP2VypImxZv+sLh73+uBq
+Ymspc3Ly7PtESo/Wrdowk/ubDI/otFZdYw61dkyuVL5902SjL/Pd/+SX8d76P+TPfsH6ujy/zSRi
+qlbR0xp8drJP3hAv4DSeEaiEe22gmqtHttEjVL5CV2XxgnmA35XPblTrtGL5++RkhAnl59iLaEer
+ypWuodwn08a0k8IyV29xfjphFaa6VsOrsMCsdBhseRYqzkYFnmJdkqokDGVxuUvxuurFAQuFY1em
+xNaaVJ2f0NLEZtJt/yhZ2VDeBOOx1RbvYGm2uP1IkI574w47Klk3dize9E8x0fjHNSIv4ObgAh+5
+eYtK2WWStORYW0VPlmOyhTzQteXyjAY+8mTRtdopm+q2d03FmUJIEZFb1YecReQn0p569FR5IJcl
+m9syqZ2NhCyXhddwB2fhSlEcY7ULMB3LZnT7jTsMR4otP+rxkZsSn9brTTJ9Iffe2ie3wqDTUVp0
+tzL4+iJETXA6qmE4PsQe6wZQCqlnbboc3RHY9W987R3uKtvSltdi0NaNc3zERN3jqVZDPGRjAKip
+uKz1yZd7XaxyO/qxQ+nI/U13ZY6/8d03mR4LyWnSGRA9kxchc9a5/FHZhD730ZdYC+Q8p0cSXj1c
+fI/Z78rzWDy5y/ffkjly2x6f/5nPAvCTr17HKotWom2/xgOnkM/OfvMrpKdyf4Pn17i6Jb0t3d1d
+Nh15VhMlxB06OYtjuTY7DvG1nX0ynVJPtSuRAYplOxN/deYNIk/zOU7C6ULirf3HQxZ7WgVIoBet
+iE9U87MTsH8q11BUGbXObT6fsiI08rRSMa0Cci0tTm3Os8fCm3lv/zENDaUuXL9Afag8nFrVqvwJ
+jjIsFdNDMl1bh9GQZq7VmnsnzFQwyFnZwu4lXK3EmUGP8SuytvLS4mgCwpQ1Cz2PCkv9oeM8fDgf
+5+N8vGt8KDyFsBFx49MvsrHWZVe336PapxirFVgWRFvKgOv7zJRr8bAS9ys4eEKm8OD4Ixu4nrA5
+18eHDE/eAuDZ3Rk/eFvc49uPhfcv7O3yqZcFMn153oBQrV865qMXxX385l2PWq2wq+nr+5MKZYnn
+5WDE4p5m7V8IGR4I5jmfHLEcye/fWYiLv2wYOtH3ANgeDNjeFTeyiAJm2sc/reXf0f27fO2h9P/f
+H+1j1a1NmhWztnhC05EHGjaoPgjLHBSBTVkbOgp0aicBTQ2V7LIiPZS5+93bcj137r1OoNyWe3dP
+OFG+gdnhHTr7cp2nRw94+SelMas3EqDM0d6UJ/fEkv768T7Znvy+MWzBbwvV20Gc8rOqt9hW3ojG
+pW2IZb67xS1uDcSj295oc+OmeCY3L2yxQt5MRuJ1jReH2IliSBZDxgoFn2MJVLl6uZeuKDUYRK/I
++fw5LMVrONo/5oePxNsYzUbE+twH3R5RS84dabhjWiHXt+Qc79xvMFaXf7mcgEq9VRo+RPOU+ZFW
+geyU31U6tmcPj0nUEzp5OCTakFDhgoZG2ztduprxLpshkxNVKZ8sOB3K2nlrNqI60YShEshc7y3w
+Lsn1xrmPo81qz1+IOVCV7uzZlKJ4fzDnD8Wm4OAR2x7+HGrV/qvq4AyZl6ytc2FTXHRvmbDUsMJU
+8gAazQ0CBdCki00aWiKkNpzm8lK89eyYexMp+7XaApByNvvcP9F21HpCx1XyVDfhtbFsLNa7i3Yn
+n6kczU9rUl2sQdXmhVvyc9i8hqsuarKTcB8JBZ4cy0I6fvKEW02VS49rjg7F9WvaMUEgD644kcU4
+Dnx2YnmRnv/igEkhv4/mGzTn8sI+WX/Ay7LW2D/VW3bMWfhQ2Bqr1PdhZvBi7UQsHQ6nMkfd7ecB
++HcuR9x5W67hwew21Rtvy9/7HrOpbLiv36nY6ci9XP2MfK9bL/m9A5nX68OYn/mZnwBgtHadYSKL
+/+JyiV8q76CWgIvUpzqR62k5b7HUEuJOskGoLdxmnlFqH0CurEkNr4e/klm3BfsHMhcHyyFd3bR3
+miOGfXnGTT1vsD44ay8/yAvmysjV3hrgaFkvsJfZUIDawsg9RVmH/VxewpSSWsOYybTCUTBUrape
+bpozVBDd9MkxM80DrUUhM63a/mCvYqbqXOu6mbz0Qpfttrj+m2GbMpDjHY1zHmsPxsPhnJY69qes
+uBprLiuxStJcEnbkJE9tzkJ7Sagrlit1rvc4zsOH83E+zse7xofCU8Cx1O2SKpgTOIrl9DOCpuxw
+0bJi/7GKc2QZVpV1cu0TTy5vYXzNNpsZaOdcnWQ4qsXXKC2tUBJYP7gnrujiyff52A3JnN+ZdOhe
+E++gZcb8/Iviqt15IyJVrgO3pX3z8xJfE4otu6De0mrAdE6ZyHX8zg++zz/6uiQKT09x5uLZAAAg
+AElEQVT29Np89rXb0+xVfEaTjnH3EoUqMLebEu6MnuZEkYCUvvmdx/zgWJKkL63dpa+JxiRoUVuF
+AasrauqMubo2Boh1DkvHwSjewnVL1nsyd5+9ouraew6n9/4ZAOnePsdKVW8CS6jgrX5YMBgIuOxS
+V2Dnrdb3aCoHxlfyU77+XWEa7u0MuekqH8bGFebXP6LXJ96TzTwOHkrXpv3MLV79ocxnt5MzUB7I
+xFmSNuReBolY8PEy4kQ9nrzyVwUe3EXzTM25DAdsrmvvwqFce5UUTGrxchajIYWqMr8znXOsmI1r
+2+9gXbmXUrtBB9bSUe1KU9VYfsQI3tNrCip5/saMWSilXVUZGl3xlPJ2iqskOtu9moMTubaGgvNO
+84odhSU7zS6bFwQvMlrcZuGv6OsaPFjKMz4Zqgr42oixehXTaEA4Eo/nudrloUKbx2FAnmu35nsc
+557C+Tgf5+Nd40PhKQS4XKna9Dc6DDU5V+ZT2toxOasyPCUBDaMmzS2xHo11hZdGa3R7susGfoSr
+XZTOMiPpa1Lm5S6dQj7z8evaDOQFPHdTdu0qHWM0cRm6F8lUfGaz8RqjTHZjPxErUixGBJ5cw9R0
+WG9/FIDB1QGZ7srbrwfc2hFL4d+Ssmd3o8/NNfFGbm4N6G/IPXmJjzOSJF+kTVRXL/dY187B5y5d
+5sdOJYHX7BiWCjE2Wc1eJd5ES6XpYjvmWOPMAos6LiSxSzZbieBMmT5TFuCheGBP7t3jWBON7kbN
+j29IDmcQbuNdEq/g089v8dKrMl+b2rjV6lmW2xIjX37oEpyI9TxJZnSevwrAlRevsra7akxT3c1i
+n9MnQs02euN1Lt+Sz14umviV5BQWJTATD8rVXESz7rDU/ELP2SRSXEu8mbFWy7O+fGGdViylz+Cq
+zOEyXzI/026MiLtyPbtpwkXVYdi6uoPSYZBN1Lp6OW+mKyX0kqpSjIvrEit6ES0Xj+clti/fm7uw
+o+XShfX/f/beJMayLD0P+86585vfixdjRmREZlZW1tDVQzW7m2Q3RalJSKRhmFwYgg3D8kBAGxkG
+vJJ22njBBWHDgAF7Y8OWAJOSDRsiJKIhkqIlNtnN6rm7xqzMrMyMOeLNw53vOV7834uqItTsbLZM
+JYU4QCGjXry4w7nnnn/6/u/DMmAn5abFnT1Z1718JZBjcKMp893fa6IBWaev3ttFw5P7MPUJ+mPm
+tCK5yLv7mwCRpyoE7ELWyJtTFwWJcNMku9KyeNbxXGwKRlvMGwY6s2iCorITBw4z1VvdddR9ecEi
+f4GdG/IiuIG8pH6m4DJR57cjWFKuI6nQjUm3tg+0Wae3m/Jgq8BDnW70Mg7hzuS4i+U7OJ3Kg27A
+wb11+fzTnxZatX/2e7+DmEConbUczVOpRzc+9TrabI3+whd/ETuvicvvbhJXEe2jb2XBh94UOmR7
+q9OCuykLaOOUDM9fbMIKLQXsCHitkpCnamZYkv5t+N4C4HUOmqK1+PZpAUW1G0eZK8izMRUU+dUv
+H01xcShVkuOhLLSxt4/X70nt/sXP7MNZyHz3ixRqT67p9p0bqG8Lo7BHCLb5ygR3PyfXOf2eD+dv
+8HzFPm69JByVwVoPHW52+SmBZ+YBnp7KC/+F119DHRLaVRsVGl15gYIMKAg7Dshh2LYa/i1qSY6G
+8Nvi+k+cNoJLCU26jRqyFZirkM3EcRwY9qJEayHurIth8NctEtb9d27tgOJTyBM53+D8AXziVypd
+YeVcOwA8yt2vnPPocojZVF5Mr5ahtSZroWMV2iwJ1e7U4bAnpDkSw5M7BUL2X3imhxoTzF7QgveK
+bNTVgzpGpAGw3CC3Xq6h4UlC1Z1rzAk3P4seINMrXlHAWWXKn3Fchw/X43pcj4+N58JTsFajKmpI
+4gqttlyS31pHfc6dtKZwg33zzXoPrrfarel+BfMrJuLSxNApOyOh4W2LlVf+Et0J3UASYeQoYVj7
+bYU+rC8WOCnaMGT7rW02cMBk19/4pLj+//IrBnMRpUNQ9NHdZAmtasCH7OL9FxRqS7FGlpBiJ/Tg
+N+XcJv+wkQYmg7dSu9lmonUM+A3+3vfgsxPRxDW4WqxRqU4QkEg0oeuces4Vf4OGQkGKstAYOITM
+Rv0u1EQsyXFNsBD7QQLX/RwAoK0mCAkDVpsemrRi1oYAa966lPp598tfQPr0fwcAHNz7RbSIPTA3
+A6gNsWK19QiuJ/eSZnRrJxFe6EuHfaerUU4FWLAM53BXuBDXQ6kpzUZZ92ajjrpa8DrrsFzCkSng
+9SU00bpARim3ksloT+dwQ5aOFwEiutTt3Q4KdjYFiYduV64/Z2fk+cDiZCHPLytKBGTo0b6Ca/ks
+Z+JhqbzAJXkm1LQGxVDjRttFg+vaGzlIlvT6XLmGaZ5iMSDT9MYIQZ8duL0GmlQVv7M/QLcvnjFJ
+pLGX9OG6Mle5GWFkVgSzLhQh8qHvoFhJQ9IL+lHjudgU0irBe9MfoFn7BLaNvMReu4TDl7gDjYBM
+OX7ThW8oBkOXzM4tsoTS4okL6rnCDUPkqXzuFjU4ETsYC6qkesDKWdKeRklXzJvfh3rCzWKUIbwj
+nZsRmZ4qOAhZ564tzqC3V8IxDlyqFGmngUZbzmMv5cGqdg1gLG4agGG2WK9tQc2IrXAExIPLDhxy
+CipHwauTyXeQwe+KC788OkeRi6iLHlEQNS3hUABGlQqVK/MWQ6HGurjXqtDYkQX78hOZy8vBEXZd
+yS/4ZhMhhVu1E2PxVF4ar1JQc9m0/HX5V50lqMZ/FQCw/YYG/pYswGWVQ5/L5lUEG6jIZGIvZBNK
+hlM4ZDSabWygPJVwJnrVh8NuRrOYwDC77pWsNAUeSr5sYd1A86VpOA4KtjVnxRhJzNo8iU50M0LI
+DbJdC9DuyfWH2odDrVCV+AhpMLKY6mOzJeYnhDnnKTSFdvwgguX6SxkS5u4l0qkYMi910WsybISL
+TsRKheejuS4s3/ElcziTAiE3W993r8I/X7sIW1LZcMMG6jREKfHKjSqC48saSpIcTsqO2EyDurqo
++x5yhmDT+bPtCtfhw/W4HtfjY+O58BR0WiF8bwy34+GQUFU/yNCiPJzxNBBK8knlFsCqQiE74+zx
+DKNMEm3N8ACuI8ewUQDPIZ9jTSHoE4HGHnWFBIq8i67SMKSaT42P9K4kvsqTp+i64j5WYzluAYMW
+yUbO1rZQ5DcBAE4zBUqKoZWAZq3Y0nWEiWEmbHDJBnAadDWLEiipklzIvdnOBGZBTwIhsFIiDpco
+Hoi7unzwED94RzAQZ3QRS+XAYZOU9hTcRH72XQfzsViYwi/h8TpHSqx53mzCYRNYe7cGl4nEyZnF
+/YlY971ljmVfrOZCSy291XbhrEsC1v/kEgstlQpfa6gW50JbFKnca8xOJfvCLsJcwrWLw3M8WUqS
+8Av6FZgVW3GsYQu6vh7xFkkJjzqXTuDA8yjLrkosF2Ie54mGS06CkDyXjqpDER4eejkcX6ymW9Sv
+BHMQaDgrtm2H2pbFCOeFeG95WYGk2QiLGgquVTWXcOC9945w4clzWlMtTKgkXl9fv+rgRKjgzOTc
+6VOZ45PpKSImF6vRDI5T5/0ZBIQua1fDIexfkwim0EuYAXkibYKUa/IQCeZU0k4tEPorwhh6oz9i
+PBebwiLP8NUnTxCmJ/j8a5L1vsxT1D1y30Ua+UIeUhYALmPjkmQjS2Nw8oQtq2vHSKaUJPdyOIUs
+zN1P74CkP/B426VV8Oyqc86FIRx0keSYfSAxc+EpZLEwFp1Ri3ChFHx23+3Wphh9IFTmi+4mGux2
+8zzAskyISh5GUZQA78PECdwGH3IOKC39GBU74cp0jmX6ttx/UsBpMvSpHMTUY4znS4wocHI0IJln
+YeHSd2w6AS4W8t2OcuBWzDucVVeAlpXrO1xO8ImOnLvTWcfDgfx8dHyItx7JxvOBniLYkUrDS0ey
+Ce/tfQYbNylqE6whYogyiaeIfHk+RaCvyGwKzltkZzjL5NpOL4FLhn+qVYchJ6KXjWBCOV7BNnO3
+4cAhNb6uHOhwxZQE6EqOp9MMWZ16lBFlAFwFw3ZvmyjE1IrUdoC4XJHQKlQuQ4FYntnjt95FSqh1
+YSw8rpF4mSLjvQ6ZM7LxCJOYz6Fv0aeg8TydX3X2RlWMGcODBbkBBuMEY3Z47rohYkLlM+xiLZB5
+KZ0IlsQ2RcESY1mhINFvFmeYn0qV5Gi4RFLIPAcusMYwRlbvjx7X4cP1uB7X42PjufAUTAWkM4Oj
+Xop96iDWgxbsQvasaRjDIwzUd+vAUH7Oszn/fomEGoCj0wJxKZatdDfRvi2Jo7SsoW7YJUhXW0PB
+kAbL8SyqWFxpW5R4nEpIMG6d4MGpwH+fkkZ9nhsYqjL/07em0Bt/DADoztZw95d47LCDakV2cinu
+fqbOoM9kBy+bCcyhVCdU+Ab0BYk8yDM4HbnIm+K27hwDAevxpa4hZvfT4f0xnkwkwXilOK8qtIhN
+2O5ajClNVodFuCVzFLp1jB+w24/9+rc8C6vFqp49vUA1lGvW2QnA6kvW3ES3Q63EljREVd0epqSy
+b+sGclZDkHeQzNho1PHhOnM+EwmT0qxEzj7/0clX8L1DCSV+tYygGPK5nTWUNIoVqwm5jaEJCnLd
+ECmtdJLPUZB2zPoZDEOMhGI4jrbIiSjIlxZOY8VKbaHJ2VDmPVQeKw1D+SxQJSrOkbEWVbkisIlR
+rSjUyL4cNVporuTIZzM8ZTNWbbmEZ8VjHSwtSrBBjl7jucmRLdnVaBOU9IRSlSJLxHNWXoUyXa17
+wrWXDlwmkst4jqORHC9ZJDAMMaIwvEp4Put4LjaF0pYYFkPM8yGaROgFexE0s6Vu4mJpxZ3zvSlQ
+EB1HQhZ3w0Obi6a1E6Dw5QHs9PvwyaHf1iFcvqRWc9IRw2EJydMu4pjsTmdL3CQL0cnxCN+Ysitv
+8SFLxarMczxZ4G3K2X+meAu3f05k57PEoMiYaZ/Lv9H6TZRbAmi5PPoulrn0RqTpEGdvy4P+5tlj
+AMA6Emzuy1xM3SY8boBRPIehQtRRz8eUXINarTpHAVPIghjEBvNAXu6pX0Ntxe7j5/B6cv2vNeUc
+w1zBj2SD8RAg2pOcSlBYOE/kOntOAeMQkLMmG0jZLNAgaMavW+iStPydBRy2KrulQsG+BOWuKhkl
+8pG8NO9MIpydiOtrwxIhCUkCm2LOUiRSuvuli5TxeawncEjVHrUiWJLUDsaXqIxk9kdkZvKzALla
+ZfhTdPlC54GHJdmgaqWHFnM7T0mo8/WnGXwqinnIUbICYI2FYUmxfou07q0CswsxLOfTEVYKjodJ
+iYKI1RuuB9ugJgWrAt5SY2+drFidG1jfFnRnYw2o8XwqsJgMFO+J4UpaotGX9T2dW1zMyTKmLbre
+ivBWYbkqST7j+Eml6P8bpdRbSqk3lVK/qZQKlVK3lFJ/opR6oJT6R9SEuB7X43r8JRk/ier0DQD/
+NYBXrLWJUuofA/iPAPx7AP57a+1vKaX+ZwC/BuB/+jMvorToXRZY/4TBQ8JuP3Wg4ZFMYjgbwWF/
+eFzlCGttXoRYDHdmsU7IaXd/By6tWS0IUBHa7KrqilatYOLIzXxoghoqL0Z8KVbncPFN/P7pYznF
+xSXOmQSio4GPbrwDa/FHlHg/6Jzj1WRFerJEzq42rSU5V2gDOxMLFCZdFFSo1vUeNsgTeJeeRLv1
+AjbZDWjKGeastFSehUtm5/mTHBPyAGa8N2ss4lju47IssD4Wa9RYA05I5NLoOfBX6kSRWJ04yrGc
+y7V1tI8P3pFuxwd//B18/1I8oZeCu/gZ5wAAsNaQ66x3PTjEb3h+iIreXVBG8FjNqMI5ygE9GrIa
+o6wjPRfOhg/e/kM8JSeDq2pwVhJelXNlpSsm3+K8gCWEuZxmaJD6vsoSlBMKykDDBcMH4hyq2KAE
+u26jD9m/M9irEKUwBRIKvCQLmfth12DJkC+sNBYEVlWwCAkSWyOjy6IqsEn173jTxehIEtC+yjFb
+yOfdZojFsXgv52fi/eY6Q5OQ/g3joE2MhV8LAALYFjOLnIQysCRNCUJUDCkWRYYFeRZSGCTklmjB
+Qqc/nkTUT5podAFESikXQA3AKYAvQ3QlAZGi/9Wf8BzX43pcj7/A8ZNoSR4rpX4DwFNIT8g/B/At
+ABNrVwodOAJw41/390qpvw3gbwOA44e4jxyfPoqwe1PyAbO5xWaNyLyGg+m5WGPVUWgEEn/mY0F7
+xZfnOB/I7quXPgI2IKG4REm9Rl2zKMgtULEDLm8G6DMrZ9FCQnTY6JvnWHfFqn4jLlBeeQirxhKL
+lbPgAEhi+btsPgKmYmFNawcOLX05k/Lm9HAT3pnUtPNco/MStSx2fhqqQdLRC5FxK/sZxiytmgsN
+TUtTuAs8Hor3M7Y5EnojFWGtxgJ2ZfkKhROW+vxZC9qIdR8PcpCkByFFSJaDC7hd5i1aGkdMeH79
+cIzTpcTczaNDnO0LFPqFt6QkGRUH8HdZB/dL+M4KduteQaXLsIVSsbRGbUTlnOPxhczF+RyIGAMn
+KoZDZtKwtgZEklQt2C1pHI2cOYzcsVDjcx6vjuVSkrWeG6JzV57VVl06WPVwjMWKMbsskfIBZvMp
+YnZB9dcDFGRTmjEpqXOLFmHe8xJQLKlaADpi5y5Lj2EVXhHQPsoGWBLFOBiV6Lfl75zLKRaUpjuf
+yprtRD7mgTy0vV4Ikwiy1Mn6YMoL82wB0EsxZAyvBQViCssUswohWcfT3F5xZ1RVBb+2eh2fbfwk
+4UMXwK8AuAVgAuD/BPBLz/r3H5Wi92sNW8QL/O7b34J7QxbEX9m7jcWF3HzsGmRaJm0ZexiSV2uu
+BBobLweoSvY+pB6ilZhI5iLxZdGYEXA2FPeymYu7tx26cAvZWNJwhnQpL2TSfoT3v8WsdmlxtQPw
+Bw+gkynD8IXc8ndx5MoCuxsPgYacJ7mQZN5T+xhmJg92b62N0AoASM/PUcWyEGybCksDg+lIkoSz
+B0dwAznH5SKHIXz6G8UJpgwbqo+ENKufi6rE0wVBX5MBtvbY+9DtwZ7JHF1QbUhXA7x3Jud+92GF
+H9wX3MSgWq7yushnTRw+EU7Hr2uZt9fMDM2ZnKN+Zx9tTdbmbgSXgjqZnUATW6Fc+f3iOMfl6fu8
+4DG0R3zAvI3El5c3zA2ciD0BKTfTIgUIU7elizNWOI5m30NyKe54wzPosurSXpfrDbWPjDqeKrM4
+SWRdxNMF9nbk+WRzg2kuG8vTE4GPV7UYhmGHVgrqw8WAbMluXBKr1CsLvXYg1zZ5D6MVh6hKcUza
+vGMVwLVy/TPqf07SBfKT2zzmewgoWGsefQA3Y/jb1ujxdOFK4MgLYWpyrKdmgofEaRTWoMxWG3KA
+IP/oav3R4ycJH34RwAfW2ktrbQHg/wbwRQAdhhMAsAuQqPB6XI/r8Zdi/CQlyacAflopVYOED78A
+4JsA/gDAfwjgt/CMUvS2zJGPH+ESMb76L8VC7770a7jXYUJpMIa3agaZXGBB+e0ZmZ/jmYNgLpY2
+XeugR0IWpxZ+SM3mpdhZ7bAsXemmAliXrgqDJBYX98l9wGU3X2nt1c6pCYEtYbEKJHwAlgyz3373
+Me58XyzezU/8LOqkcVvfFmsXVTeRUom+s+2h0ZbIStdrsC0pAbZK8TSy9x4ipMbC6XSJOJTPay2L
+b34gFmg5KMAeGHwY2Hw4jFVoNMQaT6oAxalYoDtdF+G2HLtxKn/5QRrj/EgSf7UsRdmRu37tszs4
+fyCu+6c2XsBL++Ih3FyXstna/iZ6ntyUioCSwjeISlT07lTuwhKT4LCubtsZTofEG3ghQsrppfY9
+hKnMS6HmqAL5jheQDTqZIqZYSuInaEVipfe7G5i7UhrMpiN4TAI6Szlupi2sFVNbdyy2Q+m+nCYK
+IXUzw1aIQ2oyDM7Z1XkcoguKs1QVPMr66UqhCthYR7Rl1dLwmIXe2t3C6YmUJ2cZMGGzVtdPUNuU
+9bnPkvq79weYToWTw49DTDsrlKbCNkPQve4m/BXN3kp5UNewKGX91gqLgiQsns3QJFtMLXTQJTYE
+OMOzjJ8kp/AnSqn/C8C3IVI534GEA/8MwG8ppf5bfva//KhjaWNQm+cI6i5O2Wfw8PgYB8xwB80I
+ylAJKOzAZdtrfSZuZjUeYMaYrHZ6AUU3qtQVfLbONrshQuIXLOvL1kTIUznufHaIeLESHOnhRlte
+hKpIMDAroRVZoOOyuNooCk+hRX71vbsWviMLM8lGCEfyEkZdArLKJvTosdzHaRfWlYy7mQYomOco
+M9br6woBqwitdg0V2X+qfIHDB8xejz6MFVebggaw4tlRCohOWNPuKAwKcY0P0i2YprwgLbYKd9dz
+LBMBJG3gEB5f2H4vxH5HNoDXoi40qzkZZAO1VRPYknvWOkBVW2lMhlct7lqXcCjQUxXyTEt1iQ47
+A6OyQoPEIe5ZhfgW3fxcoeawN6NaQX8rxA5x/VOgyY0+Kkt4dW7wa1voRwI+W0GDF5MYOXtCKqPh
+sLchrJfQxAvE6Rhnj6ViMCfWI6956HjyHJNijgFFXAOlYEiTXxBWPp/H0KwMNDpNrF/Iyzh2Mzhk
+k2raFI1Uvt/dl89+qtNEe0x+ybrChHIFzV4D+3UKHkUKtiYVtoqVBScrkZGVe3g2gqZJcAqgFsq5
+1xpNdBp/sVL0fx/A3/9THz8C8Pmf5LjX43pcj397Q9kfE+30/8e4ubtp/+7f+U+w5iaI1iTp48+e
+4I1HUnG4/877V7yDUb+DL70olmvTo1VWY8wGkjiaexZv073e6ibILsXCNLZ83H8kNnR3h7t20saM
+5CTLZIk5kY6VUrBsFNpu3ET7s2JBb3bEyt1/8w8RU6jlycUAF+yhvxjOsEzFUmRVhZVghE8p+mYt
+QKsuXsWNdg0t1vEbnQiuZvMXm2RMz8fDC/bH2wweM/JGRShWGhHxMX7wPZmXpMa6+ghwmJDSNsR/
+9rf+AwDAhu+h3RdOhtthikfkOvjjN74NAHhweIklw529movP7Il13Nyo4YWb4rr3+jtYEgtw8r5Y
+qKW/xJip/HfPY5ydkR5uksBSjvGD4wwVgYnmRyy3oqwE9w7AVgVSWs0F2aUfnJ5hfiJo0+OLc8yO
+CfkdzlDUZb6mgyECJvFmh8SKbPqoSM5ya/9FvNgQjoz9n3kFfUrGx3GMb39DEpMjQpEf56d48g3x
+SN1ZiBkb07rtGJ+/89NybE1Ck0/+DD55LBR8v/G19/G1f/F/AADm4yEyR7yRnlfDra6EivUDco0G
+NawRk4O4wJCq2nGSIZuyujTPkK/Ic9g5mmsXKGW9tNcb8IlxaeR93HRWpC1d1FriFf36f/cPv2Wt
+/ak/+wk8JzBnKIXKV9hpdeDGEuudTC0u+PDTrI1XuTBf699F7aYs2C0tn6nEwzST0ODh2ffRmkrc
+21s8xFKJW6pPYqRTYeZZMOM+bF0icQVY1LEe4qmEDJMMSCg57t704b4jx1i+Ji/0aPwEozP57ng6
+R0LFqrgqseLIdLWGuyLLYGn1k1vrePEVeTFveh1c0OdvGx91tnuHFG+5yJ7iaSZQYosLxIoMS3Uf
++zmJQMYlfonx7psMJQYWiPgCLt0U5wRqHVgXmwMGo50WHg3e4b3K4tm/YWGYyY+2LX5+XYhc7vz8
+TfQ64opnkYeSvIo7PcKnLys8WEguuZVaLHg9g2KKb69EYQsHM5bT2AD+w0dloVj6tQvAYdfi7IzP
+7EQjeyLzefIgxx9dPAYA3BgDUU++E1uLBfMHL7H2WhpgUMi6efqBh9eFPhINk6MWyrpI5iXubQsF
+/8ORhBHDpI9AS0nZ9IeonbOMkPTx5sviov+XVBNLfvcEp0tZT/f/xW9gTBIdXwfYZBhbdELsknD4
+5r5037ZrCRRh6v2oj4K9GOeXOd58LLkGGyUoE3m5NYk3H00maLBTs0w8FGMatcpivM3Kx7BCvVoB
+rp9tXHdJXo/rcT0+Np4LT8GvcuzNjuB0XkQQixjKd3GBRlPCg7t3NF5vi+U6uL2OBpWLbYvststN
+WPf7AIC1izv4EtWMx2u30D+UHXOoI/zUSEzoWU/2wvM0RDiTnf9Y50hZE45NDpvKMc7OchT8PG+R
+l3FisFiyy3A6x5iS5HWtsKzLlDYdjV3qOP7qp0V/8Qt//bO4dSBu62T8A0zfFMs2qRJkhPPmI7G6
++fktfJZcfu91FC5ZcQkuYhx1xLpP523s5OLaPqZ1fREWo9W8lgob53KMYH8POhF+hj/sulin5Vp8
+gi51sYPuvnz2c96LuPFXxGK2NvZgI2IMThykt8iIvBBr17gzwc1zCa/O9g5xcizh3zeD78D7OiHf
+ZoGTFRiI1/bDSMe1o64aulQoPAEAkI9XnYFv47tfk3t68+J3MDuX61l0AtzUsl66/S72Q7He7prc
+00F3F0Es66U5SGEY2tS8plShAGwWEdRUkrG3/QOZYzWG2xC3fVR38O5TWQMn3in+0/HXAQC28QUA
+wNj+Nn79W+TcKIdXZD/rbhvb9BZf37+FF39WMAkvNkVYZ7afYGcp4YPX9OApeb7H2RyvvCchwVE1
+x5MHlJOjNunLUYnLU0LX/TYqckRE5RDZhF2gRYLD2o/XfvRcbApaO2jWuihVhAHRXntwUGxKaerm
+5hZ2b1C9KapDs1RXWpk8bUN0NyXP4HzpHmZfktLLeh5iTl2DbulgWlAfcCSvTXaSYZDLpM7GOSpu
+Ju0owpwx3jIuYTTBNCO6anGGyyWFTR2DKOBGoH30yEX45Zd38bOfF13FX/iySLK7QQceGYHq7kvo
+fEIWabeyMJn8XZJIvLmdpTi6KwCa6PEhykPJAczzBMtSXpT6wR4Od2QzcR4xVInPULIU6FgPN0iX
+7jS2Ma1RR6KXIVqKW12jyOGLt1s42JCN4ParHXiRzLdrAiCRazbuGVTMtmZ2GSM7PY0AACAASURB
+VLZRg+6RlzK4hXZb+AcndR9OR17e35u+jfZXZfMZzeWef9hQSliGAKAcGqSVbJIZyXi/9cYTvDWR
+eZk+LdAkjfr+3jru3RQVqoOtW+isy7woapN2nCZep56CFxQIqM7lhT0ERDHaho/6vsydGQow7vPu
+Gt4ZybzMT9+EXeVGVIq1RNbn4UCu8WvfmyNOWYacl/AVeS7XQ3T2DgAA/Vf3sLUmz6RB2vpedwdr
+rBA4TQeaFcmGaWNJuYLNeYa9SNbvZSb3fHg6QS14DAAoqgV2+rIpPjy/gGF7xHCWwdafkbGV4zp8
+uB7X43p8bDwXngJcB2WvgTJUuMxWfeUF9A26g946GjuSgNTJEiCHn8+Mrh/WoKnoEfZyNKy4sOnM
+QhN2WyYJbEnY7ZkkiOzyApdk1J0mMYyRPTIxHsIr6myLBfv+i574nGmeYcnEWeBECEmr1ruxjk+z
+evLLv/wpfPqueArNLbl2zA3ABGaQFtDsu3DRhWaDRUVIqnF8rPUOAAB7VRNl+l0AwIMsxmNL+ZEl
+4O+Lh/QSNTPfWiZQp9SVtCXWX5Hfq6AOzOXaGuU5ynVxRfsEuazf3cYGLZhqtgF6IyZJgRUeJJij
+JMjIqVY8DiWcFunS601E7Gb96XAXL1KqPSsqvHwsocs/eUxQ0KxgT9+fGkpdgcGsrTC4lGf9rfvy
+DB6cPMDlhCDZZob6tjBwv3zjs3jtVUmIdppNdNcIYGPY5Tg+Cl5btF4B7K70yhIl78Vr+mgwMW1j
+cfHtZICtV7j29AbW1sTjudFv4pKqV8dvyT19ddCAQ11KN9IIHTnWq6+/hFdvHAAAbu7vYY2w6gY9
+3ab20NyQdaod76qb113ECBiCuj0HkSuhUG8iSd4w8BFkpM0bjzEhO3a9W8MZe2IaU4XWFUfjs41r
+T+F6XI/r8bHxXHgKutKoL+oYTI+h67Ib6iDEC2uyW9fbBTRZZUpToRixm4/JJC+awu9I4qvQKers
+CMqjDDiUXTlxLqBzUmmFEpv5hUZGXEGc5lC0GEopJLlYB09N4RABOVhRm2Uj+KwVb/RceB3JB/zM
+3VfwxVckpn5p8/MI29Q6mLGjKJnBrsRUah34JE91XYXVo6hysQxpdYFuzBLiWoaduVjx9z44x/yc
+bNXeDN1I4svHZKW1Q4WKdfPCVNhUch9JNkXQJXLPKHTrYmH9DYmBW1UAQ13C8rJAyQYkJw6ANbL/
+zFtwmV+xlvYkCGDmkmhV+QguaeoaNsSiJtZ6/S0X3i15Pi8xvP1uPoAiRtviQ0QmjIWd5Px8hDNC
+0guiTZdZiXu7skZm2W3sUdLv1Ts9rG8QCu0H0Ey8Ro1gdWCEbbJAOzVY4jqcSkGxxKcSB36dit5t
+uc90YREM5Pf9yuDk5oqsdY69ueR/5ouvyFze/ABjqWRic+NF9G/JM/v05+7h9rp4vTvuOho8X60n
+nkRY78KlcrdTd2EW1KuEgiExb9MvURDjMp4ygYsSkRwW9VmCKSHmrt6Ak0sX66gOVCv+hWccz8Wm
+UKHEAgMYx0c/EXex0dvCGolTnHQOS/p148cIKOtdb8okuV4DDttDa24XiqIvZWzh1yRhtDwGCuLu
+q1IWdPBkgJBJLVMZpHThdaHQ8ilhDsAjeZSlCE1YOYh4jm7Nwc6WvGD37m1jryuVkaC+gMtWVsPW
+YzgGTkBXNIqhSlmAUDkq8ow7NW4al3VUfbIhmw2s9aRKcmujjveG8iIfJgq9XH7+LNWDvllGKAgp
+TuMKur1qqV5DyC7BQK8hbDJTna3oxAsgZ5fdJIHblHkpbA7HkJRGu1ddi6th7BCKwr0IDCyz6Dac
+ou3K87l3w8f3LmXz+sRrcn/90sEfPF6FaObDTUEBCc+3/GCC6vElP5a+jHu7N5Acc+53fLy4I5iU
+Zq+LBsFnflRBU6xntdGHUQBqzEBbDcuKkbWAihjIGAOHVaCQFZ6uXqIYS2h2Z2cDx/eZKK1ivJlI
+9eFrJD/ZeFKg0RRsycEX+9ivSzLzZX8THeItwsTAYyUiIiDNqQEOqe+RZVcclDoq4CbscddL+OzW
+rFMaIF0GaEWy8UxbExjWdOblEP6KvWBu4bt/sSQr1+N6XI9/x8Zz4SlAaVRuCDfOoUKxAmvddTRC
+sUBFWSBfSHLF5ga1TTY2BbQvpoKmBoRTV4DLHnTHIGJZCK0KMT2ELWoJrLk9RKHs/L4To6QWX5HF
+KBpsFDIuFOvUTpfEoCcWTe7aXqOOTSoxH+xso9UW6+DqGmBXJDFiSSwCgB4PckCRvMMU6orjzazY
+P5oOSnoYushQnZOYdm0N/ZZAuqfLJR6QNLZJWG5XaRwzLKmMRdgSzyRaLjCnyEgdHrpd6nHOGSY1
+NDxLhebe/IrB2AsBh/Vvp6VhKFYDr8l78oCQSdKlQUl8h5NdIOoxMZbU0ack3WO6xuErdewcyzke
+Z+ZD3IKJ4Y/FO3j/n/5zfPOWPJ+dPUHnbgZLqA05bgt9fP41Esm2m/BIDus6Gp4ieQ49uiq3V4rS
+OiyvujYVHBiGglo5Vww1K1SlWyhkXGdn98/gZHJtabbA6YXM1/yBoA6rWoF7d8Vr/NSNz6BJOHqt
+5cFviVcY1mMEEWuOq8Ym614loOFZmHSlT1FdcTmUJr2KsTRDXutWALtqPa9EQGGjeaowG8h6gAW0
+auHHGc/FplBVFeazGYKwgyBYiXRk0GRCqnSFglndohagYMbVYfea8hQqkkq4ZQgQ965UCcWF3uhW
+UJW8FOVAFvbdgx2khSy6JItxOBI3UbkWIcVfo802WhuysZip/DtIKyxIF79pDFQg8Wej3oBDfIOu
+DHKKdnh0VZUXANQzNGUFQzr0qspg+RJmqwVaZijo7pvKR9iiPPvsCJ2m3JN3nqEiC8/wMReBdtFl
+SDTJHAR1Ql+zCOsp8RluBWspxsoKiI8Axl3BZEvoOnkwMxeKG5Z1ClhudKCQi0lK0YwHUFoNxdyG
+U7SheH+tbY09xsyG2o7/77tDzAjtddWH1CXGlPjBVyUejmffwOKfEOD1RaksrLUaSNjDsN9bIKzx
+PtCGS3IdkwPghqvI96gMUJAH3wk0LIheMhkK8moiLKDYBm3YyZhlBk5IMR+T45Cdqzaf4+iEPRP8
+bj3L4BCCvtZtgbYHzSqCk8schcqFIv28ZYUnW+TQqxc+0FdroUgNFJWxdOUA7APJaQBbvoOMIfZy
+1kLmylpeZClSbopVksKybftZx3X4cD2ux/X42HguPAVrgCzWyN0F2mxy8lADjThm0xR5Qv7+qAmb
+s7+d8uY6ayBcY31ZNa92V2sAzbpzuUxROBRauRRr5TWmiJTstL2mhxG1G7PCYMpQ4qZuwiFD8Zzy
+b3FZQRE1uBbV8UIkWegoCa8qFdZkcNtybFsx3PFymJLJx8hAU64M3gyWTVWaXlClhsjG8tnkwSnO
+lgJtPZkXV52UgZtjyJ/nFD3xrELErj/tlghJMjK2OaJIrJjjh1eK3nbl+uczFEzU5bMATkM8jyhT
+UHW6s3EHBmQUnq0YhR2UJKTJF0NoSmMskcEkbNw6P0eeEdG4Jdf78F19paHgf2QV2ovvIvktSZ6a
+rSbC12Xu9rekEnWZP0CTjMnGRJgeyzNxbjrwy5VX2IcmDsEwgautAsKVl+bBgi547sCy2ayKa1Ak
+3TFMeDtlgXRE3dDRKeb0tgbjGdpkmLYMEyZBiIDPPLRbsIUkh6d2gk1LrEpVg3Zkvip20YbNEI6q
+fXgsMjt7RQ67mlvMkRfi6cR8MfK5Albao/ESkxm5QdIcS1a5CmVQFn8Jqw9ACVRD1PwDRKve2pqF
+Zomsmi+hiQd3Ax+evxIBlUVnAbA5D7rtwAkY764vYGsCetFFH02+OMM7jHu/dYLXX5fs9WQ5xtFU
+Flg6SJBP5dxKT9H8HEVT32Q4E42wYDdj5Rl0qKVYdx04fEmVA4Cuvark5Vczi5RS9HqWQVOxScGD
+oUhKMpNqSZHFCAjLnS6mmDyQB3t7s41DEo90EWBGsJND7UDHlldtushdRIT5Fl4XbiXxsK0buBRU
+UdRXzIsKBcVb/GyOoCYvY1UU0OUqHMugKfpredyqmgEzOUbg+xgTWLQ0OeptKRfWD7roMNx6+ULu
+c9B08duM26eeEvcYQGnv4LvvC/ltzbnEpi8vU3AgPQkbUQD0uCk2ckQM0QKtEVCv0QmKqx5tvWJF
+qiqYUua+RO2qDFmVHoxH0SFTIci5cSiWRe0YdRqDW2ubeCOV65ilCeptskJpEvn4BjVCl30VI57J
+cwrqLlyC4epBCG8lZ09iGUcXUKwoaFvBkAzGekNohpthopCRnHhIseFaJWVLADAmxXJJLsbpFFhJ
+AiQVcu8vUAzmelyP6/Hv3nguPAWtHDSiBjqegxqJTBpBDSBNlnZLxHT5cy+Ey0Rbzk5FxBXapLvy
+AkCzYcRWHhSzyVVlELPr8Adfk4z977/5ECUTQPmwBImYUTkWpSdTs1hzsbGS/j6Q3dc9VFineMtO
+4wCbDUl2IagQT6kMfDpB3CHIyiNb8Pkxhofy+2hDY7PY4e89WKo8n8VSj7+YtjA6kaz2b/7BfTyY
+kOr8TYM2eb8vrUXC+1uJf6xrF3OSlBjHoBmJpQlcA0O9wkYzAIhTmByJizscXmBJjc5+M8RiLC6q
+O1mivsEmrnUHekk3dyjewdw9ReZQWGZQ4uhYrNjDZIF7dwkiO04QUwDlIqPrv6lx+4kk6N5epleu
+tKkm+PkviWX+h3kHO18SSvn+Jz8l9/nkMaYgG/Jigc2uPOtqmCDjXDiVgs+E55X+4tJDTp4CDYs0
+lWrI7LSCE8p6Wl9fh6G4TkVwls0UElYL8ipBey739/44R9aUUNcxco5uI8D2mng2pVXI6JlOh2PM
+yUCdNyrUV/D8ulx7s1dHza7EQDMYNn/lxRiqku/Mhh/ggpqkJ+dyDU0bQEGeU5Uu4HENzMsSScbj
+WQWzcqOfcTwXm4LSFq5fIup10SAPndGAYwimSRcoGTJUWY4zknrETNmWg2OsMc8QeLvw+3Sl50tk
+uSz6+aPH+FfvfAcA8N235WWblgY3iGBDQ2O0lMU4ygtYuqWDYQHflQdaZvKwfvbuOsYkhN3bqF3R
+r7vIcDGTBzeYnCJnTuTyXGBuZ3qKt78h5CaN7Ta+PJfQ5dWf+yT8RBZsNiDN+p+8if/nSNpwn85m
+mCfEw/vAjMCclm9hKUZqiWJMTQWPbnleKnTZG5BUDnAum6F2Sih3pacpm80kn6Bgy/Kjw3OE26xw
+jB5hq8E5GgHuloCz1EhequUixvfeewsA8LvzMaZPZNOb11xsv/MYALB90EDKis/+JhmdhiFOKKO+
+FXrw2FNh3U1Mbh4AAL789AL335Bn7W6urvMEJ8cSXujS4I23CU6zwJ0bsvhvvfwKdgvZiIuGbFiz
+0RHGlgQpMXDBNvp4eoGyKy/yvXQJv8tyNt12a6ao0Sh0D7ZRtuSZteIxQr7IOVuTrVfHVRybTfGQ
+m7o9HmP5mOHR9PtorsrB9Opf+9Qr+NRNOW50sAdzyIqYXiKfyjXfP3+Mw2PJK8GVNbK108aYFP61
+VKHuyRy1fR+LiBWzskLd//E2hevw4Xpcj+vxsfF8eArWgV81geUShkIYjvFQpOKqBrUNlK5YmqXr
+4eKCQi1UKu6FHeRMFk2GEdoUVnFCBykt6cBx0bknpBZ//Y6Aje5ncxQzhhpPFhhTzfeDhyMsKTgz
+SqdYiIFFnezEdW8Ti6V81/d2AHb1LZctFFRA8tZ2MSMx4YKJuuJBhfUtSVpW+SneOpL7u/3VCzRf
+lfvWF2SMhsbLZPL91F2FhHqW40mKx2P5u5pJcUIMxIpZPavslWCJgYHOWQGZH8JSo1CHdSzO5Oc5
+VaiKFBgxm36ZGSy+Lt5NI02xR3KSRvsGdt8RDsKUSeDvnwzxlQcMNTKFg77ch3/DQ2ikQ9NPLhBS
+sWhOpu1lcx23bxFDMB5DE/+QKo1WTzyTpHcT7YZY0IJ4Ayfvo2Pk92+VY7x3Tu3Hyds4uxCIcaOz
+jrCSnwO6+3m6wIjq4ZNZihlBZO2wgcqVOTpZOFinFW4R6eR1LUYX4hXGF+dw+fksr7BBTUvFSlUW
+RxiyAlBflnAswVsv9RCyU3Y4dDEaCQ5jyX6Oy/P7eET7vJdViNgzMj6f4sFjWXxPjk+vunidljz/
+4QiImBhdWgfMdyIpAEMaPrjhlQjOs45rT+F6XI/r8bHxIz0FpdT/CuDfB3Bhrf0EP+sB+EcADgA8
+BvA3rbVjpZQC8D9AlKdjAP+5tfbbP/Ic2sCvpdCFRuQSXhp4cJh8qnKFy8digf/w4Tt4ytg4yxhD
+3t3H5ylj1r93gcpd6RB0YGOxeDrOMT8X6/6H35U49PffeQdRyB50CzgtQmLLGupEzamsQs4usxqV
+jPcPtmFJHtrvBwjJ/tOoPMza8vnjd47wrSdiVU8u5Xx56eJgW2JdJ01wk6Wp5n4f9aZY1XZD4sLP
+9bbwnZoc90++f4iLgeQqclvHJpOjSehCUzLZp3cwMxVc5hQUFByWzZx8G/6OHKMKAI8xdcAk27ef
+DPDGBxIDD+MhFElnN+t1nMZy7L/mjpE0SFLKxO57T5+iRYXmaTfAw1Ss3PQR8OltKcnVHaDeINy6
+Jh7KRqIwabIhaH0d1TnFbroRTl+VuXCfDrAbynN3Hbl2Heb4gIm9r/3O1zGJJQ+UGYNkR65j88kx
+Nm5I8tf3JAfitIFwJonpmu/DMXKd7z04wZNc7vvuDYXZmXz/1mtCsNs3AdbrMt+jTg83mO94MDnF
+ep+EvjEbtBoBIlJpd7ZTnD+Uv7scLHD8SEqZg8kJZgtZh2sdOUcTFe7skLi3fwCHqun2/BRhSK6O
+/hbe+EA84EOS2L64U2Cbpd7Odg3Np/J323qCxcrzqswP5777IeNZwof/DcD/COAffOSzvwfg9621
+v66U+nv8/78L4JcB3OV/X4BI0H/hR53AUS5a/hpqjeCKRsokKWxF6i+vQFBJUmqvv4EdPuikLW7b
+RtjHdk9cvKCsgIgkI/MpCgqH+BroUGD2blfcz8udFoasFtjIQTYUN7jmK8w52WWpkJPUZUaq9m6j
+i/ZdOVa7s486iWHCposON5bbbQfLWwcAgM/2ZcMq+w3c6cjLkZoxtpkhbtzowSfFXK8tD3b3ky6S
+hXx3Z6OP75yx96OIcUEswOLpAt5Q3MRZVl3NZ0pYsnYAhxMaqQxmIv6las7gcLFFDD92fI2bXdlM
+X9zrolFjn0jho1WT6zzoe6ixTbw8kjn5qZtdLDaINzARCgK1xnWN7RZr/Zm9Yqmuv0Cqc9VHdCwv
+/PzRUxR77OQrc9xtyWby/tMjzH9OnlXHE/BSWL6P+glh5ZGP9RdkLewG62i32JV48Em0KYYSsvri
+mwjFLdmQt2YNDBN51qbSiDjPd7c6qJOHM2qTgzLsAITb9+MMIen8a6qBkBB6ww05NAoeAXLLmUVA
+uPJkcoFaKOe+t76L8K6s660VdL3uoRXIZuLXIoTEULTXetC5fB71DDaoynVKgNjauoPQSAgdGAsz
+lHXxbfUU6Qqn4RrY6t9wl6S19l8BV1ygq/ErEJl54ONy878C4B9YGV+H6Epu/1hXdD2ux/X4tzr+
+vInGTWvtKX8+A7DJn28AOPzI91ZS9Kf4U+OjUvRbvRZUEKAwdWgmlEyWg2xWaHUaCO5JvbpZTK76
+0Z1ALFtjsUTARCTcDArUS3AzeKzNN3c1Xvbk87t3hJT0r5WvIqF+9PkfPcYHT8UV/ZMn38EPxrId
+j+YpXJb7GlRM7m+tYzGXz7qNAJ5loi3so9sXq1N/pYttR47nU0DELbuIiK2oyjbiWMIK39mEZodb
+ALnetVsdtCr5eTHxcO+2kJXGCvjBW/Ldd2dv4/eYMFzt7imuimJQRsGyj79EcdUwhGQIN1jVyMXL
++cRrX8KBletB1yJwxLJ1LkbwVjDgKIXrNzgX8gw+qzpY3iLqTr8ARaHDZHqKdF2uLTlTaLH701uX
+h1qUc3B6cHDjJkKHeBIDxKkcY7veQPxHJNf5HEuTjSW2XviMPL9aiO2Xxea09AE2qbvYaWu0O6s1
+QLl4M4Pmcf2bCk7GUl64xE1CntsbASJf5oXOATy9vCKtsRWwv3uH178kWgJXjXthLYS/Yt1WCdY3
+ZW53d27Ai+S+dw7acHnuJllgK6Tw6I25tg/NZGcdHegdl8fuw8zk+g+YVA9aOfILNmVdlKhZwbj4
+gUadYSF8jar88VSnf+Lqg7XWKqV+bJmpj0rRv3Kwax00UJRDxGyt1V4PAXULa36AaIP6kdUajGXs
+X7ENt+1fsctUaYhqLC6V41tkHunJpx2s5kk3xRVvzBxkZA0K70Rw6rJKP1g2ERF4EirA1RJHr/Vk
+E1JeDcbQ/UwqmBVkdmHhU/w07GnUK/m+TVfsQQMoamVmZohkyvxJbYCwkAVkuLBt6iEg9NlTCg57
+Ikrk6HLR+EmGe9wNvs33/aObgoWByeniLh7DkrU4c/qwhGP7I+Lv20vUMrZ6jxwYwrHDnouYfSf5
+mQbJi+BuEZtRaPgJBXTDKZy2LHjXsXBH8pzCZIoWzUZOCHqhX0brQM5hak+QrfoBihTqhN2Vt2/B
+kMnRgbzc9WqJl2/Jfbyw/zMINNmiygmiHis4cFAxs+81GUrWQkSEFVsdoEVOy6DbQs4ZCyvA4yuh
+NLtAUaKcSfgXdMbovSibxnrQwfhdbvBkAU9NHYNErne9u46+LyFTt95ESABUlDooq1XvDcVvbQqw
+CpTXRwiYt1F1QE/Y7u7OoVglqdh/4WZr8NmVO4sPMWPXZT7K4a6qUqVGFjPeeMbx560+nK/CAv57
+wc+PAex95HvXUvTX43r8JRt/Xk/htyEy87+Oj8vN/zaA/0op9VuQBOP0I2HGDx1KVfCDGUzRRMVs
+ct4L4FuxDroRwiGU1s3mUOwcA/kcbZKgJLuymcXQ5G5EaeCncovWTRD2xKV0qB+pehk8dtZp1cdy
+yhAki1CnldZOA15b/q5+U6xEFFrkbPbRKkdJt7xKS/iUcHd1Dc6qo5DT7EcNFKfstMxLlCuYtu2g
+ottZMZmpzxWCLfI2qgLN23Kd1fdTzJi0KpI6Luka5rBXZ7pSoLYKlRWroqI2VEivSaUAuyRrVD52
+BkAot4dg4kJtyP8UlyfIrFjgMDeIXqPVLGTua90MOJf5jBoOVsQIfrMFlyQxRRhiRRPI/ib4eoAp
+CXWK2mfhkp9Bey6+RcTmi9P30egQ4cqV2mp24ZBqzLUxsGCnaaOF5JKAkn4daLAzVfH5L5MrtXGn
+Hl6tBS+OUfD56dhcybxbVlxQlVC0tB4M+mxuGzk1rFOmzl7INY4vh3AaMp/52gmiDXlOvfU2AuIi
+Qt+H4fVnI2lQS3yFZkUUrqNhiT1wrIbPRGnUCaFI4m3XySGxVCgZbkbNCCW9yQAawQpibQrMkx8P
+p/AsJcnfBPBXAfSVUkcQlelfB/CPlVK/BuAJgL/Jr/8OpBz5AFKS/C+e7TIUKuUC6RIlgT7VLL4i
+ITGxhuuzG9D1oEgggZLy7YmBoTiL5zgwxIMXY4U8lwfqdh2UGdtsHW4OAFzGZzrOkV0I0OV8fIwl
++yC8IEJEn3mNrEqF7yFgHOmgATA8QLBAyUXquM4V0QcCtlPnBVICrhbnT9Bh/iEwIQzZjTyW+srd
+Kap8JXbjwhsytmwXSCZcpP06FiyzaS7isLKIeVpXAYbMPMqpUIwo8R4Y6DYZfcYE6ZQeHAK93CZg
+2HGn0hIB4d/hzjoCgoIUN7GwuQvlSbVA6zZ0TkBPogC2hGBQoXgkzyfOKLhzZxttthMnxRKKLe7Z
+6G28dCZ25L2HAwx/Wo738w1RobJ6DsVKhi4reDW2w1+cYMISYFpNEXEjKyFlvPHRKRQrJ93u/hUA
+qFz6cCJ5SR2vDhSUl6cTrXQA1ZZrNmdTVCmFjhtdJGxhnlHgZrYs0B6v1qyBm65CLMBl6AKUADfL
+ZAUgyyfo7Ep1xTFATsNYJCXKYAW+6lzlIhSp6o2aolzK9VSXCZak/j9XFXQl9zTLY8z+TW8K1tr/
++If86hf+Nd+1AP7Oj3UF1+N6XI/najwXMOfKWizyEsb3r5p5yrpBzCahuhUuPUD4Cld0ARVdriKf
+oSTVVhnO4IWS4POcOVLunuOTBRI2+XRp2VylUMWU/Z6PMDgVV/s0XuCYfANb7Q7uklL8lTtC3z6d
+J3BJhFFzDCwxBnE6RY3MudZPoVdIU/p9hS6QER6sshJDJVbJ9Tx0fPEgyjmrCamL3CGxDFLoGhOf
+C4Neh2QiKkWNCTqXoK9lZeFceQoal1NJ93hLF83VxJVLFLRihGMgzXKExEpXtQQVCVeMMfBiCd2K
+fgmfRCVuQ663Gs1BnBNMNceHxGoWhhbUWI2K9XtQICY1QFnJ/a/Va6hickcMNb7+PQGcbfQfI/49
+eVZHvrgd202DkHBerUs4xK+MFgmekoimO2ogXBdrm9DjGV8eIXks99e5k6CzfgAAqAUuvJwViiCG
+YaJYUbutKjLk5E8sEg8e8Q+bXoJWkwrhFNRpVCUc8kyM8jluGlmH6WIGQ9h8WM6QkGdjNCEIDxku
+qVfaLLoIuF7y5QWWI2JkWh4a5GfwydztqQwFc/ylKdGh8vqad4r7KfESSQ6isJ95PB+bQmUwmi2R
+5ROENekN0KkLTXc2rRJYsyISrSEjvnwxZ03rcorKkfjMZFvQFDNtdELMSJxyelQiyKXkVtXFdYyS
+Aqu87Hh6gW8OiRiLU1RUIA13uji4+yoAoEOFqMl0ijQl0Kl+A7pBwtdpCUMkZKM2F3cUwrUHAOny
+MeZsfx2oC4zuS0r++PiPsUeVJafHcmvSA8Wt0JqMkBEFN69KlENxKdthgEbALsnlyqUESDYEzwGm
+E2o2jMcIiCo0TR8F1YSSWPLAUVnBsDxjBwZZ9b58d1kh9+Seire34G8IlWPF1AAAIABJREFUQLVO
+EpJFfIGEUueu76LOezW6REK6dBUvUCzk+cRPBRTk4w4a26yoFAqKG+v06PvQ5/Ld3F3H9K64zEt2
+nI7sGBuJzOs0KDE/lE7Shw++h3RKDcp6F8mZPNk1djLW9javqj1p1UZMoJpp1dCosW175gA+cxuZ
+hFUZligm8kwSt0TqUKvCtfCYx6qRBLbW6sOCYdDYx5ErRsaYGD1HjjGajjBdMTIx1LhQQ+g52+FP
+CtzalXsumhmqGRGky0tkSzlPIxNj6YchUv48ns2wJFdosqyQEhhXlBWMvSLQf6Zx3ftwPa7H9fjY
+eC48haLIcHH2BI7bR1JRmai7h2DFteiWKKnuVFYDlDOxAsqsOAFyXDIzGCzOgUIs4ugkxtsxlaYX
+ORpKrJQi92FjrYKnmGSq1VFvE4JcHqNFC/zaWg97EE6CjPwNR0+eIM3EInRaC/Q6AmhpwkCv9BPz
+HMhZqeW1qXYbVYsw4MtzVOT2WywyvH9JbUq6kS9EOcKIHZWLNSQduY/stELYFrc0iDKYTKxDxP3d
+WgOXbNZ9L8Db3/kaAGAt2kZtIV6Tiw5c+kguw4F8UWJBEho/q+CuSZIzKVw8OpLPyyJFlyFI+r5Y
+2sN6hg0+h+bWGkriIopGhZAswnlNowj6fFYy35E7hSK+IVYhlnO5j42XPoM3Jz8AAOx472NZkwTc
+iqw6OujimCpVUTDDMiUmpb+NkDX7i0mMfPR9uZdPvyjHqt9BFJJPYoCratXiMkHVt3x+LvyVkFBO
+DIJeIqWXFoYVSoaxw2WJWovYCTqs2WyAxVxeqSM1hM+eEN/rYcakeF2nCMgZ0unKud54+wx5Ip2T
+7/fOcfZIqvpq08NWW9ZqJ9/EYhUS1Bl2JTVY0uPlITAk5sTkJSxhzo524DEpnOJDKPyfNa49hetx
+Pa7Hx8Zz4SmUFhgmCvXqAoNELEq4DxhNhFYC2JJQW+RX5RarmVycpnDJFWCVgskkN5A9GcBtynfX
+mj3pGANwaemNVC1UJEe18wTLc1odW6JaQSGCFtwbcgwvkh3+/dEURSxW571JD595QXbwZl2hiomI
+M0tURJj5pNjJ5mMUQ/luu95Bu08hQJ1DH7G0RLbn9n4fDmvbSTlHxdJipcsr9uG8SjHlPWWUGmu6
+Gk1Cpve3GkiZU1lmBvfPxWLcblWoR8RAnH5Yj1/R0bmRQkW+CBcObrTkOquaj5wNVpkvnkKUpaiR
+gkylOSaMcWEATZpmHRtMz4g47ch8x3BhCTs+P/kGzhOZ+7b/BLdacn/vnvuY7U45X6L7cJaMEdHi
+VcNTuDP5O6sV2iy9FUWEndtyTd1ALOma56HRlWOUToIJE5RIcAVNjnoBrCF6k01lRVaBAFqkuUW+
+lPNlSYWKTUxGy2e60QbIuaFNjkUsx525c4SWSumVgrNqJWJi8F5jDQv+XWOrhs6MqtsaaKoVA1QJ
+5cicl8x3xIFFQA4QuyjxIJHPn2YFSnp0ASqYFcT1GROOz8WmkOUF7h8foW062H1JJmepZ2ikpCQP
+PeQU7FABYBdMNDKDvhidQq0gz/suUuIQWrcqvOgz7ChzzBl2rODRs9EUAUk8Lk5TPB7Kg/Pgobcl
+D6x7cBt1Jo/qNUkA/cFgiCqXlfLSbQc5xVAQW7hNChbOAnhNPlwupDQLEA9Y+UCFlIKvja1NNEkj
+v0oJ5ZMl5uQLDN0YhvTeSZ7glIzIT07nmJFCreDmUIsCbDRl87pTO8DhQhaSNSHu8eVeJufwqWOI
+NW6wxwoBQyatMhhF4NhsjmglstJQcLiJumtyjpbNMefmPZunaDdX7MoNlBRxXS7K/6+9N42xLMnu
++35x9/v2l3tWZe3V+zKcmR5ymhJFirIgUpZp2NAHCoQtWgQIAwQkGQYojQgY8Ad9kGXIpgFZtiDZ
+MmxapCxRFkHDGg8XcZ+VQ/b03lXVteae+fZ39xv+cM7LmaI4nO5hV3cZfgfomayXL2/ciBs34sQ5
+//P/n7ELewoWi4KS3alkC+688YBShWGGF1u8rQKq/e3bDH9Pvv9OX8Z1cAmMIwvy5dCyijyzB2OH
+UMsH29slTdW8bLbk6FdNBzgqwlKbOaUSpHh1SaZjV2QTHFcXOFcXjalhrLx607RiqgA3N5iz0ZJA
+8S3NZuVpcqZaNkprGj2Zv0XZom4qSczBhJnqbU6OJcvS7zS59v1yn+tX/jQ9XaSS6QO8QBbFo7lP
+elezbaW059PiaCLX2juYMDyR7w6nGa4edf0gpKHl3KcqZPOtbHl8WNrSlvaQPRaegq0tZVJw7Mw5
+mMg61Z2UzJUjwEtSQt3d/ShimIlLdfOGBKQe3DxkjqyS7a/1SCNZ+ddTh0o1GdbcPgdaQ19qMdDK
+EyucnEgw8LW7B9zylDG6kRH1rgPQqHzuKFT6E1ohmM5mjFXv73BasnEkKcJR1SOfK7FIVaCp/jPK
+s3u332RXvzsuR7R2BQZbB2/TUvn1gapdr7lNqhXZMTb9CF/hwe/uZ9y5J0HHt/Ips4UrrY15kcus
+IX2+fGmboXpKe+4J21PlUxj3mB8qujPUQGuRky2wDgcZh/u/D8DJgyl5Q1GfJx2OPJXW04KjZMVh
+DYFEZ25CQ8lnwgsJjXtKaWerM9bsrVVpYz6MKB7cBmB3NGRPqxKf/OpvcnT4PAB9b4XjnoxXcyhH
+vvvmkIuu7I7trXXemolHkB2+wa09+e7F0wYnA/nO+uBXAGjtT85gx5nbwllTcpLCY3tdJfLmAVZ1
+P7O5sk+nx4wOZIwfpPc5HstYbHVcAnX560RxBZOMibr7wXTEUIl/zm8n9BWn/cAG3N0X8p1EeQ4u
+RTPWXpPvnt79FTqB3OepN2ZVU5mHgyluKgHGzZ4834OZZTSS+/2dwV1uaCA5sTWeHn/KRvOsAvXf
+IkD4JvZYLApFUbK3d8RW5HC3L/nxrdPnCPQFcU0T311oTFrWu9JJ50kpm11Z61INZMBavScwCv5I
+Ds2ZO2fGB9RNORK4Clv2/IhQ4dFvDoeUqSw8a3XMzooy4eycsq04BXuk4jRVyYrWZUzKU9JU3Mgs
+H+KHyolYGGqtSmype71xsYWvkeeD3Qbdtlyj11xjUYe7rmW6zcGI4JJea+Sf4Sl87rOni2WGIVJn
+T4/OXApDnlE6dDda4Xjvy9L2Zp+pzor1oo1r9LjlyR/Gfkip491oZ8Qqxb5+9TyFEn2sdvvMc82C
+HMs9pEkBI3kZT90Yo8eDynFwtKS85bo0VPNwzSgjUvs2vx2qzPzOKlfvygsyX3keZypszfPoBKPl
+wpORHDWyKuW2kpvMBxMu96Uf0yDgwlOSXboanONPvfx90vZ1zaKMfJJKBtk5qXEVDBYXHl5zAYxz
+pMwUcOaqVzpOyDM5/tWxT7zAYUxr+s/KtTePZR5Own3ysxqOmFgrXt0iJdMajCtrlsiXBWCh/uSm
+FU1d1OOywYGK8szSgPlMmAg6vR6BiiW7rixi6+cMN157R8cnJRnJM3Gp8JSi3q9KSpZS9Etb2tL+
+GPZYeApUFm9UEzdrfKWoKtyEXF1jt65JdOULM49FKV5Tc7T14YR6R3brsNGl0Cj7aHrEeCC7Y6cT
+EwSqGKzeQ1pW7KtOwzytyLWwqdPrcFEp3zY3XyBSLHClxTdRChfWxRVfu3IZlFffUpAm4mHUuaWp
+0XdfZcCiOiDQgpreuk9zoPwGiWE406pM9SpWL57D0UrNuR+Q6N+5roGFZN18fNbG5absIt957XkC
+9YishdV98YQ2G9fpvSC7dOeyQ3QqLn+pbnJaV/gaDHRc8OeyC5r7JxjVOcyKgnRPvImb9yTDk1Yz
+Ni9IADNuRaQLPoK6JO6rW+7UKBCQQJ9jXBiuzOTv8uMTypZ8YX8y5MlrMt5fbfTwHPVkVAAoTA7x
+FR7eaZ1nc12Di6mLowVK55+6gBbVYhWTkg72GCpCttdsEDtyvKhbBZVqeua2oF7ohKTiip9kJclM
+g7jWoaMYECKHbE++6zuKvQi6bGq7lR/Q9uW6CQYqxUL4llXVosi1jc7FLS40lXGg0SEcy5ydNSyR
+J0cJukc4Vq7nx4qQHU/Ye0dh4A9Osfq+BNYh9DTIWViMfX+vubH2ffOjfOD2zDPP2v/ln/xvOFVB
+1JfMQc+U3DyRwXn1zZs8eFvAKA+mA3YWRCwqTz9sDDjV7zadknunCuHNR7RWZDJ935/6BC/2nwGg
+q8IbbjHi7g1xz7782tvMFkCm1ZAr514E4En3HNVl1UQ0sqj8xN/6r+hofcXFpz/FJeUznHRcTm5K
+RLlkjomVY1HVpvrWxexoVsMk+FriXY0q/C19CbWsthinFOqie2susaYnzc4O3h2ZjKsvP0WipC3t
+idyj2W9zfGXx4u3zt37yvwAg9ENauuD2aocjnaTTgUTy51lCFSj+3kJD3d06KM4UhhwDpf6dF6vK
+UbvFik7yCI9mV+tL2j02rlyWe5rUeOek34nCjou9gnqkZ+MtwzmNZ1z72MfxtOzXbUQEY+nLjTta
+OXn3bd65J/GOcjSno/jneydj9oYCYy+LEl9fXkchyKuNGpWEJI8sQwUW+cbiav+Kssb3tbZDFabi
+doNivEiBB7ih3PPKWpPjRERwjk6kzy9dcNmeysL72tGEgRK5jMoMZ0EH70OhQLxFpimMXEJnsWDF
++LrA9zoBmpRhMp4xUFHfVGNi06zG0SxDezXm2p+RStL/8KUf5dMXZK6bZ7ZZ02PhE1de+Iq19iW+
+hS2PD0tb2tIessfi+FDVMJrBtbUG5dvqOq1u8+BLbwLwyt0J4aG4ue1xxmuxSpAp+KXjQd7WiG1d
+8mRfVsapiclqDTQN2rR3NHddqGIym4xK2Wk2oy0OTiTCfVp2uKfQ5O4zx4Q3xEWzXTmiVLHLi1vy
+81Z/jXMr0vbtyW1WL0qQL2pVdHX3Q8U9nm408btahVc1Secq9953yBsK1CrlkZwUc9wdra7Mcxwt
+rhodJ2yHsuPnBxnuquS354eyW61cr2lPZHzKrI9iqVizAc1adqC+0+D+WDyk3C7ox3ya6jVODLi6
+ozvWw9PjUVYburqT+kpw0+91cbTA7HKzc8bdGHW2qBPZbS+2ViiUsuwY8Z72JnO6mxrA3L1HHl8G
+YPf4hCtKHMKrGfk5GeevvPGq3O804LLyTk5XN7inxF7NcUFQL5SwodKgMYonCfLybAsMpyWHufbb
+1sy9BT+kS0PxIGWqcHUyKt3ZG12XQLNO1bjiyXuyY+8jcPSBSWhevaPPcQPvVDknrFDEARRVhac/
++95CddzBKq5irdXA25CfO2HzDJy25owpFOCVKZ9CViVovJjyOOXtX5G2fyG/y4t/Q3hIt4+nVK2l
+bNzSlra0P4Y9Fp6CZyrWwxFJuorny279xpt77B/Jyney/1k6t5WgMrzNtq86C+flXH9t7TmuKTHo
+RSdmGsny2U/WeCW7DcBWEdPVwpdmLKmruW+5rkUtb3ccUiMr/lExIxvIDvTuu6e0lYLLqsjpxRja
+16XBC9kht5Uhams9pKPxhY34HGg59Lbu0O0LK0SKw8hWQ8KJ7P5ZMcOmck+nCrNtlpZUmYJme3Ca
+Sxur5ZRRJp7L1dEnGeihc6UpO3GBTy2/Zrd3j3YhHsjKeo8gl2u/WR1jFQnoKKNwaBys1uZ3cbBK
+ytAMAhod8Q7WGjGV5tu3lc7M6ftcakiMxlv16TqKmlzx6Gs5d1YcsKqeQKqp02dWZ9Q9ufdbexvs
+OjLeF90dUuVA6JmEf7kv6Uk/kZhC2rjHNQ1QBs/CywM5R//q+pT68/JMBqND8kJ2x47utNt+gKeK
+10elT7dUJKhn0Kp83Lpirmd4by5zKCRAM4uEuYOrXtOEOXkh4/JnKwlWp2nFDSOxk/Ygo1BiVm/q
+EamCesv1mU9lbK+fk3mRVYYLKrdXbK5xofcsAMnqlJf2xJO9u93AKJfdyTuvy/3MDakWdhVZwVhL
+sQ8+/6+4/VvivW7+qTVm3vtTg3ksFgXXOrTKiJKcvbsyeEfx77H/tkz03TcOeFvr9y8SEmt0/cVL
+MjEvnX+Cfk8mSqdbULOg9fbYyRTvnk3xYiUh1OBMnAxx+zKh20WCcyoPt1vGnOqEHY5Pua1R5u5c
+XuhtZ521UGnJzl/gikbAV4oYR6niunGDfku1DZvq45kcp5IXvZUE4GqWgJBCCWV6+tm86ZxJnLuO
+S2tT3qZR0qIVSv9mzYTLa+LGT8YyIZrpnN1TVU0qU2Ilgym9kLmrVOxuQaXVfK4yZuNbGlo/4bge
+kVZ79httNs/LBOuHbazKDW0oF+HKhR029ZhUBTGhwoSdNY9sUeWaeuSVLKjXHSGqqS50GDnKgWFS
+As3N75gm85a08eorUyKlLfcLuc+ov0r7WQE3fWptlcan9Yj1W8e8uyvj/PpJxixR2nblgXT6PcZa
+iTk6zmhopiV3wOrxoKgq7CIgqP0MPIPvy9ibIKRaqG95JdWKZKiGKjO/d8FgdpW7ci0nLWUuPLG2
+Sf+cuPPf8/yTzLV25WIgY9i9uElvQwKUo7TBmmYtyk6J+0BW+AezMUersnCudaVPX/rSG+yNpE95
+VeOXqjze9zlQENXJIKXRe38sK8vjw9KWtrSH7LHwFKzvUG+0yE4N+4nsLkd7Y24ObgMwzUaUoaLD
+uqtce0rcqyd3ZPVdWekTaXVhZ32Fuvo6Qs3TWiXHd5lpIM1qqm+Wz/Gb4oE0Gg7DsexyZZ0Qd2Xn
+fndUM1Kmp1ZbVvCNF58mviIrexuPWDUm6yBmXQt3VjsxkaYiF9qO1gvPJO2oCtDqOwePWnfsQivd
+StdQG9l16nBGrSjG0pSg1F6HI5c0l5RiuKNYirE9c503Gh5OTzyPqulhVAW5lRfk6to6eixptsC3
+C5m+kAvqgaz2VlhTDyvqdXAquY+2cqLFjkNLuRecRgc/VehvWWOCBfrPME2kf0eFHrUuNjG5jPdq
+F7Y6mg6+tIa9LS5zvp9yrGP4rCIin37iCt/xMcnpb3Ug9+XY9eJaH3tJuQXmHn5b7uPeRMZiO8i5
+qc+hE/ikmgJccQwHSrdmHRhpOtRbeJMmJNT8/9wkNI08tF4Qcqwe0rAj7bZHTRLFnhZhiyurMsbP
+bEZ853dLJvDpa9dpZTJf2luaGs9K4idlPiXTCl9VzG2Vw470b2NgGW7KfF/fFO/3+a0t/tn/9W8A
+uDMckyvv2umN2/zmL/8yAFcbG3zsunqD79Eei0XB1C5e0iHZ+32ijriLt18bMQyU6rrV4oVnZCA/
+9tTH+fhTgiE4v6ZS9a0+jnIjOtbDXWDS6xGO5pVtEuAECv42Wi1Z1NSL/L/j4iVyfCgDh1YhD8PL
+dqkcmaR7eiB+ubFCy9Vy6lYA6iauNPv09QVpNlx8s8DGK4UXBVbPpPgO9SICHlfUmUy2ebbgFjTU
+oVLHB4Y01TLsusWkJfc/HJ2QKKx2eyhZj6pxytHqoiw4YVWhsaFrKfVFSAtoKpuxpzXijagk0nvr
++6tcUBTOxvYmgUKvA9PAhAsKMvl7x3VxK3lhg6rC+EoLZytcxTo4McQNdWFdPQOXXULkWNbYKDnU
+X5ui4ta/Ecq3efsu5X1xn1tXRJL0+U9tsHNeXg6clOkDwQr4JmctlgX+XhCwP1X+S30GB+OU2UDG
+c2RLjJYWl05NQ2n1awPlAuijR6mynGC9vt56RqbM3FMnJVNlqXwin612Ulb1+LjiGC52ZS689ML3
+8vRVeek3Vy7S6srYBrrQGaeD0fkUxB4obX85Lil1keq2K6qWzM/19QW5UItPPCVHu/DNhFc161Tk
+Hu/clMzda8e/xXPbQlTzXm15fFja0pb2kH27UvR/F/j3gBy4Cfwn1kr1izHmM8CPARXwV621n/1W
+bVhTkoYDjmLI35CdZiP2MedlJR41tnlyU4I6H3/yMpu6ywXKpe/HPmZRPOUHoKu9E/QxtRwDauPj
+KmR26mswqXlMeV8CVYEpWNsSN9ltRaT74sb3ApcHSpgZtfS629BQzcEt14GurPjdVkhbvxN5EdYq
+YYdiHmyVYUuFWIcFxiiKMa0xim9bRP2pwWiE3M1rHN3ZTJ3jqhvccNwzfoOBchOYKqevfASpC7nG
+VttORDhSBe5Gk44SpdpKdq0V36fV0Jp/06SvOgxmDEFbKx8j92xsQ0c8szD0CRbloGmJ0R22Dg1e
+vijK8XAVLeko4tGtE+JAg5lhl0zZZoM4ZPyE6on+9oTVK/Lcn/sT4kmd39omiuX5V/kR+anyb9wd
+cKi4AFvM8BOZD4o6Z9iGVDMxWW5xFyzWDQeNhxIHPr5VT0A1O4IqJNVAYuA5RAu49jzAKZXeLlAi
+FyempQrcF7e7nF+XY+7lKw3W2qpd2Y7wlZzFi1XHITQYDY5b62I1GO2ZE+yx3GhtLJFK7sUt8Xjb
+k3WefPqy9NOtefdLt+XeswmOCsrs35wzf/r9aTx/u1L0nwM+Y60tjTF/B/gM8DeMMc8CPww8B5wD
+fskY86S19o8mh6stzjTFHD7gcCpCqn7D4/ntJwDIyhGXr4gLdG7zKfrrOqihTuLaw9EqSrd2qXSC
+1dOaKpMKPpwOlFpfoFp+5Wm6SADgZTVdIy93aOFYU4vFZIRr5PgwVJhpt1qn5yscOWywpkq43VZA
+GGklWwm1lni7vlZt+gnOVAMJVY6nLEa0CirlOfTVVa9ih8LI7715dVYNSeihAXKMY/FiedELFZEZ
+nBxj9UzqRh6+ciZmdUnVks/PtRo0tH+Ognw6HYeWloaHbkChqUrfJjgaJfeNg6el2J5Gt93ao9bM
+UNiwWD0GeZmDcnvghB656lQushcmqKk1PlEzwVc2rWgy5Tl1pX+tLjjXF9f92WufBKCx0qJSivRq
+7hDG8nz7OwEbh9KnQTug8JUYRgfL9yDX+RIGDu7ieIChVqaqvBHS1VLySutV8rLCMSo7kFhqTTM7
+kSVTgptQjygzU9N2JOO0ev4cVzeUiCbeIlYxZD8ymKYqmC3o5GsPFszXnsGqhD2Zg9HsCV5xJhac
+65GCZo8r5+UofXzrDpHybRZOTXmom136Bn7rWyKbH7JvS4reWvv/WGsXeY7PI5qRIFL0P2utzay1
+7yJKUd/5vu5oaUtb2kdqH0Sg8a8AP6c/n0cWiYUtpOj/SKuxJOTkk4DGkQpsNGdEO7JzrTf67Kh2
+Y8MZEXgKnFHuAi+vqbXOnbbD17GfGcqkhdOan3E0ZgqOyWcVQVfd9mYDdyLumR+krAZKthFajhTR
+smKVRbpXYHRnbpcGR8VJoqglxxekpj3T6jnHWUi6tam0UMeUFSZY8CMGOHrPptT8uJ/g6NGgdFIq
+rQz1vRp0x7BOzUxVsw+NiskMLdlCD6MfUETyd2Hu0S6UdzF06MTiIoWKoWi6ZpEMYZ6naB0RXmSJ
+1J1tNGGx/YdK+GeNPTsGuES44UI12+BHCuclxs2k31OtRA3aI3JV6/b8gDUV7XHWmtCT7MKz4TFJ
+WzyFjsKqjedjEunfePAu80o5CoNNutsCcOrdgZ0N+f5XJwreantn8n3z0lAob+Z0Ysn1c983OJ4W
+eun9VnVFod5N5QnkHKCcWTLVVhjrMXG1jglUA8SGNd2+BBcjv6TMch2v8kxvE72urQ1moSpHhdHK
+V7wIT7UkbVoQanVk5C4yQykdZZ9+8fg5PvuO4D6ms4S5nomSA5+yeH+6D3+sRcEY81MIHeTPfBt/
+++PAjwNsbWySTyzz8SHZpk7i+DzdUCZEHAgDEoAfdaiVpNUJF6AgF7crk9zg4ngqFNsM8LVmt5zm
+5LlMmkTP+PNsBgNZePw4w9XFwo0bHCj3XZ2lGAURLcgvoklIsq46lk2HjjKcuKWHBqQpyilWjwTF
+dEEEkuKlShLqOrCoRDQltbqroSLfcutTZgrMMSU2WcieN88UmWakVOrsVXqGHOQFIy2/bpU16KIx
+d+bUysG4CsS+1hdo6WCR55Qqv55NfUolHonynLpapFFXiLsqDa8ZF8epMZoCLbIppabsqrrGUdEW
+x61xPH2ZVDNzPPYpdCEo7SlzXWym04zxAzkSND4ecvEpIbhxtM6lSDM4krqN+SBk/EBIRm4f5gzv
+KStUCYcaE0oU918mFX0VfbG1z0Rd/zzMSKYyRrW1eKHOs4VClGvRLDlO7VDo3jPOKkqNP6DXcq3H
+pm4A7WHMaCTPbBD6hIEsHHW+g23rBoDGlDwXFPWKW2B0LjhxjNVKS1PDooKhdhc6oDWOth1zRFNr
+fqoTh6mCs16Z3eHozi7vx77t7IMx5keRAOSP2K/XX79nKXpr7T+01r5krX2p1+1+u7extKUt7QO2
+b8tTMMb8APCTwPdaq9zVYr8A/O/GmL+HBBqfAL74ra5XUzGvRtytfVoKflldD1hbk12nW3q0VBk4
+6hhcPQa4i4h84GGUVwCviVEp89qkpOod5IMaswCbKCT4YHpKM5QVvG88RholqQ8zuqpz2Go0iNVj
+mWqVmg1H+J54Jq08x21Ke27DYjXCXWcuuSt5ZauAlqZtUWoku2wYAkeH3w9xNfhZ6/2EJy6h7jrp
+yQTbknv2akPQkpRCc1JyrHTfqcrDnZ4ekMwW/PQGT11Op/Jg4cKG7hlUdpLoTjqsSPTM4KVzrGZD
+dg9L8hXZjTaMS1uDqh2tlsQtiJXXImpEDE/lvDbzK/rqNfitCDQQbHLBHbi2QaDK17PpGvOpSqwF
+hnuxeG8v3o7Y+JQ0E/S1H05NqoClk1uf5ebn5Tmd3LzHOw3xMFrzSoBiwNUTcbVHOzPeVjq9eVae
+qUrPqpJK60e6DvgarDOaJSrLHD0dUhgode5ZrLDYwBmnQVKOqSItwvESsvFI++8xtzJfmoM9HE+O
+FbVme5xGD2MWLr4PzoK5fA4KwfaCiGoqc2RvIoC1Yr+k1VcvZWObeiycI7YoUCeV3duW48kHXPvw
+TaToPwOEwOe0M5+31v6n1trXjDH/DHgdOVb8xLfMPCxtaUt7rOyqUp2vAAAgAElEQVTblaL/x3/E
+9/828Lffz03UZcn06ABz8goPDmU1fP7JNTo9WVHd0ZSToey24+Imm5qv5aZ4AUFvjWhVd6VOgZ3J
+Cp3PasYD2d3nZYvRbUF5PXhFi0yqKZvbwkeQTwbMprKiziKf467s2OtJxYqeux9okKyZxbQ9Pdc5
+hkAjR05ZnXEkDG/uclQLdiAu5HiUZMfUKhATnfp0VK24LGqsXnsSa+47b5LONHA0OmSgu3+vZQg0
+vmBOUxQuQKZEo7PjwZkHUsUdIoVKGyc/wxtcWtui15ExrJW56GhwyqAtY9jshThjZUjKS07msouH
+gxoUouuGcq2yNcUEqq0wr5gcym68x5zryl+xFpeYRFPGqmXhuw7lqTxrMzkCRRtW90/pnMhz+sVb
+t/nr7e/V9sRDKY9OuPdVYbf62c//Gl97QwVW9w4ZaMBzve2ypf17UeHOIxMzsDJGp1lFpWm/ytRn
+lY81LoHyKEQN9WxWwCib92SacbbnOs6ZNJvRtKcNGjR6At0OezGxYjNGx3PqjszfTlxSHStuZXWR
+Io1xQ3W4He+sQAtTnMWlyukxibajBZyw4lIoA1bytSM2lGLuHdcl13Genx7x9ptf4f3YYwFzttRU
+bsZJ1cWfKy/hcIiXyIu5Oz/EnkoZtfPA8KZWjk0Vj7DSgMuXBMdw5bnrOEpqMrs74HQkrtbJwU1+
+fyp/Z6byWdnyCbWE+DhPOT2Uh9VZiYhCDVRt+NQHEjBbHcuEd5oJUSj485bXxCwkeOqE6ZEsQkcn
+R9y4LzyGe68LQch+XFDoIvWJ5zd5akUmUHX+EvZUQi+Z0nefvHufV3fls9nuXZK+vBRPru3QVoIX
+p87INABX13pvQZNRqorLbs76umZDygbtjjzuC+sB631pZ+9EMzJOzXwg4313fESlrrFfjzkfyQvW
+miR4qmrlHSxQQT5vD2Rh+fXJEeldpbuPPD5xTY5Pn/zEx1lTGHqqk7gdT5nrUcQ/9Ti/ItfIGg7j
+hmwGz9mU4FCPNC/Iy3qaD7k1k7mwe2S5eyjjOSwr8pliEzKXUGtFfndLIdiHKXOFik9n1Vk2J3DE
+vQfwnZqwK5/Hmvlx3IBKlcgKE2JmyscYeySL8nJdmS/0AhpKOBMaVwAvCPBoqtT+r1cF9UyCo9tt
+rVG59ATdizJnTdCj1ueAk1Nksqjfe/Aar3/+qwB87VD6v7WyQakgtGhnHf9J5bE8dMhKpfw3MWXx
+/mofljDnpS1taQ/ZY+EpVGXF+GREdX8Pq8T7xtukmGvNfrRB85pgE27P59wZyko53Bc30tJiW9Nm
+k30IfdmhimTE3oF4AjffvcVQ6+kvqevrrVzG1JIs2Q6nNNbkOFJ1Egaa6o8PffKBXHuo4ZEwaZCq
+S513PepcqbumBt2MMFGIe+UKAHd2Zbd+5cavUkwVBt06YBJK8Gn9S69ypCIj9zQV9tZrNxhM5d5b
+Fs41laYtq5grWq3KMnzVwJhVi7x6Rq274Hye0TML3ERBs5SfTWI41Nza4Z5c697RnAdKTXd/ryZX
+jQQPh3un4nms99Z57lB2zXVVeL5fHPDVd2SwDsYljQUmYzvmlsKtV97ZxSgxbe7J342ONul3ZBdv
+tXYprQQP8zSifFM8pJWXV1i5KkeTOltQmHV46pwgXddf/i76yrQ8f+dd2j253lYU4jW1ME0rXN91
+HObKN2BNTaB53fMrbdqbck/hPGBdIdvrikx94JeMxgpvNylNxUv4DWdBy4GrgcogDDCqcp4VVzlB
+xnNcwUTp7279esyNkShMv7QqAeMf+osGx5OCr7i5i51Je7NwxL2vylh89jc+x+9/WY6j+6vyDGJ3
+xnepx3d5PaGjxz8TdYl9eSYrLcP+7ntUgVF7LBYF13Np9VqkjS2uxzIBz+00WbssLlVV1NxVSvFf
++rXf5p23pDIuU1DR8VPXz0qg250uK5FMJLfb4MJQjgy960+xdygv2Rs3ZXAPOeDZjgqWmA2ibXWN
+6znx6DYAdyYjIiUZWVFW37ib0dC6hUaeg1LH40N5IhNvf+7wG6+KEMvNt9+Q+3xwSlXry7Sa8/Jl
+eRGSezH1XI8du+JazkYTKl8m4NgxnJ4ofNif0UoXEueWfKiZD62sm09TEs2fO0HNS0/IxDO2SZjL
+57lbEZaSu54rG1OCZTSQWe6QULN4EQIOVcS0yZRoR16WK89dA+Du72ZslHKuH3fCs2zG8dBjoymT
+8cA39BX3oPSYtKNDWNcFMryCr5gFe7FJ/R1S8fl8BGtb4hJ7igyOpj7ZhrwIR7/zCqNbml3Ka7pt
+Ga8nt1v86RfkOPngVCn+d8dnx6NOGJIotLlRB2RjubcLXbi0KQ1dviYLevcwo2nlRd/zXOYKRw5C
+h0QlBoxqVDbrnKgt4+1nM0qtmTl1Z9x7IGN0XBxxdCI7xx3V2nywf8jOljBRV0mbQitRJ8fvcjSU
+eZGHDe6qRuidG/LsNrp3eSVXaPrFZ2i+K+1dX9vjdKhaoevX6XxSax/+Ne/JlseHpS1taQ/ZY+Ep
+mLomSjKe6h6RDBTvmTs4hQJviwB/JoGa860Gqy+I+9jqyU57aeUq/Z6skg3fBYUP++kxm9fkepvl
+CoErblsxll3OuHM21yRo1/QzvDUNStHE1+KZNNhn8o6s4ieKjmubLoG6wVme01DqL6eCeio7V33z
+DfqJrNZPrwl9mOfeIJ1IgO758y+wrUU7xfmQCyeyPj9QV9bJrpIqlLjpxEw1lplVIV0NLnm1xdN8
+fKVQVrcscRWKa3sd+krd5rk9HJVKC2JDdiIX7CmkdtJw2VmTAF9KjqeszCu+x2As3tTF2OVJ5QW4
+ti474vS5K3xBTmscjI7oaFDSWW+xsSHB2EtP9EEzIts92bX67SaBUuHNMxd/gRZN5/zgeXlmyTyk
+sgv26wWUusYZyHPsbrZ5yso1skHBE1vCs/DyS89z7bJ8/8pMxvvqb36JZCBjdSvN6elRarsRY3dU
+G8IP2TonXmYQitdxsbtJphD0dBQTaGFX2PA4MnJ8jWN5HlOnS6JH3qvnuzR0LKbjKb3nZNyqVp+X
+VVfzfFc+u3TpHH5DvFG3XYAyXxunT+ucjMXTfsbmymUA3r4v8+Lc1Q5bsTKCT+e4m3LdB8kmrga/
+bTzBPXh/qIDHY1FwHNxWg900IlDx1NO3Xsd9QUVeey5XEXGLxmqXqKeR4UoGslWDqVRyPR9RaR2E
+m8cESjwRveCyUl0G4Jm2fPak9Whdkmt4pcUodNeYGY1IBrtMu+zuys8bKjk+qSe0VKUpJqZQCXCn
+HeGo+Onmued4KhDXz/suWWw+Nv4u1pSI9ErcZ/WSvFijGw7VJ+R6z35WXujRdspQAVJuXRIrZ2LV
+q2As/Td1RaHioZ5GwoO4RaEw557XJFTCGeNDW6HNjSBl3NLS8IZcK1i/wvq6Lm4rAWuVTNi1IKGY
+iQtumlMub7wAwPo1cfGf48tU3yGu8eTtCF8Xp2eeOsfGS7L4rk369BXDX/Xl/1fPNXED6d/KLKRW
+oZ1idYtoIi/C2l5J8q6Mbag8IWl6SJjKM/t3rz3F/iekjXDX4/qWbBJbKxHhFXkOxZHMi/ATl/nk
+F+V83uq5HJ/IfT7/fMX5WI8Kl3s0tJS825T/P6kzbmulacsGbDoaqzAZjS0Zz5NUYxmhg0WOQZnb
+5ZLyLp5v14y08rPZjDENWRgX4rGRk+BqurS2LiaTNoJkwIaWvnfXXyLelv5936c1Jb3tM9NjtXuv
+wa0HPyuf+yk7n5Sy7e954Tv5pDKU/fQ/+he8F1seH5a2tKU9ZI+Fp1DWFcfzMUd3b+Br7no2yvnO
+A9kFm9tN4r6s/FEYU+Wyc1XIkcI1EaNSBVCGU+qusit7KUEkq3l1OKFrJIMRdWRXdlc8fE/cz9pL
+IZPrzvMJ/kgwBK43w1Oix6FWMNqkwURl671mjevIcu4kHpFqxpudlGeUwhvdaVgzbD77CelTBPZI
+Akbt3jVSRaSsP6+u89s+bfV4bK+N40pfvdMmtfIsjKucTGnGMy20CoIATwNgzW6TYiK/zznFC5SL
+0LSotfKx05DxaTcS1nXs6yTCUc+LVoajgcR47tAyEowtVPY8bK3Q6V4G4ImdW3haHBWUFfUNxTK0
+fWqFgjcDGe+4ukisz6Gqhpym8nNuGphEPLLoasBcA5SxgqnymY+jgbhge4erhXiWwbmAdQ3cBlGG
+mcuO3VJl73i1zwsfl+eUvrWLF8lzWk/WafZVm7K+RLzAU7jiuYxIiGrxBC6suAQ63pM0P6sUDT35
+/5V2m1Xlg28kPk0FpK3snOe8akwar0ldLDIYWqxnHWrNBpmZg6tZEq/nEKkwzprXIFLPRGukKMch
+fR2L4/i3uZ/LvD9IN3jqmacB2F5fwW0o7uE92mOxKDjGoREE3Jr41MdyTtvqtjiq5cFuBG2cWCZs
+FNWUxzI4jhKkZId7OI48RC8q8FpC72CGBTbVye2v0hgoWcjOgsikxlWQS+2XlBo5912PdikLxJoP
+kdVSZQVLFfU+bi4TsBjNSbQ4PG4G0FRWpM46gZYwL2TPvU7362K1xRHzE0UHniQEW6q5MJXJemG1
+ycSR40dauGTK9BRby0SrHaOpS5EsQEayKDaiGBztpw+lq/h7L8Zq7CP1Ypr6nVIj9qHXxGgGw2v4
++FpOnM49FBRJq7kBOs71XBa8Rl7xzFVx4W1acnAs9xF3oKkLh9OJiGpxpZvKmdlsFnh64VkFc63h
+6Hcsuz2lrR+MCXSKTmS4maYHFBqfaFYpVhfcjXMtIiVqSYdj3Pu6aH3Hc/psXmAVqQ3YCl3OLVia
+TMpQ9R+bjCnO6TNRPs6gjNnQLIHxmwxVwWtWjblwXv4uOJRFox/VVFo63zQhoWZO2qutszJyE3So
+ta6kymWxJc+oE5nrftzBqJakN80JdAFpXlnF1RhU2Jc5Up/k5IkcNbL0GS4U0r9bx/e4vivvUefF
+7yYJ399rvjw+LG1pS3vIHgtPIUtT7rx+g2hyj1tT5RHMhsw0r14DVtcvxwS43QVPgeANsoOU0/uS
+3+9v9qlKcSnnRYSnePBmP8R9VqvSrNb5lzVGJdDLUUqlyKO8GjFTso35/Sm7u+LmjxXiWiYuuVY7
+5qlHtEAsxSn+IkpeZZhIGZN196jzOVWtGP5pTa1qQvXOiJnSxB+rSzmsb2E10BiVhlrpz+pOzYq6
++V4xJ82Vc8EuGJUNXkt2l6ARk2jQsUozBgr0WZ94xJpdWPAThh5UugN7gcEo5j4oQAWN6KyFVC35
+PMkWILM5pdaa+O6cjtK6Bw2PzZ3LMiy9PpEV/7kVLYhsYtCdfTqcMdSKwo1JyjXlR7x3q6TTk8+j
+VI4tk+GQulRuhXaP5upC+zFhpnqjh7vvQKJU8spZ0drsM1gXl24+36fzvAarN9fZXoxzYHD0+dQK
+SArDionC5ovE42gk3tvheEpbPce1Db2HsIlV+n33fIqrEHSbGIyCz5xyjAkV3j7XSszJEE+PucYa
+jEKUnTrE8Xb18zUolTOkkLF3DLjquU3ffJNkV+b92HOYKTya/JD6dFHn+d5s6SksbWlLe8geC0/B
+AMatOLQOVnegk/uHPND4wvMbHoEnZ+o6nUKiRVMaWDk+3eV0qnRkcU2gwbNgcJdcd7zEnRMVkoOO
+9ExOXRMshFpMjVFCUGNjrCtFU/emKVOtod/S9N1hNiHQ3aXr1kzUoykGI2KtuAtMm2kqO9dCjq32
+a2plxClmE0ZjCR6WjoevO0w6lFhGYSoWxD6NoAJPdrA4LCgTDUrZnFRz2oenMm7NxgqrqpfRb8Xc
+fiDXc/KCNY2NzOsQX6NViyBqmrmEGkfoxt4ZEezwTs3YKAkoJ+Tal3VPd5+TGVxVtijj4UbSJ793
+lc6msm57XVq5npN1jJ3YkKnuhR9Z+srynEfumTbGhXMtJkMZw0klu+DhbEyk2AU3ORJcCpCGGbVW
+kt6+c8TgUGJJzlzG5fxexL2hAComXoWtZT7tlG2StsrphRWlBnwLJdcYe5AXmiZOHUZmwadQE2h8
+ZKS6HhtFelapOZhskGilbVYfYY6VFHgtxC7EZ3TuFROH2khwtXLPE9UyhslJTmNFg4uzjMCXz+uF
+WrWbUKjuh08fTxW91+0eiULzd++MOb0pXvR7tcdiUSjrksHkmMY8xVe69JGTc/N3JCr8oDOipbTY
+lCXzPXGpTkaSoy0P75O68uCYN+iPZAJVhNR9GfhxFmGVyMOmC1HVmFQJLap0QDlRrHqdMrwvLuOD
+ZESuD1FpDakTS6IZgMjrkqTy0uRpSBbK542wZvFWHx5JQClox6yoWG1hEhIlPTkcn9DU7EG5oIA/
+OKVqyT3kLZdmIhNiPm9TO9LecTpmPFPhF33pTCugUPblojCMhtLX/PSUZkd1M6seuZYOZxrMDYqE
+yMiLMj4wzCrJ6U+LjFLp6MoxlGv6Ui84I7stqGW8qZpnY+gcj5l4sgh3V0dkSp3nosCxSZNKQUF5
+bRkrPqBVhCIbD8SXXCYz6Uv6jj7ryqXZVCZt0+dQJ3zlGbIbMs6/sbfLV16TZ13evAXAumngaRl1
+y+9wWSHYp6clV0sVpnUNobJ0D1L5/aRukGldxkE+5+RQPp/VBW+pOle+L8/54uoKbRU/Tk8LBiuy
+8LaOAoJtmTxl0gElBFqQpsztiEqrQev4DquaPUq8CqM0+VQDzFgWYsWrUVQe+Vyew9i+it+RRWi8
+t8mrR3K0/sLP32I++0YepG9ty+PD0pa2tIfssfAUAt/nws4OrNwgPRAvID/1eXVXJOS+/6hJek7h
+s+UxWSquVmdHKhyrXpPpPdkRismcspSgVFm0GE/kWNF3Dqmt7ISLlJYbpwQLVeKoQ+rKLl/aGbO5
+tBHWBj/QIJC/IEGdESiisZoOGXrKNxClnDG35iGZUqwFSh7bb3cJ2/LdZDLB0d0hyjJ2b8tu/I6i
+IzvUNJRIFgemCqVt4zFXvYgyN8xmi+InVa2ONogS2WnGUU2pAbdR5TDSSsxeGeJoFaS1ultXOTM9
+grUjh+bCte35eHPp9+aVNs0dSfe2lUm7GB4wG4rburGfMr8iP8/LBuEixRkaolB241rZpW08Enpk
+oAjWmVdyVKzcgrKjxUp5gKuaEuUCCZk3qbSE1XBK3JSfextPMLkisOqdn7tB9EJHr61IV7vORHU/
+rq2t4eh2u0mPupJ72lkNGU1kHCP1tpKhIXM0kJpPKbQIL/VSjI7LiQZrx6cnrD4j3phnpuSZjEVS
+FTTVq3Dc+syrKxaeUlbiKjGx1+xSTOR+yijF6pwzI6j0xFYW4jXm04xckayFc5FNV+bsU/E+r78t
+/XinNOTZ/wdhzrauyGZDzvk5NwLtuWsJNgXOmjXWmCweVjEG7WSeyAJS30zIlYHH7c9BASY+Dp1F
+zrfImCrFecMq408aEay39R6GZ0IleV2SqOjH8P6UdkfBMn2Vjq/jM77DxAQY5WV0vCn1ojy3zigK
+eegLHH0du+Qn6jLevM88k+8ORxPGSkWeahWlyVxyowApW1APZHJkrYJYJ0I9TbALlmQF2OAYrIJp
+rDXkmbwUpqqZ5UqJH1gWoh1mKNfyK3BV/micQn4oL9CD3YxTZQVynA47GkspWnKt+SQn1SrD4IWQ
+Rltefm9cY3p6Vl/t4iqj0RnsOovJ9D7L8T6FPhuTVIS1tH10aKk70r9I5dmT3IOOjtEpNJoC0qmq
+CYN9uX+n7nDpUyrKojUl4VqbN5U/cjVxOJ7LdSedMV1l0J7WDVytaalU6De+EDLYk98fTk8ZadYp
+czNuHchLuFD92tsNWVFBodwPKDQTlVaWolCxHz+lVKGd/FiPblFMR3lAy2aI4qZo5hDr+2B8Q6FY
+FKuiRLVfMa3kODPYv0V4We7t7a8ecXcg3z0sa+yixvs92vL4sLSlLe0heyw8hTgM+djl63xl4x0a
+e6qoXCTcfUMQWm9eXOHihgSonLimvisr/q0j2YnGX/waQbzQoNyg8fRTAJiDQ6zma+sqpVBKs+mh
+BNHKRoKvcNi87JPOZKUdHWV8XgNUR7OStq7WTyoP5CBLCHQ93fIy5uUimJezcV6xB4XHTPkYmWtE
+/g5khxIw233nEFdX8HnzAkku34k1Op9XA6aVQr5vjalb0r91W+AgbeRpdiY3V6mXU5sOM6uMyzZg
+v1zgPrZ45hnxijw/wV+cTJRRmYFHpUU76eExr90SL+XeySm2La70Jm9jtsUlXtMsST7wmDgSEK63
+t4lvaxC0E9BpyDVsfQ4nkh20VuWcMPZIlZzGNjqcUxm7QTals9CUcBOyU4UKtxeMJhFmoh7YRhNz
+Im3/7hcP+fxv/BYArw6mrL8hY7DSk2KnfjBk/1iu8Zo/Zu26Yhb2C9oq2tJoJiRDlbdTEpZ87HIy
+UT6F6ZSZQs9bbZ/7C73KqXgx33M5J85uy9h3dkhV92GczWlF0p6bD0gT8QoWAfMiDokuSbuBu4pv
+JEti8Kk0wJxOLFbxCRMd1zixpAorj+oWA31m3TDjrsKmq7P/ee/2WCwKYMB1OOcZZioeezspGewL
+W83nXvsCH78ik+n65hZHnqbhZr8HwL7vc72zKNn9FLW6eDaJOYi1Su6k5mSqpa5jmbg7W6tk3oI8
+dcKRwpJfGdxj/764X3NnRKhRa6uHumJSMNYUUmE9ci37NWkBKtF+sefT0pKH4Z60cWd6j6ayjDhd
+94xTcF6dYPTFo6kpr72QWw+EnGU4m7Kmrn85N2dpxFGdUqp639RclrGYHBEE8t1G0KGjkuwn+SqD
+uZK15nOKI+2TMvQHXolV8pb7I7inn5vL6/zJa3LtF5/6btYuyn02kEq/aT+h2JXJ+u79PSbq4hZ+
+wuCe1gHEpzTWlWuxJ/Ge2q5S+vKy+TGYQK43mfskNyW20d0o8dZVg1HZpvJOQazqTlnW53RfXppb
+xw/oPSn3+R/1NtlRvdGkKS9gWtYcR7pIHTV5864Q9YzzlFq1Nxm1cFrynUTTjIdlzUhToGUxJVhR
+shjPpaWkO8eVfLZ3GvBST56N620wGkkZfVZbGjOZO47d5ngun0+0viSeu7R3pZ9VcR/fVRj/akV1
+InGwrLxPNpT28loXkLwk0zRl0r7D+fPy+/0vlH9gHbC8H1seH5a2tKU9ZI+Fp+BGId3rV9j57hdo
+XRbv4K1ff5PDB+ouPTjk9bfEnW/VEQ1fVuOLC5XoTolRME41S0Ap4ItmhltqIHF2h7WugmlaWq8e
+OqCrfF0DGlnO9yYEueyw/QurrKvU+KLQKGiWOHP5u2w04mAku1hdOKx1lHugvYbR4Ni5DVnB12kz
+1shyY8vD94Uvwnv3Aa/sKlhKd0R/toenUfFmFOJqpL4dOWTKI9hLI2aqAGmbKl7Tu0i8JkG2InDY
+18Kf1dGUow3hcjDzZ+mo/qGrNf1OZimUwXmjG5BPZWezps2Ko5WGmzVGsROOAsTiyiVW6vidLYe5
+kc+H+ZDN5kJ1uUUjlB3PjdXbcqYUSvFettaoMw2eeffZa8m9OfU6q+rJFSvKaz7g7JjjBikrF+V4
+8Hx+i7192fFXKwfjytjWibSR25LyVO6tPLpFpJT4/c0e67HqVbYc5rnCsDVwm6dzDq0e7aKATI8M
+YRSyq9wRzVI+2xvtcvtEuAuecwckJ+IJDQqfVU+8n+YVS6xaoPG6eEfO6fDrxU55hHHFE/YmCWjB
+W1DlZ+IzQaQCMFVOL5JrBO0tfu+L4r1mqeEbvQNXC7DK9+gxPBaLQlVkjA/vEuUeM83vbAcNWJez
+81q0yn2tibh0POb8pp6Te3pu3D+kbGrJ8mCfiaL8TpMBtRKXrnR8fFVLCvShmDBGEwCMq0MOFQyV
+Oh5tJVVd9dbohItKRHXtD3IKxfhnvsFb0PQbB+spMpGSluojeqphGMUNwqmcF+dJj5kqTh0eP+Du
+ibA7zdTv852abl9eoL5voZRJ6gcutpQJNKkzPH0hV4ye9S9sYhsLkpVtGtrB1e2Q+UUVBzfQXdUz
+vmoXMKnwVJ2r34lxM3Fn54cTMtWymB/cwXFl7GZ7X9VLVRjlPrRuBFqV6XVjjJYnO3FIFelUUw0F
+YzI8RynJZ0Mo5YV1d99gy5NU88SUrCkzlHU1BVpGOIria2Qxzqrc287RFs4XJIX9teKEu6/I/VVW
+/v72aEJbtRwaseXqVXl513pN6pYCpMKQ0pe+FLXGLboB4ak8x24UU2otSbfZp6UxoZ72rb5yjkTR
+pvl2SaxFkNn4hOFY+rcyhrClAR0j7Trbq/gqGkvToVKtU+o5xteUs00xRuZ1OVNuSGsZjYXEOPfa
+vD1TbcpmSKQ6pBkQqPpUad/borA8PixtaUt7yIx9j6vHI70JY46AGXD8Ed3C2rLtZdv/P2j7krV2
+/Vt96bFYFACMMV+21r60bHvZ9rLtj9aWx4elLW1pD9lyUVja0pb2kD1Oi8I/XLa9bHvZ9kdvj01M
+YWlLW9rjYY+Tp7C0pS3tMbCPfFEwxvyAMeYtY8wNY8zffMRtXTDG/Kox5nVjzGvGmL+mn68YYz5n
+jHlH/7//CO/BNcZ81Rjzi/rvK8aYL2j/f84YRVs9mrZ7xph/box50xjzhjHm5Q+r78aY/0zH/FVj
+zD81xkSPqu/GmP/JGHNojHn1Gz77Q/tpxP47vYdXjDGfeARt/10d81eMMf/SGKW4kt99Rtt+yxjz
+5/44bX9Q9pEuCsYYF/j7wA8CzwJ/yRjz7CNssgT+c2vts8CngZ/Q9v4m8MvW2ieAX9Z/Pyr7a8Ab
+3/DvvwP8N9ba68AA+LFH2PZPA//aWvs08DG9j0fed2PMeeCvAi9Za58HXOCHeXR9/yfAD/yBz75Z
+P38QeEL/+3HgHzyCtj8HPG+tfRF4G/gMgM69Hwae07/57/Wd+GjNWvuR/Qe8DHz2G/79GeAzH2L7
+/wr4s8BbwLZ+tg289Yja20Em5PcDv4hw1h4D3h82Hh9w25kBQ8UAAALYSURBVF3gXTSO9A2fP/K+
+A+eBe8AKAq3/ReDPPcq+A5eBV79VP4H/EfhLf9j3Pqi2/8Dv/gPgZ/Tnh+Y78Fng5Ufx/N/Pfx/1
+8WExWRZ2Xz975GaMuQx8HPgCsGmt3dNf7QObj6jZ/xb4SVDyA1gFhtbaBRHSo+z/FeAI+J/1+PKP
+jDFNPoS+W2sfAP81cBfYA0bAV/jw+g7fvJ8f9hz8K8D//RG1/Z7so14UPhIzxrSAfwH8dWvt+Bt/
+Z2XJ/sBTMsaYvwAcWmu/8kFf+z2aB3wC+AfW2o8jsPKHjgqPsO994N9HFqZzQJN/28X+0OxR9fNb
+mTHmp5Aj7M982G2/H/uoF4UHwIVv+PeOfvbIzBjjIwvCz1hrf14/PjDGbOvvt4HDR9D0nwB+yBhz
+G/hZ5Ajx00DPGLOoVn2U/b8P3LfWfkH//c+RReLD6Pu/A7xrrT2ywhT788h4fFh9h2/ezw9lDhpj
+fhT4C8CP6KL0obX9fu2jXhS+BDyhUegACbr8wqNqzBhjgH8MvGGt/Xvf8KtfAP6y/vyXkVjDB2rW
+2s9Ya3estZeRfv6KtfZHgF8F/uKjbFvb3wfuGWOe0o/+DPA6H0LfkWPDp40xDX0Gi7Y/lL6rfbN+
+/gLwH2sW4tPA6BuOGR+IGWN+ADk2/pBdUF19ve0fNsaExpgrSLDzix9k29+WfdRBDeDPIxHZm8BP
+PeK2/iTiNr4C/J7+9+eRs/0vA+8AvwSsPOL7+D7gF/Xnq8hEuAH8H0D4CNv9DuDL2v//E+h/WH0H
+/kvgTeBV4H8FwkfVd+CfIrGLAvGQfuyb9RMJ9v59nX9fQzIkH3TbN5DYwWLO/Q/f8P2f0rbfAn7w
+Uc679/rfEtG4tKUt7SH7qI8PS1va0h4zWy4KS1va0h6y5aKwtKUt7SFbLgpLW9rSHrLlorC0pS3t
+IVsuCktb2tIesuWisLSlLe0hWy4KS1va0h6y/xfIOPq364CimwAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.2531... Generator Loss: 0.8170
+Epoch 1/1... Discriminator Loss: 1.4623... Generator Loss: 1.2401
+Epoch 1/1... Discriminator Loss: 1.2366... Generator Loss: 0.9501
+Epoch 1/1... Discriminator Loss: 1.3075... Generator Loss: 1.3609
+Epoch 1/1... Discriminator Loss: 1.4270... Generator Loss: 0.9645
+Epoch 1/1... Discriminator Loss: 1.4111... Generator Loss: 0.8603
+Epoch 1/1... Discriminator Loss: 1.2284... Generator Loss: 0.7865
+Epoch 1/1... Discriminator Loss: 1.4061... Generator Loss: 0.6807
+Epoch 1/1... Discriminator Loss: 1.2659... Generator Loss: 1.0759
+Epoch 1/1... Discriminator Loss: 1.4578... Generator Loss: 0.7154
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVnsbXl23/X57Xmffeb/fOd7a+6unuzqbrsTx+0kdnhy
+XhACJEgkpAgJRJB4ICAFEOIhEoIHeEGRAgSBiIITEpBMIie2cRzb3XYP1VN1DbfufP/zmc+e9/7x
+sNb5d5cgdLXb5VSks17+5+57zt6/aa/xu9Yy1lq2tKUtbWlDzj/vAWxpS1v6eNGWKWxpS1v6AG2Z
+wpa2tKUP0JYpbGlLW/oAbZnClra0pQ/QlilsaUtb+gB9ZEzBGPMvGWPeNsa8Z4z5Kx/Vc7a0pS39
+0ZL5KHAKxhgXeAf4ReAp8PvAv2at/d4f+cO2tKUt/ZHSR6UpfAF4z1r7vrW2BP4W8Oc/omdtaUtb
++iMk7yO673XgyQ/9+ynwxX/Wl0PPtYnvk1kIjA7MGHDkH461oBpNa8CocqNfxdJSNXKxsZb2h7Qf
+vYzrOLiu8MDYl2kb42L1Ji3tFYesHI9umMg4Qp9Yf2dc+d358XNSNwRgN+rQ6UY6aHD1ebauqdsG
+gLoq5RmNxXE2Y3Z+MBELxmxmsxmPxTbt1Tytzta4DrZtr75XN/KMdvOsusHWtS6QS3LjBQDmObRl
+JY8zDtWykPWp5XfGFlf3NaYGHZpjWpomld/Z9mofXFfG47Zg9BTZBlzPlXHYiqa2Oj27+RnGyAJ4
+xrkSSU4r8wI4eOEVilLGUVUlVT6VL6W5riG0RubhAlbnbzBXa2uMg8Pm2bqe1tLqGjqt7Pdm/epW
+v2sBnVeg83DCiH7Ul8+ewdM9M65HNpsBsLQ+AD3X0h105LuOwdH7tnVFq9try5amkf2pWplTWUNT
+yrUWS2t1bBhcPaCttdS6P83mXWjt1fyM4+D6ciaHvRFJpyfXYxerc333e9+7sNbu8SPoo2IKP5KM
+MX8J+EsAHd/jz75wk7dyl2u+LM5OHENHFrtTZriVTH/lWkJ9WXzd5LLJOF7Ji7fIS1Z6UGgtc121
+URgxGnUBePVA1sWPBjS+3CsnJ9DFPu3s8Cdv/4yM46VrfLIXAxCOxgD8d//5f8I3By8C8Bc/+Vk+
+9zOvAeAcGPq53K88v2CWL+R+J88AKBYZUSInt6o7EMqYaczVIWwb+f+Mknoth8bBYPVweMM+5XIt
+152Wcz2Y+UqunV7OaSfncl+vzxf+y78LwK++ZVg+PZFnhzHnv/EOANOzuaxF+4B6JYzC8c8w8pHE
+X7GYv6ljXmH0Re+N9EVY10Q7cozqeU13bwDAZXXM4kL2sm5rSv2d58tLsxsktPKR3rolGMre/OVf
++Q0ePJR5n5w85PjtX5ElevM9WZeJJQ2fAzBsHeqVzN+0AYncgsCLiJRJVkb3oyxI3UD2MbOsa5ng
+5XzCJJOx1Q2YruzDtQNhBJ3br/HnXv0zAMQHIWNf9izq7fOtv/e/A/Bb7TUAvjyo+dlf+ilZt44h
+LuTZ2dkz8kiekT/JWc0uATjO3gLgyblh9ugMgJVtWTcZAJPWp9fIvhd1xeVKmPNE34U0K6lUcAad
+iMHhSwD88s//y3zxp35Orn9iTMUKgD/32U8/4kPQR8UUngE3f+jfN/TaFVlr/zrw1wH24sT2zYh+
+MqPpyEvx1mTC7Y5sUOfaNUapXI+aCYtWOKJx5WAO2pDSneg1g6sHwjQWpxCpEvs+xpf7MZbT6A18
+3EwOyso4dOfyYt1dHXOtlo0puinzsWyGO5WN+2Tniwx/UQ4N+x2+W74PwCfifYLoCIB13mAy+f4M
+ee6iqIhUiiedFZErzMnrBjiVjDPwVDKUAW0pm7/MU5JIxlm5LXVfPvvLlMOxbmGwo38dZixlTuuW
++1+Rg/m8STn+lkjdIvFZPRRFzobye3v8Hm4s93Cqmr2OvGHp7IKwcx2AJn1AqNLINfJ35419CGWc
+0dkx5aVcH3RfwHOP5dnFHN/K/HzkpdrtjikTeXZARl7Jes9bl97qFIB/8rd/H2csWljSkxfPrZ7h
+X6ow2O1yYGQthoOWbiD7Gu3f5kCZQTuQv53Mx4mEYfnzlseFjG12csnJTJjod56ueLyWc5RN5Cwc
+9R+y48k4q0OPJpe1nV6c8HLzabn+RVnXncDy2/e/CsCf+NQ1OnvykgbVAa4yIW+/ZWn1PC1lTre6
+Jd1dWe8Lf84NFXphGFGsRGN98+wtokLOu1/KvYx1KPONFtoS9OW8vVVN+Nk9eV/CTsKelXt8WPqo
+mMLvAy8ZY+4izOBfBf71f9aXG9My9zOSvKaditqz2+bYhRywsalZjeTzchqR6KIljhy0Vdihaw4A
+2NmtmJ6JpJklLZGVlzAPIVgLs1iu5Hf7ZUh/RzbDPy0oQ/nuhRnz/Yt3Abj3f73D4stfBqDvyu/z
+T6z52fg2AEGxS5MJ52/frGlekJdtul5wMZUD1jyXQzPwZ6SNHNz1IqajGk0vXVMFMievlgMfYnEd
+mceCDHTsNA0N8tmtS9aVbPggkmt31xHfS24BUDkL3jmSlyr9Ssoolpf72ekMMxOtwdEXsyktdfmm
+ruuSdSgvqTEJPdWgukMDytQ6PRnnjfaMub5ID+cTakf26aX6NsOxvLAnK0NtZXzOZv55xahURuAN
+8AM56OvTFb/y9ldkndMnvDG+Iets5GVsX95j+kAO/LAa89rLsm57gx6OkXuP/X12X5R9taqK+wOH
+YLEr9xi0vDxJdZ9OWLwpArTbe4vya6I5LVey9s9mDieXchY+8VWX9MVXAIiclNXOQwBeUe2uX3yW
+60audR4ndF6QdVsfhfgTWa9h3GM0luuXR3JmvcuQ9GWR5sViSamCIchcFqpZlr+X8t1AGNnQkXV7
+7lc8n4o2Omta+nPVPN/5Bufnd2Rd9vpksQrDD0kfCVOw1tbGmH8X+IeI6fffW2u/+1E8a0tb2tIf
+LX1kPgVr7a8Cv/phvttgSK1H3fOoV8IxXbePGQnH7I8GHMTCuZ/v+uwdC/e3sUiim16P7idlKv1q
+wKoW1S+d58wRreDiYs1SnXmRSrPBoUMyFkkbDy33H8ize9kxJ5U6g26MuPvdb8hAPylSy9YH+GPh
+yquLC8KpqKXpzjPOToWzu6c5ppJxhMrZs6bLeiWcPejULAv5/7wyuKk68Fo1d+KYUr1TnbYi0zkV
+M5dKpW5rfLxExuE5Yopkuxm7cgtCL+TN74lEXDk1dSn3a+Zdmr46W1NROduwoinVr+FCMRsBMBq5
+hF2Z3050E6sO2GsdkdzdZES3+j4A0+GY20b2qXKmmIV898a4w+lcHWlGxjupM7KOaDEjJyVT0+7r
+X32b89+X+d0bB5RDec7N4acAKL33uREOAdgfx9zbEe0nHqzwFjLOuNsQ78t6BKptEYxw9zafe3BD
+zk48CRjvyefPf8clUY3lH3xDTMLi+XMePpfP+3/qdUZGNImiHzDviaS/flPWNTt7QtSICWZvRrhd
+mcfAXsc90nPt9Ihc0WIiNYPMqwXWynrX9TF5KtdtuaCnfp43opfpvCnjzPblDEUnOZ76nL3lmrJR
+Dbla8Nbbcv3O9TnZUrTvD0v/3ByNP0yu59AfJZw+y3E6MstXk5ruSO32YUgY6wsy7RPckoVKXFEX
+R67HQO3F6NaA/aVsTHZwwfxSpug6U8JKFi3sqCrbVpSNbpaBzkgOZrjqcGnlMH7r2xf4r+phu5RN
+ufGFT3H/RO7R8S75xAtyUCovAHUCFf4ZZ+oIjfbFFmwvcwzCIFZzi2k1SuC0+KW8LI0VprLrlKiv
+j/W04nwhv0u9lFa/47geoSMqcXckL3+78Gh35f/91MG+JvNf/IpL9UwYgDN2CB7Jy1RZcaI62TlN
+faZjqAh1bf12zq53R9b7ZkhX/Thd9TmMfMtiJS/gq/WaxVLMkmw5YtWqWZU3GFWJ1fIhXa1JM5lz
+eC2mo6GIWZZzEAuzGJ8+I3lBmMEwkv3oJV28nqzMzm5AnMh6Jxzg9GR//bGDE8h1Z+MDCWqMJ5/b
+1sOuZa+DoKQayEvTHcfc3tkH4Pq+zOPZOuXdY2EKne/E/PQXxTSLOwd86vMiJE4vxXwa9WfcflXW
+J3Z2iPdkb4w12FiiQKb18HJh8J6utx31aVL1h5gbRKmsW14ZlisZx55/g7u35Ax8+1LW53qccS2U
+3/3+86dMNTISTyyDTEyeWXoNE8uYPixtYc5b2tKWPkAfC03BMw67cYdZf4pTiTTuhH0+GUoIMO2P
+6LqiMvXHNWNV23wNFR4NeriOTCUKuzRjjcezT34g0njvbMHp7AIAV8NDte/Tc0QVzVYTCpWavTgm
+XAt3XZQPqTS0aM/lvsnRiI4nDsUmhsqKF7mzvuS5RjPqxnCgURIc0XhOouckKqGGUUtiREIVtPiJ
+3NtpRYoEKWRWJKJvCmI1n0wasMz0elgwakVid63MI+qUtBrTX8Uh5/c1Fv7knCYQyRxPQpyOSJim
+fajzXxLqeEIM3V0Z885gj52XRBM4PBjRy2SclSPrOkwWdCvRzM4jcO2hjGeY0TsRbcQfPqKcyLNL
+NY+CoMtKQSSdooRW1+LJA9LHEn580Eu4rbiG3ainv7NUraxhbGI6vqjdjlPhu3I9CnfwfQ2BhrI+
+xunAZjsWE1pftcViRNgTbfFo95Dwpmhcn3oqUtw0x0TrTYQmpVBz9HbTxw9kr92OOAADbhD5n5Qx
+mBLHE63DiV0cT9bQFms2x8Id39T/72B80QJs08V1FPeSL4l9WYsqzHBDuX5zZ2MGJZznsg/jVYDN
+5B513vL4gcxv9LkFr8WisXxY+lgwBVxoOuDFUOuhmTnwTMOCd6suSaDmwSAn0NhtouCQOocokJWO
+9yKM2nX4K0IFHEWJIXoupsmThTCHLi1xV4FJoWV3od7isibpy0G5NyxZP9VwkTKYUXiBUdPGX1jy
+WFTb2SrDc8WUcIIuhS/MJNN4dRBYKgU90bR0rqldmztYDVOV6w1IK6c0srGNaYjVHxCFPlmhYbGs
+priUz4OeRHwjx2AaYWietfSvy8t//oV9/N94AEAVT3HqpwBoRBLjdXDVVzEYw7XrgsM4GHi8oICc
+wcBgduTlzpeyTx2TsIhFfQ7KAY2rL/2sZf+6vKR50aW71kjDBrDlVww1krEqI5xIPl/vhjzbFaY3
+Wp/hNcJYciP/HwYGZ60e+cuWyhecgh+5uL4Czkofd6hmkb5IjgFT69qP+ljkujXHuGvZ32ac0Awl
+qvTCStY1fbPm0sozDrtLEj1D5sWMQH1F/UMxDcJiBhoRM6N9XDWDTOAJCAIwkY8bqTqvkTFTh6D+
+FQv4azkL5rlhvCtn2Y1zLisZR3Eiv1sf1shbAd0nLmeFrmdwQbN+CEBS/zxe8OOlMmzNhy1taUsf
+oI+FptA0Lav1gvmypFKHm5/mmLGoxHkQ4Dnqoeq5uGuFq6q66DlTUFPCqUtCBT01VYTb38T0XZq+
+cOCiVhU/Thmq02p2aclVNczqJQv5KpeZYaKoufDiPgA3pq9SKedPVym7jSIBaSgUrpusDO0G2uuq
+SeDuYA91zN2APU8km8HQqm67VAk8v5hTLdQRSU0dKbS3LfBU7bZtwWIpUvr+iQK5eoeMfDWf4orm
+iUgz53JKHYgW0y7WVIolMytBB7pBRDy+A8Dde0NuqUd+/0ZEYtXkCaD2FUcSapShmLLnijTz60ty
+3ZPUBsQbLMP+kImaYPlU/r9saspG5hy09goS7dQrBgr53Rv0mWcyv2kukrQqKwJHxhYlXWwlstJ0
+fbxINYFuAFYBbv5GStaw8be5MUbVbtf3aRwxFcLdHoOprN3onmgPB9Md6kv57qTJ8Z7LuiUHAeFI
+HI11Lnuez0M6O/L/jnsbfH1g6YCCt3DdK0g3rb5+PqBoS2NX4ClUerjGQ0zlYP4eA0W1TvVewQKs
+OrZ3hwGPFrK2iV/iJTLmKAxJ0x9PU/hYMIW2tSxXDWVuaVE10TU4gUy+sS2ZqmKJ9TFqAxpdSC9M
+MAogoeNQlxvockqjoTAvbgliYRxDVcnSeoipNaRX+nix3GN94XOssOHHFwtcPVjTU7lXYwqcpRya
+QXeO0xXmVc+nxPrsxsmxibz0ifoJvKZDq88Iwpho45FPS6ZL+d1aD1ibG1an8juzE5A1suHXTYiL
+cKym9kitbOFcD3lWpayHamfSI70j92vmlmYiKrHtrGgn4tV2PTm4TueSvb7AtQe9hFFffQrBgsFA
+fS2Oi9fT8anZ4kYJrSdMMaoGTHWutm3ZjWVshe2z05G1uJgJc6gr8Cq168feVf6BGyfEscKjn71H
+Fr0q92g1UrHj42s41Q89GmXqJh7iaXzODRyceGOmbOzznEb9E65vMJFGcFoXOhvh4pD4YvvfOBRm
+tLi25NvviP/o/eyCN1QYnE9e5OXXNLqgCNPwcEZw8El9RoJRJCtmiQ1lDY0Jf4hZyFrhWGg3iRsR
+VMK8rWPxQmX2gxF5X0BwjyqZU3OxIlBmGUYNoZ6n9TJiqYxgPXtCE4op+GFpaz5saUtb+gB9LDQF
+WouTNYRuS6AqZeQZ+pvcgNAhUmx80nWJjKiMrkqG1q8xmkjV2gSj101V4ChQyVYGXz34kVEMeVXj
+FsLlm7BgV6X4uZMTqmrXNy1lrslYmrHXzxPyWCRG6MX4tWDnV3XNqpbf7XcjIp2Lo3kCbt+nrw7T
+ZgmOlXh00db4uhV7jkRcsqHL5XIDbU5pSxl7Vo1xNZV0XLuMO+pgDWStynxJvFKshPFZTzT68PSC
+Whz1Glnxdd4a209u0rkj99jd6THcVwh2J6HXkzH3KXByeXY1FCnZqSyhEYn4ZH12pbENY49Ro5GB
+ewuaVMyOvC8axvFZh1Khy1VqafUerJ4zfybrWQwibgxlfK4RTSN1uEpFbX0Xr5X9a9tMsyrA2BZa
+kfQ2E1XcOi6+akW2yrAqxWtSPM2fsJVH7is0vZC7BUWOPxeN9eRyyTcRDXHntQfE7hfkgQoxd+ob
+OL5GBtopNpc9bW2G42i2llljdS5XWWd1DHqGqFvqTWZoYzC5nBEb+DSNaBadVJ43fzDh/Uy05lXR
+kCr+ZrlYYk9k/P/0suEXXtPMsw9JW01hS1va0gfoY6EpGAeCrsXLDKXmfjdZS1UonHNpWSvUdrSG
+qKPIRHWy1Gv3KqTlBD5tqzFfQnyVXE7Po9Jc3cjTjMPjFKejdn3e4qlNduj5eD11Lp1WnKtjs1UO
+jTMjiRRqXY0oLhRrWhbEPa3rEPYI5sLFw6FCissGoxiJwGnwEEk6zR+RpiIpvFLGawOHxIjvYBb7
++O0GPrxipWninb4HtUi/fU3pfbg45VKlY4+ATG3nLNkheCQot3z4jKArz+sNPyN/3RirWIckW+Nu
+6h60LkazNcMgoFGB1802zkwoVDiuqyVnCt3upg2l5jK31iMeiqYwmMkzqv6EE0VpZusAq9mxwxvX
+8FeSfuw9/QOeXKg2EYrE7AdHTDJ5YFE2DG/J/8e9m9TrStdwih1uCkJI+JksgJ5oYU2bXdnw1eyC
+QuHBtgmvamC4RpzLox4MdkWrcC8aWk1fHvsJniNaBYloTa5TYRQeTjQFRX+2TDGKv2mjELOpW6Ha
+DDYERZBak9MqFB58anXylibHCdVXthAfx1l2yYMLOZvXhjH7+zK/smpYz2RORyu4NfoXEKdgW0u5
+LplXJYm+o7UPy0JV9DoiXcuhf1ovWaqDylWnXhIHrFWlvL4c4OkmhZ5PO9AswAJqV0E96lyce1M6
+qTiApu2cpXpvy6Sg6Ylavn+xh1W8wVqdXdUq5Wwhn3thia+4gcbtkahn3K5rpqX8zn2mTMzZQetr
+EPcLvJWOP4TmUg7QuisHZTWDQCGs6+mMp5eabptOaDwZc10GdPZFBc0UH2DrDq6rZlWxoPimHCD3
++Hcp698BIDj9PE1PgUo35SU9uHFAV82qBSW9c/HIXx6NMKpK96oO1UKekwaKqyhq8kei7j45e8Zi
+JtercQertR4aA5Ncr6uq3dYuwVIO8bpZsZr/wDz81K4yXHYwlezDs8m3ATBpRdrRoi+pyyPFZKSL
+E7oagQqSlGipkQg1DRp/Sb1UR+v5klodt7V/TL6pz+Ee4fUVnJVpVKNT0I82YCkfR6Nga3OJO9ZU
+c4XY4+1hIgEywS7Wyr43+R7LheSHFNbQRfa1jRRYxTluK45d1/MpSjmzWXnC+ljOQ5me8+3vS0LD
+W6cy/0cXa/yZjCfxLT93SxyK/+gyxe+Ks3J0fcTI//Fe8635sKUtbekD9LHQFFpg1RiytaF0RPKN
+s5b1UlTGdRVwpNz6LAzw7wsX9DQ81HQM45HG/G/WHBzdlc/jMeszzQxcP+b4uaD4Lo9FGpz7DSN1
+2lXGZaEaxGXhU6vfyw97RBuOrw6i83TKYiGS1tmzdFS9aSczzlOVOsby3kwLmTzalD57l51Dkcwv
+3bzD7kh4cn0RUGsVpuxEpF2+vOTt92T+z8qMY4Uud01Dq2ZT2VsxyuV+/m0FVtSQqynhWIf8/v8m
+93v6j3AamXex85xuJtDseCkhrajI6Lci2S9W73H2WEyfm+cuxBKP9+6MiRQdVyvk++nZY5xwqns2
+oqeVlVrP4+ZdrU9xP+eZYidKDaGGYUi3m+nzGlY6p3Vb8nwpRVauX1g6I0lGUgWE8yfPYUfW6Cx5
+lyeqUs9tQn8mc+kGPv1EPrsabqzyiFyrYqV+RkerWk3mS2ZLmevhSxndXGDayVD30QspxpIEtQ6e
+s6NO7MXiFs74ZQCMFU3BaUusL4jIdpbSqPNwdnrKg28IxuW99DnXNITZeqIp1MU+t+4KJHo8GtAu
+5LwsLgoezAX3cPHeA+4/FUxJO5fFiPsBc3Uq168ExC/L2t65OODJpczJCc+4X2/MkQ9HHwumYKwh
+rl2ioMZHFixva1y1cTtty0DNChP4mKHASm0lC+auVvhrWYSmPaLW4h113eLkspD5Ykqj9npXbS8b
+XjBqhYEUbspoIodxp5eQV3LwnvROOVtt6vXJZu2mY9yxMKasaZk/lUUPbYsfiDprTiYsdXMnzzXN
+2plyXSHPu3GHpKdqYrWk0bTuupD71suSuVb8caqInaHajn6fZwqDdYqUrlGorSsMy+muiCr57qyo
+KS5/XRaueEhrO7peO0TXNQ28L8+7nCzY8TSCs1zTZlqZyEno35C1OGy7LKby/Xcey4FfL46J1jpm
+Z4MygZuez1rzGdZxQ7KWfd3tiQq/1x5xEQnTe/zdOVUuazVdPqZ4Kn6Ad2cVI0/W7vGxpnivF5iZ
+zOPla2MuFaeQzSpyxUU0B3u4rlwPNY6/sjOWWpZs+fSci3fk7Nw/y2BPIz/zhDuJrOftz8icb47u
+8rkDUeHf6UQc9cU0vTM+w2htRmM3PokAdB9t/YDiQoRQcT+jzYThTr/zNpMzeUbXFwZ0+OWY9Lky
+sQuD6ckZKXpLvHceyv3ePqWnEYzgRTFbTubnpA/kvo/esfz0l6QATHH9GTupVstaHnDbarbxh6St
++bClLW3pA/Sx0BRc36V/rY99OKfWmoptVeNowYtlmvJPjkU6po1HVgnH76pz8cXeDteF+bK4ec54
+IB5gt3EoHolEy2h5otmFx6ouPp+uePWGJAl1opKh5t6bukMSaQJSWxGoaosi+wr/kvlEpEdahDSt
+3HfY+GQq6R9Pz7nQMZ16miSzaOkORJY+m2YMx6J2LzsFy6cibeqFOtl2e/iawdmuMqYKZ468hoUW
+b4m9hqLW+H0m1+bLCaViM/Bc3KE8o2qLq7i4jc4JR+K0unZDJEr/0qfUhBq/XoE6zzKbU5+KNPJe
+3KcdavTgiaj1wcUF8U3FXvT3OXsomtnbx1MuOiu9h0+oqFBrRGr1D13mb6lZZg0KN6A7b/heqOZh
+c8LRWiNGGl1qfB+rezPc6zE6kLnWtU+mdRmL1Roz0EjKNZ3HxQU8UIdwlpLvK8Kwbxgo9iLLG56F
+8p1XCxlnbFzKnpipfuzSVyk+7h6AVum2GzCiv8Cos9YEOzhqxuXDr7LzRE2iJOBiV53GYjEwzM/o
+f0LMDn/QoTwXzdP5nTe5/7YULHtQVZSJ3PtIFBCc5Zp1X9bw5dser6rmuXr9Ju/25Us3PhfT2k1l
+jg9HHwum0NQ1i8sZLQ2uI4vujEMirXAc91xSDekMhwOihWzYepMunVTkHYX5LhxMoZVm9gKaO2qj
+P6wINdcgTNSGDGJQGGlWxsSqZZXeCk/LQNeRz86uAqoORMUr8xUzBTTNV5fsaMjRRgGe8g/P63O0
+L+MfaI288nCfvivPnnc8TjSC4bo+rYKz1mrLtu6AI40kWc+w0gq/0/WETLP9Yr8g1Qcez+QwO2lA
+qAe3iWrq7610lR3QeoXOsgREdR0dydhf3NkjLGUtotU1aisqfNCFRPMdep1dwkjW0335HgAP2oCj
+2wJ6SqKItVbMfn5xjl9rLklb0NngdRbCvCZhyOGhFLlNnl7iahbo+bPneI9lL293AuiLqnzkaAEc
+43Bb92FwcJs9hZgHaDYi0BnFXN+R+W32w+0fgead2HLIvoLTPhE6rNQub8oFkUaP4v4dAFbVkjIT
+pnDQ2eXeTQWihSkovJtKw8iNg+loDkdnQLAraz8uv0z1hqzRwZ1T+u9pWvOOCC/voEdXU6sjN6E+
+lLE9uDUinsn1uycVvbHsj7pOMO/W3NQ8iS8fvcZlKH4ir9/jhdekGvnr41fIzbbIypa2tKWfgD4W
+moLB4Fofd+UQaIL/HSfhxoGqgNUB12+qC6vwce8Jh+6eqmTYMTQbqGpnDUuRZu0OlL5mT+7H3M20
+4rNCe9vMUve1BPhZgKMx6mW5ZB6JiheuYHxN1LnGFdFd2ohSIxhhXHGkZd0PD3bxV8Jnx7cumVwo
+aOm6JnbN9shrkcAJY2pvA20NWDoKVNJaAZ3OGl9BSL6T068UouuHJOoBrzwIGhHBnaH8f9mUVKHy
++tLi9uTZ9aTG2TguB0vqQuLm9l2ZU/zJfW5orYdibNnXXgZu0oJGDDqJS6xRhGN1qDn9ALe81PF0
+MVoPY3eMc5jlAAAgAElEQVSnh6Ol547SMbVWwq66m94TXJWu2xtHaNlNTk/OaBzReoICIk2K87W/
+wV7tX6nrgSnxWlm3jtvD74km0E+6RL7Wu1A4c9PO6O6ojTL1cbRqdrouaNVBuWwcwqO+jm/TyCdl
+pppX/1qHZFfWa3p5AJtqBmqWmfwSOrf1moergKxu/5zUl2SznWc19hWNBuSajBYMqAu9h9PByZur
++YU673AnodLyfq6/0nn26ByKJlHfHGJS1XLqjFc/KZpS3G+5nG/cvx+OPh5MwULQWDphxTiRlziK
+DEexYu7vJESKmquqCjcQtavsyKGLfY/ztUYAmh61MgjXNSRadcavHZpYDpCv+HUTFoTaT8HZdXAG
+ekhtn7NNefb9lKzU1GANU8aVi6fhpknWkqeyoXHo4Wq9vxvrhOuK4jP6ki4mS1y1kZduSceR++bn
+KYOujGmhfQUC08HdET3xKN1jeEN8A1HqcGzk2cvMZW+sUYlYIirTvRmt9hjISkOrhWMc43AVZ10Z
+ulpYJNVqPbutw8GRrlsaX/VnqOuA0lXk4eWaRjsPBVpOvefmoGaH10kYjuW+rtcQqtpaZwWXM80b
+0aiOLTPOxnL4i2che6rhLs+ekGqzm6fdA17T7M9mLS/m42bBq46YLveu32WgdrbbBFgtdBvFDoEy
+k03xGooGL9fUYneAr/ue9F1Qs6Po54SZZlpeaJ+G8ZjDUCsouSVrHU8n/Ba21ntrVjTeHubKwXAh
+XBtwwyFJK/tzcCtjtZJ5e4Pu1V8n1z4tyxWNIh6LZsDlqRz8UZvTj8T/8/KLMp4oX9EqMtPLI8yu
+mrwraJciAI/dkMFwC17a0pa29BPQx0JTcDxDNHIIcp9MMfWrpuTJmUjr13cS4n3hfOGswVeP62yt
+rb+WOZlKgbBfYTV2bctdOsp1nf6ScqOCq9rqN306Co3N3YU0KgRs3uKXIgX26pRLX8t1hQpFZcmi
+FMn38MEaR1XUjh9z61Cr+SZ9gk0nKwVZ1VFIoSW4ommO58szvGEBUzUbFIdftks6CgTykj49zT9o
+kwvWaqLkWcrZVKRjN1QsRbqg3sBoQ49AAR75tMFotGZ8+FmSQ3n2izdE8u94t+gv5XdxZ0IbiFmS
+pwXOuRaUYUGjkZRK8RZVXjCNRau4PnJxG/Xq1xUm1E5OSUUtAQUazeRr84ZQ73t7EJKq6h+sW84V
+wLZsLnBLwZG8X2rZOWMEDwCMmiH9vqxxlk2Yr7SBzYOE5p7sj9F5tE2Lt+lt6VcMjzYtFUMczWYs
+e8UVTmSl9SL8iUvfk+hLcmPFYi5zelQEUhQCwFE1p11ApRmJ3hCjeTWe72M2UOP4HpHmrqAmlT1P
+sVryzuYWx5f9vV5M+K6WqI8HDnu3Zfw7gazJy6/NuVRTI+4N6Z7IXnaGBu9Qe506Pk82oZ0PSX9o
+TcEYc9MY8xvGmO8ZY75rjPnLen1sjPk1Y8y7+nf0h33Glra0pT9++kk0hRr4D6y1XzfG9ICvGWN+
+DfiLwD+21v41Y8xfAf4K8B/+/92oqhtOpkvWrWVfi5V6dY2jRT6j2mcQK8fvVFSK2Ety4YyL9gxn
+IBx16L1EUiq6ry5Q9wFO0WA21YO1Go/TN3i6BGFQk83EgbNeP0Hzc1jbhECdak0h/3++WnKhWWjr
+WcrlM0H/LUbX4FCeMXAigqFmaGqjDz+dkWo3hyJY06qvol2d41mRbLU6lto1WG0lN+rWZKoJrPKW
+nmbf+aFDpci9qWZatalHqQ6+Niuo55vWzh3CjVMkfx/H1QakQ9F+goHFKJrPs0NcLSFXeDVrbYCS
+rjwSdeB52sLsYp0TpLKG44MMbeeIG3o/qDxUNNRa/DTXMOx7szVDrSgdujm7E5nrtGrpV/K7rmeZ
+aLivo41/kijmpesyhrBfEFotjup65Lp2/n6DUQes0Xv5TkumGapJu4PXbqR4F8SfTWQHZFpVudjs
+bzkjRZ2xnX3m3/o1AB5czLDepoSchibbLtbRhreMQLUDxw6gL/sTZAe0sZbFKxTF2q4w67Eu3Pqq
+HFvRGTPWCtvtzGPndYFbB9fF1zZe9xhpkVsnGDNTdexB3XJN4eht0DCeaYWnD0l/aKZgrT0GjvXz
+0hjzFtKC/s8DX9av/U3gN/kRTAEMTutDXRLo5vc6EYkCVrykpdXSVY1tsI44FVstJGGKHn1H1Nb+
+yyFxX9Wo3RHrlToMK5f5dNOxSV7uYDQm0MIrTR2z0iIWlyuPtcKmbdW5ahl+oU4oL+zgGu1VGGW0
+mnG3XkxY6uYOBjFtprDUnja/rdurtvSrqUOhICyvqCm1v6WJFL/vVTgaZegnPtRaVq0NSRRvsDQh
+qXZMjvS+oeuSt5sS9xbTkxPvNSm9kQKBPv0GkSNz1aLFZEdLGoVBp2TEemCLfM3phRz02uvQq7Xo
+hzq0XdPgenKTxfIWB1on0ZoA4yojWOcsFCpeaFaj61ZEMznQbs9noqZU4AVYbfzShjWJrnPkyXj9
+5Acdsta2wM0V4192cRTLEbYJzW6ka6tVksuWstFK21EDWqfTCQqMo3ksXk3UyHqFXa0pee7Q3ZNn
+u/WcZ0uZ+K+dFvzVQF/kaFPUx2A02kVhr9raG6/mqmm832AKfe0qcRjX64o6k89+v0s9fyzPvtGj
+oxiQaeUDCvZQARmFPmYgYygWF8yPRbA4YY4//4587r7Mf6U5Ex+W/kh8CsaYO8DngK8AB8owAE5A
+K0/+v39z1Yo+8n88cMWWtrSlj45+YqZgjOkCfwf49621C6OqNoC11hqzKfT/QfrhVvSDKLLtusI4
+BU6gWXuhIdFGH2kKUSvxbdt6VyGraSAx/4vzOc6m/fx9j7NWuPxoOSDoifQoL2CtmsD6Ur572GnJ
+VDUsqzNa7avYtTW2kGevmHD2VCCqEy0u+tqte9zcV9VvGRKrCm/dltVSri/XKW5Xi2zMFEbrNQRa
+ULSXWTrav+K8qWkDLSrbblrb9UEbmfhxj5FRzWWaYIPNPVrG2uru7kjCVfVwSXymFaEbQ6PIS6cd
+MveFPx/6d/C1n+Gldq0+mXkkGleP1jBVPMWT41Pefl8zFc9WfFULt16NbTa7atXe2AmJVnPe3+3i
+al2AoilptRKL09MCMe4Bs1Zkx6Mso9HSc0FVgVa0njWGVtu3jbUl+9Nyxvcey7Xd3RjUKWeqc861
+zgZVyqoVrWCt3aVnGYTab2GcOKz16GctDBLFuNiCRnuNOFrcxO1XOCvRHibLS377sZpSRY7RcKDZ
+wONtDJuq42YCzaaXQwBqStnqnFrDmlZ7RPjDI1x1ApeXhslz0bDixKenRVqfFSumM0X4KqLRx6PS
+BkeLpcviQDSW/+lXfpd3v6fn80treuUfYyt6I0DvvwP8L9bav6uXT40xR9baY2PMEXD2I+/jgtdv
+SS9bLueiApWTAYtYXu7xZMVqqM6BVUmmRT+mU/nuqk05OZeF/OYs44ULecH8JuGmYh38OyN8rcjk
+aDrt43cecP2Oxsq9OWtNpy3mGR3tpuR6OW/lcmju68H+hF/hK9y3S0ae6cs9WXEjUtUvuUGpmX9W
+vdM26hNqCrgb9SjU5A6KC6Zq5rSbQzxo0KZIdLohVqvxWCeldbQGY9gSa2n4RIPldZ1i1NSoXBdz
+V2PX37jEXcj4n8Rfold9T+6dfFnW8nnNk6WM98DMyZRBFJM1x4rFzy6nLM7kQA+UOYyjXbKh+gaq
+6dXB7XorWrWNUx/W6tOONqnV7TnvKHZh5q45VuDUG0e75AoFn0wXlO/Ivp7uybWL5wsaBZPdfBLx
+Ocleph3u0Xska7jOMqpzMXNWKqMKKpKO4BvodfG0tmVTrynnwvStbyi1QE2uRWFM2zB79Jas24Mn
+V9D1bucI26jtpRW08CtQUxKnc1W2naoEZVht417V2zQb/rE+B638nb73LstMMjh7D5YstCGvv1gy
+HwiWYfxEnjH61KuwAV5Nz4jn2rzYlPzfv/4/AHB491Wenv5pfhz6SaIPBvgbwFvW2v/6h/7r/wD+
+gn7+C8Df/8M+Y0tb2tIfP/0kmsKfAP4N4NvGmG/qtf8Y+GvA3zbG/FvAI+Bf+VE3qhrL8bxilhV4
+iXDBN9eXjDXKEKQh402MuS4oSjUVNMGpjffYtGjejYZcai++3W7CsXaxfilvoRUk2CIWjnt/cc7i
+vmgP1/p7LOdyk7PVgrArkiRPXU4rld5qtqxNxVS1AGffx5aa+JM1LNG48npJNVfHprYw64Qxbmdf
+x36Jl4npMgosa+0+Mz3SVvSnLVZb2jnenEbFykV6xlqrCzdOS6FoyaVmX9aXCZ5GEZqypH1PnWDW
+YrW3RPMP3+f5ZwWOO3xZHFlNd8aFNp+Zeh5W57x2a+JIqxJ3O3RrkfRd7WMwuL7DnpF5pkVLqa3N
+ztMBXZVyF/M1tX5eahz/66sV4Uo+z80UO5X53Xk95vGpQtMHFY+0NkaxEKkbrFJOj+Veb9ozdvoi
+pQdHId0jcbq16ZpAM0xdVb+TwMN2RGMLex0c7QHill3yTLuGL1MKTUxLI/lbrQKe16KlPFudE78k
+97u38wJWIwM0m3qQBuwGg+CC9tu0rUurma1NXVFtFIiZapD5KcVziaikdsFME8LScMnJt7Ue47Tk
+kUZuygNZt88sX8HRdolm5yZvPpD35b3LjMoRWLX/tyr8VmtqfEj6SaIPvw2Yf8Z//5kfaxC+z8HR
+NSarikpV3CpuyI8VXvtSTKVe7ZOqpC50E1L1CtuS5vAlAG7v79EPpCHH6NWMh5o6nc/foVTQyz/+
+HWEKw7DmlZ+XwzObw0S9+l/NCl5Rb7EzGbIM9OXcNBYhZq4H2sVlpKXDi6ziTMFSnabiQlufJ5Uw
+kD27xsvFmiqnllbrKq7OHdaqakYX2mg1yHAKeUY98Ti9VN/IuiDTrD03jDjRhrU759qKPjWkaqJ4
+lSH8aQ1v/aM5VrP68hsPKbqiSl82Wsx0Z4fZWnsVdqe4c1GNzx/NyDVFw/V7aECEntrnXm7xVPVf
+uDmB1tVsTrOrrlbr3FLUCnZSU+TVvQLzQNT9bBxyovb1z/zUn+LZUtb7e29+n2gtexzlamfHXbo3
+5Ng+Wp7wXiH79+mkT2slxDtvIjwtGON5Wva8bqnUb1HZkErnEbYW6yqDGOT4Cr4KzuX356tzHqh5
+EU0KvvRTYq+cJy+CFsW1joY/jQMKcKNpr/wLJsholalXtb1yO5T1Q/n77iXNPVmXnned3TvyuTr1
+iGMxsc6KBd+/L+Morsu6feHnruP0xJc0O/4D0kSu/+lf/ixv/IG8R/fvuPyPvyqm4oelLcx5S1va
+0gfoYwFz9rHsuy1l16dUR02bLphrluBLO3e41hUOPMxT7s+lVPnX3xe17+anXuCNT/8SAO6LKTcv
+pER4/2bJuKeVnZu3+Opv/hYAZ6qqdu4lWCsOzH4z5GIuGsROVeP0JcusObCYt7SKs2bW7VZdbu4K
+126LQ1APsJMsCbVkVtcNiLqb0nLyd7J0yJcKXFm6tBrHnz27pFDoa6ll4eugwl/JM2ZOJBBawC9D
+rh1qd+UmIzciEZJGIgvmwMOfarXqtqDpyDx854JKNQy+vybcuQOAVSfq83cfcbPVmPj0Cf7jhwA8
+fPAeNpVnG2dNqBGT55eyN7c7Hida55LIJzqU9e7vumQKzfZ8ONeksbk6wt96Zvnl27JWT962JGpq
+7e50+OxtEeP37/u8+0yk31xNuz9zaOifiAk26tXYx/LdiyhlNZdoRjw6oFwpSExNokVrCbTd3DkP
+CAbaHu72TfZ2Rzp8S/pc1ujyQjS61fox7/+uOI93YniplnP4c90Gq3UtrCPr3QbhlZQ1bovV7FJc
+8FQjc+uC6SM1RzU60b9xh66aaM1JRrzz0zKP1ZrXv6TRh9/6Bgut/XHrBclgbYdjHC359/g44/xM
+9vrBa4f8O58Tbfk3f+3vM3gu8/uwlRo/FkzBuC7+cMA6X9DqgV7mKb+nCK3D5VNeuPdpAKLc46Ub
+osJlavd+a3bMFzXfYXT4BstbmreQvIa3pw1K71esFA7xxi8Kdvx2k3CgWX/Pe2c8ONZCLTODo1WD
+FmnE/h1JjS61G4+5GzPoS43DAyfn4VKLrJyMCBXI8sytGCmgar+v6c3J8ApY1UwtjYZDo6N9Gs3K
+dJ/JnCfugpl6rM8DSzNX9Xv0AyBX4cU81rLl7MvfnusR7YlKbbySVz1Zi++0C6yaKGHvGoVmPrr7
+wgivnb9Emwng5VqZMPucmGPX/BT7+B15hjtiUmma9FzGNilzUl+ZZbCLcbQYbbm66sjk+NCqz2P3
+idxruGr5X7+jdklnSKml3+NPvMI9DfG++jzj0UzGtz6X+f/BYsH16zK/O50Gb0eY4iFzylD9C22M
+fySmRHlNG8YWLtGmo1hiabR5cewFmFDWs26gOJRxLjU0FH7lGKO9SJaLnOpYgED/sH/B65VU7Wp8
+zcR1GgmlgTAELRiE04K2rfeTkNGn5DzFc1mf6vtvk11oKfdOj/Nvyhq5zwoeZt/SMcfcvSXP+ewd
+qcXYCR1mY40M9Z/yZz8vc/4Zu8/vNP8pAGZvTLX7UMYhkdwfSVvzYUtb2tIH6GOhKVSt5fm64maw
+x2QkKjPZEDMRrj2f9Fmpw6k/cnAV1PSpn/sFAJx/8B6XX/k9AM5OnnOwKz3+zpKv0UlE8zj+6ne4
+d0tU7EFH8f5Jwnqikm9aEDeyHGkEZ5lI+b17NxkoCGdX8QiV63P7UFQ166/Zr0QNPlmdEEZyb6ct
+8BT7HmlMfDS6RjLSRi7hivkTkeI7yR6g9R/Hgqso3jnFVbz86fyMleIGykmfsC8S5mI2odQsuSf3
+Rbpev7fLWv2hPSfm6e8/0FVOcBUebItv4h2LihpOJQrx4u0TEu7IN4sVN7TrdDWE5XdlbO+9f8zt
+VK7nfTF93L5LorDd3dEeSaz5E0FMo7UcisJjkookPH4oe/Bs+YBZKWZAnGY4le51tWZf69C51wNC
+Tz47ruzTdN1h9Ejh6DcSDh5uSrEndDTKY6sFqzMZX9CVs2ICB+tt8h1idhzZv3DHB1/WZXWypNA2
+7+tLEavfmcJz1XhuNftMLnS9z0vsv631ELTupOEHPTppUzCbhjQh6LOdsCVSkzU8kL9Vd0h9oYV8
+FnO6n1HY/GdPKb4q47kxighflvuNb8uepasJfisazy/d/SKnCrFPRkuqd/4LAH7B/02+spLy8h+W
+tprClra0pQ+Qsfb/E4X8x0qfef11+w9+5e+SVjm9lTjt/P0+6wtxKLKsaEuRJFXrsV5rf0BNcCJs
+qBVG2hrwtV6CCR2ySjhtXlpKTXLqxoqObDrUO8Lly8kaf0ck3vvzjINQ7LNpbTi4L7Z2ZoVT/9W/
++Te40O7RX7p2iy999lUAei/GHGnFHy7Lq0KiVsNxMRBq8lBtQ9Yz8R/k6YpGE3tm0aakVkhbib2c
+nl7yRH0Kx6akUBu3WDSsNNtvGChS0q/xFd9hkz6TuWhK75YBWg+Ug8EO4Uie09ff58WaU8VeNGVJ
+JxI/SH8vJGy1ngT5VfFXb4NeNwGb4tGhAU9hzi0RGqkkzRpKDTmaUy2Cetgy/qcy5z940edrnqL4
+njzim9+VgX7qL495eyHOw+qb4tj8dO2xUJjiwDgU+eZcWHLdd8czV3DrWJ1zieezUm2krSylkc+m
+gUxh2nudLsWe9rjYFy2m3XmNP5m8LvM4TLjnal2HvX3+s//o3wPgsfa32EsSro2lJ8loZ4dQe016
+8zXPtR5ENbuk3tT48LRfRDjgBe1FYoIOsYZy08ojVhi3H3S5paXiRp2bAHT2Y9pGw93XuqwVA/PZ
+Fz9PR31lUddchYy74+HXrLVv8CPoY2E+OI0hnjn41ytazWFYPn2CReO8fpdCC1r47ZJWi4hkGnev
+ihXxpglH1aEZiv4cBDDSmo/pzGGhYKBSGaHr5xTaOj693iVJ5aC8seuTqNPqrFPSjPTzH4gX+ijY
+JdpTLMBrMQ8S8VT//LWXOOzKoWCwJlaYdqNoFadx6CiM1kQr2pU4nPJVdoV5bRtheGG8T2o33Y1q
+hu9ITcVrk5bnWoTEPV+SZnLY8lgOwWqV4ahzMc2X9LRq87A7By2N/qg8454Cv6IDcZiapy1dBV4t
+8TFafCaOeuxuSqxVQ1L9zobxOqGLVfBOWZV4WmY9il08X1E6UY1faL3CX5Rrb3e6/OxLouLffXyb
+d5/IOB/9zyV3/00B7HznvzHkY7l+UznaetVnL1ah4CW8PlbOE6SsteahNT67waZwivztOhHrXITI
+4+OMRzNxclZNg9HOWc9zQ6qRllqb3gyGp9hXtHHOpyLmqazt5fE7+Fb7cSqArI49HtfiiCzrOdeP
+JEpQrTLyQsY8afIr/2PH1bF1WuK+mAHDWzskCoarWvAzLWm3ExJoqcJ4tEm5hkYh75OnUw7HwmT9
+8evUB2pKBS42V0DVh6St+bClLW3pA/Sx0BSsqWjCU9xTi9HqvE5eiy0A+D2XtRFHU5muQGHOvVC4
+ZGYcrIYFg04H7e5GEFZ4tRap6BtCLVx6gSIhJ5ZNgd8wKwkrQY9NvAOMle8cnFY88CWEGW8qCx9Y
+XtwXCeXNQ65r0lXnNKajMes0KPAyDUWqaRO6DfTkgd6zLm5Pk4Py+5S3Rbup35f/95MxiZYwW+06
+vKS1JQ5eCejfF43l9IbBOZffDVULyC6mvKPzX5mWrvaRGOYO+UprTngl3lyh16pGT/waJ5M5XR91
+2Ff4sN/fYagJXZ0dl1bxBjOtx+A1lkazNhsccoX5xvhXZepGrmXeyL7emsrfxaNv8LUz0QjuN2NO
+M9m/V/7bx/y6NJgG9yk/r2acp2HK7t41Qg0tRvUut2/LGnbsDr6q3cM6ILou1xs1NRakrLXVXbZ7
+zrkm3l3YBk8L6hg/JbvQHpkrLflne3zzwdcAeG3qUX9Gkos8f3bl/B1vMnTLXYJSIdPtgmxmdR8y
+5ooR6TSWUFGWGyRljzHXemI+3KaPGWnB18bH0V4VSZiwUhOsq+0PXTcnVVj2PL3km2vF5ETf5cVA
+YM7uy9cw5l/Aas4WS2krEv+C+lQm3NYzUPu6TI+xqrat85xI+01WajeaeoSnL03QCUErFjVVhzbX
+gizeVXoE+UQbpBQ5map+pl4wW2osOf0+3Z0vAlDvFnQeSLcd56aW23b28XdlDHu0jEcChDE7FYWj
+amft4WjeRbhx27QtnKotG59juloluNPHnmgQuRH1s8lu4mmFHW8Fwxc0+67xuIW8KJylmH1VDbWZ
+yuVwl0orQVW0VAqwoZ/DRLPz/F0qLQmfd+SgHTUeaV8O6YEzxu9pRaM6uPINBGGHWg/0SBmecSHS
+/pl5nmFamV/p2qvenXWekmgF7kcKpKlMyvc1AvJC/bs8065db/2fBhRiHrQwS0UYXEPAODc/c8je
+Shj99es3uJvcAaC7N8PTyn+xb/C68p1qrSCkbJ+5MorkYZ+RkbF95+kxU02pT5cVgVZvyrRTWfPk
+EV/XJV72XuLz54Ib8D5xl1prfoZjOQuGNU4jsOM2ekxl5TwN3Jxamwn31yHRSEyFSM3g/ZeOOLou
+UZluryXydN9Ngx9pnc6wIc61pHwoY/OcAF+jT4u1g3n0dZmTHzN8QfZnfz3E17T8D0tb82FLW9rS
+B+hjoSk4niHe9UnP92kGIpU74ZD0UmR761tqdTRO/JbEl+vJpgFMXtCoVmF6PqGnn6s1RaSZc9WC
+bKEQ4k1JtNzixuLscmrLqtUybb094q6o6MuTNc1QG44oyu3O51/hQtGUblhz70gkcJT08BWTYLwM
+g2oQiNpqih9ky1W5hVo87tQ+jUqVjToYmxmNSoG6sFe/Szo7VKpGhn5LNZW5pqoptcawcyhSvpiX
+9I5EYi6PS8KB/O6TY0NHe1IMEnUikhI5ImmTnkdo1a4qaoxm4rVeja8OStSZGTkOoUY+erHLSpua
+2DSjUk99i6XQ9VppkxX3IuZPHkp06d2nHcZjGc/0dMphIN+5m7j0fkY87TcGcm3frTm8LWryzb2A
+fiLaWBIfEWifCT/JaRWSHmjPhqizxnPF8bcoWw5PpN7hk/6E2bmch0lZkGuty0ad0bVjWGhC1De/
+NmOAOAw/vbemq41hzrR2QxCuONyRezkWXG9Tem6Bm4vED26EDHSd99QJejDYwetqxMzx8bVZURT3
+sBuHaQFG60d6WrLP2gpPy9W5vTOCSuY65T5ffUf2+sujHknwIj8OfSyYgiRbuvj2MVWqmWfGYLTm
+3jyDlXqORybB12zFRot6BmFDJ/xBT0F3k7XW7RHW2jnpEnINSVkt9ol3QahQ0zqICLV8+aCO8FpR
+AzvByZV3Ol2I+nbYrdnVUuw73og6EtVvz4loWlX97AmJNsh1Va1tTIEOE+uscRttFtOJMPrsQAuM
+2GCHRlXY2EyuQDWkA2K9x17h4GWyBpetAn4G53S14cp87bBr1FfRN4RGftjt7vPqrnxOu7JWPa8i
+CrX5TjIg0xBaWzk0myzR0mwqDWI0x8M4Db6aDG1dbgwbitBhrTb1hcmuqkU5ldx32XOZXsi307hm
+qaZbXXl4qr+ehwGfLmWct1uJ1Oz1u9xUYdDrN3S1jHrHtITqV3H7ISbbVLDRrFu3L/0fAZsNqF+T
+82TjVxjtCdPuv3fGe+dixq0bzROpW7qbCkq1R7GS7wanr9HVELW+78SOC1qk904EZwr/jk3Ji101
+FXqHuCNZr8OerPfBqHdVmBgD3Z6e306Cp6H40q+uzCN/oLkvxYpiImds3+2T7sic05OM1UxMyLOf
+esYrtVai+ZC0NR+2tKUtfYA+FpqCsS1uXZC7IxokHp+vfRbLTcEKB0fNA7csaFvh3LE6XALTIdba
+4vFuF29Twt0raLUgSXS9DwNtHPJA1L1o4bPWAiHBMMRbqSp+fknQ1aYtcUI90VZp2rsu3GlIjGgH
+gfkXWfAAACAASURBVG3R5tdkVUkv1MQm6+FolMRRLITjl1cFOdy5j7Ov0siNCDbl3y60NPe4odRe
+9hkljhZ4YZjhqiP1/2HvzWIuy9IrobX3PvO58z//MeecWYPLZZddXUZQdgsQGOgXC6FGArcs8dZC
+gAQN4oGHbtQtIcASLRBqhPxg1LQaGowtwN1tu2VMV9lVWVNmVkRmRmRE/PHPwx3PPGwevnVuVbbK
+XVFO24Slf0upjLhx7xn2Pmd/0/rW6g/GuLqSRFpgxAqGVYKyoCXd8FDFcs2RG4Ik1nB73lqxu1MU
+i4YDTAjiMcbA4Rwnqkaf14+RQm3JO0lZOa8x0PQfavhQZIH2Cgd6QvIZv8GK8OAViQxOpjnO2OFX
+3mpRNALMsV6LFev4cc/AuSFzP5rIXIwHfXgtM/LHGsFN+dxzK2i66+4qhuqTKp9Yj6Zs4CdyH/3J
+AEMmq8PqMT7F+7794w4e/55UJSqqY2uj0AQycZuOi8VUjnfarqBYgYpmlBSYJqhceW4OVYIRvTRH
++WiJuSmd5Zq2vSKoLV0W6FEMxncc1ATchQkQxGK3w2gEfh0hOpm+PjyCrYpHC1g29KH/FMNtgUJ7
+pz3Ug055/PnGC7EpWFg0bQGdVygXnLwoB0WIYFvAY3bdWgWH3YM+3S8vUPAibhrWheGftQ2gx1IN
+MHWMnpEFn+yK23pQXKGmBmV+UmPGkKBqErTTtwAAfT9H3fGgx/Lw+0WDltoL8yJHj5vFqBej4gMb
+VhYI+TvV0bfvQXnyEquJt36Z9CRE2+zyz3zBjisYdjXarEHuM99RXcKUHcKwhOY1pVTI0tE2qpp9
+CW6KlA/3IisQECyTZCWciTywLlWc+pWHgCU2x2nh0BX3Mw9gDKtUgLaVzcCyG7CuGxhuGkpn0Hyg
+65FGvJQXfVK28Ddkzab87mKWI2BZIy0ttojym2qNmCjET/e3sEMNSb+r8JQhDNm5zHgEbcSl1mG5
+7sq0KACH4J2aL0rToqU4rhP4CBcyR1vj3lpfI/fH+OceyDX/H+RobOsWqmEVRcVo+WZerC5Rs7ya
+Lwl4aivYlXwW9xxUJGONHIMg5jz7HoJQjjekoQs2grWaWOxVsMyH6GaxVtzSCnCYM9Fsl/eHEVwa
+Hs8P0JIU15trLK+k3+H88gY27/1AQvU/dFyHD9fjelyPj40XwlNomwbFfIY8uULJLHWWOihodTyr
+UJTEHvQ8DIn971SNfV/DsiOxcTyYnMIpqkLZslPRbeEEHR6c8NNxgdUDCVfO9Xyt3mR7PXgkTinc
+HZQUjIlIj2a9Fm4h5+6NDXpM/GivguaUtpFBHUjm3LQSUmitoEMyChdnwIBJwOM58pJELuQfRO8M
+bdrV9D3McvnzaGJRUoPRei4sJbA8JtyyfI6WiUFVxjgjZV1TGcQsc29u9OGRGEXTxYVfQIcMiRog
+oNBJHSRoHSbtaoWCgjFFd9y8gmJCVZUKLS2007Yw5DkMVhFmVH8es6KyvaUQfCCe1O27E/yDUDyo
+2A/hdsI2rkJLxS09ks96egh3k+5zbwM6JgFMfBuGFYMyDKAzKkJ3Qi/6Ag09nqBQ2CA7eFE3SOlt
+vRlXeEal6M2h3PPJvF2rROveAj7FXk5PpygYSpWEIjfNAv6A4SFaWHqTiAAQe6AdHzE93B6p4TfD
+qNN3gefG69CsMQ2UofVXEdyYzxbX19MKLY8bjWboM9lcfP0DPEikOzb2thDdFi6S5x3XnsL1uB7X
+42PjhfAU0Fo0aY10dYEqkxjQbTMUDAcr68NhOTFuxiCHKxqWjcqmQVexa4snqKkjiCBEqGX3tOYC
+aLrGJPnyHX8bOpQd9f7JJVrKwk1uaARb0gRjbIXRjBoIrM2PygEqYqkngYcee/2ddmN9HSbKYdg8
+1JJR2DY5jBGLaJfbgKLeQFujSTqGalpa/zOo98hGtPgA0VLyIc3sFiqy+FiU0LQqmvwAQXiGFbtH
+EwsYStdpp4Bj6NHoAu46ccs4u+/CZSyvrYbxuqYcFw4ZnBuVweZyTyt6Ctrx1nqOZZ2gYV5GOz40
+57v2Fqiojl1uyzWsKhcbr8r9n6UZhgsyUimNkEjBy6yGWpKybMBi5wYQVswTTSbQS7GUyuSoOjLZ
+soQlfLVmfqW0CsGaPm0GB5I7aMtj6E25ptlZgc9+Qc736LfpxZkcDW1nkbeYncl8B/4cmPMYjeR7
+jPbXiM/QiUESKniOj3hIz9KM0CeTdBB20nUuFBv32qpBw9xP4CsoNp41QYGGyeFAM3/UALbp9D9b
+RJTCW60qHLwj3mnffx9bb/0efpTxQmwKSrXwvBwm2IabC2ioRgiPLxXiFhWhtH2vhR/LQ9p1m9m6
+gJvJJHijIRpqBtqrGdSmPAi16q3p1IY3JDNrvUfof05quP7vniLYYW9DpeCQTltvXGBFWK3LyoG3
+UcMPGc4UPdRZt3slMJsdHVkEn7RpnRepw3wNR1bZO2sW4MxewJIZWBu689UKObv+cneMxUyATrZ9
+gCQkAUqpkZOD0GcN/vjiCqecl7o3QuWKi661Qkrcw+pwiQIyRwMCnZo6RLPihhyXqJgQtFWLNiOT
+dBBg1TDMYQv1SLlrWvPWV2hISKPaFBk3CEAj8OXYNyioU8VTHDz7EABwubmJZiL3OpgB76Vyvkmi
+sMEqQt2fcc1apCWJVw7mKAmGCmY5DPkoPd2gHQq+oeNPtKUPTdBX6Y2Q5HJuBA5GV9IaX2ch/t8D
+CRsVQy2jNVr2DgxKhYrgprO5RdCXOaCiPNqsQTWXdbgqS2xwDpvhBCSXxniswHwuwk7CIFlCa26g
+ZYKSz4jvDoAJQzdjULFzs2XYYYYaIG192IRwCWAbjzQqburTJyvUT3603odPHD4opYxS6htKqV/n
+3+8ppb6qlPpQKfU/K0WkyfW4Htfjz8T44/AU/l0A3wXojwF/A8B/Za3920qp/w7ALwH4b/+pR1AK
+cAx6ZokpXSSjS7A3BWplQNAcVm2GmizBpmuIqlwYRyzJMOrD0QL3bLwWl6mQYJ58PV3rOB4QVXfx
+/gO8/1B2dmfSoKVHUA8TZA5l2i40QsqDNT4tQxJjRR1Br1mgIWdLrUYAXdQiT1FbOUZI2K652oEe
+Udber2BYfnXwBpQn4UEuRhDJgxTKZcKpmuGE5c3mQwflmNoStcWSCcM8kvsYOJsoKXU+ryucUy8i
+VRZlj0QtPRfjQkxhkLMTtfJwlhOuuzLrDkc1iOB2pm1RoGV5LqBHk+kKmnqVRQlUJIYpTIuwI5ut
+a+havr9sB1y7BuHkiwCAnfQRrk5krparCqCFvWU9pCu5/vZIvIDydoQzkpNcPFvh6kg8RD9LoIZS
+htt6fYydI0kme6Quq/IcOpZn5Lv/5zHeOxOP9DtHx1ANSU90jrIU5KSupIPTURqWds31PeiISV6b
+oZnLHHUSc65x0TEXlK6BJh1bU1ksOC8nyQJFzWR6T9Yg8xrsB1JyrR0PPruD27gFatL35dUa1ap5
+vV7RA1wiYNvvlVa3tkYYMeQ7OTvFt77x2/hRxifVkrwJ4OcB/DUA/z6l5H4OwF/kV34FwH+GH7Yp
+aAMdjVAcVajarpfxEslVp9IzR01yEhsP4JfyICxSLoHXok+Irmq+x9VXtCWe3ZeX6b33von8TF68
+B0vpRMzOAyy1LNBwuYN4JAvuZi4CErjo1kPNTsoNxuHn6QIuIbr+pIDp2giLM0yfyuI7VuF0Li/n
+FuPvIn0P9YixfJ1ie0cy/H7YwhnKNWdn1IHsV5h/XfpAFrB45129/p1zwHhkw6Lt+BhJzOjXGdpY
+7uN0WuCYrc5tjbXO4WJgsTjmhhVRyn7gYEaa9eZZCrerpS+24TFr73oNlKFADTeHxSpHXchmkpyt
+UC1lPguvwg4Zmm0dIGMruk+F8btRgNsbcg1fO/IQs8X76HSB0HTxT4XlFTeTMTesC4X9l2SDUEZj
+gzKO9dEOrgJ2RD41GNySdXfZP1O5BWYPuQ75KSYrue+XxkPs9WRXH04yzGQvwNvsV/ngVCElXfye
+a2Fvyfp9eAbEzGklzEX5bokxO2PVaokZ+1nqozkOmSc4aCx6kax1HMrL/6nbt2G4EWztDeCypb5M
+HFj2qyCpMb9k7043PTcL+OWE67iAYefuzkuvY2siHZPvPpvi/UOGIM85Pmn48F8D+A8B1giBDQAz
+a20XxDwDcOMH/VAp9e8opb6mlPra5dXzMtJfj+txPf6kxx/ZU1BK/SsAzqy1X1dKfflH/f33S9F/
+7q2XbZ1VUOoK7SWRZHGFnOrSp8fn4B8RbpQI2Jufk8IsSg1KZs5TlUHPWduOFfqbEhK8urOJJ664
+rncyoUx7MHiCu0RHeghhtmS3vhcOcKsvIch5uUDAHb1m1nu7nGAVUeHXtyhpYVNXAalYKD+fwWcV
+ZE625+PlDP6BWKDRHR8lUYzNxVMsZ+RHJNWaeuDgnROxfE+LGeZ0PzcrHwcr8XhUbuGSJabapUDI
+YIzZlbjJTdnAYdNYhRKu7mi8ZjAeM+e8/9Pjc1yyQ7OaVjCxeD+7mcYePRo4EcqFzMGC5qRoS1Tn
+8tlHHx1hVctcbG146FHPMW8KlESFWnYDhuEQYCemytU6AVvm1Rr+O++5KIhe9ByZK8dqfPRAzHl/
+7KHuMvURMOA96chDS+ufrSjTVxpowpnv3HoZo12571u+hypjByosnFQSjYMzWaeJewQ6AphHHjQ9
+MpMn8BXl5hg0tNbFlNWZ2PcRkdJvd+BjSPGZkTPEaS5JY5/q06vkDKuUidskQLhFjIi5xPlTadC6
+ejrFgiFbh9MoT7exMSRpT+Ch78jvJr0d7LMB61vFOa4umFR9zvFJBWb/NaXUvwwggOQUfhnASCnl
+0Fu4CeDwhx6prYH0HNOrJaZW3ER3YZGRdPT07BwPZ+I67TcZdCSLGFJIdms0gZeIm9UWDfQWSz21
+j5gVgHzvJfihPAiHH0me4WyeYEnq7bIu8LMs2QX9PhbMKSyTY1TMqPv01etohiaQlzdZ9ZGxw2+o
+G5y+/wgA8ODJFeqb8v3OjdLHFYZjeVj3H1XYE3IcVAOL5X3yKj4V8ZP5KMQfsIzQTjS+yzjy88Ma
+K4KsiqlFxnLpxoHMW10XWIzlxXtsYsSehFqNarCiA1dWFjlFU6cUdZk21Vqlqq0semw5zl2Lzu9z
+owCW8OfVMdvBTy9RGW42gxDHJ7IhHT27RLohG4EyLZj4BziXhbGYXlGKXrdg8h29noeE0F0n1thj
+KW9rR9ZmevUI7z2hItdji9mSIjNbA7y0K07pXmjgbDHvNJGXQ53ncCniW8YNTlcyb+8//BBJInO3
+PQ6gljQoLnMqJkTryX1EaYIFn6FLW2NzQD1R6lyWeY7cyHEz32LQcpNtXVxSx3I1uETKEHmL3ZA2
+neP0UMqzk91NbHmS16hnOWre36VWuCIZzGjaEeAsUbPnJV7uAKQMaLITnJOUqOd7+LHbsqH+xttn
+eJ7xRw4frLX/sbX2prX2LoB/A8BvWWv/TQC/DeAX+LVrKfrrcT3+jI0/CZzCfwTgbyul/iqAbwD4
+H37YD2zbokoTOI2PPmvwjQfE3W7Xi3GTjUbb4w30acXqUKzIhh8i7nXewUDcUQBtXYDK5wAijIZi
+3cZ9sdY3+/sALb7rDeBSWbzADNmFuM9JAwwoh95uMdk5bzBlj31PzXHzVUKN5yl6D0kWYqboU3yl
+v5B/L/fvIbIUKbnnIaUP3u/dgO/L55djueA09fDFN4Uco9hOMHgsHoS5UKjpRvpqhQsiuRJ2VL7u
+bOOIxDETc4GUKtCt7yJk8jC+MUFI/cQuXz4sI/R3ZF6tF0NRcGfc94Co6/a0647PPnUSzwIH/VCS
+rrf8CB6VtJ9eOVhmxAXULm5Rx3HFLkKVZoiMrPXArJAyZGhqDULPcHc0xvie0LD1WAFp5nu4sU2K
++9LDzT5d6dBHP5Br9kcKQSpHCZmAdgcBioE8Q1G1gVdYiQgHLfKcFO02xiklBiJyQr5/eo6C1a5F
+PECl+LzkNTQ5IbXtfMEWddlxVPowfSZr+wFGDGOj0F8DzXYoVHR6XsJhjOLULry+3IfdyxARur0f
+jnGLieJqIOeN3RiKLObK7aMqxftbnp7BEP5+cz/H3s6Phgr4Y9kUrLW/A+B3+OdHAH7qj+O41+N6
+XI8//fFCIBqtBZraIHQMDBuYnF4fo32KX6hgzcBs4z48WqmYu6HTc2EozqLbHEVKebemwbxgDXnU
+w81KKLiyz8sOvhpF0JFY5iB14cfy3bPyEjMyIK0OXeTEGdwk38JFOoO+oI7BHR97PuXo7g6xeSY7
++/yWxdNnTDrtixXYPYsQ7rG06OxgRrg1ns2x1CxDsc8/2jeITiUfcGgSPH5X/lzrFuZSPIV8O8Tr
+TLa+9UU57/TvvYPZjpzjo7ZCQ9ScUzcYxPK7Tw038OquxK0+5zL3Kpiu3Oh5GIHiJKZGJ4VWVAlq
+6jckpF1L8wqqkVg1Tw2SnG3iRYmUVrXfDpFwXQ1h3L1RjuKYSWUfoFwEepGLiseweYnRSix6RJhz
+vDVCRfRn09YAy4JbZoL+Bl09aFScF48lWeU7cGbiVYWbA8Skr+rFARpyWcTGxeSBzNFXqXbtxsAG
+2ZV/crnCGZmtDwON0O/gxkSxNg18YgnmSQEnl3u98XoPQ1fWZ3AjgvOso9wjSfFgBzMrXqzfxGgT
+MkT1NVoyPocoYU2HEZG5MLHGmI1d/b4DMO9UvzzA3Vvi9fk6QK/3Fufla3ie8UJsCrCAKgHbZlBk
+uPWtQdfC4OzHGENcKuNuQjPZU9HlKq1dw1l1q5DN5UFKXQO+E9ClA3NLDrhrRdGp9xMh6hWVpBdz
+fMSegd1whG0KaDx0CziEttbMzm/ZAKcl4bBVjKaWzH/jFQjekj+/+WGJW7uyYO6OXHty02LIh2ZV
+5dgwkpEuhjU8gnviDdm43KIHNZSsfnpVYJeCNG6eouVDumwVbn9KFv9b/P3NTzcYMYHnfmjRdAhs
+p0JIkplgNMKEwJmwy+5rFx7p1OFpLJayoTVVg9qVh9RXBslSXrKAUFuYGroh/6Bb4AaPu+0GiFge
+X85WcOjm1ryggRPjjJDwaJ5ikw//ubII6I73Rh48hi5Ry56QfovXWlnH1jiw5IOwmYVbE5Ph12gr
+ub6K0G7X1SCdIVS6QkCxmyjahMMuUK1WaHdkXetvyXed1iIjUc+3HRc5DUNQWijuZD5BQ35t1mQq
+sdaoeM877ga2KWbTqwPkA9lwri46tbAVgkquYVVlyKl6ZZw+hrzm5WIGG/Cae3Is1zOYlOwUhgvT
+k43uxsUcK1al1PA2bm+Rw/45x3WX5PW4HtfjY+MF8RQaoJgiW+VoOi1J5wYU9yyjPBifyK02gOkx
+PFAseVkLdUqXK2hRtSxDTgFDS+L0FGJyBCi6bX5Swb8j1vHyqoA9lJrws6sag5hdZrpFwU61TkPB
+0ydQgXgjT76bY+wKQjI43ULbp6za5i4mI7HuDrth9DLFwoiL20+GcJh0K30POBcLu0mpuVRdYBlQ
+gzLaws8MJemYuGc4JCQ4QoZQy3y15/L//+13z1G9TLRhL4KaiuUqFPD0Qkpv+XyJZiyhmduTOXED
+H7FHs2QTkPAHc60R0jo2KkUzYAm3pIdRASWFYaLNDdQkkvWSGnpAmHPso0llvkBrPptnqDJ6I46P
+hii+Wzc8fPOclGd5gZQdiNYV78eJfFQFiUvrHhxN/MMgQ0nODTUHdI+iQj2iP+sBXNK/1f0plKUr
+rn1odmWqngenJzjz8ba8GndnDt6m+MzTeQJPE1sQeZhQh7SDOdtyiYr129M8x+dZkjTw4R+Jt2Fu
+lkiJqbmyTAye1RhRF8LmKzRtzjlUcCgaEg76KNjR6ubiFQ77PjySwKrahWYJX6dTbMRybgUF/YPx
+g3/oeCE2BWstyqaGKgsYAlpalSJqxTVWbr2m2nLDEpoMv4o0aOVVjZYZWV321pOqixKJQ13JWYic
+WpIdJLoOAJdsz5Fx4bD70m2OUOWy4El9BWTsjuzL4i8vEjw6YSUiucTiki9N5CKeyUs2Ggaw+/KS
+tawW2A8+hGIH5+BlD6NKcPlh3SAaEntg+JvEIp2yxVsZXPYIn1Z97FFlaqFPcXQuuYGHHwocZFn2
+oZ9QU1CnKIuOSq1Zv5ClqdeK6Q6p4vpqjIBkI63aQEgR32LVwmWvRVNuoSrk2CNi7he+QZkyN2Id
+TDZlN/E2XdjOa40rJFZe3qfn8mGeFADzGa6rMfZl7h8XFmOGD1sjQHlsqe4oyuoQ/Z5sGtZT60x+
+XZ8iWcjGUbdniJeyiZpNhqNejXLQVaUCaJK96ECjNew+nBcoCsmPUPQLrZ1gkdNQKRc+OSjdtIV9
+iUzSIV+jnoeLrgVeKaxo1MLQQcuqjC1qxMzHgGGCjRP0fAklAxNCF6R7H/bgEGKt/RQ+afjcCcOV
+oA8HpPyrG2S59H7kqGE9EvwsT9GW7+NHGdfhw/W4HtfjY+OF8BSUcWEGu/DqGVpiE1w9RAtxk13V
+Byi5XVUWypFMrU17/GyBlK6j57eiZQYgrVdIuJH2hiVIyQDVMSfXBoa8CMHKw3Ag5mE+nyMtxd2d
+5goeG3RGTOZVTYQYrMHrcK0/4bgJ3IjZ0ShGtZDrr1wJRWYfXKEKxAr0sIfmhlxQkGp4fSaayFdg
+GoPLhPL0YY0pWZD3nRolKdSKpUFQSbffSzflGvYeL3GxKce677jQRO5ZAB6FXLZCH11Du2Y2vQ1r
+lMRetnYBsILjaoNmySx51CDyyYgdyNwHjgfTk7XxKh8T3r9qNRRZkJvEx7yQ7+eZhDBX8xR9hivO
+2MAhOnC0qfDRih5EPERlRL+gIXO3zQawpCOzLa8VgLoK4DKTWJQpmn3qeG6xyuK6KA8lzKvVORLO
+p85cNKz/t06GVVdhom5E6KR4jazN5jhDHtP1NxouYdMr6mBmTQtNisCiqHAyk2s7efIYBcll9m2C
+lt9v+DxteB78XQk1g8FtgIJCq1UDy05gXQawdL20FW/UqhaWzNCtWqIs6ZF6JbxYQrrVszm+cvBn
+kM1ZAQiUghlr5CuKqXjVmniiTVYoa7YfaxeW8WyRSRdhWldQZDOusjnmU3Ej05WFGbNE5gHoMtIk
+oPC9CJjKhOl8ioALur8xQLWQCY7qM4BlOJcbSGRi7Eaz9bXHbNvW/ktoieFv8keoWtkATunOHzSX
+2KZ+pBvMUD+R6zgMH6G4lPCgP2C79DJEy/AoOUswoPYj5iOA7E1F3mLRk3v62rcZ4/sawYIU6DBg
+GwicugY6L7914ddbvE75rFjN170BxazAPJN7yuGj7PgO5wYJhWKn5wxRVIGADEvWZEgSkqVEHty2
+01hcQXfiMkoe7L6OUbJvwWt89LTcxwOVIWef/IPKxxsLeam3yVak6ivEHXt0btYVqKrKsCBQqyob
+uOyhOdEP5Ls6hGVfSn5+jppCxrP9JXoRqdjTKxzPBCQ2PWbpsdfHii3lseeiIPHNUPfgNFSAIlOz
+LQcII3Yypi3OWal5/3yJe2RWGngbyLTkrhYUuc1DhfpIDN35KsdoJi96PNlYA+1U1aJhlcSwqqED
+F4rhg9ElQraMDmcaWsl9/MY7/xiPyFz9vOM6fLge1+N6fGy8GJ6CUjCugXInUGPZMU0xRkNtR1tk
+SFhR8J5oaEJ0LYE+Xl7Bo1XC0IVP9eRh2yKkjFewOYJH3v+1K3rxBLkRq5RFNSzhzF5u0bBhaBMN
+Lsin1iTMvKfPkBsxsXVmUH4g506CA2DASoM/QHsiu/jqhEIuSYPBm2RdrhMcOaK5Xj55hg8OxGq8
+MpTEmbu7ieKY1joosXxCzyQeYk7I70JZbDBp5ftyjsNZif6nxaJcTH1E5ItYosW0Zot64yCh2xBo
+Sr4tWzgEDdUqg/boKVkfnpF5SbwGzYIurCvJOb8OUQ4JTEKCkh5bUy1gmGUvfYMU5BIkiGnlVHA7
+abYgQEnwlvYzPDmRz5eTU9xrZf1eZpJ05+aPoXks3pRbtmsMRa0VNMOfeXaJU3aSjpcU8skabDC8
+0JsDZJyX7HgJxWTrzFZ4dk5uSiPPYTS7QEe8eZKXIJIaZlDA2ZJkXp+y9do7RU7MRmkq5JSlvz9/
+hh71IW/5Bk9KuZcT4mnCNAC0XO9gGKIlcc5AxQip0+mMewDPUycd6MuDmjB5mto1KZGZP4Lqi3dj
+HIVlR+ON5wsjXoxNwSh4Ixf1soKzfRcAUK+mKJd0S50Z6gsuFjQCLpJLZJc33oBjZIIdb4xudmLl
+wnIDcL0AXp+xGLvJZrNnKK4kBGmxQFGxczA/hGH6ObMaEWNOlzTqDx9neDKjCOqkwiGJSjZmS7id
+jLgDzNMVjyHfHe1vwbkjwKn5wwNURNWVY42tkO3VDDn250uEjF/HTYbvsIPxZH6FaCQIyu1BgfvP
+5HcfXck5dqMxNshtmZUFqAWDyrYoWQL82ukRtjbkOg1BQz3rYFUTkBW4gM+svw2BkFTtjY+G+Ywo
+IAlsz4eicO9lnqFgV+rKdeDxvutlAUuxl1VN+nnHQ6qJWPUHcBm6Pfr9GgFDxfy9Bie1uMGnX5S1
+Hg9qhF3OyCvXVQnlhDATeZyjosV4R+bFMmTcinowPjeyTEN1xK6jOc7nsll+8HCOgxOZ/xUFV959
+bDBLGIMFA9zihvzRUiNgRSxn301fTdBc0FU3LWqWPafzFe5zvqJ8Cl2QRp6sULt3+rgVviqnmPQQ
+ckN2XR/hFsOH2iCiClppJUdVtiU0eTWVMcgZC16MPFzcl03hWzpBHPxoAcF1+HA9rsf1+Nh4MTwF
+J4S3+Vko5wKaTL5OaGBLqbs2Th+GtfDLIkGWkuaMtGuNznF5RfludQjL+jdKiyXVqh2/j8Fd0r3T
+hS3KCsOuBl+MEJGC7UE7Q8jqg6230aNycViKRTi8bDG/Io2Z3UKVsItyN0ceUpbdG+KYwiDntwWv
+4gAAIABJREFU3M01LtCjqvHvlH28yQ7Nyd7n4I7kO4/vS005O3tnfZ2/+e1DlITU/tRLITQlzP0G
+ODyWRNrrm3Kun0yWmPyMWJT//quAOpXP3cYg4vGGSQSXVNiGCUxVO1gyI59lCTzKrXlmiaazlD2N
+gAm/Ch2ICUAlrq9OGmTsNbnyHZBhDknRYsaa/ILclraxmDgkegkCLCiM82NfsvjG/8Jqz6se5rfk
+eG9TGfql6i6cHtmsqxplRsq3ag5bildw4fawU7ErMRbYuJqkOHgmz4I6OETOvpOnFytckQ+jSpZ4
+bGWtl0dy3oNsBdvIve5FBjmoHzkew7v7OfkdBXzm+ftQ9Pjaqzla9kY0jYsrJmNXicWAIcEu+xZ0
+0kfFxHVQhHAIWsttCT6ScBXQsp/DGci9hbEDR5EvolggYSh4dGThPpJjfPHWFh5mXfhwgecZ157C
+9bge1+Nj48XwFGChmxpOYFERX2vPztG6FFOxI7hEhJmywYr6C23esSVXcCDxuY6AnFDbepFgRXLX
+Jj/GsxMqQu/KznljbwthX0peVlXwPdlJXwt6CBlTVxveWgylJW7gJ22Ah/tyPZPBEDgT6z+7CLD7
+018CAGzsbuAtIiQ/OngXAPDwGw0iEpS+/Jk3cd6X63lt9TPA8DEAIHj1x2VO/Jfxv//Dvw8AOLnI
+0B+xXl242KHKyFcePMEpG7eWR2LN/vkvjfH+P2L8enCOmkbeKgUdUkm6PcR0IbmNbUiybNBbIc1J
+YTZ9htNTUr71B+sGpf6gRnEqa7JYUgAlSWCdrsGngk9+A9gSNagp0RpYlmoDV47lhvuIJ+K5mdrA
+mcqjONUu3gjJNvTQ4kOyT/1ULtb84sshXqFXoX0XGeX9nj46g2K+ow0nmEX0HI/lHPOzGYaplIOT
+q/fxwaHgH1ZuiIxQ6lVRoMemuLNLduUWOVSXUgh8VGRXHjcVLHk7NsiENQti1CyFhp6HnM/ssiox
+YlJ1biN4lJtb5fK72+N9GOZXilIBxEg4PYOEnqXFAI0r59sM5J78gQ8zpa6JU6O9lDV7+3f/AINa
+zj3avoN/lR7i38LzjRdiU7Bo0OoZGmg4kfDJpZMZ7IecnD0HNUlWglojYqbaMttclS4C9hcoN0ZM
+HUDbczFc0dWMCiStLEZNEEivmUNRbafZatEwWdeYBpUhnn9wBz4Zk5d0xd9vVig80n6bE/xBx/NY
+FhitRHEqDPrARDafH/8X7gIA9O88wtvMiv/Cq/cweuUvABBSl33v5+W+U3kIHt8/gHNfHsBNbGJE
+6PbuZ3vQhXznQCdIr+QhVKRJ+xtfPUVFCrJZ08AlrZg1DlpS3B8phVNHXqa7TF4ps4GQClgbwx50
+LJvsNF/CofusihAV4dhdm67T9FFSLSuMLTKuk2qmMFRLWiQzLEh7Z9quc7SFakkh17uHWSPu85em
+NX6FlPEIK/yLA0n3P6Vc2CvTQ9zpizaiqVIMKOE+HoxwaOUcb9y9g+03hdKjhmxYcfMG9EJCkHLz
+HjwmDH2nhCHH5LyxuCxlDg7ZLp/nLRxf1jovMrgk9jlVGX7mNUn4PjiX3oLwaYZyXzaY6jJEj+FY
+3vpISZtm4hI2pFAL3frg/BI+VcU3B5sICCiLywD+ROarDvowXQVuSOBVW4MNumjmNfShcFcuygwD
+hhI/8akRvkqKTfxNPNe4Dh+ux/W4Hh8bL4inoFApD65pAC2WQcclkorUWDMPmn34ejiAZuLLIatz
+UNdoaP2r8zP4I3GvlKeh+0y0tRG2SKhiByxNBg5KUm0p1cBhCS3zUixXcoxb+zEcJWGK3iYnwGiM
+T+1IAqv3hQo3nsq1ffRbBVYnlILbuYA/kc9HoViSL/7cZ+D/lpTY0t/7Lbz/4AMAwJ39LyCxQoDh
+FuJJ/P5vvoveltzf6/kUQ7qM335Y4ORCLPN7H66wTMQy9wjzfs8fY5PlS1M0UOSYG/kRbtyV+x9u
+3oa6IInpITkp7uZwaNnd3ghjXyxUnMaw9J/reYkB3dyArmy5W6K+Ig1aUKNcivWfroATIgjnZYOC
+OguWHa5qGUANpcxYL2YwhFU/vnABJuL2nRYV594cU+BnNsRsWz4bRwbKiEez98Y+mo+E/LS+OMZ3
+fv3X5DsDMaXaWkQ98QgeH1xgsifH08UZskpCyMOnH2HqMlFIL8YxZq2JqSebKBxx10cpkD5+LOdm
+J+OTNoVHjoxBACCW+3NWVzBcPy91AcU5ZOnYqgb6gBiau1uwJPtp2xLISQbjxjBb5LsIiN5NMlCU
+GlWzwLNM7n8aRnhrKJ7S4NabuMMyN/B38DxDWWt/+Lf+hIc2yjo9oFoAeIUffvR9X2ix3r6cWnAN
+ANZZYXgGltlyaL1ui/V3IviX8qD7mz5KdjY2t8RtWy6PgCVj9cBA+bJwwc5r2E1lMdJBDxNSu++9
+KaKz3/6tX0XDOM2FAVx2YqJC29LtbDK0DE18utpWKahOdxEKmnFmBbIIAajZ1j003ppR2LQ12DiH
+qijQaDIOaw13LC/9l176PADgn/m5L+Izn/s0AGBr+xYuPhKX89d/+yk++tp/AwB4VJ7h8EOBSlcU
+7rWwaAifNUpDEeuhYdF2sNqPLQlfcmAdcwMW6+dPq+8RqxiLEQV1bpEt+Y0/pxD8LhWU/uYX8D85
+XwEA/KNX/gEuyZY0yQ8R3pD7S74t/96kJRyGOY4bojiSUErZBA6Vl3LPwmUIslrJBpJcJrik273T
+jgHCwyeTMeIN2TjCW6+hYfWrIFX742yOmF258yZD79ljAEBauvirv/Y7AIDzRp6F7TDEeFNCitub
+29ghyG57Q+Hma8J+tD3XuFx8FwDw9D0RbLk/u8TqsYRPH5UVSvI8LhyNO47cv3YVbE2sCifZpBVm
+HaOTZ2C35Xxf8l+BuSPP+L1Ao78l9/eL//l/8XVr7U/ih4zr8OF6XI/r8bHxYoQPnwOqrwAqBOwP
+1K1Q6NLojdEwXSONphR9baCY+ENroVlDr/MBwn/pHgCg2HkVm7/56wCAp5QGg3cLqMVNbBYZlMNG
+I7uNZuvLAIDhTon6WNz8FTsOUQNVy0YrY0ElNLimD9/v5OOBnJZ+QI6IShfQpBVrdYuAHsSqrYGq
+o5YTy933YuSE3zrWoCbMt1EOSnZS2rpGcylezHEk136QPcUNJeHKZhUheyoXl9R/gEeXknH/8Pgc
+NT2rlnPlAqByGVpr4dHii8xnR9lmUDPE0rRW1mh4tC1ZU8PYLrRzoOkp5G2Ny6F8fu+XxPL9r19+
+Gf/BTVF7/upf+3kUnxNdSf8vAgNiE9C2WD6lboXH7ksbIwjkGErPgT2BhSM9Q0B0ZmQz6Fq+3xsz
+kdwb4iaRsPFoH3ZEMphwD2ZAwpnQR0sX3Z+K5f586QGlzOe5PsEsFhd99s05NNfV5Tq1botMEXZt
+Umy9LB7b61t9DHYY5m2NkTwkFyafj+RqhYy4Edc18IjuLKsKBd2wsm5hupAtkWchrywSepteUQKF
+eDmLvQ0YehDnjsFi2ql/P994ITYF9Rhw/hLAJP4PGBb0quG0LaySlyLvpOjRIGIoUcCi7Y6TZdh5
+R9plz37//8EHZ3TjO8h96aBmua2xACr2RKQXKOdC0nejiVAPXpJzsx+icAzahoAlOAgsiTzcCh4h
+1MFghJhY+x2SgqApkLOMNXYcBNR8nK8yrCAvKQ8LNzJYUo2psBpDipVWOsclBULmaGD5Jn90LCI0
+O7+xxE9N5HrbT03wXQqwfvCVP8BqJvOmGouGLy/3M7RGw+J7YUKHo6+0xpCiv76n0dZyL7UhI5Dx
+4LHy4VcWNTe6oRPAuuRXRIED9hr8Wx/I9fyXjy7wn/49nvzmv4eIlZY8+lmYE7nv2A2wIoTaWco1
+jG4MkLBdujpcwGU3Z98pYVkmMg8a+C/Ji15eyLHqmwHcU25ue5twG7Yc353A8HMThGhyksISWJZV
+E9hIciPjCwenvsytZx4iY3nVIS9jnbuI2VE7qSJssVDT7/lYPZEXM8rvI/9Q8keLY4oGLzTuDmUT
+GywrnBiqPmUZKp6jyAuUIUmAyMvY91oU3CAuVYuahEHvHT3BmzN57pPXfwzBxo+WIvhE4YNSaqSU
++rtKqftKqe8qpf6cUmqilPr7SqkP+P/xJznH9bge1+NPd3xST+GXAfxf1tpfUEp5ACIA/wmAf2it
+/etKqb8C4K9ABGL+0GGvgOpX//B/N45CyDq360Zoqk5eXT4LTQvLGrXT2HWyLoTCkxO66zZZaxeG
+rOHWtoFlUlKhhWXyDNkRpoE0SmH+Oj5zU6zcKibQKW/Q0nq6gYbHPvZB2KLH7PpONMFGX5I9O3RP
+K0wBVgbcIEKf1GVuk+BsIZ9Pz8XyJWWKq0ysR7EsYUmeHFhgj92T3zk7x6qS36Wck2/PE/z0B5LA
+2r4T4IMToWk7bo6xpKdTAmsRkRE5FIynkbN2H+UaYJJ0HHjo70iibWhiVOwkHZDoZLSzgT7p9ReL
+HMOOd3LooaCkXTldAMRW/Mq3vizTWv8KLEOp/eMEU3ZRNm6Gnt/RQFcwnlxzeIdWUq1gqZW5ys8w
+IUjJBLuoObf2xhQ1Q8HoVdHmK+oWzsuCU3CcGs4GJd7nDhR5MS1GMKEco0ykimDNAZQR7yDbPYL/
+nlTE4v0abac7T5GdskqReJTbuxditE1qOt/DshBP7mD6AAkbuhz+7pXXQ2x7ssBv9Uf4kJD9+0+f
+Is1kPgMvRs7GvLzPz0KD1xhK3Z+tkDM5eqYtFNm/30ieQbHS8rzjkwjMDgH8swB+EQCstSWAUin1
+FwB8mV/7FYhIzD91U5ADArAf/zu9KGz2DXSnGxAaNCTyCFmFKIt6XYmwuoGhG9y2EO54AG0FBCTg
+rEnE2rYa7Vrd53snb22JfCYoxMXTHPObnwEA7JNn8OvKoGWJ1FEeNh357bbjYJtltju3+9gd3gUA
+RLskfD3bRN2K++z1XKSK0z+L4O9JrBq7kil+lsyRs13aGV0htKQ1bxoE1G/YHjgANQrnzElML+e4
+/0iy24MPd+CxzHh8nKIkACjUDTQ3zoi9DFtDHwuSl+jtFj6ltXaGAfbYlekNHQx4zeMJuyW9Lagx
+N+xFBBuTSLVx0TA3UF2OEUqlFb7zjwEAw3di/OKb8hD/9sLFu1oe7uF+iOy4q4K8h609+dxha3xT
+5wBBaGfTGVpS+7ttjNryfBcr9PuyEWMl860HLlrurK1foiy4mYQeGuYiXLeF4iapfYLCwh2A/RzZ
+eY6sI0qd+1CxhIfJQs4RRRa3N2ReX3VG62fWLUokfKEvUmDF9mxNZKrOYtQ7cg1F6mFI0h7P8bAk
+ffxq1qIkMUzukGvUhKibjqnMQlEXIqkaPHwmE77hVLix/NPrkrwH4BzA/6iU+oZS6m8ppWIAO9ba
+Y37nBMDOD/rx90vRf4JruB7X43r8MY9PEj44AD4P4C9ba7+qlPplSKiwHtZaq5T6gVmO75eiV0pZ
+/BPfUhaI6MIOegP4TKL0bzVYHJDyrOh63x2UoSRcTKrRMolWtQpO11nWKuTUK1wn1LRdp9zb78dr
+WAtlCQ+u7qBdinu5tS26hm2Tg4eA8gZAKNZsHPdx65b0yN+72cNdysPT80cdL4CGmNOghpoTc+80
+WFD5ZsIs/a3LGIfMQldViJQVgEEFnJBvYFFm8Jh0CztVYwtcnVDg5vHFGkpsqwQ1OQ08pXCPQK7e
+y5K9v+cZJMRb3PQD5OzBf3V7B4Mtsbobug9tCN4hX4Hb99DMxPIVmxYh+STSsIC5EOu3qk/QpHKe
+k06cpi7wkJTzc6eEwz4B1bRwV9Ip2tgAPpNrhl7M1WKJJ4++CQAo0wUcV843G2cYLiWp6PZihI18
+rsiv6WpAkwdStX20mVjSsqzgUCQICwNNyXiXmqZOqVGxytU3BimBTBe5xqCrcjEcHdsehgOpdr32
+0mvwI1Z+3Bn6odjGq+o96DuCh/mUI8/HpqPgenJ/J8kKzqmc73MvA8SC4dv2AhWT1Anp417b2sQz
+8jf06xwuPY/YejjvCGzKBm5FnP5zjk/iKTwD8Mxa+1X+/e9CNolTpdQeAPD/z6d/fT2ux/V4IcYf
+2VOw1p4opQ6UUq9bax8A+PMA3uN//zaAv44fRYpeA2i/76+uAaUNEd1wcW8q1vhiUWFvTL0HQmOb
+KEdUsGQX1FCp7JKzRkMx1k50g5a19e40qgU8xmdkIluPlqrDO1sWweR1OcYNwqMjD5YMzjkyVLFY
+j3i7xVsvi6XZjSeIaemdsBNAGKDhDm4yH2Zb/t1r+/DnkojrGJV17MJsirU6f/YYYCLVb0uElp16
+Sw85BWpq0qs5WYYriobkyiKVcBeZUus7t77BjMbxZ3fFs5ncHKLHcmIQufB9+UJ/NMJOj3qFkwg0
+mlBkBwoVUN8hPPxyAUtr1WtiVDfppekIO0yIDhjffstZ4W0SwuaxQkXsiVmtkAzlvqP5EzAFg4rl
+vWR2gTmFb3SRofFlLvzMoKaN6+UWhqjHltwLugWcQ3Z+7pfojGeTTWGeso7/Sgg1kvt2C7neajqF
+YsOXM3ARLuTf43AFl3weoUNV8bJeX2jkBxjvyvra0x4yIzmj7Rs3MBoLMnafTXxtuoIXSRK0l04A
+zkXzYY1gW45npw7Olh2jFgl9BwUSSuj1FgHmhE17bYUeHfRFabEgbPx5xyetPvxlAL/KysMjAH8J
+8nr/HaXULwF4AuBff54DdXnGDvBiWguHk70ZjhFsyQ6x1URQF3TntshrV5ewc1nlVT2FxxzT1FoU
+BG4cLRMUBDB0SWO0CiU3Cg2NtntpgHUyyzGfR/8leXEMBWJUAThdksi28GfcFF7dW7udURzCIRDJ
+NExwmQwOQVg68teJpkCnKLk5gS5i4yr0yXxcTvpoZ+Iah66DMTPOz0ZX0BTL3CLNeu9Co8ek1tHJ
+Ab56IvVqXRSoO8BRo3Fvm+23r4oL+1J/DH9EmjAngOvIuWMdwWMLuOeHMMRTdNeujAePtXQvbqE6
+8ddsDmhx4X2/QPCyrM/TC7n2/fcP0bD/ItEK6bzrUJ1DkzilxQZyJi6zqWyaJ4+eIGULe7gTIiKU
+eOQP4W8LpNuP4jV2RAeyScNPAcvEbbgJTTyCilNYdjDaFAC1K1uS2tiwQNOQOr1pULPkoPwMPisx
+xC6hbCzSWp63opejLqgg1T6BYbv05r03sDGUuUck19teLdGyn2dr0MAlLWDZDpCQQu/TZ0tMSe9X
+8xnKYhefDSSx/a3lOSbn8hzWRY0BYeWhAmrC9J93fKJNwVr7TQA/CEv95z/Jca/H9bge//+NFwLR
+CAUYV1TNDJt9rK+gR2L9RiaEoeLuSw2QvkaE3VwIQuzsCME+0WxuAALQcFTNUHdMwwfA0ZQoxLaL
+Fewa39B8f+wCQEHON0+eoSmlJLnS4gJ6/RCWIcpQ9bD3kljP3Z099KkVqeL2e7hhIt48uwnjk7ps
+EMKw/NWWNUAcxpIkH24Uo0cp82BDY0CRlaZ2UF2I1fTjA1QnZGBeEtHZWmyyVHtWuti+kn9/11oY
+whTv9DRusylsz5JoduJiYyweja+BiCzKTpHC82kdjQvXZdzB8phSGppMxV7toIZYJdv3AGopRk4f
+diEFKS+R+/vC3h5eu5Qs2ldyB++xXJwWBpblXi+/hGpkjbMTwY0kZ2dYsdnJOCE8ckd4wQg6EDxF
+49s1mrJl2AEbwpL52hlV0C4JXrCJbHbOdahgyXdBPljUVYiGJCr1CsipvVAsPMw7jAu9gNAtEYZU
+9k4j1LT4M6QojVzPnfENeKbD1MizsjInMEMmlU9K1HwmA7/Fzohh2uUNXPTlGJcH8oC/tjlG9kSe
+odc/fQNf+T2Zo57vYX5FzpCeQZr9oVDhHzhejE3BCvTYAGBiHW6psU3tvGljMJnR7b63g7cq+fyg
+kcXc2d3Dgpn13SZGRoWkbbuLE8Z4m2fvYEnWZZa2UbYWipqCBYCG2WSlFKDo+hev4PETeQHufkoe
+0LZq4LFjrWgq+JDFveXvoCIZymJeYEgVJXddH79AQdrz2GINwoIFanbaBZRy1+lyXahwqhG22WBR
+JQkuCaxqM4OStfkVexmassHThWwEr2dzPGmkOuFYwO20DYMAb0x+GgAw2pJrC2cVwr7cRy/qQ1Nk
+RLnuWvXIwKLmfFpm9cOet853aNdF3ZAIpPag2YE6681huH51K+HMdLHAiByNT+srON+HHUkv5JqD
+7T3Esbj85VR+9/aDM9Ss3cdTF4szCslup9hgFcAP9qAySsoX0uiiCgs/lgqAXXioa9ngk0ah5CZk
+8hbZguER8RuuH8Ph3DveEj3eR9auUKfy3YICs4NejIaxxMXsGCn7J5buKTa2pUuySBagPixays+r
+Uq3nG8UM+aoTzPHW8+K85aL/oaxVToN1eNXi9Ubu8xvfTDBhLqJoNCJHnsM8A7JkHTA/17jukrwe
+1+N6fGy8EJ6C1oDvA9x4AQCjgcFgJFb87laE8kp2x9O5QlaxeLsjVnW1yOH6HWOyi2hfqgVxfQmb
+iTexuughpnZfwZS2tsWaZKW1dm3xAA3LhOfcfxcx6/AX2U8AAAJVIDPUdGg0/Exc2CQ5RDzgbr1o
+sOx3nANE3Y37ADPurc1he3I9dVujptCHpymPphxULjv50gI1hVzKOocmF8DQ89AjddcJUXd5XmLb
+dPcB3GBU9EgBmp14b41G2GJX2ADiLreRQex1lmuxfjCMruHyu23UQ5NSSIKIyKbI0BDFl559B2VK
+hm2VAuRo1I5GRfHKCUlIdgdLnH5E61gBpD6EVxlkLwuc3CyvUB9IOPLhobTPPs7ncHisu5cJ7D0S
+layOUJFwJTq6QLtFDMFMKuJ2ZqEiljI2XWQp+SN7AVqGgmayATid6jlFbxYABoTWuvN1uOmlATS5
+JLuQt85rlIfyvC16D5FuiKdQ3M8wKQQhW5d9nFIZPmIopY8KKHJ3To8ucHZfrP9lVMAlevVytYmr
+D+VeL0ldF4VX+HUwWZvW2GBFTKsWuaZOSlkjnYvH+rzjhdgUbPu9klPXtefmDUZG4r5BsIVmQ+Lo
+6cziu2dsL45lAWq7wNKlKGe9i09vC97f6bfolfK0bWfAMUj3zrbStnLRENyklUEHdW6VB+XKyqnq
+HtzPCckpJShRJzlachF6rkbWyPnQexkl6eUrr8TVsXweEWwyLGvoETcQp8KQgKPGB9ygk63n/esx
+NNmkas9FXsmDWUYegqE8CDubG5jMWSWYUgkqq9F0D7bn4mFCWK4FNggGs5Meyh7zBC0l0MsST88o
+ZNKUiEqK+0YD9Lborqf6e52pDIlWOsXl18QVfzY/w+lUXnRPFbhFN9fGEcbccBZdWDbx4W8L0Cda
+PIKmixvc2oE376DLFaZzofn/zpWsf3Ja48YteTGnXoODJxJWYHQDMUlWpvExhkthxvJ8icGS8BSz
+ueQ10m9WsFwT/+UB1IasSdhsw+tk5RvmJ6ILdA51NffRFLKx1O4cet5t8LRmXoWclYxj+xEGR2RN
+uuHi2SUFdbw59GO5pnhDnt+n06fYekzBXl9hXskxHl1oVO/IfT9ZnSGL5BgdoKv1QgS5bKATc4QD
+Ml3FYQCPObKmUTjPfrTW6evw4Xpcj+vxsfFCeApQgGMUmsaCHjOGt3uwW+QNuHiMnDXv4skTuPtU
+VU7Ek7jMLYpL2VEvthIcvyO/+8JP3MPyPSHyGN29jbtMTHpzcSkvkwJhl9VfFmhpxZTSsIVYILv8
+NUTfEHfuXfoxrdIIeaFDBwiNSH5h5eFhQWBNNUN1Kjt3Fwbs7G5iM2H9XBvkBGH1dIiI3V81gUmr
+NMN0Jhb46ukRTgoKpEwiqJJ4iZdG2GG9/VsHcl7PbbFBCLNStxDq+/xzjYyekE5qLM7k/i4ZioVh
+gZqu/2J+CoeWphfEuHFLugRf31oh3JeOO0s9xOlJgjST+Rw0JbyODGWwh7jfVUY8FIq6oBTLGSLG
+0qFH57TQXXObO4JyWaG5CHDwgYQNTz4g10XbYr4QD+vkYAqflYh6tcSSbvLeZoSoL8cwuVRRcge4
+/7bcqy4dXDAhWj/L4RLgdPOldzC6KaHnxg4rQ95krVGZJUtcNR37TB8XVZexJrGO1ihJ237+UOHU
+CDmPv+zjnF6jfbtFREwKc+N4er7AJnkXXxoOMLonHsb++0vot8Sbah+fo63kHTjmMxZGA8yYlE10
+gxQy9/UsA7WTUNkaxer74vLnGC/GpmAFkKGsvHAA0DcegoW4WffTCxw/fQwAiJ0S+VxeoANmXpdN
+BY9+d16UyOfSpnrSs9jdlgV78o0nOEnkJT1ZsKTlKjRZR5ZSo+F0GA00DUEzxQrvPpYF9Ug3rpsW
+JcskxXCMjFiU8/kKCwJryvkcEYV5Cta3Dh8eofTF7d7f66MmCUuRl1Bk5tHMXs+PH+PwCTe6CoiY
+P+n3JqhJ0hpON3F3T67jNl3Kp1kFN5KXe+CnqMnBaKxdd+0tvRp13qHtSM8+MtBs5e5Ht4GFvEBu
+BUwI+jHhFkI+yRXReB5ShISeltBIqW4UtAkaKjapUCEksrSaUlmr7ANjceEPs3qtNYnWolkxDDg6
+x8mhVFoOqKewO/ZwbyTHvTHyMOlJmLdqrrA8YQyPFJM+SWF9CRlmTy8QbMtzc/z1M3z9VNZ9Fg7Q
+uynP0WfrO7i9lPP5Adfp9ssANRrjfoAR72lVnACs+CSsRG2aCASYIk9WmNcULD5M4TItsZglOGSo
+rNlvk7QpzsglejBP8amI536jxP/9tiiAXZ5kWNIoeawy1M+eIeW8Kt8i4OdVo0AuHFSFRt7+E3Dd
+HzKuw4frcT2ux8fGC+EpKA14kUa2akEhX9z+9AZ2ArHW+QcGU3bRta1Cj1Z6VXQdcOWa41BPDBbc
+if13nuKsJnfheYkZiVGII8HEDOGz5p21JdpOeaqp13xkTfsUNZNuRSq1/UHgrBmM29rv5fCdAAAg
+AElEQVSDn8q/7+yPkIS8jqxFzOShs6KMfOShJYejWVjYWCxMHRiUS7lXl+ClpmhRjRlKzCu4hEpX
+rYOasOKd8RDLgrwGE3FVB/kKBVWjji7n2KXVeVbUaEwnItJickOs+7yUxNl3L1LEF+LlLMtLWHLj
+3drdxG5B190r4GyxE35FU3RyH7Ncfne8SPDdZ2J1q7TA7Tvizo7HPvaUJDSNK8cajD0MvyXrEcMg
+GzAJmtWYE2KdXs2QHsrcLjjhG2gRcG2eLlpkn5XzHX91hqgkkOl0hv1bcl9BJN5d4Q4RHYnFH96O
+8Yj8DmYjxG0SnBymK8wT8TJvVOxzuUiASLyUQp/D1gnXz4dPYNyM9HiLosEe+SY29wB1Jg/zeZpi
+0rK7dKMCVsTUsH/k4LjAt3Xn4luEnVdxkOHkSp6Lj/IGEZ/VTf6u53uYr4gbSVLM6G3FBlCEabcw
+UKrr032+ce0pXI/rcT0+Nl4IT8FaoK0BoxXGnuyu99yX4N4gh355Hz6TgFeXDs4ywlXJJJTVrcjy
+AnCSAW4OJc6895kQ737ApM70GJuMuW7eYKebHuLrhyQGLRVUV3pqXVgr59DqLUSkG8uuxLy4rQOX
+O7E/crB/T5JTgzsb2HhGJOCru9iNJSlXKzlW2QJ9QghcVaLH5KKyFooiI2lM1mrloU/04w3UgMf8
+g3ERGLlv1wmxMkzyeWLZllEOl5Dhy6sMRyVRfkphn3O7t3sDPWoEDDlvYZTAC0gbd/n/sfdmsbYl
+53nYV7Xmac9nuufOt29PZJNNURKtaGLMJFKEOPKDrMQvQQIjekkQIMhDnCfnIQiEIEBgIEAGIIGh
+PFh2BsAO4sByZMeWY4mUKEpsDt3s4Y5nPntce695rcrD/+1DtSGFt9WAdBWcAoje3Heftapq1ar6
+h+//vptoaCkMRhVyFjkV5yWaHQIKaNHZd3bRPxL/3XhruL7MbdYuMfHlHlmewz7gnHPM2tUYvCbB
+mJ3nZ4j9LTNvjX4tVoX3YIHmIwm6Dd6VIPHnbRcPqAWxslokC3JA3HsV1UZ+U7Q2fIJFRwNBoY6/
+FEP/sDxr52un+MW1sFNN7R1Ye3LvtMgRuQ/kmdly38p/DpNz7meAtYVg32rA4klE23iJLjBmavzm
+/gQ3iQif386vCrDePXuGhwOZ8z4tt3Jto8eCsFfjEb7yi8JsnX93ht86+ocAxHL8bEyy2UjW8SYJ
+cLcva2w2n+GdmVhNVYerwG0/SRDfonV3IjGqH9Reik3BsoB+X2M2beGQt859WOFwSD7A1SHmuTzQ
+JLvEpSGJCIVFrARoKXHuHYR4lXx+0cMJ7rJO4MPnChVBONNUzMGnTYo8J8ijrdGSms2YAjbJRG6H
+j/HwTQEt/aN3xA0YxJOrKPNQ23iF5mdkfNzelw1pU6dXAbiilTeoZym4LJ2ulI0FRT/6aghNQdB8
+yy6tLPTI8ejoHBtWxjVNjZxUaQtzigsCsvx9yVffMCE+Wj4FAKwKg1vMRGzKDg7N7tt5gMRlMDLh
+yxoZKGIM6vsGSU7Id+yjIllI5rXAWl5qiwzBQT9CwWBtoHYRh5SXVzkalp8f4gb6I5YwU3K9sB3Y
+H4qJ/1OvD3Eay6amjAu7R93F8wneuCHfv8k5zqsOUZ+kJpmF8FD6Fh6FMIn8Nit9DAmAchakgM8r
+qF35vPfDLooLgpMGBQJPfpurHDf37sozoQ5o1floXLJHNxYWFBt2ph5GLJ3eFNKfvmthQMGZwThB
+L5Ix96MlqgsZa916WJBhu6IHVrk+RoTg3wkCTDIeEAPg/taFnK/wQSFAroLw6Lhu0Wn57aUPELuE
+uuqgCJvf2Y3wcE94Kn8PQk7zg9q1+3Ddrtt1+1h7KSwFpRS0rRGqDk4pO/8gHaJPUy61n6Gdk9Ai
+2bmqJ2+oDB00NvxYzL37gwl8X3b+7lGBaEdO7p4+QcagTEV0YJIYnGxFVrruihFOQcPwRC+bAd75
+FtNsPCWTcYzapyl+7z70TTHhkl0bzoWcDp5yESdidnrbQGNdwqJFE0Q2akrItfkMNanQqi25R9Oi
+Z+S6/mCAMYVD1psS8xmlz+cOAiMm+q2RnOzvpRmWp9L3rNWIyBzccx3cJ2lL8jqwy/PAZXVmT7lo
+RtR0MDX0Vq+wKWEsCdANrUNophw9qsV0dh8g38K6nAmZLADHTaB8OW3DJoa3RxwCcRju+QJPb5Hw
+dufz+MpETvlqeYLsI0kB64XCkGZ8Qpfow3mL1z+Sk/nHv5jA6YQPor5VY/2eWFtOWEFd3JXn58u1
+VLsLb07U4HgfB1+m7sV8gbbj6d9E8BjQ9ol6TXwP9VYLwgVqulVNPoXfk2e2Q0q0UAfo+TKO/W6E
+mm7CLfsm7F3pZ7/ocETEZk5rs2dVuO/z+Y9jzC+36t+3ccP7DgDgqZVjWbEqk65bmzc4NYJlqS0b
+E0LkVypHSfRqMnmIh58TURr873ih9lJsClGg8KXPePg/z2tMlSzAwcigf0im2n6CD5+I39rmKeKF
++E7GlcVR923ssNrv8AsJDm7IRjA/W+D2R8wVRy4uuAFshWSxrq4efmP0lZCJtg00KYbmeAyb+Pkq
+kgd++PpbKGnmT7SHXW4nfSuAnjAjkhqMWEZdDxhZVhYq4oTNrLhiItZhDHcLXU0pQmIZhNG2+tCC
+nVONqBegYNlyuUmRs4oOJBXp79zAgFWS63KKu670YXqkkG95JVsXrSY+gTGACjYilu8VToViKXO8
+Vh3sNV+aXg5rhxWTfLlDx6C0pRLRdRPYjFsMmgR6SBh3ucGW9YkJF2TTFXpP5Nnc+PIO9l//WQDA
++YXGyhFzfbJ6DMWszDEzQ1Vd4elTGf9n+zEGnxNcwfRZgUenLNVOPYxIwOMQ6LRpa+jxlvSmRhjK
+eqqOaxja8f6uBb8v688j7Lq7XAJDeen1cAO1ZiYmj/BgIH/364wznK+X+GneL9tr0WfGxO0i9Lak
+PTcSpERmf+9ExnmUt3hIN+H2xEWbyb3tdoODPYLdzixka7n2CLJR7g80jukmZJsaKUFydVvBZhrP
+KUpcrD9Z6fS1+3Ddrtt1+1h7KSyFulY4P9dwlEZQUpL8mx9hZyDsyXZn4f4DcRXm6QCfJenFEQt/
+dkyDIfPxt3ELO4FEtft7Pj7kzt5Y7pVst0cJsxoOcprzoa3Rsm6+a0LULJ7q2iFiViBebMRiSHIP
+CYNhnheg2uoAJhb6mfTJ6CE8Bk0TxSxEu8EaEiSdu2t0DBJ6ULBIq5bbxGNoCxpyrTjsQ1OarS00
+nFB+47k+0pmcNqeV/HutbIRjeay90z2ckFegZxnc5fdwDRQtIcoroh96QMTTrIklJQQAp1NEuzK3
+SfAAAQVcLJ+w7FEMl1iJppojCu/KXAxd2PxtW6/Qbd2xSk7dRfP+VXXl4a03MWIg8tGzJ9BLFjwp
+Dx2fcePKd1lnwVDe72I2Re9M/m7Y7mM+lOKpcKeCx8KmTkthVHd6hKmRudixfxo6lOuNOwdLWhCR
+NcBwzMrOaou3f45uW2WYutDkR+ySNVpaBT6DoIOmQVmLXLrVDqCJTQj6HayEltJmF6/fYrXjc1lP
+RdngMJG5evveF5AweJrVJQpaDf1cXQXCVUzNimSCH+mJVXw6n+GbzySobrQL15Ogqn8wREP8xou2
+l2JTiAONf+GzPaTzCsrii3njPhyfgit1gHGPYiGuBqggdKuQh3XsdGiJ+Ci6DBUrBqOJhVduyoT8
+xKt7+C2WJy9beTG7ukFkbSnecSWjrvUaIdOXIyfFhqAQn+mmgzcOkDFbcOj52ItIopIp2B6j9o6B
+RRPOELrsKBuK4JdKt2j4faBiVLmYyZuAsvaFBYtiI2EvREWCzk6tgEzGVHsGC8PfaNkclrMC2Zyw
+Y32Km/uS3nu3quBrucZBNIbt0kRl9Fo5LlqauwpLBKnMZ68foa/E1LYmMYzFugu6CfXyHBn7vpoB
+PgVWkcQwBOQYa4COkfOKc1mVCSJbUohRfIkNXcHG2sc5KfWj4hmGvrBevZJLCvGj7hj/bC7ztnCA
+v3xHfhsnO3DJFqXnDYLPvAoAaI/kOT6ev4/6Q3nR919tYXsypsGdHF5D1wwLdCQDbjzpZ5Fq1I2M
+L9tYV9Wq5Ubhs2/dlbknK9j5xRwt5+fo4hSWJS+mbU9R84X2IsAjOCt0KTRrtVcapBVqGF++v5we
+YxDLmBp3hqM1DyrGPSK3wUUn8/rtdIk561F006HlZr84zlDiD1Vt/iPbtftw3a7bdftYeyksBdvy
+MYke4ksjF5et7FMHnkFYyy7Z6RZBTXluB2hbcQ8ymzX/qwYtQ7LleYHvZiIm0hx5eIXVbtnlGjWL
+g2xy6oWRQkGswLqp0LRbkRgbFvkVV8ZHSQZfxyG1uj3C7YHcexgcwtnfMj8bGJ40tdGw3K2gjJwM
+eZEi3dD8rCw4ISXquxbzOQuCbEKN1x3ShKQh/QF4GEEvamQMRlbVAPEO2aqfyylyfnmBo0spAnL8
+PRhyQrr5CqdUNkZvBVdJZkfRFUPWwKYgSb1RqCxyVZTAUksgsToO4TsS7SZiHI6eY3Mkp9Usm125
+CcHQgvIk+2Dac2jyNSoqbZ9mDYaenIjpRYuJJzl0f3aAejsXydvYe1XWwN6unMDp5QnorcFrNTZL
+AeZscIxvvC/uwZuJjeO/J9Wxi1r6+41nC+wPJVDcbCqgogtplWjJ799aG9DggqGlYBlAaVpe5gKK
+rmTTpdCUuru9K2PbdS1EpAKMFVAsWYFqFAzFbLJVjQ05GY4JaS+bBid8fs/sryF5T57T7TsJsinB
+daih6OqOSABkGxvfS8VlSOclGrKVN0aho5zc8fQYO9GfQZIVz9O491qM042LxbtUfdpEyC2Z1GES
+IWHMYLO04JOxpmURg38QwNvSoScluktZHNmyxrMPZNGfpMWV6VqQFCMwClbAyGyjYOhnNwqwXIJ0
+ahsBXQJ7RBWnG4ALWWA+LGC+9T8VdLg1q3Moyqdv/feqs8A9D22ew7dlUaVlhg0rPhXp4tUghqKZ
+aJoW9YZmueWg9uW662yNhmSkoGm5yGfoqB+pYyAk99+TQgPHEvZ+/vQ2otvyIodMWVZNApvuVaUq
+5EyN6ho4vRQwVHt0CZsxn22Nyl48wLyi2tJmiZTMRKvvlQh3KGwar74fUZ/Rl19eYphIfyzvFo4v
+dtjnu9j/IRn3/rM1gpnM+Yw6DqXpcMoq0a8uNH6sEdO4qvvYIVlrsUmwmEvJeMuy6IOHAR7ee8i+
+d7CYGTHPW9h0R5x+AjXhS98wDXm8QEtgkQkblEuuz7lCj+K2l1vSnrKBM2VKtpfCois8nzuItjGf
+qsOj92XDfczsk2k6lHfkOXibHMuOqMmPalyyDqLT7pW+5ZaPE04NZ6s/0nYouSHbXgjP3ipkJVjV
+PFFesH1aKfr/UCn1baXUt5RSf1Mp5Sul7imlvqqU+kAp9beoCXHdrtt1+zPSPo3q9CGA/wDAm8aY
+XCn1twH8mwB+DsB/ZYz5VaXUfwvgrwD4b/6/rmWMi6a+hQQW3npTotOefYbqXEBImdXBY+392dSG
+LkmMQqqtumwwb2lSP7nENBWrYV60cGj6Lxc1WuoxRqwyzFsLBaW+LVjQNPfrWqMjYYXVT7A7lgiv
+ZsTdyg0ycgzAapGSSy7SPmxiIbLSYEl5JoumaKVKzAq5hxVbKJl9qJ0UFev0zRbiuzbYLMRsTYY1
+Gp4e87XCjKAXJ+ldkZasyHe4Kgs4rCPosiE+1FRrznO0ns15WaEuxSxfEZsQWw1qzSBoAZRavu8G
+AcLtKglizHlSHm6FcyYJxuSneFr6WKZyCh45c+zbFKhpYjSsD9jyInx9qPDF8MsAgP4RcPLo/5Lr
+/sxPwsmE1CXp30N6KnwC36a4Sc/VeEpSk2XTYkFX4t74LoY/xTqOWiEOBSwE920AwFsjB60rJ7vt
+j2BAarP+DWSluBiN00ETYpymAnqq6gsEnWQw6qaHcvlY/r1dAiVBWxckdAkq5HzWtRtAbSHrZYXL
+hcztSpeYEaZekpNCDzuMRqzxMAkUqepX9gWypyziaDtErIWZEVb93cUcFl0bZdvoqK3aog/HFxfb
+DUPYn4xO4VMHGm0AgVLKBhACOAHw5yG6koBI0f/FT3mP63bdrtufYPs0WpJHSqn/EsBTADmAXwPw
+dQALY7ZMlngO4PAP+3ul1C8B+CUA2JkM0b97D1/oj1AeyQ7d9e5h5w7z9N4eFjPJ6cY3gI60MvdS
+8ZvONi1mKX3W2EY8Z/HNrr5SY44mR/AyOR0v12TcrTOYbit/ZmErfd1q9/tCLvErUAdCidW/Jdd1
+9w6wmMqpEzoF7DHhs1EMZTEtlp1gwWBPVcspMTIx7EgiWWoeohvJvQftDpJbpCvjdKXnJeDJ+J+f
+PEfGsZb7MeBIqmuTn+OEgiTzqfAAODAoFclK4wa/9FNyUp7/rwWU2Z7cHpqcxWRj0qv5QE7YdY4S
+NQvFVOnC8eV+g8MdPHhNKkKtvszh5bSEM5cx9eHha+/Iye6YMQ4fii9b2/6VhmZKubkv3LqF19+Q
+IGFmNL7zSP799bufQU1eh3p5DE1i0iEpjW4OXJxeyG8/ylp8ixbIvS90sF0p/Dk+PcY+i+kw/aY8
+J/81zKayLMNeCjuTmIo/uAn3gKjB9SMURIimW+Rl3sPgDqtkFy6WTFUv5iWiHcY7iIVxNy3Km9LP
+W9qCYvC70xaeGpmv/HmGBXlAWjI2bUyIlmn09bCHmwcyR+89jXE5lSDvvO7weC6/p7GJkVYwJB6u
+Go1GbZmZHISWWFtx3waiT8an8GnchyGAnwdwD8ACwP8M4Gdf9O//oBT9q/fvGrftI7QHWN6VheJV
+G1g0feP9MXzWGlSLGmH+GADw6IlsFJbqI6QZeTJ7griUHPSBvo+dW7KIj/x/Cp+0altceGpaLAlR
+tVULowgmUgoqEhPulZ2fgDOSl+xH9iVib7cXSCAv4ZHysEvswc1kAKfdMu0amAt5uBUZlZdhC6it
+5HwPSZ/4htKgc0R0tF6yIq9/gelMrpu6NlJS1VfzAuOF9N9uCvSpq/mEJrUuM7QUvN1rYgxuSZ9/
+au9dXNCtWqUZbEbDlSP/jX0HAcM/8+wUTz6gCIuTIn5L5vAiyhGQvi27kBfXxx24xBscHf82Vguq
+Gx0quKyY1LN9ZCe/DQBYrAXcM9x7A/7+zwEAjv/J34D1jnyvK4OIQC34Bzh9QlHVHennrtVDeyLj
+qOsWv/EN6eeXdi7xwXf/JwCAE3p42nHDaVlZaP0mdh8wiKv38PqrsoF4nz9EQLeqqdyrDdwFBYUO
+byHavSvPqZ0jJsP2NM9wo5HNIqDSV27ZmBj5+37/s/AorrM5eowgl7XwPG8x5bO6syd9nFh9HL4l
+7s6wvYO7bwh7+PBghUNPrvEPv/suzrlhLekSW9qGoUSB1QEe16+KdrBD2gEnjHF7Vz5LPuYHt0/j
+PvxLAB4ZYy6MMTWA/w3AjwMY0J0AgJsAjj7FPa7bdbtuf8Lt06QknwL4c0qpEOI+fAXA7wD4RwB+
+AcCv4gWl6G3XxuDGDsp2id4HEjD7tr3BnVx264mtkAwlZTW8+QjhhZjrAUlPqrrBwTYH3dvB739P
+Akf9YAJ7T4J93ijGkjRuF4/JapwZqCuVboPGkj3SUgZxIrvruf9d/Ij3EwCAeSD5f7+/wQXEIvDT
+1RXBi2MiGNb6W6kLKybyjEHEweHoKqWXX5wiXUj/86ZDxnp7xWARvBJ+JK7IWm0wJPJyjgibu3J6
+Ok8SOIEg/bTLVFo2A3gqPTs4x+c+/5cAAB/kR7A+kPu1RYV3LmWOvvSmjHMUTGBRRXm0s4PigfT9
+PWXwmQeSyhvffRP5a3LyDoxA0C2kOP22mLjWrT388E0yEe/twCcuIh8c4d1Hkl6encgpt5ecY1O/
+AwDY/aEfg1WK5QFXwVCvMqhS+J+V7z/zXXGDlP91/DqtmPwyQ0Ol7O+sjnF3XwhVmlmBRoTCsTkm
+LVlRw6NeyO7PjuC8Li4hBi2K2WMAwCJboSPUPdkV18e2XZhOiq5Om3NcUI5tvNvHnETAH6yJLalr
+wJZ1+MUbHfSE0PR8D7ovVu3Y6sG/lGvMWRGs+woNCwHn9Qf4wlNyg2QGizEFY/YGuEUCnuhcnkEd
+B8jo56jKRbDNd/sNFvlvAADu73wRs+STycZ9mpjCV5VS/wuA3wXQAPgGxB34PwD8qlLqP+N3/8OL
+daRF33mIzeckd33nIw/lY5mQYqeD2vqIQYwhcd3hPTGNs+wCGrLofv+4wuuaD7xZo6WC0uyxRtAT
+E80PtrTYzhXPdueGsIgngNEAiVj2zA/jkOXXIX3Hdr3ADUbny/4GIbUki3IBF7Jh9cMAOTcyRTWp
+2IoQHogJu04cWASvBFWHIesOaps573kNhwpSfRVgviH2/0LBCuQeO5MSZ8f0RWeMB1gB0rW8pN1H
+fZwtZQF9If5J2G+RLn16hvnvyAZ3wTLrnneCgP5yp1z0bgpM+Ob7Fyi+KX75cbrEIPy89FOJy5DV
+T3D+HVnwe/197Oywz3s7WKWy8azXQEpWpEVFc/7WjyLeFYYhbWJ87jOyoHNTYrDVzXQaDCwx8+9/
+4bH0rX4bt179L+T5tQY4l3mZOS76b4o5v9NotEt5rjdC8a2P9BSVIVgs9REpWQvKLdF4Mp/VIke8
+IxuxP5I1pnSLs+cyV2WawWJNSHwrwMVH8iLvBfI3xszQ9GVTfH6Z49Ahe9OgwYBsUXrVQ/BQNq/N
+MSnn3QJ6Sdh87wHM8gkAIDwIET6Rvv2LX7mHswt5Nz48EUzL770zhybzErI1WlauwrYRDX4SAPDK
+ZAc6kWfya3ix9mml6P8agL/2z339EYAf/TTXvW7X7br96TVlttVwf4ptNBqbf+Vnfg6Xaw/xtuZ/
+dBNUycK6CzEZyS5YqQkayoOdkulXmw1OlxL1nkweoKOoSe+mwtF3/x8AgFsusZlPeQ3ZlW1/jR1H
+dvCg7TAt5HrrtkFM/YnBaIxXf+RzAIC37gpZxdG3fx/vFmLi3rdr3H8oeWw7LOAzGJSnKcp6S8hI
+NyiM0eNJ2nN2YBg8VU2DlkU3G0bT87JGlcvpH2mDYiX794Wfok5ZSWkKPJmyco6UaJd1iuhITrba
+CfEokJNkPU/hlnKP1rNhb+TEthgkM36HyKIcsq3Qj2R84+EQPpGj1WqDdclg5VqupWHDZbCy6Qxm
+qVhFy7xGx+j6ugVqYjlqRsj92qAiR6ELC4OR5NX/0n/y3+Of/n2xdJ5847/DPJXP6SWtn6ZFgy1T
+MWCTYs7SNvS2oEtZV/qODlGFtqcRUqgmDD1428I7VFcQ5DzLkLGIy4rFqui/8qP4xS//uwCAO1+8
+jZs3Zb3cSEJ8/jWxEJZz6cPk3/IxJ6TdfNPFZ4iKdd0Eb5CabX93B6/dkLHu7YnFcKenMSAHyNmT
+c6hd6fti9giqFffu8hvP8I5Dd+VUoM1hHWO1JoeC1aJIJFj9md4rmNIVjsoL7O7Jtf/Tv/6ff90Y
+88P4Ae2lgDm3tcHqtMSmztHwYVlBg8VSzLMmVrg4lwkxuxbW5+RoPBFz6nT5AaKhTPDjssMhF/Sz
+RwtEt74CAKguvgqLWPSKQJKm8dCMCBlWOTqy6nR1jQ3rIGy1xGwqZnD7pmwOB+YQ3X3xax2t8bwQ
+8/mzhxPEtmQ+vNMpakaiHS7GQRKgTxx60h/CIolp5xhka25krMXIlwaXAcVf6w4WhSyjMsBsItdw
+Nxt8PpB++JDU3XtpgsqVF/D4LEMzlWts5s+x5EvqJT76FDuJR/LfsLbg7EjfY9vGzR0SyvTHVyZ1
+mBW4zMR0zcn+tNIGDpWBV8iwuZAxz+ocF5fy+fliBt3Is1TYRs7dq7LU1tTYsMLv+TsXeHoiFEGn
+Z+8jK2R8HXH90OqqLkXbGiHXixvYlLMF4LiISYRLOUuM3AThWMa3E/XgBiRAaTRA9qkPP7zA01Mp
+v+7mXGNPv4X8mBWcX6pxwU2o12jk/0Du0fuXpe/TX/HRBdKf/r6HzVIOi3v31vD2JYPz9uEe3nhD
+NgX7lqzZw4P7V1WUB9F7OGccZPfOHbxPlbGle4EFN3t7Jc8DlkbaboljpGoSAHadAO6+PL/5cYpF
+cRU4e6F2XSV53a7bdftYezksBQXMbI2yKGHID4CzDWpyG+bZHN1UTpjs/d9BRyBTAYnYl2ugq2SH
+9+Y+3iPfoS48HHRyOgT5U5xTEbh0WPWobRQbuYYdJPD1VjrdwcrIybYuG6TPZLe+/FAshuE4ww7N
+4HYVI4ipuzgNMRizwKhnwatkLGNiHiaTHuxAThe/jeAmksFoqwzBgPX0c/n7tldgr5Td/gKXsCi+
+MtQ+NPPcrb1G2kgw06Ub8Fld4Bt9CRL2Ns+wmspcLasMmqIugeOBBZHYJW5Cj3o48ORau4c+dnsy
+b+NxglEn/QjuAHchJuqSEffmuMR6l8VYaYmcFYPtWYPjgVgVm28XOKKl4FR0V3QDl8VcmQrhkp/i
+aWUwfSqYhTrfQDMQvIUMu7YDTVKTwE4wjuU5jHs+NiRGmdg+diYyroRWmo4i3KY1OTocXHFT5nWB
+HouKblnv4dfmcho/KsWV9Esfx88Egv36b3wZD/4Nsch6nsKD/0j6/x0KywBzfI5rT1kGn7spwcp7
+b1g4jCVA+8qbHg5uSmrEHZERe/UuEMhcLd79Jkpar/FpAtTi5q2KBk2f1bZkMXddwGVRnW46OIW4
+CfVkjAMGyv3wdXRejk/SXopNAU0LNV1B2UDTygtbFgvkRlJB9blBVsiiKmcrdMw0GEZbta5QVkxJ
+diW6XB5G5FwipQR45Y9gEyc+8iW6m6O+qoYLlMGgxzqB0AZm9DOrCjX99eWxVAuGezfhh1QKqhqE
+jrxM3dBBqyXKPlYuOgZIkh6p2n0XuiSgxwcUadbt0MAwDmK7rJdwfKS+LJR+Eb3Y6MsAACAASURB
+VKGKl+wz4DP9uJhpuGA038iYl/sB9h5ToHU3QPZ7cr22KGBt4zVBgNFQTNhgV16a1/YO0CdF+k4v
+QsjKwJ7VQxTI/eJ4B3BTfmZZe7TGppB5263XKAgc2/RnCC5lI7uszqDelZd+SqHZutXICSJz0aCl
+e/D46/8Ym8VjAIBBCYsbwLaac2fUx6sH0k9vt4/XBq8AAEprjoApvrg/wM6BvHiBkrnwgz565NhM
+xiPAyPdl7gN080a3Yrisxvw7vynl9+X6HB88/l0AwMEPjfAZyMvtmABPvoaPNQVgyc3voXcPP/QX
+3pTPto+bt6Wfw70AXiQbh7WS++bzAYpc1mTVHEC9J8/9o/oU33km6ymuBvDPiMikwKyqJxgMZF6L
+zkeSUPeh72FFpqpY+yisCJ+kXbsP1+26XbePtZfCUugshaLvojzO0LYSWbUcGw0JScpygzyjUEl1
+Cc0TX4NUap4FXVGZyXGgyU0AXaPZisR4DSY5o+QBFY2qFcqOoJmiQm8gO+p+FCJg9UY66wBWs53Z
+YsV8/rUhLqZky+1vcHOfEGMNuFtpe6sCIrEQuoC8CJ4Hi/yQXdhDy3p623LgeDTH9yRb4JRrhNmW
+DXoBQwn7qq7hkn1YhTkqwnEt1vavTyu0e2JqhwuFlpF6tBoeadtvBBYOR+La3N4VK+fmboK4x+Bc
+V8MnAYqrK2DLERFmCFk9ahNzX3sBygVBNYsAhi5aavmoPbFS+tUAYUTwUkXOxXIDbLU77QagilZn
+cpSsAnRMhyGBYfsUxrmzexNv3hc3wNsZYMCga7AeI/TIBD4ZA3TTet5dmQvfgccAtFI2LGpb+n6G
+Wst6WBQZ3r4vJ/qG7tFvf3SE8wtZk0e/8x6qvyhjyu0BHAaje5aM44ansPOT0ref/jEbD2/IOHbc
+IXrkaPQsDU2gUuPIfxcX/xhFny7BMMYltSmfn6zhU1XcfzKH4XN4viRhjb2Cl5IMppfA9WXteFGN
+DWt+VHCJ0ehPkE/hul236/b/v/ZSWArKALpusCnP4fgJv23QsfCjyqdomedWwn0MADDkRzAt4IeD
+q4spMtMUhYUBU29WWWNTiH/WQU7uG4kHQyquVdnCkEO/d3cP41RQkZfW97CihdBv5GQIlcYuhWg6
+EwHdFsVoYUNJr67NMeTvY7LgaFjQJJ31oiF0Q+6vOoQhKs5jUZJdVzDEDUReCEWeBlV2UFuZusZF
+wBy7p+Xka3xAM0g6QwCLaUijGjiWnBj+YIAHe+Jz370h8zZMQgRkt0LTwiG60YtdhIQru0pDE1uw
+VSSxbUA50rdN0MFq5bf9SYqc1ayTgxAHrsQwulKeQW07eDyXUxdWjZbjMJv3rqo5lbLhMRDyuYkg
+E2+9/RB3tkHExEGfFk0T57Ah89wbegiYoPSGHudnAHsoa8tpu6vjsK4SGBIEq3KIminhmzfEYjBu
+jd/6PQnWLh5d4FEqwe+fHuGKHs0mPiKPbfyML0jJz2dfxD4/D00Atye/3WTPgCVjUGS60nfeQvPh
++/zOwjRnYPeWjTOm3cssQzOjtsmAUoBph/W5rKHOHeBwV+b4RrKHVUvUblDBMmIJvWh7KTYFaA0V
+xmj6PTQLMTNt3aIqmaMGoFnW3OkOfsiIK4kkVFMgJtjEiyKUkIVXri2MegTpNGuAKkUO2XsHloPK
+JW497VBzE+qbAAf7BLqYPZyT1tsbyAtkUMIayIN18sUVjVueaygSi9ge4NCs3EbOfceCZRhkKjvo
+oVxPOxGwBb1sCTE8H4HHl7StYTOyboIpGoKJosbBihiBspTFY9sVug03k6iBzei80j4UacNGcYjx
+rnw/7MtG4Vgafk7iFK+Dp0jHBsBa0BQdKShqbG5z+3rtg3oyMF6ITslmiVWDZEBdydshnJwKKEOZ
+e39TYlzL8puVCiaUDURbfWimRuyigBtRVJXw8PuTMXoD6UPfbq4o62I4qEIyYZc2/D3exyYlfdzB
+JzenNbShDO9XpDCkqI9CD81D2Vhus2Tbz3Oc7csLNrpZYncufWvv1rC5sQQUYTkYxqh2CIbbMwgs
+2fT8OoRD4WErKeGQZrDdY4XuRQNzj9ksO8IOuSbN8RqpJ79JRxtQAAqHlzL+xaRBTdxEerHAs8eS
+OXl973WMJjw4qjGU98nIz67dh+t23a7bx9rLYSmoDpadI9EFKp5mqlRYU0ZdNR1sgrLs+D76O1K1
+dyMSCKjb92BR06Cuz9HWkkt3d1dAIZZA0POhqVa9N6Z2QVtidUrCi8U5AsrLr7MaNok074/vYWcq
+gaYqYGCz6dCRGbprHeSEXdtuBMXgYZBbUFs8xBZ+60awKPCiegrotrgIG21L+jMlJ0rTWGjZ3840
+qAhx7XQLVZJgtjVotHyfU4Rms2lgka4Nfge75YmpK/Sp+eiGfTiJnCSaFoEHH/AJ/7YCWMHWLbGu
+mI2N8WBIxKJ43c4t0PE0VlUBW0uf26BFwVr/e/YSK85tweDwWa3R0VVy4wKasNzL2UcwhFK3MCjo
+jgz7lA10czh0Sxr3+7wHsBrEXC/+qIeALp1D6j2rCWElhERbLgCZQ8cL0FA7IRqPUZDspD+WE3/9
+cIBxIfOW9zqUTI1nmxw9YkNCohjf2BniLj8PHAVNub0uNuiIvDT1AIrp3G0VbOtl6BXirnbNGu59
++TwLz/H6TNbes3CByYVYLMcRLZB1h2xFZuiLJY6+K7qT3zp8Da884LsxsNFZW86jF2svxaagVAfL
+TmHyBkPGCRbhFNaa0XmlQEsTQ2+FiP586MnD3Ng1yLKOrAG8QBZCmp7Dj6jetGpQkhDq+UqqBQ8P
+95G48u9z10JFBY15OkWZixn5xnCC57flNx8c0wS0NCyqH8VRhmggv23XJVzGJbQHmPGWUZf5eCeE
+JvOQgiXoEwCdstHxZVuzrsGYEoqLvCnMVU1EW7foNoRjK6CmSbwuZU4uLlt0lvSzZwJ4dFHazEaf
+5vUr+x6GNk1tJYu/CTJYpLDXRsGlBqU2FQyrFlvk0Nx8DclIGqWh2+1mEkEFsiH56RPcYGXr+nsF
+dnryArwXEbptFIYZNxvHwoJwXV1baBi2cJTB7VBerD2WqttVgGoo9wiDEBarSkNzABfy0oQDD3Yk
+c+744uZZTg5NM1pb1pXokLYaIJRnotcFnL7EAbqaVPzlHqpUNpjWFDCQGoxl/joUKa1HPbnXzc6C
+55B5KyoQ8n7eKEFTMEPlVNDUoNSsk2lb60pcxwkTtFO6ROMChpmvV0yAky0bP7HbF/Up3Fb+btMt
+sKpk/Cfnx9ihPMBwAGi6tC/art2H63bdrtvH2kthKbSqw8Ip4KolloQGh04PJXPXmS5gOWLyR6Mb
+mDDiHBIdF8FHzIi8rjKsqfloOoWIJp6lNrggqQmVxRHnJe4Q5ht5ORRlmV29xOZI7j0b6SuW3Ac8
+UbxCo2PRkQMPPk3mQnWoGJSzxz68jhiCLTOy6WBomVh6AlNLQKnNfKzrrYYkLYmqRbmWnf9iuYBN
+tuo2U1iT/i1PM0QkubIJd14U76FitecybK6UtO26gO1R1r3XQxQEnCM5zbtNDUOchgV9VYnauB1c
+ksR0domSEOuGWQhHA4auVFGVOK1k7u1egDn5J/xbBuVXpZ+H7MPFLMPMZUS+LEErH4vsEooWnWV5
+VzyIfZLeaKtCcUn34iBAQE7Mzs9QMKAbVgZWtCVXIes2bGhK5ZlqA+WLFQcHsLfjQ4fOIuMz3QDX
+r9Cs5DmdPa/wm+8/BgD8wsM5hlxHeUrthbccBK4EEd3VK2hj0sblK3QF+SrXK6ChijeZuB3tXLGA
+m0bDIR1dogOEhDGvkgaFFivG2ch6C7sQ50/FWnY7g9YQjj8/AmZSgJW6JSajP4Nakso0cNtzfJRO
+YVsyyHvOEC4frN0NEdIPe/vuF7A35qQSgGOKC/iNfF4kM8wXMqzl4gg9cvW1ew1CgkbqJdlspimW
+JKDwrO6KLr3bGCwtiZabrMHtHYlRnNKk1HoGN2Z6q3OhUllUTruCGsoGYscaHqG5HtNROjKwKK2k
+6zU6mp+5SlGnS15PxpF1JapaFnGjOhQE9OSmxrzYqkk1MARkuT5dim6B+UL6GcNGyHTbSdVgwVqS
+clqgG8iCtalolK8Nui2wyC2hSCeuqxoVNyHlBuh0zTlkKbRlQTFm0tiAQ6ixVT+C3cnG8tH6GdqJ
+9C9eSn/q3QotN42xNnhEyLM/2kHZbZ97Ad4axaVA3oM4wyZjxSgM/JH8wNcT+NvAU1lB0Y1THudl
+VQHkz8RYw3QyR6aqv5/xqW2oWH4Tack+OJmLt++y3uP9j3CHmo+e66Bmebbm3B99b4UnpbAPHv5k
+CJ91PPWqgSKoq3MtwFDYhxkQZWKYiK9io2AzbmFXCTq6GoiHKE5kDqyMMapxiwlJg4PnNrhfY+AY
+NCM5GA57Y3hcty/art2H63bdrtvH2kthKTR1i8ujDbzaRsgaey8x2A/FJI76fTy8I+CVO4e78KjK
+qyB52aYXwOpkKIPqAAcTOa1W+RB+JabW8ZPv18ivlmJmfWdew/LFpdhzPBz05N5FU8G0DJJdDnCT
+vIMRi6Bcuwcwsu6OQ+iSFkjVwYachNW8wZSCIjWrAcPBPtyBmICWo6B5OupGwSFwqKIidjXXWFOC
+rSybq0Bcs1ghp5x9vbHh+fJZMwqf5Q48mqJFtUY2Jw/FuoDFeZnqCrOaWoqsWoRRaJcspHJtOAtG
+rGMPGjJub1OjI4ag4fFadSU0Jdk39RpLsieX5QH8Wsa/mr+GQSOW18ISU3xpQtzYFetvs3yKHinz
+UyuAQwzEwOsBDJgdEwaeLmpY5CLMl3PsgtWc6yn6OzK3A6uCSytLb7EeVoDKJTw4DaA6wbIUdQVT
+ElZtRVANeRSoV2qNJrjzowJdftVN4T0in8QH37oieHFbVsaODYI9WbPGBGhPCAWflKipXWlbPXQk
+pYkOCL6zU3jMVCivFmlqANqp0W6k/3Y9heooeMSMU9tq9PpiKdzox7gkdbzTAaBepRvswNSf7DV/
+KTYFdB26IkNbNegdykKxDmwYErfuORYs1iisF9/DmmZXR7pt2zJX6Z3BYAdjl2Qhzhynj2VxzBYp
+bGLVB0TrHQQREqapDge7mNCPnMYGAc3LYvkM59+jb8+Hcv+LDxAQzRYZDxl9wObsHBlNNaNWeMIa
+heBc/Mx49wS7E6a/4hF8h6XTbomMxDFLGm+r1RlKbl6bkyVyRpAdk8GUslDqrkDXkWiUdOJOuIML
+mp+Ja2FJdGfVGSjqORazFFks12iZbgtjF3VHFNyygEPz25q7ICgS8cC+KtXd8nXNL9foqCy1ulxg
+xrHWpoYHEqu4Fs6XrO0gcGwv6fCqS+Jdx8L7S87n3S9i/R7LOXGJWz15Id0L2WAeqRKNkTnM8upK
+Oasf+7hNDYu3772Kh69KD/u3pZzYNRrNmjIBxVOsTllSvpqiIftWHMfosXo08gRh6O3uYveOEPW8
+dvYcaiaVst9b/hg8Zyv0KPe6XYUYdC7np4+U7mGdtVhNSczrbJAMmbZ+IuOwaxvBrrh2SdyDy4Ol
+rVyUlHfaLHKkJzK30wWBYHGIPSXu8W7vDBmlBNLpBc4eyb0ju4MVbFHCL9au3Yfrdt2u28faS2Ep
+tI1BelnDbQ3SKeW2hxEmpNTatCXWJ7KTLtMzWLl83/C8ssMAvZGcqkHboSQGfDIYo/dATKryvI90
+G7VVksloegaFJeZXaWy4B/L9nxtFwIp6lPMF3nsiborqmP//4k9A2Qz21YBN01B3BWyacLXVk+o/
+AGdzudazyyk2d2R8D18J0LGeozkpsWCVZ95teSPmOD3ZSpan0LxWGNiwsYXw2tjvy1h7AVWMBk9h
+ZfJYV02DRS2nf1O2OOL4yoHGjMHBhDX903RxRfm22VgoMgZjtUYSUp2qDK4i3Pm2dqDMoJbybJ49
+nyMj8MhxSjiUTK9yGxtNy4uw3WjTg75xV6779LtwGSWrU8ClhVEHfZxF8vsbPM3DXCOhReftD+HS
+1C7mFxg0Mqa6LbDaug9HYhH4kwgdVabK9VOkj8RKMTt7iHcZrPPHsJitwbb6tPLhD2QckfsWvpZ+
+HQDw1sVvYaC3tPvMdHguskSyDGvfhceg6mx5hi5m5mMFnJ8KTsZnHYwfWxiuWRE7SpDcJHxflVg+
+kjXw5MkzPJltgW1yrXEbY74Rd8YNIzzsyXo6u5zjybscv+vh9VevqySv23W7bp+i/UBLQSn1PwL4
+1wCcG2M+y+9GAP4WgLsAHgP4RWPMXCmlAPx1iPJ0BuDfNsb87g++B2BZDVZli2YsO/irw5twbPrf
+TR8nz+Q0uswtOIGcKoFNlJuyUNMnPbOXSGgpJHGMhif3zkGIcSanyjefC3S0XmnMmTY7Ly6RUF16
+R93DcEdOD2tzjOqU0F6mytAV6Mj23AYpumYLzdbYGMq4pc/QEJ14SgGRNo0Q7MnOPswusTOWajj3
+pov2EStCyRRVmwoNi4TmlxmypZwYA8cCAjnxb+z6V8g8m0SkG1NfsRj1fAvuAQtmHvnIIrnHxe0W
+P6TEgjC0up5/cAHGPZHlNTrGbbx+iJboRW/cg/bk1Nkcs+L09BLboEPuajyaMVC3WcPfFrFBgchk
+GMKLi16D9zfyHHLfAIzLTN5+A6kl1x7MLxFStyPYk9Rx0DVYnss1husNTC7PL7M79McS5AsANNQL
+LYcMBpY+vCW/qx2sSX93dPoI+5k8h15/id4+qyptFqupBgHkBPaGLd54Q/L/D974HBKmsDXZupdN
+A4+amX3tYsk40Jl1jAEZt7KygcVCuZzWWnhWIe1LvCBIHIQrGZOZTrE+kvWS2gWWRI7GjCMtOgcd
+tUEe5CUyErS+m2eYptTiSHtQa1YQv2B7EffhbwD4rwH8yh/47q8C+HVjzC8rpf4q//9/DOBfBfCQ
+//sSRIL+Sz/oBqYDmg1g2S6SUrpUn5xBTSjeMnFxr9xy+7nwWCYdkSI9ifuombs2MGhZOqxsQPfk
+YfQP38BdzUguFYhK1UGT4t4oDzfvCPtu4jgY9WWym+w25mdi7p0RVONaNuytkaVsdCMKjBZ78DOS
+xEw7xIT2tqR6t3b6GDoSDOtOG+SKlZFuDFNSx5GbVFsGsLjBTOI+zgiwCSILHQFZvhugYpDvcrVl
+S7YxJrBokwBrqiL1VAObxDD3lrfQJKx8DFkZ2g9RMzBquRoezfJeGF1lHHpeAk29QnsoYz7JM/Ro
+RqPcIKJk/LPpCboVxXNahRtDUrOROEdXLTpb8v97QYZdly/K8QwJRXPDkY8eN+rxoZjOzmIN2xF3
+JkxCJAz2GbuH/kDGnUQ9hISIE2MGx3bRjsmJ2PgYmS14y0M3kLkwABrOwVZXtMkb1NvaiGCIOJQg
+thNY8JklKcB12I8RH0pgdHBwA9FGNt44GUBtS7w7G1lJTAqDrl1vCYuB3a4FQP1I+Ab2nvRnsB7g
+/iFdsGZLzuPiglmipJ+gOZe1Zz+u0OSyLspVinn5ybTof6D7YIz5JwAZUr/ffh4iMw98XG7+5wH8
+ipH2WxBdyYNP1KPrdt2u259q++MGGveMMSf8fApgj58PAVaMSNtK0Z/gn2t/UIpeW0A00CjOG0xY
+K2/GLfJQdtqBehWewBQQOS5MJ7tuP6E0vO9fSavP8wV8yoCZqoXlyCkQTiLsdEKc4fXl70KVIM8o
+/TUa40ZPdlQnsFGTnMVfGdj+YwCAXZEfwImht6Qg2RI+C41UqDCM5CTpJUukTM8NGXzadfpwx2L9
+LJoN1lu5+7zCipZOTVPV8it45RZt6WJ/tNXDACaJWCCR78M426AceRyaCPMh1ZArINljkdfvt5g8
+4Lzs/zaqyU8BAB5MCcG+5cGh7kVZAAkI/fVsdISQe4kHt5SxzEoZ27JKsSiJvFz6mLMoB1AgJy5u
+uwl8nugbX9zAomjQq+SsqcY2goTW2wiw5kyv5kvcJSQ4IbHtMHLg7dG6CQPYLjUQpjl80pUpO0I2
+FXbv1pYxq3oNEzDY1zpQ5N8ImwSKqeouBMA0sdqK1zgdMjI0JzeWsHi9Kp+g5vexJX8Tw0NMzILn
+DRCTxdutcrS0oNZnGSKqcdskZKjhY34m5r7j7cGiH1ckEQoWv3mxiwHTk2uS76xTGxOibbU9g7et
+Dram6JOeb7MucHaS4pO0T519MMYYtcW5frK/u5Kitx1tmkIhUsDsVAauVYDX79MX2ndxb8iaiOEA
+rE5GRxBLUVtobFZO+j5YsQxl24hIyOJYIWxHfMr9VJiP/WgPHcFNnuUjSpjbhoLHl/MsVigC6cfI
+JxCordGQjUlbAWwyBSFQ2JbkJqqP+HV5SAtmFOImQM6yZyuOEBFP0FourIAl1TVLbFsHYSiLdcfe
+hcNNqp9EcJot7XmGMJFNyGKVpRlfoFtxY+1ybLjwAqfBhozKj/5ZgB/9vHxWI1lgr9sJGhLAbDYZ
+GuIb2s6DobuSWAodsy4emZc8HcPn+BdegVEjC34SBIiZOVBWB4exgQ2JXFRV4dglnf/axdCR316k
+NTR9bufuLaiejCtmRakV2YjIIDWIXbisVcjcNVaXLFUP1jg/ERO9EWQwJocPEJMAxWobtHTNbKUR
+kGofXYeQm3PLPtjDAbwFs0RmgpJl5KH/HLvcQLqePKcbkxHCkWxuXlzDY4VmoIfouHH03Qu0S9k4
+S8YfClXDi4h5yCt0FEJ24KPH+p7KcWBF4t7uu+TrTHKkZzLOaRZjhyzfk5GF2TPp07LIMc5lA3/R
+9sfNPpxt3QL+95zfHwG49Qd+dy1Ff92u25+x9se1FP4uRGb+l/Fxufm/C+DfV0r9KiTAuPwDbsYf
+3QkLGMYGT6ctHJKJ3O+Pkbliat4afQauxYxC5sDhiV5QRVgXS6glzSzPgsUClTZLoYkqs11g4EmQ
+Lyds17dcBL5YAU6vgJ4x32zV0H1mOLIWBzuyM3tKotRtdYlG0zRsV98n0ICLoE+NhxhwNjxVyNS7
+aTK0uZwkoyJCdCjX61oPNXEImv9eWym8kVgBidGIWGjk9QO0nfQ/u9jAEE/RkfrLdnMMyX9QxgrD
+UK77LG+xHsp8vn1nAqMey/iCH5E+lj04JE5xwxobGj+5ZRCSd7IXDZE7WwtJzpM9L4RmBDz29lCv
+GTlPa3jUUGh0gyYjzJfCK1XVotpQRUXFSKlT4NzuI6ThOdlkqGmOr2qx8hI4KDpak3MNL6F4TlBB
+57Tk3nmKlifsZkZk336EwYYBt34MP5B5rlYlND0eJwIghg4acjXaT1doPeosBC5CW05/zx7D5jrb
+aoKm8zXqC5qpd2xYsYzDCgNouj/eMEETsRI2Jaz8cYWO5EJWUMLi9ay0wdreFrz58IjPCCtaKLvx
+FXdId5mjYtHczd0QFzOZz0VlsNmwquwF24ukJP8mgC8DmCilnkNUpn8ZwN9WSv0VAE8A/CJ//vcg
+6cgPICnJf+dFOmFaoF4YONrGiDUMm6enMJ4IumqtoUKae1EMj9LvlhazqDMuFF8807loWX5c1zUy
+8iu6bgybVZKxLw8o9IBkSxTr1KhYlWkWOVgwiEZ7GA1InEFFn7T0EBDEUxsLbSTf+9qDMtsKvgH0
+vjw8X5G8JF1eKUS5o/DKF+2cDTrS1oMqQE2mAaZTB0kfzL4i8ANU5HNsnRblmpTj5HBURR/eWoyz
+TZdj+S7TpZaFHiPrz7/+f+PWj/3r0jef7EGuD5vzZvI9dBYBW5mDmCQibuvBbIV3KdyYug1YlgFX
+FxiyTHcdlgjBtG6zQEaz+8mxzE9qtZh4ZCDyQzSVjKl4L4ViSnJptbjMxNVLWEW4WaxxQEKSdWBd
+QY0ts4uORCYfugqnTyrOvexuycUaM7ogceoiZim+Ew1hmK3RdgBTy4vasBx8XZ8BZIuyEcEjhb/S
+KWq6rzazD4O+A5visJYdwqarpZFA9UiyYsZXILFqznLqwRkiiug4kQ9H061KsqsNIGtTuFQ7c4e8
+rj+EnXCja4CT+bcAAJHyrrJHYV4hZ9r2RdsP3BSMMX/5j/inr/whvzUA/r1P1IPrdt2u20vVXgqY
+s60V9hMXuinRY5QWkxgrh5RfJxmaB3Kiu16Lrvm+ajQAtGjRMbdd2oDPmve6K5FdVcvlCMhE7LsM
+5AQBLBafqNZH51GCK7BQeQTeZDF8sfLhDce8R4ScvAKBaRAzG4CugXa2kFKNlkHHtharwoYNK2OO
+2unBOiBnwSaAT1bpFUlYivUaPjkcnS6DRR5EWCU0T3xo90p704nkHpXd4YgiK+cXGZhWR7cCXrvL
+YO1feA0W71dTeET1S2BbUViu4S3lHjVqTFfkDXDmKGkJ5cRTGDdCVZ6xz33UtlzXjnx4xB5gnqAg
+9yT5amCUdQUV7/QGK4rL5FGLjozJe36IHnk4k63qtOeh7mgherhiqFZFhDUtwcvNCnOqba/IpXL2
+wVPcJaT9zs0SETMqDhQakr2YNoUmZTKnEA02V4I7wyiDxYB2kcfIe1ssB62OZAzPpUuIFobCOdCF
+pI0AmK6EyUl1p2nxlT5K9iHwd6HpMhSVQUdQmlNp2DYFjwisgmlhG/ns99fIqZuZ+gb9QNxtY8/g
+B7Q8XrC9FJsCDNCWGrFxUDYEgmQdhpRnn2YbTCgwm/ZKGMqWdxv5btk02BL7dVaGnBkF3/UQEnVm
+1x58RpS/z37UQrNstmnXVzqWQRIhNDI11TjHgqWnHmsfjBVDkzMxX/pIJFQBXcdoWCZtAFgEt3Ts
+b9UCBTkFXT9H08hLb+o10kJeMkUGJqcECpZRN6rF7phgo6UHsB+OWsMZ0FztBOSz7r6JZ0+kim62
+2aA6k8XmG4MpyWfG5yMMEknPok/yj0ULPZS5yFYzrNstm5SHopbnsDreYNGnGZ/Kd+PYR0X/4el0
+jgWzFje0gUVSk6W/QnpBH1/Jv8faxRl97llZgeUOsKcp6rXErde929BE/E+hjwAAIABJREFUMnqa
+oK/qHFUtL1XrBiAuDFl5ipAu31s3fhz2G/LypnOa+2sDdY8EKr6HbsXy8shFoEkj35UwmcRdzJZ0
+d9CHtaD477RFXcu/B+EMPtPSITcK27ZgUYNTBQYd16QpM1h81brNDC01GfKcaw826hX5FVffgmbK
+0Q1dGFbdOn6CJtyiLbevbYmuIAtX3sFjrdCkjjCj9qirQ/SST5ZPuK59uG7X7bp9rL0UloJtaewM
+XDxuKzTMV1vOEEckioiqPrINK/WOO2ietul29+0KJBZp26GQ2QzqeBUccuM5TgflM99ckRk5U8CW
+/lq36EpWOzYONDUKI1+jZiQXhpDpfg9FKaeOsY5hMvm+qko422pAVcI/J/Q6IXTbaChGyNNnl5g9
+3xKjpDh+JiZxSDhsqTsMu62QS4P1qUTAPcdFQ/cnXzdISEGmefp06QmaNRWWIg+tlvEt8g6Vz/r9
+xIE1FF4Ar/eG3G9WQj2jUI1yoOl2tVYHv6J4yc7r6DP4OaJaNdoGG2Z1wnQGQ1epnufIB+K7rMwG
+m0KuF5JgZKnWWEzl2UT7NVa7dwEAvhOiOSVfxPkx0ks5mdVE/j7qOyimpI1bGhiCrBrLh6Ly+MEw
+gntLApQVrThn0odD6w4hrohjCrRQ+ZZuTgGkvm/mtBSyBpulKFAPbvbg+qIkrVQPBQllUkLUp/NL
+ZGcyjm5H7gMAXVlD5XK9zgCd3eM8i9vlNR064kmKqkZeEFyxnEBttTLjEGTEh0lZixO5aGg1mHoD
+j3PhjWIc1jL3uzEwCen/vmC7thSu23W7bh9rL4Wl0Bkgqyxop4/XiLDLyhI5IZ5Pnl1i4snu+lRb
+6DN12PAkDVwFuMQeeD5UJztm7MZwmUO3nRANg4OGf1fm1dXf5W4KEAvgtivAkpNmVaYoPLEgYsZr
+wkGMDU9uq5sgpV9nVAu3JAItdbGhxqLTyInY+AFqFvM0aQrS/uNiEcBjECxVTGnWLhqqVqu6wIwW
+hucALfUVjdXBInloAenj0bzFgjn21LVRbKtWOqBby3x+9R98Dfd/9hcAANoVS8Irc1T091e2RkUO
+heOzDRYM2jmbI9xnGtE94OljlijmW03MGsWWbLZTMJyXxSpD7bPgKZPncRKssZ8zPTlw8GBH/v3Z
+d3IsU4mJzBvg2VNJT968vcc+rBCTbSmzFCpWKJrcgcllfGn0BNZTOSm9kPRx7RwmIiy+MOi2SNcu
+uBq33QDgZ5Dr4Gx1jqKT8adTg9GuPPd+V2FK8tpnCwrg3NS4S/6K241B3ZKTwoQwjNG0Sl/R89Xb
+mNKwQLy1tpoS3UY+N3b7/7b35rG2Zfl912ft+czDne+bX809VI82NsYGEozbGRxZoMiR5TjEkhVk
+KcZCStyyQOKPIFlBhiCFhJAEpMhkDtDYgKdYEMfpbrvtdvVQ4xvqze9O5555z4s/1vfcqhfc6ap2
+vaoXcX7S07v33HPO3nvttdf6Dd/f9wsab+Pl2MWK+0O0cumCVJ27i6WHKZxHN+z2eSCvt99psr35
+r2GisbYead2k1SloKbM+t13mS5dwqqIF05kbiK0dQ70iA1MLXJ77Z+Kw+BXtSg9T4hFIOMOECd6K
+zr2phGGnz1Q3thkNSc6JRp0WtaTPvTKgIcGVXs8l59o7z5MZ1+KRv/EVPOEepvObeInr/zJmegZv
+jpWxj2rw1RBgWg2aYpLevfgc01PnMj6cK8Ncn9AUvuFodEIq97M/DAnkrpaTjJsKlcqJwybcny9Z
+dqWM4yW0BJSZV5ZEGe7smQ47T7vJG9mn3LA1LUu5+NnBguVEoYY5JRNT9mZR4e068M7Sus8/fGCZ
+3XOYhrkX8RW1uO/uDOn1VUOPEspjVStEKb/dq4jedOPz7//Rfb4k0dgw38f/knOrl/M3OFWNvXjo
+Jn9rc4/xzIU+ZpoSqUIV54a66RaT8d2UQMm1ROClIDZkqgA0dnoI00YxGxOJ2r651caTRL33wN3f
+V+eGh7fcdQyHFYNn3T1LW1c5nInqTngFO/Fo9MQ03m3iS3vUS0sKYSHS0SmVxrlYicAuSmKFto12
+F4t7uOfzinKssLHfol1ohRduxg8SQlXPaJxyuOoFavc4L3mBXssy7L07gdl1+LC2ta3tEXsiPAVs
+TZVOIfUZDZ1bF6czPImzFGHKqbogF4HPUBDVxcS5dcf37tISSUcx7nE0uQlA5nXY7KkmPPQZNlxI
+sP/xVTdZRCiyyzoNSDZc20Yn6JHXbscr4pJ22+3+XXUf2jolkYz8wUaT7A23S+f+IfuXnOvnLWOG
+ChtmYk4ezZeELZHH9jfxhIsojwtK6Te0x+rETHJuyHU+np/QUxfhbpmAkpylN2MgyPPRVKjDGHal
+ND1dVgTCbzSoIXfHbr4Gxbc9787jaXe+yWmALzKVw/kJo5vu+jc+8m1cftY14gyeixnm7nN+WzR3
+zxvmd78MwL/4zZeoNlcs0N4ZAWmyqLklvoelEsm/8+aST2g3L77Y4Y+84Mb4HxaGSG73ogmTqfMK
+ssTpK7aHQxajG+76x3OmQmTmZYZfubFLvAFb++4eG087dLGHSd19yqYTsomSuJfbdFXHjzo9gpnm
+1ty9t7z9Gr/xzxxP0IvPbtLYcp7Ep17YpCEwQ1G40ODavMnonjAbH/dJKnmpjZxQUPGsvM/JXecJ
+LIRjaVQhfkfJ79GUYuG8KTJDVTtveT4bElxwx26HKnX7JVNJ0Vdeg6cbn3LnEx9jBaE21tJP3l2/
+4pOxKHgeptXC2BqD2IpIOZXkelwsuLlwF3kxP8Xriq15T2GC51MeKadQhXhbLqfgLyGQTHjQahI8
+4wY1OpN4n5GKbDC52jsTCrV1zahwkzFsXKAjwQ0rshFMTN1zMe5WeMKtLUducfPzhzQ2nAsbtbcI
+xZTTEI160mmR6YZ2ujGtjjLkm5bg1B271gKyPAlZakHrnnZpC+Yd5SXzjuL2B+aMabmQvmIwjhkK
+un3qTWh+1U2Isa3xBBDK4g1uSA59Q7mD2A6oxTW50dvEe9qNy2H6kHM9tyi0om3ygQtvep7LKfjN
+glx19WGxTXSkfM/4NsuFO959c8hM+ZWtuSpKy5SvnKpT86kbfEnqXRc+8n0UlevHqK59nkIP/WIm
+5uekxdamW7yLMsWMFaJsd8kn7gEyBzPGmToRxa+YTV6iIRHf4hJ0tlzY1G7URNJ89GlRbqklfKQO
+1od3uSNW7cFyg+/ddZ972DKrLmu8ciXqU/NQLFzpZELRk2qZtZgVr6bfZXBFi6HYq2Jb4avHI9gM
+CJZubNt1QKGFJev4xFqEPM+NZbasyLWYtOuQQEJE01lOoVJFxx+x9NZiMGtb29r+APZEeAoGj8A0
+6QSGiUQFzbxFLChtYFrYpUgj5jHVplvL2hICaeyF1D0h147mdLty4bdCUE0/bm2xhXY3ScqnVYwV
+32Gv3sNKin1RnjA9civ0+WSLqOFc2xX0dbnMaAnCGxZDOn23kwb2iPkb7vyj55dkifMwYnWvtRox
+vVUGvLYYkZM0vAFJy53nQhWXtBqzJ75+c76NdGGYH42ISmEdbMZ9Zf7vjpRY6u0wnrnEX3PcIrMr
+PUuPnqTgFuWI8ReEp1CzU3muxvRXXXh9Gucdq83ewSFl5na02Zs1w71n3Fiog7WOUtLb7j6dH/ZZ
+iuzlZFDw+j9zNHZHJxH3TtzxXj9x11+UBWnooLi799vclwanvz/iyjl3f8OyT9Rwx4s0+OVRzMam
+9DCiIeFDsXg3GiRXnPdmzm3REZrU77gwImjOqdXYZCYFichJzDAikGZGmYJnVyhT55ncsQV1182b
+q5ufIOm4c57bDUJhyHfVENXYrshE9Xd6dJ+ulLuj3TZWkG7bDWku3fzcGbrzWaYzsrHzUIzt4YnQ
+sgjTsw7cZt0EKWnn4vJIs5KGv6PrtBS+eD6nLXZ31a1rfFKpnr9TM9a+a36U99w6SWw/dWGfu7bN
+BQmf7vQ38IZ6+D1DlbvwYJYFGAF2ug13Azd3EvqbrjJwaf8qrVB8hp2aNHMD/+DeLSaK1TY3nd/X
+DZ5iKCKU+aJi64rL6k5Mgw/tONc+CEMauqG+BvfP/ekf5dY156qeHt/kVDLws8WcQuxFJZyRkzRj
+N6nieIuWOirbpxkHTZdZzg884m03gcYNiYLUmwz1XoqYcEV60iw5ECiqkUzZuC7R3AsCNN2Ek+91
+E+KwOuF7rrlJM8lzEH15qx2yJ+x/oIXpoC7p9dwEfH6rz476PPYvtdi+5M5/y79MnkqIZeFi57LR
+YHboJvTNW1PeOHXnf+x5PP+8Wyx/9XdvUY9dNn/2O+4hXuzVjKRpMv6RmIWYjn5i+z/jpnGQ7U/s
+7PD8Rxy4qi0XOGxEDFV1agYR+X33JUcnd8iMG4vKthkIBJbrgW1txKC27fn9Q16//iYAd+6+xK1b
+ArUFhmrPhSYvfqfLnQQX/g0+c8ktTF47pKNjt5OYH/+Pf8pd00gAqyyjve3G6rmdPYxa0b3GmLgS
+weytmzy45+77cek+l1eGzZaIadtdnr3kwtzQ7xCLF3R8esRo6r7jzsiFYPUi41SVrTiKqPtuDn1y
++DR1313/xY0WW/vu/P+Dn/rpL1lrP803sXX4sLa1re0ReyLCh8AEDIINymFNokb9YxZcViXi3O4l
+/Adup5h5Y0CkHoLRbncbbG25mvBGv403cCt0VSwIJYXWIGGm8OBANOrl5m1mD5UE7DeI7rhd4NJw
+QDJ1O2y5B7UAQivNwXQ6ZZK7HWqez0kFRqmr+oz6PaxranVlrjwMr7HkVLyM4VObZNqlN4sJ40Ml
+u7acx9NoeJCKEizMiXacR9Po54Rt585/3bN0/ksXonxFuIo//Bdf4ujX1dXeuEVj0+2I87IiEE4h
+CmFnX+AdNWJxkuK3nOt7eWOX/S33fe32Nlf3X3Tv9XpkueTUMoU21iepnUt9evt3WQhiXfUCxgp5
+WpcCflkJ9Us/6zzB14qQ7/kxt1N+/u98F6hOX//xFt3nlFyMfA6VmA1UydnstohwX9bwaxa67tGD
+O9y9c6D3FMyN8976+yKqwWPYlySc8Vkkwgrkc5bq7Lzz+sFZJWElK/c9+2MGwpakfZ9C++hsXlGt
+kpGHbi7MFiVB33kKy6SNmTnsyenNE1LfeTH1ZMkDYQ9OZu7z3XYTqfix0QloSdF8a3cTYZ6YLUYs
+lQiOJT5zv4D7C/FFeDltgdaM2cfbcu+Z1SXhqZsD79TWnsLa1ra2R+yJ8BRsANUWbIYxLNwqOYx9
+umIO3h77HPfcz70iYlNaDtsbaoJqRCRiqOn4Fjt1y2tmFqRjCW5EcFUErNemqu0+WNKp3Spa+30q
+3N9ftgNebLqVfTju4UkDwoocdbTImYkFepnVqNJHbWsC9ePbyOCtGHTUkm2KAk90ZZ69TiIug2vT
+nDp0ceIVI1LZKsC2XGzpFwvOdcTyXM5p7bgS6P3TI37yF913//Spu85ffgkI/wEaACjVV5/Ys2Tl
+edPhfOR22EHXHa83XPK8yqzPfdcLbGxKWs9PaIpdOLP3qNVSHgpJ2bjvkQ8ED29tktrrAASTmPF1
+10j0oeeXvPSayyX89b/tdrDP3IH/RzkFvH8KKq0un/pBNiM3hvten44EdXaFGu037Zls4Gw2ptCO
+HhUTvKXztrJpSq+nxLRkCHeTkLJ0r7UsXBxccuP5YszmKy4n8kwz5o68s44wHTfHHk8psbt50qQe
+6P6ajEz6IsfycnxjueCrXF4tOfXd504f3qUUNZ0pl7SV78C66zjX3WJXLeLnBkMaxv19Y2I5KNVK
+7wcUapoyQv0aO6eYub8f+5ZKnund9JT9l+TdPvMM3rt8yp+IRcFYH78YUA0yuqrtD5r7hFvCuEdT
+zom6jK0uXfUPtJQYs8smQexuzDwviJuCxi4iGr5zI8N2l1qJu71Umom9KZX4DMPFlCOBd8Lj17gW
+/aD7eQgNqV/76vCbHt5lIThyZXNqEaA0Q4+WOB8D32ek7yslxjEf19T+Sl9yl8p3Dzd+l4uRuuiE
+sw/seQrJovcbfZoNN9kC06c+cjTxW/Mv87M3fgSAdPKfazRjEvXgZ1iMuiQ9G3BOrMWbz++wpS7C
+3cQtGpeCkGefcxWHzadeeIsspvTIR66KEJQxJpGAiTgNzPMjEvENbPcNz6jX5HcfjhkJCDS6d55Q
+PQV/8toPu8+Pf44VzZlXW1YKjuVhROequ09ZaIlV3/dXdMBFxVKgn8lJzsJXuBL3aGqKBEmKL9CS
+WRHOTCvaY7cKtfttQpEvDLsd2h93791J21yauTG/uZAK9q3f5M55V31q7YbEqk7UcUIhWrwIF9pd
+3G3hnXfhwyyfMLvnkrLG5HR8N2c3O9skl1e8B+6EB5d32VAFpChP6GpjybwpbfVudJcDrojn8kBd
+vsvM40hJ99O8ZKY5eW9+yGLTHe9D6QOEz3vHtg4f1ra2tT1iT4SnECYhe8/ucWdygieCzuf3U9LA
+rZ69VkIk4s8y7uA3VfNVJ1tQLzCqCRfhnECloLCRYjrOJQ5sRinRj1giM0Xd4qByya5+0WSRup/j
+wWVOey6h9qX7XV4853amtu92gbTIydThZ4Cm6sbNZp9IRKKmbJItVXIUm5KtwIgLIgxPCIWwO9cw
+hMIQWM+FDGV3gUnVUWkrPCWU2qFPc0vdPN0NOs/+YwDuvOzc/auXcn75NRG0+snZNUfGsquuy49u
+DLh60ZX99jYUrsQe3efcjhi1N87Kl8Xxm5Ryg2t8kliCX57bUZcsiY3bga9uRHxJ9+T6wZJQmIWn
+n2nyKbm8R9/zPwCw+ILHd6sc/vNHcF8hyt6nznEq3YrAS+ntum2uV4g+LYJ84UKtw2RBJvaixItQ
+Iy3zwlDHuic6RjF5nQfi3Og2FnQUgnjLEmkLkZmaWKSwDevund0Y4jXcjj/KE/aEUmwGCWW3r3Nz
+pexeUJ5J6AWzOfNa5eduwp70Tfcv7HFOxMOm6fbk3mYDI2rBKG/hxc7TK8uIdOnGebPdIm25z90U
+i9XDhSUT65U1NblCu+PZ4kxcaKeV0Rfi8p3aE7Eo+MZnGLUIevMzwEcW9LgggdU8bBFKpLVvLEko
+F11+TtxvEgoIQ73EC8Rt2Nsk0g0o5xPmnnO/Wh2BiqqIwdy9dno0wzbURtysqG+7Y4dRyWzHufQ9
+wU+9OqM2EkWxPr4Wgl60Q9pQ/8DDI6wYga1ouUoqYrV1F8Y/44xcAsUq+9xWPGlaZKpRt8IEab7Q
+LQuWC/d6yEUyycD7gVv8sqN7LOOb7rUyYlPdeWUEL+659zz9fIe9ljLxXfdasj+g0Xf9Baa2VKda
+CHKPsC/MxuIhtl4J2KiSEfc4fvM33Htfv09+3S243bDgTQnBTo6u4w3dg9Ofu/txYlt8de7c71k8
+J5TOZ5z6BNZd007nAi3BdVd9FMUi5VjqVJ2lJVJMXUQZrY6b/Dtxh1J9MWeVocynqa5N+zBnZsXz
+GLVpt91i2aGmqtz7BxLnYZDg31Ulo+UR7Lp50fQCfEGQG+LzNBcv025rES4qprqm4eZ5nrviMBtP
+nbtIKDbnSNiTRj8kEr7FryJU5KKOElLlRGaFZaYQ4xnpas5GXWYT0eZZQ09s3FfCLqXmZJSbM9q4
+d2rr8GFta1vbI/atStH/ZeCP43LG14D/yFq3vBtjPgv8GE4/7c9ba3/pm5+Fpd6oaRYbIDizCS2z
+VUNRkNKIlfXG4GkHbfgrUlaPQNnwqNciFLzY1BajjHvYaeEHLvFlJSaSlxG5UlxZa8ZEOIXFrYc0
+PuJW/8H+pwjFyZ/LZbZ+fJZ0rIucwFfyLRyzEF0ZdkEiV8ZK3MRUHvLm8X2Djdy1+mGfQUvJyqYy
+713LaFNJtrrESshk2S3xNC7hNCbSrnNXeIyvF7Oznv4pEZupe322YTA7bodJiucJ+uqe7LvdOu48
+jS9ItGmGoDEKPB9PNHRmo4LYeRjFgWrfd46JYndRh1cyesJ0JL9oOV3Joi0qYu3MXQnrZJ2a63Jx
+e8MAb8d97+aOD/J6ellBrUpRoV3XRktY6J5NxmeJ2zDyiFbUe0VJe7hCNArzMM4JRWPmtz1ycSuk
+dXbWVNUYRPj70vi4J7LaiU/4tLvvnZ0OTXkunqnZVmK2kBfTrgymdnNoms1pS9Z+EPcIJPXmtwxt
+zWVfIVUvbxNJ4Kg2YBTGVosKoye0HrYZit02qSRqdGnG7RNR+o0rkPeWDAMiVcoWxmcUvTtP4VuV
+ov8V4LPW2tIY87PAZ4G/aIz5EPBDwIeBfeBXjTHPWivhvm9kZY09XVLYgvlKR29a0FA/Q0xAkMpd
+bVSEEngx6vAzpSFsy92nxWoMqjJCoSq28qhKCY6IArgsJm+VE9OC2YkYgHsjBvfddy9aD4n0/kBc
+77ZaQLES9/CoJXri+TWKUOiFAXOVnlY9DlEVUEjRp2EHLKTqtBG0udJ1FYWWtAh7nQYXdGOjrk8g
+sI058ZiolHmxNeQwETOPsP7tZkEtFaPZwYTmOXdN21GHK2rr8+MF0Yp+XZluzwtBC6sNDV7XvTfw
+ts7gwTZqY8RM7YlAJB1fJxuv9Dr3uKRqwKXBlH9+6B42v4bhaqapHX6WV+ypArD37ZvcuC75+WWB
+ZDOxXsJy6eL5U+VigmlGJVj5Ml/QExFNUJZ4Qy1qvqFTq7NVi1EnalAWKi0vC3wBfdLUMFG40un1
+COeBxtOdQ1FMSQsXPtmgwOrBs57HhoSMI+Vq9jsZ6T0H5z5NMzyFLv1GA6v8QpXWoHxHO1F/QqNB
+JJGZOn8I1o39MhgTt9z9TR88pC0B3UqD2a+6XH5DHbHpMfPc3b8xAc9IXSyOR/jZStXxndm3JEVv
+rf1la+2q0PF5UE3GSdH/PWttZq29gVOK+vZ3dUZrW9vaPlB7LxKNfxb4+/r5HG6RWNlKiv5faTWW
+1KbYPKYjKHKvExJInyGwPoicxDRaqHGMpthr/W5AotDAb7eIxLVVhQWV+BiDtqUzkX6gpLYmeUwp
+Mgp72sJT5rioBszV63478CgvqO6/4tv3WtQ4V7ysIREWoOVvMFZ33WmdYWp5N0oAVZT44ogo87tn
+VZJF/pAHlTvPpwWEicPz9FR9KaqUlgA983rKRFDph4dvIHgGF1puZ+gmfe7MnSt+zfsKm1LrfuZc
+kwsbAkPFNaYUiEr4WhNbak+usWexStaaek42dy58YGLwpRMhl7v0pjQ2xAI9Tfiq9D0/OqzZVjfq
+nanlQCCk0Ztu59/uJ3zysvv7YprzW6quDDZDGoJE2+UBkWDD82OFPmFFPXF7lGegFP6h5QUYhUp+
+y8fq9USCLKZanhGueHFFpNdrJkxUcajqGGPc2HXbzsNoNHagEDdiWdBZuZbGpxbzc0PdifH9E0aF
+25UnpydnzNx57mGMtByC6EwXtSmdz2RngK+ws4q3sZnDr2RRg0ieVdVJMDrOSIIz8WxErPsXTiNK
+ebR2nDOWXN7THx9wbN7H6oMx5mdwDYE//y189seBHwfoNps05h6ZP8PXxIz9jERS5bayWAGOTAjG
+kwir3Ew/DkAMQ3UxobSqOPgeiVRzTJ1hFZNVmgRd61PVbqAPD28yF7Y88GNmcq/jo4KTlnMv25Gb
+KMtlihXSrKwNC+UJioUlraTUtKyJJUAa+GKTCmYci0EqiTwKEY0mgUc6cg+LP3TxdDdakCtWjZOY
+VuTi+q22x63TlbqT5VCLaFOEoVe8bX47ceXUbtinH7luxl4ZEqk9u7Mxo5wKWHXL6Q9mMQR95/DV
+U48VP3mRzajEmlSOXz5rRU/viJ588wLZxLnMJ9cfck3IxbEXcVK5zxWWMzKYTPmV+XHGF8T9ePyH
+TtgxqoJUIZ4k1eNODLUbl3Tmjjeu7ZkATOIFJCsgD0sChS6NNCBUlaRe8eIYQywyGJsbcuuuP29Y
+2sqD+IuStHC5kqoj5SxrWCr3UUyuUvQU/vkWpm5eeJkbk4MgPctbNJZTThbiwRy0uNBU/mgREG6v
+cjfu+oM6wngSoclzaom6tL2AYtUzUcUs1Uk6FItTu3WeN8+7zw0PJxzHCh/qgvMCbS3uVARDVVLe
+oX3L1QdjzJ/BJSB/2L7Vf/2OpeittX/DWvtpa+2nm6sgcm1rW9sHbt+Sp2CM+QzwF4B/21r7dknb
+zwH/szHm53CJxmeAL37TL/Rq6sYM34/PpNTS05qOvtrvBWerZ+h7NJSsW7lOERCK0pokohY8OKy9
+s93Ba/sYZY86EjSs6gkHc+fuLTsplb7Dz2JqVRTCrQnt0lFwNdwmSCOBWjTrdV3SWEg4JJiQ2xVT
+b0lD2XfEfByEb4Uam7Zm0tExvJBYGpr7wsN3vIp4w53vwMa0VyCkjQ4fTVx/fOHfwBw6V7MpvcO8
+e8Sl4DIAB4s3qQ+cL346yOm+rC7PrzUIL4s45b7zCKKyIBrcdH9PEopUnAWnO/hDeRXFjFC9AfXK
+i8nBH7hz6326xwsD55lt3Bjza9KLGc9dVh1A8A5M13J77na5TTxui4hmc/stIE9STjk9luy8Lz6F
+VkS6qsTMMzJta6aYYJfuy8Mc6K9Upd3fw8IHicHEjZJc8OD0wZjTuZsv3cYxXQHDEiGh2mWEFf9n
+EC6J5YobC0Y9EQdSP2+d1NSZ64y8kZ7Q0HVMjsf83usODNW5mGEmLqmcBAKLGQikiF3akloJceuF
+zI07z+niDrZ055ErhCt2O/Recce4sN2ibdUx2Q84lWeFiWmri/Wd2rcqRf9ZIAZ+xbia3+ettX/O
+Wvs1Y8w/AL6OCyt+4ptWHta2trU9UfatStH/rX/F+/8S8JfezUmYCsKZYRKk9DtqkomCsxpzq/Tx
+tLRMw+XZqhqIdLVudKkkVhpaH1/eQdDoYsQY7AdNErENiW6B8WlNIBhpkJUkMyEMzYiVtMTkwGNP
+vem5dozlbIkRSjE0hrnKZQ/zBVbddS3fP+NWMGpTqzxLfyXzlTRPlGW4AAAgAElEQVR5QazUnb3z
+XNoSVLatROTcY/SKI4/9vVlFU4nGna0+kXItFy9dJui5PO6Gzq3VGXJJsezLLz2gec6d52C5SfyU
+qMSOj5m/IgEbaR7cvvXPuXPT7cavFhVbwiZ89OqQ+GMuInwh2WBjKFTnhur8xYz8SEK4kwbjN90u
+97lXEm4pb2GAM0KwVQn4BF7Ydl7KM7djviT2otiL6LeVi0gTgqb7jrHKifkiZy5o73gxx+Tu52wy
+J09Vcq6PyY5cLb+/q5JrkFDJTRmOC5ZKNvimog7d+Rs/IFNuJjx292McXMcTUWxZZWcBtwWCQ+dt
+ZSuqQHtMNnG7eDqqqZV/8dMp1W333t88GXFBXbwfveg8jf3tLTa7EgyqK5byZJfBXe696jzZ24fH
+/NbLN92xhYT0jMGfiaw1L7FqgvI6XRrCeuzZCQ9OJ7wbeyJgzrWxpEGBV7aIw5WacZ9YdOBFCZ4I
+Qk7nc6anmmwd57ZtNjfpivswiX3CDeHTlxWV6tW1nbFcOld7/KrjMLy+GLM8dW7d0WjOXECmRdLH
+qIPNb/ZQEYCWWKTnVUWlxGZpK7JUC0RsKaReFASGpqcOTI1yhzaZXL+oNqTqZb6QegSajC+/5ibM
+gzs3uCncRFaWNFoOF3Dhwh4fOvcRAJbFDBJ3nv2Oqyx0k5zDl9WT3DwlDVTzri3BVIvXqyU3VA24
+rMTnqIyo77kJeH5zQGPfPYRZ06cxcmM4u2SphRtYpa6m18fcuuXG8It3rvMVVRfqNxds6AE6MG+F
+D4q6uLAPnzh219y8lPD6bWXwW+Ytpa5iQSm4cT2W+jQ1/kLjkk1JlFCbzxacjtx8mI5PyZtuHC9J
+jXvQ86jG7iGd71/EjxQS3D1iKYo5b1gSLlV9uCpl740LmGO3EFa+xQo4ZoOY67dc92itZGgzLYjV
+X+FFbbYFS77S3qaRCHrfDMgW7p6keliLYEgpiLYtXSIb4HBUcHjgKi0nN8eECkcjkQw1tn2QMNBp
+e4ldSB09tFxSeLdshjw8XatOr21ta/sD2BPhKYT47Nsuo96SZuzc2bRcEKhhqCpSBwgATNgmk3DK
+Qq5/2azBeUvkKSRLbe1D70wFeX7nGjffdC7qwcyt0NOoope7Y9TTKUHudoErWy0mhfNYBgzPiEWQ
+zqPr0VfN2w8R3ABTeYQCUXhVSSUYc0tkKyYq6CpRFYbQ1HccZg+wM9eM1BFJbG5ixnI/O4M2vQvu
+Ap/Z+iTBRXVS3jwk0k7a3RDacnTCG4eujFVMDOd67jvqIWfeTzOpySZuJ/n6dTc+XzoIOVE35OJB
+yvB1N7b7V4Z8+3e73bb7wJAL9dhWWOZtGCa/7XbKV3/nIcfympYNj7kSe9ZaVvUpo0LTnUPDsaCn
+b/zfhs2ZBG7yGUiGrfYCQmE9glz3Lj3kzdfc7nk0OSKVZ3ZyOKFMVwAHiyetjTCRB9kfYAbuvt+8
+9iXuHurnWUFDY3hu1mUQOW8pl0Tg9jPPnSX2kmVCLg6FhjGUY/fesUqWdSOkaumeN9t40qx4+eGE
+HR2bhxCq63IQuBBn45KlFu2cWeTU8iAbxrA7cJ6Ll3s0MlHPSRuk1+mwVCi1M8sxq/lpWkxWYVUG
+CHr9Tu2JWBS80NDcj8jKmlz9DGlZ0VRdNptNueuvstBN5PkSKrdgTEB74ia014wJh4rrlgG1NAxn
+85pUuopHqfIWuWGhSZV3YFMPtKkHVE0V1uuHeAs3QcqZwpbQwwr66wGRMsTtQQhzN+vnC0tarnoX
+hI+w/hkhi19b7ErdyOtRKdu/oWuLt88z6UqgdZTRzV2M29iJzliRQi8kL90C0Gm617L5nHgVSpVj
+Fis+yn2YzV1m3DLnUs+d/1fUTfdgMWVfFY57xmMqTMM8rF35AAj2NwgkmFOLX5DbDzi9f9NdR2LP
+dD63reVgpaBr3gobLqqL8HQLfu/QjXHy0RJeci5x0y7PGJIoFhTzVVemYOVZwihUB6e/IJOza4MW
+fSljHZ0cUwqgZtvu/pa14fSGW0yuvXmX35Oeo+9FXIrdohd3OsyHeoAEFPLuQf+K26gaYUEkHA1l
+jYoBlAJFjdMJVmJGVegxVrg2XloK5Wh8YgLllTZO3TE+em9G3dAGmGT4euiTuqJKBL67eJE7X3Os
+VifHbsG+f3BCqJPoxDFGorkjP2N57BaevFFzT3mud2rr8GFta1vbI/ZEeAq+hVbpkYYtfEGCk4bB
+aBX0CosRDKHd7dDUwreIXfItbHjYVYa/MlQTJQHjJakyzovaZynXNlZx+2Q05iQU/VujwVxw3sns
+NWZLl1TMdkt2r7gE1uHcue22rM6y6aEPoTobk36fRDDl/X5NqjXXV5at9EqaylqGZkEiz6TXjLi4
+47478N3Ovms36Aiqe+9kQmPb1baDQQerNN/gXJvRyLmUon4k9Qtakdt1+36HQhR0O0cR9tvdeF38
+2C59HXvvNbcTf+rTu1h5abenc+4eus89vXuOrYvieAh2SQQ9LwfOnb+7CLGXHRLye8wRp5F0Fe+C
+d+zc69Cr2VaiuNK5Xa6huek+92OTU/5m6MbezArquaC9QUitZHOn6TylypY8lzucxqyZcTJ1u39Z
+1QSZPIjdiqEAChc2nIt++cWr5Po5MV+E+26X7g23aKkc1d1sshFLUKZWAm/xeXbLP+ruY9O6TCBQ
+1z6xwiNfTWLWr5gJruxXNZ6SoJsb2zz37McAuLS1w1iu/dO7gitvtc6qMmZp8TVv0jgm6rn7vpce
+8eJld/8eiBMzyC33H7iqxm4rJlNT3MMHOR1VH6aLe3RXcNJ3aE/EokDg4w/adCYGKvdQDKiwajdt
+tTdJVKYqvZC4KSBTveoyK0Bx+6KYgDrdOC2YqUpQeRUbbcVnqm+aoMUsd8czi4hKoPucAXXsJmE4
+2GUxd4O60RbsGv8s7qush69QIjQxkUhigk55JhlfFJITz5qEYo2KvS6BWIoa3bd+zsQBGA37NGfu
+uy5fzahEZ2+zmLxw3zuez/DbbqLbFVz7wEeUkoTthFBktVXYIlJbW2Nrj+72h9zre+rKrOcsZ9Ka
+/NU36T7njnH+yiZ7okaP8Ak6q1Zd913NWcY5yciPiwIr8Nm54i6t+25cuoHHVtvdk8tPucl67kLK
+j1x2770zbdKRlmLpQ6S8hb+YYHTdteLi2MQE+6JO71qqa+486kbFQiHfdrDDQIK8Gy3X3twzXcor
+7pq2Jh/luwfKP/TnGHWaxv09uqow+aLRt40Aq3IodUm1XLXPtwikQRksdO5ZgRHXZlaGtBtuod/c
+H3L+ojuP/cEWT2sR2m24Mek1AvKj27rOyRkxTFqUNFta7f2EvYG71x1Bpg/HY66Kfj5Lp1S+QreB
+z9W76t0Ztvj60oV/79TW4cPa1ra2R+yJ8BQ8a2jkAUUwoRJMOF0mtGKtysbQVVdblPQJxHC71Ipa
+pBYTS7qrzpmq464IIZOnEHohvZW+YEOEHvEms5V0161TRpFz1bYHW8SlZNyCAalc3iwQBgGfQB2V
+iR+eYSi6tkEgAZt2BLYlqbeR2x3nzRRhjKhqi6cgJMsDylCM0eKKYDQjb2Y69118eU3pqOKOGngm
+QcRAXaCZcZ879TwK1bGbrR0S3yWnJmnB+aa7Pn+7gUncTukL53Bu80UCz3kVe82vMqvdeLc2uiSi
+uDfTY7ITN17l1F1z3uxTSBG6G3i0Cve5vY9f5uGDmwCcHC4IpfS9t+dCreVxl+P2ZQCONo557rKa
+kmY51YqUptVDmx+VmtlqH4Kqo/vXY7Ltwr+j7JSWPMcL3XP0BL0OlZEnj2jgPIKOP+I0UTKvucfm
+rqN7n80OiUfuWlYe1mDrMm3NIT/3SXF/972UaIXZVuOXNYZU98/WMBP9217/BT78sU+6c2tbolUD
+Vr7KmJ+wKNy4jm8dUYoH0poMm0rrdKNNW3MEz722mzS4duqa305Op0w7zsMaFSUPBTc/ntYMJ+8u
+0fhELApQgTfF+iXZkUQymyUorusEbYpohWvPaYhZyQvdjX04ygnHakONDZIPpDQBiYAgRAm13DW/
+XD2AsKXvWr5g4ECU8rlHvuE+t72VsDdw5cKGHqCwF+GpGy4vQdwWHBcpQ/UulHOfUg9CLP3BBjAv
+RUk+z1jxaZpGzOEDMeWUztVrd9psbj7rXksti4erkKdBKmr07DjgjbaL23fVcbkoU5LYPRxBt6Z/
+LBTmcx6jhVsgWneeo9wTEEZPXaMXE4mcJfzYx+msFqd8TiiK+qqdY8VakygI3pn63Jqp9LjpEbVF
+IOtd5tbH3LW8+fIpdU/EIKEb7/BqTj50Mcj+/kVQG7LfNNhCZd3pEVbENuR6jeysk7aaLmmrVTsP
+DFYkMtsmp6lMfGmFWLXzM77HTr+F6arE7cXEIsHZ2GlRrBClJworR0vCq26B8JMAP9QCmb7Vwp9q
+DE/Hc4zCytTL2M7VHbu1zRA3LsNBjBUxa33g7l12uyCfOmBd5c+Zz9Svky0xIsFp9VokK1KhXAty
+z4DIhR7kI8JjSdVTczhx4cO9pMkt7911GqzDh7WtbW2P2BPhKVS1ZTrPGc0n5GO3+3e9Bl7gdhW/
+6dMVoUrQqkiEgDHiNGhkJYgCvmHalJnbHZpBRCrClUYZEwkUE4tAxA6WRKFzKbtxQH7bJXtuH73B
+bOkScd+27xNviFZN3Aw2q7EC1fheTSno6ySbE87luvcaWBGZFHKHl5xhsKiwSGCIcJozM25nOpan
+cDLN8Dbllncu48s1XuQeC7kmi9Bjek8hiu+y93k2Z1sqRlEQk192t/jc7Bz9b3O7rtk3GJzH0uqr
++zLq42nXiaMhQeXOo1gavNydB5MmgYBcbfVcFNM5l3ccU/FoUbG34+5Nq9Hmo3Pn6ezwKr6StM2O
+ozYrkpRg2+E/9i+9QHgkt7sC36ygxLtUIrMx4o+sbU2gaVs2LdtWFah+Rbl0Y+gNalrVqu/EJQ4j
+v0klfovE1qTH7vqi3haJzrk53MGItWaZrkAIE+ryMgB+FGJFlkLzLa7QfqCEY6PgUEnpugBPKucN
+LA11vEadGE/0+Ut1yebDA8oTN8dqf4E/cec2MRmCZ1Asa+KBPFx12tpjqFRZ8IuCU92bE98nXLpQ
+sVoeUI/eXfVh7SmsbW1re8SeCE/BGINJQuYH5qyhKLY+SGQljrvUgnPmpU9UuhIfqSDKpiDUzrAg
+J5AHkaYLMiEIk2aTWNTOPm4lTquAUI0z1TTntCHEo7UkEok5XLY5rzxAt+dixNz3YNXs5BuCeJXM
+rKi1ss+zlEbsjl2kSpgu67N8h61rVkFp2/fwVWYtTlbiHtmZKEpnI8Io0VgeG06nKkn5FVNhIPpS
+e47ok03cuYfbHnFTJcuPbOANXULN2pylWIATJaRM4oG/opurMYrr/TyjOpGgShIStkQxduKarrxu
+ny1XbaNT+my0RKDba7Jx7FiM2sEFZnMxAamctn9hl8vnpKS902IubyzuXMZb1f1HI5Ce5uxN18RW
+1iWZFKWXZUYpBWovzBgfOTzJwXHOcd+9pxG7e7btH+Knbvf02g3SE82d2T2qpfQu/CZ+4DyyZENN
+Uo2IM1qMOiJWR6yXNImVKA7lKbSaNVPxQkzzimMlwhfpAUp34AcB1mpeTNz8njwsmYg9uxV2WIhK
+zQu7+NKUsKY8I9vNpOOQmymIxq/0I1J5nnsxDEPn3X258ija7z2b82M3z0Jr6RF5E5YLdwfizRwj
+httZVtGoVpTjHjaTqyVXbzOMWHF0BaFlNhW3Y5Hgh3Lz7YJCry8FfbWhhzdVu/TJTW6+4ogp0hA8
+gZcSQg6VPGoLrDL3Sgplw71Og3BFvBFsnCUdizynKYp3T/RZuT/D6CFeVCWe1I2ulffxT1ZZR/ff
+Io8J1MPg7V7kYtO5yfMwIxNFxWI2IxJIJRKEu5jf50RkMcE0xmuLd3GUs1JxNTtjZrfdQtUSEKZa
+fphAi7DNTqikqZhlEzwlTBlNmNx37dwP1Orc7jQxE+EKQkOx4glMHp61NR9fv0/VVuZ/zz10JzcW
+HLXdOQ9f6HI1cAtWPqmIVCWqmwNWqOJFooXpoDg7RmBiUrEy3zk8YD5y92+QdCkO3Bjcyt3C9ObN
+iHOX3TUN2x1GYz30vk9LILFeujgDpa0qSqa3jZXCV1gYrAQrPWOI1dARrOLA2qev+XY4y5gKev/1
+wzFLUcoXJqIWl+RCrNSmTmjVbvGapkfkWmTaDQ9PgDJDRKlxiUQRH9Q1J4Le+3mINxBQb9HEVyI1
+OxzRr94ds9k6fFjb2tb2iD0ZnkLg0d5u0Ki2qNXgE9oey5lcQ3+Ckf/VSUOQvNtK6t2vMiIlosoy
+o91SD74taOTSBWj2CLXbLO1KTGRCpqTP/cWIg1PnEi+8DXZdnpHz2xHbkdvlIhG+Judg9DX393Q5
+pWioIco7oiXUZDVdstR3e3JxLYZasvSezamVgAwKj0rn1pdqtTfYoNUX4nFRYDbdLtfrbBB2lWgb
+Z8xFBbap8KqMoZ5LiXgxZnnbof/C3ZSGGvXMNKZSiDE6caWr1vQN/GCF2Aywodu5/PkMI6m7koKl
+ZNWmYycz74+GjITZ8Ns+iv7oTjJOZm48D6cFHbnjplDib6dDMHPHHobfTiSYuo0skUIlk1hyISdX
+nZNRu+L4SBiLyYK5XPTx0ZLpkeTeKdl/yiVVE41FXmQ0jqRD0TB09p0Xukxzxrfcee7vbOIPVBqV
+UEt9UONJSzRoBnj+SpTCEMidt42VUFFJTyFq5dWUauh77eUvM146r+F80sCqO3TllTR65yBz19Qe
+bpE/dOXJcJlg1FRXkVFNV8owKp3bAivl9fnQY9N31zQl5f41N7ZBLyRN/3XEKXhQtQ3W8+htuQub
+VIZKLurSt/SEU6AbEJcrNh3V2pMBVjyIvh3iKxPfzKHy3I0JrIVoFSfLhV9OGB3dAODug7vcve1u
+TL3xgKfVMXgQjuj2HEcjscs8T+8WtNS1mY3rM1WryhTMAvd615ScZGJDMiu8fEUlRaOqNHT7ckVp
+siWFqIU67prFjKXk0NPu0h0HOEpyZriFoChGeGKLGolH0c5K2qHDM0+OI06cV455pab5UeU2BglT
+1bSLh44oZHL4LB6u+mIbHmWuBaI5ABGr2LCBrwTCnsKgWXYH79BN3LnJmCsb/mCRcuO6O97JeEl2
+JLy+qivBuS7V5Y+6+1GGGFVfMA2sHjw/TjDqR6kFP2Zxl/Kuu6ezZQq6D37gkzXUB5LkzIUL2Nhy
+/xdjy7yvzcKv8Zfq9qybTBvaLOYFDeEeitItzpl3n0b2nLtnfgje6pGxZ2EcZ3qdNWPlmsoSKv39
+8N49fvH/cIvo1p80tCQs7HWHGpMDumqNn2cLcrFnV5sZsTDrlpJcnKWFcAoH9w44uus20aZvycTO
+dffoiFIaqrZI8SZ6dt6hrcOHta1tbY/YE+Ep+H7EoHOOXlJB4XaXvMg5vidl4GGDjvgMTwMfBAlN
+tKt2opxUDTVUkzM2YJsdc7Jwu0CT0RlvXTpyO+lpPmGmxOakbtKW7Plo5xKjxMUPafRhugN1CQrN
+9vx3hLz6OZFpdH3KhlvBj+qIgZI6SdAgECw1EZz1JF9Qqo9/bCo4kCcQGgq9Z7zidJiW1ILqJsUN
+8tjtVsEiJ78jyfG7t8980IF2HxsGzMTbtwgOyIQKXdYBzc6LAJSTBaZWdUVy6BssqcUFkGRDfDFl
+l9MlWb5yk6OzDsxs4nblrFiQR6oGTANShSWTu6dkE7f7N4cBR+IdnI7ca9/7nUMunFPizEu5oWrG
++cvfiyd+isD4+FfdcYbKyOfFCxzMXgbgjflDInEPXLi8S3roQqXR7ZD9bXln0stIkpSD+25uRacz
+qp7bpUfZmCvPOncq3t/Dazs+xniobsfGeWLPhYQmTt5K/BmP/sDNpwd3nKseU7MZuuvvBQFjlaKq
+vOKLN5wX9icOr2A2FepJZ+ThwSlVJizINGeuMkJ0WJFFbryiKsPO3L28N5IHcnBEKb6JeRZyZ+HO
+o5GOz1S662Gfw0gxnXMKv6k9EYuCqS1BWtKJJ0xzN+nGd25y7U0Hy92ZbzFIHOil1VgwLdyNziUE
+YpKMMHE3rg4XZ1DTsH+ZrsKpKMlY3tVNkMrP0emEyal7Lb9/igndTf7Y4DL5WSdmiFEKvCFa8JkX
+8uK2m+SvHvhk01UYk5Iqjqz3E5odd56tuXDoixMynXPkJ/irXEQVEWYub3Fh6NzWyWLCaKpS6M4l
+mi13q079CuavuGPMvoZvxRvZuwzA+fCUNw/c3fdsk6TlHth6WPPwK27SbGyNeOWmk+Pw77vz2e5e
+4fwzHwbAjn0WJ6q03Lt2Vg7leM7k0E36xZELKeK6Js1d1t+eBrRUtj3ZeY3DBxrbg4yxRHI++W+q
+o3JqaEzd944Xv0dfIZ3Z9TBaqGhEeKo0JFsKZxYX6dTuAdu3fW4cu4diMjslEqjnXGeTRE0mUerm
+0+bOebr7KxKdkpsPXNx+mi0p1GtQRe2zlmkvEf1+1MUqZ2LwWDnXnjG0BQDbbrv/H95tEqzUwIzB
+r1fVgDHLyf8OwM3jT5GIN/T1z3/ZXefJ/EyY9uHhTSYHbj61/Ckzhch7G1ukTznAWK5qR7FckmiC
+l3fmlGO3sLw5y/iei+4+LKqIPQkp6Wjf1Nbhw9rWtrZH7InwFIg8zF6byL9KL3VNItPX7vLyQ7fT
+TOwR4QXnGl7yL7GlXTMUhboNEzra2b2wQ63avMlqWlLoDbwWx9tqNjp0LlmxmPPGiWTJ2gGtXXHj
+bV5h2nHvDXshw0iVDXkYz40zfuXQnVvmB2wIgj0NCtoSCCnTLXxRgrUvquMwHzARbHffj2huXgbg
+1I7wxSW4qUaeRbckkXRbdz+it6+uvtOCBxvODfbuQmjc+e84R4Px5AF+7Fzj0Jtw/UA8lnbJuflv
+AHBlGeEbEcqIGuyLR1+m89B9rtf9NHFLHAq9j1AcOw+jDn2al915dsTEHCwzutqN/aRkdN+BjPyX
+p7x8ICjxsmbzktultzbcGG+fP2C0cGqDVTZk8UC4iY//ANZIDp6MKlRHoSpNzaFH7znxCnjXuNB1
+4zU7BD9wn1tOakrNkVTVkjqdsaeqVNwN2O267wiXEVck69f0CmrRsfnBqp01p5SnEHsNvDOKd8tC
+SccTMbgdewe0JOTS73koQmNc1lx/zRE/vHbjdxhuveDe8zEXonrXbmDFqh1f6TN59ab7eZRwKIr7
+ec/j1tzdB7N0Y3+3WdBUR3DatZzcceH2xAR85ZbzhDa39rhdr0o478yeiEWhLi2z05R+0uS2UIrT
+sEWqCb9YNqkfup/LzYJKrarhQLx3VUUprQO/0SRUeOHHNQiBxnzJhog3EEb8aN4n7qxINM/T3FU4
+cuEee4WLLT/UjUnUIlvXbnG4e6eiEJV3o8qovJVOYI9EJSJbZWx3LwMw7CrP8KEB0SsuC22qlBu3
+HbAmDCLGuElj993DWNYxzzzv2HrOnWvSwT2w16dvEgopubfRwtQu3FioTJeFM8jcNYVek/KSO8/t
+WYfmVTcWNzseE5Vln1LI1NnaIJCQKouaZENt1r2QUsQx+el9Iqvseigp+tERVuClXS9nvuNc+8NX
+u6SRO4+9pMu/+4JDL159/nk3bvUOvjo/bTrj2quuCnTBQGBW3I4evpImlZGobO7Rbrvrjzf3aT9w
+x+iej0jEzpQXczjUwqmxIh9jFFKY2nBhw4VdF672CQfuczbwCQstSImqRHaO8USc4xkkfoS1MFWn
+aKKSrfXmjHy3kFdxQSQa+TjPiMZubEf/4jrpnusJubDn7k3a3WBeuhCs2+mTXHEVnvzSgo2FgFFZ
+wUEpoljJ2p8+sJRavO/NXme0Kr/mJQdtN1+Oj5dUb9dwewe2Dh/Wtra1PWLmLW3YD/AkjDkE5sDR
+B3QKm+tjr4/9/4NjX7LWbn2zNz0RiwKAMea3rbWfXh97fez1sT9YW4cPa1vb2h6x9aKwtrWt7RF7
+khaFv7E+9vrY62N/8PbE5BTWtra1PRn2JHkKa1vb2p4A+8AXBWPMZ4wxrxpj3jDG/PRjPtYFY8yv
+G2O+boz5mjHmJ/X60BjzK8aY1/X/4DGeg2+M+V1jzC/o9yvGmC/o+v++MeIhfzzH7htj/pEx5hVj
+zMvGmO98v67dGPNTGvOvGmP+rjEmeVzXboz528aYA2PMV9/22u97ncbZf6tzeMkY88nHcOy/rDF/
+yRjzvxhj+m/722d17FeNMd/3Bzn2e2Uf6KJgjPGBvwp8P/Ah4E8ZYz70GA9ZAv+ptfZDwHcAP6Hj
+/TTwa9baZ4Bf0++Py34SePltv/8s8F9ba58GRsCPPcZj/xXg/7LWPg98TOfx2K/dGHMO+PPAp621
+H8H1dv4Qj+/a/yfgM//Sa9/oOr8feEb/fhz4a4/h2L8CfMRa+yLwGvBZAM29HwI+rM/8d3omPliz
+1n5g/4DvBH7pbb9/Fvjs+3j8/w34XuBVYE+v7QGvPqbjncdNyD8E/AKOkfEICH6/8XiPj90DbqA8
+0ttef+zXDpwDbgNDHLT+F4Dve5zXDlwGvvrNrhP474E/9fu977069r/0tx8Efl4/PzLfgV8CvvNx
+3P938++DDh9Wk2Vld/TaYzdjzGXgE8AXgB1r7X396QGw85gO+98AfwFYUeFsAKfWSkDg8V7/FeAQ
++B8VvvxNY0yL9+HarbV3gf8KuAXcB8bAl3j/rh2+8XW+33PwzwL/5wd07HdkH/Si8IGYMaYN/GPg
+P7ErHjeZdUv2e16SMcb8MeDAWvul9/q736EFwCeBv2at/QQOVv5IqPAYr30A/AncwrQPtPj/utjv
+mz2u6/xmZoz5GVwI+/Pv97HfjX3Qi8Jd4MLbfj+v1x6bGWNC3ILw89baf6KXHxpj9vT3PeDgMRz6
+u4AfMMbcBP4eLoT4K0DfGLPqVn2c138HuGOt/YJ+/0e4RTBBdncAAAGXSURBVOL9uPZ/D7hhrT20
+1hbAP8GNx/t17fCNr/N9mYPGmD8D/DHgh7UovW/Hfrf2QS8KvwU8oyx0hEu6fO5xHcy4vte/Bbxs
+rf25t/3pc8CP6ucfxeUa3lOz1n7WWnveWnsZd53/1Fr7w8CvA//h4zy2jv8AuG2MeU4v/WHg67wP
+144LG77DGNPUPVgd+325dtk3us7PAX9aVYjvAMZvCzPeEzPGfAYXNv6AtfbtjcyfA37IGBMbY67g
+kp1ffC+P/S3ZB53UAP4ILiN7DfiZx3ysfwvnNr6EY6f6so6/gUsAvg78KjB8zOfx7wC/oJ+v4ibC
+G8A/BOLHeNyPA7+t6/9fgcH7de3AfwG8AnwV+DtA/LiuHfi7uNxFgfOQfuwbXScu2ftXNf++gquQ
+vNfHfgOXO1jNub/+tvf/jI79KvD9j3PevdN/a0Tj2ta2tkfsgw4f1ra2tT1htl4U1ra2tT1i60Vh
+bWtb2yO2XhTWtra1PWLrRWFta1vbI7ZeFNa2trU9YutFYW1rW9sjtl4U1ra2tT1i/y+ueZdNY7uB
+SwAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3840... Generator Loss: 0.7229
+Epoch 1/1... Discriminator Loss: 1.3608... Generator Loss: 0.7311
+Epoch 1/1... Discriminator Loss: 1.4321... Generator Loss: 0.7518
+Epoch 1/1... Discriminator Loss: 1.2556... Generator Loss: 0.9692
+Epoch 1/1... Discriminator Loss: 1.2830... Generator Loss: 0.7561
+Epoch 1/1... Discriminator Loss: 1.3029... Generator Loss: 0.9397
+Epoch 1/1... Discriminator Loss: 1.5198... Generator Loss: 0.4745
+Epoch 1/1... Discriminator Loss: 1.2650... Generator Loss: 1.0075
+Epoch 1/1... Discriminator Loss: 1.3682... Generator Loss: 0.6288
+Epoch 1/1... Discriminator Loss: 1.3223... Generator Loss: 0.6662
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmsrWl23/V73nnPw5mne8+d69bY1UN12d2e4iSyGewQ
+I5IgRQGsBIgiAQKJiC/wBRQ+ECGhICUiiSJigRocHBAtYsd27PbUQ3VVd413ns+4zz573u/88GGt
+c7oK4rja7Uoq0l714e7a593v+7zPsMb/WstYa1nQgha0oDNy/kUPYEELWtCnixZMYUELWtBHaMEU
+FrSgBX2EFkxhQQta0EdowRQWtKAFfYQWTGFBC1rQR+gTYwrGmJ8yxtwyxtw1xvzVT+o5C1rQgv5o
+yXwSOAVjjAvcBv4E8BT4JvDnrLXv/ZE/bEELWtAfKX1SmsJrwF1r7X1rbQr8r8DPfkLPWtCCFvRH
+SN4ndN8t4MmH/v8p8MXfdxCOZ33Xp+qVTOMMAMc1GF+GZwCMOf+cFyUAZSn/WuzZVbiOwXWE11nX
+UKomlCc5Z1pRWcq/xsCZouQ4oLfDGuCfokAZ/ffatWsUcQJAXAyZxoU+28HGKQCJKcgy+b5I5WaO
+AVvIZ9812FLu6AUugZF39SJ5Rl6A1QGVLqSF3MumFk9ZeZlaHN+cXw/g1n3sRJ7RqBj8ug9Amnm0
+lwIAJpMYrzA6Drl2Pi1w9LNbaYDOkesZCp1vHIM5myNH5zUrzifGWDCO/I8tyvM1K3TsAMXZ3Ac+
+rsokP/Ipcvn++DRkrSOf954+xnVcub62LO9MTFCR9widEKt/zyenJEms61Se39t1ZMCFb0jKXD5P
+vjceC7gf+nxGruvq2Dw6S10AmrU2QSU8f9cs0/XRdZ6lfUZjGUM6n5PqXrAfunNZgmvOPuv35nv7
+lA/tyT8qOnu/AnrW2pU/6PpPiin8gWSM+UvAXwLwHY/Ly7t8tjPjdz84BKDaCIg2ZTEca/Ei2Qh+
+YTgYTQFIZjMA0rLAMbIJmvWIRiQLV3QDppkszMndPrl+nk5kczg+FMKDqFYMs5kschZBOf/eIp2t
+aaBj/5t/439gcOseAO8PfplvvT8EoF0NmX/wGICH4ZiDwwEAo4fyvCC0lH3ZQBtNn3Qu07+63WQn
+XJJ7XJclPJnkFFNlPPWCRxN55/zJnHZVrokfpNQ35V2PRjK25g+vUP6WPOPLn/HZfH0LgCd7bf6N
+f3cXgN/4nfdZ7Qc6DpmAd94YUW3JOOsv/ijuXOaq2Y04PR3LVFQ9PJlyyoZMSu/pCSaU9/DTAqch
+n4uTlCIUhjQdDs83/SiWZ/jb6zRMRcZwY42hzsvf/N8u8Z/8nIzpv/rP/gqtqCXXv/bzAEz4gEsv
+XwTgYnQFW2/L+//GV3j48JZcy4QWDQAalTkA4w2P2/OejOE3B5TKAG1uaSjzyq3BevK51ajL2G6u
+8qf//J8B4Kc//6fYefEqAE5hODqayJieyPq/+eQr/MrXxEJ+8va7PNK9UNqc3MjzknFJXZaMWPdb
+GTrMdF7c0JLHcm3hgZWvf18yH/rs6NjL3KK8EltAU9/v1NpH/+y7CX1STOEZsPOh/9/W787JWvu3
+gL8FcHVry/43/8Gf4y/+z3+TaUOli/F4fiqTE0YFbWXuUdMlm+pLevLmTWOIlGmsdJvU27KRdv0m
+pimcezav8wuPZJFaVfl9L89Zqsg9hhm8tFQD4LvFnAvKAu7OpyLigUQ5ezqb8Z2JMIW3vvWUgxPZ
+HEXFp0jl8L6aOAxqctCbS7Ixf2k65+KOPO/dvOSFTdl4D0qX56+JipCpxnB5qcKRJyd9M4+5XJPv
+K+tN/mFPmM3GzZA7jnz/U5fkoLxvIq7/vGzcrzx8j7/cuSLXtvf4+3/jTQDWt0oGG7Iz04kcQP/V
+JqOJHKRuUlKoBjKfzcGVyfdnHtRUY5kLw6qEAV6qaxp6BFY2ut+tUsa6M5sJSSJzUHHkudVxxp6R
+9zt5a5/lJRn/1/+fP8vFv/x3AJiu3eColLW8fCDv7DQNrULm7Qs/tEv4TTnof39S0NEjMo9TvEK+
+7yfyvJ9qbNP2haPV2jlfGQijcxzDqTJ9zzdnChJBJvvm6btDbv2j9wFohNd4bUXG89LqJlYP8r3k
+AQDvvnXM0zfvAzC895R8KhNzIQwwNZkLt8h4P5E52mwKU3xsUzZcEWr7ccZuRZjpfVK6eqBPsu9p
+Nx+mM6Wi6jvMlUNsNSs8ncj4ncBwqtopxcdTQT4ppvBN4Jox5hLCDP4s8G//fhcPkjm/dP8dftpZ
+5v925WWyKCXqylsu4ZLrxnQnIYGy2ottOcRBw7LSUBWv26Kzsg7Adt5gPz8A4P5uwc91LwHw7Uey
+QecnT0l9maim9cirqwA04iNGDVmY5V5AXxekNHLgn80mPP4tOWDz/j7eQBY0bji4maqzS2v8yed2
+Abgzk9//j1sdHh3JYfq1R1/HUSnwfNTm5tq2vN+2MresxtCVC6o9y4PDOwB8I+3xr+zKOJ+Edea5
+SCl3VX73w+EFPHcNgM/uzOmsywF6+m6N5Z3bALRnlxm8eQTA1hXZmLdvp4RXzjZNTubLOMeTKW6q
+qnTdEMQyL2fSrF0JMTVZJ8eHfCS/qzcCRkbeu+FUSQbyvW+qAExqCfm+jD0zBdVc7vGL05i/e/VP
+AfDnvvo+1H9J3u8zPyn3yg6I1kT7yb4z563bb8h9N2PsXN51pVLQm+u8PCeM+SRd54WqMIJ/sJJy
+Te2txwXkVsfs+RSuHNjIk6OROJbffv+bMofTkqufFcZpOk2OVTt9/LvfBeDB299i+FiE8UkyJ1Tt
+IPVDWrnM84E7Zq0iv3OUiflFQqwmYTvwKaqi/bTshFz5aqU/J7GqCagZ5LoG5cGE1RBXWYTfrnMl
+kOf1cBnPhfmWUzlbfxB9IkzBWpsbY/4K8I8Qk+bvWGvf/SSetaAFLeiPlj4xn4K19qvAVz/OtZPZ
+lN/+zjfwn/RpIKxvrd3APZXPad0hTYU7Lnc92ploBS/eEIlxcafB1pKoyXElpIaoiXuzjK1crtlb
+anP9kVgwSV2kZPt+iw8ORHpUy5xTcwzAbsfwbipc9UKRk6pqt+yJZtJ/9zFHR3KP3lHBxY6wczt2
+qTZV0r/Q4nOvX5Zxrj8PwFY/5+L0VL5rWt5+Kp+X11JWEbW05YmWE2zXsaqXP24NWA8uyDixeHui
+Gu+EKbtN8RvdGosJ43jvkD58CsBnwy1qd5syL8NbdE9kvm3tmIovc3t6T95/tX6RoDwzKcZkarp5
+sxhP1dksdch1bs/CVrYoiRz5u5uBF8kclWTUA5kLtyhZ82Qcw1zmdd5z2JvJmsb9IXlH7vd/vPce
+23/9KzIX9n26W6IhVL4rc9W40WQnknus10+4tSnr/moxZXVbpONmxfLgWMyxSk1k0ZHjsenJvmlU
+fV76zYcAdDF8ppAxH7hNRh2RqocyhUxyl+hY1qHX2uPp10VDnK59gfmezMX9e2KWvv/GY4aJrkMO
+bdU2Uj/lyMqYj1PLcipz30Pev11ahmqito1h5oiptOt63LGi0VxwXPbVy9u2ct9RaYn8s3XI2a3L
+fD+Np7yi1zhexucasrd+7V+kpvD9kk0L0sdDxk5GpJGDOCmoLalHPs5oLcmGXV4JeXVd3BXXn/8c
+AJeu7lJR1d8nIU1kIrfMIdNMJn5nf87JkniwXzlRB1BkGSOrn4wLAt3qlU6Fl/RzHg2pjUQFrXbE
+7DjZ/yYnE1E511YDGlN5nunWefULYqL88A9/jp0XfgwAN5dFKdZn7EzkcARLcxrqZQndGdWmXDNP
+1MEXdM8jFbX1kieJ2P5f7g+4U+kDkBUZg0I28UYg4312b0QcyO/iKkSX5Pv6uM3kRWGQLyVzBmM5
+IHFTna7jhCKS9wtTi6de1cJxaIVy2DIbkFo1jyI5EF7pUszPHD4OZU3G2fJcUqvzmYRMNZI0P5Vn
+TOI+DQ21FI2Ymm7u6d/+Fd7blM/5yYQ8Ed+N2ZU1X+mscn1H7rVjGnxpS8axvfUTXL4pDNV4BT86
+UGdz8mV5v6WU6VhMitduHzH8oQ8A6A9rJFVh8O5BCzOS8X3zlgTPfvnOe8TH8l1x/xkPq8KcsmGf
+o0KueXRw5lOKMTN1EkYOtaqaICkccxaJyhiq7V+oCWZDh0B9B17FY1knv9J2uHbmSuwkrA9kvvxQ
+Dnclh1AjQ2nmUVEz7kItQK0xKpMaWU3tVLGk/0BawJwXtKAFfYQ+FZpCaUvmWSySUTlc5oCjTrto
+KWJjWSTpj/zQa6yv3wBg+4I458LmMn4oXNQWOS2jUsJugkqrwdITVqfiiGrsiSOu69SpjEREPT05
+pFIVJ1JYW6Yfilp9Gnk0G6q2bYqOO+n1uaChN5uk7Gng+ebqBj/2RdEOLj//BcKmSGarDimPGl4p
+v9tabZJG4rgsTA/PiBbT1phfOShxVBrH+SUuXBAz4cnwiMqh3KP0Qq42RPM4GosEm7sO/UMxg1rX
+wLwvc9RdmzMyIjGexQGOqq7OXDSGdieDTObCkOMkMvet9hKRI3M7mY4w+rlWkbnyDcwzlZRxScOT
+ewSOIczlc1yx1PXesYaLBzOXZqQRjFYHV/9ePB2QFTIvoWMZqINyZ1tU/Feub/G5SLTCtQsrrFZl
+Xlau1YnqotE5vgvLqnqoc5jcUtYVk9EeYYsXAJj153iZPGP2XMB8LFrkAFmP93tPefdIHXv5nN63
+RZMrXk558K5cG0YaWci8c3zDRrVBQ83K5tThdCQmwbpr6StuoxJq5MuxrNdkXuutkK2GOGPDWoUi
+l2ePopiKOkEbVXm3cpCTOqKZpXMXP5Df5UlO1tKzY3MI1Bv5MelTwRSshSQp8Ywl8OWFV12P9ZZs
++OW1kFcuyEG+srHN0sYGABUjXtrASbBGcQz1OjZRezhMsYX6A4IdYrUpQ8RD3JyOGV2RTdNeaxO2
+hWlcX2lxeirXjtxHHKnalTVF/UzuxfQzDdjbhKtdufaPfekzbN0UkybqruEEob6fgqnKAtOSw+RE
+Hn5dFrHM2xTqLTe5fteZUZRybVCMyDT6slN0eKBMrxJ6bIYy5pbRkGb/iLwlm9xkJd4X1RxLA8qx
+zFdZ9SmnstlaKxqjn9ZIJjKGoOqy3BAGmJ5MGKi9b6KAmkZ+yliBZUGFsKVAJ5PiaaQiqlSJ56Lm
+VusumWjg1DJZp6UrEU19j+MHHieFmETxU6irOp8Mc7otYQavf/ZVAJ5bq7LR0XcOq7Ql+ooXNvFC
+BQA44Ki5UmZyLVGKq2FWv1WhTGRvVaqnlBMNoyanVMPrANz4nFz744evk0wlwoEbMzqW/XL86AGT
+IwUnje/KvRyDU5HnNjerbKigStMJ2woiO809bmhUYqYScCmEG0uyvzev1GhVZcxtP+JkT66dejNs
+WxjLyVMRWFPfpRbK/MSNkqrClIbDClX155SRYZwriOVj0sJ8WNCCFvQR+nRoClisLXGBlnLaVuSx
+3RVu12hWWbl8DYCl7jqVpsZ5C5FExayCp7Bb41jMGY40C74Hwa06VDTom6uHP1tNWV0RBGXiHNBu
+CICmulGHtkjbWrGN2RFVMrCiJv+TW7+DVTj2uh+yVRXT5uVXn6O1rHDsIOQcYOqIRHHwQKGxjl/H
+qaikGZXk6ow8QwIVXhUzkShDNjYUgczLzC1ApUBt5hApPiFO5e9BtU7lDNqbNcjV0z3PDQ01CYbT
+GF8RhvOBiPBmkpDM5P3Gowg3l2eTllQK+Z3nd1EMEp6CtMqyoFAQWWDF2w3iULOqwQdFCo5oQPWK
+SM/9uI6pyfqV9RHuXM0H9w7Fk315v5daRJkCtQLRJC5UJ5SJSFWbpgQrIlVdzweFk+PmEJ7B4hVs
+NYoxkW5338UpZd6cIqK0EpaxToGTyXs110QCX35tm+1Y9kiRTLiYivb27jsDRrO5PkOeu7m6xFSj
+QNeDkOlIJHriO2yrVrRWLVhWYNxE1f3lmsOVLdE2O7vbWFc0Oj8as7stY+4NZjyb6x6ZCGZlvZYR
+K2IpabRQQCOhOyetyHvXr25w+jvfH276U8EUjP6HZ84x/l1yjhVH7l3o0KmqDuu2yAZycGZTUYvC
+ikPVk/CfiRIcFJ9eKUBNDMda8lJ+50UScWiuLfHciy/KtbcMiac229IWy2rLBdEKVzdkg4zvyO/2
+/e/wtjKYcbvklcsytm5zHewFfacUEK/9Wb4AuYej64oNUBMfG3o4kWygMpNnmemcPFOI78oIbyKM
+aXVlm+4tMX9iM8dqmPTiiszb0TTFVQjv8GiGpwypOq0Rt9X2nc8ohrL5A42cPHPGeLkc6GrmEnky
+9iRoU4lk807nGZHas3kghzuquzhjeUbFr2KVgeAXRMqco6KGuiAo1LzYjAuOnsg9mlFBWMhB8XpH
+LK+IH2Q+vseFSExFo2HP/iPLpS05eCUdSvXgm2SGaepElw3OkWF6aEy1wM7lecY45/h24+aYTPeL
+m2BLNX+GsvduU+GmQq0/GCyxvSPz3Z3VWe/KmvUqEnEqGgmXBuLXCKtzpqk8wys9blxS/5dXYM7y
+TeZyr80XW6zX5e+1egPfyjjdzhIVhfLnp/ep5YKWPFwVP4qDy6QnviRTBpgNjWxdznjal/Hff2PM
+dCZm4celhfmwoAUt6CP0qdAUrLEUoaVpDUEinP3RUUzbExX+layFOfOsmoRcpV/vULhhpxURaH6B
+N/dJY/k+3GjhnulUaQqaGWhcBQK5hvyK4tD7Cbkm+XQqG5iaSIGKt0RaCsdfvSBOqP/L7RGpmPcP
+wCie34tWMRogtqX9nuqqqoJNDU5VpZVTxZyZChMfUxFtw1MzAcdg1dRwp6tUdC5Mt8aSApZOqkMy
+BalMVFdvd1q8e08cXy23ge0J3++FfYKZ3MMOC3JVk0dqRtRC6I9UU2gFBJ7M91I1otCISeb658lk
+9bNswZmhXlGNKEtwl+X9ytwSqYpeNFwcM9G5lWunQZfmjnw3j31CncP4hT72O6KR+UcpJxUxJW6/
+KxDtKy9dJ5vJtf5W5Vzip+PxeZanV/UglnXITnQOVwOcUJ1v5ZhyqNiDyMWqs9ZJS0xF7hHV1BH5
++BF7pWhpXnfGt49lX7z+Yx7OgczR6q6MJ3iwSqsr1ybpXcpM7nXp4joX1kXbyGo+oz2R3FtNecZG
+a4tORU2GSo2wJqZEEC1zOhIIdT6eUozkObWmPDedDbCqsa163bMUHdw44+REI1enE/Ls+4s+LDSF
+BS1oQR+hT4WmgDWQGuLA0huLBItdDyYa/iqrGM24O9kf0p+JdIhUesa2DamG8loRuaYZm9mU0FeH
+UsWAK5LZDTStr2jSMQKfO+n2KNRZST6i2xBnVq29QYlI4ZMDyZbrJi5vKVtuZAkNDTP69RoEIv2Y
+5tia2IzWO5vmBBK1dX0PHEUCBvm5lDOuSC3P5qA4BuuF6riEXXeN6WdEqtz63UPiRKTq8y/sArAd
+BHwewXHcefyUQUOdmWnMXCW+zXKiXORBpFmpNm0RtWQ8wdYy7Qsi8cKkQqnhvSJLMaXMha8JN02v
+RUU1NzdMydTp5gYtXKMp1/MZfl2dw74mLW03GB3KtdW+TzwTjSD7vR6PXXH8lcMpW4nMoXsgPqPj
+yibeZxWuO/IoHJH4WRbjqX/BMy62pVphW661hQeaSGSdjMQVR2rS83CRe0yThDyW7ydzGW+nBvsN
+QVXyrOTK2ssy/p0EO5OxpT3ZK9c6E3Y1jfxxtkmuW2F1pUOzIr6GaeFRKoagviq/X2st0WhcPp/j
+RB3aw8kDjh6Ik/O7D95mUpd3Wr2vCNrcwVZl349rCU5f3iOqRdRV2+iseeRjdRp/TPp0MAUsJZZk
+XjDTTLBJkbOh2YUzN+P2kQByyunbPDoSj/TZ4F954UfxNHd/o3YZ6wvTSDKf0hevtRc36O/L5h0p
+PNUjJFRP/vyOxa7LtUUYsLYizCIKa2RnBV6m4vQak1BMxMkZVeqsXZQYulONoNQiG2VCNngIQNAQ
+iK4pXc6qMpj5SKu5IAxED73RzEIqdYynmzgrKQs5KLmJqB7KZlpODPunGt/vi6Nq6cYOR74cWLsX
+k58q/r70aITy/vPIkiYyjvFY5qrSKKiqM7catinGMp+2WaWm8fYwhSRXeHNVxlBt+jieOnOzhCJR
+tTXOKfVd89mUhppHob6eMQVBKn8/PnjARE0+6wyI1QmaW8sTddZNjvYAGHxxndvPJHp03X+ftKrg
+nm/3aVyWcbQ7GRVHsdKpPrBuKTzNypxaTu/LHhokU0JPo1ipJTuUQ8gFuVe9uUH1ljDeI3fMUDNi
+p84VlnRvlTqv9dVLzJShmYMYX/EIcVmc1wA5ymbMB/K8a5oCnnZHmIrspzyN6T2Wvb7Xe8bD90UQ
+/ZPbB+fFFS4oAMyplXRHcvjrvkupUHg3Log10hLPC5xEsRofkxbmw4IWtKCP0KdCU7BAgaX0DPNY
+4/iuw7Gq16fZPsuFONf2ZwP2h6Jqjg+Fc85mv4pjnwPAa5fUm8olByccfyCS692Tb3Pvu8J100Kk
+1mecZSarIgU76QnBO8JR/Y0Wa6sCY6zP13EiRSRWhNtfqaxyNxTpUQlCKpsqmZmSHgiX7x3f4vCR
+3MPtCNy546/S2RKHk5vEFL6okWG1jjfXexSKR8ifsf/135D3HD5j+FhVXA/ivkjN06c97pXyfo0D
+qfjz2sYyax25x3bX4Gplpfy4wVkhrtBOSA9FMlkNFY5Gc9qKHsynhlOF/uZ2itdVaRs2JHQMeApB
+n80KxulDWZunT7BasaTuRYQNeXboBoSapOVVFGoeNmjPNKScJ5wW8rxiNsVT53CRlWTqrL2r2k90
+94TrN0TK335vyJ2nvyd/f7jHpd8Rx91rP/Iy7U3BnHQuiloeFlskc4V5H32T9975bR3nOhuBmEqj
+2hw3kPXzejLf+8GQbCDq9/7JhJ/5URlHt7NCEIqm0Fr9LADDx79OW83O/tM7HI/l74/eO2aqRU/m
+0xlWTc/jdYFJR/7zFBfVyRtFtDWRahB2qSzL2F6/6bBeF3j3BUVuHifP2DsSEy3rzTjUMOzlts/J
+kcK74xnxWa2+j0mfCqYQOIbtqsvBMAPNE4gteAoP7g0dVtVj259b5gMF5+giz/I+R89E7bt4YYgT
+SXTBiU/ofSBVcR7t9ckUrrxUk4M06DqkA42rDy1LVdm4g96Y9WPZpJ2VAQpvoKKe3rQ5JlDbeM1Y
+/ANVk/djxndlnAenY976unjMYyP/1lP44nWx9+vbG5Qax29sXKe6JfHm6cNvA3D/997nzl1Jo9zo
+hISbwljSeE7ckvef1B9jnsk4p/syF3tXnzLoawUia2nsadHH1TF7+7Lhm9OQiasHEjlI60t1ylg2
+oGM7zGPZbK3Qx0616lPdkrkKMirUFOvPODkRWPL9d/ZxjYxnc+sSS7HYu1kwonqq6dUatcnLmFlT
+cy2qLp1Mxnxj+yK3H0g83uCSaiWqth6qeFbDn2s8vtKgFbwCQG0y4/plmaP8ZHqO/c/7mrewuk+S
+yNievvmI9E3Nr7hRgYvCLYP5nMqa7DkTC7e8brY5XREG0Z7eoYe8U61bUmkIE/WmsrGyI5/ffvMd
++Y6MUiNDO16VqTLDqZ2QKgNsae6DneSkmqpftxXymuyh1WadYUsE2FLs0ViSz7VI/BOzo4yrF2U8
+h6cxvVPxReydxjwYyfruj+dsqW/r49LCfFjQghb0EfpUaAq5NQwKj22/ZC/XmKrv0L0iqtjF56rs
+D8RkON0fcPuZcNL6kkia3WCDlZqoi25QEvmaMemGbK6Jw+hV0yQX4Bm/+Ouifk+++8sMEpEYu+ur
+PH9RpPjF1TpxItzfelexijArZyLBdtqr3HxVnv0j620anxMnX+HljLty77d+632GpTg03zwSifBD
+11bZU25+deWAclscl9XLbeZPVLV9Qzzdv/n1r/PiT4i02ju4wuy+aBtBZYoTyrWVecAcUTvv1UQy
+NAZTYi1IMzga4n5OpecgwWRaIyKes+ppNKMrEuzkaM6juRQ+Pbl/j23NCHUrFVYqIpmTMiNsujoX
+WgvgNMdRbMXV611+8z0tR/b4NrlCnrdWA1gTadyKZI5zMyRzZO6Xgg2K2kMARis7XOnJ9/dnI5xV
+GfNLf1KKge9eyxnNZW0mb3yLf/wdqYvQ2rJMw8/L85YaUBNpHJwlc5kWyTMpq5YePSR6Rd7/N2+9
+w8pjMStrNZedmUjhtV1x4D3/mQ7RspggK+9EtDuKM8mH1Fx574eeaBJv909oOrK+Ny9foLUsGmsQ
+bjK+LfviznDKiRVN9dp1MSWXs5LsVEyUWSWm4cr3eTmgHonqP1oP+NpdgTeHqWi/Wx0HR4varK50
+sZ6ckcfPYsxYNK+O79GwWp34Y9KngilYU5KalElWEKj5sOV6XFZVdaexxngkzOLiZ3apb4pavaIl
+y7eXN1lZksMftusYqyEyJ8BoeGdpeZMaskg/9ryoyQ+bK3ixqO2eN2FlUya4ZquoKUuazvEU2usE
+slFOfXhuJur1jS/8a3iu2JlOJaAayy5cWQ3Z6ErG5MYVGeel9QnLLakUFPoF3pKk73qRh6fw51sH
+wvA6boPVExlvUs8ZzjXjcpqSDOXZfsNnKZMDW9Mif0WekWrJ+X5m6ejmGE5yoqlsxvF4xgUFQKVa
+VGSeO9QUQOT6GaFCf+fJgAMFQPlLHo2pZuVNZaP14x7+GWjGRLykUZvCMaAhyXReEipjQetdWq9K
+qenEfpCBrt98f4/gLWUmUZtXtbjKq6GoyZHfoKLvN9tu8CVHAGWN+ojmqvolmhU8hU1bV+HMNmN+
+IDZ8u9ZkuSbMsns1ptSwbC0KCRMxsZa68jzPWWFTD+bR6ojnl4WpFVGH7FQY9VJfmNTFhs/tU5nj
+m9OCrS2JktSW10g0W7M6PmASqdmoxXjJhwwm4n8Al9II4z0+OSQfyTjcpmVbTRCrFaauNLrMNApW
+a7WpK0T78OiYvuadLNcjls+x9Ud8HFqYDwta0II+Qp8KTYESTAyZa8jVfCgoaa1pZtnWNtdviqQZ
+xwNembxapC66AAAgAElEQVQEwDQRntZoWEJEirvDOZmrgJZ8SqKdU9rhGhpu5/UlURG/EL5ANhTO
+bkrwtG7h4eiEUV+iC5M711n+vIJ+UvX0P5rxTIEp8fEYd3VZ7+HTviKS8qZ7kxMt8LGt4JbuWot2
+S00N12KsaAVlsYypikTrXhHJsLN0gZWbYhJ1/TaXc3Gkjg/3mB6JBHqcfsD9XKRjpHUUm94m47Zo
+Uo1JQFoqSOeoZBTKM2oO9DWGvrYl470R+hSKkSi8Llq9nI7xcfXeTpaDZkRaXxxjSTKmooVOWjtt
+mpsyMRXHZaYplW6esrmmpdK0QnV8dMTkUB14HYe8lGe39tqs7H5G5ztmeUk0hfSqeN6XuhFb6hD2
+rE/8vF47e0akyW/DwlDrP5TfPRGb0S4PKLWAzWr9Is3nxXTreFUCTf6aZ0PMTL4Pq6q2l4dkByJh
+884FLv24ZOtG0SqV6tdkDqqyTsXaXbJ7Mi/fOHjMUlc23M1rK0Ta4KZV3OTxqaj5kZaRjyd16oXs
+hck0ZzDU581TCoWvh57hkvZBWVqWueisNnGcXXn2oMeDUB2Y9yOKUrS0l5YDGu6/hOYDBvKwZD4u
+yRUodJzmPH0gQBB3MGZtRSZ+Z+MqqWaAlXpIJzNwFa1o3CWMeoh9z6O1KovsFFUirWRUN9pkJjAk
+WpfQ5im5qqjz6ZxHxxrZWBoy76kKp/kXJzsQ/I4wkOTePnYoarCpLuOGwiC21j7DykTNmJdE5fZ9
+g6dqu00gn52pqBGl0VRszRDMo5xA03/DxjKtTDMVazsMl8RXcWc+Jnqo5btL2RC90RNSW9e5yJho
++480zNgfynxWkgqtFW18M5fnrjW3sNqbIHQjjEYJTFkltrLBgjwgUBXVqOd8udUhQp635Fmslif3
+Eo+lqjDfIEoJNHKTa9ckM53S3pIxV6MUX3tOTJ0juv4uALWGYaq5EvGhMJv2UpumFtQJOiukGpWg
+0iBFAUfFEb13tIhMTcusm2MIhTn73TrRTJGZ3e55PopbNLAaZrR6NMp5ypGmxr+6dhVXnRRu6HN4
+lmmqdn3X67C6LmYJ8YT5qWYnTjwqK7Iv8kHCutqmhyMFupmYY025bs2a5Lrubujhar5OkDosa2Ww
+pgq1SrWBoynufbdONpG9fBobXtSan9ZJyc/Ckx+TFubDgha0oI/Qp0JTsCXk2ojprNfg3FpuPxYu
++ODOI17YFQdPzYbkTS0hprnyeXKMo5DhWekRKquz1iMwWhUkNDgq6T31jAVeSKjY8axIGGveebUZ
+stRVmPMuZKp9zfvipV55cMK72nnqpHhwDm02FQ+0hLkTFjQ6Gvf3RboYMtD8gyKc4mmdROM7+J5I
+20jzMkanTygui3bkNEu8WJ2ZruVkpgCgyKGhZsc4Ecnn2RmT8feat0zqIpWy/pRmqJl4WUqpGkJF
+M/WcsELd1TyRAFAJPR8neKXWTqhAo6sOzVTrIYanBA1t+tJdwSpsurpepyDRua/jnNUvQL33tT1q
+vuZDZCHHPa2qPW3j/7S894XfMywlWnpMy+o9ufWY9Qui6XluC1edb4XNKc/Kq48CorrWpdAK3GW4
+iZtKdMVrlLgXRKI7NsENtOZhnBIr3DrTPp6mKNi9LnuhuVQ/71NKlmJVs3RdMXNvfO4FgqnCmR8c
+czTUYiq9HnUtpxfUGiTa0i7UStzFccIkVWeuOaVsyNx3Mp9Ay+WF9eXz+o9ohfL5pEeu8zqYpMS6
+f7cuNLjkytjuPx1wR53iH5f+0JqCMWbHGPPrxpj3jDHvGmP+I/2+a4z5FWPMHf2384d9xoIWtKB/
+/vSDaAo58J9aa79tjGkAbxhjfgX4d4Bftdb+NWPMXwX+KvCf/7NuZIHclpgSzlwixkrLMoD+s4cM
+tXFILY6x6kgsNGEoL3IShZR2K+BoVqMtAlytZJSnPoGi3HxXbfxaRHlWry2e42mtgKKfsqbluKqm
+ec6ZJ9qXcK+T03ggWkxUWSHvCX7B7W7h5pqVaD0c73uhSvmyPE9aMXGKrxqE4waUda2QpBDsUecp
+/kxt/AtrOC1553x4TKRNPcwkIddOov1E/BqzxwW+htCysMlMy5yVSUlXy5UluUtYaIhPC7hWVgqi
+s9oT9QbZXB2wOee+jWqrga9IRg+RSrlTxyrkOSwLfO3Q7DfqWHXc5uMphVZunQ0lBByGLbxUfBzT
+eUwSyti6y89xeSphxut/doN33hIcQiMRLS04sjxEJOwF0yC6qD4jG5BqZaV+9hAKfZ469cLKPsWx
+aorVFo6WqaNWwSoEuywyUq3mFSsEe5o1WVsVx17r4iWsZpcm8SlNRNvYXpZrK+kWR1viGD19cJfh
+kezJve/exX9JHJ71awEVrZZVlqK59eIjnsWy16em5IImNY4M1CLRhCpNcLRPhldTBK2bMTuY6rye
+MNIQbzy1VC+LBvjwnfu8t//9aQp/aKZgrd0H9vXz2BjzPtKC/meBH9fL/h7wT/gDmAKoCcFH24E/
+1cP49cEJf1w9stNui0wPcnwiG9MWMQMt49aZjXCQBTAdl3R01rGnj51L3N/f0jBEWQXFMZA6oGCa
+spbhFcIAkpMJZlU2cqnP+HyxwbfqEs/+xtPHvDiVoiZ+8jylZvWV2ZxUGZV31sB6luNUtOBKUgf/
+rHxYgOtpE9eWMJLapMLoodyreSMn7ykDTKaMn0l04eh4nwNlEBOFyU5Lj0Zbxt5phMy1TmA29Zmc
+qfnkqJ+QSCtYe06A1XTo0HMpz6pjV3xcLVPnzmLKQKMPCuEtnAyvPGtV7+Hrxg0qS5RaQ9Oms/Oo
+UumKSRFOciY6x9WKQ+NUNnFewM0fex2AjbqhsSH3e/iOgH+e9e+yFcsh7HQ/hzM+s+1SjKZGj09m
+tPaFqdkVhaZPJnhaQCUthkxG4oirOTWslUOaxFMybRgz1RT+PVK2lUGW1sdqzcj0NKG6KXOUml2Z
+i1aPK8oUpq0O750KLuJxfMqulgusedXz7HlPi+ycFgZnIGuTdj1iNUeazQZ1zXYsqnVCR7E4jqxT
+nvVIZ7LHJidjSs2lefFCBz8Qh/aN+kOeKZCLf54doowxu8CrwNeBNWUYID1p1n6f35y3ol/Qghb0
+6aEfmCkYY+rALwL/sbV2ZDSkCGCttcaYf2op2Q+3ojfGWNeDIv/Q3/le6/f37o55cFt0qtr6FTzt
+rxdruavCjVhdkh+XrqWIhbOHyzXaK6IdzJMhVpFiuYbbgsCFiXxXYLBacdjNPcZasGLo3D938kVd
+URfbP3EB8y0pkzUt4PS2Vn7eyM8Rgnkx4UzvyYeijbjGI2yIuovrkB5p7n2ZU7Q0i+6pSI+HHxzR
+0Oy2+v4eUSjOqcnTOaea2DPJDLmWf0smWofCGxFpi/NKvUt5X+bCrcWkcxnbcRawqe6kvVMd+3JJ
+xT+D8BZ4GkIMAwfXKt4gTilGur5t7c68tIrR8ZAW0Nb+kaUvNiBQGO8829GohpFmCaXWkCizGa72
++3C6Dh98TSTsyuVLuFrG7GWFR3/AIflZT4owJZvImhUzn0IrcDeCFuUl7SWpyMaivopdE9MmL8ck
+j3RNnCFRquM3LmN1Ao5K0QSL1DDXorFBUtC7I/twOryDSUX6OyqJi5Hh1BeNJ1tb5uQdgazfaFQw
+msyEH1Joa0CjxWt2Olssf1Z+t+WGdC9d1ftZ0lwxCxOPQjWLJDprThORzNREy2MidWIHzWXoyNwe
+kuI7/9Qj+PvSD8QUjDE+whB+wVr7D/TrQ2PMhrV23xizwcfEVha5VGP/8PC1lSKn85hhqd1Rs5Lk
+rNKP2s6RDWgEMpFhE0Ktr+c7FndFKwyxRqkwX7T/nnFKjH+m7scUc91U3SYTBefMzAzraE/LkeAD
+hvdGZFfF+z45SvGaaiNWfXKFyWaHU5xQO0A1xHsd1tuoOYwNYgpHpiY7GTO4pf0trTzDx8UGosr2
+njylWspB3+u9y9OZXDOYJ+eqe67NRasFBNptKCqn1K5rRt3xBKOp6DWvylSRXImq89iQSOsuFhSU
+2o8ztD5NLZBiGj2cZcU3FGfFW2bYylmDGxdNasQGE4pUvevuIdlU1izUQ1NcdCi1Scnh3YJHzwQq
+7Cx/lsGX5Rl3D2d84YqsT6D4h7WTJax27HLTkkzBW1ljSHKqEaj9x0TqaxneEKbBpEVFzQdGE0z9
+rJmPwbpibibTQ1JXzVQtELMUBswVNp/Oj7h7X8Z55XJEsy1z66m/JJlO6SiWwGvUqK5p7crlOsxl
+zINhnz2FRY8Ue5GORoRG9tC82WCszWi9IGGsPqhWDUoj4yjV75STQVUBUGGGrx2kqA45nmoBmLmD
+/32e8h8k+mCAvw28b6396x/60/8J/AX9/BeAf/iHfcaCFrSgf/70g2gKXwL+PPC2MeYt/e6/AP4a
+8BVjzM8Dj4B/6+PczPJRLYEP/X8xzeg9VUeVmZKVIhGcVNXacnSutkbtXTz17DlOE0fbqVmvpNTi
+FPZMUwiS83g8eUmuDiVnOqPeFI+zOzEUqT5vSbj5Iwp69wR2vL6+gtvVzMHxANvX4guOIdMqzuHZ
++3hzrDqOypMMXyGq/vITvFAzEX3J9NutThjN5Rn7dx9yNHwIwCR2KY610Mkop0zPnqHVnNdb7K4I
+pNirN6n3FRFY36Pra2SgNHjaudg9Qxi6EYHWs0wMoIk2uc3IKtpOzm+Qa8KWrarzbe5h1Vmbz6oY
+7VuYT2Zk6miMRx65Ii5LrSiddWqM+rJOT8ZDThXTsB4v0bkn92vc9HEzma9cHWdNU+DkWogm6RGq
+2VHOLYlGNmhbXG18c/odcQIHlRr20rouTU4xkDlMbYZBpGqSWIb7cg/bUNzHyiXmWmLu6M1fZtIR
+rWLmL7NZlfnMjGpExTZPO1IPY5AbquoQ75QuIysOymCQ0Xsi2uTJgXznVifUFNE5SXr0p7IOa/UG
+mbZAPJiFVKIzc0y0uNIWDLXuxzQraGuyWTwtuD0RjecZCb424IGP1//hB4k+/Bbn/Zf+f/STf9j7
+fuQZ+m9sS45DUbnGnJ4X6Dy5peEtv8RRX4bdHxG0xLfpVe7jd8WsKEcZtiKeeK+ph8Osk4/k4KWn
+Y7JY7Uw3IFBHrRmn1Je0ElAiC7dzWuEbkYY9wwL3VLsirfYoVNW0ZoYTipo3m2r4zkJFu0Y5UUhx
+cub7aOOG2s9Qsf6V4T2++0vvAnD3+Al+Tw9Yw6Gq6vrMKzEVzf/whfUs166SaXizFlZpLAtTmL2/
+QmplDtsskR/oRldQzec2Amodmbd0+IT9I/EV1wOf6akww2zvTfIzGLN3lva8RhJr/cznVvEV/FNv
+d7FDbYbieMS61UZVGW/1eMZE3ykvZ5iJjKO5XPLSpZsyn2uwoXUjR0ajLBc3KaYTnWNDrOXLs0mJ
+pxDs5XAHtP7jREO5s3GP4FSeMe49xUY6R9OC5baYGP3plJ62nW+N5Z2P50OcfTHX3Nde5uJLMp4L
+3kVONIzqaM7FkROzPZeiL/i3GWrz3/sjS+We7IFK9xgTazsCZAw74RpZV3MusjEVLS7UcVfpObI/
+T57dw9H0cztWBluFSargruMEV9O6/fGQ2YGs+6XlKoE24PnaHa0/+QfQAua8oAUt6CP0qYA5+8CK
+A3v/n54VZ5pCVlj6mvjjhz5d9RCPM9EU7t57H45FrUvdFnkpalllI6dzUbhqMssYz8Xht/PHhLPb
+io871Tj4ySm5NkbZ3ejipuL1jv2YItGmMyvCce+27pAcyGC/O+gxfkcgutX6q+CLQzRqr2Eqco+Z
+tnnr9Q6JrCYBdes4R6rOFWuwrtrE5CEA/cMe//B3pe7itOzxo1elBuXW1hp5Kbx8dtLHUc/ypkr5
+xmpJpiCreOySaNMXz444mssczuYOxVzUy301B774I5/n2hUFdfU9BlrVPH/R5+aS9t5sNJk8lrmI
+tGCNKaAWSoTn8NYRxYqo4vWVbSIFbU3cGTNtrx4oFHkcO6D1Mup+Tl/b9B2aE2YPZcxuOyXPtVyc
+JmvFtUs4oThlKx5YrV8wfNgnU9jwSglJW5vEaGu6slVncqhO1ZHPd56I1vTi5zZpemdZrmMcrZ3R
+P2tWPZ7wK3e/A8BPOttce2kXgLDTZKyFZpq6b+puhZ5K+fWtBne/JeswyKeUuXzOpgUVzTqN92Q8
+B70DEtWEA6oERv5+kO/x+ESL1swcrr8oz7l4ScYwT4bMVduiHkFFNInbvYOzvkdMbcZLy98fqPhT
+wRQKY5iEHrV5xofzuTw1CbzA8igTNSmcn+BWRNVa/YymJG/UcLUCaXv1GlFFIWFuiNfU7j2dJaq+
+bjatzZ9lQ/qposC8fcqeog3XuhRd2bz9/lv4D9XbfSyLstJfYXzxIQDjZw4nQ7Fba3yA1caeQR5h
+FOzUfk7SbRvpEJtpzkA1wHle8w7sEelU8P6ehlzc9m1eflGe+/5Bi2VN+w2WSr51+DYA906PaLdk
+A33heSkemvgZR6mO/egJW69I+fkkPyJ8pEzIHtLTHoWOptW+eesdbtyQAjB+e4PrX5D3i4/z8xbo
+prFC54c1ZTqWU1OOMtJQU8p3pyyvyruawJKrNzyvPmB6T1RmR3drZatDpIzp+H4F1xFw0uZGhwcv
+y/fL73bpv64RJgX0lO6AJb1vOS1xjcxh1O0TaHt5e9wjUqCae0Xe+XQWEE3kEJ6spWyvCANsXK5Q
+lMLIpoXBbQlTH47k2vcfv00nFBM0+jM9Rj1hTq3WKZWGMJOZguxGpx5zT0Pn1S5eW1GhxmX/SN5/
+bbOLtghl/Zr8/sl7/XOToTQzDs78PKOckboDmk0DCnAb9MQMuNMfMdVwceQFkMqzO8sQT+XzlaMK
+njYy/ri0MB8WtKAFfYQ+FZqCDSzZTom9/T0u5Ruoails1xicY+H4337nAZ/XuoO+ZoJ5SyX2VFTV
+bPAAq92eg2rE/Fi4ddVbI6grcEi9uON+n9EtcZI5TwoaoXrqpxPKkXDa/J2D8yrAp1sKr/Z8moi0
+urQc8XZfO0Z/4+vUNgTQ4r+4iq9QWVPVvpT+2rlXO4sLsn2RSkVeMN8XSXlwLKrxw8NnTPoiJrqm
+x4FiY4s7Je+r97qYzMg1tz7Qblrjqy3sXQW3uAn2jrx/Uc6wQ3nvfgpWpVGp2ARnP+beban9t3vx
+eVo6V8Gzxzx7IGNqZSlsypZxSlmPxEtpPdXaBGtdQq1ynccDpgoAK5+UVDVZdazPdbMjksnZ2E7p
+BzK3L+0ZnBdE4nd3It5Ws2K1pzkXZo1SwT/j/A6BlofL4jlGwV4nJsY8EfsnPRRNKMt9ZpsKWhtG
+dPT9omnEpBDJO7l3wuhA1tpvyZodDQ1fUkdz177Ocl1M0LfGPa4W6nTVrNvhrGR/IFpa9mCEW4r2
+mu2d8FDbBOQHcypaHKi2q81rrjUpxQKFWkhF7zGqz6gOtUlQZJiN1cTqy3hHaQbqXK3MDZWWgs8O
+h+xrs55Ku87j+ceDN5/RQlNY0IIW9BEy1n5/EMhPghxjbOB4rHklPY27txxD2BZu12r4VFTCVo2h
+WhdJcumKOFCuX36e5UjDMUFEoPDZws4ZDc4yx8bYucZ3fZFQD+6NONUY++AoZ18dfwdJQqB4gq3l
+ZV77shRYbbdFutRmCbm2F5vxlA/UVq84JbM9kTC1tqFZ0SrHWhehs1Lh+de/DID1DM2a3M/NMkKt
+XG3mGtJMAk7ui4Nr4N3n3W9JiNCblUzHohUNOzG3tFP2O/+LOKRGz/kc/e+idXy2Bn/5b/3XALx9
+t6R9WZxZ37lV8sqqZCIe/apATH713a/y6FA1EGtouCIvLux2aCk6cJobZgohH6tUHiUpvsLTq8Yh
+rMu1aVwyOWv5Ni/OxU9F2/Td2F7h86/JvDaee5mLddEw/vt//DbRXH0wX1jCmUuIduOahJFrVNl5
+SRGbj6aczmTM4+mQL39JoNBt02C1qrUhFL8RLQXkWqXp+NFtfvuNbwDwuLjFB18VrWLruU2WEGfd
+5c/LOhVrETvr4ms5nUasX5MXefZgyP/0H/57ALRyrWmQzBlrn9PMJsQqod3AZbkm46jV25Rac2Go
+8Or+OCZT7IxroaZZkIFxCLRTdpZkaMkFSnU0E7kE+vG1L36Wn/i5nwLgC5//GaraSdtvBcTaH2Wn
+s/KGtVaAMP8M+lSYDxcaVf7L15/n53/926i/hZlruKQbs1YLeK4ipsLyxRZXugIsuvayuMC3dy7h
+awNPW1RwFGRQJCXZSDdxf0buy+Gdaxm33daQvUNtDNM55htaOn1wt2A+lQV7PO8x+81vAfDTf+xL
+AFSjKkex3OvuwSnZWA5yVuS46gTbqEbsXBZH0uWa/Lt0YZOwqunblXUcdYyZYoqvDU39mpgfhB7B
+VVWj4122XhLmdrr3kHiqzrr1iJ/RWoLBX/yLAPz73/oK3p+WefnVr73Bz3lyqI79e/zWL0s0o7P5
+Am++/VsAfNCTsudHgyFjZZoVLK5CpV9brrD9gpSImw08pgqf9dTzfjCaMR4rkCk3tFb1EFrLmw9k
+zEdFzEhrMJZqBk0nQwpt626qz5hoQ1s7W2KoIKT+uyM2XxbmdVbDceWyy4YCzuzlkHZPnteNdrm6
+LvfohA0cjV1Zzb/wfIPjaXGTwOdPLAlD7h895DAUhnr47ISKRpg2rsl+21j6IpkWg9nzYr56R67d
+GdUItanLQJ2SUzsjP5OxeUFFf7ccOuxsyiGtZfAo1sIpZ8mlOdjsrMmmS1fBd9WqS6bfj/KCUr+3
+yhTyUUqhjtuDvVvEe/+6rJ8N8WpajTstqXAGXvp4tDAfFrSgBX2EPhWaQt9YfsEv+ONhg1/TcJoN
+4eqOqIzbWw2ubYgU+MKVm6ysi3q5vibIt8rKAFxtiph7oCWzbDGgaGvbrfUZqaqi6Vy4/STq4lXF
+odg6rjLSEOj+0W32BsL9x/OYRl9UwkdfE0lb/exNJk/FM9SajhhNz9q6O7R9kTBLnTrP70qYcO3m
+rowzWsUakbSjwx4lUhfBHE7wa6L9eMrVnaiNr8hF392koh2VTd1lrupq6W1wcEcahDytiwr8l17+
+M/zCdwUJ+bjzjFgz/Mq7z9jYUMff777N3Z5cf/+hVq1OCzw1JYPQ5eVted6lH7/CzRVR84fHU5od
+acXe1C7fg17C/FgclHvPZly7oq3+DuasarbmN94b8EDX9dmJjOFRf074WN7/hUYOGowerF/FkzId
+NJbWQZOGaroXpv05fQ1PtuKMG4rYdKsRddUsyQuCSCT+PJa5clyp2C1jDwibsi822m2uhDL3h1ce
+8YY6tE8UsVnPLDsbckw27hXsab0c52Sfk7MeFoU6lG1OqJm7pVNS10yk1VZINxFTeJjNybRJzpkV
+0PIBzeCteR5dheyXiaGvqcOT0uIqJiXTgsCJY89LBX770SHZtwRi/drP/CT1oTzbb7j456WLPh59
+KpjCPEv5zpMHNOMpF/VAX+00uVzK4l/c3uTGBVm4nYur1JYl0BvUtR6iaWK0RiHVCBT6WRY+jjZJ
+cSsOrmbtWU03rpqIttZttCs5awPtSNUZMR5qrUQn45niFzwr0YLrsyPmqmoPT0q2dWM2Apfdlqj8
+O9dqrCyLSdPckGiJOzXEmuyZH04IOGtaYyjvaVbbRA6pu2twtMqPtRPQz7XqBkEih+YoHTPXGoxf
+nYrKffXx1zndF3/Hl0cB2z0Zz6PKOq17UiHqaLXP8Xc1Nq+13Atrz2smvt5p8dOvi33+wvJzVDUq
+07nZYqkpc1QoJr/rVBhpI9lGZY6rGYMXIgi3NUOxtsbeW1o5aaTPyy0HWsHYvZUTd9TsmLxCsS1Q
+4Z1ZzvqPy9ztasr1s+GUWl2esV7rstFRtbzuE2ojody1uFa98hrBMnGG1aa5jutR1YIkuCX+ljCI
+ICiwKlwaIxFCbn1GR7ElW5s+t35H5vbYX2Y20OiJduTy84KWApNca1hryfuvb0c44+/lJSxp86AL
+FQVYVfyzMp9ES+45FH7iG3oKvc8cy3wq93D0PRtFyamW3K8ODONvvCE3eX+C/5L4RkxmmTgL82FB
+C1rQD0CfCk3BJJbgQcoggJXgrJ+Az9IrohFsLEesrwoHj2otAk06QqU1Xoz1RXKZeQlnSUdVc66i
+mbF3XvHZ1Uw+r5pTK7QVV5ay3RHOvbxV5+JA1L3x3ohS49BzzcocPzxgUMrnWi3g2pJoNOFah51l
+ud9Ku4urfRCdE5Eued4kTsVbTt6nom3svM0lwq4m12gPAqYO1ETSmBxMQ6WRA6ZUk8jM6Kmj6Wc1
+nv9GXjDRxJ9ZdcBjX8yLJAmw25rN+SszRgq31qnCs4YrHZFgP/Onv8zzL4iDb2ltjVS9v61qhdBX
+M60qTsRJPqdzqL0QvBx3XeY2Hre42BQJ29qfMzoQDegX+1oeLyuItJRasFqlPxYzbryWsG7F2Tq/
+uo6jhUN6I8W6LkXgicbnVkstPQ22LCk0P88mJamjhU9UE3Kj+XnlY7es4Wp5OCecYtyz6td1lnNZ
+V1MXiT9subQ8uVfbBFz5ETFBa8dj/p7WqzR636rrULPyPFsP6TQ129EJiLWBzTKWlZrM4UpNS/MF
+LlUtteZEJVVX1IZvPpkz0cxdmxXEat6N1WzxMWy6ioANfGaFRJf+wa/9d/ybV/5bABqtCvX0X0Lz
+oaBkUqayUOq9twa6WvFopeNhEoW2zqYUGtbyHb228HG0RLo1CeVMIbWmgnF0kUzMWU/5wBdvulMM
+SLTqiV9xaGhVpBebKUfq4b68nfNkqI1fNNxUODlLqj7vVAK8NXn2ZnONRlsxrL6DH8vvZtqkIznu
+EZf6vKUVXB2/mcW4GwoPXpHNQVZgNWRlvFWcijatKQZ4hai2ldLl6iU5kE8PZENUDlO62k59dDKj
+96bMVXvpkPfvCnN6d7hHom7ymh6IF7sh/+qPSH7Fc698GT8UE2zw+ARvVcdU6+AYrQ+o7embSw7T
+TIPRVFwAACAASURBVN6pZdPzAiCe12N+ZjNvWLZ3JCJy6aEwk/40pq1h5lrgc9a8s3xWkt0Qxllr
+rXOkEYpTrY14sdVmsyHMZrMaYWaqltsJhRai8YuS7HSu38vvokaHUg9xPkkIKlqRyq7gFvI5S+fn
+RXdcDZEuByFMZZ3aLcsLyLOLRh2r91N5hF/CWGOEu0GFV9bUzHVD8pZ253pW5aVrKuBU9fdWQ6qu
+rGm9nTPWDFbSI9ITYRDLzYT3DjTEqXBt13VpqoU2dlxSdTB8/auP+PKPCxNudq8ThAvzYUELWtAP
+QJ8KTcGWkCYlobFEqiJddgJ2NgVSGjg1qto4xffMecYZ/lnsW8p/AdiwgtHCG8V8jqtt0qk5EInU
+NBP1SGMwimkoTYmnZsVSq8krr4hj890nJQdDrRKsZbLsSUlTpXw/+H/Ze7MY29I0Le/517znMeYz
+TzlnZWXW0EPRXT3QjWgDko0RwpIHIXyBhJB8YbDlC8vGFr7CXICRDLK4MHQ3WFZjaJrGRVPt6qwx
+O7NyOCczzxgn5ogdsedhjb8vvm9HdUHRnUVCc5Dil1IZJ2Lttdf6x294v/ddcFMFVbq3tkAr9dxZ
+Snqi7NBKG7fIesSxKhynEYVaGL6bYZXowyhs13TaMFfCj+xE3AnA2gyzBLcUDmWtcfq8keBcUA/Z
+PfzHAHw8TsmuK5jou3uc3Zf7zSYphUakmwrSuvlcm05VqiHt5AyV2KQc9KkF8n6uV8IqTbyZKodC
+0pBUETAzGZNjUWKezmbYuQKIopBLGhB8oy33utuMuKo8BnSr7ByKRVD9sZtUlF+wt31ER0Vdrmue
+v9V0CLSIqxi5ZB1llx6PyWKtNJ16zPSaoWY4vPoxwak8T3izQrGkFu48xZ/KMyWlGYVS3jvKmZmM
+XcJ1LVwzLoEWuWVBQqbkO75aOVMn46rSzd24VKOuQWd/NiUvybXNSxHRXCya8pa8s03btOpKaZen
+UNHiv6zC52+JtbG7n+BfEqvn3Y8Uru7lbHgy1xuViNOJzJHhasHBSILKm+llGr+DN/WTtGdiUwAo
+sOTFOX0iTuCxsqaS3XWDVbPUDSyuEmUWyZKqPcRXHkXrgrHqZwbe9zYOLwBV7DFmqYdosJp6MrMz
+rMqv19bKOLlEb6NbEdGJpPgGU00JrWdUdpUspKizqoxO1Qpkik/Pi4AklkWY6kKfxSnM5HPmsj13
+eQozolB+SFvVCs+igdH6Cqwh13tZG4IjG2AxdyhqspkM9uTv7lnCaE3euXc/xdfU1B49FgqymccJ
+ZQ0mrKgWxtVmC0c5HHuDU6paepz1faJ1VWSquqAktLlme0xkUQV7KkGZRaAoxqmlOJNJ7LQnJEbM
+7s6WTP4XhynRqiyaSW7IY9lMLyUbNJua1issE60InLTFvL5SdjDKppWbEYlyP+azHmYo946DA+ZD
+6fPeTCouzz4cYCJZ6Ful52lqTMWfVkhjIVExpRK+jqWr1Phup46nfJReaKhpVuPYK1No/CCzMk5r
+fsCaan3cuVajofOs71QJFN1YrTkEdVXZmsrzlsMKNpQNzaFEeiT3aLRyaoVsCh2/YNxT6v6u9Ek6
+nrGn8YX6wlKrq0vnVKg05H5RVnD2QxK3XrgPF+2iXbTva8+MpYAVzNFAo8LddoFflp1xepzSWVMo
+cRHBROnAS5plqFlMoErLmXPOE0i5QDdzzCShMMrRqEEmnBJRQ07BYjrCXZHdvJ832FSguc0CekqW
+Mg+U7bnvsSgrtXqe4SuYxi5iZidy4lVahiKTz+VaUeg50Lgu3xfWazAXqyB1EjxfTodC9THN8R5G
+ce/kOVatnyI5wfHUnHUMeaoCLiUJNNZ/ZEzy18VqCDyorGlg8zubjML3AYgch2Vgr6Q580URnDMm
+R+WYRCv5Sh1Dojl9b3yKq6ao01YXrLmJ01d6/WtNKqmcYuXgHh8o/ddmWsK6YtGwKif4nYbPVIOd
+jcWIAwX9LK4ZNs/kBH0428FX1ZpsvlRwXiWoy1yITJUsUwn3w4RyXdWg6JIqibifylxZrbuEufQ9
+J9uYutSjLOanxIpbodijPr6s7yeW4iSLKWkg0ika+Crgs1kEVBSctAzWZjhcuyQWUTXsMJyp0NAk
+oVyT726VOOdd7CgwKag42PlU39MSqkVTjWr4qvA1ihw+pxkm77aM07fvxfSUxs4lY6EVk5fHx4yV
+Q31WTOlkF4HGi3bRLtqnaM+EpWAMBJ6B3NLSwOC3Rj4v7SrbUKNFrkjHYjEmVtLNJYoxaIY4dpnz
+NeTqt3sNByfXXbI8ptBT1fpyWpGl5BWNOWxcoujLyVbJcrqX5JRvBjkPviXfczdRQZPVjPE9sRqu
+1UKmmrLyHvUo1H8b9oZENXmOqUqRlW2NXAtVkpNdMrVozKKG15GT3q2oxRAP8CZKG1ebUaioic0S
+koGe0tEBUSA5/ctrgiuw736Tz35Gqvq++uEjUkUQUh/iJ9K3xvdouHrKqTUSlCvkmk21fomS6hBU
+Kx5VxVCQOdg9eX7vmnzOCwPyhgRlzXRAbUueuT3fpB5JMHI0mBNc0u+ZymkWX0651hfr7s3hiO6q
+vPfDZHbeX4vaHtUduSZcE+vhihtQUcxKKXfJNP08dcs4LXkBL3bwR4r+08K20mqTmit/d4xHOlIr
+83rrHC9BvMpcY1C5mpjR3C4NU3AzPEUTep5DoFWwNpX5uBGUiBURmU8XzEc6T7MMXwvFkigmVrbq
+kaI4a/aQbFfuVauEWEfe388MVgvouibE0ZT5rROxFM8GCY0jsTDiyDBWnomjNCNVVGtyPMG/2uGH
+ac/GpoAhcF1arkNZQSMbV8DWNILVcvCVnTfxXNxMyVU0ousULo7y/dnIx1GwiTEeRklEDM45kCmf
+quBmuYqvdFbz4RGOmsnBeE5t6R50N7l0TWosKh/JwrUHLiUtEZ7lMzzN048jGJzJgLaTgGEkm1qs
+MGBrMuITWSjtoSXoyCR1m1OMv6yYVMBTw8cqfDZbxKAug1vysbkSw5RukR9JtD9Z0aq+Vz7D2gfy
+fZeTHlZ5EtPUJ1AYcyMIaCjnYVlN4DXfo5NKMLcWVfCMLMZ2UCOyYko75YBiU+netS6DsIHranC0
+1MZ4YiavtyK0pIDT+9sMI+nno33JBqQPLPc1u7JesyTqNv362T4fDeWa8f/1JvbqFwFofenzcjNb
+wFg205MZjHtybX97m+pMnr8xqnKykNqOkyMZs+5wBdPS+ZIPmGrtxvBth3qgD9ocUVIMQKmrMOc0
+weo8y22B1fExeUJNQUgVrWVoVkusaZC3cEukxRIGbTjsyRzZO5jTHgvWfXQq93pxp0qkm6mTTkiU
+rXxqh3grMn61vEPe0A3nQ3HFmrWQfk82hSgsyJauZyXi4PQJAPN0j3Qo7/JJ26d2H4wxrjHmbWPM
+P9R/XzfGfNMY88AY80vGqFLpRbtoF+3fi/ZvwlL488A9oK7//l+Av2Kt/UVjzN8A/jTwv/3ut7AU
+WCKT85qKovxI8wrRUlYi9BiqiVaexPhKsjLT/KVPFbdQkzpzWVLZGhwRfAGK3CFTVORCkYLJYJdc
+3Y7p/hS10JnnPgvl5m9kLRp3JH9vfnPJDLyg+nSJlCwz16q1xd6EPaPEGWHMzbpqSaopPvM9ao7K
+pkWGqKnEME0fRxmqTUtN0iAkncl3pI5PdirksFF9i0QrOL38BDvUtKbqKPJgTluh0n+ifQUPOT27
+ayHDXYFVn3l9Llc0daqn0tFZQndTLCh/YCmrXuHMaRFlYu56pRaOQr4LxYK4vk+hoij2IKdQ3rV0
+EBNVJZi3tVaiOZfTLVQI3sPZfd4dS0BtxUY8f0Vp5fZPOXzvtwA4HT1msavp0CdvSB9/OeHRXNB6
+o4/3mSk1Xe/BU7yW9FFx9Ij8UOnm6vIeyZUOM55IH5057D9W1umbZaZl+e61yRaxq3oQVySdWlQq
+1NQddRs5meJMDtOciic/X1br9qXNS9xsq6Tb4IwHe3KKz7OMTZUs3B+MeXeoHBjqan5Eny8ojNu9
+1cDod/Q9B7cnls7HyRk7ShjT35Vnm86HzHQJm1mOUf3PzXKFZkMm82j/kCeZWHqftH1aLclLwC8A
+/xPwX6mU3E8Df0ov+dvAf8/vsSm4xtBwHWoVD0erzIJGgGkog+8wJ1cOv5n1CAdiihUKE/XNU7Kh
+/OxWa+cEEz4tslhecb54ymDnIwAcpQ2P/AppIYPhBwUmk0UxToZ4CuM1DZebdens57U6c9PNOLis
+efzHlnlbNx63TCeX57/kVgk0PlJBNoXpwsH15dkjr421iufPffxQhVh6MgHT5C672wJAicc5DQUC
+Ba1dIs1H28GARHEBDGRzmPhH+Dqs5RsVqjdkUR0/fpW8LiZ1NHcY9jWbk8kzvPdwQTqRyRZuhbTU
+wPtROyPypGKSwuKpXP2y+nT8eI/+4G0Antz7Or2RxkwOBlQUDPXyqx2Cujzf1hfEDZh9bYZ3KuPR
+Si11Hfftr/9j1hx5punglLNQFvqDfanhKN0HEnn23sF3yc4k47D/wceksQrUFAMOFGP9uetils+T
+Es776uZlGeMjlQe4m3FT2ZTsRp1yLAuooViJcjUhi9X8TtfIAoXKL3KaimkIlX+xVMopatJvg7Gl
+7MkG2TINNjVmcnmzwr37siHt7GlmKMh52pd0wVVbcFUh4YwmnOjwzsyEyqmOqwLOBqZMrhT30zjD
+BrIBbvT7TDXz8yB8i+dH6up9wvZp3Yf/FfivYYmyoQMMrF0mAtkFtn7QB40x/6Ux5jvGmO/kzwAl
+3EW7aBdN2r+2pWCM+Q+AY2vtW8aYL/+wn/+dUvSR51jPM9jMEEYqmjEo4eiJmLgpmcJrZ4MEfyBm
+Z6kkJ3SQ+ueEJFE6p5wJPNqpn5FpZiCe5mQ9lZpfl107zkNI9ZQIKziKaAySKblGtQtOMMrrv1mR
+E//GrYDsLbEqJmWXp09lO99wHKzKuZ8yJX6iyLtcAkteUMJT9+AAS1vRlK3sKv5lMYmdVEz1/v4e
+i3e18KXlkm9J5Nl6PmksJzqTGK8jVs9Y5cUe3D3gVPPVP/KlF3hf39+vgVVm4Ml8SilTLkilVbuz
+2iBqKIqz8CjUHdv5aOdcrbvd3qT10ouAZBoAjh99yG/80lcA+PD4lLaKqTSuXMOkMk47T0+53JTn
+DJ9XAZimw2W1fl750it4Gvi8fbXD/a/+pnyHHzGYKZ+h8mDeX7lL+Eg4KB/e/4BU/346OqOrBUqp
+B62yjPVWS4q8HH/MkfIRfOfuQ7aP9JS2BR+OZZyulUe0NpSyb0PG/OXLL+ArbiCKp8zVBU1yg9UK
+xbnyak6OB8y1+Gg4mHCmCuNdhrgnmnUJY9p6TXlLvuNkELPEjSRPcvIbMreuttfhQIPDfZhoIm2g
+gfby0RGuYhc+SlKG+n4PWjHXezJmR197RPmLP1xY79MKzP5RY8wfBiIkpvBXgaYxxlNr4RIovdDv
+9hCuy2q9xrUrVZ5TXsP1l5pEkZjrmRnx+J4smuFsyn2FGL9+TYyQTv0GUUUGKEmmVMOl+GmVxYGY
+l6cMMKsyoF/5qoB48t4RtiqT/7krNdZ8MXfr5TnTZQlsWMIqVHiivIzT5DNcvS6T8aOzMe2umOjN
+FZea9ujxYcrJsSzwj1Rc9bkooqVMULY341RjG91GH88TRqOkr2m8gx6Z+vi/8e0JkzdFAKbS8Hjj
+mmxqL7z8MmFT+f5msuiq5SfkWtcQTmMGmtI63B/haL1GJ52RKma+s64iryZldSEm9XRhUM5cuklI
+fKQVk9UBKyqAstiV3/W+fZd91Y98/sdX2N6XZ8uLmAeJ+MN3DkJKSqy7Gcvf02iDq5+TPpxUcpId
+2Tj7SZnbVZn0H86mGPWNr72gKcl1l/dHSu57UuE0kXusrZUIFvLQ3YrDl/7gKwD82E/8pIzHkxFP
+d38VgN04BsUxWbfO2jKW5Cc0An0vBR7tHO5x87pUsDomYa5kN/nZlG5DYhEHU9mk+jbhM8oJiU3J
+I41X5fBQlbg+jGO6K6rzuCoP0S1OybTU24sS2oX0VebMONXS6V48ZftIxrJ3eKTvmeIrcUzFcUmV
+x3IwS3jaeyLXhj439sQ1/aTtX9t9sNb+N9baS9baa8CfBP6ZtfY/AX4D+ON62YUU/UW7aP+etX8b
+OIW/APyiMeYvAW8Df+v3+oABAsfhihdwdUt25UbUJajJjrkYJeDJz5e31tnQKsmVq1qx54e4yluY
+llsUKg1O4jIfys+Odahq1dpLV+W1+60mke7QWbpLrArMeVEwUfqvUj5npvLpZ8oBGDf2saqp2Ky3
+uap57IZbxWtqJuK0wgsvy0lyeS7PFnVBv05YnY3Cg905uboNi7sSWFs8nLBQYNWtcpmFypx1rMV3
+FOCUrxBUxJoaapDwdOjQKMvfabZRSANMDqkuNQr9FGcpSKLYhQDD1pJmvumxrmrN7eomUSIWhOcU
+mFxOUkdNiX4YsF6XYN5zXotLNwUmXNSa9AeCoYinPfyqZhGUosxf6VJGnjk+nrC/K99R2AHjM80M
+BCF3WoIhuKMQ7Mttj5Ke3I+HCbVQ+qi7GONaCWbevtbmSz/zC9JfVwTcZbMzOq0PAXi5eUhDVayv
+r9+k1RIrJEn2z3No3Y5wbqy0Q6rId7tOgaMSBNumYEWtH0YqkJP4HGm2wJnGNDXo2C4F2Eyu6fbK
+zBXI1FAsyMZWjHeqHI31CE+Dso6f0tXA7cFZcl6MlVTEemgtcrZVZGc4h6qS8kSFYX8gc8tUS5yF
+S/TVJ2v/RjYFa+0/B/65/vwI+MK/iftetIt20X7/2zOBaAwch8vliGqzTq4nfh4UOL7skuVyxJay
+//jrhupUfP+aBv7aax2sBh/dwTa5J4KgTn0ENdlKm846pS05dV5saRDR22CoO7s9dXF14z8cV6mn
+Smx6z2KReEauzDbBbsLisvIiHOf4K5oq8v3zk+S5NYdBIPdwjDIRT1JqysCTZQHOmfjD+a6hsJIu
+nLdUXPVSDb+rSs31Bq4GzuJ4juPKezdXahQad3GXJcLDI9Y2JGBYSifkrmoTmCY7C7nmSq2B35KT
+pKnMwXdeus7lq3JitqJV/ET6s1FZZRpJf1azp+dFXk5NfORLr3e4fFtO4/rVOjaRMct8j5MjuYdX
+eY4r6xL/KSoSU2mXPyJWWLI/m3KgYrPOonYuAuSczQmV9Kl+U/q4s3EJ18iJv1vtU50tA4Yur166
+BsD1L3+e1ppYPU4u/VYx79B6Xk7an4wbHG2LlRkGZ1SUQu6a06BQqbfmJQlWJ+UJo5FYPKX8RXKN
+V60mM57fkHE1Rsuex33mmpLOioz2ikyoG9UqrC5Rtg3uajymNhNLqdpqUa8pnLvkkJyK1RSXAryq
+vHfb5nQP5B53NsQS7Hge7TMVuBmNOS7LtfkUXEVefu52gy9cFXzKJ23PxKZQGJiHhurUglECjfGC
+zFMwjVNwrS4L2nUjQq3JdzQ/7GQzUA688dilsqRum09BgUPEM7y5mOhRTSamMRHlTTHxF9U6Q1Xq
+rY5TzpQWzmlNefyeDFJJXYqsZvCO1GQEpoqFqBXpubKSG7isrclCiA9lATacmJmKlARewJmSdDTP
+UuZVdXl8DQq5c0J1L/x0QlBTqLEXEaBZlJIPqrY91kq/rtvGqMqIn+fEmjlxkz5MZFHshjFvbMr3
+rGmtxWevPc/mnWsA5Is5jipn2W6DUqqVpGcVAqVGzwq579bNN0gSeZ6St8ALZaEnzCjrM5fcOVFN
+xm8JCsvcJlfa0vfv7n5MpFmQtSurHNyXDSKPOBfdSXfUveg0qSmG5MWN5zgtZDGtB3O2Xpf6j/Xu
+VfxAFrXRDIEz63K7kGforV/H2ZOF3mg3aUUdvTYk0kDiKJDvq0+fZzxW1e2No3O8TGFdCq3i3aio
+OzrPOFTW5lZuWZvJpuCVY6zqgsaDmE6sB99MXM1mJWfmy4IPd0oM1bVpbK0RLOTQWpuXGGgguFIR
+H2dRzLipmJz11RJPxvJs740HlFOZAysbVS5f/+HASxdVkhftol2072vPhKVgAD+39Fv5OQuvHfco
+3GVxUIlQFXXLzQ6hnkBuqIUq8YxMGWvLQQOnprp8QZdyLkIlpt3F04IST01YJ2jgKqKvsGOCXCwF
+W/ao9MUMnNkTKLRa0ciufbq9oL4qu3nmQHggp3xcmlG/rNWF7TpEyp1wU57dzuewkHTSfLcgsstT
+p4cXqniHqh47nQhPC27CjRKeEqZ6zSqZVsNZPKyV9/ZVm6GzETEZKcbg4DGtH1WKtUubxI++CcBG
+EnGnIybl5Y5qZl65Tr0u1xa1BamyLieTIWdW3nvFrYIyFjkzdWcOD8nVbPUv38INVf5uUaakeoa5
+k2AU3RmkerKXD3A18Hur9zpvWjm5C2+DTkvEerKjA6oKMe5qMDfqp4TKEl2JSszV2iqnEA6UkWj1
+hGKhMoJjpTybPKWqVam1RotNVS4PXtkgOJJ+thVLXKhAz0yec+b2MVtyryKcMlZIez5Oubol1tag
+LHNoYWOmmr61iwV+fUkhGDBVUZq8XGKtLvN3qPod6SInnYkVNxr3aCvefvh0SOzKPUIvxnTk96dL
+mr9gfs7iVAmr5GdLVxEuNWQ+XHvhGq794c7+Z2JTKKxlkuVwGBMoiUV+s8Ap5OcgXRD4YpaG9TpB
+W342KnxaTAPIZOJG7TJusRT9qBK2ZKJngYuj5r8TLD9vMZ6a14sUowzA9nTGTPkY+z2HqRJ5VJVp
+OUuGVCY6yesNwuUidad4mWw4RWDwNCdvm4p5SLtYR6+tTAkS2XgWaQ6yDogaCncOE1wNcvh2Bbel
+pdO5i9GaCDtMKNTFKAcqix51GBxLDOTw4JTqFxUfUEoZIe/dX8woKVT29g0BRYWNJn5Znj0vdig0
+7jI7mFFbSD/XXt7A0SxAbjQGMGqSaamvB3ih9LdTdchmyjs5mFIoHX82X8ZULG4h73Rie3w8lN/3
+ksf4JzKW8yKhPlyq52j+3xsQKwVZYH1qmzJOs90hw76WVJ+M4LpiVVJ5z8XpEXsfCO2aFx8RKSei
+/3SCUazHIk1IpkrHNlW+R29CyxG3I/JcYo1z3XP61K/IYmtNtEYln9JTbIpXcUgUcJd5kAx0YdZz
+XK3dQTepvcExQ517ySzj6ntar5E/Itd6B69dxq1otW1dPlexPnVPXbdFmVg1UjcLl5u3xX1qhZeY
+Z5qN+oTtwn24aBfton1feyYshQDDlcJlMppz71hMqu4tKDRo5XotHKOR1dQnV+IMlDSEYkJ2ouQX
+7gyvrVWNfkC8NJ3GPWKtb/e0otDOffJUabCGUxJlTI7zErMzcSWebJ+Aqv12lDLNPeqT3VJUXZ6S
+aL498hychVgCTq+KURirUTM6SVOcx7Lbm2hOWU/5xA3PJeRyV07gYN4lmYkpmhmLsySZSUPyJWhu
+mhGreM4Sll2KAm6EcgruhRNuvioIxCT5Ob7x21J96C0S3GWwa1u1Jtf7JIm8nykl5EdaJZp38ari
+agQrGzhI37qKBRks7lHTbFB+lBOuKbvy3MWokIudWAq13sZiBOA4MxYq6pL3E5TRjOndp6DW1GyR
+sKPFb/vbopPoOi+yUpPTeFwELHpy396D96h7YmUNGwHOb4uVEh/JOH748Xvs78rPhbHUBnKP9lGN
+rZf05C2XWJzK74fKRhvc3CcpjrTfniNzBL245TnsvCWuxmImv6vsZlwvZHBWg4CsIWO26Bs8zUTU
+NspMVH/EPJU+3p/NeHIo7zm1lr72m19NcJXj40rLwx+JZXW5oVaXcUiVgHdqchYrqsxtAjrXhN17
+885t7Fzm3ydtz8SmkGDZI6V8kmE0pdfb3aCqmOFxHBMqa3H5LCRVikVfBTSMtzhPX6a+JRlp6XQE
+ibLoTk4GtNpiamUrmir0fNLR0ndOQEFBWXzG/hOFuR6f4ldlka1ek1Lgew8+YvKufF8pKsjaMvjz
+PGWgIKRskVBvaCxC2ZfzU4fMU74/p4JRKvNqHOE0VJa9cU2exyvwD+Rz86NjiqWr5OSYJcV9MWes
+AjXHexohjyPGRk3YckSk/JG3rm2xqZWYD9MFexoHSQffAuCNzue5+rLGPgZjmC6BMgscTyb99Fc+
+glxM34OxSNh//Zsf84VX5J1KreuEcxXiKbXIhpppKIrz+JBVHcjB7pi8L8/59qOnFKr5GD0XcvJQ
+3mlOwf2pXPONr0vMYZ6m5C2JB1QbCbv3hWn7bDelclXjPAfHPPin8nz3NPsSLIakyvoSVWJ29mQO
+HNg+07ospo1qh6FqYfYd2UDWj7YocnE7Fi2XXH11z9T4h1tS5fnlvyu+32+djukcyKawt5jzGc0I
+LS41aGnJeFKYc05LM9ODpdbi+XXZnMP0e5qep3HAxJW+nfdyiqm835m6AxvVMkmuz5tM2VMQVZF5
+vLaQDWQ6PMGr/XDgpQv34aJdtIv2fe2ZsBQcDJEbMFv3z7n4Sp0amad8Ct4EV4UuYneIdSTC32oo
+kcbEJbVatXh8wKKkpth3Rud6CUXUpaH53XSgUF2vYD7UU6A8ZzTROv7pgkOF8+YBhEpplsUqI9/L
+2dHsQ71tuaZB0LRimSzFZXKDdyrPkVk5JRbJU/o7ct96eZWkKidzs+FitfY+7Sv01y0IQ7U6ipxi
+X++7PsQNlPKLBlapyUxDmYPzHG9PhvV4v0/Ulu8o+XVe++mfBqBtvklclUDa9r5836uTPrNDAVCZ
+0BKsilXhTg2ZamGOh4eMtdjq2+88BeDmyxFuRaLzftPDanFUPh9RFHLamtaMXHUYMgUbLeYHnGkw
+NyuqNFW3Y/rt7FwzPreQKpHOW1rYlu35XFVugnji4S7kc5ubG1SaYiY3WjeZSHEkLeQZomGdqKNB
+3OmMwspznOYOU3k9dvwBeNKPlY4EXRe2IF/2YbNgrEFF1/bY/h/EQvhr6VJkx9Ja6q6cLEhUXRNy
+TQAAIABJREFUoOjLt+rMNWA4GU6YjGSsh3rKh0UL/6p8x4ot8MtqKfVyJonM2WmRMlFSl3omVlxa
+g54KG+2ScqZW9tbtNby2ErUMzsieKBb6E7ZnYlNILOxkhuZoTrMr0XC30sSo0GY2gpny4Fk7p6RR
+6ySTgTVeRDGVBVurbmDVx8cF57rwK2LTcx7HQivr4lkPda1JhgHjiUyg3mDAIJaFNykiurp4GzWJ
+6A78tyif6IbU7FDqLAFSUxS2j7NZkCjphzOQQZw/NaQKMJnaY2ozmdyztI3RofDVX7TFlP6BEsKG
+FrumUfuzDGVlZ+FM6e/L9aeFclSGI450UgXNEmWdjDcbJVb+iNQDvLVW44N/8BsAPD4RdODgcEJ3
+Qza3qHBxY02tdVuUNAXYWr1FPJdqzWvPK2o02sRty2L0ggDVWiUfPyTR8utwkVF4WtaswYOTNGCo
+8Zdaq0H3upDNDr/7LVKl+S+ATMFeRyMZ6+3dPsObGj8yCY5yaXaigkjTlvPFLlXNglzPZGzyW5At
+5O9puiAuSbq4Ol8wUVao0ZlPWett6qn8P6yVaHtCeuK7BRVFlh7lOe+ksrG6E3nGEbAsklyZ5xQ9
+2RReO4VqWWIfizhhcizvPVWt0aJyRkuFbBatEkNlDgsCS2WkiEXHZSn01NTSf68a4S4FfgYFnorp
+NouIY1djMe9k9EJx/z5pu3AfLtpFu2jf154JS8HFoW4jJuMxIz0R/DzDUREWJ59hlvwGhcdEJdYW
+fdnT6qFLtCanuB07hG0lWVkdkZek2o2zA+YDORHyskrZz6rkhZq7xYJUTdv9ZEGq8m+jJCUfyjVG
+BUa2wha5r27JNMFFzcuVEiezJ3Lt0McoujRL9ESpuFRVyr1Ua6A4H7zLNygtswuaz1/sfsRRLNFt
+e1zl+eti+vpByFyVmN18ytlE4ch7SmiSO1xf1cxBMsNRC6soIhxlGv6p177E4VffBODrCvqadyd4
+vtxrlqbEx2K2loYN3DWFWNucsPsjAOccjkVnA+bKTvzRAeUbSv01N5hMsQKE+CrcM45Vo/FsSqaU
+6/WVGc1jOQaz3oJzrE0BS06uoXJp9rw5Na1VCP0KaVMsgvHgmHSoXAdhCaPYkmlZXa2JxSg5yTSZ
+MlfugbBSotHSopdgwUQtrtjKs61uVvB9eY/EbJFWNWsxMcx0rJYnuLXfe96etcyVAGVQFDT0L41G
+iUWstPQ9+a56FDHSl56OprQcsYQrRUiqWIbppI+rIjLhhrxHrdnk4z2Zk7NpzIb+/sVmlVlfLKHJ
++jE30k1+mHZhKVy0i3bRvq89E5ZCYlN200O8ScZ7jyWg9Npv7xPF4sum3YJQkXRhFDA4VO6AYznZ
+DypD1k8lPek7HuGx/FzuWPL8LgBZOmN0JDutr5wHzRvrBIo6HGZTBnoaVasVEqXHKofZOe/BzlPx
+zZ46Pa4qLHV3Z8gtTT3mCx+ryiHHfkLVKCpQU0XWpuS5WDn51GOhmgvtJ2MSLf4aaRqrP0w5kbgf
+lWpGnMg7he0yxUBOrp3ZKYkqPu+cCpy7G26RDcSK2d0fsDHXd3ILlGyJpKhSuSmnSvFPpE8q5TUa
+dyS3Pdy9y1zZrfonb7E5lqjdPO9TDJWy6I58zn2wy+xY+uX97xxy6Zb8/dZP/gHU+CFLfRbKqt3d
+ktjBydEJUSGn7sBzMBpc/A9vrvPL7wsiE7538josLQ2HKBProH2lynRPYeyTNrNTxSGczBmqBei4
+Moem/hQC6YDTfkxPCU+NN8O/rYxbXhtlAOSsKs97ZbzCpCRzYT44JlOOhFLQ4H9cEavov1PF7N/Z
+jDEslOrvwcmEVkvmWTWMaHZU4nAonzsdx3hKQtyphrgaM+5lQ47UKjS5xVdxoUjV00c2Yf9M5vJh
+knFH0Y0LPziPNRTpAu/V9r/0fL9beyY2BVOAN3EYBjm7hSyKx/lTHCXNaERVqq68sCWndUUGsdzS
+XLst4WpgqNy9jK8BINo1fDUDvSAjWFNmOKN06l5KYmVyjN0dDvVzi8MD6k0ZgLA054OPFJqcKu33
+2OGjsk6qCazpolhpVzgpyyAGM4/oQN6lqlDiZDQn0yq6ce+M+mWZsGE0xKspRuJEdTKf97mlBZO1
+m2t4nrpBtROGviyE3jTj+ImwqGSal55dv8fdN9W8NjE/pdyHp0nMwa6Sl3gxkZH7ra+rUnN9SKLQ
+3krwGmxJALLsbbLQiru0skHoCfooSySA58UJiQaHN27v42q1auaMCbQE2o6fkqrmYaLvv3Gtw77C
+oL/7rROeHuhG8J/+Sf7s3/q7APyNB2dkRvq+pfUCt1cMCw265nbIuiukLrONYwqtkyjWapSVPj3P
+JLuUTwyx1nM4NUNDAVKm1iBXdmg6NQJlRC435NlPsglhVfrqbD7muC/3XSkm/OJtwUv84RPBUPyj
+pWQ60CyFhGty7aSRMzAKcJt2yZQfM+ho4HuckZW0orJuQRf/6EmKWdeDY5FTX9Osi2Ivtg+mfKy1
+HSPP0FfNy2KlzGGkh2FcoaQAvU/aLtyHi3bRLtr3tWfCUiisYVY4pNbS8uSR7vdjvJ6ewIvLhFfl
+dHCdMUFNTr+qwqCLwsUugz2zmLykObszyBZa9Vap4iTK2NGQHX0+OGSspvbJkzMqx7IDn81TUsUY
+vDsZsX8oO3Cmp8/rlRqeprdu+CHbscKu9wr2+3ISXg1dRhVxH/ynKh9nQhwlamk2oKUFXxif5FhP
+v0KCdo3JKl5FTqjS6SppS95j8uGQ4YmasKcLQmVrxpHn/eCdOQPlJnjiLPjzqn/ZcnySiVhb74x2
+qZzIM2/dlpM2+3jKwVD0Gzo3n8MoCWruH7H9QPQnZk9GzBoyPlWdOlngsqZM02G4QVP1EeODJzip
+wKPTNMDTKsHpRCyb9PGMQS4n9/7TI45i6fv1O0+oLOQEvrPSIaqqmrYyLlcaLawyFQ/LFq9YCgI1
+KbTw7MwZMPtQ+qOn/BzT/oxTrfBciX2ylsyFZuBjFHtRLHLmGozszuX/49mQ6mPFFfgpxal890l1
+QfZFGau9N2U+loxhI5S+38DDU73OxmnOifJzhJ0FqXI2VBVtWi07WA1sT/BYHC1FZBLqWlSV10tk
+CnN+X2X1Dk9mDCdy36YXUFXrppGF9EvXALg0b7OfiSv1SZuxz4DmwmffeMN+9be+gU1yPuwLKObX
+P7jHx78lEfKv/cqvUV1fUq7HDN9RPkOFFI9GCbli54ucpa4U/7bezDUONWVGHuWgoGO8MKDWlH+t
+tUImuhBQbr2G77B+WaC2+Sw+n4yl1GWiOfbZkkHKZLhK1V5fiwiNTNJGJcBVvr9eMgZHFsu+bkyT
+qz7Hvyh9+Hyr4Jc+EIDN9GTIuwe/BMA/+NZjZg8kav3b/+Tr8nc7PBcotSNDpJN7PilQMiXSRYHq
+q7LczwrDD+xoV/8DqIUu3a4K8FRkYkeuQfdY1qsVMoWp/5k/8x/TU2aiztYZ730oC6+lZDCVJMQs
+OQxnU/zVa9L3vWOMMnq/f/cjikTu0Vc8SXO9xE5PTPjn1tZZKYkL+srPvcCT958AsGhm9N6V7zsb
+yLVP+gc0WrI5e07Bz/6obKJfea9HpBT22VTG4PNfrHD3gRwy7dU2Kzp+sVei7siib117nWohczla
+lzjR6PAAG8rv8t05NKVzzw63uXxNiGPKWUjts7LJDu7JprA3POHBW1ouns05UymB6sZ1GMr33XnO
+4x9/SzbW//0Xv/GWtfZz/+JY/Yvtwn24aBfton1feybcBwO4GCamYK7y8nt3pzx+8x0ApruHXFO4
+50s3Nmg2Jcr8V1SrrxH4nOZa+OJAopVqrllKbICPIVWr6F9lSSzLRtLf43l/9U/9EX7+73yPuX4J
+Ii1jSBSuWzEeq005KcKZsj2vV7ixIu9Rbpbp7WrgyytoasHMdCrvcdyf4Wou3WlbbvkSQU5DcLQg
+rD33Wda6/KnNlwH4WjDgw//oGgC//s1vUmhJ5dNiwG+9JUG39//pu8y2JbB3onyAXQoCI8+76Vje
+1tjbai1gW7EMdd+lr1wGJS1gmlt7bhEs+3r581Llu18Y1oxyIKiVEzkFCx2J7eMR620xfROnS9oU
+K+ar33jMQjkn/E2xJCpNQ7kpv5uGFTzlZxgECXmsmA3P4WhX3rvbUTRqI8cv5JQPi4jKpvT3pNfj
+6lUpdOufnpLfkNN2+ki1OvIXSKsyzx6WYv6ftyUltLnSZj5Q2Lye0P/otw4IquLSVlKHvK50e0GJ
+UqQkOEGIU1FpxEz7slzFapUvVXAUst9e+yzt4Jq8/0sRgSIdmxq4Dt55wrgja+FoMONS/hIgMPVv
+lcT1fvurx0TF0pb9ZO2Z2BQshtxxWMwLjhQvb99/wOmRRMCLdMxpLCbj4HHMt0OZsbc2ZFCOpx6V
+mQxMhuj8AVI5qSW7Xuhi5gq4WfrDWYKjk9sWFqsU6BS/+7bw5/c+5otGOvqbNj63t1Y2Wmx1xTy+
+2WnwnCqOVBSWenPzczSuyPPEjs/xFRn85voaTbXLM02lLU57jE80e+GMeDAWX/zxzh7+VPzhilnw
+LXUXTwpxE9avfp7+TH55qbZ7DorZ+cr7PHn7GwAM7+7R15qQRIV7T12fy5qz7LU9fqYjz35gSoyP
+5N7zeaqqmFC48p5utsAoesdNCwr92WLx3aW+p0NTiXFqVUmpVIspvVPZTtdKOdORlpyX5hx9Q0z3
+RTygHih7lZbJN2yT08OlfqLBpmJKx+MTFlrh6MQOt14SMtmmbv1OkHC5K3BlE8XURuqa1T18T/po
+pahBTzaOu75soLWVJo3nZT403snZ2JQ55D1JiLUmYrKrorTRjJorYxNMCkpVcQ/ycUa5Ju9vbYGf
+St8VmbxnkIVkumnUux2KscRD6tevElWUsDjawvWPtW+19ufGlM2yaHN+9OY/pLsufRW9MKT+q0os
+3DGYbY15fcL2qdwHY0zTGPP3jTEfGmPuGWN+1BjTNsb8U2PMff1/69N8x0W7aBft97d9WkvhrwK/
+Zq3948aYACgD/y3wFWvtXzbG/EXgLyICMf/KZvQ/ioLD35QT/2j4dRYneuLnloFGrXfCGYdWdtU7
+yn9gria8dCzm4LspbCnP47DssKbchvfzgg0FkywjwTXf50hpvnKbMddItcP3FHN/UHty/ICSWhM+
+sKay7Otjn8svSACr6cLt6xJUZE3M0xdfv4JVbLO7aHH1hpw6lchSKCO0r1L1i+fOmJ7KyXDvnYd4
+k6Wy9RknykX4sFzgaRDzVxVAc2X2f3M8lVNi4zjADOUUO9jZ5u5vyP1G8yFz1R1cBvsycsor0ocr
+d27zZTWDvzU7pTWRPnyrsNQUs3DqyvuvGodD9cfWcofBuRXs4KmV4jsBdQVRtRXHMM9K5JE8287Q
+4mqPO0cZcSx4ieAopnpNCW7Kcrou5gPSsVgEpycxmWIryr5hQwN4QXOFrY7yW4ZaPDd0MapdaVYy
+RgoWOnpvTDRWTc/PreLqe/+YK+7Y2XxAc1+sh51sn9muQt5NgdOXMVOPisqZgxb5UnFTkrHiPhoV
+PLXIyOY4qnSutJx4UYRtKjdIfILfEvcgilsEa0odmI8wClRT6AaldofOTMB+P+K9xnCsRYHHV/ko
+lO8YnU7Igx8u+/BpBGYbwE8A/zmAtTYBEmPMHwO+rJf9bUQk5nfdFHJrmWUJw3FKnIm46NmTE6bI
+ixlXou4A17stGsqK01mXSfdCo4p1ZUJc7vvkc3FB5lmFRDUBTRLDWCneF8p9WFrQ0DD6zt7snD8w
+N1b8kH9Fcx7k6LrDWPB9+ceVVyqs5kvuw4iKEme8oP5rWLQodZVBylYIcxlwAh+3rKXRiYKwplUi
+LZG9tfYUx8pmUX88YZLLtaP7E8Z1RcIpMcf+UcqJllPbOOG4L7qZb+3sUKhOYjCyFMomFenCbVUj
+Xn1BJv9rr3+WugJrvtQfcW9DvqP8eEC60IyPVga6lYRVLWuPnAWVYxkb68+ZKWDJNZBMFTl5Vf3+
+QUYxUlLSkiXXhX48e0o2VJP/Upcrq1qtGMjGNMpSMmXQmicnuFPdnDY3aOoCurR5iWbpOblHW57H
+M2NsWwBQ/tGIg21ZWd6rfRaPlBNy5LLZFBN89lMyTis84lgJdVrFJpNVRY66IUcfy0EVaFCp9MIa
+l1RHA7+KqatUvW/JIs00LWZUNhS0VlKq/kqIO1Zy2GYZry7v6vgeznIj80NMRTYZq1mWwF1ja1Vc
+kNofj3g8elf68Ov7VFM5nOZrPbqL3+2I+5fbp3EfrgMnwP9hjHnbGPM3jTEVYM1ae6DXHAI/UN3y
+d0rRn/VOftAlF+2iXbR/B+3TuA8e8Drw56y13zTG/FXEVThv1lprjPmBcIHfKUX/8uuv29PcEvQX
+zLdlgzg66BEpHLneCfmpF2Tn/5kf/0kmmSomK09ivgC14BnOfPJAIsSPHozwZnJa3Xu4y6CrlG1n
+8kitqEqslFiJf0rxdHkKZmItwPenKJYAKddi1YRzDEw021HKg3M69yDx2bopNQN+R9yHaqeF1ZuE
+nU0cjfaT5pCrKlKovBFFQWkuf1/b/BJ28h0Aeu1VHgw0G1AOeTQUV6LY0KBkAJOxKjMtUp5+KJiF
+9cjh/VTut7AFHaWEryoQ7A9+8QZ/6Kd+DoD2xvMEVbnH8YNDugoxTq7FJFopenoip7Wdp5xqgC9N
+B/R60t9Vd8z+XXF5tqdjNInAVLkl8oml3ZIz6WA0JdU+nDwc4+nR61ZTcruEt6vYzzxnpIG4xWJO
+oO5foxxw6Yrk9Ls3r+MkimXQjJNf3yJSNetkuuDyC/J+cfUGo7mc/mfHj5mq9mj5ZanRWKlfYjEW
+a+tS95RZXbACxWJOcnCk/SVzaM1NKcpKPx94eCpEFEYFNXVZTSkn9DUD1dLq00qXoroUMHLwAlW3
+CkJwl/OwAOXHPD/Jgxi/rYHbWZ1LyvI9O7vPaluyD7XGDfKj3z8+hV1g11r7Tf3330c2iSNjzAaA
+/v/4U3zHRbtoF+33uf1rWwrW2kNjzI4x5jlr7UfAzwB39b//DPjLfEIp+nxeML434eDNv8kv//W/
+A4BjXdZvyM643r3MH/jSawC88urLuJo6zMty6kTlmFRzvn5cYpZKOuq6/132n0rgstgesrd0rdpa
+kBJnWGVVWl1vsVuTHfXx+6fcV5qrnIJcA0muRuXSBajniGuhq6f/jfU2S5KE9ZZHQyvqypqaonAI
+1Od0jMVR1eKCOXYZP1laJhUXx1XRmmxELVCtwW6bFydymn00HLKiAcr0kTIUlReEmt5Lc0uqBK2j
+k28w12BX5DqsdMRSeO2GQIp/7stf4sYVOQVr7TUKTbl232hhlX3YyToUoZz+s1NJU6b9Cb0DiVXE
+5ZiRFujEHx/xrYGkC4OzI3YVxrxakueNG3X8mvjD4ZMy9zWeU25nTJbsTUMHVsXHd1TXwiZ36Sml
+nQ2g/IKczOWNJmFXrs3nBbmezGj62iMmL+TvpesVHK2ILRGSqubEo+17NBEL4kV3XT9Xpvm6nPiP
++jWckfTbdDbHrGrQWGEAJijjOWLdlisNSqonYecuvl5U6l4iWlExo5oGF0ODUavYbYQYxcga12KU
+h4IMSDTQpZodlC2uQv39oqBckjnS+kNl5iOJDw12yxx4P9y5/GmzD38O+D818/AI+C8Q6+OXjTF/
+GpE4+RO/101im/Aw2eHwr/09jpSia2Etf1TN9TdeaHB1Q8y5Uljgd9Uk1EWVnRWEGsq1zRhvJp06
+Pl6lrYQjNz/Xpf1AB1RN5/7BYwJV4MlMRqGKTB+1Bjiz7y0sV6vWXGU4zkx2rk1iEel6gDUzwfha
+7WZuUo1kkIIN/V0eQq5mreefqzuRLbCaDTBqzmN8vEKDbG5BsiUm5+p+mziXBbkXltkrZGH1tcIz
+71ts8b2HO9CsBWlMW6XKjxdDXtD6kNdvy303yps0fA3E1XI8XVRZv4Kv7gOhwRitQbBiqucrIyrr
+SkIyP2Q2EkKPdL3MQBWNkndP6T+Qnw+0VmNl3XKlLJRue51dIiWysW6Gr5M+6MNsqOpLitLa3hly
+0heMRacUsh5KpL7V9fHVHStVK4yVLAWtEEwnMZ7WlVDfPHc3mSX4KkWfJ2UOBuK+3jiVPmxXA+Zd
+2TjLswWDM3EZMiDVwyCLtIYh8Sgra7XXKOEo5iQIDJo8IWq0cSMda7XTHdPAhvLsJgMUpGRcD6sY
+CtIEtKLXqquJ72GWO6hTEJZk46zyCm1f3MZ0f0B55fexdNpa+w7wg7DUP/Np7nvRLtpF+3fXnglE
+43wy4r2vfYWPyzOGSmEVuA6Dipyq/rxGpOy0ealKeYlP0ICh66YUnuzmjh1TLGRHbW9EVKzQh7WD
+bfZTJUl5Iidt0mmzyBU9VqlgBnKq/MILhl9rCKJt+2SBCv/iaS7dWAkwAqQWcnUrzgqX/FB+/rEf
+d7FlCTR6Wp2ZTx38FT0R8xrkS56GAqrqo1g5BYwJyD0NGLZdqg1B4z3fCmi9JJ8bffVD3rwt39f/
+FcVbeAXF70hLf+6SnGLbH90kX5VUbX3kkXlydIUq5BLVcopAJdPygkJTiH4jBk9MdNcvYbQIqKR1
+/kW+QliTMZvut6mEckLNi9u8rrL19HosRpKQujcRyyZ7vMC/IVbKzZUu/afKGF0UlPUEHmQlyirR
+3oxUyn1/zKnCrtvX1nhuVVzFbv0K9cvKk5G3MbGYzP2hUvp1XBwlw/Edgy2rVVRe0FJdj5/8zGfY
+39bg4Lr0hdPu0DmR/H8yLtOvaoXqLKOh5DutWF3C1QZBXSypjuNgUrHMUjPDqtUYJgYKVbmOvsc7
+ZxK1ED0f4y9NiOgck0DosmTJsUs3OM3Pi+1cB9K6jFn11CGfq1txJ6J2rOCJT9ieiU1hpV7nz/7s
+z/LH/ue/dG5TLfBoaLlsUqpgZlpldznALcmAxepbM3dxFayRz3KYqC/fukpJYaeni2NqD6RTV1bE
+5Gz6EU+UzTiZOlzpio8XtAuilizSX/rVHU50vGZq7q/7cKJgHA/DrpX7npzMqG7KZJv4a0RleeZC
+efbSoym5q9mH1gIyZWGaW9xIrnU0H2/zGEfBVgRtPN14rLEkDyS+8rM3PV7tyfXvrYkwyV/YPzqP
+1BdAdk0mxJc/f53DJ/cBeGoTelqD4CheoUg8KMv3pSahmKqallsiXFFz1iZYjWE4mmP3q1XMXBZ6
+KQrI4mvyfkGBc0PG4Yv5FyiVJYfe+kB22GF/Sk1dtDPrnPvcnUWdx4mMq7Piongy+lrKvhenzH1V
+YXI7dF6U/q6EdVDch/Er5PmSoEfmUNQNKIUyvk7ZB43UO80y5VDmyOqP/ChJrIspl/nkzjs0VIP0
+eP2UhqsQ7NkRJ6ku0pL0W60yJ1IovfVjPJ17bj3DU5cuCxM85Wi0sR4yUeO88MbOXYyOgzEeNtLT
+Z1JASd0GrWcxNsMqf6RJI4JUYc7+mEVDId+1JlX/h6N4v6iSvGgX7aJ9X3smLIVZZnmrF/PF2y9z
+77tvAeCvuPzEz0mxx6WWg6P15iZ1SAdywiypzVzPx9OTJqi3yAIlzfBjMkXeBUHtXPKrc1NOkWz/
+GG8ied7ThkO1Kzvx2fZDOkjA7Cd+fJWvfVdJLQbifhTG5ZbSdu1n4Gjgk606zWUJ5ihm8FgqEZeU
+FeOjfaoqMtLeBO+yUsXVXYynjMLLAJldYFXyzqmF55J2Udqkeku5K6ufp/e+uELvvSCn0o9Nprw5
+UqnyvOCSBg+HYRk7kWBk3bHcVNXl2pm6DLYgPtKMSyvGjhWiXI1wUkXYVcsUimg0ajpjDa5qbJL4
+OGphka3R1OCvuxrx+guqDRGLnNs729v4XemLa3bE4Z587tj2aLW1YMiGoKQnZ4G4Plk8o50qSjFs
+kMylw4fDEY5yKZo0xq1oIdxE+SlO1ih8sTaKKzXI5f0cJ8IJ1JowXVrPicVmSvL/sOhQKCP0Ij6h
+o/313e0z6hr83FhXnchaHU95Et3QYEpiCbh5BdD7Gv97bqMGgW1eg8bSbVyABphxPJY1ejay2CWe
+2qgCuWPOs1Y2zzBK6uJfi6gV4k4nsc/pTDAun7Q9E5tCbCc8Kt7kaHebOzWZrD//6h3e8MXf3XNP
+OZ2ImVSZnJJoZPzkoWK9G1XW1eipbVXwNAK8KGCaSzT5/a/f5Sv//LsApCUxAW+4ZSpt6XXXX8fd
+UJHXSsSJlpv6o/vcfE0n4VellmHWPCLYl5W+5YfcimRBdrcHHHTk2q/ffZfsRPzk/YVMxm/vxFyp
+yvd98bVXeKnx8wCErTooCUehAYF0njBTTkHP6eFkmr6MM3iswKLsIQ8SiajfU4HaDQdaan6OJxZ3
+U4BTt/a+gtVU10464SVVwBp15HP3Pjhm9Y760XOH5FAm7GVnk5YKnkZmDauZllxTiI47YT6VbMD+
+4wMOknsyDouQqKbw7mGDVE3sdldSZd7omHm45FpskavqVzZNqWp8xS118avyfOOH8rvRMD6HB19q
+lFkoiOrho20yxaa3mgGXG5d1XGUjTMyM0iWVbe9dxmqeueTlzBV8ZYGgkGuiVDaxvBiThkrhf7zJ
+ve0HAJyYnBv6fqvKRVl36+fsR145wtH6mKwoE7VUC9QLSNTFcDWx7RXHuKqLikkgXcYXpufuWj7o
+nWe5jKcbst8gP5X3czyfXA/LolumompSXlEwaUrc5ZO2C/fhol20i/Z97ZmwFKLE4fZ2ld9++QrP
+adXbCz+xSfycAjoeTEkKDTRtF/QmAmO+9zWJdHebLi8/dw2Aq8EXqXfEdDKDCR9+XXgH/9HXfo1H
+Z2I1rKxJoLHTbLMIFKLcmpHtyakzywqMVurNah79txUD4UiQbD3xCXXnd3KPYFN25UkjohnK6fFq
+bZO1LcnDl1XbMt17yOhMshr33z/l0styMredz2IiMfOnB3LqHj76Dh/cFf6DGzdep3Va9y+hAAAg
+AElEQVSqWoudhEoqp/xi7wlBS8zSn1dK8u2fCAm/Iv06NRMCxWzcna3zeEcsFxMU9JR67f4HWuDz
+uVN2vyHByn4yYXIiVsqd7gZf+Ly4cc3bz9PYEmvJqKbi9HjO3Xv/r4zHu/8Mi5xKL3przG7KSXi1
+MqKrepzTK+KWvTad0VMtxcUswah1dzbuEyoD9+VOwMcKO955JNkLO1ywprKB4/4xH39H/n7vnYcs
+VP37cxtdgltiTa2siBWXFHPifXFzDqbvsP3uEwAq7Q3yREVpthIuXRV3Mrz1BgBRpYRJZN60rE9W
+kvFhPmNjQ/qiHqkEXb1LuNSVSRJSxWTk0ZhJX+aLjfPvVcRaMf39eoR3Ju8RbTXwVerOUpAuZI6P
+7r+Dq+I5QVlBT7UZRoOWuJahFSutNLiOu3JN7rdasPpDEhM+E5tCnKdsD3bwyys0FO+/bm4SKBHG
+YpJhFC9+ejbk/Yfi4/efivuwWiljlBqepCCfyqJJ033iXZlMbdPh1nXp7Bdv/CgAblBiqJWTQTUj
+mMj3BeNjJqowNNwbM08VAKRW3SIscAdaPxF4VOcyE6qtEEejzIfHfU56vw3AYCKxiPGJh6mrzuVJ
+memOipnWRzCSawb3ZXI8unvI6J6qA9kZoS6mYjxiYjUe4Fyjq2pCBytiDne9KbVIFsRgamhd1qzG
+3/0QJWzieJrj6KJ+siQzna9CJJPcjt6nqeMQln2eqskcNjtUtLTYcWQyPvj2m7z9wcf6Tj6dlpjM
+R02H0rFcU17xWCnJRrypwrzVtKDbk/EbboQcPpYUYpEaQqWln+ST81Td3r5sprvTBbGSmQbf2WGo
+cZKPxxNuzKRfZjdfIawssznyTpVqm7Iu3nf2Rnz9oycAGPOYjdsvArDVW6E50nG9oXoL0Sbuss6l
+eY1G4wO5XwqBlb5PEzW4kwGhJ5uKLSyFajvmLJgeqHhxbcHsTEZitaxp3U5EXbMFrvs6/hUdKWOw
+ucYP5k3cTRX9rWiWBY9cYzupgbAsXJJmDK5mXQwZvrOkxvlk7cJ9uGgX7aJ9X3smLAXP9Wm1N/FX
+B7yxJeb3c1fr+BsSaAxWprzztphRZ9v3+GfKk9dZlWu/1P0cXf05n5zAiuyMRT+gkkvU+rkrEe1X
+XgHgG2+KmZweDyl3lLNga5PWTTkxbj46pTQTE626UeboVMzHuSenUjb3qbbkRFkPfG6qUpC3UsHJ
+5IR5+/ge0305Yh7PZOfvrIe8ZGS3v1Gp4Wsmwrflc1GaklYOtrYSVl4VwNI33x5T/v9+Hf5/9t4s
+1rIrPQ/71p7PPvNw56FuTawqNotFskl2t7ojdUuWoMG28hAITp5sGJAfEgRIAiR+80se9JAgyITk
+xYGjBJAs24IheJDbknrQ2APJZjfJmqvuPJ175nP2vPfKw/+dyyEtdbUJSCXgLqDRl6fO2cPaa69/
++v7vAzDLJlh/Sa6tWbuOxUAsvdGTBGZYWUfFk2OZRoCDXXF3B/sJRux98C2F1k1JQDYXxcPYm21D
+7cq8PBnH2FiTe1qrbqKzXOG5Ayy3SWU3EEtsDobwTbnnjZ/dwNe/JnN08uCrmBDXcevaGn7pzdcA
+AJtV6a/IGjXoFbHckweHsAzW/2ceJiSraK8vQz8iVfsc7pykGBLO3PYUuuQ/MAYZkiYTd56DlLLz
+tRXO1aUrsGjFF7o72Lokc3/sWqixp2A8OMD2BvtiDmQNeXYLkZbnf4h3UWPVQuURCsoReKxUOC0L
+FsNAIxshphT94bsn0AS+7c0crDUlpKtTZKZVLUHTA3NWTcCWay5GZxj1pRqHpRx72/IsG0xKp+UM
+HhXHLGMZZpMkM9degEHG62w8wjSkJ/eM48JTuBgX42J8bDwXnoIuFLLQxjW3h6uviLW2XR8epdSW
+giXcZCw+/OxPITNkx19cEQt19YUtOGSjSUoKeS7xqRFUELP+v7a2gesbQrHVeEN24sCZoJWLhVV5
+FwbzEkndw+kpla09A0s1sUCDfWIFOjFKYzYMeQ40cadOUKDkkusBDVy+JcfrsAtv/YqFUiox7pUX
+FlElz4IyJ1Azlj1ZxtrcegNldlS29QT5VyTxZQ8iqJiSYPkSdgt2dh4IJuLxcoJkkfDvQ+A4k/zL
+u1//fTiGWNiOZeIrS2IpqzclGXq4M0X9NbF47aMDXFuV61iqL6Nis9RV86CUWD/TI3eOXUG7Kc1R
+N1rr8L8kH5/0ryI+lcRsYA5QYqkPCTHYtgWXitj97iGGLMO1Oy4qBHaEgxkSJc84III0SQv4JhWj
+swQ+xCKueFVsEPdw6/ZLWLm+BQBoEZviVOsA8ycl04dry9xuDQ3UXhTvZXV9CeUaS6d1ub84niIH
+S6uZg7NDqnTbDlxXLLZKycGgXzinWDNrK4glhwsYQ5gsRS8ZLuol8eQ8Nqj5rSp0XXIuVhlQFOHU
+sxTOWDwhpwLYi4JPyck85ZpTZLF40MqYIY/keIa9DmURs5HlGJs/Xk7hudgULJ2iHZ3iKeo4m0jd
+vZXXUC5R+7DRxNqrsvDaWYa1S9QPnIlbt7S4jCKWCbFG30XuyMuft8bw6zI5q5c3UGL9fus6u/7Q
+nONAYEZtaEtc1C4a2CBOYed4Ap/CKDUSYZihAUUl0vEgxoT9Gm2jQMWQB3ftDQ9jLrBX5xLpzgJq
+m/KQ66UlOFN52eJhCZkhIKSYQqvNzdXzjsu1peKcabqI+lChhFVFcozxnmT7Xz+TMOnJw11ET+TF
+y6ERvCsbZPTyFSTfEBCL37BhvSAVhZUbsim88CUDs0iqE5dPtpCG87lfRUJ3vVLsQQd012vygnmb
+Di5PZT692jpeoNz9lr6E6Yk8H0+P0d6UDTAivNof9THsy6a55EUIY1noUVTHiNT25dIIg764xBX2
+Q5iehRuX5eV/+W/dwNpENoUPZgW+8FnpNVm++jI6HQKuInHL89kAhc0E7aUOrvZlPcHRqLeEGn1l
+0UPtkrj0BnvYC5VjeEyVsdMGAvZg1BwHNrUr87qslVwXUIQiG/BQItv4ctJB1iAV3nQZToWb84Z0
+/holBxZ7SnR8DYrrTC0DZV9CSKtRQcmQewUxCMXkFJq9K7mlMUvYpDM+Q1EIKVFo7sIrfrzeh4vw
+4WJcjIvxsfFceAraVEgaBj4zXYA6E2v1dvoBrlvS+LNhl9BYEItoVmtIZ+JNRKO5nmF93suDyf4G
+HCbzYjOF25KdVhcRlCWWuU70mFW3kVGEMksOMKWicDUK8McsLbbqJezQDfao9ltat1Ds0spbFt4e
+kuDFTrG1JLt8e3MTn3lJrLE+G/A+NXRFLK3vdpDOaczu72Ck5J78KyQctW3YZmk+QecCIrnKkbO+
+HYdlNDPCbllaXatqfJcoTT0C2i/I+cyph9WyeDFRyQViWi6PKMDSBlotsVxB7QiIKpzDNjyqbYfd
+Kix2+8356DorLyMIKINWteFUJTwyTQuNKpNuqg+DLmzBcvE09+A2xBU/TqZYs+Xf47iATbKKh3tj
+TPl8QpKzuJGDV1+SEPOln/u7CE/Fm7reP4XDLslapQHFsCKbyvnyHHAbLB13ruKlO2JhJ3qGkmI3
+7lINDiHbBjsY0/4ImhwlYdZDqyq/U50ZEo8K4dSBHA334FHuHo0WND2s5YXLMH129m7YsFjbdlri
+8SjbRsbuUYUCc1Yfw9qAYuejcjwYHkMwek2G7UOT9Aa6D/dkDvkO4cV3OW8aleYGfpzxXGwKjuNh
+69INxFs59h5KIPbOb/9LjAnoMMbXUPk8Y+qShlsTl9AkE3E6OEbMXmbLdKAVa74TC4NjqbHbwRIa
+q3wIJdJ0a3V+jGBqIiRkOOh1cacQ12/fnAFTOfbMkIc17Jq4SfWfwThGeiRu8r0oQEpI85d/8iX4
+BNkYqxRxTVLAFDfZyJLzjPT+/fvIiNX3yV5nvL56zglplAMogy+bU0d2yk2hf4Lpw20AQPkRhUn8
+AsnBhyxOs6q4w7/0X/8Kpv/Lv5DflbuobsriBlmazJUCFuGzZqOFbEAW4ekZBhTh9QwLhjPnlWSn
+6ugusogLt/MyLBKkWHYJliffTUILOpW5VdS+rNlj5Ja8KC9t3kHwUPAk7w0nGFN4duhZUH2+QATg
+BGYOry0bSNtfhPGGbASNAwMFAVl5tw/N65w8+YHMfc1AnXqc/nIV3hJ7ZSYVJJSir5V9WGUC1cgU
+pYsChScvbGr34LDq5B8BVTJ/OQxV7FYTigI4eRQjLyZ8ZmWUFrjxVFYwb/2cQ8Z1NIVDZi3lJFAW
+N94sgEEotbJcYE64QjZzpQ0oViLM2D3/rl8xkcQy37H2UHVILvOM4yJ8uBgX42J8bDwXnoIyTTjl
+JjrlAdAXS/LBdhe9E0medfQEy4skxbCrMBokAKnIDh0FBcZ74mHUqk2gzGz5ZIp4X/a9eHUAzNjh
+16QLaAN5JAm1vHuK8UgsfhQHeDIW6/jABPb68nePLMqrC8CoT0tilqEzIttmMcaENM+OT7DM6oMm
+14MVp5ir1qvSGCHDkqfdPQzfkbDpJ36OiDgPH0qkZxYU8RZFESMbbAMApts9fOdA9Db/1bbc/85w
+hj5VHWeFRrkjlnQluIpf/W/+AQDgd//5v0BB4hCnPudTyM7VSYxSBoscjtnMQcq5aG9dP+cjzE25
+EWd6BfFMeBpMI4ZliTdmugYK8h2agxDxjJ2bU/b5WwZ0zgpOnqK1JJ6QN9jBhN7L7DREALJj037V
+obEezxVsUljUxqhWO+iHTwAAcTAFSJiz+3Auj3eGDYtrqOljbg/joospQ4xG3oZBKrSCFnhyPERC
+fo4Vs4GTQ/EaCrcEzS5Jt5DvVgobSOilFTlsclvapQ5Mi92ovoU5NDblnORxBLtBlKJyAJMVMZQ/
+NNsZAOIiFKtdWlkAE7cqT2CxizI3a7CIEamELorKX0OSFUMXcHWAdFpFSPjs4iIwHZJ0tB8jMtg9
+OMtgsuPOMkgomljQocTOUTyGw9g5iSeAJe76YBxhdCoT5a3JObJ+6VzOPhpMkBIgM45MVEk+knaP
+oThNHl3SNFGwG/LSTEcFrLY8jGymcdiVYw/3TxAeSChgBuQqPJhA1+UcmbcFi3H9qpthcZHdmtxU
+kqO7SJSUyqwwAQJWO5xT5F1Z6P3hNvYIpT2h6M2eFZ+X7zSATZM8kS/ewcpYNo4vDl5GPpOXejZk
+/8X6BPmcKSkykQ/l2lxoKBKPWLMhQFy+InGMKvfgjthdOa3BWONDzUrQ5EfEZAYwLzEZsx08zRGz
+7XnBN9AjiaHhuejRrR5mKaYUDjbJYZmZCr//rrz8y1//Nja/IjmY/CjByYl0wc58hQYl4Z88ZFej
+qeAO6JYPbPiJnPuDf/V1TCYM3f6mi/ZLWzIFgdz//uQe9j6Qqo0xjlFhmbxUuGhS0zFjWBkmU1gM
+A1VaOyeHhY6RDalB6U6hGW7psdxHPtUAj6uMxfNNQRcRwGdZRAYMm/0TFByCSqEZrubhBEkgzzc8
+7uE0J5T6pQ04rIg967gIHy7GxbgYHxvPhaeQFzlG0yGMcIyH35auxpO+CZ9t4+/u7OENJvvymg0j
+nWu60UKValCmuN+9wx4mDyWx0oeBnTMCEcoDlO4K0MchDLq2sYjkUHbiMHWRu8y4hwEOIrFobmGg
+Wp2TiIhFWKgqnPXFS2m5NQSZ/K7IejiiBf6Tt3bQvCFUb85UXGDj0EQ+EYvZunwJuSthjF+9Bvdl
+ObZfvgMAyPoOxvsCOzaXXGQUeEmjBAN6I/G4jEMm6A5DuZ7pKIdi0jUvNFJ/jjEwkSVyvopxB3ZT
+QpchvY7VogWbGf4iDKHY668bdbht+W736R4WtAijZBUJP4KkhWyJFZxZAUYEMEsFdMzrUCYycgkW
+5MIYPJ4gpIuryhkaixI2qXd3USNXZDzMYNM769JTPAk0vvVAgFpXvvpvUWH1JbV9HG/LXNhmipEt
+nz+lpPzqZxdQdQWb4EUVzAZy/UfBBO98S8Bga2oZM0oOuqTXP3l0D7PH7ESslrFEaHpiZ8gJMjIi
+uY9osg+PPJDaDpDE8u9OeIJSMa8YTKHYJZlksk5NXUU2EHg4JiOoDSY70zKKiPHmJD+XoVPmvEkq
+QTGXIghCxGzSO7h/gP0zWesLN1+ASbzLs47nY1PIC8xGM0ySITKWvK6s2Ogzv+C2XeRUyjGjCAWZ
+lbK5YlNcoLogwBXPdZHyBRrEPVz9rCDF4kET1TX5XcLsdnK6gIgAqHFzgOiAvH1FiCgW13dgWagx
+oz6bt6BmNjps01U1D5ca8vfg0MWM7tw7wQm+8C43oS26nM0PyTx793bxZEdw7WfbITZNcfEurclv
+IqcC64a8QKd7MyCQ6zmcDRBlskj3Hu/hB/PegLFsKqn+UBXJAKC4MEd+gVkkG8DSloQvAJAQeIWS
+hmJ8DjNGwRqvE9toNgV4ZKytwpqThpJQdaGaIo+kXGxZBXJSzRtFgYyMt6l9iGTCCgU5OJPSFEox
+HrYVJiTTrfsWQJ5KVIGQQKxj6nrMkOERCV4ebh9i9fCPAQBtbwmZKxe3qG9h5MlLf/VNKbNe/+xn
+sLAx11vwYC5LSLTyYBnHbTEo9/1dXO7J2gjIYuQPIgz4fCsrwGEim3p8dozNa1LZyRx2jlo2chLv
+6iRFmskGY0ZlJBFf6AJwfH7nKXU3lYnI3eEcxtD3iZCttFHQUJk1DwUBTorVkmx2jDyTZxpGOYpQ
+rrm/EgMt+d1URygN9vHjjE8rRf9fKaXeV0q9p5T6DaWUp5S6rJT6llLqkVLqn1IT4mJcjIvx12R8
+GtXpNQD/JYAXtdahUuq3APwdAL8I4H/SWv+mUur/BPD3Afwff9GxirzAdBghnj1BxZJddLW9jDwk
+L2PuwiyY5NMRNPvXjSkzy8n43OqUWgtwFZMsqsCKYp/D0hCFZrKqK1YrWtsBE8dwThSOCZTpqxT3
+CEjp+zmKklyHm8q/9/IMKpZjXfItXKuKlf9WMUacknJ9kOHBkezQL6wIlNidpoAtnkk2O0WHXAFF
+x0KdlOtGY54k1LACsZhuOEO/EAuWTy2c0fV9fzjCPfYSBHMBGABzY24AsIm5z3pd5Aah0n4NDr1V
+hxx/RmwDFKLRaACJJNqS8RmcCunJl7egpnJumxgE5D7yMiG6wURYhwHkOEWm2c03BcJMrn9yKt+d
+eg7ieA4QmsEt0xspheedn6VaB8fr1I0cfAhCUvQaH00C/GyXeIPbLtrETpSMHWRd8TJjVhEqyoEm
+Q3Oee3BimdvWSy/D+JokEo8e7qKqxRMo16QaMjJG8Eqkjh/1QVJpoOHAaElyW9HbdO0aFKsyWRqd
+0+QXPpCnxCaEOfKphDnWIvE2yoLdFPkUnR0BZAc3mi0YJFZRRgHMBYq0PIN0nCI/kxAkz1yMWXWy
+Zi7qFa6H3RGma3+5fAoWgJKSN9IHcATgpyG6koBI0f/Hn/IcF+NiXIy/xPFptCQPlFL/A4BdACGA
+rwJ4C8BQaz3PBO4DWPthv1dK/SqAXwWAhUYN2fQYwXEFzobstEuXl5GTYedonOCkK7txa6cFzY7J
+/ER21GlvAHeZMNpRBqNK9t3DMRxyMph7EQ4HErd1yGLkj6qIafFTtwR9KrvuJElRZ06hmxvon5CH
+QIuVW/LaCLkrew0bo0z21kvVCu72yF8wSXFCVeL1B/Ldxs1bKNXFIjbu+IgvEwaLCPZMElhzq5xg
+iC51GiZmBXlPvBHtbMOm6nYYHiFU+Q+bXpkLAIrsR7HvYdDbBgB8praK2KDlPmKcveZDGYTRjvvo
+vyNJ0mx2jPICuzkfh0CN8T5FX3T1EvIzlnjNHrxFsbCWUwEo74ZZCoPzfDyTMq0zyJExv7K4MEZw
+0udxXTyZiMVfUh4SYhK2yHOWFjmKTLyRSTHEW/e/BgB4Xd+EWpHn6no5hp7E8+PuvPRaoD4TlGqk
+e4ASr+/bv/c+huTAaPsB3JjaCey4DKcxwq5c82Rqo8ykqp/656I1M/JUuHULHhOcyUxBl9mpWDGh
+WcKNT/qIWA4tk+3ZutZAMSF+o7UOZbKUay0BgZw7iWcwWuSwyMVzKVQPoLaEMvsYZfLdUlLAtCSX
+Zi7aMEZzjbxnG58mfGgC+GUAlwEMAfwzAD//rL//qBT95sqC3h+cwfGA6ERevKa1idIqGYcP93Fw
+Kv7uavsIfiIvyIRipr3HD2A+lMXqxyYOYjlGEYcYKqFE070e9mby8F57U/59+Rd+EVV2kEXpERIS
+U5x2p+hyEVdthYJ73C4TPA1fo2nKIh2lGguEtp6ZORYZBvi2wi7p0Ld64gLW4xjjr/97ueZvmMjn
+jMFw4REIU6H+4sHxDiYWgUev38FlYvWfHvaxfU9eNusgRymiUAuToPEn5jkqU4jmvRkCJmYHAweN
+DXkhj5mcWks8GAQsGYGPYCBf3t87w+2pXH9SKxDeF1q02JSXzm91MT4Wn/r4CLj+C+ImN199DWYh
+L1Y2DtFXDAnYf3D4cBdlW84RJTFislhHuYNyU67p3fe6KFsMG0nf7ocKEYVx9mcBHhzIvI2KPm6S
+8qx+8w5qSjanzJJrM7WD7ESu/fTJAR6ciZH5w0dvwWI359X2FVQaskk6deI78nVEsRxjGk1QyuV6
+Nq6/hmBGjsmpXMMsfoKNzi2ZQ9OGZsLXLnlwSI82OOwiOCEbuSMb70pvA1YhRs12V2FfZaWiESHa
+J8HLPQONn5L1sHjrJ/ndGuJYEtMDlcEm/sHedOCxIzTLQqTuj7cpfJrw4W8AeKq17mpRSv1tAF8E
+0FDzAB9YB3DwKc5xMS7GxfhLHp+mJLkL4PNKKR8SPvwMgO8C+BqA/wTAb+IZpegNQ8Evmzh6cgjD
+FY8gzU6h2Svv1kf4zve/DQC4Wh+iWJXuSWeJCUXnOvQ+YbT1RWzaYhGe7NkoUbZ80HGx1hFrtbgl
+1kCZY6Qk7Ji0RtjZExfWOnqAGrvlqrcSjHco8GGJZdiyK5i5VADOJjgmC7QuLCwtyy7fPSvQ1/L9
+95ngavunuP6KuOLH2yNYdPEaKoe7TNl5X9zPtWvLsGsSztjNCqI6lZ2PUtwNvwUA+GY0RqbniT0Z
+DQMg/wsKAPWaWKDD1wb41/+7zMUvf/ExrpCTYRpL0itM63AjMlSrKjq3qTXpXYUmziL1mvAu8Zqq
+giuw4xrKG2LlL8dHKJFOT+cRclO8htR8fI4QTIcSUnlVA+HGNgCg9ycJ9FCe2dKNm3h8xK7MvIfh
+gA1bhPM2Ohp77AZUFvB9W7y4z1h9fKYh3sFBdB+Wx7mti90rZ30YJEtZ/PJtlFLBJvR+o4bDhngC
+9fUbyDYJU9cMH9J9zHvAap0ANdKmxYPDc6n57miHc1JgEkpY5ZfbsDoUtYkjFEyg11/YgNMUO1lM
+SE2XleBskjfBzKHach8wFuC8KPex0nof7tr8cyYRHQt5VagJT/fH8IgQXbq8ilDLNZ3s5qjOfryS
+5KfJKXxLKfXPAbwNQWa/AwkH/jWA31RK/ff87B//qGMZBeDNNEoeMKZrdRx3YQZcsGcxckJmv/W2
+izdtiaMaubzcbrOEGev48cnpuTCr39M45h2W0zI8nxhwVgj6u49xyuzH6cEA7rEcoz82EUEWx+HD
+BKeuTHbA6sNCtYwtsubEUYi9E7YhOynCKbsu0xwnVLDaJLfj491FrP6MxL2e04Y7ImlLTcEIyFzN
+yoJbW4GRzeHaGt3vCKhr93tP8fWvyUsziiLM2wDmTNNBgXNCbwXAZB7kysjH37oq4J3vhF00HwiG
+IPTkHPFhgHiyLYeqdFAymVlvBTibCHy2+OZb0G1xRcsU7dGeC2dD6vV+7QYsdiImvSeI+sSTHAM1
+Kk6FbE8vBUMM35YNYjBIcJ8b7me1Qj1nv0OUo0vAmCb5zGhaIOMdplqjSWGVnSjGKYlo0qSGFxbk
+GI1rMt9Gzcc0l7n1exFsLZv+izdvIZf3B01HIY9J8c4QdPdoBK8rz6+oV1GKJK6f1CM0EnJCEmCk
+xgXOSLdUdfswCKxSlSp0yqpNXkfJkLlNbeJl0gnMXYE866U27G0KAw32YF+XlJzjXoNlyaZQBOzX
+iRVwKvO9OulhwMVgDRLkDMfi7VMcdpivecbxaaXo/xGAf/SJj58AePPTHPdiXIyL8Vc31DwZ8lc5
+bt56Qf/j//t/xdPwEl57TXAF270Iw0eSRNl/2seDrsBrzaMzrLwq5CUVJm8+9+ZtNB3ZMRv1EpCI
+VfFrNiL2xUMpxCTv6FAu3MiAglX9YS9GTC7Cx90TpFT2Pd7NceM1OfbOvuy4X/0f/1t0D3m+rQiH
+j8W1r5UrmOyJO9cbTGFRHbnaFtewko8REhgRpQUcehteHp93Np6xkWXyeAKbNHDFpECbWX9De2gt
+Ew9WWTiHJre2xCK+9rmfxBd+6ecAAAvaxUvXrv+Fc28QEr2wuIJLrwgqVM/KUBMxn6FroEfptfj9
+AUrMqI8YMmUlhWSu4GwoaOIlKoYBg+7LQstBtS3WcbHKbLmdYcSMu1+2YbPqcqv9Gl6oSRLza//m
+B3CvyjFaa+LR9PsBVldJq2a5OB2JRZz0BojJSzgeJgAh1MY8vWVbmFFXMkkyxFwjuigwI5tzuWKj
+SUhwQXupVQrT4XzrAgvEffRjA//sd6Xykc05E8tAEEkY5KYe0sdSDTjpfQOzfelmdZdfR7EvieLm
+64K2PPngAZi3RnKcQ6/I9dz9/UfwV2ReWrqC2hVWFMgzMvXbOGRzWMky0M0ldKmsLaO/NxfzmeGf
+fFPm8//5w4O3tNYCiPgLxnMBcy7gYoKrMDcU/t0H8uKtNWt44M5FMbbRISz1tFtCeiwPt7y5wN+n
+MG2q7hQaymVsDI2KNQdUavhUIbIJUrItAwX/rjdczCh7/mJtE8czmfj7rVN8d08e4oYWd7CPKsa5
+fLbz7QNk3JBGJwfIIwlBfAMA2YaaZIo6zhIY7PrLoNDk37Fh4Ca1yPf4kqp6CWE6PSMAACAASURB
+VH82kfv3qyYec5EvlQ1MR/LiLaTH6J6yA5M9Fw+dDlZvCjPRpc/cws/5cm1fDeZSMJ+Ye96/Wa2g
+WpP5XPUU3juTxXhVWXixI3O/ul7Bb7Bse2NDXu7vzXpYZJ/EURTBsmWOhwB8hl37AXCjQRc7Y45A
+m8gIJjvYGWNjTebiy3/nBn79t0RwZW85QhaLu/46+RA9J4dPDsf1Sw68ewwFGxXkGTeC9SZyEq74
+LRKgQGHC7Hy/FyJK2M06nSFh34hWCvP3H6ZcTxA751WE2DLw+EjWheM4MBwKznDjmXbvwTbls8Sw
+0I+FWr1nGBgdyqbWOpudi8n6uxIe15rriHblGOVOhKQnG0vTWkR+nxT+KybUUDbnSi6/T8Yh0iq7
+fKcTNGzJUXhw8P4aw+2vHcOY/fll6x82LrokL8bFuBgfG8+Fp6AdBb3uId5O0V6RTKmzY8B+iz34
+wxhntOKz0SHyjlz2aSG98mrPxpGS3f7Fa00E5BioN3102FFWLjmYK3nPac6UAow5S7AJ5Oxki3Og
+1hBvw98HRjVyHGyLh9KPgbgnSbKSOUF8QvqwNIJN65+4LpoMTQ5Zo4+NGBEvomYaOCXPYc1ycEhy
+jyqTZXu+xusWKeF0Dk0rWCQJDDb+BMpAwmOPxuIi3r93H5sHIvf+hdUWfs+Q79YAzBG6Hx2KzMD+
+5k00SlIZyZ0BjCWyNVttGBSXeXirjr/9ijQ/vf+IPAzdPwPqcs8b/QAjejyzZASL86nNDFN6Midk
+Sd4opygmxDSUNE4Jf37vvSd4jeCkaWLArDC8uSQW39wvsEy+yvp0E9UtSfIuTTOAKtat+jJsSsKr
+CiXgnTGSmaybwekhhj3iIoJDvPuehKnHWYyczyEgwQ3golZn92g/RMVlwjRKoMijoCgDZ9WOYcxe
+BQAkvV2MjsUb+bM/+QAthqP6ehVbDQmRc5es1ZU1eLdIY1ftYLovXtg1r49j4hTanRoUE6UF52R8
+OoJZk7Ds4eOHaG5JwvfyRoSF78octS67ePRtdnk+43guNgU3NbB17CN0A1RP5OEfqwP4qUz23sku
+qo6U0Ix2E3W+1C6z+9F37uKI5Ro/XMWE/bsvrpeRrUmsbXoGLJOgkLnupKGhiGyztUKzkIkMKgk0
+0W9L5TGC70gJ6WlJFr/9VOMsZf9FLz/vqUBSwGAXYMUG4JLcNSIVOIpzTYNqyUaiqPXg5ojZUuxX
+5LNKUsIlxjOVTCPO5e9ud4ogZRkySVBieJSlc3d3goM/FcDWg+bnUJD0gxws52PuIpq8/2LnGOYL
+wiXZ/cEAaizPobs4wJ2ObATey5t4/T3ZtPvXxJVdLrXw3bG4qtdnEQ64+GH4GBHcMytyDAbsY5nJ
+wj6suRixfyKaAD41ET84+TM0dqUyUkpMvMHqgTWQ65k4AQ7ZEdox+kjIm7Kxsg5Sd6LSWINJ8BFo
+IILIQz7Pd2xmaPqyKTzen6BcosDsuIuAr0SVOYc0jaGGfH6Gg17MikquYHADtEpyH2Z0DTmk+pDs
+T/HBv/wTAMDTnQM4REjerivUa3L9FVfuzV64hMKX0C0dPUbuyqZRrddQX5LPk0EPyZkcI2UnZ8d3
+EY9kXq9iEWf3JBypbLwATVLZcb+NpCYG7FnHRfhwMS7GxfjYeC48hcTIseP3UTot4eEydfa2+zgg
+dVdUqkG7FBbBDGMSg1T7koVOrBRxLO7z5MH3sLgiCdbJukaHikt5lsGwyX3HrVAD50kkZQJWSaym
+65qISRzQCn3cfVlc0fqpWLmhcYKE9XHtKpRouXKlASomO76Dlsd7oQWD8tAkNt6ueOdeSsP3UTiK
+vyMAZWLCok50MdE4m8i5zciAw2x/oTUKqh5Zc/e028V2TxJRT773NgiROO+cnA+t5qQfJEKxB+i/
+I92C+6MImnN/6eZ1vHFdmLRXbl+HtSHu+sLJNgDgUXcDq0PxpMYPt6HnCkrpGUaPWKEICyQMCXJ2
+ISZjG8VEnkezZcKYyflmd7voEtLsTXJokquUGvIMhvdiXCcoaDLuYrkklrTiuqhRdt4r12HapHhn
+OGd4KWa8Bj9oItNiVRebNoYMD2ZTFw7DyYxhguFrJHNKuMRBnSzeRZADBKfpnD0jFRfWVBKKM+sx
+ziyZFyfpwViQ3y1fXkP1mlSj7Lp8V1VtmEwemv5lOFfFG9NFDByKB5xUDSR1OV7MtZd2m3BL4o0u
+f2EDu1rC6dG9Izhd8TaWN8roMEEOcVx+5LjwFC7GxbgYHxvPhacQBwrbb9lobA5ghWKJth8NkGup
+wVZ9E/Ul0nUdDVGw+3DXl93+8sIldBjX93ZSnGr598/qS3DnPPypgZzwUIPJNUC0EQCg0Ab4VfjK
+gKKH0XE1XqUQTTyH+3YTeI5YiekwwinZiyqugxco8HFlrQ1vkeeesK6+UMBX7KFvuCiRYHZtxUMR
+y3fDSHb+oNAwIffXfgRMya3wMHNw1BevaBgpWIQ5JyTztEyF4X2xxoetxzAZ7xbRvHGV931+/2Kt
+G7UOPndTPIKTr/8JnKbUxG9euo1LL8v9L9Y3UDTZjFWWI7Rabax1xULfR4IVelvdbgz7SLyYD5Ih
+AuZxIno0dlygRI3OYZTCogDKnWYbb1E8p7VRw2NPLOjtidxfYGo8YVnz1c1lGM5c68AB+LcyfOR8
+JkVCHorcgSLPRjwcIZwzRKVVGL4kGnWWwbTJejSnMFMGCnpm7YbGkOXXNCqgyaZkunMOhSHyKWHz
+vRO88wM51sZKHT/xEyJNV7negU1lalVmnsypA3O2LFWGUlty7jhF3pF8jd0fwtDkdSgoD2dMUTCR
+Dt/BpYlgUva3d7GwKO+RKi0grf41pGOzSgXadwJEwxi798VNjoMZcopfeKUMSY9Q4rMeJra8LA2S
+m4yiMdrsMmzcrqO8SUKLYAJFlmfVLH34JuTzRKOC5q6QS6OxfKwBm3p/NeXD7krevsfatrvUwOxE
+FlKc5Ocbi+sCGwSbbF1ZgCKU1mzJ9ZRLi2iukFfScbDWlJdNeWWYShZpFDFpGSWYBLLBdG4doL9N
+erTrZSywM/C97hA1bgbUsIXh5AjIcn3aPQUi+a5n5AiZaNP4kBbNZa/Fay9/AddqsqiutB4guMaX
+8dICylwmTgr0qH/JBDh8wwRI626s3kbXEwBN6XgFIybUXtEaD1l9mFPFRYYFRgSwYWLElzhvL+Ir
+i7LQT/IaKquyiWpCie1JiJZLGrNxAfMm+wu0C4OJ5zgZICcaKItZxx/PUJBmPSkXKEjLPx31AFYl
+JpaGNw+rKNhiw0XSkbkynRwL7Crd1QaUQ7Fcig8hbyOnAta9u9+D7cszXVxYhjmi2MvSNcDmpsBk
+IKwEoDCOchQUE8naSGGyZdzw1wFiZ1w+33RlijJko7CPAxSE9ztv2MiX+T48LfB+70Mj+CzjIny4
+GBfjYnxsPBeegpEDft9ANxvAPxLXeGf/EbwZXcDSABWSSfTGe5gMKQCiZSduXHPRaJGHwduET+ov
+Q4eYkCrLKntw564WdQLzQp+TbRjFhwlIoAAK+Y+qYaBJbMGRwYaqyEfMUMQGkNA13qz6uLIs19ms
+VUHoBBTZoDdWq+e6haXyImrL9CRyEzFDDH8utVYawiF56Fm0jM4iWZCzEIpydGdx49yiGXMchwI8
+SuGd9IcwK/Q8Qn3eKCVzQ81LJj7XV5YQKrHy7TUHn12X+7hsfQbxUJ5JYAYIKCITHcgJSytjmJZY
+8bWJhucJ9VyxugufJV4/c5CUOPfkf5ikEXKDdHO1AqVAjudvVbA+4vNdWcNoIM/vjEnecJghoYao
+ricf3o+fIshJbFoUSBk2pEQ2ZsMEeZVJZbcKl924nr8ANBhKnDqYUW/UYFhl+zYU1cM9ZSNm45ab
+u1DWJ9xy5WD7+HcAAN99K8KY93TdBhYoyWeoJgziE9T8eq3OuRQc9AyacHxlWwAbzFQ2g8nQBTUp
+l1tpA8WUDBpeDmdKIZ6WDcUE7SgJgM6P95o/F5tCqnKcekMU7yg8oDgLjBKKUPoIKnkHxynlwOME
+Der9tW3pIKtVGqiyzushBRwKamYKGbnxwmEPWU3CDZswWdsyUJDvT2XClQdI7V6xpdoqLDiLsngv
+pfJQvviTl/HONwTu+3D6PsqpLOK1Zg0NT67JqnhwlCyaVlkeuJfX4dTlZbTLPkxSoJuFjVKV7EUU
+x/UKE1PGBJtrEaZTARaV6h08ui8by8nkER4MWLMvc5PSdeSkUzfTGCY7Qi1LIcg+si2QvKSyIuFV
+uRxilpOlaBShd1d6H3ba38SKJ/HwyNnBjD0PTomqV+MtpBb1LxuL6DDXol75LAY10XEcVm2ErESM
+2+wMnRnoFwRI9SxowqPfXHsJjqQ24I+6CHOZO/+UOaOhC2eTYimGAZf3P9YaZoPQ5ryOgMxZIdm1
+VUXBrpK5KANKDYnnW0UXp4TLV4+7mARyPJd06lXXwozdkIYy4RI7smiaH0lIyXNWTh8pQ8z+5BE6
+xKFUrt+AfUtuykwSwGNI67EmZDnAXLbAMKCIVQE8qEyOrasKFsMRRdyLH2fICKu36hkKMo7VH1vw
+TohpKA+xNJ4L0z7buAgfLsbFuBgfG8+Fp1BEBSZ3A9wdP8DpiXgH6jBAyxSvYRylsCAJHMQpGm2x
++HPl4KpZhQO6ouUKCvrSdjqGncjnlmtATZnMIvGGleVQZFQeJDmaRBUaygQ9cKSmgslGmrYtO27J
+bKFxU6xOaezCoUu82b6CEr2CwvLh0D22CbV1tQmbyaVK2YCiS52cGbDJGOzSQ0xiFyVm87LqFVR8
+sappMsYmM/VjP8Do+5J0VWRAblUt7I9YDSmV8J/9tNTEf/23H3xszi02Qm2yWtJMmzBDOXndrZ7D
+gAdDE1dvy33o6QrcjJ7CklxDs7SOkyFRoWEItOQ+VmtNRKSjO+slqG+JZY5YBUlxiAdTsXwzZYO5
+NZSvrqLpyfnCoYMOl+jUoohONcS4Kc/BtdtAg8IqiYI3R45WMiBkNn8kc19edVHy5nyGKYqxWGBr
+3cENNo1F/RGOt6VyZRGtaGdllFmJihzAYtNYrkYAv0PFeRSTBIPvzxWlLVR5U8vX1mDSM9FlE3qO
+KZn/Hhpg1UNPLSgyMSvDlyQkAEyM82SkWSJfZyeCnpLXogjgk2+zPOwjpDq25bhoJh+GWc8ynotN
+IbcKTBcjzHb60JR1d3P3vMuuhCHiM5l5y1LwGEtmdP2TJELGG9duiGJeJfAceASWOK5CPi8dEXPv
+ahsJ0wTxbIS+Sdpvsw7FklueB3AYfwcjCRmufeVVTP6Y1OP72yhIkLK62kBp7gaOA2R8uKZFjsOZ
+CZ/tu8pRMBwuChXAYOw4RxmZYYqQwqfTKMGUL6nnTGDE8qV11cYDLVUQetlIJgbqDrv6igDfeiyP
++Lpt4vvnzR9Am63Ki0o4BZ1qBxt1aZ0uvdnG4YmUBV/93GUsV0Sv0WqNcBZuAwBsX84x2zmDPZFr
+ODnKsHVZKhj5SYxaQje//RLSksB/e4/k2sd+FYrlu8XmCH1yQvrZOiJL7nt2NEVCUNMyy2r51EaV
+czWY5rBsbs6FAdMRt9ya+bAZtxt12UzMwoA5zympDBm7aquLWyiqct/XMwdLoQCAhrasodPQwCyS
+8MgqZeeFhpJdAeZCr3P2qyKGnbF92c7QWJQKjuUvIe/TWLQygBWR82deNaDm3bzW9EOWHG0AxnzH
+Kc4ZlzTk5YczARQ3bNuGzW5W19dwMylbqgON47Mfr/fhIny4GBfjYnxsPBeegpEApd0cSe8UCCkW
+EgQwCWTqDgfwCBluNRpoUUm5qmWnLYocqSKNWeZCW/NuxxyxRU7BwMDoibirASGslkrQqBEUFY1h
+cyce5RmqhbiajtKwqPw7ZiJy01uH9XnpXV8MvoiU8uqXbl3D4X1JiPaOAjTWiRFg0suJPBgOdScn
+A5RJn6VViILUXaYjnoZqlhAfyu+Ohj0MDuTaF1o1WDM5bhSHWKG7vj8XS1lIkSdyrOlhivElseJn
+737oJSgAPlmJ+5yfb7z3bdyxJJm50JlhdU7T1lUYnIqmZWujjkpDjt0LJMybhgopuXlPD4DR98TT
+swfAKKTLv+/DYmNPfUks2N5JjrAi96ctEzZ7dtqbPmKCs2y/inAg3xmNJGx5N5gh+I5Y2pdbGdyJ
+XH/N7OASKz+VkgvHkb/dsvw+sS0EtPjDbh/b5O50/Skm7HIdTaeoR+QqoBFvJgZCWvGDoxl8Kj/X
+1z0oizgDCvUUcRX/bl/uGUmOmi8e6fTuIWpvcj6nJSgmul3+v6UjGFUmx3UGRFSadvrQxNTk4RB6
+SOEXQvqTiYX8hBT+FmCT8s4s1+HMxYySI3gvzhOXzzaei00hMzOcVXpQoxZOc1lUHVNBc1FZhSts
+OgBa/gwhuRKzoSwwL20gZwdjt+jCZOmwnQfoU3fwuDbE+EC+P30gC6WzOUZyWdpNPd1AlAqKLyjG
+iLUsqqq5BG3J9xfoUq7cWkP6rvRXnFzewdVQHmjupNjblZdlLxui0ZXr3D0QHLofaWzVxd1dXm0C
+FbnOjbV1uLlsTuGOLLQT/RAH+yKtvv9ghORUrn12q4NrpH5fLEc4Yoi1xtJqN8qQE91pGClG35aX
+KVXAR2uSZ8Ttf/u7fwgAiP9I4avUJrDMAkusnCx9Yx0/dVm6+W68uoGri+KixwQhPf7+B9jekXs+
+OuvDPqNGxHYP72hZvK+UHFTvUZvxGvMr+QgzluRmTwuAbeSG14bVlnPXVvcxY55g755890nvFBFf
+png8RpvdrGb0FDunsvFc26jj0ioJR5h3yjJg+z0hL7m/ew+9MypBlTewznstwgjVmjxrd8QO1qt1
+5McSdh5tH2FSkudTKkzMwW6a1Ydp/k3MAlm/k56BnLmo49NtlJ9KVSqunGGwLRv10qrMZevKxjk9
+u1k1oOf9KoWJQV/6UUZP3oOVy8bisGQ7rcSo2HIfxkGCdJ1y97MKzAlJijt15AYJX/HHeJZxET5c
+jItxMT42ngtPQcdA9FgjbaTwSOkdjlJk1OWzkKFKTcBsbKGgWz5gv355eoZ4Irt1u54hIVcf1kO4
+hli56b0e3v220HylBdlwTx3cui/Hrd2uoh6KlagtXEZ0yox6bQjDI2uvpkpyZMDflL/H38lQH4g1
+jrpHePuxWMerG1X4xKrrrli5o0GIzBAXcPDwEM112cFb/jK8slzT3UffBwD8+9/9FvapmhSlM5gl
+SSjdDgOk1yWTXR/h3BXN6O6umWV8r0/23iSDTxry8fFH5htATF3CgGzJdgGobN5FauBeJubqdDxF
+QvBSearQ+iXxpsIj8Q7efe8x3noqFjjMC1zqSqKtqLQxPZP5/MZpgM9TZKV0QuyF9nCkScvfMnD3
+iDT5jobN0k953IF7V453dCwu+tEshcMkYaI9DJi4DUYJpnPodtXF7Vvi6S1dEWq6fDxBcUaG5rgC
+m+Q0Zn+EvUw+35tu4+ETmYO/cVsSsI3GElZ6Mkc/MHaxsixe35WltY/QZsscp/emSN6X49aXKuie
+yO8u6yZ+cI+M0G4fXXKPFkO5Z+W00doigMpoQJGvMzp9GwN+9/B7Q2R1WTt+mZgcAxhT2qCz0oBF
+5m4NC+aCeBXlZgKXOJpnHReewsW4GBfjY+NHegpKqf8LwN8EcKq1fomftQD8UwBbALYB/IrWeqCE
+IOB/hihPBwD+rtb67R91jsIqEC4lwEkKRViqMmNQag+uYaK1IjFZp+LD1ESQNcnzr1JUSb91mk6x
+siB/Z41luCStfHL0EMGS7LTbO2KtbrdqmHZI55U4cKgJ6OUF9ILs+MNZgIgYgII6kcuvXod1KNfw
++a01GK7s1k8OFKoLxBDkCnYsu/9DJn22lgxYNuvVdoKQ6MbalnEuIbe/J7iD/fEZrr4snz3ar51T
+J+3rEBjLfVh+G4qIzY263HOQlZFFEtdOMcNg+/8/3wo4R+O5XAGW4WDJo2ZDKs8EACpGGXfa1Nl4
+YwEbr9+W8+zL81h9+gj3mTu5c2cNxqFYse3++zjkvNmmwh/ymm8wOTdqLmC9LjF1b3SGuSSmTk3M
+yZv0qomWz/JrS76w1rcQUgatlCY4Ygl+ZKZIaWFf8w0Ym+KF1Vckx4F6gcwUz+1auovNWG78G9/r
+YXdPcjf3umPUyfS9NJH7+EX7Bo5J/5Y3HLhEDbphH5p4kXkJ/ECf4uoWMQZ5BfVLcv9v9/awEspz
+/aM4xiLL4Fdd0eEoletI6B15vomcjV1J30ZGTcy1Lzfw/r7M85O3RBjp3vEZNlbEg7xhb+D2ypZc
+Z0uj4lNG0V6CHokn96zjWcKHfwLgfwPw6x/57B8C+H2t9a8ppf4h//u/A/ALAK7zf5+DSNB/7ked
+wMgMVE8cjMwU6xQZOc5mUDPuCkZ8XhnI0ggzuuDVnDLdtgOfDM6uXYZFaC/CCQqDoiA1Cx5FWi9d
+J/X4wiJqdYKeMgWfSaRJdIJWIIspKY4R0v1/OhEffLMwUaNGY55toIs/AACUpgk+40riy19aQIMu
+f4Ub3ea6RtuRhOLscA92WV4K1zGQkZwlJRCqXarihbokQe8sLmA0lBc9KiLYpmw2Nd9Bh9WHKTv9
+jlUfg4TCOOMCIRswPtr3oAEk5xlw+d3Veg1XKQ7bCydICbxZ8pbwCjeCVz7/FTS4olVF3OjLSy/j
+YEOu+UvVOmY35Z7LxxlGQwmFenGIDmHMGc/XDYcouCM9NmLMMvYDOAbME3YDqjZy9h3YqczbS7UI
+nRo5DK9u4OuHMi+ngx2s1+TaXr3yOjYuywvnVmggXBf+UMIqr/wCDJth3pIFgyGUqY/h1eQFv7lI
+gZtqBWVWMqzEhscuyYmtAArU5FOpalSKdRS+bOS3ayuoKuqKdi2sf1FAZOmkj0VKyTfW5Z4sN4PJ
+pLNyHChD1mGsUlgp4dg1FzdI7d/8DNds/T2MxnLuZuHBi+TfdakJm707pmFCr8n1P+v4keGD1vqb
+AD4pMfPLEJl54ONy878M4Ne1jD+D6Equ/FhXdDEuxsX4Kx3/oYnGJa3JZwUcA1ji32sAPuqrzKXo
+j/CJ8VEp+kqzgUn5DNbRAs7qtIJZG3kku3ZhNGH7TD5VTSwuiZVKSExScysw64SlRlPojF5DHqHU
+llvcMjYR0gV1SiRPrbdhkxw2n2kEgVio3DzF8IxdbWEJo6l4COaAhB0IMCF9VrdxikUtnYGno4fY
+WpO/r9x6DamS71/q8bhGhILIzNHmFlZa866+VZi2uJrlTfFsVoY+fGICFlwbpk+6riyHSsW1XV6r
+Y8jS6SKbgYaPhudw7GgxRbH3IYfCR0fGkmLhy/xsfGkdG1fkMW48yXFGIpv/6JXXsfQlEfyqLtoA
+Vbp9lmnXb5fws8vCC+EHGZZX5fo33Mt4iVDouydnKBHjcaIizmGC/ZzEtu9m57wWaZxgSlnAqXsP
+45QM2mQyLlUWcOdnxHOplqv4aYqzPB7W8cZPS5n4+os/gzq9NEXla8MaoqjIZ7deu4HBiSSgy0v3
+8CIEsRncG6JyTebgypJ4TXnJgbcraExVMWEWRLf2uihiImTpEajqY7z6IsOq1hvn2Jr1LQ8F4cqt
+hxswOrKuW0vizRjVChyWkXVSAnxZN/aSjU5Z5rZUL2E9Ea9oZVPuf/kDG3khz6nd2ETekOM6M42I
+naT1eogilfflWcenrj5orbVS6pNr7ll+dy5F31la18GORlwewxlRizGrwG3IAjQKhUpFXO2FhUtY
+JPuuIgFh4gJRyg0kMQFKzrdrDspqrolookomXo+umpcayF1ZKIOgh0nIrr3YQWLKYgyzHnIqCE0J
+NkpmCk0SwMxUC71IwDtrq9dgp7IBrHQ6KCDnDguK5qozTJfEvbwZGLCUvFimYcNinuTq1ZfknocF
+qlU5r+dYWOa/F1kNjscss+ujZpAshFn46pKP+r5sIO4wRJ1t4sPwkyyNMlgexyV/Cz//uZ+X61w5
+OFe6Wvu5n8TljixeK09hFrLQU1Z+Ni/fwdKC3Me0exc2w6OikUN9li78PRvJTK7/Mp+BkfXwR3uy
+2R64JmbsPoyTBGUSxwyeepgYci+oycvt5wauXr8j13ZpC/uLcp137ASVO1IxWPBbMJljmvNyFkkJ
+tYZsBKVahlaLm5q6ApO0+smrKayIIAGqWw1PJ7BMCTtv1TbhOrI59XYTFNRrNEjYYhwswOVLrK9U
+AVZMFhbuwKoucm6nANeqqpBsRQOKG6ThuNDs7Kz4V6DL89xPApeEKpq5E/vll1HQYBlGjGRIZrAi
+Q4lcp1kYwv1LkqI/mYcF/P9Tfn4AYOMj37uQor8YF+Ov2fgP9RR+ByIz/2v4uNz87wD4L5RSvwlJ
+MI4+Emb8+cNR0FcMuCd1ZDV2+5kODCIaR4cpBmSwre/vodaRKkDKlsJgnMCi1kMKAw6RclFUhmew
+dl3yETnkraOVU44Fpec6iBMoegp2tYTMlc/HkxFOqXk4ZiOLUzdQYfPUknEZUyZ1Rt1DeORcMHMN
+Z07E0uaunVyGXYiFmnlT2IF4EpiNgFx+Z1MerlYroV2Rnd+tZlCheBiW5SPJ6RVZKSYDSbq6M7JB
+hyV0KGuf1jxM32GiDsW5vAmAcyZphzJ1r764iS3KoJlrNgZstKmEXYS8Tt92kFDHMM8kStSYoWBi
+13B9eFtE1Z3kCKidcOTWMYqIDenKMx1rAx1PLN9yM8Mp+SuK4iNdrFeX4P6p3OsWOTQqQRWrLXGH
+a6aPG29IM1NwcAab3lASTOESR5JNSbYSjs6l4iqdJhQ7JsuuAqjhUaBAOJwnfOU+rUKhRe2M8qs1
+nOxSIm+wh2wq6zAmBZu3ZqNFL9Xq9uB45OeoV2CSu9LAAoqQsOicnb+WB9NmCGIkMJgENks9GHNO
+z1IH8xKNMsXym+UyUov3qfdgMXFrjsM5XQa05SBju8CzjmcpSf4GgC8Dxv2KbAAAIABJREFU6Cil
+9iEq078G4LeUUn8fwA6AX+HX/w2kHPkIUpL8e89yEWZuot5rIK+OsRFKthV1hfGhVAB6ybs4eySL
+P3UNFEoywzFrV3Geod6eE5mY55qCxaiHoC+Oi20ZsAs+cEv+PcknyPhCR5GGw00hNLuwHPInpgox
+H/6IJCSO4aHMhK6XLeLrU8myz0ZjXCnku9N+G/Zc29SZ04znUKEsTDvKUFmYL4QKMjL6OHMprDRE
+zExA1V6GxzbqMI+hmO+YnA0xYjv4MVmOnBfKsLty3OV3LRQeeyk+IQbDBlM0WAqbPu6iuM3uPWuM
+ckteCp2tA6FQjhtuEzHbwWcP2PZ8MkT9TflswSpDx/Ki51aIICLJ65MuAoZjqkRRm1GBXkIiVUtj
+iWxSjueiBJmLmufjyZhy7aeymbzZKaDPJI5WzRYMGoByq4IpSW91+hTFjG48JTSDvV0MnlKK/ouv
+wuN6UXCgz1ufe7DO5L7DUM4R9ybI6E9HRwXe/uAeAOC9wQSaoZk5EkKair+CszNZC20/QIUuvJEU
+UFWGQYjOCXzmCllmScOoUAcgyuaRC2y9BO1wEWUFFNvrFTcCZWuYU6718WPYEz7UhQWkfdkI0rEP
+e/WH64j+eeNHbgpa6//0z/mnn/kh39UA/vMf6wouxsW4GM/VeC5gzoVVIOoEWIgWUSyTazF24Xiy
+2+2mOY7YIWb6DnTOvnl2k/WKKaJD+bvqfChFrz0T/lASUYE6RckhM+48F68dwKJgSXCMCWG+JQA6
+E+sYzAIMJ+Lyl1lrtz0DSsmunXYyXNuUhNrdp7uY9OX6O4t9pPMqCLPQRjCDQyXqJLYw3JfIKjZy
+qDZ1MwNJz9ixAdVlF119DDMXD8oNZ5h2ZV6KqIfuScb7ptrxxEPnQCxt4vTOLeUnM8E5PxnEcr3b
+vad48gGl90yFChvJ2jdCFCwuRfuH6AvqFr2ZWMSs34P9eEuu06zDIa5g2ougx3IdqRoDfXo3DA0W
+2grb9+cdsQVST+41TcPza90v3sd4LDT/2UC8jq6hcXIqaSqjvASrQas7iBAXMi8TqwrbE1IZG+LS
+BcUQeUm8gDjpwY7kc9MqkHcpumMF0PRe5tTw4zyDjuTZhMEAmklJw9BQPSZHNanq1QcwPKkQBA8V
+4liqGv6NGXSF4kF5gaIra1mRjg5qCcjnwZ13zq2Q5ccwYpl7XcQwquRcyOa69SaQyrxgUiD3KBh0
+UMYkluuotQ045Xlx8NnGc7EpINZQT3L0y49RobJPYDeQksMuTnK41IEslWuok8Q1acoDbE9zuPNs
+s9Yw6H5WrBgR40xlJUhJn26wFOihDsUQZJxaCAkq8QsfUVcmfjTtwcpYJbDI3JPbMC1mhTMLQSYx
+fGya+ICLtxMtocYYo0jkfJbhQTfYOj3ew7d+7y0AQDjKYHeoUViVjLSTjLBBKvBSPUZrg+w/ykBU
+k43lxM1xNpIX4awsi+6Nto0/eCj9AtU/OoI59z4/Qb4zf/GSWH735Ad7uLUuABs/t7BIApDqDQ2L
+ykqjp6fYfU82smxRXu4V7zUEzIZjWMA95EYAF6Oh/H3WA1xL7jsM5IL29Ckisj+Nc41NuslJHsBj
+KGE/yOFpWdA9SLjypwenWL1HhbDls/Nry/saIas1rooQjUmAe8Yeh/V1NEuvyHPw60hJhhNPUih2
+aGajEFnAXZSt7IbuwuB7l2YmmkTLLs7a5+GDm4rrb/3bU8yeMrxaLNB9Rzam169eh8/u2CKeIjth
+mNNmaJeNkT0RY5AdpdBrzNvbW9CGXI+ZKOiCaF/7Q+3SnLk2ZBp6wPdl2IdjzQleKrBtojqfcVz0
+PlyMi3ExPjaeD0/B1sjXQoT9GcIDcdVqiw580oqVF4FJl7qMuozCFKvZnydVDRsO9fI6SzXUF+Tv
+druFekX66m2VwQCxBwHVgpWPQU6Ow3yIgsCUeKYROLJDT9Ix4pDMx3W6i/aHznimTKwsixXcWaki
+fsoONytHe87dBdnhc6sMxT53pyhj62XJnHd3nuKEFmGZwiPZWRl2jTiMho3RgCI5yRBRLKar8ihB
+dMhqwFNxd//f+8c4+2MJfZwMyD/kVvmho6DP8LgIMD6U+amsOlArAkTNQ4WSkjp9dfVFXC9tyXzZ
+4o2Y5iaioZCwhOkhEupDmsMmzlin30ljXK6xEkMF5ySIEZLG7rITwWcSWPUKZIsyR+6igVfIv/AH
+JBAZhBm6prjG+cDEtEwKf99FyZAEdNVag5HLOopY5zeDDNVLFNRp2ygmBFHl0ZzKAVk2QWaRt4JE
+NflMYZDJ3I9LKcAqUEUdwprTr7clnDW+dB0rC3Ifj3cPkZJGPnZO4HXFm8zrCbBOfkjyh6rEQlGS
+uMxYBLI5A3ftDJrVB+XYKOZK51S3Sg+fIFOS5MysBHZZzpE1HNhlrrkMyP+/9t40RrIsu+/73be/
+WDMycq997+nqWXpmOCslWaRkDgcUacGCTVmGKZMwLViAKMOAzAE/GP4gA4QEmTJASyZMLzBIiRZF
+26OBySE51Ixm4Ww9ey/VXVtXZVaukbEvb73+cE7kdJGzdA+7ust2HKBQkZGRcd+9775zz/I//5MI
+xdyrlYWlsJCFLOQheSwsBa8wrB0H7M32sdriauDt4CqS0MsClmLRylHbIYpF0/qaQpx5GYHGH4Kq
+Rz1Uura4hudqCtCLCavyvqsot2w0JQ/kROgnBa6e6LYeU2qxi7GWelNJY6M5K7OH1ay/cX2GDQ1s
+zlrYicQfnN2E8qLyEzQ1cDTqMNXiqLgRc9pVSGwYMotlLsVIToZRdESp6EhzbEhq8vuB63CgKckd
+HJ6JZC2+PJETI/1cfoIPMBbsvGPYn4g0nnCDakxlMrH0rVaM5k1CNLg2c3D8b8m6RW2aa3qCRmJJ
+5Dt9YkdOSutsc3SgFkF1yIHGcMa9CS8rD8FKQ7ZcJ+lRKN5i2PJo633P2hH1qVoh9VU2LktK2X5F
+rJEgG1DReEGWlgwLWe+6OyKpirW1tOHgMY/diLXinmviKu2ya70TQlR3VDAea9pzbLBHCr1WTMPu
+0Q57A7nXYzuV4DQQL6/gavWoo3R7K9fez5E2bPFv7bNzT9Ka+XYVe17zjE4TT/k+jNICGpvjehLv
+oHKM72hVrgVc2XOOl0OhVqRGe6fjDtlBV9e7ih2pBXx+GTNQBGstoqi9EqHy/eWxUAqOW1BdHtK6
+e4qdlpL1jT3cuizCxqmnuLknm36cZZSJ3LClipi1hZvScOVBme72uPVAAjXDpRanz0stQuYOKU+L
+eeVrUNLUqrAneed8WlLVAJ478ygn8lCEXkSmwJO1umz+SZmedG/KKxnrhcBnNzc3edGTmzhpp2Tl
+vKW6bLTRNKN3LBvQm8SYVB5C79wKFaUHszX53my3y7ayR4cbG9Qncs1enuBHcj3nl30+1RbFwadl
+/mlYkM1bHMKfTjv8CTkBP9ucvY6Y4lVnQKBlz80j74RL0U33SbSUt/852Yybb1tl72V5b5Aecua6
+RNydwzaDqcy/mxacuyvf11fI94N7PYZqtz/Zt3ygKcqt++w97GXJgiRf+izrVXGx3PD3ADjIoV8q
+qU12n+JA6hK2HxQsnRIF0gi3KV3F+yuZDJ/sY96lALdBj1JBZM76GsFY1nZqX8QqxVqqpD4zryTX
+svXucU5teV46PcVRivZce2l67pjVc8Jm3U1+j28pE7V/PcfRQ63sHpD25iA5xSaEERyrid/wYKwM
+3bUm5UAzCqsRXk3h1NoawMkzbKF1EuEyibog8VGTUrthBasV1Gt+1bJwHxaykIU8JI+FpVCWDqNh
+RLlUYhVSPPQ8ttpyctscqtrXYbhnub8vJ2irJTptqwxxleF4kowYqOlXTEfUVZu7gcN0XzR0+92C
+K7CVkLAr1kazNQDFIQRBjVSrJ4M8JazK6b9UlxPhaFTS1jRcXi8IEZP60jsu8NUviUswyDxqWvc/
+nZ9Wo5LpQHLs47FLoVWe6d0B3rJWCU7l1D18ucdIIbNH2TGr77wk49mUdl9OxNnkAT82kes/rZV1
+v9EdEehpl2BZ01qYg+9iQc4DjZPpjEQDmPXsLAPtUTn7N19giJyawWTCCyMN3KXy2eoNl9FUu1z7
+Pv9eU9Ka3tMu258UKyYd5xyHiiLVYO4gndFR16Uwho9rQddbgpTkxl2ZaxRw92DOVSBr/2D6gP2+
+zDWMCgpfT2Zvh5e+KNiJaHtI3pdx7gzEGquuVKh/Wu5Zf+JSbog18r6/9edpnjovn4kucdzXVnfK
+dt1aO8vBbbHi/EqHMlTTK5sy787pKEfEbNaBqezZ7imfO9uyRs40xG0JzqScZkz2BDszvS0uUaV5
+mVID0MlwTBmo+1SdMTsSCyMr6zSvyuvaO39Y7sf5nDyXv0uDglC7sGcHIf5EIbfeGLeuKOFXKY+F
+UggihzPXq2w/14G+mHWczTl3USZzsJwSdSVnb6sHlAqlzXyFePpVoqp2LEpPE5VqRrVqRIoLqF09
+g6sNXqYT2ShJ2WGs2H8nyjFakdcd9kmr2lnKFpT6kA1SMX0vVC03FK6cHkx5m5q+tajK6Q15vbZS
+otR/LFXkIZ7W9qjUxHcc3u1QarOYODliVGi8QzswNZ+o0OrLF8TnAkaKobCex+2qMAUVkzXu7Mp6
+fWNN/v7D9Zw/UoBRks/ItLntRnfC3itciXmoYd5Ud2RLxlNRts/u1nnfFfm+C09f4eUjBeTsWt55
+XrI1uzfVZA6m7MlLKmspzyTPy9if/hzP3hOlsFzNuK9rd1+Jah5kJauKwbk1M9LsFxjXW4SrEsP4
+2sc6dGri3h0NtCFslPKlB+JKfnhYw/XEXVm7XuWJc+JqzO6npC2FeiuWpdfJ6BXyenqxwumzmg3I
+BuQohN5z6Jcyv7kiN4MJGxdkv032t9m7q5mvXg8UO5OrSzSe9LCdjwGw/ckXeVCV+d94/lO8+5pk
+wUxUJzyn8PaZQum9ZdzzSk/PLTCKK8h84qe17fzgBm5VlIJVkJYJXUxDrjcdjXDHmsE5VVI0ZQ3N
+cQ07E0X3amXhPixkIQt5SB4LS8HNDY19j+ZGk7UjjehGEaGy3YZuwHpTkG33uvuMj8R8HGrrMutl
+5Eofthz0CPS0LqYOGgMiTH08rUcvtOdBpzclO1KykXL9hOZsYhNGz8sJnC0FVDXnuz8TDV2b5iyr
+Pp31PO5psc9qd8pqQ3Ll7tFtBqXCcVsaWLIhzrw4qp0RvKAmY2AIOwpz1gYi1czDtBTO3TcM+xKI
+GieGu3c1Mp7dZP9AC5euyKnV+GLEhQ2xeIpBl+1L8n2TL5kTeLdBOmvDCRExNs34Rle2w3vaEx6M
+FOvxRJX0GW2hVt4i3Je5djSS3yNhmMrfhbsBe2pVPNfpMp3J2HczmCi24MZQMzVAR70q6zmMFFDR
+sjvknz3JjfCN58TsGfZlfUxucDQo+7ufucf7zmqrtPN1lp+QveMvGyqKa7Gx/L9+UKFbKIFPEBO2
+xPL0C0Pnvlg3prOETeT7Gn0x/XeHCaNtcem2jwfc1sYxkywHdavcTPZb3Iv5wh+KC+Pu79FUVyn/
+zJfpbmln6ys/jBuqK1GKhZLcv4ntaqap2cTRjt7l0QD3qrijrn8VTynr8j1BShbTFO5pU5ukJNUs
+Sn19Aj3ZA05WYxi/wGsRY+1r5kd53eU7kbQYvm3iVhoh594ti1M0GxTPKVDpafWVPjPg+r/7AQCG
+nxhR/7dlcxS7BbWffBqA7d88ZHruJQC++pu/A8DxWpdMqbdx7CtC8d9bfvUf/jfs3ZCo953BMxx2
+5QF5+8VzVHuK568PmRxpdiHQMuTY561PCxGIm7kEoSgp181x5jbbWDbEYJqy95KY2rP6jBefk6zM
+/aMD9hS6m9oSo4xG805Ra6tNolVZl62VFh/93U8CEKUeTiAPaT+xtFZl0zTH8l0jT7phAeShj3LI
+0FgKGB8rw5BToPqUsT7E42HCkjJEtdyQsC2m8bifY7UU01iPQMukNzdFaW5dvkr7rMQfinOrLPVF
+Q/z6r/wykWL7o5ZDMtJdMGfLsgZtUUnaT06AZO1Wm6sKBoto4ikEOdAa4mglYqoKrXu4w67S5w/K
+Qw7va3VoOaXI5MunyhDlpiGOowiwSoP+SbPhgs898zVejURNl41NiUvVZhWunFFy2LrM/+n31qla
+uWdBaPBPyWFwfHzIqVWlc5/U2Hy/uEqDW7IXjoshX//ScwA88dSTuOviXrzt+l/BV2BVrR0x1OrR
+zfryM9bad3+/6124DwtZyEIeksfCfWgZw49GHr89/XaI3MKJbZsWIWdGovnOnV7jzFsFe/D7Yznt
+fvQXnuQP1FT98M+u80k1ff+TnzrHtrI8/+W/W+EX/1cpQHrPT7wLgI//0a+w3BAT/3iwQzR3Cb6P
+yZAVBTeP5e9eeGkfV9uODeoh47p2tO5DksrrVQW0rKyvs15RRt61J8j0dBx3+zg1OZlybQmX5hMc
+DRLW0pxry1pQdNhnoAzNeVGCtkkfDcX89EuIx2LWtptQaCRxmiSMlPPSC/yTWn5nVVmbZy7ekqzx
+VhgRKW3cqh8yvaS07McjCleveVdO106cnVT11eoe2jeHshGwrZyWk+lEOv4AvQO5ntNn2ySpgp7u
+WDIjfl5gDGhAcDZ0cLRNfFUtovU1F08h7ZN6lXIi17+1scHFc1pAFjaxeqKHiukImnXKhhLnRA6n
+12Xs0bROvy7uyPigR6aWRarVknmW4yjlelLxGGmm4f5sgNig8FW+t0Sp4VRVFubPby6Tac/Ln3j6
+nQDULy4TlAIGK6MUG8oarwancRTr4bYGTA/FQlhSOPP0foKj9/T57td4f/0/k8+WHh0lhjkeZ2zM
+Ke1epSwshYUsZCEPyWNhKfRd+L9rHmvTnINXQPAiTQuuvSVk/d3iCp2Lm6RNiS/8h5fFP+93Nnj/
+2pcAmE3HfOBJ0YyxrREUcoI+Ox3wQxqXuP2vtIdaVtCfUxI5DknZ0JF73/N6j6ZTetsSAAuyhMj7
+dhdoXzkQ0qplqyInwuaW5I+vXnyS01viOxpceppSrZ+rcHxPb4UyEyWTQ5pqVWy2TnOgqdVBMcN/
+Wdbl7mTEjsYtOuo3zsoxa1paO9rpMjkhSh0TqI/vJw420pRqR8aoVj3e1pDT6vKlc2yeUS6A5S6F
+09Z17mFiWaOiq37taEKp8HAzaxCm4qvf2HmZB4o8LcYFx12NtSg3wVb3CCeW7+1GcEaboxaVEMeq
+1eNCnCocWYPKThHjqfXX9qrEm2JZrZxaoaHUevnRAF9PZldPWrwZrsKA280GlVTxFsvnyGK5T8NT
+I/q6jscz7fj97CF5TfaIbQXMErFSqpM+n/0uEPIT0d9f++AZfubtkoo+U7/K5tsEc7LW0EKqWokf
+yevMuJTK3lWNIdkTzMJkLyLWgHWoEPwwTPGrste//MIzZBsSuNwK2xzvyH6KtjKY/b8Q5lwaSxIl
+ONiTC6oGDqcVQLLZWmItkht6/X1n2Y5FGVzUasmXvQFbhwJr7cYtLmhNxNg9ZiWU1zfadbx/Jc2q
+XhxL3rYyPqSfKdFFWWK/jzKYy+TOLqlueDP1OXNd6+lnHgfax9HJQp5a1+7X2glpqeJSKp+Am1ns
+TPPKRY9Ao/mO1RoPGoRKMjMoC8KBBvNqKyRb+v6BJVWG6QOtuehlGZkSmtSdCZmCYsoSahrtX2p5
+BErDFqh70VzxuHROlMKVJ86w3NRI/to6QSkKKT8fnDARp1aj4qOATLMv6ajHy7c16FqvkPiivL5e
+3uR4e87SLdfevXnE6iUBHlW8MYe7kpt3Ohmlcm828pxIg5ihIw+C45Y0K0q9F9apaO6+GUYnnZWO
+Bx38jhLmKHS9Oiqk5gGoxHWaJ5T5U0Z1UXR+JaKhGIDKgVx7cjphR2stusMuA21QNJll3356vsMz
+5wCx1jZ8OF7mqffKXNuzs6xekP0QhIpdyAocvQbKI7JDmVNgS6YKrXfT+6SarfLqst7LcYU15YH8
+0PRtOM9+Xub6xDtoaFC5PDQcLb0xbM4LWchC/j8qj4Wl4BaGpa7LzIeKsmhurnicPy9Q1OvveRc/
+dEW06vmVVa5oIxZbk/83zhgy5UhwqJ9wCLjtkK7CnC/0xnztghYVfUa6T0/rDcyxmMHWOGA1wGj4
+noVESXGfQmm5zjy5zmUNhh3aGV4oNuPWmkt1Sb5kzdOcOQbUuikCQyvUE2N5kxU5eOmqeVpzAwpl
+Cc7CMTtWTMO1sIWTKenoLGecKGUbcors9xOCqgQ+jyZ9prniIsocv6Lms+tALOtc16rFc60aW9fE
+smmtLBO35OQKSoPR0yi0FUxDGYmU7Tpt+KBt9aZuzsUrMnavU+PP+7IWS6MZ9MTd+npfcv63d+9Q
+uSUB4/U/d5WG9r9MI4dqLn9nVwKW1ULMtNxzdalOY1lb03neSUjYpFPGCvkt+wk9ZWoyCkt2DNSV
+jahwcwINjiauS6yPgReBP5FgZfuKfNek4VC9K6nsl+6EHIYSgEyHU7QGj1dSVpyk0UPDD71TrNen
+P/gWljUCWwk8nESu359TybkzXE0hwhLOllbgjgvcu1o0ZQtGG4LknClbVro/4eqy7KH+Wo17kbga
+N+58nvplcbeLNWiZ70Oq8SfksVAK1lpmhcUzhoaaXEtnqlxoizn7xKlVnjytftjmZTIrG6WqPlnh
++hi9HXaakmi3ndx1aetnw6JG94Lc6L/wY8It+/GP/lO8sWQR0myGMcqjZ0uY56a/QyLCjF2uXZQN
+tuR7HCtEd1KpsrUlN/xd19o89aTSfyl/YiVsEmojmnhl7WQ3BWEFq1WXVe1rODAejOVBmY49WBVK
+9ZvTnLglrkIlWeaMZmw6idKbt2bkgVYDzjKsKgXPsbhK3vHAFJzRHptPrMhDcPXUOist8fH9qI6n
++BUTxLhKkWc8F1dLjk0p7kVghiirPbEDTl/MZBMf454XBfLWaskd5Tx86Zviou33Si4pbV5UODQj
+NecHMyItcKyUVbrKXdh05lDyCmtN+YCXF/T7Snfv2pMGMF7VJdIYhq/U/9XaKSqqCJ1JiasKK3BP
+4zni8pVZTtiUh9Axcu3XVjvc1sxON4YlKVdgejz6U1vDwAmD99U44ANvVeiydYn1F25R4LqyD20+
+p3JfB71PMIZUe1fmKXlNRvFLj1YsbVmzicTEOsmIbibXbtoe60aqde998S7X2/K6vXaZ2I14LbJw
+HxaykIU8JD9oK/p/APwVhGfsFvAfW2t7+ruPAD+HnIN/x1r78e87Bga/dGnVPTZroiUvLNX5kXe+
+D4DV5jobDTkdoiDCq8nJa1ytUTcZVvs3mGaMP1NGXsclzzS/fdrhp7ST8KeVH3/08gf5yg2lROs8
+c0KQEgWGaVe0eOG9QJk+HElaW2vjq0uQOyPinuSNL615LLfEUrh45hSNpmQa/LrMqdJsEGkTEicI
+cQItcDEZRoNgCmnAocZspMG1mkM6FARlMu4z0OrC6nLAk1aspXkwqTlO6WRatXjQxWrBlzWWqZJ7
+tI05uc6VU3Ltq6vLuBq0tHmG1QuxpktZKJy3unnSwt4quYlxDcGytpLrNzCBsm6PAyJfTtiVtTU+
+cO06AA/uyN91g5wnVjT4Fj9JWWqvyDNLhGqXp0lCQ+nImstiSayuNWlpq7y0Ygl0LSoVw7E2qplZ
+Q9lQE70m11YmY4xRi61dw8vnzXD6JHNy76RkpI1avLZS0wFntK/k8YFhra2I04MhHnOouGYyMJxa
+kvv400+9g6vnxSpshimxusVOFOLO4avzfVr6GA2ekvonRL844Ot9cpsbZKN5m3vFdwQHNHUtjGlw
+3NGq03cZxhVtOZinZK8RtfyDtqL/A+Aj1trcGPPLwEeA/9IY8yTw08B1YAv4Q2PMVWvt93VqXMfi
+WFg+LYvz1Fqb89dkIdfWAxxtz+06Lo43vxnzLh4FVkE8xjh4c1/PKUGj3fWoQbOvD4i2BR/0Vpmp
+r37jmSqz0+8HoN//GjbVKrORB8GcPVchrsunWFvTZrOpT7cQP3n9zBqXz4qp7XvL1BR85SikuFar
+YQo1DWsBpauKLMuxmc5Fb6bvLIOWb9usJGxJpaW3ukO4Iy5PaGpUtTZ6pByGW+0qxaFUzu3v9yn1
+obEutPR2N/2Yi+dkw54+I5subjVOXKasGBBqfMXPlnGVccr1cpwTN035AgOD1Xl6Xoir4Cu3vo/R
+vpOVqceSVrw+9bbzAGx3Jpx/Sq596VSbnZsy/0rpEzfk+6rWxdeHST0DwiADbfdukh7GVzbn0mA0
+c1Pxc2a6N2bK3lVxAsbKd1/m/kn4KJyUTJmncye4cx7EsSgj6wckrpKa+BGxL98RB/G3uzDpd1Vq
+Dm87J9f29r9YoxVoOjVpYrUdQbBcYIx8xma6r/wpJp/TbudYpQkwoQtalu+mBYU2qS1j5RL1ApKO
+7MPYH7K8KfusN25iA3HTvNmIYUVcxFcrP1Aremvt71tr507Q55GekSCt6P+5tTax1t5BOkW95zVd
+0UIWspA3VV6PQOPPAr+lr08hSmIu81b031OMAdc4rDZrnFnTDr9vv0Y1FF1TdiP8Na04mxiMtkE3
+SopSZpMTTVvGHpi5ue/iaGCsSCaMlPtu/6tiHdT9FuunRWvf6zYYPpAIeWP5DJ2xFJrY2GVORGA0
+JXH1XJs4FO078EfMj4y3bJ4hLsXNCWszSleCPXVlQ3byJby2fNbaKo72jSwyg1EwEametHlKZtTt
+aO1j9Xvfce0t3A/mHIUjypaYtk8kYjrf3xuRduXEPAhcfLWqQmMII/m76+tVNn05CTM9GW2ekmcK
++ppOsdoO3m3luDqeUxgo5/0RtSFN3sbMA2a1kMifN2dZx845IIzH1lTbxq3IqXsxmeCOxPppNx3c
+UzI/v+FRS9Q9cAcst2S8Ze3DYKcOua/3PwvxJlq45Dp4NbUqihrKk0MeAAAgAElEQVRGmbR9R9Yi
+LUqqWkmVPzhmpI2Bpm5ycnKXmcVT0EE6kexFtblOuy33r3pvhkGCv4Fnaeje8gNZw0bN5d95q8xp
+desqHEtAsLXi4MyBSnnE/Dx1KvOCsQA0EGuIcGvyfYX1ceYWJFV8LQqbKXipPsnIjpVpfM3lwBGL
+rrHfxVuVx658e0TNvrbH/M+kFIwxv4TUj/7GD/C3Pw/8PEi6qHAFxHE2lQ0xHcVMFY0XxzWSmdz8
+YCkjmxOTqjtgAoOj/fnMLCAf6PsVFxOq2TYsyZXVaaMt0+5NY2Z93Rx7fS6ZKzrej3GvJi7Byy99
+gnlzP6ulspVagyBSs63nEugDFK9sUlWcedbIifV9syx+u7H+KzoMhSe+Onl0wnbiRGLO4ztEhTy4
+RdSiqSajrfgYzbkWpcGqQzxORNFNs1vcU+apFT8i1xi5wbCmEXCDR6Tm9ZonY0S+j6cVhzNvTDCv
+Q9lwcGMFvzgOhZaam4o2PvWqlLoW5tjgzGM/2QQzVaSnt0e0pkCuUBTlYTumoWxMx4XP+YuyRuUg
+5VhJBSthSZ6p778k8/Ayg6f3obTZSX1MiUdDSWwdG1JqU5fpnEi3ZkkT7bcZeDBHOkYVHFXIYdqk
+nyjjlqYsm16DQOn3/eWU2X0xy8vUUmpdhaf34PIsZOpq7UqvxNHMV+IaGlp1WZSGfCjXGShIyyXF
+auq8TCxOQ37vlgHF3CBPS0yg6NP5Hrt2gVwPxqE7IOjI3pk02kyR1+UE3OZryyf8wErBGPM3kQDk
+j9pv11+/6lb01tpfA34NwHOdN79+eyELWQjwAyoFY8yHgL8H/AVr590KAWlF/5vGmH+EBBqvAF/8
+ft/nGEPguJTtgIm20w72d4lOn5oPiEk0wDUoQJlqU1dztD2HuKUnTWVy0tnYiQpc7ZJsmgVWA3u+
+wnKb96a8TWvQuz8UMlSobereJo/OA9BY+SDDw8/IdyuwwC8TxtpfcpoMWKvLddaWljHHc/hzTqKV
+ekFdacMfDPDW1MT12hjtJF3OEkygr7UpSukkpNoSzjE+NlFtn/sYJfUIYggLrZhcEddoOWnxVoW4
+fqsY4L2owU7fAbWm1kOXuK4BUaW9r7sxpHrSViM8pdE3owSUPdg0KziRNJphIpZCGecnwcwsGzDb
+m1cXFozKI/3slJ4yW9cjrSI1MWhL9YpJaahFRzPEKlY/S0rcpnwmCsTqCgOL0eBbEcT4ma6R4zBW
+jIeXDTnWAJxiwegfp2wuaeAvD7FaRTjISpa18UsSZ5R6Mpdz3EA+w9eT1ltyWWvImdeLn2fLV2xF
+U4OdlZiKo/cjGZ/08TRLJYUGmLEurroBVq1fIge0kzRFH1IlSIk8jD/fFymlrqFlbmnEePOWd/4a
+VV85Gc6skysWwinBTl75iH5/+UFb0X8ECYv+gRHz7fPW2r9lrX3WGPO/A88hbsXffjWZh4UsZCGP
+j/ygreh//Xt8/u8Df/+1XITBEDsem45Lc12CJacuPoEXa+u28SHHmt+untolcpXO6jkNkl000JXX
+ES2MxiKIQgg0vZP62K5aG5ryGU/uUlNC0bXby9i6dpout8mXhf7Nv/EyvhbP5BP1+8MM70g7Rneq
+RBvqq3swUYqt49uWeJ4JUt9zb3+Aq/RpK+0O9RUJpFonJzmSyrhJoo1JOiW7Cq81bpeK+vCTPiQv
+yzXXTmWYOeRb89V+MWUylddhWT2Ba9eckhVFBYaNBnFFkYnaZ+I4twSujB3YkGwg82j6Z6ho85Ew
+WSXXxqwY+b31u2THst5Hd3ZJFMZdTtOTNKuZdkjVulmJ5d51sw4HRjzLU8cXSCqaj+/NSEutSkwh
+qKjPHGujnsxCXyzE0TAnUl/cGE4g2KUzBuUZKDT+FK66+Nq0Zwzs3hIr5qhzSFXRpJWgOCn+2roo
+J3tnfESIWKFx18HNlCrNC4lj+bslpdi7ulGn3XpK13DIsQYEDympj2Q8u75FounJCG062+vgxjIu
+zDChFkf5OTAvZnJJtdHxrCfBztlsxOhQ1sVPU2jI3lry3oKZ11dNDbuFFv29SnksYM6eY1iKfJpB
+jU19AG0zYKxR7fFgwFRLnHfvGbwleXhTrXCsPzNi453yEFcvXCdWrH7QO0WmvIM9e4/79+Qhy2Ya
+sEkK2m152DYuHnJwS6LFppFRnhZCFv89dfisUnXrTbbjGYcPZOzOcYelZaWAbx5x+EW5tuPgEOdI
+MgPVA1Eg21+5xaSUQNWV9Q2iJ4Vqfn1zlZE2Xzn4pnIc3nuWtvI1brav4EzkoZr1XmJaynXWOhUG
+mhnoTWXDjEdDVnUf7ZgZjVB+WGn4TDTBEVYNjq8B3YM5CMlyPNGg3n6HTlcUjz/6FOcvSXDQbSyz
+8TapQQlm8qCk+xXu3BQP8WjyNdxdeejXlyuwOe8EPqWh5DM9vd7y5Slf/bSUuw+fKmgj972MLZWe
+Zh88i6PkKhV1Ke7cO2B/IJt/kmUEgQT2VpfWaWqpfTNOSNT8b61I1P/U+mmGmtU4Pr7FSzu6FwYB
+Z7TjdVIpKZXg5O49udcXZhazIWvkJY5ExYFKkNPQmpe5stk806ZxUcYoZsvkY1Gc+WFOR2tTvGSf
+RLs31SsaPKXEbUqGo9IKMFobQbxEpi7y+M5dUnU3JofyLOQVcMaibIqsy04o7z85voRVl9Zpl9TG
+87qKVycLmPNCFrKQh+SxsBQsYF2Hwrh4Wv8/28nIVufwWY+RJ6fDgyMLyvB7846cyu9dW6OrRJ1P
+JCPMRUmLuY0uk762KXt+wN0HolWbvprA1TPkVrTvUqvJVTWle+GEUNNGO517DLTWPRlrmisZMdO+
+ANOD0Ukjl+P1Cc88kJP+wf5N0pFaHo68d3h/yBlFEsa5x2nlWZiFK1htWnLzBWED3uv1aVbF+jHL
+Lnt9WZfuNCIeiwVSOd3EqotV3hLzM408OtryrhXXuKz9IChyvEisomcHlroG4oqq8g406/iFRuXC
+KRUNYFV8w+EtMfOj9TFrLelV4G3KuPsPPsuX/0gshb2Xx2xor4OdK5us7iiXwYWIZU21rmiq7D77
+TO+IBbZX38dckVPVmbmMlPItLiEbyDidXO7veJQzHqvJk0XkWjB1UBxycCD3cjwpqFflXj65qpBg
+JpSK+nywPWCyq60Aly2NNVlnJ8nwtLoyVwLd3ihm+kD2S8/tU8yU6ASHfFWL95SheulgSnZD/i6t
+QqHcGV0/ZXRX1nBqE6YH8vmzWwrRbm5RnVezcha3rZW7kyP6XWXuvrPHqC9VklMltvXzJpG6V36l
+StORAkJqEZH6jb1hTjQP4r5KeSyUguMYojigeqpJeFk2bsCMQrupjJZ9XnhRFmr6wgt8bk82fXMe
+FR5X+Evqc/b8Y1qhbDxrXXK9iZPkOVp6Ez91V3sjfvMbGIWOFo0arZoCemYhFIpbP1/h6KWPyoWW
+2q/ywZhSS3k3rrZZvqKZjyAnV3zDN292WBpLKev2TCHI5wOctjzQm1cuEl2QzVi/0GI2kPGudmTz
+FDd6rL9XTNGv3d1leEPKvW8eH7OlHH/+0WUurMuDFz0pa7XCeby7srG37+6e9CvcCB1iLUOur3jY
+tiiLULtsdV/e5VhZm1+awFOXxcyvti5SUzalaVgQX9LSaV/iIfndT3CkjXC33tPiS/fVTL59i8ay
+bK+nKk3WN6XCr6m8k+dqp3m+qab69m0ObgvmLVgK8GcypyDNiPx5xkjWe3nNoe8qX+UMjjR8VK87
+MFQovGepKbnKU1vndB6r9Lel0vT27W1KZA987cUZz78sa15vhlzYVF5Mq9Tx2ZCxVlruj4bEnjyQ
+jThgOZOgkXNKm9yulFSqckHDKMRxZQ1f2r3PcF8zQv0RZ87IvQ4SKR0/YxIO9cBpn65S5vK90/6M
+B3tySEydAZ++Ic/AZFuuN4stb78kn13lKqtaf2+aLlmumRY3I1IY96uVhfuwkIUs5CF5LCwFCxQl
+rBc+dUeCWhUK3CXRytmOoa75++DcJZ5clZOwPZUTbH2tSkXRbLFXhUJM8SKNSY6lF2HDW+WMMvgG
+rlga/dVNxhqs/ObN59ifSkDw9ukxO9sa1f7KTVItiErHivijS3FPXJfGtSfw1DwzWYVmJqfYxVqN
+K+eEA6IyFAvjrZsFZy89CcCpa2cI1uR1VE8walYvrUmg7lpUY6uuWYSW4faTyud4b0SlqYHGMmec
+iGsSVcTqaK2uMuppW7H6Er27Mqf1qcvqspwqF6nQcuXE98o58ciUxpZc+1uG7gn9GdGMqsKjy6SO
+o4QdRvkjs6CgbuTar7oNzl2Xk3lsAzyFGi/VelRDceniLbGq9pZu8MJdcQPb4xr3PieBPz8pqGnT
+ieVqjWpFxlvRiPzeJGcplt/HrkugqNC4UqPQYF7gh6wtyTg17VxOrc54JlZcJSsZKdVfoxITKF7i
+dN0Dq+hUrcr0KzGlBm7t8QRPrS3fq58EfC911YVzV8nXxGqqVk7hKEJ03dQ5d10CiacGE1qbMvaK
+8mw0KwGZ9is1XoLWgJEe7ZApQ2CzvsR7rsh3bIcyN8cMyTR67CYpK2fl/lbzOhMtBbATGJh5mdKr
+k8dCKbjWUssyzCzFV/BIvLkkVDjA+uUSf1VAI0mU8dauZii0mq7u1PEVc0/WY9aTByteTyg1TrAR
+X6B+STZm65ws5DRJ6U608aI/YzgS82zcitm+9wUAjq+Mmfyb8UPXe3i7x/68tVJvwIpClKtOxqlL
+wh956h1XcZS05PqOgqkaS2xcE7O72lom0vI6x67ghXL3nZaYrauBh+PKBl1/MiNWivti8x5kkgGw
+gwF390TpPaldhYoHBe22zG9rtY7TV3ryVR+rwBq/4uLWtFmIlj3H7jpZIA9VkTWYg0yjzBdwDVA3
+BxTHWuXpSzzAO7vE2/ZEEbSeOIVJZMO6zRAcmXdk2jTPKC27Qn9PdwKSiWZA8ikdKw+Y70Jov81U
+FXqicCotee90fYPaqpKr9rtkVvdCUaNQt9FxHFbWFFhUEXfNDWasXpX7sXl8lklHgUOeZUl7gZ5q
+u1Q0j5zpWkWNmNlIMzSzkIkikmLHUu3J2h6qa9N/dp/1q1oH4Z2mWZG1aC8tkTVkPU+Pzp7UNrRi
+JXRJS7z+A72nZzHLui82qmz5ylG5XGdZbjuXn5aDM9nvk2s59fKVLVKFrpfHHUqNRwX1HtVinu58
+dbJwHxaykIU8JI+FpVBYGJYF6XHGdConUGFahI4GtYLgpHrSRCHuOdGwRk8ibJU8FZN51D8ArQpz
+yoh4SaHSmYOjWrda1erDaUZYlZNh89yE6JacXP9B0Ob9Z0Ub/1e/9RsY5XIoynmuHdyBth8fTugf
+aK647UFTxjh/9RpL6xKpT65pnts6uCvKfejEJyAq1zEUhdKN1eQ4KNxDIi1E8vwq7bZYSqOVNYqO
+uAS39o7JjsSy2k/k5Dtzts/N+/J3G1XDVBvHTAYeQ6X2Gk0SVrTvYLyqbdUqTXLdDllsMVYBRDbE
+lsp96DQxCvQpdb2rjXM0N+R1O7f4VbHS3HoILYVNlxOY6SmuFaUvbQS8RfkQp+WU7g09KbfO46q7
+5vgO5ZyPRAvJKs2IdqJBUhsxONYTve1ilefQjxusLouFUFfuBes5tJckOt9uDEky6c1ZKUNqmhkJ
+vSq+YhZcxSMEWUQ8lTGqdYdIT93ZbMa56xIwfOkT4sKNIo9vagvB97VdokD5IjyD05D9W0Q+RviI
+sMrPUdInK7WIL/ehnFsp67hKOR/4LkYrOK02SM2urWMT7VO61KamVcVjGxJ78tlZURFU3WuQx0Ip
+WFuS5ik79T75nHlodEyifQkzp6Rl5AY02g1c3XhzAsVk1sOdafoyaxIqLThRDW8mZmlp8xNkoaN9
+A0wEoaZAa/UG7jUlYL2xw/2GLPy1C5f56jekB5Bx5AHsHHWpKNNR/a2ncY+06Wp6i64yT1w5fwbf
+yHdHq+K22CTDaGzElimq82CW4Kr/GSgxqGM8XO3TECzFOMqEVC9C+t68c5SD2xEfNhvJWN866mMf
+SNYjK3MSrRkpVyBSX9zLx9hMNs2c9tz3C2Kt2svrTZxSHujh4YCpxhTCvoPJ5whRWSuzs4On6127
+cAWj5rUXr4CS2KZTS6muSanI1Cf2V7jTlDXe/uw9jnclTnTu2lVKVfZh7BDOadmVTSqwTepVpaX3
+AtYaWtZdrzJW+nmv4bLqznkl50hIqFntI7JRw1Q03lPxaOvZ0qgvMVZ2qkJZkchLJpp9SM2YVIsp
+RumI2/syXkXjFgedKdmBHGq8ZYqvD29Qq+HPGbCaNVxHri2ZavozjalorYm/5ONo3CJIcoJVPeCC
+GnNSz3n/0MD1yR1RyG5UAQXX1ZoBhboSNre4894mr1IW7sNCFrKQh+SxsBTAUFqX2sRS7YuGSyqW
+QDEG8WSEq70UvSjGVRNvTlZhxi6FnlBBYPByPTHyKU4qp0Mex/jKuuyE1fmoOGrCh3nB3WMx6yoX
+xoweyKk5uP0ijp42c8KLkSm5oJWBS0nMwBFLYfvLt+nrCXN8foWmZhKc2gmXGKVyIVhbYtQkxgSU
+2rvQHGsAc8k5YV82tnoSySYClOtgd/eIoZ5Ad/cE2PLSnQyvI+7MU3FApuOVE0stU7qyPCbOlb5O
++1nm9RhXIeZB7GIV71+kJU5f6yA2WycmbD4VC8VlHd8Xd8Z2p7hrAt12GwGlAsMYTxjf11oDzQx9
+NYKpNpx5uRxwfCjfd2qSgbJRB2lJ0ZJ5TxUX4hUJrnbrDhsVikjXyDj4mjmoBi7NluwX/TU53jxe
+SmlcQjXR26XH5qrM1daqBErfVigbwGE+YlbKPIa7U2a51uOUBYOq7JelBzKntHCx9+RUnt4f0WrJ
+77PUJaiItWjCEhNoIFXrLEoG+I153eAQpkpw40RYtRqcwsEop6dRcBdOgaOGiUMfVmSve1Qw84xR
+r2DiLnAKC1nIQv4M8lhYCp6BNdchdAL2UtW+fsBYkXK+ExOrv5SlLm5Vc+iJorbClOxQKxjTIe6G
+BPiC5SZpLt8R2jGlBiw85S4g8XHmJ1BScL4u/m4/P8tTK0Lc+umLBXxTW6x5ygJ9MOSlXLRye3aM
+ncrpcW/3HntatfZDT95h+bycsEsa2PRc5yQtmEw9zFR7Wo588lzSofOqPjMq0aXAXZlSDORUycZT
+enfuyveNpvhaqVdVbv/7ezvMbiur8cUQV60jk5VkFa0crFSYVPQz6nuXBy5GU2R+EIEGsCq9DDxJ
+OYb1Ei+WYJ3jSXDNVFIqYznNBsmA+rbM2fM3yUrtHr0/IdGqy/5YLI0rJuN+5RYAD54bnfjtWVGA
+Mi1PQ5+GWlNGYc7FaIZOibLuUlVugfHeFHdF6flmEVYtId+RgGO4mjEdzdvpjXlwLNe2EYLJBL/C
+ZEKaaTC2kBM/m2bMEjmOR9MZA722KHYZfFXWcFfHGgwSWkqbMC4PmPa0II6UJNL+G9iTloMUYoEk
+/YJqIe9Zv4XTVjzMYRdH+UWMUydozOMj8l5ucpKOBLGClWVKjZ9F/jI2ksBm3C4IstfW9+GxUAoF
+0HUKJscTpi+ImbkbNllaFnMpj0Kmuomr6ZRCezCSz2GtFSpKVzZ1IVPsq7uWYrVxZzpz8RX3gGYT
+TOzi5kohvnWZZCaf3TQ17r/wony2A6G6K5HSmR1MZrjamenF/FmqbckY9GaWA1UQX7+9zfo7JKIe
+dWSnRLUNNF6KUzqMlRE5+/oYd10pyesy52R6n/JI5uwcVwmq2q9xMqVzoEG7UYUzTVF0HU/GHfVy
+6tpf8TO7GROlszgcpQzuymeOag0aHXEVOhNZq4rbY7atzM7rfQJ3buImZGNxD4bTkmxbQU+bcj39
+pEY5Fu3l9gqChroMkx1MoPRgTpXSyNo+6Atc+1Z2gHNLsRWBReOepN2EcVXJUgqPIzWDrbpBA8+Q
+KKX66lKDwVBM96lvKZRwpRE5DNWspibXEGUhZSH3fXxkKadyzbuTAi9S+rcsZTiRcSbzrE1ekveU
+RKXICZWePTIVXvLlMw+66lIZSPTevOvmhKAmY5gioVnRrM2kwFWaPav1PIETUMzN/bElWRIFkTs1
+JtrIt2JySg3cesj9zTKPiYKwisEEJ5JsThC5uLpGnolw/TnP46uThfuwkIUs5CF5LCwFay02Ldmt
+WQZroqcuLfk4c5izNyEfKwXXdEqpWABX6dVIDGFTtKc7G1IeSqpn9qCHgxzNftw4IV61aqoZpwlK
+zUZYUI8U+bXTZXwgiLCitcqqmo8X6hpoHE55MVbzOop4O3ICL9ddbhyKhr57eMTBM3LChtqabTVY
+wp/JGI4TEswU67A8YzKQE6qpfP3T3jFBLOPmtoVR4pTj/Vt0A0k5OhsRAz2ZXlbuifVKxLH2b9gK
+He515HQZxy6pWLOsmZBCezKMPTld89InnIhLMDyusr6uPQZKl2lf5rfTP8RfkbUd/bEWKAVDiljW
+eP2qIdE0rClL8pHCf2tjDm+KaTt7VtyI55+9x6eqMvZs/G0Y7rQhLe4AynSGM9Hg59wCSTNCJUqd
+jcag/TFtYSiUdmyUOMw0BZpof8nsyCFT8p3u+Bijgdv+OKUWiXWaOxHdVE7vnraKy8uAgaeVuybF
+KJ5kPBuSKrGPenAcpharbtA9O8Uo5mat6TPVakdbz4n0sTPzykh/GeupdRBOSO8JSjX1auS6x4PY
+o+hrqlLJcAazEamdt81bp679KcqWh6NWbVHkuLzOdGxvhBQYejhsHA3IDtWOfEcFX3EB2XDGTFc+
+KUcnDxa6INYpcZShJl66hFV2XsvoBDSCG+Pkutm07NkEPVBf1SsijLI2p2sDboy0Eq3Xo/1eMf/f
+sy1VbZ/qfYtkqKQvtyZceac8IIPUoabgnlkxYyeRrk7LnWsA1Ft7OJ48mY7nUaqPO35+G7SPaN8R
+kzrZ65NoDMONuowief/eQYed5+XBjM9EtNfEPP7Kjmzc4/4YX4LpnMqb3FQHPJ4ljJTtd3TWwfeV
+ov2BKtYGuL7iKiYuw752hSImXJIH8nS4yUw3ZE3vR2XlLM6agoaS/KQjVx7MyJSJefSgR1frRj4e
+yYb/+GCPyQNlaeIVMnMp5p1bZ5ZSiWbGmnHxC8gVxzAKe4SaXTK+xcwNX39GFGmGQr/LTSwjd96M
+NzjJAGSDKbOaKoJKzqir3I0TmedwljHVjk2TMcSxNsyxAT1loy7zOY7BMNM9ZrOE8UC7STU9Wkoy
+Y8cZqZXxjHYtKp09KqEcQrYAlK3Zi1bxazrGwGKncm2pxjtGNiXTdYkqOVbxC56p4Wj2JC890tfm
+PSzch4UsZCEPy2NhKbgYlpyQIrFkIw0oTnOCVdH24+EUR2vCbV4wyeS0MUM5Er0yxVVUWXY4wq3M
+27yNwJVoeXl/j4ERc766LoHBcOMyqUJY3TzAU3Rccm+D9VyiuteffgdpT7WxUqatRBXu9eUUGBZT
+SoU8v+XsOqmSoRwfJjz/3Avyd3qimOyv0roqYyzV2iT6fvMtLWY97WOpDMHF4SG74zn2ok36knz2
+1lfucTCWaz592Gd/V5mWtTCq9CDRbEB7yaGpwb7eYMq2zt/bb+LHc4IXNZMzB8/VyLuXUHRkvOok
+JL4gEGs3rlBrKBfkRXUTgg3SmbgzO7dv4y0LNqOdt/EUQVrEUNEek8UltQJ+rzipBnxlL/demlDV
+jFAWuESK/quUpc4vZabWT8vxKJXBORkluMpBGVer5Mdyuud12UPGJKzVpV3g9NSE8bPqYsZ16jN5
+f+Z3GCr3xVFfx0ssM61EzIryhBG84hcEqVpFyvmQzkoGavfsTjPsSK7hHVFOqIjFJIX+kVZz6hFe
+OVVn/KK4MM7pOsG8U3gYM7uhdGwckBVK6rIl984dGTKdv+96lKFyMSYdClebz1QdgmTO8/jq5LFQ
+CqUpGXgjgr7HS3dkc3/wTpdKrNWA6QizrI0zqh4cy+J0dmSTT/bv4yiQqTaoMe6pr1fv4KTCtTju
+PuAPd+Uz//4v/AwA6xc8jMJ5Z94qEyXGJHRBWYrebk7RuCC+4YvdLwMwdTIqRq7nsDfl5WPZ8Nc3
+LrCq6aRO/w77+3Kjn/uyEnY0dk8gp37NMO3IeEefv4e5pMCTfXngjwYDBiN5vXQxJtLfOy9n1PbF
+9PVNDaulzB11iSYzy6Ulmf96xaNjJXrtFjk7A/nM156/xweUJt0/K1ugES1T1/Jd2x8wVOU8nuyT
+TuTBSqYZNV87ObW0ki+4T+dAysi/9eKIc5flejZ/4gnsvGT35ojJRNboLw4Enz+Lt/n9nsYJOOGX
+pRhNGGgF6pJxGWVzoI785+YlxpOxJy4UHY2JYMhn8qBPOn3GmlJ1UQCZe0QjkLjG9mifZ+/K68pW
+yLktbQLkB5RWYcPaWavhN07qR4bFjHmLE8ca/to75J78+h9qmbIxHGmxRu9+SjeVOX9ww7BxUdaw
+nFmGx/KgZ2PZV63ZjEgJWcIbIXiq4DtH7GtM6M7LHSpteX19S5RYUClIpkoUXC1pa8PN8QS8TNbW
+813cYJnXIgv3YSELWchD8nhYCoUlPS7Y9lJGarb9uf5z+EqP5ngWVwtw7CTBWxLt39T8st84RaZQ
+U2d1i3apQcL0KjWFPM8uN3g6EChw6kuEf5b2KRSD0G+8wL0bGsw8+CTneB8Ajf/0Dn/8i6K5b/Tk
+/8tn1vimJsIn45xZKlH7WnAe/yk5rerPuKQKpXUURnv6LctsXhMYcLI/ho7CtLe2OdxVht+xQrgb
+U7beqnDteEDvSEhIdm6NKGtiIc16KcNd6Xl5rObyqWXLBeUV6NQTgplYQpOgP8cj8cLsGNu/DcCV
+Uok5qhUSPdjj9da8sTPZqILVa2qeXj+JtM9q2qq+6+PW5TuuXxvTelqBGPXqSX/EydYNjvQ6nntW
+AUuVJS4rAOrmYYlVW6GyHBNpJ2nfgtWGOeG8cU5RUGp2ZfmoxQwAAAcBSURBVDpLaWk7uqK0RNqi
+Pa6coqZcFokCxDqTLUItMLuQ1fCWNRORVRhp1eJokpL7SnlWKEjJmZHH8tq1OY4SshQzy04mJ/aH
+1sRS+BcHGUUk137fyVhD5r/t7HFaG8449bO0LmoQc19dseYa9XWpyq3VHdyqfK8dO4TXZez48Cu4
+ytsezGH+Xg6J7HtGI0auBLYrlxrMHJlTMFnCC19b65XHQimAoTQOaZajfBd8fq9HdEcmuRSsEJ2X
+mzwb9oldURBBoRVkTZ9hRx6gpPOAxFGO/WnOSM1B2qtsdiULEF+XHT/sHuAkklk4flBQ7Atg6Y9v
+znjvOXmChl+bcKciJtoLO/KQX3dcWhq9biUuAyVZOXAfMN1RoEvg4GvHpa1Sr8GEuE192nKP1lS7
+M638CGfuiZIZx2o67q2QjmVDJw+e57kXbwBwdHuPI00xRcULfONwXoYrN37HDVlvqAk7LsnUF08L
+qCvw5oWkwD9WU/sFzRxUMi6rTxr6M6pK8OLHBY5udGeQMFOEXbEjD89wNiSqKWIzPEcj1ZqJl7/B
+RJu7Hn0jp98VF+Perowx2poy/Lo6Dc5JgR+1UYS7rqXTJiLT1NpY27a7eZVwThOfG8ZjUYY2cRih
+5dxFykAZtzKtLu0c9eg5c5Yil6mmUZ3S0NE+EunA43isMRqNNfXDhNmhAqeaLoG6Eqb0mNXlnikH
+LitBhB/MswwJ+6GS/94+5KAtD3R8uYWncZJYFWExHDLoi3Kfrm7g6TWnvQ5uW9Y28DYJY4k3zane
+s2mCOVTF4s8YjMXlW16ZkjVUgaQhXbRT16uUhfuwkIUs5CEx3+4N+yZehDGHSOOe16bSXj9ZWYy9
+GPv/B2Ofs9aufr8PPRZKAcAY82Vr7bsXYy/GXoz95srCfVjIQhbykCyUwkIWspCH5HFSCr+2GHsx
+9mLsN18em5jCQhaykMdDHidLYSELWchjIG+6UjDGfMgYc8MYc9MY84uPeKwzxph/bYx5zhjzrDHm
+F/T9ZWPMHxhjXtL/W4/wGlxjzFeNMR/Tny8YY76g8/8tY7So4tGMvWSM+W1jzAvGmOeNMe9/o+Zu
+jPnPdc2/ZYz5Z8aY6FHN3RjzPxljDowx33rFe99xnkbkv9Nr+IYx5p2PYOx/oGv+DWPM/2GMWXrF
+7z6iY98wxvzYn2Xs10veVKVgjHGBXwV+HHgS+OvGmCcf4ZA58F9Ya58E3gf8bR3vF4FPWGuvAJ/Q
+nx+V/ALw/Ct+/mXgv7XWXga6wM89wrH/MfB71tongLfrdTzyuRtjTgF/B3i3tfYpwAV+mkc39/8F
++NCfeO+7zfPHgSv67+eBf/IIxv4D4Clr7duAF4GPAOje+2nguv7Nf6/PxJsr1to37R/wfuDjr/j5
+I8BH3sDx/y/gLwM3gE19bxO48YjGO41syB8BPgYYBMjifaf1eJ3HbgJ30DjSK95/5HMHTgH3gWUE
+Wv8x4Mce5dyB88C3vt88gf8B+Ovf6XOv19h/4nd/FfgNff3Qfgc+Drz/Udz/1/LvzXYf5ptlLtv6
+3iMXY8x54GngC8C6tXZXf7UHrD+iYX8F+Ht8m2yoDfTsvIHFo53/BeAQ+J/VffkfjTFV3oC5W2t3
+gH8I3AN2gT7wDG/c3OG7z/ON3oM/C/zumzT2q5I3Wym8KWKMqQH/Evi71io3looVlf26p2SMMT8B
+HFhrn3m9v/tVige8E/gn1tqnEVj5Q67CI5x7C/gpRDFtAVX+tIn9hsmjmuf3E2PMLyEu7G+80WO/
+FnmzlcIOcOYVP5/W9x6ZGGnL9C8RE+539O19Y8ym/n4TOHgEQ38Q+EljzF3gnyMuxD8Glow56QD6
+KOe/DWxba7+gP/82oiTeiLn/JeCOtfbQWpsBv4Osxxs1d/ju83xD9qAx5m8CPwH8DVVKb9jYr1Xe
+bKXwJeCKRqEDJOjy0Uc1mDHGAL8OPG+t/Uev+NVHgZ/R1z+DxBpeV7HWfsRae9paex6Z5x9Za/8G
+8K+Bv/Yox9bx94D7xphr+taPAs/xBswdcRveZ4yp6D2Yj/2GzF3lu83zo8B/pFmI9wH9V7gZr4sY
+Yz6EuI0/aa19JbXyR4GfNsaExpgLSLDzi6/n2D+QvNlBDeDDSET2FvBLj3isH0bMxm8AX9N/H0Z8
++08ALwF/CCw/4uv4t4CP6euLyEa4CfwLIHyE474D+LLO//8EWm/U3IH/GngB+BbwvwHho5o78M+Q
+2EWGWEg/993miQR7f1X33zeRDMnrPfZNJHYw33P/9BWf/yUd+wbw449y373afwtE40IWspCH5M12
+HxaykIU8ZrJQCgtZyEIekoVSWMhCFvKQLJTCQhaykIdkoRQWspCFPCQLpbCQhSzkIVkohYUsZCEP
+yUIpLGQhC3lI/h/qu6kx5N24XAAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3569... Generator Loss: 0.8252
+Epoch 1/1... Discriminator Loss: 1.3871... Generator Loss: 0.7830
+Epoch 1/1... Discriminator Loss: 1.8022... Generator Loss: 0.2896
+Epoch 1/1... Discriminator Loss: 1.3923... Generator Loss: 0.6645
+Epoch 1/1... Discriminator Loss: 1.4469... Generator Loss: 1.4170
+Epoch 1/1... Discriminator Loss: 1.3721... Generator Loss: 0.7974
+Epoch 1/1... Discriminator Loss: 1.1933... Generator Loss: 1.0238
+Epoch 1/1... Discriminator Loss: 1.2738... Generator Loss: 0.9327
+Epoch 1/1... Discriminator Loss: 1.4144... Generator Loss: 0.9993
+Epoch 1/1... Discriminator Loss: 1.3502... Generator Loss: 0.6667
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvemvbXl63/X5rXmtPQ9nPneo+dbQVV092nFCOwkII0VY
+AhGFIAgC5DeAQEICK39BXgF5mwQDQliRpUCAxAg7HTtuOx66u9rurqFvDXc895757HnvNf948Tz7
+dJfArrLd7RRoP1Lp7tpn7bV+03rG7/M8xlrLhja0oQ2tyfkXPYANbWhDny3aMIUNbWhDH6MNU9jQ
+hjb0MdowhQ1taEMfow1T2NCGNvQx2jCFDW1oQx+jHxtTMMb8jDHmrjHmQ2PMz/+4nrOhDW3oR0vm
+x4FTMMa4wPvAvwIcAd8E/m1r7bs/8odtaEMb+pHSj0tT+ArwobX2nrU2B/4+8LM/pmdtaEMb+hGS
+92O67wHw+If+/wj46h92ca/btft7uzi2ZlmWABR5yvRqBkBaFkSOAcBgqG0tPzTm+h9bi8ZT1jUG
+c/13g3xvsbjG0Xvod9aSVbV+x/XvXNdhfQvXgqvPsa78vjXoE3gRAJXrE3qOjtlSKpsNfY9Qr18r
+Y7Wt0ZHju+56+DgGrD7Q6MVFmVNUshaO4+J7nl7rXP+uri3oWuR5Kv9mNXWV6vMsVxcXMg+c62cb
+wOhN6lq+rer6eqA14OnYA9dBl55VUeHp//hBIPN0LGX1g/VEf2eMwQkTAJJmAz9oyP08V59XUZVy
+r6LOqXVPXGPwXbl37roE+rzlotLvIPHlu8hx1tOnsjVV/UNar362OnaqGsx6r+31nuKYH+yvrX/o
+HK3vVel/YEqDca0+z+Hi9ImshcrW0q5XD8rKyv7InT92BtZr76zPL1Bda+zrAcun9beOMdf7sP6u
+rO0PnWWD5zjXc76+iwW7XsMsu7DWbvEJ9ONiCp9IxpifA34OYG93h1/6H/4uYbbirdE5AMdHH/BP
+/6evA3D38pQXW3JQvNplWcqhd3xfvnMgT3MALhYLAldfIM/HN/qS1RltfZE9R77L05x7swUArqnx
+TQhAvxlh9fB2raVt5H5FVw751/69f4fDwfMATLv7vLglB/7pg4KrhmzMMwddnmk3AdDhsrAL0lo2
+aL/Xwnflc+A61HpAvFoYwfHpE07HpwBEzS77/aF8DhI8X8aWLXOoVgA8evgOAA8+XJHO7srzsopf
+/O//HgA9EzGvZWy+AV/XKF0tARinC2whBzqjpN+UuR522jQCeSm+93TGsC37sL9/E4DnkoLzWSHr
+U6S4nVjW0w9Jnv88AG9+9Svs3v4KADe2uwDMF3OmlzKPo+UDMn2Bmp7hoP+szKnV4WYs4/z274zk
+u77L57dlHi8nTfJcfjdNV0xWcgZcaylTmYvVsVeTBbiyEZ4p6OkaEgZ0I9n3PJ1TprL+F6sMgNrM
+oZrIPl16uF253ywL+YX/5m8CsIes1WXtUZVynkaTglkm4zG2olThM89TAmXwiTJWz8K8lDUEcy3s
+HGtRvkniB8Q65rqUe52lGZ4yurp06CdyDkvfIVBmaSuoEnlPfu/u+w/5FPTjYgpPgBs/9P+H+t01
+WWv/DvB3AF556SW7WuVczSq69x4A8M7DP6C9PANgK5uwMPJCdxoNtgNZHL8jk20nbQpkUfcuW5RG
+Nq7X6BM3ZeFNmhLGcn0rGQAwzy/ZuyvrdDK+YktfBBO3mZZTAIIqpG/lHoW+3enEYx7LeO5s+Wz1
+5Xcdc85VLYepvTJ42/KCxLFOelmTjORwx1HzWsNwHQO5jLnU3UziEb25XJs/HePuCFPwgwmhlRv6
+FNCQU9OPZG7L5IT8VMa2bKRY1QTc0uVWR6V4q6aVypw+zESTCKuKXKXcQRjx5s0+AD9x83VWoTCe
+pvs+jiPjvKlrlW5V1KtjWc/xhNZcXoTRfpfgwe8B8H4+55Xejqz9c9sydtehKmStDk66XLgiDBbO
+Nh/15SV9rjJ0Q5nfVz4nY/9SbUl0/7ttDxd5ic9WLv69K10Dn61ExhHSknUdz6jbskZ5NqeRtAHw
+5hWdHWUcYUVuxrJel3KeomcaTD6ScZ6szuh7svaD9opr1auQ8d7qRywcuXarqDlWgZQbj1q1qYEP
+jr7cu7GMrfJr0kw15LoiVuFUO9W1BhEEHl1d81UuD+7NpiymcwDaUUDZkjmZJCNM5Qx0rxzuLUb8
+cejHxRS+CbxgjHkGYQZ/Dfjrf9jFFoul4OTbv0n5WA7gxf0njFcyYceWNCI5FN2wIvZVordF6uwM
+dnCs/D3bKnBD/bvbxXTkUMWLAmL5PsplA46uXLwDWYJOr4OTKTfvFHRSuda5WLKrz/twLr9rtWIq
+5XG97kuQyaFqxH3s4pFMqlvgWDF/wkA234ZQDUR7ixwHz5NDam1JraZNnYvkTuIOQSBjy290cDNZ
+C2Mb1IG8hLh9Kr0+CoSJDbOS7NY+AIu33iHUsTnBlEMrL3q6yGkaVeMr+Tc2NQfbMs6v3b7N57/y
+kwDcHL7AZCGMc+B5PFzJy5KeyUv3+IML3LGsizdvEOn84tGK8UzXzX/M2/dkXbxnD2V5koheLnvN
+QcjVPRln+FJ8LeVbbZ9KJWzf6ciUOyuiQJm3l1Pksr/p6hEnC2Ha7mrM1s4zMj9XVf9+guPKWnX7
+AWYqWlijd5PYlxfPJyTz5EyVlzK2dD6niMUSLqsZBQeyZx9c4KhmMi+Eod0eb5FZ+c5xXboNmX/s
+uriezMPJIlwVKANPNMk5GWFPhZdxiFUIzeo5ke5T7QcEKkQmair2nQZPgkv5XQ5+Igxpr9ehuVCm
+mHe5O/sMMAVrbWmM+U+A/wtwgV+w1r7z43jWhja0oR8t/dh8CtbaXwZ++dNcW5UwvvD4zvl9vMcf
+ATB/cszlWKVH6NJ3hdOGrkfkCjffSoTTtoOISLlyubL4LZmWb3wiV02NvkcQiZSra+HEr3R3iSOx
+wzpBxPyB2IP2/IqTTCSvSTNuD0UaNWfqiAwHdG6LOmz9Oe1QxrGYX1IHItFCz6MZyPdBIJIhjlpU
+lczJD3yMOqhs7VD6wtljX8YTxwmmLWMvygrPkbFROhSZSPSajDJVaeuJhL5c3iW+IfPrbXnMC3WO
+xgHZrownrDs8PRHpcajOKQYdvvbqywC8/OUvcPM5kejGb2Jm8rw822I7FPX58a7Y2b2rFpMz+Xw4
+TKCxJ993Lri6lD377jLnyXvvy57tyL38177EJBdp3d+6QXYq0v9h2SVry5hW3opDZF9JdN0aybXj
+zJawykXdf/r0CY9GJwB85YWXGfRkHdd+lLN0wY5Mn5Zpk+t5cb2SoJY/+ImBlVyf7Ml9V+OKIJfz
+Nho/otkUFR234Gol48jVhDn1ZzSQvX46K+j1ZR98Y3Fz1cziiN2uaIiNtpyV3bxiWsi92klFrT6s
+7hLmqjUMelAia99eyZ5fOoZQhsb4qmCsmtJjFnSXsid39nocZHKPT0v/whyNP0xlseLi+Ht0JvDh
+uUzm4XiKVZvMwcM6akc1IpoNGXZ/VzY+Cj18TyYehDVxY/2yWQJV25JGi546wfJaXrBs5LB9W5nN
+aI7TloMwKXLKIxlbNxywWggzqdS+DaOCFx09rHlF6ogt69c1TbUB3dLiespEHBmvcT3xHyBq4rWP
+2HGuN8LogTDGgJWD5LgV662yJsfVqARFhoMckKoSU+Xg5oDRiQw+9U9xrIx9t5GwFcmBXaRXDBP1
+kquza/BSh1e/Koxu63Yfz5W1yuxDarVhu88cECsDbLSeArDa2yX+styj0e1SZEvdsxu0HovJ8PT+
+XT5669syzkLGViUhz+6JmVNPc/yhMGEvqPiCOmEqa5l5Mv5hEOu6QG1l/st8xXuPxO908lGKry9W
+y+twNZV9ONbfF8cjWj1hPGFjQrmQ9cyaKZ115CfaInBlXxvKTE1ZcVLIvW4fPkM5Fx/MtHaxuTDy
+7bb8pshhWss+dGKXyMr3QdMS6Do7gcvBljCWQJ22qznYUu7VDBwKdbrmfkjXk3sMOzGlnuX5WNai
+jC/oqXO4mUOO+rxSCNwfOHSzjtz709IG5ryhDW3oY/SZ0BSqsmJ0NuWj83u8eyROtNOsZKiqv5O4
+dHx1NBmPfkdU0Ei5aOQ3qTXiYGwCGonwghaOrw6eukFR6zWZcOoyuSLK5R5LU+Mn4ogbFjB0RQKP
+vYwyFc49k58zmRpOzD0AOk8rnOBlfV5AHoha7qwiXPUim7Wm4BiolA/bmrWmYIyF+oc+AzgeqKff
+VBXXrm5jMJ7iCa6mWA2B1Rr+SsOC/EhV325NqDH/nXZMS69ZeC6eru3NXZEoLxy+QTMQk4EiJi1l
+H/KRwWj0JDY9HF+eFydirnR3LH4sv3P7F8yfijR2bUrrpjj7ttzv8Z3fF4n+/l0ZW/fXf432X/03
+AOg/mpJqJGlS+hz3NNpRWIbq+HM0jFd4BVdPxez4/gfvc/qe3C+46dM7l6hSvkoJHRlH4IjmUq4y
+rnxxCHrFPvPoA7nf+yX912X//GZ9jR0I7UCfN6M5l2fPnDH1SvZh5RfU1XpPZD9MllG35dpWlRAl
+6sSOWtCQM9l2WnR7GiXw1QHtpMSp4kaCnEA1jLALTTVtkn5NmaozvSeaaXe8RaZy3e9Z9hSUUdol
+o7bsdeMJlPX/B82HqiiYnT/h+PGITL3l1lZY9bb2XJ+G2uVB4OKqR9lZA1dKSx3JIY8iQxj1AKht
+COEa3FGzXInaaZU5uE5C4sniVe0hRakx/1lOmshCuucj7qn6OFX1rVmeMnkgz/bIaRxK9LW/s48t
+JXRoWgbHlYO+xskYW2OVeQmqRg6KrcCq6ketW2IqTK2xdArWW2VcMKW8kGU8wx3L+P1QohP2+2c4
+N+S+/uMlW4EeJFsRhHK/xiKgq3b7s7dk7L2dbXDULFlOyNVXkVYGB/mdCWf4K/WS60tc5w5oWLMe
+e3iBvKTlqkG7I9e+PPtp/lH1iwDceyLe8vr0jOFv3wcguLOLr1773U6OWwoX6sQB8Rq0paZYXTj8
+0je/BcBv/sP/lb/40/Lyfm3wM9iORiWKEcYXT3xjIGdh6Bqy0S4A3VZJcSyfr9InZGOZS7ObYdy1
+KSH7YaYxfiRj9kcwS3SbLuY4Cmpa4yOaUUKlXv+432A7VP+JCUFD4ztJg66GIksNLZuWwWvKfjhO
+jzSXz6lZkShehsK7lgt+sQYpZRRrEFltWOj7Uvou3rHM/14x58pfx04/HW3Mhw1taEMfo8+EppCV
+OR+ePyHLZ0Qq2ZIyYEfxAVtJQrchHL/RdIlCMSV8jf/jWQJfgUK+g69mBb5PpbDbejm7Rr+5vnD4
+IAoxnsa2swK9BYNWTZCJpDxdLK6dPSbVaEA2JVew0TJpE7dExQ3cBiaRm1jr4rjr5V3zXou1P2Qm
+lGoq2OwaZGSNSGtTOKDIS1wfo/FqjINjRCX2XAd3KBqUORNpx+oUm4um0GFIW1FuWzahHMo1UeRR
+q7Oy35Wxd7e3CEtVtbMxdd3SkdcEnohHp0qx6s13FCXnxAmsxLlWlVBr9MR4hroWdT2sRnz+GdFu
+kkok6fNlQOWKhtFqHjCeiJpsrEMYqQredAh8fZ7imbM048PvvA1Ank04XP4MANvtHa7UXKvmj4l9
+mVc4UXW/0aVULbSqUxyFKwddqNoyb+o2a+3NUQkceC1CxRVEjkdbwWVlFLBSiHGh56rZNfiemKCt
+pEGrIfeIW33aOqewkxBrVKpWMzB0EiyyLp4XEClQb7HyKZdz3ZMCVRAorKJRswlWEa1hENNsyhk5
+v5xzupQ92Y567JZrjfPT0WeCKXh1zWC55NwLyNRM6JmMUlW5MvQwDdnQZrtFQ8NJkaLL6ggS/c4x
+CZ4yk9xNcX050KWJqVUlrNVWD4Bc0WWFdQg07Jm4HTINBVnHY6KgpVUkm5ikRzSP9O/hnPzOd+Vz
+56dxfwh0buwauqqqtjXYWk2GMvshXH5Frb4BWyx1jE0cDf85tbnmD7iGUrGv5eiCeiGHYpFK1MYl
+In0iobnUntBR34HplET6PG9ZgoKl1vkAvllc+zXqqoOndmgVTfB8nUfexFN1vjbq0a4spZH5uYGP
+r5BhKodZqlEbs01PQ3L1XPbgrrnk9cELAFydWuir/yR06GoUIa1cVdDBKjjtCo+BArYKZ8VkJNGM
+0WKLp4/k+0V+F68QIZIrEtTMruiqaR0sV0wzORfTozNWVsyD9JWfJlKmTqEvWzwnXSgSNoLpTF7S
+OCjwNbwcqyCbrwytofwu8jqQaNizbQiTdaQsxgsUWamQdtfUWH+dG+JBqAIiarC0airbEuOoryzT
+w2BcVooKLbKMcim/Gy8trkaubFGRNlRgfEramA8b2tCGPkafCU2htoa0NAzDgLknnHhVuSSqfseu
+xVNOGkYBSVOjD4k6XIiIUQdY5JOXwj2dPLxOGAqi8Jr7W4XG5v4Mk4vE8OKAQnlk5M64pVLaTzzm
+qo4vVTjW3gFuX6TxxfElL656Og6fWp2YdZZRaaRhHYUoKMnHotbl2TFxJXkANrnAjmT8macQ5nSb
+lSbwBLmL15R1CRsHZLnGyt8/xlbiiV+kMoYn9oLJPfn7yVVGM1In01nN9xP5vr0yuI6soafRiewi
+JWqLVzyvF3iBrKGdNKGp+RNNj2wunu9iLusThhlOLCqzKWc4qajtdcPgzuV3S3vClYYw9kPVuuIG
+UwVkeWenlHPBSIwPXExHJOGwLulVcgY8xX+MHn3E733jLbnH6ANeCL8AwDd+8xvY+zLmp/lHdAsZ
+R3NHzauRQ/2irGdYvMrD+jcBeHAvYKXaz5ftLtsvyZ54GmWgXOEs5Iz4HUPyUHMUnAyrUaW2Ood3
+WoaOmpp7ww6lJmP5bodKQWJ53CTQLE+j+QleaLG+fK6L+TWAylAQR2JqrGyKp98va3HmWs+lrQCo
+xSqlpyZvp+lxqUlTdpXjWLn3p6WNprChDW3oY/SZ0BRcF1oth9WVz64rXPkkXl0noWEcGm2RFN1m
+QqiOtkgdKEG/S+1q2mxWslrXIfB8qrUzKM1IK5FMZaFhrqKEUiSfqVcoOJBJUdPcEw49WHk8uRQJ
+M1I7emhPiBciEZ6/vUPSEKlrsgmllWuzDMgVNTeX8WZOxvGR2MDLi0sGTbm2592iXMe6HZHc9fSb
+nCCS7+h0yo22aEr7W8e4fRnbkhnFpdiLVSL3Dc4r3nso6MDvz+Y4qUirs9DhS+p8G/S6NLXWQcsV
+B2Cjt0seybr50ZjySh1uxqFUp5xdFWRjmUswFHlSURFZeR7LOVY1jGxlcSNNCFs2GKhP6JEmtr3w
+zA7dXP5eRJbKrDWhJXWlNRuswarPI03lvu9OvsHJVByNzsrh4eWvA+BdvAaR7O+NsElDEZ7eTdGg
+ivaMbCWaUB19wLAp+5fdWuK6ClmvzrBTDWsq3sAuW0Tepe5ZSalOvicPJwzUf9doyXif3dshdWQe
+fu6vg8wUQUaqjuJidQaztdNRwqlOVBKqFjq3hkJdOGle4mrcvV5OWS1lDVeKmqS2+BqGHCQdZop1
+2NuvSEeyJ/fyjGWs139K+kwwBYtDbSJ63RA/lhU5f2qJtf5BuxGCFl85zic0LmShzn0BCu04hobW
+OsAucDTmbU2IVe91WeQ/KESSiiq7dFbYiRzyhpcSKly1VQccH6szK6tpay2Hi4liAqIB3VsC2CkG
+W4Qt9XqnKeVM1P9pHjI+lzh8ca5Y/eWIqYLVr+oJca71FuoHLNU52lJPdjx9TKRoqTwzvDVWWPHd
+p2zdkky9og3TRD3fuYxxdL6gjJWxjB2mC037nTrc92VO7a7LVztSswBltiuvoFQTzDNNakccl872
+AC/R01+d4y41I7IpTKrR2iboq/M0b1JGYkqE2SXpY8UvRB/SPpAxHx7LPPbrhFiZbNwccvdU092r
+kG11jPUCH1dTjmdTUZmnX79PZygq/qy5ZDeWF6s1j+nfkGf3d5rc8EStrl1Z46PhguVI1qIXGeyx
+SIDusGBeCVNP05DllZwpry9OUL+bXNdT4MGAiSN72my4GE1X3+nL7w/fbPLkHTmzV4sVXqwvcVny
+VKHi8WSGVYfuoaMRM6917fwu0ylEMr848sk1U3iR+ZzM5Pw+utC8nFnJQPd/8HyXgUZJ8pMlqZ77
+G57LudziU9PGfNjQhjb0MfpMaApRGHDn9g3OHlvOLyS23WvkpEtRBz8cWValOPYc3+P0iUBmU420
+PHfrIW++Isi8prkJyrn95VNqdfwsR2Mu5+KUMxqO6201WWkIbVbnLFPhxMtJSewI1x01m4QzlZqK
+mnTqHFfV/V6cYLV6EXZOcSXmSJrOMZmMM5vK31eOwV/I/A5CS1j6em3O0ooEDVR1bjg9wkORfMnD
+u5hMpPHx9IzZUxln4ySnDkW6T11V992QZKxJXonhQw1fPbILvtSUa9zzhHlPpPijS8lqrMbHLBYy
+9un9c2wsv9vZOmP7QKRxmTWIOqLpuLX83lYJy2NR0ZerI47v/mMAju4+5rE6GnvEHM9FSp83ZC3S
+LKObS/WqMl9hepoElBiilarakX+d0Ha5kL8v3F1uamLbQSvg5FRU+7Ez57X25wC484KPPdMQr4ac
+G70FnpHv5o/O+eBYVOq798+IO3LNadjnq6/cAuDlpuI3tvq4bdEKW3su8fvyyjR2Ew41C/K5oWip
+1SQmReDT988yyoVoU8mqxtQy5o/e+Yg8EG1r54EkjL307C5bkWhS/f0hsZ/q2hrmE6nb8fDkCfOZ
+jLmhZ+Qs9KgV95E9vmIrkXfAq/okrpynq3zJuaYFfFr6TDAFg4vv9gmSI5oKhBnVNVp1i7Iu6Qzk
+BRoXNe2OqIZ5Kddm05rpseYDbE8YKABgMV1wcSkLX6VTZmqCOEY28bRw6WilJL/n0E3lJYyaKzqx
++gZaM1YX8rm5hh14h9dMobw4pQ4kFm5Nk6kjtv1H/+xD3r2QDY0VPbyyGR09bM89/wZmIPdY1iXj
+D0V99PY17XnQBvXO94ipNcMx3HKYXn4oYzNNtg7kwCYaw75oPmGmAKiogH9N04Uv8pilwrX/uXdF
+xxP7ejyTe9mTOb9/IgdpdLriqdHKUyOH2x053LvDPl/6y68CcHsp5sdR/m2OH8iL8MH5GdkDeV6x
+5dFUbMF0NyPWFwTEh7Ecn3KC7E1wN+OpwoAPdh1OGnLQb84tSzUlQgXjHOx2ufWsRByi9oAi09yP
+2RHbc01JznZxdNFPJvKMyaMMVytktaKIZ7eFEdy69TnimzKmuoaoloIq2QP13u+GYNQXER7RXtdj
+rAp2YlHz+6q2+5OCkKbOsoaWjGdruMdjxV5s9be5ysXETEcyz6OnE9zdtfno41/IOEerKdOJzK+D
+wzM3pQSesyPPHV1UzDK5l53mVBqtam0BF4odOaqIp+os+5S0MR82tKENfYw+E5qC4xqirkv2YYFX
+CAcPbY1ZD2+V8623RfV/uFiyLEWqbjWEQz/baJLOxREVJxDvreP/M47eFbOjyEo6mtn33Xsiwd9Z
+jLjREw49iJq8ePM5ANrbQ268IR7pxq9f8qgQ8yHTCIe9+oBlT7SDstnHnss4XR5y+l2RaL/x1ju8
+o9WoX+mIpEkD0NIMPFNaumqiEBsKresQK+zYpJalKDGcP53xsFCMQb4g1ySZRttyqIlC3QPFClw9
+y5NtkZJPl49BUW5nb+S8/1CzDx80+H5fvh9YMRmOT+a8cyJrWFQVSRXp0pd850y+/1IIXV/MjXgg
+EvHROyOO74snK/USHo1Esp2kC/7C85J0dKcYMGuK5vGtmUjuncPXuFiXaPObRIrhHbSqawn6oc05
+3JH9mbZlMW7f2efF1wXanE5WeHrte++V7L0oe5ZYl+tQUqoRp8BlNxYNc9C4Qeu2QonPIa9kUxqx
+Q7bSbNw9GW+d3sZV7IhjU9p35IyMF0fshJq8p5GRJkueHIt5uNU5wGpm7+MPRvzBTEzJq9GMjtYK
+fXUo59GrU1qKrUlchzUo1B2nBGM1m2qXb35HtNAH9fcBeO5WDzQBq5V06GpmaLTVpfV7sraP3JBs
+S53wYml9In0mmILFklcFjV7IYCyH43hlyDRvwa1LCs01CIKQV1QFv9WXRX/hpZu4K3lpSEt8BYc0
+GkOcpmxAxw/Z6eqLsyMqt/fRXZpaKQe3pNJN3t4NSU/lZVn1VuwMZWOuNPzXagyIjNjZq7omX2iI
+dHVOsZTfDfdavKo1+J5/UezUncZt7I4s+ct3btFQFT4ezbGO1EEMFAhUlmdMH8jLthwWHKxzAxyX
+cEsY0nA74mBXU3wnwjRDLC99VQuh3L3B40vxlvfeTWgrxPq065IX6vlfap3LIKTY1kNqK7YWYodO
+mxmXuTDFm509tvTZ3ZfFO39zeUF1JIzXnlQs9KAH6SWxJ0zR3wlJrKi+t78v98ofHZE2ZZ9ePvgi
+jzXbsxWGuBq+LM6veHIstviTd2R+r932iJ67I9eeP8C9LeP8Yr9DrmndjSQmfyB9hwZtLeQyy2iE
+WqzVrwnmsrYzz1Bohah6yyPWPXMimacbOaDRrKiXsD2Q73nHYZn8NgCZFsbJb9YMprJ/3UGf4U0R
+TtHckJ2LeTTvZex7Gl4+kPOUTUaEGi5uNX3Cppzr5XKBpyF1fxyR3dao2bps/yK6LtzvJrB/KPM7
+n66YJ2JKfLHVZmblfu/xgE9DG/NhQxva0MfoM6EpGCAwDqQ1R5rI0esEeFpqapaF3N4RLngnddkZ
+aM3DQ5GSblSx1DJh1cWUiYKFjF9zc0cl6czgaFRiqLDWn4rfQH2OdIKAOhYe2Q9r5h3h+CdHXXxf
+1W51DEa9BmjxjjqvQOvhEZbcvCWFRZL2NtlIvt9RyZZs7eFrBKM5dLATjYz4Rxgt6ZZo7YKg2+bW
+aypRUg/XlzGPRx59jbokvYioIxJhrJKv6XscIlKn8WbI1T8W1d/pX9GdiaR5pd+mo469RAuBNIcd
+uhNZn8V4ylhz8MenE7pbmhx16OGEsvZWc/69docrR9aqeVDSPxE1uXerydYNMTWGwQ5vfef3ZS9X
+Is0XdY+RxGuRAAAgAElEQVTmVPZmFExZmXWTGctQswS/5fb4/pXUdvzG138FgL/ys1/gr90WsyTu
+HVCvi6ksl1Rq/uB5GNXCskqK4aRRRqYRnCS6TaDIoxZTmgstbxYOaYYKbNOs1TJb4GpdBC9sUGoF
+6sZtl0TXqKkJTqenPuxo0tJhC0edw4eHA6JE9iRbHkGme6ml19thKV5hJDG21oSoVtIAjb6EjYj5
+uWgIz1aa5Bc36TZkLVo9n4E6IC9XI5qZ1iztODy+oTgTaQfyifSZYAq1dZiXEU9nU1I93IUb8pym
+nrbj7LoUea8VsKel2gNN+zMePNDmHfFiTqkvUFBZXE8OnhenBAqW8Rb6YvZquoEcaKdR4BTrzj0h
+paZDL7zv0NB6jB0FoJhmm3Ih6ux4ekWcyKHpuYfXRUZsluNrhqYW2KE5BNdRr3AekWvW5uTsnNEj
+OWzuoTxj51aHpqZcVlcBVay5H94ZQaW9I8IOnivzi9SGTrv3Ca5EXRx2tylisUPNBbiFIhaznMNC
+BtUMNBS4igiMHuhOi1IZXTzsEg9kjZ4dtHA7ummTE11jB1/XZ7Q8J9RamV3PsHgg9vU/SZ5w/1Rs
+9KWqu814xoOnD2StPjzliaJCv7BycRoKkFpMOD0T5nPT/HlZ43CLPJOXrfBWcN1EZUo1l2ek5ZhS
+AUKzlXw3PqpANf/WdnjdMiDMdlnVWleynONqSwBvJSFCB+e6r0Ptj+ChdlsqHRwjL2HuCaMrMsP2
+ixIZ2W4OsJ7Mo57VdBSUZ8o+eUOuT85kT23YotnQrMYgopyI2bVyQ6yGyetqTKWh5qGGhSM/AC1S
+HJcu9VLGlgx9xjuyr3e+n1F+IGfuG3w62pgPG9rQhj5GnwlNAVPjOSmzheFqIhyu07W42gmo6bYp
+G+sWcSGFFlnxwvXwveumIF7YwYgmTtgOGKgzy08CalW7rcbdW/0GkYJ7ZrW97iNYpRHnrtzEOSoZ
+KUDkLBEtxl1cMtLuTReTCYe+SIygm0Mm9/ZbhqXmLvhaVs1hhuusKxWvqDR+fPL+hMeaPrBlxQxy
+ZwXOUAt93L55XRTFrS8oU73Ga5Jr/oDtivTxy2fxX5BnjMvH9LRYyFFg1mUCafkBHTUDItbt03wK
+zYcIbcULLXlG3qwourKG281tIq1L6IdasKWxza1DcaQ6y4LiRIFVboXVoh/ZRcH3jMzVBNoqr7Vz
+XXDm/WxKHWpxnTgnLrQbWNzia1+WuXyo2IXt527hKoakWlQ4Cmpz64R4KNGH9P5DCtWmnJFmuBYu
+2ZXsx42Lc5J9MfPcKqNSrSIsU3x1+AWOmqNpAycRHIadJBRdzfx8f0EzE63wbCJ70x50yRdaM7J5
+eO24rkxJWIs548cFXQUTrSqF0scFcS5zdvPgujhPw3UotDXi0m2yvavQbcVgtD2XhWZo+pFHtpT1
+XjS7lOdyln93MuXoQMNYn5L+xJqCMeaGMebXjDHvGmPeMcb8Z/p93xjzq8aYD/Tf3p/0GRva0Ib+
+7OlPoymUwH9hrX3LGNMCvm2M+VXg3we+bq39W8aYnwd+Hviv/qgb1TUsU0PSDegca62DzDLS5Ju4
+BTe2xKHi1j6e0Qq3lRZGbXd5plS0mr+ikyic142ItLyWX6wYqz+woQU+/dzB1TJYwWJFqY5G461w
+3pXY9CKf4GvvgPbJusffihCFsNoZpquVcLIVtZUxJVsxgZZv8xRLYJYedqi2ZeVgU/H8tCKPXe11
+0H1GDN+QJp63Lte2wtFqSw4pRrPl6u4SXytEVdoY1bud4A1EYkbvtyjM7wBw4Huk3g8qSRdakUkV
+KRqRj6+NV7ygIvHFj+DtpXgKxx60m0RaG8JRrETStvQ7ogUcH8NgKDJgK86ZX4pj7MhfsKfyJx7K
+PG939rlQ2HmrzPj9p+KgXGUeW20Z1M3tkN1a7e62OC13yppKk5xSd0wLORdmYCjNA1mL/oD0UvZv
+mapW2AkoFNdSTSz2pvYUCUMamsEYeG3CRNbO1ZodlQEqrb5sSnx1cieDIcumjMMqxLw9cBh6AjUe
+Tc5pllptqdWWnoGA57v4rqJTbyjKM5vhuS3dByi154ZrDdVM1nB1McdRZ/qgoQjLwNDO1OcQhrjb
+WjvkwxkXI/ns1yWNoz9e5aU/MVOw1h4Dx/p5Zox5D2lB/7PAT+tl/yPw63wCU3CA0NTs7d3kTD32
+5WykfXfAcQyrqajJW80mtTZzWbe4CJcBw65iGkYujhV1yc1TvKWCVArLuTbyWGgtwsAtqQNRF/Mo
+Ii8UMptGXMRa5qrpM/1Anj3R+nzzq4K54tOLtouzELNiWvh4SzlsYzPFVYz6TBvT1nlAhL5sYUih
+zU+d4D43kI3uaDEOrxmTrrQjzWhENRVzZjwagTLLllfgdLV8mDYWiUyImQtKJest2XYVzpxMr3ts
+xgmk67bz+sK75geNW+PII9Ksvdr4qLVG3W6AMmI8rYhdxkSatTmczfjoe9Lha9adEi6FwU2WGU+v
+ZI3u7MvLvbW3w7gUJ+njR7+NbWsxFM9em1tZ7NNQxri7K2t1YSyxNundCoaYlo7D9XCeyvzGV+9z
++Z5c8+4DGc+CIZ97U8aTMsRoFqFLRXO9hk6As65+rS+b9cbUGokInCZomfxw2GWgAK6hdhlbuIa0
+o30blwkTbRYTVEs6asaZ0rtmItauhVdArWUAamvRVpk4K1gphDoLz6hW2pWrIetW110Wmsdj0x+U
+03ucVTjqmHbCkmVXTcRPCV76kTgajTG3gTeB3wV2lGEAnAA7f8hvfs4Y8y1jzLemy+WPYhgb2tCG
+fgT0p3Y0GmOawD8A/nNr7dSsmxwA1lprrrubfJx+uBX9swc3rZvs4k9PaGi5rvHMsNIin0+cHLMu
+GLqyBD3lwEZxB6FldiESo7sXgub6Ly9LppXCnE3IaqoZdWcitbKVoR1rc5OBIVwn8Jgc9N61615L
+05FChhdlyXImnsFiMeesrQVKGz1Wmhk5nkKo4UerDrw8Lmg90t6WgyF+W5GJezs4K+3dqBU2Si9n
+uRTz4vS3vsMHD0VbSV2XOBGt4fboNXraei3eVnH+bEL5fVm3xeQMX1Gc3eUVEy3yuSpgpT0uxlqs
+1kwLKnUCGn9ApMVS3KqLp+jHcgZTX5xu/rnMqfYd5pms6+r8iGws6/2d44JeKVLz7Trn0UqOwYsK
+DCm7fc7vSyu5q+kps0yk7vEFWC0+s1NGuFpyb0sLn9Z2gRlrARSnwAlUapZTCtUELx6OmU1lLrsK
+KZ4tY/qemma7DViXK5tbqkBNzHZCoWPOrmSf3JaDceXZaTsj07W9NJe049sAOE3Zj+xiSarmo1nm
+uOocb+CCL1Le80NcDf220T0LahzVTOtmgOZ+sUrPGc1kn0bnjyi1fJut1UnchUpxDHVRkeeqefYL
+ertyv/2LmupS1lB27pPpT8UUjDE+whD+Z2vt/6Jfnxpj9qy1x8aYPeDsk+7jAJFxMOU+iRU7Mwou
+SUvZ5HycEaoNe+7MidRW29Gai++Mlvz2icB5b3y/wxsHArU9eTjnXCMAwcBIzBlA4/VvP3nArc66
+We0uTUdV7aLibKE27hyO1Et+qiCm+uqMSrMScVyWK63qm15xOZFrJnnJKtSiLg/l753zBsda/en+
+29/kuYbCrRsu6WMB6TS35OXZfqmLozXnjy8W/PJ3VS03NS96svnuly859oQ5vdTQpi7nz7PQAin3
+H9yjpZERv3vAYKVFODAYbfc+0gzQdjcmc2St8vQMz8oz2jccskwZRDhnoU12p1eneq+cOFpHjO4Q
+BHKiP7p8yGIi1x6lFbGaG9s7so+7jSGRgolSP+DwUPMWmiuclTCOpVfS0NqFvkYyiiq+rpJtIp9a
+M2Xzk3PufSAZju/dm/IXPv8iAN1nvii/79ykVDxFUq2odK7uVs26BVbo+NTuGgOhfRmdAFMqKGri
+c664jpOzkp6Gc7JcMzmrJo/PhEF2nYhCO4AFQcVKmw45ZU247oWpvpHZ4ogw09L+lwnTJ3JGztJL
+pplgPcrcwWgBnoWRtR/mXZyB+juChIVWzz6/gLnWmPzQc3mkeJhPfhOF/jTRBwP8d8B71tr/+of+
+9L8Df0M//w3gf/uTPmNDG9rQnz39aTSFnwL+XeB7xpjf1+/+JvC3gF8yxvyHwEPgr37SjYzj40V7
++PsuN4yon9FJxYm2vvKKBdlSm4J4KeMLkcbzS9EYLs0VpxfCXQOT8r7W0YsSh1QLWtwwTaKeSL+s
+lL/P51ccq2NwN1uRaoLVKp1yfE8SlC5HxzzbEw/3bCScPUt8IrvuIlxRax5/7WTXbc5mrkesvf+e
+3Jc6BY8rj0yRdLNJxqVCm9t7N6i0w0E/VSfoXUv3VXleNNhmMRO47tl8zkBxA4UfsetqXwP1y8wu
+z7CakdigpuiL1jR0AwaKTTiZTCmm8ry29mywxiVQzEI5S5mpw7NeRTRDhZM7E1CTqL0vGkhmVgSa
+kFiZE/qvizlzeDXmCNkTg6XfEU3mK69+HoBOEnLjtki5P39ywFgTwWzlX5d/6+KQqPPM0Xi947gs
+EnVBOyW1lti+eHjEb33nm/I8m+Jr6TwyLRATgVf8IHOy1p4hxnVJ+nv6DIvVqJLXlb0J3JBaZedi
+OsdRZKIXxZRdOU/FRJ21B1e0Hsp907ymoYlSdeVTajJaejnB8VXiq7N6OS/pNdSZ6RUYGQ7N3Kc1
+0toJoxmLdfBITfSFE7DlrGt0tgi6CsFejbmhz7497DA9Eg3pMaJhfBL9aaIPv8l1L/X/B/3lP869
+KmCO5Umvy4st8WS3gxy0O85kHhBoWHC/v8W9VDbswVgOv2sibrdkM571IwavvwLAVtzg5ZVWW6oD
+FgpHfnTvPQCSOsf05RC43SF9OZfM5jGPR7pJdYuFRjPylmzi7HzEYEsYSLfTAQ2BTi4dKoVeN8wM
+qwd5dKnFXYqSPe109bkvDumEYj70XvoC8ydi8YUNNZ/YptmWtYjzpxxqQ5YjLGjGaOd0QqAMYq4q
+d38QYyZiF/dfOuD9XxFm8c6Oz6s7MuaXJ12ePlW0lCK6TLiipV74+WjFyYXcw8zHNDR8Wa0iWpoT
+YmL5fRn6oA14w+WAqeY+XGY59ythnGXisaUmhn9DmEYR5gxXPyHr9vI32X9eDn/TL7hRCTMsEp9Q
+M16twtxx3OuS+XXukqppNy1CXM0o3K+aHM8UZPVYzEouDV21uU06wJPHEQwdmjfUHIsGOOvnaJ1L
+rxFTrHuQBitmCjXefSHg6qHc29VU/tJrMripAKITl1JDwHmyJFLTtKyXnJ1rb0pV912vhgv5+407
+L9NqZjrOBksrjJXU4d4TSYe+2dZs3Xib/u5tuVdYMRnLpI6ikOee1zP73oysLQLux24+bGhDG/r/
+J30mYM6eY+gmPl9uJ8xPRTt4t7ikckQFXNYjHM38O3jmRRaLdwD49tviULvz0h3++r/6VwBo753h
++lIyrLz7hKuhNssYPeCdd8Wb/9Y74pB67kbIV/dFcrX6LTwtpvLh/VMKjS4Euw6hgj/KiYwtLyO8
+SO4bLSs0mRN3OWGpauI8LwlUvcwUQDWbzUgaws3f3HuduCWSrRUl9Dui3Tg3RHqa7D3OpjLPf/LP
+fou3rkTix1VFobH7hyVErBN31OHWmmH+QItt3NmnvStS7gvtmuWVOG7fL1L2ErlmruOtZiVLq0lV
+RcmFJkS55wGBNsO5nGcMdrR2RKWt+7Y6lHOJQJ8Dp+9Iebf7kxUtbYzTS1o8+5zoxLNMQTpDl2pX
+7nv77AAvFUfj5TjkSKX4dlHiaPLbuvnyqrKcPVXI9HTMjiZPRYHHv/QX/y0AuvMm7eflfktHAWSx
+R6GNeMqq4GxdtXns0ysUTh7k1IrlMOv6FXGIq4Voxs6SxXuqpd3x0QLTlKrFlrMrAgUWNRPDvFJp
+fVkTqkkQOjC90mI2V/Ks53YSXO0aXsznFPfl7Dldh/lS1vbdBxe8e180jM5romEe+GDUEVtlJ8wU
+dh01L7hUR+rs62MWoz9eOefPBFPAATeC870ltQJ6/LfbpBqmaV7m7Aay2O7M5fWfeg2Ah+eKgS9n
+DD8vtvXW4dfIEz3Qb+4RPBB1vDiKmSowahC+LP822+zsaYv3dMLjD+Tv3/3e97jUMNyXs23OXVGZ
+F23ZlGafa6932GlRa+3D4bAHI/m+5TTI1F+xd0tNFM9w+47Y+L1X79DbkcIj1rG4r2kfBe2E5VYD
+xr/3qzInd4vAiu7n+wEHkYZi2eLDkaiUw57WrUxXdP6SVAeydcb2GzL2R70T/KXY9YO3IdRQpPXE
+hxOGMWhL9V4S0rXChB3P50KZ4avP9al8eVkCbZLqmYxYAUnF0RMeag/KvlNyum552IfLXTm8703l
+GS8+1yFXQE/8YkJzIfswuFGjKQEsovq6B2+wbhFf1eSKNZpfuEQt7QbW8IlmcvHF975DuhSml21p
+W/uox+X7mifxShs/kvN0+82X8CK9YeRjNYLuDbRMvnXJlDHNH7qMtedG6Li0hsKQLo7lHG7tbbNa
+ygsd1UNcbUGQ1y6uZlru3GpjchFEi11hWC+99jl2NH/E9TKMr12oRjVcyFmv+DVuqQDr7AnD29nt
+kSKMYDaHj55IpMIc9NnWyM5H3hLErQJqMX4SbcyHDW1oQx+jz4SmUNcZy9UHNB52ubwUTvxh3eLA
+qMNld0GkErQ9dAhjkZR/7g2Rund/a863f+H/BOCVn1yxDIV77uw0OXlXTAUmT4nUHPncyyIxI9ti
+EYp2MD56yvcfCae9qJY42ml6tp1THMg1nXsKRok7xE0t49Zqw0KuTfMRnj5jmvqkWp8h0ryEm70u
+XUd7LY5PqRVkhbHXxUJyBT+VZ29x/h0p95V3Ztf1KHebDW40xVn5tBiTa9OafYVS58MdgsM35R7p
+MUSi3dxytsgVYv1edM5OSxvbzLT+Q8PFqjMwrEtchc+Wgcd2Sz63gi2sRld8Fdf5cM5CG6vUQUZD
+KyaHfsKhFhNJygHVfRnz79wW88K89VU6e+sejC8yzUTDGk+hsade9LpBrZmr625hjinYDuTZi+aU
+eCxrOA3OiBM5O5cvW7ZauscNEY/NfgNXIyZddwCaoxG0BlSBrIWpYQ2+c/W85XVGpdiTWX6PXd33
+oPZIFfJdROsISYKvGY6Zl+Jo9Ml3YzSYQ+QE3NCCQaMP1EH50RGXY+2hWizpNmUNLx+dESowrh1G
+bN0SDTDZ1jPkWrIrxdOkUCvk2zy+IqxFSxnezul/IGN+wKejjaawoQ1t6GNkrP1/RSH/mdKbb7xh
+/+mv/ApR5JJpWMlUM37xv/3bAPz93/3n/Ec/KYVCv3rnde7+ujgMv/72bwDw5MmYqSLCqtoyXef8
+84O51Vg8ZdeBJv7sBA61q/Fvz4OGcPmrZc3ktthtD+9X/MuHInXaX9AmHL/627yo0NdJMqBtRNp+
+98hylWtVXt+w0tDh1VwkTWWgm2jVKONcw5+XeYaneUZPtXFMQcBMuwgHvs9sJpLE1i62/PS5Ij9x
+U3wNN1ttuj0xLvd2HcKm2KftrmZ7drokCvN2nSXNtT/AAOrAeufuQ+a5rHNPHbg7TR+j/T9dY2hq
+JW3frxncEUzC3s5tnIFIuUhrYBjPwWrvwzxYUNUi5f+Df/0v8awmqY3DASGipT3VqsalYxhqRa5q
+XnCksOqqqLG1jK0sKyaFrH2kVbgiP8RY+W6Jua4zkcQRthRNYV5burFmj3Y0K/NGl/7z4vv5qcM3
+GL6q5fa6Q/7L//Q/BqCj/oLj3HI50pDsaIJV7+gzB4e8dFMa34RuGyfScK/WAGnELsNnxHdQhAXJ
+lWisT8+eMsvFydn1erQ0czN0ZC0mToRRX8s0danVD3YxvcJbyDvQdUumiuT8u//wH3zbWvslPoE+
+E+aDtWDzktpziRWWvKpm7LuCifr86oTggRw8+/wWra4wheVYvcKTlJUji7MTOcTK6DqOj6cMIDMl
+TQU1dUNN+214uLF8l+JQh/K7xIu4o+rXz7zg8pMdeSFbGjn420HMbinq51efe55ZQ0rGt9Nj3jqR
+8S/w8QNtJBoq2ASD9i3FOg6dWpmFrcmUka1rJoZugVFV1TQrfufJun15ykQBPbW18Ak8vaHq/m0S
+DrVa8/6diHYgjG6xkIjEcmVRxzlN0yPQSsxMIgqF9nZ8D0/BQm31vMe7LRw185K8onoscfX0+Q7O
+TEBbNo4JtWxceFscarYoqXyt9jwq8NqalegERJqq/Llem1ki9zvIZH2epnPWXeKjdsAdI0ytKiti
+hStbUzJSIbGu7N0Kmtd4k+miuK592PQj0kDrGY6nFFrWr9vV6tLNFvGZ7NPV3pJ9LdqStEfkWvhm
+Vspzt4OMXNsA4NdsNWUeX33mGZ5/7SUAgmTGwwth6hfvCLR9UTV4Q8sC2tmcxw80n7BaEOgzWgOP
+nmY+Ng+FIXdmczIt1NP0HTx1aHvvLFhpmbrD3g7LWDfrU9LGfNjQhjb0MfpMaAqOYwijGIpL0Nr8
+i8cfkc9EUt4Z+DzznKiX9cNvcve+ILTeO9PWbrWlq2rUq502bkNbcFU+ibab66cOmZoHbe1kfMKS
+jmbtzXw4UVPiIrzg3qmq8dsVj96X5/hauHU7z1kE8vetWcjjC1FxU5Nct0Nv1QavoeXdtMBG5nqU
+GmOb23zdx4N56KD+NHLtU5GUPk8FXc3oNGOpLe9sbanXiadr79sfQTf132a/5qWeaDqeDWirKuo2
+tX7DeUVTnYQDx2KWYj7VbslITYbbw2d42hWtqY1oSm5Rs6sZjH4SU0VilqzM5Ac9IeMFtZpulTY9
+cdwUZ6VNdDou+bk4BNtFyawW5+jn5ttMLkTirVSTyuKKrmY4dvuGhhaDieMOe1qoJI1yIg3bVmvc
+wcrlwVw0unw8Z+7o0c8NF1q41bYcIu01Emk/kNrMWSayUauj96lvCEYgPclwFf2YLRWan0eU2jNz
+O2zyxdcEe/JTX/siTVHSmJ/MePRQwsijY5nzpfuUm5FmfvYGzLWP58DfIlEn5s3eLaJE9q/WsoCW
+LouWaA1H91dcXcg4svACKtFegl4DM/7jlSb4TDAFjIMTBuD0KEcysdVv/w6cyoH4wuf2ifdkoe79
+1jm//7a8pHvqKW42fP78bTmkL+zt4WnMPi0dmqoyWsegcHfONLJwaxpzoYdtu12TKkNyV1BcysE8
+ebTkSlXC6Udyr1vRgHM9KA97T9h/KAzrW4tTkr4cwnBlWVeJOTVivw78mkpbzgc5jJQJ7bfD6wxO
+R238rk3oadnv95zHjLVrENmCUrPsPo03qG6Lfd7c3qfxurxAPbfHPFMQkoJfaGe0S1m3sFXjqj1s
+ew6J9lJMlz321hl+aqfabIm7LnseOBQKle71GwTa/DbIb0NfOaAyGJIIXGUQYYGrOSpXVeM6g/GB
+f0JjKWv0/lReoNYwptGWsQ2CFlFH9/1Gh/5g3VcTZoXmI0SatTpd4R1r5ywvZq8jR78oXG7oGZjO
+lqy0AnOu71F6tGI+kO+6zRmuNqkNrM98rqAlbSlwXkzxFKfRbHR4/YviB2sfeBRaofmjh3d5+56Y
+VUeKC3n55jaOQsIvV3089ZnsDXfZ1bYErSChaGo2p6/7EaXX1Z6T/oJJqud3kl+Xjy/2C6JED/6n
+pI35sKENbehj9NnQFKix9Qq3NKRTQSBm7QkHz2l83A2o7wqnffD2lA+0BsChmgw/ud/ji3e0x+HO
+gKqlYtVAqTUEvHZKMNOqytuaUXjxA2dgtSp5Vgt6jOp9HjUUJno6olLe+cxN+V1+mfOGtk9zpyu+
+kcmYew1YqISKOxW2Eql/UyX+VicCdXxeTR32FQa7t9/DJCLxOi2tSZhZpoXM2Y0KOupwe/jkKZnG
+zbO0QJMr/1DqJSJ1Xny+je+o1z6e0NRiIGsHZ9mucDX70PgxkSdrWAQZoToam4NbuOsMvVThcSbC
+m6p5VfvYobZdC1Z46xqa0Qyz/my1Jljdx9VmKVU5xWkorDhf8az2PnQWJXdTcYRuq3pdmxodOs1e
+k32N+d++eYjTU4mYG9RvidEO3JW7ZKUNUm4kWwTr4ivljGKqJc28ilBz/Ca6NyxmnJ7JIr/afpbF
+RzJ++8oukVFUqOpstrT4rpyRg+5zoGXQ/MuMkWo6l3evqNSHuz8Qm+JrX3ud0JFITbFluLESB/oz
+yYCtzrp8X4Ol1heJNTRk/D1GE/kuZYY7kkmX2w2a2tejmM9YBH882f+ZYAp1XZNmGc7kMX/wG38P
+gGbZxW8JHPkqesg3f03SYv+Ph3NCLRAyVFW9kUSsVppNt6ih0oUqobKqXhGRKSDnUot/GCcgUwZj
+uxHTiaqR5pRdLUb6fliSaeWa4K7Ycn4nYWHl2odPrlh15ID545pGVz7vbXVx9UVw1aPd6npUubyY
+jQh6nqj227eGuGq6VGqq+IXlVItqmGKAoyqsf9Vkpv6FOq0oPsGx0A2FyQwaDRIdR9Bv4mpB1FJf
+0mTZINaQpIdPpQVlgrxLqI1K3EaDSvMAwoUyWzeGvvZ+LB+RXSiQJ2ng9eT7MD+HMy1E0tRU78qA
+lmo3xxVWsy692iHVl2wxHZFqGnVrqinbQcxuR/wdg7jD3vCG3reNqbSxT5RhKi2v7ui6TXo0+hqG
+nEfkvqZGT3rkTa19aLsstT9AMZcxNJotnpzKWjxxLzk8lnFs73cwmhNS6bPGqyVb+xJl6d7Zw9Wo
+xtOHD3j7TNT5J17NK6+LmbOnoe4vfe3fxNH09JeqFaszrdJUn1NpkWHjZVTHMn7WIXffJdLmOz2n
+Q61p3due5cyVsxpWESMtGPNpaWM+bGhDG/oYfSY0hWqWcfmrd/ne5QmXj4TD/bk3n6P7rJTUmn07
+5+2PhBufZyU7WgK8q//6LcMD7TrdHOdYhcGmxeK6+u7gvCZOhBsHtUYDwgW1eqFn57DS2PX8tKBO
+RRrus1IAACAASURBVCJMxzDXWpEfNUXvO3Bd7p2IJAlWIQvtGX/QctjWdudxnuA2fxDrBinFtc7T
+77dCukPtA9iK8dqiPvvlWhXNcS7lvlUzp1hpk5KDkgf3RXItGgXF9I/WFIax2he2oKUO2GJS4rsy
+p0DrT1bWJ1csQOAvrsu1Oe0KqxqWk6UYdZRq0irlqibQVu6u2afuyN+vRv83e28Wa9uWngd9Y47Z
+z7n63Tenv331fdmO7cQ2CbEVkIggAQmIIh5NFwFJXuCBSOEp+Ck8gEgkAkkIUSIF48Q4rsSusst2
+Na6qW7c9/dn77G71c81+zsHD/61ddcGue66v7RyjPaTS2bXuWrMZc8zxd9//fafoUd7N2mpQRmKB
+7VSsue8aIJFsuul6QL5OCDc4GssaCJSLgszV1Zq0Wg3QZzNTr6/RJS2/bWlYpNJvlIYfEabdiGeS
+dTPYCek//BaNYdLONmhauRkvquFp+VsTQ/J0pjCvBEz06I1j7NyW/25OHqKo5Rx5Kd6McjxsslFs
+GNzCCb2je9+ewN2U+/voDx3g85Foiw5viifc3d6AIkeEFzgICG3OHg/gaVL3LxVMuIZNE2puNCI2
+c0WdDBclm+m8OVqui7lTo53/Eaw+rJZL/Mav/Ev8r19f4LWXZeH+6x8dIJxKeWtuL5ETxGG3wIEj
+L9Ye+fQK34ZPBhprZaEgtXqSejhlaDiwG5yxdLjzkrQbO3EMzZewtufISBZyolIsZvLD+0mLDstp
+GyS8qKsO0hU5Gl1gg5tMpIBojXH3QzQEUTWpfDcINCyHwKlOhBHFZgMvAAsRsHwyOmUGFnUCdWUj
+tmReussWxVAW6a8bB/d4bE7P/6ciEQ7YzWiPoHpyT97KQ0sU3xpYZbldaMbfllawLXaruj48V86t
+wuKSUAUhJdC1B+TsPlQx2kZeJiuI0WayiaaLEL7LDYIdp0rryw1GWzEUJeBXVY2KlOp2oKDA3Ac3
+L9fyMSBZzjAcQHMtwPgAKwO204Phs7I9vjROC0Uh2PRihprZfu2XiEiSAssgYHUlpwKWpwwizqpd
+1uizv8AyBusWzhWZvHqOh4988hYAoLPjI5uQdr8q8blXRWPy46+9iuEtWdfGYf+FqmC4fu3ag+Ww
+RDrU6HDzNtqF61IFbM2WpQF3RQ3V2Mcwk/OtfAfeGZXDSh91/btxIf3O4yp8uBpX42q8ZzwXnsLT
+1QJ/7au/iHcwxxcV+wW+vgd8mhlu7/OIOz8PANgogICegCGVWpM6UNuyS2ZJiackvQj6DaaPZHf9
+raMUCRNNP0Wz2k6Glx5I3TigmBDOH7b4Vik79KKpEdKCfoW8jF+oMuySz9GoECtizrWvoQdi/Zxu
+CYvafmv26LjjQduk5/YNarIk1/ESpl27s7R8VXtp5QLfAfNtGA5s7FHt+E5H4WxCKjji/i9RUBwe
+WZQDJ0U9I2xarWCIPbAM2ZIDwGqZhS8j2LSU2ragWqbLzQCKGAKQphxqjJa1eeNZCOjOWk2N9J7M
+fTp9gryQRFvIBK4T/RiciND1NofF35mqRMwKTV0AHin/I3oaxqtR8zMdWjB8JsoO0DKprO0KqOT+
+bLpgrl2hoXxQU/jQDFeUB9gkfsmLHPNE5stWzOQ3K1yQN3MnjJAeSZja2d9E5rN3hXiLa7aG15GK
+wuH1W0gP5fUaREsMPynVse7BPoKBeKr1nIlWbwFDhbOmncAk4tF2bMDierHcBi3DO12QNKW/i4Zw
+7sBfYqsj83YRaoRUHJvMU8B6BpTb940rT+FqXI2r8Z7xXHgK3bbGv5ZN8KKfYjaTHfP/OvlNfPa7
+skP/i29+B++MxTKdZi2GLDMdn4jV2d20kFFRuM0bXGOsp6MMT22Wm2IFfylW4O2HYrUmOsUmu4Aq
+5UNZcryOBdy2ZBefRC1+lN1sD0kGaoUuAiaqrCFg6BFsuRE6jA2HnQHmuVhKTRhw4BoYJTF1lecw
+HSoNFyl0wZo28REIgIalR1vHsCwy+nRD9EkF98qTGiWRnr9xIaXFxby5fKgtADAXUeUAKIbj+i5c
+nw041FBAHaJlwrBRFTKqRFcn2wh6pAdrXChHjpFTLMVuUsBimdFdoFrXx+0+0huS+zh/+zFm98RK
+RztyXmdzBscWi4ncgWJDG1oH8TrBEgCGZdLtnpyvpxv06FXZRqNdexh2jYaNVDo30JzbpqSAaOrD
+adce2wAZm7js0oUhq7ITjGBZYoXtOTEt1QUGlhwr3vVhEafhJDl2CFm9RXxEHvtoiU3w9nxYM/Eq
+3JGDrbk8B+f2Dgz1PNr1tbUNjEUdjfQpihkTu24NuyvJcWWvgJXMLaitgbKCWuMplgYE76JXOZis
+RX60wtOM5K/POJ6LTSEzNb5TnOONH7bw4Exu4JN//zWYPy/dXb/6pXdxd8mQwDJoCFpyiNu3RhE0
+abnslY0xcQNzH1iO5YFndY5vUQuyZbVgJ+/BWyfX/AaLlryF0Rmm5BKLhhYecE6Pa1kce6ZERrz8
+Tb0Pr0sXzvHRECiySG0k5FJsata+TxSaiBvEVo0eiUXgDhGw+lCytdo0BZJj+e9L3WKp5SKqrEbB
+ttjECuFRVHQvl99lZQaudxit4CzlxcynBdxA6uJV3cDnQqnYs1075jJB2+TnsBcy31bTIGP40+mu
+YLeyuK01p2BtYDuyabRlgdpnMq9VcEgu0+vFGFGpqkgYwhQlTErKdS++5AV3baBkxn1DDWCxyzGi
+e92LI/T2uJkOunBjEoughsMWVOVZUOxTaSiaaxwDxb9VWqAggqhMPRRsqbbK6aWYbsDwaFEXWJEf
+btAahI68mEmRwafYT3Gd92QppJm88LOHUyw6YnwSNcHOiNUVf4Gaydp130nTONAMy+w6wiyXLsnl
+kwSd63LuuLZgb1JFjHB0U83Q1rKxmOYcqxmBWmYCX/H5OBqLoz/kLkmllFZKfUMp9U/4/28qpb6q
+lHpXKfX3lCLw/2pcjavxR2L8fngK/wmANwDKJgP/HYC/YYz5u0qp/wHAXwTwN3/QATwN3Bo22P+V
+Q3xrU3btn98uob8kLtXx8QQVEYQdy0KXJSAQSZbXNUaRJHJe2olR8kreSWd42FCrIclhkR4toEV5
+FDV4kfJoe90Im2zgydJt9LrU4jvSuBiJRcsuqDegLGiW0MqODZvlybpwLt3PNJvhuKCLcSo79RNV
+o7O5VlG+hiExCzqq4DCRNmeCsz47xTSR3xcI8HBOb2SVI6Z3szewUXrUFlgw6TWvoSOZl+Pcg6Hc
+Wu1EqOhtuHkfS4YjOd3PpqkwpRxfVM+RjOW7g90QWSWfu76GZlilRzLfKluhPpau1WRSYjWQ450+
+ybCRi9Sdl3Sw8RPyfa8vIYNCBbCcqCwboHiJgS1lTgB234fP574kRNvyOqhpEedRAUMcSooAPvkU
+XKsLDaqC1/I8WtdDW8o5ymKB1QVh7GmDlPgUky0xisUys8oMBz5SIkGT4xoHf2oNFfcwNzK3h7l4
+QeHONuyYdG7ZOSIycFfLGl/5tuhmjh+8BYcQ8r0DIZ4Z9UbwScFmey1Ajo+vPnwXvS9LuKY94Mbn
+BQrd70ii2UKFJpWOy0x7SJjMnVxMkR9R63TgfY8K+xnHh9WSPADw0wD+GoD/nFJyfwLAv8uv/G0A
+/w3eZ1NoGoX5NMDZRxr88Vdkojx7G1/7igikRE6AiPLdH480nFge4l0y1u6PV9jf58uEzmXNvlM1
+2CEWYGlbUIM185I8gE9thugy/2CZ+hJYdHN3hGPK0mOU4HApu8ztQ4qg3nsKh7JIjmuhJd15ms1h
+r/ExmzG2CIA6O5I6/rlqcHEqG52z0UG0RSUkf4DKlgc6ncoif/e7b+N8KddgrBoNV2k5qWB58p3W
+amFXEnP6BGF1RgUChhdN7GJBOfTddIGMwKEKF6jJsTiZyTmyrEVZyfUcPUqQE25dPD7GgCCxneMD
+vPpRoc8PYtno2izHu18XcZ17Z0/h78rcxghxQqryrZfmwEPZFPy+KF21y8/C1FSQsloYtj03VgPF
+MM/SGtmK4Q0kBHt4UaPk8/dwgfGbMrdjq8QmWZ9eevFV9H05n8M2+iY7Q57J38vFKRKyLpcWEHYJ
+CrI8tIQuR4TQb3aEBAcAJqixOpYNYviFLaSUq68yefl7uovDWNbQ/MFTPF4KC9NifITVGxKu/ON7
+E9QMiV69JXDtv/DFn8DoY9f4HA8xfyi/O3t8D7/81QdyPb7B1juyQXzkBjEkvREGsTz3RQXkqydy
+nYsCFTes5ZMVKnaxPuv4sOHDfw/gv8T3OvtHAGbGEAECPAEoTPD/Gt8vRb9uyrkaV+Nq/Ksfv2dP
+QSn1MwDOjDFfU0r9+Af9/fdL0Q89x5jW4OAdC3lfes2tbIrOiViSNgRGtligvnIR0OJnTArdiDwo
+SnZPmgmsQqz/+OkMNTkPe5YFs+4LoWL02crCaM3EW5eIJ7IDuz2NeFO4+PYuTmDT/B/Tko5bB0Pq
+UwwcB2lIl/GpjVPyGV6cPcURKyKzc7ESdqTQJ4Nzmlco1hwKdgNF17ZmAtBtHSi6fbE9QMMqSccv
+sYK4xGpZomVIsLUrc3Ge2/DpXme1hSZlt6NngFO5juAgwoKNYI8WkiF//OYxVuVa9jzDKd3vclai
+YX//a/tHGG6KZ3IjFMtWpQqplnClXM3Qq8Vab37qcwivy1zo89dRnMu9tLUg/qqNR/DtGwAAkx0D
+vtgOv3Xhk5Qmz1dYUXfxkKQhlmeBMhQoVjaWhCiPrQvoI3muF50x9C6h0F25tuS0wuMLKnfPSkyn
+Ivn29CzHkqzL2x2FnT15lq+4wohtbb0Ix5U1GW5ZaFglsgsbNatDBcMLb3aOdC12MV6gacW7GSUh
+NkYyL+ErhzgqxOLvTYWabjYv4JyyItFfoDwndmbpI6C9XK5SZFRCnxPLs7cdYESaisXcRk2PdX6+
+gE3Kvp7twqs+GKLxwwrM/hml1J8G4ENyCj8HoK+UsuktHAA4er8DZbXBG5Mc040jfP6+lLd+Mj7A
+o1jipduzGMgly/q6ztE/J8zTlck56Oyix0loqgY1yTSKZY2QD7E/HAKOZG3vncqCP0su4LCTrdf3
+8fJtOXfPGcAe0q32R5hO5NjFSjL5HdNibsT93MyGAIVMar1Efi4L5GiywgMSYCxYVtvRAa7vy+KP
+nB4i9heoIodLshNFANFg1IVtBA77xrsTjMfiJu91OqjoiPnaxUhx46Dbujya4h1mshd+DTeR68w8
+D5px+cU7OSJPNrU6lWuYJSnGDKXsIMCGxxZoVeMu595bAaaS43k92RxsP8ENitzqfSBtZI7H8zl2
+78gGWHk9BDtynlUqyyEubqEpqfNo70ERamy5wJR5gHDlIiSQac1ihVTjwbG8VPftp0gZQg46fXzk
+1jXOoYZarK9Tnu8KU0xO5DPl1HBjeQ6nbx3j7lTc7scDhZdW8vJu9eV6s06DzU0CvIoSBUt9yaME
+a+qSDLKZzsoCIypSGc8BzsWQXdQzdAnNvlF1MaB8/MvMDWwc3kI3JM+lO8Auw9SPndzA9ZWsvdnN
+IRZvkziFwLLNro96TpKc1TmWFCXKsxRZJcYwHG0gPWM56hnH7zl8MMb8FWPMgTHmBoA/B+CfG2P+
+PQC/DODP8mtXUvRX42r8ERt/EDiF/wrA31VK/bcAvgHgf3r/nxigrZAeO3jHEqvU7n4Nc2rtBW6K
+gp1G3ULjWlcSZq/elt13d28PPfadt+8+RgPKkVU2WFaHa/dwkyIyza5Y4Pj0BNcdyqxHGhY9iTiy
+oQggicIBdCw79Nk73LUjFx1yISCwoEmrlVeAS6l5x/Nwnc1NdUd2/ls3drC1Sa1BXQCF7Phl2QCG
+4B69JiYZwqb3cPPlGDb9rVj7KChe2VUNapcJ1ol4D562cOiuG3gsKPJStojhbtCKnXlIyPJ8e1ea
+cvr7t5BN6WFYAXqZWGDjpvhiItc2tLex5QtVuWZS1vX3sEmZOm/yKTxh+NRtYnisfHi3twFXjudS
+w7JeJfAqCixaNdqKVrCqEHJZOjGQkgcx7Ik3cu0gRpCIjb7Tj+H5AivuxV1sbbMbdXIEl/enyWcZ
+2wP4MfVBVYtrZFre/pnX8Prbwg7etVxYLr0icjxuLQ1CqkNP8xKjL1AW0BuiG8jzOSBPZBxqBARh
+Va2PmqQotVoiSuVetz41xMe0rL9gj3M/HMFjslIrjaAv93rzhRTZDrt1J3u4+Ix4UMsz8aC3ezuo
+Y/FSNq0XsM3E57eLMzQMFXVoEHXXhcFnG78vm4Ix5ksAvsS/7wH43O/Hca/G1bgaf/jjuUA0Fsbg
+blUhU1NsJhRkuRsiJmvt/uYhvJmU8vY2bLz4cUHmhTtS0om3OnDY/TrRARoKiLjdFmEjx9vc28J0
+yFiO1txsvAZNBGLg2HCJhAzqFGZEoZKFi5rNM6NDsQinbz9FTL4v3bRoWWIKex5ULF5BtG/D02IJ
+fHoMznCIspadfagCFCuWHLMECduzLba5xoMtxKxn2/ESvfiGzFVWwmETU50v0CQsVQaShCqTCg2R
+lNHNCCnzDwfdBnkj1q/sJiiIBYiZML2230e5Tc2KpzkUcxQVahzcFK8ozx24rOMbj/mQtoG3/1EA
+wNYNB9G5JOWWJ8co5yM+JweLt6RsiaFYzDq04CeU9Mu7aByx+Ms0hU+qNLfuIiRJq2KyM+xs4+Mv
+y3N3nT7sda96bV+W3nToAeV7iWKboMQGyVqb3IelyYDlpXjlpqynAgWGWtaIGpL1KmlRd9ispjvI
+HrLd/ZMpBnyuIVGVeWMuk4+DPRe9TeFLUOE+DncFjbi18znAkfVZkz7NKXKAIjlWvYImg1Jvw4cK
+JOH9sHqIpw9Fci9hE9goqLDXlzzKvMnhziiX2NsBAalYXOSwPuBb/lxsCg4MdtBg2gApu9R+20pw
+iwpKt1SDj9+RpMxhx0d/JC9nj2o+VWGhWXBB13MYuqplGiDRVIM67GKbbnyxph7faC43DbdrwVR0
+tW0XTUuq+fAMBZM5XfIbtEYjYM271jkadglGZYRgRJoydxcRF7dH4o3CKpAu6MLr6lKopkaJkvyR
+htTiA1cjYNdbsOphQHx+7rlYsZZeFO6lkLDLkGkRNLBLeVFezjtQkGSf54SwSZbibO4imsmxXXIq
+RuEuFOnNK6+ECpmUm2m4gWwKqZqhcmSeq7vy8tt37sDy5RyWb8FLb8hxr/WQ1qRAN2/g5B7d+FZ+
+p69byAkWw9sPUQ3kRa8aBZdwa+VYl8nhDhWk4ihEwGcT2wrgy13pAoakNPBraL3u3GQoNXcRerJJ
+G6VhkT3aKxx0I4ZKziaaSpKY6RN20nouNtlR2tvSKEn6MjkuoJk8tAgASydjnJM9e3tnDz431p5/
+B3ubcn/B/gCqXIcEEhNWy+8lvOuhhpXJfCoTozOQY3wy3kaPvTdPA/awuA4yX57TYBVjzASkbwdY
+0PDt2l2UGXssnnFcdUlejatxNd4zngtPoTHAzACJb5C24ino1MMFkWvauNgcyk7b3fEw7Iu7pwNx
+ZZXt4cFjSb5MKoMOE5RlWGFFFeh6urq0eH5HLEbYNnAzsSRlVKKarKmRO9CtHK89q2FH4tpa1HBs
+swxTdhzG2RYKltOUp1EtZZ/14gL+QM5nE8JrOQoty6h1A6wxXpbxsNZ3qVnzN3ULpyQ3g3JgUzLM
+szLU7M5LUSIpxFIcL1i8Ty14TC46L/nokIpL5Q4CCofMkgIx+/4bhiKoLCiWTjtxDy6TU1aQoV53
+850kSEqW+l6V33tpDYtQ4zbswSFatAk99JgkRLyHa5/+jlxnI9Zxs2+hfkfCp1XlQE0ZYqHFjF2w
+h46PLXaYrkOKVjmXUGI3iKGw1g21oLy1BKCGzc/1knjlqoRiY5du28uFb3kBwOSn1bQo+axawmLz
+ooLTl4ezEYdIWSI8+e4JhvQ2yjUUu6qwS/3PdDxByU5S98UQNhvh7LyCEqcHTijP+uRoim6fTX6T
+HRRM8ibtAi49R2d7C+EhPeBHRHm6LVTFULJsMSfZ7Eov0VI2rg4D1OMPRtz6XGwKlgIiyyBPNSrW
+gWvdwtANThqDjGGA725CkXdwTUKSBRaK9SQkC6Ajbt1m2IfNrK4qfETsV9DrBZZZ0N66uzJEEazB
+KCVsdtn5bY3hDh+YJWCTUgEVRUr0JIVmzb+t2kvshN1TcNcLkyKnUAZ+KNe5SgGfEGzVGjiObCAl
+7x/GhkvAkuO56JA+beVIXwUAnDclJsQhVIxPQ09jvycv/87qOspA+hJWuY3AkZcwrIbISdjh0b1u
+3RYOBV5sU8FnbG12LLQnXIztXXiJZM7dSGJke/862smbcoyxDWuT/QedHhT1OOHvo/ei8G2qE84b
+FDJfwD2LxQyDwRpubsGvZIPYMCECCqBUhHk7bQOH+RftOtBs/W6MCzciPLrWcAmV1ux2tH0Flyw6
+dVmiJa5F+4C77ruwctQMD1yCwpCNETsMA5MavRvy3expCySkW+MjC9oGir00Orfh0AjFsQPNaohy
+MyiGm5o9F+HmFiJiS1qrg5aufz3pQBtZv/YThQE5PXcoHdCzc3TYM6N3bNSp4GiW+Qo5K0bWtoFP
+wNyzjqvw4WpcjavxnvFceAoNgLmxkNutJI8AhJaLPisDB1GA5UKsTrsdoWypTM28UnpS4jyVuuzJ
+RYWYFtjLCgx5jDrPpDMPgtgDpHmsZGITTXnJZGbZOTISmiqveym4onvkRygr5I1Y6FW7DdeXC8ln
+IigCAJmTwKJKtbo8ro+mXHdaVkgXzHArDYe9+Q7JTD1Lw6XnEg5igHwJRbVE0zAkymaX8m8Lhl2O
+H8PpsYNzK0VxRqq4OENWkguhu0K7pkc2RGtaBWx2LRrHESJUAPV0htWSOI0HCQLKqA/JBWGqGVBK
+XR39BK0itBlLmDVJq1nAsIPP26WlTVbI77KD8fRtqM0/CQCIG4PjlkQn+TFenfL7pKnLVR8ZE3ux
+UaioK2mMQkWvoHUtFLn8zg4kNNBuDp+ViLQuYRiiKLeAzTVSpkCl6YKvH5oylw1TveiGeKIAdLeD
+rkvYNEVBsxLIxlIhmEQ3sDFkAnMRoO1JpaVuejAkY81ozbPZUxREgvpOgpR6HxnO0JLFW1+rYZG+
+7+aAIY7fg8fO0OXqAh7vvzItNDU0JycVaveDvebPxaZgGcBrW5Qt0JJto3JquK4shNNqge1EXLjH
+Dx8hssWN74AcgKveZbfg/naDQ3afWXoDRT3j3z2YTBZmekRWHcdA0Y009QJWywWYrlAydnS3Hbik
+zm748CfKQsMMsm5rVCRfaQFUmbyc7QRIO9yEKCnfmBUM+zXqNEPBHo3aJFAp6bltiUnL7gqbxPUj
+qVAxn1HrBcaplB8fPj7F2YW44Moh2MozyNj5aKUhen1mtY2FkrLldhijrhi3EiymJhF0j0I0Tg+r
+RnIm1VmDllUE3R3C78nLtHpL5jUKpjA9XqeVQzH9UCsFq79+sSZoarr/kI0C8TEuDgW6nU8ctCvJ
+NejIR58chGezDK6WzWdr64bMi2NhebEW/D1DQ5IYtCuUU66dOoHmy52yTb5JbawIkFK5j9bmfBYG
+pSvn0zpAnsnfzZpR2g3gphIytV0P9QUZskIFh/e9z7RMdp6jZP3PKgOk3LxOH88w6sjmFPS3oCFz
+lzyV63307gKPzwVA1doXWDxmzmvfxq0dAYuN3BU89n8o9oN0EKGBhAzVskBB6zWqBkiYt9h1fRw9
+k+ro98ZV+HA1rsbVeM94LjyFFkBhgNpW0HTb7aaHgq6hKh0saVW/MV7hhUZcsYIJNXQbFERy9jMX
+dn8t6OEiYUNUFNrICzZNZbSYLuC26wy5C1WxO69NkRZCidVNOmgJUjl9LLBVr6xwSoquLbWJhqrD
+lQlxQvDRUNWYkRPQrCsDVg29bljrRugGa3l5FwkTpUVOD6WxkfC4pvJRk5E3GZc4e0s+/87xCvfJ
++7+1rsu7Gn7K30XnuOmsE5QhvFCs9NnZGH3WxcflmmsxgaFeo8IYuqRrbzQcR8zO7gub0KxghF2x
+Wo0RSLcc5AYMORzRGUFp8X7aKoT2xSuwBhJqWFUXHSNuhXUQwqaW5k/ffAHfruUY95IMWSN263Qq
+1zM4HeNwT849LxcwbAIKdQyPTkhTOdBLhncUgCkThZxVG69dwTCh21YNMtKxhQpQazEeVgAy24JL
+7gydTFETc5JXS1yj7mcTyP2PVnPcJ/NzPznFYEzSmp0B5lOGd+4D2LEkDPu3xaPr1n28vMdEub+J
+7FCeSd7poMvqi21sKCbbHWqHeMa67LQtL0pkGUPlqICXcqFFDpqzD8an8FxsChYAHwplraF5w4mb
+YptkTlnZYsVW3g2ri3Qif4/oli/zBdSa428ALCnk0t2LEHYkrAiUQrKUL+VsSe5EFlAwE+xkyFt5
+SHnVILdZApwvkR2SIWgqv5vZGtcIFnJMivOWQh5OhMhm/Gk70CWRbqxkGLeGz5fR1TZCXxZY6wMB
+xUmqUq4hLhwsJ8wddBfoUEg1WLmYUIA0KwvkxLsvOW93xwn2KeQS63PMWU7tIYazJQsvXm4iMeve
+AFZAlIFhLLvMzmAYXgS2D0UQWXd/G0FHEHbutsyP041gBWv3tA+w9GZFe1BGXH+lHVhc0BYrQC1y
+9HpyrM7mEh43kOH1W/hhS97ujXmC18kgVJNyHYWFNqe/7tdoGdLl3QYBlcF85SLYkufgFwRkxRYy
+VoMC0yAhK1LtO1Br5qUKaKnUBG7YcZZhwcrP8fkS116Q46m2A5e9NH2GKAvPwdlbgrxdlDk2Cglj
+O50hGua58mmFliK0JUVdev0RegMStLZDtLfZ2ZlM4JNlazU20OzXiFy2+wNoWWY1YQqHbfeWbaHI
+SADT92GxqvSs4yp8uBpX42q8ZzwXnkKrFFLHRmG1GHliobqtjb7LWqvfBSj7PepbMCQkuaCbbF4d
+tAAAIABJREFUvGpKBESE1FYExe7C3LJRMWnVrnwsCfPNLEnO2Ok2jE+wUAtUFJFBVSItWWOf1rB+
+W6xVQyZfGy1s0rVNJx007BmI7Qqmwz4Bv0LF/oKClQpVtigZBvj194l7hB4si17KXK7h6ZNzTKn8
+vH84xM2eJJyyro+cJDGlU6Cgu3tKSvZZVSG0xJMYVy6mJJ857Gg0ibju7kYCl4zAGanq24EDw6Td
+7LyAQzm6ylsgZjdnYu/BIvbAhrj4dd6F7r0qcxU4UKRx09oArVg/SxmQHQyKnhSUi2gkCTzUMyCW
+ufr4qx/F12zJ4Ou797BFiz49okT85gE2bbm/QXiAi7GEdGkxg6/kuUZ2hJzzHJEi3Qor+IS01/Ps
+MiFYIIVL0BYcFy0v1NLrsKSLCUMwlQRYUbHc2gA2qBtqUyxnaPsIef9pZqN01ziUAl4o6yK5KNA+
+JR1/IiHV0eMT+EY8pdDtIWXvTtF4sJl4d70unIAS9dck/LA6I6zteutY0GvpQKMRb4mHWM5WSFh1
+e9Zx5SlcjatxNd4zngtPIbaAH/EV3q08NIzfjlCgS26BtDlHEFI+K3dQsNT1dCE79PndGjbFNPJU
+wY/FepisREtrvTME4m3GbV2xmK0/RZ+5ytYpUDEpOZmdoWBiK6ksPHqTZbst0sO1GjaVj+/lCUaF
+HLcbDC7r39UyR6rpkZDuK3Uz1E+YBMxahB65ArYGcFmzXp1JgtMsZoj3JbZ2ndtoGNfHwWMcnzPx
+VdbwGA/HZCZK8goHFK9JCwtVyjJr5OHRU7Hyd/o34UW8cXYWrpISDhGPVmFwQVyBPw7QrpmRF0eX
+OZj6RCxf75Xgkjeg3TbQY+ICIguK1taoFmATGtgEpbCC6jM7PFEAE7TqRojtp9L59+D0S/gWZdr6
+FBO+5ezAqSUX0fM6yGMpz44nKWZkvN52U+hK5nw2WsfkC+Rk17btCinxGQYuNinm4njBpUhMxlyE
+DQ1rRg/KXWGDjWRaWyjJzpQwv3R2/Bj3prJWAq/FvpG5bZYGplmjCk8xmYjnePoOsR6NjaDLJHdo
+MD1jvqeZYy3jkqUr9MZsturIcX23QkLKt3RpIWN+yW0V5hTYDcoG6ZIdo884notNoTUKK6ORfqbF
+7Lvk8ptprByZyDujLg5tUoTbUwQe+wcIH97bdFET932wGSPh5Ng72ygrtipv7cLZJ7yZGWSn8tEw
+6YOFAp8hpucppsfycJco0LB4ECykvmzyCvfm8mB3ul2cUOH3zu0Qu2wpXhkHWGtedkigUmg0Xcmy
+z7GCSwxBVysEO/KSRu4aY9DD+AbFQvIFpmOp4z/69hHe5cZxlBUANyGPCzPcUVixnbpqe9ii6zif
+JbizIecusiU29m/IHBGDEK4UmnrdCtzCLxkqeRYK9jv48TZCQnC7nxT3VOsN6KFsik3PwHHlmRnb
+AOy6rI2CZgKyYbLPsmIo9kyorka7ZCv6/guo1DfkO/3bSFKhSiPLHT72cIH4E+TSdBSG2wRk1S0K
+wt+dAnAYjhiLNf14E8kFk4SNhUFE1e1wcJlsbVUNQ8Icm9R1mQX0t5lIPauRUFW7nebYuy6b9uIt
+eTEfHVW4eyqb2E5XoyJFv/JsKOJX3O4AHWJVSgIc2lWBbizziY0FBhvydzHuw2c/Rxs18MHeHcL0
+G9eg5rxVTQqzXgOOi4Iao3XbQpd/SHRsV+NqXI3/f47nwlOAbdD2a2x+y8e8kl2tcAwCkoou8xpq
+X3Zw19fIuDtuaKLHtnvo7ou1Gg4Vcu6SbvcWKkuSOrY9QFayDEVoaKkylDxWm6aYsqlolmkkiVj/
+RVLh2gZdXmIoUPioILv1YBRhh1apVjmGJMCoLQeGmZ+WJC3a2IjYrOX7Bg7d/ErXcFKiG6ky7Gx6
+iMfy+2Vd4M2LBwCAN7/7LlZs2gmaCpm7ljmjnmUVof+KHPf14x70Jq1gpWGPxEPqd/aQ0730SK/f
+OD5qNkeZuYKO6Cb7MRSTjvUqwZKlNeuElrb/FOGA2BJsQbOxq1UtFJuVLKMvy2ItBQ+VCWDWytXG
+Q6Mk6fbmG9/A+EQarLInJ6AcJ8ixgnGdYZWKF5crQBEW341DZISN12EBwyRuxa5FZ9BHQN4H13OB
+NeQ97l7CxvPKXHZHomEY5ACa5WLLADqW32kV4JhEsHePBTfzzvICTSbf9TZsGDZBpcpDSaSqqxp4
+6+Y+kNh1dY4yZ/l5XiDqiQdi+S78iBwJYReeJx6EIhrTtBlqamd4doCYwNLF+Rz5iiVc30JnSA3K
+ZxzKmA8GgfyDGAcbofnZn34Br8ZDJJG0RUfBBG89lIU0MwkGhPFOnl7gdCLuGpGoGHVDBJQZnxUG
+DjUDnVGAYikT+GRRweVLvbsh9eNB3EXDev1kscDFhIutqjGrZFI7to2dkeQgwh3Jlv+Nv/W//YC7
+WaOTDNaOmNYUQdUGETv8qkDjx0cSO+trQ3x+WxaCzf6K7HSFCTPvfs/GPmPgLx89gmGc84kXP4Y/
+9x/9aQDA3mf+LQBAEAxg9Bp7oDG885MAgGT1Dv7jOxKC7Q+Ah6Sff52Yj3I1wcYl6YuF9lI5yiBj
+a3DpBWgYEry2IfmA8QqYKlnc9y5OccBNprBrhNwgrUCDER8cyrf7/SECkpREd15Eb0+Od8N6isOO
+dGBO/R1c25CXNLnPFundDoYjhiLTKf7p//EPAQBfv3cfmlyKtguAVZUOmZnCIETBBVNnJXJuClv9
+DnwCvzavHWDgr2n35dqUW2NwKNe2Y20h4t/K2cZf+it/CQDw8Z2X5Jkd/gm8eJO5nXsW3FtynZ0N
+DTySDWQyfYQpGZKabM281WKeypr27AA5Kdx1VSPJM37ugtMJZdb9MxotjZPd9zE+lbV879ohTt6Q
+8905fBl7n5Nn/I/+6n/9NWPMZ/A+4yp8uBpX42q8Zzwf4YOxYDURwryP3U3u0DdyDNis9K3vXOCI
+FshA44CNUjWxCwECxGxOGTUtLCb78srFvCM7aWcxQUJhmFktibp6o4ZN5d+8WCFoxXpMsgyLuXgN
+qY7QJRTYDZ5Fk+/7PS9CaYmUdJWFTzA5utIa/kCu49DJ4NPNPXso/z6dpFjN5Xe7ahPmjugUxIXG
+hBJkrbqPWSIexgE1M2EBqmH3pTb4Tw+ELGb3hV2ou+JN3U0ynBG96DZyjpHvIqI3UnotTMp7NQ1S
+QsH3nAbbfbH0nZE8g3JDQ9GFLSIPDdXBz0r7UrQkNC1q4rtP1h2u1TH6qTzrjz62oEPxwjLHRUoK
+uZf7EYZM8rlDXlt6gZwds2fpFKORZIGvPW7WdIxoPQ82OxHX3ZJ1nuOCzyZoaliE0F+sStg9uQ4z
+O0F37brT2xoc7sIe8xjDFdRYwrvo8Bwgr2QH4t1+fnsb+68xJBr4eDoRXsqTYx+G/A6bboCwK9/x
+eS6no4AFFcFRwqUnUDgZqlNZD4lVgI8VzZqbQbVY0VOI/AJdenG77jtoXpDzfb6KECzkPP8Izzae
+i03BaWpsLie4KIDrjZBxHD84xorZ4olfI53JTQ62I9zalCy6y4xtWDqoGNcOmxCKjEfz2RLHlIHv
+Nj5OWnGpnhI+vJjPETFT3601cuLsg3p+ScCZZ1OwHQHJ8e8t1FpHaJUx+E260ZZpccKX/jszH9tD
+WegOw5lOVqMiOYatt7DZE9f/z39qH+8OHwAApkc5fin9CgDgthHlJTcPYAWj9ZlhU53qSz+m8GWW
+Vu23PLx6nfyXBzJXOyuD1ZpmvqrxhOK9xxWQOsyNwMaMFZMJS3eTssZoKd8964jaEQAcZepSfcvW
+GgFFbhqyTanE4IFNstnzJSJXXOrPvPwyslaIYQbOCzAzqlqR1t/251jM5btVe4GNmACiPRuGOaan
+iwmcQu7vQU6NzsxgRrCbdh30Q9nUitCBRQCbU2XwGJoErWxSOrIBl9DltI9uV17C5TcvsENimGJH
+5u16p4vsiaw3pBUWNmXi52O4zDuFro1hT1rqh77MvRM48CmWk+sKWJJvs5oC7OBcWBkK9jNkDH3q
+qkDElvmVCTBjXubROzMcuTK3v/mdb8M6+hQ+yPhQ4YNSqq+U+gdKqTeVUm8opb6olBoqpX5RKfUO
+/x18mHNcjatxNf5wx4f1FH4OwC8YY/6sUsoFEAL4qwB+yRjz15VSfxnAX4YIxPyuo9IuTjr7GDkr
+XFyXnbZ3z8XXWY9f1iF6DANuDW5hh7v5Tl92XG97hIhyXbVxoCNJMk1nF9ih8GC6dQ0zurnvvkF4
+6fQYVUJwi8lRrGGirQVDjEHVtJfWra3XurnPNtYpR023Tne8S/5Ey4wwopK2MzrEtc46KSfW43oN
+dDapn2kd4pVXqG152McrBAV99Z9/F+WvCffh6iOCY+hdP/zeiaHwzbXE/d+z4fBxz2INZ90dmsk9
+TYsaHv3vc1PAYWawMhbukJSmoyP47EqcsYswXdYAQ5F2qdAQ9NM0CiZhl2tg1l+BZclxi6rGZC4u
+vlcB9evka/zoLobkLpwPZ7gRrPEQTC7rDXQicamt1Qjerpzvxm6BMUOXzhMX79wVvICm57Vqanik
+VTPGRo+NaVFniIINRiMrhK5lzsuI+I8TFwHDi+PJGeIdeT59r0HbEHpO3EB67QgbE5nvb87fRU5I
+94Ybwlfi0XiRgw47TDtr/snQviTy8bIlVpF4R+HKIHNkDnu2j5QiPy4nczZzoZR4QkXuwidLkG41
+irfk+p2iwewhO1efcXwYgdkegB8F8B8CgDGmBFAqpf4NAD/Or/1tiEjMD9wU2rZGkU9xe+MQzoU8
+8HvFCUK+NG6dI+wzQ3zgYH8g8dfWFpl9OhEovQBL92CxUhEOcpQX7LTsLjEkgUax5KTHmzg9lQk7
+zWsoxvWmaUAeC2jLxiIlj2Mv/SBThB7LSf1NWUgb13fwmiWL4wtfuA02a2JrsA8wdBkQI19kBQJ2
+gbYWELPrT1kNugM57mdv2/i1r4gr/et/638BAPyb/9lHYLFFGtpDMpPVttRz3KCORGezh5D6hymR
+lHlZAAwZSjQoynW+xiBkS7X2v1eyC1gWjYsWx0uZH1+38Nkxes1qcUEQWVsWCCtmycmg1CobKTeN
+p0ixrAT9uaqO8RFLQiFvskBxU57Z0JUMv2rGiMmr2bu+h5FmBWdZwz96R+ZQGfgE8piJXHs0S1Gy
+NNytLHQ25EUfbm0gp6rVfJZgmLBlfiQ5nF5oI2fn5F7vBZiZXOfc2kZnJJ//6A3ZHKqHY/xWKaHG
+wPFgVkThxlNskHg4drroMS+zLqc6ylzykTpuhEqzX6dtYVvsTQGgbCIyl2TFMinyucxhx01QLGXt
+1FYDRZqAcevB2eNillt73/FhwoebAM4B/M9KqW8opf5HpVQEYNsYsz79CYDt3+nH3y9FnxYfzAJf
+jatxNf7gxocJH2wAnwLws8aYryqlfg4SKlwOY4xRSv2O2bnvl6I/7HdMp4zQ2C3O35Ik02LkI6Fq
+c9+x8dKWWI9rvRcwOKRCEN0zVXbgDrn7ah/Gkp3WvRhBMVFjXA/lSiCzMckXdBjCJn69XJR4Oual
+2g5cVgka1cCQF6FIn326LCho0rvtOrLbXxt28aeuSyL1pVuvYdAjJ4EP1Bnhv2T6tYYRTCueyfys
+hpqLRTdWFyBsVe2PEL8kx/75b4mV/BHzW9gi74M1uok5vaO6cZBQ9epOW6MhLT25a3CSVtIqCvHc
+FOco1i4CXn9/FMFlbHKRyb0NVYGa3aylstFjJ+qkqDBad6VWBhPyAtisSNzednHGcyRnDQImfzGz
+kO+IYEw+T+FWHwMANCM5X7Wao6DmZefOLvpD8jOkc4TkMIyGHUQ7DCUm4jU9fHiGOXsAwsSCHbNi
+0pZ4nMjvknmCjHiIg1KeQ/yShqXYdetMUFwQHDOYICTvw9KTBPbi4QQXQ7m/7iyGQzWtEbaxxWpG
+ZzgC0fswGZXG9RJEmyNvUrRj8UAyk8OmMrmxVmjma1Zwcnc2DjoMXepJg8aX98WvFgC9qZ0C8M5k
+Dbyv/DvHh/EUngB4Yoz5Kv//P4BsEqdKqV0A4L9nH+IcV+NqXI0/5PF79hSMMSdKqcdKqZeMMW8B
++AkA3+X//gMAfx3PKkXfVMDiHMcPMxzEUqyYvjtGlzF5NNrDaCSJtqBjIWTPOytQsDoOCGKE5VSX
+RKMqtKCWhIn6QDOUpNUetQmOp2MMmQTMFLCYyU47LUrYjPeqrMHyMnO37ln73cf6m33PwUc+KlqC
++ztyvs++eAfX9sXjGexvIKY3osMIDTUm21xMt4MShqI1JkuRrpj4y6bQRH0O+ttwfvu7AIB3H4mn
+8Kt/8xfxZ/6LHwEAuFB4MqMF9lp8jBbPVgFWhEUPSCsH7eJpyy47y4ZDngZfiwYHAPRaHwqktyO+
+ofYqjEjXVqUFOkRsnjWry7r6PG+x5LNyWd+928Q4ZydqVdWobELBV8dYnMtzuvnKIZqKnBqETzf5
+HhZKolPHTRC7wi3QNiUCYk6SM4WAeZUdwrUbO4b1QPIB8VDDceR3waCH+dviQR7d/zaqRhJ3XXbU
+ZlWBiKXAvFmhHYhH6iUrBCOZl+Onco5RFaAzXdecF+gVTChudKDdtZ5ECZv5jpbsTk3mQFGOT+d9
+ND5Fh05zpMx3wNKXFHFqDUFHBoe5CAslXApIFmhgmBRPagsFy52gdOf7jQ9bffhZAH+HlYd7AP4C
+xPv4+0qpvwjgIYB/+/0O0iiFqVaw0wqjTZmQQbkDRRfohf1b2NiTl6kTVLDWdFRcKMppYWm2AltL
+KIsJnm5zqSlYjVtYa7HRSBaj17iw2DE5XJ1jwPbkpbdCkK51EIE+XbSYcN4fNNZu96jn4xW2bX92
+W679Nf8GNgmxjrwIHtWrLDu4FMFpCVxRs1NY3LCcvgPHlftrkgkahgQ67uPmF78AAPjEr/2CfBa2
+cFR/fTWX6kVFDtC7RrI/hj6RRd9S9MRzDHYzuqJVBZ/0bps6RJc7rlfnWLEJwSUd/LW4AE5ZfegC
+Z+yNOGw0DLElvmowJ3X9W8T4Z6sCZ8wlxbCxQ+CY0RZaAjuaQMHZEHxGm9+TufI7GOx/Qua4fwOO
+z47QGnA3BY6+vTECFgyPLHn5G/cpfEKso7EFK5TrXMYWdoeyWYw7HiZrkdpErsfDABY7KsPMguOz
+P6TxUbF9dsXu2WGoYVF3sq3zSybtfrCNTiTXY7kA1JqBmhu2b4N5XyhTImR/xflEIeR9TNIKrZHf
+OcyqO02IgSOhSxt30HANjaY+poSs9/o2olTmRfii3398qE3BGPNNAL8TlvonPsxxr8bVuBr/6sZz
+gWhUpoVblnj1ToQ4khKTVR6h7oiV6PYDRH0mDwMfmtbdIvGpMSkMRUhM04ey1zRYDeCKS9x4HTQV
+u8t416ZpMaEWwkUJRHTPOg3wiKQfUeDCZnGkUu/fl+6SRMS2I4S7YikGu3If4dYANkt6jsmgbJKM
+1M0lNBuaKLhgCDCE0cqDxXr1uGjgnop16HQj+Pz83rtybT/k3EM7k7KY7naRMM+b6wKBI3PkHg/g
+dughsMsSM0CxWJ6rFs6Q0m0dBZ8YkdJzkBCn0FKVOg9a+J7cU2YDE5Z7J1WDpKHFsw3ctaQjw4dp
+lV3iP2oYvEvdyUW5gE5kXorTAvPoPgAg6EmC1u566PviNTk9D9DiCdhWhA5LyrZVAcSwOES0bjsK
+A8rKzVSFtiff7bUaL+xIOJbYd/HWWErUx4/l38+8+gQBdTf93giZL/fnmxCrM4Ex71D0J1lViDps
+JIMLRU/RiiawPfI+tBHWsW5N96ApM+SUIUwaG6W1Ri6uMCFeZlYXuNGX+eoR5t+6XdSVPLPNdgU/
+krl48yzB6FhKrrllMN6hG/I6nmk8F5uCVjb6wQCDug9YMum10tjekpe+1/XhgrLfdgcg65Eh/LZZ
+BABBMcoHDNlozNy7ZP9xXQ9UmofXyENZ1iv47KnQKJCxQ26xbC47KiPPgz1Za0y+P8zZ0L3c8R0g
+YXWE2eaiWyBn7TpKK1is/7fZBBUFVhXjTG0sNBRkKWchirVqkuWjdsjRuMjAUBS3bkln4fiLB0gf
+SZ6hc3ADn6CruRlqbHAxvt4kuGXLZhlrmeNDDbxJnMJoK8DHtySfMehGmJMgpAgNtgmx7VyQwTht
+sNwgmUzVYkj6/LfbFmbd+1ErGMp5EfkMxy4wIQHKgQGeVGulri7AFu6JzrHVlzBHx/LfvciFQyCQ
+Qg9KrasyJfT6xbOM9DwDcAv5XdztoK65gRxMkJzKxjluG1yQQTsd1sBjOd67p7KBHo8TvOjJNfSv
+a/hna7GiHBXDo5TuvuOmyPI105ePgDyefnEDzlCeu3E8NDQMFasMTV6iIXt2hgkWawnOugB/hu2o
+RkWsxwXn0lUpdI/hXGrQY9ngcC/Gm+eyye4cG/Rzeca/iWcbV12SV+NqXI33jOfCU7BMi6hIka00
+ul1xtVO/Qp9SYyoIYNhCkdUNHEqo6bU374doGDLYyKE0vYpRBqtm15uTXCaBSkdc9NOZBZu8fn5q
+4SGhsd9cnmPILPOdJkZKa3thvz/XnZ9Lxnn32jZ2P3EbAL6n/bhYANtMfIUaeinV2rry0JJJuk25
+T0cFaiYtKzdDRi+lNQlyVl86KgY8MSuvflYaeDxvAUOtB6M0dmJa9J8J8PZ9wmBfD3G+TQtD63O/
+bLEMxcpvGw2E8vnk5j48knrYRzkuGCoUo3VFoo+gEu/ua0/muEtm5BJj1MyMn7c1lkTYqYDkJ57Q
+8AHA/bRFzYap5ewJnnqSGLv+wqto2PDjsXnIjjrQVIlurETEGgDU+TnskOrRlQWLSTyz7q61MiS5
+4B/GDx7gzQeSuFz620jPxKKfTXK4LJksqDPx4P4cr/WZ9Z8ZeEO5jsW9c0xTqYIEROHe6BzA26De
+BBz4PWp1DD20JM9BswK4bi2fTXeqRFWvdSAtWMQ6OGoLniPXXIQakzMqli9JMWct0FHyHDt+hJzK
+3Hf6BR67EnadRR5mH6du6JfxTOO52BSMBVS+hUz3serJAusv+yh8ydSbvMWykQcwmyq4M+LaO+wo
+3L8Fd0jIM1LYBDVZYfw9wpFVCY9u23BH3MHbzXXUxw8AAL1Zgrf6fAmXQE6gi9o0uL0v5VBzcc4r
+Pv1d74V4JTS2g3QqL/0ykQVx73EEO5Jj+UEMTYx/oyu0muAr4tdNXaOp1jTkDRY5F8piiYC5BnVY
+QpsbAIAXSGY7OPxjiG7IZ8o0iHZk0ex/e4QGkgd4vRfiDqHJIBTZ1C08EtfOZznu2bLZ9EKFnJDf
+V7wKHxnJPDcM11ZtiPl35LibtUa2bg23bVR8YQ8shSesKKR8+Ydao2b1wbKBqvyesPD2phiGpL+D
+mEZCM7SzdQBiojA9vQszfQAAqPQK3c2PAgA8HcOsE0EW4dwXT/FbvyoO9PG/eAcXLAHvvNjg7VNK
+BUxS1CR83WT2fpnWGHusUDkt2i3Kz89CpGTLQkxBoc0avk11Mm8ARb7K2mhUDI/U7PyS7KUliql1
+QuiAwKO8hO+JUZt3ZrAj2ezt4zNEBH69Qzr/3UV+CazzghAByXvteYVtVoE2Ry3ae5fNMM80rsKH
+q3E1rsZ7xnPhKWilEAU2LL1A04rr6IUtjEVVYn0L1SZDiUJjvpREYnJGYFL+DYRzsf5b3UN0Yjbf
+1Au0tLxFmSKnMIqxZFe+88I16DuiYL26OMHLb4gIyf/9yOCcHXf37RLX6b0EnR+84yoAXWIdVpMc
+476EKY+evA0AuDAKHzsXt+6PfeKz2D+Q8KK/PURbyT1VBActTxaYLMV1PK9ml3JryQq4zm657vwc
+zvp8gTzKm1YGxS46Y2n45Ivwdit8LJDQ5WBQ4+ldubZdCsDc3g6xcSBzmM5meGyRMfikxnYsXkUY
+OIh98XrcgInYRqF4Qa7tRruNL1wTr8LWu8h+40sAgP/98V2Mv0kK/mbNO2nB68u9xiv30vqftwqa
+2IqNIMDFVOagVwvWoz8qsXoqFfdvfPMX8dV/9m0AgBNMsX/zZQDAn/zRTyPeEA6BgsnTX//lL+NX
+f0k6St99ssCoQ57EqYNd6nHen+XIqf85WWtXhvfx8IGEY9uDT8A5owfp+ajpCfWIm9HYAGw5X9JU
+6zwzdPU9iz6+dx8T6lxWmpDorQ14FfkXY6BOZa6W0xp3H8k1PxlfIK/lGQc9ufaLNEJKshsztbAb
+i1dlOf4lH2W9TDE/+GBiMM/FpgClYakBdN6geiQvR/T5F9GhbmFlFjh9m/LrTYKInWFNILFgNIlR
+5vIQre0pbAJFvOMAiZEJrlcWJokQgs4uZPLy5gj78p6gMUtcf1Fw9rfvz3F/Ktnnwskxm1Kv73du
+4/jebSigpHZCP9SARDko+vLSaFNCQf6eTaboxLI4gqEFTGUFXazkPlfLFGdzOUDW+Jgy1NjyRghZ
+ObAWDvSevMgbuz8l9/zaHupCjuHUG5gx/7A1dnGwzxAjaXCH7D3+lizo7Y0BDDsYte+A1S8cdAIo
+9mBE0RCdkH0Xa9c3rXEzkI1g0RkjSwj+8VbovCix7B83+4geycb6f05kfhptkBD/uQuFvF0zQQHa
+kw1pdfQUNrsLzx4I6rC253jwd4RY5heO3sKDBxLmPJydwf0NubaD3RE+tSHwmZSlvqM3FnibupM2
+NLJCXpRf/+3XccR8TdAWsFgx8TkXlt1BtpK1ddG5wGDO0mGdI6I0vM+ybtTM0HCTRV5jMSU5y3mD
++5k8y/lb5xgH8ny2mH8arVpsdOUaem4XaiXXNh4/xPycvKGzCt2efF9rCeH6sY+KFbhiqTGxZc22
+2odDIzo5XqJ844N1916FD1fjalyN94znwlPQbYNeOkfiKQzI6tusUnikybpYOfiVfynIi69P78Jh
+RvoV9rF/9MYWau780ekx2ohVBivF5EgsSRUUMLaECq/fFxf+y0d38eI22YnNEJp69m6tZGWvAAAg
+AElEQVSvD4u1+bhWeIeF44n1g90wA4XISJXk4dEcb2ju3LVYsO2bQ2wfyDW3oY3VXCyGXo0uu/ba
+U7FKTl7DjcQivP5bJ7ifSpLzWteGfZPu7NZLUJVgGYaHBO40LsC6OpRGw0Si0jbuOOIhTYYJxqRe
+06Rvnz9I8JVKXPVFXiF2qEj18hAbhCZ7dYaallKvu0hbD3mHlGHLEX7jWxLylf4xfugzxBhYOQLC
+nD1qZiadCAG9g9TkKOlKZ+cL3D2SY7jhDqpvEqtBoJo9+jH0X5ZQ4t//0U9ApzKH//Cf/GN844H8
+beobCDcFkOSwE/XGp1/CD3O5b+YrHLGP5eFdF08glYitpo+DLbk/KxFP4ZHJscc5unhnCUXClcn9
+c9gUhjkdi0fQD31YDI+saI6sklD48fFTfGdG3o6LI8SB3MsWWcLbrILNXhKv64kOJ59Nn2GcO9jG
+gwux+McMQbfiHpxYvK39/ibsnN5NOINL7xZBD6089mceV57C1bgaV+M947nwFCxbI9iM4NdbiA/F
+uvSGL8Ddkh1z1ER46ZPyubOI0MnFGu/tyb/9Xhe6EqvsVA4a9ptr7cEEYik8WHD6smV+7qcElqq/
+G8OjLkTHnaLry+c/mR2gGktuoyhrXCdSLs1/cJekgkJDWrioE2KHwi87xCZ89qWXcXhTkIf1bAJ7
+RVqx0IJjyb1Ew4znXeAA4hEkH+/DPxWvYdvvYjiQc5R2g9iSklX11tfl/j//KTjkGFCqwYC1+0/2
+d7D9EuHB4wIZq6sJPaxF18Iem8f23A5usmz7wstbiAjHdc9b1D3GsJB58zqbAMt4kR3hxc+QLcop
+MCITUGc3wfyW3N9nqef4sC1xwd9d93xMqG+wH3YxIWHvr7QP8NprEkfXpBTb753j1o98Wq6nG6Mh
+gvTfsTwcfE26+D/9Y1+EE8vc61Is7Y29HqLqhtz/vMStMT2ocI75N+RZ34k6+H/Ye7NY2a70vu+3
+9ly75uHMd768ZHNokmq1erTldls2LMWGHNlxJiBILEAvSYzkJTGQB78kgB8CBAEcRC+xHQWBJTsx
+AtsyYMm2pO5o6rnZTbJJ3nt5xzOfU3Ptea88fF8dNm3LZJuwQwFnAcQt1qnatfZaa6/1Df/v///y
+p4ThyVd8xze+f3jBi9BOU0K9D3cWUQVa5VpLH+MNn1XV1vtv0Y9UpvDagHBX5jL8xHOErlxjU9GI
+7sE+rkreua6HURGhqBHRWlf2xk02nxHL4u67UhEbxE3KXGHXxuIr0nWyn4CyVV/tO5wsNM7xIdvH
+YlOwZUl1PsH2ViyEioFBb58gkgmKGtf4cWUzfmkRUKUKXlLOtKrO8Ne02AsXo+WAuZMSuGvzeZtO
+LA9FW2HQGz/xMvOpDGqn4eEq311xNqT9HQlwzZOnTFIV79CJ+4Oa78CPq0z4q7eucucZyS5saKVf
+s2NwlTRjWSzJNIBFUlDJfBMpjbyT7lFpKfC1K4a9HdlMvKjPblce+ma7STUXU3vNojzM3iNLwfGp
+lEout8d4Vh50D4vfloXXVM3Edgibe7JYO8M+rZH+XjuhowG1ZZGwUFcqV6bimIjODQkIb0UjdiZC
+klO0uzRVjLW1+yncK3IvD+YylnfHhvGhXsMWZLHM5X6ny0PdsTbv77Mv08PIyLj+kX8nxA5VkKXR
+waZyjd6dbV7KJFAcjobgrQEja55Ig5PJZ1vPX6PSkvofOzukoSCxl55/gRdf/jEAjt/4pszHzkOO
+lIlmiU9DA384MZ017Z8G+x48mhBuaF1KFNEPxAXb2mmymcvY1tWKgLXrJfMfbTXJVHfSMQ6JYiwa
+jsHqwdLoNXGVJv5OLWORG0tZyHtJZgk1hdOIKmZrfI51iHZ/tE3h0n24bJftsr2vfSwshdrxSKIB
+/mTF+X0hjdq9/qfwY9kR3U5Nb67oRjeHDa1ZVz3E6iyncsVNiBsrPCW0KCchbqC4B2oiT9OWmhPu
+DTw21bS32ROKd2U3z1dt+lpFOElLbK6c/WuBlD+oGcuZJ6mnTwyeZbcppvRgR7X80jGn53JSLqfn
+NJTN12mVMNHKuVKDnZ0Cp6XIt6MhflcJUFp9WmpBhI5P3ZU+e1tfki4Mhtg15WVdouTKZGeWRGXd
+vVHIzoaO51CO4mzZYE9VqQe7AxyF+eYTg6sumOlbVlMJRj65r313SrrXlW+gU9D0JZjrd0a4yrSc
+R49Zfk9NbR3LO/GC80jM3dHc4VRP471qiqtCPD84n9EpZQyTTamevVcljJSBOmiAo6exv3OTbSvj
+7LvvnXWFplOXpz5jla7eeeEO3Styf/5phO/JWNy4vY07kHnPlcFnOc/watUV9du0J7pGyFnruPnO
+WqUlY6XcBXHHp6WcFI2qTRAp7LqOqWq5v+pYU91BeQHHzp0CZ6ZCO0ELo2zjWZZQT8VSbWplbNt4
+JMod0rQVvloKzaiL7QoC2HlcXaiJf9j2sdgUnCInOHzE3QIGt2RRFV6K4ympSe3jqh9tsjYmV2iu
+DkgVW1JdVG4OnhKSmLigXuhG4LoXpmSwuRb66ICacLW7Q+JLFPor97/Gu0qA8kyngzuVwX7Xzv+V
+91EZQ6Q+JY0eYV/z2No3E4IJFBQ1P+aakc3JsyO8lnwm1oi2R4QT64LfzqmVKcivUoxGpHEjTCqL
+pr8tboRjfUyoD7HxcFVo9X45JTiVB7JbNAkVdry/L783jww9XWx77hATKCzXKykVl980Mc65XONA
+3QjvPOTqQlyNylsRjmT+3LiJ11LWo8Mmh6eyGb6jTMXLysNRQ/V+XTHX0upFmvNQ60DOyhT/SFyl
+4bPSt6eLklIh1kEcY9eConZxIeSCE13wWxZPxZ15enTCQyvvfaI8Iuy9AEB7GjEzgl+Z2ZR2qQ+W
+7qyzoKBaylg555ZipFIC0wU7Stfva7VrmjfIVnJ/SbWg0DLzTmAJPVkLprIYZaoqB3KtJBWmLYC6
+DNHKaWI/vCjzL7IaB72GblieNXiaqXA8KBW0Nk33yR7roZYHJI1LnMJlu2yX7SO0j4WlkFvLk6LA
+SbpEyuDcjbcvtiwTOWu+EWzUuKiMY50TbvsEWsloTI0bvlcQ1VcKLqdM8CMtrlGz3o0ibLUWykjA
+0xr7hyc8eCwn4bVek+u7Kln2WHZcw/sVI9fNsYaVMi3nxsEkaz4FMRf9uEszkpOk1+iw05Tj0XUt
+TlOtAg0muZM5TqW0coM2nvIDODbAUf0GvJIqXFsjStEVbWK89/Z65UehnUSk5+sgZ8awFgshV16/
+7rbPqLul/fUw67EduThriffzIxoaoNxIVDchGuCobHvg9/HWUnftPvZUSEjs2T2s0q15atlwkuEt
+1Qz2LOeq+VglC/xTzbfnAcU1LYRS1eb2o4dEr3xGrmENjlnzYbjYls6160GpWqALwS7MZhOsJ5ZZ
+kUUU6sZ4g5j2QLUijx5RaWaAtrpuC2h35ET3xwXeTUU82ohKRXJQ66LXMkxX0vf5aQ1XlVDH9fCU
+ZcZUOVa1McKOulRBRKxuUFGkpArT73oeSS0uX41DpG6c72ilaZ5dzFMeZFCs5exLlppVigOPcvaj
+PeYfi02B0mLOM468U46VkvvZ+fcIPYGqhmGDKpCHzcGnUnPcWZuOboGr9QA2b+KpL+c2wNNoflX2
+cLQSzaiphklgLTnudXDL3wDgPEk5UYDUk9LgKa231WjzD28KxgidO4DnudwYiJlflCnKX0KpJcLV
+YskiXUNRVywLSYHWKzC6QMqF9DE5h+iKmICmbhNobYM1IdauxXZrjBEff73RYTKo9bXrX0xw1qg4
+XN/3+YKDlYq/Kpy3n6zwOwqgam7itsQlsNNDVvrZyXFBUsi97KpQz7XYA/XhbcPBaIqQ8pRS3bwy
+7xLoZrDSuMyDsiZTO7ltAkZDucZhFfC4I9/b7MRsdKR/i0Rct3nymPlYsketXpNay+izxWPyibKT
+9LfJVyqm+20Bve2/vc9tffiXC1hp2q+smjjrYRlPyc/keu8eSB1Mo1mQZfKQTn0onyplfhDT0nL9
+SB/4eWFZqLzAIu9QWiXbrXMCIxtIljv46ypX7UPtw/JcS67bHTx1Mf2lS62sNDUuvpa2t7Vqc5VY
+FlPZLOfpHKvuReaBoy7KOHWpdy6zD5ftsl22j9A+FpZCZgx33QCvLihUuOLgaU0/VtMwP6JKlK25
+moNaCFZz5U46xKhL4bRcrFoV1ZFHHcvpUS4DzEBNYjVVGa5Aq8mq9CGHvyOn7t2JpanMuWXXo1Yi
+izWNm8N7loIDYi4AncihCOVEMxWsFJbq7KnbMjvA5mLC7rghnWsK6Y7GVGcqta7wVBO45Gt8xKTG
+KkFz0OpRKC+CWYYEI80CNCQ6jxeCwqoxDmfKunxr5RNqBPxxVlOq7FuuAjF922J6JieNt1ESKCjG
+FE08BeGMrl6nKFWabCHz0Ry1SRM52RvFDE9N/9KrWR3dA+D84Q84eywj1qvFMttrL1mpVF5z5VNV
+8httY/lUvHYDuCDPQatBD5an3P/KPwHAN5/B8SXan2WHHPz2A/ns/Fs8OhK34VtvfA2A8UFJo5QA
+c2+Z4771GgDJg5rfe1cCjdHBKU+VOGWinPTNXsD1kbw+j32iVMlQyhWrUtmVO7reZoaVwuOLYEmi
+0nvprMbRDEaRWGolqmElY7GYHHPyWPprOgVFonyV7oQoF6svbBjqQuanCNXidXwqRy1oa3HVquh2
+epxsilXRuDdmfPjBLOQ/3D4Wm4JrKzrZgnkr5pZSoE/SkuJcF7cfgFY+1kWO0ci539DYAPZi8bis
+KBdyW/nhE+a5bAphx4By+9FXVaFljbsmOVw0ePgtyT48yObcVJHaT3sjAsTMf9IX4nwjPwRAEDiY
+Qga94Xl0K/FJg8inWIOrUp3w3KW9rWXdg5hetu5ziHU0zbah7yUuhT7QdZVQlNLn6vQYlHAkaBpS
+lVxvdpTwteyDt6Z4d2ko/3858NhS5N272YxM01d1oVmdqMncyILvnVQUmtaNhj0aynTEcsJsX2sU
+DmTDO82O6F6Rhy0+aOL0dPGXUyr97eJwyOJUfOOHpfw9jD2uZOIqxh2fubJhdRpNqpuql9A0PPmB
+XGM/k3jP0W+VLHZlToPbA3b3ZONs5S8xvCkPy/RkxqYvG+BzvAjAYmtOtCebcHPYItWI/HHnIalS
+ePmNgPOnMh6LXL7vt0OaGgNYzo/IXdmE0iQhVN/fVR2HQbPJ01L6eZ7mjJXEdrOvWp1AVSQEodx3
+sLFW3fVpa+bgpKwZn4gblzoFlWYwrOtjVVcyUaarthvjKKtP6Eak6j54cUlDS8bfSXxmGsf6sO2j
+StH/18aY140x3zfG/G1jTGSMuWmM+X1jzF1jzK+oJsRlu2yX7Q9J+yiq03vAXwZesNYmxpi/A/wH
+wM8A/5O19peNMb8I/Dzwv/6rrpXj8MQEfMLb4NoLcvKNWptYTTvXvsVV6XQn9EADWNSy85d+hU01
+Ou+PsZrHda8HtFUMxFlUWDW/bKA1/90RZqXKTO4huVY19guDo9jxI7vki59Ry+JMKu/+0Tffwlda
+sQYRK1XsCeMIR3doWxksysE31ch7o41ZSt/b7gaNXSV9sQZXxUmaeoq4rZzVfE3XVWPVYak9H6OA
+ntptEHTk5HLXCGzThTWYhppTfRnmDr1r2s+ZR1tz6z3NFuxd69EL1Dw9HVP01JJwctAxdM2UrgY0
+41fEInJGA7xDpYafH1Ai1lh8Yxv/ngQEe3sLwpa8P8rE2ipweO6amLvb0R75U/ntzX6TzY64QuN2
+i/zTgr9IfleyRHaZEi4Ujl30aBmBbod7hmFf4NYt95y8oe5NLFwPUydidSI1A83FMWYNPOrtcKD1
+IavpuzT0taPBwE+2m8Q7Mv/JoyWUCupyA85LsSYCzQx1N3y8U7X0WpZC3YeqrnE1GOtiKJSIZX3K
+x/EG3RsyUQMCsm357fPVKa5WqC7OppRKERehmYraAGtukZBQax/G45SF1pJcG/V5+MHKBO9rHzXQ
+6AENY4wHxIjY9ZcRXUkQKfo/9xF/47Jdtsv2b7F9FC3Jp8aY/xF4hIhe/RrwTWBi7QXQ9gmw9y/7
+vjHmF4BfAOg4hnaaMYuOGK/EB9waJuS60zplk1pFUjy/R3KqOgMTec9tLXHXO38WEPX1FDM1rqc5
+9pVDpYGtUum8PJNgAhEZsYtzikKutxW6JC2lHQt6OGMN9gx1hzcGFV2mOyh4IVQarDgg0pjIuJyz
+ZdapIw3qjU+ZZeJbd9uPqabyvf7Qwxtp8DDWYp9xwCzL9Von9Dfk5KtTn8Csa/ZTnHyi3xO/3jgu
+Rk8PjMsa/OiYklqDZ502FGrpWKWYW1VTWooETcYpmDVuoiDSQjEn7zLYlVO8Vp+9JKJuyCm+XDnM
+C6EP6yUJ/lLmwQ8XtPoSwCt8DcS5JVaDlk0X/FgReO4WhVpez2+4XDmR5fOgfCD9xSMfaxGQ77DQ
+WFKQe5RPxDJhumChDNprTdB73/4ey1pxKM4uW7fEwhg/OGB5JLGkJCtpNWT8Q0U/bva7rBQ7kwYe
+jgoGOS4cH4tvH7iyPnzTob+lWg4TnxOdv+3qjA1H1kVlPVYTuVdbSKzJSWt8JVpt9rt4mjrv+iOS
+TCkJ65JcyYt9f41sbGBdjSMEOUEqv5HZ8oIgeJwayo0PZiH/4fZR3Ic+8LPATWAC/F3gT3/Y7/+w
+FP12GNi60aLt9ZgeSwagGL9IPVStxWCBnSkWoKqpUpn8J8fKqrxqMVKqsWbVolqLbh43ME3NlceP
+mClKOaolA9De6WO0Iu+73/5nfPWpTG4r7vLqsxJcbLc38RX0cjsS077jQKPWXPPY4e6WbCZf2BmS
+KQtynm1BtaZrlyjz8fwp+3fl7zciH5SmrQg9eCyLrdGUzWFRpjwpZfEsz3NuaPVlt9+9MCPLyYDG
+ljxY9ToY6NZYNR0JXVpaBdnyW2QKaBk2YnLl+8sVQ3H8YIlvlSfSGqqFvK72JxgVow1WPvMDCVqp
+fgpFviS3QpVmRztsanVlVWdEql74+197i6PHsnmdKYPzrY0mJx3pZ281wtcMRljltMey0OvqhNcT
+GdvTlfy7NB7dXPpw7+EKdygd8aICP5UHOo1rykPVh5wrf+IYOqGMVTic8vhA7m/+vXPeVWGY0bCF
+1cxA05XvlV5NqAZ1ahwCBaRVnsNc3cKDvpZAz48p1QVzzZzlQu55drLFVk/dSs+wSsWeXx7KxhOx
+wtcah0lrxUxJW2bLCbWRez3PckKtVh3trXkyK9xQ3WonooiUtj+MyLQysvuo5kSBZh+2fRT34aeA
+d621J9baAvh7wBeBnroTAFeApx/hNy7bZbts/5bbR0lJPgI+Z4yJEffhTwDfAH4D+AvAL/MhpehD
+F270DF6v4vmXfwKAwo6pEimuIa2Ir6quQxrR2ZKd8oqm2FbTkkz5BBp3ulhF4Dk3LOVcPZncp6GF
+Nq0tLRjytykrMc++9zdf46v78noWVXw2Ey6EV+54NLRePldUXdc4nGjWL2v4bBbyP0G6xU4su3Wv
+28Ct5XWs0OVrwy6d6wqZLWo2rmu1ZtMlvCKmdrmQvncbV2giZnm9CDFaHx/FJW5DT/+NiHSuGIEt
+Jfcoc4xCn42tGa01EsqcgRKFTsuU68q6bCM5aRMTYdRdi/d8nJlYMeFGh7GSo3auNTFKXtu6qii/
+/ZLAkWCn5zYIQ0VTNguOD2W8f+Obh3znUDUmFe48mKR0kNP/+2cpjyv5+4tFwKnzQD77oOCJ8hes
+lhpJjVweaqDu7eQ+lbqSQXOXrRsyD/FZD78p47nYl+DineevcHwq5ndr2yN5JGP7djZjqVRoceXS
+UWzM9ZGMT7sRU1vFw5ynLDWVm1VzdoYKlVaU5063w2NVBLd2SKgQ7DwqsHr+RjEMlFwm3lgricdo
+XBScgm5Xfvt83yNLxWJtdafUmpJsKPeEF7lYdQPDKmCi6d5qw+PWVFyJd65VeAMtiPo3LQZjrf19
+Y8z/BXwLKIFvI+7ArwK/bIz57/W9/+2DrlV7HvnGFnGnzWhLBVlWhsrKJNblAEpZeNZNcNYPdywL
+N3bb5Iip1gx72h0IvQ5lVzaL8tQDI6a5ozDhPHvKvX/8KwD8g8c/oC7ksy3HJ1XcQFFaepvyeqF5
+9zyCYaLlrcblVLUE47ZDS8vaOr6LryATu6YC91y8Tc2YTHNQKfM8cS9cDFf77jdgw9GNsNMnNVp3
+YSpsIm5VVTWwHY1qa/wC18fotFqbEvQ0g4GhUrDUs0kLvy+fCZQ6fpo4lMrb2Jz4+L489HFrQPMF
+LUu3MbWKmrRiycREzQFMZFyT6pxqprT0Z3N+/6vCBvX68YxD9a8ThTY/PC+Yat2F58GBUqA/z4Ty
+HfnMg+mURzPVeVSwVWQMS61X+dprr5G+rZD3L3nEN6TysQhOaSjQrKcVo9zcwL+ukOl3x5hKsiB+
+kVLowxuklrYCp4JANpWzqmZ8LPN4erpEybhpRU1cI5tQFsnczKMGsfIkui2HUHEmwdySKY28F1V4
+699TwFbmLUn08EqWZ/ihzNM8qQhipdK3MW5ffs9a6U/hOBgdt2Xukva1JujQxbbFNbv+8oDokXb6
+Q7aPKkX/V4G/+s+9fR/4zEe57mW7bJft/79mrP1gJeV/063batgvvnKT23vPE179HACvvrxNNNOo
+7mYADc0JzyumUwlTzE+V+bg/AD1J7t1/zFT5A4e9FpFyMgxHHdod2Wk7m3ICN6qQxUoLbeYLnJYi
+3sKSrT397Com0yBRUsjf/6uf/0sMtHIudBxWWkRT5DMaPdn9//2f+5P8zM9J3LWeKydk6z4nj5Q+
+bNshfaqS8U2HaiUnm2/EOnr9H7zJuWpAzCufF7Zlt/d3X2WwI6dfETfIVe4dFQKJ/G3yPWUyjkKu
+D4X52HVhWa2lz+f8vb/1NwH45d8QGPCPDyo29eQr8vxCiv5mbwtPNTTTqmarpydpR8awXuxz990H
+8nuVT/eGZEH8ZcaBno51kuC1xSSOVZexv7vJSPklw4Gl9GRs//r/8jdparbmLElYFXIqLs6VMTrN
+WCqS8MlBSqKcmKvFgme1UKjxDPzCbbEatq6JC5pHPudjsWgePX6X47FGSquSQIOfmWPpaLVtopJ9
+bhdQ+rfPbd3ipT8u5128eZ3Xv/m7ci/KSn2c5vieXPf3/vEh//RtKcbq5ClnmZK9VC5RW8b2lU0x
+8butDoWiIutixYGiSb/99pRcoX+L5Yw/syVzXOrcPBtc400lpCntMalCooPPf4bHakG9/OxtNl4W
+y+QX/8v/5pvWWqky/Fe0jwXM2RgPxx3RZI9XNKV352aPxlJFTpMnLE5UqruawLEs7mmq7KPH+0yR
+xfP4nTeZqcpPETaIr8v1/GwPVwlVQpXybl0JiXz1t9IJ+VRckO3P38CRZ5OD+pi+1axER2GrZsHN
+TPy7o8WUh1q1ubnp8bNXZQH99IufpLstC6i+pgCp2YqdY32oNl9gcFPMYMeE1Mc6+7rgPzv6dZK3
+viFdu+sTvyKKR+ZGgK/m5+zpOalueqVVJSUXujos3tZ10IxDGRqCpfzeyfIuweF3AYjvy7/ffTPi
+uevy4L70TJ9RW+8paOHpPfm1S7Ml92LOZWPKdq9xR6vPi3qCWVPAX++xdSCb3vJoSqYmf+e2xGqC
+U5dqIOnZYNYn3pa5SRfzC5h6wIxcAUIt3dBmRQljrRb0XZ7XEnC68Fl115ww4KCUCZwrcmdjd4tK
+3bWR6xDqcOdZRLgpLthktcDTzM22ZhHOvJjJvsRUjqMF5V1VC9uescw1e7KUz97ZjFisJKux6dyD
+hdzz01XBrFSxG9fSVjh5HshETZwMq1WwVZrRVjduu5nwjPKNDpqGZwKF7G/K5j0O93lZN6GTecoR
+crDcyL/Bn/uCuNs/1twi1k3oF/lw7bJK8rJdtsv2vvaxsBSCwOXmtQ6t2wF7m2Jq+0sPJ1WxkOyM
+yVx2yWU2xSI7bTiVU34x9pguRCBjNl1QoCIyueGKmleprSk9BSoobDWoDe1czM+TrQbTe3K9J4dP
+6CqAZHXSwm2ICdq4L32YHU35zkABPYHPM5FkEf69F5/l0z8njMLtFz6DVTIMd80snAQEWyqQ0vDx
+YvksjotzTSs/tQK02fxzxDfF/bBJQak0Zm5VwbpIJp9eALEO78mYvN77NTaMZHAGHZ+mKydzNXsA
+ml2Y/va3OXwgp3it+fGwY9jYlrG4srfF9U+KaE3T3cFo4ZmfL7CxQqyVK6CslgyU/2DVGEApFoQt
+igt5M/sTLZZvaLBOMSadVkJ9JGPRe3aP1ZvyvrNIqXOlVC88ZsqL4CrmI68qcsUQdDYtZqZ8Ga+G
+/P19OVVnb8GmcmVul/LZL7ghg45mrbodvB357fm4YKF8lL12C0dP1Uh5EuvhgvGhvD48ekJxS/Qq
+x1+/R+tIMzcLwWkM/Js8+o4AoZ4c1eTqliyqmpXOa4G5EMF5okVSG4s5DV2HaW55eCr3PC3nvDaX
+7002HbJDcSejRzI+N2836Ko4TVT6nNRi6U6/U3K3L1bKu988Jf+EYEc+bPtYbAqOExHHz9McXiO4
+IQMZ53CiEeLFPLmouAuyiER9TiJlwWnkbCkbkZkPWfrKc2gjPK1FaPst1rINvqagvKRB2BHTrzUv
+mQ/E58wnFaWVHNF+8oQ+srhRJOHDJGX3VMx22+ty8zOiQ/Cpv/BH2H5JzHyaLZx1vEaRa97W86CV
+k8YbXpRcYxxQ9SmzBiPaOQQCWKobCaH6+3Z5BmtmKVPR0yzAdCV9v3vfozdd+w9zKqVWL3Epn0qJ
+8Mlv/javf0vNeeRBee72FX7yJ4Xe/JkrP0n32lpWqLyouyBfUCuleLEn3/dnLYpaxiKyLpluhF7m
+kg3kte+Bc1025JMDrRzdganSmm+1ClqadVokNY6a2jbISTQFWujv7oUBq7bM9cPipIkAACAASURB
+VO2rbfx1Hci8zXcaD+Q3uh7bEwUhDWV+w8LS1RTxoOcTaw1Ht11AqSWvZcVK19RMx7h1nhEpt+d8
+kjAuJZ7VY4Cj9OtWAWcPVxMensoh9JsP91np92prCNZEPLgEqjfpFfLv8bTCUZt92GsSrQVrozZL
+rX0ITiyKM2OhadH00KdRySPcclr0FFl6OglwfnUNhko4MD/aY37pPly2y3bZ3tc+FpaC63t0doe8
+uLeHXWidu7fALLXeYRISa1DG67QpMwUnac47dycMFYK8cTtnFSjcdbxgqClap8wptJptVWmVZXkK
+Vkz/VtOnmaqJWp5zogGnUbRDdaqVepFcNylyjrS+4IvdHf78T8tv7958Ba+l0uDLE6zCmB2FSRun
+CWvSEMfhgpRB/rruqPwbDEBhzoY5aAW6aYxgKa5L5KXkuQSUmpWYw09+5xFn9hEAz73oMJhKLQln
+R5w9+ToA0+MnDDsyzoH24db1LZ595gYAnY1tgo66ASZlLbZtKw+UHdrXakEbWKpIcPZ1tWKVy2lV
+LSy+VnD6dUqlVafGl75PFhXXOvI95+iETE/dMpnR1AzAwG+SGuWSXLPNhR7PaH3B87u7hK7Myay5
+In4kbt4Xd1zyM+nHLeXX7MUBW8pQHTguLaW5dGMPVynQV05NYMWk7671dFY+70xkADaaGyxfl7Vw
++oUmVs31zYZYSrP7J3z3iazTtJRsDUBoDA2tkixdQ1e5OoZKm5c3LLUWqbS3urStZpLqnEeqfHbi
+TljMZa42tH5ksZrRWSmLeZld4C2KZcq+aoh6lWVnJNRyH7ZdWgqX7bJdtve1j4WlYHAJ6FK455Rj
+DS5GUxzFIWSNU9wjPUn9jJ6VvWwWi7/Vbg/odGUXjYMYRT+TzruEGpRbOR5HRvzBk7feBsArrvOs
+5sdNWBIrww7ViuBYqwi35qSzNbeA6jzWll118PautLly548B4GxuUevJNl88pN0RzIXjKibaM1wQ
++eOubQPx2S/wIus4g2XNKGqTEuNpUbzXxCoZbT2BbCUBtXuZVCe+5d0n+105SbaKmvPnvwxA/s4/
+47f/z18D4NvHOTdGEtDduymxk0/9+OfptCS37/dbOBocrRc1RtGNxgmxgQYEzwXxZ/0FpiknbTF/
+ij1RS8lbXMC8TbHCN3IqtkNlMTorSG5qEPjU4De08tW4tJUPIlksKBTe3VMk5c6wwZXB9Yv+dGKl
+yEtAwYh4taW1rVJ9yphdErzHs2ECMrVc0jTAaGC6LA2lVlWeLMVi6zQDCmVrPnAPaO4rWvR+m9YV
+VcI+k5jC19+8x3iuEnN1haMEwh1cCvXrt9sBOwONRazkutuDATuaDv7Ec1eYqoVRpgnPaJzr95++
+xmNVGD9XDdKgsOyrwM3U+iQaz3Hcmko1POZnDo3sDyGbs+vWdLoJ5cSh1uq9eu7hac1AJx2yVIjy
+OJnhqZy5P9XqvdgSN8RU7XeCC5rxZFhQ6gPtHe8zPZIJO1SqrWr/CbvKPzi4vkFTI8/nh01srS7G
+uKDsKeOz5o/zyvJE4cibt0Kiji5SP6TQ6kLP3YaGBj9VCAR8qbnV9j7YmAaPUBZpDKCis7hQjL8H
+gDPcwMl1UTkhyb0H8rWZPBHff8sQLKWfv3ZyynNfk43g67/xO9x7JAHPRhTz4o9/AYArz4l5Pbz2
+DG5bNkinSLGlisFEASjNFzaBSutGlPm5mjlQy9zg7YByVOZJgqcYAq8Z0dSNZX6kik+zQxqPFU9y
+Y5f6TMxkN6uZKCDLc1wc9V2ayurcb3WodmRdNFJDU3UXHaciCbR8vtOio+5dRzk4V4XFmjUkfElR
+KRgsd6laeo0sw9ENqdmQ96YLF1fnbLGf84b2zTk8ovtA+vxQp2x85lHqrLajkIa6uXllyZUAJQgb
+dDSbM1Lx2K07G7zyqmSirty6SVHJNeaLU/I7qur1cMXXf1MzEe+okKxNqbVqc+kUZGsKOcfH03E7
+qVZ8kLDZP98u3YfLdtku2/vax8JSsFgKkzFeprQV2WWqAFdPq2YADZXyro/HrNSV8DVt5PYi2mo6
+xq0ujgaUatenUCyDb5yL4FKgplyBR7EmLHGhGckJPOwazrUqL8CnH6tbYdcnPgyU7Xl381Uc1Syo
+y9kFVZrfXOK569I3/b5xuXAP/gVJmfdIPOV/q4vPmsjHdOT+yQIq7XPlQLqhBKrfFeGV5aoiVg6F
+m1nOa2+KbNrp8RRHhXHufP5Znv+iWAqDtkrXbe3haT0+1Rl40ndrSxwVl7E/JLpt14G/so1danVe
+ZbDdtYJPkyKTk7QsawjlXsJYSUEaMQvFodxsFXhKTFsbg1Ope+QY4oaMR0NTp428ZqRFRxaDr+zJ
+jbhFEmmQsNlH4RQiFwjYMsXXKkN/4oBybhSNAFcD1pWXU6o0YKUB2MraC5nCRZbxQlMsj0HZ5OFj
+MeffWCquoq55dSR9OzmN6SuXxw/mBfvKAu1mFXtayPfMDbEEX/rCZxleE3eu2dslqcQ6aDpdTg/F
+1f1Mc8DZp+V7+8otMduvOdfiuJ71eVSt89kVK02zftIf0HPFYnuDD9c+HptCXZDPTwjrkNO1/xpb
+glIWZnS9havw0bapGSvJSC+Sme/ZIV19ljwnJlUdwbzI8JR3MStOiJRko1vKIK2Sx+w/FpN5OIRw
+U+nOGy5H5/J61I3wNBreUVHWyIFEwwQnq2+Snn8JgCC+hllvIEVLhFkAWFNs/0HaUrz3/lpGHveC
+49HaGcVEiUzoQC0LYTU75J1vCnjpV+6Ja3C2hLoW0/6XFiX/3QOJo4QNg1EB2e1piVExVlfrQVx3
+jlGRXuv0sOpHWzunVtg4VbSmBAQFkEFKpRL3+HOM2qp1lTNXn9krSyplT3b1e4tyzm5jzQk5IdiU
+cQusZeWqGVw7NJXiqqUiMw2/pKVu3LAd4SpDlvFDWjrOzV6Mb9c6pGtcyAq75ka0PdJU4lWVP8do
+7KNwC8oLIh75XpZnHJ+p6V/WuMq8lPdLXjuSwfiaK/f2lyOfUtmtene26CoE/eB73yNRsSI3dHhB
+RY1vXX1OPjsIabhaURnUkCtjV+OEqCXzlLQCRrmAkLbbCm4KD8mLNdAppVaXIatrSsXAnHHGO/WP
+5j9cug+X7bJdtve1j4WlUBeW1XHKu40pTQ1OjZzrdK++h5Qr5rIjni2bOFpPblT1N/R6WH2dE5At
+JJc8PysJWyrV3unQLBXroCbnIvPYP3sAwJXlNhtTjSwnCaVGlM+GBbvKNdhQYpHYdfmc4gayWZPp
+u0Lk0XpuiNG8uWv7WJVKM4118NBcnFwCXVzvyeVFYI81b4JtgkqW1+cTlvfEjCy8mkSlyR59ZcpX
+fldy0POZciCyYEMj9YuzhEZLOBW3pm/wQDESVXf7gizFUQSfXTlYdbtsbSkLwRNUZy5eWywe4/lY
+1TWo54ro9FNQtGmVHJAp6UmezyhV3zKrz6myNd+D/OPXEaGSlJQnOYmvgV3H0lXDJPVLZpqVqTvy
+ZisKGOjJF/ltnL4EmAPXUqnid5DXqAwnZSjryT/3L6DStukQalFSndgLjQvjdWgoN4bR+TiZllSq
+7Ox2PZKFvj5Y8e925f6+rMG+d9z0Ilty+6bhnXcVYVhUnOm8v9IIsTsqUb8jGYdWa4NItTN8d0H9
+UCy9oOrQvyEcom5xwqeW0rejLbmuWZyTLZV/pK4I165pWGMUVn29cgl1vSuD5Qe2j8WmYG1JVpxR
+TRfkyMTuXh+wqXEEx4k4fCyTey95i/lTqXNYPNFF9UzN4kwe2GYPVqksQLdZXgiJ9txM/ghcVwGV
+kyRjdaaCsMmUsC3ugY1aRFY2kOUPnjJ7Qczj9lIexqZjKV+Uodt5fg+3LQChVVVSnws0O2q3cRSm
+3dAyXOM13gMC2Rpz4V741MqPWKm/aNz6or4g4yFjJdMIB2MWJ3KRo/yc78zle2u5D68Bj9UEnljD
+qCsP23lzj9stvY+buzgzTcMpcaXfGVwQ5ZbpCcz0QQ98cq0qDbwZnsZVTF/Lohc1jiMumhO6+DsK
+0T0e0fLFN05XbXI1u6eqXrXdtBepR6cZcPqWAK46rnMRLa9w2VKiktZcHrxV4LG4IXM6C5aMcpnT
+yHeomxrb8Qtc3ZDWakt0AupQN5hZykTrDmalS6WiPL5dEurG4SoZ62l5uhYRo3vukigt+ztewjPq
+t3/9WRnLb3w34cpMDpCt50PuPpF7up8WFynCJHVYaoXtuZHv9QcRsW6sLAdUDSUBWqQYq/23AdFQ
+6liufOJY7+OUdxdyeD09qChdJW9xHKKm3P8PFrByfzSH4NJ9uGyX7bK9r30sLAUMOI6DiTtkan6N
+OtsY5f6r8iekiNm6Oj4jPVCyDFXWzbqPGI7ks6MTaHe0+CTepNNU4o3z8gJqGwzl9NxLG5xMFUa7
+zC+CVh3bZmtXLJa3xzWtXGXfFmJJNDyHP5pKsMhPR8xSORHiswaFMh/byZxK5d0yI5qKbnsXvyEn
+m3EDQoXHGuNRa4Bx/Ej4DYrDt1lN9AR7dos3NcB369Gcg++pzmPssLslJ+mOUoZNZoZRIL+bVIZm
+V8ZlczTgSAFebrCDbb8XXQcBymRKmZY/vk8eycketa/iadDO6axwNPjp+mqft0aUS6WnLwL8UMVU
+WgYWGmi0NVbTQ+54rQ05pViL5FzL8adyas6qioYjloDr1uynyhu5/reZcvpE5ixtlUyW4rq1d2q8
+gQCW/HDIQF0sVCncjRokpfxeOpmzmIuJvsgcjtWqaJUp20oC440kyD1YNjBvqAVVWzaHOoYTy/Kq
+jN3tt2VdPLCHvK3gp+gr+5wqBXzPtdxWGfk/vrXDdiyvPRWecas+jsLbTRPqhc5NZslUYbsoC1bK
+rXB6Ln3fn2RcW8n8p42CMlMeSMejoeJI/XbN3fk60KjcEx/QPhabgsWS24zeqsGpL5ORBTOiWMzy
+qtXgysvS1S93At75J78HwFvq9x+8cQxXVKOwvYOv2PLAA6takueTCUkun19qaa6TW2ykEul2TqZp
+qs2rW2wXUn149s2KJ9/XCP51AZgUFZw0ZYA/94k+nY5EhZ+88VVmE3mwNvdWtJ4RkptkqqXFqxXH
+j74JwHB4h2vXpKKys7FHORc/cf5U2Hr+yf/+Jmd9NSlf97H35O9/61tPGQ5kkse5ZG4A3tYKUNMO
+cBW81Gt7LDSVt7rWYbCtEhwHKbOxmPyJfp/6EbVW781m+8yfyFjF0X06+rDtmFcwW2IeV4WMYTl5
+l9mRuAmT8TELXXhZmTI7Ux3LsibXzcRRJGhpZ0SOgL7S1x9TqIlu3JrcXddJxETqbqwUKWraPeJt
+LbnutXh6XxJtDx4VeE+18nNrg9s6J91tAWdV2ZzZuczDWXLMUkFIhROwsaUZr8QlVQakUB+qjvHI
+1pLxecXNRPrWenGTYKycltfl38+V2wzuifv34OH4Qn/jTw43+I/+0h+Vew1iZg3dvNT0L1eHlFpX
+Y1KfLJM1u5qds1LXbVYtWanr0lvrTIQjlNSL/kGJ1QzNopCaDoAdIhpb0r/flqruD2yX7sNlu2yX
+7X3t42EpFIb8xOconuIrUGSr+2X8ruyeRb4EFfc4HB9fnGjZQsz6g5OaypMts+fn9JWjkUmbA1X6
+OXn4Lo8OJfAXqKx5ujsgVki034R6onRez7Ro9VVJ+cY9jr8ursv8UDABS1vj3Zf99Gz/mObxAwCe
+3Juw74kr8dr3+7x8V0zb8Fkx4RfTKZ1DqQw0fYNVenY7KjAa2HODV+U+esc8fiyuRPb9IWe7yvbr
+NPjaPcmu2MTBtFWBekPGpMotz6k5fFaGeCpPHr6d8WQp9RFxEmM1h96wMoZHd59ynMlJ2/U9zkvB
+RUQnO3zyVTmZyu05JhJLLv2emCYPH7zN9EAyH+fmKdOp/P2pBy3NEnjenIErVkq9ZiRO2hRtDaQe
+RzhdOc1qQrbUpUltSaL1A3s6Zzd3BgS7Mp5HmSEciSXQWiyJVlo/kRRkymxsFBJfTBwminWpFjVJ
+KpbS4dJyKEx2bFeG61flXreM6lXuPsNuS8Zo7B0xz7WGoWgyVrXq54y6A8URc60zmKYVh1q1+Haa
+8tV/KmuocbvFqVLn3RqJxRPHTULF3qTJIftvyvweL47Y6asiF6cs35a1/OiJkgwFDa5ti5t7rdHh
+aycquOMlVAqX30s6mHStEKW8eR/QLi2Fy3bZLtv72gdaCsaYvwH8GeDYWvuSvjcAfgW4ATwA/qK1
+dmyMMcD/jChPr4D/1Fr7rQ/+jYrAm+PkFZEWi5TpGcVUdtfzs3f59a8Jielr997FVQbmqQayHC+4
+4NDvRiFD1VDobob4UzmZZklIpifXgcJSwxPLxkBOsLFZsvLFH8yLFb5CJDunMas9OZmiruSMD7KK
+XzyTOMNnv31E8JKc7jO/YPGmWDdv33+Le4Xs+D/TFgLXvZd26HU0QLl5g+62nDCuu4mj8NiRnog/
+/R+PeTX4KQC+/w8e8M5crI4r4zf5dRVO8dugXJ00FX5rdw3xY2Uxarn8w//nqwDc2xjxpVfFmhpW
+uxhXTs14Q07a3/ruu7wzk5Pk2U3LlqYeJ2mK+0iCdi/8ZB8vlGuUhQRPX3/tAdd3pBObVz/LwW/L
+PX/9wTfp5ir8cm3AsyMZ+60rqg6+yEk1zRhvbbJSucCWY1mohPaVKMaqxuKNTWWham9x/IbEMP7Z
+wX0OtJBqFPT43G2hkGuwIlTC19CR9VSYhDOtKHSdOYmyaT16POapjmfWNtSH8kjsKb9D78qIn/hj
+ct27X604UezJ2M1IzmUMn87WVtcAkwmsfO7V5IlabGnKPzx6DYDnnZuUHQl+710Va8REDqVaqVXi
+cjqXsUhnNUVPVcpbd/iWku3+4LFYLrE7YzeUfnr9G7RqgbpP65gdjSsdFSu+o3qjH7Z9GPfhbwF/
+HfilH3rvrwD/1Fr714wxf0X//78Ffhq4o/99FpGg/+wH/YDB4OHhRCGRimeWy5SVsg8vj47p9mWA
+22HIUOG4A1100yLn1lXBNFy5eZP2QMk9OhFtZVoebQdsK0V7pE/SKrd0+/K9UbNPrdWMbpFgVdCW
+bYeXA5n0rXVpgIWZlnj/6usP+bkX/28A4vOrdDZlwl9pwTMqxnr7WaFr27p284KqvtW5jrsug/Bj
+TCUTFw+0CrH7WZq1PICd/+QGzz0WSGz9yef4/PfFRTmvTjlVrcUnx4r7d3p8+vPSt9fvh3z9B+IG
+fONRhas0dZ9/YcJNV4KgWwP5jRdvbdIuxGX61PMjbrrPA3Ba7HPnilLiDzZwGrIImzckWDa4tUV3
+IA/ecOcTOD8nAdr8t1zCpZjPG9tdNq7KnAxj1dVMT0k0+7D7qR5mpuzQv/57XFe48rW9IaYjS3TU
+X4vx+uTqMj3TvMUnRvLQjFojrl2R3yA/wddMk6MBQ9+vGWgGxDENRk0x3bfuvMJElcEir0HtrgOa
+MtnDvMF4KnMWt31e3pVN6rnWkN+pZGyHfenPwklpyqWIXJdtfbq+sNdlY1P6tvfZLzHckXWxrTyR
+21c7F/UjiW9olfK+03cwgRxqnm1y7ZNyjeecO9Kfs5JXdpUlethl/i1VABtPKbRe5dPdNkfKRi29
+/eD2ge6DtfYr/ItgqJ9FZObh/XLzPwv8kpX2e4iu5A6X7bJdtj807V830LhlrWq6wSGwpa/3gMc/
+9Lm1FP0B/1z7YSn6dhyxtCntxGdiZGc/KyYMplpL3trmuZdkt77RusmxKga/rboBjeOU7Q05BXJj
+sLpLFnmKo8dxc7vLFSWhKJXheVRYrJ4ovtvGKJotWeyTTlVoJjV0lN4tuC0naQEUyMl8/943+I2/
+ITv/S59f8cVP/gXpU6fP1itChBrG4l54XbCKYnMDD5x11WVxsT0blXM28W1cRTT2K4g2lRfA+STD
+O2JKHz94h3e/L8P92lwCsZtVk66mMl/481f4H35XzNZpmfH0vpx+5eoKe39WLIErdz4JQBxWvKp5
+8J3r27QV67Cd3aA9UkSmE0GtKEzt+ovPDAkacv9h02VXGW7+5Gc/SaWllIN2jNdXP2cmFtHkqEVz
+TYpSlnhKsXY2WdLsy+nfcbpcbYmV4iqkOOx0uKpVjU3HIbylGhGuxajb4RQRtdLGrQlU3NiyuyH9
+cYINQg20Fq2Aui+COYs0o7FGWaq7U87G5IrpuLq5xXUVAer98VdJvvI7AJyo/kOw18UqnZtb12hW
+EN/rceOWnO67O10Gn5Dfayg2wYm6F0Qu1OdkofJ9rGaEvjxazbbDJ66IVbu3LW7HPLPsjVQWcP8d
+vvFtTZ1WDaqFrPHD6Yy37b8lKfp1s9ZaswbS/2jfu5Ci3+h17XzlU4YlgeIK0vQ6DhLJbnpzmHb0
+mwXbPWXVaa9ZmnIWRhZbsDwiPFeiCxqUuvAcdmhrpV68XJfvulitB3BNhVGmpyr0yBay8MbdhMFE
+HqZqKnnlSP8DOJrV/B+qHPPls4yWeQDAH3vhTxFva6ZBc9A4YNXlwfgi2wRQL6lOHurb8lteywHl
+lAz9qzixjIu3syBXmvTmoqJYySIchkonv7FiuVLCmWTIZ7Qq8VtVzcm5LJq/HU64komdO1zJptLv
+Nmho+XLcv4Kr4CU3cQm0Dtk4YCsZg/pU/u43R8QjrfeoU5pnsqSavQ5OoFF5L8UUcq+FvtdO21Qt
+MWi96QZWS6srHKzCh01hKFylMPf7+nshvgrlNgcF3lpEhRWrsWaljIuv0fcqkAfWpm3CQK4V+zFG
+3bgwkcpFgG6nReHIOFsN1LvtBv1aHujzW2+BEtwEi4J9ncpoIuvqbLZiqht9v+kS6NO1cavN8y9K
+XKn3E7cIldinOJfv2dkZaH1FXo9J1O2ajUOuiNdIt2fpIofS7rqGpRFRZOJKjmdLmMsY9uIFjzQD
+Fx5CU+EuH7b24V83+3C0dgv032N9/ylw9Yc+dylFf9ku2x+y9q9rKfx9RGb+r/F+ufm/D/wXxphf
+RgKM0x9yM/7AVtcF2XKf5XlJ3JJTLh1PKEZiEme55bSSTISJfOxS3m90NcjScTh7rFHmZYadKT3Y
+wCFWGmDPXxL25NqZsuXOnXPCVAtn/IxYRV2Mt4ntCPwruNuCa7KzW1/MjgpYKn/K/X6Nuy+nw5sn
+Ad99U2jTvvRTfxajQTXUJYIcXMUmVDVmTV6Spiy0yMvtibnYHlWYtZZFs42yjlFnBbZWQZlogzfH
+gpAcqfn96qc+xzCTa5jBkne02m8aWWaFHG0b912mc+mzoxReYbtL3JWgVtgNsaWeYl5JpSeTXy+w
+szVxhYy9H3t4WuxkuiWRunHWNDHZWmjDgrompVa70jil0ZPMjxO2mDyQ7IpvKo41ANucpzQV0Vcp
+a3F+muEOVXrPaRApjLk2BclMi9tqg++pmMt8bSr6RAOtqi1LStUDcdtDHIWjmzRDkxZkSopSu4ZV
+V7IB/XRIqUHu8+kpK9Wt+IqiQt2wwZ4vE9X3fZ6oivkpHsuOCr9M24TKsekr90ZlaworazZYNanV
+anpSn/HcWC2XboNAlcLrUteFk2LXGqqJ5ffOpJ9nvsVVaPOkSDhZi4l8yPZhUpJ/G/gSMDLGPEFU
+pv8a8HeMMT8PPAT+on78HyHpyLtISvI/+zCdsDWkK0ue5rhasjufGSYnynHnznBrFcBYrWion2hV
+azLe6VAr41FvlROqw+uFPWy2xntnOMpM0+3JgLWdHnUln+3YPqFWUZrYEJyofmTfo60cjesKwbYD
+bSXTmFQuE2VeCpYrwomYufV8TUICoJuUDbG1xEGMtwkKEKonGSvF5bea6/CMx7rO2LgRpqkiK5VP
+ZXRz8mtiBdM8o6m3F3ZfIGzrBjlf0laztLF0yNRfPuk65AfSj0xjJ612iK+ZBVOcUp/L2NZxQhQp
+PLocX7BPeR25VlwOcNXct0WIoxDluvZACUBMVlOr4latpnoQeDQ1PBHdCojGmp4zDkP9jQYOpbpb
+85l8P4oCOlpRGEZtjCPvlzMXR92tju/TUbgyZm3jN/DbSrlfZdQKdMJW+I5uHG6Or1D3XDVGbVUT
+a12GUxWMXtZNaGJYaVVpU4mEG5GDVU2B9HSFUWapdLwk1HlywxIvFneramjV5jzFTHSN4NPXtO9W
+M6WpsP8gHuB2tI5Dqzqz8YIik3ks5udkhZIHnSYcLqTPu7h4a97PD9k+cFOw1v6Hf8Cf/sS/5LMW
++M9/pB5ctst22T5W7WMBc67rmjRLMWXJEy1UOTq5y0zr/23LkCpc2cEyWb0n/Q5QHYQ0NQf/eJzT
+6YuJtxFB02plnAspkgteLeR07HUDas0slL7LQkFNzTCj1Hx1e2uDsFCqcqUL7zqGQqPhg9ttfvIZ
+ieD7Dx+yKuXay9mbNDN1H6xytzmWciHelGmUOBoQTB68xfRr4nZ0NMtg51cxKjmOrd9TmPMDHA0i
+VcsjVlrBeVorGUeZ4SkF+II2TaOcE52CWN2Vl/eaNJpyAoWO3H82OafQk6vIK2Z3xYoJbhiCaM0f
+uQ1rERiV3nNMTZnIfVbFikJpx5wkEWsBcF2PNX1gnWmg0vdwYxmX/HRGohZG01iyUE62h0VCrMVk
+56GM67DskGQyj54bUswT7fOcSrNHrU4HqwHNSLM9puvgqhxdnpfkK72PsMZXUpoyNyjeiFQZlY0p
+qEdi0jTTbexTFSi62iHV76U9uc9r201aqbz3g9mKRE/os2lGsRCXtywTaquFYuc6T8t9pk/kukm6
+YqqEGFthhVUrxG2AU66tMC0Sq8ckc1kL8/OnHCiw6qQ0FAoVP7Ulqx8SE/gw7WOxKVgcKhsQhjWR
++qfjeclc/dDcJDBTxhuW1Eqc4ipl98Dt4GZrSvIETxWg5o9OSBXoYmzIIpeI+/lYabFHO3SuymIL
+yhR3JQu9OdjBV5M52Ijx5orRn6n6j+syUlWh3TLgjmZJupvXqDtyvcffdlP97QAAIABJREFUSOju
+yAbgddZ6EvtkD9b6km+QnUrG5Lu/+Uskr8nvdXbEdAy+MCfQ9BdFiU21nsNpkKv/+ea37vLGD2QB
+7d4UtyvpPqD6rpji5cYTttvy2/nKZabsP5WfE7fkno5WUpXZOuqSn4kpmoQdioUwOm08+hytWPvR
+zalTiX2U55pxaFdUWg04Pf4+kwOdm67FNVq23lxiVMFqXMjcLM9beBrpNyc1M3UDFl7IRqGZCFvw
+ZCr3d7UjG8+ChHym+pGrc1IlIfGrhHqhNPKTM8IdmctcqfrLwxijVO4mb5AVMpf5uEHaWpvuAZmv
+6D8lxkndiHQmsfP2FlRH6lbNwdG07Q1NJ/pVxkI1Phw/p6xkLUxtwUy9mNLtUat6U6LZrMXYp8zW
+epYpXrGmD2hdrJ1iNsZoaXtSyXORjuHRWNb046dz7EpJbCkptRS/URuU04oPG1m4rH24bJftsr2v
+fSwsBdfUxH4mXMcqvztezDk6FdO4oqawCjhqxPhqEsaOmIiNVsHBqSSWmzOL3ZLAUbm0F8QUHXfB
+SvfASlWaDrIF4Ymc8lW3Q6imdkYb11GuxDwi0cDeWHUu+45htCfXevbFHZ69KQCb4mHNWSIm4XE2
+ZnoqJ3PXk93ccUZ4V+Ua9fwW4fD/BcDjk9z8lFgeyxeEs2Gr64CegtYkoDl2wxIvl1PXawx4y8rv
+/WQhJ+P0UUJnrriIFnxRqxKbny55+75iC2ybs5mcOrtHajrXJ5yv5O+9jYwolAxNuO2RaaA0SBJs
+ouOlrlhV+rihzI0fDC/g4U9OZnStnLrVThcVUmasJz+LQwqlrXdaTZaPxKrajX1Q66ZOLONSrInH
+x2p1BA4jrbWYLBO8htaMtNrEyuH/ZPWQ+AcyLnVbhYGOZ5Tq8m10YpyWBmuLkqenktnqeE1crUHx
+PbVGPI8MwZDYbIRty1xPx2MczewcK7jrqtfGTeR7cQjLpUoJFBVvH0hm4OrZU+JKrMJSuTuLomRz
+RwLMbvEcvU2x2BaLOS0lbfFqn1ID7LkGSdN6SqJ8jm9OcibKul065oIK9AxLvfY9PySa6NJSuGyX
+7bK9r30sLIUah9QJGTgOrZ6mrOgy02yi7xfEkfpqpsKk4iX5iiOdhwm+ypkNN1w6scBuixs1KOGp
+6xjmd8WaSFSMY2czhpaeQO4QNFVEI8JTAZfaDSHUtNixbLWe5/JjVj674wcsVGfi1t4G3oHm4+sA
+DXNQHovFE1zp43latON52FTgrj/23GfxOkpXZpV9Omqz3rONCSFY6zLmOKrm3NiGF0K579FEfnfx
++pLBnvSnsWfxb4tV8ad7Q3o/rhwC8yYbkbxvtHI0aDRpRBqXCQ2NWNCYdELqiYxR1T7GegKxdpSu
+zCnACWU+gv5VmqWMW7Sfki9d/YxH0Rb/Otd7KkmJTsSvbz/r0+zLfWdewAs6P6t+yO+/LTi8qeYv
+i8qhoWnbRu+M+lxhxZF3EQhuzzYINmSu3VjGarDbIlEMQddCrVW1KyciWGl8Ia/oaBqx0jjKap4w
+V8q+p6unPPcTMu/d6QBPGb2v1jJ37SjiVJmitloDrqjWadxts13LeCWHZ8yGYvXlGiQM6yYmEwvL
+2k3C0bqwq8BXvITTbRN05fd6Len74nsTVkuZ0yio2Whrmnyao3WAuLxHIP5h28djU7A1SZ6C22Sp
+uGR/O7+QX7ejAZHmjRs2YJ6qruRETNF6vmSoNQ5BsAkDWVSRX1LroFkT0RoKRdfSkwWzt3cNL5Ig
+UnPrKm5DJjTqxrgKaIkam5wfaxWdmv5bgcv/196bxkqanfd9v/OutVfdqrsvvfcsPfsicSiSJkVR
+FMlIJBLHiATFsmwFigEHspMglgkhH/LBAQwnih1AduJ4USLLlhRZWwjIFEmRFMloSM5CDnt67+l7
+u/vuS+3bu518eJ5qTduiZoaanukk9QCNvrduVb3nnPe8z3nW/z/RrEbcDCiviAk3ivIwKzn2NN1l
+2JMS1F5OylOrg5RUTepocMDoogba7IAjhXBvfEw6Kmfaz+HNa8dezmLCCeVRET9TGPXWACeRtfjD
+TbnuD82OOF4TU3V49DjP1gQBOHm6QCGWB3owSkBrQAqKS1kqucwsS5CzdatPpy1jN33wFC48V10j
+v6K19i0FLAlapEMxqW0S0D3Scuy4i1dSBRJWGHsKy6/FSF0npKIKrX90xEAzB8drOcKSAo6MDb7C
+u93YkXv20KlZAl/mXF4+wW1Fzz7c6jCn9byhG5NHlF5u+QkZp9PD0xZiM/SxeRnz0LuG0UBh6htG
+qnzDsmI1Nh2ONGuRK9Xobcg40kaJk7PycB89Lt91plwivSpjG9VcZpUhbLDR4/a+tJoXl2cJWrLO
+gz1xW9ywSLsp1y3P7tDSzI9XPCBz5ICb912slmbHWv/RtQeMc6IIi3VDRWtrdkYG1I1NgLeYfJi6
+D1OZylTulvvCUgAHmxZIinlCBQTNeTUqCrDh5VxKmgt36OOMRTsedCU4lbU9vIIy8u5fxd+Uk9vm
+DFbx9vvxkN2O/Lw3lFNr9YEPUaxqI1HVwFAtE6eIpymdLO/gtrS5Rv2BxHcoB2KN7LU65LdkbJXj
+K3cwC4aHbcYKXJqVNZdc22T7l4QF+tWbV/C0qWi7nLB1WU7Yxm9Kxfizy8c587ekbmzlwQ9iGpOE
+UkpvIKb4d77cYbcpx8DZBT25/Vukl8QUdytb+A9JR81MfY78gaQfb3aGuEYDfjPK1Tg+RlGJYwZL
+57nx4joA7dunOP2MnPg2tCS+roVkJnGX50msjGdnb5NvflX+cHLVZ04LIQcMONyQ6+02xVQPRylD
+T9ZlsAEdT+HYXJeGQuQ53SP2B5OGJrlGc7hKvig1GaXqKmFeyqNffukSB66M//EnjjFwJVg3CWDu
+XLvKSMFhc4WIvgaPW5s9ZhTKbrZWIimo+6BNc82kT+9A7mNUh9Gu3LM0s5zQRrBnrFgKtw42uKKl
+zY80FmhrG2hrf0Cke+70nEGzs7gHynzthKRaHbm5vs21r4s1UmqMWKpLatit96hEJ2S9NH07vH6d
+q0dixVYHOWY92Yctb4DYedK4NzEUtOj8DeW+UAqB53BsNs9sPiDnKPpuqUKwKDemUc0zu6iR+H6e
+Rk025vCW5sdpUlU+wGBuBqvmZ1it0duc1M5XyXlirn3sRz4MwPyxRarqv0W5EUa5Dfs4BFUF5Bh5
+JHNK0a6O2niQ8bLm9NdMgXXlqzy7BsdXtGT2+AJWYdAHObluKVlk/scFwfnyL+8zc0qBVYY+n/iA
+PMitA908n/go4Wl5oG0xJZtkm13LSOvha88t0NFrhItiqs5/eEgwOgdA1g5pfJ9kEWw5z8yctHC/
+Zl7E6en8lP2q/OhJHG1vnikvceZAHlInyROuyUNYWFnGLWjc4diarmuCr9mJmaTP9z+jbX2YO/Gh
+XrdF0hSlMFJcykYBPC1djwwMb4rLU6mWCBWb82x+kfRlMaV3NL60udfFqcs4g2LGiffLXPvrPfa1
+16KwOEfjzF+QNXBlrZY+cJrmusR27EFEi3UZR3EGX+3qfCFCCcoYjjRG0+oQlBQYx4lhQZ7oYbfD
+kXKM3qxIz9+tb8Xc1lbE+lzIivaV7I5dbnUkZrA5OOSMJ8pg9kmNYR1a3BPyYzjIsXRaMxy3mjTm
+NZtRrOCFsj8HqgiOxiP2XpU9dDBy2EhEmTR76Z1it9iCM/EH3mS189R9mMpUpnKX3BeWgsHD92aJ
+ZhJmFyQYWAhKlLQrsVAJ8UM50W2wi6d5eO+UNuW4DdxAqxGdKn1F6i1RJ50V1T9Xzzh5SuzZU+cE
++zBYCXEUbMONXKziHGYGSCaNMT2KVdHskcKHxb5HFMtpZb0hnhIXDmlRdcXUZibAURfEbyuHZdnD
+XxC8v2c++SSFVE6xbPc21VOfAGChJhZP7sQ8ZPK91nHv8CzYpE8rluDag2sF/mJbIvEf/5Cc3Iu3
+6uQfkuDi9cKAmjYElXJVUsUGnIsqhMqfGGopdeJBoiXITtahUhfLI16H7gUBanFHfZySZocU23J8
+uAeJHI9Jp01lWTs0h11iK/ek3zpkqFkgq2Q5uAnxgRKdVAMcHcfBbYtrZBz+3JCKlXWOHTl1O7FL
+f1/+Xps1uNolefyJZZyvikXWfuEyjmJjDJSBu3Z8lWhDrDu/ZPBHSie4ME+iFYaJ2yZSopWhds86
+gcuClrk7UYq/qgQ3jodV96dwQSnmugn7mr3Y2mwzUp7OrpfgqSUbXe6zt7IOQP6E1KR4tR6+zmPF
+X8KtiPU2WNzH0bHFvUM6Q7GaOlviEq2/2qSrla67oyG9vrKUJ5ZJX65rIH5rTZIYa98yPsrbLg+e
+OWH/0f/437K0fIq4JJH6E4tl2orhdGm0xfDGywD0XrnAb//uFwF4dXdd3pCmWLWXRollsu8WiuEd
+MJSD4fgOC9OMFrms1fIcOy2pt4X5Gs2eUpJHMdcUSHTUHONo+XPXER365SvX/tR5VDDYQMxWU79I
+ZeUnAfjwJwWt7iuf+z842JGCpe711/gT+vmE0Mh3e0qU+7MnlnGK4iOvD7fodGWTf2v7ALSm3jMO
+j83KDJfmxeQ8V/KZn5Gfg+MzHGTyAD2er3FUlNdr42v8y1+XaPiXD8WsfygHn1iVdVk1OYIVWc+C
+E3IwUhYtW6ajhToffkjckhFtFGeUK7fa5EPZmIVqnjPv/T5Z75V5XE0T+3VFPIoiWhsyp6P9y/RU
+yZ762H9DviDvOTQeFUUqufKymMzf6exTi+XhGKxv8uo3BP3o5s4WSwoGE7l1eiVRavtDmf9jvs9A
+1yKf5IkCbfdOM1ItQmoNujiTrJLC5Duu0LwDFDJDWYF1nVqNn/iIkNmcmJdDKss1qC/Keh68HHPt
+QMZZnc2z1pDxHJ4/4NLVb8lcduTwGiUZ+025RmQ8Ti3Iexs+vHBTxtwajtCqfgqTjmEMjqJzDUcJ
+bS1eih2HMxVZz2eePMfKg3JI/PX//pdftNYKOOefIVP3YSpTmcpdcl+4D+DhZnUCGqzUROsWciWC
+NQmc3L7wFS7+7r8E4GtXm1w/VPKR7oSKzDJRo0EAuVRMhWLqUVO+xsTEKKwiBY30phE42lyUdhJO
+5pV1+lgOqyfX0LpUQ9HcG+mfDWjVwUL0ZbnGgUurI9i2L8afk7H1Djl5KHN61UnwtKrEMfAB7RKs
+qsm5UTjC74uFcq5Upas2z+22y5ESxyx5lrq+HmgB0TiF3KacwGGY0a/JPKJxhScUqr43usCzJaUj
+25fvOmsCnlX3afEBn0BJWAa9hPhA7U9zyCNqTY0vyecPRzEvaLffbC+lqd2FV+mxZb8EwHtn51h7
+z/sAyJ/6YQDczhbVOY2GXQrwNCiZ9n16oVy7kUa42nS0uqxB5d46188L1PlrNy7Tub0u8xsO6CsO
+Q9BIeSTQLkhfOz87A3xtCYoY3OmY9G2egdZQ+E6Ml4qb0tCgXlpwMEeacXFyNBTxO/PGeJ5Cz8Vi
+gc2sLeDPi3sYPv51ep+V2pPOKw6D5wTSLfVf48WLYkGsK+S8LYcsz8icl4OQtbLcp/JSFYyA/Vy9
+mWJjPcPVmjS5DE+Lm8aBw6ttmVNnPOZ2S957+sY2x5Tq783K1FKYylSmcpfcF5aC5zk0GgXyxSEF
+JeGAlExP642Xv8XvnBcfePv2YOJS46WTdmqDp6hCM05IqCfNYpin2pApPm0XUWYu9jRhu2HbdLeV
+H2DFMsnZZK0+JeWZKMVlCjPqn2523/SchqnBaBPXjWtKJGotSSTjtNYywdi1HnxOIbPMoVYaFgfM
+a557ztRwTsi7P+QYriuRqh2ntOryfcfV9+7tjikpXNmNq/sUl+Rk38x2Od6UU+zV9Zt4WoL8hJbO
+PrDoM/u0zLlSK2E0ANu8fMRNbbZKhx6bGqS5pczfV44iPM3//5CpkWqe/3C3yVc+Lyfwd5aG/CdK
+bvvUM1oqXqkSrMspl3/4JDvflFN1vuaTaYuwXy6Tad7fqFXY27nEzs4lAPbWL7HTkjWu2QBHrUK/
+3+RIYzBjJePd6w/IUi2D9jPyWtIepSPQis2C65APNYgdS6hu5MUUqjrpbkKg+2zcT5jri+XozWjA
+e+n9ZAqPGhYfIahJgLZrD5hrSzq0ZHJ3gFm7iaz3fOLygWOCrr10YoFGIPepXThkBlncpdld9m7L
+nC5qC/+8V6S+qCnwxKVppXGrcxjT0kbA3YMeu5feLOODyH2hFBw3oFQ/RmVxBkfhukyaER99HoCv
+feMFjrYVoXnksK/B0aJGbPN4PKskKvnZZdZ0I1hvjveclI2Qq9dYPi4b88JrEoXeu3KJ232FDp8p
+k+jmME6NsQaahvEOc6ckqLY8fvNmmLUJfqRI0e4E6ztBcUfIUgdXQbCz1BEcQyDV9wb9ALeimYEE
+vr8k5mfliYjnnpEo+wtf22Wo+f8DxQ2wg4jBqpTRrrkBLygn4n57E78gXJhRK+OqNvh/5IRs+OKy
+oTBSbIIesCibKitkPHFaN94VO4EZYK+i9RGJx1O+vHj8uE8ai3ldDWKqr8rYtpsxyTWFLW9KtNxb
+rpAaUfTe8ZD5DS0cc0MIVdm7EV5XlE+0IYrg4ksX2dsRRXDUjEiH8vAGxYCilrqXTEhnoqn68l31
+wGPkygO/7PlUFYjHSWGgh0E6jHAUJCbVLIITQ1fXZTjuM1+RdQswZPPKN3paaiWoB3ixdH7Gh9+h
+viqZqO6eT3Akn7vdzjgxL/ekrlgfn/zQcxz/QcmIlWoF+loPM9ffZ9+VNToz+yDXL0lh2MKG3I/9
+/T6zyiU5osCZrgQu99pDDhTerztMGDTfHAX9RKbuw1SmMpW75P6wFBxLWEjJhyFGfYOENjdfkdPh
+1S/cpqalZrW8Q5rJ6bCizSAPVyq893Gt6Dv5FOaMBH7Koc/CmuTvC/NVfESLzzwoRaDth+q8tik/
+95MebSUycdwB+woES3AKtNR2XJ5kf9/EnDCEk5S8P8FSC6gqtFmlnAOF88rqluxIkZyW5TQb9YZ8
+/7JYNmfrVU7pnHInHHJWTqBy81t8ZV1O4BfVnC+kHoeT7sMGHO3J2J91Ib0i77maDjmnbkVNT6vG
+nMHMaU1AYYDblZ/nSmOinJaYlyxGafE+taFkI4s5xorJMFvMUerJ34+dNQw2xSpYN2N6N8S0zQ4/
+K0sx/2OU1iQ7Njx8nuS0kq8QUzWaWkwj+o5YLHs9cZluXt0h6Uig+VjicaBu01JthtkZ7VYsFln1
+ZcyJeqPjGOp5WVufgFJdA4ZYfG3S2hs0GQ3FRB9qXUQwThireVfNzYFVZuuSgx9qSb4GJUksVrEl
+vEIVV838LNklMho8XK3zQ4FYCsV52ZtnPv44fl0a4QwuQVssuuSoRKEhroSfFHAVfyE3o6X5223i
+WMZbTSMWFb5w1j+iq7BwvWjIjXiCLPXm5L5QChaXLK1iicgiMTmjcY/nvyDuQ8sZMqORVz8f8LSW
+wS4+JjfjRLbKwkPyoMz9wHvJL0ibtZv1CDxlGCo0cIpyQ/1UCWj9RRYVcKXTXyfYU5PYy5GriR99
+dTCg1BR/dpybAFu9uVmNFVMvyLRkOO+yoL76SrXCQkE2RxpYXC2ymrBRzc8F+PNaYOPmiDR/Xuse
+J1cV96f09BmeK6kP35OxbaQxF29rRqXtMVYYs+2sz/amXCO/6NJQrsTSqpaHF8r4VpSe6cdkioId
+JTlCzX8Xlko4deU2dPTz7R6J+v1uvUs6o25Cb8SMkinObkZ85pKUAp97QR4Uf24TMyNuTvZyj74S
+3o6GLsbRGEzrgBvr4g+/9GWpR1g/WCdUOL2CH3BC8/+nj82Tq8pD0SjmCSqK0ZhTUBjHI/SUXMcx
+WKNlzN0BIwWqWUhrNB0FPtEMFYFHSbsSh96YonaoDjz/TgcjSjJkmxs4s9IOT21Ecko+d/CSg7so
++/DYE49R1dbpwJf35hZPYhRT0sYdHG0/d/MF3EQ7UOMbWH1cjYIBNZwCB4qSfXRgmGnI586s1Bhs
+yz1LOwnNg7eG5jx1H6YylancJd8rFf3fB34MiIDrwF+1VoACjDGfBn4GCeX/nLX2s290DQdL3h2T
+jTwcjcL3b77KQU407UKhQKwlnBtuwsrDohHPrUqjzsm1GpXjUl2WLwUEejo4bhlHLQFM8w6Di6PV
+bs4opj4b3lmJrkbZretTVpM/O+gwKKvJuPPGGtfR7pOcccgrejLKYVnOhaw25IR99uw8K0UJYJaX
+ZxgOFGQj0kq6LMZoAC8euziTWKXbJ5+XUvDjp4+RDcVSWNlROvVXtnlJQWg8Us5obcaryYjihCex
+a5k/rhwWeeVX7GekvowhPFUluawZh2ZEqo1SZrWBVSRiv6ilv06D3DF1O/aapEauvX4zYuaYvGe4
+HfGVQ3n9PS9Kp+anPvoXCK6L9ROthWQbMkE/7TPsyaCHe0121iVPf2UgQbasHzCYBFUTmCuKuzJf
+qVBUq6EcFilU1aRXxGjrFwg0gOl1xoy0TiHvx4SuWBhZ1sMU1XpT8JlWLyLpK018v09XT+mMMeGe
+BAEngLFmaY5M4ePc/CKl8AMyD75ER1GZq8fnKVUUZ8JX7mU3A+WgJB3jFLVm4yjGcbWhb1TGpIof
+ot26TmjwNPJrCikDDarOLcxR3Zeg480sIbuT53pz8r1S0X8O+LS1NjHG/D3g08DPG2POAT8OPAIs
+A583xjxgrf0znybjgBe6mGBMNpCJt/auEb0oJuX7Q5ctrcW/FXWo9sU/O6ttwfPnVqjkZIFdv4br
+K9mGG2Mc3dBjlyxVfEQF6cAHk5PyWbc7IKe9FpXFWfZvymY0g30yXzYeSpjqGkjtxMh6fWG5g6M1
+1qEPRTXE6jn53BPlOg+elnGurs0zFygBayXkqKGdf0YV1u3BHbLSwUqFyozEQ3KuT5CI+2CreZYf
+lrjDE5tS0LP+SszpUOnXU9ifkK6ODI2cKJwfaJRZq8qtzylyclQt42uXqLs1wJZlPL7NKChlejBI
+yGZVWSrKcFhaxLmg/J9uQsvX4p9VQ0XTr8s2IdAG3sNbolQGV8/jHFsHoFx7AG+s2Jxuiq+mvV/y
+6Wqn4eA1+VzDNeTrinjtwNyK+OWN+jzW1zWcKeK5ci+9UBWWAUcf9IiMVAvHjCetywCpC9WB3M+4
+KHvI2PYd87vdG5NTFzSJCgRzqhhDRanKF3BCdR+yDtVT7wXgmWfGhEuibArBI7jKMmW0MMtxi5CI
+QsYNcbQoyi8tYdtSQ54MBsSabcvNyP1InF1cRfoajRPGCkgTuh5G996pXB7P1YPvTcr3REVvrf0D
+Ozky4HmEMxKEiv7XrLVja+0NhCnq+9/SiKYylam8q/J2BBr/GvDr+vMKoiQmMqGi/zPFWHAyi0lc
+YmWdvv3qBrs5OaF/cLHOKS2l/eZBl0DN0tXHHwWg7C/iKc+eCYtYLUV1MxeDAp2MIxLFz8uUmSRt
+Dhj3dKCH+9iSLkdzRKxsxcXFVbo9sV7yJTVJTQAK3mKsxSjeH27IjPI2uM1dCuoKHVd+xSdPLnNK
+cQjm5/Pk9TTDWEpaWONrQ1FQ8IjV7SgmA/LaURn7JYxiKJQKht6enH7fuCxW0EGU8KBaiwf1kFAp
+1h6uOpSVX3G5AeO8jNlb1gaf8gCjUGSp6+FFmp2wI8yiUr41ShjlOfQ9zX2PhqRFtbxGLsWxwphV
+Agbfkc8NjKEylHF84YrUWNR/43n+0n/1c7JWJiJUoBPPWPIKY7YbZmTKnbClFtnc/DHOVdT1cV2K
+RbEanWLpTg2FY7M7FHKJFrglJmHS/JdmDrF+X2rSO7RqrpOnq1ZkXu+pF1Zp5ZWaLRcSq3uHG1Ns
+TILYevInIeS0Ms5dwMtrifbTK3iuBFidfBkTquUZ6ebzR6A1FCT9OyX7xm2SKvaCDft4gYxZS3nY
+jVKOWnK9eBzR1yzKOKoxqYgOjOWW/3oKwzeWP5dSMMb8AgID96vfw2d/FvhZgLWVFTLHJ467bD3/
+GQD+4KsvU9D25fJKiTQvvuGCLfD+44K75zuyedxiFUfNVuMXmDQ5ZFGC1WhylnkcbAoZa1srAndb
+O7hDuRm7t64SKNJF/skC2aHcsKIfMxPIBrmm3ZInCzU27rQyj8j04c+VHMpa3GIOMzpquvsa6c/P
+woJG72tVHycnD+R4sEe+I7ci0dbdtJASD0UR0KnQ0wxAqV7AU4hzL3bIHUh6bvIwNkLDWNctPwC3
+KArkRGhZXpZ5NOZzeIpn6ASybl5axPYUVYgMb0ENwfwcTle7OedcjBLkOk3dOkmHTKHo3UobR7lU
+uBKTeHIfHjPwOUceph0liv3S9T1+tKGu236eoXJb8kSRsQLl9m7e5PolRaTSB+W54zOU83LOeKZP
+Y0munYUxrtUHCJdM6d6jRNYziQKsp6SxXsBIexzM0CXWOQV+CV/Xy2rMwc2n1PIKpDofEXfknjZT
+i1OVlKrJyVrY2OKgit4PMJo6J6iBxiKM7ZMlCvYyVjBePFC3kewAO5gY8O4dWMUoahNrr8SwL66G
+0zcU9Jzq9EYMFckr9ffwFWO01LOErbeSNftzZB+MMT+NBCB/0v5J//WbpqK31v4Ta+2z1tpnG6px
+pzKVqbz78j1ZCsaYjwF/G/igtUryKPJ7wL8yxvwiEmg8C3zjjb7PZgnx4JBBd4P/+8svANBsHvHc
+M2KKz1aX2P2GglfMNvnGQAphFtalyMN/sIYXKm9hsotRrLok7jFWEIr2jYu8fEEi2N8+kNMn3eyR
+RALa4NqMvDJCj/IzzCqabzEtkSr0ebcvJuBHjz/K//7zYkmMfv4CYU8CP5WHizyYk07L0SjF14Kr
+GTVPw9gS5LWM2XcIlMJ81KvSH8gRe3tfApx+EuBoqerYzUibmokZ9ij7kmmprj7GwFdsB+UzrB15
+bPfklG97Ed+naxzkHZbUMsnlcoSpnKr9llw3nCmSoaZ/lpBd0Nv9RbJgAAAgAElEQVQaeQSP6ndU
+lqE3oaKXz2XuEKNzGn+pxEYirlYby6kH5XoPpwHnNuX9v621G7fGGcOvq4W1UKE/FqvBlCDV/o+4
+l6P4oARYH43FOnj4gTWqStPXiX1Qjkk3qxAqFZ51AuKW3h91wVI3IRnq94479DWw108CrPZJ+KaP
+p0iGgVGa+NRQqSl4iSmwrZmIg+EhbiTFdXYswUW38QhosNrgYhWmLjJt+rsSlqs2apiu7E8/N6HH
+K+IqY7SNapiiuFijvQ5xT8bZ2dmmqdbiUUe+y8kGLChN3VY85MpI3rsYFPGUfnEjjdnLq8szseLe
+QL5XKvpPAyHwOSPgJs9ba/+6tfZVY8xvABcQt+JvvFHmYSpTmcr9Jd8rFf0/+zPe/3eBv/tWBmGT
+hLi1S+9oh+fPyyl+mPnMluScC9MthmdEkzq3Y+ILovJuFMQ3G3oXWDojJ0nBjEH9rEGnxfp1YXPu
+Xr5JNpDT9tFjctJ2T5Yp7spJMj7cozMSH2/fG5FHfq6YA1KFGMttSfxh5adS/osX/zIAv/Hcv2Cz
+JUfp+xqW7PC8fF8BAk2BPVCXZQ6HCUdqNQTjEJNqZWIuoa1pqPYtrarc7TNT13LfconbLZnr5uXm
+nUYw7/EFsoGmrzSV5mYuK1pX0eslOEpS6xaLeCcUb8D18LQWoBzK5wZly9GWnHIz13YYqy4PzzSw
+WgEatG7jqMvM0gkAzC0PuyGxGu+05fiyNAkdHR7iX1cg1aLDaa1uXNiUE+z4wOUW0kVYrT6J2xEL
+KYpcYWsBxsUO9ZGMuaBdhJXQIWjIurT2d1kYTCjRPNyqWBB+mKFAVlhlMe/hcJAolVy7c4fLo4/P
+bl8shUIKZQ3mzSu/hXUKRBqnC7KYBa3v2LtexCmINeFUHtE3+BgzCUbbOyS1KZaNodzrpfPfoVQX
+JKT80kOyxia8g/tAPrqTnezcPKR9URDHOt0WnJAk35HyoQx6beZ6sqdz9ZAlhZBr7kZ3GrpSF+L+
+W0NXuy/KnJPRiKNLl9k6ukHuhBb3HDmsnJQij/nj78c7Iz0Kjc5Ndq5eBOC2kmkEXkpdYbPzxTlS
+hV3bvX2DmzfEvK5gmF2URS0tSNFTsVKGiizqlWDAS1/7JgDlPuT0vYunH2Ko8OP1Ock1P7nxU3R+
+UlGXX/tL/FjjBACHn91gdyjjbF7aYFmbH758JG5HmwOevC1lu+97cpFjFVFky2snmNcy59Oa+97c
+27+Tlx51dzB9eYjXR0MW2vKewvpt7KEW4SzJZszd7tJWxGQndHGUi7AUZIw0X+3mx3QPFQdRMRDZ
+GZHE8l2Xd+I7n5sZ7eHsiKKun/eZPSf3xH+fzDPzXI7Ug9nvJ2y9IPfmi/sRhSMZ/5O1ELRdeEFz
+8y+PEi5/Qea0NPodrmqNxMk0JR7Ke9qlOVaOTSDUtP8il7Dx7QsA3Ghe59I1NaUrOVbOyMN26tRx
+3HiCb6kgM8M9uk2FNtva5EAVgesVmdGeAqfnM1QXalt0BkWnTapFX2F+joLurdpCRpJqgHHyFDsn
+QHlObQKREhG9+PufY/u8QNF/tWlYOC0u0elz0l352Hs/QrEhoThjXHrXpSjq8tc+y/XXtBXb8XC1
+szUZyL4/3IpoalA5DkeUIoWxi/vs6OFTtz4rNdmH1ztvDuR9WuY8lalM5S65LyyFLE3oHh3Sf36D
+0YFo3fd9+DmWzghHgnEyWk3R7K9tbNDXSrmdTQn0tDurzK2JS1B5NE8SyXeMrjdpN0WzLxXmGKeK
+vf/C1wEohCXqpzRVdLhDplVuIxtSGoh27ef75Lty6sRWgmjNH36Y5jVJBR6/+SXG6xK0+8iM4flZ
+MTsr9QrLqYJoPCyn3ObNAScrciLURzkyNZO9lRFOO69zFZN0JkgIlMdgzCLxCbFSzu50hQMTqBdP
+4a3I6bj6TbFAvu3vcXWsOX3XwddGG1JDuK0dfCd9gknQTRupOk3Y2pGTdHCUUi5NOBsswwOxYl5z
+Rnz4WUW/fkWCwDsvXOcLz8vnxlU4UivmsvV5UOtB/rgf4WqlZ0G7REteysWBnIgn//Amba3Z6Dzl
+0Nb71Lm4jqeVl8ueuCXtwVWurK8D8OWrtylrTUrZ6cEtOZlncrPUlFcxrGtzUd9jNJbgadqPcBWE
+ZNzLOOrI2GqMKCmeRVUb6eKiwdeGuKDhkEfeu0KBKJM9p+RsMOxBeU7Xu0NHux1vfX6LLx7KiZ8f
+FvmNKy8BsPY12UO/uPoghTmpiswcy9bL4vL+6leukO7J/QnLFnNR1mjT00a7ZMSMsm4/UJrh7IpW
+TXqHmB2xKmaTPG1tXoND3ozcF0oh6g25/bVX+OcXr3Fcu/7mF87iF9Q0GnXoaMbhxkET5WmhPGnp
+rc/SV4TbtDMk1dr4A3dIOSduQH6pSEk3yFjLh9uDNvOxLGTpVI/ZdVm0rTSPWdQ8d9dnrDyHiebr
+H36swdWWbITm/gN84Iw8IJV2l3NjeehvVa7x1eviNhhlispXc8ytSWfdMN6ioC2t3sExvJIoEFej
+5uVClX5dTPXtSy2u7suGXq1k7BzKTT47LpPXLrmH3yNR6C/8QYqWAlAoOMxqV18/HZF0RHl1Xurf
+QaHqtEU57Da7fO1IrlFxLY8qOnY+yLOjNRnPnAkI60pQc0MRjTauMDujbebzJdC2bpcjFuU5pn+h
+LRiZwKIqBWeuwt62KL3fGhhsdYIVGdFXl+i1w33OhvpwNsRkHu7tMy7LPCrlOVa0YzbN5Zmd1XJj
+fFwtjy5n8t6x59wheikUKxSrsl5XL+yzPpT7tFQOSJQAuK7xAtfNKNW192HgQkn2ixP1yY+lBNmq
+/27K81gtkLN2TFbSOX2gxuAVec+ZhUWKF7Uc25ExdJrg+pNYRIarax9FlsdOyntWH3yca98WN26j
+KS5aEBt2dJ4P1yrMVmWtKosNuodKJjyK2PVfnyB8Y5m6D1OZylTukvvCUsiilN7tHjd2LM6qBgmj
+85Q2TwAQJYe4Gk6uB3UeOCanf/GMHEV+cY5lhWNzsoQ0E01cigoEVXk9LOXvBOX8eQX0eGWbzf46
+AKYcsqCot4XqLDU17UtJQjqnpqhyLMzWXX6iLifKxWvzvKhWzK1LPRb3xYz3Z/PUWjKOc46M8/HT
+K5x5SiyFbivDUawAJ0zw1+SUWz4jJuXB9XVI5aQ589SDrGmAbrZQoqR1E24uoxCKtWSvyDyumIin
+FDjlZxaqXFMMxnDs4yyr5bXnECl1fWNerafjRT68rwApNqKofI6m7lIfium7UMkTFMQq4qPy3ge9
+fZb/SK59MPJJ3ycZgE9dziiOlc9xzeV4Tk68H9Qy7vPb8E01jcu+R6uvAbrUUq7IOB9eXSYzchL2
+DhRjod9h+bhkg2b8Go88JBF8f1wEJe2x421C7dY0jmZcwjF5Tzsccw4zeXlv+H2L1LalfqUQ1ihp
+aXK5PsE8sIw1wJeZFu6MfK5GiWBWux1LMgbrOJgJOvjgO+QU+OaRD36cM0tijeQfOcupF+XE37Vi
+mT7w9AM4OjbrZtRW5RoP1eqcOSV7/fEzD3LqOYmmL/+RrPHRzSM8VyzLh84e47iidZ+/coOa1o50
+4hFh76095veFUmhi+c0s4lphm7yaQ//qV57nL39Sbmjp1PfTVzDP1bBATaHIi8GEZy8mLGndvg1I
+BmLWRd4hjtaXh+5pHAWhWNKOveqZNUZHGoUuV9jXyHIl7BD1ZFNEFTjSOEc5Lx2OwQj2NTPwYqPG
+9VcF1v1bX77MalUeoJU04SdOin9pVgQ9p9RwGNyWOEg2GtPUwpqsVyPWCHZxTuYWbZ3kSHkbvWqP
+lYJsjtzsPPNaKh3Mh9iRxDl6eXVhcHlPUfEsP1GjfkHmOmsS9tT9MbMJjpYN50+KyXnyRBF/IOvt
++xGxUpmPr25TmhUlmp4MQVN2Rjvy/OMupR+QKHqYDgg3JRWxebZIpAxXj/kNyg+Iz9cXnccXtzc4
+Gsl3lUzEwkllpIoTHKsdjmtLBG0piD1Ukkbr5Fmal7GvLZSgLAqi2+8xHGuk3g2wirc5UtargdNH
+Exy4+RCtTKeYy3iweELmUqxQ8xXCXjs1nXEHR/ebNQFJImsRVAOsNwE90VKczCdNRNEfXbxAe3td
+vmumSD3U1uj+w6w8Jfto7kjuo1+tcMdoH3dJtYjugZmIxTUZjzdvqWoK+9xJ+fzuYoUlVWLVY7PY
+W7KXs6CPp525C8OYQV2bYcQDe0OZug9TmcpU7pL7wlKouxn/aaNPoetw60jMy99v7VF/WaLTc0lG
+WdmY/cghViCTvJYiB26JtKlU7J5lvyU59I0LAypKerJyPE+xqp2ImlkIKh5RIqfAMHfEWip1AzYJ
+6bgSBLs1OCSncOiegnQkFRdPLZfilQs8/28kbz7T8zl+Uky8hbalqO+vKFGLc3DEsCNjPxp28Tpy
+2nYf26d4S67dGyhM+emAOQ0WhfESM0UZe36UkUMCR85ODEU5YaOBQpNXDGU1RUsX8pS09z6Mtsi0
+oy4oLjFXkrWbWZCT1qk0cLSRzK9l2B0xd+NTx3G1HNcpDKEhJ689FFPVrHuYoiT182GNxkhOv0Yl
+T7Sl67zcASWwaV2R/4dJn6HePzMKqN7U2oSRQ5qTdcn1Em7sigXV6MnJV624zCLXKFcsqHuR2TbD
+oWIvOENGCsrjKkO1GXi4SkfnRSEFbbpKTJG81pMUc1USR65nWvLa2Pg4jo45N6So1laUSxkbxbjQ
++2EPd8i06ap/K6I5kJ9DJ4+/LO5hcfnEnea9nI7N9erYCQxh5wrrl+T7WlnAEwvHdK5F/L7iJSzJ
+nq2N8qSR3Kdk64BoUqPUKVCak/E7SYFq+81ji8J9ohQc1yFXzjP66Tq3fkUWZHAtz9cy8b0+cOsk
+40z8/UpxTNzR6r2Spms8l0TLzrKkj9XCk/3+gI764o+O25hMwUJy8l1uCMWcLGrBVsir4bQ+yGMH
+8vpgs4ezoO3QVjsKHQc3VDNz+RGOPSUuQam/wXseFC7J2XiT7FC+o64cA+FMns6G3Pzzrx6xojDk
+6UaRo4qYfr1IFMFCGViRDeF5RQoFMfPDcZNUi2YYz5BojX52XNbih2fq5Iby8F8f3mJpRh6g4TBj
+PJQHKxeXyK/JhnSWFaw2V8KvyIPujjPMSfXxr28y2BNFUJxfxm7I2tmBVFgmpgs9hdR3HPJ1KQyz
+aZHiE2Lujg/n6V0Xl+5rCl6z6zkcV0W2WC8yXlGId9Olp9WWrWabmRntUByJ3+FlcxgtJhuNIFMi
+y2EYk2pqsDfMcBJV2lpAFSXmDipW3ma42pIcOHmMuoImS1DPkkQrOjMbgCqKtD3gyJU1HIwGJAcC
+bDMey3d55TIm0y7YZ84RbU8o7quU5iTuEBTrGKMt09ouj03Bk2skX7/M9Vev6f1r8aE9TSOfmqEU
+Smwq15VnxM022W3Lveluuey0Zb3bpQFrsSihZrnF+bEcrm9Wpu7DVKYylbvk/rAUchmlByJ+7MsP
+cyOvBULLJZbbCsk9ZyhrsGepUWdpVhGFFSYtzhl8hWe3ox1SPRFWFuqQ11MszuEoQbeXFzPMHUJm
+tB7e9TGVCVZAC60PoZoZCkXtVhxrIZCxzCvX4n90apHrG9L7kLsxQ13z8ElxnlQhvoNUtHndP8PQ
+F6uiQoGqoghHpV3CopiiXcUdaHQ7WC2W8uarOFqz4WY+RvvtbX7AWMtyZ4/kVh6VHarzshbetZTs
+CQWc8apYBdDKlz1sW5mXDiWgapZzZAM1Z4dXsIpR6axa/KYGIK+vY1blpDSrElwMjs2RfVWYtNPm
+EKOYl+7xRZwjOcUdv01cl3E2t+V/N4FHH5b3PvbUKVo3lQtzkNHQjs98pcCeBlIDR90cIlINMHf7
+I0LtBjROQF7XKPV9wgnMXKRdqYFDqgApxk8nSO24oUvo6nqSYCPtK9GgpI0yorYyOh31cMsyHtev
+0D0Qa8nTL/PtgHBOGb8dhxkjJ3tQWbmTBTFx8w6snw0V08ELYCiWUKv9KvsdrV9JfEykhVFNQ9oQ
+qyHWfo6os4nZEGu6P4q5vdfUdUmYmZXPzZ6bY+VFeV6+C4rBvydTS2EqU5nKXXJfWAomKuBtPsXO
+e3b5gZ5UBL7/tSqZdoPNFapUFP5rqVTAFOT1SKHG7OEuQ0drD0YeIyOv58Zj6lWl/EoHDBOtJFOr
+wksjklj+3jvY4XpfNH971MVqcHHkOIwO5VTNKZfAcJiSKSzZzuICjzwjlsJrboH9QFFxLn6LMzUJ
+AgY1GW9cbzH+hsQM0kqMHSsEl18j1bx5TWngjjoBR0VJb1UOWhxvyMkcB2UcTTclmaXbEt93f0ZO
+l9mlY1S/LT7pxXLCTEEskFLNI5mQ2RRjEi0rjhVhyEZbuGoRpLaPq2XO3M6wuvb7z6fUArE2/HOC
+exHFY9ItjWucTHFHkvO3r+2S3daTsO7Q/CM5pb4Zyym367h8wChXx2BIUzELhv0xjqYFo2qJhtYb
+jBS5KPYtkcLjNTxwldcidUcMx0oRSMJooHgIEx4HL8VTy8u6AY6WKydOn7wv77U2INPS9L6OsxD4
+mIKs99B36O1IPGDueIP1K9LkxLLc0+rZNUJNLcfdIYPDdQDarWvMVxWVcP44jkJzJ7tSp+CUK4wu
+yrr98WfOc/2qvP7+pRy7WxIn8FZKOIosFe3Kum5v79IZSHzh9maTi8qrOch8TtTlOQraKbm6wr+9
+SbkvlIJ1MuKgy/BLPdplmdjJxMdTbMOZwohMoat6rT5+WVuKlbDEGQf4GiyyxqOsudvc/ElyE+g7
+P6TXlUSts69FLN4YtKz4wvor7B1pqXElIKelzaOCQzXSjTVUDsMUElVITm9INhbz7Fx5AZOKYun1
+BxyN5eY25qSHY3x0k7Fag8mhpf60BBLDmVlGWvJrlYq+6IcM2mIOvrYRYyMJFi3Mn8ApyXuzPrS0
+S67blAdsZu2Qgysyv/PbfZ5tCABIGn8Ho6XgXd8nbakJamXTZRdLFCawZMan6GuBlFnFalnt3mGP
+/lclUMhnVTEFA6zydVZu1Bmta8FSYqmoGxN3h/zTm6K0vnMk82vUXcKHRMm2+y67CnwTxg6pksuM
+WwOsKr26sjAZt0/aVBiz2QxHb3AYRdiufK7fbdPtqmtmtTNw7KEYM+QqHli57+nIxynIWvi+YZjI
+ODOFpkvKBseI4on9DKPKYtzuczQQpb+gKOHljTFZTdGeez43D5Tw9VrG0ZJkxE496ePpNawqLuN3
+GFyXDNZh/5DTgSjytfk6sSKTX11fZ/iyzGlPOzzTscXVrMX11pCdA+2JCGFLy+bDaAaydwiObSpT
+mcr/N+W+sBQG3YgXvnSLX082OV0UbT9DiUUt4SyuFsmqmiJyPTKttitpSi8uG6JUPld0UhyrufsF
+S1OrwIJRQk9PnVQR5NK0z3hT/r671eNALYKCrRPm5EhPeiOSqqThYmVMNknGUCORcRDw8COaNpsd
+YPYkFdTZG1IORIvHSrLSut7jfF9OrpfaXZ5QUJfyyQrFRUnlDTpyohTTFr1EyWC4xaUd+ZwT7uNq
+MNKMqjQ1TTpSrK1CMeSmFrA9PxjzV9XcjXMLRJofzwYhoXJHbO7KugWuQ8uXazQ8n6I2Ejm1kHhO
+rIKlJysMezLvgy15766X8tgzmt7Ll+n1ZY0+u9XiR8tyNB9dtVxSPIUDhSI9HXicDaQyL26X2B/I
+6TmXDhi1NOGeOTSOSbCudCTuh43KuHVxYQwBgRKkJKlPSSv3RonDstLSGy1jTGweFArOixJsoFBo
+mctIuUIzkzIyCpw7OVzdHKkrYwsT6E+g4BjzQFMsAX9ZmsT8lYCor5B+44tsXRer8dKlA1YVmLdY
+WWJ+VRjEPa2biG2Go/UI8w89zZLyUTYrKYuaI00jl00t9X55R+71k3Ml8ooCXbQ+h1pbs5D3KHU1
+pVz07hDfvFm5L5RClEbcat+i2Y7ZrisjTvE18jtidkcPj8jGaj56hpJGX0dKPW5tjIknptiQsYJ0
+DG1MrOAkyWGTKK8PhRKEOKOAntY3jLD4GkWu4BJpy67damG1hmCg5ndmMxaU2NQrF4m15TqqH8Pq
+Bru84GIcRddVoIzdK4f88Q1xYU7mPBpjdTVuWNwludGOKiPTj/F3RWnkqDFaUD9onOBqLKLn90gm
+cPVj+fyVq1usT5CacfBPifuQJUOKixqDabZwtbOvop16URZTRLEmo0OiHd3cK01cNdcL5QhfUaQK
+q/KwznQOqO7LGDrJPk3F4P2+XJHgSJmq7JChGqU1hVZ/qOizPhIFeGz2fZzQbEFschSWRcn4zT5F
+haIPG3K9bD8k0+9wcoZkQpaSunTGcr18sUBt0gaaaBYi59PvyUMVJAlWK32ygoebKJNVlmImEO76
+edfNiLQV3QJrJxS0JclRaMihNLMkB4G3egxHoewdN08jJ4dFdT6i6ikKUDsmWdWy6LzC9m+nZMrz
+2UiLtI7JtQcH27SGEmsIqi5FbZM+1dBCNa/OgmZ1bvQs9QONjWQRm1357rWVRZZSOdTerEzdh6lM
+ZSp3yX1hKXSs5Q/iMa2wy7FANN/wIMfOnJQmdjpnqQbyc8lfJI3FhGtp9R+dI9ycWBLDoYurZcUH
+29tEkRJ5uEsUlAI8G4mZlYsSIj3lfQ+cgpYH1zI6Ct0VhT59zT54SpZSdKGmS5fkXWYmYLmOYbgo
+zU/h5iaVSbRYKy+vN9ssqmUylytx+6acKuHCNrMKIuIdTqLXHmg1X7a3wQPHNIuQOwZq+pbcHsFQ
+5t1bkKBm95olui2Vdp6fEZbkc86ST1m7R9nbYF+BQzJtbAoWcxSUWGV/IyJaV6yAmx22r8r4i8OU
+mcqEukysn//rWpdQKyjLM3kSBYYpn6oRam3JrVsdqloh+H4l3Dm+NM+gJ2tlcx2cNRnnIMyzmClG
+wrESx3IKEDJSi6+6RHtHxhDkHTLNIvT29undVOtm2KedKv3evGI9RGPSCfv3KCLUrEUQxjiaSWLs
+4+oeQPEfbGoZu9pgNrNMrOjgLJZwfQ3MKjtLmBZJAzmhI1Nkpib3v9EGP6fAP+YYWVuZqzc1G9Jr
+0lHXZul9y2RXBGG8lWXEsVLa+VUKivswo1ZqpZEjVdKb+WKPR1fFGrk5jgmVQq8/6DIsvP1ckvdc
+Zp2M/7wU8fU0x4ECkH5zPMDuiqn9+dlX+agnJlqp4OBp3MFVYo79zZicksMOGZI4shHam0eUlRMx
+m9kjVmjtrC3uwKGfMj6Qz+2M+2Ra1BRal1jJQpxCjopuiiM1I9uJi3KNULEGFBSjlnNJlIXp6rfW
+GTYlUn9qTWnRTcxHPvUXAXiy8QMEj6m/GzTIKcjG2JeNMozWaX1LNsfBluXpk7LBqlWfUSAPdJx4
+9Cd+8rrM42J7h9e6GjnHIbcmD16xaomOJIPRGg4pago3mlXldpgnpxmVTqvJxX3Z3LldAdaVQSVk
+scyvqsS29bFPX12eNM4Yadq32DeMNJX5D7ZHKGo7/0FJ/Oiz76tw4Yoo+oXzMcVj8vqFA3CWZWyL
+0ZieclauaEdix0C0J1/WyQ/Ja/dhmPMpBKLUvHEO5adhFGvpc3vEWA+DJOmTU/zEwlGF2swEct3B
+16K0kaYmkySDgSqmYkK+I6+PjjziuuxJq8AryX4boxwm+WibbEEUk/dSkfKyuGm5ShGnKNfuv6Rl
+yQe36bVFwcTnA7a16O2PN3eY0cK5M/VFFhbloZ9VcqJi6LKuGbi9JKNak31/Kg3ZUqU2sx0zCt4a
+Q9TUfZjKVKZyl9wXloKfD5h/fJn0P0vZ+t+k3NP8W5dbGgFe7rdwXSlztWMPhhpoUtPpgSfPEnfk
+5CpW80SRZhfOWFotMYODnMO+FpZ4rmj2QXNIWlTOyJ7BRU70JLTktNbBDiIcBVeJ99U0rhpGCnOW
+cyyhWgrG9Rgrxt/q6UfhVaVtPyGaeqWSp/2SIEYfrh1Qqn5QJvjUAC8nxSbdmwo7RpMsk0Dkp37m
+o1QdOZWc0gGRokN7A0P/QH6edBZWuhE5LecehCOMBgaHuaM71yhuJcRKH89A3TLPIdEuw+qDDU4p
+eUnLS4iUdn5mtcawrPgTgZyu85czchrhf2UwugNqU+wVueXKd2z1M1INCreWZS3q7hqtyrcAOO/k
+WdMTOMv12GjJ9+WLLse0ASlU4Jic7VFcE2yJ7vYWjqI1e6QUFW4v3h3j6ZjspPko52MVyMVLU8pG
+vrfRqBNoF6Tn+qD3NVAuycNsjF9XUh9bIFO4tq2oSSkv8xvc0nLntXnQYHTpzClWAq2h+AtFOpns
+SW8xj1+VrMvax7Th7bNDylWxzC5tHpJo6brfz0iU5Ii+oaIM2/lJuX5pzM3PK1T9wRCFmqRYrUBX
+xvbSMKZW/n+h++CWLbUPZvzorz3GbaWiv5EfUppUKUY5RvmJT2lwtDOwqC29bli508lG3Cd01R+s
+LpAEolmiaExJ2X16EzrxuItpi1k+W3cYKbhHz0BrX0y8cTwkUx6FglYmjsYJs+qHeplLrKx5bmrx
+PbkZS/Uim8uqOIyAq84/9iIHX5H5fXv3Fs9el8iyM6xinpS5+hX1G7ci4kdl81drT2ID2dzj7gjb
+lvhB1nNw1dztqUtkg4jHziq46iDAuDKPolOASB+m0gFuJArO7WvvgF/Gqck8ckNYOyObfzUXMzK6
+tntjdq9pp6Eja5lYS7or3xUaw6zCy/8xQ64dyJgSY5HtD7Oamju8+RrHXlOq+rrLzqTZ5GCP2jG5
+RjFeZqDptExTc7lCxqKa4odei/yujLOVXSLVArdxaYynHAiZtjrn8iHesjyMRZuQ0zhArljGKAFr
+bHwyNcfH6u7YvIM30GzIjKWglPLjPRfHkzcNtYfBGQT4nsLBBx75iqacT51k9JpWem4cMl7VmMiq
+cFnUP3iSbFeUfjhzk2woXbmrJ29Q3Jb7182N8HsTIF8FLhJBb5sAAAS/SURBVN5qshPL3ztxgglE
+EUajiKQlY4qTAw6St9Y6PXUfpjKVqdwl5k+4Yd/FQRizD/SBg3dpCLPTa0+v/f+Dax+31s690Zvu
+C6UAYIx5wVr77PTa02tPr/3uytR9mMpUpnKXTJXCVKYylbvkflIK/2R67em1p9d+9+W+iSlMZSpT
+uT/kfrIUpjKVqdwH8q4rBWPMx4wxl40x14wxf+ceX2vNGPNFY8wFY8yrxpi/qa/XjTGfM8Zc1f/f
+Wq/pWxuDa4x52RjzGf39pDHm6zr/XzfGBG/0HX+Oa9eMMb9pjLlkjLlojHnvOzV3Y8x/qWt+3hjz
+r40xuXs1d2PMPzfG7Bljzr/utT91nkbkf9YxvGKMefoeXPvv65q/Yoz5bWNM7XV/+7Re+7Ix5kf+
+PNd+u+RdVQrGGBf4JeDjwDngJ4wx5+7hJRPgv7bWngOeA/6GXu/vAF+w1p4FvqC/3yv5m8DF1/3+
+94D/yVp7BmgCP3MPr/0PgX9rrX0IeELHcc/nboxZAX4OeNZa+yjgAj/OvZv7LwMf+3de+27z/Dhw
+Vv/9LPCP78G1Pwc8aq19HLgCfBpA996PA4/oZ/6RPhPvrlhr37V/wHuBz77u908Dn34Hr/+7wA8D
+l4ElfW0JuHyPrreKbMgPA59BqEcOAO9PW4+3+dpV4AYaR3rd6/d87sAKcAuoI6X1nwF+5F7OHTgB
+nH+jeQL/K/ATf9r73q5r/zt/+w+BX9Wf79rvwGeB996L+/9W/r3b7sNks0zktr52z8UYcwJ4Cvg6
+sGCtVepTdoCFe3TZfwD8bVDoIGgALWut9ibf0/mfBPaBf6Huyz81xhR5B+Zurd0E/gfgJrANtIEX
+eefmDt99nu/0HvxrwO+/S9d+U/JuK4V3RYwxJeDfAH/LWuUOV7Gist/2lIwx5keBPWvti2/3d79J
+8YCngX9srX0KKSu/y1W4h3OfAT6FKKZloMi/b2K/Y3Kv5vlGYoz5BcSF/dV3+tpvRd5tpbAJrL3u
+91XeLI3N9yjGGB9RCL9qrf0tfXnXGLOkf18C9u7Bpd8HfNIYsw78GuJC/EOgZoyZdKvey/nfBm5b
+a7+uv/8moiTeibl/BLhhrd231sbAbyHr8U7NHb77PN+RPWiM+WngR4GfVKX0jl37rcq7rRS+CZzV
+KHSABF1+715dzBhjgH8GXLTW/uLr/vR7wF/Rn/8KEmt4W8Va+2lr7aq19gQyzz+01v4k8EXgP76X
+19br7wC3jDEP6ks/BFzgHZg74jY8Z4wp6D2YXPsdmbvKd5vn7wE/pVmI54D269yMt0WMMR9D3MZP
+WqtQ4n9y7R83xoTGmJNIsPMbb+e1vyd5t4MawCeQiOx14Bfu8bXej5iNrwDf0n+fQHz7LwBXgc8D
+9Xs8jg8Bn9GfTyEb4RrwfwLhPbzuk8ALOv/fAWbeqbkD/x1wCTgP/AoQ3qu5A/8aiV3EiIX0M99t
+nkiw95d0/30HyZC83de+hsQOJnvuf3nd+39Br30Z+Pi93Hdv9t+0onEqU5nKXfJuuw9TmcpU7jOZ
+KoWpTGUqd8lUKUxlKlO5S6ZKYSpTmcpdMlUKU5nKVO6SqVKYylSmcpdMlcJUpjKVu2SqFKYylanc
+Jf8PYsKe8eIq/vcAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.4132... Generator Loss: 0.5582
+Epoch 1/1... Discriminator Loss: 1.3437... Generator Loss: 0.7727
+Epoch 1/1... Discriminator Loss: 1.3675... Generator Loss: 0.7321
+Epoch 1/1... Discriminator Loss: 1.3730... Generator Loss: 0.9555
+Epoch 1/1... Discriminator Loss: 1.4001... Generator Loss: 0.6890
+Epoch 1/1... Discriminator Loss: 1.4468... Generator Loss: 0.4802
+Epoch 1/1... Discriminator Loss: 1.4452... Generator Loss: 0.6017
+Epoch 1/1... Discriminator Loss: 1.2895... Generator Loss: 0.9879
+Epoch 1/1... Discriminator Loss: 1.3213... Generator Loss: 0.7545
+Epoch 1/1... Discriminator Loss: 1.4087... Generator Loss: 0.7632
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvdmvLfl13/f51bir9jyc+Zx77tR9+/ZAskmKpEhRtixb
+dhInhpI4iQMkzgD4KYEDBEiM/AV+ylvgwBkAI4OFBHHACLBNSIolizRJNZsSyZ7vdO545j3vXXNV
+Htbal2yHErstUe6HvV7uubWrfvWr37B+a/iutUxVVaxpTWta04qsf9kdWNOa1vTJojVTWNOa1vQh
+WjOFNa1pTR+iNVNY05rW9CFaM4U1rWlNH6I1U1jTmtb0IfqZMQVjzF8yxrxvjLlrjPlbP6v3rGlN
+a/qTJfOzwCkYY2zgA+AvAE+AN4C/VlXVO3/iL1vTmtb0J0o/K0nhC8DdqqruV1WVAr8G/JWf0bvW
+tKY1/QmS8zNqdw94/GP/fwJ88Q+7uREGVa/dIiugZss1y3ZwbBcA44BxPACyPMMxBQB5Lv96tRDb
+kk+xbUOZ5/J7WRLHCQBVkVDZ0ni90QbAtV2sFVssczBG/ixyqjyW9yUG25O2K+T3ZVwQRUsAijwm
+zcofPVeK5OV5Do4l77MceUlVFpgfE8x8bzX81o/6UcgNxuZ5W5ZjU1Vyg+f+6F43rLNq0LKkb1Vm
+MKv3YXHng3varMGzV99XYutY6CUcxyGpZDx9A6Ve920Ho/e6WORVrvMg92ZlhbMaNypK7bOxLAJP
+niuKijiV59JCnivLEnR8gkYT15E2omjCwl4NRU4nCuVbdKgWWUyl7zaWhUHH3rYocvnbx6LQ4dSu
+UVYlVDpGpqQy1vN+1HTtFKbC0jk2KkHbtk3QkLVn2TVw5W/fgka9D8AykffaTkW0yACYTBZkeaTX
+3dWrqfk+nqdj5Bkdex+vknWa1xymyQyArt3GtUp9t0OcyZqsG1+uGSjyVMZ1mZCUcm+VpVS29DNo
+Np9v8g/u3r+oqmqDn0I/K6bwU8kY8zeAvwHQbTX5r/6Tv8aTUcnLPemS3+yw0dkFoNa1sbd2ADg+
+PaHvTAE4H8ngXX3xdTrNHgDtts/s9BKAi3jG++8eAVDO7hI3uwB8+St/EYDNzg6NhkxMsRxilAkt
+Z6ek53cBOLnn0jnsAJDn8vubH0x4++0fADA9e5fHx3MAZvMLykgW/85en81QmE9jIAs7W8yxVpum
+Mly7Iv1xCAjqynguZSG5PYdkLhPe6DfIc2njYD+kXpN7tz/3c2DJIgzq0rf02MEdBPJ32eBf/ZVf
+BWCeeew35Z7FYkGr3pLx0oXZ39jkKJ8AcNOGuS/XbzZ62B35/m08LrMRAKfn8u9xVNJTJhRbGQv9
+fs8PeeVAnhvPCz54eArA06mM1XK5pKzVAXj5q7/E3oZ801s//DrfasoYTcpLfvmtT8v8DeT333v2
+NvG59NOrBfiObKZFI2R0ruvBbjJTBufI/mGZxli5bJTUikh1c0dxzDVf++kVNG15wI6k3Xa3w2u/
+sAdA2HwF9vYBeCEs+fIX/0MAvvuBzFlns+AH3z4B4Ov/6NucnL8FQHNjh8yXb3r52nV2DmTz1g5k
+Pgab19jLhHkPX9jgN+7+JgB/tfevsVOTwyfsDnjn5D0AvuC8KNesgvnFEwAe/MF9jhYL+b6Tx+Td
+AwBe/cU/x0APjl/6y3/1IR+BflZM4Slw8GP/39drz6mqqr8L/F2AG/v71XZ7Dyd4gj2VDd/u9Anq
+suBpWiymsklno/vktiwmvyG/j6M3aHRflnvNAK8nC3b+/nfIcxm0fJqw0ZGF8vTx7wAQNH+ROrIg
+vHaXYvEMgLAosVy5vvFaRlHIQq+QSZwMR1yeyuc8eXJBJ5aJqyyHcFsWW7/m09TNud2Ua6bfw72U
+vgVXrrAbCNOobfjUxrJZij1hbu6sYrYjC3N4/yl+e3WyFbg7NwCIL49o1+S0cvRfczVGD0+sMqHT
+EmbSWsBAN1bP8khcGbvck5uH1SkL7cOo43PDH0jfuhGuUYZTy7EeXsjYTmUDWlnJ3Jffa9OUUiWW
+0PbwPOnTzkHBRCU2K5aF+yh2KZby7vEH75EX8t3f+M0jpokyalPxRuv35buXrwEwWMJxJGMRzWOm
+pbSbVAmFLv6hv6Cq1eTdpTKsosZoLvdOi5yklDktDTx2hclQ99kOVGIxMmfh2Cb6ntx7Of8mR33p
+88OdDf7CV/9zANr78vz8vSn5hZjN3Olvs+lIH7xaSarST+lM2NTx6p4JY44evc3X3/k/ZVwe5dxZ
+3JE+H35AqyfSwc/t3OZBJWvnxa+qBMIuu1vCpHgRrDvfB+AocpmWMk/nj+9hlDl/VPpZMYU3gBeM
+MdcQZvDvAf/+H3ZzZRvylk03sLn71jkA4WUTuy3de/zWCY/PZaDmhcX1gy3p/Fg2+eT+Q3hRJo4X
+rzMby+D9wXe/D3NZKNvXe/iVMJN/9o3fBuDydMov/ZJoNa3WgDwRjSdNfApP7rW4YDKWifbCzwAw
+dRKOT4RJPb44Ya6T3+/WaTgy0cZOsZ7rBPL71qBDuCkT1O0d0nDl+5ydALcx1XcIo8jPplihTP5w
+OqHK5DvSsmI8FkaXJBfk+68DsOlckbEs5mTRGwDY/pcJVTQODgqejWUju1lGqy59Oo+l3eVoSFYI
+o/jiSw2u7kg/H00u8U4fARDvB9w7lX5+MB7LPMUlfltO182ySWNTVn+el6Qcy3XrCgdbIvVFM/km
+KzslGcv7RsdDjuey+CdxRbkyfluGR0uRlq60ZDO6Vo0olz5QZCx0hKuqeq42nWcF212ZB8tTBnI6
+I82FKcQV/Lh5fZ7J2mlaPeauqxfl2qQc8uzeEIDFPCKdNgC48+CMaUPars9kHf7vb/8mx78vayUz
+Q+axMJad3KVUlddKJzx+Ir1+756+43tv8mYhaza+iEkD6cM/e+87hDuyHt6cPeTGn/88AP/omhz4
+f/3KIaaQtur1Bn5dDqFROuf8QuantEqWj27wcehnwhSqqsqNMf8Z8HXABv7nqqre/lm8a01rWtOf
+LP3MbApVVf1D4B9+pE6UDr1Fj+HlOX6m3D5fMjwSPfTk9B7BXLjnp25/mu1dEZnePhWufPq7P+SR
+o/p3z+bRY1ED7IuSwYFoMa9f+xWmhZywi1+X0+q3j77D/qaoHYeHKf7KOmW7eMlNAKInM6aFcGBv
+KiLZ8N4RC9WNrVnJ9m2RKvpen7Au3D8Mm+yE8ly/IyfpVqtLtyV/B6aJ1RZR1Z5ZOKFKFXpi2ptt
+Cjmg2Ah7zBfSrqHF4kJOh3me44nUTZmohDG6IGuL/aWc5dwMmgDMoiWLRNSci8ojHslZWSvltLaz
+irYr3/GaeZXoQtSjh2+PyS0ZW3eWMRnLu81c+m6FNq1SVBTbN/hn0p9pFfPdH4jU9+IGdGuijlzb
+FTtX5A2458g5MRuNcGKVIOpAKmNh7IBP7coHNlTEv5wvSNSMWA9tVeig3wioQvnfq71NbrZEddkN
+5bT+Tese/VK+Y3K2INClnziGzZa846A74EClhrsqjZSeYZHIWHU8h7kaSrfDBo2RvO93vyGn8vTb
+bzJJZY04SZvdLbl3p7aPe0P+ruwBs0LG9gf33pQx2QypYnku9JqUajx89XCXZVckule299gayBz/
+u9tfAGCzvUc5OZK5qcYc7MuaXXy6xz/+ztfkO46ecR3VXT4i/UszNP445VXGeXFGTMFTWzabe+xi
+uTIxdtXkylURP28e7uCpVXu7K/+aVz/NyweyMAPLsLMpi7v7xT/DwbZcb252yE+k7a2bMqjJ0Vu8
+9QOZGD/ZZvOqGLVabkURiipRNpZ4M9kA/lI2+eT4hLEunqxVEai3o920aKou22qHDHqyaHY6Mpn9
+pkOzKWK5BdiqVuTOErcmRkfLVs5k2WSqwoRBEzuTDZbMC8Zj2dxBa0C+kM0UL4SBEixxCt1gqceF
+LmLT8DmZiQiexhFBTcTrhXoWtryADbV9WAOLpyfCnO7GCwa+LJOW7ZF05bpfynOdfYewkuc838Ga
+y6Y/G99l9Ej64VQRB3sydmF/xRRnuI9X3gkYWjI3n663uYNsip2wSVrKu0eZrIVxscRXu0Xg2Xz6
+5iYAhz2fV+1rMme7NWq6sUo1+39+s8vdQvr+q2GdVNWKQ/+QsRorm60CkHF59I70PbUTXPVUnMwL
+wpqoDxezCe9Eoo69OZW5eY/HBOfy3na/QaMhbfnXr9DOhFvO86cML49kvGrKKLo2rxSi/g1Hc25s
+3gbgxSs73K7LGpgHS167Ipt+oGqCFUxwVraPWQe7LvPb2z5jgPTzfmo4uVB16yPSGua8pjWt6UP0
+CZEUcsbxBX46o5MKZx95JS0VezrNLlv7cuKXbUOhJ2EYiMFx/7UZvRsiMvuVj+2KYSVrj7ALESOr
+rCJTDMCVL4ooWj+8SVdP/GGVUpuIOuKEt6jFemKHBZZRa29Pnj/LjwktkQKa+OSFekMsqHnCxduW
+R83IddeIROB4bUoVYak1IROx020NsFK5boXyjMkjgqY8l+UBNfcQgDg5J1Hvy3BxTsMS9ag9EZG0
+ttOnasupaxubopJ2a0lB01ZMQ63JYqCn9Klcmw0qWptysl2WGWkiJ7rv5XR3ZAyvNbcwI8WGqPQw
+6DToq0tvEk/obMo5U3u0yT+Zi0Hs2XmMH4h0s6Xejsbur7K4/vcAOP39O8yHosY09kKMzsk4zegr
+PqOUoSDPK2o1uba71eYLhyLd7bYsDkI5HReLOaUvKuZ7J+IWPDVLmkvpc9hr0DcyT1e3rpIn4kY8
+i2OeqJu7rcbTcpnRPhTPxxP/hyxmIlXMFhnfUU/M2R1dN9MIy6gbutpgXkg/l4++zVkiale4MCwX
+oheeHB9J3xYuHTVWW/0QpyZ96Nze49CRsR+5fdptNbZuilTpuzllpmvML5jPZd6b9W3qqmq0mmfE
+E/nWj0qfCKZgG0PDdxkuYyIxB7Doz7hyZRuA/d09BvsiJnrGoliojmcU/NK5iqt6qGODn6m4b9eo
+MhngtALPk418PRAvwuHNisl92ZgnD2LyWC3qXpta/wUA4mc5S/Ui5AqO6ZgGE1vdWIGPcdSW7XrU
+QlVXWg5BXcFLDZmUIsmfA0zscoGvBoEqNxDIojD6DuPWMLGIu0EYUBaJvmKLpapVs8UF+UzEUmtP
+xM9o9ARf7QhWUXHYVS9KbcFkKdeP60v2e/K+dwNZ/L1ZxM6+4DsON15iz5N3N61tnFDuqdctvty8
+Kv1XfEeZGHr6fcbepVYXcXaWfcCTubzvznnK8VxE7KglIvBrB+9RWdJn/5tHTFR96CQuJ44CnbIU
+HBmPWaJM2rbpKsLtUzcHHKrKs7O/y3Ky1G91sTz5lr26rJtkHHGq6sNoPOWVHfmOqLagHMv13Mrp
+28JYMplGLu2QciC2qA43mC/E61SYGddd6dvvRWp/WHokKsLHnSGVepKS43MWrjCF4nxBqAxu5QKJ
+5ilBU8bzYHOXvSvCCA7713GNbNGOcbEbYpfx7ecIP8jlm5N0QRTJO3K75OWfexUA980nfGuqhp6P
+SGv1YU1rWtOH6BMhKVSUlGZOOZ4xHQvXDRr72JWoB42mh6t43Gxs4yova7oiRgX1CsfIqeRbXSxf
+xP2idEkSta5XPTxLTqC6+pfTzKV7S9SO+P33sMZyukSTmI6KosW0Q16TU0wlfGbpDE+NT1XqEVp6
+yqcVvoqMtVoHHznFbDX2FUWFsbU/1HAU5WZbNWwj1/VgwLZsLIUU+5b1XMJwLItQRcZ2o0c1kTbm
+p2JorLKcTK3pOTGtQH6fjnIaakXfqveZP5VxnipcuxF6bNgiil93t0gPRJXYps3Qk7FtdDswVgv+
+UAyxJ7FFqviPnUGHXl2+dfTybb6SiAQRxe+RzqX/0amM5T9NT4lPBMU3Kofkhfw+zefMl9KGneQM
+c+lHaBRL4Ht8alPm5rXdQ651RU1oeA6lJfP+dhxTPlbwzkj6m1wOOYsV/VlVPAwFQ3FAyNOxSH2T
+KCWN5H7fllN5nCbULDFyU1zSbsk6vJxUPLsUNWB+pkDBeYXvy1qYXgxYKpajHD3DqNG4Kn0S9TDl
+kYLz8IhUUqzOBpSh9CF7ZFEdSD/6VoOuUSi04kka7h6p80T760Kq48YeXUckM7P7Ptm9FZrjo9En
+gymUFcksZVo4RApy7wYxm6Esxna3Rb0SUSxtLJ6LT8GuXJMJlIG2SgfbkQkgaeO3ZNHn0wmuiprl
+WNqttV1KI4vxYBHy9A1ZbPnOjMKRTRa0XQa5WNQrdXuOLkqmiuUPbSiU2UR5SuLIBERVg5on/XMU
+EWg3IVT7Q63fxfVVtLddbMW+G1VxKBJqPYVXT6HMZUOX0QzPV7flpEkZyIKeHckmMPaSnZdkEVuZ
+yzNdeG6jj5PKWLi1guFM1YYXZCHtH77I1X9FrN5Zo057R5hl+PYVaueyuNtNn2N1y/KOMJ6gPsZy
+pA9mv8lCwUbBbsirHbGDFHsFv/V7IsLWmrIZk+X7+Jcy3nQc/KXadiaFRh9AXqswvjLLTPrb7zlc
+e0HsKNu3N2m31V0cNpjHwuhq2YRoIf3YWshzs7DDTU++I6dkoijVxcmQy0pUzMU8p420t/T0YGnN
+eDaRubFLl41S+ny2ccmlgpDOz2VuMtdQixUp2ZgSevJ70trESmTNlsHoOTPw611t1+C78l4nLLm2
+/fMA1G9v0NK143chUCYbrA6QYEZND8Yqj2i35TuG0RPiTL0g0xdoBw/4OLRWH9a0pjV9iD4RkkJZ
+GZalS7Pfp68xBa3aJvUN9ZuXNSJPxE6v1UdtXLh1MURWRYZRC3g1vcR2VKpIp9gtOY2T6RnpUDj3
+ZSJtDdxNAj2ZB9t7jHbEUn0Rw5YGB7Vv7OHNPgBgoeF7l+mShXpJqFLUzY0VJGRLDX5aplR6EJq6
+3Nuod/HUyuTWG7hqXDP25vNoP6PSBdkY1EdPI8dZSD/LtoWnQCV3+oilpVGEHenE1u0vEw7kxZZp
+ogcmzdyhpu9ejFKOl/cBKN6QMf5HfED3fTHKTcoeX/2ixnlMLxnN5HQ/fXuE2ZC2T0YinracOtNC
+wU3REy7VTXDrVpP6vkgbh6Mlh4FApe9+W07g75+ech5Ju8sqx1Evw3lRokIRFVBo7MKyppGvueFi
+Itbo+ewKR0uRDq6350QXcr06nTHJBTZMIuMzN1P2VQ2aehVdfcmTAm46IvWcN6GqRAq7UKh1eWmY
+qNpRuhHHgaqSC8NZLtdrTZUuRjapxmJU1SX5SCSTRRbTqsl4WqVHx5Mxj1XN3drsYDRya7NrcTYU
+qerP9D9L15Pvbjg2SzWs53VRKUxVYVRdcVo2tYVIDeVxwfkz+f43Rr/JuJL3fFRaSwprWtOaPkSf
+CEnBmBLHS3GNS7sn7piwcJ67AE3iUKodIZ6kxBqKbEUrkQHymXDwKplQnYlEsAxSsvcUmrx8i9ET
+aWPSktMg2ig46AnXddst+htykjw6HjM9kHu2NxssC4UKv3MEgG8cxr7ob/HCkKjem1Y2K8/ZfDEn
+TITn9tUQWZQejoYL27aFsYWzmyzFuArt1XDpsu5TalizoaBYaN4Ap0apGIGgrBNN5WRqqLvKbZRY
+K6ljEeNXinLzU1Q1ZnJ8j0RxGG6q8fpFg//hf/0mANc3DBdnGp47NnhqP1ksLtl+R97ztCM2l8vT
+FvVddQEX20x25XRcVtDX8OTdzlWu7cr94/cEQXoZl+Q6WFZok6kR1AZW8VDGNoxdmYcttcD6qUcc
+y9+PHj3mxab0bR63KDsi6VyvKnbURb1U6/DezS3KM+lPfdvGra4CcLs1I1HjdjkaMqykjSe6M2ZP
+PsA80qjbhfscO7N0EnauisT2Aw2pj7PRc8O2lzi4Gozm12tUGhnZ8mz2O2Kjun4o/+Zzn6UiLPNH
+OYsdkRR+72vf4EtfvQ5A1N3At9UVPRZpzWsGeIrctBwPo+Hi5eKc9ETWvTcNuLgQW9JHpU8GUwBq
+RYmdVDRcGXTHj9DvJV1EzJ6KOJ8Vl5y1xYBV16jGp8ffx1Ef7WD388Qz8SXfuf+IC/WPXxxb3BnJ
+5F6msqG/fPM6f+6rfxaAnZsDTnSDPXznjG5Tnut15lRqfJqr+jCNj1nZjSlsSo3T97HIE7XOP4Ul
+onacjmWS99tbbCo+YLDVpN6UDWYDtvquMxV78+M2y6VsiNnohCyS7y+sFpkuoJk7hJFY5xM1MsX3
+RlivyKJxohJXneHLqORkKIzz0lgk6hGpuRoW/OYTbB3vP3in4uTN78oYk3HYksX/4vYNKktE/ukD
+WaAXwZLFUMbnZPEGT9QQebVzl1++JZiE7XpATfMU1G+KShF8MKGrzO3cZNiW/L5cRjy3NJbQy+Se
+RqkeHDviUoPwa5XPkRoEX9nqsqGi+2Znj7bwcXY+LweIExvyPRmXxHKJRjKGZ2dPOb5UBmk7NFT9
+eX1DmPfJxRbvKsQ6LxcM5yu8hMXsUpm2qoFlEvEjCEFIp76Cf1t4dXlfUIZURtbqKqL2tVd3mHek
+w/OjIXcevA/AvbMjHj+UtTxc5ByPZGx/5cVbAHzl819m74ZidgZ1somss/Eo450L8YjcP3tALWrx
+cWitPqxpTWv6EH0iJAUqQ5V6eL0KX5NttDwHayGn43Qy52IiHPwsHlGUcpo+u/MuAB+cZry4JSfN
+Kx2H7Fg48dv3hzzVHAKJUzJVjm/ncgo8mU94413JoPSZbANrU0RHq7rk/l1J7lG3G7SvSuadhiMn
+d5XbNPoydFFQEumpNHUCjK+pfooag1ANP0s5Po7OH/A4lOduPBlw8IK016i5oOmzhk+F2z+bvcNU
+T3aKFk5NjnEvSCk1srHqbYClEXoX8k3Z5IjtnvShMAMuVEQ/6Fo4mk1pUpZ4CvPFlv40izqTXMYq
+dEoKNaINo4r9UDMF9We4lcKfZzJWj8oxZ+cinj6Kx5SJfMdpMOe9EznSm6+9QFHJIH3llkgdVXiF
+0/dkfqP2Jm+ciASSRRHZKp2CZygVZ5FmIn7PXI+b6srb3t7gWKW/40fnXKr4vExLPv/ZqwB0NGVa
+GPQ5ORZjZ1IueONIoM1vHp3wbC5tbIUet3dV5VEI/S99dZP3zsWl9/BRRqwZrjIfMmQsRqmOt2dj
+VF3zaw0K9S5PF0tsRR6OkzPcVFGvgayreebQzaWNKLMglzl58+kFtaeyBrJqySySBr+v29YapPyK
+BoE53ovYloyt1zqn0LwX/eQFEkfG9qPSJ4IpFFRMrRx3atNe+fRbPrkmzZi1XC4uZUKn9pJZJAvl
+nblsoPfOx3Rui+71iwdN8qb46c3pjEohwZv9Hp1Y026dyWIMmz7HC9lU++UmO2rpbX96i8WxAFPs
+PtTaMqHdG7LAhrMxJhGRrPJcFhrPMGvZNFvCAMbOksWZLNKGRv31dvcJNBZh3FvQ1cjGxuYtHLU7
+ZE0BUC2PDeerxH7llA3NK5mYDBQnT3wOvmzuKpXngu3rZI6mdGuOWag6U6UBlqb22mvVKTVCb5WY
+ZD5cUGqItKFkpvpRaZU412RDv/7STVzNoHR4XeZgP9/i7pEmuHl0H3cpD97Y3WRvQxi8F+b0NOYh
+KWRDvPK5Q275Mjd3IkNSiSr19fPL5+pDVRjCQjZpqy3j+qXbN7i9L/MQuANC9R51OiE9jYnZajbo
+b8j8NANRGVzboZnJc7NHI+yaehfKiJfbcm/h17nUNlwFJFmPSrZU9H9geI7DCHOX0wthJtbKU5FV
+rKKUO51DwoEw2eKkJM9lcweDzefJUCojv79/7zFXtkUl3r62S+N53EWd2hMZ78ssoq/Jg3Y+rYyr
+36B2VTxwNbegUMZS79VpXZHxrOIJL/ZF3fjm9yVR0U+jtfqwpjWt6UP0iZAUbKBJyXA5o1GoONsI
+ydRie3p0wrePRFIoK4dmU06Yo3NRE0bpnPORZmruvISjYpt3633sN4R75smIaSnc09uUE2OSOdhj
+kQjeOpmxe1MkiY7bJtAkp/NkRD2V0yY6EU5tWz7zqeYV2HZ4oMaufjNaZfHi6OyUaCnvztTY14ta
+/Pwt+b7dacKpWqrbk4T6jlwvCznNSy9hqSrTsT3kvbflxOg2HQo9dQ5esGloJGa/pxiDYkGQSrvO
+qEGguQfiaoLf1cQqQ4dQg4NShd/SrOgqvLi96XE5UlcFJYOGnO6Hn7pNXZF5sVHE4MlD3JnAlZ9G
+VxmOBHY7iwrmmpX4zijk5QMZi21NyNKr2cwPxOiYuE2+f/p1QDMUr9QHpyJW78Lmtszd9sYW7x+L
+hPjB/fvPUaYvXL/Jv/UFORF72ZCaZjx2PRmXKpvjPBUcQ9spuLYjJ/PM+Cw1aU1SRpzPZPxnCsvu
+ZDOM5iwI0jlLPcWXUUm5UNVUVVTLLag0lVrjhTpXD+X73v31Uy5UwvLmxyzVSzLX+Z+OL3h8KWv6
+P375C+y/KnOz+If3eVuTvTxc2sznGvD0gfwbJCeEGlT3pV+4haspuAdek4MNWeMvbn2OyejjIRo/
+EUwBCiwzJc1T5jK+HOQv0Nasxe/OwdZoR8/aZHdLRKYbV2TTJOVb3DiUqLCtxg5pTTfpvMnDUOW5
+2KJRU7Gso+J81ueKI6Jx82afnoq7Tq1J0tKOPFmStOX+6amKwCYhUPO2M3e5HgvT8JcVnX1hLLcj
+sA51QWro9cZgm4Fa8utJQqVY/WW+oFFI38JQRNldx8W7pZmbrBaThuyUDS9jpJDooMzpORqBqcxo
+9sEEK5AF4XmGjqPenKjEzkUw3PJ6LGP5JruSTfoXP9eGpfy+0za89UiTkZqSf/Nfl4zQ11/4BZKh
+ArneEe9E5rQ4sAV2/KVByHhD2q2HV3lJ1Z9JWBCqoSBTl+xGY5uOZqI++cFDzEzhwfAj70Nucagi
+8c8rSOdGv0FT5dtur4k7lu9/5fYeWwNZF2Hk46xUL5SRR/PnSWWb1Sav7Mo8Xb2+Q5LKnCyjZ3zj
+WwJgm7nCeG9ceZHrjqyFm/0Jv34ioC971mI8FLuEFYjq5s5qbLYlFuNz+1/GVY9Qcf0pm5pG60rY
+o6UqzWPlrPGWAAAgAElEQVR1my5mSzxVQYqGYcsX9XfitalKYbLXt3dovyZ2jp+/+kvyzX2bWk/h
+9q0Wnh6iVRGy05a+eZ2U8Gybj0Nr9WFNa1rTh+gTISnYxqXhbULjkrgmolOnG7C1KyLjVzsNfu6Z
+/N3e3WGhFt6tHcES/PnT1/jCFyURRru7T5KKeHl17yqZBoZkecxYg5V6tasA7PTrXBuIBXhzv86g
+qem8GhWXH0jijNQJGf5AwStj8RN3qjaxFjJp1EpOFc7arFXUNaX6Z/b7BJox2W0pBNtPKGO55nsl
+norrzbBOlWjKs7b0x7sypqX5HK8uBxR1zQkZ2lSV1k5IbXL1KCzfl28rzZhgJGNV9ivspqZ0sz1w
+5d7cs9nRqFOj6equXmnyyq3PAbDRhJdKOeX6ZZvrLwjewG5U+K5IIekdOX36VkXtdQ38Ss/IH0mO
+hMSec+OVz8r1ToP4bVH/VFDAdcZg5FvhAYtS5t0Fcj3kbbskqel/+vLgtZe3+Gwkp/F0NqUMRLIa
+DAKCFT7azjCa6s4K5XStJj5eV1QDpxlQ74iE0apqLHT+LhyHX/xLooKEqUiCg8MaSw1Wetr9Pl8K
+fgGAr339W7w1krGLpwq8arcIFIK882KdG5+SPl8GV3FjGRerA+2xpnh/JmrJYnJEQ3Wmw/0euQK1
+fvGVWzzSeTrth1wZiATxuavyjv2d/nOjpF/vUtVWhXqe8dLLspbnF02eWP83H4c+EUwBk1NZI6aX
+OU+1oMUrV6fkubie2vU6jRdEP9vYfAnbiLi2f0UmMDMndAPxPtRrDla6Ci12uDXQGgnBGakvbeSK
+hLx98AI7fZmg0N/DZKtknS5hXcXrZoPpmYhi44W8d14tadRko9t5HV89CtNlQluLtmztXaEeCPNS
+jx5UPcaJuIcK36FZ6oQGPr6rSEYFNLlOSqC5D/NkgtNXEI5fgSP68OLylKkCmeKuAF5qdgNrSwuI
+bFg/wgEVFRUr9aHGM0vUg1gjCxfdTXZf/zIAu1cyDiaiSpXTY5yGbt50SXkkGyhNFeOfWmRbIhr3
+G20S9YwMmrdoD6T/SVYQa/g5Y2VGm9uMAmHqD5LHOL70LQPKVexDbmipK7Kl+Q67VYOB5nls1hs4
+lqwXy/GpFO1mNwY4qiraKpbH2TmZlovyWgY70LD2aI7RBLteGVN6Wgfkivzu1nfpbqkLsdljofPU
+2Aj4m19T1OCGJq6dRYQdea6I5pgnsn6b7g5WJN9fuygwmiQm9jQdPBkNV1TNlrdBqAC2tNPH/aKo
+Zre8DYz6vgdt6WPdQOBrvY8io1Tbh9+t09BU9LP8wXNP00eltfqwpjWt6UP0iZAUysoQZRbPno2J
+usKnRolhxxVuZ2eGVktOCtcx2JpQpa2Zkx3rEFtzJlLFz1nd1nYdf6T1+ra3CRAJwrslz3tBj1DV
+AK9WkczlFExOUvxAOPfImTByRFJ49EhOhrpbx6nkfc6GzUSLmsxHI05jORFem8/xNaOur0bJyjXU
+E+nDLFuQ+1r7z+9ibSo317by0uDXFePuWlSZVlhyvedHqdP2KIeCdfBrclrNyhxb4bXFYkFHr5PF
+TFQlmA+H2Foh6fyZwoePz2nZYqX2zOcxXfWGhBsUT0Qdi+NHZOdayepArfTffIjjSeq67d1dyqam
+gJ/UsH1RK8xojgY7EmtEZREf8/Sexqv4DTZV1XKMpM4DwC45C7X6VqxJbWqGQlFBgZNia6ZsE3uk
+6gUidalQSaYlYjQvDUl/U1LKB00Hd1PiDsq4xFWgltMbUOTSD1ulP9uaYGl6v+7mS3R6Mp5hMedA
+pVcr+oq8w/khgUoNl9GMmeYG6WQlpUZGLouEdiHtbdZFuk04YWNXs137DUJN81ZbPKDREcmxvnmV
+ekPaqwUqpZoSMgWyOTnZUOHY1ZzK1hR5zSknDy/5OPQvLCkYYw6MMf/EGPOOMeZtY8zf1Os9Y8xv
+GGPu6L/df9F3rGlNa/rTpz+OpJAD/2VVVd8zxjSBN40xvwH8R8BvVVX1t40xfwv4W8B//Uc1VFQl
+syymtdXB1ySYXmVYPpVT0HbD5ynNTDTGLUWPKjUQJWu4+Cvor9/m8o6m4rq4xJqoEbC7i6vuwPmx
+tNutJ8QN4Vn5PGXyTBBfsySjeSCpybIPlnzwSAOT1GWXFzGDmujc9WpAojH4l7MJ55fy7ul8j1Cj
+2uqBItjiEqMnfjqbkgfSxuLxhFJ9zJaeiPH5kqAhkkJpj6k0UtFthqBYjvjyKVMtprtUFOPAuY7v
+i1GLaIuaI+M2n+UYbXsJTEZqrJzLuN39Tsn55+T7W7XXsUpB9BUzm/EjiWw8n1mYJ1rsRDMEPz2J
+yS/ETRma3vN+VvYZKoyweHSP7FL6mSr+w38U8J5CjWdZxKYn85CVD56XdCuzivhcIz6XWtdidE5Z
+V9eynWJGuoRNSTGUuY6jGLcpc2278o4yXTLVWpLVOGVrqjaHWocyFfuKFTlUU2nDKFw7T2sEga6R
+2hJPpZ/AuUpf10D4qoxV7f4VbI06Pblzyg017Lpzn6gULEedXWJNp/bOWwLTt58tmVyX8TzLUuxL
+cXuOHj+jfKaZwbwW1kyN1JoxuwxCUp0/rISl5qdYzHqcaG2J3z9+m7Onf0pRklVVHYMUC6yqamaM
+eRcpQf9XgD+rt/094Lf5KUzBwaZnusztMR1L1IR0Nmesq8rNCoKrIgbGyYJIYcXpUAxVRatNmIka
+kDoPuP8HsriP7gdc3ZJBi8+HWO/LoFUak3DuPWWgGYz9rGI8Uez8aM4kl9iH48cZy/saGqyhsA2r
+xuVcU5DZsNSKRVlRMj2VRXiyOGC7L5szWxUXrXLGyrzGkxRPC7yMooJ8IdZ8y14Vkp1hSi1M61aY
+VcioWVDF6kXIm0RGjHw9NZ7WP+PQ14ImlQsz9ddnocVIw5OLImKqdQ5jTWN3vMw4eiTgno1rdzCl
+qAEnP/yAt38om/67H7zPnhbFtdnUeXrGmYrGVfguW/tXAZgvJtTOpG9RWqA1eLEq+f7T2XvcGQnT
+dGptyuYqLf2PGRotGGnqsfdn0s8vGIegUE9MEFDNV/iGEjSeo3A80NyTyVRDi70divIIgNnZkM41
+YaK+FeCoupVGMZbmSiy1IqxVyygrWSNeeICD5r8sI641RAV584nGZYRn2JrI5+p+i3CVbq+qEYxl
+TlrBjLlW1/JVDfYaDXqaLMeavcP0sVY/9ypi5PuKOw8JFPL9ghoqPbcErahWxQXRSL75yfwui2M5
+UHqXu7iFMJ+PSn8ihkZjzFXgdeA7wJYyDIATYOsPeeZvGGO+a4z57kxDhNe0pjX9y6c/tqHRGNMA
+/i/gv6iqamqMef5bVVWVMab6Sc/9eCn6q7ubVepZ9OvB89RRSb3AUZdWYNeJLa0wnUA10gQfqZZ1
+r2Z0BiIOpkcxpwoNfWodU+/KCbw38ChCaeOZBuQHZQVPVLR3YTiT5zK3xNdgprTt0LiuWaA1Vdr8
+G2Bp+rA8K+moXzl1LSrN8pwkBZcKM7RjTWRiaowVmj0uFnhDFed7BeFcTqOu+qJbpqLUSsvVMkfT
+DWA5HmVLMwY/uAuan8FSeO3gxmeodeTvIvdZqv/bil0yRULaJqCoyyleX66Shrg8UGnj5dOHJBfC
+qB8dLzlR2LQ9NtyZaRLXTZmbXjNgY1tOTGfQJssVMlwk5CrpTRYJkaoVrUje9yy1sTUYq2obvEgM
+atnKfQtQwHIkz735VE78f/tsQXZNC6AkGSjcmqpGtkpK0UjIR2rYi1cl0yr8F0Tt9JMFthqY7cCj
+WGr6uiAmU8kjVVeniXvPs3V7bkhhy2kchy3s64p61AjXB9+yiGwZ4057g9BXCapImataEZc2ZSDj
+udQUg0EYc6IFT779dsH4VFS0wc4uh4q4zSw4uZQ+bapK3HJcKq1cnmZTZhrMNY5Cyq6qP7VT2o7M
+D9/iI9EfiykYY1yEIfxvVVX9A718aozZqarq2BizA/zUShRlVRIlCy5jQ6abajiZ06vJgJTpgrmm
+rB7PZihKlGgqotx+z2F2LhM3t0YsVYzc3IVUMfzZIMFBRLSlitzDYUKZaxruFMoNYQT5vZKWxglM
+ypReTwvNaCaoK5sbjFINo7bAUvGzVQQME5mYxxdjDtoyGXNNT55FOSbQTNTLGlZPht/UfKqa5gHU
+2pBZ6OBk6pEICkwq9xZ2QaJMZtaNmJ9o1OLnpVBu6eSUnvrNizotVXmSWkZjlTVosqCpCIZIs4JE
+85jjd0WXPT2sY52LhTvc8jicibC39XrONNFwb4UJz58saaiXqFUfcDkSITF6NsZXgMb4rMTSMTw+
+100a2LiKD1jMUh6OZX6tH1MfADJb2jidiLr2w3TIxkzGNXQqTFdtMUtAMSf2MCGuSyOublKrETIo
+1IM12MBVG0ZljUA9RlWekmqmJh1u0tFDwhdU2HUCVrmmLx8ccXvvUwC8cVdUqXH9hAT1zvQnWJEw
+r6haPgeOXYwvSZ5p9G8i62L0tMLT50blJlu3rwKw7Xbo6rqYpyX2obRRaR8myQJnIm0kXszFpXiX
+8hIqha/XOk3sp3U+Dv1xvA8G+J+Ad6uq+m9/7Kf/B/jr+vdfB772L/qONa1pTX/69MeRFL4C/AfA
+D40xf6DX/hvgbwP/hzHmPwUeAv/OT2uoLGERWVSXKeNCuN2g1SJT40vOGDQqsVomWKFICJl6HwrT
+xNfKz33vFWZGUliNWhNiFSN9zyXT3AJNLdgyn6ZEaslPqwmbS00A0ixZrNBxF+BtyEnR0aQabvU9
+LFWTsqjELYUT12sp1ir3YRIxLkQKceZyksyGlziqajQahk5b2g2cAJVQqalxyqn5JGprycqYmvbZ
+K7ukWhimHM0p1Iq+qnNWHCXMXS3VjoWlJe/swsGKavqOCm+hhVFUGtlpWgSpnLTerM2iFBXLf9yk
+FcqpOyhvMUcCdIZat7DubFNXiW56dsLFmfx+cTpleyDSRKNZYdTaGgVaLKdqs2eJhPEH43MePZZ5
+L/95ZXOVcQUtaPJgQexJn+PmElsTjziVi1tpKT+7pOPJ2NpdNeA1IbekP05Qo1hI//PqkvRCVZAi
+o1JJz1ZpqygboEbVolgwOhHV9fd+9y5bv6reBU+knNpdqS0KcCW5TdWUb9qtdoi0inkr6fCdhzJG
+o0fSVpFPMFNZv+/dtTmL5N3b+z9HdkNrPVgeTl2M2CtJoUhillpDIi8yjEaMFhdNZkZU66lzSX72
+p1RLsqqqb/CjeLZ/nn7547SVZwXDkymzZM75UCa2325SDETM9xwXFNzSKCvGWuSzVKx3NKoz6GqI
+dGix5YqYmH9/QVlpssuzkEkqE36k2b/PhtHzwirdZoNmTzD1p8ePuK9JWsNujyulXO93VfSvH+Jk
+4oaaZAsWmuI7mDeIdRM+G5W86oqerIZsLpKUeCjfdLDVJ9S4BTfMCTWde6Gp49NsysWFLDbbVFga
+CmuzZJmI/SHPD2ls6QLR/JIz64j+VDPzuH1yZTKdOpT6rZeTgkRzJqLRl5HnsdyWMR5VCQrn596D
+9+howELD9ik1g9BkId9cb7pMVNaupqekWhx21PbYrEmfnM1NCt281qUwtLkVcK6xCpfzE05z+VbP
+h3QVtS0fLOMRy72/nR/zevGSXByE5I+00tVyRnMq8253nJUjAmcoDbgZpHrgpGOPrFTPyPkpZsWI
+wznuqpakpmRvdbpYGok6ezrCHsoamkX3OVRE1rG6hU+KJ2x2hYEs7OGPoPB+iwFiKzo1D7mvuRZn
+UwVbdQLilgCZmg2bay2BsaedFqr9MZ4taWq6enblYlZOQXOaWllIooVqTpNnJMofd7PrxIMf8HFo
+DXNe05rW9CH6RMCcKwOpa6iiiIkW+jg5s8k/LeL8fudl7EqO90mzxXIofx+dyOlyll5S7Ain3e6U
+FCM5Bb3QJhhpEZVxyVDbeHYuts8grNNR8FKz12K0kN+jKGeowVH79S0OrgqHrmuCkIfJ28yHWhsx
+LtgPVGAKDEkqzz2ZJ0ztzwOwoeXl08Tw/n1RbRYXC164Lc/1XMNSIduhJc8vy5RzbavyAvq7mggy
+N8wzOQbuL+7z0p4EheWhliHv3sDb0ACfWshloWJR1WISaSm0eAFq/Gw5Cokm4VJxA6MqwlZQ0NHd
+SxaxGA93N8ewlHcvtPhON3nGPbWHbg0sqlDGe6fTw2g+wzyBWJ1QjiNjmDQaWOmRfMfyMbGKwR+S
+EqRj0ifNNVk9GhHdUtWm8Tlm2/KOyx+8w8VYvQHNlygdBX6pOyMqwZ7LvYt8wWwi3/T06IIXX9Og
+otIQZaqydWTOAtel1LyaZfaMx56Y8P2XPC4VW6HQGtIYopmMj+2NsWzFizTG2JEGWpERVj8EYLwv
+Ks6vfulXuflZWYfT0Rhb8yz0+y2WiaigMy/DaOXxq20tl5jVKBRuP41nWJrqbrYAVAhZXnnC5uFP
+RAX8ofSJYAq2KWm7S47ziBMtvumfX1LXQW22QixNlhIfzwnVkjsYXAUgLFOchg663ybc0TqJ7QHm
+iuhZTmhzaMv9m1vvATA8n2G0FPtGc5Osrp6K4wteaIvo3+1YLOaiKixUFLXGNrECRdwQiqZc33Jt
+hqfSt+PTiDtviR659RWJMvTbPdq74iLNzmO8bBX22qGum8nVVOZVvcaekckvTUQ1E5160Qw5e6xI
+yWyBp1mYujsSOp5VQwpNSV9El8yfyb0RUyIV/T1cPrOvjBNZjPfGQ+KnsgCPTh5zc0fTiP/KVZ78
+zhEAnZpFpiHFE93kXlUxcGUF3gwPOd2TTegtFzRV5WmmTfKOLPRIIz/rrZi3jtTjUiR/qB66IkfV
+srOLhDvH8r6d3RMGGvruHt5irineXb9GuC1ivGNWqNAFuRaS9fyL53agvdsDvJUftJtTaK0OMxS9
+vwoLXK3wlZUBkXo+ruUf0FZ7RdgQYF3VyGioxykMN5kU6rVJ55SejMWVdsmXXr4KwHd+IKa4x9/4
+Gtn56wAsvfrzGpTTk32CHU3wsnfA4RVpT5UIFsspuSb3LZw581LXQquJ5pOhlraYpuc/ZXQ/TGv1
+YU1rWtOH6BMhKVRUlGXOfBqhRZFY2C7ThYjUMxY4hZ6a1RhvLidzT/3cjaBOQ+P4fWuDLBArrR0V
+jJaams3qUzTlpNzS5CWzZULfE4mAZkGm+RojFnQa0naymHL/gcbha6bpyXIs4XyAl3m4avg0nTqR
+Zm7OopinU4k6fPBYJQxT0NPsy7UDm0Lh2lEwo9BM07amka9bPRz10ZeWRaypy+L5iEWimabnDtZM
+JAtHoc3J8IyF5hLI/JhY4b7zuCBeaGXuwKMfyolm6tKuZVU80/gS53fOcX5ezqMXt/ao/4J6LZY2
+SwUk7c41mtUt2dcMzlndUFMj2tiF1jORJlp7hvJUxuhCReB3v/GA3z4SXMRwMqZYgZYM8GMeCGel
+mWlinZPZgn/8TUkF1w9TXlObY91pUrsvBr9L5xkX70qsQW1TjYizmMlUTl2n16LytMpWOCDdVu/D
+zCZbqQ+azjqajWjd1gI/F2fMfk9E/7Nxk8c/FAny8ROBEQcXDU4VyPTo3QGXr2lOxWGPpkqh7aRB
+1dGcE5o+zXY9rg1E2sg6BY2WSDm7u5ts+Oqh2g+wbY3vMVrh7MywzOQdw8sp2WQVN+SRePL3o6dH
+xJOPpz6sJYU1rWlNHyJTVT8RhfynSjXPra5sDMgyC9eS/syXcxzVW6uq5LAlJ+wvfPYK/8bron9V
+feGcA3ePWFGFdSdjOhLpwGm2mWhk3EYvI9IMvaHiDmZL2Bwows7eJtAMQ3k5YvRAgoC+9vfv8e6R
+xOE/1KxQ//T+45/4HcYBa8X9c8mAAxAg1zZevc3yTPS7XdvhcqJBQLZBs24RGtE9ezubRIpNmCYJ
+i0tNGTa9oFS3md/cp9kTA6vXEUkjGQc09tReED8huvP/yjeRkSiWo1zG2KWMUWnJmBx2euxqLc2g
+sHiip875eMZYMxGXVU6SazCOfmee55RaDZnqR9DlpMzxHDmN67U69ZrMX15JHwK7wCg442B7m4ZW
+xP7vf+3XmI9E0ssn93jr+zL2/93f+fsA/O73fvi8ZiKhzW5H0ZStNqc6P9koY5Yt9X2ryNacpuaW
+sMuSyl5dLzAaoVqQk6tEEil8OrRq5L5Cm902iaZu2/BbvHL7LwAwW4oEskwjlupGTvIpqaJeRQrS
+IjGui1GIta9o0qIosIyKSsbF07Eqsoxy9a12QKi1P1aY98CvUVRam9RySZeaFSubMVMblGPnlApv
+f3Z6/82qqsT6/UfQJ0J9AAPGxqkqPA313fcrPPWlN8OSv3xFAEu71/ocvqrZimUf4LltlgrGqeYx
+xoh1OomPMFr7L0996soA7aZsiFrp4ZlVwRILAjEuuXEAN0Qu/XO/PGf4v0iG32+d/tEhqFUO1spQ
+aIHRqMtKi6Rm7z+grunTaiYk1AjG7dIDS5haWw2OznLMVDM8m6Ii1M07yW0qjWDMZxfYmuIdT+tr
+1ickalyclSPKhaga+bIgV7l84JUUvmyQfiB9fHGjS1fzFuZZxULjSkzi0lB50rVCNMM7TVuen6QR
+kSZVzEz1HFgzX+QUOt6eyclyNQLGOha+S1tVl/nsgqjQGA7bwteqUNVFzvK7vwPAxVNh0nmRovsW
+dw6xitK1RYGloIZGnjFdVfotVuqRRU83f248UDUhrVwaZnXdIc009kZTm1VRzoVuzFSxDQAX8wmT
+sTCvaKwRmUlMTUFWcW7w9YArsHC1EldQq1NXw2WgaejzPMI2K/RaQahzOl9OWGqdUse30Ix15Ao4
+q0xBqJXDojwlWGjsjm3TXBX5iWfExccLOFyrD2ta05o+RJ8QSUFKbyXOgkw5fNu1uK6s8bWX9rih
+adq6ixhHI/Uc9f/Hs0vyt+UkMT2PsdYsuPvkFI418caNOu2aqAfDUzkFB6Nzkr8orrxyPGdYCn7B
+DDdovCInr3+jzrEG9lhqGPyjaHUa+yYgWyU31VPn9Pycjb4Y+N7Nz3mprolafEO7plFtMzmNLqYF
+Z1qYpKo3GamI2qwHDKeidiTpjGdaD2OzLrkbliFkKkaW9gWOqgyzfI5ZpUSza2xqyq9XBmJo3erY
+nE5VTE4WXChuwPdKroQitrb7AZaKtolGTnZGDsOFvGNZOSSOtDtdLqhWUZKxhaOBSbkGHDW9GuNI
+oevJjEas6cMcC1ddzj94+h3+x9/4JgDHqmqVJc/dbZQlo0jaXdoWtmZ+TvMcoy7MSt2NrmczUdBD
+x/PINOEMpiBXiabmOqhWxUhzWSyKET/pnK2qapUJjfFc4MqZSXFUUizLkkpVBtu2MIoH8UJwFC0Z
+KBy96beprfAilUWVqyRQOriqQpa2RbKSQmIZV8+qM7zUdVPGzCJZO37QoNBo46jKKMrnNdI/En0i
+mIIFhFWFjSFAPvJ6v8UX++JDv+U7NJoy4fYsp9IFYilWvzgekaq06J20qZ0LMMU/SRnr5LsXVxhl
+Ygt4ciw3L3Y92u8rbj/+Hk/vykB2v9hlt5TMxtHdX+Nq5yoAbymeHGY/8TuMMfRXurZbsKHw4EtL
+Fu4Nr4ur6tFup01Hxd3dsEaiOmUUyIQ/Xab0Q41kLOZ06jJVk8rCbchzx+MEV9d2pSHJJafUbd1s
+zg5zFR19x8JZFYOpNTgINT25pou/mBYca43NYZLQUkb2UqvJreuSUXiwdYijeRAvlTE/bSw5X+jg
+WwkXWtZ96g8ZK2NxrZRKsd6+wnJZ2uArUsn2SFUnMJUhn8rifvR3fot7yhjnyQoLUjBXSLhtlxRq
+l9gb1GmrDeNgr8nFQj1CqtrkZUW/oV6n0iLV6MKnswLbVRXDD3jhhozL985l3FppwNFc4yEq85yB
+WLbDMtbITvUSEeUE7qoiV4Wtf/f8gE5Pk540urQUkj/QsOdOq07oynsrUqJYx3gSsFAsS1ktOR1J
+n04jWX+LJGEVE1JGJYEr91ZJgO/oHjGGRG0+P3nV/v9prT6saU1r+hB9QiQFkRCqMCbTZBQb9ZLr
++4o6G4R4mibL2lrgbSnkd6KiYbPA0vJilefgjLQIi+3Rbmi9virikZ46j7VE2WfTCtPWlFmnJUM9
+8WoPnsCfF397+IW/xOcv5QT94J74x7//zwHEbDVUtTyHjiY92W21mKh82VKDUq+06HvaN2MT+prE
+0CSkajyaaKKMelGyslgHrourSWbageF0KKfV0CkoUzlVk1zQcXnawO5qzYZqQqjBPAtrQaWncU5C
+2REprNTT/GS+ZKF9qLs1bm2JFXf3SpPdPYU0t+vElqYQ0+Cwum2zp1E7s2VJTaWpd85g/LzmQsoq
+NYStSV88FyxFDwZBidG0a1VVMp+LivHN+H0czW7d0uCxFx2XyBF1Ji1nzBTIcL1v8ZIWYtnYcJgt
+NUGNImRnKaR6oneiJSM1xMW54cSIVLFpZ9zckDb6oWRqfm95h6aqNhfDBEcjLUfZEEuRo6lGw5am
+ZK6ivzEGf5UpvO0xUO/ZwV6HrsLQm6FIY32rTl2h1BYe0VIrpfsTMvWeTFKXVNueqmEzLgyZeoMK
+KyXT2pSWUxBrxmin1qBKPl42508EU8CUVLUIt7LYVleRT4uaWpa9YICrYajNrc/ilTKopWaucVwb
+Z6muwIZDpObyzesuVqYVlwabjN4U3W9vR1WR5gaNbdlAVv2Q1kKiyR42Iz6lOmV3x2f/8zIZ3m8p
+wNSMnsNym47PFRXtdzwHtyebZqfTIlIATE2VeccYGhqR1y9/JHbOk5zGKjZAPS5bocVc8xl6xidT
+YJEXBkRa9WqzhGm1SgAj712apxAJQ6s5r6D1ZiiWDoH6PauihqWQ50hLq58my+fi5149YGNfNkW9
+lpBoVatpuaTUIiqeYu67Gz02dPFPFil1Vxjdw2d9Kk1gc5nG8Fw9kP5axiFQRmDnOZVutoIl8bHY
+h6Zl1NYAACAASURBVN6NY4pK2r7tyvv6Tci25bmnZy43LIk+/MyVFodq4U9nOT3d3BdGxuK6XyfR
+JKfe3nXePxFVslfEeBq5WeHzVA+lK1eF8Vx/0Mbe2dWxOCXTGIbpyYhcgUOrYOGyLJ+7lmu2RdPX
+w8DzKZU5+1XApkKeV14dzw9ws1VZLPMj1+mmoVRwWuFb3FTm9P+x96axumXpedCz9jx985mHe+5U
+t7rqVnXdnjtuu+3Y0IgkshUlEBQLkcjiBxJRJH6EkF8IESn8QGAhFBAgMBGRSQIiiRXSTtxt7PbQ
+Q3VV13zrzvfM0zfub897L368z3e6yzjp2y7HuUFnSaV76pzv28Paa693et7nsQv5/sFZgUOWPY0C
+aMpFCGnDMcjtWMVQ9Y/2ml+GD5fjclyOj4znwlMILAuf7g1w5k/hMhtyraMuyDvs5RAhEzGuNYG1
+KuCjekYYcJJCkRuwqTSCFbES7lKEesyGqIGLO//a5wAAu9SJ7G8UcJfEQgfdH8PmVcE3nL39CFUl
+liTofxbLV74IALjR+TW5HqVgmZSbWxngKqG0rZaBkPVmp+2iJoVxw8Rfx1mHIiimgAmD1t8LTTRM
+iLXIztugRqCZ6bdzrBFj5gQGQluslW/MMWQoNHbEbW+KLmpK1839I3yyJZDZD6IDNCOxJIOuwoKo
+oLblwK3GQIuu+OZmDxGTdmbgICGjsGe5KGkKDQqduF6DfiSWtBXlMEz57CvLPkxSz711FCMuaFWZ
+nXfNGvUP4AY0s+WmchEsy7Gvrvewd1eeyXgg13m93YY9Fnc/67q4yWTendtfREUNUePpCAGTazc+
+8QU5ra3hzCUB7V11cPP8JwAA7x4+RPU6ocJlhRf7Qms32JF7SioPs1zwK+3u5/FhI12uJSq4xHqk
+TPwVBi6UrZWpEIbsrg1crLTkngbtEC1WzSxac60aKMKjVWRBswIX1R6cDue5cdFhOGkwLG05FVrk
+tdibJlBkuda+gTlJd6BdlOaP1hD1XGwKytRwug0wVBcioVFnCR2Cd/yhCW9bHhKqHHpOUA/JVa2Z
+hYwlMsvdgHdOjYAlBzG1CP1oExsExay9KovqxArgdqUb0LJC3LgjP7/3MEb5lLvTWgKrkZcwIw5d
+mbsIQll0O9DYyuTndV8hoMtoZj6OLIkNTS3u3rLrIWDn524yAlieM6oSbYKXamLyW8pATeJXzx/g
+mKSyYabRZ9L+TmcN3yDaLiik+7LOHyCmS1lOVpCbMhd6msOrydFY9FCSlxCkfe96DlYXWXG/jX5A
+UVxTATZpz6HhxYx9uWnkhQlSNMJKh7i2JPc3vbaNcFdi2WyU40zLBj8motGFB40F+5GDmKXDpp5h
+MhGdCe+RhWWGNBszWfwP5hlW+ByDysAXbl0FAGz7FmZk5zo1W/BZGvWP5BxBV2PM1mnnLRvzsWhv
+XrMa3Fdyzc10hIMHYjCWWPnZO5xgzpZ5Y/kpAuqK1lWDLJdnYrNyVNbfDxVDy0MnkGvf6i+hHckc
+hoELbxHvWwutzQIljxHMLHi+nC8uJ8jZFOLHFVxHJjolb6NVJfBy0sUHJt5JZGHklYWOIZvstK6g
+mHd41nEZPlyOy3E5PjKeD0+hMaByB004xYjgn6irYW5LhlxtbgBUBUIToUok417T8mk0MMiL0PRP
+UbJ4b5QOzKtyvEbtw7wjAB/7XHontldrGC1JRBlLO/Dbfw4A8OWzUxy//6vy2f4VuOQF+InbYlH+
+218xkJMIY2Ql2KeadccDIo9cioZGkIl1rGxxF0eeRkkgifINaLqMca6Q0H3ukBwkRwaP91w2KQwC
+qLIyQ0nuw9Rdxo8Re/BeKtf2nfk2FKnOa+sUIPil0w4wbcu5o8CAkYvlnRjMZMOE0+F8uybSllid
+oI7gEZpdN8UFnmI6O+M9O2BSHOH2EsxQrPVtYwsfEAptT2ZIiIFYJGgty8G4lvtwrRaUJR5NMzfx
+7vcIb98JsXQkS/RBIh5Gz/o+RHu9F6KkqEtlxPCIC3E6BTT5Chf5u3ExQkz1J29gwWwJiExXx7gx
+4GvgOgip/zmCXM/N7QzvErOQPtyBziScMSyNmuCYOSnoGqNBzUqUZRvwmICGD7i+XJtGg5w6l0VB
+3crGQMUKSGpYmKfk+USNmpyRdmhBEQMzyLnWVYE5n6/SGr2QazKtMCNFnuEG0M2zIhRkXHoKl+Ny
+XI6PjOfCU8hQ4R7OcXc/hZmzb/5pBToKiIYVEvICqMaHcsUKNFNJHOV1G03FzsjzOSrGwPkwASjN
+VUUHMA7JRMyy2k7zKZgud9RoCk1r3OxcwdEDYWdaq1OYjliK1S9QW8H8OjzGrO0mxEa9KBGGsBkb
+T/QMZUIEHXvbjcLGmGSfGhop78kySpCDFgaZk7UdIGHOwYGFFktzTRihoVc08B0cj1k6pRKxb9/F
+aCoJN3vwCnaPhpxjDynxBL1tG8xxIqXEcx4asD1yVmxswGYdv1YmpsxT1fUpDFs8jIMRE5tmiJT6
+Fa69BKcnHktkztBbJUVcozAj5DtnPsTpaPiKcnvVCRzmfkpvhtaSeALvpwVSJizKkjmQpobdFY/n
+2vISirGUlJP0GHEl1n08yQAmbm1+L04nKCnXNg88zE+EFyE2PGibHbbRCjxiByZ8NcrHNiZDmc+j
+8W/gaUqmo1IjYyemQe+2aRRMNkF5hgVSYKBKNNKI+S/lw+KztNkkBqXRpgdWNxWMhVScCqHYsJak
+NVyifeuc16YtFGQHN3UKt5E59OwOUkoSxvHpBdT7WcdzsSkYGvByhVGcIOLLMfJmF/LrpRtjSmCG
+3h2hojT8PGX7a/UIQcJJDz30OyLSkSznGH1TOhwPjDHySjALx2SJvndvghd/XFz/W6/8FNw1vlhe
+gJUNJtfqMcpF8vaeuJ+h6SDgxtNqm+j1iP1HBpNdmaNeFyeFuG0hs/pHcYrKlL+3LQd875DkNtyI
+tW4uqsJSsBo5RxYUaM0obuJXKMjaW6gJdjx5CTMSd7x17xrOPdlNDXcE15KFMhqXiNbkZbvvVrjR
+kWN0Ima0DQdFW1zqdbvBKKKIyr0xYnYJqsRETUbs4ZRJxGyI5S2+VC0HGanT0yzGOunDrmzfQMCN
+akEJZ5k5ugQhTRyNjC+b1ViYuUKQ4noj7LT5EhLabCUm1h1xxffSFM47Ug3I3RBjKoPhfALvhlSg
+Ir5gc2jMhxLyTLrLePNYfh8MYlxXMi8tu0FBtzuiupOpjrFsy1wcxhoGn3uF9GLjLBcbulFCLUIt
+U8Ni8nhuGADkXrtNgUM2WHTHMvf96z3MJ4THFzkMShe0mw5gsZPScVFANuKMQC4zbbDVlvt0bAcJ
+4djjfAJN9mutQuS13Pezjo8dPiilTKXUG0qpX+H/X1NKfVMpdV8p9b8rRRD95bgcl+NfifGH4Sn8
+ZQDvA+QDA/4LAP+V1vqXlVL/HYBfAPA3/3kHSMsS7x8cY5Y0IOoW6WmCYiBW5ezhPmIKERS5jSld
+5eK+WKsOauwvOuB6PTTEGGT3Yrx/Lsd4OisAJrN2j+UkffsAb+7JcT9961fwudek8adt9bAa/CwA
+YHr0NoakP3vwoezUjqnRpvX/xCBEj3gCPSvwhH36WeLjZCY794SoQa/xEBL91/QNmJVcx8B3sWkv
+iFPofmoTR6dyf6NTIFVyH0sncwzJ5uyvtmDuiFfQD24AAHremzidSDKsHq4CHUqZRy2seYJZ+MJS
+/0JP4d0TuSez5cCay/m+ejdGTKvbPBhDEwq+aUYXBCHFmN2XXoXvfldoyZ48SNClCZikYyy3b8l1
+nBwinMlSO2TL4dzVaJMNOaxbF0jOdPy38B1yKJy+kcHsiWX+uQ0JE+5PzmEQuagmLg6J+Ou3cvTp
+LeZeG2Et8xL5LOklIZ5YDAPeP8cBPU8zDqC78pmOsi6ShmuO/G600cJGQI9V2xgeHPFnA2W50OYk
+NR9stNmd2YEDbcpz1xMNrytYiLemCbDHZ7KAXec9tFienGQlPCY7VRcwSD14dDrG6Eye+4Qs301Z
+AiSWyawCI4axuXJgBUQ6Jjks4kF+L1H2P2t8XC3JLQB/EsBfB/AfUUrupwH8eX7klwD8p/ghm4KC
+gq1MhNAo+tTcWzFxwhbTUalhnpMnz0tweJdMOalMyFJj45jx5FVUSDtSs/d7LSQUcS3yDsbshjvn
+796LJ5g/Edfqg3c92OwZ+NKtO7DI2xd/d4RHb8jDeH0s9PPXgwBOxGur5tghOUk+8GCcSqViMsyQ
+882rSlnYR1UGhx2TvbGNHmvXvdBHtM6aNV3R81GJu4VsBCfnCRJ+z9EOagJz3OMSq125194Ntuw6
+PXh0/YfVGb4cykaQ+wFeGMjPGRqMWcc+pSt7/nCMx4vkQZlDu3K8x5MUL7HNelLEsNji7VhiA1Te
+4IMzCascc4SKPJbtSmFrQ+Zrb1biCdmb5tw0T+JzuAbxG1shCpLknHzXxvvvyc+Tqxq3mT8xSV++
+vWEBx/K7PNDwLDn3eKgvIMaZmyNvZI6GMTEGukS9KJM4wJRz6+YaU0OuWTvVBU5kpmRe7iz3YL4q
+7Fa/+Svv4dCT63+7HMEg3qAgXb4yGnhUiKrcWqi4ANQdEyZh4e5xjtBjDwM7f5tSw2cviWcGqBK5
+tkJnmHOzPEaMnEzYCwBZAAs11+HxZIKCuJaqKlFQVMgyIhTNQmT32cbHDR/+awB/BYvOHWAAYKw1
+ESrAHoDN3++LPyhFX/1/tMIux+W4HP+yxh/YU1BK/SkAJ1rr15VSP/Wjfv8HpegdU+mTWYGiNEAN
+FtQPSzyiw9NasnA0Fyt2/HSGb9LlXXT11brETVYUzkyNFSIIrW4GZ0122vPpBPsT2f3vUmKt7SrU
+mXgEZxUwprK18YlV+IToLr38Wbxz/3+Vax7S5WyZWO2Jd/Cq5SFqpMKxZAQoB2JJRpMc22x+OqLG
+YYsdhgCw5rroUwBmw/Mw4N/mhAPXLrBN0Y8lHWKNCayRtnBEKbxJVmJC1GfdkfOu3FR4Sr0Jz165
+0F1cbTmIavJP1AnOSKgyGcr9H05mULGcu2hyZAs2M2g8JfFIbVrY6Mq8vPoyUaWJg0OK6Iym8wsv
+xogGOCTa8jA+wSSXpGtOSXqlKsBh5cNMQAcDx/glRDOqMp/ZqPsyB9Z18XKu+1PElIPf3Y/RZGIR
+o67CCpG9Zh7BISLRatglqWN0WjLfa90B7HOqPM8KXKO3tbzewqSUe7HJq9l1V7H8SZENLD0fT39Z
+Qqyh9nCYEpG64FzUDhwmKFtuC6HFLsmkwDml5kfxU6QJk8OpvH7n1hEKYlqura8j6BC6fDTHwT4T
+unOFjGzOATVWn+Z76JQyP4FXoaBUvfY24Uv+HJP9PTT1j2Z0P67A7M8qpf4EAA+SU/hFAF2llEVv
+YQvA/g87kFIKlmFD1zX8riy237Qa7EQywUsdA+4mBVXGu7i5KZMzryX2mhUxpiReeaFjww1lUoP1
+DtqnTwEApn+OJKWIDN329V6Ep6eyCE6LEl97857c2O1vo31HSDnbLR+rWwJ6GhxL996Xd27AZTPC
+eqGxtUNREHcJgwPKzicFnj4peJ1s0/UVbpN5aVnXGHAR9zoGIkqnayoM6SiE6cs9n1ePcMJW4LWB
+g05X3Nnx8BArnoRNbZN5BrMNm1qERjFDV8t8dhwTESsqONdwFySfrBGWVXPRi9E4CsaC/6Rlg02E
+WO+YsAiourkm57CXJzg8kJzLfmIiZ/XE0HPs9GTBnpkOmmbBV8gFqmq4bCP3m/YF/2Gn+Wk4R78i
+57hRo47Z8bkic9HMPRyeyIvyzpMRbHbVXumuo39DXl48fojespzb5gtU3H+MTdYI/a0e3h3JTb2b
+zXD4WHIiLzbrSBlKfPJFAoWudLG6JM/fL9Zw9iW2rf/GP8aUojQl79kygNUVuadO5MMiVOtwOMaI
+13yEAiE/f+VVWQtvxVN8lqQ3dtmCTTDVfPYAj0liq6t1nLDjc+8euSHTIXyDLGJti1UOwGjFsIzb
+AIDAbjBS8qyfdfyBwwet9X+itd7SWl8F8O8A+JrW+ucBfB3An+XHLqXoL8fl+Fds/IvAKfzHAH5Z
+KfWfA3gDwP/0Q79hAuhqbEQRVnzJGv/8Cy/gFjPHrtfGco9Z/dUIJROMn18WAY3x4RGeMCFzxQrg
+s4FFJwlaHfGjBphi4InbeXVFdvDeldv4HMOSr99/gusUlPHtDZiNJB218RDhokmJ/IQTf4ROLBWQ
+0LfgUx7MSQGbru2S76LcYu15Tuh2fxlrbPjq2glMhke+qeEv8BkEqJiFjWV2ibb726hW5RqWei3M
+zxr+7MDg/c1m4gJ3zXu444qVfGL2YRniKVWFBeucCtteBweUlC9zZshRX7jBltJYXREvLLYUXr0i
+Frid1TAXHZMzSqu713CtQ0ixPoEXi8Wft9vwE3YJeoCnFx2RYocihGixXjWwFRrKuGXNe9j+43L9
+rff6uOPLs35JU7xGHcDsUtLvs23cNuX+W6GJoC/PLEssOAwtFyzKnRFwSNCQde5iLZL7Wx4nuEEp
+v7VWH0OGOSXh467nwi7lmfhug09RknC4fQPf3f2unIPNYR5cRLXcxxUvQOWwQlMA/o5cZ1Oe4urq
+C/KsyKGxbAWokwWX5inMU1mH8bRBTtGdwZUWeo5ch83msJOnFUxH5n6r1YOuGUJnVzB4VX5/dLR7
+Qa7zrOMPZVPQWv86gF/nzw8BfP4P47iX43Jcjj/68VwgGluGhZ+J1jAJPPxkV3bUrTBE57rs7J1w
+CcjEAumRg7Pi+xJbANB3biCMBfK8vr2EaE2O0Q99xKS3eXmWQZ9J7LhoKNn+tAn76CoAwOpYuBIS
+ohuaKDOxsIbdx0ogn7m5RkLRaY6ljngBaZHBDBYMUAECisK+HLjoH0sMuLUj3x+VKWwCMazahMny
+VeB00CzLbl4w+RTVHpyBXHvQaWNKkZFxYsBbknkZHVYISdBZphJnJkYfy58Wyzc+fg/BY2Io7Dks
+W37fhD5apAQLmKD16xpdavatb3bQ78hc3Ny5Aq3EU9DpMWaJxMZ1IZ5SZ30Vn/wM0YZ7GofSeYx2
+X2OtL8lB5Yzg7TKuJaIxCIGG928MumgbMldr4X+IIP3vAQBX1ubY8MULK0nyWs9LuMxGX/U0WtfJ
+iGx78NkQ5Gw4SCg5qHl/1voAfiZeR7SlsU3v7d/duYnAF69Bt30wFw1PL5CNFXQp8n9m0sXyNcml
+vJyewf2GrIEFHL0xK9RK5qW/08HOiqyFz6YWEooSxfYnMCNfglvIMyh1CmUukuYGIuYXQsvHFpPN
+jq2wyoavqi95hLbqYPmqeNY7/St4MZH1/c3TGLOnch1GHUEp5qnwbOO52BQay8C86+HT62vYIWfB
++sBAkFJS3TahmTBaX7l68SLUrNtOWwodLUCZ1YGLiJBfjGP0mKy7vjWAbcv3hqeyqNrHFcy2uG0v
+l5voXlmobRjQqUy8zgKoKwQ7FfIA3nz7Lvw5p65yUYxZ545MhOH33f/1Dfl9SAn4vuEjYa+Bb9uw
+SJbhuwGCZMFrIP9ORlMYzIB3lAO3I67xLI1RBOyrVzNUVJSymRlcaic4mcq1W0kXU18W7OlRgd1D
+0W78fLfAiPX/gDBo5WUXHAq3Osu4eo0cjmij05YFO7YDhKQg249lE7o2u4JmSf7eSzycEwjUtSzk
+y6yIJC6Ol6gCTRh0N2zBYVa8zA7gQf5evhyif/qSzO0bj5Cfky+ABCrVuzHcQJ7ZlWAZrYVCiqlR
+s4HEnq/A8VjFYfVhnKcXHBCWDyyTp66pc5gEDtm6hEHik8XmpbUJA7KeVJChimVut9c2kFskSaF3
+XtUKHvERLbTRIS+CHQSYsBJjVhkGvoSFC6KaLAdqHqTtBTC5XlprbUwS8kGoDC2C9r58WzZpJ11B
+j0lJo/Lw4bGEWN3UwP5YODvPp4dYiEw967jskrwcl+NyfGQ8F56CagAjAQzDxiSUXXKltGFSPdnI
+kou+eNuo0QupqccEkG5O4LA+HjgdOCbdrKCEmojbvex10N4QbyJ5gS53PMQgEAulN67CWPA07L+P
+JrzFEwJhnwy+jbD1RHYXw6FYIr9dIrbl52Xbgc1OS2UYaIhkdHoL3gSNiGVIpzFQLFSQoza8dfme
+9xb76tsOel25Nl9rVMfy2UHXxoQlzs1uB3PyIays0OrEG4gNSSKeV/t4lQS0j5oDHJ6L6z9olVBk
+IVplyDAvbAw6Yvm67RBLtXggT6M5olNyGWy5GB/LDYxMsZj3Hh3g9m1hrHp55wZu0+ru7o6xuiEW
+rSraQCzX3xyL52LbLlQhn01zDyMtx+vmAfwtNv4c7WJrnXqaD8S6hq0GlrEI83z4pOYznTZScjnk
+wzM07AgtJ+wsXPLQnC3UujsIaXXTpoRPuRfHXUaxEE5ZaHwMZ8AKWajGOUyf5KnWGVpcn+e07Mqs
+0IQkgbU1THbSotRwKQWnLQOWL9dvUAPEnFVoKDTkqRBOV67HPAOWqR+5vnMFGyQFjtjZ61kWDML7
+p+UI/ofSSDY7P0S6JyGDafnQWBDMPtt4LjYFrTV0WaAaVsgI8Yw3WwgIS21wDoMgndLM4RHpUjcy
+6WETol4wTukGIE2b4aoLVg8r8KFPiBs4ltq+cgdAW9xkbzhByU1hWGj0ntDdazUo2K14fl9OkpWn
+UKa48/nchDUlnNWqoLDorjShwXZvXntQN9DcsJKigkH3suW7iGzZADy2ypbpMRye1+pYCKbsyDNi
+2FNCYosACXED8Zncx3T0LZw+lDDhaJTjqSdx5pt7JTSxDgMnwtVwQeZC7IYBgLRyKm+QKdlMnacG
+Hlt0Z3fbiAlwOjuVf+8NjoFKqgG3VzJkj+VlOo4TxK/LdbgIsJXJ4n7MF6FRHqpa5md8NIXLUsTb
+x38T/83X/gkAYOUfJhi9dBUA8OmWvBDNkgf7jBtZXiEYyealdYyKdPCzpIAmD6JJ33lQ9HDOXoVm
+7oIQGLimhZgANh0XcGcU0uHaUkEFRXyLMavgkncxSzYQz6ktekHrbsPMySWaWYinMkdObUKzTdyy
+A4TW4rUjm7du0PBVbMwCNdvIVVnDWAjdpgUCkMKdtPWGaaE6ZFjlWzjkvT4aFZhQp1PP0ovejPoZ
+xaQvw4fLcTkux0fGc+EpNDWQzk3cn55i/4y7rrcNY0V29oE9gOEu5LpsZNx1a7pOurTRsNEmP5tB
+M/lkFgoZVYRR5ZjWsnMnJH41jAwBlYPLdhtFzorC6RFqJRa2eOIiPRXP4nceiCz6jWgLGVmEMS8x
+IlLO0R4cJowMlcHx6MJy760c9X114XqMck5ikbYHRUsIfifKA1TsorMLDWddvlee1cipYn1eViiH
+8pkDV0Kbbz6tMEzpxZhPccDGGF8Do7Z4AueugRZ1F6Mt8VCKB8dwfSLi3AbvUf/g6GSIMcRarWXn
+OCHcepox8z45xfGTN+R3ahtTypy9f7SPzpShQtSCZlJxIQat7AQZG4Mi1YNFXcW/8z/cxUOKobwb
+FIhyyt7Z8hxfuLqNyZ54Mf2ygjU75AGBjJDn87SCz4aucJWUZyqCTabsaTFBSfEgy7RgsKmoznLk
+bIRyWZFonAw1UYNlDVjkLIA/R6BJsFrL9VZGjlPyTAybM2xo6bo1PAs2KfkKy0DFEMWkBobWBsqR
+rPVZGKPLhLauK4wm8vsoOkT1mFwcJG6NbAXDpcdXzS88jKKeIGO1wzEiFOpHo2N7LjaFUmkcGimK
+aY0t5gbKqkKq5YGO0zG8iJWIzEcZ8uXVsmAq1LAYFx5mGXyyNymrhkG+RsfP4BMIkmi2vGof8YRS
+7ZMEqbVQ2GnhgDkDo5rgd7kZvDeUOA2OhXUlsawBGy5jlzg30eLiNhrAxIKtl/Tzng9jwWjUWJgV
+kts4PxwhpyrSaS6/G8cTEBOE2rVhcMFXeoI51aJmTYGyIY6empmuNcYslw1Nqw6+t88uyAQo6T5O
+0zkOtmQxbZNHsAzURSefMdiGyqWEGJs2WuSNbK856BLPX7E3ZDnxMCaDdQUXTyfizp5mJVL2XXTL
+HDXbgRt2J2qYMGuWGd0JHFPCgF++/zrG3+GLVzb4u6aUA9/bkjn8y48ShFfkPtpxgDHzHamaI2c2
+3y5MJJxbZ7RQnkphsloF24GayrPMGxvaJadlVFwYn1IzB1KuIh2TddqyEFPnMTcDzLhRLfQqdaOQ
+c9dLcmBuyGYRGG2YFQ1AXqC5AMmxT8LuoOyyH8dZguLchm0b4Ux+nsc1JoVshmsd+X7q13CpFTqC
+hUN2oo7VJnRf7jsfp4BaxNbPBmK6DB8ux+W4HB8Zz4WnYANY1gZOkgylOAR41c3hkCarLoGSEmO1
+tqDYBNMQ1lnaMYZPZGc/aWKs0L1GVKAmE+/gRRc9CKipbos1OHryAMVCrcvqIGAG38rHqNrigu+d
++ZhQR2HBUpHlLvYS+d3SqkJq0nIhR04cgtdyUM0pDMLEkoYDg/qRrh+gT26CxjAQN+T7I8Nzv7t6
+UT+vMEFCC1TlDebM5BfNBCMmz3xPLG3etFFbC1bmCjYfcV2WMEgM8wgZokquv2Amu2v5cNkBud0H
+1mPBd+gXWtBHEmKtuQYsJk1ziwnhxkLIsCRJuyhpjSZ5Cp/anBPbQdtYsBUz2VcXMMmIDdcAMrkn
+e+QDtPKAkIcAwD1yNnzntoWvDAVAFIcJykSsp+13UA3k/ma/fR/GgLRpntTum06MLBXLPegEqCBz
+FJspLHI5OL6LioQkXsAwNhuhaCSRmjUlRkyOnuztI3JksaYERRlWjZI4k+kMSKnA7SxXsogBNDCh
+1IJvUz5bJTWsgGu6mKNk9+jB3vGFJzszumgYNtSUhuz3fHQcubajcIrdjPfROsBkJEA1T52gMDmf
+C0KDHzKei01BaxEWrZIcKdGG1dSDyXJMWQ/hJxQfQQXF7HuZ8i7rBikZc/yphbwnk9MdWZhSOGz0
+uQAAIABJREFUR88cd1C3F7MiD/PeUYaKhKI3uiWW3KsAgFGT4N535WF8+OE+5iFdVGahn44n6LMj
+LfvQh9uTxVZ3bfT6Eju6cJCTIWeR4YeRYkZ2oKIpEVJlyjJdVOxVDiEboRU1yHlP8yKHSRGWrEgx
+YhiENES4JIs7H0oYkZYPYTNLbZdLOGdIBFfIPABgWdm4wpxCb8KekjJHPZJj2RMLDmnBt08neDiR
++9g9ScB9DBlXmG4NwPcHZXCGci7fq3KNOYVMXh7YiFK517tEdHp2gIirz25MpBQsOd//PWpG3I9L
+S168u29FuLUtIUU472GzR8LTPIfFDtX30wT9R5wDSo6Fto8zlyxMbgSbwsPZ2EGqYs4F4HFT0CRH
+nURz1CcEpx1VOCFVyDsfnmAWs/pA1SsFC25B/Uj4SCw5d5S0YVqsapg+DG4QyZl8f57EUBQhPp5P
+kGjZAOskh2eQj9PUUCQnnvKzzjBGSbBVENyAFcn5Zq/P0bAHp2im0OWP1jp9GT5cjstxOT4yngtP
+wbdd3F67BbfzGENyKs5UhpQCGcHaMtQiCVbneDySXXdKa71hLsHakN0zXNKY2WLZ/HQJk64k3VI0
+mCSsAgwJnY1MjCaLHbwFkO34/OEE335XfLQH0yE+FbIDcY1JpGQTQyXnKM+fYETZsTtGiIB8EEaj
+oSneMaS0V1zO0VD/0vQDFC47Ma0UbXYJ5qTwMooC47mcY5gO4btyjkpXmNl011seOlq8qV3yIxyU
+NpyOuNe6k+Maab/NfY0RTUAYuTAIx92j5S9UiJyal9+6N4K5Kh8+HyuAHAOZob7PsE0AUTtO4Q4Y
+ahQZamIrarsBGJpMOgYmpHSrqEo9jXNYWwJuWjaAgGI46vdCculkVVypj9Y0DktxmevBHA6/l/oW
+1BH7MdY2MaCwjU9tS9MMUGeSXExbDjJ6obN0HzUT1qm2YLfZB8E+inlSY3gm53g4blCx9+OkGqFL
+K35Ay97oAqfssjzNR9ipGNKZNUz2YOSGhZxQ77JYJP5clKxKxWUCFRPrYjnwKcqjCheHVDEPSSs4
+CTTaLZFT9PoBZol0rua6QMMeGq0sKM65fkaSxktP4XJcjsvxkfFceAqNU2G2fY4rwz6CVHbPVq4x
+m0tcH9kW0GIcZUQ4HD8GAHzviXQybq9s49UdSSh96O2i90j2uierJZbYGbjrKTS78r03PpR/P9dr
+o7MttWS/KjEcSz368HiEp0pKa11Hob0plmLbEgv1j/b3EbIhKlA2+tzxJ1mMuhHr4PgagSHW6jQl
+ldzxCRawibV2Hw9tsTrmvILrk3qNOICHszFmFFwpAbz8opx7PB7CZnddaHUBSqidUcLMKA7QOC7P
+8XncfSpWJc4NVOQ0CJwKT/n5AXkDEjNDw1LuG8ZDeEIyhTQpMFh07bUCtDKiLNmUlaXnuL8nzyGp
+z/CY0mx1lqNYJcfaZIpHrO8XZCQ2Qx8vs4RWOQ0Clkbr34vIZThMGAqcxxnOrsiHdsYG3uAXToZ7
+aIgmvW1GOGCy2T5mOdh04DGW349s9HJBW+Z1jU9uEPLcqtGLFslrohhTEydUlQ7rGPcKub+50aCm
+0q83YsK4rEDGNORmgpqoV8+1YJPEd5pWODyR9VDU8hw3+z3oTLzCZDhGnC+YwTyc0JMdDg9ROjKH
+ayG5GawQva7gW0ZJF9/7p5IQnsQxSO6ERmmgWYj1Pdt4LjYFozYRTUOgbWLlutykZ1oA67VZYaHF
+zPL8xMCL6/ICJfviwp7nMTqWuMzXr34JMMk1eE2jSzzBC+4Uu1TKaZNkZasAXCaq8nMX+3wws/k5
+rjFc0b5Gt0egDtWWmqGBs0WV5GWF7UquWTsaU0KsW0vZBR5iuSuhweTAw5FLpeK6xAr52OpUYbIg
+5GBmeTY8wXQqv1u6FaDLZJbRjzCJmSSzUszZ27HuyJwUS5/G3GUtvaMwZ3klzRpYVEG+b1r4MtmD
+l8nb6OUm9qnWXaBAmlIluV0iZJLspTBAxtDFWcioVwpmJC9KdaxxQuKQVCv4BHIlezMUhhwjqeR7
+d1ou1taIXWibON/7fsXh9xuKZClxGODW6lUAQN8rcDOXTe313MBpIxuOUxd4MZRNdBSwpr/URutE
+2rOdIIdTEjZ9w0CHmo5Rz0BN0R2D81M1FW6syPN7bAA5WcPzsxQvrwpN211HHlqZxVhfZE8bG6Nz
+YkdaOUZ05y2t0KIaQuEu9DoNuEsMiYouVsmT36l9hPH7AIBt6xqcLVkDHXbMFs4IPa6LN05/Hefn
+B3wmGhdxl2mg+RFJVi7Dh8txOS7HR8Zz4SlUVYmT0xO0zTYCW6znZK1EIV4piispcE7+flXAJy3V
+ypLs4M3TI7z/PUEddt4zUBXUd/jqCUz2rm+vmvB3ZIe9Tsos85oLm9Jdh+kenjyQkOGNUYp1dsOh
+Y8M4oNZBJN+bxU9RFGweitbwhHDsaKyQkvLNGHbRX2Jmh0hCvw30D6gV0J1h9Ei8hqIyEJOExF5g
+F9IU7WW55yXVhyLMWZ2aCCljViYNZhSJqeaUD+sDJjsD29UmDNbNA9gwWAL9hGWiPRJv4oTfP5hN
+LxJZ1VDD9OQcIQKEm+QFcFtQLuHkUyIlQw/lnFJyHlCdscRpNjjdl5/PrBLjOWG3RHTqqgWDSEg7
+a6NLrwEmgB9INhqkwutSHu9T6x3slY8BANN8A6upeG8oEvhzwr/X2niJCdsuuSBasxhn1qIM2yCV
+iAHB3ETKptt61iBkZ2PGa689A/aY+IfRBBZ/NgwX45mEFT4Rtrc6q6gjitpUDZ6W4kGo7Ar6bGxS
+noZP7c0WBXW0mkER0xBFHlqU5jPCEv2JPPekHcNhaF3b8v3jBzl+eyaUcP/gd76FGRuzNL4fMWjd
+wCAF3rN6DEo/Y+fUv8jhWJZea7dg2CZcuom6qOBRZCNpcrxCaOef+/N38JWf/DMAgHJFXOfsjXNM
+XWoVPh7he+9ILH7ydIxzkon8+EtXgE1ZCZsvCUde02xjbVWOcTY/wcbSHQBA/4UvwXfZUg0Xi8BW
+k19x8+oSxssUmSkKvJZIBnij5eNBTVblaYmrm6xKsOW16Qa4o2RjKsMMIYlc3j/IsE3X3uoQvNQJ
+kS76QNwE7xCu7Pk1XEPu49qndzDlC5mxR+DB+yW8Ru7pQarx9X3G+LUJiyIiZVVhwCy7RahxhhqW
+ko2w1zJxziD+fBzDqxbip8VFz4BPKvM8S5ByflRjoc0Yd5ykMAj0afcj/Oy/+ZPyHL74GgDg1noL
+40eysUZ+jAk5FZe2bmB6LC3AneoARcGqDEE8jlHCvyFqWNVYodVn12VpontTNERh+rAo6lvN5XnY
+3S50HnKOS+iUWX2/g2osP1vBFDqXtnNr4ytyLGVAOVQ6rm2A4DJoE//0ocz9G7vyb6ddISIgq1fM
+kdly/y+srKAiF6Yz+gC7sWwWm55UbYL1AQZtwqCzEhVDrHgyRTqS3MeHbz7A/hO5tsMD2YxWghRF
+Ke+LY/t4ciJWdO/sFKfsiRn4JlI+96++//B1rfVn8UPGZfhwOS7H5fjIeC7CB0CjaWoYmYEoEuvx
+QqeCExGuW4zx8y/TOt6+hlAKBqhPZU+zomNYR+xNbzr4Ulus3HeWR/AOZbd+8tYx7G8JgeAslc66
+m7duwfX/JABgy9U4G/8mAKBXfhZwxWoqw8CFp0AugLOTFPURL10B533Z+a3Op3GHjShdp8CVdXHp
+f3xdElLeSy46xDSYjQ2LXsXOi1NgJslRN5Dv+H0ftSHWJTkcYkrFZMfJgHWxxlkxxkqxwAKID/zC
+ZxLkhzJve2dTlE/oopaASUKVVc9AQ2ZjhzXsZpojZ2J0lORIiIIzAAzYMFRZS2izmbNHTouDUY5T
+JtHgFzCJLbGLDLNSrt8cJfjd7/0OAOAlSq7jz/xb2PiUzFsxdPCQSMarjgXdJmbh7SGsHj2kDpGp
+9QCK3pGhU+hEPB5/JYJixci0ImAqz1ixE7EZP4XVFpo3lSkohiV68h2Yhsw50jOo9sv8vLjlZvRJ
+XMiDWwpKLTobNcwxm9vobeGwxvqK3N9634TbkkpUq+1iTnyH7W6gVYu3aFKCzg89eCYJeZwc6US8
+g1ZXo2sTD/LabWwTIr+/LA/BmYxQduW5z+dj9CuGxZVCq0Uuj7TAjLqZzzqei01BQcFRBkpkF22l
+/a6LLQaXP/mVO9h0ZFI7Dw7RDN4CAFSHlKSPTaTffgwAWPqJFaSFVBz6ezFmZwKJfQtDPD1hhv9M
+3K9feHEX1/8zWVTlgY9vvf1bctz2Mu689td4be4FCd/Crap/T2i2T3KLW46LL31GvLPW/ADXPVmE
+O7fkBbTXgouYWyUzBB0po0avriB5IK6hOZOXzbEj1B2Zi+njPSzRHS6WB4gjeWHPTk7xMBV3fj2S
+TdPcrnBE9aeN2oPNRZzb2YV6U1Yr+JYcLyVefpZlMBkmJK5Czjbjpc0Wusvy0twKu2jvLHHu5fvR
+gYX2GVvSlY8TxrXzPEVObcO8bjB9Q67pvxy9DgD46z/9OewoCQnOj/bRoUJWo1JYrGA8io+xFQhJ
+r+nJ4s+qKconEl5Y7hI8As5UFaM4pADwzIAOF5qOsvFWo12EoVQ4zKXeBbCqPHkIp03i1cgFsu/J
+7w+/AQBwNxQsn5sGUuiGiS5ch00CXZst4mMvwYw155v9AQLCqnU6hqfl/n0vRcWweHYi4kPWKEZz
+RSxdmZ4gGcrvUZpwWvKidzeXYSj5TGtP5n5vvwHYM+HVNR6ZDP/W3Au2r733jhE1P1pA8LHCB6VU
+Vyn195RSHyil3ldK/TGlVF8p9U+UUvf4b+/jnONyXI7L8Uc7Pq6n8IsA/rHW+s8qpRwAAYC/BuDX
+tNZ/Qyn1VwH8VYhAzD9zGNDwDI0lz8Z1WyzGS8sr+PElcvElLqwOm6DOc+QfvA0AcEvKtz9NEd0U
+nEKQfAouhMn2al7jKTPu3djG64W44MlT2alP7AD5fXHbZpN7+PD/Fkty5wUDoFYirASaDSgXe6jC
+BahGKQtf2JDw4E9tavTJ0GwYGm6L8l5t0m2/pzHcu8/vmbBfEffSfS+HZvgwPRNr56604OyKC1yd
+FaC6G5ymg3Va/7qKoJmgiuZyjvqtFC2qOc/HOUB5ctcy4dL6tyIPeUo0EP/u2zbWlmQ5hB0b6xuS
+BLvRXsX1HUnsLUcWCiYgU9bu9/tdxBQyUfYMd3fFC/vtZo7dE5nPDPUF+Cgjh/rxNwJEm4KQMrpr
+iMcMGQYB4nclDHBqoLZ446zHu2kJEFfQKA1Flu/sgzMUR+JBaMOAvyXXrMcC/a1mQ2QMS1o7X0CT
+fgAAKIdnqDbJ0bj9EyhTsdLzPeo26nO0SbWPZgZ4C1c8Bwg+WmE1wLubYP0zxNlYEfKp4AYs1AhI
+N2d715CeyTm+9rd/FQDwwvVP4jrXVnZ6H/tvi7fS37oCb5Mhj3MGzybcnJDpdmuAjHT3WnUxIKGM
+X3YwGcvaSdsFyuGPpjr9cQRmOwC+DOAvAIDWugBQKKV+DsBP8WO/BBGJ+eduCqZS6FoWgo4sBAC4
+MqjRvyMvenetglkLMjHpvwkzkpgME3mRwus7KNntWPePUNGdU3GGMpIDBo2CTeFSRUDPY8wxeUOk
+Lof1m/BCUm+PD9FocW2NxgZIarKo84SGAoGJ8H0DO2yBtbYsLHe4uJWPs5G4ruffYdz+aIhhReaU
+ZoZrJAXp2T5SdrUlZxJnN6kNj8CiODfhspxmOUBJXcZW34Ye8cVrUcA0cS9yB0W7hkHi1tqqkLKz
+0clSBCy92VzQy5GH13ZkY3np5k241LVY616BRU0C2/fQGBS0TUnrbtbQDduTo6u41pPr8LIT/BZ7
+Td6LxygYgp2dSu7g73/nb+HHtFQLbvhnaLMkNxo/RezIhjPtNlit5CVU7GGAZcFdlk2vmaaY3ZON
+YH4wQsHuULfIURky93NuQrbpwc7lGEXzBEkif7eCFqJdubagtYvJnly/Ybwqx5rPUE12ee5HsKwF
+c9YmXEM2hSnPMbp6Agl2gHRewzJkcyt0C/WuHLc0H+LDr/0SAOCbb1LHdPg7OGOS6nw8xHwi9/GK
+ncFpSX4hCE2Ac49aXnhtZzCZ+4naPqpS1sLMniMp23wOfZgddnM+4/g44cM1AKcA/mel1BtKqf9R
+KRUCWNVakyMLRwBWf78v/6AUfXkpRX85LsdzMz5O+GAB+DSAv6S1/qZS6hchocLF0FprpdTv+8b/
+oBR9y7W07dXo2T4+5YuVMLAGPxKr4rVeg9qW/cuO/zTqiSQaVZsZ5NYGbAJ2mmqE7EzCi+OlCp19
+2V0Hr61i63XxJqYkLDlMA3ztnvALHpcJ7ify92veh3gxl93fDx3AoF9A9adK6QuGXA8OWqVc80pT
+w6iZ+FIVFHnpY/LlTS1cELbcWt1CSu8mM3LMz8VyNexe9EdDFNTEdIMcBTvyyrMRUvYfpFWCOevU
+k4MJ58q6oAHrLbeR0acxcuvC4bFMhQ6z3VskVrm53sE1Kkn3u5vw6f2YusbCLcrTFPDJN8CQwXMj
+WOw/yIoE7S2xnp+ZvIrSkg7Vow9SHNOaMpeJB0/GePm2QIYn5RRhT9z9UXoAnx7NmtuBtsV7sVm7
+b0IHpkG8RWYhpWucezasQOazt3QNVi7XZCrKAKCEZr9Glc/RUDezqOYoqdKdnR7C6Im3WBxJQnEe
+9GGffFXudfs1NOxXMZwac4ZebSYUl8N1KD4nxzQxiomzST/AlH08d/+fB/jVN6QK9uSAsOSNDVjf
+kOs8X0lh01afmBlaM5m3ZKbR7hMPwmpQqy4xNWWNmNUElinXXs4y6IaJaXOEwPvRXvOP4ynsAdjT
+Wn+T///3IJvEsVJqHQD478nHOMfluByX4494/IE9Ba31kVJqVyn1otb6LoCfAfAe//v3APwNPKMU
+vacUXrR93LgaoM2Gm5vLBfyV2wAA88ZtKMbJynwCa1tQcWBHIpQNfV9iryoZwaJE2WBvhvEGm3n8
+GV74D64DAH7jH8huP5qOMBlK/Hb3TOMprcv5h7toqncAAHX9AgxDEIsGm0wsGND8OVg1sP2SWMfu
+ygpc0qqluQtF2jSb1tU2bXSXxfLFc4AIVhRZhpCxYcWOy86qg84CK+EaOE/EqhTax4xexdwpL3IG
+y6vixcRNDjtj89A8RicQ65EUKZTN3Iej4fPc11ZlrpbXWwgofe14CqCsWlKlMMkHYTo2GpYwlUGt
+xqZBQDxJ4HThlGIR/TUbW2xua3SBX35dYv+MOIV0foR37i0SZ8vYPxGY+p/+N/515KRec5sADlmZ
+DS0W2KhrKPIf2Eqhasl1tIsMNsl9zbKEtcB72CTbNTWajN6GZ6KOCZvv+DDJ4IwmgmanaBOJ12Ek
+D2GtSZm5mt+DGVEfEzWudYlCXObclzmGWmxgByUuZLWLISbHch/vvv0uevQg7J5cezDrYOTJPbf1
+MtrLMt/dsoWypldU1PDoeQYRxWRCCzUh0aXtoc214Lh9FKeyDsNDE0fZMxIpcHzc6sNfAvC/sfLw
+EMBfhHgff0cp9QsAngD4t3/YQZShYYYliuMaDrH61dYqrKncpDpNYGwzm+y3YBTBRw9Qmqi0vOj6
+oIvZb0ot/PzeLt6nqMfV1zpYi2XR/Mw1ue2/exLBHTGJlgPXEpn00btLKE5Iz23N4BhcKGQA0YaG
+RZ7IXuzgBqGt4f4IJslEQliIAz5ESzavdsdAqysPdFQ0MOiX52PzArwyISfhwFtG0GJSEis4tGTR
+ZGcpLLbhhqWPibmg9pINSGcl5mRcPo9zZBVBUYYJk6FGT3m4YTHksZhBt9agCUvOZnO0COe1bQ8Z
+6+omDKg52bFdOYdWFlDJZ4s4gUGXuignACnN7rg9vNuT+353JskwVwOjB/LMjrIuHlDF6GfzElbF
+9mwLFwQvVbzQbczg0h3WeQXHk+t3EcLxWRlIXdiWnE9Ro7MpZygaiu02EXqs2tRlgYL8n9a0hNHh
+Br6Aox+EyCOKG8caFsOtGhOA+pc9hjv+gYVhKNf+RtzGvBbsifvgAd57JJWvs1OFVrGg4SOuoFjG
+gNWSwNcImOQejjJUrDisrrbgMqQFDWTYW0d9JMbCd2oYCy1NAOY7rLANZ4iHf4TgJa31mwB+Pyz1
+z3yc416Oy3E5/uWN5wLRCG3AahzkXo1RKdbKXXFgviB6jmr7CuCK263MEMglAampq4CpBZjk6V97
+inRN3KU33i9R0/18Wh1g5cfE+rtrgiv4E9UU978l1srtxDj9NXF3r/6Mh3Isu7x2Ejjbn5Dz0FNY
+Ny2UtJRLQYMx0XNXBjk0y5ZO6WFpQ86XT8VqTZcqBFqKMS2nQsrOubPpEOmBXEcroAWLchS0rk3p
+wlyW79XJIQ6ppFynNWpXjpFRnTm2PDjscDS6NQwS12YG4NLiz1WBmFoOGVWia68E2H1apimyTdLb
+oYOwRbe01Khqhg1EK7rQqJlo85wBDJbsvMzCpBQrlgy6aLWlbAkSu47nOVKWXK3OEO0u9RYK96IT
+07EUGmp/VPVCo9NGQ7KY2m6gKPOG9daF0I7pGgAFesp1SrCduTBaiw7UGRqqNZtnPpTHkObgDGlA
+aPaMeIN1G/Xb5NxYuotB/SW5/9IFq6U4OJPvTJwpUuqbjt69D31dyt3jYxP5iZQcG2sMf0k8hOsr
+rwAAOq0Qmz25p8lqif6M661XoWbYmBQa80jC2LbDDl07QXiFHk0GdOl5TlMDdiDeqaWOUbvsJH3G
+8VxsCiVq7OsYo5kLExKfeaYLvajLZhbgifulawAMFUCa9fJwF9k7AmeenbyH3Xclq/31UYZVuvnv
+jgu8/VcEf//1SB7yVwY94Jr8/ebYx0OK2z6IK2Q9OUY/eAklBVttJZtJHWhsknjlk+4aPtUTDEXP
+1Sgbdt+1HbiefL5oJM704w5S4jujvI2qlAx3UNoXbqLFWLdUCTJSjzdWCT8nc4/ThjmTc+zFBdSE
+UFoyWG91PJQUK0WsMCfbkGO7CDifXShosjzX7J0wAg0vkBd3Xs/RI96/shVqdq46bgGforANc9RG
+5UFXZCOq5sgqcldCI+NG3tSnSNgxuGCUrgyFPjfAaR2jp+XcU1Uh5KK30gaLLt6CuRoTCiCfpV12
+kLlS/XbTFip2uzdFgDonkIlgOJXVMBbX7k3RTMjBOJ/BNRfqTTUUsSML0Ro0S5jtfBsAsPLSX4AZ
+8b5VDwkJdRZKYM0kxKMTqbicnb6Op1+VKti99D5utmRdh5XCTk/Cjj7ZocKBj3Qs63t9o48okA03
+2ryC2anc3/D0BOen8qw/+Unp8nVrfdFqr+caSbbQ2ByhsyMGZWVaYrT3R1d9uByX43L8/3A8F56C
+pRVWCguzskCfXXGuUUJ5pOiyUtSUdcfxY4B0Yw1pyabHv4vZV8VTODdiGLSeG30T4yPZze9WwIN7
+8vl0WazP/3k8wV/8hJjuu5aLPBQ36/atAoMB8RJWjKYhOzKt1o7r42pbdv7XvthH6yatblzDJRlI
+HQWYPybCri+uXD1y4FFDoi7HF2hC7fTQb8lnixGbiBIXDcR62l6CgsksY71G2xILnNW7mBzL77e4
+vys7x5jaCqNpDpuM10Fgo+eLZV4OgU5LrHGnS92IxsWIWAJfW/hwIXDiz7EdSagRDDowaXktMmar
+uEJCKEqSV5hRDGaUF7BJNxZ5vQvBsorVB9vUqOnRjE9SWDE1GUwbRSVWc3Y+R9T7Ps5ADtYFdVxQ
+HZsoWCUp0xz+soQm1tEYtbuw6ITJ2AU0sQfVhoNyLveRdifIyFWR9Es4TB4GN9iVqir4jlhm7c+g
++HdVlwg9UrZRFOYD/Q6+9+3/AwAwPXgKNZdzr64FmE/Fiud5jopEQq1NmVcvbVAyeYx7QHGNuI/H
++yioU/nhOxnOliVsbn0olZUrfRcBw2PVNVCeMwmauFjalLDidvUJHB4vUAPPNp6LTaFBg7hJMckN
+eFRbMsoSzROZnPT46xh/V7jqvv1bH+J4keFPZVFNixhvUmb9imFjjQvvM34EZ42cgdMYc078aMYX
+KTKw+xvy8/3jKQqWrNz91+A4Pw0AKPN3kc0kfGgowNrzGtxZlpX5Qr0FZyjHTScNds9lc8q0i/0H
+srDOfbmelc4WVh/LlBce4LCs17FrRBQgBUtMZ5MRpqeEuPaa70NYTRfHjyR82jspcczwwGQFIMhL
+pATmzAoNTfGSllLYsuRFCMISbVYRjvcFwvvd7wDjSlzmU1XAJLHrqh/h1TvSB3F74yqWVuU8XZLX
+lJignrJt/WSEo0LCruE0hx3KPZ2ezDAZUz6dLduGNpCSKQnKQcoW7iofI2EXYDw+QEmR3YAtxNlZ
+hop8juNRiphQ8qi2AIYYrZaHkCxZviWftVSAEQlX5mcaDWQOx2UJk6GScWYiXBV3/fyJfHY57KJ3
+Q+61eX0X6vPy3B1zgLJgj8mEVZT/6y6O35O1chaPEVLgxkhqrFGI6Lp5BTuOEK8GbAgxywSuQQ7H
+2RDHX2ffQuhiN5H7fvPtt3E/kU3v7Iac7xOfvI1XrsqGtBSZSKiylWUNgn32RAQ2InPR5XkfzzIu
+w4fLcTkux0fGc+EpeIaJF9sdeLpGm65qXObQj8SKPfz6+3jnQ9k97xUJdukSLqTbkrGJd2lh75kF
+vkio7b//c1/B+QNJOE0/3MM3TsXzyJm0ezqr8fh9SZLt5Rm6TPa1BgmgyDVensG1PgcAqAlB/vG1
+bWxtkCzFTbDPXp1hrJFV4ho+fX0X4z7r/nNKi5spjlK55uFoFzkl5F55YRMvf1Yy0RXl8U6ngKYM
+WFUauHdKaTM1x2+M5Pf3RzM4JH55tODkm3XhLZH/ITQRskKjrRqnitRkTYgh0UtrhMn2lyN8aVWy
+27qr8fAD8VLOJ2PE52J1zrp7CIl16ERiJXWSIyOmoXIr+AJmxfz4Edy2NPy8czJEVi+jqI1CAAAg
+AElEQVSIFylJX5agdw3HMeHT+0tnISomh4eOCYeKzrEv5x0nCWw2Is3yEsOxPLOH0yOUc3lmA7/B
+Wk/u68VNuado3cajoTyoo5MUXleOl84t5EyU9oMA5VSOsbouyWMfEwRHgiE4efgtXF8Vt7zur+H+
+UDzVf/RbUiF4szjGuBarXNSAuyFe3E1zEw4h+2uRgrEilt6ppbq2vlOj5cv6PX1/it+5K8/36fgc
+N8kEvr62hvxE5txk52iV7OH0gKI2vRDRjIreToM4lvfINDto93802/9cbAra1KjCHF7WQmeBlDu3
+kGh5Edov+ljNZeFdjZfxXktKPTHJXO/ZDV5ICP6pC7ToOoZfseBm0om3/L/kGJCWfDiUBfrHuhHe
+Zju1+dhATDf/9IoNg/hy1/kiSsXPUPDWux5gc00W/0A7OCV6LB0PYftUmQoTnJxTxNWR6kPsW+gT
+pDO3Erw7YafeHtC9RrITLtxffescJ9QqHMYZuiQiLV2F0VAWTVqVMFlRGDNMyFsJSPeISPmYWbIY
+Veqgx2OEUYQOF1ubdOLjgxkeTST0SbMD2EsSMmxt+OiF8iJkRgp2oqNiviDLG0yO5T7uzk7xwQN5
+QdKzGb73gHToqUJJ2n2fiNVS27CYJ6itAjkp9cfjXZTH8oLMsgSFT9IW9o9U0RynB/I80mmMe1PJ
+9p/PXDic274bYMrqQbEA/BgeLJuaGukE2YQ8iCXQJavT3Kmx2ZacgcGyb/jKK7AiCV23jNtoInZR
+hsc4PZVck0ES3yi7gZTt8obysXxNStK9DRM9R1C49odH2L0n6/fAk2OtvPrHEV6R+S59B8EuW7Fb
+Dm69KuVJfX4Xo29Iz4/ZZXm6OUdpSn6p21TwWNUoimPogTyTTpXhpU+QquwZx2X4cDkux+X4yHgu
+PAXHtLDdXYGdNWjdoupyr4azEL9rGQjo7u3fPUCPOofGqli7l/oOph+Ii/vpqy30r8muawbrsFmP
+XzMzrPy/7L1nrGVZep73rB1PjjeHupWrc/f05ECK0lAESZMamyAFWTZs2RIEA5YN+JcFw4D8wz8I
+Q4ZhwJIlwCYECfIwaShQNOUhOSSHnOHk7uk81ZVu1c3h3JPPznv7x/edO9MDjqbIBqQScBfQqNPn
+7rPDWmuv9YX3e1/9nWJYeGbL5fmmnON/PUsYKpddFnwVo/yIxhhcxAQ1Cty51ViirbTnjinR3lOF
+5hTQnPfWc4scfUWyGf6C7HalhXUuaf54+AdfZKk2lx9POTrRqj3NHIydKb4GBhdf2GBTXZ7Fa/DV
+3xO3KumfkOVzURYlHoliCuXqC6cRXZWzHxdTQmWj7i62WeqqOXEiO+JedMzwjvT3sZtQ6cq1f/pH
+1tAYJ227SaK8mLO6WFWjXspOJtZIlHikuuON+qeUqyrKMwzPcQ1RoaQvFYtZOOeBtKho8L3IC060
+tiNr2ZypGvdyTSsOxx67VeWpuFNwMFW9TtthsyFuxSSMaOp8KZQUxWn5RPvyfLN8wiCXcWhveeTa
+X37TIiwrH2dXxrReRCyt/4ie9xGhdkbpbMolpXNfXxVNzOCZEYN/Ls/c373LbFN27r/2ib9A15Md
+/c741/mXvyPByPvaF06py1//r/4aAJWGzawlY9JJbJKSWkLJMwwa0s+eUvDtBRFnidznJz7Uppyo
+sI/TZaoWUnWlTbgtltXjtgtL4aJdtIv2nvZEWAq2De1Wznq4yqYjO02tsYFRlBcnARvqWwbtOkeq
+53dFV9/Jzinxsvj4L19Zorwi+f1ynhLHUhm5WF0muiOr6nIk/mapusGmBsNqwJz01r33AuZTmgx3
+YiyF2mo2DccdE6heZVHxCUey06TjMa4qN7fziIW2itWosN/Ht1ZBGYGe23yaLeXpz7KU0bZWeZZk
+d9myF6helmDXMwslRoqm7DT2mKwp2jKrMdWYQqoUbZ3QxletBA+IUtnZJiGchlrMNOsyNxSCmuwu
+V8IG+VOy055FQ5auSG5+uWhT9mX384KCKBfrx+5pajWq4A5k1+22Frm2puI6let4mrL7ytfe4kQR
+hLdVhCZOMkLlSGg2Xa4uzPs7pLGoFZGxxVpd2bCUv6Ga5rSGssMemoiSpjKfqzUxCjF2vYi6FsIV
++szOWYajupv1YUGssOrr9S5VV4KD6y2fVCtFKzPZle2sglGOjOx2weia/M6zBhSL0ncLq2rFhdeJ
+Oz8OwMPhV9n7l8Kb0Nu8zjMvyfNd+4n/gERJb/+/tyQI/szGJymVZdzDsEfjbS3iWm7CI7n2mhXj
+3ZT4QiOT/vbjGVZJ+qLtt6h2ZJ66WYXqHXl30sxQOvtTeY5+YHsiFoWSV+bG+nPU6j7lueZe2cLW
+nLg9uEU9kmBX177LxJKF45EjE2yhuomttGvNZz+Ks6WVbv7TWKof6V9e5yUjL+G9b8jkT6pHHO8o
+cUplQCfRsu1P2BTIYmFRwWilXq5M02nikSlGPpgYnEV5CbvdFUbHMjBLg4ytigTu/K4Myih4xHVb
+Pq9cWoDLKhp7eEhwJgtdUJOXYKle8FBxAzsPz3g3Ek7BVlxlWzHu3UYNWydFpPUAYb3EqjIVz8oJ
+NU8mVWqFTF05NmtN6a7oRFElrM2FLqlSpgXWOiNPnrVLjj2vB4iqmJJWlYYyMSu2y0JN2ZxbDuvZ
+nDZujzyRRfupp1bp3Jd+uR9sA3Dcjyh0sfzItVt86EfqOpaLVDqqCYmFqzUMnlKpTbG5pSI7jWTK
+hMtyXm+M21NsginjK+xj+Yb83a/V6Bq5xqVOj80thVKHI9YvyQu7UGlj1RROH8nYWF5O/FCZr91j
+aiMJ2hUVn0zxIMeuciZulDBb0q/GlImG8qy/8oefZ/O6LPDPr8Kn/87HAHj5D68DcPnFFVDimLRU
+oeTJtRdbDa5dUgh9HBCGspLXtZ6jYm6ArYtCvU65kL4qZjnBsizI42jM2JrrETxeu3AfLtpFu2jv
+aU+EpWAcg7dcouG0sG0xdy1SjKLZ3E4ZlHvfWbxCxd0GoD2TlXo2G+OtyApeulZgTcV9SM7ewtji
+VozNW4wUzvrlUzGB+9/y2dZc8tWWzfA5OV+jvg5K42VMDeYCJkrBNsh7DLc1/5/7XIlkd0hXKtTa
+ape3jvn49U8A4M0lJTuGXFWSq7XrVDSgNFqCV78k6aln18TdGaQRfiQ76WHwiOc1LVhv2HS1qmow
+nrLaF1P7TBFxVxplympSZ6MS00hcpmlkk+kx5jgnVeumrQVApW4Fy5LvCi8iVUxGEeVga+C2NiTR
+tGaeiCXVWr6JmcnuWoonuCq+cnlzEauu6t5nHr9na1HRvlzj0AlwNb/Z+EjIZuemdlJCt6Xp4FlK
+kWlBlKaTm6UylmpAVJ9qcazVh/Woxb4SlVTsOu15Wlp1Q/2xT13nS16LKKlEXlY3NFQXwU8PcQuF
+cQdyrpJvk7lCuNNY2cBuKeq1WuVIsReFJ2N+djc7p9ujFJIMZeBfef1dBsrsHPfX8ZdkLndvKQFM
+K6A4knEyxZRkSa2xBtRuyfjWzCqzSNzGkloxnomwUfcqCbDQYLPbw+8oSc7h9LsQ8cdsT8aigMHN
+XOJghtFcszfrYcryIph6AFqqbIVT7BWJ9toabSX+Nq76um7+ApmSZmTFlPyLXwRgcdDgYzWF2n5U
+Bmv7Qch/viqd9x1Tp7cpZqKXvo6lDMaYgvNumgt0xjb7KktfXa4RKG9f06ycZ0nKxSa+vliO6k46
+/iJnCgP2gj6exkSq9jJrV2VhcSxZbDa7HdYa8vyjzS7Bu5JxqN3Y4Gwg9zmzj7jzVcl5eyq53mxV
+GSuD1L3TPtWymNpH4zGuEoi8m024NpVJ3+rKBHPLFr6yPlmNFkWurEjxKYVyMOaFR+4pqKehE/o4
+oJTLS1puulSUCcmZhFTWtQS4M+Xmqbhunysk529ZBY7GM9pundt3xb/+kRsfwhRzNiGDoxqMlkLU
+baeg5CjxSi9jSQV1vNAHrRhkOqNRVqGWirhPRSuDd8QFW/ZqbJSlv+0tn0wX6lLDwnOVtGVD+sJL
+I+y24BFm6SmugkCsJKBR1UVGiWoGi4ecnoqylGNsHK0uvdZs4KjY7CTq0Y2F89mqSJzIepSTbqnp
+fxayfFPd5rMBlgwTVq1KVYljnEie33JKWCpEZBcZzKHk5Qq5r1mUTkLv+CL7cNEu2kV7H+2JsBTI
+c4gnBA9CUH09b8nHHKms+xCsuq74SQqKIMvHijTMR6QKh7XiPZS+gLyISDzVGS9mzPqySxcq9NGg
+wuyBrKjPVivcua+CLPtbcEm1yo3NHJqbK7HIqH/ErvIgtvcynl6RncINLeJAeRlHE7KKHOM2xPzO
++gFephV5kwhLd6VwL6U8U3dEvY+cVcpKJZacjukpJZxHypIqO8+GhuGK4hTOFOd8FHM0kefY682Y
+aUVps16hpcIpl7Cx+9LPqSvHpnULW2XbvdjHzLkKJxGhBmtxy3iqbs28vzOPQnUY7F5IrLoOYcXC
+O5TxSY4PmdzXQKrW/FtYLKokmjNcZ+9dyb4U13IsRRumSUgylzyLNStQlLFUOyNLQlwNpFaikLm+
+TX+Y0VNEYvNIrlu2qgzV/ds5OsT1ZdetFyntNZkjdliirEFaR+kUYi/COdFKxBOXdEF2eTfvMtcx
+mKXSx4e/u0NlWtV7K3AVnRvuRdz+nAjA3Lr5McZ3pTCpUDq2/HILhnI/40kMpzI/7+8Oqb26DcDi
+0w7BSPrWKqRfq+U2ZYX6F4xJUiXXiStEDxSang4YKw3h47YnY1HAhrRBtmYTHSqHXxpCTQcobIEv
+A5pbIZmSWwxG2wBU3C6qd0r49rvkFTWzilVyZFKMMsMDjVEcxPL3oJVyVSm5w8sFt6KPyvnsS8A8
+EFAgrHeQq2BqXlqgpK5I5BhGmqr07RmF1gaU0hKJLdd2xjLIh0GIeaTnaByTvi1/TxpdnLKY5bEu
+GpPRu9RakqaKyl2KMzE/xw9PmWi0O0sMU4V/R5YCj1KHAxVqibycK0o6e9seUNc+Oh6HPCyJ+1NX
+N8EyLkWmNQyDkJlKUhWRTaKuVD7tk7UUADYV1yYOMhKtv5imCbm6LrPjGWdjhXGbGpm3qGMl17Bi
+yBry0hw+gDcHsmD/3MRCGezJgwRvLoTry30WeUaq6Ua3VJz783klZ6S1CLujY7K+/k7ZpBYqFqc6
+/u32EoFqMPrUCTW7VA09Rkr0WtIalpQhDV2wk0pEQzeqvD7m7n253p98YVv6e9wnnmm8Y+E6+UBc
+osCq8s2ebBY/mr7OwnWJGwW7co18khMdCXHt/b17HN+TORS5OTsD1a7cL/A0dWp0wTMMyHVuOgZS
+rYlJrWOmU91wphmUvk/89Ie0C/fhol20i/ae9kRYCrnJmJTG1IYetqcchY5PouzC2DbZVHeE3Cec
+SHBtpiQr5nJC/YpkGdKTU1BSlHSpjZfLjlZeP2Ytls9fm5uUnkd0VXaBK1efZtSR3bq69bK6DYBx
+KJTgxHhizodxgKeUYFlcUE0VrtxcoDgR3+WUE8q5uCADJfObzmKijlx7tbN5XnTjVF3CoZrzGr8b
+R7Nz62A07jOeZy1Mdi6hNnXr+KpzOVPCliwbk6ocfK3c4ET5Fuqee54Lt4qCMJHdfRyIZVKplQmU
+Ci73B0S5ns8usNR1CfIEX+95lKoMmheTjOWZ6/kCg4k8QBS5DJXrwK3YfE2DleO6ckYWDbJNGaev
+JNvs99TCKCY46ge4WXYuGR9oBL1iRWT23JSoEyoDd5QWJAppb5ab5ErrkqoCziyb0Vm/DEDnSkhZ
++RnSLKVeVrCUNaKw5XMcyL37rRUiT3b8ytJzFHNuxzIc3Vd+hm3pi+HOq7RWxGxf+cDHKD9QZutZ
+D09/l0aXYCLWRIb8fRyGDPrijMSdmNazirfIKyxdlnuudirnKt7zuVmcjQjmvJTZlFQh4bM0Ia7M
+wWU5LcV1PG57IhaFIi9IZzGpSaCrqLTwDK+Q1FteKkTcE8hPUswVTWWtXJYTrMck74g5HLf8c43J
+opxRvKtxh2c96h3xBy8tSyT/K/cDbqpkkWVusLUkrst09iUKBGBicJgbVJb6t563SH8s92Ndshjm
+8mI1JiGRahLYyzaFsii5S3I/G2s3KSoqGX97ROjICx1PJkSZ3P9IKwTjmkukKbSjYe+8pNdaqGP0
+RSi6I2bWvPpQuRatKuvqZ57NRiSqBXB4FmIrT2LqJkTq2w+UbanempLNS5lrJTxlNErrAZM7kn7s
+l1PSh7LgTHwl9Ngf01mUxcRbWCXQ8vO9nYfUl+X+/bCKq2SyJ5oubTWGHN+Uvlr67DGe1jYYy3A2
+UBPdmlFX4pv2/GV1cyxFjWZpjdiSYwdjA/OUa+HS0CxQoyUvYKXjYym1ul31sGylgC+Pzmnp86lL
+EEp/1FqaFhyPKT0lIq+FXZB6em/jgKsaa3m9r65W7RFxJn2/uuLiVp8BYOFwm09d1vuoOvi5zLNI
+a2IefukOcVNZmjoN/FvyomfTEq0NmS/FqGAcKMoylbFxHYvZRF1U32CPlZuz5RIqF2ZRihke/NkE
+Zt+vFP1/Z4x5yxjzpjHms8aYkjHmijHma8aYu8aYX1FNiIt20S7avyft/ahOrwP/LfBMURSBMeZX
+gb8G/DTwvxVF8cvGmH8E/E3g//xh57NyQ7qb4ynluHFKZMpmbHkRtuadYyKsiUTzUeKRfDJldF8D
+biNgSdmTx02GM+UdvF2jd7INwP2HstPsn2QUd5T19keheKgkHPd9rq5pVM4L1VqAOBQTr7rqcilR
+9aNJFUtNezvOcI9kV3XTgkkxVxCSf73RGUlLVu2zkwFz4Z66AVtVpSNbrYBBgVEwjjuz6Sn1XGsQ
+Uq9qBWC/hKb0aZyrV9VY6oqF9fbU4zQSS8lPw3PVp1FQ0FW+xnSgFk+jReJpnntaJVWm7HyUAFoz
+ECb4pXmthSpiLzRY8jX7ULGJDmWHOhrOCJSmbaV8SLWQc3RDCYDtHJ8Q/9LXARjGGflItUItSEYK
+J88LanPogcLY7REUoXw5jacE+tzZLDxXiU/KKV3FTnipBiiPDaG6FG6RcaYUavUNl7amfLI0IlM3
+pVDl8qie4SrxTfZwyvSGHFtOely9JJ+f+qDyXX7rJp7SrrXDZymd/LH0fW7YnAhRy+LNdaL7yhU5
+EOtwko5Y7ksflturKMKeaQFZXyyTyXhGlMzh5ordKDfJA50v2YSZo6be1MY7lvt4kKVMxMB47PZ+
+A40OUDbGOMjMOQD+EqIrCSJF/x++z2tctIt20f4ttvejJblnjPn7wCNEl/h3gG8Bg2LOFioitOt/
+2u+NMX8b+NsAq40qJlvEvjbD66umQysjV30Dq9nAWhQUoxkecbwtAZ4Hqay0t8ovkF/V4FQaMy3L
+Slqz1kibKoThbHBW2gZgu6e7xzKUlIJsFO4zOROo7WztHtFAjvXLTeySVjsqw/GHXvrPuLssu0D0
+7UfMVODkMJywqBBdx1tidiQB0duvauqxdshGTXaMvdExqpBHrZzijGUnjbU+fpyXyHuyO8YVn2mi
+SsRxTlZRa8pPUHAb04miA1cr5xBff2OHG8OXAPhK9jUmPdmCnEqFzlRTditanefXaLflhvpBwamm
+OKsD/xzaWzJlkpr8rhsow/PaiFZZ0o2nwYhxU2nqajYzX4VRKg6FCt+wr/57nJ5PvmQB2iuy63pu
+l/pl6a+0NzsP8s6UaoySR6FxgpiQI8VbnFgBngZ8/c4SkSU770R3/nrmE2r6NX0UEVWlD+2zCeUV
+DcS5QwotvEpduUZkStg7qjE63KO2K+dNvDpXF2WsOyrBN36mxXQs8/e1d7+OCQR7cau0wesaHNyY
+3eawJRbu6TfEzMvaHmeZ/P3wcEB6Ks8RmD5nx9K3aSklD2RMWh3pe4cxaEDRLlVxZ2pVZBb3C5lH
+k+GQkaWEGI/Z3o/70AY+A1wBBsCvAT/5uL//Xin6Zy91Cmt5SpUyjgZt/EmbuKHBNeNTeDJYjr3O
+YSAiG99WwMvhizZ/8SXBGETBPSo7qppzKWN96VOASHw/eE0Wk6/N5OX4QKNC9CGZjA8f7fHbd0WW
+fmU/4MW/8HG5D/d5Ig1yotVr3vWMzalMjn59wGhXzMFhDbpGFhZ/ucGSro2/+YZAX0cHAVfWJVJ9
+tbPCTOHY3YnNOBCXx9JJvlckhLoIpa5LVd+JXjPDTTQSH5Zw1XzsVeR3ZXPGd8ayEM6OZ1hPSf6/
+PXQ40apEe5ySdDVYp9yWTjHELWSBHEYB9/cf6jh5vNSSBfnUG1J7JAvVrnJpLo7KvKnkH7vb93in
+J7UIW4nDiS5O1umAN07Ebdq5q/eW5aBgo2IG6Vhh5X5CTcVgcstAX17eoYKs4kmJiWop+nmTvorv
+PNo/JFeCm5tFi90lLfE+ULr7ps2SigCdlnPSUIlahjM+vSaBxAyXrJjX00iHN5YqRJ66jWmbkiRM
+qK1cIlf8xqWPKV/ll0Nu92VOtryQ2a64bu+aeywuyTW+/U6HSl+e5a4Ksm+Em5xo1WodmKpIzDX3
+EpOWcpaG9zjTStqFtlzPykJMWQVuUmg0ZH4OZyeYWGtebP7M/sD7cR9+HHhQFMVJURQJ8Dngk0BL
+3QmADWDvfVzjol20i/Zvub2flOQj4GPGmAriPnwa+CbwB8DPA7/MY0rRmwLcGOJKRLGqUNujAZU1
+hRr3K1Cap9xCrj+vTLWR4AqOzAwTytqzdPVjOErAGi7GlBVJt2yfsKU7zLOqk/iBao1bunscDRNO
+Hsmu2pwaKoUW5FseOZq+0ry74y7hX1HYbfaQ+JEG/ooUK9ZA1Gqb0V2xCp5SQNkONcpq7l1ZrNHd
+lPvPHkVEdd251TXaWvLoyc95fbpHsCM7esvLKamZ7LZzyn3ZCS9p8ZV5NOagIsfu9Cb81ba4K6Ol
+IdNYzr2UwJoW9gSxEsi0yoSKD9harlGMxWqYNA2bS5cBWLyxQSFdRLEuprgflVjWdf+S71DakZ07
+OcrpLKgux8M+r2/LD8NUKwtBQ4Rg2RZnI/m+3m3Qm4gll9YzSmN51qWmuFfh1EOhGYxPp2gMl5PA
+4dAR62AQhzQUITpTqHjoH1AcycE9f0atKoHimx/cxFUsA06VTCtoVbICL4xxFiWVfVzco6K6ofZ0
+n/VN2aWfuiYVuifDt7msxDKn0VsEbWHojsNjRqeK9ahYDEtyjLchu/mGs87lp8TSze2YSbYt/XIa
+UlWBdT9fpvK0umyKIbEaLlag5DM1D1fhzAv1Lt+ZicUyGO5wNp5784/X3k9M4WvGmF8HXgFS4FXE
+Hfh/gV82xvzP+t3//UPPlWUkowHTN3p4iUbhtywad7V82S6w9MGycoyt+eiFikTZk9fu8vaBYgGW
+e6ShmH67r93modZJBNWYbw/Ft6oMZKLs2Dnf/Nfy5r3en3H4lua8KzXOHskANNqHJMcymeKK/H12
+MiY7k8lTC5Y5uilmYP5uynFbfErroc2h6ir6OnDdDNw31KScZKzpApCaMZne04Ga+K1hnZFCcaPR
+mPtq5q+duczmE/ZexIkK8vYOZIKdhSlHSnVe2B77OwKfjYYjmhpHGFYNE6Wanyqs17/vsbKY6zNB
+VZmcgtfv8sqOuATtP6lTUU5LX92cIzsh0crWAQX9vjzfXmKR7cg9fflgwIHe0/dOT2eeafENyxsS
+eorHAbmOU9DzyZblmJoyKEV2QnQkL/HEMeRTfaEbMeV9WSBPWzOmuzImqdYyREcJYyXnXIoLWquy
+WdTDJbIFFQw6S0kyuXaicaegHVOf12vsJiR1cQ+DpuF0Iote1ZF/X4y2+GZZMyfuMpM1qQg9eu0d
+Blp30ukNKRZVNFbZkXY6Zzx1V8qz7aZFSVmmBmWLrQVxCeorS7QLWUxYVCWowDlnxE6TnMiRMTm4
+e5+7uzJmJ+MZofdnQwW8Xyn6vwf8ve/7+j7wkfdz3ot20S7av7tm5vqI/y7bSy+9XHzh9/6Yd3cm
+1Cqyu957a8Kv/9oXALj/7u8SZrKjHRzu42lhS90XE7DZ7ZCrzFlmEnyVQ28udelUJIp8ab2BHcqK
+nurv+wdThqr74KUV1q+IyWlyGOvOvPfOgFIuxzxUhtw/fOWb5JFYBEXwkN6+mGqf+yef5bf/hej2
+Xen47CmIYKQmfpLZGEdW+WkK3ZbsHm4KhboV+xqEsouYitqOG2tdVhQLEBUDdh9JwGnnuE+qy/pl
+DUiRGZZWxd2p+iX+/q99Xq49vYMTClw3zQzJvpjoyaEEbc3iCme9bbm3u/vsviPjUF5e4vC2HLsz
+POWNBzIOgWoYHg0isnkFp+Vw64pYby+trvLSR5Tl+GTKeCS72KkydC+biKEr93zrZ36cqit9/7lX
+c+6cyi6XBo945inZmX/hg6qbsOjT8uXYUrWCrTDu0ckr3P1jCRQfB1OqjoxPoJH8s1FBrpDu4+kZ
+bz7QjJAxjFS+bcNKWb4s2ZjnrkvWpnO1SdET9uXt2xZlVRsPkin/y6/+EQB5Ic/R7Swx0AzPJDkj
+VHfNtSu0tsQS+vT1WxwW4m4dKdq2u9nlM89LkdRs2OeOStYdTEc4Z3JMP8+YuDKvr9+SPi5Nh2zr
+XP76w23cPWW2nkzIFEdShAlVnUfT/uxbRVF8iB/SngiYs8Hg4lBqhvTfELMv2bvL4I6EI9LRKbNE
+GW/yBEchvXOa8nqthK1cdeHYpqX+Z9vL6SxJRy6WHKpr8v14V4U+FjO8I02FlWsEiCl+MzMcVVR3
+8VZA/1Tx5ceB3q+4NADJYMD2v/gsAH/yxa9yrGZyYocUWl48VD+6aQpmugg7WEyUE9KNhowV0hxM
+5BpBnlJRAE0Sgbcq52qvbLLakmscDAqKiUq7a8VhyY45OhITuHBHWM6c83IGWtac5yNMIhH1QmHe
+yfAuySN5kfaPBqDiqbk3Zkk5VYe5z6JSuwehZgXsHF9jJoXJaSmNvOUklBqyKEooXb8AACAASURB
+VFSqPdxDedGdu/JMRWudRIMmj958DWddJr+bPkdXJ7S17PKpm5cB2LwmRLJW2cPMyW5sF1urBCtL
+i7htyfL4rS7LM3lRZ3W53uydE4ySotxcfIbuFXn+nYdHnKjmY/Usp31VFtRiVdzDblTHWxKymOri
+HidvSx99azghzdS3V3x4lE9Z0TTRu8czUGJav5bz9POy2Pzcpz+I5dwC4OF9WaRZa/Ezn/w5ACbp
+A47f+BYAr/f7dG7LmH3zwS7TSGJsS6rOlfZm9O5qGXUfemcyd2QIvsu2NFVhnMdtF1WSF+2iXbT3
+tCfCUsCCrGLRnhT85gPh8nvzd77GI+VL2D8ZUVFor1MqsdhSgowlWTk7jsHVghmr7rGwIJ/bvk2j
+pqItlsVE8/exklGkhaHekIDTYDYgPZId+I9jm80rSmTSTjlUk1AZ22UpVWbn17/xG/yPvyRAppN0
+RqJ8Ci45mfIfTuZcfo0yM5Vfr+UFYSznjaY5QzVhLa2ozIGRenbjyYj8UCyF50seC7bcs+9NGCpl
+fC+Ucy3UG8xm8nyjYUhRyC5hijH9I+lb6+AS+RUNOo7FjL539x1mR2JtjEcBrmopptmEWqHs12FG
+Q7UUj3dkx9wouXS1krGo10krcp+lIiHcFzKRarXLyUiBQ8ooXWods31HxmP6+yPWNuWz87FnMJbs
+jpdXEq4/pXDlumaAjEUSizmfFCXKCrKymGIbOfa6f52sIbtmXkigrha4+J6YPO1mi5c2BYdysH3A
+7XsSELz3xj2mU5kvs5EESbc7PgtNhdunMXeUlt/ue/gaxMwSha4HE860P5M4JFaG7ZabsXBJKfhf
+atJWl7bdFEupemmVckfOVXGfo6mVwuvRkOO2uKPN51Z4uKtwaqXL/+3ibU525T57qSEs5nqd7689
+EYuCKQxeYrH39pDRH34JgJ3wTU76MoEqjsWlkkzGS9c3WaqrT6mRadNLqDeUxceHVluObbgJTeUo
+NK4hUsz8WHUN89whVYahceTQ1xevu1ihdCaLgh27dGL5HETa6bkhPpMJ/9n/6bf4dl8mhclcLlfl
+mIap0VpXJGQu5l7Jn3GwJ5M4L0WMtAJwkE2oKOKt2dJUX14i1hLo0dSm0OrCfj+l5MmEX7e6JK5W
+6Gldg0lTXNWpSMMpJpLvw7f3mHxDqkPHrUc4b4iPXjwU/9Y9qNBekRfoSmuVvupFWCc+piPPf33j
+BU4Phbb+g01ZkA/HU9YbyttYijk61dRiw4ZAdTnOelQmmmlYlBL31aXneKP5LwDYvzMlSRTU9cGI
+hZac7wNbWyx2X5b7UJaiIk8YnYmegsMGpa6kA5OJIZlToF+/il8WlNHoVMYpbl6nWZWXsbmyjqsL
+RPkGlHTx7lbhkdYMpKfyzN3Li2z6HwBge/fXKTxxA5LZMRWtiAyVxGTcz7GUuDaIMyzVrlzfrPA3
+PvUTAGw1X6IwyoZ1Q2nyG7fwqloRnCX4CnRq0GJhQRavnVf+gP6pLPCLz8h7cav6NK7GFL79jRN8
+LZ0eTJI5Wdifq124Dxftol2097QnwlLIKQhMxttBwueN5GuTd/o4qQYEXYv2mqzcV7oNGg0lmVBh
+Em/RoerLTlItGaoa+arV6lSVkKPkpYSqCN3UijsmBaFG/U9OU7L2PGI75RjZVXejHKOlesNETMM8
+DXn9y5L//7XTI3KlYi/7BVsa6b229TTPXJfVf1zVoGSSUlNhjn7ex5zIvSVWQdcVq+LFF2U360Qd
+dqdy7O27AaGt7CvRDE+rIL0gI1JZq4nm0id5mWXdPSZORBzKOYaTOq/kSiN/ew3WFCxlyblWXrhG
+RS0M04Z6qlTnG+u4CqH2Fm7QDGSnnCrP4DPmhCLU3dMZ4SvnW2oNsctq2js+o7Gco1VRV2q1Qq0k
+VsNWqc+ZI9bbtaUGPTFY2Nxax3HmU1Tp+LIDDt6WAF23k1JRi8V1E9ZvyG5cbdbJNC3jKt1ed7GJ
+m2sf5hG29mfNvcTVZ/SYlSb2a18G4PRQQUp5H9OWY+328+SZzIuKneIpH0Zfp1OSzEhTMeeLvEAJ
+tnnppR9h7ZZYNF4lwVam5TySgKNb62Ismb+2HVMogY8xNkaff/HGKktncu2uugk/dv0jPO0LyKxd
+vc9X1PV+0AsZaDA9NfY5OUuePR4t24WlcNEu2kV7T3siLIU4L9gJYo5371E9lt2llxU0tISi0yjR
+1HTSNInIRirTVci/YTLDU8UVk/pYilMwUQy6+1u5Q72sYhkq94UVEWvuOql5OFpXfy+CUDn0HW9K
+bCRm0FRLI8yP+c6XBENxNstwNaD0TMmjqkHOxRcjlhdlB11Btr5ZGFAsSkCwd9JmZUPuY3ficeOK
+7A6f2JI0cvXaIm9+TfLuXecR+/vK2lsqE6gF5bZsFrS6Mq+KH16pJOzOc9uTgjiSwiZ7tkM6R8qt
+dlgpyc4cKMrPXx9QsWXXIXBJXdmBvcUBriX+uZ2fYmuuvHpThWN6yxRakTc6izC2PF8pqeKp7kE4
+ihhkes898aM3l9a5/kGBAe93vsjoVbm3+mLMpUWRU1tobvBdjh7Z5U5md/jHr/8DAP7GZ/5LuirE
+U1gNysvKdlzewAzkeEdVt237kFwFYIokI58Lh1ZPcJQQtYnLlac+DUDkflW6ot3A0hR4tR1yWlUa
+uixmpLqTs57S4wUTck0/WxY0VVvi7Op9Pq+w4x+NFmk19LU7/QMAsvUPU/UEx2DZPmjMoaCC0ViT
+7W+x+oyMZbkQvYhKLyNXeb9bdoWzjliFtnWVnd+Ucx+dzUg0Tfy49K1PxKJgG2gZ2B/uM1CCicBJ
+QTHpnuXRsuWlyCyLmebvUayAVYSgQZZ6s4TRQcQzGC05LlyPZKqR7zmevFQn1wUktSaQyaKwcGZg
+WRaT09GEROnTR6q25GQph8lb80tgO8oH2LJ59il5OS+7K1TbMtn8qZbYLi3ghuKC9Nv7NJtiojcu
+bZFq5WNdsw9tq8B6RgNObplqLMG1pGljkPz/sJrwYKb4eVv+HXzgBvXflszAiUlwlcNvVDxgPZNJ
+GkwPiReua19IZ5QsC0cpyY3r4Ko2vFNuYc0l490ulkKvi6mcK5kdg4qheImFpfRg45MDOsVl+Z2p
+Mh1JQHNPMy4b3KP9nNRlfGxyTLAkC+DV1edoaZrHr6wyz7dnuZz37uFvceuWvOg32h/G0hePtEdJ
+5eWtJCfTEkzX0exTXiJS6jmra2Pr91ZUwlLGb6/WZnlDXbpI+scxFmXV96yMHaxT6Yt7vSHLLbnP
+g74EZbM0VfEg8FyLlz8qgKSbKx9ndiLu1qi6Srchi2xckvsdDd+k1JVF2FCcK5EZMwRkTpYaTVYL
+CXgWM60pSUM2K1IFPJm9RW1P6N+avduMmi8AcDp66xxo97jtwn24aBftor2nPRGWQpJl7I6mHO+8
+hjuWlbFmKpS1HC7zbB4OxBIoRSNKimIrz2QFD92C9hwFl1kYRahllk1iz8VHgnOp8kLzwIVJcbW4
+qpaXyFuS/x+HPQYjWf2rx1MOB7JbBcqfNjz4f/jmmyoskxm6qk/xk8+/RHNNLIW82SBV1J8/Dyz1
+D0lUPs14NTYXZZer+uskiHl5NJTflIMhHIo5vB5FJAortHOHXIlYqmcOr6hwyqEqUbfe2uU4UpXk
+PCedigjJ6N4pg4daXbnmY/XUJegoI3YJ0rESj/g2VqiFNvEZVqyBzWYbNC9uqnN9zRIoxZoTpTQL
+2fGSln/OlxA5MbZSrO3titvyyudPuP6S3M9sdUx8V8as0+gyN/QMNoXyG4wH3wTgH/3z1/nMJ2RH
+rHpbGJSEJDOkgWAdTDKGSFN8Kl5j8pRiju60L1GoLL3thxSKLLVo4SiHRVPVrqNJjqtEqfbQ5uBI
+5sDhyZQzRYVOzyRVmGUZlu7y5VKZZ54T8l8nPab3hny/vfWIZVUeT3Kxnurla5DLXC5MCVKF0Bsf
+Sy0a8vBc9yGvqq5mWjB5KHOkbVmYgXIrRGt0rsj3Dw7Gql3y+O2JWBSypGBwlPDuWzWCZXmAaq8g
+Uh94Oo4o5pyAU0O3LQM2yKWjF8oLVOoStS9yc05+US41cFUcNg9CrFhz+nqurFRg1Dz1XA9PMfxt
+13AwVYhy6DFLxbwcakXi2VsNHqliycICdJX6/TunCS8uyjXqzRIHU2Vzrsgg17KcoKKVbnlONKdf
+NymmvKm/k8UmrSY0auJnjkcH3BzKsWMSSqG8CHvZkOVAvg/P5JlrsxijJCXlqiGfyPWyyiqdLbnP
+mleBqkygkmYLHKtGoqzUWZaQqxhOMaviGHmOJO9hKbdjpq6WcQtSFZs17TKORs7rIaSuZoHsGmvX
+5fkePpJai6+++lX8qpjoH3z64xSfUPZkv4GtdRUUBYXWj+zdlTqDv/78jI++/D/I9ZwmhVai5sUp
+RSrPmk962ArqsprynF56CbrywjseOA2NKaRNcOckOj185Tksnapg0Ok2iWaMPK/Ktc7zAJzVj3mg
+VZlZPF8gwXJ04a0v4a3KuW55P8W7NwXfsbbi0ZCkC+XgF+RDpcCZh05MgvAXQUEM6trYvouZM1CP
+5YUvFmY0bMHkTGY9VtalROB4NML5usSzqqbLwByc9+fjtAv34aJdtIv2nvZEWAoROQ+zCQubbYJX
+xfwMJxGhmslJlhKoVNjiQna+I9Q1t98oOTS0gKflV6nqTllyLVwNgjl2Qab5XTMnqXAKjJKTUK3i
+aBUlSZnMKHrsUptgT5lF9HejxS/y4VUx5T4/GtJVPr9xbrg/kMj/KE6oKWYhU6RdqzwlmGl+vNEk
+02BeVg1RpXVqm7LC14rhOSGJ5bWx9Fwdr01vX/LVHEQU+/PqStnNDvIhhwrjdlIoWvJMy89+nEom
+OfjUW6NUVQyBRl2TtMBotiANxudR9CyNqSzJbuTEAamax/lUniNPLLKhEpnYIxKVYPNpE9tzCLKD
+qyzdlfuSnXlnPOBd1Zcs/+jrtGti/di+h2XN96qCVHU0KsvvAvDS1n9CpSXFUcbxyQPVGzXLOCWx
+yEx9BUZyf8XcBW1MsDX7FKe75KHsulPrTWplCQj6KRSOzL/YbAMwXAxpR+piXrnF9R9/DYDdpMvg
+rQfaB9/dgTP9bMotSjPp2y+f/hY7lszVa/ZHcS5J9iCx1BXzbIpUsQmWS6b8FMY2MMcWFJCmaiH4
+qpExqpBZYikNsgHTntzn3ps2WSp4n5PsiD9reyIWBduyqVfrFNYiqVYAz4YTUlUjylOHBZWib7Tb
+NJU0YmlFUmitpkNNKeC9AlJ9ubMsx9NouVuq4+lks5TY1Y0NuR5r7IKKmsa+W6Ej7wGT3OG4JzeV
+GIkzLDk/xdW6DNxzL8UcfVteyOlgiK0R/qSdYKtLYxKtWuyuU6rLuXw7w1KAjTXMMb6KyKiQDdWE
+kYqnFuGIQqvvyoHN8pIwAYWlBTp/LJWBoaadzDTDURWmIEwxyINY3i6Vdbl2dP8hbk0zDYmmFu0h
+gcYzCnuGscXHdashRtWbimaNQqtAc1cWjeR4RqrEIplbx1ZRl6Rk4xby3CavsnFLYhh7R9tyrtdH
+7DyQOMpPRD9LkanvbCzOhUFNQTT7ZTl3Ii/r4spP4ugiXKQWqaULeZ5jqxBNkeZkvgqqqBpYyDHj
+b6uu+8I2wUBKso/cr1Gui9l9a2uTZlPmiLMimZHK0QGjRDIH9WqbZzti2h8srn6XOup72lyZqn+y
+zTce3ZbHOGvwjZZkV557JuSll58FwNd40NA7oPAUfGeunpPWJKMdMlvmVj6rkuSy6GWZ9IVV5FRQ
+qQE7Y6aqVrZ9m+BIvvcLn4iLKsmLdtEu2vtoT4SlECcFu/spUfEQ+0RMTjtxKDQzUKnXuNyVHe/G
+5WssaEXkqtaVGxPiKXNyEc7OI9bTJMCsK6V4tEamHISTqUptFQlGg302FdyOrPKxN8HVYqXGfkhV
+I9XlWFbzYPBF1j8g97D5+5eY2fOqS4sillU+SxtcKmt2xBGXoFQus6rBt261gquEK2mQ4KmWZLGm
+wjNTQ7CjRChxSG1FYcfOIoHmxe34DNOSPnAnSjXXKBOpjrrveRSafUjvH5GeqeVVS8gzMZkjlVmf
+MGOmGpXJsaGUi1nqlDNqc+u4YiiUaMZSerGkHTA6li1zHMxINdNSFDmFRtTTOKZ9S9ytZ5ckc/C7
+0SPu7yr24Ph3+Nj1/1TGEguK+e6fEIxkZ/aNFAYZ0yVVgZd0do+TO0pKk4cUmeyq9dzFuNLn430B
+G+2+/irfeEdM6i/fOeSVI+HA6AcjbtTk3n7sL/8In/mPpADLFLIrD27njOvSR5Vr/4qNJRmzGz/e
+JfuHf0rgTr9Kp2OO74sFcrj9ZZKp/OEf3imz1/zHAMx+/7cBqG1e5Rc+LHwKK62Ah7/7fwHw8PgR
+rx3LvN89HdEty9z4sb8gVszyys9iRfJ8b739Kq+8LpmtR6cBocLGgziYQyceu0bqyVgUwpwHdwLu
+3Z4S+ap+E7q4tmoRtqoMNfL6rZNHtI7lBWhsiBm5VN3kqevyciwXHkZzYQMrp3+grEDePienkgIa
+7StZShUWVbuyW1miorLuJi8oadR6dtQhUTrwqXLx1fO/zGT8RQBy55T9SF5COwSnqTTix33Gmg6b
+qvuwuVen/JRE3FeWFqgrSYwTZoRHmi5V5qUiL1NWjYUwalIoR6VZS6kq8aed2NzakMl/VfUGToc5
+H27KpHknfUg6k0kVpDtkRia37yyjFd70jqSO4K13+4yqCoo5nNLU6stma42tdTnHlajAdZU1tSNu
+hztyGd79DgBfu7/DtprEqYlpa9bi6tUVip7cc/WyuFdLdplHR+IjLwz+EoWrIfk8AXvuR1u4mmGa
+hYrcnO0xUi3NO9/8Jn/0pV+Te6u5JFPJcDx1Y4WN52Sxd7TsOQnLvKskOXcHKWkmz9d21lipzcFQ
+EdauxAnqz8oiVLo+5tGOpEODrw65/smfkest6sL1A1qWF7w9lTjI4qgBqzI+5eIBD/6ZxIS++prM
+i2s37rOy9jsAbO0u8M5XJNbwxumAqWYaLD/HlCUW8e3fl8Wm8+yvspTKgjZIE5oVje24yTxpgW+7
+pJpePRcL/SHtwn24aBftor2nPRGWQpSlPBr1sNOUQmHOTl7geEppXW9RW1JK8hmcoSIjJ7JDJ/23
+qGrOu7m0TF1XfnfqMNAMhj06IFGG4kKp2xqLLfJ0rrbkwZykwq9CJDvJyorFiUKJq8cKtd4I+NjH
+hZL71Z1d4ljBSW5OviYBrmA45mFPVvmZUq2NSqec5RINdpc+QWNZIu7tepU4kGscTAWAk4QVdlTW
+/lsPD7la1kDbvsW1TQmSdW5VuPK07MBbmVgMmwOHtYZGSc0Eo65ErbJJptThcalNrNdJlYSms2Wo
+zFW10yP6Q8mi7G4/wluVnWZpbNNeleCuq5qSUWaRW/J5mIwZKMHLU9e2sFKxeibZmKm6Ma2qZA6a
+Wy7+qbgBx/59PtlWzjdjM4f2YgLGvliIWf0P5X7LHySYSQbg4cqbdBTsdlibMlBVr2ojZNWSXbWx
+JdDgaz/b4JmnFPfy+9/k9p5mbe4dEyxKH756uMPC8/K7tQWlUtt4ng/nkud/EOdkbXFBKsGtf6M9
+nsYZyV2xTO4dH1EoVMCrGrSMhZGC4kbTKXH+OgB/66c/zn5NrIo7pRnVbbWUxgXOVCyMpCV9FbyS
+sLkqY73o1c8VsOKZRVQofiVNsOYVpo/pQFxYChftol2097QfaikYY34J+BnguCiK5/S7DvArwGVg
+G/irRVH0jTEG+N8R5ekZ8DeKonjlh96EbdOqN8inNpnubGkaU1FFjla1w2pJduDTxOeRcgTsqfaA
+4w8xyoJjBwFXNiSIllf6TO9IHGHs1JjmKtpRkl3Qet2irlwBi6OMZ29qUKtos644hGQU0uhpLXxZ
+aMDKtQ2Kptxn5zo4X1DpNd+losGg3nHCSEVbKras4JHvM4rEatjunXJLtQ4aVptpWXbV6bZqF2SH
+7M3kmSf1Gt/aE0vCP014oPGHTzQ2iRK554aKm6yvXaOshTpfv3+bLFYOORNRLMix1sEQTwvBLEV8
+xu4R/buakqxV6JXl3tJsj8GRwp9fbGLNtTZ09ynigGpDArDt9asUaxKgbPmrZDOxiobjGVNbi4Zi
+GbtrtVXeaotZuNW9SXymadvNjHOBMWNzdCIVkQNVTu4sBBiNOzXGbe6taAwm6pAqR8KjhzU++gHZ
+Qf2OUp91nuPF020Ajto9tkPtl7MSB7E8S//OHv1CqNkuXZMU6uWNM1odidFceWuXiiNW2uGZSob/
+oGYKRmdyP0mQnoMJ49Rga1XtPKUZzQrsrjzz+rPPcubJ3Hrlc3sMFBU7DV0CRy2L22LlRcMZt78j
+x9745BVSrag0Tk6i6eWS7TJJv0vi+jjtcdyHfwL8H8A//Z7v/i7whaIoftEY83f1//974KeAG/rf
+RxEJ+o/+sAtkWcFoEpGXd7FU6KPkOdhaOxBPRuyqOlN1ocmLazIxnUAmY491OgruiawxMw3wNUZd
+4kQ6cNwfUV6Tc7dKYmovXVqgolF2u+LgOlpTUcuoqg7iytGI+xpxd8byXXj4Cj29RvrtE3wFyFzv
+LnBJxTsakcdEg44vb8oitVCyqCqnYLOxSM0TszUuDMGpCssGKrMe5ly9rCQkRYfTRRmq0nBMUpN7
+TsKcsSpnXanKZH1qcQu7JTOwOTU4CpCZnp3ga/AwKdskPXmxPK2MrKYdKpdVu9NxWUNeprM4Z6Uh
+LkOtvI7JpQ/zoao45YaGVjU+F18mtCXCZexlCq1B2T88oqu0eJWqvGDXnh3w4pk8R9VaoOSIK0Vu
+KIwWTeQ97rx+GYDtfTGpL908wbHEBbnRXmbpU/I56oX8yehzACz3tqjmch1jyWLJ9IBxT56/f5qx
+VRV37NJP2NwoSbn0817CbEX6s92VgKhf2WBxQ+pZ7JeT8wBl06go7Q9olgFHnz8511sGDHiKuUmV
+7blaL/F8ReDTt7of4+ye0PK3wjYVX17o9cUqfb32Wl0WrGz/lDPFt9R9n+KSulrbGbYMH/E0Qct/
+3iPE82+89x92QFEUfwScfd/Xn0Fk5uG9cvOfAf5pIe2riK7k6mPey0W7aBftCWh/3kDjclHMQycc
+AlrUzjooj5m0uRT9Ad/XvleKvlRfJeuXmJ1lZFqQMs5idGGkP9ynUZNVsO23efYjYtqWxrLbHZzE
+VDQNWSQ+JcRqCAw0lbpspeozrsuO/0JX1im38KhqoM23DNW6WB5+7p5TZo1rXZyanCPVnXb6Rok/
+fON3AWE4fl7FV154YYubV8UKuVNJqbbl+2XlWFhvb2AOVNbdP8VxVT25ZlgIL8s1xuKidKwSudwO
+S94lQtV6mKUVqnW5Rqd5GScUngWrJtbK5taLTHa2ARj5Ebnu+PZGDIHY4PVql0Tz2CWFM1edOpZW
+kWbNCjNL/l55uMyGEse4dglbg7W58lRUyyUSLQLbdIfEiaZZ0wkDpXpzvJSOkrZU1YrpBJf5xIdk
+F7zc+gSxFpvlaYpl5gVRZT790Z8G4J2HkvZcdMsYR/gk8oUeXk2LnDYTFs7+rlzDCygvqTKzMi47
+cZeXXxCyWm/6iHcUBn3cW+HKFXnuF577JE3lU6i3hMvC9lxyI9ZW98oKsSNuyVEm4/SDmuu7tK7J
++NvvDkj0mdZutvnAuvRd2xdkalqL+ZmfF2q25mbAjw//G3mOn/kVelqk9mBQsGzJ8c0lsWgbVLA1
+NbrcusXOo98A4O6ORazMz0Pf4UyxP2n6eIHG9519KIqiMMY8Li7ie393LkVfWblZnLr7FF5E3peX
+xnIMVkkefsHq0mzIwF3vVllQwE2kZmatOGKq5a/ewLD4rEyIWjjgaCpvVtpxWG3Ly1Quy4tSdTy8
+UAbca7VFEx0oLJs0lc52GjlVT0zl1UUxk09Xv4W5LRP6C/0zfuKSxBxefOkypYaYqwveHm1HXqYr
+HZkcrWqb8Ir41OEQ/EwGvFa5jKMCs8UVMaOTNMFRfkm/ZjAKnLJMDUeZm8Non6ol526oe1FZqbB9
+IutyXEDuzgVJWtieVgNWKtiaBcljV++himvL4kUyxdVYRL58RnlRMw61EvlIcQipUsc7Pm5ZJnnT
+bRAeCpjGqtZwMjmmXq0TJTIxM+3Xxc2YxSUBCvnt7BxbkltgKUOSsQrq66JA+HxX3crSZfJczutU
+1mmoQEpuV2gsyqLnli/j2PMMhp7LPaW6IRmqp//iB6kcCNDnT16f8KGPyAKwuryCW9nQ51Isi1PG
+ruvG4TxPHki/VW/p+X9AM8bmqS3pz4MkwtIN4mf/yjV+/mUpqT6IZJyKvM4ldVea5eeZ3RBG809d
++SvnLlg2rDB8JNmo6pK4qGU/wc7URcmO8B9I5mQ9+Cr3dJx6aQJ/Rub3P2/24WjuFui/x/r9HrD5
+PcddSNFftIv271n781oKv4nIzP8i75Wb/03g7xhjfhkJMA6/x834gc31HVavL/DA7pJoZLlwDJ7K
+g9XqHtfqGmU3ZQLdbYwjq+/6QpmpKgAHdki1Lrtt5ZrB+oqSofgenZasV6U5ajKu4NaUHyBPcRRB
+mY7ANMX4mURjum1xNxa72wC0K9fYPxI6NtuZcDaUNdFetagcyfXqvSkrL8kOU/HFMqm0a1iaH48K
+B0sZqJ1yCW9RrApbA3huVOCvqKVQtrG1otJ2XNB8dJIZpt+Q5E67Kb8v2jbNqrgldXJS5XPM4wxn
+QevxhwFRKv1pVN/BLqU4WlGKA74Wh5XjCFexHFa5SqYy8YklO6k3ckCL0Vy7hKV4g3Q6IFc0YuHW
+ybWYLDmTPaKysYy/dVnuwbUYvSvFUY1bKUZxD8aq4xZq0fgSrHWNg6Uwd4wNvpLrYLC1n4vCw8wt
+hVzDa5ZDoTiGutfl5pL0xa2fq+KWxTpwyx2pTARIA71Gdm5tYFvYZbGgVhT07wAAIABJREFUvOEP
+2E/1516nga0I0SvNLu6K9MuL155ivS1B0EUjrsjnv/QnPH1l3hdl/IrM37y/TbUlx5jOBs0Fde+U
+Z8SKHWxPC9pGGb0vCTr1LHCwA+UlWXToHwZ/+r3+gPY4KcnPAj8GLBhjdhGV6V8EftUY8zeBh8Bf
+1cN/G0lH3kVSkv/F49xEFocMH3yH1LqHCWQCuiWPzJ57JTZFJmZ5UkSUNa1X1VRfMTEkWqvgLWxS
+bsmEmA1sDtSHHzt91lfks21rRWIQkc09nwhSLZeOiojsRM3S/T65xm3bSCxj7/U/4Otvi5m8v5Nx
+SbMapW2fmaKvnHENTxl7Srm4Ldkg5fRdqbg77J2xeuUyAI1mQqG441Tp4p1ZhlHiFNOKKeZl336D
+bKKm+PFD+loyHQxlATr4V79Bry/VeWFuSFXApjg+OS8jt1z3XOfQRBqdtx1yFdUl9ikm+rtxQObL
+9eJpxEjrFRJ1Ddyui1Ha97zISaN57QdYyKJWJAnWWPoldpXP0WnjFkpkEuTc35bswmKcYek42FL7
+LdfJ5i9pmVyJYq18BlolWKQB4fDr0nesYy+I/uNcvSk8fp10rHUbjiHPlMS1N6a8pAA2cgqNqRf2
+Q+2fGlZZz5VGhFo3E5xoNev3tTkl/ZZtaB/LWB6PZiypLEHFLRFmsljc/j0h/33tC/dpaVVnw/k4
+O9/859KfezHP/MW/Jc/fyTDKXlUE8kxFZBMrb2hv+zvs1nQTXXVZf0Hckf5b+/gKugsfk+L9hy4K
+RVH8xz/gT5/+U44tgP/6sa580S7aRXsi2xMBc7Yim/KDFul4QqGYkiDPKCOr68wJGViye6w6Teq6
+uc0jxaNpifJApdPLTQYzxUqf+ISO2HPNconZWNmYIzW1K2Ay+V2aQKqaj9F0RqiVj30rJ1OW4FlJ
+duWzVy/x4KHQigVhyr7uQAezMluWWBOzFXCUZCNpiemcHg2ZadQ+6BcUi3I/0bhPEap4iQqMJM6Y
+dE6PNq1QshSqmp6RB8pEbLdpeyrfdiLPcefwDp5azN2OQzaR60XVFHesgb/WBpk7twSUCMU0vhto
+dQIyHYciaeNkYpHlk1MGWjCUxWLKVqoVYgQMlscZmVo6aZqRaEAwz2MszYJUlM/S8y3sZQWLPTxh
+wVPY9GCGaUi/lEpLWIqjQBmzrTylyNV9yFMKo5U/2ZhCi7+KNCWrqdcaz13FdXwdvzAek6gmZJSX
+KTmSiXBWPYza/yZU2LVTkCuXIrMEO1Jra6jkPN/X5riksDDQlGOXRy5VparvH8K+Xvt0W/p1t284
+/Yq4oLveP2DvSyrUkyasvvwVAJr2c4QqOWipBZ06J1jKM2GlTdxVGbRPbT5HpgTOodPiX2ff+FPv
+9Qe1J2JRiPyYBzd2SH5nej5Jfa+Kowi8rilhqblbRBB7isxIpaPjaEI/lE69e3zAFvI7NwarJYPX
+Ng2qys5U+DpyToaZ6Hltj2imhByWw1DLc7NKwWQoPRwrQrH9kz2uvyb38K17PRJlLPKXS9RvSaR+
++J1TTlRsNJ/KouL4MFG0GhuGgb6EbZNRqDCrpcg23zHEgcZXCgerrtLwE3Nuws6sAL8hz7p7pEjI
+eI9+Li9prV4n95WchTYu6p93LRzlPizKc6pzn0yfIzs7OWeIKmY51lUtgZ4GzJQgdujLItQur1NS
+fkHTd8nnwCPbwVJNgjRqMdVYdL0hcZ28VMb3lRXp8hS3oS+/O5XVGsiZYdky6W11NXAc8ljRj06B
+paxIeeHhdJSMtVzFZEpiKr8ib48gk3N4YYWJ5rv37x5RsWTM/CABV0105PmNs4ylxLS552CUNLX6
+YZWx+v6WyjiGxmVTyVCOOif4qluSHleIPqCKY1dkcXs29jDPyCJ92OzQe1YWjcvXL1Nu/f/tvWmw
+ZVl21/fbZ7zz8OaXL+exhq6q7upZtNSokdCAEIMJLEIOgyFCYUME2AEBtBXhsD+ATeDAyARGxmAc
+NkLCSAI1Ek3T3Zp6ququOatyqJxf5pvve+/O555x+8NaN7sSutVVTWdVRviuiIy87753795nn332
+mv7rv6Q3RlH1iV1twqs9TugZhhrjuRXAjl7/65U/QeVZQWZ+/jP/hKJ4Z8nBWe3DTGYykwfkkbAU
+WmGDnzz5Kf4e/x1WT+qkmhEp9deBCTkSaHcnz6MbyelYbGiPvLYl21PzMkiIB3Lqem1oK0V2pVxD
+GwJjjXyvJDG00cuoQ6z1/+PDDGdOtMPeaMTORLTfdQ2W/fVn/1N++sf+KQAb/7TLYCBaJXfvkvcl
+6LZ983UijUVNMQiVxgq5Eq/0bvaYYj0Pb8e4DdHi0ZZ8V6mW42t2xdZisvGUXzBiohyMw0rGUOHW
+pxfl/0E3pFVWOvh8olWHMOpE+KfFuvEnAwgUbzaWSSYcUHQ1J25jxh0FIc3FpPeU6q0BpdZJuZZV
+ZVEe9rHKiVn4FbK63pN+ipmW8WPJK2L1jLtiwtu5U/fp7aiXOZJrF2w/wOp9MG4NlJIPM80mGIzS
+vJF5WE8tvSTHMWI+5wcOtiIuSK5NdtLBIb2L2mHpzDxxJJZgVDog0uBoyfRxp5R06oOZ7BBbjnQN
+x1QXldIv8ac9Wx4gSc51Pv2DLtfLgpsI9g15Ktc/dK4SdgX532wLruD5e6+RxwKGcuMasVL0Dy4X
+xGfW9T4tk4bK3ZjKHooZcqD3b4OE1wq5p17wy/zG/nQt+rxTmVkKM5nJTB6QR8JSiIq7XIz/MkVt
+CH3VSpGHrYjW9DtL5CXRGJETMF8RLT5RCjNnAGFdPnekMkd1Tk7gPOvi+eK3VashNhNtO1ZamjB1
+7geGorgg174PRR16SqsWrR9yvSNBq962FgyxycKKzOHxWo11rVOPR0t0NiQoNbxXppuJ9t/ZFQ29
+aGIUKEerYqkcSmDsxvab+KmyIyuK84RzgqzQIBkxkTYpCYYFE2UwDg5dMg2kDXaV+brTI1bYcbXX
+IgjkFo+2OqRV5ZNYbmCUzbjod/QuDMl8sapM0STuC2uQZwJsUy2h/Samoz0e1OeOM0Ou7ZUdIpIp
+NiEN8dSjz+MejrYAdLQwKu/65NuqHZ9cIQjlPhnrwrQ/RzDBeBrwUw6NPOuQK6ux585hrPa1iPtE
+A+WqmDi4seAaxodyPzp71xnuitY8duoMJpIxJjEMt7XfR7VFlF3V69aUZZETH0oM4Na9l1iwoo1r
+WeNbt1FQbo3YDNm+KvepkRtWlHpvuONz3ZMY1fq13wRg41aHvpIsvHnrMgt1WcPgqUWGdwX1ObIj
+JmOND7li/TgDw51b8l2TvZjMk2v91z//FcbaK9Om7xDOyCNyKBSpYXLXg7TAzulTGtaIhvL66+4W
+2Y5Sny/PsVqVh7qjD0e2a7B6KHh5m64+9M2yIVRwUuI49HRRi6E8YKETEirVuamU0CJJkm7EXaV1
+f25zn96Blq9qYNDPP0G1KaQYzz6xRveO3IwXv/B1ji/LgXQvKhPqrvnqS88D0Kq3OaIQ64Vqg8Gc
+lnsPfWq5fA4FvCRexmTakOZ6Qs3RdvB1i9WDrlwOmWzLQ317Xx7oG8MtnlQwTrfawRkq4UyrS6gb
+1kuPkk5hzopHyEshbiSvx8mEQOHalhKKXSIPHZyWwpt3lBk6hFgbsuRk+FV56DPHkE40s5MPSFwt
+r57Xhi29CWOtH1i1x8GT99NxjutOMwolAQ8hJdwAee82uZVDyK3moBD0YtIhviafi5MBZkFPX20g
+HN9YQcsgiBOPQV8epkq6SqDuT5Z3CHyZh80lkJgkO+zcewWAyc3blOtSozDOJQvz7STJLDeXZK0+
+lq8RtOVe7txLuHZH9k52Q3EF1YCSskQfm7MsK4V9EIRs3RIo9GB4SLQre+PcE3IwlUzK4oo8/ENz
+iL0pBDDJcILtvuPKg/sycx9mMpOZPCCPhKUw3u/ywv/9a9g97kdt8qBLrrnd4taY68vCm/8jc+eJ
+1Jhwlee+k+9S70r652Z9F6uIP6fss1YW7eH4Lr6yFbuKtFs70bjf98Evl0is5tDdJiNNgeb5VRLv
+m5YFgF25zokTgnL7lbnLDK7J3742ucOCah33GShvyfftqNl6uLnNcE1wDEfPXsBvypzH3T2GiotY
+CKWIJvJibm6ICb+5s8/3n5EKucRp4KOQ7nzItNvYYVVM6mbJpat8C2u+i7cqmrZZfRw/FxSmWwZT
+yHhRSTkrsghHi2/wUrK+/L6f7lFoACtYSO+TdxSaIq06CRMNouHlRKlo5nZzlbGmQ3eur5MpPFjR
+xSTNu1RCWUNTd/E09Zh1O1gNGFIkWK3WNBpwHGeGYVdM/IX6UwRj/VxW5e5tMcfHnTELvqxzXJOg
+5OXtESttLY66scFmIBq4Fw85cVw0c7k+h9U0dz8WS2pruE8WyrodOXWGyZpo+WI4XflvLUVucW5q
+B/EzA8pVSXve2j5ga10g3Yd9ubY/+IEzfOIPCkfCarbCfibzPHn2LE7u6TxeYb0rLuKFhqybFxck
+B2IpRrcz9r447Rfx3VsJ8IgcChQOZlKGYszUhs8cl3BqOmYO3Y4s8As7e/zho7IosTIoVVYiyltK
+LNKoc6jMv0k2YKi05mm5YKEmLkh9TW6oa10c3axZPMbVzjs9m9NuaEvxosIwElPTm1csgfMUvvpv
+1XZCK5ExFqIhB9r1KN0NGSVyaC1WlP2ndRyj0WvP8Ti2ok1dWLrf/zJ0ZG6TZIe1puT0T59dZqkQ
+PHxS6eN0ZdMcZnfobooZ+z6NgXQqGfdimcO1nSGeNhmhFcEU8ny4j3dEXIxwoNH5rZh4R2MYR5q0
+L5yU6zvc4PDLwq+Yr3pEmpVorsrB6hlD67RmBuwSUUvWIuwfkipPoImqVEtKq5/JPWuu/CBohsA4
+FayS4VhTYF1Zgywf4GjbdTT7VPI8rDa7SXYHUNd72Sqz/DHJYGx/aZ9CS8nrnjxsT8RbTLSeo7pc
+5clVyQAUk0NaJa0DqdYoxnKgjnYFjr63foOjJ5UTs3WcXKHEm6+LS/F7SU+ZuYfFOZKuvD7VqrIS
+yz58UxkFanmJp499v8wtXOaEYiiyIKSi8QXjnuXciiiMeqBxlsYBS1W5ztd6Pa4NvzX0+p3KzH2Y
+yUxm8oA8GpaCW5C3I5y4hrVymreqVQplBp4QMVbo8ouX7nA6kBPT8URLRut9EsUV+MN9fOV2rMQ+
+lTWxV6uRR3VRgmdl7XXu1h2SwTTZbFDWNTITMdwXs/MwG95vQ+ceShCqd+tfs7MulkL55QhliqNp
+LF9Z0Lzyur3ftOTpeeXOO1aipu3eN/d2WWyJpqzUfUxXTO0o0cDZIMW48rqahiRNmc9kZ8D2ngTo
+7l26ypfWRQOvlPR8d11CdZ+iwmAzJfro75B1Zf7xHJT6igoti+ZO05S+Wjale8H9rsxuaGg+rc1s
+SkcwpzSLoN2unbmQki+aK7Zdoh2ZZ8fLyLQ/wzg9hK7CrdcVav7MEibWqsZRk0y5IvMkoNCcfWYK
+cqWTqzakiQxOdr84bNLPsKlmTCpdXF/vdXMPXwulRq64AclSSnJPeQubx/F8Xa/QgUSh5aMyUSz3
+9WBD3IuDy7ucOSlaPMsiNu4Kqc2rN8Sl+L0k014dX37tIkcWJNM0Xq2yYGTO85rtSropkWaPKose
+hVLeOWMHo1ZFy6vhaB8QpyR7ZbRheGNf1urX7lxj+1u0sftuxNh32Lv+Ycj7P/BB+4Xf/iqX7/Xo
+jmSxv/rcm3z9i8JudPfSixxdEPPZOhlhLKZmoyWbaq65xtae3Py93g5jLS3FOKBVee8/d4p2Y0q4
+ISbntSvX2FAzcTw2rDbl+5pLVfSMYbjrsaKMSx0tw/35z/4uW1vi7+9e/S3evCuniZ8Nufo18Tn3
+B0MiZSla1BTSi7f6THIxr5McGtruvR8ZltXfbep8XRMx0Kq42HPpKknJeDwmVz/Tcd37actseh9z
+gTcDeCWH3/3KcwCMom1quUKXa03caem0L7GIzJTZWxdobOfG73Dpi7fl940SnevTCPc2hzv6cFfk
+wLt5d5ue+vs28mi35f1xlDOvJnyjWiFUOHVPTdzDeEivq8AjcnJdqy+/8irjrvjck83nuHlX9sPd
+q3KfXv6NK7QVYfxGp8eNLW3omxoihRiXMoeJ1fWaKP2+kYNPXls8BXW1GxWWSkoQXDMUmsqrlGWP
+lJs+Hz8h6zm/cJzHv19IX3xvnuqFTwGwtyPNYr5+8Xle+uqLcm92Nnnhjqzth+fn+aM/Jc1l1moN
+Xv26HCxf+Yas8W6c09KakMQ1tE7JfOosUASyFq87IV+7I+7Gs6mg3g63O7gLch0Ly3X+/F/9bwH4
+wcf+AG5ZU7xOmUKbwJTK9RettR/iO8jMfZjJTGbygDwS7oO1kMeWzvCAlz8nGjh9/V8yuC6aq+x2
+cVOt1AsLfIXBnjmqwaTA3HcJbF4j9cSEjeIYR+vb+71tqgp5doyc/HOVjK62CK9UMybKxbeylVN+
+TIgwVs6NQCPuF7fEVLdFzlg5x9ffuI2zLif4nhOyPhTz89q9fUrqCoXnBEizcnSPzXWNppd8zi8p
+f2I956mG5sfnJaC2VNR4Y1+0pO3kXNlRcJPv0zfaat4Yhlqtl05BQdGAw5Fcf6+f4yuRS8WLcfYE
+YkxRwfgSPC20MnR48DJ33hDL7MU7+1S0cGtnuEWtIWsU9zLyedE6o8O+ruWEYEotXrc4CmrIsoRJ
+pFRwjbJUsgFVnfskaFCZl9fb+wcMlLmbLCXXwO721Q3u/doXAHhFrb+b3XVGaq0c9CLWBwooMwZH
+G9QcqdUoQon2F4lYEiZ16CjxSJG7JJqJmveg5sv79UqTLNSiOOWWKG3lxIo9KTUzkkTmNigZYrVq
+bzwn1tjLv3UNNm4DsNmco6lrnAdjxoqHCSoNlo+JFaKJDALrceG4uMRJcUClL/fy4uYWL9yT/XSl
+DyOtbL1V0faHhaGkGJrbtyM2XhTCHeeJP46nwVprHDBvj0dhKjNLYSYzmckD8khYCjhA1SXa2+Zz
+L/0yAHsXn8PTgijfBNiqnPjhqHofeThFcJqwT10z9qfnG/SUxWjQH1AMtenm4JBXu/IdVV81ODCn
+cYTxcEhHi0teTGIu9CXId2QJDjbkxK9rgMsJHZyRlAK/cOsGh68rAedcwWuvyOe2kz7Nmvh1jzdE
+qzbmFtneEy1+6liL82uSFrzwA8cIFMvQOCJaydtLqKZiPVx/4RqVvmjuYR5RD7Wpi29xNKZQasg1
+bQ7KjLYk5hKlCVarknzXoxeJ5VXN6phFba5zU9TVZ17+Ja5c0UKyw4KlFUnlxfmAVkOCXb3RPug4
+cVeZiUpVGsrAHS7ViXuKEdnL6Uy0z0R3QkuDZH0N5tbmXTyllSsGJdJEC3d8h2RbaMV+4V99lq99
+RSxHRxGroz3oFRpriRMGhWhBz/VZnhNLob22xLwGHetzsoZ7VzusaTu6SdfQUX6GvEiJFJPi9/sY
+DUDuKb1fJ805nkq8Kt4OcCryuSPHzpMp/eiXf1MQq1/bWqc90p4jDYenqmIhptk+hy/K5649eYNw
+TqwY9zHZK+cih6VjMt/FlQv87r+S4GHn2gYXY/ncJClAu7BPm/guLXj0DnV/9yL+2Ve+AsCf/Ol1
+XA0wG38Jh3dmKTwSh4ItIIkKrt++yfDrskkdP8K18qDXm1BTso35uRILTQGmLGonJIYxtbrc8JVa
+TmzlQe+Xy+y25SHcOYiwY6U00/+X59q02rJpOvEW/k3ZCL3QUOrJ0piLYxIFO/WVpIRJzHDvtvzt
+c/vYE7LTOxuwN9JgXg4njorp/qF5yYnfiG9wrC5z+5H3PcnH/4DEfAKvDMsKotIHvnSmytqOuCWN
+fkCcycMxGJdIUj0gixYTXzZWL5DoW9S/yk0NLOVpgUlk7vFWn+imbOL05JexdwQDsf6aBMnC5wPe
+f1oCWGu1x7lq5Prs3SbmrLhd7185yeauvN9YkY09bPss1GQNg0YOaua/7m1ya1fm5tqMPJGHbU4D
+eE5apqr8DeVgRKnQSss4Zu+S9Ip87jdvctfI2hYb+vs8IVV3pV52Oabw6VOnl3mqLYfsk2ePs6Cu
+RF8h2LXlJSZWPleqVMk04Lmz1aevHBAH3ZQDhT9bbfYSNBySgVz/aDBmV9sA1MOMzW1pJX87kUN4
+tGE5rQ/3af8Id7WT2WAccSqWe/ZE9ftYnJN5nmg8KWOseLRDuX+TYUr3UEBYzrETvPCyBFhbVBlp
+97EfP3VS1tUU/OpvSdDyVpRycEnrdXp1Al9Zyt05rFEt+jZl5j7MZCYzeUAeCUshzwsO+zG/+41L
+bHqiHesDS0UhuAtewLwG5R5rtKkpInFuijeohSggkNxPSUaiEUJqeDvfDOyt52omBvLHjXJGqybn
+ou/VieaUoHRnh3uZmHC33DalsprBWumXDodcviXa4VJlnSO35Tu2BhNQc7bklzmmzEK2JhqzZCOe
+fFrQcU8+vUBjWV77ZkShmtRVn8j1QmrLJwE4+ZTBUULYa7sFhSI27w3GFNM0m/YZ3I1qGCtzy52C
+OJPPRRlczm4DcOH5JexxWedsR7TIqQ99mNVFSfvmhIQjZQle/TCVkmjrxFukXtHO24tiGueDXVym
+naFdfCWtqLoZZUfWe7c7JFJXYqwkE5V6ASUZr1FpMqgqwereLv/8X4ubcynp4imgMdcbHBjDkqY3
+zy+FzB+R73jmsUVOVQSuXGtZqg35m7WpFZMm99u1hf48hafz2eiye08Qm1u9jOuK6jzQYruULpt3
+ZT038wn1NdH4x+bn2X1ZMCmXr4l7kWYJkfb2vD0eUtPq2adOvJ+Pfd/TACyfXaNREWi2F4nF6y8M
+8KbsT0sRHxlIX4hT7ZTff17WuZOkrFTlOxrHZI0TJ+b6Lbmn915N6XfEMtm6+RJxLNe/dHSJb00c
+9+3lkTgUhkXGl4d77Lx5DUddy8I6LPpKp21catpwpFyps5wKEKSk5pRTjL8ZYc1LRIG8Dlox9Zos
+duMgI1CCj2Ig5mLVC6kpmcYgsrSrctgMqmUOtFAvqA1IC/lh2ndx19+ic1fMzCzy2Ozpg+y4eEr5
+trgQYFalZmBvSlneXGQu0FqM0nGsdrjK3YBiopyC0zaKpSEmk+v3a5b5RfHx0+EeOzXlnbQj2JW4
+RdgUk/T4wpg7PS2bjWE0VhxCtEWg+IbtdkGjr23ntYlM+RyEqXxXmnk0A6EBY+EylUjcimg8wteG
+OpkedEl1mcN7GuFPXJyytq1fnOd9ehBsVkLubcvf7ysuP85CmlW5T2WvoKFNe+6MvsDV6xKviDJL
+SUuxG+6UyMVy4pys51J5ldWmPPTt+gqup1WZhyWSWKtfdVt46RhTmtKqrYOZltTDkXmFfLv7TBQP
+0lEAWNFd4bYeIM54xNVClMX5+jPcHMs8HYWPD/OC7X0Z8LF2QEnX6uQnDMtH5OFuVhZx9KAzRu/5
+sI0pT7EeKdWj8n1+8yOgdTcnD/q4bXH5phTwaWvID31UQF1fuPU8JpD96zVPQVMO8jE3CdAK3Lcp
+M/dhJjOZyQPy3bai/9vAHwYS4AbwX1hru/q7TwN/DmlW9RettZ/7jpPIYb5rGUcjclfbnjuGoZrE
+86UKS1rBWGvXsdqq3FEe+4QMo6SqnuvSairENUkIFYJMy6Wt2mNYUuZnCqqKpJv3Qypt+d7e4Rjl
+3GRsRkSqVQ8LObXrY8uBkRy1oSDXdvaBG/C4RruPz8+zsCIuT3io0O3jK6w1lDJr1MOZohDTETaX
+MTLNMrh+CWPVkhgn+Mrgu3L6CK1Qri9w6lzaElN76aRol6j2GBc3BAXXjToEyhnW693F3Ze5DRpb
+lBalGKvui9apVY/gjEV7RqOYkvZWCL3F+92Ky+U6jvYsmJLVZllMbVnm0z9IKCaioVzg1CnRjsHe
+IYWyao+19Vlqh2Ta1CUM5siNWF7Vw4ygJMHhiu+Qa8ZnrGzWa9WQFQ1Wzi2WWFD+hlq5cZ/GzKEA
+bZdnErEwYtel0MxIrRXg6/wNOWV1K5aOHrlP6qIIe9bTTcpDuSeX8wn1LRl7/8ZttB0GrhbuhSVD
+Seno6gvwwWe0O/ZCi5ZatWU3ACXSsYXiVLwcE8j1uUFIMNTWdckeVlsPlo48Q6KQb19RsX6zyg9/
+8o8A8H/82kX2FTm7Hf0yVe8nAJgkZdzxtIHb25PvthX954FPW2szY8zfAj4N/DVjzBPATwFPAkeA
+LxhjztspWP3byHAy4fkbV0gn2/ixLHqzXqKiEeA89+hOFKjU7VPRtNi0A9EoK6ZUhNQIKMaaInOa
+DPRBrgUB5xUfa8qLOm5ES5vBlAqYqOHUXWiQKWU63ZyRlhRPtOHM7s4X2R6LGZl2C+aVdfqp1grB
+vNzEY0cXGGidQ7klD80J02RZS4ddt4GinHFMHceReSQV7f6TlYi7WlmIwS9Pu00tYG+Lub7qwjVN
+wDjaU3FxsDfdcxhrKKycisNuh2KizVkWj1LOPyjX1FYQkxNS6Fq51sFYuaZovIMz0IrR0AOlqDfp
+FIzkEyhAjNCQ6/tFYWiX5Z45zQpBKv7zRKtWr3f6DLdkjUv1Ml4m37G7/jvcHsuhUKQOodqyy46Y
+/guNKsfmxSwPgvL9A7KZFUSaekt9j1yVyDQlbUKXVIFak15KnsoYpbJPpvB1Ly0IdezjmiWa1COu
+9bRJUCelr1WZ98Y+nX2Zf6Lfm09crMaRPlg6xdNHxbQ//dgcJU++zwnL2FTm5Oh+s7kLidLWpzmO
+r4xilRbORPZ4PuwQNjSWoDGjcHACduT+PVFz+J1d+dvOFZ/WkuzPhdYpDmLNmr1N+a5a0Vtr/521
+dqpAnkN6RoK0ov8la21srb2FdIr6yDua0UxmMpP3VL4XgcY/C/xzfb2GHBJTmbai/z0lHRdsvhjT
+63u4VZnSxLX0EzmBi0lKuSsar9qqUKuKGTWetixPLYHVZiJ2gC0ObSDDAAAgAElEQVRpn4Uio74g
+7/vxhFg1tuuI5m8OcxLlQgjd4L7lsRSG7ChEeVh4lFw5O8caDOq8dEBHqQ2zFcNSS81BypiqaIG7
+vRS3qcE8zQAwX6UaStCu0ajjKLzWuOC46mp4Ck/NIryKvE5H5n7/htRPKS1rtaYTc2Rermk7ULbg
+sceiI4HY694+ifJYZsEileNiTSxUQ9AmKlWt/AomZca+0tt5XYIFtQ62yxQN1f62wCiJjD9Qdu3Q
+Eqim9Req5Eohlyd9/FA5LuZauMrrcNAToNfmHnQV6FOqhtTnFTT0fINtNXVKZZdce11uZ3KvK0mO
+Vb7GoNIiXlLwTssQjKaEKyl2milSFmjjgaftlxOT47oK3S6nuNqx3JYiSqlmRBo6t9E26Wsyt16a
+3G+n53hd/ETuWayNikqeS6KuZLe0xLFz8l319mmcaVWmSTHaF9T21Ud1hqCfM7YBsVoutTFU1Wqo
+L1JoGW+ohneR7lPT9nDHq6s4ZQV9/YvP81dOaylA/RmM/x3buT4g/1GHgjHmZ4EM+IXv4rM/A/wM
+QFBrcT25Do5POlDCjiAgr6q5NxzTUSDTfKNORV9XS/L7mhcQ+Iq/t1LiClBkqSwyUHUsRpGOgRKN
+Es4Ra7lpSkQ4kYMnOcgYqym61KjQSRUIksrG/vrdz7K7roClw4K+o41jminVSDZxt7dNKRETvKyt
+w0NOEWsD0zgYU9LGKsYu4movQePJ/1mck2Yybj/v4SoLlY1dEu21eK+7wUZX1qCnD2awUuLgmpiU
+rjX4Cuo6/eT3k+xLtHx95NKoiatQrWrPzFJGaBV/P+nhaqyl7/YIdU5p7wBXo/aZphsDUyMPpqCw
++D5AyLHlb1arWshdObQqZTmwas0czSjjBwlVPRSf/0TCwZeUlj4aEGir9VDjSyU/YKJp5BP1EmGm
+DWDG+X0uSQdLria9pzUOeA7G1dZiZkwSy3y8uACtiTFeC6cunyuncmjWnUXyOUk9LkQB2aLcs/Ja
+mZ62o5+M5P/h9oRaTxVS+R5+LFWU1h5iQznITVoC3atpaRo/ckEPW5v5WEfn43uYid5361OE2kMz
+FD3rhQX+WN6bnLVESsW/m3TZ1IZItfoO2US59t+mfNfZB2PMn0ECkD9tv1l//bZb0Vtr/6G19kPW
+2g95Wro6k5nM5L2X78pSMMb8KPBXgU9aa98axfgM8M+MMX8HCTSeA77+Hb/PGoLYJ0szTElBJZUc
+o92lc8+hoZWRBzHYjkRTVxpyQhdzLrnm/MNKCfrTvowpRSInqeu793kUvGyqwSyO5qDrJqCrlWWJ
+k+NN8fzl6n0ClEk8ZYM+x6SjFZzLcKimfcW4DDdFXUXJhEQtgUhJM/b2cwJPNZEHmWpxJx2T6d/a
+SKnI6gGxpkCicIzRDEipVcUiFktYmsdqRHq+JtcRd1pUQvlc7hzez12H5TrGF0tg47f/EeF5AcLU
+ww8AMHQi7lwR0prNi69T0x6V7eUGmXZoLjOm6GvGJ5w2d8mhotiD3JBrlD2PS/gV3Rppiao2Sakq
+S/SJpYxcu4ZvxAcMh2JhXdiLiJeVaOZqitWofKH9LBvzDeZ9ccGKShNHLaTx2GDVuvOyGE972cXj
+KfDDMEXxBIEh9sX6yctjjGYiXHdCoACupjJmL29/mbW23KcdC3XNouQ7EU+UZOx/d1wLOjZSRjUN
+3N7chUysjewgBMXGmHAbU5f5G6OAhWyMrcp4TpJRpGLR2YMApy0Wna08hqsM3HkyDURaSmuCw/nE
+j3+MX7whkOjM3ONfulKP8d/vzdGJ3xlN23fbiv7TSD3R542kvJ6z1v6X1to3jDH/L3AJcSv+wnfK
+PMxkJjN5tOS7bUX/j3+Pv/8bwN94J5PI0gEHu7+L8Qc42i03cGr4ijYsu7CgGuZkc4FQuzE3tRGp
+14tBg1r9/TFYOUlda3A1ODgOfYz6+2NtuFkKMjL13/ykB9ospd6qUFE/bGdjn71cewdEookG+TrH
+zojLMzg0HNNYhRnF3FLG3YPhhKWeaIKLqjDzksOdN0XrPnX+GOdXT8o82hHliQYztV3w6OYiX71+
+BYArr74Knvx+9fRRFgpt+BrvcqhxkDUkbhHMR+SOYgWsS0lRoY6NMBPBVux0Co4ele+INCF/9auv
+8G8/+xsAvHDxMitNuaaG5/GBpwXTsDp/nqpqN1fRnf2gx84N0Z737g7oxjKfhabPWkO0brvl4yte
+OR7K3FaqAQddRd11D1EWOv7mz/060dYUvwETvVdLmo67cHxxWizIYNhjry9jr1R8Strez2tkuJqK
+9KaW0jAkbmkX77zMRHkY7H5GpSZzqzdqhNqIprIoZkVj7jFWjkh6r3k3o3pGYh8mbfErX5bAXr+r
+reZS6CtMevv4Fts90fhcyjBz8v7Xdgrs3hfl+7I3ADh95DyPfd8fknVtdrGbYglP2CS9LunXrd4N
+bCHfVy9LfGLh7LM4SiB8uuJyYiLxmitXOmz8j1Ks9Rf/+BdZPf3OEI2PBMzZZh5pZ558ElBoxH4c
+WDxXLt6di9lQEo7JeJO6lo5OA5GLWZ2wovUMqUNJc8l5s3Gf9CPuZYz7klm9qqmDYTZmSbMdc7V5
+zp4TU6wSlPACOUDuxTn9kW5orab7oYU/yeG8VLKlNqc/5ZJMMnzdse9bPcqHP3IWgEwzCpPNLje1
+oo50jKbxWZu0mCg4Z6AdlQ8Ot+h0ZXNktk5Vad37kz28sXxHPyxR0YdGcUA4B2Uu+FJTcdXrUKjp
+jxcTbcs8m4lPqyKHRTGRTTUYjim15IBcOFqh2JMxXhzkRMol+Oyzlqaa7nXlwby1NeG1N+SheePu
+HfKx3L9aw3JSG7+8/9Qiyy1xR0wi965U9zA9OZjMOCTz5FTY6Nj7+A1cMLouflMWa/1kjrOh7ppj
+mKjbmAwKAiVyWaFKfU7Nce3MFNuMTOnfEgq6o329DZZ5rbvI0pBWRaL55SdPylrt9ihpgPnQ2+YH
+rHRsqrbq7E4xENraMvfBUa7MeCHkG1ekgnF8LcDU5RD6N6+8Qeea3Ncd7Qn62NIGf0Vdnyc/8gTm
+ngQr7432eH5K7xYO2eyKC7Xmy+Fw+okrnH3y/XL/zEke/6Qc1Jd+IWXwNVmXu8/65OV3FjqcwZxn
+MpOZPCCPhKWQ50O6va+QxwOsdoy2lR62qqZ9nNBRLb9xuImbidaYV3O/ywatmtKOVXyO1sVsDROX
+3Ykc47fu7rPdEY2wqeQti4FDUMgY1XpCkSoWwCszUmxCvVIwVJMyyUVrhe8P+HguVsBLrx8ysVJo
+lbcdFhUmO9daoqpVgGZOU0m1BUraBbhWg25PipXqJmSiue5eqmNUF1k7JZr0yHIPRy2QSZESarCy
+dLDHXiLzD6paGPTYCqXdi3JNt737xK5uFDDQSsvK+XnKCk02msffya8zLol7cfzoGGWeIyunDDX4
+G01iVpWMdTwWN+Hm9jbru5Kyy5MITwN4T8wvYFbEOhiSEe/cBrhvuTSrTRaPi7WxbF3CsXIWXASm
+uaxcuDYA+op/aG/47CsNWmcC7lAsgrlSRletLONbWovy/nygJpRr2NBg5u7uFncPxLrZutflvFZa
+1sYBH3xGW/L1lLpubZ65c6K5f7h4nDP/iUzu+MoRDv4n5a2YRs2ybwaVX7g84sQxsSxfePMy+0rK
+s7nbpafWXaQw/R3b44sXJXD9xJOfwK7K2J/5lZd57lXBdRjXsKeWyfvOyb27fMnnzFD7rZ4/Qf+q
+rNEksqQaS7/+9/p0zrzJO5FH4lBwrU8tX2Uw2QdtyDLEUrVaUVhZ43hLbtbgYMI061pZUPBHtMfW
+vmz4thOwasW3Gvo9OsolOJx06enBUde8ervVYE7xAcsLbQoFEw2yiIU52QiDeEChIKmdoTzc9cZ5
+Qk8rLZc2cfQwiZaabL8iG+/L117lq4XEBFpluVmLR07zxGMy59oYetqIZd54xC0ZY7ApB0WvXafz
+psztyzuX2NqQm79U9fGrcn0febrNkdPyfWsn5AErVdY41RDMvRdcJIrk+m2vT1JVUFB/ghPI2OMb
+EtH+nRe+zrWXxZVYaWT0dxV2HSQMNQs0t7TG0lEBr77SlYPg4Mt3MAoNDuuLxIW8vtjpMacH8o4b
+0FqWB2C63i0vp1BMwJnVY2xtKWX6v0cuXigMvdYQX35r5y6jfc04xB5uWa5vcW6Jtqa2S0lOXCho
+rSU++cSOGF2Xh2MYJxxM5PDeK4/o3JF9djRyqVg5LExb4eZ5yPlzUqHqze3x2NIZvY4j96d6f8oO
+TKatUFdPcnFD7uXzt2/R0+xDQUFJqzXPnpGHu1I41JuqABdamLK8n17ZA82e9Gs1sqbcv4t3FfOx
+PeTS6/L7yRt97t6Wuef2fqM1nAR6txLeiczch5nMZCYPyCNhKRROxjjs4FV97ES0R8vz8RTaerzk
+cXJeNGKvWdxnDz5ZkdN+48DjtZ4e0cWIIheNUT5IMYW2MKfDsraC+4Cr7sXxc5xvTpGEUGmpyxCU
+WEpFA63klu1FGeflV0U7drde5faWUIZdf7GHV5PvfWKxRFoV1+TNA0sYyPsNbfN1ZnGeJStaYKHm
+UElEe5pwQLCj9p4WflU6VUxbfl9P6swpim25lOOUZV2OLiyx3FYorREMggl6NJaUyMQPyPbF7bry
+8u9ysC+a8sQzH6Y6Ee23fXhb1vX6mKEW/uzE7v1+Cd1BzgWFUteLKkoOTahclJudmFjt52rNIYwV
+2p2XqCuXYn2xwqLCnNeaizo3B6OBs8biPE1FqeI8x1spBRtaEfmkBmXbaUB3JO7M/EKd1WX5vtOL
+J4iUCo9RTHIgVl06rzyfw4w8ly/xCnjqtLhb5yYV8lSub9nLoK7YEv18I2izEHyz0Kq1IkVOpWwZ
+BZniZdPmpi7PnpT5/MxjbbYi2ac32tdoaWA2CCa876Rkc/xVWeMj6TJP1ATzV6q0SVNhZS6X55hr
+yr74oWc+wr4jLkg8lr0Qdta5ONBW9N0uYaC4HRvfV/dFAb7uw7fL1PhIHAq+qXPE+yTX+By+WFEk
+RYrR6Owo3iNT57KdlWktKFxZI9Ytr0nYED+sVWtRPyrm9VI1wN1RFqLuHomW3HVX5GadrUdYdUGa
+7SoNNQT9tM+B4utPzJ/liMYBbm/dBmD9xYQvvSQR4J1uh/O+pANb9TWeuCBmbp77pOpitBTmvLhW
+YikUt6QRpmSHWibujrGRzGN+SeZ27PwaR5SM9gfHZ8m0fmKSulQUqOSH81TU38dTjH+5woXmUwCE
+tS9wsCub/LUbNwg0lvKpH/8wpQWJsh9ZkL6cH378NIHWfmTxkKQqm/EDKx/gk0+LO7J6epWyUsmv
+1OTzy7UKA4V5e2lCfU42bKmwnDguLt/acoW18km5l1YyP/FhhqcNduv1GuNC1vhUyeG2pn59z2VJ
+syTL2vhnfj6gojD1wAtoLcgYtVJGW7tFDYjwNAbjlTTm4qXMacOdRr/MWEur17wWuaZLHd9jXg/A
+hTl5SL0WTDq6Vk+WmG9J9sES8UPzMrdXR6ps5lr8wFOSDXjfU4/xwTn5rg8uN1i/cxuAnlfBlMWt
+Wl4T7k7r7vHYSSkz9xoOZftHAfhTP3mHG6+LIgrONkh3JfPTU1JZ74lnOdGUmEN/2/DidSFuvbcF
+mrXH/T4Xd1OuO1XS4O8kM/dhJjOZyQPySFgKuRPTr9/Cr3nYrp7wXnI/4m4OArwVOY2Xm2UcJVSx
+GnJ8tfMKvX3RmOV0iZWqnNBFP+KYNn65PR9QLkTDHKpZ5/o+sQKMfFNi5Ihmz90aQSyaeWEetgox
+21qBaJ037UuMtQpvEDvkkbweBRGNtrgKH206VPTv5wKFXddWaE45BrIUf0Eb0ZRy9nyNdjeUmnx1
+jkXVyu5gRK705GY8Jk00EJX1cBzlnCjJe2GlyaWamJ+DNCfR5iaDyEoPeqBoljChUraJtcvqDyzS
+OCracS7fJKyJxj/7wfM0yqIpzeGELE91HNHmz5w8zq2RWGO1SgVXsxMrlTLn2jKnxfl5jFammoHM
+9zC+x9y8fFetnlH2JCCYNw0l5Xl08PmgMnavnZR7V6nWWMjkdTtoESouwNQDnLJCqYcLtDUwWdc5
+eN1dVhTCXDQLIuXSLLIRNfWJaq0SVV3/QrEZhGu0zmhPz5WP4bXEmrJFBZQm/xNn5DPvq57lU5+S
+rNTSSguvJpbQ3McDTlwQS8C0R5hY/n6igIyw+SlatYqu61mMJ+t57Ef/GM3zwm5eeAGHb4p71FgW
+68hbMORDGe81+zpvvKSFggUMp97o8zm12hT48fbkkTgU/KDOytEfIFrPyFYFJWYGk/ssPyOG2I7c
+uPYRF/ShD5RJZzGo09UDYm65QtnIgzmag+yiRLWP2nnGmtZaWpCbudUbsqjdj5w8v186fbC/S025
+/6h5uOprH2gk39uokqm/vHQmxNexo72IYyX5XBgs0NCeEvWW1g60m3iajkrHQ9BKQ790gfCo3PDu
+tCZ70qe0IOYigYMfaaly4Qt0DsjsELQRrlkVe3G0A1bbt48GCY42yWi3qjhN8ZPzuEueKQWGlome
+qjZxH5fNVowcqk3ZxEeWT+PpNokGE9JpjUlLuxwtNFlVpqva8hKu+t/VAkraH9GnitX6iHFHTXXH
+I9DDMihn7O/I4fX0XIUXlNuxaFj8RRnH1wO2XlSpLclD1fLa9xmLglqFTG3mogxl5LqNKgLmqlS1
+bqPwY1oLU9ajCZqMIqiWcALZD5NMFcThLUpnBAzm1HJc5Yq0aUJTK0mpyWfajznUljWL0Fok0DhJ
+RkJN+/mW2qt4nriTidKUjA5GBHWtLq00mJaPeoc9ShoHc8sLNJ7VwkFFqjn1MlbJjRvmdUY3lBjI
+GtwparftMjx8Z/1iZ+7DTGYykwfkkbAUbJGSRRuErQh/R7v8+FUGyrm3N+hxF831t5s8rad8odOv
+XLe4quXdcplU6+b7u3cZKsPgvSLi5DExg/OBaMxarUqWi1ay44xEcQj9cf9+85JSHHH5nmjxzR3l
+QSzuYY2c2sc5QaMqlkR5mOFr1V6lElDRCsxGTRu8WMtEK9b6vQGOcitUvF3iTW3k0VF8wLCEo9Fy
+zJh8KGtR9HOyAzFn86TPIFVW5UuiETd7u/z2b0mDl2yYE2pmYKHw8LTuP4ozStota78vlka1F5Ar
+RqRRPk2idNb7N2/QVMqzZJAx3hdtnHbl2rI0o6T3plaUCKYoEi8nSKe09RHDLNW1lbXsR0MWNHef
+LLQwSpCyn0ZEWonYHlepGE137Cm0+VibMFYMQmlEpSzWlGNchgeieeNJQaS4lnig4B4/I8rkO3w8
+gtjX98EqtyG5JT9QAFskAbw0KFM5JmscJmCR782LCbFCtgOt56DbItXaHbycXHuaZt1DMnU3i1KL
+oq6w+E3JBm1cfpPhtlhmJytnccviSsb3bjLZkjFcM8AL5HOlo+JqOeOAwaZY1hvX7+AuTRvVpBx1
+ZT9cSWPaajUdRFOytN9bZpbCTGYykwfkkbAU3KJEY/g417svEVZFS+xnIb72fCycjL7iNjM3onZE
+/bpALIbS1RrjvmiEaC/jytzr8v7Y0FMehsVSjVyLY3zlCjA2J9UWa1vDAakSs+aDiD0xGhjtL9Pr
+ydmZKuJxez0jKikzz4kKc5r2OjQRjgao3HqVQCG2ruIqbA6aZWU8nlBWpKBZG1P3JMgXG9FQ8f4d
+JnUNKHllion44pN4C+tpIHVSoxhp12FXaulvf+kO9lBQd+PsGwyHyhVwokR9JOtZPuyTFNoPYkeL
+cwbQGouW7PR32Ny4CkBl9Synl6VQLIn3SLXoaKJFaZV6SKqB1m50SEmtm8IbkTtaCOa7JOkUgitj
+2DTDKMmpOzakVbm+Y8MWryqS9dCbcKkQLZ0diuYrLxoWlXnJcStYja+MowmZ8jO4YYGSWDNRRmwn
+r5Jpu73UjXCVYtTrpwRaWGeHMVZZuxxH9khQdgnGooFzugQai7HGZy2W+NGtnlh5uztX6Q1F45cb
+PRpIajzOqjgaP5r0ejgjZUsayPrcuTlgdElYDO3hmOba98n3vnSLjjbwWWudoKYMWKvToGS9QaBI
+yCcWHucPfUqu78O2zp2rYlm+r7rIy5fk+g42vyXf0X8gj8ShkAeWwamcYCMk76gpGlqctgbq4iFZ
+WQEkWY0hsvC+blDTrFPdlw3RiQ4Ju9r1iYC2I6/DIKSp9Qy54hWssQTK+mttTqJVkMmkoKPw2pXF
+MTe2BD46UhbeoTMhVI6/3hgcNWfDRoVMI+fElkSDVSPdoa4bMtGH2zEGW1YSmaJE3BJTMy3LGBl9
+sljfS+L7dO/ZxGOilOMjMybWjk37sUShOeny5h0pgc4dhyKUjbJWWcO4SsRSiWmUtDGtHnSe73Or
+I+O5+/tcPpBDY63pM6/EKaPtfZJpUFUPWAeXVCN1aXRIR2smqiWXsZZfN8npRFpSrkHQfppzQRma
+MzejUAj2b1+wjJ5TUFcy4VakZc3PyP8fzQqKaduoKGSidB2TIiZVN6YUhBRT+v9M/rYgJ60pzHtS
+kCg0fZKklJWt25QiCg1+DrRxUG3tzP1GRPXqMUxJmZjLPv2zymNpxS09SH36jsLR3RpxqF2vxgPM
+1L0dO1gldeloeX6wOsf4qhzOr+xtEVZ/B4DwXIXKUMBSeSskO5R5Zjofr5SSp3JQjJ6pcqL2A/J9
+m19mN9D+mGbEj35CSqcv/fm3dyjM3IeZzGQmD8gjYSlUK1U++oGP0v/aVXpLUqCTTRLKGrSrlX3a
+82KK+a06u/tyint6wpdaFVzV0CXPEsSa0qlYrC/aKMkihmMx1wojJ3jgB/ebjSRRRFxMKxUnhMvy
+ud1BzCBSvn3VPkFuiGM55U04wp2XFJODTzzWvosln0oolk6mxCRFCmldtFW6bxkb7cp81zAaiwna
+UVjy0tIxJlPCzWBA3pfXEztBu5SRlGCgwbywJgjLq7cWmFPwwdX8LvlQNFS3k1KqynjdbZewoqbt
+rmi2O7deYnNLfh+Uy/QjsYTOzFU5vCXX1JtETDbldV9btEV7OYES3Hhhi1TbnI37liV1BbuTOmOF
+2u4disWw0qhCs6rfFXGgAcw/Vqnz8552fp5AryKmx+5tWbed8wscT+RzpuqRFWL95CngyxolwxhT
+0uYzA22CUc2Ip5RoJQ9fXZfcGWA1LesaS6wM0weKIDW7t+FZsUwjelQ18OnS5EklcPmadoUJGyXc
+iuI/CpfkQC29IsaJFSMy75DofgrVEm73TlH+iPJQzNWpLcp+StcDvJNyrWG4QuGKa1l0Zb5p6GF1
+3y8dO8FvXPlVuZc3dukr8c1P/7UP0R7/MAB/h5d5O/JIHAqe79JaqlI55+N/Q0kuyrvEE2VRDkMa
+Gjgd7kaMNMTb1ypDM0iZb8qiL5frLOiGz9IUx1XQT5oRaQVfoN2WnBQyq9H0SYZXaHPUaoirzUkO
+JgOGXb25WpdRZN9kDj7cAndRsQC1eQ47gouoBSFFIA9Irr0vh4dDUm36EsdDjGIIxgNINDbQ0IR2
+heMM1PRNhxGpkoJwGHI41mh4eY7DUDbx139bm76427y5e1nHLcinDWyuXmIQyeZ9+pPLLB+XCPZw
+8iUASkOfhid+dGVxkcG20IKPd2IKxYu4BzX21uXQ3p6Ie7GfFDS1f+ZcM71fMdjPEmJdz4U5y2Rr
+rHPSvoxui0gP96EN6B7KvfynX7oNUzSuhZHWhOwvKQhrv2CsD3c0d4BXl8xOMc5IFAxkTIqfytr2
+IzlknUP3fsMdp9JmopmDLPPu82OWIsuUvjPQkvJy8xQ2ku8aHewQNLQRDbA9kIPx1m2NIzV9ig9I
+zCFIlzjY/xoAntPGncYtiup90h6r2ZBqBZYb0pzHb7k4ZaVyP7qEvyh7Jzm09Kvquk0rRwOfQg+m
+ul1h8prcvy/9zpD6lI+0Zjlfm/WSnMlMZvIfIY+EpRA6Lmcq85jVMxTvExNpMBrRu6E8BU6ENxBU
+WdJKKRLRMNG8WAe9rYy+sveW0nn6WsBUKdfwtSjJsQ65NpcJg2nA0SXNZYxxYSk0iFYPKxTKWXDl
+3jpxIqaYowHKU+Uj3ElEU3aGe1zui7nu+x6umhB5f8jSEdEqsWZOtvp9Ei2eaS01QKPlaWqoNiTf
+PlHW473tbcbKOZinGWgu3fU7VKsCQY7LNfY9KZi5OZCc984bE1ztPYAZkvdFK+0XEXd7Esx6Ym+P
+ONJCG1dM1V7Yw9N1vXFxm1tjee3eXuXYBWWPrmcsTFmHt9UC29vCrUuwKwkrHIxknqNhF5QXwBnH
+jDVDEVcVah76VDX67nr5NwOslgc4FaxmEfpj+fznh/f4o4ognGu04FAj/6MDokjm1GpDoCWMriPz
+8cKQalUsmnEOubqmrudgNI8/KI0pdJ6VuhZ2rS3iTpRVO3+Fpfz9OjOP75v/fQB8vfd5Wbe9DUaK
+UozCEo4vWRu37FJXfo7OcMLmbbG24kgsgmoVqtsybt031OaUl7EY0db90om2KfXFcl76+FH93jk8
+DTqm+038tnzfqSLkB8/K+z8495dJ8jneiTwSh0LsWm62E8rHj5JekoWk06FQCOtkNOKqJ3j+k3tN
++toy/kQirkZh9zGHYjpuL/UoKSlnL+rR1OrKTp4SarqpWZaNfeZYhZ5ugiTOcbUhR+BV2a1o0xPb
+YYyY5hUtTY2XuqCcIPEwZ9JVoti1jMGOxkQ6A8aZ+KJox6qNziWubcmDebqzRGlR0km2GxGpubei
+POTXvJyB1hT4C4t86LyUOo/HhsTTEufOAc+vy8P9jYvSVNYZRHQTNU8LS/W41gm8WeeNrlC4v3Z4
+k7P2AgBGqyFrYU5Hy8w7dp31dXFXSos3+PCqPCDWifH2ZH4tJSFZ9Y9zWanMN/cPWN+XbMeJWoO8
+JX/bTWKuR+pW9ZQQdWGeQusv0smQuvr46jF+UzR+0h0qrOPgYZgAAAj1SURBVPzWAfuP3dT70Waz
+KQ/NzfVttnblw8d2ygRax1FSaHqMYU27fo2qPqEqgPZ8hQvH5T4VpgClbe8p6K08zqieEkW15H4U
+gmnK0mfwh5SD8e/KJG/uRezckgj/06dbGO2sZfwCT2ndS5lHvy/r9cI1uY4nW0d54jE5QG1Q414k
++7dFwL3Lck2dcO8+xdP5stwPt71Knsp8Lnf+DZ/7hriNF/6ziJ/6kf9KFi5cp+w9zjuRmfswk5nM
+5AF5JCwF33NYa1c4Uj7F+BOi2UrXTpG8KNplbKvkCpbZTAacXRFCka4G6oIjhpMdOWmPLq3S6cjp
+OvIOcTSA1woslbq2gZ/TKKHrsjgn2nE0jAnUrfA8w2JZTuuScWlox+ewIctV6jlk2nIdU+C6Cp91
+DUmmpmjPcPOOWBhPPyvuxdzKSVYU0LPgtHjsvGILsgqR0TbpU07CZIco0b6TJ6u0lKTE1vYI98SC
+en2yyWtfEppwZ1fmm8UW7bSGBeaVs2DtqRXKVyX6nNzbIdeMQWNBtM7R0z3MFVm38/MrDLUWq+Ja
+BsrDsBK2sBppz5RKrRqUOak5/0raptKQwdtJmaU5uZc3nt/gUMlAQm1Td65Wo6H0YklznkizIN9O
+HMWb9Acx12/LPV1orfP4kZMy/4XjuFWxwlbtIufOC4DLi7TqsTLCP1R6uHiL0JfrXjwt7itA6FRJ
+NDA72r4GwKA15OxHfwYAt1bDOGrJFjHZdbmvh1taxGcybl8Tbe3//g/genLPoqLPeF0slsZKiw99
+RPkudB+H2yNaDQkGzi+tsupq8DvKaVbFqliqH6GqxWaOVnjawCNXwNlLz32We5tipf3Nj3yS6ulP
+yjyDZ8j231nX6UfiUEgmMXevX4ewR/2KgpT2++RjedBjb59eX9ukx2XQyHdzSczrYJJRUu6/7q0J
+jpYMpH0HX7v7tB2X5hHxrQIFzThNQ6jug6l7OJE6sKWQwZ5s0olTMK8+c60sG/r1jUskqaYnrcN+
+qn57HzbV9/fjnG4q89w81Io9M6Gt4J28PWB/TyL8Ti3E78vG3NUW6bVqcf+Q8nOXKJfNeng7Y2tX
+wFRf+NoLXNWaiVh/X2BRdxljIVfEpjcacDSUwy1rtUk6Wqo8pxtwo8W1LRk7GcT4ofZL2PJIjmgP
+iNMX8I9q5eOebMD+nKF5VVytvWCfWk82/83hkP2X5W9u9vfpdWSedb05cZZgtQ7E+m1KWquA4YGY
+whSZWNYXeZRzSZF5iZvS15hCO2jhbytNfG1C8oLcv7pG7+eCGqkSubhZjjunmQi3yUizEiYvGO6J
+Itq8JffO3dvn2Z8QxeHmi6DENzkFX/zSNwAYa1bKTQxXtT399Tf2KQK5T8RllkvaAayU09Aq33OI
+O3dl/xovbYiL0jxewVVAku+ELJ2Ww6tRrePrIRMrGa/tDLi2LnP4t5+9SXJL9uSJ438S40qz9yxx
+SLy3y7kkMnMfZjKTmTwg5pu9Yd/DSRizB4yAzns0hYXZ2LOx/38w9glrp7Q6314eiUMBwBjzgrX2
+Q7OxZ2PPxn5vZeY+zGQmM3lAZofCTGYykwfkUToU/uFs7NnYs7Hfe3lkYgozmclMHg15lCyFmcxk
+Jo+AvOeHgjHmR40xV40x140xf/0hj3XMGPNbxphLxpg3jDF/Sd+fM8Z83hhzTf9vP8Q5uMaYl40x
+v64/nzLGPK/X/8+NmTYjeyhjt4wxv2yMuWKMuWyM+fi7de3GmP9G1/x1Y8wvGmNKD+vajTH/pzFm
+1xjz+lve+5bXaUT+V53Da8aYZx/C2H9b1/w1Y8y/NMa03vK7T+vYV40xP/IfM/b3St7TQ8EY4wJ/
+H/gx4AngTxljnniIQ2bAX7bWPgF8DPgLOt5fB75orT0HfFF/fljyl4DLb/n5bwH/i7X2LHAI/LmH
+OPbPAf/WWvsY8IzO46FfuzFmDfiLwIeste8DXOCneHjX/n8BP/rvvfftrvPHgHP672eAf/AQxv48
+8D5r7dPAm8CnAXTv/RTwpH7mf9Nn4r0Va+179g/4OPC5t/z8aeDT7+L4vwb8MHAVWNX3VoGrD2m8
+o8iG/BTw6wiotwN432o9vsdjN4FbaBzpLe8/9GsH1oC7wBwCrf914Ece5rUDJ4HXv9N1Av878Ke+
+1d99r8b+9373x4Bf0NcP7Hfgc8DHH8b9fyf/3mv3YbpZpnJP33voYow5CXwAeB5YttZu6a+2geWH
+NOzfBf4q32wAPA90rbVTQv6Hef2ngD3gn6j78o+MMVXehWu31m4A/zOwDmwBPeBF3r1rh29/ne/2
+HvyzwGffo7HflrzXh8J7IsaYGvArwH9tre2/9XdWjuzveUrGGPMTwK619sXv9Xe/TfGAZ4F/YK39
+AAIrf8BVeIjX3gb+CHIwHQGq/Icm9rsmD+s6v5MYY34WcWF/4d0e+53Ie30obADH3vLzUX3voYkx
+xkcOhF+w1v6qvr1jjFnV368Cuw9h6N8H/KQx5jbwS4gL8XNAyxgzrVZ9mNd/D7hnrX1ef/5l5JB4
+N679h4Bb1to9K40afhVZj3fr2uHbX+e7sgeNMX8G+Angp/VQetfGfqfyXh8K3wDOaRQ6QIIun3lY
+gxljDPCPgcvW2r/zll99BvjT+vpPI7GG76lYaz9trT1qrT2JXOdvWmt/Gvgt4E88zLF1/G3grjHm
+gr71B4BLvAvXjrgNHzPGVPQeTMd+V65d5dtd52eA/1yzEB8Dem9xM74nYoz5UcRt/Elr7VvJDT4D
+/JQxJjTGnEKCnV//Xo79Xcl7HdQAfhyJyN4AfvYhj/UJxGx8DXhF//044tt/EbgGfAGYe8jz+P3A
+r+vr08hGuA78CyB8iOO+H3hBr/9fAe1369qB/wG4ArwO/D9A+LCuHfhFJHaRIhbSn/t214kEe/++
+7r+LSIbkez32dSR2MN1zP/+Wv/9ZHfsq8GMPc9+93X8zRONMZjKTB+S9dh9mMpOZPGIyOxRmMpOZ
+PCCzQ2EmM5nJAzI7FGYyk5k8ILNDYSYzmckDMjsUZjKTmTwgs0NhJjOZyQMyOxRmMpOZPCD/H0c8
+/onzFIOhAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.4724... Generator Loss: 0.4925
+Epoch 1/1... Discriminator Loss: 1.3801... Generator Loss: 0.7973
+Epoch 1/1... Discriminator Loss: 1.5132... Generator Loss: 0.9511
+Epoch 1/1... Discriminator Loss: 1.3006... Generator Loss: 0.9248
+Epoch 1/1... Discriminator Loss: 1.4002... Generator Loss: 0.7541
+Epoch 1/1... Discriminator Loss: 1.3821... Generator Loss: 0.6088
+Epoch 1/1... Discriminator Loss: 1.6036... Generator Loss: 1.3762
+Epoch 1/1... Discriminator Loss: 1.2994... Generator Loss: 1.0744
+Epoch 1/1... Discriminator Loss: 1.4678... Generator Loss: 0.6439
+Epoch 1/1... Discriminator Loss: 1.5260... Generator Loss: 0.4740
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsffmV3/X57Xmfebjz/U9V/5pcZbtttzs2SSdNOgoE
+AkRIBAFSFESi8EAkkJBIC/HEU17CGxAFEYFQIA0CBYSazHFCd3pw2+22a676/+s/3vme+ex57x8P
+a51brqSbLre7kno4S7Lr/M/dZ+/ftNf4XWsZay1b2tKWtrQh55/3ALa0pS19vmjLFLa0pS19grZM
+YUtb2tInaMsUtrSlLX2CtkxhS1va0idoyxS2tKUtfYI+M6ZgjPljxpj3jDEfGmN+7rN6zpa2tKXf
+WzKfBU7BGOMC7wN/FHgGfBv4d621b/+eP2xLW9rS7yl9VprC7wM+tNY+tNYWwF8H/sRn9KwtbWlL
+v4fkfUb3PQae/tC/nwHf+O0uDnzPRqGPtVBWNQAuBjeS4UWux0ahaWoLrvzDYACwFv0EuBbHCq+r
+mwbbyNe1qcHeXCW/N87NDx1jqGt5dm0tG/3J90OsceWzXnzn3h1uBmQb6kZ+N19fMZmsZJxZTq3P
+bqz8vcLFsyUApQWn1rEBZnO7326RXF2rBiods+8Y8OQPrUB+mXk9dntLvbjF6YPVZrLyP8C34Ona
+tvX3dWOpa11XR9dGx7MZG8bibr7XL401N6LFNg1pofOrGxr9nes4H89L180CrquTinyiOAZgJ46w
+VS7rleW4OnGr83Nrc7PXdWVxQ/mX07gYX8dWWXB1cSt9rNNALX93IhdTG513QZE3eo292ZPNpKvS
+gq9PrC1pVug0DE23t7lYv7MEelZM5EKjzygqGl++z9MMq2toCplnaWpMLfct3JjYyv6tczCFjKM2
+BsfRtdPxBKXF+rKPrdDFjzry2XPBtADwYgfHles/eOfBlbV2l9+BPium8DuSMebPAX8OIAp9vvmV
+++SF5fx6AUDPugxe2wPgpc4uVSULsloWOB05eL4JAaiqmkA3wPZrwiICYFEmVCv53cJbYAq53ho5
+BL4fQSAbFLsh8/kEgGXZkBk5HYdH9yld2fxDV5brv/7v/hucRk5bk6cs0zkA/8+v/lX+l5//h3KP
+dx+ySmWuq3wGwIUzZDc7BeC0gvZcxjGzDkEln/V8fnKtXLB6/g5SmAQy16NWAKMBAF+7JWvyzv4f
+4T/82X8AgHPnJ/kv/+QvAuD6IThyMPdq2H99H4CvD+QgzZKK2UIOpt+CwJOXtPAgLHUgXknfk+tL
+Vw60X0fYWJlpkvHmY5nfySIja2Qy/bhFgXxudB8raxl2+/L5lQPe+MrrAPyZL75BfvmBrNc7j+kj
+h7t6QZ4xnAZ4esjnlzndewEA3aSLd9SVsV2lmIG8WM2l7G/RSnDmcm38eh9v7ss9liecPBTGmbUb
+WrJVOIFs3uW5xdmT5zXLmrfefibPKH1WP/tH5drG0e9K7sRDWe+X+thUnrF6fsFyV75/+OZb1JFs
+pv9U5nnqzAmXzwH4qPsFvtzIGfrlDy3BU1n8uQlot2XNsyO5762TivrWGICvvNjj6NXfD8BXdwa4
+wVcBGLwe0RrIuf9jX/s3H/Mp6LNiCs+B2z/071v63Q1Za/8K8FcAuu3IlkXF6qok6OnG+TFDV17u
+ylp8TxZnuNeh7espLeXAtHfiGwmGXXP6XA5338spd2WKnQIWjbwUYVsOaFaHBL5sfpNZwlilbQFp
+Iszpo3c+pH8szGn00qsAmLrBKmf3vRA/lXsM8hnNWsY8v/S56sjBTOU9IatPOXdkPEVZUTkq2eqG
+wmzk3z+tK9gamMrnJwZMJp8XL7ShKxt+/kjW4t1f+xv85X8k4zw5vcRtq6RJaoZ35CXcjx2OA3np
+I33J9zoF4yNhMK20wobyfX9vSBzKODu2YjWTz92h/N5tu/iNHNbTZznDXTmkDx6fczaTN6ywMW5P
+mGiVyt5VtuJiJhPpPr7itHsmcx2OmX4gk3U7PVoDYQYDhIk1aUXTkvXeu9dDp4cJPeK2vmw7Q8xs
+DUAeKZcdWpxj2Zug9FlOrwHw3BZ7x3LmzCLhopTrTV/W9d4bhtWJMII3P0g5vjuSNbrf4u+fXMqY
+l/Kb+9+4xy1fftcK13xYyPeD2FCpYKjWz7n8wTsAjI/eAGDqLOCRrOfk6lt818h5S9bnZHoubJOz
+SvXzpazh49jBeVPO6eDlW/CW7O9rf/h1fv3DXwDgheXPci2851PTZ8UUvg28bIx5AWEG/w7w7/12
+FzcWVlXNdZbSasuidvoeVSbDm+cZ7ZZwxzvHHgfDlwAwOvzhyMWthNtPLlvk+7JQXhOwE6ra5nU4
+SxMAklwOTLqcsZzLoUvrlEol0LqeMV/JIXaCimolhzdU1bGysErkczvKcWs5VE++dcWdnqpt7TFD
+fd6bkWz4XjtimcqY++WKVO0LpynJN9LYbvTX35oMDo4nb0LwGOJjmcuDpdz3J//IGyyeiNT9s/8B
+/OW/9I7+rsbzZA37/S7+WuY6M7JWe90ROwNZq/7+LWxbGMSwF+K5Mg9TtRip8hkpD/ainHIqe7be
+mROUcqCHYc77Kpc+yi2Oam+4olWlucfGepguag4yuUfChI8+kpft9gt94qEwmfWZzNMfGFjJenbc
+At9t6YB28NeyD24rBUcZwN22jt3Ba8lc84dXrJFxVHOfIJJ7z9OC1k6s18uG2CYh7oqU90OXFFnD
+ncpjrYJoXcv6fM3vcjASxvT+o2dcKJ/vhxCeijV9fr4icZWrv/c+AANvwsPZFQDjg12WF7K/cTuk
+VHMsMFDWssd+JN8ZN8LryBla/+qazr/8LgA/+NsrildlzOfvPeaNPyoMnr/Kp6LPhClYaytjzJ8H
+/hZiDf9Va+1bn8WztrSlLf3e0mfmU7DW/gLwC5/mWmMhKCyOLTGFcnuni4mFo5omwu8I2x13X+TO
+nnBBFsIxO/0d8rWoZ3bnAk+lhx92MOpcMgS0VRU7V7X9ZD2lcuQZi1lG1VZfxLSmqYT7l6VlEYlI
+m1ULnVxxozLXF9ecn38HgNXVOfP8HICH6SlnlyKBso2jymnT6+p96wZvJd+vSoOrDtbqh62IH/aL
+qlQNG0uhX83yJZNHau+KAKM6gS/viNZUdA6I1QtoA4eO+l1IHZaezGUnEjs8Hid0G1HRez1walnb
+0PhElUhPv1PTpLqevvoI0oDCii+mW2W0VdvywxF7cjvyScL5Wua9fi6/SzvVja92na24eCQyI/jy
+T9Nvi/nQ9gc3E7e+SPam8fDaMoYqHREO5Xluk+OqlukWe7gj2Vebi8ZnIpemUP9Ee0azlL97gYPr
+iDYRjjwK1QZd3bN84dJqyz26cY6J5Xz20pAX1Mc0b8m5aaqc7zyRqPvDp4+YqRP33K35jb/3oXy+
+Sqgr1RTUQW2LksrKM/KzBB/5vrQfO8frlkNHvaalpz6xpLjRNuf1Kf/oW7KnIW9RPZDFvdXvw/E3
++VHon5uj8YepaRqWaYoTOdiW6qVhhl/LSe/1exyO5WAeDRz21FPd+LJB/qAg1EPsOy0Oe2Jb0fbJ
+LsSur21NtyWHoq+HwK6mLJbycvc6DbUeqrkpSDJV7cOGWlX6dCYHs64K3n/2CIAub2Mnou6eHlUc
+n4j6POvPSRay+R2NhozxeeOO2KR1UrAayiafzVPWpVx7PZfvsrqW6AIQGRffVT9I4ZLqC4lxUIc7
+TSNjDx472KGoi85bQ9S/R6/v4o7kmoI1+3rvW2NhCjtxjOfLWsQmxtc5e+4aK7wNx+ncHFjjy3+z
+dUPkyTEKOh6pp+7+ombfF1Xa7Zf0ujKmh2pSddyYaqH36jiU6gQ9m5/jdA/kd8cRlbfS9VJ/EIZQ
+/U759YQoUz+C34Yw12dPcdsiOIyrTGFsKM9l/zzP0DbyItc7lnwqZ8RzelhlrgTqD7pIqCr1o3R7
+hLEwjaaT01VnrN8TJmaalOTxEwBml89o+/fk+2584/sIqpxKzY6wpz6zNKUu5cFhEKJ+QWxVUW3M
+ybbLvjpd80bGcFbmVI2c+1VZ0czkc1IFoL6Up0FF/Uufyr94Q1uY85a2tKVP0OdCUzAGfM+h70Z0
+HeGYbgFmJJyxM9jhuC+SZhgO6YzUebThvp5HMxSua52GRv9QmoB5JvdrqhJvrY5C1OwYjmitNaZv
+I9QPx7PCZV3KPQZjUAFKu3sEwFk15b2ZeK+/HAb0PdEOWtFt+l+XZ5w/fUI12gHgxT2RKIcv7LPb
+E09d05ozeywmz7eePCebyMOHkWgM80VOXm/ACy6doWzV4NrwzG7wFM6NJpCoStrsOrw7kXV79Q+9
+guN+C4DdTp9DVaUbStyOaFOxjqcTGtC/e6mLo5EYigij0Rq3qahUw6gWIrnTOt8IJaCD0bBm1WTE
+GvrtmS5VJlI6dmQtroqC8EDmPChd+n0ZTxAE7NwV7aVT9inmErqpElW193Zx1IRxiwmOSny3E9E0
+qiFZg+NqRGEg62YzDydUB17Ph0quXS+XN6FatyioVGJbNZkau8RRE6YfWi4SPZ+HLitf7ueUhzLP
+JqNWDM3CGfLKvyAaT/XQYDQyMtjLaYXiPO305BmVVzGfy1lo+ZaerlFlV9RL2ZOJzSl0HLVq0HFc
+stSwt60cskrWvi5L2LxHK5f61iE/Cn0umEJjLUVVYf2I/QM5EGM3pLZyuKNxxPFrEma7HRoiR/Wr
+roI4HB9XjerSVmRWASbThrbG9OdFTarfLzWy0KsrOnogUmeNXehhq2vqXK6dTGrCQxmH2RNmMptc
+0TiiBreODxidyQH8fS+fY1Ql3P9DDvahqJJ3j74IwOEbd+grLmD57IRJ+wKAftSmmIo9+Ej9EM+D
+KXEsY/MacGuZ83XPo7jUcFpW81hf5Eq91Pl5yuqnReXuvx6A2vhZWWIjYaZxtaITytaHGskwYZuO
+L2NwQ3B03ZwafAUIWTysJxyymcrYIg/sxuHh15jK1a3xKBayRkFQYWYyzlDxH73Q3gCZAidm7Mn8
+VvPkxtzy+wN85AXJ1/K7MLS4jayxN/Bw9MU0HR/PlbMTtAOMhn5NV8FEqYNV3IRTV0S7ipEIAjwF
+lKwma9ahjHM1U0ngeliNYKUdS72WsbUnhnfORC0fHskYXwy/wr4n+3donxJpJCJYGF65I7drlV/i
+zqvCLH7q3l0Ann+wgI6sfTdu085FUJ1czlg1cr+mY2jUHH33B/L399sLvIWMfT3JuM7Vj+K4hAP1
+u8RHhGONPnxK2poPW9rSlj5BnwtNwTgObhDj+B6DjnDXjtdlrc7D/abFcVdVpuZjybWBqoZBjNXI
+gC0awlx5namp1anjexbjaMy3EQl2Vi5o1JMbOB6u+vXDVoOZq/QrXUwukjcYCifO1wVf27kFwGF4
+B/viRwB8YdIly8TpdKf9ItHXRG3rH6t3e9gnMDIPZ9dnPpH7ff3OMfmhmCYvzQTj9fc/qGgpVHee
+O/iKv7VlDapqr7opJ5ciNYyGLRxamNUJAIvuCb6n0RwT4KsT8CDoYNRZ5yoGo/ZdavVaVjYjUhXV
+79V4qk2VtsBX9dpR2HFlDKWaY6ZqcNWxl8wzlrrOEdBRT/wtDak8sA4t9eqdJHMO9zZo09dJdN5R
+OaEdyO9W+ox+usTryNgdp02tYCNKg9+Tax3PYhVbgaJYjQFHnYROaWjWoqV5VUGSy55d1NeUiYx/
+ncjzvLJibkQTSDwYOqpB4FLreg5rMcF616dc3lMt1HhED0XKu5OPOFKw1yvW8oWvvgLA3R3Zx3uH
+McupanG9Hq1SfnfrzoCZguiqKqQaiwlm+oJ5MB+UlKlgBM+OnvHeR3IWsrpk0H4RgOAFny9+WTSo
+f8Sno88HUzAQhJbeMORwR+yt8vySTCM35bAi9PUwFgm2VBVW4aVNJ6RZK17eeBQKO7YR1AvFi2cZ
+dSo3rMqNBz0A9ayvnYQ8l+Uoa4+xqs8X1qUqZWMeFhJlyJYTioEs9MuBi0nEvHDdOYF6y8P7U8JM
+XvRWRyIOFStKnUdT9tm9KxuaTCv8I1GJdx4qmu16zSNdgGVesFZGFwZQDeVzMHFxKlEfY6PmgzWk
+70uI8P/+le/RimQe3cjSVWBVm5I95Xl5S9YiKGtKhSD7kUOt4Vu3zikSDXvGLZpCVVS1z+t1Q6MJ
+JkUyI1c/QuMbfE9+t5wvWGm0olJV/JbrcTKTl3QcuvQPZT13R6ARYLpuBxKZX3+sfhTHUDkKLFta
+XPV92KTADOWaJm0werKdpW5wr0Wz3uQwLLFLOSNOvsRTU7GpEppUfSmhPDdbO7R03Uzl4XU1Nyc2
+LCdyjw/7woB+kpT1+3JWRs2Mh1O5Nl68Q74SYZd1U+YfCsRwlYvQ8NilE358Jv1IgGP9apcmfwjA
+wi8xRt6NQ0VQlnsej0thet1JxNjKmG1Q42qUa+BNuUyFIX1a2poPW9rSlj5BnwtNwXNdht0+B8O9
+G3hqYQtCVR1vu7sEvnqWk1TEJUAs15ZJQV3IVCrTYDRDzGYVoUYU8iCmF8jv6r5IhmHlMB8pmGbh
+MtckmHo/xiSaaZlekGtSzfv/7SP57t+IGAw02kFIpdLRhFfstL8OgNssyRUsZMaaPMRdsidiHtR1
+SNwSzh8HhmoTaRjLgMf3RzjnCrmNH3GaiHo5jGO6Hfn8fvmE8A+q5P4ldYYtp5RrWYuP/ouHvHFb
+1q3d3SXWqIWtLKWCYjbJY/4gwK9UAhsXXwE5RW3J1cXdsgmOK/ez+nvPpLjqzDTOkKIQ6VlmxU2y
+WdAZk58KjPdCzbmdwGe8J+tWuSHDHdGQuqMeXQWwBXVM7Yh2ZlyRno7XoVwpPqUpiWKRlP7ODo0v
+++4kGaatWoH+vbENNhNtyhmM8NVTn01LPNUg+36HlWY5PjlRbEqVE85FE9h3Q3I1XfBqsqdyj9wV
+YNIvzr/MdC57HdqM67VETo5XbZ4rnH73Zwz7+uzLuWiVw/GYlmqmprAokhp31xKqVtyL77FWgNN1
+X1Xoac6dgQKrrnc5v6vvACWLRvI1DsYvc/0rmjjzKWmrKWxpS1v6BH0uNAWDIfQ8gsAw6oiEOk9h
+oZmIxvOwiITKzPomAaXWcMwg6GBrsdmS+hm1+hdwWjf56NOk4ar6OGQDwLIh9tQJGNU4vtx3NKlI
+1T7rOREzTX4aqBPpV773fe58RTLcXn6pwF8LHmH2vRat+2c6zj5Xc0lQcd6ReJQfntFyFBdwO6Iu
+BRLdYGhWGg5VqHVp9nkwkDFMkj3mM7lvnfu0HIlp3x13WZ7IXB8HMrf2cMTFhYb99gyBQrpjf4kX
+iA1bZSmrTK650PoOB0VMo0lA3iqnE2mcv/Lxu2LvN06B9TQ0qNmVbiei1P3IioSVQs8XueVMnXyr
+JmWh2Z/HbVn73mDAR2rv9yrD2BetyXc7+H1FUxoDiYw5WYkGkgUhvkrr2HgEmqHlDfp4RrQJ+jk4
+cm/jbXxKAUQax5+lNF3ZM7do09QSOnadHvlKfALlhYR987QhrmXM1yH4y80jXM7OxFm525fnXttr
+QvVb5FO4dU/WLWkGvDBULTS5zVx9Rfu1hCZzr02nLfNfXC/xFUFpLxo+VGStV19TreWsRnOZ0/nF
+lM4tRen2Djlaa1LgxYLGCqLz4tJw+CXxMX1a+lwwBd91Oej2Oez2WeSyKYtVTa05wov6iqdnDwCo
+r6dMCtnwpb4c48EOh7EA7U3lUK9EbfPbt7leyoK8/Rsf8O1LUfOmCiX2nQarlUDcomapkNFikVMq
+zjw3NZ561Mu1qGG2abEbyGaU6Zz1WpxB8+s3OVvKmObP7/KbHwkO/oMLeWmGByHffFVi0994/RuM
+hgrYYY6jkO3rpTzj0YMJz5B5fP/NKcuZnMZub8bwucKV45LJlazFWose+J5Hu68pl3FDNxKHUy8c
+MlDcQ20NS3W2dtXh9jSrGWkadrqCYUtzAFyHnU39iqSiTtVxpwVp3LBhoWrwWXbB8+tSP6+4mMmB
+HrZjIgU97fUVGjxy2VUz54lJmX0ouQ+dOz9LmCmIylmijn+SS4VgRyUow0qbkFzxBI7/nL4npoZb
+OjdYFhPLeXJsTKMMME+nZKXCg89rkoXM6eLyglQjVKFGXxxbkGgUBWJC/fvYdclrjbQIb8CNZuTK
+jLxOhdPIGXnhVsRJKUzh5OIZU+SFrbpypm9dV9iJMBCC5+RaPOP54oR3fkUyKeeDhsu13G9+ItGH
+0lTszDX7dPicZC17ua6XrKdqYkTXLC6P+VFoaz5saUtb+gR9LjQF44LbtTS2pKXhrW6dkRqRCMlJ
+zZUrnPbJW+9xqkk1dS1SYqczobgn6l7s9+mg2XLBmuu5SI/fnD7lybmGJJUXtqOGyBGu7YxK2pnc
+L88q7unYqnJFplJ8ryVq4pt/6x/ywUpMg9d+388w0qSi4Z1jnAeiVsd3Az5Yinr4pUOVwDNLpSGk
+q6unAi0GvNIS7si862s1P+IF0ULu+5Nf3KcpNUv08Ij5VHAI+cUZgSII7wSq/XgdShUeSZkyVCht
+1LNUWrrLtYAmf9lK5la2LBczkf6tw7t89Fg0rLp8yFodgl7Vo+2JBlF6spalsXxwLteeXE3Rkgb4
+sc+LvRcACP011ULWNrAy3rgw+OrYHVQlta5xOOpQPhMnGd2SMBc1f7An+7s8nZJr7P7BRUij+IXF
+dM3oUO49KBNuH8mzB6r9RMMBaSZa3JPnT7nQ5LYkrZipI7WsQ/YUFh0aeZ71IVT05+K6wh3L2Zm6
+Fkeh8KUW2SlquLcvZoC7zOmECtP3B5yfyTiv10s+PBcN8PlH8t9vvPERF46M907cYfCCaIvn5xe8
+90yumZwH9NScdCPRYjp1hzrQvVkVuLVsfGRz1KfKIi25rGSun5Y+F0zBNS59b0hQ+CwLzUg7GHCn
+LzbUIA5I1QOceg5uLYs2navqXIQcHmvxi45PWwEyJu7ihXIoxr7P4lAW3mhuRLZImOpB2XFaoKph
+5bjUmonpNisChbbmCmzZv9Xmw2fCFL63c48/GAkYpX9/l9ATk+D8YsZxXw6It6NZePePuLMn2IS2
+XeL6qiaXJYWq85tMv5+4PeRr+z8BwLXf5uKJvIS97Jr2fYFNn/Z9jJZCe5bKmlzOUqxChsf7YwIF
+NbXdPr6qu3Ec4mnSxDSRNXxvmpKoV7z76Alr9S8MhwGjltjt+z3wG1mXepNNmC6Yq4l2UcLsTA+u
+B1fxe7Iubo+WJ99vajEGg4pNQuzjqqZRnEWQOhg16ex1l7SlZascYRqNAxPNmLSDFrEmXlSFS6nw
+59bhAX5vU6xGoyHOkNqTCEh2/pSJFvCZNUMSX3/nrEgnsn+hwtgTbwFq448ig2fV/PE+rn1YKXx+
+tV5jc/19CKWCsK6nS0LFaYwHDh3N4u2GImTieYf2kez7IqyolxKJmWcrdve0uNDgDr2OmAFLZUJu
+NafQepaLdMGpmphrcq50DRsbEoc/2mu+NR+2tKUtfYI+F5pCXpY8vjij299hfKBxZ9fHFsJR016D
+m2qdgWzOo6miF5WDf2HHpdcW7jo+PrhJJCoWlzx5RyDID6cLzL6o8/Mn4vV//OwhgUqoqj9gGAl3
+3XErNLeEsRkyCUSa3tsXdX716Jr9A1EN93ZColDHmRxTtES1f/D8Ae9+pAVXfllrRu5M+Fd+QsyL
+u4cZjSbouPdeo7yU5JrHvyi/WfVqxvdFunz0rW/z9kNRZ/figj0tDrp/GPNqVyRXORWJGLTaFFYk
+X6eOSRWiXdmaRtXLebmkpxDj6SYz9L1r3FD+/mFV46hj8JvDAa4rGlu6rhm+pElqqlW8+WzGtRbH
+zd2IlTq7iiyjUki3ieYcaf22zrHcd3FdM9GMyvllwbCzwROsSLXOpdeOKXOZaz7RqEBk+dV/LBpI
+6TssctnrYW/Il2KRvKHvUCvWwbz4GgC2WrN6X7TCdR1iR3IWnjy/5OSheAr38hp3IJ+PFR5+eORT
+r+QZwThmNZFzGOUNoSaCeZu6cqamr8lHu/WIoiXnpkwyVmcy/2Rh2VOz4uV9dY57GYtnsr+j12/j
+LOUMzd58TqLmVue1DqdvyRlZqVbZdzOsRnPcyhJsslULQ1vNsVUTU/6Ir/nngilgLRQFznJNfqXq
+btEi6qo62/QoFLjRMft0dhVYpBWQhwcNsS9vcRCGxHpgi7rFSSkvhWcCbqv5MG1p5l25ooswmKEX
+4qmdnVyeM1fs/MoU5LXce2rlXreDHs7hpuz7mOVUQlqmXFHPRLXr+iPu7CqefaDmzCAm2pGD4jj+
+TXn2OnlMei0v04NaVNyec0TfyKEZHOe8rvX5OkGGKTRftjF4WiZ8rt7tVeFSKG6n22qoFAa9TFZY
+NR/Ictr7wgxfjcXEyQ+PyFxZl3bUoFop1pZcTbUS0ssHdNVfU+nLaCsYaR3Fw8O7fKCFVRaTJ2Qr
+rVa9WnKlobVLLam4aiIqHfskXRNcyxo2mUvRiB8ozzyMVtEqwk3ae4GrRUbypEOgpdONm2N8+d18
+GWM0Xb2biPnhuQ1VKGPbf/WYnZGo4nU0wa8kCmCiJalmPhYqFVrZiJUCi4o0JatlnzqZj6dMNNS/
+e05DqtDnatxipJD2NAgYa/HbMmwI97RO5y35+2KesCo0L6VZEyvwqm57N3U1e+EOw0NhkIknc+oH
+I5ZTYWJBt096LebDtZlTN8oUyQkdjaN+StqaD1va0pY+QZ8LTaFqaq6SJaHb5fauqOi9bsNeX0A/
+r+wfE+2JavieZ3E+FCnua078vaM+RzsiBby6vCmiUvkltzVZqYoCRi2R9K21AmjuHDLX0tu3D7vs
+7YlD7eHbDdW7amLMczzl8jsaffiJn3iBharlXmmJQ/ld0U7Z1YrI0U/F3H4gKm+igB9TNuz0Ojo/
+cLRwiINLM9CagXOREp2dPmEqku+Lo5DEEymxml0x0hoC3m7MdxYiEZdX6jz1K+xYnbKN/3FznQqi
+dn2z3p1Stn5nKOP9mS8PefORaiDzFTu6nqFxeWlPk2tqn1gzKWvFRRz3XbxE1X33gqLRas3tAQsj
+47gd1jzzyTkbAAAgAElEQVRXD3+m5trIjXk41zL5gaXXE4m4juckiaxRZ1xQa+ZqX8tdx7bNv/YH
+pLZGmqxIYjEbY8ejr7UT/DyDtZouWirORCsChXSHnRbuStb2J3cS7ij4bO6m7KijeGm1x0Lm0W/L
+PZL1iv6mjLWbc0vXxWoVHps3N8l21C6eQvZv7Q4ZKbjs5WqHWhPFolLPiuOQeHLu+15MqBGTu0GX
+mZZ133FSort6xist3lKsGCqIrCgMcV+1g2uPdls+e9bD0ToTn5Y+F0wBC5QN8U6PsSve+b3dNYdH
+suh799o3aqL3yiEvv6JgDI1OdMIOjqp7gUlJFUxjipDWsdbC9yu6A2EQ15luQODxlbYciMh1cLQa
+0ZO6zYXmM1TGYhpZpkZDei9+9TZLTxGIlxG5q6nVUUH3njCy/nzEKNykXGucbplhNP/CK10YajHS
+vGCZy4afqpl0OEgIezJ/lwVtRzb/uBvTFKIO5p7HeiVq91KRi5O6wEnkUI1GbaqruV7rUNSbKlQr
+ai3OEre0atDOC7Rb8qKkpU872tQR7DHS2pYBMxwr6+wqKCq2Vxwp5n6WeQQdLbtvYr52R+Z0Pc+p
+PxLATT2VlzttObRHCtha+LiB7ImT+3gjbcQyyXGRyMbtIzFRdtwj6OshryYEjjyjIaPuaV3GVUG9
+EKa+OeGF6UIg8xsMh4T9ewC03JI9NemCfEyeCSN7/lAb0pw/Z32uxXzaNb4ymcYx9EfCkK8VTLfO
+1lzOtGR+32PQlizZYRDQOZC5RqND7FpTzTcVeI3POtG0aKemMDInJ/RxjjSNvN1moPNrr+Tcu6bL
+5ULGdjpd8kSL2pSuQ6Gp9pXzsT/u09LWfNjSlrb0CfpcaAp13bBYrZkkM/pHIhHH7oiutoXzrEMc
+C3cMxrsUmyLnXZFypkpxtIOULWLCkTjowtM193flmqfnl3ilqrPH8nc39Wi31emYL6i1/Hq3sswu
+RcrnWXXTpsxT7cDvdImtqK3HX9/FP9GKw4sLHJWUbruFq1gARzMO3WAPjEZOljmNSvemd0xrLtJ/
+MJB5rs8L6lKcjsP+GE8lFC4sNZ++8BcUWt5spmiV1A8pL0QyPHOmdDXCUa7nN+XAq6pgnm0WX64d
+hS5xKM9etUOituZoRAmxljI3pU+tdSRiHc/u4jaPz3Kd85SRlkD3Yxc/EukeljNuH4jptVQQU17W
+XCpWoK5LAq2JOHn46KbdHE6HcKHQ3vdkfYIvrugh93JDF2djznh3KBUWnxULPK334B3KGOrrKbX+
+rlouiLTeQPvSpYk3dTSam3qUA4245PRZ57IP9WGfQmHc3jzhzoEUMikUcDZbZay0/sM8qWgiLRsY
+tYhUA2zVAd2xQN0jzQJezB7iOPK7zs4X6akiNH3tDZwrcfL2XI99q1iNaNPn0yHWTl5+e05X8TeP
+kgpfM4iLWUw92HQa+nT0u9YUjDG3jTH/wBjztjHmLWPMf6zfj4wxf8cY84H+d/i7fcaWtrSlf/b0
+42gKFfCfWmu/a4zpAt8xxvwd4N8H/p619i8aY34O+DngL/z/3cgYB8eJGUVduloypx218ZR7elmK
+21Losm8wdsPZNU98ucbR5i3Gb4GG7JwqI1CodGMS0kSk0VBRbt12D9TpM3AsFzPxL1ysEhaK6Cub
+Cq0jSqLhvWze5otvSDm2dtiiPBD71Z/HMP1Ix7GHq7n5VpvmNv38JhuyWD6jsDLmcBxSlerYU1Re
+Uk5pnE0ptZxQYcW1cYk1m/PkxPLkQtZgruG9vLCUmoO/vC7pD7Qmg4moVSI6U7BaoDRUWHmzvKbd
+FhHlORmuFfvbz1IKrTfQ+A7jkWheTb2pBn0KE5Gk6XlFmilysQhZqq+lG1i00xkrdeA9X1dcq3+h
+pmKaaVUr5xBP7eEohEbPQ6E4DKda4hrt3xC2sHqt651TrzdFaqcY3Suv2ITjAhaZahuFJc61aGwc
+sYnhOn5OoEVTI6s2vr/mXfUz3HqYkyi03i4zSi0cvONqI9kAlurbytMSm8sYcteg3d1wnRrT1S62
+Wv9hmSxJFGLf7TbYQJGOfkCrlH0KOn0c9WmZpXY29w3tRsY7m8PZVMPobg5abWmVXRNMFEfxKel3
+zRSstacgaXzW2qUx5h2kBf2fAP5Fvex/BL7F78QUsHhOQ2ZT0kjbc1PRVBqjfj4nHqhDaVVSK2R0
+9UBextaLHs5yU4e9RT2VHWjd83AvN73IXZYfCab+ZFcWfdQv8fXFLVYzslsCIJk21U0bcYvF1Vjx
+pi363/wbf533n/8MAH/qj/80sYJ0snZOJbfAHyVU2hWp1m5D9cMlzq689On1mrlmA7bXGa2XhVko
+Mprr1KFQfEA9PCLTatXp/Ir8Qg7slb1gvlJzRHMjrOvSjmXduv02tRZOKeqKdldehNQvcQOt8Jur
+t9ytsDN5XtAbgToU03GC1XTi63lDqJ5v70syhpNfWXGi6dKzswus1lIzscW/EhNkUc1YaARmM48w
+dMiMNvelYaXMeb0+IdQq3uP9NuVTOfRuIL/35wlGC+PYosFqwR07rSGSl8WfXZGdqimRy157Rx6j
+XUVqLQPMRJ5t9nZwVXDY5ZxaMztRp7S9SjkKNNPSb6GQE07CCq+RZ5xopqkBGoU2lzGsEtmbfqdN
+qSXfbM+SaTXxpJD1fvsH7/LOUzk4X3kScPxlYbyXpyecXyoM/2xGNdbCPkZ/v1xyfa4RrnJ64whv
+ZmZT4Z0g8G+co5+Wfk98CsaYe8BXgV8F9pVhAJyBtgv+p39z04o+9H80TralLW3ps6MfmykYYzrA
+/w78J9bahblpqQ7WWms22Uf/BP1wK/p+O7btVpt8mvDkfZHm3b093FgkUNTpEl1rr8gqY37yCIB5
+KlKkunwFRz9HF0vytaiz68e3OZ1rvYDrM6ziAlbq4AuKPn6gXL6qWX1fK/lOE2pVq41nbrIqN1mb
+5fE+F11tFffwjFtdDdO5LkQqmScTGtUsNtl5RZ5RXGjxi/Nz6kQ0Ca+Zc/VUzKOHEy0+6jYkC2H3
++d6KeCV/LyuXqZZue/hRwURxCJvknFYc0daY/8HObaaK0Etrl65WPo5rn5XG1pfabs5d5MSl2knp
+c3z1di2WbbJC1+3yhDKU0GL6ayJJH8yvmanm0gpiclWfmwKiQKSfa1x8DZ0FGt6lbWi7aiq5lpa3
+MZ8M3UixJbM2fiz3DhXfQa9NpTUU/KED2cYpWWE1PdQGMdGRmDe1K+HC5IlLqfvYGlVYbWNnnQQ2
+eBEnoB6LAzI9EbkWOT6HPW0+FPZvMnRbQXCDdEXxBpX16SvKNkkScn3e7DqhF31cKq3R/TvRvX7W
+BDc9G76/fo8Pvi3nZXJ5RqC+yr1kTXesmpw+1is8am0csz6dUKnWVwTgauk943hEXXGKw6drH/dj
+MQVjjI8whL9mrf0/9OtzY8yhtfbUGHMIXPxO96nqmqvZnNMsYfab8uINvwGups0a1jQK87XTC55o
+kYlnGic+Xl3C6gcAzKbOTbVjUy5IC8GRRxYWXQWvKBgp8yY3VZJnpXjB5Q8lmYJljOfQfUmzNffl
+oP3a3/l19rN7APxbr/5+vJZ4k8uLFKu2f5pcUE61eo+mJ6f9Pp6WVO/0Q2YKlJmxEnQRMLqlFY2m
+AWst9LK+cmgUYr2oKi5Us7pMGxRuRGM2ERIYx7JurargUg/gar0gNAoKCkBNZ64y8ZzbizGZ8oTc
+5DcQ7CYJWWnxmafTEltJjclcMxVrt6RRuz4+3qF7oXj/smSyaVTigeJxCBRgFK4saH9MJ3boRLIW
+1x9eUNxVUE8YEThaTUlrH9oDn0KZpZOHeC3VMkdtQsT2CoYj0NwFQm2mkjdUK1lD190FjWw1ZY0J
+5CVsspCmpY2DUaCQX7C/JybTdbAg+TWNLlHxVJsTT0utSj2OcLUH8XRVMVnKPwb7e6Adq/I1N02J
+Ku0mde/4FQ735QwxrOhqCf/TDw4ItJJ2d9wjW2r2qD43cS2LRvep5WM6mgeRODQqLAhiqmRTlv7T
+0Y8TfTDAfw+8Y639r37oT/8X8Kf1858G/s/f7TO2tKUt/bOnH0dT+APAnwJ+YIz5nn73nwN/Efhf
+jTF/BtFX/u3f6UYNDomNqdYrzi9EbXvy6JD2a6IJhElJrNDeqkiZhZpdqGpkdmdJaATl2EQh60TM
+h1EnoMlEynsjjyMUjquq1bpOKDQ+bhsXG2ltfs/BarkxM/JodYV3HobC+deHX2f/nnD+qqyoNg1C
+wohCO1CXWUOSakRAax7UpU+gcfX+8FWG2ojmIq9oFBIbqur89OFj5tpYJlyVDHwtFVeXLNYyjqn1
+qXQLHW3BVroBs1yUs9v0MBtshTXM1xrBqC0LTbrZmamDsj+lq6r9EoOdyLPLyCfQJKegv0PQU7Sk
+9j/wTcxaEYF7kctcJe10vmClKnNkoNQajX6xKfRS4WmTnH0nZjiWv/e6AyJHJaKX4FbirFyHGqkh
+IdRai3Y1xu6KFDfWYALVJuouphYndJ3q87pdrGIFiulzHJWwjrn1sarddSg0oc2qOeoPoNDGN+tT
+wyqXcey2a9Zai8JV9GuRBuyoxrYoC2abPo8moXJlH9YlLDQqEel3tw/v0eppj4yWh6fn6bgZMktn
+Oj+HRB2bjmareV6Aq0Vv4s6IKLzS8SzJtCS0LRO6rX9GjkZr7S8C5rf58x/5Ue5lHEsQ59jUkKun
+/mQ25XApB2L3tneDmc/NgnzjMXcUGht1MHPdgeQSd+PF8ApasaqaV0sutdZgqinEe16M8TaqVUCm
+bdafFRD5G29+jK+FYB9pgY0/+dN3aO6LurcTh9hUC4IGGUYLaLj2MeZM1bxEfRHehHpfXrDq+QLF
+HXF59pyJMpxGe0aeP1nRP9KOTaVDHmsr+jxjqnDWRXGFr9DljjKTzBY02oA12Td0PGWEUUOjabh5
+UdLXtPNVI3O7qNqkmu13fZ6SRRrqa+2yp9Eef+2SuVr8Vv0BftsnvJIxnK9X5PoCxcM9jBbe9eua
+Uguo+lqevVsU+KoaV7stnmsB1j8YzPFOZS06bouZhiJ7fYE7t6qYYLgJ0RQkjyQEXK0d2NUw8kcW
+q41ZZ6ncK37Rkj+Sl3hyXbN/T5639xVDpOnntmoI1up3eUFb2Ce7kIuJFSYZ+7dlrl3X5+rb6rvS
+CM+du22MMv2uE1HUwliuloZcIwP9Tk2RaY1RDbcuVw/ItBhrHVvamsJv65hkpcLAmTFWP8jmrS2z
+hAuNPl1mczyNgkV+QKKMw1qHRoshf1rawpy3tKUtfYI+FzBnB4eO1yKKYaFlwWdlRKFSM4oObspZ
+mX2HoS8OozdVjV5fuLz+wksA3K/vYNUpuVhO6WuxjfRLE87eEwdllomzrHf4U8SH2mAjn7CUYs9c
+5RcY7Q4dfbWFvRQO/PYvyO/+0n/2r3NaipkwTSZ42p7cq68IBxJdqJsSM1SordZ0OJsvWb2v9Rva
+K0aeljVvR9RaWq6tNQ12bh+RaSZmyzF4uajaWZHf1Ohz3ZBC4+LtgcJ2nRaNOqKCuE+ZiSkx6B5A
+pmXDygXrTQt7LbZibUOk4FMz9EgLrar9+DFX6gIf94dE1+qYrTZl9i8wMjRqp6SvMfTd/XsMd0UT
+8IuKpZpSj9ci8ct2m0g7pK97O0weiUYw+kofe1eci9Npha1EGkd9AYsFh3dpUs0GdBK0cyBFeEK9
+0oShoyH+WBLrYm3I0gR9HIWx9zs+danJaG4Lo05Fd9dgFZDkn2pEYm5orkXS7vZ8Uk3iapUxPlKt
+O9Dydx49utppunYb1qnu+2yO35VFGu8PMQqAevYDqeMwm2bsxKJh2p6HqxqpV65YaVSi3Yq5fyTR
+fUcb8czSKZnC1MMswFdnZu9on3SummcUc7gr7Qg+5Lt8GvpcMAWw1LakNi3uj8U3YJ02U0VrudbD
+U/BLYw44vKVNYRstYrKzx+4tqZPYaXmgXuZbZkV2LQs8e/AdRpq+W19p/8SDmFptjYtqyT/8QBat
+WNa4uvB/eO/38+RMQz373wdgVbTodERdH7QOOfvbwizOn3+XV177ptzbLmhiBa8EckDd0MW5o1VG
+4jFxLAc9DmoOKrGBJ6INM3n3lwi0HmLUahNHcjDN1ZIcbaO+OsUPNuhOLcNuCoxadfOLJQp0JDko
+uLWnoPrzmIWWwU80j8LiUmqYchh6REbU63tv3KaqhHn1O9DWddmUPS9mPolV771rb1Co/iCi0QOb
+Oi3yTa8OLQZzVSW8vveHAXjn/QnPm02/DAc/lHnv3uly/styj8ffk3DaFwYv4mn5+arjE9aaderd
+It/g1GxEiRZJufOyPDebUSjTW07fpashzqBjMJo/UOc5tQqiWCNDzU4P0xfGtNeK6Kby98urFVFH
+1rNST//x/RaH8YbT5ZSZNtB9ck73BRnPaF0Q9oTpvfKqnNkHb10TKgir6/pUVgREnoOrNubO4RGo
+KYxG+edXhkTPyMpZE7TVJ3Z6ilvJtUHTw+//lqiA35a25sOWtrSlT9DnQlPw8BjbMWVkCPvC7t3m
+msVUuzudvkP8ouSmU6yJWsLxjyLRFKpFwvl3fgOAWZKyRDQFO7M8eSKi980nD0gqkWjtvnYHOnlC
+purzW48e8PiJAnOyDF8bjvzms4fMViLFC00tdJ8/x7krEsH1Ut5uC0bi+//vh3zvUj5/c/gifc2C
+62tO/N5oRKPVla8vzplcSAOUqjVivRRJf3ImY3h6/ZxYKxK3V/dZFYJ5uLyaca0alGMd8ka1mFyk
+fBobzELj8b5hV+sWuvOGSh2ii/WS67U2uElEy9mdtfCWsp6HO0PGLV1bm3CtIvjR8zXttkK61f5Y
+lBlWpVnYMbRbImHzyRRHczcCNyfTcuhn2sUoqRze+0gwJN97/9ustLx88Af2aWnfxWRwznwoc33y
+7Ueyp+7P88KROHm9dg+Gci6cToR/Kteu8mvsuRztfEeiE/V6QaaAtPzyAhQL0e68DI6Mw6ZrGivr
+7LiiHTVlSL3QcvfV9CafIYwNr2idx0Sh3beGA9BM1CunxM/lXtV6wdMz0YSGLYdRoWAorbOxd6dL
+rtXKbcviZloWb7/F0Mj84o7LMtE8B63mfDFbkmn0IXA7rNVpXCwLck/7gs4uuHp3qylsaUtb+jHI
+WPujcZHPgl5//XX7P/+1v0aSTvnwuyJpX+r28e4opHTZxtc4/nIJl1YcNN/9x9JS68PHbzM5FQl0
+XayxWs24oJZ+hIANQ2KFKS+1l9/55Jql9jNsxzGeSvZu0L/xNYRhw/NnmtWmef7/6p/4Y9SFPG+9
+SJjO5B7Prs/IlzLm2XyJVTi1o1l0rU5MpL0tV2Q3DsHi4HW+8ZPvAHDaE1v+lX+ccaF+hPLUZb5p
+orzr0RlIHv/91gMmJzKn/Viedb064M5LMp7FrX1+/n8T7Fg2ucWrf1y0kAd/D14SM5mnOmfP86m0
+m3XsNoxfkpJng/NLCkWAFrMCRxOXKi00apuGQDsfX3PIF/qimZ222owfKbrT80lmihbdhHqNoaWf
+q8Zw1BVfy5/9j/4864n4O37w3Y/o6sSrr4nkf7V1iFspBPt6Smtfvl+vMmyhvSyul5Rdbf+m8fqq
+qqkS+bs/tGiEm9ki4/pa1sv2XI5b6nQsRAJPrwyrUPssuPD29x7J/uUVf+F/+HnZn6loAe+9/V3e
+/ED28cG736fQ5zU4hHr2pqsrXEc1BcVNmLqmVnRnPx4x7Mm+j3ttCk2kmhU5i2vBjqwacSLWac6p
+lrTz0oJ0U835h8i4YF/Vf7zNd6y1X/+nr/okfS7MB1tV5BfXTJ4/4UBrHI4Px/RdwWynzYRMq9bm
+Ucn0uXy+Pt9gwWtu35GX6XZ1QKI4hv6oA1pR1xjD9Vrbi6sj0jYVRtOMy8bS7cvB3OkMWZeiqi0n
+JcO+dnhSyPB6MWGpZbdsBOu5jKPIVyxVLW+aBl/7GbqRerS9gJ62XF97z+Ed2cV08j1+/T05KImC
+Y6Zf8xg/ElXcP2yDtni3VzAppFTY7cF9nETut/OSqJzvXvwS/kP57nt/9xnFJjWNp7z3P3285u+p
+z8rTQidV26O+lJejMpZirhWObx0QTjVnYMfF1UIfoXq6l+0U75EczGT1kGfnsoans4rqvpYgm/o0
+O3IPT1tRJnnG9Fq7gfV8lgo9MJMlk7dV1e73cNRqPNb+is7So6pFnY/7BqMgswgP09JW855Pmck5
+0sxjArcmGMvYTJ2RK3ipE4fYA3E09ssUq9gXdyg/fHHH5epczsKHj+H+S7K2CxeuNGX8+uGbAORX
+c8zpJmOy3MgQTN1Q+wqi8xwCR9Z8MFLTzgR0O1qExXb42ktiHt27e4f2UM7Q+0+WuEsZx989lUZE
+2fdOuMgf6jNaoGeHH5LztgYNknxq2poPW9rSlj5Bnw9NwTWUQ4cPfuEZP/2z4kQcj2JqrZHgehHz
+RDPHPnrI1RNtxb4j3Ne//QbHqmG0und5vBZxFAcVgWatTecZz7Rr9PWVJrJMXGZaPswpoNbEljxc
+Me6K5pEnV6gWSKyFO86vpiy1OGxZV1SKUFtMMgoNTxnXpasqsXtLy8YVczzNjIwyw6Wqj73dMWt1
+qvaO5Tfh/Ig0EDFfXfSoByLaL9YFt7Sg7fvnDffuixP05Jn8bn9wn6VmVH71D7X48Oflvk4gmYsA
+jnEItTio8WQ8QePRqMpd1j3iHe2PGXfp7qqJ5e2TIGu4rwn7aeNzFss9/qVXX+W9U3nIG3spuvRU
+g5RN64FNkhd4N04yv2gw6mi0hx7P/qZoVu5LF9xpiWRurrSXR1zgagVj18wIV2oqtgxo8Z0w9Fmr
+VhcpwtA6EaGGMm3dZzHdwJiDm8zGZh4xVpSh1YSqtXdJZyCO6fHpgpX25tz3U04ei8b69jtiluWz
+U4baO6Q5827UlDBy8R2t9TDaZdiR8d9/Ve573IQUoahKxwe7fPN1CVXvtndx2rKv9149pdEEM/9X
+ZH3+7vmUl43M/9H641C05cdzCXwumIKpa8LZit3uiri6B0AYGcpaq+QunrIq5TA+PD2l0uIkt47l
+wNw/2KejCJrwVszwRA7CsrhiqZll7U7Gvk7XRxbaKfdwjFz7ZLK4qeuQLWuGXVENfVsRaa29uNIi
+FumCXF/uhW1YT+SApVmG0XqGoevhaHTEKrY+m6XMS2Fo2WJNpdiC9czg+2pKKGApyBLWN1nGT6gW
+8vdluSZ/JGrnXnzCw4dqBywUGtsO6ev8vL32zRo3pcHZZLG3GtqFjH+p+RexzVkoPNoLEoz2V2zK
+KypVxa13TWuTDt6S31dVje0rbqDV4Uu3ZG+uPIv3RO53Wrm0N1WQlfFaEgL11GfWsFY/z/AsIG4J
+JsGrdwk3hXZGwvxM2cLVkuueCXDU34Et8dV3Y5sWUU/Wtql1Tn5NvtR6hkFKpJEItxXhVwoMc5fk
+Gl3YNIfFjokD2ZNJvyDWUvudsMezc4kenWuEq15dkCvGwvO9m2pKTfFxLZte0PDKodhEP3EsGIoR
+5qYAzN7wLgc7YjZ6ucVXvEVk26RaaObLampc3B/y9HRjB64h/22YwaZcSf1b//mfpK35sKUtbekT
+9LnQFKqq4OzqMW7f4o1F6tpyQa4q+vzKI0divvUsZTjQXg7alm33oAfK4b3GQzub0WqFtLVeYyvz
+GWlS0W1Ffk2toacSYxA7PNb897LOWC1EUrb7YwYam29qkfKrq/SmonSdNxTqqW4s+Bpx6Pa77O5p
+zVotUvLcaW5UPNe4+Jr5SdOgJQtwlU+vqvImCcpaD6uZll5dkCm8+3JV4CjOd9OSnlVGeqAt2t7N
+cdXD79QevXBT6KSmpdV+vZ6M3ZkaWip08qzBU498M7I3mZi15+PqPVobFd51OdBkrLho6MeytsOl
+x0y99l674XxTdzFRZ23h3mRwmqCiUBj7iblg747E/zM/Z1nrvmtty04rpq/FGdJVSqP7FwcRkbaR
+r2frTZFqKu3zaAOHJtaYvnXZ2Rnp/GPYaGFpTF4pFkVbFpaTAl89hqOdEY7WuWxMTVErOlMdjmGT
+oNo+vufSUWesU+f0Vdt8dWefb74oSMYvvComapyuGeyI+dDq9mntilnRXC1wW1onw1jisZyn17Iv
+ALAzhOBM3pFf6r3Pb7wrn1dFxSaoaG7+D/JPaVZ8LpgCxoDrcjjeZdS+D0C1XnJ5KQfi/fkJyVuK
+Ae+O+NJr9wA4fEHgw367Rz3R8F9dot3nydchbiBvWzt1KbThSKV48rIXc88Te99vtajP5dpnFzUG
+Lfjq5Qw3DVwi2ayr5Dn1pmFHvrx5kWtj2DlQk+a1fUyq3YY8MX06ScA827Scd9BMWBrX4qo9XKuK
+bxyHJtGD6ebUE+1HiUejpov1PMpUXl4tNYlb+WRzudd43KbRikwNFal63P3Ax1V13mpOguM0bCrn
+x4FLtOmPaA2xArxwDJ56zgsdm+OajSlP6d6UdsRxa0YaWssLQ1dTrSM1wUxrjdUXdl2ucDWt3VQO
+A18YTuXGnGqTFDStux70CDYhvdrSHSucN2zja23KpL4g11Ce8USdb1oRRsPIbuzibsISpYN1tDhq
+4OIY9QNpVaVsndBsajjmFUbHXA5dlmoKbbhpy+3SVaZ4vV5R+bI3rbpmpyd5C1/+4i1eUqj7SOuA
+hn5MrOsddANYb/a0wtFiMMQOVteudagdu8wRP/UN8Ws8+6WU9/YlEpGcNRRqmrkYXD0DVJ/Oftia
+D1va0pY+QZ8LTaG2lkVV4nQGWM0AbC4tp29Jy/FpsSLeEUnyuhOwNxZp7GvZd6/2MKHWyStyrKrM
+Xu3QcdR52A0ItZ5Aoir+UewyGmgpsShgrdlDxi5Ya4JKvTQsFT7azzUZKC0woXLdfgtfvcLG8xge
+iqbz6kuvcHUqDqj0qUi2snYIQ5XQmXeT4eeFPkYTjQL16ufEuFq7wKxdalf7Y+YGTcrDpBWo+lwr
+jPavHiMAACAASURBVNj6luVCTLBwGGJq7Vu4F7I3EAfl7azPrR1RXa9igTkv35my0ApmZeXQU/Pi
+IPZwVLUvTbhB8eKpxz6sIdS25wPfx1dvv9PEoM7KVpJzqfiNk56sVRIXhCt1wM4NlY4zdddcKay6
+dkt81dK0LSVt4/L/sfcmMbck6XXYiZwz7zz84/vfWFVdQ3d1dbPJ7iZpCLRJGLYkWIYhyIa1sA3Z
+XhgSDHhhy155YRvySubKXniAFrZpQQa0sCZTojipyBZ7qu6u+c3/PNz55pwZ4cV38r4qq8h+zaLI
+J+APoPH+vnVvZkRkZMQ3nO+cgBWFLWSwCb4KIgteM59BCwUFeGp+aNJnZDlxYcDLoaockE4Brgox
+J24lmct8RqGNmqQvi6yEIX1fuNSoAhaskfm5OxpjTMPmEitoiuvcGA3x2r58Z+C1UZEr03AOjVOj
+ARGaSkM5tISsHBpNNitCc4Z7Wuans7WHL/2MWCY3wg7yC7Fc3kmfIOGbncRqk41o6Pd/XHshNgWl
+LARuB2Z9iuWx+E1Z8jF+eF/MIacf4tV7jLgHA7jk5dOM1NeWQaFZARmvUVMtyrI8GCLFbLURZ4LP
+tFhZaXhcVHtdF4t9cQ96rR6ekJ79yXoOFTNmQE1Fy67QorBpJ11jPaB/HXRwc09KuDvdEE8eEnDE
+gEFvq4c1laBgSwYCAKLuFlrEqhd2gxT0gJzRdH+BZNVIOpVwlCxGz6tRNCg2/msKQLFSsahnUCza
+81su3shI5PG6j+1SxrLkIja2ELgAQKFtbFEhyXNs1KyvqHWBLqPrNbMWnu2iaBiBMgWLbkltNAKS
+2AbdAMqlr060qapraBKvJG4Cj1LzdTbEZS6pWKu8xGgsVbNZw+IEBWVTA8K2NqaxBWzQlrABVdNV
+yppNyqAgCUmJDDlFiF0TI+WLpy0PFlXErKamoAJqNMpa2UYXwlZdDDvcnA4kmzCOOlh2SWJ7FcAj
+s9LBfhdtxrGKOke2FvBVQRSq5fiwqS/pBRnQbQITHtQmDhDDITNYzfiMX/sYDGU+g5/6Bv7cx1LD
+YX//Et+lbolWBg43oTM8X7t2H67bdbtun2ovhKVgGY2oTlDWMaaakOEHh0jacrrsdboIWbtu93yA
+pmi+ZrCrUyGf02rQGoY4ez20UK0ZaCtrlISB1nQNlLFQkdY8WWkMaGq6fgeGdQfrRGFNnUZNVaXJ
+1Rp2R3bf7Z0AAVWJ98MeuhHJSZ6usCR1eMyglxvWMMwl11WbaAIArRoomP8m3sLVc2QRMwBzILTk
+HoUBlE9KtAIbt8PiiaKBDY5jdVmIujWA6E9vY/jBOfu/xLQlp9wlgVxP1wk0Xa3AVVhSE/HKd9H1
+GWisS0z5udUiKzNseIy3zZ0UNqnuAt8BaSWh7Bw+LYguXb6XPAczcmm2nRAhyUvcoQN9xpPtAgjJ
+gbG/K1acGxi4uVyjNgaG1aG2ccDyGKgaUORmhMVn1/Zg6KK0HUBzHGWuUZJ8RlcpXK/J5sh8ZkUF
+kGJOeQ4yKjibOkWf2JKPyaJ8p1gjX1B3MrmCF8qadXUNi9/NkgJzkgS1SB3fq1IoKko7ixhOTyxP
+q9awvEY2G0CrCabTBVUuLNZBuO0Wbg/Fdf3q9hEaFp0fLc8xK5qVRoz5j2nXlsJ1u27X7VPtxbAU
+HBvhuIN+uI1S4l64KDOMPUm9vHHv59BmINHNc9gUO9H0T3WdQeuGSBTQDNQU0GiStH4ngGWLpZAT
+u5CVBQzhXrW2kHIHnpkUFf3oyMo2jDeKfuFisQbT9biaVLjVF1hqZ89Hh/oEcRVjRjXiNU+iMvM3
+2hK1pRFQodpb1AiYWktdymSsc5SMIyhjoa6bnL4CXUoo24VprIasSTtpGFoV5VkOm/GXi7/zBI8I
+pZ0se5i/dsy+sZKx1vCZy+23PbgB04Lw4PBzxy/gki5OMygJY6GkOklkOnAZtAtCHxHjD7YFFDxh
+Y0K7w1GAm5Wc/kdThRYDlEmcIzmUOVzOMkxa8qxe7RL/EAzhsNDMh0HEteB0+9AJYxiBg6jdKFoT
+Hp3lCJn2zNshPIJZluv1xkIqVIFi3VRxMuAbAdmC1YxxgYg8G6kNtEmn56+ItsUa+pLBQ1UipDWV
+I0JOC7HwV6i0BHlTxhFagQ/VKJAbgzolaa5Vw2KaVXUCKMYoDHVHTVADNuUUpwncV2Q+x4uXsLuQ
+eNxcDXB+/ydTnX4hNgWtS8TrExQXGe588RYAoOV/HbYt1WBBpw0/pEZhDVRNsIrmlElscL0DqKAZ
+RLIqCy4DVPVqgYIrucjlswwJNGHQMTwkZG2OlIKh6afsEinLoV3mnfW6QPqhLITctWHRtdlXr+J4
+KS9bOqmQrGRBTi6obuV7QEkMwaALp5R+ZqpAfCobiMUgablYA2SGdjKDVpO1yB1YzHy4hUJM0FJF
+fINlW9A0k20fqFK5nt0L8O5SfjcYlDCZbGQzCsla2obN3WZmPPQIPththRgRLFRaHghPgMeNsm00
+FoRYn7kaPkVierqG4XyOt3uwWas8aFEH0WojothsneRQRP0Yr8IpcRin0xhvvPolAEDg8UWwQtQ8
+IJRrI2OVZ9BxYFOi3TE+FH0auxGPVTaUTRKSPIFDbdKo1Ud5KapkpXExI6R5diX/qjyHYiYGbY0z
+VtoGWYLyWIKKASs1Cwsb+PssVwgoKNTTFrKUFPxOhZQSPl5bgudd30NMd0VVOYIlcS/tLri/w/Ii
+KNY5wG/8JLXRrrTNCTrEMdzev4VzTzbk7370FG6XylASh/yx7XO7D0opWyn1PaXU/8P/f1cp9S2l
+1H2l1P+llPJ+3DWu23W7bi9O+6OwFP5TAO8DpMQF/nsAf90Y8ytKqf8JwF8C8D/+QRew4CK09zHe
+SqFjQTG2DjrYqeQ0C/IZlgyoFdM5FHPz0bacLm7pIEAjMuKgJBzZRgCLEOU6D2DIqqwIVbXVFhKS
+sJRZAtBktIIaiunOpFzCsLzQJ9M0AIBVjUZVuIofAwAWhylmPd5vViFmeqsmCtC/vYM2BUScsIeK
+isolZtAs+Cq4TWsNRMxjR+MQHqvvvFTDMHW4NHMoQqgbAIExFhTTsMpVgr0GUD0pETCVd1WW6K6Z
+GiUmwAZArwop4o1SsSkjGM5RWwfoteXvnPeYL9aYkonatkusaeIuLQuXZMS2Fg7oVcDiXLrpDJRB
+RFHlWJ/TzJ8HyOdNMHaJ7S25hqJ+RZ7OoNI25yhFklOnc3UBn2ndA3cb4YABW6IOg6CHkrDkWmtc
+XUra83AeQ6+l/4vpFHHOQDEt7jYUnKyBnmZQKxn/wLcwow5pi6nqTgK8WzKYl+ZYxDLHF1dnWDeQ
+7pmD/ohYh215ZXadXVgLEtlkc3g9sTzTNIbzgCQ4N6YIQ0l9akNr8mSOUouVU1Uu8oRryElwHktQ
+eVaewcRNZerztc+rJXkA4M8A+G8B/GeUkvtXAPy7/MrfAPBf48dsCnVVYj05xXlp8BoXYBgF6Dam
+9FxBESY7fbyEe0tWU/q+TII9tuFS+LPf2YZq6MdVipoVfldZiYtzctuRq/F0PgWUPIB2bUEbSqOv
+PZRUoVLKxrJ5sfJPTC7fRQOgIr/ij9YnyOeyeNteGyvCSmua8OnjxQb05O2n6J0zY5LbyAlasgim
+qAugovmZpjYi0qwHlgc1lr/vGR8/YD56zfLtvFQbIIxveZiRdVmFFrJmAyhSlLYs5JKxCtgGHlE8
+Hhwo8iuadoiI7sNty0VM+O+KgCXj2qiaOgI3RNEEFdIE+20K3xiNmjyOFmMf3b4Nm/M28DzUrC9w
+egZnfD6uHeImXcQbLB2vJ4C3I5vCxekMeYuVj1MP4VDusView/iCI7FX8ln7AFgu5MB5ePIYHzyS
+F/ZoNUPJTdaKAvTpQlVUiHqUaIwJ13a9Llqs58irGDXN9dWC/VUVNF3CJEuh6ErFboIl61zqldow
+aLPrGJsKex7rIAoLq3PS4BeP8eARWb/qLey+JeQrZiUbmtfahUs+zsgLkBiC5FBgfU4o/yrdxFqe
+t31e9+F/APCfYwOdwQjA3DT6XMARgBuf9UOl1H+slPq2Uurby/XzpUqu23W7bv/82x/aUlBK/VkA
+F8aY7yilfuEn/f0npei/cO+Widw2yvIQ+Ux2V28SoiA3f+0ucDqXHfOss0BwKME8bsrw0i3cvslC
+Fr+GIVldahxMM4nCPv54gve5Qx9diokYhW1QvgFa+YhYp156FXJW86XrOdqMvmuv/WwAn8GHN3UN
+bObNi6xAyQyGab47v8SKnA3p2xm2xk3A096oTvMnsG2NsjFFY415Q5uQ5xjQlL60A4RblIs755iR
+wm+IQrS9uXc9KZCwmMfWPrJ9mtVcAoVl0HcbTooufE+CYHt7IfoM5nkDBw02e00dh27oYs5T/mi2
+wvRKnk271YIibPxg0EfAoiFvwCChZdBW5C7oeFCUNqumcwwZiVdhC+3bUvQW9MR0nl6+g9Wp3Pvh
++hiX9yWIZtURXtmX7/QHEXxCNUNaYDodwmuTXVo7GFDZ2RvuwqZlWRUJsjWtQrpPlbdAljKYG9cY
+9glBjmw8hYy1dUei3PZVgA5dpovz6Ua5fL5MoflM4OSwSADz7n35rJNptO9Qdbx/B2GHNG0XQwxd
+Was/mH6E6Q8IeW4zG3b4ALeo4t02AQ7vS2A+Vgb1UOY2tVx4Yy6eCa2OH9M+r8Dsv6GU+tMAAkhM
+4ZcB9JVSDq2FA4Az9we0NE3xox/9ALc6A2S7MiGOXiM5p1hpnePhx5KqW5dneMyob2WJefqnbnbh
+pfKwNGx4t2VxrI7PcUwXo3D7aH1Brh24YkYmsxhDSq5fpRm6E3lReiODWeNGWgEYREfH+QR2vFHR
+/EQ1qg8PNX1mrf/ZrygoaMYAtJehYp872yH0hASldAOMZcNuy2ZkJS40o95ZHKPKxGSsghZ8ptO2
+mNIbd4cwhbxgVpBi9oCw3EjBMFPR7ke4sy2qQReZEI3qyRo2wVnTszkCmu1qOIRP3/l0WkH16B4Q
+oj2da1Qs0VwUBdaUPQ8cCxnhtbOljW4oL07YCH2GwaY6MUxmIJ4M09ViI69+dwB0HFnQLkFYqT3A
+2++IKE+2OsMP3n/CuYjgsNbgrZbCsgkK3LnD25XIDuWlWKQu9J5sppMHT7G+lPH1LYWcMaiCADAf
+CuThQTWyMWdsayur8J3fegcAsPsNyZi9evenYD9uql0DGErOl7qG35cBZnkXJQlcDig1cFVaeDCX
+jemVr9xCuyffPbm/wGzK+FHQxz/6p78tHWEKaKfdwcM9eQfeQBf6ROILT+YrnAWyRuZWvNEZfd72
+h3YfjDH/pTHmwBhzB8C/A+DXjDF/EcA/BvDn+bVrKfrrdt3+BWv/PHAK/wWAX1FK/TcAvgfgf/lx
+P1AacBJg6ifoMhpR2A6a+pauAm5RabhQe+i0WVDC6P7ujQ6qjDDSW4BL0RZdLZEY2fdaowhv7QgM
+1COkeB5NcLAju/Lk6QlqAmy0ZeCQ+spNVnBoMpu6iaFjc/wrYEN3te05mDMLYGmDuKE/a6rh8GwX
+1rWGYU1/3xvA3ZZocXbJE9qEsAlF9p0UhhZBXXoIGckOAwsec+SrkYzDLm1UDXW8Yzbmis6BLoNo
+qhVjH2JBtMk5qEsHTxpShlqjw4rCQBU4mZFurZzDkP8xJdWYqwAnYPXlwMOAufSu52I0lCBuhHoD
+0+7QX3NKgxaxBLlXY9i4gpmDNoPN4+0WvFrmwPIZlAxLvHogJCxpfgteKM9Sa41X78jJ6/sWShYV
+aUq5wwxREG/QHQLBnoS6Bt0RisMzzgswO2LVKC3u4rRCRjfHqRT8Wq67FQbQHvErtAicD/8ppvRp
+u7aD/pAs0W0PJeXoYs/GjaFwK+y/xnEcT5AWBKqVc5TMrlSVgbMj3/2CFyIi3dyc1aW3drrAmTzH
+O2EL0zb5FN6rcb9oYO8F9Ce83udpfySbgjHm1wH8Ov9+CODrfxTXvW7X7br98bcXAtFoOQqtHQf2
+MoFhqatt9+FGLM+N2+i9LttdFmsMqRfgW3JKbI1ChORFWF+lGxEWV1nwWBgSdIAwkdP4FfIjTHwX
+9UR22rsDBxdMw8VJiaAJgrVszIuGQmz9rM888gNbYUhI7c+PbsHcke+2cxfffSwWzdFKimTWuQWP
+adZKKxCkiZdec/DK8g4A4B8kcmpVAOo+o4Qx0N6mhsRpid1dOT1ubg0xnUnM5PRMxpb4FpYJLaWV
+2lgKVttGm9gCFCXKQk7QmijAxNcY0FLohj5GPNkdq0LGMunFwkKvI+NzGYupK2CnTzhzUiMaURC1
+3UabjMjtwEXIwp6c6T8nsDdQ411XwWXp9PadPkb35fN27W6QkF0iEIPVTbS/Kum7eJHi9kvyXbe2
+MN5mgK7MMLuQU7whYHXabXhlAx9WGPGZhvfG6Azkepk+hjWUcX9w9hQAcL5sIWs4FOoILbKGJ+UK
+xYWso6cnsoaG3QmSKznNB2GAvb5YSv2tPlYJIc1xia6RdRQyjV73bbRdxtIQwaJwcscPccelhaEi
+tA/I8cHCvrAVoXdPLKmxdxPJdyRW1i2e4Oh96ZupMyTDn+w1fyE2BUcBPctGHgRI1pRZ7yyxB5lU
+Z+TD2GL6ddo17twRUFOjcGz5PvgnwuwYCXP98TpA6soQ90c+ujTHHYu18osFWsRH5+s1CkVp+7P5
+JvuwTHIoBr7sBu4KgExaMMqCw4X3jdsBghvS51d272FwR4J4b38k1zqarNAupA/H0yUcBp+8xRby
+e6yYY4568HqELJbrri9WsIlxv3W3h72hkCRsHTgYnJAApKCOYgxUDf1WlG+yJPWqwsJuuCY9TA8k
+UNg30p+o76PtivndHdlocd6KykagiaFwbMCV79vEdPh2hQEj8rUVo7Rl/J7Xw4D98GvAIXaiYrAz
+LQ069A8noY8WeQF2Ag/b29KPZVbjci3Bs5uct862h4giQWWY4Gb3rvQnL2EF8tLnsxQOMb0NSYtr
+B/BH8uLtLxLYlIaP3B6CgczFnvca0hYBXrYcOO6jt5HPSM9uR4iIuLIjG3olm8XifdlAHoQdFFfy
+Em9/s407zJwM/QgW4e2Ob8ElZN1jZWh3nuFgyHXfdTc8n61RiN6uuBiVG2FESL5iMLfV92A3tHnB
+EOaSgkC/Oob2ZO1h5WD+zk9W+3BdJXndrtt1+1R7ISwFpSx4Xoh1vsBqIjv1QA1QkPPeciJEpNcK
+I3dTjGO7sosaa42K9fHOwIdly+edyycYduTkqi9qeLv8TkqztjPYsOro9hruFUlFPQPFU04VDlLC
+bsPsGTLMbTe1+TZuMBj0xde+DueG3GOr9xJ+JpN7T5kC/eatfWTUVnjYPYVqidn6za/fQve+nIST
+UMx6NcvxlRtyYq78NmwSim4NBxiQsacTWrhPptRtitpou4ecNfZJOtv01+5ZKHlatVwfUSVptLIr
+p+QbQRetMRGYrS0oxuf6rTVmawZPWz48Vk82Iita53DITBXChskbGbcUDjkCPLuEpulekVauMAoF
+U4g9N4BNQtv+9nDDbJw+XiAsxSrKGhEdO4Td4vNQAZRNgtZOG2CqVjuA36XFMpC14M5LdBqaM6Vh
+ukzPBh4inra6ymFRU6JHyHsvjNAUGYbtAgsS3QbxEiXRpMNITuisbuPVe4LuvFuO4JA2bafbg8P1
+ZIIALoPfHitmly4Q8BpuqwNckS3M7wPU7GwN2+huyRoxBStDAw1DGLfODTqnJAouUrzVlXG/m6zQ
+Hsk1Tp+TeumF2BS0Uci1B6uooOhzZ1aJDs1WVWbwyJ3nt7qwyDVnbMlR67ULQzgvEhddKgEVrRFa
+bZmo2l0ACbnJXObu12pDxVXGBfJcXqKwKFGQX9BerFDS97M95nsVUBG7oC2FdSST7igbISnCK51B
+JyKWWzgkWxkr5Et528qywH5b+tndGmO1EBzG+++ITmR7aTAYyEK6O3CREUMwtHL0mOGorkrkM6ly
+nDF37fgF6CXBqZ893nppoPhCpsEaEenmnC6p0+sQDe3kaXKJASG661K0EAFAK41RKH3KliRyWa3R
+YfnydqeH6UKeTQgLhnNh6gJ5yg2e3OupKVDxpVIo0WZ9SDa3sFXKPU67MTIqUul1UyE4gROKwK5x
+bWCTMTpBNSP5TjXfQNJ9UtfBcmBRbNbul1BrVjh2PLisKNROB3UhG6oiP2YZ55jzcOrMYqxYaal1
+CRajbijtXts1UNsyr37eRcBawFapAGai6qJAwPnKLrje6hZgC7amLn1ULXmm1dqGW3LebAW7mS+n
+w7ksoBOBPJcpsL4SweVKJ5jzYUY9vakVet527T5ct+t23T7VXghLwXIUopGLHB245NmNlAVFPi+d
+pE3dEqrZMQzl2Opz2dWx7zyjaPN6CPjl4PUJogfy+aL2EF9KFsAjY3QcJ1iSzDRfneOCgjLV2oZm
+cU1sOagYoNsUgZtNQSXydonJsdhl3x69g71KzLYveDEcujdv3iaBSqIw7jey7i7cXPo5OX4Kn1Da
+FlWdp9MYJ09YMTreRm+bmAdrjZinhz3oYMFgXZE3oiE2Rox6z9NGLwxQlkJNi8WKFU4CzkUmfbvb
+H6A3FnP/1sUMSxLhak8h4z2MCbCsqEzdYiHWVGOylP4ceC1sUbAkj3O4TWVrskZJ9GJJeKiKHLgQ
+V2TcN3DoEiT2Cc7pBmx5IbAiJmUkcxkubJguI+urBBUxG9W8gDUgs/VFDNUhfRvn0xnbqIhf8dUQ
+mkJDtWtQE06eJTFyYhZ0Rz4btNoYEyrdCVtY8sS/msaIaN00cm3L1MYeXcaVXSGMxYRYbPUQlERm
+GgfFCdcZM0rOyqCzLW6HqmzYI7GEsum3kLIwq/4gRHu3IQ6manonQD2j1exkCMcy5i++tgvnqdx7
+3mthxaKpow2H9R/cXohNwYZCT7mwYSM38vBzP0eX6bJ6YZBQ+j3oeijmNJPYfWs2xoRin0X2BKYl
+i+PirMAPHwkYxdQJvrgnkeoW2Xig642YyrrwYJH8onQNZjMSZxQFfCKVOt3hps+alOrpuQKxNjha
+nsM5YqyhZ6OsyFAc0Clt+ZhqgRe3hh9j5DdkMB3EVDd641Xp4+TkECGzGg9mK3w1oDlvHNgsnTX5
+EinN9YZnsPBK5JmY2n7QVLMDptAoSDhiA5vahxaLKpJoAjyha9D3kNOXnaYRNOvbMnuBbiBzUMVk
+1A40HAKWjPKhWTodtHw49L9TY6OmrV2T71DnBQIlfcjgoNPhUlxVaM3lRbgMIywtuUZJeHjhAg5r
+CoyyUJTkRAwBk0s/kqyAFcrnyULWjRPuoGCFZl4+hWNkQ8ovZ3Co5GRWCThFiE9Zado32O9IjGO9
+rAG6FYVlw2pYmPicz6+m6FJ8xhuVSFPpT3wyR4dxEgQ5arrCTx+xEjctcJH/AwDA3lYHAyVrIE1O
+cMB4R779Hsr5qwCABovk1jE0Xd58XqBg6bgyCjkffXIJKIoF49E5nqdduw/X7bpdt0+1F8JS0ADi
+GrgwPgKamduDPjKyNftujtladnxvksPq0Ha/QRKWqkIRyIl5epbjmICes5MaGc3gaF1j2nD5F4wW
+JzGu1rLzL+wUS2rOK6cEa4qQKw1NyTK30XIHNnwKtaWR0hL4vUsbC3IKDi9nWE/k8wkDbseqRl5I
+cFQt1zghuOf18BEUJNDU0nIS93ZrnFyI6dz2XMyZE587CRKeTHbsImbwKWZ0X5UFNHP6Jv10AddG
+cCRQWNQSdFWRmJnadrAgsOhwYXDExIUbLpHMaV4vU5yRV3CPXAnBeACbenO50TBzzrFXIaObkxmN
+lEQlAQOmiALk5LNWqzXsS7nuiTnEewsCj5IY55MO70cLZNgGVtKfejlHwfVidnZhxbyfWiK5kmtP
+aY215xcICpGMT+drWDu06KIRDIOSVWWwIu/DhFW5blpgh2Z7vIqRTAknr2pEVOVpKCnKrMRkRX4D
+u49+n3R8tYJHLged21jSpSnpwlzEGfScXJnzfby+LZaSNwhRNjiLbgdlzABlpwmC2tAN10dRwYll
+0Y7yACPKGp4OY6h1oxnyfO2F2BSMZaOI2jCzGQqanE65C02mJO0lKLnoV/pyUwXYXRHl1+5hsC2i
+m46fwSKCcNjNUK0FsDKfnkHRMLoq5WVZxFNcUTSkXhUoCdiJbBdguigIHAQsdw7tZ+AlfIK3oioa
+9aanuE8yzzesNiol/T+mTzdxx5gTKVc6FWbH8vm7TxVubsvnWyT00CpATNDUnpvD4ct0WTrQrD5M
+6gQrxkQMU6tWp40eZeId+xNMUZ8IQNd5icGZuFWFw5dnVSJ2ZRE/PAR0X8Yxij1YfPG0ylATCbji
+/IxSYOnRbK1rNLulSgwywimrNEHCzSuviMpTKVy6D5Yp0Ga69PFlgiuSlpxHBjtPZAMYhZKV6bXu
+AESe5tklqpCpvHQNeCzPbt2GS0HekhmjoTva6IPWxwlqIy9eVWawKFRTBgrJOStw6R46dohYsaq2
+zFASLdpyFGySqnp0bSpjNjUMc13idCVreeDNkXDe1ksXBX8XDaS/X9y+jf6AXJNRG7d2ZVN3awV3
+JOOrKhualY8Va3Fs1ULJqlod9VD3uDtlKc7bZPW6KjGOrrMP1+26XbfP0V4IS8GCQWjV0Khw45YA
+eiKTwuLu6UQ9FOSfS+8XuJjKzlfaEjipNFCdyXfjYoY8kZ09nifwdkhRdWIhodBHwSxCUruIiXmI
+IhcWzesisZGTIKQOfKSEldr6We3DZ6l6v5un8J9KwPOlwMWYMNarEzk97q/ub4JTnhvBIa69rCy4
+Rk6EVkms+1WGmLqLH12GWNC6aXubRAsqU2HFaLhDQEy5irGsZcxB91n2AQ6eWTfa4AEDVOWU0evM
+gkXYtc5swMiJqfQQ/ba4NH7hoWaQM2/EVnoFnIJcjKpGOAz533PovCEscaEJsmqUtGtHo+OK2Yel
+6AAAIABJREFUiet5FUriJT764QPMCZYa93t4vBKTv38pFtQr+xZ84jf0eY2YTNt1nGJwi6Q2V2sU
+xL37XWYfXlrD9eUELs/OsWTfLi4KjDiUuIyR0erLjczPcl3j7FDmYmVqrFlpajwLHTKFJ8xCqALI
+SKCS1BZ0JPOSwMOM468sBUNKO1Qy/jKZIWEWqDtooyLZj3Et5Eey5pamQItcFDaxIqqroOm7lG6O
+6Uz6ocMC5oQ0+MUSkwa48pzt2lK4btftun2qvRCWgm1ZGHgR0mEXbk9iA0Y/gMNa+UgFcG3Z7Xo3
+QqRzsRCeXJJJ53tTNO5+xwlR56zqq3KED5i7dru4mpDNWYm/1enuwCOrUDsMMKe/uEKxYSnyixI2
+1ZqN+kRM4TNaDMBm7OPYqRBockCMeXos59gZS3D0L/xbfxG39+UU245uY9BrhFDJYpRk+F//5v8B
+AHj33e9gQkno7p0xQh7580WAjCzQESsE0Y5QU5W50XwAAPz/amImDDK0Cd1e9gwiIiCrUQmbv71a
+P8WS1ZOtjgOfZKV1InN5vlBolHEGbQ97Nklc4Ww0LDq+izwhtR6hoFZuYAXyu6XbQz8isrKcoCR3
+hGPGKDKxBM4ZdC6CCJ5DQd+dAhX99hOzRIivAAB2fv5l+IRjNxadVdfwaSl4//LXUXz4Xfnd+++i
+N7rLWblEi7n+Ruynnp0h6EgMYKdy0W0/s8zCikHFhEVSQY56Tjo+U8CLpHDLdCr4gVzXyha45JiO
+jgWNOLZb2NtmWtTXmH9Ikl47h81S2nm5xis335B7t+RaUdRFxvVS1i5OA7FSL8LbwD2JUXlPBzh9
+8gkL9znaC7EpKM+Be2eMttPGq1+SyrLZIwfLy3cBAOH4Jmz7MQCgM+wg6spkhwzgFS93AVvcjm6v
+C2smgcYKC6wuyZl38QRnR1JxlzEg1Y6AisrOcLvoMF+dhhn6vjz8UXmAORWQ4vgTPsNn0LHZsKD5
+wU5iI7gjC33XFRbeeGUBpBD3ogqRkRd5/Mo22qRcn2SyAGfFE+z1ZVOceW1E1LasixzKljGlkznQ
+6ApSvKXn3sDphIzDBL4AgL9jIT+nmatqWE3QTVFVKQ8R7MtGsZ76sClUMuqkGI4kU9GzFApS37c9
+Bq8qBzUDdYPeeAPnNQbQNKXr2kWrolHaYuTdTtHtyj3CtkZjUQ9ubwEz+e7P/8KXcfqxPMsLHgCz
+oyW2XyWPZ38H4x0GgdMarV3J4HRu3YbFzI5N3IRlalSVvGzr+QWyRLIaO+ObsKhvWRkXcc3KXMLm
+HW+E2pEXPbQd3NohJ2aucUa1rIA1Dq19F5eZvOhZmiGdyrOuKw81wWctd4Ac4t41NQmj9h5u7Mlc
+7HZbCHrcLCc1aockKt1b2N8WkiB/lxT3qJEw+/D0/gMcfSTPZDqN8dbNLwMAvm3W2L4lFZMnP5YY
+Udq1+3Ddrtt1+1R7ISwFBw4GZozBoARJdKFHDh6/LW6CWZ8jIoMxCg1/IHvZ3kBM1cUsx9nVIwDA
+6p0TlDzx8xkwuZITZpJdwJB41Y8kwLPEEk6LDMamQs2UZFk76Go5pdN+AJdBx2G79azTnww00mpw
+AdSENyZxhsVcToL9HVbFhXsozwVG++6v/w7iLTmBL6cTDCCYhAe0flYfn6Cypb+72z7WVImpcxuJ
+JadYYQympEXzp3JinhWPMeeR4OpnJCv5zIZl8XT3NJxC/s55mi/jC6yeymnl+Rb6TBe22zZqUoyd
+x2vYRu5dMojW6hhEHtWVbQtkh8OyWMKnaWsFNepGr5EBUWtV48qTk/RWq4Uwkrl/vXsH+egZJiHs
+yBw8fPoAAPCd+/8Eo/5X+DwCdPfvAACi1gAWMQLLD54ipLRcTfo32yhcPZF5+fjt30LlyXPY3b2L
+NOMaWcSYL+XzYxZXPZysUNJFcbYiRMRn9Ds2ho9lLqwbDDhWM1zQaiiLCo9PP5Rnow/gkKBn5+YW
+egwwqx2mwIsMOhY3cO3GKHwSrqgVerRGonAEd0DpPKIc48mz/p4cT/HkXFyG2AJSR66XBCfI5z/Z
+a64aQMufZPvpr33NfOt3fxdG50gnhKXmFo4sqTLMHs+x/LYw5548PMP2W/QpU3ng3dEONM3h3hf2
+4Ntk4GkDxZrljOEK1YzwWG4a65ME0ZB26+MJdCbEHCfvXyLpyUJJrwx+9zfeAwA8XdHc+4/+Q6At
++PTptI1/k6XT2z8/woBqQcWqwppgmpklm9v0/BCXj2RxHE4ukZUyvg/wJbxV/goA4Dul9P1L8wme
+/txfBgD85W+8jfPWLwEAvvx0jjkFRotqiNlCNsuTirZhMcYP35e/7eND/J9/+69v5pmVw6gSgARK
+SGgrWpZCzR3EsRUUX/qeTjZ4A5PVSGhcerqJ0gPNXlPBQkC3orCAFqd+bbDBSXzmarMA/Jz8+eXy
+p1FD5mCS+Agp5oJbFJhtjVEHMt8d00f7Denn/kiBBFGITIWhlkrDE/ryLwe/jY9j6mMerZCxvN4P
++7hiBmYQlujydgOHm3PWhstNSmcWfvsf/h4AYLGYQ9ElyBjDmicZXG6y1qiLLWqMrvMSNoFa7Z0e
+tg7Enby1dwcA8NWbr2D4BRlTzw8BVmWGVheWTyBT0IXTbBCNUK5KkDMz9q1/8g5+8++9DQD4f9/5
+u0h4WMSLFBYZgS6vTr9jjPnpz3oEn2zX7sN1u27X7VPthXAfYAyQV6jTJXxfdlcTxuh+X3brh3//
+Q8SPJFpsH+wjZsAsoCCJ2lrDoUCGPemgMILy8mIFltBDnc2wupKglSYhie/24C9ZtJNYAJWmD36q
+wPn3xBR7/7spbr4lGYMDiWPhw6mL7FLM/V/8pTfx1j05dUZDFynNxPxqDqstJ8jZuUCbHx4/Rkg8
+wjqc4/K70o/T+f8NX4nl8eGR3HfeDnHyD/87AED55r+KJx98BwDwr/8nI2xT/u7WV7qIWBmo35P+
+/OiDhzBjOYn2vvYG8LefTXP1CSGuVcPdaBpTAdgQBGgFwxOojp5lAFwrhymIMyCqUNclnlmbGkRK
+wwBYk+fRaLORWof+DHSdBkBJg+prATKStnTHN9DaEvPG8olHqGL0LfJEjisESsadzj3cC8QdW3TO
+UZ6KBXH68T+Wa2VjPHggQcDipRJ6IdZU/54Dl1D4dmUjveTaGctpPB44iJcSoP7Bd2a48YYEuW/1
+d/D93/uR9Jmo0qjlQzXq0abEciGnvOs6CFvEVqzOsbhPIR1qT5SdDkKSzKjMx3gkQfPWwIchzZ6O
+l/DGMiYnEPcjrA1ymmBf+fIBBuG/BAC4qH6I3/kdcadtVUO77j87539AezE2BaWAQKH4bcD/Bqve
+1gY/+g1xGUx9BexI2sjBfXRnNMvkXUV6maBFBpqLZQrVEVMzvAjgdcWMjM8WOJ7KA3epo9juzxAX
+8lnb6cFakxVIxfDG8mD8cILUE/90h5He3s++ieo7shhfvRdhe5dw4lJtKu6uWh7WF48BAKcfsBJz
+cobsUB7QcH2M+xcCsvqZ27fw8YU88C/dk3+PD2d46eWXAQD3j+d489+Whz/5/jbSSCC/em5DE7yz
+UMKkZBUGNRmXkvr3l+Nz6W6BacPIduFwnGWnjYiVpkPnBtaxuFVpYoNQfORM2VpKoSRximMDZK2H
+rcxGmNaghtHP56aa/V3gAculdYWwQ2ahOa/lOxsxnPL8BPnH0o/gyyG2L8iEtJNgNZXvf+PVrwEA
+Th/M8TM/Jenn5cUcK0/mfvkgRo/uQZWH6DZKVRzIqjiDMrLewhooCd7qGGxUnxIlO6GrLATkiVwt
+K+SMy4RtHwPGNiq9jStCrI8INd8NarydSmzg4N5NbJGX0dLpRmgnqSvYhrENw/Vmu3A82TTHO9uo
+LmTTe+uNb+Dpmdz7o/cTWE0Z73O2z+U+KKX6Sqm/pZT6QCn1vlLqZ5VSQ6XUryqlPua/g89zj+t2
+3a7bH2/7vJbCLwP4+8aYP6+U8gBEAP4rAP/IGPPXlFJ/FcBfhQjE/IHN0jb8l0+gCgkM5flDpMtv
+AwCm1RJKUYp9qTG9Ibt8XUtAKkWC/IIgpfYMppATwXZdzI9IBqJSLDMJYra6ZEBehRg1st7hOXoU
+NckXLvpUeR4PEli57GtDqmp0/AEOb1A+zPfgNaIvfopFLUHF1F3iN96VoNTbvynjmEzmyHPSgMXA
+JGQ/pgk8S8y9kxnNYW2wnMp3e9Uh7n9PLIz97EPEjVBL4OKEp9VOJGM7LxOEvuSzt+1n/A/AsxPA
+uMCIp/uS0O87QYAllaSLsETQI3+iM90IXPp1jZR4iZAZmWVVQ9EgUZGNiO5BUpkN+UhhAPPZIcZn
+jYfZjj3GU5cs3SpBwaI3yxZgThJnqHbEEkyOLqFLOXWj39vCakcCt6PzEHVXLnj2WMZ04FlYpWI1
+1rrAJCForXZg0/LsjSskrITtB3TzMgsHhCir7hpbtazPcThA35WTXpOevvIsrIkTWpZLKNUokEeo
+CV8PIwuPKV/3rUDm5MF5gt4WK4J3AlRkI09TBy6tt9n6EWJ7xrmQddrpB2h58oxd+Buq+p/eGeNX
+SRsHZTZVoM/bPo/AbA/AnwLw7wOAEeG8Qin15wD8Ar/2NyAiMT9mUzDQqoLq3UBZCtCiuHyMw6W8
+IAdjhTZ19z766BAV3YA+K+uiu30YCoPaGvAt+qF9C2VKsFCi0d8SYJTpkEzkaIoz6gqMtYIfyoOZ
+zWKkrDXo9Npo3q2SoenWOMQ394niCxwomxV5RYJ6zarDxcc4+cF9uc+lLMaDwQjbfbmYXhokNdFv
+WiNgyvWQpclzr0DCF1e5Acy5/H1cz9A+kId/lFYwV/x8W/rb3b2DypX/blvPah8sKHSpvdCyauxR
+X9AiK9TWKoT1kvRhdaphSNSyN/Zh6Nd72wWuiIdqxGGTHNBEHi0N0CIW/3i5wpqckMdJuSnx/f28
+iGbDKmsf2zep4JW1YEFeXouViF3jwZ9z41lMAcaVhk6IvVz6aes1XIoJ93N5ucfbfbCkAonOsU0C
+m047hMuKT6e2EHRk3AFjVKvjGlfcpLYPxvBCao6EJUyjYMaakZUC1kxfpusKLdZzhH4PYch6BsdB
+b0SWJQKsxp0MX7kpG/nPvPwmAqaJbOSwXen/aPs1XFKv5OyJkAzpkzZ+7g15LzzfQTQk6nffx5ep
+oflgMcX6cPLZk/77tM/jPtwFcAngf1NKfU8p9T8rpVoAdowxp/zOGYCdz/rxJ6XoLxkAvG7X7br9
+ybfP4z44AH4KwF8xxnxLKfXLEFdh04wxRin1mWfDJ6Xof/prXzOAA+X5cCo5zS+u/g4mZPXdvfMV
+FD+QCLCXW9gn6cVwizDZzi5sW3ZD5VlQBIcYrwWb5pfvV6h8OQUKKhPZXo4sI8mIilAnpB2LYzho
+NA9XmFKRqh6IJbGvXHRZ7djpRVDcwRfJGh/TnF09niOzxKK50Zd98efe/DIiVi5W2QzppZxAF3qK
+oyXJWbZZyaevsCLvIhwNjzDmNE2w/FDu1/3yAhNSk50cyzT3eym2qCxl3f3mZr61bVAwUj0KPdxl
+BDtjJL/Xc6DmVCYaVigjOZkHlg+PAC/XquD2WT2ZiyvV9SrUGQsrigR6QU6DUMPj+JysQrFJOvw+
+pkIDsioMKopzjtvAekECFNMELSt4dPM6bRdeJf3YHjroMcDse1dQa8KpGWyNsxYsujY3+mPcpBiM
+G7WQrahapRLYtJCcofTBtZfQVIhylIbPGF/iAa7TRPWpO1pUqMl1oS2NiHL3r718AMNnmfc1brqS
+wbi3L7Uxrx7s49ZXJfs0GPmwfVK4l4Ci1eCGOcakkEs68u87F4/wWnEHALDrbcNiFsWt+7h1S353
+9+k2HlKiYHL+fBbD57EUjgAcGWO+xf//tyCbxLlSag8A+O/F57jHdbtu1+2Puf2hLQVjzJlS6lAp
+9aox5kMAvwjgPf7v3wPw1/CTSNErjcJYKBgM9Bc3Ea5/EwDQnq+hbogftp1dwOOJ59qMKcCBReQa
+fKBJvduZg5oSXTA1PH7fp/9eb3XhMHWzUhWu6KtavQiuI7vrvAyxICqydU4m4o61ocmyYMMY2dlP
+Dz+CapNvPwjxxkpO1Z03pLrtlbe66FuEWD+qYF4TC+JsPcabkHEffiA7/AM3QH9J+rdWhOWcRU5l
+DVCy7exKY8gCnfZEfOvpdAkVysm3lz1zy7zQwasvb3MOPfxrr78m1yNDT7BIMKXAzeGpwo1bkuJs
+ux4CQx/X1lAtwUCgwSukBTxCild1gSKWSNuHhwM8CiXNlmmNbCV9vsobpqhnTSmgTd4D7aQA4wfr
+hYLDOE9IejhrV6FDKYeR9rDVldN2pxXgtXuyBuppCFC+b5rL+Hb9AK2KiM1uCwFZp53RNoq5xK5W
+00uw+BMW8SSDVoaCGgrzSYaMYjfdOENK7oUmOJnk9eaUDV0XWzfl+R68fhf5Q/Gozxcxejfk2t/4
+ovT35mAfbU9O/9C48DQDm9qBrhsWrWDD/HWjL7n407MFFqT62y5r2ISW7t7r4S7Ffm5NFIpC5v7p
+fTxX+7zZh78C4H9n5uEhgP8AYn38TaXUXwLwBMBfeK4raQulipFNpTKysA/hdMT8yi9nUKQrX+Y2
+HEZhAyOL3EuO4HaJC19YYEAWpqgAwm5VFkCTfy9fUWwjX2HNHG6lYjTEjLWxNzTwS3eFfTL45qQ3
+G1gt2DUp0OsYVS0LLzm6REmh2/LqAruviyu0w1qLUAdw6RJ5bQC2mMa7UYCM9vVqLJuDzt2NPPu5
+XiMFeQl1sQm6Xc7uwyGnIzJ5qRanZwjIKZl+4c4zgVkouBNKvB+M0WpT83AkfXe2DhAsGUXsVTjY
+lkV698YXUKTEBZQV3L583qa0+nJ9BU1KMCvsQ8di2rfdKW4QVt5WLh4tZc6XD+UeefnpilOHxCGV
+rpCuJDrvqQXqSuYoJpb/Rr6H8bZ8d2/vLdwI5aW5M9zClifPb90JUbMI404pkvM6nQAt2UCClkbX
+k2dSmRorMmWnYQf9Zp5J01c5OSrWnSzzFN26MdFHcJmJsSkeWxcJqub59lvYZnA8vTpBToKerbGP
+N/fkmd3blvXR8QHDmhK9NNANvb6joMkIpFwLxpc+17lsMCvnCPNjCi+PUng2VbSCMXqp9GN4cY6X
+Itkg38bztc+1KRhjvg/gs7DUv/h5rnvdrtt1+5NrLwyiUdkWskTj8SVNbmcPP/tlyQOXOkZGspSp
+cZEfc2dfCCYgfGmA1ZXsntbaRYcSY96gh5z1/+nchqIUu8XjM6pzlISo+mEbRYuWRBLAYWHL2Bsi
+ukf2Z6ImW10XLqXUylIB3JUt5ylatZxMwSJFkIiJavOUzNoWPJeBIzjQvHdZZMhZzdOoZ/uLDCsS
+khTLDJoyb1WcwwppCUw0ChbHhFtycrfKLcTUGvTydUNvgNErPdzryD3udD0EzONneRMYNLixJUi6
+/hhwGMR1uy0MBnLSaANYjTvGQGXnMkRFWfuyrpEH0ueXjYfeUL7zyH6A3ffF2npf8yRWNapPAO1W
+1GSI52ewydyduREiTsiQY+63C+zFct0928brDCTbPmA5co+R1YZFxmtFboLC6sNdkCDFKVCULLxL
+QgS0WqKqQuVTt4FWgJtt4yphWEx5uKS53rcKOIrOQmOBokLNKsmuiRAUsj7jh20UdGOC7TbGihaL
+kd/pWYaCmJS5PUd8RqKX2qCkRFzdytHZFhi3zSKxvWWNzAiE/tgtsR+SDKg8xmIl706+U6NM/5hw
+Cn+0zcDAwHNcBEoKDOL2EXBbfPHeVYrz8+/L5/YKwy7LfrlQJksFi2ZWZy+EarGKLAcKiuJ4fQcl
+8eUN/F6VNcY7xJNXISpfHnJcH8PzqYk4clBFEhnOK8mAuKZGRfEVq9VFbFGpanQTPVce+KGfQlvC
+LzjakYfVctuImB9X7Q5qVhpmU42CFZhulwt+dwxDWvQlpliR0SnolChzcvE5GjMyIbXRqBjZCElk
+Ero7UHSPVkcZ/C3qFdYl8huy0PcbxufRGDde+oI8DQ2kLTIVV110KSqjgvWmrLKoZcxWdwiwxqG8
+ygAjL+nUXaMkq9N2v42HdE3UgDjpaS43AkSbk2Xm6WIBQ0BZZJUAXYKKuIEILvpUf+o5HlyK8G6N
+92AYG3CcHhziCWpb+lklBjbVpLy+gc0NOc5yeI70udcZwKJvb8jFmHUdrFjWHMRnsFpi+p8HbcSg
+G0q8QZ0A4EbhBi6slFgHa8lcFnBnO9yQwaSa/JH1AvmUYrP5HDMeSJVeQ7F+xkOEdcLnzvqK2lSI
+SeNfF1dIyTtQpykWzDiUC42s+gRX53O06yrJ63bdrtun2gtiKSgopZAYFwkJL265r8JUQlJhzWoU
+d+Uk6WsLFnkUwq5EYVuRBY8nsO12ARaRWFYBTW6WoCzhDfv8XD5MY0AxwGeP+hspc9+14JLyTPVD
+lERLFuc0RR2F0qZsmpPDJwxalzsISQ14695LmEJMu+5YIv2uVcIjQYZyXKSsPtSug0byud1oFwSt
+DfrP81toWXLSZspBSQRlkucIKzE7HVci3a3dLoK2dMLXU9isTrx1p4fpQCyBbvsQVkuCXOstOaHu
+Dm6LJhsA8+oBvIqFNoMunFROtMqeoPDE/I88cZPsQiNfSkdnwTEWT8VkPj6cYUZiyHVlIyQHZXTF
+eVMKhn2rawOHmY9CZ3Bo+lfoweHvWuyPP3Lgk7LPml3BjsSkLgsfnR2JuJu8hmrLvRdHMj/Z0oLP
+OV6tCmBApel6iJJVmZmrYVnM5pD0JdwGdO8VAMCjqsbFpTyHu5cF5rR+Ykb389pGj1Fuz4lwNJd7
+DwZd7JDy7fSyhHkgFuSdXboqK4NpLlZHcpUhAYOx0wXmrPh1+n1sUxNlQBfzfhqjoqXb9wJssbJ1
+bgosOfeZDlEsfzJE4wuyKUj1tOsY9HYpDnujh+hbMuCV9S5aNaGveQpD3cGMIA9XVwhIJOEihU3w
+i7INWkwX2o6GG8rCajT3/HyAIm+i/Uu4FAEtXQc2Ofqs2oJFQldtyUS3EEE3EOR4iaKWSQ+WR6gj
+yZhMrnJMWI8RQqoa/d4OSlbf6VW+SSEF1QqKtRYVffVRL8SU/veJB4RUI+q2bNgrmpqOQcp0aUlc
+/7jTQosmd6hKGJr2WerhC0P5jhWn8Lig9VjmZFZ0UFuyKV6+9xD9obxsXkvBSuRlCvMMDmSsji8b
+smvX0MyuWOkF5i1xtR7Yv44PLlg6vHQwI3GIcmX8daVBiUooZWCYUbFMCItl2U5g4HKJVo2oankP
+Tz6ScXz76j3ov/tbAIDdgxBfvScv782dHeyS39Jnxahp9TBdyt8n0wnSQ5mXR7MYJeM1QR1guC/u
+yBuvS1XuQd/F0JdYSyv/IbypuCh5ZeC4LIdeUkEMMSKKt9ieD02d0mk1g6FbGPg3sJjLWGeQzeGV
+qIvpJcl4jx/hsJK3frnIsWbmx2lfIJqIy6dI6nJWrnHzkYxz/+sJdrYlBZ2WbWTvSS3N9MkVzCeE
+zZ6nXbsP1+26XbdPtRfCUjD833ePL2E/fAgAuHv7q3BelqCdTgewGUQ6mgGPSIRxayIn8N0v3oHO
+5bNxOABWcvobG8hWcrJlCtATmuCMNE6WM7Roag/sLtokwiinOVwy7aoLA4uy66BwTFYukefiGuik
+QEhzbjDI4FCmbmwWOGbB1oRBJD07hEeWaBPP0SGpi69suEOa7kvp2+nsAmeUMMMi3gBQfMuF19CD
+TYBZQhZoEs/cPHgDe4zIbzsJfGZaesaC6kqh1G6kMCW9+n4qJ9ssusKjbxGWfGsfPzp5DAB4/O7v
+YZ+maMvrYI9u1fhnxRopqw4uOa+/+2uXeNcVkpjf+fY5mKxBtKPhsRhrQHi4XVa4ZKCx1oCjZPx1
+lqLNuWj3BrBoyQ0Y4EzPLwFC2utVDYdB5dVK4fRMXE+TxvD2JZgXUGQl2CtxlUug+NHRBE8YdC5T
+GyBPwcHdEUKyP9eXYtmcXSTYeVPm6ua9N3HMdRjrlZR/ApjQhfOqClHDLznPcEKtSC8aICZZSnx2
+H/sHUvyUsirI2p/CXcocX659TAq6Eus5Ts9IvhLlwJTIKloSurDQvSd9e3o0wb1CcDsr9yGOqNbt
+WzXK8l9ALUkYAxiNm32N5atvAQCsVhvWQB7sagHEuayw1NVIbNkA3uHLdvHDD7G/S72B2qBLDYE8
+V1iX8t3Zk0tcceGlLIFWrS62qLoTdhU81kR4LQ8eF3StlrACVh1uUbMgnUGz9NZtbcPmw+0u34Qi
+k1PiLDGgWlJZiFlnmxYMNcJtHcMhnr80Ghn98phaAl5V4+6u+O1b3RLTkJ87NTTlzh/gEHElJmVB
+fzK+msPZF5OyMxzD2HQfQg8dihl00jYCSs0zFwA93MOdV5kW3FujPSd1en0HaiYbdWYuMe8S3bkm
+qYt+gkfvPQYAfHt6jPceXHJMPtpDWV4v+z7msTyHhz51GecWaqbkAIWqEciFQUW/vmPXsNpyjdyR
+vr0ZBdgeMHV6O8TW6wT0lD5cbjLx4X2Apd2KKk6wW2gHskZu72jcMPJMVW2QUr9zMB7DZbqq9qgP
+alzkRwQsJWfo9eTFi50UxUMePjEVxCwXNSUFdD9GfibXWq8zNLwoq1rjdCpIz0ck5Hk02sdrd0kd
+b2FTMevbHZS7rNxdADYrUH3qPiSdFFMiRL97/xSBJ5+HVQ2/ln64UQ1HN4TDj/A87dp9uG7X7bp9
+qr0YlgJbYO+g15OTRqUVFCvWutt7OHxPiEqsaYx4LbvgEXfoZFnB8+WUOM4d2K9LkAxFgZT1DHPV
+wVUolsKcppq+OENMPgJzax+KJBWjTtQgkFGHLrILOaUbivRMF1gesWLyaw488jeU9QKGRCWptnF1
+KDvz+VTMNyvI8MoWGairChWBPrUfAgQqrY7kJF0vAdyW8auyhEf+glFgwekT/O/0MKVIWCiqAAAg
+AElEQVQy95KpaKfjIEnlfuuLM9jMm3s24FFJuizuQ9MNWG3JEli9/128PxOr4zd/dB9bOxLh/6Wv
+fRMeA2rlpIAh/Hs4k1MyvTjFlNaRtzeGcyomfBiXuO2x4tNLYDPCH6158qscIYFQaW4QdhrOxxp1
+LtfI0y1sM6i8wyH3b7hokYRkPjvB6a+JFWb5L+H2TRlfyxddSAAI96RWw7Ny6FP5XT73cdLjHH10
+H7NcPr/Z6qL1klyj0bl8aSdETIMm8wYIHXJqtBz4zP/3GNitoTDYoz5m7mNdk09jVaBVywCKrIAi
+R+gV4fGed4bwkLU9r+zipbb04fGTh8iXYmIs3DUMKew75MQ8vZxhpWX8ve4Qg0rmO9qqYbMiuB0o
+XKyETu9527WlcN2u23X7VHshLAUFQBmF7Z4NFdwBAFR6hTqRlI1pn2LgkuW57cMbS7fvkO6q5yis
+1uLjn9kTbF82DMAKmiStzjDCVkW9vp6c+JVbwjB4MFsXGAZM04UBDFGBejWFT2JOoqThmz7qkASz
+WQHTk9OzKucoc/EX43mGspTTOGgYm3pD+KzT17GDkkFHt0qhWbRSRY2GpYftjpxyC6zRTZje7JRQ
+hG4fKwuhYT8tGYdnYjg8jVPHwCGTbw8BKgbE6gyISTBaTeSz07SEKUjBNhrAJZuxVqdIKC1X9F0o
+wmfTUu4bqwK9SE65n+/u4w1H/P2js6cYvCT9OPnoEJNd8lZM6OvbGro5klSN2hXrzXZDqA3QMYbi
+87NtBmIXJaa2BPtMbqP2+N87CRyyFJUWEHhU8SbWw7GGcMdihbVcg11GU57czNC/Et8/33Khcvl8
+QEwDLAttqjb3rxbgEkLp+VC0oHyiLQ/aIfao/ZimLrYJUZ63F8iIXlWuhmuR7YriQ7qoASpX72CI
+20OJW6TL1f/H3psFW5ald32/ted95nvOnW/mzcybQ43dVdXd6pZo1FIISQiBERhbAocfbBMmcEAQ
+Ni/gJ14IggjPE9gEdjjACgmEjYwtMEhI6kaNelJ3dc1ZmZXznYczn332uPzwfed2lwKisygjEsdd
+ERV569x9z157rbXX+ob/9/9zoEFed25ItEt9jRsmOFSqLZIy48jIc6zOQhqKuTlOK8qF6MZTtmdi
+UxCtEEuVVXiam3dyj6JUeGkacPN5Mal23x2QN9RM0vqDqOEy7GsVZZZgNKpTjyNSX0ZyvVljMJTv
+bqoJ35z7oBWQnVlArFqKth/guFoxOKgoanJNmSilvDOhVPKO+WgChby8+XjE5Ehp0boFly7Jophq
+hL8Rhvih6hJGAaXWHfieZeprtFjrAWrGIdYS2vrlNnF8FRCm6YN9ifDPj0+llBqImnKvTmcTows3
+dENiDTQu+facJ9EJPWrq5tRDeaFbOzXSofRh59YqTSVWIRtTV8hwNTcECrXtn8oLlp9MqDSMvrQx
+ZvOmfN/l9Zy9Xdk4d31YrOh6pc/slwy0DNlxwOrmFtfa1LTy0TMuXcWc7NS1OnMpZN2oUOxygVVh
+mNCCrxtgVZlzoh1vLv11GmMansz7lWsdciXGWd2OcFW7ki50Uq2lUMq+MHUEbg34rsVpaSm3Lbl1
+TbAaT/ZkPjYaTV5cFwZutqd4Hd2RJil39uWwOOmfUVfB4p66s+2WoV3KeG9cctjoqET90jWeTCXL
+Ne1Y5iqa6yh4zZ2lTNTt8gKPltaUN/0cb7GZjveYJx8NqHDhPly0i3bRPtSeCUsBLIYCP3SgkGDf
+LHmPSgk1w84Wyye6I27s4y4IORQZdkyBp0fslu/jqblHVeIpgi6ux8Ra/DPR0sFmraSl8uTecoCR
+w5ZifETVV8JQk+AUytmvNGcmcyjVAklmNVIr+e/CFIRdzU/OMjY3tSJStRXKosQJ5N6B71MpV8Bw
+cIrVEy1Ts9ZvWZxAFaGrOb6zIAH1OERStcYmNJR9xDS1/n90QrymhDPLPWq+Bmt7KzQKJR3NfPwV
+OaWW1MLwwhrUlQuh6VFpMK/wewTKZpyMTjkePADgYF81MxPDqpLGRm5BoMzPUVqx0pV7PzgJGc+V
+lVj5LSZ9h6zUE8waqqEGIq/GuMGCayEh1zRxrSf93Ypr9GoyrrHj0FhapNtyMkWvptn4XJOUppKy
+mhZuW4Kjy+3gXIE6zi2Z8hB0ah7BqqRzfXUJ0id7WMU0BMGMhlH+irrD2qriXQZysid5Rb+Uat1L
+QcytLXGl8gLKjmqHJC2Wl2XNdV2dx2xIGInl2cIjasp6W+75/MCGPN/WwTbTQsbzTKsel1f2uP9A
+3AS/7XB1SS1oJ2SoVl/qjUhVQ/Vp2zOyKRgwLgUBgTqUYbHJSfkeAPXCBc3pxm51ro2XR8oINB0T
+KFFGw60RL3Dth1MChfk6+YRAeflaudZONC2euiBu7GMXGA/Po9TCg6JZo3wsE1YdqGvzwxOqakHS
+UYAEyzk7zVlaln54tsRXfGmomoLWq+NpFsGdpcwWikz1NQJPFuxqT9yk/omDq+awmVVYzZ+fTick
+hRKj+HPaS/IsqfrWE7dgV92klaUJNY10T+dnnKgEOnZKo6+cgQ1ZPO2gy0LD1498jKN6nT6UKjiT
+V5YoFHxCvaFgKi8gSxeYhoIgUnfMiyi1xiQqnzBFXa+JgoOq4rwuI88sUU++o7OyRTVX6vTK0taa
+iJq6WvGKQxyrO1Zm1NQEL4oY15Xxtm6Aj2IH1D1yxyWB5uvzaUqwLBtns/BIFhyMVYWj2QX0pXI9
+e17Nar0QR+tggrjg1iUZiyNlyOrv7oJCu+d1l6BY1FeUXI/lsLNBjZ2rgsVZ1rkbHe+RaJxoY2uL
+5WXZmBopVAN1t7YcNlYVf6OsUF98PaBh+zreLutaRj/KKx6polY+TqkrRP5p24X7cNEu2kX7UHtG
+LAXAGiLPLNjDsO2c+ntyaha8Sam5/oIKq9Beq2Z5MU7wNHpb9Swm1VPJJOcBLswcT7UOXFUUroom
+qSL7/LxPpbJxk8kRmSLi/P6YUK0Gr66BLBvTaipJhx9y/0SyJIP9h/ienECdooGjJZq+mtQUYwya
+cM9GVCrS0UxzZrlcWwvFpLTXjuk2JZBVJqcUo6PFMGH0NCtSw2gmVoHRE5VRiWtUz3J7hUhz6I0q
+otLCrmFa4ipfRFcDlWU1paYEMIFrcBczkeckCqst05JVhQEPMzFVwyJjrjn/drhKuDitwxFWT2A3
+L0j0+wZKNZZUJcW5CISlSDSY163jD9VKGUxZxMhOZxK03Bgs4RklIWn7ODpPTpCQj+V+xvrnEts1
+X/pZNnMiZa7OvfE5B6dHBWPlQ/Dm+LlmSZQBxhYzqbwFvHJMQ5GOkd/BpHIC31So9Tu1kLOZBGBb
+x+CpFTYfnZ2jV/1WE7+20JjUwrbsgEotQd+7hJOc6r37tFzB7ax1L9PuyjNNRvJdl1Z8FNxL058x
+KyQrM5gbRveVsm0yoht9tNf8mdkUSmuZzzNClRvyTJf4RYV+vhtAJDb6+HGfKpKJtsrlV7oJxVQF
+QVdbGDXLg7U2xVgGKvcc7GxRqqx28uQMo6Qg+WRMoWm9wVGCF6o+YN8nuCLft3ApclIy9TMbLkTq
+sqWVYTaQ7+j1HIwSsVSZMhc5KVUsfc6Oj5mtygZQy5ZYfU2JYT7Q9GcjpNB0k1vzOVMfNzk6Y6Kl
+sPvJKblyM7b1mcJ47Vyxqh63iWN9MWOfUmMxZjzFVXM8U8ly6/icv4FRTKXlyzQzjMZuTNPDS8Qc
+X3lJaxFuTxhrJmN+ckL7ppjJyayFOxM3YHd+Qqp+8ELQNjAumTIWyfuiJCq1mCrRjazdxteK1oXL
+kHgWR1PE1VlJuaHjdpQy0f22PJ3CFanW7OYy9s46zN6SjaWghqMHS+VDoeNZ1FsY3ThR+Hs2qjAd
+ZYIalhgl+MmZUSjRzFhf6LU4wlcClOP5mEhjOCvtJcYDzXx5S1RKqHOqzEyPH+4xmsgiqud3YUfc
+h/6jPpkqeMXZXQLnBbnfUGtxjub4auyHfv2c/3M66HN0JN/tBpyTyDxtu3AfLtpFu2gfas+MpeCY
+CmtnFEZN/Ag8K1BbakdUp2KiuyYk13rymYpxTGeWlu5vlilogKtMXKxSsZvCUCg8dDCSXTvEnrMy
+B7lLoTJngZcRqMCJ3/RJ1eQ3WpySTTMcrZXPa10SpSHHNbhoNN+0cXSHLpWboShmzNQNGk0yRsdv
+yHfEkP6amMyHWq9vxmOubIglsXK5RaxBq16tyT01L8uJh6OSZa26niiktNoS9fZaa6TK7Gyd+nnw
+rExcZnV1MQq5Nk1mzN1Yv/eAQElk7LRBoqd85WaMtaBpfFvG/snBEUOV93vpxZfoqUyfGee46rrU
+Zg6zkfRvrlRj87KkOgfVGMpFZsSx5wChgpi5BijHqVYGHntUCo92mxFz1eZMshF7j+U7jsZ9in1x
+t8bbIrFm3+mxu5Dvq/t0FTdtnZBcgT7VfAqq4hxqANMJOHcr8eZYJXspgjrziXyfO9M1FtWxE8Ej
+VOkYV2Hes3lGqkCnljdmpuu3rzwVo5llopW9b509Yq+Sn9PDOZFSem84HfbuybOM1RLM+ge0m1qV
+2+qciwuNRwlDXeN1LySub/FR2jOzKVS49G3AkpUJiulSeTKocXOFpCebRXn3DpWiHo80c+CnBYe5
+LJ6j3xjR/cQCy96k1LQeNgcFvfgLNF/pYDN1DajIC009OQa0XLro+mR3VaxzIi/Kg7VDKgUpbUcz
+XMWcR94cT5FwhVNJbTMwG6jv6ESkhcYtqhl6O+4czhicybVppnUW+wWJ6i04jZBYo+n94ZhdXUzD
+MmFD01DdG58AYHo6xWhswHMqziYSnX7cb2BdidGseAXOVBbygbpgcb2N9eXljkpIND1ZtwW2pmXS
+k5xH6hLNzuSFOJ3MGDxQbc+NOW1d/L7JeNgXc/2N4wFT3Sxc3RSM5Zw/sgI8LeUepc55lWTY9pBe
+8B0dyJrPrCZz6k6OoZRN+NRvU+vKPfJxQF9FXL+mNPNxMMPvacVo5jDVitmahUT5GMtZidV05yLa
+4QUlmfY5qzmkA9nUgzLn9btS2/LwQDr3yrXLNOpi+j853SU1MkZJVeJpjCJNcgrt/0Ff5vrs8QSj
+Zdgjd8SxkugEaUnjRA+nVxo8mOr3Hcr93t3bo7Os2TXjkCvV/mg6plzUa9iKZEFU+pTt40rR/yfG
+mLeNMW8ZY37OGBMZY64ZY75qjLlrjPlbqglx0S7aRfvXpH0c1ekt4M8AL1prE2PM3wb+KPCTwH9l
+rf15Y8z/CPxx4K9+r+9zjKHjB4SLXLrn4i7o0bYKPKPmXhwxKfREULWltLRMZnJyD8dntN7VINL1
+6yiilNnMx1cgQlFTDgXrM1UYcJn2qbTq0jMVrZ5Elk0/wV8ELpXDcH+W4GiUOXyUgHIt7g1P6SgP
+ZFb0mUzlmoW70ndywkw59ZpXqN2QZ13PCpIFm6+eBo+XdknVato9cFlS+rf9Wc4TtSqGs5KrbTmZ
+NlbFCjixER1fPovaLhOFEqejOSeaRWjGJW5bVbo1K5AXQ0ZqVE1nBb6Kk5hsmaghgKyGaXNFKdOt
+4gqq4YR7q2Iy16xh2JdnLYoDTu6LlTIdpBhVkXIWFAoJWOVIMKXFan1FXvfIS1mWbVOnpRwImYKC
+0vZ3ODbHZsTwUMZr3KgTW7Eabl17gfyajF0Zyzy2/Qaxnk9Or8LXeOIsm1NoFmEWlud1JZVZZIly
+ZjUN4BWT81oMUtg9lWvHqh7+rT2HH1ySvg1Sw6nKFcwZE6h4TjIa4WoWzGr5pRe4DBRjYEqHlmpT
+BjWHPQXcOfsz7k3E8hoqFmJ/mnF1Sdyk5dyFRF3FOYSKcQldl9j9aOfyxw00ekBsjPGAGrAP/Aii
+KwkiRf+HPuY9LtpFu2i/g+3jaEnuGmP+c+ARkAD/CPgtYGDtgpKTJ8A/M8phjPkTwJ8A2N7exhhL
+5Bg8rSAz1lCq1JZrAtZeFV82f+Rx96GcQGelpF2iTptQCVNXems4mmZLg4Iz1VishgWOnni56iAW
+/imu4gNsbgibcnxkM4/KkR26zGKKnnyHq5V3v/XOe9QTpeLabLCdaMFQ4jFRfobZcrpgb8NoAK9F
+A1+1H9tRC6u8/2WZ4TTl9G8qK3W938aqpoO/5DA+lmvfO9jjUP3aeZBzSenkesrGVFteYqzWQRD3
+mKi245OzU0xNrJix8Vn1xMqKmprSKzOMwo6LuE5NA6ZZq4av6cnSbVJX9etCsQmms8rqHenb2fgx
+uWpxHPc97g/Frz8sJ8zVIvEcHQt/wpnm7nELaCzQfxmhlTHwLy1xQzUSqplYQreuZoR6Ih7cjnii
+aVs/m4JK4RXTwXmgsKXxlXrDYPxFpWVFqfdOozlFqXwXnsHXSktXMQ3TYclYg5mV02OusZ2qmPJw
+V07pkbKCbV9tc6aWUB4YJot5ci1zLaBL5y5hpAJECnfe8Cyhkvw6hcNMx6jbcNjSuEtqAuKaVE/e
+H8j6L8r8HAuSTQMOx2JBPJmMmanOZdAMqJofrSDq47gPS8BPAdcQoO8vAD/xtH//26XojQXPd3E1
+yFRVU+aVLCrXbRIEkqOttzK2rkpV2tkdyYOf5iGrbdl70ukpI80cTN45ZHtZ8ubOrSXsQN0O5c6r
+FQ0aqpRkmiskU/k5jkrCofIgFmOmq/IyTRrSt+Ttk/P6iV57jbOWfG9UW8FX3H7m1KkvS59i5Rk8
+zEYcPBDCi2+evoerUehxOsMovLutVHHlukdLX8Ct7jbHvrJLP/mAsKZ1AM2YW7c+DcD69nPyXcld
+7EhM7rC5hlVjsPJCXGWS9r0lJqqVWFNT3HEsrprMTjRnrNWqh187ZDCQeYi8GjWtK5mqOxPNUoaF
+XBs1PF7a+SwA/apg+kCeO6g7LJfSp3c3tLryiYedaqalcimeyMvWCUNGqse5fetTXL6kNOipRtmz
+M3LNRNTbm2ilCfdGp9xXBuOy8GkrtHx9SeY6OnOJF2pgay1KFemdnQ2oa3BwubtEpPXcRVPl4GdH
+RFMFy8UltiOb1O7wgDiUzXCmr1GWWU41gFmYdZxYNo0yHxMqgChuVIw0uD17JM8/M/Y8QJ2NQxTq
+gHsUEi4rfH08YqBis7nO/0rNZ+2S1k+0G4z2lO05qGh15ZniXg3HXeKjtI/jPvwocN9ae2xF0uf/
+AD4PdNSdALgE7H6Me1y0i3bRfofbx0lJPgK+3xhTQ9yH3wN8A/g14N8Cfp6nlaI3gGOwNsAofZi1
+Md+8I8GlF9Z2yI90/3p+zubhywBMBmIuvnHax6qJ5DW6rKladb1lqZTopOiPcEdyeqxe0iq7KsZq
+8C11C1wN6jQ6DSo9mdN9hyWFUGfK0zAuc+xUduvOaw2WleugZafEihcoZ/3zVF6oFGbxrM32DeX/
+j9LzasD+3oBaR7EAI+nPYTbC2xRaORtFmImYog92c+bKPfDZWy9w81WxEOptuXay63C5p4QsQUyp
+egpJOiXuCjzYX90k0nRZqnwFy1F8bq3YtCTqCcR650W4+7aSkwQ++VSu76n7UGZjiKTvvWufZK4n
+4rQc8vo7YmHM03PvgJVKsCcn5SM6a3KP6X6Gs6Zp5JdeJm2IVfEHP3uVUJdodbQoKusSp5KvL9Zb
+rDRkjI/TiDRUy6PVZkUDvkuV/F3tUgPTX0Q5K9yFpbPVxKvLPYIA8kqtJXWAqzCmUKm4erBJqVbf
+ZB7hKeWZp9WunlOeE+tcWzKMM7FSHxzmNGtKAGMNoWILNm+JC8coIkTW73g8ZUtJhEI/xlXZuLZz
+hfLogVwzU6ZxLI7KFHa9GKPu5tHejLUVZSmfeYwV9v607ePEFL5qjPk7wDeBAvgW4g78EvDzxpi/
+qJ/9z9/72wzGGjxjWGSIM2P52b/2/wDgrOxy45GYXJ2lOc+/IoMdXpLB7Z4MsSfi9613e3i+kq+U
+Gf0jZReeTmBZF30iJlfmzUhVCMS6UyKjbkLuUAzk5Zwwx5+ratCGvNzbXo9JTQEtxuBdkYmxRxlz
+zUQEWU6mtQ0dZdiJy5xEaweaNUMxkZemUS/wlfFn6osp7hchlRWz9WhsePOxRPh9U7C0LAvs+o0r
+pOJeEkUK/rElNY1VTAqP0Flgfx1yra4c2TPqCk121A9NWy6eJrfL0qWRaT+6DbaviC+bjwakdRln
+M1YQz2qNENmEHX/EnUPp0G9+8w7zqc6lZxh48tIfK/x2Vs5BMQ8EKaXGCT6zsk7ck81rNYoZ6Nj1
+78gmNM0OiYYLfciKtr6YW2s9yomM/WxWkioRzZFuUu1HKyjim6XAB42lxM01Si2dtkZqHQBmVjMc
+tiI7ljGcrc5JFG8Rt1zWVNDVKBamTsWkko1pHC3R0lRLtxN+RyvUNzR0x1lf1ThQo4UdK9y8HVLX
+w8mPfUp9HwJnRpktSH70frUl4prc435/yBN186p5ztlUXWU7pT7+aDDnjytF/xeAv/DbPr4HfPbj
+fO9Fu2gX7V9dM9ba733Vv+RWb9btC6++SHNli2uenDTjAYQ1sQjaQUSzK9v8xuVXuXZVzM4ral7H
+rRBX97exN2GiIeC3dvfgUEy0o6slPUdOoK/cE8j06bfPOLYiKFMrSg4eS/hj0j+iqMTC6F15geVV
+odi6qUfN//Q//CUWCZYqm5/zB2ZVn2KkiDcqCkd26F//xn8NwNe/fEI9FYtn2B/T0ADlB8Uar8y/
+BcCvDuXEf3F6yM+lYmr/qU/f4xdjsVZW/+6M22q5dF0Ho6b2ViSn7qPBGr21BzIW15f54l9/V/qJ
+T3dLrJvBIVxe8P25Mm7GN1RKYbZSj7n06VcB+JEtj+c+LUHe2imEPQlaZROxMHrdNVIt1UsNjIYS
+SF3ffpXtnasAtNo7+CpQU82VE6A6xq+JKAo1S+jKyfyf/rk/w7FSvT24c0JDcQEjtfLKwzmDx2IF
+TKo5hWaSmrU2jkbqsQWZKleHik0osGSaRfHDgPV1se6eu7bD2qpYQmFVYiZy0lczhYFXDuNCTuBk
+lvP2+/J8aVqy84XfA4CrIbTa+ITDQwn2JcmUQaKW2ThhrpWyjTYcV/JMHa18PUzBmytTuOPRrMva
+mvqGZdUDGdVjfu9rck3vp0SN/flvlNxT8ppvfP0RTzTrtNyN+ZM//YcBuP4TP8aNtljGN25+7res
+tZ/he7RnAubsFBW1fsIkeURfcfsNE3HrOVnw661lttT/7PQ22b4kUX2/rew5jRWsAj46iU+iUdqN
+lVcxal5macZwpJ8vSUyif2PCbl82ikma0t99CYD7j+/w8PVfAWBw531ClWWf3ZLKO/ISo/fz/ZgF
+02hQ+lhNPZXZGaWCcPa/KS/me1+Z88or0vfW6hr+rmLjk9s8MvLivf34Tflsv8WTA/Gd/7Ov+5xp
+FV3vkkN5qvDuT7bY0mj4SiT/fnn8RSbCLM6DLx9xrrdCztmj74z5E2WqqmmVaBS5lJqRWGsE7Ggl
+3g/+5B9kBTXnfyQCJRmJNmXDprRYLVUffPAOjoq/ZnlIV0vNo1qAG2lMYFGjMW8xmsombFllpq7W
+2f4xRw/lxWws13AyJUBRt+Rob8BcXzbHCVnsA1le0VA/OsZgPGXJUrYiz5bMUTr/KmWu7uHp6T6e
+VphebRoKBbNZdVuaeOi7xt4p1DU4UkYVhcLXzwZyyLRcD18Jbb21nCCXOWkcJ9yZi/s3PsxIFf4+
+KGVd5en0PONgKVBPAovleNGfecIXvy339r8sB9boP7pE92taIn41gbfkHoPTEX/3//4KAH9wc52V
+Ty3YqZ6uXVRJXrSLdtE+1J4JS6EEBqVlqVFgFIL84pV11nw5jbrbDW5ty0na6a7iK9ttEWqIOCvI
+UjHbbeUzV1O03aoTaJVkUm8yV/q2LQ32baUx63MJFh3OS3yNhDT/SczMlYBa8eY9ihPZ2RMlfcGD
+XHkTwrbBKD1cmfTxFrDc/Iy9f/zfAvArvySMvOHWGi+uiStSzyZ8U1mCP//8TZ48kef+cS1w+odP
+dvnkjjz/vaOSz9xUkErUptET07YYxHR7coImyidx6/pNbv9TMQk2t2cMb2uXAyg0P+4Z6Dny3F5D
+Tvkb7S4v3pLnW33td/FDL4tr9upzr+Aq8YjXbFAqPX7UbJxPXlVobv/yNkFdOSTmMD19ID83dmhp
+wNNRs97ahAKZRzsuWN5QJXE/pL/Q5iQjUCq0vScC8Z3NxjjqijRCQ+oJUsExlrZmIjZjl7lmiuJI
+rBwb5SxpgO5kPOdI+Q2GR0OCueAbZp7PzmVxJawKvRwwBhX7KetT6r5iQPIJh3vKWRCJG7ifuVwK
+1bKZLtEPNYibp7Qz6U/fFPQ6yvI9kc/CKiVTDIljhLEUwHPAURe0Fjm0AhnzH/+jKifIF0ieF8ty
+9XiVo6ZYWPuHCW+pG3Pti+/zE9d/gI/SnolNocKSmJSu3WBdK8jKmqGrfIctv0FdiUnxC3JHTT8t
+afWtYXomf1dzhpSBkpr4CZOx+vD+iMdaRhxqlD2Z1zhTKve402ND2Xjuhz2u92TBntYqJloafJaI
+eWaswVPWHEqD1SizmfcpPHkhj999mz/7X/wyAN8+kZf4h2tdhmOZrOPDBE9jFPMqZasmL/dXHfm3
+Mjm7SuW9UkuZqNm9RZORxgF2uiWzPXmp500xKY/6I/K5+MAj7zuGYFEYGgulp7rlSq7ErD1daFe6
+bLwslZYvX41YXdMK1SiHSPUvvRTf1cpNs2C0MlSqJOvNTsld+d7779/hW7vi/nzhp7ZoqJiuszDh
+7WPmx/8IgN3RMm7j9wHwnNegX5P5LWY1HhxobYP6+p4PDUWWxnEHV4lubZGe16b4S1tsb8oYdZdk
+U1hurODN5fmTKOXuXYkl7T8ZMrGy6XuOw7GKsXYUmRlPuzStzNmgyOmrlkPbyXgw09JpXXtNt2Ci
+RLiGIaNjJWeZppyNdPM2FYUqn7m+bLY2KzELDLALtcW0xdBWly7twCVNW37jnj6gVPMAACAASURB
+VBwyz3tfYqogqytFm8eaBdplzORENtHf+I0v8vt/8iU+SrtwHy7aRbtoH2rPhKVgrMUrKqIgpHvl
+JgBXt7v0VmSX762G5FpfkM+OGc3E9D3TGof93BCqeenUQrbWFfBiA2al7OZ3jnPGCiDxdDefTFNc
+X07KtZphVWXSX7jeo3KlH9ODjAcaGCpmWidR5ZQLlWRvjFmYz8WUKhGTcFA9IlVQy04o9/jR13Z4
+7Qe+D4Dy0SPqChk2TkG0JCfCi18Tc/BTvTu8rbTnyckIjZvh11b49LaYuOu2YFd1Fw/lIGUDj6lC
+e8enE9RTInIDLmlgtmUyri7JGHzfD0hg8IXaOtdfkM9W1jfxU60B8DNINbDnN6Fa4DN08ooJqHQ6
+7hljDQJ+49d+lV/8denUrvuz/Nn/UCLmdUfxEeF1vOoL8hX1b9E/k7lsLcX0tuTeT3ZnFPNF1E3G
+suW12VwTNyf2G7ha4ZgEGddekCD1rd4yz21qQLcjY7W6FpP2FQsQZby4KoHro+kBr78pmZ/5IKeh
+9SEd5dg7HJ/y8EjVpaM6va5aOv2S+a5YFQt8RxjXSNWCqhyHclFTYUpq6lbU3ZhuLPO+ti6DeHY0
+PJeLj52Qz3RkvbxwMyaq5Fl/y5kw3VXsTCD3OHh4QPCcUtK/+BxX57JOHw1OGSTyrKPQ8k/fV7rx
+p2wXlsJFu2gX7UPt2bAUDAR+xdKKyzXVjFx3oKGCJH5S4Gjly2Q25dGepPjeOtVips4VokxTSGlK
+oRDetbWQ+QIVOPVJPDl5l+ey24+nM8bqGte8iq1Q7t3AYamQn6/dcKgfy895T4JvJcW5FoBn6sLq
+BGQ2ptB4R/qk4oVPiS7AH3jxFgCv/djPEDXEP686dQKt9SezEMrPL/5u+f2lT1+j/bb4jo/vl+R6
+cm30U9Z3ngfA7SzjvyWpp3kuz+y/+FnSdcn/H37lNzkeK4YgKhhpinStV+NFLe7qaS5sfb1BOJRT
+x9sK8BtqEVQuRk82XAdrxQ9mqvwW8ykoZJjCY5bIta7Z4XEm/Z//na/y737y1+S5PnldH3kF/4pS
+t32wQtZQNOVmC+dITveoukeeq06GBoyvvHyNFxSu3R9bgoYWORFxU9OFm0t11hbaCa2F9oRHpFD4
+Wm+FtYach5ecJr2eLK7TR/s8nIl1k/nSh+HtESfKmhSYio7Syrl+C6P4jFwLo9wowK/EpDtLEjpK
+7Va4c5ZD6c/SVsjOZbHO1uqaDgacSObP8R0+vSpjtH3jJnFP1t7V23e594GMxYMziXF8a3qb4ycS
+U5rEA4JCg6D1iJpaLN4wY/COztlTtmdjU7AGLw+J4xpXXpXc7mpU0Vgo6LQsjir6VFXB/TtK7bUt
+i6N1GuJ3NX8+9UG1CG0yRPcBtsKY41WZhPqCB/J4Qv+xmFy36zU6GqhK/AmXr6kWobNJpIy6pimf
+5UVKrnZ56FbnmHPfpKAS7+2VTf7NbdkULu+I+EdcM0SRbgReF0fxDzgORokwfCXbaAaX+cMrkg45
+uLXL0R0JYFbXK9avyqKpH1v+6gdqzm7Js/3Mj77A46PXAPiHpwfcfiR/t9QKeXlHNrXNKuAzn5Dg
+U/e6PPNm+wrxjmxIfi3EbSzp3DiCSgKsU4COHbkm1jOHUmHVbm2NNvKiPB86xKmMywdHR3zpr/xN
+AD77Uz8KQLCREr0idRvD3g1uxQt1o9Y5V+SEikhrM9A5eGXnKtuXxU0Y3L7DMFfMRjeiqW5Fzw8x
+iRKjhMrHGXWIFWTm4RGvLupflmhqsPJ+UMJASVIe6+bQDTnsyxjvun2mc/mONRMShrL+FlTuve0d
+qkeCvVgipbctY3ilFbBk5Dkuda/z/PeLK7W1JeMdJhVBQ37vL18m0M3N9bqgdRlfePH38nkt0R68
+L3P6F//Kfwe59LPMZ+cZjHpZkCq+wa91uPmSjNfTtgv34aJdtIv2ofZMWAqu57G0usKNtcu0fBXp
+cEMmSnpSN0PmCg19dNJnb6xpIz3ZO2uGfKJkprYi+UDJWdZG9EulUGsVeLuKTFSJtndPhrz7SNJm
+q+Uqbixpqm0PwmU5dcxsyIqi7dxYLBfmFm9BQJr7oErSvrcKiezi9cqhdVV26LCppvF4iGWRj3Yx
+SiFnoi4oXFdrYYTwY0VOrSv1NVqashwMBywtaS68FtBzJVeeaRDt+q0VOvp34eeu8Atf0jGuHGoD
+LSrbWKVSKbGmypfTrs41D4pyipfrebHZxCpfgh0XOMqgvUBxWkZU6SI9O8A71V/XHeJ1TUOeJfzv
+j78NwNlbggnYaH6ez44FIZr3/y/M5r8t4xavsxyKXOCxzfGVVXppWQKA6ytdQjXns9CjoVW1m70W
+1zckeBgmJ7hGxmtBqhu4GShxjmcLHOVkcL0Io2zba42ASvUotxYM3qMG7x+JVXE4HJIUMs5BJ6Ct
+0nO9ulga61HA6YoqRj+ZcEWh6d9/ZZUVDXxeXquxfF0syIb219gM4ynuo97AWSwCN2QB2XRdB7Rv
+gZLL/hv/zu/nG3dkXOu7I15/7y0AZpWlo5RvvZphY/WjBRqfiU0h9F1urDa5ueTiKVFg5cwhl4eZ
+lXUmbc1d3xvjPhQfqa4vm9lPqPmyGqdHM5KG+GfvHzbP4xLz+bsMtsScGx/K4M6yErsrENX+bML7
+SjhSW2ny0pIs9KnX4rgnC88pVJwlcJkp40+Mj6OT5UQzqhPppxOPQDkmkxOth8ifEASyUThpiKvZ
+ADM8xt1Q2PBE/fN4jFHmqaDZpFWXDak82qUaarT/8jVeWZHF9sbpBwAMPzikqxDs7tIOdTW/P/n5
+da6o79/JcwrNGBzs6QbTPMGbqSDuZsS8Ly+VV29j5sp56fo4mT6fAoXKwwHpUK6dJ3tMtWag17vK
+z+zIJvvXnDlv3pd7H+3LPP1J+2V+VqsMu0c/z+3L8oLcvHaJr38g4+bOYaOnPJarsshXah7BQDak
+uhsz8RRY5VuabWWtqq1QKm/iXGMScyI8VaEKW02M+v5hs4ZB5qQzOsXJZM0dBfL317Y2ODyV5zsc
+nZBqbGMYVNQUfBWonm8nrEEim/RSraKpmJuaW7DeUxGdyzFRR+MSTfl740bnsgS4HmgFK6XDedqp
+csGXuXSNrJXv+8Qmj5/8pvS3C20VFu6Ohgz1IOv2fLyebEJP2y7ch4t20S7ah9ozYSn4nsPGSp3Q
+t1RqzrrlGZnmyifTCff21D14vEujKbvnkhatZGZIpgUu9dqcYqymfTbi8VwsgZEXcvaGIhNVeOTk
+cJ9MzcFJv0HZlj2y0dphe6ZVeUUfTzkDjVHqMtcQqqyYLWNssDCfffDl2vFxzhnyeT1WOq/aClmi
+JmCQnVsYNsixIzmhjKfigEWA21SILiW+/tzo3cRbuyq3M7DywwJhvXxbZdzyQwi29echvo7R2d2U
+RvuS9jNhuix9Wk0kyj5PHCqFIldBDA0d23SOo5V6btvHxPJzlWvVo1fg1hX/MalTqGUynKXsfEHc
+g09XMf/gkVgNH1yWfv7c1wLeeP+vAfD8Spdf+JuSnfgv/9J/QD2UbMC816KnQczLW2JVXI4vcTQQ
+F63Zcqkp7mElXCHSzFWjHpIvuBrGMo/pLCPPlENhOCHoaDorMbiK/KzFDWxduSMyFXhpOVzeUJn4
+D445mYllMogiKg0C2kxdqTKh1HXR7DW5uiTf1b1+md6KjEW8sYmnWSyjQWnCEFSBG+vIfyB4dEUx
+4lSgFc1Wrd9aVOOzz0s1673X3yJ4WQKYR2bGB/tCUHTmpIyU9u1p2zOxKVRlxWyYMJ6cYZWINLWG
++UzVhOKAZiw+/vIty9LqNQCMctXVRnMOx1r2PJwSeZKyqVyHZbPQoHRIQnlJZ1pl6E6OGfUVFNV4
+TDmRRVXUcxbkf45bsawVfpND2Xiscc7JUaNGgVFp8GI+YnYg93776BFn98VEf3hffr+1Zbl+WXH7
+zSZRpYCr1EPnGaOsOvn6CXWNyDtmiUIp5bPWBlY1A70ipteRhX5zTSDK5cxn/7ZsrMeze/QUsPXJ
+q212Z2Kudp0j7p8ofHZbbvxJf4tTjeSboxPiDRnjpTDGxMobWSbnfq0JVKXJ71BaidGUG1sU9yRm
+cDRKOErFXTua3WV1Efk/kr6/OX6Cd0MW/ze/OeQP/yEpSa41NsjqMvjGndNry88bPcm41Pw2uaYQ
+i4MjDpWWPymPuXRDrqmCLnNX+tRXvcZsmFFTTsjJ2YTVHd1MGqs4GptymxGhpg47jScAtOojJrGk
+lFf27/HoWwqJniakClOfJuIe3nMceroJu7QoNGMS+Ts4ms2hcQlCTfGGWr1YWFB9TKoCFjTylcUm
+skYq16U80hTo6T0Avvw3fpm9UPpZFG1+eEPekep9h7sqs3V8N2UwlGuetl24Dxftol20D7VnwlJw
+DbRdCE8cbCQnfnNpFbsoovFi6Golom3hKQlFzReTOqvG+HU5MVbx6czk2rE7J9coul+fc1lFXTwN
+AD06S3CUcMAfZLg9MdWOjwN2B3LiX84TpotRUjizY11qC4GNwsOqQrXJJ5wd/VO59+4+J6obuZBf
+v+tYJgqg2UmfI5otTpUPCDwJdh2cPQDg6DcPcT/xdRkLu8VIzeHRm8c0t+WUv765RaTcjVZBPMOB
+y8ngHQBmJwOSSPp8egbPueJKzfyEyUBp2dUyu/vkHoFyB57OA7ZSOV0+ceMGcayU8amlGkhRmKeR
+dRPNQV2GxEx4cCz3+/X33+D+kYqhHFlMrEFOhR3cJ6E4UDdvbvnKN74BQI2AVzfE7E5aL7LckTnr
+rUig7rFzj7fuSlD18d17HGkgsb06odkVS+d33/w8Z2OljTsTS8F6TQ6nEjA83T/k26dimbw4ecya
+BuJavodnZJyDStZWXowIh/Jd+eGctFpU5uZ4K+puJnJCN6f7+Lpmq+KUvYG4aCe/9GW8S5IG2oqu
+8dqPXgXgxmvfD4DjdDCqmWncMcZR3o5iQn4kllcS3eHsTQlM3z+WNfa37r3PYSqWS9cG3FC4dfz8
+GsFjmb9pmvLmewKdf9r2TGwKjgtR1+KXYyrVBnQyh0ZLVtCdd/Z4/4PfAOD+6SndQCLu139QQEHP
+Nw2BSpY7tRKbqhqRa87LZdtJxaECOmLVN1jyfXBlIRUbIemRIhPLQwYP1PdvNenFSoSoIKXSVkyG
+YoqGniVQ8syinDO6r65JmHCscufDfXkBgwPLm5qyO9455HImvuVSFNPclqj93v4DAP7J63eZvCdR
+5s1rhxzvK8cfPtcrcQNqdkxDo9ZBW9yE0cMRJ6eyoT06GXBdy8W7rqVQ/7tXFUwi+buZMve8ftQk
++ECeaenlF3l/LPPQnD5h1dMXr9nEy8ScjVWX0RAxuCt/9843H/OGkXs/fDDGtqT/zVdnvHYoY3Q8
+V7PcnfHrp1onYCvKoYJt2jHjUrkrVy0/9NnPyfWx9H3vwR7Hx+IvH54e8+RM+jY9zXh45SoAz3Uf
+sBzIvctYNpjaTo+ZCuwefaXi9relz2/c/zor+VcBuPnSJ3lZS9d7z8sLPxt5HGqFY5/5eSlzWmVE
+M6Vz1+rTZlyjcqTvnSKmOJJrt28FzJEX2t1Y5+F7shF316QPQf42cSgbsksdd0sJaMs+udZPVIPv
+Z+WHpG/JG7L+f+wnv8RXXpeUejSa4WgK/NKbE2YKOHMKSzn9nVWIumgX7aL9/6w9E5aC6xg6oYdT
+VTRmYrY5SyWh1jPc7LoELaGWqw32sFM5Hd5Uk3O84rKzqsEgp89Y5cCPzzIWOOfcK8mVKsuo+EdU
+tVhfklPHlG2OawoZNRkHB2LOxVlITTFLdaVsd12HSGv6yyKnUunwwiYcIn3bu11yoFwHY2ULTpMx
+h7cXHIAttp5X1t7mDVxPvntZTfhPPXeNA82rry9dZ0mVjjqbJQ3lL0isR6l8hjXNYRerKdVUTuUr
+WzW+clfGYl4aCDS/H3WJZ/Ldp2dy8h86T9jZEuhzbdXjdF/+7mAwJ1ZwD7PH+ModEW1ckbHwPSpf
+Kce3AhgKrj+4WuPVVTnlWvPP86QjFsSdIwEm3d+FRPH5rjUcqYXluSW3Lkn/L5sf46XXxBqcz8St
+PLt/wHUlUDGNNajJ2B/1p/z9/1Ny9vaHZvz0j4hIziUFCEVbO+SRfG/6qQ6Ho28CMEpT5qom9UGy
+y0ZH53UoVt40OWakJDuttEVbRSjjwMHMVUo+lwDgJLdEymBdXprx6ZqM0Y3P/Ai9ZQ3WXvLIT3W8
+Cs0+zGNMXawR4wYYT7MaQKWkLtPikNG3xMr64qMFdf4+v+v67wLgky9ZHBVBYg4335P18K1DSJY+
+mkLUhaVw0S7aRftQ+56WgjHmfwH+AHBkrX1ZP+sCfwu4CjwAftpa2zfGGOC/QZSnZ8C/Z6395ve6
+h4NL7DbxmjUaV2X3rIVdZofK/NtbwntHIbrzmG9r9eB+XXboh+9WlFqgY5sxYy2eqrV85hJn4kl/
+wrGCxgKVDCvnOanK1Hn1ikZHTox5knHkiI/fsh6eBuUWcnPWllQqD+b5ISbXarhJnUir2t45G4Ky
+ELUjSaH5UUhTWaGMB6UrVkHruR2W9bQtHkrfDzyLo+nEX/nKV9H4FxvvNKlty/2uBjHrjtxj8Lqy
+Sl33qan4TOiEeJWcGLljGWtMoU7C2qr4pRM95R698QbvvP8AgMN/MD+vHPxjn/4cGxoc7d/eZfmz
+cnKvdcTaKg6nJGMZ2Ec24It/X4KjB6dD3lP9DaK77ChMvVSqtXGSES2r3z6wrC8pq7TjcjSQMfr8
+J1fpronl0X9fc+1lzlx+zc16k4ZVRKc/ZXwi1oSp2viKQ/C2xMzznIp0QTB7eMxIOQnuP3yMUasp
+eAeOX5Y+v3BHrIr66ID3x7KIhvWMhjJemyRjcCKfNxTxeuz5XFNWbTsqGayLdfTgq3+P37gnP//q
+4xO6a0It+KOvCsbkC6+G+ArHdq63QLEz9AuKhxJo3J/X+Ou//osAJN+WIOmm3yB8QYLHy/XfRy2S
+AGyVvU+hhLWD0Rnu6Jy996na07gP/yvw3wN/47s++/PAP7bW/mVjzJ/X//9zwO8Dbup/n0Mk6D/3
+PTvhOSx1a2ykEbGW97rVjCwQM+ngcMb9D8R0OshL3osfyN9F8lKt5nAy0Amqb5ENBB8QtNvMGxpw
+ObY0VP1npMIrZfqEU60yM8cVM8WZh3nBLJNNpjOtyFUNaVOhvRQQ1uRlM46DdRRqG/usKIPxp25d
+5u1HYnZGNRWTaXS4sqzS8LOK2NdKy/kEo7qRSgeIs7KOO5BFvNmKKRQgtBn3aBm5aGclxnfEzH9U
+ifmZVTVwZfMKN2MchcnGucErNaga1fB6svCutiUQ9+3mgIZRsNRSxVJb7hHWRhwsGKoDl+bxG3KN
+RtxLPyXSF/dzOz/I/L5UPj6cfomxAo/e+K3XeTcTN+1EXb95aXEm1fl4nmr03XcCXruikObOOsqB
+Q9zRuoXGGb2WfDg59cg7Cm7qtsla8nJvXunSrsnO4asCGH5wHrRLyjqOksT4USFjBiQND6vQ8npd
+y+QLg6dKZCadM9f1WbdzxoH2WQOOS/78PNPkzh0KXW/TNMFbkbX6cn2V6LqM840XNYjoz7HKxm2T
+wXdgznFJ2NCaj1rIn/7s7wcgu6HuzM1tQg2et1otykTel3BpDtuyJssHGcmWrJGnbd/TfbDWfgn4
+7ZCon0Jk5uHDcvM/BfwNK+0riK7kxkfq0UW7aBftX2n7Fw00rllr9/XnA87xf2wBj7/ruoUU/T6/
+rX23FP1at0mj1aDjtAlXtMiJiKWuph7vlVz9Q7KTDh8f85mxmr6xpJ2q6S7oDj88SJkoRdeqrbAq
+f+ZGhjU9mG5dkd9nyQZ3xDpj3+2TDfTENzGeVgEm+QwnFathTwN8BeNzCXCn5uIomWkYrbK6Kub1
+y6+NqdT8rzQl5NmE9ZbW0HdarHbktPWKAhSxWW9LjvqGNyCYq0TZqkOjI1O1vLpJtyk/r3Y3OZW4
+HQ0d9q+/43Ll0gMABuNLpBp8qq+5RL6akVVB25M+by7LKf8f/8wmZ6fynLMgo6anf82mdLQfjw+m
+eJmyVWuVYZWcnQfMNvwpf+SPiDWV7P84wxPJj/9SUONrX5br+5oObnoViR7XZVBRTLUwC8i0WMmP
+czxlMK4pscpz2z/OUfGrAOwe3ub0LcVvAC+syzO9cHWJQK1I4y7IYjI8xCK4tN0mdwSx2ew2sBMl
+UE0DtpRte+cFWW8n9xzaJwv1c0OumIVjmzDVdG7Tk3WYrtQItWgubMLOkszptU9cwuracbpNQqV8
+qy/pWgjGGBTrUq5hNSBsspBA9TVWRrDyqr6uc5kD4/lYR9d33cOcSN9rk1/h6E11t1sVneuLc/np
+LIaPnX2w1lpjzEeWmfpuKfoXttdtWLl4tYB8oqWi9VNcR0zb3kqbQiGcYa/L8qEM4EQhzKn9BFMF
+q4TeNyn2ZCFUWYBdSK67Y5pacrqmlYVOFbEUSte/9r7BUb3PRuUyUbbjWWExWr5r2zJcnhPitBbK
+SjFY5eKLDdGaAGEu9T9BWcqLGigDTzSc4amobBW7xIp8NV5M5SwYhhQ73+pyTV+EzmB+LnqyVncx
+CqV1jEfRVtWnB3JtsP0ErES93/mVCZUag4kf0lTwlu9ZQgVcbW7oAn3hExirwB2vAYpHyMNl8qG8
+QEXys0wSrYicKRHIPKS3pSw/lzfAk3s762CWJWNUj7bZe+t/A+BbqrZUnXhk04VQKlSFfkcYsbGt
+m2XUBK34dBuysbZvXuKVyVV55isjWJJ5d+MW3/ecbMhXt66ea2saLZc2WIKujNul2jItxVAsHY44
+TTR+FHjEjoxBXUlYqvlVrinV8u35IfvDmX4+w1P274GKEa+0OiyX8ncv7sBznxLoebe9Q6Dupttd
+w2gGg4YKCM9H2Lmem2UfcnnpcTzcuuIX6iVWwUlWgWxOPcBmKh47GpOOBG9xUoD3ivbzTsijL/3O
+ZB8OF26B/nukn+8Cl7/rugsp+ot20f41a/+ilsLfQ2Tm/zIflpv/e8CfNsb8PBJgHH6Xm/HPbVVV
+MpkNGTjQmGjkPPVw2rITO02IpmL6hdUAu6QIskCwCaPijEipqkx9g+mynEatakbdqvDGKTzUU6Cr
+CLX1oMGwEPNg5hxQV4ETJ5vi6imWhAFFsthpZbfP8hmO0qYZL8CocIhxPdATr5wZ1nM9pWpK0XZt
+h0r5HAfzJ3ienAhuZ4vqUPpsjcJkZ3PaXZXQizOsBuJC36dUcpZympGfyXidTeV7X975JA8fKu/i
+1pRKMzhFlZFoFLPKpsyPVC/hU1pUZvNzcg9/2ccmV+XzqGCiit7VbkLnNcn/d67IuJWHDZJ7MlZx
+94zahgQanatNKj1ho3qJW5c+dZXo5mE1Pq9OLFKLr2zHGId6XWnO4hCDclUYrcSMY5rrYo38wMkZ
+X/e0enLrFq99WuQAGzWDuyjcUhOryhK8uSJLg5JGJf25fmOd6zonjhugbHJkcyWcmU4ptIApW9on
+eVNO5jKDrCnj6fqLwqf6OUI2eTyjVOq6YK2Hp9kOp9MEIxawUW6GKi+olKbPlpcxqi1h/AirEoi4
+BqOs4I63KMxzMbkyl1c55lvi5v36L7/F/p5WAh9n/MirYvX+E56uPU1K8ueAHwaWjTFPEJXpvwz8
+bWPMHwceAj+tl/99JB15F0lJ/vtP0wlrDXkakqQpXlNfjqyLrzrrpioIawtyjzpWJ9QMxMStZyOm
+U1m4vuuwvSaDGjlL51x7jc05lzTTE6qU+bg5x2j565Wax1BFQGdzj6mmOMv+lEIXd95R7scUvJrC
+fK0BNSMd08H4UndQ9Z+Q12QzKZXwwosM5ZH4dY+fHFO7JvdbbV2iHssiHCle3lYJjkbv/dzBLIhc
+bE46EOMrK6Nz0Y+TuTxncmAZpfJ8Xp6cvxRx2MZR0M98mnKsRK/pqVxbdfYIlfbcrSz4svir0mM0
+EUPw24d9fsLVBTlVDkf3FKsAsOzUp7YkrgbNOdWib+/+Jg9OZC6Hqbx1mYUy1SCPhUIp810c6pEu
+ftcDR8dWYyBB7ZBY4wRRu8XyWO6xvBERBrpGxhWVzolKZeIVUFi51k19YqXzrxUORjkaC2spci3d
+16h+nz1Gu/J8Bw8PONUIf91C3JJ1VtMYiK3n5FrZunc0YDLRMZweYmI1oCsw+lKj6WKckkrrddzW
+HFytojQWs8hEFCUo2xfqzjIrsJlk5crHpxw/kVKAf/jBjMeJcneW8M7oo2UfvuemYK39Y/+cX/2e
+f8a1FvhTH6kHF+2iXbRnqj0TMGeckqo+oirqVIGciOkgoVIQkm8nOMpEbMdjKiWkWEiL58UZ5VhO
+vvm4oqZsuHEnY7QQ6hjluCoRV3bku1ZbNWaHCkhKxmR6CmZ41CMZmv40P4fjovHUwiQ4WuHoxxVG
+5c5N7FGcqIx4dMLkTHb/tWVxE2YP4TjR4qG79ygeyen5wjWHdktOBzuR4OTx7oyNDTmVakFE3NBg
+Zc0nG2ql3nqG05Z7rGZq4o4MVzQbMHUsfT0lTscT2sqZOM8L6oG6Lo5Gvf0u5Ym6SaYQ+xjIa0dM
+3hPrp9ifkb8nY5B9QiwMjgxFqs/3+ps09Dnc+RrDN4WK/5dvPxYCGqChvAp9k1NphapFLDwA61gW
+hYhB4AjKCzCLzIlpguIR0nzMhi8Wiz0dUW2LK5HnGa6yJ5dKuOP1mriZmPM2DM5FZtxoTKHzVPmG
+aixrxF1XjgWW2B3IcxwePKEeytrqug5HU7nWaGboNK1YidWSImHU1wpN8xJ2pu5tWFKNdZw7CnSa
+T3AW1Gz5MsRqCZR8hwtztAdKg8+x6nW6huJMrJgqcnjnrgzcILcUC68jsJx8l9r407RnY1OoDO7M
+x80MhZqOZf0UV5l0rB+QK7+gE9bJRzIQeaIug9dlZUNrBzYyqkArABMXU7UvzwAAIABJREFUt676
+j70jVhJZFO2WLJR20D6vS3B9BxKNE6QVxpfFnZdzlK6Pelt+77s+rqsTa53zicMN8DXddLY7A32R
+Ky1PTo72ePu2vOjvH8zpz4Qs453dXfxKnnVJ4yhXmg1KVU1qtlqsLGImRUgxU1/8xBIrAUq7IQu0
+XybkCoQyjTojTaeemZj1hcBsEdMv5cWa6KY5H59g1UQ3h48xrox3ehoyWNCaX+vQX1Y34NuCnvOG
+KU5D3Jn1m69QRbpwJ/sUo7cBiI5rGNXfSJU9qKiq8zC3seCGClpzDK5WHWK8c+LShcnsOIZMZenT
+uqWpdQJuZMmGikwMwah4b9BclHg3cdTttJ6Dr9Wz5bw6v0cx6mMz1dhUAeHSznCV77EsLb6mSFdX
+uxyrRujpoTxb0MoZOcr3uOSQazp48vgB3jUlac0SUBSqVU7MaujhLLRSI4tVblJbGIweSNYfQKJj
+q++CTSzZQ52Pd4/5rcfSnztJRbVICBaGvfcWQpVP1y5qHy7aRbtoH2rPhKWQFhkfHN/nlCbNqZii
+m+v+uSR3GcUkE83jmxnoKeCua6I/M0R6Wk+dHqXurkU2IHQFElqeZqBKPpGaVtadMlHNvcM0Zz5f
+MElDridzVRU0FYS0qrTZw8EZYU0iyGHTgFnQnnsEPa3ArPUwY6WC0xPlyEbELTkdbq3t4IdieYwj
+w2qg0WfF5/fimGBD7pfNK5QSkf58RuXJ/4SlQ6mCK2GlNGIrDWbqlkyPEiYaRd8/e8LlrlgxK1GD
+SoOq47EGXc8SSn+hGZlSKsY/rVyctproT1bYVUhvWzEixdkZyw/Fqoo+9QKVgrrYn/L4ngQVX08n
+tJUMZV2X3D7fyZ0bFza25X5lWZ7zPHoYviNaqWCxMORQFagn3/oGZz2pI4hX67inSldW9CkaCu9u
+iasYTc0503LulVQaxM3nFk+thvk8IddgXnYonw1Pzngw0gBmEOKrb5ONJ5SK+yg1GDjOG7TaSvE+
+b5MpXVsaeyTH4oL5ewF2SesxlCNifniX8h1B0Xmv/jDBSCtezT5mKM8R3FqHkax7o1DrZLjL4aFY
+wrP7T5go58Y0t+eUj1VlCSK1+hKeql1YChftol20D7VnwlKorMMsaxIXGUFNUiwm22HekBM2LgEr
+P8+yAhNofKFUGLH1BCMAZMMZc2WEno5yhnoiHc5zIuVWaH2nRobMkxNzLfbZWwi1nFomY7mf53qs
+qb/XU71HrA8q0UVZgb+AZnu4kZwe169e48mj2wAcjCV4OG7W2OyoPFr3MuGGEn8Wc7qLnP2ZnAZJ
+M6MY6M6fTChV08CvapRWLIHCROQKibWe5tr9goHGO5xpilUf3skDxmPp24prKZQPYjgSS6mztEup
+xK1O5eAqAo+kQaOjQcnLI9yanITtpqZs3QaHV+R7b0z7oGIx49G7nPUl1nB1taLbvgrAWV9iEc77
+opsI4FhLFahFYF08R2M0fDdQdpG+y1hL5PPD1OP2Azn9ezWIlPy0PoH5AlqeSx+W4za21CKhU4dK
+EY9eaZio1ZQ7EVbVbDIE8Xg6GlM81qAqGVYrP1s2JGgr89Kp0rX1DzCVoGWHsynjXOZsdPimmJ+A
+Z3tUM+EB6fRE3s8N24yUKcqZ7dOpKSw58chWZS698RCrJMOlFr9NjgrGmopP4wmDtsxlfmjPs522
+giT//75K8l96K2zB/9vem8bamuVnfb/1zu+7p7P3Ge65U92hqqu6q9o9YYiNITYQC5sYR4kixRZS
+ICARJCRIFInQ8ad84AMiIiESIUEhiRSZIcGQWFYSxzZOJPCE243dQ1V3jbfudO6Z9/zOKx/W8+7u
+Ijauavetuoj9l67uPvvss9c7rHet//D8n+ekfMIku0b/ukvktGZOLNoxU1iq1E2a6ekpl4J5lqrd
+T17YJX4opuZFQ6kFpA4KIvndk8rDF2vvpYAwSR7SCOLamrwjKmYVeYSx+yH0KkaSbW/2JXs+fxd6
+twGwptl0OBJ4BBJt6d0d0H7ZTdJT4T2Tw5g8lss5fYv7T9xxlD7MEue65wpbzqeGs9Ld/DsvjNmX
++G0DeGr3rsyc9ZmbCEbkJ4N6SMqJzrOlVXKt9AylEXGICbiiZNWqEI/k6YxKlYhVDqHvQpv2OY/y
+Dfd3i7fOMDN3/NWuZO3DkPYNN8aR/4TnXnRtMF4c0Uq1KrqachA7/MLwH7uwYz9a8VjJwyCtMNfd
+9W6aJY3o9OIwxig0s0oGetEeg9/lrv3O17+AfezGCJYp8Y4DPfnRMasn6htZqOLwQkDYPZhBTNt3
+x18XLa36GZpwiVFn41q4kS++/YQvzU51DAFWzvXRsKZcujHCVt2XdcrZfXc8r1xvuXboNoBemRAE
+rt3ZG40w6sGoM1Wc7GNWb7rrshveptwV5DspaU+ELQlqgtB9phLv5OJiyVwdumb/Ll84cziFflBw
+Xnd0eRZpIb9v24YPW9va1t5jz4SnEDSG3WnIzijGVs79WgcX9NU5GHjQDiTEciOAJ84lXKsZZLYa
+c3Db7Si7QYOVcvD88WtYiahkn7vO/bVb2RdCpbXhgB258AcZzC/ckvrgwRGFRD1uP/c81a50HQSB
+HewcbNxraz2Q6Aueh6fuPH/yCkfZr7jjv+J2j7iXsjhzx7y8XFF4KodGAWXpPIH1Y5dEe9Bcslg4
+r+HgYJedO84DSVLDVF17l82SVo1ZE3VzefikIh/NG7NBBLaBwZdWxdrEHFl3MtcOxSUQxQxF/eWZ
+OcvYeQfHr055cOpKp23UY1du/MMTtxOdnaUMbrjjWTZnsPeCvmOPO/+m+75+/GmSmdNkeOfIkbDk
+TyI6sQvjhzz8aRHeBj1a4UVaDF4XQshTMEFKfP33ufd+1xvcEhZg4l/lQm7yYJ3SH3adlkr81QFZ
+6uDRZs+SC+NSN2uMOtNSHyrVn0tdzy9NL1mLk2H/uU+wUiKyuHxMJKh70UncD3c30OeDz34nN+64
+Bq12+hgjmrZwco1WYkS/+hM/C8AvvPqrHL/r7kd/8hP4a+dt7acpdxxqnDvfcZPb3/Ovu2s77DgU
+SqqFG+/412oeH4irY+ZjbdcRC7b4YP2Kz8Si0NiGRT3j8UXJJ66p7lwe0sYdQGBnI8baCyYEuy5e
+HMjR6e3vMr7j+AUjEtraPViD+VXalQtHLi/vcfFVl6/oDxRvhj3mK02CYI+1BGWaOqYU3HVoQ8oz
+d9PfPtFiEvkgyLDn+9CFD6bAep2Kj2UkcpXB2E3G/mTEvHEx68NpTax4pceAoVifTnJX577/oOTO
+bRdb/u7PfpKxBEqrxQov08N0OmMgMhFPuQyWNXc6DsqiodFCUF2c0w5crdzeuM3lucMQHJ+6hfLq
+YEwj8ZJBL2Vv6I756l4FX3bXa7GsmEh+fTQWS1UzYiVmomxwm0a19KWtefV1te++MCDUQn2lcMfw
+tfKET0j16eFjn2jiXPDK1CzVS9LDgO0wC7quWKzeyybX2Ou5p2Yv3SVT34zZ2SFK1JWoluUkM5uK
+Q+td0HY5ozKkEtS9CJdcVloMvuKqBe8eTZlLvPiluEdPWq0Pipa9664CVetvdm/d4vrLjjMxMzXp
+oTvX3uEui/ud4EyLX7lr+OIfcMf28B3LJ/+gqiGLkIEQ0atZyVVVjA6ef4lQgLPVoXuvvTgjloqa
+Z19nN3eb6JQn7A3d3DuflqDQsm3e3+KwDR+2trWtvceeCU/BsyFJe0h7dsqjI6c5OLwypp25w9tf
+lgSiI2vsnJ5Wdl8y5PZkzpPTX9V3+RRWHY6nZ0zP3U54vjqlVAvcoOdW3Hm74uSJW61XqwXnl+6z
+y3JOlbvd6I3jM8qZ8zyspNr7Zp9Q7MvQYjsYdFXSSrOgPjmmUjdOphq9X0X099yOcaMZYdQQVIQx
+vmrlOz3ngXzqygF74pDYGe9QtS6htJgVrHM3htdGeIINe6Uy4G0Ogmjv3RzQ1fnnFysehWpcqhaM
+VWk5U8Z+MZkSNW739HspsXgC7eCCq5LnWy+nGAnxzN+S1xTfI9UOdHbyZXxpad67POb4vrsuR/mb
+3L9wSbCvfOVVHWfDvamwIF5F5+0Gjc/A76Zls8GqoKaj1labDsbLJwHrCxfaHO0tGFXOK6zfukdv
+qMSl8CvZcIc6d/ffCxraQAri/ohCFajlWcP90lWKfvGffBGA84tjWiFBH9RrLpcOT5Cfz/hD3/eD
+AEQdBPvGNaKJm1vV6h2Kt1zCNxg8ZC3+yNiLQQnkQEnC7/m+5xjedcde2R1CVU6asiQShDqcBLRl
+14zmEp/NecPFucMw/9w7v87lue6JsSyFSDVhywbr8T49BdOVrD5Ki6LQHl4Z009HGAF6wlVJJlrs
+UdAwWwp2WrYcXHXu8TjVgxkEeKW67OKaUerirAqoL9zf5ckKXwpPy0Jlv5XHwW3x5Y13yfRg1vMd
+ml2VJ1vLz/2jfwbAauYemv/wP/szHD9ylEc////+U3KV9dajmutqxd4b9qnE89hEUnFqLGcCwqyX
+Of7E3fxr2QAj8FIo8M/KX1GqZfvkfL4h9/AGMd9xxbmtbW6p1AdxpoTHsp7ydUFfy8ay+7JzNe3B
+H+BHX3Yd7r/0xZDBOy40mUtEZ06J1glu3L7JrQ6EFYYM1DGY7UekEmldl8L9L0v8K6oilENyLU6X
+U0shgtWxTWhKFzZNL+TWBhckCgN+5VdfZ6FO0n/v3/kcF1rIv/T1KS+IT3P4wy7O/v7rL1HMXM7k
+8YOHrBNpRs5zzgReOj2+oEjdPc7UOzFd5RiRmabjcAOlfvTgYlNd7h0MmfTcnDOt+/1qVbHuBFmK
+mGXo7l+d9vm1X3ALXWPc8bzwmSn3XRWaO7nHNHTjVS3UnjuOfm/MtasuP1Qv3XzKUrMJjw73r5Bo
+UQxtw7E2gNXKcn7uruFl6Y6nyAt2++4iR6YhSVQ5sTmJWK2Oj5YY3ML45vHFF6y1jvnmX2Db8GFr
+W9vae+yZCB8MDsBSlZbRvvrchzGJ3OA0SIi0q4QHfQ7HbjUfqhlouDOkFYtuWlfkIp6IBgnNnnu/
+WM5ZC6K7MxDIw8bsahesLqC96sY+uOsxO3JJyS/+RsGtlwQm0c53/vAhr/6Gc9uqsCFXpWKS+3gS
+jPECn4M957E0SkpSZRzoq+qmZigZuqWF3kjJU5FtLKcXPDlXh2eyTxO4ZF5arikbd9571wPGN9xn
+Hjx26/ubjyL2B8JH5CXea87DePDmT/C3/6kbPJwP+OQPueN8SYrZ67lHPHA73kvPDdhTt+Oot0+i
+JqedLGApAJQv+nrfb8i0tzw+aSgzJcNMQDZ1u9w7D2q8Xbdb7V1142XvHPMF8SvevjthJhq6rDbc
+f9Pt+Leeu8Pt593nXxw4nEM0T2nkfo9vJnjy0qK2x/7EAYCqa3tcrAXZLrouS59+h2MIcy5ERXxt
+MOd06ca7vtOnEPgqEDVdXrdY4RveuJgRn0pWYHaB3cCGnXf0xi9A56q/M/E4FB7By1pSeRM37wz5
+9B1H2ffcnkvmBqHHoTs0iircJKjnxXoTarz6cMlS5EBfP3Yew8WTd1guFKIehOwMXbg5P3/C0ZEI
+bmKfSp3HG36038a2nsLWtra199gz4SlYoMLHsKZaqn05C6gkCLoIDf3Y7brX93vcfc6ttCgBtL+T
+bRBv7cqjWOu1aWkUW8ZVD09w1jJQz7+f4yuhGBQLVqdut7rXPqBvXW4gqZegxpa+1JdPy0v+2Vdc
+0s6mcw4GbjdbrX0Oh27H3x8eMBy5eDfzXDzc+CmxmJgzCip5NIu1wRdRbCIvZhiPNrFskDXMdJwn
+TxrG3fvlhPPAjXFwy+0S63bBqXX5gOz8nCPFrZ/41ITL6W0Afv/HPMZrJcR23Jb58ZtjbqiEtnt1
+hJHWwdW9DCMqsSDKqCQbnemaeMxBlGl+WrAs3K65ai4p5m7HuzJYMRWEet64hFsvHdP3xYGRpFxJ
+BUH/9DUu/7Hbgvs7l9y87o4pftuNuxxfUCmhWEVPNsrd4TjFKie09D0OxZkRCNEa9n0GKmXj38Dr
+u2TdbpjxvFCdtk426NT12nlYi8Up5dqd32E/4tVjNy92+ks6MHYycNciX3j01Kx2y96hHrlzfWF8
+yPMvu9Lpp37vH2Bf4r6fe9mdW1i1+JIFXCxyQiWVV4sVK1wp+trNipWYyPZfc4mLXy5X1HWXPG1Z
+K7k4HOxyPHf3ta5bkuZfQpizsRA00AaWpnUPRb60RKLdik1EkrnXu70RYz1YPSXyeklCoIRSleTM
+pm5SVbahbtzDkg7WJJrcZde7XydYsSGfPLkk6yTV5xn7pqPqXtMP3ASbiFn38btnLNeuO60p4Ubf
+LTLxxNLrq++/lxNa9/mxkqdJnDLYcTFI7PmU4i1cPV5RalErRJYy7hVUQ9HD7R1gSvVGmDdYClab
+mil9UXeNlCXs+7CnRcpfZjw0btF4517L9x46qC0Hhn1du0AiJB8fT+gpSRo3CzJhOXr7Q4ySn2Hc
+YDsBm7E6J/M9TKwkaHlKLgbuRd5nrTCmeXtNVjjX/tHaLTZpek5vIG5Hk3GQuLE/drzDa2O3WAyq
+50gfueOY3XW4imCW4A3dGEEeUOs7pkWJ6SoKRcRwLAo8bSbsNCxOhHVI3yHruB/TlH7orteCHDN1
+1zzUfWyXCbm6DJumIlOLrQnDTVK/WAsA1/OoJfZze3nGY7GH/9E7L3H1Ew54dOemYaj5sNuTOlnp
+Q7DUtbAg5fXM+DQCTl2J+9jQ3as9dehOvF3+4RdcsrMXZ9TCLGT2hEjgrLk1RHywRWEbPmxta1t7
+jz0TngIA1hCnIZXq7mW9xqiZp9frMRbzbc/PGQhVnHpqbPLXSOwZLwroe4KfRgW1Gox8PyTtKTQR
+vqGaLVkqBNnxEnIhKBfHS057Snhev0IUa6fAuW+PpsfU6moMMwgTd0CpGTIQy9IkSwlVF+5r556k
+uwz2u5qxh2fUYLUTUIgtqph39fOYXup2klVWMlIZzk4PWFW6ANZSiKVnOZf3Ew94cd+N+46/4jUR
+7HvLmtJ3u3VoLP6uhFFuuh0x6ln8vtut4iYgUtOVqWo2bHRegi/uAE+UYbZu8MRanPR2CQfuRoTH
+a6JGSde9MRfStYjeUgh2XrJ7w4UwFkujZO09c8Gnv9NBopdNwWokurELUeyZAYHnxj6enrASK1Q6
+SSk66rK6oF65Y+rtKpTEJ1fysMpDMu3Wo2FGIK8prQLqvhiu5OL7pcEXKe5sGbMnVeon05JIDV2Z
+GKtu7O1xEKjh6+qEF77D7fhpr8etF91573nDTZNeKFyIsQmeuD5MDkY0IUFj8EQwa9oEM3DnN0hc
+SJFVK0rrvKqvHt3n+NwlV5/cW+Ml7tqm3iV2/S8hzBlj8COfKM7IBBNelTW1cgCF9TqkJrby6Viv
+A9GqRaYHyh1YQE2QBATM5AslUboRoW3lTrWhT9elGyYJRqGLrWqmS924qAGFMfO+m4CLYk3ccfUl
+IbUEZ9Y+BFKAykqzubi7ciPH+z3ioQspKGZ4qnxE6Q4dsfHKdKI2Ma1i3UVT0h64Y3jR7DPXorCY
+LnjwUL0Uokg3Zk2oCsb+uEej3oEyaDiZue++Nb7CoBNXWekaRg32VJWF3Rh/ouvZGIwWWT9JMDpm
+u+yuZUkQi8o87NHUoicfhcShC10i/wqBbtrjyL3n+7tUYole2RW5Fs6siNmP3AK49EvO846ZWlWN
+6ztkkoBfVi15p1S1SKhx969t1pSFsBVSo23rhlKLAm1CopChR0It192rINBC7Il3s4guKbTgtmbF
+aN8dx2VzSatrse65a3VeTtkVZV8vMZTnXW/HGesvuge2/tcmxApBmoXa2v0+plSFKjPYS012ryLo
+CcZOCMpXJDtuvOdeus7vDdR1+6UBZ6cOO1OGPlRuPviVwWrzeb+2DR+2trWtvce+VSn6vwL8UaAE
+3gT+A2vtpX73eeBP4Vr//5y19qffxxh4YYjnW2rP7cYtDW3pVsTKFiRD5zKNEkOQyB2SV+Fbb7M7
+Nh7YTGvdCvYkzlGVBmJJiQk3MAhrAgmBnNuK9bnbHdIkpBKZxuV0Suy51XokFNyyqSm1QtteQKKk
+VdiEDDReNBiyK8VnP1GmPooJOxGZnkdjO6VlA8suHJFke9sgti+CRctetxuPQ84q50G8u7LsDdx3
+1HKHV6scW7r3spXdaGR4xrB319X6Dw9Tbt50hfF4R7oQUUpoFJbEFpOoQpPu4Ie63kGKER+EVf3f
+Dz2s3HYvTQjanj4a044E7BjOaY7c+V0due+9aAoK7YhfvXfBZO6Oef6ZS+ZvqrFpN2Q4Uffnsbtn
+s1VLEaihzTT4cvVMaoiFubBmhS+5uAs1tiU2Zij6s7oNKSTas2w8RgrdqtrSdJIK6lAdGEuujGKS
+ZVxKKXtvYIlUqdgdO+/h7s27DKS5sf/cARy6MfYn15jcUfJ7Maf2xLztd4zRS0p5xV4c40lV3A/W
+GPFamCRzJMFAJLi+TSfc1bHPpgVHD1wi+bxZ0qxEvnJUEn+wPOO3LEX/M8DnrbW1MeYvA58H/lNj
+zMvAjwCvANeAnzXGvGg3fZy/uRljCD0f33rfINMIUkwq1uUwIROWPU17BJFIOEREF/RDjB7uAJ9Q
+F7vNPOq2c+2XUChfIdRJbQpylbEi2+InXWwZUoiL72K1Zr+riKjFtjQ7eJ4IS9Y1pSpdpC19VTCG
+iUdPQJhE+RDPyzdKT21TQy22pCAQHyG0HWuxKWk7evq2pFH8GfqGiVzDPAvx5K5XejDni5LlhYtr
+I5pN/iTwYrylG7vXGxIpzNnBAWGiaEDUugnW4KH1jDiNMRLD8X2LVQ+CJ2CZtXZDuIxt8cIurm/x
+dC1C25IKkn44dA/r6Tzl56cuR7M6mWJy5TBWu7wu2forszNGu65r9IH4I/tPKuyuG3sVwFylzKCe
+EUnh6sAfUvW1uKp8S9tsJAPqfkjbumuRFzl1qdxUCKHWxVow4UGQ0KiScTnzuLxwx5w0Bi913xck
+YsFerwkUBq2Nz57KzOneLrUIfiqzxJu6Y0p6DryUVys8LUJeG5GowxGTgXItNCs8KVVZsUuHTUSm
+J/jjLz7Hg3uu0nZSTXlNYjBFOyf8gFKv35IUvbX2/7bWdrzRv4TTjAQnRf93rbWFtfZtnFLU7/lA
+R7S1rW3tI7VvR6LxTwJ/T6+v4xaJzjop+n+hWdvStiXgb5iRA9uQCiba2xuzK0hz4ifEoXrlA+kL
+BuFGbMPPog0k2uwGrE7dqlrQ4ml38LW15as11bTjOGxJxCdQeR6RmqeiJOU4d57FIHe7dT9MeGTd
+9y5b6LbVOzczYnVwxn6Mr+yo6bLi05JGDTXVbAWifDPLAq/b2eZKTkUtrN2OEiY+oWTPvbaiU2JO
+BoYd1f1ntXNbB7ElENR6afMNF3IYW0Zikj4YDUiVmF3p7yNrsGoUC/tDaBUepAm+ztWEyabTzojO
+rL2oQNwFdl3DpHtd0CiM8eqWMHV7SOg5VzsIH5KqgWdpW5ZrJfsOcsbCCqxsSi5wUk9e3nzXsjhx
+x/P4ZMqpwo4bBzEjQaWfGPDU/NRK4n3pVaSeMvl1S6vdlrHPQMIx4WAfv1KoMHS/94oWFWJYlIa5
+aPPOZjmB1fwcu+uaBnv4Cmnvzy+4VCzy3OWM0cTtm0HQslb37MqKCIaayuv4HwyIerBdrp23ANiy
+hbTbh5U4NDmBNCTiwZLnxw6/8MbZEZcDqXGP+lTdub5P+x0tCsaYH8P11v34t/C3fxr40wBB4NNg
+qGxC1tW//IA4U0myMkS6oQkeoSiuA4GYQpN0oDriJCJQXsJ6fOPB80dIro/pQheptGQ9ueJF4pSR
+gNxbsJLrZ9oVnm7eUtnibDikf+Ym9/JyjlTZGfs7XBu4NTCiJFEoEHVxbxRQlCqLVTmxXMO2MTRd
+q57G8kxA1Hcuft1WeJGw/KVHuBFdDfH6YvSZumsx8kKyXedmNrnB1zEkyYB9kZX2GrMhixqoJGut
+T6C25yTMiNUC7AU+vtxrLwtAmWzbuaRxhqdJjFdtOvKMb/Ek1NJGKajPwyrsIggZiXiXMGahfIZ/
+EZKe6z5kAY+tWE1899nTRxVnEtM9mV5C3lHxN1ijvAo5g1aLjNeJz1gqoSrjyJJqIfBsDxtJzt0s
+KDR3Es+Vb8vh2wT6deLV7F9zQLY6uiRIRRmvVud2rySXA91vCu4EDsmaZYcc3nA5HC8JsWqiSRL3
+f9D6mxKoZxJ8lc9Mz8eIat9LPFAey2jRdwuYwq7LJYhY+BNXr1NJXChqjzmd6ppzn/dj3/KiYIz5
+E7gE5B+y3+i/ft9S9Nbavwn8TYAkjj/6/u2tbW1rwLe4KBhjfgD4C8D3WqsUurOfBP62Meav4hKN
+HwN+5bf7PoulxWJsvtkRQrOkzN0uvvZrSiUVc1OQCiziKZvshZBkbmUPonojGeabjHCotMniCUb8
+iTZUvbppKLWzFT0fc9mx8zak+u7HM8Px2n1fR8N+7e6AL7/WIXp8dndcMixoY6ra7RS9UYDfeQKC
+YON5eIIrh+loI0uPB6263UxXdy4LWiWy2iLahFWtHzDTblQvS3wlK1OJkKyrBcuVqg8NREp29gYe
+veuuZ8TrB1TyyGZSZ96pI6ywF3bUw/bkzoaTDZ2XtQtoO8CIXFjf0ojz0kQRRvwOddnQdv0o8Rhy
+h0nokrmT/Rv0b4hF+a236SlJWKUFU2ErXn9wzrXG3ev+wHkuT8qKxzO38wVVRaYd/3xRYaUVmtbw
+ZO48Bb/LmoUNciyZ5gZ6+l7jKloA0/mU4a67jiHCWLQxiUK7q0FKICBQO5+RStSlkwqcJkP6hQhw
+vOvY590xP/exV/Bwr8NrLb60PjPhV7zyOWphOkyRY6zCHBvTVtrlwx08VXasvErrNZv+izDscf05
+lwhv9jLmvuvtOJ6tWXff8T7tW5Wi/zwusPkZSZ3/krX2z1hrv2I/fnzDAAAgAElEQVSM+V+Ar+LC
+ij/721Uetra1rT1b9q1K0f+tf8Hn/xLwlz7YYRiMNQQmwAqWXFQWzyh56EVcXqgJ6MBSrVRuWbhd
+sumv8BTBRH4MagbxUm8Dx43KwSa5VPldt2RCVUg8dJ1vNsHAjwlXbuWuiopGO69yQfTqPYaRmlmi
+grsHbheIRxlL7YhRukukelHb7aqtxYqHIPVSyLt4d7ZJVjba+euqpMhVsisjqlIUc9OKuWrvjXU8
+AQCxqOJCsybWcQ7DHknkdpc7o5tcm7jx8lVJrrJtps66RQS5ErHVw4Z07t4/vN6SCdoc5hGk7piM
+r3xHkVOvXNDd+AWedva6rWjyrvR7gsnFqBW53WxSFGQiUk2DmN2+ciLxAfe6PEJxTql8xqPKjduW
+Bb7uTepH7O64Xbc/3qVRQjQMCzpQoKnFEGUMnhJ0RVHSKrGZN3MqibbEaUhfeaPgmhiYqggjKHnP
+zrmQuO84GdLbFzGvFKMzf0q5che/2rP4u3cBuPAC2sYV8KavrjZQ8Jtjl5/YHd7CLJRzqRYgSr58
+XbCeur+rdh4yGjqmbKOEeDOtqCpHR5efGiLPHfNuW3BFUod7ewlDq6aw92nPBszZttRlTl17eALh
+eNaQCQ8+iKAVbdjDh2tuX3fu+nTa1WsbjBV8drhLLEVlW8Nq7W7ASX7GvXvuAp8o0TgrFhA41y/w
+1jRTN7lPZktqZdlDP+wwI8wEGrr7/BX+ny+734/rHW7fdMfTiywruc/BumR64nDpraCz/qokGSvb
+HzRkvksG1cUSP3CTd7YSMcn5JY/EtFxisMoMRsGE/cxdl9gz2LILhdT+a6NN+/V6XnJr353f7edH
+7MYuOz3uL6F0D8WZiFz85oj1sbQY05jeSKwfxOyIHXu4ext/2VV51NV5+ZiFFqniuMXfcw/I4vIR
+/o6rm5tFSyMIshXV2qKesxYJy0EWcnBNrvRhwMFt55a/tX68SaqmgbtW88BuKMtnocfs0j2Enz6A
+m5nL8PvBBfW5qg5nUtW2K3JVQy7zgkKVin4d0FuqVftgwnjfPah+5M6jKI+oVlLMntlNR2hscixa
+OFSdSHu3aP17AEwOdjncceFaL5rw6jsOgjx9+A7T3J3rD/6AC3n74QlxKH7JyzWtL63Mo9f52j1H
+9nN+lrH7SYHhpup27e8ywc2FKIVcc71pDWvNncM05eKwzwexLcx5a1vb2nvsmfAULAZrfLxwwNWB
+6vUm3KC5gsZs9BprP+VMBBLD1q2AQdBDhLzspAWVylFVFTO1zp2/vH9ErSRmp0Ts0bJeuc8mRUg9
+FAFpkVPOBB+er+kFHXLSrcp72YCXrrhd0JuVeJ1idFIyO3XJs4sTaESAUZ26UlCxrsmO3DHfuDHg
+UA1diR9uBEmCQrvS8SlTaQe2QUA61s7lW5pWZdbQ0qjkGFYdR0ROoqRdmxkODkQQsneXHfFIRDMo
+lMzL1NRDNMaXV1GlOZ5KfbOzGYGufZKdEnXUXuqAbFY5SwmynE5LVkoCzk1DefwOAAfBgNFQEnmx
+kqt5yk0xdJurVzj4mNtVk/OQsfQ/bx9cI8+c9+YJPl7PlnTaJhcX51C76/3TFw9IFf/duXuDG+J9
+mIj3oZ9NqJfu2CrvhAfvujl0/2jJUkTAe/Mpi7FDU9644tCGjYFE1ztuViTinAhGKUizM4/dPY2H
+ow060qZzzs7cjv5rbcnFA1eEi4ILnnjuurz1NTcPD54v8SQJ58clNpQIUnJI0LrjOZ6/zZNfUVNV
+r+N/OOK2GuGuDvpYhcI9Jgykc2nWI3bEDfF+7ZlYFMDStJZlMWcWOjdxkLYMOkBHP+HJsXONzs9m
+3HvsMqsTkVR878uvsHNHYp6XNZm4EevlKef3HBnK2RLWV5xL/Ohtd6Fff+NdYnXvjQd9YoUuxqup
+BHppsoiLE7mMoti+zFc8J81Imx5vehgWJw3zxk3es9MLfEGaz+buQchtwF215ta5ZSFx2HA/I45z
+HbMWo2hAM3Gfvf/whPWRW9zu7A1oBYO92h9yVQpRQSLsQp1smJGTsmT3Soc9qKgEeSa1lGoXrlRl
+uHx8zFQdie8eH2F9d62+5xMDRtfcJLyYXjDypVTVU0x+mTNduoXliRny6/ec+3zy9rtcVex7f3eX
+T153f3dTsORlm9IbuzH2PLi5L9bl9SOeLN25DP2MG7F7cM6k3hVGZ1RakK5kGXu3XJv1W6dv8vBV
+t/gm2RHxngslbr/o+j0+/vw+5ydfB+DLP/uIR2cqmoWGVP0mvj9hKVHfVeQe0htJhK8cv9dvMcpd
++WXFShWhntrlzcHz+GKzvnj3iK/sunl2+X/8Bg/mbuyT+xcksVi9PuXCzuvxbW6+4I53NH4JI/Uu
+lhckCm/pRfyTLzpW8VxakqxKvrrnnpfPvvASL7vokKpYMFcO6mJZEIQd6On92TZ82NrWtvYeeyY8
+BYMh8AI867O40E5ZNDTCNM2LJadzNSsx4Kqy6Gkm6POOYS1kXpTVBOfONWzigFMVcr2kx0TJnPOx
+W31HOzN2Uof+q/MFidrJ/NbHKh7JZwvW4tk3aq6q1zmT6x0arcfygXPronBFqo7IySCjLVShmIjj
+0E841LFXbcRSycPdVQEijCmEZtsZR9wQTHZe+3hnbgyb9igF7a3CNcjVtNIVCIoVidz9XtQnnLrj
+SWqfXCIiVVkRaDwk+lKUBVkgjsa+hz9y3xuOKy5nbqfJMp8It0v5vmrifk2iYz5IEz79/McB+OIq
+YH/c1dUvGIq0pj9y72XxHveOf8393uSEp3LF9wZ4tas0XM8KdgZqkFPUMvcjzk+FTZidEikBfdXf
+o+m58/NtxP4154VcEcPzeHILLt25vvj8Je8KFm8Dy0ghqwl8FtLluKcQtHfTp1BY4s8v6eva9oOI
+YOjmwHjHVQVYnbBYddiTnOC+m2fJYMqocOGRvX4Fo3m294rzzPKqIgrdPPRgwxMZRAHpxN2H522C
+/4nbAJxeOKTkaLdHKz3Vj4/2GEXu2PPhmLOHTrTmYnXO+PCDPebPxKJgcTp3gW2xuZvwZV3RE3vM
+njngk59VTL00GKFQbgTuhu8MLPXKhQmry4JM/QcNNVy6m9Tr94hq990v77qbeSu4xVrdhzs25UJl
+uJOmIFQHXxwn1ALCrBRnm+IxQ/HoXbndYx64eHGxDtmRG3/QH3J+7lzJRN15h6PehoXJUtAIZ481
+GxKZgVphwzQhFplKENwgGmlhGbfsinK89SCz3ecFGlpA2BeMe7VibyxOSGvxKoVHdU6qCR2vVOG5
+Nd7MhrvBp+gLztwfhuRz97Atz9f0lO0OtVA0taEnHkTbhvgKD8y1HVCr+e3djzPcVyv2VG2/zEmV
+4xkMQ4rQLeSnmA2EujcZMZZgazNyB1cWE9IX3fl/5WFM/VD083sJ/TvuYZlc3eMVkfv2+1KI8huq
+A+df36me47tn7tp+7eKU4Q0RwZIQhYI3x+48nhz32d1xx7ye+0wiN4dmdsnqyI3d3HXvvXhlwNtP
+1IsQjfjM827hHOzscv9VV0VYnZ7Rkz7kdx240GdvN8Wq7E0/AeVUwmjARGxYzTzAP3TPw20R4Iwm
+I/rKKQ0P96nXbr7NTy9pj9y8Xq3PKM/dtX+/tg0ftra1rb3HnglPAZy3UMcJPWEMQusx3HO70ksf
+3+W6atDRtYpMcNxQiSwvjCmEWSinM0zX4F9HVPE7APSGOaPEuZSNdvM9HzJRjWH7nB6675u9+Sa1
+yEkuZjm1pMNDVRl8WzO6K6Zmc0hrXBIw8GpGcve8lU+cqQFHNG6DNMWqAtDUAyI1XYU9n0hJq2jg
+xh0kKZPWjTEZjWkUwuwmAZ520rypWErKzoiRJU0aIrnzdezTVxekH0dE6pmMooixEpTxrtslrTci
+UMjUH/Y2naRtlrAUOU1QP8ZKV9JUSmx6GT15HcNezFogpYPkgFRYhihs8TO3o5Vd31ddMYmUdKz2
+aOXCh6cnmFY4k/QaU5HIpGt3TuPJIcObLvk49R/w8E23A89XJ0wm7v4e3rzG5EpHmCPpvqJgULtr
+dev6dU5K57pfvtbS5u5aJCHMBJseDl0LT1gVnAnzEK7XmFBhU9riaUefSXimne5xK3H3f+9Fj8++
+eKB7tsdnFMYEsYeJhDnpq+lseYkY3rF4GDV2Bf2A0Y7zfsIoZ3jpvrvudEeHEUEjXMwwo9L9bXYs
+A1Uo7l2mrIuNas37smdkUTBYPwBb0fjuZu6m/oagdZJFXDlUDLyO8DMhFqWUFHpgxalnE59A+nrV
+dEFQd0xGJcmO+7u46fjw+giYSF2XNNJu3LUBC7E+NUGLp4A2VDXk4vSCwLhuyP6n+ixn7mYt51/f
+3KQwagl6At4IoRan4w0oirgiiNSmOx5hLqR0VImwI4LeVef2hdMSRFDqBz6luBv9lSXQA9TxOZpe
+gNjpqaZTitpN4ovFlFQ5lWToYVdSeFKps9ePidXVGKWACD2qyuOycFn0witJxARUqsfDFJZA7dJR
+v0c6TnU8KUbxvskbGonshuoCzVnRjt11S9ZHrN51ZC/eYUJduO/LZ3PGQkBeSoehrlqihXvvrm2Y
+Kyey9AZoneP53oBdzy0QtapI7TrftOKX0xk7rfIgg5T1pAvj9mmG0gHRfaoo8ETqY8KES1Gu1w/W
+lGLq6j677htuZh8D4DMjuLLrFoWDySFG4Ky6sbR1F0Ke6JwCTKAWaWtpzjpobYRp3ZxMBxGBQGue
+SvVhL8UTV6b1S6wQonEDsSoqD5YLsg9WfNiGD1vb2tbea8+IpwCmtcReSE9YAd8EIBozs4RWqtIx
+JZ6QvZ5k35umpRbEs8prBjviZfQzOtK9elXg4VboOOhYdD1Qosa0M0ytVbc0VHLX66Iikgvacfk1
+jJmIf68qLL4IQAZEBAoJAusTChfgdxyOQUkrWHK9KGnF9ZBUBrF+4wsoZfycSLRraWQxSigW1hBK
+FapsZli5rudKgiarmkI0brZtaANdz6KiFDHAetljILo5b+g8jcT3iTOFGomPVVI2ryouT13SdXpe
+E7XutVVPRWgK6LyGyuB7XQdf1FFDgDnf3L+6cckwm0ek1iUwc7+hUD/KIN6nti5pbItTmo5CTbyE
+66bEero3cYsRH+e6ntFUEkA5OWJ0VZyWCju8Qb35u6CZshTl23x9jP/EeX292wHNvOvfc+c/vTyj
+US9Gc3xCIm6MyE+wcad67vAGYyCVu5JMehvlZ79p8RSOmfKcsnYVnOKBO39qj6Zy3q0dTiF3XpPx
+A/ywY5eGuKPMUcjrLRqM1L+pYqz6btZPZpyv3Xc0/gp//9tMx7a1rW3tXy17JjwFgyXGQgs98eYP
+bcFB5uJa09QY7YTVckWoWreptKpHNSwUUwch1Uw4hknLRPG8DWM8NQH5mdCIbUlTuffqywW5vIM8
+L0mURxgMQy5UknykLsuDnZhsqe68GwWeUIOe3d2QmHpJgJmqpCjWKDutUehINa/xr7jzC4qM8IZI
+Zb/g4sxmkFGfuZ3ExtCKiLMKY9oLt+PX1YpCqstdI2ZTRazpGrsW7N0VRNfGGCU+I1NilUxpBPNm
+UmHXHTVdj+rYfUfuXRAu3GeuHk4Yi9cgVmm1eXKJEUaiWVzgq3vSrlYgtGR7mdN2nakCjuS9OU3b
+Jd9KHr3jdu7bL1RM9sVp4DfUi47IV/kVU2FL973F+TmRTrzvWxbK0SzyFVPR8HXyacNxjK+8TB34
+VOdqlDu6ZH/Pvd9vniNSrX89dXOoqD2WVYemTEDnWtU1gboSw8pdq3h8QOw5LyBuM2o1WrW9S5oL
+MVyFIavXnLdUHjqvKzzpkx4I8l3UWKmq25NzarGBNXmO19dnzuTR9g1W856kYLXStTWG+xojXy15
+bfbB9v5nYlHAOLYuL4jxpFvotSkrxQmlV1AvRV5R1VQz9zpIlbDJI9bCfeN5tKqPFxf7tJGrf/t+
+TWndzV/LNfRWJa1gzrmNWIkTsWhKIomFlKslS+EXekpg3vr48wzkPtPGBIK+hik0TfeA1Hihe13L
+xWuThKoVTXxVURy7B6GOTuEtN+kfye3rffURgRiQbdujJ8CLbTy8unPRfQJBnsOFwoj5mrk6Ji9t
+yw3RoccrSCWaW/srjFqmO/e0yWsadaU2qxOK0H1HNS3opRKzuTUmGx3oOPRwtCGhaN4IasqmE1yp
+MDMl+ZqKTuPUDtSrUfdo1EZ+cR4yCwX9XRjiLvzJMk7UMVhP3bW4PG/oCdq7GPdgrnBkdcH0sTum
+h37JxVe/DEAkOLctbrEv0FrbronF4znyY3wBucLlKeq0JuuS1aMAXxtAWVQMfD0yg5TmTC3Oe7oH
+pOy1rlrgDS1lK6zAwzVGYKji8j652sDvf0nVErsgfsH1SURJD5QEZTghULjS2hVVLvbvjlE792Cl
+hacpqPVcVCvLbOxO5KtvFNDfaklubWtb+x3YM+EpWAtNC0VdItVvJlGIUWlqsSyZp251TG1JrVCi
+0eFHBmqxE1cmwnRc+emM6Kpc0dqnUS2/LjtykwIrvbZZ1XIylddAi9UuvV7klEpWJhpvcWFZDaSe
+vBsSTdwunz+6T2ndCh1an2jtdsKVGGPLtMUXGvEyn7J6VxRcWUKdu7DBF1SX3BKp023Q5FxIF6Bc
+tVhJn1fLhkw17yB1nz1dVxyfuJ1hXZWMFWI9thFXlKx7PujTiCTCqsxaNiV1h3mIGpbylIIwohaP
+xIPX3qB3zb0eLIQ0TBZUF87riA/GtBJZsV6LJ5m2Ngypl13pVJJ3rDip3XU5m1esF+7aH82OaER/
+155E7OxIdVreUeF7zNxloy4esZCH9GBeEErzc//4nHYguTixUp9Na/qt21VXizPmCp+8QY94LGKU
+0GCkBF5IG4R6TuyL4CczGP1d1jZUHTWb4PiL58fUMzff2mpBYZxHcHJ5SvjkdQDOT6acp+4zVyaa
+YxePefzr4nS4cYehqAWNLTc8Iq2fUZ65+9PKgzZrNqzbdtawEkHM8uSY5ZF7fblYbMhs3q89E4sC
+xoAX0EtiQp1kXfhMe+IRrCoKMTKZekmtdl9Pmfwy82h6kpzPV9SN2luTAb2ewop6yUoTtjKKo9uc
+RoxH08sZc2XOay/YiIicztbUcuHCrJMv32HZuthxfmJQxzV1U1N1rE82Iq9F9KEqhF0Z/MidXxoM
+8ff1h17M8KoAK1IHIjY0QvrMm4qFFphZ4FEfS1kq9fHEoDSXgOt69Y3ciBfAVG65v1xTnkpm/daI
+fqIqiZiBvdCwXovdaNGwit0EjOIhVYc9OGlZyl0PBJAKmphS9fNqNcNIAKZtfQKFI/XydJMxX+la
+5muf07kLlZb+ikSQ56LtM7t0sPFleUSFc8dnqkRlVU0pzMOjxwueVG6FKKoC63WcnTvUWnw7la7Q
+1FjhDYrJVQZnrsIRZjOGHXO1l1Dl6g9ZKwzMmw0Dd4DHjo4/SxPi3+1AVIevfBaAG+uUVOCzRVtQ
+qWqxE/msVX25dznbcIXe3ndVC7N/QKm8TZHnMFR4WJU0hXRPm/WG2b1S122NhxUBTuCPyEs3Xv32
+lLdThc2pwZfW5fu1bfiwta1t7T32THgKBse6u2oaksKtmFmYEMntKb2ARcdgawyFOtHK0u12yU5F
+kCu763k04t8b7DfUQp3NL1bkWuUbVQAGYY2nHWWRl+Sqzbd1wImy+mXe0qgS0YjWarxjqMSIZa/5
+RHMlzzKzUSuuCzbJtVKoMy9oCZW1T9qI/q68g2XFYMeNkeuPjl5/hydyB08jyxUpLVeDjLjTyvRq
+LtQdeiT3+7JMOfXUXNPU1CeO3yBfZbz0gkhUzAXxjtsdkw21m2V1Il3N+YJGnIHB4Qhfia3gcIe9
+HZdo3HBWlDXrufNMVqeWQh6IX50TNqoerYFMYYOQmyf2ErvssAs5R9JgzCpA8HVrM4x4OHsDd61q
+P9togUZhjiIbvDgmEatyYCCV8E1/5NCPveeeI5Nq8zphE66YrGVH4UhyM2ZauuTfPFAyuooQVw4m
+CbHCfcxtg3cpDcrrzhsz4Zz8XHOsl9L3HZdDO7tPu+eO4/zX7vHu+ZsAlCfO62qnJywfufN84cU7
+RL9fnA1+Ti49D0wOYhivBNFuW4OvxHXdgxO9ng5OWH/R/ZlvDWXdhQ/fTLz+W9uzsSh4hjCL8Lxg
+o2HYxiHHekivlisWyvoOJiNio9jQk+sYJezvu8mapkOsgDmV8fCVZU8P93n8yD0gkYRHkmgIgsEO
+2gEoU/+ofYe14l2T1MSZ++7AewWALAvw1dI7PzckEiBMdw6pO1LZ/BzT0zF5blY9ni0InIfO3nCf
+yaort4UsRfEeN3pY7445/oK7s+++9Yg7n/suN/awIhSIZd2uKJWRTtYdyGdKIGr4qhlsekJW7Yp5
+B8dOeoQDQYwVs7eLlrDvJmmWVpzM3I149OBtrFS0Uq9iuSctxfu6PguP4Io79mVZkQn7n4Q7RBIn
+qf2AQr0kHUgrLkd4gau+5FGfJ3J9P7EKv0EjH/bIB4KNZ+6hivKc7KbrSzBhn9p7F4Dj05Lx0D3Q
+yeGYOulUmOTOr1seRSLO6V1jlbnwr5y1eBMHXrpx83mixK32uVr4Hy1XGOVrUj/Aizt1ppZYZcST
+r7u/We7cpJTCV3L9mP2+QqbmDsG+21BG3zUnfNN9/hfvu+P53OQm1z+rlvLrGaePJERUnlMKALWy
+l/T6hzoOdwzZIKCtlNfAY91z+ZWL7GXsd7iekOT1AafvatK9T9uGD1vb2tbeY8+Ep2BbQ7M0eD1D
+oiYZ3x9xduF29ifeksl1t9LOB5bdgds1dkXNbfyApN8lF4dEiftska9YScdxcfyIUPXfRICmpA+F
+JL+KtE+zkhy4N+ZMTUAH6SsU+y4bnP4udaxdu0KnFffKZ26xelPsu6dLkBBNW29UvMjUvbe2GcvG
+rcO99CoTycH3hxN8udf51O0YZ4/eYjd237v3iYRDAZ2wJblEZMxZRaNuxa4WPaySTU289eGJOioX
+5xF3v+M7AJhECQtBYoeBu96tvcCIqjxdxQyVnPK9llLXcGcyJind8cVKjJbnOb66KHdSGO/3NHa9
+qewU65rm0iUVazWjLWyONS5RV0wfk8r7CVJLPXfntJv06Heex568kfUQlFwdXn1uQ7s/iGGkKkIQ
+XKGZigVZcnUH3jELNdDNjk4J++56fvxKn9ELzs2PxiNWAhxxrm7V7AqeKg6TMKPMBbcuS1Y/74hM
+PvWy01B+vQjYf0FQ8Ydromvu9SdzS7nn7s+t5vvxJ+7Yok87T/dg7xNcm7hr7+VLfPFOzi5vYqVd
+OSobAt3L2v8GFkSvaBc5+bGbp0ere/ye7PsB+JnZz5D0nKdQvr/o4dlYFAzgW4+2ajcZ92VzyVLg
+l7fsQ8bK/Cd+Re+6m0xhJyO/9lgcOXfQq2YEIgltqJlKR698eMq6r0496QTmXstS4I+1zVn67uY/
+qp+QafIWt2M8PRTB2yIkqft84tA9FKk/IL+u8OCXLrDLL7nvq30GyqiPrjr3dJT5RMLWt8UT8ndV
+shusqFRyvP+qK109OPoKbd89KDdvv4DfumsxaxvyS7dgLYuAqfgYrR6qpBfgiXrcjzLCIzf5T8Oz
+DZHsbFJQPnIPaSg9gthPqdUl2pYenhEi0A/pa3Ubxpawc+c7UFS2wpcLawZ2k1OxbUNedLmfc9Zi
+rcpDITdnKy4CdzyL/BG6xGRmj9q4h8J4Lf7YXedELeeZH1JJH/K89UjV9mwnDZE6JiNvRthzD9me
+xu0PQ1ZCHl5JLfWBCHrSHtZzC1m5suQdR2b3tHkhtRaW4/WMSrmIxvdom1cB+OovOv7FK69kPDl1
+9+nKW/mm0/ZwN92omR0EazzNT+9IgLw3Timuuu9o1pZgx13vsKzx1JVK6GH1uHaVsXzZ0ARq3y7g
+zSM3d75+f8G7PacHcVk8JF9sex+2trWt/Q7MfEMb9iM8CGNOgCVw+hEdwt527O3Y/wqMfctau//b
+feiZWBQAjDG/aq39zu3Y27G3Y3+0tg0ftra1rb3HtovC1ra2tffYs7Qo/M3t2Nuxt2N/9PbM5BS2
+trWtPRv2LHkKW9va1p4B+8gXBWPMDxhjvmaMecMY8xef8lg3jTE/b4z5qjHmK8aYP6/3J8aYnzHG
+vK7/x0/xGHxjzBeNMT+ln+8YY35Z5//3jDEfrPn9g429Y4z5+8aY14wxrxpjvvvDOndjzH+sa/5l
+Y8zfMcYkT+vcjTH/gzHm2Bjz5W967zc9T+Psv9Yx/IYx5nNPYey/omv+G8aYf2iM2fmm331eY3/N
+GPOHfydjf7vsI10UjDE+8NeBHwReBn7UGPPyUxyyBv4Ta+3LwHcBf1bj/UXg56y1HwN+Tj8/Lfvz
+wKvf9PNfBv5La+0LwAXwp57i2H8N+L+stR8HPq3jeOrnboy5Dvw54DuttZ8EfOBHeHrn/j8BP/DP
+vfdbnecPAh/Tvz8N/I2nMPbPAJ+01n4K+DrweQDNvR8BXtHf/Dd6Jj5as9Z+ZP+A7wZ++pt+/jzw
++Q9x/P8d+H7ga8BVvXcV+NpTGu8GbkL+QeCncAjvUyD4za7Ht3nsEfA2yiN90/tP/dyB68B9YIKD
+1v8U8Ief5rkDt4Ev/3bnCfx3wI/+Zp/7do39z/3u3wZ+XK/fM9+Bnwa++2nc/w/y76MOH7rJ0tkD
+vffUzRhzG/gs8MvAFWvtY/3qCET38+23/wr4C0CniLALXFprOw2fp3n+d4AT4H9U+PLfG2N6fAjn
+bq19CPwXwLvAY2AKfIEP79zhtz7PD3sO/kng//yIxn5f9lEvCh+JGWP6wE8A/5G1dvbNv7Nuyf62
+l2SMMT8EHFtrv/Dt/u73aQHwOeBvWGs/i4OVvydUeIrnPgb+LdzCdA3o8f93sT80e1rn+duZMebH
+cCHsj3/YY38Q+6gXhYfAzW/6+Ybee2pmjAlxC8KPW2v/gZH80D4AAAHMSURBVN5+Yozr49X/x09h
+6O8BftgY8w7wd3EhxF8DdowxXbfq0zz/B8ADa+0v6+e/j1skPoxz/zeAt621J9baCvgHuOvxYZ07
+/Nbn+aHMQWPMnwB+CPhjWpQ+tLE/qH3Ui8I/BT6mLHSES7r85NMazDg56r8FvGqt/avf9KufBP64
+Xv9xXK7h22rW2s9ba29Ya2/jzvMfWWv/GPDzwL/7NMfW+EfAfWPMS3rrDwFf5UM4d1zY8F3GmEz3
+oBv7Qzl32W91nj8J/PuqQnwXMP2mMOPbYsaYH8CFjT9srf1mVoOfBH7EGBMbY+7gkp2/8u0c+1uy
+jzqpAfwRXEb2TeDHnvJYvw/nNv4G8M/074/gYvufA14HfhaYPOXj+D7gp/T6Lm4ivAH8r0D8FMf9
+DPCrOv//DSd/+KGcO/CfA68BXwb+Zxw38VM5d+Dv4HIXFc5D+lO/1Xnikr1/XfPvS7gKybd77Ddw
+uYNuzv233/T5H9PYXwN+8GnOu/f7b4to3NrWtvYe+6jDh61tbWvPmG0Xha1tbWvvse2isLWtbe09
+tl0Utra1rb3HtovC1ra2tffYdlHY2ta29h7bLgpb29rW3mPbRWFrW9vae+z/AzYs0lxZPjKLAAAA
+AElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3717... Generator Loss: 0.6639
+Epoch 1/1... Discriminator Loss: 1.3463... Generator Loss: 0.7220
+Epoch 1/1... Discriminator Loss: 1.3487... Generator Loss: 0.9054
+Epoch 1/1... Discriminator Loss: 1.3769... Generator Loss: 0.7837
+Epoch 1/1... Discriminator Loss: 1.5438... Generator Loss: 0.5937
+Epoch 1/1... Discriminator Loss: 1.1925... Generator Loss: 0.8585
+Epoch 1/1... Discriminator Loss: 1.5108... Generator Loss: 0.8107
+Epoch 1/1... Discriminator Loss: 1.3479... Generator Loss: 0.8114
+Epoch 1/1... Discriminator Loss: 1.2299... Generator Loss: 0.8803
+Epoch 1/1... Discriminator Loss: 1.2651... Generator Loss: 0.7570
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvcmvJFmW3ve7Npv57M/fEBEvxoycamBWV3WTPZFNSmyI
+kgARIECBEiBQgACuCEqQFiL0F3BFcU1AArQQQAikIGlBgCDYHNQUu7q6q6q7KofIiIw53vz8PZ9s
+NrtanOMvM5osVharih0LP5vw8Gdudic799xzvvMdY61lIxvZyEbW4vxxN2AjG9nImyUbpbCRjWzk
+NdkohY1sZCOvyUYpbGQjG3lNNkphIxvZyGuyUQob2chGXpOfm1IwxvwFY8wDY8wjY8zf/Hk9ZyMb
+2cjPVszPA6dgjHGBT4HfBF4C3wH+C2vtRz/zh21kIxv5mcrPy1L4k8Aja+1ja20J/D3gL/6cnrWR
+jWzkZyjez+m+N4AXX/j/S+BP/aiLfc+zkR+AaaFtAbAGenECgPGgrBoAygqKugagLuXf1tZfuJt5
+7d7r/xljCFz3tS+ttawtpcA1WEd0ZMdz6fS68v2wT+BLO1qkbZ9++AmOIzfxHYdG7+EYgzHyOS8b
+LPLZ0/saA1Uj92jb9V9/tKx/53kOgX7GMbjagbptr9q/vpdtLa0xV8/Li5o/Kq7rEPky9foPLubq
+eWEU4ug9lkV5Nc4NLTT6PPv5GLqujoXr4OgYe8bB6ve2hbyR+Qsd+XvSi/ETGWPH8eTeQF06OJ58
+bqzBtvVrY2HblqIo5HkNOL58X2Y5y1Umv2s+77OzHnvMVaNL+/k6q7VdMl4GT9dGux4rIPDkHn7k
+0vEDaUfgcHyylGfobwLHgPniWljfzH6+EK1dDyG1fmiBWttmrFyuF1/Ng+eYq+d4+sFxnKu/1439
+fOUbS6MdaUtLpasjLeoza+02P0Z+Xkrhx4ox5q8Bfw0g9Hx+4e59XC+jLmXCWw/+zNc+ACAYGV4c
+zwF4edLy6OwMgLNnpwCk5SWfn4KMLAAV19GX3ve5MxgAXC3WuiwpddHd6kcUUQjAL+0O+FN/7tcA
+uPuf/Xmu7/6SPMeuAPiPvvarJLEM3bVuj1mZA5BEEa4pAXjw6oJKF+ek25E2BPDqQu6R5jXVWgG+
+doTTCTcu444oo52thP2uLEYTh/SRz2erjLqsACiQxV0XDbkuYsdz+fjx8b82LoNuxLv7sjb2x/Jd
+B5dJT9p5/+27dMIYgH/x8AnTVzLeS1vCXOanKPXFrWr6PRm37XFMp98DYCvoUyfSjjqzfDqTft/W
+Z/zCb3zAzW/9qoxbuMOFkfk9fZEQT+QZq9KhzOXZ27HcN1tkPHn8KQA3VhDuyPev/vATfvv3/wCA
+2fSMVl+cfkf64RHQ1jJWr/IVjSqQ89USx0pffN9jpEqmUOXdd+DWWO6x89Uhv7J9C4DyXsL//Hf+
+JQBhKNfejX1qT+ayF/lXirxZ1TiBjlfdsCylbSdLWR9Z23BRyPy5Lax0AzRYOqq1x5FD4olCnSQy
+/1E/ouv7cq9ZicwCOG7L5Vi+Xz3POXBlTX7307NnfAn5eSmFV8DNL/x/X7+7Emvt3wX+LkAviSxh
+gRtCGMvgebsxd74qzbv5zXfofFc61s0LHv/WYwDqgwu5V+Nd7QLGC7FqSfi+EZMDGI8i3n1XXjKv
+lfs+PlowXcjELcgxhVz7j2Y51fw5AN+cnDI/k+fMGQPgBC2TXRn0TlJzdqqdijICffFC32J1Vxzv
+yLXX44CDpfTDayxVuTZZGj7f63WX9x0aR9o56gckRj6HsU8U6MttDVUkv2p1Ya/ilvxUlFQe+zi6
+kOrGEgTSjiDyuHZT+n3nmrxU3nnFPJLFv/Qy5pX0+aMPDzi4nAEQR7ClL0Cu21nWgG7cdMqW2Yn0
+76hT41hpc9x6TPMFAM9See7qyaf85V+R9rx6/jXmRpaLyc4ZjWSeOtMVx5kq1FC/qxacWNmhPzyu
+ye25jMvqIXl5KMPpGMqO9Dvoy1owdcMqU4untvRlKlk0sMqlLwU1sW4YmSrsF4XhLJXn/eWxy8cf
+iQE8Xd4mSuSaLZ2DaGQ5L3XOewYf+dxUDVMjr2wnrhlO5Przx6KkqsohCddroaZWg7ZqXWygimDo
+UdfSzqkajZEtqSO5x3RWcVjItde6NR+8J/f75x9nHFQ/md/w56UUvgO8bYy5iyiDvwL8lz/q4qa1
+zLOa8ZaP7w4BGO/3+PrX7wKwffsXqNqnAAw/+5QHurutdFe+CA0D1ZPNyMdbyeL2r1vezmTR33mv
+xy998BUA0hNZ8Gbximn5EoBOr8/5XHaPxauK7/6+vOlR+ovMYrlf2l7Kv0WLk0gbLh0Pz5OJ8cip
+jCz0NoCwlUnaviNm8q06pvtKdszQtDSuzG6V5rSuLDBXf5MMI7526xoA+6MAdGcuTQGu7BSO42DU
+avD1d93tkoNzeUurvMTowoycks6eLI5f2B5wcyTjdWNXnvHD8085fC7tuSzn1NMUgJdnU2qrJnYI
+ncFInqMLLcsd8lieN5xs8eSJjG09zylWcr8//9U7rGq5R/pQxvjbv3fKX/4LXwXgrGmpnYfy7GWH
+u/mOzEk/YhBIO7xI+t9p+vzBQr47+vQB82fy7DudmNiRt217q8e8L+O8O5L2nj6+pC7kdy05YSJK
+ZuA3rFJRoosqZ3csSsis1CKaNxzpkaBoh1zqG7OYn6D6nes7ehTrh+w1Mq6jScN2JGvvB9k55Uwu
+jjoGd30sjmVMfLfFJLKGhm6HrNCjTeYSbsk1e7d3aM6knU+P5V4v5invBHKvy3TF+UKtEdfhz26/
+JeM9fkp1ImPxZeXnohSstbUx5q8D/wg5lv2v1toPfx7P2shGNvKzlZ+bT8Fa+w+Bf/hlrjUOhKEl
+IcAGog3v3r1N6onmr6cejmruk6c59vgpADcj2YGHtqHrioYfdgy3xmJtdK+57F27IZ87De/Fcs2T
+XTEjf+Mb75P8vpi1YXKfH6QSMa3HCeRiMj/6w6f0fkV2rq3qPgCR79NkMnSZm1NUormPZ5ZJIPfu
+uR69gVgYi7lYFQ+zjHol19rGIdGjguf3aUPZKVo9h75/Y8BvbouN23Zjji/ETH42KxnW6ncpodLj
+UajOuevLigM9Sp1XlkCdsEPf5x3dHb9+Z0RuxaJx1CJo2w4jNYNXZxl+LhbGvUlCqM61Nvb44N3r
+0r9E+raqXRaFmNdjOkyn0o+DpaWv5m5bR2z3tgA4uy2+g2pxyO/8s+8BsP/LNxkXd2Tse0uaTJ2c
+ccG1roxBfSztzKqAvlpjg0mCOZ9KO0fvcu/XZa65fg37Uiy9Uo9wsXdMcinj1lsc0wlk7cTJFHsg
+a2C0nDN2+wAkA9ldL9KcxsqcffJ7NbfuSJ/CWc1WLO3ounqU9GE4lkE8P7eUkawR0/HptOqrcCva
+XK6/fUPmIyogV4fhzvaQwpc5c2uftJU2j6Ntljdl7K476mg/rjnP1L8wjEnU2qQpefwdaX9/4tE/
+k3bICvrx8sfmaPyi2Bby3LLKlozV01/OZxy3YtrPHy35rQ/lhf3odz/h1bm8sIlRR53jMx7KQJvA
+Z9xVp40NidS7bJYttZqUzStZYE43Yfsr6vqYW07OxNxzlysSPaIM7QO2p2Lm1lsyQaZxydWJWFxW
+VKoUMiy+lcmPogCjzq7Zubwozw+n5Lm0pzuI6CZi2n5zYHjRSL/vbEmf3tm7x+1t+a6wAU5XpmrJ
+MauFmOhZ4VAaufeqlPuelXCpTqtFXV15uksPck/6l7aGoJTFdIyM5cAPyEZqBrt73NuSl/7lWc21
+O/L9cDjklr5MhavHtTzFqkMxmkKq/pre9IBafRRbkUMWysv9fiG/e+rl7Azk2f7Jfao9WfB3k338
+QJ2mxudsJXOWpeJw7C8y6kIdsKMOe6HM3zeuO7RjcQIWcUp7ew+Ajvowno865As5/n1yErKv5+8j
+03IDeZH/YOqSlDJgp6oUu64hV2deFNVEL6T/ZneB0Rc9Gsi/Pb+Lqy9st2tAX+iocQldue+Jjbk3
+6OpcSj9C11Cov+et8YDCV2dz1nCSSf+vmZZn6v/anci/QTKilWHhedayZeRodoaDoz6MyTRm2pWx
+5YQvJRuY80Y2spHX5I2wFAAwLX7sMBKFSfPsgs8GuwCsvv+Qb//uUwDOLo6hFU0aqfm2FXgk6i3u
++pZQPflVWXCkYcsm9lkVsqs6+vfiJGW6lM/z1RFxK1r53F9RzCRk988ffcZ/fE8iEVTvAOAFDqWG
+tyJTk+kuHXqGUENSxhhiVbknjjqA/IC+RlfiIODWTXnGL4zHvN2TY1NnLhreJAHbEzFly6gLp/KM
+k3nG0wsxd0snp1qI6b7SEFviW05mskM1BjyNYMShz707sjNNcHmmpm3XlzBtbBpGI3me29mif12O
+TPexOFZ2Umc7IMykU321lC6sobYyFv6g5f6eWAcFI6xGTFYk9GKxpma3pf/VwyE/mEufvxbMWWno
+7d64ixPLLr1lWj47kkhToxbWcp7he3KvrWDM9RtixYXXRsSRfL94HlF70teqJ2O1PctIQznCfO16
+zOpCrMW9vMLvigWxu8q5bOTzOnyJ7xLo3vlktSTV0G81BV8tpGEk47OfhDy+kD6typLZQsboPC8Y
+9+WarcLhuJR29tUicJ2EkTqEfS9gpGtoGcR0KsXiELFVylxP1yHsNGbuqNXrGhq9Xzcvea5H7LNp
+zXLdly8pb4RScIwhMTGOaWgVdDHNKk4fStz5yZMjzmbS+dABL5ZrruuCT3Z9dvTYEQ88utqti9KS
+phoZqB16agZeaAxtEnhcWLG/up0Ol2N92R5HnM7ERP+D7xyw955MzP2efFcZl0zxDYsKrCqpwLW0
+GkLajgJGQ3nBe7G8bFl3QOnJs7ebLh/8CTF939q+wWKmCmtLTn5p2qVUT3Y1N3T0qBGaM1JdKE+f
+T7G6wJ4W8u8adLOWfqDgndhnvCsm9SzPiEIdgy2J8PiOR98Tn0uRBHRcWcRx4uHWMp5t7dFPpH8X
+M1mgruPQVrqg+zGNL7+7kSQ8mmloeGLpaAQnOJc5+CwFrEQ+HpoxQ0fGdifqECkmoyjPCVKZ9/NY
+xucuKWkk7bzZxkxV4W65LYdTeVl8z+fJQubyK9elDU9WAVtjOT7ZecT1jlz7+MTlHSuKcZ6nXA/l
+mqrW0PHUcGxkrMrW4TNVZN2m5Z2OHAP6+sKng5DyXMblaL4i0jBxQEAcyrVNr2KgYWLPk3ENvD7+
+SPqc9HtY9cX0sFhH/GMd1+eilLGILmXzqp2GYU/GMy9zektRWN87LXj4StrZNM2V3+nLyub4sJGN
+bOQ1eTMsBQyxa3DqgGalUYRwwac/FI2YP68INM496AdsqVa9p4CWII4Z6bnDNDE9tdvLvCSMFLoc
+x4yHsmMP115/x+Hdoe6tpXsFD76sG+xUwF9OmnP+mURTb+7fBsC1FaVY6zSlwVfAi8Fh5CmGwDT0
+EmnTB+/LMWGn+zaues5NWrF//w4AvSBgMRenW11v6b8esQKFimVBUsjucffamO9+W8boICvI1TT8
+UfCUdeQgcSMqdVR1+jXdWCI7uz1pWxJYmlYdrV5GR3EfoQnxNcqTlRfkGpuv1ZlpbQBqubhNSG8g
+O1tQJ9R92f2vT67TuS73OJrLHLzn/h5xLH3uNY+ZtPcAGCUJUUeW5WHew92VY8y1VnbJzn6P/ZEM
+fjg3tIVg4qZ5RbNGWZaGviNetcu5WFgDpyTIZPfcGlQ0F2rROV1sII64/WHEXKNDO438ri5TRmgE
+wHVZqKXnNnBjIH2JGxnj2auCbCFtuzgvGSsOZWtUMywUyerHDBQt6nfEwuiFPn6g+IagQ6qWiVc1
+uHq8czFMujJnlwtp++2tBZkCJ47alOOVzMllWVPpsratvYJef1l5I5RCC5R49FNLruG2oyag1PNS
+N7QYV5r6zqDD3pYog76CeGzHYxApIASPUF/SGEOsL2k8CLjelUmokRfTCyPaM/m8E7hcvC0v5DdN
+yu8ZWbC/nnj0FvISdjxZPK1xsZmCiaoaV8/nSTfhlsLbXOuwd1c87m/tvgfA/WGfxpPF5gUtUSjm
+fC+xLMNQ7y0LKW0aQgXpLIoGFDTk25qdoTyvfFX/2PwJX83rDjUHUzkq3draYjyU8N22Ksqm9rGV
+msmNS0cVZFN4dHtrH4WHr3kQc1VSgVNR6JEpyWpuTcQPZCKHfi2L+O2RT7n9NRmLGzIfoyhjqKbx
+ih7VWKMLowGhQtMP/SXDnlzfc2Ush5zhNBqGdReU5zJuTlpRa+Sn1ymZXsq89jT8sjAuVpVslUEP
+eTHbpKRNpE9927KtXvtKX/6Ju+J3juV3+9GQIhfFcuHN8XTe20YU83JZMJcpI3Bdxl1pQ9/3SSJF
+lpZgFJqO+lxi1xJp25Iyo1GItRdaHPVhJDbA9GWuupGMiZcPGGzJfU+KFR+u80dai706R9orVO+X
+lc3xYSMb2chr8kZYChiH1gupPctEHUpl29Bq8k3gG94aiWbf6yR0hmIVjB3ZlYsWola0cpOA0c89
+HHx1mPmuoU01c0xBHoXv0lPM+WW5YG8lw3HqtdxUeGz5pweE6mlvC31GbVnv0W7QMNBd4M7IYUc9
+53HY4+2JmOb331JTfXADWykQqAkhUoum08X3FetQy1ZTFwVuodPjZ7x8KSCd4+NDuoGMBV4Imoz1
+o8R31Cz1OtwZSD92kw6TLemfqxaYrVrmmuHYti5tqLu/Z6l0Z6vrklzBUEUqXvqVYyj0uxPPJVLg
+UZ10aTRn4JXbZ+QeAZDflyPY5PY9Is0B8J6kvCwiHdsaupqv0qkpSrGmYleeEfX2GC3kXku3xmr8
+v458uvVU21kzUMfmTOeGVXG1e/qxi6Nmue8XVOrkHcQlue6qW0azctuQ+41Yir39Bl+dyks/ZKVH
+s65aD1WV0VWsxyBs6KmjedR6WLVYXdchTtSa1OSrOjLUeqTNGw/baO5KkNA0aqV5DlUr7Wx68vuB
+AaPgrNH+de6cyD0eLXPaYr0uDJ+nBX+eEfpvk42lsJGNbOQ1eSMsBcdAEjgMOhWdXdGuybzg5bHm
+7ocQanruYJDQ01BQtI7nLqCM9VrXoxetc+8dTCOaNC0bZkZ2kvNcd77zgkUmoaubuw7npTjGOs01
+uomEy3bbX2cRyfn7XNN7qyK/cvC1pqKv6LiLjmWp7Rj1YVfT4a7tyW7X7exQpaKti7LCILtViIeN
+xArxNfuwKnJW6QEAnz1/wPMn8uwfvrSUS0mHTsIu2Zpbov3XeRMAnEDvFzVsJ+K065v+Ver3+nw6
+zTMiDS2awCPWZLMch0oh36fzmljTeo81VGjKmpcrCaPeweFyKdemxYRafTv7O1M+KSW8/N7yN2Xc
+guvc2r8DwJPrR0SZ4BFmZYldKmw4HhC1Mj9tJjt/GM+YOrILnr+85MFKxuVbA4dUQ879QY/ZqYzH
+uBEH3yoIcbsy3vmipnKlzW3oUHryu0Xt0VeIsUZWGdUuzxQSbp0+TVfu5wWWOpcxyBNpj6ksseIt
+3K2QSP0TFounfqx+3NDbFUt3OVd+h/Rz34EzNtTqJWwWc8411LydZExTnWsNxbcmYjKQ+RtGO7x7
+X8bqUX2ToxeSMZp5NaEujVTb++PkjVAKgWu41fW51w/wFCY6LQviVgZk4Dns6mCbYsXqTAbwXM2h
+y2VLX8aGbidmqPHohpbiUszcg8WSA3VQPVfP+bg7o6se4Im9SbinHum717gWykTf/Or7LBUqHaWC
++59lqytzj7blRaOm/0HOHV1Nny4bbtyRF2TrgTjRhmNDpQqp072GaQRYlfWH5Eu5ptLFP3/wmGcv
+JHPwo+8e8URBNc8uSmwlkx/bikrNx7pdm4avux4DXdCJHzEcamTneh8qzSWopb3zWUqqCrQOXc6O
+Fnq3M0J14DVtSuvIQAe6cMuswluq8811WKmJnjop2wo9b9pdThcyRom+HO/f2MI2a7PdkhQCQsrz
+gkCaiZe3DNTBmIei0C+yhs+eyFh8+MMLFvYpAEWnTyeQtr0z6hMkqT5bxtPxDOlSMQ8XJ5zpnBau
+QzvXvrRwXkr7lqWst5qa2JF29mzM6G31/E8DFlOJNOSqKPOyYaLOx6Z22NajpEk8ugr/Ts0Qp9SN
+r6+G+qLB0chJXbV4yocRWIeh5sQk8ZAbE5nb1VIxLfFAvKbAjn9G2Ujb/4O9Dqel4E9eJnMydQr/
+8LGM1Y+TzfFhIxvZyGvyRlgKjhECj84oYOCJFr1w5qjlROHA0bFCkGOHUB10mWr+02lxRTwy6gcE
+SujRjRNWqiXTsuCWUQxBR3Th13dH2Jti4u8RcOiKE6wdDejtSbjsnb0JZSxm90IdYFXT8EWypFLD
+dBem4AdTeXY/yEn/P9lBf/v7ApP+IIqZvCUa/Pa9b+FqPHp3dMj0UuPbuqP+kx9+j8tHTwEInR7f
+ekvy4//0zV0uvvdtAP7eRy+YaDs/eyVx+SrPvkDnxZUjNcFSFbrLtxGXB7LbnquF8oMXS5aZtOew
+WLJr1LnqRdy5LkepZNJlFEo7YyWIibqwatS0XxWUjpjG74Yj7v2y8FfcjBM+PpZ+93cl7Du4VzFR
+NiV3vs2sJ5bAPM+JlGUrmbg4voThrJKXzB6c8vxU2n4xfc4RCmO+PCcfKBoxrOl66mD2FR9wtiLP
+ZTf+7qtPePaZOCvjOuH+LXG6Fl2PnXZNdaemfWOxah1di11GrrT/ZHvOqc5ZV9dT0yREGma0notd
+40vrmlpDw1njU8ylf6WGfUdRiDPR0HmS0yo3BrZi4a7p5CyLE/mdyeXaenLJjS1pT5TeoD8Wy3Qy
+32bwi7KOnpzWLFeyNr6spfBGKAXXNYw6Hv2JxVvKQFdZhac+gzDyyDWleFG0tK6a4Ik0Pyoqujp4
+seuQqUe626b4muLbcwPO1DQ81izLmuf0FYW0HCQkOqilbchSybhbDjM66l0PjNi1wob3+Zt3xZPo
+1KzUfu52IM6UFSeQhfmD85RJLp/PqgsSI9GJ40lIdiEL/dsfydn6e9MzQo2iuOaU9JF48rumJeiI
+Invfu6BRbEGtFF2HZcGi+Rzs7ClQJok6bOt4Lo6nrHL1ds/Fj+KVJd1E/j6OPFqrUZKiJK9P9ZoW
+T/0S47GMyeqypqPe9JVjOTqRfjyYXXLyfWVs+tYvsaURk5NM09Pn9xgqXDm7zDgsRCkYN2IwkY3B
+r/ssdbzO57LIf+fRZ/zWt/8pAB8ffMJIj0dFABPdGOK8x/YNUfB7PfHnlMWMx0/Fj/Do4QMqIwpk
+7ixINNPQlj38NfbAkyNBbAxdcWFxVM1oV9LvcifADzUjdM3tuVUR6CuVB7DSE+bh4ZyLWjeUasm1
+vqat69ztjCfspZobsbtNHMsz0sAlP5T+f2wWPHgpQK1CafruLANcIxuj70ZXnJDb4RJXc2xuTWsu
++qL0vqxsjg8b2chGXpM3wlLAQtu0VAtzRUW1NCXbXeWc242YXmhiy4WlakQ79nzNpvOCK69vt2cJ
+Q81UrB1cI9fM6xWPTkXrnhyIWX+S+wSZcDb8J8k91OFO/MoQIru8OfW48NTTvr025eCLXMyGNcNx
+C846jm94qvDXUnPiV7XLkR55js9Kxj3ZSbP9Ec/Phfvv6YHsZrO0vkrKmS1WfE+98M6LMxKNoXdp
+ub8tO/eeWkcv5yl8wVK4pbwInV6Ikg/TJyPX/P2LXHblh0fHVMjzTmzDta4m4vQDGo0GFGFJvKdO
+MDWTMy+gVKyE7xkyPVaczUu++0DGduCMGGfS1/NWEI/x1GfyvmzBjw4OsX2lK3O3uDRrrMecsJXx
+WGiyz/nygI8P5Thms5oP5+pIxuOGHpUeXjR8TQwufvOXxSL46PkTHj2RI8Ph1CEL1ol3GT/URCMv
+fMEHQ7n+1jU5toziAb7iNwIcXizFhO93BySapRsO1QKbgauRgZ4Pl0pj8PC44KxSpGSvIonFeRqX
+suMfvEpZbSuCcuwSuDJnbuzQqLOyaX3mkWJnjPzus2xO9EIs3XFSkymcuaktdxRZa3otRe/fHJn6
+UfJGKAXHGJIgwK9cjpQwNHQ8Al9BI/ic6MTsRRmFZpltDZRnD5eJeohDD1jIhKcdh4nifPw04hvX
+lbj1nprlnoenmiDZ2qOOlK0ngFGiXIOmxWoUJNAzuaBgrji7P+8HDoFyJromINIMxXL9994AqwCU
+V6Wh1d/edodEA12YUw2JuSmOgqnGvQGZZnYmQYajJnNdG1pfyVXUA960r+dJdgNZ5HE0wFF8fdMd
+4iyUY1FZhXqRT3coL+wkyrg9Ej+Cn9T4ep73Ih9Xox2uzk1bZthKlEI/GfOeEsH2oimxjuGtGxOY
+qv9goNGOuuLsWF7So48+ZDmUyM6vfmAwehafnlm6mkmYX8i5eCvZ40/dfVdG3rQ8PnkEQDCvKHWz
+mBaGQsO9F620c5VFvFor/c6YbWV5jkZb1Hp0G5klk658v9tTAtauIZsrD+ZlReJL28ZuzVL9X1sa
+IseZ4xXqX/ADbkyUX/G8YEfnbOpX3N3SkLrm8FQnZ3i6SvLLhua6snGXFlvKhjLaus4v9uXY6Gn+
+TGFmbCssPkk8zIW0zVnOyNVvgVfSXWp89UvK5viwkY1s5DV5IywFi6W0JWkJ/koTbWqPo0yJQ1KX
+jiOatNN3yH0FMqnjzNY5rtKJF7VlS3kS3dAh6MiOfreIqZRLb67sy7cCH6OZZ8N0zkOl+nbdczJH
+TLxBXdPuKAVZuC5UY16DA6wdPMYNcTVD0x11sOooXBdTYb4iU0eWH4TUStd2vbfC1azFwX3ZXXaP
+U1q1fs4Dn0lHuBfaexGDS426zFO6VtmRn4q3rGxetxTevikm5/Y4YqHOrjos6S1l576vyWW+/SqT
+6wobj1z2dHNZ1jWxozF9Y4iUBt/VBK7xzpjTXKyYQTDiopEd6uZwh87OHQBu721zeV2PPLpz58UZ
+7YeSfVof1ey/JdbGcLdLcya745PZZ1wfKC297sB/8p1doi2pK1RPLxj8rtz31cXHoAlvNgqxHbUc
+MwF6jbp1WUqkAAAgAElEQVTb/Ilt5a6kplTCnaas2VcY+mi0y/2B9EvxaARZTaTWlFfVjJTGbmcY
+cabc/utiMRMn5lTxH3mWMUrkXm/tbuEjayB3LTtaoKdwlR80zMg107TvpLiVck8MQ/ZSuUeZ5bR6
+nBxur2tZhGypJTX3ayp0XcQXOLVGmlYOi+TfDoX/o/JGKIW2bcnSlMJaHOUcnJuGxl9XPzKMHV0c
+PZc8UjINBYS0aUuu3mJbtlT6koZpQ6bmYDwJ8NUD3lfmoqATM1PU07Onc2ZLMSMvtwqsEo2+F14w
+zuWza5SByPFo13E/YzGarbmzFXJXcybG98dstXIuP1TU4HDugJK6vCqgF6sZ6U94f18WxeEziXqc
+c4arbu8PEo/OWM643lsjJsofOEuP+ewHcr/ckfO7+bwQkshIXsKumeC44lTIXq2Iezp2Gv7a23YY
+aMZk5VsG6+zEMiNbFy0pMozi9UONyFQmoK+hwNJp2PdkjM2eoVawlOnFvGXlmpWSm1wsDcsbykCU
+W3xVzgvbXEUAOouKxw+lzfsdPa7ELm9r1MkrblL/qubBdLfIZkIE+2rRYyeU+5lAIjyj7RnHlwpk
+sit8K329P3S4e13uPeqHjJREZrmStXCc5pxoKDNuZuw6cm3ddlnlEg2oZupHGLiUumG58xJfqdz3
+3YJWI1evnJxMmaqa2efHnUqVwnRe02Yyp01eUKjvxvEM/UiuiXNF7E4SZrW07fzVcz46F2U6nV1w
+d6LENydw3luXiflysjk+bGQjG3lN3ghLoa5bzs9T0n6Fn2otvrrG1Qy3bt+unfq4WGL1To+UTvs4
+LVlerneuGk816XLLZbSmxAo9tpUFd6XddgjwNA6+vR1dwZWbkxFtIjtJcO6Q3xSN72heQ5j0sJqF
+Vvst25q1du/2hF/7QErd9RLDjVAshaUrTq9uEPJyKibz/tOCVEueXX83wQ10Z+sLZn11kLA100iM
+3+HmroJ43C7drTWXQ0W0o1yDSjgzdV3smsEaw6Avu0QUwvxC4bGZ5ZVCZe/21fysoBMolDzoXsHG
+p0VGoVmSaWFplSk7VVZmwoxCOQvctsJVXovBaEKg9+vHEyqlgQ8aOZbZ5y+YfldM+4ex4c901BKs
+XdxYozZbCdWlRGWeGMEbfPMsA0csqPF7fb7xVOHd7+/z3SMtkfex5UR3bG9LcxHOxvTUMrm8aLmr
+68Id9rizJWMf9AISddy2WiLQX0Ctx6M26ZIF0o68LTjScM7FQNmX2xG+OqCTbktH8xkap8YqB6O3
+yBjoejldf1eUKL0DaWK5uJS11WYukeY23JjEVxaG31NHpHXAyrWXq5pybdEtW9I96X8+8jhc/GTR
+h39nS8EYc9MY80+NMR8ZYz40xvy3+v3YGPOPjTEP9d+fDDmxkY1s5I9VfhpLoQb+B2vtd40xPeD3
+jTH/GPivgX9irf1bxpi/CfxN4H/8t92oaVvmWcZB4dAsRSt3HAdXHVw2dPDVaohtfUUFhtYtzEtL
+qtowbysCvdYe5YwKOZ+Z2iNXfoZUvYRhkBJpfcFq1Mc2WhchHtPuy3kx74R0wnV5L3U+hgGRhuai
+fkNPC8je2x7zju5Mnf6AsToHo75YDH7r0w9lB3pZXzLqr6Gt1yhraecgl53orUmKrwla1+send5Q
+R2tFqckzy/mc7ETi+JNYdp9R2CNrtPqydcBK2/zekEQdn8ssww0EN9C4ShU3hlDJRb2eh9EMv2xx
+zFwZil2TU2rCFpEiLIMJHWeNw4CuFpDd6XdJ1KFLCzZUUlgNna7KGY9TZXs+eMnpkYzF+GZIT8lI
+e+l1DhsJazp6zs6CiKE6c/02YK8nlsncbhE/kzEaBEve0e+dUv0k7gnhTOZ9LwoYb2ldhHGXviZS
+BY7FU6dweaG7/5lDqpbC2I+5ua+1Hbsuvnbv+aHiNPYu6GuYsq0irDq5kyjGU5+CV1hC5biYdbS8
+38K9orSzFxG1XRdZdjBKi1e0Cf01vUakVmy9pNEyi65NqDRBsHXPuXi1DlUXmJ/wNf93VgrW2kPg
+UD8vjDEfIyXo/yLwZ/Wy/w34Z/wYpVC3lvNlSV3BfJ1F2POIlJYrzyr6WrU3sLBcVzjRWnwXy4ql
+Dupp2VBpvsOrpuBEYan3hz0mSlpSsB509yqe7VdTTrXaTncXBodqBmc5oabWNkrOEuLQH6hi6nTp
+KSHJYllSpnLva+MtEgUINcq47HstjSoevz6lnSoF1405tfi1cB2B+44cl26pxCNdh9iVF/2yzrFz
+dcYuFiwUC9FoHsGNYcxLJToJqgLP01wFU2L7+kI3BUfHGsHpqIlbdkm04vfF+eKKuyVdZFcEMNaz
+jJWEpFLHb69juKMVrJ89fMbqQPAE/Z2AZKy8i70dllaUEKqY/vmTxxz8K0mnXoUR4e9+H4Bv/cav
+YxV6nicZ20MZ23yqzuN6xuJI2ty5Y8mnct/qcMpQAVfDnYZMjwd7I2l7VUQ0Cp8u0pxtIy9WZGLW
+PCzWaynVgbxSPElZLLC64exth4wU2pxSUGmW56UC0j59nvPVfXn5w9bgaf6FG/TpKFiq6ITsqmM2
+P5N+lmEJK1l7ncDiKXmL8T08zUEZlDPikRxzOgrwK4yDntBYmgBXQW1pFjBYrTlLfarJvyel8EUx
+xtwBfgH4NrCrCgPgCNj9Eb/5Qil692fRjI1sZCM/A/mplYIxpgv8A+C/s9bO1zF7AGutNebfTCX7
+R0vR206fdFmS7K7Ldxsc3R2xYDR7rYgsjeIJZmsyiramUE3ceC6NJqjUlWGqPazaGn9Hdoqz56LZ
+V6GhdNYhsgk5EmNP4z1+qOSoXw1H3FNzr1a+ga4P4zDQ51WgEFzGKbXzuZumWZvxGvZL/QpXy7Qd
+mYj0VHZV+zhi9oNPpH9jpUSL7vK2wqq3ZilqNHCUORw9Fwzv0+dHnOmWPqnkvkunpqMmunFdLpaa
+3x8MiPpybZrW9GotLKLEKmHbcqQw8IvGxQ/VAqmnpIrk9COfZ/q8jqawVnXFUsOUF0XKUvv07FnI
+7UDhvJ6PUXzDspa/n/6r53xvpaQ2mcf44b+U5/Hf42vIeTSLOVYiX9PKc18cr6hePJF7HEGmJeTm
+RYljZf5eXEKj7UuOlMimLjBab9N3IdP2sFiSNtL+1nWwaqkWpYStz72KQkOBT8KC/pm0uXFHtNrv
+VpcpTsWJMirvTSxLtSSy2RmNhqpza3ipx5FKk5lyN2SpTsljJ2YQKeFKFNNoODEPY1y1SNqukgQ1
+IU2x5rWY8UhDnC+XJccK9e9HHWLNXP2y8lMpBWOMjyiE/91a+3/q18fGmGvW2kNjzDW+RAU7P/DY
+2R9x72zF5UCBMGXIqZJfPLUliS7ejh9BpCmroZhIw2FMOtOBLHJSzYPwIkuuJCK11xLvq+mumXxh
+BotQ8ygmPltaVWg63eFoKanKnTxkYeSsXaspZ8KQpiOfE98nV9ajqqhYpppp6ZU4ygg81xTb4WrM
+PF1ThFcc5LLALr9zwtGRQH6dZ1owdLDi+ttSkWoRDYh70s6PXi04eyX08w/yS1wri+axwlpPZylz
+fZHqHD5+8EPpx1st+65AiUeDiFON4Hiq8KZpAZ7049VFwd0tjccHPrFCt18tVjQa5XmSa81E/4jj
+KwVRcDCTs7/3bJveUIFYb0VkxzLmzYUs1t87eM7lSvo0dw2LH0qfXDegVjh2PinoT+Xzy1Ta2y+W
+fHQm/h57knFZSzu/st8FPa71+h4fn8hz4lcSvbiWDHG2pB99r4erjEzGRKw0XyMvGkJ9OVdz9ZPM
+6yvexiDp8ELP8KRLbu1q1qzuCUVrSRTUhTJpgRzBgpkcV/LQMlTY/FQRUh0n4DDQFz22tCNRpkEU
+0VbSjsBWzBTGHaSi6GpbcHkqCvI7Dw45UjzFZd2iRNKYeBczfB3Q9uPkp4k+GOB/AT621v7tL/zp
+/wH+qn7+q8D//e/6jI1sZCP//uWnsRR+DfivgB8YY76v3/1PwN8C/g9jzH8DPAP+8x93o8Ax3EpC
+RvugIVoq1+FClCQJLq2az0lYUFyq2aYsuk3b4mssPaot4zUJC5ZEE57Oly7pgZjMzlh2/t6yxu9o
+QH5nQF2o+2Pra3z1W+I8m97YAV/ZemuNOIQuXT0mtQ5X+fapC7VaEOHEJVGodKHsvMu2oe1JVuPd
+xGH8rkQa7KcL3r8rbpg8U37CniVTh5Pbrni5UCdgcci0kn541iHVaM1MIeHzomClKLfGwlSMET47
+fEGgDtabd4eUhex4tdYf7BiPRkvs9eMGo9bWaNwl0h3I6Tp4Oj+RFk0pSstEzWvfdphpZCgdW8Jb
+cj/XFngDcZJ9718IF8J8mdKoM7BpHC7V2uhvRVc1FwrbYbgrzsoglPlo51OWh7JDz+eWW3vyORle
+I7mjdTEPDMMbSmqyVMeoXzJRc75uHUq1ihZujquO6cXJCk8zLWdqlhsvIFBkYnaREmjRoZFriBVl
+Ga7h452SjuIKqjrC0RJ0/W54FRHzMbgKbx8r4Uy7qBkN5bteL8ZTJupe7tIoGjZ1HC7VUT5WyOpl
+0PAs0lqSgxhf+Tj3g5D+GvexM6dvdYF+Sflpog+/zeepgn9U/sOf5F4OhtjxGHUbIoXJrtKWEx3s
+IGyuyr3PcWn13O6uC5200FUP6yB32VaP/KIN8HUCwiDiXI8NnoKQtnf61KEsuiJLWUw0U++tPtdv
+vw3Ae4OEJlCwiWLgz+bZVTaiH/sMW62m5LTUF/LCxk5NR8vZuwrxnR+fc6FnztFggK8v+mgU83Ap
+C3q3Ly/3tLIYDfW1fo2v1PCVa+grD+TRxTmFEsGeK5Hqsmxor3DODvOpRDMezA5x1Qfzza/fJlIs
+fqhkKpnT0jTS9tkrh3UN+yptr5iFttwOiXSFbKwwacdSF5JROV+2mJPvAHCv77C3I2xL0c71q0zE
+mRKtlk1zlT5iLGhzmCQJtfb1MlnS2ZbFXcWisLOTPm+/r5DoZ5Zc4cpOWVCeix9g1IyZx9KXsRLy
+nF8uqDUVP3dqGlWii8uMbqQ+o35FrePZWN0ArL0i8Nntdehq1MUfeBTKotLb1/VYNIQKqw+qkEhJ
+YJsgxKpfaVXljLVealzr8XeQkGtORcevMKq8HK8gz9eFZ5dczmTs7K6+F3WHXS3Ztd1zuKOQ9mdt
+zbYWC3Y8c1UE58vKBua8kY1s5DV5I2DOYDFtQZi4RMqbQAJbqXIquiWuamUzhOF4TUUul05fXHKo
+EQnf+ByNNBKxgEwdR+H0Eud9MdcmWk6dWwNK9bITvc+OxsQn33iPXa1U0mz1KNSUrLUke1GULDVe
+fSsqQb3Fp5eGhTr5VoenjG9IcpNRopNpZZh++7cBOLsMCJRp+On5OS/OJKKQa83MMvK5e13a+9bd
+bTyNm/eDkFOjO3qa8/RMIyaaeWdfo39pmetOcpSmdPgMgJMXd9i6IRZSo1GLjjV44239ruVE4ea1
+SeloqTvHjwk0869JxYqZL2Ka7hqzMaZ3In/fe2eX0a6At7yOi2elL9/+WByRtrVcGZqOg1VeAGss
+QVfuse9v86hVx2smlka8/U26Wuov3GpBw9ll22NxKUlhVAYy+b7SI5h1W14uZdxMVbGlx4TIaemM
+dMQWFqsIIaeUcVnFIT3ltbADS3ipgLnCo7FrYhSt93i5YqYmvtONJZsWCFNDq4lgg9IFJcRxlQ6/
+MiNcdfg2/f7VcaSNXbKLmX6eMNSZ7dyR40CZhZhdscZuzM4ZDMSa6k6nLDVCYV+4zI2078vKm6EU
+jJTV812PULPUuj2HvoYLj04WtOqdbbsJwx0dHM2m2/m6x5MzVQRZxUwBKKM9h1b9C93YZ1vNzr7W
+B8jaEaWel995PyK8cR+AW/6IIFF0X9FQq6IaKvanrBsy5WJcBhHXuuI7uHCm/OFTWTTvfitkW2nC
+/UTOpJNthw9+7U9Kl89LWiVOufjekjBRj7P6QKaVw9u3FJW322WpSLtu0XDuSKTiIC04U4IT+1pq
+5Oefi0JpzfOM3z+VsfjWi3O+cVMUwJov0PgtiZa77wYDdpdrc78g0WxOm8SMtTpX7Wi6cRGS6rXZ
+6ozJB0rWuv0ujh7dLIZTfSEfzX9Xnof5XCcEAaGa623pUKsir7yWkYYi13UxbFwyfk/a093/Gt5c
+09OXZywnEq3Z9q9hfI00+XocW+5RHcvnxy8/pNZwaLKXfF5FLDRXx5h4zabVBoRKVT8a9om3zFU7
+cTQWuc6z3hoQKzjN8Sw9V1/0IXiemvNVQ6yRD6XJxLcBriop3w9ojda6yH0aZXLyyorRHVkj3dEd
+AJbNgp5SCoxHd+l05Rg3ntyj1EpjL9oTWg2ff1nZHB82spGNvCZvhKXgOR47vV2cGmyieINlQKyR
+gXEIp5pl1yEgv5Tvw4Fi53d73FI+hdNVTaK1H13TZTJSVt79XQKl40pjccikLwt61+Re85NbfO3G
+N+QZyYBAnWtF7ZKo6T7T6EPoJlRaGv105eGoI6f1E+Yr2RHOHxiOFQo92VOOgTZAqQCwUUtaSLGX
+pV+RaOnz7o489+ad62gQgcpm+Is16tPjVD3q87K8IlVZHxoMhiv8mIWVOhJLMoparvnB4TF3TsUq
+GvWVb2Ho4mg0xwQJRjM7Iyoa3Qm7cUx2oUzLyjjsXKaEat0UbY9xpBYP21SZWj3nFf/oH/8DafOp
+tCGKgqsKWcNbEwLFgmAclFKDfh1w3IhF4l7+IQDHx108HQvrGEIlVjHhDZJXcpRaVnOcTD7PpvJv
+nvmkC6m4lVYFS90Px3MXX52LeexQ5rL7u9qPnuNRacw/zjv4ylMwbi0XHS0YowZDajx8rUbuJyG5
+QrrDriEyukaiEOXOoTsSqyIygDI0eysHo9wZzWXBza4yO1/WxEokxLk6GqMWuxDrYH//PoEeH4Ly
+GafPxXob9BZEF2IVflnZWAob2chGXhPz+ln0j0eSKLHv3HmXclEyU0Rg0mnwXXUStjWpIujKtsWu
+MxQV+eYHAYE6arxOn1pDfUeXpyy/AMdda8D1njvsBGxtyW71/qDDi5k6dbKalVJfrVzIcmlHrT6O
+v/2X/hLNTbl2/9whvSm7QG81IhrIUx4+eAnqiNq7eQeA5cWK7z0UPMK2k/POu4KaxPc4fCZ+glKh
+3bvdAYFCVV+ezHDVAVlnlulcxuKsLHEUuut2NNx2XJGWYlWFvZjPztVJltTsqOXxJF1eOXQ9ddS9
+uMxpNOxr8TC6LooWan02xjDUuHmrc1AUOYVaK6FnrkqoRZ7DvRuChfjluxOqnuyK25nsiHYUcDGW
+3z38Z895MJNsz3/5259g1UnmFA2ZIzv94kAss8+ePeOv/Y2/DsDs5Su6imIMW0OjiWlZ1nCuSWi5
+OqBb21z5MHzjMdKs0t1en6/eE2tk/8Z1JoonOFc4d3pZ8um58DScH6e8XGg90brhb/wVwejNpz8A
+4PuffMKBltAr0pJMaz82rSVYF3XxvCuGcU/nwHUgX9cOKVpOteZjUbVXWI7XRZ3uxmWg/YgDh/Ha
+WetAo87My7yk1nufL/Lft9b+4r/hhq/JG3F8ML5LsN3jqDwlUIIQ25YkiTpfWkukTisThHT0WDFR
+51yTeOz09PPuHbKXssCWD3zyqbxstvFY2+OBFqBtLSjZMc6wZqTm2QtT01Nn5d6uw/f+UBbpQrEJ
+y+SAd3Y0ypC7/MFnsvhvX5vjPJF2Pj6/wHiCe1hqNmS6uOTTI4HdvgjHFEdamHXss8jVsbeUo8bx
+bMGuckp+dDalLhU34MBcyTTKsqavqcrJ+iV1Frw60zqKRYurzthbt1yWSrJyNGuvoLa+LtBFUaJB
+FjpxS1+dU17b4mhugPUdxqoUVop5qKy9Ks/ejx2sOm7rouGFes7vdX18jY480NyPrw887r4vg//g
+n6QoEpw6PyHUvJMm7/BbStXOiSjTb//9/4vzQ+FGTGsXmyhVmuuRKwt0J/GYuqoMNRpEa64iHH43
+onJ13K71mOsRy8QlmY65Zj3j9lruOpF+13CqNVqXVUtrRFksjcxdVVWsVCk4rb3KYGxbQ18VVtwN
+cdp1no4WMDIOlT6vyQwXunvVaU6j2b+vc+zJBV7o4WiZg0nPxapCnkQOrpxAOH/YkDYbnMJGNrKR
+n0LeCEuBxsKiZbe6ZKkJUbvWQy1i6g6UpeivvW6fW18Rk//uWFCHmWevrIbuznWyiZiDx27B1gPR
+ntNOy45CiNsdNZMPc2a6RU1jn9lCtHyegRnK7vHu7T6/+7FYG7USsrw4OuXe+78hjds6Y6Lm9aoL
++xpOK1+6bCnfw/lCdrmojMi0KEi3E5Aq1mFy7R2ydWhNjxyP8kvU10nbBCidBC+yGftq8j8rGtRK
+ZuFK2+9sBXz4Qod1XrLSuPrg2hZnKzXL3Yq4o9Dlag0Jr6nVEdkNHG5o/Qbbg2Gm6MVug9Gq0S9e
+yo5fVB5dRQp+sLfNVJGCTx4seHgsYT83b+gpEazR+geRM+RXwl+Xht63hEYSe04++R77k/9UxtCm
+mOJjAD7xpL7D0Ytv064rjIdwrydr4Vs3u3x2qZRvrsVZSv8WrQzieVyx18jRpfNuzDuZ4lB2K+7o
+Das0oz9WWPGa1yN0yZRnY+dmC4cyf0FRcnwiY75Qc7/MSxq1MIexQ6hMy4W1bCn5yuhaiKfJfQu1
+XHvGAw3Fh07A8kATwi4WvNLjb70qrpCJwRq70XPZU0xH2LRoVgCr1rKrjM/lk5x6bW18SXkjlIJr
+K4bFAbUHkbIKOabhspaJ2XY81izpcSegdbUgqDLrFrMSR5mWk+MVe6EoiN/oJRzcuiPPiDNudsQL
+myoM+lH7hO+/EEBPeZpykaofoXEwytIz/STD0/OZs+Z22d3hdKY8ek6Er3yAZw/OSCYC0rl3q4Ov
+lXwOtArVojHc2Rdv8dbWNhMtjDItPMKbkgfhKCx3fNFwod7ruG+YlxpFaQtWGrO/PghxVVmulFsw
+n7oMlfr+JK/wOvKM84MlZ8VK++Fydv55sROATuTgaSrwTi/kK/fkSNRGhr6WOC+znAvljTzQc+0g
+8uiqObwduIRaa/Ksn2H12PT4MsfV8YqUYKR4suTj/1de+L27IdWJzGn01tepFNPwvLpgKScFHv8d
+idQcpgPeGetxbRjihcpLGFe0pzJ/5Qxu6hiN9uS+VS+nE8jvtoaGW9ck2pG5n5vLVZkT65orNESQ
+LUrqNb9k3rK3PnrGHtWpbBbTM/n7ybJmjYUzreGmVpCaO4ZdxYN4JmJ7IC9s1mhGcBgQRDIuo26X
+oSOdPowTdi+1QK4D7VAUbq1Zq91uw+1aHvjSFtQadVvmlr1XmgvkQv4FOoMvI5vjw0Y2spHX5I2w
+FJrWMk1LSlI0kY+FW+Oqgpu57RV3wsq2RIdy0fGB7PJF2iG4r/RZzjW87gMAhnFCeEuz9toetyay
+U5yoiX99NeHBkcSuz2ZzLtVb3DqWlcYoLkyM0axEo6XjvSYmWpt1BjJHKzePPYY7osXz5QQ/ld9V
+S7FoJn6Oq8zBkyTCKLdjL2hoWi2rtqfw4k5BpHwDZ5khVFixEw65phGHtvEoFdexVJ4Gz6mE5Vfu
+QqEEMKd+Q6Wl4gqnoFRveKCJOF0TMNFMvfE45F2NyiQDn0pTVL/74QHkgrbr6TEgjBwaxYt4scFT
+JF3HDUCdw8uivsJYRo38PmkaZoUWjplu0+xopClwWadKudNjTo5+B4Df2/4uALdfGgbbcmwcdk85
+eyH3yy5T1sXRop5L7Em/vrKtmZhmyO5Yy60tHXYVQ7LCB3VALsqQpNZCLY0eq1zI9CiRBBGOOiI9
+x3KhpCdPtFZohYP6/Wh9H0cLy9wgxNeakN2gy52J3GNWaI2QHR9XeSFubMWMfC0+9EHLq4eytnai
+lseXiqztiMXw2XFOk8saK+clZbNmj4ZHGqGqa4PDTxZhfCOUAtZi6gIvMLRaDr1TtaRfqJPnKqno
+kJj5ugDGQlNh03NOPpIBvjlZstQU0rQsqJT4suqH5GrO9zUVdit26CUyGcvZAqsAk9pYEj23p80c
+o/UD1wqr7PSZqbd8Ahwqi8/Qc1gpMUybLjjKJEMxVTIV0+kz1pRk1/GIC1kcqe8T6LmvVZ69SbdH
+o/DorZMTFpr7sRsFn4OaqlrgwkCkkZFps+BEz1rndYOvJcnLprk6BlWFxY30ZdGXoOM7bGuOxlvb
+Q/r6onecLi8O5OXNZjmpUsN76uRoC0ukEYzA99EoKuOJi3co97PV6gqGnasn/NKr+VfffwrA7UHJ
+UbTmoPQIHHnZsvOWv/8d8SXUj4SrZ1k2jDQ/Ic9WtEoqdP96F/90rRgNpRbo2evKBZXXkqvSnzkB
+e3rki6xDxToN3rDSylGtzkdd1bhKrrOM5ji6FgoMR6VEHWod+27fo6N+GafnMYxETe0MuldlCVwb
+kGjIq99XeDUdSGSTiQnY0WNHVbeM91Rpd2oGHa1UpWS8ZbniUGnmTeQQKbguszWpwt8b2ivl/GVl
+c3zYyEY28pq8EZZCay2rsiK1NT13zUJrUBoDgsBHLTGsZ3ADpYFXr84BFSMFlbyql1eFNy7ajCgS
+x581Ho2yHBcKTBkPhuyMZSeZXfosqjXHgLliM17l5gq8sw73lrMLuCVOyzkw1nNOlXRZJyMtq/oK
+MnuhAJpRr6HSZKxOHaKcJmzXJacLDSPo0SCtXTpKXlLEId6OmPOHx1O21alYYXHUw11pVuMidyha
+pfpuDJXiBprQgPJYlo29shoCtRiubXV5eyAO2ru3Rgw68rzZoeVSjwyprSiUem6hfYp8n45aY3EQ
+oIhwbvd6vLsvO/4PHxZk+jtPxye9LPkw1PFZPmeuALHl8Slm/wYA/6J6RPFEgEFZIW3wbXnF/Via
+hH4kE7U96jLZ1SzPvCHQxKVupuXmFjDTwilOBVWlrMyeWSdaUlY5VFrSTb8zsUOwlP6ljSFV669p
+oGIX1GQAACAASURBVFInb62O1mHPx8s0+hB5bCmEPAlCulqlmw70la4/V+vBaYKrEgZu0KHty/qO
+8YkVsxHWFa4Vq2C9RkZ5y1Rh9VvLCk/Xy3JZkypxTFU1mJ8MpvDmKIWsblhZg6Oe0l7XECoxxbCB
+bmeNwLP01ft6c0ew3jurmFQXd+pknCpfY1WCOs656wmlO4CWDSDwA97Zl3uUywysmvtFS+nLxES1
+Q6UcfetU2ChvafXcXlUG1tyOiwzW/P5thqdm6bZO7LDTpRfLggj+f/beLNaWLE0P+taKOWLPw5nv
+lPNQVV1DT4CN21gIm0bAgwUIHgBZ4s1C4gEMT0iAZCQkaF7wAwgsMDKoZV7cnpo2/YDt6q6qrq7q
+qqwcb97hnHvGPe+Yh8XD/8XOzOqsrszO7uZaOr+UuifP2TtixYqItf7h+78PGjFb8grlwSeCsuJ3
+IuSIqL1gu2MUmQCditCBT5Sb79iYX8mLd7mWf1VWo1It0KlBnwtW4ChUvA6oGkx8wyO4aX/g4sUH
+ch3TqI+wJrGMtYDN8OjIizB8XV7ILanzXa3ghNTr9C04kBddNT6+vicVlXKm8WQrc1sy9mlUg81a
+PvvOJoehLmhoGlSVhFuP3rrG+hF5HGeMnac2SuqBHPsWPGpQvna0h6ArL4hXFLuuygDyu/n1HAPm
+CQ61BR3wfEmBiqCfwtIoOB8pWPmqbKQMaf2iQM5VTzfN7jkatlqhxkW3K8eyLR8Oy1VB5CBk+OQ4
+ESKGNBbJVJqk3rF6VZ4Fxc5I3Who6qla2seUOa2GFTrs+8hyuU/P1i4qso9FrtotzkmDj5HufDa7
+DR9u7dZu7RP2nHgKQNo0KAuFOVdo23LR6ZGkIrTRY+LI9X24VBtqd+AmHKIby67z+CpHs6BbigZ3
+yIGQ+l3sESeumcgxrsaEYcI37ivUTD7l8QYfrGVn2mxKGEKMmQOEG3bRa1dfO0BSyThcy9pln5M6
+wJSUZYEjtfujyIZi912ocng1gUx1hTUBQLbPXg2j0SEMNgsamD53/xtvRwBSpA1SEpyUrEuX6wwR
+PQK7sXZMxA00NCsOWutd4q/PpN2dQRc9xQpI5KFRbUeoi/5YkrxNp8IJuRU2Pe58sFF05ef9OodD
+T8h/dYRDUs8djd/H//4twSTMb2Tsy9oAhGsrZe3gup3h/o5PwS1ukG6pEMV5vdsPcEzuw/HYw5gh
+z6jTgU1aNa383e4fkS5e70VQDLU2WQWXUu3r2sAicEhhgJpVnBtiE6o6BUic4sUGHsMxz9Q7YhSP
+FZd1VsDnszDdc7FHHc+OChDQAylgQzFUaugduL5BTdi5Lko0pP1rtAXTtJ6eBUWSnybkc5H5uMv+
+kUenSxT0WEyRoaDnWdcNPmfx4dZTuLVbu7VP2nPhKUApNJZs6R6hwY1ngQsf7o5cjKgNaJwePKLi
+Bh3ZgW0/RaPbGvwIH5aCPRgZg7IvXkV/z4XhTtLrye5yvV7DYqOK6oe4m7On3wt22gPn3hyaCUPD
+mDR3DArGxmFTQnttYqhGbtoOOAfOiPgEnrfj9sHNGo4JYZHAU9UGHYdNTEyA+ZGCxzjbSXJsCG11
+rM1uR0S+Ro9baMDum7VVgzQGSH0g5O7Y8bUkGwFYykZOnMUJr/Nup7tjtk5TFz16Jh4UNBW4R1G+
+0zFsPaxaZ5iz/BV4Y4Qs2RWIsH+XiQu1h689FBq2t0kPl6cFVCuYoC1YzAFsszkqS7yJ+dNLJGy8
+6pMvY9L3cDwlMnFo4Ex5T6MDKE3GIj2FsmQ+DZ+rYJ2gZMJYGY2arEiRlWLNuWuCGprlviERjWvH
+oNOK+vQUonPCpmsLDZO1PuHqttNgyPs76FiYdGSuOlaAityBtqNgc84DSqkr24JHT7iqXSCR8+We
+gUtUaw0bXig/L9obXMbwR0ShRnqXuB4rhbjVfSiB5mP8Gp/FnotFwbYd7E+nQLJF1ZeJOqmtFg4O
+3e9jQBe20h2MCXM9IL13kvZhiBsobtZ4jcCUd5sch4UsJp4zwuFUfs74MHaHDQq6zH5do6YUff+4
+hnPJrsvTGldM5lVtZ+B6joKUcKoqoTO5ofMyR5cvyGAyQp/y8F5XKiDDjo+cd8iqAEMuvsDOsWVP
+QcCHuCxLuK5cVFInO1e7sm0oPjTGqtEEH0GTAeD8GtiwVt5VFkwbgth6R1/X3djYEt68N6UAa2Rh
+QTp4f7iFJnipDEMMCQQq0gLdgpyILqnFixBuKS9jNG5wTY7+d5+d4nAjSVwTW9gQL9EdyfUd5w5q
+3ocqcDFlT0iynuMxj3F5fQZVyrV6oYzhQTfA6IAvoRvCr5nV9x2ojcyhdjUsm7qLXbmnaW7BENyD
+RsEn2/PGaDhNC1iq0XADKNmDoiwbk1bQ1a+xPyLoabOC64k7X2YCXhr7Gl3qR3ZcZ5fErWsFj52W
+dmijQyn5NXtKTF7tKPVdVSHl9xwoFAyDdLhFzsS74ULvun2kZKTZaEn0AkBjGmyKtmX8c0cPXzx8
+UEpZSqnvKqX+Nv//gVLqt5RS7yul/g+lWnrVW7u1W/unwf4oPIX/EMCPANZ+gP8awH9rjPmbSqm/
+BuAvAfgf/qADuBo4CWyEXgc5kV3VOoXNJNnYcQFXPAWvMbBKqjFzh7aV3qHqXt6PkEX3AACRmcFS
+smP4Ewu1xWYcItGSOXbdhzGAYdS6yQpOyJJkfYjZleyEp3Q/vRqoY+IRVI2KrvTsZo5zNrD0LtY4
+HshO6QXyu4uBBZdr5KTTg0WcRWW5sKs2uSTHSuIlrhdy3vcfn6PmjlHWCh/1w6VwylYmXn7Tg4MF
+6/FZbtBlZ51tAhQxZcq8FCGTZ4roxx99sEA1k+M6HY3ZI9bEvQ7usGMSyxpFREgvO051s9lBsOfK
+wXdPpUno4TsN5hNp7HntK3tgpQ4hYeC+9tFLZGyZ66BkEm19eY2rRMK/99+5gkX32ecumTY1Prwg
+1DowUAyr8mwDsDNy3FdwLJLJJkyS1jY0uxPr7Q1iwrvrpka5ZGI2q7BiIpFyC/D8Goaa83uFi8E+
+peeyYEf7V1ttQhkYkoihjEtsFuTcaBKolm1b+zvdjpp4k0hbUJQvbPpduFQ3tysLKWn/ttsREt7X
+GzaM6aLGkjoal2mJbCO/v8krtFXLBj9GxfAZ7ItqSZ4A+GUA/xWA/4hScv8CgH+bH/nrAP5z/JRF
+wdIa/U4Ax03BRC5cu4BLoo80Bhwy59a1QVrJg7eriftj2BTh0LmHXocvRdyHx5fp+nKOq5U8pO89
+ohahadDQfX6z19vFbEGvh9CWh8kyHfTeEzKNg1yOGxsXDjUD68bC2Uxi4Pee3CBfSuxsJTacV+Ri
+5oS7Hj0r0RsIMEftZegNJAxy/AoZwUvpVr7/4fwpHr4r47y8ynA4JU136GJP8SVMa4AL54Cdob1o
+A0NZe1c3uGY23b/+CO1a1SUsVmBa8pZ8W2NRyctWrwIEjYxj0gOCfYGQO9MaDtuEG+ZU8iTHOdlZ
+9jYJcC4//8JJBy/+BSH5eXEvQKuG/rtn0q/yzcsN+o48xJ3I3QHEkqzB2bvS+53d3EATUGYR9FVq
+wL6RlfOxWuDZpdzTY1UjdCWUODo8wuCOzPkdwqfzOEdOXMTbZ09Qp3K+NDYYEN5ddi0csat0yTDJ
+qjQKxued0MOrM4aSwRYF8QQZwzXfcnYMYMvM4L0PZA7fudxiTR7IhSkxopiRR6DeGydTDI9l4e3r
+DH7UCsqEeHwtL/0svsTpWp4/27AFfNzBIGA35LbAOQFL12WNsiUrMh+j0v+M9kXDh/8OwH8MoMVM
+jQEsjTFtA/cpgONP+6JS6j9QSn1bKfXtvPp8/d63dmu39sdnf2hPQSn1rwC4MsZ8Ryn1S5/3+x+X
+ot/rRmaoaijLQkZqq0gr2IGsulZoIWWmNy4NDCXIwO41u1qiz4y0W9twyQtQbzPcgGIo6y3W5G6c
+LaW5prvfxdAn5NRRQAsJblYImFCy3BgHzC7XbQUkdNBQRjyOgSvKeZXba4x6suLvDX00Vlu1YFcc
+SpQcu+vF8IlWK90QWSzexuJa/v7s7DE2XCu3OsFy03bAdXdNOcY0cMmraFMAp9/RqLmLpw52VRKT
+1VAedw8o7Acty7Nc21vzGGeUSHeqNS5nMub+YIYccn2j+10cGfFIhkNSu1UNYnZzvuPc4B/PZece
+3ziIf521/i//aTh9wqYvZbzL1Rx9cnDmzxT0VDyz6+0TbFIhpZkceTjbyjiHe7J7jgcO4hv5npPX
+SHiv394YpFvx6Nx4gVfnEjY6P3MCAOg4wFVCL+zyAlvu6PEm3SFkh80IUZc8C3TFLctA2ZSe82Ms
+t5Sbqw265OPsaRnbvcDdydJbWsGUMp9ZU8Ei1thzvF145xGO/ngzQ3Imn33Q07jLpHFcr7BeCpL1
+Oi/wLj3SIRvlTpJ0pwux1GpXHWsqC0Z9bKP9Ewwf/jkA/6pS6l8G4ENyCr8CYKCUsuktnAA4+2kH
+quoKy9UC54sCBV2kO5GLO3TrAq+LywtJjZ/ODZ4k8pBOCUbq+l28QPBSvx/ipCeT1jQZGt6Msqh3
+GPUZKcvjuY3zTB6UF8Mtoh5FRqIxONcY2iOABJshxUVX51fY7lM/MctQrWVsnuOizwzwk6c3ePaQ
+OH9bHtb9wMOgS4nz8gA90qT3Rg42Gy4KM4YMFzGe0OV8eLNFaRjvezd4kYIsbxyOMBizSsK8RZza
+SFsS0EohJSimqUsYtk7vOT6CPcJ5iclXYY0pX5RNaFATWJWmKX7nQ7knX21cjA7kPAFh0KVt7ZiH
+6sSCRWWmdSfCP/xAwrzv3/wGLKYonjGfM9/mO7GY/rSGtWb4tLFxdi4v4fJyiYhu8MsDCfP6w2PM
+buTv8wDYMDwa+gEqLhDNeYUnFFRZs7JQXKe4OJVxbkwAxcrBauPgkrkWp1pifSPHo2o9XCdCxcrJ
+XjhEzfxCZSvcpaKWxRBFlRlsAq8OfBfLDR+iZymumFNZmBRTl+5/l809aYNLI2Nb1Q2KUu5TWsY7
+Cv8EAeZ8xlPmxjzLRTyX+dQpYBO6HdkaBavBFbCj/G8+4+Lwhw4fjDH/qTHmxBhzH8C/BeAfGmP+
+HQD/D4C/yI/dStHf2q39U2Z/HDiF/wTA31RK/ZcAvgvgf/ppX0irGt+/WeKysaHY8145Pg4z7mJr
+YMHVvGuV6DFxNyX1WRTaqGtmoYsGKx5Dw96BhZSp0enI5b50IizLxg5R92SX7xgPfcqDGV0hpyxc
+mlUYMnzw2063ukG1lB3jarZEypZKz47gkpDE6hQYsjLg+/K9g9EQlkN337GQsbEHlUbMJFErgd5Y
+IaZM2Zc6RE2MhHZSHA8k8TccdeCzZv1QnCcUcYWaiaUCQN7K0jfWbgdYew0iehs/d1cqNW++7qOk
+y7n2KoxyMlFfn+E6FS/Gm/RQsnV17rdT4SGk9zMZHOGNYznuwbhBXogXdjEr8Nu/80iOR7Zj5Aq9
+DrEemxJrArm6yxmmBB5FwwhdZvaPegJU86wBikDGs+/uwyId24NgikLJvXzo3MCly+9lrD70fbiE
+F7+k+gh8Gef+HRcNqfc29TnCFqbcleM6boCk7XJduxgQut7bAiGTtT3C7pVfwudcLDcOpoFc08FB
+gT4Zo69VhQdDuX+HBIgtr9aomIYLChu60ype99E5FI/t5aCL6VTuiSIk3B1kWD+W6Qy6WyxmH9Hr
+u0xmmqZG0yYaP2MZ4o9kUTDG/CaA3+TPDwH8/B/FcW/t1m7tT96eC0RjWTc43yRIc42GbcEXSw83
+LFOljsHkjqyuXm7Qd9s2W5YssYXjto1ECi7jergDuNyB7lk+DBl1211p4GkY9uYHcYm4pVtDAdgS
+twXaYOQQCcnmmstNivhcYrlOZbAgGq2ynR0Lsn/nGMmNJN3mxGsfWDWqUnauXmgBHEddxlCEK08O
+GaeOG2xWLCcWBmOWQNE1eIkMvllZoSL6b0Gth8fbeFemDBqFmDGuqetWOxV1qZHbhGDfl4ScPerh
+4qnstOVygSgg+9FQ48FQxnxnr4ejrYxpS/XpkRvuGoNGdhfLQlqkYWncuyfSdPdeB5KNeA0B+Q8e
+JYDZyj1bJw1UKDv7+IVXcXgpzM7VvEZI/ElBfYdhJ8ZLQ0kOr1Chx+YhvZ5jxSamva5Ch/mmHjkG
+NrXCEdvk7wTBrtTZ3W5gs5V5gzsYxLLT51qO5ZQNHJaqw56P44XkubaTDrosmUcUP04LjWAguSbt
+Nxi1vdWjfZSNuHIL1NhnHmRVyblOeg5US8bbs+Dacg5/WuMFekqW38FVC/ElCMH0bezfleufnWqY
+Q/ne2XkJwmWgjW5FrlvisJ9qz8WiYKBRqQC1NnD44mVOANuVB8ixBzjqEDcw8bBsWuA9M+ibNZZk
+Ox4oG4qQ2aBjAGLcnX0bniUP06BFG9UWMib7XOOiIa9dWtfIKCKSVwso0qFDkeOvybGq5SbnqHaQ
+Ur8qEWtJiJ10ejCUqb5o5A5N7TF8m+o/aQHFhFKZ1VAR6/AbdvpFFfoOwVSqgtMqOkUNpoQdb2Zr
+LMjxvlBtX4ZoLAJArj7yGBuDXa9FYzsYs/YO9kD0tQV3JA+0GQcYGSb2FhkKcp65dg9gR2SYyfVX
+cNBltrxCgX3W+X1VIzpgV6YK8eUDWXyvrmUuQkcjYyl6UTRQZDWx/AvcfV0WrDcfD/HwQ3k5cwL7
+ZzMDl6ChPS9ARb5DLGoMKc4yTz2Umdzrivc/9AokJIOpqhK+Jde05xQ7iHGgbdj8vFrJOG/SHNfk
+PdhYTxEzIWpcCyV7SfKVjL3XV3Daao9yd0nCiZOhNtwM/BqGfRz9VavBacHlil27HjasQHm9CWyC
+oQqtsdf2QbQlfN9DzITpyA2QEArvQ6PDpKtRzUc8GnxHfprddkne2q3d2ifsufAUtKUR9bsIVzHK
+rqxqbww9eMT8NrreNcZYDuBzBWbfCHLL2UlxFZFGRaac2vHBTQBh4yPwZOfetHqHfgmfUGnf01hy
+F09qB1vuCFVjsCWUtvJZd15t8IQZTKsE6g2hsWMHPTYg9ToKQSi7Q8CmnV7g74RF8tUMGzLljLwC
+FUt5BRORRaZwSO8hd10M6FI2qoJLzgl4FgxZjw6Jjps7FubEPIdGY8tOPFMbmFaoBM2O+Vi58n0n
+c6Hppd1BB2NSfj1dVhgRVl6VBVx6TTMy/mirhMX56dsKJXdxN+ih60lo4g0inB+K53FIT8i5qvAj
+qkfbrgXN8Gl68hUYI7t8p/ttdF2K+RStkrgNVmrR8x0QdY1+kOMyIxlrZiPtURavR+TlogPDumjc
+OBiw8S7Ju4jYKVsXORyWBpe5nGS1MNiSpqrnhbjICInuKvRJwJo0gquYzWrc3Zf5zNx0Ny+NbhB2
+yaaUNvCYrP0Rk66bhcFkj2V02+CGz55dXcLpsCO018WW3qBiwlyVIRwlOAarYyGw5Rl4kDuIE9L0
+qVrElgDif3+6PReLgqM0Dm0Xum8wOaK+Xq1gs2tR5zlgtVj9Ei5aYQ35vte4qJhncEwDm6zNtmng
+tBh318BmVN26Vo0KYbfgy9JCveWLucmwzSmz1PgYUO68bUNeu0s0ZesmFkJkAQCpBvii970cISsR
+AYlJ7MZgWchTrBR2DM5pnqFIBVBl6F67/RDeQB6afjjeyZDnSQ7TYu7jBmrDGJiZ9YkOoVi1yKoG
+z6jYZGSwAIBVlePpBbEF1LPMPBcdn/mMukTDzzp5gIp5BLvZYrFhNp+dkfW6A9tb8hg9NFzcgskY
+7qptXx4CRl7YcSkL5LzJ4bAXIV3H8MJWKNXBwUsyjhe/+qdQrP8RAGDQyPfn2xohgVfaWJiQdzJ0
+fKwS1vqbGAdcnFVCUNugRHMh53D9DC67Y4OJD1WwFwFzlKksXhVxIdpawyNb82pRYUBymbEXwubr
+k/FlbPICeczwwjMwBGd1PBsRQ0/LcXfEKSErIHMdI+R4kblwGG5WmxIdR3JpZpPtujlV+1mzgOKm
+pkqFnGpnTQX0eP/SukFxy+Z8a7d2a1/EngtPwbYURl0XHdvA5eo5qBsULX9BmqKim2wZvXMRrNZ7
+cGzUTAyWRY2E7MN54qPPzLDjGQRk6rW4spfGQ94uupsCm0R25rSeI6Hu4guHPfiNQHSvSnETncCG
+xw63q42C4Qpelhpe1UJYfQyIwrugXJvKa4RstOkEFgpmAZt4g/X5inNBXEHSwFvKruqpjxS4jTI7
+WXoT5ygI47aY4HowcrCfyi6o8wTfmRHdaLCjYEuyEg/n7K5jx12QZrBV21lYwOc5qm2DPJaKwvxx
+ijcmMrebWnblUq2Qdqi1qLeojcxb9qRE9hXBQLjVFi55LUoS276zfYY4pTaitjAhv0NneoSQO/fL
+Ew/lsZC/LhlKHgYlUoYgrpPgOpdxWJdAxM+MrAKGntWYuILa9bBmdaLOC9i8PhQFHIvYmKxEs5Hr
+iws5btAYJBS+uT8M4bV4mL4Nn3Rrivodp9sGNWX/rMJGSBxCUfmwuf+u8xX0RubZ1nKsfTcH5SZQ
+NwVylg7qWmPRZdXG7kDzmjSToWmhkG75rMcpNnydl3WBiu1IlVbwPmdD1POxKACYKgVojTqWCWtq
+ICfDUFa5sPjCWoGCZnyt7JYnsEbW+jyW2jHjWlgipcZkrj24hO7m1zKRmZdgw6w+VgW2c2nZXZQl
+coLYE1uhw+678zO2TgceeuxOsyyDx3y+HMtgFss4J4slSo+8iTXBT2EfQ+YttkUX6xVpy20FRxHm
+y3xAJwqh6CZvNkuUGfMLdQOb7EVJtkFKdzWEnHdrStTMQuvQ/lS8StlUOL+QcOV73xSxFa83hB58
+BAlfEZcf1wl+xL6Lq6s1nhYyZt1qLoYaQ4eEJr0e8rUsNvHiDPm78pmgs4fZShbU761kjmdJDr8r
+5zuJXHTJxGwD0D053v1X/yz+1m+IMpS7lkVz4x5D0bVfXRfIyVw9Q4OU6lPvwEC/P+eYHsnc1zlu
+eP0WAIulb12voNkTslzHKNYMCVYy9iRIoQjqmhc5ptQhhR0gZzhqawmpxv0YW6pG2b6HDYFoVZ6h
+orhtmaeo6c4rLvRXuQubBbF+WuGKUP/MqnCwlc/0dAc54eSGpfOkKDAjl+h1WUo7MQBUNUxbldcK
+2roNH27t1m7tC9hz4SkoGDhWiYsih+IuV9qAzQx/Wdc7YRW7aVCwM7DiSlzkBTS3RG0bhH0mfoyL
+YimXuFpt4VDT7MMrJpGCCGtbVt1BWiKl62u2QEahj97xAB88lmTcdx9Ks9LXDifYO5aQYjNbYr3m
+rmobnLK+nf7wQ9xvxPU93hN4aqADNFyHi2yGyzm1JsMauscuQMrYhb0uXF9W+PXcwnIu4zR1gw4T
+WKlSSJjAm5Po36qBCyZo2bz5qbZhh+nvXcu13Z/fYH8gHYWFV8CndxNHBvfGEo7M4ht8OGcijbXy
+sYmgF5RiH3ZwedEmIIE55ea6+1d475F4JufcBVeVQcCKSWcyQUgyGOWMYMidkNuPEHL33wSskkT1
+DoLeqAJXVJoedCukJDIZ6j4+7Ml9ePsZOwu9AH5bkfACWIRBF6ZBwe7Zi5t413W6bOS8VuxhwWdr
+2nGQ0ivQUQOL1HmG1G1VY8OxCRVP090OnccpbCbHa2OhM2Qz2ZZUemEXK1IUbV2FhtRzbmPD0AVO
+gg2WC+pTsMlttkpwnYhHtFknWBLa7ITV7nud0IbPcPsRgW4/zZ6LRaE0Dc7yFB/mDdjchkkXmLL8
+U25y2AMKooYOCoqmkgIRxtdgWA8FtUOalY0Hw4na1gUuKxK1UBQmLWrUBKbEdg6HRC1LFaMbkqPw
+MsWP3heA+fmp3IB/9mSKPktodd/HyQM599l1AcNYtbQNRqyIjInb7wy68IgkzJ8Ad3hDe+4+DFF6
+QQto0hqaoY3jW4h53DwpYdPdVaGDIJcH0mrBXbMYhZaXZpP9ZLBKG1W8zQrBn17EsBx23JUR+qRR
+jw7GMCuZl9H9fTQXXJw9rjhVhNyTm9axI0THki1fugt4LF86poaOCIC6kpd0TxuMOnxw9zQ6bD+2
+Awc1y5ALvwu40t9yyOtvLA3FXE2VrOFSuNXAg0NE38uI8AqfjQ65GvtTB3gmCNNtWuzue6FK5Cw/
+pnmGggjQTiT3qU4NLIZS6TZFdyy/t2IHVcDSd78FwxXQhA9Wq0ZWaAB2EMJx2NvgdNBlFQtTfj9P
+oLTM/djpY7Anc6jXW5iWlh8VNLs1r5+xY/h6gYztkLU2GHLTW+QW9lmWzy2FtP58AcFt+HBrt3Zr
+n7DnwlNojEJSWohvCiSs4zt2gIgw0jizMMnIktt1obiW2aTeVoVG6LbMuYCZtzAND16X9OuNh6aV
+USfOHF0Ll0zgDUqFImiTOho6JDW61cXyTHbFOpfjZlmJYiljuNMP0KcbeWFfoU/YMIzZAZl8AmU8
+L9nxAR5NfPS6r8jYTIrFXHa8zJJzqQbI2H1Z5hU6PUq/90J06ZY6DRAyzFmmrL5YNkLWwadGyDP/
+IFtwx1ynK6BmmLPOkYWSGNzEfXgZE1/nz+BtZUd7ynBlOHVgfGL1gxQHJJ/p74dwmezqBIBiyNbu
+YHGscWdID8SxsWC3o+O40CQnCasLfOlI7vtTql37YQXHEch0rmvkCfkK8xJpSnfR9eFR1k5ZpwCA
++QIIKS2Ymhy6IBiszHddidM63PEhrJmI9KCQkESmO+7AIxfHtrNFN2k7Wplc3uujoneQxSlCh4A6
+Y6PtUS2MQcFnfHPFatcqx4pVq761woaey3BgQXUpQVBmcFP5jEuPYFOVu1DaVxp1W9kqDQom4UeW
+vQM7fRufzW49hVu7tVv7hD0XnkKlNGZuhExpuBE7yLSNCXeMZVbiKCbZ5f4IwZ4k1wgYxPrdC6RK
+Vv6oF6Ku5O9G1yhW7ETMMxTsduuy0ajs2NiLiHXIK/hMGFlaI2Bn40VzhitSYq2JbLtebdCboZXl
+cQAAIABJREFUkOt/GeKCNGbz0zU2gfz8ShmhUOJZOKRdSxqF+JqksfYAuu3OMwb5Vs5xMZeYO80z
+RGSXtjwLIePXke0DHAcchTKSHXHAZNizrMb1EzbwtHDoP8AaxsvVZg2PdfzOfg8JS7nV7zxC5XEc
+Vgg9ItfBtVznYrmGIhN1dDeEJompM9AYj8TzsEKgTym/Uy2f7ZYbBF8ha9QzF5pUV0aVADtCj+6+
+jr+19zMAgPjst2Q8eyECJhSd0MIdMsKeLivgRnI0KlDYkNzU8ZlXyVdIEyb4qgYWOTKqIoc/kN87
+CABSszVs+MqdEh7zAXW/wOwtmfvzRYIvPZDy6vCenGtsfLx9KnPYYAvCF+BnCpXdsmonKOhB1jdS
+JHVshSn3ZxNo2OyijJMMiozQ1tQFWuZuLf92YXbs2FGmdpSFMyjcGbCMfloharuGP6M9F4uC4zg4
+2N+HfX2KGZOEr418YC0T+WRRoTORn48bGxFvaJfJNfsVCzWFQKLQQsgeBx0ouOyM6ykbIV2xlInG
+rHYwu5GX26o2O+7DqNdBQ4DTk7evsG4ZhRWrIUmCZ6fy87BvsJ7Jg36ebgF2Wkb3fSRM8NScZi8p
+kUVMHC0LNAS6uLkF40mica8nf18UKwwZ5ljjDkL+rBoXLrH4epChZCL1kJnWM71Cwtr95qevCfD4
+gF4vM3z4RF6E1746wGAkD/rwL7wJaMKfG8CK5UGuDNus0y627P0IDpe4vGzl1TsA3XJT1OjekXt1
+wIpKLx3gcCDVGdXR2DxpezRslKxsFEGBr0/lIm6OXgYArDcfIrwnc9Xr+/BuZGF52d9gQer0wJkg
+lCGjR8h7XC2AuYxhqefwGZrWwy48ho2JbuCywK+ZqFyt1ghtQqWtQ9yMJRx57xHwJkOMIuEmdM9B
+J247ID3YVHLSAyAMCVdebdFjXLX/M3cAAI2KdlqaVpJD3ZM5LhHsBGziYo0tG3kUc7z9oETFlPHk
+MMANVdJersvddrDqWdjGn29RuA0fbu3Wbu0T9lx4CnZjMMkNrNBFtyUSzZydyEatDa6YiHs2T9BX
+Ui6cEgZduB0ERJrljdolIp1yAIvkHIPeCIpJIuPKUlusFGyuqbltocMynO8O8XQmO0J6lsDtsW5M
+AZRBr4+SjU8fVjYu2IgCY6Fh7T3UASx2taXsMlR1Cs+SsTv7AQz1A+ObDSxqF44C2VEid4jcED2Y
+A2jzl6HZaUVmtQVTyjEu0xY958CK5Dr7scGznzDnHl3KQ87xRVzg4aXsUIfJG+h2BbNgdYB6KZ5X
++ux9bJYyd42ROY68AuZErnN7PoUiKUqdZsgLxTH5MIXc1wd7wtlgTSeIMpnvbecUHYYVVW1Q8nt+
+5sPr/CkAQGD9bQDA00sPSomL7g57UPQWvcBGt0OBGjQwLRkKEZhZbcEl5iGIeiiJkHUiFyqR82m7
+3iFEG5YNHa8LTZXneFbgjFR/QVkiPBQSmYAI2jhuUM7l79vLJ9Cco+GxhR4ly0uns4M3K3ZnVqlB
+xmTmJo6xooc80AnMhGzj8wbXkPFX2zaD2wW5brAMAoyJs9kUHgJK2QVhA7f+yaXpTzNlPq98zB+D
+Tbpd88vf+AZ0do53zyS21naFb7RsznaFL/HhffnPTLCOKd6RyiT8Xz+6RtKIS/360R66Wh7Av/vD
+x/jtD8h4k+f4/deqoPkQ3+/5OCCW/chu4LHNOIkAf8t4nRSN/+bBa1gfSOz/lXmJ30nkZr17VmBO
+11elBncHJN8gS3RVW/juTStqU2BAd7djAetWHZQgnonlYOi3zEwGd/db4hgf60TG88MncyTMlnvs
+Fpyvamh+bxD18crhawCA5XGOkFWUby6vkRDOm7dMV/MYihWFXjeEz8x5UpawOUfDQQRivfBks+b5
+CiTsKSnLGg0+3wP44/bs8Rw1ewby0uCcWI7ztyRcWSZr/K+/9msAgPS738Txy+KC37FCWCMJR/pV
+gv37Em7skQnrBxdbBFthl37twUsIycD8nSdncG+kojI0DnQh53maysJjr9fYe4EL9Z27GFPC/sYr
+8N/8F39Dft+XuXylF+2UmbyOD4/MS9/7cImY1PC2FaBLpmhGrrJZkBVsELm4pjBQVSWYdtjNOQrw
+iOxaEQFSv/Dmmzh+UWRVHm8NOqwkPXjzBZz87FcAAEf3vwKHubKv3HvzO8aYn/1p9+A2fLi1W7u1
+T9hzET7UMNiqArN1CouZ+kY36BzL6vnVL+3j4FJWzGrvBKtLWXXXqTTz/ODdNVpE73SQ4uS+LMVm
+NMBkSZTXpQGYJf/IFBSTPmvLwWtE2J2lNQ7I7XcyUfj+hfz8jJiA6uUVvv4VZt9/p8Lfe0SEYZZD
+M7loHOAhZc0HjownVAViYiuKxkXNJKHvNsiYMIrJHLxsGrzOJOC8KvGU3TwnsNBjJ+XWGFTcxWP+
+W+oGN4SKP3UyHDiC4nvzazbeYgXg7XdLBBxH22pfqgI2sR720AKnAsW8gWaXXRAaRJRRf1qJt9Zr
+LBTkrCjXGUCPDaYVDft8NptfYzIlFmDm4v9+W36fJ+Rz/N0fYPnoXRmbHiNmQrAehZhMGB4699HZ
+Z7ftoYRrL9oVLldvyDl6gMUuQoMcD68IbfYuMFqL17DKxENZrF3cscUT/MW7Kzz6QLyG1eQBXFL5
+3RXAKsJBjXOSm+xHBWxH5moytuCNBZnZcywMhuSWuBBvZFFE8Jj8Pr6rEVEY5u0rhZJMQm+8orGg
+WFFLJvN2mcGtqDnSCfGolGvuYIEvH9CzenwDi1Wgz2rPxaJQVhWeXczRlAk6HbnwF/pD/MKXJZ56
+8LWfx4e/9y0AwA9+8wN8fys4+vWlTOp7SQmvbV7rGNw7vgsA+LnlHG8yA/6//K7B6pK4fMbvulYt
+dwvujDysqjYmq3HEt8I56sF6KktOSqLR7/3oGvtvilt+Vl2hogbj4MTCm4TrfmtZIWXeQbMNu3B9
+bChJPg07GL7MkKhzgIfvi2ubs9KRBTFwLA9SdbrGgkCZm6sEXz6QY8TKhtMyQDHMNF0bSyooHcYl
+3n8oi8Jg8XN4b/2WHK9JYVgF8bnQhQMHPh3HYWRjxFJtkuc7cpmDPRd7uVzfDa+tsgyaRv6+zRss
+GUqY8g+3KHzv9Ar/4l0pQ54XMZpGVoVnlszPcPkQnQGZnqY+7ruSo/gzb7yOmgvrKjHoskX7hNqd
+dtlBFbdtxjU6dPkH5hhnG3HLv//uI/w8N6U1QVpx7OMHvCd/tnodT7kQKHO94+bsDCjA24kwZITq
+jhIMlby8s1hjSoBbf6xxcCAEsttAnt/zZY5Ek8dzbx9VxnAmBrwJ7+XJG3i9loXKZ0i8Srbo9CT3
+43WOYJOq/mJro7uS8OE6NKgw+2yTT/tC4YNSaqCU+lWl1NtKqR8ppf4ZpdRIKfXrSqn3+O/wi5zj
+1m7t1v5k7Yt6Cr8C4O8ZY/6iUsoFEAL4zwD8hjHmryql/gqAvwIRiPnJg2gaTPMNtKXhQLa8L7/U
+x7kjO6X3wyv82t8Xd/f/fXaOC0ploaI7b0pMIq7KHwDLF2RHGNUxelyh3+wNcLWQY19QljlDg4iA
+j2mRIaXEnOMC5xsm+d5KccOko2azkjvt4extWeXfjbcYkl15deFj9UB2gYO9JSL2ws8Igz2aTnD/
+gYzhzvAlYNx6LH3k3G2tqVzTZvUMdS2gIdWtkZOoJdkUOCfgaBJpLDLW9NmTc898BLLapiXUS3KM
+Rz/8ED9kM1IVG8QkOgynBHRZBhG7E1VRo2TGLNAWQt4TtdVoCOMdEF57FVc4YAxiRxHeIdR4VRnU
+5vMnHa3DHh5d0QNs5ruKkf9bck9XysPPviCe4J3xffj7pNjbj7Ck9+JXewjGJF9htWd630amhfQF
+8XuAR3bl/RTW5D4A4OTxb+MDJoqdkrwKuoHPcs/vfusKhy8RsxBb6PTZJenIOaI+EHXk5+XcxYAE
+MV8axehb4sJn3TX2ifsoj+VZOVwHKOMnAIDGTKF/lozY+/dgb4UW0FYHePB1CQ+CVoNz/TZUKp5C
+NzKwxhLHFMsf4dETgakn9zMM4q99tsmnfRGB2T6Afx7AvwcAxpgCQKGU+tcA/BI/9tchIjF/4KJQ
+Ng0ukwS2X+GlkYBR4DrIGTD+tX/wbfzGI3GB8rhCySpCy9HoKI2MoKf5XR+P2crr+S4qxmTjZYOM
+ceKc0gR+CVSVHOQ6LpCUpEDXwIpIOFM48Jgl/jgr0jVf0uwMAHH7vaGP146Z1c9fhrcvi8+ylBf9
+z7/8ANlQHoiwo5FzkQnXDT54IG7u1yiRPs8sjEjg+U2dwpAT8dlWIy5kgbzaGFhtR2Tbz5FWuCTi
+r7ABn+W9xaYEdj0hFTKWcNvy5qjjoeJis93UO7WoKHAQUAlp0wAxRU7n5G3MLIOG+hvTEdCby/UX
+tYU4a+XQ8ZltvzPcAXK2aYwF2a4+qH8AAHjTCfDCna9yzBsUVOoqtnPYJGrR6hL5NcuTgcTWfh2h
+Y0so9TRZ4Ohc5tOr1thzBWX6Vqhhzkju6jL8MQolhVs73Qa9rYQKWZTDp55mfyjX75kIpsOWcnRw
+TFp75d1DZLeAMgt3IL+PmbfKCo23iSA9XjTQLH17boOzkdynQTZHeUVUZE+u+V2vg3ouHbyPigm6
+RhaQaFAivXxPrhtfwnx6/hlnX+yLhA8PAFwD+J+VUt9VSv2PSqkIwL4xph3FBYD9T/vyx6Xoq+YP
+F3/e2q3d2h+9fZHwwQbwdQB/2RjzW0qpX4GECjszxhil1KfuEx+Xog9ty6Rlib2hhXtcUeffv8Tf
+d2VnO384R9yy5JoGhqCgmrDjQd9Dj56C5frYXrP3Ps5wzfpwVBR4gd1iFVdiqBKzlhk3UEh2tO0l
+HLrEvTLDU7qlGVmbL6xm1znpWA0cKiTd7/twipbBeYOILNCvgnRlD/axp8T93G4q0CtHblc4oUJS
+wnP0exFq8iH+olMijGVnTuMNrigAs80rtPI/A7rqflNhRte/qIA5L3U+W6BRLV+j2lUXPLqiVlXB
+b3fHyMH9fQrRuD1k9DyeJnoHsc5bvcqyQkglk1HUx4akl03eICeVfl03aOgutMRgP8l5+HAD9D3y
+XK4svP1YBF4cVggwdVEb+V160+AZO1+TZobisZRost4h+jFBTRRA6QQaH7IP5vTyClctI/j2KS7m
+pxyni5i/tyq5v4mrMCGe5OlmA7sSj3WW5fAJm7cUFcAGHq5W8ix0PYX1hrR4NzfIyTFarAye9Nb8
+vYztJk+xuRYv5n1zhYodnudNg4YqUg+jS4z4fK5rmZ9iWeCUXg6uzrE8JvXg2Rh2IAnaweIaSfmL
+P2G2P92+iKdwCuDUGPNb/P9fhSwSl0qpQwDgv1df4By3dmu39idsf2hPwRhzoZR6qpR61RjzDoA/
+B+At/vfvAvir+IxS9KZpYNIcH8wULNJIGaNxY8suf10UOzSigoKmpxBxlx/v2dhj3RzXWzxmjTxd
+1lhTostkFRzyECiuuP4wwIR7l+/Y8MhJsMoMtiw/nhmhiwOAVt5hsy5gqJi8tTXGlFYJJh10R2SL
+ykLsj6TwMqI69ngyQNF275kUK9adB36Jxwm9nqHsVIXWYOUNi02IwYEcy60qNCWl57Y1WoHIDzje
+71cGBROjpgHWjLm3Su9EcBplADJHgZ5Z1HEQkNGpGwU4HEupb9wJcU0quJG9wWzbYgGoAq4saDag
+Vb0A3ZZx2LWwlc0PaZKjaJvKON9lU3+qt7A9v0I5mfBarmE2koDbsDPQUQWmgeRlmqxCPRevITEW
+njEB3V84uPAkNzCl7NwyBLK1zP3Veo179DB+aG7QY5drUW9x1JE5uKROwzADtlQEr7SFx0nr/Wzg
+dqXsOSIUufQCHHKcmWPDMvJzVXqw2D1qPAWbgjPzXck6xyImN0i6xnkrBFm7qKkfeThKkLLpbcSy
+8FW9QVVSBGhbIKLC9vlmhvBGko4f7GXQ5bc+ZaZ/sn3R6sNfBvA3WHl4CODfh3gf/6dS6i8BeAzg
+3/hpB6kBzGtgvdVICCnudyvMCf01NaD5CFlKwSEoYRKR17AOMSBFuFmUyAggWmxLUIwHjmXjRd7E
+F9kWff8FHzNWJ67eLvDrZBpO6wZZqwmIjwky8oHu1c2ObjtoLIzClkuxQEqCk8mkQV/uEbo9ytMX
+IbTTApamGLb0Z5WLfXrHPhOjxi0QMPn0wl2FKUFITc+g+x15MP/x+gZLMjsv+dA1zY+9amy39UwO
+wu/haYCoaAwZUkWexv2pDPjo8C4e7MuLl9QBnEoys9q20dHMvjNpZwBMyeE46Ye4JIwstjVchi6z
+ZzFOC3k5Ky4O5Y/jyGieXwKNnC85ncPcEC+SyPksf4gNL9GpatSblOfz4FMSvt+dQoXy+xVxKtbG
+gWEY8GCcYLGV63YvPGzJ+TgNPTQUB5oQY1AbjV4oY++bHL4lidYkVdhrBWwYBwZwYJigLTY5Kj5b
+jmXB4/3RYQ2LlaYO28XXVQaXKk6NsTAgaQ2M2vF09uDv6PlsMncPtbUTeilcDwsmrgO9gp3JmOff
+A9KnyadP9k+wL7QoGGN+F8CnYan/3Bc57q3d2q39/2fPBaKxARADKIoaGV2rde3tiDAiu8aWnkLH
+sTBkffhFyrGZyIPNrc8yNkL2tF/7Gj1qJ1iRj7snUgoa+PK9yUvDXXfhUafCtzeSRHq6zj5WRvv4
+zksCkTDAMCJ6LrfRNklOhjYGTBhGqouo7YjkjulUFQqyL+uwgE1PqOu40H0JMWyPB3Mj9Nn11umG
+yOjO68Ud3Ak/AAA8/ru/jRt6Cr/PQ2hHXLdKxdmOD6JqDIJAxjFlE9i9Xg/3TsTlnA730KMqs7I8
+mAPxBOp5gAdjmeeDsVybawBF1Wm93mLrUkRl1IW+lmNMVYgtWZfXhJ0nbdzzYzYNeih5/4xTo6kk
+JeWz+3RSBfDXdOc3C4TkP7BMje5ExnkYKnQd8XRURWTmoEIvInFOHMIhB8aqb+/0Kv2nEfbZYbrx
+SfmXNVjyNbnv38eAz+Tj7GLH8jPyWP5U9U793KwNPIaSTaVhVCt86sAi74Hhv52eixG7SFXswQlk
+Dn1otKKfhzoCUfNQrTdp2Vgz8bvKDSI2RF1WBWYP5bMfrudYXyw/da5/kj0ni4JGpjzUqgK7SgFt
+Y0A+v6C0EbGJcOJ6uHNP4utjhxndWiMigGihGtiOfPjE2Ogxdm76DfyKEOOxPKx668KmcOmkb+EV
+YgVuFjbenzE/2nwcgMN42HNgM8GwP7IRsvKxH7nwGxmTWzWoCSWuyARVOCUaLkJlYMNlGKP8DsKF
+xJFli1fWCiagq142iBNyVyoHwwP5/auvjLDmC/R94hjMj/Uc5G3MYNdgCwYCbaPPduAXR/Kk3Rns
+YWzLwuSHvR19uVeU0LYsps54C8eWORwPZFHI1ymujbiqo2AIQ9btpIrhRuLm+h0fq1TGfMMwYlWU
+v2+sAFAUGbwRK0a/d4nrM6lu7w0IKsIJyqVgD5SyoXt0qc0+umS68m0XYK+BlcsjPvFzKEfuey+y
+EFV8wxJgcSnXunesUFIevtOK8+gYESsH22EBj/evsA0MMSINKfWRJtL0AsD1q10biOOUO7Giuqx3
+KmgOWjkDG72JzGdsVRhzgbctD06fwLDcRUmNyQ1Be6pnIyfnp6O3KJjEqW+WeMJOykezGI0d/755
+/oPstkvy1m7t1j5hz4WnYFsa416AeB2DZX683I3g0lW1lglu1syAj7s4GoqbeG8gWerTVQ6XTU7T
+RiGyJTzYFAn6TAhmqsHoLhkphvL3vHTQJyw1skt846704PdrD//b99mteH0Fg0/W2IeBjW6XNWin
+wZ6iDoGdIecKnasS/g0TpQNxBzujEKlF3sbCgmIYE6gKGY+nWJGo1yVsStdtVIGMSalef4Ahuz3v
+D07gfkU+/8G3KBvHCkprOT2hpFE71WkfFrr0UtrknDMJEfjkd3A9eHSfmyBAh9R0URzBbfkmKX7o
+OD7Wl3LOdTPHlpwMddkgJbmH7wAvTuhhMPv4KK2wyiny8THIY1NVmC3lPt1UK1TzC56P+9d0huAV
+uf/xokR5I98dTlJ0xzKfnSDaoUVHgcxhNbR3WpNl4sMds9NWOYgYMsyWDl6wZEwz8kIMfQ8xWZK7
+XogFO1t16aFpxXzojG2yBrpqhYEqdKlRWTsKYdlmvDuwQ94j3XoMQLoihHm4QlzJ8xRYEZRFlm6d
+4NG1JDm3dDua0oJPHc93Hj5FOhOP4LurBcqPR5PF5wMHPheLggVgCI2u56PL7rXjvoOQVYbAK3FB
+ncMHBxGmXYl9NV14nZc7CHJv2MOQD/QiaXbimlFTYbOVzzxdCuAj3djod8Q13hu5GGm5AXd8G//S
+icSk/2SZ47wmxTsf3mFjEGYUajEaAd3kh6dbxHzQ+9MQUS5Z9C1ZcExVAuSMtDtjNAwJishFk4nr
+p8j3V5kYWypMJctLbDdyjsYaYxvLZ4N0iRO2Sd/rykP1QVah5ANtoFC3Zci6BJjhjiINr6GcO3sm
+5k+XmLGE5qoYEUVWoiDC3SNZfLth2GKlUNBl3sznWDySsuBqM8Mp8yQdR4NcMAg8B1XDUiZLhV+a
+enh0zpb0OvloXdAVFMOR+J1rFDfymbbj9LJKsH1G4tLGYEwSEiuwoFmes1SDfk9CDCcSQK2bVTsR
+YpXeYJNQtj0ButTCfPM4R0kNyS5p2OtcIXLpfscF+swfjJohFpUs9jE5KtdbjT4X0KqIsWQvjbEN
+llSDskwDdrDvyFLsJoeruMjaLhy2ddt2iYblUlMBBbVQ12RpGnY9VDP5+/kmwROKBZefnl76zHYb
+Ptzard3aJ+z58BS0RrcbYdJLoSNZtb/6Yg91RSCP9QwJd8rJQQ9DNpqsziQZuFxn0Gxp7+oavUD+
+x3NdNISErtMa6bKlEGslw3xsu7IL3DEativegfP6EMevyTHC4B/gV79FtV+6xuvaYECqLe1pXHHl
+Xi4MrjcCte0uQ9zpiWu+XcpxJ84caS5j11mCgmBPNd+giFsKcNnxH+dLhCuB3/7e6RVKJp8O7u3j
+VVYMLjOFpG2kYjJpmf4I1+ReCKBQ0zsoq2aXM42zBgUBYJdLcgMuMmwNk2uFRo9ZsnFnhPKV+zJH
+908wZIOOlbRhwhYpae094+LlHsOxsYWEAi7FYos5m6P6DO3UXgg7kmrP/IMSJbksRlYHH95IcvH8
+4j0o6iZOSFk+7UTo+3IO215iyeY2LDdo+uKiZ+4Cdsl5piuUpDUs7ux5FsPKWqLLCns9Cr9YY9hd
+eSXOZmStzgusWCgZeAE65MO48VYfVURIPKO9FPFCMv1nV1toS3buUB9i7IvrPwsVHHqeniFRja8x
+7Ms1dTslrBaolwMZYddNkcEjxuPVCZOkgzEiNuCpd76PvP70is7ntediUXAtjZMoRN+xcDiVG/Ry
+o7HoywvyYeJgPpcb+vbbN7AYw7YaA3EdI1SMzyMPLgk8Q2XQlFKpKDyNmh2DMZV2KpNgSldu07Xh
+sJw0Lht07sg48pM+fv078r0lY/ImtOAztGkMkG1kGq/LFc6Idy8XS3zwbeY59uQhOL1wYG9/CAAY
+3RlgS5K+F/oRrKLNdsu/P7wAntzIQ/XNy2tM2Kn4DS/GOpUHfp0DI2ac9xj6HNk2NgwfbAtwiDwM
+XQuGfqWlBbUIAPeom3CpFQbM1G+Vhs/kjg4aXC8F1NVZRegQkVURVbnOcxR0y5t+H+sbcdunpxob
+zlHftT+iqGf14WW7Qncsc/zOY40VSU439RK2kvvTUx+VBvceyGYxGfuo1/LCxrmFJSsOlh1As6Tc
+bH3kVAazSbZSrrZYsLv02foaWcx+hqszvEUxXd0xuBuRdr7tA4GBYr7q6fYah7UcL44MKo5tQz1P
+rSsw2sF1Pt8xbq2KGRaFvPTbNEeXPSj7WkLNJlEoCHrKrAEsSsoXTY2UYcVGOXiHVZf1Y3lGHhzM
+8MqdV+U6xxNY5wSI1T8BGfYZ7TZ8uLVbu7VP2HPhKTiOxslRiK7joNPIzu7tjfHgruxmi+0CsyUp
+2Dygw668cZe7mZmiw0TboNfDgHDmIlZIwb7/bYobgl5++FBcPLcb4po7tGM0DvuSwAwGnZ04h9+5
+A8uS3V3RU8A6R3PCDPi2QcWwohsAIQEyp+sMS2LUe5GM8yoaIn8iO/tvn16i9QKvZyUskqXogezy
+T5YN3mGH53WmULjsLziN8c5T2TGGmYP7rlz3yQETn6cubLrtro3ddTTGhiF23rVt3LsjGe6oJ/gB
+vVpiQ4jvWbrGkS/XdxC5KGs592oVY0IewOVKxlDlDWyGBNfbBh9cUBhHV7DdNhOvEBESbPly0d6w
+wV1HduX9KEHNZGwZV7g5lzGvigU6JKgZUE2ryTQe0rU/vYpxs6K3NTnB8b6MI6oSbKj72Tbpzs+f
+YBaLx/b2ZY6a1/G0LGCoR5k0G3z9QM53fCjz4xQaJanlTM/HGV10bRqkhJCXpOBbbBpEKRmXtcY6
+ltfrOw8/wLJVtm622Cct/WtT8R5e6vYwYujmOAYeO17jyuAxqeJW2sHDuVzrDSnpi57GKz15vn9+
+/AqWfUn4ns1vPYVbu7Vb+yO058JTcC0bd/tT7J8E8Kj3OPZ8xESgHe6P8dIBe+UbA4s4g7t7glob
+WAoROx91Y8G0NG2Ou0MQ5pkLqm7hlXuS+Ns/GKChfNi97gSdlpC/qFGwIWboakypbbjgLlEYIFtT
+XXiTIyXUtIoC/PJYSmB2aCGaMvafEEmHI1y0KLZrg04ovw8bD0FbvmSDi+qXeDGU3XovUnjxHmHQ
+91xEF3I+d7mCiWQnubiUsb9i2zjldRoIrwEARKG3O9+DfRf9Lkt1TGzqTo09X3ZVvwpwbyReU9Rr
+UMby+ww16lw+n9ls2vELDCPxNga1wbgvO2y+WADMNXSNgWbTGEmLUS4T2EzEDSY2nA1NPJnTAAAg
+AElEQVQFZ6olDBvFRk4P1kiOMe3xfLXCqr2mYIIuOzRfenAIjxDkbB3DragQnhKDkLlYt9Rujg2b
+UPke7mG2lbmv8jP06dG0ZKyqrtHd5PysBlMUKBIFlxiYlk/DajxYIWHn1QDWVjyJMPB2+iIVFI4m
+MkeHZGzq+hbcFiqtLSh6heV8g4bJ9qPBAOZV8azOryTHczz0YdETenWS4B8RsavU5lPRop/VnotF
+QVsawcDFi6MuCp+ae6pCuRB3rx8McPeOcNE1eYqKN2zCt7w2FXzKc3suoJhQapQLl+78sBPAJhy5
+x2L7wbALYoKwB2/XG2CqGlwT4IVD6LbuS1zEk5s1JqTS8i0PKzahxYstxq/IOY5fexOOLQ/9JiUR
+SvY+bkrJQt/3gTvMlt8vKjyiYMz9gZz41VQhXsmDdHq0xQMmXV94cw/uVE74T96aw8woAHIiY+9u
+fbw9k787gcEZw6NQKVgMY/bGR4iOhTPwmFWUYDBCw87Qy2aLIYUoq7JGj8CqoBvBdeTYpWa9vu8i
+ZRjXpICTkc7+3hAx719QVVhm8j17K9dfuAosx+OXrA5OCSaqNsD6qVCM5TrFfo9hYV8WnrHr4quB
+LJCmUUgJ1hpaCobAKrdvIV0TqKVkM2lKha4nx9pzh1hRSCjpznBI1aqwv4e9hvLwDmn7NzkcuvtH
+oy4yJqbXeg2b8lsjhqjGVlhxXxllPvr3GIIc30VUi+s/bzSOufjahC2PHb2rntm6gk1hmKgXoM/k
+uMmBkGzOb0QM7d4c4kX21/u1jX/9z78OAPjv/84S2faTILbPY7fhw63d2q19wp4LTwEAYBQsp499
+sjLP5xs0hMRZToO7zMqlSYB8IL/vslvMzjU87ly+G6Bh6a1KMliEgfZ7XVgRa928aqt2kJiPYMlq
+Rdlvo2ET6XdjNrjmDprRJaugsCZrsTdVCEjMmjQaCZtd7hxoDAbfAAA8azsjrzf4merbAIBHywYb
+Izts4lvY88Wr6GnZtfIXehhxN3uj3gDUnXR7RwBLcmoWY7lk+bElcA1L8HRwHI2cxLQWgGGfaMpa
+w11QEdmS3408B1VLTacbuNzGk9UzsFcLkd2DCtjwxSTiukyhy5zn6+1o3uzaguYuHzUlNCXVC4Yf
+szJBWTC8mPi4z67NZfkMhgI9o34fNWnoiNaG7vbx/7H3ZrG2ZHl612/FHLHHM59zzx3y5lijq7ur
+XU0PFpbdAmNA5sGy8AvCWPIDIAuesHgxEjz4AQmQkBgkELKE3DYWSBbGoqWW7aZtd1M9VFVnTVk3
+7807nvmcPcWOOYKH9cXOyurqqqwuy2RJZ0ml3LXvPnuvWLFi/afv/33ba7FLD4estsXgXHk0qTXd
+s7Smkb1rG6Exd58xy6wXehlAcmS9jbcGLiNhY9pwwUCWORaL8rXb0ai0eFncUCmReuY3lMIbZAvr
+SUyinPna7kN/sMLDrtU9FwKhcBszI9I+GvWhLQ5eZn83iQKMPB5T+iBqungn4fWi78q01z/c2iYS
+8vSojZl51hvbTsacSR28+SPIQn4iDoW2rsiuT7j8VglH9qFo3Qwz6JlpfFb9tQ0bfAmYbCdiDh4Y
+IpGbUDd0qu0WlCR1DzIyTETrXYmXMA4hUMy21eWcS52pDQIiQV+fPvsW14Vd7L76sF6XnIihJ1p6
+NNeCW49rdsXsXOclbmsz9PvaHP6+y/kr6UueO6yEy989HJHGNjxK9HD4rcfWsXUT5yuzIVyhSFme
+WZj2B1flhkTmUEQfj1YOudqFt8bf1bUZVHTKnwSBT1GvtF7irSxb3MBu/iEx0dBu/g9eFCxngpC7
+NXt7Uty6sCdF6sxJWusm741r1upKTXyXtrWH8KArySNl0eXWmjJkKIxEcdKAhH5XLwuuzsTcvb5h
+V/d1JSGX0h1QqWU5bDIc8S6GQcO1DsC2qCkEMU6OpM2ZTolVv78aDLgrIBDViK2kx0hMmKr1+0q5
+ikHR0qwFXhsEvBBsPCfn5ZU9FF5Tz4U7a4lVZQiHhgMJE1/cOAz1fU1XsSsa8ivNZ3VdMBBUPo2H
+DGUsaqei64Vuu5xIBtOoNyKYBYRiy8qGJfvYeXx2POFSlOVN/aPT7N+GD7fjdtyOj4xPhqfQwHrh
+8Hv1DW/oBN8/nGxgxXlRkQh26zkenfQZTCP9xLoAKfXO/Jaol203FUUqbr/xlLawSS5H8FTT+Bhl
+yCsTYaTOW6UtF46FGN9cf8gl6PbEz45DruaqKiwQlQOTJqTspRQXJa6aqgJBVZu0JlKyy4QVe0Iv
+rtZDRqpK9GFQMK5B1Y6kCTD6jvLlDTdyxZPWsKcfX1VK1DWwpUT9w8TjmZCJOwcx90Z2PuOwxihp
+2hS51sIQaDuE7gjPtVY+rwxzJQnDxNtQozSVtUQXp9fsSKrwzlbAWJgGr/Mx4nJwGgOFaMWEtb6p
+UuoLqWOHDl0u5F51Qhza39szQ+K+GpPaxZ9T4eq+d0VA0CMPnYAy1fsETJV4bmR1XR+qwnpHdw4S
+xsKyTJIdHM++dtIXlAoLWzlSRWooltbj81YuqIktiQA1xV0KCTuoHaqir7J4uCK4OTqKmArLMApi
+nKX9jhtJxaVpzsSRwMuVSy1vpc2gFMoUA77g7Y4QlvXshHZkPcyzy4onT60H2Tg5Uc/9gSgFtTIf
+Z3wiDoWmbVmka9Ln15y49ib+2T/9KYaKh7oGrrTYoVPjbEueW1na65czTtcC03QOlcRgyqrhzWOb
+tfadD0U23Eiy7oPphv3GbWoc5Q7SZs6p5PcqU3FfnHmRblzatFTaCF+/hDtiVtodGUJH7bLJmEIH
+S33ak7U2ZHaaZGnG0/ftIbW173Aq9/HOfRGaBNt4pb2Ous7Jlbd4en5NfWOBXJ7vbioGJ4q97/jw
+xpZ1L7cexnzziXQgg4A86rvvEswm9hdRiBMT6c2VWZGe2QPLq112tU3WXYkZ2PdzHcKLxZzUtZv0
+fuARTuXuDnxywX+DusRXHK1uYuLW4YFKtdNlyzfWtgzZzVesr+yBk7UZhyrPmb7EtlqghksGg5is
+1wXNHRIJupquZKWejjSwYLHrtcH37HovzjPe8Gx5z49ClHbiunXpViJulaBtXS7IFVYe7gw5FIR+
+1q4wyrsMVOHpEocd0fpXq5httaUbP2ZLcd7FxSUDufQHgnabQcepStz7ozWNWrV9x2NP/TNkKxav
+LDhpqFZv52hIrnW7uTjlQsCpi6zGVW+LMQajQ6H9mPmF2/DhdtyO2/GR8YnwFDrHIR+EPD03HCnB
+dXq24O69ewAsy3OWcv0z12dqrKewVk257bIN021ZGpJd0SgvFmRyYf3QxxGhSjYXP4KzwhGBYlfn
+LHtm4GXGPBOYKF1ypDp1qCTi87TEVyJr5DSUWsaubbm4st8xfe85ycBqHrayNE1acTaz1/GPby6Z
+z3o693NGV8I0qOzx2mgLX7XyV0/OeHFlE21fm6dMlezyBzUn4hp8pFBrN3H5hT3rKYwmE2JZKOOB
+SIB5cXPGPdHdIwrxJm82FOKV77KWWz7YhitBpdP0htlaCTMlybKmIhYce1HVhAvrErdFhtFvrMuS
+SlZspdp8GOR0vv33916u+aqs7db6Bt+X5T1f81K/06qSMffGBMa6W24Inlz/dVzjKBzLUljqWsbS
+hAxWV1z198zryJWY7dI1alAlXc0oVIFaijr+qsxYSqhzXc2I9B2ZazgXOOn5E/u7x7sd+VT8HYFL
+3dh72s2XrIQtWKdLMvGJBth1K4OQQPf6pqkJFEoZD1pB+v3S5Uzw7itVX+77HhOt4VUOi6X9vWad
+4giLY1rDxvY3Hw/Q9Ik4FDzfZfdgm2pxTXjf3uTDh3ukgfT3yhEvzy0DTzjuqBv7gLjbQsElMVOR
+uFLX7Cf2O9z7uwhXQhIm+CJweabQwLnKCNX2exA4LAR6KjqPXC6X1zpcSTfSiHVnEvuMRNw69Aze
+QmWoYYc3tjfxYjljeik33la/WF/kPNJDM74acS3q8AyXlRBv4UJ5hOeXRJ+2JbRHV2u++W17zd8p
+FrwpwErgtXxHwKhX2khbnsuFI9BM3DCZbGkNl+TqonNNydlImfOBchw4Nk4DFjeGQuQei1WBI+LM
+ZyuH+WOrw7AWVr9wPHoNsDxf4opkJK9cVnrAxl3FWgQgpXo81kVNd2Dv04vfX/C1S/vZL2ZzZnLh
+b+b5Bmh2tVSfwJbZAKeCpqEUWWkUdWSqqBgHcs0j0HXu7R+yJ/c5mk6Y3IhZK6/IStuq/eqmoruw
+m+N0KcNRdFypa/Fwx+VCkMbcnfNqYQ+nRmTDF/WIL+kQzrcq8ko9FeuOtXIDWZuzpwd5rZxDbHwu
+VYZcNN3mwHVKI9VJaBKP5+rdKF7Za04XHQefsff6arUgc/rSeEfeJ386NsxhH3f8uFL0/7Ex5uvG
+mHeNMX/LGBMZYx4aY37LGPPIGPO3pQlxO27H7fgJGT+O6vQx8FeBz3Rdlxlj/g7wbwN/Fvivuq77
+FWPMfw/8ZeC/+0HfFbgur01GbL1+yNab1iLsvLPP5UtruZt1AVtyfdqGWLDaWNZg/86EKLevJ3VB
+0ocJ2TVqWadc5zgLwZyF28/rnJWswNgNCELBdds1lVSBXl2uWCljPhBhyX4csaVkTxh4hEOBd6oA
+crnX2x3bWzbx1YbC0Yceb7/+GgCP3IwDEafsdTGjxCZEtyfyOg5GNDcSchn55OIeCG58JhN7lg8C
+j1SglytVHCbDEbsjJd+uYbr1OgBnyy+zPlNSNUwZrYSvl9CLm+zRqMKznDUbpun97Tt0AjXlzpK4
+sRWM2ZG9pndynxutYdoZHHkQ3fxDd9aNwMjrC5SsLTOf1bfsb7+f5yzzXkvTZS0zkhURrme/b6zE
+H7sDtkaiYIs6IrEuR8QUjjzEtuauPDZXlHaXecXOzP7dsDCIEpGiLmhEgZdlN1T6vVC8EbNViek5
+DU5vaCP72qvqDZN2Li7KLC051tqPgpDRQMnxKCYUdf+RH7MluP1S2A1Tlhz5Pd4i5FJYam++ouzs
+vhi5HYke10vR1Z1Xc8ZX8g58l5UqJ2XA5p51NXSiwYePh1n4cRONHhAbYzwgAU6AP4XVlQQrRf9v
+/Zi/cTtux+34Fzh+HC3Jl8aY/xJ4BmTArwK/A8y6rusjmhfA8ff7e2PMXwH+CsDeeMh4OOb+WwX7
++w8AiKYhw9JCQ73Ve3ylB6BVNbU62RQOk89zGsnGrS5ymoV1D+ZpS6CutaYYM1hL7bdnJhpCJAJT
+N/RI1MnnVh7nL6VPkOYcKiR7W0m5ZzVEwhBMag9fnYZxUrHlWUvRpnBZ2WTVeGUjw6jN8DNrBf7E
+OwOuLq1XcH+7JUtlKY7Vu+9sM18+sfOtS+5ti2h14PKW5pHRsBSi7Z6g3V+aOMRrO7duO+fNibWO
+J+mIF9jy3Judx1rKxt1MmIeopel6kZEc6dBggKkriG44JCxszJ1J+zDcn5BnskpVyeLGzqeOHJxQ
+5c62sQAKoBMXgFNWZK7t1PSLBb5yB3U6oFEuYl6tcbSVVmqu8nZCto+s9Y/CIQsl+5rmmkqal/FN
+w4Vgvi9ObA7kzK14PbEeZDQd8daRSp0jH0/w92HV4cXi81BCdJyvmakcPj0YUIha6ZoFWY/1EAP3
+i2rBeWHX+763y26shj18jMrgVVYTqRN4Lc2GybClXqmcmqe4akYr3Q4Hu3/P1xl9pnhHZdFgN6DX
+AHK6NWN5r+WVRyuPzfUCOums1ny8JqkfJ3zYAv4c8BCYAf8b8Gc+7t9/txT9O3d3uunWmgd//E2G
+N/amrMsab18XU2xTG/vgLZsFb0fCw4tg5PTxdzYah84oolAiqo1ariXftFrPWaorcaBaeuLHhL74
++SIfv0cnjTtKRIkVdNwRHuJAdfWvnawZCx4cJBWxYNNR7VFG9vXV04zhu/amD3/GHjBOHBM8tRiD
+q8YwkMjK+dU1s5V92NYvdYgNXhBK3WqWrqmU9X7rToiv665vrtnathvhTfU1nE5CZl+2D4S/21L/
+yyKiOfpjHKpO/+T5Ba1Iaz6nUCuKI0IJw2wNanqejtnNkkD4hjT3N1lyvcVFlrNa2O91fZeBILwm
+culbKUIPskys0nLPk12Dd2Qf9KvfbcgFZ575AUvdh+k0oczs2t2IiiwvJxvdRY+MWNyObdzhpiKf
+GexSq4ckmvTs0i2eXHRv7rHqgWhtsKnyTAdDSrnaA1V4lvspvij2brwZnQ69l6aklupT29n7tBe0
+pKqC+X6DK5DcwHcwE/sZn5xGPQrBYqZ1C0mEe8kTQy0wx8UqYypw2XB/QChjEAnIdncacl80APXp
+kqyxz8DEhCCVqXUdUuUbNh8+zvhxwodfBp50XXfRdV0F/O/ALwJThRMAd4GXP8Zv3I7bcTv+BY8f
+pyT5DPiXjDEJNnz408BvA/8Q+PPAr/AxpegdOmIanJlDM1Vyqo1IWpvsORjETO/ppJ1tM/RtGWZX
+7n7w6ft0c3u+NabahASMHeqxdTVfZkum4izw1ADj+4ZcbvRgMCZSA0tprpj3SmBdi5tI9k3kqb4x
++CoblbXPyLUuYz3IuJIlYeTwfK4SmLAJr99NuPfz1jKHV/t06i6cPa7w1QjVbVkvoJs0bMu6PLko
+efKyJzNN2BcS7iz2GaqDbyhqsA/OrvmWXM7yVctPRxbr4d+c4Bzp9fkNl+f2uv/x+zbU+sXhhIOh
+ko/EbGk+cRCxowYct8pgISSjpPnuu3sEfo8qrAhEHDIOQjJRrHldx0AdrWLEI3IdSsd6hYPJDEcQ
+ZS8fMs5s+Tm7P+HOhf27J8/te+8+XnLnTTuHPX9ENBI7tLvGPZClHO8xja119LZFkHN5Rf3S3qcP
+zDMCAbYHOyHjkeTcaRnpvvrSEekyD6+196Qp4My33shN3m74N3rkLZMBR/sWdpy6NaWSqsNdn1g8
+IVM32mhJZg9Vply5jEcqX5YDtu7ZfX2x2iEI7NqGA5elaONyo33oj2h9e/33Xr/HYNd6nvXWF/j9
+a1tmfXTu4wgh+uji40nS/zg5hd8yxvxd4HeBGvg9bDjw94FfMcb8F3rvf/ph3+X4MeHR5/GyXbpe
+NPLKUAmksl5FPDD2hq/imlAswY3ctsFWQhArRnY8GvUBOGZAFOjGOTukEgBJC9WBswZP8uRZ5ZHd
+2O+YPyuJ5AZubY948NC6ou+MtUHPGuJeFSUa0EixKTA+jkhGLrqSXbl+lb0/5M6Q3WPbfRmEMV2c
+6is8Vqq0jHUw1cc7pDObkwiaa3Y1t2TbsBIUvEgzjsTYc6GYlesS/TPDuqMUA0wxPGCSiqLeOcJx
+7dqeCufw+KwiDAVt3pkSuQqxuo5N4HoJ1wv7HZUolFw3wy2l6UlFrfyCt8rpdGClXUmtB6jy7cO6
+XLUkZ/aaq92Qu3NVeNolpXIR4+WASBiJQPmQ07zg+VP7d0GbEg3sgxB3E4yg4PPLGWuFR6kO9zYI
+CRW67I4cXN8+pJMg2kjDe8xIJTrjqJJu/DFGuYE8N5Q6QA7agADhIoQx2PZGxK79DVP7NJn9jraM
+8BRW1uGAQHsn1N4L4pKg1CGctQQDKV2NUwphGbJivgF79fT68eEWq7U1MmZ7CJV9Rt76tMfut23V
+6Th6n2eqQD36nT7//4PHjytF/9eBv/49bz8GvvTjfO/tuB234/+/Ybo/AgnDP/dJ9JS7H32XSCjF
+L+2GvCMU3+FWyLfF+nFt81S8WOYIrMjrh1tsKZu8WLeUjbUq82VJXqkrUc1DW5HBU1Ir6DpaZchH
+nksi8orccTi5VI+9Z6f54N5bmNK6ZMtFSrHpWa8RwI6iaViU9vPn0vIrmg9JL7qu+y6kmcFRA0so
+Cz0KA+71fBFVQ6GE2ryqGcjqOgYG6iKMe47KsUfU2NfFyOFff+0zAKTJnJ1Ta9mukpZ3HrwJwHDP
+Fof+x//nN3lPDVrXq2aDG6BrCMTA/Mt/6t/jP/urb9vvky76bFaSiSrNf9nym48s0cnXvv1l3j9T
+8vMr/4TF+vd1rT94v/3nf/HfoTy2vz1dGX5PSNaLMxvmXF/PefzSelCmrhgOVTmIA7zAvo6bikhe
+SrAhlvHIJQVXlQ1Nz9+QlQzlbeXGbBrdluu+e7Sj0D1rq4ZC62xah7/0S38CgObI7qvt3PCNa3v9
+7sKwlpbD85MbHG2M7XFAI2/kSm6MB/jilxzikYq3oyhK1vJCaaCQx5b0naOeQ6TXy7zB0TUNA4+f
+/WlbMfrz/+a/gifBoF/8M//p73Rd97M/8AbwCYE5A2DMR4RGwWFk9xpD39tc/GnZ0YmFqJL8+qIs
+KLDvvbnjc1d8htddzcWZyjSDgkO10Z7qNGnLjqKXE/dalgv7+r2840itt8d7Dtdn6pzT/XnoZGQ9
+1bfX0OiGj32Diew8r1cugR6AQDmA8ruaWGm7TZdd24LTs8qKBPRgP+Rhr+cInKhM92qVgdxVrzFc
+KGuvvc/nH8CTb9j5vKg8+Lx90D/3Ts21PvvuIsCs7EEXdfahe175mF0bD2d+QHNhS3nGgSKwOO2H
+f3yb8eHnAJiqBLxdxvzOlf3x+U7K2cSGWv7eFE8U55yE8NwyGVHP+wXg+41l+4xPf9Eag+dfrvnq
+C5WR5zZ86PL1Rpyl812SLftA39uPaNVSnbcuE3FPvv6a2K3ODO9f2tfJuGAS29ezyzWdiGEMDRO5
+5q2onoznESbKUS3KjSRAZgxr38aF7/wxO4ebr9U8Ucv1LmtMryUZNFQKg8zIZ1vbvNbhUAGBwlzj
+tIQyIpUPA/WdeF5LplCvVwtzQkOS6LMpvJBa1lWT8+nMVjZ+7ze+yq7fg6U/3rjtkrwdt+N2fGR8
+QjwFg3E9urrezCj2fd4Yi+34wYSvPrKncnnhc+euPqSaeInDtiTOH7xxwM9+6ecBePbslN3AJtTm
+g5wHOzbJ9813v2P/+/iCSzVX3RuHLGcCphQ1RvyQrx9NKZ4oPBDU9mqeb2Tiu6ohVjfg7gCmas5f
+liW7Cgl6EpY2czhRCDKuPRZKPvkraHbtZ98prQt8PDV87jWbRHKvW2IltS6dklAchumqpVqqvi0Y
+8fTeHjdP7TWX64KTp9ZiPHj7TVaB9QoSJ6dTM1Y4EIV6HLJ/T8nF5g4nv2F/Y3nnFV88sizBv/xL
+d9k9tBWM9bVIYYKanxpZa/XoZIVTWg/jP/q5u7wrPcdvXef8D//H79n1evlVuxb1ku8Hu/3ae+cc
+Pn8LgOeLp/itxVwU4rD005pAib9B4HBwaL3CN+69RtXZezJbVKgnjnfesuHOo+aM3UZud+xyMFX3
+aOPgSOfy2WJOIeBXrpDQxaPTe8nIo5RkIVXJ05d27zxY/AwAF8UpQWw9m2QHJjNBkN0Qo9B1fxgQ
+iip8pfAhcR2IlXwkZqWEcNK1VAJ7jIYD5iIMml8rYexW7O8pdAsyLm/sfbipKs7lpX2wquHsgz+w
+zj9ofCIOBQeIMBjjEMjtm0YBXizQzONz3jvVQ0jGjbLd+0KG7U19Pv/Alh6P9o43JcLX7nhMJ9Yl
+XpyfM1RZ6As/K5KWyVOePbMHRFz5ZNv25s/qgkzkJa++2TBWr0EpUs8gzzcunNe1HAgM5VQw1sab
+Dgx7YuZZRwoDhjFzYzfVwE+oemn7aYS/JVCQuuyKas1ddV/Otgs+O7AHRP68pCkkMDtZ0qoKUIg1
+6uU3S0ZSmSpTQ3hgr7W4qaikopWcGb7xFbuh337brs8vvO7wM2/bcPM9N4Kjz9vv2M249+CPAfDZ
+vQOCniDWteGHazp2+o3rhzz6B98A4Os/u2L/U68B8OZP+XztXfugf1OEsdfp79Mp3/Pdh4O/N+Hx
+V+zB8mj+gmIlshPlj5JRwGuKwQ53xvh79um/vzfAn6qcW2/RqvIzcW0489pnPeKhvWaT5YwC+zCF
+h6+4fmlBPem6Iu9BRHLVG5MzVHv9thNwpj6HhemI37D76Uz5jhMzZ6r+i/ZZQ62D/oCGobQEjFux
+r16YvdfsvZuEEStR1Q+imLzuOz8TCrWAH2xPuJLG5uVT+99vvrpgz9hycTjyWFibR36ec/XCrtud
+8SXNyQ4/yrgNH27H7bgdHxmfCE8BA54DkQeeEoY7vksjbPhvXKekYsl1XMNSYcMXdq313HUGfOGn
+LY7+wd4xh4IoF4M9diJJ0fsjDl2bwFoKBn0vbPl/hxI6uVxy/b79u5FXcNUn34uOY0GaHcGrL7OC
+NhCAhoBaVGFD32VPjMmfKTzKiWDOUil6Z9tnoS7K3R2P1dzO4/WdkJVc2NHEWqjLc3hrx/7uPK1o
+1RF6EidM5WrmMw+3V0RWMnPbN8yVUJuHiw2xStZULIQLOE8veSaLeL9PUN7/Ig9+yWask8Vr7ChB
+mfkxh0NrVV0/IVNPwfzEhiWtH5DcsyYq3Tnl/3zv1wAYvx/yU3/h3wfgi/5zPvXTttrxqU9ZQse/
++Xc9mvN/AkDTfkgZFhRwJQ+ieJFtOCQreY3jZEA8sNZxZ8dnKLXxo3CAH9r3o8mEtWDKx9LKDPMQ
+b0+hZL7kobpSu27FMrBuSE3DUq57Jq/Q9QwL4QrOqpqlksdV3RAr1Gt63MFlTemqb2OrYiw6+yEx
+WxP7flMMuD/WPAc9CGnIQqCpnUFIIRiz7ziUSnjuj6eMKjvPUWcziuddgSOP5jrr2FXvytXQJ45V
+wTgF/7PKQH7Mcesp3I7bcTs+Mj4RnoLpOvy2ofPAk+Wblg1PkG5h09EqmTcIXe5P7VkW7VmL+PZO
+xJuCM8dtyGJpzV9985hM3AmRH2H2rVWZVioPhRWvBWLacVaEQjEmrs+6sxbqpFwzoO9KFAEmDUOV
+se6OHBT2kSYtrXINg8Blv+kFauwy78Uh0vFg6g3ZUjlte+iT+OJk0GfZDSnE+V93Oamou5qs5Tu5
+ndv1qmQmiEeP9Ph6OqfWPFeNoRIC7+RmvsFnlMZhN7TrfLhl1/Kt12HYqUNwHDKZIy0AACAASURB
+VLC//4Zd+7QhvZIa9eyKrVCaE0r8np1d8Okv2pxDtXqX+SMr+faN5YxvPbcWKv2Fz9E2tvs1aex6
+x/N3e4JnGj7EbFy3JdUr642UzRojLYc96XwOIg81yTIKXWJJ4QXRlIG6VSvHw5c2xKqnrlt7GO2n
+Qd2w0vc6ZouhZy3w0N3ixre/7XnqgGxAFBd0QYkRRLmFDWnsjSi8w0HHua2cEuBxs7K5hjioiFy7
+DydBTKvMc49oNI5LJASlV0f4ytHUZUCtbs28qAkq8Yi4YvRizWVjPbq8rMm1Ll4VMpO+qZnf4L2n
+rOvHHJ+IQ6EF1k1DVWwS/HzTh1IPJq5DJEDH1mRAtGMv8p7ETO/u723ovRf1nC3V8V9c5SCxUvwj
+tiL7etGKmCNJSHyb9NmeTDjYsZvj2SylzO3v5XFHqPDAE43womzolO2/MQHjTt1wSYPRwdK5hk61
+cDVDsu1EmxuXTPcIhIUYuR6V+CZruYNdWOOqzn3V1PgSR63amnVr31/VNamEcdaqcx8PIuYi/3Cd
+lutzQXFfTyiEow/igKGerOkd6SsOj4grXUd3RnlpAULzZyk3LyzG4PmqZXhuN+E/+sA+/K2bcHJl
+k4v57IZzsWovi5Lli28B8L/++g1JZ5Wx7mU9aGhG832ATFeXCzqpTxUVJOpTCQT/HgxcJv1D5UaE
+Q2sAoiSkE8YjSFsKPViuaO7OywJPdHOlH2+APnRwcMcm4rZncxD25VLufFQ6FD0xTOF+KEvfdqxW
+InKZWmOzMmuMYw/vm3VKJHq/cTjijvr8k7HHtkAljaQKurrDV2+IH4akAr3htpuOSN/vKHTfx2K4
+3hqGjDwbQtf1jPWVvdbTOsXTo/0462jnN39gnX/QuA0fbsftuB0fGZ8IT6EDis6KwjRCutUOhCrp
+7QUug6F4A+5N8PbtqXv32JabDrcOiRq52q5PLsEV9+oJlUQ4tscOmRKGkercbpmyLbd0sXZJZD13
+PJ8TYQzc0pDL80hkaboOliLivFjAoTyBauHgiD82SBy2+saezr65f7DHQE05cRvhRL3OZYC7FCeB
+uvNWzRKjRp3jSUKu7sQ7+y7dtV2LV01AqhLhQAQHYzpmIzvPoOxIVO0LUkMnK7flGHyV2aaOnfyq
+TPFu7HtXN9c8+8C+/u1/9NuUmvPV+fnGrf5WL5l2d4wMIvvxgB11Y6VOTa6YZnWRclNbv/qpSnp/
+GJlouU4RKJTYGAbqCIzlpcWOIRLJyNZgQhzZtU2CEDdRN+PcJfJFtqu1HyUlzdp6hYPJGtQ8NQkL
+liJxfWvL56q03ud2bT235bpmrVKgWTYf8gAYGHhSHhdrc1GB8rdk1xmeQo1RbNgSPNqrPTzxQdRK
+npvO0LX698gQaJ9VTQPyFOrCwfRlcIWr9yYjlpH990Xb8kxCNZPS4Ne9GEyNP/vRpOM+EYcCfAh6
+7TdLWXWEAgLtTwN8wWr3j8ZM98Vn6PeiGQmJsvZ57RGpS3J0vE+oTbryDf7C3txOLmUTuhRy8e/u
+HIGqEqenS1ZnohGvOqYCC+2JB/Hxst3I0z8uWhLlLb54N2B3JHfXc4nVg+HqxjqRT4x1Hd3IUC5t
+zN3lE9QFTjsQfHpWMhIm34xdKmH8s/OGu3pQ1r5DLBbkZ3J3D/CIpZ/5YtSQqipxXBvmOghWi5y7
+O8JqiOXn2ZOnnApK/OXVt/jKr9q1+s3zRyQ66Rwn406vHCUZrqmTYDTPyNvhzj37Hdmpx8u5ZOfL
+FXXbu+s/uPehrVprEYDROAFVH1xB20fThB0xKO2MByRiShrg0qhlPhw3FIVi+B45Foxo+rCkc4n0
+4DFsce/ZNbx4ts32nr0RzwV3dqZr3jux1zdvFpucUdd2jBNLcDJRmJs7OTOBlAB2FBLsBC6mPy2c
+llpSAkZ5htB3qBUeeyXILuC2hqXCyqSJUHqExO+FXkLuqlo3C2vqsZ3bxSrgUNWqbl1YBbUfYdyG
+D7fjdtyOj4xPjKfwvaPrOhwRBY6J2D2wbu4b24dsK2wYB/a9wcBj4EpuLe4I5EbuOhX1Wq6Y6VjJ
+DXZqqQinLaFjPYXSXeMrqXU0TZgocVe37SbBk8roFMbB9MjFqANRuo0Sh31RtvkVdL5cC5G0GONQ
+F9ZKtCVUicg2GpdOrl/ZJxGbkKASiKDJN+Im3cjQCBG3U2RcCAEaK0GZ7qXMLZqZWWjw5Xk18xRX
+gix3Y59ITVy1MrvVzOW0sInYX/3dkkeXVvJuWay4Fnw4Dh0aze+n7khJ261whdYbsOTnP23vTbEO
+eLkQDLFrgJ5RuKfv/P6j6lpc6dnvJIZcSdxEDWO7/pTjLVWRphOMqPK6LgR5iJU7wK/tda8lptJW
+GZ0awoLYJxa603NgPrfW+HCckTv29b74DMs1HIyEGqUDhRqdAdOXkkZy68/XVLnYrOt6s0cSA6lI
+cNbGQxEYg6HCCM/fCOfUIbTqriSIMerWLOuGdWk9r5kSwnm2pq5EwpJlFH0C3XPwDtRsN6+5qTdt
+eB9rfGIPBehYCxs+C0t25F6Nm4g9EWQ82LfuW+n6RHJrk6FhZyCRmKoGkVmuzy9xVYmYZ2pZ7Qyr
+rGfP8XFNL7A62HzHZb5G6ulEikNdWqKe/NVxeNPvtSZ9Wm2UNgkYirHIEQ15HDa0/UFRu3QiifGD
+lKVi5rpVyBCVxNsihbnpaLUR1otio9FYZLBuep1AVT2WIetYfmbX8urMXvNjt0IcKrw5HpNJ7aq9
+se7+2pvgrRVq7KacPra/vfRhIJc/DH3uK84ZjEU6O45oJHx68CCkEMfkw7tTHs9smHe9uqLZeNXK
+uP8hYURRNbS1QqzJAE/sTY0C6dFWuAnLqDxa5S1Kv6CUmK7vNKQ6qIKm70QMcVRFWs5qvMQ+YA2G
+UCHkYBTiqjyUKdQqBuCqDyZtQrpOmgEdZGovb2/sumVejNPZkmvdNHQK88qkw9eBFFPi6lCrRTjj
+JQMcuftt6FFmfcekz0pt8BeXz3n/mQBlqk6s6fipA/tdi6LEkSxBU3V4gj/nXc4i+zCk+TjjNny4
+HbfjdnxkfII9BagE8Xx+s4KvW7DMZN3x+8/tafz+PWtRtr0Jn33rIQDD7R2qzMJuTZWRL+25d/Py
+EYVMfieyjTZINrDVs6zi9PxGr+cM1Wi0bAxrpUFd1ZXdpqEVHiEyHoEouJx5x64Yo1cHLasrSY5L
+S3J23VFLJ/EgMfiBxGKMi5uo5l2LK6KruRI1/Gn2krMX1iq9WhVc9hYhirk7sXM7EqRjNIg43RLP
+5XPD11d2DgtcXLnUq67iTYUE+1JqTuk2tO7RHO554rN02BDR7G8HvCaP5FheTl0VuGI7fvLtnK+e
+WM9jfrZmqlAhd0JqCaoY2aG8syLp3zs8YxgqgRx6IZG6OXuAWFU2XIlOz80WG2+rKA2l6TsRp3jS
+5KwbNUn5FUUvz14teXZhrz8vS6K6rwYYFkow9uzStA2+INZ7V4ZvOf370MmKr0X1fn2z2givRBiG
+PW1g5dLp7+rUI1spfvDsHHbdEEdeqtMNUURLl5csriUtuCrJdB9cNQKOTbDZv5nfsYpU7Rh7xG2f
+mI2oFj+h1YfvN/otc1HVFOIaLF9c4GBd4kfv2X+PBwNWV/bf375/h4cPrZtVhCMWM1tE+t2nj8lf
+WN6+qr9xSUKmBT4u4dGF/WxdfyhXH61LChFr9BUSx2HDs1dUBiqFDG3HU222+mXDN5XN/0DxPtlL
+fMWLX7o/4fiBfegfdDGhkIxL6Qr85svvID5UTq8rKjEyfatu+LRmsuhcfIGsOukYfP7OIfO5Zf/J
+pyteCI1YNM1mQb99k5HWvXqRKiDBhG0daHXsMNm3G/B65nIkzsAgcdjRg7UuJEbbLHgpcpqx67JQ
+teBTR8ccaW0fPXnMV89tfmFn2/7Gk7M5XfEH3VrXOMRCcg5ih3Kh/gLlFNK6wL1W+7lTUIn23C9n
+zHI750W7IhmJu1EVgDbLcZWfeFrPefpEOZNXa453xcHoOyT5QtfXo1cNkWf7aiZ3PMITu7da0+Kp
+qrSci1A1a3DVXevHAY6AVV3rbISOaypyaULWXc/0BYloAqI4o6y1tt0aM+3vU8ID9Xzcu2uNXhNF
+1CfWWKanOYnWM4tjPvu2bXE3ly1fCdI/sM4/aNyGD7fjdtyOj4xPtKfQj67DmmfALRuYWKt4I5Wi
+dTXna+dWTcl1KsYjWes24slL+/7Xv/6E84UNH26UIT4YJYwk255XDkvVm4elh+sJMx93SJiaQLwI
+bmY27t7RlqEWv56JWzIJi/zay5S0t1yigbugZSp4rTsvWD0VIYfjsTWwc8olLLNIU8LEWujCN2zb
+ggOvuQlreQfrVUqixGZxYyf5aHLCxaW6RBtDI3x929UbsZvM8TCJnYevBGYY1IwFmXbKlGZtPa+h
+X3Eo370OEobCC8SquOSlIVTSsghKBqG9Ny9XczzN7bP7Y2Ziox5iLf+5V5IWolX7rjAiCgNC9Wsc
+jz3ORa/e93OMvAGxQpHUcynFtxA2LtVaHJNrDyPFrEL3pmprFqqGtLMTtqfWkwhNi6MMfl5WeOK/
+TIU3CKsaN7Zh5f6wJPHFf9k4OMKOtCJ7cIcdjUBIe3sJrx0J3+G5LMWCfVPnRJ619EN5Oa/SivGZ
+DYlNN6VVQnTmGDIpiq2CCedr63levNeTvoSM274nKCAXUq2pc85bC1N/mc6Zl98/qfuHjVtP4Xbc
+jtvxkfFDPQVjzP8M/BvAedd1n9N728DfBl4DPgD+Qtd1N8YYA/w3WOXpNfDvdl33u/88Jlrp9Lyq
+HBzVbhPFXouVoXlkT+tBfs6BOA3OrtecVDZmXmcVa3UtXvcw07zDPbNdcRdZRyVLOvAiGnE2DMMI
+5eLYEd7gjuMzVk3cTwzvSG7taFSzlLX94GrOE5WWbqRfsRskuBJcWaUB3xR70ZtBxKgWam4hxuXS
+IxR67t2Lkusz+xt1fM07fSdlYDBC/zWSZTPzhErWbisOmKo8WXcFxcS+fhOfN3rdTGE6nlyveVcd
+eZd1Tpj3pT5YC/79xtaAe1vWyhULeWkXM9aKo1s/4L1naohqGzi31vZoGlCJcero0JaRcxPzzdqy
+XuVVjaOSc+M4LJTvSBuHY1l6f8+u/Z67z+MP7D29mN/wnRubX7o3mnKg+7M9img7ey1be1LSvplR
+zOxn63KAloivP7/h5JUk6VhyX7kbR8nOg9GAWJqlw+ABjrF5KdeFVgSrrUrni1nGrprYjrfHPHz9
+PgDPXl7xlUd2Pk+Xc87EyPTGoZC5NVxJKzNMXBCmgdalS+x+cZc+758Lh5EJ5Tmo+ONjuy+mI5fd
+kYSGugBfmpbPV4/Ju3/+icb/Bfhvgb/5Xe/9NeDXuq77G8aYv6b//58A/xrwlv73c1gJ+p/7kWb0
+fYaBDawYCgq1PkfiunO6NZl0+y4qwyyzC3JWXm60/Zwk5m5sF3gsafnE92iENxiMio3IaezEPJMY
+bZC23N1XV6a0Hak77kf2hg73Xd56qARdFeK/sBusHnjsi8HX7VTH3xrylhSgHngQKVs8KGLcA+Ew
+5vaWvDadsBIGYbALZiG3Fo9U0Na9GJLGfvfM2MNkRMMbh9LgbD2qS+H2I489qSU92A4Zag13xFWY
+Ow7HwkqMiBiLLt3pCvaO7GbbivbYUrt6Kgq6eeBzpEN6EE8I1WtycnVNrU7Ft+NdPihsfX9fYKvq
+4YB9cTj+TrYka+w9m4QhoVz/5brm/q4IXozFpnSlS692c3c7wQx6ar5dYrWfh11MMuw7EBXyNbDK
+rRHxvBFCo3O4t4tRUrEqO3Z7qvVIKmKDEF8h3Z244u6O+ituakZ91UnyA/PUx9fhHDkJgSjy1m3I
+w7v2MPRfOTxUs8y+FK28VcsqtQYinWf4PVt3WdMpib0/hC991lLaLU7tWlbdgmgosFxQUytcc7uc
+JLP3ejIZ4Bb2Wp6L5/OHjR8aPnRd9+vA91K3/DmszDx8VG7+zwF/s7PjN7G6kkcfaya343bcjk/E
++KMmGg+6rpMYGqfAgV4fA8+/63O9FP0J3zO+W4r+h40WQAgzPwiQAd2UtCrjEMv6d2XKdmldvOet
+x709a+XuTOBMDMTPxMS8NfYx8jaOTcOl3Cx3mTERScfYd9md9fVfaxHf2Iq5PxXHQhfSSorcPVtt
+mm6+MBriiOA/lfV5EEUE+veHbrfhRdjxSwZG6tD37Gn/GRPQKME3Mj7xvn3/STngrmi3Tv0GT97N
+pBY1XTLEr+XaRx0vXsnyBS1b8ir2A3dDY7Yv4tPcuDSB4NpdRyDQwvXSYUfJxWRoSFTOdXeslXvb
+bdgX4cwb0z0eH1uLuDh7wY0Ss3fJMd+xVvO1AzvP0t1hr7bJNaeckl3Z3zg4nhIEQu5dVbh70uNU
+x+hgEnA/soQ6buvhq7t0rwpZi4Is7DoawYY731rgqvUJe3YWIJ5aD+Ohc8gDIQxfXAfclQ7nXHRs
+sVdgBCs/OrjHn7xn1+WfBk9ochHfyMp3jUMjfdDVuqBK7fd+eneErxBz/pl7HCgkKBV2rbqXnMxs
+eDFgQNPY5HcwKDkQmnI4GRBLHMiVZuR5ueCOJOcv1wWuyF/zOmE7sPP/pcOHpELyfvXlJR9n/NjV
+h67ruu+v8PRD/24jRf/D/t4BDsZ2g31he4tXajl2hV1YlStqxfKuGzNXxjpICiJBTd3jI/ypXfhD
+EWH4ZkAjF36PnK0L65Y9v6k27cKVUyONEV7N1S057jhQZ6QbZeTXcuG7kqGdErumwwntpuijDt91
+SDLrdJXRGCP9wJaOdWo3d7oWhNdfYWq7ie+4JQN1agaxu4HPjmYzdB7xShnman0FuvfxdMzB1G7i
+h5GLI4BU0I1o1H7sGoVGuxBuWbKR067kYAOf7sg24iMOrnItY9eu8Tr02FUOJ97u+NKO/Y7qzoQz
+z27S9rLgXNTopboWh9dXPLxvP9u1IdfK1OdNymRoH8KgSnn5ws75/rEO2FWBLyGeeHJIN1Z3Ye0x
+SZWPuelsRgtgWwfatOKVVIPL7Jrp6B0AjsaGLuhd8F2G+u5wZcPARdVROjoo2qckgncf+wekhb1n
+A5Gs7DlDFqpkNHWzgaxPvSH+jr3XvlNt+lGqS1tNeJGm3Cx66HrJjkSWz9MO8bgQdi1DteJH/V5g
+TFTZD+yWN5wpFF7kKY9buwCT0Zg7xxazwD/+Gh9n/FGrD2d9WKD/nuv9l8C97/rcrRT97bgdP2Hj
+j+op/D2szPzf4KNy838P+A+NMb+CTTDOvyvM+COPYeDjK5E4iUJGqg9PDqwV+MrTFjGtUQ0jfHX+
+FPl4k+13yoq3d22y6rK1GfRx4m3YcndMxVrHcuflDEv73Z8+PuTx0p55L9bWk3jjYMhIbu3iJuhb
+/ll3NY4yorOu5FAn+khw13tDh4uVtf7VvKZQRn7mBlTn0hsw9oT3xiG7QrMtl+BPlXDCwXF6ebSA
+dSlIsJJeQzfmWmFA1hp2ZPGjrQmms9fnty17d6WZMbIWbFGNOFDi644XkQhDcJ7fsCMX9rrOmR4p
+qSo0X0ZNrSSoX+YkYk82W1O61IYHZ+sFy9Imbru5kohXS64UotVNxyvBeX/qFz61IbtZLk8Yq8v1
+QjLsb+2MMAphRomD29r5D/2WuRKXVXrBeWW9wjtIlbmJyOXuv1rmHOR2wwziAbH2U+e7bCn0eiXI
+dLlqyXLrNRRlwjKT7J1TbKpZI9dugJ1BQCk4c9mB3yOi3QLdakLXxRNadi2Y9Gy1phA1Xxm5FKH+
+fbHiTBwgXrRkKO2PsTpDzcBlKnzLRZszFHdGtExxVWHL246DYd+h+vHGxylJ/i3gTwK7xpgXWJXp
+vwH8HWPMXwaeAn9BH/+/sOXIR1gH7i/9SLP5nuFogR94hqnIM0d7DmFPXKnKwn60YiTeu/u7A/yp
+zW2+3s4xmVy4fYehMtWRsuK+GZIndhO46wJnLfGOIGKhG5a4Cdvb0qZM7aEwCSLIey5Fj1Ul0Ezj
+MlC8+Pkdh91t6x4Pxcw0rEISMSU9W2b0uONu1hIJFBOIlLWcO7RizLm7PSDxpInYghfZ31i6HbEO
+obKxh0m9qomlQenjslCo5WQO8biHzAa0mV3DdtJnqRuizrrtbtTSFHaeSeYzVzdg7NWwUlyrfoBB
+Bk1p45V86dOF9lAI6hJzbt9fnWaka1VExDZ0QYf3SrHu+Zz3RT7zS5Of447YbE5OOtKldTR9xerF
+OmZ/e6r5gO/aB8VvOi6kJpWalh3Rp/chWkZFIwCRSUs69S9PRwnIBR9XHb4eLE8Ze7/OqTN7oN2c
+gHR1OVoNuVZYuCVhmSZwST1Zp3VNKmBV5ITQC9OWHk5l1/BG+60rPMbKmZg6ZjYXD2TusVyLQLaF
+vcTeH8/tod0NiPC1W0e0S7t3VvkMrxdMGhv2lBP5uOOHHgpd1/3FP+Sf/vT3+WwH/Ac/0gxux+24
+HZ+o8YmGObtCmNSBi1HSZjlzYCyghzyGaTzkUMCVgyhkGlqrUp3XmJHILa4bjmrBUseCn/ou696t
+m+dM5Grn5ARza1WOHgRsSQwlG9sk4eUqZTiVC1dVuJKVi24ctsZ67Q0YCWS0Lq2HESaGbfXrN1sZ
+z04EOCpqkIXKxGG4YsUwFL1YBeGBREquQmrNrShbfE+6gyupZI9DHIUt547hyXPrdm8nMW6mpNyg
+Io2tex1g1yIyEY46C6+eLojdvoHHw5Xve3aRMVUGr5J0HY7HQt2Qg4uCZWujxXo75r3n1mq+enzJ
+q4Vdu1q4gXfTBe/Oexr9lEoYiYeHe4ylpFwWK8xKXpG4LIahg6PGpsjxN1LtxaLAFZHLvWHAi9Re
+n6NqAmVArGqIk1b4okTropyJwpXagHKmeIKxJ8CosWv0md0p3zyze++3F+fE6tB8JfCTN+wIFOaW
+frlR9q7CiFhNbCb+UDI+LiRKtL3DQt2ch+Phhi9hFDksIvFAXteUoTgYD/UbrsFRjj5zWhzdG29Z
+cUc4i+14QPdDiG2+d3yiD4VGZcZ8XXE9EHpssQBluGdSK+rKnEx38/n1DKMW57pe4vflNO+YQpj6
+Wm6dG8xY94xHyyXXud24H8xSzsRr95uvThmqy7Ee2uX6vdMb8tzOIepmrFW+qhKHctWX3Na00lZI
+lRWuV4YgsDfupGk5FSIuv1yzpTxAOu5FXtsNH+LscsZC7uC1V5L0bFFdyUpgtZlISLy8gbW4GIdT
+bnoSktxsCGeen3ccS4XpRiW7aL4mEu/iLGtYyb3uOocr5ReW84Kv6aH31AEYR4ZCAJt8mrBQee7Z
+5Sm//g17f66ur3i2UD9GZud5kaXkQqQ2bUsnJGAbDfHDPn/U8H5jMfyhwqq8ivFFIz/rctY9t6Hj
+0wi9el13LFXiu1nb63Saa0rlYmgd8pXmPChwy1zzqFip56PLeoBQzpYEdD/orviHv2/zJI+yGT//
+pr3XC/2N10KpB9B0HUu1Ybvemkahm8+QUtyj7VB7z41oFcKsmoBdld+XyZRo+QEA87om1jV5fel1
+MGChPMl6dsFSWg+NZ3hfSlXtdUY7ETHMxxy3vQ+343bcjo+MT7SnoPI4J2VNImbgVzHE6gJMdfKf
+LnIuJR1+0zZU2/aUdL2AvSjRdzk4om1fClvvlxXLTPDgPOVc1midNswUVozalpc91aD+/p89m/Nr
+r9Rr0RheU7Z4+42Ad+TROHXDShyLN6IA392rWS/sdxxNt7kRCCkASqWqU110nJQsJhKfySOurqxF
+WNBxZyhasaZFpXeu5f0cvznkcqFEXbFmKfGVK68mGSpU6Frm6qjbVTdoVTRkgiKvyoJJYK3Z0kkY
+iHAmP9ila7W2qsfHXciBavDxwZD0xH7Hlx+/4r1T6ymsqZkrq993AOZ1Td32DBVsWJ6rLmJWilvC
+jRjKKi5ET1+UBU7PTZBVLAUrTjyPUrBiF8PKt1b8g5mdw140xlenpTOMaNwPiXYy7Z3LmxVhZa1+
+zwvRdj7n6q4sCfnG3HoKsywjUd/BeiWJ+KuUWL9Re4aLQmCJ0mMgafgir+g1oFNVVpp5wSy3c3/V
+nrGSarprDJlAWPtdySpW56qqHu26I1RXZlGkrJRITbuW9dKu15fTdNML9HHHradwO27H7fjI+ER7
+Cv3Iug6n6AUyKl7IKr45sif160c71KIo26pgemBP2vEgYEfc/NGRx1rcAfHUnsrZAvKeTN9p8YRs
+S/YcBvqNalUw2lV5Usm152VLW/VYazgRK+nbzzxmao45uhOS6P3u1P5u0I0IDoRGayCu7e8tgoCl
+Ekpbr4QPWAYs+3xBHNKE6oxbO+SKv0u320jLBTrf3VWHKzRmcVXQKvHVlo4q9jA88LlObV6iE3Py
+YBJh1DBUxC6Oa691Z+jhyUMa7DmkS2ke6voTVrRCkHLd8J0zmwP49rMrOmkWjAiYBfb3LoU3aP4Q
+4tYuSljIij97fIYvtumxSqtN0FCpPNu1JYUsZUBM04sAdS3Hqc0vzCQo9LgqmKaCDOc+a3lks6Ig
+Um5ntVqRK6laS9m7Xq/IlO9Zvzyn7BGLTcuO1s5RHm9edjR94roLcIXeNL7BE3N3tapplUwOI3W+
+rucE4m+YZw6FGukOk5ix8kdNYDadpFWm+bCiEuu48WISJdu9LuNalIWruuSg1774mOMn4lBo+bB1
+us46HNF2D8Z2cePpEW8q+Vi7BdtyNVdBzKCXbW9dOvU8LJWZrhKHQNWCJIvZkRLQuIFSDLgP9iN2
+xXcX79oN8V/DR0RN+opB6ngcqHvwaBJBY//uTA/K6bKjPhfSqfNZi8asDAsy4SI8kWZcr0p2R309
+O+fua3ZzJHsDRqrKxHnJtQBJs1VPQpLwSryMF+uUKrMPxdlySTazD83rh1u4ysRPFT54g5BaHYyL
+FwtmSrSVp2ccSrMzTAY0wmRky54DERrBmSs358tffh+Axq0/7HMoWz64h9ehMAAAIABJREFUtIdB
+p3o9hu+haNShlrvcXNoHer485XUpeJWqx/tVRSN337gty1z4jEXJShWTLW+MI7zIrhKpbu4wOrTz
+uUkdikZrdJKzLyo0Z+DgqcqFXPGojWgEp5/NDLX4GA0de1Pbg+GrPf3VLKVQ8tRbu1SC0Dd1gKeK
+ghc6dJl9YAOR79x9MOFtz37Xziiga22Y19Gymqtdumm4VFLcyIB4rUfbd1QmARMddOPUI1CylrHH
+23f2+VHGbfhwO27H7fjI+InwFACQp+AUJW8dynoIvtldnnGhhNvhnS0qnZhmteBanJXLsuFipHq0
+rOvITZiMrUXxPNiWKvPKC3kYqIwTwTiVFRv0cNGPmrmWviZcU13b2ry7DdGu/b37x/bvVqahFfx0
+a3fK6qG1wPFRyezbSqReC2HZGZ5LJv70fM4bkrSbjoYb0pfxVUWrbqv7sb2mdHvNb33VWuVnWcVY
+rv9VZjiX/uPRakQoy4SsmVuv8GNrSafxkoUvyq9xzOufe9t+JrpHIOGTQgKSixeX1G1P8vqYQp2m
+467j7UM1GiU+V09Vaj1T2bNebIRx7HLaizrLa14JWbmsKl6I9GRHpb6yK2mW9voODicbYZhZBV1q
+S5VRfMCu0LD+2MJJE9MyU1nUyQ3Dnrw38IgkyuIVEUYJUTluLNyMeCi17q0CngqFiqETP8HuscLY
+VcK7z+SFOhVZb3PTEmfPzn8vHoLCkeapTYJmZotQFj8NhiTyRorWI5fcvUvE0baQjPr7ougwvR5K
+4pIpRPFSQ6C98+Z4xP3Xvrsd6YePn5hDoffWX9QN2wLv/PLRHQC8w4jjbft6FEaEqt3WkU8vPrIs
+PAKJv851wLi+iyt46TjJifWADfEZ+NbligYuuXQlT2Z2QxvPpVPbK6ZjNLJf/PAoYd3rP8YhbNnN
+fez25Bc+cwGgFrOY0NiDZ7QKqXbsw3QvsVT1A3PB7In93ftvj9nbuwtAOO3wlE3eHe4RSXY+Fj3Q
+tyuXUqSSdeHRCW8xX7f8M4mWJM9r7n7RrtHeRAKt4QhXsuZ33YihmJka0xH2+ZN4hruyv5OqryHP
+YtaqWrz42nN2t+x6Hoz3GGlOw/2IN0r1VdwVK/O3a87PPwBg3dTEhSj1VwFPv2olrm4iw76qIKFg
+y/OTOZ6C+LIxuKqM3PETEonkRNcLFtKudFb2gX3OmkaK7MtwxaGnTtqJz0DkO7nX4ug0KIXNqGYN
+awHOloT4An2VeY0vTcuROke391ccqM3+Yp6zWNjX89cc7oqUZ7I1IFZ+ZKD91C0cGumbegtoRXwT
+ljWZWupHuAwP7X7qaeSvrzN8XWfj1PiOAHWeC1q36PVDki0JEH3McRs+3I7bcTs+Mn4iPAUX6BkX
+yqZhqeaZpaTB7k8nxPKpO8ej8noqriGekofD6XAjdjKUoELbNBgh96rOx+9sF2UwKSiFhPPm4I3U
+XKKTP3QCGuk3BKHDsToxd+KQWInC8+uSY+x3lOIHSOqCdmFPfrO4Jr+w8/e8hnogi6BEXheCUpJs
+h7ubrs2yKCiuJHCyE+FKbuwqsMnM9HlKqrN+7BiM38OE1yipz8ubOcdX1hOqBf1u9jx8hVVOOMAL
+7fvD4ZBGTTmBOyY9kaBOqpr/1ZJsZhmzL9chsaznbjSmFMz7xbmHo+TvSFRqv/ilMfzWlwB4tvcN
+LldCYc6X5BKqGZ0lnIvvIahtWOYXLrU8vWwGnvgNGgriwK5zeNdhJ7dhgyMOS9MYypGde5yO6LQH
+ymXJupY2RAClcB2LpteCCDmprIvhDD3iAzsfc96Asfchu7ELu0pHRLLcIxcK3YdukVBJlGg1HOAK
+n1HHO/qsSyco/aCNCBTG1c0Cv+4hzUta3YdearItCq5LITBxN9RtAdDENjSdDLaIc3Vxfcxh/jBN
+v3+R44s//TPdP/1Hv4EJC1q5bbWzoHhmY7b52Xv837/+9wHIvvMe2yK1cERrXkc+teLMpjWMdYKc
+5xmlSDV3D7Y5EPHOb5/aDXb1NOVsJb3GsuBKsOoJLv/qT1syqS/8xS/yuh6gZ/9fe28WK9uVn/f9
+1p53zXXmc+65A6cmm+xWWu2WLNkGrMg2LBmOkwB5kCEkdqxACGDAShDAcUNPedCD4cCJAziOjdgO
+YkiyEsexOgIkuaVItiylJbmlHkh2s3nJO98z16lT8x5XHtZXh7xKd/OyxUteIPUHCNatqlNrD2uv
+9R++//dtuwfW6/1nsCc3MzccXjhhkeFrJ9jEPcqv3XtIecdBgkPFek1TgtqFm4uMWmIpfljx1i33
+3WroxghbDUJRgF9MM0L1ZWRVSSUikMF0QaRS7VK/Ne0HWGlCntcZv/4bXwLAJAusWIoW1Vu8+aXf
+AODnf+pzAPzSq9+gpfM/LpzGIkAj8FlTVeKT2336YrI6Vyb8wWjBaOjOOctqEvErXtvw6QnUVc4L
+buk7E1VqCiwLgciGtsJoIf/086/QvOquy3c1+5xKUauSvmTYtNy65x6Eeja7bLNupzG+uCuT6YJD
+0f8nAlYdn04I1OiStnqgVvQH9wZ46sA0JiDQNT+duvfWLFy54R7ea3/sJV658pI7/rWIn/+8m0fW
+url3NB6QHTrilLib4gu6fHI2oFQ+x099GnYJIV+qSb2z8fg1HEipjMWEUjmhKDIMBZIK1GmbpBFN
++foXs5JIpVU/sJSCVU9OcgYqjT88n37RWvsZ3sNW4cPKVrayR+ypCB/qqiS7OCPZLC995tFZwVRC
+GMc3H/La7zqNuOkUOHUrdHu5S9bnlxqMi7qmpW1zlI8phBX46vA+VyQh9mXVzKeTklu53Miswnju
+9zJbcfCGc8d/4s3f4PgN5zXMP/4KAL2P32H/ZdHFfz3gN78k6qt4RH+yTERdMJer3BRnQ2LbhKKC
+yyMPmgIseT6bLTf2XUGjg2ZFWx7RrMjJ1YNfBQtCEcOYKuNEzVZdZaSvXIm591X3+VEORl2Enh9j
+U+fxvPGVA/7F551n8oU7jq/g9qSEZcY+9ljX6yKEoZKn9UbEFXlI0wN3PGnWZKAKxmJRMVo2gU18
+nldGfW4qcn+plCyegsDQlwd1cFpyLj3HT6dTXrgqPc58xM0z97rbchMjHhouRINWhwm+aPK3mhZP
+4dqoCplZ9/62qiXz8YLbwiA0wyEvrzn3ejAoOJu5a17YBevqZqxEWvOVqcfZ0O3+r/RDbt9082l+
+5bsx5V33e/vub44nA+bK6a2vh6yrYhT5CRPhQpqbATvCvVC4BG1tUsy6O55kDnEkPM3YYJruOu+t
+x9x+23kQJ+fu74K0pN2QnmVZciyMxHYDnn3JXbffPSwZLd5fl+TKU1jZylb2iD0VnkJVFQxHD9lu
+fIxKdWLPP2PouR09LYaUsVv5b8xj7gsL0BNL8v1BwW66ZCOas9V0CafFLMBTw8zbdkxP2gKRcAdV
+FTETY9F+6HOmJKFdWG5L7CS7uMqdxVIPwe1Q9x8+4OrgBwEYZMf00jcAMG3DlYU7ptNOQlEv2UPd
+TnRjt0GeuRV8PinJlgjKRkKYOq+gq0RV3oZmV3H9rEEghOX9YUFfpbeqLiiWrcgqkf7RxjavNl3Z
+czGZUIuWzLc7VNpJp6dfYtpweZBC8WkeWFSFZKcb870vujj6oMxJPCEhv2udF555FoDRq46ibmNj
+wV7u7od3nPEl0a5Fp3BXDWbdwON8WRqWc9dresR77loNzwpy1eYPFwVl/Jy79iZgr6+kYuR2yU5t
+iBRntz1DLI/mWnubiYRex4s5o6Uyt3QoRos52VQJ5qTEhu6c1ho1TaEsX5sVbInlORcr0vB8xoMz
+4RSKV3hbIjHR/BZvqenoE/JS/cYWaw23i2/vd3hRZUivnVJpt17veTwndqrpWE1ZuUctDEzixYQq
+OY9OR5Rixrq632NSOLToce7u/8l8RidUArrMmeVq7Cp9vu+a8wqrbk6lZqvHtadiUcD38bo9FvmU
+uaivFrMA7/ccW/w3zmNuJA6HkERDZrqJY/Wdx/0UT7x1/bIiE5fdditgfOomx/a8ZNp0Wdjt2D2Y
+w6nHcyfuwY3imLbKE4cmp1Ym/hf/9Rk/8Bn3Gy8du8v1ex/rc0vAk9P8bWLxHrQGUCjxda1bYIVh
+n+bu5odBivGFbygq6qW6sNege83hE4ySp/ligKfeiObaAh4KJrwYMMnfcfDSZJnwVMhwc0xDC4uf
+VfiqBlhryTN3bd+6M+SLP/M2ABdD992OMbR1Xa2FUhDcfsPyTM9VZfw6YSacwu6u3mtUrJ0qe98G
+Kw7Ko8aI8cgtZE2gI+CNFRw7jgzZgao5piZTwru53mcsaHajaWgvsf/H7v42Wm0+eU09Kl7KUPGm
+F7axwkhEVUR66M5rNnIP4Pq1AF/Vo8l8zGLu7tnOCy2GJ+7afewwJ4rdYnglEQmLveDeiQslX/3X
+99n/jBi6xymh4N0nB+r8NMfc2HCJyL1Gj7ZClOfTCF+dn6k3o7ckttFGNp9CQ52fsd+mvubOf7oN
+iXUb0VpnB//TqlyJqv/u/TtLGA5BmhNpDpRZzemXJUrTDgmOV12SK1vZyv4Q9lR4Cp7xSPyYalIw
+R+W9yQVfmt8EoJGfEQodN7QPOJcbuF0tobgRa0uXKwvZVg2aRsqGXWoPNNiRcvV83e0YLyQ1Dc+t
+5uGi4p5W6/WFAUF3X+5Yrg/djl8853br9bRHLUotZguMkmtlZGk3VB+fbVN4Lpk3VxNUWPr4Qp2l
+LShUd+6HDdLE7bxp4XbzW8WUONDtGeaE+ZJ5qGA6VgdnbQm8d7o1AcKkSfHQfV6SY5fHWVqGX/8y
+ALd/5dd403Pu+NVgSTkcUyvRWuQe+dgdx6ZN6Kt02l5L6PqilguVBE3GjNWVGaQXxNIv2AhKxkJy
+PhOUbIi67Jaa2R6WljWWkm4GTwm+RtVAzX5ElU+tTss0dcewtpFSjt3YrTAjEvVU1xpKUbrtxDGT
+truv1yXCUkR9itCd05254WXJrdmgvCy5LnoNWoJTz7Sz923Ihaj3Gr0J9dvud7OdY0wpwlZ5mI3G
+PoGawOJ2my15WJGXUEqZuh57RDOxhotWzgQ5paTrtmMIE9fZa9ctlZjH1/yYpuK73suS7KsuyEUP
+V5cJLbEtTauCueDPrXmDSKHXpRbGe9hTsSjUFua5j19l1AMX93/hzd/n7BvObcuOHjI9cTHs8OKC
+WpPwzIij0ViMauyd2L8ElTSqjFICqlc3DE1lfXfVLTnZhI7i81lusRPFnA3v0t378nFOY+EehEp4
+cu+ioAyVOZ5MWSzc7z6/vgaKd6vygrJcQpCF+0+bWAmLYAz9yE2gKO4SCI48FqlINAyocO5lbANm
++jsPn6nOtaryS3x9W4pAw2rAsHQP0nxRk4voo57nHNy6467tw7tY1eGHCjXCwBCobyEMc2rF3Ou9
+Bl11ZSbTDueCAYfiH5yXFbEgtVGvzwsC+oz9ArtctAP/UkXriuC8syKjqnSc72IR9FoBmYBMm02f
+E8Xra96SSs/gSTy2IiUS3d7Mb4AVMUraZF+gpjJd8njCKJRSV2gYCN7eG5fkUuIKMzjP3OtaEPR5
+qyI6cYvCndMTRvp8VPeZqYW5d8XN2Z1ei6bEfHZ39qjUR20mBWiMugzIhaeo5q6qURqDryrZtAzA
+Uwg5Dal0/vN1n0bgQpvetkLie7scChSVehMWF+7+tU3JifI8c99i7Sp8WNnKVvaHsO9Uiv5vAf8e
+kANvAf+ptXaozz4L/BhQAX/NWvvL7zWGrSvy+RAzC3grd/LkvdkhN2u32kWLY2aVe92JYg5FdLEb
+imzCBqCdbRzX7PbEC5A16Et4I48CtlQXPxXqbrOIsYG0CFshF9qZy1nBqfxxr5vy+9qNnl2qFp8+
+pBKT9LCsuNGRnsBGi2DkPJqyKEBal5mgs1mrAivkmi3whWNI2yEnA2EERIoSpVsYX8ky4+NLpq0o
+z2lKt2JkIFHScaL85dhPGC+UrC0rFjd/HYBDW/Gzv/V5AM5OB65pBkDufJybS8JUAkNPPnyr18RT
+yFMFBbEITsaq41dhTa6GoXZjyjxQ8rDTIpZrH3cqKmFK7gnNly9qbkk7o6gtVpWf2lg86XNcBLAl
+roJMYVKrhqJUExcRw8B5LJ0gYyBy27VmzKE0EnbURXkxt6yp+vLArtEwIrOZ+vRFAFMBzeYyhHLH
+1pv4nKghLK/a/N7Q/UYjzLC1Qr7ENau1N7tsCE1rWh5M3bWdlwWB0IaVrYjEYTFVwtvP68vGp81m
+fEnjF5iaiVisN4qSsTApG6mk/jZ3CJWYD8YzOpmbIw/n78yH4XBCXb2L9u4x7DuVov888FlrbWmM
++ZvAZ4H/2hjzMvAjwCvAHvArxpiPWSu/9FuYD7StxzydMXtL5BfDMZNj94BtUZFKACXyM1LFYpHK
+ODvtJm3F356xJE33EHejkFCnGGJpq3W6p1jWTiue2xBrbz7l+raUed6c0ZWoRxDCyx2pL+naHuQP
+sYmbjN7FnHRTramzGQuJfBbjGVZMT8GSqbiw1Gpx9m2LVMAT30toRO68464DSjV7M3JBdW0GZ2KC
+utpLmSsXEZaGC5GBdBLlJ7IKqwfIWBj8q98C4N9UKf/yF92COzdQKS9Tinkq9IwLvYBur8HHnnVx
+7V5vja7ENBd5QCx2qkwVlZaXsCbIcK+I2Lui7kPvnB0tyGdnE74YusX3WOQsX54XzARzdtVD9zA1
+vICgUodj9c70rAVFtp5HoJ4KM6sJtQiRRGyJbTvImqylAjsJsLbpQaiHyTaHeOUSMhxhhm4ObPVC
+aoU0lUrEJ0VBv+tySY16QrwMx45i4meUg1pXl2TaYr/r5mlYlyCa+FYcUQl8FpoBYeiOo61yolel
+dDUvklYDVEG0BSR9gd2swVdZOlNOaSf1yNTtWQQZEwG11usJnrpO72Phgw4fvpkUvbX2X1prl2Hg
+F3CakeCk6P+ptTaz1t7CKUV97/s6opWtbGUfqX0Qica/AvycXl/BLRJLW0rRf1szXkDcWoPhCaEy
+qKEp2Gu6nWanUXE6UtLO81mTC9sRyUrS7bAmT6Fd+WykYsNtBEQSS+mOLGmqbLFW6Lo54dmpwz9E
+1YSGJ4qxs5KF6uKfbnd5JXG7Rq5mp4ntsSaG57KfElx2qpV42uWs59FUFUQbPl6Z44tApNOoyY1I
+TYKIeN3tMEu3Pq8yGLkdKsygVbl1+d6kzVYu3QoTk7Tddwaq7Xe9NXakpbnw53zuX/0/APzMayPe
+OnLYCp8K03S7x+YyjPBr1tfcdf3UK3tsJ+7Ykrh7Kd8XZZYIl0iMgraufYGPwqBiQiU4sx/t09oQ
+BHnQ5Pir7tr9uqixR1nF0qu11uKp4H6l12VXFGxl6VGJzCWQ9ka8EVCOVbUIKxI1SrUin0IgqzSE
+onTXs6t7XQYBceqOM8o9tDEzak6pJeYzelCTqOqyUJJ0M7GE0qt8ob2FOXEezx0GNEN3DTZm8pR2
+LfHS4zEerWWoFAa05AnOpgmJuiQjScp304BK4W+n9hlLlKcyFmHTSBY1p0oK20hs1mGbTTFK51VB
+KJ7Hk7OUvkLBRj1h/j6bHv9Qi4Ix5ieBEvjp7+Bvfxz4cYD9vT2qekTdKWkImPHm7SM6kVsgPL9B
+qji57YdEL7ob3iq7+rwmlruXNAyJJ2CNnROpnTZPazxElDl3JB5lbIjXl7LgLSKN4bctVqjH83DG
+2YmIXruiHp+fsH5VWWaaXMidq+MJDXVletmCmUqgC/mDOX2SpQBK2SaQqGgRBpeder6y+54fEqbu
+PPZ3CqqRe73TPWWs2LFVLzDLWFQxvr9dER2LsGMS8E9+15V4vz6ak8tlNsYQCrGHEHq9bsJL4iq8
++kqbPc+VwqrMu3yYauORthWasCQwhbHo4hv0CANVH4y9bOVtrq2zu+ne/2ToHvLX/PKSAQsMSilg
+ErCh7vtizrlKlYm31JLsU3tugfTjLr5+wzfRZXm29GoCHkU0hnV22V3YajSJ1KHoRxUThSDb+zVn
+91TBGLiH/6jKKLR6La5mFG+7OTIHGtpwwn11hrZ88uFyIRgzU8erHUwZqWM0C+f0IrcRtbylpmmX
+SCQyXhDRGi/b+UtCUQJ4qU9fYdPBkdsI6tmIStWusMypajcHtns+qcqX3q3qEuD0uPYdVx+MMX8Z
+l4D8UftO//VjS9Fba/+BtfYz1trPrK/3v9PDWNnKVvYB23fkKRhjfgj468CftNa+GxLxOeBnjDF/
+G5dofAH4nff8QQtlbZjPLRehS/Al8fAdmfXZjFLU6H3ToRYnwfqu8xTq0CeVK1cbj1hApjwzWK3Q
+duYRNNzKvZSP884sk8nS3c04FbHK4KJiiflJ2i1elebfjiTlRydDir4LRbKOIRQU9WRasiERkdFs
+dknqMpT8eu/GObbpMOlp7FGKeKRTV9RLd08VifmiIpR2oF1YWsqQR82EtVQcAUVJX9WKZfhQeQ0q
+hRqeb7glZuR5/agLWddL8Rn377SynIdujGS0QfMZt5tleUFTO2zY8mg23e8of0dhG3ilmwLtayGV
+VKDjewWBvI06LOhtuN975SXnVbwYVdwRdHtuapJl34ln8T13L4d2Qj10nsD9TFBx07mUE8yLjMw4
+T2A7zfCMYO9ewkLJ2DRcUvgnhMr2V5VP5LuxS+MTKTwI8gaNjns9PHDjDc7nXAi/svXCFqy582sy
+xor4JqxcGBAkDRa5g+Y/GEy5Jnd/cHJErjlbJQl7a85TzSJ3fWyYYRWuplsVVonINKqpBJwLzAWT
+kbvOh2cOv3P36Jx06s5jmOdkuTv/eydDpvKwBtniHRmDx7TvVIr+s7gun88b5758wVr7n1trXzPG
+/G/A67iw4q++V+VhZStb2dNl36kU/T/8Nt//KeCn3s9B1FVFPjpnXI55/asOGjy6H7GYu8TYvcgQ
+CaVYjAZUmdSctXIGaYOrHZWgmm2sv5Q9qSjUgDMfDRgL8XWhslJQRQyluzaoco6UwDovIGCpMwEv
+bTkPYVu0Vg83oR0ukYkhmZSUZ2cHDASVNlOfXB2akVB8g/mc+NTF1s0qwROfQl1FBJLAK40gytWM
+IxHG3nl4wEwIwqj2mInh2JjgkiGp67nP48GcUL37bT/isP7mSaZa7x/LW5mez2m+7hB2g8Hr/OD3
+O4+tG6zxsR2HpIu6TfxM4iuCRM/OKx7edd17Z0c9RifyTEYGXztz0osoRtLDUBLx391qc0eCNLej
+MfV8maCzGJUqp8M5gVCI1SzQ8S4w8txMAWnb7cb3j2pSJXZ7fomn61jou55XsxDCNM9mTMXfMM7G
+1Cq5Fhcls7FyO8I5EBmSyJHUTu9Nidvuu7vnLeqNSNfZjbuYZSwkjDMf3OKBGuIiL4Da4Vp219eZ
+q2FvJo9nYxIRRG7HL6r2O7wWJiIJ3bweDwoeHuj+nEiVOiu5kAc1ns44Flv1nfGY8ULq5bnl/e7K
+TwXM2VpYZB6Dhw9YzJz71cBQCu/+YD7G/7r6B5qG4dCd5s6Rc8P6vSbJrnPnezvbNKWeHJVNFmOH
+dTidP+Tk2F34M7fusNNLOVR2OxmfU6ty0MgLPGWtP9lv8/Gmy3lcJO6ib/s9tnVjbZgzLt3iNZ4U
+rCkzXNeG6wpTRhImaZkZ5/LXZ3FF6rtjJjrDE1S6UrfnYDHg7MB1Mp6fZHS6EjrpN9iXS3x3FFNL
+23GgrsbOdoOe6MpO6/cm11gs00FlzYno59u14c7X3TldXa84M+6YfL+m0RUBypn77ZuvfZmf+8pt
+AELqy67G569skVwXQYgHx1pEyi33Wy+GW/yRG27yv/5gwFRaoVEV01Tib7/V5GLkMAQLMXiHs2Mq
+gZdaUc5UPSPx0dElqG0rAn/DLWRqWiU0MVahxsXihLMDN3cOHi7YUm/D1Pj0VGG6oUUhaVluSZ1r
+OyqR8BRTf8qzHTf2mqovLEYsZu53j45GxNpY7p/MaS2cyz8+3eI5zYtKwCq7W5ArHgtyj8wKGLZo
+cpC7yXrwjdvc/rp7bTS/g82Adc3Tw7HFCL8zzWsmwjS8X+ASrGDOK1vZyv6APRWeQllnnE9vMuEe
+fcFrvxjMsbXbmbs2xJPwhikC+lvaFaV3WORzDi9cqaiqffqb7vPs4i4X2i0Pbt+l6rpkV9lyu9LU
+FAwlSJLmIRe5SDoqy0RYgN+7e8hG0x1H+bxb2U1aYnrCEAwsR0M1QVUDpkooZcMx97VaW8F2X9ra
+JA3ccU7Pc1AJqUgsM8Fg53IBDy/u4QlGO56fsDhy7x9j2ZBb6vsFvtiKu6n7+7g5JhEM2vqPryFY
+e4aZOhxP++c8GLlQ6e2LMfcnLoz5nnqHKHBIx4mUtE1YECSCY08NJ6IYC84rdlWquzvpU4roo32s
+5qP1EjNw3sO1oiLclqhLsqDRdp6OmZW8+kCeiXXHcDgoqMZqRms1aQlXsNNPCRe6T2FMR/d9KQkX
+G0tlRJV3cUqciD06OePwvtv+KzPlxhUnklMJTRm0oSmKwFNbMnwoLyxd8Mm22JWbImsdWd74qjue
+N+/f4lzJ04vDEQMREp8Vb/CMSrzPCCty/7lrbO2KT+O5TaxCsLcPT/jde19152Q2ufvQ0b/1fQmY
+7Dd5Vs9A7EdckUTia0nj0lOweFyKez9meu+pWBTq0jI9Lzi5XXAkNtwdO6Ut96rjlfzekbvJ9y5g
+dOAmyPMCf/RsjX0olp+NQ3ofc/Hb7OSYgRSJvnpuSUo3mW4eusnxxsMFg4WLF5/3I7q6qFQp80uN
+voTXlm2tqrvfvnmHzS25fv6c2LhjXnQTqol7IN+8c8J9sTdN1C03fnHBy9fcQ5UuSo4eOpcyqSOG
+Q/f67L77rddPL8ir2+54D04ZCR48YcE1Qb73Nvvsbbjf89TXYZIbZMGr7r3H8APDpbZjWXNhltUe
+OFIF5/is4BvH7pg6SZPnrrjxqlN3TouqxhfT9CC3vDlwk/9rZ1O44jMIAAAbdElEQVTCh671/ZPN
+Ni+IGOWTibs3Poa+RE9mYw+rTtLZYMhZ8xoAo9kpPfWmPBSYKB9ZboqjMxrMGajE88nn9nlZOp5n
+JzlT3bNr19znzQjOFKLcPZ7ii3b/dFDwO7fcg2yDGZkAUBtyxcdVjV0S4+SWty7cAzuZecxP3XzK
+9h0+77y+gzf7GgB1ML/sbZkVHmP1qNw+P+JUUd1Bxx3D1VNDs/kQAHP8At+49zoAv3H3kHsSjGl0
+zjhbYmBU8OuNPBbacNaSkOaGC0evXRRk6v4dFTmxqg/68/e0VfiwspWt7BF7KjwFYyu8YkyQBGSq
+/aZ1SH2ulXYxYiom3o3Ax8e93uu6lTb1C05zt2rPKsjcpkuV9kh9t7Lvd/oUOl2JL2OsxVPm+ciz
+1EraJH5AGqtBZVby7FW3Aq8VosMqA7KJtBdmE/K5ugSjeCm/QGN9zLVAbp4ow55fv8ZsqRM49yml
+13g6rTlR8vMLd9zxnk2GGDXUzG16KSvWNhDGbndI2yGe5NATVS82C4+96y7JdjafACff/JoLCXdD
+RCgnWMLQvRcYn+6a+w28E4pSvJE2IJ/ot6W16bf77O8KxxDljGp3/G0vZCYUop80SYSnyOSZzE4s
+lSoAVVzSXsrIN1vMlDmfjaHdcNewKbxC2Mz5uARwQlJOxeP50u42W2pGOvUmdDrCEAhKTttnfCLq
+NhvRaTkvc22vwZWx49iMTcr1nvOEuruqzhzMSdR9ydxX85bDkbTXpZUpXYXDYcbQd9iDhICF+Dpb
+/R6Nhc67URDOxJOhasm9i4q+Oid/8+GY1x84j+b1ownzeimi06DbcVRvtRi69xtzevLo0sTgSXdy
+t2MJ5u663ZvPGKsjdJo/nqvwdCwKQUi6tkPj/h04d+585UWUOpn1Rs1Luw70s78ec6iJ3BW8OMuh
+SN13z60lFgmLP09pPe9uUrc03Dt2E3b30E2OT+23OLTuZjXHE95UHNooK7qK2z/eD9lVbB6LZOX8
+rSHFA+fudboREx1PkETsamK2N9YxYikVNJ7rgcepSmU2mjIaq0vw4oxDpbVTLRRVZZmq43LBgjgU
+aKbdQs2c1HVMLBLPVL0Ku+0eL+g4p1sDvvD1b3/tRYhEZHwMS5hwwfEDxcmRYTMRaU0votFQV2bu
+HqrW+iaf6bmxb716n1G0pJwP6Ggh2IkC5mrRPhP01x9NOVfpLYliOk03+bumw4Z4J6M0YKSKUEu8
+hlvPB5xJqr3j5xxLIGU7CUCcnS8+s4W64Gmr9brOE/ptlyc5OLxgduRCECZz/vimm1vhc00+KUr1
+sR70ZD0kU8gzuzkkVRs5FLQk4JKq47RlA3xBtA3w4pb7/GwtIRso/DvZhws3xweq/MRpwbEe2N7R
+WxxOXXibej61vuNnllDqYRt9d7zrL2xxQ7mkajKjLt65FlnbAYnLqcVfouge01bhw8pWtrJH7Knw
+FGpbMi6OmVkuoajfeHifa5tuh6qrdVqtpUZfm0L8BoUyaWcPDnggivNG6VOr889rDxlcqHZbFyTi
+vnv5htse+wRsqFowGm7CQBBVP+NQXsNX753SOXEJw17bVS8uzhccSgbtSmKwAgJ1Ap+o43aVK4uA
+eM2NM2y77zZmLdbvvQbAvdGUXDseUZ/OptsdjsbuePJwQlGoycsPCaQJ2dk2pKjTMh2SC5ATxG6H
+ejC7yfjMHU8dvPftLeTCt9ZjPq6ddra/QSAvhijDE+twL+gwWmobStTGm1qitgsp1vYqPqFTsiQ0
+5IVskTAfCBdw4f7ulqnoj901vmEsF301PKVjLPJM/IybCgUXRpJ3+QbrAo41uhuMmy5J2Gx06KvZ
+qvYMXiTvTTRuxDOqZbdn6oE6X19e69ASF0XcbhMLT8FQvAmJYeIJ6NQ8J1PSbuiXTCT8M+6J32M2
+IhNZzlpckau573qaUMubOkp9/KYLR/3jrwBwmJeERmApk9IMXSL9woJVmFdGIdtr7jvrfXd9rvVa
+9FRpKvKai4nzfhrtjL7k+ap7BdPy/WEVnopFwfdieulzHIRDTpTRbWQeJwt3819pZRTS5bOJRyS3
+POm6i3NwljK5625QcsXHiOQ1O/IZT5yrNkt8XlBr8ESdiBthQqIJ9kI44CvyOZMTn1vSB+ylLd7W
+w3lVaL7DwTE3zyTrvvYsHZW3FpGlseQJrD1aAiKZmZBtixlD4dqrsuRc8fDza13m5+7B2knd8d5b
+b9PXA3seJzzTdu56GcV8Yt1N+InXINKCNBJ9+2wOD4buNy7KbxdDur8TORDBecnkmjveTROwtu6u
+8f2pYV2hVJE0KdVJeiq6++5eDsr0P3d9g1RkInu0yYTYiwcjXlsSzgRinpqX9LUI3co9OmIbmszm
+ZNJLOM4rPLFvidyJIMqZqpckbPjsNd1129poUCykzXmeX5ZePMVaZhKTBCLUiUJiLTI2D53IBlD6
+hs6GG+hc4WgUd0mlVJZPai7k5td+zbmqIC3xQE59j7XU3dODJOGKSHSySUBv283JG2ebbPhujvx+
+5WhI9g7G3PHdNdzdDVmkriwalg+4JXTnus0oVeLcU+6rmiYkHS1ijRg/c3MkLU5YzJbCwsdk5fvD
+NK7Ch5WtbGWP2NPhKfgenX7ClWd6pOsicZrdIjp3O8IgztnUThmHFqttY8t3K/xxu8dax333xU6P
+1LjvVv0jzENlqoMUP3K79CduuJWdaYtW6dzauLHFdSXRkuaYY2EMZtOMq1Iu3hL+vlyAoAeYIrjk
+F/Rn5eUVbaUB6bIXXruxTTOmy0byvOaFvjsez28RCfueKpzZTXIa152H8kpnjU7ThS5heEFL3Xem
+rKnUlUkm4NVxjKdj6Hnf+vYu0c2VqOJMXYIAL9WkvMysr8WGZ9fd9dzutKnVd9Ftuutmxj5JRzyP
+ccoz4rJomD5FKTx/WhJeqBqwpEGbl5yIJfrqGvR8t5sd+h2soNJZHXKxVNSS1zMxCXuCWrebKS3d
+m7W0z0RcDlm2IJRaUnWu80sDmuqJ2fIyxrXzFNJmi0jkOylz6rkAceoJmZ/nHC1caWh2OsOTZ1Zi
+L7sZI1VD5l4fqzCuF8xo9txcvno9otlx2IsdU1OeO28r+6Ib49b0gC2Jz+y2d6lqd23DCzCB84DX
+4zYvfWwXgL2uw0U008klkct0Dp7VnM0MbXWJWgPvF+i88hRWtrKVPWJPhacQRDHrVz6GF8Du5q8D
+UCzWOVcTzLVgghDIxF5JEYmWSvHddt4l7Smn0IpZEyNvw+5jn3e7+OHFhJZO13gq720bOHdeg5kf
+MxVvQGILriscf2XLZ09EsI0NSX/FHr50DE7zGVsSHPGjFOWsMDagE4p/QfXj/DSnqcRX3feYCqLc
+9CvGkfMKenturK3DiBtNt1sHrYTnrrj3j+c9rs3cbvv2eMxcuY9KO0OnbTkX9PViMnrPa7/kWfCD
+mpOZiEjJSRdLerCU9Y47tisbMetNt0sPrHtvXk0ZnrljaM8qanlvReuUxZFIbE9PGV+4eN4qlu/l
+FTc6Ljey4cW0xULVixtsKEnY3FgD3eP5A3cfd9KY0Heey5XYv6QriypIlQSNwhFj4UFClYCrGhAy
+sdkN8QdCUJ4P2JLgTLy/TrxkXV56TTbnVOItWVFQKk9i65pW4u57MBHkPa7Z2XPXvi49dsV+lPhb
+7LTcsQ3P5tTiSHi+68rl28+ccm/mPv/0XpNQVHddr6YpnYkXTZetrhvv2RvifKgzYiWmzwZnWF2M
+i1HG1GquBhFWWIfpYzZHPRWLAoBHRX/zGn/6P/iPAfjH/+vf53TmsN6nI59y5tLQ83nCQhyMpQRS
+Zv6AO0s+r7cG9Hzn7rXCiLOlLzTzGA40ST11VzZ95pmbrOEFZEocHeRTDkShFo4stdSi9q65B8Hz
+WpeCqNVszIWEbq8kUCiJafMJUwnTLqQJOcVixUE5rlvMxeRyPFtgJMya62FshBV4SwpxQ6mFgGDC
+RN2AZjJmVLnFcKrJc7ebc3jsxri5mLz3hVcYEeDBUuI+9hhUEtj14WDuMBTd2xGeqiuVAEaZ7xGL
+VbuKPU7vOnyDXy84m7lz8s4sp2fuN45Fe39qSqZzd61eqic0dqXk5Bnmao2fl5aRkoctT2zQ0ymb
+Swh25lOq5bqqLZHq8ReFR6GW+FtT1+2ZkjAUmGpWZ5TqVj08fMB0020SewZaSoSO1EI9qmbYiXt9
+4c0p9IAtqMjUGj+uliQtGcOZKlE1jAJ3DePJfYa4McY2ItPOYQo3T98chBxOXOXrl70FwYWbh8cX
+c85U7TjremyrfT4buOtqWj4L9ZpU+ZzxzN3vuJwzaYhkJg6ptNlN88eTpF+FDytb2coesafCUzDG
+xw+7+LHHzisvAvDpl5/nZ87cjv7WfExH+NJTu6AcuZ2iGWvVXlSEldth32RKc+J2q+fWr9PpikCj
+XSJwG/OFOt1OS07mbqddmy84U9PRrUHGsbKDn2g0uVu7RFSgmrGXmksy1vNhxrPKW+ahxWrnHp0X
+BJk7vuNC7rUp8EVyuhe1uKvdygsXRJEb40bbeSMPZx325WmcBB4jdXMeDwpiyd4d1JZZ7nbYU+3m
+zwU1X7sQXdfZ4jGuvTunRV4yFqHJZJqzqXLaaGS4G7jjPzl8yB+55napTtNhPppXEgLtng0SrEqq
+g4OM20MRtZznvK0wIFdZ94iKa+7QuUeTQOXXw/MBvsK/2SRns+9ev63jTLOCmcq+2WzC+cz9bmws
+Q2EnytwyUZPWmhJxE6/AFzVfZFKyrujWjjNG9935nY4Dnrsq3MpU/B2dDifS3Dg/D1kobZeXMBe5
+ykRNSX7dZrOphGjt4RXueE6yBWtCqr5xaNlN3Dk9lDhNzYI3FKIsbh8xklfYafiUQoU2mnOOFvIQ
+hy6hOjsLacj7GWZzZkJb3lsMUf8Yxvcftzny0p6ORcGDMPUghNAozvreP8P1B+7hLe7+Jkdy14s6
+p6uLM5y4m2F7G1zvuTPfnvqs77ssbXu9w7pIMWZel6nISUrF0V7loaY3xkXFUMzGUSOhI3XDMq54
+aceNvSVAiK0MgbLwZWXJ5Z7ZOqCol/TcMDZqAV7CoNNNmupbyBcez2/IXTdbmCXT8JlYd5Kcue9c
+5ricY8VKbMIJUoPHM03mokxv7wpbf5Qx0iTONSm/naWKH3JjWGrNHp1l7Or8rq53eGnZzWgMG10X
+z3c23EE0OwlThTyLSUauVmW7qCkV756mFXEm9S2VPdqUZGInvrbjsyYtzJM6YqCFurbHdFtusQzP
+3QpSJwFry1jdm9PsuPMODLT10NdVTSpZ+mbg4vdRMcMb6IFOIjZ91fHXNnk4duPNoxEz5SX8jhiU
+sopFV8d2a0SlHpM6qIl76r1pCfMxygiWrOLFDLHP40UJMy1qvbWayUw9E2rTft4uaL/o5r03WjDR
+fVub1gRaQKLEx4i6/0Khm/FK5uKlLNIYq/PwPY/s3M3DJLGU1h3I+WMqzK7Ch5WtbGWP2FPhKYDB
+9yOwNX0l4r7npSv8mzWXaPNuFhRCv7XHPudqAglEW9WzMbupk1tb9GfUQozZ45IzZYtr69PUqjpR
+I1XUspeSX9UQ+jvCI/gex0rK/LHvvcKzvjuOYt/tApP/6yGtTHRsnQxfIhz5vMJbagHECYmqAHMl
+Bm09ZXqmZq0JnPhuZTf5mELw2nVBtOeVJRDV2iIoWVtzx74Vp2xGEhFhysdaLnb52oULmYYPDpnL
+k/Kr9xYBWeajo8DQU/dpnefkI/e3z+9d51MvvuLGuxqTiN9yoe969gKrjH1e51Rn7nO/WXNjT9wJ
+I0OiMK4W38KDac2f+oQLQfaTHkYVpTt1Siq+zWDQodtw4+yLRboVxPixduOWz3ypFG4WTM/dPUsr
+i50qCagIalRmoFBiPoCmoMtJUvE9m27HtpspG1J/Phu53+o3LdzRPZllJGLdNllMVKkxS9fZ78xp
+yFdPFimRELll0yMVDmN8Ao1YzW9Svr56dZNW6e7fdpRwvKw+tAyn8vqqs4yJ767tWkdhXg0NNcr1
+FxmpkKBv5ufYXHNgYUgfYx68256SRcGCLfCCkET03kfJdR40RIc+6bFZOze516vZ60ucRC6uV05Z
+crKPxgYrzr2+LaikJlUUBVOjjHrtLu5mv0/QlXhqc4tqcguA+XbOhliBrF1QnbjvV113cefTikni
+Hth9DKcDFzsXmWHvhrsxcWXxI5XZdFNs6OGL6CUK23TVohhvNyi0COWi7C7nM8rKTcBmJ6Ch8mQ7
+bl8CaOKwIpdm4NXcff52Z0opwZnyMWArmX5rLfC4om7ORddjqDzBraO7vCCwVzS/AYm7nuWBy1tM
+eymzufoEmhUnQ3efrjzTY3dLILLDLtG5E6UZt9w5X68NaeoGNAcz5pv6jWhBopxJPR3zcCwpehHu
+LOYZgboF49Ym/YXyC54lUceol7apde1aV7XonwyY6Rrb6YIqduHIWjPg6rNuQwlMB0RXH7YUBtYt
+kmuO57NZ79E8UeWAG4zEkjVeE/vRfM6ZlF2jOqStSlRQ5njrLjxY6804Wrg5bg/uAHBULS51My9q
+H+O0mjl76DEQ2CuOS/qq8izngpfX+FpMm22PsdrLs/mEC+UrFnNLZlYw55WtbGV/CHs6PAVrqEsf
+fEMtN3rTH/Mn/+hzAPzWL1pQsmvvRkovlqKUwDYP3jrgVAIZk0Z9KXlWpCG1uv2Oqwnbkj5f23cr
+7d76PvNK7m4yxbTcip9M24zVdBK3O5TOu+Q0cF5AXVty9dCPyx1atVvB7x0d0JKW4t6zAWnkXLuG
+uv7CCGzg3jOU5LXUsRshUwFrvLk79tP0kH3BfTvXmuxuOK9pOqtJxTU4HsdUAvo8vOXO8+5pTbkE
+HxgPlBijfnS3WFYdSnEojLOae8r678UNepKOP53nDES9tv9yQdR0u3+4Jwm+qmSxVHM+G/DMdzkI
+7k53G8SrmYQlochS9rTzh0lJKO6C4eIhb4puz98+56Eg0fOwwfWW+7tI1ZD2yFCJDCYNu6xtSzow
+NBQ7oqTzt7AiqGlvKZE6n1MowXdw/w4mlyZmK6YbO08or8ATTVs9Dy+PoV26xPUzLzS4+srLbrxZ
+yPGxuw+VGo72b6zRaYqibZ5hVKEyvZTmlhujuVijV7pdfLonToq4j58J8l4eMFm48Uxecf+WJA69
+U7wN9xtp012L6iwgVEJ86hVMBAwbLkoGejaGVXWZWH9cezoWBWPA97GVwej4i2KdbbEQ5XaN0bkD
+emRJTPWSWxQi5Iq325ilIKjZoqWJl6U+tVzRvZGlFuhlw3M3wyxKImH/y6llI73uxq5nnE7dBZ6d
+wFDgpR1xOPo0mSgEOT3PaAs9ls9L7ojdp9OraalEZhtaCCwECmcWXkYtHYrjBxMK+WyetBF7pkXS
+cw9E3zTwlgg9WzEbqXW89hgLn3Rf12deVvgNodkKD6uyWTX3qH21OxsPgS0xChNKa8nU7dhY77Ol
+iom5mGE19uRuRJm7xXcRqzJ0+5xaKL58HrAj5qJ2FpNJsyCfxnRDraxr7hpOpgPmN0VSWyzYECLz
+jXFEpurD+BxatWTgb7hFsZE1iFUuDqqcKnQPilfWlCIyuchuYxeic7+r/pOWjz8T0Kecs1BIt161
+KdV1WzUMQ1HXL1WVskbNM59y5/RC/APsPeNenx13+a1f+SUADnJXho3GLapN8VyWJceVO4ZdIqpU
+ILnuBrZy80GpE6rMML1wpD2jswG5JAyyenIJcMurmKb6bS6jQs8yVI7nZJEzU34lqyz5skriV+9X
+SnIVPqxsZSt71Ix9nzLVT+QgjDkBpsDpR3QIG6uxV2P//2Ds69bazff60lOxKAAYY/6ttfYzq7FX
+Y6/G/mhtFT6sbGUre8RWi8LKVrayR+xpWhT+wWrs1dirsT96e2pyCitb2cqeDnuaPIWVrWxlT4F9
+5IuCMeaHjDFvGGNuGmP+xhMe66ox5teMMa8bY14zxvyE3l8zxnzeGPOm/t9/gsfgG2N+3xjzC/r3
+M8aY39b5/5wx5vGlot//2D1jzD8zxnzdGPM1Y8z3f1jnboz5L3XNXzXG/KwxJnlS526M+UfGmGNj
+zKvveu+bnqdx9j/oGL5ijPn0Exj7b+maf8UY838aY3rv+uyzGvsNY8yf/cOM/UHZR7ooGGN84O8C
+Pwy8DPxFY8zLT3DIEvivrLUvA98H/FWN9zeAX7XWvgD8qv79pOwngK+9699/E/jvrLXPA+fAjz3B
+sf8O8EvW2peAf0fH8cTP3RhzBfhrwGestZ8AfOBHeHLn/r8AP/QH3vtW5/nDwAv678eBv/cExv48
+8Alr7XcB3wA+C6C59yPAK/qb/1HPxEdr1tqP7D/g+4Fffte/Pwt89kMc/+eBPwO8AezqvV3gjSc0
+3j5uQv4g8As4ycFTIPhm1+MDHrsL3EJ5pHe9/8TPHbgC3APWcND6XwD+7JM8d+AG8Op7nSfw94G/
++M2+90GN/Qc++w+Bn9brR+Y78MvA9z+J+/9+/vuow4flZFnafb33xM0YcwP4buC3gW1rrXSfOQS2
+n9Cw/z3w13kHvb4ODK21S0bNJ3n+z+AkqP+xwpf/2RjT5EM4d2vtA+C/Be4CB8AF8EU+vHOHb32e
+H/Yc/CvAL35EYz+WfdSLwkdixpgW8H8A/4W19hEedOuW7A+8JGOM+fPAsbX2ix/0bz+mBcCngb9n
+rf1uHKz8kVDhCZ57H/j3cQvTHtDk/+tif2j2pM7zvcwY85O4EPanP+yx34991IvCA+Dqu/69r/ee
+mBljQtyC8NPW2n+ut4+MMbv6fBc4fgJD/3HgLxhjbgP/FBdC/B2gZ4xZdqs+yfO/D9y31v62/v3P
+cIvEh3Hufxq4Za09sdYWwD/HXY8P69zhW5/nhzIHjTF/GfjzwI9qUfrQxn6/9lEvCr8LvKAsdIRL
+unzuSQ1mHInAPwS+Zq392+/66HPAX9Lrv4TLNXygZq39rLV231p7A3ee/7e19keBXwP+oyc5tsY/
+BO4ZY17UW38KeJ0P4dxxYcP3GWMaugfLsT+Uc5d9q/P8HPCfqArxfcDFu8KMD8SMMT+ECxv/grX2
+3eypnwN+xBgTG2OewSU7f+eDHPs7so86qQH8OVxG9i3gJ5/wWH8C5zZ+BfiS/vtzuNj+V4E3gV8B
+1p7wcfwA8At6/SxuItwE/ncgfoLjfgr4tzr/fwH0P6xzB/4b4OvAq8A/AeInde7Az+JyFwXOQ/qx
+b3WeuGTv39X8+yquQvJBj30TlztYzrn/6V3f/0mN/Qbww09y3j3ufytE48pWtrJH7KMOH1a2spU9
+ZbZaFFa2spU9YqtFYWUrW9kjtloUVraylT1iq0VhZStb2SO2WhRWtrKVPWKrRWFlK1vZI7ZaFFa2
+spU9Yv8vXgo3LqcZTikAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.4447... Generator Loss: 0.6179
+Epoch 1/1... Discriminator Loss: 1.3410... Generator Loss: 0.9721
+Epoch 1/1... Discriminator Loss: 1.2921... Generator Loss: 1.2052
+Epoch 1/1... Discriminator Loss: 1.3283... Generator Loss: 0.9427
+Epoch 1/1... Discriminator Loss: 1.3041... Generator Loss: 0.7806
+Epoch 1/1... Discriminator Loss: 1.4502... Generator Loss: 0.6852
+Epoch 1/1... Discriminator Loss: 1.3364... Generator Loss: 1.0475
+Epoch 1/1... Discriminator Loss: 1.3068... Generator Loss: 0.9476
+Epoch 1/1... Discriminator Loss: 1.2938... Generator Loss: 0.9685
+Epoch 1/1... Discriminator Loss: 1.4735... Generator Loss: 0.5542
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmspVmW3/Xb3/yd+c73xhyRmZFZc1fPjTHYWBjEi59A
+BoSMhPATAgQINzxaWFhC8OBHi0E8WEJGIGMhg90Yt+223U25ytVVlZWVYwwZEXc+83e+eW8e1jon
+Iru6OrPcnXZaOuvlnnuG/e1x7TX811rGOceWtrSlLa3J+6fdgS1taUtfLNoyhS1taUufoC1T2NKW
+tvQJ2jKFLW1pS5+gLVPY0pa29AnaMoUtbWlLn6DPjSkYY/5VY8y7xpgPjDG/+nk9Z0tb2tIfLJnP
+A6dgjPGB94B/GXgGfAv4N51zP/wDf9iWtrSlP1D6vCSFXwQ+cM595JyrgP8F+BOf07O2tKUt/QFS
+8Dm1exP4+JX/nwG/9JO+3BsM3e7hIZEXgy9dsnVN01p93bKwKwBWkzkYA0AQdwFIBh2MFYnHDw2u
+LqVhP2c1ngHgbItVqchTVhhEId00ljaSA7xInm0IiAN57Xs+FrPpB8Dp86fgGnnPtdha2vMM4KTP
+UeCTxtJGvyP9tE3DolzIOIoKo/0prGMtsK3lNs+A97vwbM9BoH0LA0MnSuR1LN/122DThnWW/q17
+OqZP0vp5bS39nZZzLp690Ic0tLWOzzps++O/2zToNssBBnztsu9D4MkHvvFf9t9I35110MrnlWs3
+fQ6iiDjsAZCmfWy7lDmqZE19aowuoG0cZvMMgzHyvgebtV6vh3UW1p8bbzPRjW2p2gqAumlZFc16
+WJ9KA5l6ltUr49cTFQU+sY30tSHQPje2oa6kT+Wmby/bNBg88+p/LzeG1df2Fel+PceBMZuFsNbi
+6wIFgY/R98+y/Mo5d/Bp4/q8mMKnkjHmTwN/GmDn4JA/89/8BW727mL6+wAsTk+5XhQArM6m/J3F
+dwH49l/5Gxg9FAf3fxmAN//4zxBmsnOHxyHN+YfykP73+M7/+n8BUMwXlLWsXqcvH+/f3ufnv/wQ
+gK98+U+T3twDIDCHvHYkczeMBiwCWdD8dA7Af/2r/yFtdQ5A2S5YPpfF7cdgyxyAmwdDvnFf2viX
+fl76ubq64P/54G8D8L33H2GUyby3rLC6M3Inzxr4htgIw/I9aFt5P7WO/V2Zo5O9iJ+7K/0/udeR
+PmQHKI+iqAr+yJ//76UNA5uT7By1MtzFuTDbv/rer/EX/os/K58nYxYvrgDIyopsqlzBQqkv14ff
+OohCeW0M9HVuRz2fvZ58acffY81X0nBXfldUuIWM73k5p9Dx79+7zWtHvwLAV7/xR8nGfw+A959+
+IOPjiqQnp3E5qYiTQNcpIvSE+UY48kpPai3jy5sSE6TShzjB6XyOV1M+Hj8B4HSc8e33zgAoGz6V
+fvmeMLt/8ExHF0LvQPpzb7/L/fweALf3YvYTGeskG/P8ueyRJ3UGwKKweMo3AwLSSOfTRQRIR7wK
+lq7UvslcOSx7+uW9JMAqRypXGT0nzzveGxIm0vif+/vff/Lpo/r8mMJz4PYr/9/S9zbknPuLwF8E
+uHvvjgvHH2F3D+leSr8/qGvOHsnhfvHh+0xfyPvNdILfl11oAhmst3RkoUxw54OA2alIB/O3R1x8
+TzZF0qRcFTKpOz3ZmEcfliyTr0hbbxV0Xuhk7zcEuWyqtl/TShNUXTlU8bTm8cVU2jUFw4EcyEHP
+p9+/BcBrAfSTIwDSriyQdSEvfl2e8cEHLUeN9P/Z0tHRsVgrh9VLIxpPNlsnSHBWNkeZhLS+9G25
+MPz69VMA/hUrp7F7IyT76BqAceLhtU7nymyueQfYWl7PFmMA/t5f+i7nH1zKmNqGeS5jzQmolHl5
+jg3DefXMONnjtIBdymHr+g7/cARAndYEXe1fJZt4NBpy5aS1dn7FTPvzZnTFXl+eV5T/L2eeTP7p
+RMY5imL6ofTNdSyBLz0ZDYf0dO6aEJjk2g/Z4nVeUKTyjF63pWuGOqY5L1phJmY2x7eviECfQh++
+J89byh8CA/dL+WeQ9OnclHWPq5amkdf7DHjkSd/mS9mPCxuwk6gkYcCpZBX4IYNA+tEGAYu5/q6S
+Pu74dsNsfWOYL2UuQhoGXRnTajLjNPnpjvnnxRS+BbxhjLmPMIM/CfxbP+nLXhUQP9njfPlXOJ8O
+AHh6AS/Ovg3Ax+98xLIQMdIECWEgN/pQJzL77kfkO8IIwvNzrG48u/oOe3uyGaf5lKOetB10ZfLG
+X/4G+UomfXEasjyQKbbzK0aqSgQtNHNpL+/I51eLUxojaoCzhoNUmMKNwZDju8cAHMUd/J4wr6yZ
+AHCdj7nRkQX/w/cGXMxE8vhGJ+RpI21XnvzmKAm4HcvGLcOas0r6c8OFDCNpI7vONipI9bH08eMP
+3wa5EJkl6UZ0BLfZ520L47EwgH/4G8Jsnz/5Fr0DmatR0nDgZK6unj3jYqFifuPwf6f6AJtbLjYe
+vZ58cPjWIfcSUQP6ySFto2qV3mwHzQ558xEAfhDhapEK706+RN79dQDen3Tw5jKY0Ml8Fz4UUzkc
+4VGfI09VjaYm9oXJNKbHia51mcvce7VHkEpHd7yIqhVmGMQ1N3Z0IDW8O5Hv5KtPVyMW+umrSt58
+IP39SttnL5W1rCvLQtlpHGdkygxDXySeUT/mZChzn3Y8ejbV9z32G5nPjybnXM2kvSqSSyH1O7hI
+5jXLSlo9zTttwOMrGV/i+0yIfo9R/Dh9LkzBOdcYY/4D4K8DPvA/Oufe/jyetaUtbekPlj43m4Jz
+7q8Bf+0zfdlroHdNNUl49933APj+O9+lXciNsCxr/ERE0YO7d+gefBOAIJTPLx/NWXz7HwGwGOyQ
+jIR77p+8SXoot/jO+ZIbI9FobtwU7ntRd5lfiIryW2+3PEheB+DwQcrzWri46U7pZXKLecMbAJTV
+nGAtR0eOwwPh7LduHTFSVaJz8zWGpRpHS/m9XXV568tiXxjcekTy/jMAutOCcCGSzriU22c38ekk
+cnscej4HqYiaw3zFqpXbrAot3Upu0EpvkbIbUM1lfMaWL68xZ1jfe3VT8v7ff1fm+UzGv3PyJr94
+40sA3Lt/iJ2KTeG3v/t9+P7fAWC+qMnWBl1P/gaBT+qrOHu0x688FDvKnftfI8hFpWvDhkCEIjxf
+JKJ6npHPtA3fkOpOjPdyrC+3f352hUHGtdeXMXe7LUbVOT9vMPtyawYx1LXMPXkFKjLHyDpaD2ym
+Br5uSYU0kgZD4kReH9444eiZ9HmlotBqtV7oH6eu/p2Z9digp3OxDEoejUWNay4cbSM3d5R6+L70
+80t3ZN8c7Y3YGYlKmyaOQA2RfWsZpPJ6N/RY5iLd1RNZGwKLK+R5c2cJC+mrCV9KBk+LamO7+qz0
+T83Q+Co1bcH1+IfkdczVU4EyTCdXG+ObSXyOvy4H8ud+/o/StLLQ3/lNMdq9OH2banoBQFl1uBPK
+xjy5v6DfkYM+jj/kF2+Kjh8fiKHu7NF7/I0njwF49ux9LobfAeDh9JsM3v0BAJHZx92SBeuGU31G
+SRjJYtw9OuTGSETV46BHR42SSfSUxMi2KdR2MNrt4t2Qw518eMzlSDbNsLScF7L5w4Vs0MvKYiNZ
+/GTgg+rOi7LFDNW20ThSTzbNbCV9O6wH9PTzyA9eSvlGPDAAs8uSv3kpqll1Le8d/8wxDw+EKaTA
+t/72/wnAvP8OjXo2mpUjMHqQteE4MMT6+n4AX98T9WkvSZirvWKWBLiujLtSndtGLaWe4bSJ6Bk5
+3JW9YC+Uw3JifDIjvxsksuZDxJovEwNZLgchqLoMlHGY1tB4yhTUVtMPG+aBqmg+uFzWxgtK4lDa
+rlcVe0NZy+tI+l6dGpr6x5UIAyxe2m2FLMxVtblyLzBzZRs2wCoTTeIBb92Rvfzle4cAHO3v0xiZ
+l2Hr46nNyMtr1HbKbhvyjVJVaGUqT1c1gZXvGgerSr1ZkSVSZtoPQR1sn5m2MOctbWlLn6AvhKTg
+Wks9K5hNFywWIr75JiJMhBUfffkb/Mov/3EAvvYrD5g+k9v04vo1AM7f+QFeJBw+7nRIR3JbNYMO
+/VCG2Ovv0Du+A4BxIssWzYoik5vmbDZm/FyMUvmkpOeJtOGHf4/uUtv4kvRh2AvoBSLi3h528Afy
+2qYt8Yla35uQHcUneFZuq9YkeJmoCW2/5cv6+Q9TS2cqfbpSb4FXVVyt5GabF1A2Iinc8H12c2nv
+YC/mYSDj9gO5abqR4/FUvju0PmvE6isQCpq4YvZUbpK7PyfS0zA8YDGQmyg8XTAvROWZvGdZLGSO
+rAM18OP0tna1JdE5PptUvPdUJLYwKzjqyI2/Wk3AyXeqtSEvW1LrmF3tNurYrhexpy63thfQaNuh
++kC9uCWvpO9e0dDrya3ZpganFtZgYPCcfKcNpWGfDmEobTgDpIpfKWIiNSrv3DS8oZLJ3rX087ur
+S8a6Rxb5S4nBAaznc23AdTArZM1afNKw0OcFxKH0cxR7DGLZL/u3ZO53uz2qSkV8V9Kq0TlKRjQT
+mU/SkFhVjH0FRizrOWPFmdimxTNqiGx9+om0Mc3AVT/d3f+FYApVZXn8bMGL4pzrQjZK0N/h4K0H
+ANz/5/41fuH1+wB0Onv0D4Qp3Hgo4tfVg59FPWjcvRHzza+/BcCd0rBSq2/n9n1qBcAEp2LVn5ye
+ExQiwi/aFbXq86vn7zJs3pG2jaPbqL/dPgLgwdFXSdbicCciL+R3gwOPna4c0kHrkagdJEwVHLME
+O5HFWtYF/UP5bvHoHH8i7S1L+a6tLZmK1P7c0VdddeE3fGUo4u5JEuL3FJ+RqReigbCUMV8VL+0I
+zjmqUhju5Akc3Bd7RTwQ9Wo4e8H8VF1e08fkmcxLnFs8ddNZHwbq4kvVq1E4x1zVkuVqxd99RzBr
+Xz4ekhyqy9gfkAUylmIljCeaQdFKf7pLx0rdr27gkwRymBb1gp0DdRfqyZvPfFB7gB/28VWt6Hf2
+CUM5FGkvRuzbbFTQYlnQUe+DZxxY8WDlq3MB/gBt3eNQtgYHyowWjSX/WHX5pqVdMyxgrVWsWYUD
+1GxBmVfECgDreyGh4hS6Sch+VxhEt5GxudoncGrLKCp8BVDlLsO3si9WJmcyEZVm2agKGli8NRNq
+G3Kdw37jMdVeHWBZ+j+u/vxetFUftrSlLX2CvhCSQmMrrlYvmF8ssbmwWr8fcvMrXwXgrQcdjk5E
+dAq6juVCOP6bAzHYvPUn93j9nnw+aPYYjRTiSUOgGIGzpwXPT8Vw1w7UiNgrSRTtFS0NjcqDq7Ih
+U+5aVY7CV1CMGpF+4a0unVitxalHP5X3mzwkuRbpIBnGxFakHjvWm8hVBEa+exAZqlysxPeChPie
+wqofy98nq5pCDUfO30iqpLGhsMLLX8xzvqRowlqRkNOlY64+9nlTvDSCOQeljHXhnxMu5JZqOu9L
+36IRzeWp9PfxkmQpN/owTbnRFbG8qC2Rmi776oOnqHmhN+KiamgUjp49rzlv5Xc7w5JhKDdlsZDv
+ZnVGrgAxGwfUKm10qpJS1YPIGSYqWaVysWPLhnwikkTXD1kpdmTHlsSqSnQ7fZzOmFUVzO96tEZV
+iaLA+NLGfjykLdVLYEsShaYzkr319WXAai7ffWc+Q6eeyjkUC8WMl9S+cik7lSqM39Av156YA5Z9
+8RjZSqXiIKLRo5iXM5yuXzm7JrBiQH4+W5JX0v+ikDn2qganUkVhm43och1bAn12ZQxN9Rngma/Q
+F4IptNaxyAsWjU9vKPaA4M4Bxx3ZCTdMzkjFq7AasFKU2yCVDfrw/oCb+6JeeE2L18jB82tLY2Wj
+7CUV04FAk9//SKCs9bKmanVS/YZaV7QGdP9QO2j0/bVYvihbSisi5X07YhdlBJ7D00NB5NGsNI5D
+scFt2WJq2YC2U3HzgWy8gjOqx9LPxUjG+aK0GLUTVBbUgUHkWVwsh2kYRlxPpU+5wpZnpaPTlZ17
+Z/cleMlaqJxCf8ctVS0IwTvnR/rcFxSXsvlnFzOmOtZgHw59WZPJdImvJ2GgkOG8UxJdrMX5jFqN
+DlEHVipLH+wZjIq2Xk/me3XlmCouMrI1taL0rlYL4r6giW72BlwZVSsm8nknNewOdG3SAKMMu9OJ
+2OutYeEetpH+Nan8PrQ1pYrdxkUEa7vETkmuY3GziMaT7zjdF/v3R/ysupQfX2QsFFkaObi0Py6W
+v6pKrGMUSiwrZVIuv2JvJft6tSfspBMPoJYLIsSRZXKRzV1OO1W1Ml8xqeT7eauuR2M3sPIg8xjr
+HsjLlrKVcd+IDfFPqQ9s1YctbWlLn6AvhKSAdZiiJYl8RrtqYb37Fq+pv/1WMiJVSGgbZYxi4aq9
+L98E4KA2BLFw8JYFRi3V7ewZxq3jJBYc7IvV97Inv/+NWc1kHetTgXFrMf9l1KKFDbTXqDjvVxHz
+lYh+73srjh/IzRbEPksromE7qfEUKhwoHNv1fexUX698ikZu2NFwn74VI9LFXJ/RBLQKRvE8w1D7
+MPBCXC5tdKKUQVfGt1IYsWkb/ECsZbu9VKIDkZgKxTlBH+yVtF3dEjUhP1swcSKqFjsZJ1bmNr8o
+uPGGGHwvl+fUKpYux3KbtVWfbl8s5B3A6o3/5s4Bw5FIP8MhBGr4y8cybxPPkE1UDA4DmlpFe98n
+VOmgyT1iBUG0I8UbxJZuR+Z72oDprWMGGhr1Diyihkh/FylOIa4jKGWuXJQxiGSfNfN849koBlA4
+mY9ABb5uXeEeShtfvZzy6FzW6SpvcfxkUJBxbKDgvjMk6oHa2z8gUUBdp6N/3ZBWJanhoI/RdWfc
+ox1Kf6yzNB3pZ9XK3FsLqGE6ii2FRnvOnMGqN2O+gtislc/PRltJYUtb2tIn6IshKeBhbYfZbIwd
+yI1/NNrhpgbXJMkuy5Vcc88qR6RumsOlYAmi0NCowSVuVgQaJOOSQ5q5GBfTxFJPRZfrh6JH7x8M
+KApFQtqUiYZWZ4Xd6MaeBbXhUShacZydY1UiKAYNXMrNXNxoNz7mq7MJi0bsB2lfA1zKgFSRkFVh
+CAdi+9jxetw4EaniG2P5+15vRrwUHXmJI9YgKD823FH31t1hj5k6+Hev5ZacVD7jUObnK3sPUQAi
+ddtyuZRA1fF7jptDmaOTB4L1aL/3Ll1FBB63Oxy8IdGe8fwbDHc0Ymi5ZGWlz89/JLfVi+IxsTsB
+4ObxMWYlc9h0a0KNBmzbiF1P1ue3FbY7rBfsDuR5rnY4zbMw8GJatcuceYYmkgFEGkZ+eQZnA7mt
+S7+D90jGtOhXXO3I79poxK4aDPd2ZM3LoML5sg5RbZgsNAy5GHOh4eN2ULHXyu3tArHteF7KPUWs
+Nq8PcJ5Gz14VTK9UnPjdyECqEkg39fn6W7Lnvvn6EbeHEpl7PJS+1fGKWPMtZPU+RS6GgtBdESpE
+OTQBKkwQV9LHcZ3R0UA50zXMNKKykzmuI5VI25ZyExT32egLwhQcvqlo65LLj8UYaK9XFK+JIe7i
++vtQyia+sgU9NWDlsRjLzPENaGSx+p2WxGkIcJTglmr1bRaMrx9LG4ohz2cF+4kwlqYzZajGoHNy
+Mj3cjW0JFaLaaEhr6nlYJwt34/A24b4CaKpLylw3W9kwV2DJ06fq8weOj+SAJU3LrVTg1qFXcnRb
+gFUHVwpeigKO90TcTXYgMrIxozajn8sBqe2S8FQO23Qlz31/1eCrEe1ry/OXUX6VZXWuRsdhxuGb
+X9N+iBFt53jI/KkctjAO2FWV58atA3ZjeZ7x+jQzOeiv35H1mF72NgwrTvtc+3JQ5ucTzqaCWShn
+jp4yoRuNHI6qzCnVANtMShoFZ6WuoVVw1ukko9UowUEpfTifL2kvhREOei2JhozvdEtK9YKEZoEX
+ivG31XBj1wlpG1FdlssFpcZllDWs1ODZjnMiq4CjXelvZBy7e8JMXusdcLkj311mU54pXmKdYwVe
+it5paIgV67A77DAYCJP9R5cZY0+g/F/xZC5GSY+4K/sitQG9kbT4+PmMt78vcYRXi4yJrmZXD3nt
+Skytc+95oBG/DY7AV8i3g2n708U+bNWHLW1pS5+gL4yk0LYNtq0INfIxzmp61yKiDxYlZ6X40F1b
+cfqORrBV4lr8TTfm5qGIwfcHe9xRUXxwO8WuhO+dPpnx7b8v+IR/cCE5BH706AK3NnA58DS9lhen
+oCJsaNwGKrzG+Lahx/1jueV/8fWvMkykjTLr01Npo+779LXBYSCYgMU0o9WAoN6gJfbF4Jl0OzRq
+zUw68nnY7HIzks9rP2CeqVh+5ih8Rb9FDqfG0TU8etFa7g1FUrh59/VNCrLW9ynUGLtf9IiOZJ77
+mWyB6+SSKlRD1YsriplIbJ0v3SbeF+TowAtIOnKD1uozbzsxlzP53fNnH1Jkoq7No5DmXJ4dhjmR
+3lZRT27d44MHzC7ld0+LhnauAVGRz24qt/+dyvGxSjJeImu6XzccRCKZJaOQCnm/Y4fsa7KUNPHp
+aN6GVnPl2ToHxcDs9EcEmvgmORxQZioBloaFuh8nS5Hu6npKrVJqONjlS2+o9Dbo8Jsfi2T1Kvnr
+WK0Abh3KOP7F19/i4R8SSXD+3oTLqezrp1bS37lbe+x2ZBxhEpJsMqfk7N0SFWPPdrnZEeNo77a6
+NF+UnJ+KtPzo+ZhC1bVF41FooFQ/dJusVp+VvhhMwQE1tOXLhB2D4x1SxYuPr0+5PJVFejo7Y3kh
+i3E5lgW8vD5j555s4p+7M2ISS2j13dKRqbj6o9/+h/zWh8JE3jsXBjMuW1qnQJggJVCL7aptNxGF
+zlrUGE6rasRuari1L+Jg2raUyliur645UphslKeEXZnezkB97E1Av6OHP03oqjoSDyNmlyLaRgp9
+PXBjvvVCGNO74wlXhSzyobN0FeP+iz1Ld0c2XmedJ7Jo6XflQPQPh7iN96HFRHIgD762BxOZz+6x
+zOVhldAc3wXguZezGMvGffv5U8aZMJPXRiMGe2pL0XDybAVVI+pYJ4iINatV2E04OJSx5HbJKFOg
+jjLCJvH42EiSlZ6JeVHJhjZhj1Y3sQsSDhX0tLsjeyHd69FeZrp+lrniBq6yS5ZzsQ/tVTe4cSJz
+7/Xkb5NnRBonEhJxOpbfXf7gu5wrBuKrDwb0j5QBavhx5u/QWWMd7vZormXu7+DjI5fMq9CgdTbK
+QehzTw/xz3/tq5x0xYNT/ULGk3MJmd/Z00Q+dbjJtmT8HKtJWIaDA05uy4aykw6RRsRejhWnk/jE
+N8Se44o5L2bS3rxcUuSKC8Gj/Skztm/Vhy1taUufoC+EpNBax6yqaIOWJNF0ZIObhOp9qNMRTz+S
+AKXxbMVyohBOtcx2Rkd0VURvVies5BLjdB4xruV3C9cl3teoxblwcJ8Fba03SeAINMrOMiVXw1fj
+LGowf2lQqiK6qdx8YXeHaiG8tTdw2HXw+mTGbK6wYv18Osm5eyScPSYlVrEi9EMmGpX34VKDiyqf
+p4Xcyh+vSpZqwKw96HkywGmYMorl1uxrGrC9lU9VaI6/5cEm2L+2LWmiqlkTMDjQYC0Vy4en57zz
+sUhSdlJvoMT1smWkTvtlJ6Kbiyi9Ui9DOV/SavqwylX89juStOXqouHmiUgxrx/0Ge0qtmQoN37V
+wKgj4//ges5AJQFrW2q1nHupI0g1W7VGZbY24NpT9cnbJdeAt8bVPFdMStfm+Eh76iygNT5WozbP
+VyXvvhDp5v9795RSsRWlV/K6BrfduqWQ6a7HjgZatVlEqWJ5oZ6DV8kg2cQBur2YsCN74UeTa374
+TF5f52+Tar7K/Yn058t3j8kVQeoHjlkm2IRlHm4MyIVt+cEz2cvnz2Qdv3wQs6+ITucbgp62sXDk
+KiH6rsXz/hn0PjgsrSsIXcrtvTcA6PcdRpOU9IqGr7wm1vL6oWV6pac00wPRj+loMlfSHe4NZAHC
+MGL5XKGks5r7gYhwuxqCOl6MGS80XLgx7I7EAvzBk7f54ZmIonUF9SaZhophnQF7qSQk6R/dYB7I
+Yk2eWzyNbYh6Ibu6MRvNbNqYhO6ubKpu7xCjbrpqEhLqwToayMJefJTRKoPY6ycc6sLudAMajfYc
+DCxfUit787r+dRG1hiln00cbm0PUwqFmjvKiGkXKYjSpS932SVT4rdMUzUdDdycltpoEtF2RaTIY
+NIlJ2F1iC7Ud2JajY/Hm3NwJ6PcT/WrJUPM/7qyTmFyN2U+EKbyxV/HBSlycTV1RaI7NmavoGIUg
+J5pNKPNwCkLqBhn7qq500y5O81umfrA2pRBpApWgDTGaojmbrfBqWZNv3r/LRGNa9vZHGLXaN5rt
+eWgCNA0iNZZQ3dK7UYtqikz0rzFwoy/zMup3iDVitpmVLH1R0wbBLr7eGzvq4Uj9EdbKPi0LH5tr
+gpj+mCrTzWdg76YAyrxKntgbNZu4hri3w2vqUbm4zgi0ykESetRr9J2+92m0VR+2tKUtfYK+EJKC
+dZDVLd3RgJ/9ZbmBb+4ekCbC+dLogMFQuHlld8mGYuG+uBQR8Pioz+Ftyb/oNznBurhHUeBrau3w
+IOG4ltv0zVSCp4KwolZD1uTDKaH6uS/5EO9affqVo/wdKNGJs4wVxFOFPqXiWZvKEmmRmHAnJtEA
+pPuaN6ElQm2A9LsWo8YlVy0IEmHjoUKj93ZCpr7cCDcCQ/+2ft44djVF15sxDN/QKE/Nc/CHuw84
+/VjTdpVz3FpyjAOMGrBM5VNpai9PQWG2vSTStOfd3gqjkOFRHINGkvplQKEGwVJvWr9J2dmRORzl
+Hq+dSPBU77CDp3koElPTWXsrtGZH2+uyr/OS5Y6rudyUF/OMngaCRcZgNW15Xz1K0bBi1z/ZjGlw
+LGs5SFMCnc/W5psIRV9zUuQN5K3MYdzvc6R4g4MbQ4waI7uuQ7ROC1do7GNT4VR9KOcLgkah56aD
+Ckio/RnfQLiGxK9KGhXt7926yw1NGe2nkKoRu6vgpiAoMQs1rjazTe7RfdPdQPNX146Hmp9j9aas
+06jqEWviaNFwAAAgAElEQVT05fLiGeO54EKyADJVH1wLkf1nUH3AeBi/S69zyFduCdpr526XRhFj
+g9sjrCeTGtInK+VU3PfEA+CtDK2KSI31iGJ5PZ3VePp+WTsevCWi7UEsoqzxa4JaQ3pvt3zrN9T+
+8KyHV603WPNj4pTvQamJMq4/zhgr1CzohLQaVxE0Cb4u/roYR69niLsqzvrQaohs7a8otB+7iocv
+KOj05YC1dY5RtSTMLOFYD/fQx35feme+Jlbz0YOc6XOxB4zz8mUdiQA6voy76lkWc4lzCHcVNZrv
+sHciz8hnfXx1gXYSyPN1QtAAp7PRKmim9Zb4iazHzrBPR7dUaC2+k+90dlucAmuqpYw/CSr2D3Xu
+s5jvPtcwYxdQFOtU7R5eKs9bqh3l7mjIUBPlpmlCaKWNYBTTaLaoog1x6hNwgcY7UBG1uu4WejfF
+pexlS1JVGVpyTCi/S5S5BzbA6ppy4BFcqkrUSTah08UryVYyBaxFLuJ+JfNyvNPnUNWY2FUkvXVM
+i/zOVo4mVjtQ5Og1e9pGRhRKG7fejPFUpTG+MAfmoNojT/PdTTr8ThtRK/Ny3qsVpz4bbdWHLW1p
+S5+gL4SkYIwhCmO8nYY8EW7v5ymBgoLwOySlivNhwK7G23vK4et2xWKdSKKakyZifBp5IY8070FS
+T/DXwCGV92zlSDV1ejnus1qIWDZt5pRqBPS9T9b6A8kgPFmIr7kZeXSN4uuP5/Q6crunZQma9yFS
+33zc6RCq1bJdgqcVq1oL0Rrjr6LsTS9hqqWppnmLVYPTwb7H4aEYDKPakqulfqBBDtlHBbkanOJs
+h3WoXgtYzRJMkdBVw2ygQKCofropeed5ZpN30RQG42msvyvpa+Ebmyvcd5USqgGvMwiI16XbbEs0
+UF+5TVCNjkqLs9isoViod2X8iGydnaS2WM1cPW0CUp0jq7EKbSySFUDHDIj7KibHsJH4A5+eVkiq
+1KrvuRoXKTy606PWXKBLC8bI+Dpk1Dpfsda5tF5LmGnqvYXd5CbwOuGmWtamlKaDpab0i1YloWJS
+ok5EsCt7K/WfEcWadv5CDc01JKnCq0uPMNZ6oy6iF2j+Bi/EV+g9lcZwdFeUVtPRmZhdLSgzGpxz
+opG0pYN2LZIsP1uylX9sScEYc9sY87eMMT80xrxtjPmP9P1dY8yvGWPe1787n9bWlra0pS8O/X4k
+hQb4T51z3zHG9IFvG2N+Dfh3gb/pnPvzxphfBX4V+DO/V0PGePhRStLuEgWxNn6J2sVopufEaFYd
+apym+QrUIGU6Pr768YnMph7g8KDP6LE0cn6+orml0NU1/K90lL5mwG1qqkJusaJtSDwNOiJ/We13
+PXCvy5mAIsnfjDnaVaxD8RYa8k6n2+DXwrkDRSP6DVg1tFmbYyu5rZoaIvUXWb3usuWcQLMP7/UD
+GMnNdzPoMtxTPPbiks662Imm6GqagOG6jmAwYX2P+S34ns5b31HNRRl1mrjWM0eYVuDf9WS1yQ7t
+exGddVm4IKKjGajjjjy3epIxm4n7djhM6PXWgTgtraaLMrak1QouVmHL9fxjmmcyL+fjjEUtNo5B
+cEhsdN56DYXiE3LNPdG2Lek6OMxBqzelvba0Kln2fIhV8go7GhiVtQTeGgOSE2nBFX8U03VaHbpo
+aVvp36aadVGxUNzAap6T7qY6R9cbl2Smf1sD1qyzdzlqhZunOzFxoVJY/xhfq6I3XTWIX09wM3WN
+eyGewnrtMmOuuGnjDP2urplKkM5ajLblj1L2b4ld6eazc2bqOs28gmpjaPxsksI/NlNwzp0Cp/p6
+YYx5BylB/yeAP6Jf+5+BX+dTmEIY+Jwc9Dm6NcQp1Li5qskLEf2X5QJPDTHd3pBOqNBPrS/YT1Oc
+FohxWUOeaeVg3+OD54INP3/+mKO+Rpmpocp4uwwU9HP1ZMr7j6Q61SzLKDSBhjMvU2wZPWAX2SUY
+AS/9C/UD+h31fFRjBmrYIijxPPFNGy1J3rqCVisH25WH0eJ/dZ1Rqji+thpnrU9XxWi8lJ6Ks0Ua
+0lVxNmxDSq2urLgd/CRl71CrZ5cTfD1UlgCrwJug9fA0CtK06xTpFzTaSLOouNZCLgfHe8ShCHuj
+tEesapXTlPq+n2A1+YydLagUABYnCa2mB2vrhmqpuRunWvy3Dmk0EcrtwT4LVQ+jYcKtQ/HHH9Ue
+T3JhWkbjD4hafGWWBofT2Ia69egpDLizt8NQYzTWHpdBuGJ2rRWiqJhdy/jiIsDvyWGyQRejDHxz
+uH02Yd1NvWKpRt7nqxVXetbWmo9xUGxS9+UEc9m/ZTaj7KtB1LUUK2GsmV5kWQG9PQGGBLYkbtZA
+L8NsKsyidJZE1yGK1LrYtkRqzE3bGUajLq/8d3DKyXvtgEzVws9KfyA2BWPMPeCbwG8BR8owAM6A
+o5/wm00p+lR1rC1taUv/9On3zRSMMT3gfwP+Y+fc3LyS0ME554wx7nf73aul6I93jt3D/a9T2Snz
+Fx8AcLpzF5trqrBqii207LcH++pyXFtEXLNi2ch3q9PnFCptfPzeJX/nW1IeLfEcg3MxQFbq5hrs
+FbQKQf54/JRHzwX/UGUlzmmyVftSQli7dnb8PgtNAjrOLOGJuLfiyBCnIkzWkwLfKaDAE4mmdo5a
+jWtlBrECpx0W16zrTWrC1KAk8USMbvKKSm/HtL7Ert1btqHQWoKVFpkJRy2Z5mRY1N7GaBT4hmqd
+/TX2N65aP5R5TWcJYVeMpEX8hKjR9HftjEzBDsH1nGIl0oSt5RYsLs8Iah1TZ8A80FJ3TY91tbLW
+bygVF7HS2zGsK4b76oadBRz0Fb3qd4lUSivahkgz3ASatdk2HpUmFqltjdF5M50YpwtkihKjorbV
+VGRBbwcvkz6Hy4RWM22/+DCnvaXFYIYWTzP2dtSl1xQCdQewYcOLxzK+dy/Kl4VxePnXrdP7EbDI
+FR699FhoGTfbhvhaoKZQA61JQgKVqoJOiqfSRuKlFPNM2/PJkTmsPZHu+nXLughnPbWMtS5Ep3Ic
+ajLaxPd4176a8eHT6ffFFIwxIcIQ/pJz7n/Xt8+NMSfOuVNjzAlw8WnthHHA7bv7PJpc8c5jEfeP
+D25S1QLGmMVdoqUsYp5b6lg2Qrwu9Y7HUg9HtmqYnkkbH51draNlOTwZcOOuMIVaE7LMxxOKVib6
+7ScvuNCDldsSZ9d2BEewXnU9YcPjIQd3NIHGgwOOdhRM41tMo9F513OKpURuGl1k1xqymeyacrba
+FBbxPEOkcN4GhUkT0Cr4yQ+gWatVjcWpKlGHHl5fvhPPFR/QOIp9GfTFi3aTzdnzfALdjK71CBTs
+5GnMSKfTpag0aY0LSBWiHK0cdqDhywZCTYCi04ZrR4wUYBMan1pDoMvmbAPOMUnASjNFr/MvGgJo
+ZayDPZ9uo7k0fQ9bCuPIi5Za+9mNpZ/z3NCP1PZTOpJEmVsdMl5XtQrPMI8FcxJFomqurgvO9Hnz
+8dnGgn8xnxCOpf+JZ4kVzLbS3IehaclXoo6ejcdcjmXg35u/FMlfhQGstXbbtqzW0bPLGZ7Cu119
+QaoZoNqlJpAZDvE9rcDbOPylMkKvw84NUU1b21BpWHdby99B0hPDCmDDkFm+Bvh5POgos+gYnlyu
+JfFXk9H/ZPr9eB8M8D8A7zjn/rtXPvqrwJ/S138K+D/+cZ+xpS1t6Z88/X4khT8E/DvA940x39X3
+/kvgzwN/2Rjz7wFPgH/jUzsRhuzePmZWPOd0KbfEexc/ZF9F5nzxAWEkoq3n11QaCNWqD7rf6THS
+W8n3ukym60CTU24l61Rad4gK4ZipRsvlVcZS/eb5+VIiWgDPOWq3LrcmVmXYMGXu3XuDezfl5vra
+N75BmEp7uEOaa8kZWJsIXzMtt3M1WjY5ocLg7LKgqjScs/FoA/WV641ydTXZlBBPk5B0XUPAObK1
+cdEv2NV8fVZLySWuT7XUHBFmsbnFPAdRoNWVW0tTrytTq/XajBhoxuy642/KrCdpTLwGAPgt5YXe
+9Dsyx365oNTx+R2DiTeABFaqOdqVw2qNxlR95mU2JbtQlcmWRF1V11zJqpb5vC5ybE/6lKvBsBt0
+1wmM8Zoap0l0ys6KUFGWi/mE+Uy9C42YtwI/wqpKYVYxtd7pg96AjidtZ6Wh1LKFQ82lGYcelab/
+S2YW9Paf5DlacmETZ+TYbCH8CKx6DmyU4FayZ40JaBQq3tuXcXajnFj3gt90cLvqUfF8ltcqaLcW
++rJHjJaK89MQXw3FdVWTzUS1KZdLllrrtDSOXvhPSH1wzv0Gn5ScXqU/9tO01emmfPMXvsbrbzzg
+Wz/8bQCu336b50PZjHutw2i8g1l1qI24r9Zl1FfLa4hEr68WJUkqIl6adTnSBCd5NufZTBb0aKkZ
+jTowuNQkHXlGroetcW4DWHK8LDW+9ux86Zfe4K0bEnG5PzrB01BezJxA4bF+PN1kWapa6bttQ3oa
+LuzvOzz1StTzq40JezaWsTWTkkBLtU8mxaawyE4c01fA0tDv4CvIqDuQdoNyn1JjvQeLFxvXmsG8
+LDZroVGbSKSAmHhQc7cr2YFac0GpWa/CMOPihRyUctnSqh48WguZkyn1GvrsYhrV8QNXEaleGwfg
+a3GVNeIn3Rti1W4xfxHTtTJHV8uKRF2Lx+Eu55rgJf9YrfonS4Y3xQqfVzXLufQzyDJGWrD1oDMi
+vKsMYKCw8k6XXLQALg/PePqulnAfZvQ1ES6Rpauw4kRdk7ZxRFr6/SK/5LGe+tJa5r+zFD0vk6zg
+WlJ1ZYbLjFi9D35bEGjdz46Gi/eilEDtBAEJuSbDKd/5gKnaERYGXCvn4Vg9Fd3hPp4mtO2upsKJ
+gGE34U3tc+EHqAmKv7uJ5/y9aQtz3tKWtvQJ+kLAnD3f0OsHpGbAQSXi86W/y+JcrMVHhwckGqhS
+rBrK4GX6dYDzq5zJXPLdefklaaoRaYVlogahIpszyISr7t2WZ3SnRyy0LJdrLFYLdjj7UgR61XWy
+Njie7O5ycleDi+oCozH0gQthqOmxag+7kKtpqcFA1hgqLUtGPSQI16L4HTpqrDuIBGMwOr7P2UzA
+RGdPL3F9jTgMu4QDNcqljsCJ8dRpsE/Tn7N8T26500twOgKD2RgdW8/w5ErE0oca1ejTofO6Bgk9
+DzB6XzSjmh3/oa5TwUw9IhHr9OZDGk0Gc51/TNeogTINCNX/EPc9jNaxtGoQntUXuFgjLb2IyUq+
++2JyTaoAryROGGvEYK7gpt5gnzc8SRsXD0eYUoyj82zK5VORNqJen/BSjLxRR8YUcMaTU+nb+8/e
+Zaoi+Ouv7xPfFVxEp7CE3kznS+atzWKcpolndEZ5KlLT1cpuIiJfTVOw9rj0W59Q8150kiHdnTfl
+8+wjSlXHLp6LId2Fx6Q638FoRKuqXe7tUGkym1M7I1VQ0w1VA8OkQ6N7NitLUlWf9kLzsniS86l+
+dwfgT6QvBFMwzmDagHn1nA+MpL9+UVebfIaDBwdEquudZwuWmlTzZl8OxHDvkNyTTX5tHUbdO+mu
+z14jYvVVsiDtyIZuUzm4P1hNeW8leQI/XM7J1rmVzEtROzBs5Km1jnhoRkT6ebsaU7SaDtxbkfZk
+Eyb5kpmKpfFAprkce6yeyjjquqbJZKN3evvMnssmzpQdhfWYgaI7/f3RJiNVJ67wojVS0Md1VWAN
+hRG8+N4Fj19IXMbFwrIuYmQ9u3GtvZhbnubSj1utxFH4NqNVe8DBbY9ZIXNbPJ3i35RDb1aGoaou
+109kY0+XliB9DMBeOGTYk7mI0wq01Lxxu/iqVq00Me3s1GO8xv6HYybqiv3u6QXPO3K47x1MaQuJ
+bP1Qa2gkFx3O7svr23s32dFYizLfZT6W5/XMgHRvbedQlH3nNUz1IwB2210atY3cPr7L8a4e+vH7
+VBpqXy/UQ1XXtOrhuCDgfY0fCeOAWo0bGiGNbyDQiNjaC3iinpi3P37O0R0R+aOdjEjrOlz9QObw
+qrriUG1CdZkwm8lleJqdURlZ11GnS0f1gI6qGo1fMr2QufjBez9goUx/f7jHo6W07Yc1zfinKzC7
+VR+2tKUtfYK+EJJCY1uu8gXjZ3O8RxoTXn+IphagGkOwr1mCJ3MCjaEPa7mVvAOPcCzc/PLZkmeZ
+YsMTR6K3tEmPGA3VZ61y33y85PRUbpdpXm6qSztewb4bH3UMMEiEU3f7fWLNC+DHXep1hWYTbypM
+47WbNsJA+h74JaGqCc15RpPK+2bZUGnkX6Ieh+qyIe6qmtTZoauALc8PCbRCsY1rnFrGpyvxejz6
+/jP+75lW6O6L0RQkInSNIyuaJeGV3LAf6m1+e1BSPtUYgP5ddnflBhrPz7AvRAwOvAHxldziuzfE
+ADZYRdjnooL0uzXJSNrwmmQD+fXqhkyzZs+u5XnnLx7z/EJu0vfy1UbuzmvLR1cyB7MqY1crhnVC
+LTVdNcy10vZqZw6aWyMsp/gKPEqrnNiIhJEuZc1NP6N/rnk3mx6HA5nvk/1D0rWR1x9iFauy0rDO
+ZdmyUvVoPLHM13DswGe4rkGpumYvha9q2rlns5aF4iZ+9IPv8+CGrPvDhw8YasGZgWJ9g2VIs866
+lmXYUPrQj1pMK5KnCUrioRhPg1ikxtlkzpP3Bex3eXpJW8mYLuycC834fGlb+slPl1BhKylsaUtb
++gQZ91PmhP886PaNI/ef/Pt/ktv7r7E3ErdYJx0zfSGGukGUM9qXmyLu79M263RjIlWEw5TujtwM
+YZjiqT8xWy2pNMAl7HXw1bjUKv4h6vibcnNVkVGoO62oW7xI9P308gS7TiVWC/f90Z1vcOIL589c
+ysUjQc9969t/i0ahtLfu7mJXIqVMNKgn9mJunKg7yQ8pNWVPWS0oNWJyqMk+R4MBTtPDZWVGofrr
+IluwUt9oGAcQamLTWLESq4RGA6l8a/nP/6s/+5nWwI+ho+nMbu2GJJr81e9Dt6+4DxMQBXJzFerS
+9NuISjNMVZWl0NtqnteEGsR2cvsuNx+KHSe9+w0ADg9j3kp/BoB7RzeJS5mjP/ff/mf0PLkRB8MB
+42uxj4y1bkTYVKRD6Vs1y1nommV1RV/tLvu9Hdp2nepODXF1TrPOLeEMibpLTWDJNWLyYpGx0u9Y
+zSS+9/Cf50/82/86AL/81s/SPxDJpL8o+GO/JIFUZ7JVMB4oQJTOIGDPiL1m/+iI/ZGM6eHD1znR
+TNCx5ubLZ0us+moj65FoIZvAhKT6nWjleP9U5uK7P5RScrPFlD2VHrqhz0JrPYyvznAaYFdaw0JT
+533//Ozbzrmf51PoC6E+BJTseR/hVcdUmRgabRCxLMWjcNI/IrGaFGSxQIPWiA/UBz0vCJt1HcEx
+bl15yc7xnB6W+TOioUbDqTjo2R2sph2Lw4BmocaZyQKr1XbYbcifaabhNUx4mjAevS7NfvAdfvtc
+ROonFx/yuh5ON2tZ5FravZINvXd4n/1EOh8FKU7xBE1pKTQnYk+rI/XDmFY9Lk1ZbUAxTWg2abna
+piaIdF4C2RB1v6RcKtRYC5p8Jqog1ziB6diCgo38POaGgpOGN/cYagrzRKG6hghPIyfbNuBqorj+
+4qULJ3Yp6VyNdT8ScdcrI9qHUtVr8uJHXGvMSLed4Wlm6+X0EUuFeueFXARdP6GjKdkra+moDS2s
+Qu5pNu6Tg33SzjoVvTCNfDpjiapdzuFpfcy2ylhqtKpHw0KXfa5VnFaPP2b6Qjwc9c98GacQ5Gtv
+U0/nZexDC5HivFI/YXBfU7kPBuzdlrHu7PXpbrxVmjhmkJDo3Ps2JNKiwdZPGGruTtPvcadRUNee
+7OOhlxOv80v6kBcyh7d6jiqQZ59enTMt1+lgPhtt1YctbWlLn6AvhqTQxOxe3WdafYvJWBFl4z32
+FI3mpT61utCKUR9Pb51yKRDWOCooFAU26nq4RpOXrEpKvUm7yQi1QxGhiTdMS6iqROsV2JWI/rbo
+EWgm4tkHb9MuxF2Yq8HQK3+W991fBuB7j66pLtfZgCtirVUxu3zCmfLcnpW/O75hT116QX+4cdOV
+LdQzubk6PR2/71FqcYbSC8m0+EDg9zauxbKucFqvMtG/i2pMoarIOjvz70XryM/I9/A1vZ0NHXsa
+bNZNB9x7IAlyX9s9ItBSeJ7Ci4u63JRcb9ucQGtGTpjS1TqPSbSg0VoNRutZxuNb/O2//j8BcDR0
+JImoEmbeJTOPAJjWjrINtX+aHNdYfE29N0gh1sCfOwd73BvJDRrt7DOKRVIY57p2SUKladdGvZiP
+Z2KYvTg3+Opy3A0PeDwTlfV0LhJGOfuI5+8KXuS9k+9xMBCV4PDmwe+asqRKZQ53Ozu8dkNU4bvH
+r9HZF7UrZUlZizgRVbo/4i49LcoTBxYtE0IdOfrrhDJ+QdgTy/utt6St7nREs5AzULy4otbUgqOq
+x3srkbKXNLRrv/RnpC8EUzA+xEND3++iiWTo+RmDRH3+abwpAFKcLykLEct99dG3wT7+2fsARCd7
++CrausDgzyWc1O6NCFUcNz2FGmdLWJcetzmeJrGIkwvChRzIqrOgXmP7nWAhSndNfiWfT88uiXUW
+j2xAd0faO39Sc3EhTMbdlOd1opCO6pOBKwlVVUhLg7klffaUUbR1hlFLdzLapc0VKr20rIzm7Ytj
+As07uGZuq8aR6AZrzaeLjeuCqL6BVHWUnTAh0Xx/v/TmA954IBm2ja1Z18la2y32jKFV20hWNXS1
+aEtv0mCtqF3T5S5+LesQrGMAwi5pIOv48UeW0dF3ZHxuSa55DuerjFCZeqSZsJJuyzog/CQZcnOg
+2Y5vD+lqlu4wCKg0piPUfdP2DT2FWnuBR7JOWmNqjNpuelGHZJ2vUfNSusWcxVjwDc3piFq9Q/Uw
+Ya2cbY6cgV6qzG8Y0Cojb+ISV17qHEVE6tnY0ziXburR1dDqTgec4luyLMP4sp8SwNc6l1/XNSiT
+BUtNlnM5t+xcaXYnmk39yNa6TVj2Z6Wt+rClLW3pE/SFkBSc11CnEw7jASMVL+lZBuqbj/OcQjEL
+y6sJHy3k1ryz1JqQt7ONuJ/PCnqad7Ht1pvk+rZd0i7kVnGxiFwmiECTjPiZYx0Xs1q0lE4RdHmB
+p3iBrBIRfdK8T6RRbbe8mlz93AfDAYOV3BsfzC+ZKMJudCCSQr8T0tF0wJ4fkWgNiNAOMAqLW5ek
+r1YGf7xOHTym1Tj/ZVihAg+p12DXdRf1Ztw3EXOtZV6Yn5zRyv8d/3sGUrW4+3HIPa15eXx8gKcZ
+k/2VwVOvRF81kyCMaAutlTA2JIeqdi0W/OBMRPeVKwgVKryrFbOzyWO6tUK3uy2h5pOY5EtqtcQH
+jk16Pi0tQdfEHKhF/s7xDr2+/O5wZ49QJcR5WVEpxDjRyuWRLbG63W1kONwRKbQsLGfrPAReSW8g
+fXqjkr31dHnFhVr7z3pDzENNc9bZ3ZQTXFsaDYAG1VFeYRD4NNWSpXo4aFruqiqhyHXS0IAiF8Mq
+QqcI4hgbydza3NLriLTVjqQPxVlG0IpBNI5WRIk8o84ydnXtb8Q19TrZ6YLPRF8IpuA5Q1oF9DsV
+tQYYtK3BXmvE4FHL/IlmE5rkuHM5bB8fKNzzOmK4Lr9elVS7mgLeNdSKE/fLBrujcQ71uqhGRFjq
+62GyKfVjJwfUVvWYoznzR5q3T0V7M3HE6ubpdn16GncQhjBflwu/KCg0meprfVlEL01pVIkfdHsk
+yoX8QSJFKwFTKtiqguBQM/7MuxgtMx7VNUWkyTbamlYt0pG645odR1cjGWnXu+vHyfyOv1HkMYhE
+D793/4iTEznE/d0RaahJVIaGQBOVBN66ilOC1aQ3xofiVF73YkNPRf/racmOWtGPjoXZeH6HJJH1
+zTOzCY02DmytEHMHiepmKiUz6Hocaw3G/UFKrGHwTdDiNATaJCkDde3ade7KpqRQhJAtDElXXu/a
+PtOP5fVVuaQpZR/ta/5ILxryXM/50+fvcZgKdzIPf24TC7NR0hw0mq8y8jo45RpNXVLrnjvsWoba
+dqAgOtNU+KHCo3tWylkBjReQaN7MYDfY1O/0unK6e4MhtMKdD7yAMJP3F82U8EA6fTlpiPIteGlL
+W9rS74O+EJKCwRKwwrYt7XQNUfaoXgjne/Q0JLuW9yeDPTTLOLta9r1nOwSamyCwHp7e/mGTYkZ6
+08xXeMrlm0zTi+20eAq/9f0DqEXCCJKnGIXBkvdJnBiJVlo8cBlPyLSG39ViwonWUoxshw/Ppc9n
+5ZzOQG7bk7u/AsCwf5PYqcrQCYn0dvC9LlbNVkbvHT9sqVrpb5DPCbUcmYk8fJUAltbgafboSCML
+jfOwtRj42p+QW8MgRW4AeqrODAcRb74mqb+OH+xztC8eh0HQp5uqZdzV+BqUY1eCG4hNsgEF1dGK
+Qm+rKnd4dn0jNsw1N2PdkXGmbsaVivizfIp2H4shUoktx9FTCehAS9gfDAakGiUbxgFdTXBjqwCn
+SlE8DAg1mGwdxFbOa4x6gRK/odUSet00Jx1qbolLn0hVpVojW7v+HsNM5vPsesK3G8n4zZfe+11t
++ioosJgVeFrqbjFe0qo4bwbexgtWaEGaKozpqNhhqg6uXktbO6QKUXatx2oqHrggUFUjWDIaiCdt
+WeZEN2T8R88jrtRAuT9t+NFPWTbui8EUHASNg9Kn1Q22eDFlXmsh2fcz/JW64Uan1If3AEjnoveP
+9kMOuoIUDAc7+BpmGyQxSaihxYcjPCPtxcfyHm25EfHs5TNco9bbZkY70UShq5pkoAynknatrTcA
+oqHfpVAk2Xk85exSD2Td8vM/Iwfrxm0RmXtxSdKTzZjEey9FeFvjq6htNLS6yBoarfTkRx6mVGZZ
+VAS6HSM/wKpNxNOqFJ5p6ayrI7mlJlyHV5N8hx50NPV7L5LD83A05EQNBaPRrY061hv1N4fUtwaj
+YGdpaQMAACAASURBVKJIn2GrhrbWpKyrEk+ZzM5OzO2VjLUoV5QKrDl9KnaGg9GAuZafbxvAX27m
+wioaMQkM2k0CBU0ZW+JpoERjWjLNYpT4KYleAHEUY9Wu5Nb1Fnpd7ExrTwQFTnX8yB+S+IpCagua
+pfwu1EjUyBX4qqLlszNmWhtk9s4pHe1b/QooeO3ibXzH6bnaebhgN9X+D26xGGhR30w9HWHJollX
+2So2tS3N/8/em8XeluX3XZ+1533m4T/eqepWV1eXq7vTttNOnAhCwCBBwvASIQQPgCJ4i5BAgsAT
+DyAFXiBPIOEI5SEooDwigUjIYIiEHTu2O+6xplt1p//9T2c+e96Lh/Xdp7pMt7srldgV9F9S657+
+1zl73mv9hu/g7dD8gC0rQtXYcqWuYe1TqB7S1oaBzmnvVbBy21gGHm3z2VqSd+nD3bgbd+NT4wsR
+KVhjsGFMVmUUtZvtLrJbCoFULjcZ21gx12LA+dDNtCeqdMdehBYGwqSl1Ypuwwk2dvgFsy5pI7dS
+GGQw4g0OAoxt1dAKSlusCnK71LaHRCNXLZ7IYWn/1BIKJttvBlRV58o8gcRViL/+8Jif+7ITJzkd
+ucLYMA1JOgspk0GHz88CTCyTGOmGt8YHhbu2qfC8zvuxpdbKnbT2oB+ZisFZ5wVWHYJ+EnYKEZ/S
+zRsOPI5iFdTkyzk/PWP2wEU2J03CUECZnu8fuhI287F1F85K9KZuyKSpGLQZw76gvXlA+0CCOWHG
+ZivTFikS5zsoG3cNR6OQdOp+96T8mDPpaj4YDUiU5/T1qI6igLmuYd+PD1qa/Qn05FqFb2h0vYxK
++b6xNBOlOXWMJ3BSb5Qz3bs071W6Z7N2z0ArWTabpVjr/hYYj+ZI3Z7zit3vkmMzfBIpNE3G6sIB
+iJooxpu66PRRVmAWUiOfCJDVhgc7+/x2TTSVsRElSHzGCyye2hzxYX8eXiSjIQKWArVl1rDXpZi1
+ffoqBH86Xvzx4y5SuBt34258anwxIgUgB7zaZ6He/H6TcLlyn3/tsibU6nGCx+Cxy30LIRPLIKUy
+shVbLQiFcjOxh13JhGMX0iauUGNVqAvPEig6nO8OK2Vcb+hBK1OXo5r2qbj8YhGW3h6/K1plMfHE
+rXJbMuZSHnrj7TO+8Y3HAIxP1dIjxwSd3oKPPFZokxwrD4hWS78XQHwsVN2ihzVdBAGqT5Li0dZd
+u9P9rYlCvKUKqb5/QN3lfKI0PI8DfvENxyq1U6eFcDSZMZO3wsm9CT2txr1eeLAma3vmoMnQFTGt
+8ajFIg0nPTJBzMeD4OBHeXPdI5aTciWSlh1ExBIisJ5HKvfr1Hr01Ho7Ioa+IgFFggPjMQoFwY59
+mHcWgOXBwCfzarSAYvpdlNOSqLjIfkc7kqVdVjFWlDLfzbiRmniofxd+i1WkdJLOSUTWivamg7jw
+w7jR7vaOTHIoKO7zknbijtkbmIOVndW2wibC6IKauCbQil8VAb6wLMnQ0qwkXtt1mo0hUCE1mI/w
+FLLEqYcRVvppURB4n63S+IWYFLAttthgE1hH7mSKfsi+ci/x2XTCVj3o8N4pcxW5+jJX9bKabevw
+5P10S5S4oqNpI6paqYR/QYsLE32FYcWmxMsddDkMjzFFB4Nt6Illt3u6JF84Lb2mJ0mt53uykTvO
+Te0xEo16MOjR81yqMY0G5I0q7beuuNbi0fY18QQBUU89/2aEVXW+M4hpyz227dyYMrJM2AS7xhMu
+orElSMrb7/KIuoG9227dBt08RgL0VbUbjodMvuLUqDtpr0lyxv0HovqeDGhlYuobQy6MfuQVGJmz
+1JmKdrlLMQBqzzBSR+hmWxD0OsMZyERnn4sWnHgebV+y/L5HLQzIpA05SUVV9zNGEps/6blrkYxT
+Mp3rYreiEpsxL3KOztThiOY8fOiOqSeqd5kUFHIGCltY6zrv9xtuO0DW8ZTxreTNnsiMeDCkkV+l
+Pw84at21vS7eO1zbTo7NA6y4IWE/ZSwcitfGvH3f8SDGYcNgpGdAtP3KCwj9jmkaU2iS3dqWU00s
+/mhEKLxLlQtvY0uQ72Q/6FElKgKbGVXfPXMn+5xpvys3/2M2g+mGMcY3xvymMeZ/1f9/bIz5VWPM
+e8aY/9kY8xn4u3fjbtyNP+jxjyJS+A+A74IocfBfAf+NtfavGmP+e+DPAv/d77kFC14FeB59uUDn
+lU9/5mbzoc15XeHX+cPHpFIUtmrZZVGOzUWYsUeUUWdLX9EsOi6bjycrtFDkI5unNIoq6pUFo5n0
+YkerULRaLMgXLtqIxFosg4pGK1/ZsweCTpLDduVWrmTSp1261eZZIyOUuCXNXNgexxuGpSzXvSsC
+ac/tN67ltd9dUAg+3CQNGxXGmnZPLFVfIojVIvQqKTFnO3o6z/224Fy+EI2F2Zn7/C/84jnH2p+N
+3W2bjiLq0B37y8WGSn4Q/bhHI9fiWRgfwlVfpctds6WVT+Qmy7mSV+bFq2v2ajnerjJq9e0qnV8d
+j2iFjmQ8xFMuNY5bUomxmklKOuzs+1REW28O+gDefsh16fadry0vMiH61pcUhdO7ePOBu1bj2Ry7
+dd9dF3ue3To25Gq95lrp1mQ+IK0UhXrabmHZDsQCXWzpSd5vcnvF1Li/X+rY/MDwcK5wv43xhXV4
+dDxg2tnH7wbcOrN2zhJX5E1Dj7jp8BZrVkI03mxa2i79aRqG99y96iztNtkKbrvWcvkJxqVc4W/d
+/Qt7Ian5bISoz+sl+QD408B/CfyHspL754B/U1/5y8B/zk+YFFogs54T0BDM98o0DHwp8Q6zA768
+us3ZKQy8Ncpf53OmnSnppGKeqfo+79Na96K31QRfPev8qV68laHS39JoRqQUpd7V+ALLBMGU5EQ9
+6xPp7337OxzrpTgbW8xOL960f4CwPpz16SuuLG/ctm6qF2S+S4kGvZi5REHGvT5J7B7CrWi114sd
+hejicRuy1cQS5B5GGn1h0FB30umpHoI2pur4EFhUhOdkHPG1Y5c+feX0S6SRquvqWiSMqHQNb1/s
+2MuEJI8T+sMujUloxdD09ejs8pqbS+EUtgtuVuqiVA1R0/lj5jQqr1fqx18u1wwlOJOYkETsyuum
+ZKaJbFq0ULr0aDt2vz8NGh4LZBWnY7aNe1EuLl/w208dxfnD2xeue4Mz4QVHsS71olze3LKSwc3W
+FEzUiXlr/AbV193LOz11tZZvvf8xm5ULxbf1gg9GSvPyhNx0XqDuGhssudi1abRmKTyFWV2zkm5o
+UFhGekaSb7r93u+f4GlbZeMRdIre3PCdp+4ZenV5zeQ9dz0lmUm7ybkvB62yZw9MS6+NGZ66v79l
+Wi4WMiv6KcfnTR/+W+A/BrqpaA4srbXd8vwMOlbIp4cx5t83xvy6MebXV/viR33lbtyNu/EHMP6h
+IwVjzL8MXFprf8MY8yc/6+9/2Ir+zdOpLdoWW2wpcxla+B7bXL59VUOzdSv6ZR6QCvp6rTDyZvUh
+x3OHGozNLZOJC9GrLMMTS64pbvn2D1xR8em1E9i4jVK+duY6GP2k4N5EUOKeR5KrOv2oT/6eiyx8
+OS4nm5jlxs38Icecn7gVwRYl11pt//7f/y57ITKNVlczO2LekxZEep9GYijtOKIVwcgIcJGYiFeS
+/losnnEtD4HjNCQUpuHRYEqoar5qfdiZh70VJoCK+ypsDpKEo7n0G9qIcergscOJW4nHR49ZaHLu
+NVe82KmrUUFf0l792BxkzFpZtY/CHlXqVqViFxMaMQ6L5uBVcX82pZTsXSUEpRfVB5GSXV0RaF0x
+RNxcu5UynfY4O3fHH6rYd3Pb8vyFE2F5sjMMJGnXxEOqtewEjcd17q79k61Lq452KwqlnbusYadK
+/av1LZu9zjX/PlHfRW+1VJ1bP8DzXDSyrNaMnknPsVzRqlhZd7aCDSwX7jz3kaVR9+VimzGQZfzY
+LzFC57bfc54j80ePGAm62O/3GCktboMpue/SnA+eNyCk7nbsIoaH45BLpbFJU9KoSzQchhijbsbG
+Moq6QuNPNz6vwey/aoz5U7ji9gj4i8DEGBMoWngAPP+JW2oa7GqFSXwUzdNYn4WgsTfPliwkFDre
+prz5VYX2yrNvS4/hRiH+owFBopBzVxJIfGTxcsXTD12O/9t6+CezCdtXCuXGawrBWfvnW6LIvTTt
+5Zoqk06gZN0vsws8vbzB6oYHD78OgB9kxKpt/PpVTuFJ/LXnHqpZtWAsUVKPAb5qA7EX0azdg5IL
+vLXLKjKJjn7nssDTS7XDMuq7ye3odMD0WC0yBWdlEVDJOGe7KUjGbhtvP5jwjdfdxHnvfEipsPrm
+0r08FRHPLtyxf7h7jhUFuDcKOSmU+44TIsGYs6X7tzIltzduf1eLFR8/daH2R9viYFpD0dCTYetA
+ebg1FZEAQmVeshYEuZe0nKodOoxbJhN3fi+v3H//zadLfl0dh1VhDtfw7dMBZzP33bLyWebu/qw3
+qn1chBShe8HqLKeWm9L3P9iz0uy0XVVst06PsRGILB6MkfgRs+GE6620G9MIyWp+SoHpRhyVoC44
+mrl9Hw9OOX3TaTROwoD3n7pJbVO5BWm9rIllfDSbjZBlJOtVwfWVUuXNlo9XgnSvJJcfWc6VSqYP
+pzwSmI81VAvxOSJL4X22hOAfOn2w1v6n1toH1trXgX8D+JvW2n8L+FvAn9HX7qzo78bd+Cds/OPA
+KfwnwF81xvwXwG8Cf+kn/sJaqFoKG1DInCT0+lgZq2Rly83KFbMGgxuajYsEHiQCL41zxlaQ2f6Q
+UHLPBoPna3tBn5lWkl/6GddxOL/3NdK5m2mz1XN6Q7c6jtsj/LnbXrFM8FSMCxK3qjZVRKvZt25r
+mlbyWrM5p5H77nD4kku5YvcSB3GdRyknpy61Oen16EsrIKhbGq3ckZQwUgOnshL7E3/s65Cpp59Z
+blYuAkl8S9A51XSekbaikhK1NWu+NnEFs8f9E46FQxifPOTpxw6C++SJC+T233lOoX52Fvv0Yrfv
++ekJE8nGJUlIU7plzFO/PvK2NNKN2GUVVilDsq8pBf++3BUYmdbcV/VpMu9DVOrILaWIPb06ODBe
+t2XDy4WUQYRNeDwcsZC82GYWkUqqfTgcHZiUP3/PJ5w64NgbM7fM3z8+Yb1x2w3vHTGbuudptyp5
+snQRxL044tdeuWu7V3pxOhxxFrv7VxUxkQBuNj/+FGipGx03qgFqYTpeOxnzJd2nyRuPOc/cvo9F
+7IvKU5JjF201tc9qIWXvpKU/cMd/agyDuUuF5oqeJnUI0tY4fdhnIiWaPH9OngnrEMCo/9ls4/6R
+TArW2r8N/G19/gD4I/8otns37sbd+P0fXwhEYwOsraXvN6SCfm7WBacyeMmzlrmop9984xFv//F/
+FoB2qX5teU0hfv8oHjKIBIOeLWgDyYCNHhDPXSvLpG62H79j6ceunx0kDa3aaWxrjAp4veN75EvV
+K0L330uvZazcOGlili+d3sJZOmcgmvU35g+oX3cztz90K+0AQ6gVOOl5+KqfeHlGrj52vRcOoD9g
+JDLMPA3wJ6IvNz2+IoNcawtCI1FZrcDlas1uJ9ObanAwtw1mAXnmtpdlGYnvrtHJqbsmQT/FlzZB
+EA8w6s2f93pMB4ISbxsK5eK5vAls7pFM3PU8b1oeysX61eUTMommfuvZCzLVKDrCVF3lpJK329T1
+wctjQc251tuJSWmutRLqOF8/CfiaooOtaTBSzzbDgmNpNRy/9ZhakeN85K5nfzjCV12qHq9J5Q3y
+5nRAo+JAr9/n8Zk7l49EWuoHAUjm7XQyJ3gs+PfJEfWv/A1+3GgtZCqOv6ivmLdOdPXLac7JG+5c
+jn7G/a1/MmRw2/XcWwJZ5cXxfd45FwnvJmek1vDo2N2DNAxJJTAb1zlVoULkZkfed9GP3Rui7J9A
+NWdrLU1dUuw5AHZsDKWknV8/PmWgC/nmG28y81XtP5dc+ialVG837Y/wXS0PfzOgrwnAm8L5O38I
+4MCW9KIB9kY8geacVpj7tnhJ3klYPbolea7KeOIe6GnsM5Bm4sAPaQLpEu5ece+hY0YOBw9JY/c7
+K7B6HLUYgaJs9Ynar/F9GnE0fL2AaVjTO5akfLnHE/agud0SB52RSUktDv1O4J/bcEMmGGyQDDk6
+lrR40GO9dkVA/zom0gs01ktc1/4BwjysMtK+u4iTyZS40/7LdiAwTb7o8Ao1M3UkktOAXeY6NVHc
+oxHcemAMrcpXsQq/vXTERhoRG0CKb5yEPnPd3/tJRC3RgjRyacTjh48JZu5lsoGllsdkOi6IpB7h
+zwYHU5fhkY6z8khlSz9cbIjedgsOmz33T90xLbMGX/oMVysVl8uIUB2c+I1z5qUcot5wsOXfa7Qi
+m+RrC19293IwPMKfuxTsQizK821LNOyKuZCmLvWZ9SI8cR/iL6eYSvoaemu96z1px+FYrml2AqX0
+elTSr8t2Jdb/fSo03o27cTf+/zm+EJGCaS1RXhPEJbXofp4XUR9YdjnTuYQ405AwknfAVOHUNqU6
+djNtvxceVjyCkFB955AlvgqGqGXZlpeYsQpZwRGV9BTKUYwvd2TeCwhCFe58t0r02pCpuPmjQUog
+ibLY8ykXUhCKX5CrldcbStg0SPFlolL5HkayaQQzfCEWS0FS+15DLDWEwLS0SgnisUcj5R0ySylS
+DY3bx36Vs92rvecPmKt4OBi1VEJe3u4WxEKL9sU0reOKSgSu+XjMaKqVK2tAeJG2yqh0HIXQga0N
+Dm1PthU3C7XQih2Vcpq9MRAoBRsoqmhbFkI3Vq3PSNDLYeUTBbq/aY84cb87Gis6NA29YZd3TYil
+vmzSPqHs36qqZZ4Kbh4rZWwMlXwohucJ9dYd26Qf0y7dNZo+6LHsUshrKUWFO+ZIem9b8YYimtnx
+79bD/v8OU3UirgFeIP2J4pJ0KGxJIw2MuEfRqYo3IZE8OE0U01NkGYTxwaTDF6w8mnhYQZ7bnaGU
+TWFoA2K1ZNtgS9B8NvrRF2JS8DxDkkQ0+5pwqIr9KGUrBljZ2xGp4tqP4oPFu7mUkEavYTAUroAc
+9PDbaY4VgKg2LXb9625/vmMIGluBclLKAYSS5boKqBv3AFX7nLrs1J8lDd809LYulZjMhnh6MLPC
+O1R96ywkSARCyQQpbnNaTUjFdkMmFqi/9/Am6na8cN2AuklpGodHGEYJRv14m7cHMFTd5DQyxqn0
+8PhVzenIhap1woFTMQpCdnP3kC5LqPdSBJZc3emjOZE67oPejImCSH9gDxNAma3YS5uyEZNvl63Z
+S8OwzSyj0D2kj+IANY+48gOMUoJ24yavl0XJS/X8k0HvULYPPEu1dfcv75ckYlou1O0ZtxWoVpEc
+2YPAiZfvMaJ+G3xq6R92sGwbQLFz9+/qxQ2Z9hGHlmjkwvmB5/PmzE2SXwpd3ebVzUuu6/fdNm6W
+fPVn/ygAJ+qG/bhhgFYn1RQ5zYV7DrfpMZ6g7LORm3hn/YRIkO96XxJLmi/fr2mF9YirVySd7uTa
+nZsXW/ydnje/wIirbbcbkARb7Ifg3cmx3Y27cTc+x/hCRAo28GmmQ/x5jdkIrRgmzIduFev3c3xf
+ariUmJ7co0PZkN/s2F0oZJ5F5GvN7Bcp8UQW5nmPeCzSkBB/0fgxXk8irkWOlaFM6W+pahUdNyVG
+YW6jglpWZrynMLrawEyFxJ4/ZSffgFW2Yyp/RxupG5DVVJItvlpu+fBdVyEOyo8Y3nfHsVM4WFcX
+fPm+k0fz+x6RzFJaPOrO+r0tyTWvFyomxZFPIpRbE4ZcCVZs+n0S465tYl6xl2nNrS/MwyqgN5R6
+dNvSihzWNtXBA2O7y1hJmHa7Egy4aWg63VObMxyoytvb8lyReBJu8VMdpxCbL7db1mUnQOvhi4Ga
+B1BLgHVPgGqx+NI+a6ZQCqcQZA026qKmFY1MefLWY/l9d5yp/9vuvyfwve+5QuvltmQs06H53GPS
+umufHaXkEkDZSUD3+f45VsI/69Pn/Mq7zjX7Yeu6Vr/XqHWcL9dbHm4cWavZ+Xgj9/wWSkte3VwR
+S7THBBVVK1eiJMCsOt/MAaUKvklXaM89THfd9gH7ouvK7KHn7sOZndP4Vz/xWH94fCEmBc9AHHvk
+pU8eFPrbgLHAKEM/orUyNA2HxMpxa7W5sqtXNAK8ZJeGTL6LZeszbFxLcn40pLJuMuClm0D6+TMS
+2YKbUfqJElIeHMJj3jTYK2HjjUsZnj67JBeY5tWTa77+jgs15+cpgURGgt01aeQq+75ouHUc0axd
+SrC42vDsXfeg7LyKweUzAKIjdzMfPjymVT69s2tySdHbKsNKWMXYkFImMqUe4ugIotq93JVJ2euk
+wrylkT7ivqwwkrtvFe7vljWFgElNtoe5++9xY1G5A98zHJ27Cv5AE91muSfqu4fbtjFZ6a7t84Xl
+4lZphWfpyW9y07VOW0uiY0tbQylV7TKz5OraDFpDNpAoS+Lux9prDpT0NjgibN3+msQjFt5/u7eU
+avFmXqfkVfNS92FV7Misu4ZJGh7qMubmKVuBk25Eyd7dZAdI+HYD1YdS2/4Hv8NPHJ3uYlFzda0u
+0Zf7eFt3vZ695xaFxcJSaKGKeyHnj9wxnD56k9ennUmvoalFv9ZE3zYFRl0pGweUgkHjWwZn7rpV
+txWp/fFOYT9q3KUPd+Nu3I1PjS9GpOAFDPsz4mTHVgrOpf8JH9uakFqMwv1VQyDzjkaQ6NXSUMh7
+YNCu4citcrPZEcHsnnaywIvdir6tXcGpWg0YlW7lDvZTmi4CqXx88dSDMmbfuu931vDNNuNbWgaC
+bEf7odv3V7090+NvAtDmPrVITk2qarENEM+Ge+MJ0S+4EHRdlUzUb29k7jFNA/oCUO0XGzIVPr3W
+o5X+nolbuvAmEVYi8VIaFQzrfUYlNmPpQyXtvzJOqSXvhsJ5P31BsnfHMD7pYXYuKtq3OXbXXe+S
+Wit+uXSRws3Vir3uQ2wMFyIEvbtcUeRuBY69mEKaBYUKnFQNkWDefhyyEj7gS22Nr4huV9QMQhfp
+1TL42S8KYnllbq1HLNJRVPfJ9IwQ9Gh7bjXd7N21WF8u6DRdclvx2sidn8kj6s72rvb4B0+c2cv3
+Xroobt9UhxW/Bl4qari9/snGjJ5wCp6BWJ0vE5VMjp2agGqPbL1bfHUc4pMh09TB8GfDOUNFBVEa
+snuliFP4nXFYIwwdza7F9Nx1Gex6xN19P2vxP/7JnZIfHl+IScF4HmE/hTxgdu5O2JjeQS7b1AWR
+QEgkK5SKUqptNBxDKMWj/Lb8xLxkfkwjhOAqyUnVkop7DrjiB4ZSgpnl9TPC7sYk4IkubLKMdORu
+Ypu7cC83llzH0HrwgQBXj/o1rSq9bW1pFIJ6qiNUdYPRQ9VrWs5DF4qPb9bEP+P21xppCpoLNlIx
+qluc9iKwrytamZHGcUCobcfiTscmovRkShouKCXeUfjBoU4Qm4hWdYlWTkKVCRkZpW5NTNF1RpqS
+WqxEu9yDlUy+2qLDfkCsOhB9n0oS6U1TM1PrbTicHazRN7omifE4CjuVIlgp9A17DYXSley44d7Q
+HXMkNSbPr6gO9vQV7Uadn2xDGEp5aFsR4dKD3kJt36iiWXedrYjF2nV5dpuYrdCi03TIbeZ+t+98
+KflkcYJPRFpt80mY/aN0jTzDQaUqNT6+npGgGWGEMHxr6CbhiS056rkF6/j0mOmjDpyV4KmO0O52
+B1cvalkRBClGjN/W2xNKsclWO/KVO9Ji12C8/Y84wh8/7tKHu3E37sanxhciUrDGUHgx/bM5kaTU
+inqHjbvQ0Gcg6XSb56yktdepE/dmMxKpBK/7FWGtgqJJKTcu9N+uSlZiTA7P3O/iXkyi0DDEfgJu
+CnYEkVR5e2cE6giEOxfK/cDwiY+ghaVEMy4XrzAbSYSnlrZ2q3BRCn56UZBJR9AUMbZUF8UWFB+r
+T71z5jWX1ZoicOcxuzel1/ESTEugmDGNfXyBXqK+O7a4CSl1dCaLDwXYgIhQGrr7uqSvlbdV2hX2
+anIjjcNNTlNrFW9aQnVlfGKscAaR2HvjZQFH0sy0PW61cn1pPuBRZwxzMgGkQbly51QFGWddJOj1
+2UgUJQtbVhIcGVZ7GhnN1BPt1xsxEAfAULDveB7bHC/UXdnG5F1LpFPEHoSkioTefbrm470AUv2Q
+12IXOc7SIy6bJwDsKsHR+dHjJ/EODeDrx76F5dad983f/w6DdxxEOpVWf78ZsFOqNVxv2OxdtGiL
+q0PhPaSk11M0OBTAL2gJQn3OWoxSSLwcI6OhoAno1T+Kz/njx12kcDfuxt341PhCRAqttWybnJ5N
+DtJWTeiR7bU6jPpYryMX+Qd2ZCFLsGr/EhYOieYHFd7U9feT03PyW7cS2vXHZF0veCt/hygiUjTi
+j4YH4422Smg3zv6tObqCWvUFyY4F9Sd5pLEgUhvD5YCgK/hFljCR4Yry73yb8fTK1SVG9QOOjtwq
+4M+nRDPXvrx1nUnyj56xVGQzHs9ph+pdNx6RLN2iyDsUCq1k7Ir+guJW/gB4ByPVwmYHq2nrg1Ht
+I4xlwmI+cWgmSGhk6Hp7scXTdWt2CzzPRWzljXQHNpZSHhDmqGEjtN3D41O+IpZrOhpyLTtmT9ey
+apbkVsYwu4qXKiR8rY14euPu7/3+hIEUjweRq+uEaXDAjfjJHE9YlXJsufi2wyHsqwwjqT4z1vG+
+9HmuGs0Pnq+wQkqeBh6vP/iKu2emTy9QO1cRq7U/xrob6Mp3P4wX7FbZGBioKNYLQzayyPu+hYHM
+iSsVaKfWwlau6r6hvXDPyBpDoeJpkO459VwBci7/hzAYECi4Nb7BX8i53G/xjtzvbl9mLNY/Lt75
+0eMLMSnQtJh1SRWvaEWnbdoU0+m/5iFhKwpwlDI4URFl6V62i2/fsti7h3TiV8xeE2txX5OpkEoq
+twAAIABJREFUUJWaiumZe/FCq3C+TQk6K/oqxNzKodgzhMaFe/WFh3ft5LPanutk1BFEXUTmQS48
+hZeeMOxcpaNz/OATaXcAv+0xf991F7LmFqsXOvES9i8dyKbMZRzTXvL6O07Ca9gP6EpctS3Auv2Z
+fIdReOwrVVnf1tRrFdeSGbcyoinwQVyS4fCUnq5LIlr3rsixufwjhzU9KTH3hn2MXt7h6QRP0Nxm
+7a7VfleQb10H5/Ki4NHR6wAMzk4ZvtZpTJa899KlB+8KC3HRlsiECWs2B9/MD5MtJxOlPLZhIQOf
+sW5ZkIbUpVKtHCJBe3txSu8rLiy/XWdEAppFoYp2oSX+yM24ntew7QiFgwkvjLsnL8MN39UboawL
+v4Tmxyikd45b5oe9JDVTNMYcMCJZU5MII/GtMuPN3E1w9yQAcz4fM/Pd5zDt0XZK2+WO2LjJ4uz0
+jEDF5FQ7MTaj1eTFNqNGiwwtuxduqloVNyx3n03i/S59uBt34258anwhIgXjeQRRD6/pI40Vgqwg
+llFJ4FmI3crkU+GruGIUxE9OfYa37lTS4bkTxgCqScWRZvGo92WiLkIYuVm0rlp8WbDZfYuZq2++
+2YGk3sJiQzVyv7O+WxFPgoRYDMBR3/LHv+HSlakZsRcQoQ5z4lL2XmIqjiJL8ofcymfXLXWh4iAl
+mXwuR2ID/szsn2Igdel87lOpD9sGKYFyLK8/wBcaTybQBOs1+04cNdkipTgWeUZFJ7a6JUrdim8F
+MfRig1GhsVylBIJox6OYSpJo+5uGZCOPB19o0us960J2bHVFILm58XmfJhfK9MUF3/u2+v4bCYHs
+K5YqgHlRwESCsJEZs/PFDPQTOreAbdUpaVc0aj0G2SepTVL08U91ja733Cy0bcG4N35Ns3XHPJg/
+4Ext6+Le2aHt98FLw/VH7vuBvElbv6RLEGxrDx4PaRKQKNLriF8msMxlenm9aQ4ivLapqHTPxssZ
+G2EuUj0XiR+zldzeFI9ccnqDEKLe6wBMTIg/kA2fmLRlAV73jNnywKQtNq9YXbjz+MFizb732dZ+
+Y+1nyzf+cYy3337b/vJf+mXO/WMaq369/QFPf+c3ABiXF2Sy7c4+bghlS+9dqjYw84hb97Jl6YC+
+sr21ueHimVyPdnuMKvSn9xzW/bWTtxjO3INb5+GBo7CrLZ7YmtPdOUEg8xXh7G+/8g0eaGKqwxAr
++OntR8+IN6p6TxuUgeC3wp5fZdTKUduypLUu74sI2NYdRFcvxNA7cBXyoMdKtG6v59Ok7oGd3zvG
+S1ylenTsJtCj/RlGD11brvhT/9Gfd8duxqRn7oVdvf+ExnP783Mx9r58n74q7j9z//xwfv7UHBiH
+TV2AcvVXW5eiLK43XEpfsdwFLKQEtV3fEEhafH7+kKM3XchsY4GRophB69K50f3XaJ9/H4Bf/q//
+vQMGBICuzKFwfhKEpOJ2+I2hlcS57/kkYhQGUURfDrM9AcCqpibTxGrLklwswso0RIm7zl95dMxb
+py4F2SjN2W0NVtd+fbPnmT6vsoy/8tf/JgB9zcg31xv2Ny5de/ns27SaQP1egxV7Nsu2LMUfeSbR
+m9uL9/hYqfBHy4KeWmLj05Qzwc3fHH8dq+e6FPYma2t6or73R2MQ0Gu5u6G6cpPFy6sVz189AeBb
+H37nN6y13+QnjLv04W7cjbvxqfHFSB+aFdHqfyOb/vNU7znd/XWv5Affd5DTn+tlmJVbHevt7iAy
+4Q3dShPs1hiFYvHGEI2FfluXNK1bBfybBZV6xU+upbHwhuE145BkaZrSyP6saiy+0G/NmU+9FPtO
+MNLyZs7LiSs6ji4KMhEDb7I1r/dcwa9dtVj5VqDowKZ7PHkY+pWH6YhGaYW/dsdmZQDTrmpKYQGW
+5Z6scue/GjQc6by9NsQbSEnZSj5t5lNduVVna28ZykRlU2R4L77jtj2xNBJi6RyQT3yPSFoCo3nE
+yH2kXxbkIo0FpcdO0Y31hTScRoy0cu9nEWMpTT+jT6Hcxe7XtNduxc4nsqSvRvgSwPEv3ueiM5H5
+XdR/r8ORKD0a9HzGKrhtGo+Zqu9RGNKq2h8mKVN1kkJ9N9/uD4Q3L/CQ/ATLLGcnNepqZqlPJYyi
+bsneNIx7woIw5NVHLhJcFRXtpStA3wTuGm4uPmAvIl223zEO3Dk165Yqd+cdeBV9dbFyFQM/fNay
+W6k4vrX4wtz01jBJ3b3uDyy1SHhGRDmTFzRlp+1Zkgfu+Y19S6gbmFy8x6r6bA5sX4hJwd9FDP/e
+Q15N/wderHVIVwMiXxNEMWX70lFW2+MJJ76rKCsNJRjMqe+7lztZR+SlC8/yRUCkC/z2O3+I2/V3
+Afidj9yk8L1nT0nV1Tg+ialsZ0w7YyQb8ct3v0+wc+1Joxs0CL/J850LHT+6ioiMaxX1ejs8hczZ
+5imtcr9EFvdB3WC7roUxhArhm2JPs3IvW3fjQxPSvrrSVw3bgdvW1A4ZGnf81m4w5m33d2lNfvTk
+f8K+735XtW+Bdcdmkt9mea03qz8mUZA4e81NNtM0YnriJpbTyDIRUWBUedSSbff8grHqMuPGffc4
+DRnI4OamvubZM3UO0luu1DqO/SW7S3d8tarsCWOuXv1fAFx7YPtukv3dyWwn6HqqEP/rx3NK8Sti
+axnoGsZJgpfrrU8THo7cwrDRpFjlPgOBf07CiKu1e5luMsNGdYvNOiOSWG4j4dc4zjjGpbRPNteU
+cmEiNLRu/mOTu87R5ZMbYs+9jKlXEgu0tthdHHrY41GKWOT84Ucu9Tspb/h7L129aryJaQbyKX38
+Om/OXdo1OhlRLTvOh+T8w5St1LuK2wKh95mUc959+W13TO0WP/hsEu+fK30wxkyMMX/NGPM9Y8x3
+jTF/zBgzM8b8dWPMu/p3+nn2cTfuxt34/R2fN1L4i8D/bq39M8aYCIdl/c+A/9Na+xeMMX8e+PM4
+g5gfO6zfUg53tNuIcuHCaG99yURajOFJH19x5CwImT9y4VePTgH5jFZCJ/UoIs06YpOHDV1I1cYT
+jpZOaTmKnG7fy/wj9rmDQX+87jEVxiAuXtLu3coVpBmFVHLb3M3mJsixC7HwUpjLWjy1JfvArYRe
+HJALumvElbdNQxjL2myzoC3FzqM5NL3jLixvfEpZiQ38AUYrXlguwHMFpe16j3f7fwAwvv9z7nj5
+kK2ATHX621gxEevVEEJp+FExO3GR1clr7pxPx/d5OHG/O0sTRoWLjibzI1r5mFVLc0gbJj3hSRqD
+L0biOBsxUsdosqq5Fdnqg3zDcuO2kX/s/k3ORxgZ35TVhL5crlMDHVyoF8Obr7s15b4nLEgAmYAD
+pqwpW3ccVWVI5NfY9y1X0o6oqs4GoGbcqWr3U47EEUrD1lm6A+9eX+Il3wLgvC8l7WDHpQBp6+X2
+gF/oV7Bv3X3NBDyKwoTe0D2z6VVFLTBL0+Z0UpoW7yC3Nj97DYDWtviBi7byxOdoKou9h2+i2jZh
+ENMqND5SOtDmAU9kbVDsVpQyHyqjFmoxV4kY936f0gdjzBj4E8C/A2Ad9Ks0xvxrwJ/U1/4yziTm
+95wUWlOQx084KgNqIQk/3K6IjyXyaTP6k05cM2R85l7YWHh6L4loqq79NyDYKiSebWhjd7GrsmEv
+CfAu1+8xPYSR2fIp6da9bG0/gsbli32b0ESdcKl74LNkcZh42D/FTt3vgrLEFzW4rjZYtdFKMTz9
+rMLoQcrrAitREH/b4KvF1+twcr2GvirWpHu81v39Vb0keuUe4nGcsjMufHzxsUtn0osVMS70b703
+qdVOS/pDJpKoN8eGudCgb0funF6feUzVeu3VBWN9txc2ZErG66ShVw51fN2xQaNK/tjvMZVg6tHg
+lHdxEubb5z6Z2IyFWKLXtx8QKNQ2PUuusLwfe0xVBzhPEr6kdMWKZbhMA5rATZaxMeSKdef9lJOp
+fDZa2Mj/s1VHJQqgP3QTyOM3H0HPPWc2DvgbH7l7stjv+fBjN8HbYzlWpQmlQF8thhOJmviGg4bm
+gT+TbGAvfUlb0SylTrXNqIQQbRji67UL5CPSH084cTg1Jv0Zvdhd4ygCG3QiKvWh4NL0pKwVVQy0
+YK3LmlxAp1VdYHTdzkcD/M+m2/q50ofHwBXwPxpjftMY88vGOXOcWmtf6jsXwOmP+vEPW9EvZZpx
+N+7G3fiDH58nfQiAnwf+nLX2V40xfxGXKhyGtdYaY34kEOKHrejfeXBm43VK3C6II/f14+MUq36s
+90bFqFKRj2MiraadirKJewRSTMbvE3xZmPryBLvTzL6/JJTtvBdKju2Fz0i97YvdmrKQ6sXmhOFj
+rWJHNc1SqYlWiXThMdA2jtIelcA2MQk2dX8v1wGVgCWzVFLmQUNRSSykqEjVY6/6YFAZ/VbMUNMS
+dFXmCPYS9RhY/6CREI52DBadeYxEX14v2bsFGjNdMx24OTm0KemJuBtHEQ9kfHPm6lgc9aeksVtd
++5FH3Hfba7cVRlFK0g8PHpuezqOpPTwBtoJTC6qij3N4rZN8G42ocqdfUGWSnE+mbANpRFS3mMhF
+W2/FIQsVEjdFxUsVD4OOk7C3B8fveBhzrJRgNJhyJrZmFARspBR9IVBbz7bcn7mTPR+MGb0pLYv0
+Jd+7cIXZ7xUV1yoK97YuEprXloUUrM9sw0ahSRBU1AIcpdJe8JOUZadnua/JKncMTd7gy7p6FAYu
+B+KHuTSGk7Fgzn6Ir5TPNhWVuiAtIUagvExpZ+n16MuiYLQ3eLcuQvaPfXrqeFV1Rhv+rpbOTxif
+J1J4Bjyz1v6q/v9fw00Sr4xxfT79e/k59nE37sbd+H0e/9CRgrX2whjz1BjzFWvt94FfAr6j//3b
+wF/gp7Sib23Jvn3CfnvFx69c5nF0b0ajFbi5PiVSEpL0BwdmH5UcgPFBas9eVDsPB8AULUYmJMGw
+hdqtUqGKU76/ZyxbstaL+X9+zWEIvEnApHIrU7ZO2O2Vf3puhp4EV+xVlyh3H3N6/I7bRpNRLpV/
+764P+2OnyKZpyK/czF40LbV62j0T4CkvD4UbiKocT1LG5XZLo+igLUr6U0mbZdCbSYA0c8e+KfpU
+62f6bkiv/7MAxKNbculQHPM6D0/luygn7l6eYxqtwKEh1ApdberO8JnADwjE9rOZisBNhC9or2kK
+tIgReC1TGafsmil/92ORkXyZ3lR7dlK9yveWeOHWjmJXct05aFPRLN01GiqvD1OfsnbH0JscMZu7
+mtH9o1Puq43cpi3Xik9LGbIkdcO9YxeNjOYT+ircTvdjrMyDWttSq3d4vV3pnCBZu+P8FWN5qNbi
+OoQQicZqBd8tXmEEXd+ubil3XSQBM5ngxEkfanl4lJ3V4RRfGIskHeCrkFrXW5pEmhzWsJeLt6d6
+UMSekVrgq3BA1XdpeFAFLKX4sM1WqMb7U4/P2334c8BfUefhA+DfxUUf/4sx5s8CHwH/+k/aiGkt
+ZlfTbjxS0Xerl3sa6e/tV89gqpArLsk6MwzxBbxiiJG0eJt7eI08E8MJbe4KcfVmR6WKdKdxmEQ7
+oqYLjR8wTl2I+/2XLzg9dVXvcdvQR9bo+l1RQtwV18aPAHccZVXRaAIxtSWWLNpaeohlcUsrnUdT
+lORZdxP9g3BI4HXyWjVhIYqsrZkJpZNceLSCMa+3W26X6oh0XpP5DdPQTWiLaocZatJrU1J1CWxb
+YGRbHgg05CcRkUrdbQm1cefh9WIS0a/bskYEPoKZuBPVjlruVKaxJGrC17d7AmH/Uz8jURX9pUxe
+p0nBy70KccEYq7B96Fv2XfshhFgp1k5syAiPoO5wHzn+tPOmrIg1oQaVpVWFv05VwOyPGerF294u
+WUqPs1dmfHPuXqyLVcKi7ir10qLMWz5W5yurobMYjRroeMv9jiqQznh+7Z691WKBFShqdNxjqFSh
+KCBSN0A+NfhJH0906sCv8CQe1PgBiD/imYZALlqZJiPTtnid96ptaDTJeuRQ6Phrn6r4vY1rfvf4
+XJOCtfa3gB+Fpf6lz7Pdu3E37sYf3PhCIBpb25LVBZFtuT92K/5ilbGVdfq9yYz+SGq49Yqm6aTL
+JDR6BKi1aPweoYRAmnhBI2fjqqpp1Kay0h4I6hHxQxGbzISvnLvV8cmr7/DeU7eK/cJgQnis/rcY
+laP5Gb1tV+DbYBO32u4We1abTiSgJJOSsKdwcXW7I4i0OlY1naLMmgQjhptnXWQTeDGF6azNWjzB
+rgcPJmwlN9fLQ7bX7vs7FeeSpk94ppQpC8nkcTG1Pif33DJ/dBYzE7OxL7OR3ijE7LR0NQ1Wmg1B
+3GJUtAq9lEhWfr7IY3VdgsRwaGs6kwiTF/gHy7OU105d/nfzyh37RdviWVfs86bH+GMX2n+7gaLT
+KWggU6QQilGZbyFIm8M1molANp/EjBVtNbsIe6Timn4XhAG57nu2Aa9x1dheeszPnv0MAN+9KfiN
+hSs6Zlr997ZBeqjUFqouLbHgha4Y3XbK2FXIbiWDIuNzTwIxp/MpsZirrTc4wO3DrrXYgK110mWE
+sltaU1LKlMh67QHLEqjQbgkOwh79NuCiM/jxWvp6Bx5Ox5TS3HDNwJ88vhCTgvE94lGKH3nYZwpV
+2w2BVGtHJ2ck0vsLBwOMMPM2ETahTWiVZ3pRCkNJq1cNjUxhbbbHDhWaq3Jug4BGHYKgnzO7J/zD
+e0+pIuVyaXjoPgRS+ugnOan4AKP6jCJwdYJ1siJSp2HxwlL/0EsNrieOsA5VtsHr66WhpVEeWQp4
+RGwRQpu6BoNC9MIQWLfd2dGQlRicC2ETTLhlU6imkiQcBRJTCT9JUeYnCYme9LjfaS7ODy9QaBpC
+AafMuqCqFMLGOV7HK8k1sSYJtR5GmzQ0pTom/YhWE7L1Y07G7v4dK4+uA0tVu+PcMaZM3QRhGmg6
+f0gDA6USI9EkV9bSdLNG3RBJd3LZLCg/VmoWR0wEp5jdl6mNDbhWOrO224P2YxnXnH/d4YP/xO1j
+frB1C0Ou+1G1HPwqP9VGsxDqvu50PMW2wvOU2pYVR3L9GvkJu1THlmQYXaNI9S4vHGAiXaukxUhp
+G79H0pPJcOJTavLty4+0KmoaQa0nkz6x9DN7c48pchzL4NbeiazcjbtxNz7H+EJECjQWu6zx7Yan
+klVbZYahFC288KRTIKNKEuxORhxW4Zt13QOAAA+vEsWv2NMUXTTR4El2CxUMq6QkUigeeDF+68Kr
+MvHpKzQ0gU++dyFls1VfOS8IpBlZ+jsqhX4ZS1LfrXi95JK1lI2t0oSqrqgLlz5cFzBUqlFiGARu
+pVgLDzuhD3KiZgKZIqF+UdLXXSuNJZk7qOxk5WzMNiakUWGpDQNCiaUcD0vCzDH8xtsJvViKzzN3
+vPnNjkJFzpnngyz5vCan7dScjYdVJR7pC9qmIRRmo26ig9bgslpj1N/3/B6tFQP1VN6W+YBWjthN
+6WFSpSgpHAks6QcGf6JrLlOUeFti1C0YDHtc1g5bsnsSsqrcfSp8+PlzF02+9ppzGG8zSyLPinTS
+Y911HOoxa4Xl1Zdqgg9V0BbD8yz2eS4UZtt+Ei20BhLjjmkpD4nnr94/sFyPjMEIE13ahq1EIk68
+AiN/kVQLeIMh1APetj6+ioRRZAmFWcBPDmKQW1nl5SYnDTt044jTB4LKmwib6D4VhrnsDn/a8YWY
+FIy1+E1GUCeU0pkb9TyOjlyeGZk9tcwzw92MXeEebrOTeMk4xlfI1ZBilOMFk4RW8utNGFALB48M
+TP0bDnp47TUkApCcZHsWKg2H5h6JhCwWMjrx/QlxI2n0ySn1lZtY+vGEQDyH8XDObuNYnjcv5Bm4
+vKZVq+/yekOk2oiXLzlTtfxq4yaseX9AcurOuXlVkm87P8uaL7/pWKK+N6FunSR8XajNWt0Qy2Rl
+vc2JUsF1GZDKpaner2iVm+TX8qgMEraqgdhyQahuQDqBvDPdSWPw3KRtAoFjtiW1YMlVvWEfqUPz
+YkegfNfjk5C3/MC1nOthRB13ik0P6ImJGA3HSBKSwQjOxq4WsbpxC8EH65Z7osl/43zGUm3rTbM4
+uCKlQUwj5ay9pNMjryVRCB+FfbzKXaOr8paLJ+6Ze/HxBl+w6ECpn/FjJqHSw7LpNF/wPGg7F6Yb
+dZkWV8RKTSeTPmms+lKagtLC29Jj0EXzg+4tX+DJISyoSwI9C17bULeiXxcBtYBx/l5T077A5Ko5
+DEpn1oMz+m0kB9WmpxyN7iTe78bduBufY3whIoXWQl56BLnhcexWhrZYMJbpxfHrPaKHznfRthPM
+tazKJWu1iwyeYLTG+xKB5NrYG7xK3pRFwfZSABkVpNZVyT0VuMJhTH/goNT3Zse8e+O2t764YCho
+b6twsTcekSI2pLEkJ+oM7M9o1sIkrDOWv+V0Cb934fZ7dbUjjsf6/IJIhKl9tedCleitClzDxY7T
+jYqkbUMlUE2QGB5N5cb91cdkr9w1uFFKFFd9rARGjBcSSmfhNBpyKq2+ZAr5R66gtkXaC/S4EjR4
+vRwS9NThKDJiaQbaoM9QAJnwVOdfRdRq3q9ubvl4447n4mJJT+vq4DiiFEhqPnTn//Fmid0qJXod
+hjMXFVaTkPOxK5JN0yFG+76o3PUZ9CK+Lmm3t966R33tIpAPJ9/lnueOafr4Ecmkk4/Xs7K7ZC+7
+tqI0BEYYkFXMWozC/uwRD0+VxilSnEcBH9y6Fb/e5Ae7ewskQ7fvUPgPrx3hCStgfJ9C+Jbdbc5T
+WQ2sFze0iobnD1x0dJJOmQuTc9wLCZQ15gYqRQLkCxBOoRaQqx8fsRdpbkTLbOo+L8uKQSPY9Ahu
+9wLR/ZTjCzEpGNsS1TtCE5MLSTY8Conkn5g9u2D/gbtxV3lKqC7CjfL3ZHVDNFGqsX1C/8jl2SbL
+aQXq8ZY7roXC+wcffQDAt1+0/Is/616E14bHTBVSzk6n9F66SaFuPOrSXeBCnnxeWyMoO6ZJCH13
+c9O0h3ns/kP9m9d4YnEatU7zKuLVhUspnmc5HXmt9EMa3ehK7UuTNpS3ChebljhyD83rSUiUuNpI
+uAlYqruyFYCoOWoo1DY0acqDufv7NGwZqyaSD5uDT2eiVmYYxgwFHDv7akqG2+7ggxd48oSslwv2
+0iNMl+4426hHduMmvXdfXPPutUvtXq5yXlea1jQl7dLt5+Gpo2y/ahdc2i40XhPIcv3RZMpE4rVl
+ltNqf7UUhjYZfCTBls3f/YgXS5eaFWXNH37LTep/5M3XmIbuGhXiSFfWYySDYdOWXCv0f1lf8B0J
+nNybTviXfv6fcfur3aT5O/sdS6li7SkOnYjKWpqtjIozV9codiuMJoibErKd6NtlRS29zcFDQ9a6
+iTESJ2EfQq9zqRyl+F7XcvSpRCnfWMP7z10b9f1Ld04zryAeOtHgR2c+/Z7bbkVGT0A0j4gw/Gw6
+rHfpw924G3fjU+MLESlQWtqnFVlwBQJrJMF9AvHDX/xqyd9ZOL3GVXtEI92pN1534dQ7j17nVoUx
+Q8lk58KltoCycivJPg9Yrd1K+X8/czP080VN8y03m//TT36NX/iaOAyDBF8OSq9ul2w3bqXYSBLM
+FFtaQZC9GDxV+JNhQ5Q6Yrwdfcx149KHF9JY+N7llpdLt63romKQuHN9e9wnHrht3IpG/uFlQSH+
+/Nt49E5cNHL1dMtrj5+6z7ewCFXB18q2L8ZU6nDYKGRWuKJkFK1Ztm5FnC6OaX2XNtSpW0Vefvt3
+uBZv4+l3Qh5I7j6MWkKJ2dSmIdy548xUUGN8y0rnt9tZXkjybed7eNZ1APJ9yehI9+RIP9vA5V5a
+AFdbRjcuwujn4IuhOYlibiSGcquaXBgHjCW9Vw7uk0ld+ip7wW8t3Kp677bmtXvqWggz3ewGmIlL
+S/bLj3klDsqvfvcD3n/lPq+8kON7bkf3Y3fd7i0+5KOBznVrSJQ+VABWxd1OFtA3RGJADoKAnQqt
+F1e3LJT+NFdD4r7g33PJ6PcSanEVAi/EU9rVNAF76zZ+k7V867lLzW7V4XhpSoYLp/txPD4nEOx6
+lBg2SpU87wbrd+Cln27cRQp3427cjU+NL0Sk0NKQmS3LZyXxwM2YJ8WadOJm68nPn/Gn1+5z8eAN
+SlllDazLe4ffuI8VSabd7gnEIvP6DZGYaoaM0yO3jX/lj/2C23HdZzJxM3/vcs/4DReBPLgsiRqX
+n12+uGQs6TEzEay6N6WjA/qjnsO8AiYcEq3Vgz7/KmfV3wPgXeXDcd5QCu5qjGE8lCJTOqdWG/Fe
+5PLTWyxFKYXjNOBt9byT4ynfe+n2/a3b7/PoxOXiw0fu33ybHxy6y2xNMnVFud7eY69VbLffE+sa
+GflAzoZDzo4F+c4DIhVr0zSmL09LP/wyYcfNl9dFYxpGI3kcLq756iN3LTaZ4f5DFym8v7jg5kNp
+NagHf9Z/zPsbF/1hQuqFu3/TBOLGXYtV6rFQ+7lQi+1sesIffeNrAFwXGZOZwyEsXs5582HnkXAf
+P3S5fS3lojjlgE9Z5RG7tViSZzNeE/Pz/LUhD6S58faXv+H29yzkxTMn+Ht74WFlJZ230PiCiPvu
+Pt6/f8ZqudR+DanW3OP+hFTI07YJ8YS5mKq4Ou2nB3SkbVpM6u6l3a4OBLreYMIf/6ZjvObSGbGm
+4ebKRXzxxGD0OmfrllDozSYJ8crP1pL8QkwKlYVXeY2X1sxUFd7d3DKYuIctOZty/6vOBLRNI6we
+bitIqhcXGOGVmsgSyIXF4uFLnn04O6Z/K5qtzFz9uT10A6I/3ODJiCb0e0yHfweA1YtrVjKa6QvL
+7/cGeKWg0tZgQ/XCNz08HVuYRozUBYgjuU0NX/FQleVe75jXzjoTkvuMpbi7Wrt9JYMXvPuhSxMm
++Jyo6j+NPHJ93oYhb/yS46OtnrnJxN831KpYty0koZsg4oEljAT6SkqivZsABxIN6b9eN5cWAAAg
+AElEQVST0vQVcq4DPHlNhtuKqKcJt4gxEhEpBQqrLmuQievo1Gd+o9A/uaWvguibr9/wXBX88IE7
+tuXFS3xVyJsg4pUUs9+oKwJpNyZZxErcjNUB1FVip+44j6pT+qG71++cnzCWaMvJAx+hhik7ngFL
+jNKg3nHIz4WuGP0L7/wiTz03Od2bvsOxGJG+wE3NeEdfnaE2aNmrO9Q2HFIX1FGJij2m7O6N4Vy0
+5uPH59jUfa7wDjTpvsBbXmxpOgNea/BUdDaJR+JPdS1GnCqt2ndM2qrmKw/dNW7WBXsxgiu7o+2+
+U7QE/mdLCO7Sh7txN+7Gp8YXIlLwjCWNK4K8hy+Pxno6pCpdKObd3+FNVOSrowMhxjx26UC7X8NO
+UM6mB1PxxzcneAM3u6bNnvDn3SrsiQxjeiWsJPmWbqnkA98shpx6btvf2b/Ea2WbNtWsnG3B7xyx
+Da00BIxfY2QTvvjNS97rzD6eut8XhLyl1fPkvMfPfuXrADx8a0K5dcdxJOHPi/NTPpZmwW/9zrv8
+1pVbleLrjP1H7vM3fvExk91Xdfzuuq3mv0F+pfCSgFbioLWXYlUkDAMfKwEX756uZRiQClVo5zsi
+CeWGgwCr5mmzW4FWq813BeEOeiQSdn30jbeYP3erZsmSLHMrbH45Y9SXzsLGRWnT6CHJm++7a/gE
+SFzkUhQtodCpS7snVsE3Fpw3icdsV+48Bkc1o9ilDPORx0RRZlA3FKWixU50dxuTCs48Mi33ftYp
+eqeDU1637l6bLXh9l648+a6L0rbXPdpr97vdHmzb6VNYdtpHo2eWKKXtu+Mp84y+BGCG6QyvM6GM
+A8Kos7oLdM41WVcL7IX/L3tvEmvLlqd3/Vb0Ebs9+7S3v69/L/tMlysLlw2Wy5QwQhRCxsIjQJY8
+ASHBBM88QcIDJISEBBMaeeKywTYqBBaWjGVn2eVqsjKzss/X3f6edp/d7+gXg/XFfvlKZedLJ9iv
+pLOk1D253zmxI1ZErPVvvgYroEKS+oSdBOD9IXYjTIL8M+vrGpO6P5yt19iti9iyyELuIpbaGuzv
+d9j5CePTsSi0HtmmTzCZUzZi5NkRwS2pL1/WGBnD2OgOfqbYsOMyLBtq5VAmusbEd/V3OSaUeMUA
+PE2Olwsevd3iHepFKEOaU5erv/jmd7iaus7BeZmz1AS/s3A+g6bY0ISdeUtE6AlwFaQ7t1G/aqlj
+d/PvPpDJSmw50IPy5tuv8VrqztOkGY06HNt9qSMN+xyduE7Gvr/i7wg3sZnmxGNdtwf2ljt/79x5
+MZpqSFO7812WlqwRGMdbUAiCfZTdJujrBeIjYRIEA46jGL+TAG431HN1M4or2sq9vEkqsJEfkBwI
+CDSoCefugb58snYUQyDKUlYybSlvuUXDbgy95CEA2XZLE3cQ65xLgYmqKmCmGoTXUai9DZVShiB+
+SBa7VOPwsM+wEFSYktK6dKQRXHk4yihVRxnFHnHcwcKneKV7znqTlMUzAco+cIvC9977Bo82chTz
+Wmp1H1rPUM66+olbSJarBY14z8PMB2FI2nqFVZqa0oBe6rZT6w57RIGg+VGGqTrlqYQ46Z4nMF0d
+S1yabGjIBYDzmxQTuO5S3y+o9R2Lco71JYH1CcdN+nAzbsbN+Nj4VEQK1ljysGGySYjFost6BY18
+HpvrAk/6e96hhaVSBRW9TD/d7SS29GEmvcaTBP/cRRh1mcBOeV4mMmkDQjGa/pi2cFXm/+fv/13+
+3gdud3hZtERD7X5nTmqrCROM+t/xXkqnV23bnHotJ+lwzM/ddxHEkdBqWzPg4R910cadB29hxURc
+VAFB5nYmT7tHnV+QblxEcO/WHr8snb0q9FnsuXD3OBqzOXfXlOdSO754iSf7u3oNa3kkhNuAMHE7
+4mo7xyjlqUoh3wYHzpQGsHUAG7cbt6akqjtxmgqj8/N7LpJoyymbC5nWzI7wpcPQ2/fwz3TOXs3+
+3J3fe30XxWyGd7nUufujE9Ku4GtbThcuwpj5OaUEIjuUX2FD1urgvHUQsB+7+Ry2Fb2JtOKamupM
+5CcJjETjEYXuzaqtKBX6R/GQzErWb2vxVKALJ+6e50WBFamuqnbubxgLpbwckOCO51W0rYv0agtl
+5Y47zEb4uiemCQn0wNhMxcy2wVMKFtqAuC+y2mLBRilD0FhSXXcg0Ru/aQn0d35YEsv+zs62rBcu
+grq8WtDIVOmTjk/FolBaeFk1bFKP16KJPmuIJJFd2zGelILayynW6EXWhTMaU3d1hni8oypTGNrO
+0qf1MVIF2hkUNiOsjFlZWy5/4GCkf/ODSz5YC+/vwVC012Qto5C0T9CJi/oBjV56b2mxldh1Scbh
+oWsHXs3k99dMiRt5A9YJrR74ZvESK3akX7qHMQze5jJ3i5RnUsJbOmWzx3DPhfAnf2TgFHkAb+YW
+v6TOkQcs42FKqQp3SE6/m4q4oZSa/TaVk1DjE0pS3mszKl9zW4P0VKg3GbZxoXItNuH04mxHrT48
+aIl7ekg3Hu1IC0e1JHlF7lsHLsR/t/GxPdcBiEbHO7/DC9+Qiz0Z1B67bFj05bwtaAuxXSuD19dL
+E4UYo9pNU+9AP3bdsR7XtFogoyrGl7OS8SOMRGm2tmL10l3flWTffzi94lHV2dZ/BBdOPcNANaZM
+9Y7ouqW4VlppW+xS0v5egSegWmt8SrF1jURmFtMVyVht9NBiRnL4MiCdGmxV44WCwNNxGXwa6XyW
+25KNAHytH7BSZ6QoWrzop3ODuUkfbsbNuBkfG5+KSKGpaq5Pr1n5PumFWzH3PjOgWrmdPTuCduZi
+0WAwwUgqzYr111zMmL50u7x/Pqf3WceoDMyGZqC+f28P/0TbbeUKMpYxtK4gVTcxv/1Np03ww9l0
+51c4MB6lLOKuu9B+XdDJE/ttRKvq87ZoiYciJj3fUEtrDxGigjwi/tAVDNdmb0d2mZ/OWMvC/YX6
++fvkXDx1u1UeNjxXVDEOzjgUJsNvAq6lyZCjivUtg5l2Yin7nKto1fg+ZaPCrV1Rq6e/7SnMzi0G
+sf7aNUEmLcbthnmXYqyvCPaHOme3I757PScsO/n1/q6waSuoSzfP4fCYyW0398Oxw5vsNYbrY3f/
+8nWfRClaU3g0ksS3vk+qKCyQdBnbnFJS9WXagnZ/m9qdSUq+KFmJlegpcqlWc2oV5YI0YZW7yKUN
+G9aVy822jy+5VnT6vefu+n7waMVy2elRQtjZ3QNBF7Bo529zu9NMLJuaxnPzXeQbahV003CNJ72E
+auGiktOzBc2Z7Atv3Wa0lHp4mxCJdVnEGTmdlJ07brRu2Kg7Q9PSdKItbYtU6hinHkGmtOoTjk/F
+omDqlvhixbOw4ipwL+kv3n9IqtJBeX2El7oHvSk3eLG7YYFxQPr6ckoau7QjfpASDlyI7m8LrCYk
+nAzwZJneWuXO1YudIODmO8/4td/4xwBM66ZzdOTMNgwVxBYKW6tqRRsoFfG9nQJUFcX4pdqX8yVn
+567FWQ7lyxgveSEwzQ9+/WvkyjPXjWEhzsAHhXsozXLKYehuz8pvmevlT61P8cS9bPGJRyS6bO9E
+epYNO8ouScFi5RabqqyZy17+INgjrNxDWKgVWtpyF1KHmSGM1dZsLYnAWQUh65ks/iSN3+8fE0tH
+0IQ+tdKndbnEkyKVjX2WE7VaJRTStvfp7Qtt+NQjVMo3OUg4lFHsorFM886JSvWXtmAl5a1wHRHg
+npfKTGg6mry1xHtKw/Qylqv5zsOxKVoa3b/zac503Un3Lzmfu/vzwyfOaHa63VLJAcsaJwgEuM6P
+xGVa1RZmiwtKieH4Bkp1n7bNilyowkkaMxZrcauUODvs4+k+FNscq25INgmJBIBqfUO11MIh5aWy
+blkZbUjzJUF3bgaStbo2/XjHJfmk42e1ov/PjDHfNcZ8xxjz14wxiTHmFWPMbxpj3jPG/HV5QtyM
+m3Ez/pCMn8V1+g7wnwKfsdZujTF/A/j3gX8T+G+stb9qjPkfgL8A/Pf/rGNZz1JmFS+WNeW+W2m/
+nKxpPbHh2oJAgKOwnmF8hcFrt2Imt/pktrOAP9nZ1puHe7Rrt/vZ6oomVwlf3ogUK2rp9m3eLznz
+OqYlHbsdC3QGO7nCsyIIiTV1UX/SiSA7RytxAvzjhNG77j+spjJsSRqW8jZ8cnrKtHU/74cNvcDt
+aHuyXDdJw4ORMATemt8T32EVxrzcut0s+UHDK2+4HXRfuIFmVpL1FLnYikK7R2hDeonC9aqm6azt
+tVvVcbBzvjZtj1Zy755fY6aKDqylPHXfXajo1V9BpIinSlfkhXbdpiRRtpZncPnEnf/1gdvl45OY
+XqfFeLzHnuT3kyDBSo07tDmVoqWRoo4y8Kl0d6zZslVU1G6mxHKuDk2Or73IFyemdzCiszW1rcVT
+cTAoG14uXAH5aj3n+mUHepIHpQfyaaFu+EiHwoMq0dwtpTvZP2R65ViLzXbFSPyROEswlXRBfUul
+aKK7H70wpn/kwFSx1+J3UnnbBquOQ9WskQwp3lbRQb1msZLBUVjvJPTa5Zb1ykWsk96QwvvpXvOf
+tdAYAKkxJgAy4CXwp3C+kuCs6P+dn/E7bsbNuBn/AsfP4iX53BjzXwNPgC3wd4GvAzNr1TB1JrR3
+/qC/N8b8ReAvAgxCuB5anpYNPdmxvXgxp5+6nPx4ex+v31XtltRGSsQXHdT4hHoqObbAJ+wrl10N
+aFfuGIxuY0ZCh+ViCI5u4deuFnH1+P/g/XUn/vpxjf/u56LDI/QPCLv2lJ/QKldfrpZw5nLc0UnG
+MJMdmTwozbLllbceAhB4G55JO6LOt/Tkx/iaOuEPx8cMV671+MHylE3gEJYDr+BIc/HwQY/DeyJg
++e4cesc9CrWjmiYmwX3f0DSkMpqpmxXeVrvm1u3yfgSVrnSzKdloB/Iqy0aF0s3KMJUexFxF0vK8
+pBVacdCLMPLmPHo4Zi9yPhrBnTF7Y/cYvKGW5PMy4BXBqotoQG3c8TaeoS8S16Q+cBobwEDMwmWy
+IhLsvFpfYsQ+LKuKraKCqA0IpWDVITO9KAK1lv1hD4yKgxFMlauvphW5ZNgCzUsYgUylse1OUJnC
+Wvq6Pm8kpWoi/Pfc83Y6zbmrytTJwwyjOlbdbFley59BTM7GjyguVV+IYowAiOW6oFYxPSAglQlO
+JVUlW1l6gv+z8iklmrustvQmIvcxotqqvfwJx8+SPuwBvwK8AsyA/xX4Nz7p3/+4Ff3tyLeDWczF
+Zc12z01ks/GpCgf+qYMUOqz+4B7VlfACMjfZLB9j9HLb2RleLuPZ7ZJaWHTv8Zb+F9xEJbIsD8Z3
+aH/gJvJ5OedKofQ/rSzTKfmu85xo0EFVIxYyXJkvDd6hO4+BnxG/5rog029+HYCiTBnIJ/A0vcf3
+HjtZ9qJNSFYOOBDjQn/P9Biq8vwPPnjBB4JP/+lXJ3zpHUcdzm4fEkgWbjFzL8rx3RL7UuFs26Mn
+Sbuw9XZhaxVCqLDbxir8edlOWKYst1QdRXibEyo1S95K2Nu4e9IXjHbae0Q4VYU/XlPMlvp5CCev
+ALC391m8228AkKau2Js0S3wZxDw7a9jKzzGAnZFLs6q4Ne7rPN29eXbdEGgxXeNB6oBc2/IlSwm1
+eGWfdu0KrJMj8QHKkKZyPwdnLwkO3TWVsaGu3ZwHYcV4oOel59KxtL7EVwo2b1s6vFJUQ62F0Vdq
+E20TSs3Ly+spb0mNPMv2saVMitcFlTAUF40AYosew4n4JcOIWOItpB6VDF42/SWBXJ98VeDDKkdZ
+J3g+m6fud+erJXfkXdluG74jWb9POn6W9OFPAx9aay+stRXwt4BfBMZKJwDuAs9/hu+4GTfjZvwL
+Hj9LS/IJ8AvGmAyXPvwS8DvA3wf+LPCrfEIr+jq0nN6teNVGVKlbdT+I7rK/loCnH3xELilrQhVw
+AtxKHLNPKWv16PYx0YF8603DdutW6Lb3Ln7ownFTulW02pzx/PrvAPBffe23KdoO/bazefzY6D5q
+Lq+Zq+ddTq9pJDo6b2cUMqLJFi8pZTNn77vv+9E3aloVR+u39xgmjhCVNWPunrjVv2OAHqYJ148c
+i9BO+gxXsp8f9PC1y4V7EZV8JIb7kh1bDPA7JOiP+RaujGEr4k66yrDW/dzE+t68xjNdKtISaktM
+qx5zAeiaTcXRHVcQW145mbvg7jv4mQuZ83XLWi1er9zj0SMX+u+/NuNMCsbvKuLpxftsdR9Wy+d4
+gWOEHsQ9PKUBTWbwZZJifBWVW58fXbtzP/hgyjhWm7U/IhyqML3ZMupLeDZxkURS+2x6EsmhpZX4
+Sln08Q/lHzlbYjYSXOm7ubgYpnyvcfPZ/4jLxDbw8Bt3blPjUrtTc8ZUlnAVhpVer+lmTjBw15r6
+FUEnInPhop/cb+j250kwphHUOm9ayFyZexgN6EvjwZcgyybasFX7sl0vWchm0U9jliqml9klZ88E
+Sf2E42epKfymMeZ/A34XqIFv4NKB/xP4VWPMf6nP/seffLSYgNe4e5ATZu4m3x0PmMvp6HJ6Rib/
+yLTakgoy6glzztGSUPp11HsYqe/aWxGJ+AVt75d2eSbHys2uSn74NZeKvDtd7Qw8I2N2IWPb2l3e
+sKdqsfVjogt3jCj0OJ+7B/Yg2aeeu79b+UuWL9zNPagdvfmW94gffcc9QFFvyd0jt3id2CH9zIWG
+haCxs+tnTJ+7n0+SQ/ZD93AcHrxKWrgQ3Ds/JMkUMuNeiHp9Si4156Jd01bupffrGm/rHsYmXRGL
+zWfU1bFZjVXXJggH9ErVOyYN8/cdL+PdH0750HM/L+VtOeplxKIF7/kB2V23aLRJhc3covfyB33m
+B+6e+AJ3ZSef51KYh+g0ZClVpHQvIgvcXLzMtywFYOurm7AuV2zFWr08f8p0z4GhxgcFw0OF/rOI
+0Ozp7/SMjFsSsWSb+g5B4a5vOVxyS6ng0ycjliu5TClEb4d9QpnGVl4DmqNeFJILv5At3flG8y1R
+5JTCJ0nCNnfnPm0y9jsP+3BI1NU21E0ws5zl1N3fuLT44l34oWFg3ByOTEO8776nroSRifv4c/fz
+MtzQWHeeiTU7rsi3npdsm39BNQUAa+1fBv7y7/v4A+Dnf5bj3oybcTP+5Q1j7U+Hdvr/YyRJbO/d
+vcvB/lt84cu/CMB29oSF9OnSpOTVL7sd4ejBfb5y9DYAJ/sqEHnBztmZvAbhDTZtTt0INj0eEGvX
+KCRPZcstVoU4v265uHQr6rOLU771HQd5vnz/msVYXg4Xbhc428yItZMuanj67rcAKMs5xdZFN0Hi
+UclnIpRnQ0OFyIUEacJIuozj6KADCDK/ViW8yHd2x2HkY6UFMBwk7B26kPjWgwknb/4RAN75jPvs
+s/2fY7gn8lFbUx67lCm1Bd/6vpOY+7X/+R/x5IPfBODphw5Dcf9Bjy8fOwGUebnk9FSIx7RiELvI
+ZNN4IBLPFW7nj+uCUqQsQ8mBdBqSbML7Tx00fbPtUSilOem57/jiyQmVIprvbK9YXLs0bxFWHPTf
+1DV9ieWVS6HawM3lQVxx+7Y7n4CERIXbvp+QDtx196OQWsW8Tt9iUS7JVx2GwtulAUVR7lKJs+s1
+lXQTx/fcvw/f+DnuvOI6J3ej450HxmBb8J//uT/h5lZqyQdZRK7o4HKd0/Uq0tijVFQx37T0BHNO
+JddnjU8mRfChb1jICf18sSaJVTQGCoEkVp39oTUE+y4iOrq1x/i+UyN/8MYtHvrufRllB5iti35+
+6T/4t79urXX6ff+M8amAOfvGMopKxsYQBC48De4m2JmrUe71v8hXvuBUio6PHvBQN3+4p97NtsbI
+yKRp5ztBzep0SSODTr/0CCcCA3Voo7AHpWDFQbLD6m/KkLt77qGvb8P1uw7y+kJV5l6xYl2KWXf9
+Q1oEba5XhFL/oYZOwi/UE9gUBr/vzmFcG/bUnhvHAWuFlJ1DVFDViJxHaQ2+WmVF1Oww9+1FwZPc
+6QuOK5c+Pfyj9/BeuJRiPfKYrF1IPZvNWP5Dx7rsry5oBTK6L4PZA2uppMyU5lt8gYnIYf/E/c5b
+98eE6gI9UmpT+wckF+4hvlifEcol544Xk95y3Zf3Tp8xe+kWmbkVKGgYYaQUVPzo67y/cefz8FZE
+JLjyevUtGonJLuUGdi8cklWdvH5Fqhd6NPRIxbuI6O2q8uHQvcT9q4o6ci+QqXMiUcfnywobus9H
+ccy1FpHiqauZzIIPubXn5rC5nxFs3fnMaTns2pqCdge2oJbSVRDmpGIzjkPDUtlDFrQMU3fdfS0O
+/jBhqPZl7CWU2ljiwKdWylN7BiNL+VzMX2ssiTpm1eWc08J1s8J4yb4WNTs9Z+H/dDWFG5bkzbgZ
+N+Nj49MRKdiQUX1MGH/Ac8FMg4MvMlbR6is//xZv3XVS3v39QyYyvfB8FVwCQ150eCmfUmCUsiy4
+FDFkMomcbDQQi2RSe4Yg6boaLYPA7Yij8DFHsQsZr7ffZD13EUuF+9512We1/R4AL09n5CqS+XVD
+oApx4DdU0icw0gLYH0UMZL4y3J9wR47XSRTubNs3SjVSL6ZQC2Rra0oVjtLKkMm+Lq5ClpXrx88v
+HT7i1//vv8kb6v+P7n0Bz3efP/q9r/HNbzgIrrcOeWPoMAS9E5deVPmSy8Dtxkl+xX15MY6yPW7d
+d0XMh/t3WMk382HPAXfKcEV01xXJLmzI9ZXbZ1bG8LbnvrsYPOG9x24Oxj2XJtzNXue3f+jSrmfr
+Ga0gytH2hDb5rpvbF0P6Fw8BGAwlpddraAVXT3sDxoJB98qKTFGD7/n0pKUouAWDcLy716mfUdXu
+GKOit0uF7qcjYuM6Kd87dxHk9YuAV+863YfT+j36kg0fjMeMW7mCSyV7fm2pBY8eeiGSj6QuKird
+v4EXUUkyPxTbMUxHvCrsjG0slUxy/EOfSAzc6+mKc5ndmFRgMTPEKgqYXl+zraWb+f5tvv34fwdg
+f8/gBy5i+6TjU7EoeD5kI5/e3m22wqRnQ8M7X3IgnS++8pDJvqvUD+Jgh1vv1Jaa2MM07oFeFkvU
+FWIZBPh6YVe2JNULZ4Mu3KuxUq4xQYs/Uii6hn0pkhwcVaSPhJPXTamKJVO5Cq3WM6zQjXEcYyP3
+8Oa1j98JiXYiHGnM/kh8h15Eo5rJ1WrLci0uQpeHBh6R+AVDYtadcWmz5XLtjhd6hn2pBvUP3csd
+jkueXorNOfo2wffd9334G9/k+dylZm+9dY8Hkjhv5HT1dFsylhntaODxQJX8V95+wMErXdsyJ8vk
+SXDgagNJ+Ixm6uoZB/maR305T603BJ4WltmYYewWAxbu3+vTS9pcLbTWJxVs0EQVRrLmdV5gE3e8
+UW+oc2voq3MyiTwGifuOzGuJetKrrEqs1z0j7qWKD/uEqs575RZrFObHPiPkYUHGRqCfQ/E5VhfP
+KQu3mMbtPqWcnGwdM0m0aCudWdgNcZdihh5GgfhVA5UQkps4JJTDU6Fn5eB6hpm48x2nEVXkriMq
+PezQHfsw8hlE7h3wJPYz8QMe5/LKxNCcOwCcjT7EvyNp/PM9ov2fDip0kz7cjJtxMz42Ph2RgmfJ
+spL9kx5WMdfdLxzxJx44NeMHh7cYSPQjCKLdCmytwEFeSalwsUwiNnKuXlXnVLX086oFlQpbnuh5
+xosxCrm8Fqxxf+cbQzyUqnI45I4gqKV0zp59WNCoTx+E0Oi7DS21qJQtJb7SFdQZ6ROSKKqINhUb
+6TMstjW5ePGjQDtfFpFop+1lPR7P3Y756GJJvpAQS7THJHXh7Grm8A+jaoAnnURvfY+n738NgG89
+/h6D5KMKt/Glw6Bq+rE/JZAgydiOeeeW25UmDwP8PRUVQ0N6Lkn8kSvEmeSAIpAb91WfZs9FbPMs
+o1YV/f7DQz73wn3+WIYlz9bnJAL5n2QFrdKqpt2wp6q8byak4lJMfLerHtYhnRRjP/BJJL4SmRA/
+l5Zk0OIpUki16yapoVUxr/ECgrU7buyfUYmDUQ9zetrFb6mD87LZ8Ohdlyq+lr8CA0Hvo9tsVaxt
+MzeXSemThGLaeoamUw+nwUrT83le8I5k2e/r337r4+Xu2Rp7A7x7AtcVCYyVYgbH7D9wkcD4sUtz
+5027048MNgEvpDPx3WcfcG+pIvYdS1YLC/8Jx02kcDNuxs342PhURAqBZzhKPE7qLbcP3Sp4K2m5
+n0kSLYnxlRv6no+Vik2HwGtyy1Z8dftixVptM9bhzjG5nQfMJb3Wk4pRlO4TCK7Yxi1W+mn+8IT0
+1OVh+2/12X/udrTLhXaBZk0gkn1k/Z3yTuSHBGIMtqWhL/Wio77rNR9lAUdi7QURhJ1cV53TqpbQ
+U/HxsB9wKPh0EPTYNu67r2cl02vlp1nIeM/l33Ho8vo4uKIQ0jM/sDyeux093VoObrvCn0l7O9Wf
+Un4a9/eO6MkS+uGtiHSsXPz+GwSSo2uiEfbBhebeHcsrAjxhE0w4Z893kUt7tmUtuPLxyT6ff1Vq
+WE/dHF+VBWkHSPX3aDpjFH/LRMzHXgKB5m5Phbo4NfRi94epSQhVd6ltvdMeyAaH9BNJy0m6jijC
+bqUI3kZUKi5WyQG+VI/8siZTTWHSd//dVHssBEt+dvWEk8o9e+O7b+ItpdwtvlFUBwQdcTdwyFiA
+emtotP+aWc17nducWrlvvuETSVx1dS+jH6seMjlmT5MUvdIjkWK3P3DfO33/CalMdNL8gljo1dNN
+SSvUYzHdkA//EHpJhhZO6oAwuCR75mbYvLMm+aG7GWVakm0d3LO9fQhLiVsIdjz/x8/4YfNbAJz/
+ox8wPXaT88pZzmKk0Dbvs7rnmtfDpQO/nPyJz7C3pwds8hZW1WQ/uqKfSeJ7fsT9pQvN32/FF6ig
+UmGssA2xFh7Pq/GFl4gsu5v7qnAVd9OIsezs87JiqEWjLp3cFsCRzGHvnYywSuKJGCYAACAASURB
+VClSP6UvavXRfkIsdmiv51MLe5FV7txmV5udMe1gMicWOOv41iEciSZdZ/Q8p2mZWfcwHvolB+I1
+DPdboqHrc7P1sZEEbJolXl8w5plLtVoivFpajHaAX7rPo8DHiOo7SSLK2+7t/O6VC4HLjWHdwdiX
+G+rMfd+tw4xEOofXmzUPem6xS93aRxYNif3O0Bd8SaJFbUyg1LM3PCCVfLova4B2s8XuBNpzgp4K
+s5dTjBYZjxWBjl1LyCcIIjwVqF+eLnj5A5c29o6e0xME+6mk3c4WlnKs1HbjsRCFP177bIcqLvqW
+N0SZDo3+jYZkKqTGfo8wcT9H/ZDRgZRq4pYw1wK47xbyYXPAA0kNPFs3bNV1a+oVs8ItatfnG072
+Jvw04yZ9uBk342Z8bHwqIgUbthQnG/w5vPyyXKJXL7l83SEJe8VLwomDbbI2lOoVz88djPbCX/Li
+G06z4JurKbMPdNyTHn1Znv1WO+Dykfv9X3jL9ehHVz3ikYPUetsZnnABzeyaeelW4+3y+2wm7u8e
+Cm/w7V5DPRe0NwhotANVxicW6Srz4EiWdT0RhvzIc0odQOR7FE3XfgxoY4WXsfv7aV1Rq4A5SgvG
+8lAIyjucFuLmm4rllSsuRfIfPL2eUSvVGh8+5kjWdOmthKqntGJxTjmTmEjgoorxnRP6+4Ld1RPa
+prOfj7GeDE5WNaZ18+JJNq4p1lTy36jrFYnavWm2wF93VnCWka7rdRUJf3e9YrnW49dmeIoasrhH
+4gk70q8x6tNHirqMLXaW615g2azcd/Qzw0AVyHjcw1Na6Cmi8wcZRvNdlRGeWrzZYEihXTUvWiLZ
+D0apjluDV7qd9vFsxlrmQddXV6w75ueV+5uz2OJ3gqley1o+pX0amOtZyGLGA3ev3jx2x/rM0SHZ
+fRcRhQxIdB3h8RGh5o3KJzqRg7iisWURUk2FbzlZc1eeIe204FSFzXUO85cuhfyk49OxKGCpKHiS
+z+m953qwl1nN2QvHPzCjBa+/Jvv56JBvvetu4tVT5WF2zvW1iy/PHq2otu53r9bw+cyF7tvtExJV
+0ZdbeSOGd7CX7lgbPkMrD8P19D1evO/gw+9+91uspg4gdC2m3vXWo5KaUtTUGF+W821LJv3AvdBD
+rXAaqQUDlGJ1Wz9F1o6kkYfa22z1MOdzSxK6F+U6z4kzmaruxYxUU5lfl1QrcRTkbXmx2OBrMWpK
+y+Fd90D30z1yhbnTVR8vdce+NRTFeNQn1kPlRQ10lfwKsJ1Go4eRzb0Vlbm1G9rW/Z2lIpQYyjAY
+koeC1xaW7MAt5MW+w0e8e/GYeS1hmfSA1x64hbpJnjOUtf3d0JApvesW1izziWQGFBhoO6hwPyMQ
+cCgwDZ6o9p4g5l5QYhFcOaqxGzdfHuvdYuHTdvg2UtU4NmVIJK/MW8u7zKJOX3HMP1F34dui3Ocb
+iE3naAWNUsLcg4HqJFFTcHjgru/Vd9xcTN66RzKR0njUkqi+EIwDjCjQJmmwLHVPpFx+uyI5dGnX
+g9EIc+rm/gPvGxhNfdSLGCQ/ncT7TfpwM27GzfjY+FRECqb2CC97LJ6uuH7kVsnF+oq5doF/8Lzg
+UIIl5/UTTCVvx9zt4PvpENCuE/UJrCrEJuG9zK2u4yTGO3Mr99a6lXZ1PKP3jmNcltsL6kv3HR8+
+WfPdH7qI5ev/5CXpRpVvYRri4oBWUNQgSuhL37/fhkRNh0xsacTWPNPu0RJwspQK8n7G/oGKUmFJ
+pZU97rsdI0oHNHRSaS2+UokgGFIJsTjot+QqjtZDFUkvp9B1FpqQ8R1XUGtsRfPY/d1B0GCFrByP
+XFErjRMC6QGasYcXShMND1uokDos8TL3cy02oL/qYRRq27ilqT8ioCGGqt3W+IIpn6xc4ezw7Ipn
+8ljoeSMe3He75vX8mkHfXcso9BmK0BZ22ITMJ1YUYDH0DhTp9AYEHdrSGhBewEs7i8AAfysNjCQA
+FSDroiGqdTyvoaiFP1EBMw0izEjCK6+fEC10z/ohT+U6fd4h7A07HIrvex+J8vgeQSm4/WG0K3cu
+V26OF/MaI5xGsu/jRZJds/ug6zepRyP5ts7vJAn3aYSVyFiTvuYi4P7zMa/Lb+60Sqk6I51POD4V
+iwKBhcOat4JDypF7cK/XDVPlel/dP2Sq/C3wRpxKSPR+p7xUW2q1yCZRxh+54170++OWbeQm6tgz
+RBPHn7A993A8GR7Qip14sh1QVI6me/34u7z8nrN2XyxyTq8khDrQ5KZDhqoc27ZmYNxDFQU5VjDY
+KgzoK4zfU+g4iXocH7r8YXz7kFAU7m3is70KNRfu7yeTFE96hkVVcb4WnNlUpAoTbWToqxYRRu6h
+ukgCZkoprmYLuHYPzfriJZuNyy1vH75Jkki8ZCw1qrqhUajqL5bYRB6bDbTqgpiwgtylad5c7dm6
+wle3w/qjXVhelOzahdbEWDEY6bn/nmUpVwLsbLxzDg4cUM1LnnB3pEW0qkiijyDdAJ7tUwk4VjV9
+hnVXl/BBc1/WPtFWUvtibdKUqKtLW21plAY1q5JVxwglJ5I+4lIvVR0FRKLcm36DrzbkIGhou/qQ
+Ur8K5z0KEHiWnt/dG4+JxHa9KEKaPLxUGtv+qCHbc3OYLO7Qpu64BWeYnjtGvTXUldLJqVrrJxGh
+6OB1e44/cs/Aq+OAxwt33ZerFbbLTT/huEkfbsbNuBkfG5+KSCH0M+7ufZn+q+y0ARkH1N93AKJo
+P+LkS67a7flf5JnMVTanbpc/uj3ETrUqxwP2VbHtjwYkuXb5MqcS82+TuJ1h2/YIBINmWpMNXA8+
+uQq5WLvfOV2Vu0nyFH4mgwxEdvK9La0k5/3C0CqC8LyAoUhV/cBFEuP9lH15Kh7cyej5sr2zLZuJ
++76VwDHFJudMEnPfvZpx9kRkF7uhmEnm7bDPnX13drfFHB1aj7WKlevzGa1MX56/+wx/4H7O3qoI
+TSft5SKQcpxBBxvPDYF68N6+T3nuMBBRnOIHrhPjd4ClpqJReFpt1lSJdCHW+c5duV2H9LuUTnoK
+R2FJo5B66y8Jmk7KPWSoPv0qzrESj2gEFivtmlJS/BvfML1y2+5wNWJYufmkLOir6BhnbndN+jGV
+7t/2ako+ExmrbynpYOo+Rmo3uTQ5itk1PQGywqAkk3ajt7jgUFvqc+UDlQeJnt9BENCXLH3qeTwU
+xPy8sLz30t3L9Vzaj0dDbOIk+15/26c3kZjKYsOLU9fteb6cs5i7KGWrFPSVL4553bgCbTDakn/n
+WvcvpmhE/jIJE0WFn3R8KhaFIAoY3R6zNxiQ6qEK04jBl9wFh7fSTj4RGyRsFu7/vPame4lHd/dJ
+9RCwSLCNm3QvBk9W3c3pCK7dBBe+A+6UtWEoEY7s8JhKbLfNcMX3Zu5he7JukBcrfuW+945nSfUC
+eUFIboSBLw2SF+Tt0YSDu3pRNy7kDg4NPVX7wyQjVUhpjEckd6atmJhFYdHX0c8NV31Jyi/6eJqj
+Oi9Z6Pcn8sy8fTAmlFpPGYGJBAAb+IzUXUjDO7Sq/NeIfzFbslV7i03AphW3IfSJxZMY7e3TH7rj
+db4XdRmxFttxO29YlO7nD6czeoITToYBykAIJHZ6dPI2Xug4BUlkOLrlqNgvT3+Ap/bqpDXU3ULV
+VfWLLb1OyKa1rBbuui/tlIU8KWK/IZfGYq/nPhtse1i1i6vC0CmOtXVCqzpC6vWoFTz7jWoOecFK
+PhO9OsZTdyU0Az5Qxid/XUJAGRNB0DLWYnl/kPH5I5feXi/gfat7XLsDFNMF3//Rheai4K5qO+ti
+wfJMwq1+zdHIXevgNYnc3rnDqCcuhvXZHn7T/XxV0DW8RpOA1w4do/WTjpv04WbcjJvxsfGpiBQ8
+vyUbF2S+z8jKobl/RHLcGYFEXH8ot+bNnIvvOlmx+QvXITj8Hcu9X/hXAHgY5fT2HdDJW79g07qC
+2fTyW/zOtx8B8N6L9wD4IZZf+ZxbaX/+9r+LtDL47rsbzj8UFqBw/XCAWPXkvRhqFQ/9egsDl4Ic
+jDxeFTDlj711nyR2Rbyt0pYyr4h77prawKNYicMxqMjVfQgFzDo83NLvuYLpQe+M8JnbYScPfXLJ
+f+21M85nAqlIxs1Yj7lCjNK0+H2BlGqfVtLhredRix9SyJV6O29Y5pIJS1teuo8x0w85vuN2moGp
+acVHqbqd1lRUS7ctnc1z3r10mg0fzj0eHrmLmoUn3FczYyJJ+d4yYiBc/6SXEovDEU7tRz6Xpb+T
+M49UzfPxaa3bMeulZe9EHpv1hF7uCnBxmNGTUnKwg5LP8BR1BCZkKU3Ey5dPefTYRYXHA0uJS12O
+J3LEHvZoFQlyGGEupMlx1JKVOnYna+8bxirq3Y5DtsJILHL4vUuZ63hrjEwhjSK+bHIIfXcfLzHs
+yVAniIbcesVFGy/nBUap5VNpXw6soXxN8OgsJb3nnhd+8CNiRcCL0rIufzruw02kcDNuxs342PiJ
+kYIx5n8C/i3g3Fr7OX02Af468BB4BPw5a+21McYA/y3OeXoD/IfW2t/9id9RG+JLj+zQY3wgpd7D
+fTLlr0FyRKh20/rphvYtl3++//VvAPCy57F810UNr/78Z0nVOgz7n+XqpYsq/vav/zq//X1XS7jc
+SE/h1gOeZk7d6Y97E5i6COLZxXfp+lctoHrYLsdvq0NyGV/ltmWgnevwbsRrJ/JPPOzx4ZmUfZ+I
+Wbiy5G+6737z8BbxvnAB0YC13LEFc6DM4ZGUdH77N7/O+1N3PlkEX7rjGId7x336t6RQfKn2Xl3v
+dv9lMiRV7/6VO/tIZArfbzFLKQv5bud+fPo+c8GnPQ6wYnsOg4MdzLdYLEmEEPWE5ivamlxCq5Xn
+8cFT9/PptqHJpUbNYwb3XLSR3nUR32JYsREWICxjfEmpJTbCa4Xci0IiEdNs5Xbz6+uW77/4tvuO
+RUry3BUXjw/X3JXIaz+KsfL7MEJCluvauYIDeVPSGBd5PZlFfCCvxcslNHMnxzb5ilidEw8jktTm
+es6VHLiT/SOE9OZAhjyhD+HYPbNnFmoVaz8sCnqCPwd9nwPBza8VubxVeyQjB3Me9QdUV0KNngx4
++sTVdr75vXPeVdGxd+zuR2JSXh+4e713OCI9cc/QO198lRfCslwsLrhc/nSK7Z8kffhfgP8O+Ks/
+9tlfAv6etfavGGP+kv7/fwH8GeAN/e+rOAv6r/7Eb/A8vLRHEoQkJ25RiIMAXyIjoR+z91BsucOa
+9kh6f/1fAKA8mHOQuqLj4ZvvkMVdABRwN3L97/2jA96Yuhfkz/yCq/R+/nNf5e4X3Ut8a9RnZVyV
+/auHQ/5xJAv72u5AKN1R44OA7XNNndcQqfuQpPchcTf86ionEpX3SKIhyWHKg/uueJoaD+P/mBqw
+XrKqdQvBYgVFqbD24W0mJxJAGfeYjNzxTg4zIskEP+WR+/sPF/Qz96IE6WanJH28P8GosFmtcnLB
+mD3Js+8nIw6OZHu+SLBNB7YZsR/L5zAPsfKs7EwV23ZDfunOMzApn1fx905dMFCHY7q4Zm/kXhCj
+dC48qzhW8TAOYHrmHv7INyCwVJV4eGJ51nqpGmOoRX1/cH9MP3PpVtpL6HUsycQn2Ao7YrQB9CJK
+wYa25wuC1P33z7/5OodD91wUZcXZh27jSMXAzbyUQuZDm8WSUGI/tpxzv3NzEUbET+Czt901z1+W
+PNJG1lQlcejO47WTW0yEP5nsuev8Y6/f4bW3XFcqjjwCYa2TUUJ9x4G6rpZbDt92afEr992/73zh
+CyQqykb+gih374h/L+b1191z9O7vzLCl7tknHD8xfbDW/kNg+vs+/hWczTx83G7+V4C/at34Jzhf
+yVs/1RndjJtxM/6ljn/eQuOxtfalfj4FRPPhDvD0x36vs6J/ye8bP25Ff7Q3wJqKqm1p125Vq5OA
+KFAIlx3vYGNh0/Lmg4cAXI5dKBqFhxzccmjFLOlj5JGAbUnGbtf85T//7/Hi1B377c+6SKF/PGSQ
+uVXbVhuKQ7lVJznLUDvTj51z55vTbFs20vwvqUj3XUHxtYOMI3HzDw4Odm1Sv+d2sz49jLwyi/X1
+Dm9QtxUrXFjaib7sTXoMRLT64iufYXjUEXsykkpqwE1JeaAIQwyY1XsLNok7RhaOqEXcCl85wJOi
+bX72Acu5+77O7fjW8R5BX1Zpxx6NrOh7/YhE0OblWU0ly7ZW0UNdtuSdzNk24pbaoaOs3ukl/Nxr
+Y8LKHWNx5qKxy9xnf6xdPkkx8kGM0nQHMW7zAluoXShBhTRO+ONfds9Ff39AT/Jo1t9gt+6+J2FF
+XXfPi9uhvf6AZuFC7bLxSRRSjwcZvsRsiq3PZ/9VmdmEbsc3A58rEdd6TYyNuvswohXa8K5apJ71
+ULBJZn16utcP/D7/+ufd3vjZt7/K6Han+yBJvEmKL6h1wAqjtDIejXhF8O+TN08olu734wfuudg7
+/Bx+IHTn5WOapWtfensxt9S+PNgf8PCuY8p+0vEzdx+stdYY81PbTP24Ff2b906sMQF1FbEV5NRU
+hrYVfDZpQAxFL23xlurNa3Jje4s4ln13UzvVFsA2Wzyx3e7evcOdt91DmAhI7ychiOFmgpxClt3P
+X0YkW2Hcd0h16BDkxvMwOh/KlibpFJsm+AMBYSqfSC9OuBFQZpTTzBW2E2GkwtQkFdHWLUipUh8v
+Tokl+x35Lb4+r2Yrwr4KD8sVoYxM0tItbtd+y1p4i/3JiNq4a+pn+xi509RRgJWQSXQoA5h4gtd5
+hgxi/GP5GW4LUM3EBFuaUIuQQtzWjslGbpEa3u0Ri3pszTFr+SdG0YLTM1eDmc51XK/PZ09cnSEv
+DNd6oE/6HkWnZOSHu0UyEAAgHoe7Okm/n+J18Pe+TxOpDtREND03557SAKqGMBW7kCWjV918ZZ6/
+g3rnVxvCgQRJVCdp1slOd5PJBnsptzAv5ahwc1DIQGYdtHgSwCmt5S2Zvvziq7f5yhffAeDwaJ/0
+oZ5xBBlnQ+c+H4xS8N1Cl+xN8ARKG9x9i2ajllDmzpeyAEm8e74H2gA5rWkeuuvYf3TFvsRzPun4
+5+0+nHVpgf491+fPgR8/gxsr+ptxM/6QjX/eSOHXcDbzf4WP283/GvCfGGN+FVdgnP9YmvFPHbat
+yVfXbIOMYiGUX39MOpC5xWwFQoe1BYRivsVaRYM22IlmGLyP2gR5SCut/yjyQZ5/vsg1bZ4TBMIK
+1DBUj/pPPrjH3/qG6wVHM1DNDUW1xHGPnnj+xdWWjYp917MVtySGUnqGXLbmPbHs2qKilpRcGxjC
+fgcPNqSdzqMMQtIUfPWxvcKCpLh6/ZBGnZGm9NmoGDmrXdlnmuc0OuFx9irth67gxFFNq8gk35Sk
+KnyZdWdYMwOZ5IRNi19Je2CbUxbqBrDc0RUreV6CR9bpGPgBZul+tw0tk71Ec2RYnLqo73Hh4utX
+7p/QrF109MHmlKUgvCd9uyOFFU1LnAg5qs5BuwFf1mymDAiVNtkqwa9FHioMoVJIX8VO2tJBXIHx
+0WgnUxdS4yvViAaGUgjQeuGOW6chPal/18GQxlN6u71mpQ7OuwqUhzm06tp4NmZPGhiv3x0xPnKR
+yd6rLf5IaarSx7JuiQNFNuFDvEEXyY7whW4kSjHyE21EFPSSwGGrgWbdUCmtbEJDLgbmwcEhD+78
+f5w+GGP+GvAngQNjzDOcy/RfAf6GMeYvAI+BP6df/79w7cj3cC3J/+iTnYbB+BF27VEfy0Aliqhq
+N8HB5pJgz02qHwdQqi3WgUYSg9cRwYzFNnrpzZRWKkT1psRTftkorPMCs6P3UlZE911YducrX+Ur
+X3O6jNfrKSaQ/LpucjoM6K1k954MSdR9mNY5MwGADpMentpi+VKU3XbrwjwgHFma0j24sW+Je1r0
+xISrK3bXFMQ9rELUtrWYtnNLaqnP1Kq7cKWc+XXOO6869uheWlNoIVzPSoK+wEvFjFznFoUuJE3D
+EX6sxaZodnWZNmkppQaTb3OQiKunhaAtC8pSqc1yhue5RcGrDF653f3OfOoW2XLl5vLCHzKVYtPj
+qzX+gWOovnn7AY3cvkzSUEtMN5dIb+xtMGonGg9aKwZjWVCpT7QuVtRbd13BvDODiWnXbuFs6swx
+cwF/zyf1u1QoI9AztfBVSDAtfuA2i2RjSDN3bqvrU5ZiSUp/lcsAXhGILI5bfNHTvUGEaBC0XkLY
+2QCoLuX7Cfm5e96SaIwR8KjdzvD6YnkWTyHUdWsBsXmA6RoLkaF+Ka/J9XtYsWrvv/Y6wwPxez7h
++ImLgrX2z/9T/tMv/QG/a4H/+Kc6g5txM27Gp2p8KmDOGIMNQprUp5C5xWL6BFO4ymt4NMST7ZYJ
+AzzpACJzFuoliMlGU2H1u+32BY0ceqlTthsHIvKWbhXN9j6LJ+krY40TAQB49Uv0PvePADhYVzQS
+LRl0NSs2u2LfvWGI36UM8ykriWXMBxXDptsJ9B1egyfpcbsweALmNM0WNR8wEgdpNwFWEUHZNLSK
+fipT41ddQc2lLADfuRLPf5LRP3TYi9r3aWoXSay3EKjiXlYFG+1oI8mXl9bfFdd806fSbl0by0bR
+wbIyhKjgq12yXFfYndbBlkA6luE6xJuIYDbLmQkw9kIhdzWf8/JKugDG4450Cds4JlIHw9tuaTos
+hyIzr9eSq5gZ1EAnK7lqWEnmf7resrqQyIpUrnsrn0R4g7YZEkVd2piABGwIM6yIYn7m7lOZG+Zi
+eDYt5AJkXa8avqMml+MmgqkBMTHvtjmXjTufb798zuTIHS/yDUbpiG0VSc7OYSVSmfd9QlkReCML
+jTREk3Nsh4sI3Wc0ayhlZ197lL4r1l4uA3wBo24fWAL+EEq8g8Vrt2xWBqOHsQpSCF14GZoSv1UF
+3LcYIdCQmKctc5quDbkpKGVr3pxfMr90dc5kcMC5cmN/I7rtZw7Zk1ahnxqambuhVx9O4bk7xrFn
+yFWjuJawa5Yb6lyaieMJNlHOSUwj5hstVMqHN6rUF2FDLLERr7Gg8NpkHjtBx1wU8XCFFTBlu1lS
+qDxdF3OXQgHNZk1eqnWo9czEGZXy4ceLDW/Io/B0uaUvaObgdki/1HkO9WCWLRZXvY77FW2oesBm
+zUodk7yy2D3VbnTuuTUEaScd79MKhEXsU0mpalpYVrY7P6Vueylp4NKcO8N97r3xBgC2mlFqcYrb
+irrrLutFClKPhViZuRfiicFZUdEWXWckJlGrVZ1FvLzGqnWYjIb4WrCsXdP6LjWtqxWVVJ9q8VK2
+xpAvRAcPF1xey3OhDljpUn/8ldPU8wLD4dYtXs9ernl+2933LHxKjV5uHTe/yIkkBtROPbKBa196
+TUMooZ42vEMrQZUWt9CZbYk/cpths96y1bO3Wm8Z3ev8UvsUq486aJ9k3HAfbsbNuBkfG5+KSMFg
+CE2I32xZCDSzniYEkk3LFxti2cCbxkDrfsfMVcWdn1HLyr344AOuVFxa/tYjLnAFrl7PMPryz7vP
+SxcxVO+fMRSPwvcTirnrrP7wvd/gw0eu8PPDdcFQO+xc3Yfcr0gmbkfZNguK3O2w6ThlYd2OsFl7
+mEKYA7HsvDxgLU09giWR8A1JvEdPIKp13jlV5zutxWqzZZ67c44Nu0Jqta65VvGs44Z4rWUlma9n
+K4tn3a4TrWdsxAMY+/eJG7fbVKuRznFJUymSqlOs+u2b7RarsNtUBYqId2le3m5JFu53h8OQ1bVE
+YrwKq23z4nRJqZ03kDFJf+8AfyjW6SimN3Kfby/nNFv3d8vGkqljYCWssqot+CoibgxZqlQj+0g+
+v58N8BVBhAM5RXlbylaKyrFH5Mk/cnON3eVuNVbmOI2cve2mYb5yc3yRX7CUMMq6zviD9t+dsFvV
+UiiVsI3FqKu2GW9QxsryqUs8zqZLioXAa8M1rxbuue7//JdpjEtnguCz1LXTn5A8JolnsHM3V2Xh
+UVfueKOwJZUO6XY94/y8A6B8snETKdyMm3EzPjY+FZECQGsC/GBI2nf5YuvDXD3v8eECAreym2pF
+2wlwGkdese2a4oXb2RcvHjNXe45hRiat/2DfEmlXjcduJQ7ic2wjSbTKox26XSUr1rviUlW1XHR4
+zVZElbRPU3RGLi1RJ7RpDZ4iGi/znRMHzucQIEpyEvXB17M5S7lH29UKc+h29CVS8l1uicMO3ZiA
+JOjWJsd2PpZtw4F8CM7Verye5WxVwFvaljj5jPt5FdB71d3uqijI1fOOVi6SIgoJ5dOwrQytCpc2
+svRTWbOVCY08CpNOaszmtFJbqnMfo5ZrMd+wWrrzeHR5yUYY8YGivyjtkS0kzfbgFVTXY3nBRz4S
+0YqVtCM8STfFdoOv3nwZLElF/TSFR29PXg/biFoKqtWpri8JCHTNZb5gY6RZ0PQIZXxjUovVLpyO
+ZT5TL8jP3HVsr86pBbev2SKSZBdnuHPu/vUB3T/jQ1U69uXqIqOZCoWp5zucr9hcu+vMggYjuLZ3
+nWJH3Xw+oukEZK1Km+l96pl7BwhCempZBuOYWK3M57/xm/zg+Yf8NONTsSg01rAoDbcOjwi4D8CK
+OTO53BydT2mFLcBCKfBOJR1F2pKqUe/24IhJ5roLfi8mV6chfz7ltz501OhSbYRf/vN/Ftn64cUB
+3tIx9cr+E9ZiDy4au7vTMivC9+OdGjK+TyNcv63XLLWY1Nkt5rVCwpV63ouQ0b7C02aJX7tCW9sE
+tCcqfM0eAWBsQDQWy3B+ytkLd/NDz+do333e81t8LQb3tNicz55xrmr6uOcRHYkF+scSgtbBatvt
+lM3yQufRvXQ+jQq0ZeZTqahFmBBEHVs1Y5DqhRRXgeseG9m+m2JBKLn3ugmYXXfCItCT0Eyi7szk
+9oSDE/cdsd+QeQ7yXNqX9GSMElR7FOoeNOo4NKsNgbo9fmuopaXoFyGtSuWFHgAAIABJREFUCmrG
+L7CSU+v33GYSBAF53uEwWtaVUo3edgdViesEX6lSE7p7Y/INz4SxqJYlbd+lW+tqhUDHf+BoscTC
+xUwyj6mETtYfPONAePJB5EJ837ZMbkvaPwIrzMr68deYP1FnZC+BobumwYFj2vqeJerpGcLDSKkn
+PQHzvvAUbcyL5x3Q7JONm/ThZtyMm/Gx8amIFNq2Zr2Z4pdD2srtAmtb8Hzhdu7jwX1u12Kt9Su2
+Z27lnk5V7Fqes1m5qGH55EfUz9xKu/WueO9dFd3sBe9du59f/7xDLv5SfIg/cKuu6eUUM7erFB9O
+yJZuasqm2U1S928aVoQyYbFNy0aQ4aANyCUOep0nIKaapzaWN2pp1vIjCO7RJm413ybHpGPH/Gtq
+F/aNgpg6EFpxMWcjPYGIAm8kT4ZeH0+eFJfyI3iU11hRt6pBglHDLB722D5VFOMP2a5FVxGnP/Zi
+GoVEvjFYFUHnl2cEhZuX1INCxLNi5eZycbnmfO6ijnSccXQg1F3ap1Aprp/0uX3sopTr+y4S3Ltz
+l+M7bpdrrlbEd1z6ZD/8Rgc2xOYVue3CdUU//YRQCEPamrYUVNgLmL7nIsF2HdEPO4k0oQMHPpcX
+biddrF6wd9d93+e+/DZG4WLbbiBz51lLDfls1vLyqSv2jcb7+ELIXq6bHUHux5m03TMysoa9TKri
+ccLo4HU3R/EZzcKF/z/Ss3DgjxkoXUkfvE7ywP3uNqior9w8b4YbCnl4DN5yUZUXjbC+0u2m3KEf
+6+dT5o1LV777u9/m3XlX/vxk41OxKNRlzezpBaeBIY3dhW3LMdutvB2XLblUiCIzxLPKS6WzV2+G
++EjJNwpJHzhOVlotKWUcEj0e8q+Jcvsn/9QfB2AveQtP/opNYVkvlPclKRvZqPdy6ASyVfRnvWno
+x+64+XaL33Pft6qvqeVe9O70ilvSIzy+464p3HoE6t03WYpRatMO4OoHTlWnVr3Dmyzxc/egHO/v
+UUzdg7RZv2Sr9CkqS37j0pnW/O0P3AuxXOYkqfu7V2zCIFQ4XM2YScCl3vSJxPmIEpdetNWUQmrP
+QZ4zGiqcbe6ylA5kPw6JxY9AVf1+Nie+dMfKw4xQ4ohzv+FK8O62FzGP3XW1E7cgT44T/IH774Mi
+IlUaFGxrcgGLVu0MUypt7NoeB4d4OyWsIZGekfEoYbTn7sNVe42vOQqs+95iVRDKscvWLakWN7vJ
+od8Jxlhq1XlmUk16d/WSNpOZbpqwrlWL8L2dklXHETaAppthP2Sk7y7LmFBS9elrcPl92cdrQT/1
+cr4Uu0Xq4R99jWQinEK9IZLo0KbeEAiGnoXH+r4X1AK4bS9XFBtd3/qUX/91Z7j8nYsr9kTd/6Tj
+Jn24GTfjZnxsfCoiBQ9DRMx25VOpylw3z3jQd6FcYkOqREjAvMVrXYEmbdxqHj2MODh1UlTez53g
+zcXwm6w5+YGDSlfH/297bxpzW5afd/3WnveZz3nHO9Wtoau63V3uwWksG4fEsQHbkTGyxAdHkXCI
+JQspUgIChbT8CaR8iIICQQohFoEI5AwQEmJZgo5jQpAIntqOyz1XVde9dYf33nc84z57XnxYzzld
+19juqnbdqoty/lKp3vsOZ++19tpr/Yfn/zxTgrFzu178Vx3zc7p/QCvoaHn5YCv97o26pNIN8KYz
+ArnVHXVqdqIU5ROxwRCv+iafYS1NsDJuqWbOdR3tO5d50mvwavE1rkNMX8mslcVOnOcRi2U5me0R
+bI6gtODaDXc/D746ZwMPfH39iK98zYVSd6TZsK4a+hrHc00N6ihNqyFj6TnOyxNMX8VycT3UARhx
+I7Y2AWEsknFEsxD2YpZvNQ9rNVRVdo3tuC68sZ+zkkjG9HTKQCzX06ymEBT605KGv3ntJVIlxuxx
+SCFdUM9EeJsmtnVEa6U3KV5Dr/ExOtmjtkRym3gVRGL/PiLHriU3p1O+DhqGkpA7nLzEoCfODR9q
+oUJtNGajbjfXc0qbSw5F5DLoDzj33eeNZ1eMNaZs85gC+Oxt0cA1MY1wIY/nj9mXjmkvOWaoRriu
+caFBuVhw/yvuet3LLxC/5PQbugWMPvOdAAwntwhfcWt5Q1/S1COaK6F72yXtlbve+cVDHmhdLJqG
+l0bv7TU3G1GMD9OGg779vs9+mtuDj3H7BVdCy2dfYi71oqG/JFVZLF9aGqmxNnO3MC/KnJVyCqfr
+Ygv6yUO7xar7UUQqzsdCZbxVVlAbN2GD4R77L7vFnQR7NGrfDRcJ1diVLSuVdtr2Dv1USk/XPkK5
+dg90NBxwbSKwzNGIh6cubk/EaDSKYroC7ywvlngKRttlzVxgm7fecqXVXpzQOVC8f9wlzN29H14f
+sjd2LFPp4R6++kBeHDk3c+illJJAZ57zt3/pHwPQjzvUioLz8zOmKmUF6kQdT/ookY8XFNsO1F6S
+bDUvgyR1uwdQS77dC31GQze+OO6SC2w0tTVzrS0zuM7eDRGKCGzUTWKO803fhcfq1C3un/0f/gZd
+qTTNgyVv/6oLiy4L14HvVzmf/Nc+DcBHDz/CrUN3GPR9aNEGV9TYjWqXSppZlvHowoVPd88uyJZ6
+Ycs1VyLNrf0Bhy84It/0k24jt1VIpTA1nI2w191auPzn/yd/6z/7cc25OlErn2zuqiXf+OprZPfd
+s2wHGdP77gDLykvO7rp1cTJ16yZfQVdkw9brEYvnsylbwg0cKkig6zakqapZRzc67B26Nbt3NKDy
+JR/Qt9xYue93yx7Z1JXof/w//skvWGs/y7ewXfiws53t7Al7JsIH31b0mzPC9kXW+a8BsOz5XL3t
+XKDjqEPPlyR5st4wqGGVOTdvz7HKsh8ElkaNL5GtuVLN1wSWjpJZlZp5TqOGbJO0bNakV+56s2hG
+EG/gxrcwj78OwAO5mS9yhV+7ky+fv0aujHW4vuTy0nkCg2zFTK5vKOKNqvMCg3STZd4jVfNXEgU8
+vO9OjTNl74PHU86+6E6d9q0O3dvu9Bjs/yHERsZBVdJEgtCIbMUOQqwOlyxo8DOxQ1cDjLAcS1NS
+ar66ier/dU7RutMsth6dRFgAUxAINBP5BvT7VjTyYTCmI3hxGFVkmQhZyoSVugAjf4Y5FWZh4D6r
+6o6IN4rKVy1fEPR8kD3mYum8iavlG1SR0rya+6ODF7gtGbSDGwccH0oMxc7JLtSYFcb4ouErddI2
+dsFg5J7pR9oxi4lEZh6uqDJpN7ZrlmdqoHtTXJpHQ1rjnm+Z9PEfuIrYCR7tVIlp0bPXdy950Lrn
+8LV7b3GwcuFBnTVMM/d1sVzy5pmeia5lwy6NWKk7fou3dp7E49InEKT/oHuNx99wnBOvq5p1UkZ8
+xlO1KrQsjFu/2Tzdcm/m67vcr38n7/LvbztPYWc729kT9mx4Ck3IYHqNVfSbzO67nS9txww6atNN
+QxDaLu76JErwzGfutDJtQUfEnmPPp5Er0VqPySbRZkNWSqRNhY4bGkt302hlr5hJK60+6NNfuPLl
++eJXWSv55/fciRAvxlTBVwG4OhnQ7bv7zFgxb9xu3YQN84E7YXqpawu+3oc9oRGTUYeJFK/bao2R
+eMdJ+7obWzFnLRjso9e/Tlq6HvpP7bekmgtvr8epBFz2I3etbDGnmLlTubIGTy3SRfYGs6n7O99P
+CXSCxsIjLKeXNMHGGwu3MXmQpFuNhE4UUEocNUxUOg2SLWluUCyJ1HuTNRX7KsmW9gFzJRVN6HIx
+18qGrz1Wg88bd8hnbr4vHjc8uvrnuqcVbcfN+bWJ+/lnv+dVXn3OfX14cEAqCjLfNHTEw4BNCZWw
+yQXLDm3MQDF5OhxxpvKzlw05mbpy7/wyZF19CYB7c+e59T79Kl2VFi+LL5KrIaptLf6pO1PPzn7J
+Pbt7OeePJOSS3aOQBOKj0ze5UA94vDbUmfMAp9LQ6F1LuCbEblbNeXTl7qfoRhzGLhmdz+5zLjap
+hby8wdUxbSEF64sF4aHmdtFy583PAxCVayqzIVt/d/ZMbAp4LX4nw/P6ZOox7/XnjKXX2EvYEp0k
+lSHZyISLQ6EbW1KRhiRJjFUhu6jbLa/ioD9AkHIi8f116gornsezOmOpykH+YE2jjK3vzyAXeCct
+t/+34jCkqFk0LglWNS3LDY6+M2IycRiAl7/LbQqfvPkS1/dcLd3Dx1cCz69z0sq91E3jFt2MhESV
+gYtlw+KLbrP40mGH8UvfB8Cw0+dY7nUcuPq/9RtyMfwGBGQC41ZFia86fRBmDNtN8lA4+8uSi6lb
+rN4oIRVpjT/ZY9P713geYqYj6QmiW9VYT7wQXksvcn83zM621HK+79OsNmrar7lncONT9CvnDi/D
+HulE4dPiAcsz93nzbMmxEomf+UMuC//qx465cSCKd7PC+hsVKp+k58KKtlxRqYuzFW/jYNShFqbB
+b+bsC7C06Pd4fqwek+WC+5qDZe5etnVxRfLpH3Jz2LyNxY27n0bUkWj+VWVarafU6sodlhVtz62n
+pgh4dOY+d+CXWJH57I/cuhqYDl3xNjZXMxo58OWDBdlNwcNjQ5q5uUgKl/AO7IxM0PVFHuKfuaRs
+v3NMkipcmUcEE/HOv0vbhQ8729nOnrBnw1OgpSbjuDtioNMz9mv2xKyUVNn2RPDbhlpybAMpOO9F
+0Va1uYoCur5zy+OwBKlAl2uPvdaVk8axOzEuarhUY4y/iplJ4fjSlEwzt+N7dUAsQFinpw7G1Qmp
+yvyX6xXruRSq85WKYnAwiDh61YVCH9PJ9vzxbRKVmzCGQLJppknwXnAewid9B3EtPjLimk7x1+89
+5F88cGWlf/aPLpkIC9D7QZ+bhzc0he40SOsudSx5tEswEmTx64pELMhh2CXaUNaJ+PXi/ISrlbyt
+1HOs2ICh3tJYt7alJ4hxT4IzbdIhVDgXlFAnSi6WXd5W01WnaSFwSVcj3og7F18hkWZizy7JIucm
+k8/JBF3uDCOOXnalwdvX3N+9ON5nb6DGJ3oU6sSM2y6BSqCFF1CKyaoR65WtahDkGb8mUoPSYa/i
+Ow6ce13nZzxU8nAtfoO1zXn7N/+hG9PgFigBbdN9Vu3bGqu8lfyMIBD1Xq9HIDq+vFiRCW1YJCWB
+8DejrvvdXpQwvxJVYG7pbijm+gmlvF5vHbCnROmqdut4Vtc8eiC6ueMGRMGWtvfw3ccx6BZUzftM
+3PpBmIdHx0b0G4+h8OJhAoFYb4u4IRHXYqqQAsAI9hkGCeEGztsLSEX51e1EeB3n5meLFhM4l6qT
+SaHH1gxz94JdzeZcKhwJlj6XAojM2gKz6XI83oDyQ1I98GFlaUUXvioLen3nEr5680X+yK3vAuDm
+nnuISRoSqDJiTQDC15s4IVCrXu/Q9WIkJzXeR52S1af+n1/jt99wi3ixXvO133LZ6c9+9oL4tntp
+PPWMmEFDtNACHCZ4hXs5+m2L2QjmtAY/EdXbSrXy8zmlNoJxf4RVPqA2lo5c7TSK8QR6sqKk9+oE
+X63hppNga0Ge1wvMhnewThk73A1R4/4+aDp4L4qC7K0ZlXD7SWiIvQ2dfZ9PvuJwAx993oVivW5J
+5G1a32OiVj0xPpT5hn7dgLpcy4FwFcuMWi3XYWVB9PoGjz1R/X18nXHxyI07070XVUMi4pW2vdy2
+7YfdMfmGB7JwOYBOJyS07gRZ2xUXmvvpIqfSvY1TGBy5jfxopJySBw9UfVp7DUZ6lZ0ywM9ERR9H
+GNHwRQL4dcuafOrmuzzM6YkYxksjoomAVYuGOt6RrOxsZzv7A9i3K0X/l4F/C5eBehP496y1U/3s
+c8BP4ZrH/qy19vPf6hrWg7bnyEsv1Ql2c38AYvKlCrbw304v2p626jEhN2CkuxgOI6LAnSR+2hIJ
+lpqmBiPZ9cWV+BZsi6fTmkHF6Zkyy4ndYldXyxWZaujt8JFuJ9rWv4u6olBoY/HxBu7U+cTLH+H4
+2B2P475LAgbEeJryprF4CoPawuIXyojK7Q0OjrCVOyWe6z5PUTnoa+IZ+so+v333gqMXnfs/nOjU
+Lfep1xv3ek2k321jy1rSbGHUo9Znn5+7ROTjYoYvuHIY3aavTL1vU8xGtMD6IEqzOhfRS+Dhmw0h
+QcvykZur+cWS5UKu7dEeyys3L3uH6hysMu6Lui27OGM1VcXET6gFvYhHAbdTYRIS9/e9MMGXrlxd
+1/jy0qh9inP3LK+yjLYSLZwa4op5gZGcXhvUWOHUy0UGtTAZkeGmRFtee+CSxzasWKs6UXshWJGn
+3k8oSneji8xd4/LilF5PsngYphrffHVB0BGhzOFNru9JFk7VntP8YkvQOjUtrajn/DigLy+tMDNO
+Hi40Pr0LoU9HsPPTizXxgcaxqrYViqpdUM7f2cf5re3blaL/ReBz1traGPOXgM8B/4kx5uPATwCf
+AK4D/8QY84q19ve9K2MtYVlR58GG3IjH0xnDvmJOv6YSGUiT5BR6iJFi8jatiUWm4cddQuUGwnCI
+FXzY+jGhWnlTufjlNMNXT0UYxjRiX04aw7Hgz/cvzliLa+/ykVvkwc191PXKomrIKgGHfJ+RYsrB
+XsyhWIaM2bD51RgBfQI/YqNOaUzjyBeBQDFk07a0KifevtXj+/dczL1qIREH5fn/9RqPu26BHfyA
+458MOMNTm7HX1HhabHHTOqgssG4qVjO30C8u3L2365Jr1wWv9T1qKWsNOl366owsWrOldg+0oJPU
+YgXCovToCo5+uO8zP3P3P728IllL01Jue5kOyU6+4e7NT8lat2E1xpDgrnfY/wjDiXIbHVUTwiss
+31SNKsT9WJkZtXgq63ZOlilnIil6L/RBILI8X20ZkoIwIhO/p5+DJDs5Ev3+w7ohkgpVfrEg6LpN
+inlGKbJEqxxIYGJyCedkZcn5pdtYCioOYumeBgmtGjZ8hUlxbehEbkwTG5F3FR6EKbHyavlljaIR
+jNZb5CWs1fNSrHweK/fTH/do9MthFVBsCIHepX1bUvTW2n9srQJJ+GWcZiQ4Kfq/a60trLVv4ZSi
+vvs93dHOdrazD9Xej0Tjnwb+nr6+gdskNraRov99rW5bzvIVkfHoKoFlCAg32dQoZCwJcJsZjOCx
+vsKEybBDMHI7cVFZEk8ddZFHvUns2ZbYc0mpeKDTxyZU4hCILq94UafSW+drZh31+scTFto6C7sh
+gKnIVe7wTYEnPQhjDLGfbT/PSsXaDt2eaoseVjwN+CO29BxNTrNy1YW2ENho/ojLR18E4GT6RT45
+dt7N2fqSeexO2ItpRF06DMT6zNXPk3BM3d001Fg8ufambTBqMKuaAqO5tar2HO4fMBYN2p7XJ9b3
+B50ukaToo7pFWCDCSGNuAhJJyNVZRSo6s/6iy3Dswqb787cYb0hEzIYluiCcuSQp43zjlbPIs63s
+/L/ymRsciTU7zEUQs3iIidWMlR5SiBauqjxHkgLEUUUtkpUN36PHklIAIq/+5iltKPBVPYiGPknh
+1tH1Qxf6ZVdnzAQhr9ctrXgoiuKKTM99I5wTlpZcsORlU1FtNDmChERhRRT3CfyNnJySiNk39SU7
+C4vyjLSVj5FwRRiXjI/EvH0iLdVOSqXwYRjZrar4o4ucrkSHRiNLW240Fd+d/YE2BWPMzwA18HPf
+xt/+NPDTAN0oIGg82iYkkAMSDnw66hOIUw9PL3fteVvh1UTtr0E/xYjEw++VhBLFMHEA1Ybsco1J
+lO0XW4o/qkiE/ltGsHfNLcZVPyVSHGOCiOqxA7I80udWD6fUai1uGsuoJxc9Cdn3RrpeQla5F71Y
+upejHVvCDZt4bDClQiJzSa1cykbF6uq1u0xFOc8qorvvFtX8UUxv6hZpmva3giJU7ufn4QW1SEBt
+5wZW6XJLuc0HhE1NI7KXNFT5dr/PcwIKHd/qYdpNd6kHagEOohDTU6VB68yz4ZZaPdnzaVbuc4+j
+A5BY7tlrZ3QiLe6hKOcDw1p1s9WsxUtUdSoNSU8dod3nSFSNsqoGtG2JjSTFXhd0Bm4zbL2GciX1
+rfNTgqHo8VWSbMoGTxt5m8b4qi7ZxKOjjtDsqmGoPpYb19wcLhJoLr6JnM2FJq3iLo02oaVyHOHa
+oxK66+rkCk95i4ODMf0DF/4lY5++NqpGIU7h260IUkG57UptbYyVENFet4NJ3PcPlO9ZTUtqPd+9
+YZdWZLXJKN2CzLI2huT9zyn8rmaM+VO4BOQP2m/2X79rKXpr7c8CPwuw300+/P7tne1sZ8C3uSkY
+Y34Y+PPAH7XWvpMq9ueBv22M+Su4ROPLwK9+yw+0Br/x8L0ZM7lW1/0Ovvi263WPJtgoLVtiufyl
+6s6drKAyLokWFyHVpoaeV1i5mtG8oBCev7jjkloXiaEWA3DSDXkwdyfCfBxhhEIK9iakS2X4JaVW
+/sbrvNmqj8AaDhMBeqKYW/vOzc+zkvMzd1Kk/DYA4/alre6iH2V4G0KFoIcV07CVVH0wDEg7Dsh0
+8HKX8tL1WrzQNSymzsV9M59z79RBXm+dub24vXHE26I1932LL3/feC2tiOWawCMRM/Bex91vp9sj
+0Em0zDKqXKFPW4CARfsDQ8/IE1JSb13n20qMV/lbivSMNaGk2UYvHvJYEGMjbst+WTOfSMwnX2JW
+Gn9gOBy707oTN3iigW9a5zUFkyNYqPITpgSxzpM2JhC8u/BCaiX8WvEpBCQESqG1TU4l8FJraq7W
+7lnayMPrCd6ujlJTpwQrie80UwpVGup6ilG3Yr10VYGzy5xSyexFeY5diwwmLVgr8Rev1jxWD0Mi
+EpqiavC3pbSYTN6IHxUkSpTnTUBPfSpetKn2zPDWbg6joGWpnh9bVATCQNRkFGvFI+/Svl0p+s8B
+MfCLxvmRv2yt/fettV8yxvxPwJdxYcWf+VaVh53tbGfPln27UvR/8/f5/b8I/MX3chPGtnhlznLp
+YdXVWNuKppRGQpCzVswZeR1CnW5GsdkqCihWou3y9/DVqBIHPsGGuHUY01FN237CJZGS2ZLlgdt9
+H51ekNVuZ5/dzxgMXFIntBmTfXcKNxK2XYcZzUbGLPa4KXLl4+vDLY3b7GLKaOROvKV6+/38Lr2b
+Dpln1nMiJd28eoFRs9LGe4gD8GYSXnllTPd1l5QrVwGPLhxe4vV5yfLL0qr4TneinGVfZHGhcdTT
+bYmzbQy+0KBdA40Sd5E8qV4vpBfrRCkLri6ct3H3/gVt7RqXPvHRl7l+4O5z2Hf5h2x+wUInZdZM
+iUWVltoRicY3aUpOcjcHJxcS8Nk7orpyp/FovEcsSZVu3OOFI4fqvPmRT+J7LklbSUqtzmfUSpLe
+f/yA03P3eR06xNKkOLs/o6txhULCjvsdckG78+WCB+euXLguck5U1vMDKCPnha3UtDQ9W1MrQVnl
+U0wjQtsywJcwzEAJ7GliOb/vnke2mlPIS8uzPqXG/Y3lm6xUktzkyfY6E46vufXWp8EXR8a9i1PO
+5b2W1hLKs+oLtmyDZsveZeseJ+JvSCPol+4++/GAxntvJclnAuZsPAh6hn6vu816d+I+qOY9WxUs
+7roFdDI/p1Ir61AJqevXRxyqTdm7XjJSPdr32MJgmyrAiOarkGv88N6UVStuw/UQX0zM8Y2URhRc
+L3ZfYjB2L3J01z3Y137t85wrwZNaMMZd73a35WPX3X30b97a9koEWkjz6YoKl3zs9rt4uLAjiGtq
+qSxVAlBli4Ly0i2IK5a88ctfcb87n3F/Kubqsc/oeZd0y9pND4dhmbmfr5oOXSWqImsJzSZcCeik
+bhGmykjtdWN89Uz44QvUYjM+y6YslBC7WMOhRFaaDRak01I9Eh5jvSa7UojWegyvuUp12n+Rz37a
+baxW4eGy9vCu3EbXJi2l5Ncnww6f+oRo4Hs+gSDN00v3d/liTdk6jEVVVhSnav22S5ZrwdGbBb7E
+ZfJIupOmIbDu+S/OKy7uud89WeRciIjGKz382L1YZsOqXeVk2jRsXkJHYVWUgja93sTN5ai2fEkM
+3cuy4domRItDIn3efDrk8VT4hbVYsgeWg+FAcx9RzdxnRFc5C6Xr9hOPZJNIHG7a4QPmgubv7R3g
+q+Ly4PEVB6Lki44iiva9VR92MOed7WxnT9iz4SkYn9jvETYeqdBzUZzCxt33PNflBjRBSDRQU43K
+XFU2YykdvdEqw47VtGPsFubaPK44WbgT4belsfBwbdhXp1p8veLw0CXUot41rNBq8WFEV2QnvOi8
+ADM3LDY9Jm3IzbWQl+FNSkFQZ2+ectUTrDh0BZhH64ZjdbodvPQKk8KdYn4nIT9zJ+Xbj90pMX3r
+q3zxDff1r3/jy/zmqfMkem3Ordh5Hn90OAS5u2/d+S0AsqDiRKSk5fA50o1egvUJ5P3EvkUHNivN
+6+LhKWf33d/1oxPOryRqc/4m/VuOKDYJzjDd590cCSlJf0IydCfR1y/PuHjs5vZrjx9zMHCNYMef
+/sN8RuQqy57zbK4uT4mvK/E5tSw67vNu3Jiwt++eQzqvqdQp6guDaLyGtUKJdr3gpRup5nZI/YZ7
+vnkZ4O+7+fQjB5OJvSXl0p2wTTElyYULqDIe6Pk1rIkVmkQbjUoi120LzDODp+dr05ZWjN6l7+b4
+cppTWucd1HbB3tB5RzfHY3KFR6lXYnGhV9uRd0hKP3XX63YHWJ3y0+UBtnJl2+XKciopv4f3pTRu
+SkYjd43nOhXd1q2L6WxNJdbs2njk0Xt7zZ+JTcEPPMaHHUapoc1UYzcGFMs1bctqQ/3uxRiUMY83
+TDopifoILqePibpuMVL4BFshj4pEoPruTQf4OQgrjgQ/9boNo4360X5IJX3Ih03B2w9UVe24B5BH
+DQO7ad8GPuo6+cY3XmGtUOLubI5VlaCQutPSrPBeet797mVFpTZcao/TR26XeeOOu9ZyFrFWLf2x
+GeKJTKVpApZ6alXYY1E5V3R5T5x8w5bTU7eZ1suAA91P0AkJlZcd7CDGAAAbxklEQVQJ4iMqbXpr
+kdMsbcjgmnPbw3hCOnCfGw4mhGKKjpJD8tJ9drm5iSRirrbgddTlqnaw6/6LfdaX7uXO6pB1657Z
+qnILO2xhEam7dHVBvnAvwq20S0cN6LbbJQrcSxb31M2apfzGb/0KAPffukTRH4WXEwnjknZiEs9t
+PrcP9ZKvJ8xrB1N/cG/BXamF3bm65JGqS2lsOPA2kG0pa/ktxUpVBlts+1G8esLjh2oN3woBlwRd
+t4Zu9I/pDR2G4mJ6j3s6V1YXMy5UiUnV1p8edlm27tnspyHzUmHcYML6sZuvs2bK3ak7RFYKFcPG
+o/DdBnFydUCQKgTxY5a1G9PJxV1iYS/ere3Ch53tbGdP2DPhKXieoRNHjNM+azW+1HmzRXaFrUeo
+JphrYUKgJNdR152k4+4LxIE62ZqAQJnu3t6IclMxyOZ0W+cpfPZluVlrHzNwJ9hy/jaxmpmCoqa/
+/xIAl6d3sOqu7Ev0pe/1KGJ3kuzvpbz4nEuoxXsJ1Vyw4X5DZ+I8lvVMsGyv5AVpS4ySlFip48D4
+7AnGfFy63f6Mh/RT55ns3b7BxZuuGnC9XxFKxu07bj/PauxOna+IwrmYLRiKC6GKLOhUKYG4t+l8
+NNSa217ikmSDvk8qdo9gYJgE7j4HL3W3WIbW6+DrZGqUvEpNy+GBOgOTY577uEvwBf4Be2pSCwcx
+eM5rumici2+CHuHSPTMb9qnFOBztHeA3Onn9Dn7oTrwk0jNoZrxyw93brcl1Hii5WOY5ibyJa/0B
+/cB5g8r1knoh5YWg6XFKJTLJvaNj/JG7jwNijODYrcKHZbUiljvfzmp8EbWEZc1F5v4ukQtvSBn0
+XfVhFE0YSgm82r/Otch5JnmnS1+s2V111CadMYkaxdbWo/KFfuxljMVYbjv79G67uW0EWx6lCa1C
+g8nNfVZKVh/O9slXat4jwdTvDRv4TGwKGANBSDyaMBAAY+ovsIIV9+OEA7HVxJMuvuK2gRSb4nHL
+auG+Ps8W+Iqz4xhqQYmLIiHVprChi4/2W5Yi0Rx1A9ZSi6oOwabObbsejTm/qZeiEQVT6m+hsaEN
+yB84ty6aPM94rKx13aMzEgmtYm7j+/QFjukMAvxMHZx5hicoaioy18HhHt7M/f31FGqRZhz3B0wO
+tTn1DskHIvz8ZacdGNydcqlW7uW6pNSL3gtKglIAolGPoXHudRW6MfViQ9BXXqbbYyVJ+V7Ppy+Q
+Tb5cs566hddM3D1Y/zqjm4JxnwU06mwtozlHkw25TJcLbULPSyj4wXJJ5233d9MQZuoD+e7xjCDd
+9C0sCTabT1+qYPF1Rp9RZaGyvCTg0WJ9ykib7Pi55/A2QjMr92zyoiB4JEKW/RjPup+PK8NE+Yze
+KCDeKErJxYeKx5cbcp1m29tgmxVfft2JvdRjd93hrTHU0qjstXih+/5eOiEWoC7sJJTiG41Eox/v
+9fAUxrVFQZi6ryfeNXqq2pzeXdFRKNRG7tlMekP62ngbW/FY2p3nF1+lo56J1PbIw/e5S3JnO9vZ
+v1z2THgKBneKNk2Nr+pDv1tSKKk1Sjr05QfGNsCK98CkzrWszqdUIkWJTEsiyvVgmuMJsBO3NeFA
+MunyMKJ1Q1e0ZI3fJ9xTdt4+h507byO4dkX8VXeiNxPnrRynXe7rxLSVRyPt9HVUMpIXMur26aRu
+LL7gqenIbLdhr2poBZDxU4uv7rzJgXr+Ly557jM6aX2Db1zGOvJAqGrq5YJmLpo6AWhMr0spvomi
+32OshJnfRKSJ+7yUlkSUdZHEZ+J4SFfzE/sJI4VmZhGQC5OxWq8odIpForEbDixGLvXtTxxsohVM
+fItYvAhN9E3a/a5xv7tvWu4fu7HO7lyxVC6sW0+olGBdXs7oixI/0BxHRzX+SpTy65agceObjPeJ
+/LF+N4S+gGHikswenhOImq+/t89129UcVgyP3EMZBX2SxLn594S92MtjnttzP390taJWErBtPK7O
+nZfyhpifX+4cEe3JC83ttkoWBikDMeGFQYCnud2I2VdVoa5Z6PRTbObmJYlgpVf01e8+JpDsodWY
+/HXIsOc8l7cWM3JJC+4FEwpxdpq2pow+wC7J98sMhrj1MVVGoc6yNE3obLoh/Ygocg8g8PugjHpZ
+CCizrjCqPgwbg79hRVpOsQeiKg8Tkg2nY+4+q2g6dFXVCG+EXOTuIS2PGnqK1Vp/j7rnymzVhoiz
+bpBkIokP4ULkLEVDrL7XKE22hCqJSlZencBM2Hm/h6dORS/qEW7VlNzqee7wgIHak1MMVuxPyahH
+Xbv7vLz/iNO1qxJk1hGOnpsVb73t4tpg9DzeLfeiJ52EVu51abuY2i3+1Ymb7+FBvVWWCvoVoQg7
+6qVPofh6ulgSyuUldDvTqrRMFMsnNPjWzdu6bglUZisqy3km0I+Y8TEel+rEvLi8pJQWqB3OWBfu
+Gl7Q0qn0/FRm8IoRXrtR/WoI9tTbYYbbdvAiW2BLF++3iQs7gjrAU65lb3+PsKMuybbB18Y56QUU
+elG7IqCdjRqMKlHx8IxMh4HX7fLg9A4A3zh3+Z6WnE98p6PfjzoXGKN26bChFplwZBs8HWrFwt17
+WaYkQuF6fkyquV/YiF5nE3Z4DPQcNvocyTBAexTrs5yZ5jMZdllJ03I1fURVKLHyLm0XPuxsZzt7
+wp4dT8H3aSoDUnuOyhJ/k/VtS6zCAOPXtMpOR+LIq8Y5XLoT2OsUpFY7435LT7LfVdTbcjcG0kM0
+eYkv/kFTxhgx415Wd6nU0JDlV6ATxhPtllfXFBum6b4H1939ZCZisvHUKn/LNt2sRLHdLjFinbbN
+FARRtZkl2RMZyLlLXs2XU3oDnYieRTKY2DKnsu7zHnzpLnfmUmO+8VEAFnem1MJQrC4uMc9JIMWE
+W+KRwPfJ1WG6RtWZ85ZDuckje4wvHIPpdlhfirrt9OEG2UvntuvgNNOGXF6Dv5zhKeMeGkAajedF
+w92TjWCM+6z98SHTcxF6RSGxPMS87hNtVK4HI3K5L1YJPn/5gERKXb5tqRdiRKbAeqImWyyoBcrK
+RdeW1VPmG63Qi0vWC6lq74FRJ6JPF1/XSZXYXuantJVLVjZZSaw11AkPaSo9q8L9zclsxqtd91m9
+g5u08v5a08cI6WRNTKUejE0FaL2aUYnyr+wkhJHzlKaXjyiv3NfdAXipg9vnql5MBl2W4qJs2pLl
+2w7j4nldkNq68Qck/nsLH3aews52trMn7JnwFPzAYzDuO3ZbYQGstahl3xH/qAToNThRCABxE9Rz
+n5lO8eEwZrl2iZ9eFJFnqnlHBcUG8SuUXGgMrfIPVXZGJoUXv7ePL9anYT5k7G86It2pdFVmrCRd
+1/U8FkvX67/MRqzFPhwEU2rVh7PSxe/+6YJ4MtD4OnhCAgbRGaX4/U8l7rHIVhyr668djEAltsq0
+TE9dc9SdYs6XxTY0VAOT7exhcsnbeSGmo6RcEOIXSlDFOazd91fS1fS6U/ZQvqBuaQQPz5uMhTQP
+33r9TaqFTuOpFMGPr1FeuTEd7g3oDTf0YTdYqQx3fn7GqRqvOp6L8ZdXM5YPhdwr1ng6NdNrwy0N
+X+BVrKWXsJ6LYDYFqw5PE6YUSyUll3cIxSxVVj4rze2icLmWy7MZX7/v1sXyIkM5bKKLKYka4bz+
+Cl+4FStYdVZkPJJHsy4rgn3nQfijkMmVWw8L8VQssjXm3D3rfj/A0xpa5jOiwI2vsiXz5Ur36W7i
+bJ6xlmThsOPT1xwuyjXzuTzdXkIlusBUnBTNwmwb6fK8phCV37qcEYpubq83oe18Uyvl3dgzsSkY
+PAIiqqLCsxtAj6HeJNq8mkaQ52IdYcTr0kQbOrOW5VTuYgZ3Lx2gZby3R+q7lyw+uIk3VEu1JMs7
+PUMiwFLtjYgE7ummx8Ric14cNDRaIHHgHlYxy8m1SV0uPRY3nEv8aJwzStwDDzoNUamKgPQlo9TS
+iGCjsEsqdV2agz1Wp84VXelzR6MOqPuOvgHV1asHp9ydubF+9UHGQ4VSiV7WshdilXT1O+Ot0GpV
+NBSaw1UbsFkntSjubeaRCyBWkm8FZvNsSS1Ny4gKQUdYPHYvWxgbhs+7e6vjgFaJuqxdcHXiXqZH
+WUtPUNuZ6JKvpivygboPH2T40YYHcn9Lt7e8WpAbYStySR4FLeWF29Rt2N2S06z9ikRjOVv7BDoY
+qN0LX64bTh+7UKtdraka9/JftleU4q4sD2OeS5yL3ohS3i5TpqJxC6KAgXGbmokiXhi4EPMyc/e4
+WhdcSDPzuNqnzd2zJvVo1ZsTGkurzlVbKSQqG6x1n1FVXRqdXkVVkej5NeuAVhDxtebKX52QC3tT
+FSWnSi5WRclInKWVnxBt5Mzepe3Ch53tbGdP2LPhKXiGoB9h1j5VthHC8EDstFlV0xcLcmnWGCXJ
+itztuOtVTtl1p8ej5QWRGIUzf75VRx7mc66Js7/dJKRaj5hNaSpgLln3q8E+Q9G/XfkxGzGKWCXG
+uqql5QzLPOO3vuwUoTu25vr3/zEAelOPJnAeTcdXaTL2tqIvnUEP/yMiJZ10WGssntzF/RcO6QgG
+287P8FKXEMyzK77+ljulv/R4BhIvSW876K/XLuhq/E033hJGV3XOQki62Pdplfhq5Wb61qfWoEpT
+oFwm83W2TUru7R/TCQTR1WcF0R62dJ5C4odb1fA8tlyqlHdVGc4aEY6Uc82Vz1Kei1kW2xJgTY9W
+LMjteoVXSKvizHkK7SAmmbvQZW4sfWlTpntDKlG3ecWSR+o2nUkj4cFb9/iGGphsY7fdletlj0pc
+DstZxLnKq7lQs68zZ55vyFpLvPKO+92zh3zs1U+5exq7OXzz3sVWeCVYVxihW5tysS1nR3FIb+hO
+7qvHKntTY5fu+V8GM8q5aOomHUbiCRn3gq13ui7VAboqyFWevDO9ZKFkbacb46d6JlGL//9HkhWA
+1noYW1N3RQFeNBgBUzq2oRBc2Usymku3mDLRvpdtQKyM7bXJ0Za2PE4jfN+58F6VMb10LuUmGXuU
+Dslrt0FcsmYhWLEZremrvn2WXTG56dpvI7lvNdsUB7UHj6TGc5quWYhfr40HGNXWA6XsfRPQO1Q+
+o/ZBUFTzsCC+rgUk2HU7v0umFyKOU2rh9ovFigePH+riFUORcKRSljr2U8w1CceYFmM3LccewSZH
+0XqwDdPcXPbjfZJU+RW/oBCRS5vldOWixwQ0gv+GQxfaRKVHT5ufaXN6fbeI67pmrc20DEqGubun
+gI3ql2FcurF6BwmxDoArfEbCKcTdfXLrMCK5nmm9uLdtAY+MDwKAheucSOQj1UlOJMn4/J7bQNMa
+bkiLsbKGoN300sTcX7ucUBWuqQQLb33lfqqYnniF1zmE++7e+mXJ8VhVED3nyvYplX+alhkj5XBM
+HRDpUAsbj3AD5BLkfRxGVKHrLs1XOZHUpHITbNmsTRRtAWOtnkFVB+SBW29x6xOXAj2lXUaCm8fp
+kFAYiHdru/BhZzvb2RP2bHgKfoDXP8B2KhLx/VX5mlyJmKBt8eUHN2VOI0RfJaaTZDCgH+o0TsYM
+xWRL16dUPfrsrOCx+tFHAhMU64iO7068zE6JXnAn0MFHR/R77utPrJ7nRNDcsHCu6gMDm8Yz08Jc
+LuOj2QmxePLiwz282u3iq8aNybusCWqRcGRH1HOh7i4WLASF/vprr7mf9wfc/owLH46u3cBE7j5P
+zxZ01MBzazAhPhI1nVBwrx4dcSXI88X0klrJ0W7SJVQirmBBIOBDvXL3FvlLlA+lMmuMqMKaixlx
+qTAmy/HmmvOOu5/Yq2Gm02xQk6jbM7UN/lJJ3rbBqmkoEIrz5PGUQGzH/nJOqSrI3mBCKMh64J0z
+v5LehXT6JsuKdqyTz0QoEc/lyZJYmJPVhcdaOpbRQFJyfsxzx64B6+ziAVNJuqW26wh9gL0wYiSI
+uBVmowhOt/gOz1uRq7JTZgEvfNx5kHtLF5bW4Zf4+ht33DUeRIwV2vim3nbumrhBjZ/EWrP9ussq
+1piszyJ2c7Q6nzJXiGW7PZJNh6bIhaLYUCshPOp3GE5UlWuWxLXzdPpNQRO8z2zOH4RZoPFq6kVN
+q/JX6dXbTHbpF0QbDkKSrduVifhyfX7G2XJTIThjMXYPYzRMWCpWn52fsJYbfGPsXFnSiCu1aufV
+dSZDBwA6iD+Bp3bp9HrI5YUAVV2FIhtpSJnwKvSvekSqWnS6KbZwi221dKCSy3XB2T0X16bBmvaO
+Kg7tiDO9hP/3A7di5sU5P3bopOiv3Z5g+mIuKn+D4bEjQ+mGEQ8E3jm9dDcxO6pZXLiY8xtFzjUp
+E/X8El8EslUVUmuT7Yq+vRMfsNhURvKMkXojru4tuHzs7vPy7G0S6+Zl8NC9VKdYCkGw/80f/WO8
+cuBIcQszoEndBnI4vk4euoV5X+W4aj1jqD6BxbShDBSW1BFWJdyLq8W2HbiauxdoFfoM9GxM1bJU
+p2mRl1wI3p23HYKNvmfXbV7FMuP80m3Sd++tCeTmj677BKGbg+ODMbe/43kAvrZw892eXSNUNWA6
+z2hazaGXk0zcmJKgq3Ee8Ztrl196cHHCx15xnzUehyy1+Tb1esv/+fAtlxtZXOVML8QT2elQtiIM
+Mu1WCLe3X3P0gssbDaUv2fM7eOovuVwUZGsXVk7CliR0a7asQ9p2o/D47mwXPuxsZzt7wp4JT8Ez
+TtI88wsuGndKdNYdIoFCChNDLfhpBIm8hsFE0OAmZX4umGxQ05xJHg6PmZiNH13O6UpkxBoRqzQp
+J19RJve7Aqx24KWN2Be3wnkFwWKt6zmXrPZABQV8Dyp1F3aiMd2+YL5tnyYQ7bwIWQL/ANu4U6ky
+Oe3AJaoO/TGREmavFO5kXGAZyuPxO9c4ye8A8KvlCYu5NAV7LQ97qqcP3Mn4z377HsuZO4Hycsxz
+n1QC0xgaZcBDbwCFm+d9sU9HvuGx4NjT1ZSuxnp46xBfCsyjUUIH93njvhKH5QnTiw1ALGGtOv39
+dM0d8TfcGkbMJCf3QGCkxCzJpUpt1nMCVYGuLk9Yiv7sZPYW40sJ/5QuGRiN9qmFG6jxCLQueumI
+ay857oHLtqQUCC5J3H0WRzP2xIswOuizUiiZDnosRCJzkjacXbm/+8LSAcQWqSVT19He8Q3m95yH
+RG8PE+tZdlXpGEy51P0sFx5TQZfjIawlEuPXJUNxIOwfu67VMCyIxVwOCTYWaU25oPbkbR1MSDdy
+cuq+vapqHj52XuiXz9/Gr+VBDdItXZ7fz/DXwru8S3smNoW2tazzCjtbU86U/Q1yBlIs8lYr1oI3
+Vo3BSB+wK3LRJvIYjtykr+sek40uROxhjNqB6x6diZByffci5XWBL5BK9lZAIHWnaFliVQUJ52sm
+aueuhJIb+z5rPcP9PvzAp5yr2Wt6nM/dgj5uFsRSkQrEF9iLR/CSym1nEVe1st5nV9TqAn1R5S2O
+fpBbQ3Ec+mvO3z7X/fRZaZOqWp/9sTaRc7fBnL75+hYx55mG4LMfB8C3wbZnxG8z0r5yDXbD/pMw
+UeltcZUSikZ/dHtE3Dg3eV5CR+5qqLDkleQ65uU/AsC1/hGlMvWzh4+YLN28ZNWSWh1+t9QnUM67
+eLlzd0NjyaWM9fC0pH3b0eAna1iMXbjVl2akKSLMRAQ3DYS5whk7JBAZDo9mnE5F2DtT+NEJ6fsi
+v+3twcot/VlbkUkNqri4zuuvO/Her95195DVFwT7rs8jvEwYJ0I09noUmzbqS7dWOmXKfk8dkDXc
+e+Tu3SR7+PlSc2+pVY2KR+4ehqWlXrrnkAY9SlUfjhJotXb6SUSr7spa/TynpxlvfMOFdmeProg8
+jTW4QTV044+zDr7ZbDjvznbhw852trMnzHxTG/ZDvAljzoAVcP4h3cL+7tq7a/9LcO3b1tqDb/VL
+z8SmAGCM+XVr7Wd3195de3ftD9d24cPOdrazJ2y3KexsZzt7wp6lTeFnd9feXXt37Q/fnpmcws52
+trNnw54lT2FnO9vZM2Af+qZgjPlhY8zXjDFvGGP+wlO+1i1jzD81xnzZGPMlY8yf0/cnxphfNMa8
+rv+/N0XO93YPvjHmN40xv6B/v2CM+RWN/+8ZY95b98p7u/bIGPP3jTFfNcZ8xRjzvR/U2I0x/6Hm
+/IvGmL9jjEme1tiNMf+dMebUGPPFd3zvdx2ncfZf6R5eM8Z811O49l/WnL9mjPmHxpjRO372OV37
+a8aYH/qDXPv9sg91UzDG+MBfA34E+DjwJ4wxH3+Kl6yB/8ha+3Hge4A/o+v9BeCXrLUvA7+kfz8t
++3PAV97x778E/BfW2o8AV8BPPcVr/1Xgf7fWfgz4lO7jqY/dGHMD+LPAZ621rwI+8BM8vbH/LeCH
+f8f3fq9x/gjwsv77aeCvP4Vr/yLwqrX2k8DXgc8BaO39BPAJ/c1/rXfiwzVr7Yf2H/C9wOff8e/P
+AZ/7AK//j4B/A/gacE3fuwZ87Sld7yZuQf4A8As4caxzIPjd5uN9vvYQeAvlkd7x/ac+duAGcA+Y
+4KD1vwD80NMcO/A88MVvNU7gbwB/4nf7vffr2r/jZz8O/Jy+fmK9A58HvvdpPP/38t+HHT5sFsvG
+7ut7T92MMc8DnwF+BTiy1qrThUfA0VO67H8J/Hlg03y9B0yttZve1qc5/heAM+C/V/jy3xpjunwA
+Y7fWPgD+c+Bt4ASYAV/ggxs7/N7j/KDX4J8G/rcP6drvyj7sTeFDMWNMD/hfgP/AWjt/58+s27Lf
+95KMMeZHgVNr7Rfe789+lxYA3wX8dWvtZ3Cw8idChac49jHwb+M2putAl/+vi/2B2dMa57cyY8zP
+4ELYn/ugr/1e7MPeFB4At97x75v63lMzY0yI2xB+zlr7D/Ttx8aYa/r5NeD0KVz6+4AfM8bcAf4u
+LoT4q8DIGLPpVn2a478P3LfW/or+/fdxm8QHMfZ/HXjLWntmHWvIP8DNxwc1dvi9x/mBrEFjzJ8C
+fhT4k9qUPrBrv1f7sDeFXwNeVhY6wiVdfv5pXcwYY4C/CXzFWvtX3vGjnwd+Ul//JC7X8L6atfZz
+1tqb1trnceP8P6y1fxL4p8C/8zSvres/Au4ZYz6qb/0g8GU+gLHjwobvMcZ09Aw21/5Axi77vcb5
+88C/qyrE9wCzd4QZ74sZY34YFzb+mLUSLfnmtX/CGBMbY17AJTt/9f289rdlH3ZSA/jjuIzsm8DP
+POVr/WGc2/ga8C/03x/Hxfa/BLwO/BNg8pTv4/uBX9DXL+IWwhvA/wzET/G6nwZ+XeP/X4HxBzV2
+4D8Fvgp8EfgfgfhpjR34O7jcRYXzkH7q9xonLtn717T+fhtXIXm/r/0GLnewWXP/zTt+/2d07a8B
+P/I01927/W+HaNzZznb2hH3Y4cPOdrazZ8x2m8LOdrazJ2y3KexsZzt7wnabws52trMnbLcp7Gxn
+O3vCdpvCzna2sydstynsbGc7e8J2m8LOdrazJ+z/Ba2AmJPuW4/EAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3093... Generator Loss: 0.8099
+Epoch 1/1... Discriminator Loss: 1.3734... Generator Loss: 0.6992
+Epoch 1/1... Discriminator Loss: 1.4837... Generator Loss: 0.5886
+Epoch 1/1... Discriminator Loss: 1.3110... Generator Loss: 0.9667
+Epoch 1/1... Discriminator Loss: 1.4147... Generator Loss: 0.9200
+Epoch 1/1... Discriminator Loss: 1.3674... Generator Loss: 0.6883
+Epoch 1/1... Discriminator Loss: 1.2852... Generator Loss: 1.0330
+Epoch 1/1... Discriminator Loss: 1.2894... Generator Loss: 0.9344
+Epoch 1/1... Discriminator Loss: 1.1895... Generator Loss: 0.8798
+Epoch 1/1... Discriminator Loss: 1.4130... Generator Loss: 0.7914
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVnMrWl23/V73vnde797/ObvO0OdU3NPbrfb7W4nbiuW
+lVxhbhIIEiQCKdwggYQQEeKKq1wAEQgJFEEQSMZRRBIyEBKZYAc7cdvVg3uoU9VVp+rM55u/Pe93
+fh8u1tpfdQGhq9wuu5D2ujn77O/dz/uM61nDf61lrLVsaEMb2tCanD/uDmxoQxv6dNGGKWxoQxv6
+EG2YwoY2tKEP0YYpbGhDG/oQbZjChja0oQ/RhilsaEMb+hB9YkzBGPNnjDE/NMbcN8b85U/qPRva
+0Ib+cMl8EjgFY4wLvAP8MvAUeAP489bae3/oL9vQhjb0h0qflKTws8B9a+371toC+BvAr3xC79rQ
+hjb0h0jeJ9TuIfDkR/7/FPjKv+jhOAptN2mDhRoXAMc0mEakGON4NMjnoiopy+pDvzeOg7UNALZp
+WAs/FgtGPnuOwXOlbS/wAfBdl9CTKbC1xZp1gxZbS3t1WVNWBQCV9ievEupC2+3mQAlAs6qp61o+
+2/qDDl4LYwZX+1DXzfXXtrGYNXted8KAozzb9cBx5XNT1TIuHasx8nzgyZgcoDKNtmEwdt2cg/Hc
+6w45Ol+u/t5xnet+eq57/X1DA82PzGcj31dU2geDdg1rDbU+7Douvr7P4FwPy2rfmtpidHye7+Fr
+/8P+CBp55nRySbZYANDy9SWNwdf1C/0I15GGjQWz/iwd088fzOd6fI2tqXV98zKnLHQs1uIGkbSd
+hDoXPq7205YVeSPPNk1D0ukC4Ie6r1wPx+gcG5cfWfhrskCjG3S9jtJfc/2dY8z18+v+2x953v7I
+3z548oO/1BYW+Urm8GpKnS4ByKbzC2vt9v+jU/83+qSYwo8lY8xfAv4SQNJu8ed/5ZdpSoeZ2wMg
+9uYEmRw2P9widWURn1485dnxFfAjhyZq0RQyCWmxpCrl2YoGx5PPW+2QUbcjn28eArDf6XJ3NJDf
+zUpqXxc0KKimKQDjZ2OOr54CcLWUDXH/8k8yfyrPbv+pd6m8MwBW37piOpkBkFczaj1AKKMwJqCX
+9AFYTJaUuohZXhLGuulLPUiuIXYTALqDhvagLb+7HNPohs6KFZ4ephe2ZK2jBi48mQvHjwl0Lnxi
+3L0hAE2V0bUZAC39fbsTY3Rjbnc7dF05FKlNsTk6nwVNKlvmspE1qFYO3Y4yutJh0sjD/bjH7kj6
+7zsRtS9tZ478PZ9X+E0MwOCwz+72PgAv/8q/Sb2STfxf/N3/nnd++xsAfG63JWPKfHZv6LN7L9Fd
+H97SELbkHW7jQilthDaQzntgK5mLVTllOpE5un/8gNPnMpa8yBkevQTA3V+8C8Cwd0TXkXfnJxc8
+Wl1IG+mSr//8LwGwf2ck42iPaMWyfx23jVUGKReWMlMLWSM3SlnLfnKxYPVyshWBI58NDq4j61Nj
+qaw8X2i7IT6+u2YL9poRTjPLP3v/9wH4q3/z73P5vW8C8PY/+CeP+Aj0STGFZ8CNH/n/kX53Tdba
+vwb8NYDtrZHNSp+ivYRnMvByu0PpyudW/ZTzM1n8qpiSz8fSRltvuaspblcYSJbX1FYOdLN0cFrS
+xnHqcn4hXTjM5gDUX7lB4slBGW5VFAs5mCbzyV05yLPtKf1L2dzFSDbav/T1c975W9KH47MJaf89
+AG7Fn+VYzi7LCw830D6XctsFQZejwW0Azgc55fIxAPOLOUFXliLXE7jttKitbNxOMsCN5BD7SUyK
+bKr6whAqI4sjmYu5ragv5e9uUlP6ukkHkGQ6+b0BppgCsBcrw/J7uMp4kyik1xaG1VskVD29rUqf
+PJDfeZkw00Vo6Lb04DHHTKU/rutS6o3eC2CZyefAKtNzXPyBHLBgOqK6LeuUTZe8a54DcPrttyim
+so/9ra8BcDRyca0yiKLC1NIP31zCQtq2viEwwnAiZZC29KgDGUeLFnYkk7E9G2Fvylgvpw1FKnvj
+6vhc2nX6tCJp96pzj73nRwA8fKEhHMtlEHqyP2LPw3EjnYsPbnFjHNbilothUcuYylzaHYUunrMH
+QGMKfD2Wxv1ADrC2xFOmHaBc2gQ4Rva9a3wcI78btQx3bspGPGrHTHZ+rHDwIfqkmMIbwEvGmBcQ
+ZvCvAv/av7ATjsOgE/FgMiOIZZCL0wWlctSzYkyeyQG7qpZkyjHNXBbTayxFqVKFaWCuTKFxmc7k
+c72or2/YMJBh998LMJ5MWOq22OrJgl6UDlzIJm7mMHhdDlb5WNr6G//pGzjLtag2ZxTdAuDo9Rb7
+r2wBMD2viHRb9AJZ8EGvIbor47j9nQP+q98TDetzdYeHJ8JwDl3pT2EcBtrP4sow3ZHPSQ/OWvLu
+RephHBVdQznc/rRglaroXNYcviR9n2QFHRGUaC4aHF/G6rZkjntNjW9kI+2FXVrIWAc7bWpXpBuD
+w4UnjHO7L+8tq4q2L787r085GOnnaY5ZyrO1MexF8vKxlXn1Og6T8hKAYGtK+UwP3tdK/tFvvCVt
+3H9MZ1v6ebcna+4kfbZVAmk7Lk4mh7ewUwaxMIgqD7BGmUEs4+gmXZpa2srrkraK+b27+1ym0t77
+Z2e8eXwMQOeBqAan5buEzW0Z/3aLw69KG4vHMyIZCgTCHNzgda71VSywViFd1kyhbnLenf5vAETv
+yVyZF4eMgp+WJ0NwVDrAljSVqqbNHFvK83UgTMFpSupA9oLvbmNU8nSN5ZXODgD/wZ/7Jf7Lbwsj
+f/Ov85HoE2EK1trKGPPvAP8YmZG/bq1985N414Y2tKE/XPrEbArW2n8I/MOP8qxxavxoSrIoWe4J
+l8yuTvEDuWmWRclCJQHX8whdvUEdtSPkFS29lW7sBng35O/TecDxWD5n3orlQjjs+iZ1G8PhC9qH
+2sdTMfDOScGkLzfM4tEI51B0xiCeAHBV9HHbohJ4y9f5ma/J35M7W5iWiJe325d0kfZeuiU3bZD4
+xG3h4O8XK/7s/Ka0Fy9J3pCb6eYXRKzNLl2Kz4j0sPp+hv/ZdwB4dp6TLfQGKlMKNQJeqX1lQUWt
+hqw0bCgjmbcoE4MtQNnOOFLbhjEyTjcMGYXSxrBl8Uq52fvb26C2FHfQwgmP9HcqjSxTzEh+508P
+MZGI7U36gMLKu1n1KHa07ULe58Uu6VikmHzYJ5+JOnY1fpcn35Ob9NJO+dLWHQB+6uufB+D8foVr
+VFJgibc2QE9bzFX18ldj2m250ZuJ9CcLAVU7vcCjTGW+vbBmuyXPxgfbNI5IctO53K67zyI6nz8B
+YHZVk35O3h2V79G/8xCAQfmnZJyOw1o6ELvm2rBrsKryNdWC3qW8440HPwBgq/1lmpvS96Aa0Lgf
+SAJrW4NXJxSzua6DturF+KomWSoMvq4NBI708/b+bf7tz/9ZAP5H/iofhf7YDI0fotrAMqAqAnI1
++iyKEjuXiZymNbUvG8yxEW0VYa0vG9ePZrygYvm/8jOv0/Nk1h7H8Hh8H4CTN2Z86x1ZaKejasRh
+TQvZ5AM/ZKWHyT/qEj6XxS2Hu5i2bOQ4kUPsDH8Hz8pGemlU4G7L37+432XUERtFEh+x1ZWFCdUq
+3pQrfDXKJS+E5C+IwayxKdGflcVd64iZdSgbYXqTr015X/vjZ29Sn+uhNzW56p31pfyu4/lUHdlU
+ad4QzOR31SpkZeY6jphYRdCeGtE6UU13S8a0NYhpVdK3yF3h7wsDwYloIfNcu3LA6tGAfCr9bOqG
+vBbG2W9vc5kJ4/SLkEANjG4i7wto2GoJMy3tBdZKu7/12494+Jti++nZJV/6ovRj3Z+4nuOkonZ4
+bZ9K7S7FvCBU42IZ9JjVasfJVO2YzDFqxG68Bk89IwEzGr+vY/L4nDLL9zxRI/IGiofyvn7ngGIh
+9oDd7mt4p7IOjhpBaeag82kBrj1QhkbX8vHqXd5+JMbonS31grUGFLky3nyMq5cdYZdGDb40hqCv
+3gq3q82u9RfAlsINpMVrA2Uv6vHazYaPQxuY84Y2tKEP0adCUqiqhvOzjEXrlNWVcLV0tcBz5Qa2
+1hDUYuGOgoawK1x1aeXm3woNr9+QW/nLX+7jdcTY92rV4tSKiyn/4kNuvSVegkdX0u6BN2TvrjhJ
+IhrquXBwZ2lZRsKNgxfP8Ge7AGS7IkZSlmS13LoXuxWfceXv7Rsd4oXcoFvtGC8VVcAdra3THeza
++Bi3KNX3busBfiA3/dqf75UlVSHLE7b7ZKn8/dn2Dg/fl9vYGINNZSyl3vIr32IrxXo0lqxQbEV3
+ginaOtYP3Fd1c6nP9ml7Iq249CEUAx5mi0bVNUoP60713Sq2OoZGgQpufIFfi6QU+gsShZNk7RSj
+Rr7GyrOF61Nsi/eBaQIqubz9zXtkU3EBh3sD+v2XAfBbcgM37j2CUOazXuSkhYy18iY0pd689ZKy
+0WdckTzdZR/riCThpjGBlbX2g4C61pvZj5h3ZF3DRo2kdcxsqe7ZnUsOTqU/2TYUKpE2nnrBCDDX
+0oHH+s5tGsvlpah/733vu1w+k8+vv/gqALOnM9y+9K0IZkSqghTF+7QHr8l8Ow6eeoTqWg3s8Yha
+jfGOyTGmp2vjX7/bMS6+q5LMR6RPB1OwBeP6KbNVQaqgi2WxorMG1kSWsC2bcDhqEam46rwkB/cg
+S/iy6nqDukcTq+g0+jp7nrpvFl9l5+7vAXDvHdkoT8pz/Lbs3BCXmx3RX6eTK1ZLeYdv2vi6u52n
+sgDWvcLUMunJsuJuKAyrO89o1C1UrCocR4E6widYVilBVw6mmwwxjWzoxk2JPBUTVTVyZxlpvVYZ
+LHuJ/O6wv8X7wQMAHqbV9eHOLqW/eWJw1PVWlA5NoiJu4TNvZMObSYFti+jetKW/deRQ5dJW4S9o
+fPneicFXJmNsRe2q+1HFWocGP1Z8QNihUnuN73gEjTw7z1eUjYwlCGUOi9ywyPVzvYSFML2Ly0ty
+9RIlRcMwlzYWz07l77ZiJ1PvkrVcZMIg7XxBS/vc8m9c4yGseqpwM2whfbbODE9dlT3aBIkcJs/m
+eIqzqF3Zb2Mycp3DZpax9MVjFD13aI7EY1AqqM3Dv2b68r2oMGVleaaYhAcTMOqJmb4n4wh2ah7e
+/6cAXE5OeG1H/t576RVW6Q+l+/Ep3cmBvOdI9mmTNeSOzGHo9IjitdvzA1yEMQHBGn/zEWmjPmxo
+Qxv6EH0qJAXqBmYriuWc1VJE7jQvMb7e+MMWPb2Zdv2CQn2zzoXcPuHOmJu5GAz9HYvjKI7BHNNy
+PifvGLjcKQWBZg/Eur31OKWXKPgnH5IpAnEYtjG7emPcb1G5ItqhcNimDqARLu/YFD+X/gSdhnCp
+HLqswZHbvUFFbm9AlqrKUEwwCjiyRUydyFhcucyxUQapvC+vmmuj1daoRe+W3CSH5wkXmbSRG/n7
+fOajyG2cDLxCRdFVQ603WhM1LPR5UpVy/IbGkzHlgYPny/d13cBU+9/2qEv5vnHlpvUAX8FL3iwm
+L0Qsd2uLpx1p2xZp8YHRTf5ZshCBjcxd4jey7t7YI1Dsxd7uLvNQb1O1yPuFS67ic01OoMjMyEtw
+KpWQ3AtQQ1ucyr4pjcWoyuAlhrgQ6SAILabU79subVVB2q7c8jERRSRjffKeywJReSarlKj/p6UN
+elyTekAsHnUli3n//Jz3vvu/yrBPHW6ph2oWiko1ef6A8bmoa73SpejI98HZY8LhQPuzg6vGUZuJ
+8dxtvUyrWcPmJziqVuEKKFrIwbEfSC8fhT4VTKGxDctsznKZU2YaZ5DnWNVl+1XITYWzLsoFK9Vr
+w7EM9qjrsFSMfDZ+BrFAVKM+sBYjibClLO5BR7wI4eESUvUylBmtWBGIoUdHreEDU5ErwCcayALV
+VU7s69+jiHxt7T+pCbflwDrFEhvqoVYEXlPm16g6TIjNVH1wMqwygEDdWNW0plK4b55lLFFmWUwY
+qIeiNfTxJjJHTS7fxWXOKpcx1YWlruV3TmMIHfk+8hrIFI48FBWs23ZxVe1yjMVRG45xCvDUHlBb
+Kl0f21JvkB/jqirlliVRV8e3dGj0oNe1oQoVt29knD3XJwvl0CwiqObqetuvaOmB7e8llIlwDv9U
+DlJjLjCqlhV1du2Sa0xDGKrqEvTRR2gUYRg4JfjybNhYrFmrLjVG3ZDRwpI0Mgc2FoZ+mWeUCobr
+Rg2rsaAt7dgjqHRdNVbDqdPrs1jakulEDvrZyTd5/wei5myNEno9GUv3XFSiMu/g7eQ6xw6dlsaE
+OD18VfMq6+E72ngubm3aKdYRe1ZgVthG1sS4JR8+2h8vEnqjPmxoQxv6EH0qJAXjQthzKXsu9rlw
+3yK117dKXWSclmuxLSeYCZd/Giqg6SJg55lw5XbYoxx+VxpuasKWQFDxt8lcsbSrLZM88wjUuLa0
+Z8SV3PKt2qVeKsx5qyC5ktt0pYAl4xiCSI1a+21sS6HGQw+vUrXC+BQqmaytwjZ2sOsApchijPjE
+M8aUKw1M8DSuoYnIUzUeTmYsI7kxz55mXKg4GJuGdiK3X6XApJVfUFyJOGzrBg3qo+k2xKoqOK5P
+Ww2MtpEbzK8SfK/SviWYQIPOmiGNGj/r3Cev5HZzlgqT9jLQKEETLNGQETynwnPW0ahL2v46slMN
+m8ZSDmSs8anPRMFQXt7QVTDYwWdf4yKTBncH8o5dz6GY6a06Cwg6sl/a4Qxf19X4DaViEqyqDIET
+EaoB08YxtaqE1hrqStZ16XfwIt1zqWJWspKrc/WIbV/SeU/hz4mlyPRGr9b7IqApxaqc1yWPNdbm
+4Q/vcfFE8A1J/BnuR/J5W+1/C8Z0LqW/rSOPXGNlLmclnoKewv0DEoWhl46chfZiF9MSw2fLaeFY
+kbxsHVxHjFrbUCvw76PSRlLY0IY29CH6VEgKfuSw/WrI+1cOzoEi7L6dUU6FW1fzirPncjuUScG7
+F8rlNVnAxCkYPxVu/tu9C/xAWPAXfzal3xH3HYfxNeJru3tb3mESLtaGo8niOvzYD3aYq6vHpC7u
+lgZbTTVXggNWkYRD2yJU3rq6nJGP5KbpFi5lIc8MdsV3n1UpcSvRvkc0anSbFivaLQ2vdERamY+f
+M1UEYhJYnJnovds7Pidj6Vu2XdOeK4KwJ1JHfnrM+Tq/QQWtrkgStetQqoE2psTXfBBGXZqzoiCY
+SFstNyBd516oKxrFW9iyIdMdE2iwluM6OCoRNb5PoMZhayrcQqHXpU82kdvPqn0mjPv4hTzbeS2+
+tg9tObcZJhrt53a4GMvvtrfEjjRrLEGkmJVVg6/Gunxyid+WPoeBi9EApNVC3aUth7Ate8v4IUvF
+S7hVjQoQVEvDxVjmIIr0Wa+Hs6d+/rome0FtVGczJqdy47uIZGN6DbmR/n7r7Clv/LN/BsDbv/Y+
+S0WAns5/wFUt427pzf9iErGnEPqtcZfeQP6elivOM3nH0+VvcScRW0SyL3YEOj/AdN4G4LN7X+X2
+Z8WQ7vrHuK5gdWoiyvpHcnt8BPpUMIWihOfPLC0vYKEJVKrLEke9AeemJNNEJ8GiJtAF9TVXQs8x
+VGrdno0NVjOg/Oqv3SNdG/Bahr1IMe56SO/uddl6RQ5pPx9yvCPPtpOCruIiJqeGZlfDua9kwZ2m
+odJcDzZ0WJ+UBSX5ufTjyfGYtJR+7FyJyF3Mt+jdlt/d6m3RPhDV5mA+IgtURJ0Kwzu9mHHyVET4
+1dk541IOZhEsuJioOOhGxJWGde9qLMK0g6fxDKWTkcTCZKxtqNZYgbQhW1v71ZvgT1JmanF38pJ2
+VyI77WhBW8MBvZ5Pp5YDkKunoslW17gQUp/Sk77l0/I6Mc4qzZlWGqq8VBUtmHKYy3rYKCRP5dB8
+5bNf4OZA+hnHKx7+nwJkqjqyF7ypx6On0k/mz4lb7wMQLuGyLYfsYGhxlPm0QsV/ZBWTQtTHSVUz
+ORNG9vziDGcpcx71DZ7R/t3QWBRvi131gLTtAauuvPv0/gl2Lr8z23IAq9xwsRBcwTtvPOC7/+B3
+APjW+xOaUtZyee8ptlLGot6Z79cOR9uy3/Y8H1vJ54U552Qs8xnYit/SuJI9jRxtfMOBeq2+e+MR
+/8ZfFMZ58OIX8WNVY1YHpM3HUwg26sOGNrShD9GnQlLwjcd2vEP1qKbliMHwWcunUohu0glo1AhW
+lBV7iXDKfcUY7Ax8fPX5Z4RM9SI5qWZYva2vZpZIM+9McrkxTF4QqBpwMOgwHMj3JAlBpe9ulxh1
+AaL5Fuo6x6ohZ5EvuDLCoS8fzng0FpF/OTklncnvDpSz70XbvJKIO8kedohLueUzp4RMA2YmDwH4
+/bfv89ZjuSUlAEzdr4OEn000g1KUc+9UVZdqHUjlUXa1D1c1iapSXpngeOrixDJfKjxWXaTdXsWl
+Sjz5csJ+qQk92h3cngYjRR7FXPEbqc7reM7VQlSw6STDV+mgsh16A5FCgtJjoOnbwq7cbC2vodtT
+l17kUrbE0Pb1l48IMpEqnv7w93j1y3Jjbw1vA/DmgydkjgZatXySkbSRFTm9RMdnHdoa5ZmpCzVq
+uZwsZY4enzznyakYICvTMPREwmhvOTgLxZwUMs4Lc8lIVbedfkKp7ufl4AZzNVJ3FiIJxp5HrgFo
+tuXhx7JO24MrxqciTVWuJdSMU2sDYOFUPL2UPTRxDUlL1So3IhnK+t52hyw1OvJA8S+Fk2Pn0sbp
+6RmP3hIDe7+/Q29L4PsmSMnSjxcQ9algCtQWZ5rTOk05V1iyY0t2NHPPRb1kcan+9pZ7DV666KsY
+lTdUgUzUV/b6dF+XCWk/eMpUvQFxWvD2c5n4QCPSplXNqCsL19pziHc1lNepyeeycEW/JEnXFn5p
+S/IdSh863TZ1VyPjnBbJTGMJnIC6rdGYsYbvZnMWlzKm6cNTkpcVXtvJMOrt8Go5jLd2B9ep0hZe
+xpVyunkdstKNENPmqpRovoXq1s+ilOZC7SFOTR3rQY9X9DTrUQ+Dr5iFQpPa2NRwoQ6Qoi6ZxzKO
+vWcVfiBz5HsB1lX/vtpXxqtjTs9VFJ8scTRewx3AViQ2mrAa03Jkc1eZpsoLIqyGU5un/nWexKIM
+ie7I3LbMK+wpLmg7En36av+YR5qmrrrMMEaxJdEMt5R3V1c51basiavZu6zXwlPGG4QttkQLYica
+kATCvOJOnywTa76nzLRYljxW3EcQ32P/RDEG+23GZ+oF0Euq8XoUyoSK8QnPNWWdU/m01UaBreio
+GpsogCw3Szq6NkXccLgr/Xnl8AbPKtkD79x7F1dzl4VtNQjVFcdqHNp55PKDhTzwwmRFeCB9q5sB
+pd1ESW5oQxv6CehTISmUZcXJ8Zjl1OdiLCJg1XFZaYqxeVOQKShrt9Xm1V2Nw28pmq9vrjM4v/y5
+L3PrlgSOvPziq5zV0t53vvsW3x5/BwBnpZ6DUYgt1Bo+7BDvy+8qM2KsCToXzYK4Ldw61/yRgRfg
+RcJP41ZEM1XRvZmTqnhcjR0U9MjTY7mhLm1O566I4oNVhftE1IPBdgcTifegrQlMh2aAHUof3vrN
+b3DvkYioNq/4horor790RLcvfSsXMk5neU5ai7RlcCi3ZHynl5Y6lNuq71ryc3l+S70MY+NyrMlb
+rgg5UzXo1RsxK81XeCsr2NrSlGCuvtfpkmmM1MxUXK1ELJ+MJ3R8iSpNgpqXXhSL+a4iBsMqpVnK
+D4sgI9fcGWmxYHUp63Nnv33tSVqjNE8dl0fvyI1YzU+5txTI76LI+MKhGPy2Gp+djqavC0T9yFYV
+K73FT06nPLyS8f9w8RhHg7z6Q5+tjkh1W75IR+1oyFJVrfFkRXQgt3+QNbz1jgTYRSgqtFMw6Knh
+1rEYzQU6CByOEonWfXt8RqjGXXS+/V6HPZ3P1vaSV/ZEwrr7pRc4/9uSiOX+akmgwWFf+4Wfkz6c
+nbH7kkgBo8kho5sixXj7I2a5GmA7X6UO/n8YJdlUhtWlz6S9Yj5VyKgpr5NIGM8hVt13P+jx2pdk
+EZqJiGGPmdA7l6F85vNdOpodqDWYcVDKyey2HfxIDtN0KaLxS/FNfuZPy2bdSV4i1MWIVh6djqgK
+8+8f0OzJu1e1uDfjbostVW2GB/3rhBZHcZtqLu+bBxe0Y7EfVAqpTZweL7ygsNRFQStWfb8KyXI9
+yDPZrEMnwgaiF7/+U68zTCQD9bI8Y6DZeH72tVu868kzJz+UzTPJLflKw2bdmvfuyVgbE9A60MzW
+qWF3X7wLLYUJt5OcvkZ7xltt/IWK3z1D+0LjDkYZZar6rIbshp5DVzH3txLo6qFqrgxuIr8LK4+j
+RA+OismtELKZMNkrd0W5jmFwU+xz1fdfPqK71OzYVvM5Tgr6Q40iTFq4GhNh5w4DtTs51sA65sNo
+9ugsoFFRvGt8tj2N7dgd0KplPuPYEmqKd28dBdtYcg1Iyc4r7PY6/mVOdyp903wuVKsLpiu5AB6c
+vMPsufT5vB4wm4unKe9AqbaypUZt9uKC0UhdssmAz/+0qL/BYUjwmmaZmoOrsPlKbScvtw3hF27L
+vGXgl+oR8mZ4CxnrU+8R8UqT5HxE2qgPG9rQhj5EnwpJwQQOwVFMJ61IVU+YLmp6PenebhwTaVDO
+nYOY/baI2Gi6s+DK0tnWhCTxEW4pt0PXg0CjzLZe+BovvCwi3PlExPZ+N+P2zV8GkNveXQfDNLgz
+FfGGVywVJBUHcvN3jGEvkXc3p3OGA7GG9ZKYVldUm3hnSLurUW253rqmQ9zX/P+RS6Nw5dRalipq
+VupFGA636Gdyu77YbpO+KjffKkvpa3z8YLiL+1wCdJ4tNPfjt56QqxHR5hCmGkU39hkpHsnthARL
+6dOtQ/ldf9dQqM877A7wPXl31WlYdQRY47gVZakGQ4UJR0GLF440b0KU4PtqXH3VwzVrMFHMWoIt
+1dhnMkg1ijCIAvxA5nB+OqZYyvtWWwU3RpqefKrBWk3Ijb7MfRLMCVVysZFLFGg0qxOASohuLVJF
+6E2vC+beNa5SAAAgAElEQVS0/V1u9HWsXkNnVz04Gbh2HQUqTdWhuYaH50HBqlBYeBmTGlXZtjXv
+RxwwOxVpcjWdM1djbJWd4GlgVkyPbZVGTCLSw26nT0ujgG9vX3Lnpqgu8Z27fO7zokJ+50HE8TOZ
+u++rl6EaDaj+uYgp/e4Of/JPiPrUCXbwr2Q+R9Qsyo+XT+FTwRTAYm1N0eToulE5DZVCzYY7Q75w
+QxZ3f3fE0aHo/pnWdDjPHToq83i5oV7nK/HaBKEi2uIhR7clFftOLRvN0CZoyQIY61BpJifXujTq
+0jI7fcLHWlhE1Y/K69AMZGHHaUHck++PQp/QFzE4KcC3IqIH6kqypUejrryi8Wg0zXrpGK7Gmk1K
+0YNbrk9noDEOZZeqJf1pqhxfkY5V5nGpyVYnjeYnjHvYsWykxtaodI2TZpRaF2HqTekocyoUxNR2
++0RDOWCODT+I8DxbXUcBGuNTas0BU4XX37WVQXZMARq27uBeV29y/QZTaRYiXadF47HU2JVg6eNr
+DMO7E4/ddfr45pCJ9rlS912y7ZM/k3fnU0unqzEKYYbmkMFECX5HmXaxzhPZIde5arkujsbB+Bgq
+vYh8QppARfB1aQXj06yrYrVWeGeyZlm/ZKTJV4a3ZYGDaEYzlCN1Y7/Ndk8Y6/liQq3z1skqmlD2
+2UATwLSLU4axqpihoT2Qi8UfDnlNq6d8+XbM7+hYbimadHt3yTsH0sZ42uIXbkjylcA5oNAo1tX4
+ORcnknHso9JGfdjQhjb0IfpUSArWWsoqpcxLagWEtDz3unzWqDKEU03/FSekU+GOqeL+0wdTzA25
+/Z/df0B7KLfE1tbLmB3h1o5tcENpL+AVYC0iqqhdNTi1iFw1Oalaj9LMXmeS9tSfTWTRgDzywifV
+Wywdz6mHctMcDW7Q3lKI8UreMZnNqdap0iyUegPXWc5Kx322kBtj++KU9pHc3K3tLdxEpI7ydMFS
+pY26X7J9Ib87eE0MmC9/54x//lRdMbVzXdsxtAn1O3IHtF/ZwtW8gvWuelZyB0/7VuUZ+UpzFqwa
+rMKcbeBRFJpQJVRrudOg+VbwWj5OJX93Q49G8SBpmeOqWlRpMZjCbZhcKIakFeKXGuex7TIJZC13
+A4eVSr6XK4HtTt2UbqJqQBQRa3Rlnjl4ia5P5koNTLguIhTEDS1NHGMt4CvWoSiuPVee17DSAkOp
+r5LXwqOK1UuStjltyb4oTku6HZWKcvFItG6+yme6sofaf+YWzqV0/hu/+japFqfZLa3kswDOFCqf
+uhazNrT2O6DgpqB9k719ERVuHD3iVi0Gwzc8mYunZxkrjdCNVwGDQNq4yN6ikTSQLL4bc7UtkvVH
+pT+wpGCMuWGM+Q1jzD1jzJvGmH9Xvx8aY37dGPOu/jv4g75jQxva0B89/SSSQgX8+9babxtjEuBb
+xphfB/4i8E+stX/FGPOXgb8M/If/Xw3ZuqGY5FhbYTQzcFWbDzIIuQ7HGu02f3pJo9mUVlra7OGz
+Oc1COW3kcOCIbmV8D/u+cPZw1+Jp8kxCLYRSj3HbYlMo5xmpFgAJ0xRH0WbOsc9MC6qkS41XL33y
+S2nrLJ3SU8yCu5XRzjXacVZj1rURXOlbHHWZaNSfCTxorfXsgEtFQs5PxM217BuaXKQca32cM4Vm
+e1M8zeVQlRFniqG4+rb08f6sorouyl1TaybiegSFBvv4XYdGMQu1JlctcxdqzcNgIowq/8YDVzP6
+uLVHW3MnZPpdnec0mm3JVv515Go+ma7rmFBlNQvN9LSuiJZmBagNoEngfKy2mM+12VWjm0ldnj0R
+w53ny6CSq9Y1PNjPl9RaT5TUpVSpwS2gUvek62optcZg1H7SeB6OJn5wqhRFv5N7gSSRhevEvUEd
+kWuihnJR0Wgw0/l0xmf2NFL0uaa5e/WUQOuI7Ps97twVI+73bh+zeiq/m3Ybkq7sv+OZYE+qeYVt
+S9+6b/e48aJERtbf/8fMNb0fYcW9iUZaqvv6eV7z5F3ZF+4dl8+8IWjM115ueHwu++yHTyNebf8R
+GRqttcfAsX6eG2PeQkrQ/wrwi/rY/wD8Jj+GKeCA26npOBFdtTQWV801cMUpLUZ30Mw4PL2SRehq
+FuFRLyJs6+Yu4GomE2yKJ3j7MpGhf4hzKTUKfU3J7g4DNKkvzazEOmINLh0HR62VQbiio5WqzEBE
+ufHs75ON5X2dQcxqXbGnPqSq1OIcTHA1s7OjFY2qKqOn0XDjtEZr2XD6NCVWw9BEw4Yni4pioSJw
+eI6jno+mcCi0tLhpe+RvCSM7LtQPfj7D0YPpWZ+uFioJ8gE7Lyi38BxKhX+j6dpK6+AiDM3zDL4m
+Q/EjqDT0NgwtVlOquwr5biIPd12evWhA4d/W9bDrjCt1g1G8QKMGSs+r6LY1eUu1ZKzAoqPOiECN
+ruXiVJgHEKtaslqVxOtqS75/nSQnLyyepsn3g4BQU+s5qvK1g/y6MliRz1hMtHxAk+MGsibkBaTr
+2o3y76Vp8PW7lgko1AAbBR0mJ3Lwmp/WJDPTHZyOpvsfDblxW95x6ysPWGhyms/strDrwi+qul41
+BW89Vtj4MuPeqazlK3fGjG8rcOq7lxxfyGZ9X+uOzlKLYr5IpparFwXy7r/4Cq8uZEx3f9rB1Srd
+H5X+UGwKxpjbwBeB3wV2lWEAnAC7/4LfXJeiD4OPx8k2tKENfXL0EzMFY0wH+FvAv2etnZnr0lVg
+rbXGmP/XrJE/Wop+sBXb4WstLgY9bqt8mf/6fRpta2ZdBqGK5dWS1aWWklfxLYp8zDqLbp4yVfhw
+98YWqZZG9+OaSi1iY5U07FlG5EsS17q6JNZy8B03pLKK3BvF+HM1vfiKDixcUiu3xNQ6bB+IYWx0
+MMJTuHEchdQKIW7UdGNdh3odMLNKma6kH0+mJ4wngjcYqF+93x9Quqq2NAEmFYmgaBrcWm6gyarP
+g56qUL8hEtEkzWk0a7P1GpxE2hh3JvRHYgTr5CscNbQdX8nv2sbBUYknXzj4qto4aU2pGIrSTah9
+gTzXGsBkvA6huktrd8lyoleXY3BbIiY3dXnt+F/XQlgYQ7al7zhdsVKUX3ZW4x9oOrJxj3Qp82IU
+S23qlEbVyrTOKFTVWBXntDQYSxLgKHagUndyEl8nsXUbi6e+2qKy1JlKXq2YhaqkK1VHK99eZ48+
+c0+IHqsxc6dDoTiT8UrmYjTPqZaCLG2aL1JoBu5Os2CvJ5O7u9fjTLOQ3xnIftxvtelrRuy9jktv
+qQhSs2TwVMYX+z28W/K+paIf33sr40L3k1NbRu3PSnvOPmkgMOfV7W2q+WM+Dv1ETMFIKZq/Bfyq
+tfZv69enxph9a+2xMWYfOPtx7fiJw8EvtXnn/oLRq68D8MrScPJD2RAUBadLsfDaMKBQ6Gt0LAxk
+uwqZaCbf5ZMZxaF87mY7JAOx2ldOw1KTVxSagGOy6LDSw+jblDu+1k8cDkkr1e2LKaj4nC9UZHZL
+rDKslgnYV9XFyy2XoaYLP9vB35EDWSrEtTYNnkb4uXEHx+hGLxLutAXPPujImCKnzUxLtdcTj6nq
+1FymNEPd8C1LW8XxRH3l5ckzxhppGfsx/a/KOx59o6TvyzgOOrdpcvXcTGVMzy6vOLRiX/H8klLx
++YEr+SYBGs8h1y0ThDKXUadNo/aHvHIp3XVEaHWNETBuiafZhjJl+sa3TOZajambkKt1/v2r5wSa
+ccupx8SFHM7jqYjfi7NzRnrgg5aPXa8NXKtuzWJGqVGl6yJC1liMeie8osRvrUOrxywVEl3VFad6
+yCYKV+7EEQv11Dy8LBioitI9rjnRHKLvf0v6GFR9hi+/CMD04jGrQmI0Xrr5GV5V/MODZxNcBT29
+cCRZxztuxM0tqXQ8vAXxSi6AXnuHC0/253Q15yjU4keBXHr/U3Wf5wqmGiYtfu6z4mU4n76B80T6
+dPTsZdJflKpWH5V+Eu+DAf474C1r7X/+I3/6e8Bf0M9/Afi7f9B3bGhDG/qjp59EUvh54F8Hvm+M
++X397j8C/grwN40x/xbwCPhzP66h+azg//jfHzMr2py8LJyxfzHlYE9RgyfNdU7Awqk5Q4OG5iLi
+7yQwUHE37q642xODYHWzS+3K7WdHK+y5GN2qWC3S5xfEegP7C67r8rnLC5KBSgXHLiuFW59eqChb
+G1y9PZPtHmUgYnLnIIYrUSWqzFBpXoNaJZM67bFGnHbaQ7pbWlfSJHS3pG+LJ4JGdFoFU/W4zOox
+/bZIGE5nTNKVG8Hu3ODYytS7moLN9lzQlGduO+L3/pFY7xc2QmOfGL53xZ3Pido0P8v07wVj9aiE
+w4DWOlmKZ2hSrVbduSJ0BErrbilicJHR+Jo/M0uoNdlN5Lik6lExhYsWqyZU1aaolnSWmq5tWIFW
+j56Xx9iJSkulIc1FvfE1JVrsVFit1dG0yms1x8/A0/wVbreNryXk2o7sIVxDnov04AbuOl6K3DTU
+jhp5i5LFXOZjqanrho2hnGtehJXB09Rzp+M5XxppOftz+e7x0+foKzAHFWapyMRWRbIre/Xmbs17
+J/L85T3ZF+1em8ee3PhnJ1PsTOaiv18TxzL30XaLwU1ZwLL7iwC8dBry4L1vyd9f3eI73xDvQ+ac
+UqhLZfn0W/zCSx8Pp/CTeB9+G36kcN6H6Zc+VicKh9GjhOaBz9FUJurMbbN4KpsqCg3dRDZ6kddc
+nmhhFE228rm9fW6q+Hz75dtM1OL80tEQv6/uQmebhcZETFSXL/wV+6HYQbvRiqEmJPFGIa5G1EWD
+Fakn7xt1xQNg/BIvkkXO24ZVpP0cfp0mFp07tc+YZet07tL3apkSKmR2PH9Gt9b6l16bUMu2OwqT
+ncxWzLR4bOymZOoK2+0P6QyECa3iknSlOuVnJIryhYdHPOgJNr5JW3QWavV/4rF/IarNyeGI4D3Z
+eCMtDZ9Oliy6Cu6ZlVysROVpzBJfsfoHNgF1r7YiEY0XS4dabRyzfE5vJH3zfEs8lE3s1P51cRbr
+r2MSYlK11bQ6U6pUE402bY7V9eYkY4521PORSj+nzRXDNQCsqakX6wy0NSNfbCa+P8RTtaFRvb4u
+K1LdrUVR4CigrCgtVtekl0SgKfhZSt+em5xMsyl144SZpxmSGsPRHTn0h3fk4lkta1L1DPmmw2Mt
+lDuMehSRjOnVuz9Nbyhr/Ttj0fXrfEHLkb3gXViapd4cTs5Cmcntls9LL/3LAMRjsRc4nR26t2VP
+3kxc0rlcKNXtETceyZ7b2dvCjz7eMd/AnDe0oQ19iD4VMGfKBud4zsFlQP6OcNqTwYL+QsSziVcQ
+apDM7gs3cB8JBz5XPMKpgdvq887Dhv0j4eDDV79E2KgFPBxRJA8BeP/bInJtz0N8TaXW8X3oyLs9
+P6Qa69QkDr1zkUKWiZaAjzpEd+T29Lsu5xO5gZu4oKvw3+WVwWpp+9VKE3400ASaM3ACbU0S44Yp
+di6cPVVRdXo+ZzERz26wF5O4MqZk0NBoDsazVZd735PEMfMz+Z25fImFvreqn/GFUm6SOHXJZhrH
+791nZyiSxTtqye/7DiO1kC8u4XglY/Uay00NeKq6bTKVFDqBiKTdpmRVaxRodkWuQT75OMMdqFE1
+sKCQ3nWhSzfu426LtFLMcgJV3abWUt/UepSThDwTKSzW4KlBXJApfiOs4WqxzkeZEu1pSrPGsEJu
+41zFfZNZCk/addKATKHJVVNw1Jebvgh9ak1vVmgAE4VDpSrRdG+J867ss3ndEIUyt+FtNYy+V1P2
+5XPbRHQ1TdvTyzPaD0X1jP2EsXo2nn5PVL8337viUPfh7W5EoIVzoocOwbbC8O++glVD8Te/I797
+/3e/j9F5zS48Hmv90194p01LJct3zVP23lOU2EekTwVTyMuG948zXs5DvAea9GMcUQy1voGtWF5p
+Iox+iNdSpN9MFvbbb15ySyMnvcmE+FC8CIuLEjPQIdYOppLFf+lQYx96GU6gVZhKS4wcimLZ0PRk
+sudpQdjV+pa6ALs7N2l1RdydVhG/eyai4c/NZxz5Gmk4fAFby+fAyOYI+xEdzZdXxQsaBd2nF1Os
+FjmdaVxD6ZcMNb9/pxuQJmtMfocztVX8zpNv4j0SMX+umYRebs45XynykoLnZwoKKhOWx1qYdRHz
+voYn05H+XCZtDp+IGnCw26K1EGZ6cXzGZSkq0XI5h66qcSriDpMIZ6ih40e3cDR0OqeiUg9Nk9d4
+WmVqjSrMq5xIMwmdzCyxujiH7YCnJxLNerRXsFLlP9CIWacJGWjJdc+CZ/VQ1A2nGp58uL1F1ciB
+3Q57Op8FaJr5i7MTLi5lvrYHbUJ1dzemotbMUFW1BkgVDNZh8oXH9K4ctvajBakWGZ480WxNyfY1
+gIpkSFsjRrePSx5oFanv3HvOkdbocHtif7L+gueLdV5Nj7uaLWriLMm13smDxymL35dy9X/n7whT
++P1vPeWmqrFXzy/Y/wX53X33Hl+4Es/G4Js7ZD/38TIvbdSHDW1oQx+iT4WkYKyLV/R5HOa4qXD7
+Wzs+p7lwz9xWXGrqq/tnc1qN3NJOrHUbazjRClGvfO4Voqlwxrl7RTFVf3OyR6x4gpEj2IWZ9x5R
+rnBeH2qNADTuORZpI6k9pplIJPlKfOU3dn8KR1WN3Mtpngu3PluU7PfVAEmOo1DbSOMLYj/A0bL1
+YWuF0fTyXtwi13wDPc1B0Km7eFqSfVVeEWleiNXc4/RS4uN/43/5Pg/VeJipUfO79TtUaEVoA+lC
+brl3giW1QoW3epYTjUXwFH48LCpO2xrh12+RJJr6axbR1vRnN27s01ZR2lOJiA4Eitd2WhVLLaxi
+4wp7XZbdUiomo1LDX5kHpJEY7ZplTTqQ+d4ejPjFtuaLcA64UkivX8k6hm5EpR6Mc5uSKnS5EzfM
+3xF165utR1QLTauvcRlVe4hj1LNQzdC6KiTtLVwtFVBOyuv6pY4mVHCXDlFXxrodW8r5On9Dm3ZH
+MSK1SHR1khCrdBCM9nkplP373s0Wx4/EC3T8nRxvSz07W9KJlz67h6uC2wud9nXlKPtyAZpPYnnq
+8bf/6W8C8O1viZehmFZMFFg1GN3hoeb4aF19nh9oRe9l3LD9BQ04+Z/5SLSRFDa0oQ19iIxdB5P/
+MVKv07Jf/cLLeI3B1yCaqmquy2sFtmGoaLRRu8WupmHr6c023OrQ01qMcezTqH766Owhz8/kNnJo
+iNVlFY/k98PukEUm0sbx+YSTsdw0ZWz5rN4OL/ZbDL4s9orBjT8FwH/8Xz+gUuPhsv42ZycCbZ09
+uWKZ6y3tezh6+6fKeltOn+2RtFt5XGcj8uqClUbnFRoBt1itaNTXvCoKSv3swrUPPvQdXHUXruHT
+WVVc96Fu4L/5tb8jz1LxYKq4gcUpj96SSLxLLUe3t3tIfyA6aVl3aGu6su2wS1/TtEXxTWa1GMzO
+tbL1znbA7e0vyQBjF40zYjab8uBY1uH+Oz/gzTclwH9x8q6M6fKKWittG89cZ9X+T/6z/5ZKId2/
+98Y/4t69bwAweU/cd7NVSaLSWD9qUa5v9tIh0L2TNZYrNf6WhdofXEtX8yLYqrku5JKXNVWp7klr
+KRQNqvZigrbPUAOmhqFHranu8EMu03VeB9kfNw8hW4ntoJwtSDWHhKWi9tVAOUnxNOiv1xZJo9Xr
+0Nbo4ND1MRrtmdUNpxodO1nlNBrlG6pbN3Q9HLXV5Lag1DveX5WUamtZ5gXZuvzi1fJb1tqf4cfQ
+p0J9AHAai3Wba4NUEDfEuSzGaOCy5Wi0Wydhryfi6qgjm7jdbeG6aogMQnLNKTiadkhHCmpaQjxS
+v7kmwvCTmCQTsXUVrViqt+O0TJktZQFObp+SPFHx8POy0W4fpjy4J+Jg9uht8lwMcSFQaYGTcOZd
+pz4frG1PXkXka5ixs8JBk3c4KYFuTFcNVU5pmOmzXuVhNN29Czj6feAHWEfVEbWyZ26Ne6bl1GnI
+z6TPZ8MLIjVQcrNgX1Wwm8oU/NY2W3eUYdUJqZZw7zY+R4cacu3EuGpsjfSw3XnxgGgdf8AKW8uY
+Dm51yWJVq+pLwkfC9NyuvK8uI1ZGxGB3xXXyGcyKi7F4h6Zv3iO7EN+70UPVG0Qkvqxjq5eQKzN0
+m+X1YUm6Pr6mXhtrYdsOLt1dLcLSODzT+c7OSvJ1mHVWs9Dx5Ro6b7OS2hUR/typaCuk2w9L1KZK
+aMXQHNHC0fRwYbBFGQqDr2Y5lWKofOPSKGMJdf392Kelqfl6LR+nVGNnB/qapu54uSDVgkilhoM7
+OJRq7LSZc10o1/MNhRpjnTNDzccrMLtRHza0oQ19iD41koJ1gbKks87N70eMFOa80w8ZOHI79MMO
+Hb3x+7FmyA27WE21Je5u4fY72wmJq0lHCSk0A3Ev0NRmgSHVxGpnq5hI3ZDFKuVKxfLLB+fc/Bkx
+NE5+X2623/17bzJ98hCAdHFFd0dVm11YKoT6MmxoKWx4qei5oeMTaISnHTrEXeHyqV+gZSSotaJ2
+0vGINMAnjfxrtaKXeCwU/uzHhkpFw75WXK6mFTZRt+e0oNbCMKfPprysyT1qb8HoQOtDatLZXueA
+PNKM0ROfRSRG1W65R6VSyNCNSTvitl0H8Lixj6fSykWRc3sk7TpRdV0d/MmzbQ73Ze6fByJh9ao2
+GjCJN/QpFLr8w995k7e+LdWan779CL+vbj1d89p1iXqi/oVNcJ3shbRkX0vUH+wnTNWId6bBVXe2
+DtjSupNx6XKuEa/TmQNaxfqt45z5qazJewtRYXK3IVsX1zEeHcUesGy4upK9s4Zg3DkLufNFcacm
+dxqmmrk6i1fMVJXw3IorrXi9pehVGpdhT6ScgzjkYFsStbRDn1Ln9nJ1zJXWRLl/IpLpYlby7Hxt
+VG6wrLNxd5hcyTi8loOZXGfd+Uj0qWAK1lqqvMR3XKwCenrDNgcK5+16reusvUkTkCgAxi9kkU3k
+4azUExE7NIWCZkaWlpZtd7cjson6rEMV8VYltqMZec9H1NvSxteeDwnVu/Dk7RbPf0qhwt/7LQCO
+pzXN1jrLj0dyQzbjYMcnU6V6PluwmsszkeLvm25CkWluw52KTCP80rK5th904nUe9gDFATEchMQ6
+Dmc7ZqwVqaxTECh+Q7Ug0hMfLXiE55bUIzncB48qytvS4E1nm/27Es2XGFGZTsixaHj27V1ez2Vj
+LpoFR1rQ1AxaHJUSxbrQ4FfPiSm0IOoWfS419PjAtgg0YcytO7ucTEUN6BmJuXgyPmV2LAzGb/Vw
+K2nvrbd/i+cKD86dFcme7IFkKOAtr84pNc/jarJcwyzwuj1+9o4Asu585UvUC2HkMy0wfPeFXUyj
+tTu54ulE1ndRWE776jEJZrw1lH6070kfAm/GStepXja0Nb+iZxpWyux9ZUBVe5/oJdl7ptWmMxUb
+letaivG62llIonD6nW3BzURNj4Md+d1B0uHgSC6t0LrXdpB5fsTCU5zMmwJzfvtyQqEh6ZeTMa2F
+dz1vxTq5ZWyh+Xh2w436sKENbehD9KmQFLCWpioIgpBtNfsOY4/tRK2+QZuWKzdQu9fB0zyBnlp/
+jS2pvXXgk0Os/n/HlHhHWrbYi/A0Km9Ram4GJ8DXKdgKEqwWQFluuVhNfXXo9Zk/FYu7vVDEYzDH
+0ai+rX0XtWVyd9RhqfmxwqrBqkFp1BfRcK/jkWgJuiZ0WWpZd6c2OJqQJdECKttRiKeRkcnIRYUi
+6jIFRV5O2wVLzSi8eCLGrj0/JlVMQ7PICaWcI7OyQzeXG3FwZ+faF77SuarnDadanGWV9kg1+Otm
+2Gei2IKocFlqopLOupy6Z5nPZMxPlx2clqouxqOFPOMHLV5vC3pv/oIay56lzPvyDoIVy5bM0Wz8
+nKuFGDmTtkOiEtTRtqL8JhX1pRp2TcWNfZEgPr/X46tfkdycO5+9ged+QfpRqATph6h9DssNtp5r
+4JpZMGnL55v7HQ7uq2Faka6PjkuqU00yY6FY4xdcH6vRla6GgA4OXVpahKbnOLg9MeYuZpZIRZqu
+8XHUeLivwWO9bsLhlhizB3GHgeImOnGHqKX4jdJQOCLJDe1r8t77b5NoNu9vlitm2h//uKBjVTq1
+S3xfdVNNY/fj6FPBFAwQAKFn8TXJpOc61znsrD8n0BJDxoRYtc7WnkYINgFWvQ/WCXHaMjmhvwPr
+Qh65hUgPoXo1yrTAKrAojybYxbo4KBDLApx+7oLW+8JYVppT0TYpjYJGWkc+4bb0ORhF2DPpW2wb
+tjRS7+5IXU+eIenLOGZFQUvtDyd5jq/W7ibXAio9j44WSe2ZNot1RSPTQfPEkoQNxxr/MY41359Z
+4ikAzPFcTl/Uwqx7FYOOVMhaZTkDjSX5oWYmOq8rjh+Knj1KujzUfTRPYjwtNnsU9Xnmi3h9qElW
+LmdP6bqS8ee7+VssKun/59Metw9elXXyLaloI6zelTXz9/rYQlSK4rGPSeSA5FdPcDQ9u7ftYXZ1
+XdUtOn34nFw9Jnf7W/yJnxaV4bOHtzl8VV7ibt/EdTRkvljnn09Bk+zUqxVxT+0EC8NS0/n76Qkj
+9QK8oFmqTJVhNBX/8SqFQl2A3gdCdlHIHKbbPplCwvfj22S5JgI2GXEsDH67B3FLGOTWjjD3yBsy
+VDd5hxxf7WPGdEAvCcct8UrpW6SFY/Z3tznT4sWDWZtS621etBoWCuwL5t51IZqPShv1YUMb2tCH
+6FMhKWDE9x45BpbqLQgrrFqvy9ol09QNtmhwauGU1pV/vdAQtDRoxbi4kdZwDEKsvxalKzIFuhTq
+d846huVYuOuqXq6LXFNHLtNS2jtNz1hpdF2oUoB1StCbYisY0PGEKz95OuWJ3qrDTpvPK/ffV9E3
+n3vEmlp9K/I4U4CJ345Z5dJGy11HABZEKim5jsuLbblVLqZLFGmMszCo0MSFGvg8u8RqpB/Wsv+a
+9FATVXoAACAASURBVP03L8/obQluZZq+y1upqBvfvngDgPTJAVlwD4DevGF8KeN7pzjEuA8BCB5l
+0BcD3M/vfF3mavAux1qt+juX7zE5FSDP9M6MJ2MZa7f06A7l++SGJmRJczorEf0LclYTmbeiqWg0
+GKlTdWk0svPZQ+nvs9mCRHMjHu4l3NkSa3/3xQ7eQLN0x11MKVu7aWkqd+f/Yu/NYnXN0vOgZ61v
+Hv7539PZZz51aux2V0/uxI6DsR0hIBiFRBbDBaBI3CALiQsIueIiSEYCEQsJcwGCSBAlyFJMEgks
+2yQ2Vpxuu8cauqq66sxnz/ufv3lYXLzP95dbctLVKbtzgvaSSvWfvf/9Detb33qn532eMQwp39pN
+g4rSe6YxiFg9mpYh6hMqkhNk1gv24RJYtH5+ik1Hn1waKJpUU5I78e0K5g3xMGbLY2gCsuzSwqRH
+zoZgil4XNgypQK4duHwVrUjD8sQjMHaLkjKCcGqottNLFZd17G9wjfDwxwMXa9PJDGq0DJUzFKir
+fwGrDxqA3yq0DVATEaJqCzYBOz3V2/L3q2IDQ62DTjxU2RZqsvz40xB2SYINp0LLCkDjFdtqRUG8
+f5mtUJBHUJctFKgt0Hg4p8bD6pkLBDmvU9zEtnUQERyze6jggBngusAe18zgIMRr+8IA5TAMqAcO
+rC2xrQuPaJOs52F2wWwxhXRHjoLFeLLIajzOBZAV2yEalhE35xUGE/l+PWd3ZaHRkijEbg3+h1/9
++wCA2cMRPvqLvyJz9EvfRvy5NwEA669LhnxRtrCo2KTvfw/9GcVvwyH0uZQR42gPw0yqGX/3FSIa
+BwaL18mO9H6DhRLk4ru/tsTy534eAPD53h5W1+RluXsu17lnWwjZn1DmLRzqZUTDPmpu+q7b4uSC
+eh4rMmEtC2i2yd/dm2L/lU40dgrNuq4qCqiAYKmGbLR2AWVIZDLawKnlhUw3j1FQi0OrGjbJZG0y
+i/Zgoe20LBqFTjpENS0qalAWoczFQs+RkJxlCGcbElp2ib4lG8H+2EMcUxqehkVXDbyIbGCIt/57
+mxtYPf6jGaO15Z4ars0o8DCcyrHurkJEnuQwFmkJzKj3UWf4IbFLV+HD1bgaV+P7xwvhKQAGSpfo
+WzZGFFAZ7gYIyQzsuxFsUqIp7aKihe04FQENxey9KmrAla1R2S0Uuj78GFUtiZ88EzeyLhs07ORT
+FeDG4kbmqBETLHQrz6Felr3z0JL68a/+/hO47IC05yFCqhRVvoXhgVz/nZ0I455YP48MxrYVwSHr
+tHFrVH2qYWUl1rYcL2MtOqk1ihWJQjIDxYpL1isxYpVkvGNhQ7UsiwIjw6LCplNmciyoX5dr2Pz+
+M4z+L1KopQrVe+/yXon1Vy0UwQ7qGxdwicvdjQxAsBeqGYZ9Sea9Vsk1HHr38PgZn022h1u51NAf
+6gBP/97vyNy/9AX8zG3BN6jXmTg79dD3JXywhhUuSGPmlRpgn8tFUiDvdDrporu1wYCJuOHEhUVK
+Oyt00WXXlR9BddesiCzSETqTqdR12Mv3AABtu0ZBLoukauA6HVxe/r/KK9gF1auKBjn5MKAAm2Ho
+WIvXcS2zEcRMCJcxDsi7GTclHGJuelEfoSfX7BAUZgY2bFaD4CsonkMPGtT0eqs6QcPqEVt7UONj
+9u8bOyPUrK5M1DEG9HrnMGj1P4k18Y8eV57C1bgaV+P7xgvhKSgADixYA6A3IN7AM7CIXGydFWzW
+gmvbgspZZiSXvl4o2GNabmcMi6y+yhRw2DyVXhRoKHWWUbyjKTKgorW2KtgsdWlTA6S+ujzI8PmN
+1NOnPyl1cFjHILANazvBnH3zE+2iR+hqL/QAJszWxMHGscKATTCN4yNq5Nx5bwmfHs3GiGUI5hs8
+IzahShoQ7YphrrAxZPj1KrQVscLsdl1bDVTBOXQVBs/IxFykyI87leMCaGW+ukSlDwepkrxFkDod
+byn8wkFD7oGg8dFQUKd6KEH3O8cniPiFlfceasbZyl/gpYXMW7P5BtaMja2vEfGZAIiI+Ev1NonW
++4UYuk8l5SMD/X8KRqRHC548L+ETkRpFLtqyY4c+hs7EYqsoRtfMqLoV3rQAuQ7QFDAh18BiA4t5
+LCe1kBA2rbsOx3qOiusltwC7S3grwCdHBEm54ds2klIwMJblo8tEun0LumOurQxahzmMTLwDywnh
+sMQLuwaaTuDFQt11OTUNyq68DgrVVB97UL7bYuCTxi0KkUXiFTfrdsvw9UnHC7EpWJZGr+djYtvw
+fVmspjSoyB+Yez4cwjlro1Fw0jLWoINCYZQyyRS6UGSs8KfXoVxx1YydoiAP4giSLFukDSxWO2aL
+C6gNu8/6QNiTBZbMTpG/Tnc1EHfXmAolS7+nDy9x+2X2SSQtWkLjw7yGx4yzcj5+sA1fes93oLW8
+9a1pELmSELPZLu5EHgZMytXhGg4zXOdJgYb3P8gcOEMCnHpkQJ4ZlF2Cq1b46EzQS8PWwRmBN07j
+QHGR3qSYTKGAL7pS23/UrnGDm1dWtpgSA/IcOYbE7X+gJbH7kr3Ct1I57o+F+0gI3vnK/l18NRMy
+GC+vcPQ32ar8ZXkGC2sFj/gNvedgwBb2+z/r4Le/KmHO3r/xFXgHcn/l1+RF2m8v0PPlZ8mxxkkp
+ic3iMsftl+mW20vEUzJvV6TM9+7AUPG2ts5QtOywvfkynCNCostjNOcknzGErnsx9nfEbe9lGSrS
+wzVls21hHxFLMDxrgX25hs2whMo6+jsHDUOCGzd8NDlZw3e6hGMAHgraVnC59kwLVJ5cf7LxoRuG
+yJlcY24aLNcyb3XuYBTJ3B7sjGE9Jg2AaX/0MGellKWU+qZS6u/z33eUUl9VSn2olPrbSnE7vRpX
+42r8CzH+ODyF/wTAdwH0+e//GsB/Z4z5W0qp/xHAXwbwK/+0Azha4SBw0VMBOunJellgSd1CKx9g
+SZcxW1ZoIrGUCRuDUpPiBl38Wwc5nFw6+FRbb0uAKhojn4mH8PxcPInHj84w7BqJVjViUpDtNSM4
+sbjHw8JGSgmy+qkQkwRwMWT/fxX5+OisCx8AFUtZqPUDVOQACHVXOvVgiKHQGw3XF6vblCFSyn8t
+z4gwTFbo/PZZVQB0k5u2hKGLjqDBXU+8l4AivW2lkHd16UohagXFmIUZnEQs0LTnIafVafuSZAuz
+AbI+objJFIkrYYdaK0R7cv1vFArWjlip46cyJ/+4TdEY+f2TsEVTyISGdyr4T3b4fE7woCsZs8ur
+KhpcPpZ7rh0bOpOf/8p/8w3kHzHhu/v72P9fZT6+9LpInyX5M3hKzvHwnW9jtiNzUTUhFrPfAAB8
+4f7n0E6pL1GJB1Y3T1HxuW/qc6w/krVzPj+CzXLnPJ/h7FTmoyAVYGQ5nZA2qtZBbcj23AAOiCcY
+y/UmdQ8Dho8ZSnjM+J6v5mg2cr7ByMaAZLLnTGCOkwW8a3IMqxihoKxcuVzinPM2O93g+XNZk9lM
+PJtNcIniUp5HHPRwk52vrvMMLpvwbJ2hrH+EOAWl1HUA/zqA/wrAf0opuZ8B8O/yK38DwH+JH7Ap
+WEph4FrYC0Iw1EMe2ijJZzhvMnhcTHVpw2L3nU34ab1eYsa/mzzJYZPB2YqBzYW8vN/5xjfwt//g
+HQDAySUVf7SH+9dkQd8ZTdHQLQvdCExLYNy0SBimOGOZ9Mmtd7G3S0puxDiqJO4trDFC9isY1Hjv
+VFy/PrPUU8dDwziyORhhzE3DawwKq6t8yLW5ew42M9bjpz0goyai16BlmFNlCSq+TPeJVzi/M8Rb
+J7KwZ4sKk5t0g42PMfsV4lEK48vmVTKpkJ+u4TJz7o8N9jmHO6UGJQ9xvXcDxpVreu1YNux1usQq
+E3uwM+nD5gtbTStYA5nE5ZkL9xr7QAjkcEoHdibusylr5OyZsP6BjfaRvEC9by5QTWXOv/tMmJcm
+BwE0KwP2zT5CS65/trTQMKar0KK5kHCqex+K9RKrUwlLToo5Tt+VZxNYGtqRay6NjT4rRtSqRVFp
+7AWyhu72HHxAmPOqbmF3dP19MikNG5SE0g91iJg4k3WWISeuZb1uAIrb2sdUrBoMYC9I8Y8Smozs
+syzFMVu4j5+doSbHpAnlWU929uETs6PSNUpWam77Mb5wU+Dff9DWOOVmSEaBHzg+bfjw1wH8ZwCz
+ZMAEwMKYrukXzwAc/lF/qJT6j5RSf6CU+oOk+OF2sqtxNa7Gn9z4Z/YUlFJ/HsCZMebrSqmf/mH/
+/g9L0d8cx8ayXFg7atv5aPsBbNW5xCmenYp1fHz+AOEz+c6KfHjjJkG4IxtLduEiDul+pTXOL94H
+ADx4ukCRyfdHI3HfRv4E49uCOswXZ1sv5Xz5GGkobueJD+ydMQFJTr2b/RHuf1Es7YEVYPWWuHWm
+TuFPxWpalwolcdNr3XWpVXCZ1bZmFYb7cg5vUMLhfQdDuqRVjYqWxlYG3kDmInJjGC2egIqMJJIA
+mEw8FPe2hZhNUrNFjREbfFynxIgWzx6NYROavNdKPb+/s4eyz846t4L22GhzUqJgZDgIxvBusXN1
+T+bwOPVQncr5niaXW73KE2PBY7JucC2CQ5xJ6sg9F02LZSOYBt8EsHgO5yKHS3KZfAXkuXA67pEn
+caYdDN+UsGR093UMiHXA8QfoB6xwDIZQFAEyhKAnVY3VhSRd/eAAhlWui6zAOhPodptECFm5mfJY
+qyyHRfq+5LxATIbt2jEAsSVN0DXEOQhZRZiOJnA4h3ExRMaQYJ4p5JV4MTHZl/XZHAGTzsG0QFHK
+ccvlBUIt97F3w8eGmJpiLl6aq3bgsanMGdjYLGUdXjYGtaC/MbjwcHbxw9n+Tysw+/NKqX8NgA/J
+KfwygKFSyqa3cB3A8x94JK1hxwGU8qHHJPSoC5S5TM5lpvDuIznMs6zAhsQZVk8e+Bf8IQaBvKTu
+BnDzrqzZw6amkEkQwB/KgjxlN+Djdo3JuWRp+3aN/bE8mN1JiDO27x5vSoAvZ8qOtMEogjdjWeyg
+QWPLNYfax+v70hloxwVAiG5SyAv/9OwIPoVH7NwgVwTb1AEUqwBeKAvs7Pgcj0jYUs03cBkq9Q8j
+TOjg9UIPYY/uI6nM32x6aNkZuVweIRrITjfyNF4Zd112MQxh4wdUXpqUATZk/0ksH+9+JPN9bW8H
+O2ShGt/twx/w5R1LTDGFxndKqTJ8cP4cEyroDt/YxaWWUOjGYIq7u9L7MOOz+daTj3D+XO7vMHYx
+oEs9T0oEjNXtpkFBUppn5FR0L1v8v+8zI79/jp2R3Pfzx4/x8z/2pyEP5R6yhTwrY7HEaFz4t78M
+AHj04AzvUWr++TuP8P6HsvF4SmHAMvFPfFbeqsPrB9g9Zz/EQYITVoH02QYZm1D6BCHF2oHP5+da
+QC77AObrChvGIydZDthyr58fCOFM6Pl4xtAmrkZwNfVEMweZS/3TIsN7X5fw98lzWdOwDEa2bLz3
+ru9iQii5k6RbnVJ/vIT97EcEXjLG/BfGmOvGmNsA/m0A/48x5t8D8A8A/CV+7UqK/mpcjX/Bxp8E
+TuE/B/C3lFJ/DcA3AfzPP+gPtDII3BZO4cAQgFEvSgTkVph4Lu69Ii7jrdQCmO3dI5XYjXs+hhR1
+sewGQ8JIVbZCOmOCqwxxM5CddDkR6zFcGwGLAHBNg77D7srWw+pUKhXteQXNnpqTp5Ls2p/s4w5Z
+m5ubCue/LpbkIIhQs0uydsYY0dUMCcbpm2sYD8WqjBIgYPIJhUZJWKpbdwQxFvYIfqluDeGQc88P
+YngNE21xi91Q3P+a7m51pDAZyTVoAzi2HO+mM4bLeSsXCbxWzNjigjLsxmCqxTrqwzN85kAse/xS
+hINLcdHjHQNDcZIqJry6Acb7TD6WBzhnV+Lt6A4+/4p4b3ERQe3KdYyfstGqGiB7Ts7IwEfN6kpb
+20hYgcpMiD1Sl2FbAUgx5Hx+9vB17NyS892xvgyXyVF1CVgj8ihY8sx1amAuJXwIyg2u53KMxSTA
+5AnJXvwMe+yu3DkUr+nW8BoWBD0dna0Rv8znUyt8mBKcRExHPXBQkzrei4HK0PvzK9wiP2YVa/Qt
+WbdTel3BJkVLaH7shCgJSMqzJWrqTrZljZjVhTue4EmUnaHHdXNzd4A+E+F5nqB4IN5EXZk/1IT3
+ycYfy6ZgjPmHAP4hPz8A8ON/HMe9Glfjavzox4uBaFQaAzdA3/G30Ob+OEJvV6yqVw3w5oQcCDm2
+dFY2sQKBX8KifFi2XsP1mGTSa4yIJuy9eYjXMrFcP3ZBpt56gzUbiqK2hCb11+nFHC2TOrf6Cru0
+QAWVjLUNrNjq6z5pO6Q0ksTAXFL2bTeBRUJTl6Ks0WQfiuw4uldtEXatsWHIgGSFkqi6Pt3B3oAJ
+yl4FfYM0YeNwa61VUyIO2X7L8p5/O8TqSKzkdwfPUVeEgo81YhaJZnEJwzKcRbbjaWBjfF0+7937
+Esr7xFOUBoaY7mxhoVwTv1CQYyLcx42XBf5t4hpLtizvTn1MAsGL2MpHrSW5Vt2gJ6FKnO3JMeZ1
+hgvKzVmug4AEqwfagTcgB0IpFvNG7OEvsNz201+K0b/9YwCA8tUVLLa2q3pOenBsRWZUXcClFsLh
+jdGW/PfVO2M0b3xFztHPMKB199fMSxkDnchz/zOvh7gk4jRcvIP33hdrrFu5pzBrYEUkFdYODilW
+5E36UFGHVYlhMYFuu0wSxzaaFaGwdQXNpKwf1+hD1tm08nF/j57Vm2yOWxfw+EzDxkLJ/MnEi3C3
+L9d5dPEMgdPhB8nN8APGC7EpGABFrbBxMwxaMvxO+3AoAONHFkK6X/FIwXgdMIXCHHGNgmQqTVYj
+pe6gOtcoqXMYwYEe0s0N5bsXjYdmxURUu0Kxocu5Z+Bm8p3LsY3BQib+nCpOZ84QvZf4YBIfOfEU
+zlShmVBYxJ6iKmQxKQJXKvsUHuHFltV8jFmwKjis76emI5npw2IoBeWiq/o2Gwt+B4UOLZA3BiXh
+4TMnQ8Mk6KhnY9awUjMokXET2oQ1DscynyH7+Pf6NsKhhAyBDuGyaSAPUqTkbkTW+5gJu5br1drA
+4YI/vOlg6MtnN9iBRwXnsl2hKTtxFWpCZh5GY/l9cVHAVnL9Ljz47GwMXY3oFqn7udmObnn4/J+V
+cKZ3uAef5/NCBw11M5OLr0EbCTc74pwirZFT2Xk4PUT//j7nWaFkUg5VA3smm3btU7SmvoDFzaRQ
+NSYUu7l1a4TyLfnumi9b62Lblamh4ZP3YhSMYChgYzk+VOfOd4pWhYEmRqStSzQbNm5ULSziEALf
+RUyCnoq4l8zPoLk512iRpPI5CxWyKeHY7wIuw+JPOq66JK/G1bga3zdeGE+hVi3yooBDwRa1MkAl
+O6PTd9CyJuxf2wGJlFGxY69da7QkxizrcIsY82MfVkpIdF5hEtMFNww/htG243BxkgAMHxxt0LjE
+JqTAKb9zfMIE3790G8uUSaJejsNdCUvGuofWFeu/TmaoWXs25Gaw6hIOkYt20AeNONJls2UNcrir
+t42C1iQJrS24lCsr5ik0E7BeHqFl+GDY4VkXORq6kdPQx1O6+0+PgTGbp+rIxWXX5cm5cuoIU5Z6
+vXIDw2TmslI4XzGJmWeo6bHsD1j+akJolv/soY/QSNKu1CHOlqzJtSViel4NdRQvqgRLZg/TqkZL
+bySwW/RJKzZpKtwzMrfpRH5/68YugsEX5Nk0A5hMrKOxNQwZp9ZWH05n/U+ldp9XBnXnRpcAkcaw
+3AFCJu7y4wUqig5pJgzb/hg+n1/sXELT5XePGlTkb1gTEj2f5egzmZs3Kaish7ZpEMYSHjjQaDoa
+N8hc6NDHhnwSRdXA5XMKkwh5Lq5g0KsQkLEpplyiv5ghZYm0Rg4SVyOwp9v5rB2D0P/R4RT+2IZl
+DOK2RpFYmNUkFmlWmHaQYXsHLdmVm5MMoLtqMbyocoXyQuK7stEI2IlYeOm267BBCp3LAvOG7E6b
+z5DzYTjIUBZSV1dFg+xUfj4rSyyIEThayf9fbqcYEWRU2y0UjxH0B7DdrpmiRlyxjbhr9a1jOD7z
+CDpBW8j1axvIGfs33PBUqKA1Y1JHw5Bx2PNd6KITFW0Q5ZR2b+UPN0/WWDAGTqsWNsMAr3CwYCue
+uy6ghyRXKWWD2bQNpkuyXV+L0eQdfPgSD1kXd/whBi6p1lklwdkaFF6CXWhkJCyZnz5ES5pxtShw
+SQ5NK5XFWiQN0iM5xqbRMKSDN0ajdGrOUYAnLueWczm2K1gkqrHXc2iqRZlqjGwleIPy3bcRXPus
+XBTBRPPNEaxTcnveNtCl5EGsXgzDvhPHaVGRpbskf6ZOC+TEB2+ez3FWSFXqa28fAV2LOrEZjaeh
++MYrx4PT0igoDZXyHKM+PPbCFDwX6hQOsQna8pDz3HZbQWmGoNiHRaIdK5GwxUKxFZjNVwkU10Km
+lig33NRatW3h/qTjKny4GlfjanzfeCE8BSjAUgamqbFhR2IYhvAiynJFDSzWcWE0GhJdYCUWxZ6G
+cOmW++sHaEnQaaHFxVosl6U3UMQFaGbn3L63pd1qk2wr9HF5OceMaEKvVnBGcu4D1sd7poXpuA4u
+Nsg61mmrgSaBauiXCGJmnClw4xYKRpNgdQNoqhJrFSIkl3/mcU5sBdVBogMLlk8dgtbAYR3bRr4N
+JQIi5kaRxpSe0sLvYdip0GnAW7OxK3QQMjTp98iybBewRrTWjY2cXsxykeP5EUlaezVql+QkFbks
+9iIszhl2nSaYs6VwWZZIaUGxthEE4m10lZrFarbN2u8oGzNqYFheg5DErDVSPH8k8zJid2l+7KE+
+l5Cg2b0HzQjFNBugIUlMaCPoUReUc7XrAuqeHNcP+lD0RlSTAoSCq34Di4lnK5Hz5pXekrOYusUH
+l3Lud08zWAwxQ3qsvQSw2azneD7UFt5ew2J4awc+UHVy92zoKzRAj8DkNiKK+SwKZ8udYbclVE1P
+1hnzuDnqXMLGstlgSXi7CXLYhXgTrmtgqLn6SccLsSnUjcFsU6Dt1QgztpAOPTRkNi6qHPaarriT
+oSaph+kIkF0L81MRf53NN0ggICNbK8yOxV3d2fVQ9SVrXSWcyHyFli+H1XpwGAS6JkOPsfpbVgWb
+C2tyQ9xBPzVYVRLOXNbAxbm8IDcPbWBI17YCLL4gTccO1KRQjFWNVaItGX/W6y0deEVmaO252x4H
+tD0ovmBu2IfNHIfnRDA1odCcy+bAAb4nm9EaF4g0Y+tejozhSG15W2h2x/Rr7BYlOSGXuYvckQVY
+Xl5ixCx5rxduF3JEZiq1SoE+8zm7wOZCfp5sKpxT2DSwC5Qs9W0onDJLSixaYvgbG4pNE6PQQUyF
+JOgSKz73gICmx1mNM9Ke9zZncDhvum9QkbxQqxqKmXptdyU7A1tL+GgrHwjJxLzewCTsqESMZi3X
+VzGcq+s50lrCzYfRBd59V571SbKBofHJu2fQd7ZGxtQ2SuZrisZBQZi2nbXQ7HaslFxbmSTICJrK
+8goWqxnpLNtWZQp/iXQjz8xUJFBxIqTUzFzPN0Ar6+VomeE7cwlzLs42UPYVR+PVuBpX41OMF8JT
+MMogcwxQOACTekma4WLDOnZvjrFFZuR4gNIVF65sKMH2zhrniVidjx5nqEPqTtoO4muUra8tFISl
+rijusZo3ABtj6mqFDemzor0ewM/LswIjNrkMWXdX2gMSacpZfPgAoBu5fL7CUUzxRn+49RTKpOOG
+XMGmxTCqRMXs86YuP5bFc8V78LRC7YiFCpRDrAJgihKGPAyu7cKJWZsvxHVu6wJn+YcAgIfPz/D6
+5wT6ausCBTkjx24PzC8idylIY3w45FbYpGtUDjv8Rvt4bU8sV9TfRUuV7olP8FZ8CE2Yb26AiPJ2
+JzhH3tXNFTB2u0QiraO/QZZSlbtv0FyIhT4cxbAGzOqfNmjZVm8Th/GkTvGbv/MtOUYdY7gr9x31
+asSeVD6s69dRJiRL0WIxQ+829FiYqHUJ1PT+SksjYwa/XMzQEOPSCbwUiHCei2f2/tMFHpXkboRB
+Tf2QnFWb4iwFBtToLHM0naZpbkHZxA00MzgRG764phFGyM7E85xnOWqGpogtBITva6VRXBLXQN6I
+pkmQsPKxrCrk9MaenZzhwRNqZa5z6H8eMOdPPUwLXayQJyGsgVxSs0xgjdkZV/SRD+Rh5OkZKrpa
+yZpZ9rbCcCiu4Y/HPfgURy2xABK6vjsOCgqh5n15yFm5Rpmy+pA2sHa63MEY85U8pPDYxo178sJe
+C8Utt8cu3Osi5W63FZJHzBCPcpwTpDPRJTKSqq42LDetajhUKQp9BwFzJoN4AsN8R8mQwgwCmEuW
+N0cbBCTkCIYK9kLCgHyvRp9Za6vPHoa5A0UikOfLHD/JfIZlD9FY8nJblo2ApU+3lk2lMCXsUu7Z
+WC2yLqZeZhhxoYd7GVBL9aFiBt0sVrAGMj+bzMfKyAbYy3ykbB0O2wCGYZpLVztQCUaUlG9qH+u1
+uMQ3DhyYPdlAVJNBbWRBX78rL9L93gRLliyfnyzgsG2hOAfSUNrkdzc9+K/Ii+NtBP2oejdQL8SY
+tE6LdCkb7lm9gDOT79ZNjqb7TBCSUQZVS3LcqsUuy4GmarBkX03LHEFla6Qkee2HBXxqlmaqgZvJ
+mnMHDuqMuS0Sqnq+j2vXBbQ3Ti1UzBM0uoBLVqu0WuCSbdIN+yESU6BMOwp7jZg5r/SjHFXGTahq
+kFdXUvRX42pcjU8xXghPoa0aJGcJWlNgf0fgqVYcwCM9u1sqaLrom6zFfCW7bi75IXh2gwHJPa7f
+uwcEhIOWLlYLCsCcpbg4laRMEZMXwbfQu0YSktUGFfkbECg4qZzvzYmN21+WbO+eLRyH/VdeR+6K
+W9oLXwLeEVf1+dMSR0dC2LF3cA3DgfxdTX6tvFygZE+FZ00Rd+5jqoUSHIBHqvOsAOhRI68W4z0c
+WgAAIABJREFUcNgT4ff6cIjb72kHddhlwMXS7ExCbA7ozmuFPuv4w2CMDWHXbQi0FB9ZNWK5+46N
+OVmgm2SJkglRVVaYdyIqZw6eHIsH0XQ0GZWFnDiMpLFx/Y3bcj17Q+zUcv/lLAfods8oBehWPmxf
+7mlTZniSyO+/GChYFDhZ7w5RPpNnNWf4tP+ZHQyo56giH7NH8vvz5BjTNSm2Xxliv5br6FiU3cNT
+5At5ZggHmJ3K4pnrFRSBX4HnovW6cEq+mtUaFi367d0dhD1ZW2cnR3jye9+Rc5BP3nUt5OxbaNQY
+YNbfRQtDOfjWdZBt5DnYBKQFXgCfNOB930PlyfMttYOEbOP1soEbyn2npHq3tA1NXoygdnDJNasd
+gx4rKhuVo+4EbD7huPIUrsbVuBrfN14ITyFrDd7dlBjt1LjbFbJ3R2BojDrYwGdS0YscNIx3Z6kk
+U4LCRVhJwinU30bNcpv2HDw5Elag+ZmD40ys2+5ArM7NV16DRXixRoJ4h+i+gUE2lKm52K3wZs4Y
+97ag4NbFAh7VoQ8Pp1iy+Wa5OcGzxxK3Hg017rmS2PKI5ju1HmH1kKzNYYJkJj+vvBDTkVh6ZbGx
+aT7AxUoSanbkILTYKGaGcGyJv2s3REAy1pzsTu+Nn6OcdckpCwmTh97eBvYFvRBlbct3ETEWWVYB
+VL4u2xanK4lfVaYxnLPkdugAtjyH/X0yNT86R1rwM57ieC318TfaPlwmQWf2EmuWezWtsudpbNgB
+uXxvg5zW82TcwAqJ6Bs6W8mCWSEenxcb7NyUZqbdazGWJ/RoPijxkAnhm+cNikGX0GSJMfFQLjpm
+5D6epTK3K9XgNchzcj0ftZbztMx36DZHwnJw6RfYIeHt6nKDmrmEpWEuJgJSYmhWZYmwoeflONDE
+HlRZsC0jLsnOvGkfYjyQOWxtC5pIx9Rc4njJ5r3yOXauyX1PJ5InmWcb1FREt2wbLYVoFrrBepeI
+xguDpvnhcgovxKZgjEHRVLhMfJyM2N754DkmfUp1xy0MM6jB2Me1A/l5FBIG7EaYejKp/tCGzypB
+Ztm4q+SBnzgZmjNxqQ4oPe7GPbSKHH6egemUiIMU3i71GvMQ2ZQhzVK+u1iV4LuIz92/g3vXJWl1
+dPktfO07XPSnLu70ZdOKKfpReQMkjFAGUYRoh1qKQQCnL4stYAv4LNPwDAViGgWEnbbhMVy6q5Mi
+QEo8RUbcwOI0REYXeNizoFgrvzzxMIgJk20qbDqWeHaRhpaD1pdKxTKdYwZi55dLtORa7C028Mey
+mRSXTLiFIWz62n19gIEv96p2/a22YVbGWwGTrHOjywx8N3Bep4ipBZqcKYBhgN33tjqc64VsRh89
+9nDzpc8AAKLwZcSvMsGaGqhHEh7YL92AOhCqs47e7uJ5AVPK/Z1mM1wcybzduXsNEfsSCqdGTt3F
+kOsttW14lIYfVztICTiz3RV4yTDsks3mGQxDsJW/Qb9i+GsHsCqGfyFgYlm30SHh+nUNxW5PJzTw
+SQxjLcdY2LKGzEkCm7D/lizSVqtRbaiw3hoUrTyn2B9hL5dNNrFTGBqw6hOqT1+FD1fjalyN7xsv
+hKdgKYWBrTEqAYvuZbZKUZOKS9XBVh26ViUCV8xtR1RZLhXaWlCMxTMf2pHvtmqA/KITHFG4eyC7
+qhuxrOasUdMyR9pG48q5i0sFjx11X7JDGDb/bJ5SJ3CisLtt4PFBxC8cu49Dmo82LfB8KZ8PYznH
+3nCAZijubGtZ0NRQ8JChpeu+JuIvUB6C2/J7e7kLNSXBS6rh0+WHamEI2W55H+6zM5yTtdhkBpEj
+cxUEIVJyNqj1At4umYS7Xnsrgk3m46gqcGvKJKEXoiRmIwyCLR9EQTq33kGMyYShTeRvxWCaKIez
+YPIMGiVhyg5LoLOsRUsRluw03bq449bH4lKe2SJaoloTQt2VXM9zqFK8EdsZwSdD9Wt37+PpE4Yg
+35vBnEsI2baiyTGzhghpPa/f6eMlhoKBGaPuy3faixYx76+quRbyGVD4vP8GFycSgiRpg0h3iUS5
+9llQIO7iHadCyQalwmqQe3JPuu2hRyIWwxBFtQsUbBTzsxKblszk5QY2k+K39yew6LEYEgM1aQOP
+DYSLqsb5UsKYk6Mlcl6HZbWA7vgUPpmroMwPKT75JzHCIDAv3buNnhshZmvqKkvRUDLethX63CCm
+kwlevSEx1d6euPvj/gSeoqt2Yw8V200Lb4kVexGmsQ+bk51QYer65B7mK7n/9x49xVcfPAQAzI/O
+cfpQ6McX80ukBKc0LAn/9V/8V7G6FFf19x49hWE1YOKU+N53ZaE/nF+i4GaSZsSvawejuBOgDXEy
+ZzXAKARs9X08k3xBa9koqfQURi4iW0KY23cOMfEltoxuVAgvWN/el1Bk9eQCuMkMeG+Cf+cv/gWZ
+l7bFY8XvepcYXgosvEmoE9mmSBbEVWQLLMieXbUlJgzTdqcR+iQLcbnZOI6FgGQwVVpjTdhxvtmA
+CF1UWb1lbzpZy8vx6KMnOD4V1/g8yRBwo/6tr34IY6iFGTsYXJfwb/+28Ee+lBjoVgzA5WqFhJ2d
+k1GEf+unpTPyC195E7s7rwAAvD7zKNk5NOHaQTxBupaN4OzyAsORzOdo71XMT+S+j5fsPjQr2I1c
+W/tojksyMZdlg7/63/9vcg6bod/1e3BJ3z4NWvR9tmrbBk+ecW0dn8OjMTjsy3F7voWWLemxY4Fc
+OKgM4BIAVRcNZoT3pxQsDm0Nnz0/bs9By2pOUykYxgplU6Fip+xvfe29rxtjvoQfMK7Ch6txNa7G
+940XInwADFRr0DbF1sOJAwsOFaOHEwsjkn6MbxzgxqFYzYhIup7tgnkcNFWKmihF01TwG3HLYjNA
+QQ/hsGZ1IjSIaoYB7nMMUvajDxq4tDAmrwDSYzWd3PvZGeZ3xKpeexLg8a4c92QWAJFYCnvpoqWu
+Q9C5zpZGj1RyttdixI66dBxh95ISYzZd8WyFC/I3xEGJyhLLNlAWEk+s2WFyDdFdsRQvhdLt+J7a
+wBBJ6fTWOHvKysFLc4y/ykz1Tx1gHlJXshIOAvM8RlKIZ5YmJXJDlGaWQfWpxWGHcNHpKMhc2MaB
+riki46wAQpdhtaiqTv06RcXEXUhP8Pp4ggtbwjHvbYWSdGRW2IB0lVC6wip5BAD4uXdkDuefAQZs
+JfWmAXx6hXd3HNy9JR5NMIzhsMJk+RJKxTpGTfi0LjSKSLyN7OEa6po8B9/S0CFRsuw4rJcBZlR2
+XobfxPR7b8gxvqShyAWqh3I9o9aFf1M0L30cY6zFKyrWI5TEg1QWts12e9dIROEACUONVZFAM9e+
+thqErHxUqFDQU7U6FXOt4XUKrraz5VawPRcZQ+HpeYSia5X9hOOF2BRMa1AVJYoyR5xJGDA49DHi
+g437Dq5NZCMI7AGGnFRFTLoTa1ip3Pj733sHg0JmSp2F8KmDqNUKAVmd4pG42svsEuURW2gvGuzs
+yWRvli3GLFXODdBQaKXu+ByrGpdvybFefbmHi0fEuPsx1gQ13bweoyEM26FgxyhW2Hdko1u0FSqf
+Yc4oxcNbsnj/49+WhfbXVjMcMndwfNHCYp/E4myOg1ji/e/qp7h3V+LId11xT+/Xh/jNB3IN/bDB
+7n8r9//N52vc+Yxc5/z952i58I5biZEd3cJhN6eua8SENo/i4VbAxi0TtKw0RFrm2FYWalL+tEpt
+uyg1LDw7l+vQmcaUHZq9CY97u8LpkZTk9A2DBfMW3YYASNOftZL7TihIc/9nC2TPWEUZAy9N5Dpu
+RiNcO+SGGhqYVOYlIew69icA80TKcmEyWQOj6RinLbUrL5a4YGNqANlkn2YFckKtz/MKd1+WF339
+0MYheynSQF7o3TiEy0qEDRe71M1svRLP5520/QivsPr1xRsSthx5wPyBbJDvLi5hKJiTAKgYgqmy
+wS6JfbrOScd2kDIvMXICFFwjQRyjWsoDHt7soSI35Scdnyp8UEoNlVK/qpR6Tyn1XaXUn1ZKjZVS
+v6GU+h7/P/o057gaV+Nq/GjHp/UUfhnA/22M+UtKKRdACOCvAvgtY8wvKaX+CoC/AhGI+ScOY4Cq
+MojjEBahzcEkxnQoVmUShphSozHaGPgdjyGbh6D72ypDP/Xgklo9OAxwSCm44WEAcH8i7R+CRyWW
+YznW7myEUovU/P7cRdk1vsDZZm8d1nuH8QXUh7KzZ/encBqxeJN+iOmUCa6JC0NyjklB9mldwKGb
+eLRewxvIzv/AajClFfub1Iz0zjXm7MIzrbUVRcmwh+XLYs6qU4W3IJYwZKff4+gEl6y1O/eHCK9L
+QvGN352jfY2y9O/8Lg5vSh1/di4WvHUUhnuUo1v0sWIC0g8MdEJCFccDqg5CTaKarATxVmhLjSKQ
+Z1an5whJeLExQEZ25BukPR9GCxwcy4NI7jRI/4CgBXzc0dfCx02btfwb1PZEjcJ6BAAYPAdufVEs
+/p3+FPlGEpd9pWHdImUZ1Zxx4zmGlVDO613AtSQMOBk8xPW3pZEqvvUuSuJdHhLodO0sx3pKjMW3
+JtjclfOdffhNVGSjjtdi8fPDERx6k59/5T58AraCHR+H+7Iu7h+O4UI8vYAexrXTNZ4yAZ01azwl
+t2W0UEg5z4UB+mQh3yW9mq9bKPKnZE2DGzY5OF2DTU6Cn7gPzQ7iTzo+jcDsAMCfBfAfAIAxpgRQ
+KqX+TQA/za/9DYhIzD91U1AKsJ0Woedj2pcYfy/2cX1MQtTeAGNmjq2ogUOtQUMtP9uqMDyQl/ja
+jQm8jtvQahCOZHL8fghNhhy0jPX2+2hWsuBvhX2kjSzimdfArCkCChcNqd27luX1hzWmStz859+b
+YZe8hQfhPpwDubZgAuzFzF2w3NauC5S1LLbewsWANOu38xLHsdzLQ/IIfkPtI2bYodcNhiO5hvHh
+ANGhLMa33jrBQ2pY1OyWVJsczqt8MXsG/UZefjW4jeMTCTHavR0UROMVXfXJTaBJWhr2KoDdflZz
+CZ9t3WVTwyo6tiiWiN0YNRNBbdnApZCqsn24ffm7gWW2mgulJiFNb4Kfe0Nc9OnFt/H2PV7/t55B
+EbTV0x6GpCr/0muy84wPDL7+TJ7DtWsGd+7IGglPatRnMrftNITO5dgOeSkdY2Czi1CrAyhHNqlx
++wTJbcmvbHIf9lO574HPOvPdKYInks+ZBhPUhXweD15GrPksHQFT6U2G6K5s0m/s7KI/Zu7DXcKn
+IPH18VdgVRL2LueSz9mZ1NifyWa0f7nAo1yueX5R4BHZ9U+LAk5OzQm2kZetxm4qayQLFIKePMuh
+8uGxo9exc7TDH85Z/zThwx0A5wD+F6XUN5VS/5NSKgKwZ4w55ndOAOz9UX/8h6Xo6/YTQq2uxtW4
+Gn/i49OEDzaALwD4RWPMV5VSvwwJFbbDGGOUUn8kEOIPS9GHnmdcaLi+RjQV6+K6EXxyATjIoQjh
+1G4Mmx1zbthlmB00VN2x/D68jkbd17CYoFI6hia/YJfmcJTCIKZs/cEJ+hueW23gjSi4sUxQbwgQ
+YtL3raTEMpafHcwPoe6Q7r0fwGcYELg9qJJcBkx22YHBkOrYe9EAPoVjqrWHXXbM+cNH8rNGoyTv
+oj8q4NHjCXZDNJ0q82SJkv0fDTksV8MK+7Vc+7IqcX5GufQ3LuE+nfC7l3BG8vM+SUxWtQObDM7G
+VbB86fZUhYZm2OSZACC7subzqPMSposfdApNSLRCjTAUT6hUa5QE2ZD4GknRIrspYVC4GKDpix3R
+yoFCJ6jjIhrJvNz8qRsAgMv+U0y/SSCTVggNMSA7NirqgrahA0VswSYi1LwKYFE4xdEDGEs8LFfv
+I28kyduuXcynBKgRrhzkCrVLpauf+jbcJ5K5TiYbtJl4pNk1CR8P/QmuxbK2Du+EiEn24/kHCPeY
+KA9GaOhtdZUv3XfRcyhw8+eWuHUs6/D9x6cYfCDX8fvrDC25Ex4T99ILI6SueL0DYwF87jqYIHhZ
+Jtpb95HEHVnfJxufxlN4BuCZMear/PevQjaJU6XUAQDw/2ef4hxX42pcjR/x+Gf2FIwxJ0qpp0qp
+V4wx7wP4WQDv8r9/H8Av4RNK0SutYAcufNuBWpD52KvRsImmgEHNJIttApFTBqDIsWA7DjyX+pHQ
+MMw/aDeA3SNDUFWjJoTYYkJGRxqKDM5e6GLnuljx2yMbJ2S/wfOHaGmBmlzOd+kYVOxdL14GUva8
+I9Bo6EGEnoFLpGNL3gflK1i0YMFkiE4txOgN9tnzHn1JcgB3ZjkSogOLtMKMjE3a1ZhlYoGmwwF6
+C4rkTGR/P8rWWP2E3N8A15CF7AxMHeS+xMO62GCXOZE1782zInQtiflsjaIgK5Ttw7c+7t9vank+
+Nr2xVjlbAlJL27BJPOvYMQwxDWVRYJnIz4sBSWdLA3slP6tdoOR82hpwjSTi7ppdvET8QlJJ1+Z7
+j49xQg8qTg0ekzQ3Uylu7sjcZlmJVfldAMBqLpb0zrXPwOmLt9LMHqP2BLHa5ruoWF69yB7hOUlh
+b+y9LtfmNCimch9Lewfhfbl+cz4HtX4QjeTvXTVCNaS+pDuGf0PmKHQH8AJZh7rWKErJ7XRsW8Gg
+BnLJAw2tMfrMqcQHDW47cuxy/zkefiTr4cklYfx5tkWkGivGkAzVK3eFlF6DikpUJOT9pOPTVh9+
+EcD/zsrDAwD/IcT7+D+UUn8ZwGMAv/CDDqKUhq89eNpC65FIYumgHVGxqOmDpWTYbgmHLrqtqcVY
+W2gp6KF7HqyMiTFVAymz0EMLVtVVFBhGrDN4gbxUQ2uKli6u53l4skdgTdVHQqp1sBX2hu/gWSGT
+fnk2h5fLuZubA+y08rK52t0qQ3WMHaoJ0BDua+pkqwZlWT40WKHI5FhwnW14VIRrmJl8XmOJgAQo
+49bBnpbzfcS6fP3AwnIgC9tZPsD0ZxhWpAmSS7mnw75Bjwnd2amEIqvzFHHDl1ulcAlIcl0Nn6GN
+smpoYha2OiZNDY8iJa4ZQnW6ilUGmx2KEz9CyeqDz/bM3qhE/kzOUbotesyoG2Ohohht5u3glVuS
+MNzZkY1w570CqSOf/5wzwp2Y1OjBAWyCxNzzCzQUAx7xfWjdt6AYYmnfQC3Jklx9iHYhoYupLPhn
+ch0XEJ7LQduDw7mKvufAm8q5N1kKDAl/5mQ0kwSv5rKhTe46CHtvynNon8NqiRVwT+EYqVZAiRNt
+J1OYqYCp9HKEdigJyCg6RJ+t3NWJg2ItVZJjGpnaAORjQVE3YAQN5WdQF6TZG2dATcHRTzg+1aZg
+jPkWgD8KS/2zn+a4V+NqXI1/fuOFQDTCtGjaFE4dI6TkuD9x4LGEGEY+YnIIWLaLlm5uSSSddhxY
+dlcsr1ExSaSbDGWXdExKNJBd11Efk11qfnYBjIfCAtqkM+zTso2dBgndREpNwnEMrrPJ5NEyh2bJ
+rk0UClZSPHsNv5Fr2tJhmQI1rW6ZNkDEzkZ4sFgi02TkHXoV3CWbw1xgyVKermo4rVjr3cDHvZJN
+Y7yl45WN9Gv0eKIWYJnVrOYwRlzmG9NX0HM6clQSxaoFSjYiBUbDo7LxoG/gEA+iWg3F7lBFGG1l
+AhjiIkzZAHxOjufBp95A0Pe385x0mUbjbCnPhrrFATknLKvFMJLmpzdHt6F61Fn4kM8ptvGVRCz0
+4FUPOpKQT7c+Imo12CHgEU7dWfHArqFtQVgqew/lXJKLVWtgyOCMPEczk7m1A3lOjWtDE43o1iUC
+Xyx+PN5FriSk6aeCbIz7BbJXxYxb9X0ouxM9ddAwtWbVIWoj51ajDko/h3bFI7LzE+i+fDamgUt8
+yuBojS9/TjyM9VyO9U6SQ1Ga76BvoU/Eo185qCk6FMBsS8OfdLwQm4KBAHRSvwA61iT7YwltTzWw
+yQOoq2xbiQCrCa0DMEEOhR3YpFZvTQnDGLGufViefFbU+FOmgiEvo/YzgG3bDmz02BarrguQCABS
+gnFWtoPlQBZ3dqHhkVNwGLvwWf+3tb0lcOlabxu3gEsCGB250BS61W4Lm/qBDRl6rLyE8YkJmGew
+fUKs1wapJcd1VQ/NS7KRRQ9kw4tbQM8YohRHuJjJi9AMT3HQSJw8nr4Cl/0MHSX7ZlOj6aKdfowo
+ZnZ+laHmfWsrglLkF6Rmol2XgN/1j1RomO9BU6EmW5by6q2mZegQH1HW2PjsKTkeQHHTs2Mb9W22
+/f6UjeNSXPvbrrjlqu9gOpZzx70+QgLVkqhFckbBYcuG84yMTIdyveW8RCUFAJjVHJddH8uDBRLG
+psUTF34sc6u4maqLGKA0QLV7BPVIKux6YsNihy3eEICYv38X+5b83p5GAAtvrZtAUXfSWDbSiOvi
+gnEAHDgUcqmrj+AVZOQq51hTzOgkn+N8LmtuQ1Ww/tzB+YxhkqPg7XQxnYXMkefX24xgxj+cQtRV
+l+TVuBpX4/vGC+EpSPXBApQPhGJdQm1DtZ3F/7gu7voDIBDrUFpEwRUKFa1ZXqYAm3ICN4KhjFdT
+FyhoCsOcKsF2Dk0LpZICNl3mSllAKGZlaHyk15hFPyp5Xhf5iXx2A4MeJcr8JgbY+Vi3aiv2UZKI
+oVYWQjazNNpBQz7+Ahnsrr5PVeomN8iJjygrg4Dhwaay4bLjblFd4v5arMDvrMXL+TNNiN+gi14X
+Lkr+oe0e4u7nxO2u7RItNTbPKVRTAvBoMdtNiZxhkOU4sAg9rqsWJbklehQ0cfsD1IbXWfpb1WVj
+KiwumVyz5nBZlWjY+aocg2xJjNuwQjSTufD/FFA8kWt7ZgA1Fdf8XSotjyuFVSrHSIsYa8ZND77z
+DIf0yKaDZutldXyI93d2UZzKz6rQxTefyXztRNeBc0rZqQbrMzI7k89yOHZRBHI9+QbYMHPppils
+ZvYihqjTsEVzQFn6tASUeBBOY6HMxGOrzQl+7e/9XQDAvV0JE7C2scPO3dnzb+PlG+Jt5KbG957K
+uf/Ruzl8wuJ9rr3HiwoVYdVnDwq0U3qZcSOCRQDs0IXdudGfcLwYm4JRsIyDHaeP3oRQ21LBZikM
+2kK2lJs/zz6CYYykE/nuym8xsORn8chCQHBS2iyhWb6rowA13cB5SsLM5TlswZ/AL0roCbsynQMM
+WA498KZYkf8w4YvSL2ocSziJ0irRJ/W26jtIN3Kdaau2PQMWVZWauYvzqSzAwUmBpkcx2spH2UFw
+c3bWmRwNSWFSk4GHQosMLeGuqnHxDtu9NxD+wd8el5gn8nfTkcIBlYlgDxGwh6OZn6CO5Rgj5gOe
+JgX0kmIprkLTyMabOgF6dPmVThCQQLaoZKHlq3Osc5njIgXOyWOpFwvUfbmOng6BgRxv32MvQtgg
+pKhPO3Rxxnmb/24G5ch1fOvxP8bZ+3Ljr74s8fTQLPFdita033iES+ZiNvMKirmbf3kvxuhzMucD
+dl9+ePIM+gPZWJYv+VicyLmPesfYfIubGjR2SllT5yGJbasWzkDyHThvsb5BRbGTDYLRNV6TbLbr
+coP+hyypf2WJfC3X+eEH/wjWR3Lus+kGD35NFs/f2UgL/E80Ee78LMWL367w229LyfLRswpvk2in
+KYAlS5gROyOzZYmMvTQnbYmUxiBwDIJcnlkWlAjKHw4xfBU+XI2rcTW+b7wQnoJjK1wbergW9XGN
+ib/+MNyCX1aLDKoSUMjlWQM0kqAJCQSq2hZZXxJRQzVBvWXpwBbEsVqeYnkprt/lUlzKeVphhwQb
+B/YALmmuVHyOQSA79Jev34FD3j6rJTDHGHx2yjDA+Pgsuw+N9nE6l8TQan2OZEnWXXJGxo2N6Zzy
+5LsHcEx3DKDOaYEyOW/eZJiz2Wnmn+OEoURVVxi35F3sATO6/D4bkZzTAuq2nON2PIUpxTLHQwc2
+++pza45As7OPUucWSrQEgzWOBWsj1+OHNexAPKgo7CGfUZadeIT5OkHKysLqokZvSpd69zp8egrt
+yoYm1LtkRcKvasSWPD/dVziNmPCtFLARW7X7Gxuk5LJILinI8upnkNCjmVuXiEPydUaAMyHwKLQQ
+uoR6E2OyzBqcPJQ5OjqqYMiCXFUawZrXOQbuRaRR76iavRg2tUdrbaEj5LRCF5qycVPClf3kFM8i
+8QheW9zDquvpyWKYnlQcrmMPP/0y/+65hBSfue1tJQnV6xozNuPNLme4RUxO41hoyIRNXwsfWg1C
+0oNrS2/BI6HyUHctAraCY5GB6BOOF2JTsGBhYPcRegYOM9kWJCcAAK2ToabsdztMURv5zuWa8XK2
+hEOdyFoN4A0pLw8HaSIu2uZE472zRwCAVSKbQ9QbozSCOd9ULaJOhaq5RM2uvuYrBsFvyjQFkZx3
+WRhcG8vD+tzrL2N453MAgHXhIdp0+pa7WBYCQqkWssBOsznSVI7laY1xzFZer4RhpjrTsvldPNvg
+QSGb18V5jdWCPJF2gciTRbxbB+j3qU7E7sqH8wSBLQvCn1q42AgYqto9xY4j7buT/mehyAD10g0h
+MP1e0uJ0JYjHTRJisZbrmMw1pvvyHKYHMVqbOpx1l+8ooElPbh3UMOy0zOoayOWZWe0KXq8DZ1H/
+QLkA6fyD813U3HBRN2iMvHhHZQWbMfPdNenZ10ssL+RlStISd7nJfDS28AZDrHqocMCVPfqsxIfv
+f32OM6ImZ1mF4xX7KyprW34dLgO8zTb4z7HUWTY5SvY4NHsJ4iUp1f0QLlWvzsbvAQButEPc6LHH
+YT9EvJE14vczlCTNjYNDLL8im9OfP5Hf79y5jkks56unHrL3PwAAjPceIzuVdfhhUuDoSIzL12Zd
+l2UDEL174Fm4pFZo2+RIWDLuJz3U9pWW5NW4GlfjU4wXwlOAVlCBA+No1FSJbic+jKL6ka7x4Fzc
+r+88usSMLvacMvI3xyHe3JV6/Vn2Pt74gnSyTaM9tKG4gzNniTPy3T09J1Bos8BoKZa1tqz9AAAg
+AElEQVR0N1b4Uz9+W64nUrCncu7TzTnWL8su32eIEh1doke8+81XPgd7LNli8zyHFcjfrefH2LCO
+vWLPRF02sMmRsFdbSHP2YlguQGr0TIt1vdQ1LitJcD1dJzhnWHLguDjy5fMg9HHfpl7llHwFqxQ2
+dQQP4x5+7923AABx6uAXfpIcA34ITaxGSur82fE5Pnoq4UrgpvjOd8Vr2HVDfJGdeCq0oemK5tSy
+b7XGo5ncx6OzDU4fvS33XzTosz7+ym4Pn31V5nYUiyWts0tkCclL7BkqJnZr2wDEetR1jcCIlT5Z
+iUs9uVR4+Eye//rQwjFd+LMqhaEQzSCpt8C3+335+x//yWs464nndvmNCnOHIKSNC4dEO6tNgYAI
+tWc35dXY8QZImNhclyUC0gKaLMfmXOZlv5BzvHzbRUgPMksW2FQy96E3ws5YAFlFEwBnkki8+a/8
+jMx3EMGjxxoMKvQOfgwAEH3xbTz9dbnmt959Dxnp8jpcjMpauB1jtufh2Zxw5irFnFUwuBYC/aPr
+krwaV+Nq/P9wvBCeglYKrrZhtx40hTLKpIFHi1hmJXKy+7y2fxsgBHXkyA7dn/poqJeQLOYoj2Rn
+t1+7BZdlmrhpcW9EGixqOzYbGyqW+HQXIcbcUFt3iAe0zE+/c47vkvD1pULKcddjYJ+xsxP4aGdS
+YrJUA8US0WAnhPJvy3lIqRU0tzBgM8++9hGyGcuGh6qSenRByK1f2rjjizV3bhlMIRbKc3Jch/z8
+5t4ECZuRdsjd0Pd8aMNSrlEIeT71wRrL1yRe9to1nJ5YLo/xO4IGkSdWcncYopjI79N8DS9l379V
+kxoUyNkt2eRL+KQZ3oGGuide0/XZxzRtPnoo2UwGMm3/f+y9WaxtR3rf96s17rXn4cznnjuTvGST
+TTa7pW6NaUkdRBkMKYDhJEAebBjwS4IAeQjiN7/kQQYMOAECOECQyUBsWVGEQFMSB4bkSFa3WuqB
+U7PJy8s73zPus+c1r1V5+L59uunYadJ0ZAo4BTR4et+916pVVavqG/7f/++To7Fj5p05k6WmGRML
+qoGQ4NBVBOVgS6pHs6bLF35MxnU0GuDsKXnvaYZ/VayN7e+7vPxVuXZvT+I2cR5z66Gsl7PnC1qP
+xVr84KDNxlI62tt0aSvxqquowUFdEWm6uDUZUg8UPj1LCJTFef9M4gG95zvsHirx8Csu7Vz6WT7X
+IFxrejLl+dVPy/ToOo7cPfxNhSLbNraWOJj3LGFTA7evbg1pqYXQPVcNy70ViVaflqxo6OxU3ZqB
+QtMzt6SZfTJE42diU3Bdh+GgRTNs4irFml97tJprkZUtrqsADHVIpd+pFKvfiELOjmUyp4sJoUbn
+vWhGS82551tX2EoFCPNiIguQnkORidk2HDUYeGJex2djekvNf/9BSldpv7fuCK/fwe6MfVVQKuYL
+XK1x8NKaq01xY/b7IblSnK9ZB93CodKKynZYE2jAtKwzMhQYpRiDZsejvRRzeNh3KEbyd7mIaa/v
+160xKrbbUkDXlw6GPCzls7l/TmchL+mqmnLv0RsARO4teuY6AE8eS3/G0ylGA3EHt0Zs3ha3yzHn
+jEbyrKEfUGjO23W0viKA6w3ZZK92PBoawHRfDUm1BoXMEmo2wNEqUbNq0OlofUFryU5X5te4DlQy
+Yh3j0Q4UGNSX5/uLP/4qnRvSH+uucFUr0zlwad2WjbV+aUJTKexD7ZvXLPnyl8Tlu/1TPd79A6kD
+2X82o3FD7rG7sY+rdSwto9msyCVWUZ/T+dMLuHLtN5mOpbKx3FVtyDIi3FZ+UHeTeuMr8vz5hFrZ
+tiPvNSJhgcdT/Idb9DHqVlbLJ9Ra8WpLj/BA1uRzmcMokA3+VMFWJ7MGZ2fKnt3sEymf5Tx0KNVF
+9qIWVutbPm67dB8u22W7bB9pnwlLAQwVHlmZkhXrUz6nQnb5qBsQKooxXS2oHNnLMkXoxTmc6u46
+zgsWigtg4VNr9WTUimgUqhjsy2leey6e1eq8psGvFCFpByRjMeGa5DQnaqKvxFQ9LZo0Nf2zGde4
+CnhzSoemCs6Epk+qbsfamq8p8SIlgyniCx3LwqbES1WV1kq/jjekUAslxKcbKEqz7VFbJZ6d1CyU
+JKbSksN0wyXXfPxZlmCGCiseFSxU2/FolpI1xG3KSrlflBc4Kh1vHRhpqtPzIwKFjdvao1zjIZT/
+IPOdiwK1oF0RaqGRS4nNlYfBSUARkqZYP3NErGIw5tShrazbjrWsQbkFNStH+ve8UvAVLUNvQ10e
+06a5rRRz3SF2+T0Zg/khjpLkFD3VpDiDWiH0A2+TV39BUpx74yMyK3+3cUGfz7palTmtKeaKaRhW
+dJUYxnE8rKe6mddkrNqDEUEk1qjb2MFRARyCR1hNzzp+D6cv116L9tTJKZwrPL6aUhzLOsuexvha
+xNW9vkcTCbb29ySQ3r3/DqWSz0RRRGMk67ozDzkbSqDY1C72Qkvy47XPxKbguA7NXpu6MMxWygFo
+myzLtYipD6dKVNJqYjSqnysBxXSa82wiE1N6XUo1GeclOGoM1blDoyXm1Toy7YyacCQvWF3lFwQv
+jWrKk2O59m27xVPVo1yJxcnwRoNnT7WU2Z/SUlAJWY2rgq9+FNIIxQStlorDr5asA8FZFV4wRxWx
+YZJJP/JMpqQwNbku0LbbAmXVMbXLNFUwVZKzUn9+2FEiEG/F+JosgtNTB7ejXIV+xanqHDJvkXTk
+hXymsZio1eFqTzbQZtYgUxfE9UbUlWwmcZGTLPQFaKmbZwx5uV7kKY6CZoI6xKpLtEoDauVSrLRG
+pTI5ChUgDF0a63VrwLBm2bL0EsFIzM+kv63zMfV7El9ovW4xT2S83O1jJm8rfsGai3FpnsnmYMsG
+mqghj5cMrlwHYNB4nUrjB7PDY/Kp3Kc6kRc2beScaaXm8SIBrfNo2IDlWF7IVMsvW24EqvCVpQmu
+L9dyiiYoFsKWK1BG7zpWkplsciFKNFs9pHikG30woXcu1wtHOY2mkLY0euJSpIuCwZkyb9Uetc7N
+48UhC4W/h8YQfDLv4dJ9uGyX7bJ9tH0mLAUAgyVyfEpfTLLxdEKkEudlDEZPHdek5Jort4qOW5Gw
+msiu3PaHbA205t0FU0nwyYQ1VhFmlcrOcXSMqdf0wjVZKfd+eFIyOZUd+Kzl0t0QHoLwJSVACU44
+eSL9uWfus9ORnX9zsAVrl6CucGYalNNioChrkRjpT7WIqbvKs2D8C7hxodBov+XS0yxJ6PgUdi2N
+vqKYyCmYVEsS5WFQmQaqwnJ2qgVTC5erW/JMWV5SZQrz/uAxw66Y3b16bQ67eCrD57ULKhXnTG2N
+VRRjmZZQyrVVlQyTZBh/PYcdaNT63QmValHUac0iW3NAyHcJXFQ1D7PRoFK6srqCtQOxwsML5PmO
+G3KTRTwneiyiPc2oh7Wqjn0y5ejrEsyLj8cUXXHTtvYV61Gc0l3IMwfPj7jWUEUgd0Klwd38dEas
+LuKZ6sc5UUamepX+U4fVjuqNLlegAbzOB4qK/YmQ/hO1dO8kOKW6Y1WOs3ggz9rZplZ3ZXlfAr+z
+Z08xDbE2zk8dDufy3fKtx2w+Jxmjvb1N2rckoOk8kbF004qmYjrGVUmhSN35PCNNtJK4ldD+hK/5
+Z2JTcF2Xbr+PMzH4VvJUsfFJlEuxsilRR1/osgGa4nOV0CNq+QzVTL6yZ+j3VWi0HTN7qsSkw5pK
+XYVivk4hWkLdIOo0x1H/OknOOO6JKUpyQqHKFfcUPPJKuIWnpv/Dp2MaSpfeCic0S+VozFYYVf80
+muaqi5RCy56TMsdTMdZ4VeJlCtPWtGCaLi5Kcv2hodZ4R5qdcnIkG+CsWl348xtKvOLMLT0liGk0
+XSLlsXRNztPH4me2ui18rYN49kQ3gjijqZHzdtnGZvLWu35BUqwFY3xqjcvU62fyfQKj37XVhanq
+ll0q3cgdL8dR/sfSUeLXhoOrpKOeV1IbhUqHHrXGIhpYYvXLUyXdbQWWyFUwWDW7IOMtFlNGL2ka
+zoT4B8oitaWCOyvLo3dkjGw+ZfC+gII6/ZJCRX4IQ4ym+FwFdU2rlFhrcEyzoqV5VrvhkpfijhWa
+BWsel/gvSn8842F8rVKoC0xPs2d0saX6+601YU1FrKns+ycPePKO8oouCqbvSD9Wj0sGH2q6el9i
+B2dHxyQKoXfLlFjfl6jfIFDyxrDRAv+TveaX7sNlu2yX7SPtM2Ep1LUhWTkYf4nR3L1bphRqRsZ5
+TqSq0mV9TpLJaVMo30BdOXgayZ3FHo+OpUBlIw9ZnMjpMAi3MBocmx4r9dXJEZvXBNwSVTFxISes
+68RM7klU8b3zFVc78v1Ec9jnB0PiTSVeWcKJKyf3oOrgKCO0k/pUGlCqlECjqGKsBtGc0FAlGqAL
+4ouAaFNPHZ8Kq1gAY0dkhYrWVJaJlSDobJXT6ssULrQ4Z56XzJVwxQQeZ4/E8ipvnLFSxmjPhYUG
+4hytAHRNSa0VpVllcHw98ayDW0uELvd8gtaaE1ILuJLyQpzHd1dQKvisnlNo9WBVZlQaoHPVZUjy
+CBvI83XTPka5CM2uiz1SgFDgkmsmabQrpvjJ0tAbqKz74TnLp4o52W7T2VGdyo2SRIO77680Yu9W
+aM0c7bs1H/bE1RhsZJR2bXlcI1Mm6aVaIPE4JXPlumXP4vtKROOEtJQmv9zRMXafuxAlomqDEuqY
+sLyQj7YVWHXBIk+Jeq7eInLl+V5wE7rKqWGWbRJXLLnVakGmVb7LSgvlvudCV1W1K8tK3eqycnC1
+wM6aJU4h6+/jts/EplBRsyAmnhsaawSiMawmsrjP7BleLaa4G7VIVQ8hV7MuWVS4kfz70d0FT1Qz
+cTPs0NMKv3K8YhhJ1DouZcAe5U84fVsAKP0RGHWOz8uSSjeeVZpwV83jVSzux6rzCtNTebnncUx2
+qFWL9YxELcZu0aYVaXQ5VjPTzfGVEcd1SxQrRF1LOgvA99eZhRJHp2c+TRirKtDRvOBYS2TjvMJV
+c3451hclrlmp316uDN87FlO1UVhGCvqKWk3mWuX4WGGF+4UDidYzNBakmlFohz2qel1+GLLUjTrU
+rG/tG/I1wVIDUvW/cUISzWDExsXWa50IjTM4llLZmEoHHGXF6v09n7OvrUlhLa3rkuI7UtO40/Z4
+cqgxEyIeabZgYFdsKWnLB1PD178rL9P3I5mz55IhNz8nfmCjyi4qSc8fn9EMlO7evovV7E+05p30
+OqwCrUpcVbQUjxVUS9qKvk3Opb+HT5+w/YqssTJb4HlaM1J2IRSFK5vOsUroGlz9t+ViWPyVGu1d
+h+1tiTkU8SnH78kB9yff+ABPAWpHj+UdeX98SpTI7w6aTYLW2s1LOZtKnzp1n0bzk5VOf1op+v/U
+GPOOMeZtY8zfN8Y0jDE3jDF/bIz5wBjzD1QT4rJdtsv256R9GtXpfeA/AV6y1ibGmF8D/n3g3wL+
+trX2V40x/w3wV4G/8/91rcpWzNMFaWqp0jUtc4LfVkZd06JcE9UWUxZaJblQfsHSBnS1jmDnlRbN
+puy01BZH9QNbfUulsu2ZL5/Z3DKOhUevGW3i+uuTbYETKFVaKyRR8pVUMwPN+RlTxe0nWUExUxDK
+Vo9morwP3YxMATS1EqGkcXFRtxFUHaKmQlFTQ9lUvsaJXKtRWSaoCVhCoVWJ02SK1RPROpYHS412
+r3UUPZ98otPaMbRjOeUWmcVRV6PTBC8WrcienuZFbVkpl6RXpRRqmc3iKUa1K2t3hrtQHMVAP8tK
+Is04JGV0QdPW7hSUsQZPi5p5taaPV+1H39DWGg4ch7ma62f/xgLlb6HyKh58VzINO3cEsPPW/SNu
+bkrmoKZEBZoZ5yHRUO5xY/s6wZfkOz97rqzcvkegpDyhm/OdBxJIfnA+Zs9fiwe1aSpXZryQedge
+tvC1z72xJR6pC5os6PXECr2OuKBHXsDiW2J5dn+qZq2tbJs1rKH1HqCapWXyQPpWOVTKJVpmoPFn
+5vePOUzEBR1vGabHYnk8faxZpCxhU+HRh42U28pZOk9LFlN1b4sF3U/4mn/aQKMHRMYYD2gCh8DP
+I7qSIFL0v/wp73HZLttl+zNsn0ZL8qkx5m8Bj4AE+IfAt4CptXatU/UE2P9n/d4Y89eAvwbQbrVY
+LQqW45SGinjYdkSsOgyLbEVXhVOqIqJWBuZaAzJ+WBKoxsB20KEdreFxGXEo/pRX1hw+kJz2aSrH
+/NyZsX2gyshlju1JQOn4yQy/oZwERDwMZZc3S2U5Wi3Z1vx+2nPwGrK3Ot0mpTIr+UVCshZiUWEO
+m+bU65J34+EtVOwlSRlrfvx4vNAvWLaUoszbd0kvRFggUALP8wymGlAL1zRnXs1CdSWTMqczV2vK
+rWi15ZmWeUVjLsHD8VQ6dLPjXMiuefikU02X9UK6HR3P5ga9plwj3JN7LGcQK8Zi8jjDhEpyG8eg
+MRXHVERq3VVKL4ZjL2IRnpNQrPUwMpda5b3dHIymZf/n35M5+Pd+zuXqUmI04X6XK8G64jVjFMrJ
+3OptsdmWqqNcT/nEJiKKAZzX2ww/VGq6rQxXi5x62wHZiTxrO1V27WZAR+d65pc4mg4Pm11uXpc1
+19+TuEdj9YzlTeH9qMwXMCpd59V9UA2TujgljmUd+ko9WBep8CwAb37wHqGmvh/cf4vzu/LcD+KE
+JxoczipZIzujBiNF6bZrl1p5NFq+4biU7yzLmvRULOOP2z6N+zAAfgm4AUyB/wX4xY/7+x+Woh8N
+hnaxKMmqhIWVCQp9h0qjwqfTYza6EqhptWqSubwAC1eFPu6OmVRittWHHfy+mHUdmhTOul4h5NQT
+U0yZzXhxeMCV2zKo0+mElb5A5/NjzDXFL1Rt7LcFQBJozcVTazl5SUw5940WhZKs1J7B7a7l5V3q
+Wl9IdXPmqxnTQ6VfXySg8Ohl5VAqrVamXI2L3L3Isry8v4/XVYn7hYurIrbBuMDVnH3Ule8+rXxW
+h7oYjUu8Bi91EiLlh5wnMVEk49JQQZ0iSXDVhJ3VKeNz2Tjd84BDpaFzMp9CK1cH9zQDZC2J8hkm
+s5j9AwHheF5ItRa4cXJQEdpAGbqT0qFwZAyDaZ9YA41uUWPVbaqx5Pp5eCbP9N3v5XzlF8Rcv/nq
+Bg++Li/hn55P+I3/UoKqVflNWpHcJwykn9YaluuamLBLPFI5+DDkJ164Lh/3NjjPZcPJ9EDyKueC
+59L0jwlSuV5qINcKW3tN1pU/DikUY1Ekc1wNKtfV8mKDKAvwU7medyCgOOf0HFerS19zD8h7Wvqf
+LKifk8179qff5J23RTR3V4tpPtcf0N+Stb6qEwLNXMVVA6OCzKYoyRZ/diQrXwPuW2tPrbUF8BvA
+TwF9dScArgBPP8U9Lttlu2x/xu3TpCQfAV8xxjQR9+EXgD8Ffg/4i8Cv8rGl6CGMKmzRJ9CTy7eQ
+KnZ3MY9xn6ioxy5EoZhJW0qPllxr01NY7u7eHk2VRMt9h4YKv7Q6DfKjBwD0HD0lrrRxA9m1z1Y5
+h5rrz+KApiv3GCYOTx0JWvU7iqrrwPhM3A5/33D2UE6rRWuX7W3VigxLVor683tandeJCJVDID6c
+E/Xk3pHvXxChVudyej4+OUVrvJiVIQ1PLaioiauQYbdR0tcqyRvXpD/Ok4Lva1C243qEgd5vEbKO
+PwZhyKFG6JJQzO923aNUbfVGzyVWK6VtmhSaDozTFdOJXHys+AZT55iOWi5Om4aiSW2Rs1I5+yyu
+CNUKQXUojHVI1HxO3ByrqdXSWkJNVYrNICderMHVfFnx9l117a6WOArXjrwGB1+V8bxetxipKzjS
+4rnjfp/VQwkqv7Wc0ZKfsd2OKGv5bh7nzBdyv7bS6gW+dyFPmM4b4P2gCrTOxdo6UfKWQXufJ4r/
+rk+m3NwU5GHXbYEv/BTGr6jU9TIqEW/aHUorJn6j36T8vlgK+9tf4Vglpq+7BwxTCY7e2pJxa+8P
+sYrr2bAbVL5YmfMiZ6B8EMfphLrUyO3HbJ8mpvDHxphfB74NlMB3EHfgd4BfNcb8F/rZf/ejrmUs
+hAUE4QpPK/IWdUKpakTkBecnAk0+6QUMNcs5UjWltFdg1c+iWmByGWw3G1KrRl93sMPrfQlv5JVi
+3d0ZsWY7TLZgtZSXLc5LEmU9WiYxjubmm0158Ta3Q358rKZ/36XzWCauMUpZl+I5bkJD88Zt3cQ2
+oyZ1JX0edzKKk7Xw7ARK5fbTir3GlSbX1bcOAu8H5nfg0VNTMx/luAoQskMt2T0+ITIKfqlcrm7L
+YrxWJKzZ5bNJTVcpxTdi1cEMG8T6nI4TcmMg/SlNg6ZCk9vPNXDX4Ar1X5fZlFoh04YU1GXKsoy6
+/AHJjFU27lRrHGzh0dJ+LheGxF1/t32BP4EmN/QlLDRDcK9esncsGYlr32sw3JS+/czr+7z/QF6m
+ydmc2YeygTfWgjrvL6g6skY6+w6fD2Wj969tk1di/i+XCV2ds2YlL3ojKMgVZDasc4paN1O/Ykvj
+GXto7Xx3h/ZKdSWDJq72mUYflFzGL5uYQFys4smZXuuc4kSrSPPvc/oNlTMIn/HWd+TvZ9UR/Uw2
+rKCtdABsMGhrJsq0mDqyaSzvFpwmsibHZ0vMuor3Y7ZPK0X/N4C/8U99/CHw45/mupftsl22f3XN
+WPvJOOH//2j9Tsv+zBdfpljOeHwqtetn85w4Vg1Ga9HgOr7vsKb7C9SkLm19cbaYEgKl/mpH3gWv
+Xb2omOupkesJFoQeHbNG1VlmeuJVLvRc+d2w3WCwKyfB3hUJdh6eTtEgO5tNn5sqDHNna4OgLadD
+YDo0taOdfbEelkdzOsoMHbZcBkMxLyMvIuxp1qIrn5moj41l51+8/XWOPvgjAN78P+/zh4+VGKWq
+aYzkd101qqpFwbmCOpzdFn9wrid04VDouJRlTluzAFFfTq0rWxtELalK9aIutTJfr6YF3/7mH8rv
+ggYnT8WN66glMZvMibTiMKwavPIlsVj2bjzPUCPjx4u7nJw+kP61ZA52/YCbXfld97k7GC3i+rW/
+/TdJtf/XWi4/eSD9/+IrwiEw6DcxVqyA5tUdWh0ZW3/YIRpJxiHYuwZrbU5fiSIBx9cMjtsDlEyl
+LnE8PenxsCo0ZFWCzlpLrZWv2ekfcqJWSuF3mD+VsZufSIDzrbff5HSts3E+w+SyaHuDkKa6cWWc
+0tKsVEPRxwaXk4XiTQpLvqbmywyFZjuyVc48V1SnclbsbrUoFIVquy6VEgp1u22Kubhj82RFqWP7
+N3/rjW9Za7/Ej2ifCZgz1FgbMzMrrEJ4Pa+ioRx2pTFEuit4oaHUwS5UucnLDGYtP4+5KOUdtX1a
+2woKCRxWOqjBTPH+TRgO1fzuWIwUzjHLCkrdQE6TGA7lfk1doHVekij0OSqgt2a86dW4GtX3Ko9w
+qNWTCof1nZIayWSUZyV2TZO+W+E51wEwGjV3PLBdhXy/fJ0rm8rCFAfEb4lYyOHjglJNWy0S5dvD
+ip0HGtdoZFgjZnA9qOgru1N1MMDTqrwrqpkY+S51Q81o5nw4Veap4y2a16WfvSTGzqVPAwXxmHbK
+q0querp6Rnek0f7NDwnPxGCMm48opvK7RKXTH1QlRyM5AF6MC7o/LqI0oQGvIXP5Qi9g+KLyH24q
+QU60YrAlAYFwp0tjQwkPWx5BT8hmaW5irOLNHXkmY4ZwoZQUgbJXGbcFCvrBhhglyTFWXiRLhDHi
+ljgbh/SO5U1ejuDJG9L/80pcBlxLaORlDBo+3Q2tAi1CKsTfH3g+vX3ZsDwlV104KZtKBJyXOXms
+5dBhRVvd27QHK8GbkStTT5hU9Hvy3WVl8HQjK/Ka+VVZhxtvtyivXlK8X7bLdtk+RftMWAplXTNe
+rZiervBVOGXguDSHsmetrMVTVyEtKjK1JtaQ2qbrsuYe930H05bvvnp7wEi1Iv0Z/NETOaWdvuzQ
+p07Kz7wmgbiVqXjcl13+u3ePcKdaoZkUlKESnNyTE6PT3qAM5eS76Xe4YuT0qJIc21CykACGsRzf
+TyaSX24euSRvyu+COwNaIznFGtsdjFZgGqN03HmJaWrOv/8CzbacgnvRdX66K31763fu86E+91Lx
+KUlY0hzI888eWbYHctI+TMdsKo14GpeMenLabg3lWt1Oxkzh33/wR0c8fCYmcV1YPq+Q5+/MFwxL
+ufbDUzGzb5kef4IcYduEfPvX3wRgP9zGtoQzMXrR8lC5KubKM5gfp3Amp9yWF1B9Q+Z022/gdGQe
+fu71TZrD63KNUEz85sEZnS1h1Q527+C4iiEYdjGBWBvWczBarYnKsBuzgVX+DVMb7LqC0fSxmt+n
+zKn1pHeMZgscKxWPAO5VWi/Iib46vscsFlfq6J7yQK7KC33Q5qCBKuVRzmMqLbRymhE9dd266l71
+GhF2IGO/jM8wS7XMfI9KCwSL6TGByiEel+sKVYdUiWw6dUigit5hK+L8ngQxtzcc/MWfQ47GqqyY
+nk6JsxytoKXXidjYlhekPc2Zq281PbFkZm3yqjjswGOoJCx126GtJcB3nmuy35TF3/1iwf49Mf/v
+JjIB8YcBN16XBe/7e+xpbcTgHwY8eiAv7Af3pqiFznks//7KLUtvppmIVzdoKiFJowGT+1oHsOkw
+VQUkR6F703xI0ZKJvdOvCXtqai6HF2XWZq1xv9eFlfjn+BNMrACZts/WrriFN18+5bnvyPVOrkjf
+7n/XZ/llWeSTRyXdF6Q/g6chdksWdPex5fMvyrjsr4FJvR3emCsJZXlEogB8Uzq81ZCxtc4mSVdp
+2eey2TzpliQLucaH/jlGU5ls5Kwi2QC2X/Px35BB3NYU8dStmSoz1YPTGVuFxE+u7Xo858kme+3n
+XqJ4T8z83q6Y393VPt6G9N2xK5y2IiQTC774+2bZwLTVn1q/0P4MoxkefIPR7N7hj3YAACAASURB
+VIJlDKrZab0UU+7q39J3JzUYpVYPsivkXZmf6o17lDOtK1Ggl/EabOqB0zUd4qn8LrU1I3Vnrm40
+aCnIt/uijNvW2MHdlgMgm9+Akazv8knN2Hwgf79pmW1rLOV9GYuYlLlmn2jVbGaacTE5wTPpx+rL
+lvo9zcx9zHbpPly2y3bZPtI+E5ZCXVuyOAdj6LdUA7DfpL8GrkQe7ydyYjS98sLF2NHvvnS1wXN7
+GhhKXNbSRP065SUprmNj5zp3mvL9142cSicHpziBgpDYIOpppeGtmvRITv+TMCZVKwXNsQ/KgHZP
+Tr5+XOL15cSzOCy1KjN/tkLjmjRUTGXHrYjuaAR8M8B2NDOS1HAqp02teohMVhA/1AvMcDQQhe3Q
+2pKo/e2vvEi6IcGujXfl9+FGzlEgbs6tXY93HLled/sauRX3af/zQ76wLQE6f1fOhfawQTeT02r6
+eI+zUvuTugyVTLF17Rp7+xrMOpeTr/TPeO+BajEmNWks4zbNUqKRjNH+9CYDFUypFEjjLR9jlA6/
+XCVUnvT59X6b51SIpjntsWro2KoClrexiaPVl0QOVqtAqSOMSXS8ron/BqBQY6oMFG9BXcn/QAOO
++nnhsk5t2VIAabWNMApucsMDvFz0ITutHukjGftiJvdtdkuapWRwem3oah1PO8nYvy5je+Plm0SV
+YkqGYv15Bx2MAvEoPGwkY1TtxGzFYjUd1A85Xkjwd6yguA8nKcszrYYMKlKt8xmdGe5oxqFZrMha
+YiF/3HZpKVy2y3bZPtI+E5aC8cHftWSjgNFL4tM5ZwmdpjLNLHLC+5qPb5Rc00DilRtyCn75c33q
+LfnuRl5zrnnercqjsyvFSr3tA6KrcmoEpfqknSkL9WvnFupEq8y6EV/4nHznKJgxFSInPN2J4xxG
+W0ou2gk5XaioiXVIBuLvlcfNi4KYQKnLJmaBq7Rc6dwjruUU8NsLmGvd/0TTW9MNcqOMR7VLcHZP
+vtvfxoZiNTkxNCLppxlpevY/XND/XWWkcnJyvXe+v2Q4Ez97+8YmN1+Xe7e7N7UPTXa8VwE46G/w
+2qtyWs8ePqahwcqdakCtsnh2IX04ok+/JX17dz7g5K744rO4ZK64jwcnBbtX1QpLxfrbvDHELMVy
+qc89lhr4czc8glta+eg3mBoVZTlVNfItS3WulGiBi2YLMYMFDkqUa2rM2irwFeJrfbgYzwi7tirK
+DraQE9uaAZWR8a8KsRR8t4erEHrbO8eeKHuX+WMONTZlVZMj8JrkWg2atxNKFQMadfvs7Kr10+nj
+KcVaoKzjZvM6Zg1aqOMLtGjdWeEcaYr0hQT/nvQjiiX2UxQT3izkmfykpqf4h2zYYvGizIP7uIdV
+QZyP2z4Tm4Lfga2vuixSy+QvSBCm+daYuYJG/FVO6MjCDCfwFcXXv/xjMtCD3b0LkIfzaIrpyILd
+aG3S3hdTzTa2oCGBvU79AgALvsmjd+WNf3KW0kMmfLR3AKWYx7fNjKOGmsRWc/BuilY68yiec6h6
+hovckp3LizDyLD3dvKp8zec4I+rLRtYyfZJiLY5qMLqgrfJ9xeMxk7FsNj4hGxua2z47IlRlJceP
+cLTK02vKgthownRXFmu6NMxUOarle4xuiNuxt92g15JKw8a+1meEV2kqbHwwuMb2i9+V8V5a3K6+
+FKchk0qyC936dXmm6IRf/skvAvCH97/BH/5vkrX4Pz78kKaKAlf1gquRjHmxI2Myrh9zrmXRdatJ
+uq6M9HwK5ZM7Th7z8FwWd29TNq8iWVJppaahh53pi16NaWw+J2PR8bEK8zXKh2i84cXLZvMV9bpO
+r/Sp17SKyQOWhWSKTCHmfNCbEwUy3nXlULWVb3N0nbwUYEutWIKqU7IWhSpnPo7SoLlRm4arkOfD
+FOd5VYvauKr/3YFCjXa/LxJdgGm1qLW0PbrSxdXsUL2nwjG1wzBT1una41iJdoIq4cNzeah+OWFw
+pK7Sx2yX7sNlu2yX7SPtM2EpxNOa7/5mQml8qpvvALD8nzLOvqC79dtL4qEWF506OK+LydxWeKqp
+WrTOVfvwekBLgzONzSFNT6DJbrcJmZyUqSLwqsUGy6WkmLaKmuVKzNksLfAGcrrvZh1mioBEiTc2
+rY+n9fYfxCfEWkhlPEuoKbfzoKA7F0ugyDXV6XUJlXDFGc8IVN6t4VzBKC1aqQStflCy2ZXpWc1q
+igdi0fiDilqZrYkqPHV5vA25R+v9HsMtOa2fvVPSD5VibRrgD1UKr3eH1nUZQzcSd8DxAnBl3LxO
+TVB/We7RW2BUB7LeO2QzExfDIKdWZF6m/aKY2l/d/EWK4O8BMPmtDrmqR99qdtndlrELVcK+c22H
+5L48x2G9pKO5+e3IJ61lru8/fML8kUqs/esqvpGNLtyqYH6Cs6V6EVUXq/wNdnoPo2Iw1PLvtqgg
+FjfH4mBq6Zutlpha3T+voJHKOK6yt2W8kxbc0DlLRtTmgVx3/Ah3Lmsur9e6HgWp6nqETsUolbkZ
+7PsY5Wmg08F1xXo1Wmlqzx9imppCLWqsXdOaVngdsYrNEuxAdSOX8t2NqMFAKe1O6hWOyt1PqxXp
+U107gxg7+2SIxs/EpmAqgzs11DH0/payKlUR1Tfk5e2YkJ5iEwauh3tPTCa/K35ozzSxV/XFdTKc
+SBdgY4DrqlluB5TKk+dovYM3i1lYWXTtYpM8l38/fbakq7GBaJmy0VVBkkjBP6cr5k/kd3lWEGzJ
+v18fNikm8ncVllSh/D3Q341Mg62Oxka6Pm1P/m4MDX5fTMqGTon1C7I1W5Gd4tTrvPo5JlBGJieg
+UhZgZw279jN2XpIIeGfW5P5Mq/42C17uSH92rzTw/IGOl06CE8j/QOoQdOOEEMvacd/AWIm4Ozqu
+dRJTKT9mbVY837sNwOPnM773UIlcum2ujWQMPMVjO/cNhy25bnziYnSsKteSPhVffPx0eiGjHqhp
+XCY/WPxEHuiGS9TEKdaLPwItRbfrSk13DsrOhUlAdUod12B107PFMb7OSThea1+usKosZYocpymb
+Yae9wwoBZ+UKomuMa4xuws3AY9SXDanTgbAhm5rbcS6UpWytUGsTYhXcZVxfAFMALhegNuoj7ELr
+VTZkLbT3cm4cq1BNIyJRJvCT1CGvZD00PZfOBZvzOR+nXboPl+2yXbaPtM+EpUBtcXNLWZYX1V2x
+v2L/VMzZrOfQWWn+OPJIP5Qd9l3nAQAvpTGdq0qE0r9CuqE1+7a4ECFhlrLmmijUdEy8iP1Egj2H
+82OaighrDRKO5nL6Pd0B+4GceOlU7tupRsRdLdyaWnyFXfc8w/imBkefFWSareh1xTLptkp6yq0Q
+bTeJjJqMngNalemka+LCEHdt/SwmoIVL1bxPohJxdZ7gKslKpjwHcQ+iJzIW7vWC5Otyvaiu6W0K
+aMPt7lA76yIgPWlMjVFOCmtLKrWgjF1SpWK22rpPVaucnn6WzTKmU+nDn771LuexjOGzZYKvvBdH
+2SlGsyRbmxJInn4wJenLyZUcFzixancWhtgqv+CkYFdJazwtZko98BRKDnP8lcxfFaUYZYTGTTHZ
+dRnPaB3Vd7G1WCY2q4XEAyBPqVWq0CaWylcxn0QDlI2c4kwxGxsu5TOtNF2sKDTD4SuexncDUi2f
+bYUhdV8rLWcFxZaSB5WbVMjYOkrUw8jBWNWcry2oJCGeA3M93U2GmcvYG1fV0ZcBtQazg7TA9WSM
+5kVNrLVh20cNOqO1gOfHa5+JTcECqbW08KgKVRKqGqhVx60iZLqWXPcDzk7kb7cli+DdxyuufU3M
+76C9Tbevpqo7uCC0mJ89YKYpA3thcXZYFPK7jv+Q6lAJQw8gGCoU+uE5s3UKrBBzcMuLmc7WkV67
+htfzZJrx4L5M2O1hl+Zc/mEayEaQzHI6V9XPtEP8DaWitwuMxkecHVUdqiPQdFPgxVTqkz59ckhY
+aaWiM6T5ig5irjDZ5QdUTV00JrjQF7wdRmwcyGaR1wXlRPzraSEvd9u7Te5pJePyKc8eS6n2RusV
+xpW4DGQN5ve+JZ/3xRxOHln+IJZYzPe/9QjNirGdGu6pL5uVCXdVPv3mL8rL39kN2DuXF/bMTJiX
+8qy57TObKZeiF7G/LeORzWUxlDH0hjK/drRDpuxOJIaWZqvM9T2sI5tQvc4+OGAV0FNV51SZcmy6
+IUah8GU9xaq4SrWQ/hgTUOhh8OjNu7hD6XPT2yZU0du1pukyS+mqiEzDa3M619J/L2R0pHIFYUZj
+R9aRo0Q+nr+JUQl7mqeAuAf4IbahYx/s4K7Ztdab20tdmieyTrPTc9Khpm+jHvWZ9H81dC+UqD5u
+u3QfLttlu2wfaZ8NS8FCnRkWxhJr4ci1RkjVlh3z8W6OOZId+GnHUmgEe6U78XeKQ175thKPXNkm
+PJacdjlq4Z4IGGW1XJGcy3dmR2IOnjHj/J5Ezhu9lLYqND+/8xx/3JOTOX4Sc3oo3282NBgUNelZ
+2fmLoKRYinn24XLJUqsr3zrL6Stt+cNt+d3VoElbg0EbTpNoS+GnuUP++AEAmXJFnJ5OKA7Fsnl8
+OidV9ZlJPCHK5Rrb3Wd8rlRXoavSdMcp856cHu6zgj09ga64NwhK1UR88oC5VgMevicFN2+9/xvE
+D2Qs3k9WpApCet/8NjdVDGb0esjBTPr3tCHZkMbE403lSqiTggd6yhtjyZSTIsDhdwvBN1zbVjn1
+dsksleebHi0oFca8ODnh5FB5Dv2SZiX9r7SK0J9CphWH8++9hdUgrzlssOxrgHX+lNbn5Bq+4gPq
+zMN4ax3IgFJZopPjQ/JzgZPbdEylEn/ZVPrm1hVTzQJ9+GzKaqkSgcERoVaMruXv5nWOORPL5XuL
+exy0FZMydJj+kVhk3s88j/NMXKjqilyrXac4V9W3TbsXeBqSCShxCklBXUhWyZ4oSMac4SlAbOEu
+4a66M9cNw3WG6krG6ujPoftgjMELPaKqw3Pqc3eHDm5bzLK9nsOZLy+WnzkEfWVFUrltu4w500lc
+PjmjvipmeSuvqRQV6ddt/Fj8xbb66uW4T/dnZNBv90b0rt2R6+Gwpz71g995m6lGxq8NJEWaJimK
+Y2IrbWAUOJUH5oKvY3cQ8NJQKzRvaer0eIBfCmjm/HhCcCJ/R82roFLtJ+/JJvZu9YzpUyVQ8Xw6
+PUkdNm412dRMhD+dc66pvI22LKRGu4M9UF7CpMI/kmfd2vWpdcPJipTxSrUanklcY5nPubIhmYPG
+xoThFQEbDR/d5aCh2QXr0lL6/Ju6qawaMUMlC8nDiF1PhX7T9Ad6Cp5DorXdb7z9bQBevNJnT1OW
+s82Qaal9W+UXRCcvNjp0fXmB5vflZT2fxFTIRvbO3QlTT16moAzZ6MtYfP7mLV5UFqnOSEBarrMC
+xMVKloc8vSup70cffo/vfU989X6U8HmtCHV9jQ1sdsiUKNc+Kzjfkc8XswWNnmzqptAY1bJgrBvd
+TrPFlS1FkL7SxV3KvYONJom6Qt6pfDccNjEzmXeHHUgV0TmbkKxEumB+b4zVjEm+VBWxzQl7r8vz
+/YQbM7ui5LZhRl8XqLsfsTRrGZaP1y7dh8t22S7bR9pnwlJwMDRrlxGWlppL4TBhL5TT81mcsxyr
+unJgcM9k5z7uiOm8Uxl6moqdj6fUhez2TqNFcq6ApGmHk7GYcM+O5WS4f/qUrkJKR6/eYu8FCdmW
+2y+xk8lpvHH9Gu9/R8y2WCsZq16DQPUTy9BeBET3N0Ju7ElHbrQjGpUE17p9JexogK/B8PzZjPkj
+OT2DVyaUWlHnDuQE3zkfEgnuCr/uYpUa3TMHpAs5YaJRTaUAqUklF05ecOgfq5x9J6M4VkIOU1Jr
+EG3R7WJrOY1a1+UE/mL3Fl5L/n6utU99U6yUV668TLqrdfxxh6NaxjBQTMBJMuP6FbHuxiyYHYkL
+8o8Px2yrZbJ7EDLUoGuhafd46vNQ4eGVdXF13qfzOeTS//ZVn7G3dqEkuPb2JCY/lXvH8xWpI89x
+mq0YHa+rWFOagXIyfEH5ONsbEEnQbnxq+cffFUvh7r0jKq1+zUqPN7QC80uBWjF3ClYnsp6e3Iwo
+31BAmptjlCuxVh6OTqfmuYa4v6+/dhXnplg5z+4t+JOn7wHw8vw2eSBW6F4mFmTQmuOMxRL0etlF
+fUg6vcvRfbEKjsZj0loDkxrg3HGfx9wW62i3bXG/fV/H0KN+Tq4xjH3y1idzHy4thct22S7bR9qP
+tBSMMf898O8AJ9bal/WzIfAPgOvAA+AvWWsnxhgD/FeI8nQM/GVr7bd/5D0A37XsW5/tVPyfnbJP
+oCw281XBmebxM1wqrYV3Nd10/cYN2pHs0E8XGa8qgaUTRDhDOW5PHt3jjw/Fh3/r9zQdt2nxTmQn
+nnSfcedrPwtAs9HED2VX/unnR3x9LrvxUhl5z+KYgzWqDnCVPNMrQ6ZK7fV/PZxg9HTvPJOT4fa1
+DV69KViBYm+Hb70l/fipqz5BohqMuZ74YYt5KX27d++UQ02Bes1HbKk+w/XdDlevan5bx+34yYR9
+La6pFwG5skUtVgnOQL7TLBo4A+VOUFhvzjmBxirm4zFzxJf9/MF1dm++CIBdlrj6nabOx67r8OFd
+OYGPj95htZS+jdouQyWmHW43ef41wRNMPLnfyeGcSOMhV24NOFVLIj47x1fy2toEnCse4skjSbF9
+/dkzjg8Vrei7tPVEH4xcKtVheHMc8fu//ScA/BVF89260aXclb8fTc55rGrN42mfpy2JS3z/6IyO
+8jrc/sp1APa2X6Kq5N5vf/O7JDq/jcLldc0crpRCr6Kmsy/j0t06YKrVlb/23Xd4dyFz8s2FQ9SR
+633t2hqi7rK9krnulgn4Mjexu8+5L7GGB/WCk0PBLDyeKqOV+yFf1OrY514Y4rwkY5x/84xKVdPz
+pcfyeI1O/Xjt47gP/yPwXwN/94c+++vAP7LW/oox5q/r///PgX8TeE7/92VEgv7LP+oGFqgql3lk
+aRUK6PFdDpTNeWs4YPemcvjlr5LfEBPuqgJe9l7ZozjXHDUnNH3NAwcj/Kksqo1mlztdMefKnxDz
+s9He5lpLXIMvHjxPc0vcB8fbuah2e/nn/zK9juxrk1pckeUkJ1XtxkY/ZKow1zRxLui7Fx1LU0VC
+2sox1zYbpIkKwZbgqub64tk5DSVXWT7Uazlz3Eye0xw4XDOy4KPBgGAhm2Fv5NF1BC+QtqScdvSs
+SbUni+b0/hn7G3Ld8Ngh1cxAUKwIduV6VyP579O9jL7mxxvblnIhwdp2q7ioV3BHDkEiLlG9kHEt
+5iualbhBm86Q5Kp8/nz2HFc+Jy/CfvQCw9tyjVcValzc+T7v/6EqGo1cIhlaDt/8EJNoFeRGwhsq
+8PLuuXzXmdegwjHNssGmktb8zNY+GzeV0/Jsyfvfl02NJ+LOdH6ix2oh473ZaPCTGjD9/TtLhh+I
+6f7e8IjbKl1/66cV2uxucO+KBDa7b/tMVBd0eVhRK53efKIitn5MeSjB6Pa/5mFKudZPf6XPS3fl
+fps/v0H3TOpH3C3l/Jw6BAfyfLYcUFnpc34yx6hMWG/hMFU+zR+biNs8+PyCa6po1bkzov1Isxa3
+5+T31F3brAhP/yW7D9ba/5v/N2j6lxCZefio3PwvAX/XSvsGoiu5+4l6dNku22X7V9r+RQON29Zq
+ZQwcAVrCxj7w+Ie+t5aiP+Sfaj8sRe8YqNyMzUGP59qyM546M3rK4PvVg232NmV3dAZD2BOXwCg/
+ftSOSLqa284D/FDRY5zjbYqZuNW9TtHSkzIQhFram/L5banB33vtOk6oQiyOg6u6itc2t/nl/0z4
+Ar77+2Ix/MZv3aWtCtQ/PmzRimRX/tIXtthQ1KNnLZGSaFSqLREkhsZA/j0+X9BQPYl27RIoccpA
+1ZA3d0b4E7GaPl/HOAfSt2IY4SleOzIOlcK/w7GcSp1uQqwswZujkCqTsQj7HquxnEDtzRuEbZW3
+Uxn5TtcjUJ6CVn+LVOnYTGHwtSKUuk+ayfSmuRztJrXsXJNAajRKueNIP1f1mP4VSfEuFzN2BnI2
+DHyZxyJp4tyRay2jhDOlt5v+I8NI4RtxVrOn/X/xmvS3yyb5hlpKtkv3ZVkL7X6X7g0xn5PTlNWR
+MExfuy73a/R9XMWZdO58nttfkr799NEHLA7leu+8N+OqunzdnddkfOI2N62kN4v9Pl9vy5x9++EZ
+I3VZM+XbiGYpfSXjDrOK/p5YBHt3XqD8ObmHt7tPoUdsoc/mxRNctTBNucLGSpxSVezsyedb5YDX
++7IeWp8TqyM88AkaSizT2SDXNHqZ/z7XlItjOappayAYTeX+qPapsw/WWmvMGkj+iX53IUUfuI71
+fQf/hSHPb2sUdjnm1oZM6I1XbjHcVH/JW+Kq74jq6GWFhVgj+VEFjgI3TImnQqHtdpOrkYCaRk0l
+zRgMUOgBUec1jPrJmBxQ1qRwlxu3xWR+9I6U01Y1qAQj3VHAaiTf3XJG7B2I2dn0h1RKS3/BODww
+5Mjfy+o9vGotRpvRMMrCo3USzbBLQ8mcvWoXu64STD1cfTw3dMhTrdbUKsusV9C9J1+I9n2equZl
+4q/YsnIPO2yBEqCEWp/hNlKCprgPQa9DUMuLUNcLioW4I/V0jlsr/Hepojeeh6eVfOH+PkZN31X3
+ZzEq/T47P6VcaKXlSEu2i03cbdl4nOOapisbi/UdUsWWdKlw92XR742kn9eimzT3ZbzLbk7QlLnx
++zcpFah1UvwBnXW16UgxIr6PWc+HW+G35LpB92WaN2RDHm59TdR9gGC9eXWXtI/kfhuv3aT49TcA
+OJ9nLDOdk4nWXJSGpKNme+jRVIhysHWTEE2P+W18lZovFY9Q11PsXF702plQxfpaljkdpfl3bpb4
+W7qJhLJo3dDHaH9tGuOEcj933iN7Qeas/zDE6/zZkKwcr90C/a96hDwFDn7oe5dS9Jftsv05a/+i
+lsJvIjLzv8JH5eZ/E/iPjTG/igQYZz/kZvzzmwumBZs7DfZek0h39O6Em9fEaui12yhQDjPoYada
+qab0VOUMskghtXVEqVVoxaqN66yDjhmNpsq4vfwXAMi8FL9YF98EAiQAMAFr7TmHjM8d/JR8/O/K
+bv93/tu3SfWUSOOKna7qR9oWlUKCKxc8tSVdraK0q4Q6UjRl0KMOFWlmNqgVdeZrLno1tYSbSstV
+OXhdOQVNElOrMnc5KakUAeqsod9HMZmRcdlyBqSqmTguHHaV96BMPJaZnFaeVvoFTo9gQ+9RG/Ay
+7XNNoWO0mC5JZkpzZtYnX8FyrlWU9hn9tmRXes2I80TuF1cOs4lKqBnpT3Y0YbqWWOv3CSstaKsM
+DTU8Y3IGWmkZZnKyN3cDGnNVjL45wqg+RV0vqZ/K71blio4VC2FwUxB/JoxwVJOiXuUYtRSceIDp
+qyt1YCjWUOKFslUnJxRWC4qOF9ip/F1kDrV6VUurkPEnS9rKdTH/fkLUFovUjfcuCqnsbAmsSVu0
+P819ai1yq85nlJpdMpGPUbfC76f4lfTZ7cnzk82xilmp85paM2Z2t0lypDyPaYxd/UsmWTHG/H3g
+q8CGMeYJojL9K8CvGWP+KvAQ+Ev69d9F0pEfICnJv/JxOlHXkCSW1bEHWno8/HBBpBBX0ppSJWTN
+eYLRmggvEtMqPz0hG8tibd7s4npiihZpTb0SQ8Xf38Eo4YZ1lPh0NoNAJsNUr4ECiKgrUJ1KHA9n
+JRPjTNYGkaGhkzXa6dLRF7ooFni5Tkzt4TlK8KHmcB24VDr5q/tnRL48U7vVxd3SiPpKXrDU5FhV
+mHJbPk6sPr7jUy3ENy6rGWiKLJ5oavXDE1w1T+vzMe1NBU4dwoO+YuaX79M/0LLdXKsJQ/+iss7d
+2KVebwRPT5l8INqVS+sRzxXSvZD+bHU2ae3I4g+5gq+gLvKMQElxbQKxEtsMJvLiur6HO9bU28hi
+lFCmLEuOHmvqtBuSNzSzofUF8XmA0foRfxHBhrxs7rAFWjVb/5MFrZvyooctjRM1axzNmBA5EKtL
+54FZpjq2Ls46neuI/108PibTTbjwR5zMZYzO5wW5QuvnK+nvUVVSP5Y18qXjt9kp5PlsusI+VkKV
+Tu9ClMaua/lP7l4wRJksovQEXGcfntM4UOasfA9HxZFMrM8ROljdFKvZEVZrgfLVgvnX5X7VzYTs
+ic7Jx2w/clOw1v4H/5x/+oV/xnct8B99oh5ctst22T5T7TMBc7a1JUtKpu/PsP+rkqxsdog1Wjwo
+A7ye7Kqu18FsyGmsDFY4zZjVWKPGh49ZqYluV2OsFvN0oopaORitK6dVVde0tbLOtqeYYm0dtEHp
+xqgt1RU93VOFmWKplX345N6C4VUxfdtbDRylVDdOTu2szX+5VD4pqNtKvOGnbLTk1An74DXl2tnG
++hQx+Osy+Ogcp1YTt/UDfEM9S0nn63uoGTmbXgSfmoMtXOVdrHsu5YnSz+82MRrw9FtanOOAVcKW
+ehmTJ3L6F/WYTCnMsX0WlboKylCdujO6sYxhOPRpqPlcOktmyqU4O6nYvuXr9dR9qANSRybQjSty
+Ffh5tsrIc/k7SCvuqMvn7hU6NeZiaqp6jtdcq0uXeKp43RjBblNP/0xcSRYpVq0VmjVUShZTl1jl
+WLTmDFIJlBorlpdbedCWdTM4aV4EOdsfuEQd+Z1qtzDLciYLmd8P33vGq1+Wa5j8HAIJ8tZFjdWg
+uLFKCed7uKpKXTk+rVh5GEYurlLRO94KR+fSqIVsgwVmoeNStyjU7UpO3qK2ins4r6mDP4dVkhaJ
+6L95/pR3K3EDBkmOXUq6MNup8Qud0IYjuoFAraazX8LmSD7rDjdY817W8xJHJewd61IOZDXNvy8q
+P3W+pNn+eelDUmIDLft1O6xjsKlTc6YTc6aRdc91WWg24LRX09MS6WtLHux3DgAAIABJREFUaLY0
++JG2cCayWhK9VhxXxIqETOMFbldM97hcEOkG4GgqNHIqPCU7reeGQtNUVRBRKitQFVdkS4326+/G
+zZjbau72nttgzY/S3IG2Iuy8ThtTKWGralMYHEp1GcxqcUGCGvmWrQ2J8Get5gXpaOzLi+I6HvWa
+ArAOcFzZIEtbEJ9qdWE9lzJgwAbiEq7ihCTQSP95xkrZsMaZJVf3YORVlCqqutCKy/PxClPpxuMt
+CQZaTJEuqXuyQbTzHD/q6fXEDWiPe+SHqp0xvoVzVV5SJykoYhmkdGypplqtmMk8xotTinN5pv/h
+yfu8/aZu5J5DUcikeZsKIHrqcraQdbE0JYkjL72fetRWwFT1dAOj8RrM2tVaYlWLpO4v4Fg2Xtft
+4wW6mPs+FKozoT+zaZO6kFe4WlQkx1L7cPwEHivQaes0o1Dh2Y/bLmsfLttlu2wfaZ8JSwGgNpY8
+r3n0+xL9HXz1i8T7YsIucgh9MZ+tH1Cr+ZWqepDb22VvX8AogbdB3dPah51XsHoaGRtSK//hmVYU
+LhyP5lO5h7fdILSi5ozTQWn3mGVvcc+TXf7oeUFsX7n220xVDvw7RysOtCLv6GyMu9BMxGJ6QaIS
+B5qpMBnpiRyrnW6PRKPz9dMT+koj719Xd2bpUGvloI1yCg1mlYuEXJ/b4hLtyMl9NBar5HFmaSld
+/M7DM9JAMzi1j9cRCyI3PgvFz7OtQdRyH5oCMCrSJUbrTqLW52jsSj+S8YLFjvRj0/856UO7ID2S
+McyWU3Zq4YeLnYRncxk3b9Am6mref00ik41JNKjaMl0W+nxFw0XLQKgriFVo5ehYsyHe+OKEbXev
+Yo/kZLeupXhH5iGvW5zeU5qysRCoFI8qasXWXo0ecyv+gox39yZTNf8f/ZP3sO4DGa9cs0hXmtw9
+k6DdN//3J8yUYm6jF1Dr6+PGmkWpLLH2/f6xz2wiv/Nb96kdyYLgTQkVAOX5qlF57QbFiQaHjcFp
+qUjMVuciIGqzgFoxJVaFiupJjl0L7gQdsrZYdMUoZ3xf5qSKLSSfrPbh0lK4bJftsn2kfTYsBQu2
+MpSR4XAgJ/CPFcfUDUEgVmnBSgMnwWnMrJSgWqKna9gKCJXY1b6UYo4VxdYGjjUaeT3EaiUalfy3
+V+9gPNnNKe+gWS/AwWq8orY9/lSrJLvnkpobbffoB6rfsAurexLM8t0e50vxI+PDc3ZCuXessOvV
+pGYRygl2NWhy9WUNHPWvQUethommDX2X9ExOxMxfEmhKNmdGoUSyGQn9dYDyifiTj+4uuPWaWCj3
+3jsm1Hr75MkG4XW1CsyU5kD6FiQStKrbS8iU06FT4azkGjZa4iYrHeeKrVj1J7bVsa0C6psatykH
+rFQp+3Ts4cZacbh7G1d940yLuZxyQX6isZoyZr7UAC0llSI9va7HmdKNdRsy/6M0ZOLK3/H5Y5SE
+iGfvjy+o7G5vDfEPtEIzFAtl0R+Taco1HuSs7omV0vhcTv5U5nr87G2aGidgoPiNeod335OYw9GT
+BUFTtTDzLplyWTzTYJ8NDK7GZIsgIVccwjIvcWffl7G9uYWnmA33igKBzxL8tqR4vdX/096bxlqW
+Xfd9v33mOw9vHmrqGnrk0GRTnCSRmixRkhUYVgwKQuJBgGDEgZ0ggGNCyYcA8QfDgRMbUBxLcWzE
+YCzbkhUrVGSFpBRaEymyxanZA2uuV1VvuG+48z3zzoe17mOXLJrVFKu7gNwFNPrVffeds/c+++y9
+1tr/9f9vwxMyf8v9E4ySrpYnDrmeIjuqwF6OppTqYU3tiPy+yhBWJjyRyNyaro05uac5jIe0x2JR
+cBxDteZztrbNVk3ZjDshnor8ORWPXAenSFxmivO9qdTb7q0B40gSbhdmm1Q3FbCSO1hNNBYHKbtf
+l5faQVznyralui0hg9uo48wZdY2PUdDMsrvFf975CAD3Zjox3/cVrivl+O6Xr3HDSNvOjeBiS9o/
+qx5zoK7mSMHuN3enp7qS7vYmW+vyQoaegxfIxMw1UWVtTKzn/P3sLp2uamku13ASaX+YVUlScSUn
+E100sdxMlRm5KFnXd7fdcmjoRK/UQ4K63DvXM3qf4lRT0fGqFMpnyEkOisOwZQ1P3VWj4K6ikmFc
+6fO0P8Gti9u9f/wl1i5IUrHVqRMoKGvakQV0NnXoa1l34jgMVROx4hiCef2AMRhdUB0NfVzjkGob
++nsxfQWw3exNmL+RS7WQd6guKJuqj7naZaCMyuXX7zEqb8m9a+vEWhq9/WSdQGn+MxVe6V2L2dXn
+8NRSleVVWbwv1qqYE1n0Qj0t2O667E/mwDqDr+XS1SVLHiomozckGara2UjaFlQMHrpB1C0mmKtX
+JaD0cMXIw5SKT9GNzGn4OEr3H6QNYi0ND4/6+L787NRzCBckKwtb2ML+FPZYeAoA5CJvXvclWTL1
+dgl9Tc5EKcVYkzZBFTtWnIK6Ttdu7VBMxG1tlCNWZ7r7bXRIXhK3rV+PmM3EvVp+XlblbmONoKvk
+JYFzuiNCcRo+uGWdVI8Wuze1qi+Z0hrITjnouHz5huwulzb22FySIZ3enBLVZJseK+rsJM2pqyfR
+7XRIVIdgNN3FO5b1eVJIP+K7yxw5gsacmYwVV45ng+YSRU1c33RiiXNJzJ6oVxKfd7F35VovpjM+
+rBV11Y0xti/39jbOUuYKpVVUZVpMCbRgKJmUpAOledvZo6E7YdDcws5FLuoKA77Xw5wTF31KHzuW
+cakf5Ww8K7tcs7FKrHiBUlmpi+GIXHUmyoOUyUhdeAONNRnDhqmyPxEPIlZi22M/oZnIdWdeypGK
+4MQF1NWbqrsGq+IrpRbPVbbbLGuC1ll/mllTCWbv3SNSzIlfL/C3NBwbybi99DtXuarJ6OBMnfMb
+0tfJUUypR6dHbfn7bcenqqQn08iyU8oR6Er6EcIlPQ6/3mf/QEI9o1CJzYqhogrU1I/xltVlO+kT
+e5IoLmY3CVWfohoISpWocnosmmYnMoeBKK8wWFe91Ds1jlQu8WHtsVgUrBWhomqesPd5eRHcYJXk
+iriGTlLgKuS3tBHBivx8TuXXPevR1eByVkTkgca9J1XMmpTA+uOM1feK69del/qKiX9yel2n5hCc
+killZI5WAWYtwp+XiZAMZRKf9Dqs9uTLBy8ZTjRE+dwrE5pKEb5eaRBp1eXbnpV6gKBym6mKjq5f
+ukjjopwcuI2IWBE5rYHkUY4bAwZfkhdiuZ2SN2WyjUY9JsqyFGUBZVVBW55c9wkXdrQM++bemB9T
+Qduh26KxrgvZ7j5ZRVLxlYa6516dIlJMfSUgd+bn8WtM9Nz8aDw+JUCpznTihl2m99Th3OgyHMpY
+nHTOs1qV/vWnxWldSU8p/A/LA+4pWjcooEiknWvNKu2aPKd4ZhjqgrqnilZ1vyRSbsROtUpnU0K+
+8+0ejgJULlxco6qKVJW6vFTTvYLGtrTH81bwe4Jf6DwzYnioMgAnJYnyPOZaqXjzyLB/JP171oGR
+Aq7unkz5vnXlh9T6Es/12FT6/ey4xmu7sghtpQNWlF6/tvkUsV47UfRd2aqSdbWGxTfkCkjznA6z
+WAFnXp3IkUXdKkCs8A0zhZ3nNgBdFIJKi0yVpY6SE9KJvEcPa4vwYWELW9gD9lh4CsYa3MJhx5sw
+KRXRGLS5p67mdhhimBNPVPC0aKWuK+P22U18hQy33Q2crkJciwZOQ1yndn0bV11eW9FkWTzDupIM
+M5lLpomqpByQK5+Cuddj5wvy/Z0fl9/f+Y0D7nvKNB33cZV1+ubBkK8p9drq+TVqHVnZKy3ZfZ4+
+0+D489K/vVe+QnxDvKL2mZSirQI2Y90ZohHr29KPcLKFaSs8+PgEX+XkkskuR3q6Esfy+/NRyT3d
+7QqnpHtuLmO2hlBrgj8+IlKtwVBxDAQ1bDDWz87it3W3dtuUc246Zw2rrmgx09MJb4qrmpg5MfdP
+JFNfCTJMpuGKO2U4k3YY1XdIhymhJvonboFRgpeW41FX8pwyKPFLZW5WWTlbNCh0LzNNaIXium+s
+tIjvynfG+7sUe3rSUIoy9LA/ZaahXfudT1O/IF5DEWQEQ/m8POyDamF+WYV4Xrs7YKSS832Tk+jJ
+VZ44rJ6VMfqgclbkazHJbR2qM1BNpG3J1+9i36W7v3eWlTX5u9FIBsCtZERdeQ6uG2PrmvAObtC1
+ith0KjgKTaeqPEZZhOuLV5gPDXko8yUZZKxoMvrw5Jhx/sYKoozUML21Vg89+47NFplxqLa0Eswv
+TtmNIq9GXbPQ0wBeuy4QW50vVD2HSLO0xopuIEDbr+IpjLey2mZ0Vx+CTqQvX7vFqJQBG4xTwkjv
+MZ3hGBXWKBMamr1dP/ssABdWnmJ3Rz47+2MTylRehOSlEQd78tL0kyEaqjKezbP3nAqbZiWUGicH
+rktLaxCsvoCTScw8wxE4louqP/hXP3SJpz8klBVL57aor79d+qe06LN7n+NAQTN26Szv+KF/8I2B
+nr/bfkS4KhOvY2V8ksk+rt7bK0tKVcCqV1xq6s7nk5RQj2K7NZVsDwMmupAdT1P607mEu2XO9xOF
+Li0FSbWVDOYd9QbHSrwyOJox0VOLOH+WkQLAWs8cMdWKwP6rOsnHs9N8TyX0CXx57rWwQasq4c+N
+27eIfbnfQEO+VifEmeuG1iNItRoyGpIpzf9sMsHx9EXWyeX7AZ6K1hQ2JysUneR4vOftfx6AvVtK
+APNTPQYDIeKZfHKfvuailkKPd2/LeD/9/vdzGAsj09PvlcX09tEIJ5DnyBQ6a/Lkv/BvvkhckVxD
+Nhxw5fuVQ7MlodELGxdZeruM1Vq7RiNUYiATnIYAuTVkhVyvEVVftHaO0PvmtggfFrawhT1gj0X4
+AFBiKVYKWirBNdqo07snCZJuPqB0ZJUcZJPTTLSjZBS+FxJowiyPS5JcXQi3pLakvAeVhEgzy66G
+Bpu2z7inmeUStlry+3sdl+8tZGj+bS9mXT24ysVXAHjuuZD2b8iOkR1k3CiED/BCvsn+nPpq5uBH
+suZqtEKGJddKTGNLXF2SHd/gaZZzHsI4qUupK3xmDHuaZf7cdEg0Fk9ppVgnzSSrHxh1HTtfoXpV
+dpLhhRPmhYH2dYxcZRlz+UhFRrbk79p5he0tOUtfNZaZ0uh3/JBUQ6JKMiZzlFdS6cWOgxR17ggS
+B6+nBUxpgqsuW4FFnQlm2o9m0Od4Qz68NPApluTzsytD9l+SZxIfz7CueD2rDaWNq7qUSnDTqAZ4
+qtPndGusutL+IkqoTST0vKYufKtRcFzIWD3VXmVPsQlB5jGaA+MyQ18rRZUKA+PnGKX3K5ycUhOJ
+GMO7PiLz4bV/pidVvSnHg1sAXMxrTBVA1Ulg64cklHr+Sp1BX74TrEjI6NSuseTKqdu12Q3WXJHs
+O/OjezyxJ57Q13Z+j64vm3yl87vyd9X/hMMTOcIIyieodMVTCoIl0Epgl5DSO0XlPZQtPIWFLWxh
+D9hjkVNoRb794HaX+17Ouy9KwuXm/YTUahnqeMhKXbbrcVJypDF6RZl3n2jUqeouP86mDHRbqkce
+z0QSR948TIjmVGmKb1hOGvzqyS0ANgvLPVUi/oDT5VOF7FZvw+dlFG2na2it2caMNIFnU2p12Zm3
+LrnsVyVOvntnRCVQXgCtdZ3GJamribYsO4XEBpHD5pKqI+vjiKcFxypsmiUWqwIpL3SrbP6Q7Igf
+ec+7cFelHefV+7l395NcbsgOdG0c8f0/PWfKe9CeVI2H912UWHe6scH3flCSrk+HIdO+NK610cZq
+6bgXpwyUxs1occ5+L8HXfEaZHTBQGPMXdw5JYtmhvngyI5nNy4WlH1faTW45cq3vO9c4heJ+ea9G
+MJDxahaWlSVp56V3yXOcdA3jeQVTkjJQTMogzUi1+O3DvWX+9x3J+D2tNHWvljEVI2O07gSsRjIX
+bmZTfEWTTh3oueKxjNUjCKqGquaXYltiNTFTWkNbxW5SzYE0bUG9Ku189yVD+bzkM8ajnD/3NlVQ
+/2LB0yfnAXjtVeW92GyzpyI6Z881+drXJZH47Noyv37jKgAXEsOnZ+L9zFG/l/wG9r2SPB4ENV74
+7yXX9JPLP01rRY7BHadFMcdhuOFD5RQei/DB8aC6XNI9MuR15R4YT3BVRTjEJVVgTVHa0zPyjuLp
+G50akSeTtGIKsjksN7F8VWsiplNLrhTvTiAv1U7XwxvLC73jz5hM5fNP1zzyqWSnX7QHZIUmjDS5
+1ptZSiUyacWXufS8AoCaIbEqToW+oaX8iRXV8mtnLr4uZMaWlJrkdEuHC6vK4edrTYVX0FduvZPZ
+hOlMfl667PN0ovx7RY+tl2SyNd8j1zp8zXDve2TyHH7u6I+NtFLi45NGktjaf17G8h3dDmdVmWjr
+XBdXadQrayFGT3sICxI98i5U5XojOsJvy0sxuNtknErWfiVyUd4bTm4OuXNHFtmWhhF+MybakRd2
++rzL5Lq8jEeTCqGCvorpEt/1YXkOy5poZTig1PqDvcGMnpYl7p8klIrV+HiRMFMhnpc1MTqLl+m2
+5e/6WUS8roQyRwFmXReZoxmOp1ySxZy8JGCmzyxOIVClcx/D/kT1RH19+dMz/PlnZOF51/tWuTFR
+WXvviBf/SPgbsmGJ15HF945S8bN+n1euybU+e3ifHRXcfDHdZxTK870RHnIv1mT0ksyxwyznfc/I
+C3/7C7d48jckrBz+hEOz81H5bvA85g0GBIvwYWELW9gD9lh4Cl4BSwOXmtfgQOvgy7IALRwJCxcv
+l91/LYhoaH3/+WU50tnuQk2raGamys6uHGPN4ox0pkjAasixJu6cFVlp44FLW2vs7SyAjqyR/TLD
+trSqL4N0ft4cqQucWFwru+rFDUOsx1crfkCzISv71HO4oK5vVQlh/TgErfZzHZeBFgF1So+Wog3D
+UqHBWwFraLETA67ell2gCCznI9lpru4ccFvJSN//y7K7FEfL3PkdQevFt1+fYPLwNTm4HDn8+HPy
+/R98lwAWWpV1Ll28Il+d7Z8KzviUGPV0bGSolDJlcl9Citp2BKrv0C7WmekYx5VN7uuZ/08et/i/
+N+XzSSrXjW4XnFMJuepBTL0uz/Iz3gijbv6TFw3LClNeaslnUQ47E1XBNgZPQ8yAAleZs4qZxVMm
+7KYrf9ckYnVJdvya79NUSrfsTIHRZOW+X7I+k3l0X5/1uMwYqc6lb1x89TwcF0xN7u0WEoJd7loc
+5WbbXt7kySflWV5/reCszpFBdZNc5/Lm8/L/g2spJ1pUd3BwwtKy9K8b1Fh5QvufudxuyjycF8/V
+Ow4XFNPgvSdkqKmAO5+6yuZPK4tWEIOdHw4/nD0Wi0KO4chxOW5PyA5UzNUaOo6eFdcDQlc6Vo9C
+LqjK0prizC+strEavzejKs+tieuYFgl7x7LI7B9n3FbNv0QrIHeDjJW6coklBam6146TMCkFezDI
+Z6esu30FsRjfJVPQyPFWk65WqnmNJcJMfj5Xr9JU/sCunowUxuJmWtLqlYQqgW4cg6d123OaSK+w
+JKp5025tsqF4C7fhcTuWCf/ya1Naigu42JX+37zc5/APpH/TdMopOAFRvpLxdBlflkXtUPMvV1YN
+kwPB6ofNOp6GSrYssIUCjzIXE+kY5HMuSp9SYdep62FSweU7QZ+2o+QzF/o8mUjsO9Gz++PWmF0F
+mbVu1jCrykuYZiR6umRbJfU15dPUsujDwRGOsnI7rktL4c/VbpNCY//MFBSaY6l4stguhS6Nlvzc
+qgcsKTFOu5bRi+UFWqu53B/rwjGQ53F9nJJZeaaDLMPT6+KEuHVpf5opoOlim+0Vafv2M+fI3UsA
+XEh/i3BPdp+uCSm2pY5lrBR1N+oF9YHUsEycgJZWQ97pHXG+KQtEWvN4SoWC5mFEw/cZa7XuU09B
+1FEx3fMfpAhVaKdMsbrIPqwtwoeFLWxhD9i3K0X/d4E/i6haXAf+srW2r7/7GPAziC7ZX7fW/ua3
+ukfhWE6iAsd4pF1ZDeNeRqk1+2HDpamJxuWqd1rV1tCCqAKHqmbyO1GTcFW8Cuv4VNriXndXHNiT
+3T8/kdV8s12hrxLvse8SJyon59dJFen3Ui/hbqL8BHpft5JSjmU3dnvQ3ZIz5mCQEugO5PuGtiLM
+fF16fZPjN6RPpnQYTVS8pJhxpJReaS5fPkkcNp7Qqs7AEOgpg/WrHJ5Idno0demrK/qbE/luepCT
+hLLrToYOzhzFSIUNxXp8aPk8543sbn4pibyjkxGhIvvq05JSrxG163gqMlImGY7yWuRafFR4CYqo
+JfZLCOfeSJu6krNcNilpJIm2V28K70VxkDJR2rgbtRFNVQ0vbD4HgtI1XRwloe0dyLM7mA5wNAHr
+2QJ/nsx1CtqRegVuh0IxErlWdfplRk0TlKu16LQitGZ86nVl/G75rIfyrK+vSXzhzY547Za0Z3aU
+Y614B7YAo+5//hn5fTryuPjdQlPnTirQkf49cf67Cd6p43VkGA9lF68rtPt7og0+dSQ3mZUDVjVc
+XW76dBoy9quXCy5FzwGQNcQTrmUjDtWjc+27qT2vQkrd8+CIJ4hTxeG00u+h7NuVov8k8DFrbW6M
++TvAx4D/2hjzDPBR4FlgE/iUMeaKta+Hzvz75lpDu/To5yWFHsO5GViFFzuZD3qU5zrF6WJgZuKe
+Z5WMUAfPcx3yROPeYEJDM7y+W/C0I5DR8YY82MOjGE9j3DIaYibi4hkz5n6uIjHDEqujNFcjmh6X
+oEpHvfKYgyP5+cnNDepK8V2rNqgH0s5Is9c2rGOUIGQyybHFnLEooFAATaiLkePMmCmL8CydkWo1
+ZNiGilYUdsuYwMoDr2othjNpcazQ4ON+glVnsCRmrC9I4g2ot1RjU6nMSyqEQ5m4XjjANcoVmXuk
+x1rjGxiIlTdSqy/LNMOrae4nb5Mrs5TpHRCprmISNthU2XZ3WRambBZTfl03ADcl0fJs53W58m4r
+o7cvYVp/oOxWeUKWz4+kfYy+pLnj4CtIJzUxmTLGpHq0GLqGVj6n+Y5JxhoeBfEpIUnczVlRuPWc
+fGZtfQ0vkpe4/+WcmW4ixrr0PyVjMGe+3knvc+v3fguAD/zgB/BUILhyYZvwnrZztXJKnDLIlEHp
+5UPWNDdQqx6Tani7ZkYkyu+5ctDC+6AeE+vCGl7KCSfS9lH962wOhWqgulUn0LL8kvKU9fxh7duS
+orfW/j/W2jk0/7OIZiSIFP0vWWsTa+1NRCnqu95Qixa2sIW9pfadSDT+FeBf6M9byCIxt7kU/X/Q
+rLHkToYzsIS6k7ZdqPmyk65Zw1JD1q+Vbo0zmhzsqqvu1FOqvmKRPXA0UWOmEDR09/MrGE8FU441
+u+uWeJoYcrIOY6Mu+KDgQl12v7zZIFWKtGNkV82mBkcz563dktkZadvgTsam1ti3S4uvnoIptc7d
+OlrrKZoVnoYXdeNj1c/36ioxZwNi9WLimSFOZFz28l3cE9l1G4Ul0XP6ZKJEMPWUItRkWdvFmycX
+zRk2XBmLPHSJ78h3lt+jydDChY4Cc9zKadhRJlPKObFKWuJp+OB6Ot6+oUSr+qZTjLbTuBUy1fCo
+plU2A4Hgeuvy99ev3mVZNRpHTkFtLqVtUtoV1cUc5fSmt2RsVRbvXNtjRedCUhj25ryS44RDhXpX
+Kj6eepYt5XEoHMuxjn46dqhr+DPNM1zN5ndmEW1P5tSGCg7ZVpVEhUyzWcIXryvpi7WYuUqqckpu
+3Sj4mp4AvPyJlHd8j7I1x1PMkiQdqRzjpHLvulEpwM4aLzynPAzn6gx3ZB42DqaUmoy1acrks8KO
+faTP3O+n1M7J6VHv+h7T7BcBONNsE2xJGOM4lvINAhT/VIuCMebngBz4+Lfxtz8L/CxA5LskicvU
+Jig5DhmWsFTsfyWmWZWBXGq5dNry4CpNGZwo9wlryknnReSFurZxgqtZcqdIQQky5sdqI3/MfLyS
+OCdNFB3nDxnO5BpfDS1WhWArZo7rL7EaD8+ClGMV6XihskGg7l49+sai5sZKqmFiPI2R2+2Aophn
+y128oSwQocaIJ8mYWF3fk8zhrq8L1s6U+z0tX05zPCW6necvbn8I1j6jeYuqc3pM55qEXMdol12+
+pykxvt9Sjr88wLW6Egwj7KoqE1mfQF8am0egtZul0oab0AVFNOaeIdYTGo8UO9DqyumIxNewKJO2
+V90K6ar0aemuT64nA0E/JdpWVqfViFeuSVZ+TbU7Kw2Xip5EDBlix0pc6pfU9YVeXgnxVSSlomCj
+MslAqz29LGeg+Y6asaR67UYMY6Vfr8W6uG0nbMwlKN/tcyaWMeyVVe69LC81WoNztxiR7ks9xE+8
+tMbhM3p8WV9iMP4DALKyYKoxfktZoxx3QqUmznZzuaTj3ZB2XrhA3JfQbbB3AKkssjszmW/VQcHU
+ERanyXHBzj3p0zu3/i8q20JhT8EpSO5h7dteFIwxfwlJQP6A/QZW+qGl6K21vwD8AkBrjgde2MIW
+9pbbt7UoGGN+BPibwIestdPX/erXgP/DGPP3kETjZeAPv+X1HIsXljjWkswx515JoPDaPC6IlXbK
+rLgYPZv2VAeyahsEVcnIFsWAgYKX6vUWpQJTyjDDVehrwxGPYHInIVZw052jA4aqIlx4CdU5zfjR
+mFDzU74Ciw5WC8odbVvfslYRIZrQJFQqKnoyjBmpcrWXqstdq1B15nBtH1uTne14NqPdlbAjVs+m
+Wi25VcpOlHohhSYBZ/2MfdWEdGaGoirfr52Ta/WGMfywXCs6jHA/I32yPZeanna8zV5ivTtPJFZO
+x9gorsJfBqMlnF5uYA6vDV1yPaHxq0pjV62eUsU5NqKufaJ3yPBQdrThJMHVOoHqpuAVlp8uaX9d
+fj+eJpTq3QUNh1pbvKmJ5xIp7DvUk4PZzCFT3kZT9VnTmpGpA9uzViB1AAAgAElEQVQd6XfoeXhN
+8QoOFTdS9aoYTYNlgcdkrqodl/haS9MsPe4ZadNSrv1LHYqOhAFv70Q8/R9Lbc6X9gpe+uSXpR3S
+Y7yipBjJHPvt29eZfF7c3u6nGhy4ct37JwWXmnK/JysfBGDrR5corapkNwaU7nkZY2eJbEfGxR9a
+0ol4Lysq1NO/M4R9eb5Xb+d8OZQ+/fov/g5/+QNS+1G4PukbU6L/tqXoPwaEwCeNFLh81lr7V621
+XzPG/EvgZcTP/Gvf6uRhYQtb2ONl364U/T/+D3z/bwN/+w21ogBvYokMZBr/jAvLQGWXg6JCUlFF
+3f0JEy2OCfpa4HRlCWcg8VaZDajr0dMkHRGrmMiwjLl/KLvG7rF4Cr39EwZKrzU5ik93xw3PcKRM
+w4fjhImWLoYap8Yn+amU9K6B1akgAfPOU1w/luOi7DhneEPzAMr0ZEqDr6SjXT9geUkTpUH0DSFR
+PdO5dXjI/bHE3/ePhhydiHcQpzPqc4akqou9Idf+3SWtVPp6xt236/n4tYBkTz0sp8d9BUy4gUvt
+nKDqXBWTGQ775COJT6NxncCRHdFrltR8Ga9qPcRzZAeNtZpwei9nEshYjQ4iRorpcPcPmI4lH5CM
+fSqO9O+MenfLWC754lXtnulxVysGy8SlHGlV3/sznrgn7Vtqz/UPMnp6BMi0wNHI009Dxi3FVmQO
+oXpQXeVCcHFxVXAmqQakSlF2Yzri8L60+ag9oztSJipFWD6fXWZ5Q57DUlwhPyN588iJ+CWF4FjN
+KfTxWdFE7B18yi8JnmT/fsbdWDys3Tjnoj6H9ars5h/tXWHjWcnxrG+eIVBmJeskFL583j9X8vJV
+gbqfqHjNqD9kXdW6e0nK4bF8/lvhiB9/9RMA5PbDDKtvjE/hsYA540BZsTStR5HJwxhOCzTXh+tk
+7O3KwB+Q8HJPK/Gq4vpeur3DU2eVGdnO8BTWaUYpO0fysvzWS1e5eqAJM4UPtxKPSldesKjmMtSz
+6XuHBa2qlrLisKMTL3Q0MeQYbK6Vmo4l1Yf8tesDzitG/2ujffbuyWTL0I4khtxRJmY/4HmFY59b
+blHf1PBBk1BHaUKmXIWJV1JRINfKcy7vD+Xvgs0Gv/+HMvEq16UN914r4KosfuN6eJqFLzOfWqoA
+Kb9B/xWVVlqXv+sd3+DmLTk3X1oOCcey0G1cqfLEWcmcV1JDplWnwyN5Bl896PGqUt/39lJqHVno
+zjgtairbvlJdI1BeSYV00LQe7zkn5+r7JqQ2loTa/auHnE1kIVj+Q4fgjLwghaoxxZnlUJOER8OM
+QkO0FT8k1hP2otZhy5Vn3Q00DGi6zJSirX885EQBbNf2jthVHc7bI8M7dU5tLsnzKJ0BTYVxd5sN
+ojVZLDuNJdRLxlWsyBkTsFHX/q8s8+4zEnb01o75wEQWk91OxuZEk65D1UQNDJmGzblTUFUW8Die
+MNBamV5vxmFXFqe4p6chG5bNylx9zOVLGoJNj6d4X1RsyZMvM7Eyhg9rC5jzwha2sAfssfAUHCAs
+DLZqqSodm5d+A9k2ihzO5FoXvxQSu1poo+Qf1Z0xjUDc9lVTo7KuRJvTDFvIdxqNButaYLWxrLRd
+WYZR/obJvUOKUgufipRkJl7BsObij7VYJ9MqtRLmrKyJzckKWcGfKba5nshuu7c7Y6SkLXZe5eR6
+BErcavOU1Mjv690K3ZbsKvf7Sg0WeEyMXKtIwdOKvLN5SHZRdtIzm1c40GKd8Rek/+O4wN+X7w4P
+juYCzbhlSqJHb3cne3TXhPJr4IlLOuof014Vd74a1ZkgO/PBvSkrpXgCzeACnkrPFYpsLJIpoR7V
+RrWQRkV2WL8VEXlyPTvKiNZlx07Hct1jN2KvLuO6HjfpKS1c8FqfpK+cFN02gZLLtCJJxN3zj1hV
+CTqvUpAp9Z7neKcUaqMiRSEQWB03p/DI1VspJzknGh7aHLpN1YtoeHQ0vFvX4qPgTMBkT9p8Z9jj
+2VjVzYMruKf8FPL/rtOguirP993PbuNrNe85VkFp6J5trlCbSvjbUjTmMN4hslq2WWakU+l/f3rA
+UNP4SVCyflE8tqFyPnQrdTpK6vLCjTt8Vu+x6Qe81LwFwBONt9FUHZSHtcdiUSiB1BWeRFcxBK5b
+YHQS1w10l2Qg9/0aLS2z3Y9lQk/9KqUjbp+3FFCpqRs5qZ9WuJncUFYVFDOWz3rDAXZPTwicjFzP
+6euRy7a6ZWsZvKQvdaLuomMKlpCJGdqCxkAGPUirrJ+RSfHanQGhgoUS5qpKDuuuljhnMwK9Xr3r
+saRS7fvK+HR0/T63B+JeTnoZUUPFSe7NKDpyKlHbqPBUQ16Krz8l1zK3LQpjIAoiHF14fCp0lMF4
+9WCJ4YHGwWsykW7ulxx64nI2g5Seius8u91htdAajqMRnUj6mufKHNxcoa5qSvfyXXa0vuT2dUvq
+CGvQmlvh4kjc7o3zEiMf9PYZIM+h7oaEiYy37wd0S3XLr6f4uoDPsQs1k+Jlcjrh52OGWgcxzBJK
+PSbyXZcy0jyO6jLGsxIbayjleJSphoRBSLUiY7HaahA0NCfS1hL4EdzYlwXk2I3ZSmXso2xyumm1
+ldHr6SJkeyJQ+nbUJFaRoJtXd7mpIc/w1quEGsb8mStyet9YDggcyYlFdhuNUsmi7ikT8wyfIxXl
+GddkrnSTGu26SiK89yw/pbT9s5sem3UZ5yiIQLU+H9YW4cPCFrawB+yx8BRsaYlnOWkpeg8A8ajE
+qLtPHlFVToKnqZIrIUlwV1boQXHCeFf+rrW+RhSKqxnXh9S0YKRRTtlShtsjpWvzCp9EQxCbWyo1
+laGbBaSKRpzgMlE+hXwO4S1rTObQ32KNi6ql6F9aJliXVfl91sFHPnfUFY/ThDUtEur3h5xfkvZf
+qXXo6DWschBcb/bwj2TLD6KStWVxE5+s5LQrsotdXm/y1WOtntzTsYj2GCm/YklMoScmxs0Yl7Lr
+essuRUth3Cc6llWH5ypCsuIsWZ4ppTjszLmQdVTSrgqRUTRpJB7GshMw1Yz7tqlzvC2/T3s58y2v
+Eji4ikiNNEnq+LsEt9XzevYbxCG1JMDRitDEr9Otqdq06jE0whbts6r+PWgzVUZoE5c4vvS7OrI0
+I7lGqXLwhVNgSq0WtDFLjnhstTWXVqQJ0XMBl1S+7/zbZQ7VOpdZb+iO/4X7jK/Jznwve5F5pVys
+Sc0j1njP2yThbToVXOXmrDYd3uc+A8Dx8/epIom/2qaGPgOwpXhmyTTGjeanOQk65Wg7LiebMobn
+jqQqt755n5Zya2y+t8qHr8u99+s7+HNofdWhVIGlh7XHYlHIS8vhNKWau3QU1+7jzDk+eXs94L0X
+tFy0ukZ2Tl6K2ZKSvF5/ma7WGQTtNr6SdTaqVS5sq/x6tUqmL0uqHICMDJNVJXW5M+B4R9zn47zP
+qsaZSxNDTyf0vAZgJ4b1SF6wy6ZBc0UWjbPnKnxwXSstz28w1eOw/R2BrU6GHhWFTLfOLtFsysTb
+iJpECvMtldnnueNVelqdeGhTlrUq8ckrXV54VohZV5/8C4zu/EsA/uDHZEzW9ny+MnwNAJu7lBpK
+tZxtruix4Dgd0dvV8OCiHE2+a3UDUxEXvazkhKru1F5zcFPVSnRK3FRe0lDh082lKlcU3LN2p0qp
+dRfRMyGJVoFWwgb1YK4opbyM+0s0lCcxKRPW60LOcvFKwgfPi1vddroUXcX5a+i35KwTNVQBbNWS
+6oJlRg5JqTmYMCfQxSTX98F1E/JgXjpdYUVzV2lY0mpqn1yfJ87I81t/QnIuRa3APZG2XXlnhS8N
+ZKG+cf0W2iQuqDv/QqfBpvbpwrpP05Hj12C7S6lzwal3SDR8LVTjceoMyVQJqmI8bCxhQmXqUl2X
+BcTPG5zR9lfn7V3LWNEK3Ghjk1YgtRGj3SEd3RiDcMrBzou8EVuEDwtb2MIesMfCU7AOlIHDNLRs
+qeiHiUvCQiXWlny21mW1LpsBniZ2+suy4m4ehKx1ldtwuUM+J+GYWEqjHkZtinqS1DUlX7g5Fa3j
+X6pWqarWZFJMaWhh0jh3WFVpsopKj3/hyMFosrPR/Aa348jklEqf1cpbTBWw0l6bu6IZ3omCbUKP
+mvbVcUPcqTQuUvm4i9ttrh3J7uHfTNlS2bEnNpp0nxM3sdrx8d/5XgBeuPpHALxSdrimRC0xCb6r
+UHA3IGpI/05aRwzm6XndiRtlSDp3rwuIavJ7Jw2xetKSpyGpApWSyVzmLqKhxU7VJ0Jc5XpIghpW
+gSahNadhYaHM2LXWCgNfkpKbs5BkXcYifMnHLikV+2qXiWI8ikT1Iys5Qa4hSjamVZG5kNXAUQXx
+opowF06L9YTDT6qkkVLHN0KGqYQd1SRkpoVpbhlTVsXrKbXtZeBTMeKun2ynjG4qxb1pEnpaxFaV
+ezXOhbRUcKjZ7FDR6r6a65COhA/Cc0JqS+JlJipb4JrklG7Or6WUJ+JVOf6YSCnmnGpCTUO3oKbF
+aoWPo1wQZuyTaxjjNzKGueqUDnsY74295o/FouC6hlbb5cgtOFRgX73qEyqpSZzlTGIZiM7yConq
+NmgBHG2vRWtp/oI5p6QS1mtRq8jAJ2ODo9nuMtfstZeQa11C5vpUtIrwStjG0xfLMqVQUoxWQx5K
+vX/C1ob8vNQMcB2lC09TciWbLe0Mv1RdzExRgLOvY3WSR1mTOTorC09gXufRlxfXMz6VukyOzTNN
+nKZ0Nqidwc/njFP3eZ/i6H9P0y9PBxV+N5EJlhuHaFkRfUmVQlGRrVaFuvavTLV2oDDEWuFZTSJm
+M5nETrWC25B2FF5KOpC+5gqyspGDSeRnr27xQxmXml8l1/LqdJaQqz7DTE94bCuCsfTV6UJL61Ga
+dUM0VWp/v0YRyTXyecwdw1TrGaKKT6b1HCUxxXTOyASl1kwUWquX2QxPBYnd3GDnhYNeFVcXvYgV
+Yi2TPtzTmplyiaCmzFpuxMXnZJNZilfp1IRQpaXHpqFNiIy014lT0NMHmwcEyxJimIlPqRW29lBZ
+rAqfbC5PP57iaDs93xAPlSB32SdS1J1bl4XL64eULTmBy6ZT3Ka0zW2ukbtKO79znZ09Pe58SFuE
+Dwtb2MIesMfCU7AW8sIhDHy8VV11j8DTpGM1MziaIfb6I3JXd4RCVsCg3qM8Uez8+hiUQCN2BrhK
+qOIXCRWlGHPm/ACpodTvWpMLNwBQTmAwVRboGVSUJbepO3cYTqkplXvz6Sr5NQH3OHGOq9Wckzw5
+JVfBl2u1vTapryRW45iJFhTWKxtYJQuZTWWHPhmdnO4kB/0BqwPp6+zsAWYku07RGnOiSas8loTh
+r7i/x44KSDZCy0wJYuJ6j5piDM5HDrme6VcVu+A7Do1kTuU+w2qNgy0t6fBYx83F12TltNTqy/0c
+uzb3fhp4TfXYJjNKPY+3w4TJRNxuv6LclnFOo69hXAPGY3lOTlQST+V+s2ofb6QnPpqgLEqfXMlU
+7CRmXgZhvcqpF1biiMIQUK1qqOEOcGOt5vQLqpnWOBRjHPWa8maGP1VRnrpWdSYh4RzgFkN7VRKR
+tV6Vpt6jUDKcfnXAdCxJwunQJVRPKYsmVAaaNQ8DSh3zIpMs6PjOfQglodhohsS5eGPZzJL6ikNw
+tnCUN5OTiY5FijtT72fdhVQS72UQER5L24bNGb//e0L5/7C28BQWtrCFPWCPhacQeC4by018U6Oi
+rL47/hBULCV0XWaHctzEBZfOpqzWmdb5O+Ma7kBWz7wYk4015sKQol5FtYKnu6bb0jg7i5hp9Z3T
+GzNTzn+HkkhJY5fGMyotiX3D2lyMwxKpFmF4UGGmHsSgzJnqNdqVFEfPykMtknGSjJkmlAbpDHYF
+HVfJqjiKG7h6UzyFw2BGfyY5gL3eMUMlWH3PQY3sjIxLsHeIc1v613ha8ywvHhG25OeNisdgnl9x
+fGZGrncyc0n12NZVr6IWZThKb2cDH7RAx5gBU9WH9MwI43X1c2WeiiZU5roQfnHKWeDUGrhaSeoO
+Zvha/lkoqjCoWipVJWt1Sownz+xCs4KvbU5em7B0ThLMmVKp+ZGLVYEXm1gcVV5zYzCKc56OMuq+
+sk/Noel+hNVEcTmd0MjVIw0hV8SmF3i4pV5bk5nDZERHE7DNKDpFy87CKo4eB1Z9uUe6M+JwWXb2
+VtOlWZW2h1FJrlKH1rNkCpu3jvSpFlTwtDAvcGo0VKa87wRkucDXOTnAXdGcgnrNZTkFpZsrY4fD
+I1E/P+kfcuG83Pu1QY9PvSp5h4e1x2JRMA4EYclwltHeVMrurDidsL0xzCbqGroO+eGc/kvPz4sE
+o8me4a2UnpYAg4QlIC9mpDyO7mzOdBvgqbPklwWhus9xEePNhVqCEkfZg6eaDEs3LS89J23YPruO
+81mpKHSKDWaOtKlp64Ra7WZjDW3IqSrwyIlc7Ewe7owJeV8exWQqi186SUk14TSbGazWZaQTS9yT
+cMTaCs0tpZ3fkQn2+Zd3KLS9/lqVtCn9iN0TWucF39Dd73HmHUIHPtVS9QZ1FPZPOoihoguBVxGy
+S8CpbFPWFYQ0VDq2widUWHJRy5gNNAMeDzFar1DkCU45T6pJ2DGKa9x3JExo7IG7JN8dmyqxMmkv
+R4CeHjjJXIRljKenUoVxcbTuIHYSPMVFVKOIIJPOpJ4qXVn/tFzaBTJlx/ZiA3rSRJmfMmEHmpR1
+m91T0p58+T5d/z0AZEGFgVKxG4XSNyoRS7k8m+3pMrNQCWnGEaWGvGmvSq6CxYkSpOTxMfld+bu9
+2iHdioRuozRgsC8bR3Md6r5WjCqYzpKRqJBN1suZJvLyV32HA63BeO2Vkv34jdGxLcKHhS1sYQ/Y
+Y+Ep2AiKZxwO/sBQvSZN2oi2OHDkrHVsMvbuyUp69uIGqdKUWWX9rdafJG/I7jIdTBnozpyNfDwV
+C8mLnLHuhF09V/cIKZRDYRIXpxoBy2ttMkXx3bPH3DlQ5Nl9WZVnHx6xe+NrALz41AW+b+0DAMS9
+L3A8ELezWfcIFTY9niiRSzqjFWgo0mkz1DP7/iQlVaLQfaWHGxYFnnIFJEXJTI/Wbtw+orosu+6V
+6QqtDz4rbdt/Se4xSQj0iK3Z9Qn+G9nxRv9Zn/0fUJq6K99H7olHEppV7X96WuSUJiNcZdW2iX+q
+BD6xOZnKzIXqBUTLPooex2YBhSu73zixlANNqgYT7FxhO5Jk7/G0zxdvq1BPAasD2W0JDbGqOWeD
+Ia4nnAtp/xuCPIUWMwVVfy6/QZwkOKq92a6HBEotlypxjsk8Qh3PtD9lMpW5VWY+FfXIJqOEo1La
+1JiIRxdthQz1GDaND3nmCUENTmY5hcK4J5mM1eHxkNmx3PfQ38VXkl4/bON069rOGZliPeYaGI1o
+meF19UajGGekxV9Ly7QUkzMtE6ZD9Xpa6pntpRR11SKxhvyCzL2t6IfJGvIc/O2SaF1DkJe0Uu5b
+2GOxKMwGBV/59QFTPyBTIMil2RSjh8m3TjKe3RbXPx6PCVVSvjxRnsELBfVUXKvKE1U6T0gmfhqP
+GF2XFzKtG0b3ZeD7yoxcHfbJNO6NZyUjTybecqV2mhne7xdcU07E3an+/58kEAnw5uYz/47nX5TJ
+sXp5iYMj+buNbpVYz9aPZzLRkpGDVVxB9ahJ6cniVXU9Ul9yCd35GXVuONYz+GrgUmoYtJcf865S
++r30dAOjcebJi3Jf15QEKgg7u1Nw9Gf1rUkM08/IhP53X/44H/5vf1LG9qqcnJzprGIVI5+blDCX
+lz7vJISFYg/qBgqpvrPKfhUfjdHCT9IE4mAuwnLCNJH+O4MSq+5zXSns43GPREloiq2QuweySL39
+Qpt9XUtMs0aiRCSl1VLnBGaJPD+ngLmeVJEb3IqCkAoPV6skQ1WZL6sF9kjnTTA9xRNM8z6DkXxp
+nAyp1OfYCs0N7LTY3LoFgN/oMlY26muv3j8FSDmecjy2PUrFY0wmJ5zsKQipOaGRyc9TJ8HV6t/o
+vIrtphtUvkcZprJVZgrUGl/r4wTynaQP+yfC1LQWK/6lMsI25DkN914l6AmRS+v9CaNUMAut9Zx3
+bMv3Zdv41rYIHxa2sIU9YI+Fp+Aah45fI9ircGlJdrl+x2W4r9ndKGN3V5Nd72pRX1aXShnF/DwB
+TxJg5tBlqrX5aREQH2tm2BT0+spjOJVdYKvaJWoqGi+cESgP/9F4RqqoyWlqua3ItJG61P7MpTyR
++zV+ccK99yts9Zalod7Nvf4JjaWLALRUU7A3TZmUco0gKQi1ws21lkhhzp6i9VpuREPP9pt+TqGU
+cOu1iJWGajlU2hyeiMfSV/HsJ6/UybS07ngyhulc1cUQfFJFbdot/tWviPv8F16Q8GM8i/FWVrVt
+hpOh3DtKUpa74h2YlFPkXVHK70cnOel47pZWmOnOlnkpflca5U5nhJr4i3UXtNOCFS1air3kVBbw
+Hd91mf66eDSETRpzD/FEqelGfQr17gZpgadoUx+wqocwLizRaC6qozBob4w184pJh1ip6RI7Y1iq
+wIsPnoZv7pL084Z/g+9tyg5c3dziNuLRDNoBT2zojq4SdYVbkCivaCVsUtW4Ki9TCg0lrHEIPUkk
+1rRS0yYe7ol6XkVAVgi35eBkRqEkMfE442QopwuRkuFsry2T12Us7pk+y3PdzMoP0nClnc9dfh/1
+n5Xiro//03/Ew9hjsSiYvMQ9nrA6LnF0cowqE3z9+SDPuRXLCjBaKqgrljvJxM9M7gUEfcWkb6yQ
+pEqsstGAjrjl08Kjr8c7DYU2B60OhZUHOs1GRIEMarcOY2WrOchn8CWF0s5JQNMMr9Dy1t4u129L
+NeOT57+HW2M5+Uj3djl7WVgvfK1YK8K7HF2VB7s/zggVp209l0LBSzuJTOzDWcx1zdjH04znluUF
+2z5X4qrKUr7m8dn/V6C2n/nqLQA6f2aF/kviXu/89hBTziU1LLnmJQajPcyrEhK82JbxeffTl9m6
+LKXT6c0p+VBCnkFs6QxkQR6XFWa5/Dw91oVucJd9iUDI1k7wFP58odEh0noUExUUenxnNFczzEuG
+W6pSNahxVUONaHWblTMy6Wej8LT82pkfF1cycn1x02SCVar2/nBCel/mQJj5NLtazq5HlkEZ4Su4
+KTUZsSqABb7HbCR9yqspd1V41tUTno7XIFqRFzpdNxxdlZe0YQIKzbXMFxvqDkc696IGmIr8nN4b
+M9XcxmCQ8LW+LMj2ywogG7qsPitzJPW6dJ5WoZ71Y7yaHC2m9hrVQ3muNVVIs7VtXtv5EgBffbXH
+T57VitFgwMRKpayTXeSZjXdI+3i4RWERPixsYQt7wB4LTyEtLHdPUi6nEfU9WQXXPJdjX9mQM3j1
+RHbpz366xwc/JLucGah7WqkSnJHVOi88ylJPIr4yxtFz+oP7MRN1QSMFysyKiFJ3mlngUY/FfY4r
+UxwNK6q9AZHK09mBnjXnlg5zGGxA8bKs0Pl2/1Ru7tV7JY1A8Avf9ZyArZrtdXhSEkBmOKWhACgn
+cAh0tzmvJxW3BkfQF0+oWIqpqhtpMp+ehi7ZzSm/80fS173r8vcfcVp8/rZ6GLk5lXX3Madn+jb3
+uKGhVDBQ/MfNLZY1S71sQvymtDl0HcJt2XVdU8HXSryKnlQ08lUq58Xb2LtdoaqEK07VISllvIrE
+oaKYDa1Fo9mt01TV8Hh9TPKS7J6NzU3G8wrHbEA6lZMmL1CgWqtDeqQutYmxCiUvwxp2TcVsEkvY
+lZ232p7DnVu4er4/nY6YKmR4cDBiV0+d4pGlrMh37igseSOc8F5PwsODex5ZKCHhUnuFteZvS2eU
++OO1l2J2VZDl7e2c7WXFwFQ6xFqJ2dxaOT19uKvcj41ayOFYad9rNQ5/X8Jbvzbj/uEtAEbuhK2a
+tGOUK9N0FvPKobT96v0WR0recqZc44YmJd1qm7qeMD2sPRaLAtaBtML9IGaq9N5nVmocTmWAo8xQ
+KvX7zZsHPHNZBme7Nj+RsEwUpJLdnDHLBcQx7FWYLMnCMjiJaURyvUqgmfV6Rqb03nVcmkuKxTdt
+prG4opdsxEFT5dX78/b6DBXRl9gK79mUxekaoBgrZqMRX9SaiHXNgTyx3AI9Xcl8Q6ovpO/kJJrh
+j5VhyFkzvE2BTpPcI42V/YkaXk/a+auf/wy//4fKU6kY+K9M+9zVF77MwOjilVNikMVk3ffoR/Lo
+X96V65azV9hZ1hLvZ86jhZGku0P2VHi32ClxVHsyK7TqL0igKc9mq+MSuNJmvzElV21Ox01J9dhu
+FCsobJZxuSptuDdOqcwVqYKArrbTqaXsH8mCMxxp6DadQqTgNcfBFBryeQ5OXan9i5KqYp3qqg3p
+WKCl7r4fUpnJNe4WKWMFAMWOg6dEPHOpgeXhNnkh860+PCEp5cUb7h6T7Ss7l9afZMmEQgFNX90/
+YaMj4+mG5SkBbaXpc8HXo0id61P3mHIg/ZhMvko8kcVtODlmrKjd5RWPdlv4H4umaj3sHhBqfcyz
+yxFu/7z83bjEuSN9moQDspmQ/DysLcKHhS1sYQ+YsW9QpvqRNMKYHjABDt+iJiwv7r249/8P7n3O
+Wrvyrb70WCwKAMaYL1hrX1jce3Hvxb3fWluEDwtb2MIesMWisLCFLewBe5wWhV9Y3Htx78W933p7
+bHIKC1vYwh4Pe5w8hYUtbGGPgb3li4Ix5keMMa8ZY64ZY/7WI77XGWPMbxtjXjbGfM0Y8zf0864x
+5pPGmKv6/84jbINrjPmiMeYT+u8LxpjPaf//hTEmeIT3bhtjftkY86ox5hVjzPvfrL4bY/5LHfOX
+jDH/3BgTPaq+G2P+N2PMgTHmpdd99if204j9A23DV4wx73oE9/67OuZfMcb8qjGm/brffUzv/Zox
+5of/NPf+TtlbuigYY1zg54GPAM8AP2WMeeYR3jIH/itr7eDmg/oAAAOASURBVDPA+4C/pvf7W8Cn
+rbWXgU/rvx+V/Q3gldf9++8A/6O19hJwAvzMI7z33wf+rbX2KeAd2o5H3ndjzBbw14EXrLXPIYxo
+H+XR9f2fAj/yxz77Zv38CHBZ//tZ4B8+gnt/EnjOWvt24OvAxwB07n0UeFb/5n/Wd+KtNWvtW/Yf
+8H7gN1/3748BH3sT7/9vgB8CXgM29LMN4LVHdL9tZEJ+P/AJwCBAFu9PGo/v8L1bwE00j/S6zx95
+34EtYAfoItD6TwA//Cj7DpwHXvpW/URKB3/qT/red+ref+x3fw74uP78wHwHfhN4/6N4/m/kv7c6
+fJhPlrnd1c8euRljzgPPA58D1qy1u/qrPWDtEd32fwL+JjBn0lwC+tYqBfKj7f8FoAf8Ew1f/ldj
+TI03oe/W2nvA/wDcAXaBAfAib17f4Zv3882eg38F+I236N4PZW/1ovCWmDGmDvwK8F9Ya4ev/52V
+Jfs7fiRjjPlx4MBa+8YkgL9z5gHvAv6htfZ5BFb+QKjwCPveAf4jZGHaBGr8+y72m2aPqp/fyowx
+P4eEsB9/s+/9RuytXhTuAWde9+9t/eyRmTHGRxaEj1tr/7V+vG+M2dDfbwBvjCj/4eyDwE8YY24B
+v4SEEH8faBtj5tWqj7L/d4G71trP6b9/GVkk3oy+/yBw01rbs9ZmwL9GxuPN6jt8836+KXPQGPOX
+gB8HfloXpTft3m/U3upF4fPAZc1CB0jS5dce1c2MMQb4x8Ar1tq/97pf/RrwF/Xnv4jkGr6jZq39
+mLV221p7Hunnb1lrfxr4beAnH+W99f57wI4x5kn96AeAl3kT+o6EDe8zxlT1Gczv/ab0Xe2b9fPX
+gP9UTyHeBwxeF2Z8R8wY8yNI2PgT1trpH2vTR40xoTHmApLs/MPv5L2/LXurkxrAjyIZ2evAzz3i
+e3034jZ+BfiS/vejSGz/aeAq8Cmg+4jb8WHgE/rzE8hEuAb8KyB8hPd9J/AF7f//CXTerL4D/x3w
+KkIq/M+A8FH1HfjnSO4iQzykn/lm/USSvT+v8++ryAnJd/re15DcwXzO/S+v+/7P6b1fAz7yKOfd
+w/63QDQubGELe8De6vBhYQtb2GNmi0VhYQtb2AO2WBQWtrCFPWCLRWFhC1vYA7ZYFBa2sIU9YItF
+YWELW9gDtlgUFrawhT1gi0VhYQtb2AP2/wFGxyLacxrpswAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3060... Generator Loss: 0.7511
+Epoch 1/1... Discriminator Loss: 1.4302... Generator Loss: 0.6394
+Epoch 1/1... Discriminator Loss: 1.3354... Generator Loss: 0.7412
+Epoch 1/1... Discriminator Loss: 1.5316... Generator Loss: 0.4699
+Epoch 1/1... Discriminator Loss: 1.3360... Generator Loss: 0.7564
+Epoch 1/1... Discriminator Loss: 1.3748... Generator Loss: 0.9328
+Epoch 1/1... Discriminator Loss: 1.3011... Generator Loss: 0.7149
+Epoch 1/1... Discriminator Loss: 1.3411... Generator Loss: 0.7330
+Epoch 1/1... Discriminator Loss: 1.3431... Generator Loss: 0.7667
+Epoch 1/1... Discriminator Loss: 1.5367... Generator Loss: 0.6442
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvdnvbNl13/fZZz6n5vqNd+x7+3aTbErUENvUBMSKgzjD
+i5GXwEmQKEgAPQQxnMAPEfwX+ClAXh0kQQIECIzYSQzE8BBGHiRZEkWJFEWymz3e8TdX/Wo689k7
+D2tVkW2RUksUrU5Q6+XWrd+pc/bZ4xq+67uMc4697GUve9mK96fdgL3sZS+fLtlvCnvZy14+JvtN
+YS972cvHZL8p7GUve/mY7DeFvexlLx+T/aawl73s5WPyQ9sUjDH/ljHmHWPMe8aYX/phPWcve9nL
+n6yYHwZOwRjjA98G/g3gBfBl4N93zn3zT/xhe9nLXv5E5YelKXwReM8594Fzrgb+V+Av/ZCetZe9
+7OVPUIIf0n3vAc+/6/8vgJ/6fhf30thNh32KqqTtOgCiMMAZ0WKMdTSNfN9YR+D7AARGfu8MOCuf
+667D6F7nBx4Gucg3gGpFrZWLrbU03fYz7HQmY3a/290YwJP7RvEBlrU8r2wIPPmldR7OGf3c7m7o
+tD3GdNDJl85ZLH+wlhb4MjzG93Cd3MNigW7XNM/XJutzPWN2W30QhHzmzTfld11F21R6D49lPpd7
+tPJ+rrY0237BA6vvZDs67aPG2m2vYIzZ/Rv4nj5718UYDN62C3F4OmZ+GMq/vrfrW9O14Mm7Hh/f
+wblCftc50D6oNku0Y7Hy+tSdpWl1XrQtdd1uL8GZbUu1v637zneOj42r0XEN/ABjtD+0P/Fge6Wz
+lmj7o8CnI9Q/fGdMjc5Z3/PwtAMCz99OHXzj765ptV8dbve561q6btuJZtef1nV0ugY6+11zcvsO
+xuzeQ7R/bQfgjHxfFOW1c+7o9/34X5Af1qbwh4ox5heBXwSYDDL+q7/8b/L1j77NbL4C4OHphCqS
+TvDLlvOzWwDONh2nkz4AI33ZLjJ0Mo94uloQuET+ftAj9GTghoHDbxoArteyoNdFzeViA8CmdNQ6
+AsaLCHW1ma6mdTLZgqwHwN3Xf4Hc/jMAXr17xmEif8+7lKqSaVPWM9rtgnOpvEe4xCxqAGpbsO62
+g9t+V7/Iv54XcDicAhAOetQbeafCFTgrC6StOrKeDKFnpS/SICLoyefxwRG//A/+obRt/m2uLp7K
+u5LxD3/7f5O2XUl/1y9KXuWFPiPDFtLOYrNiscwBuFwt8XTiJZE+N4g5nki/JJ5lO18965PGck1r
+G5LxQNp0dKxj08ev5b7hbE7bOwDgr/zVv05bfkPatCgwU5nD7/3Gl6R/8oZ8JZ30crHkxaWM5cXl
+Nc9fzgBYd5bWk3HwnYx5WdTYQMbUdrJJAvitIerL+EwnhwRG2tS1ujR6jkLfqahzHuk42dGYTXAq
+11Zyr9oWJJ5c3EsShjo2h9mQKJV+G6UjwkD69nYuvyupmN3Ke8yWM1YLabOLot1GXRUbZpeyBta5
+tNFYi9tuNl5E0JP3aFuLQe49stCmMne++jtvP+UTyA9rU3gJPPiu/9/X73binPubwN8EeHh36oKx
+5dgP6GQd8NaDJ/RiWSxXsxUPdcHelDGvnYwA6EI9JqsFi4V87jctZSQT4mjQY5TE8nkcYfT6di2L
+aj0reHopE+m3L3PWlQxWQIyX6omfG247XciVbFLr+h0urt8DIC08al8WUx3FmEoG0QszAie/6/wS
+AOMHFJl8rtYt3zmDvrtf5F9Ly6qUSRD4IYmVd3JmQ6sDHgeG0MoEsqEMfFs3VLn0m5f0KT39XHk8
+v3kXgFH5szxIHgPw4a28R8eCUSz9OupanG6mmyjhZKqb4dwnr2XKxAPpi14y4bCvGo0XUjSyydIk
+OCvvWuYhdakTuZbfDaKORMdsddxymN2XPrQLzFreNd+8y7CTaXR67wkAV08vaKIrAEIvIKhlnPJl
+y6qRZ0RtTBPKs9tK/r6pSrxc5kJNg6daRWMcfdVS1sGcnvZjifRrz6UcjWSc2sse3R15v0fHj/nm
+mdyjXS/kN25OL5nI76Y+B7EcXv3MMcn0IBtn1LqpVUOZN307JFWlI2gdQSjj3k+PCSJ5drGsuR7J
+Rc1SNc9uwXIjn0tTYrwMAC+q8eZ6OE1vSdshfxT5YW0KXwbeNMY8RjaDvwz8B9/vYtNCcOM4zyGQ
+A4MXVzcMQjld+rZheConzGeyIa/dlwmy1oEtl095FslCGQ9T1AKhCQ19VdVO0kNcX4/hvkzGIktI
+9fSfVee8M5fBzauazMrgLsyCTtVAq5rGneo5T1/JBFxWGzxPFlOvqrFbVTIwNMg1sZV2VrbmNJDG
+PTW/f0P4bnEdFLqA+vGSCtlgfJvjtqqma9g0MoSB6tSts7hK2rm8viVq5fPvvtdy7UkfvfuVv8W7
+H50BcFtdAJA5S6xaQK8X4fTzeJyCTrZ1nLDOZVGEifThwKUcjOX9l2W50yD80tEEMonnbU1ZyQLI
+z14AUCdvsNFFEUwz8qW0zcuXvHwlm3Z4vOby2T+WvmvltHC+pcmlj/PcpwzlcxMHeKqRLTqo9cTO
+b+W+nWfJte0mMUSltDMaQaV9OCtqLq6ln4cTeT/XbqitHkLxijCTfnl6tmTVycK7nssituSEQ/lu
+ugmIx3pQhRG9WDYkzwZsrLSjrkU76FoP9F6daQl9nadtjRfJGgh6jklP5qQ3UvM5fUD6gZiBs+qM
+fCH91pQZa180QBdYmMs8/KTyQ9kUnHOtMea/AP4BYtb8D865b/wwnrWXvezlT1Z+aD4F59zfA/7e
+J7oWg7MRLgyZtqIF2CikX8mO+tnX7zA61F3SjjkZy8k11ROxigwnU3VaVTHhVFXfrmSzlNO/T4Sv
+WoNtZFe2h45yJKfVyeCYf/rOhwB8+dUlnqrladhjk6v6aOQUebm8ZqDG/8LzyWp5no039CI5VdbA
+sCenW+HLSRLewI16qqaJZZar4+97KA2e9x0Hnms6BhN55zoMOFF3V12sqJ0MYdmoX8IZOnW+1V5J
+fi19+Hcvv8zq18T3Oy8uKC/lve+8JidRuDbEap+eDHoMJ3KyxV5Eo/6VNt+AGQPQG2h7ah8/lPc4
+3BisahidF9JVosL3og3BTPpzpdZFuZ4TTOVeQf46VSbtXz19wbvX4q8ZnU1BNSu7lLabgyHnufTx
+xeYjrkp1+Pox6fFr8uxyzfxcTIyi0zmyrsk7aQ9VRxKJSp2VPfxU/Q5Xt0TqB/FK0TpcOiRdyPhx
+b0hayJhu4or1R3Iyl62cygElUSR+hqjnobfC8xPmnTpB53Ou1G9WqXk16g2ZJtJv2Z0DctVMbtYw
+DOV3STIhSkSNDmMxceK6YjGVh7x45vGRJ6bwbLWi9lSzWjjySDv9E8qfmqPxuyWOIx6/fo+2PmOl
+A3A8PeB4IC/22v0BWXYIQDoI8HwZULsUN0U7GeE79cySEfRl0TTO0i6kA7u6xYvUjszl76G1FMjg
+9wYtldr4JrR8+1qu9UsLvtrlar/eNDG3rdqTDRhkkEMvZuFkkjZNSGlksRi0DV6FbdUGbMCPVEWt
+7C5i4KvXO0ojnKrovpfhOVmkjx48Jtq6313OZiYDvlTH4KapaXUTs52lCOXGT86u+D9+9zfkPUwf
+fyITL1D79qgXcDCShX407RMHsT67hko202UcMcp0MY172vaaxUbu0Q39XfTIWEen9jmNw6nanbS3
+eq0hXUg7R0drRiNZ0FdeTnat4xDdEkayEBaJTFVXethENvqbfMmrM3l2kfh0PTUZ5g2hdAc3Tvon
+rys63SCMM7jtOLUdRj6C31Jvr9E91rOQHMvmlSxDTHMJQG9wQL6WhzRrGeeIll4gm+2kd4eg3ka7
+1rSFbADXjWVxeyP3buT3UzLihycAxA78W3Uaj3xQP1gWB/R6ao540vceAzL1v8R3QkK99sX5gP5G
+xuHam+HZHn8U2cOc97KXvXxMPhWaQhh5HD/MSLM/z/Pf+nUAkkHG8URUW5whUzUySk53MWQ3/CwA
+cWgxoXrnN2uchoLCNqczsgM39RV1LveLfTlpjO8Tx7Kzd17Ea3fkpGyWD+gSORGenXV0GuTo1rKH
+5qs1nWoVYeSzDSn6QYLR+HdjSrZoUc+XEyEKQ+p6+znCqdc7HFhaPQmzXNXI6XCnHbQHJ/zkmw8B
++OyDOySNOJfyesHiSk6gK404zK9r3r44B6BYXLPMxZFI9hbnt6Lu2rjixx+q2aAnYy9qefOBOHN7
+cZ+qk/bUbYfV0G8PQ6aahylVHfYtVrU02phAMRRx6KhbdY7GMYFGQbJYnbyuplJHqusl9I5FA/Ha
+a0aHYkJurr9JoU5ccyD9+vwbL3j3+QcADBxMUrnHkHvkiWgmfmZp+9Iv41ZO+SadgZ6eBAbPqvOY
+Cs/o9xhcJ9+3nmgYVVGy0HH0Gw80/GzinGYhJkrTiiPPTWJ6Gi2I/YZcw8Quv6DSuObCNNwu5Pph
+Is/NjgzOUzPn1lE5jdSkfZJI71GBMaLVxhreDOgIWvmOcQWtam+xh/dS3qPuam6r/w+aD74XcZA+
+woyfEj2Uzun3J0SFvFjvMIBOdDyz2RD05OWtopdMGOD1tiiedAcK6myIGcj33qLHDmuiPgVv5OMH
+WwBNw5F6oVeHMY988WE8vX1JeSPmwdYk7fuWpdr7tqqpNf7ttxbPl4npeRWdlQe26hVPiPHchbbN
+kQ4kTHV/dEQRysA1uqiSYMgwlEUan97lc699DoDPPT4ifHWjPXfDRkODF0/l91/JPJJb2fQK47NU
+/MY3qwtKXZBJGzJ7KRvL+HX57nRwwkjDnobvgKE6F+BpPw/THlGofa990diCYSSbqfNCUL9NYCyJ
+qvCNsWStvOtQN8Jvnl8yz67ld9eQhmIenqQ3WB3fxD8BT363jfbM/Ftuz+SlqiZgcqiq/f37rGv5
+/sVtRp1rOxt5v01TkgdbsJjBKJjK2hY19/EDML5uxBox6tqA7lJetjiZ0+h7d1choS9mQ+rrfPI8
+mk7a2+QtnS9za35ZUDQKyLIepi/zoghkDq1XNToNaXo+QSdtHyUxnc7VNoZGQ+Zu62dpwUTyToPa
+p4710Ltt8EYyZ9sXBY363j6p7M2HvexlLx+TT4Wm4AUh6eEJ46jlwVhOxJez5yS+nAInqwz/ONOr
+DS7XWHgq6psJR7hK1QDPQCE7o+f72E2lP4uIfPUoDeQ0aKoaAnU6di2enng+FYGqjFGX4PQa3ygg
+pvaJVc3sfJ846uszHKFGKIzL6BTS21ltg+vwrTrf0ppUT6jKlRym8uxC8QhN4XBTUffT1ZiNQjYr
+e8n4WNXEtWWzlhNhFgru4LDwSVt5xmZgOH8hpsRHz96mc6H2t6NQNfc4kD5+8NqboFENr/FJFcYd
+9D02tbxfGPh4tYKlfMVjFNEOzmvbAqfft7XBqYkVO59KoxVxJBpY8PKKyw+lzeHwLjPVbtpNRan9
+7KI+vt77pZpB3XLJyaG0xzU9vAM5HU+bKxYaiblcdAzQyMdAHZTdkJWCtPO2w6k22dTNTrtxeHiq
+qUVGx671qXQ+mdWK1BfHZ4DBNOo8TeQ9704OGB5Ifz67vaHOdSxth59uTZCW1BfNcRTLva7KiKp9
+BQDXMZND+XsdxjRO8RtNQzyQ79eFmpgx+OrwNlFArEC9XrTA3ujz0ohotTWPPpl8OjaFKCZ7+ATz
+YQ7qsR12b9L1BZXpgkPs1rC3S5wCU1yj/3ZGjC7Ath1ma/cpKg+A1mI1tGZ1ohnP4pW6gcRDArXf
++odzBvNYrylwCnPWuUMXBDTbcKH1MRpTDCKPDl+vcXSqMkbazVWzxCiYKHAxXiSf62KDsTKBQlXV
+k8zDM9o2b0Z7KYu/LCy39+X7ofXpanl2eSUL+usXr5jVEpqqqhbjFOX27ALfKmAnCoky+TwdyySO
+fEPcykLyXITVUFjgwp0foGlbIgXhbKUMSzqNhjgMxn1HRd9Or6apsJWOXyubzXhyTDSTdjazD4gG
+Ameujg4o1mIKDccjNlaiFT1VtU8OHtB/SzaCm/dnZBo63JAzTuX7e48zylKiGaUiTJvMwz9XX1Rd
+4DSE6xuBEAM40+Hx3TkmYEKH0Q2iKT1i3ZztxuDrBhiqyRH6KY0iNxvTUKHXliFqVdJGEbbRMVYI
+tkschY4Nrqa3kefV7ZyFmrSZB6jfQV1mhH78nRQOC576QZIoJfDlMHA3OZVGpj6p7M2HvexlLx+T
+T4WmYHB41LjhKdczcYC1xvLQ192+qEh92QXbwQTjtl4uTTTKV5hQwSaupCtFvfR7ASYQddW6c2wn
+p2Zn5bRyZYjn6W5egwYwOJyccDh+BkDfRqQKZHI92e6TymOtB1+QxGTBNiszIwnl1Fi2lkKTbhrF
+0ScEhKrODuOEXk895ybj+EjvoRGHIByhuVV4SYLxRX2OvENidT7VsSOLxBl5PpX25s/n9Iw4IvNu
+jTVyYoxO79K9L6DSdRVynEtfpKpy+5sco7kPlSmolpoHElrMUOPcrmChHnxf39kLA0LVftrW4FRF
+D4KWptT3rxxFJ+ZBXCtwzKzoZ+oQXljaUaK/q+n0d4vLOTZRs6ORvx+M79MLZKyH9/pYdaLdq+7Q
+DkUdv3t3QrtRqLdqBK+WByxU23p2fsGqlM/rdcP8VrSRotyAws+TRE7rJGKHFYh9QM3UOvAY9Lbz
+TN5pcDQl1iSG2fmMUvEi3sSjZ0SLue2W9I28kzlSB3QXUmkW7E19SasJb8n6iGKL9QgcvrYtynRi
+hC2ulf5u2ppQo0SDgWVkZFyfmxYb7h2Ne9nLXn4A+VRoCuBh/CGb9QXrlZxQ8SG4W9EI/NNk5wdw
+IfhGHXuKkmPjIFYnWuVj1F6mNnQahnJ+n0bRbV2hMfbEx8zlHo1b0yg60B8nvHZfsvM+l89Yviff
+X5TqfAwNC7X7mq7BhNIez0CtPAQmCfBUoUl0o87CgF4mp85BkO44It48zegd6om4kb+fvHaHspST
+4dqljDSO3zYBS03cmtwdkqeiWf14LKfO7UlBp6nOXu0Y9+7K92sPFFbtRY6pL1pBeiC2fJE1tJoC
+HLqY4ECRctaj0zYzG9BparFV308Qhfh68gVZiy222YcrQg3VLf0F+VqTn/pybTJPaHtyj9E42p2C
+9aqhU19QEkIvVaewxua90QanGArbWkYTRVimU0wonysv5OZZo+8izug2j8kONR2eCtMIWjavWq5W
+ok19eH5NpUlKgSYlBX5G0Jdn13VLp4PasxEjTW1fK+Jzc7vBVz9Q4TfYVN4/cB2Lmdw392ouQ8HA
+hLVoYP6yR6OZveW6ZKNIWEdK7476xYxHpungjWpjayJBnAKh+c5STrwx/aOZ3tujWf7ByXf/onwq
+NgUhmWh4+uwp5+qFPokquPsFucBYrHZasfJ2E8jXVOZwcoqn33mZh1EwSec51jOZpLezBZsbhdgq
+KN3YFZ7eo+sK0i1UetVCKPf76cf3d5P0n35DMvwmSUa9kY3AhI5W1TZT+EyPZLGVvkeRyYQeaEae
+TeGOL/e6am9RuD/hsKNv5drDqSyagzDjuicXPJw8oj+QidczHvmlgmWKilAdrPFE+udnbz/LV+9q
+irBbQSfPOz6Y8LbG5m/zDW1f2lnUGr34yMcqEMN3DeFQJt703im+pkuTlmTaTiL5tzABleY45POK
+jarrVb0hUyeZ1/gEsbzXZqk5E+6SU0/U/enBF6h0bKI3HzHSLMI4H3J2Kwuo0Intldf0Rup9twf0
+lDsjsQ6banbl0qc9ls+x4hQm2Zgu0A1rnfDiuWwWL4qG8a04WzO/43qjADaNQuSNI7Wy4d6Eczba
+92bkkwViElhf8BZl5VG9lO/y5TWRckjM133mM4kuuNoyPZTN686NYDOI+3RWcQx9j1vFm13n5/Qu
+5d5eL+XhnXvSt9ufFRWHumdkkQVPTc+o5sFQTO/3ezkftXJwfFLZmw972ctePiafDk3BGerW8PLt
+d9ks5OT70Z/7d/EOxGHWXCy4mssuf/3yFa2GJENN5nnw5wpG0zsAxFGL05BWUXpcXcgO/XIzY3Uj
+u/z4nmSyDcIMX09g2pRiJafLZjmj1RCaS/o8PpI2nR3KaTavLNlQtujKOaItQnLs+LCSa9yVR6OY
+hVCZbzxGLDQMZRvDRDMRh96IbQaPr7Hrd2YXfPldOTKS4+d85t7rAHx2EpFrVp5vxxinhCTqGLx2
+cP/JW/LOV9/kcCzHygRwitKbZhMCJXspbjWhaJLSaHh2UdVs3pb3GK/mHE2lTYNhypaSIlYIXt0s
+uJjLOF29usaog26QZjSqVBjfEjlRlfNM+jhaZwx7cm1gXlDNFAsxmRAVipA8DLELUbtDhf6ejgeM
+AzF5/GBCqM6+zWZGqGHr3FsTaCgy1TjyqN+j1RDhVbUhUBKdMR42kblTOI/r5kz7RfqzMh0DZfpq
+bi2twh9jPyI+ku8X56LNnL+8pd+XvkiDAQNfTLeh12G3GJE45iiT/mwida7WtySqbbpusIOQV5sr
+rmcKi+/FmFIh5KXc97Unp1QKH0824HQOxW1I/0A0wTeO+7z//o5E7hPJp2NTsBV18ZTF1TX1Y1W5
+P/+Y1TOF8OZnhApoKcOIVS6Tt1GAiv/82S4jzfRinEYAynZGraroZt0xu9XsQR347OgeOv8wWIJU
+seXtGBfJZKxshOep5/tEIhnZbEWhi3FdN6DgkLCCTLHUTQYHoailQwWjdNYnv5L2TA9SULq2592a
+3iv5PHxNnnF1WfHhXBmGmiV//keF4vLumyN8ndxVtdltdIliJZIiZ6MwYW8U0uYyYcfDQ6JMqeni
+mFPNPtyaXV3e4NSL3lUljDQl2xQUC1E/rW3wlUEoUixI0DZ4ajJEYUym0N+DXo+m2WYcBnQTpSaz
+sqBbf02aTbTvYR3LWPaTkCQRtfv5xTsEtzqhffnOLyYsNDW86j5ipnR61+2SVDeISZpxMtG+15yS
+tO+zWcozVouKUtvZmojEyPiOD3t8tpP23fSlj9sixOimvphd0WjmajKISCslSdHNn6jBaE5Frxcz
+HMjv3n76nGKjGJDYMdWciNWFjO+6WtEbySJ+eOcENFdhmQe0mpsTtBBpDo3XKnPYZYBRyPOylzDU
+LF4z9Rip2Xjv9IS798Xs/crv8Ylkbz7sZS97+Zh8KjQFLFC0fLh8wbEmRhpTU2s0YF1tuFSP+2LT
+0Cqs+DpXZ5Bf4iGnzsM7LaniCbr1gEUhanDehFxrVto7Z6Li/drvLRiq0+akN+BzTzRuHIZkdptx
+d8PiVtT1SIEMZnTIQOGl54sVValhhsmYk768wHA8YPlS2twpwQhJhh3ICRU0HUbp0fKLlkq1m1mh
+KuVdj4NcvNsVIa+MnP5HLQz0cWEc4Ss02faUnbm3Zj4Xs2MzzzGJ/P0qv6TTrMSgbxjp89bKlny9
+umB+oSpzY7B6Mj9KE0qNbJimpFU8yFyjRHXbEWril+vnfOu5Eo+8WzBRTEbQhZzqc9JYojotCWGm
+fBNmQVtKO9OmJR7I/fojy+JaTubekWSJbppbXp0p3uDDDRdGHXiF486hQnsPYwYjVauV2bQlJtdE
+o6VtmCmnxvnyinPNKvX8EmUDxIvVrKo7jOLRKyBWfMbYRIxS0RT6K2lvXlUUmuSWTft4GrXIwoZa
+oyidMVwr/Hmgjs/bfIOnCNlR7zG9R8ohUeVsFFYexj3Ob2Q9PH8qkYVx5u00ovuPJ3x+LGYQS4vV
+6MNkmHCosPBPKp+OTcEDP/X50bsH3EtlcV+cf4OLXNT8yDoONPSUDGPaQibs3RPp1OMAglRJUhnj
+rWQiRZ0lVpaaxKx4MhRb4QuPHgHwavGKTEOANp/vSEAL2+FqTZ2tDDdbIM+WWdePsRoC8vGINEPT
+FDU5ymG4KYlUq8zuSnsPpwN62vaynlE2GolYzugpiGiUyMR94+CUx0+EXPXy1tEpA/XViwqrWPzR
+JOGmkI3HalZjmN2nnL0t3ZrUFBv5+8zNCdTePxyljLep02vpQ+cGDNS/0q09TE+jIHFKoKzaBvDV
+7xAHCmGOfYYaiRj6Ax6fysQ8my+ZdLLjNsUNBz25Ji/kXuX6Bi8Rldn3jhh0sjD9IMJqP3tXA6ZH
+avufaDhuNaFQaHOLz0RBP8WyZXiiALauo1I4ddVoNGFWcFGKKj5rLYFmX04yj1wzCvtNKKwqQLeU
+94xMh9vIwXLncEroSb9N0oi2lrnVV1/NJu7xsC/+qgd3D4gD+bu9e4+joYa7jSVIpV+27OFVVzNS
+022cxZR6ALz20IJCqYPxIaUCmSrlhDyyHn2F7j8YjnE6vm1b4N9oCrhvuX8o/fxJZW8+7GUve/mY
+fCo0BWMCwuCAn/ozr9NpwkkUD7ku3gcgHsYcJaIpNIsNyYGCZWIlMekFDFrZJeN+gENU6SAcka23
+6npFrJTrd1PNDOwf0iigx48zfN1p880t+Ux25bPFmiuF3U5icUJdzHJKhbv2/JhC1efM+LS7E9Qj
+9eVUfL1WvgXXESgWIogTfI2F94d3uXcqJ9ep8iM8SD0apTxLP3tCpDDfOOqY3YgGtSwaPHVsDjQz
+cr3ecLBVVQ9P2VSKkXg+YxDJ6XF/NCHW82CiGaPhNKNS6K/t93bciGHgEyggy+tnRFr4xtOpk3U5
+VjkWfM9nqFR4x0lKmSvrsHeE8q2wMsqh0A13NPJ2AA8jIczxBwcYBRONRgGjgWiOoTof+8MeDx7L
+s29e3nJzLW2eN9eEoZooxtDTJK6uUrCVNfRaafvnDk9o9NTtTMvjmbIdz+HGqGalJmM7u6J08uzj
+UcBd5VJMex6/+aHMi7lTTXHUJ9J3ytIJkWZi3j3sc6uJcouqAaVhi4bqVD894f4bElFIshFSdREO
+jiI8ZXBeWpjG6gieyneP+kf01AS9N0nwQyUPmsdcaxmDwTDi8UQ0zk8qn5JNweInOfd/+t8hf+93
+AVjMIwKtN2DKJf1QNgsz6OMUnBT3BfefDgK8bQrjck2z1s8nOYMDsWEHmyW2kkEYj8RmS+OaekuN
+3k9oNcdc49IlAAAgAElEQVQBk1ErWrKY3dCttkw4sqhuNxtytQut50jUHkzGPXorGbj+MGSgfom+
+bl6LqqKv6a337x/yTKnMg/qWuwp0Ob2r5Jy9A0YDYY0anZyQNkr0Utd0tWxOefeURDlFC83qC5qK
+8ZE8Iz0I+fBMTIkqsIyO5N4nJ/foBdK3cS1t6Kcd4UjUWtNjV5ug5RoTbtOeU0JFYW693nEypVUO
+S1PG+Kr6Z70RsZIf1lVG5Ymfo6/EriZsOVfg2DTIiXUDrNob/FJBOD1LOpTFss1LiVyGGYqZkIY9
+Dk9kIaxXB7hMMxs3C6xmPq6VE9OZxS7L0PR6VMpTYyfNjgi47uckS7m3p6CwfnBI4ymorfSZHG0j
+AIbVlWzOtdabGBz1idWPMC83vBFL26PM51gKdXF5dUuuFaAqzam4c2fKm3fF7EibPqGGFkdZwr2J
+zPHNecJa518/1FC8s3iRRjXIQetBlFG3I/IdGcvwQNmZPqHszYe97GUvH5NPh6bgIKwdBCHJY9kx
+F/4Z3gvZwQ+P3S7+H8QpRguOOFXbws5gfd3tmzWdqvDePMcp+/BwcEIeSMw+0lqFWTIiUHW9KAy1
+kqGsy5pEMQaD9Jb3F6Ly9gbqUKo7Cq3FuG5bRpk6s9YVnUJw2yChViKWlTIjO6/acThGfkKgp7Qt
+U45PxRl0T4veBEcZsWYRxomPQU7xtruiWGmptzXkWhBnOVfsQrjEV6DMOMqolXH49tWSUHEK9AyB
+goF85XWPE49Y1VY/ivFizXzMA6ySAQSeIRwqtkAdeXg+RuHMLilp1UQLqoBISWtsscRsQVuatxAF
+MNGkEFv5lKpS+0tLUUp/T4cHRHo6Gs0G9OIGr1EN0sRkyqEQpT7VrTqHYygulYE7Vk1wWZFEcmJa
+F1I6zT/IQyI1CcqFo9T8lli5NYyX4WnVKxetdzTPq0VEXoiH3yrd//Jixbkydz+ILe5UAUtJjVWo
+ezIaslqLhuHn8rts8iZxX+ZbcZ1jtapVNE0Iavm+fy/gRCtOTbfz+3bJRuem8VvKUnkvNiVeJOO7
+aEKMmhKfVP7YmoIx5oEx5peNMd80xnzDGPNX9fupMeYfGWPe1X8nf9xn7GUve/mXLz+IptACf805
+99vGmAHwFWPMPwL+E+BLzrm/YYz5JeCXgP/6D7yT52OSEYYGTwup5vVLskhjrVGfVHP9u5W/QwIS
+KG2Xd4BttjX1Qjx16pgooLhSEtOixleWolDj7q4Z7JiAPFa0G3keuUehZbcILY2GqWa3+vfW4rZE
+OWXHslB23q5ioJlxlB75toSclmbrO49opLiBy1vqpSDaorAjnP+IXLvN9gxnwDaUNKNR1t62aGmV
+xLamoNyiBrf1CkxApA6pzEuoCiW8PbSYtbzH7e0lMyMaVF/h2p7NdlW+bd7iq91q4wmlFjTtXEva
+KSOV+hk2TbXjIAgcpIr+tH5Gq47LzjZ0Wp/AKVTXxpZIHX9udLDNP8NYj3Yl42onIV2tdTg1e9Ey
+3hWf8W0fo/4Mnwar5QDLlw3trgiMaHRVF2K2FGxtuUuwjUOfcpvk1S3x1e+A9rfXdrSFPK+KGrpS
+Q8qmotYsVl+1iiz1KFWreHr+lDdO1TnKfTYKCw/WFzSq0VjVKtIkoFK/Vble4HRMnR0QjlVLYcKh
+zttAw6x+5PCszMm2rrFbdmy/2VWo3hQpZrMtbPHJ5I+9KTjnzoAz/bwyxnwLKUH/l4Cf18v+J+Af
+84dtCgC+Rzef4wpRg6fNEXFPPqfZEdGWkCN0NJqXUF4rLj72aLTMurM+nZKabG48mlYWZLFcsdpo
+oQ5Vv6d+xLYLauNhNYpgzWaXntredpxqYc/JSMBEN+RU20rUfkHdbanLAlp1InWbhttKvOgDBdB0
+ScThtipxb8PJPTEJXjtIee11cQLuFuDMo861aMh0QFmKCrhcXXK7ko2uaTqaQK7f1po0JtpV456O
+M85W8n79ImK2FlWzuinxxppmrDiFkopM1WjTT8iv5XkmCii2baImVQ9/rDU4i8WaQrMrJ1l/R8gS
+dtAqt2XdNJh4y7C9pUgPCdQ5Oog7xgMpMBsejhlWspC9uATlRGz0ncIyx2VK+84Gs2WS3tQESrWf
+DZJdpW9r1HHbzXZFW9ZtzUoXZhKHu8IwBn9HbzaIZMyXbQnethBNSqbJH4NRn0PNfTCaIj4Y+uSa
+Rt05H6ckK7VXslFoelk61lrzsdNao97NiqP70s62zWlWWk2rvWWlxW4u7BVMZQMY9sUBHbqKlW4g
+YV1TBfp+eUepUZDDgSGyW0jWJ5M/EUejMeYR8JPAbwAnumEAnAMn3+c3v2iM+S1jzG9t2Zb2spe9
+/OnLD+xoNMb0gb8N/JfOuaVRpwuAc84Zs40Vfly+uxT9T/7YW66tFpRtzvVLwSYM+qf0VVOwtNht
+KMxF1KpNrDUrzr+ZEag+H/cPSCYa0ze3pK8krzxffECpiMar9zVWPrjZ1WkobEunJc98Ajol+G/q
+ir5m8w01Iao3z2EgJ2KU+dQbrSJsASXJrNPVzlkVKf9/GhhyZVFuXMf8Rnb+vPLxF6oS9rQEmZdw
+8JbWJTQVrSeOsW61wGqSV+sCVFEg3PKpto6B0nxlWUpzoad/r0dQq0PUiygUOFDrKWiraofmK5Yl
+rdmWjfNoNPnJNoZSE34aNUucc3hKidbhaLaZegG4LbM1MUbp9HpD6e/WWgqdGWkakJwqPVzfkj2W
++F21vKbcQsgVsVk1FrNUHErPx65Emyo2Depfxfc8Ii06Ybd1UMIenpoUZ/MlGx2n6PwMozU+2hCs
+9kGba7JTuaKp1JS0hiTbFvCJeHgs4zNUaHvv/oSzbwjZ8PPFJbeKSZkkMWGlbNVuubN+XaWZv/P3
+mf2WvEdcFaRqahRhhtPiO77n8/xc5v1BJu+fpQ40Ocy5klAh2KlvaVU77Yf1rsr6J5UfaFMwQnP7
+t4H/xTn3d/TrC2PMHefcmTHmDnD5h92nqyqW773HN148Zar1DEeZT/NSTQI/3xXCaOqARsE0Tok7
+bhdnNJoHMR08oNioz6B9xbXCgANTc0ch1IVCkdd5QKA8iUVZYOzWXEkZ6OT3vArXinp4O9ffGaiU
+pagNLNsSUnm9wSlDVGpjPE3fzZW1+XByAOpN//aHt1RLARmcB4YPpvLs+z0h0njyJCS5UU++uaZW
+38em35HPpM2rdc1cy9Jvq5m6qGahVahC8l11p6Dqc3IgJsNoFNMqF2TlthmnUCh1OKmHP1T4d5lT
+qm8krDpuNeoSK82+SWIGiWaPDlOsFmRpuw6nB4S1LaUSsQQK4zahj6/FUTsbUinQKSTDKVTfx6fR
+xWs0KkDW0BT6u7zDHyjRSezwSmXnSluqpxqVWcpiu10tWKj5cHl2xVpzRrraMAhkHOoISt048s22
+JijUapbYFK7U9g9K6B+IOZnqMvKsT+5rsdrOcrFSJqv4Ek9NrKKoOLwjOBNbyQZ5kc+JFpLJ2Bqf
+SDfOfpcQaoQt7ftUemhdXss7TaYpqa7gKPCINULnp+C2hEFeTKXVwz6p/CDRBwP898C3nHP/zXf9
+6e8Cv6CffwH4P/+4z9jLXvbyL19+EE3h54D/CPi6Mear+t1fB/4G8LeMMf8Z8BT49/6wGzlnadoN
+z55+yOizqjquX+1Ox8h2bInzmyqnU3UvUAdgZEIWyoBbPXufUpOAwiDAU/SbbyDua3GSTJJ22uCS
++lbV4HZDqEkpeVnQqI7nexEm1dLnL9XxF9SsFe7brjvc1rOcQ5TJ86IoFm8bEGkSUUlDu1ZN4rDH
+UHEPk7BHpByNkdZDrNYNHzphaD5op3gaH7cRBJGcjm1cMc/VyadJUgQhjWoul6sNncKji/ScVGu9
+2W7IZS4qsabrMz2Y4CungWk8IqUxs0cZvRfSz2Fck+hJGivtnFcNMIr4s3VOoGZHYx15rjgMt2Gg
+6ai19pvvz7kTCyZjdP9nOXv2kdwvMnhaZ8EzR1hf3FMuUIdak+A0g7EpC6jVAYlHoCXkvHWIvS/P
+eTTXMR3FzJVX8zA9ZlWKA9qagMsz+bze1HQadXGejEcVFkStJr8FIWkmfb/YrEi1TNtWc13klnCi
+jOEX53StlpVL+7iNare2ISrlmsFr0odx3ieuZc5mg4BgIG1etVOKbRXvtsZoiGaLdbG2w9vWIw1C
+Nor16PuWvkagnJlStXLvTyo/SPThVwDzff78r/+R7gXUzuPynW+zeiiq1cPsz5I8lJfxZiFGy8h3
+ec7iUtS9D5XPsTeBRDu9/0bMQyTNNppOcerJnd885+2n6tUeiE9hQEKo6vxgMsQpWaeXt3QKR24W
+Da9efVue91IWzcFnH2PVi05XECsZaddENHqPoRdR6kI2St5yW4a8cSJ+1/nmipFmCWZJymtj+bxW
+E7oyt6i2S1WuGGh+QRiG3NRbZh4fo4Z0pQCqznQkCgMvvZSlcjCevbqgWMrn7DMlw56ovq1Cqb00
+xGjWZu46rl58CEDyYkSl5CRF38fXjEhPCW68zhIcKrNUF2HUHKOLCDXFvaurHcHLlhVr8vAJ45Fs
+7nHwgtPXxfdDMIS1hGr91JD15PvNXIubdAG+psBXtxtKHd/OdYy3s3FZYjeyIdXbgjx5yFA3ND+p
+STN5/1m1Ihqr6ZV3NOq76G3Df21LMhbvvQ0NgTop0jRjpSUDloU8y187xlPZ/HqTglwZorzsgEBD
+1fXljMVGDpdprlyN2T2yB0p6YwsSK5tb6Jdcb/kvW49A3XOZL3Mh6/XwNOoURwaj2ZXhMNz5K6I0
+p9v80VKn9zDnvexlLx+TTwXMGc/HpGNM/4Dffud3AHhy56eJjNSVtME7ZKqUDI5+hLwVlfLVt94D
+4PMnj3ntR4T5OS58qpVoEsGqpFJV+yzv8fUrKWH+o0a9xo9HRKriRWmPutqCdHq0Sm6RFB4zLYBy
+XuopZxsGGgfPhzVTVTUHJ47FCwVDDSx3nZxGS6VFX5wv+EDV02rTcaVl73r+jFdL0V5OehpfDyy+
+1iictIcQKYAmCCiVuu3MvyFVKrGequVmA0tN5tl0HtcvvgLA9WJBqvDupy8Cojvyu0dalbv0YkLl
+nqhn1zx9LlpVuTxnNBLn59XTOQsr2tlU8/hfuz8luZFpNOhPOL4vp39/EAk/HeC4R9NqQpu3rUvp
+yPX0r8uY8L5kSeblDLOlLitWpJMfl3FN5PfFZkmmcOzkziOamfTbhxcvSH9V2tY/8AnUSbtUU8qz
+JU499c+eXXB9IPfrhTGpls6zq2vcNqKlJe770wHx1lQs16yC7ck84eW5aF5OOTkYWXqtZnXGV6zP
+hADm8vUj3ggP9P3P+ODFuwDM1Oz4Cz9yTKT8BwPnmCnPY2AHbDSKcL28YqjRrLsnYv6OJylupaA2
+V5AoZD2NUoxiSzZ5Se2+n0L/veVTsSl4riNzSyqW/OaXngPwmebv82M/++cAiBuHGYnamviWe4+E
+xPTP3Igt6J1vSJ8oicXwiEKx8fm1w2XbrMUhP3oov/u8pqn2hx35QvMZ8gpP/QiWDl8zJutBuKv/
+SLktHmvBbdOeE3wl/VhuFnSaDr24uSVW/0IXyAR8Pj/n6a14mV8/vs/xXTEZ3DWUau8Xc2lD/zQi
+VCx7ayqcpl/fNjXPlOfxpl4yTmWyJZpTcDmbsTGyKdrbjiO1P8em5dXtlnjE4zM6T95X+zYbT0BN
+lMCz9CZK1krB4I701+GTIV27re0ov5+OhgQaFzXGkhhlp7IWz8r9mq4g77a/01oI+YR2y5Tkhbgt
+YrFaUWr4+fDkdTxdhL6+X1itsIp6DQ49Bmp2Pao7jFLq94ZjzCPZfA63Zo5LaTpZbL07j/m9F2IS
+VpuCWIlZk2FNMdsWJNY6E42l01Dfpu5INF3TD/u0WrOzVv9LQo+1mkfzzZqbc9lYZ7/q43/x5wGI
+joZUvy2df6YFdsvXPV7/Ccnm9auOUAsIb66viJ5L+2Mb0lPSloGao6EXsbRK4ktNjMaly5pSzcbK
+68i38/cTyt582Mte9vIx+VRoClWT8/7L36RYXRApO++v/OaXSPviwHn9ySnxTCnXWdD05PSYapXd
+/GLG5jfFMZZ+0SPV772DiuJCVfTlNY9O5bTKFH5KE7LWOH9ZbnZVgWZ5wqWCZuqmZa3UXFs4c7dp
+qFP5e7eIqJToYr3Iabd1LjcBL5GTqdbvrC2oN3Lt+8VTrlcK2LEd5lJOzVvlOXjkMu4ojLbx451n
++dlHay7UjAmMR6tQ4RfKYTmvlthrJRbxLGuFXYcG+oGq0m3LmZKTBKloLgfvt3hTrZYVlxTKEZC5
+jsuXH8k42YChcmhGU+WPNIYu06y+sKDRKVVuWjaKxa+Wt5RqppWKi4jtJb2RqO2dm+IrAcz67IJG
+VfemOMDo+9XllX5X7EBm5gq8qZZ1TzJMoEza9SXFmWicG81I9OqUVjkUnvo3LNR08eMxjSJq89sl
+KFYFTytILXxKjQy0I8OoUNCa8Wm1glmhkZFiVbFayOeLpzNu55fahorf7ik3xqghOpIT//ZD+fsv
+/5P/h+fvi6YQTzqGPdF+8llJnsmcHU0nnCptO1pQaHG7odK2pZ7HWqnpbtcFGyWLKW4KXPqnAHPe
+y1728v8f+VRoCou8x//11Z+hexSz+OA3AfjqYsGX/ud/Ln+vwFekoImTXfXoqpXdvpjlNO3fByBv
+wGmYxjpItFRaHERMtkk8W0oxL8TuAIHBDvprfUCZa7ww4OhAwkz3P/8TAPynf+U/Z7MWOGtdVxw/
+EMclL685nMi90+whTsldA3UQUa6pNZxYVCWhlsILbLlDHvqa4BNFPp3mwc/nOQvVFF6/vSVXEteN
+9cm1zmN/KKfIOEiY9sXZlY7u8OCB4D5uX/4O/+CX/3cAbtYZ33ghjExrhc5+VK15vtZScCuf5bWc
+gu++/IB8odqPcbSN1lhU8zUIIpw6ZV3XUmpiWmvZ9a2lIdXQ4Y2GcjVw+fvk7ukBrYZZB4MePU3S
+OtHw5XACqdFw4djn4UDCepODiMNUcA+9QUZf8QTbIjuz9RU3gWhH33j/OS8uRDsYTHr8yBNxcr7x
+xn02Mzm933smc2vuHNfb2hJfW3B4JPf9wk8+5isf/IcAnBV/TZ7xYsHrE60FOrxPFIuD9qZ5xbtf
+/78BOBqn9FVD+rm/KE7UB4cT7qiD+XDa45Wyjc9NzuV70o53v/U+f+dcTv+NOihHvZSf+cITAP6V
+f/tn+cLrP6ffJ7z3/m8BUJp38EZPvk9vf2/5VGwKjorKfsDzt7+CPxM10TMNhS8zL/YiCnUYmapl
+44nHuVE+x8aU1OoYtDicOtcMhlYzJk1gWHWa+aeTym8snRK2NJ3DqaPR1ZZ6W2imCrjxZZAGlQxy
+ERSEmn2Yd29zuP48AP03hwSbbYHZGk9rEPrqXGu8DqfFRVMzwtNnd3VI6yt4Rc2HKEoptPBINLzh
+MJeJFNPxQqngmvaKSaULYav0LQuWOqr9yX1yTeWdXS5470wmWG/9kJNEVMpxJv3jxRsSTace9IfY
+G1lAva6jr3Uns8zDKn1dptTx4zgmVqxH1fk70o9l7vFyLgsvaad8kIiZYvOtJ/x7psSAcSjzOV5i
+uduTWL/6EPGaENeXNqTdgEMtuDKNDxkr+CwLMhLt29JqtKBnOHbSh+Ukp7xV/ELaMdVKTb0EbCjv
+ejjVzMkzw5WOzfpiRq6ZjQerDc+q/xaAjb5naB2VOknXn+nzZCYb2WDZMtEIx5/5YsbdiZgSnz2R
+sTvuP2Z8bwurj2k+r2UJLgw3b0rE7P2zFd96Jhv519+Rv3+0nvO1r8uYnv7EJT8xUef3scfpRtP9
+7zrC4Ph79/X3kb35sJe97OVj8qnQFEy5Jnr313jv977JQkuWh/4JkeqoTZuQKwlomZ9jdDeut8zI
+tsOxDW9ZtjlCxjnUz4ZXtxRalTfR0yP3hrRKk1U1PmjosOs6nO6X1jUESs4ye19P+arjqxfSnoP7
+C+avvgbAqPdF0sMtoq8HStxqNPPORimZkxPaN8Nd8lATFkSqVQRqJuEMbpv1Fh/sMuvCtk/ky0kx
+CPskauaEWrPiJi3pa0m02atXu7DoL//qMz5Sx1aat2R90QrGWrwkbxuqSjSpYRwSa5uPJhmDVE7r
+o+mQvmoKW2j3mICB1pq0fsPNUu730fMrSnUSdiuf//juTwLwP/7ul+X330dRKPIGX5F7SZ1wppre
+0ZYqzt/QbolXxn3CobQtDVP6uxKAjmazLWyjGmTZ4Ssk+ODwHo8UI1J3G5yydXfzlFbH2ipcu/Iu
+mQvcgOuuJriWMXv/y9/gR/OvA/DrmoxXW7hQ9uxHZw09RY0+/EzLw4H00ZPRY75wR07uwzui2fTi
+kMRTrERs6RTOPLibMNH3Oz2acaIJWMNO5tuLr615oUQ9X/nKR/zMgWhjo9ETXmjFcqoV8/e/9b07
++/vIp2JTKLuGd5YvCeuUVBdFFnrcaM08jyXlrahorW12+k3ELkWMB6HYoaVX02lGXVE3hFrRyIs9
+AvVFWMUEeIFhkyuU2oYYtdXwfazyNUahj/PkHmv1yF8/e8HLXFS54MUBc+EH4UF9Dq1mwLUBxUJU
+8FQZfqM2wGmmpmsDnBPV3lQh3ZaQRM13381xOnFdE9EpeCtqbhgoBHtdWBZqz/eUQKVOLWsjDSr8
+BvtNaeeXvv7POH9H+vPBaymTRPwgB8eiLofXHZ0uoPtTjx8/ETt0kP4IQ82unGZj1A1Coanlm+UF
+qW5+kRcza+T9Pur1OLmWOP7vvnvJl/uyif6UVu/651s8978oFtrAaF8E6JCx3LJEhwEml43H3fMJ
+dcwi12G1ulbZtXjbWpEaovfbAE/zSqamJrovpuD11YJa65RW7QpPP6e53HdTpphGVPQkHrBR0+3p
+/EM8rdc4Uh9VFVk+eyqw7J94dMJbD2URR13Anc+JyXDvoMedvlYim6pa71l8jc6Y1gidNlKWPtI1
+kB0c4GueR/avyQbyzbNrbt+VKMvle5f8k4fSziCqWelAzc7vcLNN/f6Esjcf9rKXvXxMPhWaQtvB
+1Y1j1tpdafW2KHZltVbVilo5Apyz+Mpm66v3yQuhUiTdwf0pdSM78elhQrTUjLtxwAutwVdeifpd
+bvJdJlvrih2Rh+8Fu+wzm/Qo9dmdFYdjczTg+KWoZz3vkoNEToF4PMHXIiMEK5SbhFC9zX67AeUw
+bFYbFDtHhyOJlftQef86G4FqNHEU4Ru9uj8m2sjOPxkEVFpWrNH6i+FmRqg0cP3hhI/eFt3Xv72m
+0dM9KBxoZeoold+fZiHHSsZx8vCA07H0YW86JVHPXxSfYELN1jR6BK/HOw5DL/EY6Wl9dJQRfeul
+vEtR8ysfyeevqapu+D6uRgNbm68qS3LFlAw0galt6h3xyqA95ljLCcbNfFcmHuftoL1OezkO/B1f
+hheFqDVCOxbuBxkHi1FimEEq/Xk/q3kVSV+Na0tule+w8um06I6n/A6PTgf8/I+J0/mtP3vMRPk0
+wqzPqTpKDyaH3yFq0amCLXF6snvGx+jzoMM0qhX1GqZKsZa2omH8xc98jq8/E5PwdrXk61/7dQBe
+f+3nOYpEs56/uqAX/NGW+adiU8AZ/DaADpymfDZNSaOwYq+zbAmcfGOY6ER4ck+yITmOGOlm8uCt
+xyjBEvatuwRifnHVrxi43wDgbc24dM8tTa3URVjMNunTqwm0Ms+9w5/iQovJ2pXYkNNgTXryFgCB
++RbjVG3/saGayYJs2gv6B5KPESo/oa0yrFVIsAdGGZm6rqTRzSBS1blrocgVuh10GA2XeqbC6gRK
+uwXJRM0OpWT3umNcKBM3mLS8O5OsdlcNCJQI10UxPfWitwvNYbgDb70l7Z3EI+JEJmN8PMRHi4kE
+lcZrwbGlfDqgi5U8tPFJEt1YOeKNsfIVPrjk2UL6aJ3JSnhvVXxPv4LnoDbbtHVLolO0U1Oq6Eo0
+G54nd6cMFPrb+n38Tk0+PDot0LMlZTV+vSMhab2WVP1LwdEEX0lU2qrGKZ9hNxC1PVof8s6hFs31
+hsw7jfx4PkcKPotPtBLYv/oFfuyeLNjT0SGhUx9NmHEwEbOyl/QwGiZH/TNedAQ6F2xnMLqIjYvx
+Yj2QSp+gr6nRhcz/L/70z/BT1+8A8KWvnrG6kbl3VV6QNfK84TDl1hS/v6P/ANmbD3vZy14+Jp8K
+TcFZn7IYc7U+wyo3nmdafKt57sCWusH3IjKFoBr/EQAnxyM0aY8pD4gUyBMtAz7yJZOyOc92MeiJ
+mgYrb45RJxHWB2Vo9r0hoZodH9VHuFA0DIx4euPGUZ3oybwJiDXO7W4Mvqrj82+v6G0Zlu8onLe5
+odXQSEuL0h5gsj7dZguc0pPYtrTelkPCESqE11UbwpH0RfnS7rb1bfKRf+CYKZw3XtfMn8spkfsF
+KO6hqjtWcznd3ziWvnzz/n0OVTtKRj0Czb70Ix9PORRM5+P0tN1qDGQhZvuedY1RKncbN6RDee9J
+dsxfeF0cur/zLXlu4nsUW1wI3xHjmx3du9cZKtUaTKPEOa2hp0Cmnkt25d28qiXYqsmmw9vWd9QI
+Tmc9jGoEpvXwtM5n0NX42xZ4HlGq9S+32Y6vdbymiWS3owsaLQdvuwmlwrFPdT4+qCcMlHglDSIy
+rUHZz3xSZaAOIh9fcTJbnIrp9TB2C5ZLhAMOIPDxOjUlwgrEl0zXk78fvDXl5y+/CMA//urf5Vrp
+/d752vu4H1Mcie9g/kejY/tUbAplV/Lt+bep6pmUiwI8PDq1ZQMiQl3IWZrQKpW1j2LSzwz2riyU
+6/Kc/oV2cOkRKN4/i2qOnohHvdLCta9VUz7cEpFWJWYby7TFjsmJ/L9jxyWjDEOsc5zalr3pZ2ki
+mfzL4gX1hYKeBsfQ1yKm27qTi3NQ4pHa+rRO2p/YGEbyfauMSI3L8attDcsBnd1ujfGufLw/uMtS
+K7azqt4AACAASURBVECFykXo/7/svVmsZVd6HvatPQ9nPvfcqW7NZBXJ5tRs9mQN6LYcIZAHwQOE
+IAngBHrIk5EgARInT35IAOcliZ6Sh0SJH5wogRUHgePIllqD1a1uSuqBQ5MsVhVrurfueMZ9zp73
+Xnn4v31IQmyJFJV2SbgL6L7Fe8+w9tprr3/6/u9bldi+INWHsq4R5bJWSQ0osjBNshmGiWyaq0R5
+bm5egx3IdShdwaj43dpHzTKbYZVQdJ/XJdvlEvW6jJo3bOioFilgy1qElYWjSFzeEWP1yKhQzTKu
+hV6HEg4sZCx3Bm0fDqsy8ULWyjVM7G4JwrS7EaAiia+HEja7PEvLgCay0iZSNIeDJYlHVLWCR8Wp
+IOygph6jlZUIFDUyeaDVucLFLTnIHtxaIbClbbkONHqRuOgOBY/3thUcclum4zl8IlldtwOzWa88
+h0UyVoNCx1AeFIlmVV0DJMyp0wSMiqEWNQzmfOpEcS1CfOWlrwEAXnj2+3jjXenzWJ4eYfEeNTQv
+9GF2ZO0/6TgPH87H+TgfHxlPhKegtUZRF6i0hmbmWakKFs2Hsuo1T0FWabRNmfY8F38qnhZwqDxU
+BTFau2RwPnQxIuPzeBji4pYcuzerLwAAvhM4OE0F2DEdn6Fm+FAUBdaqIB8edBTCzUvA6X0AQDtZ
+wi7lO5aWva7jd/weTJKr1AeSCa6MMbRDcZJyBtMSF9VECpNubs6kZR0DFZNkGiHyQkKXuhaXHgC8
+wkBE9SmrJ7iD2jjGwBDL57V2kVPnMU0SJKQj69QBdm6Iy9vfFq4E0wVURglreNC0cjUsGBRLgeoA
+DjXlDf7MFXTa8A/aqMzGVdXwiafwL3ewFcpnXzsQN3pxCCQkBTHyFHWDMjNreA3IzHDgUlKecBLY
+jkLHFrfc9TUsm9qPtgOLYY5jFgAFWkreU51p+PQ808RETUq3WhVwuJ8Mx4NLr8emytj2louw9QoA
+4CD9LTygO7+9ewX3v/N7AICNgh5WZSFJidmwUtQh719ooM6Y5LV60KT5J/ZOOCO01/wHwIqSMkyg
+yREaNmDJ92gS8ph1gRazrl97/ivQuQDDVNiGYnjn+hby6sekJXk+zsf5+Is5nghPAbqGLlbQH6pR
+KXyQJHIsE3lDweUpBGyS2WNDSe0ZMFiOag0rXNqWv6NTohOLVTFXcwQT0rC9Ikf0X7/8byKvxFLe
+vvdtzE5JDhp9qOb9MaPGCYJGHm02gSoaReQSc5q04eYVWI7Mo2zdlznop6EcllbrDkryPlQALLoh
+rivxchXn8JicStwUVipxYaIjlJnE5VVSoRswORiwdGWM4MTUeuhayKhDUNTAIhOzs9PbxRdagrxr
+s6NSVRY0BXWqLIWi+IKZxtDEVhhu8kGikT+VVUPRElVpiooxeZGWiMfiHWSnNsJMrOYLN28AAI7d
+ByhI7XYc1cip6+EbHiySNvR2NhGQk2FlM3vqKLQo9tMq7XUpz/E78APyLBTB2kNY5Uzm2XP4EI9O
++TF02aBbSyCnFgUSaJKiGiSgDfUQzgbhyreuwAvEO0DHgxcwP9QRr+Q4AZ6hSI7RMlAweaiVibqk
+J1CUqPM2f8+l1A6UarrAXKBu2J+shgAKVQXostGYJKNXcQgVy3753OUdhENJOk5PZ4ia/FdkYF7+
+OUw0KgAu6jWYB5CMtMEFKWsDltNQVnex2ZFE2rXRTwMA2s95WEyEDv2ibePKJcEvJKFCfCQbdlx1
+MTuRhdrJrwIAgnYLX//JnwMAHB2OUbXkoTmNjv7Y+dpRipp8hqUVr1uEjTkwJLWXqk+ASh5k1Sc5
+xjKGQci08n0YFGktiimImYGmS62tDg4OBHhU1StEjRhrMofflQMyrzOEirVyCqKilQKExubLBbyc
+dOHRAjpr9BMVAm7oJkmYp3OYpNF3nTbMblPhsWGQ0k7BgmLoAuIqat+HwcqJ8lwYC0K3wwUwlusv
+wgm8Sj57xVbmV7afwiqWDsBZlaMmDsEOHbQ9uZa+EUC7ci1Ncs4zTURLAews530MbhCUUuYwSMVu
+2Q6Qsiek6Z4t9Zokx1AeUuJF8qJeQ8h7fgegy18SI6Jbc/SuS99G67KP5DfkoOsHMVaVJIWHS1lL
+IyoxJTbD8QtYFKfJkhI28RuuZ697Who+Hu07H9x/qwBoZOBYAK9J1wXKCZXBbHKJpi0UpazFwMuR
+8J61AgcP9+U1SX0GK/90HI2fOXxQSplKqe8rpf4Z//uqUuo1pdQdpdT/rhShhufjfJyPPxfjz8JT
++A8BvAOgqXv81wD+W631ryil/gcAvwjgv//jP0Kj0hqGBpr0ngkDJsEHgWfA4Nli2RYezuiuHgjz
+89WihftTYcg9GnWRU9RkMHoO33l0HwBw+50fYjKXpNx33her9TNf/woOxuxmUoeIE0rGGw6WhBXr
+8o/C7lYLDacryUOlNzFnydHNpzATcauTqItlKdDebCpUcdoE3L54KWYwgOmIpTGqEiVlyY8fS6fb
+vf17ONgXT8EyPYCkIZ5ZYJiS2dhSyGgERuwibZk1tCchUZTliEkxl6YFTCbPfKvCgkm38akkrWqE
+0Aw1XC9HKxKrFFzag6NZK/cTqIYPgcm5Kq2QsCwYHURYch7JNMJpJOsyny/WjUmbxHREWwVeXUo4
+t5jOccSOUc/uokvPym5rGCydukw+Vlrh3j7LsMVd1GTm3u16sH1Z20Ib607KakGvCQamZRPaLLFo
+tDDNADnRmb3AwjCQeXRb5GaoezBN8Vw+v/2XYNf/HADw9uuvISSWY5pKGKRbEQzI3OdLCzUZqFtR
+Dym7f+N5BZ/498518TCscgLLJEEtABDyrnMTJTt6s/EcSSLXFI1J8xctUJPrIlEJsjsMFXtLbBH9
+uNAdpOWnQzR+Vi3JPQB/FcB/BeA/ppTcXwbwb/Ml/wjAP8CfcChoKBTKRI0PYp8aNcqGsKJKURO/
+oJcJFFmc9x/KBv19aBDVio5v4/BUOsc6W4/w/jtvAQDO7h6s5do9PoC/cV+h9CSrf3B6G2nEjVIV
+aHRy9ceg9JfaRXlXFnpjkKOmKKxltxG0pY7ttHpYnck8liReWa0mMHK54YF9D62GvqjUKNnBNx+L
+O5jPj9Hl5umHIVyPMXDQRUmm3latUTRPG93erExgaQqwFn00mjVK1ess+1MXngOY2yi4xtWoi4qE
+LLmtkRyTG9A20O7Juri5D/D1zYKv5jMcHct1/vCNewDjfVQmkim7D00LWZxwbflARw4OprLIpWOs
+BYJzx8SS+aHkSKHXI60+OwtV7SImfuP48RLvEemUlUPoDbaUw0Wey/sSvnYBC0tiNh49nuDemVxT
+Xlbosofm2t5l+OwedblH8mUMixT+w8/18fTzfAh/4GBF3kirkgM7OTGRjhgamHPkC0L2sxVqyAHf
+8tvwC3nNpitr2e20ELCPwlKAwe8r0wpFJOs2PT1B5cvhXDOn4LUUUlbXivfHiEl3H8/1GnOTXLAw
+mzA2+YTjs4YP/x2A/xQfGPghgJnWunm69wFc+Lg3fliKXv+o5vrzcT7Ox499/Kk9BaXUXwNworX+
+rlLqa5/2/R+WoldK6fpjMqR1g1PQgG4QfaqAYTbsweKqu7YDj8rOg24LT197CgAQGCM8rgW9CAuw
+aY18KhUX5QwPTv+l/Hs+h2oqDuoDEhBDSauU/Fp+mdZz2JT7XVYzDHOJnIyRQkl9iuo0wmJGxWPy
+JqzaAbp00VWxQkWhD/RaqNjzXtZUQM5bODwRj+jN+3+AnLiBROXoMRt+c3OITUq4o/Gy3DaqmOzT
+ZoYWxCK2vQCDnli/q1d2gaXMPwkaDsMZUnY7rpI5bFZDdpwcZkz8wsYIZth4AvIjXhzh/m3xTM7i
+HIEplnmrvQ0MKH+WpTAKwp8ZEk60gT69jbZtIQroCc7nWLJLcLSlcNGTezV1xdXub/RgkbU4rGOk
+iXzeQRbBviVaDp2gB4sQYtORtTKjMyQnMrfH4zOsIio0mykcvwn5FjgmJDO3JKs/HPRR0/Nqty7j
+33jxywCAH9z+TZiEb6ShzL0zCDAw5DqnCwsdNu6Fm20gItoyLhDZsh/cQ0mO+3kXFpOEle/ASsTG
+FvMYY1r//YdjLOj2bbiyrlbLgs1GOd1ro8t7XUQpjJL78DjC8bHso086PqvA7N9QSv0cAA+SU/gl
+AD2llEVvYQ/AwZ/0QYYCPANYfehh/LDvoAEoi52GpYZBoItJOfUw6MEnGOXG9hZ+9qtfAQC4aoi7
+B7JRpocHyEh+mpOy/SHOkC1lo+i6Wh9CWgM221RLANCNmAZpyI8OoNiqPGp3UAXMsp+kiEOpXDy4
+cxun9+hi01daxRH2bAGxTHMHF3blgQ4dA5QjRJRRyKZjYamlXHhrvAJWcqtcc7EWcTUdAyUzy/Ws
+CdpjxOz8jGDgyra4w3f292EwZJiXCUZsjV6xxHPr8AEe3pHNDzOGx6rGS2WBeiAu82ZdobUrn5dE
+snFPjhJg0JT9RvjObakonNz5HjpcI89wceOaXFeHm38393HYla3hrUr4BNvkagqb92dZp5i9KMSz
+24Qle4YNm7ySoTmAZ8v7FosF3i9kbftt4IWbQpke8jofp0scLuQhXJwm0GQmWqQVlgfyvvEkR9iT
+PXKJ1OnXbBc1yXWMoIuNS+yJcDSCbnNPuDeTGjFraOHAhOLvD8dnmJLCX7cU3Lnsl5osXdZRjqFB
+VSjdhU3AWV5aYMEMvz+e4Y27QhYcpXIo7AQG+pa84KvXO9DMRbiqwKQkNPtoglOC5z7p+FOHD1rr
+/1xrvae1vgLg3wLwm1rrfwfAbwH4O3zZuRT9+Tgff87G/x84hf8MwK8opf5LAN8H8D99kjdpQ8FU
+HyQnLMOEzY5Bx7XWzS5a1zDpdm5QLOXqizfw9AVxM7/47E18+Rlm+Dvb+Hfzvw0A+Ea3h8eU8Tp8
+RDervo9jYk3zOEfF5GKFGprdeU6lkZVNQ5Sc9nlWwyLMt10HCIg3cEdAixRd119+Fd4GSV2W8nNu
+ZvBID2flBUyGM87Sg0dORKctlmanKrHVFqDPjUsXMD1j4m+VoCLQZ3fzOmaJVCsmh5L08jMTSW+X
+1wHsbMi6PH1hE0bR8DdMYRK80/PYgBa20Lkpc3MsBz7hxReHPtym1p8BiqGJYrLTG3Zw2RBPYrTZ
+Rp9qzd/pvQtnziz67AyDpqmK9HEnboRr5B88OzvEypPfz+cFTFq/TsvHiB2FV+hVBXaAUVcsrakr
+pKYkM3e2fbjkSnRNE8MeAWyGWPm234ZDEsewk2JrKPvJ9TYQk++iVdrwiD95dleAcbbdgl7JfjFd
+C52YcPJqiR0Cv1qk27ty0UDA8MoqTITUAk2mgOowqVwZGOzIWow4H60XSKYSdtjZBJqNT6XXglfI
+a5678CL6G/Ld33og3lg7SpGxEpVFPpJM1vDx2XgNrDocFxgvPh0d25/JoaC1/m0Av81/vw/gS38W
+n3s+zsf5+PGPJwLRCKWgTRtQNRpfwTCwlnGzlI2AvPmu28LGgCxLG9Jc9MVXP4/rO4I3uHplhHDA
+ltbeAK++JEIfnb6F2weSdHz9e1IeOrqXwnXEEh1MjlHGbOVdlSh1o2WQQzf+S+PGuAEy9q67uoJL
+vIFfWnBdoWYL3RBqLq+ZpmKtgw0TlkF4dB7D75CHQa9gMLbvM19QhSEyxu1equERbWh0K3RYymzv
+OKjOqH5NLoSTVQ5Fy97yB9gmOejnLp1gQSXii76H2pA593yx8rsbl1Cxdq/sLmxHXms7BhwiIZUB
+wBFrbBMyPKgXqCi62u1qbO1Ie/r1TRPTA2bizC0wN4ijpcTq+ZHC/YjSZr4PU8l3ZEYKSm7Adfq4
+zKTq81dZKrQNpKQ5K1ZLhPxgzxnC2SKuRVUwiJwsqQhdwMVF5leGQRthi+U92wCY5O53ttBi2/KA
+FHquOYVJiHJdLeD0ZI/8/Jd+EvlYPEDmnOGWHlolKfRCHx1PviN8ugczbfgSWigh72vazE1tIyMs
+djVO4A/YEOZr9HmvnQslrijZ11dNeX98nIO3D95GF4/oLZppiYOmlFnHyJJPV917Mg4FDZFzUoBt
+NHLaCmFbFmTY2sBTP/2XAABfe/4l3LwgNyzoyYbvXttDj2CcttuG1YAoLQc7FwXy3N0KcWNHklaX
+nhag0+y9L+De2wKAevvoDt66LYmo3tzG6wtx0VAqrPkUmIhsexUs0p5XDmCSkKMqcyRTZpZ7BVpD
+ceNVKX3umTWEMggkSV1YPHjSeYacepQG3dcUNqpcvq+/18aulu9L0yl0Q4FeFnBZdZgk8nM/mqHb
+zPP6JSwpPnNp7ypODyXU8BFgsy2HiU/BneHAh2PJgabrGJoEIcpeAeS81GkC1fAxshMTfgCTFHrO
+sg2PtOXdK88i8uXwTXSOCcVXpuQ5mJQT1IQBd+0AZ8zguwsXdkvm1LmyiU1yM1ZkMi4dwGaXYej0
+AeIQLMtDi8zUjq5RU9UpS+QQc9wKF7bEcFRbCcolAVlqDseTkC9UGjzfELR4IFsbqKiqXU1LoCvz
++Npf+xm88c++Ie8byFpVRbYmiPFNwCPLdatlw2VYYRgV6kr2r9GS761Njfg9uU9n+48QDOXvPec5
+eG3Cv/MAfp9qWYHwQGaDCVJfDEAapxiTym+VmlicUMBmnAP+jxnmfD7Ox/n4izWeDE8BgEnfvCYe
+oYCJkBZj2N/FFy8/DwB4du9FXLkoVpP0+AiNLsxGW0H7AJtndKahHIrLZB30qF/wiicW5ZHzADSU
+0N/IMKvFsr1fAO1CLNRYP/4jyiV1VSBhd14SacQuE1FBG21iHbLjFSwlFsRyaRHSFDXZkLXpo2IC
+M7c1sjMSs/Yb4lOgdMQdbOtN+JRHM/M2MsqYzZZnOJ7J+04jsYixZ2L+WOqbbvgYbVJ4Fe0EuzeJ
+GzAKWJqK10RSum4AmxbfVAo1E6a6tFAR8VfHxwCJUpGT7kwVUPQk7M4SpsHmoNBH3ZPwLrt7jAZa
+yQgFxtxCj8lDnQBqn40/bgbPZxdg5CIpqY7dY6J5GoM9ZTAswPIbCwyY1OlUrgvFpqqGKNfKfQRD
+djVGLvIWWb38ECFhzK4BWCzFGo29LDVQsXwbHcFis9Llmzdxu/WvAABe0xFnG9ABNUENa71UZlnD
+cptmLQ8AG8xsolBRI13dBwDsTyr4d6U86fcNmH7Dt5fAIWuX4Qhq1mv1YBCZmaVnOEnJ3J15mLAD
+N68rtPm+TzqemEOhrhUMDRjs2us7/tq97rUjLE8Fb7C6NMBRLGCMbiYPeb4CgjkFWwY+vJixel0j
+m5CJFzGyoqkeNPFmigXj/lvJIaJaYrXQX2E/aujIqj9KRR5VKNvitkXzFhTbep1ogpIVk47dWTMQ
+g11tqDRK4g2qukZliQueLnLMc5lHPeOOd12UzCzH4zlqYhaSNEZBKfppvMKSNf2CNOV9GLCuyaZJ
+5gZM0ppXWQC/ZNdmJ0a1bLocm97cDSh2VMKoYDKMq5QDvZJ55lUJh+3XVUm25NkZzAFFdb0+FOnt
+oFwoVomcwEB9xO9jTiFRc1xWV+S1ngtQjNdEgDQn3Lz/EFXrcwCAR2zD3jALtGMCgZwaJcMuV2nk
+xDpUhUZNZamSkGLDMlAuyNpcrAC2KrtlAMXv1kYKkPK/SuSBXcUlKk96V7JphmAkIWtg+GseyIK4
+GcOykZCSHuVyrbJVBRZqtmRrq153fFZsk0znCg9OZO89Wk3QjSTM8R9/D32S8gS9NqyKeBAevEpl
+CFn5il0HLfJ0vm/Gay5QU1trJvRPOs7Dh/NxPs7HR8aT4SkoQJsGdK3Q2OWsLlGSIOPu4QyJI/z2
+x0mMn3pWrEddCXqwtGuoqbi7+w+WOI4kSZiZHq50JdRoORaMjlgPhzHDbOri1rEkeN45OUUykZP/
+cJ6i/him4WZUbgvxoXgg9oUaFUlFh+gjYabKDDVcxS5IKgCvyhw5cQrIYoD/LqsMdoOKq8RKHo8P
+kc3Ewwh2K7RP5LVZNsOYXkW2shAl8h05Xe62uwuLNHDLLEXOGnV8kGLvGSYBzRAVrVW9Eg+jzFfr
+zWC0QtTMWNemhYpwy8pIUNK91j1awbGLaCGJ1MDz4HYlNDNKrDUhlVHCodR6462dTCaYTIjo2/TW
+oZTl+lC23Kc3vnMA/0g8ky8/JUli7Y4Qt8TCdowUbSaVs6yE0ZXrdlYVQJyF03jfgQFFnQXAxSoh
+FFynax1SVbnI6XEhl2vWWQSjFkiqEU7QadPDCG0cLWVtJyfiuW3tdnGRYY5rVsioC5G0Ytj0FFx7
+hJoM1IRsYBE/hqJAzPboKsIB5eGSEorVnnz5GJ4tiWCrQfT6BmpWWZzCRZfEKrUuEJGcxmsBl4by
+vtfffA+fZDwRh4KC8PiUpoJD9uXQxwcubJlhdibAo82+idN9xtcD2QSTAxPvRm8DAL7zLx5hquWh
+CUoLvY5stlF7A9tcnA470pTRwjsHcigoZSIlm1Df7uCwOPuR841nEWJD7mhycopRW9zyKhijw8wy
+zBIImtKZvHZhmCKHBaAuCiRjuabMBKyU4iMBN/OqgHYInzaWKDSFPpI5TgnSqWZjlFfkWhaMyVN3
+ARWRJ7BKcHsqB9ZiVmDynmymr335Bdhk9ShI6lIkKWy6nMpIUbP6UNQrxGwNXh0toQgD5rmDxMiw
+PJbfrdoK7lKuyaziNV2/rhyUFmNcPqSn4xSKNOt+HSBhu/tu8FN4f/JNAIDn+khYDrxHdukrRg6j
+lrm18wwFW9wz5NBUasqNHBGFYOfM8dS2hU1XXHHYFUo+kZWVIGB1KS9ixGyDV3zYhmEfFclUlOXC
+HTwj1+e2cUwCn7tTORTH2Rj9p67J32sLri9zCMs2mh7BMl9As4yaZNxjsYPdi9Kv88LNPjq7VBSL
+IpSZrGG5Aipeq6FZwYotlBHFg4oUCTtpH4/1Gr7//IUdXL8uFbhf/Y3fxScZ5+HD+Tgf5+Mj44nw
+FExDYdCxsMyAomx47Vx0mPVOMhea7vziNF4ToLhkQ97d3MA+GYdffjHFD+41FFwZMoKBtPaxbOS+
+HXFxJ5MznI0Vv+MMbc0QpNbr7HPdtAN+aKRGgWhOOq9Kw2ejom+PkLAjskozmJqQZlYqPKcNRfbh
+VaJhEDKssUJJE6op19buddGm6EeyMBGT+3FeVsgpR2bWOcJSmIYX/N1xOYTDrkbXnWJJXoR3pgvM
+ySfx1Nn2GiCTEM69slKA+hVte4jKEmtULEsUbVrg91KsaKUKVjvy0EUQynrqPEc+Y0epkYIYKhTV
+EtGpWNsmoaZLhYQZ+SwtYDJ8MJY3cLwv9X9tHmFBxeefDUWmz97wcHGDHZ5KwWSnbA2stTEWqxhn
+idyHfeJGlnGCMJB/90IbHSKO+l6FMqN8mwZWnIdJkpZu20I2ZwdqN0fVkKHoGoo8GtlELnQ6NXBG
+KXvX0EjoHaRxjJj8DNAGMupPZEyGFl4Fn2zVK3MGeyJrZdY2kFF7U9comuR3IPvKLB1UnryxPs5x
+xsrIwVmElPgM01Vrr+iTjifjUDAtdMIBfHu17oa8MGxjGjGDrCJodvh1VQ8HvsRGV6cCDlq1+njW
+FEDH/esFOq4cFlbXgbGiUMfuHoqCbcmVlBvnpwdYsv+1LNO1PuJFv8AtouY+LqmQT1MsGsy5Y8JZ
+kGTFyNCx2ME36KNknsAla1KwymBR2NVOOsi2yfG38KADdmWS5ahUGYpK3MvlPMUpeQmr3Fy3Bau2
+j1kmmfHfeEfyKCNrBdunCztMkBE2dzbPYEMeisfTI1xyhLjVblxSNUJOKGFRaWiCodAq4VLIxXj+
+CtrkY6za8llFaqLgIuXLI6SeuOh1sUJdkxim8rGq2M3ISkan38ZLBJMleoVbR7Ju3/7hL6MknT0K
+YHZPvud36h8CAL78IrC5QX5Js4OoIbyNFQpyVzp+Hx1X1vGqI4elsoBHzO0UZYmUkgAr08WCD56u
+CoQsngz6ch3aGWFVkmTmbIWcfIxZukSH+aO+zV6GzXDd4q8NCyFDQVM7WFI8KJvHANmrKpbRc93C
+opQKVv1oinpfDudWR5ErC7AHOWrW4LOV7D2rKmEU7JgNdzB9R0qSebLCkFWJS6OLaK1J0T7ZOA8f
+zsf5OB8fGU+Ep2AYBlodH3Huwyds09HtNWvtPJlhMWtq+ofYXomlvx+SN+D0m0gPmdE9OkRGl9Jp
+WfDoFewfnwFDqd/vXZGT8zDvIpmKVcrhIC3kBD5J4h+hky5jpmwsCEEu4hRmKRZj0HXWOIuNOVAS
+J5/QfSvrEu6M2fLpFDn598zMhMeOOqOhVxtWWE3l38HAxkDLdRxXB3AJn3bTDXzjW+/KGpBF+WA2
+wXIpcObBMMSeL15TkWcYJxTJSVvo3mB/CKsdFVKUC5lDHJSYHwkWpDZstBtqcd9Y85K75JswdQl4
+jT6mi1Up3piqAhh0jzOzwBGvdUy4s+o4uDQSizhXJY7GpMMvDj6y9gWTn+NHcq/vmTN0exRDGW3C
+LWSeUZRgp5YwRq0UlC3zjJnMdDKFfsNNpxUK4hTGZQkjJs6ib60l/mxHrs8wirX8vKH6qE3xPBbH
+KUJSoW2Rkl45BrqUCty9NgILCiiVQk6vVxlANuecbHpg1Qo9drZ6rSXaIT2MUwupId+9zF1UFOIx
+6fG4KoZBucHav4z5VNbleDXGU1clIdpybZSrRln9k41zT+F8nI/z8ZHxZHgKUAjgwh9a8Lpy6j4F
+G4sjiaOPF3MgldNunC2xJG++dyqWLzVnWB6LBer0AuzacnraT13BDvvUZ2UMaLEkp5F8bnY6QUYK
+K53lqKwmvhZ26R815nEOI6UsmZmiYoInLpbYKIXGK28pgN5ExoRiVueYPyI6TpUSXwJYFgW8pbzG
+JKvQ2SqBZlx77ZkdBAOxKhtxDpAq7J1yjgnVoxcL8XiidAUvJ5PvdIHUFw8jMB0UbJ5KKhe+9Qa0
+GgAAIABJREFULcxEKpNymunHwLbU42tt4v59sfgP7ryLnR3JP1xojxBNZL16HTag9XwkZBdeujZ6
+I/FM/NCDJhYgzQIcPPxDAMCUv+v3R8iItjxLHDhXmYv41vxj1zzj/cjmZ7h7T2LnegyApWEnnyOr
+rgAANjttKOZEbMbtZVl80ICWFogaIZ7jBD1qdfhmF4HdNILJGiZxjrwlj8lw7yIKYhIeLx+jvSld
+lyN22lpehNEmEYhGFwbnbJgFKnbEmsrB4kzu2eR96dqtlxqtvpTLVaqxQc/E97soTVnbsWtg94Z0
+oBpMQKvShoZ4GLPlGG/c3eecI9iksVOlB8P7+DX9UeOJOBSgAG0rhJmJHpMiZwBGfZne/pmJOZmB
+k7yCkz/i26iUVOWomEHWAPyuPNwXj2sccBMfF2PUzESvXueDN34fVU0CirKGbpiNGyz7j5quUwOk
+AfNsE8upbO7f//5dRNsyz1ee20ZBaG84aOjDNtC7IcmifJnDuEQNQrVctwvXZD2+d3gHc0LWrSRF
+TR1Ie9TB3QfyUDw4GON0Jg/1Jg8T1DkSfkaamNjh5j5rm5gyOfrtN/8QX/ySPOg32XGoXBcelYla
+nW1c/5y4nxteDx4Tab2ti9h8WtbLIeeiRgR7zi5JK4GxMppJw2Lvw9nBPdx9T+4ZKrnQ7baFW6lg
+GoJQYzCRB8hS0m7wRwZ/9zCKUT2UzzqrDteVnWcvXccWuwuvXL+MflsMQFXKwev73rq7NM9LPJpQ
+ofnuY9SpXFOY1HApRZ9FpGvL30O7uCJz+/wF5HN5SKPvv4uLu/Ig97qsBuRd9Cz2gRgmwlYjomPC
+IA2+3+qg9aw8sKt9ucGRPkDHIihs6MJNBEqtzBWMXAxAz7HhLQnwYjXI2thDTCj9D999BwdzMQwO
+HLSI2mr1Uhze/3QkK+fhw/k4H+fjI+OJ8BQMBQRKIS5jpIkkSwZeFzOWbkLTxJJNK7YBNGjVpq/e
+MBVMak0qXWOSyGm+eHcBg52Wi7iEsWKXHL0DbSTQrCtrXaybIZVSa+jrxyUcs2UGTYuSLD1MVjLn
+6XiJgtoCbWWgQ6ozn+HDRpihz5LdarZCkdJ6GjkCc5vzl1+5WwNsFOwQNCosiYo8SEv84R0pyd45
+mIhIJIC6JdbnsvLWzVVWb4QTNt3YobWWkJvMTvH2H4hCcetlQbuNkg0YvixsZJdgYyc2RyF0RW6C
+1TFMSOila5bsLBMlIcxGmqA26aXkJSZjWaM379zCZCUJuqRkaRJL9NnAU+4nyJvGJUNBEcarsWay
+gE1RRcusMD4Td9ix7XVi92w1RnQiHtRJXWNBNKlNjoGwbsO25KIW3QT1RObpOhpzIkTHyxQthiPq
+vngYZ3mC4QvigeytTpDO5Pofu7dxqZQQrBNIaBC0+wiqJumaokFMm4UJgziT1K7gsc443JI9Pehs
+wmpLotWoVzAjNlppHyl1QU01RUY4fUW0aewucfqu4Di++QffRkqtCz8IUHfky5NViMr5dKrT6knQ
+XBhdvKj/5n/0n+BxZGHviuANrm92kZHuvNOzUTyUBTycPsbJjIIqtSzI42/cxYPT7wIA9o8jVI5s
+jjRS6PcI4qhcdD0JTea88bVWeOoL0pL9cz//V7D39MsAgL3tDSgy3liFhbQJJ5biUu7tDdfkJseH
+Zzg8k0Ph3VmJd74nLtxbv/K/4fbB9wAAq1jc5Lo+wceXNT7ssH383y0KuSiYCPwm4+zBa8lh4jOG
+tL0M7Y64n63Qw//6y/8Nrz9FTRUmZXSRMp6/+0A2zJu/+i/x7beFfOZbt97BfPkOACBf5QiZDe/4
+F/H0nvBfGh12To49nOVy/WmRIF3I/INhjIulvPYnXrJhtMXV7rqysR+9+TreeyTfHWxbePb6CwCA
+rz7Tx+Gp5F0Q12jtynUdjbnhKwctdgseJgtMKK5jmgq1SbFce4E2GbYr5p0OVnOYlhyKFy714Tch
+UW+IITkfNzo+5o+EYfrRkYSdUyPDrOlxmETY4wPt7l3E6Uq++90jeU+d1djdlTlkRgHPpARBauGA
+ObFZVqOOZO3Gc7n+VZIhY+ha5xXW4IQPaw1Aw+fhMxhQM9L1kLEvZRYXqHhAal0B7IMY2AH8DdkP
+3/i13/qu1vpV/AnjPHw4H+fjfHxkPBHhAxwH+spFbL01gRGIlb/qXYXdlZP9uz98G2pCafGDCkYo
+Vu7oQE7oif8IU3oNeR0hjcXFC5WLgnBV192AyzOwzWSQMnMs9iUD/M3/t4WvD8SdveC52NoRa3x6
+sEKyLRZhk+IenqGgs6YxKIGeiCfwMDJx9zXJst9ZvomkIEpNN7z7P8or++MTm1A1KnbWGba1lnDX
+eYmM0nQlKcGcqYmUCDzlPAuzYXBexShJK2bFNWZERT44vQMAeL18C2+OpalsER0gy4hpAFCwsxG9
+o3U3o7GS8MEq5iipcj1bjtGqqHI9m2DqNijFHF+4IolLRV7GWQlkRCOeLTTcU5nz117poE+o98yK
+0LXke9p9+dyTswIZocZFUcEjn8QBMhi13OtaJ8ioA6IVUZq6hsMQJDqKcULYfHK5QodeRVkAOTUr
+jUC8g24dwg+YBV64SJnE3un2cBLIeu6eydwOwxh7W+LpVvUxTg/oEWRnSGMx/1ZdYkUof0okpQ0b
+iuFarUyU1BlRlYGK0PS80CiYbG6k/kq3QkF4vEINW8t1aDuCZsFh2Y8ALRWKTzqeiEPB1AZ6SYB3
+LY0N3ozXvvfrmJ3KQ3pS/y6sW7KAp/kcxqqBFcui73kJZh4fPPcME+YJiixCrhrtxhSGEjfq2pb8
+XJ3GuEcdxNPj38FFxtef39yDsZIYL7/gIiCRx8mx3IAbLRuPYpnnu2ev4THLnq//09u49d1/DACo
+qxiaghwKH1RG/lRDrzMc0EWFjAQnqoxRctOYZ3LNuWXCn1GFKTuAyy7Ik8UcxVQevNPlY7z7uqzX
+29/7PQDA2eF9JGMBQtlVipwhk641Cn5fEa1wynzM1lDW5PHiDC1y41dVjqKa8rXAeCVrOzE9xLGE
+dyZ7ANqVh4luiFpivLMQoRO81MeCQB+zb+B0Ige8V5AJqlgiJk9knlYw2InoVQFmp/LaWLVhaGbc
+qTalyhxxym7VMMMp12uSRnBS5ja2NrA4Y08LqeHrpY1ZKQfuzJ/BZq/I/tEEec5eGVa+zEGN+3fv
+y2cZGjOWvsfTOVLmSVzHRZQ08H1qPJoOXLsh8dUwWeIuc42CwjClzpE38yf/ZFmWiJivqo0MJsVw
+YHtIeJ/ctEZ5+KM7fj9ufKbwQSnVU0r9E6XUu0qpd5RSX1VKDZRSv66Uus2f/c/yHefjfJyPH+/4
+rJ7CLwH4Na3131FKOQACAP8FgG9orf+hUurvA/j7EIGYHznKosbJ2RIH6TFWvyvW7PbsCPE9SXzt
+3byAU0t+X5cTXA/ltL66JzXcC9eewd9OXwQA3Lr/OhYkQFlNZkgvvwQAGGkbVzfkfCLJLordEX71
+l+U0fz8/wfuPXgMA/OHjr6AIxPUvrQ245PArOoQoGzna7PPftjfxXVLFzaPXoQz2za9W6IakyiIX
+QBHXa1CUWv8ftTIbvZnmlyZg8czWAEwS/mkDUEy0lXW2/oyy4PrUNixSyqf+AjWhzavxArffEd3M
+W/fOcOe+WFizQ1zIbI4dAsfajoWakOdVlsKje90OLLRNdrGSQvxzm13QMcNXjBAPV+Ip5HUJxZ7+
+XFnwSVO2JIehZ2mEJmXbxwVmpMIzohJLztmMXGR0pc0pLf+2gZIAo60NFy4xGa0wwNQjeU5eYrfp
+UKUGZaVsRGy2K5YFjAG7D1MLtwi+aqsYJUOMmHR187iCjmiZuwHiuGFMnqGEfN+kkDl2HvXx2JZ5
+FvMIaNPNr+21gNHuoIcW95FrXAEAnJYaFzrirQS1j4ie8IPlBCePZX9OZhHyJuwgI3a5LAE0XJoG
+EoZEXp3C8ah4vaoxtehBfMLxWQRmuwB+GsC/BwBa6xxArpT6eQBf48v+EUQk5o89FEzfQe/ZK9j4
+7VuIvisxedyZIyT7T36wj2ssp222S/zUQGKk3nPMAQzbSOm2vrj3KupEsrPuNRvWgrHccBs11aCM
+Bodu+rj5C/Id/+D/+j8R35PFHsw1tjYlxPDMGivmM9qMw0PXh+7KZ4yGAzxFfYrf0y40VaQ834BF
+t3rA7Pa8TlDbDCVSDYPsR2VRwyLWviZyx+uGsNgxGWwMkE5IUlIkiPnQVOUHoYliN6SGgZKZ7nxh
+YBmTzDQ5weoOS1O3pnD4QI4fNkCwBDnXeyPsrlmTwq0araWsfeVoROwNaFHTYMu1EHZlrcokxc3r
+pGR/lKDuyKGY5RoWH4oZ63RDe2fdRZjEC9jsSl06CRTBYDXqD0IFcl9ulB202J5euwn8kaAw7cDE
+zjZDhURjMJLwLyOpbnnWhSLBjdMP8HgiB8HBgwlWfMgSFaAgoYzmIWbkBdI29SRSEybJdxwdYMW+
+khYP5PnyGKZDwpbKhjOXPWKVOTY2ZA+8MNzG6Cq5K9nCbxYzXNmQ1nDVcjGh3sfs5BhvDCTH9s77
+D/H+WOYWkyez1h8QHusSkhQBUJmtNb18XZ+izn98HI1XAZwC+J+VUt9XSv2PSqkQwJbW+pCvOQKw
+9XFv/rAUfULy1PNxPs7Hv/7xWcIHC8ArAP6e1vo1pdQvQUKF9dBaa/UjqGQ/LEW/ef2GLpdzDFvX
+MBv+3wAA82SJ51+SxF95GOGZ58QCffnKdTx9SSC6zYnmbXmwtNBZ6VrD9OUcMjd3oAkvrfMcdaPS
+8zQp2asRvnJFTui/azzAv3pbKhjR3jcR2BJ22JkL3af733QGGga6pL6KByOkpGBTxhI7A3FXn+pv
+4vkdqjbRJV2dHeB4k/Xo20uUpPi25gnmO3Tdl9RRfHobu0quf3JjA8nvSyLueBrj4J5wJ5xNjlGR
+bswIqEVYuzBtZssNjSgR17GaORjekHWpPAuz+yQGmchr25bCNinwB60+LlBtSXdM9JYy//GyxCoR
+j6wfSui21e3BDgl5zipEhFsPhxohiT60FyOipxczI1/5Aea2XN9iNsO9sXQ7GmWBlieex8nRGCvu
+nkHDr7nU8NviEWyEfbQGrCQN+hhuCwty4IcAlZpmc3oKgykCj/0FyNHrymfsbtjIElYo5gbuk4J+
+xUSyqgw49BBzhNCUml96JbJaPK+wLWsxn91HQmRdyyngEXCwvRPg5evi0Tz37C6GoVRUOhSvUbW1
+JotRRhs9dnCuBkMk9AAXRYLTUvZyedp4Cmr9DBg6W2MatFXgg0fb+NRP+WfxFPYB7GutX+N//xPI
+IXGslNoBAP48+QzfcT7Ox/n4MY8/taegtT5SSj1SSt3UWt8C8DMA3ub//i6Af4hPKEVfa4U4s/F4
+OcHkvljM8HIbD+9LvPS1aw66bOwJKhdGKZY5pMKxsm2onGSX3YswGJMZhQkYcoqbYQY4tFILsfKq
+ZyBg8vGv/81fxA8O/hcAwDh6DsdMNG31TJgV4zOy9dgtA7nFZJ8ZomKRebl/hCFl3376J1/Eix2x
+DlNaz9Xd5zDxxSIephHMS/K+zZMLmF2S1xi0yu2dEHu+6GDWvo/8S4L4e28+wS1Hwq3v3PnnGI/F
+g6gyWgnXgCacty5q4EAs26OTCgtbEIaDvTbSH8r7YkJndVnAackabm5v4RKvqdMHcrImdTsJggWj
+QUK47f4u6kLuTVpm2KJmQw8+whGFWkwHI4+ycRS4CcJrWPaIVjx1kCwouHISIXUaUlWFFkVd7JQw
+98s+7Jhai24bw4tXAACt4Q78HhuT7D40PQVFzcwqD+GQe6CMcqAnf3e8i5jN5Z5MklNoYgG2ezL3
+/eMVFM2adXGF8mDBtQ0RM0cVZXJNK6tEwbyFEWvsXBDPa6vt41ooa3t1Z4BOSzwhs8uMt/IlQQAg
+zmo4zANNWzZqKpMbjg2L97jkd+R1CrsRejFMlJqsWHEOxV+XqkSVfTrZuM9affh7AP4xKw/vA/j3
+Id7H/6GU+kUADwD8wp/0IWWc4+z793Bw8i1kXbmwvhXiwlXZjJf2buIFto2GnT6MU3GJGyETe65R
+90iUGCVNQhaqLAH2REA50JQtVxDQk1E/A8Pkg/D0l/G3/gN546+fJnBIyJEvYrQpGR6xIUBDrTH3
+dlXgEQT009q28MWRJEFf2X4ewVDe5+3Lpql3DOwtKQF/rYDKJCQaPtdB7ct1Z32Zr69t7O7JfMNB
+B9VFuVXPl22cPCvv837tp/Dr36bcPWnStOHBtTd5zVN88y2p4Oy/8y9gXJMwaEOHOJvL78tCfrdV
+B+g0xCLpCsGWbOiRbeGsRZn4ZQGY5AmM2H15MsWpJf/ulgmukXAkyU2cEs57oQpRUwdRE9evvdsw
+cEPe19pD0CGz9SzHY5+Ht3LgsjZ/N5L3X0uAHhWS0AaKuMEj1DC7V2RuaYXKkXUpU3ngy1UFY0E5
+sDBGGIjr7rZMmEoOLD2NQZ4WaCZBb51WiDM5hPt5B8NNCV2jcoHjQ7nuOQFSWaEBJfvCCS1cH8p9
+uvnUZYx2JbTxCgsO9S/NXA6FqgpRueTdjJP1fNx5iX5f7snucgePRzKPCQFgiD/olTH8LpDLNedp
+DEJAYPodGKQE+KTjMx0KWusfAPg4LPXPfJbPPR/n43z86xtPBKJRGxq5V6J8+yE6rOlf74zwk0xm
+bV9zsGHISauMo3WzS9kXq2seGlCUYLPCAIqun6rsD5rzjRSafAFm54r83XOh2Hdumh6+clWYkVV+
+iM2eeBB9215DEVmihwKbTgAkZYLLoOu7vYcvvSikqVs3NoETKV+5I/n73AjgEdG353hwVmKW6h0T
+pkmU25yakqYDd5uNL4NN6IZcNKrQp+fyN/7y83h8/AYA4HtvvsvFzGGQRrm2HHz3lhSC3nzjNUS/
+K3N+upVhNSf8mQmuw34FVyp9eLnjwGjq9dpCTY3G0AgRO2KZGyLWRwenOCa9WBkk+MGh/H3PcHCD
+MPWo20UaUQQmE2u2+WgPS1K0tbsaFzuSfDtUCzw8oufh96DJpLpNbg0r9hFfkBDFmWhsMWmnlyXy
+Gdcwuo2S5eDVRKyq6w1Rkd5PxxUUsQd2GCJw5T602xM4TCbPGTL2tyYwley3Vd3GlcsUajlxcbfZ
+EKRHc0wl3imA1nCEjQ25f3s9E+22XJPX9z+QqSOxLewMtkF6vNEKFsuwZXuMNjt7N/05drgnxy2Z
+z8NsDCNpmvtM1PRSigJr4l3PK6E+5WP+RBwKRpkjmO6jbQXo8oG+ea2N7Zflhm9YIxiXCMxIX0Dd
+502g0qi+uoBRUwfRbUHzIYWp1+zQerkE2PNQM5MtXXOs80OvRVw//+LOmlhEASgpSmOS8UkpDdOQ
+h6LUJhSrDze+8Aqe+hy59tJq3c5tUYSm7QHmJXHtndqCQYaoqsxQhYS8ki/QVC3UZF6xe91G+hC6
+M8Ugk8z5y16OX/jZrwIAxizrHh1NoRV7H2DhwZEcGrfHh2iz+vLDWKFwZL0uKo8/NXyikCK7hT0y
+BdltBx0K5ap8BS9r9Awl0N4xLcSMz0/PLIS8N1Wxwn5OZqlZBG+TrvuJXN/yqSWiE1mfg9iAbjUw
+ZoBcMKiwXIdC01r+brU8tNituupaODllq/1uH/pN6exM8hlyxt32RVl7J7QBQo1Xx1MUJMlRs1OU
+pKtPZxra5zxcee31wTbeWrKvBkCxkHVJfUAbcuAoSgNkaYFuSOawfgd7A3nQ+8E2fIOhQmJCNcIw
+pYQ2pt2H227axduwCM7rYIg9MjenbgcXGY4et+T+Pz49XfN8GpWGybqBaQM2sTO2crHu1f6E47xL
+8nycj/PxkfFEeAqV1oiKDMEgwKgtp+Sm+TmckcNu+yeuISeDc1kBHrNBNOZQzgAmky91YgIPBa2m
+wgVMQnd1rpDPyFBMxmj3cgsWWIlwZ1AW68aug5z4YU9ZaySkSX0+5ZhQRC7aYY3hhpBtTL//OrAl
+FrjeTFFRot2lxXB7HdjUI1BeBZMwWbOwUBtsEWGi0SgN6Ca0USYQ8mKdPhQRjSNcx6ufEyu185ui
+i3B0eIIV4cWGEaA4Fgh2Ms6QZKz1hzY6rGmntAuGrtEe0KPpD+GSyGMwGELL9NEeuzhmg1KbKLn9
+boCQdf67ZoSEOITCdhCOZb1Ct8ZzrB5oSux13BqPZuShuPMANr/vwq4B1TBbGxq5TYIXeg/1zFnr
+XmynA5QOXe1IQZNv06iBFr/HZnNROjvDIpa5xYnGgi56u13BJe4B1giHB4IBSbOmI9HHBV/C2Dej
+Ezw+ITbBaiNJuYasvqi6QEgI9gXHQzuQzzVbJWCJV2j5LVgd/tvgXggU7FT2Qu2kMBJ6MbZCSL1R
+r20Bj2X+Xpc8DaGH5ZQ6n6sUIKYBNaCp/l0ZORz8OZSiV7WGFVfYvNDBC7uS3Z13jzEjLHfrYYTZ
+6Q8AALeWCif/jzwAdSBZ/Z0ZMCCIxXUUrg5kc+w88yXkm3Jz4+MS9xMRqcVYbubVL97E1o609Ho/
+8XWAwqZWYaCk+wWrXLtlJzwoNtQHOpcX/Q08uCK/z4zLmDpCnjl9TSNmu/PG27KjBze3MLpMoY/2
+FuoeM+d5DdUIzDJRXJoZdEZXtrUBg70IVZ5iTSFZnGCDZbafuCG5jHfffW/dDah1hQfHDCXKEjUP
+Ajc34XBjjUYEPakAi0Lc2nfem6BkGON6Co5PcFY7wKgSUpdjblYrPsSb35Zr3p+u4DIXMSiBR4y5
+r5sWTLrYXiUPa1IprNjJtz+NYJly0H3d3oVHUFdl23gYyfzjM1njyxcB25aHaXK6xHhf1nZvc4Y9
+lif9gQ+fpWFFkd9lFuC9w/sAgMVpiUP2aLTtHvwew7yixAHBWUEi13GcHMGg4UjNFaZn8mCaxgKF
+loO8kX23LAWXCmBRVeHBCatO0wEqU747rBQ623LIdK7JfLtBCDXkQZ5pGA77SxIHOWnkH+5P8dY9
+CRHnvL+O8mCxzK6z/IP7G9joMcSojACV9elKkufhw/k4H+fjI+OJ8BQq1JjXKwQHAfYuNZTVCXYG
+coKvLBsj4/MAgNB7HS98TjoiHywEb/Dg5D18+1ASatfSAicDsX4vem0kVEQ+/t4+btG93GZW/yX1
+ArzrUvM29BwwJKnl2hVc0p8Z2oBi+cE1yHz8obm7gYdXrknVwsi+gfnr1FrUQHIkCaqiJeZ//NYU
+ZodVlKJEkDO0UQk0Y6HVXCzm0YMD3H1bru/y56+jDQltAmsDqi+WxFkWABNUFxmWuFaFmhgLpVxU
+bFaqJ7dQsnmmNHIMfLGkW65c/1N7AVo1M/y9FjKSqcx0iRZ5K9rKgUF4cJfzDYxH2N6U9bbcFnav
+iudyOfPXjMJRlK8z7nkgIUU76yNfsNGsG6Bg8m3qWqgISotyIKYb7G7JfJcG8PBQ1jjJUkyX1Hw8
+M/GlSqzqU9cuQBHr4FI+LSsLFBSnef/xAR4sxAv1nQQ75Jv0TBuOlmvZXzZck8CQlQ+sWqh9cnlU
+GjkTt8tYfheaCq227K1uuweHSdx6axMWk66nyzne+oF4rFtj2Y+7uwH2IgLyDBtup2EbyLCIpCHq
+hw/fwumx7IeSSVJd17CZ8E7KGgZBC07losgbIZ4lHCaKP+l4Ig4Fy7KwNRwhcF08HIj7dmEeYGQK
+Nn764DYmvMjNsyVstkzfiCSWf3XTwsk+M6z5ES7cFCTh1ee+hOkZiTb3juHHcrkvfFWQgv7Fi1B8
+mLR2oJjitwwLJV2xGhVSlnpq80OOVdP2bJhwOyQutduwPImTd60+zGdks8WBbMxFtsDJfYlZER7D
+2BNAlru1CXqgGB9L3uObf/Aa3rvzUK7j9D5u3iQ6rrMLj6QfjmECzLVYPAh7gY9ZxfkoD+b8PV6H
+hmU0RLjAYEse3i9dlDleuLqNkclN7I+QEgjTChxgIRu6TDQsdgzarjw0N0abMJjpL8YJQl++eyMo
+kHPTW36C1qZsdK9kec+vcaUnvzO8XTycNd2HJsg1i7Miw4gqSlfZWrw5GCCeyj1NjDY6LHsGpokl
+qdrPojOYhax5sUFEax7jaiAHZDaaYpHKdbQ3Q4xcSZpMzXxddnaJ2DQyZ82aVJcZQnJ3ln4Nl1od
+VePOj3p47qKgRjdCHxsEcrWUCZO9KaXbRYf5KLM157pOsJiI8bJ2tsDbhGgS486bEprNTpZwHVnn
+kS/ztc0KzoqhDypU7L/3vACGlg2VJjFK48fXJXk+zsf5+As4nghPQWkTSrcxK07w4JuE+Q4LbD0t
+LtVo2Mbd70jf1f6t+ZrZtya92lODFTqkCO/0u9h59a8AAMIX97D/DeELOIlMfPOxnKSPf1Xq2e32
+87jakR4At9XBmihPfVDXTcsKY/IcRg2G3Ptg2bQGcma17amBsEOdy/t3cHJbEkP3Sde1XIb4/EWx
+pC898xRMX6zj5vYmCupR3nmb4jXzFI8K+dzf+fYfwv0NcjMMLby6I0nFr3/1WWxeE+ueE2p8YXuI
+tCXzzVYtBKm4qkdQKFinXygDe45Y0tCT+TqegfuPxUubzo5wwm72Z5/pYIv043U9RruWCoVVUSU7
+HWB4Ta7p2E/x3jv3AQBvzJYwzYaK3cWrWySXGcpru2ELs4zJvjJFt6Egq00UPhW3ogIRM68pZJ6T
+NENCGflpbOLGdZlPkTtISEeWpgYSEp/4m7w3ykKrLyHY6OZVdBz2oIwjvD4Va1ylMUBuhc2OXF/b
+DrEqZW1vTyfY9OU6eo4Hm0QmLjkULEuh5VNXdMfFwYlc3+P9t3F8SHUu5PAc8SCevyrzsTpd1FoW
+3OkOkdHjOTw+xoLgu9YoxP17sl9OWAFKywoF+S0Cz4VuaKBNcx0+lGUOI2WPxScc557C+Tgf5+Mj
+44nwFGptIE1aeDz+AeqhnGov96/DI1rrinoZu18nt8D1OfIDSeCdkrTy6t5lYFMwDb1d0hHWAAAg
+AElEQVTTTfSvigdhh1ewcVm6C/duTPCTG3Jy39wWS7txvQOzZI3aNgDiEepSr2m58jpDSUjvAya9
+Xv7Q3FVd4diSE/yRtcAWuytbeojyuliCjZLlu9DHtau0tHaJlHwDxdkCtUOl4Y5Yhs1rQ3x9JPH+
+l7/aQUqIrrlI0I7Eym11gZowV88Sq/zFC1dxaffLAIDo8ATf/j1ZK8dYQbHMeqkVYEjtiF4glrGa
+KgRce9/po7cj8xn12vC7RHKedJHRMmVkULI9Ez2XZdaLNXZuSA7g/ffeR3VAGrvJAtmy6dAhojE7
+wTa1GEukeJ+s2ss8wWJO5upCQbX4enaoTs0lqrlY3bpKUZXi8VwbDOBaHd6/FBm/p6Qat++3YfVk
+X/RzA5e2ZT537y9x+5iksWmNLrUatn1JwMahhYw8BqNBCI/Jn7gs0CFNX8hOzq1hF312ftqZg1Yg
+a7WZtWBfoRWvNTyStI4I0w9aAQwmtuskhTZlzoWpcPW6XFPYewGbLdlPj6ayj5eLBVYLWZcgsDAl
+jmYSJchJzWbUHmyfe/wTjifiUNCmjXKwjeyBgp7Kxt18qY+XrglxCq6HGNRScXDa76JHbEESSh24
+1duC6ZLOqsxgeiSssEN0tmVxvvrKq7gwEzfRDdgNmNQwL0niSClHdMIBwDBgMtvjag8uGXxb6o9S
+sSsDuBLI5q7b72BFsNTg0hDB+5I57jGRlagU7ZAU4p6PJJYMeJlpmF05ALb2WOefPw1KB2J4qQM3
+YpLQ82CC2W6rjbiWDbR3T75jPpjh2tOyFgf9PXzrjbcAAOZkDBDyvdNq49Ie2arZmjv0AIdusHNl
+iJqtxy1LSSkFwMqJ4Toyf4d0bDg5QsuWhGHSy7FLhaytjU2siKFIZpvw/j/23ixWtis9D/vWnoea
+69QZ77kD773dbJLdLfXc0RRLguQYQRQ4hpEAeXDgwC8JDOQpfvNLAujBSBAgQIIACQIjgAZnghEL
+sgAjimRJrZa6yW5xJu987hlrrj1PKw//t4tNqZUmRUemgLMA4hbrVO1ae+01/MP3f19AgVhGEXVt
+45JCqlo1qAljfjJf43wpn6l0gzSWhVP05LrDKoA55hihxGdHstBHroZqJeEjA0SQw/NJZNPzYFIE
+6ODuIVrawnQTw+EiNMweOpwbti/XKosSdUaOxhTg25gtGwQd2Qz8onU7a3SZAfA6BrrcpI73bqKk
+aItjuSiInfC5OYSeDer0oMozmFzEveYKHZazT/Z6W+j5xRPJQpzNZ8hJsnORAl5EYeGowoY0/41t
+wGjd4o/Yrt2H63bdrtuH2qfCUrDQYKhTPE+6uHWfKrtdAy4pyhxrCGtfXh+OvgTTkF2w3wh2QfWw
+pUNukqfQS5qfgYPw9i8AAOzuIxhLSQGmmuy9t3wYbW2+3f+gQ6qBYkF6pTUILERo/NkdVxnW9tSc
+fOY+XOoQOJWBvZeYbya+QZc+rH2y7CYVEpp7ZQ2oSoJHkx1BdGJfo4zls/0bE/hKXps6h2qvpzIY
+PEnDL4lJefyWuyWYzXcMdIg8DAY2fFNOzTv7Xbg9+UyPqbK+acNlXt2x8q0FEfoNFBmR80UfAFWc
+WbRjHxyg5tHi1x4CVjXuHHtI52LJrTonUORqiHlPYaOFBAfAgyyH4ci45Xm11TeoDAMG1Zhd/jtx
+Qrx4LP0JwgA3ArGwKlQfWHqTGk3UXo+FQXkDgwVtrmdgfyxu5Ve+auBFkr8W8wQPqfeQrqk3avro
+NXJaZ0aBJCCXx0IDRC/2WbU7NkOEpsyFcRjCoasRHnhQBq0YNKgysdJKRWh+rlDmlOGLK9StBWW5
+8MgZEqgexi+QW4HCP/uPn+IkYbr4+RRXKQuzHA2HgVuj58PusRjtI7ZPxaYAywZ2JvD6B3hIk/vp
+nfvofk/y9C9/cwNzIdgC03NgmCQRIf23zhrUFEHVFz6MkWwsuq6gOBHM4Ba6pbgP/pKRcN2Donmm
+lYkmF1OstK0tN2Baa8RtzQMLGfEDWBCljK0/aBQBuh3yQ+YKNhmE4MjCLKYxDLoBM5goS+ICQg/N
+knyGNe3Ijougywi65cPptJWKBTQj0lqnMLhB7vm3AQDZzUd48r5kHJ6vl3Bb/P1pCM1sQO4EcBoy
+XCn5e7hjw1ZkLnJCgIzXtukjpex81TyBKgmSohtQGz6KUiZdGKTo7Qg4y4wtWBTRqU/7iGmCmzRl
+Q+XCCaTv5XqDKpVruHUNkCa/NkxUVH2q2ypRI4PjSt/Hno/JETde10RRythFa39bgQiX7mGpoCg6
+6zYO7LE8zGNjguoLrD48e4qHf0Ah2IQLEwrdUJ7TUitsTuR76abGTpf+XRsjOB4BJPIxamPbT8/p
+wrVaViggJxgiY+1EhGrrrlmBj4bEMZWyETLTZToxPLI1uzt0bbIDJKdywJ31UqQzMkAtarQejWkp
+NBSP+ajt2n24btftun2ofSosBQ2g0gYmBwb8VLIMb579CXwSRRy8/Qq8Y56UoyNoTROuZi15HCNe
+SfS6aEwMSjEHraO7W4KJJn0fm8WfyPtXPIGPXoGmJBqcEg158tLE3BaiZKi2W+dTRrS//qf73wYg
+uxZ0TOXfskRFZKFN0hOnFyLbiLkXb+ZYMZLoZpfo9OX3amYTVLKCRd0EQzfY2uh1BsVgXtNkqGnm
+1q681ykVLp7Le4+WF8hMOeUNO0MoVi6Uk+PZRtyNG6SeaKID6JAB2lqjG4o7ZTgBrs7FYpvHESY9
+ui4bscay9Rytbr3VceG6rBItFTTNLdtSMFlVaTODk2QRIiJIH81jRKnk6Xe6PgYMFBuWDdCcd3hy
+792aoO/LjRiqh3Jb5NWAaGygqNG0up8Ui1FWB/kpdRnHC5gcTz/cR039yO5OhXAkNHVran2Ylo2a
+OBXXNbYWZFEWqAgF73TEnZm4O9tAahrlCB3C4jMfmmhR6Ap2qw2R0uXNa1QmOULyGppR0mQzg02p
+DiNtYDLL0VTEiDQKJTNYm6sGy6ncX2OkGHSILQk8rJakb/uI7VOxKZiWhf7eCPGjPdy9Lw9xYNo4
+4A2nWKI4ZYkvLmHP5IFdrb4t339c4ySV2oerd5b4zI+LvPyNH2+QUF+wSc/wzru/BwCY0M82co3+
+z4r7AOsIoN+a1Rk0UzomrC1P4F7LdPKnmkGwk2vbyDIu3sZAncvitAgUyldnKFkDkJxdoqBQquXf
+gNdhHMBhtZzlonHIu2im+MAe9KFBVymu0BjtJiL9fXYe461UXLAi6iOlnmFUNlietFyKF7hDt2Lp
+spTZi9G0Jq5rIuam4Vga63MSmy5P4UKg5RZTmZkuYVss3412UcxlcTd5g7KN8wRDWNwsNon8XVUa
+06dyr/uqxou3peblZBlhzIzBJgM0U58NAVm+5cILmVrQGQpDNq9iHWHFCkY4OawWps5YTBKbKKlC
+1Zx7MLlg0x2gJvBtupihw/TChKGmBjkWS2qT1kCXpLh+r4c5S9h7FNWtLHNbi6AbD3VMRqpOAYvE
+N3XjomC8o2ZNTYEM2ZzP3TFRW62OZ46UxDdWDWi6sQ4zQOlsjflaXIZsWaAgO1eZVNAUv61WBYpN
+K3D80dq1+3Ddrtt1+1D7VFgKnq3w0r6Pz/7MF7FPLrth9g4GlJSv4hrlHTn9vDxEoWUXdJkfL1dX
+mFJr8je+/T382rsiPfeVX/9NmEMCdjBA90hOOfcVOR2dz9toLuTk0sEERUVTHCWWLPKptYcW32QU
+P2wPVVBt1Ns2UHg04dI1PPIp5Awc1Q2QZqQuy67w4LHs8vPRFV6mrmBnTKk4o4ZtioneFBlA9WhU
+Ck0q91/bJ0Ai99JaIG+cP8Cb3yV0+84RbFvgwVmdw7Fa3kXgIeS0usNYWVyW8Hmy1Q5QkUNA2yWs
+rpzGfnkEnyzPBvkKgnAAqrihCQ1kEYug6hKakfFC52hAc5yAsyLJ0aFozfiVQ0yOJEB79huvwmfR
+0cyu4NpyCncI4dVaoY5pgXk+ykgCbbXqAGRBbowcOTkzZh6DdvMTND7dALsDj0VF0aML1C5h6JcR
+Gl9+Z8wit3rjYOmKheFXIUbkXSyqBr/9kIQyVOC+WACfJ+2cbVeoLbpSRYJKMeisbOSkaq95Jtd1
+vS3EKndcKAbQUTeINzI/E2sHQSXzxXJkjN99do4HJ9K3yyKBaiXkjBoWC6LyMkaJNkL+0dqnYlMw
+LBPOsIsmtuEGMsOc9AW8/Uwq/L54z0T0nphAmZ/DTMXkt2+KjRf+rZcw+F0ZhJ//eoX/87X3AQC/
+GZ3BuZJb/MVXBvjGF4RS3N+T1GQ1nUEZMtDlxQj1UEBIa8vAJWsRzLREStBPL+HCHP/w+8jWBcqI
+uoRRiWIhdRcVdRsN24fZJWFJeIQOTeJVUeHpudz3iKw7N/Z9uCPGQ+ISZSwP37IDNEyDVIsOanIJ
+Xl7I97/17jkeR2Lu716M4VWidJWvTGRKFumJa+LuhSygx7z/ndBDRLn32qtgeHK9ujGwJNLTUQae
+PpXnYIbiMnSCEH0uoI7R2aotVWi2WpnNssYqk8W71fMMfDgE8SRXNZodxhFKGyvSzsMoUdE1m7cZ
+CWRotu6ThjFuNS5SWMzywDJh8XAJB9R68EykLIv2rQCKbsCmZ2FDuvomCOCw3sQj01XkGjiIWZWY
+ZvA8cXPyZI11JCCispR7Wh5MUZGQxihLaKYsyxIoyDzlej2YHc4tjvfeUQc1maJSK0ba1knUNtrC
+3KKssGHmZ82akTefnOCtqcyLtFBY090uco2GlZFVx0Cz+UusklRK/WdKqTeUUq8rpX5FKeUppe4o
+pf5QKfW+UurXqAlx3a7bdfsr0j6J6vQRgL8P4CWtdaqU+nUA/z6AvwHgv9Za/6pS6r8H8HcB/Hf/
+nxczFNAx8cItjcNAzNJFnqLbJ54gG8Nk/b4dKGxII+5YYlq5QQe3dl4CAIz+HQd3DsRNcHAX6lBO
+ncn9z6NKHslNkw5+8fQd1JGcpIP9J2i0ZC2ytYsnSnbjsCmRGWJKn5jy70/+mRtgZL0TgBR9yB7G
+iFqG6U3O/jhwDuT1/cltvHJLLJdsOUOPLNYFo+V1plGcSd2CsTdAE8t9NJ0rKGII4HqAfx8AkHpi
+VUVXNcKBjMXO+C7+6D1xpZp6vSWLKRYl3mSl3U1G6eOsQTBgUG9ewDkkOYuu0CqLeG4IJ5RTrH9A
+SvJiiHQlJ+a8PkeHuXnLKkCWdRRpAcU6Ac3ovFlWeOFQ7r8ch5gxmBn0bayp8JUU7pbXoGmtb8cE
+xuI2NlGDnLUN66aG53X5ewkCZmVC0rN7/T14DCjWTYqmJjnLYICaZDDlqoBLN6WgyR2dr+ExuxLU
+HRi0RspYo8hJcFKI1bSZxijIsZD6Gh4rLpXqoLF4iqscVionfsvVaKwclBb9h/McimQxYZEjZ3/s
+eomSLs/TJ+I+PJiuUfKerKCPopT14NkGXFZibqoKihmcj9o+aaDRAuArodUJAJwB+FmIriQgUvT/
+7if8jet23a7bX2L7JFqSz5VS/wjAUwApgN8C8B0AS611m7s7AXD0w76vlPp7AP4eAOwdHuNrnRCz
+JkGXElzDO8dYrF6W3wpzuJn4+89nD3H1fbn870NSjIt/ssLViXAklLMNOqFgHb758gq75wIb1le/
+j2IoQaKdz8gu+mwaoFeIWIp1+nVQQQ7rMkY6Zzpp0oEfk+ufCtZ/9l7I4hN0kQ+EeUd/rkb1WN7f
+RNRkSE8QkngpXjRwiIQrI4V9YhIcBqf0qMGagc39sYFOwDRcowCeiM54D3ZX3n/8/4jPfqUtfOEb
+UjD20s2fwbff+kfyNaCVuEBUZFhRf+EZqca+4CmklVzL7FSYn5zwex5CIum6kzEs5vStlKnV+RoW
+VaejukFdyinmWgFMpiQL30FNMtZmKSfw6M5dRC6LvOKH2BmJxfYHT55hkYvVUKOEIsuUSUbsTrAL
+nwI2pR1jupb5skoMdHbkt+0ygWJaryY1nZ/GW/q3qFpik8hvd282aCqy5aoMLtGLDu+zv/GQMPBZ
+JoDpkt5vuUDE2EfFCs6TxRkq62ty/zuASV1KaBMVBXWSJEFJGjczEitn+XCB9FIC6ZdputWzNOsa
+C1ZuBV6A7l3px1PK2F0Vc4R9WV798T0sS0Gy5qsMFpWwzU0CVB9P4/mTuA9DAL8E4A6AJYB/AuCv
+f9Tv/6AU/ed/7Et6J1BA3UNJoEgXCfQtubHF1SmWEwmIDZIarw5kIf/B74tpHD8+x/NSzEWvdjDK
+xU148O0FdqvfBQB8bq+HH/9pIV+5qCQ6v5g9xudGvwgAmE1TqEsxg2dRjE1OtubxC5gWko8uyh8y
+XAofKEhZLnp9CURVxQL7R/J5j7l0YzpHNJfgY6xsRFeEu6oYDgNfQ0EJY3k+xXwgoKHe7Kvo3Gih
+2TZa9VCz00Waymbyv/3Bt+S6lw9x88Z/CAD4ws+8iOy//CO5YPNBsEk3wnUIAA6Dj0u7wbidjO4A
+WSrjWaynAM1W/W6KVSILNmSAM/AdzOeyIS2tCscjuf9+EKBoc/PKgUpI9z6Qv8MLoEk9V4Y3Mfyc
+uBKzX/tncBuZ/BfIMIhZr0ATP69dOIUsaF/dQu4zOj+7wJ7PismxteUlLAiK6owHqHcYhZ/v4HQq
+c6BZxNv6kG6ooRpu1NxAJ10XU9LBB8kSV6b84f3pKWruJTkhyossBjFkqBsfNenT4JkwKXScLTd4
+9Fw2AEUklOcDrqabYPegSJij4xNEj+X5nB5bOFy29REtT+YY93flAFh3QtSvyXxLkWJnJve9tito
+6pTiI6KdP4n78PMAHmmtr7TWJYD/HcBPABiolqUTuAFQzfW6Xbfr9leifZKU5FMA31BKBRD34ecA
+/DGA/xvA3wLwq/iIUvSA6FjUuYmUDMd5v4Mil63tcvYaktdkx8zcKZpHYgl4sQR41ipHn2jEe7u3
+cIfpnYPdERYPSdGVeHj8VPQi3vi/xJw6z1b4xZ+iaEj+APOnYvpt3ACKVkr+1grzOdmDW7qrrx9/
+0PEfzPYohZr19EZioCYK0SesdXd3gP1dXneZQ7F4SpnFllSVHLHo7z7HXiEndN84gEm0oWpC1FQR
+LpcmTp7Jiff0bbFA1KCDGU//83yF+0M5rd6oP9CUqBVgECn34LFYQS+/f4Ipq/3cpkIwlH4eh32Y
+dA9C+xDpTKwCTXdAqwIuqzIHFRCSYwB2iXwlN7PaZBiFLZ0cCVUXazy5lEDq8bEHtZFnma4ut/eH
+VYxzyrLbb8nYHxYZbo1l/A/dAMeH4vPl6xnit8SF9Pe7sFqNyZSCLasVFAN7gdfB/WNxK8u0gM/j
+XVUWIqINXT7HsvaAhcyh59MTfPc75DJYreG6LXRZ+pYvE3yHz2F4MIa1YID2sA/Q0hkFPtRNGdv4
+kmQxRQRnLMQ/Xb+CSVXqptiDA3EJntnA7AkxGezj0Rc+i8CVa52ePsPztbh8WVXimccUfr6Bjn84
+EvfPa58kpvCHSqn/FcB3AVQAXoW4A/8MwK8qpf4Lvvc//qhrVVWF6XQKJ3dQE+Qyr4DZQszPxWmF
+hlHmMtUoCP+9Q//PzVwsWOGYxqe4zMS9mD5VSHi9s0bD/BZFR8m5Z6HGq2+LyIyb34fNeEb1ygRX
+068CAIzqPVis1ItIcqHxxW3fxXtgTriuEVMtaPk4R3Qq/TA46bzQgab/mc83H/jtTYadsWAuBowX
+uLsOen2JsvvdDkw+qrJpUFKEdjl9Df/LP/sdGS9mEwZHn0WzlAnx7e++j5h6m4Glt7yFrmuiA9Yr
+9GQCPn73OfRew/GJcbtP7sa9IWxCtzeXb6MkgEaZ0je724ciFt/BGTQj54urBtO1PD+zqdGQXGb6
+jvx7Mn8fS2aPuq8dwY4Fpn4ap0jLVmuxRsQYhf2YVOdujLumLELnloEmYh3A7CEQtdWVPurbdIVC
+1j5kDUDXoI6XsMJWTSoHMrlG2qQoCC4zCLWubIW3L2RTePPyHE8vhGVrUVVAC5dnbMT0FB5eSdDo
++99e48Vb4s4YMwcB6xZqs0KX9Sj9XelPnGosnkjtzno1hRtTTctxUQcyHwb1CtNI+tHi2Jpigndi
+GZc/ee99bGoZz7poUCvZZHWdoG5TNx+xfVIp+n8I4B/+qbcfAvjaJ7nudbtu1+1fX1Nafzy00/8f
+7Stf+Yr+o29/GxIjbynPLFTkxtssz/Hmq78JAHjtrN5Kn7uURPMurvDwQgJjv/XuW7icihmZRTU6
+Ds155WIvlFOs1W8ITAsxA3CWWSPlHrm/00WHRUxNbeAiY0EJK+u++WN38M5znpiWi5t3aOYf9LAT
+yDUu35viyVx29sFITFmVpwhZGDTqdLFgLr1RDeYLOaHWlEjP8gw1C1w818V4Ir8ROgF0TQtqE8Gl
+NdEbMXdfrzCgVsLwxl38/b/zHwMAorM/wsO3vgMAKKxj/Fe/+T8AAN79DvEBywIp1YlvDA6wT5cn
+L1PMCZk9WycImTHZ7YnZCsNGyb8niyViVuRt0hRz9j9vSlSsjhwP5P5vdPvY6coYj4wMIAXZs7jA
+OXkgj8YjvHRXfme0Kyft0FPIKDn/LIqxpHXgKgMB6dhsXWO2kpMy4HWdjo+qJW+JI5QM7FbKQrcS
+q2C9WuBiJWN7RevPP76D218Qeru/+VM/hxduChp24HVxe49cDvTL0vgSiimsKLnC6XsyD9/+k3+J
+Z5T9WycNnrwu7u/VXKwO1DXOGSjXsUbHkjE2PROK87POc6Ssjm2zF7muoIlc9GwffWaimlojz+Q5
+LFcrZBz70/PZd7TWX8GPaJ8KmPMHTW1NcQUArDiL1leIa5bQzjQ0gTxXD8Sffm4kmDKFOFufIqUa
+qak0EgovOm6DZUtgYsrkMRobKWG3RZkiJM6+LEoUHWLYCxNO0/IL8sEONe7O5eE/7a9wbyCuhmMv
+MH0kiyyKV5hQ1el4Tz7bM/egiFsfuUeoj5l9WGu8borv6LfU6cEKUUwtSbtAyA2pVhVUIQukqhYY
+5axBYAouPq0xH8qG1bU+h8Zp73+AeUfu9eq7MxxsZEIvGZLePS4wcsRXv3O4g4IT8DKeIz6TBWYl
+QM1NtiEpqakaaLQiMx42FO81TQWDZmuV17AZLMlZD7HwTPQZczmfABOmYk2rwkFBVqg9jZfviPrW
+gOb+ZpnAt+lb+13sEfJt2zYORzLe3XCAJJEN2XBkoezsBqgTAo/SJaac+T3fgsvxPHl6iu8/k4Va
+X4hrV7z7BI+pLPZ7uQX735PNuRsMt0phuiFLl1HC45JynB7KiMxaRYp6TvBWA5SxmPmzhTwnx9iB
+y4Vb6nrLJdlVDTQZq2rDQkWmmcIgzD3PUDBbZ3UM1IRNq8BDTxM23VmjTK6l6K/bdbtun6B9eiwF
+JbtoG36vqxSLSzkR3nv4Jt59ICb86uEZZrWcjqdT2c3DElhspAhqqKbImUtWVYOCAJq6KLDkTjps
+xGJYOyYMnjR50qAAC3RqhWPmmB3TguUwd00tQuvSwYNYTlhvCLzzjuAlUl1hU0jAT6/XOGaRz54n
+u/YkGCAYMuCkfbD2BrGZwZnTXXFJy+XtosuT3apiFATbLGYbJKxmbBwDESswrZX81spJ4ZHDMbua
+waAQzcnUxMMHAoX+P/75I0yXJLCx6DLdGOEFU07BycE+npLCy8lzRORlhKUQgicej8lFoWCzYAxN
+jXsH4io9m55BMYKfzxUagneySn5vE1d4TLj5vcLEjOrSutDIGKn3hhZOn0q0f+pRGr4431arKr+L
+2/sSzAsqD/09Uu9ZY+ySJ8LsiQXiBIDdJemNe4QbDGAaSqEkAMp+4S5Wa5kPZzMZz7N8hsvXZCx+
+Jwdu/htCC7hTdXBv0uU9tdwbNqpMrID52RVOWaz03mmC6kzmchGMsCF3o8GgZu2u4Ftyfx0L8GyZ
+n5bZh1YsILNN9Mk0XVGXMmmqbTHWerMB6RbQbWysW7q8TghV/CUGGv+VNq3R6Aqa0ftpfIrvvS4+
+8JkNPPu+3OR8vUJWyACnpDp/tlxjbFBS/cYIeyTFOF2uUGqZCDkyGPQdNQlMd7ojzPkQTZWg5MNt
+SmBZycAfOT3YjvilNvHr8aLCiqw79YMBLmzJMmSbGAcDWdyfu30XX6EQ7tdfFkRSODRgWQI2mc1n
+qHivrnmMOzck2n95JfepigzE/mCRrPEmN8iTd5aYraXPYdBHRFJOc9rGL3yUSt4730xRsk7it197
+E7/xK7KJvvnsGXaIjf/iL3xT+ts7QEjuv51BiAFFUx+6PVSW3N/J1RwuGMFnijStF1BKFs0g7KIT
+yPtfvTfCs5n4zn1zg+eMr9TMeniwoFvFqrUNMA1pWSF0QNTk8hYuQlYtPpfvj/Y99Puy4LvDEV5g
+inPk++gQFVr5ITySwjpMG5oDF5pktGZToi7FrSjLFLkv3+vO5gjuSmpwvZLfm2cZsqckoH2/wpvf
+kzSq+Y0FvtkIcjZj6rzarJAwc/JscYI5yW2nj0tERLX2zQAdblgx63yOJgPcuyWI3SAAbK7u87RE
+QeBUmQIROSh7QxnDzqmBsxUBTbpBRe7OokhgkIjFN3wYQ8p9PcZHatfuw3W7btftQ+3TYykAMNAg
+J647f7bCm0vSbT+Yw+jIsZk/XqGyxUQNBxJYipoU9UJ2yds3jpENZCft7dl4/v3HAICLxdWWH7Ek
+L2N1FW8DcUlVoiLWIS83qC05aUb39xGwSs5gwOb+yy8gpcjKm4/fgGYVYelbIEcKvvTiHfzMlwUg
+s7Mv+HTDWmypv3wTaBqB/FphFwe1mMH3jyVqXE4bZLUE+J5ejBCyvv/syTOcMdCamA7GZOeoSdXu
+5DYKwm7LOEPF03i4eoy3Hgr/oKobDI7lZDokJfmNo304dBN21D4uR+IGHTp7WBUSfAv0BBekR5u2
+vJN5giGtju7Ig82xKmYpxuSnWGaAzSyXoqmubA8Nx6KoE3SYu9/f20Ucy7NeXU0msmYAACAASURB
+VD3fnlqqy6yNbeEOKzsPbvmYMAsSOhFMsmpbFmDuSyDV9AgKs3vQPNG19tHQFXRrEwFdLL+pYNH1
++vJG3IRkscZjU6y0al3i8R++CgB46eilrfJ4x2YAuonhpWIprVML56+KhdWYH2hUBpbCiGUsndvi
+rr20t4cbR2J12OYHTMzd2RTPLsRtLjsVmlhO/yBhAHrXQKJlvpyvIlQk9XG0D4eBSycE7JLknB+x
+XVsK1+26XbcPtU+JpaChoaGUjYoMzedlhTqS7k0fvQOfvv/nP3OMs4xKu/o2AGB/Z4IeKa68QRcD
+BpSeuCncUzkdkijacvKXRDkqM243cDhKtbQBqOsSKZV/y6iB2pUTP/RIOjqw0RkIj8Em+SMUlFUb
+exN88UCgy3/tqy/i4K7I3pmtirXuQ3dIGZbvoi1bbAoFBPSBC5LHZhdoYrmP0W4Dt5GCoduHc7z7
+/Iz9jGBkFDvpM7YQGlt/umwaRKkg5WbTG4jo71qeu419GG2hVHyCw5tyWumugVsrIb8dHq1hUWTk
+on+Bw40M0rkpvnUwMBGOicI0ulA2U5XjFSKyVfe9D6rGVoRXG10XNgPGjtqgLFvyWwX3psRgoucP
+YbD46TZNsH7g4cax9GH3YBdeW7XZTNpMM0wzhMngcKsgrrQCHDkxdRVDNUQjWg0MBvMcfw6XrF63
+j4huffkl/O5CLKWz2RqzSwnWbpoUms/PcsRitcMBMhZzNZvnePxIYPVNPMM9WmbdnoGxL3NktCcp
+4HvjCWxar1qliMmt4BQ9xKSv21QFGsLXK8ZAdsIebMZfigcl5hRBatwaDVOVRaNguR8vJfkp2RQU
+FClAtCUPv9gAF9+RjMIsOsFdgma66xqNLQ/uZEXlomaKZ4zMHq9CxAwolnWKiIPqmw40tf02pWwU
+aVrA5qSxzA4qZi2aRkNTKPYy8tDJWX3GCHq//xlcWL8t7+U5GobiXaPGz35NgC47B0ewWBdm2C13
+ngFN7kOjFwBUBWoCoGFgT7vyHdvvwuKCDbMSWU/6djjeRdilpuDFCquOTJSAZnlaKASM6ie5iYrm
+7BuX30JN09jRLnJWaOobsvl1DIWKY9UxNJqujMv5uyc4o3ZhvVrBhrg8I2JtN2mFes68+tjHYCLP
+r8462NvhhvNWH5c+qe0dcYm82oPJyW0gRUP3wnRtrEkntygKmCRtcRu5/7svjNBnMNPLbTgdckmW
+BRQXpOq4UAw8b8EElYLqyLgZkUbDIDWqGsqnlHwQICBUeBzJ78Z3LUxeE3f1Mk5xSZjz9I3ZlsWb
+pQ/wwn2sWRvy6vfewXwjr3d1A5cl3H3fgUlW8MNhq3TltsJbqBsFhwdSbtsIea9ZZsJiFqEgTL/j
+d1FW0rdxP0GeSt+STYmCWId6oWB9TPKza/fhul236/ah9imxFACtAY0GeSw73Lun38E7jZhq99wA
++xNJ2cVZhccPpAhkvRTzLE43eE7E46w7xDAUS6I/CnBIFehxECDmbv3wiVSyrdZX2zywr2ooogk3
+qkTREL8w+2OszljNOCBqcJNgM20JNAy0pvHwcIJXbspn3WAC3cgR0mx591MglP7oxtiKyOhEoSFa
+raY7UChsA5+lTmEWstt3+zuwmWY1zQwb0rdpR/4+hA2E8nfH1YiIpzjP0i0ZjEKBKY8mj4FKPxzC
+oMn5+J03oeJ/IZ+dTbF4Lv3PHWDsSlrzNnkjFmYIk9bNsLtAV4spnVYRrI5YZi/c9DClTN3JlZzW
+yrNgMGVnFgPUuaQAVaZw1ebvPWtLj6a7TLElFrxDGePatlHNWHSlUigGNpvcBFgQpOjawbMAaqI0
+OgFIfqocD03U6kVUsJiuRiiWhBn1MaaLorNmW9j1ZP022jO1rWxVcPAkFw6Md7KHCCiRd+OgjyF5
+JDqhi77PFC6t4iRqYFA9vKksaM1Io64RBDKelblART6PBTEmnirgmwyq9/a2+BWdzBHTTUtzB5Zu
+VXI+WvvUbAoAgLrBmqKks3ffgMfagEF/iOFNidTWb5xiRCagB6lM0LP0g5z+eRTDpj84qm7h3j2Z
+QL4ZICVOfNCXgXz3nRLLFU21qkYr4qPKDxZ6mjRYLGTz8QoSYZg2vFxmmDLVlpL8lS+/gv7ubXnf
+UgBrJhqWRcNUUNQ2hK6AWsxHbcRoKF7b5C1IR6FAK0DrQBFk1eu4CFi2nOliW2th+zJ5FDK0yuOm
+NjA6FFzEK8Mx/lAzeg0Xu8yS9Grm7nsTLB9JhZ+aKlxcyuuZq+CHMvkHtsbxC/L5Q47rC8pDwQ0t
+CH1YhCvHmx5qMgvZ4xCTqZi5pvcYADCP12gSGfDOjgtVUI9ytwvzddm0HaPB/r5sLDuDEe8zQDUl
+jL2nUIBgL6cCNmQ0CtZoDC5Yci1qOABLkqFzNPThlVWCGCvo7AO6/nZz8N0FdoeSyXCfatQ23Zje
+vQ+o/QmVr+sC5ox4CydBz5M+Hx7bCCrp28jvIDDIME3AkqpqkIgZplWg5uZtuGorBuPaHZiMNYQD
+llxXJjTrLkxksJhpqdYGaq4RhfiDTeYjtmv34bpdt+v2ofYpsRQ0pDpSYZGJa/DksgOdMYd+dw+9
+hDno452tzt89R06rbpZAx8x/eyEO98SMvP3iAQ57rCIM+2CCAgf7cq27owG+/4a4KOdRtN1dXaxw
+mfAEsgHHYJ26JgQ2ewSTyD5lWwgJY/6FW5+HScbner2AwXr7RjGImBswHHJiaQfgzl82FTTNeEX2
+Yjup0NIhG34DM26LuJZwezIu+vkHLMm7PTmJO9YaBoNMKzTQhVhebucArsdT0Ajgap68lDU3llN0
+J0TarTcY3JRx2w19dMbyvbC3h+5YqgTdIyp/lwolUXdVUUDzZDb0BjXFZ4xZhqEvFsRdskDr2RoZ
+eQP6jYsylD6HZQ6H2aM628CgUEvQMNhbKngVBXwaE8phheJGozOZ8l71Fgpd6dZdaaBYYKfqAnXT
+iusUWwo2nV4gj8XiKmsJEpobBwOqf7sdExGtgtLrYsuwQ4shqRa4okL3kXUH45uSJQqUwnBItW7D
+hu3TJDXkWrZjwqCuRdIomCErNItd9APBi9Sxid0dmZNhhxyVWYZTZpTsskaHLmTHNVEzawNDwfA/
+XqDx07EpaABaoa5qWAQIjZoSNSm0+90BRhMKpa4VXv5xSZ3t5vRfp1eo2miz0UHoy4TeG4/gcwBN
+w4JDf707ECir+8UJdCmLYnj1AMSz4FXrfTQRcfupRr6RaHiVyWSdhMdYx8J9aKKGQcBOv8pgdeS3
+tRWhbKsy27Sfp6Cp+Wj4CkQHw/QUNLME2pROGIYNh5qJegOUOzL593vHGN2WmEn9OrAgf+SLgZC0
+JImJDglo3aaBRwLPXt/DmOXC2gU6FLLt2zJZB7f6cLV8MfhqH7UjfTdOnwETGS8j7cLclc1Ekeq8
+0RUaxjXKNAUIFQ/HXegr2SBWXoIbjA3c5qZhI8JrmpqXZQJNRa7+8U101iQyWTQwcwrN0AIOuwNY
+TD1aRgjHkPE2QsAuW2HaAUzeazv2TWyhIfehUQ1QN7LojdiC5qJXuo+GdOgZdS7NkcL+UDbAbt9G
+xhL3iPT7ALbCxOWmgmJ84jDswPEoPuPXsNpK0p0uTMKtW7KU2gSwkPGp/RwGuTJLW6PjyRzPgwb1
+hmpghOAXlYKm9IG2NVwS2vbKHUSNZIwaZcJxP94yv3Yfrtt1u24fap8OSwHMPtQaNoUbOzdDOMPb
+AIA4X2OTtpTVNpyJnB57rI+3D4YwCBMurDUskmn4fWebAYiKFU4fycnkjgR2vNtxEd3n6ejdwtOF
+WAQv1cc474jZViwMrMgtUHHnjxoDG7oSqrbQZ/CwGIVYPxdsRWgMUfF02EbZXQVzKDu/VSooxWDl
+5oPsQUOXoTCBBenMpvM54iVBTwMTXslIdtBHyYIY35WxuBkEqAndPs2XqEm2EaCHm2MBU10mz9Eh
+RNwNmH2ZHMMvZIzt/R0gpTvz468AhNdivwBSaqObH8i8VQE5AKMITeui5D40+RM77hI3WM13fkEp
+vds2dqnKvYk2yMke3bNq7NKl2dE1/C7xFC6zT5ukhXcgW8Zw2hN40MHwloxLUHfREIZdr3mi2y5i
+kig0wRp1WxnZHaGeyj1VsYmSwe3Kk8/GFw6Gntzrvd4ezvnZ995/HS0IomagNV6fw+rKM7v/tVcw
+f4MWyCbCirwepgd4tJasXRkfXdkwumJtuHmGgpHiOK8xZ/HbYr6GSeus41Mp/LBCRar+IktwOZXf
+GHZM5CU1RJWG1n8FtSQBAApQRoM+TeO/tvwpvDt9EwAQLBJETCdeXM6wPCc6bklFnB0XgSsDMu5X
+WwYaJ7MRc+K9+uRtfJ8ak5Mdqd477BwhI9FTkSbwIQsz6/ZgJKR1VwViRolLApqQmwD9aMcx8Llj
+ifC7lkbCSZUPPcRLkoHE5PCLFAbkDvS9OSy6PEZTw/CoLcEOPb94gG/98dsAgIeLxZY7MHBewJTm
+dVXksKjXaHPzCycKK2YwMK9R1bKgjwYFDu/KYrtffRXeEUlpaA4//f4J0gvB9Qff2UFOoNbNb76I
+IJAN1+40MFjvbbDasU5SRFQmevz692D0SP5amvBDeV01CVYrjif96PjyBFXKlJFXbMe2s7OPvZmk
+Jz0zwOfuCxgsGImvFZ1FqLsy4WfZFWZnct164eHojpDu3BkOMRrJb9fUdNDeGDNyW56fvo03Hkvm
+KnI1Rq64IAc9Fzf2Zf653PQ2+gr7Q0EefmbvFH/8QOIEk6b4gLOXm4JqFD5/LEjQuM7hjiS2dVG/
+jvkj2Uwen1+i4dw5fkliYjuj27BJklMuU1RtuXTTICmkz0XZYEj2KZdjPwj2oClFMD9fwzJY2bmK
+t3Or08mQrK5JVq7bdbtun6B9eiwFXcMwLZiMlIaHx2jOxfS7yJ/CYeBnnqXb0y0mIUn1vIBbPgYA
+JDdv4uZNqVWwGxfPKLzx1htPcUGq7jWJNBb9FaINMQ2hDZsAmdDr4u5Y8Pcn9nPML2nuhbLjTqev
+wSEuYBwe4ue/IXRsO+4RMrRCHytMaaVcsh6/KGuMV9LPm0MfwVBOimCyB4MBqoszOcHeenKKbCMn
+xqRnYLgjwb6p0UdFvEGn30HIgNrhWKL6gVViTQBYbRTIcwk4TfY+ixv3pA6iGx2jzqVP64n87ux0
+ioochw9PnuKsFBBO99e/hy+Ro/Do9m30bsjph6nc2zq7wtuvSgbnvdNTGD4zO+PPostIqgcTOYNn
+A18Covs9jeWKZDCuicsWRFZtsDcik3SgMBlKnxpaQrG+QhOJa9dowGMFqjnQsAmhrpILnCaCERke
+S1DaKipEuVgS33v+BE+JdRj5Bg5eksyNZWgsyX+oQO1S7aMilLy7M8YOsQe7Rz50y5JMmj7nwMeQ
+lHbV5SkyxWs0NkYT+Y3pbINFI31bvCWW6+1BhJv3qfQ0nCBL5D6S5RoXF5QjuIqRPiB+o1Uk6wY4
+pBvohB0cGMScODMsScqTWApl8PHAS9eWwnW7btftQ+1HWgpKqf8JwL8N4FJr/QrfGwH4NQC3IXwu
+f1trvVBKKQD/DUR5OgHwd7TW3/3R3dCAFgJQn7LNKR6hodRYUmd4tBCrYbGIkNjM5ZeCCfDrGtNC
+dkN9eQbH/2kAgON0cDITXYR4sUGHJ+GK/mt8OYXdeoa2i91KduDJcICE6cCrPMVwyIozBjZ3HR8u
+8QY7kxDBPoVaghw8aHA5neH5CQt7MjlpDDeAorgJmhS7WnxxZzRuQxR4/5mcDCdXM8TMwT99fI7Z
+lZzcUVYgIptSUq7hDeTkbjEUtjuC6YifrWsLHfIlPN28Dcx4bSPH3a6c2Lsj+qSFiYw+vnlziOC5
++NnzaIn3zuU5eN0F/IMd/p7EQJ4+yvA0pi/rOkgJlX7jze/h6FzGcD/o43DElCuL2awhYKYSc3Gr
+Cl2S1HZdF2tbxsV3GhS1nKrFXO7v2fIMDx/J7z3LFKDkurdu7sLI5XRsUMAjGvSQaMTlLMWDM7Ga
+bMPD3mflxE8yA7/3rsydQVCjQ2Ldo5G1HR9mADEa1vjpl+V6N27dgq5bfQp5Tl1rDMske3jdIG3E
+mopQY0McTRk6yCKx9OYXLDQrfRx/ViyF3n6AiuN9dT7F5TOyMqsG5wt53cTye72+jTgXi/bWYRdm
+zfltpugOZIyKvNpWTH7U9lHch/8ZwH8L4B//wHv/AMC/0Fr/slLqH/D//3MA/xaA+/zv6xAJ+q//
+qB/QUMIIrDWMsmWsjbD/WWYZzm/jgOy0J6sLVARmmFpcjcrMEJIhN/BcWKQI126DmjLyR3d3cGNH
+TDsjkIlZ5pdwSAPmrCrs9cXEW5ce1qyknHshYmLfR7cZ4Bm42CFV+U9+5T7u3pYJ1rUUdnxZpCMn
+xoT0YPNUZpUNBwHZeZt0DZOmtl0bUA6hwrtiDu6HGSwGkUZugIsxeSWjKS5Y4ejpfWhWhy7pDhzC
+wKQj95m/UKIktPvp4gzzc1mE9r6BUV+EcPe4WCepgYQ0brUT4MbnSArSu4Halgnd8ffgNOQJpOJT
+5QAhA4q3wy5SRs4fOI8QscR9uZrjyBX3ZpXKuJp2jpf3b8s1dIJn9Tt8XWDFsXdXXUSmjJ1iBeek
+E0DfITDnKkMVtbybBW7siZvjWGvYxKT45EO0xiMcjmRcdswl7EMyYjeHeA5S8i0VdhIJ4r58Vz67
+zEwsTqVvB66J8tZPAgAGox7quoUS83mYHoqaRC5WhCEp6wwjhNWjFmZpo3tT7rtDUJs38DDwKRqc
+WDBbyLPnYu9QNvUXOhPUzFCtSO0WNjZMEqsc9HtYkttSaRdRvs/xnKN029qbj9Z+pPugtf4dAPM/
+9fYvQWTmgQ/Lzf8SgH+spX0Loit58LF6dN2u23X719r+ooHGPa31GV+fA9jj6yMAz37gc60U/Rn+
+VPtBKfqbx8fQWkGpBg7zw3fCCVYHZGX2PZS3xBQbvhmgYG5+w/RY3buBPZq+Ti+H1yEFl+vheCS7
+67rzWQwGYiH4hM56+THGJM90AZgku7yI10ifyQkTeA4+vy+n3OBIdt8fu/dlPHr9e9IfT+OY1Xle
+mMMl5LVvT9AlWelkKYGjRpfQbsARcGExYGg6JpQnfdsnPqLv34JHXMTLNxTitVgCp48mSG9Sek4F
+eLSSwGS/Ju5g7aJ+ke7MEx91LL+XXi6xpGrxN25NcPMW4bh7Yn6qno/8TH7DUB2UBclIhw40IebB
+sIDN+n5FWOHB5C0EnoyPHfQQjsW8/onlGFgRY1Bm0Ey51gykXsYVmpxB5dBDtJL++8EIZvIOn1+O
+oS+w6jxk+tnrYMRUX6jeRaZpWSZrKMiJ2A1cqI7cq9kG38Ie9obk2RgcbE1/a9TgRiqWnjdQ6JHe
+zXHkeajkLTixTO9FU+Me3a3u4QQNqe4sVlQpS21lAVVaY8hUp39cI7kgliWvUDJAad2QcRsPhuiS
+W0GVGay+jPFB04VpHvM+QuwMqcPZSH+sxITNAi3H9XCVyPPzjAkuCObYnxfoUp7utyEUgj+qfeLs
+g9Zaq5bL++N9bytF/+Uvf0nXuoCpFZQng9ffvwX7jLnw7ikUhVrGXxojSSmeuZAHG3UUZlOJOVhO
+B1bdVsgt8eKLIiaycqbwlhRO6cu1dhyFgFyMTdEgI/V7CQvTQ7n2S9Vn0D2m6o/X0rrXuPeSTNbx
+cAiH9Q6O8sACRqjQhO0ImMajiV+sNRDStal9oKX6hgGTNuPOvkyC9XoGi0Auf9fGmOI0O/0OElbO
+XaUNNg8JcCpk0/xsdwcjm751kCHSYg4vCxeVzxqG7gHGoWRoTJYWB56H3m3JcMCsUaU3OPYaVcb6
+icqCzlqYtmx0o8mLcOm/ms0QwVgmtNXfhaL7k8dzxLn0c1JQX9G+RDpjPr5roDOg0eok2N8T47Lb
+pLAsWSxhRxYQ6gYWwWD+rfuYm7LJVGUXAx4G3n6I9CEp5enXB34Xh7dEAzTRl2hWsqmFexZczl4j
+KqD6dE1Z2Wr5d2GR8SibLWHXsml4ni8l2AAU1cSU0rCINwjcADkXeqcpYfnS/2QzR7SROTC6QZ1I
+20ePS7EoPVim/H2newAnZDlrYqNLl9XmRqeyHJr9TLMCoZZ+eJ017FT60R14GO58vJjCXzT7cNG6
+Bfz3ku8/B/ADkszXUvTX7br9VWt/UUvhn0Jk5n8ZH5ab/6cA/lOl1K9CAoyrH3Az/vymNVDWyMsK
+DvkBLFfDJgTXbEyYRGg13QY2Cz8s7tRmkWPpyS4ZahO9XTGvmiSGa4upNqpCuEM5Pcaswuv53bZQ
+DZVK0VCX0DIbHPtyyscvr5EmEqBbncuJePylv4HX+Xs9rwR8msk6gWa03zRCGL5YIT5Vq027QpWQ
+i88N0dD9cYMeDMKbFbUP8zSDRRo33wLMiZiint+Fw0j2Sp+gILNzyCDTFRaoT2Ufvnz/Kfo/Qegy
+VogIm7acAjV1Oi1fTjPLsmG2nAymDavLQGK8hhXIPm+rDCaDtHoj3/e7BiyyMjdNBadqeSBDaIuQ
+7dhFFUvkPKfJXa1MLD15z1/GW4m1TmEh5/PpBw0cchDmkYx3EF4CpNtzkgB7N4Urc72YwWJ14dVV
+A91Sy5GuzY3jLemLV+5AdeRZBrDh9OW+4ZkgAhkNYddKBVuXwuzuIKF16rgZQBXvVs/RcG0oi1L2
+vo0gYJVo2gV44FvGAKYpFmdNjoywO4DdysM1EeyYUHHHh03i0MLMEBK9GpB/srIspFGLqyhbgjlY
+ZYjuLq3FWOQTP077KCnJXwHwbwLYUUqdQFSmfxnAryul/i6AJwD+Nj/+G5B05PuQlOR/9FE6oaFQ
+w0Iaz2C2TCeGiSpjBVhSQDctkYWNhqCQVmuyKBoYS4I1nBwxfWe4CuWcBKxFBDNodSplEhRVA11T
+pGSVIeMiTVKFrCUgbUycP5eF94DS8abdYOyRpztWWLCKcuQq8HnDgAaYMWlYko1aQ5G3URcNGir9
+1LkBReJVRT69sixRRLJoPHsCVbP00VnCZHluFVXIY46FI9cde32cEphzeRFvuQgd128LGPHo6h3c
+v0tBWlZLqqIDRbMUbgNFYg7DdICWkMTxoQj80i1ByNqEIu27oSuYFGpB6UDXTBOXBXK6INma9PRW
+horclmeLGMsryqwHGpO2rHnZxeMzIbjprum/H1gIlDy/Gg5SkuRow0byWOZOamqkJEkJrshBeXAD
+5YIpwmoOz5TXpvcCUNLny2Nokuc0pMlvsgB1j6TAlYWCJajJZYqaY9Rw0zcqvZ2nhqVhkcmrQYqK
+BD26qWBZravA8u5Eo2AmosgbFNxY82wJi8CoUuWoS5Z10x3VTQFNbs+sjtGQqUvb2KYh4ziDWf4r
+rn3QWv8Hf86ffu6HfFYD+E8+Vg+u23W7bp+q9qmAOSsApmnA2pRIuEM7joeKxCPlMgHp7NCkCUDK
+r3QhZnJZWlhdSVhjlsyge3KK9Y77WBUSgEwzjSFpd+MOOQfnCcBccxavEVMU5KJOkaYyNFWdbeGq
+4yHl38IOhjT3pvUTOI3AnGGb0LQKmlqjJUxoWjxFtIJJJmalG0DJqWpkNYyWX55BLa1rJIwg97IC
+FiG8ShuoKWqSXG5QZKT1hpx2T9drPCNYan2WwiQL9lEQomNTzbnoQVNtuinkFNRNubWalDWBjuT3
+tKEAmv7KDre8B62YZGNeoaIJouoPAl+6TNBwepVlCtQyBtqS391EKUqyEvsGMKaF4YZjKIJ0yrKE
+27SFSeL6DJMeTI694cZInkhW4vxqjUzLJLmI5hi2kPWRmP7BWQaD1Zxp1cBsC+GMCG7V0pU12/5X
+tALyagpbk/uxzmFxvA01REl9ACtlNWzPhK75d9OGadOiaVZQfGam46Eg/B3kbihjDdA6iqIVopK4
+Fl8h4rWTfAOjT5q2ilYqOtiQT0NVNpRLXtFcISHz846n0B0f4eO0T8WmAAUYBqD2A5w++z4AYNc7
+RjQnz71ewqHPaRQaObUH6DaiRI2VKYPz+p88wsmemPlHi0O4FBg1KhupJ+Ck+ZyIOUMjI7pxXURY
+JqR+t30kvLg2G+zeEPz8hhx4dROjT+2/9159jCKSTcboTraAFtPIoEn6ka9lsSVxDIOL1yyBogVZ
+OTbsSDYeRc695dNzzEm66rg2RtwgK6WxpAk/i65Q0i/trsV3XjgFnr4tYZxUp9C1ZB92XzrEC8+Y
+yhv6iFdyL7FN4M1qDafXrpQKBUudy0YBdAOc83yr+dh6RECBYs1KxLADPZWxtYwKVSp9S7INVnSF
+soVsNmaDLfV44BrIyO5kIodD0djZo6fIWvJb9vNiuUDF15ZfI4u5Wbp9XLE+5o3mFPZ70v/v7UlF
+7P33uti/K9e9tbcL84XPy7PZLGAy9auqCCWzMWW7aRoBCqZnC5QofFlgG7tCzoWXG4zVRDP0mZ7V
+hUZFBqw8yWGw/D7KcsxW4krZjBEYhgLBrSihYbgtQ5SBkhmxrHSxoatoVvJM81phviGESJWoSN4C
+o0aRy9iPBwEmN0N8nHZd+3Ddrtt1+1D7dFgKUFAwobWJgnwEq8US81iq2tQmRoeUUk1cIGMQJSJx
+RW30YZisdOudIic9VqpMqFROgV7PB2n+kFQtU3MKTSr3TZIhSuX9TaPBNC/Gdwc42iU8mrBVpT1M
+W91vKKxZdxFGFwgCcjTGGhVPmyKlUItRQJNxuDRr1BFdDReon9JsNeXkO5tdImvVh2camU2rAR2c
+T+WkmF2uUXAMVqypcBcHcENGsmc2DIipaRoW7u3J61m0QvZAgqND/RIAwO9GaForpzvcZgnyqNhS
+xNW5jYLVnCVdlFSlMAwGZesKlcs6jxooGIA8fX6JS/Y54liUysSmJhdCnoNGEYpVgsVV6/Jd4Jwa
+oadn4h4edExkttyH33goyZUZDvs4pML0/OkN9O+T+ZiW88AMYPTF4pvcxHu3swAAIABJREFUugWb
+gJ5m7iCZkdvRN1HmchpXrZs0dpFT6SradbCZ0k1dj7FzLu5rRJGdXqeLsNX2bApUkVgQSZRBJzKv
+s0oh5bVbns+qU8PUbcVlDUULMtFA3nJCokREesKK1yp0jSu6JXZWw+8ICOv8/ALnG7EWnd4e1JzE
+OB+xXVsK1+26XbcPtU+JpSCCGrYTotsTfMDV/HVU9LPdfANtSQqtsjUUa+/tiLJxdg1UPDH6IdJL
++sBDjYCpOhsOQP+zbq2OukZDoIJjGwh7Ehja5C7sUk6BL/gT3Lgn/udiw9MzW+H5XE65aeIjrckg
+pAewSGJq9DpoWv+UPis2Bhqeqm7eR9anPxxlUJ02qCrf6fV8BKy+sxvgcs1AlZEiWTM9qzyAvv9Z
+I37qcHEOh0G7o719gJaH7/VxY+9r0o/uq5g9kjG6KsTn7pUDVAySmU0JrYlfGLnQjOE0agnDbAua
+CB+uFDQLxup8g4I59CxOUBCxWVcZWumBgjBwszKQtBWjFZAv2pr/ChtaJqssRG6IRQO/JTbtoCJ2
+YbXQyIkR8OIEnUAQgl/ofAY7+/J8BrvST3+0j82Kgc/CRn0qJ6k76KGIScybGPC4JDRVvKumu0Vv
+Dotd6N5tAMAj9RApg4AXa6lgtQ4OkDCCqcoCBTkdUKQomVo0HBM2mbhATA4iGzkZlhorgWIf6rqG
+2eqmugYaskAv1jI+VRTDZODT6faR0GKJ0gzn5zIPd49CLC5bq/ajtU/NpgClYTcNhrvEi6ufwBR/
+CABoLhRMRl7zeY2GM2yVyo0v0w3On8uDvThZ4pIiJPNFhuOJTI7hXoBeRx703iHrBFwXLe/7olBY
+UEAkWpzjq6+IqXn3819Bb1dcgj5x/dNH7+PqTZlUz/EQHesnpM/7OzBo2sEqAQaXGir7xHGEgDBa
+vYlgMR/tOCHcEbHxPWpDpgppIb8XlTnsXK6lzBJ1l5mRaBfnz4Sy7fGVbGI37AO4VIjK0gBNSbfk
+8iFufkZs6Tudv4l/6f+W9In1B6mrtyW59TrbRq/7yod5OeMtFag4ZcKWsrwqkJMTM2t8pNwIq3Qb
+iQRME2FfxpB7FJZ1hoI8kHaQoR/I9Rq/i82GZDCDCfKOXK8mN+Q4mGB3X/qchwWunslmcrpeYHYq
+BDfPH13iQPY6hKw1+Ux/icphSfKuQtCyXL8cYpf8j55dAHQt1UDG3mkquLsCkKo6I9zg5hSuvoTL
+p2/JZy5ZWt9NUDrcTN0Smjga7dtouHnHmxJXVLXqlOTdXGgkBJOVZYqQbpDpaRiE4Zs6gCYeIs94
+CNkKDqtSe90dzOhKvL+eAiwXKM9OkXX7+Djt2n24btftun2ofUosBQ2gBiwFg2Zpf38HuhQSzEX+
+BmxCfvthgCRjwKWS1OPq9HIrlz4ejOE7cup43RBOJifM5ekFVj35TOjLv5Ogi5piKmVd471HQhQ7
+vUrxjb8uprbb81AxWGmFsoNPdYKokd36yfvvYfXTZAYO9oGE0Gu/geeItRFlcoo/PL3AZEady0kA
+c029vzCGsWElYlfcp6JOEbPvgVvBPpRA6upkjo6StFe+fA+vvy6UXpe1BMB2xzbcBevu3QUSiwFB
+7SGklbK/93kcvS2n/8z9NgDAsGp0B8KxkMUrnJ/L9QbOAQYvyftWqlDRXLVaqLlTYH3yGADwLHuC
+6hFJSQc78FuJvNpBXUgwT9FLqBsLE7TaliEyn7qa8GFr+b3L9DHshYy5x3SpiRwGqcb6/gDLUE7g
+6PEpPBZN3fvMEF0G8ZyxfC8peuj5YjXOovr/be/NYiXL0uu8b585xhtx55tDZWZNXV3d1RNJsWnK
+Mi1KECnIlAX4gQIBixYBwoAAyYYBWQ0++UEPAg3ZMiDJJiybgEBJtEnabhI2W2SLU7d6Yg+srnnM
+yvHOMUecefthr4iqZE9VrcqqNBw/UKibd4hz9j777P0P61+Ll1vumTz50h47H3VhRxh1yLKlCJC0
+FeI+HAjmXeVUvgvNti4fcHr8svu83kTXGHOpcdXNZxUStcX47eUEuGucjY55QxRrbXFBHHT38eVJ
+5MaDWN6kzRmP3Ok/HE0pVOJsinthf6NJnLlxzoKMV5513uvo+JQPXpBWR7PkUOrXb9cejE3Bgi1r
+snTGCiReTUhEe904rimHynAbVvLc+5ecWzQazBkO3EQ3bESrJ6nyZkUt5tswn9KOpCIiz7asDcht
+PX35BT73h64derMdcCAJ9DoOiLTJGLn7nTpnrnj/fFzzB7//BQA++MTDtASltmUTT8qjvZ5zVT/2
+4SfxcvcQNy7sYoTIqqIJkXoivKY2vDpl+IZagbsb+HIHeWifZ19zm8XnXniam+J8XNr17in9Y9GC
+ZzlN1auzC+eEnlLx9ZTdC258R//WvShHsxd49Ck3n9s7PXw1AcTTjM5l933vQnfVr1EL6BX4JduK
+h9PnRjQS9/NOI1zFzLPpgEqYk0zqTr3aZ5y4z0hP73B8JJn1/IijsXvZXnjthGtN58YLr8bQn60w
+GUngsSHW5a1Nj4XGure/zcUrLg+0ceCeQVQGIJKSxfyIofpZtvKKuNSm32wzX3ZEvu6eR/xIRShG
+7KobEePuJ4xCGlpbvqpW1utQ6CVOdlsE6mEwcYtS5DLdhs+emKb91M1Pb7uDF7jO0KyakAjIZqqc
+ynfr2g6neAKa7XRdiLK9vcdcebfR6YA7Ry58ClLD5lW3QUat63z5M9+7/eittg4f1ra2td1jD4Sn
+UNcFi/SYwWSEV7iTy8ewEPJwNBtQ3XWnQBRF+H1lyZcqys2KibT/brx6zkSuX5cmW9JH7O006HSE
+bhTJyng+4/DIncZ/9PzzjIcSTgn2Wag+HNQBtWrrmVh0Xzx/kUFX9faZ5SvPfRWAH/v6Izz2iNvx
+m2GML76/SriJJg1EHUh659aqa6/2LJRCS97VSTU5w5cbmWclgXEn6Y1pwOe/5lCfz738NKmgwqFY
+nStryOTiV2XJeOpConhW4cllnhxBPnKnRy3V5tMX4ULH/Tx6wqchJJ299RpzXcNrbFGoicuIBmxx
+NGbBkeYto7flOlQ9WzEWknM6vE4qUZOzgRvHscmwEq0Z3B1xNnXXeOHZl3j1FbFDv3jCDVUBLnRc
++NTvB6CEW5JMGCuUOF3MQZ89mZ6T3Xbhihj2aLVjLrRdWBJeyNhpS3wma2I01unsiNmR+7ul2nNQ
+BfhX3Tja9aN4atgr65LJ3I27kAfSImCixi87GlIIv1KNFiwGbi1kxYyeukCXUnLFcE5WuXmd5icE
+AtQEhcfA0/tgLH3pmYSqnqW2ZKGE8K3jm5xO3TtQBx4Lze30cJPKVwXnbZpxPUzvr330ox+1n/m/
+f5vs/A3+8DO/DsCXvn6d33vBcb6e3UlXoqlFZZAXT6y238TE1J57AIPB+UpaHM/g+yqnVTUWIZJU
+FkvaLTYUnxkvW0Gbo1abXls9CuWMQO4xsXP3L178CMOhK0PdPj4kVGzcjDxmeiH7W12uPH7VfYZe
+pOF4zkxQ6iRosVB1YjZLOR5Itl4t1M0kJGoudRQT2rVz4ae2JC3d747Gc9oKNy7subj3p/7Mj9P/
+sY8BsHfhGpHKs6fjF/j1f/6rANy4cZ2hMtX5VNntsmKYufkZphVlLVHdoiJUqauoLN6yy9Moxjfe
+SirTkX+4Xa+uC7JimScoWK4zFeP4Tqvu9pd+k/OX/w0Ar/7xM4wD91b/1vMu9/HSS2NCkZScBzCd
+qQRozapFuAp8coGvEuU+POMj7h3yosLqPnvdLk98xOWunvrQU7Rxz+rOLfcy3hmd8OqrLm8zm2XE
+eiHjqM2f/6lfBCC1Lnz8wm9/lq51G8jxqGQw1cZZTImk4HXx0jV2D1zeKO65Q21xfs6Nl9015llJ
+rVna2N1lp+kqI63NmEOBoWodhpPbJ6TqO5kPz/B0xps6X4nBlIFFGkecv/b8V621P/gdpn5l6/Bh
+bWtb2z32QIQP2JqqSDl+9Q3uKIl28/lXSdUEFfo+oVz3kHolvhHo5O9GEWZZw419StXKvdCjqQxV
+WqWo1E8QKfnkBYQtt+vOhwtK1Y3tyLAQX0CcQyqilkidgaPpGXfecJWPRZHRbkqmbrvNpT3XdPPw
+wQabXZFQtd2pfHj4Gl7ukmRbzasYJSUPjw6ZT5c98u7nvX6IZ9zner2CxtAlK18+vMXLx8713Y8L
+CoFbbgj09MUvfIHLogGb+1tsK/n29S98jjsvOrd8NBpx4qvaIW6GCQum6nIqihmleBgC61FIss/i
+UStLG4h7wTPFiochCOwquTpf1FizJJ950y/4Xn5pHVh88T/e2g4Zf1Oe18R5aU2b0+i86V7HEgRK
+K7MKR2zlUSvkWeI0woYhEL4lLWYU8v6G3oi7d8WluXlMUwnWUorhQWFoK2FazDMydfGasM1QodlL
+L/22+97hIXnixlw3t9jSvNS1z568gw/80GX2xGzdEi/C6LDNQezGefvOGXv7zjv4xGOP8dhTH3DX
+zg1Bw117PHTP7HR6zlefdSHfrduvMD9SWMyE2W2FkPWIsnhnUvRrT2Fta1vbPfZAeAp1bcnSlG+8
++CJf/oZLot0aTaklCGqIyEJBnmtLqd7zSuItRTBirsA2MSGZWmvxoBQM1loP31+y7lpdt2CyFISd
+15T6DFtMKSQ0EzQM4sskl2LLWXnMaCrWJFtSK+671trhkpidtx6KqOdqmT53H7BRd9nsud18MzIE
+amDZxlBedh5GX+3Eu7sHlHYpjpowOHIQvWRWMBXDzs03RoyFlqylLv3l9EWmvy/UXdYG4+LQP/rd
+z/HcoSPaDmkR6MxeNlT5rRpTL5O8JdWy2ayulpkYAq/GyFsK1LJd49EJRAnmB2xIqOfmoiJQ3qG0
+b+YSvheFaHl0ztMvuHu++fIZ33jF5U/GktBLU59UHsE8LSiFRyhMBeI3mBflimXKU8K4ymo83Tue
+QcOjTAsmx+56d3p3yOfu/rd2lvRoc4q52LC8KcWSxaic0n3BeQh3nnXIxvPRnE7hnv+lJKDRcJiU
+1uWKj0jxe7vRw1dZN9TzDTuGnQ33849eOuDgoiPVfaS/zZa4EIp2Bg2hFMeSlUuv0a9dzuWbGxFf
+Lx0L9uJOk1xis1m6YMUx9zbtgdgUisWYO89+lj/6g6/x3OF1AMqsRuudg4M29XhZaUgZ5O6BbQgQ
+kjMjUPbe71h6LfdgTLNBKareajbi2pZ7CYvCvTQnRcq5etRnoWWqnoJZkTpiEMBELcrK/V3YdA+x
+GIxWyTXjGYLYbVLtZINHHhPbb77gTCuvLS7CgwsX2Fq6vpFFjGDQv0wdqNNySQ5ShxTiEhhVNU1x
+NKZXJ/z7giZ/ceM6t7UxHo3cy9OYzRnfdYv8+vCQG686haxX3jhmql6DZpzhG+El2oJM+yUXpKA1
+yRdUc714ZUFb1Pedhke/rU5RvXUH222a0tisC58ThTMf3ip5+sRlw+8OcjKFfHHtnlNq7bcNJdLj
+I77wJ+4le/q5Y26oIhSJIOfgwi6ZlMPKaIqnz7NRzGKZdPRzUKLRi6S4bANKEaiYEiIJ8VRFzWjq
+svPXby5oKcPvD/TzukGo8XUbXaaTJeDK4xmR/DRq9/J3E8PehgtzdjYa/OiHBJV/qMvOxTcxImHq
+5j4Q7b0JQjoKNVo724QdQcJriNRrEgQb2K7mftNVuIrhhKt91+X6yOsxsapkX5zfoliIO6SCYokY
+e5u2Dh/Wtra13WMPhKcwnxV87cu3uXFyk6WQbwMIVC5M5zmB3MDzcU4u7YHXPbHiYuiKOfhqp8+V
+x1xSp7PdJ1KJ8PELP8TVq66GXqkO/Oxr13n9FXdKvHB0yGtq4qmCkqX8nl/UlKK8CkQGWlauqxMg
+9CJiMTK1d5srlqKSjK566zc2nOdyZb9JY9kxWcwoPZXLogI/EgozENtzXhHp71uJxYhbYb+xQX3g
+TsGnth5n99S5iZ9/QYSoRcFWV6rEZZvfe9GVTkfpFCMnPrYhDUG2A5V6u54P0ovob0Rk6uRrtWFb
+XZDd3RZtoe329t2JuhPGBCrfZgtLHrr5Pnl9RNZU+GBmzNQ0NVPTlVfb5WF+j905WZBP3Hy/djxg
+oeTnRlvNYdNiRXxqDRjcqeqZEl9eQZJGeApjapWfSyo8dW3GfnPF5JSxwKy+n+DpPuelex5+ndIU
+mjRuRJTiwDCBYSJvKhVvRLfV43LfeZVPffgKjx+4MvK1C31aokozpoknusDId+vbb9QkSkqHsYcv
+Tg6TTvFaLinpRSGmrcYmzVscXaQppq6PNUPaWi92Cn8guP3YZpRifH679kBsCot8zjdv/AlFWhLq
+5dhob5BLo88rKozAO+0i4bYqA5oPGi1/Fb/2Lzd4bEOqR1cu09fLe/nA46E9584VWvwHe9u8fuCq
+CNvP9aBybuv1w4JU187TmlwxPL7iaQy+4LVBO2K75/IBB+0mgVqjW37J1pbbnHZEbtJotYiUkadq
+UyibXBSWIJJorsRParOgEGYhMhVex7mo5SzH5A701GoF7EZuo3v6yIUMw9GIwHcbT//qJqdDt2kU
+VUWojrtmo8GmXqyuJmg3DNgULqLbjqkbbo4b/S4HUrhptlpEkft+a0NdhrG/woUURbpqEy/3C3Zf
+dve5F1/nxkBUdyM3b18/nJIqvKrfsjk8e/oyN0bOBZ/mNZ7mebkJV1lJLiKUwFpsIJo+E1MEynMY
+SxAt6d2cLYocqw3NS+pV92RonKgtwFZ7A395M9ESW5KTK8/TKiPCJdzcK4kEb05id4/tC30uXnCb
+wpP7O1y+pEpSt0UUKFTwahCLcyC+zqjVJoiWr6KPVbXHS7YxwTIbk6zmyGgzMXEO6stJthIefvwj
+APyFH/e4NXJENef1lKxeAhV4W7YOH9a2trXdY9+vFP0vAv8RLq35KvCfWeuoJ40xnwJ+Dgdt+9vW
+2s98r2tUtWUySZnNU5LA7bRbzR6oCWoQzfAm7iQ8sZZQp3FDXkU9m1BIuisYZQzUDbl3c8Z8S9oQ
+gw61SHDbm9p1Nz0ebrpM7ygLOJyq260oGcs7GHgTqsxle0tBalvNFvlSL8H6lHIf82JMKPqzJIBN
+SX6124Kn2hB/KStnYoy0AIKwXBHHeLGg2KZEzhEmrzHiemh0OqtOQ7+sCZTYelhJ1K9PJtw9l5Ta
+SclM2hl1XRH4y17/hNJzbtaGEo57vYT9troFe006SuD1+j0SdZImmzGJKMESJUx9E6+qOdlZSldH
+et4ouPqI89jCPOKgdBiJz43d3x3EHjeVyc+LBUufeHRWcHvsmoBMmBAJvl3nbvyzOGUzUcOQCcm1
+hKM4XjUH5WW6cj9q4VuMhWCpTGdimkuNyUa4mtuNVoT13DymIq2x3jmVSGZMPyFR4rrIIyIlIDua
+kzC19BpuHW53YrqqPsRRSCjyGUuJkeflSwoxiNoYu5SajjDlkoQlYoWL93xWmXf3OKAOMJIm9csu
+iXPeuPTINXqPuWTk7NkXKOfvfvXhl/lWKfrfAT5lrS2NMf8A+BTwXxtjngR+GvgQcAH4XWPM49Yu
+R/ztzVaWfGaJQ4/WpsRfexkvqaRXDUb4mXtDvBKmVspKit/7oU9bmfrj2RTz6ucAMKNdwkP3QLsf
+PGC3uwTk7Ou6HtGWm9UPPHSZwcjlF24eTykl1lkWIdNKLr1KO/1kg1m83Jg8uhJyyeqKWpqJnaRN
+0laHpkAzASm+6N4rfIzi75AAT1TsywdovQqvXrJCxZRzsZOEFWHs7ie2CwK5lW0tfjurSQXeGha3
+CZYdd55H3FySd5TUC/EE6oXeCFtEzSUrUkZDEPJmnGH8t7iuWjFWm2Ja5HjLlzSsMAvdRzUn0IJO
+vBDt9RwI4//0cE68FDrhTVDTYLZgqvJqGJW0FBYuNL6W59Gu3f1MiopKoB+TVfgSjbWmWpUclxqd
+gfFWbrFfVXh6IU3ukSyJU9oNNuWa31ZS6ebRkG5bG2h7Azu96j6kNaEnUFZVKRRLatrqr6lrS6V+
+j7rokoskiCAnVh7HR2pbpUctRmiTFniJumfHGVbao16Z4hmXV1kRiEbhqsqCZ/EULG1v9/jEJUf8
+89nm04xHN3gn9n1J0Vtr/7W1dpm9+CJOMxKcFP2/stZm1trXcUpRf+Yd3dHa1ra299XejUTj3wR+
+VV9fxG0SS1tK0X9Xq43HIkgoYpjoBBsPTqmUcT9LUyaZuvOymkhZZl/97IvEQ1T5bFUNwliZ5XbF
+QdMluzoLYKbqgjQlTRUQCbOQBBWbynAbFuQzd21b+/gtne4C0CTdbRKdAlSWQKd7x2MlsRZ0CxrL
+5NEyI+pVK/lyL6zxl+6gtVidHsuEaplZrHb+OqrwpFHZKCFtKtFagF+7rr69wPmORf0cCxw+oJzF
+K9EWzxhWGcEcAnVgVvIkxnWGEY5hpx2D2IXnZxVhR56OF4Cguam8NEofo5PLmGqV7LM5dHWSznYT
+9icuu/7VG5JlywxTzcVbEQsH+z9MaBwIpyp8SiWF/SVU2hrOlGXP8gwvlyhNmTETM3dlDZilaI24
+HW1JIMi7SczKNbHGw5eX6Xs5QexCkMRTVSOvqSRJeOTVIH2K6a2MhfAES42FLFgwPnQJVe9K8Wan
+bTChqeShKRLqpSr4EtKVjVZdsjasVtJzpKfYTOC0KF6t6+XvGq8DSrQaC75Cm3Z/jyd/2K3PrT+6
+zKnYnKcrHejvbv9Om4Ix5heAEviV7+Nvfx74eYBG0iAJXBdboQlu4jFcEqJ6JaLqI/NrPE1mQ5PU
+b3fYV7fgfj/m6oF7uS9e3WNX8Vtna4soVqvyspvM5uTKzpfzmrRwsXhcVxjFxnvNbY5T95ItrNxa
+U1MImxf40BAXnxdC0lN5stsl8ETOYqV3WDYwLT3QOgFPRLJ1hLfcONTSFiQZ1WLpclb4KnuW5YS6
+cEGl157Snjl3N1I7eTMKyUSAcjy6TUeCtXUQkwh4VOUepd6KpLUUMy3wFKDOc1btya2tFh1vGZdX
+lOIw9FRO9a0BuejVIqKSjHpYNZjVbnxRmTPZdd//cxfc5vDZ6XPY4lvxjb63oAqWPQMVVqpPiaai
+ERlaevmzqHbktUAjbNCSDHwdGow2rVCVhTLPaChEa7YiZgJ9JfjEqijsh81VaVtVSprNgJnQm1s2
+Yi4C3Wl1RFm5ys9kSXcfelSqWhVZTi1xW0xIKYYkvwGlNCbtUjWqDjHLTcNuY1SJMrWHVbXHlOAp
+TPH9ZSnmDCMWLuvPQEpXXphzIXahyQ99cp/DYxcKn9196Vvm+9vZ970pGGN+FpeA/HH7ZsfL25ai
+t9b+EvBLAP2N3vvfv722ta0N+D43BWPMTwB/F/gPrJUevLNPA//CGPMPcYnGx4Avf6/Pq60lz0vS
+s5JECbw8bHBRfI3nvsGoh2Gah+y1BTuV8vOT/ZjmZZc8/MGLF9m56FIcnl9TeG6H9ryNVT96ayaa
+t72Q8HQpBnPM6Q3nck3TOQ25nRMfSqv6t5JrWTYlUkolMOCF7ndjY/Ek3eXVhmpF1KLkW7Ogniub
+HhXUmTuhaq/ClHoUS+DKqGah5GJ+lpEv5e7PaqyvzsBBCkry7QjH0OjGHN4Rxdqrr9HZcF5TNjrH
+5MuOwZJInaKZTq1J5ZHKne/XCYGqOR2/SVtkMHHkrTyFSuQElfGwqfusRTHHKEQblQXzqfN65uen
+PNxyWI5fKv4YgIeNx3M68N5K6XFp9wpd0aSfFnOSlrv2tiDKYdsSyn2OqhZR23kSm82NVedm7uec
+K6RryDMr0pR0ScRRWzyFF37p0T9Qrb8TYYVv2ND4u3faK+Xysb9gOHEu+PxsSqbelUy0/lHcwgqm
+btoJ1M4zy2p/5XkEpYcJlUBOpaTdjIiCZahpsALnlYXBlyJ24aVEd9y1w4sOwhyWTWwgyUILRvR2
+demT9NxzferKFb6y7zALr/H27PuVov8UEAO/Izf7i9ba/9xa+6wx5n8DnsOFFX/re1Ue1ra2tT1Y
+9v1K0f+z7/L7fx/4++/kJsqi4vRoSFGO8bWHXI13aF11u+S1qoURbmCPDoXKO6F20b2DJttShN6/
+tsv2tku6eX5AJvXomV8wvusoyAalO0n3SWg3HXS0GNT0xGUQZikLxYZxGTFSuF/5yyTgnFDx6267
+5UhKgekMLjcFZy2aDE6GGp87ESJTEewrBlwsqIW9iGIPTwm8Sl5MWZZUklIrO32KQkrTIdTKg9RV
+RbFkRG65+/nY9hVOT1035K3D59mSqG4QnRPEbtxe2aCUcvNInaPzM1iqnHpFg+1tN45PdlssUnd6
+ZhWrE9YIHZl5NWN1mp6dD1goQTvIzpjp62heEavx6mrDnZ6vtKEjMZXxW1yF/laTa5J3Oz6+ixUo
+Y2Nf3Ye9gK2O8B1VE8+XZxWEhMsEpK3ZEh9CoyW8QtlnOpJY62LKsUhQi62IvcTlZfoX9tiJnEeT
+q1vtlcstZqmg62nB/ETeWzUnm0tsV+rR/Sigb51nMy2iFYltdh5wrHh+sEhp6rW7+NBVALrbJYl4
+GhrNczzjvL6zabGS7KNqEPeXiU3H0tRcJHhd5ylstA4I5Dl7UUZSufl6rLXPtjAsb9ceCJizNZYs
+qhkXBSO56FcuzHh838GEQ9q0d+SCn1sWlVuwx6fuYUR5QidbqiSHK7GY5kaPUNTh6eCEwzvOgUrF
+vzcbbtPaUBeeb2ltOlf7A5cPyG+4F+/WeIgVcUYkdz/wPfoS2Di4tM32tlsIG90ItMBy3zBRi+ts
+mSTLKprCCrSihDoWp+AoJZfGZK0O0CqGTLtRGMPszC3oyi+Rx086tPjq/2jk7mULW9Bqi58vNTS3
+3H2G9hYnR8J3bNZcTFySLCzczxfenEhw2LrpceNUydX5gEcP3EZ27WCDRKCmoOX+7qzIOD13iayT
+8xEjYQU2vGgFCvI7AZnGNZ67jXA787nBsqr95qZw7akn+PgPPQ6xL5HTAAAfQElEQVTANw+f5nLs
+XtKkt3z5fRKFRGQBRbDUbqwotVFNhvmq+3NLzMdBlJAlEks5yvBE5Z5nObkSeLYXYaSSFYpd+cNX
+DcyeBeCrX32FQriJkHiV8A5F+VZ7AYFwE35ZcNflrTkqbvDKq66icnJ8TlvkOU8WbuP5SHGJDdG8
+Lc5rMvF/no3mlGfuesezDHPJfeD0ltNY3U4atEVCeeXqgm2FDL0LH8C23Gdc3erxMx9zdHOf5u3Z
+Gua8trWt7R57MDyFuqQcH5PmC3pyxT+01+CS1J43Wz6VGmPmXko1c55CT/DirVZIY0dJFi934osA
+taESzHd445zDsTQLRMZxsHeRjjgEomSGL4zEo/VT3Bl8E4Djya3ViVC23R7a78Y02y5EefLxCzy8
+4dyzajFkLALZo+eucypP5lys1FGj4MKRO9E/8YEPLWUISI1HJrk5eaTceXnO84cOYXl4eMKVfXdy
+xS2fywdqRuoHNERdNj5xJ3BifBoqQ47IYe60ANKipKF5iT1LoCaeiyotVq0Oe23nttaxx6x0977l
+ZzSXBLOTKa22O7m9JZvzyYTbZ86r8m3Ebs+dgpc3Nrh1043bL2Yr8tpPXnNNO984/iKxt8RKmBVl
+W+TN+OC1JwB47MoOuw3nBnsrgpwFC5UATRRzOlUZsihZlCr9VhWeOCAuem7ettoNRkJTzl+Y85q6
+S+cRXDpwwimNwqfI3Tx6ffcc95rbTC88AsDz33iBOpEmZJwQKeHrqYy50+/SUEJ8OEhXNH7p2Skt
+KWVn0exNFKlIfINquKJYq8MNGtIs7ZQJt2ZuTK/dPiOS6/How4LP91o0Veqd2hRP3l3Q8PDTqwCU
+s3OuXnTP7O3aA7EpBFj6fsFdW1Mv8fDti2xvuUVazFOsMvxzE2C0uHs9l0fobyUkvvv58PiMhrD6
+kdekEBlIa7fNEx92fHcvjxQDt5ornrz03GdXENVqJ2Jv103kM2c3mC/cYgpVIdjuN+krK95vNzGJ
+avM2xBdxxlmSUvadi10l7u/uLBbkJ8suygGPXBGjTw0DyZ0vVL2Yd1ssPLcpnuYV5xO3wLaygrlx
+YcknH9qi1XCfYZVz8bs+7U0xWL9REokivEHJSLXwud8l0T0Nxblo0yG3FAbMspqWqgUf3K0oVImx
+pwM6m25DChXLv3x+h+u33ctvgg2KU/dSfa0a0hCQqVEZruhlua7w4QfY5w3f5T6olhgNiJodGuoo
+fGz7AKv2+UIU8I1mQKjKyWRaMZbISlUmK2WwLB2SLXMK22oH73XJxdbdaV6n1oEym1YcCpNxZnPG
+AwcJPpiL53JQcqL2+9zLSVXB2iy22d1U2CBwk83PGfqORr7XrimLN8FUG6oqxds7NBORqAj8ND+t
+aSyVrvwhiXJb2wcHcOCu/UZQ8eoNV90/e9Hdw24jpd9x2MALV+6ycbGr+zDUodsg0tk5o3mfd2Lr
+8GFta1vbPfZAeAqeCeiG2zTiMQeSfHt4Y49YLLTNVo4pxKew1cEIJtpKpAwcBRixD9++lTO866C/
+F/YvEog/sdlrsrHrOiKvqipwOL1DrGRfA0NHp9/hYkGn7/bLRzuXeCl1blseuROl22zTV+Kz3yjx
+K3dClVFOR2i0Tn+HNHEnSdFxSb3CQiLW5dHklCp1p6BX1uRKSmZ9d2K0/S7/Xs/d+49eeYyv3XQc
+jVsZRKnLODfzmOFA8nVq4GpXfbbaznWO/GcYnrtHnHgxZdPN24WNTQ6WHZHyury0x7ijKsPc0lCT
+V9cPaKoppxhYAtXQPU+yekC0lMfrBuxsOe+tZEElN38+qNhqu9Mt99x9/sb1O2wL63GLNyHYdZXS
+lqr0Y5u7vDxw3kSn6571bj+iGeiEjSt21Kxmkza+vJFbd2ek4tAcn7kQrBdvkhfSpQwD9rbFDp36
+bC3xEmlK20haEHe/E8+nIT6FvZ2LeLecJ1GZjETf3+q7Ob7QaLNRumvki5xYuIi9Rs7Ghkuam6pD
+a9Pd85IFfDo8I1X3KPmA3rbja9zaOqC3TGZ+vEMkZOVXXndrIZ9aWtI1OQieIpC8naGkGJ1r/GMm
+S53Et2kPxKYQRAGbF7e4UA/obbhJ3eq0ifbcAkxsQldiGkHgEXnOTUKinHVV4StAvxWkVHKAgqYh
+DpS1x+I3JALTcotq73QLFOs2ntxiIULYTR/2h871/xp3mAtfXozdomu1E+YT9zIOzZRua5nP8ECw
+21Yzpk5V4jtz09zpt/AP3INtRxGx2rqL8ZSG+iviSrGlrVgM3Wa02d7mR/bcYp2nQzpLPcNewmwm
+PLvARGUYkuDCo9ALuTFyL2FARH/TfUY76BG2lJ1X2BU3IzYFK6+6Law+IyqhpX7vQS9lrpj7IYFQ
+r2zmFOfuBdveaLDVcy9CI4uZSImr0UvYm7nY/g+e/rcac82hQrt7KOCth1k419c0Cx5uOHf8SBth
+J26zpbzFkBEzNY/O56dMlXcoFz57ewIfVS4EjaKA/T111bZ+kO3T6wC8NJ5yTfDvj+22qUWvfiwa
+/XY1Z6FW/POTiGrsvj6bVuxdcCFWQ/dQhT6etCj9uk0tMNVWb5ddtdHHcQt/WTFRO3gStxhviJh2
+4q+g2b7vEy3FfqoWP/y423B7hapucZPNLW0wl1oEdhnG9hnn7v7T0ZTtrZh3YuvwYW1rW9s99mB4
+Cr7H9kaHqP0Q26Ko6jQsnVo0Vzaiv+tOuaBO8OSi1zq5Kz9nrhr7fFAxHTjX6SOfKPCbro5bTYdE
+S+3JAwkMdneox+4krcoKKwGR7jSCpvMwWnVEKPqvmYA0sU0ZqM59NvUIxRuQkxAJ9R03fTZ7LnNe
+Sa8xqwwdnbpm28NmolnPcmLR70Xi5MsKy8ZMDV87MaZ0J984r8myZQJrhF+oMUlw5zCsmR+Lwizx
+mKtSQ1CSGDfunY2YbePuY6ft5q0b+pil1FidM5grGWZqNvrumYTDko48i0bPuaQfaf4Q7Ymr1Myb
+IZcEO+51W4ym7nQPenA6c1//2PCjAPyLu7/n4qk/ZYvsDuik3LywixVU2AzldYQRKgIRtxtkgl0f
+5hXF3M3zpW2fg55bLz15mK3ZnIY8jHCnj7/l7jMc3eBRhaGXLxxQV26s2zvOWxnfqpmKZCft7DHb
+dd8fFEP2xcXR6YrBuwiI1O1a1gWBOmbjzRaB6Nmjql5Bl0sltm3RYCpg3fCu5fCqe+69MMZXg1kz
+aXPxogtBeuJqXEwzfHFgtIMGS/rP0qQUSkYnvQ5R5+Bb5vm72QOxKcRhxNWDS/Q7T+K3VeYxhjJX
+Rr3XxGNJVOITq7pQd+XWz1uMFW9Nj2c0um7SykVOrRKnwae2bqL8VF2NzYi60ts4mWGFwKtMSa/r
+8gDn8R8yHEtGvVq2wvrMhEZcTKYknnMjW55PKSIW2ysIWuIzVPkvz2uNAmwYs8jVcZc1iNRpGAsD
+H9Q1/oYLUSKvxBdtUFm1KSoXMuSjlFyf2C7dQks7NZPagVvS+WIlQOtVPsXEjeO09Lmm9txYLmxr
+zyNQ92XuGYrCbax5NmY4XkqjG1Ac3BTRTZAldKRTEJ8P2dAAo2bFdlO5lrM5/dSFfL98808AuIbP
+M54Q8G8haYzykJFKsa04IdcmWt1UbL1bYdVeHloIdlx4cGADylfdfE7Smo5Yplr6eT6es9Vzz729
+1aFuuRttbQQEu+4aSdzGV54gUodqsDll8rpbN/HlmJ3cbRrffLmm1xLLlipHpZmRieQ2yAuCQICy
+wluB6OIwQJiulRbG2WLA8dyFmi+dnXP6Rad90jWbXH3CoTttMCTQodaVBmVVnmLVV1NXGfmSi3GS
+r3otQr9P0Fle6e3ZOnxY29rWdo89EJ5CEAZsX9zk0d2rTDO3Y56MXiLSDpzFNeVsqdjUXlFtBQKE
+lNGc0ZHzKk68KVfUDWj8TWYS+lgcnxKoN93TBwRJH+tL6amAQteeTguEOqYfHWCtY3kuVQHJFnOm
+6ZIma8Fw4E6EcdNQK8TojUMq352O84V6HDyPWj39pAWFiGOSqCJS5nhx5hJESSekLSxBaCtyHS9V
+OqYWD8G8ril0yo6F48hKy2IU6OtsRbJSG8uSVS1Kp0yV7TY78gImfSolVNNsulJyyqzH9K47KU3L
+sCNeTL9wLretc0jd+MJkDOIY8EpLpqz3oio4ypzb/dRld73PDyvaimyWmjgAQbNJf9N5GIvzlPPx
+XV1bUHMvIjQKKRKoF1IOszF9hTlmVtFO1Nm5kHdoOhSZW0PVaEJbgKNpXVMORMTSqfFrdzd+7ubn
+bAALdSLaosXpsVtzC1uTLYmfhU0JsppK1PLFvCCQR5NXMNF813G5Am3p8XM4y/nmqfNybuUjbj4t
+L3Q+5a8VHweg39/EU+I2lTdTY/FyN6822qTKliSUBb5Ae9HuJfTl27a1p7C2ta3tHnsgPAXjGZJG
+gp1nJE0JoNRdCpUT89SjEg6hLqbYQBoIaiPMFnCuEtN0OifeEawzT8kLl0jMTEohiTgzc/FyMk3x
+Y/WulzAdu69v37rJ2djt5qfTu/g6hRuK2WZlwELcA9N5jSdpr6264mii/nd7yAWxP3fVqNIwAX5T
+ScfxjEWhnIkXIFTuCmqdFwtYlmEbTUp1TOLXeBJICUzAUAjBJULx9nROs+E+48lrB/zBHXeSeHjM
+hIo8LXOeENYjPxf9XXdMLQ/KCyMWyqNMxlNOlItolCHHlbunTui8qsF0ykDsy0kcMC7d9eLRBqWa
+tby2JbgjAZ6X3c9nU5jYb000Njb7XJKIylE2Iw9cTD1WU5KPR9JQDD9JiWO5P4sJwVzYkWnI5odE
+2KuOw8BvUIzc/eShz2Di7oOOJZa3FbU9IjV81b44C+yI7IabozuzlFwoxbgyK69vIKLZhhfhL2ne
+vJRKLLfTuiQQe7YpQnKtpztyFf7k+CYLdV9ar+QsUDlxEXAqz5FgRFyIv2FJ3Br4FPJevehNzpEg
+7oPYo6O4oo7e2Wv+QGwKZWk4Pgvxt1IWd9W1mJV0lwmSsKBeQmH9gLpyE1gv3AKdpFPOF27yWlGL
+QomjV89vkSwX3nhI44Kr89Zjd43CjghE9zWpcm6fOFzA7dszXjhziyYqMvY3nCt6IkjteJAyES1X
+WhakcutGC0uth1+cWOLAub7oPr3WDpFq1xMDo5n7jE6jRbliHRbV+2lKKoCQ7w0xEhAhy5mJu3KS
+L0jVtXdz6sbUCXr82Y9/zN1bN+IPP+/k0mugVrt0i2DV4jxWlSXMIVzqMnoLAlGw5TZEuTWykxln
+0it85VibTVwTCmK9e3BxxahMXa4oxoqGx9C4zfmv/sgnAPi13/z8ijLyrVtDHHkEH/gBd/9RzPh1
+B0Rbyt0P0ymJiFwSWlRK7M4nYITViLdDDl9xf3eUKHSrQopElHVZm4kCpPTuGZcuq7Nxq4X1XHiQ
+Wvf3w8MBX1F37fVXJwyMnm9cYpXkLUppW5qcPm49RbFrlQeocp96KWBkDOOR+7rI3GdtJXsEH3AV
+jB/odYg2BeNfFOzuuw0yCCATKCMPltct8ZWu9CNLKOBVHcxXDNslIWn1zgKCdfiwtrWt7R57IDyF
+PE+5/foL3Hi6x8UPuN31Qs+SS5ylNjF2od0x9/GWNFdyvxZVyVbbJbg++fgm8QV3ms1HJwzE/Xpx
+dxcqd/rNpAe48GfE6lg7Ohxz+7b7uxuzAYcz94ejNF/yYVJNhYsIaiI13xR1RiZegKDy8JbScmHN
+udzVzaVSczOkXJKOeiV22ZTDFE8lsFBlrH53i0Cw5HRg8dWRV3oW1KE4GY84li7mcOjGdPFam4PH
+nctcmd6q7mWAWo0947JkpLnvSEW51ynwvGXbpmUu+HMdG4zKnWVoKZXkHOgeEj/ikR2Hx7iwuYnN
+JnomIwp5N83xiD83deP6cu06Eq/UL/LCil34TQJXW5iVjHxzp4t/6D5vIGEgG0xpFk3NRUnQlRuz
+a4jPPM1LxenA/d257+anbXISlRDbjQJfHaFJ21KL6m42nlPgkorpxD30m/mYo3P38xvZkKkIV6rU
+cj52v3ty6Nbh7laIJ7m9oDbEohMMjLfkwSVPfArpXfihZPoaKb2OCHaJ6PXdfG90G3gNich4NUgu
+sF4yf5cQtt+k/6uXxLVFi8JfltFnnJz/fxDmjBdiu/u8PnuG3qkbeJhsYRK9TNZS6oXGX5CN3Eqf
+KX4fmRmNqYsBo2sRnYVbQGkyZzJw/HTFeIzJHY7e33Ldh3Y+Jw+XgJ2EheLs4zxnITc5LxIyhS7L
+jsvKZKSVugt9GOnndVajTmR6ZYXfcu6cFTIprgMyxfvJtEO6L1f7JMNTC/dSIr4IDfO54L5ZSCHm
+58UCFkp7m0aTgfASM3ESPrqzyaULTmrjeDCkoZJDWud4QrcUpuBc6lT7IpCpyghP3Ie+b0kEbtrc
+a9HSoho1h+TLHhRtNn5WECo3Mh8f0RST1dl8yjBVfmjaZJC4Fu5LH3D5nBf/9RF8GzF6Ewd4ItpJ
+8g1s7MKfnX23VDeKBGPdpjAtZthbwlPEPu2Wc8HbRbwSRlEjIr3OHr4Ys72oQ9gVQKjzMLmqJ4Nx
+saJXHyh38PRoyLkEfrygSSBRmtQfM1xC3XO3ObSGhkIKUzbwKJpL4dqaudili2mNFYigEvBqmnn4
+c7dNxz2f4tg9s7pRMRCFf1hDqDC20AZTZ3M2lXfy6phZ5h5KsjEnzMQAZWPeOP/Ct8zzd7N1+LC2
+ta3tHnsgPIUoTLh88DhffekbPBs5l/LjOw/RFLVZtPCp1XRUzEoW2vGP1exz63zE8NBlpze+UjBS
+dtcMJxj1v7f3Yg4uutp6/+NuL9zuXMZIFOW4Ba/dVVixKCjkd9vYrJCAKBF363DEibrwEi+kKem2
+MgmZKZS4O6oolH2/lOhEqMaUh0JYDmbEEifxSoMZuc/z5Drn+xG14LBekmIlMXY+ustM3YWpn9Au
+nQfxyCMOVdjb3cD4DpvR7veImu4a6awkkwx8ljeIpW+Z64KjyhLq66S5wUJhxVmWcnLbJUzTeUmh
+Bh0j8o/OPGBHKNo7ZxnBpjvZttlkogpFGcTszR25ys/+Eyfp953ofL2oSahSS7Db5YPt/xCAyZE7
+jcvTKY2OO8WP7tgVnqSc5QSiMRvNMjKFJJXkBoP5kLk8ms12TrEU4mnG+OLJKDglFQbiJJCrnld0
+Wm7usyYkwkUUxzVjSdGLCZCzecV+2/2j2QpXHk1hKvJqmXQsmYzcvIyzN6HbvnQaFicTcumMnBYp
+tZKYccPnyo9+0H22OEA2OhFRw3lmXi8hVkIxaPcRJIN6ckY1a337yf4O9kBsCgCm8jkcTSisSohn
+HyO4qtbofLqS506SLZCWoK+H7M2OVzLxRdVeNYrGkWFL+YXF+YLDD7rJORAQKt5qc3buFuCN0wU3
+Th2JxXzsUQgG2xhWDOWKhnrB5tR0rdz8TUMsqHTDVGxkbiObemMO9aIPz7QgioJnv/41APLpnKkW
+4yhNiURI8nBL7ucWbGy7hfL4YxdJYlVJFgtmqdssxmbG1oarqHSvOrKNfJYyuCGVoyc2V2KlgR+B
+oLZ2NGeeu/uM1WdgvQV16uZ7XlmefcG1594YH5JX7jO2my0SgayKwIVrvU7GeOw23jJosaMEzFaz
+w+jQff+5IuU3W26z6EmR6ujbhA7gsvNWIjpFdUwcXAXg4kOOjek1voKn8Gm/c5GWdZvFrRsDTm66
+9XBuKyptapXo4r3+Bpf3XO9AGU+od9yzbEwqanUoJuwyPXbrZYALd1p+l/BJCQrdXPCqNuT2zhYU
+bp7n2njaTUuhcvE0bdBSiJl75aqt2XoBniocKLNj5jmxxH5ajT6tPfe7+fw6iyM3juvREP81tz73
+H+rpHvpUgnP7UUaoXFscGGaqcNyZvEHRXHJhvj1bhw9rW9va7rEHw1MwFXUyorGY8KKovT6/fYP/
+WOwX2WSCly2TLzNCJbuaPXcKbJx06Xbdzn/lw5+gc0GqzElBaCXFPhkx3ZAYjKoP6eKMW2cOm/D7
+X3+R69ed250FHg3VoE/qBb7o1sz4TVXmWt2FvUbN+bHb8W+fLtgVGUgnLrBK/t04cXXujx18hL/4
+o58E4M7xhIbvfnfqD7ECLUWCwNbRnErVh9gEzDIl1BYhae3G5Aclvor9naYo3+aHtI07GZ5YPIoR
+RXhYl8wH7hR8MXyNYMOFHfsTV7VphjFWXADdusneVXca8caM+UQSckmLq5vOI9lRs5Y3XpDVqo93
+mzR0Up6kQ05Eqf76nTfoK9n6+aUG5XeybMysdGONo60VW/VG4jyorTqivON+3mrlJF3nVZx3x4yO
+XJjTTBpsP3zVfa1TeefhDVqhxuR5nI9UZZgd0lEXoWnDuajdz1519+mHTRJ5preLGbG0JoN5hyR0
+89wVhqIqCjJfXaemJhW3ZUxApq9LO1vxR/Z1sue1RY2/hJ0SjFtnvd5jPPSQG99uK2P0hrtns3Dz
+nWxeolYcVqYxnqoSeRYx9ZU8HdzhjZfefSn6+27T+Zg/+upnOJ7eplbc+/yNl7nwrBvkw9sdylRl
+mNmcSnFWpe+F+Yi7+nr2pZvEHbdZdCYeOweuXNZ8ap+WQDZ15Cbp9HDC577hmHReOr7LQDj5ovZZ
+qG2bJCEWmw4q+URBiL/pHiiVx1xkKpPRhLlIWzYJsMvSobDs8TzHuyw3f5EzzFyoFG15MBE/pJCJ
+eRQRqhfjxnHK6Zl7OW7PxtTqUegGDVBOZDhyG1M1q0DApPON26QCOmV1sUIsZtmcm9flzj8iXc7T
+XTYSN4fn4QwVSbi62WCoRex5Psa6TbQ6VW4hDqmaS13JCXPpKZwczXhGZcHnJiOmQ/e1J5amythv
+V3xgNn6OQr0rQTynVNdhph4WY3IWGlM+mZI1BGpbjMjn7qUZn50TqIqQh+4FWxyHNGuXJ8ibYDtu
+nlt0MCK9XUxLzs7UC9OQOO60YKF58ToR3FD+qGTVvBBqc0j8iEJ9CaeLklidu+ncX20Qi3lKqKra
+pohXvKbH0cTdez69RahehSsXHyXcdZ/XISQUKA8hFOeziqX4eyOcYz33ufNywEszt0G+/PKU08Fb
+Rdy+t63Dh7WtbW33mLHfBn/+nt+EMSfADDh9n25he33t9bX/f3DtK9bane/1Sw/EpgBgjPlja+0P
+rq+9vvb62u+vrcOHta1tbffYelNY29rWdo89SJvCL62vvb72+trvvz0wOYW1rW1tD4Y9SJ7C2ta2
+tgfA3vdNwRjzE8aYF40xrxhj/t59vtZlY8zvGWOeM8Y8a4z5O/r+pjHmd4wxL+v/70yR853dg2+M
++box5rf072vGmC9p/L9qjNAv9+faPWPMrxljXjDGPG+M+ZH3auzGmP9Sc/6MMeZfGmOS+zV2Y8z/
+Yow5NsY885bvfdtxGmf/g+7haWPMJ+7DtX9Rc/60Meb/MEYcce5nn9K1XzTG/KV/l2u/W/a+bgrG
+GB/4x8BPAk8Cf90Y8+R9vGQJ/FfW2ieBTwJ/S9f7e8BnrbWPAZ/Vv++X/R3g+bf8+x8A/5219lFg
+APzcfbz2PwJ+21r7BPBR3cd9H7sx5iLwt4EftNZ+GPCBn+b+jf2XgZ/4U9/7TuP8SeAx/ffzwD+9
+D9f+HeDD1tqPAC8BnwLQ2vtp4EP6m3+id+L9NWvt+/Yf8CPAZ97y708Bn3oPr/9/AX8ReBE40PcO
+gBfv0/Uu4Rbknwd+C0eIdAoE324+3uVrbwCvozzSW75/38cOXARuAps4aP1vAX/pfo4duAo8873G
+CfxPwF//dr/3bl37T/3srwG/oq/vWe/AZ4AfuR/P/538936HD8vFsrRb+t59N2PMVeDjwJeAPWut
+WFY5BPbu02X/e+Dv8ib/2BYwtEsA+/0d/zXgBPhfFb78z8aYFu/B2K21t4H/FrgB3MX1DH+V927s
+8J3H+V6vwb8J/D/v07Xflr3fm8L7YsaYNvDrwH9hrbTkZNZt2e96ScYY81eAY2vtV9/tz36bFgCf
+AP6ptfbjOFj5PaHCfRx7H/iruI3pAtDiW13s98zu1zi/lxljfgEXwv7Ke33td2Lv96ZwG7j8ln9f
+0vfumxljQtyG8CvW2t/Qt4+MMQf6+QGsGEXfTftR4KeMMdeBf4ULIf4R0DPGLLtV7+f4bwG3rLVf
+0r9/DbdJvBdj/wvA69baE2ttAfwGbj7eq7HDdx7ne7IGjTE/C/wV4Ge0Kb1n136n9n5vCl8BHlMW
+OsIlXT59vy5mjDHAPwOet9b+w7f86NPA39DXfwOXa3hXzVr7KWvtJWvtVdw4/4219meA3wP+k/t5
+bV3/ELhpjPmAvvXjwHO8B2PHhQ2fNMY09QyW135Pxi77TuP8NPCfqgrxSWD0ljDjXTFjzE/gwsaf
+sta+tY/508BPG2NiY8w1XLLzy+/mtb8ve7+TGsBfxmVkXwV+4T5f68/i3MangW/ov7+Mi+0/C7wM
+/C6weZ/v48eA39LXD+MWwivA/w7E9/G6HwP+WOP/P4H+ezV24L8BXgCeAf45EN+vsQP/Epe7KHAe
+0s99p3Hikr3/WOvvm7gKybt97VdwuYPlmvsf3/L7v6Brvwj85P1cd2/3vzWicW1rW9s99n6HD2tb
+29oeMFtvCmtb29rusfWmsLa1re0eW28Ka1vb2u6x9aawtrWt7R5bbwprW9va7rH1prC2ta3tHltv
+Cmtb29rusf8Xp4rS61SqtBEAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3587... Generator Loss: 0.7105
+Epoch 1/1... Discriminator Loss: 1.4629... Generator Loss: 0.6467
+Epoch 1/1... Discriminator Loss: 1.5333... Generator Loss: 0.8289
+Epoch 1/1... Discriminator Loss: 1.4835... Generator Loss: 0.4824
+Epoch 1/1... Discriminator Loss: 1.3330... Generator Loss: 0.9264
+Epoch 1/1... Discriminator Loss: 1.4501... Generator Loss: 0.7557
+Epoch 1/1... Discriminator Loss: 1.3579... Generator Loss: 0.8731
+Epoch 1/1... Discriminator Loss: 1.4515... Generator Loss: 0.9340
+Epoch 1/1... Discriminator Loss: 1.3060... Generator Loss: 0.8987
+Epoch 1/1... Discriminator Loss: 1.3188... Generator Loss: 0.6393
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVnMJFl23/e7sUfumd9e31dbV1d190z3TM9wOBQ1M5Jl
+ijYlCxJsA5YlwJABC3ryBtiACQN+15OfZBgQbAMGZEuyIQkWbUoUJS4me8TZl16q1+ravz33zNjj
++uGczO6WLbO4DFUPeYBC5RcZGXHj3BvnnuV/zjHWWja0oQ1taEXOv+oBbGhDG3q+aCMUNrShDX2G
+NkJhQxva0GdoIxQ2tKENfYY2QmFDG9rQZ2gjFDa0oQ19hn5iQsEY8wvGmPeMMR8aY37xJ3WfDW1o
+Q3+4ZH4SOAVjjAu8D/w88Bj4DvCXrLXv/KHfbEMb2tAfKv2kNIWvAh9aa+9Za3Pg7wB/4Sd0rw1t
+aEN/iOT9hK57CDz61N+PgZ/5l50cBJ6NY588rzCI5uIbh9rI99YaHFZ/1Kx1GyPHDLA6aJxPHTcG
+szrZWOqVVqT/OUCtX1e1Bf2+qi1Gf2iNoa5qHYec++oXv0RVy7G0SEiSEoB5umB+OZOT6mp9o1rP
+NTjU+lAe1Xo8FQW/F31NOYFjwBqR64HryjHXUFWVjLeGyI9lnFUmBwDHOASenF9UpT5zje/ItTrN
+BtWaLyV1ueJL9Qnv9FxbW+oVW2uoVkyqawq74q7FcWTUlc5Ns9FgMBgAEMUhvivXW86fkOnPOrFP
+GHb1oeVgWUGVJgAsFjMWy0Ku6zp0I3nWOI4w+nylDq5IC6pCnnWR5qS58qiw6zXn4IGV47nOmWNr
+Kmv0+xrjyLk5hv1DGX+u/HFdS9sPZDxUZJnyooIyT5XfBWWlYypW68qu599icMzqfp+sT2uE18B6
+TXueg6N8i8IAR3lfm4pEx1RkJY7u/UmWX1hrd/hd6CclFH5XMsb8NeCvAUSRz9e/9iIfPxjjW5nk
+o6DBMtQJyDxiK0Ot85Ta0wWpC9N1wF1NTOyBLwvCcwOCWibZeNUnC0HWBlFVkamuNE4Lqky+X8wz
+jK8TFhtmlzKhVSmT9dvf+jbjuRx77/RHvPWjEQDffO+f88bf+r/lgssJlc0ASBdLGZttkqayaHbs
+mKkKm0n1lJKV4FgtUAerx2rW7yIuBk+lQitwyMMQgGtteXmavYjRaAJAlta8svdFAN4f38NWIrCa
+foOjQQeAk9lYxjBfstdsAPDzP/0lFioWLvMxyaW8hNP5FFPpIozlvmVSsdS3uEphWujCnM84LYRH
+OCVxJPM3VOH1ta9+ib/4F/8yAC+9dpO9VguAH73x3/BRKhP0c69e4c6LvyDX82RdjMYVw7tvA/Dt
+7/863/nuqYytF/Jn77wKwKtf+BxBX653nsm1ju8eM3t6IfP0wSM+fCi8SJ+WRFaeL3L3sOUQgAez
+HIBGPmNh5VkbzPADeb6PHZ//9L8U5ffRUM7ttgv+5N6RjIcJHzyQ42YEZ4/fk3FMjrmcCW/PjuW+
+RVaQquCpXI9YeRRjyHTmcxeqVH7nqdDfHjSJek3h4a0jWoXwaOYseGcka+/pR5e0axn/Dz58+IBn
+oJ+UUHgCXP3U30d6bE3W2r8J/E2Adr9pl42A2SzB6D5YNDNauSw2l5hMX9JWM8SXZyRw5AXDdZkV
+woTQtVj7yS6YOfK7pqloBj4AJpbv67oizuR38xyMEXbYRk3dkHG4aUXpy+eV5PcNPJgtAPg7v/SI
+9z742wB8+MYDxqfymF5psEYWRVXJZNXVJVkpnwtc6jpXbjjY9d4sVFGveQF2befVWKzVv2pLL5cx
+5+iOOa5ZZHLdIoMnj+8CsKSAWl6QrZ5H1hdeRLn8v+23eeWOLOgX/9gN7n8k62f6gVlL0bjp03Bl
+ETZ8ffmDmqXuYGni8vFQBNLcr0HHYa3HVLWpXDWTH/z4Ec39bwLQub5DO5brDv7Un+fxm/9A7nf1
+z5E17wAQuHLdRXlCtiXP/DCcs/earIEXwxf4qa//28KXI5fttiw/k8pzeEXGt9ITuW61xNf1cppP
+SZXNTvqEhifaxsKOlN8ul5Xcu+EEHOqL91K0g/vln5Vr/DMZb7O8yeOO8LBd19S9S7nW5Yi7qXxO
+KkumgsqP5MaLyiF29XNtCXyd39jHq+V+WVbTiORZa51HN7YQ62ZoQy7aqh31A5KzxzInWcliIdd4
+VvpJCYXvALeNMTcRYfDvA3/5X3ZyXTnMJ22cKiBP5GVLswo3FCa0OjWRqt2B79NvyQJqq2psgxpv
+LL+zdfUpdWmJVWbHYZOW7+v9ZLGmjiVTKRqZmlplzCJ1WCzkGmVa4TtyPxvKPWoM3x+ey4P+1j9i
++va7AIyPT9R+AeuV5LrwbCkvfG1r7MrsMJ+YAdap1nrip80I+6m/Pn08V1V66sGyITtexwovgsrF
+1rqonJKnhSzGma2JQpnuVlRxlKuA25JFvH/Y5E++9goAWebyaCk86rgZVgXkttfh6uGWHA9kDEsq
+lheibbyfTTlP5QV6P5lTepXeO2NlCyoruDx/wuXvyIuX3Jhjf154fD4J+dLs35WxjQvy+MfyrIUs
++OPilKeF8P7Ya/LHD0SF/4U/94uE7UMAnlTnTHMdf3INgCJcMN/+LXm+1pzBTZn3WV7wWJ/VnZU8
+FjaSLuT5zj7ldZs6CSP93ms+4k8PRRu59eSPybH4mDT8jjxTOSDoyMlns/ucXshaMEXN1UCeFd2k
+/H7C7FK0yVZhCFUolHVNVQjfuk5MoyFjdnQ1DHoBnbAPQKPMmSx1zCeWeBgBENchS3/J74V+IkLB
+WlsaY/5j4FcAF/ifrLVv/yTutaENbegPl35iPgVr7S8Dv/ws5zrUNMwcJzDYpUhGW1scX4bX8UOa
+oUjPw26f9qANgKe+A5NlBHKISZVR5XKNyKnxXNn+mx2fTiCfnVKuWxYOi0h2idAYpolI82XhkBVq
+7znB2s9hHLlfZSveuSu73MPLIcuh2LVVleC7sjs4lcWr5PyVSlBaqHWXdx2DmtlYH/Lqs07QwAF1
+cVB9yqnge6zNisAzUCoPAjmWp5+o6LaqqfVct3YJ1KxahjVxU25+Z1d216987SZ7nSsAXMwfsROJ
+fyHud9lX5m5vt+kN5PlaDfneeBX5XHainctLdgJR14/GKe98/BSAB6cFF+onWIXAqyLjo7FEqKf9
+BUUtu6df/Ri7K+NIW++SX4hf7B+nog4H+S69WBjzC6/8SfZfEc0lb/c4K98E4Du/8y571+X5jqKb
+AMx3L3jlg8/Jva8FvOTI+I8OAt77sfgRksWcC3X+zdX0W8wtVaETYQ1eqI5i16M5l2e9vCprZTZ/
+wuBUePVx75yDUrQYr2U5DESrCPqw3W7r8wnfqsc16VLWntt0yAq5d5161LrGu+2YbkeeqR/K7/t7
+EVc7PXl+MpJjHXtzgbMt9+6FBe7TlU465lnoX5mj8dPkYGjVIcaxqC8Pz3XohaJe9VpN9vuyGPe7
+TeKWvNzlKioQxDjuylvsoI5eakK6gTC1E4WE6kvwKrlWbQvKWv0MAbSXwo7K5uvjy6TCi+V+K6fk
+JLF8XKrzkEtidRItjQtuvh6HOobx1FcRGQ9P/QFx6PPathwvggavt2ThPVWV8sjO+c1zeabDpOBN
+ffl36oqnqorHheGJOliZrsIvNZ6r163rtQliTU0rkOf+YveIz1+RRfPyF8V8ODjYwi5F9XeThOtX
+5KWptzt0V2ZHv0esqm3YUFurssRtue6tVsC1A1Fn73w84itteRH+/geP+eYHIkQztR8sMBqfAXD3
+u/f4qS+LIKje/iHfi39DHmVxg9G2+B2imfDlZOdrzE/kHp87ctlKRKh9aL7HB4/keg9P3ybS4/u3
+RI32nA6Dm1MAvrHzEk5Drnf2jos3lucLFiFPNWIyujsH4FGroE5kU5hU0KiE97OlR3AugursUoTR
+ySJnuSdj+Bz7LDx14k4rdndkXVzd6rGINbpQCi/HZoHvaRQoNwQrp6NToNYKpk5oqdm835U5vbk1
+IGqp38b0MK74TParJnetrM+y3sLbUvPhmdyMG5jzhja0oX+BngtNobY1i3xBscxxcvXMBi4NHV3P
+dwiDSI/7RBqSRHfg2vFwPdm5vLogQyRjsjS4usu5gSF2NGyxiu0HOYKtApuXjHQX3281cFRLGZ4s
+mGvo01dVbpjOMW+IA895GpN5q3h0SbWK6Xs+nsbmG2pFRI5HX0N5VzsNXjoQ1S/yLTdfk53tjjpM
+g9Bl9paYJVt7Mf5Hcr/ch/xUIy3dkLMz2d3rSHlSVJQaNqzqAqNy38WyE4p20Nn3ePnOPgAvHKlD
+KgiYTmWn8UzGViQhS9MyBJHs+K1ejGrP69Bp5Vb4vuxgrh9iHdWUXEN8JM/6xy3cfSLzusxl9zTG
+rOdhfPIu1VBCp/H1FsunMn/v/dKPWHZl5w4dGU+Wfod3+jLX7T/xs7TNRwDcP/mI6qGM7aA8ZV/N
+H3OmDlWnplvI73o7V0gSNQO8MS8eqeZ4njJ/IPc+b+pufVoxUqdslhqWbqhszkHV/1I1ifCdUxqB
+hIaLBlT3Zf7OZnMGqum2D12Wp/K7s7viJB5dLnHVOdz03HWkxqtqQl9D6mXOWM2Aq7Fcq9eybPfF
+fKpyQxDI52HpcVuWEaOTp/jOynx4NnpOhAIsC4NxDO2mPLAfujiqRvmepaMvViNy8BXcEatd6/ox
+idrRBSWZKl1JywUjxyNKfBUiDVUdgyimzjVC0K4I9XsHj3osizdtpphMY+watahGBqcji7Xz8jWK
+74v6OK1na89wgKWpGALPkYltO7CvceU721vsbyvYxo+IkcVktuTFnY0u2InUUeJUHO7IhD8ZTulF
+Mv7cNcQNealHC3mOyPHW9ysA15GxeziUKp1e2rmFp/6FupDFaJYJjtpHQVngqUR2Im+9YN0yxWmI
+gHD0utQFNhV7zbFgHBlPr7fD8lR4cavR5agjY346UR8IhsDIuVV4yFCsCx59dIJVE+rlW6/xdx/+
+tvD2W7LK+3tL/uqf/U/k+86Ee98VodC/kfPV21/T+fuIVP0c6VD8Gu32HdL2MQA2TQkVs7Fzs8/n
+usL7B2/MuKGRgawhz1x0HY71Wq7rs1z5eYzh7J48d4SMt3Nrm9FIIiMmdSjVafT56wfEbY0C3Z9h
+FYfQ0o1nHoJZ6LqpS70alJFDrGZxVZUUGhJPFENRJwnN3T0ZQ79DPVXg1HTMclvW/dWDAT+6p4C6
+Z6SN+bChDW3oM/RcaArUNVWyxHccPE/VfQxWvY6R6xEqgKhhPzElAnWc+T0DI/W+VwF1ILLOKTLM
+Ch/k+dQKBFlhAvzUxzoaKzZNqlh2o31rSQq59sW0YpmJ1pDobuxPUr68sw3AR7xMxv8FgP0UBNt1
+DLUiz1aStxEFXGmKNA8jh1wjLUHPxS7VO+rKeJLjEmPkfnEREXfleJrmuKopZYOAJwrzDUrVeAow
+Gl2pq5SOYizKENot9V5vWVoD2R1L9XTnnqj8ADat8RrCpKjpQeHr0BwMK8es7HZu6lAoNLguUqJY
+NQzr0A7lnMM7LV4dSRThxyeya2VVQVaK135+8YAHj28AMM0yTjUK1Lvtsf2PRVt6fCI77Z1Xb3Hj
+z8r8b9eHLG/I7v/KS19l9+DLMrb8Z8lOfgTAck927iDYxhvKOKf3jjFHujN3PbwzNaHwibfknOtd
+OZZNcu4jGl9eu3Q1uvSkvmCeadRiJnPguD7ZUGb7veUZL9wUjET8qkf2SPjy0fGcQE3IzMic96oI
+6yjAzYX2noKQFpaZRkOKrKRS3MfFVHg4nS5Jh+IQ9bqttWaatZtkilux3oeU6e/tNX8uhIK1Amrx
+Ax9PX9gizXHbCiwKPXqKZY8NqNVA5QqTXOsSKjosLHMKxbgXSb7GqlvXpVLQeF0qjt4rCdTn0Gwa
+AjUTxtMFHV99G92EZCi/m+Y6cZeQGhEggxsf8/jbqj4bQ6R+h0HTw1XzZ0snqxN5a1RlO/DoRXLc
+b4BtyD1Cnfjt602ChdqWXZ9AhWXLN5xnU+Vcl3steVnOhvJMga1wPAW5lD4dWdt4rSa394Vx6TSl
+6MliqhoiHBb5DDeXl9Tpe9CScdrMxVf0otdoYdxVPodGERwHVBAY36XWCayXFa2+CKRgy/KFcznn
+//ympMSczXNSjeDce3JCohD0/IGhdmUc7sASfUHMqS/dEl7+pT/zOj+9/Q25HxW7A3npQ2cPTwVq
+7aQEO3JvJxfzwXVDHKMC+VWfTH0f6eIpodrc176yzfB9iSgUd9VPkBn6+n3fcei15R6jiY87E+GV
+LwU+3e+UNPbVh5E79K7K923P8v6p+BeGeY6re9O+gtu82MEodn1eVTTURHF9hzRTBKyBWpGjoYbI
+ndqSOwppn8NyJHxr7MSYQC4yni9INbz+rLQxHza0oQ19hp4LTaGqLbNFTplbUJCLY1I8dVptex4D
+TeBphOF61yzVNqiLDhiRjIGxxCuNIIhZYYK6fZ9Y0UCexvmNn62dciEuXiU76ayX0/bl3p/LUz7Q
+ZJxMnZnZ4hF1pQknv/mDNXbX81xijVT0gohuW3amrViu1fYN/ZbszFuDJt1d1RRqD1edh55irVsH
+Lp1LjSIUKWVL1fmX9kCPJ5cZNkk+M7aqdjDVJxmlLVXtj640IRGNJk1GOA1xUDkKB8bxSRXIFXke
+oZpmpmxR6a4TNT3I1CuvfC2rBDeU57OLDFvL58LPcfqCJwizLXbD+4BEQVZkV5iARcFbd+X7vX2X
+seQOcXp3hB1qAtXLkgPxxZs/S5DJPNkwp+H8mzL8cAalaAfGWeL5PyXHXVk35JYwEM0kunIHbygA
+Wy+ZstxTR9xkj+xMQhgnF6rFVCmP1cEc+BGNQkzJ0AFXTTr7WG+x7NA90jnfd2moFnr+4YJ7D2R+
+goVLR7XeSPM9+v2agR77aLikoViQ8zyn62nUbVYyR87JNflnXpacq+kSeh2smthRuM2uRnlSE7Bc
+ga+ekTaawoY2tKHP0HOhKVgMaRWQlDmu7lyxD+iOt9XxaKuDZzDYorSr3Hq1df3W2iFTTcak6ojr
+NnrYpkjJQSukoza8u9IUmOJojDl3C2q1jb1lwONLsdUb27u8cim/OzkXW75YTPjg5A353JrhaJKT
+h1k7AWtjeeGaYAGaGprqWjhUB1+v0ydS34eJXEJH4dGa9ebUlta1A7lW5FPkmrU3qygqGU9164ib
+mgL89mMZW4WLrynnxvjUTRlbHuQc3hLnaB07FLrDmCM55lYOWSW7XJqUuAqPbvb7uIrfMEmBCdSh
+Y1fozymrBM9w9wrWk++rdE6pYd3l5Izd28KLgx35/3h+H9/IM5eu4f6FPN/AzMlD4cswc7m8FHs9
++a48U9P8Y/6NLwtfzK1j+m0Z/6DVEGw44PgedXFfzlG+4sS4oQy0nDzCJqIpjR4WLCvxS9z9Rz8i
+1/U31/V0vszJ1In7vptzrk7cpWNJz0SbMAqrryMI85UzsyJXjEFS+Pi6W5cVdJoaklxBrf2CoTqx
+twJ/jb/pxA73H2rafuVwshS+XCzlvo9OH/BnfkbwHa2gIIvkuotxzn60C8Arg1MudsUh+tGHPBM9
+F0LBqx32s4jjdEyqL7ofWrbCVQp0TaK47UURUuXqPVMV3+QZ0UpQNANCIyqj23OpVIWripxH6rVl
+LtcadCLcVDHnC4PXVBXcLOkqFDVftOhqJmKs+QXNquD2VLzp35oYKlX9qGoWGnG4LDOejuVF7Wmh
+kJGxOFYdfGlBVz2mnudQhSvTRlN3kxGjkTgzx8OUupbPpoKBKx75eFDQNisBoGCismC/ku+HdkHP
+F0fd7d0rbPVEmHRbPdAoyCwTR9VykXA5kwV4MTmjpc6wqy8fc9CUF6g76ONqqrYNhSeO1yRdyNhm
+kwnDsaRqT88n1Et5/lZvh0Jh1T2N/7vGWQueF2xMdfwBAOmOw3Qsx++/fcHwTObnoRGM7sXjBW98
++L5cd5bwpc8LAOxrP3WVG0ciLFqdbapaIdvqgPa3vgi58H75/ptcXEiK++WsQ64Ow2DQwNUIBbEw
+wPdcIjUJm8agxggLC6Gm5ZdqunouMJF5OK3mXNsR8+m4HjM6Ed76WCYKvmvoxRLHY6nO73lW4nRk
+zOenBU8nGoEKXawCGPyVEHJjsizVayR4cirRoKJuy71vvnqD2ULW5xvffDapsDEfNrShDX2GngtN
+IXQcbjSbTAsP4yoWAJ+Whu9K1zDKVuHAhOFckk6mustvN1o01ZkX7XXotnU3c+Hxqewwi+WYh2cK
+sdXQ4+vXXqDTk98lzpAo0yzKWUGiyVamW7Azlh22pRmX7St3iF+Rne3oicvHY0Xp5QWa/s4yLTnW
+3eGBkV01L0vuqRPpdrfFtSuyy/XziN6uIAWpRZW993TEmcJdZ3lGW02eRjNg+1B2Zs+mtGu5d6Co
+w5pqHdYMygh3FUJLDbcLNZ/SkEqLy5RaNGQRuTiKQahsyYkWhrn44XsUA7nfC195mXZPVX51GM7P
+5mS1PN9bj075+IF43fYKn05fS6k1j3n6kfB8eq5xdRx8LQzTCEcYJCHqcjpfa4tlDpmGPrsabgz9
+JdMT+d28snzwSNT9rbgkT0UzOWye4RrRFHov7uncfAALVefbU7z+5wGIkzaPf/RdAM5Ky1wh25GG
+RbecBWFL+NaPDdd1TX5rUtDUc1vqJJ4tF1wq5L1l4VQz804uchYK34/icO1g3HpRIdFTy1TjlKUf
+UqVqBpHjazZru9XB1x1/CzU7uxFOJfc+uLqHqzUnGlcGuIo8fcVs01Ot57/7OzwTPRdCoShTTs7v
+gu+Raex2J25gOjIZdWVwtCJO7XiEoajuvi6CnAqnlIXZN9217yAvS/KFLMIgq9jRVOa6K6ps2g3Z
+H0hqbTQ/ox7LC1l0mrTVkxssC+4GMqZOWwRIPbnHIJXP6XEJ+Sqa4WFS9YkEMbF6ibuxTH5VFcS1
+LLZhnbOl4J19t0lbVdT3L8T+e//JkKVi7ptRkzrW+ontgKIWyHM5mzNdiKDLNIri1DBO5Zlzx2AV
+E5XMRlxcyDhaV5eYpuiulZZSM2Wfy/K+8MIdUClGYuD3GE/EvzJ+cknDE96bWnhy76N3qFMt8MKY
+l7tfknt3Unau3pBzx3PGT34IwFmm8OMqozQyH++cPCacyVy2Xx9w+aHw4GK6wCoP9gZa6KX0qRQg
+tCgyRnqNby+m1I8UBnw14eZPi2lWZjrn44dkWnru3e8f80vfEmj6D96cM5srtqJKiTU9HgVQpZ5d
+vyU3bIivc3rY8SFSf43muzx4OCFUc9XbavH0x8K3+x9PKbWUWpXl3EOjZorTMAZSLdHWN+CoUB/O
+LIniqs8vxkz0Gm0FmbUuRuSp4mV2A15/7TX5vtHHVRxG4/CQnYH4XZ6VNubDhja0oc/Qc6EplKbm
+zFtw5oCvNQ96LThqiJoVR31O7okXehhMmZ+JFOyrVH79ztV1kpNna8JYd4nJiEh37jLe5ugFOf47
+35YSX9995yP+xG1xOO34fQb7spN2DnqUZ3I/Om1utXQnaKknu1Vx7H8bgGUvJxbQHK6NSNQZtExy
+Hjiy44VqXhz1OvTaskP3ooqBoh/3dho4irycTGW8x0+X7Hfk+asiQ/NlWBzDZSmowFdvXGNLUYMr
+6V66MFL8RlHBRHfmYuzw4pGqxN19mMjxY80W/MEP3uB3zuVBstOKP/aCOO2ufflz9H0xc5aPxphD
+UUtjLePWsm/xjmb9Xbtyi/sfivnwznLCjR8Iiq/bcOn2ZLdqqYOzHlkKxZuf2Yx6IVralY8slWoH
+DdcjV4zIsUZLZmZBR00mN3B4eiEa4vh4gd/VubYzjg4lylGr1mjPC84KGedvfPOCyYnWg/A6vPZl
+WRfZaEgxFB6V6sueFzVP53LvJ027rpmYxj6zsZwbKJbAGofxWO5hK4+paltu5dHqypiTRc5QozJG
+tYAgKOhoAtoXbxziqQP6195/xPhcx1PWqA+bUoc+zQseTiVq887Dh+xdl/XbnTUYtESjC5vXyLua
+MvmM9FwIBTAY49Mo7bo45Y3tPlcPxB50vZBpJAvzeqtBVotOPNCKQL12Y13VtvRSUi0eOp1PcRw5
+px00aTZE7W6rV7hXwmQkC2lWPKSIxKw4bO1TN/XtLtoEbc2J2JMX4iIbUZwL071LnyrWIqjWUKpT
+oSotVjMQQ03r3m3HdLVAzE4PWgpu8uuArCsLYW9LFvPeTsKtnozXa/hc2dYCGwcxmQKctu7sMP62
+erK/cx+AZW6J1B8ytuW6FHuS5FhXFmzcalBPFdI8Vxg4HjuOPN+pO6GteSfZaETzttrlYYxXiZrv
+7LwAQNC+yvzsewCcz0PGKry2Kh+/EqG4bPcIVP3fasrCdTlf56A0cJipf2GZlCxXoVrPrDNbFwox
+Z+oxOBJe3fRjrlzX9dJs8OJPy5zsN1wOXpXxRQdS4TlzLuk9lN+9cmPBlT1ZF1/oFrzoyTk5D/Dn
+ck6ufqC/92s/YqS5L2lSM1PgXJ5XnKtgnUxkPdq8pkqEAUu7ZE/NxsOf3aYzFOE0XpyjQR4a+vq5
+1vD1q2LGfuH1OxxrSPnodMxHgbz0BQV7WqMx1OpVnXaLLa1CdrUZU6nfzWsGBLuSd+FvtTGJrKNn
+pY35sKENbegz9FxoCtYYqiCmSjKu6K56PegRRKu4eszWjoJ7ioD8psZ8te5dsxMxPBZp7oU181B2
+s9kyZamOy639GKuJJF98WTzd2eEepWanOV5FX6VuYgx2IfdYOO9TFLJ7hAqD/nDZ56NMsvN6IdRa
+ddrzvHWyVYxlV3sZdDWxq7vt0lKnpWMiPK32WzRAIRBc31UV8I8fcTaVsV3Zyel2RYNoxSEDdXB1
+u7tsxxKJ6SgkPHbTdablNK/xtYZCbS2+brZRWRL21CGmfQy+0bvNz11oGfU2+OrJ9rdKHAUZuU5J
+ZuR+Zi6mgfUtZSAXfugOefnzsiuZZUiyFH71ux4mEnOko+AeDw9Pa10MXMtCC8NMZgVzzfzstX1y
+VSfaugNB4nHdAAAgAElEQVTvDuDPvS7PetiIeeXzAtKJI8PeHfkctls0r35dxqGJbaRjtq7dAuDn
+/52IhydiKvmzFKPZsfWTPsubyqRCd/lvhjzV2hGdynLkank3r+ZY+4GMLmW9+UCsZsBBv8GVgayL
+buxz4QtvTeTiKxisrSCm/Wibr39NysXvtQJagYzHPbqBq7w4n1zihVp/Q3lyZbeFty3jfOlOiy1d
+Z51WiPFlfigttaM8eEZ6PoSChTStoShpeVrlZ7/FoCsPOeh4BJ6m+ubgdLUqkHqC82VJpZ7cRmeb
+QKMBgYlx1C73y5quqvlZLOpwUl/Q6IogqB2fWhfs5STBNSogTsZcLsWbb7Vg6Ju/8ybv/NoPAKnp
+PxBrhJ0oZkdRkYOgzVFPEWs3dDLLijgU2zpqGW4c6CJuVuCoWr0jZtKet8ONuaiwkd9dl63vbx+Q
+I6p/VedcJlItaeaoJ7uy2FUDHGtp60LKlwvCpRYOYUanLYKxrZWNrmQR1aEWdWn5OJpSTeGzVL+M
+CTpUq85Q2tPBa1+h1LBY6dU0U3n5vdYpV/dFhXWjgFPNJDxBhGJhinX+xLAscbUKE5G3zuqbJbCl
+xXt/9pbM+U+91ufP//k/JcN89RZhJL4N6zzG0bwLggq3e0M+W/G/uGxhtNR54/Dz3NB6hwF90qGG
+A/cbZImEsO/9WHxNd+djzhMtF++EhPpC7jYDHuu7NlFTbOB4HGwJv6+3IvY1WhWkDlsHup7KkMuR
+mAQml/Xx6rUrvKTl9XFP4YEImZde28LxZO2dJRGjR7LxbWtm6+CFPoMtWS/XrrxMpOjVMNjBQQRS
+MZ+R1qs+U89GG/NhQxva0Gfo+dAU9J9xQwKtP/e5g9v0tRxyc3sbb9XgxAtA1S/PU+98nXKRaw2B
+ZUroy67SjgyFwob9PCAMRTKvIhU77YiiXFWGLik8kdCXxYxEu1M1Wn32dbddqofoe9/+mCePxcte
+FyltX4FHXsG2agf7/S57fdkdVuribscl0MrQroVGU7bKrr9PtYqeaO2F+WJMrMUx2t0enpo5gedg
+VCvKbYRdqqNQ27Wlbo2/bjdn0dQO2vGAfJ3b4eA7aoINRN0Pui0qrZVQjEd4RkvJNWNczTtJl0Ns
+pg7WlqhH/Z4l0UrTnFd0X9bKyO3rNDWrr7AjGtpCLm8r1NxaVkpt4kCpCRRhXq01nWRpaW9r6Ty9
+xZ0vXKF5cB2AqHsbZ7Wt+bdg1QjJ5JBo6qKuESfcxajjsxp9n4Yrz+GaF3Dad/X5TqnqoY5ZoiHJ
+uGShmbuznsOZVkmeVxWlhoTyVDWN0OWG1rZs25qmwt9vvXiTSh2X753OaVhZFy0ra+HqQRMzUYwM
+E4qFth2Ip+wGikMpEvqHMieLWHEO3pR+S7OHex6uFtQp8lNCRxyttYnItNvVs9LvW1Mwxlw1xvy6
+MeYdY8zbxpj/TI8PjDG/aoz5QP/v/37vsaENbeiPnv4gmkIJ/BfW2u8bY9rA94wxvwr8h8A/s9b+
+dWPMLwK/CPxX/38XciyElUNpagptypozw6xy/sscT3sI+E6IXZUb00rMuWNpdTTRxosIglWTmBxf
+Q1mFzcnVnm03VtWZPZxSob9FSXqucWWTr5vDmnlEYnTXUDs6+vgtTKKVfG3JRCsW7duIUm3jVpyw
+0BJxqVbM2TqMcVqryjyGfCr3K7szanWkVbNVFuJCmr0AzJ5CpHUK6opCY+Wz5pBQk2ciTfypEig1
+0chYi9F6Cp3AJdSYf5E7FLobR8pDJ6ww2lSx8pcYhULWeY2CBsmWC/xY5qGYaOy7qGlqqCyoaxoN
+9Z+YmkA/O44h7slFvnRDHKbv3p2uW8h5DtJWGfA8u8409TxLS9F7PQ3Zttvb2G1FB+YLHC1tRjXH
+Kr9MvqAuV34VVR+8G5AoejOb42/dVD7PcR3x7fhJQaL1Ip4+EEfdKCnwVhmjeJhVM5jKkmerBiNa
+v8MpMZrhOfMq9nXLbR24XD6U+Zvdv8/b2pn8ivqPvuHVlKWssUU2XvcFtWNLqi0bel6T/hVF1LaU
+FzaguysaTxR6uKtks7iJQjko/YxaHb7PSr9voWCtPQaO9fPMGHMXaUH/F4B/TU/7n4Hf4HcRCsYx
+eKGP4/gkirk/fXjJ4YE4VMLawVVnn+O6WK0fmEzEozu5mDK9kEXavd5i2ZIXejFPuVAnUWM5Wndn
+sgozDaMOttLGn/mYxSqLbrJk5snELRsLRk9FzTvRe1x3m/Q0TXdkLYUKi7jM6ClWfT7NWE7keoeK
+MUgKw/x81UTGUGil3qlb0dIKv6aW3xTLJVYbuzYaFk/7ZvqOQxppbcNLD28pv2urIJxl1brdu+e6
+TBR/3+m0yDTdeVwt6Cg6p9TiLF6ZrRvsGjemXkFxw4JcF5V1Y2bq/GQofKvLkDtXxNl3tjhmpF2b
+XRPjFyJATKPGmYsZsKsO1VbgM9cXN3ScVdFtXBvQbMqYA2NItbxbo6887hc4RmHe9hzf05e78rG1
+ODPr1KGuRO1eNfE1YYWp9KVqb2PVDHDCClOI2WhMTqkgoyfaVdx1PZqKG8jCklob8jptQ6gRn1Zn
+1XU6YL5q9jNP6NxWB1/q8mgp5sy95YKFArGivuZJhH0cR6H78YJ8Js7jIHRp7wm/JsklkZpxRrES
+/e0+Zp0ZPqLUCtV+2aSMZK1WdYi7sr2ekf5QfArGmBvAl4BvAXsqMABOgL1/yW/Wreg9Z+Pv3NCG
+nhf6AwsFY0wL+HvAf26tnRrzSeMJa601q0T/f4E+3Yredz07T1MyaopUdsEf9+9ze6i7StfBU32o
+ZkyVaV+HiZybL2pSbfPmJzUNNPNxXrLQwhS+D0tXpP+qWYrjzPD0d3WekWin4XlVgra5dyYz3h/J
+rvhAk4/Gw/fodsTBM5qnhApRzr0adId5XNbMtT7/1p6GApcu6cr5VtYsE7lua7wg13CoowlK/djg
+qdZRZZZWV3aBRtjkUqHQc2/KD7U68lzNBKcEX7M566pgqo7I6XzB4ljCobOtBFd1W6s1K7L5lErL
+jhE3qZUXxbCiWKpW0PAJtFZDpRmcaZ7S3ZJjjsnITmTXHTYKwheERzYLyRJRj+eauFY4dq0dSKFZ
+0e4GPZ9Es1gX85LLmc6fs5qzJZPTt+RYt0PgiMuqyk8oJmqOVU9YjFWN14za4mFA2NF7XH+Zdk9D
+qo2YOlv1Cu0yeqChyHdlpx2mBYeaddssfNJAUYWRTzXVyttqziajgpNaeLUV+gyPtc6E8zHf+oGM
+4+J0Qa5O7Fgd6dPxJYsroinUC48gU755M5qKWynuDcnHWpJPTSK7bT9xtGYtvFUvDtPDVWcmngeu
+OsKfkf5AQsEY4yMC4X+x1v59PXxqjDmw1h4bYw6As9/tOrWFZWVIbUGmi/uD8yWnwl+OXuxRJ6uC
+FpYqExXbjzX1uB/jNfRkx8FowZXdvX3clbe/DnBzeQEcvcc8KzCl2tR1Qa3qZVm7pKvU4jjE6Aty
+fqlNTuOcJ7GoyZ7r4qjXPnYrXM3mNElKosU3Ls4VhHQDZtrzsbaWXAFSQeXjKgCqjmQx58biaGlx
+41jmWgK+ShIul/piRR0uNFN0LCwh9GLavizWSWmYaqOWk0lAfaiqeNSmGKmA7Kx8J+tetZj5BKch
+i2q8HIE2GRk9SYhaWkPzWF82M+NiJNeojEu8LS9p6uRkl8KjzB1CSzpgTUtZoGVu8LS8fm2rdfPe
+MGzgKZ6gqAtOVfj86tvygK+/lXHFl89x1AAt6lKmTZ4+1MrGWcUP/pHktxTatOf0wZIvXJMX7Pqj
+u7z0p6QidHQW4GxpXkJacq6Vu08Stc+DAF+L+Ry1m5QIX3YbMd8bC1/mlXj3l0lGvuomHNXMJpp3
+Ucx4oBOU5AWfV2xBV8Frk1nCh9//vlyjDhgPZfNpd3OyUy2uExjCUDbBa03FPMwntBs3hIdFRqVV
+p11yAk/uYcIuuH9ENRqNqAT/I3DXWvvffuqrfwj8Ff38V4D/4/d7jw1taEN/9PQH0RS+BvwHwJvG
+mB/qsf8a+OvA/2aM+Y+QPrf/3u8+CEO/8jmrF+vY9dDJydQznoyX2FWvxDLHaj8/Rx1xbhdizbGv
+K1BtjrgV0dMMv2RRUSgyL1WXbpHloDX5nMoDX/sNFCmJFuQYFwHDubQmG8/k+690m+R92T0eLJbs
+a/foo3aTVHPeazcjW/UY00L+oyxhuGp5t/S40tRqvo0WnpoY1UKlfbpgBSpMTYLRWPmkyJhp+7B6
+sOS909UOpPd1cqwr5yZppTXCYJmllBpx8HwHR9GiKL6hblaYsXrqY4eylOtmoxnuTHa05qzi/n1p
+Hz/UGgo7vXDdUMe2HdKZojB3YhalOruMx8SKE/CHWoQlK2s81ehqBwr14FdYlIUkgNFaBU8U8v39
+x5fYr8ux65Mulcbux0/PmOXqBP04o9cTLAMTue/BF7r0HEFxRs0Fk2Mdx84ezaWo6/Nkxj99SzSP
+s5Fcy1Q1WqqCZeJTtmUNLMqQazPh7YWaiSkVlbOq1GyYKHZ9shySrjImPcPhtmhLW4eyNh+fTphr
+tMfUFlTbmJ26VFpcZzsMCXvyudRajEni4iXq2A2bGNUQ66oAxc6ErRamXjWiezb6g0Qffns9+v83
+/dzv5Vq+cbgaNFkUKYWvZchnlse64F/+gktLC3AWlU+i4J1itQjG5yxn2jjGW+ApD/LxnMcTfbOq
+mhNdIKlGLYIw5khrBzqNJVY98Q+Gl6RaeadoNng6VPtZi29+9YtfYWlEZTxKx8zUvPByS6+vLeyn
+PqWaJo+0Pf0WHRZD9VR7Flft+UVZEDyVa8803BhEMUm9wtQXzDRUlpQJS3XTBMWAE7VbK82rbQQ+
+sUJcTZhjVQhN0pzThWZG+jFusFKPNQyZWuwqZFk6LBWCO1u22NeoTefFG3hTOf+kljnwFwGuppQf
+zy+4UNh1/KBL9/aLAGSNC37wWPj14JHw3tY1oUZcXOOgDnVcz6Eohd+xcZkrFnquOSXjqKB9T1OV
+b55TLyXEGW679I3kNrTMOVc/pwVQ5mKrT08tEy17HgxuMXGVt3lKpuHu45nDG3e1oIwK9J4TUjur
+Sl4LrurL1tyq+OJjWXPngfD72M2oFfIdmYB2T3s7pi5X9rTZTVUyWkUIlzLG1C3XDW8HLYdWU3w/
+zRjipsxTVtQs5lo8R82LnUGHVPMunGiAsXLhKG5QhiIsHJpY7TT2rLRx+29oQxv6DD0XMOeyzjlb
+PqLZ6DBW5WNqc35Fq/a++MIttr8qyTVedUZxKtrETB1cUfMQvycSvN1/eQ0sMmmK1xSJaROH5UJ2
+gflMS3y1PUKt7eg6HmPtLpzhM1ZnZHS55FJtmkITS67UfX76tgBe3r33Hr46ASdhzQulJl1tR5Qn
+sovPNVrwQT5jrlDp1PcJ1JNdL2JGKwyFltGazaar3i04TR8NcFCEAfOl9iV8f8xYq/2ueqw41mNV
+9jcMSmpVvxdVwb2PRbM6O3nIwc5t4aeOwYugrhTOfXHJ++9K5d+7x0/5UusGALepCLfEWRenWneS
+moWWn794PCOIhVm9o5D2Ldmlx8dNfvO3/ok8lzpXZcDq+MUQrmLpsxxPPZ5pDbVGRDLtu3l8NyN/
+HR17kzyXOR3Oc77zv/86AMnHCa629StTcXx++8lTmpoo97lth6uaKRt+4wvYVySJ6923P+S9tyR7
+stCaimHsrDuXx0GAUZBYZH3ePpYy/wd74kQ9Tsa0FNxUxg6TxwqWO9zm1r7M2b3zc54+0ciGRola
+XUO1kB3fmojTp2La+G5MU7W389wwVWflnX0157rQ1bqb+bRF85quZbeJUVBe5S2pq99bQtRzIRSs
+gdytuHQneFfFs1qcFAwV+fXhkxGvs+pFH+JopZumporaKiTSNNVGp4fbEZPACyyDE3kpzh+e0Uvl
+nMGevNCdLf8T+9tYPAX3JEXFcKYY/oWhowVHZoqua19L8TQddev1l7DflWam85MC95rAMv709R4f
+HMuCPbnQMt0B3OnKIp1aS0MzAPtdn1ZfhNp8JBM/W0zpqd2fO5ZjBWFdnmfMFUc/OSmpVVAZFaZF
+XXCwQvk5LU4UkOXUNYta1M4Hj7vcuSqLrdpSdGfZpVCgD4uY611Z6PUooUR7L1TnTF1R1/2u8HDs
+npAdy/OFTYeDa2LLe22XRNXjk8tTPnhL/DJogRAHQ63RmR0/oqGFRuskJ1DebjfctTCY67XeeW+K
+PRFbnDDj8UiyGlM34ua2HP/2h2N8DUW7rszBzxw1yPSlOtzx2LupIKRrh/xY293/D//rb5MtxcRa
+9fwcRA67irbMiwybCG9nVcFZQ859t30fgOiojbfQPIii5DSTdXqQRByG4rd4MlqgwSo6midz/VqX
+4nBlKrYIbmr+y3JKmmt0aDphqy3nO4UIleQipXVDAX7bfbwV0jddEHiyDq0JKErh0bPSxnzY0IY2
+9Bl6LjQFDDiBy36rSWlFI7BRhVXny0fnDzk/FVBJb6tJpWXY6kwkdZo8Yf5AK+BefESkPRrHVc7l
+A9kpLsfjdTv3rvY49FotllqHoE5qLjQqMUpyFqsGJ463zpkI1ZP/NMvZ25cd0WlPSDQ27yZTck3/
+elI7XAnkj7ER51vlQVPH0MLFU0915VRk6sFeQVj9Vguz6q6dpszUQblYFAw1K+/4YsLKbtAQNU7g
+UK86UjU8zrThcOSCXZU8KyumCmPuJNq5OyywU3n+qipwtV317VdvoxozXlAQamObRBW3NB9QBrLL
+xW6GH2oUqN7hvjoVf/O3fsyFOsdWZo5xPuFn1AhptFXTKUt2tXT6ME3QivhrFXiYLfnxm1Je3xRt
+hgpXvrI/IL8lDHvtomR+LNpNrA1wOllIcEcdpgcxjb3rer0xb/xDMZUefDykVieu661qE4SMFI5e
+WIujdRnd0BIMRI3/8p44BuduiVMqTiV3KbVdwdPZEEJ5pt3Ih2CVu6OZqvWAWJ2dodulaChg617A
+o0tZO2lREWmpv8TVXqphTN2QNWZLi9WanxWGWu9d2kvKVDXHZ6SNprChDW3oM2Ss/f9EIf+R0mtf
++Lz9B7/8t1nOEupYdpQ9L+b8Q4Gznp9DpyWS3aQXJIE4D0+GspsfH59TanmtR7Mxb2vDkeRyRkN3
+YLcV4qgd/PINqXJza9Am3NVKxXlJIxINxBQFkcJOM/cKrX05Z1vDYn/jb/wiH34gFX0uzs+I1n0f
+HEJNXHr54JB2W/EJ6icoPJdUwQfJLGOipbYWRUWiu79ddbB2A1oa6origIViDHwnxFOnnBOF1Ktm
+LlqtaGkdCoXR9ryIb/yc5NUPJxVahIov9bdJNSSZ6O5z1D5gR7EgBy8dYlZhXVORnwl6sXRTztWB
+5QWiSQxyh9TI851XMXEgvOoUJY72qjiZZNSKCixU45s1Uh4mcm6RLNiaylz+3V/9VTJVQ9rNkD2t
+65Do2NK8JNTaGstkRFnLrlpj8EOFutuac20RaBUe3+/06Kx6ecQ+q2JEodfA1ZJ9p4s5lxqujtXf
+43oer3/1hvDz9Vd59bokf73W2+Fn/q2/CsB8KXP6w9E7vPVPPwbg+2/+Gh9+T3b5W9U3+DnFstyd
+FkxmUgl82pQ19PY0paEIRCfwCbVZzGw8WveVpLCUCoueD4WHSZkQxrJm9w9v08hEK462co411Ly4
+f0qvlN99+813v2et/Qq/Cz0X5oNxPcLOLu9N32DrUpi+3HJAzYCd3g7FUiZu7MxwtH5epWp9I9rh
+cldTbHv7XGtpBylzwaUVAVKTMdBK0cOFeMvdTsU1u+rAE4EChEzHW8OODQnlVJxEaVcXZj/kUtXh
+dFHia7x9r+VzfVsy+L54rUe46k15IBM+ySyFNmo5n+Scz+RFeDJMpM4cn6Q9O6ZilUaSlwVWBdLC
+lGgfFhpZg0z7GdYaRSnGM2rFLMxLF++qCLdq9ACbabZmZ5dWSxb/NU0b3m516A7k+50rB1SV2h11
+k6mnuQ1BQrzKd87lWFnmtKwWu8FgElWfO/U69bvtGbKpzGW6q41JIovzQOZpUbkYrWdZ1pBpD0rK
+iKCrTJjqQxuHXJ3OrutjtCVAkVVYTVufphn5Cqa+yq8w4DS1yI5T0Ii0uZBXrBsVl2WKo1iVdFVP
+PXS4r8Vu9p8MsSpZHa/J13VoTzTa9Q/+1ge8/aP/HoDxBxOaav997P0T3hiL83vy4TFWq4pfnkne
+YNfuk7dlPdm5wdG+om6dUmnJwaKeki1kTU4XMnd5sSTTyFdEzLQvz9Rq7JGfisCxocdslUf9jLQx
+Hza0oQ19hp4LTcFaab0V3rtGakU1Op89pOm9BEDnKCJPRTqeL318LUt82L4BQJG59BXW+vj0GO9M
+JGo26PNuIqGw5TzlKBIp72rhldEx3NDiH41Wb93oIxkHGK0B2jOWQNXLJNBd/jijSjUUWNV0NAT4
+cmOHf/2n7gBwdLVBorFuT3efRWbJlqI1XORLPn6q2YdOTaih0ZGaA25ekjjaMxKPVMN3dcVa9U2q
+GqsVUFaQ6FbhUTgr7IHFqSW02DULfE2OYjaipdmFe3dkx7/W2SeOtS29Z6hyTSTzQlyNp1eeQ1vD
+luUajZmSaXGtLbvEKAQ9rUpC/RyXhliTcmbncq2FKeieqo1SlmR94UVR+qz3tSwn15oURj2UbsMj
+XNVd8z2MPqtfL6i0X0ReVFTaSt5ZhUDzAr+h/RKMS6CmhF+XTFcaZ+iRq9ptK+3sXBgmiu+4MjzA
+aju5xzc/pFat7p2nMk+/8ct/l+TBu3LdwuXoliAsl9n3+RVFHpZJhpaWIFS13m8+4Uok2m+zbrKv
+GlteNVmoWbmsDCNPk6MU3k9VgyYGZIs5oeJTluQMpjKv8zgi6gz5vdBzIRTKKmc4esw8/wAPwZ53
+k9eotBuRSxtfQTFtt4sb6hurWY2tz2dsqWrc3O1SXpUXcza9R/+xxGuHdsquqtjDC1HbPr5/wVkq
+6vV2EGE0Nh2EUFQKgPIjXO2wE5Si9uUXZ1Rq4weOy60dUcG/9NO3uXFbQFadXoivcGOCVQ5HRq5q
+6UFu6USSTuy5x5x0ZNFM1T6dJTMuNf03s+W6J2RZ5RjtiZm5+boisquLw23218VkPALa2sLe3e/j
+zuXF63oxfR3zdlOeqbHTxldHQuU6ZJqJ6nk5zqqiUdhbe7htKepuhIej/gWTNahbmsGad7HaUKfp
+lSTayUj7o8C4ZLbquJU9oTMSYWmLbC3oyqqi0MY/gc55O4yJNTLQbmxTqd9iPmqyVHMsdyp8BWX5
+Gs3a6jXZamtW7ZWYpnrwg9DQ11Tt0WREpMrzUNXy4TIhHwqY6KEfgHZ3Ch9fJVeA22/+4D0Azh7d
+w9Vozk6vTX9bTIZwvssrWo9x0h1SqT8n0A1pe2vAzQMBUPlFl6OOYEEKP2a8gt5PZqBdyyaKpbC2
+XkPpx/MneLn243R3iNty7+ZWxVZ9Q5n+Fs9CG/NhQxva0GfoudAU6jxh+fBNxu77awxCf+eCWwPZ
+zTAummRGp2PY3r0qh3VHDBoRteIKrrgNRnPZgYfJCxy0pNZep3WbidYq+LV/rj0Mj7/FxZFoCsXn
+X6Qbyr2DMqNSVN1ZeU6eitSNYo0ijGfrTLDd/QFf+LKoiZ9/9Q6dHblGFFnQzEW3uapV6BGrY3M2
+z+nui8Q/zHvr9l9PTmRHPT4uqJuiqmapy3TV0CMzWtQQunFEog61FQ66doq147PRqOgoIq5yUsy2
+3CMcxOyqihq3dWy1R65JTkWaoxsbtenDyvEVesw1CW0VIfC8ik5TdvFFmGNqccqW4ZJY60pmvofJ
+tVCJVnO+6Vi+P5MdeDTP6TZUw3AMyzVmu15X5eprBmAcu+z1RN3YazeI2qLpPI4vuCy0NfxZzEEh
+plComajXXzigq2pVz4sJdhUPUmUUpfBoOe3yZlsiBvaJaATz5IRSM2lPL+5TK1Kw27ZcalbpvYdi
+MgS9CTtaMu1Kr0MYCd/ivEf3imguX77Rh0g04Be0cYzXGHBF27ylpsP+oTxTVTRATbD3F+e89Z44
+D0vtnn1xVnKmL0aR5FjkuFs01306nWUTd18TB5+Rnguh4DiGoOnSHzXxTuUBuoN9HIW41vaCTCMG
+7ahHu6Xl0P1VzbqASsuwU3g4Ru3yaUm1pSW3rxyxo6r7Ry0VGstTjh/LAhtOF2xtibfYCw2ephTv
+NRvkWp7c04Y0jrH0dJF+bnDIzVVTF8/DrgAmWU6gzWc8hUmbKiBV86DMC5ravGSr3WekWP1GS/5v
+dxuk+m6UtsZBG5c64GhYIggiHBWMC/VJRH6NpxGJThwQadWgQemheBbCqiLydcGqAMnSnGUi9/Zr
+F0/BNpacXM8J8pJKW7tPx2KnBo2YUDtW+bZirinOkyrFVTi6bz0SRVc1tJGJVyfc6MjLYS+eYrWr
+leMaUJXZlDVWwVAtTX29Pdhi95q88INBE88V3juB4VAbWZbdil5HXrIrB5qeHkYkhbw0raiPUf9Q
+PqlIXDk+aUx4oZIXMrsQvp2Hw3Ux3iIvWI60iO+ky+lSwuDmroCpwlmHeFurIzWg1HM7YcB2Vzaf
+G3shWwp5HhzKZlOZBp2BfK4rn5b2EDVeg0hbHnSXfbpWeDefyEZ3l4zFI3mOsa2oNf3c8Sw95ZtZ
+XuIXcr9npY35sKENbegz9FxoClVVs5gm+PU2B3tiGixOPuQkVTBKEBIbkfxho8Sk2o9xvXMHOIHW
+58sTUO9sp9+nWGnXuUOk4JbXX34ZEK1jciaStrUM6Wg9PM8NWCxFM2n4DQKNWsyMRECKwtDRtmPX
+bvVpe5qRtsjIVvBYv0G5Knyi6qkpfXyNO5c1lLHsqg1rqNRzPNa6f5Fv8NV5aE2Nq863uq4JNaNm
+q9thWcluuzyXqI2xJbEmBgWNkFrVWa9h6UWrjMqCLFGI+ELj+QXMNP4dOQENX3gROG1qzYKcznNS
+haJxOWYAACAASURBVGb7ukMXiwuW6sy0zWCtTWWTIbm3chI22Qrl/IVWTs5Nxbb2x0wPAj6e3Jdr
++C18ranh2hINErDVEb7sH3Q56Ms9omabvJJ7d/wAd192437Q4Ood+Rzo3LTiPpOZOOq8ekSu5eyj
+3SatQnuBVjWLtvDjBVluPJn0GSoQKnAdIm0neH62ZPRUC6M4soZu3m7THgqvktGC1t6ql2iHK3sy
+jq1eg92e8KK5pRiSMsJzVgs1JNRO6YQN/EgbBlUOt2+IBvHuQ/nd06dP6Wv/zyzJSVcYi9mMYFc7
+q+/sUmpdjmel50Io1HXNYpngJzVaeZzl0uH/Ye/NYi251vOwb9U87NrTmYfu0002uzlckneUdGVZ
+ku+1YtgxPABGkAHIAAN5MwLkwXHylIcEcF7i6Ml5UBAYgQE5UYDAGRBDsiJrsETfgXci2WSTzZ7O
+6TPueddca+Xh/2p384rWbYqy3AbODxC9uc/etatWrVrrH77/+z4+lkrEzvpNbG2Lq+Y3W9BBS+LK
+h83yYSpmzusKBctGtuUgoCpUXTVomwAVM9lvXLuODz++BwC4ffcD7O2LC9eLLcyIehH6dk56Emoq
+AEkirzeCHiyiztIyRayYf4gBB1TsIT4flg2XYP668tAQsditO1D08LaX8v1sPMfEkwdXZTl8En14
+lg3DjEajDaqi1ZXkuOkGCWt6fhPDpbJU1pSoWJJ0Yh8FdSYWU5kwebGEzbZv3Q3QVDLR644CxaRQ
+1Cmm1KP02qaE2sYZKe67SqEGlaAWCmPmOZJeBY9VAtvI2Jc6gzJy0V5wBuuBLF51WYCETNJczdAF
+DEUiy4fXIYmr50GPqO+gYnQG8ts723sYEL1qsZW5XC5AwCryu8Aok4fXgo2YvSK2CtHvktzWSEjR
+HXQwYSWiyBV8tjiPTY18IYvMGqss55M9nDAUWZyP4JCyaX/NgU1x4iKvnrRfsw+mXLrQnG9WoGCx
+58W2AUVgmB1Z6Hjy+sZNWbHef/cODpm3cVwbhvIIsyLHOUle3QjQk8veh0u7tEv7HPZceApC51yi
+sjPktuxQiyBHl8kSL6kRdkl3rmtYdImpu4GmyaBZz25sg5ogpNrkaLgC69pDw5U7s2Xn34kSHHZl
++7gzOcdLF3cBAK8lL8AiMOCsWMChLLsmhVdRFKjY1+DEFgw/6/s+bMqy+5EPh2GDT8x642g0peye
+QVBDkXpb6xKaGP8ilGOt73QxyukaG4U5s8zLyoNhJ19e5zBVS4FOMRllUBI0Fe5aMAT3LLIxHN2C
+oYbw7FYtil2idQaftPbdfgTDBKZO0xVs2oMDn9L1Z6Uca3m6xPZAdrCZb+Eio5x9XiDp0/2psxWd
+uQW5v72uwT2f+I3MQVaSDEcvV2Q2rm/gtVAPAo+038DVLdX5GM1ArrujbOxcEfe6l0QABYNSql3X
+bo1ySuxBZ4RZQe9vBmRk+daOi8qS8x8wXElUgTyT+75sGoBs3XFZYTmXcakZltmdI+hHxG8ENXob
+5Fdcs+AwsTuIXVgc+5q9O7ZXIWDPSAMLTcGkst3A4jNgqRIBQU9bBMu99tIADzLB3GRlggWp6rNs
+uboPe/5VWN5n62+69BQu7dIu7RP2XHgKylbwOjas3F3JcnXsBH1qNvR6m3ApN9bYNgwRfa08mtVo
+lGmrp6BREyqs6gYNk45+EIMhF/wp2YdtF4MOUY4n5/j4NklV+xdIHMJ88xJw5diKOgyObcEhmq05
+ceB0ZGXvuj66bLrqYIi6wxg+lB2zSacoUkJYvQV8jn5ddKBJzOr7snsMkxgHa5KIK6oJHnEHi5IA
+FSnWqrRGSkRbQN1G15akKgBUSyBnOXV6cgTPk53U2a/hKPI3VG15M0bQJfmtH6FmA1ZVK5R8DaVW
+HYUxcwtn8xR1JLucPW4QUGJu2Wjoqeya80WIvpZGNzZZIgwb7DSyi09UjIZxvVHWioLNsROERKEO
+iPXohQGiSPIublTDrVs6vQXWEukItT0gZH0/oweVTjXGEzl3z+3Ag+QUiqbAggndod9BDfHeAvIi
+dIMBjCONW1ZVoWJzUWYaZD05/y7LhsoMMHaE5ShOIvRNS922BWtdxqW/9gJiziffJyfHsIRVcL7B
+QFGBW1UaHp5oY6Bh895QjnXta1/Cy6eSBJ0fRSsJRBigoigRFgG625/tMX8uFgVoA5PWUGUXNV1u
+O1iiFayuUwVN3T5ghiyVTLvNyej6XRi6xtVcw+FEseCgMVLTLcZTKE9uXkD57u39IdJKElJXO2eY
+UQ58/GAd/mbL5tvBLOdDFktSr0hr+ISoxlGBfpfZ/sCDG1DpBzZqaimWBSHItQuHhCyY1jBUuPLs
+ECQBRpf6gka7cPdlAVlUNt55IDc8XWYYRvLbpW1gaZn8EYena4VYECBlLBdTiolkM4PBNfmRQdBF
+vWD4w0XM8304BLxU2RK1ot9eeaiZaMsyC6EliTiP6lXH3iOMT+T6YhdQLjsR1QL5Qh6Knb0tBAH1
+KEsZ+4v8HAMtD/fAbRB2WhryALaRc/YdYJ2twRuufC/2++Caj3yukBHaHJoI5Rpb5lGhZWex2wcW
+BoUjY6gW4WphqUYT2FRcymczxBxb/4rMi63JC1A/EhyCbSy4DF0bU8IdyRh0S1Z41irM7zHpXAAl
+KxX3z0q87gsn5KkxONjmGO4Sj2BS2K6Mt6kqGFKyW+4ShmPhBDHyRu51HAqASt89x5D4j709H3eO
+5LervIHHjdNpFGLTPjvPZp87fFBK2Uqpt5VS/xf//7pS6i2l1IdKqX+kVDu7Lu3SLu3fBPuT8BT+
+MwDvAWhhU/8dgL9njPlVpdT/COBvAvj7P+kgjQJqNAgME1HYgVqS876TwzPUanCwKkk6VptcVLCY
+LIKdwecuZlACKRN40CgX4mFM2TzVjzbw4ossCykH2cN7AIB5fYqeFryE7VgwEZNHjXgBxlJwmBiM
+hj68DnUnLQdLNswUkYNFJWHFxbHAedNKQTPh1ilcOK1CcRTBJSlnwBDlomlQ1uJJnE0maBrW/E0J
+y4gHYhmspM8DUphFnoOC3BM6LzE7ld1xucwRJCzrhV0UDXvymQCzFDBZsNFmmaJis5nn9oBSPuuo
+EJ2EuzF3QX1aY8ZO1DpUCNbEhXciCxn3nNIeoyLNnNUq9VQB8rba6NXYdlsdYrMqudoG6DG7tojl
+vdNsDKcQ17/rdVGyvNxBgCnLjLHVQTGkYjc9lzJfojmU3z5dnMOQ/HS+0LjisUvL81Cx1u8zkRob
+hYDzaVlaCJjENk2FmiFIreQ+Z8UQFj2i5WiE9x8JZHrt8QzZhXisyo2wRuq16+8I4e9uJ8Lm/jUA
+QLLeh9uGRyZHeiKM5lNT42wk3xt9IPRxh6fnsNsQRblwmYAuVIOCye+qnqDSa/gs9nm1JPcB/NsA
+/lsA/zml5L4B4N/nR/4BgP8aP3FRMFCWRuUtAJcTeqDRNtGhdtEQgDHLPVQpRa1JlOFPE7gUvAjc
+Dqp2cCYXmF7IZ5UXYMFM/eEFH5R8jnAj5OsQ/kBuaJMtYVzScDsGhopMnXDKCy/ROMzI9xQKcg2W
+WYaQMb7rbwKQ47lyj7GYXOCj70qIMvRtDDfkZg2dNWwSZGMo9W6QY1nIQxVvJbhKBuPzJIfDDLij
+PTwm5DVrcycqhMt8SOTUq5bzjbUOeleZ2+h1oMg1eEoB1ov7c4xJcW/OFqiZfQ+Mg/0tOc/93Ss4
+Yp7gMTkQ0+kF+mz7bYyL+ek7AIBpkWPKEOXi/hD5iyIZH5ChuuMHmBnKjFYZNtcZ2lkaOd35xqng
+kuXZLtrOQBcuGbF9x0NECvR5PUJ6T87fCYGrnixOVV/Gbb48xojMz5aqYHEP626G0O2CpSqkhn0C
+vNWmPkaHreMXqDHjeHpw4VBS/nwuVauT0QV8ktBUKNBhL4abhNgPJH+QOQs8nMg9+93/QxaFruXj
+CkWJ/uI3/gyuvSZjlWUTPLor53z3cYGMmAvFdnK79qAJemqKBRSrMo6l4LI/ZqYLdJzPFhB83vDh
+fwDwtwG0xPJrACbGtA3feARg79O+qJT6T5VS31ZKfXtKKq5Lu7RL+9dvf2xPQSn1lwGcGmO+o5T6
+xc/6/ael6G/eOjD++gDZRx/CTiQho/MclZKV1usAKpXd4cHoHsYTWURmxBv0lqfY3xDXfnvdgcNM
+dr0ocTQRF87WCeYkV7l3R1b749MMa1RUPji4ASeW306dKbw5V93YhWZaxGMD1loSwabb3Yy6GHuy
+47nKg+PJzlY1JWZz8VLmxCaMP3aBStbhk1JjbY0oRVhwSBxS0VPKCoWThdTup8cah+wCzZocexsM
+A3yNM4Il7ZbV2WlWQiZGATHd6/VeB4OctHAXj1fahi6h5JveNobbvDnXbTTn8vdFdoYJWa5nH5/i
+jMQwp0fiOgdGYzSgAncWYUEFm4WZQzHLrrNNeI6My+CW/EjgauiFXOtsdIGoz2x/FGI5ljFKVIw1
+JnSHpEFzowCK3Y6jdIL5iKhW34OhGnenilZq4gMiQbVj4S7hyNYohU/Sl0VVoqQ7PwgThOzKDJhw
+POju49bL8tnDPzjHslX/ViWSq5Tku5Df+vh7J1AMO2xdw3Xk/X4/g8+w2NvsIBnT89qR4/7oZIz3
+HskcunFvjL0D8XJOTh/g7e9KkvNbH52gvyUh1s6uIBr39zrYKcVLu/vhd6DYCGfbakXvNzmeIOx+
+tkTj5xWY/StKqb8EIIDkFH4ZQF8p5dBb2Adw+JMOZHSDYrnA0NtDaMkNah6fYZSQpKMJcH4oE++f
+vv9DLJjVb7vsQivGzBd3dtDrwOOD6QUFMJNLPJqlUJoKSGz7nekaxaFM+BvXGlzblkH/weMCM9Yv
+t5wEFcukZSMTcNgdYi+RRarxU8Ss/ii/WkmVH759F79+JDf03gfyAO0ECi+SBLbrNhiQbGPQ2UQY
+i/vokfHn/v2PcM7Fr7ICeAM5rjNXeGlbMuPbvS6Oc3Grpy35R6FBTVJMygW+NBQ9x2t7Oyu6c73M
+kPN7FnMZdhnhwdk9AEB9tkDIjjs/6cEly9R5fYEJtTldutT7cYR7dGfTdAHDNmvtaTj6CaV+SQKQ
+6TnzBXUfs7Es2A58KArt7Fx9CTUXwLUoQFcRLETIeJoVeHRH3O8zU6Egc/xL+3t47ZowdRmvgKda
+MV2yLQUdbLNKMCoNgqHMkdOLEU4XLPcebKHLys3OQB7M7rqHNyMJNd7+/g+RUo9TKQVDSHrAzavK
+FvD4MPbDDq5cld9wxzn+QEmIUX7c4MqObGA/84WvAwA6wzv47R9I+/X9x4do6i/LGCkNTdKWYZgg
+nzKM/TK5NLsDnB/JWFy9eh2dj2U8i8USHnHzuikwZpXrWe2PHT4YY/5LY8y+MeYagH8XwG8aY/4D
+AP8fgL/Bj11K0V/apf0bZv8qcAr/BYBfVUr9NwDeBvA//cRvaA2TpViqGNlIVlS36EKR0yAMfBh2
+3O0Nb2Duys61Ddl1e4MGgSYM2jhoCMipvBgepeirYoQek1avX2F92UkwWspW47oRXO7G1QODR6Rl
+7yU9eGRxdpR4Eq+/sIGDLjs0mxmiRIA5xjJQlRx7ZmtsurKjq3W5ju04hkfehGGQQBOCrE2DgonL
+hl7JMi3Avi4c3OgjrMQFPMEjbG9LmLO2sYHgtiSiTgrxlAK3WjURqQJY25IdY2d7E2HYwnkHyBfi
+CRXUpRwffoSiTXIWNXw2+USxgrbkGP68wgZd0a0t8ZR2sI3kviRP3y0/wnpH3j+rH8Mw89/tdWFF
+MrZkuUMRV5iRnTjqazhavnd1c4D6QsYtUgaa2JKM9Gqh8RCEsvNd97ZQ9+X8v7BzA9v7ZIQua2hC
+2qspIcUWsLlB/Uh7As+W3da/FiIlXHktGaKmB1XYMhZxsIkuu2TDcB2OxVDJtmGoYu61dPCWD490
+dcOki2uheBJ1UEC5cv3xmoU3bogXsnYg19nfGuL+mYxhEnmwQ7mOoXoBb3xFXr/c+HAJ3XYiqYx5
+Vg4zkOtbuB1oViK01shYrdqxOohIHfis9ieyKBhjfgvAb/H1XQA/9Sdx3Eu7tEv707fnAtFotIFO
+Kyzzh1BUXU6uR3ihR2hoL8SwlBVzre/CInXZ0VhWc7cpEZMdpxsNUClZ7WfpEr1Y4rdbBwEa9hf3
+N6QgogYTXC9k948GLpaueAfaSrG5J98Lugpxi0OgfNjXXj0ANPkI0gVqQpM3h9swFIb5cu86XtuQ
+ODr7kngrRydjXFywB98P4LAJJlUp2KmMhgy/u7sdTA4lqRfaGSyfrEHGw5KNWR4AsIEqpDfTSzws
+W4ouk2FrjY025QKF1fJT2PDX5Jp8JiCSwT5uMdeiNnxYTFZenI5wci6/149cxGQNitfFO9oqImy8
+LImztV6BNJT7tzhZx5BK4OtrVxCR1UpTyMWLbIxriYFPF3MsLiSuP9iJoRdyT5SVodsnCpMeQRR6
+GPQl0eZqBa8nnosf1vASNv7Ma9BxhCE3g6Ub7G2S/ej6zkowpylzlCz9whiYlgqOgjsPF++t0LRW
+RyMhZH2jt46TmYxLj/Rwu7tDuHO5kZvbEYZs0Hqh30dEZrBkN0BM+D7YZj1ZONi/JsStSd9FSdxD
+5G/hjZ+Tcc4WBRy+f3Em36syhXCH+a6P8xWZsGWAhA1tVzd2cPCy0AX+k2//Bp7FnotFQXk2vN0u
+qvfPYdvkNChCxA4Zaf1NqFAuMk660GTojVgfzy+W8Dpt736Cgje8rgyW2+xHTxUUcfAej3ttJ4Td
+kxtUlRUeHRII5Hgr3Lpn+fCIQfYiZuqvbACnAkjKxh302IMRBwmcLpNBTbHS/DPE0/fgY+HJzV/a
+OXoJobjKgiG/QcWOxGR3F7t8uM3EQkz3OVrvrRJ4y1GGHmnhQNVq10tQke23zgtEXXkYVZGiXMpn
+bTtdYeo9LqYqADzStgdOB02L9bBzjNZl0dtK1hF1ZMwVCWKgS3QY5ry6/rN4eCbMxkszgsOxtYyN
+rVjq9MYnVZ5b4oWFwH2/Parx3mMBQH3llZfxhRusYk9qbHDxGoayuPmuBY+EEY7twifdnmtcOKxw
+jMzJivHaJ9+AtyihBzIvOnYCj2OvGhc1F3jlB6hyksAsKSnfeJiR1GbTsZH25PfWt4Y4HQvHR8g+
+kivxEC2ieCeJcDCQ69u7sotuV64j2ehB8R7PWOEqmrt49VV5cB1oeLWMkbcdISLMe9Dvok7lnilI
+yGgnQwTHspj+87O3kZGP06gGHkO3Xe1ie5NAmWe0yy7JS7u0S/uEPReegqmBaqzRVdehA1kldTpH
+oVuhkwwx8Qu2yaDJ6OOyBl/AQanF1TZlA13TI7CH8CmEMVumGBNX63eJyO5U0OT8L2Y1ipQ9/cpB
+zY60IPVg6D7mhrqUJoTvSrJncCtFyLACszGsNZap9DbsPvkXRrKar8d7WBswETl6BEWhDztZh6b6
+TOiLi9ixIyTERwSDCPmIW9B8iQ7FcOwwxLUr4kq/e0926K0mhtuT3zipfTgs6enSRkXoaz2bwLCT
+ssfmsGiRwKKXo2cFPMKL+8MB0oVcd1gbJCscBvEBaoawJ2FcurmG9X25Tw8+HMFK6TKHLmKHYx7I
+mFyMzrFGr3BdAb/16HcAAL/wxa/BIhy716vgu20JUL4eJh2sMSS0ggBKiTflOT5s6k8ssgtURKF6
+TOZadgCLXpVjSniEhbt+BMMxKpcpbL622aBVpxlqojh7ToIbLCdubHr46PYDjouMyc1Xr2N+TBh0
+6cFlZ2foBHCY8MbSg9U2fJFJN83mCCjDd/PNNxF12M2aK2h2kmrTgU8W6IjUfU0B1KXgG7L8Ai4x
+Fg1s9BluuWs2HhX38FnsuVgUFDSUydEMAszOZRCaPMZ5JC56ZA0QJOwWc31Y7B8wnJhO5APFEze5
+IsGEdhxEAXHkfg3flZvbsAJg1U/aqed2hTmJPnrKQc6gdFov0Se3o1KSLS6XY4SBTDZVWVD8rPEL
+mIxMxIMUQU44ciIPVWXNULP12As9pDNSqRUTGMKc9ZxQ1jzFcMCbr2v4TvtZBZvMzW4P2CXH2AdH
+lCS3HHx1/1UAwHfuf4CykAe2MQ0KjkukQ1ieLGRMHSCMU7hkl/Y6LhRdWLfjYIN8f8VyjrNjcfMj
+yrAn/U04XGT70RBTLiYdZwMZedUcp0RjST09pC6lHSXIlSwQVXqB83tynmUzXylLFZWFiNUHi4Al
+VTVwGIJ4QQBDYhhjDDK2XFtuCLMkFX1FkNV6DEXVKK0tNKSN8yIXirkWR4doKPqbk0bdryrkS8l9
+RHEXDkldIivBO8dSKbu5Ja6/Za+j48l8s6sFSmIajN0ApMJTXgOXzNSVkXENXAsVxXIWD4/g3pQF
+S0FDM8TwfI06kGtSJFkpsxIXM9lEjk4m0FTDMhZQkVxoZmVQ00s250u7tEv7HPZceAowBnZZIjXT
+1a6abXhY7zNhqM7R0PW3/BDKZuMSO92KbAqf7RbuYBudiNqHagSfSUmz08Hje7LiryTfUMGlvoFe
+FnApdx46LoZtkqyjoN2WrJRiMMUMc6pS9+MaeSsXjhgWG7fUFKtaeV21kOEZioxdhgsLir9dOQU0
+XRaX6LmotnE0YoPS3IVmpUWHAXxqJAzjLqap7GjeUn6rG1uImPW/nhzAIhlMgRQ15aq136wQiQ2b
+j+raoKYHYtkWbHYq5sspyorowMggYALWsIoA2wJiSZ5mVo3xRLyR9z8+xBabDxe5wg5kx5+NBPo9
+OrvAhE1Qbz8cYzISD7GcTJDTPVahhQaUpJNLgmWX0EWr0J3CCglpb7CCY6cXM6wxuWZc2YHz8QgW
+KwvKsZEW9BoaF57PJK/OsGio61ATrl2eI+LfzXqG2Rnp/VSJsyP5bH0u43PtZ0PEfc5Nv4TPMLdp
+SoB8CUoFsGI2K7FZ7/a9U5wdynk+nKd4lUS/rhOhx+rK+voSLjt+s7nMhUVhcG8sgOFZMYVFxm9b
+1VjSS7uYF/D7cuxntedjUbAtmF4M9zSH1bADLnJgz3gTAxua0ui1CmCl1FjM5IEozysYYs6V1UPe
+dpG5wFxLSLDIC1wcSrY4sCS+c+JypZTUcT0UFlmR/Go1+WPVgW+oS+gynnZtOHM5nyqN4MXUnXRc
+GJdsxdYMDYFBBVuSR4+naDgZje8iajsRvXgF0pnxhk/rHOenssDMzwq05Edb6z4GPXHBh36NY4YE
+81quP4h2YV8QvBWkaNirpqoaASsOvmvBZdXBpsud5dWq4uBYIeqSxCpnFS7mMvGSzgZ6vsS7IbUo
+K6tGo2XCp5Nz3P62qBj9s7e+jTeuSxXhK68PUdB1H/M8R6WF2R0JD9/5we8hTeV9ndjIKAikFwHC
+bYaI7DhUro+CLenL+QTKkvLlUo8wOZf3F6nGzq7Au0NCkKdmiplhfD5poCnem44a+FxYXNdCPpfr
+ThlXTUoFe01Wt8HHJ3hnKvOpyM9gsZr1+ELm1Q9/uInXbrFl2wO8FpBWpWi4sNh+D4qhrr8gm7Xr
+4oICRjibYvpdCUssx8Grr7A8q4dIuIlMyyfXefqhnO/joxkyqgx7jg2XPTaBCREn8r1ntcvw4dIu
+7dI+Yc+Fp2CMQl3ZsMoudCg7w+hiDJv16OAiRsgkX6Nm0Dklx8kTaAchQFd9cv8EjWEGubJhD2Qn
+nR1l6FF3sLxgDz7msJWs8lYwQMgmJ6CARw3CrtVFTRKOsmWPrkMoKjQHMeBTk8DR1ipNbsYuCsVw
+g5RhSdSBYt+8pSs0XM3z1IXJZJVvBUuOf3QOMOk4GPSQt+FFYWCz868oLJws5Bg+M88vr70A+1XB
+BOTjBhmh4lVpIWGCS9X5qjbvWaQra3wYYj3SbA6LHAPLpsb5sYz3cXaKa3skc8Gc119hzGRf5fsr
+4RSUORyeW2hiTE9ldxtTrfrBg1OcnlI4ZZHBsIowWyicT5lQmxyjyVg9GJC6bdtHSZxGpoAiExc+
+7A9WmIQ0f4DzQ8nKDynIYmIHoCK7UUDDpjpHNZgRTx6mAqSTiyG0Xec4fyj35jSzMSfnRDpf4uqG
+jPNZJpWvw7O7+OKOeCiuKmCx8qEKCxbDgPLRORq+1tRN3e9dQbUvc+/azhAF9T0HTg+MNLDMXZiJ
+zIcFq0+PxnM8nkkCd1FXK3Ka2AuxR6EZHUeIV1Lfz2bPx6KgNapiicZ1YVJ5iMN6iJphQpYAS82e
+iDMHBTvqnLrlXOzAIrBDBw6UJuHIWgQNyoGbORKKgcwqmUjNTGFEbUd7eAS3w3Ki28OcMXq8pdB6
+XxVVk+BqeGRIsvwCDuSGqlDDMIehgxyGuPuWbsKJI7isZFRFhYrtuyZzULGiAja0mbqGzc8aVYF4
+F0yKHKcUIXG1RrFk6MKwJexexTUuaN+dKcyYhUfawPWoHwkfFmNObbf030vYc4+nUCFnZ+R0tkTT
+tolXwHgpDxupNGG7A9CLhtIuQjLj9L3eCglo6hJLdiuWLPWOqhp3qEfZoIIFOcij0TlGj5krOj+C
+KsmVGAsQaLGYo+GC1pQWqO+D0I3hsEPTqV0cHn8MALjgg2TFMWrmX7TCatELLBsWcz7LWqNhmFMP
+ec/KAGdzmZO3H51iNiKasG6wdiDz5d4xcwTLCc5J2757tYecHZOL8gLJQsZWxwkq5mjmF2zxbHJc
+Gcgxdvwh7C7JdYo1mEgGelmeY06+ySVVxE4nNd45kbmcL2bQnPeOnaDbkxBZJwt03BfxWewyfLi0
+S7u0T9hz4SnYCugoC3UyR3ZOHcFBiYRdi6YqUJNR2VuLEdNT0I2853oaFmvNtmUA9t4XRYqMvfmq
+v8D0RFbuxxeyew4sH2BvwNgCkp4cd2h10bS4gH6OgMk1y5NzizsBGmoYLsoMFjkje8kA4E6DsoFF
+d5bJdKR1DSuX3WGZG3jUMNT2HIZeAWq55mQ4xITs0vNRhgmBTv2uh/G5/HZt5/BySVr1SN9erols
+bgAAIABJREFULR6hal4DAEzKBjlBPMquUbDPoTAGAXfKVizHqg1qJsbKpkRNYZH+1jqu7wj+Prm2
+Bqtgf4jThjslKlZGxv0U37svycOzaYmvfF3uQ9bVsCbyO2Oya8/H9zBjw4dxunB92Y0X0xnmTHIu
+sgU2xtR5DEmH3lVQTOC6frTiuMjLKSKKz4TxPgb7FIzxWHHxDB6dythmdx4iJdfiyXmBbQJ9dva2
+VvqXLUz6Yj7Ch/d+BAA4v5gjK2SMXEvjm78kDAGPTiURe/rwAT44lnB0b89GTabtqrCQ06Nz1zqw
+24qQQ2k6L8EWmbtrU2N0m/e9PkUTyn2duY8Qk8Z/wV6cuWvhguO6LEu0e3wc2ytJxTiroAPSFz6j
+XXoKl3Zpl/YJey48BTgOnPUhigdLZLnsrnGoYeek0lJzFCzZJW4Mj1z3GRVj62UBTTEY6BxlSizA
+2QTnDH7rxsH9O4LGa1I2V90YotsSsGqF4UxKaP6mAy9oGZMDNPwdn1Bkr9tFfpdMvhMP7oHs3J0q
+glMTYRf5kgkDYCC7RDHJ4TJpaTs+/JBMw1mNgkm38wVLdrMM0zP53bJUWJCuLYCLxyPyHqgaNmWZ
+NROcv3P7Ds4eCx6jv9VDKRACVGUBmyIy3iCDxRxEzNyIDg1aQYVSl8gKGbdkngJEL3rGgkUEpWLu
+ZKmWmJZyrLv/4j7ee0di+bQB1nosI+YGdibHTukRjeBAsazrGgWHidumcZFS9FbnIY5d8SDGY3nv
+2sYWHOZ4jKPRsIQ4Py9w9eY3AQBXbkYIEiqBEwlZT0dQieQwzuMDvP8HPwAA5NN3kLZcD02JmN2R
+huJDo/kS9x/JfcjHC7T7qDYKX31VIN2/tfEGAODo9kO8ryTn8vLZEOtM7C47ASaRzMmuvQ+nlnvl
+joWpOVvU8GxBodqVi9NCEpfHHz7AFudQfnMD4w02o1F74+iswIjdpWXZIKLG3sYgxlQRyzAvsZG1
+mZdns+djUagb1OMl0nMfPglUnPkE05zAIhe4uCdup35phv66zPRySiZbrwNFOK8VbCDZlL93Dm6h
+Q7bj9+7MYFJJDL3+mtzEztYSk6VMmp0wQbzFCkdzgqYk67BlwfUlkVhW4u5ZTQKjyTW4lcEi5Xh6
+UiLcYo9C7qHRrLezihBWPgz7JPyohE8SjsAJEPKcdUj2ujKEKdjJ2IvQm0v9fzqawWlBKr0+tjpy
+ng8/lO99f3wHupLJM07WsLt8WQ43B3zSpKfzAjap4NwBKyelj5LEHE1aoyFlft7MMa6IyZiNEXRI
+gEIylex8ipSkNrvTDXzj1s/KbzR3sbMumXg/0ZhygT/70dsAgEcfjhHPZVHcdhQqtslXyxh9LZPe
+2SiwGElH4Du3ZYxf3FPY3WT2HhqhTXCTY2PxoXw2+OIQZc3VkECvOlhHuhQ3Op0usMVE6Ztf/hk0
+vjzIDUo0bZs0QVOPj0Y4OpP76GgL2x25740/Q2fjGgDgS6/+WQDA7R/8c5hMksBnpwvoK8QYVDbG
+5KNMjk6wucfk6aaMTzF9gNlH8r04iXCwTmj+8hgew5/ySoUZoeff+fBbAIAH7+bQhE/bCuhQTWtj
+sI1lRUxOP0GUXuIULu3SLu1z2HPhKZimQTW7gNVTUOQVaPIeqpjNRfMKj1gKM3d7cOl2+0y85HUN
+TXc3tlIo8tzXTYXx98VTOP7geysOgWCb9GJ+F3klrlpvPUBJdN98ZMHlCp3sxGgcJtcaWXHz4hxO
+n6jKkYJDnEJmlUjGROD5hWBvARgmRlVswQ1YTnV8WIadhpGDNJffKMdyvtPHh0i5Q5eVC1AyrG4M
+NGm51hMLKUVbphQxqfIc75zQ21IK2TpVpZFjOmeCDgEcqhm75J4wKFCxGzKbZXDdFlYePRFwcV2A
+zWR5SUKPooFTU79hy8FNojQrvAyLxLoqUDibyY52PFlwDEuoLrtgiwb+giS8nQbRnrjzRdqDzfs+
+zulSn5+jM5QO1a5y4QVyHmle4fixuOOztAMrFM8qSOReT7IFDim+Mv34Ll54UbyR7loXhpJu5/kZ
+KsKxD0nH99HhIWyGblGni+G+JKatJsTZXM6tS9TlRnIVM5IEZcbgYibvVz0FQ8i6D8DrkMWbiufx
+Vg/5mdzrzEtRTqlQff0AOTtbwzzAiJiM4ztyrEeHJyjZuQulVsrjmZshiuW61/pX4PqsxT+jKWM+
+m0z1vwpL1Ib5Kv4afge/gqfZ5EJ2OMZOH2lOfr3mHRRUVsqJRlmx5UD0IxVxAfqpv1nSc8bPPzF2
+5OLHR8GiE7WmLLxEGOyPmMuYLT4/O64FBZ89ExaaVU6gFdDoWz4s9iUsYAD95Axb6DIsoCJoaYdi
+kle3utgdimvvhD7euy95lONlhYCLVGIpnDKLXhHu7BsLIEX4ItfijwKwjVqxQzdGIaD25C7JVgLf
+WkGCx+kcU3Z+Fo2Gz8W5E9rYCcn65BCvUDYY8bgxgK9clePdzkKMGRZ2HQWX4zLjb1RQSNqqjqlw
+PqU6U2XgsNcgdBz4PpWa2JVqqgp227XpOtDMYez011bjpcMQTc0qARfLh5MjVFycTdzBN/6MhJ5/
+/qe/hr/93//vAICcMXvHdzFwZNEIt9ZQcL7cevEldEM57sOTh3jnR9JyfXYkLn6RLrHKVdR6pVJV
+NdVqYjq2i24ixx62LOZRLCpoABzHxmJJJS/XwGbX7dpwiFf3pXr09/6X/+07xpiv4ifYZfhwaZd2
+aZ+w5yJ8WOAcv4tfgYVg1eduQUFTS3FZNygaSaQVxqxWRxAFZ9BAcc9vUK9eP1ElBJqnXj9tn+Yn
+KWDlVUyMwmNiBA58WXF/SDqsZ7Unv6E+8X+tPJoFC4a1a5dJxEmTwaEnkZsGPtfvwjSrDkZHG0R8
+XbNL9GxawDGSEO3HPdR96jmOGuiImezSQLW0hPy+9i009BodKLRjCwXULQ9B0yCntzGn27qwXWhH
+duOibtA2T+oGKCljljU2HKLxcpv3N9Gwp1S5tmw8ZKNYUdVISSenQ29VaVquqkg2VhBKaLCggrKs
+oEl/5wUBPNLTOUvxGLJqgpLo1RIVDH/v44tjnLNaFXoOInYz+kRelpXCkh5UM5njuw8lWbnz6hIZ
+0aJjIhOrOEDM0KfTcXBlWwhwtpMujlxJJE6mBY6PJbSpWGmyLXtVRSq1AYgdUUaDfXnQpkZJKuwl
+Gb8r10BT4Kipm1VTmRMoQPOzWYGElbRntediUQAAGYZ89f9aGWgKyFamWD2kn7QnwcbTIcQnX+NT
+X/9R9vTnKjS4z4lu2Z9tMfjDx3vyqsaPhQOqfV+uSZaPavWZ6qlrbbUylQVYLlt52zZdvUTMJ0WV
+CrMZSTosBXaaw4JBzdkWJpz8DbAomPuo1crNh+2g4fE0DFyHCy4fnnlRoWTIYAMIYnm/XpRouCgo
+10VISnzPFbf2ZDLHkM92XQOKcOviqeqZrgxKrl55RvUjx0LGRbqoa9SEDNtKweOC2vMTrEcC+vED
+HrcIMQklL1EuNFKWmStTY8xcSu5WKCOqUzFP5Ft9TNlqH9YF0o+lCvLuv3gf87azsxWdrTsYkCdy
+X+3jy2+I2IsKU/z+P3sLAPDRhx+gTuWBTcgEtTaIwcopZlkOsszLvOBt1xDyWQCo2TFs5hUaQpur
+UqNoS8qVg4hPtjYF5tROfVb7XOGDUqqvlPo1pdRtpdR7SqmvK6WGSqlfV0rd4b+Dn3ykS7u0S3te
+7PN6Cr8M4P81xvwNpZQHkVn+rwD8U2PM31VK/R0AfwciEPNH2sqx5o6pbKBD9uQsV6hXu6aBopvb
+5t60wWoTVgpoc6fGPOH2s9STJN7TrsDTicb2s0+/hsZK7hvFU28+w/Xgqd9++nw/9bd/7Pu29eTz
+5qmoQ6knp6+M7OoAwEgEpjG44C64nBuo8EkFpGURrjVAzRoEfGGFBk5Ob8M1MIqgJs/AFE+SoA2h
+5S2Yaj5aouEO5TnuKrOORQ3DxJ5tFLyY4KUZ+QYiC/vXWpqwHLiQXf4EBq4n992ybORVS1fPMawN
+eFjY2oMiDiF0PGxvCi6gv76Lg1uCBTjoCjx6+8oaZoS3v3Xnd3HntiRgz8/GWI5Ixa8UajaC1Rw3
+ZVUISJfuJT40sScX7x+hYRKz4cllYYYFz32xaaOianpWFxi9zxBjUqLrSRXrpRfEm/nC7otgFIAP
+zg+RsUJjlAMwrMzqGgUbrHLKCDS2hZKM30VRoOBBHEdD09OpHSBbPvHAn8U+j8BsD8DPA/iPAcAY
+UwIolVJ/FcAv8mP/ACIS80cuCpYCOp6NRQNE7QPkukgaccV0bCMggUQDjT65/BfUXdyCxoJy8K8G
+Bnf5oPTKGhd0P78cKtzmhL7GuPgDKOwwcHmvAPq8MYcNYKgRUcOsFoWq+bSsxKdbYgElOx99vpdp
+AzL+owTQeuilMasse5vpj5VC1jIFoQ2vxLWruerZBmj4BbYAILcNPH5vatXw2my/0Sh5/r4LOPxe
+zpZzu1ZwyTBlGxuKbrTjeajnjHFhoeaC2E5Go+u2ax2wDVTZqmmpVb6iQYUlS5g98guu2y7WGYpE
+/Q50T1zi+z/0YLiKNsaFbvk2GSZYjgOXFZCuE2LQZWnRDfCzf0E0GHfDDl5/5YsyjpsStvRf2ER2
+Kg/bzz26hv/z934dAPCP/+/v4vRcqlhVY5BSWyHw26qMi25EmQBVY56zbB2ewKWj3fjyGPU7EdyY
+bfJNjW89/D6v6T2cHgnSs+MDB9QC/Qu/ID0q214fc4d5ix+VUOyDsIoOcvbgqNzGknHFgxHZpKYF
+zlglSo0ojQGA0goWq1J2ZSOr/5S0JAFcB3AG4H9WSr2tlPoVpVQMYMsY03ZgHAPY+rQvPy1F/xxU
+RS/t0i6N9nnCBwfAlwH8LWPMW0qpX4aECiszxhil1Kc+8k9L0buWawJrDYXJYUeEeCoHJTPWfgns
+bcqOMM8fwaOnsMnt8YtXd1GRU+/rPz2EdS670cHVLbz/rmR6b768vlJ/9piR//CjUwxJh+5+/xD3
+2gjldAImp2EMVhlufAJF8YdN4UmosL8eIqU7nrcsu4WGy+Gw9VNJ9MrAs9vYhaI2ngOwVl7BQNFz
+qYx5EjZBofWlM3ozWWOwpKekUKNDP2WRN2D5G2Vh2toCbOIVtOWsWIYj28WSVHjGNCv32IIDFC3O
+QHZM3Wg4xCM0pX6yK2kNi95PkdW496Hs+C+QpGVt4GGbnkI+0SCuDJZlwSUgq0hrlAVPmtfvWkCH
+xDAvb1/Ha6+8BAB4ZX8XX/qaqBWGPRuDA6kUOX577jHqTdLW9xL8FXbSjm4DDx99KOeR5ygoqDKZ
+yzVt9gYrj2/dKFyQ42NWL2HTmwjaEEYl0PQFba/C9/6J0NaP7h2hYQa1txHitSviKWwELYGMBTOW
++xB5NaasyvS7QI+iLnbiYLGQ157TUvjPYLVcmlkhlRkAlmPDbuURUILUlc9sn8dTeATgkTHmLf7/
+r0EWiROl1A4A8N/Tz/Ebl3Zpl/anbH9sT8EYc6yUeqiUumWMeR/ANwG8y//+IwB/F88oRW/bLrq9
+baDSqB2JNiyrQpYL4qsX7mGwcxMAsNX5Ipqz35f3WabqD7fwhX3pcHzt1hrWXpZdwt+ycOuL7Fps
+HmCHyaOMBJ6b3QMsyFnQdSucXkjJsTEJDGv9ktZ7tvhGAQi4qwxjBwPqJ94/nK7+bjP30bEdlG0p
+yxgQVQyPSDvPBryW/g0aNm9VUVerszFPpyifisFaZmilgEUhB65rBc2SljZPEpsOPQ3XNmjI5jwt
+GlSt7oMbwdJPErtt3Gqzdq+MganbzsEKbWFdKUDxTLVWqBQTnobXMUlRGvEKO2seGkKeS8tDThm2
+WjXQqwyr/BM4NjYp+nJrex/XB3J/r/Y3sU4V5yCK4HgtjwSbxwIfitfRlGOsM8f5V//yz+G370h+
+4fDBKdK6zd7wfB2DRS55gtDqwHUkRzG3l/CoBeqwzBr39uEw3/HtH97B4V3xQNwG2GBuo+daSLos
+B1PMqFEK0wvpbJ2VM3iBzJvTWQZfiQe8EQ8QMHfhklvE6SuEJM3d2+ji3nnLHq7Rpr/63U246rPl
+FD5v9eFvAfiHrDzcBfCfQLyP/1Up9TcB3Afw7/ykg2jYyOseaguAkge2qAAm0VF3fMyYwHJTDX8m
+WducdPB78R6+8IU3AQBdt4Nwl0kiN0HIibC4n0L5go2PJuJ+dnWBi3FLq9YgomZkoZ/gAz6LdTzA
+bxM/toV6xvNnGKAUEHLSDIIAU9a5DTR86ji2ArV18wQI5MFGzetwV4+a4Ab+8HJloOgAGqMR0PWf
+NwL65keg6Nq3wZ2xzEp0VcGs1pjGlKsHHRCZcwCwGCfZsFdhQqMU2FEt8GhGW01jsKDIygeHktTb
+Xg/w6hWZfk5Soh7LcXvaQlW34q8aDgMdj23BAz/Gzro8jNuDLm69JrLum4MBvLCFMWsoApnaCzSm
+XtX5rcZBMKQo7Bc6+A+/IVP0H/7jf4QPzk/4PSZU8wygjqdRBvuUF7ibTxHHZPlmD063u4mAuIGP
+jj9COha/fWMzwe6mLFhJMEM0II0ge3DOT3OcEVhl2TZURCbp+3MsWXFIt4AhafZsPvED30NngxRt
+9hBzhnbnk9GqN8XRHiznJ1fLnrbPtSgYY74H4NOw1N/8PMe9tEu7tH999nwgGpWCCT0U9Qheq31o
+ncGiWxZEPhRJVrzmHO62vN5nE9BgvwOfQi5Kj1AtuAv2SoC8CJYXIVqTEmdK19I5srF3lZJnH3lw
+NDUgcIzPJp8hlkCtymkdrVEzszfxSaqhLby2y99LAjw6JjlqXqHD+OFsSXGWykafO7BTOajJ4JsZ
+B2P9pHZftUnMdigB+CwbZgC69LtTmFWa1OBJMsmhZ9OPPaRMOtrGoGDZ1relEQoAiqaB5s4budzl
+HQ8lMQthY2CsdmcGStOsXjdtopQ7mF8qnCwkRNvpJhjcEHhw97GPBTteTWjDh+zMCUOG3e4Ab7wo
+XAmvvrCJvT3BIcRRCLv1tpwaiplLo8X91kY9wU30XIShzIW1To43v3YNAPDdd3ZwLyX6LyfsuKph
+8bVyx9jcFC91rvpIqfWZLeR+dPpTVBzvZbpEQzj+9df28M2vfEUOe/Iudl4QpuXQabVNZwhclkLd
+GouJjOfj2Tky3gfHsxEYQrCpP5L4ISrqaIRKYUiG4bPRBUrSBdZ1iSSWMXxWey4WBcu2EfS6wPEY
+pSYWV1cI+9cBAMP1DWzwVJ21PjYoDjqIJR/Qy2YwC+k8U+ubqLVMtvqDU9iE8brhAIbtu+C/C3UB
+N5G4Nm0qjCGV1PIZwElPW+tc1461wrBHKsJZRyZFl0QgutE4iGVSFdYSVznBsvMpFHGujSMTLAw8
+ZEuCkOwSfotZ0EBBvMWkqVfu/9Nn3CKFLQBHzeIP/V1MvhhwUegpvdLMtGx7xQkJJeIigOAp5uRu
+3IrYFu3ZcEkhPk9zZJyky0zDd1seyAyasVBBtzzoeEiIxfV9F+ukJM+zh0/cfG0wJNCnyzbjl3a2
+8fruKwCA/YNtdAcSSrg9B6pu8ejWqn3eUGzYFNWKvt32opV+pG0M9nZkYfn5v/QNfPexVKsuTinK
+UyxXNf+xCnBCJasw6eMeuRkz5pGy+RRrQzmfYjpdqXNdSV7E3jXJeZmDCGFAsaJG7n/USbFzVa6/
+vHuK8yPJQY3SBRzO++PjEexaxvnKFVLnq2DVX2IvbPhceI0BKgLVTqYTlMVne8wvuyQv7dIu7RP2
+XHgKcAPY27fgnZ0izyU753kJYldW8AP7JWxuy8402KjRTaUqsRZIGLGRdNHQzUrvnCPZYdixcwCl
+JavrhwrxLVKTkeF5kNuYfSwV06+/Bvy+HBb3Kzx799RTHzUauMJGISs0uBVL28fD++RzNDZOJnJ9
+lmuh5Ouv7vhoWOt+qRZXry4qLDbkOh4cLTCxWFFAiQUVo4M5MP9kshwGT1CR5umT+zFrQ4m2+rDl
+KwxZd7dcFwT/wXIU+hznh5MUA3IYvnRVcCPGNsiJNn1sLFRzUn9ZU2Qt6tEAusUms5auHBv7G7I7
+1mmJkDyJcHOklJ+3lAsnJG6DyTLL0XA2eX/7A5iylX53VxqNBgYmJSuz3SIyAoDiPKacwfbYRRlu
+YZeNSW/MM/z8jd8FAPw/Y2FwXhQlGmIyTm1Au20jVYNpQWIUYsyXuY9FQ4GbPF11vO6ub2JrICHP
+nQdnyE/k85UnHu3JYoHZfQkfelphjwzjvc45zi/IIB77GJFTYmcmc+Qwz5Hb4lU0CJDzca61XlVt
+5vkEpWlxtM9mz8ei0CioqYdap6gryU5r42GaSyb4sTOAx6rEm2svYWf/SwAA25cbMFhL4BGHbscp
+mkAGstTnSJjt96+uQzHscCOZjNHyq/C2pQx5y47x16nO9GtvTXA0fTKhP6Xq96lWA8gJlnGsAlt0
+91K2rqb1Ei7ZnXYGIdxWwn3bhtuTiblby8OWNWPcJd19GLhwCNe+qEZ4QPc6z0q8fyyvxzOWG/UT
+AFX9VMzw46dO3BB6dOG/uJ+gbzFW3Ynw4YWc51bXQ4cD8MHdFBM+Y7fIM1hXDd4/ocx8AAQEGy0z
+BwrtQqZWuYi227WxbHR35ffy4hz1mOMddOAtzOocXeZ5CsKczxdTnMwkVNxPN+FuUri17sBi+7it
+NUAilrY3QrkGLfW/qXM0VKpqsgLZqbzO50fYIgS5tyHfP5svVmPWVEDRkvE2Nfy6hZDz702Bctn2
+e2gkCYl+r2f4zsM/AAB8+/d/gDqVebtGqvpB2EHAkmx3GGCyxU7T79lIM5kDR0WFhmxQyzP5fhC4
+sGyZ92tRZ0UM03H8Vau5D6CsP1vvw2X4cGmXdmmfsOfCU7A9C/GVAOq0Azsl2AQaa664/p3OGjZY
+fYjiE0zH9wAA3Z7sqmrQXfnDbuSiJrPzZPQRFiU9hXuHGPniIRz/UEKGuw9OEDPp+H66wDbDlV/6
+6gB/8D1pYHlUNSspt0UL9zWfDnh2jEKHQ7rZ7+PWUIBY+1flfJr5EC99RZJkgyaD2ZVzi+0aC+pN
+Vqf0lHxgjfqCe97uil25W8/wiLv4zmyJ3+iK2/k7t2WsTsoGFuvY41KvEpQ/Dl9JmBy7QfDP3pqz
+omhbPjjGEROm3qyPU1Y7Ol2DF6/JmP/MT92SMRlf4OBUkrWLBsgIR//Wb34P7+fiec3G9apDsSpa
+ajMfFcOSa+EunD3CfP0IF1SENnkNm4CdHQqg7EQetjYl1Ij6m2jInnw0eh/xI3G7g60YnXW5lzYB
+QrYVrKoPWmvUFGcpnAw56dB3X7+OLwfy/lt3JYn44P4YxYoerYamArkyDvpMpGY1vQlHr8hrhnEH
+L1yXsVjLNQ5/xK7Me4foh3ItV14XisG9IMH6jiQdvfUQD771PgAgXaZw2KSWeF3EsYQgPzOQz3aH
+XeQ52b+VjwelhBKV38eYyXZYwJR7/xTPhnd+LhYFZTS8MoWPIRT1Gl3PYJOCr1teibiSmzE7H2M2
+kofi/o+E6vqHv1Hg2lVBMd58Yw/DIePTCw/f/eA2AGBycobbMn9wMpbLvn5zGzfX5MZsNjaGiTxA
+N4yHhwsWJd8rcErNR6siBfq/pDphWRodljtNpXCHOn8ThgyN0uixZbfZ7+LGUB6m7c4uphO5ue83
+UhI7OVziAyIh758WcKm38KXhBnp0xYe3NvFaJQvcW+z3cB6WWLaMHUbj0xxHC8CVRCbKqzsSwnzx
+S69hSe7JcTFCSDDR4rSC5oP50kaEn/qK5GX23hTQ0LLawM5cPvvWo49w+7dlUfvgZIycTEHb3TV0
+2RPw8FQWL69pUEIm7sIeQrNPRHkB6qwtZdowmZzTVbZCf+nmK3jppT8vx4hqNAXzEnWBMdGpPatA
+6MvY2swX6KpGTYp+UzuYL+Szs1xjRCKazZ0b2OMi+fU3RLv022//EAXbt2utV92eXd9FTmp/l+Cm
+HDUiLra9zQ42BrIo1LMlHnwsla28dLBk52otQ4HlusYrV6XSpnWNiw9+DwBwNk1Xmp92ncJ1ZLE8
+JSo0vNBwOhKCzZt7SDzJNZxbU5Tsq1VNg9i9pHi/tEu7tM9hz4Wn0ACYGRuO5wOBeAqJncP22Xvv
+bSEklHS6vEBxJln7aCkrse93EFBGfXp4iGFPGHcdewh1ITuXs+zjmy/JavzWx7JEq2yKDsRTcLIG
+m9dkF7wRAaMzJvmGSyzPZJVftvqL1acr7ixrA1XLOY9mBdQaWaDpoqz1eqh2qVzdDbEg35/z+osY
+DuU8rHcl8Xn8YYbIkZX/w8cXyGrZlcr1Ca5Siv5g53W8+ILsAjH1B2urRMWd7V+WF1U2cHVTjvHy
+tvwbRQqDF78g1/zxHSSk/PJvdjGbyfXu9hQ29kQMJerJDm0VHYxPRG3p5L0xjslb6O9sobiQXe6V
+gxfRjWRcfvU3fxsAcDbK8fjbcp5l+BjOdbmOMA6x2Rf3eJzNcYUiMXEtO+K790f41t//Ffl74OIK
+ad5e2d/DC1dkPliWQpXLeNiFeFiO5cKQi3E2meN4Itd0+/sPcbuQxOXLmyNkI5kba/E1uebhAB+d
+yP2ztFkJ5mSVveoVWRKPAGVBWywHVTW8Sn77g9tHGFN06PTBYzzSpMA7lPdefr2LWxOZmweDA3z1
+ZXn9O9/7COcThluWxv3H4r08oMe74ygEFPM56Mdwd2Ts7VGOGasynqWgrSfJ0mexS0/h0i7t0j5h
+z4enUFSY3D9CmZ7AdsUjKKIcUyo058US2pISi6e62OlJ3PeLb74IANi8PkDNfvXZ8SGKCXvip0sE
+oSDJoqGPl/ek07K/Liv4mYkwqMh2XOXY4nG1ozBnA9a4XsIlq7Jj/dGafApAEDKBt9nFjstE0nWJ
+21/d38ErvyRsO5ZS8CrZoYw1xTyT+NtJ5dw2O3P0WEt/9GIX81PZgV7r+KtGqaaXwfetrLqCAAAg
+AElEQVQkdlbkQjCwniqdPuFNMAqrLWBtzcK/90uSg3lpRxrJgr5ZCcRMwwTb12UsGk9jPSRD8bqH
+gAk/qys7lDuxMeUuWDUGr96Q2P/6eB13mLf4+b/4b6GzJWPxG29LEm1Rn+LQpmaDa6HH+NqvnRWC
+sKN9JESA2mRsqioLdx+LF7De3URDxGbQK2EFlA7U66g4HzwmnU0cQCnmmppmVb7cutpB9oFwMnh+
+gfOW1+KmzJv+1k3YZ/IbTZ2vYnxV19DEITiEgTeNRpWvaKhwMRbgyykc7BISXXR6GHMXj8hce23j
+CvYSOQc3GSIPxOPZ6Qwwp+6pox1YDlGmrDW7nougYaPYVgc7lJubHtxF9j5hzqX5RGn6Wey5WBR0
+kyObvosin2KDcNeu1UUnkskW9s9hmOAxysYWM+Ca1FfKyTGfUZh1/hGKLpOS6RIVSSpevBEgG4pM
++q6SRWPHSlaw5GWaICS/3rzKcZ2T/1HgYEZhjS57EVps/o+bZYAdJhpf31vHOkVGVEaotWchjCQx
+WE0yGLJVF3mGfCKvE09Co5/+c+tQ1Aa8edrAreRh0z0PGZNv65tXMWeL85sDcaPvH86weCq7OGDf
+xVIpHPD1l3sx3tyUKkh/i6zHN300U2LyXxgha6shnQSQ5wrDnXW4bDk2rIPbgYVBCyWvl3jllqg3
+ZdcMvpj+AgDAiWxYG3JSb/45+fvRRwUM8f6eiaGGhGNPQmS5HLvn+nj1qnz+5dfkoVnvR/hmI+Gh
+1xQYUOlJo4bX4xKYF3B8qUTAlsXBNAWUJ+eZrPdhyJ+4sd3B1V25J1H0KkbsernLWxz1wlU3ZKUa
+JBQZ3uh2cX/Bp433wC4VGoIWsnkGxepE7FhYj2WBr3a6UHP5/I0tea8Ta5QtWcrsCCNIfLC9uYMD
+JoLz2sX4Qs4tZGv8te0teFTkenNrC+4+if/mFR460rZ9nhZoeWqe1S7Dh0u7tEv7hD0XngKMBrIC
+Tb5As6Q83EGDTfbN9+MY1xPWfHfWsPMilZLb/E4aoyBxihtcQ5lKAi80XYTXxVOItvvoBbKTRLG4
+WaYpoGxZrWO3h5IJulGRIWUHptcouHQpA+uPhovWxqySctd7V7FJKHBwQFn08hwOk08qyOD0uZvV
+pyjbXYcJwPWdPcQsN1m7X4AmP5zTW0NdnvG6Q+SpvF4M5Np0rfA0T7TDWKPnWRhyx/vKcAPRnuzA
+4T6Tte5VmHVBJka7r6NYynGXRwsgYideEkGxbq4WdMtdC9GmuNov3jpEr5Rr2liPsfYzQqQ6Mg3M
+oZz/n70qpcw7Zo6xkp1PjwO4ZIzO8hoNBSrCOMAeuQeu7YsXkyQxgv+fvfeKtSxL7/t+a+d98jk3
+p8pdXR2ne1KTw2EQKdOkRJsCbMjhxUGAXmwY8JP0pgf7gTAMGAYE2C82ZAGGGGRLoinaJiWG4XA4
+oaenp1N1VXVXuHXzuffksPP2w/edO9PDmelqtkyVgLuARp2+99wd1l57fen//f8d+ZnxLBwVfSkL
+izxR2LHTxdEwznY03LFdSoVxWl5IK1zgDTwh+AHc1jLNqZxn/s49APZP9igVQVpzHJYdSYi2Gg0e
+DiWJbWmDGsawYB+0rJKwKs/ypUqHpZsagvUN9Z7Mc0ul7Rorq7TWZQ7H8/s4IznH+qU2X7whzAT1
+iUtvX9a1X5P7bzYbZIGE2EutkJnC3+fbDayvKhFwbpFlP9yz/VHjKdkUcop8RFlOmMUSXHbKS7Tn
+cvM1Z53GsiziVsum7slCDvUlnwc54w/eBCCOHTpzcbXdZohXUUWiiY1bVUESVXoy5ei8Z8KxPFLt
+MjvbndG9r1BSz+HyurjYRrPX95Ul5wdHUsCZ6gqWrQR/XR560VMWI7+C31x0TNoUp+K2etVlWlW5
+zgW9OTNwtB/ArSxhqZ5lFseYROG6rQ5lXxbFwa68SPMyx9OYPC3Nef4hLc056/CNGzt4yUIiSgBW
++XRMpm3GzD1UrhPb8bB1I7DiFMuRDbVIFniMnKKnqkn2DpYSh5RBC1fZh9N+l5VNyaj/zNbPyD39
+yRGP9/UlLUtSpYTOkpi5XnTo21S1umBnmi/w27h1/VngYYyK4eChjY8kmY2ZKbZEIeZWEVJo/0iZ
+x1hazXAaDdxChX7tkNJRshOtfGXzKa4an6W6x6a2XFdXmmR72nWp1SBjziU4ISq5orL1zz17icqm
+PMvCG9Jel8/rGl62aj75WDaY0f0UW++/46TUpjKH6+t1thoS8vlLKsCbnRLp76u1NlPFuBy8c4Kj
+kxG6JVGx4Ll8MvKgi/DhYlyMi/GR8XR4ChhMYWGRE6WSsb0/8xgbVfA9bXF1W6yYE1bJE5VXVzbc
+eZkx1+aUeNrDtMXij5MptVR29mkwolWoWJUt1tW4AaUm1PIiZTIUD2B4us9+X3ZdO4W6Jujs7OP3
+0FDRatUixlZFZEt728sCirbSqjkhcSGYhNLKKBfMx9qeGKQ2RUcJYqoOVq6svun3FG7iokemENtR
+XyxplhaE6imURY4m2SkosfXvGmshllqbUj2z0lhYKgyT5xGFqi87fonrLpRjXMpUOw2Vt6OMQ2yF
+YLeWoAjkhH5QodqScyyRsXxZwsIFPV5j7RrJLNafnWBO1EIn4TmjcpRm53wIRisxth1jB/o8KnXQ
+ezV5dK6B4dY9FuoqC67N0q5gtFpQpj7YCvx2aliaPMS2KTXxfHhPEKbRcEauMOemXcdVPQzXMud0
+gYsONDsw51oeqZWhFBCs37oKivtYmXQZaSI1VQRt4NYZdSVcuT98xJ2778m8jEsc5afo1AyXFEfi
+tMQbi08NU+WzLCtglIF7OJ0R6zyHTkg/+svlaPxXMhzPZenqJv1+hplrqnt8RlJqBryoMRzKC515
+XW5lEie6HXHP+qMjuicPAaitNThuK0R5bhPl2qpMjVkmsGF3riQXZYdUF39/fMD+gbjzh8dTaTcE
+Uqvgvir2nEYfAwIpwdXNKetNmdXl+5VQiUjTOempAle6Azxli5pVJkRzzQk0lI1pmuM81M0r9XDq
+GopkM5JcrjM5a5IopSDzBWFqgaPubGAcrAVhqoF6uaADzyhHKm67ri+d5WFpmOAsVbBVKSm3E4gU
+KjxJSRU4HStIyXOX8FoSzuUnj/CQN8Eq+rgamqx1atS0WzUanOjvUzxP57OwWL0sz/ftgxGpzn0/
+nZFGCsgJFtTp7rlqElaJ8Rcw5jnZVJ57PJzgaehV6EZYxqegOSHjdUh0nZl0DItOStMi0jL4793+
+A7mnLGW5oQCqdZcqGj54oNNMpmxTgeVjFl2gFKSxsj7NxwwsWWeT6ZRIVbKaDfl94M1prdyQuRoe
+8tyabFKdKwE17d2w3BlYkudJNURNzeA8pxAmTTItOw1PI3zt1s3zEGM+WUBwET5cjItxMT4yngpP
+wXU8tpZ3MElJhoQPpR3TtMQTaJYWp2qtR9mE658VANB8IC7+bHfOvTPZiWtRRv1E3LPJ44JQe/Yb
+N64wrap16GoyzH9MMVY8wgcj9rQOfDAdcVZqHX5i8XDRPJP/ePBSDhxpM89kktGYiFWM65IwnQ3O
+KLVrczIYEqhFcO/Co13xaBZ6jyuBy3Eu5z05i1lZUy/HqTNVmPUsP+HtO9LNeVJ8j6vP1+pDYjgX
+C3HLkpkmB6fTKWkgFi1UpJNxQ4y7cNUNNpKFL85OzpNvmeuTTuT6UvVsEm/MTEFP6XiIkcvEtGKq
+1/XcjTWKROY+mki44sYRxal8OUgdlpQlOXcmhOrdTGcpu2cyB69qo5FxOHfXyzIHrRgVszmFdryW
+WUzpS7K5XFjuxCVBpdozmCgXY2mVuJ4mdMM6h48kaXr7GwIpjtKEJU0MrtZb53qisZ2fq3EvFMHn
+eYIWeKh5DlOlrjvdm9J1BCNz1O1zdCTuXabJ3rbvUZ3Ld+u5y9WadEOWTkyrJmt1mvQ5+EA95PqJ
+3r9zTnBjqhN2j+SaB8kUP5E1189isvKTARWeik0B26Oo7+AHKalm+EkGTGqykFYqL3NtXcArE3eX
+WMt+jba0x2ajExxdHPN+zLqR7LS/1iRsLjj213CNLJR0KDHW1Doj1tLaIJ4yi2XSh1HKyUKlx26A
+rwsv+vGTmwMHSnQxyPo09OvhgcbOGxWSSF1Yq8/Jm/KCTFKPU1UewtO+hVmJP5QXr1pJONX2Xjed
+Eul35laVfin3Ei6AVaFFtCBJzaGq6XAXQ1DVfIYbYi1YkZTFqEjz7zGvDDPRcABKbxl0UygmEzBa
+AkzkOaVxwe63ZGOKcdnqyCL2W3WyieRMbKfAqcoGn2mpMy0OqGkretM0CRcAoSLH0fxBQcGx5mVK
+1fjACihnC8KWCaA8+qWwKAFUbYdMyUkKtHJkHFD1qihKKBNXn5lDXlnICjg8vCu5hP5YNo0wsHE1
+JKpUGnSH2gdTuuSWVig0XzDJImxbXkbHs4hU4v4g6jPTPomz0y6eoht7I7n29++m7D+UjWLeTTmz
+FZy0scqBkuc4mU+YyXfcJSU3TufUKkqBX9lhPFSJgsiwsnoFgJmJ6WsYyo/o2fnB8Wml6P9rY8y7
+xph3jDH/yBgTGGOuGmO+YYz5wBjzG6oJcTEuxsX4N2R8GtXpLeC/Ap4vy3JujPlN4D8E/hrwP5Rl
++evGmP8Z+FvA//TjjlUWJek0ZTQ+YByJS1mvFAwVuvz+yYAikE68FVYw29ofYRReuhPymQOhaAvX
++jTq4hF4mxVUtJjYyTkZi7XtD8Ra+bMJ46lYvMenA8a2nK+fpsSKcR8mMa5mn4v04+u8qidDkUaM
+jmWXD2+oi57nWB2tkuympDN18+2ccFNCiUDp6VPm54zKxvRZ3pMD+5WAuQqdRKsnoJYiCfXv+oaZ
+Yi+yvMStyXFbjuGSAoFMyyVXN3fh/VjtFNIF4GVCGmvlxGmASpnnU0NZyM9tTdqVk5AlTb6ZOjTU
+1Q7cFuVC1r3skytGv9/VLtDvnpKoeEm0MSCLlBdgnhIvqOHzlL2ePKuZhlX1eE6uEGUr9s4rMUUx
+xw5X9KZ87HLB5izW2K6v4UWC7yjsAq+mPQxtC+ZS5er1h/zT3/8juSZ91KvLm/gKm5/O5wy1/6WY
+F9RKOV5aiKdQlCWlLcf16wEnGlYsd3fJZ5pULs05OYuDJrD3Z+xp0rnlV6gpLiaiSame4Gg4xV4k
+yidKDV9vYAULBu4eRyNZ33NTMp7L3K42Gux/Qtv/aRONDhAaYxygAhwCP4/oSoJI0f+NT3mOi3Ex
+LsZf4vg0WpL7xpj/HthF2L5+D/g2MCjL88zGHrD1w/7eGPO3gb8N4PpV4nmXKC3wdBcNy2WhKwbM
+jmFyKruytRpTaCyXh6rfcHwPryVxYYUST+vR07tdDtJF15pLdyoMvcOe0m9V6zjayXdQxqQaniZj
+QxEvCFgLCs1ReNqlNo9/OK2VBayqAGTNqpxb6XSsyUcr5uxdsXwf7g6YafvaqMhpaMIzSWXnr1pw
+VqgG5XHGRkvu//KWx1jVtp2Zg6V1/GKRXMxzMmVeMoAJ5feu77BRl3g/n5dktiISFxiExKG0F+jA
+ksKX82U2ZBp/F6bEUr2HWElHTTmmuXlJPjcywg35+aB3SBzL3wXugFEpjFPfeU+6JO9kxzRX5NpW
+44LIlQTz2ahLrp2rlgPHw4X+gljV1aB9zgidk1MsPALbJlfUYzoqMVpatBQSnJx9yLES4ZqiJA3E
+K6xFZ7j6nb1hzptnkqxrKO/FipMxmwnacBYGNPpKU1e3uKzJzHku50rs7LxaGhQuDW0Ui0sXqvKL
+6qnNUPUhK8osdvNKm5svSkkyjzKmA53DwZjxoSZP7TFjTXRnOq/rXsxQ8wTJtORYPesszUnnkq9y
+al+i4ktz1HyuC/xjxqcJH9rArwJXgQHwW8AvPenff78Ufeg3ymT3EUWc4irFec/uU12Aae4alrYl
+iZR4B3Tv6w23FdCzf0ykYCOCgOmxTMKdRydECjiyPZvTmbzMbiiTOvQ87JG6wWOfVDHu3Shiplnr
+Ms8IS1mQvm5GP25MlB9x6dmrNJc1+6wAlPe/dkKmzczL7ibBhlzHWVpwfKDU4AqUysYR++rDprMU
+x9YW2WlGqRvHyHd5OJDv95QSLU0LzPljzXHPex9sQlvc60k8Y6phU31ZFjZOABqOZbbNsC/XM+uP
+SacqnNJw0W5vjk4kDDCHAfWa3F/zhZtMd2VjieczrMuSRZ8NZtzTjPs3viNJyWqes7xoubRDYhWs
+zVODg8KOy1Uencnf7b8t4J6rL1zHUbr3vMhIY9k4k6igtOX5JKc51aYc2x7KJpa7DRLlPzNJTD5f
+pLospirW8/7d97EmMi83N6TCdTw+pK/4B6+bUtGeCbtlMxy/K1On6lVxUbKwh4MsYdZTTEPoUdUO
+xn035+gDSWYuBzL3neeW2L4kLexe0GS4L5qnDz5IKXal0nA0Lznuy712FOad+QXWUJ7vxM45OJL1
+nRQZZ6pT6h6/h8sn00b9NOHDXwUelGXZLcsyBf5P4KeAloYTANvA/qc4x8W4GBfjL3l8mpLkLvAT
+xpgKEj78AvA68IfAvw/8Ok8oRZ/nCYPJQ0zu4lTFmqXTM4aaLNndj3jtsrizw26duxPxCpY0AfT5
+X/xl9t6VhijP1Mi64u6FV6scHIi7PmZObQHtrcix+uM+3Z5YpZiciq/WJbOwygWnf4ynTTeLpM6C
+X+AHhzFQKgS3suxgq965/SdyrMv+BoUi8Jav1fBWJKE06udc/YxYlYcfSojD6ZR2TxNK6y5GUXOz
+8ZxRKBahNd1i90AsQqQNQKYE1yyUoS0sRbPVTcj6JXFXa3WP0Z783cqGWky/CtppmvcyyoFaxHlB
+rDX95DjHUi2LZSP3NuwckSnx25Q+ti/ewX6/R+uBhoKJy+xMzldVglYvcwhVI6HqZUwG8tlLfXx1
+wVthyVhl1799RzyTV0+nuOtyH3l6SjZUMZTYI1Kimig+IkslpGmvyHU6rTZLyHdnUQ1H9Rzaa88y
+mMuxv/H2t+jYYr1/6cs/D8BvfP1/Z3JfLO2W53JVO1sd33CmEONJqVgB3yVVT2GajDmKJCy5XFo0
+M7mOgIgN5PpdTfIueSvYfA+lGa5K+dY6eA+rro1g6ZzVQDs4le06ynOMqwS7/YR4rGFlahFp0nyW
+PMIE31MNf5LxaXIK3zDG/GPgDUQH5TtIOPDPgV83xvy3+rP/5WOPRUGWRzh2wSyVBRgXfRaK8B+c
+vs/vvaNU3Y2IylRe+k5TxUopzzPdx90Pya3FRM0Jq4s2WxtHwTk9hZ/2R1PORtrJZ2Cg4J7EmI90
+vmUKD/64yXIdaKnu4oO7D1nrCg16pHXs1JniaKdaOnKY98SJSgOHw6EszNGHcv+D8YhqIGdsOj6L
+psZ5lmO0w+/O3oDbh0oJv9B9pzyXX7csw7JyI651Wlga41ZqNoniFyYDqbvbtRJL8yg54Go2vAwc
+2kaBTPX8XOjXKuXlWS4vMVcXPnNrzFSZqei5jKsSw46cKSenkjNoaYXjaBgx0CqJtdY+fzZLbZ9A
+u0CbzZD2QnwllDk8PfgAvyHxt1tYlNr6bns2RgFc8VlCrC64Uygga9JnfiT3muYp1avSKRs5KdOR
+hiu9mCsbsunt12TTSEY+Rg1EY7nN1itXAKjFGbmCqEJLzpGVBZYqUmWZxTiW+9/tHWFZCzr4lM6y
+8lEqiVDUnzLYlWsz1QEDDVfmuzGPTuUYTljFr+hc2Aq2MgEzxUXs7XUZLtaAU54D2CJiTKnG7AnH
+p5Wi/3vA3/uBH98HvvhpjnsxLsbF+Nc3TPlxWmh/CePVz32u/OOvf4PTccrIKHHFmcP/9pVvA3Dn
+tz+AE7EUh70/pkzFa1hTncHlyjUmIwkfDkZjYpUEq1ZdTrVb7nNXPssrL0o/+is/K5gGUwxwlOMw
+siKeffklAFJmtFQ9+GxqEYTi5tVVk++VV15knsl+utSpsqMJruMsOQ958tjBybRxZeEdFDlGE38V
+4+ArH2VazkArGwtaLrKSSGGykxgCrVdH2OdNSU8ynn9VeCznVovPvCRs1X/j81/mmZefl+OpZ7PZ
+7lBqJtv3Ug7HmmjMS8ZHYoH/8b/4P7j9zYcA7LTFPfc6KRW1joPpDH+hSeG63NuTJOFpf0Khff+x
+ogpdz6ba1Pr/MBZEJfCln1zizffEqxiNprQ17XWmdHyTeYkWTvBsc65sXbE8ploledmycL2m/p1Y
+4F6QMjJyvvUwwPblWaZ5TjqTeR5GKal2Fzp63jjLqGjYWK079Icy91la8oVf+gm9JjnuUtXhVkc8
+qNVrO0wVVbm2skRNvYa4PubBAwkJTnry+9UswfFlDtN5Tq0uxwuTgonqWB70z+gr+nao4fHG1jKe
+dqKuLXU4fSze5psPH3LnWNbs9Wc2+Y9/XnQy/vO/8999uyxLYW35MeOpgDlbxuBbFgMr5U/vygN6
+sP+7/M7f/ycATE4PQWPAoCyYa3babghxx8ngQ1wVizkY38dW4ZHhfErdlbh99/Q2y/sqYf9deeEb
+NY9JRSa47rY4U7GNYNsmbSvsljOyeOGiaqW1HcKJvpi2z1R5Jb3xAYHSd0+SqWDzgUDdy5rVOIcM
+b4ZLVBQAlDnLZKW44IW2/w6KGWenEiMPTZ9YN+/sE2aSi4ZChnsR1YbE+/7N52ita4nXl40pmNtM
+KlqS9BoMIykhvnMn4jvf+iP5/IdvcdKVvMNMlayWV5aob8t8RuMelqo+FWGNWJmQ8nxOlsmxc7MQ
+fTFESkqalBmKTSLZqXD2TZnbKIKxAsYcFiViC0/bm0Nj0dQQyzGGgYYPXVx8hbIvNeUltThm01qI
+ypYYBYBR8c6l5D0DY1XJynVzSB2LvCKbQlYYIn2mSVLgtLRzdyghn1Vdw79yBYDmhk8Y6Ys+zXh7
+LhL33jxg/0A+9/ZlDv32Kutrst5WOk0aVVVJKxxaulnWPJtjDUEeJFpaDVJCLaf61IhVDCbtVIh1
+Q374uM+7Q4U5P+G46JK8GBfjYnxkPBWeAoAxFl7scPtPJPv+T/7Bf0P/nlglJ7AIlNFiGpTY2p22
+58nvg1nJmdJ5FaZA8TyEtsWoom5r6vKVE9mhu6XIjQerFSpK0uGEKwwsSU7tmJ8kUzbn6tAld2Rn
+njYXnHx1OgpSsueQabdfkYW0K5r5n5RkuuNvqjWrr9lsWMqYvJWzroQl3w1j2rFY26H22l+KGvyZ
+ZsvNXs6TtbL8+ZHO1b10XHZs8RSWZzHeWCxoUFO5Odfj8alYvGgwYvddubY/+qe/w1tvfwOAeXd6
+TtRSqCR9GtUY7Ev9v24q2AosCmuGwhFLajkGXE1GqqXNJhbKSo/vNs8FS7qPXFzFik9myfck7B2Z
+7+W6T8XVRKpvaKoXNisy0C7Jd82MusKN316R8654Dq7KBDxTadFQXsVaGDJWzoIohMOJzP9MG7Cm
+8ZxcadsnJRj1Qm0nAldCz47ytbW8FlWtZlVqm6zuyLXdffcub35L1t5atYp1qlWHWIVc1up0PHnC
+nSWLdqD4DcdlOpE1V/XrrKgwjq/XO8wmVBXoVWs6rNUFJ1h2J1SUmi07m/L+d2RdP+l4ajaFkpL9
+NOOrtwUQMng4pFCfsmJXWFGp9tVak41VdXBq6tZNE/r6MLKliAVB0o2NJtWKLP5Ku+BkIA/0UlMZ
+nVpVqkpqMi0zJgOJZR8Udwi3ZEH3ChfXU8rxkVJy12xCPe5s3KNUBGVeb1LVB7pctrixIV2cX/iM
+ZLqX2jW2X5YHF7oWubrGPzUckurnwVBejvhghNOSDdLwBkfaDp4kBekn4PF3HQ27lq9y5oiu5rC4
+wWEgP98uF/Tlc+ZjWayPhl3+UFuIH8338FOtOFSLcyanRS5qNp7RXKAcbQdfKxxJP8V1lTnKuBgW
+PI/y93E2Z5rK8/A88PTl9twJln52bEOpL6+n/JIr7ZBaVatBmUOp+Zp8brHclIu77Bo8LRMGKklv
+hzYtfZlurnTOFaeaFR9PeRfnUcLDY9lE7g0kfh/vFozSBUmOoVC0aJEbjDKAhcq76XpdYhUxPm2m
+BJasoUG3z0S7IFfXt+iEsplc/ozcx/Nr16jUlKbJmaHUo5jCwdEwp1bNSXSdFTqvh4MTjBqcsGVT
+9OX+14M6k01Zn/3TLr37UpZ/0nERPlyMi3ExPjKeCk+hLCHNSu6nE/YO/zkARZ5S0V1wfanOqzuy
+A1/aaHJrWXbgVIk5srJLMRWrc8W3CXXXXb/RYDKTRGNeDDlS17AVSpJtFkBLuQcOKiMevSUu1+Hx
+e+xclb/rBhOyrliSa0q/vlkNGZ2TH9bOqbw3WjUajgBPvvyzP83PvCSceuuXZNf2azb+qlx7MY8o
+lUW4M+hjLei5B1qFuNSjvS7Hen65zf/zunSJDk5PeOtYVazzj3cZllQMJXd6DEqxaPfNbVYUutzT
+8GEynjKdizVrzC22NzRh+MpnKToCMY77JXtd5bRUghvP5KypqnFScxn1xbuLgyk1TR6mVn4u0Z4Z
+7aMoE9Du0zL7HjdiJw/Q0jsmtnE0fFhd0nCu5tDRdeFaBelMvQ074/qSzK1vFWhDKBOtTqyUhmZN
+rvP6+goVlQyoLNu4sa6nImV5RZJynX2pFgwGE+4eSwiaZCVmQd1WWDQVfxKrfmRcdRjZDwG4lq5z
+byDJ2j9849vYSiv3ueearGgPSkf7WRr1mFB5F9PYx1swaVPi671mWESFnHs2XtD5O4yrCk2PSgKV
+mr+8FRAjbQHz0ZSJCtw86bjwFC7GxbgYHxlPh6dASUbG3e+ckH1LkoemLKhpo8mzlSYbbRERubTe
+YHtTPIRAG+sLq447FUuzttrCXZVd1/XWqGryqWhsUO1KvOiFKsUVndG4obJpgzP26gp5Pu1iKXlo
+bVwlGkhnn2lLbb+z3iBQkkxvNGFNa95tbFYUTfncc2tsfk5KprVAG7cqLpZK0B0dHVAAACAASURB
+VBVulVK73upOSalq1SaRpJ1fNlmbyd/FN6/xzFQaYx58JyNYlAWLhI+DmVTUZCZ5TnRXrOBgvUep
+xJ6DQqzI/sHkHCtQSxOc+9qVeGyIPPHS+u4xFS2H9bV016pZLGlZbIrNQDsKo9yQTWQ+82nGibJm
+Z8qsNclyCuVmsCwbVxPFX9p6hvCOUtYlc3wVdXG1lGnHBVP1HtqBzbImipN6wJrmmHwnw9fGpVWF
+Em9uL9FcburnNUJXPod+lVi7BxOqWIsSp5a4HywdMtZn2j2ZMV5wTpRQrWtpdKxcCElIoGXPuIg4
+fFNyQslgwJqWHK9t1VjSHMYiaWt7HpaiDgO/xKgHZYzBV60SjMFRL7LvL1iwA2zFXiR5wZJ6yHZg
+qGoWt7d/QLf3I3D5P2I8FZtCUUKUWZyOBziVBfWXwSlkcThB5dwdrNcc5kpz5erCtmYWDYXf2tUx
+ZSQvkzE5ZVVf9OEUH8XzayaymCfEKsjSdJq8siyAnJMjODuWiVxeXsffEKWjWHur48zQ1POF603s
+2oIOPSPUVoLV5ibuwg9ecIGnBtTVNk5GqQ/fNiELkSHLlYeZFANcMj2uj6ckJXHpUdVEU5pnJOWP
+DyGyXBZgWIR4FXGZHz56yJ26tuoeSdK14lewtHv0/sExNT3H1qXnOc0l+bt/9zFRJJvC+pIcd63V
+wnJVzHWe42rib5ZNsZR85GA2ZaDsya6yu0RpfI7jMGTEev+r1Su0msJnmCcps7GcT0WjyKyUpnaM
+NioOdU38NQOHQCe/Gtaw6uJiWwOlMwt9mkq+E1gOni592/GpdaQqk0YRrmZxrSvy7G52Nziayhqa
+eTGKJyMqClwU6xEoGU4JUxXGObY7zFTVq1pb5aXLcm1+ZqBYtLMvmLRzMt00jR/glnrtFQuj0HO7
+EhBU5RiBkpmlWURD+3jysMm+xmjJ5JjNlhi7F6/c4uuJAPuedFyEDxfjYlyMj4ynxFMomaUpo71j
+0pHsU3kBIyUzGQzHxGptk4Fhoh1lfk3cr3Ylpraq9f/qKlZdSj52vY6tFFyeNyNXd93WHdVYLo5y
+JMTlKTTV2/AtumfiBgfeMoV2pRWZHCs2KZ42pdjVEs8SSzM9GdG4Jed215V5GMg1EWUK+1zmrCy+
+h5rLy5xCmYZL5WOYxSnTTKzk4eCA0ZHc8353xGyhDWg+vvvN1oRoTEKkjTb1OzG/3/06AJ1lKZt+
+5nPPsK2e1EmRUFdEZ6PV5ugNCbfms5Qc8Sa2LktotO22zp+NV5SstLVpp7BJVdp5KbBJVGciUS/N
+KZ1zlmELi1KlzTqrPmuqwTkcxcyOJdwwCv/eqfgESliyTpX2ktbua4ZqfeGZWcyV3DXVbkHiBFc7
+TbPMxVJiFNsDWxPWthPiVmRuq75Y+Vs3tnms3BO7w4h8pF2ZWbYw+FjZonnM0HTk2m/v7rH/oXih
+DSdne1vK0lnh4CzKrx3t2qVOFEni1i8yLG18ctzgXBbUtixMU7kxVsTjSfKCRNeTnc+wVMXcpA5B
+W76zdnnKelc5M1AyjI8ZT8WmkBfQn5Qcdo+wK/IAnMRhyZOFeX27zo6Kza42C5ZX5HNNXfjGqk/o
+KDdgew1T05c+rJ/XxV1CylSzsDrRhamT68IM622yY8kWj47eZ95UlqW8zvZIF3pFcAyz7hlKxsSV
+jRpGIbhF1aaxI5nsTmtpoTGCrfFd4RYYFizDGYV22SUMyGJxr2cqODPOR3QLqS8PTodMmyos03Yo
+04UCVPF9UrLfGwv3rwDMXO5vOIow2lfiNZo4M3kpnFQ2uivbn6WtnYpWuMZIeRmXKjnl83JP/eNN
+Hu6La78Q6203PIJAJ8MtuVEoG9FoymikTMzZHmlPQz1VrznCJj6HLueMlEGp3XmZ4UgqLb3JiFki
+119RLc2VisNLWwoaCkP8luYAcu9cVLZiDKUS4+Qac7tBgHaUM8tn5EqsUmQJRa4hHSmWthlbClFu
+1Sw29TnWwhwloCYrS2ra8zHXCk+Wz8maMm/V1OboRObq2S2Xhr7o9YaLqwzajm5SeTHEV63hvBxB
+Vbkk0ymO5h9SM6VQgh6rKnMSrHlMugtwXspcafRbNcMokg7cig83bshGhejbfOy4CB8uxsW4GB8Z
+T4WnkBU5vWRAsnuAibRDrJKwrR7B9ZWA601xV4PtCstajw60RusXFq523FmdBqhmgSky7IXWYLUN
+6v4b7XCsbB1QKMd+ng9pqPjMYX9K80gztmGXWUUsk7Umu7a71ibRhpvTkw61QKzDCxt1nrkibmLF
+2WAhUWipbkBhRxSaBM2s8TnOII1KspOFPuRC5q5CdayeUGuLcKGe3YooDuU7xcckGQHyVK3ZZHjO
+O9lObZY0EfXsNbnelYqL35JrK0uDEyi8dmJxqSbWP37lCxglMlkN5Bmsbq1zZUk5Gq05SSzemGOu
+MO0pJ0FWcvtI0JSZIjfHaYSjz2ZOjqNovfomHJ9IdeVsf06uqNZQPYW1ikugXoXjeniqY1lSUAw0
+9KzHVBSmnigXBpHN6alcW3W1fo4RYbJM3paQoFJtgibxAs38Oo5LZV1C05v7Xd5VOHZZFhiFUquD
+xbKzittUMqBiio3qQZgK6bKsz8r2Gq6uOTdVC+5MzpPKBT6OrpfMn+OqB5ylU9KerpdFuFKEpBp2
+JNMQ4TuClXaTWHU5iqLGTqgL8QnHU7Ep5HnJsJcRDQ+pNGXhbmUNKoVMpBde4UzduWbhYCtv32oh
+sW5laRNLhU7MvAcKLzV+E+NqTFnOMaH+fEFSMY3IRhISmHGLzWcEgrz2zJ/yze+I+/VXg024Igtr
+eqY8kGOLukJ0DTa2ZpF9Z5vtlmxehWWRqGjNIoZxLLCUvGQ4G1IquYc1B6cpG4+XKKvO5JiwJiXQ
+ZntK/qGENtHIxpQLWnP4aLDwg5/gZCFxPh0T6i6VFhOGexJf2tdE+NUd3MRo7mSSPqK7r8Sn1jZR
+LgtsNj2joi9hU0O7laqPnSrt+1IFf9F1GRb4KuR79aWU60LNyNdSvee4ZO4oyUr5PZKYdnmN6VAh
+3VlKoCHWqiPHbVfreEoq2+w08RZdlK6Ho+I5uZVzpOVXry6/Lyo1poUSpiZN2poTsSpNLHX/3dIj
+VdFbuyprq17kLIWySe2sNthuaiUizbCVeLeh321UG3ierMPXj0ckuiGvVdZYzmTtJb0YXzs3cy25
+F/MhtgK9wlYbu6qU+bOSUvNfbvUqua7lRZ6lPx5TMZrPYk6pVYt4PqOppVHfqZCuLwh4nmxchA8X
+42JcjI+Mp8JTiJKE9x/vkUQnoPwHA7dHrtn398/6pAj19nq8yVx36wVyx60Y6lPVh6xVMcVCZtzH
+KIlIngxJ51pFiGSHnuy+ST5XN3m1hdV8BYCdrMXvDgV4cvvgAY1NtQS+7L6BD5avjLrRCRUk7Ig3
+oD+T5GB2FtDdk2s+ySQLHU2nNNtyf/EHpygmiEvVTSqX5X/Gx3KN3/yT7/K20rV9uDfh8QcCajo5
+OSXNtFheFvww7NL3Jx+j/qJhqCRWHorMD1m6LHNw7aZ4CsU6PD4Qr+nDew85OZB5GTf6VCLxBGZH
+A45ULDLqC/S5tReQrShEO92nFsr97YRL+KEce6uZ8O/9gnhZzz6We3r3/T3uyyk4GmTESoU3i7qM
+ByqvXpZYGlY0tLJghQ5loOQ1UYnlSYjpOg52R66zWjrYl+U63Lp6R7UEbBVZ8V32VDil9+Ft6mpV
+Xduh6qgXsq4VIztmfVWe9VG/xcaS3P/R1D/HiCzEXdxKhqfYhTw+pa4hzMYLG+xlYt2P+zPWNJnc
+0bXgzCdgZM22kxlWVbyKcf+MYSTJysK4TNWEJ4dyn8fHE/yazMVonpwrVNf8gHpbQpNao8QuxQt9
+0vFUbApZlnHS6zIcDmnU5aXYylvkSg7RKOasb8kEri23z7Pohz2JEQePvk2nJje++fxNqlviGtrJ
+lGSigKNelw8eSVz78IFWEU4HdDQOu/H5y/iXZIHtpgMmirx76/aHVJUh6cY1zQdMRqj3TDU0FDrn
+VzcbVGLNRA+OyVJdeKfyInzr3lscfyjXfLXm85MvvAxA9FLJ8LG01n7ra8I29d7tLlMNn6r1Gi/d
+lNi/s77FW2++B8B4NqLQWHSxORhgQUQfAXNF/VheyGpbLnRptcNPvyIsU7e+IGxM40HMV9/4PQC+
++7WHlFqqrd66gl2X9LvnzFnfkA2yr3Tjt0/eY1nJXB1/hYoSkri5h6dIQN/UeG7tZ+X6G7Jp/uzz
+t/l/78tz+L9/Z5+jSMvFXhOjyER6htqC6FYrC0ejCXksL950ZUgUC1ioKC0ubcj97dQaVG8o8M2V
+FyXLfI7P5JoHu2e8P5GX8OR4xjNNCRs3t2t0qhIquKreFXg59aYiVis2a1rx2qk1sRR8FmlVaymr
+4SzJ9W5u1sCRXIu/Cv/y978FwKQ7Y+ezsj5XbLn251ehvZC1N1P23lYWseMBD+/JZho0Nxgpgc91
+3Wzc2jbxkdxHreZS1RJ9vd6kWOhe+Bae5oqedFyEDxfjYlyMj4ynwlMoTEpiH5ANzohU3GRc79FQ
+xuFedsr8sXIGZjlzT8KKwYFY82rvgMNcrM54HPPMl8WS+Lgcqps77ie8/Z4k13JLfh+ZBu6KfJ6w
+xvSR7K6PHu2dJwlPo1PuKL14vS3ucLjcpqIY+HiSkxTiubSdhKCp2PesZKBdmf5UOQbuWixN1OvY
+bvPSX5EuylbrOU7vfBOA129LyHG4n2Ip1DizUo7UK3IL/5zarCD/c+FDCeeELAbIlTWuWnXo2FrN
+ubTJa3/tFwHoLO3InMyOGO4pjd0gZ5yIVf1O0me1Kdf/mVuXaWkG/K1UvJXX7+/iasCSZwGpL67v
+cjPhlbp4N63KhEZFPD1b78neuEVlV+a7KH2M0sw5tT6eJiMdbBbtla4r11ZzfCpafWo3GvRi8VxG
+Z1MODxWb0Cm47Iv7vLbABCQJs1NJUM/imJYWl6qdbTavSbLu2a1L+Co0E6iylpXa+C2FUruPqbXF
+83iGAk95HRYT7pdV/AWJTqNHqCC7/tcy/vgryi5tGR4pTd2Xa8KfmfzcDpWbSpXntqlOZS1/9/Yj
+7j/SkDef01KLP2zLc5wkjylP5VgrlTqvfVmg61vNHKcqHst8GmNXLhKNF+NiXIxPMT7WUzDG/K/A
+rwAnZVm+qD/rAL8BXAEeAn+zLMu+McYA/yOiPD0D/tOyLN/42KtIDcWBB0lBrD3hO0UTOxWLeHKY
+8Y/f+xcABNsOzlh269e2xWJsLF3HqyrEs4yxColxrUYFu/8QgMP9Y4Yqw1aMJM8QuQX9x2Ll3UmD
+my9KCXB9ZYsS8Q6KNGbQlzh494FYnQFzGjU591InJJvK8W7f+ZDcE8vVe2/An3TfBmA6lPu4/+hD
+nm+KxVxpN6l70vPu5VWqFUlW1hQ766QJbW3gefvuA7qpKg5nDqkyO/ND04wfLUlGuZLR2h1Wb8rx
+Xr75EoVKz2WaiCzmETXViMjHEf0zMaVvHcx5dkOu87VnbnDteYmHDzVmvf1wwNt7kgSNC0OciKdj
+nJxv2rcB+Im1JW6uCpz61c+L9zCKHVar8rMrlTGRlvcCe4fVhngvk9MhG8qzsKTszK2NJsO+rJH3
+Tvv0u2Kma6XNjXXxDtzVYCF9QVCX8rNlGXbOxNOb7u3TUrXu2w/2eU89wflze7yieR5/VebK1CJc
+JdPd3L7Oa6pivl8f4SgtYMOXc9RaO8RGvJw7334Amfzd1Rd+mRsvSOJ6Nkr4wmdEku7ltuiChGsu
+m1vSBUyakF/6aTneyQH2B3K89c0WrivPobkma332/pxjpYqz7RjSRcNfSqiK2HalRmqenP0bnix8
++AfA3wf+4ff97O8C/7Isy18zxvxd/f+/A/wy8Iz+9xoiQf/ax52gKDLmsx5pMiZV1/i0aREgrvZV
+6uTiXbHcXGN1Q274uavyIq1fblDXfofCNudts97OOi1Eo2+zX8FyFaegFGx2WmWmGPjtq9usvSjd
+kC8/TPm//kygtpNpdM6gHE/kX69SkKlbOxtGVBYMzmeG2aE8xD5TdnK5vselJJQuh0tsvSDXeeXz
+P02lKYkoy3bIV+QGL+9I4q9ITnnhVaEQD2oN3ry9cH2PiZS2Psuz8/6JHzUs5Ux0jEVdqeJWOiWh
+SrGnSltWLC3zcy/9FAAfvvWA/a7SwpURlsK0d648y+qmvFhL2o679f4p7xZSXcmiHMMCkJUx19X1
+4GzIWlW+03sk1+tf2SYK5fneevYW7oOHcr7Tx2yuLNTA2uijZKTKQKvDjKnec0iLQLPvG7WQKwou
+6/jBOQ0b+t3Cr1Jb0orE3hGpXlzgWVRz+TyKHR4+kqTwaqls1ekyjrZT28tLXH9RNqTlsxbvaZ+K
+MtqR5HvUMjlHq9OmvSwb4E/84i2K8MsAvPGtD3hlXapcN35G1se1nVfpNCQkiNMTIl/W1peSz9MZ
+Ssj7mS++RrEk78ZoX55HpXybyfuyoW2GFXJtE09zl0IJZYxJcHKlenvC8bHhQ1mWXwF6P/DjX0Vk
+5uGjcvO/CvzDUsbXEV3JjU90RRfjYlyMf63jL5poXCtLNX9wBMr9JLLzj7/vewsp+kN+YHy/FH1Y
+bzIZz0TxzJKdf7vt8TOXxapuv7jGkqdlqtSic0Pcy4WWWrhq4ReS4LGbHo7Wd6zCEIayS1567SbV
+QznG0qbsU+XEUKol8UJw27K7bl9zuKShyd0HR6SqC2B0B68ai+GeJIMSY7Ot7u6Vm89z45pY4+db
+q6Q9xROsi0v66P0Drt8S67C8fonA06RkOWCm9f+bt1Sw5tUKdkt+v7JkqKvm4+nxBq/fFXRj9vjR
+eQPV95ckFw81BQq13L1Bj1JxH1ZeJdNe/0pTztvwoalPcefms3xXlb07ns8Xnxdv69oLz2NrEvD6
+JbGIxfPPMuzLNRyMuvRisVZJNGGnKta63fbZXpe57+yId1ANfRrXxPXdqqwTTTREOzjEV43QF15Y
+oqGYjK1tmYsrlztcUiJZEyWgNHZladGqq41LPXwlk0Wbx5JkiKVdm1ef2WGsxCrr9jplpGzbyx2q
+KuWmjH1YtQJXCVA8IpJSvutbLk1HFcu19GwZD9ORZOCXvvQC7+2Kdzco3+Hahvzdi//uDo01WavP
+PXsFgGbYPNfLYFSnraHWT/7cf8AXX5PnUBSGcixeSs+WEC07qvK6Ijrj+pyaegdOYUhngm/ABJTN
+vyQtycUoy7I0xvx4H/aH/925FH17e7O0NjIcK8dXMpHt5SV++otfAmD5ZgOvoi6QbTD6sqCt1SQh
+uVKAu802ttZxrcLFW5bFa+IqvtbQAxX4LCrDc7x4aRVkU1nQ7arPpa0rAHz4qMtcGYQGM4nlL11q
+cXYoi3gYWbSV9CPwShoqKttc28B9RhlXtCNva3uCs3j4VoZdk3M7cUx1pkzRyiK9vHSFYCYLd+1S
+m0ZLQo1RmWL9ljy23+3uU/5AuFjy0Q0i1D6BWTbinfdlofzKl1/Fs/TFsmUOg7FhpyMx+aXWEoHi
++mthhWc/L/Hu0so6US57fqcrYcn1l7bpncrPbszW+e5dSSHVGxusrsuVvLSyzAtXNCdwTRY8dZvi
+VI5x2pvS1xerbBXc3NBWZvs681zw0ddVKHZndQVPX9IsM4upxXIclAaRWdYjbCzIbOSFMBmUSrnf
+rK3QCSQMyqOAVLEslZUmnifrzNMXPiemVINkTh4RZVoFMw4VDSELrVTMZzG+gqJM6nN39yEAq3/g
+84UvyPO78uwNAlWcqoSCjzBeed4ajh1habUqbK5hGmIYTZ5QNpU9WkWYh8cGd/YVOfe8TnZr0TfT
+hsWr4WS40SerJ/xFqw/Hi7BA/z3Rn+8DO9/3vQsp+otxMf4NG39RT+G3EZn5X+OjcvO/DfyXxphf
+RxKMw+8LM37kyOKS7r0Cx7RpaU34tY017JFSaTVW8XRnt8KcIlOrokk0u1ZhNNCoZTbF3xZ31zSa
+WIUKtUxHlD3tlV9TLQBTgLrUWVySucqBdzTl7EQSkCb9XgdfoA5RNk/ONQenszHDvkzj0f23GW+K
+D17zU5xwwbuolYOyic2CzXhIMZKpKW0bt6Pw54eqa3jnIYHqEi4t7eBWxBU9GMyoqytqrB/uFmbf
+93lB6pKmCQe9D+TccxtrgbxU8Rnba2B8lVHPRixp4uuzLz/HC8/elO+Oe8QKeWauCUrvgE4gNsHd
+qPBvrck1D4qUSyuK7nMN25cl6ZY3VHilPyMOxPPqbNpMvi7H22GL2qZk5WsbZzxWBGjHkvmpLzXw
+VTPS1GvkyhVp+R5BoOviMALVfKSmXA+Rg6OaoOQWTiaeklfxCYMFzBk87VC01INM4r1zrgssn1br
+ln4+oj9VnIV2vo7LnO6hJKjfOso4eEc8M/v6z9FaFUyKPY4Ja+IhLJTNyQbkuqbnowNyI9djZSO8
+SO7foopJlVeypgRAaylLdfGIOlUfS2HXk5OH1JV0CKdNFGhT4BOOJylJ/iPg54BlY8weojL9a8Bv
+GmP+FvAI+Jv69d9FypEfIA7Mf/YkF5ElMaf7H4BJsRUyezjP2ZiKk9Heb1BeWXSUOWBkIZTIROax
+T6Z9ENF8hK9y9pbxKTUGTqNICCyAcjHptiFXVzTK+pztC8T16MN7pCpRX3E8XIWPruqLYlsZmdIq
+pWnCmYYXd8/6XDmUeL++AvaZMkA1FlLgRxS5Bu75jCSThzXNpoyGkuc4fEOl5SdHtL8kWeokr+M4
+cv9WNj7XOyzzHx61fX/vQ00rB5MiZap9Hgene0ShvOiubm6uPcUoW1F21iPS0MxZ8bCVLSZJjuid
+SYw7VK5GM6+weVlc4/b6GisbSmriL+MvCEmilFpDXurS1bbf2j7zU3kOB91dxnPZDJe3Kiy7AsHu
+HeziaTUgjuTZ5cMAZ0WBUMalUGBOFEfkCnbzmt5iiYCGR6VVUGinrbFzSu2YdH0PR7sV7TzA1hxM
+nu3pvOTkWhFLJjk0tMQbNJkN5ZpSVRAb9k8otaqx+9ZjDhRM9cH0a7x8InPYuLGJpQCpQrtni3nG
+7FDAYLvduxjlJu1YIVW9p8raJlaoOSHkHakOA1zd9Gu2wVUCydgxNPXv3PacNP1kMOeP3RTKsvyP
+fsSvfuGHfLcE/otPdAUX42JcjKdqPBUw5yyKOL37PnEUE7Rk95xOTxgNZEdMszGFSnnjBeckKoWt
+DSnFfXo9sYI14zCeK6HH3gRTXVBe5aQoL4AmxozrUSg/wMlkl69+XaCo37p3h0Zrwd/gYykZiKf9
+/6kV42qTjDuwsVQHMJ1HJDMJFeLRlEibeXylgKcYUag0ejS1OX5PrNgje5979+T+vvuGgFw21zo8
+cyBW0mQJM31S86TOIFqEPH+ejs0AvnI3xsD2s0qQctqn1JDh3oO3+ZlH4gbXV1VF2apzNhAv4DQ7
+w3ZUiKdZo6IQY9sKCbSJp7mssvXL6+SKKyhKl7bCvI3XwlWNxXweEygwasHnaHo5ZU0xHcZBHQW2
+tp4nq8l1HA6/zofKnbF0Xdx6U105J8kp7AyFbDCdTBgor6YzyWm8JOGKX5UwIXdisqlej2szVrIY
+N+8TBnJ/lbYhV8yFMRpeZRGZLbMbu33SE4WexxmluuvDRJ7drDcjCbXDMxkxi2UtfP2rt9kO5O9u
+LP91jPJYxqOHAMzne+y/IZyZ/+zxWxgV1LlVrfHaK1+U+2iXYInFL7QzNKqdMVS6951qzEyRAy37
+Emld7s/Hxzj/6sFL/7+PkoI8jymZMenLAhvPhniXlKrdjsgWL8Koh1WReClTDHk8sDhTVp3arRcx
+hXbGJYbEkhCjmOVEimicHYprGJU5As6E796+zZ++Ln0SDw/PaK1Jtvt61uJ4KC7jgXbZtW8u0VSi
+i6xeJVDyP9uaMXfl83h8hjZXYi+owF2beCRx6L177/Kbvyub1/3jY+7taSgRK4JtrYY9lnN8ZvsK
+k7ZUJSbFmIM78mKm6Z9/2IbzjnIsY9he09boFKaqVVFYYBz9kq+dn2nChwcClDnYGzGfybzFD4cM
+X9JNtLF+HgcHsYRBqdUlV7Kbce8DzFjOV6mm1DsabjkWub7I6aJEEKxg9STM25vcY6LgpEZ7iUSv
+rTzY4XQmm0JXy7vj4TqFdaoTe0Y6k817MBzQU3RjYY2oqaqXPZO1kLvF+YZkZTaJVjtO910aymrV
+njcwKKGr5pGSPKNQunh7OGWgnwdZglFK/ORUNwLj454pu1WrQt2WzeL4/WPuXpP7sP+dFkV2qtch
+Ri2fZhyoDmT1KCfpKnL2RolZbGrzCSaVOYw81QB5d5/5QN6BrGxThIpu3M7wZ9rCXSnB+WTMSxe9
+DxfjYlyMj4ynwlOgtDBZhSKHVBNDfrYFZ5qpp006VopwSqKBWPpwWTyGtFGjZanakLeBUy7o1xMc
+TTpmZYw1V3IV5d/rzh8zmYr1v787oKvMuKMkwvTEIlypLBNvShiTaPKt14+oOwoZXrcxarH7Ucnp
+ofJBVDI8zfp6uVgl39rAaV4BYPOWTfstwTo0YsOyJk0b2kdRMSH7GtpszurEgViXR7spJ2dK1W1K
+jIYKCxXoAsi0SlIa6GqFphpcwRjxMLKo5GhXEqKrWtVx/Q5HpzIXs2jEUk0sbZJb3L4nWAF3703m
+VbFiB5pkC2spp325/5pnsRJLCHbl1hZlIZ5Oc22TRbtGEi08ohJ/WboE1/wTbJW2t0wAtpy7fXNK
+ItKijI4VPj0tiTR8TAOYqohK0GrTUA7K0Sih+44S26wp23PdPheISZPZ+RqphD1mQ9VoHI6pKZ1a
+oL0hUWtIMVfW6e0mqUKMX//2PS59RqrvVV1Ph+M5/Z48p64dEyiXQ2pbxGNZL9NHR1QQrEauiWK3
+0Wb5RVnLv7T6bxOoJ1xpVwgUhp/lFfKJhoKWPP+337tNmEnos3OjSbslAU7C7gAAIABJREFUyWrP
+NviehE+R22Q+k3DsSceFp3AxLsbF+Mh4KjwFY9m41RrlKYwUNfh2v8u152V33T7bJ1WUYjGpUdbF
+kky1FDjOU4oDsR7Hs3tUFMXW2ljC0lzEtFIyVfjfUL2RZOjy4EgsyvuHpwxjSdTE45hTtbxlVC7Q
+1EQzrYlnJbmlxJ5eyGpLYudk3OOoK8e7emWJ4FwvQuM755SFpHJ45PKTz0i33KsVC2tFHsXbr4tl
+6J/dJlaI7nA6o3cm5/5wsM9EaeUw36MrQ3v3TQmOph8z4LCrJc5hhL8gSvVc+kPxoJwDsXxldcLs
+QPIySZax1RBL2miHzI5USzGd4Swk1Bas06MNqgpFTqMJM2XYHs9mVBVqnM2nsEjAKXmsa3wcFWHZ
+euUq63eU8JaMQuc7b36B9Ru/L8feE6+qNxxQd+XcJqueS7OZ4QxHGZsqE5dMS5LTuVju1voOaGI6
+Nx65ZjbHpWF2Js/MSiagnlDZUCLgGCa2PL+wbDGxpQHpjfuHLD8jkPWeat5N3TNqmpT1DlNQmPfw
++JTj++JtmWaLzNOEtXoK2Z1TkvfEUwy8Jo1rmtjN1ylVL4J8zigVD/nwaw8B2Ht/RltxPVurPrWF
+3FyxRKGCQbVawMHpJwMcPxWbgu16NNcvc/boPeba6vvwaIybyKSaJYMzlc9Op0NSkYcwVvjmZDBl
+HsmCaI+H2KqkE8/PGKhCVO/NxxwrbdbhWGnCvJTBXCZyv99jOtUFVuTMla7rOIvPpZ5quuiGg4hM
+a83LYZOmL7/fm8JuV17qZ7sDVlVqfAFVzZOY8YEAiKbDEc2uPOT+tMr9u+K6v/5Qknrz8ZDUX4il
+QK5iOFHfBZVzt8riXIjG0n89y7CslYMTA32tKMx6Odc3BMbcqHgMp/L96mhBw57R7Mj1LvtVxlox
+2bi1xnpN/q4ZNYmMfP/KSKDPTjnGVjDRWRbS1XvaPz6l3lFQUFni6HfmsQrzVh2M6lFuWje5+fxD
+uQ/LJ0mUN3Mj5+oVIQ6ZZ0pr30xwM9mwpl7AUKsTj4+PWVJG7Ka7QuOS6k1qZcgJhxz39UXvTjnT
+jezOB4dUY7mna5eWsery2VNimUFU4mvvQL4c87U/lPu7d9Dn55UmPq3KswsjC7T7dOUqHOyrjPwj
+uK9hRenWIJe/O3xdIMrm+ID4njyn43KNVCnu49mbVJQXtH3LIdKQ5+FQw7lqRKMhRiaLI3IjG0s0
+sVhRbIwVpZyNLsKHi3ExLsanGE+FpxDaLi+01nlomcUmyoPJGd257NC+s4m/rMg0p6Cnu+7RW2LN
+tz/3U+dyba1rSzgLdY7JCNtSqbikzvtfF1LU7ESTj1eqjCbiyk1HM2K1nlFWkmoIEscxvsqkWyqV
+lkYFM7WYSXCDM1c6JvN5wpF2Cd47GvCMwoMXwMN4NmP3O18FYHhs0zuVYzw6tfiDB5L4O1TL7RYF
+tifu9cnKiEtLkmSy5y6ZknsUZXleK1/wOluWTamPtePWmJ7JhEbRjKEiLPcfH7LTUvivsl1XnBnP
+LMs5vhbafP0DSUq+85UPqHxZw6DREKsuxz4ciMcTDJpMB2K5ujOfvX35/OKlTXLkeNnYJVO3PNFG
+LKfaZq7Sdb1JfI50dBybJBErHz1oMw/ECvdnYtmPHvUJNpWp2LKx1TJP76XcuibdqOvPXKKxqrB4
+XVDOOIZ1DX12EnZfF/q7u/cPuKIeUqPp0NqSJF+ujXl2mIA2uY3vDPiW0gIOpglG5d0SDbtmsYet
+HohXuNS1BJ6WJZOBeEh+4uEESoASiQW//407+HVFMWYRj/9MVL53Tx+woWjE8J1lBkoF9+5d8Y7a
+WKzm4hGMDjOcTS2/5yndI8XydI85fqx6JU84nopNwVgBVuU5Wv46g+whAEWacfeOuNLpX/8SFeX+
+myYjZg/k5jtKluINzvBWdBLyCoWtDDSdDSqqZzi/vM4rx5L1bbwqzDb3p9/kDV0ck+GIiqoDZXZ5
+jgbKywxfYzWFIFFExTmgqb2WUslloR/0J8SqFfnO7V1evqKcjrYsxrg8wNZ8SH/vMZHGtXMrZkc1
+ETNl+bEyi7knv9+fTnmg3Ye9s+hc2r4sCxYCidYC5k1GoZn8WXVKqv0j5AWBJZvobDbmtKf9GNqp
+uFJfZklFSK6v7LB7KMc97UY4WrUJlkPsUj6vhPJiPk5eJztRl9tEvPqazHHLMtipxs5+jq2JmWSm
+OZ5yRtNWTcisxopyKZIZAktmeuvFLcIH0ifwppKeNOtViql2JVZGdIyEVY2tgExxD15wCVtBa54v
+cb91uUkjlpcwPxyyciYv5n/yV36R6pq81GEwpaw29V7kPmfdIx58W+bNOoOVA/m7pdBleCIhjd3V
+8NIYUhXGuXto8DX8XcYl0s3iN//gt/iVn/r/2nvTmFuv677vt5/5zOe883Bn8lIUSVWWREuyJKBF
+k8B24KYt0BYOXDSuDRgBAiQtAqQW/Kkf8iFNkTYF0qRu0wGFm6Gu0xhOWtdRXaeuLMmWSIsUeTnd
++Z2HM5/zzLsf1nrOvdcVRVLmJW+RswCC7z3ve5797P3sZ++11/qv/1/6VF+XGo+kfpv4Dfl91Dth
+vaLi9wz+itauBCPeelPjXyeyYG+GPjsqatrZalFolmt0v898XZWjxilf3/9gi8Ly+LC0pS3tEXsi
+PIW8TDif3qTW9hjPNRruwoEWwezdvYd5QQJORVKCRtFdFWSZ9N9m+qq4Wdl3XqGrrvbci0hPxZ09
+G5xwYfsKAN6advt4jbMz2QVcxyFsVtrxdYYVDNbx8FTP0GayQ3ea3oLQwvdzfN2Zy1ZIOKlcxhn7
+53L/u0/Ljhj5O9R25OetrMlxKTDm9Tij0Jx+GIqrGmdTTrXwaVjMGJyoevQ4obB6Pw+JwVRwZxeY
+6ZHCzR1QiC7GUngPJM+OrUa7tVoQcqzy/XW3LvBCohDtVo2sr9oLl3eINOhq13WXfOs67liuu1lr
+sdaT+8+cEpTgJnd8xsoNMVQEXr3Vo1AMAanP9r7ArpNyhFdTrcWhS+3ai/L578tuNx6McTqaUZis
+UVMug2zT0teiord/t89KTTyE1acEC5G1Pca/J5yRhwfv0NtQjYgXnsdblaDpPLtPoRmf0VSPCQPo
+H8vPsWMI12Rsnyu72Klqlq4oW/dgTq4IynE6oKV8C27Hx4zFQ/rNX/2nXKwLT8jmulROplGHd2Zy
+fNw3sKFA1QubV7A7MucmqeE8ViSkeqmXey22n5LisGA1wlFRm5kdgGY4Dg7OMJMPln0wFejl47TL
+F7ftL/7ln+Wt17/HwaGkfMzUMNCXMI0NTdUSzLKY87nCdVUKvNVqUqvSO37JSKvFZrM57kwx+pFH
+pum7HSUT2WoGhJohiPOCtgJ2Jod7eCvyvRsJmIEsEJ9oy3nz3/nzn+frL4s7v+af8MWnBJ/+D2+8
+xP/wd+TBTfZiripRiVHCzbP4gGNVr0oLCPR+rLUPYgL6PELHx2gRdIpdEJF6eEz1nJxgF1mHh8la
+H1aXvH9XYhhHxzc4G74k91y2+L2X5dj03VdlOalNAkZTcdFP5jFxKlfME4PRdOckTYnTCiosv6+5
+JZ2GvPwFJasqxnutGbKqRKqDtE9diUWmml0qnIJ7ZxUZbc7OirzEr/V+lBvfkPucDG5jRlbHThaF
+Mi1wdGEyNsfoz45r8L1KeDUn14XcUdasMKzRcFVYuNFclBkbHCIdWz94UH7sKqPT2SRlqsIwq7Zg
+rgtrP0vYO9OMiHJ4vpuZBxQquDg4WvJeU11RJ6xzoS3H45X1Lony8h/v95kmc23DI/BlnCN92Jmx
+xNlk0f9ZxYp1tUfzc5Li/fLuKl++JhvqT/3cf/Jta+2LP/BmWR4flra0pf0ReyKODyUlczvn1vE+
+e3uyk7hFXskq4zoRRSUp75QUCkJJUvm/k9SY6G7mG29Rj+67JYXutsbkBCqFNlEP5CAt6M5UTtwP
+8VP5fbm2RX5Zdjbv9dsMlUJtT4uBas9/iRsv/1MAmnaL/fu3AXjl5SHnfdlt50XKGwp/DeeK8XVC
+clVX9nDIFa7r4GKpPlfgUZngKR4hJyXSYOfcxjxcG/n9xOgr38/BYDuyAx8ef437xxKUu+n0uTmQ
+cR6qu3wv6XPel2xAMkrJVNuxsKCbLtaWRJV346mL70Y4vkbWM5/hXHb/81qd/EQ+bzXai2dVKCx7
+VrNM57rrJilFIGMUd2OOjsSVnveHWAXhVAI4BvAVIxK6Ia5mmjzHwau0Fgu7ODa5uu85TlERO5Nl
+E5xSn69raSqfgrElaNbJURno0Mk4KFVLc15gqipPt/aeHkJlFovzkP/machaEyesly0uX1C90uYK
+qa0IcOYYTwONuUumAciJBnDTPMNm+aKNuc6td0Y53VckKFm2A9L9ihjt/dnSU1ja0pb2iD0RnkJa
+wP2R5Xg/YXAoO43rGgJfVvNWw4CuiLOiYDaXFTHQHbMcTZkpNrbZdNj2JeiYeAa3J6uywaOp/P1o
+inA8d3G0zp/MxQ9UoHO+wsGetNE5ai52jagtK/jbt8ZsvCIFJ6/ceodXarLD3riRU1asUGXJRM+n
+M6sybybBVJu8KVGdE3KnwNFdbKbLdFjAyKsCnBBXqccCiirA8C7hILv4v2WoxTzDb13lrTeFNe8g
+eYPRnoxLuCK75HQ64c6h9CPMHJqqmzAbxVQF2qHJ6WqpdbMu31/tRdR1fGzDIU7ljDtxM1QhjqE7
+YVMDqIkWGh2ep4xGmpKdZgy0IKy/uU1Z6RfEc6yO3UJh3PVoKrai26oT6TPzeIDujHOwyqhVFYyl
+ZUGgXkBQZBSBzIGaMaR1ea4bjsdAH4qvEOxxPCVTxOOoyBdxW5xKnO/9WaHxJSzEyoGQrykseeuc
+Z65IMVPduHQ6Eg+4Uquxr8/k7mTE3qnEVc60uqwsCnKnmm9moXExP5zSHoh3m6YF58/tf6B7fSIW
+hTJNmNy9yXD0QCmogYeKJZFlBaVTRVccbFWbr9FdbI6r1Nv1esS1LYlk7+60MDqZJv6Ytoq2nI4l
+mvz6zTPySpEJy6QKgnkJdXWf891d6jXB3a/nygI9/jqnnixew2jM2Zsq0tFPCXTChkGOo5iDQCer
+7xusTuKm7/LpNXlrZnhE+mJVLGJ+kfLGQEk84oI40+o9x3Kup5E4tbxXnDg5F1f8jeTbvHYiP6e3
+Eu7l4hJvjCW4OtybU46VTMSUpMrBOC+SB0HOyGejLRmfz35KJnG00sFqNWjbqeF68r3ZxKGcyziP
+S7MI+LnKCRkmBVn14tqYZKwL59EfkmqguLTlYjFw9eXuhHW2OxIcvrqxSUtBP1kxJlXwWVLmeHWl
+79PNIo+LRWAPmy0CuptrDdoKQgp9w9FUlsBYM0p3+wmOK/0zibOg/TPpewfoq/W/G8FuR551vW7x
+9D5eUMDd7uomz70g1P7xJKRTk4zCtctXOL8jL/SrR0c07siLfkOPA/P5lFQBUnNbUuixw2RTJipL
+H3NMf7Dxnvf6sC2PD0tb2tIesSfCU8iznJO9c/KyIPArsQ0WkmhxkRMlsnL7xlaxIExFahlGbGol
+4mevP8/zzwpS7PJnr2DmCgMe9Ek8RZu9JcHA46OvYxVVOG3n1EpxGR0zZnVFdsRZEeN6qiTclB3j
+1X/0HV6+I4Ice3dz5qkG2iKDp4IxAQGqScPzWjDlNHy+0pPdcXN7k0sK1+2nLiokzJmSp3J+yO/c
+F5d6ejLmDVW53nRyXtUjQTxMuJeKb1GJu5Q8eKgF8OpLknr8/Td+lzfuS7/D4RhPWaX3htL/+HyM
+UfyDaxrMNXXqYmmr93NppcuXX7gOwJ/8GclsNWxEoQxRthkyP5cxPjm5T96X3epeNiQ5kfubq4fS
+oFykOqeZxTfSnt8PHxR5mQd6CDVPdtqdzgo/8pSkeK/22vQ6Goy1HVxNUdsU/BVJ36VzZatuNXAz
+PYJlc1w9B2xvdPB89UbSgHem4qJnyv633z1ncChtZK5dsHgXxfcL8T4wB1BUPP/udo3mVcFCPLVR
+khXSlxevCB/DuL7JimJr4sDF6pHOlE3Wlb2qfn+NrZ6ke9NC4Ponhy5Heuwq05ysqJCuMFexm5NT
+h60P5ig8GYtCluecnJ2RTTNcjdhnARQa6fUSoC6uk++5hFoXu6oyPl95/joXn5GJ8olnPs2Wvmw1
+t4td0ZdlrYuj57qVUF6OdL7P6ZEcA85mMYOpvPSNus9KV1y4iyZnOpYBDpV+69VgxvgbMjlm+3FV
+RIljIFQ33wscLnbl4X55U+7zE5stPvUVealMvUe4og95VGK0srGrcunxbocvBAJnPdiY0jyRh9/y
+DP3b8hLe6Li4h1qD8dB4FvoiWSzHWup76+Vz+m/KhK+FkOWKAZmqfLu11Cv9RZPgWLnGbrPJU0qJ
+f+3qBT77eRnbqz2l0a8lixfE8WHmyNjmjKCmXIrnhmNlrj7O5U3J3Tq1fenTLCmxej9leGNR9osF
+35G/X9PjwPMbKzytgrc7GzXWu1qKblwCjQ+VxiHsyaI+1zLzfGIgUEWxwKNTlw0gCkIyjfDnyRR7
+otWc63Is2do/5NaqZpROStJY55P9wfLuLQeu6ZHhp/7tP4XR+6hv7VDTY1NrW+DOtaJOqJTsftjH
+KJ4izSxOKIvCzmaOo1DxT1+SI8V30oSZCgSbAnKNQZUGnEqoZjJhdlAB1N6fLY8PS1va0h6xH1aK
+/q8D/xqyQb0D/PvW2oH+7qvAzyPe61+01v7me7VhS8t8mmGx1BsVR31GMpMVPKqVRBpo8jyXuuam
+n1YV4Ree/RzPXJXdo73ewqn0/po1rFGF5vGMspTrraiW3/WLG7ga6V5ZiRifykr71nBMPpAdtLmz
+SqshO/o4kxX65NsxrgaaumsRA4WwJrHFq8tOcrnR4gvPiAv7jB4ZrlzapHVJNBL8+gWsqy5zWVKq
+e6wbJkl2n5WKpCR0FoVS40mGo5HvyWyOsdV4jR+M50NpibsvqTscJzSV9GM6mzJX7oiyUnD2Ippt
+rU4cp9Q1Cv+jly7y45+6AoDxE7qpjJ3RoqWg2caqKIopPbwNeTab6U0SxRZ4qz2sBnkPRgpdP55R
+6jNtBhGpIiXnmbNAX7iuQ6S6FW3NHAUNQ6By77VaHeU0ITAO9ZamO8omZabHHx3P0jckemRq1iyh
+ekX1Xodc6fRim9JWeHBdqdQ+cWGNl94S+rOa6zFRlme7CCN+f2tFDs/uyPzs1Jo0rkgg0cQxJlSR
+oEA9qSygUJEdv9kE1SWJ53NmpxLkns7OCVeks5dV+ftsMGSipDf1rk+qXkOap7h6tA5rdYazH+zV
+/FH7YaXofwv4qrU2N8b8NeCrwH9kjHkO+GngeWAH+GfGmGes/cG+lrWWMsvwXQfjVkxCdjHw1roo
+docQy/a6RG2fe1Zw7RvPtKjr2ctvBqDgpTyb4lXXsw1wlLFHcaJPXX8OVyf37ZOjhSx9ej7ljmol
+NhtbdNZVOCSVl3yQJ5T6QG1e4qpLnKYPWIP8Cx6uDm+g4KfmxiqBsjQ5brjQrsSbYRZALQXbxA5G
+qyQbc5eaXms0zxcPLUosUyX6eDc7Hqt6U+6R1GQBSPsJqb40i6kdQpHqRKpHPNWVMf7Sv/EsWz1J
+vzKY4q5qCXQhx64InxI9dniDBd66Vevh60tqgxkdZbmuT+T4YfdvY5WFyslj8uqMPhpVhZ84xsPX
+GFPlDhdJQqYvZplCobGWsmHwq7SfKZlVSCU9fhBMKTSbM8kcwg2ZC8HMCmgJwEKkx1S3VtUX9Nhc
+VYbxUYqjJdzvlvZR3Rw+0Q3Z0OPm8eEZm6rBHBUFfk+FdJqSpvWwlFTzyV1IEaSxYaLVlXHCghKg
+2iwurq5wEMtzHKcZTa1j6Y9LdD0mn6eMi4c1w97bfigpemvt/2EXCWS+gWhGgkjR/31rbWKtvYUo
+RX3+A93R0pa2tI/VPoxA488B/0B/3kUWicoqKfofaMY4BF5AaiTaDQJrdXTb8XyHplatXGjU+cTz
+ErW9qoQY7VqB0UIVTEBuZRezTrDgBCyaMVb5GotC9SJMQaks0KU7odS0RhimHB1JBeM4m/KiJ+3V
+6xokO3HIlZzF4OLrrpIXDp7m4y+su3zikqzoG7uyO4ZRjzKvwCbThUuNl1FWGRUVUIm6dQKlkvO8
+mCiRa62VDrV7Gu3PQyIFsjzsLzxcEBUeSxtus8AdVhDlfPFHVgOKft2jq2reF3p1nlENxmc2V+go
+IUveyHGMjKEbynElLwOItEjKlgtKMBs6+BsqdTdOcc+UVdqTgFstsByqtkThWQKF/qbWp0J4+QZ8
+nQ+hwqvDqCAqFGJtZ9io4qEoKbRThckXxDaFBhHT3DJSDYmx45KeajVuuUq9rSrmvo8JKwIbGdFO
+z+GKBrQHrRGnY/U8chaAsyoP4ToQqKfXKoOFfsV4NqXbF8xGuFunUPGZrMJjBDnGkbGaE1MoJDzJ
+M7ymeFthfYaXKF5CWanbnRbXJtL/cRww7ctcPk0TUvUOHLeGV3ly79P+WIuCMeaXEH7QX/khvvsL
+wC8AeK7L2TBhWmb4KgoSRNliUXDTlCiQF2t7e5vrF5TIQ1MtRT+k0HOmCTqUlbs0i8j1pQmCOplG
+gL2Bnj1tQtCS81kvTUiPdQEpzsh1sTg7PeHGXZk0W5dlctimR6KyufM4pdQXy7HiCgI85XW4tHNJ
+71OOHf6mJdtTgdbGGMfTly2OKStAlkoeeYFHq5T2yJrMa0q0GrRJFEw1YkjyfaofHv4k/JQsnL2T
+EcNCiVnTB4tBBe4qEoe2gma+ePUFPvmpSs/wWSJXkY67zxMjHTeJjJX1cgKtr8jnezh9PRLUR9hE
+3OM0vk/AFemKkeNM1nJIU+UizAz1UCb66OhskRkprEU/xtFF340zfH1JvXp9If6bJAmDc/28ky3e
+WA0jMR7EvHNf2l7r9XBVdGeaXVgsxLUowhgd82rhCS2rVyWmdD1OeOekAjI9pMqli5hxDDVULCZy
+2ULmWbN7AXdLFgt/pYGrcRJT6Ovn+2hICTO3zPXok9uMngoP5+cWJ9fMiFK8b+y2SHRc+4cxrzuy
+GRrXodT6iCTJmDnvr0ajsh96UTDG/CwSgPwT9kH99fuWorfW/jLwywBREHz89dtLW9rSgB9yUTDG
+/ATwV4B/2Vo7e+hXvw78T8aYv4EEGq8D33qv6xXWMC4CRskYR12neuER6Vq8EQSgkeOLtWtEgeoK
+ZrITRZ0IR1fd7DxZRLWN5y0guEUJjQ1xLYxyHzJvkh4pRvx8gP+MXO9W/DTv3JVdJXAyRqcSUnG1
+Oq9IQ3KN7hbWLKrvitwsMAsNd4PtnnA0ukZ3F9sm2BLvwNoEO5HPHeNiNTga1FTGLfLpfVpCNXXf
+J1Kpu9nRhMs7yuZ7y/JeIuO7oew0x2tnHJwqz2VZYKub1jFO53NcpW6bzcc0U4mW28DBMeJt2HlK
+py335CIBUxOmZP0j7VNAqYzDpXfK+JbsB83iAnPdgds98SpmL72Bq+5zVLoEOhXjPKXQar+SkkRx
+K8rihuMEzLRs82xiOVYN0TzNWWtqquHMp6HPoaN4hdZqG/ZlDEfDhJUdgWnP6hZPlZu9eUqhRzpX
+CWLagUsT+d56t8XVVfFYv5fGC8yCfSgzFlUSbXOfT27J/tjZ6dFcuSLjZdKHMjcyJklR4Ct0PQwj
+Ir+tj+YWjgK81i5cwm/JmK/NdQyHZ9StSB3mFyP2cxmkt/dPFgHkssxJ8w85+/AuUvRfBULgt9R1
++oa19s9ba79njPmHwGvIseIvvFfmYWlLW9qTZT+sFP3f/QF//1eBv/qBbqKEjWnJPMtJK0mwpKCj
++dwYQzFXkZR8jxUNuvmaVhsHIwJV9S1WIxyFkZrmDFfTXkUZMxzIOXI4vA3A6OiQu7dkpT2bZVzY
+lpV9p5ux+7SkkPr759hc2ZK08iwoShyriLEiI1fvpixLUi2wumsOuH0sEmpNpSjb8A29toqVZilG
+U4TODNjSM3AqbcTnGW/ceQWAt28ckykBLbllpDDnXtNhqtDd7F1Qt61AA5Q7W8TflZ27yB6wQFf+
+QlpmpKXEHNx2QepX5/o+vhLTuu0mjlZH5Z6KwZzPSVL5Xn4+gJ4i86Y+juowzE8P6N8XjEeogbr2
+ZkhD+R3CmkOzlGd279YJyeLeLInGhzIN4B3bOaVqWdw7GjDRWEwyG7GrrMsXugHdy7Kju6H4UqeF
+x50juc87e+/gf0/YqGu1Op/fkfTr2lqTSFPNF57W+HieEmm553o748KqeKw3T30SLUYymi71HRer
+wcWBb3mjL2O0emefWPEwcewSKIvYyqq0W2tmROtyv57bABk2XKfOrC9xgtO04PA7wvK8rg5RVgwY
+aHHf6tpVrq8pVqdTZ3qiAfSyoMz/f6g6XTMOz0Z1zguHsVOJm0Bbob9rUY2tjrpOXo2ZuoH7Sqce
+3HW5tC4Dsn19Sk1x7/7MY6oY8DwZc3wuA9gfi/PS9CMaqkdZ9I+YqQiJH6c83ZWH9P/cP+DwVAZ1
+W2GmxjTIM/lbW5qKCwbfcdntSNvPXdikG8lx5fzgNgB3377HhRWZEOudFVoXlTm4s0qh3JT7e7KQ
+vP32Tb72hxJQPB1lPK+Q6JWdFhcVsHPtYM5MsfrnOha5hQrpkQMGOY4UcbmoPqwWBGBRW9Ct1fjU
+riyK7cxjNJaXuD/LcTXA1fDG2FBc3kzxEenkPlaDZO72JlbL3UejswUgK65F1NdVpduR7z3jdriz
+rxWqtiTU88GuFzHSI4OUoatozeJIGHL3SMFGwwHnKtoTl5ajodaKrLW5UlMdRyXZeenmIW8fyFjt
+7fexilnptupcqlSn1y+TZLqInCoAzji0Kxq3doMrGzLPbuz1GWrb1cLqYai5mllIC8ZD+f2N05ir
+Sihz7/Qeh0oReKUCtW2t8fwXhK+xc6HBVCXsX7t3k3t/+CoAX38a+/0kAAAerklEQVTrkNOh9OXZ
+jsyrlUYAgdzvajxjEst86UZdDjjTeyvxzAd7zZcw56UtbWmP2BPhKcyKmJf6b5IHEamSqTRCl/U1
+WXUvdOtcuapBm+bKojhqGIiLlIyPOR/K6royWqO3Lru1k8+JR0r4WQb4GozstGUXCRudhZ5Ep77O
+TLUkwzwm6ShG4ns+Q5XxCvT7heeRanVlkiRYdRW6rZAvXdEKvs7VhZs/TsUVPUoOKdStmw7HXKoJ
+mcZafZNsJF7MK9+9AcDh4YhVReut76zQ1lK3S+3LJAM58nS7lqivUFn1FFwerPQu4K2KSz0cp8QK
+McY+AOlWiM+12gqJVntO4oTcEc9keDgmWtHU2nobT+HB6ZmM6zyZk2mufHq2z7e/838B0D/ziBoy
+LqutDTY1396si3f09FaL4RXxHo4HR/RVtOdecgeqYh77oCK2pceuLC+YKax8Pi+Z6/FilkJ/LJ7C
+W27O8LZS5x1Keyf9Ef1T2blzx6IZbHZWPC5eleDpc5/7DCi8PS9lBz6a94UQAWiOYTpT/EKthlF6
+viowmuWQqKve8ALU0WWzs4OvkNxa1KaTamo8kzl0fnTM2YHs8vWNLaYHMhZf/+1vMhnJnLwwafKF
+5yVwfekLEgTerl3j4J4wWKeDQ/JQOS5a+QNC3x8ir/dELAq5gVO3YFxOH4LdFjy7qXLhQZs3Xxd6
+7r3eAFdFNrrb8v+numsEgUy6rIhx3IropEN0IlfsZwVjVdN58+U3ALh9esSuSsqvXFhjc1MwC/UL
+bUpdCJzQp1Gv4hLKapwnlEqKUtoHjIldt07dl+PIf/1rv8OJAmFWlA/wWrvGtk5As22JBxIn4akQ
+IrlKfCYTJkt8rjwreIxvfucW33pH4gufXT3kdCKTvxfV2d7SUmVNQ8QPHR8ssH8guIKzw5MFlNo8
+hNsPNBI+LzPeVMryqD+krXiDjfWVRZwkHU6pBaqPqRqc2aklUfc0d0LmmSpPOSOsSsPPyjmlkeuV
+XV2yag7XL8t4X93w+c4N5UE0JdlDSIvq5wpL0Kh5TJUMxl8PyZV+0HFzNhryLEsH7u3Ji9Vek0Vj
+s9bFvyz3eetOn0D1SLNZwZvvSN7/kytrRHpMdZTUZ7XRwpnLeO9lxaLuotNxcDzJ7JhM8RZFiaex
+Eb/waClL1fHtW7yi2O1bb+5xvS3fe/EzomhVa6XYvj73wZz5WObFWpbTaciLvv3UNkf3ZIH4vd/5
+vwH4TPMdYVwCultbbD0lP2evvIOnC5bvOrR1/g7iIe/HlseHpS1taY/YE+EpuEDPOMSlJVe3p+55
+PH9N3G7XXYW3lTOx3iNV3cELKgffCF1aHcUe+B6OVoOUIbiRBAzN8GyBBfZ6EpFvTy1WKyqH05z1
+prbtr4Iju4Nba9Pt6CquAaL5NKGIVaq+tFRlRd1Wjc2m7DSv1Rtc64mns6keyqVmQEsp2NY316h1
+FPI8m6GXo9YUT2O3adjdEk9h71NT6ntyfOgFDonmnbefaVEbSb9vHoj348QPtCCwhr13BNNwcHpM
+WVGbOc5DuXVVZ4567OoOtrHe42Kjiow3qKkPauYWq6Q1VrkLovYORSpuec9f4Uc/o7vVsEVrW7yG
+7HRKK5J7jkOtWnViWpvSv7zmM1SiliY3FpRnJRZXYeOrPeWG9EIKfdadVpctIx5G1GqydlHy9/cO
+xhyeKHZCx373qWt8Vgu0Vuu3aVR8nfmYrl477HUJlSrN1mRuOYVLQ9lSGrWEbkeOYM9GPt+5IfOh
+r5J2JSXUlRejs7HQyBgNYlZ6ykPR3uAzT0sh39XPyPExDAxxptD8MsZq6efVy8+BVS4EE3CnpoHw
+qXx2agp8V44r9XZKPlMhockIzxUvutdssaII0Lvn7099+olYFHzXZbPd4mwyp65AkjUi2p6qKW13
+eGpXeBe9bB27UlXAKay1CUlfHkAjDLAK9yySFr5GwzevblJL5UVf35BJMNg9Bl9eBL/msKXVl7O0
+YFWh0LUwIW4oLl/jHf3jCVn5wMVVbQ+e6a3z9CdkAl258AxOXV3edWmvNCk7m/JAzckM35fFKTOD
+BdnJpoqdrjy7Q6yiIF9wNpltKNzDDVlXcZLLWxusDOTvb7wu2YIb5zGuolqnObz5uiwK50en+LqY
+2jCgevSBgrAubEX86KdlLC53e2ys6cK5FWIGMp5Ou0GFCrKq5+iFEatKwpJkJZcLGcN8p46jLmzs
+ZjQa8vfnSmTTmltGjdsynuMcRxeppxpNXtEyYktBTascG5oW9VuGXSVv6foB5YqWQ9fbOBqDunM6
+w+vI91b1+5ef2ea5TdlkXviRDSanMl9aKzkNvc+VrWcZ69FsMpQzfhTWcLpyP+P0lCtbEjOa25JP
+duWZ/MGpENkUzoy6wsevtuoLsp+m63BRgUc0HWqbMh9sSxbT070+XkOeTT2tUSSy8GxubLL+lBwf
+Jmc5O1d1vqhkQOG6lIp66qxd5OWbwsFpXIuvFZoXeju4fMhVkktb2tL+xbInwlPIipKD0ZzAcTC6
+sq+3I5rrsmZtbLbprkiAriDFBupS6QqfxAXnY1GodsNgIcVuChevq6IfxqPhqrxZTYNI7RZGWXsd
+t0agrrQtx7Rb4sa3Io93VBNyMlQKs7QADToaYxaUYZ+73ubpT4qb307Wcbb0NqOKF6LESaRP82iP
+mhYSmXxIoJTizYvabiNkRSs8c2eLXInWwyBiOlKIcS3gaKz5aNXH7Jz7FBrgSsspQ6VcL7NsUYiz
+0uqQKRV9psCrXtHmU9fl3nd31miqOradG0ZN1blcbRLotRfM6wzwIz1qtOoYpRorygyjAK964GLl
+EjQ1CBz0fCYntwG49dY9xgpOm8Yxqxo0njgp3Zo8k0T5GIK05OqazIXV9YjWRdmN66XhjUO59tnx
+d9nQnXl3Te63NXXpXBDPM3R38C+LJ+CNm5QqRONFdaYjFcQ51mNSZ4anGaqVMMHbUKBSavCn/1z6
+FMg9jtLJQiag0424ppRvWxfX6PVkx29u1xfQ5Vx1RwdRQbcr0m44c0LFlnhbPp26HAlWIw9HJ9RU
+PV6v6ZPPKukDh8FI55nr0lQPuaBgnL+/AGNlT8SiUGAZkzMDGprH2WhHuGOdCM0N3KCSg+9S6GSz
+ihILohrzSNzofHyG7chDNL4DRfVCznGUoj3QtFrUjLBalVlMgsVo2CzFKqeg7xhMXlWtyaKQxfkC
+seI7Lm2l027MI9p1OSc2L/q4dSUVzR/w6HkNrZnYP4ehZAY8f5228vlVkuX2vKC+Ki+mt9KhVNBP
+Pkswes7OQgdyZRBSYdNrzYiTinjE+gQT+V5ZFgs2oY1ejyMFcqUKQqptuHQ7V6RPZYGrZ9IiG+Nl
+ch/u1MUNFdWpbCKln1KM5BpOo4YfVcQqhjKRhczJg0UJc65aD0k8Fwp3oFb6jBItW49KZjUtxY48
+GppLKaeqCbrewdWjgTUrJIfSXnvDI88F1DaZTdlRjdCLq3JkaGytUcYadwrmuIVmGfwA19HnVBbY
+9EEpuQxQRqo1KmHUoOfpYjk+Je/JGCYTLScP/IUUgZeOQcez19tm9Yq80EEQYCqeygoVenpKmcli
+5LNFqyH3Zv0UO9TNoNclqMv8bWjtT5ZDHGsK1S3oH2kZ/TihpcjMXrvB4EiyI+/XlseHpS1taY/Y
+E+EpYMEpDb4xWPUCiAr8Sg44HeKqYIdjXYyu5rYCj7gBdY0gz2ZzMgUWee0GDBRzblwyZDVur8nu
+4dkaRabgn8aUVLMWrhcseAY8U2emoJ40qaL65aKGvhYErGpQp/ATHHXXXX8dVwErRjUHyyLG1GQX
+iFruwvVzgjGlEr94Wp+R2IxcvYCgE+Aoxr8wxcLzyEZDmqFmWjS4eBBPSSt6uDin3ZRdJax7RErO
+EUT+wtvwdF9oNQ1G6+5N4GKVAKWMoKVVpb7fAOVkqHAaThpQKDTduBY30mtQW5CeWDMnHchu5Wsb
+cy8nnevxyEyxsYzn1PXp6jHHOharHslZBWk/H5G1tT4kOCPX/jH1iTXCuloLcZWaraL1z9NjBnvK
+lbbLYrcOWnVcHbw0M4yHMkdGWn3pl5ZMsz1+3aFQnoUytQRGArNNR78z71PqUTFPfGqJeDdRw6As
+/wTGp1RPKNeainoU4uo8JBw+AKKVDawGaB2nxFupmHH0mDvPoSb3dnLjjPxMx9jL2GjKUdBteyR7
+y0Dj0pa2tD+GPRGegjEG3/VJHGhriswdBUwVlVtOM8q5rPheGGFaujMXsqvO4gH9vuy02WjCfH5b
+rjvuUhay49X8DoHSpoVTgcGZoIlRxFwcD0imilhM+mRKO3bmzyoZy8VgGWMwup7WfR9f5Y7vjEfM
+Z3LtRtoEDbpV91Dk55Sp7Crx8AxHdQiyRsF8LP0eaDouDwpC3WncyQRHd4dkPiQuJEg2HpScn2gQ
+SQOG41nCQD2aWhTS8mVXbTdHKMCS3J4RqYdRIeJO8zFnt2Xces/sUlRVkk5OVHlvzTa5IhPTrEJe
+ZhilqcuKGF+Zax3fW2w5WTYkoypc0vuNPYalxFReujvFP6mKkjqcahC0E1tC9QaHY2UtBvpnOkYt
+H5UKxXWbWI07eDWPeSJ/8/pNEe0ZnU65+IKkDp8qP03RVHGhdEamQjTzccn++FzvU1OWJqDUeEjp
+QRpLpwazmOYVEcTh1h/IvWUjjOp+JHlGNWyBV1JWkyi0lArjHk4kjby/f8TFpyVW4bgOhQZV43xK
+XlYK2zX8VMagum6SDjjvS//2+6e8fSjIzMDmhBsynrfO9hjONMr7Pu2JWBQKaxmkKbkDx7lM0ren
+p6CMwWXepywlUFMUM3xPI+Dq9pmZJXRk0P2dK7Q0G5CalKFGcnNmZFp9l+5pKWnYp6aEFkmSL+TO
+B7FlqO5lcTQnyR6QfoBAlUwlsFi6lJqJCEzGTBeT+KyPqzTptqYvYAxooO3s8B7xVDUMdzxufVey
+CHfP5AFevdDG17LvdF7gakluPskZHkt7o3zInX1xGfd1MclSIX4BmOY5nevS/25eY9TX741Twopm
+XOkujm+e88qGlBNfurZKvaELWu4yz/UocTwhmctLM1DZeps79DZlQgfNOslEruv4g0UNQzosSCtV
+Kw3azgOPey/J833zxgGBUy2KEWcT+Tx1oKvl5Z7Whk9dh7NYA5vukN6ugrpPp9RLJUZp1HGVBXpd
+MxnTIubsXFaQnfqEVI8a85s5tjpiFD7DgYq36oLsNFI0kI879zhVyPPxJOf2Hfmb4bmMRRGXtNv6
+EtsMdGFKJwWelqU7NXdRKxMfS9/eOjxiogHaaxsXGZ5oANOxbK/L/KzVjdBXI2UBAOkU7t2Stt+8
+dchQQWStSy0CR663f+8u4/iDLQrL48PSlra0R+yJ8BQskGIpixIVnebt84TTA3FRr7/QIlONQtNq
+ko/E7ao0IlxbY0Xr3Gur64QdyV0TRWwNBNEXj4acHspKOk0rIZQZ6aTSEHDx6xIETINz7tyUY8Dh
+MMHTevpEb85xvEU6LXNScGXHn5/lxHrkmZkBVl10ozRo7Z0ruIHgEFYv9+nf0ZTkrMHWrnpCvhwN
+mnkHM6sYjnMyFbWx0zmxpvpyp85IYbzHSqXWDJqsajoqDRwYqrgJAQNbVSXGNOtyvbqm49Ii4fS+
+7NDx8QGsSd7cHeXE6q/ODg8xuvOaRO7N65YoBw21ep1yrClef0qplMpF7i7IYwote7xx+4BXqyou
+QuYzJcxJNphlslPO0oRYpeub+r11TEX2jBNCfKbHkt2QsVY4ZkmBo+jNXIvRti+u4ffk3k5GdylT
+ZUE+jAlqFWQ9olC17SCspNtiSj0S5FFAHku/x7M+90biIU2TigAnpq9o06OppaYEN1lmiZU8iKMh
+jpLBuFZ1H8oaw/vS50ljndqG4CkCUxDUqgB7h0R1P8/m4h2ORzMGWh08meQLjYvBXk7rgnpsxvnA
+pZJPxKJggMA6xJSL/P+kzCi7qoo0HhGsSqTazzxKrZir+PRsmVPogHHSJw7k7Ni8eB1fwS+lt0l3
+SwYw1GNCNm8zVXac2MY4TeXqGzU4PBJ3/s7eKYm6q6jr67kumf6c5iV5KPdzWMs5vS+Y+1pzjbrG
+CazGQLJihLsqLq57aGiruEduHNZU4crokWF+HjMZa6mvSTEaAS/TjInef1qfMZro/at73XBdXlSg
+17f694lVTWrg5iSL6kiXIq7o7KUfUy9mOFE3OgQScUvLhkNtImPvNXzchtxfT8libFlbCMW6xqGI
+ZMGyiSFJtN4hnmJVbddM5EW5sXeTl74nz6nMcgrNxHTijPNY3WRrKZxKGUbG6jRLWYu1uvSshaNg
+oXw85U3l1by1f4ZTMTb1JLdfv1zQHQqA6B3vjKnSCTt5wepQBV1rOZEKz1bHw7KEXHEfszRnrJWG
+o/6cxkTm1rlmp0priRWmX0aWYSb9WJ2fYpXTkhUf/0yZqbvSxpXLlxa4kHpYo72jLNijAkcXrMl8
+RIw8n5Hqit7rn3JbKyeHxRk63OSeod/XBTJ7WGX0/dny+LC0pS3tEXsiPIUQh2tOxI1yitUVukjB
+pJrHXW3jVi5jYCg1wp2r27Z35z5GC4N6O01W1P0aH90iU6SfDTwO8ttyDd3ZTNijEhaw/pxC2878
+Ocd92UrO+/1FRLmlEScn8LGKFSgs5OoRjEYzpsobGRSGsKtQWUd21XF/yOhQ4NhlbHB1x3D8klks
+rvTsQAuH4hm3lSXZKx1aKhXW7YYknuwCk7mHHek9KXKz62Rc6sgRZc/JGBzLUWvejzFaipkXCaUS
+oHhbMlZ1z5CWMp6D6YhWqgjRs2LhBtu6AQ1MZueyW2VmSNiWv01tSTFWlee4wGoePjEzQqMiHRrp
+f/P+25xroVXdqxEp38BFU+fASJ9ib77IjhR6FInLkkOlJUuaMWvqzs8OTuir4MrlusP1T4i39MKG
+/H/uznhN6dhev33IsQYz12pdrq1IMHaj28RVWHyqFbFFYVHKCfKgYDTRLM98ygpyTN3T+7UmplQP
+MplJUBHAZiGxBqNzd4bXlWtMNRGzG/QId5XGLYspbks/TkuLqQhcnAETRafOtVAu9krOtPhvnMJQ
+vcbclgwqRuyyWBx/s/z94RWeiEUhp+SsnOAZl6xyRR3LQaBCqtOMjSvi+jmBj6vlpPltjS3EFqOH
++d61zxNGUrIbBin3X5M0TXE4I9Zzd/eaYsjdFFcZ6n2/jacAkkkScKIR/nyWPWACUlfVSbKFMGue
+wZFo69IcdujrBHEbAbVVcRnzUh7iyZ1DjCsvdK+8RPOLXwFgfniX+ZlcI5vLxJ2NppzuaUoyCHBV
+ZrxmDIVqBo7nY24OpN9TrYQblCXZucB9L62vMHhavjec3ONsUAm/QKTxmF6kx5nUcqRU9Kf9k0VJ
+9XyY0fFlAag3A5xcFohSy4JNCMWq0uhbQ6rxGltjAdjxnJBShW2OhuLuHtyMyVRLM2mUFLrIHo+/
+QxTJuMXFdCGWO9PFIRunhJoWbXYtNRUZLmYTVlzlKCwdXlQi1Egh6KPDIfsvSwxnNOkzV1h1vl5w
+VtcK2yyjrouMhpqwXobVFGkwg7NMyX9nMSfxP5ExcFRhykKmL+PLwxO+pMI/1y7WMJGSyJRznGPN
+ns30Oc4SerESzdZ2mOYao0gPSJS49TA5Ya6sT0Zh+kMbU86Vln9ckugxh6DBdPpgUfO1dGD+g2VH
+F7Y8PixtaUt7xJ4IT6EE5iCVe1UdCpbJnrjwyYs/RjbTKKxbwyplWW9F4cr9GuO+cBtO//lbOFeu
+yOer67QVCDJd3+KZ67IDBdfElR2cfpeTe6q+XNQIe6rae3bG2bm04TqGjitu8IWe3MNr908XknaG
+DKuu7XSUUpzJ56ldodRdNajJdXdfaJBpYKg8Pib7w9+Xvz2LyJXbr78vYJTDfsHFmnw/2u4xd2X3
+6B/PF8VRo36x0IgL1HOZRjGThgTcxlFC/UjJNpKIOZVupiHTzEaoIjtXdpvE4qQw2DPsdMWTaIVt
+Ug1iBnFEuyVjUNsVSbwyHizUweu1JqXm6dP0jGSqATjTxNe6/zsKkDo4nC4m32bpkfniNYy8lKEr
+0XXTCskV9FMpe1vr6x1D12kz0UrDnd4qn9uU3fH4cMjeN8RDjD2ZQ2mWE87EG7vUCPmSzgFvrcYd
+Ffs5PUkXgLgVFYMZzWJMrqA3E2DONYAZJ4w1I5QbcfcJPUodq/l8yhsKef+sscyVor5/q8+aFltt
+XRQ6OsqSYF2816gbEkUyr7O9kvNSPcvDOi3lijzKNIt0b0SuEIQVY/H1OUySHEc9mvVWl7W2tPft
+d94fXuGJWBRAePVCz1BUkvMNh5FGZEd795l2NdMwa5JolDzS9JhdyQgLGVSv0ca/VnH1BdTXPyUX
+nAwJ1uTlTGJ5iDZvUypzjx/mHClx69FbQ1yNKK806uxoteKu6vq9dv8Er4rkmxJHAx6BB/cGEjPo
+H/foNoVhx1Zy6NMUqwSltrTMqzRrb4q5JxO611UwVTbD39DqvXDKWHkJx0lKogtBP04wCkJaUbDR
+ZjOs8FFgSlY7mmK70sIGMqGbWFx96YOGupmNgAtI2xMyxiq5HoVzYqUqz70aboXFrw7ESUGpC6/f
+zHADvbk5C3l1ExoOzoT1563vyfgEpqTVkeextR4s8PzfG4/oqcs/swUV46RRHkhjChLVgeyP55h9
+1R515tS0rPlq0iPTepNAU5PEHi9uC9NTZ6NDd0deyJvDI/bH0pdkWjLRLIinKMbMOhTqosdewlTj
+LgaXRAFXgcaacltWJ01ya7h9TxbnN27uUdc+9U9PSXo65vtVnciMV39XNoj2Wo1cxWYHw3PGqdxb
+o13DaDtjLeWf9gf0tYZ9OE1JlAjW9eZ01iTr8tTFK1yJ5HvfVi7K97Ll8WFpS1vaI2YeaMN+jDdh
+zAkwBT5Y4feHZ2vLtpdt/wvQ9mVr7fp7/dETsSgAGGP+wFr74rLtZdvLtj9eWx4flra0pT1iy0Vh
+aUtb2iP2JC0Kv7xse9n2su2P356YmMLSlra0J8OeJE9haUtb2hNgH/uiYIz5CWPMG8aYt40xv/iY
+27pojPltY8xrxpjvGWP+kn6+Yoz5LWPMW/r/3mO8B9cY85Ix5jf031eNMd/U/v8DY0zwGNvuGmN+
+1RhzwxjzujHmxz6qvhtj/kMd81eNMX/PGBM9rr4bY/5bY8yxMebVhz77vv00Yv+F3sN3jTGffQxt
+/3Ud8+8aY/6RMab70O++qm2/YYz58T9O2x+WfayLgjHGBf4W8JPAc8CfNcY89xibzIG/bK19Dvgi
+8Be0vV8EvmatvQ58Tf/9uOwvAa8/9O+/Bvxn1tqngT7w84+x7b8J/O/W2meBT+t9PPa+G2N2gb8I
+vGitfQGBKf40j6/v/z3wE3/ks3fr508C1/W/XwD+9mNo+7eAF6y1/xLwJvBVAJ17Pw08r9/5L/Wd
++HjNWvux/Qf8GPCbD/37q8BXP8L2/zHwp4A3gG39bBt44zG1dwGZkP8q8BsIv8wp4H2/8fiQ2+4A
+t9A40kOfP/a+A7vAPWAFgdb/BvDjj7PvwBXg1ffqJ/BfAX/2+/3dh9X2H/ndvwn8iv78yHwHfhP4
+scfx/D/Ifx/38aGaLJXd188euxljrgCfAb4JbFprD/RXh8DmY2r2Pwf+CosyJlaBgbWVCNtj7f9V
+4AT47/T48t8YYxp8BH231u4B/ylwFzgAhsC3+ej6Du/ez496Dv4c8L99TG2/L/u4F4WPxYwxTeB/
+Af4Da+3o4d9ZWbI/9JSMMeangGNr7bc/7Gu/T/OAzwJ/21r7GQRW/shR4TH2vQf868jCtAM0+P+6
+2B+ZPa5+vpcZY34JOcL+ykfd9gexj3tR2AMuPvTvC/rZYzNjjI8sCL9irf01/fjIGLOtv98Gjh9D
+018G/owx5jbw95EjxN8EuqYqAXy8/b8P3LfWflP//avIIvFR9P1PArestSfW2gz4NWQ8Pqq+w7v3
+8yOZg8aYnwV+CvgZXZQ+srY/qH3ci8LvA9c1Ch0gQZdff1yNGdF6+7vA69bav/HQr34d+HP6859D
+Yg0fqllrv2qtvWCtvYL08/+01v4M8NvAv/U429b2D4F7xphP6Ed/AniNj6DvyLHhi8aYuj6Dqu2P
+pO9q79bPXwf+Pc1CfBEYPnTM+FDMGPMTyLHxz1irVF8P2v5pY0xojLmKBDu/9WG2/UPZxx3UAP40
+EpF9B/ilx9zWVxC38bvAy/rfn0bO9l8D3gL+GbDymO/jXwF+Q3++hkyEt4H/GQgfY7s/AvyB9v9/
+BXofVd+B/xi4AbwK/I9A+Lj6Dvw9JHaRIR7Sz79bP5Fg79/S+fcKkiH5sNt+G4kdVHPu7zz097+k
+bb8B/OTjnHfv978lonFpS1vaI/ZxHx+WtrSlPWG2XBSWtrSlPWLLRWFpS1vaI7ZcFJa2tKU9YstF
+YWlLW9ojtlwUlra0pT1iy0VhaUtb2iO2XBSWtrSlPWL/Lxp24vjnwJuhAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.4152... Generator Loss: 0.6163
+Epoch 1/1... Discriminator Loss: 1.4929... Generator Loss: 0.6064
+Epoch 1/1... Discriminator Loss: 1.5524... Generator Loss: 0.4964
+Epoch 1/1... Discriminator Loss: 1.4786... Generator Loss: 0.7433
+Epoch 1/1... Discriminator Loss: 1.4437... Generator Loss: 0.6628
+Epoch 1/1... Discriminator Loss: 1.4178... Generator Loss: 0.7201
+Epoch 1/1... Discriminator Loss: 1.2742... Generator Loss: 0.8131
+Epoch 1/1... Discriminator Loss: 1.3555... Generator Loss: 0.7472
+Epoch 1/1... Discriminator Loss: 1.2990... Generator Loss: 0.8521
+Epoch 1/1... Discriminator Loss: 0.8481... Generator Loss: 1.3975
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmMbml60Pd7z/bta+1Vt+6tu9/b63TPdM9mZmwcWzZW
+sOMIsBEBBIojAgpkE5ZRhBRFClGUiD9C2EJiDMSYGIjHwTKMlxl7Bs/Su3u7ffvudWuv+vbv7OfN
+H8/zVc81GU+Pxw0d6Xv++U6dOuc97/rsi7HWMoc5zGEOM3D+XXdgDnOYw4cL5khhDnOYwyMwRwpz
+mMMcHoE5UpjDHObwCMyRwhzmMIdHYI4U5jCHOTwCHxhSMMb8gDHmhjHmXWPMT35Q35nDHObw+wvm
+g/BTMMa4wDvA9wHbwNeBH7fWvvn7/rE5zGEOv6/wQXEKzwPvWmtvW2sT4B8DP/wBfWsOc5jD7yN4
+H1C7G8CDb/h7G/j4N3t40SzaLbb0rxnnEiLMBoCDpazXVTJTAFA4cs+6QEWu3XYFLxBcZ2IDfX0t
+NhDPvpjpb/4NvXCA6en/Cw6lF6WCw0DuFmVpN8ssRSBTl6cOhSP9aVcauLFcJ3lMOI31K/IdmxWn
+o6MAY7Sf1uI6jo5enrAOGCPXWWGZcXR+2ccURmaiFBBUZdyVii9tmYAU6YPNodc/kOvAJ8/0G6ag
+W23IfCXS7iSbMh5OAEiThBkDaYw57ZMxBmPM6TWALSwYfRaD47py7Rgq9QoAgXVw9JnCSt9yC46V
+ZwsXbC7fGA1OKOSR0+/+TjCPXGt/HHCMee+J2aUOJLf2tF1jeKTPRjvneQ66tSgKq/19rw/WvNce
+ODydPf07epaB7hvYJ9R12DcuvpH9UndrOHa2VgkAsU04LmJtoSCd7QELxencf8On7b85PwbDbPiO
+cfH1D9cY0LGMi/TIWrvEt4APCil8SzDG/ATwEwBnOcsLfF3/Mzuwr2H5Pr0uk5jHAbDO05x4MoHT
+ymUAko6D99glAOo//CTdLdnw/i0f8zn93i0PbikSyGeYYsB7u6cMvKLXR4zdvwHAm+ci/tYZOUzj
+K4IdeicFk81FAPr7ZZJyCMAPP/UZGu8KYrk3vMPrL92W95yR9PNgTDpjzqKCwFdEFls6lRIAsSKQ
+tJ7jeqn0JkzJMrleu3qG8lgO0zNXtth8Rubg+uNnZMylDfbysczVEH7+n/8t+fbWKsMD+V4cTPjx
+j34PAJ378r2vHrzEb37+twDYefiAIpaN5AYBeSHPuKUA35MNPfuNwxinrIcqd2h0O9oPjyc+8wQA
+Z6IalZKMe5LI/EzynCBuARC2IRvIun/hF3+OSSzXWWFPN75rTrEKnvMeWnBnBzpwqQXSJ9d1QKaI
+PNMDESVMYx2H/x4iCDwfvyzHYHGpjpno8kSR/MYZikuxxlLkswMb8PXD2Z4VMJxg+dvSTf5H3jCy
+T/+nUp1NZwGAT7Y/ST1dkX549wG4nT7gZ+J3ATgkYt8KskhyS6T7wTGGfIaodEx5kZ8iCNd1KDky
+jmqpzooSzJbvYENp40uT7Xu8D/igkMJDYPMb/j6j907BWvt3gL8D8DFTt/Bp4Le+aYOBnWHgL7Ja
+nAUgzn4GgGLwCZy78l+v9FN4ue4IZwUm/0LfuwpGJBhDVe8NmO0eS8570lSBk8vU3Hzg8+YFWdCl
+3g8A8MbwBZb2PwpAzd1jyZUDOXl3xKBxBMBr//JdDkYy5DiVRcniCEe5g7qxlFM56L6JiEJBCgNX
+NmPYK5EV8v8oz7C5vLd/54BWXfr/pVdfZaN7DEBz608AcK5T4vxYiMGg4/PWWN5bPfw4dVeYt43a
+08Q9Gd/+1SEAL/zsu+xu70o/o1xIL0BR4OhhcyoB1ZVlmcOBbNzCzUgTGV9uDKNDaS+owf4bewC0
+n9hgIN2k7+/LfI9qrCwLJ1E/WSFflPGHeUE64xSMxdN+lGZL6ro4ejB9z1BWrrDbqlMpyZis72D0
+mUhfTO4PiIsZq2gxitQKwCqCKCJwGvL8VJFDbAqMkuiyE5DksiaFzTH8oLb3L/md4ABXlPv7b9se
+S7685zVvk4x/UeZ5Tw7uJDjmYi4EZxqkdI1yWw2HUAlHs97ARMpZhNLW7nBCmgoCdQzUHOnntarl
+k1b2kZcbXlei9H7hg0IKXwcuG2POI8jgx4A//k2ffrYMX7kGwdf4Rpb+lAMEcGeUwgdHJsdPZVNN
+SvdwrWDf/Dd/Cbe9Ju9/zyL8eWGfOV/HfPoPynXpRfmNHVAxwCTFI8xYoazf0LXsvysTf/zUS/Ld
+cZlR/DYA2YWCMwwAuNcbw9fke/n4hHKkYk6grJzvEejC1VyPpt7vJHUiJ9d+yGbth1Niq8gkzzGO
+bJRwGhJk0m5ayYleFgSZPit98KIr2IpSz3CCN5Tx9aOX6F+ZUfQJ9ybSRviLQjxODm9i9DT6fkC1
+2ZZ+dho4i7JhW06X61vXZV4GgkB6kykPbwrFc9OCyEg/TOZztC+IvO3mmI6KSvvCHTjVKYP7Mlav
+U9DMmgDYjFM22Lgens5RoyJ9aJZKeCoHNALD2Y5whZ3V8xS+7ItpNqac6zxaQTbp5CHlVDi6ZJqR
+Fyq6kOG50rfUcfEneiQUGTmOwVq5LqxLUJX30jCH6VV5tvpV+aX/DXvWEGzKWJf/yFU8K+Nz0gXK
+/0w41aIrB/fj1sVvy/g+uVii2BAutHZmAWcq+7pz7TLHD0ScfjjZAeDNNw/ZOxAk/M7JLn5Z9kuz
+WWasHVkuApp3Z9x3yPuBDwQpWGszY8xfQFCoC/zv1to3PohvzWEOc/j9hQ9Mp2Ct/SXgl97Xww/H
+8JP/mkcVf++pHDEGPOEKCt8jcgWrJgsiv07XV7CpsO1v/fLPctUKe73+65dwr21LG/517GdExqf9
+GflNfxm2pQ1u7EHW1r73+QcqVnw+M6fKw+D1VQA+da7OLecOAP23UkxtXZo76XM8FT555CXkFRlB
+U9n9wgY0fGm3XK+wsSLUo2xSchV57KHwrQ/v7BCP5X3HB8+o0s7zCKdCEau4mFRY9Foq320Hu/iZ
+UChblPmMtwHAO8UdJq/K/FbPPsn0tszX2/dFxImKhIov8/r4Ry/zg5/+IQA+Ui9gTebo6tIlzIJQ
+G+dYWNi9YcatV4RyPSxuMrwt4sHr2z1CX743GO5T96VPNVeoVuBUmSbybe+gjaotaPgeYa7PBB4r
+nRoA11bqAHRcaHtCBpfLZS5uiZRaaZVxQpmvpLxAEAgHcRgLVX6+XWWkeplo6nBHFbDpNGWs60vV
+MM1ExPCUnZ9EVhSMgFPkeFaOzNRz4O8q5XWEWlM0sa5cF88tki+eByBcvwIVFccmBe5/LPvISYQL
+KLoh17ty71q1S2VBOAW/2cLJZe4zryC/K6LEILkCwKeuDLg3kvn+9Rde5vaOjDXqpOz2pP8305zD
+un5bVE3fEv6dKRofgcITbRMBIBvefqMG2XXIbKz3yyRlGXC0LL82KzCFyp6jkF+ZCNv6R3r71Ha3
+pI3NI0wih5ctlS2rPwXnvixt2DLFQzmQkbvFjWPZsC/bgnaq8mdHEEH144/zRE82606xwsnrwoKP
+vYiGJ4t44bzDUaJWgFRZ52rCxqocjubCKtWqsLaeA0yFldwrhC0vHZTJVTlH7lMYldvz4vQAgcP2
+RFb6Xiab43ubAU4g4lPpOMN9QsZ6addjVBbdRyU+Zi9SS0NfDsp6s0ZnSXQRf/FHf4hnrohY0m2d
+pdSSeXM75j28vaUHM0q49LQgnrT3LONhT/rzzh2+8JaIaV9+EVJZVvZj+W7gWZoqKsWtnGomiMAP
+DI7K1LVGnetLgji3VIRZ9wNWmyrOLDTpLMo6tEwHc1aQr80NUV3mvjWV9dg4b0lVDxKS8PQD0Y1M
+vQk7D0XkuTXpMVFxa7kiz94dWHZPpM9+bqg0pd3K2MX6omvCfkIn5VW4KuJV9n1lwk2Zz3v3HhKq
+YWt5c4HmRUEWJbWoBWfO4+vBdbwqrivEyQ2WMYFMuDcNsRdk/cqe9LG8cYvmkcxFpwRfvCdE72vv
+3OZmJB90DRwW32iv+dYwd3Oewxzm8Ah8ODiFSQ5fHYGTitEaMOTYGd9mXbxAWfv6BeodYbtqG2Ky
+dM+dhxsiJiw//Bk+paYbt/aHoaMkKm3C05+U65pQQR4fwesfke/9oTHubVUCXtjmr//CPwbgvx8m
+THKhKoOJUNV/1N3i6Kxg7fyNfaZDYRnrHYfrW0JVy40O7kC4jemRvHdmdYOzV4SK1xeXcDPpW144
+jA+F9Tu/Js9Ow4TUEZbZr8DRQ2FVR4M9oli4ivEoxCqn8Gv/QqjyH/rEv8+ZmnAgQatEx5e+9y45
++Pvqk7A3IJ6I+NBU+/nC2RU+uiZs6xPL51hZ7QLglX2cmmivjdvCqDZ/pnwDh1qwJf9fSqj3ZZ2c
+WpORavIzt8Rrt4XLOjmRcWSFwamJSFSKLGFTxtTyy+SBcA2dZsBGS6jphnIKm5UaiZoWvSyhpMpm
+dz0nncp7phrjI3vELAjFzMcevgyJSr5Ea0G4yZ03LYVzIv3wfIIV6XMcy3c7ecpgKGNNspQ8VpOs
+a+GLF6TBxb8rv4dLsCfUOu6dZX8sa3k8iciOpG/d1WW8ksyzEwrXYZJjHC7pfNdPGWTjJIByDU2P
+Qrnowsp+K1FjYUP3SLNB0pJ9fTAxHO2r5SsbUxifbwc+HEhhdRH+8p+BP3Yf+F/1psspI2MrkIrN
+25jn8ZY+JfdPhBUnboKaaRz3P4FU7VejKkTqQ1X6BBwK28ayHBoGZRjJpPJiD9KvybV5HPgrAFRO
+fpOK+wcAWCy+BEDfhLhDec/JQmjJhj2z3uHM5kXpkmsYKKucpLJAS80K1zeF1c5KDnmk2ve4zMmC
+9DmvS1tOqcHq8pYMwyv4ylsvS1uvHJOO5GDlqT21w09vy+Y/2DWcaSlSyGN6ibDGxcTDJrL5Tcdh
+0ZNnqurE9PiT57jgy3ZwJzvYvrDdzqqDUdYeJ4RCnjdqDXI8H6PWAMfUMBU5sK1WyOUNOZgV+zEm
+apY9HMucTI9jCIXdT9sF2bEgReMVVHVbLjVrtFb1cDbKOt91KiVd6wJwBJnYURu/LvNpC5/Uke9k
+I0Eaxk9xcxlz4cfkPXV8C46pNeR7NTwyR+Y/U51SpZZg1KISpwWFavBL+PAn1R/v535afvnPoJDD
+ffKS5WZZfA8GTo1mU79tM/Kh6kz0u+Q1iETMM9UNnLJq07wyp347aQUTyDOOirNeUMYY2Yc132Np
+Sx4tXkg5GQoiL9KUUInI+4W5+DCHOczhEfhwcAoPKvBfPA3mJbB/Qm/+DKfds2Wwn9b71+GO3l8R
+DM4rDyFSDXA+BKMWh8lZML+q76VQEkrJjnIaN3YgUZVs+6twrDjyxXtgn5Nr8+NQPCnXofCfjcO/
+R78mCrUHeR+MUB1veQNPFVFV6zNQg/u0IfcWSj5d5QQKUyX31W5enpCqMiiNhEI3LlVYXxON9HYY
+8kxPxl+cGXL3SDG/W4grN1DtCkU5v2qpVISSJrYg7wmbHLsRd9TJqB026Wjba570/dJWl/Nlody5
+PySOhMp54RTjiULNZDnGSJ+NaudNqXGqELb5BFKh+L6TUPGE3V3f8Hj+IyI2HamY9NbkNmRiMcmm
+bQbKeXQDj9RV/45ySrMsFLvRVAeqkx6lXH0Xqj6BWnhydxcy6b9TqZC78vxo/ED7UyOoCGeZGUOC
+iE+xM6Kh1qyFJcu+ijdpLn1wfQenIvut6E+ZzAg3wJfUcnVGnZe2L0P6AgCTpMTdHeln1MzZ2pLx
+l/0y2UjmtvDknr/WxZn505ldUJ8G45axhXA8tniAnQrXY1XbmwcRWSbWp3hoCVLhGoppSBIKp5Bl
+ljR71Kr3reDDgRQyDw67YJ8H/iu96XLKOtkUMvV2PF4AV2Mi9tTL0cmkDQACyNU5iV/6BofxEKKZ
+hVS1xeMpoM5NByPeC5Rocxq6YTeBm3Kd3JIuOFP6ZWEHk7xJ7gnr7rRypqH0OY6mOCW5XlHTY3PN
+w6i+I8uj0xgOrEPgy0Fvr8ruaNRrNDrq905AeF4W+fZxmVuvS7vRJMKqk423IP1pLixQVnOcyQoG
+uW6IhQoadsCEmHPq9LPclY3mhwV5S+MImiUydbFO0gwnFMTp4ODkwsK6pyqFDKOmzDyaUqilJS9C
+/LIczAoJa4siSiyui6NT/CBimqq4Ep1QxNKPTsXgqtWllBtyFU36PRmzN7G0da4muUPUF8IQVIJT
+C1UahETqfNabyNp00oxc1Ut+vYparSlcy75V5OwUjNXF2KbSVjRNSdXzNPUgUZfhmARKKqs/VF0V
+/xu4gih2+2PeyKWfTqXExaZ8o+1PcZsy7pKG87iuxVE9kHVzrJXvYQrwlIhMLMUMWc4W0kKqLuFp
+ETHRvr9+f5tQrQ9ZbkmTby8Sei4+zGEOc3gEPhycgr0FxY8C3yxe4wSKr8hl+uWZKwOnnIRpAio+
+0ADT03Y9lNED7r73PKpQZAQotjcpzKLXSIBf1GeK02estrXHjxOOhPrXugnhgVC5ZOzSKwlFaDge
+bklt2iWhyu3WKnEkVHdqDVP1Y6jVKhilCE4h/2/UrtPtiOWg6U0oKmLteP3eLo4j7GeS5Fh1Wth9
+IEvZixssqVLLVGGkvhDGrNJcFFbTfZiz3BBWc7MlLOxCxVBWFt6vVHAryzr6MZkqrTzj4Go/jcYc
+OJkLpqv3qthQ18HzcDwZd9Bq0FYN+Lkrap35rSonynVUkjKJxiXUOw2chrRdDrxTsaqXCuXzs4zx
+zM3EjilrJGkdl0g5r/7JPnE6W0vlqgaG6oLMVS3KsCo2TROXk1gaDIcFvYk8czQUKn+cxEQzPxPr
+YNX1vLDAHXXSXf9z8vvQUOQiun7dqXBXHZ0adY9X7kg/GlcTriyIlWDme5KEe5DIenjVLtaov4WJ
+AF2H8gWynvqWeLJHwnBMasQ6kQbH9FR0mxwXZDpvhf1msabfHOacwhzmMIdH4MPBKVyrwU9/HD5x
+BEy+yUOqELTmG+LIZxh8CGpOo9uBE1GiUQqhUK7BsTCemWZm75c4DfQ3AVTUAzFMUac65UoeVdQU
+kxxX5T6v0qBQzqTfH9GuCOZutipkHaGK9Vjk3kopYLEmMmdaMkyHwlX4lRqJq9dleWe1vUitIvZs
+W12AplCzpVb5NPcCltM8C5EqyMaDjLwrnXeyHG8q1547wah7cGe9xbPn5DtXuxLU0yIjV5NXNuqT
+6bw4QYNC71sbYjQE2JnZvr3yKQdlcxc8nePYxVU356JwqGbCsSyUt+TZmkt5rDb/BkyGMvf1co2K
+yupO0yPvyLo6U12DoEaYzuRoS6i2/qMkIRzLMwfTPq2qUNtmSfrr1X0mU23LTbB6nScuo5G+Nxoz
+msi4j49lzOMsJVaTbIGHrwFacZrB96ub4s/+h/Lr/jx5V0zOLyQn3CuEpT0zrdO7oCbQoolVz1LV
+b2LqNRw1Lbr+Fqak+gW/RKFRmdmkTlIoJ7CnodXWnnJpTpbjjKQ/iUlO94Xkw/j/o6LxRgSfucl7
+SU5+N7DiAg3kqkxyHQdnVTfpn/pj8FH1PfA3YfSaXP/GXficWiJO5LDh7Z4q6mjmmLOqTXZy7H1V
+5mwXkvEEmGnqOscZ8ZL0Yd+EFANxtc0aHqGn7HMzYClU23pTLQthhnNOFrwS1wmW5dk8MTQ1WcrK
+4pZ0rVUjztXPYejQPxbEMtpNCXTZHM+FVMOIA0Vog21sIUo9awI6ysIOxhDmIgb80PqzbC1fA6Ck
+MQne0YjkRDb/dnRM/VgQXXOlRTuQ+IKsOWaGnE2huSCKEnEkrtnj/hijzj1hFjIJ1NWWDKtIZEsd
+bDYby7y1K+Ji2ZZBQ9VXqyXiWZhx7NAMKzr3ivySiLE6kbl+BTSWgv0psRGEtBi0WFzQ6MnyrJ8u
+ZpZbYRwzDsXyYXOPopC+JVPLaCBzPouZDTyXaqHKvixFXUtIU0v+ljgq5d8leTjsu03e7oq7+b5b
+Ir0rCNmPXR4rSbyCkzcY3ZdvdNQiUapcwqnLOphKjZkfuwmrYMRilkYhxVQQXa+3o0Me4XTk//ag
+Rbcposblc2v0HsjcW8eSJt+eQDAXH+Ywhzk8Ah8OToEO2P8AMf39f4VyGVAPPKqLOHptE6FmReBg
+LoonoXnuClx/Sp6teFBVn4TPRNDUpBhtcbklq2Pf/nW5DCLcJaVsz1zH/rxi//3eacqvMJbp2k73
+CarCrg9vVxgeiqmyOLvC6FAo24N4n566MW9PpZ/l3OX5QwmYWV3vcu68iDn1SoBJNcnGVMY/mOzw
+9V9/HYC04nJ/TyjQvb17uJ5ySK4l09wKk1g5lPKUJFP34rGlNxKWM6xOMKoQtbUTmolcx29rHga/
+x7Qv87Iz3mOyI6baRrfE2ppQtM1rZ4k0CrIaiKKVkmXvSMb3xm/coHNmUfuW46zIdcspU9NMVTM/
+hs6qxb+tSr1wTKFK3lqjREm5inKtRE3zXRypqNELU/xAcxr4OUmkfgx1lxJq36/VqC8KVa1lypaX
+DeGxfu9onzv3hFO41T/mgfp9DG0MibQ3M4smSU6syW5wAfX1MNbybl/W59ZDmStTOeHVC8LOP/zt
+e4xUzJmEHQZ3NMvSnYiv7Ilo8qk14Q6e+PinafxRidy1YYTdVbNuv8+d18ST9af/4VdZr8u+76mC
++s5izuXLsjbnL3U405F9v/pYjdLr0sbgJKJQn4v3Cx8OpGD3IPtrfFPxwQCahIKOS6ZIIbXC1prz
+DeJcZGR/khBU1cndJtBXnUE6hc9sybX3MfkN7lBsqSa4so/RDW1MnfxJcXPm8zGOIwdoVAirNk1c
+cj384/F94rEcZH+0QlP7mfYSHhTiIHPnrhyEwWDC23fk3sevLfL0s5Lj7+mnnsZVV9R7U9k8r772
+29x6V9jEcrCMd0azNMUdKktyCO2uh1VbeKjRjvcmHtd9daltFKQanlgcQysQ8WHJOcI5FkQ2Gcim
+O/qVKV9xZZO/NvU4OBIk03J9Prokz175rstcWBd3845VkcEd8spb4jfyhVdvUf+aHMJKe8iRJ2LV
+U+evcP5AkKG7ptGOpbNU26KpNyNwSnKIu602tir7oOaWaGvuyana4CeJz1B1AOPJhJNM2qu4Ae2q
+bP6VfMLwRA5FRw9pNjVMW7J+8YHhRInPUT9lkqg+x6/Q3BJxIzqRfZOYAf5EdTSuJdGMTY6b8E8e
+yrre0IxPC26bu9uyvicnOb5GQXbtIqG61OStDi1HEOsrr4r7e7b9s5zZkbk/98d/jPxdWafD4Rf4
+p/+XWMpePDrmzWNBeuua28VJlhhOBSHdvWtoXxbr0pK3yLmWiBJv9baJ+faQwlx8mMMc5vAIfDg4
+hQ0X/nwLfirhPV+C98AaQ6zKJWvgyBNK6bclwKlZvs5QWXTzi2+wcvl75boUYEeCPRlsw/8pFMFW
+xUMxck/YCwWzN557hlpbtMLmkkt8VijbpDmkolS8X6gbcJgz2tMgmXcPyUNRHLmmQ20ilGTn4Ij7
+AyEP+z2hfHEY43qCh+/3Ah4LhY0sZWVyVZj17kl/j273iaZCrY5OHhDuCLXaPTliquyu64OJNUOz
+plJbmqxQVlbcJhFWPd4cm1NfFMp1VO9x50Co9PDzQj1f2nmTW5pg5IU4YxLKNxquy0BdZpu765xf
+EQVe4slc9u8d8eWXhFrd3o3pq1eod2zJM3nv3vaEp/syF89cVZfxyYB6IpyE6cQEswAr36Gsbr6u
+75GpV2iuHI8NC7YHGiVaZIy0nzgNnlVrT2KnTHvyfNoXTqJUthBq/oNaQtsXK0FeucV0oOny8gz/
+SK0Sug9t4WCt7snMx1ffEy/KeFW9LG8OZqn0JuSeptAbpTQ1t+UZ19XoLdifulzU4L72kszV/vAm
++Qsizpz/gQ1cDeKtfPVNPn1BFMJP1Dy+WAjnOJvjPDeMdC8srxdUVNQ6X68w3ZREx71+jzuRrMP7
+lSI+HEjh0IG/W8OaPYyGS1ssVn3ObaWCaQsb6G49x0IsE1h0dfPstmiGwuKGN/c5dUseL8FADn1x
+94Ri818DkPyyTNL95AYvKvt9afIKW9vfBUCn/Qk40M0fhEQa1fbbmlR1KcvZVlk9GyV4qr3Gdciq
+8ky1bjjnC9JqtOXguWOHixtyEJ5bWuCipkDvdDIKdUJZ2pGNe+lCyEYiLHwcVThRs1j3gWGvribO
+Xp0HN2Wj1KuaoLUVUnb1AAUZXc39eJA4TDT560nW5hUNrfUX1MKRVFmpio7jMROTHIn2vWNjntN5
+fqrT4exjIge7sSDQou7x9KGww1urHU7UvXY4iqlWBJGtL1R46gnR+Sw/Lr/Jb6a8aER7P4wtZU1Q
+WslzbFX7n3h4mmm5WZV5s42IjbEc9Eb5DMVVQU4tLnB1S+auaUeMj2R8FY1wbNc2cXStrbNCqCma
+LxabuFNZv7yc06xr6L7GTgzGBT11g64ElsiX++N+xLojczBuanj24ZhU4zJ6XsBzXbEC/cjZi7Q0
+QLdGG78m7XWPJZ4lPfEJTzQc/NoYxxVLRTf9Hp5bf17ma22fMy/LPvrCXckPmnQsLbWIPXFxg4uL
+5+Qb6wmb12WPH9zpcN+8z5RLCnPxYQ5zmMMj8OHgFBol7Gcvw50JFqE6BBXsU6Is4ZPX8erCGpr2
+R+BNrR3woihhDC/iqINN9dMtONREICs+XBYKZM5ewLwhSrDgurCy59PnWNfIwGTQo7oiVMxptCg2
+hW07LH8B0xWsO9gTBV5UdRkpO2xde5pboLlQcHVFqK3b2CDUoCmbC4bPJhlrTaG0F7tdllSpZS1Y
+I5SwuSW+Eme7kPREZKpX29ipUITRc+c4OhEO4uXbd/iFRCj9lbNCJVc3VpilrXdCF1ezGTv+CGqS
+HqzULLGsfhFOXb67fG6TYU04gktxRObL95aNxzNq2Vk6t0TQEOqH5oxcXVnlhzZlne7v3Sa6ozkc
+qz5LS/JMc3mdZkU4vSgRar157ZClO6IorrpTKrm069spVvXN9bbPohb5Kaurebdc4eqC9LO6tEas
+QVWVUonQ6ijpAAAgAElEQVSWFuspZQs89IRDnCn4ytUIozSws1HlXEnmolKusKAOSYNaSH2WdXks
+nXgziuhraje/WiGaSP/TIsfzVYmta1MNfCoNmU+nafnM45KE5fxnP0J7VfdFB7K3RUnrna1pH/4Q
+zZp01LGXMZ70wbv2GO5l2Ttev8GFKzKmhQtyFnrpPnlNvn2mvErgaq2HlTKjkeyLJy+2eW1Xrm9o
+Bu5vBR8OpDDK4AuHEBye+oDjhmSfVX/wH/gRnA05LGa4BFc1WemaaL2LX005OboLQNU7T2lZnZDq
+DoX6wNvuMu5nt6QN9ZcnS6hq0YyaTTCzCkoVQ3ZdDlnpM88SaS2Dey+Lr3vgdynUqcRmAwrND0kc
+U1+Q9+or5jQrvYf0p24KfKv5/tyIoKHOK7UVrHr/NR1Z8FFucdUhyTUOblfeqzobbJyRQzp06lR+
+UzeYLwcsMu5pdSNbLnCbimwyl+ZUIx8HlpJGbp45o5mSTMx0qEEllfM0dI4qHTCxHpSls3hVOUx2
+Nod5Tqcr5rT62qfIPyK6inwUUNGw53KtRDGS+RqfzKwldZ7virVnfwwHdf0G75KrV6Qd5hRa1itQ
+XUy70aSkWyQgJlcvzWrd4GiinWjUY3Sszlyu6JoOHmTkvrZRP8fKmiL48QRHU6PXTInyojwzVLNu
+vWW4Fghyq7Yq3Bjm+m2Lp9Ghblvmov8wZVudzJpLLS5cFcLCSpW4JISl1rlA+RPSXnYsa9f/4tcJ
+Lj2n65hjNCakiMfgCRHxz3RpLsu8lDUhy8L4GLemBGAwJdPoUS8KWDqNO1lm7Yz0+f0ihbn4MIc5
+zOER+HBwCl4OCz24uwaa/ILuOYpVSYPG+acwTc3EvGhwNK23MWKwLSY/i/lVUbilwTq20GH1HYqm
+5hZwHExVuQ2NazDWBfUtN9bHqi0cJyKaZU9+8jI9jcc4eijfWN5osnMg5KpXHOC4Wtpr4tMpC8Xb
+7HSoaAETUwj1cNMS3iyC05RolISKlyoNCk3IUhfCjXtyTDwSDqW73sJxRcwp5S6lQNj1zdUxlzdE
+KfX0dWHP/cgjTTT3Yxyiofs4kc9ElYB5PCTT/vuBcDatpTU6s/yL/hQ3lo74PkxzdZlNHFBxC6tU
+0gXjqluyY/Hako6saDuY8UjnpSDVtGjTu/JsftxnGVVWXo0Z3RPFbTyZwok8MykXNNTleVZ/0bE5
+lZbMW+BklEtCpT23BZq0Jh2fEGgcQKaOUBMbY5RTGLSmlFVZudhepNlSR6Z7x5QyLT2nCsPFxTL+
+njovDQ0f1ZyWx7nLrvo6pEPZY6PCYrV028nhmIc3JCfH889eo9TQtHBegFvIXs492Vc7vSHdSBXG
+OymOuofbrAd1tYyUm1CTNS43NBYjKGOUo8krO2TJLFM2TFSsOvPUJt/ly5p94Q3xSflW8HvmFIwx
+m8aYXzfGvGmMecMY8xf1ftcY83ljzE397fxevzGHOczh3z58J5xCBvyX1tqXjDEN4EVjzOeBPw38
+qrX2rxljfhL4SeAv/64txRbuWmzpAKMppeziFHNBsWupJCQLIChBLpjPfJeaWvbOw2+Iu3KRpxRD
+MU+63jqurwJoxcHUNduvM8sY/V75YWvGp/URU3fCQV+o3LEZck/j7d9RncMPtdqcnBWOYGc3IOuJ
+/Npc61BTartQTam3Rf7OM3WTngzxM6FyJAGOo4rIYoqjCUPLgdjPq6XbHEylD+tJnXJVZf/KiHJT
+KNfFSZNPPCbU9twF6U84/golxP5lqg2uqKfgARN2jzSQzMSYRRnreCScWZ2U8rqYtHJrmRpNV/Yw
+IswEr4e1CY6V+56rqb8855RTyId3qKRqsqyeoVA39Dx8h+y2um/viufeQd8yyUXG3d1ZZezJvAWD
+kD4iG5uJS1sVmo46qTI6oaYcXW1thUBdfw198mNdXzOirslPe6l8txkbKkvq9Vqr4mmIYmB81pDG
+j+sn+MqpVWJpd6NykSITiu/eGLOkUaJ9PyIfSz+yqtZqHJlTDsPzHXrqy+Ju51Suybetcci1zGB2
+V5PqHhTY22qGfWYPU6i7dmmCNbMjOsYY9bhF95AfYfO3pI29KZmaX6fjQ95681UA3pk2qS2t8e3A
+7xkpWGt3gV29Hhlj3kJK0P8w8N362N8HvsC3QArWteSNiLRXo6ROPMX6x6AiCjUbH2EC9Z0Pyqcp
+qqwW+pjWtrmv6a7OX9glu6MWDKpkLXVeWlsiCOQZt6q1Jv3kNErSDiKKWNnr0oR9rV60G2TsZfLe
+g1g2+dLjSzyuiqrX7jxkpAerUjeU1bmFERSaK3Gi6bz6hz08RUhuVuCpFt0bg9EahZkm0HArAWms
+6dDHlsiRA9RobTBVheFueEyxKM9ffkqrbi+ew/c0HX5WY2tJkMLCfokri/JeqZnjq89C1BcRZdCP
+yXU7hH7Mwd5d6Xs/ZaCZmF97aFifCDJYXxMnpGiU0nlSEMHeyy9RSmWd1i/VqKvfQPjSW/R1nm/c
+FC37wMb0+jL+/mpOeKyIM4oYq4Y/KHtEGtmY7MpcVBtl2up7kRUN3FBlwdCjqAvy9qMapU3ZR4uF
+9KFcL+OUlP1edIm1IvR0LyYciPNVhYxKrNW3Cjl4fh6z86725yDnk00Zxx+JK/zNoeyRwUBEH8dJ
+qaliN88mvLojLuu90Ta1oVgf8sAlOpYYkwf9GwC8fniLJ9dlHZa2L+MsyvpliYuje9Y6VVB3axNo
+2kCbEd2UvZccDgibghQeDN7hqzclAtUs1EgjjRt6n/D7omg0xmwBzwBfBVYUYQDsgSbg/zff+Qlj
+zAvGmBeOvs2AjTnMYQ4fHHzHikYjief/KfCXrLVDMysZDFhrrTm1/z0Kj5Sir5StU2lg6mPsRDBq
+ducGcSJUxYufxFbUjTk1FOp1moyErdu/sc9RX/7/+LkGpevqJ5ouUQyEjYonFTw1T6Lx/8bWTovP
+FN6AdBYtF+5z775c74SL3HogbQ9UWmk0z7O2Im21N99mckuTX/SnWP1GnE1IVdm1f1eowMlwD08V
+dLYYcXgobGdnoUmaC4UZqvvwzcMpA42b95OIalcofn1aIioLEv3S7XfZ0/Rtbl2LibRXcVGxBI8f
+/XOirLXTKxT/qZhwc36BW2Phpk7K8t1eqU+i9eJ3JhEPEun78WHM1/ck/dv9nSGBKu7qlS8C0OoW
+XH9VArvc0iH374pJcvNrlmcuCjdRLhfsDGSst7TW5jTq4nekn43sElMV7frjnGNNGptPcnzNaxCr
+OLCclzhQBV9tZ4hdVh+CUp10JHtv5BxxrO7NmQaBBQ44R7Lu64tXT021B+/8Gm/dFM/KqOLz9KIo
+cR1V1mbHQyo6V/+etayF8t5DZ58EGfdQldKeU8JNVHFNwEkhc/jGy69TXRLOJFlYIdbK4zvqNfq5
+o0O++pL0/c9c+hptrYGRto5Ya35W5jtYxqbCeRSh7JF4kBDV5Bujm7uMPeEUvvzlHW5pVfGnL1+g
+qGjSofcJ3xFSMMb4CEL4R9baf6a3940xa9baXWPMGqfpkn8XaJYxP3CN4G98AoPUdvTLy6QPlA2+
+4mLDmY//gEQPQvzuXQCq8U0+pk4/lck6zr4KoKaEY54FIFgBqrPDoqXFsxCrjj5MyxSBhh+7LUYT
+FRkexvRSjY6chSfXOixNpI3Hn9ji+G1xOx0lKUaTnsSFJdpTH/1UEVpew9ZkA00OPI4SEW22j/vE
+6iyUT8Vvop8P0eS9nHQjDtUBxZ3ETEbq/j0JObskFpiFmrD1Vad5Wss9zzJYlEKx5vvquHc1jNh5
+kwvHMoedT8lmfTEtOIhkMx6lEbFGA+ZtQ6QJZ8IUJpoHMdKELdRblDZkLiqmC/tygL5894RbqcxL
+1S9RV5NPNJTD2vMGZIdywLyV+zQ02UuWGzzN7OwaS95WO3wk3whNRqExKIPpkLI67OQTj0zT/Ifb
+loeHsu12YzmkTTOgvSTr3w67VDXzUiV1yaJZMZscV4vbzmqTfuHgIVUVKT4SlGg0NU/irk+uVier
+1aTCdEBJx+k7OaG6pt8+3GXxllzXTUajI7EPW2fEj+GxC2/TaSuCPHOezqI4Pfnrz1BuaDWpcpsi
+H+scyW/OADNV/cPCwqnF7Ddefcj+SNr7A6stxu4sf+n7g+/E+mCAvwe8Za39n7/hX58D/pRe/yng
+F36v35jDHObwbx++E07h08B/BPy2MeYVvfdTwF8D/okx5s8i6Zn/6LdsqViFyX+N+ewx/Mp3A+DY
+f4ivXoNmMhCrA2DzjPxQMWUk/2+Y89TO/Gl5L74IQ40++VID8/2a8/H1IUVD7b8tlT8mPmm8r+0e
+kOypX0G3wnSkwUN3H/BwKFQnH8u97qhHv6n28cYm5VTLnveOub8vCp7L3SZ5qorNkrTruTmBL5jd
+X1vAjYVTiFKHhlpUnAW1CkxdHLWZe+UqmVYaHg+OufWmKLi6ay2+96KmHdMoPIodMq2vmFoLf1WU
+tYwDOHhGrk8+R1CVMZXE4EBzrUTJk/eOOcFTK0KpHfP8ihbisa+SaLm8JVW4PXV+hY669vanDs6W
+WEOSexN2+sIpVbIR52tabbsl3whokDaEsq0tLtCPZPzrE49qXf1FoiqlXBWweq+WezhaNq7qt1G9
+LYVzTDoVrqfWcFkqNAfGtsxL2Y25XBH1VrNUJUxl3VtBnce7wpHeyGOOtXDKiXomvnEnxVFl5/ct
+uuQ12TtfrGRc05wFnuaojA5HrFU0kU2rTPhQ9ufh4Qn+sfiTlD+5Sbkia1ZrCXf7l7r/DY4v3KSz
+tUK1Pkvjt0KhWcqz8QmFRp3m+7M9ckKq6QYnB2VGan057Kecq8o6/YGn/yCva2q99wvfifXhS7xX
+LP53wvd+W405AVQ34dfWwfx9uXfyDPaBWiKuHeK2xFRnHOfU/TcItJR3+SLmnuozXz8HPydyK14P
+/oFc5+3PE/6YbNLoB+VwOK0RrrKt6X4ftAR4HI856MvBu/vgPiMNHfY0+tCpRNhUDkJqU9JAkNTJ
+Sc7Nd0XuO/uxszhoNiV1S3WmBXlVaylOPSZqcuzHGVbdnye+Fj0pR0TKyD28M+Ykkw1W9wwnqTx7
+ubZOqjJsqpYTnIxMrR2TLGPp/9BTE2cYpBCqzX4TMxKzbelF2aD1H7lGWfMu5kdH3OrL/6MiIBrI
+NmnnEQdq4jvekW/8yu4uh78h40jLFlfdlfMi5JzK7fYogOqsRqZYAzqLFa6c+4Ny78Iyu28IAvVG
+/wqvr7kwyw6mIlvscKjVlqoZrsZBZLWMsYZIE1py1bXEhY91VV/T0UN+MmZHD7r74JD7d0RfMzkO
+mczclSsRO3uizb+lJulRHqF5XvgcOZtjWZ8XCpcnF2S+2vuC0GK/QlCS7/XCHnGmeikLoz053Csl
+h6wvup3xbWmr+sBnfCTFjt580GMhlfYWL29xvK6JZGv7XLggRYwa67Kn3XxMf2Z9qHa4a8Wq4Xnw
+3U9fBmBzYYn8rXmSlTnMYQ7fAXw43JzHI/jyr0Hnr8CRVvI9+GUGLz8GQOsTKzhqv6dcg1RrN1aE
+e/CWPcwNtd12evCjvybXv/CH4cJ/DoC947P9BcHWt1fvAnDx+sdprmsgTtcjeihs1kmyy/G2UOaj
+4zHxLDV8MWPn28RaX7GY7JCNZkE0Y7Y1d8JodIGqaoYj5TROehOy/ZnN2DtVmMV+hMYwEQaqvc4b
+TBx5/9bNO5wMhJLWlxqs1uW941LKO1Y4k8UDYWU755bItLFhnmCy/0G/91eZRU9CDlZdmlHtfHWD
+YSTf6MUxxz2heNFkyqZyaRu1Fh8/L23fvydz9dXoIeGBULwsN8xybNbbLYK2sMedcs5grOXyWkLZ
+LrQ+Q+2aKIQXkuuUr6ri8m2HsgYYhf0Ib0fmfjQrsZfUGcxqd3ohniY1sdZlqglXhlnE/UMNiNK5
+OIkMY+Wwdt+6g19WF+NpzFD9G/K8wpv7YqF4qH4R1jqU1V39Ts/hjiaiiUuWTihrua2JYC4cNdhL
+hAMxpTJtFRuno5Cv3BGl6/oL1/E1/fzJbaHs+ycD0DKnQTDmzkTLvx38Kl9/RfZD0fFYXZW1/t7v
+EevamdVF8osi/+3ePuFLX5C+h5llc0O4iuxWg7wx89t5f/DhQAp+Cuu78PXvAn5b7plF9g7UYWXa
+wbdL+rDFr6uX4orWfegsw/epJLO4BX/9J+T6L8fwz/8iAM7W/0PrkmzIRa2KZIKQyVCzOKUVUmU5
+p8OADStsdSU35LNagkadf9xLVMcaDTc9ZKGmmmzjEUcaldc75LwGMiw1ZPO3/EX6I41UDFNyFS+i
+nmGQC1JLDtV/fbFCzdckLK0OmebZKzll1hel/5947KPU9X5VXf6csMkwljZ6UQz/VP32f2QTeKhz
+aCg0hHPakj7EsUuiB+wsLZYvigb8KIooqzepM7UsOjL3iysiGuzd7VOvyOHIlwImZY3haJVYDdQi
+lEwJ1jTuJBQkvFh6lvZAtO9ptURLPQx7piBRc2JgM6wWkC1G79VwDFXLPh7n1LqauzNz8cqyZr4t
+saQp492qfG/NzRkP5EBvnl+nHOXajz0GsSCy1/aOmRyrZ6HqEbpNny1Nv/7G4QFVTZS74FR4fVs9
+Ex/KOt2c3mWsRXWzsuGKL6bA840xJV+9ZXuWxfOyfs1c/t+79zV8FbW2gov0ExEPXt9/mae0PMDC
+sxvEGttRTDTX5HidXEWf3Yd97j2UbzxpVlgNNGU8BbW3/81sZr8bzMWHOcxhDo/Ah4NTmPYovv7z
+2NLbGNW473sRN9T+u/z61yg9KYoTr1TDapEV6wirVrz1EAcpAW7eLsOupnDno+TlnwbgmHXSBcHG
+CwtCBet5neE9YbmGbsjwUDDtwbDPSFniJbfAnWXw1elq5Qk9I9jc34lp1TW3YzEg0cpC+0cnnFW7
+eE1dtDuVgNqMQpse2VDai9s55Uy5CfW5LwoHLSlI1uzQsDLmqp9xriXPrvtdurVZBahZRaARSU84
+nnAcYv+2ilLeQ9DYDZyMtztC3SYaffhgGNJtChew0CjTWpW5Gu232FFnr8OdQ3Z8Gd9epPkW0ohn
+msIRbV1cOa35OE2rVJR1PyiDncp8rimrfu1CifXLwsVMHyQMtcpzOgwJM+F0jocZ2Vgo81SdvuIg
+wlMnnepSE3eq/itORh7Psi47VAPN8tyRMUUnMWEi7/Xf2SWrqKZ+kNDXbx+NEwKNU2lrGrhPrp0h
+GMr/t9MCq1W8fT8mPRRHraP4TQDuhyMitUQsJA2G7VkdzDrdQL/3xtssaNIeesL9Rr0Tbr+t9UHp
+MSsU3q67nF/dkva21sgm6nSHcAFRP2ScCJdz+PIhm5rK8PvPrXNNXsM/vINbl3iT9wtzTmEOc5jD
+I2BmNef+XULJ9+1qp00YJqTqw1xYixbOxeY5uVKdAovrKnXQvruug6fUvLvUxtX7aZJRaJZjSpZk
+JBjf1bj67kKVJ58W+7GbxxjNPDRKU3aU2u4PhgyPhfrNKiB71hIpOreFlOYCaHfrVJUTKFGQKOaO
+Nbmm40JX5d5WpcZTy8JhtI9dLq0K9t+eirz4ue03mWquh9gUuJWZjFxlTRWf3bdy/roVSrjGP9TZ
+fIr3Km1HnLsgHp3DKGSi9SbzLH+vnKbOq2McTmtzOoaZc7pjOE1j5rguvnrs5c5MkVeQqBcgVrJt
+y7WDq8lPTWGwp9mRNfOx6+BrtuNyfYGK1k/843/+E8QLwiK9e2PEWLNtP10TxW7l9oR6Z1aHIyQr
+z4q3eKdJf51ahfaCcFO9HeHo9h/uEatL+7knzxCp7T4ZJfjq99BdXcJX3cWSer/GjeKUnx5HHv2J
+mridFn/vf/knMsu6vklUSM1SYO2xdfJtme/FckBtRfp/9pPnsalwLO9+TTiNiZNy4aLoGZaWm7TV
+BDz0pty8Ka78B9t9xsfSXpS9Z2K0uSpw0+x0TXNrMLqujVKJmmYXu9U/eNFa+zG+BXw4xAdrcQpL
+QcZsN5Y9h0wPWJJY0Go7ruPgqeHY1cg7YwrKZWEXq80SRlNyj/YiYi30YTKPWP0F8lDrEuYFS31Z
+oNVL6/SmEmsxbViyRNqrZXXi4Sw9lmwqKeCpTJZr8dWxyCsZggXZTMluRKpFaGM9VHW/zkpN+v60
+tWykgni6tZCFbXnmNU9CYYfjjKGWPTdll0BlCX8l4L9bEfHn+XtfwkzUhZrv0clMOQ0Hx9JLRHTJ
+whJ2FpJcODArEKKFZA2SJEVuGYz+38HB1/smeC9VuVE227qGTJE02XsExjMenjqckUWk6aP1P2VC
+9fk0xKsJsny1X9D1xQ3YLV6mMxD/k3yiGawvORw8kHHEtQSjofGd5gIYTQfvlIm14lbaEkXjaPcQ
+qzkc89xjpHkZTyZjTCjz7DZGtDvSj/ua4CY1Hm1NCeJ1PDqRhkBXuoy0UhP5LJ4lwtGcmMWooPuY
+WIRKYzj/MbEYnHviGXb2ZZ9tPSN9j52UxzSi9PpjjxPsyFgf5kM8VeK21qYcvnEXgKESpGgYEmsX
+pv0JjhItt8hQPEcpSyicGZF4fzAXH+Ywhzk8Ah8OTsFIijTf9Skp5arUfBJN1ulOUnJNdOKWPBod
+UWw1tPiHW4rwNYqu5ATkSuWPT25gtDZgkmWnIshM7EjSnPu7QhFqC2t4LaHGQWipaR6LUtYkWZbn
+86FQdttzwJuJJT5BQ6h/a6XCspqWslLAeMbOqknvqUtP8ty6pgfbfcAlTW124bUn8c6K6HJ0LArV
+/zt7E6Mikdeo4V4TSlL1N2j9SakZaP7CDcjUe/OKmmkvnzDjFAwWG8+Cv+JZ0WysY1VcgECTu/gu
+eMp/uo5zWu6+6rr4WhYt8z3SmdikyUSKqkMxUU4qK5i5EwSVMr6aGbNhgKv5CzQ1AykW9BuFW5Bq
+3c3JIGV8Q7J0D+uHNMaawVgjKqvRMkFHuQBviWFVg+Nci68iVh4bBlobpGWEU7h8eQujSlV8n05J
+qH/SHhL4QtHDNKMUCRfiaC6M+mSFulrD/XyVUVujPNMRRrmfU79e3zvlpCoLi/zI8+Jz88mnHmfp
+rIipb+1ss6Lp9LpPaDRvVmNBq4NfWrtI3L4LwEZmOFeVZ8Kp4c6mKDT3Q+Ektt/ZozeU/t43e6Sa
+NjCdGGItAkQBjjp9vl/4UCAFB0PJulB3qagdvL7oEWg2HpqWdks2xer6Clc0IrK7LHJYYKrcC4WN
+vr9/SKGOOdWg4MG2LOLJYMBUo8tmC+dZ93RDp4cDzm1q5rggJilExh+4A5jFEqi82WkEhOjuDjzW
+L0qsxeYCrHbULt6pYR1hfetbMqYrq09yWSPy+uczmjXZeEt/toPTl43y3LYUsvljv/UKL74u7rDn
+nl7GycWmX73WpB9o/cvyMzg1ed6qvsRs9eGBtGXzXZa0yMr+dEI+08GYgLIrS1+qquiTFcQqdnVb
+VdY0AczmShtbE+TVHw/Z1XyFySyE021iq5o+Y1THqGWglDRwFLEWVMkmgnwnqRbyKRLQOAMns3ia
+V/O7x11+ffIlAKIbI+oN9T0oCyGYdDO8uhzuUpDRNFq0x7VUZqez1GCsoc+u9vP62TUijWAdFilT
+zfbsba7iabYvvILDexJG3exKfzaXU84sibiWRCO8B+qOPsxYr0ifBmhuyNynpOLMZ68/yfd/+iMA
+XLv8FLNzGU4DVg8FGZ5d1AQwlSZ+ohauPKalmZgbrsuqWswmzSMuFRLHsq1Jfe4mTW4Mb+scNhmN
+RI8y9I6J+/LFMJyQqhj6fmEuPsxhDnN4BD4UnAIAToFTeNTqgqc2K03cqlCltZVFnn9Wajue33yM
+WkvTUilhyByfdXVx3Yj3SFKh+Ocfu8yrt8Q19MHd2+y9IVGJuWa9nQ6mjDT5x/a9PTZWhMLmxYSo
+Jpi2mZZw19RWPtKgnSpUS2qJKC9wdVE4ibV6wDmtXH19+TrNy8IpLNbEFbVUblHXugkbVQe/qbkm
+nRI2Fmy+vinP/unHr7L44lcBqDgVVktCuZsbDTZXtI7lv2pR6UqiDybim2EP+1CS/xOWcbSQSTkx
+GM3351sfN1DRTLnMwLXUSkKtznXrPH1VuLDHrqxBVSja7Yf3GWVac6OuykcvYFULuYRpTjFRarz/
+noiRpSGtqtz3pzLmozARxTKAk5Joibzt6jZt1ajbhRreLA9iRbNrZyWWS8L9TDyXTNn9sutQqcn6
+9aMYqwFYFc2YXa506CcaEUtCe0XWrJufY6x5JEKbkKhYFSfye3e4z4XlLQCWwgZVtZKkb+5R2ZR+
++Cqi5W6FBbWSfPbT59nS93y/xnis5er9A86fE6tTpytZye0oI9dSeUV/QqEFacx0fJqhujxKMIsy
+d2u5Vjxf7pM5wq00LwQ8UEX6rQcTZuUv0yIiTL5Bufs+4EOBFBxjxGxStpQ0FjZLpyxXZJE//dRj
+XLsuLLpfMdSqsgmdQJOYRBmxWgA2KxeJNe13tdrC74gr6WJ3mTu+yGTjvogR77xygzR7L4/igyMN
+p23EWJXxoqCAkWywekPurZdb+MtqDSkqnNcNe+6JBh8/J4e0vbpCpbUFQFmrRnlegOPKguO6OJp6
+G8eA9rmu1gk/XuDHlgSBvHXrHX7rS2KVuLpwlZIj9QW9Zz34VzP180/Lb9lAT8OlnTdpeLJxx55H
+oaZYt7CnkX/eTMzGY1HFtcfXl/j0ddFtLC5tMtTkr9MDnyvqFm00eQlll6Eim+bCIk5fnHAOTiyh
+JnSNmkPaGl5+vyma98Spk2jh3nLuoNZLsmnGoVo7wiylrOvjqi5jse4S6SEMxyfkoYalrywQabxG
+XMTk6grt1uTZfm9Mv6eJZI2hPnO7TlwyO0twA66KhW++Kmx5verxxCXRBwTnz2BVheO3yyzX1U15
+SczI0cSl7MghXlnqUG5qFq7hLmOt3VlxWiysLZzOOcC03COPdd0rBemsaG5pSBDJ+PKVgsmO9HOs
+8T5z08kAACAASURBVCp+DmfWZD81mi2afY2bGcY8UMuWTTPCiaYP4P0pF+biwxzmMIdH4EPBKRQG
+Yr+gbMunWm/P+KxoLcLz64/RbmmeuTwGTV5itFKz66WUVIk0dk9wp7MiKxkNTdLRXm+y8VBSl/UC
+cY9+0N0mOVJ7tIXxRNjdwPdx1YkqHfp4WpBklmW+4VkWc1HqdDeqLLaln588d5GzT4i/gNetnBal
+cTSqz6GO0fFhgvfkH5xT/wxHHU1KpZSlhrpPn7e89KK4cZcic+qT4fhXYOVvShMPVJM9HnCaxy1d
+Jsw1wCc3p3PrYgmUTS5pZuyKa9hoCGt8eaFOXWP6bZSDclZRf0htX67DsszP+toiy1Y4t7IXk6tW
+f+vqiFs78u2dXYco1+A2tXY4zhhvVlfP9bChtPewN2B6MksYk2HU3bhakf54fvvU16GS1qlqDoVO
+sEis0YpuDNX/l703jbXtPO/7fu+a156HM59zR97LUZRo0bI11JIsO7VjuzaKBkHbFI0L12iBNAhQ
+FGlStIA/pEHcL60RtMmHpkUQxHBdozFcKXYsW7Il16JIURRFirwk7zycee+z573X+PbD8+xzxcCO
+KFF2roHzAsTd3Gfttd71rne97zP8n/9fQWJtT7MM+QR3WeVqAjzlWsycGa5mXdJkfhrwtAoQerA/
+5cY1sSB36h8gcJQrM+lTKlS6qgQxjUrJ+U1xH1v+KkZh+rZMTmnjaq7Fs2ohusqzUbp4jkq+sYKj
+qtTuvMKiVAm9SRNjdt/1zKqhi1XSnjJMWDQFZNdq++wfqIq3E+A4353q9COxKBjAN2CDjBNFIBbj
+CT+yJlHf5lZCrD5p6RRkGsFPEBMvWdSYKNnIyWDIPFOCkHAFtJai5dRZbInvOL4rA9aO2mQ1RSsm
+KbkiAQPHoVCz05lMGGi2IlM/c2O9QWVDzlsN4GpDSotXdp4n0Ki9E9TAkYlpk+VDSUARiMb1eJjL
+egj6MafpRIOji1CljPjhqsRUwnaJp2a5aaRCTgNwLPfM6hD2lHnKuUWgmgaFLUkUABY5LlZdiSVI
+aTWKeG5HXqDtlSo1nWB2vkCD60RmTmtV7OcN5D43woD6kzLJJ8Mxb31TTdXQQdnVmacLjoeaLlO0
+jYkC0DgKaX6qx+mPhti5PKehZ6jVValqW142P5zR0OrSOKqRKN17tZKSKkJyJWyjpSJUIo1JzB3i
+lpyj7y6IQrm/AEOYy/e2Osco6e25trhuw3SAnQuqcK32gEzd0eGi4HJTnu9hLPc2cTI+sKq6HY2Y
+rNT6mGxOI1BXIRmTanm1p5oWhoBSX/4imGF0YXIjnzKVsSi8YwKtYq2r75fUHSYzyUSYzNBQNyhw
+PeJY7s+fG9ylJNp7bGfuw1k7a2ftXe2RsBTysuAgmeBmISrwy8IYzIrsqk5Uo9DgUuK5jJcgDWV4
+HmcTrvclkLN7kmGW0GbfMNLqPNMKiSMNWkXiMpQNh3lfTLh8lnN0LEGwwkR0UlUlJgHFJyxK+X1c
+cfHmsqO3q1usaMag2trCIDuboXoKISZYSs5bzDKiZnk3md3SWFAsgUHUrqRNWbmkZms3gFjr6gcO
+5qoEFU0q98H/MziNWLNosBTELvIcq/oaRWlPxy5WRauVesjGuoDB4pUmfldwGtZvUOxJNqMT+Kys
+CW17TbkeKit1aufl2Ek6ZziUYN694zETDQSP3RkzFbaZKbopGWfkahVWvIBUn9nAL5mpy4ANKNT9
+q+sAVYKQSCHWcT2mosAqi0PoP3TTyqoCx9RSzKMZVeUeaFZj3CX3Y1Ew0/EaFRV2SyUfN9L3AQuM
+qlJHa+vEc7GmmvWL1C6IpZojFkNxPGOsFsFwuA8DddFSi6dELWVgmRyLBRGpq2X9GaW6BNlseopA
+x4DvabbDreNXFVOjEPswT5nrHKl4DslYrKLEZiz0+yJLKb/NEn0v7cxSOGtn7ay9qz0SloLFkOUe
+UwrQHHXV8+grTHjgeiw0xzxbLDicK6pOg3PHgxmv3hIW5UHfUl1Rya/FIdN8iTfYoLoifnBwoH69
+9SjUBU6TnMGJKhUHBhOp9kDhUsx1BS7VZ6vVCVT3oMIurVCCfH6rhhMvSzsdUM1Dq/2lKKC6TENm
+EmwEcL9tbdZ0HLkLVn1O06aYyW6UjCOytqpuN/u4rqIwt3WH+8AErn1EPmd/DIVYMaU1LHV5PGOI
+tThouyNjdfnCBvVlnGR7jUpbfpf5PuWyMCuPWas+I+NZl7GYzieEDfHJI+9Zmpd+C4Bv9GCut5rW
+DLNQnk+qKUTXL+nmWsTmuRwpVdokiZjn8hziuiFqKZmsFrHlDYOraVsnzPE8tSZ9l7myKaXhgtIu
+v9dqTuOT6udWZYOaPiebzRgXMi8O5+NTAt3dY4kDvXPzCN+VMfqxfy+goerfRaeFG0vQJIhlLFuL
+B/TGkp68fe8O5zUeEnkuZSnnjfIaFTWHXf0uS0tstgxK5nhq0VkyXOQ+4kZANtKAr17PyWZE+vxn
+kxSjuI96rUJLKd+OmFPYv4A4BWstaZaRleUpHbqz6jNXwY6j45yqojH28iknuQ7OSEzAB8dH3NsV
+s3UxzHCNmFxOrSTPl3JSJwQjLb/15bxR1SVT8EuR5+RaX7GYVFms6DFFFaP5+0Jl5H0n59K6BKKe
+fHyDhpYnu/E5sIrtdR1I1RVQyKmdlthYKxUrLiz1uEPz0JVQe98UBY5SysdOgzUNcPmmRhDqYjHo
+UpaCX3AvKpv18z8JSlnOb22ABuIqgUeuZmdccVhT8pGNrpx3Z3ObzjlZ3FYbO1Q25bMbGry6uhLl
+CXXF4nuljNUoHZ/m+b3Ip7MqvJrVtR7H+2rCu5BrkDZTs7wsM1Rfl9Www1gVjWajKUb5HCs7bdrn
+BETl6T234lWqmjlxHY9IyWVwV6i2VXAmt6SaPcjKJYjJUK/LgHfbNarqamASPJ0D8eE9YoU8F1rX
+sEgsvWNxL8bXCqLHNcNR5FQV0hzoHGvu7BCoe1RaCGuCs6kGEcO5LBbzyZjGeutd95SWU7KJ/N0W
+NfxgGWhs4S/fh7KGacu53aFeYz4gUxxDWYxY6HwJqzGl6mbm5pCi/HNmczbGuMaYV4wxn9X/v2SM
++aox5rox5v8yZrkdnrWzdtb+IrTvh6Xwt4A3QXNU8MvA/2yt/TVjzD8GfgH4R/+mExgMsRtQlim+
+5u4v1rfpqHDI7r0bRJpu2uslHPliuht1NeaR5XGV2pqdG7CqJl5uDEFPTLRqJ8TzxcJwtKv1SodA
+wQf5vABdUY0tCKty7XoxYaQVfravlYNxwEQl5d2OR9wWxBt+hYfqJIC7FJfR3T+agxYikTgsUb54
+5WnF4GlMaGJwJprHbgasRZLqsgGQ6TWKADJJP9q3pTDKPH0bxstKzSHGFdPeCwIKFV8Jigb1S7LL
+VTXYla+UDGYy9jcfPCAYS+caYUypMNl6p4rTXCIopaNNs8LdvgTcXv7al3j7nuomvDljaGTs02PD
+ErLoF8o34ZzgKIqvZ8dMdYwb6YJMeRm81SrNqvIJ6PNIRj3aq9LnRn0Lq1ocZVmAozssDhU93mo6
+uWYCOm2FlZcZSSapvtnAcjSWfh4eHTFZSFC1Vi5T2S6eBu3eOfgyzfWPyznchK6VeXhbodjpky2e
+RebhzmadUPkk3CDADuSZ3JzcJ1GLralye4zrZEsWbDfDnpL5GGYTsU4HixEnhxKgvH5fxnh/1GNp
+YsalwaoFHJgKF7sSTH/7ek5ZfHeBxverJbkD/DTwPwL/tUrJfQb4j/WQfwr8Et9hUcCA9Uqc0hAG
+yqTjwFvviJ98I91jMpBJc+vkLjN98dY68nJ/9GPP0rigzMkbmyR9Bc0c9Jgoy03//gJnKL5qqUAR
+ky8ItCQ3mecsa4vL8YIlL4UTBnjOknlIYxyThH4kuIAPvPlR7KbiEBoWbmnkHxfu6fdj5eT73UPQ
+xY0E+CkFHH2tgE+oL3GgD/DXXiR781elP3UHFCAz912CtpDnBD/+GM5luUbZe0X6uIAlyV+aN0k1
+/1295OCNVR/SKxgoy9QLhyKHfuN1H+uK0Fd/vmA2WYJpHAJ92T75g5v82FMSU9h4QjIVWRrxjz73
+OQC+8tKNJXM8OD7pqWoVeEsvTsVjs2nKQFmyHSc6hSzMsjn+lpj2ncYWdfXnS+1DOnfoDZXKfXqf
+uSMnnt04ZqxcixMv4vxTEnc5py6DiSNO7qr8vB1x+5pA3meex1AzPgMm9BbSp31dHDLfJVUm5huv
+3qFTlUW42vQZpqox2tb76K+QbarmZ2eH3ZtyDssRd29Lbcqbd67xR7qIrG+Ka9RcjXjm8Ytyf8kJ
+FZ1CiYXbx7IAfPErr9PXDM1ClbCymoer8gI7qyEVdTWGlYxcgVw2L/90yaY/pb1f9+F/Af42D5Mo
+XWBg7bIcg/vA9p/0w2+Xoi/L724lO2tn7az92bXv2VIwxvwMcGitfdkY8+nv9vffLkUf+L71rU8e
+gqc5/UmScvOuRKxtETDcE3OvNzpgpFmCu5p3fnC7z7MfFyn258Nn6VQ1/zvfOy146h/ts9AKPnch
+tz3P+1SV5iyLEhaaJbCkxEaCa2XYJzmQNS/X4FWvTKl9S7a2O8EXeWIixzrTFyj/338ln2/MwHlJ
+zp2JNZKbOZ4GIl1Twq/JWLjhJvyT/1L+x/s/ADg4uc4LyH32e/5pReELWD58X/r/qTeeohHLuKxv
+yLi5n/oQLJ4HIGj8FqFmUVqVNaZV3cUWNQIj5mWwrIhyciIt7AoxjOaaXZik+MrS8cIbCZeUG6KT
+CQT7RnmdF18X9+FoMiWK5e9R3RKqhRV3G8xUXKVUKXu3nFNV0pM1r86RSsY7Xky3K+futgJKdScP
+DuXvbpQxuq2QducmN+6I1XBydHwq/FKLS8a6w8YfVdeuXmdhZTwPD8Y8OJFqzyyvkLTld7NejZHO
+lyN1nxJTMp/JPHstOaZxX+XswxpjDRRuKKp06ibsK2/E7Ovf5M51ucbswYBbR9+S896fMV3IeDZb
+cq4PPtZiciQYmcc2rtBQzciD2ZCb1+WY/fvH4Mo8WlVRdYKIqYraTPccso6iGIfeacC+GkbU1WpY
+JO+tIOr9Csz+rDHmp4AIiSn8CtAyxnhqLezwUIHkT28GjGcJSp9IUylJlGCHClwJKpSatnMcn1Rx
+8uVMH+D+IdckCM/T29u0LolpW+5ssEhkUu3dP6C/qyWyUzG5x+OUofqTeZZTLFNB1jDT+opgGJNN
+l/UKMnmC1OCoRmFatum/KGm47kGb4m1hDQpsgqMGlKeVd641WPUdc5thVLHJmfcwwe9Kn6dS6j0i
+obMkUjWWYzVxb2M51lTnb+bf5D9Rz+Q/0PyfP/ZhUzkOh8/QRCC6tdjB0/SVt+awiYxRe0P6MJov
+OBqKORy7PrH6/vUowVGzdTuuEZVi8mr1MuG4Sc1XPcqKh1HK+WqrQiOVPjWpclTV6kgVBTapxVX3
+4umowd2JXG/f93A18j9dTGhFcqGmCqbWiipvH4op3hsfcaunYsOOy9XzkhHq+g4rXV+fgy7IacJQ
+61wGgyGzqbw07/TvM7uvY5AW7N+U/nmxfFeLNkm11D45Cri3LudwjmY0dFGoViSDkzoDbrwjz2//
+5oTF27Kp9U4GTHQMe5MppbqpSw7S24cZ9Zq86RdbASN9+YfjhNv74iocTgoWSlBzU4luatEAT5nF
+Kk2Pix0Zq53Yp13Ks37qylO84Mgr+OsjIe35Tu17dh+stX/XWrtjrb0I/IfAF6y1fw34IvBX9LAz
+Kfqzdtb+grU/C5zCfwv8mjHm7wGvAP/kO3bCOLTCmDI0RJo/T5OESku6d2mnw0ZXdqPR+AJ5Imb5
+QnPba50aj5cqM+6XVD3NFpgaXYUEu66PUWsjVLnwclYwc2WlzZKcUndjpyxwjJJb+NBTmKvVKspG
+AGuuBJyC8CbZruAfipMpvu4wTlLH6I5uFAYLE7BiGnvcBOUP5PJF+Km/K7/7Ixmuy9/6BuddCWbm
+xXkOMgkImrKgo7nwKS7n1Rpxl5RbJxMYC6eDcX6bc8pnGObxqVDJdvMC9alCwV35Nx/2uRqLqT29
+fMTjE7mPVRNQGYmF1a64PHtOxminKVH2ViPm5/9dGZc/uvkqnsLRFyR4vny/EgVE6jad9Jc0aXOe
+8iQz8qnKJjerAk57c6OJ29HKxuMTKmvyXDe68l29GHBhRQufWpdorEkw2vUabCut+04zwmoap6UU
+8E7oUWiVVNQydJR85lI9ZqyWYH/SYxTJvbYuCHz86tZVbt0QKUPHSQk82a0H/ZCPK4nK+iV5jm/s
+W64fy32acZ9NJd/ZudQgH8u8fu3eNebKDv74qlhrP9Cp06iJdbC9XpItxB3dv3/EXEVwnCDALMRC
+DjQzYkxJVS2zi40WH1KwVBzAyly+3zz3MS7Hailce2+WwvdlUbDW/gHwB/r5JvBD34/znrWzdtb+
+/NsjgWh0XEO1GWH8gFKLiDzHEMey6m50WqyqOvSDxZw8uwiAq47tY+st1tdlda1EbY56kgoqcvDV
+arj45Arnt8XnbEVy7MHeXb5xXfzTm9eOsammKoHdG8oivAluouIymu8dTly4JEGkSvYU/lNKgvpD
+z2E+q1iHw1dhsExPKpa6SCFUXojuX4NPS3qPX/hBeFYpg/ek4Mj9wjfgNz8v41P02BnL7352cEip
+OgMsHMKpBPm8xVKV2UAh6UnyHRqelOm6lTnnWrIzXY3qLLR0utvWgOpWl6FiKBY57N6SHbhtAp5+
+THbNxipUA9m5bU1RfHGTT/y0oBgXXxgxmWrad54QRxpUTHPGIxmLuqLygjLgSX0O55ttVg9lpxxc
+8VEICDO22FLYZ7shu+6KX6MZqNVofNZT+XuSZESavmzGTbxYLJIlg/d0XJKr+Otqt86akqNuLkpG
+M/l+L6kRKp/FxXNq/WU1ZhfaOi4LFqkK/EzndD4sx9R13Lorj9OMBS9y5flNPnL+UwBUFj4HU7Hk
+Nt+qcWdP5tbjikJ9rnaO2rYm7PKYyUCl6SoerbZyQJQtVrcFer6msYyommCV8HWrXqNTlb6bvSHZ
+gbwD3WdS6kcyzu+1PRqLgnGp+HWKFky1IjEfT7GqBOTFPkbFOxr5jHM7Euzaqsu/F9csk4WKcCQ5
+riruHt69zf5QAlytlQ0uPCUPoaNBtKMndhgqEcbR7qvY0RKiaii0tmG0H+I3ZeAzNdsHzYDqsbzE
+s/guTqGiOz/SwPyU0JjxzmfgwRfl801lCzYvwtZ/Kp8/4sEPC0Mz7ZpwrAM09ZFc+CGcTwspjLPq
+YlW7sTbYwwn1fAf3cP+nfy7HvPKb8l3vAIxyK5jfpN35pI6xYUPHsFkZqSAMeLG8HA6z0/qCoB7R
+vHwRgNjm5ApN9qpbBAqhHp8oa7NfwYsl6/zcRz/BsC/5/+GshS1V03I2onwgL0JtIWP5ZKfDf3Re
+AqIXu09y6P8BAPXqJaZKrz4+OcTR4Jmr2RcTgteUl79eFFRa0k+vLHE1u5DNc4pCgpyxakqmw10G
+fQkSho06q2vyQldnGWgEP0wabH1AKz6n0vdv9e8xu69w7XPOKeXbOBtiNMCaKy/j0d27TPZks7hQ
+X6PbVDjzSoVc2cYvLh7j/CUZr5pyaSbMaa6LW+n6AVb5Nitem3NKdZeeL3lMXaiGBmibfhtHM1Rm
+YokyCbqa6oy1WD43MAzWXuC7aWdVkmftrJ21d7VHwlLAWBw/h4WPFsCRdmKqrlgKQVYSRqoG3Kix
+qkwy61pYEvqG2mJZcWaI53Jb5SRhrmnLS37AeWXPrWugsZp5rF4Q83rtWg9fzcBROiGZahFTaKhV
+tapSU4jeOKGhoi5ltEO+rfnfK09AJLsfzzhwoAnlSJGNf/Rx2NJipSdjUDNQqiQVdrZcpq0BvX9T
+CaGx1FnYOOVFSLwq/kf29RKaI5x4UNFjjy7TUBcsKFOIlI06CKjsS5/jluzAue/Tbsn9RVmFiVLI
+Rck9du/LjrbmdTFN6WCumA/nwMGNxUVbz33Wtn8AgDu969y9Lc9k/OCQsboVa1vSn+daMe1zsitX
+r1xkZfdHANg9fp1U4d2dzSqOBitnY7FWmn4HX5m0A+ucUs85afN0Mvft/BSdWgvlGnltgf9A7mM6
+hNWmnsP4tHzZgYezITVXLEBPqzN5dcwwk0Dduel5+p6Md/+wx0SZwCOl7vMzh7WGuLldZ4VYi8CC
+qMBRl6d1NX8oAah9P5ocUVE2qUZQZ1aTAGxWDHDVem3VAza6YpG0VPciKqsYTUmbSkZixeUrswNM
+rOd4LGH2FZ1n77E9EouCa6DpOgydjFQhzN1ah47GCRy3JPJl4NtBm8ZFjQ0oECYbT6ggg3AyP2Ix
+k9y8SXJCJUvxfJfYXfpnKiZic7YV3LN3qYLdE799sXuXtFTfOLHkapYu6xMaicFRX248P2LqSCYi
+v32E/wFxV8yievoiW62S5IkhbOpC4RvMUmWqLB/yNS5FQvdGFLsiIe5Wnjhle84O+0xvChz5td//
+lyx+72UAPqNw7kodMLf1Gvfp1MQFSTPIfTF9s8ChsimT1GhdQ8OfsuqJopFtDIm0bmF2d0zcV2r4
+Sy55T6LvdUfMXfsYODqN/CqkSlhSz6rEoZjMu8mYWMfinCdjdfHpJtG6ZDuKSh37QXkxW/eHvN4T
+rEBhN7mk2Zx8QxbTUd6joc+xWT+PdTUOFKS4ujDWbQIVmS+emtFdt8GmwuJNw6dak2dSMxUyjSME
+ZnIqK1CfiWtab7xB8UBNdPcevrJ8O65HZSBjNK/KeM+Gk1MSnRkJ/aEsIOsmplMT19WEdVCotzNb
+Evl0TglnMjMnVzdvdFilp3yinVkENWW/DuVcnh1ijPRtMSoZTiRzcj85ZqEalPbgNq/Fktl5r+3M
+fThrZ+2svas9EpYCBoqwpEjBaMCt3ghpr8juUTa80+KKIlpQjTQCrpDUKIzxupoByFYY9nWXqznQ
+kB2xW28ym8oOVKmKVeGUC9Yui9Vx/mSVhRUEWv+4ZKFVgNZYqkviSyUAOHEyuu0lyu8y06rYcInv
+Y96U3cFtdSiHAlFN7wnGoJzNiZ5Vco+KRz7Q3Sys4l4QmHY51mBZr0/yqkBjw+MGyVACeK+/fp3X
+3vkyAJ997Q0K3R18HaFP4hKGEmgs8xj1HrDVgLJUDEie46oCdZHIWMX9gOKC3P9stOD+NTGZ7+8d
+MtTCrptHR1RzOceVx1QUZvA4Gx8VC8utBafoz8PjawwmGmhMDec7cszjFy4CcKmyRTnTnXuzjqvs
+2PfmRxyou+I0QnrHMkab26r3GGT4IyWDiRei3wgU2ZRCLa9RtjjFiHhTuUaWz5mri7bWhlZdrhfZ
+eMnty9Sp0LsullBSkXGdDFNq23LA6vY6mxpcrDgR74zEIvV1TCamTeGrIjZTdk/kczhp422oy9AJ
+mdyVgGeq9H7HgxFuQ/qWpQHHuzLe94tDjvbE8rSbA7JDeZizdfkuMDAfKFL0uGRvIBbBteMBt9Xw
+jEzMS9PvTkzykVgULA5FGeNWC3w1552witHahsnQ4YGVh9SYezQb4lbUdEKUyRTjaeQ8atJREItf
+OMSHYsLGpYejpXiDgbysiyBlpgtIbXuDqpJt5Nw9NeMdY1hkMqiO1nndHS9I9uTlHXgJpib9CbI/
+oK2y5ZVpm5O7YuafvC2Lgr8R81hLIs/TN17g5POSOvS9CiuNvwyA68qLGX3yJ4l+9Gfl/k7mpLfk
+ZXvpi1/iK2NlBUpT1jLVgtTUq+v4GKs0yuEdkprGBqpN5gN9SRcpRa7Vk8ocfDw6JB7JeO/2B4xV
+NWnay7llZeJdf6fEah3EygPp549c+hY/9fjPyX1sPk++93W557vH3NwTH3d94wJXN1VktyoL+tZO
+hXghn93FGv66uErjlwJOFrrIvpFTn8nYru3IouBVqvjqzs0H++RjedmcqMJCU4vXj/aY3l/CrfXl
+Pgm5uy99vppfotnVKsJswkxBQf3+gqHS+d+7JdmSa8M7VD1dvDoNolzOu3GpyYuvy/kmqnM6cxuY
+nsy3skgxKiAcVlJ8BUuZSZOZlWNGh3Ldt3ev44ey2DRX1hkr2U/vxgQpp4V2+Aw3xuJKzA/lOXXX
+Z1hNwx4m97g70HLwiUOiBDD35yPGZ1L0Z+2snbX30x4NS8Fa8iKjGaxQXZddLuxY/LqsiO0yplRX
+4Xg4ptEX89LTrIBrc8pDzRZUhgzuaXVeckQ7VKjxfEDLl+DYUsr83mDErbcF+jnOHSbDpSZYiTJ+
+kZU5o4FyBqo03f29Q457YgLeWWkwO6c8/vNVVqZiJjvJV3ntNTH5+0PZEVbu1ol3xfR/cO8VtobK
+NZjewU3/vvTNEyvHiX8RnhQ3x82gsbgNwKcnGxw7rwLwSmIZWNnxN5YiOrGPWZEqSe+tVawrAbr0
+KGHsy64Tug6NQoOmKpqStCxHM9nBEmfGVCHdO5e7VDXQ2FlJmKowzKZKkP3g2jbb6wJecvorbB1f
+BODx6dfY1yrI896cD27I92uB9KHqruLuqsZFVhB9UcYtsynhEsabFUwUvn2i+K/NSgTI39MEUKET
+Q4daRe6pXeviLc1xNdHDuGBr7bzef4XZA/1dPeI4EcukNxiSK6XZkmzFpiFRrDqYY5dKfUmh1uf2
+HbE4TxSjllUjAgV1VUyXtmqeZo0KR0r2E9QmlEqCM1NQ23C0YKDS8bX+ECeV+ZZ6lkuaPWkwY01p
+BocTmbP1xg6uwrgLa7Gukui4PscTrYwci/v23bRHYlEw1uKlKcadkiIDVqGDq8CNaujiaJVcOcqY
+6QNL9SWt5gZflXLyPKXwtNqvt8bOttYXbDdwFXjj6iTfGx7y4K74Yf3C0iuWYjANrJp72SRnXiip
+hS5MlQAOx/L5oBjywUAi6odPjKloPYY5GbJVaFS+kAXkAi3aUzFLN9Mr2MU3pT9pSVgqRn/JHh0q
+qAAAIABJREFURPG5Eh6o2fdWjOkKGvHpgyr/jhLR/KHNqeg1vCUpjLsF039f/qfyWwQjMf2PTqY8
+0IPc0iVqy+LrqphIno/xJrqA1EqI5EVorDa5cknu7/lWiNvXqsWJ+NMryeP4Q/kumY7x/lDO8Zh9
+gptz0ZX0XMP8rrgSzomQypqtFkYrLs3Xmxgd28txhLMm50vjCqli+I0C2QKTEmjcyXcCskBFguwI
+U8jvzq3vkGvtSqEI2aobU6ob2BsP2T0WgNBatI0WytLPZ8QaP1ryLz5WCbA63oPhO4wVFFXOPIyW
+2mdKajMpclpjmW+9TsrKTBaeJK8SxXpPhaUMlRNyIQvQVmuDVDMVi1mVtl77L7WfYGVbq4aLNZpK
+nz9VpajpdEypc3PRz3G18rMZODhK8joJDfGGLCxfEQ/tO7Yz9+GsnbWz9q72SFgKWVmwOxsS7uU0
+FZO+yI9xlBxiZ6WFp0HHgb1H/67mpluyC1zprFJXi6CzeZnLWvsQ5eaUbCI3hvk92SmGQ9m19t9+
+i2Qsu/jEKSiVIrzwFyyWdOHpQ2o2o2voPHaYKs1ZlLe5Xsj5NsYXuH7zPgCbLFhX6blMKw7t4AGB
+0nk5gxvkqewOSdlXsTJAFYWZ/S344+Xj+Qj0l1wP/4JwyfXgljyvluFquQyhe9BU4M34g1wf/joA
+fWfCYKB8jHZIoAComVLFNQzEF2RXckuHWCnrFuM+uw9kjPYO9rk/lmv7t2Un+pR/nadekL/zoEes
+oJ9gI2cz14Daaw5TVav2IrFc3PoPYF5UIFclwHlHskBP/eJ5rnoCgHrl/gGDW5IFaWju3uLg2NMo
+MKlmFPLhiF4u5/anNbxIXMH5VCzPuyajr2N4lLzFvQPp2xMf/ACeapbW6nUmc+WV1GDfNE3wNW67
+fy0hq8l5Pa/CVDM+qQYD87HHSK3UaL/BrmZDnlxvkFXUZShzkqE+v4o89fPbm1x6StzG6bikMpGg
+ajiOOf66ZJK+9voLPH9Onk9rXfkqY5hPJbh4kow5Uii1hyFQlutntmo4Gqz9Z0i153dqZ5bCWTtr
+Z+1d7ZGwFIqiZDhICawlPZLdszZ0cTXXfL65zYqqB8+cKvfuSyro6JZYBPmlEbVMd+6oQRSqeMnd
+kFwhxhM75fCB+PAPXpMd5dr8DoEyPdU8n8yXVTcKXea6I9qCU9mtU2mG0qVINF4wSogOZOV+y45o
+pmLdDAcPB/drSrT5SdMgGAvU2mv0MUeqxEyOc3p2tRT4Ag+pL/8QNKAIhi9q5V8bj4+oD9xYXszd
+h4FU6hH8PfYLQQ2miyYLV3ZBM3E51EKaqmoWDKc5563EF+aRB6qMPL01ZZzLeB4cZ2yrpFu1qqrT
+81tE+3re9jHhoeAtTip3MDWl58zmVBQ5GSmTtukew0e0vv9zn4bWPwZg/eJHMZeFq+He//ciu2/f
+BmBfA5xr7Tr1SHzuzFmAUuvNpilzDfil+Yw1Vyo7m4+JheIXAW9V5XpvfD7nSFOHaeUmz7SeA6BI
+ApyqjO3d1yQesmuPaO1Jn0fJgMW+jEWzlbKUFAmUtblIckqFY889l0lFju0dTKjruC2KkKlWTLpG
+TNCL7SZbOzJunU6T6FAJZtM9XvoNmQ/3Zy+zcVtiMGxIf6x3nomS2B70eyzUylmpr9LQojJDRLv7
+3e39j8SiYDBEjiEoPCqKB4+rLmakbsIoYfOymF+Oe55AswFjBXbMj2cMO3Ls2rjAm4r55a87FKlM
+Jm+xIHxbXqDAyGLzTH2DFTWZbzoZt1W+++hkwGKp4eenp7UGSzq2+SzHUT3D1Ck5Vk691jSCTGnA
+ggpG9SZ/LJIswpXGFbwfkAnoVp/BfVWUpHnna2CExgutdMMLYK76kKYGipEYkPOqLlLfLAt+2FeQ
+VUNdBlpwQV5uXnqGOFGNQsfBU+nzGQtWExlPT/Edae6wCJVmPYzwQzHt1zfP09PqwqtXW3QUht5y
+JdC43h9Qe1zKvYvP9snr8n1QdnEVL9KIPPyWsjI/phP7oxvwG5IN4rkJ/OFPAFBrdilHWpsSOdS0
+dHgx1rHoVig002I9S92T+654beZKJT0fWFptyTS0V6QPAzPgnC4s061j3EIWBQcLCpVOghbZkZZc
+q2KVN4GK0tofDhbMMo3wx5wu5K7WOLjFAqvVnL7NqJQagC1T0EXx4k6HrK08pHM5V7ezwroqrFc7
+bbw1faEflFy6INd47mtNzl+QMVxVusHri5LBQJ7NeJJQKlS61sxQz5uw6uEvHjqn76WduQ9n7ayd
+tXe1R8JSCF2XC602RT0kUDXjZhERKUmHMz+hVNRZx+2wXtXdpqnaeUGNNeR3jXpIcF5hyYuATElI
+Jnf3OWhJKmcwVMhpw9BWNOJm2eO+Vlc6c4PR3G7k+hSaJ7Sa26+WHmPVAUwGKUfat53uMU9oHnuN
+kEoku845K+Zeq/oOtqeks9kYHKWNO3eIUyiNl8KPcWtwrLtjGZCrafwPU4dXNXc/oeQl1dvsq4DK
+6toBDD4LQFG7wwXnYwDcM3OGb0mfLS5TXwu+XM2rA9FCdrBGxdJuKPP1ICedy7jduveAiSPn2E6l
+Dx/oNtjUtGH57IyDPbFMDvyMRGmHizhhUsr9LdQ89yYDnI9J0K54NWTyvBR2rfAzDCLV9LztEhux
+WCY9scZ2XZdoS3a+RlTD04rBRr1GYyjXOArf4vBlCfj1D2R8HvR8bpyI2+kelVxuimuT+wHVgbgj
+g91dDu+LxXawr8rkhXuqM5FlJZ7yUMRFBUcxELkGnbNphlGhFy835In8fTKfUaq1GZgqrYb0ua7a
+G2b/NlPV5Cg3I4Yncq9vvvJ57nxLAtcHmyUrl8WFdhRu/+DwmJt3xFo+WSzo1HQOOS5VLUALFgW5
+VrG+12bsMpL7b7E9Hzxt/3j1VzGHFqf8ZQBG3pf5G648mG94KT1VzXE9l7oqL9WVbORD6ytYrSK8
+lZxgVW3o5v6M8SltO9hMjunU5MVdbTaptORzNpxz50gm0mSekmnprXXAVTyEq3LhgWtJMqUAX2SU
+qixVbcQYfTlrYYCvvqajJn6WZYzV50yyAtf7NjWppWy9eipe4AsFEuDmhm4p/fwRf51Yqbodc8JL
+Zkl3Ly/p1ecv0f30TwLQ3VzlV//2LwFQ+O5pKTaeZbWjEW5NHCR2xnQk55rlMxERQVixjIKFwmqD
+yC4p2qXvbdvigx0BL12cl6yWas66I/ZVxHXiL9hFvvdiOe/W+Q2e/qD40VeufphqLNmHv/qLf50k
+e+iuWXXZ6hqpD31ohFo6HIesKKX8moErJ7LAfzrf4dng0/KsjiSb5dZG5BW5v1vhy7zRlQXiXwyP
+uKc1GmMLqW4G2w1Vxw095oqhMBWPuY79Ivf5+/xXcsiuMj8n/4oX/NsA/N/BiD1lDT9epBhdvDtB
+TFzI8U1dYB6jxuMKj38qnXFJIf3naNJCSspDbmJYciwuFXf+c+AfSt84ZAnqghGwVGu0LKNhBudl
+a60yAv3p7cx9OGtn7ay9qz0S7oMpdwmS/wFavw3HS6GWlK7uDtthSENlxC+UEedWNGOgQajWJOZG
+U3b5pq2SO7Jj1Ke7KIMaSVmcchZkhUqNZWPiVFbURWhPA1iZecjt55YOpUbflwmCGZZCd5QScFTS
+LPA96uvSNzsvUC4NSg0+JllBoSexxsEuA1WBS6kBLGcpUWctzXC5k2T8F4XsOs8Zw54rZvc3U8M/
+0wj2VF0b78Yx4YfE12hc+iDzpeWRVjBV+R/ftik0u5JqIdW0l5Aq/gFrcZxlxsXBVffJd0oanrgP
+W2qRfsZO+bhZBjPfJpzKjvdN75B+KQn+E2fCvqL+7ImcN6zldBMJBlaDlEituwwoltkeB3y1pnzF
+fLRaVSJ1XRqhodWSYz9qI/6Sajue238Zz+7qOEtu3uxuM62IdVC6W9xUQZZh08F3tWiMlHpN7mWs
+10icDHdZuRtHdErVkYhb/EQiBW2T8yIA5H2jxl5TMmLrUcxINUempqSh++/jocNKIWP4l1OZN58x
+UxqadXKdHkGxzETtATf1c3qan3rY/vt/7f+X1ZDRv37gd9UeiUWBcyX8UgI/vwbIy70Z1nn2nIqE
+nlvF00mzVnF4uik4+fZAJt20OyNQRiA7cVmoeTmfLJhtyktTTErCVEEfCjsuc8NU3QC8Gl6gOPmy
+ZKH+oLWcWmuF+pCmcLDqXhjHxdFJ01rv8pSmlpJsxHgu5urBsRLJFgajlZ2hb04l7t3Co9D78/Rl
+DKOApvILPhfmPKXW4GrcpvGyAquc4Skh01D7c9SfUXlNXoiofg+jt1d6i1OST9w+E3UbPeV+zNLF
+qRiOcR18R6nFPYOvNPiuX5LrYEwirUUJ4L5SiHNkiF2N6luP21bu+zixTLQqM1UzendvRprJc3An
+M9KRvhTWcPpOuC5eVcarptmV7Y1VMvXh8+kC1EVZ1GrwlK5UkxRaSmH/vylpqTcg1ij8veiExOrc
+SgLcusyd1UZMkMl9z6z8fp76WH0mziwgqSkcPSrwflpck8Z/82G5VniXHz8nbtAL8Yw7mqGqlgUd
+VeK6sLHO6khh73XNtK3keAo4c3+/BsEyluTA0+ryvQhohSrLoseAh+vA97G9L/fBGNMyxvyGMeaa
+MeZNY8zHjDEdY8znjTHv6L/t71dnz9pZO2t/9u39Wgq/AvyOtfavGGMCJIj93wG/b639B8aYvwP8
+HUQg5k9vo5Lyd6eUj4GZyk47+fCE+KpQWv/wYItmU1bX1taTFIUEYmLVnfSmlktKA9YpMhIN4Hxi
+pclqV8A7jpczUxP28JaAe/74pRvsqqJwVKvgaRCtPwoYj7ToKi/IFSOwBKs0w4BxKddz/IB1FSz5
+2OXHuHJZmZbTVd64Lzl7V92WbhTiq3vg+gGzmfRnNB+g7G/K1wiNRkysXISzGnx1XXaog6gFRn73
+u28ZZida2anBToqULQ2M7bglVcVbJBQUCt11vfg0GJvqsWVZLtnmqEYhgcLDS8DTYJ/rFqRaxHOs
+7tzvzHP+5WypUA2+Wq5usbT5xCJLyiXdnNUxnjG4qcIxjw3JhmJSd+MKY021mMBje0Pl1BTGvtUM
+GQ0VFJSGZGoV3o4jvqqBvUVcpXqgYj4XFJsxM7yjFP2fa4QEyqV4/kLEeV8AZSZaMExUUnCiXB3T
+klTHcFHOSNXdLP0q2YvKC/oJef7B3Q+TPyHcGSu9E3YCOYetWGYaCL/ZNYzX5J6+si3fHVUtmw25
+xmanwopavUG9h9kSTIJz8Sb2CcUb5Es1cw/zvy4r4XiIriuAZWHd99Dej8BsE/gk8PMA1toUSI0x
+Pwd8Wg/7p4hIzL9xUciygsODPiMTs6bqP9nHP8OW8uuV53LcnpiBcbygKOXB2Ioi++qbdHVQqcxx
+1JevFCtU67Kw5MkxY60c6yuQxiYJL+xJCsqPQ7Y7Yg6+fR/uqN/n5h6ZvnDTsfxb+AWhkry2ttb5
+sLoMT15eZ6WqfYsM2QUl2lSseitsEsTSuclRzsFUSm+P7kaME1novFy59dyC+tINCkpua0rvuOGS
+qn7FrekCq+KnZrFUxbK0u7IwXa60cPXl9cryNKXqlimFko065TL1anAVmFT3PIxi540pcdX0nWUp
+uQZpCiWsGaYlqcZcjGOJZ/oyGYdC4yQllvA0Aq5+9izhS298BYBq0yNV+qOgammo6xI0WmyuynPf
+WFK9+5ZaImNUrRmqNXm5K02Po5nc95fcEW5X+nl9Vc39UcE9fdEr1Yjuc/IcrtYf45JyfqbHI96+
+K/2r6RwZBiekWpLdXFQJ28ptuaiQ7Gvl7k0Zez+7RVAKIG0z/ho19e0cY0lUfr7fn1G7Kuco2gJq
+m1+qM9dYxv6zOemRbEjNtSdxddOyn7I0RzJX3YuqHdKcYr+sb/+3LGYpPPth4HN8z+39uA+XgCPg
+/zTGvGKM+d+NMVVg3Vq7p8fsA+t/0o+/XYq+l72PZe2snbWz9n1t78d98JA16W9aa79qjPkVxFU4
+bdZaa4z5E4EQ3y5F/6E4tM5uSnPbEtxRso3j+1Quykoa2g7eOVmNzcwl0eqz1Y6YVpHxKANZUYPN
+i8QN5QrABVUdziZrBG2F9k5lEWo1a1QfyA4UNx0yVTtu1kqaVdmBxtkMJrKL+Rq1C/Fx63LsxmqH
+ekf6E0aWRkvW2d4kZ8WR653fEaBMYzVkqtmVw/oJzr6sl+2ay2SgVOSpBkbzHFd3a8qSRST9qfkV
+umpGPuG1uTsVXc1Xb4mxbnGYTpRApBkRKjFH6swxy0AihsBTXU19Hl7pnmYcHMecZgB845IqBVlZ
+OCyjroVaHVmeUZaK5ccnUoy/h0eiLkqRGWK1QnIN1hZFzvGh5vHfeovGljzrRhDhr+iOXqux2lGr
+QeNtjSxirAHalXaVVkN+l9RcKq5yUNa3ODgQ0M9MuRir6w6xamJ2Nxo8e1l29I11n2ooz2nkOnxI
+vb+9ffHnWmXKRLMEWcPDqn8URwGVQ3WQPij4Aef3rzDv/Y6MReFRVQr/btjmZCIB2MksI8qlz6st
+sTBrQZeWCg614g5BV9mssxplJn3O3XOEFann8G9rMDd/gKvK1WZtjD1U6+5L737lvlsk0vuxFO4D
+9621X9X//w1kkTgwxmwC6L+H7+MaZ+2snbU/5/Y9WwrW2n1jzD1jzBPW2reAHwPe0P/+OvAPeI9S
+9I7jENYD7LUt/I6srvX4HCu6G+HX8JRqKm/MqAXLykbxBcNugFEVGTds4VckjmC8klJ3XpuDO5Ad
+prKiBT4XNrnzQFZda0L6WuEYeA6BJ9fzZj6lynR53tJHLqga8QvX11rUq0vGm5A1LcSJawvQCkaj
+6UJvEZD4CiEcIwI4QC1fp7Iun1ONDWTZmEwpurAZYV1Rc506cSn3vXl1wTNTOf72gUiDLTLLTOGz
+2eg+qeasnNzBV9SkcUtC/RwqxsC15WkAMysySo01+KFPqLt7mpUoxINUP5RFxjLCZYG8XCIPC6oa
+G5jjUFVUqNVYRFE6lHq9w9GMUKs2XSzVRO61c75Gq63pQrV4OpVNNpQE9ny9jqexiHlaMlARlSCe
+cX8hfR7qPtnplDRWpD9PRJe4tCnWW73jUZSaDs4rFHOxBMKuBCqP5g16I6ncnZQe2bJQLktxfkZ2
+bn5ZrA7TukekquK92U3mqkGKG2A0tpMnKdlQJd3qYh2utGNqoSBMo7iL58pnKhnZVK2fg5iTA2WH
+TqVv5XiTaImOHN2AUuMPieG7tw8etvebffibwD/XzMNN4D9DrI9fN8b8AnAH+Kvf6STGuoRZk+Lq
+DOeOTHib96g1FDtvhrgaXHTiFazmiqnIC+16bdyKDKQJLHhqnBQtTODo94a8KtkAR7Hs6ysbfPgH
+xDy7fXJEYmVyVKYuQUODcrPFqe5ioCZwJfCIawp9rnh4anI2V2LabVVLCuukuRzTn0jQ8mTYZzCX
+CVEUhopSjJk4w1FYqp/KNWapy2Sg5mnmE8yUjiz0qNdU3agX8NQTEjl/43Xhg7y+N6KYyP0PB318
+fblTU5yCpWLj0aqrma/35hT21L1YUFBq313XnoJnfbck1YUzL5ekLxZPXZHSlgwXen+5Sz2S7+ue
+h1HXpFDyy04YcM6XF6wWORQq7FOLXAiUXzCb4eZKxd7SwG5jxGZLFv1Ot0qiFPC94zGpun/5Xhcz
+EfehFioV/ahCU/ko20/WcR2FXWc+8VxqafLGhEWgpdE3dXGLIzLFmyTFMQNdbEgjinu6SfyElFk7
+X3IY+HLdO2ZKrs6ZdWpEOnesTUmG8ly9E5m/rQsR1XgZiL2LO9TNJAKUYdxyHacnY+tqJsM1Lv6a
+bjLXfXAVtFA4SArie2vva1Gw1n4D+JOw1D/2fs571s7aWfu31x4NRKOx5G5O+XqErWuV4HSNvKem
+f61CqqugH42xgRJ1pMvglQulrqJOm1J1Do2fs1wxTdXBLSQA6TRlNffziHYmq/nuzGI0CzJvLBio
+VFiaZ8SKWHTUBMaXtCRAnpW4jsqkrzVpqPtgrENZiJmXzMRSmFCSqUtRVgvCUO6jKBMiNfPLJXza
+ZgSKJMzzhKkr9zcfRVRV9yA3cxZKNktbg3MDl7KqpvNgiqOBKLc4Vb0j9h3MUv5Mfx9bTpGEoyQ/
+xRN0SsNEkXl5WVJoxZaj45pjT1O2pS1PU+UzCjKtngxNfuq6eIpodB2HQFOu58IupQZ/rZeTZGrd
+TUt6DbnXWiam9mPVNSpV5WbIQxIlQS1bBVVFbJ5E94h1PH0lRYlKQ6ToyMBJGcxUO+Jok+aqqlH3
+I4Y3JtpnGZek12cxk7/Pg4IiUAspd5nr9eZfVjlCe4M7ukHv2YxpVeZWr+wx1LlAUXCSKX2b0rhN
++zMco1RqzohAn4mfV8lTJWYdLXAv6zgvdSj2FtiaWjQfs7ipkhSPUvj6sjLS8rDm8b25FI/EolAW
+Jdl4xmLtLp7St6c37jBeER3E3MtwlKPPOXRhTR6coxH5+fgIp1Da95WAQKPJZrz7EBoarmKXgq7K
+/Dz1JvSGMqgTv86xI8zO43s5pVYJ1t0YX0uVHaWLL6IGhRrVu/v7tBxVIcocUNbeLM3p63O5rQxL
+B/05s0Tp4isVIn3pK80udiDEKKmyRvl5dFp9iU3JlT16d2//IYBmntMv5HeZ+sJukJAqK9Te+IQf
+r8tYvLlIWa4fTccw0phAusQ8ZTnzYlnNaU6BMKNpyVwTSIvM4jhazq7PzoVTOnxTejiOZiKsQ671
+I7ktT3kVS71Gq/R5QunJz9uMY4Weu906RpE343RKZU8+jzRmVDRiZgoVH2RDBrMlJNhjqLGB/nDB
+TF+Wi09/HIB2I2OmkgDTrIpJ5XpHh99iciyg2+nBMeFUqZ2P5Xq99gOGQ1mQ5h2LrziTLM4Y3xXc
+wIPu12R8bpR85Y7Atfc7NYY6P/vHPUZjjVdFhnBNrte3KkQ0OmRoVJwndAnUDSqcPhjJxuXddTwU
+F+HIHDru7FNxhZzlqXqbbl+rat19TE8XoT7MtQqU8r0tCmdVkmftrJ21d7VHwlIo3JKTRoJzo8FC
+Td9yZx2U98CN61IcArjNDqG6EpnqQhjjkuluVlsM8R0JThknAVUUtoXFFIpGQ0VPegUPjqWYJ5vP
+efBAVuteNjjNp1M6XKjIOU6O5RqHYUaqEvezyD/VUGg3N4is7ALj/IjxSFbr8Ylc4+BgdprHN3ZB
+7MpuVvE8fE92oFgr9rLhMScT2RGGg4xCORrN2CVbCLFGYhIOT1S+bixwZ1v6BKrUPE2O+RvnxVL4
+8rV1zmklzf3gkM8qFRi6c89zy1jdp9SW5EuroYDQU5MfSxBolkfxClFpT12NrDSgMnSYnEW6zDSU
+2OUupZmYgwyGCrHOThIO9xQq3Y2IFYeQhw5FTZ57TV03M/UpVb9ibA1FLhaiqVRZq6q0/VaDy3O5
+3uUPCv1dK64y0QBmttdnuie7buVCyKQvgdm0ERIPFZG6IdeoDdtMVI+zklc50YDgIF2wW5cd+PbL
+csDbdo83tULxaAH9EznvYjShUGuzGtS4oJZCt6YanCsGoy6v32oTKOFmEZ/DX8LNnWcxGnQcazak
+WMzYU60LJ6lxzoiWxeqFJtFPyLmL3zvhzcmS3/O9ka08EovCooS35pbWJcsiVnWgRk6iDOD5dEqo
+YKJG+zITxGybD9WPbkLYk4Odmot15AVx8hhXBUGtYylcifq6flX/LU4x4i++s8+BZh863grumph2
++Y381D/Nleu9iGocTuSFz7MJV2uyKNQaMbNcFoBplpDoCzuZq6ag67K5Itde93aodDSOkGc4Rus4
+tD6jttWiWVk+HkuUyn0cmRN6faUvH0wINUbhKOzaOi7tSK5Rb0Zsfl0AXhf9HT6gCljT/D5G76Wq
+dRkHeU6oL2zDcZgsYzHWsqaZATcOSdTkn6mGpbXlw4XFQqDGZ+zWOHTkhZ3Oc2Y2W96KHluCirPk
++YB95NhaeJ65pjtKpnSQ1KGnG8TIDHGUzt7N18iUtKWcZXR3lOHK26KpKd52IJmFyBnRKiRTM21X
+uXnwplxvu0EUyGIS7Wwz3pTahelIOjodRJiGZMSC9ORUrac48nnhUFyQay3590ZakGr6er5ImfXl
+nhdZiqMBm3qzQbsui8Lm+Z3T51Rq7CSq1PF1EY7LbYxqgS78CslIXMUHR/Jsrt2wlDOZ0yPrETck
+ntWapgSX5FlPnClfXCoMLXmAv0M7cx/O2lk7a+9qj4SlMDWWr3o5GybgalN31VYLT7UWC+tSnap+
+YHGPnio+X78hmop+VlJT3r7nV5/BjGVX9epbWKU1zz0Yvylm91RVlF9/4Q2u3RSTazicMFPNx+Bc
+wVSj1keTk1PswLoG8IqdklwjdGHhMVDl6v29Pkf3xGR889Yhb90R+Otb92WFD72QDykFWXilwoZC
+cINZhcSVe/LUbak363RVUdnGE8Ya1h73+xweyfVG88WpC0VlyVRtaG3K7lIrVnAGsgNfHu6yilSH
+PpfBF5T/MlCimicCl/NKb1dpVLh1pNZIbkkU5juOS1KldFuvqkthIFJ6vIrT5sqOgqGSgM/flF33
+1oNjvJkGK7V4KnA8ZmrdOE6LQgvQFkVGVlF+wcRjFqmwT6Am+u1j7ELu9WT2LW7fV1XpXsJzV8US
+eOoD5/ihH7wo/dDS1vSkzWhfIOFvJbu8+KoElS/tNAmURyNYc6ErVsPefbEaX77RZ/sJMb9b7Tre
+Qqvt/IQv9GQe3T0WS2GUJiT6PHrjEdniIRWgo/vvSW/AH78s+m2Xz8s8/fCVp2muKDYhGbHYlzFM
+irfpXRMX5Xd/b5dA2bZv7Ip1sJsdM1PynavnfdZXJejY2KnRUfzGfjvm5VtL2YD3hl14JBaFxBre
+SQ292GLb0vHHUx9vWyZHJ1jFz2Wgjk/e4YuvCAjp9VfEVK+6lgs6uRtBxrM/KBHnMus1njwxAAAg
+AElEQVSTq5+8/80T3r4nTDnfeFEG9+u9V5mM5Hedi2t4XXmIm81t7u7JglOUlpqqRfnqkxoiXEXg
+pbOUmyN5SBu3blHMNa05n1Lb0BfvWF7S0fExL78obsdi75ByfBWAJy9eolCyzn6uZKb7FRIF/AyP
+c8bLKsgkolmRiTubJDiK9PRU/yCOq4D8vWj6XC5lQVoNvkllIWP4wKasN2RCzlvy77RWcmDkhS8r
+VfqaFnSqKT2VTC9ilwub4o60lAzUSSNq6vcSJEx68vLeGN/intY29Bfp0sPAVVckDB3OVeXatZ2Y
+qprrheeRjuTco2zOTqEsWqqw5EVNem+LT52nCXsHSuRyPGSoFO7O5AHbNRmXnY//qDyn0S53VNvy
+tz/7Oq+qQtTgC4buliyGVzZW2b4sILjrx/JMD+Yzgj3Vl+yskulziF2Xe0N5ZoepuEGp42LzZb1H
+gVlyidbreMrXubHdZk3XlckDmQvTtT1abVnQKvUuw6Es3oPxlHtKIOtUdimUmuTSj8oY7hw8z/5I
+NjV3YTmcyPuycmypIsQvh5emONcV4NR/bzGFM/fhrJ21s/au9khYCpktOUgSSutyoJjhxy4GxIoN
+T3qWBz0JLr5y9w6//6rStqtL8fhjbYE3A7P7C3KleLcbPvNDWc2vPbhB75qc74EjQSaK5imEt5q1
+qa/IGtmv7XJT6cFyLOc1572pFOh7jqXiqSR5BCdz3a3Gc1oqAFJah3ysoJ98GVCDVkd3vCA4xR7Y
+fkmWionaV3KQoBPgjWWnGfanZAogSqzBKsCpUq0wU1UgT9W01uJNti9LDUC+yDFPqAT6S10cIzvG
+xbBGqfyXI43qjyaQd1293hF9tXi24y4zIyZ6zXHp1sTlaStu4HD/gLsa7Iozj9sT+TxfzBirpHqW
+l9hl1aVWuKYlvFjKGO8kbeZqxTizHE8xKcaPyLXepKY1FRWqJOsSPByMArpXlIhmZGivqEVGQHKg
+wTXloHEcS9mXMW5GFS6p5uXL2R4Hx6rqVB6xeyCW1UpVuSHjKhUNTDfHMYeZ3pNN6Sn79XipQWrt
+KTwoz4pTfop21Maq5WISn6mO7X3l93i+GuM7Mu9rlW3KtkLJB3dZb8v3rSvP8OK+uKM3viHMBElx
+n0D5P1efaFC/oFiP3MU6Mkbrm1fY9pd0N0PeSzuzFM7aWTtr72qPhKXgOQ6rlQqtsIGrpJWh6bLM
+X7lOQqaw1I3oPD/8tHwfR+L/Xa1XeFqZdorBiGQkgZxG4xOgzM6NskJxRXbmnzMikPLO7RsslEqs
+th7iNyRF9LVbLbxU/M8Ih2qkJJ9KNdYNI2KtlpvXCyKlbY4dh7on59uuu1xYFTm1LdVY6B3Nuboi
+fvROWWFRlV3cD2d4GjMJJ7JLNP5/9t481rLsOu/77TPec+fhzVO9mrqq55ndJFsUacmSTEuhTTpy
+nBhGbCFOkBhBJjiGA9j/xImC2AgSGEgcxIbjAZbkOHAsa6JMSqJbHJpkd7MnVnfNVW+e7nzvmU/+
+WOs+qo3YrHYjdhl4G2jU6/vuu3efffbZa/rW951zqXa0688pc7crVqCU9ukOtOZdVKmp8va2lgpb
+Dszp3HYqCcPb4nls+WMWU5nnvWrK0kVdFy3Cn7gFlzVRdVS/xfUtTdANYpT8h8WGw+bSTDpPOyrT
+kDgSi9+NCgZaUo2nBdWKYi5SQ6yvB+qZ+F5OoXJr3XhMb9Yx6gCZxuL2lPlE2YkUEdiq9lhQNOby
+6iUuzEs9ftAeslGX+7QRJ8wvyBo4vuwbt6izVBbF79//wgphWd77me0+W4eKl6gMyYZiVRcUe+JY
+MYRiYafmiHCsnpzxmakJziQ7MDa22tnCSnE0kXpxaZHzm1IuXDtXInxfcmFPNKWEumkWqSos1HLG
+BDMofLuDoxgJc7EMb0sjWObKHstMgUnFs7lYq3ExkByV3fuA0lSuo5y1+eSK1vb3eaDxUBwKlhF0
+sKmE5BMFHqUTyoUsgl3NuLgpXIsrgcdGKDdsxi/Yth3aCtgZFSGD+7JJg84+RmvCq1dXWdEehVk1
+YXNlgXSWMCxldEO54SdWxK8pGKrs2TyxKQ+yq/Rb7/opJVX5SeIKvib7FqoBG4oRsMsOdkOuZa0r
+D+NecEBLOwPnfI+pr0CtJONIQ5CdqbjqzaJGxZKkXn2hSUdpx3qRYSfWpJyfcKx9FagmpGnH7J5I
+qHUwbHJNsQe3Eo/nFZuQlNtU7ZkuoazFxcInKcv17921cfUh7YdjRtrCbbXLhEPlisyUqXqckOhT
+McYi1tcntkVF6cYKewzqdtvatToZhNxCvq91aPietoCfm8Z00ZDIrzCIZF3yQg7WllNjY121QitV
+Eu3tGHtDWprB6+TgLGpSLZ7Rw1VprMt98nODdnCzUV4jfkIOr0m2Rah7qqwduv37u9w7FLd9a3tE
+uS3fMV9qk+ipkBWzDtCEvJiFdh4VDTE/cXGDH//MSwB47Qz3ihy+LQXqNdqLGN1DySgj0TmbckFF
+cRhW1eWJF+UZuJxuAhCVEoZdqYBUi4KSMl6n03mm+8LoXW90eCIT4wQz6pN/8TgLH87G2TgbHxoP
+haeQA2Fe4CUWU+1Ii29NiJ6SLFGzPIenPPxONsRX/Ue3rDXxOGXYlxPzxv6Yee1O4+4dXEeSMqXz
+V6gviuUyWkoaV2KsQFxK3JDwunRPpncCXLUCa1aV8yVpzBqtyumbOMEpzZmTjfGUPNXOJ9TV7cxO
+YsKhiI8ks5AAKGLxBI4pYQrxPOy2z8mB8jrclnLrOM948XmxiI3Qw7bl+mpBneVNmf/hdshYVaq1
+mY7dvZiu6i04nSaBSs9lbp88Fe/gzrRLTzU7VxpybdWazWQsNiKeX6Dmilv66uEJk4mSekxKHCox
+yt6JytNnIbmKnhjHA1csbLlsn5LCtmsO0xPVZ1D4cdmNWItlDU2Wnzau5a5Nqk1OWTFheqKkJiqW
+Uu9cpKn8FV4wxXiKeFz2MX3xRjKmFIprsLUJLo1PKPT3eaVDc008SDPokWr4Uy1auMqkPN6X+fT8
+/qn0YDtwqdpyfUHJx3Llvs8eonj6A4XyoF1lXWkB5/2cwVBKh+vNc7Q74m2YQtZ40rsDnoRBbjln
+qjwL3eE2pXlJGHZWAlzVS3U0h1qxmrQXJTQo9kckkfxdmPcIytpAWJvHfUlCYd7igcZDcShkWc5g
+EOKmOU5JNsHd0T2aexqTtjoEGgN6xsZo6yypLgg9Rl3NAB8mWCVxu02ckE6UjSacYiubr6MPrplY
+xBobp4MMM8vwV49ORVTm6xatq+oGf0N5Is0xNjM9Qxdq8nqp8PEThStXPXzFDviBbvhuhakKoHSP
+I1DFpuTegFFfYdPq7ocnIcc35IAw1Sp1d3arJtQ0TzKpjumkerBoBnxgRVi6a+LpDjtl+Xn/2GdX
+xWSDZhtXxXRjDSk8t0Km8Nqldk7Jkrr5fK1Hr6cx7CjhzrtyyN5VafhpnJ92fkbRCVXdpG5m42qs
+3UhsLGWEtsvyABapRV6SA2LBCnBSrZKkOUGqNPJFROzI+8vRrPU6wFe33MttHAWnubQpmrrOZvGU
+cMRS3kbPlMibcvCarKCKPJjWYolUw8a0F2NrK32MYCFMXmZZc0qJXSZpqYiOVZxS3/uKQZhM49NQ
+olmu8vicrOGFhXPUZz0v2ZhSRfdnpuJEkwFhLAeE6bawyrq3Evu0F6ZidbAV6u0XArvOJhFGxWuS
+6g6xttQHc/PY89oRvFbF31OGqAccZ+HD2TgbZ+ND46HwFFxjWHQditw6rTIM93xGK2IlavEWpVzc
+vdLSCkbReMboCX83p6ydTSsrJfxAwo72YJ1iRWHT5TquMi3bKhduSj5pLG45wSG51p2t7Qpt5UX4
+5PwiC035bmdDrEdn6GHPEIFJD1fxBLZlyGyxRmWvdip5VpoxSgdDrEPlbzgcgYYgRSNl7lBO9seU
+asxb9FhYku9YdX1c1QVIipyJCtGMpjXmKpqVV9mxPPZ4dFEsUZcaMVKJmQY5d7WD0RBSrakbP/OU
+7IjqrCt1Wqev4UPZdimrB5EmI6baxGQpzLuUWYSaXEviCcWJWrlGHctXdGeeMhor6Yla+U4ZXkaS
+p6vlnNc0UepbIqkH4OYF3lg5E+szrzDC1kSrP7+BrS584YyxtPHMKjpYpRkfpaJQY4+gLaFiEd0j
+N7K3fLeJq4rXmbdHmsp+cKfiCVVMyIF2OB6H+2R7KiNfq1NSFfKgItc0DidQSCL2fGOBJzfkc9uN
+Jm5ZvK3a6iKWraI8kTb/zbexexraBHtUlFCmVO/gLssauY0CZ+Z6OTNehZxEG/DSoxNQLzR2cqaB
+NlglNvFLM0GIBxsPxaGAgdzNSSsptiUPSl6aEg1lI02Y4Gt5q5QYLIWVZsqdF/sxWXm2wOBqGSsq
+x7gaA7q1AkvZkoyW2CzbPuUOjA8KDsfXALh1+AGLCpp59HxOQ10/60QOh1Fln0ZdMfxpi1hzFFGS
+n7INxUWMo6QehfL+ZbFP6Eq8H9ZzqvMKdZkYLBWT9VfkOpYWPOY7cs2toIVfb+nnDrF3NHYsTxio
+UOpM73BpwWN5UUFPt0LabZn72qjOJT1EB6nHvR3lNkzkIUjiCCuRsCx1HUZHWkVIpih6lusnhwRa
+ZhspY8sk/AG0Nw9ThiqjnkRTfFW7ck2Oqw+9o2HQZJgTt+SDm6UyLQV9Wb6NX5H3ekcBA71ZRpVy
+7UmIFWiFKs/JXeWHjF0K3RduJcZuKmfnVEll0wFZLg+NsWySqayFTxu7pWpfQY1sT/JGmYYr02nI
+VFmTBkOoKf28lxpi7T6sK6lueRAwUfJUd9mivqBCREsBvbGscykZ4eset1WQxytb+E3NRQ1cipFW
+kioZnpZZ7VIbS8mC0YpL7nlke/osJCG2tpRbjYBMy+RTJ+Ig/2iP+Vn4cDbOxtn40HgoPIUU6GIR
+hjZZLImjOVx6HbFWdb/BxBVr7PWuYxJ1JbV3PZ5GdIdSOZjSprQkp2R/r0d+JNbf3qjgtwWclE2k
+jp8mKWkinxGV6my9K4CP/lvbfFE9rk8vvYKbPSvfvSKNKtsfRPRsJa6wXELlTbi1fZdWoO5s4lNS
+Wu9EZc4mWcRAG4IGWYGHfEl1sUGgHAHdW0r6QoBxxHrGSYSv+pmO7ROo9S+NHKYqvz7UhGrVJOx9
+T5t5ehmv/HfSHPbK1nPwZ+Wzi/CXeXxL5n+/L3bh3fMpJU0+Dpiw05MQrB9loBiQgoJI6+mZkrTU
+7IQ19Yj6DYduX+ZhJxEzm2M71ilJTlld/w4JLypEt1MtsaBUd3uOfXpPbBMRDOX9g0N1k+frJAeC
+wrG9LtFQqwzFlFybhyb2FEvZvZ0Zm/WNiL6tAjFzG8ytyu/t4ft4me4np0yuVjyeSEI1M0MS9Rqq
+DWgosMqreSS5rMtACVJyt4KnOBTLbRCeF4/1g/2Qb37r6wA8Fl3hJ5+R/RQo8/V4lFAKFJNTdhgd
+yf6MT3rYHak+BH6NQqHshXaaptOQWMFZ03HM2FXyIKeGoxU6cpux+9Ho3h+KQ6HIIRlnTA3k2kHW
+r4V4Oj2/UsVzZQNF0wRHM9KFchUaNyEoa0Uh9/GVsMQUJVDdAN/UsLQkRy4PYxzfISlmVOcJu+9L
+CdGMpvwBlSTyvt/DHKre4q8rUrB6gHciLmVtrXNKurrTH/NIX2PRBY9cW63DQjbr+CgjVLBUGOec
+KFHLcDwl1Q4/19F406uBVkOcsovR+ZgcAu2W6ywVlAdy3SOtBhzkQ7JDcYG3toaYH/k1WauNJUws
+XIIcvMtGJhto7oqs5c6JxYkjoU1aiUknsxZni7rSz4cmJVFNCV/LdBd9my9p7FxEbWIlmH0/2uZN
+jcszLyXVjkGvKi5zo7AozWtXXz3gsoYutwYxsTI2RR6MfHlPN9IH/jAnkeeHpBuTeGJESmkDJ5Br
+SbaHREqPn9+SmDxOjpk0tftyWFBRRi5v3iK7J/chqwXYSstuqWaFM5qjqlod9qTCOFAAWDYkUiak
+fKZt6nnY5RmXZM5UdRoMe7x7T8KHhXvncK7K/IPTPpkMS8uJ5eAilfNadfMtgrqK35oyM3WuAplP
+YUd4uSqqzSd4iRwg+XQCmVxH6tnsbWsDyAOOjytF/58bY941xrxjjPl7xpiSMea8MeZbxpgbxphf
+VE2Is3E2zsa/IePjqE6vAv8p8FhRFFNjzC8B/w7weeB/KoriF4wx/xvwc8D/+i/6LNfAcslh4sBQ
+O+TqkzJWW7OteR88cfFs2wP1GnLVWixZHucuikuWejkoG65TcvFqcnqaVpXCku7KYiQnrpOl8tlA
+986U4lgs1OfTElWlgzfXn4V3NGmVivWJpxGxusEWdcoKlMmaDsehuLatzMVWcM4wEesyNTGWeg8b
+5UXChliS0cGUREONjWWl1FqpkSnG37FLmEyl4ycT8kA7Qkc2jMSquAofroYWa5qRP6gAn5aQyeBB
+/qOyntO/hFF74G3JZ60GGa66af3UoakQ5LBcZUGFb/pJQV8z3E0VU/mCfZ5PB5+WdV0/j/uOrOdy
+9hsktlRrbhUhh1oFKI9lbhMTkR+rTSpBq6Zdm+MJE12v0HZYscUNPlR5+u1kiwVkL5TKLurZM80m
+tFxV+V55EvOUMmx/QtbVvWEoHpO1GrsnxCoJ4OUVUHr1tHdMwkzmXe5pvTbiUH5NYfeYqIfR61sY
+7eeY3Ru3BC9efgyA5xfXGPQl5KmmJR5HQon1S21QKrtSo66X/yRG1bzzWgVvKta/NOdTaKUpjw4h
+kXtVKG1gHo2JESxLEaX4ipGI0xArkVApTqrc3P1oyo0fN9HoAIExxgHKwC7w+xBdSRAp+j/0Mb/j
+bJyNs/GvcHwcLcltY8xfBu4BU+DLwHeBXlEUM235LVDmzX9mGGP+NPCnQZqOhmWIkoSKJWWXkISp
+hkLT8pRKSfMEzRqJJhhRCrMs97GVH8BxEhKla0t3jnCXJH9QmCmpxlZ2Ry973mWq2ITv/PZX2FL2
+5J92DX4icTtHH4A2DxHJyT6xEtDatd0LKTfUYqTz7CtHQmswImuIRxJq9800muIq229jzmduQUtM
+HcN+T6XSNPF33Ouzd01OeHOpzsq8WvzAI9Jy4bDospeLpUDLnkWeovQPLE1bsKUSe0UB/DX5DLoU
+iMycrboXfm8TK5XvG9QM5UBeL1UC0r42+YQR1VCs0ZrKyp0LHYI72mj27g54ssYXYodGKp6Qa0KM
+ltEijcn9LryfynWcT1zWtcntILzLVC1vOazTb2uT2rH8/V4+oRCnECoOmXJcJHYXV/dLpZ3hepLn
+sLVEbDpdcm2YcooJsZKumoaLVlTx6obwQLEj2s0ZjsfYmlwttyrESrE2cWCiJVBHMTK1okM3kbzG
+/Wyf5oF4B53LDk9efRyAS/NrHGwJTZ11S655qe1hqyxg/PX7pApzNs/bTDe0NMqYqnZaWg0Vk0kT
+Rnvy3ixziWzx0k7Gx1hd1Zwoj7j2znU+yvg44UML+AJwHugBfx/4qQf9+98rRV/33eLoKCL2Mlpj
+pe/u9ripLaT25vKpnmNOgTK1k3WVWXj7Hpkvrlp2bEhRWO7dCK8kN2BS9ll4WdzOKsKTWJyf58ZX
+hHDlb716nSuKi3iscLGMElI4fxeit+VnSxJ1Bp9UXbnouE9/Ihtza3VCWXHtO90udlmFQzRheDSK
+GRxoj4adUbklB09jeRNLk2t7Srxx0LtHdE9cwNXyJo15pTELl8hcWaOjWyO2d+X9hbr+e92Em+oC
+Lyyvwv0/qCv+T/45d0I2WKt0wkjDoM6kxEiFSiYnGflMpHZi8YgnD9PTyk580VyjpvqYRWkPTpRa
+3LvNUiGf916eEuoaWD3Zcq7JuKpCsk4XvKpk+6dRhmoCU3JSigN5z462mc9Zh3RvSiXGuuQyVoKX
++zf38K5JN2PJXqW0elM+xJKkK19zubMpr33TzZiravfs+XlWL2v/x8o8QVkTwdtyQHb9MaEmHW07
+PcVFVOI2aTrrkpR/zcGU2+M78vu8RbAgoU94uIizJms4HbR5/RvfAGDReReAn2zep+TKoR9lv0Wx
+LX/X9bY5vCWh67SZ096XLsn243pwlZskmriNDiNOFIh3uDempxWK772/x637ugYPOD5O+PDjwO2i
+KA6LokiA/xv4NNDUcAJgDdj+GN9xNs7G2fhXPD5OSfIe8LKRWskUEZX9DvBbwB8BfoEHlKJPspz9
+fkiWFlCSkz/1C8ye+IOrlTpRQzkU3Bhf4bEzxGDtYptcxUQqj23i54sA2I/tEyvjc7Pi4+aSdKSt
+fefjEb/4ZWUc7k6pa4PLaBHqyyq79boHVQ1XRpsAeOaAfCbPPhmT+DOptIyKJj9N4WCpGvOitvW1
+Gi2m80rAGrrML0qjSrDcoVDtwsoH0k1nxl26E00G2g6mr+QmtQgFwtGfjjGRvKejDT638kOmmgTd
+mLTgf5eaN3/aA7X4v3fkyuo8qZeZqLaEY9ksN7XMaIeElqztvG1zKREI9eMLsnUqowhrU9/7mzWi
+qrq+UYNUm7HqvsMs4rOUjq1h/NPk47xzQjyUe2qVJqf6BCkpmYYdTeUYiCYZR5OhzmeeBSWwscdQ
+nJLNegTKms3XNmVunzth/qZ4fwv3DuhsiPWfr9UphVriG7fIxprws+T72madciBeSj+bcmcs7z2y
+TyhSrf8rN0OcjZkoDNw7ymjVJOm4/kiN9RlcuZqw21GY/lQ8qem6obIsPwejz1E8JxZ/eHxCplod
+pdICoSVrOzlWZvMJOCoilBNCql2UjktvV+Z8+/07dPcfjIZtNj5OTuFbxpj/C3gdwR+9gYQDvwL8
+gjHmv9XX/voP+ywD2GlOlMUMNAYsUlhWqu9oOgSlAHeSxqlGYUlJSpK9hMk9iZHvfvMtSloE9Y8g
+VMDK3Poj1NoqYjoQN2x/5xZvfqBiGmnKm5rJ/utDlz+j2el6I8IK5CYVMxfWtDmayuZIi5Syakku
+1eq0E+1kK1s4Co+1MnFVq75HVWvTxgmxlBreqZbpnsjn7b8vD3H/xhGRYuR7k5x6KBuiiGGkRCZ7
+RxPCRDbFirIr23bAqt7Waq1L8fPXdJU/fCCMNQfTb8hiveYYPE0F3ckzUs0NxH7zVH5+2zJM2/IE
+TALZxCsLGa4qEIXzx1zPFcjlTdnS9wwch3yq8be64paV0tWM+xv9Y0YVFeJxA4YzuHICVcWWrOk8
+G3lC1JW5JdECQUkO+kYLYj3HE5PTVx5PS2nRi1HK1MjcNuY6NBQK3vHmMFW512H/gLEmsronyn7k
+TIm0n2FiOWRdhWbToKoKX2E+AxUl+Lr2n6xVubom1ZdFq4OvfTDTg4QgllzDeCJO9NaxzaAn7w1q
+R6BdolmlzoIC3Py1EEs7JmcgpuRgQF7XztDCUFY482h/gncg+740nGI/oLDsbHxcKfq/CPzFf+bl
+W8AnPs7nno2zcTb+9Q1TFB/tFPn/Y7xgvVB823lN8QV7+uovkZn/E4CR32H46L8FQKm+QT4vJ+ZU
+ocGHyTXeuCUw0t/pXTttfKmO6vxsItRXP2r/F7hTxSyo24ozJq9KF2Fy5ZCdJ8Vy3/Df56ApeIOT
+KynXX1NSlkckUfnTF34fIy1O/OPfeoO335AQZHRvyPBt6S6suBCp2EGoeAOrAEtDlNyzmT8vru+l
+p1ZYUF3BfiBW93v/9B2OtPpQJPlpw1RzcwFLO0lHR1O00Y4X/vCLAPz3f/4v81RbPCFT2PzUH/sZ
+ALr9KXtbYpnC7gD1jskUgem4BkvZh52Ki0nEI2jWAhYXxVo98/xTtGtiKXfvynXu7XS5fSie1GSc
+UbNnnZiGbi7XksQJiZK9JFrbT/LiVNnbsW1c9SAqDUOoybxwkpGq19BUVmM3NtS1gzXDp7ooSblW
+4NAPZV26R2OcmqJhdYHq9SqjSDxL0zI4Go50pxMylZTvPL2GdUd1Q3NJfMblKl/8z/44AD/73E/R
+0qpMEua8vCkhbaRrlabZqVr30kaLaiBe46V2h8q6hAzH6QRfq0Oqqscgj5lfUsyCl+HuaXepn2Kp
+pkYSxuT7Kl2v5ZLIwKg/6wJNT/VUa+0yxldkZZRhcpnHq6++9d2iKF7gh4yHAuZMcReT/cdg/TXI
+Z1DODFshyHXLptrRmKDhURxJtri3/w8A6B7UeC+SB3Mr9/mUkoz8KWeXS6G8jvk+WBJikEhph+wu
+ViQxcvy9E965L3HoX8l3KfSGXzo+x4lKwm/s/TgAv+7s4/XlMz64F3H4u9r52D8mz1QYJC7wtHNx
+BhRKixyjnYOkKbl2DC5vPM7aghxYuwMNKSo7FJmCpZIpRuPX3m4fWxl4ppOIXFuY3/lVyZ1YP5dh
+lMzVsS2OtL9gmnrksbiUeZyTKe8itvZnZODp3CzX4FXlIezMB1y+JDmaJ55YY2FeDohzbSmF3qsf
+UG9qb0cBjJS41Rlh95TJCYthV1z0SMuzxSQim2Xvo4hcJewzU8VO5QExZoDnKkmK5hYWL7SJjpUs
+pmHha0v52Dac7GlL+WSI0Ye+UpXwKh7mhBo+WL0Gli0HRH+3O8O6Yd+exyrJA7f3XWEripyE3/na
+ZwD4Ey/adHRPhp7Q7QNk2l+BMbgqjrtyvsZnfuJ5ABa6BROd57Vel8xVvsllmYM9GVJXId21jQ0S
+X+5T14pwNMTym0sMbYHhe5bCsicxpTnZF9vvHzHM5BAukphaRa+7m54aogcdZ12SZ+NsnI0PjYfD
+U3jGha/OQ/sikpIQ97NQ6C7ryxSrcqqmr58w7or13zkSF+8fF9vsq2Zg5OTkDW0Gmc+hqansl6bw
+V5TqWrO45C65kTBhf2z4P5TW/a7lEI0l4ZROymRz2sFY+nUAkm9UmexLTfz6268TH4srbWX5aVde
+YBmM/uyqO1wtVWjUxGL0rBKb8+JtfPb8KyzNSzzymydSUy6yb+Ba6gJaEZkmV7kNeisAACAASURB
+VKNuiKu8EHZmkWuNfNZ888t/529y8b/8CwCUnQqpNjPFR8dMR+KFZGmEpUvrKrW473n4LdWgbDV4
+8olHAfj8py6xoC76YtBhcUHmnCxJo87guZhjBdDUizmmmjzbTo74ztcVVl6v8O6BJFAPJ7Kue9dO
+GIUSluVZitHrcwuPXBN3uW9wFCS2uCHeyic2L+Bf0qpMUGJ3oLwQ4yFD1dPMiwq+gsTmVBHcKyeM
+JuKB9Ed9Qu0qDcMhaSbrfHT/Bg1l/J5pW+Yjm+7XJcQcfikm29TGrhQs9RQKVe42ZY/KgnzHZz/z
+af7UZ/5tAEqlmAP1kFqvv8bygqTcKlXxBO9FKf6x3MdLa2uEc4KnOC5BdKiYk2CN45Z4aQe5eDPD
+rRFZpJ3CwzdJNFkNHvlEPe7AIZ7OpOgfbDwch8JJSPELH0CwBVO5+VQOiJ+SzZiELtfvygZ6P9nj
+l3sKAFLk246BkrLSTH346qykFRr+zLxu6IGF/5J2s401LtyO+Qvq7n41yemqa2icAnIVdN07wNc4
+Of+mbLRL/pQvv/cmIO2ts7ZgC4NRuvOK5bCs0MJXNiSv8annf4xHLwmybT/KIZDvuHj5aSqqhhUM
+5L1f6/46UyOHDa5NpHoD2AlWJje85lbJlOhjBjA6/sqbHH/ud+W19ae4HIv7/Zb5PqgOImmBqy3e
+5bps4lrg0J6T0uLzr1zgC5/7LADPnLtAYWasSRa+ryxUc7JB51OLzVXVlXRzzEjCoHMnO6x5GnP7
+I54/kAP5lmb1v2W9xTuKtOseHBEr2OZqbZ7tSK57OMlp1mU/PL0gmgZPPr7MqlLHJ0WNwUj2xdEk
+wULek45bpJaiWvXQc6YxB10J865tbbGnSlBZMWGsD6QbRcQzdGIg7ncUj2ntyqFQXP86ztyn9e9K
+LKi+5az6UJ+v8sqn5YH/oy9+loU5+X3J38TS9vonLuc8ck4o19tK3hPF8enhnkVjbKWRn0ZTQmXI
+sqwIp/0EAId6zcdewjAVo+aYTW7eEFBXbEGioUR/YHEczwDGDzbOwoezcTbOxofGw+EpFEBcQHIO
+tLOOjUcYahXhreyY374nJ/v1ccZ3NJk31tl7lkXqiQXyLY++0nndLrl8+6IkdR6Zq+Ovy8ld2ZG/
+v7+3wj/4FQELbRcFJaUYM3FOqlTmaRCxGspnUJK5fWdnQhYr6YvrUuhJbIxFoJbpE36dP3L5GQBe
+XBcF4MqVJZrPC3B/Mw2ItePQ5C4l1Zh8dF7mcKFtk/cUzhousxuL+z1KUtAOxjmnwaryA74zFhf+
+ZvcGN157HYCFvsckkNetcYKxZvMscANN4qpK0cZShcuPCFHBlz73OE9ckJ9rlQX0krBKBsuSec5y
+V6KIpPiPOAWlBJsvVXC1u3IyOGG+qXgC7YDcq+zRV4q1NJ/SOxRL6qwbFobK4+h2WNaqzKOPCdDr
+ysYCyzXtWg3ajFW7Eq9ExVcLm5cZqYjMwbH8Ox71sPbl/k0aIW6gYdOW4W6o2ozJEFuh2fMdmcNJ
+EtGLFU/wu7/Gxce18WKaYDeU/bojXtBTL7zAlz4hyf2NjTX8GWtAblNWoppHN5botMUrdJRp2k8z
+ciXtScYWqBq3H8akyladjUYYpWbzWhKC1RZP6B/LjajWm3SWJaR9//iA6FhVy+oeZDMp+gcbZ57C
+2TgbZ+ND4+HwFBILdmvwaA5vS42d++/iqQZEZgVoCz22ZZM5GsMpXdkzS1WoSYxrNRJyRRD+5Cda
+XDonHAKdi1PKnpQyS+ppNPqHfPF7fwmAv3FniKUmMS5ytN+HcOxwoN1nSSQneDkOT/MIvmVT1XxG
+yy14wZPT/9+rr3BJhU9aqskQ1DaxPbFyxQXwdhXd2PGhLJ/dlPwe/9VLX2L4pEK3WwFvffXvA/Cr
+3/0aB6mqNVs2R8ozUFOMwU435uvf+zYAzzXOYSlbz7iYnMKjIcfTmHljTZpYL240qM7mGbYJXLFm
+Vv4DJWVDA0vpxtAcAEWOUYi1qaQUlvb62zEV7XwsLTm4ylacKV7hUnOVwTNa0kvHTMaSlGwH8/QU
+C+CUymxo3P7IiljdtWoTr6I5nCympLKA5UoFSxm3JraN1xePxJmT79tLx3S0zJpkS1iXKzp9m8P9
+gc5tQK7lbAvJr+B0GSrL1q++9nU2PidNTM25i3TK4mU98ri89+kLqzQCRbHaGY7qQWTxEeyqtuPS
++ikrs5lhhLz0NE/kVaoUWi5ObYNR+jd/wSPpablb8Tn2pExnaZZ0zYhdxTFEKT0t8frHJaZ1PtJ4
+OA6FSgvzqS9R/A9/GMOfB6CYtCire3mpXvCdSDbewJ3izXDpdXntqcevYF/U9t3rI5orkhl/trTM
+3KacJo3yC3gtwSQ4+vCnlW3+g2d+HwBv7P422ypqcmAsjGaW0ywnUQ6+qCSZ4HZmaGoH56JXYlX7
+Fv5Q0OLZqri551Yfgx0JTRzlDrRebcFlZZ3eyzEzlt0sYHYKOUrf/eQrn6UI1C2vF2xekoPg5b+X
+8603pfry+qTLWMFAFXX6sgD2jrRasnvISME/eYRwuQGWZVNyVXexJofRpYV1mgqKsQcj7FDW02oG
+MMuuY2MVypc3kynCMAPAGVzBJgN27KIM6DjWAkVV1mCqAsKeDZWuHDzVRhPbl/XsRf1Zjpe5OZ+N
+c7IeS22ZZ7W2gO1qO3hkYWli1yGg0KqMO02w9F4WyhlJMqGkALC5WgPHls8YVvao1xV6XQQkei25
+Vh+qrs9Emxtu9ab89m9K0vHpF208vb4lI4ff1bXyaS+G79SwXU2au2NKTaUONBlM1UEvz/pkrFOK
+ccs0KBQrgXHIFSpv8jpFIMbJCrXqQYanMPdaOWZetUf3dvYYH6vGqBWR6EH9oOMsfDgbZ+NsfGg8
+HJ7CXQd+bh7MN6D4D+W1/M9iqzs4X3RYV92A9yc5DbUql5vy+zAds7IvlsQqF6xrTbh5N6KhbnXp
+0RbWhrq574nVdd0lLlz+AgD/47td/uoNSdC9mqXEqsUXYZMr+s0Zi/UsFy4tdQ2rocvnPbF4n5xb
+ZvGiqAv71jOYsp6599VaffIt8NV6BAFofZz2ALTz0yjC0LtUoqiryz2IqXqCtsyfucqLFUmMXX9z
+wrGi+Baa4g4ve/PYy+pmZhMait+wTEah3o1jO7Qel3nMrYrr++hyi1XVUWwv1zCu4CUslsHVuVmq
+II10gcobfIzWzYt88IOwwo4plJwkL+5jqctbUbbkshVSmRNUpJdZ2IrJ8NOMUJORo2xKvaIhiJYh
+jRPhqgti+TWSoeIJzBHFrCEq8UlS8eoyxG0vZRaON9N1H+D78nnd5jLxglKslW8yTZVcRr2A7lGJ
+0oJY6Epkc33vt+T3+xU2m7Kfmsqu7RTQaMh3OKUAPNlndmZhLUh4m0xH5Er/ZufKuGxXoFA0ojU8
+3ReWDdlMJNQaMHtcjTKCp04IqrHpeVXmlPKt0aqyuyNrm6QRiSqFP+h4OA6FNKfoDjSjPeuvCqQl
+EBGV7asLt2+mVNQV6yvTb2Zshon0CTSKBukd2QhF1cV6U6ChhWlQXFd9QN23LFSxHhH39EJ8gT/o
+Sbx4L0n4fjLrUbAoaRY5UNWhjmlSnRGyWCWeVJGOxnAN5w09ANofQFvcOb6om/HwEmhnHcMCFvRm
+dW2oa3wZq3pVw6JQ7cvicI/sjsz9qNvn9q58xv3xhMNENlOknXUbjxVcNLI5rFHOSNms7WoNW9u9
+jWVwWvLZzfMy96iVk9RlLUprLfDk9cIuYzRcM34JTjMMMyezkN0LFLFFMevtyG0KR0FkYYlMW8oz
+7fwsz1cIBuJqD6+9Q6IS8L1kihXIphdacwWdRMpPWMrIlZzFBBaWwnnzJCQtlLszjyk0zDHa71Cy
+eiQ654ZniDWsWmxkHGinoduqkynoKZooi1EGgRonajWGev1vvbuHo52bSUfvqb8AjgrVuCVQZbA8
+iyk0xjeBI/TlcArpx84wruY4kjEokKvICixPrymtosJmFBrG+jTIZ7RRiUemLM+NckCha3h875Dj
+sfJOPuA4Cx/Oxtk4Gx8aD4enwG1M8SeB+5y2mVGAEjiFjuFNdX19r0p1RU7ulZpYYivJyQ/E1Tb1
+Kq4mZMzugMQR/W3zxjVMSd7jdoX6yno2pnClHm9tfIvGjpzgW0WIbYLZLDDqBpPJfF5oeuS+nL4v
+HU1ZSsTVDqJDjK2JoZ0XoKnw0l3BKfCsA/fVIkyuwz1BqJkLHlRUzKCqSaRJhtFElD3XJHtKRWS+
+vcvJVL7v1mjKnlo801ORlSOPzReVhCZK8CwlcnkyIP5AGsLS4z7hRCzp/lTWcmeckkzvALBwcZ16
+RVzbvOiDci2aonKK2Jzdp4IUCvW1vRJGG7QoZRTqxVhBiWQo6zVRaxZPqwyrKhzjd7AD8YTizMPR
+Ji7XyXEmEh4UKhdPXEaRyBgvBSWZyUxKqnDe0fCE3li1IwrV0wgtMkfq+yb3KWvSseI1sDTEwMzh
+Bho2zhK0gcFG+TDqm7ie7IETN2NuTgleLKXdy3okSomWpwvY3qwd0iZV3ERhW9izFlXVACkyB9RD
+wXZBu2oLOyZXLkwcGxRHk2Yy98kkx9WGMcsakWmG1s1aOCVJsEfde8TxR0s0PhyHwoYFf74E/1ED
++IGrU2hgdzhncUeVgg7sjLaKfPZVU3Gu5HNxQ2GymwELiTz0dufwdJPmi1WKD/RB92XzOKMp05GU
+7/benvIt/e7QFCTa4VgYyNRdHSqrzuhCRrAnD3e3EjLUEml7w8Ncl4OqCFxQODWzWPb9FixqO/S6
+j1lTMsJgFTx1y7VVlrSgGOrmSBzMQK7jqD9i71DeezwNiXUj7Gu4M59NeCaWsu6db9+h8+ObMveR
+R7agxCmNNhPdeCVNoa+163glmbtnStiuPAi2lZPPHEoDxih4yf7B4V1oTsHkFQpHN3pYoVCxXTOd
+w9I+lpISbLYXxjw+lCpR+DLc3JUwLwsTFufkPec21mmrklOloq3FtTUcJYjJC4vclQc6GhZkKoQ7
+SXIKoxWfvoq6mIRqPuuoTMl0bSuOoaPXsjUZE020IqBVoHJsmDryWe3qGo1lAWFtnRxRU+GYcwpR
+by428JVg1/bKWM4P8iCpPmr5eJ/CmWmMurqCOUZ7PCynDrOqU+qRJie6LjlZIganO57peCa0lOi4
+wCMb6yFc8dmsisG5uXyfu4cfjXnpLHw4G2fjbHxoPByeQt9Q/JoDSxH0VMRxccDWFcmM//b7U+4n
+4kbWTIXjY7HoPa3hth2H1YpYnbgW4F6UzHoa+0xuK1zZSkiWNBscitUN3BrfvvcGAN81h/xthese
+ZAWeJoaqVkC5ro0rQ7GC3+yOZ3kvfief8qzSnv/hSs6Fp7TXf3KEtagNQdvCdZAPfw3rjlg+t7+E
+84iSvixPYdaTr41P8beus/Orf1fms2MzHAnM+7tbN2h4CnMul7jXE6sxw8FYx4a5VQkZsqWQl+YE
+nFR73uLtigqLbOWUF+QP7m1rhWDRZV0BYHneJJ3KWlnlFvas+uAYTjl5ixl4yaLQJGGeJWRKdDIJ
+xwy0MmDyHRytrpQ95WOoruNeEk9ven+e7zbFS6mMDqna4vpWVnwWtUnLUTqzzBpTaEVlEufcuCVs
+3Md7J9i+WuBeiDcD9VS1OjGt4CgPwXQyZHdb3Pxe4tPry8+D/pDuUK6rod/bnmuQKcXepSVo1cQr
+Gp0Yzi3Je0NNOg/9Hn5J3Xm3ToF6gsbDC+Rep45PEU116ZQFOquAJmIxySlmxaTODEFOlgxPvbO6
+AsvcTkaaSENUMc1P+UesZIpni0caOM7p6w86HopDobBSoqBLQQ13Q25m9/Pn+ZWuLNQHts3PKGvS
+0twKWU/c+Q+USajpeXSUjDVPC05CcZcapsx4VW5AZ34Tpbijell1GTtzXH5Uvu+D+Hco3pD3pnFB
+MtGN7o9ZLuRB7mo33K3BmG5fNr+f5UyrMp+XnQb1iriMS09cwF6WOzq6IQ/0zq0TBjvSw9CKmlz5
+7NMAuFULUmlJTncknLn2D/8h//A3hVL+yStLrGmMv7LeZE6z4c9PM97TWH3Wi1Aaj/G7Ms+FWzWy
+Py7XtDRw+EDp0vs7+ziqY3mwI5vq1cky+x2Zb9Zc5qm2HKzPfOpHCRbkdbc2d1oRKmZhWdIjVIr3
+g/feJ2jJQTge3CVRrY54WlCvaDwciyvut1wCzdG0GiWevio5levf2aXzCd38dodCXfREY+79u1vE
+oYQBW/3r/MZvyYG7f2+PUkVzEc0yTz4p+6Xck4c0qBXEypD0/YMb3HlbQqncLZgc60OdT4gVGDTS
+HE02iZhb0z4RJyXSUmdwnHLQkAOnclVyPMtHF5l05PflcoAWe8AU5KmEUkkyId0R9KbvSYhmNcFt
+yf3Px33QQyiLe6el1dHhgET3397WHQC8RZ/+gewt165TqAjx0IvYRcFLfs58WUufDzjOwoezcTbO
+xofGQ+EpjKaGb77j0K8VXFQ38+ZbIb+uqsTPLT/D5SuaJDy2OVJ1n2eU8GOtOcf+VLwGq+GQl8X9
+HNtH+FOx3HGtgfsJJWLpihUcHt7gfl8s2/qPvMznAoEP//3f+T6h9ke0bJcvnBOZ4Gt7YpVe7ecM
+xpo5tyDO5fvCKwtkbeFDGJcdBmPpWnvtUMBG346nzBLSjwxHrGnir7m0BtrPEKZize9fgNonJYlk
+L5zjm31hZb62k3D1Kfm+ptvhfE8sQhyK5X/FTShtyDU7z/0KefJjAJQvnKN6T5Wph7epnZP3/Igq
+F507CfnGzlcA+ODL36Kt0ObP/JNX+dLPSM/I+Z/8EziBuNX5RKzS+OQtbl8Tcpp33/sWx3ty/8bN
+u9RKwhuwuFZi0ZEEXOOcisHY61S0W3Ch53Ppglz/zXfeYi4XoFYT+1RwZTgS78+exETKJ5AUxyTq
+it8/OMHXLslWOuWDD5TCfUUsfvnY5sauWOjvf7BHpPDwUurTHcvnmQJaLdlnA6WR99Mxj2QSjj1Z
+jkjHWnGoHPC7GrO9PJD5ZtU90p7sycjfxShuYvD6Lq++9b8A8Hd+6Q7jgVj3z6zLXvniFx9j7adE
+R6nU3CDbkqTruLjDe98UarYvv/smX39NXh9OJNxZXl3k6gWZz1x7iYuPzum9qTBS8pXpwKa8qpvu
+AceZp3A2zsbZ+ND4oZ6CMeZvAD8NHBRF8YS+1gZ+EdgE7gA/WxRF1xhjgP8ZUZ6eAP9+URSv/7Dv
+GCQ5X94d891JypNKfOkt+CS7ypW/EHNwVwK0w9tdbh/LSRloCa1YrlLriNUpFRnVXE7GyK/TDeXz
+Du4d4Eh/ErfuvgrAvUHC9/XUfW7jEi+/Ir3ybw/2ufOGvP6IY/PT2rj02LEkyX6TrVOmHFPYOIti
+XUpLT9BVqO296zfY0v/5u99XNGKY8WllLKr9xCp+SVmXnTlQ2jS7JvwBzXOrXKzK9X3/3Zu8uiOx
+ZT+KOR/LPD/5SpXIkc/+7pfFCuZmCBPBI1hfXeHgBU3Q9lLO1cSKb825PHfxRwD40iWJ5b/+2j/h
+3i1Z4+s39nD0PriBwzN3lL2qe0jN1u5BNSf5ns/BffEaTm7a/Mr7wvp00o3xG+IJPb3+CF/4onIk
+ZEqaOw1pDMXKWUv7bKSqsWlKmFTmnJbrZIr7SBRPYZkSx7EkKK99+4gb15XmbTwlUJrkUu6RqbJz
+d0u+d+TscWdf8kAn44Kqoi1HWchEcw1+4eAoHDtTbEYWgn8gOYOV7z1FdUlKsqsHT/J2+bvyfWW5
+d26jzslAPFav1MYVB5H7777G+78h9++dk10s5d+4bcm6vfO929QuqW5oe540Eu/g8O4+h4Xck836
+Bd5YUCauXfH4Gk4TSxXUm21DognPaZFQ6sp6nT+3TPdY6QcfcDxI+PA3gb8K/K3f89qfA75SFMXP
+G2P+nP7/fw38AeCy/vcSIkH/0g/7gppx+Yy9wom1z6ORJskGNfxz4j7XHJfwloQMNavNoyuzh1Ae
+sHIKC77WsUcpXqYMuPs+TFXK/OQ+gS83NFd8e290QGSkDrwf3yMciJv4TP4yQ/NrADw5dVnblmRV
+rBLgAZ6IzALGscgseVAanRYt9dQazUVaCGjp370ilRHSBk8tict59dmfIqifl8+wC9CHwnEkQbZy
++TkWr8gNn19cJo3FNR6f2+HpZ5UxeO4qP+WKC3r9q/8NAO8PEx59S+aZrezxaCTX5K4lXGyIm7v0
+qT/I739JbsvldQlFgnRAZiSsWj3/bTbqcjj97JMbPPKo1LzdtIbRtl8SVTlqVKjOidv6+OfmWb0q
+33f7YEJNBblWOxWe2JSe8NqCvHe0v8ugkHCs6JVxUnmwXj6/QFMTkOn2Ad6yhHfhUOnFxvfYelcO
+Qrdc5emrsoabqXP6cJ6zGtCRdQ4U91EKr7CXa0VlwWa+I9dXzgpu3b8ja2/neJ7ch9FEDqMgz2me
+aLWKKXUFzJWXXT6j5Cpzin/wDkKqCzIf62gEbQ1B1x1e/ONyCBc3lrCPZP/+yCMSUl1aXqRc17Ck
+mZDfb+v1TdisyM+Xnz7H8uOyj/bfEgOwuNEmnsrBstxYIJuTw+3ujW2sEwm3sobB/YhJgh8aPhRF
+8TWYQb5OxxcQmXn4sNz8F4C/Vcj4JqIrufzRpnQ2zsbZ+Nc5/mUTjYtFUahzxB6grX+sIljl2ZhJ
+0e/yz4zfK0W/7PisNBd5uT/iUa3dVmsVooZYlYFfsNaSevvKpfNMVDNw4stZZY0KPNUtjH3DaCRu
+a70+h+eLWzZf9ulsyCn+6GNyQj8VHbB9X0pyI2ubobILe6t9LrwrJt+NYhhIwnC9J1YymUsolbX8
+Zbt4npzKvimYuyJL4Z6skM6LNVoayfeNnR5LsXo06+uYqrJfpDFol6DtikXsLC+cNiLNWSXWNuT6
+nZJF4IgraltNKoviPq+o69wLLbKRuLDFtRrv7wvB7Dk2efIJKYFe9s9z9YISxrhi5VY3L/KlNXHt
+v+BcoZzL3FrVjFJLiW9MTKGe3AxX4TVaXH5UvIPCqeBclet4pebjKMWYsS1KSj1mlHvCrwfYRwpd
+d3LmlYk5nV8hbYiV9r1zBJpo9JVRmpOrVJ6X9QwHDv1NuadxmJFr01yj3maYy94ItazdKw2ozilK
+cc7mfE2utWynrLXF0xt7h/SPxcvaH8jfmyTHUbe8hIO7JuGW+/+UsS6I5znO5R74pc/hq50Nlhex
+bfn94lMVqpF895XNl4l92attRzyG6nzjFLMQDwenUPDctlndFE/BrtfoKDHt6LxcczbdJjyU/VbY
+Bf2BEOHeH9/jSPEiy615Oq48Rw86Pnb1oSiKwpiPqDbBh6Xon662ioV2nbXxiLwrm6o3GNB6RDbj
+BatKVTkF/fKUIFPJdH0wE8uiP5S4KfczPG0tnRwfcHhfXM1upU7nknyeq9DeICxYV5fMrT/G4L7E
+bL13b3F5QTomn9sfU40VLOVI9jeNq1Q0PneqkCtuotwsqASbANiXQ/JZG0BVHuhOsoithBcmyikc
+rSU7DrnGu6P35SHukTH3tFQGnI6Lp/0FtuNjKfNQ1u8R3JPP+PxVqXl/4raDtT+Ddn+Vg6EI2DSW
+LQZag2/lOWieZHbwOM0K1aFs7urco7gKzbbiI4quMhO5Fm5H75/2GeTTY0p1uT63VsZWcFOeZhhH
+uRTtlEKz+Wks7m44jvEaEhIVe+6pnHtproqtLNBxeICrh4Wti+k3fKyaEurUpngjebgtP4IZozeG
+fKyt2g05KAZbCWNtyU77HsN5JepptrBqMrc5e5FtVw7Uxi251wsRfNZV5aXmCSZVabCfeINIuyR9
+JTcZTt4ndzblmljD0v3pl8+dUtXnyyvYejDaaLu0XaFQfVCThowiyWfd39/ikYufB6BcmSPYlHvi
+RiqsMwywA7G3hwd93rwpc37v+7vUtEV95FtUOv9q2Jz3Z2GB/nugr28D67/nfWdS9GfjbPwbNv5l
+PYV/hMjM/zwflpv/R8CfMcb8ApJg7P+eMOOfO7JSzuCRmOD2CvacnJ7us8uUFsXtaZaXqagun0ki
+jNaYjSPWeloMSLUxyIlishM52f2qQ8PXRFU0hl3ttNuQU7RVOoeVKczRa5C1xE288/Qu68rzWL4T
+EHfFWln3xIWN8ykLmr1+9uojtFwJH+pFC9tWshenc8r56MwgrJENahHySR+jlZHi6IhsX0ojg18W
+FGN3tUpHr9/ptDGq00DhYVQuPQ2nRJZkqheeFTey+hPr0JCwJf/Lj2JUTCSYb7Ch/JGNdosimnEg
+qGvslPB8dY2dMo5K0xWmRToWb6vwbREwlIsCwKZy6rbbhY+lKEbL95iRQprMJtf3FwO1jge3Ma6s
+VdoKMV25N3Ec0UQsrFMLCJV8xNI+OVPu4PsyB3tcptxUFGpWx1ViHDfJsdIZ9ZzskWonwlNk3/b2
+DuFAPLLO88/gzwh1JiPGR/JFicr0+Y0MVxOY5sqn4We0ue0vPsfQ/tsAzAfiEQWdKqnqh5JYmExD
+poqNCbVj1p9iGSUcdWY8kZArL6MVLFNflfWcG5lT0R6nKLBmyXQlVpjmA4yGo9Osz/2+rOH16yes
+Lsh3OJ0u0Y7M40HHg5Qk/x7wWWDOGLOFsKD8PPBLxpifA+4CP6tv/1WkHHkDKUn+yQeZxCQp+O7+
+lKPLPiVXNmO7WePcvFyY7ZSxlJDCLlnMoNxZKiFDMh3iz0pkLYeTu3cACFhn7UkB3rQnPnZTy3PI
+zfBKc+SOkrV6huhNcduOb9QYOdrB1nGZJprJVSl7d2pR0Rv05LlLrGtHXuDbzJhwTFwFzRwXKlpT
++D2K2xrvdyekJ9J3wfgtrGtSRTAn/xSAcvMl8khKXjafOj0Ai2RAGkk7eLYd4Rba1vuEOGhp7Qky
+FUfsv9Tn8bKEIEuLOU3tVOx0mtjagZqM5ZpLkYurmpAm8MHS0GYwAe355ivTMAAAIABJREFUsJpz
+mLLWXPUfq+xiKvp3Xo6xZk0YLiTasmv3YCh/YGu1JykG9BUMNthbZKx8m/U4BuUoHO33CJTdyK7N
+DqNtmM5IThvMNoNFH0/vq1+tU/Hk/cpZw/l4mcNzItRyNPoVxhoSJeEJTb1PE2OYKLdjxZb1qdkl
+BjMK+x/NMbPn64+GtN+VaxoMxFHOfUOmpfEii8kyFWqZ+FjB7A8rMMu1ZDMSSx9bW9KNE1EuVGhn
+4/HTjlCcDMuowVTyXM83pMruZJHhnszySlPsI/m8uc02lfZHCx9+6KFQFMUf++f86sf+P95bAP/J
+R5rB2TgbZ+OhGg8FzPk4ifibOzcpuR5XO+KKrQQe5wZiHSflDFshpbaJMSU5jcO+Nk+FOaYpbnAS
++0ynKkJiRTTymSiGRawcAvmJMir7GZZ21sV72xzuiYbfJN/nOyMFmwQWl1bFzR0rliC6e5etkzsA
+3P5gmXNXFP8QhmQqOGLKaxit5Oaq5pz27xPPNCrvXOfwbXFhp69PqM3LiZ/Zkjhz5z2iO3JNgbFx
+bLFsBSnZtgqEYEjrMs97hSTwauvQWBBMQNSbo/vu7wDQumkxWpPsdS0r4WnibnhX/i6vWFRUQs6i
+QqaJwSw6IuuIR+bPL4GZubzqtrv5KbtyTo5RL8V4Eczk9oZT8lSuJdMOx7yccUvxFJNql2wkP7dL
+hkyZjb1KjTRWnkqFXaejBGLtNIp8Uv1cYxc0lE7Oq1ZPw6NMqeWd5oCFBZnzY1ab/TfUheiHDEKx
+9EfplPhI1rZe0sQgCRMNffJxDcuVdStulHh7W/5urBWZzcYi9bF4ggv2YxSqV5lZBZZ+hnErzJon
+mXFSOGApUQu2izVV6LrXn+VOoXApUtmHGLkOp+QyTTTpSMhIQV9xkXKsEoFRWFAMlQv0AcdDcShE
+ac7Nk5BGzWNuXcKH1ZMUO9DypL1AeiSx88jaZ2LJgzWcqKDmwRatttzkBb/M+SsSi7uxQ6CkJlm0
+TKFci/lUBWGTMnGo3WvRMQfX5Od7uyOOCtULGC1ilpUodF1d6tvWKbjlmzevcc6W7PMzT9+mrJvJ
+jAagLcWJpf0XkyMsBcdMLtUZll8BoPljFj6bAAxcqQAk9jLVhmzyyNwhVcr1rHApyvJz/842b92W
+jPNvXVOil6WnaV4VN9n7/IDsmnRd3ggPSN4ScOnjkw02Hhcg01j5+w5279GZqh7G+idOKc733voW
+33n76wA89xN/jPMXRPVquieH3/7JWzQviuR6vdTCjeR1J/AxqnGRFxlpKAfkIJIU053rXb6v7eDO
+bnbaHZtO49P4umQiyr4Yg7Q3OxQhnWrbtrNPokC0Rq2MGYvbHZpdDj5Qdqpdmc/9mxnf/kAO4TE5
+y01Z2/27OyR9lZ+3EpJEK0mKbLy1d8LNjjwmr+Q5HMtTmj95wrdflwPn+IYcmte3I+o/In0yGws7
+VAO513bkYNQgJVkFV3NMDjKH1MuYdrUbspux87r0krz+7td48mdeBOCZZ/4ATiHPQKqhdDyOsbXU
+O9jf5q6WUaPQItXwaTgt6KzNTpYHG2e9D2fjbJyND42HwlNIs5zucIwxAZaR035hsUJVuRGrSZuk
+Lqfg4c0h72tyzI7EY5iLDuloZ+Rco409lCy6XSnDPTn3CrcGCwKuLJQbMHcTcjX+5s4UWsqcO4y5
+9Z5WCdJdFnLF6Psq2EFBrG7wQa/LB/FMPTghT8W9zpyQoiueQlaVeTrlRRylFZuvP8/is+IyWk4T
+oxDqhRlbSpZitPPRREOyVPkF05TCCHjHW4r5jV/8dQC+8poArH7mUg9bcRo1p05bFZbIOwwPtBPR
+32XpgmA2ak0JxcahxU3tyKvis9KWtQrLPjfuSoLr4Cu/wQtHypo9kHXdLScM9gSvdmXRUGi1IOlF
+uMpvYFMBpcAzyhuxN36NpKuVgScabJ2IB3GyH5GpQvPyRpnFFU3cqZvslFqEasomo5AgmCXROowV
+C9C/0ePGPZnne69KH8h792+z1RNPsFxvk2p1garDvZF4Z/f6MbWS3JOeYoN74ZhBqMpSaRVbE6bZ
+qzbdI/FejhXw1M9jrik0/1NPhVQSwW+kzoSpUvEn1jaBJodLHdlPvev3ef27ModiO2LYlTB2f3RC
++z25jovntigry3OkHnIcBLiagM7KDkmsCUjboVZVcFq7AcksXnmwceYpnI2zcTY+NB4KT8HCUC5c
+AmyWlMD06uI6Da0rF34X3lecwklOqskja9YVV2tT730KALu1itXVHqwvL8BLClf9zhg+rW5BX3UR
+LnVBVYttZ0Jb1Yz/3/bePNjy9Kzv+7y//ezn3HP37tt7z6aRRhotjCRkyRKgBYEBk7DZxoYUDiaF
+nUqVgwqnKk5iVznYJE4sY3BscBGMsDCLSgloBbRrNCPNpp6t9+7bd7/37Of81jd/PM+5M40lNCNP
+z3RVzlPVdU+f5be+v/d9lu/z/d41cvlcrF1rSUG/LytsVYVO1ipVtpVvwSdDw2gGCyFJpHkO4+B6
+ilnQPAJ+61Db0U3msPtaUi25eMqt6ZTkN4XfJTdTdt4ITwUB08kl3KZqOVxMWf/CZQC2NSZtZBOC
+UG6r61resHJCNrzms3tRzrU+DvBVkq+kDEtFd0K5JMc+GKcoXQSV8lHe86bvB6Bj98GX/MmiQnxP
+HFllrEnQ3IkOOQ3o9slHkjOw3ogi1Wuu8fA4toflyYBVrl6Te3Nqd4NrO3K9R4sLrKjGoqOlPjcd
+kmiiLcSQK7PxJBsQX5Jt9EYD9m/ITcmVuaiK4f5QxlB4rI5ViHHDlAlUI2Kc9YgKZfDSMnJSuEwW
+xVNw71/FBpr4nNugot2OuUoMjhopBwPVInEjMqMcH4lD6CnKNPNwVSQo9MW7bbUi7l7VJrCTHpVN
+SQi/Y76KsxbqdwOcdIpVEa9qst3BbeoYy31KU82QwKes+4v8mNjq+HuBdltMCmDJTUoaj6Zs4kyy
+Pm4mYYDpANPOx7SE6csNLx2XB6U9ejWu8ta55x+AT6nLbHP4F4p6SZ+Cz6owzJICaFYGjJfFtdor
+H9Dri/v8hRtXGPdk4FWLlPtU/LWmQiDpsTpDbRfujMZ8SdV4vudr15k7q8CT5gmMKgdNE2fJ1tdJ
+91W4dX+TbFNCgv2vXsIqJVhoZaBcGZ6nU5dtPfBjb6dxpwJe6hBfld99/LE/5sG+vG5rYun0d9x9
+KAJrEp+spozKl9o8c0Wu287WFxgtywP72mMymYbNCEcFU+1gwDO7X5L9XY4IFeZ75K6AxeNy/CUE
+FJYVMFT3e2sw4FRdEm2lakypJNvL9hMyneCGyvB8o7zLlT25p4ONK+w/JvemfDRjN5L7k/UyLu/K
+cTbVrU9HMYGK1MZOyH5PJpDx1V2qzWl7dZW14/IALR+RhOrd/Ta+0tpf3dvi8WclBE2qPonycRI6
+h5R73d0p8Ypl+ZRMCrkX4Gk4lo+rbClM2b9DIcVdn88/K0nCn8tHpErpZoxD0JRJMS8O6OzIONOC
+EW6cM78o23Aq83hNuZfOyjzDoXxp9+HzNFVlytF7XV+qM1FNzAPbYb+Q70ZzOYWeU2evw2JZFrsX
+arPwYWYzm9lNdlt4ChaRGevlI65fFteot1kmfbXMdvXVJfyr2ojZv0h3S0tW51VLoHye4JwmU5xn
+YSQlNC4cBU8QgqQVrKNycmNJ9CQTy3hTZt+LFZfPb8rq8MTWLt2pLoKT4yhZykZVPr/r+N08XpYV
+Je702dZS3ucevMCZNSUM7V7BerKfIlQ4706H0aasEpWxIQiku3DlVT6ZhiC78ScAcJ92qSBeQHy9
+TnKfegrjE2wmsnp+8DcfZneg563yaZOiRl3dWSoOQxUy2fUzzDVZ8R49d41sXq7nPT/5dgCqc3N0
+lmR1vbHRobsh136crrOyJa+X73kbniqxpNsS73x260k+9nnxKrxJzPt+RFaue0/fRWAUjbgQkW3K
+97e1e+/64xPO3ZB73VpvstsTj64Sl+lqBFa70cUo4nRQk/tRNxEThRKnBezoly+t91gdyjU68ao2
+rlLkKTcqQWeBzV1Z2b/27BWuaEfskl8ijtQTtC4bmuTsa9kzKDwqJ94NQFIYvEsq9fZAk+HHtLFu
+PPViBkykEsqz16/TUAIbb2fCUElVdye7bF6U1wuqkXGkOk/QFs+rNBeBJq4P+s/y+Y9/BoCtZx/n
+/vteI8d8nyRra607yNQj2NreYaAhj+83SRR6vrHexz+hYccLtNtiUjCIy1LKA+Z8cQGbR5YJ6icA
+8GqrOK+TQz3ufS/vGIorNtGHxou2KeYFUuq+6iz8wl+SDR8bw7pSxpdjiJWqXGvUTg4TVZ7aOehz
+bkcett0sncr5MbI5lzPFNQxlgC7UVzhlJeewlRfkKvG+sbHLSJmUTXsVV13eknb9cccpzEmttz9+
+9VBByTinCFbkoWldEYxB/WyBX5fztEcg25ffxdWCzcdlcru+0yfU/or3hpLp3nnwPPN33aPbBUYS
+ny73AwaBTCD14AT2sjwIyYEMmPJqyMmm0sH3HYqTqqzknMEOZBC3g2NUzBE9Jpl47t5PSEsK3nmg
+xplTwgoVeAkmlXtps4RY2YU3n5D7dPmxIRtX5Tyy0pisUGr4TkQ2kHPa9kZc0a7ZpYkce6UGqYLI
+HJvQVA3GEyt1FhZlBlgoLeJprF1MNMcxN8L68tDcd/eAQgVVnMRQUkh7bW6RvW3hwsymOj2ux+a6
+QMh3HgtZOquw6o05tIMdq303Ns4ZBXKely5v8/pVqfBEy4vgKPV/Y5GlM3IcUWs6PloECul3khCz
+KNubuwFvOC59LAfdPq1lZabWiSCNRwxUcn6wuUNF1aQWAp8d7bVwqwZvKNf2hdosfJjZzGZ2k90W
+nkLg+JyqLGDcjMWG9sFTJlBdP6dqQRFm7kqP8pLMniOtfRfDYyQ1/TzyMX9PVlWz08aeU869nSZp
+Jqt7uiQufuoMefqCrPJf6fR4SjsGh9YezpYJBeuq4adIW167nxAlKixjnpd93hsxPC8JM3NqDV9r
+3Y4i2NxKgumL15DfMSTXppV87Sp2T7wY9w0qyzaYJ21LqJGMUtJd9UYeeYqPflKwCSYrOKlZ9P9K
+WZkXd/cpzslq7JwOWVbVZW8lZu8h2d9K4bCWye+GCtWN2w3qmqVOTRVTFw8qmlTwanJM0TDDZBJK
+OVoTbzuWN3kqa39smdJU0duBdH9Xr3OHnYckPjr3OekC7W7s4ifTripDSZNn0dAlzxQpeCE/lAtc
+8uX30VoLKsrQXPJZWhLvYM2rU43ktV+aEO/LNoaOeGDXL4/Y64pnOby6j6OJ6+2DEVZRk/4oYhRr
+7KIwcOO6tDXp7PV2SC6qK+4mHNGmuL66lcnIo6lNWd6lG4zvuAxAo34PfqRdm7ZBRXVCnOFUAKZH
++pRWyaIJaU/GzeDZL5KqaE255RGqpshUOGZ/Y5dnLgmxyqVLA5rTakY5oKpPdhWPuejFVR+MnYJl
+XkEzxljXcbA4GG+qQGSJatolmSQU6qIZG7DYFHduTbnx1tIqb56IW/tO/820tXznuZfZKKQT71f4
+Ck964rZdcrWSQR2jZarWJKBsZSC9rpjnB1vfB8C9tfdS0rZk+5QMsK9/LGfsyu/+4ec/xFf/zw8D
+UHEM3YekBbpeDrDK556qMhFpdoj9z3xoz8sEcWRhibNnBJBUKKjm6sXzbF2QB7YzHNFXHL0JHFwt
+0y3O1YkacsPbWolZWpknVgKUzPj8ye9KfiXLLWim2tqCMNLJUstqtrDk+qAUtiDT8mthLUbFZEuV
+Ekbfj5NpW7TFM1OdSMsR7Vr8Eeb5cSO05QtFgMPH5V4qQa3hbcCPyHVhAlqqba7+ENmc5mI4SqEd
+iqff/D0AeF91GWXCUbh79QlGiVQ+SBJcJextcZT/ofIPAHjfRKohK37EVG4pjsaYQiasYnCRSOn1
+fb4H7Kf0mH5V/65h+F19XQa0Vovlw5+R1umiJffgS489jlW2sO+64+189MNCYhttf528Iuf9iUe+
+yOZXZeEYa2m9KArc6bg3llB7RvANeSLXOy9yNE1AoVWwIPDQyBXHQqGTU1YUFPqBLRAdAiAZpw9b
+a9/At7BZ+DCzmc3sJrstwgeAAgOYQ5lu13UJUABG5JCOxQ2u+Rn3hLLiv8vKbP/+zGENcUu95CGs
+FcgvkzZ9R9yv15syuXa4raoE21k/JtfEZuhe5O6hrFZ/ydumMS/vm+hfwyP/i77+AAA3kj9jqCCd
+ZHeN5JI2ybhDokVxH0eDHN/XjkBNWiVxQaEZrHxiGCj3wni5we4lxRMsqFyZCaitSLIsv1EQRepy
+OuawK9EaS89IoimY6CrpgadQazdsEk9X/xyM8gC6jkOuDHpeSTyGZJRQHMrMOxilR3Mth/ck8F2m
+C5qr0NmaU7Ckv3u3yflZJcBZsNcx7h/Il83WoT4iek8NjwMf1PdcrC6Dw7SN35NGMa8W47jK//i4
+AL2CE/vs/OlFvZ7XsBraOTbmpDJs/0FxhdOlj8r73t+RXXR/FKLfAMBPvguKj+nhNA7BQPA/AXof
+mNo5oMY3sid7/xaA1aYkFOfuKHN3IWHcWrPKD7z/HQA8+odwVUNBvhAw1AasqZduHBej4ZMTBIfC
+oCbycVXR3MY+mULhUZIVayzoWEhTi+LCsIAp5DuFzQ/1KF+ozTyFmc1sZjfZbeIpGAyOzJi6cvuR
+h+fJjFlxAkKlVlpwA9qLSjqqnTFOO32OhD7axahkGM6ARRUovTPwqS5K3P3MiqykZxYixnuyrfJ2
+m6ikELPlEH5YccefOQn3qufxuwKfPjAuFy9offzB8+SFUp5Vfc6WJN/hHHUp9PJevCRydHE6wGis
+5xSGVBObo6s7dJoSXzaUlHY5LLPSlhWoP79HoExAWQG+wphv5Lt0tVfeaLLTrnt0tVbm+AVWacms
+AWuf8zbQUqxRgk8nADOlBPOc5+DYriHwlaG4FuGp3HZFPZBjIby+KvmVBxJDY06Tsk3gfi0Hf9BC
+vavv67L1P5fgrynsnJAplZOTlSgmcr3jRpVgrDR1KsHmPZiTj7V5KB1hNE/iey5rqkxdXQsw36ux
+/796h/xd3IJM0I3WewSGCps/0YOhjpc3eJiP6nI7DeBhqtPzXDpBLR0rLmAi+33twms52pIxVso9
+zgYyFurf805+80OSq9i4sIOdktQqOtJvV+XGApV2lYpmtGMfxrsyLpKxxRlrM5aK/BrHwVGPwA0g
+n5LDGkOePpdsmAoXvVC7LSYFxxgiL4LAw3EV6x1E1Moq6FEkNJTPzi27dAM5yXPKj+BmcP9JqeHO
+JQ7V01rHdcuMKtrNeHwJVzvHVqdUZH6Fa3Ulo2gYdm9Igm53lPLmr0qmetUd4/aF2Tm/Vwbod+YO
+j10R8hLnsU/SdmS7ZxeOs7woE0vVKXNpR9mR9WHMLIfU6KVyhBtodWKY4vrygBxpyqB67b0nWFqR
+gdvsnCBXNvjBxKGricvhZInOvnYwKjy8N+pQ1jr/YASRJq1SYw8TTpEXEIX6MKnrn9qASSrXqlwK
+KUJ5uEteSKOuCkiuy9j2DrcBsLBQorwkidhr4YTLKs++2hwSHlFI9w8NcRUezavVjf6ORfh5ddsv
+rMCX5Vod8dY4cKV6kly7RqAalFkufQTrRY+J4jsym+P5EsZFTgOzIOf09bd8B/59MqHW/0dNpGYO
+O//P4wD806sdKlrH/6mjPidLMs6c5RJ2RRcGZcw2dxRMBZz5M26yv7pwAoBaTUO3ekTFk+R4USSM
+ygrjfiTmT//fX5dzGuzTqEiC+a1/Q8Kke9ZeT9wV1NPm5U0cBTUNt2MmTa2S7KdMNvS6KMnMxPbI
+vWmY5zB9nDPHZ4KM6zQtyHTiyJ8/0f0FNgsfZjazmd1kt4en4BrKjQCCgEQrpIHnQi4ros1yGnPi
+Pq7UyhytyUzbnJdZ8nTTUm9ISTKo5AyHupLsT3BWBZNw0q0ympdVY3mgpJ4GUHmt650J2Y7yIpQ8
+nj4n27i41eXeszIz72/Lyl2425zVPv43fs8dZA+Lu16+o0Z0Sbvrwh5JV5eYXFbX+chhTpF2taiM
+LekMnqccV/Tia86Iy3l65QiLLVl1wiMlXC01D+Ie3b7sI/OOsbUqiVRP6dPanQpWEXqjnYzPBpqU
+zDOskqq6WClxAZ56LkURY7U2Xy4b6gpnrtd8ap4yQNkxNS0D1yPZ1qlWi3ZDuw+rPpeVEmwyamOv
+qpZFy+P4hlyjuRVZ+d2HEnIjK6y9t8vlvoQaYTpgrqdNSW5IqIxNo1R1I8YpniZMHXwWavK7+aM+
+3/2dUn5cfPtfwXmtCKYMerLyZweP89RYZPw++xt/xKCm3Yx7BT+lCNfFdUuoNG6ljqIU8eAtU1f8
+eW64dTiypCxNWpIM/CYon0Rvd5tL/1owBJ9+6D+weVW8zMBx+Fvf/wAAP/PX/7ac52DM49u6Po+7
+OFauy6Q34EATum3Ho1iVc+0quvWpXpcsVdbp8nOP8oiYssY8uW8ZjrQh7wXSKtwWk4JxfEqVZfI5
+j+JA6dGSIaVQLvaxuTlOKavv0XqFkydUPn5ZQEyrq20crbuHlYVpQhYnjElUlzBJxrSMUrRPBAgz
+6DrEueIKasdJvksu+rHHqkRWMOfd4ZjsURnonZK4b8UkJ1mUh3fxrgp7qonom4xBReLhtOuQKjin
+popWjabP0rJ0rNUqz7ngi/U2zRU5zpMLMjnMtatU5zQ8siUKXx6wstug3FTh3TwjUIGXoCPxxaAW
+0+srKGrUo645mo4ZHRYAXAeCkj6QqfZw9BMtakPN81hQUczAegSuqjrlHp7mD8p6b5rNFuVArn2p
+XmM4kt9tJCGJTlTpOCJWLsXVh+QehI1dRo9oviP3OKdVlKrXZr+tFYV4QKwT4GE1xAVXeyo843Fk
+XiaZd73xr/L275acj9M4wkShveVQ8ghe6wxhUyaKs3PnGO7IRHBixbC/p63RgydpWwlT5SyBG8Bn
+dUC5GaQaBnkHzB9ViZNQxiH7ATiyX/cjn+Kh3/41AD60fZlRIudRDkLepjD0mivb2nSGPP4l6bQt
+rq4TZ3KjmmaEG2tXZrQF23Jtc1/bwQOPIphqmtpDzUjXi8imDOJ5RJ7IRDX880mRb2Kz8GFmM5vZ
+TfbtStH/EvB9SD72AvC3rJXucGPMB4CfRtK3P2+t/di33IfrYupNwkmKtspTmIRIkXKrc3UW52R1
+WGuGLNVV8ksRj0HYxBglyQwq+CoAY8oJrq7GXm+bpK+rDdpQ08ohFR3BXuxhStJ95p56CjuU1WM/
+P2CSCx7i+kj2e6dbYnlOvrvUWuXhTFbKx774Z3gDJYclBhWa8VQf8ki9wclFScqFtYyKrrB3nLqb
+ck1Wo2pVVoNqycdHkZllF+sKys8rUlwVUQmdDLQDM0mVY2LUIlWylMzsEpZku17fx5t6Cq6FfNpU
+I9ckTXNCTUT6roMzJTAtefiKacjJqCrrcLWpHkPkUlG0nskjxop4tG7GUOXg9yeGvb5ci/1ENTS2
+Mx6/IiuXl0w4r77t4M4Opq8iOhOIfXk/0ox8ySnTVPId1wS8K5CQ4R2VVZaUyu+gu0H3nDZjlZS4
+145oGLmG7yw/wJORJDabWw2KOXm9u13HUd2HWtLTfVjY1ixvmnP4yGSrEMgYYE+1GbaBp2VbwS9/
+jmBbqk6TfILqzfAar8zCDZU4vKSI1csPceUhIZVdtDnJdCxUCkqeiuGMynQrygOi4UVlMsEod8bQ
+mil8gQDIE63i+QWuq/iGF2jfrhT9J4APWGszY8w/AT4A/PfGmHuAHwVeBawCnzTG3GGt/QvTnp7j
+sliqsZ/vECuPoDPJiE7ITay0DMvKVrM2F1DWjLpJJGNr9gp87TLzizKWy/L+OhiN4c1oQNGXbHd+
+XWeeOhgFvwS1IZNryoTktUh8FQvxDbuJPDhf13jxvW7IyQV5EM4nKUVNB78pyJQ9eGHBJ1CX2ZmX
+B8kvOdimUpk352lNBU/rUNOJLtL43QYOGXI8TlrBUeZf4wKanfZyn2og2z5QskmfvUNY8jC21LVC
+0w9SjMKjK74LkTIWdXVbrkNLhUdWFkoEOml4ToG2lZBbFy+Xa1BVifdyJcXTECWd7JKPp7TmzuGE
+1IpjQgVteWj3ZRJSUrGY3WHCnsbGZn/EKJZzGSYJYSHu88KcTEKvah7lrcclZDhqK9xxp4rdvLGB
+OdDs/MOX8Ofkvldfp5WfT24e5l3eUWpxell5Pp/aAy0tZgwJtMtR+VOE3Cef5hIKRPgM4HOwrhdm
++sS/FdBJKNr7KlUtHS4DK7rAfVd7kYY2zcab0pEZX71OqGXtoGFontKcmRMRj7WF/2iMyWWMj3e1
+H6JqGXT1epMSar7H+AWBkXMaJwXGvLiA4NuSorfWftxaO8VJfQnRjASRov+QtTa21l5ClKLe9KKO
+aGYzm9krai9FovGngN/R10eQSWJqUyn6v9AsGam3zXB36zApYq3FU/8iHhpMpDXYwJDoLBgnWmNv
+OjiqP2jcCXZfkpVO7GF2ZSPJaMB4U91qxX2aUYjV3nQ76FO0te88L5EoyUgvKDHSppQrymvoNdxD
+SLBNSmzvSXjRnVwg76u728xJdds1xSOkoYunPl5toUGpKqt8UXHodrXjs6119zyk0NXTqZTwp94R
+LslUj9FJGeSywiSOrojlOomutHu9Plbd7jAzWDN12FyGuqKnSiYTeOCqRmOW5kQKkKq4lpRp+ACO
+fidX3rxu1yOOlbqMEYkmV0dhiJvIPZmMRzSqqrSsepb9G0OGisvdtTmX0ilwylKMFZBTjvB0G8sV
+CfPOLC7wnW/4y/Jea47qPcqN4fhk6sVUkirauImnfAru6QUCTS66LhSfES80be2S1eTYnokNgXYu
+Lupf13qgYZyA7ASzYgnh47qm/h0tmRWXYPB/y0vOs6SQ7u/1DMfgGNGRAAAgAElEQVQq4rnMn2wR
+HlMPw5Nja5xZ4QENMZtnqxypSEI0akDOlJOiz3BX9tPYl6rG7pfPkZRkvXa2A8aKLYn7GX1NbLqZ
+IclfRvCSMeYXEWT1b30bv/0Z4GcAvCCgP/EYjixTfU7XuJTU9a8er5KOtV10OKLc0Ay2cgqOnTFp
+oiSgo8khGs8UGWYk7twkdkkqSu0+EJc0c2Ckun6x38TUFbCSRXhH5Ia98dg8yZYMpqeUytw4VTzt
+gEyGHW7sCLBmdHFEPNbqyVWfPJ+SqCjRx3yIzi/YLCfRjPqgE+ApaYvb0bz3YomqkrTkeQrqmCV9
+S6bdnPGkYMeKy3xpQwaBUw+IBzqhZRMiLTl4fopNp5142WGlYYryN56D1YnFBs5zeYIiJ9WyWG4s
+jel56zXezxOi6cQydBhpn0SYOXSn5KeeR6A3tq2lIXfe5e6u3IfBZoqrJCql+gKDieQdXBPjadix
+qNWle+ZKzGtVxlvoYfWBzQyoLgzj8gRPSXj9VXHFg+MV7LZc4/JeCzOnE+DmPazHcl/PD3ZJVZ/0
+tdppysAwFVU3aQys6HXbxCwKwI03vVP+finBIg+sgzlsOTjZarFwl6h2LfwX91M99Wa5zvMyoS/s
+H8MelTE9X19iYUWOPXMqh+GtzVx6JeF/nFTkGq5t9UmvyjNybbJOmKpEgTcm0EUkySaHuYYXat/2
+pGCM+ZtIAvJd9rn+6xcsRW+t/TXg1wDCSuWV79+e2cxmBnybk4Ix5j3A3wfebq19PtfTR4B/b4z5
+ZSTReBZ48Fttr8gLBr0++WjEdOXyGxGeahsyiTDBFNrpUNZkahHoapYYCoV75mOLY7WWHObgKn6h
+NsEdSbY4belK64Kn2ZKh9ci0u3Ay3saNZH/Ne45RVvm68oaECa7xcJRavJZCpaudatsT0oGGAb3x
+YUdhqqt8Pi5YUNr3kucfStGHTUN9KgOvVOaVwiXVmd8Ze8QD1UZ0RjiuZNn9SoG/oxcxFgiv3Vgi
+rcr5bXd2KbSj1E8jUiW5dirO4XFOtc5dxz2koAOPTPEdcWEopgAnm5B4iqLSRGVuHDx1f7ayjIOu
+XDffjQ95LnEiPHWlB0rSshQUhG05oKO5g7cn7w/SPl5ZfjeIC0KjnkdDK0d7VYZfV+/PGeAviadU
+OXaWWPtjnEqLVEOQQjEdeeBjVpSV2TnAnpOxMKqPDo/zy5euc0k9uTfrvTu9lMG7VDH63wU8h2AI
+4PvUNfmhafT8fqaPlMEyN12hm4u494n6uRPeSfmEVi00vFyuZTRGylc5fxeeApH6qaG3K4nrySBm
+3JP7cDBQ6P5+Cr6MEd/UGUTKD5eHuHpPKoFlPHyJqw/fRIr+A0gXyyeMxKxfstb+19barxtj/gPS
+b5oBP/etKg8zm9nMbi/7dqXo/81f8P1/BPyjF3MQxlr8NBXWIZXZrZRWaZ+Q5FK6EzCZqgvv5SRt
+ZU6ShZuF44uUNYZ0Q0hjnRljQ6CJrSSEia4IsZb9bqwPeHJDltrxMMdOk1q1Cm5XvlvtdbAaU/d8
+md9KOIfMNqVRTGNfu938iFinwCiClnop9xyTiOrEQoWTbUEsLrSOYTTus16Zckn20TJCtOrMj8i0
+ey9oBVhHttXp3WCYajltHFLXhq6y1soz/xKplvH2rsY4yZRtCCbaRRjkdtokSah5hFIYUFZIXL0S
+EiiqzncdqmXxDsZ5gNGkVa4eQeQEpOo1hDZkTUurrXqLzUJW8YM9S6rIyUA9kDDPmS8rY3SRMlYI
+cdyLmYxk22M7wKbiWd3Q49yq77KnZcOVaInSmRU97028howXk0B/V8rV3Z54UEHPx9MS78H2kN5Q
+tRf8RToVKZ557SfYviaewGesuJCnxgZzTpPKzQx6gpCkuAqf1+apE78vfy9/N5jfkOvjRYSarB3f
+e5R+X2XvnD7NnkTU5bZkQ8s0cUrqYbgwUl2IjccmPD0R3IOXT6goXPlYVcb6m197J+cuyncnSZ+F
+RLbX8/bojGR/w3HKi12WbwuYc1FkDMe7mFJ6CCwqnITORUnkXDx3g8c0qx3lBalqTM4ZGTA/9MBZ
+Xn/3u+S9tyzDhvxudHCeUVe2t31wid9/UC5wT5Ns28MEFXMmjwqaSjgyPjrHQiE34Nlhh7Z2maF1
+YEtGnmrVIrcs+npsfp9UQVR+nuBr0q2risn9IKJvNXEWO1TaSnAy6TG8Jt/1W5KocrMWA5WJby60
+GGqFY8+9xpVnVSBlsaKUzUBNs82NGl99TEAze3vreNpum3kGNBwZuTmFttZWQnmv3gpwkyldW0Ck
+Edh8GFIJZJLpjbv0u6qJqLRyWAhULGd5fpGlMxqCHWTkG/LdfnJASdvgfQ1XNvoFjkYwYzLGGqKM
++gdk+p08MQyVKq1xQqDE5TtXiCrifhepw74Cc/aevYDjyXUZpn262km5vSVueSkIaSiehHpI45hk
++I/fe5yBDoJ3em/ihiv7K74iYyhxcsJCB8lwDlQmgGIVe00v0uVXy1/+GSAP7IQDei3Z36dv3ODx
+J0TTMv/KV3n9vXIuP/T+H5BzWyszt3xCdrF+la9/RrQkv37+swxdKd7dHS2xdLf8rohlASgmBTVl
+wb6jWuJA8TQdwGqazksKnBf5lM9gzjOb2cxustvCU7BAXri4Xp2wqcjEsMJoS2bBdhhQ1pWt3Mxw
+Cm1sGsvn5764zvH0TwHobDYpVyQ8yNY7dCoys1+8fgNXyU/nGjKbr5506OzKJbg03OHaJdneZP06
+Z9Qtf5+zSlPJUe+bU83EoiDXFTorwGgz0+LiKVxVNq5W6zQ0PJhTjoTmnGV/XbZRzx/DdaRMVfZ9
+hrvSmHVtW1zj/e0Jewfy3fFOSO2IciiMS9imfOcga9E4rtnBAym92cUaG4/K7ybdlIrGCdZ1D50K
+D/cQKadIbDr7+aHy9bXtLhPlZJg0U84e03JZdYGyZjb7e/L7hucSuuIdjM0eX/hTWZk78R79jjZr
+VQvqqtUQKkt0c5LzKk1aXvF85rWD84pTQwhSRd/T1eOPu3IMI7/B5x6VlfTCtSd4UrU6tnf3adbl
+vp+cX+XutwiNW2VekrJpssMoEI9md29IM9Rk3ScNf/SEEKx+5esXWFHdz9c0NCTcrUIqiU0aQ9iX
+hCE8jPkdSTDa8s/LW6OAabFtZH0eHivS83VLXP+47K+zu8XOSLyQr12RY//etx7jL//wDwJQ9+t0
++uItdrd32OzI9bxuDMPPSqi048rn5y/3qSiXw/Jqk5VVCU3brSpMxKvd2tsg0DD2hdptMSkYDK7x
+cYwPU5x90SWNZaAv+RUypeGeHKREKqyhSuA0SjGbl+Ti2UsdjrVPAVBaLeFqKXmy32ZVW4of6clE
+cW1rjz0VBdkf9Im1gtEvF8zHSiyyNGb1jGzvvkeVWTezh4y6vpOxMKXWDutUF2TCmatVSJBJJtX2
+5d0Dg9MQtzboGkq5si3VjlNflW2PnhDXP0kMc4lMJo8W17j2lHYLNkLOBvL+sRNVyiN5XV5UPkd7
+mkogLD9uzeKNFNSUWlR/FENGpvV/G2tt27X4ymI0SlOubCnGIPNoKb5jccGj7kvcTkt7PPoFXcVj
+dK/6XNg70AuekSlj0VxeoqmgrHpJtrs0Mcxp63S5FHFKWaCvOi5uLsdkvRKON70P4jq3kjOslyRe
+7iURHQ1j9odjYnV8S84e9xxINSdoaIUnmmNXAWKPPn6JpZJsbz1fZ0t7EbJhTlCT74QKHLPtG9DW
+8OGpEhg9P+rYf6yPz2t+Wt/7j0JhBXyBAdeMXLc7u6/nq2sytg76e+z3tJKWyuB88OmABxSCvlxa
+4q67BUqdTXye/KQU78Zj2EO4KadJgqIoaGj1YWmpRlOVoBrrBd1E2beSHN97cRX/Wfgws5nN7Ca7
+LTwFz3FpVxqYUjRlK8M1lrKuHn7Qxx+Ly3xnucLJNcnm20RW2lNxhSV18YejMYvSOEe19VbqXZlp
+DzqXSMqClJu/LDP443FGX4VHcAu0aZFjTgPryHcq+y6liaw6rRVZJdwsPqyS2GLEyBGXxW9WCMdK
+zTVfourIKh41dAb3oa2XPMpy4r4cj39njUi9CW9OVo/WErQUxdfeqLOj8OnSQkBTlT6Or95B7Zic
+d3xZjmHf2easMk2f8xqsVGVV2S8mh/qIZdcnVexFoLizasljoSru9x0n57iuSc65ekCzqViPAlyl
+gpuiHD3fYaTYkmAuYVkFZQaDlPqyVhrGPr5qbcwrrV69klHaVNaPXsEx5YFcCJeINQM5HDuUFNex
+Upfr6rcLwimvZmWJUzVtBBuHlBXLMt9uo4BMGgolxvi0Q7mnrfo2TdVuPFk6yrmyIiSDjKPagVrs
+SlK62Mhxr2lS1Ywgn4YPX4NrugL/goQzfPDH4ZjAnOdvhPzYq1X34md/guVnRd37S5/5EI999TEA
+Sgp/ePc9pzmmx1lpl5gMxds8Gp9lbVth4+kO9yoiVXvgcAKXJW2qW1ooC/QaeKa4wY2rEvLcIMcz
+L+4xvy0mBeNAEIIbZGSBxI7LeJxckcG41gtonJKbdffcKvNKVMKGDNxW4TK3dC8AzeFl3LHEVt4p
+j8qClJvuXoohk6z1vZlc1fdu7PPQukBcn4m3KFSFyY+gfl0e2K/Fmxy9ITfdvzrtPtzDKG4/MZBn
+03zHhGCinY9lWNa4tNoS97MSG9DSqrWG6oJe/sAhUeSQXZVjW2qElLTDcbG9yJmqxMZekeJpabS+
+eDdGW59t+IR83jvg/jdK2SxeOcLlz0gnXj6wBEpxXzGGrk5qDaV0Or4aPcdiVK5QP6EiOSVLWaHS
+6ThmOOVHVFhyfxJjdPKqlivcuyC/6y01qGi+Ik9SmmW5Xu22trg/GbPbk2vcyFNer2XUP66MUeQ5
+1k8PSU4HHZks+9crjDR8HNU8SpmEbpXFHK1wcuLICjU9l2k46lRT0FDjyMkWJX0ijzdcykvC53h6
+eZFIy5blz6nmZxzDroYPcQF8TV6zAPeLbib/Xjo1edsTcJ+wKdX+8NdZ+fH7AGieXeKtWnJuZG/g
+vruVJayQ/Mx9999PdVkFhxyDq4SuC6fP8MaJhAE3dpocUzTc3EkJfZp+lXBRCW1HDoOelCevdzeY
+rnBLrRLjWO7fxW+ILf5PbRY+zGxmM7vJbgtPAcfDrcwRzHnUtTttoR2w0JIZ89gRS2U8Rdv4pK4C
+i07JDO7bhNyR7H2Gh1UF6qRbpefK7DkM67Tu1Blak0ymt8Ubb0gocleSk3cEUhrfSCisUPe+9nKI
+d6A8CxVZjbdHfapawUj8lNYp8UYW7AhUaj0oYK8vvytp3d2vVdjfkSTn/kaPsmbG47RC4WzqMYs7
+HNcrtObl2ErLI8KRJOjC5gK+Ehw4WZWi/mV5fU25G4ID6p787v475tj/vHg5gwpkylrveTmqPYJV
+0NBoYOnksu+S77Cm/JEOLuNd5Ud0LPFYVtCtffnuOE2o1cW7C8o1ctU5nHMdSlXVdqwkeAp2qqon
+XnFHzGsI4vhlVrVKVJgWVOVcqsOSCJ4AdpolXasxtyde4cnsPBMjYUDFXqJQOLnn53hKRJKrmMpw
+p8+GErl0iDgxrXAsNTgeyTiqLTpUtcHsjFY1/Ms9cLT64ExApenguiDiAH5Jm8t+7j1w5R/L8Qwj
+/D3xWE3g4yin5cJKi7WGakVqU5kdTXDMVHCmTlqd3qgWJ+5/OwDH9nssNFS5WmncorCMV5UbOd64
+hI3l/boTcEIh5HY05uBF8incFpOC6zrUWyXMKMeUVXmobmioiz5fbxMpqUmjUaKhYJko0ZjVT0iU
+oHMSD3DOawXjyB6Zuu67V/uUyjJAwtPKKrSyxFomF9K4DXrLMoGsz32NcCAPfeqnDKy4cLt78jBG
+nSGOVg7KxnJ2ScKSpJvQvyqxaL0UU8RTLkHtk/DL5L6WS4OUujO9cUOsdgzWNL6NUp8o0EFQncNt
+KsIwquH62qlnC+z1VXmJgGO88TFqTXnAGgsFn9aqTTmJKFSKPSj5ODrJ9DVX0/R93Iq8t+xWOVIX
+tzzBYfNAHpBBMmSg4KV9pUgnyamreOpkb0BJYwYbZVQUTBOaADdQVaOGhk91h/SInGu12qC9Kcfh
+ug7ZvrZn+w4NFXF1R/LdyTWPQHtXIqeMTeV3p5ptUldRr3l+SFI7VQXeHVmGWwqKKntsDS4DcOLY
+PUTK+ZgPxxoiQKeprF9vGeEpsxZ/7InAAkBagkAmPX5Bexn6l2D7bQAcHT7D5NOin1m87n4cXyab
+0jBh4Yjcs5qiNXFdjMb9WTqBVMZINXQpK1DPXfGpVXQ86RgiTXH0ePOsjnFFo7LlOSyVZMHZayck
+vkzqL9Rm4cPMZjazm+y28BQCx2Wt0mDf2aU7kFWgu+8zWZUZehDH1BUbXgrr5LqCJoWSjdwYsrt7
+oO/1iVSEY44Ipy2udOSMGV0TpuWmisxUjh4h1+x86l/BuygufBC32FP6tziJ2VeQyYNGZtz/Jsnx
+AsWy24Bjq4rPX6/xzLZyEFZS5jQh1rhDgUV5fkhScqS9TKMmySUnjwlcWd1bDV11GpYp44EXjPBL
+gpUwfh+LKiQNXfKa1K7Hvhzj7v4FRrmsRCsrJ4iUVg0/R9nPqOBipxqE2uORpylLvqwuK8stqtqE
+X0wSRupt5GOHgW5P4RY0ymXWlN+iXAmINLRbqDbw1GUe2QHpruw8n/IxzEUMB8oPGbn01uT9BVOh
+UxevbzxMaQXisdRK4kkldp/gurJLp30KhW7n3vCQ7CVKa4y0SnL5Kcmu5UVIqSFh48LRNtVcEsxM
+trFKZ7+fb3L14mU5r025H9XtgBNK6uMcTeFAPZB+Do9o9eRHFAzzX87BUdluNHZJH/mKXMOPfZro
+TeJVLJxcxXNl7FgNHzxnGaugjsHuJQ7UM6stnqamQjxRYXGVys7V0CcbbTPeF1xL59IBg6F4Cgf7
+MTtdwe3sZX3iyVTi6oXZzFOY2cxmdpPdFp6C40E4B36ndKjEm6UOI9U23NiaUC8kXizj4WgReqSk
+o+nk4JCWrLZYxo2VrHVpmeiMrNJHRyHd85J0S7aUmcmPSafdeeM9xjsSn5p5lxu6oj25OTjEMqxr
+kqybrNPoy3th7U5U/4R6FNMdaaKQAaHO8l3tbitGY67tyKrSLJU4s6L1fZuQa0ku9TTuTx0SzQxm
++23842O9WE2y67IiFGbCSBGEaV1jz94CR7TUWXEqxFNmpcTBVbbmzBaHNGyJ9vSPTM7YKIOSfU6j
+MEsLci0/jvMEV4fMqiIFG2FIW72HqORS0np7qSLK4SDl0HJDtlfWDOf+lsu2oknbRZ9nhnotjg8p
+6f0pwhxf8wSxdhbuPZzT0fMoeWOM5kFCO2FPUXxXtrfpX5ZtX78hq26lFLJ8VO7ZA3cY5iriYaQj
+n13VAYlHPTpKznug3aDLZZe2sktX4xCnJtso+m3cZ9SX+/uqp5B+HK5oBjd7nLSjyMWvfwJnXsqW
+tvEmskiZsa5KDmt//FU2PyVezqOXnuHGFTn/k29Y5Pve930ALC5UMPvibRSujON8krL/hMLKh306
+fdU33bvKxT0ZZ26SH2pDvFC7LSaFAofElDH1gMZ4qnvXpdeTG9D0JhxsaW3eQuTKQ28juQHu9Yzy
+nlycajpHVZWjqqMO5QOlyF64g+U3CB4+flwqFXZ7k/5I6+4LIZkKpHhFiUShqFeGlpHWeXsKg764
+2WWtosAkp0eiDlcSzFFT8dq8O+JyT27SwXlJMpElXDsvD3TNeLQ0QXn05BqDvgze8VDVptpt5iay
+rSi8cii269ZLFK5MEMn16wyV/uvqOa0WFJZqU5SQMrfJWJN9Q5LnOC3TlEk6VcxR/kgssYLF+jY5
+xDRMIgtKU1elQegp27ZCkYOwEGAHEPiWUSYPqdc3hC1ty14qU6hQKrHCvId9FhSC7SURda3Z5xMf
+tJsxnFhSxQ0MleYujBLMaBpYTXBVxzMLC6wKDvfyIc9cF/e/25PfT3Yt6yPZRtFMeOBVMhZC9yqD
+nkxkYyfFmZNw09WZPj9wSRU4YX0gk8+L2jnc7X8nh3G/6ETyxddD+kcAGKeOq5PsMBnQf1omtbj1
+KH5LJpHzTwvf44MbNzh3Ra5brz/kmuL3z4+vsHa3JJW/o/SdjPoPA+ArwU8+MQxVFLlzvceWJ1WS
+85c7bG/rBB96hwnvF2qz8GFmM5vZTXZbeAp5UbA/GDHa6DDSJEzb8ejmsnp0PIPREqC/P+KIQpAn
+V2Q1X9vbxVWPoO2U8NZlG1HwCE5dmkvMwn1wXZtrKlI/zrcfJeurVmEloRNIwiludfjqlsJEsxGn
+a9r8o7Rrnc2tw/ZCd96FWJJhXWePMJNy0aDa4bp2XRZdSQwuOCGdjqwIB2mPRx5TCrXAoaWcBVGk
+f8sFrqeEqVlKrJ5QkC5gBlPdixTnQBKQmxc+B8Bc+W4WXq9eQ14wTFW+fGIOVaWd4DkF4mljl+cG
+7MfaaDRpUlFuiRyHJJbtjbIxWgHE+vK7InMpKcmKTcrkVvkbwgkl1TMMkjKOQr0JZGUrj6BV0vuR
+OyTbsvpnR2LQMn1KRqgYAqurdTGpUmmoVgVV0kxW1TiLWDsqnldQXaGpkPSDK3J/N7qb2EyVtpMy
+gToujfk76dUFf2K2q0Tqaqc9ub9d28dRwRX8MvgqYb97FH5Wyo/8r6+X98y/wFolYQmGhwQ2aRd2
+DmQ8HTzucfQ1Sq6jhDOtICLI5aTnWm0CjUbsvIMfahK3MXfoZdmBhAZZMSZ0xZOImhYmEl7s7D/D
+ZlfGWasJ45sVGr6l3RaTQpEmjG5cY6/TO3TRUy/AqgrRYJDQMjLYTg2WGcZy1bJrMhgrN2o0SuLW
+BVdPH3aqOc+OMI9oTb+dPtft9qh817nr85ihuHXdt+5z7biEBMW1RTYUc56MC159XLLWlzULHexu
+sJsq6cmwTJDL5/XqcVpHlF33mYy+Yl6qd8p28zhkbVkepuNRg5WjMplEGCJ1mVtt6UIsCrDKs+dW
+cvxYyWfWz2GMZqGvLxIuyQBp60BZXbnB4tJPAjCMOmQ6EeQ2J1XYcWgcQqWdjycKDjKWSHH/NnCZ
+aIcfXsacsil5dYck1Jp3SY6t7ZUpN+Sh6cUd6SUH6IeMtP3cOpawL/uJCgVeGY+6qhg5fY+a4hj8
+2JKobiZZRq4gpMLRbVUDRtogE3o+cyrMm1FnWcVrT601GPZUUOaMTujrO1zdlQev0iooOwoAMmWO
+xdLC/mzvHLsD1b9UkNmRDY+xVmLqhQf96TW6Dp9VR/tv68P6L/8BrAp4CVvFV43RsmOod8WFd966
+SFRWvdD75J59f/LXeOebhClqFN7J8mRbr3cN58hAL3cZtyLjJRvqRB8PSRLJh5hxRtGR69aPLSXF
+afgGJpMX1zo9Cx9mNrOZ3WS3haeQZCnr+xukRcFYyfv38xRfIbNRLyaw4op120Nql+U746cle5ua
+HM1HYbcuYGL1P20Ofe1Bv/E0FJfltdFs7JchqcqKP6kEdOtSqXDP7nFDZ91RkbMyL+9n12UnFyd7
+lFZkBXriSsR4U8KA9/9gjbkV6aJr7m2zWxHX1tPsve+7HFfo8l3LberHJNQIE0ui+9sZKs2bM6HQ
+ZJ+NPdymJiDrddIvKPpvNT6sDFTXZAVb/YF3Uj4p+Ad/f4me8iYUIYfiLDEFVnUYplyNeZEwmsgK
+tDvOcd2pHJnBV1xIBrgK0c2Uh+FiL6allZoiTkATt0UNqj31+roZ7WV1u5vKMny5hBnI+ZdqTe4e
+qaJ3GFN2ZUWM0y6ZokH7miR1+zGeEqREpRLzx+VcK72E4Z7yOgy7jHsSejpK5GJ2xzTUM5lc3+ZS
+T1bm5aNzh9iRpy9e5dJlGTtHVQruVbbAdVTKr9OEqngYTmcRNoVPk1+dakF8GLrvlZfOr5BPm+ZG
+e+zsKFtz/yTuPSpac028wvaZEu1EWnuHNzrwWfFS7NouPeUhzU/3abdkP6alSd7E0t+UbeVRmQMl
+HQqspbmgQjM1/1CF/FFemN0Wk4LFEJuAwvEoF9rS64BVGe56ZFhQKM9pP+RIJO54sCBAkZXRMl5F
+wgDHOQNGT3+jAVYZ5uM7AcneouU/a0YEVvoh0rCJiaRFeudBh15fHooVG3FyXeL2Df+LAKyHIb5C
+UTfTCxSOPBRz5bOceINMFr31Dluh4vk18361f51YBUAmnQFnCuEJbM9VMZm83x3Lw9GbpHjKNrXS
+TnFPyXfdZYv/WqUqv+Fx/rxMSBcO5Mbf1TpzKEbrtl20W5qyF1LyFGtfZKQK3bVK0Eoc0ZvI/rr7
+BbkqFvV6QyZatrXWpaKAm92BTHijLMO/IQ9g03cP+zxeXYooWQ3XyhOGPZmESprLqDSHVNZlEnK6
+EUEobnCWHyVqycM7P/JxfJkAykpcW675GFV9qlVdWiqwGwcbdC6IWMp4vE3oyEReUw7/cT+gN35M
+jz1jXvtOwvkl1jvynQvXt9nWsONORz6/p1qiHmjlx13H7Gn1ofwE7kf+mZzf4m/I3/WzMBah2MJE
+hz0HT95o8Vkk/HnrM7t4Clm/cunTcgyf/ThxR85ptHGBveuyv2f8Ds825Xq//21v5NVvkq7L2lG5
+vyYICddkAt14dpuu8pi22iWq5WnLuGVlSePGF2iz8GFmM5vZTXZbeApYBzeJcIoyWaR18CKn2ZCV
+5nSryZomTqKVMvNHTgBQelr64L1aCdMRd5DvuBP+SEWq3rIFF1UB5UgLPqwxhp61EyS4RwUSXL6v
+RpHLd7908UmysTYgeT7X48sAjFSMo57P4UyUJ/LaJoUyJqfbKfVj4hK+9t7XMHpaVtC9TH6/k3ex
+A/nuOBgQqnLz4uoajmb4zRVpbHKHE0Jthjk2f4yKrvJFUQVPVrGivU/6NUlyBh2FIh8sUSg+wCbp
+c/Lz1sWfshnnhlSBYX6oVZTAEJbky82VkJKCl4xfphzK9hYLLjQAAAdOSURBVBaiFlVNOk6BMhs3
+DggTWV1HnQG5JjYno5hUVawDDJl2XQ6RcKYc1Rm1tJSxv05XyWmsBU9xA8ECVMvqKk+FwsttAsnT
+UY6aOI4cfyVqEcmlYPPxPcjE47JD5Vqs9mj0VLm8fYSVu8T177Yto6+JpzDo5BSaeO0qxmCvHTNp
+iWfqP+5h5+Sept0l/HcrH+CfCi4E8wzkMmb7TsIFK8d+aTxgV/krzl+8Tvm0vD46JyHDYPAELRUf
+WvRPcOo+pXLfMdyloK/7jp6lsqCJ2ZFiL6wHsezPcw3tupzrqyoTNrUjNIw8PP/F0bHdFpNC4Pgc
+iVbZd3bpbGrZLCrRVmDNQj2ipN1gA5sw0gqF8ya5mW5xZCp0hLNQxvx1CSVMtAiJdrJtLDFtJih2
+JEyIq5vsKLV20Wiw+5gEcP3zuzSM3NCzkcf6UMKUq67cjHcELTR8ZSnxaVqZCOZMF0cp3okDoiWN
+qS9M9R0ixhMFqQzH7F6W46x0UpxI2X/0YTtqAhbvlJs8v7yGV1UewaRHopyI/a0JVrUP75qTUpjn
+H2CGqmEZwbJ2GWaRYaR6Cr51CEPV01ShWIs5RCAWIwc/nOLlY6yGUlu723RrSqJyILH3qJ/ghFPN
+Ag99SWYzxrq/3HcptNy5r/0Qu9sjalox2d6JuRjIZNk0VdyGTBZZWsdN5PtBqGXBygTPists/T65
+djhWVyKchvIy+pCp9iipjJHScInSvapPEczBoky4zz65zuMX5f72RjFxLsfxjMbyv0fE3o6MyVcV
+PfJYQVG1Ed/9cQlHTVsAaQwMuSJSL2cBzyihzqOFw0DLjxfWt1h4UsZI6bRUS8qmwXXlDW3NpyyV
+VYr+9BmWV1Rs9kSGm8jYSJCybn8TdrXd//LVDvGBaoCEHsd0Law5FcL6DLw0s5nN7D/DzHPasK/g
+QRizAwyB3VfoEOZn+57t+/8H+z5urbq1f4HdFpMCgDHmIWvtG2b7nu17tu9X1mbhw8xmNrObbDYp
+zGxmM7vJbqdJ4ddm+57te7bvV95um5zCzGY2s9vDbidPYWYzm9ltYK/4pGCMeY8x5mljzHljzC/c
+4n2tGWP+xBhzzhjzdWPM39X354wxnzDGPKt/W7fwGFxjzNeMMR/V/580xnxZz/93jDEvjmXzxe27
+aYz5XWPMU8aYJ40xb365zt0Y89/qNX/CGPPbxpjoVp27MebfGmO2jTFPPO+9b3ieRuz/0GN4zBhz
+/y3Y9y/pNX/MGPP7xpjm8z77gO77aWPMu/9z9v1S2Ss6KRhjXOCDwHuBe4AfM8bccwt3mQH/nbX2
+HuAB4Od0f78AfMpaexb4lP7/VtnfBZ583v//CfC/WWHnOAB++hv+6qWxfw78sbX2LuA+PY5bfu7G
+mCPAzwNvsNbeC7jAj3Lrzv03gPf8ufe+2Xm+Fzir/34G+JVbsO9PAPdaa18DPAN8AEDH3o8Cr9Lf
+/Et9Jl5Zs9a+Yv+ANwMfe97/PwB84GXc/x8C3w08DazoeyvA07dof0eRAflO4KOAQYAs3je6Hi/x
+vhvAJTSP9Lz3b/m5A0eAa8AcAq3/KPDuW3nuwAngiW91nsCvAj/2jb73Uu37z332g8Bv6eubxjvw
+MeDNt+L+v5h/r3T4MB0sU7uu791yM8acAF4HfBlYstZu6EebwNIt2u3/Dvx9DrswaAMda1Xv7Nae
+/0lgB/h1DV/+L2NMhZfh3K2168A/Ba4CGwjh2sO8fOcO3/w8X+4x+FPAH71C+35B9kpPCq+IGWOq
+wH8E/p61tvf8z6xM2S95ScYY835g21r78Eu97RdoHnA/8CvW2tchsPKbQoVbeO4t4K8gE9MqUOE/
+dbFfNrtV5/mtzBjzi0gI+1sv975fjL3Sk8I6sPa8/x/V926ZGWN8ZEL4LWvt7+nbW8aYFf18Bdi+
+Bbt+K/D9xpjLwIeQEOKfA00zFRK8ted/Hbhurf2y/v93kUni5Tj37wIuWWt3rLUp8HvI9Xi5zh2+
++Xm+LGPQGPM3gfcDP6GT0su27xdrr/Sk8BXgrGahAyTp8pFbtTNjjAH+DfCktfaXn/fRR4Cf1Nc/
+ieQaXlKz1n7AWnvUWnsCOc9PW2t/AvgT4Idv5b51/5vANWPMnfrWu4BzvAznjoQNDxhjynoPpvt+
+Wc5d7Zud50eAv6FViAeA7vPCjJfEjDHvQcLG77fWjv7cMf2oMSY0xpxEkp0PvpT7/rbslU5qAO9D
+MrIXgF+8xfv6TsRtfAx4RP+9D4ntPwU8C3wSmLvFx/EO4KP6+hQyEM4DHwbCW7jf1wIP6fn/AdB6
+uc4d+IfAU8ATwG8C4a06d+C3kdxFinhIP/3NzhNJ9n5Qx9/jSIXkpd73eSR3MB1z/+p53/9F3ffT
+wHtv5bh7of9miMaZzWxmN9krHT7MbGYzu81sNinMbGYzu8lmk8LMZjazm2w2KcxsZjO7yWaTwsxm
+NrObbDYpzGxmM7vJZpPCzGY2s5tsNinMbGYzu8n+P8fGbpO5FWLJAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3439... Generator Loss: 0.6907
+Epoch 1/1... Discriminator Loss: 1.3787... Generator Loss: 0.7232
+Epoch 1/1... Discriminator Loss: 1.3393... Generator Loss: 1.2362
+Epoch 1/1... Discriminator Loss: 1.4625... Generator Loss: 0.6389
+Epoch 1/1... Discriminator Loss: 1.2682... Generator Loss: 0.9903
+Epoch 1/1... Discriminator Loss: 1.3393... Generator Loss: 0.6646
+Epoch 1/1... Discriminator Loss: 1.5688... Generator Loss: 0.5454
+Epoch 1/1... Discriminator Loss: 1.1573... Generator Loss: 1.0297
+Epoch 1/1... Discriminator Loss: 1.1199... Generator Loss: 1.0091
+Epoch 1/1... Discriminator Loss: 1.0635... Generator Loss: 1.0411
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmwZWl23/X79rzPPNwh7825sjKrq7p6qJ7UkpCtFlgS
+5kFEgG3ZgRERROiFMYIAFPAAPBD4iTce7AACAgM2YBsRgWSFDNbYqNWDuquruqYcK/PO9575nH32
++PGw1jmZaQ1Vre6S6uGsh8pT5+6z9zftNa//MtZaNrShDW1oRc6f9wA2tKENfbxowxQ2tKENPUcb
+prChDW3oOdowhQ1taEPP0YYpbGhDG3qONkxhQxva0HP0kTEFY8zPGmPeMcbcNcb80kf1nA1taEM/
+XDIfRZ6CMcYF3gX+EvAE+Drw16213/uhP2xDG9rQD5U+Kk3hS8Bda+19a20G/D3g5z6iZ21oQxv6
+IZL3Ed33MvD4mf9/AvzIH3dxI45tv90kwJBWBQAVhul8IZ+rAqMaTWEhK0oAVlrOs8qOMWCeufez
+etDqe8eY9cWuI3wxcB08z1n/KC9kHB6WopKvS/19BRi7+myfPt88HYwxZv1ET5/hu4ZKH11Zsx6c
+77q4/mpMLgBZluNU8sS8KsnLp/PxdfyOa7D6jLKUQfrO0zmX1pDluVyr416vhbNaDat/N+u/Gwvr
+4TgGuxrzMzdwVktlzPp7zxg8X8bveSHWODp/Q6XPKQu5WV7mBDqPynGwKp+qIqVlQgCWkSGIW3IP
+6wNQr4HrBwC4rkuVFzqiilznClBm8jlw9a/GgiPHvVjm+DrByshZA8izEse1TxcBKEpLuswAaLgO
+xWrjHY/aTlfXSL7K8oo8HQOQDFKW6VLumxbrPXFcB2d1zir9NnBxdd2cesB6CI2AhhPLJX6NSNfI
+INe636dM/+Y3v3lurd3+oOs+KqbwgWSM+UXgFwF6zQb/6d/8K1y2HvcX5wAsCfmN3/8WAPPFBU4p
+mz9cwsPzCwAKfVOKyj5dX8/BXR1oINfv3WeYRT2URTWOR6teA+Bat8bOlhzAMis5uRgA0C0yBpmc
++nEpd5gbl6iUG8+KJZkedNd3sMpMAtfBuHJ4+7UIgEutiKWcbRaFh03l816rSXNfNt+6MobDJwdE
+yykAB/MJR2NlhJVlO5abNNsBRSn3Hs1mAOzHkCvnGeYOjw5PZM4YZs7qQDv40eqtlrnVcMj07fdK
+y56ukRMYrH6eLqs1I25Eyrx8n/lc7nEp8OnudQDY2rpN4cnYtpsO00J+N76Q555Oz9gPZB5prUZR
+yVolZ/f4qeg2AG/dCbj2qb8k9ysuAfBjn7O0tq/JGDotFqeyT14x5ejsGICiNMweHwFwpSXPS70C
+25Sxnb5zzKUdOfoLz2HhChM6fjyg2ZT9KyJZ78Ew58Fb7wPw5VbEIBQGYeI+n/93fh4AX6bJ++dT
+Du/9KgCv/693effdd2Uv75+S66Fs1utEO3LmSOQZzrU27b06AOEXrtHNlJn+c1f5cvNTAFzf/jyv
+IGfENW0AWgSwZhQ8Lw3/CHKM8+hPvkLoo2IKB8DVZ/7/in63Jmvt3wH+DsCN3S3r5jOmtsNwJtz1
+/L0TkokwiKzIKPQwLvOclegulRMY7Jprutbi8pQrrLkuKH9lfS+KgtliDsAoMtQzORwhYN1Mx2lw
+VUqbsdzBb5YYfYFca2mqFHPLElvJNbXA4ZXernwWgUKTFkFbx1Z0GHAKQDvsUd9qAhA7cnG9GJDO
+ZHv8wBKUMoiL5QhfGUscuMSFvliBjBc3JFppFeMSfXfpGB/jyIG3yxjfFY4UqvTc77h0+3Lo5rMl
+3VJ+OCwcTocr7a0g17X1a/K7etuQZvL3SQbRQvamZXZZTIUhWadGoC/FXl8O/3R+D5vI8/zCIU9l
+7W/tOzjtLwLwqd4pvf97T+b61+RcxFWHmitvoedDoAwrdUoaDWGoQe4R3BjK+HR+LS/ELHtyjxsn
+BG15XoOYvFRp7MwI+nK/yMo+XPFz8vT3ZBF3r9M/EkG7/L2YbvhJudaVd22y4zDuKmP9lYAtV17+
+sRPgB/K8WhjilKLFVEbWYrvbY3f3SzKenYBOLPO/s7fPbf8WAD3jE+gJVh70RzACwx/6CrDP6csf
+TB8VU/g6cNsYcxNhBj8P/I0/7uLKLplnd3kUHPPOiZzo6XLBXKXxsshIUjl4gyRnqfpqqZP1gNKo
+Dus6FKoIR8Zg9aWIXQdHD2bNFQaysDBbyn1PkyW1c1H9jGeYz+WlOTApe8pwBpHc1xYhjqtqq7XM
+Xdnkfi0gVjX+5m6d61fl855KqLmZs9MVLu9ELsnougy5WxCrtEpWO5LvMtkSpnFp0qVzTZ73ztsF
+c1/GluYJuVFmUMgYHjpzdjK5ydAtqHStDrtLQnmvmDWn3OjLgd21cu2r/W1qDZHWT+YH/EEhzLKR
+GQ51TgaHTiTjjFXKz/OIyWSqi5zi6z4dtr/O3pbIhdbVGeOHcv0iFckeVDOGtScA1KsuTl+O+l7/
+nEc/8Z8D0BzcwfxV8U2/fPM/BKD3Yhdh2+AlMZ5qIAsSai15tp8G+EthENGOrKEPmFLWLZrfxtlR
+8yDzqTJhCrGtUcmtmQ9lTr5XcnMk+/dN/1d5IbgCwNs7A35i/F8AYAefkPu+U7E7uinrNvyPWKg5
+U+8FWJX+k4XFngkziLdFaPTGP0/Lew2Az17cYe9VWauXjcPK0vDtyiT9Z+gZ0/WPUxTMB6kQ/wx9
+JEzBWlsYY/5t4NcQAf3fW2vf/CietaENbeiHSx+ZT8Fa+yvAr3y4awOq6grF0GBV5fTaPsEkASA1
+UKmpUJbV2jzwVPI71rLym4Wew8pcbrgOYUumuBX5dLZFIjRU5X6YJdx9JLZ4mmTMVlw5donVxo3r
+/trh11QNZJZ7K58VEdBvyv0udWIuqWPo1vU2V3duyPNikRhR8hK9qyIRzZUe01N5tkudUB2Ms548
+Y7sWEdREApEmHHcnALTDh7x3IBK2mOTksVzfyxoy3jCnUO3HCw3OUu6757tMFzKO9nWHlzoiEq8j
+v7vcjakuyTwOhgGdpcx5kma0PPUv+Ia9nvzu2lWRdt8ZTfFWhlkFeap+l2WJsy2qSX//ZcxUJG+m
+plvzsInvyb1M5lJ1RS23/Divnsn9zlLDtX/5JQBamTid04uQMJZzMewbioGcF9/2qEWyZ+ZSRViX
+uQSpaGOmtLClGxy5mErG75QF2e4IgLCYU4xVAxzJd3l/TGvrMgC3y5/g+L6YtM0v9/jeSHwNeSWm
+ipkMeetcfAp584S4vQVAvZpyvBQtJTcz4pbMpbUrz1pE/5hmJHv6tvcCJas532I3kHGCi8/KXP4j
+yJqnSsMzF9j1fz48/bk5Gp+l3FY8yVNq8ybNSJyI3rLFUbFcX+OvvL7WrL29PfXYVqYiUydgI3S4
+EsmLFzYdLu+KHXmlv8vnr8nmJpWYCRdOh//9H/8uAIenI4aJvDRumtOJ5YB1EoNbqjNn5eh2nbW6
+34hLutviD7ix3+El/dzba3BpW1RYdy7X7rdeJLgs5oPfv800koNXj1oscnGSNeM+AAtnQb8jByLJ
+D7kcidra/MI7XK69A8Bvv/HrJIkMyg1l/ru5zxly6NzKxavLur0cd3iCqO779iVeqgkz2e/L37e7
+FVFfvvOutPhSKM+2n5hzN5f1PHhk+dwnxI4+nKuDbxhCJS/KVXeHoCnMZMv7GdJzYZD9V76MefHb
+AATKee/ufZWORpcuipKuJ6p0/tqPspcKM9y7dcIr9suy9pF68usZSXEmz6762LqsbbMZ45ZynIOw
+Qdke6zVqvy/neOGO3MOm6z3NiylBImtbuu/j+PKCB22Zn8nqDLZljW+MdgiasvaDw30e3RNH+EtX
+ZO0H+9+jZr8BwNQGpLE4O/27Bl/PcsN6bO0Jw9q7KWZJ/XZJ1JFxxrtzhtVdAE5Mh1Ylc4rckHId
+dXhKT9//p2++tX/Yq/D90CbNeUMb2tBz9LHQFGxVUk4GjNOUZSBcLUkzbKh5CCmshHRZWOw6Viz/
+RL6Ho/62vXbAjbpI4629BldU9Xvp1R1aKvHKTP4e1Epee0W8zGk14/RC1bnE4ugNI8df5yeoD40g
+cKhFsnTb3YjrO/KMqze2uFIXlTG2LdqVOJ38y+K0azau47Y0tl2v07quUr6ymJlIjcqX+8Y3fGIr
+0sNMW6SeqMw7VyqshjjbR3XO7otEDLqyJm7u4xVyDzer6Hh6D9enrp51ZzqjflPMhvpITBhnEdHu
+iGPw1S8cYe6KBA4aBXs1GduTK/fo74nEG7wu5sBwfESp4yw4Y6sj8z88+W22Lsu6LBvbBI5oHm5d
+Q5ndipaaTDW3TtkS6flCfEJ9X9XulxqUqWgFjp6FPD8hWknMPKG+vYr2FJhiFWpe4qjJ5unJKQNw
+HFlDN1rgVKLOu2aCTWW9jPcEL5L1aFi5djkweKVEF9xunciXdSnvFXz6lmiTbEm4tBtNye78ddmn
+iyGDA1mrZe3bNC7Lenl5SaMp56IuSiHu/h2imuxH2dkmMTL/zPXBkXUpcdZawTqfhOfpj9Ia/vD/
+fTB9LJhCVVQk4wWpWzA8U9urKMjVrl0UOTP9nNtqnbyzVFs/LC2OJ9+1mx5uV9TSXiuk21e1zPXx
+A9lwT2N6tazOlbrYsld3xgzO5dnTsmI0laVPayk9tVeW6oWveTHNWLz3vThiry+q706tiePIM8Ka
+Q1wXU6LmignjtBvgyAvt5D0C9XoTz1nFDo2VA1HmM0wk8zBOgVlouHTepqsxqetb2xydyuFdaB7D
+srFYJxYVJqXQ6MPD+JB4LGs0rZ/Q8OWoHMxE9X/BNkh9MXd435DrId4Od+hvvSzfn5fkS7lfP5Gx
+FTmkypCX1mJV3d1t3aLhqE+kcRMvk/vt+nL4n6R7lG0xFdu1gHpTmEbHcTH7soY27RDWZY3yqe61
+u0fqycvYHG0TZTIPf9sBjcQ4BFSFrJerESOqOY6j+QGpu87O8rySMpHfeZOQPJXIeamvngkqavfk
+XsO9nJmV8ZjulE/c+jH57MmcB93LRFbWpdYZc3LjPVmj7pgn+v3W2NJoi2DIK2G2fuclbKImRXgH
+X023rfkWM31Fa4Eh07d+FWY2sM7Jsdh1lMHw1ASwQPZc2toH08Z82NCGNvQcfSw0hdKWjNIpRV6u
+Y8mEltAXnjVfGqryqRLkKnsMVYL7jksYyueGE9FHOG3N2yHwhbNXuYtXilZgVI2M9ixXlqLDjf2E
+owNRxSd5gl0lRhXOOltyNQTfdalp0kw/rhNcUt6alMRNkfSNuENlRPUtMtEYioGLE2uqbesRZqEJ
+V0WFsSL9iq5404uzEle1I7KSrCZOq3K+wHPlHpcvX6G1kDh8/p5Iu91gizwWlfnMQj6VuXpnDlkq
+EsO5XDG/kHW+7okW4wUFxYWaIh68UIqK27x9jfRFMX92zmocz2UcTdV9a50hyUAllB+RTzXv49MP
+SNVxnrVeQKfF+Kp8aPZrpLmaaMAoEEk6Yoj3ULWGPZ/xDXm2d6I5Bsn2WlJO+gc4yPhreedpclo8
+wuj9rDofHSrKWCI4VTnDKVbmVkbRHOn+XeBnamIuxNG4jHLKHVnDznIb5qJ5Lj+/pK4agtV0oo5j
+aCJ7XTW36VYS+dh69Q6FZuTa3KfraNQlkd/NQsP5TNaw1XTpa0RoVC1YLOXaOd46zXkZ6Pl3zCor
+/mnCHqIdPGsylB+T5KXvi6rKMl/k1KxloYZ7w61zoB7bWVas1eBnZ7yqI3ADQxyuQog1Wl19Mbc6
+dGui+prCx2mKL8FryA/7foi3J97m/v4djt+Wl/h4eMh0lXVWVMw13rnIZQw7rsdue1+e3fVgrupg
+6BBqaM2tx7ixmBWZlYMS+T3oiMoYsEOKHHgnM+TIXF1lIOnyglhDgUU8w3PleZU7pK52+c72lJsX
+cvin8VsAXO31GZVyyGtLy1Tv2zQxw0AOd+3sNnlDXqagparqtYrt9h0Z7ywg3BaPvI2v4o9uADA5
++0e09RAO9n8bgBsXJTYTE+Sl658hD+R5N7x/nfdPZC9b9icxu7LOW2ojX9SmmIWo6qPJklqhfoT2
+l9ipvwBA0k7ZL8TPsfTV9ItLcs3MbNLG1RRz0wjXiVqe6WID9aUYTQ8vclxHfQCMMZ4movljPE0f
+roIK2xVm4Ms2UM9TbKgRo0b/qe5+to2rL+/qdezgrRX10lgqTacN4pcJNNqxyAtqK3PUl7HFqUdx
+LuPtZC7DhfpBBoZzXc9G3eNE931L0+dHfUNPs5tMCCpvqJyng6qAZf5svOKDaWM+bGhDG3qOPhaa
+grWWMi9IqEjVWVQmDlZz6rO8Wqvuxhg8ldyRKxwwCDx6WpWy1Qjoa5FTu96kqkTCBK0mlCKZ/UAd
+f3UPLxApv7RDtjSnIbp/uq7WLAvDTO0HLbyjHrn4WsMQN6HKhZuHvTqxcnHjR2RTTU5qi0SoSvA0
+b9+WpxCJ5C6WCWj6cFnKvZzWFJaaNxF5oCmztW6CW4nTLu7E7G2JpB/syH2XVU6m+RthBZk6ZW1l
+6XREjnXsnK0XZY0uq7ob15u4VtRrN85JJrIPC++CSU1i7+7eu7iaqLWn+RSvOQGtnqzVC3dq5Lon
+d0/+T4qeSPlR1aTdlhyBoCPOzNYdD/OO/G4cJiSO7E1UH0FHzJhOz5AX8jnUJC1jp0SBjN2QENZW
++nOJo882mJXTHqOREes54Kw2EKhWkZ8Uk6op4YUYNT1XqUBmBm5bEousawkiubF/VK41hGejAE+l
+7NNkIgdDbsT8sTZlmcl5SEay16UBR8eWFy7Tkezv0WiA0TP7Ni0eDVf1H6KNejdaXOvKeWttNej2
+5XMQeHiBjMQWlt8bH/L90EZT2NCGNvQcfSw0haqqSJZzMr9krvZUHiwYax17VlRrJ5Ixdp1iXDnC
+cT1cfA09BW5I7svvcgtaWUxZuZQdDWVqhaBb+OQzTZmtxjiaSrsoMlKtxEyrililbamlx7V6TKsj
+EiPMQ1o1sUlbps4iGetIE0wh/gNXMyXLdkpdJVutvk+ly+/UItZyR7WSKjFkNZ1HtsRazWOYBCz1
+d8ViSdeKxO4Y0XgO8ntEa2iG+RoXIt2ZsaXVPp/4XMgndjX8mEgmZTkMSdVZm557nJq35R6TiJNM
+7P1m6eN2RYptaVHZK5+AViWlzNdebVPVFOvgOOLRm2KL91o3iGOxv7crGecg3Ka6KprJ7eYl/IZI
+/04WUevLvcu8x04g88sWKmndiGqp++DVMKva+KZ9RsS52FUW6qqktKjWFazVzGDV11DMCqpUperQ
+YEM5MPlirGvsEpzJ2g+bBwy09r10B89oCH/4k6ksnjq93JkDWnW6HCxx1T9SJRKS9W2LtpW9rvs+
+lSfO3Hz+kOGFXDM1BxRLOWejE3XQLm6T9SQX5HynQRGI1jfeabC/J3ty+NDwy//ThO+HPhZMAQyl
+dbEVmGqlatt1xMExrE0GB0O0SvDxV4krPs1QDlXo+Tilur2NodLQ9MxJmWhZ9vJQNmVi3qPQhJdZ
+ccGjkTKWwMNRjACvelqdtkq1bjdaxJqME7sW/5ICmWQpS016mj2uGJSSrlrL1Jl3KaShXuitOwWR
+OhLdRgdXD2wey+EvyzkzrVRMjlLmmhs/P8/xQjmwo7Mps1gcY468c9QWDdxVPkXf5ehMGF1jHnJT
+KxFv/fQWjbkc9PxYDt3ZwZRxJuvyxndmHC/l2UdpjqP842duvcidS1LK3NsRB27tC4ZtxaEwvYCD
+b8kBPCq/xVFPGMRR84vc1jWoLmkKdr2NDVdraBjG8mJeVDPcM3H4duKc2a6MycjPsLnB11yBRXxB
+bS7P8OYFniNMiMYFJBrZ0bJom5ZksaxbOUuwaipVw5SyrWacs8RRHI10KGNInDFGc2CCoYW5jCd7
+bclyXZir5od9mmRnM0uu/2cWc8ZTcf7Oji5wPXm2pwKw2kmoh8L8PK+ih8wpu9rGDOV9KIMmzlwY
+eNoXUyyux3iaVh7kY2bIOZy+u+Ab58JYHvzDJ/zab/9XfD+0MR82tKENPUcfC02hspZ5VtHFIVdp
+XOYupRZ2OI6DClWKShCOQNKNAcKmT6mx+bDlE2qZpBsFlEY48MVowsl9kbaDmar4ps3QqvZwfsGZ
+SqhiWZBa1VgqSy1YwWfJP816g6ASbj0rzmmdiISqOgXJhYTZHOPQdmTQF648b/6kxhV1DHap43ZF
+xQuaPjoMjEKNLTggPdeU4sEBsSdqd5GWeEtRf7xggD9RLUYh6ppRyKzULMdJQVNxDz7Xvs2iJvPY
++94vUAX3ARg+kkq/b7z1Dr+rGtS3znIK1dIqA6FWLQ6Hp/wr9isANHqCrnf95CcZPZBwqDf2mQx+
+E4DL5t/lvXekeKhx8OMEN+V+nUq0lWY8oabHb74YcGNH5u2cXyYei4Myj6C7kLXN1OHmlBnzuaxn
+o+ox02xE/Dp+Q3E03B3sCrIPmX+eJFRzCREvp2cUC1E98mJKuAoDJ3Oyi4k+T/51Co+gpqHlscXX
+MKJ5sM+DJ1ootaOmgQNW0zvPZ3OCseSQnF+ckz6W+711dJ96KtfX27Ifu8MXGGjocWurxnQqc51N
+64xC0Q4Gjw8ZjCXHY5TKPk1ff4/LPUmxdrsLdpoStn7v7AR/Jmfr977xDvP7319I8mPBFEBMBItB
+HbMkWQHl0/TMKNBEEeuyty0Hpa+efi90QPPXy8zDVnIQRtOUyJNFXUyOmJyKCuc15EDs1gL223rt
+wlJE8sI2/SUT92n0wdGXW10YRJQUWnGYsqRytYLPi3CPxW5/bzEnV6dv3peD0i0tKKagk0wJNAkl
+sA5VQ55ROmpn1ub4RhjMrDzFjOX7pZlhn2h9SDilUuSoYEvuW0+2yY2O7cmUV9X3cbPnkd/RhJ7D
+b5C9rCAqWlHZDD1uagLV236+rvOwBqzuw3B5wle/8z8AcO0n5RnZ6B5feywvf3BUkdXF/CmSt7h0
+S55R3/ssvcarunjy952rEb6aFEPPZdJVJjy8z9SVMfWjiMXs+VwAY0YoLgzz6Qzrydr6ywxTk3NR
+5lOcQOZitUqyrCakiplYmQWVgr0sszOSgabTl2fEmjbudLQSM62R+MI4i6CGKYSpZcdjHi5FRTfq
+Z+hPI8aaZDZ49xyTyTOODo+5eCD3e3TyNtuat9K5IwLLSVPiXTHn7HHGmaR0cDaa8PihMO/JyYT3
+KmEy2yo4/NacqpBD5i1qDGsjnfMTlo6cnax4h6R8wPdDG/NhQxva0HP0sdAUKmtZFjmuZ5mqt3xW
+WhKVUBWsY7upKVlotmGkWWl+YtDEPvanXfZ3NTYf1VnORdL83veOefxYuLVBpMswPaGpeaLGJAQr
+DEOnWKMZp0G1hnGzmho7ms/YzhXEo3IoFM9xnM74re9KTP/1ByMGOqaa4i9+/uo1mjPJ1tu7uMSy
+IZIiutTBqgPSm0oMOnn4LudvyQ3ee3iXx+9J1uCkXBAo9NrelTqhRjisBuZbnyrpGvmcvZGxrT7X
+4Mfu0h1KHkb7sw8JShnz1wbiDB2Ocr6zVCCTomKu4sJzZH8AXGMwXY0CFJqOPX6Bt998A4Du1Yjd
+G6Ip7XY/B/fkebfqu3gKRtEoZD+Wfky9Jqrvds+nUPvw9d/5R+QNefjRyRX2IjGVirmo336rxmgm
++3j+cEDUFqm51QiJKrlmr94hqCuYjaajLyfvM1GvfZqkpOpoPDk651BV+9hN6CGf+2oyBr5D9UCj
+Fi/BfKhnJz/g/j/4uuzfrRcBuOcdkk9Eck8Xj9gNJVJzdnDAW49kzGdHM0YdNeMGYiY58QRnKvdw
+/QnHA9EITu9XOKqFNi9ldPQsB4VojVtBhyuXNKMzTPA0r+XJ6C7VTM5AlQyo7Mr9+eHoY8EUjDG4
+xgEHXAVL8XkGW87adYViFAeETfEo78byQqSzOWNVoxflnNa2fN/qdDmUr6msXaP+WPUKpwZSffu7
+1uHKrhzAq7tbvPlYVL9kluErQxqoKjqcJIyn8qK3Sphc0bTb+YJaU17qKJrz0p6mPHfk4N7ce4Wd
+poJ5Zm/iamJVmOwSaCrtLJDnztzXKUJ5bnu7TpqrebR8n+aB+Bde2nNAfSa5lhbfaO1zmIq6O/Pf
+JG6JPXz5wT4dX02Xz7o4XxVV83SpJsM448Zl9dSfpQQrPEe/WqNV334hYF+Tr3a1xH25dUC3JevS
+273Gp2JBDbq4HpLdUD9I6OIq5uW4L2OIo5BmXUFqfZgpqvT76RHLB6LCN/0D7r6mCTkzTdI5bzPP
+FSSnX9E1K7zObUIdZxJWeJ6GcNWT7xYVy56sy+j9FKtgtGcPTjjXlOhakdJVhOWoqWnJzQm2q6nG
+eYf4RMb27t4hw8d/AMBv3fs1+fv9jIWGbK+2r1BtydrHyxOuaMRgf+cSzUBe7k5T065Dn8KX/RgM
+CtAoGTsTtqww1jq3ud4RhjRqKNw9NRpqPsV2TqHnrGOvM+loDRFnGHdVNrsCAfiTaWM+bGhDG3qO
+PhaagrWQVRY/LXF4GvMNtNijtNBR7tjtN3nlsnhZb/aEi55dnLLQ5BCCFr1Y03y7LtevSwz9K7Mv
+8onbApaxdVmm3bi0AxOR3Htewmj4EIDRyTlnWjHoYDgc2fU4AC6mS7xUflfVDaFW6vW2L/Ov/rSo
+xD/zF26x3BHp4Co0fKsf09RCqt3Lr1BoirHvNShXKduOaBdx7SrRyyLB9msvsrAigdKzfVqp4gR2
+mng1qcQbPhTJ1+5e49235O9m0eByTfIKru99hYXGx5uHXyKJvwnAT70qmsanb72B2REJ9bWvTTkb
+aDSnMuxuiQYRbu9wuSFqbvNFcRz23B/jloLFfObyF/E6Iv0v8SMsD1WDunKZsimSq6mOOj9OaYdi
+ajjG0NOoUz6/gpPJuk0uw+5cirSWOmfHXdDeVgi51h61UqVjs4Wj5mSztc8KfGBVUWt9S9tolWzz
+FEedcltX2jia9p48ekjfExPKi1VaN+vM1AyMF/tMS3EkNt6+xuH7YkLNZnKveyffwug+fuozM7a1
+E01nf4uzKUV7AAAgAElEQVReXbSGtEhpaGHWykna6r7KYqI5DZ2Mw4GYGn52iVpLxuZ6lkK1nlvb
+nwHgYrwgrMn8RuO7hOtklQHNlfmQPcCzYkNmaDXoB9DHgikAUMFkXrLUxith6K1x7h3H5fY1OSg7
+t65yfUcOU13Nh/EgX5UOcPmFDpGqXE33KiaUzWj+yDZuKqAY4QuyeF63RVCJuu6eWc4eyIvyzeOH
+RG3dmIUhVZts3SEpT8lU5XRjn1C9yXt7l7imaEvRtS3snjCI2Vg8wW4agiL3WBeiWE5bWRV4Rsek
+mZm7V3fwSxm7MRWpJmwV7Xs0tLdCVc7JjbywzW0Jt54vHvNkphGXZM7122qCdQucjtii6YM3cK/J
++HfP5L693c8TduSlcX7i1xh9RzEMPZeFAsNs7+e0FdvRTRRmvf+Am59TX4w9ojLio3gwfIuDC3Gj
+v3X813ihdee5+TUI8NSf4xhLpmaA5z9kGmh1ZSNnOpXxdZqa8VcUdFtX9dmXCEthllHoY7RM3glc
+1gWRGp4NOpBm8qLXtwyh2udO1EHz5Wheh1CZjFvJ3ixPD0g8ESYLNyBXH4X/yMMxYvu/l4gf4Ww0
+wtdq3TRNmWhtQz++RNjWCt3hkLKQudQU4CcIcrzuKmqT0FIAn8pUa2baOiuxPWHgpZGXOwinLOby
+jCSDi6Xs+7Q4Zamdqvy4QFtOsNDo7QfRxnzY0IY29Bx9PDQFa7GVZZGXpIpZ4LoQaxz/Rifix7+g
++fUv32F5ounIHYVoO3WZaNutm81L0HoIgOdmBAq5HtfatF6Re6xyUS0dvEy4ddV7k85DuZ/vPSY/
+EklxUC0IV/VuKs1mWUbQEwlV80PymkZDojqKWo4ftvEz0Vh8hV2rmimlVjsWtrN2dplWRKVpzo42
+MWEJnn50PAfPivRwohqextCLwwNKhSOzJzL/92a/z9GhaA2OW1KLFD7ss38AR6K52BslgWobo1Ck
+ec32OT3V5Jjv1En7ImF7QcVC60DyYY32a/KcfCrPKOMWHe28tPzk+zCW+Z2dWh7+lmhI25+6yY5C
+od1wRGOIHI9AcQUsEK669kzHLG/IPSbHLa5uy57UHKm/6F1yaKhG47tbmESeHXbCdYNLxwmxqSYv
+aSTGNUtChTyz85xgVVcySTCqXrun1/BuypjtuUj8ZeZTfVfm/+TqE85OZJznxuK58ruGFps8rlbO
+PVgsUhaxrG3P7+BrZeciT6h15R6x9swM3Ab5WD7nJqehUPXJaEFzqDU2rQmJAsYsRUFhOLOYQDSC
+wWJBOZI9mxydM1STpl6zdOryOy2+/ED6U2sKxpirxph/aoz5njHmTWPMv6ff94wxv26MeU//7f5p
+n7GhDW3oz55+EE2hAP4Da+23jDFN4JvGmF8H/g3g/7HW/i1jzC8BvwT8xx98u4qyqtZNXfwQthvC
+iV/Zu8ZnromEidu7FJE6I1ti67bGTbw9sZeb/jYs1TYeN/EuqwRauLiaHhxomWVau8Bo8ZTvhgSp
+/C4D5iqBXUJmGtOPPHU4Gm9dtRiOA/wXRUKYKsdTkNaACNNVROFV78t6QXWh+RbOXQqFY/MaTaKl
+Ara2xEacTxLKUqREu97C6SkSMRGONm51wzqVK829x4mIgfMHsDhXm3XPZ6IQQv23ekQKx1b+i0P8
+t0UTih+InerOcwLFWY3fC7mh3Z6n0ymOIkL7jS5mosVP2uT0rHyPNBH7vDu7TX4h4xhsfZd0T0OE
+Xp9AofCqFb4D2gkaafBc6udZFaModrjjOaf7EvbbLcSx6eQ3iBQpiWi6dvRYMlz17Vh3uk4/raqn
+6dGmqw7YeYrRSrman1FoGJHc4JUaRj0QrWnBXc4dddAueuSKkVF+1tJ4S87ArJSzFwcRqFR2az5u
+JWvVrNcJW9pgthbhq0NwhQhu60dkI0XZ8jPiXH5XdAYstdFMwzRoaOXnqZHnzYsSk8jZW5Zj0kCL
+6YylXm/p/Fyu1eWMPzyW+X8Q/amZgrX2CDjSz1NjzFtIC/qfA35SL/sfgd/gA5iCRQBIYtch0002
+JQTqye3u9clrcqi2uIH1tQrQlwNt+xWf0H62LpZQm5863R5G4bzyRYt8Ii9spfUA3vQauebAm6rO
+6UTSQbPjHokmKqVZTvxMb0oA129z2fspAILe++wFX9Lvd6ChYClxm6hSdV1Te93SpQrlxMfss0BV
+92obR0t8fYX4mrlTFgOdRx7SQjti5wUm0HqARYDRsuW0JvUHs4NjHMQx1lhE7LZ/XNaw/hmSphyK
++vAF0ko6IodXJAGnVa/T6WhJ6bVvU9OS5Vb7lK6aNMvUozX7CzLvXe2A/PAqQ6OQdhdbHGiORPib
+/wLxA00PfuNfonxBq1i1O5fx1t3eKZ+peq7bfeIzrVFoJNweSLPVqL4qgd4lT4UpRlmNcv60sW6h
+nYA9L8YqiIpjNS2ZksARiLm0OsCq6k7LI9bqyqJ2sS5dz5uy1/XzLp2OMF5z7EFLKxwvGtQ9YUK3
+mxIBOq79+hpF2mZNAq1XCbu3aIZS4uw1U+oa7TAK2+/NO6RaRt0KLed96Z/Zm8RkS3WEey6l5k7E
+lZyRbHqOq1BrhQWnkJe/pEujIWenmmfs+ytl/Zt8GPqh+BSMMTeA14CvAbvKMACOgd0/5jfrVvSr
+sugNbWhDf/70AzMFY0wD+AfAv2+tnTzbGddaa41ZyYPn6dlW9DXftXXXCJSXZh1WpSVRKK3ZfE46
+FY45iEviUDltIc6UJJuyXAgHD5oGq04b24rI5/cAyLw6k5H0uK2sFtlUDo7KqGz+iJOhSM+T6UOq
+SDsYZ5ZScRtWZoDjlBQ1kfjblxwqc65j9kgPVNUOSorl801rLDnWiHSosjmuVnaWkxTrazHPUuZU
+VhPmGq9eeg3ypYw5igOipdwjSYaMZiKlp6ei5Szcd3AVk6E6n9O6Jveogkc4TS0CGh1QxQpT90i0
+ity5jNVEjKg/BC32CfyISSGhrqxcMlVoL6cuLdKHRxmJdsHOqDOKRBU/ODrhOJdnXEsaBPHPyLxY
+ORcNxaoToIVMsQBc+wgvkDld2YmwhRRb5a4CjNx/gF+XsG/lBKAh7GJucRqyhqaorfspWtQ0MAPy
+pTa+iU/X8HaV26bU4jZTy8DI9U7tru7T+5hI+jcEWw06K2DeBy26igJ9XGrxUVkyW8h6j4om+Vz2
+KV9MSTNZw6JekVr9bFcp2O9yqv03mmnA2URMl8PDGaFC6FWLDvQUtGeueS8ND89boRj3KFPRes+6
+C8xCNLPcGsZzTev9kPQDMQVjjI8whP/ZWvsP9esTY8yetfbIGLMHnH7gfQBjLe3AxeqLl1Sw0Fzu
+5fycNJFFK+cNSoWBLzQqUGYTzo5kw6OBR00RivPQJ0TLmp05dfX6Tpay6NXYslR1sXCPOJvI5p6P
+51j1zh+WOS1F0FEoPzI3o2jKPawTQ6i2XDUlV7sumcWgPgGrUOBOvSJXdXD+0GU5lcOf1c+wJ3LN
+YiRjnyYlvZuagMKCqSIjL62h1pC1GB2fMNEmKQf6ch+ePORMW8OfJEPSmfpUrv0Bi3PxWzipxdUc
+/UQRlSN3xlQrC49fn7Eo5eC2mwFpLqrv/fEQ6lK1l78tL833ThI065jWl3x2tCfkw3zC4YkcxrPh
+fdJcmPYqXdtWHoEmFiUlOJlGX5IJZVfxIUfXqDfkHvOJ+FqCWkx1oE1kvG08zSdo3dzGappzOaqo
+FGoeTWRLxwnLC/l7VcSEWhRis4zKyLyraQahNhxW7/28OSc+E0Z2fPUh0YVWxHYPOHsouTOVYokm
+iwmTqfx9cpIx1aSnUVLinoupkacpvuJcLgvZp+nY8viBfC7rAyZD2d8H9yfs7W3p887pnss+uGom
+eImHE2h0KR9j57K29WHBwpX9mxU+58cfLmlpRT9I9MEA/x3wlrX2v37mT/8X8Av6+ReAX/7TPmND
+G9rQnz39IJrCjwN/E/iuMebb+t1/Avwt4H8zxvybwCPgr37QjawB6zj0QpdQPeSDAOpaEHUymJOt
+YLL6YJDUXVuummokWEe463Rqmc1Fdaq7GYum1psHXebTFS6fSPOBGVFkwrXH0xPOz0T6z80Cq36t
+y6VLqpGI1WLVuyEn6pHeziO2Vi3OWUCieuv8hEzb1BWamRknW/iK6pv331+rl67toPUw5GqWRG4L
+P9C2YlnBoCOYie40YjnQi9OcSvsTLLVHxtkg5fRCnpsXFe8PxAyoP8pxtcGJ+5dntN/Wvghjmdtg
+8oRpXzEs/RSjYxslC9ItEXmTWcnwVLErn8jf3yGh0DZunZOYg4FIvHvdfN3mrLyScjoThbGhzQma
+YQ2flYlmOFdTIylzIm2R50UnnHdFmwpFoaFa1MgRbWW616avjVhqyW2iSCHmgilW8TccrSj1yVh0
+ZS2cqUel6X3GFqQdcYha38VR0JbsQqMarQJ7VXEwZw2yY003/0LO+VuynpNzGWORFlhHczqyC2bn
+Muds2OFU8wn8KsArZNPcTJ2kWyOibTXtli7BSFMsdxLGiZxJNw3JFyL9ey+KNuZbQ6Ad1qcnM6YK
+5rMkZaaZjqdPhlwohNyHpR8k+vA7/OEelyv657+fe1WVZZZmhMYnUZOgZXwKvf3ptOAdTSZ6YbmP
+2xWVshXKxD2TUVMsvqWZEWnTk8Tm+HPZgFmar9F2zlbIS27MQtNgs6LkbKnYhycZKwA+m1dUigC1
+co/svvwJrC+q47AZ8qIrTCrwcqyWF9pGE6+pXnRNUhofNQm74glu2k9TVvKib+12yI1CmUfiLxkP
+zsnGMvbj4Rm15qcBKKohtUDs2kVwH1f9AMelHJ6zwYBCqzkzW2Dn8jtv2OcRYhvfeucaaSbRiiL+
+dRnvwhBqJeJknhEmCmTTDXis3aSODqfrBqs1bVb7s86n+d1TYWQ3T77C4+Q3APDP9km1TsDJvkSm
+ZlysYcNHScGe1rZki4RCQ6q1+Co9X5j6zEJ0KutRlFq16tYwVn7XWnS5UHOkftGhdGVtg9oVqqms
+p+uufFQDGo4yjXBAoYlAaXJBOJeohClnazxGX0FrdoKXsFYrcC8qZk2ZU/XQZXkh4dJ2IesW+hAp
+8M+4hPNMBMe0rOgu5bykTgBWojm5UT9YeolAa0I6rYr0lqzxjvsqi6XC7i8WzDTdPszEjJhXKWhY
+O5ke4mlLrtPpEmehgMTLGVMtif+wtElz3tCGNvQcfTzSnJEclIui4kKxC2xi6Cvm3iTPGQ9EEj40
+KZc1+rDUvn6LBJaTVcx3xlyLWoLjEa1Si0h6OYuxqtjqYW7U68SaVzAfTxmOVxDvJbHiEziVw3iF
+w1AID92qNxmUYjH1G5/EuSJSLBxsE2sMPfRqeCpVzVycgEdvP2DvS18GoHm1TdAXieFeGFx1Ri6N
+Jjx58Pjt/xeAkTflauc2ANuxjynkedgpr78nDr/7b8i/RZWTqwprCoe49zoAj0cBky3RDo6/t8v2
+NXleeKZJQ7WKYiTzOz874uSBdnnuuLw5kTEdzDKu1mVt+9sizYdnb9FRh5nvWF6+I/d98M53qbV1
+HJfeoN+U6spRrE1fihRFvqdMRtwbSdp05M2xGvlpxBmV5iE0FV/SKQdstW/I2OoxhYK9nF38FnEp
+FaiBF+FNLvTe6hhMxhjF+3eaJa7miND2sMGu7lkdctGKgo5K1/mE3FWNp5Xhe1qsdHdMXZv8sBSt
+abfdwl1qQlpVcPq+jKH4TIpBPkeOz3T0UMaUyVpOswvmqr06paHS9O/JxYAql9+ZC4huaHLWRMwS
+J8gYajWvzcfYuvyu7qY8nsj4Z+mcPPv+uk5/LJiCBawpeeJnKC4mVVCReuq9dxweXYgdPfUsQXtV
+DScbeP/+ySpniNutBqF2GMqnPoNKATvcLeKuqpJDURHnswq/ubKjS+4pMs95WXBV1cj7rQKNslHo
+aj38zhY3L0mG3Wj8hBMrD2/1JxSuqHvlpEtTS/WakWzmtVf6PHlbVM5a0+At5DDZ1vuUAy2nnUvC
+kzOPqanDIMJhO5XPxp2SasjxO99+nb/7mxKyS47kkF/4GY76NfKgXHeTCj73NtPvykHphksSPZDn
+aqzPFx7nc/G/nAwzTtuapZkYCllCnLLiXGHJ378rhzUrPTpqv9euTngiXzNNE9JDTQBrX/DWSPwA
+n2vekPHkPo52A5uWM6qBYmXOzvC1XX01T6lHwjjHp4rF2E558kTGudtt0I0UyGSvJJsrOGyrj9+S
+dcx6Eoae3l+wfCJr5JjL1BVQx0Y1gkRUe7scUmooZa7RrJwhxV15CcfBgHMtBx85DkYzE+MdMZ/6
+NsdoxbVNQ1xdw6S8gLlmjoYxnp6LXGsl2skOiQK+5lXCyVD2ZHg+p12Xs9XZXxAruFBZynjmU0uq
+0ZeFl+MoGtgyTzgMVs2ScxSyFHQfP4g25sOGNrSh5+hjoSl4jmGrEQkGYEM4Zrse4Cju4DS3zBWO
+bTld0Fyog1HzGNLUEirMWXzpOpfaIlUH9oyRctLpIiXTdOVilWdvlpTqIj+9mKNIcDTaAaFmv9yq
+SlKFsaoUWv40fZ3F6a8CcGkaEFwRzt2sblJpF6a4mxJ4qlZbcUQ6SwfznqTMPmke0G99AYDavECh
+KRk6/5+M7Z2MrCnz7LS6FJGoyXlpGDwQbeTrXz3k6EQxErRTUuRFLD29mQ9ZR1PB7W2iI42ufH6C
+OVMouLGk6I7dU0JNhNm/4XDzWKSSt5WSabr5eOFznska3DuUuU0qQ62mpsRFxBvvyXgG+RwnFIfo
+G4c7XNsXiX6sKvx1QlA12cxCpploEmQlAzUZnGVJ4otZ5KYK1X7sEygUnO18m9DRmogkxzuXe8+7
+v0+jElg4oxWq7hTmOzK22qwijTVV2i/IFIE5n2RkI7lmciJrlToTpieyVqOi4OJI4ddvZGxfk4Y4
+LVdwN93yAscRCT09SSi0T2myKBjWRPq3vR7hyhyttHGQd0yi0az5MGM20ShCVLDdEedhq3WTQtd+
+pCjR5+cJRqsviWvYUutqbBcnk3EWdo71VvGADweosNEUNrShDT1Hxto/Mgv5z5ReeemT9u/+7b/P
+jon51l0pBqn8bf7L/+Y/A+Do/e9Q00rFeW7JtUNvovkGRVliNXwZuM46xRUDlX7vIu3gAOrhCrrN
+J9MGB4Hv4GoDmCKDhbaY7riGVbvCmQrg7Z0mzRVuQmHBXVUtVvJj4Oh0zlydpivO67tQaAZlkluq
+VYWfhUDrP6wOPvIdOooo7PkOnvaeKAwEqkEFTY+rX5Lw1E/+4icBuP75r/BydQOAR+X/wc+2/zYA
+DQyJagK+79Nshbouigxdq9HZFsn2Yr3DZ25rk5G6C4FIq/lJxievyfdzTZOeLD3eekOk/DUvYFrT
+mv6szr1DLcAq5jweiU/o8Fj+fp4sqGn4OTWGuVYAfu6l67i69rNsTq7O3SDUVoGOJY5X6EgeaJiu
+6bkMVRKeTUvq2oX8ek+0xhsv7hMrqO5seEGnEA3y7uCMdw5E60lKy9WW+Ana+/K7xTzgYiiS+XZg
+OVdpPCtdfu1bmjuifrzKVBhFk9rqN9nXAqbRouBCsQ5C7ymCV2/lGzMOvmqhNxu1de7J0RASzWW4
+4vmk6khcaB7HsMq5qvB3/Re2mB/JGr37+F0+qw2R3iwrDk9Fazg4O/imtVbU0z+BPhbmg4MltktG
++ZI3n4ja9r1/+sscvPddAJbZlFK98yWWUp1dxTPotC2FCK8H7lr1d1zDlkKEp05BT5NsHK2SjJoR
+5+qdrsjINXEq9QoKrbvwHIdEn5eu8pzLikhNERMaWholySaGRaLAKUWJr4de+QBFIZ2wAJaFxTVP
+odNXKdvuqhKzsOvS4tAHT1OCvQxagVYDpgEv5Z8C4NbFvwXA9eUNOnU5VZPgCN8IU6i7Hp6eXi9p
+UNd6haYjB+zLNxu8/CXJadgNMvYUfr7e7ZFXkmMw3X1nvYaE4ukfDUu4KS+Hn0Z0NDI0u6iTdMQh
+OLlw6Chq9kLXfp5WOMoIwtIyWipzLjNAmWlV0dYGNZ6uRdeNCfUebbfOXFvV25lLrI7LTuZSj2Rf
+2zqeeqvGjjrqxpGDMxSmcWkWk2q+xKm9IA7lmo4Cnew4dcpSkpvcsMWu9q6s7nnYVUWvsn3PWiJ9
+pfw0Z7DU9Pg8o8z1rFroKFNXvze+cXC08rflWCJ/heFYMYplTr2ghl+TvTpXJ3G6mBNoCn4ny+hp
+784nwwlZIM7TS1PL8eRD4rApbcyHDW1oQ8/Rx0JTwC2hPWPm/DL/5KuSYXfw8JC5tlhzqxJUUqbL
+nHLVrn2V/Rg49Ovy9+12SE/r6l/ca3C9p46Yok25VHPDE3V4kXk80rb1k2LOqcaE57ZYayFHJmNV
+iJaqtJpOKnql3Nc3CzKrvRuzgkQ9hjnVulfFKkq8yC2lZiAWFlaRIsdYPDVzVo1X5qWl0t1p4+Br
+MxXjSQYoQL8V87Irzq473c/Jd9YFzWq9evY3KO2/BsB5K6WpTtVxY8DuvmT3vRLLKH7stVd58c7n
+5Xmlwd+WOfluCooLsHX+GuayfF8OFXTWMdyaiqNt+sL72IFI6GlwQk3zSE47Y2aqwh4r6ElSFsx0
+ZXrGIdfMw5wc4ykwiltRqFbUaYkEb/gp7ZYiX/sLahoCPKpG9BU8p92J8WK5fltj+33fX6euO8Oc
+RUs00kaesF3TtnHnEdG+SlXNlFy6BbGGC79l3uZFdVwetDLyx5pTo47dCEMWaYasG5Dq+TXVUxCZ
+7UZAS7WmazuaPRm4lFr5SKsi1MZAnR2HeCLSv7g2oan4GsGOghtfOKR9CYfee3xO3JS/e4MpX92S
+769kAdO6zukpWtyfSB8LpmC8EK/3Iu/f/yzl7HcAmNUKooHa3FGwKnZjjl2jc3Qj2eXX9uq8+oKo
+S/3tmEu6cS/f6tB+QUBWTFmu891TTcaZ4PLwQg7gyeyMdx7JZjwcQK6mQuKU0tcSeXkBijIhcxWX
+sahYpKJwVdZSqQPC9wy+puMu8qfNalemhGvWID1s+84KS4jZytQAPL245VTUtYGirbk01ZQaLhfc
+ffPvyf1+SzjBnaO/grsvabtf+/2HBK6s0X4QrBndpU8FfKoptvOP9uTam3d26dxU5ONpie+I2u1V
+LlxTWPbAYh15yQrNs2/dSYk0t6LuXWJcSKLW9s0lzkht/2XI+KHk7deUCRcOazj/qrDrdu6eE+CG
+ilJkSlpq2686efmFT00rAzvdLu5cGHkn2sEP5d7BjQ6zM+EWkUaD3GpOWZdn1Fo+tdoNADJ3zpVY
+q23fPWeiuSOZgpf4DUtoZAw3wzrpWPNeeiUOKz+BmkEWatqbtCxBAwBExnBrT9bw5Z2YLc3DaKkf
+Ke7U8StFfm5sEcUy5uJWwOJtmdOgnlCowIla+q+7JFUfx5NhxgpvJrINrigQzyTLaV+Sa0YDTXr7
+ANqYDxva0Iaeo4+FplCWDuN5jVv5X8Rt/LcA7C6v8dCTmH7YAkc5oplbulpp9xdvSrbbZ35kh1du
+i8QLnDo3Ozfkd7vQ3pPGGXZm8T1VE8cCppK6hkiz3K6e1YhV0sTHlpGy3frSMFEn0FJV+6LIaern
+SVHhaqbY3MIqmlNVFblK+pX54Zk14DB1F66G8j83AlgVxm2pH6/KDcEqbwJLr65I05XBaHx7kRUc
+pDIX77v/CwDu/LeZPxLn4x/8/jcItIfAy9Fljh1RKV+u/jK3VKW88kmBc/P6Ln6gn5shgTYvMRW4
+LcllqEhZA1sjkt81bfwtUcVDW5KHUv/vTvewdUmxdooTxk3RLGZzbQEfFPi54h9QEah8MoHPtmox
+prlgq6+FVIp5YEnQQAT1XUN9Jg7P/d42qkzg7d/kXCHiUK2iMGMibYxTsVijbs+rJ/htmXfRe4/q
+ZKLjk99XGbjatGZ3BmeZAtgk3noz26qNLcucUPMwUlvgGNmzfsfhi6/KunxhZ4+9tlyzVKCXKy+/
+hlGMxq3mCyysRGpM+xWODwU8Z5aUHCSyni3zoq73t5ioifbYfZ10qNotDrfORXOmGFEr5fpH/BM+
+DH0smII1FZU/4/3R9/C6ijk4HxLEq3x3B1/Rj3y35PIlOSg/98WfAODya9dpBnI4onZOL9Cqt2aE
+XxOEINPJMNrPMJhrTYIfs3NZkmOWdkCiqv90fMilnuh+40nJPNE6CA0xxn7JuR7A0lqSXHPOPYsO
+E1sZlqvYqP+0w5RqxtR9h7r6QQ4SS6nMJNYElVd3DYVmU8VuxUL7Ae40DE/0miK3XCiEfXIqYbXR
+POHSnmDxfe3uA/r6bCeo2FUbtjG7R/+ahOfqkbzcQdLB0fCs22vgaOKVsX2wWlVaK1ghqhhtZIM7
+wGwLs3FmKX5rVcL+GBsKswiiMX3tc7inHbnG8xQN9pAUrJlN3Xfo9bUxjh/R1oY/K19MMPOoaS/G
+a/U71GXb2a71QSsfi60ankYfrPpXlguPVZ5vvFPHqN8pND0SrWyNuj51rX2YL5VLTw2p+gnq9Rhf
+wWDy0xJX1f9KI1+hW7DUhLrAN+zVhFn8xCf6/PSPfgWATtdQn2okyZVz2Ny+hrcva+iNjqgrs1i4
+3yG4LuHe2qigret5NtRajMYQ5Vd0ZxXva8n8oErQvsK4ocfcKoP8kLQxHza0oQ09Rx8LTaHKLcmT
+lDO74PCxqMPZUYajyLiNpqWmMfYwCPnKpyWF9fJnhBPvdEPirrZ19x3CvvBJQ4irEOcUGWiFXtBc
+hRNKajOVSvUBV3ryu9mlBl//rnD/ebZYO+gKRQgOai7OZW0Mc69i/iza8wp9fAlTdUxWitXohhCq
+dLSxZaLmxTK2XFs5ibbkgitbPtENRe8dezxUUTo5q6jtKLZjUnEvEcldPZRrD69/j8+diuq4mKa0
+VDWZbh2zo/HtK59ecuuyrF1Noe2s60OmGLtnDlVDYb5qLs5Yog92uoBVS7NMUfamMfZY79GaYFRq
+ujVQ8DgAACAASURBVIQEMzle4yyjVHwKz3u6VmOtCPVdgxalEgbxuro0qELcXPYvURi7wECrsyo0
+26XpKqR6o7POX/EmO9QWClnWUw3MmWNdUeGrucVok5zQnlLpOOPUrhv/PDkWqdt0QqqxjPlBb4Sj
+e50GCyJt5ReoaWdnsKroixyXO5dkDJ/51DWaTTFdwvnw/2fvvWItu9L8vt/a8eR4Q91QmcUq5m42
+OR1mOs20RiNNsgXZEGQBsiBAsB5kGX6wJT8ZcIAMGLZkw5AA2RYEWYJkSU8zGE3q6Z7Qze5pdmQs
+kpXvvXXjyefss+Pyw/edU6wm1U2KUrsE3AUQdXjvuXuvvdbaa33h//3/OOuyDr1ELdYA3Jn0zT87
+x+2JW1yYMk1NJZXODkGVtBc0bv07GRPlA8nijNBd9KPgnqvU8G6ZJP+AaQdtj8SmkBFzyB2+953v
+EI5kspK1A0LlLUymGU3NLK6s+lz+rGQU1tckHVexDYzyJBKHmLkuiHodqlq2F/uYXEuVFRnmtUcE
+hQy0l0KmEeet7YSu0p0fpBGjXGt81R1YXQ9x1V9MGtAWb4bKxKei34lNTqwAqIWU+5prqKjJ6dfB
+0S43Lby4LX3a/rz4m9tOlfqVBbTN4WJdJv+NP05xNRsyMikKaONtNZMvljZIVXsiio/oVGQsWlOf
+dUV01p4DT7MH/kwv4I+Iim/IZ+thJqrX6LfIW7IBFPkYZ75gRdL4TH1IrmSlThxQLuQ5eqtHpFO5
+RxAXlCpqYmvJnnPfZUE8Pi4KcpWiD8oemlzBywxTrfabH4mvv93dZqOuuoy1KUY3PTsFV7NRyeoO
+7rFS9y9UmMo5w1BIZrKjCc5YF1SSEKsSVxoc47rywrmFDOiBHeNodqGZ1ohzcTWcakwYLARy5e/n
+RYHRNHRuHa6dkU32wso2eSGmfxRFBEfSf1+lCPLy0ZLHk6yJ31Ja+tUhgWYwXNvANKUffdWRrc/H
+BHNZI4PhnL2xxjsMVHSci7ggrn8wCfpFO3UfTttpO20PtUfCUpiNCr7z2xM2DjcZVmRHXfXXuKdE
+0NN0TEeDS5+4cp7nLooQS6OjbLpFRLkmABpjDa5CdCl7OCrYYdN4yR2XB0pckTWxjgp9WHAczaWH
+TRLNdqxYjx2tiUj1FLiwfZlmTc312S61UKyNLJ5S0yj5bnRCK9d6hQWYquThKZNvXjRpK23cCxub
+PPGimJdbPyPPZk5CQpV7n4dvsjLV6Lz7W9zSakfy27h9pe5yxHoo39tg7EsQsTRO6XQk8PfM6lkc
+JQVZG/4pnKqYl6atWIKqix8K3iBPUqjJM9lKC1eVu4n6uPVF0FSDc+4F0pIQpJiyR6Ys0C22mWQC
+f27Vhgx8OYHLU/nXM2AUm+A6FmMXQJ4aZdWNtLUZiQKubEn8q/ZWk9oZCSTXty5jIwXs1EOsjn2l
+0iAJ5bmDUK0Hb0jVlb/rZ/eoqjr0yBkTurK2qmUPf03uvXtf1t7JYEygAjadNKCvllAlruH5AuNe
+1WBvP8wIjVggpRKsrEjWJpq2aGjFqFexBBWVl1c3t2wfxyp7tHduE9StqjkdvEAkCpxmQJCJpfOY
+BsTNykskGqDePT4g35WxSgpYV17QCQVmrqasEr38uHZqKZy203baHmqPhKWQO3Mm1eu8Mn+NQvNU
+e8c9kpH4kVmcUdYE/mf+w59h6/wFAFwN/JnEBUc5FvwQg2oGem2MOnymZEFjA4tgkUlS/Lae3HdH
+5IEySR8c4Spise9kxMWi/l36W6JNyb8NQNgoiFWjkMxyMpeTcELEsSpoX9BqOb9qaa/IaX1n94Qn
+2zL8F9YTNq7KaROsKy3XaoZVibJ4//vs9JWObvY231UC2oFJyTSyaRQpOBm+uqzqPE6HfLErKd7y
+Wkj9rDJi996kWKT9NJAVBGs4WmXotQ2OovzccgnjimPrNiMMOs6ZnD42mGM25O+K2ZRQfe75oE+l
+oWSrJ4dYR9GGWuG4WXPpx/I5cRMcTfuWAkviyfMFboKHBhXXFK7cWKdSlZOWOMZVyLC1I9yFSFDQ
+J+guwOUybv7cwXrSt/pKDIogDPOITJmPw2qfBXVSvaVqz2OLqynLYVIQ6bpwCgg0/zxXBrBqYXH1
+lO+2QjJN36apw3Asa2Rrcx2/qjDtujybcV/DaalITnaMo5J9uXuEc0nFfIoJaU+1OhQq7tf7JJpC
+rZQcOoFc99DYJdzedxxm6Ycjbn0kNoViPmN8/XsMs28xHSwCMjm6vggdlxevSeDr8bUnyZT51paV
+pjyZ4uij2MSl0AAQcb6UIrd5gfVUHFQDYNPeCKPU6HNzk3ksExBXD5jNZLDvTcfEhcKUdTMZ7NSp
+7f2cXuurxCcy6PN0xFgpxYeDfLkpOGM191oWfywTZzqWTlf6ufqnLNXH9CWtqd7l0CHVKsIkz5nG
+8oLtTqbMNqVvwxs5YwW0pwqgSt0Z6xpcKwUFLaUAr5wf4mlQsdsqU5rIM0WqYYmNcAoVge05OGdl
+URXFFKOL1MnauEr3bhUrkUeWoq80YbUTclep06shZQUyxbURxUJQJ9TIe8ty5ZyM1XObHV5SUVyn
+8PH0Rc9GlpIv196uyfj4fkGqACKTtlkYu6bWxGo1iRO3SGbyEjo1Mefz+YxCr+sbn7Qta8GMC1J1
+CUZ7GWlZafe1atOmKZHSvu8XPaqxrjPHoNNLbjS4mEKtLPfYaIaU6/Ks6dzBKUmJezRwCSsKm9dD
+z13ZwIk1bbVuyGe6ZsdNih1Zn3F5j2RBJR8p4czdhIHKCJSylGa4gOEX7KuKWMP1KNyfcKDRGOMa
+Y75rjPl1/f+LxphvGmPeMcb8M2PU0Tttp+20/XvR/m1YCn8deANUFhn+J+B/tdb+U2PM3wP+MvB3
+f+QVXBfbqJL3WhTK/5/GQ6ziA9xqyHpHT53ShMOpnOhV1Y+sRT5mU83dOMTL9OSaGhKUBHMv5u6x
+BL6++9sCFz1YGXM2kRPoymqFekdO6faFkEvX5GR+czBjqOSvC53M3d7X0JorotGcmfIpMIO+uj9p
+ZpfoxYULMx5abukzzWoFP1A34PzXj3FvCHeE+xlBWJrek9DRYOB8wupjMryf886xciwBo28OBjSV
+fGX3SF2iFNJFgY9TUDhyUtYOGlQ17VV8Zh/vjiLzVCBnNq0zjVWa7yinfiTjYm0dV0Ch1DhPtVB3
+RVNe8+4d0kRP48mEZE+rJFfHzFyFNFcsti4Wy+aWnJjd6ZD1FbH+6n9yyLGmZ80/spjZglshw9Oc
+fqFkp/G8z/ye6nqYd/BiJc09E+BrAI9yRnasyuTqBrglj7ypPByTkIlS+vWvj7lflrk+6Pcpx2K9
+HBwr3ZwdUUy1P46zlKZzWj6eVq66GsJ2PI+VdRnjM2sN5sryfDjpEaspd3xnh82hWF5ltWgbF96k
+U4gVV7Et3IqMUbr16rJCNcwcSuckmB42ZP798CL+oayX3Td91LjBFCwRomlhcRekQh+wfVQtyW3g
+F4H/AfgvVUruZ4E/r1/5h8B/y4/ZFOIYbt5yqPY6GISd2M3MMrIahj7uqgxkLTlPkcrCnKpC6XR+
+wnpZIuSBDQitLNIMh9lQNpPh0R7j72qkXqO+X9z8NPdVV3LWO2JyIpHz+lqNoCQ+c9WG5EsaXJn8
+O70RJR3ofpYy0VzyNIeSTkYpcKhpBHhFWanNyJIrgUhtUuOGmp2/+5sBTZWov/xlqVvYS96iUZO+
+Xzhn6TwvWQn8XWo1WcSd7rc40nx8oL7+vO4QKGdiaTZn1ZHNpDm7TE+xCbVvXyPWupJ4IhHtkjui
+mEg/79yakA21z2dLXJn+gtzj0mMEnuCKU3Vnip1n6d+TDW00K5goBLfce4yjkVRMmjDAV6YmTxND
+z32my+Dz1wC44n2MqicY/6/9g2/hKc4kdwtqM3FprKcxnqRPT4lxeqOIaazQ7ZtVWiuy8axXN3G0
+dLqbq35m4GH6El85uXsTfefp9wfUG9IPd3gT9mXOxocyPqNRwTiW+W9aKOuG60w9aqHc76yK3+bl
+QzaNCtJEkChbWKMc0NZnyoqM3dc0M9KQ+b88+CTjNaHwt0ePQ0OFcN/+GDNlds4jl0zjSnkhWan9
+/kv0xrJ5T0czxgMFtRWWstZlZI5Fhbg+cPuo7sPfBv4rHlAGdIGBtXbRjR1g6/3+0BjzV4wxLxtj
+Xk6T+Ufsxmk7baft31b7N7YUjDG/BBxaa79tjPnCh/37d0vRNzptW64fs39yd0G5R1Hky4rCctmj
+phVuE3ufdkVOj1CpzRzfwRg55Z1qF6OnsU0jCuXia3XmdJ7UANyqVE4GpsOGZhb6vz9lsCoR7uE0
+WsrOT22OxshwdQ/NioLbGmXysoJI3Yoss2itC7MY5op1ONYCF6+Arhwu1EzGBYX0HUxnjLTg5817
+Kj+fWw4WDF43fVa/Ky7Pxedr1Fbl3u6mQ2NNTiBVMGPD1nFVDCZ2p9TbmgHwDYUvp/jJm/tEV+Ti
+wV0ZtyMsg0Ppw02GWC29X/c8Sio+kjc2yQ8eByDtiiVxdP+Iqaokx8cZenDh7x4zSFX4xZujsVou
+X5QA7Xr7Oa46UiRUa0LdCNbhpewrFJolcktzUq0eHJRlYewfzdlRte7jQYqvCtXO2LCu0oGsHHJx
+VdaIny4CdTlRImM4SnqMF6zNxZSRKnYP0x7+QhUbdVuSGUaRqWnuLOnWkllOVSnbMpUvbIWGSEtm
+R7OEtiJPUzdkrFJ+WRgzUm7RhmYIdoc95uWvAzDtdUhXdU0Oc0aHYinceW3Ivqt/pwQ/cSWlNNPA
+9jyipC+P50LJWbg0DlH6k3Mffhr4FWPMnwZKSEzh7wAtY4yn1sI2qFP/I5otLFGcYU1CpikdgyHU
+yPhj3TWGOwLueCtaoxyI7XdWiS/LpZxKqgCN2CVYlZc/zSYU+2I+D/ojZkdScfb270iXvv32HS5s
+yZv55BmP9pZcY317k9aOmNWFn7JgyM61BnqeFcuqxsBCvGANKlhCdF3LkjJ+gTy3JcjVBZmWCtY0
+Gj4+V3BwW++h0fujCObqohR5RqF6gMG9NdansqkVk1eYnOgmsqjqqybYE305Rl0mfRmr5NkDuCPj
+krTuorgwvn1PPtw7SnlH3aAhltWm9O3jgxK9iWwm/d/PufpJTc/el7G6e7fH4W15wQpSpnrrycTB
+13Riq1TBa8uCbbYVpPWpn6VWWVDLQBnZbKajPqmK3XhxSFyVv5tpHOHWcY9JJL8f2JC1UObXOjGD
+ewoAcn2uFeKGVS9rqGsK8UBdkP0b7I/lu+/sH7KrZvdoGLGqlau+8i9O8lSYv4B5mFLSOc2cnJWO
+bDxNNfdLfZdw8fLPCxqbKv7ay7mvKdmDeyfEyiV5Tqnqb1VmPK4Zlcc+lTPak2f91hsD9nYlbmbc
+DE1i8fJE5rTjOqAbVpsH2bGygdRXMJtxseYnpCVprf2b1tpta+0F4M8Bv2et/U+ArwB/Vr92KkV/
+2k7bv2ft3wVO4b8G/qkx5r8Hvgv8Xz++Fzlua0y8l+KpdeCu5rRrsstfefYiXU9ztM4+TU922lCZ
+mts+xGWxAvIYAg0uMklIGnIizAYHTNW0u3Mkpuq9yRGjXTkey3GH9fNi+4blMnXlZDjfvkPUk3ur
+shtpL8LRE7/sQENra0YWFK1L6BhStSZWtKCm6jucXyRozxvO1cUSGI1zhhoNWhg8FQxnFPRUzw0q
+UcmVcznVsxJxTm4WZCjdmJ6C9TTg7aH092g25vZIKkqvXV/HU6th9vM5q/cVZFOV8b7dS2lvK8vw
+FK6qOva1NZdeS0+/miXOZLzaiv+YdXsk+3ISuXNDqAzGRR3ClpywlzpV1s8pUclloaIvB9lDkuWL
+z8NZSk2rB1s2p6+m+cGx+DOzqKBckmXbvdSmW1MYu3Vpazbn3LMXqV1VJfCFcAwzxgoGC95OqUyl
+/w3js+8p1iPMSPSVqConaL3pES9EylNnycA9Dyzbii1xAplU32HJpWkPPcqJTObMH1FuyN9dmDVp
+KMdkRyPU+5WcqbqE9VqZalst3UtDgol8tzIxrAYLjk2tLvUdVjTQulayxPqsvSyh0KyULcAEP8Hs
+w6JZa78KfFU/3wR+6t/GdU/baTttP/n2aCAaU4/ooEtrZUZNscTtTpPVphTofPZLT9OtKqNwZ50K
+cjTXS7JTp/ExngYiR3f6OHp65N6QZlPSN43Hr2EnElC6ckUe+y+YVfo7Emg70wiJte68FLS4ePan
+AfiLP32N3+H3AXhVobFvD16m5i4KgyxNLXzCB9/qzh4UdI3c55zSdT2/0uZMV/7uhRc+xfSmnFz9
+rM9++VUAjPrhh3nMigrXVo1la0WeY7S/RsuRsbh58DVOVNG10FhMPJoxTxYqvYZoICbGWvg4g1As
+hZXd53ALud9Tzwl09okrRwy0Unv/hkPFl7HfXFvjmcc+LX3aPaE5U2FWFfHdLLZJu5JO3CSGqloN
+lXViPQnPb6xSXpO50mnCVfXmRVv4sdE0BqvsR4ED9yWQOlJgyKVuhwvXxBI88+TTZIqLqLY2qDTk
+fpeufg5P+RI0M4kp77KhMGcec7nYkfTek09WeTGSvrz5/Vts6am6eyy/P0wz3lL0Z24tVgV6yoHP
+lSd+Sp9FJebCe6wuipVqPuc7FwBYjab0FU+Rt8dUmvK5fUWsihcufZbpiczfZu1Jpp4wZ72Y3OT+
+uhREVSr7KAkVz3pSCDgZH9PWgPjQ9jiaq+RiyWOtIpN5mKV4WrB39+g2H6Q9EpuCMQVeGJHlc+oN
+XcQf6/KZZ0ScZGtjjbYvQJdwpURdaXI95U50TZlx/7Zcyx+TWXE7glIFq+ZnpVrBD8UlaLrKORi0
+WdkQSXln15I7YnZHEyh3JIC3UYermeS3e6+KCbvnGZRekSizDLR6MvQdShqUerztM1Ns/FNt+f3T
+tYjzT0hwKs9eI63L9Y6GEc2GmIEn6kZcvmCpKg6jHjp42rda7Yj7Sps2GE0ZLfLRync4iA3jBc28
+zQmVT+IwPMRdkyxBdDdg9aq8eBsqpV175kXcc7LYRk/+gGwiYzVPYHQkOIRqO6LQCL8NlROgyNk+
+I/dbK61QWVOorTlPnsmcNCttSmVBQJWcj8k88f4mretaAl2WB3lKrGQnXc3UnG2UePKKBCXXLp5n
+UpdnKtwJ9apsZAQ9FlnyWA8Zzze4q0oy8/wFXOUwnCW7rCg9n5/fxD2Rz4uKy5Ob4KorkU9dTHWR
+nTCsntOAb/bHAByZCU5LxnWrF9LdURdtq8TZTObduAPKyoXpnFG4cjGmtbUAJFm8HcU3bIWsNZWj
+cq9N+dLiWeRn6awgflsD09Oc/Z58HnhjfOWzr7gBY1Uw+6DttErytJ220/ZQeyQsBZtBdpBxZv0K
+cSBIsvrJU1SOxeRsX1il3pbdrlw6gzOX09/XdBVRtiwiSUcNTHdBzJpjNEJn5gZn8f2pnLpOEOKO
+xWrgwg3ckezA855PqafBnrDPOU1V3tqXk90AKlpMMgVVXqOUFijCmmSeEiuF3D2x0Ln0pRmxypzd
+n+aM9uRUSbZz6ioAkqtWxHmAFXnO8mHArC7BUXdep5vJKdbL54zHcpJqNpGpLZYpVN9lqWHYe+EN
+wgPVb1jbJQjk5NKYHtlsgh2IdVC+f5XiqqbC7s3ou3K/Ul7BrsnPi0juODVtQoXzmifGVDNFI24M
+cI/kNC519ymduQiAt1DAWY7kw80WCX3Vo7x9e06uFZrbJSXm7ToYLcZy5zU4Udm/2hBKmkacJiQD
+dUMvqc6nk1JMlWUr3se5IAHo2ttT2BTrbvv6WUZPiovZfElO+eN0tEQEBhV3+QBB1qHsfxKA9ED+
+Le3+FtmBpLuT9Bb5plgxpcyn8bRCsA+v4m6rtsRILpz44E9kHTqdGVixGjwDbStugPvTO1T3xc2Z
+NCSN7O9X4OOyljuvGMrnxVK4eC/kLU17xuMmqxW1oHjzPeP9fu3R2BSKlDQ+Yhqd0NYKsF7xfYbD
+zwEwGsSsKHTXNCcYLT21qgjE3CUKhNtxMgqpKJy1VuqSbUrMALOO8QQKvSATsW2HQst3TfAY2Uzy
+8bZ7g+S+vGzNygqh9ungs7LQ/tVvQagghI5j0HeUtu8s3YpO4DJS5SFHTXTnng9dWWzz5gyvKc+x
+7gScr8rb+QNf7lsrytTVbG0GLrFG73sHARMVhSWFhgqpFrHqSxpDFGpenZwJ+vvbKzhjBVE9M8NL
+tUx6Iv655wa4DYkHxOUMR/Uj096EfiIxA2M3KU2UUGYi/Zxv3mV2LOOzMb1CpSE45qQ5xS/ks3/m
+Kq4rL7XzkNvwXnFjh3BR1cx0NiNX2v25xhnKRUA6kxfhMH+N3lDp0J1iURlPPD/k+Lb075yRPqy1
+rzBek6xNMK3QcoT2PPC3qW3IRscTbeqKRRl+TV3Cxpx0oLiJwGE+X5BpnrA/+CoAx1YwLc1ggK9V
+oElRJj9R3tC2R1gV97cIU9IFuG4gB4S7tU9QFXUuv9qiqlmEev0NRieqebn3HGXNbLCp2aDDB67E
+NNmhptmJXhEvxYVGmUtyZviecf5R7dR9OG2n7bQ91B4JSyFLc453enSnq0xLEiGvv3OVewpBHjc3
+mK3LKd8cdbCRysnN5YSKerfwDv8DAMy0jx3LqRT7OeVIrQIK0Io5agsasCpo/Ty5gbZYFbXsRZKG
+nCq1Vod+rC6NGBIkObSVRXjq5tSUjuuxSolQo97GyShPZec+W5XdvuNefKCf2OuQ6UmROy0O+lII
+Zidift9PhxSKKkzClLP9FwE46r3NkVKa7U969CIl/VCoXZ5BW7MdhRPQDmTcNkcfp7+gD9v5DFat
+2bQspqhpdinHQoTrlvdJD+XvbPr7VHti+mfMqHrynUwj/dfsFYYldcdO1ok1qBXcfgIbLQp4Krhb
+ihf4MSlzNyhzRVWz+42IuVGXx5X+FOV1YqNFUne2uHNLXLqV4zbuuprMezXuvCaWwtpMlNen56u4
+d0Vvc5pEhLmKy9QdKn1xc9Kdu5j8S/rcf1vuG8VYV8z5mIK2Fj81/AaHO6onml+Qzvu3qWqxXtAu
+LYuynOoV3JnSBbqQHIpbXKQy9p34Iu5CSTwvE3bFVdwcfZ6hFq4lOzGTbRmX8htPynWLOxQDsXLm
+3q+R9xc6nbcJ5jLB03gGb39CR/e7P3rwtT0SmwI2J7MD9uIJDRVW6c8HOOY2AD8Id1l7/JcBMLmP
+70l2wVHhz97kPvsHsuDdVo0sksF2/TqxMt6UW3WKWN0OT8uCzQS7lG8asmR1YUZpXbIPpuKSHYvZ
+dvMVSQ+5BkbpwkQvmE/k7y61IFPp8PHUcldf2C0FuQyCG4yU8OLo0HKsakSzQ9gfyjUmykB0ueVx
+oryMZypl8olEuHfiiP1EFnyUPpBzj5RGPsAnV7eqcHI6CtgZ+G/hb8riPrmbU67J5hPuifmdly+Q
+admwKWd4yPOvXG3g9VSopJRScmRnLJQJadZ7BWdTrpEeXmfvSF2U45cotWSe2nYTdywgKuz7G6dL
+R8J18JXi/eJWnYmCd8qhPEfU22dY0vjL7C6JoxtP1iBX5FcynpIV8mKN7sqcraw9Q3Qiz5yahN6R
+6jFeXWWmLth0fkiay7NOCtl4fcdSViZqL7HUtB4+KFm8jjxrr/eyPoRLd0O+6yQhhR5eM28HxnIA
+ZPhMVf9yEutLfOsPMAvux7JPouxdu3eOcNoaP4mmuErRX4u/Kn0olzCLWsTqIX5ZrruT7XJ/JLGN
+kRNiF0W+H7Cdug+n7bSdtofaI2EpWFuQJzFzk2KVYyB1fWZTOVXbvsszu7cBKKXXSBWWakpKS9bf
+4e0bEgw7012jeVlOlWS/Sa0lJ1TgF1gF+BRa4VhMmihRMyYMSBMleEmqDN9SwMrF17lzLLvu118V
+XYRpmrOz6HwORq2NV/cjrnTFZOxNEu5qkdKrt7S67acC6spKXTpbpncoP/fdAqMFMXPt2+4gpbyi
+WgmDlKGamvdPMgazBTgpZ5ovgDXK8eemlNVGD3OXSK2YW6tv0bgr5m6lucP2UMzLUSKnfPkQrKoy
+54fgqMQcRUo6lmvPezGjVQnoum/Iab47z3BGcr/65YJiItbBPE/JNYhbLqeUN+Xn5n3BzQ8+3Tvp
+k6nYSZBYFgrtkapI1zJLtVD6u8RjOpbPpY2M+ERcifFOwd5dOTXdNflZ/v0hSSDP32issbGiVHeT
+MdFInvvgziHHViyPkVEMSRJRKBjMoyCPFtZUwehYsxm9nwcgDV5lorLh5arHyrqMUUQGimWxaS6m
+JiwrgucHOXv3b8vPrCUNZU729qZsXRTrZuuKh9Gqy15f3LX8XsGsJT+bxz2GGpS9259wT7EsXlCl
+3jriw7RHYlPAGHBdjOdhkwWu25KpkOofXb/JM3/8FQDCLx1Q0boE/TVH9+7SL8trmo1HtPcvANCN
+Aoal78iX7Da6ljCuovI6DiZeSJVDHsuLGVX6nJRkI7jxg3f4xj8Xf3/PyouZAhO9t7GWktpbSVHw
+zkhuMoosfTXtpoqUHE99Ol3NdlzK+ESkBBn5lK1E4gBf0TSVN7McLyQc4zmZVlQW1kElCLGpYdVf
+UJjLz9IMct38RgXc1rjGmVs1fNU5LC7NmDiaWlMuv9HkkPl9zT4kUNvR+LXNOanLC5neDykNdOOI
+pJ/3nSGOpizNpEFLr9dv5EwcebmbTbC+jKdFov5ipL43+3AyibEaS6pm4J+Ra/dPxNR+ZZSwPhT3
+KQlyAq0rSc06qSpVHcVTxrF8p78rA3P75C7BWd0UHYfY1z5PBxx60ufd+Zi+isAM+joWRSYioEj2
+wSoHY6nqMTSyto6akup7zMJc4wiRv0m/rxuZP2au4r6eCfAL1TR15OUfd2ZMF8/UyyglOvEdB1MX
+N82ZNMkjcd326/J3k7HF7Ws6OJ7T03rcUVpg1QkolwK6VySewds33jPe79dO3YfTdtpO20PtHrsl
+XwAAIABJREFU0bAULBSZpZzmS4hu29Y4UUDPfFLwr16Tk6YeXOXSJ2T3zGI5iYbzlK1QagNK1lBV
+cY/CD/FV/uykHzFJJKDkKFagbs5hNWcceAHxXHZgp7zBUEExt7/yJvcHcmpUNGSfFHsolwgpD2Th
+DmZQ1UDptMhBg0ev6G7efKvFsC9Dfn5vld6+mIFemNJsSD+eVI7HW1GODgUHqUOzJ3c8iCPmqfxi
+kmQouplUEUs+sHlGTqvhSUqutRQr4yrXC5Ve+85FDrbldEwj+VnoBZQ0azEbwVAj5ART+lOJ/Hvz
+mJUtJVlRYNVjvRaHx5KpObxfo9cUs3tt43kGim9IayHTbRnbxpLX8P35fGdpTlWZtHMHumMZr4Xk
++sk8IlEzuZKVCLRC8eRgQqhSdms2hJrc56pWc05KGfZErnWYzzi4oy5Bw+BYedbj4WBZpXuinIr3
++zFWKyMxsKmMO0GQYxLJcj37mFiu4/5bOFrvkYQpqapVn4wKKqpWXZRm5APpf1mva0ur1NSdqV0c
+UVJZuc3mBifKtHP30CHRde2rtVUyO+DKdW/3rnNwKNc4nFvOVOQal7cvsvFZodP7g9/4nfcd8x9u
+p5bCaTttp+2h9khYCsaBsOwS5ZZMi4AGUYavsmKzLOH+oWAIXjn4Hk8rOiwqJN1Yq5UIG7KLrgQO
+wbHSrpUgG0vgKKjOSVSh2U3Ft0yzBM+TkyafWKwnp8t0+BYHr/weAH/89stM9MSvhZvLPme6naYW
+UvU5Q9ditf8N3zCyi/7LifGHO3do9DUG8MYtPA2vVYsHzM9DpeRNsKwYLXZyHWZqFWU250hp6FLL
+0ndcpL8y41L4GnQEQpVr201ipoGc4nfuRqxXZQy6M+WmSAtKcwkGdhuHHB+JZZaOY5xcLQg75+6+
+6iL05AS7d79HrDkvJ/RYP6eBRj9lRUlO+/Y6+duCBVh5UoVQ3n0cvSv2mGc5E8UVN0sOiTIZ+UpU
+MU0tZY2zXlhbZY5YcUfTGSgwsWUsJ8qtMJ0u+h4xixSl2jki0GdqbYeUVZQlcFJmM+nYkQZoh1FK
+Q8MrVa+Eq0rTeeHw7J+UdTjOJfiazzL6Kyq2u9Njqkqw9WqB19JCsWmf4YlYTcfahzkx07nAq/23
+DGsd7U8xIq+K5THYGxNXpE/NmsS2VsopE9URaVZgkKt4kpOjgFRWzjRwW3yo9khsCq5jaFR9vPKU
+6L7MeOxH1MwDqflMo/Kv7R2xP5BBqSi1+rB3wrrSXZUrdbJzslkc3i+T6tvb7JylohTfuQYA7WFM
+osEZt2yZqSvRH1f4imISbk7GVDXvX12XjSmxDzYFk4H1lVXag1Ct7hUPzEwJOZQIw61ZcnWJZlO7
+HPzCcehrKDpLF6QgFmdDQVZuQU+x7PkUUiXsSCeW+8mC1FI3prOWsr7oswCOlQb/3lZK/qY8d+TP
+6Tqa06/LSzc7Mmw3ZSw281U2fkYW43yvybbSucejLmNkk+kpi7Tjumy0Bc5bWTMM1dwdzSbMjAa+
+Znv4Cm+uHgsp15WN2oNMxLvijfM8xyoltpvnZErNZpWLcOKlvKHBztUnhlxU8ZwoH3P3Te2zE1PO
+tGKwrPiGyDBXgNdqvcFGZUEGY4k0M+CYjJu+XOOtY30Zs5RQKw7ThsXUdC5dl1VPAGVPd+XfVw5+
+g/vC9cPNV97BXZM++J0yuapQmf6cM0/IS7//qoxhMp3hKYt06sL163oAhDO2ldTz4k+FDPZUdEYr
+XeLI4agkG/33d6bcHOphkUP5sgYgz5fplD4cnfOp+3DaTttpe6g9EpaCHwRsnz9LkOekM0kt5qFD
+OdUKRx8yRS/uTsZ877Z853Jbd9dhzG5ZCUrHZ/CP1dTsHZLobm1OCvyySrNpOjEqnZBqIMuNfIaJ
+nEBvnVzneC6uRsUPWeloKuuCyqe9tINZ4Bsci9L/06o41FY11WcNVVWCVmAifsnjjAaGzpShrhZI
+aB1cDa711OqwuaVcVUm01FLXist5yaGjvsbdUYpV/oZ0IVIyDDjStNnUyZlZrVo8LNPXNF1p3XJ4
+rIzJjnQ+DFOqi8rCTZfmVAqlymlBuiEnU5b7WCUksZn0t1jNKBQjcTyKuK+pw7ToUS7JeM1Oakz3
+3tbxegOAs3/pRcruAxNhcZaFgcvCdIhdS6CBvVz5DTxTkJkFd4TFuyQWT3vsMq0qcWnNJ2yIu3JO
++3bSO0E9A6qhyzxUVuYcEmW/niYBO7vj5bUBcscQa5VbUQ7xQ+VbKAUUbR1DRbGGUZNbrrgB40GP
+tBArplQOcDTVHhAQHqswjhKhbJ+b410S9pnCJgzelADt9WCMAisZnARY1Y1caFdmlTL9iczHK7fH
+3FXUpPV87MbH5X7nPoG3vqiS/GDNWPveXPFPur3wwgv2Wy9/672/0K4VFLzeEzKUm9HXmf7g+wAc
+rMhS6v/BHt/77usA9L4dMZ/KJI+SjEij+V7JoaT+YKOiL0pmiHVB1D2fkoJNxnsphycCfrniFRwo
+kcnxTBddNkd5Uzh0C6zy4WUGinzBqOtgXQX9KCQ6ye0iWEz18ZCf/asStX7mz/8CP+PJxFVDMUWv
+8XFAy2Zxl9yPxjrvV3H8PjAgcUFOCjEvfRMw0xdkXBxwOJRFetNK7OB3f+vv82pDFvRTZovtz/1H
+APxCWOVSIMxLWzRYGJeLVRMzwmhNXkTCD0ayoG/tfYPf+B//lTz37R3eun1Xvj9XJauKobohL8el
+xza4H4kvPn1pgK+u4kmasCCRShZ6jVmB7ymUuF6muyUxjHN/4VN88QviPH/ymb/CZ7mmo7HIcjgU
+6h66uMv+W4rlZw/nXT+XljJiqhvTKLnHb/3dfwzAO++8zu/8k98G4HGdnDfCgnJdD5BalXIosZY1
+v0qsm1A4TfFcZVpWl29Y9cl7Mq7XOiE93XAPXY97yuZcTCOaCojJdWPq1GqcVZHicrPMYC7zEI9m
+fOGMjIXtdGlfE5zCn/7P/5dvW2tf4Me0U/fhtJ220/ZQeyTcB2kSdnpgt9jloWesg6lcACDNeqQK
+Ez1OZKd+q7/HvgbA5hSESv83G2ek8weKwJ5KzU/V/PRLDihhhymDFysKrOKABoYSv0JdrY1FgZJf
+gZKa/qZIUb4RCjcn193f+HapXZ8ug4dg9VQ5O2ryeP7nAPip9OepK87iHGLFuD9EV2be8+E9//Oe
+ZoCqERPWJycwiyBZh2lZ+t/SYpmS+RP89NPyzJ8ofYmLFTlpWrbDqvbFvOuWizuXaCyfCRxWa1JR
+Ob14SPcTFwAYT/v4t2Xs2kY5F6M54V35XA5rUBJLwbUFoWZdyHKsBmlxHvBFLDIRa7UK3aacgteC
+yzT6fxGAJ7m8FO55d3OUM/NdSwu7VILU5/qh5/OoUnYW1aoesTo6yQBa6jbWFedQY0Z6T4VaVibk
+er+t8xZHOS7CNCGIVOpOLdMVp0zfVXRjDFb1PvyBR1V1QI7zgomu+7JmuJxgTGHEUqo1fPxCrKI7
+wS0O1MRaT6rEL/17KEUPDybh3WbwwrMxWDZmsrjHNz7L1/5QFt7R1z8FwI23/nfyuZJ7mFeYWBmE
+Is/J1OxMcoOrNQFG4b5O4ZEVC0n2kOG+piyrBcVEJv87tQGbOokjlfomhv2G/N6MxHcHCIxkUkD2
+g0hTlYuNwsNwRvngv3j+v+Dj1b8OwBOHAS3NWrj/uhd9sVuaH7cVvLsZPL23U7j4GkvpeE3ynvy8
+9KoAYT7Zv8Qn31QymAuwrTeprrCks3+oH8s7PKhmKFmPTRWmDXe/wLfuSfzgYPxr5EbG9gBxy4Jy
+hVIgm8Lw+EnMWLQy4/S/Y+zI/Ewzy1xv6OlGUcal5MnLVJ+egV2pben+2l/jl78kIKu1xUD9cHsf
+T/k93/qh77j4hDqGld4Vynsyf8nhHvO5mPlf3pLN1O0bJl1ZexdWW1S14nU97BLnUu7drezTvSLP
+/di6/P7IafCdl1TuvjTmqtXsxGrObKZ08Gct6xp42dHxKdwStWt6qA1mxC25RqlX8JIjgLLPDw/Y
+dV5974P/iPaR3AdjTMsY8y+MMW8aY94wxnzaGNMxxvyOMeZt/bf9Ue5x2k7bafvJto9qKfwd4Det
+tX/WGBMAFeC/Ab5srf1bxpi/AfwNRCDmRzb7ng8P/qcAeqlgBP7wy7/DH331/wDgzh3hUBhPx1TL
+i5MkI2dR/17ga2QvwWI0gq+FhWQJmEVBzTAmVk4CkxS4mpuvWohUQqyiloLNLFUVPZnmOZ5G0R3D
+8ugpsEtTVLFEtH2XzVX5YVr6HofO/wPA4fwXqCgZTFiRLzvvISN5kNO37/rRj7MabqrUfMmBsnJF
+HuQDjlWnrn9LszZmzJv3L8h3O20yX8zSC406VY3VvbdP2iXtUGohdxRMZO/RPit+VffkKpWefK4e
+KkTbFNhEfJeD2b9kR7km22Gw1J3Mi3TpRPk6mKFnyFRh+97kTUwmJ6It/RGbf/Q/A/Ar5S/R3tBx
+/CF34Ifbjxu/AojVXD/mFr25BLn3dyb4Cju+pnR7x3FE/apcsZVmFMobWm2nPPOzGoDcqXGmJbZM
+U+8+Wevjvi5rbNvUuFASS6j8YpXdPxKrAi+hdSKWibMt/247VTZdmadJFGE2ZC67pkRF+zRNDO7V
+D8fm/FEEZpvA54D/FMBamwCJMeZXgS/o1/4hIhLzYzcFLDJDS2fv3btDwf25RK9v7v09hqoJGSiZ
+nxdmrNZ0IL2Q/kwXm2MoKXQuSS1GCUms+vo2N3jqUjgjiLSmwMkMC2nGxqBEqnp9Ya4kHW6fLY3k
+3yLH5prWNHZZFlvxLCvhYqOSn336apmLH5MU2ie/ENFtCgjrbPOIRMlPg1DcoNy6S2pxa8SFer8x
+s/qymId/vGz/7O/+FgA//3yVWVU21r37v86NI7lfd1/M+d1yg0D5GgdvP0FN+RWL8ud4fEV5HI27
+3ODefY9Mx9Pkc/JAgGON1luc+5iOSyXA+BKjeONlyXb40wJP1ca9wCfWiHte8gjUXHeNXRQokul6
+mBc5qQK28sxitNbk1r23+Mf/6G8CMNz9AU/+yl8C4FPnmnpdh/LClXSgtnwOb0l0++5xXMioF9Zy
+nEkG5+TG3yeuSTr8pNgnq8nzPeuIa/ta9Zg6QmGfzvdIOtLnc+drvPixnwFENauxKUjIbCbVt43u
+8/ANqUvYWjlHuS4oXLv9SV7fElHckpcwb4g7dn5F3OdiPqecy2brt25xJpV+9GonRIeyOYemINhr
+8mHaR3EfLgJHwD8wxnzXGPN/GmOqwLq1VonL2AfW3++P3y1Ff3T04eq9T9tpO23/7tpHcR884Hng
+r1lrv2mM+TuIq7Bs1lprjHmfI+5hKfoXXnjhgVH8roDau+3kZkW6uvbUFWY3ZLd25mIWzfOYUFMO
+bp6S7SkMuAYllYEuBXaJWUjUTZjaApR7ICXHUcjzLLNEasPmJiPQ82OmajxNF4ZqUqYGUvVHXHcZ
+JGet7LG5Ln16+opYMS+8+AKPXZFTovPcz1JrSV7ZD+v4VmHMqhDs4FNowAlrQLES7w2MqUtj3s8Q
+trx6958BMOlssaliNscXHXLlCyhWZM+uHRt6FTFrnZNjXm8LhuLpTo9xV8zkJiWKxTmiR2lhiiWA
+qrAFrhI7VILHuaiq4I2LKwyP5T73doQnsLd3mzHKsTmekmi5p80NkX4uMMvAravHeWwNSaZsyIWD
+pxiCYRZw6674R//kpX/JJ88rRXtTOBfP7aySrogFec5fI1U8QQlDqC6k9z7ORGxS0kSsn4PSNscH
+8nxRXGUNpcVTLETWcjjauQ1AezNnUxm2Lz7+PKtbgptwtw5xp3II+jPBoXjNY174C3It9/geidbg
+Hud/zOrT6h71UvzVRWWr8DfU1zLCkeJiKobJibqCxREltYoiG3J0/cMduh/FUtgBdqy139T//xfI
+JnFgjNkA0H8PP8I9TttpO20/4fZvbClYa/eNMfeMMVettdeBnwNe1//+IvC3+FBS9D8UEfohpGW3
+Kn7Rc7+wTiWWz1YDgByEZHpaH7w2YHNb/rY7NahxgC1yVKSa6VBr25Oc6YKazdgl449jHlQ7Ttx8
+aSnkyt0WAX01gNLiAUTXwDIwVtiCQmMCvirEVLeOWbkimhSl5oRQMQSuv78saCrykj5+mcLTz/gY
+PT0cfBxnocP4rj19mb81Dzn8b3zj9wEY7ZR5wxfrwBu6rC6YhgNZAl5a5u2hMAmVZh7bq8IifJR9
+ns1cxrvqOniae1+MRVEU5InmOu0cm8tp5VgIchXzaWxzsSXxmO+XtXrxcMJYGYxLxQO2qDi3zHRE
+CyypPoyjOhtJViyfL7eQq6WQJDOspgjHuxPsVxUhWcjzm1lKnErfLzz+PJ97UhCbq41NzlYlXdhw
+nGVAcxHDiArD7kQskLuzb+JVJbWabgyY3pD+f1u1NONDcEuyArbiMufUUlypXMH2lS3sKKTYlNPf
+UZ5gt71KLfuiDOGVMd4dTdse1Mne3pLvXp5TOVCLpS8ox2Evp6tr3T+0pFOdm12Yrymqd3/GRDU+
+Pmj7qNmHvwb8Y8083AT+ErJS/19jzF8G7gD/8Qe60g97Ge9yHwyGaijY8MdXf4nyz8kC86YC+Jjf
+GLNzLMHH8dGMllVx0chjoOXQSW9OSYODEyXj6LkZh8r9N+VBhWKcZUuzFWOWPI65eWCqm8VmY+0D
+o9NCSRdVq+KyuSX3u3BFAnXnux+j1fgz0vdGB5OLSe24yYOXfoGvyR+88I4t8WC78fnRMfOHx3Gq
+tR2D/YKVTCLV/rMNEkf65EQSAIyOJtSUoq2z3uGZNeEdfKr5SbqO/J1nguXVF+CggoSiUJZsfNxM
+60u8KaYiblMQz9lsCTnL009IsPPt7w2YKSQ8TudgtewXQ2Clz7HJcRZQd404msI8yOp4LoGKtdab
+ddYuiF5l43yLjWc/A8DKGX0xd9tEZTHRneEWRSTPVNgamdabZIHBaiZpscdOi4Q8lk1jWHycoiYb
+QJj0GRZCklPZE/chTAv6q7IpHkUz/kRFAoLlep9Z+Tfk3kwI+zoPXfmX7gGFuo8mmuMt5OyfvQFT
+2URblU18vfbNq3Lf7AdlUi2Zr/gJmfJRhocO9VgrO2cRr/sxH6Z9pE3BWvs94P2w1D/3Ua572k7b
+afv/rz0yiMb3nn72od+4alZvJJ/CHYlJFdTEnLrJH9Etyc/c8Btsrcvpl7oDXEc+T+/3qNblVNnR
+Yqd4kvNaIifXJHcYDpVbIbEYNQVaTrhE1eX54nScLy0C69ili4I15IqLON/u8suP/yIAm8/JHtld
+/wwEYoqbSfVBVDJ3lieUmT9wbYzCeQtjcQrVcvAsRuXBCEVc5OFhezg52b+vlXPHLdKSnDrtyXlm
+65pmRU6oSTKm0xSRkWb3WTaHUpgVjNt4yhSblcDRe2d6yhdJwGgkz+HHPomSxDj5CpESgDiRT78v
+JnM3kkBrEAZkYzn5Zmm61INI0gJfLbLC2qV7sPQmjaGsaeZmvU73rFgHl9afgpJ8bvU+zcGXZY7P
+/IFYRMP6iEp8AYCdzoDed8Qy6V2q467K2tp/1rKh8zrS4rfJXZ8f7IorNT4akE0Fi1f22wxzObGf
+V7GfQcPAWCxam+SsXBEYe2X7i5RK8tzz7oSwrUHxlo6P18F60h+nXJCek8K0s876ksOjsRYyKsRK
+eV6rh7+Tf5lSqoHp9m06J9L3wBkSK+pxve3zrAoX/Tr3+CDtEdoU/vXNAnPFJJzkx5jyawCMc8l8
+5qXbS6bbC1crNLeVTWnQYaKLPnANkXIQapEdx1VDRQU24kFEuy6LLc4c5ouXy7UEGq+YLbIM5gET
+cc4yHAAOdJR1udvy6IeCrWjwbQAOj2LqRrD6Ya1EtSYbhBd4OJ5Wbhp5zmwakQ2UyrtUJpstIvKW
+CmJ2umfWKa/qAlt6Gw+7D1mmYjDFGFdfPOOMCXdlLBZeSuH4GKU13x5G3O9pP3Y/zXwm9wsrlWVd
+Sa41I/NkxHwkuXIzi7CaJUnzhPlUouXZZJcil3hz5imBSJEx1lhElCVLrkwfD89ZYC+Sd7mQ0nzj
+0NGS47PrZ1k5I6CvWpAxV07Ik+gGqZE+3daS61paJZrKhrUSwPWpuASzI593clGOWj3apLmIFR3J
+y/b28Fvsf1cqdKNwxHwmYzSc9KhqjYKnasMTf8j9O9KHyppPkLwkffc/Rl5Skd51u4w7GKX7t94A
+p6Y10tFdvJqqYgUR61U1/dOYhW4RQ+nDY88P8O7L53Q2X7KIVVYK3GMlIGo4+D0+VDutkjxtp+20
+PdQeHUvhPWiGd0UaLXiKNqyaiJmy+VaNVpnV1mlmq/rdPZqrCg0tjSjlYj47eZvZhuy64U05aQhH
+RNeV5qvpUlFyljAElEotclNCtRp8f2EdWGJN1KfFA4s9dKCulkI6ShhoLfz8SJBr82dzGveE5q21
+/QR5R/pZvdDELeRzUgiaLT24T9FRmfUoZ241J75zCCUNtHrPE9TERXGr74ayPqgBTNS6cYBEOeR6
+8yHhAgarJ3SjHNBTXsPJUUD/Dfmcx0PoqORbqcHqiozdVKuritFkqZdRLY2YFjJPUX++FFGZTQ6w
+rjwXjp5syYRpquF3W7CYa8dYjErr+Y5ZZgEW7kPFs1xQkZwz7SlrZZnfpj9kZBbuHRxMeno9vZYN
+Sa0EprNxDVRLcz4zTOb/VJ7p62sPVKcVx3F79wSVwyAeB0QauB3Pq9SMuChfb6kLugsDVb4u+yUS
+xYXnowhqYmJFxhCWxYqpLBCyaU4xUoEjv4+nMuamVMH4Mt5p4xg7k2vMMsmGNMOrzC8KIsC8E1L4
+YpnFGRwFSqgTBZTrH+7sf3Q2hfeB8S4WQkaBp+SgPsdUVSc20XLpTj1g4gl5ZjqwTN+UQQs88Ldk
+Yiqra6w2VVT0KbnAuVLE5aZM0MHkLr99R/zeUemEI+VMlBdewS0qEW7iXARpkeyDWsx0Q5fPrstE
+P7NWYe9AXoRmrvqS5ZgzVy4A4NZCguA5uUZ8gtGaAa+hDD0bVyFUPcN6mblyNGZVH9RtSqOEXKPT
+tqIsVebBWOa8e3swqGgQEZCpz7MoQ7eJ6F8CfOden3sTAchs96ec2ZCNbHvlKaJIxq7uCgzark4p
+pnLv49mMnTckFXj9+BZlIwu6tmJoqUBseVtcpkb1Oocj8Z0Lm+Koy+B4AdWlnZwueTND/X2nUuJ8
+Sw6ArAgxyjw0b7ic0fRqeKbOxVhIdk1NTPRyGhOpuR+nOTO9R3znTU5SWReTmUNX2XNyTVuHpRLO
+vrquoyNmE5mfyOaEWm8zP5K+9ScZiaZLerOIuwNZh/WXf5v9i38AwO2XIemIK9WNdc6euM/HciGy
+OfdkhVp4XufpJrdfl83i60df5/qXhUioeE4Om8vDLa5dlveiOMlJujrvb7n4E8nAjScJrLwvfvBf
+207dh9N22k7bQ+2RsRQK+6CiDTS4qKZvFs24t6/qyO/UGGsQ1a1IJNgvgz/4gvxwd4/ZVHbom8Nd
+2qEUouR5Tr0h35+olPtkfIX7+5I/Hs63mKjkvBltkBeCe2ilVQoF1jiKf0ickyW8NjEWR/2Hkl+m
+ooSNbx36XO/LfXJXqeh3C365IgUzz2/9NKxI7t5YQ6ruComcbNF0zs6BmMblnV2OhnLS3nnry8yH
+csJuXo548RefAcBrywns+w/SEQaJ4AMEGMYL3ckMYv28sMZKbrbkiRxMIqo7YhG8/s5N6hWxCjrt
+N3nhMZGiv/yxC/Ize41vvywm7P3rb7K7I307STyKkiyvlfYWnXWxyLZWJApfKXvUArUwTIqr2ARj
+ci4q/fy3JnPSYoENkX4Ok4Sv35J5KhwH11fIOw5bGnRtNbtcPSsgsfNVxVg4ITaQz/fv3ebua2L9
+7N/dI1WL5lYW09SEUJTJ2EdpzihVfodZwVz7k1mYKz5DKRoprKVQrswsatC7KxbN9+ZTsgO59x9/
+6waxrot5U4rDrv3uJ8mfkrXZ8T7NvCYXHN3e5v/+3wT7d/3GjIlS6/2Z3l+VeaocEO8pP6T/Ft2p
+ZNqSkk/vQCUQE2hlC+miYz5IeyQ2hQJIAI8HL1jCnPFAzO/h8IAffE10GN74zuskx+LDHbsS6WU8
+FVUgoN3KaKkk+b3BEdMTkXAfejM0E8mR+sDGlsg02l/NDZn6stZxQHn0jDdlKxTz8p7Shge5i1Ff
+NXpX6CMzGX9wTxbTcdJjoBj9rrIcNYIRX3nj1wFIk5s86X8egDX3EieKrz+JVIvx3i43X5bneyO6
+QbMti2o2OqKrnI+jvMH2p8SkrEQXAPC82hLck5HjmEVa031QHWohUCOxG8jLv70ast6QewyDhHSg
+9R42WeoUODbh4I5sjOeuyf2O9m7y8tfFNN65t4sJZIyutS9Q3ZCXtF3LcKqyqdU1c3Dx4nmO+/LM
+vf4Eq4PYDEr4+pIWdsSijtrTVGjTr3C+o2K1hUei85RF2XJjadgSZY32N61sPEnuMBzLvE9GoyUh
+bG21jp/Jd9oY4omM/3SotRY4JOoqpjxwcgv74OXRaSbFLkFdP/WMwyUVij0uJbxzS1zTZnmAe6gb
+nWpWJPHvsf20bHSloEJ2Rubk9tsvEVd/TebHP2blqrimA++fyDwdT/jaHY1P1AwrGmtq1iJq2tOo
+CtHkw2nRn7oPp+20nbaH2qNhKRQwiwtqvmGmQbkoPmL3puDW3foOjivmfOniiI1N2UmdiZi1O9+c
+cTvXCrIY1n5VrrH+ez6/qXhxe2xZv6yPu6+mqj/n6EQ1CvOcIF5QqZklK/PISykvOBb1dImLjGJB
+rJJBprntw3nMjtZMZJl9UJPvLTIZBfU7EmSqm5A8Eiq0z//i4wxuy3Fz5xtiHRhvwNOrYgLPRm1u
+vC5Yh8HskJnm4N3LNfo3/zkA+ecVRGoWpiI4uMsTOHNTsXmR085VYZRGTX5Wrzrcj8RUlokhAAAg
+AElEQVSsnY5UPh3wSwZfVa7j1GNi5URzKxLsdKMmo6GYYL1owGwmz3E8e4OzM8EQPH5pjVJF50RP
+8/VSRE1FbaqhS6YcCaFfcFIRCzEjZ4FzXqhLz72YSE/Y43lCR6P6iYkpKdtxqxGzXpf7dAS7RFKu
+kN9WJatRSqDEEJNBwvFI5erjDE+txZke/+MiW5LsJFhCDczmxi4lARbkM44Fo+vi2kYTU1acye0+
+ka6du3sT/JqqUY+VWOXxBpfOfEL6/rELZKo72coj7rwiY9FqzqlrReSJqlXv3JhQCzUrd2I4DsUS
+Wq2mTF3JPrQybwm6+6Dt1FI4bafttD3UHglLAVOAm5AaC1Z2u8Q/xnOkvp98lbWuntKrlrM9STJv
+RbLDT0Y17l2U0/NCVmGrK/5kb/U+lUBOh+COw1Md8R3rerq8sTFh43tiKSQmx9Gd+GaUUo9kv3R9
+lyxb+OXSnaIw+O4i9lGQLU7j/EFNvnWhrKfR+prcdy2o8rRqHVx4vMbWswuBkNtcLsmpcvuCMO20
+JgbTl9jJ+WnMoRKCllodnlqXANZnnuqw9eQvyTUWsEprl9wKczIcPcbcwqFYnP6Bw1ZTLIpV1SMY
+RjnGlb6tdny2zqiQCfDOgVgC89SSKHNxdiwnv9eZ8dg5lY0rQaDaj3OvQXVFtRTbXc62GvpZju4r
+tU1mej87/gHXBwrzdQPQNGOZEzzVcWypoKObucyVdbtT7bC+oqjQaUGzpRoerS71deUqKMTMMWlO
+EaoGY9ujyCSwVy087umJPpzPyLXSshsuLAXD/kDW0IyMsuJQSAsyzfGGihRtO+C05fcb10psBdLn
+x56pcO6iPP/dbx5iFbJ/uStr9tyfuMq582LpBU4Vf6bIy09O+OwXZbyqbplttWKmm0p592SJrmp6
+bo4t+yqONHonojVXUSJielUNenzA9mhsCtZgMw/XOKQaaHQna8SpTFYz6S7FG9eOfp7kSGDOq6EE
+r84/P+XFLYGqnnz7DhsjFc1ofpln+7px+G+R3JdJOjGSS+/OP8+N8dcAuNSoMqtrsC+O8LUuYd2r
+0s/E3HOUTCO1Iyq6EFwjlXsAvuPycVWTqjctG+clGnx5XcA/jbWQC1elz1fP/SrpVO7RqZY5qcuz
+dlQQ9cbRmJVCNoWViyv8Z015+e/G+1yuyKLwuwllLUV2FeJqzQN25dB6S/fBNQ8i+BUvYKUrL83l
+hgSn5iVDZSE62yizpj+fRglpRV7uG7d7NBvyEio/CJVGlaeflYzE015KksgG4rX+P/beLFa2JDvP
++2LPO+fMMw93qro1dXVXz92ySIoSB9kSDNCQCcGCX2wQFmzYEGDYhvWmFz/wwYZgw4DtBxuGQJiT
+LUAEJNGSSIpikz13sbuGruHO95755Dk57txz+GGtzNtFUq7qLqF9ZZwACjcrT+bO2BGxI9bwr//f
+orZyvd3BFnMVbA3aMgeR2+bnX5QHwRlnDL8vgbisKon1YfM9Q09/b28gm1S7ZWlrzUCv1+b6NcEj
+OJMErykbwLWtl2jE8r1ApeVPZiNqBbgVNmNNPxs3IwaV4ALuHj/CUZWl47GshYW1BKrIZVyXNc2o
+FPOKkdZ53FDcfDdyaHZflXmY/wIv/LRkmtztMdfauhmu3yVui1vYVS3U/vZfXpXHGmpqX/rwAp/i
+F/99WTv+/IQMIahprwthy+TBETvrUmtR1ncYqZDlw/Vzyrel/4UbMQiWGamPln24ch+u2lW7ah9o
+z4SlYAwEXkXhpJTKPDFv34M1KShanA+hK+alDe/S70iOuY3sks7GNcpcCErr5uscKNvvRXjGhYqM
+dK+fE82UhKIne2E0/gbtvpxsUzNeodWmaY4qc1FF2TI7iV3W9FtDpgn+2pgVvuLl9Yi/cVOqNf/c
+X36Vxpe10jBUuPPFGv4ttSriBVEueery3GNRPgBgXMhnt3bP2ToWk/Pav7FPZ00qADdGF5ihCo7Y
+S6qZWB7FWLUHwjZLJtLC5KtKy7K2uFo11W002O7JydWNxAq4ubXOzs2b0t94hKdou/sHUyYnGuR1
+Dc1IzW4j99lv7rLzkxIwjXOXoZ6wVWPAolAdjQSKhgbl1NrqbXaI1+U3tvZfILgj2JJWUTHNxGqK
+WhEvbUvl5s0XxcJoFzWDtlgBvRvX8FpiKQWuT+ua9C2K2oRLpWUtgqrqgqzSnLQ/xi3Fiot6HbTQ
+kHbLW5Hc+Av5t5FURJ6si0HQIFBimGo2WXL00lB4dW8tJlco8sYLQ/xA3JWw9xpxRyydbmvjKXeG
+K+aW8XOMuna2LjHLgjAX9j8h42wu+phQNSEDeW9j8w1MrfDpowqVmqS5PcV7LPd3VtZkRz9ekpV/
+Jc0xhsANcExIqWW6TvkylPLwZzffIj9UCffejIYCfaLrAm0OpnPyUvy+NLFMtuRhK6pDMkfJO+aW
+9BOa031dbvuse75iTM7ilLmumdpUdHWiZ5VdMSu1taTiYl6vBF5sDY5eY6sXsn1T+tHbXdDcVtNe
+F2DrBRd00Y29BzjHWlF462t0Lh8AMF2XqP61S0P/L0k2oFe1Ca//gYwLA+rOG3LfR02Su78HQPOm
+gLTMhkGRwbhpsHIfrLErhSg/rkDJZ+qm4jT6GXFX3xt3mChQ5mJ+yHgsY2ickFCFWKya+Hbao9UT
+Ez1aswRaAbgomoyV6cjvZhilcw9LWcR+FuIU8r3O2j69SOJHveiUy1rdo4XLzedkE3llU9yEZqdm
+XTc0r9UlPdI6ga2MtkK9g2YLZ6qbdlPuIx5XBEjfvcRQrsv7TT/GUVbmsm5gQxmPSmvSHz1Z0NF7
+ChoVhUK6bZZh9PB5ot9pJgXt16TOZWP3r4HGH4wTYCrZFOosxDQVA2OWupyWFW21m2OtzHtdRRiF
+sTO4R1DJGNWhlj2aHfAELMbDO7xzT9ZIdFSSBPIZm8BhfiVFf9Wu2lX7GO2ZsBRE3K+gNg5GySFc
+v8RuCgy4yNcJe8J5n4QpxVDMxEQpvKrHM+7nEoTJ0x77lQRycjyayr1QJxHpUE6VRFGD4xgWYwn2
+BLnP8Wh5StQrXkbXccjVHA+Vfdk5m6IwBlyeZhl2Nlr02mJKB7M9fD6pd6e6CMYjG4l1cxF/BycX
+iPJa/Sp1IlPRXddI+FnAc5cSGAuj6wRW7onmOpVGxrN37lM25SRJHn4NAL/zl0Aj9UezMcuSKGsN
+oQbE1vpt2k05bdcUKr7W3CFWQpqiDXFXTPTzuymVK9iEVuDT0GyFv9BId/GI9kL62W5s4SgOYdFP
+8VQ2zfoO7UIshLqt5ryTk6jLE3uGtc/J34P3fPxjfR259G+Kib01EDepEzdY18zJrGOxKukXdtZx
+tegyjacrEpg8l5x+lrqMQxmrUV7ARL7XKB3iXXHTdvw+gZLg+Ev6O6+Fp5yILafJmWZJ0jrBWwrU
+KIO1dQwvbss9x41jHA06O3ZErRZwOr3AKWf6G/JbTt/B1BLsNEyhUDi9d5/RWOa6aa/hZGoVbYqf
+YPMethBr+uStI949EExD+92Izq6M2/nlHMd9Cn3/KO0Z2RQMrhHg7VIE1DUBvvq7RWi4LD4LQDtx
+wIqvZi5kcA7vfpX3/lAW99373+NnPitR7Vn2bcIz0SiseZ/wiXz+IpUKwO7pJrW+np9bLhNZVVnh
+UyjgyFmEtJS5Zr8lBCkP3SHuEjJc14TqJ3fYpm7I4q32X8EJZYMwHXlorNvEqtT89vl/xOgt8aP9
+jS6jSzH9bp8K9PnJxRuMlEew7bzA2h//vIzF6Ah0g5g/+F9IE3E3FhOBGlfOdRYaZX/jn01Ybgqx
+FxAoY7xbx/ixjK2vKT3bHxA64qvGjqXjyoI+XD9moyv1ClHgs7Mu1x7siX8bNPYwkfTT8RsURsY4
+LtrMa5knm5fUCrhqxrIZJVkueVvAeDO2qxfkehs+17rCbXh39JBNIxunG8jvxWsNFNFONxjg9+UB
+C9yYzJPf9hZ9Ls9lXEIj/V1k7xMn8tsXk0dkqtFY9lN2ItkMM5NjFODlakYpNFJtC+CFGY7+NrVh
+XeHvn+rLAzi2OS/yt+T747+Gp5WkeB6OlsbbRZP5E638VDWw4GKT+Vuy9tovXqeMNE0+/xLDe3J/
+tVMzVWh9f136m05SylRiTXf+8CHZ63Kg2PYpj++K+9B1LLH3w20KV+7DVbtqV+0D7ZmwFH6QGn3Z
+oQBDU9mA8yon0ej1hXOPcEt2xFwj1ncvvsnvH3wTgOPZCY9eF3h05Jaoghx7axGbugVONJo89h9j
+Enmd1AWVhhQLCiaaXWjUJbe6suMHvtKhGwMaJCqwlAqDPa+mnN6XPu1sHhIPJPfuq+Xv1jPCNQ0u
+dQ7ZGEhAybu8hz2SPp8eibvT2h4Tn8kJNOUtiodyf8atsRcPZCzOjhiq3Nr0G+8D0P7u2xwU0t9f
++dpXV9RmXugSNpT/ER+tCcNP1fRNK0ot4Ak7MVMlKTk5vyS5lPeLNUMylAGd7cgJ5mX38GOxMLLU
+Z6EBuspMcBU04xQzylDp5lx1d6oZxUKucZJcMEklyvvF7T6RVmWWJxWZkuSkDTGps+EU2grMcRbU
+qu9pzBhbafAzeUK8Jma+L/8wiXLqB5KJirwRjvJCJJ2Iy5FkuZzIXQkCpWpJGBvj1HLPw3HF3TPp
+c1aWfHJTrKzPxyqJ510wOvwdAOalT3ZfaN3t/jXyjlx3Xp0QZCoGc0NU07P8XRZb4gbYgxb1hgQU
+jw9OOByKhTV8/xBuKK/HA4Wy5x6zx1IQ9607f8R7c3EfYmNpX8hYZGsBi3QJuP9o7ZnYFOBP07YG
+uBjdKhqOz7yS15tJi+MnMhDZXB6Eer7g+rY+bHVIMxZTrFEZFhp5XVSWoifmnnOxLHtOOa8lXZPW
+JaG6DPPSruoEpqbk/kge3htGwES1rUU3EsBCqotxOhwxbshiG03OaCvPoaslvf7aLraSxRGVfcpM
+6hyqwSGuCui+o1mI/YuKrU/JAxE4LqUCWpJjAzr5o8kFXzuSvh0ojr7XPOXRRB7AO2fjVfYhdGuc
+pbnOnKKShz5VotgsDyk1DTmdFxycSIbj4OIe5xN5YI2N+ONQxv75gaLuomtYR9WN1iFRnL2Ttlho
+NWfcDKl7Gl9Q5qLZWcVU2bSmo5rJQyWLiZvsXFNCGbONr6hHTzkhjeeQL/luzx1ydePyxmKlR5kP
+HciVAqmW8RmdPeTbd5REZ7pgXzfnTuhRauq0zKFQbca5xiIuxmPONdb0JMk5nanmZV0TqMZoMpCD
+ID2qeHAi7uj46y+x9mlxV+bJhIkMEeXZGde/JJuFp0Aoz18n142pat4jUxWte7/7Fu+eyRjd3nNw
+TyWDMTqQe3p4OSU5VZqAoyEPzmUMOyNWjFs3Sh/f+eEe848rRf+fG2PeMsa8aYz5VWNMZIy5ZYz5
+ujHmjjHm11UT4qpdtav2r0n7OKrTe8DfAj5hrV0YY34D+PeAvwr8XWvtrxlj/mfgl4D/6Uf5jSXN
+WVHX+GoHnkd3qCPJBS9Ukc7dGfF8U0yqV/dDHNXwc7Mx376UXb4fgb80nzUglaUOnkrKN7yAdCYn
+lG9+QEbeQq14/kdzrbgEyqeyxCt2mPEiYXqh5mVyRJbIqREaZZFOprgK5i/iu6THckLNpt/h+Pty
+L7Ou/NbJMexdyH7ajhJQFyQMYjKlOzSPYc2Vz1wqZ+LREO5M5JQblTlGf5vKhbl02lkrUQwOs7mM
+TzQd4Snb9dnkkjM1n7O5T2+jreNdQ6oRcGVwnkUP8Qr10Q4jEdQEikEMara6TgcnlclcqAtW+mNS
+rV2J7IxZJNbIk6SieyaWlTks8L8w0Wtoh21IqNbDPByTLdTSmzg4KqIyDCaUcw1A+qrzOA6o1VIM
+jSVWTMOGG5A05RrzifBCArSU8y+0m0yV6+LOWUGmE2+BkWJV3n0o3zmfVXiBjP3b9/8R1Ylweax/
+apeTazJGG909rN6LWcic140e7U9qP+ctgkAsmvjlS1qpVoReBpQnqlSVKT3cKOX0TNm65wVTzXJN
+CotCITiiJPghj/6PG2j0gNgY4wEN4Aj4GURXEkSK/t/5mL9x1a7aVfsxto+jJXlgjPlvgUcIF+g/
+Ab4NjKy1SwjVE2Dvz/q+MeZvAn8T4Pr163/67zzdsTwMU0XHJYdtck0Bzlw5rR7ZBZvKZtwrO8x1
+549GET/3nASt4q0OtifdSpVj4Xw84e7XZfe9nGdoMRyOfUoU2vMcSg0q5hp8zOqaQF/n2JWm5WGa
+8/ZYgqCvNUIcZcLJL7RoZb6Br9RuRJ+nVlqxzuAm7ivy/n9sJfV4/PVvMzzQQFy6YMeT99OjN4gy
+OT3SbE7H0Rp6rZD7/kWOHsr4rsNCeQFM5jBSBB7nJX5HJfcWGqsISzq5XtdGBLX89tq+Q5hKIK3X
+7fPavvjJQShpyObmc1wojdssLaArP96y2+Sxnu5ei1kuJ1rUlpRt6jn0dxVPcXlXUpTAN995zE6u
+girNmjiVNHCqllCrVT0t7Ir7JIkKygwLdnZlvPcat7icSrxm97paOc677EdfBuB733qDseJT3j89
+p+XK94KmQ+ioCEyqVHphQaEACJfiB5lwOdI+z3VKF1gaiVgY/+LtDPuCjOGr7THdmaSnh40t3FO5
+7w0EmxC4n6BSFuwqOOd8JHNTG8tJIFZxdTwmWlfIukpEjMuShVodqVcy0WCLBZIl4tZxaATawY/Y
+Po770Ad+AbgFjIDfBP6tj/r9Py1F/2d8Rs2hjJTjSxE/fS97n1dUG29jS3LX5isL3n8si+7Y1gxU
+E3E6zohSeUifdyN2npOortfQQXJ9qGRxv/mNCU5DqxYdQ+gqjXzuYDQA+XDyFC66RJNXlqccXQvw
+NHfdrlv4CoSxcwFezd/6KtFtWRxRZ5P2FxUyPCqJviT5+NmhZB/a40tO/4kGsLZdpmeCQ6i9CdNz
+MbXz1JApA8rlpYzJwbxiES4p6R1SFdC9rBd4Op7OZEH7UFZWrO5Hp5vSVMq0RlRTTOS6pyPD2Yku
+0r2KYapVoEoZNhndw9GH388Dgp6yPHNJoJRodZESagDW1VqF9Szh/Qsxnw8fv8vpE9mo42zCez0J
+Zu5HHeqJQNaDlgLHTE3Yk3m0vkM5Vzj2wydc/8RfAGB3/zrPvazAIK138fY2SLVasLfd4B//faH3
+Oz6dEnbkM2thh3qswjZKuDOLm6T63rx4SpwDoImrVU3MwrWQypt2VvHV92UzufWFJtW29HNy+Taz
+d+VeHodyGDqtBtPH8hvWTPjuE3Ef3jm7YDSVlbZWOivVoayWjTdu1pyoyvy0Bu0yTVdAWQCFY1gs
+lrm9j1ZC/XHch58D7ltrz6y1BfD3gZ8AeupOAOwDBx/jN67aVbtqP+b2cVKSj4A/Z4xpIO7DzwLf
+An4P+EXg1/hRpOhXzaLWGSfThK9+Rcgq35zf50Esp9hPaBXiz/zkT/PoHTGDG6djWgM5lYYP7uKu
+qWDM/iY7nxBU5Ol7cnJfnBacq+z3uFnTuNT01sCQncmufBbn+HNNYWqgspjXq5GzlehJAgyDnLNN
+OQkfV4dsLyT33PFFg7f5qRvMT+XUNW+MSI8EdXcxe4fTb38LgHemcnpGyYRbX1B0Z+xiA+nn5PGE
+R4EqG89S/oGmnoqR9P3At0QaPA23LeMHqu/gZXhqd4+8yTLVTxDLPfXdEHepYznxcfREtycXpBOx
+wmaO5WznAQCLkSBIG/WA3qa4FL3rISOFqRfDKb6vp3u7ItXTqqfJqDSZMe/IvZ5MhhRWTsrFfMrD
+REzm/XiPIJRr+66SrNiaXOXu49kGm5HyJtyY8v4fv679nxIsNBWt1GXl+YJjhXRWxxPcZRS7RMo4
+gaIuyddl0Z0fyin/+mjMuQb7pqZ8Sn9tYK5jP4ll7OMcLjWIPZsb3lbD8v5vvsNrr4nV93Kwzu2f
+Va4OJYWJT/Y53ZLfTeKC6ETSk+VFRKUu3dFty/SBjO1ID/w1AyctVVB/UpEpFdx201C2NUU9N0vu
+W5be44e1jxNT+Lox5v8EvoMM7euIO/APgV8zxvw3+t7/+qHXAkpE0l1dOhbW8vh9edB/9+zbvPUt
+1fabj5j2xOd8XixuXuq/TLj/RwAcP7EUhxKeN48MrTVZNH1/m/lQRVk0qn8neYt7mgdOWyGFMga7
+nmWhD9Air0h0c/LiJQ15sTIZrbEs7aLauMxR0hJ8picSpGi0xXR0zx/gP1IsfmNMoSIkbn6fXltm
+bPc5ZTR6q0XUlwtHF5apkqnMp5Yske+dZ+WK7OXdmQq7ljWu1mq42y7+wbIM15Bp7KPRCwiaqk7l
+ic+dVRGXKrAbtgOaKqG0sx2z2Rdzfa25Qd9RRiNf+pOFBnRhzuYZwyNx1y6qCesDce/63oBQS4dT
+Tx7+JFynuhSfe5pAri5PMw6Y5FonYTv4nWX1oHyvyAzVYimtnhKHslmYsEU+lIrBi3vfoqUEPI5u
+IPNkwbkn62ny5oRUmawGYUQZycExKw1Ucr3xsuT6Mmc6UXxHaUHZjwyWUHkjlxyIoVcT630kBZRa
+zXtxUXGqma1P3ApZr4QkpTOS+7xwv8rB+/LZ71+c8+AddYXjBUZx1cVJQZ7L/JW+9OFy7JEvsyHW
+4KtI2OBayMs3FOB1FybKHPYNzVR8WPu4UvR/B/g7f+Lte8CXPs51r9pVu2r/3zVj7Z8Z4/uxtmbg
+2Zc32nQKOFdMQBy3afdkJw29iuFITpXLacE412pGRR36riFbprGtXWUOosCl0syBMZZaT/dGsIT7
+uiu6ru1+i5dfkcBPEGyQTOVE/1K/TdmW0zHQ4qFvfPcJX3Lk5Pt6cEGKBNfOz89Za0o/d7aewxg5
+jaeKb3CTKdNUTry8tIxmsnN72YJUIdZVKSeD67s8r1ySdbdLjvRhZmoGDc2393pcqJvz5ExCN59t
+dhiva0XezPK13/sNuQ93jeMbevpf+ykyK9f7wqvy73OvPE+mnJi/95XfplKth0ZQkYdimd1/eIed
+ltzr7jXJSDRf+BwFYh1cf+Uan9/VAqTA5Tt/JNDsk/fvcf+7UvB1dKR4k/NLfvIFoS77wl/5q7z4
+eamI/e/+i/+Sn3KEO+GNmz5BU+jIzhStd3s75eZNqT51gi6PHkpgtjh9wmUuVkGWubhqcu4r7Zq/
+1qTWMSzmC66rsnPR8Sh0zMfzBdGSuqwp/04XASen4rp9ZvcG0abcX2e7z+gb4vJ9UfUef/P4O5xO
+ZW6++/7rVIl8b7/XpKhknRm/YqDJuULBIsNZjVXG8P3YpaWcnqXTxlUr5FPdGGdP4c0tsYKSqcOa
+cjkULRgei2X66P4Rn9FK2sd1zkx1Lf6H//ub37bWii/7/9KeCZhzbS1ZXpLVDrnVCrKZYWxkkhdl
+TpKpSk9eUpZLSXhN0ViLuncYLEbfr2rhhAXIbfVULWlJye1CUSyFYgPyUk3KKGfmy4N8Wbh4c9Vf
+fyCLZ9DLmfg35VqTKSffkMkvt85o+vKwxJ5Lp6mme1av+rPkoAzykrESaCySBZnGJZqeLJ7amVGk
+yurStHhKZhoGQ5q6WUSuYe26LJSp8llap0U7U+DRXX+1yPvRF7FKnFL/3jqzF4Qy/sjKA7i1fpty
+VzaWZH6Ep3h/p7vHmlaJXm4YmrXy/PXl4dgZhORt2WxeXO8QDSTW0GkU3P7zkmkZ3FgwVTr75ELi
+OcflI05O5PcevHmftYZWX3Yd8oZWxE7vkPyBTuAL4mc3+tuEpZLRBnPcsayRPM0J9QDo1i3mOn9R
+LtmlThDRrGVDSzaOaWspswk9Kt0sgm5BrBuup/GQyjPMR8omVbeoxvLZMs0IXdngHhWSWrw8XfDm
+VyUeMvPP0UvhOSHX1qQfs+mQrpK9aMaazdRnGslvbPltdlV42Ck3GIWigOWZHt1Q14bs14TtU4KB
+9Gez1WRdYepp+j3crmwcz0+7HPzRFcX7VbtqV+1jtGfCUqjqmlGyYNyoMYWe8o0zag3EzZOCQoMs
+WVXjKf2ZwhGwFVSaQfaMETgu0KgthdagF9UP5DfUGvEcZwVISv2SJ5r0TfMCO5ft+A827/AZIyfh
+Aw3kbIQDvrP7dwF48ocRw5acqq/svkoQivvQMQmFQm3nEyl2GiYnVKn89ugyY6zS76auaKpVMdQT
+OsgMxwM5VTfdNeo1ucbm+m0G6zIu+byk0oq7wVxOre+0/yFbC3EDRoOchprr//iV32B7JgM2+/RX
+eHlXwFDzheA/pkfPcfxEbnDxKCDQk6v92gmzmZzSLd+w1heL7dXPqtzcngOunOy3NrqEy0rSymFv
+oUFJx9Jfk9z7oVaGJu8teHMseITizgg/lOh85Nzjd2/+stzf91tk12V+Prn/UzIua+d4rlg8ySUY
+hTaPymM2FG+QOXPCJe3+hsq2N/ZoNWQs+lUT05W5Ces2da0FdGUXVzNMc/VHnbqkN5XfuOP8Duu1
+8D5kRZ+O8yYAv7/1KwB85Z9WHFu51iDqEGnR3P6aByoTv2k8FpH0YydQjMlawX6pkm97lm1dqfPe
+gtapVgrfOMFNZR06myqhl3eIr8n3zKFL2RHXdOuRy1eTPwbgc7bDe9flfcSI+dD2TGwKYHCMS2i8
+FRmm5zvMpjIxdf20FiH2HVpqEjuqcjQZl0tcB64DrSU7kGPQPUazBPI/7VgeqkYjWAmYBpklr2RC
+z71zttQP2/Gvkyzken3l7q/Hn6d3IenGx+cHtPblAWq6Of5MHoST+IA6kAmbZPIgDGcV4+HSJaqp
+VA2r12jTU42ETGu9vdqh0VNy0SrG03hA7Ib4Vh7I0rXkDVmw7UjcgA2vyWIk123c7FG/J4vqRrHO
+nSeSlWl82eCmaoN6ijBsvcP+a+LXB7tr7O0LEMizh2SxuARZPuFT1yWGfE3Jb/3eDTzdnVuxITBL
+ifuS3qaCpbotvjSROEDVFzdnOFxQFjLeyazmwmqmxvw8t8/lof/e8IS+KLTT1XkqIo0AACAASURB
+VHyac9FipCpV0y2PuhLzemPzOr4SuTSuxThjud5aLBvkoNOjpWPoJRXWiCvhFB75QA+U4YJMadvr
+Qmsq2lM6y0043yJTkFj5+QGN81+UcbkvrkYy/C5uLJtm063oOeLmNbySVkdSq0U1Z08Pmb4yQV3/
+TJ/WuSp2+T0C5c+cvxCSaXYoqX0CPcyKHa18vSjwzvWzw5J6R+5/rbnLa0pxP7w/Ze+2rFv+qWzM
+H9au3IerdtWu2gfaM2EpWCAzhj18hshOWy4CEmUAxhpi3b4cz8FRufVY97SFqQhVCclxoa0ZBc+D
+ueZorW/paOSnq5HlIIpWGQnwyNX0K0cwbsjpET16qgWZGQ0+vXiDvUMp6bgX/TrtCzGfaTgkrpjz
+oQVHT5tczVO/zOmo6FWSxDQXSud1+0XiHTnRmzOxGC5PzrnZkMi7MUM6kYAyrGvxtRJxEZ3iT8Wi
+cW7K928frvPYSgCvVX6Ky9vS9y9GX8K9LjyOe83/ikZTzOr2KzJWf+ELLtdfkGO5/gw02iprXl7g
+B3LKzfKMdaXIs0YrSoMGlQZPQ895Gsw1hlrHuRuGODflVOxtKCHLk4r8geAKRhdjupqpuXezyc6R
+WD23Nwp2MqHWa6xrdqZ/TOXLidjubtF9Tsboxvo6rmL8/c5NMsVAbKsUPXFFZ11N/9Fi5V5M81Mi
+BUCN4xPhSAQynUc7j6jX5J5vzJucaVVmI9vickdo6p4//jcB6ITfWcHDiXxYU6j8FjQ2FEzkXuMV
+paRzFXuysfdljBhsBF6TLBXLcy26wbyvNRhlTOLJnLmJULDl6V0K5Y3wmvcJ5nKvWd+wrzyXUVnw
++OEPx15wZSlctat21T7QnglLwQA+UBpDrenCRZphNGAYeWbFmGxxWWgasdRgkOcaQo0N+L4jgQW9
+sFFmnjh0GbTkVOy3VVsieCqrZj2X4aWceBfjGVmuNGZ+xEWgugaJvPficw850Vxn3qxYnEkgrktE
+Y6H+aTcgCGTnXg/lZNts3WDjBfntM9shN+I7/8SLn18Rqd45k1PiqFmxuSbTk/ubeK4yNnUMgaYv
+y4WDkhfhFHKiBL2IXkNOoipyGWjt/Sxs0HlVzBRn/Bb9z8kXX1Ur4MbG52i0pQ+uU694BZpsUnuK
+rIydFcs1RiwU47CKAxns6rU1NY0llYOx9PqCbiz0tHv59vUVm9a7jQPmGgRt2hNSTafFvS6eCsOs
+uEfHc/o3FaXauE3nyzIWe402Xq15/EYbe0NxFrXqNGBWMaiyDY6esE7eWJJsUeNQaIlpNVvS1Hl4
+1QO5/26PZlexDllBvyP38qYjvnrdiCkVEu7EsKNszYPGFmtNsZCu7RuuteW11cImL6yxTa2uSie4
+qnvh5PcJ1hU2Xlv8C1kb07MHOpY5dSCf9coxxVAG6SJ9gt/19BqQPvjhdB+emU3BdWDi5iSqr1iZ
+P/kBhY+mJYluCkYf6MiYVWrBAUqlR3OsweqM166hUDx8ofneoLbUGnwMHB9fJ8lULhMtrS3iBWGe
+6fvygH3Oa/J2R4Arw9MRsdKMt8uYMtBIb21ZLBTPfyn3tHnNsrcvOe3nG58muRTY7dpOh4Ev5nPk
+yt85G9BSzUTr5mAV/xC0MWo+lxW4mvOuz6Vvk40pzlT1Gv0Frifvf23jV3Dflodmbe86NyLRf7Qq
+wDtNfOozhdTOalpiGRO5Bk9dM6/yMP5TGjqZA2c1WcZ9WjBqaoMmfnAIiPX+eo4AxDaS25ieZlSK
+DYqWws0PC2aaMQhnES15rmgprmAeNKlT2dza9Uu0NNDm3AzxFBTksy489YAKWkEJXkMCfNU8ga7C
+ybOMfKZgtqJirvR8gQrpNkJoTWTjLLccnEqrL72KKJN+NG6pxmherWj8rjdb7KtL1+s2uLZzE4At
+LyLalfVXnWog1h9QT7XWpPFURcsrbhBNlLm8Z1Zy9vVU8SbljLmWlNuFC+quzu8l3NMAdDiFsfvD
+bQpX7sNVu2pX7QPtmbAUMOB5DkUOVtMufmCItJIt8B2WcOzC2tVrR82DwDMr+fVm6BKuBFCgVPeh
+icVXxNtSKi7sBCuBFIJgBUU9TGYrvrV5YjCaZlvoKXmWH3P2XTFP83FC8JLmkrsV+3M5ParA5fJC
+C7AqGeZPLW5QTuSkGW3dI3bFTO63fdoKOz7WKsTGo5xaC7DiJGQyEOvAxdJpabDPlgwVVr2s2urT
+I9L7nO96XPxzLQj6do9AT4zkZ94jVbGUHdUNmJzd5d5ENCMvzyI+VUmwb3dnA+vL9wI3WLFYP+Wt
+rVd0dBazMhVqU2PN0qLLiZQMJOmIO9Dez8gOhcB0mzFTdV2ODk/hTObBd2rmN1Q74lKRmUWfpupE
+XvjfYKHp4uhoHSKxkBbtEQ2FDVdWLBTfdChCqUq1aYKn1Zq+tRQbGgguJrQyhSNnct1xfEzdkjGO
+si5BJZZV+UJAYsRtmL8lAdNO6TLfUCj9boNX+xLYvLXZxW6IVei4Lt5UgoBLl3C+9j7MZf6cUQNX
+C9fma69j9fT38g4hKn50Uywsjuc0ZjL2qZ2wUKswDy0beh92kfPE/YjlkdqejU1BeZa2PZcjT+ME
++NglR6OtVq5CUdcrqohV6MB7arbWWLxAF0TB6ho5oHwidLWmIu428RVWHIYRvj4o8emEi1J8OVNZ
+Ntvil6r1Rl502PSk0u0oPiFIRLzEJCF1Qybfjz06sUKTO/Lwb6y9RKDR9+7kpxhqmfRguE7Sl9dx
+psQb4ZuryPncP6WnEfI8zUHdH8/1GVi5tq8MzpthzDiShZt7A75S/xYAL69v4L4k1/tc9z+hh2wy
+0aY8bH53wL7mz1vXLL1YXlvPI1qqrxjR/ZQOLqnVzaqaeDUJgGNdSsWem9rB6ng2M4Ez+16XrvrR
+2ztblFpF+f1v3mdXax/SeIR3KsxLVSXmdeNagLfEb7ADWqptoj6ZloG37P6Kh9P3ZLOp6xLPaDyA
+MY66I3W4INLYVYmHp7UEU81keKM2uboiO619/Frh0ZMXMMqO/apKAPzz4HXiTFyNvfVbNF9QRqed
+5+m2ZY2ENsZTPtEqW7KO91gguBcnMBDLpteqBySlxBEazT6lL7GrdY2dTJwjojUZ43FWUZ7JBtJN
+W3jqCh/WCdnl8jG/kqK/alftqv0I7ZmwFJZxxIx6pQpT1PVSUZ2ssqAmVY1ZyXi1FK8QRg5KcYfv
+OhQa+LKmxlNToeW7eIGc+G09+Tu9zspSMMayaIopEXd8vIny+1cOU+1UpXxXtrwgU0jpYC+moR2y
+3YjyQk6YNPJoKh9hHCkXwGiEt6sFTPZbdJ6TexqmD6gK6Wemp+1gvyQMxdROQ0hzeV0YqNU0DNot
+XNXTLDSPH4dQaZR9VI6p9GTrxbfZeFFl28tDomvye0vkXhQFuIor6HsRjUhNZr+NddS0NT7WLnUx
+lymHGrOM8lrLKuJraoxyD1CmWCVRqTTj4s1nBIrfKINNTKXVoTan7srYN3sZZSqWV1e5JZqhIVAM
+SV0d0Wrpye1ZwoZWGNkDXDULy1LN9jimRlXAY6gdLSCrn2IuTJRiKtV5VMGgsJpQpRL4LPIAbyBW
+QVWf0FLE5teOBVLsNAtMLf3c2e3SdWWug1aMo8V9UdPFBMprUCsvRDZacWY69QzXV4Xq4piwr+rR
+4QlxqRWRGsCuw2yVqQjrklgzJ8POkKJaZknmLC5/TByN/ypbjSUpC1JjqTV6O6krvGU5NBBosVxV
+W8JQ05PeMmXprFKZvgvzSgYhwqGhC2iwHhN0lHLcW/q6C+qFXCshZzGT701GNZn6pEVV0tBNplJ/
+pVcE3LipvIXFDt1SFmA0zzgsBEpsT8H1ZEKDmfzedOce+2c3Abj+0mfol58AoHn9AhtKmDxW8FJr
+HlMhC7SbVwQjMZkvRlO8riyabJ7RKSTVVx4LUelka06i/mlQhVBIH+6t/TbjNwVqbAYPaTbERC99
+8bOL05hORx6wi7yityWD3yEi1liLg5R0A9TK8uM5zgoAZhxWzETGOtRa51EXFarTQjlSv/+85nwp
+1HOYMSl1c8IjjeV1MW6zp27Fppr7rZ5LlmrWycbUmuzJ3ZRI3bXAaWJVPKZSwdsys4SebBp1VoKy
+JZXpHAqtSrxcULgKmFPT3st9nKH89iP7iLYeEEHTobch8ZhuRx/yhUNf87B9v0GoGpN+ERKEGmua
+ujh9ub9qJr9RlyHFWONkbUueCnCqLDzsWNfezpBCyWUqZQILM5dCK1jD3CVRiLV3YHk3knU4yQxn
+jt7TR2xX7sNVu2pX7QPtmbAUsEInZaxDUS8xBqw4Emr7gyy6Fl+zErGqXPiug6+mQiNwcfKlhJxd
+kagUpY+j1NgP1ETsLjJMIbtvHRQc35f3Z7MMq9Bdz3FWFO6pRtLmrmUzlYChYcT7sYi+1CcWX+mz
+rg/aNG7KaeyeqbXix7S0Tn+9F9HvaFR/sI2fSj+zl9T6aVtczVczg6ma/oMnDvn0gbw9XnCu1XeL
+Wsxkc5Fj50o/v+VRKF/j6N0e55kUEtVfmLEzFrrzpsKujx8ckHgiw3f0zpzOppyeg/UNrt0Wa2Sr
+f41mT1wv38ipnPgZJw/kxBuPzulppqVxw7CjRUeBN6WaydimWrRkOFsBvY7PX+d0S3EhrsFO5MRz
+qwr/M2Lmh7mqNkcNKhXDuZO+T6yB+GwvIktlvLt7RwTKgRHZJZlmTNqXftq5C0pwU+cRaawLowwo
+F2I5DY+FI+M8OyFXfsxi6gLyeuN2i4tKXk8fybU2nBat19RS8nJmQ7HSmm6D01J5ES5juudLXIRi
+DLZy6kQsT89vUih3SNqZ4CzEgnRnOZnSCWZa4enOPMxMsTCjhEKfBzeMuabanXerBE8FhpAufGh7
+JjYFi8QKmsasAthFZShZmqJPwUle7eCZJWeeEqoCiUL7Is9QqemfFxVLNr8kh1o/76p/t+E3GauP
+268t02Vl3LzA0+fReoaGIiELzU7YWY+ttqB7vjH9ZwzPtBrSm7DZVwKYfsRaQ1Jusz1F1fktXM0o
+xM5Pkc+0WrPfJLfq+05UFn1ySUOr5dLyDMbiVhTJAVZJVGa2pEjl/XEm39+t2pxmS07IDoma82t5
+ylQ3w+zR5xldFzal5I6MxYPTN4l1XB8mY+b3ZOQG+x7F7C9K/7/QZMtdUrgrUnJa88ffk8j56Dtn
+dCTIzk8cfZbzW1qjEGZ4oYydn8nDurv1MrEqT3XchxyU8vrNwfdY9+Xhnkdj9p3XAOhFSppbDHn4
+RMq909GCR0rxPhx16Klg43OTT9PoyJz1EpmbrFyQHsvYnz65y+xIdpOECR1PTfvmiDKR6xktby4T
+F8+RMd5trXPpqELW6Q65Zn5uzX4agLfCQ14e3ZR5HLuMfeWgPHuDaqJpba9go5YMxcYnpG9rrZeI
+dyXLQuyQP5DNxjm+zuhQ4hmTyZx0LBuWq9kQJ5wSqo5I0SiJIlk7/a2YzYnyibbPuZ8skYAfjaPx
+yn24alftqn2gPROWggFcayisIdUahloAtPr6Kb6+xGL1M6lmFrKsWmn8hYVLoLUK07JasS6HXkGu
+ZshSOv6inHKRyMl+Wtak+r5r3NWp2aHN1kCjyJ7s/K3OJY8VtFCZc1LlYGyuuezWKrlet5mqEtV7
+l4JBuNbcJ3hVjtK8fBd3U0zDsmpSaKA0z/XkK844P5WA05PRCPSEyooxg6UvFdV4KvoRhioQ47ss
+o3qzeb6idS8XJbVmAObjd6mVdXqmVGN1fs5YA3iDtrvC7W8M1mlviTXRb/p4yiScluK2VOEFN/fU
+dZm5eLsKU/fuMJlpXcmdGk/ihTR74j61NzyiXCyz/TBkcaa1CrOSuC2uUqvlYB2xeR2Ve89OHpMr
+gKiqF2ztyrj0S4hCpeybvU2hkX8Uv7EwUxINbKbzQ8xcxsiNZ/iqSTqfL1CuF7wt5WaoW8zUfQhd
+lzXNPpTZEKchfbuIl0pfGWdTAYDNq02cC8241HB6Jvfkt0uymfzeIlIci5PR2RXL0ylqClfmZJ6f
+cnIqvtLw/iWnM5kHXwFbnVsuu5VYoY0XGqu1MO1fwJKtu3bpn/4YVaev2lW7av//ax+6hRhj/jfg
+3wZOrbWf1PcGwK8DN4EHwF+31l4aSV7/94jydAL8B9ba73xoL4wKqrj1qh6/Mk8tgryy+MtiEANJ
+qcUeocYc6polLYLxHAbbyuA7djhRBp5xbukoJ7+j15rkBWPV38urGlcDF65r6WqAstuHTzz3k/J5
+TQs2ueBdI7JjB8fHeNrn24MdYtUCSNIxZ6rofKB9CFgw0FO8SltMDrVKbjOj9DWYOdQ+LByilvy9
+Y1ymj+V7DgU2khO27TVWcYn5SO55FF1wOU50YAPmqhVZOxMWU/Xr3UccXAoE9/49iQccHz5eqW43
+2h22dsXvnRyUNLzPAXAWBDx3XRCJE2W7Ls8c5vckHdoZDMk0pXq8GLJQBu6e69FTFF40EXYn6xcr
+piDHBPS0mMmpDJ4yZ7l5i8ZM4jLLtOeoiDk+kXjA7HxKOJb7nnYsPRWM2W5s4ikZKzvLcc1IVCqv
+nFfkahLMkpLDg+PVb7vKqbGufnvXNmkM1dq4Ua4KnirGODrMa105zfs2J1/IXM9Ou0RabPZkOOGd
+Q/HnB+0YKpnLW09kLLc6PoEnr+PWFsXFdwFIHk95ckfVuE9HnGjfjrVY7znjc6qVtJ+c79Dd1srV
+tx3eWpd7cudQBAqF/4jto9gV/zvwPwJ/7wfe+9vA71hrf9kY87f1//9r4K8AL+h/X0Yk6L/8YT9g
+MASeg+9AuBLbeCpe4lCz5GOLfZem0nN3mtL9elaSKl32jWtdPvniTQCOT0csHivefVHQUpzBEvzk
+TBxqq9jyyqGltQ0dr836bVlgvWTAK9fkGn/QlOh86r9CcleCVmZ+ht1XTsi1Bl4q35vmC4KumODX
+O/Lg3t6+zt6miuP6d1cuQ30e0+uJfT0fyEMcZxDX8hu3O00e72pC/tDQWOL5w4qztpbqKo3bYjyn
+VtM4fqlJqkFXOw0xit9wYphrCN/quEWtiKZS2De2G+ytSyByvR3ibwjGP9zeIGjIGO6r/Pyjjce0
+lUqt6/VpNFUtywZwIp9tnsbs7WilpfIkumlKfaEB4yIjfElej98aE1yqSxD6XPTVZdMgcZItGKwr
+VLrfplYFpW6rT2+ZaPAK6rbMa+zIRh5EDUbbcs/lUYarrMxOMcVVXUzSghuJjMGOytZfehZ3yZuZ
+RvjKCZl/MWP2WOYqPVQuxqDF5Wua1WBCVcpaGIQp13Zkk11vhfQdWb/XbqihvnFJ6QsAqkwjfMRt
+XDTPsS31FS89Sg1WBkojQNPlVi3rZq8ZkvR0bLcMW9ma9mPB1jXNPoiA1oe2D3UfrLX/Arj4E2//
+AiIzDx+Um/8F4O9ZaV9DdCV3PlpXrtpVu2rPQvtRA41b1tojfX0MKJkUe8DjH/jcUor+iD/RflCK
+3nMMnuvTcWsuVnJsK+MAYwyus0wn+niaL+xqesz0UspM3ltf2+XTL4nGwLVr4A3ek44cnGErOW1D
+TfUVbYeGmt+76w473SWxRk1LrFbc2xm9G5pGfEcCS5PDJpvKe/Bd7w5RIieXjbZoqZbkoL9PPJCL
+RCpF9tzOJiaWEyoeXOfJgezsYTOkbCqRiyuYgNpmtFrLASh5LpCg5Lg4pBFo0c5sTFuH3mnKibI2
+a5I1JJDVrHapNdC4HobMVCBlfbBHtyn933xB0nHpq+e09gR70XBDdrblZCN3ebEvoi399i7rTen/
+pJJTcr+1z9myUsw/o6EQ8lZnm1Ll5jY+3SeMlqQ18v3qssQoj0F5Nqar+fg4OKPlyG/nUcG6I/30
+lBLv1kZOs6XpST8mP5Xzaq/bJV9iCJov0NBxHkQydyfJPdZDsQJm/SNwxH0YDDboRkpeOx+yLVMF
++m+EQ5DI3ze7+5wqHN2c7ZFfiuU4UOXyerPkugI/2qaiowQpPTZ5vi1rK7js0F+TdRu2FSG79gLF
+hVh3QZySarq3EyQ8P1cdDSfjU119rf/6eYNXtiWV6e71wdff9h7SOJD5GXPE5kJpyNEo6oe0j519
+sNZas6wt/uG+t5KijwPPhq7LLC9WICXXPt0IPKDZUFCMH9PV12ZJaFIZTCSDunejRaz8glHgs/aK
+Unn7CwIrRstlLQ9j1/dZ09947nqX/EgmI3IGJBoPiEevsbihUvQjEVB5OT/mTKvw4o2I2Mqgr20O
+2C9lQfe3XmbtVYHB2rks1qbbJFPjLK3HdPtyjUYUEzUVFOQqcCeChro7bmONpbJI4HSxicKDM59a
+8QmRajROO5cMGloFGjcINGvRiDpsrctievX6F/j8LdmwNmJdMO2I/ickzlCki9U9lW5JpyvX2+h0
+CbQyMErVdfMidgdS2dkwrxDvNrTPLkY5Cr0ixaqkOlo7YcNjWLJFrYWUgbpNUYZVMFHc9qlijUt0
+dHxmGXFbHgRvrUuxI3yUXhqRqCSAEwT0t+TzgbpPrSRikkkcpO91iJVv09lo0VZxHVMFRLkSoJQy
+Z9WlBY0ZZB2fSKnYq6xFvCbAqpmKwniRh9XS8vW9NbZ1NcfRGpG6PM7OExqhMth4Cl4KFkQ3tL4E
+aFbSn6Jzk4ZmKHYG+zhaVRppmb3XGuIoOYuZn5GqlmYRPsJuy2eDiUP1lj4nH7H9qNmHk6VboP+e
+6vsHwLUf+NyVFP1Vu2r/mrUf1VL4LURm/pf5oNz8bwH/mTHm15AA4/gH3Ix/aXMMRIFhWlaUy+J8
+p8JfBgZ92FDu/SiymKWLsTytcbFqnm60O5gteb8/uqSj4h7NqEvreTkFPucIF0K8tYe7lG3PTpgO
+xfO5uDxgdyCnUfz8qxy/Jwi749+SCPorX/weTiT7oF+4tNQM7PoxnpIHN0NLd4mmS1RGfd3iZ3LK
+23SN4lx2cP96iKMwbXcmFwg98HqaLalC3EyxEkPIl9VhNsObK5nGUE6Uk9T+gHbGHF+Lv8rOkFA7
+99x+yq2eWApbW2JVhesD/EDM5DzJcdtLirEhMdonYgyqS6EVh84YwiW0eXODwNF7LUJsptWHzowy
+k/Eq52r6P2pQNSUfn1YBC61KNWXISBm9Ly4NAzWVw0R5L1o+RolxGl6fRMEnzobFjOUzndYmDSsB
+OLctFoh36OAp3ZzvGsJtmYcyWVC6cn/etEM20ACs4lfSvI37WMboaG8KrkoI2gkbypGQ9kXUZnTs
+MjDiooTjJranjNdxQaOvmYHRTeLbYk3WSj1Y4qJLGbefoDAMoszi6XjXnx5Sv6dB+JasG2/Sot5X
+Epp7LkOjgc9HCY+vi/ViDxzupsts1EdrHyUl+avAXwTWjTFPEJXpXwZ+wxjzS8BD4K/rx/8Rko68
+g6Qk/8OP0gljDFHkMUkdfF3RQeASKDdg5PusKRijNAG1MvqkWlmXjgt8Tybrewdz/DWZRKYxj8bi
+95njJs9viC+3tf4ZAMKuofaVc4+aoQqCmtqjnCl9/P07fPdbsue9d/rbAHzG/BzZRBZgI4spNmQC
+knRIpD5ww3awDZm8JdmpF4S4as4mwV1stKSLWcddaNorEBmfehHR1PeiuEuu5deld0lRitlapSkX
+gSziy0r8RT9JCLWSMWvPcFSfPJ34GDVLmy2PrtZdLP3apmNATXWTZ1h1q+rxHKtMR7YMVuXHVvH5
+ef4Ee6ruWtnAH8h4lutT9KNYnpAfytlwWct8ZCdzylAfzLqH84pseqNsTjpX98HEjG/JGGzHEmvx
+vICOxgaKbop/qSS2k4SOeiitboqn5d5Gge5+bXH6WhF6EhJrnCcio1rTDNXIEo5k7JID6duJ+w4j
+K+PiHffwdU7dzxvyRF6nhzIfzmzKSVvmYX1+i3aum5AXrCgB3CYYZE6irmy8aeMYZyiurWPaq3Xv
+bDyh1HRoY76Bo+C5rC+baXFU4D1Q+v1HR4xaskFM85K2Mnw9Oh4zCXVgPmL70E3BWvs3/iV/+tk/
+47MW+E9/qB5ctat21Z6p9kzAnF3Xpdluse0VVAomqhxoak48aECp1ZN1WeGp+ehkykWYl4yeyMn1
+h8ff5+ybSzlxuDgVMzkvDhi/IQGxR1uymz8f/hSmJTn4fjDhyYXQXQ3HI/w1CebYx495cE8DQqlK
+lk9iejMJyp2536Y1FjOyzm9gNsTcNWubNH0JNJZaDNSMmqSeWBXrPM8wkHx0x+tSKny4VanMeJ3g
+WjFrq8DH1wCXxcNTaCt1jjdUk38mmZFWfYPhRI7o7PsuYaCgIS/mwsqp6S1uU+Qa+tFsQGEjfFUt
+ruYuuc7DyeOMzqFYWGkvxVF35GQovzE6NBjlfqyTmjSXJdWdtFaycKbYY6wArnwhojZn2X0czT6k
+vkP0SPkqgxqj7krRHLFTSebDKeRk97s+pVKNhXmT2UxP/3pAqRwJG/YWdqSq2Xr/mRnDVBisk/yc
+UCHydX+N5kyyLoVzTJ3KGOVNcSX945i8knnqeWvMPaWHPtwmUnr8rcubAHz90a/SUdbt8+dLnHVZ
+L+1Gl66uBb+5Q6AU70Zdn7ZtURUKFS898oWsw3j2aRYTqcBNp9UKI+HdEzBdtXiM1Tmre++zMVSO
+i+wu8+8rL0fyLhvzJaX123yU9kxsCnHk88kXd7lMe+TKVWergCCSiXNyS7Yk7FhYKgUZBRonjWOH
+eqJgleiSy/LrADRnLoVWPta25uFC0pPHJ7IYH7v/gEjjFm2vYqpoNJM7FCMxxap0zGEiCyWrlDgz
+PeBAN6YsnkMpn83dc1C0mW8SjBWz0teKPZwMqw/QIj9hqgCik4MmlQrMTmVt0OhDpgKt4WADq79t
+4gmOVSx+IyFyBbk2ysQsD9KITFOv9+9XdJdjWGdUmiSajB8zq2WDjI8lpbcIm+jexei44nwo171/
+9yGNuW7OO2u45/KA3JtL/NjOMzo3lI1o2CfUCsCB6RJrFsQ3czJkLJJKy/uxiAAAIABJREFUCW+d
+U9yFbu5rfeY6xs1ohtPUOoGOAV/6UYfyG4vTKamvWYa0YKh1J3k6wldX0Nx9RDZVv1uZqYaPHnF0
+opWog2q1AcbrAW4uD6FnDMrDQsOXMc6GCbmzrGcY4ypZyuLAJf6cjMV7E3H5HqcPyMdyH9s3d+gc
+S1wj3ekze6KqXbdyHM1GOaFkJLLyMVWtqMPFhEQFfefHZ3gKLqvnNVOVNnBVINXxLJG6zZ45JHXk
+npP2Yxaqy9HyK+q3l7XCH61d1T5ctat21T7QnglLobXe5y/80r+L01vw3O//PgBvv3FEoey0djRl
+eKH0aE5OvqRpsxrsqsA6yl9XuViWnIIeJtCaAeuvqMEdVSaaLCYcLmMwtsTRrMY4Lak00FbVUOvn
+lzzS/WjAqSvmenFoibRuwa1dqoZaFdmEOtOcvFav5a2aMlWzdjLAO5fTYdQ4wF0qp0wl4GTcDgvl
+Cawm6Sr7kBzWONFSir4gO1HY7RM5XeeDEYXyEw5nI0y9VNHKmCmH38nhmIsHyjq9VJNqTmk8ktPl
+5PJwRSW3fT2gpSTAxltQq7bhpiN/P3g8wXaU9nz7BDOVgOD5MMBraaVex5Ap3j9b8pmcVkzUIsim
+DUKl9p+PRsRKq5ZlMZlWNiahBgwbEYkrF/HOLIEjS3j9xg7W1QDeuGZxqIrWivu37phQdSJ7jR6t
+dV0jC8hyJVHJHeZtjfar4nke5XSeyLjd23iAd66w8EZONhd3pDFQd64qeTJWK+0g5fpzcn8VPmWt
+QLvxq3h9cZWqofStPPKYHH5V7tm8z+SeUu3PxrQ1SN3djkkudP3qnLqOS7Iu9589STgK5P7O35jz
++rpMWvuOx3jFf/7R2pWlcNWu2lX7QHsmLIVmw+cLX9gn8bqka7JPbX3pLo/+QE7js28fkSTqZ82g
+oak8qwg2awoWSsEWei61nojEhlAh0XHk0dfce6YFQ9O5Q60BTL9ysfraVC4L5WfIClhoBV+9FDrp
+Akqp1Sp96k05lVxnQZ2o9Np4xGVL4iNeJO/FeQ9nqUOxe4ZXqO/MGmYmJ9RoQysAp5c4p5ouLCbU
++xKXSIMprvr++fiSk0jGyCx1Diqf0ydyAl2mI8yyqGxRsDSxjpLvMky1qKqSMWk5LYaB3NPUXBIo
+XmJ/I2KmaEmbz3FqZU7yxSKY3QyIlay0nAV4ikYcNxeEmtOvJg6+YtYTTeml8ZRE+RbSRYH7mvz9
+/PvHhMoUZJKC9/cFpddXue7GSYKjcO1ZlLKpmhpbbBFqvGbYeoTTUnJU1YsIspBwfYnvCKiUZ6Gs
+x6Qd9bmHEM7lkUjGGouIRhSa64yLLrMlMvNmwkEpc3X3VPqY5S6ZYkguJwvOj+Q3uoP7sCFjH3gp
+zrmMrR2JtTas3yJ5pIxP7hRPi7zKcEpVhjq2NY7KIZY7y/Fpkqs1PRyOOPIkXnO8qHGOZU7O05rJ
+EtfyEdszsSm4bodO82foOZbmrkb1+1+jc+8rALxxesi9h0qMUiSMVIq9rVRUxjVkOmDjtCJSIFBa
+GWplWraeR0dpzPJldaI11Er5llOs6NaOi4pa3Ye0sqtg5LIW4/bmn+c51T78vx7+H7QvxQTPTMTU
+yIR2Ao+mpxh+I4vAt01spYCWfIcskQh33HcolXCk2xKG59Pzc6qZKk+FARuJlBxn9jGu0XJwe0yd
+SGQ5VXrzZpqwmCV6fw4T5UYcEDBV4MDhpcN9JQAJehr4K+crTMPh9JD0VHP3ZoPT+ZIibsjNLQme
+XSgNWhhHDBVXcDSZ0tIsyXbnmmyegBtElK6SlhgJrpVxSlfHoiyhk0l0/mT8ffoaVL4ox0wT6dNw
+Tza/XjpbbbI4EbNMTH9OPNo9MdHHkxsMJwJJ7yjGYD4+owjFBTstLwnUHYs7Lu1IMlSN9gJHS2iN
+rwKtFy28hoz3bhpx3liKxGwzfl82va3xZ6UPxR3CXLIkh0nJw4VWeE4WBPf1IS1HlFp+nSkfZJl1
+yDMVfUkcnFrmdN25SaKCQdPKISuUx7EUF3ORJgRKHxCGJbcnWvnJMd0D6f+byYis1kA3Hy3geOU+
+XLWrdtU+0J4JSwFcPNPBIV8VAUWtmvAzYjL2T67haGAv8CPCpSCEmrKFk+FGWmXYsESqA+h7Fbmi
+H2On5CjRwJ2qR1vf0NWTYTa2nGtgcF6ap5Lq/097bx5saXrX932edz37cvfbfXuf7p5Ns6FlhIQE
+wgSJIlAYyoY4NsZUkZRdhZNylWMVfzlV/oM4ReJUOTiUSVJ2ANvBGAjGYCEWSwhJ1jb79EzPdE/f
+7rudfX/Puz354/c7d7qFBs2I6Zl2+fyquu7ps7zP8j7v8/zW79caFr7IBXflfRenPHdDMwxNRGsk
+fctG6wS57NYmcXH1JHS1YGbe3ifXKkLfq7J+agEmkpNoYdJsLCppwAH5UDP7zpzHeBpCK46YDuT0
+cNMRxars/rWmaES7r3SZaHjLIcRZOPCceEGPyXQ0p6zZoN5wwWNQprih0M69A+av6fjO7nDqomgH
+1VINbyLthGpSDCJLqqQmR1dehqLGxBtlGkVRFYpujgnVrNLYvCnMGCxChHWPOJKTcja4xlAdY/Fs
+RKJ5A2drmsXY9Sid1nj8asDBjWcBaO09xeZFyb2oNXbIjIR7x2M1/RhgpzIBmZ3ROZD4fykO8LZk
+fPV0lTDR4qhQQU4dl6lmBPqVgIlqlvP2jPWH5OR+9kui8QVhSqY0dsk85eBQvvvwuTmjTELGpckt
+eqOFOSZzEkVzJi0JWbYmFrck4+/t52SLTNYRmLrcq61ItZUgJtJ07Cwdk8zldd92mGoo2jMw7vwn
+SEWf5zCbOXi2wDjW6rz0h8lf+KcAXDtqM1T1upfPQcEm6o/KYGtDl/G6emaHDiMl22jGBndFCWvL
+Pp5WII6ek/fmVYdQaaiOwoxOSx+gHBY4FpkrlPVyEfmzvnaKrUByIZJ0jNWFPpoPSEQ7JmaC0Xz+
+QBGcXa9GW8ul7csO45bGplf79AeyiPOJqJ+e43H6QS2xLZTxZf3R3x/Tn0nM/6h7xJEmVPU0Pn7N
+tphqzUF5JWCgXnS/HpMpKc20fJOZL3kKWU2WQOJ5cCBzseU3uPxfSIKN46yyqeZPOEnp22dk7rqy
+eY1mMbkqnKcvlMm13Nuzc1LtR1yMiTQd2ai/Jx/PcE/KNQpxk6IjG8TNVo/aliz+XrfLrifvT1Rl
+PnUqIFd++XCckihSVfWCx9yR+QzqbbyObF5HVnxRvb0pfYVk31xZZeeMmnyOxdMc5NFszHCo+Iia
+xp2bKlFH1031Gu3ZYnOecbn8QQDu+9gt7XvG4UgRoiYZR1q5O/HmZEN9uCcp2zsazRpLnsO8mhFd
+U9/A6ojWV+R3+0HKWSXB2bpsqWoilhfKOKeDnL5uoJ3DmF3lv3zlIOWa+lfWZoZpQdfvm0xXWJoP
+S1nKUu6Qe0JTmMQpn9/tMXXGlHXHfLX+DN2XxHv/2n5Cq6/cC7OYRPMGAn2v5BRxfdkGU9chVUru
+wdyl7os62J4ZIl9O6YHy/TnGMNBrzQcZ8YKQEruA4sMxBqu2hNHZmtWKjPe0ujCvMV3VU27UYaMl
+71c3ugw0rbim3IHBNKcwV6TlYgerJoM3T6kpdmNHsfUqk1WcTDkFR0N6wXUADtsDuhP1Mk9y2pmc
+bN2eqpGjgNyVeXGrVapNjah4FsfVuHmxRF9xLmepRBH8tEismZClRoHKTB1xjsO8JKqvnddAs+oc
+JbXBmzHbVarzSkhZU5CH8/4xzYAzfp02LlPymlpQpYRoI67bwC1LfyY2IFGt57BjibQIKFcqNbNy
+lnouZs40T0gKaq7NA04p/RuZhaqcsAVNOzZOxtxXkpU4YqJFRyE5Y6s5HhOY6fg6erJn3hBXoeAm
+MUxVXQ8vQFnxMk52VFNac3n+jECS3noxoqMe7RudOXWFqZt6lqAofQs7aib0hmgKAnG3yFg5TJy1
+nHpF7sP6TomizmdUVATyG3MOVDN5dRjRUqq7gxhijSRNLKzW9ewfvLnCKGOPecTfPSkHnn1oo84p
+fHarSh6aBsw0GhC4OedOSPlysbR6nAPeVDzH/WGXsdqLo+mMVI3nYtGQWl28Wcrpkub566ROkpCe
+es6boUseyHV7RzH7XVE1T4cwVOCMjpa0/p2PvI8fCSQn//fWb3L+41IMOr+ece69UldRaZwlDUTt
+vvKF3wXAOxhQURhyUyxy9U++BMBWpcKREp7u9uSB3r014dEz8sCefPI9lNelveFhSrGqlaRrVdqq
+8r/8nADwfax5huRh8aZvPXiZDz7xKAArhEy0vsL1qjSaooJv1DTsWSzgaOTA+CNqGvbr25SWwpP3
+hx0cLf1u1OW7/XmOo7ySzYLPxuaCFaqGq0lWG40qmzuaHjyU/h7svcZ7C0qis1LAr8pc/fTP/jyh
+IjnFrsVzlExXyX9NljLSkHKcQ0UXf7EQsLUm93fn/We5+ORZAB76i1K39x31Cs3id0nfafK6kmyw
+LHBBNSx1u5g//dZCfuIvvw8AXze09GbMTNO1u/0Rk5G8bo3nTHSzyVKLp3FpR01Xzzj4wYL70jKd
+L8CELUVNp6+EPiUF4vE0F9sUS5xc+Fo8h1lP+rE/alFZdLJQwhjZhD7/3Je+bK197xuPSGRpPixl
+KUu5Q+4J88F1DI2iQ6OwyUSxAkYHNby6qIzr9SZbDaXuWqszGWlxkDJGrxDSUM7Am9kM1DxYLxe5
+cFLAUmIzYE0JQmaacBBFDfbHYqLkTn5MdLLu5oxVLfe9ApXxQiORv1u1GSP/2+TzbM7h35T+9H7k
+a6A4j/dfuo9sQ52Hnmgj4YkYzxcHl3swpNhUbIIsY0NPyvGuqHitUptpKu8FQY4bKTx5YUCqfI5V
+t8oJZYp+uS4w60dpRq0vGsbsixGppsTmjoefq9d64mAWNPeJnj41Q1CRz8edCQNNrOrMYg4VDGZu
+I8qLJCRNimJuCIoKekOZTM0Sz01wUoWWC8qEvjoENQpxUGzRVmo+Z1Qh/armk2BxlYjHtSloUtpc
+aeujNAWNADjmmOQaN02PYer83YjwIZmDMxPJe5mXH6P0+vl5hwZwrCHY296/Q4E2X/8TLBZftdB6
+Ljkb45WYtKEkM52UTM1RN8tx1FntGovvLqgJFrwEwFwTubLsmHndGAg0X6biCfUAQFEdh1EyZaaw
+eMZAGCyK38bkc8U0TRKi5LZxvwlZagpLWcpS7pB7QlOADGvHPLvyHLM9xf9fPeCRc+KIurxSYHtF
+Yn2VSoCZyCl1M5JwU6lomDqye24WS6zoaXRie4v7TgnWQdGZM3UlnpxlyvDsWSo3JTTXcUb09yT+
+HwUDCrrLv0CHmiL4DjU/opR/lV/80F8DYPZUkcoP/zIAj93/P+FpWKjdLTN9StObE0F6qjw0pjAR
+p13XvYmvHBHzE7cop3IrwvPiGN0ZrFJ+jxKuODfJPUnnrUyaBEpj12v1SEbSt5Mai/7M6V/g4ysS
+IhuZsxjltehXDwkVes7UZ2SB2KKRhvG8eMB+WxGt5im3uuI8tVN7bA+7xqApIKyoM8xicNRmzQpz
+PEV5NuR4gYZXi1Na+1LkE/jyXafb47dLTwPwnqzOTXeRQ5IxCNX3kUCi+RCoAzPL8+PT0zeGoqJM
+ZbfRCdJfwX3uhwBonPh2AM7/JXOMgKUd/8byde+/kUvBYGBvW+dL1tCg/xWmmpNy1J4w1hyZSZYf
+0yE69nVnpqNI28ZkuOpnSDKLp9qYYwxVpUBsNAL8gk6++iKiYcIklDVihwmory2Ppwy1mM6PXWK3
+9waj+MZyT2wKjgkoBztU45zXFJ34wv0FHjwrD8KOu0K1IotpGkqtA8DpkjgfV9YhLCu4STqnmInH
+eW2rhq8MOqXYo5E9LO0pHkOyFeArLHh54uKp6uumPvZIF2Ywo9NRDr9Ab4bzU3z4imAzvHh1zpn/
+URb/+ks3GP2RLOLiQ0+TPynqf+GW9M2LA/pXXwBgUJpQ2pTrrdYfYgFlefph6fvWbpFCoLyFnR6d
+i58HoOrv4HUk5Xl22KO1JibKusKgPdn8MfojiVpsv+8JSp443yqlgHkkG4BfcIlV1e4r3kTBJFhd
+dPMoJo+lb7M0x9HoS8E31Iu6SNUB5gYuRUWaLrsejnrq/YIlUOTmpDU+dgQ7NU3LTepsqJlwoxVR
+OqmU62aApwlsSZIeF5zY21TtBcp3tR5Q0roLk0B/Lp7gVzovYY/+vszBUJzAawc/xNYJMZXcRdbv
+mxB7G+O54c49ozeRXBWTiqk1m86YOYq7mFsUahJrDY5iWbiOwVmk2evnaQ6OzlVoDI1QSXKKLqcV
+qGX7RIWSIwfGvh5qcTLA8TQBioxYTejQ+hi9RhzlZOatPeZL82EpS1nKHXJPaAp4Dul6hfV5mQvK
+Tnyu+H5OVuX0K6zNCHTbTWxMfVNCMmuKwLO26REU5IcmK9FUrSHGUChp2m3sUHLld4nGvk1lleSk
+OBQb0wRKEiJ0OwXmJ2Vnf6Sd8IwnZspQQ53T930X7z+QEyg8/1nOt4Qgazd5itZFqYvfXIWdgkCP
+mdMyjnx8i+K6aA8bW99F2lQ6srUA+mLabGx/VNoYp+Rl+V2v+yxBJnyOJD3CguZCbHq4sTjURmfF
+KXu6sI7VlOhm+hgVTV0+7ZV4JRftpp422R2LhjTRTFFKLiU95VfLNWqeaD+zdEK5Ln2uOw5Nhchz
+NU06qBaJNB4fpvb4lLFjh0jzRdJRQuSLZlJsy8k2djKKu3LiOZOIWLkmvcBjw5NlecsmoLBp1i5M
+GIdCQdrbrJfYasrpWbOWTizt3XeqTnFVvnMulXk9vPUiKyuiKbq4xwDB1ojDEoTd/E+fkla+hGoJ
+x45Iy2FLHIwbiqfRjWLm2cJhyHFaucVSyDST07EUF5qQlrum+evaz04xZGddU8mr62wpqc17Tu/Q
+Hkt71UDuuZMf4YSqPUyuYiaKrRHOqaeaFZonx4zlb1buiU3ByS3VWYpxLcUdJR4pVilqPoEdJcRa
+vhz4ZWqbanfXRb323RJhTSHB3IjQlwVmezFeLovQbXpocAHH6OeepbqpOeKzmMZU+QNnEFmx1fLA
+wVH2Ji+SPjx+sse0rGmytYhnv/yvAIgKPcpHUp0X7NxHmOjvFOHX5K9SOSs5BK4zhxUFXInHWE2y
+whFoL//sAVaJSSp2zLAlG1O+WiJTXMVaoUG2J5vljX1JmtlcO0XjAaHvTMyUZlke7pgZc7Vbd7st
+Uq0q1XpDNkshOxsypvpqiVX11K+ddPGU6cqaDume5nIoPNxobPE1P9/0XBwlvzUZmHCBGZhhNJFr
+3tbYfT6hbGTMsWfpdsRsDD2HfLERGEOeLzYD6WfoulxQ/MwHzpzloffIA9IYlqmtyQ1eeeBJChXZ
++EtWTMx6OCNSMphCWMGxuiFlkC2eghxyZ1ERe3vE4Taj4baXRoFx9rW+pJPmzI83ELtwg1BwQHF2
+cBzDSEMmC1ydNIeG5otc3qzz6CPiP8uKJcqam0CQ0XCa2k2Z45XVAomvKe2zgLGR15NpjNFNwfcM
+M/vWSqeX5sNSlrKUO+RbpaL/h8B/CcTAK8BPWCtHqzHmk8BPAhnw09ba3/1mbVhyEjPBlCdsKVpu
+dadPTavsxkc5Q83yqvjbrOruWasqp56bE5QkypCN21hf4brCFFc944W8eHyKJZHCvA0cnKlqCp49
+JjWplItEbWVP9gY4yOmYKSZAvVKgdfnXAXjhXx/hCSA0Z3eKuDMll4lPMldM/qwnGkHl/Q/hKrmH
+610gbysF+mnIFYk3T2Uc3tU/xnlUoeTyCtlETtXx/gAK4tia9sHOxJSoaNp1byXF7Sm2QsFhoykR
+nL3xC3RH0rZNLeq0p64vNlZDVpqqgQRFzp6XuV3fWsdvSK4Hgw6DLVHHm7uiVb3WnuLO1Jm7k0Ak
+J/BompBpUVGSREyPqe4U8q43p6/ZqzZxaGnlYMFzGDsK75blx8A2rmaV+q7l5IqMb32jTN2Vtk8+
+MWW9IlWSlc0cR0FkKCrQTRqTdhX+LD2Fp6ZpisWmC30JPNXYrGZSus4bPyLdSLWYQE2GFGZqEriW
+46crtIZcndtZBpFdOE/lc+vDyRW5Dx/4uOGJRzSNO1ohuqmanjNnpOjYwVwBYlqG8oLMJ+kwUa6S
+bJAxUE6NonXx3jDU8o3lW6Wi/xTwSWttaoz5WeCTwP9gjHkQ+FHgIeAE8HvGmEvW2m8CEmfIrUsz
+r7Gj/oDimRrOTPkcTYFcH0h37QZ+SVTGMppSWygSVTWc2O/iK3pO6FVgTR+EOAENLfo6SdPGAXmu
+JcQ51DQN2lZb+Hqj150qY8V5LNblLs7CCe1Py3WrX7nOjZ+QyMHq86t821ySZcruU/RcoRcvKWZi
+8+hxvDXZsOL138HG8r4bn8bPFe14U0p63f6MwD0r/Q2PaD0m4btw1+KMZNxea5+bG1qWreOvHzyG
+XxckoH4jwRZlfIPdiCRd2LAZvtrtW0VZYCebdYoaWSg3AtYrkte/Vm4QbS7CjwVWjJDdpDWxb0fN
+FvFNWdBrjRBvplGGdMC0rck0UeFYde9qElLip5Q0ySwZx4wXJXyuQ6zISzkGzOuJPABl41NXH4cb
+GDQbm5XkAdZKMl9u1X29INDKQx4MSpia9NPmFquuf8cWsIvcJeuz8IosNqE/Sxb5W2b+upERqsFR
+cg0mXKAuc5w2Nc+BSAlsdB02Kh5PvE/W/fsf+wCnLj8JQK82Z/g1OQDiNCe4KutsaPVZ2B8yVgRn
+J88Z57ImE6CiJb2ONaT2rRkE3xIVvbX231trF9VDn0c4I0Go6P+FtXZurb2GMEW9/y31aClLWcq7
+Km+Ho/FvAP9SX59ENomFLKjo/0wxxhAEPid3NqAh6pIZNI4JYCrblmYo0YVKaYu6woYVG6Ke23Kd
+olK1J6UDClbVwVqGpzXoeTzFUS9xfIzwXGUey35XcDNcxeoL2+vMsusAXKw36SpO/64m/2TT+7hY
+lwKlF9//G5x75RHpm/si0Xkdfv1+NkPRPNxQkltMbQPUJAomp8jUmek6q9gNeV3yRB3MVyMcTf6Z
+F15ic/YDAIzn10hdiTRMN55hdSz7cUe50ysbNbySzFUjeg99TWo6yTrXHaX1zM3Cqc9c4cVspUBz
+R9o+s36Zcxck4cp6IauZmA8TjihotV9nTfItLjiP0p1JQtZarUhakr7ZkU9XYaAdPyJS73tvJr8f
+t3NqjvRt7oFVDogozQidxWmteQIyewAknsGWFGNg5T7WT94n426eZ64QaoXRWfK2aFCuomtHeUrp
+SJyOybiIV1HSzzDHUU0uD3I89WjmVemnX3zjBCbN38Jmui4yg6vaT+obXCUrClNLstDg/fwYw+Ns
+Q8b83suX+MBlcQ7vPPrjFNaUuXzugv+cXM/mJHUBlFnryPgn8afJWnKtvpNAKs+FY8aotUIEOP43
+KPT6M+TPtSkYY34GSIFf+hZ++1PATwFUCgH1SpFSeYWwpupSf0ZpU25WvWgor8hDXw6rFIOFK3qR
+YJQTz2XBB5UpjhEV3S03cNCy3lJ07Dk+trHilKJyKjIeEygwRSmPqGiJb+aXycoawdAagPLGiD3d
+TIL0JfqvSn9OPRlSeUWJSmyMpypoaVN8B66TSyYcYHkWd1X7YW/gaLKJdZUoZKOF8WQD8dx93Iks
+cr85IHSU6clsYOeaOFOUNoq2hL+qJdJ5n0RhzQ/pYRZYk5nFV8SpspGNq1SqUa3KYtzabOIqWakp
+lLC+mEHltQCrNdCVhm68dkpwSjc/G2OVQLZoqjQVgt8desTql1lV06BYnhM5izBdhkkWSEGWXB9M
+zzGkCzLhRQJRDuVQH7YwoqYArabUQUstGGf7RFrN6Y/l/ofVMu5MvhDaMpH6A3CL2LJcL80z3FAp
+6mPx7dS21/He4JlK1X6Y69/Y5niLrMIMlImeWQb5gvQ3dQiO+y9zuL7qsHpCo1mTFq5MJ7G5Dmoe
++iYjnKmpkCg4TanH/lDWoevGBA0lYcbDUzxSP8+I52+/T+EbijHmryMOyO+2r9dfv2kqemvtLwC/
+ALC+KDZfylKW8q7Lt7QpGGM+Dvxd4KPW2tt5rn8T+GVjzM8hjsaLwBe/2fVc16FRLxOuTylmou43
+N5vU9DRzHR87VYoxExLP1JNdVZaS4YzEagpr6qHYJLizCHeBqxb5JCWtJRjLaTcd+8z1WlHaJ50q
+nJdfpIQ4qF4b7uFOtL5fQUjcTofiWcEv2PuPDg0FPVnrraMI75Su90m3NHqiNHWF+2/i9lUzqaxj
+dAd31q5iNDUZpTc3tolSKuK62/jKVOzvWewZOcXdW3MC9ZKVu9Lfg9o1nJfFXDl1dpthW747Tnpk
+2SJebamoZlKTA4qVqsNKUU/J3Cc1Cls/tngKZJLPM1KFEc/0NE/GlrkiLkdJh1gJTsbzDJMt0LYj
+fH8BiyftFV3DtaloMckIErVnDA6Juqsya4+rIBdhiIIH6zoXG/UNQlfmLai6uNMFhp7BQWnjKrpu
+fAc31YrRLMKtL3KQc6xid9rRkFy1RdR0I1qF8uvYC7fLVB23jtY4GAvJIrU75RioJ3TMsflQci0G
+OdHHA1nf/esnuPkZmZjG9ha5rr1G/bsIPMlxSUv72C1Fpo7lfgSHAXM1QfqdiFxzFkxu6RYUSTpy
+aWhquWb0f1P5VqnoP4nkvXxKkzw+b639b621zxlj/hXCZJkCf+ubRx6WspSl3EvyrVLR/+Kf8f1/
+APyDt9IJx3EpFBsEcZlQiz285hB3pCGd3D2O0/eyKf1XxI6KfTnN2hOP+gOym6+bk1RLMqy8X4BN
+TbXtukRa2DQ9kH1qXJmQ6MldMDMW21daTiltyg58ay8j7Ssyj6b4tm3G7IuyW594KWT8VyW1OX31
+AjWFiMurY8aZZCFGX5M+VF5zGc7UEXl+xnomqEiVBzfwi2q4VuSZG4Z8AAAgAElEQVRksL2HQVF8
+svYRk1UBTI3CnFCJZ/P2LuMT6ih9RexMO16lfSAOqeCSx1hj2/HQwVc+DFOw1AKZz1ARsfNZwH5P
+nITxxFA7VKbsYpXKtsbsZzUS9bX0W/J332sz2FVIqrRPqli0pfWQZqZOVTIiDVWuaYryanlKR9Gz
+W1mMOSbCdYVyHMAa7MIw15ik77kY1SR2u68xU7TmcZSxUZBQdWFbSHEA/LKuhdGMXi45C9PXLMPn
+FPE6mZFtyneryQqbF+TEDvqihUaFHsWChIAXeAYAMyuobwCeBvFymx/nHmS3GcSOtZTVn7NV9Chp
+LkMhkJO9O7lCb6xUf19xaTwhKe3p2Rm+FkHNc4+4JX3b68s4BoUEvyK+tul8im0qy/dRQFG5UUIH
+nOKiAmzIm5F7Is3Zcz1Wak3KZhOjnIHh/hpW0XCT0RHxTFwT817EtZaoxLO+qHjpdgH7nFKWbwbk
+U9G7YzPG9sWjfni0C2NljdYkplJ/nblGALIkx1GotGKpybqmUM/6CVeMetQ1dTR68Tz1jkQcBvZT
+nPwtiQyw9SrG/wwA5UpAGMn3x2fl4Wh95ohxKDeuduN+plvifimMvxvnolYdzhUUZjdiEfONgjr+
+0cdkrDdfIduXa0xSh8JrkjeQVSThpVqoM9WHrdb6TmaTX5A2MsNQ1dyS9TGq/rfVefXywT51xZe8
+WZwQqOd8Y6fEhaFEIpxqAT8T1b2neH+93gadiUDcu92MTJ28tfkGk6KaLnFOpm2HWsl3YXONMJHv
+fq7XJtWE67lJCbVvQ/Jj8yFbZDHZlJuHWu5d9diPJJnKeSbhzI5sjBfb76Vck4esphT2w6M9big+
+Zvf6AV95UXI56lWXj1z4Xmm7PCB5Wq5BQ+pETl2cUv2O75RrbZaP96uob4nVHKto3cLUWqIF+wCv
+p2YXjaGi+RbVssclPbSM4i9WKivkSEVwPXg/1pd7ymvrtJ+TKtjBJGXQkbV6YeN7AFj5Cx9mrhGu
+f9v9Z7xyS4B2bk1fpJFoFCSHSq52KEe8GVmmOS9lKUu5Q+4JTcExllKQkzl9CkUNBWaZ8IkBzvgW
+V6/LLvfaM11e0jTYVeWI2CoXmWloLt7cJJ6o0y6E0UB213H7gLwjp1velC08zfdIlWevlvj4W5oF
+NhuxouQyo5pHqSbtNBS6rLL5J7zWUifT2u+wvycq3vlzm5iXZLdudUOsohwfaHgvvZJTksJJRsNb
+VB+UUF/aewqrjsZEWY0noz6R8gGWHztNrFmMk/JNQo3PlZpz7L6iQEdqMmQliqtn5VrmGr4iGM/T
+HE/j41nqkOqRNlfAj2k9wq0oldx8iDdQrahepeuK6VINVzDqVOzHorl1JxOyRHkQhzFpU9ob9NoY
+DQEGPQsafvW1EMktJQQL0NiCR6TqvuNZJvntuQlaVKV9L3ohuZoaR7MO4SIOGc1INMcj4Xmsp0VF
+SsEWJQeM98UkWN/2+B51UNYu1FnTalU76TO5IpmlY6Xb2zv4DGsKPmP59uMciS/tvoizgPXT3qYc
+RyFxOaYJwTgw0VyG10YRIyXUeaQsv8wLc+qXxSSI0j/EKUu+X2xuMXFEo7ne/g+MtIJqvSf3pn6h
+RnVN1v1HklXizypWxzwj1IrXJIXOWKm+36TcE5tCbi2zZE4QJszHou5VqxZXU5CvvHrEb35FCgxe
+ac+OuSBLikl332GdiyXNLX/JYHc0imCLxOozGPenxIks+vZNuRnDnmVDUYlXGg6NQBZVsRwSaApy
+EJZp1ORWVwNFFO5eZ3jmDwEYfXXOtgJd1HOf3kxu0qtXD/ncy9KP67qwqw148EWtNahPqL6mUYkP
+XSMayILdvyl9vP70PrXHZeGGBx2ua5n4auRw6QkFPYk2CbaV0egFRaNaaTHdF3Vx+9SENWUVusWE
+ROsPUjJSBTgpahWlXzT0dH5M3xKp9367OzpGvz43LlE7L0s9vKno2dd6DA/FvErTEWZPltRz0R5V
+RXNeMS4nmtKnuSIvhVNoa4quNZZiWaHvRzEJC1an1w1zX3MaqnUYjeTBvTGb4quZs17xKWhNQJS7
+VBS70Dup5sNBzO4NBZmhTm1V5u2pLx9xvS0VptXcsF4VE3NjU1T7zL+B2ZPxlRpDoqns6nuDK8Sa
+n6AWEcbelo9gQAM8FIylo9/154a8qJWtiW5ofpf+DTEx4498DbcsCVkWh2EqVbe5n3DtJUkS+6OZ
+rJXVp3f44CeUYzTKqE51w40tYyUcxho4Tj5+c7I0H5aylKXcIfeMphAlOfWBoVQXVS4/XcNT9OSu
+Z+gtqiSrheNU1PMlURE3yxCp4ta1XUqq+q76BSK9HuMEV51kNU13HucJeUN260LWZEMjALNqQhbL
++xumga3I3jl5jzIH5xPmvy8n35lxn8Lf1KrL/nWmWhzTbs8oqNlxRmPfD1BisyqnY34SrBwIpKM5
+bk9UvGhFFVAfCorKnBbmtLbF4VQLVvFyyWUIp5ajFVEvIyNRi3jSZKWiOQ3nDjl5VrSNg6enWKuw
+cr7B0zS9jaKYJZulIgNdDlE4paaYFOfqAWvKP1HbrtBQ6vdTSkJy61yHTJmWvaGLr3Nb2ShjFYl5
+o1/gdFVezwJN0XVSVhPRzKLKiPSEtL33VJdUoeAcazCKvVlUersL26uc3BTn2qi5QkPnu7lW4X7F
+06meq+EtKi1HiqswneCekBM2q7mEQ412TEbAIiuyTEWxQC825P42HnmULJTx5+PLuJsyz+7ZKyzy
+FtzbwFQWQaSGZ6iqydOKLQU1NVaLLh+8T7AdLz8k92bgwkQZqp95NmBtSzST8qU5iWYsen6D+zfE
+Admei6bkrLRpa2HbtLfPXkHNuAxKoazVSZoyzd5aVsA9sSkY6+DFBSqNDZRqkMpkCxuKSvX4QyM8
+9WqPSwmpljWX1basVjYpKUDIbOrSqItq6JY96grr7mRlgnUlbC3LoroY1qAr1zq3WcSrK0KNqTJS
+9brqrXKzJDdhYOTz6ugE72lcBuAZ7/c5d/RhAAJ7lcYjEp78vvfVCc8oPPl1eWiiYYKnlZ3r7/s4
+WU/TmOsz5kbswdM1ZQT69iobFwV0NK80eSyXCECy16GitR9x7SlOaDqrCZRrceUS6aY8eCvZt7N9
+Tsyure6QyVAWXsFzObGuD/1F+Xvh4UtUy6d1HD7NsjywXmnAxobWtBmXsoa6zET6e9lp4qcCyroy
+a7GtVdYmDxm0xZ+z7bnYqrQz0D06z4o8msjDtnm6z35RHprPxS2K6jNxHHBVN6+XJLry6COPcOaS
+zGHp/Dah5q9tnaiAmiMrJ9/PbFdNBUfMuVG7w+MVLTMvbrC2Ld+tbhb5qP8R6Vv3Jhu6+eQFMSOc
+IKJ5Qsp38hMrFLT27wPu/eRWIu8LH0+Mxar9UHFd6hqGLFdyNjQF+ZG1Ot/1XjFBvKasWX/jYb70
+h89LG6+cpP9h2ZiKvQdp+rI5ndiuMj8lYzpvpYJ1NoxY1ev+29HPwZG8Tj2HdQ3bpK6L/2cx2nwD
+WZoPS1nKUu6Qe0JTcExGudgnKRapbiklWhQT6sldXTtB83FRmSsXznLYExTd/FBO4HES0+or2MYp
+h4YvJ7BTCEk0+WOlMaGeKOCKIjxnxRo2Fi2gAOQLj/woJ1qXaEdgUzYWKa/K/bdy/3U6RW3P5NiZ
+xOkLjzapfkVOseLaedwTchImjX8v/YnOYtb1JEoneJui+ucjCHxFJVZ+yULzNcKqnP6m9jy55mkU
+HhtgR5qYU+2TdrVg5rxEMrzgZVYe+EF5nQ5YUZAVv+TjFXU+V8pcelxU0YeKovGc3r6PyrbkTZT8
+iIISiHj1SzgVjQy4ESiLdVDXE9Evc14rVLdaDYpNxYgI6mytS/JWoe/TVy5Ip7coHjogXFWNplpk
+eqjzkuXH3nzXdY6JfU5eVjzOQsAD9z0g47h0mWAm18UpkSiBjfXGlE+pGRrJ38ZmmYnmKRSSGzgt
+vd5OA5PJGllb8zBdje87olW4PIHTPaFzW8HZkbVQdPvH52+sDlHrcFy5tVVyOKcFcR84W6X0HrmX
+FS+isaGmTSx9v37td+gOb+kch9QKihfhNmk+LJErfz6krolfSVlyKQaDq8wGMuZGLcEWFH+kDjNN
+zbcWouStwbHdE5sCBowP1XNj/FxsVnfFp6SltZmpUCqr2jqfsdKSm5SeULrwvQkVTQQpO008XcTY
+lMBobnweUNhZkIrKpJvCJkRqiFY6zHqiiya2irMn6mW/dAOjpciJeuQr5ttwn5SHMHluh0pFgTwO
+M9hUXElukWk2ofOMqImF/6qCm8n45rmDvSVmjPfgAKtYi6kvD3Fw4MGZP5a5aF3E3daMxXYTe06z
+HvcDnHXZAGsvyviHl/uEiSTe5JkljOQB2go+xh7/n3w322B1Lup485xWojaqNBuyodleir+tdRm5
+j9GaiCBdY14XEJjGQE2qHGpKl16+f4jfU2xDP8GPlCB3q0/hhm4WGg0pXyuxq8AyfgSDeKR9zsg0
+c9ADXI18nFmTDevcB06xfkHGVKidI5tr+HndwdM+Zb6Dp/6KtCbJTcV6CvvKLNYZ45yTBzIc1Anv
+EzPHb50kX5W5SweyRpJkn2BdkpsK26vkZgEccz+WhYouXfAyyNTBMCEjL2sW53rCyQe1JuJwnaiq
+QDM3FNuxX+H567K2Ll30iaayEVTjGBZ8qmfAH2h1pael5fkp9qr/QfqeHOKqzygbJRxmsgGa1FD0
+3wKmPUvzYSlLWcrXyT2iKbikpokdlikvVP/zEe5QTmAv2cLTCje74uFM5YR1lAAlbt1iXJNT3o3B
+UUpyjxKTqpSG2Y7BacspHaiTLVlpkWnuvI1mxFpr0S+9wjTVKruhTz5QlfmM/G5enWJa6jCcxYw+
+LFBp9voGwTPiaQvKI+xFZQdWxmU/fwLUO0/4J6DjcN0zUNBKvZIkP+V79+GNJQbt1RrkDTFn8nEZ
+cmkjv3mL0VlpuzORz81wFatpM7PHrpKYorbxPKtqBrn12TEcm8nlRC2lDfIFS5NJSIaKr3jQworm
+S2BOQE3mIollLrP6a6T7BW27hK9RIFt1ybs6/sgwXzBFFyWKMspn+Kq254OMVMFQLAZXuR19Y/HU
+nV/c0BOzViIsaGlnmJFq5CAZTMn3NGX9RI/QFccsmqSUj1exBdECJk6D+kRzUpwCruKCUihhKmKm
+xvuideTrhnwmiUWkTUyohDnB7Dh5aeHI84w9JnpxcdnU3BInr1DUuQ/PbuKsyDWO9kQjeHnwAoOZ
+nPKTeZPpLZnj4blrBJqmX2hVjzXceEPWdNz1mGXSRhKVSdcVln83Ios97RuUyktNYSlLWcqfQ+4J
+TcF1CjRLFynPzzN3xN6q3zxFXhInihckoOm6JdZgRUE3lak52apR1kL93E4oaZw+jxJCrQychDlZ
+IKdGlCib8401kpFClDHFlsT+LGcbzEoSZvNnMbHy8mVd2XGL7g/jJnLKDcxXqF75b6S97leIylIF
+GdTuJ+Q75P0dOdny7BJEyvzc/nHmTykr9YkV8kxel1zRQMhnOBOFm2uC09NwaWeGHWgKa7VF+QUN
+3734lMxl6X6m6QKNZ4vm5DcA+GDto7zm/xoAG+k6uZ6gpiztJeEG/lzj8cmcbFdzFqIeZiChyll/
+jl8Se35/KqFOv3CWuCMOxanvQl1OuXL7BBFit5vxlDxQNmrFlkgmIxqagTeIcyLFg3Ach62C3D9r
+UqorEr6rxlIQ5he/h9yVsKA/L2Mi0cLyXYc0kpO3ON445lU0jjhSo1cblFvS92j2NINrapefK1G9
+LiHXdLiHVRKc+eyz8vmwAsrvmRVdjIZIvaRwzPlY1fD13KbYTF43/DKlQDXEtccptIUwiGxEpSDg
+vpPGHwBwwVQYrsh6+9hj/zU7O49L2/unSNT57cRFpsplanvvk/vxbAendD8A43FEQ4l2CsnnGc8U
+O8LNqGslLLw5Tsl7YlMwToxbuknkJ4SrmljU2gdPMXBnIzyNm9tpglHwldyKGuVXLLkn6nM6y4kV
+uiwHoqkSlAYpUSKLd6xQ2M7cI9S0W9+NcYrq9+6PSDN15rkV3EA2nLnWJfgnnmK+Iqr/ePbrXL8u
+C/DixSbFz0idQHz2JGYBd1sVb7PpvhezKrp4Yg9xL8kXbMElO5L+Z0fyoARrPlkkC9pJq6A5Erbp
+YseiJjv1DqYn5sb4ff8GgFXzAKX7RHXudzaorspi65gppUDV0nGbwUQ92Prw95sV5rl8NxuXYKob
+cs2n7CpRy6UKKECNHUvEpX/UwjTlu8lBxLAo85nGe2iYHs+d4AZKroP+zruFowlS88xh/5bcJ99x
+CF2NwBRS7ETmaJ5Kiu/hjSJbmnZuKv1FdTnx8Aa55hWPDi1JIn2O22JWvrb/NQ5eljY2TqW4kUK8
+RxvEezJuN/RxVf/Pp3LPCuUKRistTQRoOv0gPMRzFqjTajK5HFdODvM56VypBrjONBamg9JWjeFM
+zJjZVMwSP9unvqY5JMlzxEpKNNt9jkDJity4gq1IexPNrZmuRSRqdpQqE8qh3NM0TPCVuzN0nNvA
+dd6cLM2HpSxlKXfIPaEp5FnOdBjhbbVIDxUow7ckmqKa9zrk6nxykgp2rLu5otRG0w7xQFSkUdTD
+LyixijPBqiPGxcWofyrTUyAdu0RWtAOvMiGbyIkXDXL6GiLaK+5RbkiG2UuvyGn3Q6NPwJbElTuz
+HeKOaBUzJrg7yi1hhjie5CE4+xJO8877x8CtgXOJVLkdncoe2YFcgxXlOYjXsdsaYps4LACs0tYh
++bqCqvb6TLSysfNF6Vv0F79AuachPXOJ3qty3dzt4Hky7s5wzt4tmYzzJQ0BH7So7iuStHNERTka
+sxmkc8WfmE1BC4ZmY7nWoNfBUQo6pzYj0/kej6ekmqIcujHjscxtV4FfB3tznmnItaJdw26s17A5
+fau5IxP3WNsIb6nz+NqjHCgvpT9pMBqKo7W8OcFT02xuDxgciIbRTuQ+tY4O+dyhaGPv9evUTkgb
+7iRnWlJA1EEFVULJCgrWO0zJM7n/fhiS6f0rR6ePQ5ILjAWTcowb0Z7H7FXlPh2M9qmoc9SZF5iW
+1Nwai8kYzWDekd9dvfUcc6O4H94GqxMJv7unZkyG0t6wpWBB3ZxpQdZFxIBBT9fFeI7DgiPR5TYW
+jDcl98SmkObQnaQEu1NMUW7c6BFLUb3+Ns1gLJOW1yakSq1tNd05jjNGgZKiDFOqjnp6TU5aVFPB
+L+Boea7uGRx514+rKMPYw8xk8Y/thEGsXImzKQcvi6/hykjeO+xfwdwSVW2tO+CZTwhe49kX10hu
+yfWSRptMy2UrO+LRZtzGhnKNtPoq9qYCpIxHmIluZEVZrPHMIT+SdnFaRBUxlbLpDHoKmnH1KjfO
+CAT4F1OZt1N7TS4rTUf6oc/w4q9ofHw0YaLJV7GX0B3Jgnz1Vb0HwQGduqjUoRfieJoLMh1zJZFr
+d9sxJlmUs8umMawecVrh99fq4C7qCKqQqwqbT3NSBVSJctnoo9Ay68qmftCJFujkZMYQKzjNNElw
+fPn+V298CoCS/SqX+mflyxtQzmQu1uZ1aspveY2rHBzpvVR/UNRPGStK8sEkZ2som1fFG5NuaU5G
+mBMYZV8aynzPxzeZe5IvEscfwyhAyqAQ4apPIdS/GTllrXcIrWE2k7XXGqSEh7IRlFYmeEYGOx+p
+v+tMh6irSV0Ty+SmPgMn2zhKYptHTRzNgeg9IA//PHYZ6YZ888Z19hJZO1GSU9DdNMshz5ZpzktZ
+ylL+HHJPaArGurj5CgVOMVYMhcatDSJfvNd+6RbkStiRhbRbWoevtGRxmGEVcso1I8KSxoeLIRWF
+q5qnGfFAC5McRREeb5O56gDLU5JcThrbn2AypXcbD3m+JafV8x11RL7yPVRGog4+3X6O4LNCS99L
+/h3T7NPSj16Fwq54yUdDOXVLnSdxNUvTK30nybNiPlj/AvlIMgWDNfFMZySAlFEmXgcGosKa3at4
+pyViMF+5yYnrfxmA+zu/CsDmjY/Qacu8bZ74fq5d/0syn2lORwEUs8zjYCpjDVw1S3oJZTWfEhPS
+U9zFjWaJelGwJE24y4oeI62B9HfTnqA/kvb2OyG2ICdso9KkpNnhTpCRKLGPh+JXBA3m+2pSkNEs
+iOkyYA8vlz71bYZSXPDiLbnuqD9jUJST/XKyzkyL39pfnVLL5DvDwv3M9bS9cFHmflL8Y9Y0+nSw
+3+alkSz92cmIh30ZXx52iIzMUW8kvy+uxVQ0jTsrxYSOOEe3HIujjsY1zXLsmhyr6NqJdbimxDf3
+mxrpoWiLrUOPklU7tnodgDOzi7hVmcOLpzbZ2NL8nPrDHB3JfYiTCgctWaur7iek74dXmDvyu6dv
+fIFhV56dYZIQKpS067tY5z9B8wESsLcY+xa/pn6E/TlmU23jHAoKhmKCMoErdvIwF9UqpEBQEdvY
+yRPikTJAzSYMc/E+z+cJUyUx1cxnKtU6qwU1RcaWsYZ8RvOMmQKODDqWG92F/SwLu/H+z3DtBVEB
+r85/m5YCs9YfCSn25cbFZR9nrNiOsTyww1d+GV8rNIO1bRwFVsm8IpmGX+1Lvw2AW4rwZ7Kgg/NN
+cl/G553N8JAIhndySu6LB3v9w1KdOe5+mfCE+DB6WRnHk+vuT3Pmx8i0HgW1h+OhLKQw86go6U23
+O+CwK5tiuHKG0ydlk90prhBfl/k6I9E9BkclqqpwvvD5Zwlk76LhFikosUoYG8pK3LPAqGz4Ma/m
+0ka14XKyIeG7a+3DYzQoazNSRUKN5/K7fpJQ1PTp2kpOyZP7dHC0S3RC7snZ7SK1hxUqv69coeVT
+XD+QB71aMOxf1dCQU2YeiO/HdWIiDVZNtWYi66wyuy5ViXa7gaOQ8cHxDMKAhQ8LrCK2mjwnjhem
+6RSrSVuhZ2gdyWHgKgTAoO3QnuvB82rKcCL32q9dOa5nmHUTUiVHoiRz6VU7vHxFqiuTZEq2IEeK
+IdZ9wDeWZPQ2c0kuZSlL+c9L7glNIcsThtMW/kaPYKgAIY0KsabBkrvMVbX14xmegnd4Cms1mAxZ
+ralTL1jF1VTceOhSTUXda6yFjMeiYUwipT6zBUJNPIkrE9qvyjHxUvuAcV9OsWe9IWOF0srV00uU
+U/22/0dePnODRioOutFoBYxSuvkJ47HkEJiWUtzvuIzbigd4c0A61MjHZsLoOXm9r1iT68UVzvyg
+nNy59QgjMRmGr3YpPqix8q+1iUOFZb+p+QEfukVVKdF2tnvUq4qSvH5I8rQ6Ab0ET6Hn/KpCddUj
+ornMd8EUOf2Q9Ll3dMjU/5yM75UZtwLx5kd/JH14LY+pFlSlDi3Vhryul0Mscn9mfkK7Ja9bVjS+
+545u8Fwmc3HC8XnoPXJyf+ZGzkxTvvO2QLUBRJqMNK1Z9ly5NyfbU1aaopo89IGTdG8pO/jNAe3P
+SFLajalcd2+/xa4nc3zatzz0oKyzYKNMXtUU4/2I3bE6VQfSXq/7WX7wQ38VgFNewOt4JT6RrouW
+MpTXLESqbTquIdHCrj846vLR+8TJfam5yWEmjsI//pw4BruRPaYX6G4M2U3l87XuKXZWRVts7qSE
+ZZnbmqZa7/b79Gcyh+15SuIuCggtM12rFc8e43Qy4E3JPbEpSMCwRprVFjgZJOttJoqgFI998rKq
+szUHo5iIxhM/QtaPOQwkGjD0LAWlos8mhqwodmYzWTlmIVpwPM78Ppkn6vxkMOdA2Y2GmWFqtOR6
+7jMoyftJUT352+dg/P0ArO7+HkcflWzC3afH7AyUJj6YUNLvaxPQ8ilFSg1fbjNQjojpdEZ0VRbs
+0UkFkjUVYuUadGcF+p5UZQ57ltkzYnYMDgcUHtCEnAtiUmysPkiYSFTD2yqy9sBZANJbFXZd2aTS
+YsrEkbZXA4Wyn1eIUoWf94swkU6fCRxWFR/Rf/AUm5n0aT+W+Y6DjEyxD53tIusKAGPClIluetE8
+Zn8s/bympd6tQUzBSB921tY4/4CWeP/hALcr17PMj0mZEvXwxyZgFEkbB304pVWp9bFhK5W5JbiB
+pwxQGwsy4anBbMh6au772IpicE4zBsqm1B1aWrHc9131WyVuhac/K/d3+9T7qJ6WeZk6OYECzixq
+H3I/o1ZaJMYZFhXLrSjl+p704+xWQKUo67aipo9Xz9koyQN/qrZGqMlZs+qYkvq//LCC0YXUjuX+
+746GtDVc7JareJpYlSQzyo7yeObg6PMCinDzTWRpPixlKUu5Q8zr3LDvYieMaQEToP0udWFt2fay
+7f8M2j5jrV3/Zl+6JzYFAGPMl6y17122vWx72fa7K0vzYSlLWcodstwUlrKUpdwh99Km8AvLtpdt
+L9t+9+We8SksZSlLuTfkXtIUlrKUpdwD8q5vCsaYjxtjrhhjrhpj/t5dbuuUMeYPjDHPG2OeM8b8
+bX1/xRjzKWPMy/q3eRf74BpjvmqM+S39/zljzBd0/P/SGIVYujttN4wxv2qMedEY84Ix5oPv1NiN
+Mf+9zvmzxphfMcYU7tbYjTH/pzHmyBjz7G3vfcNxGpH/TfvwtDHmibvQ9j/UOX/aGPNvjDGN2z77
+pLZ9xRjzvX+ett8ueVc3BWOMC/xj4BPAg8CPGWMevItNpsDfsdY+CDwJ/C1t7+8Bn7bWXgQ+rf+/
+W/K3gRdu+//PAv+LtfY+BETvJ+9i2/8I+B1r7f3Ao9qPuz52Y8xJ4KeB91prH0aY2n+Uuzf2/xv4
++Ne990bj/ARwUf/9FPDzd6HtTwEPW2sfAV4CPgmga+9HgYf0N/+7PhPvrlhr37V/wAeB373t/58E
+PvkOtv8bwPcAV4BtfW8buHKX2ttBFuTHgN9CknjbgPeN5uNtbrsOXEP9SLe9f9fHDpwEdoEVJLX+
+t4DvvZtjB84Cz36zcQL/B/Bj3+h7b1fbX/fZDwG/pK/vWJJmFOsAAAKZSURBVO/A7wIfvBv3/638
+e7fNh8ViWchNfe+uizHmLPA48AVg01q7gHU+ADbvUrP/K/B3gQWS5irQt9ZqVdJdHf85oAX8X2q+
+/FNjTJl3YOzW2lvA/wzcAPaR0pwv886NHd54nO/0GvwbwL97l9p+U/JubwrvihhjKsC/Bv47a+3w
+9s+sbNlve0jGGPP9wJG19stv97XfpHjAE8DPW2sfR9LK7zAV7uLYm8APIhvTCaDMn1ax3zG5W+P8
+ZmKM+RnEhP2ld7rttyLv9qZwCzh12/939L27JsYYH9kQfsla+2v69qExZls/3waO7kLTHwJ+wBhz
+HfgXiAnxj4CGMWZRrXo3x38TuGmt/YL+/1eRTeKdGPtfAK5Za1vW2gT4NWQ+3qmxwxuP8x1Zg8aY
+vw58P/BXdFN6x9p+q/Jubwr/EbioXugAcbr85t1qzBhjgF8EXrDW/txtH/0m8OP6+scRX8PbKtba
+T1prd6y1Z5Fx/r619q8AfwD8yN1sW9s/AHaNMZf1re8GnucdGDtiNjxpjCnpPVi0/Y6MXeWNxvmb
+wF/TKMSTwOA2M+NtEWPMxxGz8QestdOv69OPGmNCY8w5xNn5xbez7W9J3m2nBvB9iEf2FeBn7nJb
+H0bUxqeBr+m/70Ns+08DLwO/B6zc5X58J/Bb+vo8shCuAv8vEN7Fdh8DvqTj/3Wg+U6NHfj7wIvA
+s8A/B8K7NXbgVxDfRYJoSD/5RuNEnL3/WNffM0iE5O1u+yriO1isuX9y2/d/Rtu+Anzibq67N/tv
+mdG4lKUs5Q55t82HpSxlKfeYLDeFpSxlKXfIclNYylKWcocsN4WlLGUpd8hyU1jKUpZyhyw3haUs
+ZSl3yHJTWMpSlnKHLDeFpSxlKXfI/w/9ljCbs+pmggAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.9337... Generator Loss: 0.2622
+Epoch 1/1... Discriminator Loss: 1.2560... Generator Loss: 0.8398
+Epoch 1/1... Discriminator Loss: 1.2232... Generator Loss: 0.7815
+Epoch 1/1... Discriminator Loss: 1.4801... Generator Loss: 0.6008
+Epoch 1/1... Discriminator Loss: 1.3070... Generator Loss: 0.6860
+Epoch 1/1... Discriminator Loss: 1.3470... Generator Loss: 0.7634
+Epoch 1/1... Discriminator Loss: 1.5236... Generator Loss: 0.6591
+Epoch 1/1... Discriminator Loss: 1.3827... Generator Loss: 0.9191
+Epoch 1/1... Discriminator Loss: 1.2797... Generator Loss: 0.8605
+Epoch 1/1... Discriminator Loss: 1.3069... Generator Loss: 1.0828
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWnMbWl23/V7nj3vM77zfe98q265qrra7and7cQODkag
+GCJFAgkBEnIIUiQUJIIsgZUPAQkhRQghvqCgIEAgWSBLRnIS2wQPsa0AjtvdtrvaPVRX3bp1h/e+
+43nPuM+eHz6stU91IUxXu7vi++GsL/e95+yz97OfcQ3/9V/GOcdWtrKVrXRi/6wbsJWtbOXlku2m
+sJWtbOUjst0UtrKVrXxEtpvCVraylY/IdlPYyla28hHZbgpb2cpWPiKf2KZgjPlLxphvGGPeNcb8
+3Cf1nK1sZSvfWzGfBE7BGOMB7wD/IvAM+ALwbzrnvvo9f9hWtrKV76l8UprC54B3nXOPnHMl8L8C
+f+UTetZWtrKV76H4n9B9bwFPv+X/z4DP/0kXx4HnelFAXTuCUPYpz/fo+do855iucwCKqsW3BgBr
+PPnec9S1aDxVXdMpP55nMUaudW2LQf6O4gCAwLPQtPK7pqXVLdIaS1s1ALQOGifXtNpe3/fQjzAW
+Om3LtY62bTe/63QwbQIGg9W2OxyulSustXi+3TwbwDhH9yJ129J01wJGr3G4D5+B9kVT03T3aj1e
+/9Tr8n1rMF13ti2urvU/+vvAQNO119FU8kVWzalXlV5bo91CXhUAlGVN03R9ZbDfesx8Swdok/F0
+zJqmpnXSF3GvRxxK405Ozzf95Zz5lptov3zLvayDKAw3feus9H1T1rhNn2tf2E134nCY7m/jaLVv
+u3/1h9Jea7s/AYPnS/utZzg62ENfXP+pafUhvpP5B2Csh9N58eEoshm9tmk+fCDS/yDzyXz4ocwJ
+oNSxq6pq0zt162i6uVy3tLX8XdJu5lndNJfOuQO+jXxSm8K3FWPMXwf+OkAv9PmXP3OH80nLzbsx
+AKPdEZ/bPwTAlTn/4MvvAPDe+ZLdXgTAwA4AaMctl5M1AKfnU6pSnrGzE+MFsgHUqxzfyuu+8toN
+AG6Merh5BsCL6YIyke/TKCE7mQKwqh3XtWxIuc7Ww3GfIpO/g6GjWMuiqMuC9UKuXTcNlW4coa/X
++jFRIhOiaVqqXBZbEsfs7KfydyDv71cVbSODeZ2tmK7kpWIgieSadVtT67SIvR3pi9kFsx25Vy8b
+8tu//VvS35kl3pVnV1lOfXkh7SilkdFxAHN5fxtWzE+lP7/87Dc5++JzuUd1welEFsVXT98F4Pnz
+K2bTmfRPFRL2ddo3YHRiGj/AT+V3w6gv7zSZsnYyNm9+9rO8dlfG+j/5L/5rQivXlpWPQzYf68vv
+AuPj9aQ/e7XjlXu3tF9CyljafP38ikY3jsjIXAl7Hi7XRePV2Fza2UQN65UssmWxptVNz4/kBv24
+R9gtPRsy3h/LfQeWn/33fgYAl8nYFMUlWSN/jwuf0a6MQxT2qXJpW+0MGBm/ysrDFrMpvpO+bx14
+oW5ueYmvh50XRJhW7nFyfi3/npxS6JycZjmThfTV2SRjeTmXa9o1eSbPubiefMDHkE9qU3gO3PmW
+/9/WzzbinPt7wN8DGCWhu7zOuL0/YBxJR6bTPm3UA+DxfMKi0Am2KpnraeONZFFdXrRcPp8AkLc1
+PV0008YQL6Qji7ZiGMvvHumEn5zM8bpd2zSULpHGLdYsPOns3TQhb3SSzuV3ZVkw6EnbbBASFNLp
+a6DxVWMJHOOBXDPoyWk23t1lNJLPAi/AZbLx9HojwqFMXqcDm6+XVJW0ob46I1/LtWkcMExkgZhm
+QZF1R738bml9/Ebev836lI9kcvRf7WErGW6TTQj0vcN96UO3jrBOFrfLBjSRzJ9drya5K4uX5g2G
+t8+lzfv3Abh/OCY7XUk7B0MW2s7VdM1stpT+Mo5M1ZB2qp95NUNdgKuLS878WN8Dikq1Hv4S8Mvy
+p5N3btorTDEEIPMc64X0Z8Y1xZW8S1Y0RKp51L48JCiHWOQACBwYX+bTurI0qvU0jdmoE7lqShQZ
+6EZ+P02o9exuFoZSN8Mols9GqccQmUPDgzH9WMbdD3vUaxnf1sS0VjYh40kb9/eHREbeqXYZXqdN
+VgXkcm3rNVRL6duqWEgfZjFnS/m+sS2Jtr2XwFLXiLU+VaGa3seUT2pT+ALwmjHmAbIZ/BvAv/Un
+Xdw2sJ7BrRsFr/pyYlQevHghi/APz2ZMLmUyzSpHNZOBnixlI1iucvJGOifwI47SkdzDVCx0AKrS
+Iyvld9XJKQCnzoB23v4oIW5kkMvWo9adu09OoIspVnOmXnuM9bRKGrhU1XBVh4ximdw3xpZPv/oa
+AHdu3pXP9nvEkbQndoa4J6d7XlXMp7JnPqmvAJg6n6yVd543LSvVNoZRn119RjlZUJWqagaioSzX
+UOkkzV1FotqP306g0E2hLfCGovq2uhG68hlNLRNzfXHB9Fqsv+DJnGH/gfaLZTSQBbJfSL/FB3eI
+Pi1j1vQyrh7LZvLBxYx3TmR8Hs0uaC9kQk8ymaCtNZRIm08ev8PkVCa6OezhTv4qIv8zqDbo9qUv
+2pnD35ONgMpxdST3qJ4VWB1rL4lJ78q7+AtZmIEFkIUZGcN6Lie6a9asVD1ocRhV+VGTaFlXBK3c
+owhXBNfSt2sbUMzlXULdKHxriVPpz529AwLVUpp8QtSXOWnSHlbv54WySflmgK8HSL1aQyOfN3OH
+G0g7q9WKLJfP+4EeNv2c84n08fpqzirTppctqIZRupDC6RcfUz6RTcE5Vxtj/n3gHwEe8D845/74
+k3jWVrayle+tfGI+BefcrwC/8nGujTzDvR2fqh5zkYuqejKf86IWVfX9xYU4BYGkZ8jWou6dz+WU
+sA7iUHbrg90BRdzZnDFFT3b8nSSicnKSuM4H0DQYtV/nRU5Vq//BrIlUnZ2soAnUxHDybxxa6lhO
+gbrtU0SX0o5hRKyOr8+9fo97d/YBeOXuPQDS1JCoWeLqFWlPfD6VK5leqiMklraNz2qu1tI245Ys
+K9GaIt8SjeTzfhZRWD3xKjlRR+Gc86JzxF4R7ks7TWtxhfgRrG+l0wCnp6tdW9bVNwBYzF/gFfKu
+g1fuYDzpF39SUuppe3AsWg7OEHfaRmwJkXc2hxbUacysJfFEm3qSqamx8MlrOcHOJgtaf61jcxvM
+35XfDe7D+om0z5P3C4KI4eGr8ozVU4Y61v5+yu6h9OfQg8PXpM9ZivZXFYb5QsYpMgmrXNoxO1vi
+x9IXRVaxyPVkrqRfyrahRT47WUIYqM8k9HBWNIWykrmSDA2BmgzGGFpP5mc0HOCcjGsyGOGF0odB
+76a8c1ngh7sAtGkO+rwyvqBdhdrNDV4p/RyPZNmO8xHhWDSs4NKj0j5sqh5JrGbHPMcYtdM+pvyZ
+ORq/VYLAcOdGyOV5xvO+NOnJ2TVJKZ0a4hj1RP2ybs0yF1Wy8/reGvd481gcQJ957S6Pnsvgm7DP
+/VJUrZ1hj6yQ+xlfOvfqckquTsSz+ZomlmeHeY3vycazyiyDVO7RqOPvYC/G6UTwbwXsrWSB3O/D
+G7fFlfKZtz5LXydQf0fNjzagt6P+gHoPXydHXTtiumtkEtQ7GZcX8v1JYFhdywKaFwV7Rtpz55Vj
+vvhYTKErdVq2pqQfyuTI86TzaVFMlhCdAOAVtz6MRavpnK1rVpeqtuKT7Mk7hV6Iq2QB2UFFvVYz
+zZPx8PMa68n361VNoZtJkve5tS/X+tERN2JR53unz+T9bhjOz2UcpmvDNJdx8G9aXPU3pI/sz2M8
+6a/B96k5d9Jj51D8JKEbcONANsjjG4e8cl82hds7+wz25HeLU/XReHPqTBZhENSU5zKH1m/WnD+T
+jePk+QVf/EDMprNW2l43NUbjTuuiwummH9QW9VtT1bKI28BStOqjyeZ4A/GP+UmMh/RXPOzj6aB4
+eq/W9LCRmhRNIF5awJQBjRXzoCymm2jGMBbTLz6as8ilX+29ksW7Mr7ZfMG6kDFJQkOh/owMXTff
+RrYw561sZSsfkZdCU6hrw/lFwGDsePFcdujp9TV1X3a4/aiHn4h6Nb18xiKT3T9UL+1nH9ziX/qJ
+hwAs55a1RipiL2U0kBNjZ3iI9WVHTwPRKlaXZ7w7k/DON55MmavmsS5bVhpOc7amrtVB56ka1gYc
+HsgpcGN/n/CufH5wY5/b6REAR4eHRKpt+Kn8PvIgSuUE9sIA10EFiivCQk7/vWN5z2o1wAvUfCDk
+4kgckI/OzxkcyKlze7THPJMTtinktMuKlIU6JZ2tqBbyzo2dECGOT5M2H2IglvL+xVXJ1bw7gSO8
+SFVRF+KMaiF+QKgh09CXPvTSFXXdhdDOCKyqu8mKVMOIu1FDOurUfz1115bYl9PseeuzfCamonuW
+QvX72i8WfyCdlM6lf0Z3D4jUbKwqR7ovp/8br+9zZ1fCk4eHQ0JP5k7fEwenCW7iKVAjdQH1PdGK
+2hKm+9J378YfUFkZsy8/lX45LSeU2lee9QF5vwZHUUrbdscaXbI7hEMZszCEKO4cmwlWo1W+n4r5
+Bli9l+l5H+JMvAoqjVoMa4wnmkBUXFOvZM65fmcOBNw6ln71TMUH74t5OC8zMtVqC+vh+SHfibwU
+m0LjHLO2wdQRTy7FC39e5QyCTuVKyUsZ3IuqJKulUwahdOSrD/fISvn7crYk7XfoloLGl3u4QcCd
+gaiXVpFHveCIuQJerPN5dHEGwOOVoV7rRG8Nq85cUTs8Pj5kTz3gB+OIwUDU4Lt3bjPU5/n+EqM+
+iFAXim+DD4Ep5Yc4hHbtYT1ZeLZWsySJaPU961HJIJHP+9GIKJfPZ2aCp/b8eF+95Ysr8kiGNYtj
+rD7DejH4S32NMRpcoVzL4rhafY260UWTpni6gbblFWh4LggjUOCXqzR8WXnYWt7TNyFVJPczXkSg
+7zq0Q2jl89FQJra32+c1K3313ouSi0hsY8+FeK2Mk3Nfxsvl/fqtbDBHQUzZylzou4A/f1824U8d
+H7PTkz6KbY2nkaKeYlr8OCCoZRyicR9Xqg2fLRh5GkYu+hStRIqqtbz/9WJOqVGrnheigSbypsHX
+CIV1GiI3NVGhG0g4wlOzwwu8DUbGmBas9qHRMHsDeLopOA88PS1Kh/E6/1BKMNQIjJp5RRsQ1NKf
+prximKhpk6Y017Lhll6FUz/Qx5Wt+bCVrWzlI/JSaAoOR03FB1O4UORXTktfYaS37vSYPRbV9uvV
+GR2W9vYN2e0f3jpiupDvfd8S78rvjvcTilKuHccH+D05dXxV+1b5goGquPmh4ZWgw8ae8f5ETor5
+9ZKlnhq1HhN7uyN2x/Ls3rDH/kh264E3JE3lpIgCR62OUtOKVmGSoAuY0+QF6Clv0h6eagrtSk+f
+MiBVBGJdreh7qgI6x7MrUXFv7rTs78kQNtfyzmfxksZ011Y4Pa39ZA8PPd1NTjNTTUi1gyTo0a5F
+Za4KiNUx6CeOUgFJjhCjMXKbahRhvsKpumvCGF+jGcUyw3SQYNujp7jjdSFtXy8s2UBOsDd++B7h
++xK1ePL8jyia9+V+pk+qmpyfjHRsetx7INfuehFvHIpj93BvzHCkoK16iaeO4FBBTEGaQCNtjl2K
+SQvt+zFBKOP0ML9FGHUwbJmH719fU15rNMAIJB2gLVusgo+iWMcxbHCe9E9ZLfHtnvZbTl3q3Ari
+DZTdKmqyrdYYNSUwZjO/mzLDtYG+dkqAArXOFMRUgvqXWRc1I53fg6jdIF1pDa2iLD+uvBSbgrWG
+KPaZXuakiv5L2oSfvC9+gttHPl8vxab2zz/gUBfTX/4BsZHf+syPcf3kkdxrtyS6IxMoMSMq7ZBm
+XVGWnZ2sqmW6y/5YbFkyy85Qnh03PfKFqGjrqiV30vOFhqnu3hpymIpNvbsXMVbobp+K0MrfNs/x
+VG3zFYpqMVj1TmPDTovENnaDa0dDbyassZVsNnUTsp/IIh1yypOlbICzNGEcygIZWpnYaezTU1W0
+9cyHuPdlhe0pFr+KcDPpz9VS3v+6XWJ6qtovGmrdnP3E4KWiznstm03GtR2uf4jR0CKl69IncDYg
+LNUEMY5KkyaaSPqina+4tSPv9Mrtz/Cp+9K3//APf4Pe9X3p++LFZlHotGCvsvzQ4SsA/PCdu7zy
+QMKTcT/GKiQ6zxt8Dcl17TGmASNj48cOazUC4DUECr4Nbp3iqaOn2JX+eTDoc3KtkPC6xVdzJHKG
+RNX1di3vtAxKyrVuPEVO1ahvZ5QSxKrmew0tGpXRTaWtPTzd/NpqjdF54VyKF+gb1OkmEpNE6nfq
+VyyeSvTp5GzK+ULu2/NaRrGMz9qHovnODIKt+bCVrWzlI/JSaApF1fLkPKepKw4SOSlvj/f51BuS
+4eebJQ9UO/jxyw9QvAo/9WP/HAAH0R7xfdmh07xPpFgArKVyclrNzi9YXGjsWbH1cRrCWKIB6SjB
+KnBlt3VcKmz4Kix5qgkznbfZcxFRX/HuVcJoR9S6eJDSoWSt7ROkco2nwCofs3Ek4teYWr93DW7d
+OZoUxNO2WI122HXMjsa0x3HCs2txOC0vWkK9vjqT4yWfjzlvRBsJKrh+LLiA3t0akx/Lvb0Wo/fr
+cj+GqzGtNg2zRFMGCLIUEypW349pVQU31xpl8FfUqpkYW9AKRIQwqKi7SIWDWiMY4VLeeTKfYvvS
+b/eSfcyRQtCnr1CouWbdIa0R52+UiUY0OEh584Ek3L725gPSXufMq6FS7WVYgZpuXpclW7YY1VIo
+a2wX2SkCrC9tM7VPqDiZPc2TeWMw4GuBYAUmrtnko3heSKlZib2eRpnmPslBl8OxhrKLyiQEA/nc
+WYePjJVVxyeBo8vEsgC5zpeoxXTvFLbYLvqwq5BodmlqTUw7mXGRicY2TEYcjcV0uSwySiN98XFl
+qylsZStb+Yi8FJpCi2NRV8QefOq22K+fe+1VhoFoB7UrqBZi1+3vjLg/lAScvT3NaswXDD2Nmw9S
+4l2xVdts3uWWYP0YO1Inl56016t2gy4bp5ZIobjejT7HM7Hb3rnKqK7FQVfrbh74dpPiHEYW1yHa
+ihgvUMcfJW3nzIs0ZTeq8RXl6JoKo6FKz3cQyr2dZrQ5F1Lm4vibNpdcaIbf4+uCa/V3fDNfcf9M
+rn88V/TjYspCodtDc0jY2c7lAmJ1fDqPNtE2VXKvk8k7mFLaM9ofEnY4Bi/6kAMiL+iICJpAbO5q
+GbJWDep6esazZ4Kqa9uQaCBa3zCBspDnLOounFaw0izR6fIEz4y1D9/CtT+uffizLDVD9dmJpM5/
+/+7rHGnI1SznGDXAzTDchPis+5Bfo1b7va0XMNXQaZxgdzRUbbNNurTzPHFIAv1Dafun3zjiSxMJ
+l85eXFNVirwMUlzn5VMHH7Yhu5Z38nwPY9TBNwRffSp+HKLDjlWnpqlL5VQA1xYYdda6GroccOcK
+nGaxVo3M4+npGROdm++cn/Fc8TvHux6+hpGHUUwbfWdn/0uxKRggMg1elHBwJOq8SR3LQF549vSc
+adVBcA3TVDrw/TOZgEcHEXEjk6M/PgJNJ3b1mqVmll2uM06vZXCfnYtzZpUVDFJZNId1xO2epucm
+A/bGMsHGO1dY2Y82xCppmhCq+l1HsFxpOnBbEfiawedVuJUMRjFQ51M9whr1HC+XBHHnvd7HUxW9
+uJTvl8uG60v53fnJU840s/PCLvlgprBjAs4DefaZ5kZM6wltEGl7aoJUcxuSPVBnlmtLnJpKFjW1
+fI+Lc+nPy9UJBzuSO7C/mxOWiqEIIsK+jE+jJsNVPuNEIz9n12tOVIUtyiWhkTaNiyG7ao80uiBW
+rqDW+94slwSDDv/wqwhRF0AEminabTzN9ZSlApKe/fEH9G7K+A17O5hErq2nBevqSt9bHbcscEsZ
+j3yR0V7IweIHFcX0WvscVkpg4pfSL9HRHveO5XlfvZzhqeNWNnLdnAJ5qcqrKZSzIWhqTCSHU1Xn
+LBQkF4U1USOqfdBXJ/jqko4Bx4QDfDUb2/WSZqpp3a7CqeO20dTpteczUfCaH3s08jGTbMlAnZiu
+DrD2O0ud3poPW9nKVj4iL4WmgIEm9DBFTaCOoSYL4UyO6LKY45Q3gMYnP5eT6Svl1wGo7CvcVsdK
+35bURZctVpEpycr67DnzK/GCtaoO93ohDxSuvLu7yziR0yq0fQYKDZ1cZ3xFk2eeacadH3rky07d
+m1KspG2hV2M1zmhDWK01Dv2BMjf1IzqwZdhrOHDS5qK8oNFEo7NLPbUuJsyuRCMIDxNu3roPwF+w
+MF0qFdoiYF5KH3l6Y6+Y4WlYM212qVWDcJMS/4aquU0IMw2zzsWJVlcFthUNI84KLs0LudRG7CvZ
+zdA00A70epk6y3zF+fty7aOzjOhQNIjR7og9VZnfW0xZOumDINI8/3nD4a5cu7M7xNbSF2YwwWaK
+N2gaOkKyVSVj9wdnDcnv/BMA9jPL8VMZp6xcU6oZ1/fhqFOANhBzj0rh74/ePeVENY8qirGqvhTl
+jFQ1i4M9MS/GgwFHA/n7OA2Ya4h01N/DKZ5gLdMRu9MyVa3RTeasp9K3zShikUuDiuU165E877An
+JtP+vR32BuJI9euCspKxya/mzF8IZiPLaow6ZjMlTpg/vaCn4cs7O0c8muq1eb7h0UhCj8p+Z8v8
+pdgUXAvNuiEODWtZdxizxumCrV3LVPHu88spZ4HSmK3VA+4idn5U49ltTE9talM5GiViKcopRuGo
+VhdgEroN8Ug/8Ah1EZtmSVuJmrzXf8Fbr90GwDvRCbhYEagJE7uAUG1AYrvBQFxflawuZII8XcvC
+PTgYsVQT5vDhAZWRRb9zvEebT/XeOiFsQ6WELE++MuXs0WMAZvWCQ03rvhpYVjMFRuUdA08fELV1
+7uYUl9Khg4cBLJSwJMxxags5p3Rns5q+4u/LtmWg0N/8MqMYSpvW/R3sUmP2Z7IAr9473XA47oxS
+9uK72m8tNpX+OrYVlxe62Sk+wrYzjMKHV9cFcU9XVultuAYNA5yR8av02ifrBasvise9n1eMNUqw
+O7BM1fa/vRfxxj3plx/5/KcBKJ6ecfFEFtPT+QWVJ4uwdBlGAWVZXtPv+Ft00y/yJZVCho/jFN+X
+jXPc67Nayv2iHfUNZT1GmuHo3XP0I4Fgr4qG9QsZ33eWzxn64jebPBKT8FPmDtGx9Pfu3UNaNQ/m
+pwsea1bpoxdrHn8g8+mDZ7IJ52XGnuZXTKr8Q/o7D0LFziwbh1G/yseVrfmwla1s5SPyUmgK1hh6
+YUTerHlyKbvgjR2fspTmffXJjN/6mnifnTEblTLoknrqhoMdOTEiEqIj+b5crViqh/ti1vLsXHbg
+d5/J7ps1Hnc07/4Hrnc53lF49E4fVM0dJCN2SzltX9Vr27KlUCqx0jWYWKMMy5jJVE7Cr31wxrli
+D67Ugzy4mPPpgV779IrdQJ1gwxyjnmyn3IHluketjqOvPP193n4hJ2YvDHnzWPkd2iXLhZxMd94U
+Mtqnf3hN7YmZ0OTHRH15hq1aqlwISygGlDtywrz4upx2v/5bf8DJXLSGwd4OrpXn3TkY8vlPC4Kw
+lyaEVlTe8/orAEzKnLYvnzWzit/8/f9b3jmDOzfkNP6BhwPisEuakmc8fGWP2zfVZFgXVEpKOvjp
+N5n/6p+Tdja/hA3lVI1uKOKvaalRLsawpVAn39XSw2oy0uTZjDdviyPRKGlpc+U4n8lcWM8CYmka
+8+uS3IoW06xbntXy980uwQzLspYTeloWFJpg1uuFzJWDsoMXu8RiFQpv4lu8/1y4GX79N77Eu1PN
+/o0Cel8Qbeun7svY4CyBJ9Gu0WFBrVrFow++yVdlqlLWIU9LGZP31Xk+ilNMx/Jdt1yrudYLEtbK
+G1oUC4xRs/FjykuxKfjWspNEfON6yfulDMrdZo9dxYGctXNiBZUc3zhkd09ZqtXTe9Rb0iq7TNnW
+5DLvqJuCVlN1+6M+++onaBO5NitaDhMxUZZeQOFkkCsX0s7FHFlUNXvq4U6UrMIYR6Phyzx0xMqZ
+2LYeSyVuJYzp35BBv5/I5O8NbnLoZGEOGkMYyfclEV4ijS6ajqgzpHcs8NtbF4/4shLThqFPo7yD
+s2yJUVW7OJFnjKMrJqH6NapHROrVN3FIo2xDVT2DRhZN2Jd/737+VfpXsguN/QMyI/DhPfscdOF5
+1t/kcXhKehP6pyzVCz/FslazY7pe4CmXZnjt8SCQRX846GiWU9pKyWrdhLqDT1++iR//kjxj55BI
+wUuHA4WVx457r4mJYlrHKJfPn1xeUyhIx9iIowOBwLuBbg7xjECJTA52DXZX/S5pQK3hYNsmOGVO
+2u+a6WpOlXL48WJFbeU9+vEl61zStjMNf07ygpXGwMfeHgudW8ntIYO35L6fOfgU92+8BcAd800A
+4sYSxrKp50WKVTDcwjUMDjVSsXeEf1PMwjKU391Mj0h1I3CrSx5rOHRWVDxZd2S0Db1kC3Peyla2
+8l3IS6EpeJ5lPOoTz5b4RpNyBnvc3ZPd/Mfu3+RHNLowvrHHWLPSZkoscji4x3BPHY3W0awU1lkH
+9H1xGA5HjluHClN+RdRh2iVpJKdVEUCszM6RB62quTfaPqOeZiA2shMPQw8qaYPXelgn90jKggcK
+2Hn9B2/g+0qQockwramJGoEae8EaXzM429Ucu6leohl0xYrXb0vb7/3kj7NcSXu8RUGmXHwmD6m1
+nZUyUSehz1hBSnZ5c1PAxlQFVgk7XL7Gqrp+e08ddT/4eViK83AwGDHJxdsfFMeMx6qZzQryXDQW
+byWn2bh3yFBhuwd3cz7/1k9JOwBPQTPnFzVV+1jauRKHYlrk1EqyUpY+k4VoiG+82Sf0/zN5XPXf
+MVxJUtzrPy5vsvPccUdrZIQupplq5mpWEaTiEL6xE/HwrhCu9LpaDzdfZW8on00/eMZzJVOZG7up
+MxEGlnTY4UzknZazBVHH1oxjpy/PfvPOA9pStILlufw7eLhDFIpmsmstr74m2spfeON1nn4g73fn
++x7SqEarV7AOAAAgAElEQVQat/JuQV3StAphXlxg1OQ7Pvw0PU0ETHYsd3eV01GGjpt7HkEuGkh5
+7fOF9+QeJ/NLKjUf0l5I2NHnf0x5KTaF1kkKaC8JGKnqe6OfcrgjnWBfLUg0rTkaDwjVTiTSMI4f
+YjoVaXGNVYx7k39YTSeNh/TVBAmNqL41IYEi5kzi4RRMYzyYKnnmoJfSS2UxFXNRE42piYby94gA
+T4lHYj8mThQRNzjYEHtazZY0fkWgEZO2v6aaaPjKazCaE9HXzLt8WTFQ1qi9bMy/9qPiRT959IR3
+lVq8LAvmL2QDWOmicsan0gSMKFyynusEDApCX/ozHEZYxdf3W+VOrHuwp/yL1zXpntr7zmLVk13X
+C4qF+HyMqsxJZCk0NHeUBoxHt3QcXuD7GuI9sJSKEK3U31G4DKs5GvW6pNFNsfyjh9SKfiwvag7u
+yL335rLYDu4EvKZkOWk8wiWysbx5c8Dermz2492IWMO2tZK7tN4D7B3Z1HfiHiMlg1lfLwnGipxt
+m00+ylojR57xCRVluhslxH1Nme8fUq0lemDG8pti7knCCRDEPYY6F4aHdzl8RdoT2X2czi00CtYE
+OcULMZMcITTyu/RGiJ2IWXF464BIgXG3buhzVw3zmWS5nnoBA7pqaDWJkswEfg8/+M6W+dZ82MpW
+tvIReSk0BWsNaRxxfJgyUPX7RpAwPJITPYruQCZqZ2JT3IFCd1U3rmcL2ryryxduTijPd5RdfcTY
+Iwm6RHXRv2K/R9vtrn7EOpNdt5jWNAo7jXt2A5VG6dQ94zOMlLDFOdBTKTk+JLGiwgVhi2e1GEhX
+x670KEt5j/Y8x21qMNbYrrhM1HmkV6wVvJUe9rh3U2LefuFzuVIuhMU5hd473VXSkDmMFL/fVkOq
+lTgPozDGHCqAyAugp5yJyvpbR5eYa8XZxxWe4i0MHigOoaWiqTVDL1Fq8dzHqZbmNT7LmTjBfD+B
+hWhhua2pNWdgrX05uVjgaSbqqmgp1MS6fPfrHOZy4pfNGS/W8i6fVUfjw+M9jjUK1LMtFeoE3h0R
+6skcVlNQh21yoM7cagamI4MJGGshnqif4ivtXVWZDZ17hmhe6wzivmhNbzU+zVCcv+l6l8cTKaIe
+dpyf1YpStc2jowNQ6L0BelY0DNsPcIWWEmjERGuWbnM8G+tjQjUVy4q2y+CsShItBxgi77Sulvhq
+dkwu3+aRaoV5bUhVUwgpyNTc+Ljyp9YUjDF3jDH/2BjzVWPMHxtj/gP9fNcY82vGmG/qvzt/2mds
+ZStb+Wcv342mUAM/65z7kjFmAHzRGPNrwF8FfsM593eMMT8H/BzwH///3cgaSxymlEFCeKDQ0V6G
+K7tQUU3YZcaZEk95VGtl6HJ2TVnKh349wqZqc5/PKCYagy4bbCC7dZLKTt261cZ+a5sMNBGlMBm1
+2pZh2sfoaVoolsD2Qqzaxn7Q0qhtTNHS9jUe6oxk5gG+cv5XXo5N1I7Mcjr21Da0mC4Rx+uqJPso
+Fy3hYEyk7MT1TkQSnmtfLDdVno1iIny/RgmcMeYC13EdjIfYQLNHqyVu2hVuVZbhJKdaiWbirosN
+1ZExlkZp2srLlrXOmK5qcxjFlE1XcyLY1E+sigWxlfPAHxpKHaxSj63r9SlNrTRmObSB8l5kbzMt
+hc05MIYdJYidKHR9cZmziqQ92XqJVX6D2STgxv2O5TuDSP1GSrpriiXVomOBLnBdZuQqo1VUYLlo
+MJpdWGqS16LNSBN5j0Pj0wxFK6irEqvh7lVXzzH0aRVqvby+oLghfpC2mNNa5aGYHdB4neaoGIry
+ZFNrMr7ZxyjWIXQF/iYr1VKrc9g41e6Khkzfb3p5tpmHzrWsXFcuESq9/uPKn3pTcM69AF7o3wtj
+zNeQEvR/BfiLetn/BPwW32ZTaEzL3FtiZn32f0icYSdnAUYnUG/HYZcdd5kP6sDrGHcr12B0BcWx
+qvTAfLVkOlHVfnZG06n/BzJYcb+HVZx5RUOp1YM9atIOkBL4zLvMx0A6d1UumWeywEZjw0oBLUm2
+2mRPuqbG08mkqRb4BNSaThukfaw669o626RgeqE6p/oeQalqpG/xI3GuDQbP6ampYduIVinIfK8D
+zUQEI+mr5mqE1XwO21hMl7adWSiUOKXDRazXVJ1au5xRa9+Hw4hQoy/+dE6gOQzKYUJjPTgVJ1mT
+hhitdDRMethOFV95VFrPcKrZi9kk2/Ah+rs9MoXotsOn7GeyoGe5x6l+/kcnshFWixWLhZgPI+tz
+pGQ3SRiRLXSzLyOqy/cAuKx0A5kVBMqlOfIsNF2VZ0Nfd9ZkvWSi1cOeatXmZ4uMAzUV944qgtua
++7FMuJypQ1uBbIt1w0yzee9eTphfStbpICiwVrAVbZDRKl9jVzWqzCOWubJZTy3BWOZWMDzmhma5
+Rr6Pp6nhTaGM3/EF2VTBeVcryrIzoVtKBXhFkb9hoP648j3xKRhj7gM/BPxT4Eg3DIBT4OhP+M2m
+FH0afWe89FvZylY+OfmuNwVjTB/4ReBvOufmRrMcAZxzznSFDv5f8pFS9L3EzWY593dT6gsttTay
+RMrgG/X7tGoemBDySccurLfOW5zG2OuwoNWMu6vTK07PJHwzSgNuaq2G2YmGAu9YEiVyMVVFovH2
+0GtptZZDVracXgik7blW+A17Q2LVRoZFwFA5GWxssVoKjrLF6SncKMqvdQVG2YwxBtexAXsptZY1
+D7T2ZZyEG2KO8oMlhZWT5PrZFbUiE9PQ0NcNtYPaEgecq1niooxatQ3jFRjVbjw/pNVEqMYoccfJ
+glWuyU6LBU5Do941FOeaxx9YEjVHOkLcqipZaHGTgfUwqkq3YY3TZKXWLfC0jkaqZeXiQZ9Uy6oF
+cZ9GiWCLaY+nHbct8Ub1vdBQ7nxU8s5zafN+Y6j68rybR4f4ofTR08kLnj0T5OiVakSTywUPv09U
+/3s7A3aVqMebF3QQkcY1GwKediHPbRtDoclvoT3EBZpIRchMaf0Wir04bMYcKZO4FzQEysBdX64p
+FUrtpwGB9q3x5Nq8fszFM8n4nV0d0U9EE8qimpFiXQLfw2rFbnzV1i5WXJxKZuTz0yWr4kPW8VjR
+u8PRDmiIl4lmG34b+a42BSO0N78I/Lxz7n/Tj8+MMcfOuRfGmGPg/NvdxzXigfWGc9bPBC9+kce0
+OzrR25LlSaei+jSaS9Bx61kaciUbmT2qmCsJx+/97ld5R1mKHuwdUvalKQPNTmxNgT0Sc4UAnC8T
+KPRD1o0s7re//jb/5++LKop6yHd2fYZKyd6zBQON4zvb38TYXdEQdAa//lsUFdVC1OfGRVjNuPN8
+s2HxKZdKoLI0BLvy/ZI5meZUXBU1z5RyfLKuN/kf8Vj+qHIYaIpw4w2wnS+5KGmjrqhNuKkytH5X
+Jsrl5WMulBk42uuDMj1drefkSuoSRocYJU45m8gGMpnO0P2FO3dTjJZOb1wfO1PilKRPtpYS9YUW
+1AniAU55ElfrglqLnjTNnwf3k/I3f4tcU5VPC1E+31ulvKbKZ+TNWZSyIedNQLCU9176llPFX7hI
+/QjhnJNLaWha9+nf1YI5szl9JwvWlAMWysB8WinoyfhkyuL9zRdPWWaaS5LdpdGoS93KvfKkpD/Q
+LEkzZnIth4mpdxkooG7w4DbEmtmqzrHeeJfBobzT0lVUkYxD4B0SmO7AaT6cI2v53YvLKW8/knXx
+/tU5y6rLhjQM1bezEycbFqqPK99N9MEA/z3wNefcf/UtX/194Gf0758BfulP+4ytbGUr/+zlu9EU
+fhz4t4G3jTF/qJ/9LeDvAL9gjPl3gQ+Af/3b3cgz0AsaTuoxl6VoCjtnIddaJbr1a9ye7OBVUdNq
+s5vOgXcQEhWKwPMNqtXxxqfvsa8n+g3/YEN/ttSsx/bqHE9V/3hvgD9QdS/zWK/kpHiRBWSa5DQc
+ieaShwmeFTVyUV1TKiVW3PdwdYdvcBsuPUuXlJTiNKHGOp929SHvf9GIJmAKaXwwCDdIyHn+gicT
++b4uEzQwQjzwGWktgFTxDbO8JFJTIokiTNsh+xKs4htcYGgL5VbQeH191KOt5ATL7WijxTzJv0w5
+VQ5Krng2U2yBcgMGtibR8njt5RE3NXlq6GV0VSDa0iNTU+9qqijGsKG1XYJZg/U6Hf4LwK+hb4hR
+jcU50eiaRctFXyMDU7ixL+13y5BMi+f0ewd8ysh8WGhF6YObAQt1Rgc7EY9O5R42zCmvtcpz2nKl
+aNm5AsQLE7Jaa4JSM2OqyNFyMWOvp1wcXWm6ZIRTTsxFlHE2VczJ4Yy13iPCEGrWYqsRAj9I2Lsh
+8PddG+HUpKvXNZ7ye7aeRSEZlDqmy7zk/Fr686Koabual54lUFxH6/epyo9XbbqT7yb68E8A8yd8
+/S98RzfzHF6/obqouPeDAus8P7WoRUB6x1KVWpBktt4UmH30NQGPmMZnHGpFI69ioIv/5o/+hU0L
+g6Ll2ZnYmV9XCGvb83GayeaqmFa9wXE0IlQq8z36fO7zPwbAbC6D+HuPHjOayWS8M75i1ldYKg27
+muHmrT1KTddsOqhtuyKOZaIk1hLuyGYR+DHOye/cUsEqNGQr2UDe+/ol37wQoMvrd+9zsK+FWy96
+6HrEXysEu1hvwD3jcMx62QGklvQGcm+PPr4CuQLdhMJ2D6MAo2y+5sk3xWSqdhJuaVrvrYc9Pltp
+eHVXFv+Ldz/g5O0vAHBycsKpesMf3tjjlqa5Lis4O5f25+qd96uSWgFnwSCh6VxP0dmGH7FxZVcH
+l/Va1OQXmeGykrbvtyEfaH7FZPKcQS4q+F27S1zLoneaH9PmQw611qRf9MnmEhkIEker4KSzizXv
+n8qin2hhlWm4Yqg2vnUFfa0A1mR9ppmS1E6UlHU5odaIE9OQMlXwWh0zNDLuu+U+7amMw0rH5uKb
+M1LNZ2n9gkWrka+q5MZDLQCzXBEOlZhWixEvZzMKhcrPs5ymi4JZS6igtaUfU1TfWfhhC3Peyla2
+8hF5KWDOTeGYPC749MOW12//MADv3nWgnIHWNsRRl1c+Z7UQE+NKuRN7o0N2X5MddfAiIFR+A28a
+bUhLltdPyCcdZFS997v7BJpxGcQRkfIOOleDwnlfe/WYaiyq2B8/FfX76a/+1gYLYFe73H5dNY82
+JVJugdAmUqoMWGiJ99VqRa68i6thwmCqkY96SaDO02ytQKBqzlKjDBfPznB6ZC6mK2aqzle+oVKv
+fkcJt5tEVLkmw0QtJ4/EO337jQHNuZoMe5Y4ksSlKpG2jasbXA8lujI9yZhqRmivuUe7J9rUsHwV
+25cTLVFK+cv5DWxPYckvHm+Yhoso4UojFet1Tn2lmZvaJy4asjeS7wtnaLtS7LWhdh37cA8Ubtwo
+KGjlga8JSkVdkZ5KvxxHjsmX1ZHcRNzpaO4VY3BpZkTKn5m9v+KOlow/2tvB6v2eXF/ySKMOZVdJ
+27cberhe0CdXnMbaxbRT0XpmyvdYLwNu7Ul7V2VCciT9na9TPnVf+nB92ZLH0kdf+9+FkOarz3Pu
+KEQ73UvIl8oLMR7SXsuce/X1VwhK5YaYi9Y0+9Jj3lO6vWlRbcwHg8c41IzJssFrvjNN4aXYFMBg
+XMDRjZjvH8jiPxwUsK+dnfvYRsOILaRKYf7mj2jqaTLm5vEDAKK7IZ7WKPSrHq4RT+7ieUz5VDpz
+rUQYAYZSy7onicGqLV7lF5uJcOOtY1wmG8S0lgF4fnWG1h8lie4we64gnEGP/ZVsSCaOcFrjwNd0
+6J3xHn4XRq0MiZou+H1QRKOnoKDVpOVSwVlBtEusdubZfM1prmCZoqJRlFutNnDkD+ipB3xVr5mq
+7Ty+FTE6kHam4wBvpBuHIiir96YkGiI8eljDoWwaxemSQJmlZtkV/r7i/J/KeLzdXrDS94vSEcND
+TTMe9lkqb+TZbM1zJb31NDzbby2eesjXOGJFp3rHKc1zGVd4BIrkNFqnojGQ6IJv2ornOhDj0S5v
+BrJoVnlOqGjKg3258aCB+VrUfUeKd6yHzO6Qr6tp9uvvXZCrCr6v/qPY1PQ1e3SZl4xSOXzCgyFn
+Zxp+VRP1rFxz9kL6fhSFpBfSL6Ud0qzFZ3C4v8/hK+KPeuuzUuHs7r3npGNJ+072+rRq+thiTagm
+T3Qc4zRH48mZ9OVvz6Y8K3UTa6pNiD5MI/JEIzuza9qufunHlK35sJWtbOUj8lJoCkkEn3nF5+7w
+Lfo/IQ7F4EnDSp0o/cM+ocJ4m7qARE6rQd3tgCXXj8SJ2AtioolmAO4Yinc0nj4/o1Cm5NEdcZzl
+paVQGG1VWlpP6eBLSzCSk2YnfkA1kpP5aC6nS13XnMxFA2nPa15XR9yNGyMOd7RtLDeFQ7poicUQ
+ak1BmzsSpTTz44SqUtIPdYBNTy94qjxgSz9nqtRmZ/kFJ7OuFHlJpRqG+lYJ0pC00NM1yOgdKPCm
+Psbf7Y7jPZzyVgSIRhAd+aQL4cEMU59xrLwItyasT5XAZtTgGnnXYijtrN5/wfKZtDMchezFCu0O
+Qq4UDPVsec11K33olFgm8xbMlcq971vCQk5Pt0rAE/5HG/bwVSuIlaY8sRAl8n6LZbapgh06j7ov
+7XyxvOLrSoPeTB7r782mzue927s80Htc1jVfuhAV/Wy9JlNMcKsU6XdGIyItE58EMZWq+aM9xzPV
+CtbqoF0WFbXC5q/WAVWtEPvojHQsWt/3Zw/ZKUSjSW52UTQfJYEmn6wIlZ+hduCrVtzMY+aFaLpv
+vy1Apz94fMKVZg+3uI1TvfThLBMNwrmKlq2jcStb2cp3IaZzYP1Zyp07x+4//Jt/Da4Cjl8XuzY3
+Jeax1izoe8RKtFmua2qtAh0rMqwNWrK5XNu0FtMVv7AVi2t1jCUJ/VBO+tFNTVrKPNQnSd0GpCPZ
+atvC0GqFj8WqotH6BYGV5x1++tNkj+UUOO0/4Zf/x18B4Btvf4FGHYVEHrU6D4c7XdizoVQnWlU0
+tAqDripQWACt7tOxb+hpvUo/8SmVVixb57RqxNampVY7s1XHKIsL6jtKcrreY1WJBmWbgJFWRG6d
+odYiOZWWIksHPVJ9z904oqeqR9a0oHUDjntDIoVx14pBWKxLLq40k3G25lzDqKt1TTzUcFrQI9ea
+j4sTcXxeOo9I6cxKv0+sdSz/2t/+G7TPxD/05/6dz7CeaPKXlgg8KOH/Ujjv27/5uzx9LtpNvbYc
+vvUmAP1isXFoLi9VU3orYWetma/1NQvF2d79i29yoP358BDW6my+8WnReAZtj+yJUCo/PUuJ9fNo
+6Xjr8+IUbzTEeLb6Pf7Lv/13AXh88Yh8qmHGtt3Qu42GMaWyThcaql6vHZ6iOxMTkug4TJcFS8UY
+rPOaVhOiOoCOsaDDQJj4DEJ1tkcRu7dEG/nP/9v/hleUhu7wwa0vOuc+y7eRl8J8aMqa66dn3Lt7
+i3DaEZoYYiUCGe1C6Hd1HteUvvI4BlqIs/EwmnFXm0jq/AGRTRgq4Ugv2WGsDshUO6/ZqfBs18EN
+tYLgK7Og8lS1jXJyxeU7TQ08/9ozxmOt5/fN93ny9h8BUMxzYlXhFnGPWBeTZv3i++0G8jxMIyr9
+PgoCgrhTUWXyJL5PT9vu+R6tLoSLxYq11mvMnKNUWva5r2zQK59QocH1qk+r6eBp1McqZNYvG9C8
+i1ArKHn9EWkkv4vidOPY2zOOVHfO/bRHrDkhbpMiPMdT7/tyXdPMtJZkVW+4MnPTUocylkvNTrSh
+JVOnbNK+Bpoz840vnfLTPy2TeP+9NVfvPZZ7fE368PSVr3F2Kt+b098jv5ZxHx94mKWCzFYTprU8
+b/+G5hxMRoxzdTTnjtu7Sik/Z8Np2VwPObypNHyZtDPNLyjVRLlz/5rMicOwNpe0K1HRIyeOysvH
+7/HoG98AYFqsNmp4Fafc0ipbNvaJdNxXS+2Lut0UwjWmx2W3eTUQ6mbiJR5rPcA7UpiyaTc57HnV
+0GYyJyfBgkgxEtPrCXbv/zMn8U+Urfmwla1s5SPyUmgKpjUEq4iRPyWYKuQ0TuklQn1lfYstZDeO
+Bvukqu6tNMxVzi+JlQbN74VEysqcjgL6VpNn9vc28VqrZcLqpcUpeUtTlDS53G99XVIrgYYrndRJ
+AGYL+V3ajrl+KkfX7/78b/D+qTgdk3CA1Xa4MidXz1agiDLfGEaqSgzT9EPuhNgy0p39cCQnSuz3
+sOpcLRdLSnWi7YcpF5GcCFfzKeeqxVgNs87LhlaTwLL8hDv6rsNBS6Thu3nRbsyUkWIwTBKQpvLs
+gGDDz3A0jnlwKOGyUb9Pq1wW80txzgVJTT6UdxpNW1KlElusq40TNG89PC3/VihFmXfHUWRK5prm
+GCXQ3c8GvBH/gfRFG5DForo33Afgt770hHfe/00Art0STxGWe56hX39Z7hes2VNkaJjL7015jVM+
+jXiaUIda3fudFbNb6txOjvG1RF6o/dksKjJNghrfXlGffVPfaYRVPEj+VObHL/2nv8A7Wj8y8iMS
+pfRLI0t0IH1btAFrLTjTFXJZU6GKFxUr1PKh9Q2DWNGpUUzRan92hDOrNZmGG8u8Yt7dpIHHWojm
+S3//f+Hhv/of8Z3IS7EpeD7s7EM17dFoboAxOU3vQ1MiUjvLBdfYStTOXl8W0t7BA4xR1uZgD3Qh
+JOMhppWOitIbG7adjmykTJegLMplsCYIlbmmXNNqqm7jD2ly8S/USqaR2Et++be/CMDvnD4jU/CL
+TS1eLG02pSFX8FHbl+/3h0NuHcoiHA/69NWM8cKWnUQp01NdrL0xpRKTNFlIoaCZdGmItHJW9V7N
+VNl74rVS2fcXnGuUpanO8NXLvmotLpe/M7PYEHlYTXsOvJjJlSwUbu3x2oEs2AcPj9hXrEAYjljO
+JtqHmhZt7aYsfZD4xBvwls9CPfl1m29yLXqJQq2jN6lH4kWvey3MZfH+858ZwfV9AE5fXLBQRqZH
+uWwUX7p+lzCTDenw0BLo5r0bgTfXAj29Ai+UjdrXwjl935EpRqLuXZFpbsteuGSlOShpGBBcyeeJ
+prC/qHLKK/FnLM0ehRUTxOwUNJVszv/4dyRX41cv3qesO7Ich69Q8Du7u4wVADYvK7wuS/JKDzfP
+0CjkO28r+p3JMAi4f/seAMfHPSKl+T9XJqyry5xn6s+pqim5bsI4WJTSjl/+jS/z8HWZqx9XtubD
+VraylY/IS6Ep+L5hd8/n5OSCdE9O5WG7z0iZisPUx1NVrK1aIlWZYy2akQ5S/EhO/CDu4dRh6Mcp
+rlWykDimVdISo6eLZ/dAPbpe1tI6RbEFCb7pMAsNvhJWdNwETTTnS9+Q/P4rU2A0ljzE0NMTNHeW
+pfL93dS6ED9895hXXxGTKPZ8vA7d2FrSUacmKibAb2gbOV2KJGe21LoX1ZpUsyeTm/tcK+/g9VS8
+1NY6QlXxi7KVytqAyyvKA/VerwL6SpLS19oSSy/jQgu9HHtj7t0QTeFofEiiVHFBaygUNpzuyPfO
+BrSa4TmdLRhqMZVF1lAq2s5VBms1u03fyZs8J1bHZ1O8oMylb9/6qRG//Au/J+/y6owdfgiA14y8
+02f3R8w0qex+BDtG5svZvOGDa9E83CogVG6MTFX8VeBjVAvzrwtq1WieLUr2lQwmpCFU2rtL1dKy
+xSVGYaYnjyekNwTSHVYe14oX+flfEUfzRZVv6pze7I/58c/ItW/df4N0pPD1dU616HgtpA9XWcu1
+mmuPn15wfEs+fy29we17olkmWQ7KDTFT7eD08povvSca1lc8x1efaxasczQ6ZpmxXF6oWfEx5aXY
+FOrKcX1RMdptuDmQFFk/9QkHqpYai1X+uca0+EMZ3DBVW90ZfC0A43sh+Er4akLoaXl54wNdCqks
+BBOV0BWDsUs8jQz4kcO1HcV5RtOV8g7lef/o//hjlkZUx17ap1BGHL8XkHeEoEFDTzeDB69KeOhT
+b73OTaVqN2VGq5DauqrpKVFLT30Hke9hNd6UViGhgo2W4YJAwTImDHio6bkds9H7teG8UyOpqZQP
+MOlZRko2u5+2RJrZ6TT6MrnK8BWk8+DuHXaV9MPzR3iewnmxhDrZqpX6bQYNjQKSdoqMYacG9yuU
+PpJJllOo/dyoX8PMDNRa3Qg2m/Pv/4McO5aJfqd4nWgqIcz5gSyw0ekToj3pz/Lsi7zTUcebFRNN
+RS4Xl1w5ZXUaaNEbb0ycy7MbP6RVrsx0f8xKx/LCReTqVxprpad6XjCbKqy8nBNqxbFgXPPNXxT/
+wjcu/hjQQ8OX573y+g/yl/+VnwDg/iClcLJ5maom1cNlfF9qSjbUWF+el0+vSQ4UAJYXNBqSLPML
+6oV0aHlTNqPj0xGDsYK3vtZjrt9fLYuu3AyTcs0fTC75TmRrPmxlK1v5iLwUmgI4jKsJ/JBaub36
+LsDX4h1Nm1Mo0AUT4vT0a0M9wZIcU6kj0i83gI7GLrGZskBHLa1GAYyyMpPXOHU+0VislVPC5MUG
+Guq1YDv1WVu7ahtizbH3q4JzNRMWuSPx9HhsKw73RPX7/JtS8u2Vu7cItQBM6Qx1V8Mw8vEV5+p1
+2XlBRKDD05oGF3bvHG40iNKt2dVamJPb4jg8zEpO1xpd8T1qdfalgWWUyD3ydUCi9QUniiU4u7ji
+tQfCOPwD3//9jFTL8RuD0WiOcwGuK2uu6nmzCOgdyqk8Xs4ZafXs5TCgUXZo31jOlgrC0X6NPQ9T
+KFy7NRt25cJWBE5h6JMJE1WlVyeiGke7EXUu1G4nR7tMvyIopLDXfOhUjoeM1Im7t6/M1nmDr+Xm
+QuehihlV3ZD3NDHN1KilSKU1HJdNTq1jUrgVpx8IGOzY/wGePhIYt8I0MMYQqFnyg5+7y2fuC3Tb
+yy8xGiXxx9DbE5yFvyvvZnKwmozG8Q5oZm8zXVMrp2cdDGkT6a/1tc6924ZQ50VjnnJ+JtrGPz25
+2FSsWz8AACAASURBVGBE3GzJ9O2OR/njyUuxKRhjCIKAYTAmUU5/z0KhNqnnR3gdCappaWq1VdUm
+82JDrbkDeB7dyFq/2dSDsHaXjlTWaKiwrjOMU1MiDAm00o+fziiVzy9rHE7NCk0GJAC+T8N0j1eX
+lHOtVd4UhGqfPtjp8/qrMvivvCpl3aMeRGqulJXDVy+z8QI8BR91CMOgDrEavvRMQKiLJg5D/h/2
+3iTGtiy7Dlvn3P7e1zfR/fh99pnFKhZZVSqUSRUpjmTDAgTBMOyBbQjwTDDggS175IENyCObmtgD
+N5BtAbIgwAMblBtSKoGwVEVWn8mq7PP3EfEiXv9u3xwP9rovmQKt+sWkyE8gDpD4kRHv3ebcc8/u
+1l4rYc+HHwY4lctAeSGb5kV3DSsn6Ckz+54RZTkAXd9wVCIlfdPjS8kjGDS4dU8ONjw6QrGRBW/B
+Qkk0pe1GMCQTspmrCN0aKpYF34kWiCJxk6Oth4YL1sCgz3KwYQhnaxfJfvPW0ATmlPUWviWbU+Q8
+wWwn2f6YFSeFMV4lsrSMn8G4cj7b6uKNW3IdWV1gwE1hGAjYKDje4CohqYttwQ3bnMgGCTVGO54H
+dyJh05BguJWxsCKD1nxhULSlSqvCkpUYixusahJQmAnT4wM4FEXuJA2qXcJnGUGRbNcijNXoDXTD
+sNKt9xqkxk6gY1YiPBuWkntqOq2BDGFO5LN31U28fl+e2QerLZ4QWVtpg00tc/i84zp8uB7X43p8
+ZrwQnoJtaYz6IcrsUzCG7jZw2M9Q9Bwouteu48Mx3GnbA+QVFHddVRd7joGmsqE16/FltifvMHWr
+M6GhaAWACpoW2NE+Knb47bY72JQfr0mZdu/lHs7eE6tz1mxRMWHYs0ocUMH41/6VV3HrhiRNB1QE
+6pg+Gv7s2F1oYg8ay5YEKQDLlbvSBqBRQo0CNUMmu6rgMpHo2M3e6wmYWroz6mHB/oQP8wyW1bbf
+1ShaWrjUAgpWV8jNMOkFeOOeWOggrmFD7rnTdVGTJzAMPXTZgWkdyLyun3ThRq1k1xBVI88sbzJY
+c7GkjqPgRiRcIU9irTbQtIhBBJid/D4KNnj3DySBl39ljN2clvJdSQYOTxfYOuw/6KU4nTNZqUus
+WcHwmxoBiXR0n5yKTYAee1uKqxyZkt8jcOBzPfRyFxFFVJhTRr9rwyKb9YN0jSm5FA91H/+EymE1
+OyMtW8MmZuMbt6fo9Kht6R/ApqiLaZpWiAxti46xj6F82udKA21VyvRhRaRlX0eo2lbKWubeD1Lo
+gvR43QZHA3HjPA04PMnBJMJrJ+IB/194vnHtKVyP63E9PjNeCE+hqmvMl2u4fo0uy2K+bYPSh7C1
+DZclJrc7QECdw4B6ga7S+zhNAQDZblVsYEhWaiobDrEMiqUwFQKGmAe7ydE6DaHrwt2LNBps1yx1
+sV79+MMUmS2Wq9yWsFluC30bXzqV/MFrb34NxwMm2sjHb9k2dFsiDCpoIzdYoIHDEqi26f+UBjWt
+eRMruNQYyFwHnpLv1es1cpbhLJYFO4GHLml/Q3+JjCrIRdTAreR6ep6DHTvtOsQd3L59ivs35Nrh
+NAiZoAysLuDF7a/hs/SrU8K57TkyQtO7nRrWAx5itUHMZi1HW+hHEg8f0KruYoWCzM+FE+zVnv/R
+988w0uJhbD64gYfPhCg8ssRLmxZjBCyjWkWNYkgZN10hZ4dm7bioidTMeB994yFrOyeLEhkbicZu
+H+M+G+VgYeoJcbDFMnSTufjphRw3tIFV20A3sLG5mvH5ke26aeAFbRPfPXicW10EsLqt5FsG3bq4
+BSn9IgPFnIoxORQboqxQw8Qk9PW20OdMfpP3o8yqPSN0vS3RZfdlUFdw6YbYThfw+CI953ghNgUN
+Bd+yoWBQMtPfJAkaTjCURk7X39rFaJhIbJjpr5QLzfp3VTV7wVODDIpQY/gRLLquFsE/duND8Qlp
+q4YhjFnpZi9Fn8YlVmR2rlrgjaWRruXvy/kCJc836PXw8j1JYPmqQjmXRVhRAMWy633y0Mv7qJh8
+c1QDxURjRVZmxwn2PRpZfbmHUivbQ8V7rZsSFRd66ZP7MC32zM6Rb8OlAGvkKQTsUfAV4GqZw6Mx
+CWk6PnZzCZlSN0fDjJnfFHA499oJ4IUyB6UjCb5sDiwJMvvw4yv8+P0PAQDvPVngyUY2zkngYTKQ
+74WsZKSqQs1uvyquochgrGwXTk+y9vYAuDiXeRmVzNRPA2xSeRnTkYcA8r2DrgVDRmSv40MZArwm
+8lJVrkaHydXR3SEU9Tg3SQ5lZBPtdrpg/hEegWy7eIb+S9SgfGTh4kLuKTuqMO2QefxKcABlVcJv
+18JBFzY7bSv7CWrS7DUFYBM701Y6lAOYokUWlFBOC3YRkhQAUK6Nxha4dbaUOclqoGwp/M0GIG2e
+61swW26Aqzmy5OejeP/c4YNSylJK/UAp9X/w/+8qpb6jlPpQKfW/KqWuhSKvx/X4czT+JDyF/wDA
+TwG0Psp/CeC/Msb8PaXUfwvgrwP4b/6FR9AK2nNgey4s8uo3lo+CXoMpFSpa62RXYckMXHcl27rr
+7GA3sisbnUD3pR5d11fYzuV4l8kSdSm/D3usZyuNQV/KRr3RAA412Mq6hqL7WTYBUjIUt9RYvpMh
+Zo192xT7vJAKPSRMUG2Wa1ywk1D3xBofGBv9iVgdXeTIW2ZjbcGlBV3Ts0l1gYLEp7v1CjkFZ9zI
+gd0KzmgfIG1Y5YvlThuzLxcGYYCIyTDtuHsPywksNJBrKpds9qoaPF7KPVnjBsGOqDpviYjNZl4w
+gd1qVlpy3O1mjgdzsdyLYodWxy7Vxd67O49rwCWKr22YykLYhkQTdbVXz743OMDwRCzwcnaO88eS
+EGyOadmrESJSwp2ffYSQvBCN28PBWDyMaeQA9ARs6oE0pkLM+LC2IlSm7Xj1UdNurco1iguGTTRl
+RttgbxT8/gl0LeXn9eYpIgrplG3jF2rQCYWJHOSVWOhyl2D1CZuqnBTDviR0h2yMsqsGyuq3J4Qm
+erWJ12iIkGwaDwWh5Utqm14lK+Q5cQq5C0NSnlJbKJmGN00D7dBNec7xebUkTwH8qwD+CwD/IaXk
+fh3Av8WP/B0A/xl+xqaglYLnu2jSBpqApLTMURAjnuYxamIMbN1D+UR+/7ElIJYqTXFIAZS7Jwfw
+O8QmFBG2pNi5erDAli5vQDGNg84A0YAvpmXD2C2lPDAkF9+4u0JRyrGLRrK4VsfCfCUbRVPVsLgw
++8pGTdfvYnuFmJqPai0Z5IdPzjG6IYt06o7RcfhSaAdrYuZ/8kQWz+OHF5hv5PvDYxeTiFnvcouU
+L3ff0vu6OUhNPum6ALPeaWnglBR/rXx4hCsvUhvxTKDEu528dN5Zg09Cmc/03CCmi3vrQR/3b8ti
+vFM7qCoeey7Xvlqv0WP8Wvc6CF9+DYDgKR4/E7d6mZYIw5biqs2/FLCZIbe0gWJod+eXhnj6Y6mr
+/2j1GDEX/UgeHZ588ARzooXSzRw1W1+77gITzu3LWR9HW+ZM+vLCXqRbPNrIpjeb7ZBy6vNBAJtS
+AveGChGJeI7vs5vVDXF6LPN9PPwavhjIfXz8QYyHt7h2fkxqdQP0bPmeW9tIyd61urrEg2ekft89
+xdoR6PbrF/JMjw+OEfUFp2BrwPLIPrZMsVvL+r08m2OeSnh3RlEiL1vjgt2QWtdYlXxfygqaG/Lx
+pItfvPlLcrP4H/A84/OGD/81gP8I2DNDjgGsjDEt+P4JQGbQf24opf59pdR3lVLf3ZBk83pcj+vx
+Zz/+2J6CUupfAzAzxnxPKfXNn/f7f1iK/s6NQxNnOTqBhs9kntYxUib+qgYoqCiskGJNdyjUYrUe
+fnCFhOiy42EPh1NB5jXna3ilHE85MYJYduOCO+r5skFVb9obQkeLC2ecBjVaKe8ICXvkE1q589ly
+jzcI7AANd2soIN3I9+ZXGwRM8lWs89f1Dpv35NqtyQb6WKzK4biPdE7NRGotdj2FzoFY6IP+KQ6H
+bNyyA9wYUOdwuUVOhekr+q1h5GBbMnloElgEZw+mNvyU3BF5jm3JjDrxCF73EHc6El41oYPDAyZg
+yxo2PYGryzM45FK0Z3Lcss7RGch17kyFZ5TkW20a1ExWdl0gavkw2MBUlTa6FCxZVTEKEog8+GEM
+OMK7aCcGAYlRJiSDmYQarY/u+TnOaBFTXePZA9KqOTtcsqLw1oCI1bhGQV7KtCwxN62Yj4u7U7kO
+Xw9wMpFrnmZyvVOlUX5Ehu6vK3gMUVQnQvq/C9mLUq21tjB5nRL3ykFNgZ5818ClV7RCgslY5nlF
+6HaAEnkhXtWgdxtqIV7fk0cf42Ip6/qqqABqfY6oveG6h+hpwtQ/WqHYifdaxyUUq2p548A+agH6
+zzc+r8Dsv66U+ssQ6csegN8EMFBK2fQWTgE8/VkHMk2DPM0AY8GnvHxZbJGw+7DcJfjkiZBmXOUW
+Vq3WHsFG8cUZ3Cfywr7y+pu4Xcrfi80CF0/FFf3BA42zC3GP1xtxyTI3xNd/WeLXbR7htbvyEofK
+BrVP4ZUeasiD0Wy9josG0wGFWesFYvZUrLMKlw/F/b/YzhDTlbTIa6i3MYasmNRrD92OLCB/YiOm
+cGa6lsXRG05R5fJgv/eTd7AkbPVgPMHrt5gNDzQ8V16a3kRegkh7ePpUFodvGezos3kWgEjOvbhM
+cRmTiIUb3rPzJyjI7RidnKJHQdsv3TuELmQy0qJEQVKXMpHww1IG4YG8KGGp8WjxMQDgJ4vdvqxn
+mXoPpjFMst866WHNkqs1T6Bdlom9LqpS8jzF4sdQBD0dn0jL+TQqsPhEwE2P7Rj5Rv6elBUqIy9T
+Dg/ZpZz7Pjsc02qLivmVk/4p3mSY8PbVFWJeZ+0oROzM7bIE7CQZdkNCqfMK0Uqe2WF3jJduCsHs
+D98TEpNNtsaYHJXa91Es5Gb/2W//Y/yIAi5XVYOjjqyn33hJCFR2VomKKmLdVwyKWMKcx0+eIqOC
+2W5V4kfvfVeOwZDq5ukN3Ju0ORAH8OX5eo7eq5OZfItTm6S+zzn+2OGDMeY/McacGmPuAPg3Afwj
+Y8y/DeAfA/hr/Ni1FP31uB5/zsa/DJzCfwzg7yml/nMAPwDw3/+sLxgApVFwG41NJrtrUZQoWiHi
+cR9TuokFQozI7Kwq+ffKz5Gwc9C2IjR9ynAPYuhjsaqj7Q2kTOx5WtzrYdfBG29IT/vkzgFIHg2n
+qNAk1DlMMsC08mCsSNQ1bjBDXro2HtPFK5GCgsm4Pz4AmDwaDsUiJFdLzJ8KGOfZJsUXINbB6r2K
+ZiUeTU6Yd3cwRM5SRhXOYJi2MX6FZQvIsYFeTyyeYb16uzPQ5Arwej6aLcMAJ0DCEGzjVahqCSXW
+ayZMywZZJd7Bl8e3cNgT6bbDOz66FLvJNhfYsUHHpmU3RQIig2GHIe7ekRTSVVzCYnLYqSt0mcIf
+jwhtLsd4xxD8U9XQBICNBykeXsk5Ot0uesQ6LInjKLI1tpyXvuWgmoolvaE9EQoCcLVM0ETUtKzF
+VdfKBciUfXhnhKEna8Tp9jFjVeLmKMRhR7yeyZRdm6sNbD7fi4cu7h6I59E7OULXZXLQbStfGyxJ
+zlOmVzAk8zm428OrN8Va31n3MT0S+Hvk0Yk2DQx1Lhu3B/Da1+kWnZFcv48uhreZuDwTj6esS1ys
+Zd6GUQWPDXauZ6Egj2mSZEgItHve8SeyKRhjvgXgW/z5YwBf/ZM47vW4HtfjT3+8EIhGGEA3DdLL
+AooCpevagsX47JXjMY5ek1LXfJHAEMUWk1C17PwySrYI3zx8CZpJO8cO8dKBWOl+x2Ce0BrHYg38
+po97jAsHnRBkdwMqC6bLun+6Q74V879m6SrBFv1GYtzXbzr4zh8I1//8cofbX5Ap/dUvv4Y1k4Bg
+3B4PHWwtSYI+Xizg0xvBBvDIIRCGtFQNwJwWbo+/AM1gvOvaKNkQFnYMtCuWydkQJRcZnEzEWruV
+wmzF2H+rER4wuZZ56BIV2T+S5OqvfOXLuEvJu7e+/itocrGIkaXQUCh39lEKWBIba+pU2IFCSC/l
+dOJh4L4CADjsj1Gy7lcmCWwmjWtCcXVT7IlGXaNgs8Y+utXHOx+SnWmq0SUbt7NgYxO2CEmrFlke
+huSIcCobNclvK+RwN0ykMqcSjfp4tBML667n0Afy95u9PsZEL059jYDt3r4tHpjqFdATyXF4XRfD
+m9ScaIaIZAmgQ5amrFnBKeRYTR7DzeXkX3r9i3izaLECXRRtsjWR4+o6R8F8t72pUDKHYzCCsxQP
+6+X7Id569Ztyr2mb2N7g4qnkojblAg/SmNdm4BEX8guvnGLUJ0zzOccLsSk0psY23cJSDoJly77b
+wGI9ftwbYdCh5qE9g0qYlJrKgta3PCAifXdWwiIoRlkhokOZkKjq4YDgJYu4A9PRGHZlJ/C7HuyU
+blbHQ0nc/nCoUCUywY1P/cFna9RHTOqUEfoE5HQ0cDKQsGI0vIVOIUnHfEXm4KMBlm0Wxy0wPZDP
+2t0AXtKq+5AGzqkx6Mr9KdNFk4oL2/d9WMQhOI0FY8niXfRI97XZIBiQLt6EeMYsO7oNanY4nmgP
+GJMJm9yQX3rzazgmh+NI+0CHkPCdQkkwlONZ6LAnpEpbvssCisQxw05nPxeq1nuFpJV9hZY8oWYP
+wNWPdvBqqk0pF76S5/Ct//cBFrkQmZxObqKnJMRoWPlpGgOffRthVaNqqPgdlkhZvzerGF2PPQg3
+jzhXGkehbCypqTHmJuToCAc2q1mDAZwWf8sNPdADlJ5cu9d3oCuCgsIMKbEHBwMmj9M+3FNmqC+X
+8E44h9khTCVzYeoQNbk/mowdjvYG5QWxNaEHZIQ2mwwON6ehf4TAIQdEI8bNsvrQB5yXhzFGKXU3
+awtTCvgcnB7B7lCh6DnHdZfk9bge1+Mz48XwFGqDeFegtlJoIvCMNuhZLQoOqArZlX0dQo1IymmL
+Ncg3l8hnbHwaDdG0lD5uCpvAKOe0A68QN89updKUA9tvyWEBRR6CpkxF4BFAtS1gWLIKQcu3SnHx
+VEqkb9w/QYc1+NPxGEdTcQmVlcJjZ1+pxDJo2C0qGRMrgheJu25ig4wSZGVKi9EcwWlLgfkFXMJ1
+ldOBZg26znfI6LqblgQWaq/EHIUKKev/xtEIPCZMjYeK0N4OqciafAMvljKrGjXQkGuv6wV2G5nD
+Xa1QkrPADpnAXOVwjwm7dtSeW2IShVjxOrQZYEmBmu2Hcp/fe5jiciG/qyqDkrR4fqcLh81PulJw
+iTewY3YnFgYbX9bC1jhwmlZQJ8ByJeXcbtDF0YCUVAwNqjKFS6ZtxxoiYrK5sbbw2G3rGxeaMO2M
+AkBVVqEmrsAdlciM/L5JNL5+V0JPeyHJ6v/t+9/Gs3ckXEsdwMupMG4uoIh7qZ0GtqamJzO01TJH
+U5JnIt7CIidDpGqAGBdXNWhS8RAasnc1lUZNLghXpVjG4i1vtYLHMKjXKGD+Z5Bo/NxDKWkZrm3s
+WCsP3QQxadfm6x26ZHBWpbcXg0ltyZbXZo2EClLu1oX7ssBHm9UKxVqgoU3QgTPkIlV8GasrmITY
++NB8ijlPc5RbqkItY6SMr222bIeeQUIv8cJVmAzletyeiy1dQ8uyUVfsVyBxSlLE0FNqRcL9tLW2
+ZwC25/psnbZcDY9hQIQBDN3ZTuDvu+HyCqjpxufMoMeNgcPzBZGPHuHabhQBpFSHncCxiJnnPSfZ
+Cjm7CLV+GRY7NAunwiyWDXBTZbC6rHkTE2BUhozxez7Q0C02wdrAZ0a9Cqq9cM+6Jy/0KomREq7c
+aIOskPmejgdIlbyEdqFhs/tzwS7RsLQx9eXvqMo9niSHhYAqW01qwxtzM/DkRbGbEn261FYIOHyW
+ncDZt89rr9zH8xXBaYnboGvLz9o/Bcgh2oQW7nxV6OdLUs7/zu4HOPtE7uPx27+H3jd/FQCgPB8V
+CWeUHsIwD2K4SSXLS2SEj4fup8LxtmOQJszL2BpdhnT1phUcGsIJ2V9RayTkqKxRApaEhWXuYLa7
+ZnO+HtfjenyO8UJ4CqZpUKQpluclrNtySWZd4U6b1LmbodFMEnYnAC2wciiWstCYnTFkCOfwRpJc
+aozGhtDQnilgk9C0rEm4WXhQPe78sYIBSTnTDA1FRopkAc0wpqQVn476iHK6ol0bdkc8kxNYIN8n
+Gt9AMfFjOyRabcyegsuaHsIinkCVDjok+ewPWPuudvBqcWu7gwEstyXYyJGSDKaoaxS0aCaX++8F
+XXQIg62sCKM+JfZKH84hJeSqBlMmB28cyd9PJz1EfislZ9AwmVc0BvGGiTinA6tNWm35nKIIGckC
+0u1qT0ZbZREczqFxgJyoz/MPKaJjzVCTVVtVCiUFfG7fC7FYShiD2zukW5mXMaXSQk/vm5asaouQ
+CMs8d6ACma+grzAICN/mEndGg70J7HsWOvRc3AZIWBkxaYyY4Zbls/vU1QDn1tUBgkNJeOdFH4OX
+5TOTO3K9X/zOa3hCnci0qJA8YkJQxdD0muzQhiFXR55T06HxwaZNlPEOMRmc50kHfkbXP9nCm3Jt
+kJynKtcoKD2n3QoXK0FCZlmGl4nPef2lW6iyPXHhc40XYlOomgbzXYKtyhA8ozy5o2BCsh3nLmqK
+u9bdFDlFPRxmhW3b2vPe7ZI5OhT00Gm5J1Fx0YHLOBL8u9ExTEXiFE/BcPGbIka+lgcWhDUqJdcx
+zyhiW1moKKSKR8Ax49DxQX/fXl3OK7gUpWlakhXHwCVdeJM1iFsX3G2gSbgyHMnGFXghAkJcPYSw
+Q25e0ND8nlcCNZmNU0Jcu10XIVmOsiZE2vJHug10Jvdx7NUYsW33tHfEv/fgRg7vP0Xd4pHzBg55
+FxENMOjJC5CT6cnth2hScj9WFbJl29mawyNE2S1yqCu5joc7OcfjcwczVlxqVUG1bdZPcgwn8iJY
+5QlWtcCmw5581ukUsAt5Dr2qRt5WC1QOL2BYWOTYtNVgKmd1NzYs7th61EenJxuIhQp21bJOY9/x
+atd8S00PFftntNVAUcdTuwbYkiSHjF6/+NZL+KePReQ2X2RoyNpeP8rhd2Vd1MUaypI/VFfMs5w/
+hjOR+mYFjXgm92/0Fi5BYo72oDh3iv0gSgNoZON1Nw2OWiUiKNi2rJ1t7kBFRAE+57gOH67H9bge
+nxkvhKeglILtunB0jiJlF6Fx4bIHf1VsYS5kNz+oBjDsvXc8Jg7rAkEgu252aWE5k371CC767JgM
+On1YUUv/RY+gzqAopWZUgaYgXHmzRJLIbrxKLFztpFZ+sZKEWz/sw6N7uVg9w6YQt20Y3t2zTl/t
+LjANaY0ME0SeBVARumwWe0uT24DF740GYsGDcIjIJ9EJUtR095vKhqFStrET1C2NF6sJympQ5K36
+cI71TryqbrRG2xfjd/pwKHGeEfBSpDGUTwtWZ8hY85+df4yPH0qiypk42JHjom0uMkahR9q80M1B
+LxlNWqDKIj6fEB/99CMAwDs/FQzCWX4bpbncXyfYrRm5HRRaqkRpMsec2h9dwrl1pXEek5as19tj
+JKABzWqH6mkE7MAcDGmuqxSazNfKjtAq66kGULqVCHTg+C1XImHl8SXc6asAADuMUZMR224GWM8k
+FIoccevPfnyOnIlB694YyUwS4Uhn0CRRUUUHhh7keiahxtPLOSaWhKC6t8C8kPW22+1w6+Y9zmGO
+mjiahpwd5a5B0WoPOTFiYkgukwr1U7m24w9/D2+9LgnP5x0vxKagtYVu0IXtBPvyl1dp5AeycC+S
+ErliKWitEIR0JYnv9twKvUOB//WnPkqqN1lNBUU3OE9X0Fx4VUgOx90OFktWjtWBCVq8u4uC1PC5
+8vGTD6RF9sFMFsyd4xEcSou/8/h9bAg2uepqDOnCjewMWzZCeBHj7yZH5bE6kVp7rj7llrC5iFXM
+jr3GYEdSEOPnUIydldKgB4tSGaTsmMvY1xHHPXgsnRq3hEUGnkJ7SPhFz7egulxYnMPcJEgJkPK6
+DmL2kiz8AmbUbjgx5q5sAC2cPmpKkPkeqttBwyqQ0RkK9iI0WsPJ5cWpaio2mRUAhiXQAOP6oNeB
+7suzPPtxhYCVn4ybPlwfvi9/b8oSGV/euCz3wKqBq6D4jMuW29NSKNhpOl+tkTAkGPgNOszq27qG
+5gZnKBOwLjQ6mbzc6eYVuAHd9cMhnl6JkTAk0/lhfomIocEuU8iosuX5IQoasrIo9vTy60Dme3o6
+Qv9IJrFwHdgMNwfdEAWp/y+3CVISB/vsni1TIGNZs2gCzLl2kqrEjiQz7283eGvY1jOeb1yHD9fj
+elyPz4wXwlNQuoEOCqQfF3AmsuvuGoX6iVjKXSeHZ0kSKXG3QCI74hlBGWefnOMGvYexDoC+uGqF
+H0IRzup111ABQT0HpDNzB4hGzKbnNhoCYZpiB4diIsX8Eu9eiNU8vxTLd/ALHVTnYjFCA1wQN4UH
+a1ydintdbxXynliHfix7b7zcoCJIp2NF8KmajTzfu4HlTu4/rWZIqVFZNAlchgeDgY28TVY2O5SE
+MWdXZGJWCcKpuN+58bGjxcvfv8DR/ZaVusYJs2DdY8J96wqGVOXx1RaPyY14sVqiYQ399ugEIQVH
+wltMjG1KLD94AACYZY+gWMFp4EDxeEl1AgzF2j5qnvDvNdCqeUOhtU9+t8CZ5OpwlX2ILCPTMvkR
+Yn+FmpTyKipRJrIG4rjCnBb2kVY4nIkV7pBFeqA9BH1Wq7YlClLfows0taydvg3UJb0zei5F7iAl
+Vd724qcY3X1D/r5u8Lf/l78LAPDeJpmKeQqb8GrnvIdtQ2i308AwGV2UCa4eyDU7I/GejqZD9E7I
+Vt3UaLpfk2u3zlGu5D4uZxdYxuw0JdzZc0skC2p3FjGWdQulbsDoFl/9i7+E06NfwM8zrj2FH3T3
+/wAAIABJREFU63E9rsdnxgvhKVSFwfJpAT/I4bBf3wdgGNdGno1sx/jbKVGTR+GCTMQfPz3DeijW
+8WvjMcaH0hkZFzaW5Gc4e//RXqH3tJIy3Ph1C5bDBBAKqFVbmkpRbsRCJXWGBeXrYjJGf/C9c9wd
+kW/hska1Eut4fr7YJwFXXog3bsvOvSOFWZrmeEYGqW4nQp1S/DTsoGpomTKxdus6xmol563zct+4
+BLuPIKJuRerDZ6KxZpfkYOzg8SNJVC1NB/Nncm23egPUF4QSn5RwSL7dswkZ197eW6lMgquleAom
+C9HvkdItvIEuG6ncrfxupRao1kRTbhP4LkuqBy4UqeDOn76Ptx/R6yPRaqtn8OkQr+nDj5dYbX4q
+17mIEG/Fs3hGglrPL7El5PlGUWPJhHBTVqhY//cDD3WLU2Drq+f2ETIxDe3BqolSXFTAgEzhHReR
+RXGZDa+v1Fjz56B2kT6SecmOzvG7/+R3AQA5n1nXH6E3IUXgJ1cYH1LouAngEW1Zr2okiXhNRqYE
+SWeJ2xfkkLjTh2aep+9PsSH1YLm5gGkbvqikXcCDYtJy90hjQdi4ox3cYGL29uhVDE//HHZJKiU6
+fN3eAdZrgltWBdZKbjI2HSibbp1xcGcik/0qVZL1ZYUOYbmRPUWTE8eQ9RA18gCywEM4pUAI4c5W
+9WkCr7ZKJJmcY71d4ZIb0tuX8724yuEhE4oPU7ipTPoqz3DJUOPH8wXuc1HlZoklcQF32A8xiSJo
+yl6tSweHNinqow7IrYJdJptGWlQoWLUo7AJLpsubVYEsaXsYKsQbJq3Yk7BaJfjRAzmG8gMsqXbc
+8R1YFCqJ1jbiMUMJ9o8kuwYhk3O9yRQvfYGcgh9fQRXsUVieI2EJI7lgmFeXaNg70I0ahOwrgW2Q
+xvJznDT4xH/CeaHr7+o9NZsMeREGnQnUyesAgNnubTzgJoNLAoiKEoBc+y714FBJenI0gO23L4uG
+suRZZ7FsNos6bYmk0Y00wm7LGWkhIZZDX5VQBIMVHpN5yRrrSkLX1AEuiXuwOzvMydZMcScURYmM
+oK6Pko/w8rkYp3pQwaJuqFN6uPdlIbCxqeRVZQUiS0IJ/94BmiuyXKdPUPL6canR5fq1Avl3t66x
+JhPRjxef4Jz4Hd+xcXpb5u2oP8ANfv55x3X4cD2ux/X4zHghPAVLA90IcL0QDRGBcbbChg0jq3mM
+aYvssgcYjdgcxBLT4IaDeiGfrSY1Kvauu2GJEVFuw0EPDrtn/DFZbxsgazkUTICE5Be7ysF8TRGS
+ZYLJXXHdbTZSbfQ7eLiQz15WO6yo3NxsLjFvxNsY2C5iJrBaqq3MKBh2vZVJjQu66FYTo87Y7Uhz
+Vjkahvds20BJ5uq0qVHRvUyrHTKWrDKa3Wdn55gtH8s9qdEeHn0ZJ2g6RICmKWKS0mwoKeZgh4ac
+Br5jYWgEVu7dLJFf0UK51l4joZmIlXS2NbqHLKcG1r4EvNmmmNGLeRqnSBli+UyYpsbaw8qhbYAe
+RHBiYG8lrNpeLFE8kXvZpBSqMQoDyrzlusEhk7led4CIJeVkW2LDNbAheaofOCg68jzc0EWf6NY6
+TJFkMhdeXcJXvD+67VU4gqEXtlkqJJRu0098NHQRatLjGZNDkUvlo8sNnhGleppViFka7rgaAb0t
+i+FMdrlBnFKvtKpQOyR5/eQZdiwTdwchokOWS+lVLjdn+IAJ5o9XS2SkvzuYhBhFZDSPn8EY6eZ8
+3qGM+fkgkP8yxnTYNX/1176EcqfgBvJyrIod1meyYF0bGHQJ5DEGMaG0NrsBFUpsybR8uckRkhUo
+8h3E/P1stQOIr3ccdggqCzadpUG3C4d4AtfzsWrjt0xBndyRc1eywC6XjzGy5XomfRsLAoAmBwfo
+EjufNBmqXBb9cCif7Ud9DEk5HmpAsUW2ymts2Bq+uCQFT12gSxamztjdq1DlVYaYOQr/MIQmwMvf
+ELhjjTF8VSoDTmnwW3//f5JrzxtUBDJd7naImUDI2W5bmgJc29COhkNot9EWErafp7aB13axEtor
+CkQEVmUVFPM2gePhrfsCvPnyS3fxF/6yAGg89lR8/3d+G996R8Kc6HSMGyPyZh4dYfdA5uDZs7fx
+/R8K2ClmSiWf51gxPEyKDDXn0NM2HHad2sbAYmu3y1Z83WtA+kRUZYGctPyNblAzhKxsBZstzl32
+oKjdFbZdOXmwXSC2yJa0bXDrSwRcmT7ncofVjhB7B2hymVs3sGDaPIFrwaUYsuu1tP0KAddmr9PZ
+d3suVgnOLuSlz+IKMTs0EzIv2QAidrl2bvfguXKdmw9nyHi+0WEfVSSf/2e/+973jDG/jJ8xrsOH
+63E9rsdnxgsRPgANGpXh6HiEho0onZ2L6WEr4xZAEaXYK1MkJEOxO2KttnEMtRQzsHVKaGbUM+XA
+IbqvF3nQjrhtbf3cJA225CFoDJCxUqG3BSoKfDg6gCFnQUV8QJXl2NE99TDBqMOe/byGcsRie40D
+P5BE4oh6lcejCMNQLJBn5aBWDMpqh04q3sSQeAvXsjEYiwWyLRtVy/NYVcjoapeehYZe046NX3dO
+NQybnTTWKBjaVMscScuPmOYwppVsk2uojA2P7khZS5MSAKRZhYL6GjB6zypdk3dg7BVI+L3Cavau
+eLot8N1MmpkmToSXH8o8hxuBoK8Xc9zwxAPZZB5qIv6SqxmqDbUzljFSakNsZgxL0hwZmZ8bNHvR
+kwYKupI5dLwUtmElwqKXk+o976SBgee1UvUKTSs7X9ZIqYRdarH4rgEsitrEWReu/pLcv55DQUKa
++FKOm2QuQpvJTl3BC8mX4QfwWRnphRqjqYShNsOdbtBDz5ZriMI+4NGrOJvtuTgW2xw1q2AJ/92q
+Gh4TsZuNBd1qepoMr3bFy6wO+9Dxn6KW5J/YqC3oZR+TSYWEnWOVdnH7tpC1DsIjXHAxjaIDFHyb
+dpd0I5uP9gQhrtNDSkKS0FTwyKt4NJxiGrQQW3mRHl3EoE4s4l2Mkos/Ve5eBl7bDRJ2VSaVHGuz
+2aImEGrg1XuBUk9L+RQAVLfBkIw9B6dSAg0bCyPG8l7XQ8MYsNwZaLI3ecyah50hbMacdZ4iI3x6
+Z+x9vLzKFthSeBZGNkJTzFA9ZCgRDrCYiSteVxUsxgclKlicry7zFhVq5CQ9KYoaMeeiqmopDwGI
+HAdgbiPscBH3NKxE7vlyblCV3EyKGstcKj+/9f3fRys+PqWb/GyZYUAdT3exwJbw59iuEV9Knkdf
+7fYw3ra1ujTsDgRgKxdOnzkAM4HF0MXtuPBtboDMBwQFsONG51ouIqJ7/FJhw7butKpQUkQlTQSu
+nRoPo66Eh5vlMeyx3FOzybC4lN/TlsB2Y6TsxXAzH10SuRhLwWeocOPoNqYk5XEC+eL9yW2MKC5k
+DJBT33OjNfo+RWVXGbo2hXbm8sw3mx02LitG2yW2rFSUjcbGlYVtf6igun+K1Qel1EAp9Q+UUu8q
+pX6qlPq6UmqklPp/lFIf8N/h5znH9bge1+NPd3xeT+E3Afyfxpi/psQUhAD+UwC/Y4z5W0qpvwng
+b0IEYv5/h9YGYa/GqvARRrIzBj0P3ZeIQ0iBezfuAwAaq0SkxMVO78nOOHnsgRKMWG+Bt8+lJq7y
+DYZdsR6vHd/C6Ab5CchS4n7yAPUnsqN+eLVERbKQ0q7g8xzQxV4Gvt1C66qG1fI0WF3AZ///uEHI
+rPbp4QTRgEm5ATki6hqdbtuslSIgRgC9DnzWkttGnED3UFGEpio1NLUf7dSDotVUTokFqcMN8QgX
+6x6sRjyhwXiEnAlFA90m+LE1JTQTdCXd/a5l7+8vqQsUdKMtAAN6MdODKSaslQ8cKnHrHbZnrL70
+K7R01VmVwDD7frZK8Fs/+H0AwN0DsRFHdohsQ6usGoQbybKHToCnpMJbphkqeilgctgzDnqEEkcd
+D8O3BAvQ8yo4xH3sdk8Q5mxCI2FJsauxykhIUmtYrGzlW8Dqi1cRNAorNuQl/F6NAmUm1ZCDlzSK
+AxFySd/+zp4MxWeCs+v1sc3FylduS2oP9F0fmiGBc+ihcyDXf+dI5vAwHKHfEW9yl63Q7TN5mjYY
+TSUU3EUV7n0kIUHuflvu6QMLa3YVx7HCjpKESls4JwO5la3gmZ/PU/g8ArN9AL8K4N8FAGNMAaBQ
+Sv0VAN/kx/4ORCTmZ28KXon1Aohekzh63JviVbpf3sSBE1DYM63hkl/Qp7to3b+/582PtxnufyQq
+THkW45AP7PDmARTLTDYZhg4tjV/sSXb3f/79HZ5ReUjVGn3GzlVVoh60HW7ygJq6RMEW6ODOGh3y
+4d2NAoxHLVW7C5chRp9vo+8EiEiB7llDdHrsOFQ2rNaNb+m96xI5cxnasqHAnEpgwc3aeLnGLiFh
+TCj3ttjmcLmQqgIoyBnpVBYckp50S2cveMpKGTynQcl+gDwvUTFu73o2Tki+8sVRH2+eSkjU4w6y
+0H1sWYebnZ9hRlWkDzZrLBjLzvIC80RetpDqT4cHNS6JFIy6h4hZlej3PVipPDN/qOBfyQXeJqpy
+3OvheCwv//HJBIODdnNzUJH1aBscQ0M2xoaCv8XUYLmWa14vYyjmHMqphausJQt2cEgy3SdXAqI7
+W8dInoh2ZW/6CiaR5Dtm1RGKXJCXDcNKbe/gkHJeNQl8rrPeMABlRXE0sPAGN8a7t6U603E8+OzO
+jLcBrLZsvRrCZqWiKncoJ1SWIuX+Owcf4B/+UxHjzdwK/pygPFRohdytjtqjgZ93fJ7w4S6ASwD/
+o1LqB0qp/04pFQE4NMac8TPnAA7/qC//YSn6pO3/vx7X43r8mY/PEz7YAL4M4G8YY76jlPpNSKiw
+H8YYo5T6I4EQf1iK/qAXmc0yRf/Axps94cC78doN9EOxSqpI4PskLJk0n3ai0b22zQT1qVid/ipH
+d0puR1N9KoSxy/bUVaYWSzN49RaGrGB8cz3Ht96X7O3ZKkVN3H5tubBD0qszOdXUDWyfvIuFhxsv
+iWXr9zqYDsVqdNHAUFnEYd3Z9xV8Ctw4kQXHIxUcyr0eoeWRUbqoYOyWHq4LS0lSq25ceOR2rFWE
+ASsNm5TMz2EDi4CtqqyRZ62X4iIitXg3KKCIT4lI1ZXHDTZM0NaNgcOyxNG4gy+8Kvv6690RXn5d
+rJtPN/vOuMHyA8EbzI7vYDmT793drPHTM5nP715eYM0E3FOCwiyj0O+SMTrN930u/sxCjx7NOOii
+ZuLyNbrtX/yFu5gyAeuFGuuaQLSJhWJHkZjQAyMo5KTsq8oCUXsOBbBxFXmucINJ5VKHe3bohBD7
+y22Mil7O1WyGzJNwRXkKBbEhbkuwWHfQ6dDTsxv0SZgzHkaYUNjn7t2XcMLqw/jgjlxv4EGpljgo
+3hPm6MEOuiJeRG1QruQzX/1Fef7H4x6qUtbke1dLrCqxxavSwNB7q5SDovgMnvxnjs/jKTwB8MQY
+8x3+/z+AbBIXSqljAOC/s89xjutxPa7Hn/L4Y3sKxphzpdRjpdSrxpj3APwlAD/hf/8OgL+F55Wi
+NwZ10eCg76N/KrvhwLgIaDWNHUCTWcjFCB75+xXrx9rzYZWtpkMJh3qN8Es0VC1ueg0Mobt12Xae
+uQgGkqt48+QufvRYko6PFynA0uIxHCxCOfZyy1KS5cBiadG2amDVouAijDyxDk6TQCnxBFoth9Dp
+wKHGn9vYcGwmDJUL5i2hGVtLLxR1Grx6D58tTAGfH26sFJ2WLaqhwA1KzLZto064Txgqt0Zkk9eh
+sUDjiJD5mXWxxpr9/5ZSOKZ38/XbR/gG9ThPDg5wwuYuayg5jjx1oW/Kz1GywemBHGOz7sMiFVry
+rovvnQtceUeRnafZDqMOC1OVxo7ltGg0QmivObcaj1y5l4BltRtRDiemWjcGOGAStCkqZFwDi6RE
+TvwCw2/USY6q5WAzFvw2WWuVMMxNOVYFlx7UAeuej2x7z/BcZzPUc1mTXf0FrJmWodwCjEmhCcEO
+PQejfkshVyCi93Nr2sWQaEmHKtiqVgDXiLJCaFvmQucdWNTNVJUPdyweiwFL3YfAr31VcBOHH76P
+b8eSP9ostpgQWn+RZsja9tfnHJ+3+vA3APxdVh4+BvDvQbyPv6+U+usAHgL4N37WQbSlEHRcjCa3
+4LOC2QQNSmZWqzJD2KGSkRWjabn/GBqYUsMQamwSH3DJduv5gCeOilqGaPx20ct5a2uzF5aJugd4
+icpTDy82cOl2ZwMD35bPDIl5WHoePNLLO76H4Yhag117r0LUCyd7rkSPHIDa0fAozoKmBghFhW0B
+molNQqPR2AAhrFpZ+/vTSsOJ5P67gYWElZQtqxBJZcNNmDAMAEeTIKRqsCYwzApqaCbaWkUnaAWf
+nx26Fu7Rxf3Ka3dxclPu9eTgFsKDVo+TlHbbC4wq+V087MEQZNUZxEAki7/jjLGistI7T6XOb8Fg
+x41uEBQwhBoP+wEMXebHuwcoK4kDfCYw7SDCcZ/GIrSgS9kUEssgYgXHLnOUTMZmDDHLCKjY7Tre
+5Viw5TrLU2SErxutsCLooOtQdDb0kLMfJa9E8xQAXO8ZNOerIrahLIaYHlHRqd+FpvrYcOhh1JNn
+Fnl92F5L/U+G8qKCbjntAg2LRDxGbaHBjdOroLjp2dywAivAwYls0mlziEfPBFsxqwqYLTfAnUFd
+/nx0bJ9rUzDG/BDAH4Wl/kuf57jX43pcjz+78UIgGpVWCAKNbhPCJYHILrUQrMTKu+MOFLvXLHsA
+w1RIy0Gg3QSAJA+tgYFiH7+p1tCUhdP9HFZKz4IQBEvdgl2LW5unNToUzag1UJdiMW4GCjaJSy92
+spvPuw56bJyZ9D2RcgNwo2uhT3cWpb23CKAVtBtAM9kFEwBEJlY1UFNwpGorhVWOmnV+ZddoWibi
+NEXV6kNa9T4x5pPTIWw8aCY7022xtzq6qADqBQSlj0MK1Cii/La2gksswMj28bUTudeT4SHGpLfr
+9SJY9JBAcg8/VnAP5NxOXsKw4Uvnhzi+Iee+EXk4e/oAAPCICEttGsSEk1rKgkc8wsJaQj+l5XY1
+8oQCPCSBPen5mPalpm9QAby/blFhzY7RequBgPOp5XdlMIE9psW/TBFYch2zosQua8mCaxzR+lt0
+4V3Hxr4bDQ0UsQCBZSOmfF+fZLbaLhGTMHXQNbDIddBpNMYRyV5gw3MZYnotGtOCRa/QavS+0Q+V
+t4dCm9wB+HvPk6Rrf+qiYsbuKBzh9VsSCs/nc5yTMAja7L3Q5x0vxKaAxqAqGoRdBW9HvHjfhj9i
+16LdgWvxpbcCKJ8PTrViMf6eXtiUASx2WmLjwbR4ysKFou6eYVzYNPO9RLqPAj7x9yarUbTcFqs7
+WD95BQAw4sNscn+vUuQ3FrxWsSgK92xDlhvC9Vq4MVWKLBeukhcMbgNFEpWmyWHo8ivN+BUOQHZi
+7BRcHiO3AmhNQFKs4HPhmVYHEiWajDqCZYayFTVRBg47Boe+gks4dcMFP3Fc3GCuZjyK8LVXpd32
+zp2b6NyUUMIpaqg2Pl2yN6SvUV0xfvdc2FtWPvwNzEY2kFGnxhtH8vyOPiI1flki4bWZdYUeOQyf
+PlgiZC+Cyhr02O14eyJ5ouHkJkICp3SYoKAQjfaAWrENXinkFOzN2kx+ZeCw57jUDQK66z2tYbFA
+tnALKLeFP8tnp8bGJ9wTDICKrnjmGoCM33HVSg100euyIuY50G3uJxxhciydq17Qgc1qha651kMF
+m1ybsG0ohpWq50AR9wA3heEGqVkx0pWLhr0hVaZh7VhyyYCShDpVAaifs55w3SV5Pa7H9fjMeCE8
+BQ0LEfpwkKGgPxTlHTjUhLQtF6WmTt5qCzUTK+dNaa16h3sMgq5raNKKwSuh2djTzKo903AJCRmq
+0sHijJz+iwyzpUBtZ5tyz8eokwU2SzmG59yR41pduLm4g9k8x+JDHqN/hN60rRhUKEmb1krKN0GJ
+ErTyaYOCXAbGKWBo5Qw5/Zs0QEbvJ51dwZBp2tY2osO2VKH2XY42XWdLF4ip4BxnFVrX13P0Xscy
+VhVsNiA5hDQudIYBKy4HnQk6h+JiuWEHYUScRl5ADeU6cmohoKqg2RnoBjlQs/KxAxIiDFFPULeU
+buw+hbJhmDhLihimFA/q0LOQZkwa1zkMOynDY1K+TYdodrIWqixDRhRqYTdoGIJ5jo2a4Z3Hjtq8
+2iFlEtHp9nGrK15MsN7hkmzddq325y5ceiC+B6hWIMZAc766/hew4TwXqWBrYvsdPOGzxtqC2+Pt
+qwr5lawtfWMKpcXTUUxKO9qCpkSgUtY+pDNlBWV92gVqwLklIU+VzpCTwXm1foAZhXo2JZAz4VuW
+OZqmlZN7vvFCbAqOa+HoNEKqPaxbUdIC0DWx/7mFLCeJae1jp5/K994jM1H3XdRzedGHB8foHYvr
+GwwOAcVNQVWoln/INQewvHiG9+cCWz3/MMFGsTfC0lhwM3lWXCEq5NgWZGEOxiEcAoHyQCFgZrw2
+DQoqCJkMWM0kG1x22b697sKe0K272mJLJSDX7iOg2Gq9pTKVMXsNQ1PXcNkCjKBBfEGuyZ5C3RKH
+uMw2Vx66vPaN76HDUpexNLYMTZrawPJbDkJSpxug4Ev1fbPBxTtCbvJN7eJLXVFICoMBFBM52ieA
+ajcHtMyLTrto+qxqFFfIyZ+4XC2RcGG2m5BnATVj3byssKPYz2h0G4/PZNEv9ByK4d08lPOtd0tY
+tSz+i/cuMaNSldcPELJiEEQuQGp3sF3a6bpYM/RZljssWE+8uLjCozMBX2VVuXe1jywJL4ZBBYfA
+IqgKLZek39vBPJNzN+YDznEfDjkerVEXNvMEbujhnNWO8WaDgiQ501Ag9oHtwnOFeBhaAaxm1Mkl
+FNcZlIIhoY4hC1XhdLBtRHlrnRWYr2S+07pEwZwYtCiw/TzjOny4HtfjenxmvBCegm1bmE6GMLEN
+sPlovdGwM9lJ/cCHT4rzprERVeK6X/kPAAAPv3cOYwnEU2UVXEOAjXKgSUWevvM+Fk/eAwBckC13
+EcQozsVCeV1AkSugsCrY7MdX5jGGQ0kSne6+KNcwyTFgFtozIWqSl5TbHdZ5Wz5I8fQReRhquTbL
+9+AMKDiTJAiZ9Y6dJxgR3FK1YjCZQtBnw9R4ioqWZmQ5qCLZ+ZvQg2LzTI9Wy3dCeBM51vIyw6BH
+6q/MRk0rl5YGXXIptjV6X1kYkBQE6xwfU1ez7zxFyNDk5fEY3pSlm514dE0G1IV4RFmZYD2TYyzz
+BMOgFeLJETBzHnWZLNsBOYFa2vdRU8V6fNLDZkmBlzRESW8i4LxenJ0h41x89HSBRwvx4pZFg4OJ
+eFCv3zjBlAnD1loXBjhfS8jzvR8/wB+cyTlKXcDhc/ecBm0bDuk4kal6X3tQACZjCat+4/XfwONU
+5AR7peA0ynKJklBrHScghSievZ/jE5BL8Q9+gqIv5/vSy1JF+Mprb+DGHbl/29aoE5nb7cUFwHWW
+r1LML8VDeHQuHqZdp3i4Flfxk/M5nlLIpqjjva6mazlIy3+eTv9fPF6ITQEGaPIG/UNgyL4Edxwi
+JEgnNRmefCgPf/bjD/GMeg/elLTmn1zhlVfke6WXQPVIpLlJsXnnJwCAx29/Gz94Iov39z9mtyQy
+RARAfWE6RL/bZvA1CmZvzxsHWBHJ2EjY0gmjPXrOr0c48sm379UwFChdPtzgyYcXAIAnrQvbt7Ge
+iet7b+jh9ZdlMb164xaqK3HXFyu5t7M4xicfsTdg8y4CAlZu3ZviS195GQAQZQ68riywdqOwtY1W
+rHXYK5GzXTjyNfpteQ4lDkg4g5CqSZaHI1ZXllGDDl3OZZxi9kjca2+2wq0Os+hoy1wF5pnMyw9+
+usFPWoYlx8PxoTyTkwMfIVuHT5h/eKQyuASD2UGADXUvsmUKj6XRoHRhEXBmiFA0AZAs5bO9jo0O
+DUCy3uLZYz6H1UPMerK0X2cVpUkVdlcy93mT4TbBWYcnxzgYs3pUuNglci+zMzJ5VVnbSApbWQiG
+zG3caVB9W+a+BTx16imijuRRgkghcNmv0xljwOuHX6E3lnm+WLSci2skW/JV9oao2T26vJwjSWUj
+e+9iie9+WzoiE3JtTo4dHN68AwAoa4P1mgYlBVyiJXXjQJH6/XnHdfhwPa7H9fjMeDE8BShYsGG7
+KSwq+/R7Q6iOwGQ//s4P8A9/W0g6nqwdXDJz7MzEBbydZbA/ENfpfvc+nB1BP6sVasJ4P5772CTi
+js8a2ZWvshrHhhTwvsEBOQaCBggC8gUkBvM2ObgQjb+veY9Q22IF/KMCuwPxOXu5hzKTazqfneER
+XfSHWav3mGFDsZuLmYcvvywWH/kW6/fFis1atz4P0FdiUZ5FF6hAinfj49mc9PMmQ5iSXp0hld+z
+sUvIoVBbyPf04xX8FpvgAGAHpyLO4Q8uz/AuPYVYu5j0xYu5e2TjwzZh2vgwrtzfSU+AMhhHOH8i
+ntQnZzt8dyGJWxs+7rMe/97DDW5ULViKdHSOix5xGKZnYQ12DoaH6J/Iskw/2iD224oBKyDrGrOP
+Jbn2YJfj2Y4YEG3DYwL58eUGh6nci7nDKcYSK3qYkekiZDg6X84xOxPXfhpGmI7kWR4SI2MWHkb0
+qkxW4wskSHk5OIQiSU5dUvTFexcFGcF3WQ1HyTM9O1vDpSJXcHOK5l1x/79yT7yc+cM1BgRTmbjC
++gO5vycXz+DfFnIhrWucJfLcz65krZ9gisdP35Vzr2d4TGBYkgG3BzKfYZFhtQ+Anm9cewrX43pc
+j8+MF8JTsBTQDyxob4SS0ODULRAxmgsPJ/jqr0s32Ou9I0wy2QUfWWKt+89+iHu3ZH+bHvwFaLLV
+2Pc2UGQ0Or39DFZ4CwDQZaLn249mOGSz05uHN+CXEr/9wrCPb89ZqnNjBD0pF1nZ/w2KURbgAAAg
+AElEQVQAKBvgpbtidW9OPVRMGFp2DcPEUH/Qw00KlYxpxR+f15g/YT7EceG7ch91aSEJxMJ6gViU
+G8oHeH+njYuEIINXDk7QORQrGKgSaq/NSD3ETYiSTVKRF8Fnwmmd16AGCTq+g4T24KcryXs826bo
+jcUivvzGXXz5vsz3cb9A+YHkO8pyhitCxQ+Ix9BhgP6JHOt+HcE6lGO8/7jAtC8n9LYORh1Jun6x
+4kXsMqyYaKzXDQriG4b9ECW9mKl6hmAl92JTXbqaNPAieX4Hrr2nt6urENtE8k6XpYbd4RoYseFt
+O0YnFA9x1O+A4TdiU6PeydylZYqrJedzww84KQZuK8SjsNvJ369WH6Bqu1Grt+Xa/D58Pv+k0CjI
+w+CFATChjunpTXSPmF+5yaSr6aJwWC5vbGQdedarRYJjntsejPHWL0lpWD+RdfrS6C561NzwNgt8
+8i2hadsVW8Qk4W2gfu6S5AuxKWit4LsWrBSImMgJ3Dt7uuw33ryPt776SwAAAxeGmerdUl6k4Nd+
+HS5fGlVGKOeSkFH2IUb3xUXz1l3cuvkj+b3zFQDAN+c7BNQJhL2DzkjwcnEFey4u851ggr/4DYE5
+q9/7KgDgqvrbGK7lYUXDAjvStBl7h06LnQ9C1KyLZ6yld0cNxge/CAAYDh2EJFapsxoHxxJKtIIm
+qohxEcs1jBsfO5LBRHmCXiUbR+do8KlcEBOYi0rB89radgAy2qGIgZKhRGrbGBMDEjGp99pkgG+8
+JQQq3/jGN+DbFG6d1XgWkfvvyoZHLIBLuXtluQgJZHrz1Rpv1iJ7/is35+iR2l4lC+iF8Bw2JCbx
+nz7GNpEEWKEBUzOc6ZW4BwGt3bz1Jn5gfSifp8DsSA3QvSWhTbr9VNvx8ewp1mwdng4CHDDc8En7
+DpPimKKrfu3g5kTW0L36/2PvzWIky9LzsO+cu99YMyP3yqzKqu6uXjkczsadEoc2IFIWaAOGLAF+
+sCBYLzYMGH6w3vTiBz4YEAwbsGHAsqwXUbJlgwIoCLJkQpQokOIye890d3VtWZWVa+xx93OOH/7v
+Rk8TkqaGbdNFI09jUDGREXHvPffc82/f/30dVNywFmWGnPh2srjhyVJjxGvWEfCTb7wHANgZ3kVA
+7EREcjFXXyEfs5sXY6QM137qx9/D3qFc/+tbx+umnQHbwVU+RX7FFn+vRM7u4Mx0YNgdu9Er8JU3
+hN3888dy7wZdH2FG/s9uiu89E27Sk9/59pqcJvTWZNwvPW7Ch5txM27Gp8Yr4Sk452DqBv2ehx7l
+2IJYwYvY+KIdvNGxfDgEYNgodSCfVVUIRySZe/F8DWe2TzyoTbG2QW+IIZtSvKnUhzdfK+Au5LPG
+96Aei5fyZ48+wEfPCKXtb4GoWuy9LgmpF09LOEW237HGcMTOuQLo7IrVCN0cAbULc2IQktDD9r5Y
+Gm1maFhjLhOD7kqO7UYs9a228PqBeC6BtbCE3Xq1hu6LNdJTA5tQRIXErgZTJGzUge9QU8AGoYHH
+rstebRETQ/Au9Qa29kf43NsyL4f9FCpno80wRNoRi1+YDKMjyvfxdwtVwqOr3hvtI2DpbWsYwifx
+S61CVKW42re7Yh03Aw89YgkeLXM4dm2ePTrH7c/J9b0+vAV/X97foieYjmLUxCzUagHX8i1EDrcH
+4vX1tyPsdamFSdSxKUPsbbJsbXJoNmBhViMgYQ58IGGT1nLRNkRFWPCavPgunl7I3O4dTlGtWDIn
+wW5gfFSaUPooREKy2e3tN/G5tyV0HQUDOCIyA4+eYs9HcS24EKM0VoW8n+sKdSCvD/fuIiapUEMd
+Et8Adiivl1MPXZLtQgGOokQq6iFgcxjW/NL/5vFqbArGoZ5X0E0Gx0n1FxpqwAy3cbClxL666QGU
+HFftjVUVsJBFbM7OUE1brPoDKHbwVYvJmuzEpbJR+DqAZUyu1QHcPuu/j0f4c196BwAwXRa4+2XC
+bh9Lp97lmQelZaKVDrG6JlOOjkF9DzSegk/8eUJ3fmM0gqHcfVlO1iIiUD1QtxUBmxBj30e3yy47
+XcGbEy/ve3Ak5yirJcyKAi5s+16aHDUxDaGvYLTMRT8MUbPl+jyv4BP+PGBYMuh1MGI23Q8NAuY7
+spPnsFxTzvVhc0Jtd8k1WTioIfsuPLdu1U6CdN0FGiYJHN3/hgpYmZ9iTCn32lZrTsg7yRZ61EqM
+PYttLfenc0tCiggJDOQBijo+0kwe9C1doWiZqz0FcqvAtOeuLCI+VMUEqMh/2VQreGTD6iKFn8ix
+J2dyvuUswDMSnajJEo5t3aXaQVXKsVGKIWjwf+FsRVp/7UMReGWQoe8+gWB7NUNWYtSLKkJDpqRm
+peGzu3LxYgV1Vzan1LPwfeZXyKDllIOpZeFk2WMsyIMZefGaRMercyh9U324GTfjZnyG8Up4CkoD
+fseh8oZoqPNYZSW8a7EkKvTgUT3aJM+h5+KKgQhEVTi4XJKLZlpB0zVWeQzLzjIdapicNFibxKh5
+gEcNAUR9gMi87tv7eJtu26OoD6UkAXf0Y7T8Dzsw3NkzreDT/ZxmM/gZcRahhik+4UcEgGxZwu9S
+d7EqUFPqDUGBtCVkoSvqxR5qmjuvblD7YjECL0TIUAHdEHOi+yrSF1sE0K18tKfRC1oZuniNWZit
+KsxoPDaZaPSNwaoQD8q6Q3hxSw8GTM/ZDVivMCA7ctXqTbgaih2ctvi+0AYllBFPz2AGxYimYGVh
+Zio8ZSLWarfWxnjri68hoqWso1OkCxLfEJVnRho+PUW9WqDXp4vViaEt3ayyhE9kaeGLZ6ZmFob6
+DmVToG4l5DQQk8ixdN4a5qzJbzD3Vpi23ndZYzehx2OewVSPOQey9rRKoIjM9OI+YrJ1T6oZZqVU
+PjbUjyDkbW87WIt8uk4MRtubUHP+Rj3BYsJO09fvI3BMzC4lKamaHpTfSgsaFOSX9FIPPpP0zlho
+/cPZ/ldiU9CeRtrrwGvUWvh0Xi7gPX4MAAj2E+hY4l1ttgGKrLgpW0zhALavertzeC1Jx94A8AVY
+Es6uYK+kdKTD9sG7A8UF6NQMDd1WvTXA/rkcb+8rO9j86lcBAIvnsvh/zVrMZ3LjdnZieI08pAM/
+A3LZ1JZKISQhaMobF8Ye8hZMFPbQY4bbSxL4XEBtbB17NfIV43pt1iQcyhTIJ/J7S5Nh1bDVlyIy
+jerBHzGXYX20z/a26kLvyAP0ndNz9ChoyhAf23GMsF08dQmVyma5yhUuCA/e6XbXhCQtfNpFKSLd
+MkTVsGQxVXoAxxi2XjZgGAyrZLOdFNWaEWiYdLBJZqK7n7sHVldx+WCGK1aBBpRhNx0fjlWbGNGa
+ylw7H2pJ3siks3ax2/uhIx8RY/zwskCx4AaSeqhrhjmrHDkBbOfs6nyornFnW67153aBt//Dn5Nr
++t0t+CPmBmaUoq8yGDrfgfKh21yEMaiWbbhZQFFstqXZMo1Fw7g/9JcICeDze1vQDFMjWyMima5P
+6YPGzFBfcO5DBY+GKrEOA8oRzJ0D/hjFYG7GzbgZ/z8cr4SnYAGstMOgaxCWshuGiQ8wg4qmgZuy
+f793DRSU6epKNUBld1E9l0Rk9t2nmD+U9/WkRkByDr1v0RfjD5XJTuvKaxhmfevLczSkam9WGVRf
+QobB1giBlR24cyi7claUsOQ3KM8P0GNXYhOGCOiKqrlZu3AFa/4mG6MYU0ptnCNhdWJjc4B+l7Rw
+7Gr0kyFSQoKbeQ7NJBOiBIZCJcGqgo+Wpo1dgZ6Co+UzQYgJwUb9kUPErP3IBzbpxm9RJi3ppujG
+TIBFHrKFYECy81OsCHN+vJxDUR/Tr2SOR/feQLwl1xGEXXg8hotqmIaVnXyFgrJ+Fy8E5j2rM8SE
+Jd/pjxASkITAh7dJjUZPo7vLShO5ClU3QP1EQgKdDKFjut2lh4QhQbk0WFKtOumR92K1QLFsdR4B
+n/qK1bJBwpAoW81xtZLvZSTDGegYG7vixYzDfRSZ4A2qzQq2JKiJUBHPbcEjGVCaJjjcJbS5dtC8
+1wg+WRdNS9y9OseKfCDRMEX9XP7QiTUSgpA848Mr2bDHShvKAH5KVfFHEaZTlskc0KOLWHk+QmI5
+Xna8EpuCaRxmlzUGwRk0xVo7KoUJWEIsx3ALxobBJjwrmWgwk63SRwjeklIffqdEQ62Ab3zwz1F/
+LOW0o70B3vwL/L1cFrR/9RjzR+TycysUijHbNIK3x/JW/BCoBbzkGW4wzqHgfvX0+hoFs/0Dv4M4
+l5uodYEeM8CdXLLXtSmRtbT1vkXDv6t+iIosPGFK/kjjrftAmnKBqmE5yXShydZamxUUs/0htQjL
+DpAt5b0GFcasygSYrrsPr/MCXQJyDkgqWy0L6EP2HCw8zOm2P35ygQ/m8htDF2KPLnqnT57BxsCn
+sKvFEi5qVbSW6zbi1WSKJdF93z6XBXrpNHb6cg5vHm5iycx5fVEiCHlPyiUyshu5Y+l2bMZzLGay
+FkIdoiABSlHMcc3yclYUWDZkSFrKgzlfXmM2n/F7EUabBImFFrNMzuls3ODMMM9Botgdv4eD92Rj
++e5qhP17DNewh/BIDEf+4U/Ld+zfg9qS4wW7EULuA4mfIiHLlm4sarI7lRMxZOPzS2gt681lFnFf
+1tDhsofb2/d4zhUUy6WGZat6WSEjCc2Hj34fz9jbEQQdxBFDl6VB6P8xhg9Kqf9cKfUdpdS3lVJ/
+WykVK6XuKqV+Ryn1QCn1d6gJcTNuxs34EzI+i+r0LQD/GYB3nHO5UurvAvgLAH4JwF93zv2qUup/
+APCXAfz3/8bf0kDQAcpkCL91jYoaqRJX066G8CNx/dzzSySvU2OyI66cSo6hDS3Dl0fwfl28jbe3
+NvG15/K6Mks8+HUSihzyvckZikysf5MF2HhNrEfWU6hPxZ3TX9/HQUI+gUOGLUEEX7UiKwuwwAEz
+iJG2ySVVImDmv6p4vBVQ8PUg6cJCXp98UCIYya3oX8kemoy68Mn35+IGivRaqbpEQBUqlXio/pDQ
+x3ISrolTbOKtlaemZY26zazXDUB3vSL4KfcbXF1IKBXuWCyZ8C1GfYyY/Ox2Q+Tkb3BL+bFsfoUl
+s95xf4jNDfJZ+hoVrbjxO5jM5f49pQdirYXnixd3XgWoCJYytsKKZqQMJwiZ2DRU+jKegaFi16qY
+Y8Fw5WqW43xBHkTPBwgXB5mv57bGJTkwhl0finDrpq6wpLVd6QqVo0o1w7E4TqFqgaC/8bk+4jZ8
+un0Ne9Xe6/8DABB099DtyTl0Bz0MqX9adjIsiEmoqnJt8Suemz8MsZtIbBtv7wIkjjkaGISkka9Q
+wgX0EKzMRWmAZSEe5MnlbM0qPdhIUTHpWDUZdMsI/ZLjsyYafQCJknRqCuAFgK9CdCUBkaL/dz/j
+MW7GzbgZf4zjs2hJPldK/dcAngLIAfwjAL8PYOpciyfDMwC3/lXfV0r9FQB/BQD2NvvY2BohLAy8
+oKXiChGOmWTpnaMmiSkyheJc8AQeG6KweA3N4+8AAK5/82NUK2l8+taFwu4RmZy6WxjT84gzos7C
+e4ioHTGuzzF/TLhq0MOqEotXBe9j62eER8GSxu1iMoNfsUEpmsPO2TFYlVhScKUaL3HRk108bglK
+pwWSTeou9jpIUpK11nPUc8mTUCoBxekCmoxOxbJARATigW5QE6ZXlxUMiT1dKfO2XFkUrdZm4yOi
+Hmd5XaMii1FdA13KioVbLPXVCUqWEMvrK7Tm2s8NZmcyF74fwdBrSCM2rm0cIjuRhqhFOluzZw+j
+W2uZM70IcMYux+dLIlYbB59NWctlg6ZkUjabIL4SD2Lnx76Isz+QJp9gRum9uEBID6pwFm4u+YBm
+VWO0KfH8oJfC8xnQswR82O/iFu/HfJzhfCoW/YOrazg2KL0ZR+iQ/XnJcrLqBTjePAYAbPdfx5sH
+kl86uVToHEhuI4A0uRnzLcQUIurYGoaIzdVZB9dsUb21O0dDnMFyQp6Kaw+7r5MjJFvBGKqqRwEq
+kg0v3RRRX+bTZvQgvQoZaeUenE0Q0Fve9h1WU/ntvFYw9odDNH6W8GEDwC8DuAtgCuB/BfBnXvb7
+3y9Ff39/x81PVjg63EDCpFy85SFAK88ew2sIkAGwGj8BAFx9Sx7y7/3j/wW/cS2vL4oZehQPjXUX
+biKEFntxBz9xJJO9fyzdZi5QGDOBaa8VFGQxfvvxIzw1soB+8XAb9SNJUJXHcjOuJ0tstC2reYjG
+ax9uoENI82pWY4cby4TUbpFn8fDb4ka/b57hLqnQ9jZ7uGa9vU3U5bpBj4nB7c0e4n1JRC3TaC1M
+a5pgTQG+oiZmqKaoKz5gcGiIUrIwWLaU8wBqdgNuBPJwh36KqKUODwyWrWJVkaJhUvJo9xBvvfYu
+AGDYlzZe1W0wffRQ7pPNMCgJXqqncFT1WjVjfO9jmcPHK3mIPeXhTaabrhdTGMKgzx6c4P4X5Dd6
+1duo2REZvBCXugoqBBRxtXYJRZWtuGrQkJQm80t4JcMAYi8iNUDKasd1pHFZyHp5cjlFv4U03Bng
+zojdrwwftPMwIiX7lt9Bc84Ncl8jJC19bSS81EUH3YTAouUSZkvgz9nEQ7kj871a1ghaYNSJbEx5
+ucTeC7nO8GAH+lquSccR8ufy2y6v4C0JKiGWorxyeDGWpPnl1Qp12eqRpugy+f24rKHNH1+i8d8C
+8Mg5d+mcqwH87wB+GsBQrdEZOATw/DMc42bcjJvxxzw+S0nyKYCfUKK3nkNEZX8PwG8A+PcB/Cpe
+Uoq+aWqcXbzAYCuAT1XmxImIBgA47cFNuUMPaoSFwGf3viRexWBjE4f/UizJuV3igkQZcz1FXsku
+n3QVwq9IJGP7VEmOHmJO/EP0+bs4Jy3X9yYzTOdijes7S9QdEn42YsEq5yPots0wC3yPpS5vZXBn
+V/bZTi/EByuxGgVr32/tbOJgS6zcVVNjqcUzGXcSXE/E21i2tf1Aw/Tl7/ujCI4IteWkhGEZ0moP
+mgjC6xW9h7CDCyIhHTQiSuxVtUZOZpFMW3xUyTW9Q23LxDdY8dhB2cPtYzn26exjDMgSvDjx8T5L
+qmj+QM6hB2yN5LNv/8gRNl4X61heK1y/kOv+zhz4jVPxJnJGloM4xKUv56C0B0VeiM5CY+MdCQOi
+ZBMjeltlT+Zn8eQaLyZiPVcLi4yJXd1J0fIuV2UMR0/Oj+VeG/gYUzvkuq4xIQRdewqWjXVnWYP+
+YatPIdb8/m6M+z8n3BHd+w0ijx2vro+7b4qncFqKh1KXH6CkvN9iWmHTY8OTZ3F5Ip7JdGMHtw4l
+qbi1L+tx9vQpNJGeKjBQLDkndomAXbfBdm+tY7mgNsiL7BLfPBHP82y2wpJzOLbVJ12s5RKu5dx4
+yfFZcgq/o5T63wD8AYAGwNcg4cCvA/hVpdR/xff+px/8axZAhkVhkPQpub6MYCOZVK+Kodg+aJoY
+nX1CSWPBHST7t4CUQrKnV2g+kpChv7+LFR/M7Z0O+oE8yFNPmISefHSFJfkc+70hSgJWZsbAJ8sz
+Lt+Ea/HlpsUEOFTMuC+Vj5IAE+MKPGUsvuWlmLGe7Gq5jrOyh1vsu2jOgRVv8vnFDCUf0oo5gjdf
+38fxseQZBhs+Wlj/LJvAeITr9iPUBLQUbXyeNSjJE4k6gKGrOV+VaNijYY3DBdmZPs5IDZ9oFCxP
+xHGO/kweoN3XOkj16wCA4eEeFLkpTfGYd26AvSOpAm3cvgNF7cdqluDkRPIBv/Xtb+G8JFKHvqkN
+PExLWay9xMGxynD7T48Qxz8CAFA7CYIrOeelpqpXFKGOJQyclos1+2CvPwQYgqzmBcAQarWUh3Fh
+Mzx/SgWw8RTXvG6kIXLG3KWq8YDHe+e+fH/r3S9j8450x3rVMZBSK1I1mF9S+MUTY4JIoSHEPq8s
+TqdyH9LNM/hLOefbiwW2SOPvDeTh7+xswJEtzNo+MJJ8gC4jhMTi+GGEmvmcfCz/fvD0Bb72SIRo
+FiiRc32+WE5g2TFafZ/AzcuOzypF/9cA/LU/9PZDAF/5LL97M27Gzfj/bijnfrjM5P8bw++lbvjF
+N/GFrbdw/aFYifOTv4nee/8xAOD+TOPFhuzGZ9/4dWRsiEFDC13EWOb/Ut6Ch5Z9yqkYit01yhsg
+oF2xlPLW1QW8UKxAJ3Z458/+JQDAz795hHu36T4/U9j7WUp6kRvv2W//t1heyW6/qGf4+JG4s88v
+rmGYwItCDUPXtiDk1lRuzWZcOqBmQ0ygA+zsiAXuMnxq6hLTKbUH5hbzlVi5lVYIuZfXNoFr5H3r
+U18TBo68CGGt8J0P/7n8/coiuiXzdXZ6hif/9P+Uc6IG59QUGF/I8V7f6WMwlPPJp9dYsCPSjgso
+dqBeURvx4dUYKfEYB7v76BClmM0XePhIrPvTyxxZ6877TETmJRw7Ub04hGJ32MXJBXJ2s25sdzEn
+EnCxEi+gD4c54aTzokBNrgANjZTWVgMAqwcN8RTWs60aG7q9EI73yY/0WknaaUCx1h8ymSsch/xe
+muLLX/kCAGB3e4Tf+JpUvCJWTjpRjuuFnOfte2/iiOI6tV1hci5zsQgUhmjxG8RS5A1iUrOlXoJo
+Q865nlgY4lO8IEHliGt5JEjI75ycYcbksYs0umTjrlYlKEIOpywKXvjT59e/75z7En7AeCVgzp6n
+0B+EePi7RwgufhEA0KS/jXIsk/DNi99EdiILaLx8AnBSHX1q6wK4tTiJ+4TQ2vnr1844lOAGkbMd
+FTl0K2Zal3j8T8Rt+/rDMS7vCrX2u3czNONW3Yh6hldTKHbfzcocizFLhFW9BqZkCNaAo5yMR9o6
+EH2LyAGVaq/fR4W2eiALJdAOU5b3VjZHSXly5xRybiYKKRwBUAotWCVA6n5e3tPPsHoged7N4x40
+y5DN+SN0NljKdKx6ZH1s7skGMRpsoEsS11HHojEs740aNGxbPtyWBbh9vQGPGf6N/UNoVlqKjoeQ
+IU3a/4RINF+QSDbR2CaP4jkslszhZEWF26QnX9UKAQlsYhqvVdVgTPIWv3Fwuu0HwJq0JjE1SP+I
+kvc3txYB70NWRejwnJsmhlcS7OVrML+PnICfqKywZEk5VhqTiTz03X4Xizl/u+AG0xvgiCI0m8qg
+b+UkxpVGyRufBj4GrLpsaMkpxMceBj255rhjkCQEvR0WUFR3arTClMfWzIfMlgUsWZxWCqgyhhch
+cBzJ3F74NfJZ2/v9cuOmS/Jm3Iyb8anxSngKOu0h/bE/he8+/PcQ9oVGPei8Be+97wIArn/tFE0m
+yUNjMuGEB/CJyF8FEJsADcC2lzXHet/TC7TOhKOuHxxg8AgAkJsIz+Z/Q473MMatiTTg9Pe6+MID
+caUnGxSTOV1Asz5+9WKGyxlhpxYIWB9uVgUyegpNS5PlhwhaxKnV8HluntPotPR5tGbW9ZGSi9Jr
+ajT0FKznwYIkCbhE6zXpluFruotll8QyqkLCjtKwmWL+dREZuZqfII4kbJovyblYPcT2jiQ2O16E
+hFRyvisQRPLj+rALR08gIyhoIx6hYUjgBwrVREKsKkzQu3cMANgtMlxdyfvTc7F2z5c9eCQNseMa
+aORaV1MHL6VKd2UwIVDrOmuJUAzyholUTyFgpt7BhyPByaQuUbHRq6Sltdai5ILJFwYlKw5h1cC0
+pDRKw1B7sqXSm1QVKt6/SztD/7GwS3f9FLOJWOkO9TwHSYRNhgR7gwH8lmj7/ANM2Cb65nuvIywk
+HL1HmvzdO7cQ0sMIVjVCVprc3MFRlGg1m6FTS9J03pV1eHywjZpNdS8mE8y43kzpo1YyR34Rw1Z/
+AqXotd9FvPUTuP0f/AMc3RcSi2f/818HnvMG1XM0be5DOax7rHQbMqRwjg0IqgPQpYbuAbZtGw1l
+8wCAT5xEgIhGhRKKnXHlfI4Zs8jf+O37iL4iC/q1bXlo6uUUJ8Tiz5cNVuw1KK2G5UPjnEPdyraz
+jJXGGr1UHsLSLOFWdKWzArWRGD0lYEmhA1fQ3W8UNDMlxoYAJPxRKhHNewDayiam42fQe1LGUpMX
+SA8JZLpoMJlLabHIlmscPUZyHZuZh8iT167J4dhaHfRT+ORJDJSCoVpSP5JjWGugqH5UuQJRyI3Q
+ZmvVql6SokuWrA8iXudphgnJP4wxMLxuT1f4eEKC0lWFMUFk4zabXpXw2NARaAUHrgVXwjKQLusG
+DSsKa3lYrWBMq5Zl1n0CtXOAa5mqPFRlW5JkFcVWYBsIrlYl3KmEmGHvYzhuJprVoCpIobq8fm+B
+JyT/XWUVBgSl3d3fw/GezNfRjoSoXlMg6cl8auegKcZbLa6grWy4XgSYWu7P3btSku91+1ADQfXW
+33W4qsVwurrGJTk7VzZD0bQA45cbN+HDzbgZN+NT45XwFHb7Mf6Lr76L+ORnEYWiGfkPt/8Uyruy
+C/693/sDlCSvCIIECam8m6LlHijhSALoRkOYsVh05X0XrmRHZTyHYwa7DT9cnUKxdq+chmYiUhsP
+ZS4W7aOPv4vgrvS0V2Nm1m2BJZN2c5ujJMa/shUsa8VKKbSQ85j8i10/xh57H67GJWqfdfW8wpIg
+nbxoMRglLBWUGlXDonUBV1CtDrLLP3mdC/ZCWx/+pYRdrtDQXfm9Bx+c4ONMekLK+RD3YpnPlvsj
+SofrBGwUhUhJrx8n4dpy+FojIEGI3mJIoUK4Sq6jynw0zNrXiwRgx2uoIoQ9cZmLTWpDViVcJede
+bnYR1rIUL4Ma1yQFWfU06jkZkSsmRJ1DwgpIGIYIGI/ZqoJr72Wo0evI+RfMzlewWCwJlsIn6Wil
+HGx7o0yznmXDipFt7LrTtKgNpgSGPXpyicGW4F56FAPa33LQRatjWmLCrs3xfIohE7PHvS5e2xUP
+ISERilY9BH157blIFFwARLoPW7A7uAihR5Q/8OTahsk1fALZuhYoa/GWz6YZMskTkPUAACAASURB
+VCpXN4kP80NWGG88hZtxM27Gp8Yr4SkEzsOB6+G1t2rkJ8cAgIuvfIR/9mtS/zdKQTOR6AUekj5L
+OUPZMStPwTFR5Q7uwj+g6IdOoWnF3GKBkrJipWXdfZ7BUeXZ0x7gMTNUFihZC5/6OSanklN4dCDW
++HJawxJvECJZ6zlWjQFYytKeWudDk47s8Pu3bmHzQDASSZygeS7lwrwwaHNBDcusZWFR03sw1lvr
+UzhoYG3PNBxzH44ehoEPM2dc7CU4PxVr9Xz5AH2Ix7McLBDuMs/BEqjNMyiKk0QbKcJWj1On0Ozp
+1yqEZiJO25bTIQSYO1CdGfw580BJCLMiGzcqaE9+78iJFxeUChUp3UyTwlvI3xcrD5o0fEGuUJUs
+OzOnEmkfR7uSP4m9Fa5X7f1TsCwHqybD4Y7Ms8ec0XVlcPL0A5k2p9Z5aeVHMIV4EMYpeAQlNDye
+shUUX3sKoFOIq1WOzQNZf0e7kqM6TIaojFxTVl7CI2w81TWOdtjBubODmOxaIecEiQePTWdwDTSp
+4tA1awRpqJYAS8ONIguu62B/n01s3gHmL2SdfrOa4xmh6fAjVPpPYKKxG3n4idtDaFvD+mzvPfsC
+nvQFJttp/hnYFYqDzj4+d18WxWvHsjnkdYWINONHX9rFW5vC4FzMhrh6KA/ktfcBCpLpPX4mD/fH
+T0K8mEk22TYe8kaOl00Bw6Rj4kewpJ2fkO4rNxYgiYWyFjnBL7WxUCxxaKOg6Gr3CSrpdDcRplxs
+aR97B60QTYALipUuSHfmaQXHBeq0XefClK8AQ/puVwviBoJfkBcrgG3dRhkYsgQrf4DLWqjIQ8RQ
+kAe5WrJfLV8hZddmZLvweB2qWUEVpI1L0vWmB7qk2oQAOzWV56A78j2vsGCrCFSxgMe2bNJdor/Z
+RY/Z+/mLGTxPHpocFn1yoPvWYd4KmbTAozgGVeTh6wFmS1alnEUYsTs2SPHWbYFe3+5I+PitZw+R
+jWUtWGXX4jOlqbGoeVIAUj6o7ea8bJSwAAEItG6LQ8jzAl96Q4C7YSlVgWJyiQ+v5IFVlYal+M5u
+F3iXPI87nT4CYiQUWoq2ECrgD1sNR7FZlAZQ7XoK0WKdwowVqtjDgPc692bYIYt3Jy8wqGQ+i6JY
+G5SXHTfhw824GTfjU+OV8BQABc8LAG3XrlpnMEW/SxqwcAv7HbEeX7z/Fj7/U8KHkE7EAo9eA/KB
+7Ia3B7eQanG54oN3sByJBa4WbyCfy1b7lXvCCXBaGnzjseyoi7MKK/L3P7p8hutL+V43GaBTkbqr
+JGWYs9AshS0rtZZjs86sLajzAiSBWKDe7jEAwBv56EfUZRxUiMlJ0E0uEJJw4+ypWB0vDJFHraaD
+Q0aIq4WPlsNG6+5agl6RRsy6Eo6wVuWWa0GZ7XQXSMQTmk5zXFOCfoPw2o2gRsikpR8peB2ZW99z
+MHTRFSKAYUVLGGvtEtpSXTrtQRNe7JSBR5i36wOakm2W3lOcezBOEslVNkPJ9z2tkJLo5HJuUBEV
+qWmtVRRCU4ItNkt0QzmfeWmRksjkoAfcYgeqr3n9nRTH24RuWw+GSMg6L2BZOs4rC5/hUUIsSGUu
+YRmCWGtB+AKausJrr4kokc5k3q5Plkjo6ebOYqcvlvvdwwHuHEnJ0VOAJXbCo+S8aRqAc6V8QFMW
+0FY5YAkFTwIoRfh61ja8KRTUKe30Uwy68oz0kxR5R657WseYkwruZccrsSkoBWjPwZUeFNVJ4vkR
+PnckAJvPb6XojCRk+OK793BcymQvfMnMZkYjeSqLo/ulBqZhfbgu0TC+Vj0F2wpy9GWijyYJNt6R
+DsAPvYd4wM7BoQ+s2F2YFzNcVhKrRZfsZTA1VJt+0A2almhKfZLl9f0I20OJa19/W47xuU6CeJvV
+h/MErfbKqXZATJFWZtN3B13MWTN/9ugKH5+T0clWaJs7lM4AVjvgtZuDgSL7E6y/Zh6ajCdYENMw
+q0rsnMnmE5JHMN0cIGBI4VUVNNl/4Gto0hKrwIei7qLm5qeCTYDqXa70AcPO0DKFoguuVQjwnAKK
+ykaqwpCgpzjUWDE3YnUDw9xPEJWwJIkh8RSGcYAOsSeNrREzzCmVgmNOaFlFOLuSEHExI1S8BpKe
+XNOWn3xCu1/3EPO3Xyymn2yMfDLKMMaCcHMY0+75cK7BnbuSu3rxewSL9SzqsWwgm9bikHDl4+3X
+sLcn4YzfrKBDtt234r+VBx2zDdZogBgK7fpQIY/dhIAjaxWrDy4eo1PLujFpgO2hzGfn0Qo9QsFj
+v1obiZcdN+HDzbgZN+NT45XwFJwDbO3gXAZQvGTVPcOM1F3hKMJ2Im6+Xzd4OhErF4XiFnWrfXT3
+xKVcrRyKUtxkmwMzWsfqTGFcisUPLyQRWaoroCSzbr0CmHCqixqzFROeKFBTIu6AVn68UAiINFsV
+V2g7TZVS0FS23tr+In7pJ6Uh7Rfekc7PQQyAStLT0QZOzyRTbXWGIJMd/862WLM7nS3kS7Ee36hP
+sCIa8axcomlr7J4P2ybwWmi3UnBoE2cNHj6V5GLpMoRX8pmh1RgQL7ERSiKu423AZ1bcg4KXynno
+0sBRrkz7HlTLCK2pxBx34Zgwg87hnrFqMQAMm3K0WUARWGpqujm2RkDLrhoPC0q/FwWwZKItW3lo
+84wBUaFbyTZ0JJY2dZdomPjci1KU7NZsUON90pStyNkQpRvrKkoQDdChi570tqAZExTwUGdkzW69
+o55GQz2FhS1Q045qpfFCIj6U5P3AI4UBvY5B6GN7R5CHo9FtDHbEUwjSCF7ATlHTqqCr9XXCKCiK
++bjAwGVt+GTguCYDhkymTqBDWSOqsrA8T1Nb8SgBFI2GbrEsLzleiU0BEKipcoApCTBxFeYUCd3e
+7GChpEfhyfMMO7fItTiSBT0Y+UhJsqKwgu63GdkJApKtNoFDjwuhmAgxRb1skFPKPU0CpEO6uycK
+DdP9nvLRIV24z5btadYg5GJsXIG2tzbp9XF4WzLSv/ALX8UvvXUMALh9hwpK3hIhYc5ZESIieCm5
+6uGYQKUWJt2JNhC0PeALg0u6++U8BDlbULkxCm4GPvHyRW4kMAXgEMNO2TNgNKaXVNSyK3TfFdry
+iBTvVllYCrYgGkAVLYRcQ7H7zoUKij0DrsfdoSnhiL83tkTNRVovLqCMzJtWBcDMuWKYEw97CHsS
+Gz+6muOSJdXKWOSq7f9o1m3LoSLct1a4vJI25DT00GV78t5WFzE5NrW/wsWklW2SB8iNLPxSzvPO
+0T52W/ISp/GMkO502CC7ppCvlirCdJngbNISlixguOE6+Dg/lfnyzmWTmi+XyPhEvba/j8Md8kum
+FpaVFlN0wP0U6LC3xSmoFq7tK4DAKVTVOn9k4NB4NHDc6MrKoGbHZFktURJiXhkP19zcJosVjLkJ
+H27GzbgZn2G8Ep6C4n/GONhcsuLF08eIu7J73r/3FvqUHJ+ef4xou5U7Z1Io9ZEtxZczcQm/Fghu
+EPgIQnInqC42M4Hapu+IlTh/foGMmdyryzkOG7E0D6NLKNUm0mLsbwngZuuIdW6zQkHeO0+Fazm2
+gy/+NL54SxKib73Zx+NTIeG4IOIlWLk1Dbkrl6haAZthgkFPjr2cUUrsbIrH52LZT55dYFnLddS2
+RGVbfsE+koB8jIpKxHiKRrXu4gKN14ZBFlkir8PrT/QP65aHYX6K6ROZi6YToN9jx2TaIKGZ9/oJ
+kn1JmoJcAauTD3B2Led28uQKV0zKhssC+/ToRsMRNrfElbaaUnihRU2PpnIVamb4YS1yNpjVBmBz
+KAqqZD+YPsdWXzL5g67F5q5gVfqRh+MDVkGWPRwdSwJVET6d9hUuxwLk6vVSOHqIxnjIxuKF5baz
+xpkMUqH6M3gIz2/Zoy2cbpXOAetToIZgIp2k2ApJwbY9QNnIms3nBXJ6FdiZYaM6BgAk+wy1mgCK
+0gZ+PJKOXgD55RkWszHnKEVNyHNFYZ2sKDFn8rypLXKyWQe9OYrnlChoajQthuUlxyuxKThrYeoC
+JjNo2MPgBXcx1ILhr2yDFTu9FqXC9LlQvD9v6WUeefDYOTkMPahUXkepglcSubdRwvLBevaRTN7j
+x08RVnxvnGFJYs8ValhmnDfjAd77srjatZbvWVWvJ87zIvjUGLiTjnDQJyjo+QNMrymUSrHWr59c
+o5yLC3jncBM/ek9KWv2kD1PJgzC9kgXx0YMP8OSUZch6iaCVGU8qeG1LtWdQMA/SOFk8ShkoiKvq
+oNZcks8+eoLYJx160oXH81gxrs+XUzx9QhUjO4GLxUXv7QQ4DGTD2d/uwo+YX6D2wvP3n+Jr738P
+APB0YhG9KQ/V5w7eRRrL751nC5innIteC4ryENFtP0g3oQg8+hBP1/TzytPrbL8meqmwOSY0HB5S
+TKv35fori+6Hsnnd7W/h8DYNAKs64+cG5yTYvfzgHFckHimzAtczOc86UDjaPgYAdG/Lb22mQxxs
+yn2fzjVKloy0UVjOZB2ePeNcFQvcoqisl9eoQ7kP9baPhnT31YtzPHkkOS8XyjXtDDaw25fNMtwe
+orqWc/vwyWNMLthyrmP0d+SctlNqmhYFKoadlfPXPRN7GyniNu9gFrCf0A691LgJH27GzbgZnxqv
+hKdgG4viMoNOFvAtRTzSBeYPBML63DxDdUY32Na4mlCKnFvao3GGg5Hsnvd2euh3ZUedPpmjYSLm
+9P0aJ1TTeXwtO3isFLY3JNQ43g2xbCsf4xIFa+/Kc1CGu/+AtWSn1vwOntvFiBwDQ2VhWf/++OIa
+3TEVkwkzvd0b4gnl0I1rUBBwdLCRImJhvCTT72IyQUjoa14Osaql7p5lBg3JNHyXwNGNb3nenFPr
+4wEOzy7Fil1l57jly3WM9iN0dui6slNvcVaizskivJ8gpgLU7MEESU/o2XcP3oVmQ0czFu9n/K1T
+9NmJ+ebwFrZDCRMGDRCwvfDNo13U5J9Q9ZLXUWA1Fpc67oQ4Goplb772rXWfi0aw7gL0aL/CMELS
+kfBhmHqYVnJPLmZneHwuc/s4PEf32WM5533KAfS3kNH9fn55hTkz+cusxIpcDbZScJpeyAOqVCVL
+WPbV+F6IWrdCPA3OK8n2P5sKVPw1N0LIalCODB4xG8WHJZornmcMxF1ZL5OPpbdHDc7gvyOYlv2D
+XTRT8SRmzycIyEOxPRihIW7ljEzbWTZBtyv3tLGAx6pFqDprRTGn3BoY9bLjxlO4GTfjZnxq/EBP
+QSn1NwD8OwAunHPv8b1NAH8HwDGAxwD+vHNuopRSAP4biPJ0BuA/cs79wQ88BgBPKWjnQxOtlm5G
+UNTX6zwLMWf89uzM4rKQ3XO2kB1zVs5RUVPw7Vs/hTmZhp88uALIeLNsIsxzeZ2xe63QHgK2J143
+FSwIY0aDkKW33VEPgRELsxy3XUkKoqcLoF9C3ZeklDpWePr8WwCARw+eYMH6VMvgHEPhVk928FGY
+Ij0WK7i3uYmCHkJRyTVdzHNcsnZ/sbjA6ZTSdbZZMwEFuoLPXIr2W2hs/QluwvlALZ7AcqlRbYll
+s2EX8KWMli9k3n7rd38Xv/NIEptbV3dQjMU7WI3P8dqGWLa3f/Jn4RGFOGdZ8HJyhkkolvvk6TWe
+PPgaAOBxvcRPvyHW/y/+mTexsSt1+rBDklQdQbW8AQAOGVMDQIdQ6jhOsKrlukbbYklTnSIjjdtV
+Y5DXn5Cnttc9zSrMSiIkmQS+tbWJfNWyQvnoUmMzbxq4tvXRKSyoB3GeP+R5DuDIrozrYE1H5wD0
+nFzfwpO5yLdmMMw/mJXFOSUJPzq5xAU94EvXxzat+JGS63j46AV+mvdpozfF+XNJfH7z44e4+6MC
+yW+uVvjGd+QZeDyX+3h7M8BOV46xc2sA1ScaNtxCxJyQtYDv/T+v+/A3Afx3AP7W9733VwH8E+fc
+ryil/ir//38J4BcBvMH//ThEgv7Hf+ARFKB9B7gAjskwzz1EzBsahyk22p6BdLpO9pxMyXGYamAg
+kxp2U1wzTDBRDU1MfTgIQawT9n25mdfLFWoCV05fGIzo7nqhxib5GIO4h3wsbnVEUg04BUVqt8Fo
+B1HKrLDZxnhO7MHWBsJtqVYMSSbiFh56ibinTbqBYEvOQ7ktmEB+Y8KFa7sxwlBcw0T5SMmGrJyF
+4zmncYqIqJeKYiMz1zJVAw4GAbEZnm9RU+7eG8RrANTSEVcwAHYO5fqO7txGsykP4enTMQyTVn7S
+g2HFYG7l4QnvRBhtCRz9KnC4mggP5PI8w0dn4h7/449S/CQ31oMDCUuqhUFJ+uWjOwMMN+XYSimE
+xINEQYwVBV4U+yF0VKPPMKguVoiUhAf9roHRcp6rZdGy7CElLmRj93VMClkAO7f7oK4K+lWDkzMJ
+U22aIO3Lb795TwBufVPDTqnkdf4PUZIm2lRzgKHE/p7M8dbmEIbrV3V7GBCCvrPqwRAMdhBsIPLk
+nMOFJGhXoUWwyd6Q3gimx3b2xEPUlc8aHaB7VzaAuzOZn71OhM6WbKbbGx0M2B80vgowpSqZ1kDA
+hC5yVkB+wPiBW4hz7jcBjP/Q278MkZkHPi03/8sA/paT8dsQXcn9lzqTm3EzbsYrMf6oicZd59wL
+vj4DsMvXtwCcfN/nWin6F/hD4/ul6I8ObqFBg+Z6BVOIC5tPN2GfSQInbArcfkMOcXi4hfuXYjWH
+PfGj+/4dHN2Rv6fQiLjVHYwO17t5lTnYEYlRErFsqjGomWR6cTVFycRe7HVwu2JiSwFj0rS5CZuE
+YOERgTZoFuisxKre6wT4t39K6vhf+Px7iDJJYnpUoj558j143hfl3HWAgzel1InCYkXOgp2OeA8/
+80YfJpVzOJvVePhQkk9VJ4GZ05L6JZ6cy9S+IH0clIPWLUFIjLvHcj6XZ5cIKKHn3CZ0IeFITCbq
+n3/nbUwIqe33Buuklnf4Rfj0DrzaorlgTX4hOIbdfheb+3LOo+4pfvLen5Nj1GMMGQY43UPUkeNo
+lpzT4AJ3DsUKbs9uYUF0X+MMDL0iPylQUkl5lYm39trBLXTo/b24BJbkvRgEIXTcEsNYeHSZXxvJ
+PTja2waUhHm9MEVFhe3zq2tUDDXURoJ3jqVz9QuvC/6hKXNMKS14cnEXHpOxF5XCwQ691xfHAIBy
++REqJvs6YYWYhK4/89YbmNBzCZsIORvF+gfvyffrKyQzojjHS3Sc2NHXdqa4Q4k8s2FxkNyX17l4
+IJ2ORZSIJxRFHkJ2eKrmDEqLpzCMu+iTOq8l7v1B4zNXH5xzTin1wxVC8Wkp+h99+123vJ4D9hki
+xuph38OQMIQra2BaVvZeiCaTmOouIb73f+zLGO2Ie/ni/WcY9Jkhvx6g4uS4JMb9DbL3dORm2nqB
+84zZYguUbD/se3MUzLL7UQ1DuG7cX3PEwxDvP54Ax2/IZ18bHOCn3hG38/X7X4TjZmKWslB2Njwo
+Je6z788RGNau4xr+tRyD+ijo+BvobQo+4L4L8fq+vHZqgHwpbuC0miP0JCdwdSEusO8FMC2PfKkQ
+JHKeG32NkGxJg8hCT+U3etQzDMsEm0NS0a98hNSx9IJbQEIJe2cAsiJ1Ri0H4gIR3f17d/cRKXmY
+6myMhHNoUwDXLRybuYxQoTeUUKqz2UGQMSdigMa2oVKIoG3RZqiR2BS9gTw0qqyRkbTG9kJkDVuj
+qycYsnfh/rZ81tRAzN6I7e0ddKkVujU9wNYeGZGDGLc2ZW2EFOfpRQ4bz+UcPnd4jMsrOUYyVqjZ
+2RkM5D57Vz6GQxosRBjtyQPbCRJsJdxYVQLF2CYg+U7uPNhHrIilIeymfG9k9rA5lPueHo8QtB2R
+7OHwsgZGE1tyNYdiXun5tMYuNT3roIc0pMIZHuJlxh+1+nDehgX894LvPwdw9H2fu5Givxk340/Y
++KN6Cn8fIjP/K/i03PzfB/CfKqV+FZJgnH1fmPGvHZ7voTscoFpdw83k42mSIt+Q07v+sMCwVYFe
+xbCULnvj9tsAgN2tDjSl1LK6gqrF29jY662tTjoYIibk9/xa3I7aROh6coyD3QMUZMPNggaGDUFe
+H4hpefsb/fU5KzZM1c0VyplYo/3tLRztC0ox7MXQlVh/E8rvht5tNGSldjPAS9m15xLEgTR8wYo1
+U6GHIBULtbXVw10yAOc6hiHD8aOHH+JRw2YeYjO6WYKaXY1ZtcLuQCx39cYU2dkzzmEFR/ERx05M
+HXqIW7e9EyL06RUVDppCNWqgYRm6gLBjL1vC02IpO3YIa8W99sIYaOXtCkBxDs2KLqxWa2yFaTKo
+otWExCf8FM0npDseod15XSFwrJIcbkGF1N6c1ng+lt9OMo3RhoQmW2yaM3aFciwX0j3qYLMjc+SH
+GXQj3kvhO3RIuFLncowwC6FT8R4+F9zFNT2dr/kWAzbKLTvinpdXpzi9ku9v7AA9VkPCLEcnlc8o
+l6CzId6GbWTeihc1QM/ELgNYapgE+QI+G9Z6fgTNpjJND8OGBtWSnlQ4RTYT76/WJW6N5Jwvz6Y4
+J+7hZcfLlCT/NoA/DWBLKfUMojL9KwD+rlLqLwN4AuDP8+P/AFKOfAApSf6llzkJpRWCNITTe6hW
+bYlpgVNCfxcuxnZIcY+6wD5j2J0RgTLdGGNeeOQFuLLU+zMGKUlB+nECn+XAe5SWn8OgnxHoVGeY
+U5zkpJoh5GYRDTbRp/jp7QOWHpWG46ZQWqDWAkX1tQdFCKrSCpbiraqk26dy2JKtzm4CR9WjKqjg
+uGH5lmpT8y6qLXlgzSpDh4vDlT7mWn7XDwHSKmIrEXe5ux3ipJQFluEDBFz8g/1thJQscuUElpWW
+iFToUZTBXrWcgRYZQVhhHCLgxorlEOhz42BZODUOPjcTvROum3RdWUKBZIobFnYiJ1oVMlf1dALL
+PmzVxFAxeRB9vRZyuSqLNdlJ+/dCZ9gnSYkfKSTM5LvtAhWz/Xk9x9FdCX+Ge/LZcjUXdTEAURyj
+32e1Y2GQ9eW3X7y4wGwp13U4IBlt36B3RRGgi2ugJyfUiQIU7Rrpibvv4gMESkKRebnClImEdODB
+kpg2CBRsxo2O97QppjAsEduehaXQb1TUqAi4sukW/LbUzHWliwTQJJypC+RcOxVivHNbenCeZh/g
+o/OXyyW04wduCs65v/iv+dMv/Cs+6wD8Jz/UGdyMm3EzXqnxSsCcoRV0GiBwXehb4ooXD1K8E0iV
+oNoqEVB37yAMsUHuw7Qn1qeulnAEIYWdBnYq3sbFYokhuxK3zDU67CTsHImn0ZmssLCyiy5WS8wr
+cX3LwCFhuiVuYoxel8pGh9LpaT9GYEkgEnjISJCC0EMzl9q8G4TQnngNrks3elKgYRdedpVCKTnP
+6XyK04lYh4KS8jbPkUzIcTjoww/E/QwjA8ME67CnEcfi6YT0frp+jKuatPa2QZc1b1S7KLfFklw+
+mKJHdzXtkRswK2CZva5XFQzVoV3kIxhISORpjYb0+M01FbXLEP5cXO1gkiDoiIX19QgubBOtFk3V
+6j+KpxTFOXTNhO+oA8t2yM1OgpyVD6scNLkaRmzEurUxxOt78r0gjBCz0uLbbRwN5D7sbqU4HIgH
+cbQj59OUEXJa2LePR+jEYt3noYerCzm3+nyM2YIhBsMEex4iIrflpPMBqpIhpAsxZ8L7xSNZQ8Pt
+CpdjOZ+jwhPGGADNcBNhT1z+WH/Cf5kTcDdfeFCBfHagHAy9vrMqQIcAva15Bhsz5GGyGsEUakEt
+TeNQk+W56y1x756s9fj9U+R1K1T6cuOV2BScdWiyCjpcIWCfQWc0x+5b4gLdfTbB+Ewmx9/poya+
+fJVLfrOqLF4w+36dZTAZO+oqIKbLPPY9dAecHPLne3a1bitd1QtQhAhx6aNg6aw/UoghE+wTCKTj
+u7DcCLx6gZCbxTJv0JAhyMwLtN6za/uUzQo+uxqdLrEkyOpstcDlpVzfogVQBTMAcvObswVqurhN
+AJSFlB8j42GH3H7b1KV8sMjxgtUJ6yzKObUbhwppJg9Cd3+BOJVbr8kb3lxfouKDW2VniHelimLG
+FcqFPPT+Vg8qIiNTLJ8N9w00QWa2rNAsWPbs11AJ3V2dIeCD7iiOWm+E8HySzea6fX6QBilsKzyr
+RJUKALr83u7+LtJY5jvxOgi4GYaqgeGmP89K1BSbrVid8eGhw++h9uFzow6MQpPx4byerqny/Wsx
+OGd5hn/xQPI9Xz9rcO1zU1zNce9U8jWZk74UexZhoOR+rEwIR9JVez6Hc7IRFBsKCXMmFfs2vvnh
+Exh2ie43Bg0Ri1PVICNzmDMebE5gG1u1ndfAUTc1KGqEDCXUqoIiKc3zqkJLMfmy46b34WbcjJvx
+qfFKeAoKSii8jYL1Zff0wgjRfbHWy+84nIzFhfO9AF2y1sak/y6rCiuGD7NVg5VHemu7xHRC/sC4
+g0Epu/GiERff1jkWrDIU1kfBzHmjPWwSPtrZ81A58hNosVZleQt+/gUAgFH/I55fyfHG9QLZSnbx
+jo7hE5fvWsZl5dYiJJs7I6Qd8QQ6lcPGpug8PnrCBF7/NuKENG/KIc/FeuQTiwk5IMp8Bo8Q6lN2
+eD6bXaAhoMc5QFf0aLwCIRNbKvNhYpnb7Fw8icv3vwejxCr1tzbg0eUssxKzSixh5+Iu9LbMRX4t
+LrN1FeJhSz6Tw+bkEPATeEtqJcYVlNcyO9MO1TGqUq4jn1mUDa1/N4LmfVpVJWpWHTxKzve1RUVi
+nE4cAtTpLKAEegzAaYcZrfCT53KePaXRJ1w76sWI6HmszleoqMR1uSyxycTr9aV4K+9/5wS/vyTM
+uXwIA8KuMURDD3AybUlhLjEj/L3TT7BJgJQxGTxyIcQvBiiVnFNJirbuLR/LnKpmSQcFw9iwCTAc
+SgitfcAtqUSmSA7ZpKgJXS6bGQpibsZji/GlIAEuThewzQ0d2824GTfjBlLUJQAAIABJREFUM4xX
+wlNw1sKVBZq0AS6Jg/ILqFwsabB7Cx7xC2WnhKVVJLsBrC3hs3bVHXXhlRQyCWPM2cCyQIaPiIrs
+UUAjqmvkrINXyiH1xHrkXQM3oNpxNkBC3vzOAWPn/ClqK1ZOe1tI6JlcW4PlTCzFcKtc18VbzgLl
+B/B5bNWUCOl5uAbYbt6R14R1BNEAXsAyZTDF4oQUXHaFgKzLTRPDsDOuJO3YvM6/T6HaoWV5XSYW
+fiPlwCDxUZC16nIm1vVSz9ElMnEZKmRMsHjuHCn139xmgZqq0k3LbtXpwlHGLp9XqH2WXMdLRKTC
+C5IemMOEY8OXMQYVWY0bWyAncjH2YgQd+e1cA5rn74gwPz8vsbXHEq8r11atahQ0vTDd8RGyqF9R
+OCYY+OiyK1MHMQyhzXVqMCH82w8BR46LRS3ez6R5gGVDBiz3AlgzZS9h1s12ch3mulnL7ZUbXVBy
+BJudLqgKiEVviRVLhAkRiD/yhc8jikTns4lirHLmKiZniIlvWC2X8Nu5JQWbbRYwVNquegEaMoNV
+5x7+6Td/T+ZrddFKf7z0eCU2BVgHm5WoLxKkd8VdwpVGMJJJf3evRrmSh8XPDKjXipIu3qSoYZjI
+ikyFrKBohuogpGJPJwxgmHGZzgm2iWLkzO7Wi2INqY17KSKq+NT1HNGh3Ji6XRz6ISJN2LFZYnIq
+D9P118c4MbKoklghLNsNRxbB9HKCMGE357IDVYiLZwYx8mULV24TYy+gWBloUCPmgxd2FFDKE5IV
+C3z360ywVgIZhqqgWzZnE2FB2u/5RwXu/IR8Lx6nSBLZcC9LYbae5j6agszOyzmeX4qLevz6DvYO
+pX8i2OgjIOTbHklFInt2hWYqIcjSWTwhMCwINY7usstz7sORoThgNyvmM6Bh5aQ/gE/q98Ir4S/l
+/HuuwRUTqJeP5BgnM4vmkkpJow14fCgWpoIjJmWQpri7I+to2GfYAqBPDImpV5gRC3A+vsajJ0zc
+FT4WKRm7C5nP0+YpLBPJ2jSw3BSU68AZ0vc9kA2md2u+np99460Nx3jagd9jd2UunacA0LCr8/5Z
+hI4WWrlVDCiK/k4bHyHPI3QVFNvOE0128DiD41y5xsBfyOuzDz7Ao3PpVm2aCu6Gju1m3Iyb8VnG
+K+Ep2MZgdTVHOPTg54IabLolPCK/BgMfx4lYprH5YL07wokFvji9xNPnsqNGNkbC3f7SLdZaDdfV
+Apv9Vo9Q/l3Oxnjadg76MVJSW3mVQk5y1EFawlC6LGMS1FUGeUPqMmwgZfLp2fX31qXM2joEROFN
+z8X6/4uvP8W9Yymz3rp/C0N9LL+RGvTG8tl0V65pdn6OF6dixa+fn+LWIfEW1QiOsOn5Y+BDytnP
+y7ZXPoCiBYIFTk8eAwA2NhJEYxGl8WONkI1nKUMRzAAbiwe1qAzKimzNVxqL14nZ+K0L5Cm1DJi8
+quMOCpbppvMpnj0WRN/e7jaMo3cTpGunu000Nv4AAZuravuJ1mSzqBFRU8OvYmh2sV6ROMfLHK77
+cuxbVzModpB1Ot66FHvSnCLfldfv3WODVqeP8owEPo/P0OvKunj+OMN3vyfK40VQYJ9o+rOJvJi6
+BrZpUag+QAJWpxxInYEuNU9HxSHSWEK0q/MV/u/23jTWsiu77/vtM59z5/umeq9GDsWhyW6Solrq
+jiRbjh1ogORESGK1YSByLEMxIMDOAChu6FM+GIZjw4kDOI6F2A4cKJITuSN1JDiKhm6N3S01LTWb
+7CaLRbJYVW9+97073zPvfFjrPrIEtZpsdZEV5C6A4Kv37j3n7HP22XsN//X/b/TFg5iVc0J91WYj
+l8GJYlKUWMfbKrnS0karKKTQcvf6mmVxLMcjHhEq4jR4SK7Bd0OypdzcmxVWy/Z3XtznSNHAZVWe
+o2/frT0Qi4LBIapikmsxrsb4TuBh1f0OGg16YxnkK192KJTiOlFBzfUrXVLNLG/3L7N1QSbu7uER
+47lMpthroJqhOCo4s58W3DqQF+zCxSZtbVUui5K8kBsfbq+TaliRKlYfl/M4unbGzNRt3R2cnGd9
+i50r1J6cp68dcs89f4lQY92WOyfQ+Lx2Lf6GhChNxTFMzszbTEEUlKovaDsxqQKEfu/gVW5NZSLU
+Gi9XlQFHJoF1PHpGjnvlqT49lS+qPSs6k0Axkcl4UuVM78iicH27w+aWZNy7nQ3WLqhwzLU12JRF
+2x7KdZ6efo69l2T8R3cXLKEAfgz5mcK4GwtQDUk0h5HNXejItU0XM3LFgISNJq1Yfj88OWWoGIlM
+q0uL+oRYq0EHC48oleN9qHGVWKsrdw7n3BpIWJUoXHkHS6KVivbaJsGOxPCv3/gVdlP5bFDHbGke
+oO4pcOLQof1xuZ7TL3pQa/hQJ2w0ZMHpfEhg0NVgj9dmslIczTKuLfVBvZzRTZmzV6IGH/n4RwFw
+a/lep9PnwgVpiw7WCvJcxXWKVxi8KvPX5tBUkSNfe1uySU6qmpHTsOSzB1Il+kw94Ohc9LhchQ8r
+W9nK/nT2QHgKTmQInoxwnC0IlwQhYBX6GSUX6T3yHQBsHRfcUn2GYqla3HIJh4pZmE64+Zqs1qPy
+mKNDcdV62zmXOgJvtprIOT04xipM1joRvlJmLU4nGF2NfX+TWHncak2G2dqcQ5SNH5yrTeczGCwl
+xvIBLKXDFY79kL9GpWNKTyecZbJDOVUIm/LzdFfGdvdgn9ESPtwyqHYJB4MTXv2KuJS/ffNFFso6
+XS2Xd1ODhg/Gy9l5Vna5TuepJS0j9axkvkz8BZK1bV3e4EQTsTMvItSe/7La5+zGLQA2H2tRvylh
+WuFL4vf2r7/Im8fajJZY+sr70Gz3hHwRqKzLQlGoCqGgmGc4oQqWmBlVKhcXhDBVhuq76YRMORGX
+TVKTvDoXw5mHLh2tDO1NpjRbqlAd+ASKN0jVAykdj6CpVaBmwmAgc+TWwV1m6ZI3MmBfPZJI3cqL
+7U0WrnSoZskt8o6Mz8yPaV8Sj6xfybw6S8HRKtnYlhxox2tQlLhaaTCNgETp0bKhICV/94XfIFV9
+kjov6T+kXA74rG/LHLq0tkl0WdGwihhfzKdoUYMbt0750p54FXvTKXm95JK08B49BbMku/wg7dmP
+PG1//Zc+RXmSM2lKrP6bn/o3fPZTPw/AYlawbC+YLioqR328XLP7fkWtJbZqkVEspbxj91xMxKsq
+PA1NllnqvCgpllTuNbRUkSoIAkZDmZhlafCXC5WqVP3G73yJcqiTZx3mS2jv4RGogtCX3nqBX/i5
+3wZge0MmT0jC/qGEF/MKHn9YsvrdbheUkOTugXYRjs9oau/EtesbJJ5Mjl4rpNQXNi3mvPm6vKSf
++50vAPDiV99koZ18F7qX+cwLv/+unsG9ZjAa+/tJk0T3jkYzJFYBXa3eYqcVC425ncpgtBowz0sS
+bXFf63T4D//LHwLgua3nZMzDlD98TcKA6eWaeii5lH/+L/8Fa6G8eHHlUmjH63LBbns17a4CgUxF
+NtHW+CSmp12sW52ahipRNX1ZHGZ+xtmxVgPSBU4ufx/4BUdHsvjOC+f8Pm8/LaFBox1SnOiYxg5G
+X9LZ0QGLVEBwYaEVmWxOUcmYtvo92i0Z/4V+myjUylbtkuji5CrpS57N8TWPEicNliKi88WMWoVx
+PD8h0wVwMNb2dM+joR2a3e0mQ082Km7XZFMZd+uJHrVK3n/iL/2NF6y1onr8J9gqfFjZylZ2jz0Q
+4UO1yBl9+TbtR8E7EBfo4HO/Qz1V8I/ncKb1fyedM9Nkaq1u+3xRnVO1F8aecyHMcotfaNLNs4Sa
+BVtoyFDk5ZIHBMdxyNXfdTNLre5eN4oplSa+Uk9j8cY+LdUqdHNwh+L622ZAdqb17TsLjg9vAbC9
+IRWHzsUOZU91IGcBW5dkN+o2O5RKauIur+HSNhuqL9lea9BSyvkkiXH0s7MaGjrWsXIm7h0MuK0N
+N9PhH4W3LmsAX69DxpzTpZO7lEuK8DKh1gajYqKJzfmMucqex55Pod2ORVowVpf/Uidi+oaMa3xb
+KMEmUcalNfnsm0GHxUI8hdB6LFT5ubZ9dtoa0mjy1I8dklxCotBfEOqzbLYd2ouGHmNKrF2O4UX1
+Ys5CUm18CquYPNR47MzgaBJ7niU0trTqcFfBW95dBpnsujtbCakn3ZfNVher3BCpAtaqLGatL2NO
+2iEX1uV61hpNokCO2/AbJCqeE4dy3Kps4WlI6wRvu/uzmUutcaF1PRZKyrKYKXgrbBC2lWQnCKnP
+xIM4CRM2lP+sjiLsQkLSd2srT2FlK1vZPfZAeAo2r1m8NSFpH3P3X/4WAHcOjrnQlh32uIhxp8KR
+nzsOueYMSk04VfOMaln/NoZAtRCCssZdCoLaAEfVmP10mYRxsLosOkGAp/kFx3HwtEHHdy2Zourm
+Cqz2Zw5+JatyPc+oVQoN/xFevCW//5mf/dfkqtrSuSK5g47XJlaW6ORCREcJOj1iqlzLl9uyw8X9
+DboqXBtECxwtkQatGKPj8KYnZEoJdumSlNjazVukp7KDnc7fuUP8U+A/+5Mew9tmAEVFVr5HFWuL
+81pJtlDPS9mW5mVGWakgbGVxtfW7rmuM3s86mpLuqUjMQoVboydpXJf8SvzqGSdasswyh4XmjLqt
+24wV65Eor8JmOyIvlpBvQ68l98ipDSbS8l3QpBUqZZnW9sv5hPGSusx6kKmy83yOwgXwI4+Zjqs4
+vSXXE4CTimdSbUwxe3LNtpGg04ijSpmSgiM6inVpJTHNSEmIqWlr+bydJDRjTZCrBxoELYziNIrF
+gkKvLQh71EpjV1Ylvt7PvKX0b5GDp3R0buDScIX346RIz5Ww7e6cwpPPv1t7IBYFxy1p9U659btv
+8PJbyjhbwllXJuPJ/A4NdWfzKqNQMPd5B13gnGscRo4n+nlAhHtes3d9w1mqrLualPRde658XFCe
+g20mNsMoLfswrxgqgKasNLl4MaRWirVy7lNYpY0bH/HZiWDOg27OZkuSUo2L8rDCqqCrSke2rkl0
+QudeQFcnSqAvxFrvcRwFWYVuSTVXyjfWqDRr7zqWcEcm5FbxDACPfnTOVz/zWWCpNrV8xF9vQXBA
+cfsEAZ4ussnFTZp6vq2NNs5MqwiqxXjqHbKYLN39mmqJk/Eh1pDnbGY5OpBk3l11Tp/yEqbHinWY
+DYi7OtFtQVOBRRtxwhwJK4yGfsNBST9RGr61gkBhxbFvznkQ+0lBQ+dAqbwRjVaH2VQW7OFwyltK
+cW8XGYF2hzpOQr2QZ+3pQEZJBrUoM7n7fUJN/ff6HXI9R6srY9pqbtDvyPn8ZnhelQpb4Tloyw09
+fB1f1JTr9RxwWfbrBARatXCmE5akHFmdgW4oTZ33CQGqDEAQhkx6cq8CA6Mz2VyqyYjWtpaB3qWt
+woeVrWxl99gD4SnU5MzrO/zyr36WO56sdlF3nXVtcir8mrKpKyaL87ChrW7t1a0OmhfEFJbxVHau
+pNeimCmrzqJgV0U0rCLfesZFiZIY5c4SrIbJS6JIdo+zaU1ttCuzVAbjrstoX1zVuXmZfW3snE4t
+1aEkGp//jue5viXJv48+Jru4NxujwEvK3CHuaaIpq0iWoYRiG7xGiyVljuNZKiUrNU5FqdwKfquJ
+zaTxx9uRHez5Dz/M3QPZ2V587YCDXe295z8Afv5rP4QQHKU8C3oRLW26euTRkMe0Nn/58hrT3ULv
+hdyf3b2AwUA8sIPB2dLXIHRDrnS0AcmPyV1NpClQof/wiH/7RbmH/UcfoeWJN1XNF3hWdzZ3Rl8T
+r13kOXYTiOySy6HEG8uOH/YbtBQB628EtBR67SoMup6MaDVk3oz3drm1p6VfH3qqPN6tJxyqt5iF
+KhyULdjsizd253iKpyjM9MxbqolCJbt8M87oqDvfCypiFcNxfJdaPVYnMASKr/F0Mhjrn8sb+rWH
+q7qTdeVh1TstFvZcgbvblmvwfZdjlYczWUpLr33zwpSvvqUQ6h2HQiHP79YeiEVhepbym596mdcm
+RySqkNRrhswbMuD5nkOosVXHC4ibMsjtjrwoH/7QFXraLpxnluO5TLwgalCcyEt6a7xgvoR+OnLz
+WrbG6MJCXpAv9WNLn0KFNzw/w9OsvaMx3WxcsjAqWV48wjQVDd1ZldNTbMHDjz7HE1dkovdVfMaN
+QnzNOOPVeL7y/ZkKN5RFwaj4qHFCwbbKN3EizZZXOW6gsXoOZlNWQ0+FSR7eaLP9lOQXDhYGXn5N
+j/HpP/7mK9bfBM45FiAxa2xflTGvr3W5dEX6TrYbPSaR0pEpCKnfbHN2LIvUWvuYTLPlba+mF8nk
+rZuwc12g0tXtWwBMj7cgkWN1GpcY1rKyVkVJpZ2t/iJgXZ91kelxk5JESWbCkeF0yYzcXsBEXt7C
+idHbyNpTCjzLoFICnJPhmOmSEbkIKEZyvEH7AvUyZNMqSpov2D1Syjp3iltK6DIbTQkVWBUpHiPx
+OrT62iUbhwT60sf4hMmSUj7Gakv8EgxnQrA6J3EzHMU9BB2oM82POSmu6oX6sRK9uAEdXWwCExA1
+dH6Xl9jaEAyF429xNl2RrKxsZSv7U9g3KkX/94EfBHLgdeA/tVZ8OmPMJ4EfBSrgb1prf/nrnSMt
+Ml7bv4U1Lp2ukptYl7N9Wc3TfAqa1V7vN2gpoemVS/L/i/0WSb2EK3tcUvLN4/GYXRUvSaKSLd0J
+h6UyEZcWV3EMPT9kvFA6K2oyDVF8J8LVersm/bGLBcaRXfBwfoPdffEa8nZMHMuu8/h2i/Vl9lm9
+HMcLcVSv0rEOjjYxGRNjNFFqNOuPAyzdaFNjKj2552NQr8Kd4qvGQ9VZ1ugdeorZ2PAi4Ire5Rki
+7XmvLeXgO92Yi9oBeHEr5NLDkhDtriWs67mbvkvoiNdDKLtVict6T869vRYzztS7Gbv4TdXZuHiR
+9gXZ/b74muxDt27t0e7L+RbZLjdvpzpUS6weUsODmSbXlnwLjgNTRZDOTJNsKrvx8NhlpNDzrm9o
+JrJrTu4q58bJgDdUqOUodTg+U5i3V+Fqo5g9Snn0oniqY6Tbc6+sSTTJ7ScRmSvjroi5oEQzLSWF
+2WjGdLXhLSKgqcnqMDQErqJlCbHqTWVKyurVAU5DO8lyznEoJndxtWnOtTEk6p4pUMfELk0lv7V1
+ARoqmzChraHgYDHGUcGgd2vfqBT9rwCftNaWxpi/B3wS+K+NMR8CPgE8BewAv2qMecxa+3X8F0tN
+ziNb2zhGHtaXT444nCjHYVnz0XXtdus1aKjL370kE/TyxibFROJTz1uXTC3QqyuKztLtdnE1Lm2m
+4qoNpnOW9ImVqTFGQSFFRu4uKdMtvorJ1krektWWUGPHRmE5G4mrdrxocP3KE3Jt3SZWwTK5akn6
+jRh3yXycznFU+5DAgJZRqZdyTI1zcVxjDFa1K7E+KGORcX3cplZX1M1c+A6+Zur76wFv6/3+ERly
+zYZHGsI8+WcvEukLu30horNQ/ct4g96GfKbrNc8Vtzx1d6t4gpmr0Gq7S7mQv48nA2a13KNuu8Pl
+LVlEb8USzhzOz2iVStRyOONY+yc8x3CmcVwyhf6aPMtlfD7Pa3x9To4HUUcrMV6fVqwLfGSIm/JS
+VEZ7O6IFFzva1t7M2Joof6STkWX6sgVTdO3hoYagf06GXyVf5pKqAOdUWamzDO8hWUDaKgHfCKLz
+dnAvNoQ6T5M4xtF+lNq3FEvG6CVrUjrHKJGL6wUYZaiuC4vRblwvauIo8U+ueQabZ+cKWX5UMdMN
+cMGEUqsZ/uiQhf/eSpLfkBS9tfb/sXap7cXnEc1IECn6n7XWZtbaNxGlqG97T1e0spWt7AO1b0ai
+8a8B/0p/vogsEktbStH/iea6hm4nJCgtizXZPadvndFS1940DVvX1Cu40qEzkpW211925DXJVfbb
+LYtzGjR/rYuvYUfXKThUspQzdS3bUcXBseygi7lIlgHgWUqVd0saLSJ1sUcq8xYEHgNl4j2xE3aV
+jmx0csp3XJMknx8HpJocC424sF7pc9466HTOPQHqXEQOAKtwbuNUUCpQyLXiIYDUvpeIKxyMJ7uf
+rwm5rV6f7TW53tNhH1SVmLoBqoINb2s7hlZ2n+BNw4Ye98NRn9a27DTrnS5RJde21jOUqpTtLRO7
+0z6mKfezmiaUfT2e06Wl4ViYDsl3VcovkM9GxRlnh6qMvJEwHapcXpnhKSmI37D4c9n91tWD6pQW
+T5uyLsQh0yOVE9wes7aQHXvjcotESVmy5lLPs4nCGPhwf4arbNYnpwUv67lPpycMHAHJ2bYCjHDJ
+FEw0SGtC5WeoFimNSDwFr1AocrygVI5HN0sxodYnvPwc02COLYXSqjUbsT6LCKOZW+MaXH3Wxkkw
+Om5bu1QagniJNmAtUqyGmLVxMCqL6ByPmCizc9UzLObvY/XBGPOTQAn89Dfw3R8DfgyEP/HNw5St
+jZpMsUvJvOSCqvs0/JDHL14D4OFLW9h1mejNpiwgTlXheiqAUnpY1Z0MJwFWnZysdmhpDGhVL7Dl
+ulQNeQAj32OhoKZGPeOsWBKrgNHcQKxx2vHJgJmVUt98N2RyR1+8Rkk/kTXQq7v4ysLjGpnYXpjg
+KJGojZy3GZJci9EyFP6ytuqAvrCU/ttPqkaYXgFqF6NqWa6vzD2dBnFTrmFR3Hyb8ZQZbx+kPMcp
+lboQzodzwnWplgR1kwuhHLefxLiRgmXqBGv0i9ov4Hvlebdq2YXxQL43PDjibCATfdKrONmTStHu
+a1JluLm7oGgIOrAsH8bpiJufFW+cq13FNmJLf+7pYrnWaHBR270bVZvDruRweskWfkdFftKMabGU
+h1c+xDIkXywrEYbtrox1OjpgomW9oS04mCxDCb2ebMhgWc1yKhYjFQBuOpS6aVUtuRfJPGJtW8Yf
+hzHajIubRThK128C6REBiEqtRAVvh2NOneMo16LxUioVgHH9OeZUF1TVHc2MC66Oc+rhq2LV4STF
+hLoRDTvvqGK9O/uGFwVjzF9FEpB/3r7df/2upeittT8F/BTAeiP54Pu3V7aylQHf4KJgjPle4CeA
+P2utfWcG69PA/2aM+YdIovE68Htf73h1bZmkJWtVg6lSpTmJZfuC6Eq2nZqp6iPOB3NCpU2rzFJG
+3cdfUnOnKblKo4/S26SqDTgrK5IlhblKbZ3kLjMVS2kEPq7SfDm+g9V0S1ZVBJUcr6megls55Kkc
+a1SNOVGex8s7DxGpfmIx3SM9ldXaV5CS7zp42tVoSgeUps1UAWjtesnObllgK13hqxI0FDGuj1l2
+LRpAa+WoS21yn1Othtx6eR8iSXyy+AngP2b5RaNde76CdDpbPtNc7vEJC9Ycca/DRUZbKyKpm9Hu
+aM1ela+rqWW6L9cwbtzl9RduAfDC0YJZJuO/OItp9+UelQ3llkg6oH0UrSzChhIevlz/Hm0F91y8
+EBBpAjYqxfdvtQ2fPxPI9I07B6CVpMcezQiUP3I7yQhV0/HKJSVAmae4S6Ra2qfT0+s4y4ibMpYw
+DfDUe0tdwTTsZwXpEkTnWmrdgRvENAKZDy29Xt+v8LRaEngujvY2zOsF+cmy47dJ1VKVbuWFCCcV
+lXq3JTm5JsRd45wnGmtbUqHUe6n2/BQBpQrgUJWkqXyvzjxqJfMZ5rtMy/cGc/5Gpeg/iQTHv2IE
+7fd5a+3fsNa+bIz534GvIGHFj3/9ysPKVrayB8m+USn6f/YnfP7vAH/nvVyEYyyxWzApUmyi8X4e
+Q1tW0uO7AyaV7A6zo8m5UnK3IbtZs+kQKfdCHvjUynITNhymKjabuYaDYznGeKoyb2XOPFKOBFtR
+a9LGDTLiSJt8nAItFROGS+JWS0ubteojh4YyNq0/1IVI4vmKYw4HNwAYOYoIzGo6S4GXyuC6srO5
+YQhacqxVaswaB1trAstJKWayIxivgb/MI0QtKFVPQBu06iym05JxbO44sNjTu/zDvDOnYHTXbGoy
+7MLlLZx9Fc6xBa+9qGxEH3HZzOV83bUYMxCvx1P16cPbdzkdax1/ELK5Jjvz9XjO0Zl8JqsN2225
+B8GaPCe/E/KqMlE/823rtHdEhu+3P/9zWO389NodfN2xx5k8j4O9mq+qWvOkqKkVyXnwxjFxLWO9
+3PN5uifIw+1cvM2rO12OVULu1D1lOlfPy3hoqokqrdhVCPnOUDsjKc8T0IUtsdoIVpo5UST3oqmJ
+v7Dj4DiKwDQlSojNfLYgV0/P7U3ozBTe7olH13QCApXQI2jhenKMvHKg0DlnDKnmF/LJMilbUKjn
+GQUOZ8u5bkpCxfLEsyFF9M3HKdx3sw6UicPB4YStS6rXWDgUd+QBnaRnhKqZWIYR3rG4fn84kxp8
+FVk+vCkP8XInpKFuvhN45MrD/eYrA15XGGypYJPECXGdZdLOI9GXe8OJSZW3bz1pEKr7f6TSyFE7
+4viGPMSYnEo76pr7DtHT8lJn+YLdO5okOnkZgDtewNWRJLguNDbp7Si9tK0wmWpani0nRMpU1a2y
+esZsrN15622SnrjardqeK2M5sWb9d6Zce1Ou97nWx6D5q3KO6bfxdiTnEnaUfl1hsuW0Zi3Stud0
+xpHyJHbyHi1HNSZHOUeFLBZvfUVwBTfu7BNoOLMdJYxjeRkrHzY0OXgQ1Vx+RPpANhQ+/e3585y8
+9hkAHv3BH8ZTMNjf/W9dnlcZ+ecu9Dg9ksldlyq8Up1yuSX38LG1JkYxBr87GzKeSeiW2wLHV2Xn
+SubQ3p0Fbx3LovCVm7cYzOR707JkoAtOnqcYDcMWtfZaeDEosczhvCQPllWunL52R+r+QZ1NmSpu
+vD4dkSnewk0CfAV9hWnFTCn6sz1lqO76dLVi5nQyHP3ecDgjG8s4UmdBpTyXnbYSvYQeY1W6qiyg
++KdgPGQ21CToVsJsmTx9l7aCOa9sZSu7xx4IT6EsLYPTlMCtWaglKq0SAAAe7ElEQVR76lOw0I7J
+clowUfe6GZRMtGx3NJXdc383JT2Rz9qrGzx1WSC6FCm+lsUaUUmgbZDTSlzmPEuZKqSxExsi3fG3
+HIeJIug2mwmFos26Wq+fnY3pqBtc3LpCcyY7bBTk1NrZeHJjwLGiLCPVf9hpXefktvwcb9e0duQ6
+PdOgGqvrPhCXcjy5za1DOced3TtUCpl96KmEp6OPy/XXJbS0k3JZ0qwNayrzduHJPciUwpmbLB+3
+MQW+ll/pL0O0IXvKyTLLp2hOlt2jAc9uyc58eMHl8tWrAFSK7hwNF7ylHZOjxYDhQhiKm55HU3fd
+Z57apKFeyFYiODd/w6Hdl5Cht3aJpdhk4gTsbEpYeKW1TjAXtOhirp5i4hMUcnHlac1d5R5oFjWL
+ZfaqSHjlUBOhLSkLx1WXVMl4F56DE4tHmqcOteo+pNYlMcvOReXnKHOGit9wXQl15R56aH8cqSvH
+bdcRjiary9ih0jlbjEKGSjeXhpaNNU1GKoNz4nkstCQZlzX+kmB4mlNpeHs2rymOtFv3QLy047Ue
+vY2l0EYDo+KViyKm8uXcdpjgma9Hv3evPRCLQm0hKyEJPVz1xbKswgnk5d2fDdibykM+67ZJVI/x
+zWN5ALNZeg74eGJjk/Vr8iJkNwd8eS4u5f7QUOpLPS1kMRnV9XlGuolLo7WEl3p0FnKz58Wc8ZGC
+cAKZSK6FSHMHxfpd7p6KS70+/xCTvVsAfPbzv8z/9QcSYlx8SnIHa8WAC46M70q/ww89JC3VYVQw
+VxGZO6/LS3UahXzxdZlIL/zOl7h9qkItz+7w7c+Ky/g9z38LW8t4ti/XVjoRpq1di60awo/JTS6u
+Af9A77hDoqQulxO5l50A5l25F8PjmEUuL9t6t8+bpxrSlDHToTIJn2gV2Q253JDFrR2kpLUyHPs+
+m6pXeXVniyuXlBilrVqaawFlIXkG0yrJz97GIVzZks/YIKJQ5uojfTlOhvDSQu5xSptlA+Bms0dP
+IdtOA8608jMZqy5lp2SowilOvMUVDZ9Oj3fZXyo25ZZEwW5WN4gjKtIl7Nq4eAr6wvHPQzdf1cSK
+eoFxFYRkfEYTWThfeuMNXjuWC51mPhc3BAPx9EV5BlemEU8+LCxjXc+VPgYg7MWMNC/xxit3+OLN
+LwNwMJLnf3Wrz7Mflvu62e/gacXBqVuE2u15NDtmsWzaeZe2Ch9WtrKV3WMPhKfgGmgHsNFtn1OQ
+HRZzNrRPfeZArJJZ/Z111pV/71STLLY+oNIdPwwjJseaLHJhsEwCxRCrC76lXX/tumSzL7vL1lqT
+Na2J+6bAqcSNf2lvzkx3iv7mMovrEKvAy+6NMa/eEXcuHt/khx/+9wH4lvo1XnNEo3BrR7QOttpN
+FvuCAr81O2W+kItrbXWp+3K+eU+79/wL9K7Kbr1VL4g1K/7wU49z8XGRGGtd7J8nKCulJcvzDKM0
+aG4bmH9Zr/nToI1djlMRaMfo1a54MRvrCUencu6r/S5jVxJ/zz1ykaZqL3Qyny+9dQuASGXPN9cD
+ttbFa0pnHs9flPDgrcUpz6mL/mj/Ibyxsj+3NUaZrONuyDWTQq1VhrVmyPFsSXAywGvJz7Xyjn21
+zLmp7MleY8LjHfFS1q88zrqi+zZxOYnEYxnMNDFo4FCVvZvrWxSuhqlnp+c6IcY1ONop2tR7uKhU
+eQXw3AirMO+8LMlL+UyojU0VBn/5SoUxvlZRyjog0ObSTdvm6rUP671TOsHFjHyhOp7zddyk0tti
+WWjjGt0eaw+JZzVTD7nRjhkrfiOZLWi42rjlT/Dn4i1Hi5T8nMX73dkDsSgY1+A2QrYbfbyeTNyb
+kxkXu/Lg+s9coKNw3e5Oj4a+TE2l2PbWL7HVlRf+sVaH+khc3NL1aCcqQOqVtLYUXqqMQFntESsj
+0NZ6l0Q764p8hHMsD2nPXRBriW97CcXNp6Snctw7wwF76qo2P3+DzR+Ryb/z3PcwGco1XdoWkGfk
+ekzjJwF48819fHXUzKzEN9rb0ZEFJEo8eq6M7889/RdoKrins7FOoLG6H9USewHWyjWYskWgEO12
+/DimLy+HOX2WuhYyGKxDoGHM4lBCFK8ZcV2BSVeiDkYFZa50Clo67lsvvcpGIfH39pa4ra2tJr6O
+f7AxpFDCldKbESmXZmkOKGayiGRnMo7IWDzkGG6ri1WB3Sc6EeFQXnrnQkg7kRLn8xeVkcvCs5dl
+EWpHbbYvidvdqGsC7YiMwglv3ZBnfGcuIVzux3TXZG49crlBTxfFS83rvLknpcy8rNnSTss1DasW
+ucOpSspnZclMWbhmkwJPId+Zwo9NtMCoYG8nS1lXQp3v/9aPcFuh0u14jQ0N9bQIxnyYk2s15LAe
+0mopHD+I2NDKjt3p8MQjz8t9Hkm4uukkZKn8vX/RR/FPOLv73NWybR4a3qtC1Cp8WNnKVnaPPRCe
+QllZzkY56VZFU1e+hxtNHtHdqNFeo+crf0HXp9qTnenSuoJc3CbhRHaw2ckM5UchGxo8ZXDuxAmX
+L8iO3epLNt3rWEK0Uy0McM7EVXSTJpOu7DRPr0+YKHFGW/n5dl95iZ2HZXcYHsww2oS/GBWgXXmR
+1+djz8kulmRSo3fjNmNlF442OzS9ZeOLIXA14bclyaI43KKzKeMPvD61Iqh8z8NRTgbKGr38c4Xu
+qD09F7WJ2jPsVKoFth5xLrvNlCUNcLqjVPZ+k3UVn+l3WnTasgtueFsEKvbiHeSkhWIncuVYsDGm
+KzvflWYIgdzbq17BfCxj6Xk9uL0kl9HMWR7BNQUCTUrKSDtCrY915Tl05k16HdnlGiqEuX59jXhT
+OlGLvMQqBHuRDmlPlP7N8chbslNGCnl/axLTUFGU+d2KJ59Q8pK8xfM9GcuN4ZhZofLxyvO56S04
+Vdk1g6WoFP9QF2QKJnIUQJWYmEYgoZaXRjRVbjCJ+wSheAKtzhZRLJ/3Kw1h4gWFdgt43oK41u5K
+39CIJTxa29jAV1bIYVNp2VJDvuSSLGZ42hF74jaxoSSuO1lEEL+3ROMDsShUVc3pLOXoZMzOk/KA
+doI+/R1Bo22Ec4yWwKIiYaHEGt1cXpqaklSBKwub01Fq9Xq6z2DJo+d5BLG6Yorxb7lbeEqhbZ2C
+XEks0jSl0sUpMQ5Lv6yrykqvfemQ0z15sCevvizlCIBeSHEsoYu51KKlakK+knu4tJirLmF/UBF+
+m4zPwVAvNSUUreb3NnGsZuG9DFfZc4xbg2bkjWexuWpYaA8ApYcx4u462QDMM3qXP4HQXUBtDQPt
+1xilSnEf1EythCvTKqSrRC5nzJgdy4L7m4d73NUXaxJLDmQxiOkqVXnf6zHWnojiuCRHFsNGfpmi
+q58/le8nV2ucM1XWaji4iUx+13OYK4nI/nSGK1EDNpRnuna1g1NpZ+T6GngSMpSLLqPyFgBHkxF3
+FKhkYln04lbFXPMgPgmOvrAXH+vyXdU1OcaNO5wqXX2oosLjICRX3hu3LIi09XFBQGGXoCB56eZZ
+RjFYhm4FXQXRGbekm8j9TAIHFNSVzrWDt+BcZ8MuAGWkinNDsq7MU1503h8TOhI+2Spnnsk489kZ
+qeYU0jplpp2fc2YYVbh6t7YKH1a2spXdYw+Ep2Cx1BQMqxpfFXkvNrPzvnHPSYjbcqnlPCNXDPte
+JruSW8xw57L7D7IFCtWn8OfMlx5E0OR4qAka7Z0w1Ryrve35fEyuu6B1DJXSl02A9ExZglWYZG5f
+4cZQ/PYsz9nsyco+n815+Vh24O+62MaoULhJxWNIy7ukruyO7lom/OIgZITqXtal0pfnBkdh1w41
+dSXXY6smRtmOramoc7nmSkVarKmpl82VpoT0N/Qu/zyck5JPKAq5n7l2iaYY9s7kehPToaN9HnHz
+lFePbwPwWjZgV+HW3kSx+mGKr4Qkg3mX9Z7s+CNzyGwoYyqmIcljEo4ZFVupJmekKsQTxAGewoBj
+z2M4kwF0rcXTvgPHk+/NpzMiPcYiz2jsSFjhxAWVVo92R0ec1XKdY4UJ16lhrB6iTSwnJ3Kd19YM
+V1Tb8eFmH8eR+3lFq117ZkJLpQFS16WvEGvPFmTal2CVIKdBRCfWkKKumOnr5WbFub5p4Hqkeh0o
+FyN5QZ5pd601NBeqtF3nVBP1MqMQR8lcXEf7Y1KIfP1sK+ZY8STT4owlpaefOuTBe3vNV57Cyla2
+snvsAfEUILUwOJ1gFV5auRENTWq1d7rnXV9VMiPXhGCkyatws4+rMefspbvsjTQ2zg2VegJllTM8
+kV2gpVRc9aLAdSSu8ysolcygsiW7b0iZyqkg8SUm21WKtjfeusv+qeyqH/vux+krXHlwe8qLv/Ul
+AD761AZWa/3WyPWauiYxmuzzLp1zAVTTlFI1DFNN4CVujaNwV+MHGO3gND7YJb7WzrBLyrpA5dzm
+sNAu0KMzA4GMw+SPYa1qQFhDrfqHI2UmOhl22VKsRzfI8BR2TF7SVU2K73jocVQKkmeekKTteqfP
+ZqxeUznBLiHfRzlrSqC7Z36D21+SY1zoKuv0ekl6JqI10YeexnUVQbnRoFTk4aiYc3Am+9bjPdm5
+nXGBr2XKZrRNMRTPakHCQCHmNWBUmi3UuN+PXVzFaWzaip42Uvn2Gk4oScnr3pyoLfc/UGqzeV7y
+Vq7ajr7Dw5fEUzizDq5RncpcXqNpMmGhuqP9IodczuF2Ohg97mIwplJ6v7SSv6eLCqss2KlTkCpp
+rnUauAvJxWSzJq6KWQQKu/eLBcyXHZczbLL0IHIK5fsI+o33/JY/EItCbS1ZUXJiZhzclkTd4x/q
+4KmARuQlONrpVWYTFgorTjTps9m6znj2OgBBENBZ0mnblPUrkuzqb/kkDflZYeG4BoxKkptFTXCq
+nXOzEVN9SEGQkEfadam9EydHt5kpVVpcX+EplZT/rYNf45W9V+UYb3wL7W3lD5yKa+gHMTaUaxjt
+HzBtfBWAcHudYqi0Wk1V/KkNZglcKQ3GW/Lk+phlZ2f5diWiUkHcmgGTkUykg+NjUKUnyzFLKXpL
+QaUw5kyhzZOZR1fr2ekFSBUePszh4E1NnrYMT288DsDVDakAdNolnnJNRmnJXHksszrlLRVfuXN7
+xk4sIKn4iiymk/WMckNCBlPEWIUxn7od0CSgX80x2u04XwKy4ibFXO7RaDGiVHBTXhf4hT4zC6EK
+0zYUu5DNLdmZhDzjxMGqEKyxMNZq1q6Z0dIwdcmDOR0MmWumseHEzBdy32zsM1fylUzjtWBhiBrK
+qp26dLTF3UwtmQoIzzzvnE/HLBeNWUamL7xnLaWygPuNiHRJ2kJKpRyjqfZqJL3knM3bmVXUqpS7
+8GMiJfCJ2l1q570FBKvwYWUrW9k99kB4CgaDWzssioybKvN29axF+IjS7zow3RUocWEzfKWoal5T
+mbNGSmNNfm75jxNq59/w7uscziQR0/bWSLRTb0na7AYWJktXbU6t1FbjdMbrY92t4jGRSsg58TLB
+F7Ek6s3qXRqenPtCuMYfvCqowV/6rT4/8OFvlXNf1TDCaZDnMr7d0VeYv6XyYJMRflPc+MQI4tHa
+AqsEKlQVy/XbhJYlQs3WM6y6trWGEdPBCW/sCbz6ZFQA36l3+QeBv35+x3NFgw5VZj2vClJNYB6f
++swL1Yo8O+Q1VYxuTGJGrng3N39PdqtrD/e41pYdv7sRMtBy4f7QcsdIWHVYJcwRZGU8lp17pzuh
+uyUlWa8Vg0KGp6VhQ2mXO75z7i0uO1UJa4Za1qSxOO+qnTKlVlRgEbjM1aXvRvL/0+keAxVfeSK5
+TnNHG9pGLq8vhV+mJVdV2MZXspRZZXEUQdryQw7n8vwmow7f/qyEEkv9yIgMX70mrwmpltGP6hRX
+uThCP8LTEmagnkJqUzLVbJiZCcqXw7bXpBtrk1cE04E8h1P9/wXzCNrMy3R4zFSfWdJfJ1RilXFa
+U9ZLhc93Zw/EouA6hl4jZJQV3FAI56OzJgsF0IQTSxFpxnloCS5LxjnUzLN1JmQHCrCJPYZojXlU
+MFVWoNqdkClcd02z5VmWgfe2rHumMfX+JKdSMM1bmYctlbtR47TLaxH5trITD8ckiQJPntnh+PMS
+435h92X+3HdKh2KkOYxsnDKeCzFMnTRxduT2D50O675iKzQOT2en1NUyX2AxpWb93S2MqgIRBNTa
+flulMs7RmcuB5jus1wT+jd7lX+BcOBKLNXIPalWYOjUL8n2lwD96C0dRUYPsgNxT7MHA5cU7+wDn
+CkwP7fb41ieEB3J91KcYyks1rDJuaDg2Oj7kme1rcg+Usjw7WYcduYZytjjX0BxlhosdVQVwYlJl
+eCLQl/TIkOs9Or07YaxQ8OOFJdD8UfdCTbNWUNOSGT/0aDhyvt6VPiMFpJ0u3uD3d+UYkeeBxv5j
+la3PnJKudqI6boORMnwtygrHUY5GFX1pegH5UhTYycmU+bnOM+pKvuf2HRoLhakrQ1gUeLQUYBT6
+m7hGFueo2cDXMMcah3JNrmmq3Ze5GRG6Mg9rr0Kbf/Ga3nmXq1OXhMEqfFjZylb2p7AHwlMwjsGP
+A+w0Y6QN5C+8fMpfel5c0XKjoCwV0pzMqJRW7e7vSKZ/984JJwrbdU9nVNrYUxXluVz92Y0ZLWV2
+di9rc1WjSzVU9WFbMVJRkNFgzkgJOQwueXdJ/KKM0a4lPFT6tGSP9LYcL4pCFiMJA174w4r9b5cd
+aH1N3OtX/+DzvPgFce17ziaPPSTuZzevwWh1Zax0blXKYCZJS3cjoqVcAU0vZZlnxMxBWaxR3YvB
+nTc4OlKIr78DZik3F7DkgQTOvZ/pUO7JV/7tjLa6tZ3EJVesAHHIhvJaXGx1OPZl595qypie7T3F
+I9vPyrkPDzk8kXt4NPWZ6q7pNlLevCPhQ64NQ/3sBOdFgeImlz5CqQwpX9q/SXZHru35rYwTvabW
+VJ5HYGYMRiopT8mLR3K+ydQl7up1Zj366ulFqme5eaGF72loUHu8dUMo8n7rjRvn3bZ/ZnuTSKnZ
+Xt2TuTcZ1fSVQ2GejqApz8ytHZraPOWrdmdVjSkUs+CMpuRaXTLGYaaNa2evnWC0irW5KcnXzfYm
+LVdRur0O7SsyF+anGemphCuFtbyxJ2OtlAgm7gbU2uFZ1wGJzjNqw0SfXxUYHPf/gxyNVFBNStb8
+gEONvYbzGZMjeVhx10GRr9RVyUjbTHdVJDT3N1jrSRy6fi0i7itQZpiQleLu7u2/SaiU4pnKK9q1
+IXWk2fJpwehAf57kOLoAbIche9ouGyoA5c54zEe2xO0eHMVMNVNfOWcs9ezv3B7z6lDO/aT9dwDw
+Oy0qxcmfUmB25XzJlQ3qVCb9caqce4cDZlZewK6zQ7mtxKxFjROqGGvpYBUezUhhspNTylwmxLwu
+eDtkqHm792HGOZe8/rkqc4a62CwyS0/ZrYp5zYFOMG9R8bF1qZ6017f1ejIGn/99OYYpSBwJ0d46
+HrBnZKFyFj47VlvNPXGBX37lhPCSPLP2a6+R9eV4x3eP8RVW3spbXFyXcOxEdSk7jRL1nplPK06W
+i2jmn7cq516LmcLCey1Z0LvBDpFyTR6e5Ny4LXmEO3eOSFTcN0k2ONbw9eRM72tdY/Q1qcua2UAW
+C+P2mGpFzGgI2rY+oQqvVHFCoItFXSf4UwXDDRfsDk71GJJzufh0D3cqL7TtOdRzKbk6rZwzBSTt
+73+Zo0NZRB95RMrBfmuT+UyOVWNxVSUtJcGESnbj9ajNCry0spWt7E9hD4SnYIwh8iJabcvkSFbG
+w7MZnzuRisOf2b5GobufYy2uNrZc+bAyP5/VOEpFdfKHc25oBSMoAkpPVn4il82WfM/ryFo4xjI7
+UNit53FTGXxv2ByjJYp9J2deyO7QVOLCqogJG7Ijxp0BLyr013CGMnNRpDW/8EufA+C7P/5RAK4/
+8xgPta/JOMZ3aW9KksjdCahfketY9yWcyS/AWl86HP2WpR7Lyl/WMUYb8Z2gQT2T309PxP05rqYU
+2hlK+BJ8TJN2n/u7wI/rHXcxqo5sFBOw3m8zPFMewawi7Mn3nltb40yP59Uxm89JdaSvMnVlcsZG
+V7yH7vUux6+Kd1SepWgDH8nVj9BRHIKv4czG1THmQ/J32ysolUdwls6ZGtXpNC5n2qDUUwGUqwQM
+1S0/MwscncK9jYREdSoXdYqnfBFTBZ41N2tmhcyX0fSYY22l9V2X/oZWYAg4ULDTsXZD4nq46iHu
+1zNyhVI3uzHOUntvKQUYVBSa+J1NF/jaEDWrMgr1zLrthIniGu4OZH5v7HW4qliYfrdHs62hZDHn
+SFW8h7bmsYfEs9h+RLpvqxKGCoke5yWBygnOC4eilrk+LQu85L295g/EouC60GpbstzF21QX6Kzg
+xVvycl+82qWpbnJSQVN7DaJMLt9rzZgcyPf2omNOtGXXS0qMTqYkjphqx1yoD6hYFEyWyL6i4KZO
+/t0czrQkdTwtibQv4VTJTteaIQMF6ezlObNSMfWhQU+H49a8qi/ZmzfFVX32iSeJr2i3493LkGj7
+3bSFe10eYrwQ1zGKXRyl66mDE7K5LG75IsOgi4kzp9Seh+Pbkr/Ij4bEsfYJzFrwirywmL8ORuGI
+xsFR8o5eQybgtetPcvMlibNTJrS0B+Xhpx7l9dduAXAwOOLTL0jJtavqXFG7wXd9m/z81MkFxopo
+zMuaUIlKLvcszzwtLm+AvIBXgpiLE/mdcR2s0pdjLIeZ9qCklp4yS80TFaONKox2s47Kmr4SpzTj
+NS60Fb0at4iXkvCVMnnt3+L4QM6xv3/AgSIh47UGuXIYvjE64PVD1apg2Xfjs5vLvBgsFtKtCIT5
+ENcuVbZkHlauQ7GsrlAynslLWmQ5rvYfdC5t4mkVZDKVZ3Zya8Kj12W+RScO4ZpsQrPDOQ3dDJ5e
+32H9SZkPnoaBZyc3z1u8i8CiUplMqimpzlXr1pgl9/u7tFX4sLKVreweM29rw36AF2HMMZL9OvmA
+LmF9de7Vuf9/cO6r1tqNr/ehB2JRADDGfNFa+62rc6/OvTr3B2ur8GFlK1vZPbZaFFa2spXdYw/S
+ovBTq3Ovzr069wdvD0xOYWUrW9mDYQ+Sp7Cyla3sAbAPfFEwxnyvMeZVY8xNY8zfvs/numyM+Ywx
+5ivGmJeNMX9Lf983xvyKMeY1/X/vPl6Da4z5A2PML+q/HzLGfEHH/6+MMe+te+W9nbtrjPk5Y8wr
+xpivGmM+/n6N3RjzX+g9f8kY8zPGmOh+jd0Y88+NMUfGmJfe8bs/dpxG7H/Qa3jRGPMt9+Hcf1/v
++YvGmP/TGNN9x98+qed+1RjzPX+ac3+z7ANdFIwxLvCPge8DPgT8ZWOW4Nf7YiXwX1lrPwR8DPhx
+Pd/fBn7NWnsd+DX99/2yvwV89R3//nvAf2etfRQ4A370Pp77HwH/t7X2CeAZvY77PnZjzEXgbwLf
+aq19GhFK+AT3b+z/C/C9f+R3X2uc3wdc1/9+DPgn9+HcvwI8ba39CHAD+CSAzr1PAE/pd/5HfSc+
+WLPWfmD/AR8Hfvkd//4k8Mn38fy/APx7wKvAtv5uG3j1Pp3vEjIh/13gF5EexRPA++Puxzf53B3g
+TTSP9I7f3/exAxeBO0Afgdb/IvA993PswDXgpa83TuCfAn/5j/vcN+vcf+RvPwT8tP58z3wHfhn4
++P14/u/lvw86fFhOlqXd1d/ddzPGXAOeA74AbFlr9/VPB8DWfTrtfw/8BCw7aVgDhtbaJUPC/Rz/
+Q8Ax8C80fPmfjTEN3oexW2t3gX8A3Ab2gRHwAu/f2OFrj/P9noN/jbfpsD6w+f8n2Qe9KHwgZoxp
+Av8a+M+tteN3/s3Kkv1NL8kYY34AOLLWvvDNPva7NA/4FuCfWGufQ2Dl94QK93HsPUSz7iFgByF2
++KMu9vtm92ucX8+MMT+JhLA//X6f+73YB70o7AKX3/HvS/q7+2bGGB9ZEH7aWvsp/fWhMWZb/74N
+HN2HU38H8BeNMbeAn0VCiH8EdI05Z8G4n+O/C9y11n5B//1zyCLxfoz9LwBvWmuPrbUF8Cnkfrxf
+Y4evPc73ZQ4aY/4q8APAX9FF6X0793u1D3pR+H3gumahAyTp8un7dTJjjAH+GfBVa+0/fMefPg38
+iP78I0iu4Ztq1tpPWmsvWWuvIeP8dWvtXwE+A/xH9/Pcev4D4I4x5nH91Z8HvsL7MHYkbPiYMSbR
+Z7A89/sydrWvNc5PA/+JViE+BozeEWZ8U8wY871I2PgXrVV56bfP/QljTGiMeQhJdv7eN/Pc35B9
+0EkN4PuRjOzrwE/e53N9J+I2vgj8of73/Uhs/2vAa8CvAv37fB3fDfyi/vwwMhFuAv8HEN7H8z4L
+fFHH//NA7/0aO/DfAK8ALwH/KxDer7EDP4PkLgrEQ/rRrzVOJNn7j3X+fRmpkHyzz30TyR0s59z/
+9I7P/6Se+1Xg++7nvHu3/60QjStb2crusQ86fFjZylb2gNlqUVjZylZ2j60WhZWtbGX32GpRWNnK
+VnaPrRaFla1sZffYalFY2cpWdo+tFoWVrWxl99hqUVjZylZ2j/2/2pBfRe05gTkAAAAASUVORK5C
+YII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.4493... Generator Loss: 0.9787
+Epoch 1/1... Discriminator Loss: 1.3226... Generator Loss: 0.8376
+Epoch 1/1... Discriminator Loss: 1.3657... Generator Loss: 0.7043
+Epoch 1/1... Discriminator Loss: 2.0819... Generator Loss: 0.2141
+Epoch 1/1... Discriminator Loss: 1.4158... Generator Loss: 0.5562
+Epoch 1/1... Discriminator Loss: 1.2893... Generator Loss: 0.7726
+Epoch 1/1... Discriminator Loss: 1.3175... Generator Loss: 0.7764
+Epoch 1/1... Discriminator Loss: 1.3040... Generator Loss: 0.9346
+Epoch 1/1... Discriminator Loss: 1.5212... Generator Loss: 0.6124
+Epoch 1/1... Discriminator Loss: 1.3328... Generator Loss: 1.0946
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsbNl53/dbe65dc535nDvf2317ZLNJkbTkKBIiO/BD
+LAGGEcQJAgUIoKcACZCHCHkJkCc/BclbYGSAAyQOAjiAHUS2RCeSaFEhm2STPc93PveMVafmPe+V
+h++r02wkipoiW24HtV5u3X2q9l57Dd/6f9P/M9Za1m3d1m3dVs35F92BdVu3dftytbVQWLd1W7fP
+tLVQWLd1W7fPtLVQWLd1W7fPtLVQWLd1W7fPtLVQWLd1W7fPtC9MKBhj/oYx5gNjzMfGmN/9op6z
+buu2br/YZr6IOAVjjAt8CPx14AnwA+DvWGvf/YU/bN3Wbd1+oe2LQgrfBD621t6z1ubA/wz81hf0
+rHVbt3X7BTbvC7rvAfD4p/7/BPjWn/Xljc1Ne+36NYyFuq4AyLOK89OnAEymM6pKrruugx8EANS1
+oJyyKHBdkW++51Er+qnKGqNizxgHY4z8rqrl77ZiBZQsFmPl78axWP3sOQ4GfY6V39XWYpC/u46D
+fsRzvct+WAu21ufov58BZdaC9scAK8S2+o759LZYwOq7WmNg9Rku38noi7rG4HqujoWL9eTvtqqp
+SxnDGovnyNS7ofwbeAGe/q4uckp9epokl/2vy4r6svvSB8d1sTo3dW1/6rqD0XepsZf9N47c17EQ
+tVoAdPp9GjqnZ8sMUp2/fImt5PtZOpVrVU5lq8uBcXS8DQ72snc/NS76Hs6nw0Zd15cDbbGfzosB
+ZzXq+nvXGFw3uHzeaj05riEPpR/lXO9VFbh+LF/I09UtsKZmNZuua/A8HwDP0zlzfMzqnXCo6lLu
+W1qsrjljXDCVdlPmqaqry/tWVYmjnas/7T5YqPUeWV6cW2u3+HPaFyUU/txmjPkd4HcArl69yh9/
+90/wLKSzCQD3Hoz57/+r/wyA//3b32E8l0XRbTa4ev0qAItlDsDZ6TGdpkzG3vYWSSXXJ+dzglAG
+ygta+J68bjpP5O/JBbkKiNJWOLVuptBSV/K512wQlgUAwzIFIMlzXFf+3o87VIEsip32gG5b+pGX
+kGby/cVsCUBRVKzWX11ZCKQ/flldLvS8kP4EgYuv3y1dyJa6UByHOtfFSE3gBPp92WBN12Ow1QFg
+e6tD2Ze7FPOU9EzGMHFy+o2BjOcNWSPXNg/Y2u4DsDx6zJmOxftvv8dEN2QynLPQtVsV0p+436QY
+zwGYLTIqK9dbrRau7tFFVVAkMhZBMwSgURie+5VfBuBf/9t/ixevXgPgv/7xfcwHco/Zw9cpZtKP
+ex/8IQAX4wdMs5nc2FridgSAZxpUyLzbyuL78rtAhV8QemSZ3jdZgo5zUZeUugYcxxDqhnVc+V0n
+DOl0rwBgipowlt3WbAY8vCXjcv49uW92cURn92vy3cN3UBlL6Sc4upG73YjB9r7Mz6ABQLu1R5DL
+vWzdYJyeyn3PSopCxrYR9Kk9eW/XtmVckzEg8z+aDYl9GdusBj9Q6VU4LCtZfx/ce/yQz9G+KKFw
+CFz9qf9f0WuXzVr794C/B/C1r3/Neo6hLgtyHclZ+YAkk83okl+ewJub21zbvw3AvY/fBMBgaTSa
+AFy/c5ezwyd63WdjbxeAbrvJxdNzAE6NTHw38KkyGbAsWWA9WWBh1+Kr1O04DcJNuXdjKcLk6N4T
+PFcmYPfaPp1OV+6XBcxqmdDpzKACmkCH2aUgV8keOIZyKe+X50t0D9LSTR55ltiRZ5S1xUbSn0Vt
+KYsMgASX5ur0c+VeTeMRlvKMsChxExUtaY2/vQHAQdMj8mRh+ZH83Q0CGvL6eNvXOT98B4BqYchy
+nQcvxOiG9F0RPO12kzKXxT0avU9ldTNt9egPegBcnB6S1CIsXV82blIZnt7/GID7T0+4desOAFeO
+urz3T/4bAN5rnvCtM7nfqSO/K9s+C/mIcT3aDXl26LpMVVbU1hIpIol0PbUaMX4sY7hs+0wnMobz
+JMV6sqlDz+A5egrr/PcbTeJYx3C6pMxXc5lR/nMZg/Tp9+WdHGh/8qcANJoxQSR9mBXVJRLIaof0
+RED04YX0vVU+plTEViQZy1wRZl5Q+nK9647xVNA1SlljUTPAzeTveeUQ96SfvSDE5opY+w38pSKd
+z4D3P7t9UULhB8AzxpibiDD4t4B/+8/6ssHgOi5lUTIeXgCweCNlNpTF2Ir6lKFIzN7WBnFHBidD
+JjYIAjY35OS7cfM6+1dEsnuhw85VkcqR6/H9730o1w8fAJCkE7rtHQAePjqhTGSHNaIlG305Nesi
+xBnIvTNvCMDORo9MT5SFG+Kk0g+7OOJwLp9La9F9SiOWze34MXFDPnvkjM9lFVsLcSyCZ6srf2+3
+WthabuBbA8hJ6+chh6crU5BL5Sik3JBNnqUF6VLGavg0Y+dbggTG8zm1I4ut4cds9+U5RVOeG7Rc
+6lSEXp6NOXx0DMBZNqJeyPUw8ikcGSOvof8WDn5D+lZQUSpGb3cjbl+XZz8IKmaJ7ORmLv+eL2fc
+fyTCe/wP/gdubwhSePu//J/44eifyesZ+KizKe/97HMAHHgDLuY/BCAOHO6+KAdEGcwpD2XsG92a
+XlMEYBgrimvtc+e2CKnl3Oetp7IWZh+P8K2g015rk4dLmZNirsK73wJHBNphMgad6/HCcnL4E7mf
+qi1ODQk6Z1lMTwWSSSqaen0nD6jm8n0FrvikzJt6IOUVkdXNX0McynU7WRLXsrmbVu4V9Qecd+T9
+8nFJM5A+x/0mIMixjEPSY5Win7N9IULBWlsaY/4D4PcBF/jvrLXvfBHPWrd1W7dfbPvCbArW2t8D
+fu/zft8BSpvw+Aciff/4R+/wQPHgxu4e/lgQhLEFjx480WfIb7uDNr0tOWny1NJVleHatW36GwJL
+n5wOSZ33ACjLBQBh1CHP5fNgEJPN5dQNOjG1qhKVnWPmI/mOGh+rdpdpSx4+Oj0hUCRwvJjiF6rv
+ly7WWVk55V5bm22ilXrglHh66vjbHTYbcn2rK6ik7TnUig5sbvGsnOjL3NDoCSo4G6bMSjkFikTu
+5ToRF6UghbLZYHMm71fWC/KhXI+6e6Rqg9htCgSuZy5n+RiA2eicQu0nbc+j7kj/6zogDuU6lUBu
+3zd4nsDgXqvBPJXrnUabZk/UqmcbPhcTQRuOr0bLj494ciga5dE77/H9f/QdeXb8A3xH3i+eFdjr
+gvoytQFsBi6hUTtK7HM1knnYu3mH3ZdkjHavXqel9prcqtoxTmhvCnpYzpfsbMg9vGsJcVfmaTqa
+cfVQoPkPP5E5dxKXiSNrLwwbnCeCKvyWoauW1ELXRYTDdmsbgEHnCi9ekTF+eHqfG2rb+eaghX93
+T95vIv11Bw6Ovp/dj7CPRKV1o5JY1eYqhvoTWVvOriAJZ+5yL5B5L954xELXnlnkRF35PKsKGuHK
+OvX52r8wQ+NPN2stZZ6zTEr+4IFAw+9/73WSVGwAUX+LQCf58OgEXcfcuCKDG3QiTCSLcVGec3NH
+BMHBnS0aRjZs1Ovw6IkIjgefvA6AOS/Y2JdBHzQ3qPuyaSYXKQs1ri0mM2JXFkLqyIM7TffSIJou
+ZjyRn+FZQ7JUw6UpabdlUex15N+b+1vEK09GnbPbkMV0cG2ftuqU3ZVdw7f4pXx3uZxwNpJNdTbN
+SdTwWZmceanGs6UuDiehTEUQLE1K70w27EUyudSNF/mEZiXPyaayaebzY8ZjEcJ5OmevIbDdNs8Z
+q3fB8Tw8T8bAqpcobgRsX5XvDvpNpjPZTPvXBuztKITPNphtyFzOpWvYOxnv3peNWcwnvPnu7wNw
+nC5JVBfPGy5VJv3f3ZRrZ5MJnivjcn27w6vffAaAF7Zvsb0vG7LVGuCqfWRyKhu63MsJC5mHYmMG
+iwOZsxcTHNW555sPqBdy70ctURXfPx0zGcsmneGTZLIuZrnDuZr4g4YIP6IIZ/NvANDbmmNaMt5/
+7bmYb9y5BUBz3yVuy/ps6poOGh2wMr+u38G+KILJIaJQo6MTdqmeOQOg0kMmmR7SHkkf/BdL/tk7
+92UMJ0v6oXqdXJdMbWGft63DnNdt3dbtM+1LgRQqWzEpprz98Zu88afiNUlNRlWtLMELJnoSTuZj
+glBOq5effQGA5u4OQ0QamiSg6ytEN/1LhGHqFoE3189y+oyTQ7wzkeZx1KeO5HmescRLkbQL11KN
+VeoO5FRNZy6VQsYyMZduxiqrKV2B11kGewO5961tMbi9cP0qNpFTx2ZL/D05YXf7e0RqJIoceQ/f
+hni1XFt2IKwF8Rg7oRrKM0auS89RI5+Kd1vkWF/91VXNeCQndOk7mEJgedhuYxXRTAI5idJphuup
+MWyjS6snY9y7tkNWK5wtfSa5npoTQSh7my122oLYko1tzidykja7fQJ1qRgXQNSiK/tq6Z/W/Fi9
+OpPhjKOhnMylm1DNFQm1m3T0xHZn0vfZ8gxP9catVpuXn/0qAFc7A5qxGBLdhkelXp7YUcOv5+Lq
+uvCLHlYNpU4ZkNXyjPQwwFgZz7ASVOEuhqSF9CfL0ksvUV2WlzEXVKoaRR639o8AuNO+w1c25Xd3
+r29x9Y6otPHV23i5rItgS+bUjTYxapS0sxy7LevTsQFVKu9tRzl5IPcrU0GpzDxMJO93Z2eXT54K
+khiPz5hOZZ5aponr/Wzb/EshFGxdkc0nJEczljOxelfG0ladNJ1YDk/f0y9XHLwieuZz3/zXAOhE
+Ho+nEujU7rTZ6d0EIKwcWgrL09rl7rb4kI+eE0h51DFMh5k+ryZbyHcdP6LuiwAIxn2qrsDqlVvp
+rMhJdRfmrqGqV4EuGVbdgYWtL33yV+88C8Bgo0MxlWc3nA4bm7KZWs0IJ1ltPHXdBQGOqgbBvMRs
+q7+62yTwpR9paXBUxTCh/DvJcnIFgBkFhXpJNnb2CQfqWm036et1qzi7G/bwfblHs98g9sX74gUB
+uUJbv2GYTmXxuuqFSBcFVj0gzbSJ15c5y21JHcr727SipUIvtLJx840LdmLx/JzEF+CJkOpYH2PV
+nea6lKq6oHp9VUGusP2lq3e4fkuEQqMyBI6MlzHuZeBU3D7Q300xlQrLhk+2kPvlo4T5scZh2Jwq
+WQVDSX/aXoteV639WU2mUuEimRKpvt/S+ICo6dI0Mm4vXO9zsCWCpTewtFt7On8HuHqoubFsfuP4
+oPYn69VgRYBSWFxHxrMqRhhVvVwNlqoii8llbvrdiJdu3QDgvZMpBunT0q2oyp/N+7BWH9Zt3dbt
+M+3LgRSspSxyZuUR47Gc3MZYvEqk3fnoQ9JMYxJ8jzCQU67f1+gyv8eOnp6bW1v0VAJbY/ACOR3b
+fslzXxcIF27/dbnvo2d4/Z+/BsA7H74Fidzvan+Aa+R3VfOY6VRPvLlC/8JSKszMc0OkAQme55Jr
+1JzrOmxFYvjqhRpdR42bycnc32jQ68nfo8jF6mnjGPm9U+eg/nPKiK7R06oJFkFKhTuh25JT7sGF
+elSaIQp+cBcz2mrkvLJ/FRtrVJ3fpd1VCKuBXAU+3YacYMv8KWi4bmVCBi2J9UjsgjiWUyfL5LQK
+GzVpIiimcpcUpUJ0QioNPjN1zSryrtJAqHRYY4uVN6RF6Olpaxesoqgy4xM2FK8v5HdFvSRWBPbS
+V3+dhgZ4+Z6LyVYhyhVodKPrry61oOFczo2rc2YqX8KegcXsMXas/W/KuG32MpYap2GimuVcLf8z
+wyokyNX7kkDbyhrpNw23bghqiBsVgZHT36HCDfWXpb6b60uQA2ACF3SNWDcHVXmclotVlca0FFUW
+CaYr825yh/2OrIu720+5t5T1Oa08jKren7d9KYRCmZeMnlxw/FFOVclCyeYFdSkvM5xOKNXa7RuP
+oK+bXmFmox+w1ZIF3WqABtURNDwcDXCKwgatLVEr+h3Z8Petz8Pua3pfy0LdbEt3RhjI88I8IPbl
+HiPdmK12SHIhzw5NgruKYbcenuYXuMbS3tVgE422bIYh0XXZbN3+gEA3qe83LiffKNKrklNsKlC7
+qk/JVwbkyCEK5R6DdsU4kx8Y3TRulhNroEzpW+KujFVuLuhYGaMkmzLLRVd1ClUHwjGpI79zqoJU
+3VvdbsIilP6P5imJvt/p2YmMRe2R5ytbjcGqS9L13MvIythr4msk46JSda3MqTX0d7fTorMtzzj5
+YEE3kD5nHhS6uI9HoquPFyl3NmWzHVzZQON4KKhxVRAUk4RSN32Raw5L4FCM5V7GqyhW7sRqzkTt
+K8uLBROVw9OFCNuTOudsIRuvcgLyRFTJpEgo1JrUKnVqBoa2Kxu31QrwdPM3WhE0df68kKrQfAwN
+waeqLlUwY3ysHgymdLGr0G3bplY1zqa6wF0LhQiQ4uKUSuc9jjw6KmTnZ1Myu1Yf1m3d1u3naF8O
+pFCVnEzPePvx95hrcg1ZySKdXf59FajUCCK2twR2o7A1jq/TctQfP7lg+fgRAL2tbaIDOR2DKsVU
+6omoFDqHBtuW03in0WaoyTxYFyeU08Mlwl8Z/IJVFqH47AF816WnIaomgSSQ73q+x+19iYvodOUZ
+DePSUo9Eq7UNrno1Zjnm05hXAKqkgaMvXTsNvKb8fVkmWIXgvoGWJu7E+u+EgsDRfAbHobzQJC5T
+4Sr68fYqTg8lUMm4EgiWDUtsQ589rTCRhAFH0Qa9m6I++HHA8kJ+V52pF6JcUihiM2kJGmfR6IQY
+7VPDzS9zIpqr3ILlkkKTqgIvpmHkhG3EHepVxqsfEqmXZ5JI36uiolarf1UahhoX0UjB76yMvAvG
+2r98epmCRlnKSTuanpCoB2dZjSgvKh2jhPlMUVamRsncxdWw41m+oNCcAuOBq+89z/QEH1p2X5Hn
+RiWUx7p+ywV+KMgsnWT4mgXpqHfCcT2sGjCdFAgVHTgVVaqG1kVBrQgCDYuwJz71UoPTrGWs6IYq
+w6pR0ikM/FT26Odpa6Swbuu2bp9pXwqkYCxEGWzUHUI1uCxNhXOZE+/gqAtsc3PAq8/9FQD8hhgO
+syyl4clJM0nmPLyvLjLzFLULceNgHycR8blKWsmzmkFXfnfav8FpfU/uUWa4uSCMfqeDqyfT6aFI
+/gp7aSzyrCXXCDPHXxBFojw6QYhVI57vyxEc+QN8NRq4poGjBrWqLsBT16K6GB2g0pTXKmtRqSHV
+CwOKpZzWHiFttVccHMgzhk8POTsTd9s8KQh76jotE9JAjYTnBk9TdQsdiwSfrVT6u2ykqu3DxobH
+N54RpEDQgi11h93SiM5pwrEmYF2cT7hYyAlcVxZbyXdniwzjyrvUGuad24q61qzFKqGhkYtxM2aa
+r3Tg8tP4lFxDvrFM1HX69jtv8JwniVLB3MVbqD1jYpg3Nd39XN/TzxmfSjxCki04fiBoclbldGqN
+Ij1oMVX9u1IXqlsYdnuaDu+koKgiTwyFnsBRKe8R+Q5eKMe43xmQCTigChwI5B5Bf4CDxiEo+jO5
+Q+0qCghdzCocMytwFAHXkYtTasxCqnaSzhyGcq3Ohzi6nX2nSbejtp8qoR6vyBU+X/tyCAVKjHtB
+4Y4oNB+gzOpL0CPIWpbpdm+Hw08kJuH0qUzslVGbr96SmIWDwTbzO/L3dLjg4exI7xdwdiqQeHEq
+C7oZuSxPFMJ1SrZSMWAt6gmBQtiG6zBWTO/muiHSglBzFVI3wHMV2uYVjVXoarPF6Egm+viaxPg3
+OjW1hheHjiXQvGcn9qnmGqa9lA09my6YnD8AYDpfYtX7ErUNRS1wdW4sjqtGzIYGEy36fHi0St+e
+cyuWDPZpMuNMIXXZSIna0s/+hizi/c1dNgYiIPNkwXghY+Q6Lq5C92t7bbw9JbjJJCBrkS5pHEmf
+zy9+zOETzZC3lq2BCFY/dPHUY+SqmjBfTHB0I5iswrHKs+AsWaqAvJgWkCkO1uxLzwOrxsPXP3yE
+05L3aOHSz+TeAQFGxyXalv5mT1LuPRFV6fj8nLmG0NvMZ9iUtdX+eMHIquBP9Zr1ONbDyZ8bjArR
+yEC6SoNXg2E/bnBLczX8boGvB8Q8dMgeyxi1mh4NJb7xKllvjmdw1NCMX4GuEYulzjWoKTAUqoLl
+GuZeFA2yrr5H6cOF3CMMDXammZuVJfZ+ttyHtfqwbuu2bp9pXwqkUBQ1Z8czhhclqXII5HV1GUZq
+XfeSJmtcFrz1rmRSjtTQeP2TDrcH1wFoNK7Rawmkeu/Dhzwdvw3A26//gMVCpOpoLJDy5ZvPUCh5
+h82CS36G8XjOUH3o+WZBrmpHrgbHvC6JMpHgsWMp1ChV1YatvpyO3/zaHZ57ScKwg1Sk9vFwTNdX
+Kq7a0myK+uM6IVkpfTp8IoazWT5mdCwnu6kruhty30bLIwzE0FqYY/JKsxnVr751O6B6S975fDKl
+qRmcJ4sx5UwQRtmc09uQE+3Z25Koc7CxQ2tDVbCLMz74UMZi+fSCT+oPpM+ez5VdceuiBtXZZMnp
+SNDB+HzC/FTG2OsGEKnxMGiTLkTlOawEgUzHIyaKfmbJkh1f7pcuSyhlvEK/4lgTtjIErfTCGDRC
+73B8SPyO9mO8oM7k2b/x8nNsf/2bACxcWQuvvf09Xntfku3SsqBSg2G73eJOX1BPa6sNI5nXUSlj
+v8xqOqrmzco2dSHvkScFgZKebHblGc+/uk+3LfEIyXlOpYl7W+EBi56sJ3fprUIu8NUIHHca+EqQ
+4nsxVrNjs+GCJJHQ5aefPObpPSFJSZayRjY2QoJS0MhwPmGWK69HYLCaPWktRNG/hGHORZlzfPaE
+84snZOofDzxzyTlna+eSzmo5m3B4JALiYi6+8myyz5FaYW/XBReJbLDN/Q5RX2Pmz0csVMm7fl2g
+6MbeAclUwqqd/FP+wCq3LGcCI8/HE7KZ6vuaU2AXFZk6yJtBQKxZe5NFRUsJObq2g5PL50eJQEfn
+bIm7o38vSqymg4dOmwJZbHEs72/dJs1rAi9n2TF1Krrl49EJ4YX0M6nnlxZ8T4ln0klOoWpOkhe4
+mdyvETTR7Gs2+htcbYlA2oplTKIip54qkUvQ4Pq+CItwvwS1vk/PU86MqGZhexUOXNLXzf+Va9fw
+as26zF0ayrZUzWaU45U9R//NLLkGBRUlLLSfpoZKVYUiB189Qp/SMhoKDSt/eDrCqcWzszi/z/mF
+bJbnn7nOzb70+ewjzQcYHqEsfexvdehGIgiarZAdZaSCnG5TYbxS3lVewNyuGI1SLjRMPZktGWgY
+98or1U0HoNR8i+GEjUgyOOOOxSlFTTs+G7J8JGPguUoc1N2+9PC0ooI61LX39CkfvSe5QKOHE54s
+xOYVh+LVSppzttVrkUwtua6FpHKYqfrg1g6u97PZFNbqw7qt27p9pn0pkIK1FUk5YbflsLMhx1no
+94nU/390dMxoIifpcp4y8UTSDici+qMoYWMg0rPV2eGOGmrMxhzXkVPgzfff4q3HAqnqC5HQ92f3
+SBdilNsN2uxogtKg0+boTCDxex/fZ6JZaUZPhLyy1MqTmDs1/UhO23bH8lCJYd789ne49aYSyzpy
+rVdW/BtfeQWAK1se6Yr7kCW1JsQkC3nG0Ks4Usv52x9OePddIS5NyyF9NcEebG5x97qgiZYa6vav
+3GF3Q2Dy+08NpaoPt7ev8yCWk8bvbuP15Hf3lHglO08Ya3z0RVjwvBKpvnjjOqFZwfwFy/OVQVBp
+3MoIvy3v784jTkafAPDx/fdXQZo0PZdA4zD6GoKeFjmJnsa4lnZP7nsxqy+p22rrUGpsSaWEl51W
+jKskva3OFhvP3QXgwbcfUCjauLJ7lb0DUd2KEyk1UicLXrguPAbPvHSHcEPmenz/mLNj4SFoBwFt
+JSS5dbDyuIAfyBp6/5MPefCRGlILGGysQujl3yyaM1eVtrHd4eNK1uyTb3/E+2/K5x+d3aMTCbr5
+zW/+OgDfuDMh0bHf793CS2UtPPpkxhsXgsxmmcvHAoyZTYSbtHevpFMIctlrD7hxTdCP4xe0MnnG
+1J6yTH62OIUvhVDA1rjZgmvX+hRKhPHMSy+CK7rzj9/4J/zghwJt+3GPuy8JLOtNxfvw/Ne/xpUb
+zwPQaRqKlT5Yxgx08R/sXeWJJxPjH8gkFpnH7D0RMOck+EqCudPtkCuha+B8RK2Zeh3NxbCmYqlU
+xWXi0uwq83PUI9A8h7QoqRyBs3e3VbmsmxSoDnxR4w2UQKNxQLaCzJoCvnx6xEz7M9iouXZTSU+m
+FZkySQ/aDbY2NrRPct+tdkmpAf91DbEGCw02Ap5eSP8HfufSldlUCvVkYIg1i7KbZMQDuW8yLymU
+Wrx0cozmIlShhpp74F5oME5UcfWa/m65hR8oM3UY0tFx2WkLjJ5enBKtGJdbEc/sibry/Y8f4+oY
+pbkB9VBonBeBU7O7J3r7frvHSx0N592PyHWun7n7ddpqu4mtks9EFS/flGdc2z6gUndxY8+n1FDh
+IC8v8xhCdZ1utGPm6t57lg2++5p2JHS5uy8bLxyIUAzGoJnzhGFJqmxTJ+Nj7Ka8x/NRi+qG2GUK
+X1nFZw0aurmDdgFKiltt1EQP5NlFp82zm6LyPX2iAVkXE+arUgRNw0DtTpPhnDjQlPrckClB8Odt
+a/Vh3dZt3T7TvhRIwVpDUboEzj4vfFWk7o2bL7K1LajBKV5mMZGT++tf+Sp/9Tf/VQAyTVB6Yf8K
+W2o5z6s5ea2Zlo2UWA0/d25foXt7ZbXX7KJlwOMrIn2HTzNOP3wDgHGWkqmxMnQMRqFroqcOnkOZ
+aDhsvWR0ISfeqy/d5fnn5H5/K47oDjQ8WPnjjIkp9fSogynBKpTWm+MUWhchlQCbGzs+dzeEC6B2
+cmpfTrmLk/vYQk7b5cXJp6HE6oou8VmeCkKxdUWm3hzqjOsdOdn8sGJDw60PBjImxh7QjOXEHOcz
+fM3jn81GlKU8Y5H61EqHvhofW6TEGiPxTK9Py7+h/W9cZk96buuSvGRFob531GKs/JibG02evyMI
+44//uLzbAmM9AAAgAElEQVQsWmNMjcY/4ShUcGrLc9vSz6+9us++grDN+lW6kayRjc0G5VIzmwrp
++51ei4YrY7/V9ilWHBChz15HkOd4fIbROIpSQ+xrL2BHA9VeyxYs9T183+P6gcD11Mrfl9kUluIB
+2b59gzsvy1i8wC1KDcgapwm+Gh1n07H2t8ZV5OU7FWUlekJx7w1evi3ztLl35bI2iPsrotrNho/I
+RrKGnDIhU2S2vHeCpyzQJsup7L+EWZLGunhlj1l2yoUy+uyEh6Sabho7LpuaAvz1v/ktvvGiuJuW
+mrHWjAdYTyY8nQ9ZnMmCsKFl0dSiJe0Gm2oN90KB4mm7YqJZmeOz96gyGdR8OCTVbL8gimitlONV
+VlvtM9XsPVNbOnr95vYmX92XBRb4uWSxASZX1qQ4ouzJJl1kI5KJZldODLahOR/KFNTwegy0toR1
+PUp1PXbDHWqtATFzNshUbRgP5d/TNz5gvJB7OY5hdCiLNGjuEBWact4ZEFciIJpaLKUVeIRdUR86
+84haPS2REzFRJiRv0cDTICtXCU0sFVYZsqKsZE+rPrVDn2K2CkQrSdX0vyi0ZoFps9GQOdvbv0o8
+EGFqqppyVeGrgpYMAeVC1Zzi06pI1/b32FBBF86hv6WkJWbO9KEErZ0+FHuBTRzSdCUgazoqRCPT
+ItVoUSfqUuoaWGpVKD/0WU7l2vijD1iqHQjXoa3Ri0Zdq6OsJlNbU+zFNJRhqREaEvUuRUmbYEcO
+vmtXNPq11cKqm9yZpZhS+t7pTOhHNwDYDmLMKkhOt+12fJXSk+ellJxm8rt87HCUysGQ5iXFii7q
+c7a1+rBu67Zun2lfCqTgOi7tsMPyXoZTCXw+bi3pXVMG3OYzRHsCqa6079LsCtT0IkESgWOocjm5
+5mVCtyuGnFl1TrrUnPdOg6ZZ5aFrNl1ZYDXe//SThEMtRFNnM8YX0g/fhMRqZU4Vy/ZCn1klJ8Zs
+ml3C65gGIRrGHLcJNSfCaqEWkwWYqcY6zGdMCoG7zbiHq98N1Nre6vQIVnUgHZ9IjYflZpPlUE6B
+wDoUGoIcrQqyLEsCPeV6vQaH6g1pLy3+jhy7tR8RaFGaoJa+tbotGg1BUmVckU3VeBhWhHr6TXsG
+r1yFaatBzjUkelo5RU3saoy/45Lr/ZJxTTkXhFCeaL5Hs8nmrqhHYb9BqQbPRsMjMUoDH4FRTgbX
+V3KWMuc7b38EwCvXX6HzLblHEPRx1RBcjucMJ0rfVqx4NnoUCxkLzwtot9UL4uZECu0TJ2B0ppyW
+mopQThfYWPpzPplfBtFFrQaDfUE36ZGslY4ZU07UEJk3cbS8mx+HNHcE8vNC65IzwxSqSroOuS9r
+wTct/EACy25svoTTUaKWfoO6XCEd7VvdZZYIArGlJVO+0cPlDJPpmvUWVOHPdvb/hZGCMeaqMeYP
+jTHvGmPeMcb8h3p9YIz5tjHmI/23/xd9xrqt27r95befBymUwH9srX3dGNMGfmSM+Tbw7wH/h7X2
+7xpjfhf4XeA/+f+6kesbelca1NsDnI+ViXlzwEJ9772DksFI9LfIBARKSqmEQNjaI1UaNOYOppJT
+IluOmORKq7XVIwhWxUPVwOd7tLWwSOZMKSZaPdnmZBraHDZcfM3A7GqV4V5ZM22s3EkZhZ40My+j
+8jSbr4gIY82YUwOejXwS5TooHyfURnT/2q0vI/ZMpQauJMIo14PvRjj+KmsvpVTGpsXYxVV9PlMb
+yHDxBKthrQf1AE+ZiM+OH+MojdsN12K12EtDIxqDsI8XKCUcSwpX0EhZuwTq3us4Pnm2IorVwr1u
+REONmVXgYnUeAnwcRVZ1lGFSRQ3IfT1KrtwUQltrMqanMt6tCLJVPMiiJlDauGi5qjReMD7TrMyn
+5zQjzVS8E+Mm8i7FvMSpZRw7WzJu3bRDrs8oiwmhlqOLG11yLZ7jjmakTUEQk7n6FouMizM90b0c
+X12rHS8mCmRel5ol+uFyzt2RhsTnIxxHS975LTyNTTCBi1VeC81rw8ymON6K2dlSK7NW58oz1Pol
+v7MB9apPmj1sp4RtQQqTM4+JwpvhaMnCyPtHrZh9NXi/zucr0vYXFgrW2iPgSD/PjDHvISXofwv4
+df3a3wf+iD9HKDieT9TbwiyaPF1K2LF9/Jj+dZlkL9pmqy9BI8skJavEKBP5Gp7qWp4eSzjrd7/9
+GnOdmJOjT9i4IjkR4fZvclM/W6XRcgtLqFRrQe4xyeUe0/mM03OBhE0H2pEslKItE1eFHk2lDPOZ
+s1BVoipyzHK1WfJLhmI0dbouHOZqnb53esoqD7RlahKl6l1ciIBZcoSvVZxamw6uUTO7s8RRI2FW
+njFJ1Kev/IRZGbCp7IHTZkmuhB6T+Rnbamhc+FvMM/E6rAKoGuUCk65iLArOR1q5e3ZOpNl5VVl9
+WmxVDZ9V7TPNNETb9Ehr2SB2WVNotmNRh0wXMrbnSjnvOz7Blmyq937yLs6JBOms1AgAvwajxC/7
+e0orl8UYDX3ubxpMqrC7sUlpZV2Mzp5g9MSwtfQ9TCznummmwwsqLQIUbUQY5SbJg9kl/X+Syzqc
+ZRnLRElUnBYtnfe4E/BEaenf+FByEk6mF5x0lKq+Lqm1KnpdzakT9QaELaqZ0sblWrfyLKHU+BRv
+64Ba1cr8KZTdY+3/Dq4ydq/4Bk0AFbIuFvYxHzySkOj75+d0OnJ9sOFw5XmtPv+P+VztF2JoNMbc
+AF4Fvg/sqMAAOAZ2/ozf/I4x5ofGmB9OpvNfRDfWbd3W7RfQfm5DoxHH7j8E/iNr7dSYTyW9tdYa
+s2Lg/2z76VL0t2/fsIUX8sEbr/MnfypEqt1OwM6eRvRtOcwei6Hxk8m77E9EWm9rGbfKcfjuT/5P
+AP7pD36PXc0c6213KbV24ejxfTpGDT8Due90VjJVboW4mdNuCPJYzIeXCTFLFyL1aW91BdYtxxV2
+RZzSCC+LwZwfwWRHTlDHcfAipfw6lVOyTDzGanAryIm00nTQjggidalqhFo+thRKPlo2EoyWASvy
+BeVcyVqzijKRZ4xHCi1tk6YSx0zTBBTaVkFNUzMGwyjm/ERO9+NSTmhbdi6L5ZxMRzx4pIbBckwn
+UEZhDyJ3RRCisRvJIRdaf3Er3iXaVjWnjC5RwyIpWB4rdA/lFMTpMnwqR/TDpxNmroabdz1G5/p+
+rqGtSWoNPfnzbkp0ofB6MKBSwpU6MczVBfro8UdkuZLgZILu8llKHCs5bupRKYlr2eASsZW5pcik
+z8VyRfxastRScg0vZmtL1khre8CjR7ImPzyXvs8WCe+M5NqvnRcMFIUa9xSrkaDF2RMWStZT+Kpq
+TM/I1WCcthIqX+bmj/6371yiiV/7asD2N0UdWVH2ZcmE9FzeYz73+VhpCOfzhLAre+PO3Re5+8Iv
+yQ/4+3ye9nMJBWOMjwiE/9Fa+7/q5RNjzJ619sgYswec/rn3wRA6AbVxWGiR0/Ii5qmWjJ/YiJNM
+4vbb91vkX38VgFqLeyTTBYVSnDc3N9jqS3nydhCwNdBF6gXkKy49TY+tZylL1a2TiU+u4dFl5mCi
+FQejh+kpW5Au0Lkt2dwSVWSaV0ymIlgelTNOlJyk0XQItMCL0aCSqghgrAVXbPPSKDKzJdFSM+2U
+5aez1SVQvsM6gWJVIt1YMo2RyK3Bap9MT0lBvD3mT94H4PD8ghs3RE3YOLjOoqs8j7VLV6H9qmBq
+NHJZaQazfETckX7EYUxrVU2KKYXi+TiT/mwGPYJdzeysPCIFn3mdUml4+HCy4FAzRT0VKrlvufdI
+hOXp8ASnqfkjWy1KTR1OWDI90+pMmhsQNwL6V2XBnw8LPLev8+cwfSj3mz05I+2JYPR92UiLyKej
+LFT96/tUyuSVnC6p1LtSVg6VBpTlKiis49LZFXVtw/gkGjfR6g/IE7GPNFsyVqN5wj2d/yeLOfsa
+Ku8sG9RqoypbU5aFxtEUGo+xMAx1DZ1cfMxCmatNvODmTcnXqHdS0gsROJUGus2ykqk6JJ5Oznn4
+VJ6XOYb+hnjmhmFBXqzI6D9f+3m8Dwb4b4H3rLX/xU/96R8Dv62ffxv4R3/RZ6zbuq3bX377eZDC
+XwX+XeAtY8xP9Np/Cvxd4H8xxvz7wEPg3/xzO+HARtNy+4Uu/e/t6VXD2amebPl9hodyyifPZizH
+IqETX0DIZL5gb0fQwW/UMb0N8R/bMCVSf7zxCtJSUMjqVMIrCBcqF+MLjMYbmDAnnGuhmTDEU8NO
+qDBylmcYRyR7GcS01BgW+BVOU35XezG1QlCl2aOul7hK/bV//Rq9K9oPP8bx5fSLyhX91gSj9SSW
+wzFmqmG+rYq6pSpK0aKhffNWPIP2jHcfiXFqOFvya78kWYR7+3uXUW5unuFvaQk55Q4scckjOf0b
+YYg/0PsV7mW06G67h6dZhF6h1nSvpl0rOcv4DNTb4dQGtfFh/YKGJis1NArwbPSQ0xOhx5sWGbu+
+WMgHccKGehwWk5hIvTGpcigkZISqPjmmQTqTNVAvZmShQP+rd1+i2VFvRaSl6fIxkXqfrFeQaubr
+okzx1GNSOQ6pJim5WkSoE0OmkaWeP2Shp3Q0qbnSlWdc1QjFi9mCXEO05/kQsyvRrcUCqlL72XQv
+E6g8zRh1dgZ0PPEMlVXFvBB097L5FVZLtSwcilTWb3qqRuBizoUikPmyotmROYs8l0KjUBfHE1p3
+fjak8PN4H/6EVbzp/7P9xs92N4NrPL76wl/h0b8jE/CT114njlbZYA0qLXL64I17fPe62A9euSIv
+fvj4AW1lELlycwtXN8jh4Sn3HomeFW+1eO76DQD6u7IBi3nK+ELZbI4nmEJjzmeZ1PQDjC0uiUNy
+zU48nE/Jn8i1FhFLHcbpxYzlUIdkuyLR3AxWBBuPjxifycTeuXbnsqZgGHUoPbVhHArMTsuUsZaf
+XywfsRnK4o69ELRQrB9OWCgByrZC0bcP7zPUIq9JVWHaqlu3FgyUhKMu5njquXFVQa3bBbkGJJ2M
+JswUqpLWxBoK3eiE7O3L79JCvAjjj0+Yzc61nxlj5di8u7+Dr0V7HMfBs8pkJXclyU7R2CY6qU+7
+rbUpbQO/KWPULw2FBpx5iVLYp/Vl4ZsiGHN+LN9dnC8pS5nL25vX6V+V8Sr8lj6jxWKksJ2Ekapd
+y3xOPVVoX8FSU9c9dYvaMKBeynieX5ywPBfBWncqdnRzP6Mpy2fzjJnaNY5PnhAkMu9ud0Y6ld8V
+iWHFxJ5PZc5jz6fSnBK/jhmrS305PmPc0eI5bkBSioAMjdLBN1osNaT90YN32OzJeAeR4VRtV/ZJ
+QeKt7P6fr63DnNdt3dbtM+1LEeaMW0FnwrKKufOiGFY8N+fB94V916ke0FNOvezoCQ/+VI1dAzld
+nvoez26+BMAsfcjJAzkFnhbHPFYq7/i+vSwMct0Kw3HoxoxnAgcXRcZiVc4rcNnUWpJRM6RU2nKr
+odTZcMkTzfNvhgUdDZkdzkLuD+XU3Mpb7PUFEq/K0Z0fpbz9+GMAAlsThnLiXdQDnIZ8fvhAjISn
+90eca0ZlZ9PQ/aoWwKlrKuUrTK3HYEdOwsdvyunyzusPL0u7VViyIzkdx4MtTK4nye7Wpzn7Gj5c
+RQ5G6zIulilPzsRot3H1gKsvSLj5RreFVcbkltqPg80NomPp58VP3mIyeSDPc2ramthkrItRHarS
+Wot5ETJR2jjckKUGdfnNgkEl7zo+ecJBW4l2FspzOZuQL+T93vnJY67ekbks7IyPHgpSaBylzJTP
+MD+Td5ptlnw4kzFiNmfhaUm3uENHMXroQ60eqlVSVts4HKnn6+hoxuGpGkzHKb/+sqhQSSj9vZ7E
+fPCJ8GN+8tEJy1+Td+1tOYSOoIlsPGf4UDw+T4eyvrtxg7Yimqq5TaSZvUfugvQtmYe5rYh3ZLs+
+86Jwhzhlg+lY7rGcVvSv69z4TQ7nGnyXDDjRZ3/e9qUQCi4hbXsTr5lwQzkM3+8YvMcCe44fdWiV
+ymc4bBMpzfhS1Ytn7mzw4i/dkHs5d0hvyyY+SzY5vSM2imkxu+TbH45kUhYYzo5FaJwfHzOaygQk
+RUKs7D7tyDBbASpNNhsuEiqUhMX3mCoff4OEj05lEd686LLbF8v31oFGJs5u4k7l2Vxk5E8ER9Zh
+Ta2eir1MI//yQ7yOkr4ctIh7qyhOn/lYyWLqkqMn8vl7n/wIgHsXF58Z22EqiyMapcRbmgGY5yyN
+/K6jm6NJk1hdls/dvI6nkZ6JLUj1Hqnn0++oYKkEGlsbEA3knaorB3S0ZmQcxJRauNUJDTPt1+m5
+QOBhfnFZ8HX7YI/BlizcenhOigQAhW14qBD7XD0Ey6Km0hqjf/zhW+y8I+rMQR5zS1PUe+EugwO5
+flpL0Fv25BEtZXQ6rlLqngaAFUPmmgbvmIjSWRUIlrF/eDLizSPJtDydL5hmmmOSLrmyJXYAT/Ma
+0pPXeGcmv/9oecHJU7GZtLcPCNUusb3Xp6O5KVfOVbUrGsR78v5+swPqEaszGHpCmrv0DVFfxivY
+lLFfjIc8VvV35i4IM83+rS2ZpvuPvYREeTU/b1urD+u2buv2mfalQArYGqdKaSc+Xlck3HP+NezX
+RQI/bM/ZuRAIW7ow0fogVV/pvy9y7r2pEnVqGBUCqYI6oFpRk0UuuTLxzqIVh0JyqV4cnj4m0UAR
+xxqsprJVlaWpFN9tNXpVVU2mPvi8qsnVq9FvRDxeCNQ8Pd1gvimnkafeBzecMFAquLDYoXtDUEzc
+jajnGg8faYbj863LU8mLXALlpcxyjywXleBinPHBAzkJ//CHonY8nY/REocYC61YCVLOTnFj8V3z
+9BTnqhq5Mg0VT3M8rWHY6zS5q6ff2fiC7IGe+LsF1VxjOTTkNsuGlBoI1XQt3o4Y+LKqYKRxIWfn
+55yfyml1Xyns68jSjeVkv/LMTTY9geCnn3xAqj77yWzORC3tqfIvFtSgodBPzqb86DWB641X91gq
+E3hVnvPWdyWu5d1Hclo7pWV7IO+//WLvkra9rCCpZWxrh8tKZHNVK09OH3N4IutmNJ+R21UhlpqO
+xn04M0E2dsujqWw3VZHy7j1Zkzu7HsGmPNuENYEaVaNEUGG2yLAryjTnAkq1wJoZsaKCqMpwlEio
+1GzP84szkrn0rQwqPlbUW1rnkqgmn2SMXlsjhXVbt3X7OZqx9v81Cvkvtb3y1Vfs7/2z38d3LKVG
+682XDtVcjHbDWcUPv/N7ALz9k/d494HQplmlwQqpqVQXnE4zSg08boUNNlVXDR1DrTpuqyFH98Zm
+l7Zy9+eOw0xdVo5X8eBjkbpJZRlpmPJCE3H+we//AUYl+/233+GHP34PgNMnH5KrP73MFnIMAVml
++nCWk6fK2OQ4rIwUFqgr5elXt2fgBwSq1xZVylhrGizzjLkaLqkd4s6q4rOcUK/evcnL14RjYBYb
+/uHvSwiJV1jqQmsyFBmuxkBo8iVnkwsWCw1BNp/q1K24QTNSP73r01GG7UAzKquivBzXbJFSeysE
+UdHSsOIgiikzeXa/LUhpcnZKpfPUDjx2DuQU/PXf/s8p1e336K0f8949mYeHx4II5pPZZfxDWVi0
+HCVV6eHrXNocmjtaPEcNhzgJtlaOgeUJUa2Er3ZMpAjwX/nlb7D/y8IC/Y2XJWr27tVbtDUb0mtt
+YjVs3qsCbt79htxamY2i5tZlBu7mq3+NV1+V2JnOMzfJzmTet6916G/ImnxOkYYxHoeaKFdNPd67
+kDiT8XsTPppKklN/scnTTNbn0+/L+i/sEK9Q92QrouUL2nrxb/8d/uZXBIV+9SstCrXHfOva4EfW
+2lXM85/ZvhTqg60tdpGTB5ZKi3G0woqxJwEvRx/9EW/+SODxg+H7eJrZF+nmbrVbuKHmSTRyxnNR
+D4IwZqCWZWM8cvUVO7oBe3Gf3T2B+MV0ysTRAp7RGM+TAKjJ2RJnVUxjJFDNzUpmE4WMscfGptY+
+PGqSzJSSe5bg645bpYN4VYlRcOa4LrUGSy0XUxT50mhqPkQLWppkWbgBvhLLJLMlreVKeEW4jU8L
+zwIExpJdEwi/32njZmKArOoKT6OsdkxLS5TDJFVG6Qo8FUih69BStueNQY/troxR4BkamuKL0rW5
+uWWhwmbpJJwsRWXo2hn1ikxkcUapocDBTIyr/ZaLuwpaiCOaWlmJWcLDN/8vAD5+9BBnNNbnqB/f
+g0zLszvGoPWICY2l0FTt2HFxNMtzkQmEn7oubU1xdxoeqW6mosqIlEIun41pPVCvSl/WULI9oqkB
+UNYuQGtUGkeo40CKFQFEey1u3PoaALu3r+HfkPVb+UtcLTu/iGt2NbT8vtL0bbYmNFRtObZTXC0S
+FFyb8YpSxWVnGV6p5EKOeB+G99/FPjm9fI9WS1SNnnvGXMllEmIGwV8Sycq6rdu6/f+zfSmQgnEc
+vFZEepFhFXLXjkN2IafK9/7pa/zgvR8A0Mfhxi2BRlvKpovr0FGDmuuGPNXTxRITKZxLlzmVZjY2
+IpGi29ttrhzIvea9Lk0tZW4KS1vZdZ9UQ1LNZlxqmfKw1WJ4TyT08N4IpezHqTMWM4HgSb7AdwR2
+trSegu/Gl6qNKUouZnIaTZKaUqMGayXndIAyUK4DE9BX41T3dp/HR3LKzZOYraaezLtaTyHr8/hc
+TqKiDKndVXm3AkejHwvfElTyu6miqqLO6ChKu3ntKrfvyOl4pbNBp6nsynnIQqFooeHYs3zKYClj
+dR6mdCeixjw9KomU6+B8nrOxkO90t9Sttn+Li7GgrdSm6OHP/R+8w0d/+GMAHtUTtPwCjUBO3dqD
+2ophsNNw6WtG6PZ+TKxFW+bDEx6/J8a4RxOB7S3j0VtFqXZ6WDVoN9OAF67L2L3w7O4q+JQPx+Lq
+41FN4xlBMWFZgT7buC2cSFyArQ1BQc/96m9x8I0bAPwSDV7bEZXgZuazpQWDxllBSzNzy47ct1HN
+2NyXdXinGTDbF1dn37NYjXQ1TsmDsbzr07uSjHd4f4fv/4FE906Mw9XnXpZ3ahk+UcLasKr4yqFa
+uj9n+1IIBcdA6Lqk/pzlkTIPxS0+eFNUhpPhfVwtnrmxf8DBHdHVeurHr+Y5PdV1S7dBsy261TLL
+QKvtZMGSjtKWR+ozjoIYNSbTils4uuBnzibVWOwITlBi5qpuXEJ0mNei9x09epdEC6IO5yOMTmKr
+dtjqizCIFZ52dpqUmg05W0xI1H6ysd2ho5TcUagqTJVc+sz7xmXQVMYmU7J3Rd5vOK5pXpGFGSnL
+T3Z0TK0Lwlw4OEoQknkVifJWtFo+F0ovXin09wzsdmR8nr22zzOaXdlceMSKJ9PIu7RznIxknpLT
+lPNUq0zNK1KdpzrwyBSINnYDKrWoL1V4mcTFhupLP0tporkI7uuc9pQT8mlCoHOVOvIevSiiqVmn
+13oNnt8T4dW+c3DJYejFIc9q4Z8ilg2UfFLgPafCOfGo+vIe2Sjnm9+QHIXB/g7zTMb5wVD6c+Rs
+0+2IN2tj8ypurfkTrZjQypw1DsQzNl8mGER4TVserwQyl7e7lp2ujKefTrB35LOjKozX2qNdqgrS
+9fA8tROYgFrnb5KkzDVWp7crz2g/njB57qsAvHvvxwRa83Lyxvc50VDq8hXDlV99jp+lrdWHdVu3
+dftM+1IgBVvXFOmSunAYaam10VvnfPiu1MxLTj1aoZy2cbdDVzNpYrUg20aC76mBrhlgSo3+8z3s
+KkPRaRNrzMKKZ88raypNWrK5S6mxAOPjEfdOBQksZynJSGDubKyZmrMZZSKGulF5zPix0o4tElD2
+4Y1Oi61NUVP6So3V7XZZ6DN816fbFPXnys4egcYnVJn0ZzoaE2hUYZXOMXp9DvhmVSDEZ6Heo6WG
+8J6fneKX0s/jLCTTcOyqqkgLPdHPgFWRGIXtG62IV56VE+zVu7tsavXsYBs8/Zy6JfOxjO1Ya25k
+Vc5S6eiWdUWmxrcFHrkW3XFMDKFGRU41+tMdkWoU5zJJeZjLGCdekzNVweY2pbGU8dxSFWy/6fLM
+rvTz1rUeg205NZ1ll0Ats3kU8OyenPTNW1K7czl/TBDKeNv0At/qempV+EpIUiwWFGrx7eRyr5Mn
+D5lnoiq+8Iqlp7ECtjLkgRLY9DQMPjzmcCmqzze7N3leIx5vdHYJV2QV85JRpDUodQ0tIxdPCxg1
+wgY+KxZvF83to1F77GxqUpjyZA6uWr6xIag5CyaEmtmafJTz0VOpJ+pvBzw5FXbzz9u+FEKhqism
+yYSnkyGjI+UUTOYcncjEut2KlqOFVPtdyokyEnVWmYwOvhZsCWxIoTpBXTtEyq/YiGPakQyar+HR
+aZpSaehrkWak2cr+MCdV+DhMJmSqPtTqhTgaH/HOY3FDntxLyHWD1UVJX3kcW/02fa3z2NP0baxL
+Q4lBW50G+5vy936rh6OCqkhkcSxaQ/KlBuNkMYlarONkTqZcioUXsEofiDVluWxaqkfqFm0uWIHB
+qq5hxRBeFqjpgn68qhTV46UrotdejXsoxwyBH2FWhLepQ6Yp45ERQTHotbBLVeOCnPO5eiXSIefq
+qnQXcwplbyqMCNvSDah0A9rcJVEy0/PRkPlYdPGktLRULdzriDB5/qDNsxrQs78ZETdEbfJaIY66
+Q/06hh3JO/FUqDgbB8T6HtmkjafeKieA6ULC6YdnCYsLEQDZUjbuOY84uq9ZsKbgW698S37Xrmmo
+W9bTGp1my6c/k2cUowTblAEfz89xRJbgZEsSdWuuigVNioqFcm0WOPRVTcVxLwsUzZczpkrXf/xY
++vj0wydMIxmreJhS+xKodRpOMQ/k2cdbP+Kt4lf5WdpafVi3dVu3z7QvDVKYTcdUE5grnVWeV4Tq
+X23FO5gVzE8sy1hr6mWa6eaby4puQSMi0OsDzydqiIiOei16avjx9UTJgxnLWKBfluV4Ey05v7nJ
+Y2QeeyUAACAASURBVFUZgqrEtgXOLjTXPpstKIYydONkRqSnYKfdoqPhrI3QYDWQKWooZI57lBp4
+tLu9xfaBBBm1vRCjtQvZ1ESk7SWlxnMnoxNKhf6j0YRj5S+YLCD6v9l7r1hJsvRM7DvhTUa6m9ff
+sl1VbWq6x3A45NBzSKwArbQSoJUlBEnY54UAQdCaJz1IwOpJ5JP0IEFYyGAlrVYPFLkSSHFohzMc
+176qy1ddb9Jnho84evi/yGED1E4Nm0vWAvcAg75zK2/kiRMnzu++//sIqDpiVtyxNhGvS5I0qxKA
+SSuzKpGy8lErrOC4O2uCQbi2PcB6n3Xubh8OwRWW5QDs39dGicaOOLZYx7W1DuYEJqWLJUZs0Dqs
+KwTsLh0tlzC5FlVBK6nKFUW6Mg0kbHKqigSqlHlaCuh0ZU5v3JZk3t52Fy2yMnjwELZIo256sD0S
+wJgurKghgSFFeuDBroiBueGhnJDfYXkKHMr1TKMGMjJa55Kpyy8KHJB2Lf0kQESG7c/3uvD2RL6w
+vcOQwuviZCmw8/ntEN9bfAwAeB0RQtLw+YM9+JSFy1kt8TKNsCchjFEukFLCQNfWqsO2mA5x+PwZ
+AGBI1ubCaSM8obZllKAzFmh6683XoVK59ig7wpP3JVH6suOVOBQAA8r0UBonGB1J78B8ngDs4Isi
+oM64gXSNMif7DUE1Hc+HS1CNYzowGrXVDFCEv5lVBbsRiKIEemC1YdeNWlQLrkOlp8jHjFn5B+oB
+Hj0lhx91FJVlo/JJZJImsEnY0dnYxO6WxK1WUSEiHbipmu/zYNKFbblduORgNGDAZPnStLixa4XU
+5UERhCuiWDtyYA9JXnI6hdcQfvLwaxtzPK/k53g6hZk3ZCIpKt0QkFrosyX5DVYvdjcG6NrU7oSG
+TyCMLlwU1BtwbANYlX4bVawIg0oOzTR3sM4qydVdF8dEJh4tZhieyzyXpC+PCwM8VxAnJsDDwjCr
+FTGpWSlcJVHqmzekRfp634ORyovu2QYsgqjcXh+aOgpGbcBuZO7bUp0wLBsGGre8hMm+mVr7cLiP
+WuUUg6sSpqZkd6o8hYsncgjPjo7wMJKwae/WGwhImT95KGjL/HGBKJR1efekh5tfYa364ipMsl5t
+fnSE8As0ZkOhCdjaHiAg0ax2ahHRBFCWJnyiJZ21AW4w5LOa8vVJhos92YeDQx+5RxKZZ5uoWrLO
+le5jfvzpztkfNi7Dh8txOS7Hp8Yr4SlUVYnZdILj/TPsHwgJyfRsioKZ/PhshOlEXK6oF6EzoMQ7
+wwsrAPKaII9siTyX01UBsAhYiacWcmIWoo78e2gqKBJa2JYF11u5Eugxk92KWhiORbtwOGt0+xT8
+mn0XtkZJmrOsyJCzf0DZNUDvxScJixt6cAjNdgITNRp6NAMWE2omxBLV1hBWzs66oIYiCMd1XbTb
+Mv/ScnA2FSsWnhJUpCysM1G179QrpWw9r1ciKko5UOy7qKjbaFYVCrIoV1WNlNl3ZSRYkkY+qSuU
+Tb8GE5wVFCpWSUyzh6Al/6ANBXKCwJ2GcMnf8GIi1x2PpljMaRELvRKtiVwbNr/D8gw4DDHKhMnh
+rEKfawVLw1yJ02hYjYdoOKhJb6atRrlboU6b8GmOipn/ogaSjF2SGjDpvTVYCNPRGLNr8zy7wJRU
+aW/c2kVhCo4mHUvI4PgK3i2pOBiY4w4rA+vTAcy35Flak2NsbMvv1wzxYhYqRZZLVyfMPkzNyo+p
+UZFbwvd72KCq1ZPn4nU8PXoPNWHQw6cGxqRuM/ae4nVb1LfqjoX5SD7zB3i5cekpXI7LcTk+NV4J
+T6GsCpyNj/H+R+/i3nel1FcrIGeZLR6dIonlFLQiGyQURsCykl1qGIx7l3MNkJ03dw1YKTkXVI1Z
+JdZqfYMMxn0fQUqBEDVDbTUQ4zkGPUkOds5PYdfs/GuUreNzTGOxGHlSwaCox2Lq4EITVmsH6Hel
+nq4a/gcnh8WyqKodFLRWTm5AMzZutCjLLEPF+9C2RqLogUxKVOz1r4waNPQIIiY+Mwf1IRWjaw2T
+NcuyLsGeG7R9BbeBPzOpl6clNOPXRQ3YzJ/Uxg9k0xaZhsGOQsW4VpkKM/67sayhKZzjKRsWtTS9
+eoGc81gyKTmzEoxickQkFUDL7BkWtGIiWdnYY67EpTAOCgug5+L2O6jyRgIwgRWxg9Fz0BT467Hs
+Gx3EqIn1yOeTlap2Xk9RKWI5kgKq0fSk56nTKYpzUtplOcxM7vvJg2NUZ2LF65Jx/Y1NXLsjtIB3
+OxGu+5IHaSNeyQki3EZYSJ6kVI2kXwx7Krm0MsrRIbtVYTowJ3JtK1SY0CPdP5aS5P1nT1DJ1DCc
+TlHW0sS31bMxId3e3q022puk8nvJ8UocCoZW8CsDZwcjPHgq4UPLa8FhHXcW58iJo6+BlXBrReHT
+Ok1hkeIqS6cY86VxfBshlXmcsLUSReXeh1kZsNsENKkQDhNmdaVRsQrwZfvH8dFHzwAAzyhPryvA
+KyU5VdYVigU59YoLlOy03Flz0GlLrXvzGiGu1jpaTbiCaOWCl4aGQ6HYFdlK6cHgG++maqUQlWXn
+cJlZ9/IlXNK3WZFs/sho4ciRTs3zsxS10UBmDVRsKSyUCduSRz8n2Og4nWNtyArG+jpKKmQVVQwU
+8h3KKxDTBVcE2FRxuiJA0VUCW4m7HoYWPNLZj2Gh18i91zL3hQbOn7G6UqUwC5lP2ArgD+VFMAwN
+g6zKig9tWsYwwMTvyIZL0d/UKRGmhK/7NgijgME9hKyCJtN0pTWUycRmbUGRBcdwatQUvmnazJEp
+TEiPlhcFTpcSrj169hAVD6zXBvJ8/6V/92ex+7YAhb7afguLUvZLsQ988HVWjOpzRDuC1Zg6tBbH
+OW69LqHB9c46EvalGLMcGStG9jSBCmX/vnZTPrvpfwnf+r5A2o/02eogbPV3YYZ8R7I+lodNO+rL
+jc8cPiilTKXU95VS/xf//w2l1LeUUo+UUv+rUupHI52/HJfjcvyVjr8IT+E/BnAPoPwt8F8B+K+1
+1v9IKfXfAvhbAP6bf+YVlIZpV5jNn2C5kDKcyjX8TSnvbXgmpiOSgmgbCWXXy0I+W4YuemwkKnSC
+mqVFo/bR6sjvzU6IdkvKTQ49BkMFMOhJQFUoqdxsKRcRQ4mga+LKTan/uu/LqWxbNUxfkj1K1agY
+oniOg41IvmOjO4AqKOayEAvV7pZIVFM6VfD7Ys5MWDBZOq0DqihXHsDavaEDeDVdzqqP2VASW+bM
+hcW6egditWo3QcFEXTrP0CI2IwkspCvKL8BiUtUiSjOrLRyMpXaf7p/CpPts2jY2e3LtTtRCA87M
+ycdyVse4YLdnli9gUwuzqEL0yNlQGjlMwtB31hoPpYJzSM+lqgF+381WiCnDu0Wi4FDsJrPlO6Kq
+jZS8EGEVQpHo1zfWUNGSlkUIwglWuAgFD3UD6TRd2KAqtb2G8kAs9zJZYkZJt5LhVcuxYRCZmZcl
+HDYx5cPpqoxoOnKf/arGW8V1AIBTWNh0JHx4FD/FxJEF++1/+ifYP5USZpOIbts+/toX35E1/jct
+3Hnrp2XOgcKYDYL3Tp/Bonc3PRUPwxpq9NmtdrV7A3UoN31yf4x4IOt1p78Os/ujveafVUtyD8Bf
+B/BfAvhPKCX3NQD/Hj/yDwH85/ghh4IBBad2MHDXYFCQxG87ePtz0r02m8VYxoQVX0xhvSuZ2ueE
+K+/t9fDzd6VbrL++jrjZ3IsFzsYMuiYhlCFutUPXd9Nfw/Z12VS20YLHjedrD1Ykn2n3X8PW2rdk
+nsz0IjOxrsQ96/jGqnOuvxGhE1HgparxcCysQTgW8Ei7dYY9Qolfu3oFBrEJlgIS9hKMD8nyVE8x
+J2vz9Kxa4SnW+gXmrETMihEW7B9wujKHoL0Gk3FJWqXw+mzvLQKk1HNcLjOkzFSXNV8q38DCk99N
+H+3jxekx16XG194RFqI7X3obBuv/Kevuk9k5LoYNmGoJl+GKVStEAfMBVgjPY9diJVvuta02Pnoh
+L+bh6Rw5Ex6DWzexQzr/Q1zgfCbxc/tUXqCineL6GjP86w4MT74jqxTSE3l+bpHA32ZvA/9dG0ss
+KByTOxWG5xTTPRpjRjBYtBbCqmROoNpUqzfAtR1Zw8nhBTTh6NGmwtm+VKX0Odvsn2yh05GDIIs2
+UXUJXgsKBNfkcG5fi+GQzn30gApn1jF+82PZp/b+JrxN6exsOX38wQd/BAD49d9+iGf3pNpRUcXK
+dxR6vFZiWIg9CVeQPceWKUQsZbqAAxLYvOT4rOHDrwL4zwCqcQJrACZas/YFHADY/bP+8E9L0U+m
+8884jctxOS7HX9T4c3sKSql/BcCZ1vq7Sqlf+FH//k9L0d9+7apO0gtYLQNRV6zn9Z1dXFuXBM6j
+yWNkbA6alqMVD0FKaPCH911sUUb+Cz/7BbQpHJJlM5xT1OTk5AEWlNhqsvobm+t480wwD7ev3lpV
+HLyBDYsUW+lyBNcUa9qwOs8nz/Hx4UOug4vXbkjyrN/ycGVNrEq3E6I56k6pZv3w0ROcUedRxRo7
+RAX6tYtlJdnnp4fiVTx+PMKSXs7x9AyOL2HJa1cdbJEXItMFTF88E+Zccb6coS4bPIJCQARiPFZw
+Gsiz1ivp84ZHsqpyqJi0cssYx+eEedca+VsMaWyNesEkLpvOzKpehTk9J4LnM2HYDlA0Gpy5XjVj
+NZ6Ev76BmztiwZ49Ha4o3RzYUEy0xdMlhrV4d/2GJ3IZ4f6xdACOFxoXfE55aWLXk2vsdq9g945Y
+251teb6GscDxcwm7HhxeYP9A9sXB7AJRIGHqlTrFZij7yG6g1kaG9VCeb+DN4YYyj8jtAxcCYx6y
+qe63vv4I3/ymeGNq/wXGWp61uSgwY9NcYLRQrjNJS+zF0gLOTsTz+q1730F7TebeN0/x/pF4m8f7
+h2DhA7Yv70VRjDBitSp9PsXcY0UlKWAytDG+/HnUGwyRX3J8VoHZv6GU+pcBeJCcwq8B6CqlLHoL
+ewAOf9iFDNNC0Brg9sYtPO6w+tAeQKmGYajGlO6150Rok6Vm/lwW5Gg6woPHwpTzs3/9a3D22KOQ
+TrFXymcvzs+RMU+QshNxcfgCI+Yl6hooE3H9fDOHvcZcxMyBzwT23ra4loYa4OQZxU3GI7z1hsBV
+77x2CwVzBu/vP8b8XObxgG3YpyeHuLkhD/SNt17DCd3urXCAnKXReERFI7+GUcl88sTHizNZl/O8
+wk0Kw3TcDfQtuZec5Tbf2kSH7b1WYOHqrriRs9Mpch6GUEBCQpKaB9PZYokNW8A048LEJJWDteeY
+DR0j2p0eCiWVgeMTOShMFcIkS9U8szCek2tw8gxr1DaMHBMOw5/NNTnQ1nav4MuEHX//w32kY27o
+zEJnSchvXmNesXTKF3emYnzyXA7QPPXxiBWjq7vX8eY1IV11fI0htS4dSmJGoYcFu0vTQMHckOsN
+pxn2yQB19ngKX0ve6NqWHCZ3b17FzjWpKHRnExQsjfqGjYJ5h+ZgPZ0c4OnxRzK3wxkSlmoVfnAg
+e2sVIlsMh97mvcXlqu3ZdQfQFasF0TFCCvRsv/4mcpZGXVcOtL7eRcFKjNHpYEgw2Gy2QMpw7eNP
+9mG+2zjuLzf+3OGD1vrvaa33tNbXAfw7AH5Ha/0rAL4O4G/yY5dS9JfjcvwLNv554BT+DoB/pJT6
+LwB8H8B//8P+wADQMmuE6xYCEl6YNTBmoi0rh7DYdLQ56OFzdwTCqVOxWs/jOeKGfzCZrTryYBpY
+o3XvDHsoCGG1I/Ew4BlwKAdeRh4ys+nvrwGKniC8QMsX6/b5DbHcvY7GkjXoUtfYbdGSRB0sSXlm
+ZwqddbF4n++I1bm4uoX1FqXCrDXkuXgQueOCbf8wtikVdxJh0RX3+tb1LpIncq/2xQT7M7EOznoB
+n4nNhucgCipsDcTi3xxsoEvl5xoxCi3358NeVRHWehJ39FIfbZtsyL0Q5lXxsNpmgYDkJGYVr3TG
+C3Yq9tcCVFosn5GWgMGwolRoM6YxQxPblHa/SSq51tVNDDYlJPo/f72HMxLYQJ2j3CWr9NzG9o58
+5gqrFpHlYzGRe67XbVzns7nypZ/BdZudhpMTTAgo8voyYbsfwS1ljfpnAaBlAT73jsLZRLL5ETLh
+h4d4FgDQa7sYTcXStiqNKYVootpeJXQXS4KwnpZwmF4rCw3mzFHXCiXxILPzBaYEJJkNLWDXRmcg
+Fa6fuavxOisqxZmP11vkg3g7RfWQIQ3xFpaOEKzJO+JXPXToEHz4R8+wpOr06dMR+kxyvuz4CzkU
+tNa/C+B3+fMTAF/5i7ju5bgcl+Mvf7wSiMaqqjGZJSgTBU1TdD4dQlcS70/nJSyWwmzLgsnfd1iv
+HvRDhCzJxUkJh6d5GLZA5jLcvbGHfeo2WEo8ha2tDZgdOZXX/W10aP3slgXTbpBwBjoUTrm6x9Zi
+Zx0m4b51XcFiqdLVBZy2eAJf+tIODNbsswZViBYaCQnDKhCzPVd7KVxm4txSPI2r1wJ0NyXBVXsG
+fvxzvwgAON5/F2opFtGqC9g+qbto7YxliW2KolzZbqPXE0tq1AZMlv0820SXDNWv9cnaPAihiB83
+LQdfnMvvz4/34UXUHliMoJn7cHlvplWgd0WseTfPUGXiYUXtDlQq99Rr99Hns+r2xVNoDwZYkvh0
+LbCh6cWczWLEc7Gqa2GALVPW4Ma2fMdm38bdtwTOmzomlrTingnE8Rl/nwrzMgBFZKZejlGeS55h
+u+tjY5OkqlUAtyP3lC8uYBMVaNPjCXsD+EfPAACzeLbibBhse2iR/bowyWkQOFjfI4x96aBFirXR
+PMOw4cbIS+m8AmAyWfXWnQhf/pp4Yz/3xVu43ZX7O+9+CB2JB7VR3kCVPQYA3P9Evm88v0BFgZth
+eoycnAyJMYMyZZ0T8wzD438BVae1USB3jnHRuUBF+KlrtXBBN6moMrDtAJZtQpHGK6TYim/5SMix
+MDy7QNuWDaQMDc+Wl2LvTgveoWRyF0Mm5+I5XMKKjc02FK9nuA5ybqoiG6KkwlOPgiW2k8AP2NV4
+scQpk6DrsbNaULtWsNnM12O1QK15MOmWTosRSh5SyWwDLg8qw2k4D9roshMzbLVQs7fhqmUjoZbg
+dLjEZCYhyJz09Bf1FDYTfzevXsMJ12VRFVAMsdotd8V+7XLGtnKwHsq62d0InZDJrsUC04wq3cMW
+DNKU1Uv+u2Giw4rRFduGYoXGtF3Y7GdohyEcJuW8QA6sOtWI2flaA1AMDyfnMZ4dyfdp0wYR3eiQ
+I6LtdOC2SbijLJQkZYjTclURGacxjCV7MywqSiOGx8O747YR8l5h5ACp4FS9jiY+ytnDUmIGtSTY
+TQO6aMhiPKx7cg23qbjsefj8DcHL+NkUm6YckPcfnOKYCeH91IJPy/BaW17Wn/p8H1/sStVjLY9g
+LEm+Uu7Bx/cAAOniAu9Q0sDkc3z8R49xfioJ9jxxMAxk3fJ4gWIsyUj72hbaPy94H/xPeKlx2SV5
+OS7H5fjUeCU8BdMo0QnOEF27j951cbNm8y5aU7GU00yhohVQlcbaFTlJw46c1MOLBSrqDSwMA21C
+ns3SQEj2I6/TQofJtXmfUNbjdaQhG2BsAxlhyUmcwvL4fZmNiFiA2qdO4N6buP26lL+ODv4QGcVX
+qo0+Wq1wdU/ba4Lb8rbE3OmiRk7cRI5SZKEBuGENm8ye3lDcU9cN0W7L7xwzQlPoT9we5lM2F+Vj
+lET/LdnepypnpRehLQ9Oi0ImXgcmuzkNy4JD2PF4Ke5pWqUrdulWdwsVXd/47BOULtWv34zgt+Qz
+PhW8De2hG5IroFtD6YafwoFJuHm1jFEX8j3lGZvOjAJ6Ife65XcRkhdhmWSoeU/KqbGgSgwpMlDH
+BkBoumvYsLt81pMMBlmgl/MMJUuuIZmuHNfFJr0Ur7e26rStdQiD7Ne1ayOhrF1NdickHkqf5L6q
+xoL47udHJ/CuXQcAdBvOikGCnRuSdL0z6UJ9KPvii5ubWEYSFuq3t5BZ8nNvXRLUQW+Gbe8an83r
+SBmaOb076FEv41l6ipow56vrfEduWJh9j+9Iq0Q2ZfOY0YZ7XcI/e/s22sRZvOx4JQ6FpCjx0ekF
+9s9SzKl+FGOMWSyuaFpOkLFdNl/EiFhbX9uR/z5ca6HRRkWSIaB7aRYamqAYX8do9SSj3nEJ/tgo
+sCRlWKprFOzkK4oKJcEmpq1XLM8e+wiibge337kOAPidr/8xHh+KC3/teh/9ZuNZHiLS0nukb0/N
+HGd8CYenF7CosoROjspn/btpH8sWMNkK67s2DMaOhjnFlJ1/SWajIsecTwWi0gQygrt8x8X6deE2
+7L3ZwQVZkmsvhN3QgVfkKhxqqCvypvjACi/fH4Q4Y4wf2j20Avkel0Q16WyBmrkDv9WB3TifbKEG
+AMtzkZQNoYp8tpoCNft+O3sDDJ7z5TYq5IHcn1OZcAjDzolH0LBQLwOuhQ0rYLt0N0TJdan3n6Nh
+Xmvo22FreGhaynPYFnMtjguQVq3MYhRNb8eCPRxYok5oDFoBzs8k2//w/mN0t9/hPGQtlsf3YPBA
+Xn9zEzv/vig2dew2DK5tOW+h6kn4t3zEA7K1wOhU/m7yjUfo78l3dHpvArXMc2Q9RXoq+8zmPu3r
+DuIdyaNMHs+RzXi42W+gfUWo7b/6S7+IG1RB+2283LgMHy7H5bgcnxqvhKegDROZ34HzXhdTUlv1
+e9cwnD4DAMRxvUr8HczOMWGCbo2otMhxQDoF7Gy20e1S3MObomb2WZlAzcYl1RCapOaqGy5dlKhs
+qifbBgxKwalcITeYBGRd2TRMXN+R8KHX9ldIwYvRCJueuGp2qJDkcnKbpXgM42GOZycCsSuSGOs7
+El74ro8lk5XVUix4Vo2QkoQkhAeDFQzlApqsxKVaYEJ3dsI6vyo0Mi0WOIaPDYYuX7r7VcyeigVy
+Cx81Ic0xE7hlGqNQMgfHARyfoUS/hadnMudkNkRAjghN7+l8OIPXFw/EDQNYxGGYsFEzK1+mJgo+
+P+qYoI5z5KB3MC+hSZt3sogxn8nfmaaBY8r3JZmsVRJ7cBo27lrDaky74aIk78E0MVAxeVhpqkQj
+QUo4sqHmMAyKBxkGkLBbEyVyysalRAoen05xMpT7Ny0PNZ/1KBvi7V2Zx2Ygnounb6Hblue3rBRi
+VnY6G5tAi5UB8xBnEwm3LnJJDH74jcf49nuilXo+muJO7zoA4Jf/ja9g9015DrcHIe4xtLz3RNic
+Dz7Zx8FEnv/x2QwZPejW9Ru4fVe8mDudEFtEmb7seCUOBWNuIvj9Hobv7+PoVDam9eI7sAK+6VUO
+NIQkqHBE2LDLHWYsF+ix90GlKSyGDF6owPcHKgfykbwsMy0u89lwDtCdzfIENUtFdQBUVD2yW0Bl
+ssrB+LRECjC+c1SAcSEhyMVJiAvIi5f0PNjMTs/oth4eDfHiTF4gsyjhWPJA/ckJqkA+czwWmG2Z
+VFjLGT54JkD+xDgbI6EoSJVrJCSfSUiNn9aA4gsYRCZaoWxG/8o6ulclbtWjMxQNQQ1j7/NZipps
+PZWpUY9kjSzHRqblBXn85BnUZvNI+OL6FjT7UupZgpSfVZYDxTxQHpfIEorV0L1ezHOkVJAaZiVy
+9jDorMQiaw4FCxPG+8sZqwJGgTiXudWuhSwVI6KNMebUtLy4eIYhey38VARS/FaGmCFo0HFhdEhC
+0h2izH8gzpIveVgSEn02HGFIsJjpOTC4bkulED+W3+OuzK3b8rDRlmdm1gaqp3KgLZZjGKwujI0l
+xiM5ZD55IiXGf/qHv4XHH5LFSac4JBDt3nAfX/sZ6VD9+Z/7Seyx72J5867c5ycXGD2R+4/nCxjd
+q7xXA1XWyCDYqK78aK/5ZfhwOS7H5fjUeCU8BV1XKJIJ4g/aeIPnlA5sDF0m1OoxUpJwJEmKlDXk
+zCbnVm3j5FxO3/fe+wDLXbEuETyEVBeONqMVpVn+nM1AowWqDsEmhoslLY0240b/BEXiriC9bmuN
+83UQWOLW7W7s4uKhdEx+cjaD57D/PzHhM5QICSaCbeLmOtWhl3OYldzHyWEOuyf3HfKRWDsbyJn1
+jy9mqE0JJS4Ws5XW4jirkFDSbsmEoVId5I5YMNuxsCS8tr/+Nn7ir8m9/t4/+Q3sE0xDhwhnixiz
+sfwuLhRIeQmnamGNwDHTs5BwYRp4+GbbQm8g1hHKQjanirWloSiSU5YFkkaNugkfIoXFUK41Gg+x
+ZHJ0K2wh4LWTrMABqwEjhleJb8Ai/kGpDAW9uCRdYMYW/FlWYZ+Z53HxLgCgF4bos9J0d+8dWFQs
+z2YZcsLltddCRp2MJXkqZjWgG5EZeCiJpwhsF+9dPAMAPPm6/PfNL9zAVx2ZQxi0EJPH05934VXi
+CZgKCPj8fvHnfgwA0MYY375OirZH53g4p9y9KnCSyNyejI+x3hIPodOTPTa40Uf8nWY9LfjrAqe/
+9bW72GPYsb7rwxj+IOn7MuOVOBQsS2NtrcadX+lh9LsSO7Z/oYNvvEswxvAhNNlvVFljdiQuuLcu
+bta1m9soJ+KWOraDupKHuNhtwQuk0qA7LkB2o7hPAE1twWyYT13Ay4k2UnqVPQ+7HjQ5Cn0y7BhG
+BrNFHP3ebQQjeUhWnGGeU2UKFhZLCSuQRZxbiDaReV7aR2uNxCG2Qs1uwNqWWNczKhgWRV10gZKs
+SWk2w5RirGWtoAgcCpiltkobGWniu7WBlsGOw6sJdjzp5rzxy1/A4tfFrY5tEoWUQE4Ox8nxIcCy
+YBUGuHJDNqPbMeEwt2Gy23FghzBDuqomQNZB6LKCrRjDBxou5IA0iBStTBNpKmKsHx8dIV3KNXav
+RCiZDzi6WCDNed9gibDTQkaGLKg5cnalZqVCydLgnbtv4SrkkDQ8IWQp1ALbHYnlww3ApALWEPKO
+TgAAIABJREFUeWmg5kFglBkStp2nBlmRwi5GFO8dn52hIJOTU1iYzBpCV3nOxXtL3GlLpn/9dg+B
+z7yGYyBjyKP8EsEtisGEsi++/NUv4+qOhJ0nb9zH9/5I9tPTxTGsx3LtD+zv4LUrcn/tlvx3cRrC
+UryupRGxe9ZKXGx05DOFodHevAwfLsfluByfYbwSnkLteMh272Cj/wV03pGTr5NtYDz6DQDAxw+x
+SjTGVYFjVh9GpNT63J0dWJQBa/U7aFPaq8wsTHJJ3AVrt5CTqffsQpIzxTRB2JbTNV8olEx8uY4H
+s+nwqz1YHXF9G4huldbo+ZJx++LP3sLzY6GHm508Rk0F5trOMddy4lcTcY2VLoC5XCubJ/Aj8Qq2
+rg9QKHY7ziU51Wo7cKlOHOfLFRZggQx11XQipjCJ0VdcoDzJAFPWooSPKUFIv7j2Y4g3CV4qMnzz
+25/I9z1nItIYYjSSrPbG5hW4gdxfNj5ZEav4XguWRzDRiO61nsFbb0hfKtQGqehzwNXk1UQXpssa
+OstEHhY4PBKPZzyZYEkcu9XzcLsnrm9easynMv/hSNxy4/obsEiVluTxip49qxVysnh3W5sYDOT5
+eRRQyZIIBZPKyzFwci4Q60kxQtjAySMbc5ZjxrHMTXk2OuQ+fH5wH0Uq37F31cHRR+KxGgShVfkC
+o0TC2OWwi/4m5xApKF/25PH4MZ7fk3W5//CfAAAe3HuEimrrrutiRBh7jRwPlXjLkR1Cj6RCceNz
+ck+z4Qus+SH/LkC0TQrB4AKbvoQS7wTAJ+MfLXy49BQux+W4HJ8ar4SnUMHA1PIwfPQ+XkC8gK+t
+3YDtsAPOtqFjiroUFUY88Q+eioU+jVPssmvxYPIEF8nvyWdrGxbpyG7f2YXDRqma9fjN/iZ2yfmv
+S6CgZXMdC4rKv6mRw2W5rKLyae1OMJIQEO3ta9h9W+LI06OnmBdi6Qf2JtqEYaeMU+PhBAkTmLay
+cE4o7bOPZ7Aj+buGzKeTtOHTuqblbMUYbGoLBrkAXMeGGxFowPU5bs9hpBSxtbpYuypW8FmZ4M2O
+WJivfMXGH/8/4iFMHwmlWBgqGEQS5r02imOJlx98/AliEp62WyE6hHxrTvQ0n0CxPu601tDZESvf
+7rbQJqu08msUjRBNJFb37MEIzx5ToTkpkDNn1A9dFMyrdDsKy5l4L885h6xyYYWSfAv8LnKK0oyf
+HeP9e3IvTvUuuuxGS2z5XeprnDa8B7MlWHnEjfU+vvLFLwMADM/HspZrT5jD2VsLcPJc1nMUxyhI
+02a76/CZ20kd8QJc10JVyrOZJRViMN+TD2G7klMxMxsvnog3cf5IPI1B1UHQF+9nfet1LB35ObQd
+nN6nV6tNXHMo6kJEZzLOsWQDl92q0TYk93M4KfDBP2Hn7k99gg8PmJB/yfFKHApWAfT2DTy/t0DJ
+voTvG3PUJBlZFnqlmFxXwHJOsJAvrmV/NMT6m0I7duX160gpNZEUF+gFQoziXTOAkirQTAaaboB2
+iw9xMofRuOVVAYMdk8oMUBM+2+guhtY2qjbpyBaHuBLKw/rAtJDxGifjc2yeyaGwuyeuXLedwaB6
+9PbeNfjbVKh2QxSJbPolJcuPzx+jpvpRYGuUhDOXVgWDJDJ1mWPagGlS+XsnaGPp8hCyC+wYolsI
+L4NNNzdwNrAGmdMj9pdMcIGn+7IuV9/QWGcX6I/fvYVlT5JdrhOuoOB5KQfrVrCLukWqsc1t1E3L
+eaXgk0ZeqwQ2w5imq+/B+Uc4pXpTDY2K5CSDtS3Y7NdYXiR4QkWpDw/l5Xh8cB9vXidjtmOixfDv
+xrU9OCQWyRYWNl+TiojjyGcnszNUicznZH6EbJut01e34Q7kEJo9G+HgVDppUz7zuOjjIhYXPisq
+NI546S2QsKXec/mSGh7uXcgcHKNEQDWwet5C75rc6/beDfwcwVf156jnad9G+VT2U6lTHJ3LGsV5
+hZ099vfUCQ5SOUQO9uW6H5yeISfGwm55mJUM/7Id/F76mwCAb/xqieR7LDG95LgMHy7H5bgcnxqv
+hKdQI0OqHsG9uUD/VE7MNIvx/Y/lxMzIZwAAWmss6So/X0ioMc0zdJ/J6flm+xqivrjzW/4ONlti
+Maw1ICe0V/2pLryE9FthVaJsKJHrEnZJvMBiiZKaj1XlNZNAQMu2VBUCsj37ayFqwk4dx4Kh6HmQ
+7LPyCsyYHM2zDFuJWPSwHcKmZS4WYg3sPIdBtt/W1T40uyTnRY4ilu/I6gqzCwk7ykzmk8ZDmORa
+8+oSbeptBtqCQ+i2VdqouoJunNFT8qcuHhyKR3D78QcIyKRdeTbQl0RinBTImcS0aR07V7fgsDxp
+GTbiQtzvPE/Ar4OhM8REPR6zqecbf/IYz0digZWlQSgIQk+hnrLE5wEOUaQZG6K+8+wh+pF4P1Y/
+RD1rkrgW6p7ci3JNVKTeKwx2cxYVfGpN3rlxA2FfvDTVD7E8ES/k4PQpTok2dDsS5pwfHeKMz8yE
+hiJmY/RkjiqXuc1Lme9saSFlmdJf9uEXUl5v3XLRKkihlyR4bV2a1GYL8QKeHUzw9W99FwBwf/8B
+6qau61SwI/FofFvjjCHNPglcF5MaJRvCnHMTeibJ48U3HyGPGY6mGlW9wI8ylNY/Gi76n8fweuv6
+2tf+dYTahuPISzw9egiH8hGhqnFxLq5ROjuFIlTWYXdaksQoSDOuNeC5siHagQd+BGmaoeMxxq1k
+gy7ydKWJaHgu2h43elchZcZ2ux3CIab+hHLpJ/MajR/ZDny02S7da7UQBGzrNS2YBF8ZrIM7YQCL
+3+d3HDjMeve2BrB4Pkcdciou5uh0JQ4tlguM5rJx7330HId827JYo7Mp99rJ5Fpf/OKX0WfmvXQc
+3PylX5E1NtQqHBlOUzz71jcBAM+Z3f/ke3+MhOCtRTzDgrDks5N95FzbPK9RM5fgcmHbnRBtdo+u
+b3Sh8oZ8pcSAEGuj2wI9ZvjUJZk/y9DvCBjMcWOMCU3/+re/DZAr08oDnKckomG7uGGZ0Fw3xxPw
+EQD4jgmD+8VyawRk39rssJchrVDyTK+XNSKGNmezAhcUbrVqE5shxWDYz2AsaxQUCq5rC3euU0Ws
+ewN/51d/DQAQEjaBPMPJqczn4umHuKBoz+joPkxWh6rlCA73mVEsuZYlZhO5T8cJMB3zUESFk1M5
+ALqDAWxWbjpkC1vGOWJP9kscK5hkyFqOgJ0dtmV3XGiSEv2nf//vfVdr/WX8kHEZPlyOy3E5PjVe
+ifDB0A6C8grq/BT+62Id1279BIyHcmKODv8ASMSKeaGLHhN7yCS5ltoaBbUUVZ6vUvgb3Q66dL88
+YwmroXRjOJIWChabbzKtEQWkwepmsDrMhrsdOAO5xhU21Pz27z9EzVCj1QqwtyVZ/X4rgtaU9IKF
+qCtWx2qg27aBkKSRXs+FSaQkqgxe0+FGxKDT7q46NaPARMtjJ9K2gvFCkmGl5aMwGvZhItjyE8Sh
+WIme10J5JveatDUMeh6hXcF+TRKzF7/5PwIAjo+OEFHbst8ZoChFlCb0XLRZYzcrAwvW6RuG643d
+LtZacv+L8fmKNq+91kKH1rZKC3Q8+Tx/hehaCNVY9t42dm35efPDj+HTDdv4sS9glEm4kXxHQo2n
+swmWjYSe6cEnN6dv17A1+SOjCtvNsya9n+f5MCkF2OppHJHbcRAaOKInZ1YaDrs1NXUa+ls7iGtJ
+VhfzJ8hTdj6+9ydQxDTEjWfjKfhrJJMZtRCfiAbEdP8FfBL0hIGHbiUeQsDQVZsm+j15NqrSuErc
+R2k6uOLJHkrnORTh/S2bcPs1DwvC3FU7wnyNDWiuC5fCOKo/QPgj2v5X4lCwQx8bP/42Tr/vYn5A
+UdbgAB2GDIuLUyyZnY+UjzfekMz5+Qv57DgZ4/Vb8iKoWiPsyG1dW7uCO9vshpvmOJrLxqpYbovW
+FmiTq+/+6QnUVD7b28rhs4vu/BSw+TIhkIy7dgxYPsuTgYvYkp87RgHFjHRtWehQUKZL5SlUNnRD
+Ie4HcMgKlWUlSr5NbZb8HMuBabD1uvbhMKvf/akB1EC+4+lZDKuWObVsMvAsDaTnslZet4WIpKrj
+8wzFBcOARYLzexJ/vvhYYs9pmqCdyhu7seGghtz/Mo1x+4as96DfQcH7dgi22ogCaFYI3nueYMH4
+u73WxmCdZc+4QMPg7uUMk9YCzBLmCzwLLRKmvvH2G3j+VIBFj54doq7lWV3x2AdiWGj5sm59x1yV
+Hs/SGCbFX3fDHDuBvOgnQ/k7XVdwm7KoqXDI1mjtWrhLEpxO28FTJuo7HfnwG3c6uCIFBfzvXz+B
+OpAy5NN0CJ8guTm7b+NZipqHZnIyxBnLiQfnT7FLUpfNW7voefL8HFPm3o88+BQmRp1Ds0FEwRCi
+VwCffPghLkbsK9FDznEbDqHUM6eFviLT0M4AueLeMioUTX/8S47PFD4opbpKqX+slLqvlLqnlPqq
+UqqvlPotpdRD/rf3Wb7jclyOy/GXOz6rp/BrAP5vrfXfVEo5AAIAfx/A/6u1/gdKqb8L4O9CBGL+
+/ydh2dhc38Zy42PMHgqUs7W+jdFYLF5dzaALEp1YHWi6ey5P3F03xOdek2Yfx14gaot79Wb/OtqN
+lS6XuMbGJOumnKjtrgGqo6G92cbzj1nrb0fQBkVLolPMx6wIEBPgwkDd4AbyciUrV1kW1kISbpg+
+LHZxKiWhjalqZE0hxbBhODL/wG9DNzTx1IwMfQsWqxpmnsGh6+vWGq+/Ja6/UT/FkPMAWZbVLIZ1
+JBbFNNbgu2IFM2+G/Q+lf/+iLvDxQ5GhC9hEs5G1sNGnLqUfImJycXstwrUdaSraWe+gtyNVC+bs
+YFselguxlAVKLDpSfbiytwObrrgyl9DU/czIkWHAR8QKTzlarkhyNgchTkgKkj18jk1WZU6ZqXxn
+0EOL65nnC9TEjtyKHFgkz1jvG+jzegO3ScrWKPmwizrHVZrDQCt0CHBDaKE/aghzxLWZvzvDHw/k
+nt755asYf1v2YdFWcMnKfJHLvx998D4U3f2jg0doNkZL2+i3KW1vFggDPmN6Cp3Qg+9THdsD6kWj
+BVoi6smNVMUOrIfi6ZrkcDRUvqIIHJVTWIEk6QM7gEnPzE3i1ZxednwWgdkOgJ8D8B8CgBa/OFdK
+/WsAfoEf+4cQkZh/5qEAVKj1BFEcY1rKZp0cLREoSqePYsSJxGG9KsbVnix2dEXiOzVP8NNvS5mq
+uxHBIgGrF1kIDer2WTnMXMpQtSEus5oppASEjPYdmEw6nJ8vMSRpSXKxgEm3bEzwS6oq2DVjNhhI
++QKFCGAoedAt31z1CefMrBupsVIVgmmv8PCB0V1VVAKHOpChhkXSTsufw6SEu22maFfyIu9uBqjJ
+JnKcyXyHwwlapLB33Qia7meVlxhC2ogffreNijVAs5QD1DDOsaDG5prdwY1NKaftRh62t2Tdtnc3
+sBHJYem2G4FaE8sNmU8v7GESU1RVubiYcBPHDrohOR1r2a2zJWDN5Bpde4FlJvddF3PUp/J3A53j
+gi/I2315kW46XeRaDu9pqeA1mhyej3ZA6nfTwgZfHFQNgUqCudWUZA0Qs4WuUcPymHfIayzJwnRM
+A/BRdog3u0KR/nFZwrwjrvuifg0ptSIX53Io7M8eQD+U9ZyejbA4SnlPGqVmRUgpGJS7t7yG6MZB
+yeqEWXpQDjVOan/1nLY2bsFivunsgZRNp8cvUNvUInEDwKWkcQWEPVnPqsxRFuSpfMnxWcKHGwDO
+AfwPSqnvK6X+O6VUCGBTa33Mz5wA2Pyz/vhPS9GntDSX43Jcjr/68VnCBwvAlwD8ba31t5RSvwYJ
+FVZDa62VUn8mEOJPS9F3tl/T4+czjMwldConXBg5ACG/cb1cwZzbbQ93b7wOAOhvswdgusTuHUk0
+hl4f9aJR+9VQqlH8caCY4UZzahtnqMna2zYctMhfMIxzBKQWPzFzmEPKncs/o8pLWBRTUbkFo8E6
+pApmm/Xo0gRqOdl1Kr8rVQEUTR+FQkVQVKkKuG0Cp0iLbsCASUITZdYriDWKJQziJfygjQ4z8WrE
+Lsp2hYtHpGRfG2E0F8v27vl9nDyW61l9A2pMDH9PrO7sKIfdFm/My4pVR6jnDNAOJNHqpC4qmwk6
+9oy4ngnVcEEENdY8SUrOZ1NMjtivMF/C65IaniQlusqw2GRvx1jD7YpXdPrdE8zZX2DOXKwTjtyd
+y/cVwRSziazFRt/BpiXzdNYcKCY86yyFxUSb7pIb8vAHvJt2aSKSvB/SkYbVEFBrE+tdWdv7z2Q9
+W6rChFR5wdMJzncYgjx7H6cTyUC++0SAR2cfDOFTtGexmEPZ5NbISvghFbjN8AeCQeQKtTodGPx3
+FAZMhxD6qA2bbDdVrNBnUnGRybqdffMF5qxkYGMLji1rZZtA0aVKmh2ioDrXy47P4ikcADjQWn+L
+//8fQw6JU6XUNgDwv2ef4Tsux+W4HH/J48/tKWitT5RS+0qp17XWnwD4JQAf83//AYB/gJeUoq91
+jVmZoI77qFw5ws1+gNEDifcVTNQUO9HKRe/NnwQAXFtvOs+O0Y6klmyH7ZVVNeoZDEpswTJhNpUZ
+NvMYoYZBS2R5LRSVsBAVVY6Pc8FIeGUXVZ/8BKwT56jApkXkpsaCSMEwKLGknJoZWCsOiCogrFo5
+iAl3NZYFSIoEx/dgN5bX+AE9HJpwuDRgkAS1cnrw2IAUTYBKEYdwVf4uerGNx6lYrvLgFLMlO/mO
+bYzZibjTDWD2JNdyeihzG42/DccRK+99ZRc2G4mm6T4KJnbLToCE0F57QSEbw19pUAZ2hKoUy5U7
+CiUt17wukJJirO9JDLwwE1Qz+bmMpgjYgPbuZAIb8t0XUYIbhBu3eJ9ZWcOn9qPvtxAMJNewmM4w
+nRLX4Sgsu8QsjEm0ahhwKGnXavlYUMDHsIDKpUdnWjCZCNb0iI7qCn/Dke843lli41TyAe+/SJAw
+Eb44I1I0n+GWkpxC29bIiTI1jApsQEXUbiFkMtn1uG86PVhke1baXD1r248A5jt0asBZyB6xbxCT
+cn6BRyfiVeR5DZdeQ5poRIVA/Wu/XnlnLzs+a/XhbwP4n1l5eALgP4J4H/+bUupvAXgO4N/6YRep
+tYm8iFCUI4ACMFjGcJlNnuoKJnW9+/YmWmxvjUgE4kVvwW4OE8eFoqKPLjpQjR44cqCpTZeNWx7B
+pO/ohiGiNUmibccxRkwq2mWCA9K45RdymLRtd6XBqJQFn9ReVlUBZEfOkgJ2kyWmpqDnKJQlgTIK
+8JhQinwfFg8DGA1lebXStoR2oHkoGpYJh1gBv+6j4Hdfy+WA9LdP8JAYi7PTcxhknTbqcxRnsmnM
+tQIB77U+IdnKMkNNnURVlOh6Mg+jtQ6XuADb0nAYKlTMnNfa+YForqOwZMt5ENS4siHJytPjU8zJ
+vN0nFZ5t+wg0CWmKNmoybHe7Lnz2cVi6h4i4jiZf2PYD6ELu1WubyAlZr+0CmSnrEpY/OCzrLrP+
+ThthVy4SVMAG19kYGiB7HxZ+DVIsQpH7sTIVxrYkvO9+ZQ/T53If83UDXAqYphy8uJghjVip8k3Y
+zd4rohWMXZcZLGpPmpohzgLQEUMmz4ZZsmLmVFi9z94SGqTqK+VAv373LmqfRDVFDa3lYAk7FroN
+xNy2oZy/RI5GrfW7AP4sLPUvfZbrXo7LcTn+6sYrgWg0UaKNKWZjC2VFsRTdRs6ONFQAaBEKe4qa
+JT6Xwht+6MEgTBgmUPOUhwmUhIGW9QIgD4MmCtBICoBWvNItOANxZ83ZCeoDKVvO6xLLqbiJB/zv
+oOuhruRkL4oEKRtb4hgIrKbTcg6bVFkW0XqocnQi+dlr2WhaA5NsCs1yqMnEoc5zVAU7Na0KJhuN
+tLWEUrRAVgmQ5RnsIgy7IXab0CB+Aj1tYqYKdo8ahLMUAQljnn0ohaKz6QV6W+L6WtCoWeiOOvbK
+nYUukXDOJRXB67qAQXhxuSgRk7W4XFYg/wkc38V80RCSMMxRfSwZatjGrGGpw93tHgqyIA/OcwzZ
+zLPgWjiGDd+lGrkVImHnYFkoRKRmK/xq1fQW9OlB9VJoel5mnsLLxZJGOsU5w6pyVmE6lGvkLE3O
+oxJLJqCx/TmEvsC/1xIbk5MxHwMh8VsBsoRhbrpAsEoIeys9QMNyUFKXAyET3lYJk26HLjQ00as6
+sVAzWYnUQxULvL1i85SpFdo9cjksYpQk3CkKBW1LqGuHNVxXnuvLjlfiUNCVFn49dwlTy01uWQ6e
+sUehqn9QwIiCNnSPcucUL1G+BWXSFUOJikxHZV4iJWhmsr8P85jafY5s+HbUhtNibXuZo8rkZ6uu
+4dDtTqol5jNm9qkDeb3jY0nJ+DzTUGUjUosVRFUrGxavYbGi4LoOAorGBu0QmgdLli6gybWIxuVU
+JeqGXboDgB2eVaxgcZ61U4tQDoCKCYxJmQFeA+5ycbGUTTxRJVo9ASHtbgyw/z3BAnxy8L7MoSiw
+TsGSoOXAYtHIcTz4kaxtmQG6kPVMDdnQhWmjWHA+ywJ5k+H3DSAWF70TKMxG8lLMhjy89kKUvP96
+rKCYJ7COE+ii0bQscDtiR+SS7MpdBwZZpy3lQjEuL7WPmD3HHdOCTRFah4zJfr+LgnTp5kjBbnOP
+pDX0gnH5rMZ5LHtuxEMlKl3MyCR9dG8frR+Tgz6KRhjOn8naFgwPDR8eWyYnkwo5n01oGej4Mg8V
+RFBUp6rYo1MrDwXFZzA1gUHDg5+jYkXFzJ0VXFm78vd1YcEshZXbqRyMWXUzrQpL0gQ4gYtS/Wj1
+hMsuyctxOS7Hp8Yr4SnA0FBujiSrUPhU31VdTAltLiFdjABwNpnDhLjHBeGwdlDCYhcaHB+Wz4SZ
+Ya768CfLFEkqlqQYi8XYzWu0ia91Kgcm6dr6vR3cvEuE3TjBwdkfyDWO6cI7gNu4+UYJGhf4nok5
+tSu1lUNTQ7LV1KAtBx5DG7s2oHmNynAQsN4e0LuAF0DRO7AdDdAtVTaQpxQymQOOJjyW3s9GexMb
+60JO840nGhaRkH13gHydCbG6gycP/w8AwHBGURgAboeEK60+Vnp7Vg4zZnberVdu/koncoqVa5zX
+WLFgL/McJTP4TnQdufOBrCGhxt4wRW9NYLll6KCinsLBDCsZ+dN4iRY9x7eZ7ExKGzqSf58mCVpc
+rrOsxpLkM8uFjR69yE5f9kUyt+Cz3KM6FiZs3DqNFeY5+QncAi+o/zmvGkh0hg1m/cvbz7HrC8Tc
+iu/gGRN/ViFewKI8RtlgWuIxKiaElWHgguFfPVEoPFmXASHovhVBEfdS1hrQ/O5hjYTyfMnwHNOZ
+7FuDrOOZuUBG72GSKyBlWL2xDs9q9DASZOMf7TV/JQ6FuqiQnM1QuwtU0liG2D+ESf45U2nYdIGq
+qYXH974HAOjQRdoIrsINmWVXCnbTwVjk0OyP0L6HbFs2U0xuwIm5RLGURbXtFCUz0q7qYmOHJa1+
+hd/5I3n4jZCsmZXI6OZX2kfLbshbajQwkUWqETOHERMDP2j7KFl6VLoNp8WqRQootn5Xir3FRrHK
+LyzKAiWFZI3aRMkuudwwYOak+CYwS1s5UoMH2qyETSjtOhYY78t3z8sTjGfyUvgE0BiWjw1Lyrrz
+JIHfaGLmFix2Eeo0XYU8INHJMs8w44tUJBkykpuEjg/TbwhlbHhteXFy5ggMDUQs5U6VCVs1MO8M
+x1R3qjIXNSsKjwhzj+wC9YLErlYCY1PudWujh3xHjIV5MIPH52eyDKlrjbyh39cKWSFwbCdwEHZl
+buV4iojU9RaZoqyixneo+fhLgw6cz3NfXDfQr+TgIX8rDg81YJKe3anQYWdkARP3HkruxuqeYhBK
+1exqSkWnTQcDll6ddhdE0OM8O8bpU4H5PDl8gJwhW9SSe+r6CxgEmS2RoOZBPgg0Iv4+L3OUDLde
+dlyGD5fjclyOT41XwlMwLMBfA9J9F1b5DABQpwEsgkpapgufGeKf+vw1FOfy8/uJgI3eMWK02WVm
+dTooyH04Hp7hg/c+BAAcHp+hKMSirZM+zei20OqS/izrQ9UCWFKOQsTr9V9/A72NPwQAJHRr56aF
+5VLmUBkpDGIrqjjG6aRx0Us8Ik/gYF+qGuvdAfb68t07vRg7b4mlcJSNgiIis1MmMM0c8ZwkM6dD
++KR137zSRduVudleCqXFsgWsfWuzgymrJLN4gvmZ/HucJUiU1NBfPB1SwxrosVKxzOc4HEvYEbyX
+rzoAa53A3BMgTLS1hoQYj7QUyzdcJjigK75YLlAuxAvrhV1sXZPM/07/Cq5uCWDn8YUkPrXfQslk
+plcvUdA+zZfZSiquUBUumFS9YDXg4HgGwhvgQsGyxDq+sd7FFkli7gQW+uRg9KjUfPzgA0yeSda+
+rm3McrmPcVzA7tALGUT4CkOli1hc1qNKI6Pr/95Bio0zSdD6F+uw3yKLNRPJQatCPGeopTRsErWs
+b4QwiRuotYKdyZwm53weZYzMklCqvalRNwzWn9zHiOJAvfUIPj2PjauEnesp9p9LdaKaXmDOylA7
+CdF2mIDWMQKXHHgvOV6JQ8GsFbqxi3hxhCXd5K3RBNqSDVYqA+BmPF4cYXSf7lMDGhqPsbklXZJ2
+nuL8iSzUk4cf4hG1BSylEIWM4Yj8yrSPo4m8jK1htALQWCbgb5H9Jplhu8kcs9IZD3NMKUCaKxsG
+wSET6FX2PSsqNASRBw5jQTyESfDSZuThzXsSn77x+tsAtSieHch8R5MZMnL8lTrGBjUKz4oDvBFd
+l3l2FVoMR2xfvneaT1GwyrLIKxwPpYw1TGoYfNm6kYZbNASsck/5zMS3PxJtx8cvOqtYXRU5ii9+
+AQDwhW4foNR8Uyyp4hSnj6Wz9dnzQyyYdPBDB+sPZG53rl9HxPyBRZ1LI9OY+8zalzZF+dC0AAAg
+AElEQVQsciZOzuc4oqLWsjBXL/2c6k95nsEh4Czy2phSUOIbz5+juifP4e6aD5MH/PV1di2eXmDE
+6pEReDhdynXvTU3YZPUyj+bQjMvRsELBxlkqP29caDz+phxqcfIh3rn2OQDARSzXTeCgtSHXDU5a
+MFm1SIcGava5DE0XO5bsp+s9eWa9/hbgyUGfnk1Qe2JYokij9tnCb5tYULz3kw9kz6beIdRhU7Wq
+ABqcNNG4mMs7YAchLOYuXnZchg+X43Jcjk+NV8JTgAaqQmN+qpFnYsVHdbii6bYtF4/Gcjrm751h
+cFtOxOmJWIMyLfDFn5SEjE4DjD6Wzz4/PoBTibVKtx28oBv4rW+L2/bRg99HBTmVd0yFqwOp4//0
+53dg9oSGe2fwRbxB3oaQLvo8ybGcMXnj1DhkJ1tZFogbUI+q4JBkxSC82jU9WKxdF7MC5YlYnf7t
+DVyJxCXsd+S/p+UMGUOG6bTGwZnckzNc4hv1PQDAlfYAP/62WODrXSGZcY0uSojX4VomFlOxxvPR
+EilBW+3ODrZ35Z6eE4Azio/gMHEGK8GsQVjHSzyndudPhBG6xOIvFo0y0Tq+975ApadVjQVDtHRR
+4Gwq67zUCm/Y5N68wjAv6KGJAypvBp+utmG4qA1Zr7gq4JFDwGWosb1u482bEhr81Nt3cU5P4X/5
+jW9iSE+vH1q4dyRew5RAqJu+jYVFBevUwdbdzwMA7h+fIJk36lsxLsgHkXJuw7LC22RMfoEjzL8h
+62KfBfjKvy1rmAyFNvDoxQFu7orHurd7baV5+nsf3UeaiKe3ny2xSeXxf/Ut8cB+dsuCS6SXZVSo
+CVdeC7uY1rI/v/loig++J17f86fy/E13iq/elvBvc+8WNllJs10TdSHvyHy5RDe8xClcjstxOT7D
+eDU8haqEno2g+kuQFAm6VyOcyQldqVM0yGWvLPEzdyROnF2VBMqbG9cxW7L5yHiGjGVIO3Thbkpu
+IBlPURBt2L0hJ+o11UU2omUP/z/23jTmtiw9D3rWnqczn2+89353qHtr6qq2u7rdnfYQx21LOIAw
+IciykZCCIvIDEBISKP5FhMQPo0RCSEggARGEH7ETBsUBImEi49jtnttdXfOte2/d6ZvPvM+eh8WP
+9zlfdUUOfcuFzI30Lal0T53vnH3WXmvttd7heZ/Hx4DCKPt7u/A9Crm0E7hkLt4L6PcVORqygLba
+QM36/zyt0LCAp9UKCmL1OEwzmmENxxV/0jGAkBwKNw6GGHmsaiN92NVkhHOXfv/AxZKouXJSYxUT
+CxEZ6A7lpHCI7Ov4CXo7Mj5XHk8BMh1VSJFQS3F/CNjXhH+i96GcOq7toM9KvatXh/ANeb1anWFr
+RwKiuljCJiuzR8Xsrf423viCWCm66yNZkNLOtrCYifW2f+M6rl2Ra1wli5NqiwvC1zxRF0Itu1sW
+CqbeHBiwQ7mvEVNsB1dtXGEK0fNmuM5g9L/05W1UpYzFa0EfoStxnGFflng3fAlg7OPeaY5RIPP+
+tRu7OE2oqp05iCNZOx8eiZWDSYGjSiyQm2sDMS3BeH+F3gZt6clvJckaDgvXHN/CFtPkO56DmpRv
+u2YXJq2JkPoP6bxAS0GZyI6QEk1pej3YT8W6G7oDfOEnqfhtiIXZniQ4Z+o0mK/RZ/rZcWIMWWIb
+eRH49jO352JT0IaByvWhzC5aXwJtTWmhP6Cw59JAj5DQX/liH7/007Kga0Oq8PTZAsdPHwIALHMB
+l3ng0LQuYLkv9UOAhBuD4EUAwNYvfwGTheSgXdyCk8mO1BsM4bCmoC018EQWSERo85OiuYicN9BQ
+HEYvtGFQjLZAi4bBvA0dm5lqOMwxI7DQIbHIAD4iLiC/kEW+98IIHdYzNJVCEMk1llUP/VpM8YNB
+FzeukLyEG0hS1OgySn2w60GxdLzIM2SZmLOr5BT7I2Y+vI1wjoU3Pi/8iy+/dA11Iw/eYmphn/n/
+stGIiSHo7MumsTUI8NNf+qKM23gLdStjGIQjLKm2tD+6hYj37TNom8Qaq1Pps+NUKDluVuvipSsy
+tld7Fc6YfbjFTeGl3QG2WT+yvRPCJF7ier+LgJu21Y+w+oaI3TiufG981UJK4pyiNjEkJ2Z/sYZj
+yOusrrC3Lfe6odJ7mp5jwODoaZOju9GKWXVhkDxnyAe+3/dREZxlFkuMie/4hc9dxfaBjK0z6sOx
+JHioSPkHswEYuM6zEsrccGzO8cqe3OtrW0OUSub1az8tbu6T738HJXku0zJBTECZuw6gWLvih110
+7E/3mF+6D5ftsl22T7Tnw1JQCtpzUDfzCwLLYbcFtVSg8whzip5cf+UAL7/xVQDA/LGc5kdPTvD2
+kaAcu2Yfd6g3EJQxVtRSHL9ygJ61KbRhPXsd45otVocTdKBdmm2BjfyUWpGPfoiC5K63RnIqZ+YT
+TLokcX1YYIfVkK67i5d25XQ4X69RJGJWbmj3TceCz4o7368QEIGWnZfoh2KibodyGuyOh6iZQlTa
+gmGxmCsp4Zly+plxDrUQSyjmCfVg8hA5Kdj2+1cxn8vJncQ5kBOxaYVomTodRNKHURThi1+Q4NvL
+13Zw/6GYqPkkQsXUcLpcQ9kytkNacZYR4tqNA7mnbg8mKyqV2WB5SKyA716caOWaJnCZo+KJaJQ2
+DH+Di9CwWrqNTYMD6mLepoXx8v42HFt+o2c4sBmMrv0eDHJStJgisjeCQbKeLPMKIk/cGb85A40D
+RJGP0bZ8bz0rsCLV2ZCm/V4U4ZjzF2Um2orsynaMw0PByRwyhWo6AUxafI42ENANMpSFPqtgB20X
+ID9HRej2+ewjtKdE1lYeLF9csKYbIuizgK6xoIg+7fG5GO5cxYT8pqfzEuqCjNfENJb72HF6yDp/
+tiQr/580wzQRhBF63dvQipx0Vgcm/VZ3obCoZULvvjuDEYiJ1+vIgjmduLCOSb1920S0SwKRsxwW
+6x26YQ8Dinds9CXNWQ96a8P6W6Ip6KsvUjRaHrbqrIJHqvXbt8RXX59+hOyn6Kv/A4WEJbk7/T6u
+7opZftOrEXHRW2Mx99OkwWou/TmZPMGID6TnOlC5XGPclXvzht0Ls1ZbQElYch0UaDbw6aZASc3L
+JRmOg8LH3kDcpzrSeADCcoMSBce2bm343uahlriM0z1DyGxJuDWGRQ3D89m7cOliuF+4hsiXbEfL
+Gg1fdWB3CNG1htDMfOishn9V3o9nGeqSfIWEOVe1gmNSqCes0PD9o1WNL+3I/M3aAkVKmPY2Te6m
+REsSlnW6Ru9AxrteTJAtSKKTJjBY81Ez9vPgO99DSd3MaDRCzYd/rUsEKxlPIwNWh3IAZIRg+5aB
+27aswyf1CibrbYylj3NW4JYNYeWGjYwVo4XrYbBhpIpCdFqZV/9gF+1Kvtc8ln/tWYt0I2vvWGjp
+EkVFAJ96m57fApxLa4MR6Y+hSS9ftYCpZZ5KH3DZp1mSokMcxbO2S/fhsl22y/aJ9lxYCgBQmyb0
+zQrePTk9+nfG6LTyuljdR93KSXJ6fw2fhVKgCRvsucCxfHYr9DHkSZOVp6g+ksBlvjiFvf0qAMCl
+aai6BjSr0FqkaMkjqJvqorik7sZIGXTaVnJKfuO9CtmHhCMvNPxAAni5sULnBis4VYubW/L5zjUx
+oxvHwr33BEo8W5xhw5JltC0UswQm5e7d2oFDV8NAAI8FM/VyjbIj7kGT+GhjOSkcniKq08Dw5fS0
+bIV3JwLLnccnSAk+0HstUvJPdMINKYjGmiIy1kuAQ0n5VhcXmgWe66MXbkRwyHVR1vDNDWbDRbOp
+orQLuCQLScz04gQ1WanoQsOkpRTPXGhaOhoVskbmZGCHCHc2xW1Et+YaLbkVtFmirBe8bo2ayMT5
+cYyaPJ0OEZaLxoBJohbfsFGxolK3PoyEHBEwkUD6ecTvxYsaS0vmxlX6go8yesFEDRbIKYoWJS0S
+ZipOnRojU67lWX24RJ56qkXLLFfekWuV8xKuI+8ps0JNi9WwC1gWX5sRDFLgGRBTN3AqeCRnKSoF
+KtbBUDE6NCdsqwvD/HTph+diU9AAWtToDm4gfF26dPv6bbwSyqAf1wd4y5LFvdVPUWfy2u3dkAv4
+Ggv671/ZeR1be+L35osB3icx0XSxwA4hrBtz1zJ8aPqn7dxBSRBSUTQoSFhhlAEaLtKjRh7+Zheo
+pKQCpdbwHTEv09MzPHoqrkK/a+EjGmL7VIIyeltYxLJoZssYI/rLeVNCK3md0U3Qbg3DJ+lLUcBV
+rNQsG+Sn1Nss10iTzaYm/zpuCEuxXLxJcfRQNqGT+SlMRuJn+TlGjQBoGvrhq/UK9x+/L2N/2gEM
+lgtDo6SwbpNXWNCt8ui/lskKRU0GocJEwzE0chs1KyYXy/KCDn3DGVnXFXymUbWRIGEFapZWaCkv
+v+c7SB15uMuK9RBLHy53U8t2UT3lhtxGyFl3sNANwE0hn/DBq2sYLMu3tY3FnHT3RobE3MwlkJIf
+seTudlTk0CxVn7gW+jwgrKKHo3cFtFRnskmHPrBg7cdWx0A+p6KYH6Mm+W/bKIA6nKtEUqSHT6bY
+uyYVqk1coySs2oeBYiVul2MNoJmu3lRRtm2FttyIBAEl2WGzVYnbZBYejk349qeLKVy6D5ftsl22
+T7TnwlKwXAujm2NkZyuYoWAPXn51hAGDOg+NGoaiWnHQuZDrAuXajp9+hCenDwEA997v4HokYJpA
+TzAcywkTVhbwhBJq7ow/7EOTtr1ZZ2gYyc+TJfKFnEYpGsTkEkwJqb1yz8bbm++1wHpBxFU4xoD7
+rFt1EDBAdfpYfq9RKZ6eyGebskJJ0y9fF8CYlgDEUkgO16hoNSRGi5zKxj03h0HTv2xLZCSfKRJm
+TgY5QGjzSVUhJg35/DwGLVSoLMf08UOOnZxWybrAR+eC2Xh0dg0GA235+RzvnMtv+26GFz3Jt++8
+LtZWmg3gLinac25hVUj1ZLpuYLCCLD6ZYU4m7EbJfDhdCyURaU6t0bIgqkgqfPBATt4vXx+jy6Db
+upDx7oUlmjWp4GwNe5OhSnIcNpTQi1OcE5Q2IznNGi1ujoi36DUAVbrLeI0hMRSl1aCkZuUkYxFR
+YyEm6Uk31Wj8DSfDDKcTcU3jTH53bHsY7onL6KQNMgKkVFJinG+4POYophLMPHlX4OGzySmCAQuf
+0IU5pMK2aWB9KmvHjscwd0k9R1h2sppAEThnNRYcAu6yWGPBwru+M4CmvumztudjUzAsDPwx2m2g
+2xVz8GgV4eyx2Og/+OMzTOj7v3d2ioQS7gGj5VHuoWE0+el0hrc/Et5BY1oiGsiDt/3iDbgEwChq
+FjT5Aq3JgU7WyNbUh2xrtANW5Z31sExksBe02yZejo0wD4AL9arHlcYDag8ovcD0ETURD2Qxzmcl
+JlPqDp4sLkzNk+kSr3woi+n2vizQwcEtDJkCRXKIh4csVe5q2ARy5a0B2yF/JHH/5VGA03PByM8r
+jc5IxmhkDVGT7CU3bEzPxK04PGbZc5FfbFgPz57ixZfeAADYro/ZmZj+bx89RnmVac3Hsnlf7ycw
+tPTd65soHkvf1tMVFraM17rOkFKn0iMO3/YDWHS7apVBM25hmgbemcomtGpL/PQtuXZIoNB5qTAc
+Er3qGhvBLRwlEyw2XJmGB2OfbsMZYyZ1jROiH5vzEibdg75nIPHot6sAR8xygW7lma6xTbWpeT5D
+n9mQUvlYz+X+Ci2dWBRrjNYC2IJRXjAeXRvYKErZnN3ERhnL532Kw+75QEtCW9Xz0ZA4x7MNKH4m
+zxewz+hKsYw8M4El3aS5CWQs5y/tECUJg+ZxDNf9dMLvn1WK/j9QSr2jlHpbKfV3lVKeUuqmUupb
+Sql7SqnfpibEZbtsl+2fk/ZZVKevAPj3Abyqtc6UUn8PwK8B+BcB/Oda699SSv3XAP4qgP/q/+1a
+hmWgM+5gPfwI7QPy3l1x4ZxLFVl+6++jlZgOlnWLJS2Bbl9M0v7LXfyV4V8CAFy7vQcVSoWj7hmI
+PAn8Bb0aNvEG6ZJkG/ESigGsxszR2j7708Kgh5KpI5ySCkwVrJybXYg/yXX4b15XKNZkT84T9Bgc
+PGTl52Eyw5JRraHnYWtHdvDtrQgh4axbNwWCPdr34JK1OjvrY/6m9NmrbXgX1XAR7A6DmAyMNnaB
+k4Wc/tNFin/lLwogaddxseRpPe5HWBFAoyLhTqwaXOD6p7MTRITgXr+9jYjiMi9cC/DCizInoz0Z
+V2vnBShmMDyjg7QUM1hXCUB+zBotPBLbhLRcOg5gsE5k3ZpwtIzRa1suVsRsNA3Qkl+jpiQ9+h4K
+siGjrFERHjy3c0wKZlfqFB0S0VzdpzT8JEUyESttUp6iu3FX+iOEtKAWzQqLOV0vMnhbZYMJXTrX
+BCiWBXugEfSkTzEDsVleAgZJbZSJMS2apnSRsiak7mooWhbuSPq27QUwQulbbdiw6Eq6jYK9ETZy
+zYvsQlJKfwqjABMqsJwQRHHDMnOYGa2YSY46+rPFKVgAfKWUBSAAcAzgaxBdSUCk6P/Vz/gbl+2y
+XbY/w/ZZtCQPlVJ/C8BjABmA/xPA9wAstNYbBZKnAK78Sd9XSv01AH8NAHrbu/DHFn5u+2eR7Es9
++7YZQZsSkPl3uv8m/rM3/xsAQF4lePutrwMAOq+K37R67xABfdLwJxRCpiR14AAkZrVMCzn5/aek
+HcPZGgYVusyyi8aQ3TpvG6xjOZnTsxgN034rkxBdBxsFOt4L/UGd4+G5fG/Q0XiH5KcNfb1J0iDw
+pG+DvT7Ge3Ka3dgeYOhJR+4+keDT9okH2/0IAPD7f/w+jFj6fu1z2wCpxIw2xarcaFXQdqk1YlZD
+KlejcyBW0+eGPo5XEjNwdYC9SKyU8lgq9p7cmyHN5US5f+8J3tgWiycsAYepsGHoo8sxsBgkPX78
+JvJGYh+hKjBPJTCWruaomCZt2xJkRYNtbDgNDDQUctHOEoqB5OH+EK9tvqc1GkLP3Yx0bVrDYKxi
+5RWIeY15DByvZf6mRoHhcqPHKGPsmiZaVkCeHpcX8nw7VoPjlLn+SYkFFa0boia1acDhyW75NvzN
+/Rs9RL5YPb4h450VJUD0Z+g6qFaMVw1qrDfp5dkEbS6WXkVZwbAOoFiV6ocKFSHh08Uc+Vwstu7g
+NpYxMRmMd2RKoyZmx0WBfkfWxfnCxGIu82DqGOn5p/PgP4v7MADwKwBuAlgA+PsAfvlZv/+jUvQ3
+Pv+a3n0pwiJtMe7K4Ox7fTSsIhzfcPFr2bsAgG/8wZv4YCE3fOWbEoh8a3mOa3PCOv/2IdbV7wMA
+ykDhXiYDudWzYHZvAAAGt8RNONi6iX6xERdtUXChm0aN9bkssPeTGQ5J215x4ZqFuhBfkXuRRawB
+qFpMuyoB3L7cS1yTIbgx4FHz8o3bvwzzGnP25gCnzO9nrGs4mpdYnsmm8M779/H6y3fkngdX0MhP
+oDKbC93MjJiAk8k9pIzUh6MRuixJXvg+hsPPAQCuDXzkx9L/1Zn4ZVff/BBP6FYtpgn+8JtvynUD
+D2OOy6OzBX73jySIWROA0zQVXrwuUOmX915BuL+htQ9QJxtwTwMz2AjnSt+bukTmETeQmjACWbi7
+3S5GLClfnJ/DIQbC5aavCyBh9smuTIxJQdbtOthuNzqXFsaNrJ2YtS9pSsEhAN6sRMhMxMAO0GF2
+6X6cIyfl2YKVrVarYFBeftC42Jx2llNCU0w43VR4qgC9noxFz+rADjaUfQUa4gZqy8cp1cfajYtz
+sA03EjhzlvcwuEk/4Po+zAWp9ZIc01zWco8Hi2GYsMkb6rkePE/uI1ikyEtC4VOFCQ+OZ22fxX34
+JQAfaa3PtdYVgP8FwM8A6KvNtg9cBXD4GX7jsl22y/Zn3D5LSvIxgD+nlAog7sMvAvgugN8D8K8D
++C08oxS9DRtXzD2kWzle8WTHNKwO4ppQ20Efv/Dn/zUAQPJgDKsV899IJNj1ht/iym3Jn/dfegHZ
+CSsHF0/x+kxqz586c/QPbgAAdvYYnLMdWNSnj1dTqFZ21PPpFL/34DsAgGW5dcGd8LQg5dsnwowf
+t1orHG4EFLMMEc3K3a6cqoOBC4My8tnVCl95RYJ2w5s9dFhRB6arHn30PTz4I7GIugfbuPGKnGzb
+Qw8rT06M5axCQ63M6Vz6dnI4u2CY7ez4GPck0NgfFIgCKjS711FY78i9GFQwfmkXybsMamlgxiq7
+qK7xpdeFL+H2z3wRYShuR5rJHDi6A/BEdJw+XArAFJWDhNDtumrhkAqvaT6uouxQqr5yAE3oruoO
+ELISMbfWOJlKn30Gew2zhmPTRHdMhMRsOH6N5VOZy/N5hpzB3Ywp57xsYZBXYNTrINoiorFp8M6Z
+jPN5XOKURVU55HdrU+PAlH420HCYA60XGo2Sa9QMjM6KDIoQ5aEu0GExli4L5NT0vPLCAEZfxms5
+l3UawsbWNVm/7v4QJiteq9TAdC0W8vLwEGOqlIchBYA8oOAYZ3kBTVdqnZVQvO81ari0Xp+1fZaY
+wreUUv8TgO9DRJz+GOIO/O8Afksp9Z/yvf/ux13LhEZPa+w3Poa1DPqyseFQS7FRGt3RzwIA9l5u
+8PQP/kcAwLdMYR++oUMER7KZmL0pDOadw+1dDK7Jot8aadjEN1iM2LfN/KKs2VplWKaycD98dBd3
+75MmvTlFxcC3JsuNzxv+uP+bppBl8WZ8UNCvLVmlZrkG6jMxHZ/+t0/x/d3fAwDcvrWFnZ5E7cu1
+/G4xX6KmCfvG53Zxuyd+e40S9VL8zDJrcb6Ua8+n8r18HaMzkkV84I0RbUhB1L5osAOoSx9TKjn1
+I1mg128fYM4H4vR0jnOa1PHKwnePHsh9fzDAaF/M436fZrLpI9oVLIFZ1GhILV6kGUrGPvy2gsn6
+iJwgHpgmrJJjZSgoljgnaYl2JW7M2apGTIAayaYQGArR5mE0W3h0JdymRseVeQ36HYCSADbFf6vz
+EoqbebcbwGaNw9N0hsMlS/CTHOckKskJVDMsE4s1s1yhe+H/pL0CHR4WZSrfdysLZsJKza6JlipT
+WdFivpKNZzsfY7BFkZi1rKJkmWNIsFWdzNEQlhyfnsIgx+TAMdC9KlBok7GRdZnCooitRoUVYyqO
+spAfSWzO6EcAsQ7P2j6rFP3fAPA3/qm3HwD48me57mW7bJft/7+mtP6TTeE/y+a7lr69F8F0DOzS
+PK06GmMW5biuh8lMToRUJyhXchr1GcjzVXuRK48nJYZdMT9nsxSJ2kA/awxpxu8Ect39gwgqZs6/
+42FOnYVVssbJlGakZaFmsY4KZIf+j//mf4StbXItVldwbykBwer+Qyyo7fit3/8uJmsJp9z9ofwd
+ykRLVKRWLQqamo5t01gFNDn+iqKBTTxC3bYXVaKWaWMwkJNmMBzhYCyndEz2YcfrIl4yj50s8Wt/
+8zcAALf7XURKrKbvP3mKe38kVsqTQzld3n37Q1SxIDBnkzOYzKjk1QoVOSnqqoIbiMvmkvqsKlco
+mUB3/AhVvuFLWABEGHZfuIbX/4V/AwBw52dlfncfe/j1v/yrAIDh2IVJObr/8D/567j9igREf+rK
+a7CGMgZdQqLd3gCDDReE5WEyE2j2B4c5jj8S0pNlluCM5DKNJk6lTDHlqWuhwIwVh13Twu3XRX/j
+ywe3UN+QCtM7tNxGgQebffPc6GNRcCj842/8DgBge0usuJGzC4scH26tgIDw4qa6oOxbTu4iB92m
+laBUK9VgdiTumBpeg89M0mjURzWjuFDUh6Y8n2nKtdZHEyyJ0qyrDB41PdMHh7h6R1w+d4gLfM6t
+l9/4ntb6S/gx7bmAOdsAtpQFx/PxwhuvAwCioYfgRB7oxycfXjDl7HdH2KbgzXhLBne324XRo0LS
+aYKc4I5mnCMjDDg/T7Bi+e6Iaapx2IMdMNJdNugP5MLVVoAh8eeTSYkl3f15I5PSTiq0EaXKX7yB
+lytZeBPjCt75R5I6fXL3MfJKFiEzRbCNFi4BUq2nUNO9SJvygu7comCNYbcoaX6WpYGUoBmzUaiW
+1CvULpybcvGDgSxMnTcIIlnQ04kBZyELouhY2DuQTfT10RVY818AALz/9m/KPU+msMg1OPAjlG3K
+uYmQ0Zet1xkcjsXOvmxG9v5LwEzuYzU/hc5lLBbZhyhnFME5XsB/h67Cz7OfRo4npTzEXfN1OFzo
+dqrgUfMxuh6gT//ZolupjAprKoAZToPJhGvkwz/E3e/KBrG2wgsB4MVCMlRFZqBmvMdvWySpbICr
+ykfyWF4Xry1w54FkV258RdzV9Y0uIsbjHXgXD7cCUH1IHk9X7tm8HcJjgYnWFipmvsokg8Hfzhob
+VSK/B5aDK9dAvyubUbw4gsMMVZM6sMjqVczOYJCIpeHOpEIP5rnEkjwrACgiYwVAXD2Un7BvYnAp
+RX/ZLttl+yzt+bAUHAtXb4wQGwGOyV0QHgHjleyoT2cJWu6S13dG+OLnXgYAjAmZdQMTfZJ/WK2D
+1dlDAMBqliIk8MY0LLz1gVQEpsQmbI09hAGDMOsUbSWneIQxrt6Uk+adj46gGIiqzmRn337tJcSs
+e8mfnl+QbMzf/jq+/XsS1Y/XMSICS8Y0KS1PQ1HcpOebaOjOHK9iKBJhDHkUjwIXM0q0rTIDbi2W
+Sd4ofEhJ8hwFxszvX9+XQKvVHyBjAc8Pf/AWXntDwEvTsxJPTljQlZxh8oN/Ir/9pgQRjbZCr2Yk
+P3IRt6wMbS3s9UgRZ7kYs0brz33lawCAL71xC11PXIJv//DbePxY+nly18Hde3Jyv39yiu8+Fnel
+822xtr508DJYqImwu8TLpNG/9blX4boyr1XaAgT9OHSf3j99ijWdrendCd66+wMAwL0//AZm5Ifs
+2DZsZhqWMwHAtW4HES0eGAYeHYu5XtUGBmt5PZ3ew/8VyhgMc1kr/zZ+ET+9L0aEyV0AACAASURB
+VO7MHCV6PiHKysTOT0nGoCrEwkyrFi7BTSgqPHhAgpsyxs6EQckIqJkZSUnCsh9oWErmfVk1WJEP
+w55OAGb3V6s1SkrodUjzpywXZLHD1KkwWtIddTrIlmIVVtEKjv3pzv7nY1OwLOyNhmjaEk/eFZPS
+iDr4YSpuwMC1MOiLiXZtdAX9HlGBjA3YqwYRwSGGbcPqi/nct1IEQ/mMaXvobYkpNv+QlXC+hZyV
+lldaG+tiU33no+EDeX1nC2uW3y758G+PD2BriRecPfxdVNR1+N1vfwdbBI8NHA+mx8kjgCayaxSM
+stttg5YL/UpgweSi2CUr59bQRkAymOm5AdsjOGmmkFKF6bxIETOYv7stY+Luj9HSnWlqjSvUcjDL
+KT64K0K5R2cxvvnmHwAADvboXqwb9HuUqk9ybLWyYVWWh919eWBD08OQmhI/MZT+vjC6Aof3+Zd+
+6auIKZpaBp/Ht/+B/N7f+u3fxslEzPKTB0LyWnzhz6M3/Z78xoMZzDs/DwC4uXuAgq7L/Xe/g/n4
+JQBA0sicLedznDyWGM3R6RSP35GNZ5HEKJhd8HstUtY5lDSd+w5wcF20NeJZjCtUmUqeZgi4Oa/X
+a8weyxwfP5RM+j9Md3D91wU4ttUmSFldaYcD+Kw+NKjTsDq9B7cjG0WSzVFlkiWqzybIWGuhK3XB
+D2kyQ2K5Diy6T307wIJMUMoNYJKKPnAs1CTo0ZQa0GYFk6Q1mCeYd1kR6wYIUlb5PprB3P90pdOX
+7sNlu2yX7RPtubAUAI22bTA/mSCbyW54fxFjZ8wou93HdVJdb29H6FHWO2yYBx82cAhiUYGCXTNY
+N0jhuHzfq+BqOaV6r8jfk3mKFU38vAQswpGbtkJryenh2R34Lck0qGjUJiusM6GUr5PH+L3vnLM/
+OVQhfT5pT8CgNfZZheebLgxCome5vhj8jrKx05N76YZyAm93+hgwen3lWoEpMRLdoYujTE6o7OkC
+GZWFjIHc02svvoJ5tanv92HR1FbtDPXyuwCAh+8q7DJX3q/EfWr750iYOXjptoUxQU1WmSKhEpIR
+teixhmEcMpJfxPC7FKSxXQxeYNXpahfbf1nu5f5bP8A/5Il+0Mh7+d33UAxlvP1uiTIR/gbVTHB0
+LHUX9x68BcMQuLXBWoTVcY5sLTl4u29jixWFt2+8hHJNgNO2DZtiNh4p6PZ3fWwzY2Q0IWwChJbn
+ZzhMJUD36MM53kvFlUgrCWYu3/oWvr4v490bd/Aiq0M/d/3LKBjwzYg7t9QpzmJZm7WZoT3jYKU5
+qlqu4XQsjAlvNm35nlF7F2rlKjYR0JqsagOVS2X12kGgKRhDkpl1u97EKuGZJcw1IfLrY7gbi1RN
+4NufjqPx0lK4bJftsn2iPReWQlU2OD5cYl5VIHgMQWRiVLAg6qoLx5W88rjbR59Wgc8iGzewYJmb
+BLIFzSIhQ/tgfRHQ1DApOKIZDPL0GgW1+nwUSHM5rZ3QhVPI6XiCHE4kO/smD/z4/vt4rxTrof2B
+Cd8XP7tcrKAjIiGTBjsU4fC4m/tj+yIw5JspDFo03WGIcBNoZPA00CEaR/6uKxPdIQOpuYeDoZzS
+T48TzJiP95kKK7WDoC+vd181cU5qtu8ff4jZmx8TnvrkVmiHcv/zeYqtiNWQS43BLTIHnxYIKUE2
+UiP07kjgckCi2Xo+R70g1HZ3DGvDfhROganc05e+cgdHrBjsXKOPvzzD2avUcjgxsHxFvvfRW4/w
+rQ/+b7m/t85gdeR0Nyv5e4E5bMY7tpSDa3ukh3thFwEYE/Js2LaczGZEJu4mgAWJMyjloeF9e/kQ
+61TiFea6QUDkpVHI/Z8fv4Vv/w9Mp36pB3PwcwCAvV97GWeHEtt47Mrfr017cHapKzlfXzCslpYD
+m3GLEAot0+Cqlrlu0SIju3RiNGg3oBVoWCRxrRwNMHiqaf3Fh3NUTL0i7MAhl0N29BTHhJ7vpiH8
+/qcriHouNoW8bfDhaonGsLGhxnM8DybN1sD1Md4AlSwfNlWUFINzqm1gsBQWjg/FKLSBEpuSAmU5
+0IVMmMVAnQMD/ki+Fykg31Q4VgaKkK5JoTEkrmGHgb/vPr2H5bH8/e6jd9AdSc6+is8wJdmJZwIt
+3Zua2IOiAlZcuI4TICLOwlc27Iiy9Z6Yw9rpIqduY9kaMMi5V3s1dncF7rq7Bh4vJLD33qOHAICX
+X/0y2kp+t9N6eJ8Vpe98e4EfkF7+1ut30CMGomKQzFUl2oqD1Q3AkhDkXoh9isP2b7+B3U3gkkG9
+Im0QRwz4PjyFQfIWZwQkjHrffOFr+CrrGd68L3Pw7f4Uf8H/JbmndorzpfTzZP4Ej98RUM/pfIpb
+Y5Ynk3BmpMcoWRo+snrojET4ZjDYu5Ct3+qP0FAId6PR6FQJ/FL6njgmXLI5K0wxyMX92b5pwiHe
+5XS2oQFvcd+WcRsddfDNc9GovH76BRxTe1SVciicIcENwqfbJkdNopZOaMCqNtkHGxUrMW0C66xG
+I+W6qAsTNsnKVKmRkiYf8wwmgW26oRtrKMzI42kUKbpkB4+LFOlarnfYHmFYynp51nbpPly2y3bZ
+PtGeC0tBtYBdAI1ZwiL8dOTa6ISy2/VsF4GSU9UqTDQpkXI+yVRSFxQGhtmxYIbEHpTNhRQ7bEBt
+qpgIn1Z2jc1GHHb6KFm7n6xKlNUG31Ajp2lu0+xQjsZ8ITnoapkhae4CkCKpLpmBoya6kLNPae7Z
+toZL+PAgstCLpJ/jXoiAefrIJR2dZ0ieHkCbZtC0hBzXxJh8A5+PbmD5SD5TM1jWNEBAkZGtUYT7
+53JSHp1/hOxYAnTNTYWUAU2bUEm/6KCh3P2T5QwepD9Xdrcw3JLg2u6oD9dkhR5P5WpRwiXvhaot
+lBVRfBOgpj6D19foHQiUuD4SfERwVqH+oYxbcbCD+Up4HeZPJphMxZwvihoBLZKXbkmQ2LAbgMHo
+8c4QI7qVoeWiT3zDuL91gftAQMsrCWGyctCJc5S0bkw1xu5NCs4c2Wh2hxwvsRSSk2OEZPbG2RJT
+4hjefffriEglbdLnXWkgjSUNaRslKoPFSq0LZZO3Iyvg/Ag6FRAuDwJv4XgOVEENSqOGvRF5MBvo
+DW8DsRKdzELBVHXTmqiZLu35XXiGBLTjzEeRiDX5rO252BRgAI2r0JYtOqTYth0DIFijbQy4NLVq
+u0BDAdKKJY5mz0BL5SGFCqom1LQtAfIq6nIBvabIKfnrqkSjIcOiagKAjD+Fp5FlpPquWixZWpuy
+Yi9MGmRaFnE/qHB4yih6puFSqFLZFc6ZY94m8Mi3++gQsKJtDc9nNWcYoNuhCe7RDUoyFKx9aFFj
+2cqDEMUehiN5SJ3uFo7kazjkQ+qFXaiNAVgBi2MxxZvyMbp0Yxb3ajhkTPYonmu0Cg4BYC+ELrYH
+4pPv7V5Bn6QeZjxD3VI/sSIztjJRPJSNIK0K1NyDq9ZCS7xBeDXCDvECLfkjHz46x+duS1wmOG7Q
+RjSfmxjOBpbrWAiZVYmYDWpVg4AEL8OtMUYkNelGPezsyLj0oh6aasPwJOPSei0sQo3bIoVFoBac
+CjZBa8PtBjHJSbb78mA+KVaoFyRTWR5jV1P45/we/NFrAIBJKesjgokqp2hsnMAlp6KtHOR0JW0F
+qHZT5clgU9OgIT24anw07UaRykVLMF9Ta2i6JgZrZmyjRYeZhWW+REMXJSk1sljm1XcMGGrDIvps
+7dJ9uGyX7bJ9oj0XloJu5dRvDPOiQ0PHgctTPrQtuDwpbNNHjQ3tlny2bisY3H0Rl9CNnIKwNJTB
+E2iVo6U+YLGp5KsqFHQTqrpGSVO1URZaagKmaQlsUGMUAnn44btYPmKwaG5hd0+i6MWTyUUALikq
+WK6cco7NzEBdQzM63xt42Pbkex3Lh8eTqyX1W5ab0CVPV6ODDgN8dVMgm1OhuD/AwTVyIDwS3PW7
+j+7jpTtiqreBjcMzwQesZin8HnksX+yj/L5YEPVGOj6psOPLqbu7P0Zni/wOlot2w4KdJGgWJC0B
+g1pVipwYirICzK6cpE6nC5tITr2uoGjG731BLJD8+ARPa5mnA/M61haFWuYrePyeZ5jYiiSIOxyy
+SKip4ZO3cSscYdylpeB20fPFUvB8FxUttpJSgY2uUdcbYk0Dmiez6bYAKUUjbaBONxap3F+/a2Mv
+Is/GssKcCtWLaYu2FpRmQtfONGskxLoUyQourVfLMNHQdTXNCqRhQFMT8WoqbAQsVL2+4H3Qlom2
+3izy5iL7AEvm0bENBCRyKVsXZUXTBA1qShUm8xrL9aezFJ6LTaHVGlnTQMNAyXThPANubYu/WLgG
+ChJoFI2Coq9t0Dy3TQWDA2W4HoyCA2y30DQjYbuoGVTYJGhK7aFxZaGUWY2K1XdNUcPiRFtOhqgr
+A++WMsnT1EZKqvbjeAK7kodmK83wmPQrTdvAOpIN6QnTpY5jIWAJ8JdCB84eS8OjCCbvT1MbsHSz
+CzrxVLswubnBj7AgAa0frdEFhWcHhGLPp5jMJb7QcWycEnNvOdewDqTOITlKMCQRLDoyxq1jIuci
+zoMAC5ZD+2GKei0PpNfvICNpSc30RJPHmBGlVaCGzTHUukJvSJN/aSHckU3Ni+Xh/2iZYzyRDeJp
+N4FxIubuPM9gbtKdlkbODFPIh9+Ehs/nJHK7iLoEAikHDTMpbaZQVfS1WedS1Qo1y++TYomGxDeq
+qlBSAapFBZvpbpssXKGzg7mSSkvdBDim8K567xF+8idl3muSmBROhpIsTHWZw2DfwjCCv3GLXRua
+qeiALrFdBchd2SDXeQmTmSHHAOzo4/nRFJutC2YtQo2WupRuVcJkJWkZxiDdJtbJGdxTydA8a7t0
+Hy7bZbtsn2jPhaVgAPChkdYlaubYVV1AW7JjdpwO1lT+ffrkHJ0uufZoLmqrC4shXQ8KtGzRtj7K
+Unb2Iq+Qs8hpQVNhvYqhGcgpqhxUjUNZtJg1NAPjFEuHJCskv+gNDHz0RE6afJ4hteV1XLbo0xLI
+Gwv2Rkac9FqO2cLs0LwOtmCPZZfvDPsoNE9pioZMkgoPSKn1aFWgZrDSVi4G2+RQ8BVsBtcM3vTj
+o4e4eoUng7cHv8O/Bzl6C+lH1UnQDkgvz3/btQuPqsyz1RJpSQukNND05BTb8gOA0X7ToLWmO8Ba
+XK3D2QpJKZ/NaqDP6snrNyyYxBNYdIOKAvje974BAHjV+km8PJTKV1QF2o1CQGPCh/yex/mN7A5q
+shNXRYMV9T99z0BNF1MHwCbET9gEmtpF7ZAqrQZ88tTpwIXXyIfi7CkqiuAUmXzfzBx4DHxadouG
+rlJ8soRjb6ww6UO1bLG2qbtpa3SY4alKhYxFTrBadNTGGiQhiyrQ0M1RtUZrSt+qxka5gdbXBeZT
+Was1P2tZJRQtU+Xa8BiYr/Piwv19dLZAXn867uTnYlOAArRtoC0aZFTbOV3OgUzQak2l4ZLUxDEd
+JFwUxVqivu50imIs9tIIBxdlqFV9gukDebBWeY4jiq0mG/l2RyFg9VqbmchNliTnFUpuTokyMJtS
+L+JEJvzWax46TIEOejaWzGZsRT569Ps8x4LtSz88agE4no0u06FzlaGgCav37qAqpG+ztdzHNI0x
+Z+yjaUvYrPa0LB+ZlvfPTiYYDQU4s6o2pdctUoqOdtYx4ljSezuuhibCcpK72OVD2mPa8CRJcHgq
+/VkvZ8hIsGoWDfYHskh/7gsv4YVrohMRF7Lofnj3Mb75nvjWH02nqBjoGQ0i7NxgnbW3j9wRVa/j
+U5mPMl9Dc5N9eP8BdgZyH0VbXWwKpqlgOaTPp5bkLJ/BItnNvK1hxEwTdwbYjuX3toY5PEoFaFYf
+xvUCyZwqTKiREdQ0/TBBncu8jrr2hez8WkuMJl+36Eakl/dCxEQepnoO25FFoJnBsrwaupVrBVEP
+Hjf6s8l9zAlkSqsMimvLtTcisA66I+mvgwgtYy2raY0ptR6Ojo4wo5aDb8laGAwDDENS3Pcb2KQX
+aPUC81xcyCcnEyTlj4iUPEO7dB8u22W7bJ9oz4WloKBgawOG0ht2cvimh64rwSVPacyPZZd8Ms+w
+3piENPFGXo1qIQGspCgQejSJzRyLmDqO8RwVv1gYBLE0PcTM//u1cTEYVm3jhDn2s2mNfC4nwcmE
+pv3TCRoSGUROiIBgqXFRw6Q5FyclPLIOb072ZWthyYq6bmHincdiFZyuvo8WPKUJk06LHD0CbIbD
+HsZdMa9zH7CoVh36BRpe+5zm6ePTEncOqCXpNSiP5HSMywL7eyLWdWN3iPDrDwEATxeC3z8/XgDe
+pv6iA028wTR7imwtv/HWu/cRzxloZFDv/aMzTJcbrINGyGv0uh34hOCevnuE9li+Z+/y1EorrFiL
+cNv2EFty4mVxgZyBtK7rwWPgL53JWBmGhk2Yb6ULnK4p2764j7fX1OncH+GAYKcglDW0XhxhcizX
+OJxMcPRYKjEfnJxgw8e9PzSx22E2w5P3BvsRTFo/VVOiJuBoXZTARomLVomCj8BjULk10JITslyv
+MZnLWCwWKxRcO4qZjigwMNgXy227t4WGqYq7x0/x8ImA5JJJDBhy3x0C3eZLCzFJdna2XIx3pO/+
+0IVFNxUw8ZTCRs/aLi2Fy3bZLtsn2o+1FJRSfxvAvwzgTGv9Gt8bAvhtADcAPATwq1rruVJKAfgv
+IMrTKYC/orX+/o/thQJgGehYLtJNoY7jIGIQ7XFyhLt3pfikrHw8IuIrtGWX/PLWCOebOMP0HOOe
+WAKma2LNU2AJB3fJjHv/VP6N43cQEjIaosZVwnkPru3A16R66yaYLujjEh23tG3oWqyDk2IKFJtq
+xvzi5DZMAw1LIleEu/qOj1GXYiqLOd4+FL9173SFcIuBLUesnPvLCofUd5id13AtsVL8noNdSsG9
+cOsArztE2N0gw7HZx4o6BMn9E8y479eFi+Jow/izwJVqU0jDlKyVwyXM27Y8BBzDbW8X8YmcNKvF
+HI+Ja+iSxq7X8XD9ijAa+d0eHnNsyyxGSYagozLFsCtjd9sRi+cfuW+iplk47wDWhOzLSiEnl4FV
+lYgpIpPRL35ydIxJTLh2YuOQEOMsLxAwN/+ydQO5LxbEnauvSt9sB0tqREzyGaKRzIOdpigSuaeT
+LMVZ8lDum5wdV80WLdOTs1wjIWSz1QoJ0c9XWHzVOi1qBiIbv8F0Lfd/fDzDR08ktvPR2TkyppQ9
+Wjy7wyHUSCyirtlHTkshzxvMqWmatBVqckAsShnjbqUwoxU3SQb4856s2Warh9d/4qsAALOq8fW3
+yCb+jO1Z3If/HsB/CeDv/Mh7vwHgH2utf1Mp9Rv8/78O4C8CuMP/vgKRoP/Kj+2EArYcA4UNHMcy
+kEPlwLdlcMJOF9ORTLLd8bFPYpDeFZbxFgoOAzy1WsMnCKe2PTiRXKOXtIgI893dQGeDEP5GNMR1
+LjaIyDaRWfLQvOAMcPRQFuGaoJlRaOAkoBl82MDxNvTzDfoUjgm1ix4ZSRw+mEEUgZwumCxW8C1Z
+HD/1uVdx8zUJ4LmcWHz7PZzQ3DV1Bps4BSdvsGJwMF6c4XQlD6TjyMIcX78Og9V0C1Ug4FhM5u8A
+C3m4B30LLc3/LVYf6uUYE4iLNnuyQEn8wt6gj3bEkuuqxu7OBtQj1+1YJgIGYFOoC3iwAY0u+Sbv
+XO3j9m2Zq8aV914cbuH757IpxndX2HlJHryBa+Ocgka2ZaDPAGrDB6ipF5hM5PdMw7zA86iBg54j
+fbYiwGa5d29AaLAOoTrEqTxcwe4KKOoLn7+Ds5xS8+sMqpV5HZNG/+bQxYQuytn9hyj4OrACmI1s
+SAWxG01qQHGC42WBkBDkrYNtqA2oyzNwdCIbYDyT8a51A4Mb6HgUXUDsZ9sz7E1krS/sBhkDrDYx
+D64yUGxAWJZGQyq/nq3wys2b8jp08dHh/yqDhAd4lvZj3Qet9T8BuFo+br8CkZkHPik3/ysA/o6W
+9k2IruTeM/Xksl22y/ZctD9toHFHa33M1ycANpCpKwCe/MjnNlL0x/in2o9K0buWgRQtotZBd6O2
+YRXY68nOvz24ijs3ZGeH4V2IqBguc97JBBlJPCIzgLmpTV/MgFxO21tXe9i9Jp/PMzHR/chFtmL+
+uDFQUy/CNHwMlXzmo2WCGSXZYp4SVmNhd5s6BQsfS1ayXe24uM0Toe966NygPFgj97EqbXz49KHc
+f0/jJtOCr/zkAXa2JFBaQU6Rr77SxW5fdvvFIsH2NUnZTfMWDtGPg94W2h2xLMxSxueVOzeR8T6C
+pYOwI9NhbXUxm4jZuTxbgtIZ8Ili3NpqoHMZnyvdFK5FTEMzx4gyZXUGbDM91xPrG8eHj6GpWZGv
+MrhUz+4OIrDGC7d2tnGwI+M5Z7Ga32sQrigRGJ/jw3vvAQCubQ/wcLnBChRYVWKljWvhdOj2Qrxi
+SOpxvO1BbRSYtXlRaVjsGXAZdIxYdKSVDZP5/WHPwY0DuYFRL4AdyjwslyuElvTT4f3brsb6gw8A
+AOtFioqycO3IwOGZBFhTwtFHnRA1OSQ8WLhGQZmelUAz1b7sO4hn0reYVokRNuj0xCLoRx1YNlXK
+jwqMt8mzEefI6Y72iH9QRgMQ/fnK7j6uMtA46HkY9WXt2XaLn/+iWKH/8//2bTxL+8zZB621VmqD
+5v5U37uQoo8cS6erBk3UQDFiP7BC+FoelGEQXZCBWNpCQ2HSkqwzqW9hTt/TigOAg92WK7Q07SJj
+D0MCbypiyy3bQhNuKio10oRQ4zDCgjqI+dtPcEruv4S/UZYZ7EJW/N6tLg4GMnE3Vxn2qVOp4wYh
+WXtXxKSfTddwWcpsdRp8jnh+u7XgaLJFUT+z0x3jJhfm2rIFHw+gUzqwPHkoOr0hVr5sBlt9EdKN
+1xlyQzZC3bsCNSXQJ1bYf0nINrztHQzuCoX5hrgD2Qo/QVDYXn+InDUa5+cJVizLDhMXMWnuHbpi
+gRmhYnziejhAL+RGfmsIj2ZwV/cweyCfSQLZYLtWF51QxqesTSRU++m5IWyChZpGIyU3o008yRV/
+BwUh6H0/RBTIw137Jmw+LGakoIhQ06Q6b1SOfkSuyaaPg0geoPGoD4+7SWtHqAgsmi02a2gCxBul
+rhqGJesiCPpYTiROsFEWc29cx9aVG/IbaYKWLg9SEx7jElHSImQJ95AHyM2rW3j1imz6fcOCQdzD
+lmsiGkr/tzz/gjcy7LAmyDAvyu87QRf+hvux9BA/Edes9oCDlwTv86ztT5t9ON24BfyXnOk4BPCj
+NC+XUvSX7bL9c9b+tJbC70Bk5n8Tn5Sb/x0A/55S6rcgAcblj7gZ/8xWNi0exQlGWoE1HTjTLVgw
+iPI8hzOg2WnY0MwSVJQits7XiCesO3cVwpHkqKvzHDbRhm7Yh89gHYluYZkAelQRPp3BYg66iAHD
+J8lKaMJlsVWfOejYCtCQOOUsmcFn8c1NT6GmRsSgY0Az+uwG5Oczc4Cm/1XPg0dT1crURSFUxyfj
+MHIUm8KnbgNNdWFrO0RlbyoVNQxKnKcHrIqLUxR0Ca6NAmQbjT11gGnDysjzCaaPJE+/cOTkVlUD
+n7RzHT3AnDqYZ3mKJS2oYSdAwOudk1LMD6/CJaFH46wRMnviaYXuUE5xvVgiZ459vZS/J6aNPQaK
+D5cTnE3FunnlK1/Fm0fUiDiLUbLCz6qondENsDWQ03OnO4ZBrYqqruDwvmFkaLdYHMS6L6sqcbUn
+Log13kLPl+8FlnuBhWg7Fs7J0Nw00p/VrMWTWCwlx/DgkHVbBw6GDOw93OAxltMLXMQ2DJjZpnCp
+xlVWgSrHhHsovzdhoPXVgz1cGYqrETkhFK20XaePlEFle2xhnonFajHgHXl9DLqyXpTRYKMtqLZ9
+tPXm4VG4Sjf0WduzpCT/LoC/AGCslHoKUZn+TQB/Tyn1VwE8AvCr/Pj/AUlH3oOkJP+tZ+5JDcRZ
+AYfm/Fj5yDkx5ngElZEpJ3RhEB5rMFBf9l3oe+yvV8OlWW6kU9TkMGyyHM6OmNqacFCj+Rj+6UYe
+kmQDilkgI4kIshg+mYzA93rDEFPIw+QUGl2W7aUO0CXxqrMK4fKB65GANdzdRp8pSadoEHLjiewW
+Nku/N0QgMGsErJALbBdK069vfRSUez+LM0yZddmKaC5vW+jTjOx2Alhq48sfIaVClnXFgWIK16s2
+QC4XE6YnLX2OimZ763TQYwFBG4ZY0KxuAqZZ4xCaQ7UqEzikH6+LGDWh2Z6nockmNJnLnIaNh5LK
+WVnsocrk/Ss72+iSROXwZIl4uVGqkgdv1/LRsWR+u04HDrMZqVlh+VTi4at8hYLjWa0kRmNFJqIN
+mWsD6AtBFg8e2aRKy4LDyL6lWfmIOYol6xJsdVEG72YKr/2ElKjPfyAQ7unyCLv8jXzHg8m+BY2B
+kA+vU6cwhxsORrnWnr+DgbPhIHVReHKvHc8BaskYueEYrr+BujMF3tYXimN5ZqNgzMFeJfBI4W8G
+IwS9T0fx/mM3Ba31r/8z/vSLf8JnNYB/91P14LJdtsv2XLXnAuasoaAtA47loMPcto5qpJuI7UmG
+jr1RQe7B4EnZ0PwuFfAeLYLO2sCtV0nuYRt4OpUTrV/O4CVkgWaxj2pt1Fp25bb20HpiOi4mGilI
+ohIOcDMQ0++QUOT9QRdXDxj0WW5jQfNz9zyGTS6HsozRME/tM0DkOD3YSzIAWwYKRu2n6xqGKe8X
+tEbieIaqZtDRt1GQk6EqE8xruV5WG7C2WcEHsRh2hkMMaNZGtg27FZfhahfI9uSEWdkGBpH89lWC
+sM5XCzRkz86zDDlZpU8WC3xwJOZzuj5CsrkXnlA3xhHuvCgWWN/XOOfvDXoDoBRLqTZ7F0VoT5kB
+mZc1upZE/evTCYpYTkTPdxGRa9E2DRjEqlia1mFlwd4EBssUVS5rYHm+xlVMWAAAIABJREFUxpTF
+X4ezGYwlFbtZcJmoEntdWlOqgs25GfVy9EMJ8jWGi/WmgpaK4U8mMT48Y8jMVtC0oKZ+if5I1kWn
+kXF7uJihGcs61amJmFhxq2vAq8jnaDmoU1ojrJIMuwFMBpKV7cEhmU8el8gLmde8cuDR8miMDfGK
+wpTFekrrjxWqDQdqLGPrDU3YSu7vWdtzsSkoAEorVK6FiHUEO94VFIXcZN2UyGjuGnmJkunHGjJI
+md1gRbP0JC6x2/kjAECDAotUFubZ/QhhTI0+hzh820BLMc86B2pGyyvd4pQAmaqxMLoji355Kg9u
+O88ALb89CPbQGZNU9vwjFBQrzRZruFSnMljGmyYzrMnnmNkFKsYc9MCAlfi8J6ZTUSMg7XttatTc
+LIs8RbKUxXZUJ8gt8hIOSUne2nA31Xu2BSvdKGC16G0LZKQ78mE+oJaBJSa3lbfwiaMv7QZ1zLTZ
+qsWKqbx5WiDjhnQhHR+XeDmQB/PqVh8dS/rRH/UREMizPC1wzviPyRSiKhe4f1+QdnULKN7fZDn7
+uKrUtC6yTlXKqlVzBZtpTaMFEtZEzCcxGkPWyK6zDfsF6mRQKWqWPrlg3JrMUvT0hukqQZmTycip
+kRMsVVH3wdAFeAZhrTVqTc5HDTipXKOx6FJNaixC2RSHjQEN1nOghccNIlklqJeyJl2Wn8/PJjCp
+w2HbPaxZMXt89AHOONdQa3T7Eq9omYkxmwYgw5TrevCuyL0myga5buBaEWpmTJ61XdY+XLbLdtk+
+0Z4LS0ErjcpuoMwchzmBOTPgSzdkh1tPSiifJ6Gq0DIXbjCiW8XLCz7As1mB37krYJNo3cIbMXo9
+iNBQ01HTpmzKAooR9zReI91o9ZWASdlya2DAYcS8zFlZV5qYvSOgmm/iHAPSsvePHiEkzVmvb2Kb
+FZ9tX06U6UmOjMHNLCsxJZ4iGJY4nxOQxQBed3sXJbMMabpCNZUTKFEuZsRyTPwWY49Y+1D6uMIa
+HcLAgzLHGZW005UHK5IxDBYNBoQBPyKpixdZMByeQJGLvBRLYjiO8DOEShcdCxazOTEzKnuDCF/7
+qrAah45GwMo/Oy9g0B05PTuFH1AkphDLpjQLOW4BeJ0ALvP48yyHTer0RhmYsGp0nhHfoHN0fQnm
+NbWNirRwbaBQ5PJ761UMcueg398QpLgoODdbXQ821cd8q0aTkUdDW1gQ8jxh0HkxbZCQRt00HWRM
+ZxQZsKTVtJdJtmtuJ3h6KhamZUYwmT5zrRYtVZqMroZFmnibbOTZYoLHS3GritRFQ66Kpgac7scu
+wbQikzbZpWuFj5+FtsSVRMa2vzu6qMvw6ga+utSSvGyX7bJ9hvZcWAqGUugaFgrLwIxbXNxbYU2N
+AbfXomKFmG8o1KykRMgdHj5ee/HzAICX7hQoSWY6QYY7LNxxezlSYqwqMt/4tYJBijZ0NdpMduW2
+iXHMkyJbJjikBbFO5b2+Z+M4F188PslQhySVXeW4QtmxTHtIiWhbnUl/G1MBHQl2+qqCQ1Xp3miA
+NXfzjUJwO7IvVJSXdQHWbaFqFArSpildf4yyjNf8N8eCoi56YCPiWCzrxyg/lM9Gty1URIuCsQg9
+N9CSSHQ6mSMh9sB3I9g8Ka/7DjwqMM956u5Gu0hjsWLCsAtNERZr2KImtmDnagfgyfzgHmMA8ylO
+GcxrO9sY9SUw1mRrKJeMymhRkFvB5nWdjoEmIzGvlQEbQRWVXbAZP5keIudcrfDHAICODhEybtHx
+HWxtsXDLslBR76NuLVS03vT/096bxsqWXfd9v33mmqtu3Xl4Y7/XI7s5NIcWKZKaSUWR4yBA5Cjx
+IAVCAEMeEsAxoQ9BPviD4MCJjDhynCgJEjCyE0dyFDqKRMkyZFPizCZ7fvN456pbc9UZdz6sVZd8
+iiR2U/26H5BawMO7t6punbPP2WfvNfzX/6/JTrfm4ps5h8KUk75C4XOHlsoIdr9fduj275/lSK+F
+TePThOCIgjb6valPaVlLi0o1F/QhVZU7v57Rn6nn6S2x5CufRJ5ypMxRE2VmGqZT8jnJaxEyVciz
+8aCkjWvWrUNJu/TepD0Si4LxHNzViKjnkyRKmjEpGCoxx9Qro2rnTNOYVHsNTnblBp2MDTWFvob1
+MmGhABoTMdW+hc79KX2l5w5UdPTc0jqa3KaYRcTxnPSkj6Ms0NY12I6cx0hr5ufXmlQdAZscVe7R
+n2o9egSxZngCUu6VFQtgNbMcRpTn5BeFg68y44PJBM8Rl3+UKER5r0mh4xxMx3iqUhU4lkAnQiOo
+0lyWiTdvsQ1YwVOYszdyWdYqyfbyBe7FIuvOxBBqT4g/d+HxmXpy7UdxSq0uE92MJxxlqomYetRq
+qteooKGpO2H3lkzSu2GPiyvaXVlqEaIQ87V1xkcSBtw7FBGd3rSLr+FKKQQ3kkXqdm8X4+t1c75N
++zZRVzzK1nGV1nxMQacni9PBaEBHW7zvjgZEc7ITTxnBKZiqVuikZ07h0zgZVSXlqTVqp23LbqaV
+r8kAV0Mbk2UUOi/8yGemieTV7LLcg8s5+1+Sjefq7h5+JmNaawUMIwk3PQJGKiw71wrNh0OcIzlf
+33oME+2STPpsbEtokhi4c0/C175yYlYbZWJNunomoykfhUqNXCnbnDr49q0tCovwYWELW9gD9kh4
+CiRg7sJJckKi9Vqvd5eDsZbkms8z8bXUU9nkZCA70O1rUtJ6efeQqjayTLKCQa68CJFDzfl2ic/V
+lf3cqnbWrYXk2hA0SSyHygzsmxJ9LVVOjl3u3VMqLZV1j2xCpLtLK2hjfHXhRlOsCo7E+YQ62iao
+jU3dkymv3ZAdOrMZaTKXD/MxWoNuNuR7Nyp7uLrbVU1O64yURZ0owigaL6xFjCMtoyYCmc07a0Sq
+nTGsVjmzLN932Nvk/DPqhbgNRscCJXaV0o7ZAF/Loc2lMkPtBswnQ1a10ejZs0ssbSuaMJT/3+h2
+mNyS89k7GTEbC5TYPA6hlh+LbJWTXHAk3aGS6DgpJV/cv2pRItPvywYdRkNFb+KgvUHcP5FxPrcT
+kVTkfvh5wQi5J/1egl2VevwHP/A0O+uSdAwjJf9Ndrl3V+Xejzocq3hOlk1ZVy1Mz4RYLX0eKx7D
+pgWofF+SjUGvkV8pESzLz/Nr1axeoNkSzoKru/fYV3bp1vIyrrq6J8MJt28KxdqtPfFi89xBUexM
+cE5FT1v1MidGwtTD4TH3b0u5u6Q4lIuNKnUVHKptVgnXtRRdiznWsDKiQhKeatu/KXskFoW0yLk/
+GYiYh4J03hj0sHdkIq3WX+WyeOv4yznr2+La1mLBzu+slDDaDBxEAekc5OGOqecaW5kpiWb229rT
+a/2QyYEsMN3RPbqKRb85AK8hE/pWfIf7fY3lJvM23BIldffvxnfoabxMd0boqFhIkjHRWHyrLQ8m
+XjHXXqFVWaelPRylRpNU8wBN5Sp0/YxUwTYrlYCgJhfg8GBER3MttaDCkuYgcl++y287hO68G9Ch
+VRfce1hqcaJu9+2RoTOWRdbMuQ8nI0JfHszNlQr1shzj/gC6d8WVHly5Sldj7rlD2mqVaSkT8fPb
+a6xekimVZzlJIZ+ajU+4flXEZHdPZJxOsEy0KQ/xKHcZHd/VMfXojHWhS3OGyrj0yq1XAXhiw+MZ
+7RKNijLbG5qXKcONnvZzDE6wd5Qrs6XAo6OUqWqQnnTuUxrIQrazU6OhfTVxahjmcv/uHIq7f9gZ
+cHQi12KYpHjK3bhxrsGyf07Gom30zWqPg1vy2jdHr3NNNSZjxswbJtc2W+ws6b1SnEKpvI7XlvmU
+JQVjZXDOrUui1aqQHE/xBi1dxNprS5gluVZnztTZ1I2qWqlg6yq864Z4+aL6sLCFLezPYI+Ep2Cx
+ovVn7Kngyiib8sb+VQBMvkf+lKDxfL+FtdrJNhdXdqZMxrKy+26NXF0/RhajTYKrK2ssR3N5cm2i
+GXfY7Yg3cuXuXa7uSa14d5ozO5DzOOgPGakrlurKjk1x1HtoUSXV5OjIy4hVEzCwHiNd5bsjDWHc
+jGSOYkt66IZPORkRagNPUlKZ9bPLtJQnMKwuM56qPmY2o64dSCulFUqZMl63NHs/CrB6jDgYYXR3
+NN0JjuT4mHQnDI9k3OOhJkZHBkqys1WKkJUV8TbOr7UJ5o042dHpuLcUSXf58jpnnxMXfWVrjVzF
+S/qdKScz8cLu3N/lG1fkXh4pwtQGVWIlKQm8iJlKuB8w5r52TA6znHSm2o3KGL3XP+axbdXgXPJx
+tAri5RmrCmUZHifUlDvBU5VrxxiKUM5tNbRkm4qzaDYoqWzc7vCQ3X1x7Xfnc+FkSKJo2swUrK+J
+N/XM0gaOcmC0VbzlaHpCrs12Now40HtWGhTsdsRjieoFns5DT7Ewg1mH2nyOzGKsemPM7KkytZMk
+nFuXY29tyvjPrlSoKft0s75KKRZvY6m8QglJcnopaJ77TZux9i3zo7ztVqmW7FPPnCMyIVtLkkI1
+yw4rnpS/VrY2iVwtt9iEo125cUahtjbLQB/Mw5MTZpo5Ho5muOpK59Y5vRmDgZKglnzObEsIsry8
+xFThvE7uMFF2p8bSFtVNeTgrCi/97O9cZXlT3LNnzl3kqYtCcHLpsXWacl9I4hlbbbl5vlYO0gyu
+XPkKAOWKjxcpQUZti8GRuL7GkZXiGy++yLoSp+zdvcnd+18F4JtfvkmurcPdOzHhkny+e10mz4Wd
+87xnRzkqGwG//ltfAMDFYUOpwZs7FS62ZJHdPKvsQE6Jlmo/Du7dx9cW7vGgg1EuyWLm0tQJOezJ
+AhJ5BV99UXI77VqFJJaHeJQPSDRfsb7eYKiAm3nZ895+TKrEu0kOnuYt8toOhcKAJ70uSl2Io70o
+1RXLE+sSPrz/Ezv4Rh7I9sUPkCokuNoqMVPyWr8hD0eUG6qrMreK0QiqKmg77lEoqcvo6CYobHzv
+Wy8C0BsOefVbsqDdG8bcv6Nzr+qz9u//AAAHr2jO6PpLvO/5fwOA4Ovf4Myzcr3f/8Ed1i9obiMM
+yHP5OUpkzPev3mBSk/Dq9WmZ/g1hoaqf+wS9r/wuANvlcwzHcp2PtcqyH0+ItFv85GCIpwmY3e6Q
+ur5hrMvRWK7nH758/WvW2uf5LrYIHxa2sIU9YI9E+ODjsGnqBNUKT75HILPL261T8EsyusdU69Gh
+51JWSG+uMNPQ87A13VKKJTraMemXnFNyliKtcnwsxCGO7mbRWp2VZYWGlgOqSsWehyXKqjBNmuKq
+R+Iqb4KXVOlpc1Tz02WefK+QTZ1ptsg1O21nPSIFKoXq5Qym92iVld+htsaSwoCL5JBlVY2OA/GO
+PvYDl5gN5O/Wti9zJpPzfM9jL3Hlm1I5mLhnub8su8eqVZyCM6PYku/Yqm2y4XxNxhqWefZ5oVe/
+8MRZqr4kq9Kphl29CcFUdpp6u4mdKEjHJswUp+BkkNyWa7jsa+WkPOJxpZzPx1NqbfGgstYKju7c
+kzRmvaxu9ZJWV2oh9w5lzOPUZaTcnIODHnv3JYPvBrCjnX+tc/J/pRlycU3GV11ZZ7Ul136W9XG6
+MpYTN2K5puAsrQZVgxVc5SHwWltMtVu1mI4xdVUN39pmdqjCKRvn5BwaR1xQr2P4rWvsKuGOwSX4
+qlSE9j/3DwC4Xsu58IZ4fBe+/xznLgk34ur6OuUV7VoMYaBw7NsduYa7SUb3ZfEKv/U7v8g3VCbg
+fOc3ePqDIuBTThJK2zLu7afkPh7dO+D4tnBkpK6hUHq7nYqP6SusvBngzJRC9eXrvBlbeAoLW9jC
+HrBHwlMo18o898n3MkgiOkoeWhpkVFUKbVTEBIpYXKqUSLUJpFyaC3wm5NpuWgtH+KHsEpM4RUvF
+DMcjYlWS7iay8i+Hm7h1JRdttYkz2cVms4RItSGmcUoxlvjZdrSRpRLRWJZy2oeefoZn1iQvETgu
+qSZKMz8kcrVUqdRtedKl2padrVk5S6CcBUU0xnUl3nV1nMZ5mmJZeQPSCWkhOxtnnuPymd8E4Oor
+lu2uKl5XxCt59bWYe3ckqVWshjz/feJ5TZ2IYkPO2S0iKiqUOlKcRlD1cJUIN3IgV1gx1RLLvlwX
+Y1MSbR7S7mbKUZ0VzdukQKrt7GHdw1mVY+zvnpApZV1JUYztS1UcX7ybfuzQGcv7V1+/Q68jxygv
+RbQfl9zH5e3H5O+eWCPypJxaL7VobEh+wesNGa7J960vr1FZFREYdXLABthUz7OIOT6Q3MAwn7Jj
+Zcf3N9Zx9J5Y9Xj8coijfAk3ru3jhDIXHM/j65/9rwF4TeHa5WOHvmJSJu4HKV2U8N1GUzx9vVTa
+ItJ7Va2LN+btfYObvyKaSX/wr24xP+UDY2gP5P585GdXCc+Lh1A5K/djabUEKqLTLeUs1WXuZJUG
+uTa/uYVPPNcAfJP2SCwKjvGpRKtUGHPvriR1ksZZjrWTrxW2qehC0K775Oq6GqXOmvRyrLp1XrlF
+sKNUWrOYRCsR6XBKrtlwX5NdnucyUen0qHqWrZa4akeDguGxuvk4uJoBD+Rtnm1d4Mwn5AF7rLmE
+NXNFEhdfmXo9Z67lBxRyY0v+KtWKgHtCr43RrLd1aziOPoTz78LgKkm265bxrCQPbXDCmff82/Id
+wdfpjOW6vHJNFtDlW99gf1fDpK5hXem9C5swUpfZcSOOlLRmzZfvdY2PVf1M14NAYbK1eoVmMCcF
+AavVDneeIc8mNNU1jifj06ewiAKK6rxfIcLRsQwH2lOSTriwLse43ynIFdJd8SaMl2Vy13wfo+I4
+fc3CP1aUcNdkgchNgpPKNWwubbKkicSoeg43kDlgle9wNtwnH8l9zIqYktb0x4cJeVMeWCdz8JrK
+m6m0gFm6wkQrA63VEHtNxmHyhE1fzvlgvmGlAeXnPgJAGjdwtdoTP9WCsYYukaHQuRqopunlcz/C
+6r8l13VnJeOl35P7VL98hs1t2UTKj5/B04W6oaQp1dqA/gWZlL1kTDa/N+kxJYXCj8oF58pvjWRl
+ET4sbGELe8AeCU+hKHKmkwG9w/vUjXb45VBRRqNW3qSmpb7Ii7C6cxl1SYtgzGiiQ/FythX9N05O
+GGvP+lG3IFQPo5i7sCsV9u6LR/D0WkJD3bJWFJ52Rh4OpuSh6hPoLv7Yh8/yyccfB6ARRASaJDPG
+YrQoXFiDVU+g0B04COp48/DAODDv0HTKYObsOPP/C9BSmcEF7aI0toIfyM7caIckQ9UvyCTh9Fo8
+oLsnO01Wyfgw0qyTjQe0VVshTQwlbczKxtqVaUbEShMW1F0ayoobhCmpNgE5bna68yq6GCcDO9f2
+rAYkudKm+SH5VCn0IpdcmYVsSZvDBjmx1ukroaWk5KK74ym+dpp6zRIjxQi0taS5d7jHjoZ54bNt
+HE0eRtEmXqmulzACbWKy+dzb8nA05MnHE3xHPluvDyhpWdNxHXzVFDGuJC1tnBNEytnglIm1c/Uk
+txypdmWwLLt51lwm2RJPoXGxzrGGWmfSkLJ6ZDhlUBiAr8ljlis0fkiu64c+/iman/4deb98maqV
+ULC5tM7qss7xlrJBv3HExo4kKLudWxz3tSvzJGWQKrvTgWWs2hhv1h6JRSGPZ5xcv0o3zDEzFRPZ
+8igNNeO8kuLM6b9ch1yFQscKlMHNMaoWVQsKMu1FiLyIkgqlbhy1ueFJpr5R0orDcomj124B8Oq1
+a1x+VnIDjZU2g4nq/U1TvKYKivjaAbi+REPh2K7rYLW11gBz1EeBJdd22TzT8zQRjoYutjA4Cni3
+jgUFwuQa1ztphtVjOEVxKlXPaJ9kJgtZsmcYJK/LNTpRTsnVAv9F+btOFDM5FOj2pFxgThTjX0rI
+jlQYJxJMR5pkRCXtxMxdtC0DD0NakWsfRg1yFT6xLQm7nFKTQAV4yQy+Ut3lZZeSwqbjaQ/jqLiK
+ZtYbZRjo7IsiQ10xC3X/2xexcBzcOQ2d8vIn/RFTJVaJR0sY5bEsEkPhyDWydiwMJAinJUCaDnAC
+eTB9v0x5SWnSN8+BtpfncR87d54VnGW9glC7DIOywdU5kDAh12qU0Y7TIiyxsiUPsReXCbTvJu1l
+dJpSMWraNWaOzD/X045ME2Ha4uKbkylLT8ii36xExIcq/NMqgcoHnNz+HAAvfuXLDI/mWI+I3Znc
+a3vjmFTnmZs3mCy9td6HRfiwsIUt7AH7XqXo/y7wbwIJcB34K9banr73GeBnkZT7X7PW/tZ3O0ZK
+wT4TGtXzmCVViV5tUCt0zXKk+QfAjR3iORagmGvqNdlQyrPCc8jUbZskMesNQY9Nz0Jdu9My3Yoa
+RZuKIvsSr8SeypqvlaChDM6dKCHQrjX68nePrWzT0p0tNA4ec0/BgBEvJSump67rXKAiT/vMJiqY
+5QUEviSJHK97KvYyx6TmJiKfqHcwHVIU6s7mJYySbOTZLlVlCT7Zls9upJe5f+ZVHX/OsCznUwmX
+sU25Lp4f4iip6mwmO0rZDSkV2jxW8shj2QVjC81gTthRI9LOR08bscrVFrYi3zG7dx+bqLpyucBp
+yLWN3JDhsXgKlZm6zit1hvtS0w8SQ0sJQhquT6pQYRMWZEqcUlO9jDf2+txSQplPrW8wO6eeRPc2
+JWWodlyDNYrI1CY33IxSRen46m18JWgNCp95iaqIapipNpNp4tevzCguSHjw2N33st0SzywZFTRz
+xa/U5Zqsn1/mklZ4dpYb+HVNJDcCyrl4I4VTIVYMSFDVOU2B0cY9nAlLLRlrOS7TPi+eQta9T5Ko
+TOuBzAVe6vFVxcscB2W221JFaTgO90KZy9WmoRy9tYDge5Wi/zzwGWttZoz5ReAzwH9qjHkK+Cng
+aWAT+B1jzGVr7Z/qvzjGoepXWMozfEeZknIH9dSInBpZLKeapSNyZQUyWgpzKWGtPFRlz5LpH/qO
+R1Xpss9uX+DDT8oFfP1YykqFD+dUuDbFoekoo7Jp4VXE7WxVG+TK6TgpK2ApCOYdtIiHqL8YTh9q
+i0Oma0KWiLufDvsUEwlhvKxOruXQqJrjoA+e1fKfZwjmAKE8gJFMjqJI8X0551rb4BUaSkyV8ae5
+x+0zstjkVw+oK7tTNc2Y5NrKnaQYzYOE3pwxOsTVGNkLJ5T04c9twGyoZdvmiEA7/MKyMnq4Ca6W
+XoPikHSirrhJiFY0jm45ONqZOonlfLPjAWVP4/dmFUf1KC9tlImbOi3LJda076Kv3Ignoz1O1GXe
+2RzR3JRFtrpUIuuW9NxCsnxXz19ia3e8TKZt5s5kgl9WcaGwjVFR2DTLQFmYvEgeTEMNP5eQYOfp
+FTYfk67T2Y3rFErz3DqreZ2NJc4ozn2p1mZ9XeZCvdUk1E2mcDL8itzXvrKMNXyXqYbCvntMrDqY
+yTQ45WusNw1FT5XR1uT+Dmavcu9QW8eThKgn4zjwK3Q1nDzaH1Btavj6Ju17kqK31v62nT+F8EVE
+MxJEiv4fW2tja+1NRCnqQ2/pjBa2sIW9q/Z2JBp/Bvgn+vMWskjMbS5F/6ea4zqElYisYmmFslJP
+4wkz5divZ+a0ju243+4rLykmwAYx0ZwbMJlilHgiJMfoLj+b9NlYVdk4hRAko4KDrhKnbEacZJIw
+OuuOQTn+RnGXTJWrvUR3cXJ8rRIYHE6b0CzkGgakyZCRcgH0d18BIPCrrDi6Y9RGeJ5WCaYzQk+S
+nEYJMYwdYQJxRX3HI9E6v5l0SYp5Rt0lVicsU72JwjNsNyTBd7J6SMlRlueih6ehzTg1ZCrZ5qgg
+jUvKRCXrmrmHX1EPo1zgK0bAFBa/qbvtXCfR8yimsmcE9S0czaxPnAOyE+k0jFZbuNo9mmn1Io87
+TK12HxYpnup9XDpbJ1C5uWHJx9eE39VXxCM4OoqZKqvx/miP167IzTx75ixt7YIczwxxX8KGWHUv
+GmsOkZKhuPEMZyIVqvLKGFc7SbPRDFVewwkV/EOE68rxyvU6jz+7rO/3ufuajG9dm64uX36KVkU8
+jDCYUESys1frbYxiE7JsilXY+1Ik5zYwfYJE2bqHFaJCzj2pJAQqnTgcbVHXztVsLOfunTP0viGz
+72AyYZQpjsGfMFFszOqWZRa/tTbJP9OiYIz5BSADPvs9/O3PAT8HUC2FFCcnOFEDdz5JfcNU6cLH
+xQyjC4Dvl04n8pwY00k9jFK8J04Vo0KbRSkk0wx+nBbMNN71PLmok0qMfV0WhbQWMFE3eW80IFdg
+SlYYEq12mKWyfldOruU9B4OnWeY8z3n1UMBXr/7Sf8e/7orG4PmavP/9H36WVkncbruSwZEShuYD
+GAjePyvrQ1dx8Taelos1GXK8r+I0N66RqviIdzzj9r6UIscqZDPYqDG7Ki7noDdm2JXJliSWSIFc
+0UYVZ6KhgrI0eW5OpF2bhAGu0tZbYsyc/7JcJz9SrsSWhGhebZlCs/pOUYamPLxltnA97YycghMp
+Tq+jT10aEORyPYu0QqqLSWCrFLrMjg4hV+WrWCnbJ0VKPpP3791N8HKJr01RId+WRTSNU/KOMngp
+CWzQzRmrwE/ClFpZaeRtiKfkO+PuADOVOeXOK1ihA8rziJtzZkfuST/v0bmtIsSq83ly9xD0Eo76
+A8LrAlSbrN2nWFKq9iwF1aOs6OJ+77hP6MgilHROKGmIlVfqlGtzktdjBjVl1HpROmY73xoy0bLv
+YFowi9WhX6pzpiwbw/JBxki5Kd+sfc/VB2PMX0YSkD9tv91//aal6K21/8ha+7y19vlId/aFLWxh
+7759T56CMeZTwN8CPmGtonzEfgP4X40xfw9JNF4Cvvxdv891MdUGS2vreEYy5DaBULP+mWOoK+FI
+JYgwJVn9+2NZqX03YbindXAnJ1OtyKgZ0lIqqllucDRx50fKN9Ab0Vd3fbPRxCpdmTeNSecVjizD
+LenaqbXyuCgo5m6w45Drin97cMjf/8/+JgD/169/EU+Zndvr4plDWAEGAAAgAElEQVT0XrzPpmbR
+t8sprJ8DYPK1m5y4chmPb8o4TlaaeIVci671WNPdatJ0eVJDonI9AKWiz8+pO1ypMlwXl7N7GBMr
+hsIxlpmrGIJZTrmsnBOKf3BtRKxEJ35WZdyT61KuRZxblu8OGsso9OBUtAavQpGpxNzxdU72dJdz
+D07d5NXSAFdhw3TkGMF2E3NVkoAnkz2GQ9li84rLaKxQaccw1gJMPgd9MSNtyJj6swkHWi2Y3rjF
+tSPxihqJR/Ws9seoxOCec0BxX0O+jRazTHbV5rhNSzVJi4ZhdltChZZS7J2pnhBtSBXFzQsaS0pb
+P1rjRlsBWZrMzSOf1/fE4zvZ7TBIxVN8Y32dWKX13GbIeCT37M6unO/o5T0OCzmfFSdjzuf//PIW
+6xcEfLYWBoT6HV5XK0Zhm2YintJ9JiS6N4eETDWMvT8u5sLVb9q+Vyn6zwAh8Hkjk+6L1tr/yFr7
+ijHmfwNeRcKKv/rdKg8LW9jCHi37XqXof+VP+fzfAf7OWzoJz2Vttc56aJhpg481Cb7G7YFbBU2C
+uaGLoyi3ku7Wx0lK50S7JP34tNzm1cpkCoPNcp+pcg4calzcn/Xx67L1VWoh25uXAPEkpocS9QS+
+R64ipa7ujnFuiVG5NRuRGznnk90OcSjlq4rj4VXl/Jyh7NAv3Zviam9KadkS9qVjJtlIuPmS7Nz/
+cip/P3j9GK8s57kSNuGc7Er1QZ07S7LTlKc+yy1VT1ZRlFYVLixL2exO/YAl5Tow/YxpOhcmnaCh
+KL4ru89uPuT4UK5PVC4IMrlu62ub1LS5a7vt4lbFa8i6yoI9eo3br8g4vvmtP0DTGZRLHg319M63
+M7a2lDZO+77qcUF9RTAkvWmXsbI71cchqeaSehPIVBT26FjVlR3LTLkusjRl90Duwyv9K5QUN7BW
+bnHJyHmeOS/JZXNkeWMqCbzBl/ZBS9X+zSalVRnfRnmZ45kkbLeUFDg4W6Liyt/55Rkz9YrC1KO9
+pIIy6khee/06t28IcvHgYMpSW45hfIe2Nj8dX9/jzq1bANxS3ZJp94RAvc27pZydquZoKoZgngfa
+ap2iYaur0hD2ZNTiY1ru3vvGPbrKL7LX7zNVrsIgCwkV+4M4Jt/VHgmYs+M4lEoV8ByiYF5l8ChC
+VRoeJ8SaJLqzN0ULEaeZ91F/RkUrCqVmk51tKXj4gcNYYbn3947odMVFu3ZDbnxuIo6nkkE+e6bB
+irLk1sOIdKR9Er5Dmqqk+EQe/m7/hK2leQ+Dj2JtqJQtP/WxnwRg6/gLlOaZ+BO5+aurPi+clZTL
+2mYPayUkSlttpukfAnDlhpxjfejx9EWZ2O9bvcxjH5bFy3QDBgqbNuMEHBUGqcl3NTcfw63IJLh3
+f4VQsQmxP2BJF1bPm2G07dzV/N9j7WWe1eO5EYS5dtzNJkRlufZZmmImCu7RDtb+zesYrU584D0f
+5v2qRlS/tEFg9O/2+8S74lb3R3K9p7OMQFWMyisrRCOlGBt2T/UvZ0HAXa0Y7GoF6PhoQqrgs2k6
+w9e02GwyJdaEcLI5YFtxAaYlC/rB0V2GoRzj3jRl2lN+xO4Vai25FuV6m4aOZfkDqhg9bZGpUMuQ
+PuN0T483RNn7SMtz2no4CeQYQatEc1MWpA2nReRJWJJ5KefX5F4lQwVmrVZYrkqlYuvyMtFU7tNS
+1afVkHDT81fpXdMKzI5cwwvv2+DjtR8G4Mt3fo1BR7UmbUZHVbRakYdr5i38Pd6MLWDOC1vYwh6w
+R8JTKJKE0e1dgtynEuoK12zgJJrsC2JKWsYJ84h9TVbdvyHowP27uzTXpYnkg/UWviIa2xsN9pWc
+5M7eXa6/IrvVSFfoaZTR3Rf368ULVT6hkmG1oI7bVrjuYAmrnoKrZc/UZozVVSs7Lq6WJFtlS/aY
+kJo884EbjGPdYfYkGfT0yhZrl6RMtby5ibuipbD9ER/4qLy+dkl2kZ0zm9Sq0onpGQs1Gd9k6zYr
+++LyW39CrIS13jwpG6eUQ9kdz26do6/kM3HiUlEClGqtgaPnFijJikmPqI7kGGMLM+0GrOcZk7H8
+XCp7GGVj9ppyTYLQp6SYDb9eJdNa+fj6hIFqRyyP64TnZVx2V8uNeZ+JciuknoNVZOVwcnAq6Xat
+P6bTleP1DuR4vdmUTOHvhRnRaIlHMC6GpIomrT97luc/It2KumHSeuFDnFVuhutXrvIvfvUlALqz
+lI52lRa39wjVzY9UcGW5tEFrU75kkhnoyrn1xz3SeZ000bJ3PGPDkWsYtj0iDTW+eeUat75wS85z
+4jJSsp+mQsV/4Eee5j/8me8HwCmtM+6It7h7u8+NPRn3q7//B9w9FC/l8n3xQNpn6xR1uX/ba3X2
+FRczsDn1Qjy6SiOitqwxm4A8v6s9GosChonrsOGVKWmbatkPieasSSNDoXTps5Mxh5olv3ukyk1x
+RlvblEvNJmur+oC1G5QUNv3Pp3/IJJeblKoOYuiWmCKLwsHYcKDioWvVhGqwrJ/pEKowjLenAJTE
+MFO9PxslWI1lcVyYygP7xdcOuHNDXt+8KK7h9I4hSWUCvrCxTa3yXjln7ybr25LPWL8s8aLbbjPq
+yfH69w958fdFB3Lkj6grHXy71mZF2ZmiZYFBO5MIE6iCVKlCqO3edjZmor0drXSFUEOznsbv9250
+uHtVlYviIaGq1jx/8TE+tq35gFGbkqM+s3ZUevUdOgNxZ1+5fsTtG3Ls3dsHDFx5/aPnAt5zXmL0
+rTWZrNWVNkPtJRmfdJimco0ya+nrQjVMJ/S10pK438aF4KoiVxhw4aOCGzg4mWG7ko/55I/+JE89
+I271/kz6K1brZZbrTwJwbv01hjP5u8pXv8iG3t87t96gd6JiNwqh93yoVM/Jtdq7SprINfS6UFWo
+tNGe7JPcZWlNxlGuLZNPNWQ47rKsRCfTSo53orkGhXA3y6us1r4PALfZIg01n+X4fOmGtFHv5w06
+sdyfI+3LsOYM/WPpc2n4ZZaXBFi87YT0PZnXpaTAH7y1x3wRPixsYQt7wB4JT8FzDcuNEKY9Mu2W
+qzUinEAJPaouByfKduzHVLQGW19VtuRmQGtHdqKVpketru5sqUFF69vnqiE9zdRXlzShVslJcqlB
+n3nsAq1wvlt5FOoyL9WWmI4UjbY11zpwKDSJRhqg2iyMe/Di1X8OwDdfvIVq1tBuyi5uls7hqXhH
+Mg7JDyThaaYJaFNRpSE7ytHdG3T17/v9DnEgbvT9owPuanfozqBPJZxrS8iHg7JhpMnVIE3Y2JBz
+nxzm9FANRlKs8jdUlejEyaHUUjXu3MNti+dSKlUYKFqy2jsiWRP32JmoqnE5INiXHbpcbbL5uKIU
+KyPc2/L6Hx70SHMJ9cKKcgVkU5ptqZJkTkCmMut+kOIOxHuZjGfkmdz3RKsrbmAxypVg3RlLqnJ9
+8RNrrC9/CoAf+8BPsKTj8pTroFzeoqaw4432Bs8+LztpET7O4+pZrl6dEiBewxN18RR3zm4ymsi9
+rvgjdo/nsvU9Kq4c+15P3r9394iNmkKb/T7+ijxez+9c4vFVGevEj7k7kM/0D8WTeuGZZ0iU/q7h
+p0wz+bndrPLUWbl/ab/ExUsy7qd33gPA+uYZmlMVM/pmjfZQlb0nM6yGwl1TsNJ4a3v/I7EoOMah
+6pboJwNg3k3XI9RSWOCEVCPlV8yrVM/IRFnXNlXr5qxtSMWh0ayTOqpX6I5o1eQzz//IU2xcl8+c
+DKRs1IlDDpfkIV1bjvDU7a76Hp05AUrkE+vEmijH33jY4QmVap8VGSaXiZL4R4wUkDId9Hnfqnzm
+0++TheeZD72fViTjKy/VcQoVmy36VJZU9DaVB6ndjGjvyGdzp877X5BFb//uLZxMFoLR4U2qVVkY
+y3N6eiCeyvd2jrrsLMux+8UxZl61GY9ZUphyuynv75xZJVCXeUICgbw+7R3jKI4+SWec7KnSUSiT
+0XoFLW1J/uDm8ilLU/aRVfavaO9G6oJqbDZbEl4Fwy6p8jya3KGkD3FvFJ9CjFfbARMtS7c1pOiN
+AxLtSymvBTz//fJA/8AzP86OcmyWojWsakLmys7VKDXwlKhnZXWLT1Tk2p5tTkhTOc87112agdyz
+lUuSl1lrbFIditt+tVtgAmWTypo4RnspjFKrN0o8+Zycz7OXz7N54QPy2bLFDWRjGA/uQ1k3pVgl
+7oOQXM+3e98Sa3t9s7bMJz8sC8Czj1UYdCT0LK3Ic7G1UiIx8v7Wxdd45YbkzByTkWkLwHLTwwzn
+1Yc3Z4vwYWELW9gD9kh4CjbPSfs9rFfGV3e/KJzTKoFrJlhF21gT09Q2R6skFfX1VVytFefDlMkN
+pRqrhDgVeX1tdYOsK7vGqmZss3aT5VybpNyIXHfY1E7wlYl41inIlehjXBI3scqMQS4/16JlQq2M
+NKo+m5dk13nvWoVnPyG77Xs2pQmqaS3hTHkZT/bIE3Hns+4YRzUd87ImCZubGGXkdU2dxJXwYae6
+SqZdN7VRm0KrIPlQs/rxAam6s44b0DuRHT0emtOEaGZSvFx2REcz1mk/YagMz37ahJJci3qtoK9w
+7WSSY1Uf0Wq3pBuUCJWuLsgzjIZ8cd6jXlUpeiOZeYCip+43IaMTbVDqD7CadGvUYrqKQzi8PqKi
+IJyZgqk2qoaOL2P94Y+/j089/oNyT6vnCdSztFmfREVu6ChPZjnFRlppMSlVdf2fvPCDZIr72L/+
+JV79gki2DUKBFyfP9chj5aBMHCqFHGOQdYiHKveuuIJqWOFiJGHCZu0SNe1QLYcutizHi5wzpNpA
+5SrRzSTukQ3lPhzvH9JVgZdks8vOM++X717xQfkZ0AY0100p9Nzc3GOo8WpvGlPRilDagTSXMOXN
+2iOxKBjPI1xdohq08dQVS+IYb95nZVxqqviz5Lg49Tl/v6L1qj5pVzn0a3WmWoHpHfVYcuY6kBeZ
+5YK8q6zLTTnXvkzjg/Lg3h11iJWNNEwKjLZtB6MTDlVS3Ivl4Y+CEu2SgEoi31DSCVaqPc5HX/gr
+cm73DtjytYKxKQ9gMT4g1e/wVkskSpSazBJcJVmZZ4r9nU1c7dTETvE0ZMg3HGbHMtbQbzDThWya
+yzn2OzPGGuYQGWYqtBq5y8y0cmBHIamX6t9pmJS7VBLlMDy3DhoSMSqYzcS1jZMZrqNowkzPMy0I
+1yt6jCmU5P0gLRHLM8HUTkmHco3cUCZ2nPZIR/IgjGcp0TyfQ5/7K1pVcixD1VtcUVCUY0I2tYLx
+kfd8gK3198nfWYeZEshOejfIRspINJT7tDG8ybKryFQ/wlPmLNeE6BrDkrvBta9Itn/OA7rit3C0
+ypBNU/KBEvyMAqyCxHIl2E1Ty0BLudnIp6jL+6a9IgxPQNCKSOc5E+3wzA6GjGJ5oIthTve+bACN
+mU/ypHx3pcgxFZkDRlWv8nxGLNEcrokIVD/SjS2xhr+93pg8nlOfvDlbhA8LW9jCHrBHwlPwXIdW
+rYqTJ/Q7sqoN3d5pL3mz2qaiqjqVyMOZu/YqPNLpx4y6yjLshrgKXe6nHqjra8IGRpNx0678vdsO
+aJ8Vd3//9gHTRC7HFMhT2cVTr0eSaw+GrxRtXkqey8+RWcNRenbr1GisSKLphR//94hUHTvUHZO0
+T35Hjp2MHAZ74rmM7u5R0068cGVLzzfGqci5OUUNNBFlujMiVf/JayWymVLL6TnmXnzKcVjzQ9DO
+zzBzmSovYWZzYu0bCRVWXmuu0dAe/MpqHW2uZDIYkdzRpKIzxVO+RlzlkLAZhZHzcaI6jnpNlgJT
+Vu7KNKbQTsJE8SY2n+LpQeqlJXzdrn//62PuKIHLoAuZqmudqFcYBQWjY/nsjT98iaMXDvSe7fGt
+r0mF4/UrrzAby7V3tYr0iU9+kA9+RDy36vLOnD0fmxqSoWy347sH7I6U1k55Ng6qu+TbOo6sw1h7
+EWw4paJdt3O2s2nskqkWZW/7hJomVe0whXkfzAxcrQKhHJx20qPky9yLlgPS86JuFdZaeEpE40Sr
+WCXaKU5bDCsUVeGMTIsRnvax5MW34czGc3DmzAQxb8oWnsLCFrawB+yR8BQMBtf4zOIRxpVVdJZa
+rGoghLMp7kC59bPqaeKv05c47MbuPSZamy/2Qip2LliSsr0pdfGlyxvs3pNS5HAsu8DG6hNkA/l5
+2jmkuap4AmsZ6Hcf9aZ07wgqrtWQ7+qOBrQ3tD/eRLi67TiuxRj5zPbm86QNwSHMrvw+AF7lcfxz
+mgDK7tP0xDsol2tYhbyauZbhZEahtHPGKSDVspKXYAfKPVAcMdOdJFHkn19UCHxtVsrHuF3ZJmJ3
+SqTx/rQTM1uXXadllcaumGG0HJenBX4guZh4usud6zL+LISVpmoWKJNSjoc5UX3F3Cc8JcxxsKGy
+GferGHfOMiXvWlPC190xaHgYFfgZRx7ZTYVsmwIv0qa4WLa5XgKJejm//Jtf5A3/PwZgq/kB9mLx
+EK9d+RK9K8rYpLX7l75+wM//RTneh/7d/4BSKFDhyeyQm1/+CgA3rr1MoeIzVw9kTF+48SJLXclL
+dMcn5BOZF81ak4rmvAaaJMwcjz0ruYzK+A7hrvIsxAWVVlPHGpBq5+ckkTntGB8v1ESYt0GjLerQ
+2TSnrzR7rrdGofDvzp7MYyc1xOOqjgOOFEEZFwWOliS90CGwSgfFmyNWeCQWhSzL6B4dURgfqwy3
+1YYvIHygSMZMlDjFeiFTZfbtHclFiAJDeUdhvr5MThCexKguFy1JDclMHri9W+K2v37rmzQzuXG3
+j0asPSmTcZQW9BWffpgOsHqBh0rushz3mYxlotiSEWp3AGuwczCAC0bFPgqlHi/SFoPX/wCAk6+8
+QftD4iZGeRWTa4JVXUtzCKancuorM4q+JrUmR6QD+eysO2XUU5n0kVLXpQmpPjRFFHGkxCOeX6Kq
+7mdlo4U9ETc/D2VCT4sAR6sPSTah0LBkMNol0QSlm1a+TVuvCkyD6YSRAsvSkk/l/ESP1yYfaJfk
+1DLWhXis1RfrOIRN7TRNUrJEtSvvhxwos3OWQzBR6jl3zjNoT93neDzk3ssSPjzx/T22NuSeVA6q
+vNaUD7lKorO90QJlos6nYzL1pU8Od/naa/9Sjhe6nF+RZN7r92WBefHeIecn2l+SZcyzkmN3xlif
+sYGOyU9ypoGMY3QQM9Ssv+/NQBdL6ybMhlpJS5XyL8oZKc/l0b3XufJlgS7fONzn/LaEdM8/XVB/
+Rs5/0JV7UI5mdPraB9SxDAYSNhi3oKWkPLnvolw3oAWZ72aL8GFhC1vYA/ZIeApJnnN3NGK7fRY7
+J0jxIwLl6Y/TgrJ20TmpASVjDdSjrjd2WFJBDr/qndKDVT2DcqxwMjmhvSm7gC0pIjI8y9cPxRU7
+8T0yTzyJ7mifmhINBNGrVDTR5BQSGkTROp7WoKf5FMfIQUwRn/L0u3YEygvgLwuyzR2UGeyKy/iV
+r9zksgqAhPUmrnoKS2uSqPQ3n8TorkMak6s8ewZMThRhOcxwVMAFLT2aNDrFP4yLnNc7ylDdrjCb
+S8SNHFxNRnUTZRYOcoyKlKS5QwnlGAgbXFiXJq1xnJN6CjfvKKah7uNUNZwJPKxS1k17XUZWS4vj
+HokmfNVJpqhacvVu8nGfke7cQeEyUvLUkinmt/pUadwPitMQJCwMNdVpME5OLZCGoLNs03hWPMfz
+ijzdOfcY29U1PXhBpqXD9GjGpU3huIgvPIE/lbLs7d5v6fUuWFU4/Sjbp6n3vVprcTiWa1hXbzSz
+OZ6nDWg1S65eY57USU80menVGRzrlq0ZQI8ae9oB+a1Xj3jtJfFkD9MjZqHch41ph+S6enp18Q6m
+0xI37oq3fO3wLsOZXIvGUh0nkO8uZhnOPGv8Ju2RWBQoCorxCKc+wuq0cQwECsDwahWs/jxLuvja
+4bfe0Ie87JGN5YLEE4d0DjYaQRaq2+qPaZyThWN783kATmaWSEk1VldWqVclxveiOndjBTj1LT11
+12lKRvtiFjMZy8N2EsQ4SifkZAar5CP+sI7TkPPMjyUm7x//Gnf7AkXdXK+ws6xVjQ8uE1al18Bp
+ygQM3BC0VTsbJcTIpCoyD1vSisq0jCnLgxVqbGmSiEmu7n48oKRQ4mRwctrWHPpdJrqQlcryf+6V
+STVkyjKXrNDFLSxRVmBRZdmjUDhyPu+5CF20ERNrygyVDOdg7zq5cls6foATiDubhXP26BhXp9/Y
+KTFWEM6FlYBcW78nhwbbklDoutb2Y2dCpgtPWqSnPROXt0bEhVzn47xHekt1Gt8nIcXk3og9T1x0
+U79CtCTH8JZbPLb8o3qNpsTbcp0bFeV4DKoUOsC29ZjOId+VjIayJYW+XJ+ZdfC1EjXrdLAXZBEq
+PHvKuhz2fDxt/7VKfhJGNZZz2Qx+6OPbvPfDF+WcB2PqCr2fNgpQ5q9CxWqdoElSkurDcbdDoDmo
+qmuoaMg7rhla2guEYKK+qy3Ch4UtbGEP2CPhKWS2oBtPWRtPWFJJcpuXKSLtjIsNRTR3nxMKDTEC
+ZeG9e/Mmd1WXMMfBalffvd4BvmZkZ4XLsx99DoCdbdlJh77FLMlO+X1PPUFrRb5v2OthJuIVvPbl
+HsOhNqKorP3TPz9ibS5Bli2RoLuSTfEypdoqT4mQXWp4ICi5z/7Sb3N8W3bgH//oGu77xC1vPvl+
+vLLsFPPKirFDcoXBHt55Bb+vCbpgSqpMw4XbY6Yw54l6M8ksI9cOyDhNKRQpGVRzWlolyYucoK7U
+bArzNqWUWPeIyXhENpDzrJVCMt01TRbhZvJ98VSFTIYnpyFBpbXN1T3BChQMaGglJTANPL2XiUq0
+TfsTCsV9WMcSq5cyrNdpryieYGdKRyXs61aSdgcjB1fRemNyXh3J33UO29zcFz2Ef3X1Or6qUV/9
+klzPjfYxz+WCtzjiHJeeFDjy0kYJVyUJ471bDJU/8fwcCVotsa5Yj6snOaakycHDEr7iQXzV4WiU
+K0ysCgBNEhxFi3ru7NTzikcdvEjwJ7NE7tnhndcZnCiRTeSTzlRL050y1ZCvVjRIMxnr8hlF8jp1
+jMbHk3hKns8h5jkV5fdcj2qnXBRv1h6JRSGPM3o3O9y2LSIF0DTXyxirsE03RVsiyBKYDTRzPJDY
+67VrL3P9psRkUZrjqWDoZJrQ1/g6MB63vioTtqYu+vDChCcuSV9Cq3Ueoxdv5sLVm3KT7r0+ondH
+FggbS6nouZvfR6csx1hfmbATCNS2hjvH9GDylAQl8RzJ3796q8P7npMw4T0//zeotbVUFLdhLpwS
+6A2ctTjRrrerr3yFlUjc9cb6GtkcterVpUwBGI1PZ0FKodUX4wUY7R/pVspUyrJgLW+vEyrc2lem
+K6fsn1ZtGI3p6CKTFj75XGhnYOlrJr5SVbhyw2G5kPj9hIz9A7lGS2tnWW6qpuXYx23IQmY9uU/H
++zN6WnKeziZ0VIB2eaVCMZbXb3lddpRQRQtRDArQQgwZlkQJZF//ytfZVQKbZetSVQjyksK1XX/G
+leEVAK7/+kvE+9Jm/fE/9xiFhjR3brzI1QMR85lqpaYZBdxUbs/h3pDuUDP8dpenPiBufhppX0Pm
+U23Ide3u9eh1ZBFqtauUItlw+pMJIwU43bwj59s7OOTObemMHGURQU2ud2Ojxop2h7bPVlhSDtFS
+SToj0wxee/munm9OpAAxvxZilKQ3KRWnjFRv1hbhw8IWtrAH7JHwFAojdFt9Yg51ZS8XCdFc2C+c
+ESrnnOf4WAW91LQe//7VNbaMrsSHfcaJ9t5P9nh8+wkAts9uUH1MdrTjiazUjbjC5VXRvy03Anw9
+9sSxNFQwOchGxF2ldldelX/9hdcI78kxPvixhKWnxfWvequME21mSbs4qQBkdpZfAOCZ577OtWPZ
+EWa9CfW6QprdZK5gj6MI1bR7m9lrsmtdbDeorqqGr+8yUiGTaXdKop16uXb61ZttJl3JbvfjCei1
+8vyQE6VHK2UblFR6zSq1ukks3ly8ZWWFtCceVv+kS60lHsZGe5OLTyloS2n2bc9yfF28g/2T+yyr
+OMvljVXOPC04jGHvGtNjCeOG92Uf2tvvnVKr3799yJWu7KqPlxsMChWMSRxe6cv5HWlN/yjJSRTy
+bIGehnSff/0KF+qS8HvmwjZVlQCMFdpe6e9ydkfmwtaf32D1GaHCq9ZzOspT8OUvf4HXrsk1WllX
+PUvniIYGSHcGHfZPtIt1FqDNnKdqokEppqzU8sO8w9GhhLRbWzXChlRBaqFHobR/Z5YkeXpx7Sk+
++qMfl+8I6ozMLfnsrImjGpRdc0Kg+p5DR7yq17/1El/+pnR1Jqml0VaCIreK6ynDtJ2S6bx9s/ZI
+LAq+57GyuoKdjEn62g1XHpNr23NIDVQm3XcsFY2dPO08a595jqcUc5/MuvS0XDbuHlBRdp/qyjb3
+j9TVOpBJENxZp/whifvLSZXMyMT0Bg7ukTxY22caVLoq8b0sT+z+9RsMvyE/rzdbXIjkJp27CIG6
++b7bPHW7o7GEDN/38R8i/GefB+DG/3yF0qfn4CsXV8texR15zYnGrF2WxcZrvRdNDTA5PsQ9ltCm
+8BIyb95LoIjA8fG3KemnCasK6HFzQ+jJz7U8x6mraK5WdabJFDRP4tVaNFfls/FsRKalyqCYoRwj
+uFrtyEYDWg2lfU9CypeFMLV9cYdQ3WAGNSauXPOhq+3S44KDq5I578Ujxvrwj3Po9pRlajijq4Qq
+fVVVyovitC/DwinK7/hgxGqmodC5mKEm3I+H8pA/FtVY09Lp1tMfYGlJ4/rBgNe/IprIr97q0clk
+Y1hz5AFsTB06fZ0Xfk5Tqy+2AtWSokGtdpQWPsVM9UAanALZBt0+uoZS+CGUtByqSlj95Ab+XQWO
+TUckvTkJ73VaW5JfKVc8irNyzfe/LmWE3/6/v8bdjoSPaSdjbjEAAAS/SURBVG7xR9r/Eg9Bq2o1
+zz/Nsb1ZW4QPC1vYwh4w821t2HfxJIw5Asag1MrvvC0vjr049v8Pjn3WWrvy3T70SCwKAMaYr1pr
+n18ce3HsxbHfXVuEDwtb2MIesMWisLCFLewBe5QWhX+0OPbi2Itjv/v2yOQUFrawhT0a9ih5Cgtb
+2MIeAXvXFwVjzKeMMW8YY64ZY/72Qz7WjjHm94wxrxpjXjHG/HV9fckY83ljzFX9v/UQz8E1xnzD
+GPM5/f28MeZLOv5/YowJvtt3/BmO3TTG/FNjzOvGmNeMMS+8U2M3xvxNveYvG2N+1RgTPayxG2P+
+B2PMoTHm5e947Y8dpxH7+3oO3zLGvP8hHPvv6jX/ljHm140xze947zN67DeMMT/2Zzn222Xv6qJg
+jHGBfwB8GngK+AvGmKce4iEz4D+x1j4FfAT4q3q8vw38rrX2EvC7+vvDsr8OvPYdv/8i8F9aax8D
+ToCffYjH/iXg/7HWPgE8p+fx0MdujNkC/hrwvLX2GcAFfoqHN/b/CfjUH3ntTxrnp4FL+u/ngF9+
+CMf+PPCMtfZZ4ArwGQCdez8FPK1/89/oM/HumrX2XfsHvAD81nf8/hngM+/g8f9P4EeAN4ANfW0D
+eOMhHW8bmZA/CHwOMAiQxfvjrsfbfOwGcBPNI33H6w997MAWcBdYQqD1nwN+7GGOHTgHvPzdxgn8
+t8Bf+OM+93Yd+4+89+eBz+rPD8x34LeAFx7G/X8r/97t8GE+WeZ2T1976GaMOQe8D/gSsGat3dO3
+9oG1h3TY/wr4W4C2P9EGetbaeTP0wxz/eeAI+B81fPnvjTEV3oGxW2vvA/8FcAfYA/rA13jnxg5/
+8jjf6Tn4M8BvvkvHflP2bi8K74oZY6rA/wH8DWvtA7zXVpbst70kY4z5CeDQWvu1t/u736R5wPuB
+X7bWvg+BlT8QKjzEsbeAP4csTJtAhf+vi/2O2cMa53czY8wvICHsZ9/pY78Ve7cXhfvAznf8vq2v
+PTQzxvjIgvBZa+2v6csHxpgNfX8DlBDx7bWPAj9pjLkF/GMkhPgloGnMnOXwoY7/HnDPWvsl/f2f
+IovEOzH2HwZuWmuPrLUp8GvI9Xinxg5/8jjfkTlojPnLwE8AP62L0jt27Ldq7/ai8BXgkmahAyTp
+8hsP62DGGAP8CvCatfbvfcdbvwH8Jf35LyG5hrfVrLWfsdZuW2vPIeP8F9banwZ+D/h3Huax9fj7
+wF1jzOP60g8Br/IOjB0JGz5ijCnrPZgf+x0Zu9qfNM7fAP6iViE+AvS/I8x4W8wY8ykkbPxJa+3k
+j5zTTxljQmPMeSTZ+eW389jfk73bSQ3gx5GM7HXgFx7ysT6GuI3fAl7Ufz+OxPa/C1wFfgdYesjn
+8Ungc/rzBWQiXAP+dyB8iMd9L/BVHf8/A1rv1NiB/xx4HXgZ+F8QhoiHMnbgV5HcRYp4SD/7J40T
+Sfb+A51/LyEVkrf72NeQ3MF8zv3D7/j8L+ix3wA+/TDn3Zv9t0A0LmxhC3vA3u3wYWELW9gjZotF
+YWELW9gDtlgUFrawhT1gi0VhYQtb2AO2WBQWtrCFPWCLRWFhC1vYA7ZYFBa2sIU9YItFYWELW9gD
+9v8C6JUakGWNzJUAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3074... Generator Loss: 0.8244
+Epoch 1/1... Discriminator Loss: 1.5227... Generator Loss: 0.6513
+Epoch 1/1... Discriminator Loss: 1.3824... Generator Loss: 0.5856
+Epoch 1/1... Discriminator Loss: 1.5399... Generator Loss: 0.5881
+Epoch 1/1... Discriminator Loss: 1.3308... Generator Loss: 0.8962
+Epoch 1/1... Discriminator Loss: 1.4280... Generator Loss: 0.7422
+Epoch 1/1... Discriminator Loss: 1.2792... Generator Loss: 0.9444
+Epoch 1/1... Discriminator Loss: 1.3914... Generator Loss: 0.9070
+Epoch 1/1... Discriminator Loss: 1.3808... Generator Loss: 0.7781
+Epoch 1/1... Discriminator Loss: 1.4229... Generator Loss: 0.7619
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVnMbVt23/Wbq1977f5rT39uf281rirHTUWOHUMUhUZK
+HpAiGqEgkPKEBBIPGF4Qb+GFN1AUESQeAAspFgTJBKJgCyexHcuusqtu3bpVtzn9+c7X7X6vfk4e
+xlj71FVw6parrn2R9ng5++xv7blmP7r/GMM459jTnva0p468P+sO7GlPe/p80f5S2NOe9vQJ2l8K
+e9rTnj5B+0thT3va0ydofynsaU97+gTtL4U97WlPn6DP7FIwxvwrxpj3jTEfGGN+5bN6z572tKef
+LJnPAqdgjPGB7wF/GXgC/B7wbznnvvMTf9me9rSnnyh9VpLCzwEfOOc+cs5VwK8Cf+0zetee9rSn
+nyAFn1G7t4DHP/D/J8DP/3EPH04n7u7tmxhTgpMuGT8E4++eca7VD2CxALRNAYBtWny93jw/BSPP
+Gudh/OCfb8OKdGQ8j05Ssq7GNdKuwYBptO0KP8qk7SAB4LsffIhtpS0viTGNthd5UEkbFoft2vPc
+ru/GN/K5dTjjtF/gdQOw2lljcbaT4tyuz2HkYYy04RmPUB/x9ENVWcJI2vK8gOcXGwCiH2ja6u8B
+Ii+U7ngtnvP0syPyYgDi0MMYGbcfQS/oyw/DRscRYkJpLw0ifF/60bQtdV3Ko75PUVUAlLoEq3xN
+kcvf26aSMQLOOmLtWwM4s5sB6a/xuy1CZDxMKg+4wlG2tXxuoO2e7wZqwHjyQ8+HxJcxWc9iOt7o
+g9E9161NgCHRRmxb0Ut68p8g5OnFSvus+9G9XHNnwHSdNm43+c5zGB2U0y+dMxgdoTHe7t27BQNZ
+c08nQ/eCw+Fhdp+7cRjPI+jGGvgEgXw/n88unXNH/BD6rC6FH0rGmL8J/E2AOzdv8Ft//3/Gjz6C
+5gCAcHQbEw31aYctl/LJwrqVxVhcfReA6nxJvy+T05t8EcKFtFFmxJMTAKy1NM1MPm9k84RJH+tk
+c+fFU4pr2aSRDXHhFQDF5QP6939e234HgF/8q/8Gm5X8PXvnTYJLacO/mWKe5wBsm4r8eg1AkOrq
+Vi3JWLa8XVdUnhwUWxuSoW62QjdKWNFu5NIzWOxW+nZ6p0ccybKlYcpJI5s4PZExPX6y4vatgfS3
+f8B/+d/+NgA3PdjontoGCQ7p893kVOZyuCbL5fKbD3Ne7b0mfz/pEwVvATC8Dz99+OelkZNrALzZ
+TcLb0ocvH95h2Jd+XM5XPD97II8Oh3z/8ccAfCx3FL/5zd/lO+9+D4Dl9WNsI3NRbSvuySO8AOpQ
+xtodoJvJCDeWz7eSjOQLcmKr9wu+vzwHoL5wrJw8c08Pkg0NUf8QgCRreevgiwDkyRqfVF44MKSx
+7LnKk/k5CnxevyttVIsnfPXNr8izh7f4z/72bwDQrGXNV23FSte8MYZAmQWxxW304ug3hKWMqUHW
+tCkNJpBn46BH68kcUkGr65R4EWR6XLfybO2VJI181xqL5/V0fD3GsZyj4dGYg0MZ39/7tV99yKeg
+z+pSeArc+YH/39bvduSc+zvA3wH42hdec83le7jpIWYum8fLArxQbnPyFmvkIphffo9v/sHvAxD6
+cmjunE4xxavasiFsJwAEaY2zytGCEL9ULmekK23b0DjlDNsVtpJNdfbsCossbn55yeFcFi/4C7Kp
+enEAoVw2vY+fMVdOMjgbEZxLn3rpmki5v1F+1foeA+UCud/i641fNi1BIe8rtLtZZHBGfp9EFY0v
+/RzUFuNJfwa24rkVnnjPysKHrUc9k8uv3w/JdFZqY7DIpkmt4fhU3u1V8vvj9JQm2Mpitce8fiAX
+y+TkZ0g2Ml/Hd16jLZ/L5+NbMrbrF5yevg7AYZbSO5V5GYdbbt4fA2DXlv5rcvncOZcNP3i7T/Xf
+yWF7L3/BWuc4Bs60zxWGttbxneqXuWOcyMUaHqdMliOZ28M1T59LGzlLDnUetypBRTZjvRVmcev1
+PquN3E79aY9oKfssrxdkI9lnh7r3ns2ecVLIvrGzMR8+/gMA3s6g2MpLylIuBVttoOmk0QYPWbOg
+9KmdMoBZS6vMoJNKEgO+Smz90CdMZC1rr6ZAng29iADpU6BSmrUJLpD3NWGEK2T8NFtWrYy7t2xp
++jf4UeizuhR+D3jDGPMKchn8m8C//cc/3oK3odmkhOltAGyRgJPJrotz1hdy2L7zW9/kH/yjfwDA
+X3rjzwEw+fd/icCXAxu4JW2tk9c7pO1UhqKhaWXTN5UcUlsWVOsn8udVQbnR74uG+dNLQLhq1pNL
+Zv1EtusrB2NehNKufx2TPJNDWJZzmpEcw7e5SzGQA7Cq5cBfGo+hJ30bhI5cR7/0PSa+bApPOVTl
+SsqhbI47wYCebqTBoOSJkWW7W0UkPVVXlLlwu8+NWtrKqhjdRywKQ+bJHN4eR6wWMtbRVDb8Ol9j
+Mzmk/XTLbCWHP1z8U1axXCaPfmdJqd97fyR9G7RDvlgIx38zvskbf0X4/HDwJkEs7ZXnNdN70sZw
+IvMz2bzO+/+qSH9nl99hPZfD2HigEjghjkPVIGfX8uHWacRSVT6/cBy/IuswKgZ8JZCD/tHDNS+U
+M5cX0ofIq0hUjH5xEXD3QLmtC4luyrNHScpKp/FuLPvp/t2IppV27a0xQSuX0IsHJX6tl0Et+8pt
+wKtUtcNQd9qvg0aZU2trjJVnhn1dpyQm1TlOQ49pXy7kVd1S6eEubUurF3gbq3SIodhKW7EXsE2l
+H3kLnsn1c8pmW/Gj0GdyKTjnGmPMfwj8n4AP/PfOuXc/i3ftaU97+snSZ2ZTcM79OvDrn+pZfCo7
+ILEzjBWuSjPGFqK3t4zZPhaOfnG25eiGcLe3f/EXAYjDV2l9uRld5VEr1yyXF/gqigUB2EBu42Ir
+D/hBgCvlO2NHzK7EY2pLaBuxxwyoWFYiIZj5FADv+BZf8eRWvrTPeSFSKVELeuHjJRmhjmWSiBjt
+1S1lLhzDCxyp6oCD1KE2OarOSBokZGqgzPop456qD3HMsdoUNusaTxgXcS1i+/G2IRjK+J5crqg9
+4Y5NCKG28eHGEA1EBF+qyHlwMKHtVJTxAcdTUQlO34j55vc/knWofdZqp6quhftcbT7kD1cigYTb
+9/n6858GYHr8R/zCnxNbjNc6XslFgrCRcMeGnNfv/BQAvzE9wvBA1o+X9jXfh5ka5YJY1vHZNuA0
+FSknjW5ypHPYv3vMK7/8BenT//QNvEB4/pOhrIFvLG0gbZSTMZeBTNwXRq9gezLPq/w5rpJ99G21
+T/zsW1OqSqTX8TpneihS4YtNTl2rgVINtyaytFWp4/BpOmN04O0++37AWO1Hd09FGmnzlmEv0aYM
+qRoJrd9iVH1qW0ujkk5bqlE5GWAGKilan1r3dVNvqGuRbq68AD/+0Y75n5mh8QfJNjXb+RleMiYd
+3wegvHyMp6J4U4TUPbVeJ1u+8NYvAJDeEV2pLtastuLs8M2QppDJi4cjbK4GrH6Ct1H9TE9uvl1S
+OHlHVa9Je2KcubY5myM5IFXjkakcuFi8B8DRaY/1c7kJ4nhCMJXPo+EJx4ksbt1aSl28NhVxcDAv
+iFUH9LyI0VDeHVvDupC+zXRTlS7isCcHNxtE9IYypmFvgN/Kxh0Oa9a6ieMbspThyrHeyHuvTY4r
+VNTGkG/l3YHzqRZyqWWJ/H1TtyTmTQCKcsGT4g8BmD/rc/5MxPyyuKSo1CYSyXs353D9SM1F1ufR
+/JsAHE7e4lvPvgXAl97+KueFrE968y8AMD0MmWTHMm+j17D8nrbx0k9etxDqgWs6fdmvqUuVy70+
+R6eyZodRn+t35ZfHwSn+TJ73Qvn7ILHETg7hurL0rMxXEtfUtVwyTWRYzeTSHkzkHd++3vDKoaxf
+HRta3SNpcI315ED6rVz6lVftDNetMTRWL4iywbbybOIHjJVRpWWtQ/bwdP3DKCSMZMyZ7WFDWaeq
+rHfG4bbWfWVz6C59u6XR7+uqoFZvT5HA1eqlF+/T0B7mvKc97ekT9LmQFJxrqOtrinGKsSKK5p5H
+L5AbmNDDTp4BkGQ+k7dEnPOM3NqtW+Ibue29QUp/IGK+SWuc3tAECU596J0RJqbFRHJru6am7fAL
+65jWCvdfrnNmahI8UhH2sF6zOZTb/OGLc0oVcV+/OcAthat4vUtGkYj0g1BE5pkbsSgfAJCaAe8c
+qcdgMOZiIeLqfCn3dBgFuEjbMhvaVrlHVONUunEsaGtRpXwnXGlFxKX3AoAKR6sejMAYkrjDSET4
+vkghyVTUgaSGXiYid5QNMQPljvNntPlM+5ESqYHOLlp9x4pW2/LSkFSNZ015TR2LiP79ixZTiEkp
+P5dnv/6zv8CtGzI/P/sX/yLv/xPRNNflJR2UI/TAKd8aqKvQGx/TC+SB8aCib2SPHN84YOR3Vv17
+PKtFCgmdumeDJ0xHbwCwvawJxuLOGKeW0pe1zgPDM1UxIjUGrmyNUa67jHxsJepIrzZ4RsZq+yql
+zRsaI/stIMYpF7d2uZMgfC/gQCWFni9zaA3UiqzwgwGB/s7FEYG6VuNhQp1vtQ1Zg8La3b7AWoyq
+nng+Tj0f9bahDTqT9qejz8mlAE3V0mzXFPUFAOvZjK2TDVSs5nzwSGwK54s1U9XnFxvxEIRcE4Qy
+CQ0lxsn39eUCE6k7ya8IVVcrc9n8xpVUlVxC5fqS9Vo2v2sKNksRmdOsRyAfKeeysNcvnuGp/ja1
+AUYPhbnwCBRacSe8T64uvs51NfUdWSQH5eZRyCCQ/pwmIaet/LAcSn+3lUfRaddlj6aRTvgbHz+R
+RW5Kx5Evl8HZUwUp9Ya8ag51nM/xVcQNY59cQTZZ5Ih6alcpZcyjg4w79wWbsKShLcSl/eLFimys
+uvhBn2wu+IS12kvGicdanDMEVyX5izkA7WDNH35X1iG++hbv9WXO3/ia/Dv9ssdJK7iPL96dMnxT
+PNjbf3aJnnkaB71E/lMrs+DijPBVOdCvZCnjoRzM6WDIbCF9e/3+q9z19JCpN6ffjDi6/TYAq00B
+vszF8uqMa1U1irMF00bWbN3Id33f56NHsidvHd3m8Egusqa5INCDbBWwts0tTi+ThgqnF4SPj9H+
+DLKEVFWinqKiqtYjVnuBZxwEspcjz+w8FWFRE3aXiG4Ln5ZW+2utxbMdaI0diMxPPZz/oykEe/Vh
+T3va0yfo8yEpENDaE7ZlRViIpb9aGZ5tfweAj5+d8cHviwErMgl37otl/PrGB/L79SWHp/cBKB9c
+4SLhRvZiTXYoaLww6dPGcjM79Sv7JsWu5NnN9TXL56KiOOPjNwo8WjouVXopr+WKrtIjmpVILpNk
+TV3JNFb+JXFPuerGo1KUWqKGI+N5TBNRbe7eHxBvhItFqSOJ5X3+WCSF1WLL9VK4/7Ja0R+IqhSb
+hiBQieWqwfWFe6ROvSh+QBkIVz3b1jt4cDo1xOpdqM5KJolIJpNU2nr9fobXKAJz3PLgTNpogppA
+rdeDxnA+k7nrQEV5YBl6it7KwEtlHGVZ4K/lffliCSpqf/O3f0uevVFx6Ikxs8cxt+/IuJ/9LqhG
+h/Vgq1JWHKpBjQWrhbR7/9ZXOZnKu0OTM+nLM+nREXUlKkGgAKHUBKSZNDxOYopc0aJpSLiWuaiP
+S3rX6t9/IFLHwoasI5EELzdzhrF8f3bRsG2kPU+NrngO1+q+8QUWDQKh900HY4brSqS+VjEr6SDF
+uc4YaEAljKax+KaDMTfEKg2n+u+2sWxUvTBVTaGGyDCMcE6NmEVLq9Lkp6XPxaVQNw1PZ5dsnlVM
+hqILfnzxEd85F3Rj8XTG47mIpa9MT3h+oS6yrRyaoX/IulWIq+/TrkQkDuyAnqoa3mkft1BxTg9E
+XW1YrWRBZ5fXrBUgVCwsG13Q59szSt1AVU/dY9mQSgH67XrAWmMwDkyIu1RXHde0gSxMGsniH8eG
+o77YA3phQP9IDkLsmZ2XxNN3NJEjUz9l24Z4Q7Vel338UL7vHUdczmWs17Ec0kU+e6nm+HYHl0/P
+4TLsNqnPi6XMJ0b68O3zJySKEH3x3nOqSkXRniO+VIiumbNdyzOt9iGoIq7mMod+a6kGaiFfeRTP
+ZO5DDPO1vNvXDf/rf/t/53gg9oD0TkL1TMFbBnryM84Nu5iWopbfB4GPieTzrV5Meq02qPWWfiwo
+y2FT4Xp6Sap7Oh1lUHWIRwfqlRlNM6x6VNLKsjlXCPkN6c/DD9ecz2WfPV/4vP9YD3cCUSL2imKl
+/5YbNHyE2lqcxmIY8/Jwh84StertUDXAlB5hT96Hi2g0QMT4Dl+9JAM/oTMfbDtPRuBwocLirUer
+gn+er1HHByZp2AXIfEraqw972tOePkGfC0mhsY6LPMd5MVsrHP+3H77PB+8KpNYPN3hq7GtMg13p
+XRYprj2s8TSoJQsjGCsHSgZEsXDCIA7xVH0wamSqXNPFltAODZyLWFbkOa1yvOrC8ehaWFcZyc3/
+s68M6b8qXOnscsnZQxFVI8+jp2JgkjSsO1CTiot1NNqpEok3JlQMe5QkOP2+Uo7ikxMMpC2/9nCK
+6YoGHo1GNMTxikSNnJECdL2Zz7VayAtr8Rppb9Nz+Ko+tIXdGbPmnbHskeUMUZ/qusHoOOLGsDb6
+cucgku97KjpXpn0JpvEtKrDRugaUS7exw1f8sI1LHVPMvBIO3FxWbBRv4TmYKUeUoFM1mHWBTfgc
+OVnT0q9ZNtJwPx3Q02Az0493cSOdpd4HFI5AXXs0ahA2tcWkGhG57OEdinrgfyiSRtBcMFIoeW3h
+QlW+cl1SrUUVpNdJcYbNVtWHpsQqHDvwIVaPQmwgUGxIolKT8X08fdYEhsjTdWrNTh0zDAl0Qnsa
+JrpoSnzFxfirzU5y9quQ3IkE5RWWRuHtn5b2ksKe9rSnT9DnQ1JoWy6vNqyDJQ++K7ruu7/7ERfX
+oi9/7e4pr94UV96bbx9zcCA39M1jcWPFoWF0ILYI2jUe8ndbbUH1+aJqMbHctOWVoiNdRV7I7Wqq
+lLwQrtOagkqlkXXj+OChwK2VueLuG0ytYXhrH/dcPi+Wa+JXxSB4Lz6kacQY+WwuIsMgGxDH0p8k
+Tl/6lcOMdqbRmoFwUrP1SRBd1SYBRuHTJmpwirAcFlPisfTz6oEG7biKUS0cM68rmkC5UWmoVI82
+vkesg0m3sgUKv2AwEu7fjJNd4gOv9QgVZ5FXNZ66zjp/vFezU9z90u0gyoM4pInVx25BI6A5Phro
+mIc8V2xGU9eYLq+FB6omU7cQKWihSy0R+T7TO9LPmyenTDKRCsMkZtg/0DYCylJDmDXKcLH0yPUd
+q3WJUcPlcl7u1rVtcjINebW6V7702pgPH8u+OG9LmpW0t902WCtGcbcRyaVtg13ih6YuxL0ITKI+
+A81xMR4OuX+g2BC1SxnXp9SAqcIzZL6Mb20NTqWYUTTCqYSbefL3uFhS5fLdyoY4lSCKvGaja1K7
+Fld20XKfjj4Xl0JrYVU6nl0t+OB9MSI+fzHDJLIxb//0bX7u9pcAiKM+kabhuFyr6BhkpD0NvU0i
+PBWpmqqh0tj1J48e8eFc1ID5c7ls0l7NNJMNn0Q+TaeOuBHRWt59vV7x4FwOdVvJwv070ztcLUXU
+DtyKlVp6XVtyV3dYsbYsNMoTTfRSNI5AYbJBmuKrf7tZOyo1pBFIf9J4QjaRcWRlyfVWLqb8yYal
+60RwS6DY91EgKsXZyjGrZV5ezEra7u6K7ctMJS0ksQZVj+TLfpJhVDUYupitjukwTmlVdO9bKEvp
+Z96T+dnMCqJYjYRRzEDjK8I0YqzGMw84Gcmlfusr4uc/fO02/8f/Kh6lq3cfsdWAFWvZRRd6iGoJ
+7AA9QeLxi298Tfo5PaJV43HZbPEaWSc/jsGX9rrw+9XljItSvTKPz3mhRkCPkF4sk3R7eEA2lUtm
+UIjKt1ykLJeixl5ezLBOVcG2odb160/l92mTUBUzOupy2URBQqOi/1UOkap3d3zNdxIZYgVnDLMM
+o4fbS2vOZzLfT6MZzspYtq284yjqMdT5Tr0RvVhBZL2YxUfCJIrl9Q5u/Wlprz7saU97+gR9LiSF
+pm25vF4yXyx2/mNrHK8dy036c8Ovc3IknGZdtywKeeZC0XW99YJqLbfvdDJlOJJbt60MV8+Fw67y
+LavHoppMTmTYk+SA0VTE2aZa0Bbq1ltckGtugehgiAa4UWoCDdMb7qC/s/YIZ0QMnt4YY9Rv9Gh5
+zdkTzbOgSLM06vH8TCDIvaTdoRtjz+IfaMafLiouibjSZCmLywu+90J+t2lDIhXSh/0Wfy7P16ci
+wh4MDI8eyDiLpKXLy2sLt3NpxZFhU4nUFKxEXM4XpcjuQG0XBIFiOiYNPQ3o8o2hVY69uZT53m4K
+uuRu/WRIolx+4I/wFI2XeSmRojftmehBHz2/xMykrYKCQqNHDRB08w2Eu+x1OpDKcX0uz37jG2cU
+iHRQhoabqYz7eJQyVVxHp4tstg3Pnss6Lesc1AB7cDrgSPNlHPYHeI26V61w6PU2oChUEswNGJX5
+k5pIjZieGhdp13jmJZ+NVEoZTfuEVqQmPzZsG3V3alCa8SpO+qISTCdjfIUoP316xWIlY7rcNPga
+XZmFumezkEEme6gKGxpdm9T4+Iodsa7F1j+aS/JzcSlY21LkS8qiIlOL+8FozE99VUJhD74wossT
+8dHFQ5ZzWaQxAnc9OPCJVHcOQ4OXdHpojVFgSa+X8XYoCxooyGXZrrneKLS53OJU7Ka2OJX94qbZ
+Ra2VmkAjNRW9Y9lI7dlDaoWfRo3bhdMar6Fn1MStYd2BF7JSeX6+ygmmqir0+zSar3Bdqn3CXbKZ
+yTgLE3D/1l353Hfk+r0pzylX8nmil1g47hGE8l1VW7r0kNYDP3i5YQfqFq9UP/VMiVNxP27BUwiu
+axxNpVbvXko8kY251WxD23WOp0k/wizhaKqekTQjVG9PVSUYq/Ya5B23jm+wUHfAk+ff36k2MS9D
+pyMPNMcIqfY3G0248uV3NQucqjMTN2KmKsE4aukdy94I+ppVad0QamKc0jlsq7EBZc51F68Qlp1D
+i1Dh72ld0++p5yCtONCLrs5TgkbGajQcHB+2W4XQG4uvl0IcZSwUjhzHE+6eyFomrUSXemvHie77
+k15KW8u7jwYBYSaX/WvxCWNlYFZVXn8T4St8OqhyVgpeir2QRC9yN+MlLvpT0l592NOe9vQJ+txI
+CpvtgtAZolDEocnYcNyXxByPHjzl9/5vMUp99/ycsCc39FduS8DNNH2TI416c64lVINZW7aYLtPu
+tmCuUWbn78rvPlqd4Sv89s64z82xxuYfDHiyFMty1rTcUKzDi1raKhYLclUlsnUPt5Ab+nm9YPhl
+MVSdZOnOSl4pouy14Q3uHIuXJIoKmqUayfyaQpPJFlvFRNQNVlNtzecNLxbCVUpbUqk+k5mWQ/WR
+XyyFY9780invKGLz3F/xUCMjTeXtLPjTacyBiqBeJsFTeTWnnykyr61p1PA5CAy1ivDD4fAlB1Lj
+Wr2sdtmV0zRiuVXxO3fgOmReRKGBSZtGIONB1CdsFXxSm50NdJhBqxlmt9YxTNTAqIlVjkYDvvvk
+Q+nz+5WIE8BBGvPaWMZ962e/RDLRvAepcOtZvGKgKoX39AlXGvD29OmGZSNGuQMXMVHD3asjWcfT
+g3vcVOt9ma/wmw7jAltVadYKR6+dI1YJq2wCYvWcPLt4ylLh2klmGaj68Kp6Yg6jlrEadid+g9Mx
+zVY+KHwdGp5+JHtyozk6QxOQBSL9no6mpJqB/Gr2EF8lkzAMqHY6ZJfj+l9Mn4tLAQfUDesazjRX
+Xy90Ozhv1J5w/xUJl37nKz9PoaCfY/XpRCYgUI9D4Dd00dJtY4h1UyRLy2Qki1Ceyu/eHB2zyGWi
+13WFSqX0pwPiK/l+cXFFrGnEJ4WCn3qHpLWoHcODhkoj0ub1imIruvYgmzC6rWnSu4U7mBKrNyTw
+k10G49qBbVT0U5F72A5ZqYV/snWE924CYOsaa+XgZYEh0QswUhF/6PfY3hPVZvi9C6zqp2HP0Bur
+2NlL+PKd+wBMb4r7Nk7vY331YMyuKRTa22srjHpEmh6cb8SOs1RXbpYZik6lrjYEGrVYrlZ4Kos3
+ZU7ZyvcmljH3spCbennFcUCoLkIXONTITuJBrjaKoSa/PRkd8sY9uVhv9e+Snuia1YZG41hu3nuV
+LJWxWL/zWvR47Y4wnCDq8aZG4J7nOYtLGdN8/oJnK1n3V0fyu+ytMTeHwnAuLp+Tah7Io8bjfb0t
+a1Vd69pSaYJWP+wxGsj4xn7IWF2L48kh9w/koN/QPo4C6CXqLfALglIvwGFMpKA8ZzIGqvZ2div8
+HHTfDJM+ub7bsw2VRnn6JthFin5aJWKvPuxpT3v6BH0uJIWmbblabdh6DYu1WFvXrWOo0Yw/deM2
+X7ghol8vHaH2HWpNi263BSooCAdWLtZsC5xmwD2YDvAavYFflX/9tkcZayb6ssIzYuApLi9o1Hft
+hTWvHMkL3zeaxCMIOBhIKrjrjaWuJfIvjmBqhbUdDSwnmXCjwVQ4m+8MaIIX13qYRGsabGqMGr66
+SJbIGQ4Hwl1ODw5INYlMHfnEiVrDDWwuBS+hjBgv8ulvhftPT1I6sSkNImoFusReyEgDhr54LBGn
+brBlo2ncNhfPCDXI69XbPfJcxlEdVizeU+xF0+Vv8PHUul2VFasrNdZuoFGxNU4SAk2nl6u34IMH
+3yNVo12/H7DdyAAGRUug/oxz54gUtFCpeD3YNvyVL0seyLujt/DvSnvFbIPXamDaqEfQBaypgbqX
+xMSKzeiPB+QrmeeTfE18Q+arze4z1CIxQ8WIeNucSAPszg49KiNtbJoVQa6qmapJVbPF6n6zTU2l
+0lt/POBVVbteuTth1GVj9jVH58DiqYHZtVBpDow0zjhVg6mtEtaqZAWqulpSXJdyow04azvMRkup
+koLxX+Zg5SO6AAAgAElEQVRZ+LT0ubgUjIEgtDgX0qhIvVlUXC1UdxouONEJDI57O+RWq0lXN8kK
+pxh+z1m8LtmSq1hrAsukiMg6l04XhXYUgrreTOvIZ+KymtmKhRacSfwUfyR9Gl6q98Hl2CNxl7YX
+z3YW7snY52QiG+z4KGWgoB7F7eBKj0LzC5rA4NRybCMI1E0QqfciHkCiWYAoK6wRHTjyGwxi46jm
+Fi+Q9zWBbNymNkSxHNwv3ppgjaAq68RjqHkeR9kBk55catmxiLgD67HqvCiv3MRXoFZ/6rNaStuP
+K594pOtwLYfReIaxZh4qgVDMNay9LX4r3/sRjHQs/lajGs0l285FisdIVbsIyKKudkLLLFePiIYL
+1ycx96aiSp78jI9Xy+fKu6LtwqQnQ2yhoZaaOn8QG5xewv1ogFOP5WmV71RPYrPzNJmZZt4qz0HD
+0w9HU0Kn6dzXCR+rN6tSj1IWNyxL/Tstlbo3+3HLn/+S7Jd7RxP6Gj7v+xpHUS1ZzmReVtuCSO1g
+fjphpEl6/VGfTF3xtcal1I2/A7sxn+E5navIw3ZVpCz4XSqrT0l79WFPe9rTJ+hzISm41lIvC7x+
+glHxO3ANLlcubxMCTYEdei8r4USpWmarirrVzLnNS398nPSJZ2JEas058aHCSjUKzcV9aoW+2rKm
+UWhz5MGx1kwMbjS8qyCk2UJF4MUVwZlIGP065KQv/Xjn5j2O7wkXGJEQquW/w8vHWQzlS0uwrbs8
+BXaX0stXOKxfecRqJPNCi+vGGjgaFbvbkF3exfJSsRebGelK+nAvqLBqcfadJVer3fjugENNLx5o
+mGhpKkINUAjDHlUoatyDy3MW17IOZ15B3Qh33Kq0VedbAi1aM0ymNBoFOQ6SXb3GLAzo8rB0Kcsz
+r9lhLN4cjqlfkfl+9O0/ZKwcf7ZxDPs6z56I7V/IDog7SLsZ0mpimGQ0ptBU8w52+RF3RSVtgLfR
+SFIsvYNX5M/1CtPXXKBFA8rdK023RxCQaeKU6Sig1rntBdf0M00io0CobR1gdM381jBV4+mdo4wb
+iu9ITURf4zFiNXZetQG1lghMrEekqt12MWeplaOO7g7oKQCsVvWjaSz1WkFojcdEj3MWBERdDc44
+QIW+T01/YknBGHPHGPMbxpjvGGPeNcb8R/r91BjzD40x39d/J3/Sd+xpT3v606cfR1JogP/EOfcH
+xpgB8PvGmH8I/HvAP3LO/S1jzK8AvwL8p/+ihowBP3BEXouvxqnGejxfapbd9ZxJXxRzz8aYViPg
+cjV6VQWRQobbZo4JNeuOq/D1Vp09OaM3khwIg6H4sVwV4xXyjmpr2ebSrvFSxrEWcIknPP2WFIl5
+rpyo8VOqrcCOEy/g+LVDbXeAUT06b308dVklqqu3JqHWEmRBGNIWanQ0vMzaq8FT9WpFqZym5/UI
+nLqmqgKrvlPjbQjUouZvFVI9D3BGPpvA38GcnWfJRvK7xNUYNeD5WWf4NMy1vuLlcsb7Dx8AcPbi
+glWjCErfZ64GsblCdPNlyWqpSUmHNb2+v1vT8VDGcnM0IB1pdesLxWHkW2Zq/BlkKUM1xP1hbUlv
+aERhL+T5C5EKEtX7XbrFU+nHs/4uu1Fe1lRll0Ogj1Xu7itKs7U+5YVm54rPd/Uaw3gM6mqmvMbq
++rUadBa0N4h0PgkPWSGw+bUpqdW6u0N/bnNKzQuBNRjde+Nhj6nu3xgfLeJNq8loA99/KQn3+6hX
+m0W9otF+RNslme7rUPvomhrndxbmFl+N44fjKVkmQVxN/jKJ66elP/Gl4Jx7DjzXzytjzHtICfq/
+BvyyPvY/AL/JD7kUrPPI25iqZJdmvQkdvobeVustuV4QEeUu/KzYyAI5LwTF8jfOYhca1di2rDXV
+1vPVnOxCgDNRohme8/VOumyrglZDXYt1QTuQBbjaXLLcRTDKP6bYcjiRTRz7CTc1xdobr93hRA9I
+7VWgFYE9LepSbUpKNaS222ZX7t1tHLZLB66pxgI/Q93O2MTQBRjYIEWboG4N+QsBNW01DDuclMSR
+9P3sfEWoRqZDl9DoAVq2Fc8fvq8DV89Ir+ZiJhvpvUeP+L6qTLmt6XKM1W1Fo3NUKxjHNpZSC1m2
+riXXkPPQ8xnr5i3CkCP1pESRHKAiafCvFbDFlsCTwxS1gKY8mwXVLmZgtpGTEm8Ezw+wfv4cq0lW
+Li9meJrav2yviBQXXSvsuM1XrLr6mBc1QSSX/vBWiN++THDiFDzmSrXkLxY0Xfi972gyWeu8N6PS
+9G5dNGTb1ruiL8YYDrTo0I04wde5901Dq3D5SlWDPPRgoJiF/ilksq+fbUuOuviKdkmh1YeN5sws
+bY1Tr1VJS9V0ZQACQi1HgCuxP1qG95+MTcEYcx/4GvC7wIleGCAFhE/+mN/sStHHaqXf05729GdP
+P/alYIzpA38P+I+dc0tjXro/nHPOGPP/Kbv8YCn6fj91JQ3zRQ1dxlr6hCMRy7eBR64pzfKLSzq7
+iVXoWzzsYTR+vqTE0/j/2rS7upJYx0ZdjqtMbvA6tHid+NUaWpUINq6h1oQW7bym0KCbSANjIrsg
+SPRm7xnCkZhN0mmGr5Bmd7Wg1rYbVQls6+GpbOII8dRIGFtHpM+EypWDoSFSd5PphVjN02BTh+uc
+03mz4x4ukz4mTYprhdMUVYWvZqN25PGqRprWRcRFlyB2KRx4tCm5nilXrVomytFTL2CtnDmwjkph
+vtOBzH3pG9T+hfM9+gpLzst2JxWVoWXcaJ1KhSsXucfc1zqPbUSjCWFHZsA8U3UkMFxroJim0OCZ
+D7Vyx4IlC02MU3gNaJDWpS1YzXRcqkbEdY3CXqidYbuRvRC+cJiB4lbihHat+2gjhtaSnK0nxmoD
+9HWfjXsZVlOe1U23pm6Xgs14jqanRtJhTGu1/FsvIYrEDazeWc4urnmxECTld7ynnJ/JXn++XPGl
+e/K+d5Jb9HWNm640Ye7RaoLdti5p1IDehBn9oRjVZ9WC5ke0NP5Yl4IxJkQuhP/ROfdr+vULY8wN
+59xzY8wN4PyHteNhiFyIsQ2NZgdKghZyBdOUJbO6K7m+xdeN189k0tuiYTPXA3+xJNUwVOcBG9ko
+o2yIVd0+n8uhaaKQRq8sz7KrtejYkms8QzNfc6CpuA9OVXdcN7iNbNZlaSku5d3l1W0KLWI76k9x
+ulnqUsXvpqBSf3SUeTjNnZekQ1JVMQL1S9OWeHpRtE2L1ZBctzTYSg5CeX1NNtTKWLrwxjoavQi2
+l1vQMSdLwwdqM7jx1gmpRvA1CsA5a+aUGorqTMxAzcPHPZ9c1YDZZkVxLeNuFPq9xWH0kqoCj6Sz
+XVuHp2K5Wxf4x2rn0TWrZobmStrt9SKikUYDvnXK2YeaLSnYcnQqz8xX8u/5bMOHV1Ko5qjs0fhd
+xqaAai4XwWW54kLT9aca6nx02GegF9rhyRirzGs5X+LWsXZ5BQqAalR9aHqOfKGRtmXJ7akAHHwT
+8Wu1JATqPDxB5BOoB8c4R6DMpKxLFgpvz9oQr68RvTpXk2zEWmHz5YsVvbGqHQbGN+V9zj9hpbid
+1blcGo11eGpf8glpVd+8Xsxxqm+aMKRpPl3MQ0c/jvfBAH8XeM8591//wJ/+PvA39PPfAP63P+k7
+9rSnPf3p048jKfwC8O8C3zLGfFO/+8+BvwX8L8aY/wB4CPz1H9aQZwxZGLLp1VzPlbuYhEKjyVbr
+nMDTGg+9IZ4abdpOVakaklhjzYclRlGKbVUyOhQVZGwmoDUg5itpa8QRaG2GPG+xpdZTSFJK5Tof
+PnxOo5zwlZ68ow1Szp6Kge+qNMwvhHN/lH1IduvL0k9CPA2UaTU6s2zsrtCH72rQ9xWhh1MsQxqq
+EbSe0QadldmQTMU04/sFfqBFRCKftK8BTWuZt4VNefBCiuT84/df0DEJ77gkRNSH5fdnPHtNPDEH
+B8IFT0xJFYpKVNuKM43gDHx/B6UdRvHO+NvpZeN+jyjVgCDAanbps6cr1orM2+Q1PcUsxJ70N5k2
+ZIoEbIqQ65k823vtdSZ3pE9j5/HwfUFkemv1jBQV3/mOSApf//qXCOgqLdf4aqy9M7rFYSdxpfLv
+0IRMVOxO0wy/JxLBclXgbTtjXUGupv9dGbcgINUclW2bEI1kfMvrklox1I1iawz+TnUFj1L7sygc
+I83K7EzNciuG8Map18NPuDmRfXr76BinUbBNscEN1LvUbHfBYSqk4jxIdrVCQ2KjOIwgYq3S5PK6
+3KUR/LT043gf/jE7m/g/R3/pR2nLGsPGi2nqGsXlsCo2PFG3X9O8RqBxBC54mfY714jKYTQg0yi0
+9OCAQH0+m+2MUoueGD9kcaXl0zcifiV3Unx19QW+Y6Ohp/Wq4YVatc/qS26fyDM//45EKi5dzZXm
+bfzo8YpSLcRX8wnXsagmJ7dOu3KFzNSiXaxLDo469SDHaMIRuympUgVRqVvUeh55V0DFa/BGGu3Z
+RvharLQ3HIIm/LSqv76wVzzUUN6H63xXrLV50nIeSt8Sz8GHco+/vZbLJj4yTBSuvFhseDKTNlIb
+M1Tre9W0BHoAWrWdDP2XtpGqdbuann7fsF2pR8QL6Gu4+0htEXa5IVVX3lVZcLGQ931tcpu0lcv3
+abXA5TL3q1Lw0/35igfqfn37fMbJibRnimpncZ8OPbxU9PZIUze5sMFHlfiooNToQr99mXY+tI5Q
+57ntPIvzhg55Fdw0OI0fCftQKBCLjkn5LcZ0RWVbcgWqrY2Pr9GsVeix0PDyUl3rphdRX2jOSOvw
+tQjQ0i/xVlq4yNTYLjxGLylnX6aOX9cVs630bdEu2ZSy7+smx7kfzSW5hznvaU97+gR9LmDOAPiO
++KDHWJN/XD8859FDkRTOvjTjphOx0wszPK0evTgTq/Bscc7BUv4+HvfxlD0Wi5ILLTe3DJY8eyA4
+hUxhyafDtwhUNKkoiDpOeb1gthTOlJdrXrktPu3pPTH6PP72EzZanr52ayL1MkRJTRVIn2IOcGpF
+Ly5FUriYX+AnqgbEOX6mInXSJ426tOwa9Wccq8cK5+0Z+l1a5r6HVQNe6BkKta7n6pNpthueXWkw
+T13h1Hd97vtkmkDRbLbYUC31ykQGzZBUU7NFaUKo9ShjG+yyUbdtRZgoYEdF5/N1jlWQlhdHJF0O
+hQZC9dbcuzHhriYU6ZKtDHt9bioYx7lHnGvU4uD2K2TKbbP6BbnWdrzQbNZtBKEaoItwTtSTWqF5
+/oDihazZcr0g0+QzkY6pyee8UPzKhb1kq96Qk+EJt96QEvVJr/8SvBQKF7++XGC6dHqmxRnZT0ls
+8RUv0HbRi7bd1QQtGsvSiPRzsVkx03U6yW5Say7Qq2eiGq03oAIUI9Mj01Ppt46PN2f6jgA0AOvg
+hqpBvRRPMSnF1QzbRV8OpxiVCu2nTKzyg/S5uBSctbh8TTaJqDRv37ptWeuhP3v0gvynRR/u2Ran
+WWVOpnIR1EG+y8wTj8Z46poJMm8Xbnp5eb1DKZ6eirvG0rDRdNt52ezy9vlhwYfffwzA0PncV6BL
+rEk8ysstjWa/mRBy7ImIN8xhoxb+1XrLQMOds1RVn4MhVnVWW65ZqzuQ6YaNLsVWMfCLfElPPRmT
+0zsYzcZTkmPari7jgCYSVejxe98HZIMaPenV3OL0gtwuG6wiJG/cCHHXCgrSOpGjaMvd26LXZv2U
+oxPZTLeyPj0FZ13PFp3HmNsaL3C9KSijzkVYMptL//uez0BReq9Op0Sa0cioW2NrKw41JXkcvsHZ
+TOwgp0d9mkRrPl4ccDSVQ3F8JWsd1lsKo3j/3KPW9OtH09uYUAFu1uBrnsokkr5HicdWKyhFRczR
+QC7n0WsnmJ7Ovd3SrDUqUQsMMwip1ZXtNf4O/WirlrRTzbTiU7s2RN2luXWwkn4+Pj/n9bGGZKdX
+HI3lghwsZR+eFxd4G1nTUy9gcCRze7G4JlE3eW8ypFJAlu3SpbSWRm0Hvm8Iepp56cFzmlz2hWn4
+45X8P4b26sOe9rSnT9DnQlLwcMS2wawMvkJm4yjEV/DOs9WMxVKj7+IKTwEbfqZVmQMHaqha1uf4
+YwVxVCGVFnT0to7RVC3KkfqgtxWbhUJ/8zWlxlJcP5uRqOX8cDgg1bRaXbKRto2JfM2Bl8RkmuvB
+HYx2lZCezy9pNBtMrdOc9lJahev2N32SQ0291k9wajzN1HA06QVEB8Jdekm481pUyxJjuwi+JcVW
+OIK7VoPTZkuhYKO459Nea86G2NGKY4PLpmXga2o6TS9W+ylzjcsIHUxGahhrGnwtjHK1zNnqM+ut
+Gt+ct/P2lDQstQp01bacjAVCnR4NuK7Un66p3BN/SNCX9d2GllszkSC8ekGiasXCy/E0PuLwVLjn
+YlOysbG+D7aaf2KUGiorqkK5XJE/kfWJxsJJbV3vojJ7oxGTeyJ5JlmPtgNDrVbkc9lTXeoyz9W0
+lfTNDSraTBPcNCljVR9qzYux9n1qNfxmWUZjZa6uLpdc3Bapd7iKdhmjIxHMuNEOd8CxsldjWul7
+HXoMFW5vRgarsOpdjU7Pp1QxYLttuNa8k+XFjGKhyW4MeBq52bafTpXYSwp72tOePkHmR3VXfBY0
+Hg3cL339pwkwfPxIjC/ny80u4WQS+3jKVSgawmHnktEMNFg8RQ0m4+GuoMrZ03NylSCSQbhzgXUZ
+fhMCQkUErtcl11rgxNWWQBXGuqmotBhIrTkE/uq//q/RqFvJpjFO8zAEwxCjBqXBpIevqYWu1Ti1
+vJqzyuUGdxtDdChcwHcRs7W42eqlFghJfEJ1rbaugp0rLKSr7bFaVfQ0oenoSP4NPcerb78DQD8Z
+8Pi3/y8A7h8c8J4WQ3l41VCofm0UEXh4Y8DdgXDa3kFErCm8emnESCWdIDb0VXobJF3auQGRWsnq
+2nKl3HizqDlXffjJVc2DMzHylhppmkcl3rVCgsMNh0Ph3P/Vf/OrXGkm7d979wG/89v/BIDf/+Yf
+yO+3212SW2ftLtgsCMKdO7teb6nU7mJ1zfwoJI60xkU/5XgqOI3+wYjtTNen3lKpRKoAWvwaikY4
+cFsVtOoubMuS93/97wJQXf4/Mib/mJ4v43h29YT1ueyRtslJjcLYj0fkGjS3ruXvsfUIdX+3dUOj
+QVWJMTi1OjZFyarbG4pzCKcJhQb8bdYlx2pUPboXUl/KfhqcHJEdiDH2p/76f/H7zrmf4YfQ50J9
+cC00K8N5OaNRa2naGIxmr+31EmJNpWUinwM12nhqOFxWJZEKPbfuvcHlWhZxvdpSLeSgp77PsNv0
+ajgLnGE81eKgdk6mFVLKZUncVR/ZBrRdqjM9mIMgIu8g1tWaNuoWMSRS3LTZrvHVnzxx0t/xwTGN
+puw+7E9Ix2LsyucLPjyTzV3EsuDxoE+s4KWqbLClGpSSMdcbsbL7RcN2I8/XhSZhaWPWT8ToGN++
+y+lWxvcgrNlW8o5+UaP4J4yCjW5l6S60emwsYy04knoet47EyHdwIyHRSyTUtHJBPyLROJDluiDU
+UJdFDKUa7S5mV/jqHelKw4erGFQVGTUhtebHLK9nPF6LqP3o/W9zdS5QYqeBC0lr6Cx8rvIxmq0a
+DB3KqnENXtOVgdfw9dAn6VKbOY+Rpnx7/eiQ3hv3ZZ69lLOnHwPw/KnsoU15SVjJ+KurDbmi9m3h
+KNdiHF02kkex/u63WGnG6JIYN9PcdNE1baxl68uI3HTyv6o2UYxvFJpPs1MfqjAiVNW0KXNMIx6F
+WJO7DJOWMpZ5GxQ5y1rmrb/Kdjko22JBb9Klif90tFcf9rSnPX2CPheSQmMbLrdXLGdXOIWlmr6h
+rynR4iDFlQpzHkScjEVC+NJdqUTt05Kq+NV4PS4DcfUM1zHvap6FQTbkzhckc/Fx2xV4qCnXmprt
+dMJwLTd7MTpnqcEzjGI8rfxba1TfYJBhVPSzdoCvmY8jP8LTzF7toiEcakZoNQbdO7rFwUDGdDQd
+02h5sPxGyn0tCebVXd2AhChTg+raY63oznVleO9a+rwutyzcI+mzRktGJweErXKXueFck7g+/Paa
+eqxcc9pyZMXK5WvtgcZs8TV4Kk1D3rgj3O8kS5keS5LXwLaEY80XUGiuiMTg1H3nxxGJL+Nz1wuW
+TubrYDRhPhPO9VyRdjYoGQwV2pw58pmMaWtWPPlAxjR/PiO+1mrM2i49n5G6VsteQ6E47tzUu1R3
+pm6JVWIZaETs/dGUXl/auJ6vuDsRzvyXX3+b1195TebA2/INLSJzUT8A4LtLx/dfCAfeTvtEKvpf
+5WfUKr2tAlVnDk6xj0VNIlrjjnU+N4e7qNrN7AyTKPQ+lTnO4gjQCNVRDJVC9m2N1f0QDEKGkayD
+gldJwjV51KmgGTeszHe5LbGHCo9eT9ic/SlGSf6kqGktl6slBQYt90cItGGHDXccq875C199nft9
+GfAb70gFqUl4E6uZfVaXC2woJ/OXTl7n2V+UZ64fVdx6TSb1luIOmuOIj74j0XQHz5/zkXoLFmVE
+9UIWKaxblqozu3kHQY6YqK7X+hVDVWPC1uyqQRnTcONEFnei4u7N2zc48DVW4SCk7XTZcsztW3Jo
++qcKIKpfRhSaHNpQ7ASXT654TWHav1m+RzqS310tdRPkjuhADtjFasEHM7kU1mOPSA+9zX189ahk
+oVyavhcw1pDem3149UQ8B0fpiFgzVFNsKbpISk1fHodDKoVxG2KMgqyipE+iWIFhDEdDaWNW6sZN
+avKV9HlYRmQ3pW/f/d4TPv7oj+R17gWBqjTHWjksDAJ6Rg7Cqrlkm2sBlNax1rTzPo4bqhb+S1+W
+ebuTHFD7MlezzRATyFinvs/NaRcmHnND8TDvB7KOB48CIiexFvOi5pFGl87nF2wq2XNDhZV7yYbV
+WL1AT3I8BcnZuI+vUPCxf0gwFbh8l6Mxm0S4lbw3bxoazTcaZD2c2jBMkuxwEelUfme2c1oF8i3b
+c3zNS+I99TGFMrVkxVpBW5+W9urDnva0p0/Q50JSsG3Ler6m9cGqNb0uLPgiXt+5dcjbt+Qm/eWv
+vMOp+qyHWkI88Cag0XuTUQ/UsFcfjXizFg61fGtNX2/5wBMObgaWwVjj3L87JOyL5+OPPpzh623t
+bRxm3eXBM/p7D6MicxYnaPU60jAh7eL7k4p7WlPhxrGoM0fjA0INrgmzlioVbtwralq1kisAD1d7
+xGpZp+fTdnUt7jrMhfTnnS/c5xsfCRfoaxReUgdsCuGeq2bO5Zn83YXgOmRlE7HwRFVKki7rlc89
+Dfz60p0pdw9ExI2Mw9M4/WYQYQqN0+8yq3gNW+1muSmxXXIaWoKetHdga/zbiovQrMzf+HhDvZT1
+3WZbYq3Q/d13/ynLpyJBTU9u4ldi5J1kIuWZqMapMbqcpzS6JmlpqFSNC4KYX/ipLwLwL3/1bfm7
+b1hqfoPL9YK15s4YT3O8nhjwovA2E8WnvHpf3ldZx0Pl1sl2wYszna0woN1o/RA1HI7iMbOLBzJX
+Jwk99Tj0vJxI8Sfu6CZHY6lVESlcPei1tJrmLVptqZcKG08q7FCLIMUBkaqhXVRqk0xw6qkI7Ijt
+Voyg7Y2IZq04hTagUBX609Ln4lLAgPENkR/twl9zW5Oo62ka+rx+KpMzzo7oZ3IpBBov4NUVtlHw
+S1DsUJ1hP6B1UvY7SM52MQqoxbqNY8YKU7n7WryzCr//+JRUMz3NrYftavR1l0K9xR+OtaWALttU
+Lwoxmjw0SFr6mdZ0zDTpbODTagrxZp1S6fvawuB5nd4nF4iJtrRdUcymounguh4MJtLurdM1F2vB
+7c+XsrGvc495IVbxYlVTKZgmsiGZRhGWpqZVOHajbtrxwZBX1XZw7/YxiaZtZ+soNl1R1Q1eo249
+DQu3JsIFmpjW9yi1hHvhR8Ra87NJHX29ZCfq1ekbn0s93Ne15fVE2nty/hw7FNHfhGMCtYOMxjLm
+zYsKIrGv+CbBaG7HJIwp1BGRJUMOtbjMUjNZnb94Qa2hyhe2IdRqME8uLxlfinsyPN3QqhfA09iW
+aBJw38gF+fDZgN5GokuTJCZNxdbQaLzD+SbnsapP9w8D/IWC6OKWJBL7wTBLSONPlq9yNsGPtFR9
+GGE0BscLhrt4nDj08br4F83tGCczXK17ukoonLwjyC93AKm520Ck9phPSXv1YU972tMn6HMhKXgY
+Et/HJmYHzHG0u3yGdw4mvH1DOH42jEGNS04lhdbGkCg3rw1el2DDBhjTleTOCLSWYKP1Kl2xwi+1
+tFviM/GFS7zSb/go78p9W/qa8mrptAgLLQmddTslVPFyVWw5UhH9KBu/DITyuqIvLZWqCa1X/EC0
+Y0Dgi/jcAXCMi3Ca36GtLbXmd2jKlkyX7dbggOVExOt/diXPHqVDylzamm2e7hbYCwANSgpsvTPQ
+pYqb+MLpmJMD+V3Q+Nha2quKmkLH366iXfm2RgFitWlwKnn5dUWgFZH7pqZSI1hbrxno3KMw4e8N
+L1isVfQNLFbLz4em3oHLQixbhR0v56LuNGzph53EsyDXDdMzDZNExtJPfeZrkZyePtMU6U2AUY5u
+MTgrXoJHH3vEqua46A0GrquELZLEyTjjeqaYhaDmrga/fc9Ztuo9ChWk5gcpfU2Bv7zyGCueJmk8
+YoVE+6R4XdXojidHLY1Gfoa+hxfIXJkkwFdId+AHmC7iUQ24XjMC3d+NZ0iNAq+Kelei3gumbFf/
+P/Q+YAAP4iQCxcY745FGCv7pD+lrWvbQphgtNtpqEg9brggTEX1ds8Bp6nSaBr8vYmdrNzSK/qo1
+es2mQ6zW/rOtT6ALGqUjEs3tV9typ382XSrwOKOn4J6wqQi0opG1W6JE3ueb4GXqb0U8Fq1jqxmW
+knEfo8Vmo2FKqKKtr/Uu7WZDq4lI26LZ4fONM6DnK2otB6qaTGP514UR66X87mq+xQ+6Iq4ZkaZL
+X3T4Dn8AACAASURBVNc5fheerElLj3qjXQUib9OS6/4rlwW+iuK+KXfVtdqii7mOCfRgRr7Fharv
+VmC1IG+ModJLKBnKQTmdRFxdSlvzZUs5+n/Ze7NYe7b8vuuzaq497zP/5+GO3e4htttJO1EiJ8ZC
+ARKQiBCCB0CR8hYh8QCBJx4YwguQJ3gAoTwgAgoSihCKkhibJHZiJ+0e7B5u36Hv/c9n3mPNVYuH
+9a19fSUT385N7Gt0ltS6p89/n11Vq6rW+g3fwd2Hwd5jjh644716mvPBU9eeJOg1BwMGuo5lcY3p
+Rfqjj1mZo0kM4pJspEE5TQNCCa6chCmxdD4/en/Bs1euUDDen1On7jynUkKaDva5l7v0wvozfu2J
+wFStIQ1duJ5vHFMzX3+IUU0iq5dEZrqbw8FUC0dld6IunRYHP0xAZslNEuBpsQij0a6b01XslLr8
+wM0PxYZWC3az8QklO5D4gESJgnLJZqX6wqccN+nDzbgZN+MT43MRKVgs1rSYskMFW5IkIpSoxDzd
+p5Y+1vb6BQMBUjwB1P04BomTeElALypvvQYayXNHIZ167KgL0dVL2t7NuOgoJTt2eb3gSs48le12
+PPxOX9xhdqrTha0ZKEz2wsFOot3aklx6Cd5Y6c5ltutgmGpFpOJZFMQEKrD2XpJ1YrEK0b0kppOT
+Vd1Bp23cDhLsudspejOYkoqzhcLdesso6l2XE3JFJljwNNG3JQWehIbtlebKD+kWnubNY9D3/4cT
+WjE0w7h3xvGp1dMPB8kOCm6yLZHaMpsgoUncdxcqfNZlgNfL2Q+gU/GMMOHVR+48v/fuhyy3cmdS
+1JUwYOWrG1IUiACIP0pJ9LycLz/mGnSSShsOh1Ryom7tgHWvHL8fcyZjm9HzU7pjuWipNhfUMNnT
+fapyVpLLK9tiF01dCvq8vWypBbUejgdEigTq0KOVN6U5CakFxOuk8OxRYoXJCUMPX1FOYD1aSe13
+ZbWDdNveUCj0acWI9dJ9mkq+qcmA3LoUuchLrntjyU85biKFm3EzbsYnxuciUvAwRCYkHAaMol69
+1pAINWiHPoi4s80rPJGjBtqJvKjGU/HJH8d4kiBrmxojZpxp6x0jcMe4tOwYkN7A4on5t2o2dIpM
+mjKiN2TsGaWmLjBC/8XGYlQPiP0WX1tXOEx22IJckGifgKEchdPxPoO5nK3x6b29TG8KE4eE6uk3
+YbPzx9xkJVat2gADqXYgFTvPLxu28i7o2hbULpwdxMwlD3e2HdEIOupLDXp//4RKzM98zxJrh0om
+CUGqluowxIiN6qv+YExMqO/tmgFR2JN9IjLhAkLfY6tW+eWl2yXb0MPXzh427a4WYUvLVpEJtHgq
+7nqq2/ixv/NSrLrO3USgqizDY5Hb6pqt/m6uOkMbGDbCWLSeIRE5jGBFfely7u9drWhk9RfXPW7k
+kEhGPHMGTOTZMIgC8swhHQNBsNvJGS+Uvn81jPE7OaGPPUrl/nXZ7sQaPBUGrTclGskYyE/xFLES
++HSenk+T4BQk2MHKrddie2fyyKfL9VwnGd7GPfe1n+1amJ92fC4WBeN5JHFEaiKSuUL7dYOnULRs
+KtYK/QaH+9BqAdAL5ocDfHEm/C7A9C5NuYcXi1Hp+6AiV9dj5GmpGwdzDhgSSuYt7aodHRovQ5E5
+RjFs11l6F5kgihnQh6otlW5iUcW7/n0hK6BROiUcuBcsGicYhZdeFGJV5Ox1+Lyug52xSEhPcG+L
+FaVeNo8ZU31mcyH13jpH1AA8DK2oyln+hEulGh4zHoi2PdG8bVfZDj5bWp9YvXTf38NX0aquVnhW
+C3XbO1YZ2qUKsZ6lXMjsZQvLpY5ddcgvh6vlx8rWsbAO8SgiURckK5fkMvAxZUOssNqYXsrY312f
+73l0WngrA4UW1CYvdxvAmYrSq2VLLbDRsPQY6N9r20Kmqn205YMfua6FL9bpbK+lkbz8YDThi/PH
+ALw7/YhaK11h+7Qk5JZ02IuLgkaL1L43YyBOTN3mJLErYmpZIkqHeEoJDB227A1uItpK6dgwxm51
+4XHPEoVWpjWUPkaycN2pxWghCO2UQd4vsp9ufOb0wRjjG2O+aYz5P/X/Hxljfs0Y854x5n81RkKF
+N+Nm3Iw/EOOfRaTw7wPfByb6//8V8N9Ya/+aMea/B/488N/9k77A9wyTccr8+AAjEY9tfkkhE5XL
+wrC+VB+OmlzqujW99JVhIraf12YY9au96ONQrFvntEpBSomnNm1Bt5YYZmgIdozKOYkijHJb0e6K
+hzrfKNoJmHqdJR6qDZe3bLRybxYZarfjl25lX+/ndCr2JUmDJ0iwN+g+/nKFvfXpNZXmIs8ttWzL
+i01Hpo827Ya2c7dwXz6Rq+c+tYqggRewENvzzaBmNHEnnT+75k7i2mlx4o738sWWXJJv3mnEaE/F
+t0nHfuO2uajJCXtLeM1J1rRcPnEtvU0bYrSNd01LJa/PZbnhdOvi6mu1hQkj7sqDMy8yYsG0X60L
+1r12RBQQ1UqxRA001qevJLempVKIDj6FrjsrcvK12p2ZOzevqGiVVkZJStcrMBsYqxA3GE7wExfS
+vLcSajALdhFrlX+fhYR+Uy8kUBpbPhXy9iik2XeRSVav8Fs3x5sXFakctucMGO67guDe2GEh0v2I
+QBGBP0hphIq1JiW/dBFgvqhYn/daGxIf6hKQ34lpyt29Ces1rTA3bbul7m3KP+X4rF6Sd4F/GfjP
+gf9AVnJ/Cvi39JG/Cvyn/C6Lguf5JPGYMPgYp71aF6C8zlzD4dfcQx9Ygyf24MvSTe78KsCqaj8Z
+jYn7bkAyp167m9jWFZcfOG7DVuH8Vb7CExjFTwZstWgcjO8yPnZpxerFGY10HC199yGkEJ7CD2tM
+1jsIeWyuBFedVJSlboy8LcvlmmyhlIGC45ljy41tRKgaxVoK1lVVcS2j0Wia7FBN7WSKVfrQljml
++vAndx3c+dn2BzQfyj9yGKL1j+67He+JETnaH7E4d731+Uxr+ZFPIBqyP7Ss9bLFpw352nUATsZT
+hoKK+1pss8s1a73Q69zjQIzDfNUQKPSfx1NiMUwrLfRN61NLMzBJPNqg7wJV2ErpWODtVKB770dr
+AjaFO5/lckEnY5wgDnn2TD6Qbc5QL9Yg0dwXGZ4W3mqzJAnc7zuvoJR7VVJ1LK60IMXSvvRCQuXw
+bx2P8WVUMxwMuF6587zylV4E+9hJLy0/ZnPhnqdld8X2hTtnmwy5XblF9ovaLOKBz1BmOX4d4Mdu
+Iay2DZ2Yj1cvNoSCfNe1QGbzlKqQghYltdStvCRkLYPcys9oJr+3ZjD/LfAf8rHO5T6wsNb2EKpn
+wJ3f6Q+NMX/BGPOPjTH/uKp/PMTVzbgZN+Of3/injhSMMf8KcGat/YYx5ud+3L//7Vb008nIpqMA
+3/cJFZfHYUAioYjjxyNqz6UELzfvs5ZbcZwrTdi7xshWLNg/xhds1TMrahF0FqsrFkIvrqWyW0QB
+gXADYbRlLvOS2/f3OHkhK+/rkjDW7p1LMqzckItIFOIRNHKjDmuSsRymwxGFL6hwK949x4ynLn2o
+Fks2QhVO9h/hKQz0Knc+m7Jl07hwcfvsJYVCe+MFGOknmmxNKHTbiTAPd47mJPKISIKYQhX3lxPD
+YKBdvrQEtyTwImahZURfy7NZy748LSfxllaLtu/nDKWt0Fuus9iyUBH46fk173zooqqLdU2gQuIg
+GNGpO6K6HzbyaYUatVVNj28Pg4iBukNd65GJ8FTKQGVVZ6wuJXqyXe+s4saTCa3Oo9qUDHV/JrKB
+M+OITEra3bJmK6yAbepdcTgpCzppbPqKGtPpmH0xGNfJHiexO4+3ujlJ6lKNoVzJ31/nbDWJj25Z
+ZkfumvZGr3OmyOxqlWEa93lf4X6VbUkFiTeRB5o3Q4fpCWvVFDNTcVTo0Cou8WTE4/tQKn2qvQJ/
+6O5ZcVWw8y/8lOOzGsz+WWPMvwQkuJrCXwFmxphA0cJd4Pnv9kUehoiAqizYCkgyjwO+cOchAM9e
+vsf/8r//DcD5azRSm/nqY/fvf/JrX+Fu3Kv/NMQSmLBFzkZAnleLC05fuPDqvSculLvYvmQQ6GXa
+n/ITr38RgAeHxxwKvfJ07uFLas9oAVkuV3h6EJLJhBjBeW3I9cY9KL/+7d9ksXLXspXBzZcf3OUX
+fuYrAOwNhxQrgZosWKUHjdKSMm9ZXbpz/8ZvfsSHS/dQTUZDpgqJX79zj3u3FGoqLJ/5HQ9O3DVN
+BzF/V0a49TpmT3/3xs+Myc7cHF7rhf/oH30PlId6cci9iXtI/+gbd7g3lcafNwK17HKZ1lQVbF+5
+l+36Vc1Hl25uN8s1rRa92Fvz5UcuYEwE8V3mSxBzMolyrLDbySDYtZTXyy2F8p+sh/h2wY5J6xkH
+EgK4NzumqF3o/3yzYaqa0P2pnMNan3MJtby7ec4oTXdzP9ci5NMCSisECkrjCbVS0+3ZFUd3nXrX
+ZbekvhCv4sid7/V7GyJxGAaj1/hR5xavX/0bv8LlqcJ5E/NTjx2PZ64NcPLWLVqpgZloQKOUoGk8
+Ftdunp+8+pAPftk9Wz88dUZF1gS8dcdd36Nbd7l/7NKSrlgRKy2O6wVR9+MlBP/U6YO19j+21t61
+1j4E/k3g/7bW/tvALwF/Th+7saK/GTfjD9j454FT+I+Av2aM+c+AbwL/4+/+JxaPGhqfpUwxkmnM
+/ftvAjAfjPlAbMDbj+7jnbjTfv2u6xkfxCNSyRM3ec1aTtN+F9JLLQ5MynzmtPgel64ifbvcpw1k
+P59WDBJVdffm3L/lCFbff/py19/uhE0oa59KcNaLy2s62Y4djfbYGzkBjUejFbE0IEod483Hdzka
+uR1zPIzAup2kbirMtocVi33pp3j3vwpA0c64nblzr20B1s3RvaMph72pyan7u3/wK6dMJfX9U/fu
+87f+nuP/N/uG8bHb/d976XGA22GPDtxOMwlT9vdctbxiw20pXh8nIZOhnLmbkELs0fUm151LmQzd
+rvT243vMBu4+XU8yKhU266wllgVeNBHOobCsC/ddp5c54dB932T/EUbhbtPlu0r8SK2cNBkTaY7a
+as1UVu33TobUa5melGPuSw/ilv79JNnja8fufsx/GJNt3LktNhkDFTFDz8JUqVfrrmm8d0i+FTS7
+ylkaidp4KbEK3r6iikF+SVar+FgbyrXwNPmAtx475e5olvLlE/eMTEVgCvwxnsh2bdPSN1SMH+Hr
+AY6ChwSKhm/flt9qdsVTRZaTcsmD0T2d25ClUuXE1jTF7wNL0lr7y8Av6+cPgD/8z+J7b8bNuBm/
+9+NzgWhsu451XmJNRNO4ZXJrY+ZSuP3K/S9w+1/7MwAkXUB0KL65cnyv8ei9tNqmY7sUIq7N8IQQ
+nEwmu0Lb3li6AqYllOJPbAJ81REyco6O3M4WpB2tqmNW0UFZFqwuZOrSlnSV++ytZMzrt12b8cEf
+/zqJ1Kgnd9x/I2NIesYXNbWUeWy+pdD20AmnMNkbMZIqc8I9jHABXZyTqq+exCMMru6wMmqlhVv+
+xNvuHO4+uLNDxIWeYXHpahiTMGQtL4ufmvwRAO5/fUyzFE4h3u5gzoOuI1TRyrMluQqXmXaftrUc
+zuR/5o3YV5E3awpaYSvCzlCIApw/d9f/NLRstANfbbcEmbQXoiWRWplZtqZS3WHg9RZ6AZVQg9Pp
+kKOZm1svr4hjtzs+3h/y6J6LCh6Jip4MBty7736XjH+aX//ub7l7ma0ppYC0aCPSjeon++46YlJ8
++XrcHqdEEukdzOOdQ3go8tzBwX2er1zd6nhY8sa+Ew1+YzrnbqpjH0575UB86TGkodkhctv1NZ2g
+5LQ1Y6F2X49jHt5ybee461vdPp7g8UHQYK6cIpVNfRrP3V/vZMLiUn3pTzk+F4tC0zWcbU6ZTO+Q
+yuhiu244F47cCy54465UjvcOsNJg7Ojhtw2ZNOmurja0ggzHxRZfSszjcMZAXIqRjEDwwRNPP0yS
+XZU9qEbs7Tv9xNl8vlsM7A5nX+KraDcKPdoeJ+/BTC/QcC8hnvYLi8xnghG1wu/VxTm5FoIkbwlV
+JGskRjJI9onC/meL6YUgqTEyVOmWG5bSTjiVcEwaB7z5yKU+871jbK+lGIVMtRhGXYIRRmB46F6k
+N0a3KYanOkZKItxEVWbU6jR0piDXYlAq/L5uWubjvd15zmSAU+FT2Y+hyWt1bvakWnywqFh1gmZv
+oQ7di7nNi48BZ22xs1IvZAbjZQGeNAoPkwP2JY3flAWJ7453bzbitSN3/0Yjt2AlXoKfuM8ezDoe
+C9cRTQ0Xz1wl+WzbkgnjUp3rRZql3Jq6F/pgMmcQqltzcoyfykpeIK0iLUjlEDXZO+BIEPTjScRE
++gyBjQik4mz61aEB6/UuVBYEkOoIiKTpORoE2EwSeLF7VkwSEYgT4+cFpToYa/+ctBDgrl3uzGM+
+7bhhSd6Mm3EzPjE+H5FC3XD+4pIg9LkWCnAY1vginESriHC/b28lPRIYL5ApSB2Qa0fJrxrSo560
+M6RauFW1Sq5Jhy6sNrFQfHGM1Q5muo+JKG1bMbahzqPdEaE8FaTyfCN9M4iTIZ3nVvbtaoVXul06
+GfhEKq4ZteYiHyqZe1RNRVf2/fgWIwkuT/34brsm3FOkMRpghmLiFRlII6AxIV0sjMTCfe/98QGj
+PVfUOj9/QSBIsDUNC0me3R627CnyGKwlFdddE5teVSknUnTT1R6tiF2enxJaF5lkkpUrlitytQhn
+6YRQpKQoTiiE2LOd2RF0xnKoDjpDJWUmE0Mi2bGqznbpGGGKJ/2GqlLfvbtgkLodPwpbzs/lmdj5
+dHP5KMQRjVhsYylTVaOGq8yRnbygRkh4qg9bXr4SlDi07Kto3EgvIvYDGrX0vMBjErgdv4gsy7W8
+QsNrzVvMXDBuc9kyPHZzOBruEQ3c33lejhf0eh7ShfBbSpHHPNuhbASvrfHiXr1qQDfQfEoqrus2
+aAqxJoaxS23aF8+wG3cezTalXUhH41OOz8Wi0HWWrOjYFhs89ev9rc9y2XvtZUSBCwejcITpxEoL
+e2XhLalxD0ocvGSw5z7rLSq2Anpsr68ZHrqXJe7VbU1ER48zD6kyiVSEAxrlwOtX2Y6W0Ft629bf
+gXc2WUUnH8Tz1YJcduH7HOwWhVB8AJuOMQtpRkZDqt7kNRzsdA69gfr/y2vK1P19PNwTdRaMaWlF
+HW6LC0ymnFm1ivTkTRbq4Dy9uibRwmmDAIys38vtDtKdB7r+smVw5B62hgVNv3jVGZ6q5Ka1JEoV
+UrEdr7MVS7EIR8M180iSYEFCpPC5sB6dQGR7MoU53DvFCvPgtyFTLfovNiXE7iUNQkNsP5aSByjL
+lkIw76wqexcA1qHPfXE7usk+z0WJ31x83332OuFcdaBHh49Zr9yLfHZ6wVb3bJt1JJ77wmTsKvkm
+Tnew+WWZEYviXa4TIrEVs0tR1b2KTKnk/tkL7h9Jyt0PiDQvpmtplFaVUnqxfkS96U1fYqhUJ4gD
+uo1g+vGMQJuZlzhgXZcHvRgdTX6Nr9QzyGPWStOyxTu7+s+nHTfpw824GTfjE+NzESlYoLWW4kXF
+SpDicpBw+tKRkrqLBitfxjY/xWjHq9YO4VVbXAwK7N07YiaLuXacsX3q1r2qKimu3XfTE39Cn7ZR
+Z6EsKSS6uV1ecb6Vj0K1YpCqGi6KR+f55BJzzcMlGxGs7o4m2KKXG8to5B3hycauXCzJZbFWNQ2N
+yC62tXSqBZUS8PSLjkBEJNP6OyOQ1nS0+kxVluRrSaXNFTqmGy6F3Pz1D95hX0SbJohZCjKcVz5N
+5Ham7Npd0/pBxnCrYqaxlD1i0auwckH2gpquVG9e8NvGyyhWEiuNYwZTCaskLZ3tuyslXux+HojV
+Wfg7ZTrSgaVTd6UoNhgxCsfTdBed9d2XKBywLty8ltsNRhHbrcM9QnUanp6d86v/0D07m7y3tDM7
+347Hd57z02873MfRvQPOpadQhiWlzIhGE7crF96AWkjIpm04U3RjvJakR85KPo3II9v2GBlLKDEc
+Y1ra0iFSW+uxvXT3p8hl1OP7tFmP2Gx3as+2a6ET0nG9dKIRgK/iY+eFdIJrN3lGfe2uuWwbtuH7
+AATTgKpQOPUpx+diUTAWgtqwDDtisdfiJqBTKyiLip2ab5PltEGvLCS58aqklr9gY0rK3N0YmpJa
+IiO+rTG6Sa2ouU3X7VRprO/vHIbOs4wfPXc3MTAxswP3YlUvpYLjGbZtL7MNKI+sfEOuTkXbdrtO
+QxfIPLSJsOJDeEFIKGq0NS297wuFTFgSi7e7+RVF6RaIzrRY8QQaryALJD6jPNsbzHgaOkWgV3VO
+KDhvFXTEMg7pTEw4cA/9VqI8pogoIvEyugwa9w+BH7GtpP+4ucDo/gS6zsNwQpUq3LV2Z86SNYZ2
+p5UZ0ejhrcT7zkooVe9oGkPek+I6j0pUX7+JiJR6+b2uZtgiPBNlbQlTqSJNZsyVa79z8YRV7e5P
+qTpREnjE6sSESYenRfHQjzlSy9i+7LCe+45c7NN0FO+UurLSI6rdfRgl+wxSyUmNRFsvlmSa4+Qg
+oVNHpbM5lbo1V4trNgvxXNROTvfmMFIHq4lpZVhble2OM9JWTa8ttNPxZOQRdD3Pp6Lq3CLTjbaM
+avfMWi5Jj388SZOb9OFm3Iyb8YnxuYgUPM8wGoYUeej0GIHRvYg9dQuafMRqJX57DVZwz1DQ16po
+WUqp+dWT5ywmLjxLBxM8AUSmh/v4vV6fil513WKa3v6sYS2QxzpfYlX42U/GnKoL0L3Ubt02aKNk
+U5QEvSai8cnl41h1DaaXFVPoa3wPo93fNDVGJB/jDUkOJZU2UbEo8fFV4LKdJZdISb0pCGUSU1/n
+2Lovurn5GXkjjLwZfD/BkzXdkJRV6X4OIstUjsi+ipa5sXhr9x2mrQkFXvIjQyQYb7mFsLfIO3SR
+ycTEFLKcb22EVejbrLcUIrfVxpLIA6Evel29WIMY9vHYYyNrukka0qmo6HsNM0Umpi9atsFO+ToM
+fGJpIVTWstL576Vj0jfcszPQZj45TPCEXzkcDMi2CmOWlnHnrmV7BKPSkcnGM4nQDO8xUIenMs8I
+t67I5w9DTKZU47iHQUd8+cTdv3E9oVQEmV8VbAr3LCyqBVGjqFY7fnPWUNaKbv2OeKwORjsG4TM6
+nx3bMRc2xWw7olBsvdWWUDicbFmRCLRWBQH2+g9g98EYjyBMGZ0Md8iu9UXH07F7IJZsGJ7rQR9P
+acRtoFYVtvEwEqbYu1tgBVKxXcf+3YcADJMQPxIwSpVp6w+oeoHW2t8ZlqSBYXTgvts7zTj9rnLY
+jcBNfowVyrHKS1rVItomoFEnIltmxGKt5eoyrNqWSC/CMIyIBYCKJhAof4hlvOLVGxohAqu8xqAF
+rV3R37auqhhrEWnUWvzB+8/4pe//EIDNpsD0lOvUI6ykmpR3FLVUkSSSenllGd6S9MX2kkwV+ci2
+xL5C0WgAatkFfi/PPiVQy7WxLZUAZ7ZqkTYJVQtGx8llRV+0JRYXai82NZkw/D9xb0bZus+Gsb8z
+b41Eka4bQ9KnXWnKWLRm6pYzpXxh6rGvDePglhCtviFQaL9twW5kwlu3WOUjQ3vI/olcq7S4zcYe
+Q9VzNuuUQByNqqzpxI7dvnC/m06HPD5yXYvAC9AUcnF6SiedSxM2GNVX0sw9H+EgJtI1NSYHSSr6
+gxmBFsjOj2iFAPXERfHNdrcB0G5JA9HavZDrjXsfzrInvPMj1dI+5bhJH27GzbgZnxifm0ghDGLG
+/oRzhdyb8y3fS74JgFe9jjnohSm6HZ4/V2gVhQkqhjMwc4YztwJbMnzhEAw+eSYZr3NViCdLgtCt
+ro21bLQqr5aW+Mh94Y++ecFCYWkrgNSmNdS1W6GzYk2zdVvCj5YXLLaOKz8bdYzV067Uj766uuz1
+MzCDIbFAMW3rkavoVi1dkbDpAjrtqpWxpPuSWU9i2kqFuEGEFazYSi341//ub3Km6vZ8NOOikgS8
+3VBoR2zLgCdXzo7tzRPHRH1VFtwWp8J0HpcXSjXClong0eSGSAzMQtfceCvWvStZ19BFOl4c4Gv3
+G9kR3sTN59W33bmtuoxcXfYmbKjlK3lyNKdqJLW/yuikw9n9tkKkp+JwEDUYYQVWyw2ZipnDSbTr
+CL28ktT7MGKs8NrPfTxhVRLrUyqCbOsSr3bzPFcEZtuARtFrFi5ZC2ps4pbF2t2z8417rr4SJCR3
+3bmlY5/qVDiESU0YCguxzDhX5+rRgSv2hp3ZYRD8oKPR/c2e/YiNp/TBljsRnFkiURgvJFDnwx/H
+oKJkk8R84L3r7lOTcbH58boPN5HCzbgZN+MT4/MRKXgGb5gS7c14oNXznV+55qn67c+9c15LnOJN
+OBoSqDi2PXM76eqsoLCOzJMmIcXaEVWG4Yi8c5/Jwy3FhVZMqfKc3H6dQCHGxpZUIhpxEFFqaqzJ
+dpFJbwYTxv6uDZVyxXLr1I0uri9Yhz0WIKBW3cGfCOVXhTRCaS6zmr09wa1ns13fv1b0sw1y1nIW
+Ceqa9J4Ulf2QSopOjCJWMqHNVm5H3F6cUkp9eP9LM979tlPpCacBM3k5LIuMdenm9kXrdsTDswn5
+A2kXDPYhcNHR5YuXvMiVk/olI5nroOLp5WLFVeB26NjEjOdu3h6//pg94QYqf8pzRS/fOXMRSmFr
+xorGti8yatnJHYwiGt/NV1llVIrIUJvOtilWrerIBKRCtTKwlKrLFMucTvehVts6aTyGtdSWOtOL
+TDGeHnKtqIJgCWMX6Rnf3YcguSZSOzQdDOlkANMS4g+FuBXc+TyruAodvmXEBKtdPggDzMzVqMry
+CafPXKR6+/6XAZg8iBnefgg4YleqKMx/40N84W+e/b13KXIXkewHrogapWBU8fajAblEj5fNR9aL
+AQAAIABJREFUgjp353G93LDyesXrTzc+F4uC7xlmacB4Bq3AGqmxbFQQfPIbL6hed0Ww2IsJBQM9
+Egtxla+I1LtPpwmjwL1Ayf6I7lIGLsWW4Vi8hGOHQ7ehz3rlBFcWWU0lBNHz1Qu+/8wd4/rlc+gN
+OXozmConHQnDX0RYVZQW56csrhTyT2LW8kGMFH4eHexR6IHuApjdc0Wp4UGCJ6dsr5C0/KsPGKji
+nsQWVHAr2g2tL3CrjVlUrvr8i7/xHQCy0jJV+hT6KVvBtTnb0kmD0o8aikt3HqfvSAfyqyHXF24B
+8Q/n7KvgGeUTFr2U/v4h85F+P5M24vMrwmu3KMbWkvS+kr7BF7/CeDmXT92isHzhFpiossRTF2pf
+Vh2eLmk2GlFqng9HY5YKtevetT1oOLDuHLJgTWd7ERaPmSeQUVbRqLg7FTblrknY66HkB0OSI3Ei
+TMdErMVN5bEnwNVIdPBs4LPoq/1BSxw5qPxltuaRcrbtUCH+Imf9W24+L+++YioPSlt4eMK1HO4N
+mb3hrrvfvBZdSrN25xCnI1qxgJfrKe/+xq+6e/JqxaGeWz9RyjjMsb3PaTmkxD1v3/kHvwmyGmiW
+hg8/UgvmU46b9OFm3Iyb8YnxuYgUDIbY9/EryNXmiYYBofq831k+50+dO1HV0V0fI1m0SO7D8RzS
+a/XdrU8qfXyfnGEved+EBIeyWwtkBrONWJ66n19mK0oxJq/Pl1y+41bx4tVmB4+ll+3qLJ70G2zX
+EPSaBZ7HB4KaPjoaMRcvPuhcUasZX1PLwqtYL/lIorLDeUSndpkn6KvxLeJD4Y9SPKEjiyynFE6h
+Klacf+RC0Y0crv0uYiCFZzOYUWrHMInZOUJXRbfzaHzZup1708zJEnf966uCgTwqm6ChDVc6RocX
+KYx/5q5/vbnaKWIHYYsv5KmtC/Kl++zZ6pIPnrk2aStcyDQO2FNYfjWIKVVIDNMBqdqoxWhEqONd
+C2PQVS30JiyVRyMCEpEhldhsmvpcC5Leu5WXjcEbSJrtzXtMDtwcvfvhOavWXV/t32UjSb40cvMZ
+1fs0ah23JqeU/2O+bPGm7vf3xMS8elFx8Uoo1rAkiN2xU9MwbNz3jccHGD23az178Tciojd1vEMf
+q9RnvFhzvHXPizdvmblTJhTi09sCSqXrbsH2hXsW7NKwlcrzVdkQDn6819z0efLv55hOJvZn/8jX
+WC5XPLtyvebNIsOT2q1vfWwPc85bWnUJEIS19rew+dgbcGL6WBNi4daDqOUocezJKwladGVI3rMk
+S4/SCrfvhTuwTNREIPpqqrz/iz/9Z6k3Lme7vH7O4koch6lhpPCzC1o2osO2vXLR1pJ77iVsTisy
+LYDD2qdQ1d7mou6ahkPfrQrroKSTIWrVFQw6+S4G1Q4IU6oGMKlTWiNDmmjI++rd123J3/qlfwjA
+f/Ff/5ecqftAKUn9ZMT1qbDzpiKW6ctwEtLkggy3LehhiwUgGyUD/oV/8ecA+PrDN5nPlbfXhvde
+OYbir3/rI37j+z8AIN9WOqylEu6jKDY7oZo/8TNf5lDqRt/90Xu8EO23EYW6a2oqzVtXWDrVgUIT
+4qmLEPljerPCoeYws2ua/GNnqd7Hs/MabNUzSQ1RJRctqX55laWRBPyBDbij7/3J13+aXyzcxtFV
+biGfzhPujdzf/5mf/zJfe9OlGoOV5eB19+x1ZUAg4Z8ocpDqcJRSSnCnyRaYztWa0tkxOjXqoqCS
+61Ugi4Ju7x6RKPWmGuKpvlT45yzf+zX3feEhSPjlS3/6L3zDWvs1fpdxkz7cjJtxMz4xPhfpg8VS
+dy1FVWGEDvQ7QyBthciPdrxxz29pVFEOJYSR5z6BhE66BjwVu0w3JNKucu9oRKPI4ih2BaDON+SB
+24GzTUnQG4H8NlEQ33SMVfhpajEKl5dUW7c7lKstba50JANkEddFkJgeHuz+Lgl9WPbSbJBsVVlP
+G9pMIisKT2lCOoXOw3ZCEktPoBvvIqXIHDCWYIepXCG25SWHiiRe1hvQfBZNx4vn7wBw/vwdNisX
+xewPXPF1cBJy/ly7VVGSqii5lwwJpEjS+B4jMVR73ME8Cnm47+Zzvh8zE5LQsw3+2FXXyxrOV26+
+Tl84ZmvlB2QqvrV1totG5tt9PsrcZ5vGZyqjnW2gSCGKiQSVbMKKRjt+EgUkE7czH8Qps1uKEGvp
+RpiGWoI7W7/CKF0ryxpfxdiGCDz3vKyWUl9mTVdL98Js+TquO+FdvMQO3TnFCkvGxZZUxdXHLBk+
+cd87nmxpZZvnTfYJSt3rykWbRenRpLKEe74gyuQz8fAEf+AijPbDBWHj0oP2jtNp8DcdftkbA1ku
+9Q6EkU+3dL9frv8+h/d/1+DgE+NzsSh0XUeeFa6lovAt9EOi3jDTH+woxVESEIm+2kNc6/ESK7hn
+trHU6qCNw4bhSGpJhWFfyjxW4Jhu2jJu3MOzTQuKjbsZ66bYcRuCoCVVK69QG26Tr2jFlqvaikag
+pkEQkoonEEewL35AK8bhwQROr92NHSRwqdZi+6qmUMvNT2RlbgMShfZRAMHcHXtbDBDVgs4zhEfK
+o4Xlr9ctuWou3sLuTHjPzs/5xb/tOhT5eUESqTMgEZZ2YbGilAfWcKD8+yt7exwcOR6ArT32Xxfc
+WmnOdG9EVAjck53TXqljMvBp1QK8N3rETz10L9a3RGW+WAMSVtmUEUYQ82f1FWdPXBpTTyyR6kOj
+Sv6JYULbubn3/QAjHsTEjHjjkTvP0eMh4/ah+45eB5OQSqpRtvJo9PLXmxWXqte065CtXvBq5LpS
+GQ3tQtD1y45nieui3OoCZoJpD/fdfP/C/SO+ovbzHZsSvua+a1jPaIcS/ilLmlIvtxb0vDP4uIUw
+e3WJGbr2ZfN0SzJwaVe7NQRTtxiEkp8Pm+cUp5IBiF9nPHKtyvbijNGxBF7qQ7YCon3a8ZnSB2PM
+zBjz140xPzDGfN8Y87PGmD1jzN82xryr/84/yzFuxs24Gb+347NGCn8F+JvW2j9njImAAfCfAL9o
+rf3Lxpi/BPwlnEHM/+do25bFYsFms6Vte1OUYBe2tk3NcCpzkr1jRuK3DyZuvYn2tzz5huuV+2/6
+vJTd2klkqLSaNw2sJZl+Z98VspqgxQgmOqxS8lDFvtWarfrfTe6zVrUnTNyqvF1c0fSeuhYCFbsO
+5iNef92d01vzOXeP3c93VZzy9+aEvkL0HJi573ixviJq+h3dHWN2YHgpktNBm/DRddYfjlMBlRpG
+vFwruhm7z559e4/5Ur6aB5esli5E/ccfvMNHZ99z8xkGjAW+Gh+63ndz3RAo2prOE/70H3Eh5x+7
+c5vRI6kgj8fEU5emVCLnxIeHbERQy/KOstAuSEUgQNJganhDFoCNOhXv/OgVa8m6r+MtmeciiR+t
+LmhEhEtNuWMaTvdk/zawTLQbD72QwYkrtD289Yg3ZWZzVXs0KmiuJF5y0Hkkc0U8qytypWnx4SGX
+gqF/tI0569TReuDUnhfvP6f8gruBr37lm8wk8FKMDbGKin/otrumr//kmNekazEyUJ+L0Bbv0Ww/
+BCD0EtrUPX9Xz929qckYS9vS63yuVtKS3DzBu+uKkcHoFviCnlt3Dn5iSW85GflyscaXVodPgiet
+02A/o25+PDXnz2IwOwX+BPDvAlhrK6AyxvyrwM/pY38VZxLzT1wUurYj22bUdYv1VQ+wUIhumkYw
+n7vJ/tqbb3Ew6JVB3MUuNgVDYf/LouJAtFffLtm4+eNifYFR1bYVPTsgwdfxhl5IIspq1oSUa1Vy
+q5xWkuK1agtx59GptRaaj+nEt8Z7fEFS3g9GCa8JRHS47x7ceODhW3duwXFIJ7+Bx6M3IRNARhyP
+/OqUQtdkGsNr4lFcFBsWejhWDCkFvjr7gUN0lmdPODVCehqPp+98CMB3fvU9TDTVhNc7A9VQgqlZ
+c0kiHsXXXnvMv/6zblGYpg2jPaUoR7cJpTVoxYEI0imZuBHrRUGRCmzElkLOWodpyn4qjonvrvPl
+YoGJBTi78DGaw+35kk4UYW8YE7bu2InalG/sTRgcu+O9vf9o5+WxP9ojlGrS2HisNUfHMtsNhz77
+6vE2oSGSO1WTJsyEPO26nPbC3ZNz+U2Mrcf6Wy6daRanvItqA1cTvv7IpQQ/83V3o778hfuMjKuZ
+mGrGduHAYMX6CZlUmtqJTylB4lDpVeX7dDII3myfkmzcHK+rHLtw1++zYD+VeK90PMPwLlbXEaU+
+tbog4dFdQrU9q8s5k8qlW592fJb04RFwDvxPxphvGmP+B2PMEDi21r7UZ14Bx7/TH/92K/qeenwz
+bsbN+P0fnyV9CICfAv6itfbXjDF/BZcq7Ia11hpjfkcgxG+3ok/i2Aa+RxiP8H1x17db2kYrahBz
+vOdWyTfunzAdyXxDnYXwxSumA+crubE1K1WcV8t3iCTXZU29E2UZatf1E49AOA0zgCBza+T+NKKQ
+zHi+KallnCLsCIVvaLWTRF27K9odjA0ziajMgt6/2DHf3IT5hApbzXCIH+sbNx2mp0+mrsgUzYck
+ku9uFhd48g/0rrck0jmLn5/z2m13nq+u3bUtoglZ1TtkVXzn1XsAvLAvaOWyVNsQPMGtVVyMRoZJ
+5VK0P/SFh0ykc5m2K3zt1l5b75h4RlgImySEgj6PvQTJHtDZAziQq7bvkax6Mxh3H++fnPDD54KB
+z2KCl5LWaxqMuktYg6fzHPh9ajPknjwc3757lwPNSzK6hRGWwbNjcgnpWOkapn5HorSkrvOdwEtz
+WTB/4KKN+jQnU0jfvad9bTbjWurRYTClERgq7gq+tudSpT/8NddlGZo5myvHTgyOQjYbqW7HlyQD
+V/gzt6aYc+laqKOUbAMGt901heu7WGFOeDohdkh4/HWKDWWec6lz2K/xAve81PYMW7vIxUTnoKjQ
+pi8IBw/5ccZniRSeAc+stb+m///XcYvEqTHmFoD+e/YZjnEzbsbN+D0e/9SRgrX2lTHmqTHmLWvt
+O8DPA9/T//4d4C/zKa3ojTHEfoiJI0rVCcqrC9AOfe/2Hj/zBVf4OT4O8SQkalQUigYJgVpoXmco
+Fy7PNrVlNu538du7Xa5QsShJLKFIUFWT0SpK8aMBF3JPDhclnsxQWvk4DIYj6lIeEQZKuXdUeY5p
+eyHVfYLerkvFMoKSLnW5owm8HZoQk9PJ38BTPh0kU0yv/NwmdFI6mg1T/Cu3zg72Jzuh0HnqEIqv
+koylYLDbosUs3HXEC58yk2BtlbMVkebebXcO4+aILnHXP52NQPx/E3oYwXm72uBLSLUVWY2kwIvc
+vKRpxMdcrQiqUHOU0envboto9VGUMgjcdQwbg6/ro/NAhduyaeitNwcjFXOHHm/su/rEgzszRkNX
+iIvmAVZK0cHkgE5KTlhFNGWNN1Q9JA9AUUVRt7vzf1AZsnNXsN4O1OLF4g+EgA2udjZ2eRfyJw/c
+znzYMyqrDd2p+96rZx2Xjcvxj5oJnvAr6xdrahnppF0vJDuiqdz9iKKI0BPpbB/aXqS3eE5bqC2r
+2ld9dkmgCfInA7xQxj+bLZ5UvFPzJfLi99ZL8i8C/7M6Dx8A/x4u+vjfjDF/HvgI+Dd+ty8xxiOK
+B4TjAdtX7iFuypowdA/pyfEBD47EfOzMTnJ8rZfca1sGe70UduHQMsAwNdzZd6HaNBwQiEJ6regs
+8GJqqf4ur2o24hQMBoZpDysehFyowm+lvluHLUXPmKwzPMnEb+oNTSOjV5tiRUtuZNvehlOMzEZM
+NMWWl5qAAKsFzgjnYOolvir1vt8SKZUwYUUnmGxzUXJH9ON7F+5YH74asj11i2JdlzRW8fxRzOLU
+vYRN3dCJdemrzx+YmnTUV84/rlZ7wYBg6F5CyoJWgKOu7SvdAWF/TcEAbyTDkmAEqRyQ6hDTuDm4
+c/wWAHdfy/iB4OHn22LHYbCdwZfBbFs1NL2Eu4q1XzmZ8PiBW1gnsyGJ36czAUYYg8CPsJqwTpV3
+61WY3lgm8unEpYhiMMILMLVkktBbrNWFGFquRdG8SlMuc3cdnsl58Au/AMDgrrv+YPsBwYfCkyw+
+IhJFP8taMoGXglsn+FoYfZnXBOWQtpDatV+TVtpwtjFN711ZR+QC4ES1ux/WlthbsgEoZjvMiWk8
+POl7dukC+2PW7D7TomCt/RbwO8Glfv6zfO/NuBk34/dvfC4QjdZA4xu6bU2u0L6zLVHswrqfvHuf
+Voq0TXtNpzairzbPOPVJVXyrmpKpCCxHe2Me9Kty6BHrcgdy++26jrKUuUdrMLIyX9DBUuSZxtD1
+Ah/adcr8euf3ZwOPUmYbl2cbKonEbPYyBhcuFE32pQbdeLS+VDkvtpi5fl94+EpRyN0u33o+Xd2r
+QHfYngRj2ImxBmkN6kfnKs7ZDmoZDDZNy1bGDouPKtYLh8Zru4ZaMm255O98m7PXS7u1OY0kvMzR
+CDLdE89ghacIhbz06ks6FVe9utpJsJmuwSrSC4IBiXAdnpyRT46OCFLHnMyrNbUiD2PBE4y5MxWd
+cCY9M3A/nROplRtuXuKJGek1Hp5g5d7mgrZXjRWK0ZQVpnE7rfWanTVbFCa7tDI1LYcK8+8k7j6u
+qitKtf2SKsIL5fVpYXTHpQ9N7VqPZrFP1brW8Cb2CV5ohz4OsWpr41kqxKRcuPu/MFdYQd6NV3C9
+1jV7NelE+hr5Gg8xgXv8x7pjVco7Iggx8i0Z3p3SXDlMSreaUSeuTf5px+diUTCAj48JU/rU0sNw
+NHcV1LfevMtMN3xbN3gC2URS9pn5hkqGsDEhU3UODscps7kLNX2/QsRHJjI+LRtYSH64jT2KZ8oz
+y81OqCX01ww2MlERFyFIJwQjd6IH/oCVtAGN3+BLM3AQgVUu10kUw0Ql8pGlymqsVKG8boiRgaqV
+2IoZp3iSB7JtiTfozV9rGl2IF3SspPo0VmU92Z/Cj0T1xrJq3UJghgYv6sNIg68as6eXNR1FTPfc
+Nc8GE3zNcZhGOzt7G1QEklTv2XkEBk9y8bQRRimarTI6pSGeGe7wIKHqNnfvPeKrX3TV9P/r//k7
+9IadtrOgTkMShUzUMbktnEowawnlrNU1DUY1miAe7WoR1lpsLCp5rvMcdhilY23XYLTgenG4u2dd
+nhHN3WIwO3bzFjUNw9Q9h9EgxORafGlBpjtB4r4rj654GrpFYXXtY2Xsk5xXlJkUnB+tqTfu89mV
+6wxts4Zp6s59Mk1Bna0qMkyLfg47TL/YC5C12i4JpfJdplt81XD82YJo3AP0noOYwp923LAkb8bN
+uBmfGJ+LSKGzHUW9Yeh5O9hxFIZ89aHTZTwep/hXbkcPD6Z0jUgyI7ftRgTkWl3XG4u351bGYTwi
+FmmqqzYEqRh+jdB4cUOxkYJx2PGkcNXi/KogDvoiX85AG6GV8/PJwy+zrhzjcLheE2jXfDCMCMa9
+jbrP8OShO7bOt4o7qBVqE1NeXOrYNX4i5txAPpiRdTEq0Gyinf5g3uSIT4PdhLTyXTz70O1aR9Yy
+FyT8cmG5Ovt19735CCubewOMBiPNnfrjRc6e/AiCQYK3pyJpWxD6iraMh9/PpyKaNvSpldrZ8BpP
+MHVr7c4iL6SkzVVIjFyIG9shrz924fc4DrlQBNF1HYGwHKEfsTd1O/dcyEtTpXTqDPjHr2MUeplR
+B0bGMX6K+EXQFyLbkE5RjjUdfZukiX0aXzh0b02YCWId9HJ0Z6TGpXShtyBVFyXroDxzz9N17Ob4
++//Hcy7ec8dbDlIGYuveGnpYCZ2cnq0Jtfs3Kqjuj2eEYxG7ksFOzTntOhrB7Ts/wEovoZHLt5fW
+O7vBWQgzRaT58wZ7X7qi6306+sjx043PxaJAZ2nKEhvDSKHj/vGMr37B4ewjLwKZtGZ+yPrUTcpS
+L3FAgh/0nYiSuXDyYeuzLNxisjldkEl8ZSixz/k8IFEOWW07MjEKc2N31e77yxlXFy5vG0zcpH/h
+jUfkMp31Xi7IBKm9N40Zz9xLvdkMsTLvqZS/NhcF45l7yA/3wTM93LqhlhPSlWDL598+5dXu36c7
+YE7QJXidTFO7dOe32T8wdVZzLLt3i2F96cLZvLkiVtjdlhGx2nCIz9HkPtet+/f3L3KyxqUld6cj
+jh9It3AwwOjFqsUoXVxmfPubTkfwo3KFJ6/I6TRkpk7EaNoRdK51OJeu5mw84yBwc3V4NOfJD891
+zh2xlJWOjob8sfuO2v36fZcXh0lCp7Rqsy7xtSia8xWBUoloOCZULcJT2oIXYvVs1csNa3Xp1kXB
+6VJtW+vT4K67XLlnZbwXcmftztkMx3RyIrs0FVXh7sO3fsXBoN+9WNC/fwsbc1eCM9frkGDW58UJ
+Z+pWnZy4axrujZh6bnFrV+udHqcfz2nUicg8Q6ZWfK0W96iKaEU3TIuOUt2e8rJipWN4nFEuf+9g
+zjfjZtyM/x+Oz0Wk0HYdmyxnEG8YqmB2/2DGm1LDnZopm4mLDrarKy5VzFpI4zAyPmPtpEfpjFGP
+kqVmeeWiicJrSGSrde+Lbtfysw6/L2p1Hodnbpf4wdkZS+2E0yYkHiv8V3X+5//4Vzi/kpKv9z5N
+5na5Jq0or933be5ZltIOWKswdJ5tmKs3/2jV8WhfAicHCe2Vi1K++8PfAuCD8w3rSqHs4QX7xp37
+cJAQKCz165ZSVfa10qeuK/jy6+76xmchV6cuUjp/3u0KrfE4phZuQJKR2LnPtaKNb773hHcHbld6
+OJ3ypcxFG2998afgyuUuW8nfPXnyIecbt5udXzSMH8oTkg58F21l2ZqgdX/nieQ2iu+yd8fRYn7i
+D73Ft/+RK7qZBkYqbN4+iHn9rosmhPMhX8bExxMd75QehvH+h0+4hYP8noz2mM+kZdArW09GZIr4
+FufX/P1vOam4D06veXLuzn9vdszjE8HJO2l55CG+fDUvi5yhis3Gi9lcyIFa0dPXfvYtsidSc25S
+NrlsD1NDJu2MjWkpVTD0xHxtOp826QFNOWaiwm5jyQWheHXWcClgVK/zGScjZhIOivdCcj2zYeyz
+fu4izjgew1AEwk85PheLgrWWpq7pPMPrkj3/2XtvMD92P/szj7Zx4dW2uOB65SYzl6JRGmypxAqb
+zlKO1XEo85bVVjl3mbOt3ctb/9Dl8sttQaowMxnPaHubeGtZqU013o/wz1w+27fxDvc8JjrGqjnk
+2jjm4+rikk6h3bMXCwpJv2cCsZQFBKpOv3+54dFX3TmP9n+eMnHX9Oob7gF91VQcT91x9wNDFogB
+1zRYK0r56iWZHoogEPhp4HEkNh2PDS++4x6Oqy4jVqoUbEKs6dmM7nsH4TFroQA3xYajWjTzWUeu
+xc2UBb68KqqFy422m1MapVJHexbU4r2MC2zrwFJHySFz1QwCITaTaUM4c/f07bduE6eqRVQeqVb1
+u/NDTg7dAmflxcjcUAbu+vL4grUEbIbpGxzK59HaKyrJwKehO986O2Nx4eb+6WLBOHW/f/AoYE9s
+x6ANmY/ccSJ5TdZTGP6WUJNpTFLK0JYG33OtyNuP3N8EyYjv6RkZFhPOvq++dViRSar+R8stq96b
+cuLmde90xPMDN4cnace8cte36Uqebt0z+94PcsSk57FEWELvims938moZPhxc2mnMdqwxu/BZ59y
+3KQPN+Nm3IxPjM9FpACWpm2grXj8lluV337tmHnkduP1ds0v/T1XzHrn2QueKAwcCuv9+O6Ux/Ja
+HE/2GMRuB8qur3oyHNlHK54LhLOQvJbxOiJ99u7shDuHDwEIug8QFJ0q7uiEu99cCptwdIuXTwTo
+WXaEC0UpbcCycCHlN3/z3Z3L1IXCz/thxPRYGPhshRWPMp6/jWncLnZ34MLdzaJkc6Gde3nBpaDE
+B4Mxs4nb5d6Yz9iTB2F8x53Di+0FVaQd6jqnlQM3z3MYCfQU1ITqpMy0Y2bLM4rO7bpZVbAv/ctZ
+MmLgu890jYfp1ao3LsIYmTFHwueXfsiHH4g7EAVM7ijUNhWDsduNR1OpZNuYSOYso/OWSGTao1t7
+3BJY5da44dFrrhgXLNx9WhRLfu1vfgOA1VXIU8G/f+L2Pidf+BIAXn5Gl6g4PHDPkOdFrM6udZ7V
+zq15c9ayqNxnYxvSiEn75SOX2pVBhzdyhUQGHRMZ2JjLkFjpqI3cbr751jmja3fuC69hOJBhTptQ
+Je76VuWGB5777kcP3H9f/fB9PhIEfZnEPBYf5eV1xllY6T4d7fg9iTA0Xjrg8tJFK3fPffZuud+f
+ba6IB0qfzB4mV5T1KcdNpHAzbsbN+MT4XEQK1jrR4a3tyJ/LY2B/i526VXIQTHj7rpOdunv8Gu89
+/QCASGSexK55eOB2lGkc0CgHrtuWw6lbjbP9msnECVuuhmpBdR4MeyThx96P6VFCJauwzEashWhc
+STLMtIZWjMJrzmhl3nEyHPFQ0NfE+nRqMxWq89w5mpCK+RlX+8zGruVar9/HypzlJ3/65wDYP3mX
+9bn73WW74PpMRCsvolMfv6tXxK6cwe3UFe3O37vmm991u/VV1WDlYVgfDPCljl1kJZM9tyN+8Utv
+AzDJt0SBbMkGGY/uu+s4bhNm+iz1mnzpag2e8A1333jIsfwv/XiPh7edDkGHT6I6D17NQHiB0Ujy
+YvmK4kP32ebylCMZrrx2vMexrumnXz/haE+Rgjw35vYO9bnITgcnPJbJyuRWQimbvnGZQixUoPwV
+w6YEqWCnW0stpaeT2Zi51JUDSoz8H9OpPEeCkOGh22mbj8bEa3f/wsGWeOauBXlTmFsdF4pCl6s1
+owM396PC3zFmjw/nzO65Z9JKHrDdHrP5oSvEJoMA8fIwo4qHkbv+LvB4YF1k0kpNao8Bjc5zOkjZ
+F8S+7QpqKQ/beEmz/oOIU8CBVqptwZMr90CfXaa8pmLe/OA+bz9wIhWbVc7JxMlsW2Fez+87AAAg
+AElEQVTI7TbmcF8Kz6OOUnLhkVeSJKpe3z/BCrRUCwbt+zUegsYOYq5zPSiXA/KqzzvgQpTjczHn
+muqCUTvS8U44e9cBmdpJwcEt9yBMv/YV1qrmWz2A/mDFWPRrr45IbqnKvDil3robPT12L+nbd2ec
+fddxA8IP19x/4NKAnCGBFiSvtgQSmhlLzstLYs70u8y2HOnlnccpFxcu5K+KhpGUqf/oXH6Wjyyx
+Vch5MCAQPyTBw5coTVtlNJeilEtYZjga43l6WJuG5MT9HMcjEHQ38GKsFg6vt0unZHPuCr4P94/4
+krQt357e5YHk1u5Fc1LdS08pWGRivihRk66KsCqw5uWG8tr9HEQWpA7dYzo69hERldF0yFTzcr0u
+6IPrKEhIEnf/TNCbwuREYlcOiOgmLszfXNVsLsQVUQeriY4oA/f8dkOfrnYHnBwNSQWQ8g6O6dQp
+q4VBGA1L9o7cdz04GBEJ1PTq2SUj0dZtVTIV52FPnpmbrOQLY/fz8WsHdEpvg2jEUjyP2MY0Pa/m
+U46b9OFm3Iyb8YnxuYkUfD/AGIhUADJBTCDV3viw41DYg2FkmVY9K09x+VFK1KvhWounldZrNyxr
+WdRH4W7HS9RCI/wY5UYYEwQu9DsaDVlJmHWx2u7cqGt6Y5WCYM8dOx3t8URmMMNhwVZGLLMkIZXQ
+RTBXO645oZGPZbu4phaHvvMDjEJCEqUwwwG3/l/23izGtuw+7/utPe8z11y37nwve2B3kzRJSdbg
+mAJlO45hRw6gGMlL7MSB85AgQPISv/klD34IEAQIkLwkcBwkluVEieQhhhPFpuxYoimSIsUme7jd
+fae6NdepM+55rzys7xyyBdvsNhO5BdQCGvf0qao977X+wze8+joAmzubVIisleVklTvX/OJ8jRdY
+IekaD87nEhaJDeGmNBKWAZO5C1Fnk2K9HHg9V2S70d8gFbS5tzF0no1ANluuQ9Eqt6w04mIBB9qi
+JVKrMwwMHUHPvSQCtXjxQpqrlRemNC3mz2kEMR9sW770+YcAbIdbbMmIptsbsJDsWyRZNc//vuo0
+nZqKlfDuiJ7YrPUsI5M3RCspuSCqaVeRZW4YKq2KsFjpEITGEgqy3izcdVk2I26/6s7v7uUZ//jr
+LuWZ1TmLyqWxJpIU3mzME0VEYadLIZu3ytYYsUcjZiynahdKBdsrWna2JZaz3yUVzHlvO2JL0Vbc
+6WPG7pySROlv5EGjSGK84Eq6CYugBD2HZV0SDaUA/BHHJ2JS8DyPJIoJIshk8jryGxIlV37ZYtTn
+TjsRRkapkezgm9rHkxcjxicUb6HxIpiucPkNsZR/A+Xvcae7FgtpSdhRJf/B5jbHJ+44sggC0YTN
+YkVPrghq97uDzd21ss3585hiT04tXkK0I8ERMQCjXk0jeG2+HDtGozsSVnorGFdN5son3rjn/u7G
+AbUg2PH4jOBCzkReQhOvQDGib18sKKfSnYxgKD/O9O4tjg5PdPwXbIh16bcSgMlTAs+9EO1kRqJ8
+eH4yYzF1mIT+VkKgvv9a4b6a0MxXAJodrKDkJumubdKtl1Gv/EhEp/ZLnyYXc7LoEMlXcvelT+H7
+7mLUpcWfKOZv9eKGDSi0j/0UU63g3/WaBZmFCVmm3/e1sPS2aWe/A8Dp6RHDz7hzDcIGf8XAXBhW
+qOhW7MMgjIl17KOuvzLcoix9FpJwz89denn+NCd/otRgA9oNpS7TTYwWpOmLKzoSg+lJ0HLkVVxd
+uMmr70V4ivZ3C59YtPyN1AdpjzYXUnOOfXLhO7JFQyE+yslyTiKuSLfpUa4Wvo84rtOH63E9rseH
+xicjUjAe3TSl20uoZXnWhCGNWUl0NQSBQv46okWGI1rxs8mUzkBFxBqQz2FLw0LVfr+sSSLBhtXv
+jbweRitsZQNqCXJsjPYZdd1qdXx2xXK+goqtnK236IxUrLy8pJUYzIuzKWeCFW/c6+FLnMMXuixr
+A+aCBGdRTZRrNt9KKeV+PT5zISkXNcnIhaqbNx/iyWfBLGsSFQkLP1vbrY1lQ/7+2RWZClJbfkoS
+rqKimM6uO85wHNEq3WpZdRMMYSzmZFHRSh7NDxs6266zkcSWZCCWpIpv7WJKrkjC63YIBW02VYxt
+XWGzLX0qaUWu9CLqWcNU9vOHp+9y9MJdl1dfs4TCpyyDc6zOJZEvRjhKSeU9EcZd/FSEqE6fRqkJ
+nk+2ocLkhmDOoy1GLznF7/dO/hELFfsO+gcEQpxar6RVWrRcuG0FkccQpVVel0B+GbvbIZkwLhcT
+F4HN2imntViZFzBqXSQ4GS7wpir4mojuhro5SnOLmSEVwzH1evhb7p7tmMKlbECcdAi3lB7MZX/o
+x2ujl3HpUQ3EHl5skxn5ZkYVWfZ7ZAbz/+XwfZ9+f0gUh0TyTCyXJc3KsGNW0IQrSfICdAGLmcv7
+SjKsbmJ3a0ClirUJI0woERUvYKkXqBeu6s0LWt0YW1ZYQXDTNMWXjHzdAsK7V0v38xpDk0X6PGB6
+5S7jomh45djtb2sQ01d+2YncwzOnYCl9yXJhCRI38fjViKpx23jnbTcphLMQ03OV7rvzBV2Bmqzt
+oAidRV3w4ty9kG+L47G8WvLSXRf6379zwEzXsJvP6cj81PcNoXLV1NdL3vHJlLO2cYm9FC4/DmkF
+157XkJ+6F0DdPcr6BMYSnwlTfPkqmkVNIwWscrokFwV4sWJtxiUXSwcKOl1c8J2xAwD9RDGlIy5B
+7UE5c3/nW3fssV2CPrftHCsAFPlsTTmuwoSFFoxeoXytiLAy4ayaCjtx8G9/p0+gtmYTptRyi2pl
+a2+aEivocj7P+OmfuA/A3t4+rURyQs9NrP3NAHMi5aU2ws7cdbm9UePJsDgcdPHi1QKmzklcY3bc
+/W2xa4OXZV1g1REyQCBJADzpmMYeS/mmtknL8syla0WY4imFNm1EVzT5jzqu04frcT2ux4fGJyJS
+sEDjGRZ5Tiikz3kbsVSI2/FrfPV8y6xm0bjVOFeF2A9yPBVv8uUl6ZZITjRcTpWCVAvKWgrN8oak
+qLGVYLs2ppbGXVnERNIeqENH0gGo1JGY2YiOQCPeRYinynJWZ7xYulXjznSBp5Wp7Wkl7XToj1wo
+fuVnXLaS/Fpe0fRlyrLq//94xLB2YKvW83g6cyvbMOrSFi58nl7M+fahiybeEQah8Gru7bpI4VMP
+7vHr33UYimdPl2TqkkRpircy1BFjzxY9zhduH/1uQ3/ojtO0KU+1uj9+/jYjkaZubjlw08aoT08C
+IVHagpVhSTWjlvJzXbSYSkW53J3zc+acC2w0rS7JcnccR/mMgVKC5mTCQLDj3rbSNS/EKNBrm3at
+Sm2XMeV8xUo9Z3wuqzdFa91oi/MzpReDGGUE+Ck0Hfd9kTXUrVZxaYXO6oRc970fdzm4IVn3z30G
+77ncCzoqLj6BpSKUnYGldY8py2VBqRSFBBp/9Ry6r85O5yTDlcdoRWjEYM06pAN9bwIaScm30qWc
+5SWlhGUm8wwRbQmDJYlddfFyBtsO4/NRxydkUrBUTcN8OWGuaO/R3jN+/KGDtg3CDkYtqbwuyOYr
+2Wu98HVDV2lHOy3IpfVfFwVG4XzhN6RdMfHUumqqkFI25EUzw7RCIOYzlkeip9YNfYlnXimvvRhP
+KFdU17ChXOX4c4+LTbe9y8Xy+zp5akgM+n1CybMPwoSFOipVPaATuAdrtOMmN7/okYtC3FxkXCzd
+han8hoWEY6Znlyw1WQYKT+syX3sF9OKY6kLHk19QiV9g6wbJSnJcuw+9JqSROWxSd0E1Gt/3SLSP
+HT9gNHQvxfZdF5J225jBnkP2mSSgvnDbq2NDo4mgWuQUE3UipId48v4xRlL8myYhkvDu9959xsGe
+23YnaNgQK9MKddgklkZ6h01cIj1b2nJGobpMaDa5sevuT6AqfHLQoa9c/l47ohKSqSjs+npNxzNQ
+63cuX4tl41GL+Rn7I9ItN1lWlcE7db/zdC5dxiOPriYCr/XWTMWBHxN3JPc+8wmCFV/d3euksRTj
+lc9GvVby6iaWVsC3WV1QXGoxk0JWWeVrxyqvqUlVE8qNoSs/s71ogB/+HqYPxpj/xBjzpjHmO8aY
+v2aMSYwx940xXzXGPDLG/HWzEuO/Htfjevy+GD+K6/RN4D8GXrPWZsaYXwL+LeBPAP+ltfYXjTH/
+LfDngf/mn7sxa2mrmuUiWxeL3nl2yUyqt0U6w8jzsCVUiwGWCpcSG4DMW/ykovKEjY9aEukVQkYk
+Tn4lN+QyKGjUUagxIO9KY6GVMrDX6bAUtDeTdsFl0eWFQpqDs5BKQFk/CGgyd0knZ9CVA3Nh3fIx
+DjqEU9dR8MuIhZyHjvzntF9z4f/xiSTmNq8gdiFwMTecqog0SjdpQ8nGnU+YThRaddyKGvoJC2kM
+nJ9ZUjlC23nLdKF0pmjIxYN478ptN7ys2ZJ+Q+EZxmMXNfhRyo0bLo250Q/oKnxO1LUIA0MoVmNT
+npHp3kTeJkiKvbUtViIwXVXst4OUy5mLRl40J7x77I4t2nrBswv3/U+8foNEuIhwpZCeBFQrrYM2
+om3cNazbDlYiOPHAJ03kq6jj8Rcl0Uqmzm9YzLS6X4wxkQxg5gs8ma+s1J57vSFTMUaP6yO+/Y/U
+HRoe8m8+dOdij9zv7o5iFhOloEBf3YDKGArhJsgrcmFErIRu/H5MVLkTNBFYRZ5xGFMpWlxmMwp1
+0sJlV/uIKcQU9iqPvlLeoPKoBLHPugHhdOWC+tHGj1poDIDUGBMAHeAI+DLOVxKcFf2f/hH3cT2u
+x/X4PRw/ipfkoTHmvwCeAhnw94CvA1fWrlQMeA7c/Kf9vTHmLwB/AcDzfSaLGXVZUak49ejxU777
+1Dn43tn8PCA/R1NQy6F5hZJLdhIaiae2pVk1qTB1RZlJbakMGZcrtqP4892EQexWj046pFLNIT88
+YSLTltyHdgUxVmvqXjfhcuFm6/NiyoYEQ2edXS6v3ApbHHjMYreN6cStGJNnzxjtaDVb1Fy856KC
+bR+mQhZG8iqMuylnh+7nX31vzLmQmXdu5GuX6+PxFany4eENl7POLpZUrTuGZ/MnEMoMpzOh0jaK
+LGdhdH5Ch/7myQd8/pZbXTsHXZYnrtVZtg0He+4a+VcLwEU9y9a1U6NeTSbMQjm7XAUHcKNwmGvA
+tBWBFI1yrfJlxyfaUV7/bo2J3Hk/O5pwe98dU287wVftJlRExxJafWe8nKZdqUBbatUBTDyneCpF
+6EhSeuNLvI4jK5VZh6cTp36dW+jsuutlipZt0U49EZH8ToQREvZ7v/aYNw9dpBC0Pv/O618A4PVX
+HUGvmoeMX7jtTqOQmXQ28lHCTIWleuDjrV6PVTQae4RCKwZ5g29UP+ovsYqA2xpimQB5QxWpLj18
+1YmiTrQumjediEXmCtBBAeHHRDT+KOnDBvDzwH3gCvgbwB//qH//g1b0QRja1lZEvYhQ1duiyPn6
+Iwf5/dmfepneXJXjqEfYdTesWUoS7NIwieTz2FoQxt/PfU5Ucb/yLal67wMVDvu7GwxWLk1xSiG4
+7rPFOQv15ge7HcpjwaYVvm3u9Ik1MW23CxY3BZDxEvJD9+JdzltaxbwbN1xxKjARvdLt+7w5pzXu
+YZsELbsj9zAGW24fNz7/gPTU3dhZ+pj3Tt05be2knL9Q0a5u8STjFcl6PB0t1q5PTyeH3Nq7B0A+
+2ud4LJv1pqVYiuJbuGs4vnjBo9Sd8/0bb9AgybvjY16cqB8fT1mcum14ejGrZcFZ49KckC63brk1
+oNO5gxVDkbALmZimz11jvWimCHrBKGkZ7LhrvNXzeOlzKpINNrlEEOtwJTDi4ZuVeEuPRrjj8eWc
+y7fdIlJbw3BTgjh9sTY9w/jY7fuD8ZjnR+563nrt7tqFiY6lu+merY6MW+vBNrlVmhCFnLyQuU5o
+2RLLddR3oKjFyYKvftcdQxR46+KoDSpa4dtjIjx11c5FHT8+myMle3aDLl0dzuISJpogaCP6u5JV
+k0BOulUS55Lox2MqqLg1GbFMdIJ2Ad7vncjKHwE+sNaeWWsr4JeBnwFGSicAboE0s6/H9bgevy/G
+j9KSfAr8pDGmg0sffg74LeDvA78A/CIf1YoeS1C3dLeiNVJwno05evYcgNPnM+KbbgaPQrtqhTOU
+S0txPsX03YrRiUIizy1BptMwk3ry8vmEnvACW2K1dbwugcQ9qqBlOXYr8PnhKZ6ENg/2t/EWK5aM
+GHn9iAvBWb0U/PgeAPeHhncv3TF/59EJr+67lWIYue3u3oiIJex67+FDwr5+vrsFc3ecme+Kmstn
+52wI2vzGYJeOIs7jx2OWEvXYa2MieUNkE7eiduOKF++6/c1PF9yWj+D2bkBH1nqFgXom9Jug3XEQ
+Mjt2xbfL7T4vPXwNgDs3tmkvFMJv36UvNJ7pulB98fwMM/m+GM7wlrsneTujFo6kLXyaUqHtwH13
+/o1zbhp3/ntHHrtiTN7tpvRjt78Xjx+RavXbfEmIxhu3MVKwruucShDrze4mwzecedDk6SW9vvAe
+isbCJKAM3T3bGWXsbLrVvX9ji0reGW1e0iryCCSgkkUF7773HR3POxRT3fcIOrufA2CWuhW/fOst
+0pmkAJuWrtqi/aZLKEOg2WzJttio92+6Au6mnVC0LnLb6Qd0VxiSvKQWOjfZiOgKul0Kum4WEEnZ
+uxv0KYTePH02Zbuj4qLX40oEuY86fpSawleNMf8L8A2gBr6JSwf+NvCLxpj/XN/9dz9sW54xpFFA
+4kfUkrduywW1Hvjn8zP2PQcvjTEY9eQTCVfYtKVTuIcx7kSketmMrUnGboLY2W6xUglOpNiUeg1N
+vvKoLDi9+ACAbLkkUY7XnsyJxEv2RW9+MavpbAr63DEM7rj0YNKdEZ8IKNKeczl2N/q2aMN1b06g
+6rY3D4iEV/Y7l1C48Do5lEDK8oKk5/Y7sh439VI9uWqoJes73LBsCwvxolB4XRaUgnMvTUskKG11
+XtITi3AZhqhBwcnYhdS78ZCOrNGPJjXb9ZFuzj42dCHz8jBgIfMR7/nK/HZOLK/F3mYHmwgDkl2x
+nMrVKs/WsuRzyZoPyojgXN2ZfsKDXddLT4ZbhKVo68uclf3l2aEUkQYViV4EU5SEKwZq0OJ1xYlo
+WmphSppUHZcqRPo27Gwe4KsrsyxKzGzFGO2SiRMR6e+zecTp2+5aLGbLNUXd+IY5ivmVotLf4MFt
+Bxz71tkV6Jm8Kj2QGlingbwvbEnPnbO/FVCduGsxbyC/kJJT0jLYls3BRod6RY2WmpSteviqd0wW
+FTMB9fy6Q7lysqJi1n48kZUf1Yr+LwF/6Xd9/T7wEz/Kdq/H9bge//KGsfbjhRb/f4w33viM/eW/
+8StcLs74la84pd6/+8u/yKms3G0VrgVLiqwilxhKpR59Vczx1NtNR30iFVbawK4r7qUt8KVW7K/k
+zGxMI2aPLSwLFShta1kxIq0xbL/8CgBf/tk/BMBgcY5fu+La6OWf5FbPrZS3b72Cp0Lp4dGMyYkr
+lNbbKj4+ecyj9+VUvCzYeCA/AaLvk45kcFMGFk8MuIKWuXAKL5ZTzi/V1Zhm60hn5ZxsjKErGKzv
++Xznf/0f3DFMfpkcp3NJWXF46NKc+TN5Hy6ecPjc7fvR1ZJTkZySUQSCNi+tpW3ETJUX43JaMW9X
+EYHFCLPQC0Jee+ik3m7vbLAzcmnMgeTDCjIQ2+8oO6OVld8XfvJLmNIV6y4OIZPAy4NX/wAA/cE9
+fu07XwHgl37lVzgSmrC5XBJoGbeZoUrlY5kLFRrkWH32U7iRuqJd6xWEcmW2cUEtbMxUhcHtqM/m
+rjv2ZeMx3JD93SDhz/78nwLgybN/AMC355t87dedr+QH40Nmj10quJiO19Gb8ZygELAmLdnW4vkr
+/YcAJPrSFO0aLxMQ0Fh14FZIXsDoebOeWT97Jg4JFOlWzLAqkOfL5dettT/GDxmfCJgz1lJXBWdZ
+w8WT9wCYnR9SykizZ4bI45R6vqCcrMInAV5Cj56kzDe2drihinMdREyPHZ5/3tQkEufweu53t8OA
+SA/rdHrO4+fuRZ+enVJKfKWuSopjFz6+/95TAH7mQQ/ju3wyenIIn5P5qRcSn7pjS+JDvjd1rkfl
+lba1uODWyB3D6OZNBoISB0TMxOco+oLGphG1VIMW+ZIrtdbCq4iykChp7rFcqg2nB4LWo5ILU+sH
+FJNvut9tbjD79f/bnd9tw5Vkv7NvufRhkpzw/Nhd74sry3m9Eget6chqPbUeU10XLxefw7YkZgUf
+b/BXGqF1wXziOhXnXkNfvcparcmhjajFGN1nyFj5fpUdY3A3uw2eMoqcyGlgXKg9vrjgO8rx33/3
+bQqZoWwNRnQVSm8MEwqlU1PP1RzmsxhfXJTU9kk0aQTxYA3jtqOIhWDvC9V+5sWSjkR06sZgVPPK
+aKg9932+5Sa/xTf/CSdH7vmdP59TzQVB9ltCXRjPM3Qlvz4Q/b7OCvxIC5bnrxmay6uMSg6ytnUG
+v8Ba5NcLfawmaWtafL3OvhcRKFUMvSHjRi5pfLRxzZK8HtfjenxofCIiBWtbqrbg+ftPOHzLpQze
+pCINBOjodPAVDtkqo1mx/QTKuLO1x8Mb9wC3Yty+7wp/fm2Yfcp9Xkwt0lahr0bwcDMlqFxh6PHV
+c+7E7vNb3jc5vHD7GLdXVEu3ok1P3LHd/eIXKEcu2ujOErbGqnTvLsnvukvae/8ufub8ChfPXcow
+6Az53B2XiuzuDJiNHQ6hc3tEk7noZtR3q0hASCH25ezJCU9E4LHVlPOuK3B1sohZ7ULUVTGpCANS
+kWjaxtLOXQQyKUqyXbeiZd95Hxu58zuTZFh45DFUz3vh1XSVEqX97z8klbX40keMu1oxy5ZMAJql
+TeirCOxVNZ5g4VleMZ1IW0Gu1LZbM9zU/Q08vKU7f1Pl2NoVT/dGL9EJRIhStPKV3/5b/MO/+f8A
+4E8bdqQP+aWXvsitu+6+Rnsez77nVscPzt35TdKCshDbcZBwSwXaURARieWYNylHO3Ip/0ARQ1Ew
+n0oxuzeksu6z70Vsq4t1KQBcLx5RHroUrFhkzvIeGMUJvjoGie9xY2cl++f+ftqWhNK1mHoNkTQ4
+D4MJV9r3oi7peNIe1XMchV2WAogFTY0kOWjLAk9gsG66QXblzm8qVfIfNj4Rk0JR1Tw+POb9p2+x
+KNyL4g1SBrrocXdEK31Er/FIZVX++oOXAPiFn/7DHMhtJ26h0Uthc4g2JJjpNwwO3ASxqRtAJyA/
+dyHza7N93oldlf2Vh6/wm7/t9Py+/u43yNXqmZ+5B63q3OBldTiqaExXOWna5Jxfujs2uZzSV3W5
+v+PO441XX+HhyOX1yQ2f4sXKM9AjlBHg6KbblrcIqIW5z0ywRizG/hG9oXsI33zrBXHfhcFSC4ey
+oJXI32xWMC/cQ+NNXzDqida86XH5gXwt5GNQtT49aV7+1O09ctnIL64aGrUtwVALQbhC1xXGIIIj
+QTfGiFGI9blS++5qWTNWNf/9U5eCfWp3j0gvxSgcMrztQE/Hi3PS2F3v1t+nve9y+EffeAuAX/of
+v+K6AMC/8Ye/xJ/+wy8DsP/Ky/QDt43TxVMO77m08f1jJ5mfnZ9xoXSz10nwpeR1x/SwPeXqVxX9
+c/d350/d8V5VGVdKV3M8bqYO9Rk2HkaelqPYTcy3N/31i2lsTaj7t512iCWz/tqNPq/ccPd4y7h/
+q7RiXLifXxmPoboar93f4d3n7njOpnMGchTzJJB5nIe0qxc9jPCUauSdkKpw70veJCSJ0FAfcVyn
+D9fjelyPD41PRKRQFTmHj97m+O0TSNzsGwY+g97KWGTIydRV8qPA46Vdh1n4C3/8jwLw4M594kjV
+3cCnleFIUbVsSDE5irtsbLpVJ4xcgcdEUKjouDHbIDJuhu4+f8EVTnJ8UV3w9guHX6hat93hVkLe
+uJm4ww7gZvNk/ydplP4cTT5gW+CdBy87qfbPvPHjK1k+8C+oH7joZlTX6ypQnAhI0+2CeCBhlRAp
+RM1f80nO3IofdH3efkfCIvKPDK58JmJwVm1LUUnfoKoZjRxg53n+G2SC9ka1i378pmYwclHOxp1N
+wsQBa8r+FVa6i02TEAvjYVXsrMwCSSWwtbmF8FGUi5pzRQKdk4wXYl0+OlJHqclIrty92X25g7/h
+9n14VZCpqBgNRhy+7VKv/+lv/R8AXFwd8YV77rr9p//BL7C1KWfyTkWrCCLudEhblx5sy1K+2Ohh
+JbJzdnKJpwr/YBiTil05CxYEUgX/YNtFgk8fn1AFbjW2s4qy754XO+ripYpCl25fVXOfVvcsTmO2
+lWJt9AI29aZ9dnuTP3BPLtepYNLdmKlASmVjaOMV6K3D4Zb7PBlPyBQ5F2Jivj1Z8uTEncdJXZFL
+Cd2vaow6FJU/ZvkxcQrXkcL1uB7X40PjExEp5GXOoxePOIvmLGXXVpuYOHWzcl2BJ/HUUTrkx3/M
+rbwvv+xW856FolmZqcR0Iin32JzhyOWZ3e0uUSqjEm/Vz03wVr4BnYZdoedO8wUPrCsImi92mHki
+85y6VTWyOc2Gy++uDsfEghLfTAbEgYsaer05Bz0Hu7390BUw44EPEgbN5z5e331Oog42Xs3mqnfY
+OatvmnZJpVt1MEzpyQovjGumV271r+WV8EG95Cr7Pt7CGFejqW4nvP/CHf8364ahWrzzD9x17WxZ
+hlJVGoXJ2rxk3kvWxJ60Z7GNBGsVMUSmQ9JxkUnS6yDtW/LYw1NbL2gLZlpBn7zj2r7N2NB54H6+
+11xwc9utgv54xEAF0VE84n//1j8E4Lfe/K475xb+/X/dtdr3738BT2rP1l5grYvMTGlIO2rPIdvA
+pk+gtvXOzS3ylfBzmeGLHTuoB2wW7vcv5Gz+zskhZ2r7FlgaRaSdXkrouesZ3v7niQ4AACAASURB
+VHaRwrGZMl15hUY+bSuyUuLTFQ5jey9iJJHWRNDmZHOfoWD6ftSjvXDbtek2G7tCW3ZfppYt3LOx
+O7b87ccsxVotjy84Vk0hMOFah8HzGxLv96GXZFvXTE+O8U5zAikAU1dr2bWN7R5UrkjY26l59Z4r
+MHZSgXQoCVcXoW7pdPTS24h0oBAvStfhGivwh2fwfVXLOyFDvSlbvWPef+KwCZ+5ucflqStmfWXy
+NQCa+YRQh7nV22MpbbN8vGQkV6sv2H+FrnUAoUhdFOv5oIKT5/kYeVp6nZhWijFNvJKa89Y3JyQi
+kSV5nYVsiglbtR1eF6vvcuoemI1eyHLuXuKsLbh44SYFe+TjaZJtnxd888ztb7jt9vLaxhZbOzJF
+CaN1IS4IN/H6KwyEwYrzsXLLMqlPYF1a5gXlGjcQWZ9E6db2RsyicL/zVF6Lx8UVC/X8aUvyS3Ew
+aIhX3RPT4+1//G0Apheukvqzn77Hl/74nwXA70B7IX5FUtLIyattS8J61dmQ3qOxBKm7VnEb0xPc
+vBovafUit3lDJDbm67fuAfCZg7f5R++4ZyE0LaFm6qwp8FaCMksHoOqYbC0R2FYBrbgPo3CDuwcd
+XYstUvEFI2ETkv2GyLqFI/Azmr74Jbf6eMttXdsZmfQtb0nabXHcxVeKmp1bxqL4l21GqhZFmIQs
+848HULxOH67H9bgeHxqfiEihauB4ZqjCmkZFFBqPeOBWo0EyIlLLbmeUsK1WTyvWInVDb+BmWlud
+kAopGDYecU+rsV9C8X3bNwBMFyO4LoFPHLuV62D7AR3PtcDaIiTouiJfP3Szto12KNWmW14+Ieq7
+sHvZHrIQj71TF9iu9j1XZNJtMarEBYMudiJLOy8A9b8DWdc1TUYrBF4Q1HhqozZ5S25dZBLVlk2Z
+nRw8k0RbMuHd0q3GTdsSqk03mT9jcezgw997f8KFlrw0chFYloXMxHbsewHlcIWKjIg3ZfAyy6hX
+dnFaleNkiKkkZFI01IrL426Ip99t0y5nOuaVW/XicslvHbqV7+47G7x220VVneQVhjuukDh9b8Lj
+J2LeC+L7J778h+gOXaHZLiZQKLJqDfXC7cN40Zo9aPWI13m1JraZpMFKOrTaSPAaXdtoQqMQfHfL
+RTZ/7I2f4FsnfxOA82nNkyPXfpw3HjZ2x5kbh2JsvKt1yldSUC4kLJPWNKITNNOCecdtY2vonpXQ
+jwlKlzIYAvxoBW1OMYL0N0uDL3JbT0Xgm3cr8Nz5PT2ccyFB17qxBIqWJ7OKvP6oWEa030/AaJqa
+2eyKumqppCREbInUBw59n1zhc5u3zFVlrTLldAEEtYxVwi6pwkSv9TCVCzut6VILmuvJKcrWM2y9
+cloNQaxML4TZCuY6eYfJUtiJntSdTMNE21qMc4apu8kD+2lu9KUGPDGUJ66+UA/dttoswJNkuT/P
+QHUJk1ta5X0rWXgbW8zqXnoxSLXXDvskys+7O7vEx+68L8VOvJrOKKVz2WA5lJnKB+NTTp8pry8z
+gsDte8+och5Drkk26Ydrj82ibGmvFM77Eb5ezlUu3xY1nuDjNA1GEN2qKWnilXx+y+BAOJIPnHLR
+3LugERvwt4+f4L3nJta9ewdr4+DxGCZzd21T9dr/5Be/iNG1aLIprQxoq1mDXd2/PKOVx2aj+5R0
+OhipfLdlha0kVDLs0hRqmdiUVs9WR25M+/d63Bi55/Do4pwXYnlWaYYvU6IQdw1Pnm1ipbVYYmlV
+H5vVGeen7rqdpgH78ulsxHdpry4wm27h8QBjV65mp1i7Mk6uCVboO/mKDneGLFUT29ka4pfSq5xW
+DEv3d0ftlKJayWF9tHGdPlyP63E9PjQ+EZGCtVDmNbYDQ2kkBP6QUMSR1rckG262Tv2IRrDaTBz1
+wUZMOnBhd7K9QxCsEFwNVj3aJgxgZXwij0qiHyCU0GKNtPeTluGe2953X3yXo1MXbVQrK3NbsiEY
+cOQtGB3cA2BrJ2bQd6tALxjz9vmbAPhCY3b2WmT7gA1HeD0V6/waK7EXq0gpDCuMzqPxlxRj3aqo
+gbWS8oJYvPlP7bi/Pz7q0Kz60tYyq12Ibv0Rh+VjAJ5MK16TDF2jc2rCPrOF24eJKgoV+6JuuNbC
+jKIcX92HVFGT33iwskvvdYkl4zbPWprSrehVG5CoyBtIcZgK5tJNeHZYcl96Er1mTkcp3+hTJ9/3
+RhBRrPfwpzGKtur5bK25YQIPT1Dwxrc0zSpVXBGGKqwKfLYb4CXS4vAzapmvLJqSRmzbZialZQOf
++ozr8Lx9MuZK9oR5Wa6JYvtbTkPjD/7cff7nX12pNjfUSkW6oy6nMqo5zRa0oRSxdyXws6wJhq5j
+FHu7+J0VQxesSFdeN8UXk7JQmlRnc4yAL9tbHhvCLCwjj1rWen4T4Om5/qjjEzEpeAa6YUBrG/oy
+MO2EJcFKLCS+JBN1eGt/xNm5e9Af7KzSiwHJhrvQUTLCpApbFzMnCQ/Ui5w2UOVcdQu/36etV50K
+S1toQoos26It+4sL5jNXfa5V3Y1Z0tPN6MYbjGSIemNnB09hbq/a5Nng/3QnKEUTbzDAyLDDS2Ks
+bMat8WglO28kQ96WpwQCcgVeBB1Jh2dTEMNxYEZUI7X1jt1+u2FJoPzVYDh95B6qSTJhqY5DP/Px
+z9x5j/ekeDRLQC/u6QLQhLxhe6SxqOj5zEkOAVEgb0sspViEbRtic01kdUVRuBch6NVsRu7FubuC
+h9cQiha82Y1px24imB/9PbLXHBTcNjdIxVO5vecm26hraRfuBSobw0oh2LOWVu1cayuyuWt95nqG
+grqDSdw9nc5m+IJp+4WHpxb2eH6CUY0im+jVyM95WT//zo0u03ckCNsYOmpxeqLZf3bwCn1dn7mt
+8HqqfRmfhRo4TxcN1abT47TqnnkDu65lNF0Pz3fPMmmPtnSTpWk6WKUEZqTFpFqSSCDmwXCfd3qO
+EXoxK0gTcTR2NynVypxdfjTuw3X6cD2ux/X40PhERApRGHLzxg51VbEnr8W2XTCeuUJi7aVO4xoo
+C8OFRD8ePXUreK8zYkthIlUOCrtt2dLKU2/x9JCFVulzaRwm6QxfYaYXtWv34CQJObjr0oe7v32b
+J6krNHZFEhp29tjeE5mnKhm+JNbiYEirEHW5HGN9t5qevuVmcNM+YkPAnN6uIZRBiOkkawOQVoWx
+7CyjkBZZtlhgG1dwY2qwEmwMcksoluDNXXfd7j2fY3GdEwvY1G13cdSw05VS9o5PV16YXV2r0yvI
+hSvAJtRa5U8XLbuSyOtbg/HdalNJfrhOQ8bP3e9eLC2VhGxKb0lfcPJuGsNKHbvjuiH72x9QSWfC
+Zl36yqveezHjc4Jee9mS17alOSGthHnZ0ly5/Z3Px8zEIqyKS0LhDXw/ZzEVfsF3+5hmF5w1jwF4
+8t4JYwGH+skGe5JzHwQD6tBd51xC1Num4Zas4X/2pdtcKsocdHqEqaKl0D1PW5/aYuuBWLmPjkFi
+KqfHNTP523mdlG+LSRmpmzUY+gSKGuP8klTaC21kyeS3mVVTCnWxCkWs9dSnUGH6sizwhb2ITcZI
+70M03ENPDsdPXvBRxidiUkjTDp//zBc5nTwhWnzfeciqIp+0W2z9nAMsxUVLd6j8VDj11oPpU9cW
+soMdwo67yYQpxUw2457H4sjdvN6+ctK8AQFIlvXVur2VTSBRq277ls+N3IX0t26ubuKQjtSN0nFN
+qBpFEPewpRBteyn9b7j9vDt0x/DWd4+4c+Q4HHcPPO48+Gl3fl4IAlEtJDQ7LnLekVx4k5S0Yh+G
+0YD+TN2H1Cc8V51kW9Trz4/xvq5ugIFMaVB3APc6rpW1tzEm6qom0rq/W+xkvDheMQObdapkFwU7
+G6Kohxskd13IvNALeHU651Tsw0kTgWoKi6pgVrtw/mDg49lK19O1+vaf3uBw4tqQZZizFCjq3eNj
+/v5fcV5CY3q0mxKCnbpH+7t/9zfYveG2+9Z7j7hSl6FcTgj0MvW9ir5au4HsAJ5dzfmdd90zsvDA
+1JJyHxnC0G2vtzmiI6et6ZUDJJnzGTd27rntDncYC2R2XhV4kUsVYnFDwijhNTEg87Km1n06yi8o
+FqoNeAHfeOaegbkYji/vtGyK7zAa9GmENp2dPOdCC9gkD7iq3DZmamUHfn+tHHZ8fsz3RPef1S37
+auHTn5DYj4dovE4frsf1uB4fGp+ISCEOfe4c9PGSbZ5/T5p75Ix23SqxlQ4JVbU+u3rMTBXwL77s
+dPtG213aleRUMcHrrnq7Bk/9e7+zwfZAVV2pMi/sFW0lFehmEzN2+87nNYmcf3duj7h7JbfiV130
+QJ3RCd2qG22e4ZltnYnFk9aDV3gEt6SBcCR2W1OufS69cINIfIdowyM/UZg7c0XU8/qErZtuyUj7
+HqXC4KAd4C/dKhAtjykEl9kUI+9ueQu/s1oZLDuh8BuDkFh6Ce1oh451x/ToSJyJdyqe6nPV+msH
+49vDknjqrlH/Vk1/U7wLrcrj03Mus5WIR4YkMZkuc6rWre7PpiM+ve/+Lg7cOW0OO5z33D2tPI+h
+1JXLr1f8k8+5axDvdbl85sLuRsW1782ekI8c5qHctWvvzmwa0xU7dtlM2d91EcnWDVfgnNKyeMdF
+XsFWyqbvIqRhv8OR0pXptKRzpVfiTKzcecHD++7n9z59ly9b94x8+/EYI6i3p6LzYLPDZ/+I42XE
+7zzmzW+4cP308BRpy9DpJWwIDBetPEHnJYkAd52dIZVSgjFTzhNFusGQDfEnEsm4LRclyxWWxSxY
+6H8aaqbqCNVFgVkRUj7iuI4Ursf1uB4fGj80UjDG/PfAnwROrbVv6LtN4K8D94DHwJ+x1o6NMQb4
+r3DO00vgz1lrv/FDDyIM2du7xdUi54MXTs2567e8/FM/CTh8wN/52669dzG+IJUcWVetvl79Ohsi
+PnXLYq1GhB9B47632ZLZ3BX83vptx9F/PrmkKwm2u3d32VCe3R/kFDO3ym3YgLs7WnVWSlBRi6dm
+WDS8TdBZXUafRlDUy8cv+LX/zcmGvXPucA5blaHvuf2ld/YJh67o5kURefEYgPlCDtUnEyaFW6Gu
+nk84K90Ktd3d4uWbrqV1Z7TFqKvVRsy5h3f3ud11+emlmdCtlZPXBdGlWz0e4NFI5uw7ubsmb37z
+EYVwA5kfspm683jQ22eZu+/nk3qtML2Syosqj8mJNB1KnxeCShdVy0ytysfPx2z7bwCsEZ+T0xmo
+jnB3v8etvrsWh90F3a+rAHkrI0jkk1GJ+ZoM+fZvu2fk9MqSK1IqFufsquV489ZgLSe3vecihdH5
+BZsiq00mS+rUhTSPvnXGeSkBYN9jRxiJT/Xdv5sbAWGrnn/ZkEykOVFbjOQAjV6j2Av4jJ7NOw9+
+jir73wA4fvcZISsnaZ9ItYZIEW81KNnd/bS7rr0OlZSebh3c4fIr7wBwFpTYC/lRBu4aZ/PF2jf1
+YdrnTJJvVWDYUjT8fFrwMVHOHyl9+CvAfw381R/47i8Cv2at/cvGmL+o///PgH8NeEn//UGcBf0f
+/GE7MBb8GpKdHmeZdMX8lFj4/L3oLg833UPz+f1X2Xvoqr4vP3TU5KDxQeaxVbBcwzrDyuKLGRg1
+Hp3I9b9Vk2PYuUulanqaWvpKO5J0C0+W8eNHh+wN3AP28m2XrpxVMzLZ1g9nfYyKOk2du4kIiLod
+vvgzXwbgzqmqzZdj2qX73NnYXl/8tolpkG7kyE0axQI29l1aUkbn1Pae+74qiGRcG3UKIrEk68fu
+Wo3fPsKoU2OAfYGwjibnlAgGO/OIhe1//a5jgF48K2gEoMojjwe77jjuhAmBiodxVFEpTWtVDesO
+NrgjFO2y6bAt3EQbJOALTFTWPNh096zsSco8tpxrAqwvtxmt+CxlzOm+20czrbg4kdTZLXfTbr90
+m4dKNeog5jxzL/fs/BIrOPrD+ze4JfelVPyT3Z19vvBpd67fff8Ze113/kV/wI2Jis3NOUIus7ft
+/u72nW2WZ+66PXn7Xc4lzbedpq7C7c4KcLf+Cz/98wBMTp/Sq50IUPUgw0gy/l/94qd5eM9R/pPG
+4S02bj5gJLGffHaK37prsbv/M7z0kvu7wdJSxCt+hLvGVeJh9SzYWca2lMJ7NuTgQJiGTsrR0ceb
+FX5o+mCt/XXg8nd9/fM4m3n4sN38zwN/1brxmzhfyRsf64iux/W4Hv9Sx79ooXHPWitfMY4BVeC4
+CTz7gd9bWdEf8bvGD1rR725vcjV7QTj12O26FSqpp2zJCOSg4/FTL7uo4MHtfQ5ed59Xvuf1vKb1
+FGZaH2RJTuhh1B9P+z1SCV/2P3vPHUPpIWsFvKhZERVp8xOWnqzhu1Ne/awTdRltulX35PyCvFQx
+bxhiV9bi+XINRe31+txJVSi9r7l3b7ReMbr9BF+iL9WsoBJmoRO5fdy+M0A2iHg7Q1Ix51q/wBMa
+zzY1pYRGL2JZ04U1D264COPtx5d4Qvnt7BRcHql91Yx5GLnze3XD/Xz7y19E9pk0nkFIYroeBFJo
+rrJLIvX3WzlKbyZ9Nj7nVvHZVctMVnmxF1AqhB0Oh3SE7pvP1IYsM5BY6cH2kjDVPcs8JCnBs/KU
+TAInjF3E8Fo8InjNrfIXx8/YkrjEolfjK508uL1DfyhBHRUfNzoJL+04y/jY91keumuRhyUPbqil
+OOixL7RhdyD/zPyc2dCtiafvPmdvx6UjwWaClZK2EWzZ81M2By4i8v2QT+26gumf+LHb9OWQ/tkf
+v0HaFZKxkSL4aAO/41b86tn7dA/Ufu9F3HnDpV2jZ8+Z6ZkMJCaTbXhYhTbnj0q6sXsuXrmzxSuv
+OZGg7sWCduAisr//NScr+MPGj9x9sNZaY8zHtpn6QSv6e7dv2qMXJ+TdlL564tEkodBTWkenPLzp
+DvXO527SGwk0kit/TWoW8kQs6zmtsP+ebUAVXj8N11XisJTOYOKv8fBgaCVYMZ+n5L57mHb3hmuH
+oFC+fu2Tr9MmgqraCzyx5Ag8xJwmLmDnFTdXdmVqwxQamcbGmwdYsQvboMKv3EtWWIFqhkN8CX7E
+foMn7UbrB3jaRnl2xES4iIvKdQ6mRzmbCuH3RzFe7CaN1ouoA3ctjmzL/VApj8A6D271CQKXJpnA
+x0jjsLRjlsfuga4i43I9YEWICEYtcaJqemfARiQ1oUEHLxRt2YZkC/eCTE4FNS8yBnqgtzsjujvi
+fMRDZjrOqA1pVLsZCf5eby8ZCRTm3e8jW0W20j5Gtu39/X2CVRAs2/p+6FP2BTEP7oKeM4+MdNPt
+uxMOEN4Ko0r+eP6Uk7m7hiFw67Z7Bsz2HlU203krH6XB668+X7GI3Fp4cD/gzoZbyHZubeDLY9Kq
+RmXChGr5VNfK0LLC30yJhu4aDepNujL1bbSSpYtDatGzizAkHrrP/Y2W0nfPMgW8fLDDxxn/ot2H
+k1VaoH9P9f0hcPsHfu/aiv56XI/fZ+NfNFL4VZzN/F/mw3bzvwr8R8aYX8QVGCc/kGb8M0deFnzv
+ySN68RZdVcXDKGMuNGI87LOx4VbmJOzhq0duE8X+WQnSDDRtBJVszwMPT9qN7XKBr5nUrrT54x5G
+0SlevWYDEgekMonp73hYWd5nU8Gdk4pald6CCBW9CaIe1EolbIvvfd+oAyDc7FGORXzyDajv3LYL
+6mIlbuhW7sZW9G87dp7ngdmUlNpijq1EkrEhkeL84ANFGEWHzwcuTP5OeEQ9lv08M+aylL9RGJoL
+d7KZlIr3zQ6pbOZtW+J35Lp8YakVvSQk9JVuVFOlFGdLFlcuCugPDdHAhfb+sIetpXtQtJSZ7okM
+YkLj89qWO6eHr++wOBE5atQnUPQzSyEUG/NTEl7pzANqMT/p+gRKD+LNLla6CH4cYVawceWEZW5p
+LtyKb/OcnQO3ctdtTSLVcLICs3Chx/JU98GriRcuwuzvjmiNpNCOLrCFi6Da0j0rfhhhZO3WDW7R
+pC5C9Nt4XTRv51NCdTiqmdtu45/SGkWFu3tMz13UF589wleXx48MRtcw2HLH4JUJdeiKj6PA8MXb
+LtJLgi7GbYJtu0sieP5HHR+lJfnXgJ8Fto0xz3Eu038Z+CVjzJ8HngB/Rr/+d3DtyEe4luS/+1EO
+IssrvvP2IdsbC7ZldLIz2FmLabShJV6xC+tgHdrZRO5BtASh+3ldzGiVJrRe5/svXrGkkWyQ50vi
+vY1gRYduYmytB9bbJN1yE5LPS0yeuZu/kJpP2L1F0JPJ62VFrFZXYvfxVvDgKscTlyKy7gErFnOW
+kl+P7I21JLklwSg8bioBq8YT+nuSbe/3MZle2Pkl1rrPxeQxqFJtxfBsG48zAZ2MSYiHq59bdkaC
+6J5bJheu8r2iJi/8yboe4puWQuzRZT7GSjglCnz6W25yXspAZTK1ZEfuwVxMj9lWXWYYdfEFA67r
+Bk+8io607G/3egxV4R+/8LhU2cl2U9J9dz23jj2OxWF4Z+yu99V8yd6mu49JOqSp3DNSF9m6lhQs
+hzS1/B9VRyqyOTPpRy6aKd2FO+ZeMsRMJfJqJ2u9xqZ018ev+3jih/TihGcCNb353oxP/0mlHaWr
+k/hhgudLGzKqCFI3qY+fHzKWBP9wGdCE4mUIpNSEQ1p1arz+gMtD+V/+zle5uesmr7DT4MeyMRAT
+1V8uaVWLaqkYCDiWm4YiFJ19MyHuyVj4I44fOilYa//tf8aPfu6f8rsW+A8/1hFcj+txPT5R4xMB
+cy7KgvefPGa+2GWz52bBsq3JFiq/Z833e7PzM4w09eqJTF+Wc8pa5heVIdBK6dUFRgITnhfjST/Q
+hKuWwwKrDkdbZbTS8GvtgnhbfWxmfPN3HFjmW++6VeJP/amfpeu50smMjPGbXwXgC+kBkbT/mtLQ
+ytq+Fm5ifHYEgfrRV1dYCZ0UWUUrfcgV4Mebl4xkSx9GHTyzkv0OqCUJllvD7MJ9NgeuANY0U3qn
+0iYIS1qFnCEBiSKdvJqRycU6XopEU5xQTa+0vw1qWb41cUGwsuFLcryO208y0ko8PqFSQfH86JSy
+o+KvLUgiF84Gdrl2mPaE49i42aco3er/3bP3eXIsZed6iK+Irsn8tav2s7PHAEw+OGer71IJc3FG
+o+0uJ0si6VWa+SUEsrUT67T0KzLJ3YeTEpO6wnRgWoJKkvjWo5X7d6POVj7O8Tdc9DqprvgH33Up
+yDcenfPvLZy0nFc7/Aqtj5F2p8Fnf9et0N/u/BpPvucq/8NbC3piv9rUFbDr6QybuL8bn09555uO
+5Xp0dMwf/ZL7/sadfVI5dtOsIsxmzZLMq5JSqttpN6LK3LXv3umQRSo6fsTxiZgU2rZlscy4XE7W
+OXkbhBgBXRq/WSsElfOcSqw8JH5R10vsqrc4CDCrooHXYBUem34HoxbgSqe79Sx29cBXBk/fmyAi
+7rsb1tQ5v3Pibv7Xpg4J+ccmnyEcKseP5jx6270gDz97zrYeWM/PsLHy9nPpKy5L0j3Zj3sey0u3
+vTDo4gtN2KzqIXVGOXEPYB41eKqcE0ItxmHdlmQr1SDta5GFnPh6wJY52aoGvOlTF2rDNZZBILFV
+qSNNx1Mu6tVlrUnFFhwmHfK5m5Bm5RH2hTgaytW/8+Yz+hvud6PuALqi8laHpGqdhXWHVm2C1f0Y
+9rZ5+5F7Gb92McXKK3PZ+PRKd59u3t7k8IX7PM7d8X7l/Cn3WpfCGNNQqG7Rtg2lLAEWvQWnzx3v
+oMqUDswyZkqZNnseZuURYQxF4O4fdUhdanKSTmLVMRjVrvJFypHssBatxZfSkwlXr1HmBD4BPwoY
+7Lpn4f7rB3zw648AuPKhI0XbRsd7Vi8oZq4e38kHPPgDrkbR6S/oq9kfbFQrCVFqLXR1U2FyMYYT
+byXjyVUIuefOb+My4rz8eMpL19yH63E9rseHxiciUjC4A2nLJZczN8PdPLhJJ3CrxKKpmQjbH0Ux
+njTsjABEuakJ5MYTlBCNJPGeRgRSbvbDiJXmeCPQjPUDrLjmnoFGK6kNaurC7c8WzbqDkUhAZD5d
+0pXm3qSoeVMr/o/PThiuQuagS37lVoL53G0r7m7R1YpftHPKx9J+jH1MKomtTNX0YY8qVtHxbEKw
+arvbiCZToWppSCVvNnvXHcMk9Lh46gqbl+clZ0/dOV0eXtCXNF1/6a3doy+lEzmYlVSq2NcsCWUm
+0vyA09Pk3NCoA8PIbete3CW+L8ej5hYdtXPOXlwxy10o7gczuiru+nL6eu/pd/n6m65A1+tDELnV
+cTl+iifsRFnu0lMRdyEMyXvPnzKeCpgTWZYzrdZtSKNi5OKqZPHCld9L8TaILZFC+zTdI1jJoS8q
+Comh1MWSUvyYQvc6i3Iu5Ky1vKzZ9V1UtPnSHt2Rgyt7ZmVqU2D8ZP2dH8vufuMeWeV4MPPLlmLf
+PZNNu/IKNeTS8YwGFcORA0Pt9y3pUFqSUZ9cGA8zWFkcpNSNOhxBii8ezMUHBe8cOa5MlFyRDD4e
+qPg6Urge1+N6fGh8IiIFz/NIuilV2XIkv8bXb9yio1Wi9OH4xM38G7s71HJSrrTqeLnFpFphi3Kd
+e6VXHfybboVq6wmI1daoeOUNtjHKz20Q4akWEcYbq04f46NnnDxzK14/dDP/+dTj6F1nIffWeU2u
+KOabX/1Ntr/kZuVe08FKUTfQZW4jH2Si0om2MAeqYXRDSqEzN+8Jm1AWa2/ESbVkMJOIa7eiElOx
+aWtaYTb8wv18Pi75jQ9cfjrLKkopWH/lG0u+/GNSGNqPWaysNxN3fRZjKOT7kHYalo0Ku+MlqRSj
++52YpXAB6dRd+72DbRIJmNYmwyzdOVe55VKaZlvpBm3ujnksn8uLWc25Vvnnc3h4363Qy6ZgRy7Q
+W21Dorbum9bdg6+99xbPP3Cw89e/8AbehYsqZtUEX5HjqH+T0atix8YSlE2qaQAAIABJREFUkl3M
+8eUREdkU1CYuzRzU7q2ahtlS/iKq1djK4s2FPC0WvPGKw4B0B3v4sStG28LVnKx3gPFW4rFzKkWv
+p497TGTmY9uQqlhZxrjtJtv7rKASUeqRoMhj4ZMLt5JNjqhU//EuheXxDNlcpLMmAuuez7qdcmPb
+RRtHEzjPfzd16Z8/PhGTAsbDD7q03pwjiYwcTs945Z5jRpoaFr67oaktqGWDvtDLHRSWjpRuvW4X
+rxBMtLukmbgwqg0MuR7SRhX51G/w0Isbh5iVj2W7wO+5l+z4/C0QzXZDYKSvPTvk7OJ7ADy/yEHU
+Wlr4/PbPABDd2sEXe9IKljy5mhF3xNokp50JzlsG2EWuc5XLUQrFdEVZXuBFMq5N96hEOc6MYXIm
+89vQfTfvmbUMuW0t54IjeIEl7UsMJI4whSC2+nd032deyovQD5jKJzEJGzxNCh2vx+bKAUupWL7I
+ePpb7vM4z5zvJ0AcUwlzUYeGwneTzLlUWA79Kbnk7DudluoD96IkQUPynrsWR8FjNgauy2MlNXb0
+fsWvP3HS+a9/7lNE6kqVuUdXIjLploeRl6QvZFnrJ9RiZQbDJZmk98qji3XXCdPB0+EXq1SybKnE
+4Rht+RwcuGeys3Obcu7k3QKzUlzexHTc4mX9TSJBtMukYCGbgPm2R6tu1MqoLO5fgGTe6nJKqc5P
+m51S1O6Arjy7qmHiI7etosKo2G6H6ZrCP2hj/Fbq3v6YtyWe81HHdfpwPa7H9fjQ+ERECp7nEfdT
+Ut+Qqf11kl1QrXnqIZ6kr7IgX8Ob27GbatONLolEXDfv7ZMqZAxDqCQismhrrp67zz2JpiRxZy2Y
+2tbZGtNAlKyFUrPFc54/cu2t/m23Ep3PoJK9WOLlRIIxPzuZ8/b4KwDs9L9AKqxDT7ZjVxeXzD9Y
+2dsFTDPZ0dVQTlW4lE5DsXGFnbhzjqOIrU3HzvOSEO2OYjnh+aWLrHq6JoYFiRCIeIbXXpXN+naX
+TRUaD9uSXQnKrOpw1odQ6Y7xPI5zkbgmOZOpxEt2O+xKF2FDyNPlNOek71bx5VFNR9HG6GaX+yN3
+/qZOmS5dhPBCRdfqSY/Yl+3fwjDtuO8Pii52z13nG0mCXbrr0d1y53F6POE3/uG3APhzv/BHScQI
+Tesn1GfyPZgu8EJFBVqN/Trn9NKhJpvNKTOR7ap5y82HTnsh7kNcuTBf6G9On51TyYYvGTbs3nCR
+Xnhwg/zQpZCJtB5M38PTOut5XUL5Zdx+tcf/9avu2Tq7KjnouqgPibleflDRkelLhwhQm9nLOZ26
+Ymy3O2Tzs67AWiq6rYsJvtCWCQmFisejW126pbtP8yDEnH00v4fV+ERMCsZa4qJgtBNxkbsTLk+X
+XEoz8eCV/7e9N421Lc3P+n7vGvdee95nuvecO1XVreru6sHtSuMJFCCYYCMHFCUfjJACwZIVCQkS
+RSK0/CmK+IBISIgEJFZIkCIzJJiA5Sg4HiBRgLa720N319RVdevO9555z3vNbz68zzpdF2x3Vbtu
+1bXYf+nqnrPP3nut911rve9/eP7P8zythXABhU8gBt/hTsOIA7Xc7vT4Lv6WArRViywTBXg9YSxB
+kc6BcwErPydfuxvFy1uEgTDp1YTThw6o9M++8hVuSSZ9O9PFKgv6fbUFj3vshe6hOVtm/OrbLp5/
+Pt7mhhiQWrtumsfrXVo6RmB69BtMQxVTqI4f33PAlXsPz2nVYv8Zd6hUN1/OHlLosi1nK04OBQ8W
+dt476DEw4vILPZ5Thjw8uMfk6+4Yo5VPsqsKzkg5lRPwgibcibghHsBs7uNJWKSc1NTqriyzJvbO
+6MzdDf/JdkJHi5M1AXWl82RFpdVnJuWlM0rstngZlzXB1C0EW69ssb4lqfYWXH7FLWRv/1JPn1tx
+a+oW1qOvvsv1z7prun1pn7otjdHlCun4EjVlmyrBP3LX9PCtFYlIVuKtkEot1/kypZB8gC/GruHV
+LpM33KbQiocELfe5IF1QnbsHsr7hAF1+9Dwod0JYUqmytd3bYqjqy+F0TrHr4v0rLznSF3O+vOAH
+jWufWqzN1UnOoFF6GiUUgoivpWq2PPfotd15tgcJyVCYnNrgt6WsNekSFw0/0/uzTfiwsY1t7Al7
+JjwFz/PothP6vQChNml7AaUalIrVmragyUleEymx11LizIYB4dTtuj6lE4QBKlMhWgTanke4I185
+FzlG6ZNP3K5Thj6JZNrKxz0eHrq6//HJkrCUwIuQa+0qYCTxkkHQ40jceTy3xevSC/jVN04YXXHH
+6SROOr2977N+7F7zJodkR5KTaxtyZaSXUpEOs4DOyO0IYeLhpaIdm6w5Fzz4+PQRC7nlkS9SkEPY
+lVDL1laHTLJrY9tjLYZfU8JMjTSB6vGTZUWr0yQUoSeMRNXyiOW9ZXlNoQalxelUc1kzGKrDbxBg
+RDhTrc1FaDLNMg6nDZzafe/eVszJodt1V6aAF8W0PRhSf6HhOih57Q3V4RuJtuSYTB2lXzl5na21
+c/2Tbk2uxKW3NBQTycL1vkWVFzcUclHKcN8dI25HF/iUxawkKzSPTQWgtrQ7zhXvtwakS43PCyj7
+bsdvy52vjcELhIStU4pMwjKFz4Ho75bLOTocZahGq/G39D8NM+xaScLoCp6c3qofs1Lj1lJq1mkW
+kepz23FNHLs5CipzUeEobHqh4/l+zVj7gflRPnQLo8BubQ9pRaGiXiiqgL5utlE34kgx7vp8QShq
+8KFo2Me9AVHtLn7h5YQ0MOaIYiUMfzekVNVhqXJbuSzxxUu4Tg1LgW28rOLmSzcAuLa3RVcZcCPM
++meyf8ny1Lmia6CW/HgRtslFOX6elSzX7jj3RYG+SLlouV7NCyqRrBgbkKk0morUkwAClUiroMIK
+rltSUehpq6joiN0pEcV7r9diNheIZ5bS+oTrZ4uCLXp/8D9wx/7VR1RiFjL3XF4j6Jdw38Wv7U/1
+yB+6RfF8+ZiWqhnJp67TETPUudSdqrNHLCTxHpQ+q7XIQ4/fAC2yyfgqlz/1PQC8fCCykasjdvVQ
+mEsD0mOXnf/81oRvPngNgJ/7yq/z9V/6BTeWuboWraGndnFjvAvlKM/6WPW8UHuEEoGxDcAtX7Ge
+N4K3BWjDqWx1cf2sB8Y2gsPfslhkMP1ewrUbbhF64do+f+O/+dsA5IXLZbz+1mP+n3/2/wLw1bf/
+Jb7mYtAyFOpXae8FVLofcmlpFrOctUBfWQZIuNZbViRb4o9cpLTabkz7kXsubj63SynlqfkiYFdj
+Sg5OefCqC3m+5/e/zKVrjnb+cz/yX33VWvsFvo1twoeNbWxjT9gzET5Qg13V5Lag3ag2e/4FaAaT
+M5QnQBAwVsXg+ZFzl/bHI2KJsAz7Hl7hfs7ylLXcq1nhcXLkVtVTdUvOTN1IHGLSDKtE4ny64ME7
+ApikS67vuzdtX70BQN8uaI3dznz67pRDVUZGY4+p6vAFloVcuFwewzo3BKUbU2Sh1O6fm4paLmFD
+yGKsRyQX3rcBYbuBY7eZe5J7T6FaawcS7LgTR5S1c9XThYW169RLd58j/iduB86jM+oHqmmfOxow
+uo/whCuwr0VEuPBgu5qyf8V9x8HWZbqVuz7pQE1Zn9hhLQVqzs55+Nh5HrfL+0zO3XkU979GKV7J
+s1DJzvRFij3nG8dlyrZYl6ug4khUcCfv3mJ15jyEWgw4SdxiKA8yqHwC8R2WRU2q7T1dFxQCIZUK
+B/IyxxYCrXkWXw1hHj6Ns2wCn8ZzbuRTyrSgFO5l4a2YnTqP5nTUxTQcoUrsFuenPDj9ujv3u0cM
+c3du4fMhfqhK2Qr8lngm1OQX7nXxxWY9X9dY4WWKbkEkqvnjrMC37prkAkhlqxMIXOi6Y3PuiGfk
+xqFlPXHf8e5Xfo3dPckovk/beAob29jGnrBnwlOwWEpKwsKjsyNIbTuiW7pdaZqlF0ScV0d9Xrri
+auyfveogp1e3+rQUW0eeT6FVt5qsmKdN41LOLeGf44cuWRZ4OccqZQZ+QKQS2qoqmIrLYL+zT++y
+djRJnnUWEIid2B/36CtGNHHA/JJwAWcB+cp990qcDbkfMpKgrQ0qVoplMyBK3O7hq9ZsQo+OdsGR
+HyHkL0m7y6PUndvd22c8aAhr526XGF++wkjeFuUxuy+5ZNjDzGBXKofev0ctb6MUC9DuakgduSRp
+sbxLYt1u9Jnrn+YHPvmCjj1h+7Lr5d3dd95D5YXk4oC4Mx2zvH0DgJ/PZ/zzxZcAOEtnWDEWbR25
+tuftEvo7TuwnyXtMBSiw1uf4vivrnrx7SCncQ8MK1ev3eE6M36YdsFLWbhoV1EvlD6qUvGnV1i3e
+jVoY2/DmGWLlJVpFxVJ0c8YEzOSpeaKNW7GiUFt7XUKhJGe1KjHyZFMxWJ9P2hS3pJi9KshaSlrO
+duh23AUM/Jpe2+Vl4lgly9GQXM1aizC6UCMv8ilzMUhVBz3GK5VfO+qRLiaslTu5lo642lPerTqh
+eN49I/1pzPLND5Y3fCYWBYAai99pESjDX2UllcBEdZ4Rq958ddziQBn1bUmrx5VPR30NcRVQNsku
+QuK+e6Bb50tsT3yF8qayrCBrSEg8n9QKHmxhIWnwO6e3eKl2yaWyUYUaR3QEXuo+b5ieuof37tRS
+LKSUPWgzUOgylmx92O3SFZPvNF1S0dT6DUPfndRgy33XoBVRKyk5aveJBdeNww4TpcF+8Rd/jUWm
+hUfEJPOzE65eEwFMVTMNnPLQzb2MR+VtN7cnESPjPpcId9AuM/YG7ny7peGmeP0+93tucnnf6PU2
+oSjdko469nohZu7+vrc1ZqYelMt7f5DrCvN+4e1/zsOZNDLPnXv9mRd+kESkJ63QYxm5rP3J4ZSj
+lQP3rPPFhTsf6r64cukSO2P3Xj+EVKrL7SznKHcL5LS2lCLdCQVRHnUTBDGgGwUMe26eD+KEiZJ1
+54v6IrSczNxx/bnhXIpNRVYwW7oH8uR4QrZWkk/UbXX0mOS62zii08ML4utBu8NzBy7U7XrQUnWo
+VLgW7vl4c3f9x0HrIqHt98fMzt13J6s54UD8jwOpfD8O2c2FkfEzOvsKTR+/wLbAcPFwyeMHG5jz
+xja2sd+BPROegrVQlzW+qShV5w5zQ6Y+o1YUkLQb+GiNFaJrrRr99niA3yQlky6edvkoGFyw+ZIY
+Ermao76SN6sZNmuIVisCJfOClU+qRNVkseLWQ1eqe150WOZSQiLMQ3/QZem7XbA1CGgLwepHJftt
+V5Ib7jpP41J3j1aTRMrWLOWKJp5HoERc0lNjVxXRSRom3xaedrzCerSVwHzl8zd5+8zttq8/cjvK
+IkvJShcSbPciopfcHD3+RkSaO6+pbt2h1RK7jxCdw2HAbuxczpt7Iy5dd+e+11tTGtF5Dfcwvtsd
+Pdt0EZZYJcPCxYp21+1Qe1nAD/xxpxh48o+mzN5wlHZngmVXq0Oiyu3454stUuteL6sl87vqgs3S
+C52JRIza18ZbDMSXsd2N8DUv67zia1L0rrMZS3lpQ3WBHuwN2BEOY2+0w0HP7dzDUUSmDtU76ykP
+7rlxvaXGJmMSZpKmK4qapRqpDqenFEpMl+fu2g23n8OfrDSmnOti59rZ8bnUlybDoEdLY1mp8cn3
+K+yOm4tO3MY04Uo4J+q4cG047WK85h6QMrtfEk4kFTf2mK6kWXrJx+/I0zFdMgn3vF97JhYFz0Ac
++8RxxLClB74sadLCSdJmnKjlmBZR4xKKqyputbGVaM78gkK5A2tqMtG01V5NHrr3eJGL08KohUfD
+qVczEFx5GcYsGkhpWXGktu3da+7CDUY5LeUZbNniyLgHMmuF+Hro48Bje8v1K1zZdS7lcDBirRxA
+uoppCSvg0cZkEhap3MMYtS2tnlphVyl54UKQspxhRDN+sDvipSsOsv32Q/f5s1nO+MzdBAfXxjy4
+LfGVq5Z0qn6AYEhnIGBU6nIOrdF9DrpuPgef6BHF7pwfLY7wazFNDyzj0QuaLzeHpu5Siql5uaxZ
+K49SdgdQufm8+fL38uqZoyObnbmF+a3Hx7S/S5Tl9phdwbG/fvsWs1rU6bUFdTB22hK0DWM6Ao5t
+j4f0ldupbYCvdue9wBKJym/QVZ8LhkAomJsv7LI1cLkNv8pZyM23y2NsKCj7fffe+cKnOxTupZxi
+BDfPVjmZdDUrN4Us3yn4uuY4r9agnFjiB0SeCxnanu8aTXDkOu7cRwxVPQuiEKswtrYjxm03zzYe
+kKo7+HThvisxM4wYBpPQsMrd9auqGY3G0bl3Qq+lE3yftgkfNraxjT1h36kU/V8B/j2c3O47wH9s
+rZ3ob18EfgyogD9nrf25b3cM3/MYt2LaXsl6qlU5r/ETt2blaYUv9F9FTizklhK6tMKISjz3q0V9
+sdIZLyaXsMhinlIt3S7WF2XWdtTi2HM7UVVmFw0sW1GbVSTuBWtIxV9w+lD6DS94hInzCFZRRDZ3
+x358XhHL5T8YjNnbdiv6qOO8Ct9WlA17y8DDqGsntAVVIgo5odlarR6RzqfyPRBZCgXYRq7Mb/PK
+JbcLfLnj2IJXtcWU0hCY5JS126HzewOSwIUMuTlifurm87KovV5MXiYciYl5Ab4SeGuvTakqyZA+
+qbocK+2CRWeBkfxZmq5ZFm5My/mUw2VDTuNx49InAfjazHUWHk7v0Zm6sW5fOuBYNfv08AT/UBDq
+BpvCt9TqtgYxB0KyXt7rUwt70DYeZs/Nxc3dhEjaH+1Gw/L07IKm79J4m44UxueTOZ6Skr065rLQ
+zRNdj8dxzZGS3Ek7ohDJTJmVVELA2rWa0eqS2UO59quaa9+9rfO8RKCk+KIq6UqLIvSU5G618ASr
+jmwNXtNUZrEiz4laFQg52hHXRbEKCCW8GRBxVVWJBw+W7ElacRRfJ29Qsu/TvlMp+p8HvmitLY0x
+fxn4IvBfGGNeBn4U+DSwD/yCMeYla23Fb2OBbxgOW/hhgS/YSIghjhuFVTgR820n8PBVFkPZ+bqy
+eHINfb+kkkoPucXXxLdbEaVCiUqQ6V4rYEflO8+CFelJu21YKrcxWS+ZCjZ891CtzsmLpAKKlPk5
+j0SG8s7hgsGOWwDGJiTfcsdenelBqu2FClW4m8CxLq7nYUV93uDXq9qwyFQ2LAtM0uhVVnhaAO2i
+ZHvXucGfuuLm5Gv3Tjg9XWt61hyvRQqyfURciCuy7BPuuDAmVkgVxjl2pvJsnZKKsKNYz0n2lLav
+LIgspfTEkuwbEGOT7bSplJ1P64xIy3M1imn6vVdqF86LirfuuTzD6LltjO8eoOXpjFKMWlDj6Vp2
+lbdIYkNPrnbLb1OrtboMLAMtqOPdq5SZKPF160W9Du2RmyNTrjlT70aRV2Riwwpin44EbHpHIlZp
+9egpJLTtipVK3Pm6JJdA7Dpy4ePj41fJ1o2oC+wofPTKhGzhFpAuCaVCZE8Vo3arTSSIth+2aJoj
+ol54wYJd5iWV+jk6oQvtbJBS4+7f9VFKvOvu7+1kRN2IIBVrJrom79e+Iyl6a+3/bS+klvkSTjMS
+nBT937PWZtbad3FKUd/zgc5oYxvb2MdqH0ai8c8Af18/H+AWicYaKfrf1jwDnaAm8CKmpSTljUUk
+wnhVwEQMxt0YCu1Sjf7iwmREUpL2485Fg0ttLJ4EUGIbU6paUUoIxS88fDUSmTJvFOawVYAvOK9N
+U5aVaN+kDL2yHplc28nDkMOp2x0eLwoOdgVCiguyhbokJQATmIhK4Uq09KlGoitbrfDrBrzjdpRl
+VlOoMmKq+qLe7vktfDUBRZ4hUK27Jw6J6GTCuZJ9YbkiGCurP91mJa+p9kr8iXv/OvgmAPfLmLGa
+coaRTyyXOur2GUkKrdttYXW8omq8tIqor6x/5RG23S62PnuXtcBgi2LGmRpJj+WNJN2ahwIp/dpv
+3Ma/qmRl5kHDaeB5WM2Xrx2/XRpsE0qlE2LtupHxLqjXRlHIImv4NqWHMQ9o+c5LWSwBhR155tFK
+tNuWFZFCuo5Ysk31kGUpkpLYx3qNTFvNau68jenSeQp3b63wdW/GUZtI2pZn1TmJvNDwWkSwliSh
+Wnh7cUVb+pK+6ZCX7vuCKKeQDLZdlljB3lHI4EVbrOQF1DuGhcIKbzji0dLB172pJRNN3fu139Gi
+YIz5CaAEfuo7+OyPAz8OkEQ+W52Yuu9RiW1onlVkQgJSm4uSnKm8C069XP9HgLpm8auCUJyIVZxd
+6L3GcU0tzHmhzrlyWuAro5uva6xc3LQ2JKbRVfSo9XqhSc9siRF9dz1ecf9VudRlRUex48HlDkd3
+3c2USEg17vt0mgXNK6naQutlXNzclVqkCcoLoZqw/Z6L6mdUQhsGccaidue0P3axZ+h5F/yC6zSl
+kBZr+7rFf+y+e8WCvsItW7jPVeYxeAoZOgOskHLj0KMvAFinHZMF7nOlGLICb4Svakjl5/jGOZW9
+TpsyVS9F6dHSjVwqflosah5JzLV3eIe+5ihdnWF0reOkfRF2NIjWIkrxRaRYrmvaAiHFsUdL4QOR
+Ibns5q5nXUv99nZ9wdjVv9winbjzHywqSuUXupV30da8KxBT1LF09b2rIsDXg2nWBmItcELQjrcD
+GLq5aFcxM7W473ljFo0i13JNI/Q0aoBq4z2SXqOQZchjIV3zmkiVLWs8Wio1pMpLeemSumn4CC1S
+HSCMU3q4985apyAGqPdr3/GiYIz507gE5B+y3+q/ft9S9NbanwR+EmDcjT/+/u2NbWxjwHe4KBhj
+fgj4C8Dvt9a+FxnxM8DfMcb8VVyi8UXgV97HNwIeXh2QaXdZzQuIVMc3Jd3QrXxhZJlppVzKhW15
+Bl9uX1VVJO0G0zDACkq8Wk5YqCuxrtwumC5zJsrkV7WhkHdQZQWFMvixH5ALruuL3yCbrIm0e3fM
+ZczyNgDlwiNS0qoTdhjuu88lO+rLaO/RFb/DLCuZPHL1+MpE7GZup7S+dtSqpCvpurAT4ul7U7Oi
+ED97sEpI9P5E3k/X9+kp8941EfNAoij3e1SFG3d5FrAUxVq2576313mRfk/iJElClLj5Hl3ZoTNq
++jJqSr/BFjT9HjG56PHW05ysFF7EWPLauSm2igk9lxC14rooi5JwqhBkWTOZOw+j3eoyFIR6Esfk
+EoEp1ANhS59STNLJ9hahuDmTxKdj3LFbWwl+6caabDngmMkKqkgkOcWKVLvn2qaUgs3nixWBwoOD
+kQuDnhu+wze+6fa1YdiiUGI68zISycRPlTwNyopYEHufmpnvrm8x6RMEzmt4dDvnUy+4uR1Gzovp
+bffx5aVZz4A6HH0vopKrW0WGYtWAl9z32lVxAb7z0oCOei3m5ymBEU1bOaKe/rZ5/n/NvlMp+i8C
+MfDzxrm4X7LW/ifW2leNMf8b8BourPiz367ysLGNbezZsu9Uiv5v/Tbv/0vAX/pAZ2EM1g/xTYmn
+2DqfWqxkzDqeTyW9gXYv4lREw3fOXYzsd0ckDd2C51NoHaptQKoE5GxekpUivAwlpUZ2QcTZYY5d
+NgmugKqhIzM1K3EgNKt2pzegO1KnXh0z2Har8uk6gEiCqIc1xyplrWbu791LNUa0YpWXUWQOVlxV
+Kxah27nbgmDXuSVXeTazEUvpQoTlkBzVx6kJlRB9bs997wvdPlMlGiNryMrPAbBzsCA9FAoxhN5Y
+sbEESR4czVlIm+DtRxnVG+58Xrx2le86cB7L9s42ldCLnii+vDzg8G3nEdw6ml80ZkXlilis2p1W
+SFBLgMI2jEeWydphwnfTy5RKfPqBpVDDV9iKaKtEaAIhCU2NZ91YT5cpiRqXJt6MS2PhELaGdMYj
+fYdyJ4C18gge3GGSu5/Pi+KiEzNY+SwlwLNQx6UfJWjjoyo9ArWrGgyDjsNF5EpWd0Y9anU+WgNG
+XGrTo5psqALeAu4/ctDy/ReEDvU6GFEEWtPClMovBJZ6rq7MILjwZI3KkEHVvRjf+uw+RmTC7aKN
+VTm/SDJOp9/Ce7wfeyZgzqW1nFUl4cpnrmBkUdYECh+6SYfenvCcZcB98f1N5TLnVcDOWNjy9oC2
+QhC/U9DANspxj/nE3UCvS0Hpq7cPqaUTOQpbZEoC2rC6gKKGhASBmyajZFEedZjP5cJ3OhcPSKcb
+MnvsBvAvsmPePHQPwlLqQJcvX+aTLzmXcbft05Erem0wxu+6462lJXnml7x+34mNTA5XTKZuJN3W
+Fp++5urto3ZFR4nSRc/9f+ma5arc6PO0YLQr2O1xj1yU8qWXkS0lV++7uRiMt2ipUGSuWG4fuez1
+V2/d4413HADq6pV9rqrKcfmSc8vv37/Ng5kLUQ7PJqQNLDmu2Bm692xHKSdSR64aCvSQC7DY6/fe
+xOTu3PaihKUAUnj2ourS3Kl37k1Z2dtuTGWJJ0h72IUvFNf0uTOMPtDT+Xh+l/mhAxY9ykvu6uc1
+sFSrsteOaalXppF4z6qA4oIyPyed6R7xLHHXLTwDCc+cHr3GeuLuzfb2gCB1CdH8k23q20oODyJM
+3z30YSyylcl96rpRHV9faHouz8/J9fPs8DG+1MQbJm2/06VU/wSDmJXCjnCnRa77gioAVYfer21g
+zhvb2MaesGfCU6itJc0r1rG9cNWwsJ66lfG8lbGjpIwXdEgjlcOUUFtWFYNMSaueRZsSxSpnmqrM
+WFruHTu3+m3BlZepRbkiFn51wQLtGQ9PXXaeHxInDW++GlnSCr/t/n50PufksViEY3jxBbfbhoMu
+l0/dLn13Ke2JZU2pMS3nOdevu/fuvXCJ1dQd+413HBnJV4/ucvKg4VsoGXccZHr7UkQdKlmXHrEW
+KYun+v9LV3Y5qdxudfR2Snbuxh9fyogeStnYazOQO27ErjxbveWgtMBO/RIvX/0MAIPyTW694byG
+b77xKt7vda/vG+lHJmfUor+78tIWRv3/Ewz1yu34s5Mlnn2oayYyvqQNAAAdHUlEQVRUaVUwVeMP
+yxXxVDD17Yx16r6josAIvRgqSRh3Q2jEfkyBUcJ31N+mK2zBigXHj93O69XOxfe9lCM1K70zW7GQ
+BuNpuWIqIlx/1SJUt+1W4byfwSBmuOXKz/M8paWEr3cGvvASge6P7o7Bquzd77YojLumR49q3rnr
+rmuarvh3P/tZN2zdC36cYtTJ6Ic9Vmt3PqfTkkORwk7Oz+noNpTDSndQwAW602d5JtzObEIpDY91
+ucCajxCn8GFZXVmWixxvDZnq520Ts1hIjNQecaQLs99NaCeKP3X6SatNRwuEV3gUynCfHc64e+Li
+1qXnEeHc2dGeYw2a5/lFNrxazOmJ6KTIU1aqPmx1xvTFinQiePTD4xP6AvoczVvE+o5B2cFY90A+
+vnvKvUfihxRuolzMCUX00t7y8UUdH9EiFY7eCKfgn2ZcUsVl6fksxAacZ8fMcK7v50ddAgVIjx6L
+vfcHPs+/vXJz9cZvnFENdTPevYwnzL3ntyjVHZro87O7t5l77iF9dHjGwci54i/uDGjj3OBpfcaB
+Khg3dl3leT2vmKzc+SwWGfN7Lr+QhvaCACZJdvnM0OU23h6+BcC9s1NqzafFUAhYthdf4p6wDPlk
+TVC769Dfcsc1Xsarv+4Uolp+wlxz+NzBlOuBE54NKkMuwFGv7eYiivtkK+UzVpCm7hhvvXPKsUBm
+2AqjtPgPvui+6/qgB2e/7q5JJ0Xob0LjYYTZMOLg9KYVsSDfo3CbSe0Wltf/v9uuDRyoQsNDxfgP
+1eOxLmP6UhHzFhVn2iCm64wzAaPuHy5Zi//zZOJyEknERR7l5YN9Bjtuob71YMKw50KbtmmRpx+s
+dXoTPmxsYxt7wp4JTwEDxjNga2K5l3XbgrroVqslp3fdDnT5pRFXtNNvKQGU5GBazr32KIjUUVnX
+BYV2mihI8CQrNlITULq1w2LmVus6SukIzluY6sJTKOwSry39QDVUles1uWS/PUl8AeShx1oJo0tm
+xI3POI/mPFeGeG6xatTZ6niMx5IU70N7IkqzPRdSfDKHTJLz00XNSjBYW0MsVl8/sNie29r21+7v
+3VaGkYxZr+WTn4lfcviQqnBYgV5tGO+5n5/33Thm0RKrrsb2Xv+CPqwfjLm0I1KTdcD+gTvnSJ2R
+V5/r40fyhOya8y1XqbB5RpoJh1FlROJkuLznzu3B6ZSFILpbo216wmSMhhFD7ZrFKsVX1+Xz11wY
+8PmbV7kRunMb7Vwh8zVv+2P2Yve5FoZKysypEJ/Z6pxS4d9ONKKz7+YltwnrmbvW63qJFUfCi8+7
+8623t7n0Zfe9bz2aEyn53Q9DrKDgpfglvcQn0XWasyY40jUJE1658SIAV652ufncvwXAcq6Eajug
+LR6HKg5YCsuCV9GVqvT1K/sUE/eeTuJeC+w5tTy+IitI+kJxRgNiX+FWtGIx+ZBxCh+FecYjiVvk
+Rc6qEOlJ6IHyC3VRs5TaTi9YsavSzG5L3WLe8oLPMTOWSMCaKvKZzNxD22qXtJSpHukhb28PeCB+
+PmNGGMm5T2cBQeMaBj612q8bpZrSeEyUZc5mPugC2HnO1dqVGXdvblEI+74jXcZZZ0WWugdobzti
+NBAF+MIjMBK2cUMiLQpOT1wZqzUMGajUGYYJHRHIdsOc6ZljhbI7KsFthZiHbhw3D9q8JYakYn6N
+0koZy48JpHm4PXZz9fJLvw9Pbvu1723TFkCozksCxfOvjF6iI2KYeu7Gv1VEjF5xClirO6c8EjV8
+tjohELg193yW6rvYe1NKT6Ghkf7pDEf0RmJW2jpgeeDc3TfTGlbuOC+M3ee+96WXKFVkIPAI1EZN
+aPFVOs7SCWnTPq5qXFlbqsrNy3B7REvZ+WB7i9YVlfqCkrFajocSx12lD2n13blfK1oM1Sdx23hk
+Kv3WAtOF7QFe5ebn5GRCLLf95qURf+DTbo52X9zBqvX/dCUIc26pQrdIZfOC+cS9Xvol7UDcnX1L
+MnLn9ukmR2AOqAt1g9ZrsjMXKg/GHUqB9qqyJAs/5C7JjW1sY/9m2TPhKQS+YdwNOC8Ntfjy1rnB
+azrkDBh1PvpRi5745xLVX/v0qOKG+7Cg4aLqGogFhMlWc3qxOtG085U2JWpUlP2SWt0wfsCFuMw6
+97HGvd6Swq9X2IsGne5Bn30lhrLWMWbk3jvoJKSC4K5EzLHT6mHVMDXaGl+Ae0wEgd4bZdrBRwmh
+auzrLLsg2Eja9qIBya4XFIVz5+uxqOwXJUvjdo/PvrLNT/+CCwk6+108VUNW2ZpSLaimFrVbZ83l
+Tzhvo7/XwVdZppyckku1eOdyG1STz2YOV+Bvjy4Sbt7OkANVNQgSCu1Wed3m7h0nQ3coD6Wsa2Lh
+O66PttnZd3Jyw45l56rbEaf5NpNHkoHX99bFjPHIeQdxd4zXkRp5HrKuhMlIQ6qmi1MhZrVYkYti
+r9uybCdurLvdDoHIfKJOq3H6QBwYD84KYnWl7vldPr/tPLb9rTZLhRqpwqdeLya8IkDT8TnzifP0
++q2EXVGuj9sJy3N3nr46eKN+TL5wHlFJRUvzNl3ldCQ61KagLeUiXwQ/Xru4gHMXacWx5+5v/BV6
+dMizjMD7XQhe8j3DsBOzmK9Y6YGu6xxfD14UBwy67ua9Pu4y0hh3FSN2TZu5yC0Wy5wyaghRfbaF
+7Jqua7pBw0so3H66Qrqs+J5lIQShzS2BZrUbBNRBo+mokt6w5L2aO+PL6nZb7jMQAYZHRbuvh34u
+uvF2hdUn2+POBZ29sTXVSlWQY5ex9rIlW0JVnq1TbKOEtKwIpQrkVW3GW0JpqiQ7O1kRP3Q3x42d
+mNXp/+lO0vsB0lkjsOqzVM+EIi3iOiI00iPICkIBi4reVSIhM0M8ELI0kEpT7q1ZnUqPsuzQUek0
+Dz2CRmx2ljM5EfBm7sYXYdjquAesNdrGStehPxgRnOk9pU9PRDq5SHZsWhH2GlalIVYlwiosWIls
+0QY1kdiQQl273HosJe6blBGDA1WrQgjaAsZlJZXi+WLZ6JEaAoER+0nMTsvlYpZnx2Rz933ntZSb
+8iV9hQ+0dihG7ny2koRQ4e3y9D5R1y0cw7lbKIK1hw1EjFN6+ALUtdIULxUattPFV2XDax7+IsdK
+pcrLArrirlwdzagbqqplgH38wXIKm/BhYxvb2BP2zHgK/U5EQsZEdNrz+QKjbbwTxTwnSrAv3LzJ
+lW5Dfa7MX2ahdjtwYC1lIwQUFlxRgqq1juiKWrsJRZZpiPX1s2cpVVevqInFmVfENbXcr6Dh1rMV
+a1G8m/mcx3dcgqfsD6nUBW6KnCh2GXVjlTiKImrteHUOdS74aXtAISKXWSbAz+EjhlsOsLXV3iJK
+3A5MNYfA7cD5cooXiNvv2H3XN2+dcfWSwESjaywnrmqzff0OgVzqeF6TiK251g4Wbo3x01ONrw/q
+FgzjGZ4qLXWdUa7ceyphGmrPcnbkcAqxNyLRblUtYhDv4nyRc+ee2JyVOIziFlsan1lnTBsat9ww
+WYsLk5K2SEaQQrPve0SedvnSo5LCV0GbUpTrodeBSNyFsZu3Vjcm2XHfu1rMWK/dmAbdPn7Ta5CX
+GMnKF6GIVVYea+3Qi5OaOztqvPFbzCS4cuqJYi5fMxy6nph6FGHO5G1kIbNHbi7GJsP01ZsijycZ
+Dy9UprxWgvLghGGNL06RIPJpCy5vlHy05dyxngPZ8ohQVIDt85S5CIhm9Rm3zsR5+T5t4ylsbGMb
+e8KeCU/BWkuVF8SmZJ03NFk5CDewf2mL77rumoAutTp0JQyTa5XMDVS1ugiTiEZKOopbtOUdjK1l
+LGGQLFZ8lxrUGElWlBRKzmTGuyAM9fEhbPClbvXtRgmV5OZevX/KvRPpKRTQyV2caAYBVSldAH1+
+dX6GJxzGanaKkVcRtYbUXUFps28AkKYwW7pdOYktoaduv7hHUboYPiUlPRLKrVbNO5nwwisORjvc
+G9PqOqwA7zwP4RvuO3otzEyq0Yqdp49C+qJaO/NOmcxdM1Zra48qct89PX6HxdQ1UG31XF0wzc95
+LEGSF59bUKZu9y+Lc4yUpB8dHvHN01PNuTtuvz8m0d/fOrrL8vgWAN9z/XtYnkpVez7DEwWeb9wO
+XC1SrMqo+fQ2pRKfq3qB0TzbNMcopvZEc+d3DC2VuP1BjKf7pUojgoaE1tZYlRcL/e8vCvKG4bmY
+8yAVh8L8nM/fdslTf8vt4ONuj+f33JiK8DJdNUxV9pyzczf+1jCifSIkp9Ct62xGu6/u2awmbDzT
+uEUl6H29tvhKOtpYatcYjMrIfuKTSYim6PnUHYn2mBanpqFTfX/2TCwKxoCJarIywOoilnVNN3EX
+dm+7x/5lYdiHYJWg8ws3SVWxIhfc1ZYrrBKQnoFQmfrAW1LkqtnrhvHjiEocVkUNtWnow6BUVWKV
+l01LBHGli9/eZyHY7TiN+fJvOChx1A44F7R1WF+iUsfdOmuSeiG+qMPrqmY5ce5sHdwjFTXZ6kQP
+RD6Fc7fArHsZVvyCXpRhC/mX/gmnM/fdx8LqBx2PrrD44c4OVjKCs/17dDo39csadqRkpOrF2j/m
+fOkyasu3W0xU27aTBacrQbrzN2jVLvP/aS0UZeozEVSgaLfo6Yatu+aihfv++THHi0NNooBQ40sM
+dxzsPKWi1uo8KybMjTu3tMoJqwboLxq4KKVWcq1Y56z1cJtoSCgcShnVFEoK56KHK5cGK5c6HHbp
+qIU9SnxsQ+bjeVSKDiqpNB2XE6rQnU9mDMXa/fw4XbMSJqOvKlI/iBhcc8C6thcSDdwCsZzDg2MH
+Q4+qLnty/5m7sHKVdcl9N6YkbBOqSmKNT+g3kgcTimbXMko6e2tqtaL7EZQ6t6BTEithf/dwxrT+
+YIvCJnzY2MY29oQ9E55CVdUspyl+nROYppPPoy2KqkEcMNYOmy2XhGpGqpUAypdgVAryQp9YYi+d
+TptQXsHJwpKLcKVsyluVoaqaen2B3zRoeh65UGdFXuKrE68hJEn2D/C12nf3Skol5c6mNYfnbvfY
+bncIdB61ZM1bUYegYW32zAWJzOzOkkI7XqKOw7LtUcmtnS5rUkFpMSsiEXsWa5+eGmLe+DW304y7
+HoWIOfKjJXVTTzvcYx45z2T3xi6extTSmNbLgvtKVqbhlLWauDrXY4warA6u3uQzHcfmf+33OITe
+yb1HXO8qYTrrUksYJ21bTsWz8PDwPqmoxGK/oSsrydRI1I33KGJhDM5T1trx0iwjUmWtVjdnaQ0L
+JSK9sI0VPZzxS3KVQNNsRaHGsrm6ZK21dFXWHg2HJD0lbusVtdi26zSlFNZhce5chun8lPWx7jdT
+cltQ48fHa3piqw51H5Ku6Ko5LmEbVJIs5wseyiucxwFXDpxrlaohql4tWWs3D5IhSatJKHpU8jKN
+V1LI80LHyPIVpmq80JJQMnTFaUarJ2Gj3KM//V0qRY9XUaxz4oZ0JoBYN27YCZguxERcBHSaVjbd
+EKEPychVJ4r1Givq7DI32KoRiWlRVMpwrxsh0hK/EjbB8/GbjHsNmTALeV0TNLVn5TC8Xoinh/Fy
+cJP22KkeLR9PuCWSleutjMHYuY+BdeOovYJSLMHtehtFNvjdEqzyJ4IGdyZ9Ut3QWeFTSO49SSJ8
+udL1+hRP598wNlVexINTd6PkJ2+TCWrduTKh4wlDcbokDF2OJu25nMNsHbAtRp/arFgJ1z+qEnaG
+LlQIEkNVuS7Hx19x7vByZhlfU1t7dUQhvkZ/2cfTol5WAVPh/JfKEYzrEHGNYMMBvliK8urrpJoj
+P/IaJnbWumbT+Sm+Qo3uVgwt996qarHSYlEUHpW0OXN1uCahx3igEDRfs1THYV2XtMQIja0wuJAt
+lXDOydmUUrDidV7zzrHL58wPV/S1UaXq9pyvUtYLVRkuB9B3x7vzq7d5LLrxKyUXLn+gipLXLrFN
+VSoBq76SwBo8dcpWVcFSCmehuEKNZ7GBqj2zQ1ojdz5bnTa+MCdnuyeclBucwsY2trHfgT0TnkJZ
+1xzNcyoK4oYfv+3RHYlPf9xhrkTc4uwRvb0GCSbmYFsSqW5uijW52GuXi5Rlrcal1CIYApkSVbMi
+ZaZqR2ZLROEIcQDSeMA3FHJLs1TdiStDhTJ4acCLknD/UnXC5ERoyc+38BvGZIUi8/mCSPDpnAn2
+kZKHXodMepXzSgQwucdi5U7osDilt+d2BN/2CUVD5/V9QsGi9553O23ZWfHNe243O1kf0pLEXDTr
+cC5dyTq4xtGZ2x1HKqysox7pwO1K02rKrHCTtZX1WTY6Encf8PqZ++6JXGrPg4MXXChxfW+LKHFz
+QQvMyLnlRTu6EPPJ9L2m7WGEN1jVPWxHFYXzNSMlCT3PZ6Ks/dHahUGLZUx3KC/OzqkEm15mFXUh
+0hM8eom6PCXD5w9qwqzhL6g5P3L3U136jPfF8hyPyQt37zQQ9Vv3ptQ7Qtku28zuC7pdleTyemrf
+vff80GIqQZvHn7xArDLqc/yqCH6iQ56fuWRkX3DtKi/wgoaB3L9gfq7qFL9uMBSGPJX8YNsdN7RJ
+A1kh6AbUUvmukgGTSN7yugUfjGPl2VgUfN/Q73sE/QQrQEh0DrEIVuu6IOm4G2G6PmO3EIGEkvDz
+d484EYAoKxcEmWbBr8kFGV2ZFGsbMk69llYshfMtQ+9ChSjwLYGyt5HnsZSmY6YehipekeXuZh11
+b/CJ73YP5GvTe5wX7qEhfZlqR+SZCjtWk5z51H2HNR4zdcnl9UM6lYvbU13lagQN01M+q+gq47y/
+370INcoi5PSOO/9R37mOvueR31DM2c4Z/LQLR0yyw6jlsv12OcFvu+7K1Hc5AlOdEnTd/PQXLSaK
+cV/90pc5E7Cqqkpi3Xh17M79UrfDVKSjv35yygt91/p99XMvXgjHhJe71CKmbVSvfD9k1dF8L8/o
+9Vw484mdESOFOY8WOf9CJDkniq2PWHDQdm55NAixuk7L04zYdy5zEOcMtgXkUQUrMjFrtVMvw5yT
+h25B7rbalFoAKr8mU8j67l03prfmj6kVgsSBR8PCYm1NkWuR0WvLcEGtXpt+lBFLmelzn7nBV15z
+99zX755wtfcaAJ+6+Qn3udkMX0xJaRYSzdwcz2xK1JWu5CIky92iMKjcXPnd/ELuniokVYdxZauL
+HNskXZB6m/BhYxvb2O/AnglPwashWVlMv6an7PW+8QjORb1tM/ZFNZWdTZgcOVzA4Man3HtfvEJ8
+V6IhVYu4ljBM5HE6cQmndLGiEChkrU5Mz9bU2v2TdkJbwKLFasUa0bnHHmvVtxt4dD8acaiafi/J
++NTArdwPxpc5ydzrr996k892HKVXXUoeLe6QC7ZqY5/ksnO1wyIk6rqd/u5dBxp6eOeEWArPMYZI
+EFc/TPBR2GF8dm+41++/4Xa27RstLg1EprKesP2C82iyx9ssxMHY2Q9Zih4/V8NQMG4Tl+58sihn
+v+9+9vKEhYRq4p0VB5Uo6PuqzmQLFqEqKlHIaNT83eNEjVLh5Ii4bJKYyqYnKT0RsgQ7W1jpQH72
+0ueYbrlr8pVf+jot8RWm0uv0co+84dKsR/gC5uxttckXAv30wSpMsQI35Yv1RXXCT5e8cNmBoVrD
+HTLPzcUkO+JUhCvffM3dY6vTKbWYu+tuQCRms9IarGjgrby7eFJciBYVdommlis3LnFD5Ctv3j0j
+F3GMkYfRjiNs06zX7RI0lKATy8NH7vqNTchoR4lJ0d5XJoNCHZdFiCc+kHy6IBwIC/Egpzr+XUmy
+YuhGISxy1gKrdAKfQJ2BnrXMhJqLYsOh+ArbY8dVN+ztUkUiRJ3WpEIericVU4l5zjGsRcY6U3/F
+LJvhN/KDscFrqg+RRy9XHBnCQq+XKqeZuOLAV17DL4jUf7D76ZfhnkMk3n44Y3/HPRRdyciX9YJS
+N1CYtfA9sRj1fYzyHP2GbSnwqAVc2QljBtIr9EowgfoBshN85U/GAxefdr0BtbQWt8Y9Jmv3AMXX
+ctpJwwB1hoc79kngQEWT+Cqt2rnqgWkzviR04NYOrXM3h6fnOd/w3MNS31Frcragdd2588/v9/FE
+Yrg4OefWN90C+Suv/yq56MmbXo00vEq8o0UxGFDO3LUOWifsSul3OPC4oq5LVKrO5q4EDWAXBb6A
+Q1FssUa6FichS/VopGqdDmNLpPJzP4xo7Yvhy0uxgizeuf2Arz9wYdXjd94FIK/ANG3m05yeKhV+
+BkttHEa5jFYrxA/cmOYlVGvXd+Jlfa5suzk638+xjbLUQhIGcUCoUCvpdWn03Ht+Qan3RolHIKIa
+2uo4XXpYVWryOr2QsPdnPplIgBZpRhB/sKTCJnzY2MY29oSZb2nDfownYcwxsAROPqZT2N4ce3Ps
+fwOOfd1au/Pt3vRMLAoAxpivWGu/sDn25tibY3+8tgkfNraxjT1hm0VhYxvb2BP2LC0KP7k59ubY
+m2N//PbM5BQ2trGNPRv2LHkKG9vYxp4B+9gXBWPMDxlj3jTGvG2M+YtP+VhXjTH/1BjzmjHmVWPM
+n9frY2PMzxtj3tL/o6d4Dr4x5teMMT+r358zxvyyxv/3jfmAEsEf7NhDY8w/MMa8YYx53Rjz/R/V
+2I0x/5nm/BvGmL9rjGk9rbEbY/5nY8yRMeYb73ntNx2ncfbf6xy+Zox55Skc+69ozr9mjPk/jDHD
+9/ztizr2m8aYP/I7OfaHZR/romCM8YG/Dvww8DLwJ4wxLz/FQ5bAf26tfRn4PuDP6nh/EfhFa+2L
+wC/q96dlfx54/T2//2Xgv7XW3gTOgR97isf+a8A/sdZ+EvguncdTH7sx5gD4c8AXrLWfwXV6/ShP
+b+x/G/ihf+W132qcPwy8qH8/DvzNp3Dsnwc+Y639HPBN4IsAuvd+FPi0PvM39Ex8vGat/dj+Ad8P
+/Nx7fv8i8MWP8Pj/GPjDwJvAZb12GXjzKR3vCu6G/HeAnwUMDsgS/Gbz8SEfewC8i/JI73n9qY8d
+OADuAWMctP5ngT/yNMcO3AC+8e3GCfyPwJ/4zd73YR37X/nbvw/8lH5+4n4Hfg74/qdx/T/Iv487
+fGhulsbu67WnbsaYG8B3A78M7FlrH+lPj4G9p3TY/w74C0Cj47UFTKxt0O5PdfzPAcfA/6Lw5X8y
+xnT4CMZurX0A/NfAXeARMAW+ykc3dvitx/lR34N/Bvi/PqZjvy/7uBeFj8WMMV3gp4H/1Fo7e+/f
+rFuyP/SSjDHmR4Aja+1XP+zvfp8WAK8Af9Na+904WPkTocJTHPsI+OO4hWkf6PCvu9gfmT2tcX47
+M8b8BC6E/amP+tgfxD7uReEBiJTQ2RW99tTMGBPiFoSfstb+Q718aIy5rL9fBo6ewqF/L/DHjDG3
+gb+HCyH+GjA0xjTdqk9z/PeB+9baX9bv/wC3SHwUY/9B4F1r7bG1tgD+IW4+Pqqxw289zo/kHjTG
+/GngR4A/qUXpIzv2B7WPe1H4MvCistARLunyM0/rYMYYA/wt4HVr7V99z59+BvhT+vlP4XINH6pZ
+a79orb1irb2BG+cvWWv/JPBPgf/waR5bx38M3DPGfEIv/SHgNT6CsePChu8zxiS6Bs2xP5Kxy36r
+cf4M8B+pCvF9wPQ9YcaHYsaYH8KFjX/MWsmHf+vYP2qMiY0xz+GSnb/yYR77O7KPO6kB/FFcRvYd
+4Cee8rF+H85t/Brw6/r3R3Gx/S8CbwG/AIyf8nn8AeBn9fPzuBvhbeB/B+KneNzPA1/R+P8RMPqo
+xg78l8AbwDeA/xWIn9bYgb+Ly10UOA/px36rceKSvX9d99/XcRWSD/vYb+NyB8099z+85/0/oWO/
+Cfzw07zv3u+/DaJxYxvb2BP2cYcPG9vYxp4x2ywKG9vYxp6wzaKwsY1t7AnbLAob29jGnrDNorCx
+jW3sCdssChvb2MaesM2isLGNbewJ2ywKG9vYxp6w/x+UAP6dGLoegAAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3483... Generator Loss: 1.0495
+Epoch 1/1... Discriminator Loss: 1.3219... Generator Loss: 0.8450
+Epoch 1/1... Discriminator Loss: 1.3759... Generator Loss: 0.8250
+Epoch 1/1... Discriminator Loss: 1.4419... Generator Loss: 0.6523
+Epoch 1/1... Discriminator Loss: 1.2803... Generator Loss: 0.8917
+Epoch 1/1... Discriminator Loss: 1.3013... Generator Loss: 0.7574
+Epoch 1/1... Discriminator Loss: 1.3968... Generator Loss: 0.7108
+Epoch 1/1... Discriminator Loss: 1.3129... Generator Loss: 0.9058
+Epoch 1/1... Discriminator Loss: 1.4153... Generator Loss: 0.8783
+Epoch 1/1... Discriminator Loss: 1.3831... Generator Loss: 0.7992
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvdmvbHl25/X57XlH7JjPfO6YN29muiozy+WyyxMtg9pA
+W02r3xAgoUZCsoTUUiPxgMUDTyA1L/wBLYHEQwuE1EBboqHptmw8tIcq21WZlZVz3iHPvWeME3Ps
+ef94WCtOOUGmbrkqu+9DrJcTN+6O/ZvXbw3ftZax1rKlLW1pSxty/lV3YEtb2tLLRVumsKUtbekL
+tGUKW9rSlr5AW6awpS1t6Qu0ZQpb2tKWvkBbprClLW3pC/SlMQVjzN8wxnxojPnEGPMbX1Y7W9rS
+ln6yZL4MnIIxxgU+Av5N4AT4FvDvW2u//xNvbEtb2tJPlL4sSeGbwCfW2s+stQXwPwF/+0tqa0tb
+2tJPkLwv6b3HwOd/4d8nwM//ZQ8nYWBH7RalMVi3BYDxK0wuUkzRFGAbANZZQVmWAGxkHAdoNi+z
+YIx8NBiazVMW0O+dzQOA5zibh29e2FgLKkE11v7g3ZvBHdwDPwAgjANsXQOQLzOqKgPANxWVle99
+K+1Z2+B7rrTbCgiCWNqoS/J1CsB0OQegzJubcQSeB56j/fUw2jeLJatlLqi074BRXm9dw26/c9Pv
+xupYnQaKRp/fjM6CKw26xlAW8t6ibjD6M9daykqedxx51gt8PNybObyZWcdidNw0DY32c9NfU0Kp
+80NjsKGn7+tSN9JGbcFo/5xmM+YGX9tuDHjaOceA40g/jO9g9NU3S23BevIOU/GD67BxaFxd68rS
+aNu2lgktqPF1fK4Bx0g/beDRLBfyWRsJvAA3kBf71scN5HdO4GJ03amh0f2LK99Zp8FY/VxVNKW0
+nddr3MrbdJMglP3ixIH+PALTbKYea3R8uDcLYRswrvTpz/78O1fW2l1+CH1ZTOGHkjHm14FfBxi2
+Yn7jV3+FM98jH3wdgOhgTPBJAcDj1TOaWhbgnY+e8vz0AoBaD2PLc1jppqGyOLq5Q8cjtTLBpjY0
+OmmRr4vsOozaOtFGNiFAXpZUpbx7nVdkjXw2ykD+y7/332D3bgPwypt3yecTAB79/keMrz4AYD+4
+YFwvATgqpb06X7G/2wdg9LU73Lv3NWnj+oSPv/seAP/b7/1zAM4fLwi0n0ejAe6gDcCwu4OXy5ga
+U/PB5FT6Jl1gTk2AjKno+/wnf+tXdb4b1pl8bzor6ie5tO1IH11KbCKbreeFnD2T955cL3E6MjHd
+suFivAagHfkA9O/ssuMMZQ59i7NRR6OGsJBnmmzFen4OwCfTEwD8C8tZLm3bzKW8MwJgcPfXmK9W
+ACwri0GYZWslB6ly1hwm8t7Ms+woY41cl1YkDNA/jvAXyuB0h5vSoRjIvPnT+ubw2iwk68r3y6uK
+1Vr6VC2mADxrJuxbeW8ndIj9PVnL233y3/sd6UcYAnB3eIfeHfl8WN6mczuRubrTx9mRzyxdirPn
+0qe27IWitSCwMv7i6ozl2TUAj2Z/TnIlc5vHDrfvf1Xe97W7sh7d18CT+XGwVK5eQqYLnoy/LsBJ
+ZO/ESe8JL0BfFlN4Btz+C/++pd/dkLX2HwD/AOD+wcC27zgUyS71TBZo/nxJWwf8yqjLBxeyUa5n
+c9JCNnSvI4PdHSZ0lUFExtCOZLKtb5nN5XdOU9PocAsjbdisxPH1BvZ9mkq+D0wttyngWoMp5Zm6
+kg0f3x0xGUnb06bi6Xc+BODs029xpFy8yCxRKc+He9KH3vGQoBJJyHdqiqVIFabxoZHPe1Y2/Dys
+2O3s6zgMTbW5wQpMRzbYbDW9kWiCgfwdFD3cnvQtDDxcX+Yq82MapI16uSDqyVgPfZmri8U5md58
+vZ5H2ZbNPX92SkihfQ7od2STEuvN7RmclsxPz++yduQw2coQ92W+fXeX3JsBsJPLIfbvlHSuZXxL
+v8BLZF7WHYfTUuYQv8Rb6sdIxn+nM8KPZEytynI0lPcl/QOM0RsYi0mk7XZb+rsqZ/iNvDfqr0mS
+I5mXOmOh6xD1cvrdQwA+9mTNzLnF7cjeCt0DnKH0Y+gWnO9Ia6knH5aBjx3Lgd570CE6fBWAfLbG
+LuQic1s5ZngfgOmnnwBQxTVRXyVM26XIH8s6zUM4Fsnx/u6v4ezLJVGsZPzVICV0hWFV1YRmIy2G
+czzvQP7hNjgqNb0ofVlM4VvAQ2PMfYQZ/HvAf/CXPZw3hs+yiKP7t/FV1Kk/DPio+gyAS1uTLlRl
+qDKSOALgV96Qyf3KvQcUmWxck8S0d2VY2bUlX8hGX1KyVGby/Ew27mo+wVGx3Hd81qkwoaxqqKy8
+I/YtjYp5zeYAdu7w6kgljFnO/EwY8KOTdzlHpYPhkD09OMbvShueg2OkjebUcpW9C0CZTslm0qcH
+sTzbP3bxh7Khp4sVjl55Nm+ofXmHm1bcsgMAUuEDrNySXi3zE4UNM2UmSa9DTxlOOVlTmqX+ThiM
+w4B1KhrfpXPGeio7bNcLKFbyO69XMDiSwzuKRAp1ancjBVMmFfFamIyp17RDGb/XbrG/lk3anch3
+6zBl7Mna7OddvFAO7GemS68rcxFNG1jLM6Una1c6DZ1axtxNYJjIoYiGLVqV3I7lLKfRuXfb8rdT
+jfAzvUn7ezh9mTBn5XFdPwYg9wIWkdE+CXOIp4ZKZfGJN+V2qpygWTFOpe29V2Vsx5lPXMj4eXrB
+eVv2RXM6xW/J/kzaPcId+dxqy1o3pqFYqXpcXBL4Msdv3P0m8asyudHeEc5K9mqpe8heTckHKhHU
+BTSqgtGlcmT9PC/EOvL8i9KXwhSstZUx5u8C/xRwgf/eWvvel9HWlra0pZ8sfWk2BWvtPwH+yYs8
+67uw161Zn1zQ2/spABZvnJKciAhbnWU4RsSoV27t8uZXRTP5G98U22Xk9WkqUaqbxqPUG2HdLqkr
+ecfFfM7FiRr8RsLhs7imVFWCuaU1lNsjXUE1F+7qNT5q96FUo9err3S4XIm4N+OSi1PRjBJnhu/L
+Oxb2lENHbrS+1VtpPsZU0rcs96nXVwBMrzK8UL5/5ZuiNz70OyxnYwDGi5R1KrfndWXJ1/KsG/u0
+9WbzSrlJV9alDuUmanoOjtpByvMp3rHcTEWWUVci6Syv5SYuioxeLM9GfkTTl/GbIMIL5BbvJgOS
+obyjHeq7qpQMkUzyRQa+tB20Ckhk/OQerYE8M4hFwsiWBUZtJuRQ3pe5zSZr6qlIhUHPUKlFsOuI
+RBNYH78tY/UDjyBUw/RsRSUCDf7AEGg7fqBGwEEL35U2ovaIppT1s26JE6huXxoiVRVNKc/2nZpK
+jZmX15dUyD7M9n265yq9TUU1it84wnz4sayZF8E7IukevO0SZyqRDlq4qsYFr8o+tNWKbCX9rNYe
+4UORPHzX4gWyd4xrcA9kzTxVY03k06QqvXbb1KXMvRME2FpsP9ZfY5E5elH6V2Zo/IvUNFCsLDvt
+hrML0c87ozmHx2J8aZWXOI4M7Gs/dZtf/Td+CYDDPVEflmfPyULdgJmDUcPeeDZlVoiY7K0qklA2
+27iQyXMjg185+rkGtS8s3YhSOcGyzHE8ORQrtUyHccbq0RkAv/m//kPcuQhB63XGcab2jv2AcCTt
+2LU8WyxS8pksVng4IlCmcXznkFhFycHrwvDqaUU6lc0an15wMpONVF0tb2wqU1Oz8KVP84Uc4mpe
+cZlJG5GbsDq7BGB/eMBiKZ+nxYr5XC3nF7Lp1q1UXALAMKjoK9OoWobOjqo0nSFJInaALJdnncDg
+q+HT4jIrpG3P5tix6NFJ1sWoHUB5NI7fkGzsJ86KyZXMcXn5OV4jDGQ2u6Kv9gpHPVEd28FVJXlN
+w9VU2ojyGLcr697zhkTqXYiMMBODAd1DtrG4Vtqo6hwvUobju4R6JGrdF/MyY3kuzNtfGS6sMADP
+G3D5saz77kDW7D3GtBGVofqjmMNJD4CLk4rdr4va4V7PsJWohe6nOid3h5iJtBdYbrwh9bCDWagK
+Vneoh9Jnp6u/b0Ks2j6aMgT9nS2vsamr6zTD74r95EVpC3Pe0pa29AV6KSQFx3OI9zpc1CXzltwC
+fTNgYeQWGHT3iWK5VcNRQOiLEchReTGM+zeGqrJcUquRJe4mpKci7lE7FK5atdWY2SxgjbTXcUPC
+SLhr2zpMArl52ya8wSS4uTy7aODbf/g7ALzz5DPeUFG0tinXKoruOiM89SGXhXDwpH1Av69tfH2E
+V8k4+rf2cT1xdTWN3OB1dY3nym0dh9CzcpstmxWZL/2YrNfkMxE7rzO5MVZZjgnk2d40oG5J3/Ow
+xCTSt9VqxvOxuAhjdenFtFiFMuZ8XtBRsbXbT4j3VWzNPFwFALihtGvKgFolBePW+Kqu5JVPula8
+gZsSKS7C9aRvVZlRq/vP3TUMHVnL3K/xVBLyRzsEKrE5qrplTkmwkm07D1JWS5FYur02HZUEmnZC
+tZEEQnmv63s4UaDr5GFdVY8IbnAPxqmpG+l/S5/1bchEpbt8cY6/L1KTZ0tsV57duMOfpZYmlT4s
+zBXf6IuksJPtYk9VbQxXDF95Q+ZcPTm2bVkovmW0P/wBpiMbU1zKHEU++LVInmEge8X6BU6ork7X
+ww3UVTtfbCAn4CY0TcGPQi8FU2hwSE1EkeXcc44B2G07hIHqXt9o8VB93vPPxlSXsqFztSyHfgsc
+WcQmsKwbOSDtVUCu+lSPgEUo72vPVX+rLQsVg9dOQ+jIRhr5EY7q3xfXK3S9bvANT89W/NGf/zEA
+7pMP6Y7kcKdNCzeS98XtlEgPsmnJNPcHLsO2qETt6GfxjoXROb0YV9vY6PpOWpPnshkDL2YY6WYL
+Q57Pxe2VT1ekirha6cFcrUta6q8umgoUNLMqYGctB92e1SyulJGpu9SvA9xrtU90fZyeHMKduEOc
+S58dU9DooQlUnLeZpaqkjcuywqqhu1nWZOoxWIUFnUbVIwWhGadh5ukFUPUIa9ErYi+hVpXojjug
+peNaIjajeVVCqmrZBFbKpMygoW9kfMEiwaobMVPYRLsMIJPxFXZOZdT2YUP8DbbCg0ZtO34geyGo
+mhsA0XmRcZTpHGYVUaYMLpA1P/y8xEFsGZfrBu8VYcLB2ZhhR9yTdeqTN6qOPBKbUSv5aVx1Zdf1
+ArtUP2xesT4XvMhVUdNRz9RBW5iCiTo0nvTHcwKMUS+RiZgVYueK6eOrevSitFUftrSlLX2BXg5J
+oa5Yzi45ee5hHgr33OkccEvRY51WRDxQTjqbkDjS7UA9DkES4zhqcQ862Jlw+8qb0Q7k1tnrdVlW
+CuTpyLvWGSRqbS5qQ6aGxt2OYc8RrjxfVRSKsGspJ/7DP32HD94To12rXZF5Oo1ecGPMDDs+kd6K
+3UBu+bjlEe0oN08WuB0VAxcZtRHvAqWIlF7bxZ/IDZzHKZ76/Htxi30FL13MZ2QTGWupOI1pWTCd
+icrUaWrqRvqzmlWEoeAQsrQgVmjuMFADnh/jtqS/u0mMuvGJhi3iWNqo6xaVekQahSUHLZ/IFyt6
+0syYq4gbBD9AgLqVQ+mrC8eqVTwIydQImJYN8YEaK59HhFZBRkMXq5b6aKkYi3rBTQyf79EK5GYe
+tGOcSPo5q69J1uolUXWs7Dds0MWe5+GkKikFGaaWcQfhkKCUtS4LEXli4xLoe0OndQN7LYYNuY5l
+tpL13X0lY0/BWcPRK/Ruq4H2fkCQybqa2yE23Ui6IjFE64f4u6ruLEryQiMEnnhEqhbX1TnhSNrJ
+J2LMdHbu4uYK7W4ZyNXL4CwozuTdzsE5QfMmPwq9FEzB1g3FOMWPY1LdEHUrZ28oVt1k2OLJ9z8F
+4JOLKfduye/C8K6+oabx1DVVOrhq1abxaNqqXK182uoaLHOxVZwvF+Sq90ZOSKo2g2ni0i0VX5+V
+eIpLpyvT9d3f/QOqUjZEa7iDswGKmISOiteRCXGHIj56oerF6y6NAovSNMP7RBbflm2WapeIRmrd
+blwcNdWXqUOpK9Ub9tlfS9vP/TYntYwly/TQFRWpDnld5Jh8M8trJkt1twUr9oby7l09VCbKqXWD
+taOIqFTReG1ZKFjGy1KoZH3SQhmFt4Ra3W1liF0LQ1rmc3rqtQnrDpnOwaSWQxfODazlgC18n+VK
+xXVr6UZysMoix3XkJPv6t1e3yVyFaNcevl4Q7jghVzvI0hY0ueoxK+2vrbDKIBPfIyjVkk9GsQmQ
+KBrQ70vdC6tmiVXVbjduUVbSxtW0YYSMJSpkLofBAbcfyO/7Zkh9Je+wDnhfUbVkeYUzURVRgWDO
+YHJzuIsP3mWZi+jvxS7thayDO4G6ljOw3lc3pHFxFVjWRA51JaqGY2tasYw1XWS0Fcb9orRVH7a0
+pS19gV4KSQFjcUILSQd3IF0yjYvpq+U4d/A0gKWqC5xY8feKe3fzmkY9B01WYDeW8bBNNZebt6wL
+MqsqhkYLzhcF80xvj5ZPTy3ciwJsJM+2+y3iSm7mpJE2vnM2ptzgH/z7NOplyIH+nhjlRt0Wnb5w
++aglEoPb7pDlajxttShXwsGrvQonE8BK0FZ1YGZoBgpGmfUIujJWu6ho70qf9+ZjBmdyEz7XwCHr
+GBr14zs5FBrtZx0XZwNucS2toVjGE19uOZs2rNXzQxXSqLpWNHPCUkFdVY7XlpsQNQy6dUOjMR5R
+KyCZyppVxiVXCcKJLFWqcSUd+d3MzjEaX+KREdaqVrXaWIWep9bDz0UqCNRjZCKPcCHjiPZ6RI76
+/9shViM7Xd9SK/goTbSNrMRTr01aeFhVCdzCxbTkd3Xt3kSjGsWkdJIOgSviemk93I6sX7IqWKrq
+6XWk78+sw7HG3Sy7Q8Ja9l5jUmJP53kQ4um4ne4d+eslNJl85w/btDIxXHtVdjMOE2e4yW0dq66H
+7eL31ZNWm5uAvtnZMxJVTT0/pilE5XtR2koKW9rSlr5AL4WkYHHITMhO3+WhJ5x2mCc3rplsWDJI
+5AbebcUsp6K3tlZySziTaxxf3S5pxpKZvrig0SCRiydL5noLoIEzkd/mdCoGvnUxp9Zbqb4ucA7k
+d05e0tWgIl8NbpNPPqFW9F/76pKkLyi/slpgHLkR2kmfvqPYA/WDO9Yl9OSGDqo9jLuJ7w/w1d+s
+6SRo7JLlZ4qLuJwQtBSxGRvCWKSG11+/zUThzU8/UJRjnhKoPcTiUjWKdCx9DhqxH1RNQ5PLHM0a
+cW/uegEtRVjGLZeoJ3NrZitQ92OeFSxTRUtu4v+p0LQBWK/DaKAQ5XXCQkOjy8DiqhvYiN0PYw0Z
+0ufE6eBn8pJWr0VWiSQwXISYlcxnlolRufLatBSOvWN28RRKnTUFXMlYyzqn0nDm82uRUEZBQNLW
+EPagYuZKGz23ojXXvBb9gkpvfUeNjx0zYtR9BYDT4gP21TVcrAs+PdOArj1p4+iTiihW97TzLa4m
+0ufO3CFayzPTxYpOS6W6d2QuRrfukrVkHzplijmX/e3ZLks1puN4lKtH8r7ql6W/ezPKXbVLzC7Q
+peb66RPKPVnftj+EWI0iL0gvBVMwpiEKUvw8oPJkUcxoSdvIYgVZl9VAjDokNScf/hEAn39f/h4c
+HTA8FGbS3blPz4rVdx1OWH0glt6LxSmXGhq9TGXCei2I1Kc/W1SsNAY9Ci2+kfflxmLDjW9d/j+d
+j3Ed6duV1+fJXDb3oGVYqR/7vbMr6ucCb846qhKtUo4Ux/D6w7fZf1OswpEX4mqka6keh+nsU54/
+EYPTs8tHJI68q2m3aClwIvBz9ruy+Ue6kqvUY6FAocwWlJlstuWiRe++ApJMwPVENp6veSPmHQtT
+ETND73UWlxLW2/YTjKN4kMi9yU+wyZ/i1iGOzlu5mpFqYgfjOviq8qXLFY6ChWqNP0jChFxVmwoH
+p6+GSyrypTCeVceju0mYE8q7wmpFpjiFuf+Ybluhy1mJHSpmofAwruyjRBlP3Q0oNX7CxjnVWMZ6
+YacEGqE4KI9hrh6TQH9XQ28oqtQ9Z/fm4ig6DZ9cCFN4pIzHfQC73/o9AJ4vC9qKnUmZ8vCxMN/z
+VsjFE8kpsVZv2FeSd0hu6/4tGtrHOhfpJZepetKqjLSj3qinalDsddn/nkD9D7/RpRrL3J89WzD7
+TPZZf7/P4M4DfhTaqg9b2tKWvkAvhaSAdWiKiKHXZqWc+Pniins9iRiMOkNWa4XzBjXPnsmtOXkq
+4ml7cM1bb4m4++ZXukSa0WY2Lfjd9/4UgCffvwCFDacDuRliHIJNWjFjb9xs08yy0sQbLQfanhjX
+NkY2xykIktek66OI6wtxC8ZFQHGl6kj/AZUwcQINZvrs82/zxyrjxe9f8W9diAHr5157i+ZKuPzv
+/v7vSh8/esTVBm3Z7XJnV4yre55HvRSpybo1jcJ572v04rKZMlOfZFbmZGNpOy1rJjORFLJgzfxK
+3WltmYur0uNKffTm/SsShQLu3hmyuyNqx607e3RUxVhbWadnVydYFdtPry45rzTdmu/T1mi+qI6w
+fZW2NBnO0U5AgEbBYm7WtxuWhApfL/M1mUZEeks1Es5rpuou/ODzZ5Rr9ekvG3Zuy/hanREj1SZb
+rszbusi4UinGzmekE8l+NJuNyTWSsvfoMYcdEf8TRZuuGo9uqKpNGHGm0Ywnlz6NjvVBS7NiDVpc
+qwE6ODV09wW/8fDhW5RDkW6OzteMn2uwmaaP+7Y757WxvCvyW7R60vYiyCjORaJ597LhnUzWslSD
+cJ1bXvNl/N+YvM7ggbTnuGB1DqcXJbWvCMkXpJeCKRgXvMQwHQWE6lONnRFVR0RD1y3xVcTzF2s+
+vRbR92IlYuZe0iZtyUQv/AprZXMvZvMbH/rKqfAUq+705QA9Oztjops0TlpE6vkILSwD6UdERF3I
+RugoDDrs9YlGEnkW7+5RnD0FoFqFoJF66/NT9u+K/aB1KBsz9L/O13qizjx7dMonuYiUX3MizFpt
+FEMFBR3t8IpGxVU7h3iqgoRODzuXjeJkU3qaTchX3/1VmnI+1cQymaVG3rsMHdJU2jOPZ9SavOSO
+4jji8JjdXen7SZmxmG4iSR2SlkKoy+omGtroIfVwuFTvzNzmNJqmrdNrExjpW2B8WhsYr2Ie8AK8
+XPrQ1DmtcqPXRxRdtQ1YS62ejUZVokVd0dasUO39Hf7wu8JYn4yf0VOd5sHuHcaldHTYlv6s01Mm
+E1EbT5+f01M8SX9Y0e8I0ysHLZ47mtVJPRmdwzZNKt85hcswkf70n+cMYlmTwwPhQIeHt1g81ijJ
+AwebyCG+XI0pFFb+9HzKdBMFOdIEKVmLEwWRvfL6CBvL4X52PeN7qmI0rqHW81BpbMTMzZkoVJw9
+h+CW7Lf4k4zuSMZvrMWLfrTQ6a36sKUtbekL9FJICkVleXZVspc85zUkgmy362FLuTGenc945//6
+EwD+6W9/QKmJVFO9rfZHFfvBPQD6w31soRz6yQU9X26gg/4Bn2qA0fV7InKdX80p1Ln76q7HcaI3
+exlQKvIuLdY3CUidWLl2dYxj5F33n4y5WiseYd8js8LFy/Elz0/EoPSHz0Q6COOQv3n/dQC8rMvk
+Um785N9eEeQiFby9qyjNw9f53iMpk/HxH7zPqcJyh72QI819eHfUY19xEb4u5St3L1mqpbt2PGaq
+ErmmoVD/fjFd30QGfvdE+jD59ud8pBF5dejyykCkm9u9I3pIkJqpCnyVBJqVShKrBlcTiFwsa56d
+ivp0Ov2USAPB7ox6vP1AjF13djRBaTEjNzKmromwtebPjHzijeX/oqSciTQxX8t6zJYLZp9rtmMn
+5uK5SD9ZVRDMZA6fzi+Jbsnz3rFGuGaw0IS/nz9/zqVGx/ozy+pjMdz1hj3BKgB3eiI9/LVv/Aqr
+WMa84Jw7bZEQd5OYviaf2evLXPZPZ7SN7KHLaMGlphCcff4I7T7vrCv6Grn5hnqqZpOaU1f200k8
+Ztho4uFJzXiueRGylGcarXmmwlbpWuY9Gf8vvvuYw7d+Qfo5HJO1NUfltcGPf5DR+0XopWAKTVWw
+mj7hk3ca7v11iSa7224RKnb+cnrB2pcJ+ZnX70KgIrYvk9duJwxeVWtzq8+TE7EjfHz9hFFXoxK7
+PfILzZCjKkXoSgpzgMN+n15bJi+sU+qFtFE1llIzdmzcf9Y3rHUDftqruT2SQ/PVez6JRgZenoeE
+R9L2/USYxlv3bvHGK/cAeP/P/gXuWNOIX05pHcomdDUBRzRM2C2EmazrHrc0m7OloaWZkZtWROip
+rt6S8R8fjDgXdZkn+ZLVUjbNNG9hFf7tGMMru+o6W8i76quK49fFBTKe5RzcFix5uNdjoS7ejtsB
+hWOreQa/lzDSMONXeg5RW8ax65XUmrFozzPEfY3/0ISqThlR5Cqoeg6MNE2+rUAh22Yo0ZsA7UBj
+Uco1M1VtnH7IsJFDehQHVGqPGl+OKcYKLrutzPTOAZ9r3sX2YkWlcQt5XeFrLEwVt0CZhT3QzNDt
+El9VQvOooCyEsbBTsaNZr04vNWnubcODvoaZ54YPl8JwP7guKBQwl1MSKiT/SjOtFuGKawXRnT6b
+85raRoa39wlOxA7yvdRyqpD8QBPP9LsRDzSjlb8PxVwyiVvHZ/pYM0S5OTv5Q34U2qoPW9rSlr5A
+L4WkQG1pJg0mmXP6TIx2rx3cxWou/GBVEqs//cFX97jdE1CQs5LbY+GnuI1wV3e4x+pKbpJPPp1y
+9HPy/W13F9R3vdLU8Hl0F6s57hzXZaUyXlqndDa3au3x8UTktbOp5mloG+xabiVjS4b7wq13BwNu
+KYbgq28d8DyQ2+q1+78IwK1bLVpaZ+LZ44BWIKKmv2ixMqLSOCqttKMed3dFAtkfWryRArVqHy/V
+TMzFgjqX28jVYKd9d4/ecCOBrJleSN+vs4K1s8m1aNjrCeDq3lDayO9F1AOZb48OcUslFheiSnMu
+JCF2pYE7mtQlCC2JpsLzPJ+9riZW6ca0jaxPFIcYzV/gFCJ12EVK4mkmapuzGMv6NtUSFP7dJBWu
+SmmJJmdAvB++AAAgAElEQVS5Nzgm390YHeObZCj5ImOmIrh/N6SjBtT7OwINdgYd3tALcy9pMxmL
+OJWtlmR+qfPcY5SINHH/ra8AMNzrko8l4rD2Q57OZe6frUoGis/oaeq2IIxwj2UvPPBfwzjfA2DQ
+8XCOZC7qMxhfSITtXNP63zlqs4ljbH9e8/xEpLfjNw/4d35Z1K5XPj7lmQa3Hf+07Jtb/fs8+Vie
+nWcNTy+1RkYzY/mh7Ivzjou3u+ZHoZeCKfi+y62jDuNogB2oFyGrmGjEneO67Ggq8tHtY/Z2RO/O
+NerNuZjR1g1dVR5LDd/Fq4hrLchxnPBAUY/+Qz3wns98IZv0Yj5hOtGaE5cRXqKutaXFaBGZTeSc
+Gw3JkYUt3R6oBdjr+uwkcpgG+wm3VTWpVL8LQo8LPRSPry4YqLXc7BzfgHeiQAE4jc/OgYjwVehg
+NMy4bhpQVcG9ylmNdQldjdRMLVNFfMZFTa0peNa+w9Rs6iVEZCp2fl115zAa4Q3lkFoCMvXKTFaL
+m+hJJ+6wqcUVaHRiU1kChWHud/t0N94F25LwQMCLDXWuAKfcv1k7Vz0jjklpq9Cat/qkK1F5nNCl
+q14CT8NE/VGCr/aVdepRaci8346JNKFM7Djs3BLGHw1kTL1OyK4y1slhCqf3APggPyGfyVoOvBB/
+V4vLJFowqHZw1SPhZRlWGWRiC0YDsQmEGt68LiwXCmRz95fs3Ja2d02f1o6sZXZ3wuJM+vZEEY9X
+13NO1ZPWdxwONanLblPiHkuff263y0O1D7X25TvPiQhUpV0vKqJYSxScRpR76q1aBngbyOkL0lZ9
+2NKWtvQFeikkBc8P2Dm6RxDCXS1u0l/3qGcKMGFKqEadZOHgxnLbBpF0v+e4hKlwa4cUpy23Rzev
+YaLx+MeGlgJoNqnNos6Aq026suUVjhb9aAchM8UNNHFDpJZ6X2/dpkgpc7FYH11a7gQioubPxzSH
+KsL1EiIV/0v1Ly+apzjn0vcqtdw6FMOe254Q5yJe2lREQ++wwdMMz1HUBeSGrcuUrBCLfOi6PwCy
+qAcgK5Y0c436K2smM/WJhw1tzZJcrS3LRyISu5pleBB4BGoAo9fi/FIrGk0XBGxuXajUkOrnWiwn
+C3F7mnG426aF9J/ao3Q3KeKWBFpcp1ZAlpuWzDVtvcXH1xyNpna4WMkcHqSGQvMiLDV/hRe1iVQy
+OUgG+Bq0WZQF++GmZid0tABPkmiKvahPpOCzo6MDlgdyS+/UIybnGl8QeZRq8NtRS2rUQKFp3D7I
+HtNRA3ORuTRrmfNZqYbYtUOtZd6uLh+zScMx8gNGb8seaewBflsMguv3ZH76ey4nJ/r5bZ/7rkq3
+ZkhVKWbDW3KoqQrNWqTiOlyyVLj9rdjFV9Da4+cf8eSJzG3i9bh1Szx6L0p/ZUnBGHPbGPPbxpjv
+G2PeM8b8Pf1+aIz5Z8aYj/Xv4K/axpa2tKV/+fTjSAoV8J9Za//MGNMB/tQY88+A/wj4LWvt3zfG
+/AbwG8B//v/bCc8wGhnWlxHFkcb/h5Z4gzRblLj7iipjgVVfcq1SgOta2ppvwHgJZpOubRdMW11o
+UR9f9XmjZbQcExMoOsxZuTe1DExV4yiCbuVaCvUbV5quLZ2tb/Tsq7bPVAu8HEWGtf5uXk5Bb29H
+I+eY+FyrvjwKA27ti7Ev9AagUX1otFxtRlgtHsugxlFDm+N5+CrRFG6DXWn2Z9W9r8uUiaaBCwbh
+TWHexcTFUSTgulgwnsr78qXmmBg1OBr5aW1BpjUbLmaXvHpLbDhx0sFR+HetEkPlp7hozct8itlk
+wPLa+AoPj5o5hdoPjLr8gnYPo2i9fJnT0lqY58uM8bnMgbtT0p/XX5h7b70mV/RmHFiSvszt6jql
+0JwLTpURaSRpr5H+BF1DpZJJVViuT2RuszrDqVVy7MVYhRKj9RTqS4unNqPZOGN5JfaHatSnpzDn
+q4UGed2GOJL9mzoNsbvZhwHF2Ucy94HHu78ltU0+uxIj4Z0kIVZp9K7X5c7bcrOXdsnKFSlm+m6G
+8aX/XcWp2NDH17KIfq+geKSQfeBpKnalRbri1lLaflH6KzMFa+0pcKqfF8aY95ES9H8b+Nf1sf8B
++B1+CFNwMSRNyP7BDpNGFvPMv2DXiGibxG1mC9kISzy8fBNyrAk9Sp91LvEQ/qRm+q5EF85Pas7b
+MpH75+ckuyKuGxXrKuPhaCHEruczUWay8B1yTys3lxlqX2SlEWuBu6ZS3H5qDE9XslGO5wNq9QLk
+7Ya1JvXwHVnY9OxT1s/U+h54OD2tfxkmlJpAcHmxqW25gA28dn5J2JHF94IQo+J1Nl3yeCrv9lVW
+rdMW1pdxLBcVnU3UKZalGuiW0ylPnqnadFf6uBzmlFpVyKwzsjMNPy8bik2J96jCpHLQl2MV69cF
+yUQYnee2MJph20tq/EjmyEkdmkbet6zkr4PBy9VzYCvSueY+NFdQyME6n8yYamh7ooVUAxPdeGiq
+rkOtlvyzp5/TKFgqbkd0VHWp1chbFwlPx7IvJk/nfO+D92UdQp92Vy6fh/UegXqjVsq811yDMs7L
+ixUTzf9pqpKfUrzAju4FmprrQtWysaF0dfyVx3QslaMePX3Oe49kTIuO/P7OTo9IozLjXkyhxWav
+p2sm74oX4cOPMoZ78sxeRw78YLmmuZb+Xjc1H82k7fxZQTUWBrHwXMb5Jqnli9FPxNBojLkHfB34
+Y2BfGQbAGeg18v/9za8bY75tjPn2fPWjxXtvaUtb+vLoxzY0GvGV/SPgP7XWzs0mCSZgrbVm48/7
+f9FfLEX/6t1dG3ZD4iRmrbUV7Dog7asrqDF01IhUBxnzmXzfVv/44PYuxpUb7PK773F6Jpx0XdfY
+Sr6f1zWB1pHwjHDwmoY0VaNcbGhpIpBJblmp621VwkTVA5UWcbvHoAkv1rXDxVJE7Y9PGjo70qf6
+8owDrT2TKdNbFxWL6aYwguG7336u7/0+bTTdWk9rJ0Zdgh2NapyNaakrL4gj/EbrKcyuWc80KacR
+cfdqveaDK7mNFykcHmt6Mc8njGV8q2v4eCLP/5niQn5ht48ppD/T64JMC+c4sUOja7oYG+qZYiQu
+NClIz1IqQjT1KuxCbquw6uKGm4Aol0qzIxtNu9bYCjQtWT6/pttTzMbeCCfSGw9DpXVB56WqVZVP
+ZyF97+UwVgnyej0m07ZH0T5pLVJmoTUpdldrnl3L+OZTmGqG7cnkjEhrQebVip2uRNj6ers6QYWv
+gVvZtOBaUYodx2AiTTarSV9cz2GhRt6rqiBWZObF84LrVPr8W4+vqFS921XJ9MOLOWGiRuxqwXAt
+0s9HJxlnGkhlOyEnY+nn/kL2960Dj/1ajI6L6zkXqmqtm4KdVzQdW9SjPfrRysb9WEzBSPWJfwT8
+Q2vt/6JfnxtjDq21p8aYQ+Dih7/JpXG7JG5MpxCrcSc1zK5F4AiKLkNHw0LnFbn6ihsFnXjLCq+n
+obc7Q37+l74BwFfu7ZBlivuuW1xfq0Si+IYwKSlWMsFlmRJqhZ1BGHM9F5GxLks0BeNNyvImdai1
+6o73+flN+vIP148IFAbr2wEtDcmd6QHLWJGE0p/dXkSomPRJ6WP9jXdFqwZFUGg4sS3hUr0BkWPx
+PBWNlysa9dMvpzK205NLxlo0dt0Y0koLkjgQFqpW+B5rFYm/9VSYwqDfwtdM2itnwRtvfg2AneT+
+TZ7Hi+9/ylQPaW8lhzFaDnH31MZR5TfAI5MuaDTLddWP8XNhxJFmVcrJWWk0p5sVeLpMTV3jdIRB
+hGdLFlpL8WqpsN3KEug9M5quGWviHMdAqX78z6qnfPDxYwD+WAsIB5XLpmxS3I3pdYVpVFnBpYLW
+irzG3pbnQ7VbJd2SVO0hl+mMcqHenihHI8PxNaNXsyppSs1tWVpWGqMTpDkLrXqVmYYg2VStkr+f
+XGcspvLspVvTe6yFdhwPM5CJee3BDtcKovpca1CeXFd85ZaGuKc1/UIulu4rI+4evS3zXM05uivx
+Ni9KP473wQD/HfC+tfa//Qv/9ZvA39HPfwf4x3/VNra0pS39y6cfR1L4ZeA/BN41xnxHv/svgL8P
+/M/GmP8YeAL8uz/sRU1Zkp6d8Mlywp2vSobb4WGLoFFRblHg3FdL9roi8iTBSbV+DICNxpTnyrbj
+Hp0j9at7h2RqGJrW4CnUtvTk2Spfsykv2Qo9jJE24rKiPpdnn4ynPFOpwaq4t8oLKkXoTcKCWtOD
+HfZ91nrDXDgZ8VLE1TzShB15RrPJY9AbsndX/M52z8fR322wZ3k+x07UEBn/ALlXhA2VVrZeexle
+S35Rab6JP3p2xTgVUd0PAnJFNz5JG4y3SRLj0NPfBZqH4iTP8RxRDeLAI9Ggs+5Ol1QTfQQHfTqK
+9PQKMc61ipyKTYbnMbnWxiiaHFNvLLoltRbJMVrxuwktpaPw2yCkGMmaXCxy5mMdd+CwUvyCVd+9
+ZU2qiNUn6cWNOhJHfSqt++D5FZVKclNVNXKbEqixcse28TyRrPykRaM1+0wMlUoyLc11kDQB86V6
+AJYrGhX988gyUwlyUxeiO/LxFPrspAFrTSt3WWdMVfcchjG+5pZYqhSzzioCVcE+D3M+0IjKt+91
+efgzskf8rM2u5m04n4rqOp+u+VCjRP2BRzDQjOe9OzzS4kF7wx3ioVb2eUH6cbwPvw+Yv+S///qP
+8q66LJg9P8V3dnnnU/EipHddvqYxCiadsrzUpgZHmEo2r81kQ3/0nc9YpJIFs2v6zNRt9HSZYz2t
+83d4RNwVPauMZdYrY/HV3ejVIb6mO/eGDb6CmtZ5RaWQXzTtt60uAGFY6+oZnrqTqqTHMpdDWKxH
+nCjz8ZZqeV+tsBrVOE+nFI5mbLr2aenm7+yK/mcbQ34lh3/hzXBVh2m3PQqNVFysStxE1C0Ta4jx
+ssDRilWtJGCqKcKX65zK1WKs7YCoqwCZUJlt28NqQpNsbXn6vqzD4rRhoZme2t0Ogw14RyG1ee0R
+hupaHHvkmovRepaWxi14TUCjIv/cV/VhObtJCDurLemZ2gYuTim1OtcidvFUpfPVG1SOG2Ktjbhw
+StaqZ7v1mn2N/Kz9GmckbR8sNdGqzWk0Q5JrHFaakQrb4KoXYTQKb/IqhmqLuG5d89131Z2YVjja
+H+sHLFR1SdTbUwQeK4187DQt8rUCj+KAjYPCXdSsNI5ns05xL+GN+9L3LtBTkN1Xf+lnaKvK+tkf
+vMO7T4TJVJro5dALOP1Uvvu/M4eHvyg2/YfDER+fCSO7aAU8/OltKfotbWlLPwa9FDDnioYpay6O
+Qxbq207LjA/UOHMvCnHXIvplt9ugVlYb6i0Z7TFXfMPsNKNU0NByWuBqsYzL+ZJAReaeI7e8bdlN
+HBHW94jVUFWFLebNJvuwxVHgjF5yhPvfpLiQEl4mLrGVwo6jAakaQVdVyu6RQFsLxRuEnc5N/sHB
+V24TRMLZi2DFUUvSiB+8Jjfx4toyfyZJVtZnT24qDseDDrlay+OypHsgBrN3HmmK96rG0zqPh3GP
+hUZR1sbS2I0XpSHVhDJeX4vQxD32XhEjZ7Nc8/RcxvHkdMa9NyVSL0iGnF+KYbI5lzaio4r6UsZv
+05qNr2lwa0i0KUhpM2yt2AqVzD7+3gmOSmP+UYyvqlLZ7XKuGIimCWip8bPbUdixNSQKbx/02nw4
+/y4AF+ucWnMoJKPwpramu+nQwgeFOc+mBauFqi5uwxt3RR3dvTUk0rT0ffV2XbcSPn4uno+0rOjv
+iZTS8gJyVR86CiaqK4epGlqdoeEwlrXZMTn7alR8cn7G8+lGbdDkLIcRzzRpTb7TYtWoSvTE4zIV
+Y/vz74z5RMFsbx5rGYSvj1heyLsuC8P+N2QcnxURF2ORkH7+p3+J3QdaZ/EF6aVgCnkBnz6tuPVK
+xi8FOuluh9mFhBN/mja8pvkVw8Ue7loRaAdyUI7vvs7rqnNWJzUXS0lPnj/7nEax7/N0id1kU1qr
++NYEoOg4Wh5GKygtno1ZKlrSVhVmI4Kri6l18CZeX3Rq/9mHpJEg1Jw6oVCEpC0s/VLBK6pnhjsu
+9kqmfNjusauxD/RKwpmmKleXlvv8GjV3cKd1TDDaZH3KKRVh53strp8LKu7b74n9olgXeHrYFuWa
+hWb/oaqJNGrR2pL0TFSscSwH98FOwI4mMrnz8C7jHWEmn/yLMQPNTdl3umSKIOyqWOtFDQurMQyD
+gv5m/dreDW5/na3xCgVLnYpa8gfvnXBvTxj9zzn7fHQplvXg+IhgJGM6nLjMFNRjl5qrsSlwtG7H
+kd9lPhS0ZZbMaLfkff3921TK7YtGixDveSQ9me/mICCbC3OL44j7D4QZdiOYjdXLoXEN5fWY82v1
+dtHwalsT11LzTO0Vay1F/6bbwtU6I8X1FPoybwe0KLrSt7yqKRwZq1HvUisekataGTs+PS0vX16e
+c/a5qJipWfILe7I+bxyIGzI/n7HS/XbHS3h1IZfQ2ckpKy2K/OatAW1fmMiL0lZ92NKWtvQFeikk
+BUONw4KzTzJ+4ReFT3Uy96aCkM1qVpFw8PblczoD4fi+GnW8qIu7MT4m65ubLfcPyTVVeWUXTLRo
+x0qLwbRtjKdVg1jDRBOSfPh8wlP19V+vYKk450KNfXF5jqew5LLlE9QiXqflYxxVY04XZ+x8KtO7
+d/iDuIX5c+H8VXlGvSsSjVN1MVZuNGcmt0HYW+IN5Hdex8Ep9KYpJjcxBfM85c9O5MZ//zO52ZdF
+g4YwgFPgqHG0qow484GqsqRjTaXfknZPhld85Y7cLo6zg7OpyNQ8x3yu2ai/1ua+xmhEewqrXlm8
+odz+tecQato864fUuQKOSstU8SCPT+SWnM1XnPpawXnPpR4plsNMcbVOZbgXsKvVqKcTgSV7RcBC
+s3WbyQUtLTiTW8v1XOY2L0sijUFoFKjmRBXFuUxMUZbUgXy/ExQEqdZ59Cb4nsz/+FLG/91Pn5Dl
+8q7SOHyu3pNOt8O6lDlvFDI+v+dvCpOzWC9YaOSu52cECn/vrUoSVaEm2Saf5ylGM4ZnZwFrNWZ+
+dj3l0aXMoe+67I5kD0wvNO4kXxH35Nm9qM3JO5LU5eTZjGs1IDezMc3ipvT4C9FWUtjSlrb0BTLW
+/mjBEl8GBX5gd/q71E1AMtSK0pWh1RHdibqm3d9kpK144558//mn4iq6mk3w9Xrc8UPamvLs4cEO
+HU2eOrBtBlpbLTCiv2dBSqVGyzLPaDKRNp4zY7+RG8ONLH4pOln9QHMPvPZr/Mk//68AuPyTb7G+
+1kSaox69PbE17B/dYbSnBiGN2KsbcwMJjnsJTaFRfV7DxkJnNf+/n1tcjbKrMof3TiUG/8PvP6Oj
+t9/tY4dTraR9u1aYcNgQ1WLAzN/Y5f/4P38fgLPzS+bqGl2vSwJNqrqxP+yNEtoqBbRd7yab0PGt
+Pbo9ma+uH93Ub/A8+WtaLm1Nc9eOYtDUZOPlmO9/X/T2y/EUakVcqg585A9oaVXxqOqxHMnnv/nr
+/zXf+cd/F4Dz//33mY9lPk4129TptOJUUZUdHCp19VVpfZMhq2ccrhVP4Gm27gxLpRDE2kghIIDc
+sRjNgOW5ho4aBBvFabjYm4C4NC9ZaT0Iawy37gjqs1KDuOPkLNSAmeY5jWbecqMEYzeu1YYiVcOs
+Zv528W4AKrausYqgtDVUbNzhVoH3ECuepggMaG3PrCkZqOA/MzVv6FxcWXtjEJ3An1prf5YfQi+F
++gBgjKV2fWojk4f7A9HXCWNKjZzrtDx8jWwsNRWVSVc4iuvvtULefl2ssLePegSRHMgEj729TYFV
+BbSUAVaLvjjhESfXAgoZjDOSth5eO4L9TUFaaff7V5cM/ki9E1WOF4oYfNAP6N+Ww/ngcI9Nzdvd
+9lDbbUhnGvvgu6ApwsMgpLRaCFahsaHv3qgJRasguZSD2YlcWhol+Hm2ZKmhvncV2BIXB4S31BDX
+cUh1A04XS/JSw9Kx+OpRGQ6kv71+h34kG6njBQx6wtAGoz6+1tts+YZRXxm1YjCKekVLIdqDdodK
+i/Gu6oDjrjBW3wQ8mYrxMNDQ8mTXJWrUwHcQc6CegXcvpvR+U0T3pyYn1wNQOZoerqwp9bAVnqFQ
+9XBgDZX2KTIOjhqHfT3wtWOJFeZcGwjNpjKY2YRg4HoOyiuxaOyDcQhaCuOuS1xl6lVjb+D2ea4p
+4SJucCgmBzfS8Qd9XF+ZKTmtSEFWVg3XXpuVFrytlzNy9Xw1RU2BqkFFRaVjCdWYW5saz2zg0xUt
+BUPMTIGvzHft3cBrXpi26sOWtrSlL9BLISkYHHzTxotCIjUyuWVI3Yg4b6oSRzMDB/6cQvPfX4/F
+b2vXFfcUSvy1h7f5xV+QtL373j5mA7p0cvyR3DruUmtKriegPmbaMVgxYMZTi1W8ROPV9DfBWOqy
+bMaP+F4uN1/3dZfbmgvg6NaA4egeAP1OyAbw2WjthWZvRFeTpbhEN7dKMEhwrGaY1sAmPwxx1J3q
+lPDwWMTExDU8U9dq9jRnJ5fbNtVkIl6SspfrOOYw1ajGLM+o9fYMjUuiKtZuR8Y29EMSo2J5EnN4
+IO896HSoFGfhRoYdre6d6G13/vgpZq2RfCPLQKHgOT7xPZHMekWD+1SkrKVKSn7UuqkrmRQ+bS3v
+dvbJR/xxKYbL1U8ZOJU5TxcyF3XVUOkt7ruGUA3FI9dysCfP7FvDp6W870ArZseJx4FiUuahT7gx
+CK58xpo0dVobpipNrVS1sw74oZZ7r3+QCLe2hkLh5g0q5a1cGpXVPTck1oQzjhvTURRm1O7xQIC1
+HPcFabhz3GZ8Jf387Oy3eXwial5ZrShVP8qqGYu1jjuRvrWsD9p26FTEsQZ/uYbsuSYbTsATW+UL
+08vBFByDFwU4wYhQN6mZz6kVolxlEPTFat3zRxRT+Vwoxj8IDN/4afHR/uK/9tc43pfFaDsJRis5
+uf4+qNXaGap1uwu1KnN17TJScTe6V1KpGrPIFgSqXxaHsuCfvf+cI/UMhG5CW6s7rbq36PflEIZO
+wmou/aysZoW6viBKtOhLz8Vx5HPUAmcjGqrs5oRd6kL63jgug11N8R5ApeAes2vZ6ckcDXbk/5dO
+iiKCcd/yWWpmnnVa3tgPup2YnX3NhakZq3Y6LfYTmZek36Wvalc3ifH2hAGEcZ+9XWEKcUvVFacm
+Vys6Zk2oqs1+2CNV4JGZpxSK/W860veicrjUpC5O7ODfl/n+9vwddmKZz8m1SyWlGTlV20HuWEIV
+cHf3OuzeEnXmZ3uG3o48FKfw9i3px1f66i3p94gVmNS0OngKNV4vI5ZX4rX46HTMB5/JgfzeQmHc
+WcNMk9PUDXiaK9PxG4pMPCqNqhQ2NPiBhlOHbRKdwyh02Fcme//+IT9/KFxhU2vUVg4XtzSpzR8+
+JM3fBWB+ZZloYEWxMKCxEjia3aoTE9bCmOa2YFzJ/i3aBYHGbjjTbJM1kwUvRlv1YUtb2tIX6OWQ
+FAyEgQU3ptQc+yZeUC209FducCtNXuJOmV+L6O6rBWWv3eLtr0nd99u3eriaNq1qLgi7WqjEB0/h
+zbXCkm3YwVNos/UMVqG/uVtSaqx/bEPWeiNU1/KunfMrLh8It36rd5exlh8fOTlFLrf/JMs4U7E6
+UXzDbusOpRoXCYY4ill03R6l1r/M9XZ1ioZmrdLK6opKc/f3D27jONKfVb7imeaccDeZkacOF7ua
+B/K0S6a3eG0hUp9+1Aloqcegp4jGfjdieCS32dCL8VQNcBzDcCTqSCvu0G3LzdxoMFA0GBCstdAL
+u5SZpnQbJMTqHx91Iq470o+zXOprLhZT8kuZ+2fdFWlLoLgPiiF/sivf76xj3ldVKV+pNGJddrXM
+28+8ts/PPRR4+N6gReIrjiTxSHZFcuy0N16ZEqMWRY8YtBBLuVyT3hWp4vBij4fHorocfiTGzj+5
+npA90UI1WUOkekfpeBuHCrUaLYMwIIlFMgmciLYnbe/vubz1lvTnG/cOuadVyH31dMzXS0oNwBrc
+6TLIROqbr8bUmlW8qCo2jkKrxuNuFNJoSrfePGGk0Oy9+gFp5z1Zs5XhbJP44QUdjS8FU3AcQxRH
+rG1zk1PPWRsKDXV2W20iTViyTCdcKLBo4xa7v7/H6K7AXcs6vkkq6qQOgeq+hgSj1nVvk6zIqWlU
+h6x9gxvKZ79MmCsYqimm2LaKa9rfs7zGXijYpN0lvBdpf4YYDXF2bEhLQ1n9c/m7imBXXZ2248GV
+9K05THA0ms/xZWz5oiTXGo6LrAKj7lRrsZqlKbQuK7XBfHgpzybrgv37Mhee39xY6i0QJprEtJ3Q
+aGRgeyDfHY5GDHrSn7YXE6k7NGlHJENVFfwermYbctUe4PghxtvkmoywtTDe0q8oVd9dXy8w6so7
+vZDxza4n1Ev5/6TXp6dM8X98/zt0JeSDyYOahQJ9UnUttnzDkaowr9+6zehAQouH7ZpI7QdJf0g0
+lJqkoapSZr3G00hUJ2xjNpdPdoKTCbP344A797VAj9qR3O86pAPZF+OsoFSvTcfxb6DJaKxCkCRE
+GolpyoBAyxL0do+5fSCMtXtwgAk2OUY17B1DpgVgnHiXViJ7L4oqGp3b2hg2TslKVZilqWElbeS2
+ZrwSZjMfrEhSLXzDc5yNW/MFaas+bGlLW/oCvRSSgjEeUbRDmaV0rrToSWqxpd60YUl/c01b76YU
+/d5QroG/9c2vs5MN9V0Las3D0EoGhEaAPK7JaDZFjtWI6FQulaf5D1jhavbdsF7j6A17Wub0K3nf
+Jkrylr9m8YrcVnuHXaozTcISGGYKme25Hu6VPJMWWv7O6+Cpga563sc1m1upR9TWyLnncqPmV5Zz
+FXeruSXQXH3zVsk1mlijNPgKvlpm8ux4veL4Wt4RRM2Nj9pzDX01gvWMT1dj+XuxFlbZPSRpVL50
+lhNW4dYAACAASURBVBCJGtSJ94j+H/beLEiy7DwP+87d7809K7P26q7qdRbMYBYSBAFSBEWCshZa
+oq1wKOgI2wrajwzZfrBl+cUPdoT8Yot+sR9sywyHbEqUI2RRICVuAAGCwAwwmL17eq+upWvJqtzz
+7vceP/xf5mAiYGKGQ5GtiDoRE1NdlXmXc889//b935fREzIzgJgEi7wDltZQc6nzWgUGa/ZWlsAk
++crQ2EUxJdQ2Fu/gydEEI4Y8O7ZGW8lcBR946G8KdPvHPh9j+B6l+qj83ExMvLIpc3wZJhxCqa2K
+j0ZNjuGZ7YVmpZoTrzgBTGIeDFVKzArAqtahmPA0rRRmKS7/xrKskfRGiSOCk47PxzhiOJaUCjYF
+YwzOyZLro0aswGl8jpIJwRv1F7DMUFGFM4Rs6LIJ9JqOMoR8ZlF/inAsIdN0lgLz3GJZiGQggCIW
+b8sYJlC8tjLN4dmyLpJhGwGlDZKqscCOpOnHix+ejk3BUDA8A1biwvSZpc4N1Dnp1XoAvyMTPDyf
+wqLbtU5X7fILzwBthhpFC/SSYVccFAkpt60qFHn+SnYZKm3DYFznpAVikp86dgmTgJWkD4xm57wO
+Iv6aS6hSFahacZGxmlHmDmCRd9DwYNMNXLFlY6o3I9gOSVbiD2C7ImLqTHtQFZK0kneycAGTrEmh
+zhCTWMVIamj4dB8xgkUsfk4y1yE0plzkYTBehJG+68FhGVIFFiosly0FzNjXLZjzc6g2MNc/WNIw
+AiLvrBrUlD0mPmnflQGLRC/QIyimTAzPhO3KBng572BWlVyCTRJcpSIMCD6z+wO4vLbHRh2tOzJH
+3znPFilzjy9xpeYAtlx7vhRC5/OX3oHy5XzKK6DmjE0zIlbNDJqVKNttLASCoTMYNkMJR8Gc0o3n
++brtJlY6clPZrsK4J/PtIINJdKfnMkfg1zBlhSMaDxEw5HNXK1BNAqssB2bOvAsNXV5EMF0iKcMS
+sylJcWHDteehqY8im/LzDAfidNGVm2QFevN8R3aGGjfZKBnAzT9mMoHjIny4GBfjYnxkPBWegmUq
+LDdtHI0LxMxe+3aCgJl6pwSq9N3P7RQukz3PUsQjGgwQ0e1bdn2AQJE01ogJMjKTE7gF8eyk0zb9
+FmwmfXTkLpKOuRMv9AqLOMc+OfOqpIhfrVWgyeAbqwwxKdhanoEwZuefNUBpkFugIP6h10bPpyBL
+ZwNGjwIgQRXTmVjsu4dy/zVVYNYji/B4iohW3OxYqLP3IdcVvL3/OgAgY5edV2lif0CF58MELjEW
+jUYdVYJ7yjBDlUI1bcKZVRoioq9qJhZay1R9cgPktIiYFgtGaz2HY7fr0HqODa4CinyHTgM2uQfa
+KJG9I6Q0D84kuz9NYkwG9GimJUItIcNP/bu/gD/6B78NALD2gAbDH4/hUa3+oZ6lLhXYaAk3q0HN
+lbxSBZuEOiW/F8cjOExMl8qDJn4jz/ZhMHFr2eaCz6Jk8tBYdrE9FN7QNXcP91gNmZYaVULkU4ZM
+cTlBShXzMI1whUnumulDURfUVDbUnNOSFRWn1UWrQq/jzi4cAtn8qkJhMBSejVHmMrf59ycOGfIZ
+UOgUcoyxU0Ewk3UfRAXiC5jzxbgYF+PTjKfCUygKjeGkxCQfIWaZp2r5qC2RTyDJML/ULC5QZcdc
+44pgE8rMgm3NIaAOvKbs/Hmqkddp8c9NhOaYxyNhZi2CGbKD0VNQZM3x/CpmA/ms5xQozyj7di7X
+tnHzc8hLoipnYyhaY1g2XFrKYOggfoYMUTPxYoqVFnzmNRrdBmz2/KeeXiDlrlwXtJsKI/RyalKo
+HBHhrolvICSKLdQGyOiFSIvVbTsryFjGypJzeCQVrTWq8IjYTGZnKOeWqc6EoekhZXONX63D64oX
+5jg5KNEI5Vsw2D2pieIzHOFOAKTEq5jNNQIXislMOwvg+Xx+RDZmyJAQI2IgQkZP6eu/MUSF4b7l
+L/Ri0KKzEiY5NPkNdK6gIzJyXa3BqLIZqQQ0S5jaZLI29VDQwyxzwJpfT1aF1ZhjIAIUy9QWpUZG
+OVIoIM/ftEzQUUKZaxicmGLOQmWZGJNFOilKBCuCvVi/fh0B0aKGctDyyfExZpNYu41jQpuLRguX
+OsyZtQLsj+TcSTSeOwVIiXTVjobD90KrHIYjayfRCUqqmKdK8hCfZDwVm0KWZzg5eYIojbFuisu5
+3mzBIrvwODrDeJ68ngHd2hwUIi90szDhTgRU47QdaGMudB0hPJX23fM3+zjki9PlS7N5/izqSxT3
+qFY+zE6nNqotmeBg6xQ2tRunBFP5HpCQLrya2Tjry++zcISTgh2KyFEbyDVpMm/oQsFN5JorpxZK
+uokj20aDMOcOKyfJucI+33ijP0W3zkTkxETsEe8OjYRtxJMJgTBBjI2QiwY1KHaURtkYVydyzZOk
+BFgNMKigFbRaMMmubKUlYtLWe6ENTXe1KEJEhHfX2AFYTgvkM0kiqnoHrjGvqOTAnErNdNDqyDOr
+UE2pmy3hoSkJtTItURJ27Fv7iLjB/+LngegNeZLplhz3+LhAlSFm/ayKkNBz23fhM5ufjyKkhbxk
+Z+kcN2LCYHVpODpGhT0cZr+ENZZrM7o5wjn1GgFuWeGg6sgG+SMbD/G9A3mWgyyFZTDBPO8/mFVR
+zD507a9ekhBso1aDYcrz0ypDTgyMoWiQBiWOvyfJ7Hfu38KVhiQuL/ubGFOA0bddqArJegJZVzFi
+ZIZ8L4OF7aY8a69oY9OSdX+WK0Tsq8DHDCM+dfiglDKVUm8qpf4F/72jlHpNKXVfKfWPlZrnoy/G
+xbgY/yaMPw1P4e8AuA2hrAeA/x7A/6i1/jWl1P8C4JcA/M9/3AHKUmMaJygKG2qV9cQoQByS5itU
+OM0lEaWyEnVSTR2fyN+vrm2genVebqpgeijUXacnB4jYUGO2LdyoMMHoEc6sjxDlkkQqJo9hkUrN
+qSpUGXZcV5fgUn798Zyp18ng0SszOworqRxveHoIl8i8SZShj28BABqnO/zwDLpLtOFqiEZHfm/X
+6nDdOdGHIPRi8wg7pVj2XllBzjJsMowBdhoWs+OFhdXszfdsC54lntJoJYNHeop4WuKIUm9tX0Mz
+aVWQZXmqPKQTaQyqdTZgxfKz7qyg4GctI4CnJWzShExPz4eYjuQ5WP0KfGpCVrIOLOIG8jACJvIg
+rtbFyqV+hntkHD4bxZgycTmcxFhhre7rd4Evcp73SNyqfI13DuV6Dmbfhn8kxytUjueuydzWKk3Y
+LNtW6MWNXIXxsdTxHz7Zhf5ArHytkeGqPz9GgGQuUd8junXVAxoy99c+fx3dWwwlpxlAMhSDScn+
+NEXMuTJ0jho70w5nU9x/JIlWY1yifCLrc6tOC15dRjiT+fzcThuVljy/cuIAmazrQT/BNJJ3wKfX
+USBCkbFD0wT2iEItNi00TJGNy7/1TTCXTn/mh49PqyW5CeCvAvjvAPznlJL7iwB+kR/5VQD/DX7I
+pmAaCm3Pgu0so94Sl6sI+ijIqRiHISYk5zjJQgwfirv6hHHY6TDCX7dEf8a5oRAyDrtzt4eMcfL2
+qL6g9X5yX7737uQQP7pMGu7qFtrXham4plpoNeSBLtktZF15eScUPbGTHDH1FePBFGEqLlw6TJFY
+7MrrbMDaJmx4QDUms4uccXael8h7Io3u6hxWl6K3pTBYm7lGlfmJ0LYx9OmWjhPYID1500dOQPyE
+nXxRVuKI4rFTZWKb3ZD7kxhTyta3mgF8ystPp+wDOa/grV3hjBx99U1sV2Vedm6sorspPzerHqqs
+SsTMB7x15w1MH0juY31zDV6dYcdSgqUlAotKBYsvXkJ48da1VXyR+Iev/MFjnHGj+7d++W/hW//D
+P5HrHADfoMvbOGXOqAI8IobizijFCpW8hrtTvPCizOcXb17BzstynXlffrf/KMIfvSes29YoQ4Pd
+nPjsMvItMmcZAc7m7EVUtLImdWxdlr6FVvA+Lq+IQTo4juDwJUwY7E/Lc0RsiKhYPjYYCutzDycD
+ma+ujpGQe3R3LE501yqw0RGb+tzlyzim6MtX7n8DRw/l+o1ihHZb7unm9hZ/p3HaF4n7k36BqwxH
+livPo1X5LZk3fCwx14+MTxs+/AMA/wU+jFaWAAz13GwBBwA2ftAXv1+KPs/zH/SRi3ExLsafw/gT
+ewpKqb8G4FRr/YZS6kuf9PvfL0Vfq9V0Y6kJKBsmUxBxni06l9rLHdRMcUVryRlCir0ME9lxX3t4
+iFdoYb1kC6M5CsxxoElQ8M57t3DnruyZJbnx6ttd5DVJ6pg3lpCxcxB1B5pZcqf0sNKVXdyoECuR
+G5jF4opG02zB+eh2UqwT8tusVOFtiXXs0zqmeQ3LNUrT1Xykc0k0t4qMZCjTvriIZ4d9KHo5pVvA
+IFS1XA5gRzIXDW8FnTXpHjoaindw0h+jvSEWykWOGsOgk1mGlCQry0sNtHbkM1MSx5wfH+CQntfr
+tx6iWpefPxfk+FFm6i/56/Btub8yn0vAn+G9PZn7GTLUlujvp1U4x3J/lys1RKlcx9amNCpl9Qku
+jSTUWL/UxoO74rb/3u+eY31Gyb4gh/ggAOk1EVRNnEwJE7Y0YiZSTx0T7xbiCVWGd4B9sdidmMm3
+cootRqbHSQ7lijU+OT5BotiYVQTYrEoyb2tDrs0Mmpi15LNe1sEG+TbrGMJhFaxkpcLw0oV5tCoB
+vG1JVleXLGyT56057goVHwCdcw3lCRyiYpeqXZRtWac3WztYf07W589dq6PRlaaqQbQLAEj6No5J
+NLHU2Icivd29pRzX9iQsHhUPPrHl/7QCs/+2UuqvAPAgOYVfAdBUSln0FjYBHP6wAxkw4Fk1aafl
+4k7KFA7BIdsrHnzmAfb3NI6JNY9CKenoGPjm/yshxfgLX0eb7ce1SC1i8VnZRZ1COTaBPpa9hMmU
+dNoHZ9ikpHxLBYjapFxvGKh48nAbzGgPsnjRcVczqzAIJk5cd6Hhlw4e4eQ35XE8GEp4sbW+hr/x
+hZcBAC6uoWzIYnO8EXTE0hlbemdZicMzEXgZj8/xwbvy0hwNBwgqcr7Pf+YmrjdlIbxTyN+Pxz3c
+GJHxSC2hsU64bhTDifiS1pbQIlPTg9vyvfNogMFwLt7iwmvLxpolHm6/LzmDfj+BxapDuy7Pw4kN
+DE/ENf7m6AHS9A6fjYmYXJhXNwK8ekPk0CcDCSn29oa4yR6Gn7yxhBNS1ePsD3Duylz87S8A3xPe
+WbRYfUiPSkyp/lSPPdyjdmWzY6I5krl40Juh60kOY+36MwCA9XEFM7Zcj49TfOdIrm13dIrA4rPs
+uHhxVUK+n/9xicmbvgt7QgCU9Rg1lgs9y0A2b30nf2SQW5hogsWqS7ALeel73z7E7Yfy82wQYRTL
+c32OIcOr11/Fzqa8xJ1rS6jVqSK2EmA8kk3q7bO7eOe3fx8A8Oa+EBabCljuynNYXW9jh+/FnaMU
+QUlYeWAgmycTPqZD/icOH7TW/5XWelNrvQ3gbwH4fa31vw/gqwD+Jj92IUV/MS7Gv2HjXwdO4b8E
+8GtKqf8WwJsA/rcf9gWtFDLbgC4cREzwuIkFz2MSaaSRkkOh3QxQDWSHXfuipCu2Oldx+57svhgo
+tHbYXdmsQrH/3ygteASZdJS4DMZWA0ZGV84bIbBZe3fOkdOS4v4BKjcItybdVx5NoMn77bQ0nD5D
+ArONwhVLmIwMdF4Qd2/yUCzYpStNDJkwPTh6jGW6z3bZgyY5R/+U/qer4bODcRZaqGwSjKMTMKrA
+1EzRoMakR33FSVJgRpKZ6tUU6wQb3feq8JjMvHTlEppUnZ74khUv7ptobMpnR/Y5dshPUWk0kcZi
+xWfTCfSQVaAWm9Wa17B5WTyomZ5hmrLb0SkxmrADtSxQ0u1ub8rzePJwhJWGcBQuX+3id26Ly3xw
+eIgu8Sn/4m1gc4FPYeWoq+AzLOlu+TBncs2d7hqCZVZd+keoEQOimb236h66TSFkGV/ex7Au66k1
+rOPyNQnzlteXUYvFq4NH8R13gJxgtwItxOdzoJLCNJprXpKLM7MWMvM3Lm2gtUWt0Cpwbe0q7/sQ
+S7Ecb94x6z1Tg0/IfjpOMQvFe4uGAUziPiqxAe+KrPebdUn8OjiBLtiAV23guC5/7+EB6pasuSx5
+MCfe/tjjT2VT0Fp/DcDX+PNDAJ/70zjuxbgYF+PPfjwViEZTadRVhjjLkFEZ2fULJBMJgu5ND3Ft
+Xazql65dw+a2xF92lYhBy8WAlGjtdhvdQKxcND1BzsTlWreJQIvlDWiVmztbC/FYI1lHTGLXWXaI
+rCeeh2EZKA9kNyY5EiqFD01WKJUolExa+ZmLVzsSO1cue3Avyc/4SZKdOiZy9sqn6C+4DlQZoByx
+BMaW5FpcBcN3tB0DqztiST678yxaS/KZarON3b035XxvivpyXGTYZ7PP5JGJn/6cWMfnti0YhJJ0
+17toG3NkIpO2wRjDhvz95roNh8xMvl0HfDl3kcTwHArhkuH50voK/C//uMz3JEVGT0g1FOyMTNH1
+AAGp7p6cS2I0niS48bmXAAB75xlsisO++KUv4f4/+6rc3xR4TCv3PJmMNRTWG7Jsr6+00CTdXmR7
+qPNeKisVdK8RA8JMZW+8D29LvJXPtC9hY8oA21ZYeUXmqOFdhgol9s/YAq3rBowRUZ/GFAb1GYZF
+iYSYlIxJ4HrdxHJFrPhPfPZ5XPflnsMkh8ly9o32c3CiZwEACYVpm60GzK54NuOHR7jzvqyR+koJ
+dlxjvd5Aqy75kdFz4j3UtYuUuYPBwMI6MSLrpo2Hu0LH5uoCoz8PT+HTDw2tCpRmgpxgd2WbmJDb
+MM4UCrbDrW7cQGtdXrIyJbR3nGGdgiteoKFJg1avrqNkEshsd9Fgp6Fi9cHIE4x75FCoW1DGnDeg
+Ds1Kg5l7SEg1n8/FO7o7yDBXgc4RlKQrawTYqMu1NRvLUC3ZtPJo3oWnkXbk2ganI1jlXIjGga3I
+Ks1NKilK1GqywBKVocHMclEP4c0p5qwU4bGsepNwZ7PMYVoEtIxNNNcEe7G9soKUbqtvB6h25SVs
+kQhkaxsorTl2XqGImfA1E4z6Mhd9FHB4fwGBSYkzQpv6i421AJkz5wrwFlwVFQMoCDibpbKh1y8P
+USnknlrtEpuB/P0335rBT2TRD30953QBE/xQhrlge/6Z7S1UduTco2GGJ+w7MGcBzqZ8UckbYVaX
+EJCvUS0D9ZCsyxsBgqrU/fMigc0Esks8SRK6KEhYkpseBn1SvOdAinlHJXs8/AparflOvgZF/sT1
+9XV02CdRZj4Un/uEGBGj6kAz7BhDQ9uy3hq1Nlyuoc2yAwQMY0y+5XmI6FSOu9ZOMGWH7lt7b2Pw
+WAxfmOBjczPOx0WX5MW4GBfjI+Op8BRKrZFkOZI8RhDPm4ccxDOxVqaR4Do5EnZW2gB31ZMxXfyZ
+iWvtbQBAUMTIRrKz1/0KLIfINc9AYslOOreqeZzDDihkYhtQiRy3GCei0gxgYs7g0yNxDcKguxWc
+ENFoj0MwkoARTOF1xNp6VRcW4cYFLyFXCQx29VXtJnIiDFNjAJdERzaZhMyqBYfJwMBQQEol7SzF
+NJZkZqPWQHd9TqQqx53EIYIzzqFvoqSFajsVtHy5nkasodgFatTYoNVqwyaKsYCJ6VSSXd40RM5k
+V5kDGdmLtCZLdqRgJERFVh3U6EGYeRUgwxV0DpuWt81uwW3vGZy6Uq1WJxZutOV7v/PWB0hsMW1/
+44tA79tyiOYa5/gwx6UNOdYLl1cxJuHpQB2gRhh6YNbgFpRyI2y+W27AdQgfNkKUBD54Xhe2IdY9
+0yNoCr/4ZI3S5wOkJJuNjUMookwt00BBFimLkOpLy2vYrEjytJpkULO59FwM3yeTuFEgmV8biQ5m
+0xk0E977+3cwvCel9s7UAXYYTl5ZQ71F7guPvBBhil4sSd7AKeFDIOjrBxp2nSV1T6H8ZKLTT8+m
+MEsjZGkOqncjjyOME8ZsSY5jUlENT3vwSVpy+lBAM9pysMJMb63SQkH8ArQJbZB/r6zCmBGXTohv
+YWRwmRUvdY6Y4jOOq2A3qTs52oXB+NrZYvXBThH15WFERQwzJMFJex2K1G26GkBXCPMl4MdIc0Tz
+HSSZfUg5Hmco+OLFpjxYx1gDyMWnLBdg6OK4PpqENjuBCc+Yt5zMwxmFhC6wV7XRP5W5KCY2Gi/J
+S6FcBZOutEUQksoTYE4T5lkwuCFn6WyhjFVEQxT7FGYlCU0YTxFS+zDb70MVEstXag6suZhmPEaZ
+yfECgniWLys8+UDOVwQ5Np+VsCL6ym3UmGv57XeBGyS4Od2b61wW2CRZTuaOMRnLvZ6eRZgwfHhx
+s4WgIRucRd1FN3ABhmZlqTBvu9VOBuWTYk3VUZ7PadrI8Zj04BCR9P6jY9zekwvSSqOkYVA0IDEs
+zMjGrcsUObs5s2UHiuEYHBsq5NpRkr8wi2WwZQLWrMSpZrja30WFm9fyrRnWdpY4t6TcbxhosrN3
+WJ7jHtmxD93HUCS4QXayYCH/uOMifLgYF+NifGQ8FZ4CigIYTZDECXLCVmt+jjk/hA3gJJR/3Hly
+hLVCLPDJgVjlYaZRIYHITmsF2Vh24r4awqBWZCUawib1mN0gOYbpLCTCMcmgmMBJvBIG4V+GbiLU
+bGiCeA9uWYVPubX3PzhCYyoW4RkrQaW7DQDwvBkMKikbtFYaMfK5y4gZLCZSq8urSGfy89kT8X6M
+3gE8yql7jSW4TTmWbWnYGff+cIqMrMUTeg+BZy90HvuzGU6JFNzYuoYknROAKNi0mnOobpGIWw0A
+eb9AePoQABCVBgpyPQReiuZlhjec71Jp5KQwe/z4AVZnMi9r14CAzM0eNBR5FHKiFYu0wITnm+QN
+FERQvvCLP4EH//Cbcuw+8GjeSEhVtXZFQRvybHZPp6gxHCnOZ5iSrXhYmSDYEfdZJeKlxZUMc28q
+nxmIKUSTYgKNOdzcBlLqdKby/bCqEI/lmvfuzDCj3VW2CUW2cU2SnRwhTsm6fXpyiGx9GwAwUzOY
+Fc536iEf8Wb4zNPgDKtkJg8+dw1LqxKCHIx7mAyIaymOMGWz1eUVejwzG/lU0KZhpLGWyr2+0HkO
+aSzox4qh5+1zH3s8FZuCYSgEVRfmrFzQsOezFBZd6sRUOOyJW/ade4/wTCDZ4oNcFt3R0Slau7JI
+XSdASqiwnyg0V8VNjmGgmIvB0IU1XAfFWF54q1FHRoZmz25B89jKU9CQ4zl6DlhqwmsIUMQ2DlFg
+rtiUYkpMaTXXKOfMStQlzKGRJeIyWoaBMqUQTeLD9Ml61JQFGvdspKQ1T+IJnAFbo6spatSEVGWJ
+kxPSelONyjRd5EQ3RYMIQUsWSrC1jWCFVZtJHxOGZjpkfiVLUCRynecHEzDtAqUGUHMYd2aCivJw
+IymFxWWJVHODWL6KY5KTFPspqmR57jTrMBn+5GSNGkxivPuBHCxzD+FTQPi93zLRorhr6pWYkhRp
+nnDvAXg4kmP5HzzBjEv4dDDD6YTl3lzDqEpl44UVmZ96asMh41E0M5B48mKFk2jxFjjVCmw+64Ls
+TuEwwvmR5Ff2Mg2PlQ9Pmxil81CDQpemg8FQzvf12/u4/qx878Z0Cc2mxMWzOIRBBm09pOBQrvHo
+D6W0/HC3WHCJBjsu7I5swueOiynv7/FI5q0SmnDmeqVJhGEhn7032odxQHBarhbKUh93XIQPF+Ni
+XIyPjKfCU7BsB+2VDQT1ElUqNEfTHo6GAn09nQwwi8QJerB/hJxdbRk1DM96E3zrex8AAB49PAJo
+SZ/pdrEx4667pACDicszEoEES8g9CUFUowObpCZlOEVCMZhhmKJGjoQ5tNl0PKxdE7l7oz/Fo7ff
+AgCcHEaYGbKLe7kHi1Da0heLULgGSlKeJcMCA9JE96NdNEh/NjojM7AVwZ3LkSFFSe7+bBZCrYjV
+qaD5IciG1gV2BpO6D2mWYVbKZwPXXojaxKM+QsKV501CdW2jYAPPcBaisSOJOh0qHB+KV/DgwRkG
+mfy8Rm/Lc9fQeF6ShO3VKpw5Z+RhjtGxhELl+hJqDSYmC7Fs33j9Pfw/X/sDAMAGArRW5HzmdBNj
+ugX/8V8FfuefyfHm0slGVOKI7rf9Xol+XU5YC0wsu5RtDyf4vW+K5X2TruezlQCbTTlH/fkaaqz/
+2ypFcsS50MewPPIhkOglGk/w/n2Zl2Zm4pUN8Wj2YoUHAxK/MHlsTEPkA/n+SXgf77wpye/q9jpq
+BRvMKoBB8Fx9iYQzyDGx5Ger2UOQEXx25iAlIOskncE8F8+jTco+u8xQmvI8gtjCKqtr+rCBPH0P
+AOA3FdhMvADL/bDxdGwKlonucgtRZMKgr2M5GSJyxQySCKOJ5BEOYhOdqbhGJfskjKqDnKxKbncD
+/gpzAJ0tVOgym8YSFMlWp1V5iGl5AocvjZ72JcsPASQVdBON6RBug5TcNbm2QudQ8/6C7S5qp5SJ
+nxZwFGN8VcKMhSfPM2VxGJGJnESqruNj5dqcV1JBEwjT7sk9R3GGEZmnstICcorP+C0o5jiifIjd
+Q/m9ozgXysWYsWpu5Dg7lrLfvVuHsD4j51ZqBLcpiztn7HwWjzCeS7XrKpaYG5gkISYTmdt2sw2H
+cfmUm6ZVBQyGZcW0j4wgrKlxBod5F8MvAEqjh7fkHI9efxODU9m8IjuFRUBZnvbhkLTkV38LuMww
+5pyiMJmp4TDGH1gjWOz8LDtVNJeJWK1asMfsfyE5Tbhmwbomf6+trCA9l/UUuzaSSNYFTAcu8zUG
+2WMHh3u4fUdexhMrQ2LJ2hslDkrmfHQkocb+yQAWn4NOc4xo1EbOOk4g5XPPWIU9ld+bbLN3Jh6W
+r0m+qvWMhyKUa/ve2yfon8v3zvu7MBzyR47kPlodD8VU1vLB2RhvrMv6fVu/hVZC4N+shD3H18lT
+DwAAIABJREFUOuHjjYvw4WJcjIvxkfFUeAoKGg4S2EYFR+w8a6/7+MyGdDMOJ32cD7nzlRkmhC4b
+Q9kCz0chUrqJTmeATUvc2W7VQ60pCcFqxcOYFq8/Epern03QzAnbnVZQVMlabGVQ5Fyo17sgJQOi
+jLJk0xQGOQozN4DblZpw0ChgRgxBDAvxRHb06RnrzskMQVUsm6VMgFUCt2IBhLwWDA0CK4dxW6zA
++4/3UCMjdHe5gZI++mj4GA8GPV4zE6a2RkwvRpcm9vfE22i1+jjita02/MX9lbM5ftiEUZW/D56c
+4cmeeDmOYYFUD/AbK7hOrIZBbsiivbJIwD54Yw9/RKv67E4Nr35GrF/ba0Oz8vHaw+8AAN58dIaa
+S6+itNAnu/SL//Xfwft/T9j7jBBUzcSiMzSzgQn5HA1DocJkrjlN0SKEvIYaNq5QXp4JvtWWgyXS
+qlXrAU4PJDm8vz9Gp8tnXQHIrYMRRV/e6E+wO5STVzoezilBcDYdoaRXpFmRiMopGoRVlyiRRBTl
+USYyJiP1eYjzc/HuLPba2JaN1gl5N32N8xNZL/duvYteLmv17HSGjJW0UhxTeJUSNhml86qBV1P5
+Q772E+jHvw4AaGoNMlV87HHhKVyMi3ExPjKeCk8BSgGOjaMow4jJwNWihiaTYH7FBainoGEgNUnZ
+xjr4NIqgSKW2v3cKOyK3wJ0zJC+TBisAnBoTd6RoQ30ZeSFxb+Iai7KXajcQkb8hMC24rIWb9jze
+DDAdym6fpDUE7NSrZiUKSDzY8jsInpduzd49+eydvROsZeLF7Ly6gTyXcw+TQ5i5WDbNhJoZVdFn
+Y8x5NsGN668AANpba5hNJIG39+4x9k/nTFW0mIUFvynWSuXpQmBs34zR4vmaloHAn3f+UW+isJBf
+YkPU7F28e1di2W7rJirPCcY4TAIc1OQ8TepsrFyvwc+lrp4dmOjdku68v3LlFaw/84JcU3aAnJYy
+otsxzDK0iCa1N+qIjmS+3/s/pnCJi1BKI+IzWaO31tdYkNVWXAsum4RMx8aYcfRyvYaxJpKRUON4
+ohCFco675SmSkSSY94+P0WwKy5LRdpHTimcTYh5i4CHEy3lhYxn1tjzrk6O7yNkIZbBZL2g5sGsy
+91bkYFSlTJ9hwd2R566mGn0yR1Vn8tmrL9+AyQT7bDCASV2Iq19Okb8h87Y/u71Ajt64JB706qUq
+khPBkzimi+o1WUO3BofYSOVZwh3C/IR6ME/HpgATha6h09FYOZMXtlHzYLPbz/NMNAOZNGiNCfkM
+a9QLfM5pY7kuJBYba+tYWZMHt1apYevKtnyt4i4y2DO63MlwCoM8idqwUKlJ8s3yXQyKua6kj8sO
+E4UmobE6wMyQxdNMcwwImsn6BSJKkdfSHH5BUpNtCWFGxgw2tSKrfgv+qhy39DdhUcikJDDn9M19
+DIm5f3XjJrYJhLF8E2en4vp+++vvYHQmxzMYPqVIsU548cR14ThUCirH6LK3wx8a0BYVicgA7Dg2
+Gg1ZSK+8+CpS4vPHh8fYrMuGdGV9B9aqHK9VsDO07qE8k2veudbBf2r+AgDg5ssvwCbOYjo8wdlj
+kuCM5Li+4yHx5SW+YdgYMfxJR/8ZZsRFLDeBLhe04rMbDEGoEdAqLdSpDxk0FK6Riv3F9cvokiSm
+syPhYbfSgsmN8+DtWxiaspnu2MtYalHFejjB4Ylc55N7koD94PYQM15D/3wERWzBZBihyOZrR7be
+etTGYugU/SNJYD45nOHFl+QcneUqfDKF610xIEsrTTiuuP7TmzXUnrAVX7egTyQE258M0UjkmV1r
+syvV8fGYEPz4eIZ7fyihWXs/xJkrm15sqY+dYJyPi/DhYlyMi/GR8ZR4CgUMTBGmAZZZgzVKC2ck
+vEgzwGENOi9KDIZiKUdkxb3UXsKz5ONfqdvokKC12ViCQUizchKEVPlNZiTSGOcwO/K7RmUZJl3R
+eDRBTtIWDSBVsutWWFocxaeIT+XvFa9AXYs1ivxDtKiIXJZ95CReDba2AQDXVgKc0/pPPngPJfUi
+THcVqEnYFL8j1qV///fRYtPO6s4OfJKiJOk5jp/IMR6cDpEQL1Cw7pTmFqZ8rM2VAJUqdQvPFSKi
+KTteAyD4tULYsfIAeybHra9WsN0RC/yNtx7i/Otia6pfiOEMpOFJrYglzd9SSEpxYbdSA9WrYi19
+30eey/1FUwuP3tsFAOwdybwFgYmolGc6tSpYuyY/P/7OMTxiBOJEo8Uk55vkWEgNvQgle0UGxa5a
+NdU4YLemHbyByTFFXfYk9MkaGnFP1s3xw/soCVGu7zRhUWIgSjNMpvL79+5xjschIuJT3r8bwWIZ
+MipL6EJ+P+9CPJ6FqDGksNICx7k8/1t7t/HiQ8G1dH7kBhrUi8g78v/w8AlmVbm2o8djjCaiv9F7
+P8PjUOjy1HAA15frzEYsOVtDTJ7I+n370T7ePxUv7KR3ChXLZ0fT8uOqxS2G0p8UA/mvYbz6yiv6
+W9/8JsoyREJc62R0jnIiN3l8/4/w2m/+cwDAo70RBhQSNZmxL3WBMYlAjrIQBuvGlmPMvVXYnoIm
+q21CV9XUDlJmkK3CQDIn9JgVGLIzUGU5Csx7T+W4P/ezX8bjhwJY8kdDDAlIMqFhmawM5CZ8blrz
+3odsNsOY0OYvVS7hdkOOu5M9QW8sFzckUD13gSgR97RXGrD4AoWGCbYRwLVK2MQsaDIieWWAjHyN
+GjMcnbCTVBmo1Kq8bxu2Lz87xlwoViOmBmUaTxfy6oZhoM7rT+wcZUj4N7MVShso53D0tEDCF8WC
+hdxiv0MUouDSrNE5naAE0wQfWbS7r99HRGr/42kVINX86F+J6Mm+vocdm5mSzhI8XqeLBDn5LZ+s
+jOEcEmdSZd9G2YB5kxWj3RIRmabTqY0hvf6t9g3cfEY2vVaX+Jaqh5ILY5wWOBnI+Uo/x7M0qQVJ
+EMPEg8VqCHoFhmRFGj64i6+89Q4A4Ltfex3DM9ksN1YkrJz5M/TuSUirDRdffEEYqZae8VHpyfuw
+e3qCVkBxoSsSHv+1n/wFrGxJKOH7JsaZbIT+xEXpEHMzqsF9QfIZzfbaG1rrH8EPGRfhw8W4GBfj
+I+PpCB+UgmEqaG2iIG+dY2gMzoTffvrgDMeEtp7nI9y6Ix7CCtFnfTdEPGSCLslQ1bRQfok8Jrff
+BFAlrSoteOCqhTBHiQxZykYjR8Fi7bksCljkXzC4hx6eHiHrUSDEyAF6G7ZloN6WDPByZMClNSYa
+FjtOF69H4navti5j35Kfd47OcNcRt3pGz2bDVbij5/3/BrTBR6VNGKz5xypDyUfomHL/0zKEq8T0
+VSobKE92OckWwKRqURhQdJkNWuUiBFLKseVFDnrwUKa5kHMvC42CSEaD9XoDxqK7VBt6MceOFaBQ
+rIgoAwXvJVssuRTEiYJMA3KOVopdcgvERYT0HbG2mS86Dl+8/MwiLEmzEhTjRqX1DLKuJO7qZR1g
+XtoMJFE3ng5QDGRtVVs5lj3xCAazEwyPBMsxVg/gmDd5L3Nv0oTNZF7DtlESW1EMEhSKoSCTyyEy
+VCjZZ7gxbELWB8fn+O7XpPOzf/QYuUvpPXpVcRjCdOXnTucynDZ5KAwHalWe2bPdZ9FLBQm5F8ta
+ee/8fSxv/CwAYJoNEJOM0a1N4NiSaNU1G3aa4ZOMp2NTgNC8K+XAJky0rOY4pA7gr3/j1/HuibxA
+fpbDIN/fhD0QKQpovhR+YGGF5KehqTEgueYoKRetyuDCtosUJnkEM1UuJNddqwKLOe7CSlDyhbS5
++KPBAIkp19ndcmCRAnw5r+Hlq0ICmic5Ll2Tigi6cqxrN5/Ff2BSct0ssD55BADY/58eoctzOA/l
+PmY1E12+mUViw1dzspAq0pHEqmVZAKyIxOQcrJguXENcX8fxobhB2p4Jz5A3KEszaM5dmCX8nYYm
+1Ng0TZgWy6+WuSDTLQsNmB9yaAKA0iU0Y2Sr0CABFEzPXIQ2RZEh45z7/H5cAMl8rSpAceM8m5lo
+kJa+4YYYnsocHYcCpho1HXTaQjJSM89QNQgVbjpw6lIazYcxcpKTxMyZVMclEuaBDN+FvcRHs3oV
+LZ85jH6JcCaxfX2JVRkYi/b63CyxxJ+HlSlGFL21qeLbsQ0UBcPf4RjvPJR+nN+7fQsHPdnUqvBg
+MI6tELxUKxvIViUkuHSlgxvLspHVGwE0ofz+cgPbkbzoj09EoLb3zkPsV+Uc9TUbNoWNSmsJkc2y
+p1GH/si2+8PHpwoflFJNpdQ/VUp9oJS6rZT6caVUWyn1O0qpe/x/69Oc42JcjIvxZzs+rafwKwD+
+pdb6byqlHAABgL8H4Pe01n9fKfV3AfxdiEDMHz+0RpHHmKsH6kmJ6FgkyN587xRnibjrl9dbuLJE
+WbRSfnc8G8MjMKm53sTLV8VSpoWDd96XRNs7d48wITS3pLSX51pIaAWzMEcxByfpHAnxrnnmLNxj
+j5mxOB2jahD8ow34NUqVX9nG9lxpOq+j+5xknBvXxXtoqRY0qbzNogVdiDv4ZbeKbz/8DQBA//+W
+hNPveRn2SF5SAFi+LFn0xPgMpoV0F46KU+Tx3CuSa0syEx7FVBqlhzkXl2FUUKh533+CefU6oTx9
+UWZwWfO3TQPgvKgyX/A8wgAcJrAc8hG4hbmY1yzLodXcw3Bg0eJnsxCg5zG1SbZo9XF1JBbsXgEw
+CkClXuLwgSTM7KrC6JJcXz0W78BzLJimuPuOAwTkRLTKFIbNprAlEzYTswYrTQlmMDpioe2iCkWR
+GMcqQZQ6zKU2LF+uySXvouEqGLbcv5sBKT0zp6wgJpgo7kk4423WUSTzKtAYj96Ttbd/6+to8U27
+vlLD7VMJheNc7uOZrTZWV24AADo31tGkrmTdtNEkO7TtNuFXJJnc7pJ4pfcEp493AQCt+g78ZbJn
+1ztISESEoAfD/z78xMcYn0ZgtgHgLwD4jwBAa50CSJVSfx3Al/ixX4WIxPyxm4LWBfJkiiwcIGep
+bHr2bXz7DcnYJmWCFaIKf+RGF1+4/lkAwMRlJrw3xM4L8uJtXnoFispSZRTjp/+SPICTr97Dg315
+SI/25Ryn9hjGRCbytaMeeiQqmRgxypjdk0YGi9FvSMz9emkj9BifVrvYJuX8lz77HLrLsnjNcoDu
+y4Loq3gSRiinhEHxW+VV8RIoz/4XbPylS7KBvGX9mkzK7/bwqwXRanmB2bmUKq/URjiwxLUdzYCY
+DEoqJ7mlAbiF3NOkGMHji2lYFpJMNlzPzhYtcwueQaXhBfLZzaUlHM1k3nRSwK5RH9H0kWgmPVhF
+QVWhNZN5iZJ0US60rQQVbpxFTSGndsJWQDLT6k8hrf02AKD5JAfDfaS9HjxS6r9z9w10udm3KB3f
+zGuwKCRrGhZycnda1hJKksQUMFCypFxGzJNEOVLGKEUxhpfJPUVxuCAC9qoWbFfmVn24my4o0k31
+IZ27aZeYvC9uvO+IkTof54u29YPTI+x+73V5Dv0IbsCcV3SEfl+uaUCh3K2lAKtNmYCg14PFGqe7
+3YGtSMWvTGhyN66tykveMDIUFZY18whgmdUcP4RdyjUlxQjuPFb6mOPThA87ECKcf6iUelMp9b8q
+pSoAVrTWR/zMMYCVH/Tl75eiP2OJ5mJcjIvx5z8+TfhgAXgFwC9rrV9TSv0KJFRYDK21Vkr9QCDE
+90vRv/TKZ3WEHOn4ZEHZfbh3gD94U5IzcVri+pI4mI3mCgrCRLdoHNuXO1i5+TwAwK+vIiPoSbsG
+fGoQtv7CFVw+lbrw8Fws5p27u7h1KC7cytDGmNn3SapRsBautYHCnisNi9VqtDx4LJUvr2/ix35O
+YMBra9dRI4jIGLXgOnTbXE6zqaCsOa7fWtBkWeYVeF2xwFev/jQA4OzxV3HpvmS3s3ECo5AP76Z3
+kaYEXGgfOQk+5lgBQ9WQu+KtVJsteKdvACA7ITPcOi+RMjyaQ1tMw4RL8o/W5Q56u3PAvF5UVFqV
+OmJmsikIDq0SoMpkpWkiZdjRarUQ2aQiH2go0r0PtLj74XIVtZ54d0N190PMQtvHuCcVh6KzjO48
+5InlHElNw84EEjzDCSxWjOyqCdOk2IsPlCfEmTRJSOJbUKU893xaIiQOJR9lMMkC7bk+FMlqbFZn
+lKEwdxWUUnBdWQNmVmBckXPnxZTn6CILZX6+/f7reO9E7qOMDexT7WzcD5Fx7lYchprXuhjQsle2
+AxjzuozXRMwySqQzmOQCNT3S8tdWkXJ6EpQYsyo1OB9is076vqUd6E+oBvNpPIUDAAda69f4738K
+2SROlFJrAMD/n36Kc1yMi3Ex/ozHn9hT0FofK6X2lVI3tdZ3APwMgFv87z8E8PfxMaXodVEiG08Q
+Fg/R2xfk2jd+83fRP5ekj2UZ2Lks1upGpQuDLERlRfa0pXobHkt2mE2RstZllBkyEnAamYsKBQVc
+ciFUKldQZS192j9HnMjOf6yMBQQ3K0PkcxJTxpM77SpCNt989rlLuFLZBgAUg32c8HzrnS0oxto6
+kdJqWfdgkFxTGRo6Jp4CCQwlSaTqung8z6z38RcvSdKxe6LxhArOe2dDqJLWWiu0TclXTNjA5DgJ
+fCbAKqkNm/mHJJnCi5l8Q4mSVmVuFRzHhEWnbjQaYG7OPMtAnY1UlYqNOst3XcLRkWoYpnhYG40J
+HHaw2naA3YFcx5kqoFi27DhSTo0OUrRaZF7yTCmvAnBKG5rCMVdmTTRouUe5JM7OlYLJ+r6bNgCb
+sHLLgsHlbMycBf4iowaGqQxo6n6k4x4GZLqyUsA7D/jZaBHsziHMyJUkEwAUSsNkrkEbCgU1IkJ6
+lVthgZTaC/vvnOO0FI+1ZpeYEAkZ5hoeE9qvdsUj+Lz1DErC0a1Yo1GRtdxIPWSleIvnaYKaRRLe
+WK5dmRoJmaWSMEN8ypKkGePcl3dkPU1g+p9M+eHTVh9+GcA/YuXhIYC/DVln/0Qp9UsAHgP4937Y
+QbQukRQxBvdmeOddcTy++s3HUJT4XmvaWCanXuaeo1FKc4NNymvDtlBqWTSqaMCke11mM4D6kKoR
+wAj5CiTsZVAKq1QbeulsGWd9EocYCntcQIlhIqWoh6Y60nJ3HTUKpu7s1GAF8sAnD4FoKvX0xK/A
+HEli065J2OKEITSpy8rIBGxqTBYJlCHHqNflwVd+9sewtEVSlH/+Vfyfrwke3kGOaE6sUeYAE3A5
+AS1m5TkMEiYo99+GxocEfcX3kZOU3OnmjqVpKsRs6S1OJjD4l5WlOrZvyjnW7SvI+QI0GRI13Bw1
+rrnjuI8aodnad9DdF/f59kMLGc93xs0kn0xR8aXunhQncHhtpcrRoz5iayVCQlrpuQBQ0R+gqBP7
+P4qhloU4xYhOkXM569SB5otnEndQFikMit1YlolGwtq9F2BC2rTqsAl3IEbJYIVLqwzKYLK2wAKn
+YWgTMyb+jCdSvog3T3C4Rwq23glMdr7mRYaQfKJloVGtyPF2NmUNDWpH6CayRtz2aKG7iVCDekGI
+zo9gL7G9npJjjpHDZkkstXLM2A5eeDZwW469slIHzE+GCvhUm4LW+i0APwhL/TOf5rgX42JcjD+/
+8XQgGosSejhDHkxwdCIW+ngygUeU35VubaHbdx4nqNfEpWor8fUS24BBshSrnCFl0462rYVKMkoT
++ROxGtPhvKffQHdDdujgQQ/LLdl1JxNgFNEaJxFychzkpMN1DIVKne6+X0eeyQ5tY4KlG0LYEdSW
+4Xbk2JZNnoaKA8OZi4n4AF1tI63AmssrU64O+SpcNvN8fvQYCVFzX//WY3zzTO7fVz7qhFg7piTf
+3OIUtin3GUY9BITohgA05skzvUg+GQudSL1AiBpjjc6qHK+ztoaV5wQSvDltYKqI3qvKs2lt2vDP
+xBupTZeQZeKxlZ6FaEyqO8fGbCrPLxjTu/Ob8ElKGpiaNCaAlwBrm2IpzyYRKqa4we15QjRwkJIx
+OfJsePOE4IGDok5ei3IKiwlrg6527mYomOy0MsAm/DkuSgyVMHD74QhOT/ACxRo9LKfAPFeulEJJ
+hCiUwvChoC01j1sceZgSmq8Qo8Zms1mhUDCT6iiFuin39+ScWJGjMTpNCRnsSRtGKX8/rh0BD0mU
+O8thpHwH2CQWjAy4DUlm22tNHKeC6zF7GeKmJHHPDktYjU+WaHwqNgVlmbC6bdROm6h64uq4prng
+vrMMG40GZbjd5gLvby3Jy2YECaZsby6OMwQUTbWqbWiyIuXjHIkhldIsp1JS1cF4l+xNJwk81t51
+bKCoEIdguVBzfj3yRwatNVz97KsAgMrKOuKBnDvfbKCyKTkB11ZQrbmeoVyD6XgAXzwYFkAQleEE
+AMVQNLP3MCIo5xkAQOvZYzzTl7/fPRrCJzdlp34ZbvUaAGA8lnuuJQcoHaoNJR7GDB9c14JDkJVO
+EszXtmaIZphATm7HvK3x7CvSWffTP/5ZJGRpsicKl5h9X6pKOKfdBO1tOV8aDZEey98Pe08wZRhX
+NwyM2CvBpD+ypkYtECCTOe3BVewZ8G14JNTJkgmyPkM3YigQRrCJMbA2cygye8fNfCEc7C65KNgH
+oBsEIWXZokfFsiwoVjWsfAJOC8q4QFIlZmHeG2Li+zZQE8Y8PNcaQ2b+o1BwE8djB5MjCRnHeQiQ
+BzMM80Uo7NgKAVW56kty7dWuiVpdwqDlV2ooZzQWEwc5gUzdLQ3fJ6sTJQPMrAHdJDWAUqiys3My
+OcUqRYCcCpBnn4yl8aJL8mJcjIvxkfFUeAoGDATKQXGlhRevilX6Ss0BpR6godGeQ2btALfuyM43
+Zxm+eubDIfVZapwheFHSHMFyA6YrbuK0d4beiPqBI7ntqtuBye47t2PgMRl1jxKg64glrFdSnM0R
+hLQoluOjsyZ/77YvYcgGl/RxBjOQ3dxutqBcyqklcw3HIRRht4bbAlhFsGstGObcBFEq7qiP0YG4
+ovlhiM46LeLDEKfUg6gaMV5l5eMyod2V8gbODLFs48MzTKcySRVbY5Vszb10iJKyeFrJnNiWA4uI
+OAM21FiuORgWmD0UNOV+f4q8K3Px5R8V76fltvHkNUlsajOGWVDifmphnsvLjRSKoAy7kNCnmizj
++bZ8Vl12cc5krmlacNjNuZMFUKHc95hNYLHXQIfu+lK6Ds0GulD5cEPx2Iw0gEUgSZLJunHyEuyZ
+g+kkKCpk1UYA75yIP51/KJzCxK0RrUC78y5QLLpHTaVw8liuOTHkmXaHQ1js1nUcC6E1n4AM8+iw
+EnioM4T0yR/Zvl9B9pJUqNzkJei63NOoF2G0J/NVu7qJkqQ9ypCQoZcM4BHpu1SrIGDxP4yAEdWv
+l89OYLV/IH7w/3c8FZuCMgDLNeHNukBNbnLZDuC1ZHIudzoIDVmMj/bexp0Hslu8cUse/M9+6Tpe
+WhdQTNCpI5sKW00SV1GeykLplxHsqsSizZtSxqs5bTQDiSGjyRn8t8X1m6QxtCWT2uguoxwxD8Ae
+gEpLoxyIyxitnWLySB6WYfrwuIDgWSgJQZ4k8hL3Xv9dTMfyIljmKmrPSdzXtU6gGKMXkWyK50cP
+YR7LZ/2VAA229P7UX36C3/1H8vSfzEZ4d7gLAHh5+0cBAO5GgPRE5sc56sImF2PgBPBYMfHzEPb5
+PE4mbNe04BGC7JYZ3n4g8fLz7RU4m/JibV2tYkg2qMdPRIHp8UEbJxP2KkymWGkSvFSvYXVF5i3P
+SqSci4SiPRVzAuPSZwAA47dPUKMCVq5niIcMJdomvJBhQykvghOOoAJ5juUYQFUqGOnBXUyHDFeW
+NlCxGWs7hMJXAD2QuUjDFJMzuebZeIYjwt+XjSbcdamOWM5PAgC0ngCWbLhFWcBcxA8GhpAX+eyx
+fCfpujBn3HlmGRIC5wZxJv0kAAJT4fGIXJ+QXMYXVnK8WkiomBXHCB/Jcb/71bu494Bl6fc6aLdk
+g2wui7ExbQ86J1TeTOGRmHbDAaaPJb+QP29CB+zW/ZjjIny4GBfjYnxkPBWeArSCqW04jQATCKNw
+bJX48k3BIyyv1/GAMl9f+97pAtTkEPprfuchrOfEq3i1vYMZLcaT797D6ERwA+kwRCPgzj2QBGCW
+v4WSDLl/+NVb2KMEV5aXGBCE0h6N4bGHb8SisaddmAQITfuPUVTYF99YQWaTkKS3j/cfSkPM/pvC
+slsxmljeJpx1ZQ0WE3ghYpgZ6bPYkdm+3IT/PKnHjRAFk2TPm8/is9+Q4x7df4DziGrFt8Uynk03
+4ROY02v48FJ2hPoePGIk2ssB0plYm5AgJcM2UVAZ2imBlBTnX9s9xC+9KknVZy+/jA9OxcK++754
+RydP3oXFxp6NShNTj9UMVSJNOV+WjYL1hYDJwO76ClYZBrWSGQ4YCup+iCrneX+Yoj5Pqh2LZxZP
+chiU97sbnSK7K5qJg6MeUi7nxvYZnrsinuNaV+bbahs4n8qznh0+QZ/XMYwSnPNZJrMIyVvyvfp1
+4iaaGg4xK6XpoyCDuG0CmiQqKb2D80oDG035+9JSHQ8ey/25roUasQnbS22MSQ5UMiQ+tXyMON/B
+4wKH9+V6fufkCD49upVVD/V1SUauvyLeUduswmECM7FLHAXyTBplBiMiC/RugfoNelsfczwVm4Iy
+FAzPghM3UQsEt//S9jZuvigu1f7BI3zwnkzw/ukUA6oJ+cyEG6cTzGKJa0/LAX7+8/8OAKBxqYYB
+GZv+1Xc/wCFj491cvr/UqCGOxeXSgxjlomQHpASpjKAxJxWvkklp/dpV1Hbk2gqjD9ecbxomij3q
+X8a38C+/8jUAwGvMgXyhewMbviywRjZAjew+V6urIAweYY8kK66DLWomRtkJbr8l7uD//hv/F24f
+SAw7zQtouqL3A3E5l4cmHEsWzcalVzBIJJSqNJbQviwh2NLYXJTWesfkZYwimOxwNGv6tVtUAAAg
+AElEQVQBEtLWP3i4h/un0u356ucq+ExT7rvjSe7kfXeG1/9I7u+DdAIVyzFm5RCP78sziacpnHnb
+OUFKT2YDPJrIdZ6nagHSgVMi4hqeIUV1zquZMQdixliuyou7+kIdx0wspdUGVphfsJcqOJhRHqBL
+dGfsIWQO5zSawSYCtlEzURpkUDqNcMru0Op92fy2Lq2h4LpwDPVh96TWSBkSRCTyMYMxklCex0yV
+H7JXaQMEnOKoBDqspF1ekrBkueHA3GB79moNzzSeBQD8J5tDjKkSFgc1FARDvfdQ5nWn0sRGR6pP
+lZaFapeiuaMRVtj5GbRd2HqATzIuwoeLcTEuxkfGU+EpAABKBdOpYoUJJ9uPEbMzbnf/GPtDSRi6
+hoLHLrH1FdntX77exHffk+Tbb712ir/8giTo1pLLmJEU5JLdhnFdsrDakL+vBCme7Mq++CQOsUlF
+aD+1cRRymx/NYJDerRaIy+3ZPkxKlicHIQaHbwMAjvanGI7+MQDg248PkLKakZOFd7//AWY9sUAv
+jafYLcTaVjpXUSG5x/EHcm0fRDGuVyXM2V++g9MPRHnpnb1HGFMZCyigyG+gJ+I6TuIIDZ8CMe0f
+hUUKNscrsQb2h1RLjE7kPCeFzLEqADNlL4nfXCgfN4MA69iWZ1JWYdL8XbLl2pLuMr43kYTZxK8i
+Y1+KHSeYMruuqwpNUrOdTuiKnw6QGAQ6mQV4asRJuugfWO0nsEhvnbIskOQGBk/E+xl9J8Thkfy9
+vrmGzRWxsJVGgMNQriMk2MpNDCQ9si6PFQ7HUnWaHoY4JZO26aTwNuUZXz7cBQBcHX0O2hOrm1ga
+NkNWZSj02ImZRHKu7p6PmMzOs0mIiKAM3zCgCTwa9sewYnkmGy3Na7BwaybJzm5Zw8ameFAvtX8S
+7/pSgfrDbIjJHaFe2yPpZzMAfvbnxYv5sdZL6JgCljvYPYXalLl9RV+DN/tktv/CU7gYF+NifGQ8
+FZ7C/8fem8Valp3nYd/a83Dm4Z4711zV3WyyOUgkW5RlRbKjKBIsBWYUOXlJZEAI4MBA4of4JXAe
+gkAIAiQBAiRAEiWQHyzbshBJlgzIokRTlihKJNUzm9XVNdyqW3c+89nz3isP/7cP2YYdVqsTuwTc
+BRB9eOucvddee+/1T9//fRoaWhfI5udIKvEIdhtb2NiV/MK16S4mtFDRLMeE0/7C54St6NruHo7V
+PwEANB6scDGR5FNvtYmNTUHm/eTP7SMuxUrPC0nKJdlTTN+TPMM3v/aH2CK//x8vYkzYJXdiGtC0
+bpuMSWfzI5SvSxw6zQ8QH4qleP3uISZPJffxB48u0GKiIG2JxdjSJr7x+xL7OzcrqIXElNc2nuDd
+uVjbN/+57PDfnMTY5925u1rh/EDOcZ5kqOhN5VpDkYx2xVi4cCyU7FoMT04Q+nUyrEJJWHXfbOH7
+bsm6hMQujNMYq1O5vqubm9i7Lp7A3u07+OS/J+XOdncD548kni0sWcsb29fx6g+Tfrnpw/IkeTpZ
+HuD2lnh037r7BG9lsl7psqZuu8ADcghMJyUsXtM8P8f5kXhN3WEDoSt4iJBdq+nyESo2bo12NzHc
+kTJr/6oLh81YRVuhu6iJV9kx6ztQocxtsDGAQanFiZejZRBvcJ6jf4UJwRfk2curQxi2JPiSRYZw
+IN6drlxkbJAr2EW6cnIoJrnTcYSMeZle08H2SK7DGHRw+4Yc75Vbkg+YPn0fwyX5Foo5tgmnb33s
+Jm63SM2GHOd3ZZ2XbXqHqcYnb0p+pb8zxPyMzFMVsHwknod66UWoDu/PM47nYlOoygLx4hTILhAz
+A75Sc/gtuYk3rt2EQ2hoEjhQM7nhI+L60/wIVwN5uP/iX72KrULu+IM338TNn/xBAMC19scwORA3
+3x/Li9e49nGUQ9kUnPFDfO1NQlRzDUWR041SY2xTbYcEI9OHJ2suv96whSt/QcBSP/iFEKtEEoI/
++c772NiT6smTiDTq79zD3Yfy7zvqHGHEltulwimJX4xUwqD8tFhDkfuqRIt9EEornFfcFGr1WAC6
+khvvmgV88gjO8G34xP7n8QyTU9l4Pvb9N3Hz5mcBAB8/p/jto6cYryRheHW4B4+4iGsv7eDKluAp
+XM/F/A+/CQC4mMk8P/UDV/BDnpDM5HkDpiEPZt65inIitf7f//r/ja/9olCcW5y77XoYsjP0TFdg
+pAF9GGHbJaVbXiGgWK5PNhEnDGFusgu2tw+7kudCrRJExGcUKBDSiDgMH/Ioh21KCBMOttFjfX/V
+ynBBQ4T+AkZCLkzSUh+Nj9CvCIByu8j4DBiODXshG52igtTxUYwm8S2LNELBpKrtGPixL0hos3Nt
+D6YnL3LI58r91DYGRyQRKjYwX0oieWPwfdh2mdhNxsj6BMn1CDJbemj0mKF2C6yOpJM2c2LcHoqg
+zPIsR+l/OIXZy/DhclyOy/GB8Vx4ClmR4f7FAVrTFAfUTJz1uvjaU0HVhfMc1/fFcrX7G3D7hBJT
+afnd03fRaYpbe+WTH0NTs0b7+DG8QJI2lmMhDMVt83fFbbO8DpaVeA+NMITDstF8kqHgfun4TShK
+2dlG3XCzCbshO3Tr6gaa+7Kbm40mehCU3uYnT2HSR20figeyvDeFc1OSZFeqFtwfEte3+qMTLMey
+m3sTsWauKrHN6+tcsdFbkSzE1lg8JZWYARh1kw+p1EzlwW+I62u7TRiFrEtlWqjBgVGmUHgy/+1r
+siatjT6WFHLpbA7Qoss82BjCZ6I0Lgq8G0nYUCzEdf60t4um3BoUsxJu3e3nuUgK8cK6b4bwzA/y
+NyQ6xSSS689hArXmRr+JjJLyZtiAza7LOsHphS04hbjiVegDGUMGbcIaUSMhKaFJ3msRN2EmBbJa
+xk+5aLZknlY2gYplHsbQxyqVz0/OxWvqDq4BWry4TWsDplFrXlfIyC2Rq+8I6yxiWZeTJF2jRb12
+Dzc/Lt7knY/fRloLGx1QGfp8AdOS+Rj7PfgtCW8Nx4HRZXObGsLX4g0qdt1W3QyK3aN5biIr5PPx
+YoGX7sga+UELVfrhSpLPxaawmib4xq/fhfuZh7i1K+7Xa7/2Np6cUTp9pjAkcH17OITrsR47IK5/
+YsKBbBSbGy+jN5SX8YV0Bo+EJKbXh79PfUG6lKWZQiXyt6llwKkZaiwDLR7Py3McsWMOZGzqtRvo
+sAeg2+zAoJuo4gjaJ0w2aMIkv16vwe7EzxS46hAcc1ZgPmZxvjmDpo5jb0P+9uPXB7hCjP95I0X+
+hHHhqYGw7pMwDQS8voydnC07RqjkIXB8B42UnXXtJrbZRu4GIfSC9XS24W5t7GLJEEbNXdRM7K4f
+rhmfDx5+C7/9W78FAPg0OTP1+RiGIVWdVRyjvUFRm2aIiqCmC28Kqxbg4UNcFhYUmZi9NlDWncpW
+Axnp/NupDfOcosClvKT2zhZMXr+at9cKYFG6hE8whGG4UOxtyLkpuCihE77QToqkZqqCD5Pao4XW
+0CSP8ciy1TwxkTN7f3plhX2/Zs1WMNlX4VmEV8cu4EgYe1CdrFu1b7YcjObk+dQWSuY53Lbcm/5y
+iCZp5LOqgFXT5B+3YGzUlSYDJjcZFLVGqY+CzNcqz2ETs+IvG3iyktxV6KRwsg9H8X4ZPlyOy3E5
+PjCeC0/hIs/xd58c49Vvvw/9E1KXLd88wRMiuGA52LDErdu4/h62KnGNsimpv9oufuIv/zAAYHjj
+8zDp5rdenqMsH8p34EBR1wG19kBuwgnEqjYvcoA7bV5WcByxaIdmhSV/Z7LWHsfnsB+KlWg1u99B
+tPkOnAuxAroZooQkjODLMu++/B9iunxT/n18gDgQ66fONjFeiXUcU/rL69vYbksFoOkrRLG40Xc2
+JgCtw+O0RFon0qibsKgCxGO55vZqiu3rdJ/Nap0tPz06gEltgZ4rXtfGZxrobMjnzAzR8NpceoWU
+idKDB3dxdCTzeN/KeKxzeNdJWDJRiJf0UkIPLpNu8ZHGmEi/pEYEVjFOa32KRYlaSDpzHBQnrFTs
+ZDDP5dy2J2tRnh1Bh+SdzAuottTmvTRfc2iimK/DDTfm+doeMooHlVEFEIdR2QY017NcaBg9Pg++
+eHRJ8RQZw8D4QYwem7EC1UdB5eqCIIu8WUDHvBGFRllQgTwH5vTe9gtvzYhta6lUhYsYfo9hYLIC
+KvHoqs4RFM+tJ2fQNdmLS48OPSiutzJSBNTZ2L05hFdrYGig3KhV059tPBebQsO18fmbG0i/9Rs4
++h3h9ZtMM5yndKm0xpe19DB0+z1shbIQAdVzbmxsYLAtMZvT30dFTH10forxsQjK7G8CZU0owrbn
+Klth9m3h5DuNJ3jEfy8MAzlBKuU8qzVo4dB1nDw8XBOPhIcDWH3mASYurJEc20gcWOQP7PgyT+Va
+GJVSCiyvHWMwl+tLPvUm2n8qVOyvvSU5h8ZkiD7Lgje29hCx5fjm/RR3D2XOr78/x6NTeTnvjVm1
+qWYw6ky+cYGGy5brssD8WI5hdSN4bGXeYHksM4D+luRf7LIAMrk+SwXQjrxAN4IufuTVVwEAn7oj
+uZPWrX1YJIiJvATLUn7nZSsUzMqfnk+xZJ6AXCtw/Aaa3Fiy6jvMSx3lojEacE4Zlh6FfCfM5Ns+
+oqjuShyC7xo8AEUia/D0+AgT8kP2QipyWR6esrXarSo0Q8bloYOYnZtFoYEz2aiWfUKi0wmMmbxg
+O6NbeMJNsd91UC1YfuRGYLk+2i05VhBa6+ufJhkuYt7rbIH4qWwmd99mvqefoPVAni23P8R8ISFd
+rj0EtWBvCwB1TxVvsEqmqOV5i2iOVMnmNuz3EbDUPJsniJLL6sPluByX4yOM58JTmCYVfuNbKX72
+nw8Q/A2ptT79Tazr8askXcNyX3t6gpfYEHXtCskxqhUU3b0qX2L+8KsAgAdv30OjL5Yr9x2AyZyS
+O/h4+gBvvyOW+d33I8TcUBd5iTgXN9F2XDRMEll4svu6zRBWU5I3maHWrMyl20DJ5ilDAYYnVlr5
+8jsoA6qWZ9eb0EzKhfkGrvyw4AaGL4sUWVXtoYwEV2CtHPiOfDdoJQj3BRfQ2ngI6+tS2Rinsm7W
+okBiyYU0zACKTVdZnIKSMDha2ViSMn1jIJZmMzNhUDJNGxaqWpzSBGwmXbtXevirP/1FAMBgXzwM
+y/ewYlOSl/kA1y1PfSwKMeP3D85REZCjCb4oiwLxktTqZgRFvoX2zj5K0s+vihSYSbXDXIqVjzMH
+HqXZvMBAOqVr3N2AbRF6fl5hRi7IFfUxnTgD856YzoFiwIQoNBJ2cxZFAaNdczfSq2q1UVGZ27ZM
+lKySZNkCrkf26Ij6kUaFmJ22FkzAkjUcrxK88ZCVtJ0hju4LuO7sQO7vX3jxJnZfJJ/n1i0ErjzL
+5qaGnrAyEmbQSz6gfGt1VQAgw3OVwevRS81PYZMwJy6X8FBXTJ5tPBebAmIXePcqfq1zF//tq7Lo
+A6/CU3ahqVgjZwY8frDCu/elD6B/nSCmzkswWJ1YHNzDG7/xewCAg9fv4wt/5ccBAGa4gbrslVCB
+SF+c4vGBPGB/OF4iIdAnzRRMkzx6toWQ5asr+1Lqc5RCg7zmjk5RXbCnwFsip0S9afhALA9WpeQ6
+tJVCZ/KQl4sM5ZxZb5wDKxKyPJKHp1yewaaEvXZLuC4BQlojZ09Iu5njjk+3tMuuxWyMlFx+w14b
+BvkoHa1QavmdYRbr1uhlVAvCGsiJNrR8EwV/52cxNJmQbN1Ci/kY7q8oZzEyVgDKVQplSBnSLDVS
+xtrH5xNUDFcMTYr0skJlyvq4TQ3uGbAdF6CyVHflYXUi1zJ5JJu37mkMLuSF9/svwmDuIPcM+AQZ
+DTu7sK7JRn5YiSuuljlCLQaiGllYKla20hIzhibLNIPblRdvsJS4fpB3kVLOfpLEaD/hM7nnoNWU
+FzZRssE2cw8eDcSxOV9zO/Y8C/pEjvuV3/ldPLgvG/iIObPTr7ax/xk5X+vWVXhtedGV3UHVImpS
+aSi2uWsivYrsDJpqaSrL0WBPRWS1MCtks/dgwTFplJ5xfFQp+v9cKfW2UuotpdTfU0p5SqlrSqmv
+KaXuKaX+PjUhLsfluBx/TsZHUZ3eAfA3AbyktY6VUv8AwM8C+HcB/A9a619WSv2vAP46gP/l/+1Y
+GYCHhcbegxh/62/Krtx/BMwadDlLBU1gxml5ivlDKaIvp2KVdve6UFRmqqDQvCM9EZ/95KfRJTGF
+0dtEekSaNkM65NKjFRpkbVZJgVnMdJcJ9NgR2d5pocjrkIDmLFkhnRA047WQmFRB1n0oKjllsGGs
+5O+18lK5vMDiWLyc1WkDETPEeHCOJwv5+/GBuJztYQtbZKvOixWcXbGOTjLC4om40m8fvo7ZVCxM
+asl3NzobWFARuxm2keZyrbP5CgvWx2GZsJdynnuPxGptP2xDkbxke/tjaI4kPLI8A/kZadLNHqKF
+1L+n55LsfHC2hLkrOIXmykKsxTJ77TasirRxpzFKhoKaUGNtAqhJZlYxfEYByvZg8j6ohl6LunR2
+a+XupzDI0I3eCr7LROl4hvO53N/4sIBiV6KZiRWPswKJkjVs5HtrISG4HhS7YNPSgNOOeO4t/tcC
+SE7z+MG7WJJd2U1ewM4madpOxFqbdo7sqTyTbeVi4lCv08hxMJdKlDqP8HQiVrzdFns5zc6RFHJ9
++fx12AWf2U4KTf1SFSXQ4jShJDGQNg2AVR1YMWxfns/Aa6JaynMI24P14SgaP3Ki0QLgK4FuBQCO
+APwIRFcSECn6n/6I57gcl+Ny/GscH0VL8lAp9d8DOAAQA/htAN8AMNWaXU0iQrvzL/u9UurnAfw8
+ADSH2/j5/+oH8Ev/wRLmn/DfNcAyMEwTKAiTPVzkWJRiKZ0HshNnV09RUQDGLMfYHVCHwGoic2Qn
+9dITnD2QBOQJ+/E9dwCjxQmZFSZE3SllIWeCLh5HUIxFp00qYj94jO1bjE+TEnlQ60PaMMj8XC5W
+KALu4iTUrOwchib0+UYPG20RWVE/eI72Iynx+U+kYcp9uMIxGZYenGdonMvOv93q4vBcrv/JYYQ0
+k3M8mRA9qJdoGRLrjgu9FlqYxRlieiyuayJJxIq/+V4tlBnhB2wmVPu7aCm2EcLA3BGP5+EffxW/
+/X/+qvzujLXyRo5//6d+FgCQNzdQskmtjMdIKKKTpEWNTsCAlm9w/fvQSF4DAOgFsKjPVql1wnOe
+zRG3KSHXk/kYpz4Ml6VMq1dX5PD03ffw5rflvprNAsZc/mE1k1j+pFhiSe9h0I5gKTmHt91FRB3H
+PNWwDyU/tBywFNi1EZ2KtxHNLYyJgQkXOYbMKYxnTKieLmFybhsbIWZMup5eREgqySUFqxRGKuf7
+NnNKZ8kZ+lpySfOpgf4NmXP7igm3KeVZo8xQ1s1m5NAwLQ+gbmqSpMhKaq8qGyoRt2I2u4Bh/mui
+Y1NKdQH8FIBrAKYA/iGAf+dZf//dUvRm90X9v/3iMTZWFjIualwCIWc30xoVkzbLvMJFj9nUTXkx
+x2evITkgC/LsbQR3JbHyXnoIvy+4gHlvgu6CtN+hvPw71wOcURTkIi/XakmdThPuVbZXz2K4dbsi
+59AYdTB6+SUAwLB1G0WDxXLdh9tgj0JloxrKQ1OytdbSIQJiAcowR0FsQXVRAI+YPGJGvqmb6BK8
+FOwaSK9Lx2X+vg2s5IXeuvIC7hNHn1HENpkDHvfkIPdwVNOShQE8Wx4a03JwwQqMmcvDP7OaQFea
+GEqvv86c68rAciIbzzu/93uYU6L+UcTeAHsX7x3KPfvMlTEafbnmWVHgH//Tfyb3LKltBHB1INex
+eOXzsL8slRP483XfieX7yGtNS7OBcFOSuy4p4JMLDw3S2pvbTSzeke3kVDWwrHkeswwGMRln7EVY
+rmaIGXVMHybw2mINdsMAzo5k7U3fRm9b+lhG2/K3ckOtlcFauYdN9t10WgGaPTlgeSKbtzYSOAzL
+/MBAn2sxP5pjGssG0esF8OLviBwBwLXPbSEcvCLH+uQmDJ/NJFddFBMqzqgZKgLb0CZcv+whU3L/
+04WLjN2l+TRFTJpBpS245od7zT9K+PCXADzQWp9prXMAvwrgCwA6qu4EAXYB8mBfjstxOf5cjI9S
+kjwA8HmlVAAJH34UwNcB/B6ALwL4ZTyjFL1TnGD39H/EsVmgR+nH1hQYc3Z5ApSsJcfjBF/9XXGx
+P8uddr/bQq8jHsHyjQs8WQk12fHiAh02GlVxA+lQdl27ILz28X185SuSaDudxNB1WSzJ0IvEPU6i
+BAWtZk46r9DcRoeWZBBsoKjEehRnCxQsEVqGBbeWgmMno24ABhNcbjBaMzcvm0DYEmv6iiN99+W7
+Kd46Ee6C8DzFHVrY88kjzFNZmPj8HMaCGAGWND1EaBLuq0oXTw/FijX6Dq6zM9KychxMxeKP2QSW
+pBN02fmZaBsLgjY8x8TGhljrT3/6J6C0IC/VfXHVP/GXfwymKS73u8abCL4uOAs1Osbf/aUvybVU
+GpRVRJSLl/Pi0WuY2fJZ2QYUvTjTtFBQdLm3TFHalP17IPdsqnOYDMe6yRAOk3lXP3EN1/fFMmdF
+hSnxF3cPJURJH89gMnm62jaQhOKNNJohNgImKwMXw6Gcb6fPcCWwcZ/XZ+zluOVRF7RTImgJpuGl
+a2QXP11hTMOOSuMqSWbUqsQZPZ2W04VP5GGPKuifdn4Au9c/BQAIb22g70pm0Om0UFHZOkojKCId
+LZLTGKpCTamhCgcmYc5ldga44i03lQvTIufCM46PklP4mlLqVwB8E0AB4E8h4cBvAvhlpdR/w7/9
+H9/rWGmW48GTU3QMEzMyGPsFcER24aLS0GSxyVSJ6L64xP/7b8nm8GN3tvDKFVme6CzBRSQZYMxK
+jGNx7T0jwpyciC4JVF57Osbb75PLL6/WePlVXuD4UG50qnJovnAXjrzQnjdD+UA2Df3SBeyEOAR3
+DGMlD5Bu2tBTeShAyKmRZFDstDSSBIrCMUEK6KaEGMWpXNs0+RNo6hKuihjzewJoyaPHCGYS4z5e
+TXFcx86GPDwt04VBGGyMEjlBQeOzfC2GY8GAToiLYDR/dnKB174hOZfKtjE5lX+/stuGV5EF+nYT
+Lxhkir5DvspWD/NCQDjvv36EhyeUn/+nMxxyDUtgjSd4sJS5PfrWt9Bml+F8EYHpDuRVCc1Kkulm
+6FkSQp3tSR6lf5RBmwwlDu/D6stbuGk4QCCbwsXBGWaGbPamI+viDEoEzE802iaslsx/VRZYxnIv
+W+0hOgwtDZ/8mlPAq3VFSwNuh0pWZRd1JiQt5Dqa7SYsAjgW5RxstUCzEWBKlbBsMYPivUZD5mDr
+GVAJjN+aAhUFdarDBFUgm7pTlChZoqlJXUr7AgZ7OGDOYbG9PrdXUKz2mM0cZrumyn628VGl6P8O
+gL/zL/z5PoDPfpTjXo7LcTn+zQ2l9YeTqf7/YzTbA/3pL/wVHLz+j9COCOUsTXSZUAl6bQz2xEKN
+Nptr93hCFWidmgiHsuvuNTfgd9kTr02YtCRB4WGeyq46fSJb+BHOkB0x723ZaDMzPu8o3CRsutH1
+oVZi6a0XxW1L/8mv4o9qUZSDGEu6g7tViIh8AVGZYk4V6ymhr3FeovajXddBq8mqRVFgzE5Mga4C
+jmFgHjH7phQ6DVmL7VEfDm9ZoQuUTOKtmIUukwqg6M2o4WAykWOcx8Wa4CTKCxSkGzOMWizGWVsX
+rSrYRGGGrQAElsJ3DGSc56qU88VJsm7AKotiDcfOygKKTWW6qNbVh/O17DRQMbG7OxjiY69K4vbn
+vvhz+NPX/zEA4Ff+4W/j4IiIxJrSwjaw05J1G418dNkwtFQVYsrP+y0XO23BGSQ014cXUxwTJtxx
+g7WOp1FWax3LIinXSbadjpwj2Gzix16UytDOjX20HAqxfOoO/ru/9V8AABj5wGuFcIiraMNfa1yE
+IQAiZAPLgUEP2OmIB1YYGhsmuR8NBUVEZ2xkMNlIlcFAyXsyz2S+cVzApoyf6y3xhB7PJxtDBCHx
+Lu4unE9IAfCL/+l//Q2t9ffhe4znAuacZinuPXgfnYsKTcJoM9fARo8kFrf7eGlHbkZ71ECTvqYm
+b1+aVWvmoW6ni06brDpWgZgdYp2gjS7bUDttcUXtIxMFuwhdfwPjTMIOvVzAdAkW0puohmRA4s1/
+3E4RM8tcGQpg+JD4JUbsvrs318j4UufcKJRpCuAE4lKn3JCVLpFzMyjpOmvfRMl/L3WFGTvxnDhG
+h1T0VVVAUUUlZzm1QAHUCkSZA1AgpYBeC8DAMGARD+9Qtr6EAYNCuUWZQ/EcUZqsIc1pCRQsgUbc
+hLIsB59VOMpE3aRbKaNGKyNsOIgYrhgkrKmsas0aldgaKftZCm+M994RYNTFNFqHPy7XbdBtYbAj
+uYG2X6HDnLZfKpxGBGcZFpa87jHzD5M4wvmYrdxehtBji7plgA2csABYjkz6ghtaFGeYp/K7zSyE
+0Zf18lUJy+K9ckl0AwVd93h4BbokXMmQwrYoKtuwoCxuAGwvsc0UBuHxjdYOMn7OoxxkjIdltHBC
+8tvzJQFWqkS7JfM5z+frluzSSRCaV+V3XYWtD9f6cNkleTkux+X44HguPAWUGdT8CY50Cuea7Khb
+pcbWruy0252bCGkpGnMXcGWnHGxLlnan30PBRKS2HFgdSs2XDqYTsf6uZaI1ku8PqN5rqgxmeVWm
+0HPhvk+dwNxBwQpFFlRoU+ZeZeJ1/OnBEqAl7bd9bNM1TMMUoPvczjwk1GI3bbqiYQMJKw7RKoLF
+UMKwFHzSgC0KWolSw+B3y1KjKMRNWcxjICTFnAZsJvBqGjFLVajozxeGRs0rYxtATl3JBkxUtG6K
+FZwij5CTEbrSGjmBPlWZo6hh2qZGSQ8hzWtatRIGraBrK6CoWZRN+L4ce+i5iEiOT5wAACAASURB
+VOj/u+SsmKoKCY+FZYZiJWt7//AR3rwv0Oz5MoPBwKNLi/j9N2+g36jXaoZ4RZEY5WCL1ZNUl5hS
+ezSmTqRSFVI2FJmmjSEhxoPAga5DpbaJgs9ZQn1ILw8xy+W4p1GMnRrfUSZYlfRSSPriaBsuNSoz
+HUGTY9OCBcMjR6XWMLlGPtdCr1xYIyYrWx7sVLxNJ8+RKrm+0srQLuX5fJrL8++6ARTBPOaJh81c
+wtvC9KA6lEbM2nBmNVvFs43nYlModYV5sUTP9dAihZBamDi5IbHQ3u0Cw1hKgEHPQGHIxY+Yc+hv
+9ZBSwzDJFayUWXY/gyJ9R1m6MJlT8ImYu3p9H05Aqu9kBY/c+0Go4WqSiroJDEsWuCIvo7Gc4Zh+
+8qC9h/ZV+TxJY1gzeYE2d0K8eEfCFcsVd9GExvlCbuxZtsAyo86hLrGktsQ5FZHOJjMkfHhMQ6Hi
+i5mUGUp2OHqBswZ41aUp27NR1ZyDKOCQ0miRG+u7HXgNzOmX5iSlLXUCg/6s9Z2vQkEh4MudlTmi
+GqzK0MZzLJhkofEsC4ErnwNHwW3K5xZcDJqyEbcs2ei/NV7g4TnhiFaFmSOf3/5nv4PHDxc8n8Ye
+Y/tPfFZKtb1uEz22VpcLEzFd8I6jMCDduWqEePOhlKXVKeeuM/gUdNU6x4K7ZdNysb8lL2HftXBU
+56mIaB11TfQC2UDz4hToUrC2swvf5j3hhu2GrXW1Iy4UKm6sVlPBo7GwbQdOrU3JHFan5yHskmjV
+KhE25Xxhp4+c4VGWpZiSV3NKotm06cCmUHC3nWKjS9mBRmedwzG6BfS179ICeIZxGT5cjstxOT4w
+ngtPQVUaRlRg4gI3SV12nGm4Wd0ZeA023T2r10KL+oi9oQB6DMOBTcKLAgVqPOUyWeCcIJxqPsae
+IdTuRp/Mwq1NNBqStHLLBNG8PkaGYiJehb2ysQhkB25QafloVsCB7OaLVoReT6zfTbOL9kj+3uv7
+ePlVqcy6iu7n8hgXx6zddyxYM7FGhRVjfCF/f28h9eo3vvo+Hj+VGvVkUUgCEYBRaWRM9hVGiYrX
+bddEGkUGt05ENm0UtMZGYSIP6ux8BUVM8KSQ9bFUhg7xFB0/hOGKvWgGPjZ35J4MDR/ffCigpSOK
+yIz2WggZXpmmglULoHQyqIjwYMOC0yKnJdfHeWIiiknXlmUw2V9y9PbJOukaeBY+9pJY5itk6EZU
+YJrJuYNOgFs74k2OOj34g5pSvYVrOwK4uvtA1vP+2RjM72F+luKCidtVOsYewUmjUYgRPY/H9ALO
+EGFnJuEMptt4PBJMRnfrChbEHvj8/apcwaxIv58XANXNEyfDDATAjWME5NX0u2LxvUYAm56gnVbI
+Id5ic9iG78q6FPMVDHJ+tjZYJYsSVLyOxMwQU+4+LmZonoWc/xj5cY2oerZx6SlcjstxOT4wngtP
+odIacZHAKm1E1PtrDz30NgUr0N0s0SK7cLPVQJuUWTbLkLblg3kvlEkFi7UwQ9no7ImFSaYLGIw5
+DU+8AxVUMNj1puMSIaXp7KmP05lYBM+K4IK8BppIMs/CnLt90Guuj5daFm5eF4m1Gy/soc1EqMU4
+up9torydcA59gNajWCwwo3Lx9gNB8IW5Ru8NWYtH4xnmS/ndJEoRsbnGTCpQeQ4GLZvvmNBkjx76
+Dcyp7FxaFixXrr9QLhal/N1jkq3hN/ACeRG2twfosbGrFXjobcicLMfD1lDM7VP29O+N9hE0mZ+Y
+RdCQuS2WCyxm4qVEqQeLaMLAkLUwmy4Cr0abAlZUU8VF61Jle9DF7U98Tr4TUPl5foahkvnsb/Ux
+2hd4u28WaPhkQvI0lmSRur55FQAw2unjlUi++/rpIf7ojwVnokoNl5RtZjeATcxCt6yJW0u8fiT1
+/xs9E1d98h7YOWImLlN2Q1bwYTOPoHQFg/mVqlJYMO9g6hJddleGA8kj2J32WvHb8ByUYO4DLqqa
++VkZ2KLWRkJ16bdefwtvPpS5uf0GmqEcd5ovUWm5v6E3QvtDmv7nYlMANIxKA2GJnrzv2F01MNpl
+MmsRoEHSEz8OYZKLL6M77ykXmlnmKimwZMuyFacwFnSD7RE0xU/pIcKIbFDXFFEZoWI22CqBmoPj
+0Eiwx01GRaxHGy4sj3VzO0QjIs68nWBEwc/hlduwuHEYNgVBm32YJjcmM4Rms0XRDOCuyI/I0EDP
+Yig+mBvjEEdn8gK9++ACS7IL51kFn7DplK5sWNoIujX2wodm8jCwSgQELO1VDmwmD30mtT51ZQ+3
+R4IF6d/y4Vhy7r7bhWvL8VIvxWbvKgDgZZcS70ULVpMah/ESihWF5SLBipqIE1MjYfdnxTCobxdr
+d36gfWwQkDQ5qOAzdHlxf4gbLlW0SJBStQfY6ct6j9pNOEpetkIrgKFQ/ngFNZdNyx3K9V/fuoGA
+be0bb23h6PHvyPmOU4ypQXkQ57i5R2o9tpG/9eQUJ0xcGwXw/Y+kmlXuaqQ5N8PFimtvrgFglVUi
+puCMUSi4dRdo24bdlmMPXGn0aeZNGESkmZ0ATkwm7ahE3ZCjmwbaFN15aSRrde6e4NemQs4zmnVx
+jZtJ6pe4IPiiMYlhupeJxstxOS7HRxjPiaegBAFXWjgnVdW+bSKcU7usdQ5NHbPcmaFhSIIRY3H1
+FkaBiJdiOCXcRI6RLVYwHBJrjK7B8MQiuAb74B0NR8vuGqYKpSHWxUaKgIQqi1WBvMbpEcG2iiOs
+YjFzTmcBh+i4O1v72OoThdlsAsQcKFoonS5h+Oy4M0vAYjtgEqCiUMugLaGI+bGryD057/nTcwzo
+5RiTFMfnYhGzVYGUCEGDJbQz14TNdfGTOaqalNny1/Rfvu9iN5FE3OaeXMetG1exPWDo0nShEhLZ
+2O5aTs+2Oihr4gOjRmlWMKn8rGMNcuHA9iw0miSU0R6KAeG6Nf/DJIaCXJPfAbYY5j25ex/gGt2+
+uQ1NE1tlMod+aCMgn4bOE5gkS8knZ8gdOZ5tZNi/zk5DR6xy+8o+mpsCpd6+eobckPDw9776Pu4d
+yJx6DQ8v3JB5RHT3/+DhMeDIGjcbFvxrdG86QMry+aruN7ISmPSg7NKE6dDztAy0CT3vNjvo+qRv
+o1NiuAlcW1xkMy7XpVxlVHD4rFp6CtemVmZDPIwXtxtwU5IB+UtkM1KwuSEmJN5tFiuExik+zHgu
+NgXDNBC2QlhVhbBgJ2KewmcdvxluQjOTmzabiAgKMenCq9KGP5JFdUoX0fKhfDdewWnLAvqdJsxQ
+XoSqxuIagKJrbDrA/FA2jXvnDxFfSK08WcaY1sy/lABPswol6/wXOoK1kJfUNkewPdm8qjwGDOY2
+6NapRohaX16bLhRqcQ97jY23u+Iad/3ruLOuKLyBWSrnuP3xAV47k4d4Ga3W+AWPcXE1T5Cw+/Jo
+WcFm9toyPRgMGQojwgYBUFeYn9nqeHD4EJtVCjMnhDwvgaY8jAEsWCSlSauahn0KxHWPgwGdkf2n
+Y6NpCgakbXaRsmF0wcpP+PpjaIY8zc4Itz4hLFRvfPnr6Axlc8pdD/cv5F7eLuXlUO3OuovS3Rsg
+yGS9PM+AQ7EU5/YVhBE3XALEnO4IzpBqUu1N/NhP/pTMQ/0mfsUW1a6tcoQXr0ifw2NWgeZJjEDL
+cRvKxdkpQWtXMyhiNSzC2Is8h8E1bDkWFIFQganheoRHmwYcPg85w8NFmUOxghM0WkgZglmhg8Ct
+xZRtgCQ5TktCqmuvfhYvfemPAADfOlrhjZXkSbqPHOSa8gGGj7x1GT5cjstxOT7CeC48BcAAdAMd
+s8CWzSRhw0OwLa500AgRMKlYBA4Ck1YgECvvmw4CUl8hNWDbYhECz4LTIKS01YemNVbULit1BVX3
+DsYlNPUU4uUSq7m4z7ny1p2BNT4Ajg3N7HxSxhifkD/SNGGQs99wWyjNun2Qc7MN6Jzaf1qjYk1f
+Kxu2W0OXa+boFK1ALOYoDHGXitBVVGCHHZPnToSc8O6C3IjQDiqyRBulBshUXNgWEo8dSpVC2pFj
+b+4IwYgdGkhXDA1sBy6rQFU8hpcw+xt48BpyHwzN0O34VKDXAEqtoFfUZLAUXMqou702dMosOa3k
+WbRCTMhww4nRYgiyM2ijd4vydZ6HxVjug7kv93rn2hWERCYGvQ006P25TgAo4XJwh9tQRKQahszB
+snswTPr5AdC99gUAwL/1M5uomr8u5zNdDK7KehRzsjb7X0bKTtSW20JAhu24WoJwClh1lcHQqHtR
+Tc+GVX/W1Vq3pDBMjHPxQhdH8jw1hyEMVguMMIRJb8x3HdhNmb9t+NBEhZvUlvB7HWwSp/GNp28i
+GVPHtJHAYdfltJiil3442/9cbAqWaWDYdeAWJbZuyEbQTyyoFclBCw2/VceIHZgsXy15raFrQ00J
+182xdu0bwQiqphQ/j5H58tB7DvUFHRua0ObMMpHRJY6Pp0goHZ5YOWzmFGYUPi0roCKrrLMycXYo
+L8Lj198B/m1i0b3uurSmCMKqDAcgkKlclKhSyc6XrgHH5svGvgWjSsEGRnR6fXi2vKSPz6cAcfSu
+BWS5fO4Qul0UE5wxY72Cgsn3vOEk2KVQbM/qImAOwmAtphduImY5Lc1PETEfEmYaxoW8sKXKUJAH
+0CxqdSQPBlmqdKsJg+FaaG6BFTtEsxmqgpyBDP0830YEbibnHvILWYtus40wlDXoLzLEOd1gKjbB
+KNFoyxo6tofFEyGiWfRcdChRr/NjoBQWQHdHSoi230ZFMZzs8RhpJZuzHzbwme//AQDA/L0Y/kg2
+9b2ebHp3Bj4ePJALOcQML1DpqWwYsAhdNuq+E8OFymuhlhIZN/1WO0TIcmmj6UIz95ER2r46rOBT
+Lb6aJthgdcKYKFhtbiyGB83NQvOe+Zs72P2E/LD5TRO1zTqbZFAU2ulbDnyyST3ruAwfLsfluBwf
+GM+Fp6C1RlKU2PQ9JLRAlbYwPpWdVL14AWURopxN4Q2Zob+Qvntd9LBIxXq4dhse6KoOt9d1XrgK
+Jv0vw2diDCV0QFDJNIZevCvfrUx0SdoS+TMU9Exykqm07QwHTPSOjwtkBBOtzk5hg/RY9jZK6h+a
+Drkal49QkVcgSy6gTbqGhQkVCL5BUbdQmQU0w5ZyNcMWgS7lao7Dicy/qwykrG/36MKemAGKKdfN
+BjIeb7G0cEJPyQwXa+mxkICM0lWobPk8uLIBl/TsrjfA4lzc8jRzkB+x1YaQaSseo9mhYnKzhEUe
+yCgfo7LluufLYzis2ftkH95wGmiw0WicRrh7T9ihN3sFDsgrmbdWeJlyeB6xHpg9xnEui2+eH+Kt
+bwol3/H7B7iyIfPY6Q9x9RWyMq8JCRrIqTuZRg9hEIdiNq9hsHmDl/QEHklnFAFp/9Hnb+MrviQd
+jbJEl8zPbqddE14D9Ajt0EGLxDEmNHwmPgszh6pIezdZ4ZS08z6p1zfabbQy8YTVYoZVR54FD22c
+nwouYvZwgayQROKNVwTQ5fQHuLkpCdpu4yuYEzbecM21DuuFEWEW1gT6zzaei03BMoBRaKAx6mJ7
+IO5bNEkxKFm6MfbgUMsg7S6BmSxUNGUfwdEM5VB+17YaGLx4FQBgOCEqkqxonaEkE1Kx4kPnLNbC
+pigSpAVbb1WJzpBcixcGzlJxNY2ZuJTDbh81Zebb7x+jZB3uzugGYIn7nM3meOfJlwEA4QF5ApGi
+tOShsjst+Kx82P0+SuYziliuLZnPMH4qD+P7hxM8JmuQNjRC5lQKM0GD4KXNW5IjOH/7ERR75ErX
+qDlWEDouQsqkW26OHnsmlgRFHY5jfPXRn8o84wxXAlnPnZttNEJ5McOOizClBoZH3srpU2T35BiT
+KTBhJn5u5thn59+m0UVIwE2zJ3MYtAK8elNyFV+9u8B7idyTVwMXBVu4D44zXNmWee6wu3Ru5djc
+p3rTvovPm3Ld9udegWLOpDFwECSy4Xib0l2pLB9Gh7qa+R50Uzbs6PEjJCFFcaPZukoAlm/vfOFF
+tAeyuR2tgDHpP/W1dC0JYDFs8VHAY7jaMhUyOuKTeYJ3T+SH46dLnJItyWLLeb/h4A7Rtre6fez8
+oMznOAgxY7jy/v0ZKoKdvjiSTazxQgz9VJ7Jk6VGynW72m3iyUP5+yMUuDb/cCwrl+HD5bgcl+MD
+47nwFAzTQtjuotPpwjPFbdvaMeAEsjMm7QqHZ+8AABaTHJP7AsZYHYqF3m6a6DAjbW4M0ffkdzov
+kSRU6bn7Bgo23+dMcAXDIVRNMJhZaDhidVqtDp4cyU6rjQQRk3mK6sr7o87aep4sl9ALsUpXf+Tm
+d4BOGVCxSPLNg7sAgE64jU6D8OEe4DKTrx0bZULswbm4iO/9yRs4opv56OQMj0/FKzqdLBBQwOXV
+nSaO2Ofg0UqWoV5v9bay1nX6IPDR2WXPx3mFgoCrOSGw42WEFqHGB+cRzpaSqLu3PMUnX5GegVHr
+CqqunHtKFeWVMnB4Lmt1fxLjnBWF4TDAYI8Ykb1tdDpUeiKGwilLfO6Tcqzj1WuYHsvxGp/YgF2T
+l1jA7jUBqg2Y9XesDsKefC7dFKteTS5zA21iBCq3gkM1ZsPmNZs+FBOiq8lTGAStZdMlFqWsxcps
+IGhSGIb6kU53huCmeD+3ZlvwR+LaT51iTb2WMaPq2BZKziFzTLQbMoctL0SDvPVJXsJlArKufNmm
+whNWJzabDkyGI8v0FF966xgAkM4S3Lwh62lRB2G5yvGNA6GwH09idAP5Xb/hwvHonZoFvDqEesZx
+6SlcjstxOT4wvqenoJT6RQA/CeBUa/0y/9YD8PcBXAXwEMDPaK0nSikF4H+CKE9HAP5jrfU3v9c5
+bFNho2Gh2/YxZJKl7eo1h/5bX7uP9x6L5Tp7nGJOmrMR+9F/+oc+i5DSumWcYkVWW527mJ5KL/zh
+cYTDsbDxXCzoBczeQDJh0qcDfO5THwcAbG/5eHRExWgTiEh/VWtbGo0A25tiaX58eQUl8Q+jYB/H
+p+LFTN9/H7/15a8DAB6+J/Hy9s0l/tLnXuW5DcCW42auA5C49OyplNjuPTzG41xOuEgTPD6X+Ryd
+ztEi3PWLr9zBPWpozkh31pxqzLhu7baP5VSsku8W6B+THBQhumyOWjKJ6qYORoZ4StgLcf+JrFtW
+mTh6xERVeYoZ4+B4wm7IbIXpgqSzpQVViLWKFj5WJJDVhYKTEtMbkzzVz7BzSzAGP3S8jd+/JxYx
+92xs6JpOykJnHvD+sCxq2bg4loTw9OszPKVAj7vZwMd3Zf7d5j6wWdOfERdSVTDZJer5G4jYPaot
+FzkT2uX5HHFLrH83FKyEF7jIz9nl+mKIYYPajkm6poqrafNCy0HLFK+x6VkImEhuOSZmp5LPcJWN
+LTZ0dan70IyAOTVF2oMAXcg1T8YFcoogjbMKj0/kXt//9n352+wpvvQnom+RZxncBnkd5hoVaeFa
+CtiviJN5xvEs4cP/BeB/BvBL3/W3vw3gS1rrX1BK/W3+//8SwI8DuMX/fQ4iQf+573UCA4BnGijm
+JZ7yJW3u9bDZEtevHI6x4Iu3/2IPB/fExd4a1g9KgMSlrPtKYflEkjOq3cZsLA99o9NCz5bkUsX2
+3XMrxoJZ8UBn8Fri4rXyNqIzUTo6XM3heXKjG+xB3fNSLI5kU2hfvYFXPymVkevXXsQ8kY2nO9rF
+j/812eDufVM2tNGogk+CGOXmAqoAkF88RpnI/JfcHDq7Q1iQhyqZd2Cxzm1/+13YZPPd6G8io3T6
+m+/IOfJAAzNyA2oTnDpCtw2HuoytKkaPPJbUOEVhAm22JxtThcFL4rZLkUHm2XJc5Kwe2B7dXddH
+SDHalpVBZwSU7XTgspcCSYWUYRPbR6AWJfRKNojhRhfXeQ5/tUTBJOHWrS56V2XOg5GAdEprgvGJ
+fDfaGaFB0Za93SasUJ4XJ2wBIB7kWDY31etBV6xaOQks9ofk0xQFKy3olHDo5ttUVbKMAF1DNueB
+9tEYSnhRpDlcbpB1QjHshthgmNP0XPj8d5WVGG21uc4l+hvi/g8HYsgujp/i4oT9F66PHjcNOwjx
+6c8SJJYuMAxZJenJHBJHwyZA7MYthZcdOd5Ep8gsSWzavgF0a1z/s43vGT5orb8CkPLlO+OnIDLz
+wAfl5n8KwC9pGX8E0ZXc+lAzuhyX43L8Gx1/1kTjSGt9xM/HAEb8vAPg8Xd9r5aiP8K/ML5bij70
+XViqQrRaIpmIG9l/6Rp6fSEsGQTXcfvzhCA3+9CfqBuJSECBbN1NN34yhmY/+qyaIWW3W6PZR6Ml
+O/RuS/bC2NdIztl01TTQ6rCDMV3gmCQis/MFEsKKc5aeuleGCDtSTmv1m9i8IrVi22ihb5GIpTPD
+sCfW9oWdz8hxNRBP6K24GWzyN+SzJ5hOJOyo9QF2el24gRw3KkvsJ2IdfvDqDkBknuUrjL8ldfoV
+k13twkVMRKDTMtGiRN5+2IBJYZR5lWFDizUKiLZUfgq7IRZ6W5lIMrGqYd/GyqC8ut9AQWqvsZbr
+SHyNfWoYdqER3JBHYeOFPeiSUniL+TqpNlXkGFjkCIgE9L0Rru0Th/Ha67i9K57Htf2b2Pu4NCg1
+PXHnlZ2iM5CQ4NrgNhS7VQ23gI6JZXA1QCixYhdhGSVASEVwc4iwW4clCk1CxM2ogtNngxwbkSzf
+giKE3t+8hYpw7Bw5eoFctyZCtm268Nmk17ObcJhUNVslOp4cd3fQhk/au1qrxHLm6HflPl0JBqgI
+IR9e6+BHbwocO18YmK/kO0sIbsRPG/jc3i0AwBkSFJF4NPmTBG2T57AaWKTs7HzG8ZGrD1prrZT6
+cOlNfFCKfthpadNyUcFFVrGj0FbwNsX99H2FIbP9RrC5xvtrshVVyDA9k31nZuVQvtws13NhGeLk
+GHaIIW+uJsNxqXykXXl4nHwJu0nikKMcsyXx6UmGZoeaf8STD0YdzDvyEG9sXYfDcET5JpyWzNm0
+tgGbf6eGpVY5PJKa5GWEYsEHN2miGVJ9yiWn5NhAy5cXrGWtMKipYTY6UHP5znz6EKtHspk8nsuL
+gqaHJlE1lgbafBHcfhNuXzYWP18ibtTwZrkOL1AI2NLrbyoY9QtduWhacuwiddDY5LUQIAXXgUdS
+lD17A2lAcg84mBEqjQJg5zMK5gtKW8Eg5bx2gf2urFt0tYvwhmyGG9c/AbtuNWcru2G7sPrSnwC7
+A7RZg9cF0CDrdrFAyReo7h033T50zcptmrAYmirbgGPUePICJrEHtZJNZViYawFWlcsEppJ7WuZz
+dOpNoUGuxdABKP5b+oDns73ermDUHbNbGg6fC82WdGtmo6JqlNUM0OyJc+13AgB1KBGgTbYsFIK9
+yDsV/E/J2n/t7j28eywhw3wWw2fFYdB31hD4Zx1/1urDSR0W8L91w/YhgL3v+t6lFP3luBx/zsaf
+1VP4dYjM/C/gg3Lzvw7gP1NK/TIkwTj7rjDjXznKqsQ8WmClYrzUFJfbPraAkfzUsHdg0kobcGDS
+MuV1gqg0as4PrB7HmDXEtd1qtGFOaZmDMfRAwgfHYmNJYa/p0TIVrRtbbB2jon5DXKbImNlvWPLv
+ORxMlhLmfL/zKYRLWgHnGIq7vGEGUCQAqQViKq1g1RqGcGDU8neDFAETbd4jKl8vjpH35Tq6fhuw
+a6IWGxeFVFfyUwOo4brkJFRejIyUZ7oMEZLGznUy7BNC3jI3oMxa34B8DLGJNj0FxwKqWhItz9Gk
+8vNKrVBTZfctXmdcwh4wC28rtBK63WmJIpdznOgcFbEVivlgyzSRUpPDKUyEbPbJHQ8+CUl6XgBr
+woa2QmyLbg6gKK4DrQDFG68UUFv5MkdJpKam52VohYoddMVZCT0Sr8lqlFB14xosVLZct6qFcbDC
+0y8R8fgXfxPXB1KhylQTvi9hTt2h2mjawJyNefEMJa8jNBrwPHoYtoOCqtku8RHVTCOekfLtTok+
+0ZQmdpDlMv9CR+iYJGIJud5BhXhbFrR3F1jVydPYgEXuzU3lIVh8ODbnZylJ/j0APwxgoJR6AlGZ
+/gUA/0Ap9dcBPALwM/z6b0HKkfcgJcn/5FkmkeclDo/G0FphcU1Cgnl+gXQpD3ToPwWasqiwE8Ak
+YIUxeVnlKMayqIU6R0MzhtIm5mSdaZc9lCupDFQgXbhRwQ5rVSh7rYmIqgPbZcZ2XiLnC5d15Rzp
+6ggHd5kVfinBiEImcJpQ5AkEXGDNyCNupkpiaL7QhtmGRVdbL3IUfKDDnpwrzyzYrIEqsweLOo/a
+cWExdl5cHMMkK1CP13FYlEjYqQc7RbdLEtDEQkSCkxZK5HTts6JWf0rgUxPT7rQRTeV803iOTpNl
+ODNAXtYsVIyHgyVQw8PjFCV350pbsG2Cl5I5Zqpu55Z/9xptjCey6QeehtOkMtPZKfg+Qt38DBTB
+QJrH1ZMnWJdUlA+QFBfQa91MVCbgyDwNhlW610DBPhDd7gCFxOV62YZi5ye8JhQZixQ1JZ2VCwzl
+uXnwuwqNH2GH5ugabOaaQpPw+WS5JlqF7q57ZlI3gENeUUMbsGNWzZgHs4oMLYaHu10LrZ48nynO
+oEh2kzw4Qhay1ZqqXkqFcNhp6dolwPyJ4wWw2N+TWhaWrf+Pex+01n/tX/FPP/ov+a4G8Dc+1Awu
+x+W4HM/VeC5gzmVVYRZFqLSF5Vzc8uNTB32bli34NGwSgCi3giK8FAYTR1GCiGkNzw7QHUlaI0+B
+89pdLS1kZEyulZqtpgEjqYlQSmhmyA2zgsPGGM8v10y7SzYtpacWNrclQXe8OMUGOy2NsoDVFxfP
+MFbQhF4rUsnpKkWtugaVQjH5hLJAFotXsJqIJxEvKvg1NNZKYdNFVdYSFa3709UMF+wC7Y0k2fXw
+0QUupnKSrmniCv8O1YBBePfCWUBN5Hdmwt7+noMpwxlzFeP4QJJW3z67wS/r0AAAIABJREFUwMdu
+04pdDRCfkX+iIKO0o+GSZ14VDgomebVWUGwU87WJiMY4acl9bFgOXFZAlkmGrBILPBsvMAgEm7B4
+9BD+dfEW1YQuc78HTSEbZVvf0cuzsA5tlGfAbpK+jUniam6gckhgE3XWz0C5yGCyMqDSEqrmoGTC
+u6xOoUhU0L/Sw2PC3y+e/jH8Fpu0TuTe6eQUDun2VkrBJ849CVI4tPiGWUATGzOfiQV/MDtC9kQ8
+5OEfdDF/+gdyjKBC25Nw2io04pKhUIvU8NEYJeHmR9MFjpYE7cHGNpmfp4WBJ8d/DrUkKw2kuYYy
+AJOIsNi3cUZ03FCncArKbyODrnsRiCRTZobuhtzYpgphE79uuxo3b8kGYSUjmH3eGD5JVVWsOQ4d
+K4SmBLiCxnIhC5mmGiZRZQVx/b1hB8H1O3Jcr7FGWLpBC27GeWYlDLqlxURuuHLbMB3Gr461zjib
+QQ/2WIhETbqiJwdjRNsyt227gFvKA1YWKRaxnGM8TnBOEleQCSrKqzWNuqW8tW5mVlbI6grGqkKa
+Mgyo8xozIGIO4GJi4/1Dce3vH52iw16Dogrx/mPZfCv2EezsDtAfyrqEzS4slmSNyoJmqGDaCjYp
+x3Oi9TID0AyrijyDwQ15YyuA/6LcM7M7gLZI3FpvNo4PsGwL67vy5MpAHf8py12vva6GvMwxTJ5P
+uy7gc/NWF9B8HspiAZNkNzWwzHCbuJhSkWrhILGlEvHa5AnKhbycBjdyx06hCE5LlELCnJeflYi5
+FtAJDG7q6Yrit7DRG7AU2nexYHVt5RWY3Gde6UofxkrO7ZxxJ0wr3D+Q0Oa9d46xiNnir3O4FsNU
+3cKWW8exzzYuex8ux+W4HB8Yz4WnYCjAtQ0ErRA21YnN0ociPdhiEiEkVl0hgKaqk2rWLplCw7sK
+ACjCFXKQKs120MxIkR0EiNdWnKrUdohySaxDu4JN/rNsfoKIWeQ4T5DVWXJuoSrs4eYVAVYFWQhN
+FuhkcYA5Va1aiQ8zYhKPlQpr5MBkSIAsh47ECmTJHJqdc9EDOVZ2MUPclB3eGHmoCEI6ffwAh3P5
+zmphgKXpdS/CchGhQZbhW8PWuktSJyUwoxU3DWRUtI6peuXbwHRMtzaf4MGJeATpMscFaeiU5aOi
+dXZqFuxsgCQhN6CTg2JRKNIUuSlrmxg2zLz2Uqiv6BXIaI1HfgNd1vqPMhf7Hdb98wzVIzlfGcp8
+lNeA4RJwlZWAEfGm2ADDI2QpUDBUZPeo2uzDBbtnCxt5JgunVAE6VqhWGSp2nYIh6uzxW7j/bfnC
+E+M92Aw3p4UDzSoCaKE3RzYCMnDHqcaS69IKclSUg09VjIyVppJkOe2ihe0tgXG7DQ8dkvJ00gYW
+I/ZMBB7ysVj/SU4+keUZvkXOjck4hsmQZ5UXuCCV/jA0YIzJ9faM49JTuByX43J8YDwXngKgYZQF
+irTEoxOxnr7t48a2NBqZwQUiIrt0egaDDMUWVaJVYAKxWAmr1V5jAcrUgFPDal2NggrUCS2pWUxh
+ERJsa408kp3/4WEMi6jHruHBIwtR2CGa7+IU42+L9cetbTibtOhOfy2ikgdtlJoWrU1mYbNY4wL0
+8hQVIdhGGkEzmRn25RzDqwpOr85KuiiptGzZBUzSqm1ebeDjE7GIbzyS8xZpCY+amH4IVOzTX6xK
+gNwJyvGQ1vBfdjIWVoEFrct4Osec2gpBy0JKWLVjAoMbhEezy7LXayAl9Ve6WMBhT39WVtBc5yxa
+wSA/gUfvb2kZSJmL8bY3wN4vmHGMhDJu+uVd5IRm2wHZlrKn0Oz6q4oTKIuJ1MUMik6Ynk6gQvEK
+1p6E9qCrmvzVg6KkXWUpQMtn7QIVy4hlJI1Uj796//9p701jLcuu+77fPvO5873vvrmGV2NXD+wm
+u5vNbpOWKCmKSEVRECAIZAiwJQsQggixExhwTAj5kA/+YDhw4iC2EiNOgsSyhigeCAmWTFKyKHFo
+ks1mN3uuqq7xvVdvuvN8hp0Pa91XXY4oVtOs7gJyF1B4r+679+6z99ln7TX+/4TKP1J1LL15OhSP
+/W1Z83mW1pSqxBVZ+1qxRGFO6hL7pFpNGRqX2VjWdqAEQNVGhYbGwRxnQlAUKyxYdXGnkp6Mwinj
+szKn0f6RznNKNp9fnBPk8jwE4+y45ib1fTpziMD7lIdCKVgMM+MwS2fsjCSY4hzAR598EoCN2UkS
+DWCZcYo3mQNZaLR129JXiDa3v0tZacgHaY/WRB6gVSdkpoArQw1UFgoe9YoGotwZ05mYqKN+h5WG
+uB1h2aVYl5uRxfJA3GwdcumkbOi9QYeVopbruhlWsSInwylWyxc8rXnwndpx5Nm6I8gV1r1UZNKS
+fPpIr7EcehjlqDycHBwjOBeLDVa0CGnasASKj1jc0wczzxl2ZIyrez0+sqy4jEBvDj3HjERRrPv6
+sxPAQF2Y/eEQo1Dz1g95XWvq/cMR1Zm8Xt3SoqhaiXimKNijEfva5er7OVGqWYLMYLXrcFyTB3Pa
+H5Klolg6OYwUhvxWZ8Km8jiOJ1M87VAsGMW5nEakR/JQeM4aVmsTTDg7do+s4xyjdKOuZHLnKokW
+kXl+nenhVblPuSU45t5sYDTC32tdB+BOa5tzW6J4zq4X+cOvaBZs2WEylIetnym5r7U0NeOy7EE2
+EU039hMGmu2I8JhoEPuoJ2tx6uQ6XlmuN+mVuXWoSNS3QryyvMfmHom+Z6b3sbxao3lN9ubU9plq
+HUbR83GUx7Q9mlGYE+Pcpyzch4UsZCH3yENhKYDFWosb+9S1+7Bvprx2IGbbo082ieeIufUijp6U
+w45o6sH1q7Q6ktI7/NYOzprSz+8eMTwtJ+lRvk38hJigde3CK69VCPQEym2LvKdgG6llph1uW/Ut
+otPynr2OmGq7O12iT4hWbnp1Rka0+SD0KWtjTyEukmiQa3QgJ1t7/4BgIq95ZQMzDZ66DihQSawl
+rp3DhNSZ98EbKmckWOSEEfGKEpy83SLVxqaudnACuHNyktSw3dF0WbHARINn2cwh19RoqkHUZJbh
+aYWedVwGmkKLnRJLpxSi7PwZtk7LGtZK8lp9NWbYlfeOsteIu4qb4ISYknYfziyeUqjlWsfxnbfv
+SF4SaMaPoLeUatHDW5PTz5ROk/sCKJIGclq7VQfHqMtQL4FaGNk4xVGXKI8TaGntgDbSUTyNKYk1
+ObjcpztUNyBMaOqeM14THEnFTrVE3WxWOff4ss6pSrEp8+6EY37tVQnyZWoR9TtT2pr+W12JqBW1
+uhNzXOaMtXQVO+PKZWkoHm8PSTYUjTwtgabfK80lDq6K1XDiRJ3QUbcikJ+pk9MvyRiDLMVRvtFi
+s0CkLooTucQVrYe5T3kolEKWWYb9MbmZ4W/Lzbje6vCNg98H4DNbF6icl83oE2HUXPOUxai4UsN1
+5cFd+tQzTE5pnKCVEW9qS+44JltRE1w7yCKngNHa+WliaSkm4q1r+6Dp/93wJssVLYvuyYP31k6P
+zhtCAf6frz/HSHknt197m/ijcv3FcIk4lg0UKT/kzFjcmeaPwzJWwVKmswxPUYBLvhLbdt7lTkk2
+9sVTKxT0ITzqHbCncZfrr+3w1puiDFM1nYsuLGvJ8+likdfelTx2s1LhfEHWIggSBlpLPPNlTqU0
+wNeOSbuUcqggHV46odJXZXJ7n2uKjr3mih/eiWZ0tW7fDTNOlGStXC9hrAVOThgd+8zjSN771o0j
+YnXHPuM38LR0t93zOFTzeWnWvNuqXJQH0Dt9CU/L3B0TYZRA1noGm+7p603QGE2ubfLuNMMtiGK1
+UYtpQ1unzYRIFY41M0bKC7r9tmCCMnA5cUn6HUbRDs9rPCMpXeCfxP9c7p+OFQ6nJEfy+Wxpn0mi
+CNaVBl5kdH4tGlpz8cK5LQCMrRIr52e16dCoSCwtiEPKyiEargegNSxJLD+PjlLGR9qjMklAlVNh
+FhFqVmk1cljTZ+N+ZeE+LGQhC7lHHgpLwVrLNEmZHeW84mrufjYjV/KL7779ErUVwTYMDLixko9o
+dD+vBARKweY2CsRl0cphvXmMzZ8Uulgl+nDmHYBTn0RZQUZHHd58WVCXL+8PGU3l9fEd6CYSlY+r
+2tiUT9l9RcbrPXeEk8jvo8kd0pFkJVKnjD+T08goToOXTckDfW2WkhfEavBnEyjLqRoO5Gdppce5
+JTUpi1WszrV34wbf+I5gFL791h7Jvp6I8+BTHLKhWH2lis+72zLGaNynsi6nai0PmSkxzpyqPQtS
+TKYo155DoazAMq2UzqHMv3O4j692flfdnJAIUxA3Z/PUaSgqFVzmHJd3F+oxmVK0337nuqzPUY+W
+mr63ju7gK+9Bkg+49hWJ/K88XaKkVkGiFZFxPSJvaLfgcA3K4mq4qcVqJolpF1vSmhSFdsM1mCP5
+PTjVo+nJfcjTGWiWZDq4yvWvvQjA1a+KpdCwVYzep3qyTFgXS8ip1EAp8FJtJEs8g24xhv0xRUfG
+OBiOKBlZi/ZBh0y7bQvzbl16DNXa7OEy1TkXwhKZurHZ1Dl+Wic9WYvd7S5XD8QFbY9moBWrve6Q
+kvKsJibCid/f2W/maaMPU0pxxX7k7PP003NcePZnAZi0ewzbEpHPB9cpFGVj7rVu4mt8oaBR7wuP
+XsB4Yma1exlJX0zmk80ql4/EJ+0dJpTV7LrYFDMyWvK4uC615VvNAmg0vG8T2gcKTNrqcU1r1A+U
+w/6r5/4nDv7P5+Ta0vmtkKbIxrx7NzIUdYck8Rz9BzItJrpUqDOsKpLTtE9iNYKvzE19xzJVn3tg
+BRocYJreLVn1PIdcy4Nj3Ui5sRTcedwi4Cee/xwAo8pTnPmsoBj5pFz7uijfy2/K5t9cMeiU+Q9+
+7BEa2tXnOoZrSss+fOeIksKWX9I29NnqkP53RHnPsiFGMSbb5SPGuwoz7mfU1U15c6qdkbswjmVO
+3qBC8ogohV955BJpTcYYuRcJlhUB6+uiKHZHt+6SsdYqnK5oVmKcHLsStbMrlFJFHtJO1KSfMI20
+yKoHeKK8WtsddpXCPcahrH57qq7PiJxAS8xnyZjRXAHUC9z8+H8DwKu/8TcACF/6IrGuYX+5RksL
+4F476h8XeIWRz3BO5GvmcaCcXNO6aZaDAsLm1uLoexwMrhaO+Xqvfc89NvVzIMvmXKApqf6e2+Pq
+b6az9CVr7bN8H1m4DwtZyELukYfDfTAOmRfTsQE7LdGM/b1tIoVWb6Yh6aGCjEx8glhOuWpJGmdW
+kiW6JTkdl9q3efTCxwA4f7rKiR0Jnl2/+gYXlxU+vqoadTajqnUMjfgErp7GVS+noSf3sBzjbctJ
+EWlJ9O3f/i1+UtPgX+Ruo95JA3v6e8kINTlw3NVZ9TwG7pxqzD3W5oVeznZRTpJUi4LKAYzmyMc4
+GD1JsJZMMwN5nstRAMy09Nt1HFBTPa4WibakW+7aep1iX97TvnKN8Y5YU+sVBa+pZTR07SfTDhMr
+cy45MwINupqVMRc3BArMd+RFM3KpPiLvrRUvMugpLN5RlUlZgpXl8hI7fXUJ1HqoPuWQdLX89lEH
+o6dxsJLQK+nrrRG7V67p4PJdj26uctRX7AU3paRWWGljA1+DdV7NI0ZO/EJFAtRJekiqJ3QSj3Ct
+rIuXvMngshaf5Qn1FXn/TC02P59S0AyPsUvHmQMbhHxpR+7ZR78hVs7lzTHBkbg2jZOWl78pgeLx
+ZIrV7snU2rkhcNyVa/O71rrnuuRqvdvs7nuMcbBzw0KtB+s68yp2jDG42miVOi7peL4rLdbezUzd
+jywshYUsZCH3yMNhKVif6WyF1eoTbHRE2x1MN5i2Jeiznb+D0bxrEFqWPLEETinz8ZmwiauY92s/
+8wJPXJSKxvpyg+e1PHhwsIu3JN+R3xKf9OjOFZKesiE3C8TKSuwUY0YaoBx6QxxNIcUjhRp750/4
+kp6ejgc1PdHHLlTV5Z9a0Epp/KrCoHkOJa1dKBdTunq67/Uz0Jx+Xa2LNA6oqYLvZC6OxlEyk5Ip
+U3Se3T1h0uz4V1w9ecpRTBYICOr5uMBWKp979UabnatCIlJUINbNyTnOn1P0qr0hVyevAXAhbnBK
+g2Sl6inqCr1WybSnPzV4oZzs3pKLMxDU5X7ZMJpqKqzsUNxRkpWBQpvFZQ4ciRlV/DIogtR2K6Oy
+rEjZ6zHJH4tVuLMvKeBts0VTa0Hc9i3sVIPKlbPUFUPB9g1eXcbzVySFGKZ1sq4EDLNsMC8mxS1c
+ZLwjlaz99gFGyWZLWoFZmzWwGhC1vos/0DLumaV5JNbWm4EEmhvPFagoCfHldo2hBo2naU6k8YCo
+EJMpOpNmDXFdh1DLpwuBwxyUOk0TJhpD8jyD0TPcV7Ifz/XQDDaO65CqpWDTgDTRUv/UHhMO3688
+FErBD0I2zpwjiVaITsollb5coqTU4rOdJln8VQC2/GU2NuTmFica4it8l48+KvgGqz9SZUlJT7ww
+IEYWu3qpTt6Xm59pxDpeS8mmOkZrh0iBUxKTEiribj7zCLVbcUWZmrNTf4H1a98AoAu8oPP4QghL
+c0XgOrgKJeZpwLEeFnCWVbGYCkVXv/eEx0SzIE1VCvu1AE9LZmcD8DVjMrEpE73J1ubHgSirYCsZ
+kM4xDQpjzj+ldPB9Q7CsGAEly/pHRNGtVoSy3d94BzRSX4mOCDoSXK08csiqLyb16lqDXOsbHC3M
+CXIoN5XgZmIxqiGD4jJ1zXCkY9DWB5ZKWqI9nhD05sVUMNxQpRG6JNsCleafO0u6qYxSOzKnsDum
+vCYaubLVYH1TSuGLTUOgHKJm3cENZQ84eg9MGOMo2Y2XrWATUUg+ESc/8TgAnRu75POuUe12dcMx
+VtmtJv0OqeJ1Us95TWkAnjwrSmySVUi/JX9/lzE9ZWkyGTRqst6O7zHTJ3nOXF6PIk6fkL23vFol
+m+g+8zLaLZnrdJQwmuNk6MGTY477Z2aewddCrmQ2vKsIcrDmbnD6fmThPixkIQu5Rx4OS8HLWKn3
+mLQntN8Qc7HQuI1zKJqxMv42aOluI+5yrid5+j++dR2A29sZ5wt/EYBT6xUcTzvk1lKcirgVZhpi
+S4oC3JVwYNisYAayBKG7erc7r1TDnedxJn08NVFnChT749EKb9blc58upXxTrEgiCz11O5YDF09z
+/RdPinm9Vi1TCyXPnQU5jysS0PBElXCmDUrKH9nE54qm3nByUrUaTNthrPblzN4NRDH/mVpm2Rxq
+zLBeVeDSQYGvfV1M8NULfQqJmOBLe7IWw70JWS5UeW9fvcPFWNbKvVnFPS1B3mR0G0dBRyeR2rjl
+iHFLCXeiynE9RVyp4My7QEsukQYE23pr8tuW1bFCyA336ezJeo6qY0pKxLJz+8u0BnJ9A0WfXkvc
+4/Lv5vIpCksaBC6fxWozk1fawiigq+NpY1c2wXgKu5YbUAIXm+9T2HwMAD+ukQ5kHSda2Wg9H6uN
+T4yHZHr6jxzDM4ri/e66/LzkPcbXPeEP3b0+YarmfNn3aTTlOhthhVu6Lr6e4B9/5jTPPyGkLo1K
+k1wRsNxqmemR8on2xxx25F525kRFe2P6Q1nvzmTGgQbmp2MXrSrHcQ1Z/v7KDh4KpeAEAYXTW+zt
+HTI6J4uzOTbsT6ST7cCZsqHcgFPT4ltdWbShop6cKjVYfl6i4sNHmsRahxCEpWPornzSwRgtdKkJ
+oEUwHZIp2awTWhw1z6ZOQq5EJWGlSpiJu1JBTPGvTgq4GgH/hoH8OJfsUNdCpcCPeEwj/6e2xLys
+Oi6Zlu3GfhGU8NTJS3hGbm40kI2bARvL2u9hOzhGHpTWcHRs3hljjrMP+dxcdCDXSHdmDKYsazEe
+7NLblAf5ufIWKAdjuy2mesHWefNdwQbs7e1y9onnZR71M2RNUQpHo5y4PB9PIdmtT5LPo/MzQi0K
+8qyPo1Bwrm8o+FogpIq+5w3Ic61jqBpKmhmxq2X2DuWa+jPD4Ugh1hS8pHGySdNI3GLk30HbTnBX
+I4yvPQylKibXngetjzB5eAw9R27AUxj1bHZc6u6VXGapdOkmbfniUX8ACj6TTifYOe6kW+GPx/K5
+4ruipG4+MWVfWW86vdnxcOViSDmUvTfGY027Mpcaorg++bFn2DojoD0m6RLkWh4fFEkq6o/Oxgz6
+ohQOD0Qp3Cm1mSjAy063h7+tzGeNjIHCB6QmBS38SnlP4OnPkYX7sJCFLOQe+UGp6P8u8B8CM+Aq
+8IvWiq1rjPkc8EvIYffXrLV/8H3HSD3CVpXI3uCxt+WUWD29RtyVk3nX9IkmYkZthTFXpqK5T7jy
+87O1AhtDCTj58SqOAquY6RibaC95FmM14uwoiYeX97G5QqllE6xqV79bZDxS2rhiAlPt/NM8+PLb
+/4Su8hSc33LwVEMPSz6x0oZtnWry1JKcaJ5mHBKGFJw5n6F7jHac+gUKgZwaE+UCyNIJaAQ5ci0V
+jdS1+yG3VZXPsrs577n34FvwNf5qJoZCKmNPnA6fPJDrf3J9nVSrO/+wKK7Y7J0dPM3w1KISy035
+kogZcVtO4MPxgL66OQUrVk7JS+hpIcNsVma5op18mQVFubaBj6eB26JiS5Qr1WMroDip0p+J2zgc
+jLilICL+MCa7KRZUfVlu3uysS6CgIdHOCcyWZgaCGq6uIXnpPSQx88W4u0aQHXOHOEEVkylYT+CR
+lcVdsbfUXesHzGZSuj6yCUZNf29kOa9dvKMfF6vkqdUZ1/5YyWSMxZtzjxZCjN5LQkNNeSw/oU1+
+J4pNnEzBZNyIuCBWRcGrYF2xUtPEIdSsQ6DXGwcOQ3U3C1FEUd3bmZvRUTDZqT1Gm7tv+UGp6L8A
+fM5amxpj/g7wOeC/NsY8Bvwc8DiwAXzRGHPRWvvn2i2JhTuJYcIyy5/WQpjvFGgpgEbRLLMZi1nX
+G47Yz2WaP6IIzucfr5EvfxcAZ3YBV8lFiQpY9T+dQgEzRz3RTlLHjfDPyncUb+4waWqqKGnjaYai
+d+sa+wrgUlS+x1bSY84Vc9A1nNDiENd6RBrhtiZiUtd0p2I/OkmNmSIhBcZg56ufJMzmG1Y3c2Rc
+XN1UoXUoRbKRTlbbvKxP/WiWcVzRMi+vdgwzNRc73RZ3RvLAtnpVzjyuvmwEibZqJ0cS9R5kL7Fz
+Q8zSi2c2aVQUaZkhHS1zzvICjnJs5g3NCiTBsULzegNS7T+YZBFuSQujxgm5Ny+u0k6+CCrqzvXp
+HmcwRndu8rWvSZCmXOqxVRBT+qKS9danXcKGuGX+mZQwOCVjZDfAFRcSZ8gxlFM6h0V6T1rO9e7C
+JXkZZr4fasVjWPY0UuzObAJFOUQ6reu4quCDRsoAyVA8siPX2PP3mR0o+a+1oJye1vE5ykWBbBbq
+nNZsRfOUFN/Z2OJkovTSzDDSTJRNMyLlq0xHY2YjLT3XjVMuV3BnWoI9GxMriEzPnXLttjwvrf77
+VQk/IBW9tfZfW3vMYPB1hDMShIr+N621U2vtNYQp6rn3fVULWchCPjT5YQQa/yrwW/r7JqIk5jKn
+ov9zxclHFIevYLNz+IfiMrjha2xOtcw573PCUe6BkynP3RF9dC5TIIxRjrcpp46f7uEEErRhNoPy
+HLjPE5oxwE6VoTkwOFYi4F7UZKZ08EP3EG+gTTlXe1zbEe0/iuTEXI4sE+0WbLgZjgb2ysbBUxKZ
+UtPBs3qqaOnyNLXkquWHJsfXw2o4npIpKaSnefVqZHDV3zEmZb0mp9XSp9f52h3t0ByndyPL+d0s
+RJ5qJmOUMx0KkEfaH3N7X06uM5UdKtpMlvWuA3B0x8VRBuf1Rgl/X9a70IShp1iCrk+g0HPG0eBi
+1VLM5gVEFUI1VjMvYdZTA9Gx2EABbHKlSksc6goll+QVBoozQatPqsjde6nLhU3FrmyoteVdoDuQ
+U7Cx8Qms8kraSYkcXdCCg9EaCaMdiZj8rrWQ22M2ambpMT8FXoyj5CvT6WUALu9cZW1Nz7yB5VBx
+PPOpyy88L1v7xd+XcWevtQnVTah4DmNdo85sQl27Y1eWYs4tiXVjtBz9cDhE6+qweZFE700UTvH7
+ihnZ7ZL5Yul6juzpQmEJMrHY6mUfsypr9JHM8lpN7vvtwzGZfX91Cv9OSsEY86uIy/LrP8Bnfxn4
+ZYAornIrLVLo57yhC+ntvU69JJujMYhwFFL8TNFSuSjfsbEsYBRmLcApi//urlRxVlZ1kPAucUhq
+j31K4ypyj58dxxFMqYargKdRoUaiGyxs1FiaKNag4ifa2hZlbaduRtkxyUzmFakqcIiX+6i1d5d5
+KstwIgX98Dwy7a8cpjnDmabTZkqBbt3jwhSyKWPF3CvGDuvaqXjTGZCoAmDeZ2HvGn8RhpZCku/e
++RbjtoLSLDXZWJLvmAyV3CVJOb8macpLj14gUiCa0CtRVeSlvJvg1fUhnHfsBS6uL38vLcW4+ZzK
+PcEoXmXmjY4rD626RJmX4GhGKQoHNI7kQbjT79IfaLQ/gkFP1jZ6VDM85pDyVMzvoGpxA/mdOAcF
+gDGuD6lS1GvchtyDOSGx9Y9bWy0WIv3P1GDVb3dUqbhuymh4R8eu4PmyJ9NkykCp5tdvyjzazZSq
+FriFuWGg+2aSJgRK3tvYOMHKCYkleLlC4OcZ43msybOM1T2eZCFmTkSEh6uw/LGWypokxCjSU8F3
+iH2t6Lww5ezbsg9fu9FnOr2/rMNcfuDsgzHmF5AA5M/bu/3X901Fb639R9baZ621z/qamlvIQhby
+4csPZCkYYz4D/E3gR621o/f86fPAPzXG/D0k0HgB+Mb3+7586jB8N6T4iOGFSEzj1volTmvRzHS6
+wwlF+D218Ukmq3Ki+QUlvNgaUKg8BYBTqGHceWG7h9EiJOtZ0F4Ba7s6cIzRWoeUBGuU5bkd4Dni
+jqydc4lcOWFvKKbimdstnED+vrppSJUufZoWmGogkZlPu6VIybFB83VMAAAgAElEQVRiMRpLOZPP
+hVhmillwNJ2QabdmpFUn+4MJrvYclIkpKP7g6soazzwmJ8K33jm4m3yYn9DkGC2JTk1G0lIsiOtv
+4+1qifILn2KirMyNVbmequfzSFNP3ZHLQGsz8CZUAjnZsmYIc7dBiVwmRy3QzNCoX8FnHhyu4sZa
+AFR08RS0xtP1ydOMqS9uXKFYwjGyRrutFvua+bnkrVDdFwvxykvSZRk9XeYZvWZ/uAWZYm54TVyF
+pafngsK558pR6Tg5zK2opIv154FIi9UT2M76JFNFim7KHstfy+kcqMvT7FNI59dfZVUZqmfPyFoe
+dgoEip5d7UTsKbejO4baWHsmRgPa2qFandd+5wW6rsxjYjJS5cScTQOObspeHRUtG75YE6eVgd1b
+T4jDOYCPwdfA5kb9NB99UtzNr15u0x9p5uM+5Qelov8cEAJfMOKnfd1a+59Za183xvw28AbiVvzK
+98s8LGQhC3m45Aelov/Hf877/zbwt9/PRaSOz35hkw2/QnJBSm0vfW3M1jnR9pfLy+y2pa9+euOr
+3N4Tv/uywof9Sv00sStBQAoF0KYd3BCrOV1rhyS3xZPJ7LcB8GcXBREYcNMjPD3R8zw9xiQoDw3J
+OfEdI6Vom5oZawUJxA2shUTJPdKMSJtdjlJLUbkaooJyQbQGTHyFXbOWXPOQs+lExgR8Tdm1J31C
+fS0Pi4w0ElUuFWhq40+eW+y8IUrX0r6HbMTNc3Yuywm7ffM2xbKsW5pM8bTysGAkHjAM32XnDQ1g
+LnncUG6FU5WAJ7NzADSLj5FPFAFrKGt5+9s77LblvVFeI1lTFOxwjZWz8rnITgm0U2xFSUqCcp14
+qvMftgmVwbmzc8Dejpy2Zzbh1MfkRM/3FEh2uE8Wykk57nQIAylRZnIbQkV8ToegiMfzWhDrxFhd
+l3y2TaogvGlrG0/ZwW0xJu9KjMWZSr1MeTljuKdl49MEL5a1T066HEWKuhyJ5Xa0V6C3L1bThHxu
+mDJOMlqKP7F71GaipclPPyqBymo0BK0nGYyH7GowKpgFLH1E3vNCvcbyKUmBOgMFhw0Mhzek6pfE
+pVCRAWuVTZ5/VsBm/+Rbd9je027N2f2dzw9FmXMYZpw/26Vz9SpOQbwNW9lh75vykH7naMoNNQ3P
+L0HrumyaF0eyQcfZHf5ySR7SR194nGhZsqBptUb3unxf/yuv8Pa3XwGgsyQPwl/46C1qpyVqaXZu
+0BvKou3ePqJeV5CN0COzinenpbZu74ieUsPHPYduX/EOXYe6IiI3Gw1mygRNR/EVxzP8icyjlfdx
+tAU6dRxCzenPIVtWV4rYIw1QOjFTpTL3ihU2lxu6biGZnRfFyFhhOmWgwalxDr4yHVWiJeJVCffU
+T5VoaIC1PZFIPpM6N9zrcgVvdbi2JwriSuhyQ4lq/uIYlldl7IkyS73Z2uboHXmQyusDvJZsXLs5
+JN2TDRtOElaMUqNrfdGSO6JcERP9cDigNZDvaI0yRhoYe2O3R6+i5dSvy7rdGe0wHWtWZ9Vn+uXv
+AFDNfZYfUQj/j2xRPiHl8n5NFag7Yff1L8t3Xd4hXJX3uquPUlQ8Q9+xWCPFSz0N0N4+yKnUtQBs
+HDFSukpz5LCp3sjlczJG+a1lrieiLK3xjvFBsYZOTw+Dq7e4pW0jo6Hs36eWmpw8Ly3uzmRGZ1fr
+bPIetUDu0+5+yOGOQAvuDERBrjguzliDtRWPWlE6Q30KbF4Q6L1PPHuDL7y6rwNyX7Ioc17IQhZy
+jzwUlkKawX4b4qtf52tWzLZLwz7nFOG434t4bklyxR//6AZ3fky049NtOR1PbFnaBTFhX33jm3z8
+Z38aADcoMyiIevxa+zZPPC6l0I88o80+KzmTnhKION5xXt02fbb7clIG45iRqzlyVfyJyUFZlB0s
+ngbfHMcnrsqJ301m3HxLUlm3bsrpE/gBzZqcfPVihXpT8Qtyh1y/fKOppvHU4Z2pnPJX3r3JtkLF
+mdCnogzTy4USPYV6a6ur4ab2OPXm5FBeEsul2tyjsiknc2JTOhqAbc3EUhjvH1HWwNdB5OFrJV25
+VCLU07YXGS5tSQ1IXV20vfZNrnUG+t6Y0pqS1qyEOEp8Ey+tU9Lmn6CmgUrH4CvacTEas35cup2T
+aCfRwVGPK9fk+pobYgn6yRGxlb2wdukCX/32ywB8bdDmUxps9T0P21Ieheq8M7aLG8l1dpwprkLy
+5QxpDeVkTg4P8fflPY4ra3XysZNMNWA8GLbIlZotTmJuz1Oc12SPdbKAPJP5DbJMoPEA3/MxWhfR
+mjgMBmIBt16WcYcnx/z4abHioqCMTeQZ6LVG/M4rfyrrMkxI1Tp97iNiBRUf3+L0eflctQDlZa3x
+qTqEyr26USlS1eavzn2aCg+FUvCzIev9F9l1d7nTEYy/p8p1Zq5sml985CQbj24BEG49S2lfUXU+
+Jjdl9VKdnT94CYDf++qXePoFiT8EzYs0uuJnPpqfwlcyUvflt+W7nr5IorXj7cM9PG1N3Vjf4I0D
+Kf74avdVqooPWToh/p2XZ8d17VHuH3fi+bHPRItNEi9lrCxFQzXn40qJpQ0pXGlmCWtF+dx0NKKs
+GY6awtdPJhNaiuLT7va5/q4oiH/jzTgdyaZ64ZFl3rwlD81MaxCWzYhJqLUAHUvkSxS6sdzipMI1
+z8YZ275EwG1HFNdgeCTQUQjoSU397PMrmzxSFxCSyJRpNEXJuGrif2R1DVdJdq5mfd76068BcDTJ
+qNflvc89/QQNR+a96ot7YbwBSSgPRWx9An3YOiNzDElUnXrUrstcmmfl52+/2OHrf/hHADz55S6/
+eU0QpApLdZ5a/igA48trTEsyr/IZjTnkGbYr233SLfKVLwqy1M1X/4B39uQ6isWUzQ2Z93/6zKcA
+qKycZBjJd3XCEcUjRVCaxmS3xQ/orcp9blzdwWpxljUOrhYNOTYn1fllE++44Gygsah373S4cCT3
+8WxQxtV4RyFNKRdl73WNh1GkroNtcR/e8mPK+lpz8zSFkdZyVIbkNY0vBB5h8P5apxfuw0IWspB7
+5KGwFFJi2tkThIVbnD6tDUE7FZafliCgXz1BXtFKssMW3knR5id/6tPydz9m9vI3AXDSI9IdMbmC
+pXXCgphXJz/rEGXKaO2I+ek0a2RaSj19d0IeibtSoUNNTftkO6DnKXNxKgGb0M1JFWwjLwUsq2m8
+UqkQalCq3wtwIzFdi3oSra5usLSslOP9PSqaMxiHM4qadfC1ZLgUFNg6L6d8vbxEfVkCdZWmT3JZ
+c/B+wG1HeTMDOc0aZzbZviGZGN+mDHKFIKuswJJcZ9w1eL5aITty6kSuJdbIerxcYaMm58VacY36
+KWVrtlNmhwpjph2QtcZZzp4Vk/vwYMQd7ZJ85NwSZa172FxvEComA47m7r2YfFeDcnlG+ZRmGaYz
+cq0baGxUaDyiTUcXtgA4a4aE3xWL5uLP/Sif+JZYAs98osapolgjyWQX05Z7mStIixdXsUggrnG+
+zse07Lq6cpYTSsc26fcIR3pNjTmeBmRDmUfRvUYYyLwHpTu8ot240y8KidArr9/BaDNaseAy8ufV
+q8ExdkKtUScJlW1bMSNN4uFp4Lbr9hhaWc+VZomqks/0gpSyVoD66qLW1hoU5vR4QYQJ5J56SYnk
+qli6wSTh0mlxhd65rfU530ceCqUQ+oatNZdR+Ud45KOyqG4FTEXM/VvvXCF6TElOnwpYqclGSF+V
++vRJcYdrexKF/tTzlwhT8a1mV7+EUUr5eu08yaFEcqctUQ7hSkSgUN4rtSV6ataNfB9nWyK9Kzbh
+ylhuQlk76HzXI9EWYmNzlmsy3oWNTTxX4hyjoM+pdS3uPCcPZhRGjAcK+x37FLTYJDpymCjAbJbo
+hghL1CNJ6RXONAjm8QfHp6NK8aXvjnj2QGIfbzsyz6dWDugeyAa73AGjSm/dPYVRTMhxvc2ygqoW
+NkXZhHaVwhlVQm6V2FUEpSjGa6hrU43wFOJ8fCBuXrFR4EJR0l/L49P86JNapLS1SZDLdxd8B1eL
+mlI0sj7oEaivOxpCV/E2l2KfxrxQ68I668/KGm6sSAfkf7G8TPcxuX/m3Bq/GD0DQGnNQT0vpn2H
+yZyivqdp2Mk+gfbBlE6epjIRBbFi+oyVgNVLfcpFuX8O2nrcukY+ljmdXH6aO5ko353DIWfPy9r+
+3i3Ze7feHuGpom9EcKDxhakLJ5ZFKZw6s0xbn82iMkQ5rsvSpmRDem8fMpqnlJsFTmgsIl1dZnNd
+cRzVjY3dCiVNSbv+GJRNKp30SeY8nqbIiU15dgTl4PvLwn1YyEIWco88FJaC8TzC5SVMv8dBS0yk
+24MdoqE28HhVjkYKXtJ4hPhR0fLzhptpL+TUCTn5mo0mzrowYznNGnmgHWLjGeGSIBcHGkzCDckU
+fivcPEMxEXNwlI6I1+SUO+OUaFnJbKwURNt/1/PJNEJuXEOtLCZzpV6m7GtDU+rhKk7gLJLTJx/D
+WIt0uv0EFCxm4kzIFcarrEU3UaNEQ0lR/FqN3FeatmBI5TWZ92pwyCuKLrw9EzP5VucRDlJxH1yb
+4Cn1e+YbJhocbYw6eAoSs+TLqdOoxyw3lSClUiBR0I9+MsbVYuo02GQSqCvlyalVLISEWhJc8QII
+58VUAeOhdkya9LiTNFULa0YPN9WsTTimoefT6c0it7X0PAmKhIqrGK3KWlarZdZPC3I3zbNwUt2S
+bMZMS5v3BwNSbcBC6z9cN8LVZibPjYkrcv2l5SHW0e5Za3GUBj4f6pzDiFn7ps61Sax0c8uJ5Y+3
+JXhov6MWKJaR1gcls5zeHH6+AE29D421JkmmGIuJ7JXG8hI3B/Id/X6PkmKJnlzeOMZeiExA3NCO
+Xi3RHk9SEp2+zXOmGihO3S7mQN1wx/D8KWkW/Iffv+NAvv++3vWAxc4SJtv7OFGdYF9ubCE5R+9d
+8dUC74BiS3EOwyUc5RywBY1eJynr1R8HoLzSwCiMuknHOFZp5CMfq3670Y406yREChI6cd3jun7b
+HuNo51xYK7KiKaeTc3CPgg9jJYJ1I2quEsHahKLWpRf8Go624nU0ZTewYyLNREzGlulYQV/siKWC
+3N26+vXRUolAfU8/SwgV13s4CdhtyPc1bgd8XKPPrw9kTTznHaqn5bXOAZiprkVuuaAbtpGVybRw
+Zo7AVMhdKso9kLouh7viHiSF4LjjOA7bdNsydqwPcdYv4hfl99xx8VXxMAvJzLzSEzzt+HQH2pfR
+HdLVlF7RlIhmyryU+MQKCvuUH9O4oR2tDWWK8lbw1F1znBpodD6b9Qj6Wsk68wl1aZNcQXqzAuFI
+06WlKY6vD1hcYY6yko/6zJT6nVxh2I8mOMos1UpuYTuK0ZjFnOrJd+dPy3dtDX2uvCgPd+47RHog
+FV3LaqJ9N72Ebk/iMml/TsBbx1PQ2WHaZb0o++lcbZ0VbZl3SgGFeA42q9idZgKh9rOkAUlL7uk0
+O2I8J0WOqqxrkdz9ysJ9WMhCFnKPPBSWQpI77A1jitktWifExFtrbeF7ag52Uw6WJMCz+d2XcRVe
+3cu07Ljh4UdznL0uZioRddN4DDRjYG2O1VPKFJU0JHMwWkYa5iEdI0G7vDfDWxLt3x5dI23IKVZd
+UQy83oC+sv/0awlprOXIYYFcAalykx9Dq3mBsmTnPl3F3COdYvUEsoCvQbBcT93Yr+Nob0dGjqfR
+8nq5yGAiJvVBNOaL6+JKHV6VEt7XbobcUtYrcng30ROvPaR4cg4s4lFT1isNoDMsDbhzIK7UwMl4
+7Y3rAIw5ZAlFSb5xmdKBgohsqmVTKrE6k2yOSScUy3KaeeVVsjn24XBEIpeMHckaO37MVNnB04J7
+TNd9pz1gokjaxfoS/qq6f46ekqNtUCYo/A5GiXbMbEKmjNZm2qNYknviaVGXdWfYWGsznAb5QFwC
+427guHNX0OLOOx/HWhrcG1NoSgak379Jpve38pGQb92S9zypTNXDUsyeHrNHKeRaIGaCkGtW7nvp
+yHJV+zhStR6eXlqiVpNg4FFpn66O0c4OCLUDN+37VCpy+oeK4ejHEelAnoFhdoh/JJaLW47p92Su
+s9kMT13h+5WFpbCQhSzkHnkoLAVjxwTZa3QOLGFPTpJucpvtifjyZ7MMuyLVaoetCf7vCcdkcU0C
+gOaJGpe//q8AKM9uce6TPwaAs/QKSVV8tdD/CEYrGnMlljF+hNH+eNfpYXI5/Q7DXTJFAmLgs7ki
+ga3yqpT47nUGDDRtNEgmtBXMdGNYpKDwbtNsghcok7LyH+SjMbn6vbN8RKBdi35eJdLGn0wRlmZZ
+gq+BuNyEJIn2zfdSQitxi6GT8JNaKXe7JKd1JbtCoLnyoYX0UE6PbLhEex5f9XJCBQotj7TqsrTE
+kfq6196+ChM5zUZ+xtWhfEf3T/coKVzcksZXllZqnGnId5xcrRNrN2c0hEjLw33HkGnq90i/N+9O
+qCigbTILGClS0iSzlB250PagxetvSddsaSDxgIq/ipnKegflHAdl4B4c0tUmrt5Bm/VTEvx0NCCa
+DKcMh9q4FkwY9+Q++DUItUrTkIN2Ek4GshY7o1sU9WRmWMUo/NukW+UT2h2ZPSf3/DkusftdIdwx
+jks4VVyPOGKnJZbCcndKqStj9BUVrFYd8xOflpRr/+AGuwr+mjXKHLRHui4hrZ5Y0eWSspXHRcxY
+/p4xYKTkQfVBRqIs3vu9EUc9tXruUx4OpeD7uMtrTG++wrbiz5nBIacUAXg3PElPgSJG7RmTstzQ
+E54EYQbfHvFPX5ZA1MrBDj/lykaqn3dwz8sUm3FA4bzCRboaYc4nJD1RLMnEY3Ykrw9Sl10NtE3T
+Ime18CbWUuTpLLuL/RcEDLTzsZNkxIgycdLsuHgptfMg4Yjh/AZNclLd0IW4SKTBrjkOyOBofIyH
+OEsGoGxShVWDf6Dm5U6LL7+rJLVtca/IQiYoWAyWWPEFe4dTpqEE6Eaz7buYiKpA+nv7HGmp7TsH
+bT7+iGzSM+eWqHckv90/sU61Kg+nW5K13zpXYqUq9RSlIhhVkGaSYjWL4MbRMeuRow//cHBIWNao
+P+Pjop+4FBJWxF0pFwvsdbV/RN2H9eVdytol65ytYbuyF8ZZm9EtKQUPIkuizF+TlvzdpCP8ZRk7
+2XfouQq3tn+ZaqoFZRRxFIYv7yo8XuAyPtQ55R5lLUhaqpX5l+r+lb4jnxkvgbFyn9qDKRWtC0nH
+M/b35D0HlYCCtkx0RvL5l17Zo1CRcu1vvHyFZCSfe/z0JlEk33c0PqKo4CsU5NoH/R6hle910w45
+ElQeew7pSNa2P+jQH91ne6TKwn1YyEIWco88FJaCm1mqg5z+UoNyJid0GD9GPhJTs+H5mD0JnJSW
+TzIZiFm2uyQn2GBnynNnxMQ/df4TLF0Uzd84c5Z46+MAePUGWarAni1tinc7ZG0pB+0dTXADrTzs
+OdzYltPWBg7n9vX9J+TEsI6hol2EdULSvnYqdlsUFWQlqpSPTdHBQCHmRi2mmshud8dk6roUag6x
+pgvTnujpUSmhpK6Em2X4NQWN7ZZJPJnH1cMdwjnkl5JZuFEPraSmexN8TWRP3IxzNxTIpe2y0xar
+oK7s0RWb4msn5sm1JptnxCR2Sj6rMwmkrZzbpNKQE7upwdpydYuldQ20jXp4usY2K+CkyqPhZrhK
+CxdpOe8w99mbaSOR9Um0ZqPXN7haC9Hcn9I7kuu7HIv1t79e5fFAzGR39RQzV/aFOV2jrGb++M4O
+N98SHpAjPUlPzALiklRHxheXMDV1x4xleijfl422sUqL52hzkX8Uc2dXgpIDJ+N8VVyGOAXv29JY
+d1WBXau2xyiXeQ57KbmmZE1g6GsdwiT18Yuyd2I9+e8c3uJLfyp77PKV28d0cy/+6zeonJLTfzZK
+CDVFfaqojAslj2pV7nsNQ6yNed3xmMFM3YrplIO25mfvUx4KpWD9mNn6JU4016goTuLRqyNGoTz8
+B15AsiIPZMf0GGqdwd5V8XWNGbCqBUIXP/tjVM/IoofLjx8TBGVZl7Slue5IxnBHA1BU4jA8ZBRI
+dLc/aJEoPLvnOyi1H710XoqcHuP6jRyHoa8EL06Er+W1iQupkQdkMlMfMXVxKhIPiIxHlsiGL5Uc
+KGrJq5bcRkHELNFS27GlFMhDU28mtK/Jrnnz8A7vGHEJpkbe63geLQVnqeUwKighSyehvyGKZXpl
+SEkV4EhRpPOow7IyYJ2+sEqvL5v0a1//Ew6uKNJP1MfmWsOvqFI/cv4Z1i+JW9YslSloZiiwHoVA
+S4aTMRNFHpqzdy0tVeipm5MmUyZajtCZTpjsyD3ZbexTKWk3qqItxdUe40DiBQfuK+RW+xlGJaIz
+ch0bS3Xqp3UNfDHLo8kK5VjjNn6D2JMHKy3USMbiQmYmx0/V9VK4++LQh55mlCYjvGUZb7Z0RDuX
+Nb99RX6+2mszaakitEJyBJCMEmaZEvEk5hglq6T1FoWggD+V++EUXbJc5n/bdnB31Z2ZjSkWFPhH
+CYDqYZFoKHtoVoWiItznScpwJm7qzkGb7x5p7cV9ysJ9WMhCFnKPPBSWQmgs592cdngGbQpj5LZZ
+G0vX29rNW7T7Svyy4eNpkG98WyyF13tt1rQicOnFL7D/ee3Hf+JF6s9Kp6UXe4yuirmXNuZ4iAWs
+dgnm0zH9O2JSv9neJR/JCbt18jzb+6qttVYgsZa6nrRZmDKxmqnwcmYzMU167TFWuRhyZSeO/BJR
+XU48v5pR1WpKQwDah49aEoHrML0lp85Bu8Wy0r2btRLTAznFspHlaR3jzlmp6Hx29gVeFowOWkBD
+8SImsyKvXJU/1OIuaxrAim5KV6BfiRnV1dV4c8BXvi0ZnrfvbDPSXH/Bcyn6Yi0FNfm8fe0t1rfl
+O86t1Vl/fAuAZnWJQHk0nIlhqhWLA6XxK3kO4VTm2kqH9PuynrPMcDSWk+0ru7d57jHZA49eFFMi
+aPt0tIrR2QmxY0VzPjTH9HVZMiCqyvWdOCnAOqaQkGtFoClEzLbVaurtkyi1XFwuEyvpUObI9Sbl
+O5TKck+LK5e4pd2X2UvXONOQa7upwcfO4RCrLtjKWsi0K/d0t51hFbAxzXJGx1kQGbffgYmWVxcy
+n1D3chB5lDVYnbgekaNWiDY+ucYhtupiOoaWlnHOjvrMtAz1WpLy7p37646cy0OhFPxSxMYnH6Vy
+5FM+KYsQ1/ZxdsTXSw57eDUpIBlVDijpYhYm0p0XHHyBG33ZSP/id3eJR4pn+NJ3+Pdva0FLNWb1
+nEaZE4mg29gFfdDNxCVTjEa74jOdiLuSWourRB7zIqUktcfmfnGpQaJgGWMnIp9zDbo+Rj83UyLc
+dDJluCc3aOg4OBpxH40z3JmyTGmWoduZcNQSE7CfjKieEH84rFcZxBIHaacjGmXprutHMqel1nNQ
++kO5hrHl1McE++/6ZBubi9mdDbfBl3m3lTUrnxVYuyA18odX9nCqYiavxxUe/biM/eSl56gty/s3
+ffmutHCT2bsKVnv9NtYoCEvBh1jWe+ocknTUxVIMyyRJSeZt1JFH0VUSoCAk0xLyzAG/qe7DpsSM
+poVtVjWdWv3EUxxeEaVw/cYB6Z48FKvNiKq6ZqOuxCKCdpnsI1pBNTRYPViS0ZSKuh1BEGC0YMxM
+tHQ7LrGvYKyrqx10melv9/FOKFGNFpwlWUqgDGDToMY4E7ckh2N2Kus6TPX3OX+o50UcaomyTS2p
+gsxUCj4zR93GZUOsSFVl7R4OqhGOuti+72B0DW9PdhgqpumhmRzHa+5XFu7DQhaykHvkobAUgtDn
+xJllSk/7nOyKhjs4fZrpi3IavT68RnUoVsNq7Tx7+4KjkDXllPioe5qsqdHy0TZuQRuGaqssKQJz
+e7+DvSSnqulKkMlkEZ7CpY86OYnSwp2yVbyynHL76YgtzXk7Qy0ZznMGY7me58ZrDBUYpdveZ01h
+zMJyiagk5uxUtX0/dgmGmtXIQ0oKE2+KUHLUbfDkJB7236WvUN7rK3WCaF7rMGB/qHUWyYzb++IS
+NXJ5bTvep6LlzPmLKZuKmfj08x6tP5Uz4M7GI5T35LuTorhMceaz6cganjhfpVkVU3X7Wpdzde3U
+uz5lmCj680iOudPPX8BVEJbk/ArKEk8QZeSK5TDpdUhHGrjVHdcbTtH6L0puFauszEv1BlMlBCqk
+hu5tmfeeK/gWzBKWtCis0Ctz4aTskY2tVcbfkZO5sLlB+aRYNzMFyAnGPpSUpu/V2wwKcqHL62cJ
+imJB2HTAVK23dF/2Qv/dXcbzYqNCl+ahBrkPx/SU2zH3ZK3i1JBpg9rkqE//GPrZ4isGp+9AoPUp
+6yVZkye2nuKoKd81cKYUfLEIKpXw2AoNvTFVLccuu7qIkxm5BrY9r4KjRG0HY8tBX/kIbk1J5iQ5
+9ykPhVLA5thszFIfgiVxDVbaFW4uS8zAvhaxnwim3vaLN5l5kobKi7LpTpywFItaYfhGj1yzC0l3
+yq7oD8Itn9me+NSO+mbuoEGuYJ6Tzj69nrgaBVODRB6g8cxlWlQiVFeuzXcdhl25ATujGRtajehm
+CVaLXwbpGDO+l4zVneUYdS/iZEo+1QIiF3L1tdsDpXIf7+OEavbFPgPtzuvutfnCFyUF1hqPSXP1
+KTsKPBIU6FyVDV91crqqLMpBTnxJ06h75zkc/xv57kNVIOkddl8XpbB8qUioyrTECPe6+vunD9hs
+ifkfnhBzN9o7gbcivxfckLEv9yydxmRqPg+HCYkrD4g/0KzNuE9npmk6MyFUtyTMHcoKqU/gMtib
+t4bLjVw9XWOiTEnsvYN/SVzIYpZjlVODvXew2jcTKLenW7DMsdXD7Obx/MJS/bhgKc0HOJp9GI9l
+jw0Pj8i1g/Gw7eJoG3m+abj6umQ2br0hn+lMU3I1vvPRgNYcy3EAAATqSURBVCTVrlwEqBZgmt1V
+HLsK/V8fvkuUKZOVDUmQ9Rx061jUBbEugbqvPuoeO5bUigJpT3MmIzkkB3c6XN6T7z7ojEjmiMP3
+KQv3YSELWcg9Yu5yw36IF2HMATAEDj+kS2guxl6M/f+DsU9ba5e/35seCqUAYIz5lrX22cXYi7EX
+Y3+4snAfFrKQhdwjC6WwkIUs5B55mJTCP1qMvRh7MfaHLw9NTGEhC1nIwyEPk6WwkIUs5CGQD10p
+GGM+Y4x52xhzxRjztx7wWCeNMX9kjHnDGPO6Meav6+sNY8wXjDGX9Wf9AV6Da4x52Rjzu/r/M8aY
+F3X+v2WMQjA9mLFrxpjfMca8ZYx50xjzwgc1d2PMf6Vr/pox5jeMMdGDmrsx5n8zxuwbY157z2t/
+5jyNyP+o1/CqMebpBzD239U1f9UY88+NMbX3/O1zOvbbxpif+ncZ+4clH6pSMMa4wD8APgs8Bvwl
+Y8xjD3DIFPgb1trHgOeBX9Hx/hbwJWvtBeBL+v8HJX8dePM9//87wH9vrT0PtIFfeoBj/33g9621
+l4Cn9Doe+NyNMZvAXwOetdY+AbjAz/Hg5v5/AJ/5t177XvP8LHBB//0y8GsPYOwvAE9Ya58E3gE+
+B6B77+eAx/Uz/1CfiQ9XrLUf2j/gBeAP3vP/zwGf+wDH/5fATwJvA+v62jrw9gMa7wSyIX8c+F2k
+AvYQ8P6s9fghj10FrqFxpPe8/sDnDmwCt4AGUlr/u8BPPci5A1vAa99vnsD/AvylP+t9P6yx/62/
+/cfAr+vv9+x34A+AFx7E/X8//z5s92G+WeZyW1974GKM2QI+BrwIrFprd/VPd4D3B5R///I/AH8T
+7aYFloCOtXbesfIg538GOAD+d3Vf/ldjTJEPYO7W2m3gvwNuArtAF3iJD27u8L3n+UHvwb8K/KsP
+aez7kg9bKXwoYowpAf8P8F9aqywdKlZU9g89JWOM+Rlg31r70g/7u+9TPOBp4NestR9DysrvcRUe
+4NzrwH+EKKYNoMj/18T+wORBzfP7iTHmVxEX9tc/6LHfj3zYSmEbOPme/5/Q1x6YGGN8RCH8urX2
+n+nLe8aYdf37OvD+gPLvTz4J/Kwx5jrwm4gL8feBmjFz7O4HOv/bwG1r7Yv6/99BlMQHMfd/D7hm
+rT2w1ibAP0PW44OaO3zveX4ge9AY8wvAzwA/r0rpAxv7/cqHrRS+CVzQKHSABF0+/6AGM8YY4B8D
+b1pr/957/vR54K/o738FiTX8UMVa+zlr7Qlr7RYyzz+01v488EfAf/Igx9bx7wC3jDFK2cxPAG/w
+AcwdcRueN8YU9B7Mx/5A5q7yveb5eeAvaxbieaD7HjfjhyLGmM8gbuPPWmvfS8LweeDnjDGhMeYM
+Euy8P2roBykfdlAD+GkkInsV+NUHPNanELPxVeA7+u+nEd/+S8Bl4ItA4wFfx6eB39XfzyIb4Qrw
+fwPhAxz3o8C3dP7/Aqh/UHMH/lvgLeA14P8Cwgc1d+A3kNhFglhIv/S95okEe/+B7r/vIhmSH/bY
+V5DYwXzP/c/vef+v6thvA599kPvufv8tKhoXspCF3CMftvuwkIUs5CGThVJYyEIWco8slMJCFrKQ
+e2ShFBaykIXcIwulsJCFLOQeWSiFhSxkIffIQiksZCELuUcWSmEhC1nIPfL/AiWGDH1aBMUnAAAA
+AElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.4230... Generator Loss: 0.5748
+Epoch 1/1... Discriminator Loss: 1.3313... Generator Loss: 0.9232
+Epoch 1/1... Discriminator Loss: 1.2939... Generator Loss: 0.8525
+Epoch 1/1... Discriminator Loss: 1.3392... Generator Loss: 1.0006
+Epoch 1/1... Discriminator Loss: 1.4827... Generator Loss: 0.5120
+Epoch 1/1... Discriminator Loss: 1.2983... Generator Loss: 0.8530
+Epoch 1/1... Discriminator Loss: 1.4825... Generator Loss: 0.6011
+Epoch 1/1... Discriminator Loss: 1.3184... Generator Loss: 0.8947
+Epoch 1/1... Discriminator Loss: 1.2802... Generator Loss: 0.8693
+Epoch 1/1... Discriminator Loss: 1.4412... Generator Loss: 0.7739
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvdnvZVmW3/XZ+8zn3Pk3xZhzZQ+V3SqjpgFblqw2toAX
+M0jIIFm2hNRPIJB4oAV/ABYP/AGWjMQDEgKBxOAWtrHaLdrC7arurh5qrqzMiIyIjIjfcH93PPPe
+PKx1bmTKXVRWV1c7Hu56iRv3d+4Z9t5n7TV813cZ7z1HOcpRjjKI/Rd9A0c5ylFeLzkqhaMc5Sif
+k6NSOMpRjvI5OSqFoxzlKJ+To1I4ylGO8jk5KoWjHOUon5OfmlIwxvwbxpjvGGO+b4z5tZ/WdY5y
+lKP86Yr5aeAUjDEB8F3grwBPgK8C/4H3/pt/6hc7ylGO8qcqPy1L4ZeB73vvf+C9b4D/EfhrP6Vr
+HeUoR/lTlPCndN77wCef+f8T4F/5oTcRRj6JEywBJlQ91fV0qBXjwQbyves6nHcAGP19FIX43umh
+HtT6MaHFePmdCSzGm8/9zvUdbrgJD0EQyfnigNrI0KRxTpzGcp9Wjs5368O99x1EkX6Oodo1AKz3
+Ozq9/TgK9T4jgkA+B4EnDiN9/vDVvVn5kbUGawK5NQdGh8VY8L0OUdnQd3K9bVkCsGt6en2qOA5Z
+7ZtXD6gP/lnj0JjDJz5rNdrhD0Z+Kr/zGP0+0PkIjaHT37ne4T/zs8PJ/av7d06O7Z0fTivX1f98
+8MEvHm6w6z0h8rCr3QqApqoIevnOGRgumEQRRsfLWgfBq7GTWzAkaaRjGB6ezxIQRHJz3tpXY9Dp
+OESGdnio1tO3rX7v+Oa3vzcM1j83sKGFQB/aG3P4m8ccxjC0ujbNqzUZGIMdvv/MKa0xtM7puMjz
+d84d1q+1lmC4D2sOA55mOUksa+75sxdX3vszfoT8tJTCjxRjzK8CvwoQRwk//95XSG1BshgD4Ja3
+3BiZGVN60lkOQLm8YV/KS5no4F3cP6dZbgDoTIev5XfJaUHcywsdzcYEjRwfqIFU3V6y00Vqu4Dp
+/A4AZw8mfBzK2P3Mw6/w5s+/Jd+nWwB+8bf/PoEuuusrz8N7MjU39wzf/Z2nAPyD3/1trmv5/v59
+OdfdswtOTk4BGBU9b1zI9U7nJyQ+lfvM5CVO44A0mQHg954oU6VRWLqVPN/NHz5jdfkYgH/yrT8E
+4J89uuXWV3LdN075B1//SEfcgZXF1LbgAzlfNCxAa2ibYQVCrAoyCD19qy+hc4T63NNpAcAijbkp
+5XrbbY0LdDwNGD0WZwhT+VztawCWdUenL55r64Oi+/W/93/TtXLMzU3PgqWM59f+LwA++u63mK5k
+rnfGYHtZwm9fXJBEEwCScUM4GgFQNkafPuG992UewuyUJJJ1Udgp0zsZAH06pu5Fufor+d3oIuRF
+K8f2L1pWn34KwPhOzQd//t+Um9ZnC1wH+uKeFJZZnABQRzGuke97DHEgcz2fyHUzY9BpYBrHjHL5
+PgDaTn6XmpSXpay/y/UtAFdlSa2bYTYaMRnJWJg0wYQyP+9++St86R157v/6v/pvHvEF5KelFJ4C
+Dz/z/wf63UG8938H+DsART7xhpi8uIetZXWUYUTsZPJN6PFrGaixDSgyOWY0kgc/z++RFrLCyuaK
+XSmf8yRjnE4B6POQ7UZHXpVGmgSkvSiYSZax3MqC3tozHsxkUd2bn7Bb7gE4GYnCsuch5yN5ocO0
+4kUoL0XV5nz7o38MyGQWoVoFOsrWQVHITpPlMbouGZuQMJdnjdWyCVJDotPjRxuCRHfBbIy9lXMU
+9zxVKPf5weh9fbZv8aSWYzsX0Ha6sxlDpLtHFMquCGBD3fl7SyRrGGeg07fUuoB4UByBwequEwZy
+8yZNGelOWrUtTu8/DGJQq6ezDlQJJYVcJOl7Wt11CQKc3qcJA/JQxrmb3/LsVsblihuZs+aaVv7M
+vCoYPZR1sSgKVo28LK42RCN58fpKLIxnN5f40c8CcO9Bx912LuftnuPnD2Tsc0OoCu6x+5bcZ/kv
+kaoCzCPD7ZkorHa5h0Q1ma633nB4udvSkeXD40W4sYxbkgTcmctL+uaZbBDTPCWN5PlHNmdeyLF5
+muCRuSzbhNVW1urHz+VV+v7VC37wTJRmQ0vrZGyDyBFYUW7Pnz0jzuQd+KLy01IKXwW+ZIx5G1EG
+fx34D3/YwcYY4jgGU9K3MimursmRz/vOYHT3i+KAJJZVEccyYA9HAflEJjmk4GojC6Fcl/RGXmi7
+bCl0xlwgg5elhpGViemqnngufz9LDfGZHLNx3yS7kkVxbU8AuL28IXi+kOulG1yyA+A3fv+f8P9+
+IjvJl9+6w9v33pBrp3KuUZpwVy2e05MxZ6dy7WS3w7aysCJ9aUIM7vb78nsbY8eyAE0PppQXJDEt
+07ncW53JVM7u38DNcwB2E4MaQmSRIVB3pfWGcaxKQXfzJITAyN/jwLAbXtLAkurCTKKQTt2qLIv0
+9xFNpEo2jg9z6oOEwbj1tKR6vXonL1XQOryawcbbg1uV5gHVlcx1Vm9p998F4Lt/8AfyfNc3/Ow7
+b8sYjkYE6m66/RJT3eo9xdT6An3y4RUAlStJvieDUfWP2N6KVbE4i1kHMl7j5l2unsr18rvnABTB
+lm0t8/uDT77PtJaX7Wl9CdUr93b4V/UgqTHYTuby7iLlzp27ALz9pRMeRLKO3j0X5RClMUUoY2y6
+6mApRGEOiSi3m5eXNF7W/Xt3Zd3cffSM3f73ALhcb/CtupLXLV2rjkVvqJvXQCl47ztjzH8M/H3E
+CvrvvPff+Glc6yhHOcqfrvzUYgre+18Hfv0LHeygrw231Q2rvZhD42BM6mWXj6KCLpBdZRpmzFI1
+eXW3StKatwrRoiaKKFR1X7cd3V5+tyk3OPU5KyuaPw5G9BqfmI8mlL3sNGb7hG4tmnbp4NaIif5w
+dgFAPhvx0SPx4c3TjheIdfB7v/vdw275/vsf8HPvvAdAH8juEgU9U+Tv54tTIg1wBdMYu9IdVHdM
+v1lhWn3OMD4Ep/zNp1hkB4r8mFhdl+Ba7n0U9uwSjcv4GqM+btWaw+fZKGY+kXPEGpt6OJ9woe4Y
+ked2rfEcA4n6wD7qD3EHo1Za6SFe6T10IZXes7dQejlH6AI6dSsSdal669G4Lc77g90dBhHXgcz7
+i8ff4uM/+k0A2j/8EIDinQcEXuZx27cUS9kd+3DPbqduTrSnfC5jTiV+eErMs2fPAJguR7TjJ3Le
++h6Xaw0cf/IR5wvZxe3H4rbctt+grWTOfut7v88kkWc6md8h0bdHwwXEwH0NKl+cBby/kHF758vn
+fOmN+wDcf/OCeShWyGSkllmYYUO5X+MmBOqO2Siga3StLiKaRs43HYu16eotzy/kXF9tWlaVjEW1
+31NpwCawIS+eXfPjyL+wQONnxVlHGVfY0hJr0MY0W7qRfI67jjyWkd+0z1iEYg7dH8m/98ZjuvIF
+AIVNODUyibOLc9aX4kpMpgGPX+gAOzGN+7Zj7+VlzNOOu0O03O1JkUkKkhyTi2sSaPzh2ScvWMTy
++aNdyNc+kmDfy/WOL78nbsUiCokbuadYTdxZmJOgGYfbS+KRTG4UxNhUgoqmlJebaHRQBDaawVpc
+Btdb1JrH4DBLUaLdWl6kSZTj1CctK+iHSDdweAvbjqSTMTpLdREHnkmvYxHlnI9lAY7mOUZN0c5Z
+drUcs1H34qbe0+giDpOYThd66XqiOtTLedpMlEldiZL23jPkH4z19E7PERumsYz9D8qaF38kn9c7
+fRmjBL9XV7Lf0auS8U1Hocu5ut7QbOT+BqWXJJa4k787PLm+vFvfklYyoJtdR8RO70meI09y6kzG
+4vTkjKgVhWS2nljjJKlG/R+EYJzM9YmNOMlEOd+NA2bqx82jkLmVMUxDDTrsrgg0hmWNYUgpdDcv
+QLM8kTOEmcxZt5Q1vRiHvHUm6+ayq3n08lLGag91JeO135W0W1kbX1SOMOejHOUon5PXwlKgc7jb
+LcX8DrFTSyEwpF60dh81mEajs8mYu4Vo7jfvSKBmUkSEpe40YU97IqZx13aYcznWljdkah1bNHdf
+t9hOd67e0aqKPJ9FRJFYAm+/+Sa7QDR6s5fzjruGaSpBxNQ+5kkvmvj+OOIvqstwb2aYFHLBsBRT
+dJykxLkG5UYzwkI0fxBPwck9GcR8NcaBkesZ20Ot6ck2ode0nmt2xBN5vvMzDVRaiEZyP811h9UM
+eBRkRGopZNYz1u3o7kx2sztpyNlEdkEfWNJCPmdFTNDJNSpXst4pKONaLJeu97SawTjJJtSa4N93
+AV0jn21oiTSt2fSRDn1P0w1Z9vAQtTct5MiueWfU8/sTOcfPnEnw7f37CwK1BCdxhlmLJVEHOZ2T
+z3084yyUMSq9rJtwXBDp73AtmY59G9vD7h7ULabSQ07lupP5KXEi156+/RXqTlzF73z78ZDEIlSL
+YZkafm4IKj+Ycn4hAUFjDKaS+2ivl7i5jsG1BDjjKMZX6u7E4cGlNd5hdIyCeEyj8z6kkQsbc/9c
+rrHLQ+perJF12bDRgK6re7r1K1zNF5HXQylYCGNHxp5MzczK11gvL8qu3HKSyEA+WEQ81Fz/TBfj
+ndkYe6ET3sJyJ35knATcBDLLNhyTXotvFajfu/MNodH8cVcyVvOMfkeOKIKg3pNors5k8m9brni0
+l3N8s3zGqbo5b4zukt+T3+VFQKEuTzGSFzrJPOlUFnw4ygk0i0JZHnx0zGA7WtzttV43BY2pmCCG
+UlwGax2BuluZ5rxvbyoajULbxBzuPUtjfC5jmxrHmxdyn3emMq53FwW2V6U3jegVNTOJI1rNHPg6
+BHUxxprtqAmJdB5cbOi9ptM6j0WUyePVjlrPF0VO7z3EDeAEAzBkMzxOcQotcKYWth+JW7barXkw
+l3ma2oS+0N9tG4zGc/rQ41MZj/1GcR+jFKs54N707LeqQIzDdbL5NHHC5koU+EUhFza7NZOFuI/s
+Ky69mO6b6iVOFW6ga7M4TSBQ/Ma0IAzkWU8nE8JU3QAL1VJcxFRjA23f4ddyjqZZEWra13UlQShj
+2G6vwar7UMtLnqUpWSjjej8NuTyRtfXytuCpus2d79js5fm+qBzdh6Mc5Sifk9fCUnDOsd+X5EnB
+vhM9lcUxjQJPitgwPRVz9t7iDsWJ7I6RFSuga2pOHoo5v98smRiF3YY1sWrdev+UXHfVGyOWREND
+pgGuySzHd2o7GkOpwJqoXuIiCWhOJ7Lj3zaXvFzLvT29XDLWqP2Xfu4tHqoJPk7GTHWHiTUKb9MM
+q9o+HN8BDTiZ5Ay6QZvLjuHNFjvSHco0YMWq8G6NV4vF1zXGy47oFXgUjCuapUb9x5MhTkWYGHpF
+WBZFzLSQ391VhE0UjRjr7mqNJ9D7JMxII9mtGteiMIUDfHzkcxq1xkwSs9mL6zKyAUZN9KvNjhsN
+UF5rNqj2/Svor+vo3WCx9ew0cNn6NZOxjGf8joKU5iVBreb+SYRbyo5vww5nhwxHjlNEplHLxQRg
+B4unhdTKM3VdDbpGLB2pBmY/0Yh9N5qyi2WMNuuapy8kqLzug1fwb4VX93WAneq9hTFxLjt3H8WH
+eW/aDmPkmdCxyLMRTaBrIYgxkQLV4vAAPuuDEDcEiq2ct663GLUU6hAKtSqmccg4lufftB3rtaz3
+LyqvhVLweCoaHAYTyyCU2y2BvkzTKGCqC9ZEO9JKF4qa+3GS4lqN9I/GhLGCP0YR1WNBdi7HY2wp
+L16hcYRluyQfKVJyPCJQfxcDsU5iEo6JdZjCTgb345sQO5bJOB3NiXKZxDDLSRtNkzqPX4niMBcK
+rArCwyJ1l5dYNffAgWYD/JU8h88D0MXj9+C3L+XzKIBYlJDfWHwl5q5XJGjgpxSxmMybfUfVy3gm
+9Z5kQBWakInGbgp9OXwJTS1An9ImFCO5dm1a/EbGbe1WLDeaDVDkX+Q8O/Xbx96Cxg583WA1bmF7
+j5YwUFeDIu/xg53qXtWreByxlXGLqinxXp514+RlNKs5baQvwvrqUCcQeIvXZ6qXW3wuzxWk+eG6
+3Qsx230R4hUe7foGo5F6G0AfifJdeomZmO/VpJma5R/t2D6Waz9bv2SoWAiHLFHh6PQeQhcQqPuw
+X1WEmvHybs4kUyWKPNtuV2LVBQ1sLxoMoGzoogEZFeBbTQM3Mj67uqVXzRT7gIBXaNkwGrJ43SEW
+8UXl6D4c5ShH+Zy8FpaC8ZKHLfIxgZpI+8iRaFBrPhuhmB/m4YRsIsckqol3uw1pJ6UWWREQzCUo
+1Wx6HPJ5dFHjOoGaPk3ExB17z0J3/GKe02zULQkCZqmc+2yas9fAXeQ0D+4dDyLJfNSzllqhqHFk
+Dyaqw0AsO4wrNduxyPFOdgmfZniNzpsiwjdyjNcAl692h+CidwFGXReMP5iXZuQg1+i6BsiKouD8
+Xd0xn60PcNfSdsSKSTgJY0LNrdfqJvnlmgZ1RXJHbLUox4d0oYxXvSwPVpMfipkwFAq4Mp2jr2U8
+m33HyslBM5txraZCoCCLvuk+U1RoD+WMrnVkTp714uEdPvmBZGN2fyCFXe3FnnAAbxUTbCXP0acV
+ZifXSNKIph8wJwqmwjIUI7Sbjj4awEKWSAOFfQWBupapVsL2Wc7ym1INma/vECVibbQ3t1g74DN0
+/gk5ncicp0lCkSn2pC2p1LRP+o5IA89DsYnNAwK17uhj6sHqsxM6rfkI7JRmCNZqoVyS7JgpaK9e
+rhkv5HfzTUkWy/W2VYtzh1rgLyRHS+EoRznK5+T1sBSsJU4zTLDH9KoxI8f5iewSsQ+I1O9bZrcE
+N2o2qK87Oxsx7dQ6IINGdoEqLnkeisb3a8/HV6J1P12Kf562DTcrOVfeL0mmWuzjR1glSairPcYM
+0FxJBW6c45sK7U1nKWO1NpI4ptHgYRP1NP1QUis7QvX0knAkn129weruYS6fY4YSxWCAF2c4LRFm
+AmhsADeh+1TiDi6u8Z0WfClq0vsSqz5+nHTEarmMQ8vFXOHab8w4CSXO0Svfwsm9Eeh3RZGwWsu1
+k13AttVgXmBxmjLNEhm3uqkYayqwqVqiRK4X2wwnxgbt2jNt1dpQkgnv7SuyAGMwVq0N49nvVzr2
+PSM1SXZoyXUFkzsyN91mi1Hrp7rcHywh50OsBix2t7Kz95E5WE1t5FndKIS+iEkGwEEQ0dzKmppq
+BWT3fMP6VFPj1YZP92JJrLfXRBrMO1WsyN3TnCKVz7PxmLIZoMaWRmMKYWt4+lTm786ZWK4ua8k0
+BV7tm0Mq09kK08v3+2qJ0TEvNS7TNLBXfEtiDWGomIbAkCgHSNYkNE4DOl9QXg+lAMTW0lQ9WwWS
+n8/GpJkGqsqWFxrgGhUFL2otqValsPMViQJF/LqhrCSA8+z2Kd94Kmbnkw8/4nYrk1Tpi3c/SVnk
+WjffzplomfXiPGK71HLo3YcEC6nKy8eCMz+ZRWx1oK83W1otXz6/aRhrLvz5pzucmnN1rXDYdEFU
+y2IMncE0orACOzpgMkJ1H7p6jdvJ9DSbCtSF2S8/JEwVdj1qsFqW54ZAna+obrUWIcq4q9V3RZZx
+L5O39GwyYxopnkKVX3pyTpIrmOokoH+sefN2T6u4jn7viVbKl6Bpjdk4Jc/lXDu7O5CvrNclW+XD
+eNFXfKRYhmpwk3CvlIL3r4KOxtBrtP/q+jGXW6lRCAfgESFX1wNAqoJag3aNpQhkHkbpmFLrALwG
+lTe3JVYrUJ21GKObTxeQKvdCvV2R2oHBRs1vt+XZxzJPL3cVn6gyeWl7coVYT9UdyOOIOwvldMhT
+XCfPvN333J3K9+EoxTo9t1H3ODmjUh4Kn6U0mqlxrWe7kettdz07zVCEOsY35ZIokmvXI3eoiWlc
+Q6bzPioStqqcvqgc3YejHOUon5PXwlKwoSU5LfCNIdYKSNu2tGpSjsKEe6fiHlzEMYGa63srmjGN
+U5bfFZdgO81oVNftm45kyAXfmXJqJChz/x3Z8e+SkRZyrvb2Gqd5/OvNkm0p55iMF5yMldpKcRE+
+nkrqCNg+KUm9WC4344hM8+Y+crinijzTarnElIwy0eCp8cSaNsuyDKM7dvVUXJw23lHt5FgXZ6B0
+a9HpFK+7QFMa+mt5bqO7lnMheaocCz3USqzyS6MxF4lYQne7KdO35Zh2KdbW8uU1j54q4jMN2Wtq
+zQZbukpp6OIE3w6WidxPGGYsFWH34dWG79+Iuf5yX7FXaG8YB6wSmdfGfMaU/SyN2cHS8dDJeK5u
+HfsnarrX4rptrydc91r1t64OhVQxlju9PMtpUGHU4hpYo+p6T6fFZmtvqLeKEI1XVDdKSuMdUy19
+bBViv11X7DSYWe4DGn2m05MJz8YDwYusw9NRRKEVtVEUEg+sXklEqRZS5jLUgDgwT5W3K5TigzBI
+SBQqvb8tiSe64ycJUy2w8rGswxkT9rpmq27MS4QLomx7TsYyv+OTgudXYm18whVfRF4LpRAay5kt
+cEmA0+j0PEuZ9PLwo8gwUDc6YvqV+nsaYXY+AcWvj0xLPBMFst7u8QutpIzmuFwG9XwirkbQeahk
+oDqf0CGLu6kCQo0NFFnHSisUgys1DcOcWJmHXP99dlp9F6eW9sAJOeKlvizV98UE9jbljsKc37yb
+M1NSEzOZHSC/ZSX+5vb5c2528nz53Qum+kzhyNK3MuG92Q+PTaiR/tikoC5RVgakqhR+9sFbzGdK
+rGI6GuWS3NzIsbe73YHP8aMXNzzaKRMUnne1YjJKQ07nMoZD6Ofx1YaPFHb9sgTVH5zlBUutdiyN
+w6/l+UItW+xLD58LiivTkw/IQxnn+Tjmw7mWCV/LC3bV3NArPiDeWWqNH3VhiL/UUuRZQ6ovltU8
+fleWfHoj87FyHUbdp7CGeKSAucSTKdw80jhDPx3TKQ5h4R0GiSlMThZ8rKQ1icZUAmtRVx7fGLwC
+qPqmpdF1Ub24JlE3da6ujUkdkdaMjCcJvZILhaEH5eB0dUmtmJRGlWnXVhitrnWhJUfOu+9LwkTG
+cDoesd284MeRo/twlKMc5XPyWlgKURxx8dYFty+uCbUqbDFOuK85X3YtJwpRdmlAqQSsTklZ7zx8
+SHMrteSla2k1z1uEwSFwZ13LWneV65VozsjGRGoRlFVDrtVrYWUPUd2Pny/ZK8JurdFdH3j2lQYB
+Y5iPtDDGGL79QqrodqsORWxzpRReWVDygQYGE1tTKPef9zWdUnvtjZinT643/MFHsgMnj65JtADp
+fBQz13LP2UnGbKqFW43CuW9b6lZM333ds1CTczaZcf62cgFUO5zS3j36VEi3l33FSuHF6WnKQne/
+OOzYaD1+0nreyDTYeibz8dXlt9jrnJk4ItLn23lHolmbpq1IY2Vl3iuHgjEHPgUMmKG4KDIHTrOT
+8YI3ZhJU3So/ZrO9JR5IUCPDjRYX3lYduXI5PFmumesOO0lkrIJxiLtV+HTjqe3AtxDQrGS87swi
+FqlySowV/cmUqJDrfePxJclazvcLb5zy1e/Jzpyr9TMuCkgUYZg6ls/EXemso1UsR9c74kYL9nLN
+GO33TDSQGmyWTPRZnXGsd/KAH338Kc+VK7TVvTweB0xmYj28ff8+sxOFyG88N07WTnEa4/jxAo2v
+hVIwWNI+I049qxcyWW/dzXHZkN4yVFbJWEk4OZEBPHmopdMnC14kylx0dclYTfRm03NxIZ8r05Op
+Pzww5Zi2pVe/3adjVkoSGqUGr7DjoNuxc0OcQ92ZZsteF9V6VTHTqH3VmoHDk3XoGCuEep7IPZwl
+F2ShEqmOAupGTeb8FKOLOJnLxGcXjrdjeQFJOXCA231MO1DKdwJwAfDJ4CO31Ndyji4JeUvTZUFh
+MWsZo9ksxinc+uyBLPLtTUCh1xiNC8ZatxC2W1aaZkzjhAfn8iK8fSr39vT6KU5TgWsHjdZu3LqK
+Xl2CZG+YKxCrV3blK9/T1q84Dg9KIQwJx+qiJI5sppuEkpDsfUs4UPjPDeOFuoKlYTzTTMMmQC1t
+RvpQ/SocEgqEPmKswLIwD2CAB2cBfaTHN8q0PB4RJDI3d/OOs5k8/5tv3meqMa9O5/H6xYoH7wmh
+r7UJ+Uzus2waLjRDYfIRDBW4uoZ8vWFfKzXA7AKrMaEIC1tZc/n8jEWuadSBvj5NyBTyHkzmTJWZ
+KUkc/VLGbXVZsVHagS8qR/fhKEc5yufktbAUHD1lsCH0I0YzjQSXDTvFKcQ+ItDIedNuyDuFqypT
+c7X0RFvZGvZtg10pLJmEVFl9x4sRpSrMMFcN37Xcppqd6BO2ajW0+xVec94EI8bKi6DIWH7w9IZc
+d8d5khBqHrjf73hnLjt3G8VsV3LtQkPL5+chcwWgTLqWuNVgWFXitAgmUAqzs/GEkQaRstEYq9yI
+4Tg/ZD6CvSNYiSnqInnmoLUHiDJNz0531TzqMUZdqbokVd6KB6OhWKvio6fi+rx8cc2VXq8YJTzQ
+KPtpOmKhQS6zk8F8Nx7TBUoQUzvSU6V56z17tZr6IKQfqvkUirytS6GWA4zzGH/gmCOyajUFM4LN
+QLemXJphwvhCzeR9RaQVo0lRgLJDz8clN0q4Ul8rb0LdklU63lFDFso1ZsmIbAgYGsMoHHZ/ubfy
+9gXJiez+d6N7jGYCqLtfzPEDR6MSpLjK0a1kTaaxZab3ZtOCXgOXRWgOPTcwWozma/pG8St1TZgp
+l2hVUygE+83FHR702sbAyZyHWcpIIdZJkbPUsoDT0YhnLyU4fnl9xe72xyuIei2Ugu897bLm4vyC
+TrH4oYORYu4nQUSs4JckmFFoVSI7bcJSvmTv9NjEkquvttnWBBplTjuYX0gE32rlmXUJJ72Sk+xb
+ak2FBXFBqqmsMLUY9QkGmv+vX2+5c6Il0nFCouCdcZKy0JJkGwaHtFitLEAj6+k1ZkAREgYyucZ5
+jE5oNBZlNMkLZgPzku8JUy23rSusEZPR5BV+rjUMzySLYgMYL9S/6HIqBfG4PiZWc953PX2pTUa0
+P8KZaUnt1IklAAAgAElEQVROBWE3jl5y4YceESHnys40iyBtxJztGyUjyc5YFhoVD3pi9Z1P05zL
+Sl7kzuasFLR1rijOb/WrV92bfHBgXvLOEyjTVTFJcafyrOnH8kyTJCTS2o/R7Jy01e5b2ZjNfiBx
+Tbiwukb2isA0HZGa5TeVJdYUsPUdcyW7ieKWQhu4tJp+DsanhKEqunfvM7krPSJG8wmNprvHygRj
+y+2hR0ZuI8YKagtsg82Gcuf60HOj1zqfOAzpNA4WB2AVvZnlY6ymNaN5RlvK94tQXCbna/KhmrVv
+CLU258vvX/DiWsbik+2Wuj+6D0c5ylF+AnktLAUbGJJJTN85Rpk2Idm1h05OV/WOB1YBOT7EbRTo
+oyZX4BpaDQJGJ2fsFVcQ5gleE/nFyQmt9iOcqznfJo5+L1p+9fx71K3s3LkFEynoZcuhsnEgtFhV
+O9aXouXn52MWSjU/n+bkak2czDLQiHKsu/FmtSLQ6suwd8QjqezsbvcYBSQF6l7E+RTWCn1ejA5/
+pxvTaMaAgAM5i9Vdp9puQWnV0sKS6s69vb4le187HbmGSCPm7UBXFhh6rel/NzzjRjMmcWgOgJ4s
+zbCaUpkWymNQJJxpk5LV5ZozdZ+a2JHVUx2vBnst9/ciHhrARDiFihtrYcBsGEM/VKWGOSN1MWp9
+jrbpGSvpS9Q5AgVD7cstiUKF8zTDK1vz7U54EKsgIGSgR8totflOFgYYtd5837MpZY3MBmxGCDbV
+2o/Gk2pQ0mxvcWqQjQMl4XmYHrpCjYqQRHf/ST6i0pqPJInZKHQ5KYaKU0sx1VqUbYlVoh5iS6YB
+1n5bUWjg2ivMObAjeuUIyXJDoG7uvTvvYEYKVGo7omToJ/rF5E9sKRhjHhpjfsMY801jzDeMMf+p
+fr8wxvxDY8z39N/5n/QaRznKUf7s5SexFDrgP/fe/64xZgz8jjHmHwJ/C/hH3vu/bYz5NeDXgP/i
+/+9EfQfr2x5fbHj8RPKr9y6muE7z+6FhpWnB3IRYDSihVYhV2B+gdGWzY6SBNm/ToesWXdfgNLjU
+Oa2MDKc0rewMGEOrzTodMUutlmurLQRyvoniA8LYsNMU4up2SzVUPraOQtu45UHCaCI7SKBQ1SRZ
+sF5JMK+rNvTDbps+wBnJaUex7hK+JpxrOjEfYbQfoF89PTS37bcb/FBRp0VLbVceWJTDxRljDZxt
+XEXykYzn2XmBG1iCNXjVuoBCfXWfxQSKGl1XW0qNPxhXYzS20SnyLw7HBJ2wW4VlSxErxuBkzlKD
+vLum5lqDkX+oAbCyaw6IRuc9fggwWIvTitG9L+k1EJxoWjdOXnXE7r3BZBrAa6FSVKc1GYFWF8aK
+U9jtr8kG3gTDoe9D4j3REF9wjn5AlmqgNcky0MIl0y9pO2kI5LIJe+214c8Vor0PCRcKD3eQjDWt
+nSRMvZKu2o5e8SBDStqYlq0GyifFGeVeU6SzBe1WdnwThQQauxpo3tpmie+Gal6H1TWJ8a+YxDcb
+wujHe83/xErBe/8pSGsk7/3GGPMtpAX9XwP+kh723wP/mB+hFDocy77C7QL2WsG4ulnRjtVEbz25
+Hcy5GjTwkww03WvLlTZ6ebJeUq8GarKOnS6UfD7hl9//OTnfPc0DtxvChUxo9iImq2SAr7db9vqy
+zOqCTNmDB8hpRUakIJXbquGTp6LIvvLegrTUdvY2wm/kpbAjLQEPoF/JhK9ebJiMZVHFoxd0GkQy
+nbgGNnVkqSzAZF5jMwFnEXvQBjb9ZkN787HMRziY1NmBjq7fdlx8SYJSd05nhFsF0+xLQg3GWlW2
+WRBRKhW4j2O2Wmm5LPdYXcSTSXrgGlxrKbBbvWCjZe3PthVj7Yhtky2rK3HH6rLkpQL9b7vBVPcH
+M9j4Hj9kIozFDpWK4QyzEzzEuvttAAo/o9HMT9vv2WvT4Fn2qr18nIE90THQzlu9M+w1G7QtS8aD
+nZ/HtJViQ5IEe6grUaVff0IwUvbw0ZuHjAnXWy638nyVBlHvjkKqjVY4jmsiOyjOFK+bT1xkB/5E
+o+7TZrel0Y5knd1glPNzt3yG13Gm8STaY9Ih53JNidPAvGuh1NqO28s1bS33f34yo3FDhekzvoj8
+qQQajTFvAX8O+G3gQhUGwHPg4of85leNMV8zxnytbX48n+coRznKT09+4kCjMWYE/C/Af+a9Xw+t
+zgC8994Y4/+43322Ff1kPPWu6skeLkg2Sp/VtvR7Jdocxay1bn4RjIm1aKrTf5e7ko8VDtqu1pRD
+lZCHudaxn89jpvfls4bsqKuStVaQlTTUGnBqfX8IghVnGalaJpmaap2DM0VNxk1Fdyi66YkUQWni
+FNRkbm+UGdilUoEDJKOIcMAbxDk4zSVrsCxMA7xRF6bcEGnq1LcRfqhGShqMmse99jEIYghHWjE5
+nhJ/R/kU5smhfV1iHK2ShRh9Jhc11ApB3q7WXGrvDA/k4auqU6c9K6/UdDad4aWi7oIi5HIjn9u2
+5tGt7Gh9HnP5iTb2UVfE956hCEquOlRPGmJNI09mE4qffxOA/CMJZramY6e/KPoerwHmNg8lj42Q
+sxi10lJNN2Z5A3agoo4Yab+IIAzIhvVCj1OLZq/Fdv30hIkWmE3OTrCLTK+xYqfWhNdq3SQNMOqC
+Nvs9obqPlhar2IS234NaG1ZT4JPY4O/JPJ2d3CdV1Ghf1zjlDunagFbn3Q6QVtdh1epo/I5Gz9sb
+x2yurmvvKQeq7y8oP5FSMMZEiEL4H7z3/6t+/cIYc9d7/6kx5i7w8kedx9qAUTaialpMIAsp2HaQ
+aclq5ZhNFaJsGkItgb5Zv6Jkn2h5siU+mLPR6QlvLwSb8M69OcEAq83kBdy2NUuNst/erNkyVCp2
+WAXQXN3ckqqfXETywnvjsPp5fF6Q9gPvYkxl5WUapckhI2IHkxNHPlTnLRJ6zZLcLlfkCiKKT5Ru
+3AR02lq933b0qltNGtDtB/ehw6jCajVzsn+8ZrTQGEDdstJF1a/H2NnAdmzoFIdR6cJ2OMpKATtR
+RavZjJGNDzURz9drGqVXN/r7LjaM1F2ZzU/QYeHrH15yrWQhadCy3CsbVvPDWIAU5hwYBkrBLJkw
+VmKYK72HcbXmngKIgjSn1fG+3KxJlFlpww2lug0De0vvHa0q+sgb2mrAi3garSStcFTa6zJWRRB4
+h0/kpVrGnqnGs9qNOxDNDLyiwTzHaseqKPbUpWazplN6dZusA69ZNdvJPd5sS6yuvXr9PU6uxbjO
+HhZYzbq41hNoEKbUDIk1hq7XKtFuz1bdmCCwtHpskc8p/I/nEPwk2QcD/F3gW977//Yzf/rfgb+p
+n/8m8L/9Sa9xlKMc5c9efhJL4S8AfwP4Q2PM1/W7/xL428D/ZIz5j4BHwL//o07kfUDTz8nSOddP
+JNoqXaZlF4t9Sqs4BRcULPdKarITkzQhOvAUBDblbqit0s5nZFrg7kJPp9mMutZekzZhv5MgYR9Y
+eo0Qm8xRl0NArCFwCqvWSs2m3nG5vda/J1zcUe68rqSyYraVjSdVnIJTnsE+Dkg1ar9IE0wjO0XX
+JISKT4imSqTReqwddomWTmvsacsDj4SJDVb7HIa1PH9ytsFotaA7TXmilXqfXNxl/kiud546jJqz
+A9ptu6+phoh7khApGzCTlFQdrkUdstdAaVlv9ZktbqbNYvqQlVbyfWd5y34IgnUxT7QVXNf/sIq9
+oWLSYhS92rk9O80YpZNEn3NBOZA/TjvctXyuwvqAMaCqMWqlDP04G+OYaCYmCEJqzdZ0e0+k+f+m
+WzOkRDZaPLdx7eElifsNW7Vcbi86wgE1qZD49fOOuZL29K6nUXN/v9uRa0asty2htovrtf/pSZpQ
+67j0Tcxtc63POiHWoKMpAjqF9bdqNfl+f7j2zvRUipBdRx3FSNbTdhIThn9sWO+Hyk+SffgtDrV7
+/5z85R/rXKGhWsTMohmJmvtmecnWvSI6aZX95mX38hBTiPphAmqWytdowoBeCVDu2g6rAJHbdYTV
+CjgzUq7G/Z6tlkiHoSGuBmx5g1GTsShHzBTm22ul5st9R9JINsDnCecanS6p8Uut1jyvDibhwFxU
+Xl1RxKI0TOLprPi9sc0JY4EYmxfqp44d6ML1taHfivLy3mMGSvUmwWzV3dJjR6O3QF+gclXz8UoU
+4S8sW8b3FPS1a0iV/idXIJeLDatKF9W+p9cK1Tv5DK8mccIIk8rCzLU9u20duxsZl+vNjqVCdKvO
+0Q3tqWr/ik9lWDF/bKQJjOFA925IyDbyUqy3Mr8BCaVC2stqRb9RqDAhk1TjB0lBo3PcVwoZjhKG
+pEbdVDj9T9V13K4VIo6l1/hJpSnbsLllv31Lrnd2j9jI+oyC4uDPh2hVqu0x2mTGOkM0NKBNAlwj
+99+VsK3l82hIM2PJNEMVJSmZ9ko1VXDIRNTblttLjVE5URrxNP0MY1VyaBnQbBqs1tKc33uTjh/m
+sv3xcoQ5H+UoR/mcvBYwZ98b3K1h/s575Gjgz0XMemFiPhsX0o4dGAXnJF40Zam9429vrzHari2e
+wtlctG7bG1DcQFsvKRear72WXa43kA3t2RcXGK04tMGCXHfNbBYRKcFJUA+cguaw03W156UW5VyV
+CZX2PHSdJxgphbdRApiPb6kCBbwkb9KrdVNbQ5jLMXEyFORcEqlLZNKAYKKWRH2DNVp0lRqMVc6F
+rQQtKYMD+AfvuNa8+dJ5zmptTRfGJMqR0OvOX5QJ95QG7cX1kudrBTVNRlw8FDj245fPaYOhuYoW
+KIUF15VAiR+tbnipBCKhgXysvTs7c+B21J/BDync8+5Vn0rje9q7SiumjMtpuQYtLlrkb+G1XeDz
+F5eYgegwztncyH10WlDUzSxeKehutsvDwvfWcqZkPjbryXI5R69WY3hyQT7V9oWBpd1q0dUsB4VK
+L9XVzCJzcA0C2x/aGoZJTLqQ+bObS7YrhaQr+Um+uKDRTA1xitPzxVGB28u81nXIPpVnmp5J39So
+27JWirm9rfATXQv9Y14810ZJ31ux3f4Qs+yHyGuhFIIwZHR+TteWZFpuuiPBXol5Ve8sI8WaZJMI
+X6p/qamWi6yn1lbfs0lKqKSrfWKItQdCOCoOvQAGdpwgCMm1JLtuYaz+uaGh134JbRnQadegWPtZ
+Rha2CvppypJWJ+62NuzXYvrx5hirrd/zXny60Twk1thBfnZKnEi6rbnckp5/voIzM+d0Qy/KrMcO
+naOigP6l1iWcjTAKrGo7RQFe99CLObzelYeF9/GLS+7fkQq/cBoT68u9f6KZGucZnSvjT1xQvZTF
+/+AsZnJfMiPjBxO2VwqyUkr6q31OMBMT99ObFTfaqQoMTafZDtcSa4T/R/F9eBylAm/auqRWItur
+J5oVaFsWqbwI0zfnNMrMNLoNmUy0x8WswOs4r640VZtEOH3maXxKrzGO6SQhVdhr7ftD3cUbb8jG
+kt2/y04JVx6v9+TToc/C7NBs9+pKEmyjcIIPtM9pMcKVGnNILGjdTDS9Q3Qlm1oy0TqX2ZjJ2+/K
+2DchxR1lyIomBDrvyz/6DpHWT4SR1pcEIaVmuPq6xU9kLNZRzdeffgjA/pv/lGwx9Cz9YnJ0H45y
+lKN8Tl4LS8EDzhguN48xan6ObYRVKjHXgMYZuSxrvHY8RqOwWezZlWIG75ZbnG5HTQe5asnzhxec
+KH6+1qYaneXA1GuxNIcGicGBlbmKOqJ+4BaQ+xlPUqoB907P5VPZ0V+mKR+nEhA8P5kcWrhbjXr7
+WUyluWmzXhK9pWZgsMAWAxRVuzK3PU4DUUSbQ1OTLoPoQhl8E4NTnHynpCmuMPRqNbncMFa8/NP1
+mqcr2aG+lD5krzUDPpddvmkrBtYQe9Iz1qDV8sVjylrZrEdjykYzNzpPy5uGRwrOWpblIZDYGUes
+0flg1xBq8DBSLoHO95/pJflKqq5jqwjX1eox3/tQ+jiudMecbRKaSJ75pgxIlDBncX9CqpWftA6j
+7hFaG1JWJSYaagpyQjfX+6lpFAPi04D0QKOunZlKT1sMGZcdtQIxTpxnphbGVoFzV0vYbsTC2J6E
+RDr/xnUYdXWJA3qd6426H83VNWamwLrJKV4zP76rqa9kPdUeXKJZMA2wr52j1zVS7R3XL2Tt/ODR
+muXjx3pvK7j88cBLR0vhKEc5yufE+D9OXf8Zi7XWp0lKGkecLGTn6hrHSNmO58WIVCvDQgMXumsM
+TLff3dRcS8yO/XLJ+Gfuy+9IKCvRtO+6mvNctOpjLQx68nzFRvPtWZqRavpn+jDj3/lb/wkA//av
+/OvcX8j5Yk2x/cq/928d0IjJ9RUvNUDpvTsEyUoT0ilDrNfKO4slVL82sJZAEXZZFJBrn4J4YIDe
+1LTN0B6tYauptc57AsUvjLOU+Uz83WR+T35vUk7uCqHtdGz42m/8UwAiEzI61V6SyZjzD+SZvv17
+3wfg0foKq+hO7x1jrcS8m6ecaXpy4kPeG8tOWAw5et9SKsdAXYc8WisuYrtlqei/Pa8sveFnrQ1x
+SoN278E5b9yXuMun/883iNSqaLqKkcK+R2o1jpLkkKqNRpYPHnxJvr844UMt/PHjiHCkmIuZsjhV
+PVdXUpKz/OhStl6g2ZX84Erh9EXEpVpe395ru7aril6rPc04ISy1spGM//kvfEWeRX/z9ZcveScV
+K+fFKOD6UjEg3e4Qw3naQqOw6lttq1c3HSUDySuHfp1FaBkpKfBq3zFTXoufV1yIN5ZOg5Zt23Kq
+LNiPOody97KiJE5lnL92efs73vtf4kfIa+E+AHjXY4OcJNEy4yAm04q8fD4lUz7DsYmZjYYSWTWv
+r59iI8XWpzuuH30LgFsTMM7lZfrEQKGMz0PT1funluf7gYa8pQ41G1B3fKf6PwGowj/PvhuuJ4Nb
+tiueP5FqxrgsD/TcvTWEhdZE+AhyzbcHauJ3HBrXTjB4MzQubTCa887VdFxYw43yIKZNRMfAZ9i/
+qi60EbVXxZkPlXcJa4V/J+HiUAp8Osp58FBevLPxBxgvxzzd/TMZ/6Zm6LgT945cO3VdLHIeamPa
+nxkVvDGXKPpIA3mdt6A58eXlhjfWkg34wdUVT5Sk8ZOqZKWLfiAbsYmnVDfv5mpNoM1LitRzXmsJ
+96zgucKp719IZD1NUkZqfi+CGW/9axKs3dUpZ+pW7vKep9p79PKPviP3ud0Rl/pyr2vuaqR+cndO
+PeAXrKPS+bMfKd5kYQ6Nb9x2f2jEYruaplDFqSXni3XMV1WhvZWd0gRSldia+MBsPU4jXipQKVew
+nLeGXJXwNu4INSgbBCEX2gynba7pNZNSRbIRvJmkvFDuyn3veKY1GPfHIYUC9ZbA7R9ffvRD5eg+
+HOUoR/mcvBaWgsEQBCHTScRI69wtI06morPujsekGqgpJimJUnC5vezs748Dfm8p2ncRVWxuFaVo
+YKqWwM/MY75U6i6lu+DNbMqbqVzv4/36wIYbV4Yn/0gCXL8+/Xv89b/4NwApbAGwT25IlQbLGQi0
+Pdg8yUgK7QhtXjWDGWv1Xtl2B+bgzvdk2iX4udkQaa9FoyZgaCLmSsLSJDAay7XTyLLSgigbwulC
+go6IwUCz6xh1sntsTEti5XoXsxlfeeMtAKZ3L/g/fvc3ALjV9nB3Z2MGxrd6W3Mxkd3o5x/O+dKF
+WBjnxYTJ0L9TA7TB5AynzxcFjpn2g1hcnHJHze78+ZKlFussNSX5tCtZDyzI9ZbSyX0ktT20Wyuy
+nC+/J6nat96WoOxJGnFnILd94328civUV7fc6eV6H5sV7TNJE7aa1rV0hEq0uphc8KWHkgIcZVNO
+L+R3y2XJ5koswHNFef/geYnVIG/fOYYqYOvg+08Fn3GNpKFXdUiiaNLn7FiqmzDNI/peCV5CGKP4
+Bd35385maC0avTcE6j6ENmev1mSWp4fjE7XMugBmun5DU7FXH21pPKaQgOk9QlK1kH90aaLI66EU
+DCRBQMiEtVKc3xt1nE3kgUeJZ6Z+VNhweFkuIm06O+75hQeKcV95/tGlTPL7geFeJC/pO4uOmWYf
+Zgv5zoUFH63FxBs/hU9bWaTr0vDon0n/x9/o/i5/9YO/BEChEel+v8Kp/5pGnjNlW5qfnjIEwEdJ
+TKLR/FozCsuNJUhaPUdM2eiC7SJazXw0GmfY73YEA924zQjU/A4zy4XXqHfg6PWet0/kpXNdQKeM
+P5NmwQfvCTbhrfM3sLmAkFq/Y/tUmpEy8CSGhokm3quw5s5UlMLbxZR7Wp49m0ck6ouHWr4bekdd
+yXMk3R6j9Pvzomevpu0vzAueKHDqU62WXF5uMdoAxTUR21u5/3dHCWuNpWQu5SsP3wNg8b7QrJ9E
+CyYa40lOUla9XPv+aAbasPf9NODT978BwLO1xntub1i+kPOOso57i3f0GiPO1c27Wl4Rf1+h9V7O
++yD1PHomL//jNaSDJe7DQ9errz0XTMD1tifV+pF2DQPCbdSOOdWqy2QS0mnWzChz2LxIcdlAGRCg
+w0Jn4EpdohJDr65Xq+Quuw7uK+AsjzJW2mD3qqvwGne5TYMDG/UXlaP7cJSjHOVz8lpYCkEYMjld
+MJkkeG2lhemYTGQ3GjtPrwUlBT2J5oqHPgaBd3yi8NPZJOavNhJ9X4QV043sePfyhGkkEfp8rgVD
+mxqjLbvd3JIrAu9qU3NzLdf+o996wj/5va8C8O/+ZTE595U/8ESOJzmZwmuLNKYwA99fxUbdm1KD
+T/vKHKLQ1zcbKuVA2JUeQq3T17Zq3joSBgKUmvBQGefxinocj9MB1kChAbL1ekuqlXwTIn7pX/5X
+AXg4ybns5Zhu9TGdWjGRZp9cVdFooHEWBryvbMaLzJLJtkfUxCTq8lg1T6muGXh1AhscYMyuWpJr
+Pj3IwCteYnD9nl1aHg9FR7llpDyYNRs+uK/EMZzgNCA6Voj5pEjI1ESPmjUnWkgUnExIT091DNec
+nP4yAO/vFG/x8Xf4bimZFudgrBmcdFoTK8J1nJ4y0TV1pgHqrzZwpgjK8FFDr9mHfR3zfW1kOdNC
+uU8IaBRj0XQdiQaEo6hlrhmz8/mCVF3IsUK3jd+DBh3bvqdVfMNmvSTRBjbRZHSAUxtFMe43nq1C
+n52xFFqV+qJ1TEIZw4fjjM78eMxmr4VSALAuoOnOCVOJXp+mIbOdml/nENQykG9cTA8pudtLZQ/q
+e5QJm3rjMYMNn6W8fVdcjNO7I/JMItUkSgcfr6gUI79ILOuJ+OeTbMSlmmLLb9d8/Tf/EIBf+aVf
+kWt0PVYrNdM8Z6JVlPMkJRo6Ia3BKRS613/3ztMo+CeZhHTboZJvT6c+p1dYdpHHjFNZ8NbUOH3B
+uqphpywkiQuYaNq2b4aK0oyJlpl3ruMXP/hAxu30XV58KgSrf/DRh8TK/Tc0VhlHESONgD9MAr50
+VxTovCjQinFi+gOxCKqkXdeDmszWhKTKsRnkxaGCz0UZsZZ7F9roZdM6brba3DcKmWpJedZ23El/
+Qe5tMmd0R+4zt3K9eGGIe0kzR+en9Jq1sUGEbyS1GBYJfSPnMxpHYBxwqqxJG1/Rq3Le3JZMtF7B
+Gsf0VJ67dgqlvlzy0SO5xp6eUnOqSZowVaV2mosyqrsdf6TuXIRjrinEN0bw5Xcl1pInOak2pRna
+1gdmRKd1pHXt2O21FX2a0k3khR6HllIbJH/jU3UZ9xXa1IxpHxBo5ep52HKhJeMZlmX2473mR/fh
+KEc5yufktbAUjAmx8Yw+Cgh152vbDbO7mtNvPfMT0YI5lkALZmys8FObE6jKNLMEq6bY/VnOnUwL
+Zs4mWO3eMVT3Ny0UWmkV3rbMFQx1GyU8nIoL8vvsePqpBCOvb5XOK05IYm2PdnFOptDYaW7plKo7
+iS1btW4iBeA02y0jNeuiOB2Ywqh6S6a7SqeFRu/cucfZuVgKN58u2dRKl74zPNWIem88M63QzBV7
+UW1aXKYgJNfx3jsSqMvTAqcJ+Zs/uGSlGIKBizJJAs6Uh+L+IiHWHnnZKCIJB5fI0+011z9UYqbB
+obelaVoCxWF0jRsQxtT7LSNtmBMoI/HP3D/hW58KAOxF50jUYkniE8qh2u/PBRi1QuIHMhb17ppc
+qwQdGwJ1pcr1NUGrjVOa6AAt39xKyL3ZdxglJInKiE2lJDmdJ1Ea+cnpewfY+OldcRWLJ9fsPpTx
+3naX9BocNOmULpSxeKKt9J4lDYVCzMPOgnJ5nLw3JlXSk3GUHBoCxUP7TGsIGmWoZkuhTNuEIb0G
+bj/tN0SK28kUwv1sb6iVKjANDU5dnwsX8VLd26zZcq58jV9UXgul0Pc9q/WayFtyTZGF9yxeee0m
+2Yy5mnjjKGH5XF7SRBfxIonpRzLQNjLkhURkF9OESBd04CsCBRkNYJsx55hKFs3Z3YbdWn3gtafR
+YxLjePI9AcD8vd+UNF4aJ8x1oM/yMUPZws5XpBrBb6OYJJY/LG+v9B4iAi2nnZlTEgW3JIwO/RLq
+QJTGvXnKYqF+e9gR3mjcIrqi0kq+F5s1S8XfF077Idqe21tlRWpCxg/lBev2Hdfqz5f9LaUqr0Ez
+dZ0j1Sj8LLSH2IHpeqy2bTf09Fq74ZVmPgzneI2H2MTRqcsQ+QSUAard1fSD8lKlMU4t95Qz8tOr
+mo0Crs5OxwRzOfbmu9f4mWQlxk/UFJ+/SVVpV6zrOdWVZAb2uwq3kxfdzQIije20SvHfl2B8pt/V
+NJqe3lc7qo3c/y55TGYEnHWtWbCPqy0/2EpZe7Pb0CpYrE9qbCa/e3krbsvLtifNdG0lIW9OZW6m
+xYhCTfs48BiNQRh1GeM8P1Tu9rZlr7yZWZBS5+o+dCEv1eVrdIwfpob3NC6RhwHNVuNOeceZluWX
+UcZo+uMphaP7cJSjHOVz8lpYCt70uHiNbyuiXPP1ZUSsfArZO+ZAa92u13R70WWpQqKzJMFvlBRj
+lkppZioAACAASURBVDLJJLgYRzlBJTuizWcY1YE2HAJLG0LN5477EXN1CVbdhlghuHsDaOfmZx9+
+Q+/XkyhuPQ4DMm1bHtoa17+igW/rATYtz2laz5l2CU72exql7nonP6MplB5MW7VTxPx/7L1ZzC1b
+fh/0W2vVXHsevunM5863u+1uu9OOx1jYfoAEeCBCCMQkP0dIPIDhiQeQwhMYHsgDCOUByUThARGF
+AEmIEuykHd/udvv2ne8Zv3O+cc9711y1ePj/arcbOenbvqZ9LX1Lujrf/YbaVauq1voPv8HhLh7C
+g6fZ349c+K1eZbFARsn13msyV9XKx5o6iY1NoQmKSs/OkM4pY7c0WHGndNoOTl0iI/hF98VxCQB0
+qfay7lXXh2ahsGAz3W6v0XSlQFtuPBSaF7tcoKSUoqk85NR1MNWKc+UhUm1KmCNnRX3marxMJTIz
+aJBQY/JgQO2MyUOkNO3JPnmMC3IUtHJBXRQMXANNQR2PBTfrKFjCxk1VwKvl50laYQmJrK4/WeMw
+lpSGRX+8OD9DvuM1hT5MRXZs7uOSAj6WlVhVWxgaBuW+gkMDozwHktmCcxshphu1JrDMblZQXWqE
+mBCWheQyW6NigRmNC13JHIUEd21QoUPwkg4MDCNI23jYUXNhFBuMhwS4fcZxEyncjJtxM35gfCEi
+haZukKy32ClnL9PlxBFWS8kRVX4E7bYqPoBhBWtyIPDbAA5aJS7TLxFQIwCNhfGlQAVvC+PJillT
+H8HrHcIyB0RQ7HX6bR1jTmio2xgkhOuefvABAMDXPjyiCm2tAKpH59kKKber3QZYcno7bAlFCNAv
+6VDsFQhZzJv4hzhnPgzm/bcjF62CWVkFqChWOur0sRvIjne60JjTbm7aSnjpCADrBVqjKelnaGNE
+hOO5hxOo1oKFO5EyCiNPrmMcOPDYV3ecCFCtT4EGiORUbM3l2tszDsvAQc0tVnkprJX5DPwOwDxa
+s12cb1NMqU8x9HeYM09+dj7D4zO2HKsGb9LP45i1oZ45wXJL/EadoybxZzC6jcGRPBeHgyMgpOeG
+lagR9RaK3hpJ6SFlVKRGEWru+LPZKZ6y+Pn8XM5hcTrH6oqiq5WCZStaY4FdzRY2vTxGbo0N59Xz
+FBzed8+p4be+otqDS/yN4UOrGgOw3qH9ErwNKMsIlq1fXwMR5/z+VI516QE5FaRuRT14xG8k5QYb
+IiHd9Qhq82dQjg0AbK3hBx10JnLh5SaHNyago2mwpMQYvBDDiAIZpOx2pjE0ZFKN9pGt5IY6gYal
+iYxSDsqMpi0RpdqTFMqlhLYtMaBCSDpscCeX7oMJHmO7lc/Z0Anp1uExDltxj04Mx5EHfnOpsWxD
+baNQsBCa561Z6xwPt/J59376LbhrwU2kzhY9Vpwr3pFh38MnjwWzoW2DmsIwcdDBkA/3NHiJR1tZ
+LC43cs2dsYuYCKLSWDRWHqpCZegErf18tV8YFV/4fuTjwQk9E+/0oVhF98bO3rzXlA6SRML/isy7
+3HWhSjL8tgW8KXMGZRD35HjFcofIZx+e6YqnFMqqpSEbdFiAvVhskfI+BaGHfl9e6juDN2W+wxFI
+r4ATHKF70n5GCt9ti3UKlvyCoiLozQ3gsN4WlwdY8uX3IwezuVzrpBdjsZH7pynak+Sp4I0B2Ap/
+iKEaoiD2Yk4DV91t4HGRjT0fNVM3Pw5gWBAMYw+29Q3VLQXaoi7k87J0C8V50Y6DKCYJw1Q4jOWZ
+VCzWBp++xNNLgeMPAo0xpemchY8VcSu7+iWc7Y9ZzVkpZZRS31ZK/S3+/wOl1DeVUp8opf5npdSP
+Bry+GTfjZvypjj+JSOE/APA+AHqo478E8F9Za39LKfXXAPw6gP/un3sEa2HrGjb00SSyo0SRA3/Y
+FhQdLK+52nkpcisr5Y6tsLvxAP0J23e2Amg5XtYlQGbZbrbDqlUoZiiLwqKqKbrZidFMJRzsXFTw
+JxL63h7FyBIJ7ZczaX/9yz//87BsN+6KGa6vZNfRZYXuUM7D9CZ4m1JwAwqTXM8XeGsoO1807eLo
+FWl/VUUDh8WjMpaIaLtpkOxkLta6wSqXCGO7usKQDMaH947xkmxNy2JovlmhoC+EjkLU5P83TgdX
+kDToPL2AwxSs9clsYKApJFrUFrudRATdyIFL8xXdaWAWEhUkrSRe4CLhMRJdIyUMOBr09zZ8Osr3
+6ioNPSpLYxFRHg22hMvi20ArKEKif+pogF/72p8DALz+lbd4n8YAd1jT1XtCWBWovVWadROUJF7V
+JGKh48IhFqROMhiSpzbzDbZG5uXyYonKkWv1XiVE9sNg3y5E1aClvtZujkGHxUGyRJ9nFRIyHLtW
+45BF5/PzEgV9Qnqegz7PySVaMfRjWMuCqVVo2E51fb0nrCWbDZYkinVieoc0KXJGceezLcZsI/uO
+xdt9me/fXZQ4W1OB6DOOz+sleRvAXwTwXwD4D2kl9y8A+Df5K38dwH+GH7IoKKPg9wz6ToL7tJ/v
+VQr+OTHbzhrZgjejyFBTbeglw/INctx/8QIAMLg9hceKLPIS25fUT0wr0NMEPkFBQZ3AI9UZyqIh
+zXqzO0OHYWA/jNBnr7itVYTTEQbEFXzw8RohwVJpmCMjiMpfLvBsJguO97qc553BFG4jD2O42UAF
+8tl930VNw1d7wTBSr7HJ5HzOVjNURsLa1SrFYdz2wnsYk/Ow2rP3SoBsusgJUfDlzYqX0ORXl/MC
+LEFgQoDNQS/A5opy8JsaaDkY+QYT1k/i1+5Dkcexfr7mcV/gDzZyzp+ua5QEAt2Bi7v3ZJG91R0g
+GrMrQ9q3aRQGY3m4D5MSawqPeK6Ht1jN/wuvPMArhwJ5djKClJ68xJZOUMv0AiCmobR9BJMWbp1j
+2JNQu/WiNGWK5KnM4bzJ8fhU0q3Hj05xvRJFJmNqTIYM0Sks43Qd4IJz6wOmaJ3DHCzpqznl5oRd
+CU22a+Y36LAO1j+O0OPvuh1vn0IVBH3Vyxm4dsMNQ2jyXJBskTN1LWuFeCj77pjAse79o72P5zzJ
+cElGqGoc5FquI3RroPwhEtr/n/F504f/GsB/BOwNgMYAlta2Cqg4BXDrj/rDP2xFb5s/fUm4m3Ez
+boaMP3akoJT6SwAurbXvKKV++Uf9+z9sRe/4xqrAYrbe4GOGQ28PQzSu7NAdHePWAUO/MkJFHTzr
+iz5AJ9QgxAD1dgNN+/Gq9pEuae9WV4AvRakmYWGscKFmIqoRoQMby67adwdYKUkVDlwP1/QabBGK
+484QY4bM2VGGKypk1ImDObnu2W4Bj0XMTz6RYhCOHfh9CVcOwwfwOvTKbLpIqRu5WUoqMrcZLKva
+uvHgQXbVTt/gJJJKm+lnuKCv5vdeym5X5g1cqqU4ToyS677BAOVarql0XCiGuR47J27YlwgBwFDX
+6BBZuVlbdNhD92Y5SlbfW1GU8+ViL6vW644xoibige+j77Hj4Ci41KsMurLbJdkOPUZCfU8hoVFL
+z3Nwqy8RxsGtISLC20095+c2cKhEHbkulkyfPs4zhOcSsdy/P0HDBpQ3FoKTzdY4p0/po6cf4zEr
+8utdjorheOhn6DHla6iN+BOvHeC9J/KMNKmFpRq1qnI4LrUrQzlfa9coiMKNfYUNLfuaWuOamm7p
+0wwOI9lbRywYxx7clkimir3JW5lUKJgG5VBQLGxmjBS8WGF6RKzO9Qbbsi3cZvBJDgtrg6Zhh+0z
+js9rMPuvKKX+JQABpKbwmwAGSimH0cJtAC9+2IGMBUa1QYYcAd9u34v2/AMdxQjbaqppcHHKfHYo
+D2b37hiWnQgThGjarwcOarpBZU2DFzPall/wBaozHHXluIdBibvkXTRugH4mC8GuaHAYyU0/efhA
+/r13jGIhD+mrr7yOewyZz08foQN58QLnCEOmKcolN8KPce+AFGIU6LryMBbXGwxuydc7GstM3R4a
+grTm0RaKef3BoIt7R3Lce/ePUbHCPWdN4fR6Dsv8PQgdpGynbvKX8Afyu4HpQrUCMEbOPQh8GHYi
+kshFTsZkeHQEW1N2Pk1bpjYs6xqmStFPmLcH3T034Cot0T7eQZjBI0W95UN4wQSXFLgxpUHM62jC
+LiYHIqgSP/gKwgcSaDp5C8d+ieSFvNCrLMXHF5JKvCxz3BvLfcoLC2sor08YuO54KCl0clW5yBx5
+Rq7KZs+kdLWPJQFOb/bknt0+jHGXYfsn2+Velt4YBxWvtSAL1h36uMPOz1uvHOCTC6lFXX4yR86u
+xaYpMQ7kRf/zXHj82kX3kAbJpQYFm9AYB9creT4/3BaA24LSZHGzdYpbTP9uhT76XHBNFmHGzzvS
+OWznn2X5+kePP3b6YK39T6y1t6219wH8GwD+vrX23wLwfwP4y/y1Gyv6m3Ez/oyN/z9wCv8xgN9S
+Sv3nAL4N4H/4oSehFCaewioM8TojnTdPhjh4S9IDXWmYpO0lH2NwRC9IhlFOXe+FPpQXQwcSYTTb
+HTSpaOtlhUlXwu5wQBh0lWPIyrEut/C5clvrIteyKodBti8ORhQhuX18hBlTiSgoMBjIzn0Yayxv
+yWfXqwYxZel92pA7gQ+1ow6BaZDP2LUIOnCIs4gpg7baZDiM5XzLWmFeSTjomRK9kFDpYIDDI8E6
+TH2BYF8ai5o6j2Vi97buDg7xrJada2dXGA/k1h8fyPkeOl2YjkRe42CAQ09C27BvoZZyrdqrESjZ
+QT36efa6Lhr2x4cmQtUy/2KNKa8/anI4BOc4NK2xhcWoJbn5et/NCbsh3nzzDTm2MdDEg7iuANXc
+4wqKJKeRuodX7t8HADxwDRS32EGo0QyopxmwUl9oeHcp/Z/kcLZy3MFDF3Gn1XqwiEKJuIZjuaZf
+DHz83kMBrT2/VigTnr+26LT4FI9w9ssGDydyncPJFP0VpdT6De4wBXX9GD3aE8ZMKRqt0TDq0LVF
+TWKTH/noduU87vrAmpiTNaMZz4ngEsdQ1goB07y8nGFCaHY+DJH+iHoKfyKLgrX2HwD4B/z6EYBv
+/Ekc92bcjJvx4x9fCESj0QrdIMRrjoe3KZUWFiGwYOEv7sAnXLlZ7xAQ+mq5c9e1h3oluTOut7A0
+mHWcGM6amvzGBf1h0eTyRbG5gu/Kaj8YduCyz1s2S/gpXXtXClN2LbvEnzq9GAfMC93kDvptSn00
+xPiartNBijonRZaf0aQVCpq61PkWGSG/nbIBSlqMUe7Ytzm6lKYbHx5gyx3KjzQGXcJ8dxkOiNyb
+nkg+rU7nsK0itIu9gGejl8iX8tnNogCIKXuTPP+7E4NqI18fBR4GnIvm5RY16xzlJIKzllZe24bN
+Gwc9ohv9XoNuLMVcu91AU0Go3+/C1Cwm0IBWGUCRLn2gIvSoSfGPr9aIPKk/xIMpNHEIrcFuDwcI
+Qoka5k+eoLbyc9UbokvKvIsG+x5fTW0JHaILmaOHd18B2dewlYU28j/dOETfo6oVd924W+LesTwA
+fryGpSp10xjM6AcSzGVOYqtAECv0Yo6v8Jq+cncEELEI14PLGlOZsia2zRAwQtTWA6064JQZDon0
+jDohwIJmNpNnXTsuOhQjTrYlqnMWF70ADd9sv7Dwu38G5dgaq5AVBmnQQ9NlWDfsoDuS0K87OICb
+MNTa6X3/s1WpSLMUJqZ2YJrvpbxN30GXTsv12ScIJ3JzNcMvd/Qa0BBL4DqoiCO35QBnc2Hquagx
+iCWNCTtyPr5xEPdFvCRV5yiJWVCpAz+UB1qHFk1BcRKG8IXK4NKFqXFc1GRwKhig5QlQ9KW32yFk
+ZdmxJUZHUnzTKoVDfkG+nEMTmj2mQUhoFAJ2PTpeBJ8ahj1ngOQemaTHhzBnjwEAhxMJqbXnYEDd
+hOMggq9b1+Y1HFbnnRVQ7whBJrZn2EyRN3Ri7vbBdwrh8BYUC2NBGEPxZbJcjLLZBn324+8c9PY2
+6n/tux/jFzNJ7+qP53g9kBd2UspCoLuH8CnNNvnSV9An+9LqEE0jc2jcGMVKuk5ggbbazOFS/2yA
+LlzyB5p8iyCSFMz1U7gV8RTUpYwnE4AQ7TIvxPwGgLIVrshG1VyEJ56DY7qYDxwXw0O5l14D6IIC
+ME4N1UqvURgnDkd72XZVld/XW+iNYHO6RdkahuevIln8miaFTrnB+TvkSj6v2u6QU3uh5wzRqBYh
+8NnGDUvyZtyMm/ED44sRKUAhhY+t9oBj2blM4MOnck8QanjsH9dpBstiV3Yt7SiTbaAYqvm3Jiif
+yo7hmGYvb+ZVLgI6RQdj8tm9Goo7UDm/gsrleLmvURVyvG2VQA+k111oCY29OECxbmG0JRR79kCK
+kh4IodODBiG4Cwq3zq6gaFgS+F3ULmHFeYEuUYpBl2i3wkc1k+MaTwNMEzw3RlFKuGpKBSeWnaRm
+mpCWDWKiQiPPICKxyXRiPNBSwOsf3kIcyW58666kLW9/+evYfff3AQCu9VBs6UlRG3Toc+mOLRYf
+S4c5oKyc0gqGu7FXGXQYBrs9B0ZLZFZdz+AEraGKzJQJGxgW1EbRADlBqKdrg//je/LZb/xrQ/if
+vCfn/OdZMN2kcCKJmrxII6AYb764BPgMNNaD6b/G85PjBkdD5OcS/dm8hkf9Bn80gSJGwLFDVCvB
+JCgK9ybpElEpO3NVqr1SlXZ91GS0ekyNgnGAARWEO16AgN/v9nwQCAqrfZRbeS66xHR4UQgKdiHf
+bOC0bdTAIqaPJ2Y5QsK7ETM1arpYEt/iO0BI/MN6rfEik2e9VAlc/Gj0oy/EogCr0JQKZW0wW8oD
+oUYdtGmo9TVsQRx93UXttiwy4umNhqEGYJk0sMz9KzeHsQQARQ4UjVhaZqBxA9SF9HwbvcOO+Ibz
+yzX+4VIAR2lh8T67BCjlJf13KgXLz5tfn8FZ8ri7NVzyDkydwqNOYPtC9I5GSNfyvcYDqksKjgQ+
+TF/OM7QEwnR2aJGeStXQQXurXLgpaxRdB8GW9ROmUt3YhWVYPltt4NNZytEhUmpaPvzSAZbP5OXt
+HP6EfK4bw6EuYXX1GHUmi1deZ9ChdCiqqoJiJ6U1r9HwWzY1vBhwiHtwEEMz37eugmVnQBH6qxx3
+D0JqvAYOw+frZIV3Pn0kX/+NAp2RpHdHHXlJD25/HSCoy3VPYEkTN3EXO6odAzVgKGN2eF8+Tzfo
+BrIoLp7/NnRNdJNVMMSL5NsnqGhInHLRWK22+JRmwqqu0La5alsi5gJREsQ0qV3c5kLvhyUCrki6
+qaDBF9opofn9qsWaey5QUu4+iGHJtdDWgct8rOv60MSfOATn2VqhpsR7kRXYsVCS1inGaKXoa6z9
+Hw0xfJM+3IybcTN+YHwhIgWrLPKgRmk3qFllX3hAvpVdqTYL1AwNsbtGTWhuTdinVjHyS1nhrRvA
+JQTZqaawlAfz+r54FACoi9ZwZomG1e3a1sgoq/b+1RmevpTwazmKUVVSlDt/T457vT7HiKrEHe8E
++VZQjMtqjX5BrQdbQlMDwenILuH6Pupczj1bnaGhIIdRLppW66FmQc5oGF6ncTtQxE0oFzDcdUpE
+2C4k3H1B73HX6SPosujoOvA4FwoZulpC21/56q+hXgqq076QgtxZVcC7El+I5fklwlOqPHd7qOYs
+2rnxngVZs3jq1cke0VdhgJoSY3p2CUuyEpRCSRhztaZwjjEwrSCs8VFQSNb6BbLi9+T6NsBVTVm8
+CxbU1FNgSijy6mNYFqNrjKC81t27gM9KqCETU3suKkKt3dBgx0q9s1shpsBqsb1GsZZrXbNz8u7p
+B3j0qUQKdaWgWy9Jx4fl/bvVwp0dg4YRrZtne2xGUxdotkz5jnqwFLFt+K+q7T5StGmNQjZ/VNkl
+SkqzaRPsPUvtGU0h4xiUyEBVN6gWEnmmC4umYbrtZsh3bQT12cYXYlGomwbrbYqirjGdUWW35yJZ
+y42rD0720F2lNSzDREs1HscPwQ4adNeDF7BS70d7Vp4f5OD8otac6EqhIYAkKyqsCnkw08THe+fy
+IGwvMxjdyjpJePbh73wXb7x1X/5us0WzbwGGAE1qrWfg0evS8KEpiholBRsbBbhUXjKOQVGTJVnJ
+A9psdjCU1msqg4DtNmVqlHw40tUOpHHg9pFU7+P+ARSZgcpzUNKxyIHZ03PHh3dx7623AQBX77wr
+17Fco3j0TI77+CXuj+R49yZ95FXLYPTRuSVzW/EemNiB36YPrgul20muUS/kWqsqQcmuBbhoVFkJ
+w3TNWhceDVLqssDzp+LklCwMvvqazOHVB5JS9KPbKIykdqZQaLbygqTdKVRMJmW9gUMNySW5LdAu
+tmdyjCydQYdyfZ5TomFtqql22FL6/oKq4i8fl3j8eLW/f4pdGTd0UTRyTT2/x3Nzse+NVd+fI+0a
+KL69xdV6Pxc+mZhu5z5s0qadO6iUgkLWwibtZtBAsdbgUBqg2qZQbL36gQOH7UsvS1Az97YV0HV+
+NO7DTfpwM27GzfiB8YWIFCwUGqUx7h5h/LashrtZiRYJkpUrOLa1kW9QlVR8JvS5qVdI+T1cis4d
+AMT9CSx3aeV5KEmIckK6Hdc5GrLecrfCaiWr8j9NN0iJ181r2xb+YXLZJb/35HtIqR85GMToDFrT
+lxIl+e/udAjLKKRkQWq9WiFnIasuU4SGBSPboCChqSFyRcUBdixwRvESNXEIbucENZmRVuV7rcW3
+X38dAPAyL8CNHVWWo6CzdVYW6DBct76LcSwCL5elpEaX0QrbGbs5bo6FkrkKn53DjqXoGK58jPrE
+CxAmDRPv+/T5bAvVb7UfazRUFy63GZhhQLMQWykgWbQCFw0q9uB108BpHbZdjSXVrWslu/HiegOv
+FCIVGgtFdekk3SKn7Njvnz7Fk0/el99ntNIMQ0y5y//0Nx7i1RNJO8oyx9k5OypxB4WR+zejzfw/
+La5RULrOWgu4DN/cBwiVOHdP6DquRh621NPLmgYNo78AAcBUN8m2SDP5jBMQkLdNkDI6KDMFp/A4
+Rxlqsoahc3hVW1RkpymvUJgW32/g0h086ORYECSnVQG7511+tvGFWBTQWFRpg10wwZoiI2W3xIZg
+lF4awmHV2vOjvTZewnZjvqtQkx662+32D6M6vUQYyMNkgwp1255izB11NCqG1POixu/4kh789kfX
+2DZMA9IMoDlHwxx/dtaDoeHpbNmgR9Wnju9hSiEWZ1ujx3ZgkchCsCkrZAxrgyaAZV6ujQtFoQ7F
+h66pNyiIdTe2B5cl/iLbwDKMLKoMTcy8lcfthz1csR1ltYOaufxltsVLQ5lx2+AskYf+ml6MyZML
+VARhvRYF8MkyzKoz6BVx+a7CLmObleaqoVVAr+0MVahJSXYUoBhel1WDiiAjTXpvHVkUDtGdaQ1D
+4RxfKbhEE94JNF47Yd1hIvOapDnWNPDRQYCMgjKJjrBlfchJG6yeydz93gt52bQCDo/kWTi5c4JR
+l50d6+OK54TZBRya6vyjnpzbb3/3Ajndlqy1QLvgVgnGHbqETUllrzYoNN27Kg8lX94SO/jsSijP
+RcRn0hxR9Sqo9mpSSjkoIrmXThMjYCtepQoktqLgZpMXBUo+QxYV4Mlxg2kXB2SVFqixrVtu62cb
+N+nDzbgZN+MHxhciUrBK4J91cgaXYirreY5NK53uAX6P/eF0gayUkJGWkqjho5nLzlBrB8lcflC7
+DUruKv4oRs5UYR3Lz/sZ0OJyt+sM19QFmJsSe311VwGsgNtKzme5SfCTDwhoKhvk16xwDxvsKBfu
+pzkaIzpeJUM8VTZwGuIQ0gRbuhZnixm61GyoGglld0WKSBGiC42SuyC6QElPzHRtcX7xKQAg8WjJ
+boZSeAXgOhqbc6mm77YXuCbIKGoyXD6TeakJ1X3+OMGI9ajrXYzdWlKUnziZ4IjpEQYTFORmOAQm
+qbRGne+B50gYpZlqh5jdB2XMPpJrRUN05sM6cowmLJGt5P45jsbrHYkKfvooABbs3NDGLY59FGSt
+Vlm5T0s25SUMo6n63CIn63Tqcqf1NQ582dHn5zUepdJ96d3qw8lkh3XiEDnP4/EzSv1td9gDZiIH
+2mujoj4s9TQrwtXDwIGl7XtdNYh8cmkKhXRFqTQdwj+ktgRBWNiuYVzu+H4Dl1BpnQCWxWjtRdix
+C7Q9X/F3HWi+I0VmUOzkfPLKh6kpxNLxERHg9FnHTaRwM27GzfiB8cWIFGBRqwbnl5f4Ngt8r9+/
+i5xaB7UWwgsAKDOAof7P+pqMtbyCH9FkJdQYDiQvdHyNhMXFJrlGSQfjaEgh0pMJwEjierHGFXvl
+buTt5cZQYY9ig5UV2i9znBwIIQpNgjmZeEO/wsCTKKajPXhdQQJGLAyudksYKlG7/SNUrDWkeYmS
+y7OnCA3OCqxLKWZOfBce4d/VLseulGt6sbjCjE7L01FLugrRkBAWDH0ka9k9uk2EXSY7zXXjAGyB
+vSTO4TK9xtFYyGOxXeKKn70Oj3HvDVFU9hKNLX0fHMJvs+waOYVUm6yE48gcdyd9qJD3rwYMoyLV
+b3f5DQwLlLXnwhL9qI3Glj3OjQ0xOZD5KInu9AYDBHwWrBPDz2Uuzp5tkBCabRBiyFz7YEozmGGI
+0BNIdLReIyEcPThy8dqXflKOh1P87x/9EwDAp9+SIqKy2CNLkZaoOffKfIzZRqKm95XM8f1hDyes
+geyqbG/Y6+kIbkfOY1dusb0ko1exWH30JjYvRbNhfZnCmcb8uwGWL6U9622v9piaoE8MxsDCNWRX
+RoCmWpSfFmg2LerXx/Hhj0+O7U9saCgE1sAiQ17LQzMrgcZISJwmXfSoBuwcxXAyOvPQnh2ooCm4
+4vcCuA6FQFSD3i25GcViiGTBfvuUcm2OQdaVn2/UHKOU07E20G7rQlSCxfD92jDoxcj4AHaGGhN6
+Qvq+QbMjwy0E/D4LVMSkLx9fou9KeKoGPQxfkZfQlDEU3ZscCr1sn/qYvyu4f/92Cbcv51PowQrV
+JQAAIABJREFUEotHlCS/miOcyByFpNXudluE1H7shWMsl1Kpr+sY5Zn091fK4sVSug7JTj7vzmSI
+t14TubkDN0XDVKouc4AdBRMp6JrAKNK3nZELt1XaXucw7Py4/RgWrSdiAoeGKS5FU7zYx7p9cC2Q
+Upm74wWICQ8veyF27C7sWumzbYPOhPiOCOjekcVZ6RV8FkqbeoqfjsU8BgX1Fw8K+EoWVmVqzD+Q
+uQju5nAnci1XSw/vfCrp2/I5N5OygVJt96EGWOTTJoWxck7LVkfxJERIfcl0vcSK3ZCjiQvvmJiV
+5ACzjBRnvsTOvR56x1+TY3zrHP170hna5RYb0rKT3TleeVXSn+FtAraSEgmp6H7owY7Z+bhokKYU
+jnHmsJSt/6zjJn24GTfjZvzA+EJECkYp9DwHm1rt+VyF0Vh2KXjquOgQKedlBoqMwsEdWTmtKVAS
+uVipBBWdf6E0XMKRg6MAuiMrdytuW5kca4bam6KQOBfAnaCPp56swElZQ7XRF5GNR8cj5CWLNzuL
+cYeiGXUNtJL9yoFZc1fp0WLuVh/FRrAH11ffw/PHhAfXIUK3ZeoRbRlZ9CZkcx5MULMQtS4zvNzI
+7j9bnaEgJrYgHsNYvTcTSfIaRSzHrdfXeHYhIfGTTYoLtlkvNrLLvX3nEJuaqc9EYXRMvYHmBc5+
+/x0AwHBwgJpmPSVDZ6X1HlvRuAaGhdtCJajYA6409r3yikVSp+vtWX+b9Brbrkzy8aALzxLeDRcN
+I6gFfTg62wSGXoy9jgNNebfD+1Mo4kG2mzUs3aFrpnOhCWGIvaiwRWfSQrC/31795DvnePSICEkK
+Beva7AVbaqVgKIxjIg8nVNdZEdpsjIOM86o8BYeu2jtfwW4ZbY1cBLeph0BNCmRzBLckOjj62bfh
+Ehavnj9GPGRbt46gaXdYk4xW9xsY+nOk1QpFwui2U8AjJsOaEJoIyM86lG3laf8Ux2g4sL/6y7+A
+cnOJmpLdi02OkoCkTuDBsklbwSIkbbdLfTovGiBg6BQP7+7pu0N/gOX8YwDAO998F5cXklMnhI6u
+kmSvUmStQp86gffu9nF0RxyJbvdvY9qjos9Qbvixp7E5FRrui/deoqZF+KACXj+RivLUH6M3IuyW
+OeTVxmJBXIB3AhSsaj95do1eQGepPnP2dYAH7CLcjQ9Qr+Q6nn10hc1MXsgXqx0eUqF4RlzBaQI8
+JmUbHQd/KyezzjiIu4ToOhbBWNKObrtoOgEUF56vfPknkRDmm3kuHAqWfOfDdzE7l2s54oIdhhbT
+kDLseYOHEwmTj8YhCnowJssE2Y64AKYXXreH56cEk006+NrXHwIAol//b/GUWaHX1dh9KHP+3/wv
+TwAA1QeX8GgzP/vdfwRFMRGVpbAMx8vZe1AOmZSxhPNB3EOxFUh3vdnA9YlVObiHciXwZ783wpBd
+rrff+GmZ+zc0eicCDHNyjf5YPsMtr/Fv/9LPAACaXOZn/q2X6MYS7l9e/T9IPpZnWc/mcGn8U7su
+wG7H0wumc7MzfLSTzWK2NHiXUvznyw1WvL7aanRYa5nE8gyNRjEe8JrHURd3D2UxXTsRokTua5Iu
+URCl/xu/9X++Y639On7IuEkfbsbNuBk/ML4Q6UNdV1ivrjG7vIZHyJhSGg41BILJCF0W4LomxuSY
+vWdL34QA6FEqzU48uDQyybISSGRVnt7rSHgPwCbUW1gZaK7ElapbSjvON8ABEYa+DjBLCfNl52Dk
+z3D+qYSZ8+0MByxyntwe49ZEpNviu2PYK1nxu1+7DwBY/uNncKievK7nKKkx8CxdwqcE1/Ca9uz3
+ElRb2c02Aw9+T+zTvvT6FRb/UIhLm0cNfpfh4y8fCm7i6CLFSMuu852mgqI1Wd+LEdCjsXK+74K8
+I6x3Xe/wwJdCXN6t8egZYbcWWKzZobha7yXrrmu5HyP00O3JdXSWFrN2m7EuvCE1ElYbrPjtKpc5
+PDgssS1kV91eZIjOZCd9tdJ4yRzs/KLES2JO0rlU6rUzxIZ8Id/9OVjzOwCAxnsFFY8X9P4imo7s
+/qBWQrbZwXVpVha/AzeQ+ax2Br4nRT7jvIewfxcAsODO3d2eYOzJZwfmEAumf+Pa+77XSC27dTAq
+McsE/1D3puiyw2Hud/dISdU/xvpMLuCgoM9INMWdhJB3N8UDxdTH1vDI6N0Uzd4bwyfuxYdC2Dpt
+N2tc1wJBv38whcd0O40rFLvv40g+y/hCLApNXSPZbIEq3ec/YdjFcCCh36v3XsERnYLiYIARzVBm
+VFIyyLGmhHav4+H5UvJ9V+XYhITjdsZw2Q2ICUZyxn34bKElOwsQmDLtxJjVvKGLD3DACV6M5KXq
+L1PsVqwpVA4G1Dk8Hr2C/n2phnv+DruRLBDnWh6q9+IAVxekOqsMSUHXoxLokOKdMLR89/cukY7l
+cx9sP8Hd+/LgTo96GL0mC8DX3Hdxj6CeS0UAVR3gVibXcRUt4S1kPju9DjxKrmdaI2BuvKNIS+AB
+4Vjm8NHlOd7/A5GMt7pATj/KYlcg0uwiUNRl3B+iJPT5Mtsio7Dr1jZ7KPj5YoMeQ96eIx2Xj67m
++HQpKcqBTpBUNMnpAi+fkzPxbIfTd6UbUHwg9RDXHULNJCz3pxFMKXMBpVFyRdLTGIOxpH+7ndRf
+qmwHhx6V1r6KgKpeVQ4EA9aoJq8gttSgJO19FK0RsdayTl/CsP7wwlQoK0kVy4/kOayunqEKnsjX
+L0ugkt+d9t6EN5aaQVmVWF/IBhATJn37zglC8nii/hVe5QLxi04H12xDvvf4GVTV+liyFa9KFITj
+D8Jor3lZNVcoyZkYuA520Y/JDAYAlFIDpdTfVEp9oJR6Xyn1s0qpkVLq/1JKfcx/h5/nM27GzbgZ
+P97xeSOF3wTwd6y1f1kp5QGIAPynAP6etfavKqV+A8BvQAxi/tmjaYBdiqYO4ZMtdzDu4jaLdpPD
+CHFIWTVH4/FMdo/5S0pm2QJzggm222tczWQXN4UjLEcA1WqOtMVFk9Ti1iW0L+tiP/CRsWq/y1M0
+H0sRbKdm2B0SF0Gb+eR6jRm7Fp4ysCwAzQYl7ErYef7K4MMXggX4J5cSLn50vcUikdX+otwhb5V6
+A43pU15fl3DtvMDpmaQtX+/exRub78rfzRO8lQueIJ5+CQvqDn54JmGrk9Tosztzexiik9I6vilx
+NmcaFBqUIWG1hD5bHeFiKxPz4v0PcfFSINoKFg53oKjjI6K1Xof4gbrM4TOMnqVbbJbs6ftrlCw0
+WuVgm8jfnXsSKS1XKebzFrPh4cklcQHPgW9+V3bH2d//O2he0Phn9YS/O4XqM0Isb8HUTHPqHZy+
+7D+T0SvwjBRmW79l78DAg4CUSvcUjWUBb+DBiRlt6PdhSbKDJ8/Qcu4DjkQb2daFDXme+RQNJfwf
+nf22zP0H76ATyfWHgwjDu1+Wr2+9jSYkJuP8KQaMONHaCnoOOlcSmcbGR7OUYm33pIOEZK1XRz3M
+KKJyTYGcJlmjlXB0rIanqFt/XsKSIDevutA03fms4/MYzPYB/BKAfw8ArMxmoZT6VwH8Mn/tr0NM
+Yv75iwIUrHEQOQ361BTsxh2EPQl3PZh9mHR1dY0nC8nnnzyWUHVlc2yu5EFJsy0yOvNUqoHDqrwb
+OHBLVuIp2a0aoKRnIrwKA9YtQvSwYK1hs10gIxCkt6Ap6WKNHv+s2+0hp6LP4/cv9saZzxfX+PBc
+XqynNBpdWotdu0g1BTTZnig1KqYoB2zZDYMD7OgDeZ0nuH8lL+/zXQTryosQ5zOohqF0LQuXaRRu
+kzvQwRbOgCIrVu11AGtdAQTemLLVEVS4fCHouasnL1FSechxHRiG/gM/xAl9Bloqu5dv0Ivl8woV
+4JrYf5UWWHPR00qhZNty0sgDGocxLFuITeGiWnAqpjWS51LNVyYE6JtoRvLi+ijhdiRFK6+/jc5U
+xHTVrIuAaeVi/gjTMWnEmibE4zsoyODsnHwV1NNBY+coC0nvHP8YjpEXrq9Yi0gWCDx5SVU3Q0Pg
+3Hq5Q0ZP0oOO1I5qcxuK3palu4N5hWjM5QJqLk9GvVEIWM/R5LYUV88RsC51pLsw9LgoyyUcygco
+t0LEFaC3Zcs56sJp2A51DSIqhxVGo6GUvhd6WP2IykufJ314AOAKwP+olPq2Uuq/V0rFAA6ttWf8
+nXMAh3/UH/9hK/q2AHgzbsbN+NMfnyd9cAD8FIC/Yq39plLqNyGpwn5Ya61S6o8EQvxhK/puGFhA
+Ifa+7/IzGU0ximVF7Y5iVNQFKKsam5fyddKq4TYGHWIWqm2Jks5KsB5aJSrPavjULgwIEAodhYAy
+3b4xMHQ9CoMIoKCFzgNEhLl+H0NR4nAgOPr4cIyMuIDN6SWWLBjqosG9A0k36D2DTbLDMJQTeuXk
+Nt4ki+52METnLn0XyerrZwXKK/l5f1zj+pHsZqcvPsX/RqXhr4d9fIW4/a9Ta67eLnCLuIplkMKZ
+MWyNwj0ArDN0YWjgUrhy7lGjYAntfpaVUKxwT8ch3rwn1/rwZLCXltOULVNVjgEFYPqxjzuUm1vN
+NoiXdPnOyv08d7qy004bD0uamoxCIN2QrflOhQiyU0YnJ8h20q0x55IOBJNorxPvj34GQyvF2svu
+CKMDiQru3XIRdyX0WLQ0g0qBtAus1XwPECoLF1oCOswXfxdNTK7EWK7/y7fvoccoZ3R8jOW2jW4S
+OJeSHsbRl2Tuv7bFYi6pRr+KYBS1HXMLzWfPcxuAjuYN5dqcYQ8dytRZb4x6TU2GuYJOWmv7GA0L
+jd6RFC2rZAvftDbYzV7ApklLNNzvCw8o1Y8GXvo8kcIpgFNr7Tf5/38TskhcKKWOAYD/Xn6Oz7gZ
+N+Nm/JjHHztSsNaeK6WeK6XesNZ+COBXALzH//5dAH8Vn9GK3jgORqMx+v0+pq9IjtjxfIyowNMU
+OTpsVfY9hVcfyu/cTkkG6nWQ5YTDVlu8eCIrtB8qVEQN+q5GwJ2Z5Et4uoFHYdOryxUMf6BMjgkJ
+WCgC+ERLau6S8fAQ07cE8aaPYuCF7GJHX3WQsd3Z0zEquhJ3jyVHHj09wzceSkvuGz/3czgcyorv
+wKI7ZrvMIxvUMUhXvD2n5xjfkZz0+e8u8OJDiRo6ukS3lGN/5avSgtu+OILbk/OZDgKYcxafkKPb
+besVHkbELJxeyd/bIkNlZZePYhc92vf9pZ/7SXzjZ+Rao1LhbCvbakxoL1QBtzUuNAYNjU5exAsE
+fTnP/GLRdntRE348vd3BdEeyWjGDsW0LrcBY0WPyVRdPzpn8bySq0PUOQ1DkdZqh35NIIth08dod
+RovHLo4O5BlZPGJ0VBd4zj3wJx92sLyQcz595xwvj4gizQ/gMErpx61RTQ9vvCbPWeQNcXgkx0jd
+C2jIPNenbAdPPESgjN9KY3ktPx8cHqJDj1S7rZAvBWfSwrWNE0CzdlDuVqgKuWdVnaCmglJeF/AD
+Mmz3NoTN3o8y9By4bBcXFZC19pFuBQavn3l83u7DXwHwP7Hz8AjAvw+JPv6GUurXATwF8K//sIMY
+rRFHEcaTQwQM1bQpsaOcl28dVCzE3Du+hYfkEniRFHLi2MVqK7OwTXbIviwP5jju4D3KYdtkDRUM
+eDz5XG0Bs5GXou8qnM7k6ypRyKmYnJTFXpW4dyAvcXQQYdCnQUqxhnNCpuH4DqYEEcWjEA2Liq9d
+iGT5T704wVe+Ii/v9PCr0BFj20pDtxqTdL9SYQ4vpomrcaC38nA/fNDF/FzOZ1lt8Z1TPtB9matb
+3hYrehR2+reASl7iWgEei0+dIETFPrxLRmKeF3B06+0Y4c1XJWX4F3/16zikoQq2Swyu5e9MCwRr
+FGq6Kemmg4YmOa7SCAJ5WbI8xxVFRrY0LLlYpCBUAmdpjmzL1Ozv5YjuyMK4WM1REn+RG5mrnjfG
+gmnZneYYi0Jk6b2pweWQPJB5F4sFfTobmtqMXbx83GJSdvsU8zS9wouPvgMA8OvvIGBqMt9REdzU
+yLXM7ZfvhahHcm7LvAcdM0R3+BI/eb5Xq96pGq5iKukZWE/muc4ygHyOVl9TRwffVyhHgZqMUdcd
+wHYJ4y6avRlMa3YUekDR0KIgA5ygNTeu4JGJmS41fEKhP+v4XIuCtfY7AP4oLPWvfJ7j3oybcTP+
+9MYXAtFY2xrrfIXOCnAY4iM3iMmPj8MxfMpSNaVGpCXEG4aENlcKGXeg9SZFj2zGOrGIqdCcaoWI
+K7BPEdigzGBpe545fczYmrq6nmHOaOJiV2BgJSwd9WXlz4Y5ykYKSl7Pw9CRn986GKN3R2CywXSM
+cku7ueK+nK+p0A0lFK1253AzVkGVRraSY5dXIrZhhhMYT6KRxeUS9YVcXyd/iIcsNH1z9jGuWKB7
+95mc+8p9iPt6wc9QaMioi9wAdbu75xkiFh0vyS69uN4hJGvvzeNj/IW3xUJugg48Fs90bDBsPSWI
+QSizDTLOD1Cj8eTr0F8gIrak64VY0epvQxbhi6tzKLIh46CDmlbt6eEl3JRt6eMutn8gu3/nbSki
+jsshRvfk56fvPUHgUwxmdopyLfN5fnaNESOWkGH5R+snuKY0XTQxMJXM7XL7IeqVtBST5iXgUOX5
+sYT4z3anOB79AgDgLBogciTliZoS+Uu2XD0+F5cxjCvns0yuEGv5XWdeAtn3OF8uAka4KqFIb3mB
+hhGbrYO9MVCzWaMibVhpF01Krw2mDE7HwKfnxC7bApRdU14XpuB5qgzbzZ9BK/q6ttitMzwvGlyw
+Xz2MvX1+Wh0pdHZys46O7iJuqcxUDyqrFTzyHY7GFVwKgSwWK9w9koepcleoCqogZ6QpFxlqKu52
+uwGOqGhj1yk2DOe3ttjrClYp8QjbS5xeyAt/2Df4BYqlXM8M0kL+Lpz10TCMT1rl48J8/7ONg7KS
+F93uEpRLmn4Q81DNz1BN6W61NMCBXF9/ucatAzne1yrgA3Y2zk5FkCVtXkAdSQrT9ysY8r6juAdX
+nh90XB8h89K8lPRik2zRJ6z8ra8d4v4t0tLVbm8K65UaTsh8nuAYp+iidgi7rTVqpkyuO0YUyXn2
+RktEhGOvSL3ezXeoyVA1fg8Ob2rzqYNbb8lnhIGHS1/mIJ5JmnBd1ljxs9OXDZq1gMXOFx+g5IIT
+a4tNIA/SzpFFeJdtUVKFardI4USyKKDxEDOftFmJsngi80IxlWenFzB/W+o5m69/A1uPi/7hWyhf
+l+vuxpJq9V5tcLak09dmhs1WcB/h9G04Q2Fa6tUCDkFLINVZlRUUoeSqVnANuT19F5Y4FOP4KClI
+aSkN0GQiUAQAGhYNuRGmrhBR7CX2eojxoxUVbliSN+Nm3IwfGF+ISEEpwPgOqiqHJdGmhN7r8rlF
+je5U4qio42F0JEWg9FT46MPYQxNJFOA7x8ioRDw0Hiw1BDTu4tFjWblLFnWSLMGQHY6O28GBL2G5
+l5dYckd/njeoDVWXiUdYz+bosXC0cnr41lPZlfIX19hefnN/TX4gO8L9WxI6v+U/ALjzYTpA3Fql
+XWzgs8fsTBlaekBJC7ngYR/pgnbw/THq9yVMfFV5KFqbM2oh/P7FCo8YxbzVxHtU6LDXheMT35Fk
+MCzWttDv0PdwzHntRl0sicDrDQyaWqK0zqiPgoYxil4CyS5HytSugkbNLkJiSuSenNt6naMkpoTg
+SKyMhw0fv043RI9iKLuui9VC5jN0LNJUQvt0I2lEOrtAfiXRTVOcIQoEK+DvDPpdue8PRq9hOhHU
+42oh51baM6QDmdvOSQiaVWNbbhFR62Do3MEnHwmcfFXJ5+ZljYKu4Y8v1kBfCteT0RQliWeza3Zw
+1BZpIoXNpaqQs2g+thugxcscHwEkOWkKy9iyhEOfz/LsApr3UlsgoJRftdjAGbRoWImEjRth81zm
+wgkqKJrMNKbGroXyKxf1jxgpfCEWBc84uN0bYlYU8Ng/OYy6uHMgIezx0QF81hF0U2N7zhvWhuJx
+F4YUYMcrELLd5ve6sLQ7z2YZunwJC0q8F3WFxYrtPddHL5Iq7d1bBwBt4mNnji3bb1c72qFXQGcs
+4VnUaDw7k5f02ekcL+gclRcNRj35vJ+6Zn73WgfqIzlGvrjGAc8zcIYIGK4b8isaNLArKhdVaySU
+Eb/+9jf3AJoyLWCMvCw+KdRlucFjpl3n2CC6J6lE4zfY8dlwtMb1hhwLgp680Ifjy+Pw/rtPccr2
+68j18OZt1kHqBQJyGEpyB1brDc6Y853lFS7Ldr5LXJLjfLnYYUM2alPKeV7bGg2BOaVy0HSoHPVU
+4+pE5v7Djz5C9om09RKmRygrlKUseqG6h4g1jOG4wVdfoTDOwW302REoTuTFu3Pyc1gamcM419iQ
+E+J2PCzn8nnV7CPcdYRXsk5lEX738gIJRWfXl59CUSymCocIfknmYv1Crnn26QtUXbmObn+IUasE
+dZHCHchikn8yh+azGo5kXjuTMbRlPSCMwCYDjHKRbCVFOb++xuoD1pWGFPdNrhBTf7GqFBp2VELl
+o9zJPVnrGN4xXa0+47hJH27GzbgZPzC+GJGC7+Hug/twz5+J7x6Au+MJ7p/I7nkSD6BYSCyWKXAg
+S2lWsj883yBas7LeGyEOaRWXNUjZr14qi2vKsW0Yys4uzjGjs3WkGkzIAMRijRmZlllawHA3GhAG
+HLkKD1nVrsMutpcSwrlVhQk7Ed1xhNcPJOx+43Wp5OebHU6pPVA+fY7oddnFO0djVATpFJ9KpX/t
+lnjxkeyIiWNRXZC15zToBCyYKg8Ftf+0YjrTGBDHhU0BKKYHu8UKBcFJZVXBkEjT+nKGOoBHXMh6
+WWFBFugL1WD+UiKz+0dD3B+yQMd0J12t8D4jpbMayBhh+E2DHTHmu8piQ7GQXdWSshTUUE7Ut0vU
+KcVwfnWHZ/+rdGCsegKvlcE30g1AWuy1HX39HoaR7OxRWaCccTfebtF9SzQuum0HJJiheSpRXKYt
+rp8wyjzoo2Zq4rgpQnZreiOJ2NJ0g/eXMm8pDBzKtnc8g2xHMpYnz1XtTZBey7lfzhPcIeYkPohg
+KcmX9i1mn8h5mIVQhI6f+Agp89bt30GZ0rTo5fv44IU8W++frbAig/MuWbmdYRcD4mk8p0Z+KXM1
+mI7QjYRyFPgO7PbPYPfBc1zcnRyjWKzg0e/w7q0eRsw/3aJGWrFyvGxQ8alPqdvoZgVi4v11U+/b
+msvTlzh9IjnnIrXYdeQBsW7rAXiITk8m2FQaJpeXIgsGKJXcxI4GNBWgRqy8DxIX5VYepNhXOKJI
+x73BGIfs6z186zZisuuqLh+q8yFCXxaFiXHQP2IIOwlQUo3n8pFU089mBRYU/gwf3MPwnlzf0VCj
+msl56qRGxJz50Us57i3XxYTajirycEY9yl2eoybash+7KBkkeuxOTIY+Jq1MvLPbh7i+1ugxntWV
+hmIL1zLPXl2p1kALtdLwbZvvApqW69usQcJ8t2THoVQN6oo8AtWgR0GSN5Yh1q2sebBFZuQF6tEo
+V4ceRgNZmN7qvY43j+XvfO8QB12a4lYJNNOHyYHcj/XlOZakHtehxu0HspgMRndgGWrbqt4LxZYU
+xkFWYVmRORlFcHoyR7eP7sKw2u+C/Ax9jfOESMihhwlVn9x7t5HNZcHKkmu4ZJWGXbm/tnFg2CaH
+CxRLWWQWV5eoc7l/rzw8Rq8VfCVbt6kSxFVrYZ+g5nGNqhDRK7MsGzTujcT7zbgZN+NzjC9EpOD6
+Pk4e3sfpyw8xiaUCfnx8GzF1C21SwtAMpO5U2KXy/dVOVv5X7k/htxLwCJBzF0+wQUosQDzqQLsS
+omXctS7n16DqOzwnw4CS3bozwBl9+7KzBlOH/pCy8OOOHeN6JbuL0ho//2WpdJ+c+NA5fQlDFwXB
+OTVTIm9cYjqSIlFXARF3dJvuoFo4K41lorse5pRmO7vc4FNWte+sLIYsxg6Ug0P2vCt+736tEVHT
+oIx8PF3IjlchBzMMbHcaA+oqDgbsTvS7iLkLmqrBim5at6ZDvH4oO+Ek6mBAKXLL8OB6/j7On8hn
+XCqL+kpSHmUMtuzylKpBRbBDltK9KjBwaBbjeQ004cFf/XNdfOtvy/G8+YcgmRH6gZzbSTHEg6Gc
+83jcwNC2HabAa3cFQ1BtLpFuyd4nU7PXc3F9QbbjcLKHxT/99rfwlPdy5G4xjelwxWehF3cxjuQZ
+GowGCEc0ZJn4yGu5J8kVuwhuhrt35P6OplMsLyUVLB59D2dzSbFm2xQHA0k77g7k3mnfwiNjVMca
+hliPYDpGx5UJmMcRZhSimTGdKfM17oxlLu5PhwgpMWfXJdYzuqY7Bay6wSncjJtxMz7H+GJECo6D
+4+kYD195Bf1KVtTp6AiKyK0drqErMvzCDiLaI4/Y/HW1BU2GkRQ7eI6sxNVao6I4Zm06UMx3W8m3
+uNOHyugcXDioQKfeIERrH+jpZm9w4nCr/dmv3cXv/p7sEtlyhldf/QkAQH8yQrUmUy1fImxdkAlh
+NcMTuLnspG7gwtJCTjsjWLYRvWPWOGwfk0aKRZV9gS0VqNM0hU/U4yB2EB8R6VlLzvprdoId+9Jz
+5aK5lCJhgQamaaXCXPRGRLwRDvzm/WMcHQoyz5ojFAu5D4PAx/GRIDaPbh8iDuWcyrVcx/HRFR6y
+cBvlFk8vWXRLS3iaubPbYEeF4lZCVDsKtq0ZqQYO7eR6Vwa9qVzTs4uneDh9Te4Z6zNH5RgwNEPJ
+gdCV3XPYjVFk3BFthW0uGJauFfu4ThahO5ZIoWhKVERY9qYR7rZwelfBs22NQnbgUVijO5eoI3NK
+HNKgJ44CeB0qf1FC7+L9S9y+L/PTndxBdS3nVlYlOkNCt/u3MGG0GPqcE9XA8hmrkwqWTtud+yNs
+L1l3yEJcpFJ0pBIeOv0RuvTccP0uFIvcqFYolxJFZ6mC7v9oNYUvxKKgjUHQH6IbuXD7EPUTAAAg
+AElEQVQW8iCpJEONFisQQHFSd8sNWsMbw5BLWYuGdNPlpkDAF73OS9gVuRS9AprS56OOvBDdToSC
+2HEPav/AJssNQGGK0hrsyJnot0Cf4UPcikRC/N3ZDsW5vEBerw+zpRqwHqBh/1v3ibFQDVDR9OV0
+BhWS1hwBzZoP9Bnp1rpEpyffO5i+im3xgtcawwspeaZcFJfs2RtiIfQSmhJrccdHQY0/oxVcgmJ8
+7eCYRakeF4Wx6mFi2xmwcGI+3K6LA7Lyht17UFwt65ovjaPx5kRC6rf0AI/Znbi4eIon55IG1KHB
+ZtuyMSlw4xrkZQvGqaAI9HHeyMDGDjpBHxF/x/DueCrB3YksUvXlKU7uy9dw0j23ozm7RLFiulIT
+3NQZoDqT+fT7c0wohTae3sG6kgW+gt57kjYuJdKLfJ/azBdLHFAHsaMsdpcEX/nyWX3vENW53P80
+eoQuXbF64wPUhLHjoA+PMOaMepauncPQhKaeJSjpQWmg0Q9a8R2LSV+udctuUGUbxIb4jqKEc0Wm
+pe8iIQUgVzVwSaTWZxw36cPNuBk34wfGFyJSACyMrTAIDrB2hE22thk65P9Hw9vwaOnWPTxEQwkq
+5cgun6XzPdEoLpq9F2FhGgRuK5wCjA4EF+C1gqFuB6ZhZLKzmK0kTEySCqwtoh8Ge/MNt2LxZtUg
+YmtOVxewDOd10cAQgup2A2jChhU1hZUzBlxCUZc56pdkUaYNNL0LfUYoxm/gH9GpGA4mb9Fdud6h
+oUFKlZ8jdyXqqam3EJQ9aFbnUrcEa3lwPQPXkeuediL0Q5mjW13ZiTpOiUFf5jM0HsKawqeHXYyO
+JYR3jYuSCLuSlvTB6ADj8v9l701iLUvS87Av4sSZz53f/DKzMmvoLvbcJEUKlkxJpixItgEKsCHb
+OxuCubFhwAvD2mnjBRcGDAMG7I0Ng7AhSja8EGAuZHgAJVJuubvJ7mZP1VWVQ2W+l2+885lPhBf/
+d251EZI6i2WQKeAFUJ2v77vv3nMi4sQ/ff/3Ue7++AiTFWXs9kJERmDl5aZFRo+kJoISnkLLhHGn
+FFpyCzz/bo4xS8p6Fu9o3OJG7nkWzuBRfn4UTpEQz2u9IVqIx7ZqruB1bKQKJZQ0rcKEuIminuPk
+HYr2eCnGb8u+6MoL1LSwN5WEY7lrcZgRet9FSBVJWKIA9UpCpT0KAGWjFV7KpWGiA4zp5+vIoovE
+0zVBDEcvyx+JBdd2Hz7FddpyiShgo1w4REIodZtG0Gy2y7iPXX0Lr5Hv8HQOR0rCZrtF0odmwWCH
+T3nV8VocCp5nkE1mgPJhyeLTFStg2rvdJaKRLK6zFUCceMG4NtAdLN3SJJghLyTWM87btaE65YCS
+2IIBpSiCGl4h7vxN/QK2oQCpamCZLV+1NUIGvwkBOPn6AnHJ7HSWwWes7uYGWMt3e0MDj9UTzRDF
+rl8CZMdxyxJuRYKMcAXtyTVp3ceZDXQuD1uQTtFzeatNgEJJVlt5NQJFQddEHprr59eYFHIwJacR
+NB+E8SBDlLGLLtS7Q2FE7MW7bz3YAXYSs4/mSu5jMkzggwfVIMH2XERZ4pls8jZTMAyfTF1gX/PA
+0jkOxrJJk4FGWsr35TxAkzTElh2TntXweejPi5sdCGmWxajpdresvmy1xrGTrH2XVWgbmdvV9gJh
+Ku9p6g6HZHkGcREmUQBbq0fTFB6VroJwi5psUJFOkXcEohHI5SlAG7m2srEYkLDECy1WVwRDzdlz
+HmroC1LOL/1dqDSMDhAwDPKzA9Rr+Tsbke+y9OBPJASrnj6BisgQFvmotzxl8goRX9cj+Vetxqiv
+5J7DxEdniAXZlrjmvHnjAuUuMH61cRc+3I27cTc+MV4LTwFKQRkP/t4Y9Zmc4HVZoM2ZTR1oWEKa
+vWAMa+X0DFKKs2xW0Mxet3WBMJZaujIrZETK3axX8OlexSmzse0AbUcr5yt09ECUv8KKvflda2FJ
+SF0S5txeXaMN5bMeTQZIaW29yMCnm+iFAbyY1opNWdqfwlWkCZtpqJzaC/ERNDPS6VjuY3O1QLQv
+9+HvjQFqVthqjoCNS3lu0TaUHIfMSYQClroWGETw6JYPswyGWfbIKJQbcf9DdmXGyQgePYLWv0E4
+EQvcdBEULWk+f4ac7np6TJmzfARHSrCyvISh16RWFWyx4DWXKEga2OMVmtog0LRJ2qEk7dq3/+9L
+rIl09MsK9+h59B6RrzvkDLXScgDvuIdpOzTEQChjUFFjMfHkPjpfITuUcGy5vcaUiT1/6EPl5NHw
+c0REJBZO1tT3gC1h7MMgRDgkWjSOYPY8rhU7I89eYrmVuTrYVji6/1CufbQPr5twneYwC9LUzeXe
+ws+9id4+m2wGN2CiMRghIldo2OboScoDjw1Ogy00dU5RdNBEwOpxguCZzP3tKsdt9OnYnF+PQwGA
+hcMgcoj6HofzLfJUFny8HcD5VCSqPoKLejoaLqzXoepbAB3gRzLZXpTBO+MDtF7BLRnD9UCRJkfn
+UywFCoou5Xp1i6dUU1qUFm+S+67Xnfzue+f4Zeo5HsaP4J9xMw6fwXjEw6862O3/K9c042vxx6VH
+R5cbALQ2UCWBJ5cUlX3xEhFVikw6g1qIshKGFh4pwl11jfmTDwAALfMr9XIfyqPy0k2OFVWMVNMi
+Sniw5C2sEffYJ9CrfHaGlnMR7HtQdHfXNwuszygw+6zAtz+QPEHEisMvvHuEs0pyMXvtFBG7PAtv
+g1u2WTdVhYahV09C41Yl2AYBrYCWLeCP25+geS6EKu++GaP8iGvGh3zfK3HElnS0t4gIMTa2QMUq
+weP5S1y87IFRkqm/NzvEZimufZ5vMCHoaXK4B8MOVJcqhAQLmZqckqs5rtkZ6fwEDbUmVduiuWCY
+5kuodbGwiJhUWEw+QHUp+ymabQBfwq52GsLOeRjyQLdPX0J/nmFJFqHj4YybFjUJbHSuoFg9wkLu
+w4UeDCtDZV6inMu9zpcOl1t5r4oMqm1P3vtq4y58uBt34258YrwWnoJzDl3bIkvewP135RRcXb0A
+6BJbZVFWTLo5H44EH47EKlZ9TITSzK/ROrHMeXuNWyYXQ6/dCcb0cF/VKigmIltbY80M96qyyMkX
+ANOhanrqMrEYL4sNjt8UKfPs0VvoXool9cwNFGvTSBbYcVvkkhB16GBX8n1d28CtWIlYdbCxJNea
+lpb0tkBzINiEIM+gCBCy+RYtrXvVrHf6ly3EGsRaY5BIPduGC7RtTxDjIyC8O4g0IiZBQ8q/bYtb
+ZITXFhcODYlTYBUUxXCyKRC8oLfBxi5/MIBXijW7bRQidgOuFwWe38jrL9YVKuIb+s9qrEVHejGr
+FBTDiu2zDIOpiKsMJzdwC1mTPcr4zQYn0BSwGbkYhqI2bTZEzQasyXSEkAnbwVi6JY32ESU9/+cL
+jD93yDUL4LEC09Y3UKTQqxgm5QoY78kcpycHKAm3VwqIqB14kgh38egrZ/gDLnqklqgWxBNEHhSZ
+svMf3cJj92x0//MAgM4WKL8hIUqTb1FxD3njFBUBTqm/D9fJfVuPHcHFBh25Psp8ifma0oq3awSU
+Q8yGUzTVp3vMX4tDQWuNOE3gKx9hJhPVrhfYUGr+svwAQ+LyQ0/DH0hs2NXitmqYXTeZHwyxoVR3
+q9Wue3I6GWB5LVlfnw+CdSU6lnzmiw2WW7ZZVzmuSfi6KFqErErUJDn9ig6gUllYvT+DoUupqy2a
+pbjznh5DkXXT8VBRXQVNrUwTD+BYDq3ma3R0Ke0By7DHj7B4LoeCSTJ4SX+AbLEhjn6z3sCyzFgs
+5fdni0vcD6h7se0Axu1dV8GQAMRYYEJZ9oIxqaebHTvQYD9Gncs9j0fHqCO5v2QcY/K+uPaqlXnN
+NxXmBQ+9JETayHW8XG+woHalazVC1sh6wpLI91Cwv6KFgkende+dfQxmMs+fe7i3MwD+XB6IrXeD
+/Zg0+CqHT2aiZblBwNzNKB7igByMPlGVXuzQQh7+GCN4JXMfzRYNS5lOeyhZ5WhYfaiNw/G+hIqb
+OEPKTtIwHCDLZC2XLLc2W4UDCvfePs9xzf379VBh1IhBqswaivmhIJDvslGDnNoRhe3gj7hH0iGS
+jKQ7t0sEI/m7mvtYFUBB0F4Fi465AzfwsKXlM54V7dBPMT6rFP1/opT6vlLqD5VSf0cpFSmlHiml
+vqGUel8p9XepCXE37sbd+BdkfBbV6VMA/zGALzjnCqXU3wPw7wD41wD8l86531JK/bcA/iaA/+af
++1mehyAdwYQKFdlr7ShCQ7XnyndYkEQlzhz8NTP4iSScPDj4tNwm9AA55FG0FaxHl9HmKAm4uVnK
+v3EaY01LubYdlsyc35Q1tsyWa+XBEEraZ8V/5ReOEN4T6+E1G7hALIJzPpwhXdmihmYLpjOsLwcx
+DD0FzwygHojVMXULS9cPvvx9W1Swv/vb8lnlAuZI3GBbbvv8FKq6RsFs+RWtQdMWeLESa2yN6cv0
+qFuhfAcAf+BgKGTSNvSIbrY4OWb9v23gqFztMg1F/sTl02v85AWTmPfFO3jxtMT5nDXxaCWFfQDr
+Fwu8IFfmbdPBMvRyVOwqPAfbhw/WomZy7eiNn8e9r7ACs/zujqczNcRhlBYxu0739RRrJtFuthY1
+ORcm2R427LUYcv21bhGztwWJQkMLW1YOPvU7q+YGDZPJFfEkxlcoaYHjcYKWHlYXJjj4soC6zl/K
+tS0+KPBkX352t1vMWDFZdQqYiSfgZQ/QJvwOEPq8MRi8KyHfKAp2/A6d56Gihio8g5Yep+2FZYoC
+jrDs9XqNFUOwzgAVvd4PXYc2/HSP+WdNNBoAsVLKAEgAnAP4VyC6koBI0f/1z/gdd+Nu3I0/wfFZ
+tCRfKKX+CwDPABQA/gGAbwFYOOf6IOY5gNN/2t8rpX4dwK8DwP179+AZA9XV0KSRGpf3sOgEueeq
+AJZQ4eK6QZGwJ79jVyN8gFoAm6ZFs5afuw6oyadfew26mjRu1Cosl1v07fh1W0OzoWazWKNo+sad
+ECFLXS11HgeTdxCm8t7uWQS/zyhOSmDNrjZjYZl0q3rNl2iDaCYW2Kh61xmn3QCK+hOulISjChsM
+OunwK68W6GomrUwBxfvwmxCa5al2QaQgDjCvJOa8fbKBZU5hvVljNuZ36wAxE2YjVrk2VYMlOxyr
+7RS1Yn4l6BBs5V6/99El3mNNHjU7Lr1L3OZidZNBiPhQJvTaVViTmFU13W6nWXoKutLoW1G1AjqW
+Kk/+0hCmL6cN3sJoLPddzmRe9ONzdOx2fVl8BFNKafRyUWM4FI+muNoiUOL1bJjvGSNFQQ8RQbtj
+TVLaoKNXYQPsErOOMfm62aAhYjE9GCFLqErdhfB8yVuMG8kjXT7bx4zX/hIGqpDPWFxfoJ7LdVRR
+BRVIPub0vqxHsL2B7bUMly3amsnoKsc2YuJyMIRhYtOsmIw2gEftPT9QuKVE4mrtYc79NAy8XbvA
+q47PEj5MAPwagEcAFgD+ZwB/9VX//qel6L/+ta8627bwvRFcwNDg595A/AOhJku1B9P1VO0RurVs
+lMX78i+sRjpgm7EtYQlL3lYtWnbDxZ3Gxi74HpmwaH+EgLLuaxWg6OT1lTUYkho+8ENodrXFrAlr
+U0ArwnwDB/dQzj1v7mCPWVeHAlpxL9Wl3MfVd55g9Cb5Hr/wNXQ30t7rrq92GpJeIpu825zthEa1
+NvDfEAKR7uYZNHswomPAsPqwR/owt9YoLLEC/mKnD7kuLS6uJVn3iycT+MyyI+1deIWyZIt3WkFZ
+CW3yq2tcrWQTR+sG/7IvlZ0/JKZjk7c7mrOb3MNBLA9KZVss8v49FsbvDxEmONGipLiOc7qPOvC1
+LIUiEK1oHcpf/CoAYP99wUe4dIV2KH+3X38OYSPzXdR7SHXPj6mxN5X1CwdyOBhr0fIwbZrznZiN
+rSJ4014afgvw+vqwxakp9Eyufbh3hHbOzz0aQJdyiE6mUol68PPv4cdzqUTcWwDRC0mEp2GMOJYE
+en5zhm/+WO7ly2v53q/+9X8dxiNY7ryGT2p8my+gb4Sbsry9hmUInTyUOdY6R0X8R2cUtiToCeIO
+EVW7Wh1ABX9yOIW/DOCxc+7KOdcA+F8B/DkAY4YTAHAPwIvP8B13427cjT/h8VlKks8A/FmlVAIJ
+H34VwDcB/F8A/i0Av4VXlKJXUPCMj2q5QUhC0LCdoyOxyHbbYDYTU5Ltx2hCIQPJryUh408tApKr
+pukpims5PZ1ao2TYYVyEOBeL5zPpk6Qhqp7m7SJHw+Si72vE5NOvjEbBWvEbE3aydSnOf+/bAIDh
+19MdmYjNNDoSqZoghPo81YGPRMo9fv4UPpuxbOrDHAt5qH2vhp7JZytyKLR/MEJRynk6+LkMekw3
+8fAIwRPxePTaYMMQ5JZhy4UHhK2UEw9chJChlnYWOV30xbpFcCLXYZhQ2xumWLPTskaIEanC3n70
+JrbkiKh+5ONDJnFVJdd2sTfDvUPxpG5VjmvCw89vWtQlsQnW7axPS3vR5i26HtIIC00L/bW3fHyX
+CFHvrMKcbNOeEU/JxXNkDAn1YQK7YAl3YJARB1z6gOvl1BgfqniL6J6EFynuo16xFDvwQMcRhTPY
+cA56EcdwGKNhQ9ty4XDyFrskRx5Y+cZ2Rb3LkxXeGMjn/uisw0tmhMeTCtkv8rubn8fKEgF6TLq+
+aIugz4fHDoZoUxt3CBvxSFXRwGcSMyTzM6IUm+cSPi5vHDSb3FJP44ohb1lfAsTDvOr4LDmFbyil
+/hcA3wbQAvh9SDjwvwH4LaXUf87X/ruf+VlwaNHBmgYlmYnqqkGV0YXHCG0P0XUpOoq5JMfMkHct
+SlYRNl2ONSmyXbOFZja5DANEe8Szk6a7g8aW4UOhHFZ0xdq6xXgmi1jaEAOSsuwPxXUO3vXQPBOw
+SfVSI424YdMK6pgajJGDItmJfkvi0MmvKlheZ7v9DtpNyLlMEfAwcQT8tOMc2RfkwQ3ejIAh48iy
+gyGbVF1UsGu5/vRt+a4H6xghXfiiWyN7yYOgWKNlnuT6tsBVLpstJB1+kDi0PEwaDyDEH/FbB8hy
+WYf8nX3g9yXkGT6T9RgMc2xZOXAfLnG7krg27LDrbXBGw2e3pusxUZ0D4SJQCjtlqXnrUBFu/ZPN
+CgGFbOdkPp5GX0SnhODmuvPgs/8gyyIk5Kn0ihpr5nzKVg6v6LJEMJX5DgceLDEiLm53ocLSOqwI
+p96wcoAOqHvAVbtA3VIZa2NhB7JW3UDCiLr7COpGHlKcNDgOZd+4kYVlxSR7NMIv/iUejAU7Kq+/
+i7qTObQrD4a4GB1bWBL0GG8A7fddvHKfnRdhSXj/2eYKLwm4Onxrgj1WjC7WGpek63/V8Vml6P82
+gL/9R17+EMAvfZbPvRt342786Q3l+uP6T3E8mM7cf/pX/iqGvsZjWsqj8CW+eSNu1uOXNULCXL00
+wjCRE3jGk3hjOhi6jlvXomSvkR94mDIcaUONza387JNBZT9zWDLrG2497BM2/fU//xDRyZsAgJNu
+hI7cCb3233vqy4j3xdIs/TF+5SETY7HGt84E8Xf7h89gyCT9+TcfynVenuEHPxIrF+dP8PRS3lvP
+DQ4o6nF8JNbMyzLEum/E8jEkNNbPRvBYKzd6ioQaFn/4+AkA4H/8zf8d33j8TQACJf5r/8GvAwDy
+i2fwLsRqPn1+ho9e9tBrGWEU7lz8KPLh0V4kWQiPNG2xb+Dtwg1xYQ+PJ/jql74gczWeoaVVmr/4
+EP/gm/8EAPDDFwtoJnH/1V+TxOHX/uK/iSNyLLhFsQublt/6Bq4fSzb/w/MlMiYoe97JUBtsmSTs
+lINP3Qrfy6DHsk5NWaNVbKZj56tzDS6psemFCn4l391qwNPUirQFHkTkgtwXD+ok8/ABodZXT0r8
+cCVrtlYWv0/cQBAxtC23O7KfceDBZ6i07SwawpGdBXwK6QzJK5j6Ghl/ToyBY6NfUdfoKNpzldd4
+yVAqb0k7pxRa0u21bQMQpg7nAKqKw2oo1XvUq2855yQT+s8ZrwXM2QQGk9M93I9HUFr6CH5UzTGj
+Kx7U17glsGimBxifsreBGfB65bBh5jX0fXh0P4umxi2BQ7MkgW8pBlJTaNVLMSCL0zhrsWrE9b24
+3sOXGD4EfoqNlsPJrBnOfHGAd0h1XmnAUay1My2qhXACNgfneLsRtqRaS4b82c37eHr2Pbm2coWM
+D9ssSaEo217Qlc2Ugs8SaaW2O8IODMKdHuNNu0KcCutPeJ/isOMAs6EcbufzHKUSl/P7738feiGn
+pVc4aLaDG6pNxYMBfIK0EuOBHjNiL4Sv+83vYRDKvOyN5VDYG44RJFSFinwobmJ/FOHn3n0o84IP
+8YxgmnN2eP6CdfBa2ay32Rm8WzlAl9e3WM/7sucWC4Jz+gdlvV4i4M/GBBiN6aK7BqqTa7uq10j4
+gGhW4+LQhxv0nJ41tluS8fohlnzIsK1xU8rBsez7CE5O8M6+7ENzdo6c3JYfFTlsKNe8vmZI4Sz2
+A8rLO4eY4W9sfSiiyKaxhwmBdg8m8lmJbzCJ+87fAIZyBZuiRMs28SerGt+/lms7r0gHkBeovV4O
+zO0IZdABaPvYrIOznM9XHHddknfjbtyNT4zXwlNolcXSL1GfaZSVeArGLtGyuSYMffwST+vQz7AJ
+5GQ/v5JT1G86BFSULnQDj6fnvUGKhDwDhdqiYYJmwL50t92iZlgSZAYP9iSxN/MDFK3Uki8uGsyM
+ZOJLmp3ZfgTLRqp7ex0sIbg/vD7H8oW47n/ha38Fg0Zc0X/03u/Iv7/3O+hYMZmkHgYT+dxp7CGi
+RUzoDquuwHJDURTtYdt7N12O1Vp+jpIEdkSLBnIhjBNMSZwyrxqYp+L9+PNrzJd0wVuDhImoNJP5
+SdIQGXkj0lTDksH6MB1gOh7xPQn2yJcwnsm9Rb5BxGu2myuMCJQJ9xMcHP0ZAMDp3gF+vBErt3cs
+Xt5F/h42z+Sa9wONK6by33v6DPU18RkwAKtKFeXbF2WD0z3xbvb8ITJiDBYrHy09q8NJgoCYizdO
+Jek6TYaIiVlZXK7xxJdQqu4cdCPW/b2rBZ4Sy3I6YyK1aPH0RvAwfqxwT0k4oocegh/JWoKhgQ4U
+AtrZ/TjBwVi8yb39AG9MxJN7+MYEY2JSBkzmWg1kDAOrvEJLfofWaRTEepystpgRmv5/XvRs5SVK
+VnAa5+D6Ys5Pjz9GduC1OBQ0FAId4JsX38T6uWT172ctAnaf3Zud4OANtgO3DmbLhaPLraIJskxm
+uIFBwyz6nkvhEwVWtgZk1EZH0Mxts4ImucfewQwJy5BFtUD1oSz4yItgdU8aKyClg8kUq7Vs4ufz
+HGEoi/j0d/8n7LPLcxgfYc3w5nu/J2Qr8ycvcXIsUz4yE0w1qeaDDkHXKx0Rv77YAowhC+sjz8lu
+dDOHIddiuRlgQa2D88cyJxdXVyD1IRwU7IKaBYUPywcriDQGfLhj5lEGvsE+AVt70xAnU9nEp/sT
+TAfyIKfDBIMRW4558DoHVKWsWVeHSK2s2Tib7cqC+7MDHDKfcbGS99bPbvF8Rbl0N8TgTTn0dVVj
+zrb1gyyAx+rRhqxKvm8QGZm34WwMv4/F4xxTMmq9ef8tDJjzGezJtSf1CJquf7G9wumH8sDmLys8
+3why1toKzy7lgdvjfQyDLV5eiPs9aCOA4dY4CjEOBVi0IenPRAGPBvId998Y48FYrufR3hjHM7nm
+YOiQ9l35fIpDY6AJXhoECi1Zn6xtsSwk5FW2Rk2Oxn/7RIzJ72KLP2Cr/UvboabGameBn04Vfkzc
+/2rjLny4G3fjbnxivBaeQtN2OL+6wTQp4Vgnvpo3GFCJd7gXImHixFW3mK8oyc0qxN7BHnwKaFSl
+RReL+6xqDwGhwl2ncExuhYL8fHsbhZfX5CYoWiiGJSd1ipI/ByZA0HMBkMfgMPbg1+IprDYrbJjJ
+v61bTMklmd8WOPuJhA0XzyUkmoYabw/F20h9g2FKV7vOd4m9Zkml5rYEYe3w0wDass7vLPyWUvR1
+i8u5WN6nF2JRitUcMbkkAz9CSwxBV9dQfR7KWgTkYRixg26W+JjEYrneGU7wuRPO7XgfhjT5g9kQ
+Aa2iY5K0rdboyCvQbJudaEtTb6HIdRBHDm9TZWqP3tiT+QItQx7PDOAWYgVfrCo8HIqL3iBEuREv
+rKLXNU4iGHpT63KOpJDvmCYe3pmJ9T+eZkgoZhOw+uClPmwnnon2DRQ9pa2nkVzJdw+bU8TM5nck
+lzhtBrhxvXJ3h0cUJTK6hKbq+R4x2vuR2b02sMC9QNYpNQ6WLN/KpagbWTNQ6EXnBWJSBHrOwXik
+6C/X0MRpJMriIROT17nska8cJAh5vedphx9cUgGrs2CESavPNXnFSuOdp3A37sbd+MR4LTwF1bbw
+b2+Q+CN0sZzKF6rCF6ZyKr8xHoIt5PCnx5hGJDQln0I2DhCeEj5c5FBsBqk352gsZcCUhc9k3mIr
+CbzV+BBHPBaVFyMhLdfxw/soGCfH4WDHSFSSuyDpHLyey6FZ4bEWRNvDyOIRGX88XKMhEvBt6v2N
+jk929f1EqZ32QBvEqNkZZ1iaSmqAcgPQPhBR2zCxDbQm/NuVSDYsLfZw4C5AR0IJDeCankfdWCii
+CoMoRkKOgMlA/h0FAR5QgfrBcYbpQK4z8hpEjMXDCDCBfEZLmjs/NAjIhBRmHrpaLLuxBorehJ+M
+0UQSSE9GFMi5DTG9ZldjV2BJXoRQA/uZ5AHmqsTZNZPJtMZRbDAgrdrheAhDvdH9bIDjd2QPjO49
+hInlOjUTg269gqL4jHIt/B4tW8/hmJg99TRacm4se7k93+I++4kulcOAbFFuEKEkQlQz51REPu5z
+rceDIRKiMP1GQxtCyFcrKLIuK5bO43gIVoMRmASOye/IGNR078JshNL2miAybz1AvbkAACAASURB
+VLnNoMs+1+JwU8laP1nXILofHQC/Z81uXw3Z+FocCk5ZOL9CtQJyQni/cLqHt9/5urzBVohCucvR
+fgCTycayxJbH0xF8Cpno8gDFtTykiMfoKMvukKFqZcEDUq9PlULBDfj0yQc7CXBX50i4cN28REOI
+cQ8OCQxQLmSn1JsluhsJHw4GX8Dj2x8BAPa7U4QMMfZ5bSZ0yCgOqhsABPRs11so3fcoUDvROORb
+uWc/8aBIVR8kIRyTS4nRSOmiDvgQFLpDQWq6tq1Q/ZQikx/yQUh8xARAxdx0h6MQexN52AZBAK/H
+GwQRQh6Wvk7QkrZdMZzRmYaJ5fC2XQ0XyaHYlEsoHl5tuYUf9A+CXM/e/gmaNeHKm+t+KuCZCGtC
+jM9UjTUPlpNhT2dmoQwfbs9iylBjkg6RkTXbn6TQfUVkIQYApoONZP3tOtpBun3fB6hCFZzeQ/JC
+3r+kEE+rS2SJLNoPlrcIO6lEHJchQOFgzWTnYJwh5gG6PwoR8yCbJhEs5zNJPKClQM/0Y9Uon/Gj
+q6qdYbAIMSSL+Wadg9wssDwUDnSHDdm6TxqNxwyL7bKD7aXBlIajihhazsXPGHfhw924G3fjE+O1
+8BRs22J1fY280sjpDk7e/gImp9JFaLbfh2VCKTIRvCkbSoiSM2YAP6F0V9cgSElHZgcI6O52+Vbg
+hwBanuBJMsBoLL9PTYmzc/Ew8sUKUSjWvd4sUFTkU9hnj3pbY87y3ofXT9CsxAJ54QAF0YYXT1+g
+c2KNOibnoi5AUVGuzBo4eh62A5bsRAwdr1d1MEyu1tsGtSa0uS5A2gDUpY8NQ5CeLdnvGlS0Hp1T
+aKkzobQHy5p2V7XoSN0Vk4g0SzLMUgmJAmOgGa95foAgEOuvgg6m10Gnv+tPTuHo5Rg1g/N7Xc0x
+mkYskxeP0VbiWZlQ8A2tbqBnDIm2Abzewh4GuCpk3j64vIKh11Ax2TtofeQsKfupAqcF6d4EmiGR
+0hlc05OxUv/TeoDq72MFQ0wGwg3UVuZAhy3uP5Q5uF5IubGoxjgmnuTd+RrvL5j4HLQAvTDHi1Cl
+xuxIPvfgYIK9VO51EHvwSEcXDjRg6XkRqGAQwSkyNVsf1nL9PIWuTxVqA8v79kAmbliE9FLOoxzP
+SYZTw+3qjw4Orft0fAqvxaFQNxYvLgoMwg4tQSfJbYlixT6BdQgdknr7/AZgppaCR6iqJRw3EtIY
+isG43VrYrYB3KlXDso3W9S51NYei0tNgcIDhRiavvaxRHVE3sh2gobtnyUVouw7r+RMAwOJyi9lW
+Pvd8fYb3/4nkIrZqgYw06IdUJvJvHTryTm7LNTreQN21yMs+o85Md9vBljzcdImUoqtBaJDFsplq
+XWPFMKbN+3ZxB82N5Hk+cnaBKttCsbJTlRU0BWRDUuObzgE8YBSGMBTh9RFDtSS+sTEM8weaLMPa
+U1AkhrFwO0FbeCs0LbeX0XDsQLWkKY+SASaRuPuF32FLxarSeViwNn+1aTDq+SgrMRZlq+Az5Fkt
+FxgFVAMrHeqXZHpavw/FPhUQ/m6tgi3k0G9siKbvH1gbNAvZIyZukY1lrR5mck/PP1qjTviQ+lO0
+pJHP193H2XwCy1TUAgx3JtEIQ2IafO12hqNV450mpK3k99vLLTrS1gMeOuaXitsF2o4hpFNovN4Y
+9h2cGjHnOPAjlFQ7cxa7PJhyDqqPzV5x3IUPd+Nu3I1PjNfCU7BWkmpfGI4REmpcBx6qvg9clTDr
+3mVMAUJwNZOScAEUYbLNooJHXQjnOoAWry4tFGXg+1Z5azQU+9x1BEQk01jlt7A3YoGDZARFq9J3
+5JXbAqkTl/PB8RjtmvXm5znefy6JxrUp8CtfEZjvvUisajdqUdEiqkECbEi7BR8RdR0WPWVYtcWa
+CUPbtjvLrXwPRW+hygo+lYvNRtzIqUtQaFYclNpZCU8H0L0KtvJ2wihDQphVYuDxOv0kRDgSTwKh
+kbID50izIapXs9ZmsHPZtD9Ex+y9MwE8wnmb7Rwl9RvSVObNDwLEQ7Hyw+MC8x8K8vLlTYPrNTP/
+cDC0jkzIY9WWiNlR6qsJQmbfvMwDMpKWmAaqV2v2SbXmYmjI93njFs5Stj1bQwc9q7KDZpgyuy/N
+bB/efAMdkZdH5gAbLfd3262Fcg8fNyd6rcI+sRnOduj6fbauUZNPtM1z+L5ch26pLj0A2t6jKwM0
+XU8skwKGLb9NAL+/bzagdUWDSd8xXDd4Y1/u9WpefgLR6CiY86rjtTgUlFIwgcazHPjyuwI6mZxq
+KM52vdpC7VG9SMWIGdaurxgv+yuolC20LgPqFT/XAhR0tVBobmWjeP0mVxaa/RW2dQgHsshpNcAN
+cxtuW8B6fWzfZ3RL5AxXhjrCTUWI8WKL2042zclkD0eDXriUsOR8jVFGrULXAB7DAMx3ZKVg6eq6
+zPGcnYNwLVZ0I0dNh5hlxqOpD1UTVsyVXI18jJixrsISikAY51loirhGoYfpSA7OmHyI4cjAkbex
+C1oserjvMdCRhchX+0Atc65jitdgDjg5yKv1c3TMg2w3L+Ac5eVfXECxtT3suxazGRSoNWlr3JKe
+fWscNGXpRyZDwLlPySpkXI29sdxfcjRA0ku4Nw0iS01I7e0OL8uwyktqYNhzUI5RXUkoUXRrbBk2
+hmuDphd3nbHa0W7ww2tZ67cepFBsh3dVh4jXmRJklw4ThFNqnlqN5VL+zgSAC2WtoyDBlgSyQ7Zn
+u4FFFgi4q8q3SNnW3U0yYCF76OLibHcy1iSjddrflSkPfIeKkP1Qa3S9UpnSH1diulcrSd6FD3fj
+btyNT4zXwlNIfY1fOh7gouoA1pXt6BQVmeKTMMRizkwvCgRLKjf3ff5QGDEMaEwHQ6ipK2vYHmKg
+WjRsQKn7cMB5sMQuwHUI15JlTiYJlo/JrmtzNBRw6VivbiwA4gIuLpe4WghF2TK/xt6EnXHHM8wb
+8VjOzwXHkFqLEdmF0zYB2NiUbXx8tJAT/4qNVmULRMwst1EIn5bSeho0wChsgBFNr+/Lv296LcYM
+q4LCYs6uTHj+znpEnoeAybqcnITLqy3mQ1YRHucYkIfiZH6Fd7/8FfmMwCCKBCPSUok5b57j4h8L
+R8Sz+Q1+eCHWMdA1aiYoR6Mp4hnxJ7zeTs+xohWfX12hYbo8sjVqhgyx1yElR8AwJhzbC+Azkaw8
+h0Urc1xelihZ4ZgcHcNPCeois7Xbdqgr8Q6e/qNv4Nk1xWW6HFOyf8dxiTcrodaLKWytfYdtSXm3
+VQ6PILLOz3GwEIsdELC0n2WYDmWvAAY1N59qIxyRGyR7cADTUQQoYULcbeERAGW2DVxFDVVU6Fih
+OI0H8ChLXxOktJlfYH0m/B3z2mGPzN4fhsBOKc467PDtrzhei0PBKQ+dHuFzE4PVXCZ4duhhlcjN
+f/TeOV6sZMFvlyt0fACm5Pm/f5zi/lIW/2BvhohxptMKjsi03JYgvR4qx/JPVe5cxwQaPstCnldg
+3RGZpo4QsWOuItHLdrtFywx52YbYXMpBdn3xEilBKsVH13j8WOLkqpOHY9gYjBh/p8kAUOJ2d41D
+3ku1EymYOwWPMaSJgM2GhDI+UNd8aHSJhrFoxhzHwWCCIUVzuybHtc9Qqi53NOuDMEbZyeddXsj3
+rlclrOsPQoeQW+NgorHhwfKVP//ncLhHMdaKFYLvv8B3K2Ejen5V4Rwyh9NOoZ2wnLhZICEQ60fU
+iPjcF4A6J+AqD5Av5V51YNAw5IvgY5ayGkWf9miYIdmTdVdViG4jv3++WqM6l2t648kZJifkxWR4
+4fkDrG6kG/K9p89x0bJ8OVPwWOVRncbCSdhUE20Z2BgXSuYw2a7hM2zqKos4JeFMyo7KJsZgy9zP
+qEO3ILlJ0GJOIwPbIvDk9Wg6431sd+XZwDl0PCDr5RKmJ8ssQtTMtdw8F2aq29USL87FiFSBB8Nm
+mUAreAxZvQ4Ay9xtD3P8GeMufLgbd+NufGK8Fp5CoDzcD1O8MRvj8Uu6RrqFIZw3sCHeeFNO0nfs
+Q1QBM/WsXetOI2btVysHzRNTVRor0oFfzS/h6CZWzASHUFiwC0/7PrZaPs/4U/i03MMTtRMISemh
+uLbFICEdW1ninDwLp9srvCRWf1Eu0NISjmihrdZYr4i3yFJEVFmygQ+fyaoBrY8zDiv2cISeQTAh
+g2+TI6J7nWqFw4l4CCMj8zPVYwwKduR1Dt+d9wXrEJpeSht4KEjv1qJPLhrEhCVHmQXIdmxR7by0
+++c3OHgo0PM2FzzG2eoF4kKs1cyPMR5SS9MZpAR4lYFGx+oJ8Wa4Ob9GaIRKztkCvk/25bLANROs
+48DDkACggPd8vc1RUm3JtholgWqhl2BMuatDBTRUfVJ00V3YoXpCluT1BpdkPg6qIbaFzNFZfYuX
+xLIc7st8LtQQCudc0w0ekSk7Cjs8JMP4KT3W00kAQ9xH1HVIR/JeExuAVaX11RmUkRAjXUqCejQc
+ow36fThEsyTLc7PF6j3xQsuNxSKnZidZwlUa4K0jCeeG6Q1Kduh+62YJNnaixce4jlcdd57C3bgb
+d+MT42d6Ckqp/x7AvwHg0jn3Jb42BfB3ATwE8ATA33DOzZVSCsB/BVGezgH8e865b/+s7wh9H28e
+nKJpgGQs1vrh/SkUocvLi2s4YhNyD2iZdKwYO5v9AD4TUhoeFGMokxg4xme3txUq6gcmLAsu8hor
+SnGtUGM2Eks5Ckos+vj66ganZLpRLEG5rsaq11tQJXzScY1mxzgbSsNMcQMkjIfH+2LNl9cLNK5H
++QU7AZvQ1zCss/ZlQ1W1aFiGtK5GR8GECB6GhCBPsgRH1IfcI1xZdxk8En8WtkVyLTHpR5sFUmoL
+OGWRM4lrWEz3XAuPMLhyqaB83t8wwYax6Lzu4I3ovWiJ1dP7M1w9kc/68PwF1j8RjyaepDg8ZDdj
+fIiUZcQsJMQ3DFCvqQ/pRyBtAA79CAvNLlhfYzqiJ0QU54vlEmsyM433R/DY7ZjGKR4cSYkP8xUC
+T362tNyB5yPmOlZPz7Hu2Y5bHx6Tqqg8vGByeEJ2J7Ovkb2QfNWF7TAJxPs5TSM8OvgcAKCjlkc6
+NDi8T/yHr9Fwn6ogQMHSt1YxFK9/eErlcpRomdtaLi8E9w6gWG4Q8J5cNkFWi4exuhTPZZgcoDOS
+xM6iET5Q4lXcSyOs6L2pBjDq09n+Vwkf/gcA/zWA3/yp1/4WgP/DOfcbSqm/xf//nwH4awDe4X+/
+DJGg/+WfeRGxxsFXU2yuNqi/IzeTRgFa9hyEjY/nrO3uHQ/g0bU74WImcbFLrHTW7URMUTt4hJpG
+iQ8vlAcoigiQ6m7hMnlQQhOia+ShuHV2R8PdNLcoqKuYscUxV0BJqvMXmwomIrCqsYhIEf7gjRjj
+qXzfwVgWvx7cICPlvIsT+AS6mKxDkInL+HAqLud8CBz2y9NUiInrH0JjQDzB0DikEanfSZbSrq8Q
+J7KJH+zdh/9Ykmtd22FLYdIs8hFQfr3PnNd5jpDh0eA4xfHk3m6+M9vzP/qAlrnzCAraf/B5bIht
+3tvmWJeyMcPYoGRCtPU81C1dXnZnzucLlDXZjAOHg1Nxg0ezGJsthVfhMJvIQzGbyPcm4xSdZdZ/
+OEQ0k8NpMDrCgGzdNlVwEXtB2AJuihTJgdzfo69rdCv5jJUJsFzLQx0sfQSEKZ8SQ9D6E2xvxbX/
+1uNnWFIafpJl+OKbcs03t/JZzdkNki/IvCXRCBVFXcpNjsGAict4sqOFM31vvGuxvpb1r1wAQ9g0
+hjU618PbxwgjWb+G4ZzRNRRhzkVZ4x4rH3vxFQZsC5A+iP+fwwfn3O8AuP0jL/8aRGYe+KTc/K8B
++E0n4/+B6Eoef6oruht34278qY4/bqLx0Dl3zp9fAjjkz6cAPvqp9/VS9Of4I+OnpeiPBxnWL0qM
+96fQpJyyT66hRnLCnUwyDKh9GNcWAU9dxdq93w0QENnlmhpeIS6s9RR8SnIP/GBX4iPxM4YuwtgS
+NzAMkJPmql4WaDW7+TBDSbZfc0ttBuVQtlf8PRA5uZ6niwJqLdbhZLYPw+7BiqWwg+AQGV3xSdXA
+HJOC6+oWmaM+IDvg9vxu55lM0iOEdCm9QMOQ3MNUFcK+iYnagaYzUAxRogZQTJIqZXfaEdtFhXJI
+fcxjMjUfTqGJ4nz75B4OWS6b7e/BY3dlNE2ASqyqpr7BcDLAG5DafqpG+Grbs5IChig+rwKqWuZF
+EboeBzG2C8qteWPYWO574kX4khHv5fmyg7kQqzhkTfJkMEaQyXbLtyUiXpsq8528vN8aGPKReT1L
+agCYWn7/6P7bCF+KV7GpKlxdy8/aaByTpHaPcm3eXGO9J97bP/7JS/hMGA6HIzSEoU/o/dxYBZ9e
+gz7SiG3Pup0BucybH4ZQ6Lu8WHq8nmP5XOxuGAfw6EEsb3IkmSR0k6lFPJF5ZuoYlarQ0EOuS6Bm
+CHKchfgRIfSqqaGYYF+SfOdnjc9cfXDOOaU+ZXoTn5Si/7mDiSuKGxy6U3gnsiil/xiKG2wy3MeI
+nIIuCwCCcAwoBJJW0HxvV1U7oRPYBvAouhkE8BgSDMK+Jr6P3L/kBQVowOxuliLkBhuaGIpt1IZd
+hmprkQZyneHEQ+PLg+WdfQ8eQTY2aTAbixCuu5TvOBplCJhnGO3dh6Lgq9f4GGZ00QmU0jBImDuI
+nEJItSi0JRSh0Mp1iCiQ61FByYdBzXwAWg8xQTHjIENuWNNuO2znlLN/Rz733v4E+4z3D2aDHTW+
+Ni1sRDh5l+00H7v+ITc+EoZMx5+bwpIMJowm8EheUm0r1GSVbsgvuTUFNMM1z7RYsVJjYTHyCI82
+xY623XdynRNvhIQCMGrPgyZWyDYDFCQRaa2B18i9avYRYLnZ9UHEocEbD9+S6ymv8fBUqiC+vUV4
+Q3hzLRWcHA2mrDK8EcfwGHapzqIlk/bpqVSfuosCJQ/WZKNhKDysfAWTSQipbLvDDTjVt143aLdy
+MMXxDKC6VdhatEs5OOMHD+CRbzQey9qYukZDJNvZ9gKW1ayPNjUKgsvKzkcQf7rH/I9bfbjowwL+
+yycLLwDc/6n33UnR34278S/Y+ON6Cn8fIjP/G/ik3PzfB/AfKaV+C5JgXP5UmPHPHh3gVsDjb74H
+75gNPmh2TTf1eonsgJyIaQyPYUPdYwxMC0tXTHsROmITPAP4hCNHVYpqJVYsCWZ8r4Xfiqm5XNwi
+pTdSewD7iFBWDpqyW+2CrmMaIS/k1N4PA1wyXPGiDI16AgA4PP4yTlgRaFg56KolZiOxKoOkQ00N
+wuYHN8hIBqPoBYW+D7/HaWiFqOdrNArVrVhB33QwTA72UmONbdCyUrOxwJhWpbAWt424qIv5Eq4n
+UaFZOJ5mOEjE4k/3j7CZyzlflhUanxoRRwEWZyKSE+2JC1+2LYKYmfxVh6D/3K5GwMRuUy0QkdjG
+o5dTFi2aWwmr4tM3EJBPoFgqxJ18n4randXs56UuXmLkkcbMWJhQkn1Vcw3bYx2ebhAQCWgppuKP
+BnCOoVaSQlP0JRvN0BJP4VX3UFEmPmQDV25XGLCzMw8MLrbiznuXFQ5O6CGUMsfZ6QBejy8ONTru
+zzAa7fgYVRGimZN8ZkycSrlFHLHK0NTw2NznKYOa3aXF9RkGA5lzpeV6TauwBWHsRYUr8nRe5gG2
+Pfu51lDkzkD/3p8xXqUk+XcA/EUAe0qp5xCV6d8A8PeUUn8TwFMAf4Nv/21IOfJ9SEny33+Vi/BC
+jcmbMc6+f4v6fcZhD/egCfQxQbsLCXwYeGFPRCkLX20/1sqzXQuvpyYKDUK27yZhAcO4NWBIESYh
+Kuo8piuNBbPlSGJUTc8mtEZrJb4MKTiTxfGu8+y8qlEwv7DdrpEzfrNlhZL4+3Uhm+DQD1Cu2Btg
+EuRrbrxKY8GcyJAdcsga+Il8n24bgLmRrq52/l2VWyhPNkjDNt6yKNGSqLOOBjt2pzQb4OJKDoWi
+qNGxtbifC2PiXRlvu1phfSt/9/jpB8ipJqXU9zAla9D4bbqw0T4CQqaz4RhZyqrGIENT9bG6gzWM
+0clMtZ4/xmIj19C8vMKWHaWTPYVRJj+vbyskPBjNkq3sD4ZQ7A0ws0dQpHsPkinanA9evAUjE5hH
+ImjrD6dQW3JXuhVMXwNtakR82Jx/A2qxomT3qeosdCpz9O5wgA+2stbfmd/iz1La3iPs2v3QovSY
+MykUIlK4O+2gWA7WI4OA/S8NO21NFCFkV6YxYyhS38eTFnZDGYDRHnyWn9VWbq4yBSw7Iz9sWvzD
+M1nfD5Yr1CT0hTbogr4R4tXGzzwUnHP/7j/jV7/6T3mvA/AffqoruBt34268VuO1gDkr58Fvxjie
+Onzwkl1vT9bQR9R+rKdwfZNT+RIdabwsTWZrFcAET1c5OEqNdRugWcnJXnY5ql4whpRanq3RMHuv
+PR+WwKHbxTVuFiTbSI5x71AqHynhrF6YIGJIsM5DZJ78vrQeLik39736A3z+gZzczUJeGyaHCEiA
+Ujz5EBXJYJ6cnaM0BC0NWSEZjBEH9Gi8EPWtWN3abqGIw1Bth6Jkdr2nGS9aOGIzlosVlnQ/y80S
+JTHGtmph6c4HhIqr9QotuR0r26C4EsvcrAp8+EwanubbCpqeh/c98RSOBikOH0pW/EtHBuEJRWJs
+A02xGw8tdMN6OiX/lqsKt9dy7dvqGk/OhRPxrYsCn5+KJUxqhTVFVNapWPm9q/voWEXqTAG1J1bX
+2hAdRYLyH1fYGFn34fhNuacXK2x+Ih2FxXiD1B1znmMMJuzMjSuAVjonn8SLH/wIN4F4bEs1wdGe
+4CJcV2HBMO7ekfwb+gO0bIKqBhv4XCdjRujYaanjEXwyUIMVo+JqA6/3TOwGmt2O1aqGzzAgaEqY
+kB4N+TRgazgr833bdnhGvoVm24K9e9Cug60+HR3b63EoaA2dZjg8/TrigcSsi/kTRBnFX6sAXkjk
+nfbRUiOg21UZqp0Mt1MdgkA2bKMrWFYRkAPZSBZXgW657dCu5cEtuwoLIgiXBdDYPhs8hCupVTCV
+xULZYkKa+Mgz2Bhx6x597hjXv/8TAMBHt893bFAHdCnb+BgdtSeavMPqQ2m5bhSgjXx2eiAHTJgl
+COlGY7OBYryMwqLrPu5281lyrNmrUdUNGiYKykZhQaDLZrVFzVZlqzV86nT27budBSz5IcuihiUK
+7uDRKbID9h9EIzSkF9d7PaoywuxAYvxhPN01N3hRKJl2AJ0yqDmfPBswm0zRHMo65P4Km58IEvRF
+vsbbB5Krnk4iXHly/R4BW9blaDv5u6D2gDWJaIY+NEVa26EHRz7NzY/kIMAgwiaXPEmzrBE/2ON9
+t6hv+2pNhM1c8uJXT+SgeLpd4vEV2Y/iMd7I5BAqdIEXL+Tg+MqXvggASGf7KNjboboChoei9h18
+8oqaYQRNpG4fBgVJAEdAVhyfwIUyb+n+AHZN/ZG9vd39maS//wphzLK2p3bsXF1j4Ujk20FBqZ7/
+8dXGXe/D3bgbd+MT4/XwFIxBMJ1AxXswJ2K54+56p9azsSuMCIntAgfFXFxX0kq0DnXfhVfm2BJg
+YrwQIIBIOb3rn9AhGXm7BjWBPlerObaEg3baoOUp/+PlDQ4bcWFPWrESbyX+rgZ/MhyDEn64N3sX
+33v4hwCA84+8ndx7MmGo4S0x8+QzVqbC2Uas4816iYw19NVKLFzkOczZFRfoFh37PJTXoSQwJwj1
+TvOwZTjzbFUg34iruoTCipyQV8vFriciSA0iIrh69egwtWg24kkM9gyans3Lszi4J27r9PABFPES
+MWHHOhghZOJ38fQxXCXrV9lgxx9ZFyUsk5E9u7KJfMxtz8s4x+WNWObzyzkmiVjr/dkQAbs1O4KU
+5mUNkJ/CWzuYmBfa1PDJjDK5P4FPr+h8Kd8RNxMMmRCMhqfw/J7nscT2gycAgM3tFj0r3uVarHUZ
+xzhnQnC+2eAdJW+Ihx0uN6Tzf0F6tWkHRRh01XYoGUoE9yM4JkRhO7RXEo7ZsuZLH6twWWwQECPi
+TAEdyNzbzQaa4SRidgfbCDXFacK2wyL/KXBSjxxSu/955XHnKdyNu3E3PjFeC09BBx7SBxMoE8Pb
+8JwKHRpa9khVaEvG10EB3ZPTBr0VKVER0VhBo2NHWrHJmT0A6qbcaUl6pHPzsgBr0jG5wOB2Lif+
+QvtISe4aeA7hmCi8mDE5IiwJR863K3SWoi8x8PBtoS4r62+gmsm396SjgMO8T5y1FbYUCMnbAA3J
+UT0mFJerDfYzub+4KZD2giO63qHmaqUR0vKuqWVRBS0uKVjy0ho0fRmuaeEIx/V9DwkxGVVHPYVF
+iYCdg65ud3Xui8dn2HRkylXfwGz0eQDAgEnX/c+9CcPmI1t2MEySRZ5CQ+EbGA2mK9DwOottjoZe
+3NVmgVtiSw50hxXhyLNOoSAGwrH5zakW6+XHnxvOWapWI2jC4qf3HmCwL3mg4pJrnVikx0RChgHU
+JbsWwxhrI5b75naODRuQcnoSK99DTi80CwB/JlbcTx2ShczdLdmktpsVYp+oWBOiG8j6VY1DSvEZ
+iwYt92E9F0/CG46h6MWpVYi2Je5FB+hYcra5Rcf8l8c8QmPnqOndfujX6Ahjh8bH+hvAp3UUXo9D
+wUGhswbe6jH0XFzAuElhQ3H9/FUCl/a4/eWujtsRlquNh45UVYPRARxd4gESVNcykfmqguYDtIwJ
+Ynm+2iUiXaigTC+jvkLRyHVMk/1dD4LrmOzyGji69oUyGDSyyNNgCFUQRDU3SGbM4PdkKksFy02T
+Xy7wgrj+tqkRtPIdZc2H27UoGUqkqkPMQy+L/F3SDQBayweEMGDfDNEQ3HIx36LjEvvKQdH1VY2D
+JmQ75fdFwE6sdXlW4/m5bNifvLjEj59Lom3tA62T5OjghMnV3/423v68GMyb5gAAIABJREFUZPI/
+//YjjB5Q+Ga7gmabtQ4+Jkkpl7IetvJxRMKSzaHC03P57ov8DH/B9v0MB0h4sIy+KPcX5h2FOAG7
+bVBR2r66ukHcnxWDIQy7HRM+3Fhp1GxxdhHgUyxYeR40D5ZaeXBstZ7fyBw+Xy5QEMsx8FI4HpYi
+ES9zMAzl2teXQMTktzuxaFglsMbbgZC8JELT61uSfl8rB8fEr059GJ/hw02Opt+TZbdLsGtS7JXG
+w4bhSll5uySuUgD4d8rhY1h1z/D8M8Zd+HA37sbd+MR4PTyFpkV1cYWoPgQ8+pmPjhBdiivd6TmI
+5kUQv4FtTrguCVO9ZAQvpUvlgGJFC+VpNEzsqVmJ5oYdZUuxNHEMdPQqnPNR0QL7wQSnJOM8TRN4
+VBf22OHYLlcItHzfprmAzci3UCewvP4X9gpvmS8BAI6mkpS7rq+Qr8S1L7oJDgi8bIJox2WgDMuX
++RWMZXOVUYDpxWCAhJ6L1hqGnAyWkuTtvME14a5lW+Penljx9TLF3LGGDoFvA4AbkyXahNCaPf+j
+G8S3ggR9536G+2/Jvab7h7gdikhKqun6qhUesoNxPD6GZuk03h8j1BSzcT6agM1KrPlHGtjmtNCF
+Q8CS7JuTAf7Ml/6yrMPhKc7m5NQI5vysOSJa/yAcwtEryl8uUbyQfTFSj7AtJAHX62LYSYbISNKu
+KD9E3EejLoX/SDRLxwp4RpLaPopt4hH2qAcS+tkOyapMCwqaYzSSZqdUFWiYiA10sdPYrGwBPxR8
+g+q2sFbWuiUTtc59qCUh+26MmkjQdgs0SsqezovgkYzW6zHv1QpLhsp1nmM4oTdW1bBFv68V9Cdi
+iZ89XotDAUYBewHOfu878On2ZHGEgl1oTWVhAgJB9n1EW7L+LMhlB4stAUIX82t8dMHqg98CJCcJ
+PY0B5dAfnhJjoC0qdi3eLucYsQPO94AOBJbkOSLWhzVj/KousWQfRWo2aOjOqgjYLokReF6iPWDd
++IH8/fH0EKMTAfqs1w2uviub5sfnG4AioH1tO8wyhP2h4CpYLn4HoK3Yg5GF8AmlnbOef+Y01gRp
+1a7Dr3xF2IF+/+kS1+//gdyHbtD2Bwez7Ji1SPhZgT9DznjXX+RIjymEmhpk92UODvlUHR68i5Tz
+im0JTa5B3wNAOvT84haOcFzNtuCb9Rbf+UgARudXZ1CEm/9LP/dFuBN2h+5d4Jiw6aaWEKa8ETZq
+APD2IzjCwv2gQzJlN6sZI2Z7dXdLfMtJ1ouFQT0+RXdNRuUvZcA9EseUc1y+lOvcksHZ3zc7JagG
+JapA5nlgU2wJkvvh958AAB68k2KckXDHBrBk4O6sQsfwT/kpOp4mQSbhU+dV8JxgPdql2XV+ukED
+0zA8ylt4PXcQ+2Qurxy+d0a5g42HrmAfjK1gfypU8Ah7t68WPdyFD3fjbtyNT47XwlNwqkMTrLFW
+1/ArsfjeYoiW7pDxx2jCnoPRwpKXLzj6/9p7t1jbsvy86zfmfd0v+77PqVP36u4qu213OsbGARsI
+xI5CEIIHR5ZIiCULKVICQgpp+YmHPESJAkEKAQsDUmRyIRhiHKDt2IlEHNvBt9hd1V1ddapOnes+
++7ru8z4HD+Obu7qM7a5q96k6wPpLR2fvtddaY84x5xzjf/n+36duOXK6Ymju3pgydN4cYbOhUmY8
+Trp4Shh1Whk4CnLVj30SjGTLG89geq0Qy5BG+oLxxLnXi6wmtW7nStMZYe6OYxkUrK/r2GP+uerf
+gwP32gvJAZEo2g7CiN6/7H6evrdhI72ESshNGwRkV4IBXz26XverToDVjk43ojDiHlCD03K9IG8T
+sV5AMna75+hqcY36LNclKwmZ3HnPuarP9HpM990OVIcBsSTW6sASi2m6/+IOauZjONR8RyEmamHj
+Psa0yVhDUUnUJOiRS0fj7MLtwO9eXPD4UuFMYul33G79mX/jeQLBT+pgwUilpkcXStr5PdZKWhbz
+FF/iMnZirht/yvIRwXqoOVDD1+UGq++q9xriHTeeOQqw4ojIOpYCd11PJWSTzkN6O0oo4jm9DiBM
+YnbkvV5kLvmaPO4yCp0XWnUjkFJ42ZSkSugGkYeVZqfXEZjABNRS8WYcY9UERrmhEB9EEVfX1Zpi
+5ebi9vk5ty+ct3yWl+yLvyH3NtRtNcOAJ9Trh5R9wNiPyN/2JOyzn/0O+1P/2z+mSFPeve8m4Rl7
+xU++/X8CcOcrl9xUSc5bLhkeqR36tpukdxaPGVzcAaBIGjpLcQ16Bbs9dTvujhgX7mFaZy0pacbg
+RQepHd14hn5H8NpvO4YDF2eWs5KV8gCJdNH3P/+9mLFgyU2O0YU7jA1+Low/5ros1BUP4k5nyK5K
+eXWVM+yKP7LukinL/kD03qu6IG45dpqGWmCpJvSJrwVWYzJBqd8ToGmdFnha6Ezo8dKeI3oZxDGl
+RGGnXY9CbEJVpXxBv4enealMQbMWjPn4iJ4ekGDkE+RuDo5vfQaAb3v1GZZGhd8ixG+huF7JvVYN
+ftmQXrpjCgvX4/D68oz0LbcBZOkjGs/lA378x34cW2rBPV0TGvEcrl1OoqwqotQtskGvS6NF9PLR
+goXu5aPdLqF87fJK+pKXKQMJ2dR+SSPRFntVELv1gTzxaFT5aB5Iu/M7n8Pbc3mUqImuuxL9Qczt
+yn1frlAsDkNqlYP/yv/6s5y8/o57bzDirbuOvzg7f8DqzC2Gy9qdWzVfYpVHIAygJYYxFl89Kp0b
+hzx75MqssUqr3/uZ1/ieP/g5AD732e9jZ1clei+5DhUaDIHg5tNB91ettZ/n69g2fNja1rb2AXsq
+wofGQl40GJtA47LG75kVm1apd7HggZIzk9M+8catxrNzRwc5e+/sWsJrHMKuGoKiwKfXc+7ccDhg
+cOFeTwbqFkx8egPRo9kVq8tfB2BTTujKDfbKGiNyivKqpYTLsZKqX3qWWKv5MjPsiRjGBF08gVQO
+99wKftgbMVAIkzYVRwpHgqYPoly3JxJsyTesN9K+pKTU0p8kEamSsasyIDOSIt9TKvx0QSM3ubLQ
+FRXXwWSPVF2gjAZMdlxVwvrynpYruqHLbhfJkkZu8iZLr6UI69WCvthnHj9wjV/zTx0Stt2QVcTJ
+ubyK/YSonYsc3qudJ1CJQGX/FuSn7vzuBpd0Nm7sJt/gNUokdh9DS4wir6N3UVP1XahVexnLpZuL
+dx58kStVl1Yvfprp0B3n8NJ9PkvPqELnHUW9IdE9ieH0Lmgze2F/iK/qw/30V92AS5/J2CWHjden
+8Z3HYuaGTLT8GyV+H5UzHp05T+9+c5dUSeylqQl7rlvTxpdkLT/Fey7sMN0Vti0Q1Bv3QAB4XIeN
+6eUD3o6cBzW9cnP4S5P7fGfmvNu3F3eoJ58GYBxUJOqaXVU1pt7CnLe2ta39Puzp8BRMQxpsuP9r
+c6bWxZwnzR0Wv/YmAOuH9zh81vHp93pX3H/4ZQAeq6T1rdGIl/dVHw5q/JmL1fqdgEEs1qSooJJ4
+6EC7cuj38a5a8Za7+HuqA6enlGvnhczPH+BnEpSRarG3qLBiVMYIQQb4gUeksuXx/pQXnnGQ4L2X
+VbK7WBGLsyDpjjg6EPmr6VAWLjfw6gsuB3B2ds47991cLJdrMsXIhfGwKk8OJzFp43a5tacW6U1B
+3epeGMvuUTtGwrAj4ZTxiHok9h6xP+2MYowamy5Tw0paB0ObMhLa7sHZI5bKXXz6OUdFVtz/Tdax
+mzdWAaFITut1j6TnvJG7d+9wo+OOaeFL0HZ+yf3lV9x8LmdkojErWVE+crt1N7DUvpqfVmo0mj+C
+gTQZ0hlf/MV/AMAvfPHLvHzsYu56Oec96Ws8ozLdMEooLpQviO/RvOWu5eBGl3gu+Ptmir26494/
+ddesay1l6nID2ekv0/Oc11DYFZvn3RzcPhHz1uk9fvHENcSd/sa7GOW26jojmcgTWk9JfNcOHdbC
+I6Sba4EYx5QlT6Gy17u8LSoaNWZtEjcXs1+t+Qf1PwXgu3Z2eebAPSNB5JO1nmVpWDVtX8CHs6dj
+UWhgsfH4hYe38d79eQB6dc7Jl94AYNr/FAbn+l1cfYX6yj3cN3T0r326x5G6DMPVimAkworSw8/c
+ezmrqaW154mX0fczKmXAgyghWLsxstP7lCt3Y57PV/jW3YS5KiCjGBaqXXetZUfgpr1pwCs7bhF6
+6aUjpvsC3hQugReGAT2FRNO9I3qi82pqH5Oqxi5cyiAosbkLDfLugCxTP0OxIRLUdpE3dBN3My20
+EOZZTiV/v7YNXSUB02bJ+am7OV7pdUlFEGJCN0YVHzIa6oFuUpJd9WvYiETanc9VXYyoyXxVdR7c
+v0uAu8kbM+Vczqd31if9qnOPR91j5rjjXy6c+725fJuHcrUhJVZ8kK5L1gL11Itz0OJbGnftTBFi
+zt0De+fyK/zaz7iNI+oEfO4POhfdqxuiK/dQdAeiNsst5dJxQuaPa2JdS3OZkK9b7c0F4z33oPcz
+dw+V1SPKuyJhKefQsksfPcODe+67/+7Pu7Dz7X/yN5mduQUivVhiw5YF3Kd/5RbnIFvRiO49Clq8
+Qo9GmJy1qTB6oG3jY0wbV3iELaBKUeDZow1vRO4Zyf7xgOHILYqfufkcgXpFBvg0YtD+sLYNH7a2
+ta19wJ4KT6G0NY+LK4LeI/yRdB/ePOVtyXm9NsgwKos1y0fE0gX4VpUFd8uYSiVJU3eI+1Jl7k1o
+UiHhUo9IUGG/VY9OM0ym3XHfpxYSMH78mOjgZcAh12rPrfjpY7e79mP/WiBlx4NSJKBdAoLafffm
+8opIJb7hjvv8JOowUXjRNwWxcR5IVmUYKWVX0lOIreGZoRJ/g5BSGofR+Tl3BVcu6pq2ZbQVfSny
+DD9umaE9MsGA46Chq9248Woy1fR3emo06lR4iAkoMhwMRddWD2galyQcPPM8k7BlwHLneX42Yz1S
+aDB/hzSQJF+64pHCqmG+IFbi79oFJsSEQmmuLDO1UdabSzpGFHnRmOrSjX1Zu3PudPY4ve+6Gm/f
+ewvxufKvvPocz0qMrF5fEkskpSfRGt92aArHvJQnhq5Yrv1yiJF7bTODFWdBnYk0+EFMqfnsjZ8n
+wsHmq0clp7vO87j66s8AsJqf06Ry1UODEUs0XkBTufswGXgE0uqYjt29XmYVkcKnk/sZtTp3sw3X
+jU1l5bnMMWBti0a1PLztPBMbvc4br7rk7zP7t9hT5jKIB3xU2MFTsSiYoiK8N+N79r+NeuDos35h
+kfHi2+7wjoMNXbmUe6OXeKZ2OYVe7Vxf1hUduWrjaUS3I5x5APTVLpxuQLRpnngQ8TpQO9fRSwIY
+qrvy6Bi64kdMdinVfleozfh82VApLZwHcFNkIqNJl+NdF5p0kpCh8ARTtSzfOBgzkt5f1Othhb2I
+ghG1HopS1Ykm8UluqFsuz6+7Hf0qp9ULjbI1Z8K4I5FbrI/fxqEGEKZ+eOOYomxJOAx9tdkefsrN
+ib1KiRQm+X0Po/PPry4ZJw4yPAxrjIBfLQR93I0xM3fDmyTGiIOyzvoEUhv0RjfJ51d6vxtjGm3w
+pQO55JQHolWzqyWjsSDK1ZqsFOZk7sKyqjgnD9y9MB0d8e983n3vZ258ip1n3f0QpzuE1j2QbZt9
+ExV4tcvX1PnSNZEAftfD7zhMilmfgUBw3qEbL9ybEKVujGgyet+3nhe88dMONm4fu/OfhCGbgUSK
+sw3hyL059vvsHLvQZmArvFtiLKfttN1gFwp5iwWnS2EW/Oa6azMuI2wLdlI/hx/V9NXWHx6EdPoS
+MGrW3FDVrakh8z8eMZitbW1r/x+1b1SK/i8D/yaO+vY28O9b63C/xpgvAD+MK7H+WWvtF7/eGL6x
+jKKSzd1LhmNl1qucKHSJk3fnc75FCaejyZTOPbcaz1LnDu70hyTSlwzjLrnIQ8MpeEq4hElMvmkh
+xO+vhZ6ISFeLDZ2eoLTLlEAwX98vyZVcMmXryhlM27XYDwnEyns06XJDrY9HkxFdeRD7I+dSD8Iu
+HcnZe0mMVbKyqHMCuajhVKFP06NYy732cjYrdW2+GMCZS3x5p5eU6socq1YeBB5WnZa2hvFIQgaV
+YSC4dlgvCKUeXZ8pJBpYAkm+BTbg9LHb5QNKitDthOvUZyVtBV+6hEHpSHIA1g8WNIlISkzBSrtu
+eGdGvKPGHbnUQ88jFBPxPFtRlG53jANDLQEXP4op5OnUjTv2q4tz6rXk4A+mJJ0/5I4/SegJPpwc
+9PEEK64vXBIU+z50PfLGVAt1KA52aVHA9Mak95yn6rfdR2uPoKVHu1piu0I02i6zK5dg9Iw7xqPd
+mFy4iTzKGKtz9+WXXmQ4cR7iaDLEl8fZiUSykwbcv+8qHA+v3uSViQtR5qsSJu7gOsGIifhD5pqf
+eNcwkHf3wvO3eF4Ykv1kQNjibOLo/fP7kPaNStH/LPAFa21ljPlLwBeA/8QY8yrwg8BrwDHwD40x
+r1hrf8/+rKq2nF1mLDcLfu2N2+61oCLccxN8GI7YGbjKQK/jM1P5JvLlhsUlSVeqT4OEKnMcf8Wy
+IZE2o+0D4nSsWuWleAK6MB1bUakfoFwVWLmzjfGuM86Dw5ZOvLkW7WxKH2/gbqDuKOBwKurz4YBE
+mpWxOjVDL6bWg+BXIbVuPN93kGSAKBH3YeARSyClqnO1HQJBwQ4qQ9qMsTzKgRaQfhxyKcWq0hpQ
+h1wYFqxnguNOGgplwHd99yD53jOEE3ecj+/eZS5KcmM8jNixl6sNK83FJhVT9WBIrspI1DPcn8v1
+HY9ZSetz2os4EQOUmOw5LR6TL9xxpmV2nZcxJqQWeGf5+B4b9XRkmrd4Z0CsHoD+cMhgz81XkGeg
+vEvQeRZqVTZUhrU5+OpKxGvwOiqjGoOXqC2xWtA5dmCg9LHLW5jE0Age7jcbSN0JBDcOePjGHfex
+XLT9tmCj6xAaw2jgPjfaH3PzSJTypIRjN4Yn4pWxDVjlrvz82c/ewOj8r7KKQSKSmYPvwAzUjyOW
+rm7nlCEu9Mm7Ff7AXcuTesHQuPABzPsyYB/SviEpemvtz9g22wG/hNOMBCdF/7ettbm19l2cUtR3
+fqQj2trWtvaJ2jcj0fingb+jn2/gFonWWin639MaKjLvisv1OUv19weXcy7Epbg/OCAei3X53S/R
+k0LzVLX9UbhPU0h4pZjiD1wCq0jXrE9E/3U0Aet2h1Scesniio70/OJ4SlNIgu3RIyKBTWxlMBv3
+faEISQgMvjoO425JrWzyZF2TqEGJvEtYuWOOO9KbaAKQpF12eUXTdz+bICLyxVmgBKBN4msSD2Mj
+AjFUx2GXqSDd2cBy76HLOJdKVNoGGlVGqrJGrGSMOzHhdXbUZxlLbzJtSQYuYC5KtOKCUFwOPSIW
+og8z3ZCmdO/vSV9xkWb4GqSsQ6YKf/LK4I/cfHYGEb1MGg8raTUGAQZ3HXa9AakIWXyg1o5frCN6
+hXb6kRKY6w7FldvFbRRjfEHFywpSFypVD2/jq/HME0eGLUo4lX7DzhhPHYU2txi54zYBT2GV57v3
+VnczmrHzVgh62Jm6R8sZS2kylPK6/I6HyRXyRRHzhTvm5cWSWrv44SvHrC+lHj0VPH7zmKMXnBcT
+jHewagJLHs2vPZpub8r0WcHs125O9vaPyK50ntOaTuDGGDIgU1NV10vw+RirD8aYHwUq4Ce+gc/+
+CPAjANPJDsXDlGe8PSa33M3/dmfA9MKFAUN7Ssd3a8uNm68yXYu2+4Yy+XlErJZeg08jzkTPRpSi
+Mi+XFoWl2EoAnF5BIy5zr2uuhVKDSRe7lqs9vIkV+43x3YNgraVUZ9zyCiQgxdVyTdS6waFPLVYP
+W6hs2A2pRLxBJ8YrW2TlGE8twCgHUpewVDydLTfXeQLT+ITqgux1QvrKWxiRwA5NyFpBZF7WJGrV
+nI52CNtWXmuhdjdm3HbVFhld8R3uEhG1HXcbGIhNqNjkpP32IZOKUzykV7nv2vRqlqrCHfcPqATa
+Wpk1dtWWbd3nF+++w2zp5uKyLPE89/rq0RmRiFV8b0V96JxQf+Ou+cXJG8zOXSnwtc98O4nIcGqu
+sH7bSWrwJMVuFIebUQNIuDb2wG9vhgIbvb9YWqsKTVviiRu8Vnfz+JDCXupjJWFb4hU/pt+kWIUa
+g9ASSRz3PL/Hi3Lzbbrm5KHLWzQnCiuHIatc94rnsdQidefdBeMbao0vHrHYiNNSSlCvLA1prJAp
+69AR9Xu2mWHFyLUpDUHY0hd/OPuGqw/GmD+FS0D+kH2/EPqhpeittT9mrf28tfbzg1YJaWtb29on
+bt+Qp2CM+X7gzwPfa63dfM2ffgr4H4wxfxWXaHwZ+Gdf7/tsXVOtr7h7mnNj7FbXSRUxi5zbPueS
+cOl26/50Sh26XWx+4l67eXOfoKUCz2GzcKt5mIRY7Qg2zFnP3YrvT9tau0+v474rPZvjT8RDcOVh
+xZhbNu+975Z2JOVtfUxLuR4bSs3ATjemEkVXFYbMlXVZrt3xFCcP2ZPCcX+4SyJuR9NAoN6AQsnQ
+9bzgK192ocGmNoTqxBwnPpGSjnEc0dXrQ8m/3Ux2adoM+NklkSTK8soyVsJzuczo993c5m1PgZdT
+NG6bD/sBpchQbGBYpC19PKzO5B43knNbQ68nVuKVTzJRdaU5x4jzsT49Y3ckubWN81Zur0ouhbEo
+wxBP8utZecb6nnPdQ2IWtZuDR7m71nd/4zav3HBVqcgW197I4uE9+iJ+qTYJCLzkb+Rhdc21x9as
+AspLF4J4SRcv1H1hJlhxHPjqLzBrKLR3bu7fo1R4QOzRORT46KEDEBk/J1RFaTIa0xUVu5kH/MZb
+rs/jt+7NuPe6G3ssSPzBzjP4N911Gqada6r9pip5fNfdRLOrf86lKOQaJQ5fH4UcTJz38Nx3fzsv
+7jqvqj6GhVw2L/SI7UcrP3yjUvRfAGLgZ0Ub/kvW2v/AWvu6MebvAm/gwoo/8/UqD1vb2taeLvtG
+peh//Pd4/18E/uJHOYjawlVqyXyff3JHHkHos44cDuGmPyWN3Mp9tclg4yKSflsK2hi6pURgvRWh
+1qGqzAn7LhdRD/sg+beN5NHmecnV0q3ax+M+ifIINqjIr9Q85c0w6gL0DxzPm63r65iz3jTk0ko5
+D0p8yf2WQYiVzkSrw2D9gFoCMXT72K7+frkgv+1W85V1Jb2LWUVv5MpYey/sY9RdWawvsNJ68KuI
+pIXrvufmai/yMTtuV77MCqzEaLNmxexcMO5uxVrErMZ33/Uo9elpt07fvqQoNF7ZEAoyez5f4ikB
+uVRZOC79ayHZfi8kmbkEy2uHz3N/JsXvxKMxLqm6Wbo8ybvzMzIxUBsvZqg8QlMUrDI3X+usZLVw
+8/HOQnoTfcvkpq5DZEjVoVg1K9Klc9m8ZkQjirjAVxlyltHUSiTXGb44FOy0h1HM3aQpJlDiLpWH
+WV1QtQrc9dW1KFH00jHlTM1aKsRVi5x+7OZnbWuQYFBR15Tnrht3+fAhJ48lGvvAzcmD6Zyd99z9
+OzkYkom34+zkMfmVuz55UVOLwSupFPUvCtbChRhi0H14tl5TlBIoshMayel9WHsqYM74DYwy0vM5
+sZJoi9NzHj90E/LsZE3atvU+fhu/I8l0tf0G3SsmkZJBh/sYgWaaqmBxz4FCvnqRcVsKUL6SkoeZ
+z+G++P7SDNMCXlZXRMp2R4N92LTafnIpAzC64IFvMZUAJsuSM2H7Z195yIXUin0lwGJ/QCoMwQtX
+luqlfY03Zz0TVbke1qvIp5Jbvnq3YnbpbqooLukoY9rtJ1i5s7kk6QfhjBtjd8OfdIfM9YD15iEb
+VUaKK59USbB0IdwBKcvH7vzPZgvKolX0rghbHE9TUav6YISxyGvYiCquX4+YJO78H8zu8yhtcSFT
+Rrtu7AsjspW4wtPiHVcQ4MKObF5B7o65WIXkgvzOz0SJZ0KWFy7p+OCNE9ZLN8e9JqIWeKea3ae7
+K5o5tZ1aa2hyqWqPG4zCrvzhCfXMJS6tn9IX0UyQ6V4o5pQK14J0RC2xyfRkQa5+jFLXvNeNGEgR
+ejIegPghs3VK/kgdmrUlUAWqZc/u2IaVlMFCLF2pk33L4U2udlUlmkE8bSH06pOYzVgvJIyzmBMb
+d8/GeXMNyiv9gGqj6smHtC3MeWtb29oH7KnwFLwKuhc1Hj6Xcncv6oIX+tqVVkvuS1eyzjeMr0lM
+3YqaZh69SIShZU2gHWN9NufOmdthH68bQjUj3TpyIcVutaLrqZnHM9c7etAdELY4hF6HOmhXWve9
+1ja0mZK8Aask5+vnKcuNG+/1y7tkKncGkqZ75egWier4B2FOJV6I2CbUldtBV0qcrWcZ78xcmLQo
+YEeCMsZsGKs2Pykzul2XrGwTjR3TYygqtf1kTaE2wmw8oFbZKysy1iqNzgtpE1QNIwm1BNZwoV3e
+VM21zoJXG0zijr+jrk6TWgpBl1d5SqrWSLs/ohTuo7tXszkV8Y3ooCeDhJk4EigaOprQsFgSNeqo
+pGAhyrO9lrzl5ILHD90xPHwr5UHmQsKRH7InD+n5vSMSeQiVGuKadclaXtPFlx5zf+bKgksMI1HS
+ETxmXxiYsXb8mJjyXJ7AjZhAXoNvgutOWWrpWUYRx7vuevSKmEczF9KdPLwiE/7k4OYezx+5hODN
+I7mspaVYujH2BhMm+yp9V13mShgupjlF4I5zZyxaueS3CJ9xc/XCy4cMRvs6dp+eSvGX6ZII9/qH
+tadiUbC2pqzWvPfwAYGYfO064Eht1GeXD+DSvT7ohly1TLzWXcCg1yER/NRcZcQTKTkNwzb9QInl
+cSqNxbdcRrvJMvYC51K+Nunw/KGb4L3RhKblR1zMQfFzJsEWY821aKfvQyONySr3ua2F4LKpONxz
+N2lvz13841vP8W3f/a0AbN58m/m5cxkHkyGNaZmVBOIZhDQzd2Mm+t1ZAAAbqUlEQVRmSc6FNAP7
+3ZpHa+eKTscHDITL35GrWgYFUdS2GdcEUhOKbECt8Gezrlnk7lxmYlIahCGV0h3HhxNStX1ntmEw
+dg9m1w+J5PpORLN/+/yS5X2BE8IaX1T1a69mLOWkOOpRieBmI8KPg05CJEhwlHQYSIC1KNc8Fu08
+TXktZ2/1UJ2mho5ISLIwALVqX6zXiNmeYZURraT/qR6NyOuyFsf53WXNozb3EyYstIl06iH1mURc
+j1wc7nci1KjJ/GSBpz6XCI99ic0a0f13fCvmJBjfGDM6dt9x/OyKSizRzz53i5mu+6enAiYlCS0b
+Y1FvSHQtN55H0HUP9zjaua463bzlMA9/wH+RQvqfz7x8i77AOp1BRKMbtJ/vsvE/GnhpGz5sbWtb
++4A9FZ5C3cB6bZnbiocSt3itd8jgwK2Sptqj9p17FYYdpsa5UV2t2nGnYKYEWKcJ8JXMy89TkkNJ
+o6/63FIjUS4Rj7pa8ErXuWKjjkG5J8oswLYiMZsFVoQk7EteDFe/B+j4cCQXdXc3JhQqcMcf0BPn
+4ws3nwPgO579TsbiS7gf32e5cee6171xLYCykrZE4B9yy7pz3q18Vmrc6uMRZOIN8GMGY7cbTaTw
+XMwLWLvz63VjSoUHy2zJQlUXD3st2tJVBWQ0GdJXJjtqYKQsurUVPXEEHO5OGXhu7JY2LqvgShWF
+BkMmNGUTpoQD4QnGHTKFMW3irywgkncURTU9wXl3b+7w8OQOAO+8c8r+vuC/qvkPD32SvioHxjLe
+da5xNwuIQiXrkoBaO2U4ONY51UyOHK7ukClWHt3pvGBTSMcxTfDUWFdXoqvrDa8p4er8gkqELGFv
+lxf2xfsgiPlqdUaihHinH7B76PgbRoOIfObGm+z0WQ6cJ9cXU3Vv1CPW8S6XXUoRA02jPqFwL4Sd
+62a0WonY0FiefcXxgAZBB18NdDQrEI4mjVJCNb19WHsqFgUvDohe2sE+PCUWlPje7AredSf/Ymw5
+7juXKb18wO7ETU4vEpHEeslIhBXhyIBkxE2WIipB9rselfD1U6kRsa4ZShx1Mo6Jlbew6zWhSmSG
+EfnGPejVpWLHwGMsMtObHTiUS3yr2+HgwN0o3WGfWt1sI8XkzdVDVnJVk8ZgjHvYkuWMpTgTL0+U
+Zd9pGPTce2/sHVKrE9GvPfpqrU0CD09g0mcFRrozu8OliEh3BgPeWjzSXG3IKncTBjXE4nHsym2P
+6ppsprxGWWHUzzHswG57rsMpnqoEkboPvcUVS4UXyxJyOZ9xUxMkqb57xK5cbRYCZw0TzLqtcBT4
+0m7kKsLXdaCe0Fc+Rrgjpt09brwo6HPREAm6HUzHRLhFKCGl1EMdFgor6eIrxDwYxfQEOHtxN6aY
+u186A0tX8+LnqjSlG0wrdmOepdRDatOIzsBdn5HyHpfnJe/dceXGaH/EgTaWrh2QCILssaEnKHTQ
+QunLGtSpmYQVxUZ5mUVKMmwFhC07Yu3ScMTD6furcxiiNA+9/pSNSHD6cYL/EZFC2/Bha1vb2gfs
+qfAUmqwh+8qaoDIMO9IojMfsJW53fM6MCKzLMu8nQ8ZyjUJJxy/WOfGoZU4Go0z2dAhGoYYPxEN1
+kbXQ4L0D1yMPRI2P3xF0NwnxBcjxgwIpoRGM1e3oG6wIS/AbnpXrPt3t8akD56IGI4Mt1CU5EmFL
+nlKIddkzhokg1t7SEBRyr4UfsEXJ4bOOwqsXxkQiaiFbELSJzSajEIDrUKCo5qxHLQm6oDvkzUfi
+VqDEaNeMw5hIcu5Gu1ZTlqDvNZXl2QPnsdWh4bjnvJBJEJEJG1Iv3bgH08PrYzhf5+S6JuPhlGt2
+j2FAruRgX0zbyalH4yuZ6SdEogxrlgtuKqQ7j09JxY9543kphV9ZBoL5Tl76NLHCoyptoFazWTwi
+Tl2GOWx1UOo1Rl5HzwxRNIL1ckI1XUWBhxXpTOULutwrKfSaP3ofe+DFlvDCbdkjJQOnu13mV+69
+77zzkFc/51gDSt8S9d31i5qcIJTXIO/OhB6hsCeFyVmv3bz0hyEdzWd/d0zUdeP1RdjiTSJqhSuV
+WVKpAS9LN7Ro7M16zrD7/8LqQ0PFxrsiS1fUG7nRRclrcm13d3x2BCqJBymFTv5q6dzd4SRmokWh
+8OZs1u4Bi+KEeiU/sWfxxW3XV9wede11Nr04uyBQrOEFUKokl15k2LYkJ/x+gMfxyF2gGwcJzVIu
+uDdik7qFbK8zopZg7UgqTpu6IBDrDr6hq9i4uLwkUcmxo7ZuawL80n0+iSJC6SWE/T75lVsgA5tR
+yDdscwPpABbK6t+KAyKVEMPSw2ghgAqjsKNWX4NHjdYMhknAWGxKg06HSdgSoKSsNbc7cdvjYDje
+cQ+sb09olOk2nmurBmiiGQMRxHZKPfz9gPq++jl8Q9hVu3dYs1y7+Vplb7A8ddd4svctbox+w8HY
+5Ql63S6ITWm1fkAo/9kfdalyEQC/e8fN4c2Da1IT2w9olF/pJB6eEjqB32W1dO/31A7uxRYjspzy
+aomRvkbYiTg6dojTbrv4JQW/ceUo189nc84fOu2QGy88y9XChRVJGRBpw5lEap2vSpq47aidc3hT
+gCsMkYBMq80V47X0JcRN6jV75I07pyK2sFJiatQwvxJ9PLv4tWt9/7C2DR+2trWtfcCeCk+hrmE2
+s2RNxZVw5GMvYBm4n5vuPt2R24HjdEpz4airYkmdM+tQ6r2bvCBSbd7rBsSFXKqsJAikEKTwwi/6
+eNIGDKmoVbXwOh0aJYny6py6cR5CL3RJxMAzDPrutcO9iH1RyidRTSEW4aLqMBBHn6+egjjok83d
+eB0/xBeNOtUIX8m6HYG3zh9fkM5cdWKaxHRaF7DIaLSLp8sCsaHTaEfpBh6RPJSw49FRVttQkqpj
+sN81FKnw+q08se8TttRtxtBoVz2b51jxP8azHN9TVUIhU6eJCLVbndcVS2UER0HJStiKTugTS7PT
+V3ho05hdhVVpVlIqMUZUUnTdHN25XPLokasorMWP+X3f+Sk6qv9H4wOqxw5zElYr6qWwCc2SWnTu
+kZKywfERoTpmi+zqOrKJw4RAnav1ak7ca/kipGE52sE2bpevswd4qvx4kx69QNUVqZCd5Rf4ofOU
+Zqdz3rrj7tNBp4OfiZa9MRz3nFe4FMV9UFuqU3eepu7Tm7h5qVIHPgKoTU0jDyNWJWOzqdhIr7L2
+e+R1S2N3l3VLG5dcsttridE+nG09ha1tbWsfsKfCU7Ae1N0aL4gJQld6bOqGx6KUWs9K0rVk4cpH
+zKQ6XSyFLtsNybJH+lyX2rRlLINdq6FksySwouDqiwDz4h6edmtblZRC8eWL82sGX8+O8YQUa9Q8
+5HkBvoRj6qxGmjVs/IqRFbLQzMjEkBNol8w2UKvRKKOmFtoufXhB/bhVuZbWYrnAnKh2XV2SSB/S
+H3YwSiQ2m4pMegqNmIs6WY/X1OffD8dUIgGN8UhiEdbW5hqn0JY0qRp8/VwYuNC81YFhR/Rgq9iS
+ChXKpRici0vaCuL9y4JIMX6aLkkkuhMTEubiTlg4GHiTbZh2RS7ab6gEXb//+hkbNXdVpk9H8O+H
+Z263Pn/nmM3Y7cD++JS89RabLo0agrw0xVrlmMSqlL13iie0ZVrP6AYumVl3O3ilQ4g24Qajebai
+0ssffZVqI5ap4BnoK++UQ+emu5/8uw5XsNuJONhx3ztfnbB4KI8nfpc95Q9G4wH9wOWSau3s+XzF
+HamY/8ovf5lA59EbBEQiC/6XXv4M42Pn0bQNTnUxp2pbdDvwYOnuhcLP6aopkKxkKdaqD2tPxaLQ
+lA3rk4L9yUs8p0x3WvQwy18EYJFlrFbuglarh8xEorIj4E6QjEklJOoXOY2UdIr1kkBw3EnSYUed
+c92BbtagpFKysqk31JJqt35MqvChkySEkpIPdlyyMysb7mbuIgePKw7lBu4WEWVXtN22AnXP1Y+F
+2w86tM2HD6/OuHzoXMbZ+YxQ1/Y51fPj3R499TA0XkWhlSdYzK+p4MpqSaXsdK6EYRJZ9o9EU3dw
+SPlPRRzjBwx1E3Z8WIrRus3O51VFIgyFyQsuRaM+6HYY7roH/XK14qHYmi8Uat2IJpxroVhXJYjz
+0QYhtBUcv2G9ElhK/Srd3GMwaWnjBvQF+rr3zl36qhgd24DzfQfOOX7VfT5p1qzP3EMcFDG1sM02
+GtNoXjbnc4xUlpYP3N/TnkdP1aMmK4iOJPhKAYIKs0lpBRsrWs7LDlYS8NGNG9d0/pSWxFdFSLD6
+/uyKW7dOdW0yJkpG51lGVyzdx8OEUNWK1Zl7b5GHRCKR6Yw8QknVv3B0k+6Oe+/BrX36er0SR2ez
+WmBbDspBD2nPkNdX170PzcYQ16L5/5C2DR+2trWtfcCeCk/B8z16o4jxMOFfeMW5SF89G3P+putm
+vDO7TT92u+6kKonENlqJV8HUK0xfUGRbI/YzvG5EMHSJoR1/QK0OxErbtd1kZIVcrjwn1nbtdzts
+lGjLq4YDdSUayXYVtcdciL/704T6gYhEn2/wWy3JBnKh4jqxYLI2I2y0ykcBjbwY4w2J+nL3VIse
+D7q0gsM2yrlQaS6KoVy3+pGGUAIoqXG79WVa0M3UPBUuMCIX7dYhvuTn/axgKILYLHXu9dxaSsFo
+vaCiFAx4amKuhENYFWtKhTdGmIDzKkO5XMK4Sy2vabUuCHaFETGWQFJvpRWmY2+C3SiECfYIBaW+
+ePBbvJ21IY3PvrojD4zzftJ1TV668DE9mWMHLRmKpb5wn7u4vA2X7vgeK6FaZCM+c/xpAHo3BuSX
+bmcOmwWNmtCaIKBuJ11J16A3Ank3+fwKL2qvmc/Nmy6Bd5m4v4/HPomIVDHNtWL07njI8cB5gBE9
+Hly50mLb2JWz4nDHJS3/xaCDt+vCqmGnS6Lu32azur4fkh012nVvUDTO+xntTa/hz3gdKoXePTO4
+lif8sPZULAphEHGw+yzdnQlvSKCz6a6ZBS7+HEWHZJ2vAnC58NmbCAevcCDNZtS6wQbjAfVEtX7f
+kKituTEBpdqF85ZQeZBcswyXhaUW5XhQ+tiR6urThOgFsdT33Xh1U7NcKDT4rYLhWLmBKiEV6/Jq
+OacvGLP1RJZiPQYdHWccMTl2N4LtjIkl7opwDF5YIwV4IKHsuder9ZJaD6aPh/A4xMJ0HOx7jI4k
+gT4Yt/qkmNinEidiz7V2urHVObhJcyItFOQeY93o/thc4+hXBVxt3M2W9NXiXafMFTJEYUhH1ZN1
+mRHNBeeNMlJVlVrY7klt8VtSm65lVz0Vt6YDipn7vouiZC2e+1oxvteHK8nM272KnhZhE/cJDqRv
+2TkikBZkpAVyXXj0FJ9H4ZCmdMdm4xIrev3i4hwj2LQVgMi72cUI1GWZg3U5A9PxGO24DayR4NDq
+YcFg5OZ+97hP0Go++jG9Q/fe7OEDNqqCza/cNU/zDV/+ihaTYIAn8pb9UU2i8HAax2wG7rz3Ju67
+iiQh6UmgKPSxvvhGi+Ka7r3pxIR8tN6Hbfiwta1t7QP2VHgKfmwYvpjQnOesrTuk7mZFUrnVPvZW
+lJ5bHaPeBXO54Oe3JfU+6BJK6vs0q/FrJbMOQqwolb2oc41TKObK3tcnNG293kKpDHliLoki1+HW
+Hz0HLb+/Kg4m8vA8F3aE3RorFN/exiNSiBE2EZ4oy04eueN8lK54Tj30o3pDZ9+5iVO7YaBml/W5
+e+9mtWSuZF9jakLpNEShj68emKYx2FwaGD3nzYwGtwgKt/OVpxu0kdKJvGtY+NBacnVSNsIddD2P
+eaVuwSLEylOobEIlZOLVqmAmXoOBkmgRhqUSjb06JYnb8KnACJ26XAREHRdKlGrQSucx/QN3zONk
+yNIXGi8Y8+KBO8699ZQgdMm4UetpFB61mJaLdYYRJiW4uqLbuF2+b7pUHXdeA4UrAz+E99zOnHdX
+xLF2z6yiWbkqgQ2q6+Roo4lrTt4j2HPQ9U73UxjFSlVW0+u58QqJrUyeeQXv3J3f45Mr/KFQhbZL
+XrhwJQjHJCfOE1ietd2OhvpY8zbP2Zc6elBk5OLmXMXT6yTtpXg9aju+xl5YIja1VL79CYlUw726
+4zhFP4I9FYuC11j6i4KahEwafp3xLf7ADXfTXNx+gzOx3Bze+DzJpQsl7s7cg5n4YyYHnwOgyRds
+NtKE3PjXLbJrc/p+fCY8bxR3Cfruwvg2ZC7gTbh3RHdfQh39KU2bUd91nzemJkvdha1PLN1n3Q12
+kW2IRaledTx8uXDjwj0cV4sSXyw/N175LMmObugmIRKMuaxadifD/VMXPvml5aVbzm21cYXvaSGr
+GoK+i2u7InDthBMqteSaKKcWiKWqU9ThzSj0rwlHNqXCElMxkmpS02uoVE5cLAoKCb9czHJa3VWr
+BdSSsNq467BY1WQqSU6SLta2/JEp1ri5m0h5q2gWlCJ9WVUFnY0b+zAZMD28BcBlaVk0kqIXL6G3
+3tAG13HvBn7uxp4/PGeprtTx/kuUj3R9FB4ymhCJ2XT18B18z+WBun6X3g3lkswaX4vXqmo1Mffo
+9LTQD0bUl27hxC+vBYITkb1WTcbg2B374elvks7dXGRJTFO7hXrYK2Dqjn/5tltAZo8s69wtWGN/
+StG4vo3Lbg+bSojI1sSRSFoXgpU354zHDmp9tlozGbmNc5os6Yp8Zv54TaKw98PaNnzY2ta29gF7
+KjwFsOAXlFcr4qu3AchjQyHSj/eqgsm5WzG7L79M54bbYYbSGezFHsGBACHzCF816t6hRyF4aVh5
+bhygo8ysF9fUClfyy5TRxK3myXN7BKHb5bNliSeBmjZbHhp7DfTxjSFfqoYeTLBKmI06Hj254D11
+yMU0jLSTxrsesbgc8mVBLNr2/k13zoPhEUbkHpgZg5H73nWzphZhSbeb4B+Juku4gfTynKTX1thD
+4k4LnCoIpMcZxF160rGcCfq8b33KvtvxisjDVxPXKPLYVCKXCXtcqWFrKLf93B9yIPdhnc0oWx6C
+oEPcVYNSAxM1Kw20qzbWoxQ/RadT4Ancw7TDmcK0vH6bWDDeVAI3mbE8ozp9d+ST5fI8Hnj0Vc3x
+hh2GR+74s3tSBL8xpZFMgL94gfxENOtHMYEa5KzXIzsTUYuqQUHHUElhfPPuXdrMre0N8dVtm8kT
+TDYbaukFhekRi5W7Z+uzBb5Ech6U50Ti53hx1+3y9/MFt2+7isSD/Aw27ueDowNGNwWVp38dptQL
+3RdhTKAOzmE/pIPz6Hr+PtVKoXAU0FEo8WHtqVgUbNNQblIa/4SFdA+q83fIB+outHMatTV/afYe
+n35BQKWBe6hWzTmc6AY7njDs6ebwoJSQqik86sy9vy3frWxDNFSZLujSJALTbGq8IxF/ViWV+PwC
+AZMGgc+ypScHInXRXUQRPbXADno+tXIb9dR9/iAMsSu52u+8TiEwVJ0ajO9u2LgjH7/rM33WuYme
+CVlLyj0tG1AYZE0Nuik6u60Lu6TUvHR3QqbKhm8aQ65SbN0LsX1VV5bC6pu0xR3h+QmV8iSXtmFt
+hUKMGnpy0YtAvQzhEinDU649bOC+pPAbvNiFBHHjE+nG7Kj6ss7ia+5LzJq1USh1HOCrXPjoy5c0
+J+66N0P3XYPkiJWSKkHaoOgCM7LQVQiWnRJK6yDeUeiXXlBJyqs2DfG+dCW7JZVIekvPUmsRKpUn
+aR5f0Dtw1ySa9jCRULTDEXMtxIX6lIt8Rq4cTX24S7N0D+9mmfPr7zoUZl7N2I9cKPjKZ1ze6ji5
+ZBmIP/LOIzJtBpsoZW/PLRyMYqS3zKpwC5oJLBuxiT0/3SFUeHGZ5SBUqIlCynTL0bi1rW3t92Hm
+fW3YT/AgjDkD1sD5J3QIu9uxt2P//2DsZ60V0OL3sKdiUQAwxvyKtfbz27G3Y2/H/mRtGz5sbWtb
++4BtF4WtbW1rH7CnaVH4se3Y27G3Y3/y9tTkFLa2ta09HfY0eQpb29rWngL7xBcFY8z3G2PeNMa8
+bYz5C094rGeMMf/IGPOGMeZ1Y8yf0+tTY8zPGmPe0v+TJ3gMvjHm140xP63fnzfG/LLO/+8YIxTP
+kxl7bIz5e8aYrxhjvmyM+e6P69yNMf+R5vxLxpi/ZYxJntS5G2P+W2PMqTHmS1/z2u94nsbZf6Fj
++E1jzOeewNh/WXP+m8aY/9kYwUXd376gsd80xvyR38/Y3yz7RBcF4xhA/jrwA8CrwJ8wxrz6BIes
+gP/YWvsq8F3An9F4fwH4OWvty8DP6fcnZX8O+PLX/P6XgP/MWvsScAX88BMc+68B/4e19tPAt+k4
+nvi5G2NuAH8W+Ly19ltw2jw/yJM79/8e+P7f9trvdp4/ALysfz8C/I0nMPbPAt9irf0s8FXgCwC6
+934QeE2f+S9Ny4rzSZq19hP7B3w38MWv+f0LwBc+xvH/PvCvA28CR3rtCHjzCY13E3dD/qvAT+ME
+7c+B4Heaj2/y2CPgXZRH+prXn/i5AzeAe8AUB63/aeCPPMlzB54DvvT1zhP4r4E/8Tu975s19m/7
+278N/IR+/sD9DnwR+O4ncf0/yr9POnxob5bW7uu1J27GmOeA7wB+GTiw1raUtyfAwRMa9j8H/jyg
+jhZ2gJm1Igt4suf/PHAG/HcKX/4bY0yPj+HcrbUPgL8C3AUeAXPgV/n4zh1+9/P8uO/BPw3875/Q
+2B/KPulF4RMx4wQm/yfgP7TWLr72b9Yt2d/0kowx5o8Bp9baX/1mf/eHtAD4HPA3rLXfgYOVfyBU
+eILnPgH+LdzCdAz0+H+62B+bPanz/HpmjPlRXAj7Ex/32B/FPulF4QHwzNf8flOvPTEzxoS4BeEn
+rLU/qZcfG2OO9Pcj4PQJDP09wB83xtwB/jYuhPhrwNgY03arPsnzvw/ct9b+sn7/e7hF4uM49z8M
+vGutPbOOeeUncfPxcZ07/O7n+bHcg8aYPwX8MeCHtCh9bGN/VPukF4X/C3hZWegIl3T5qSc1mDHG
+AD8OfNla+1e/5k8/BfxJ/fwncbmGb6pZa79grb1prX0Od54/b639IeAfAf/ukxxb458A94wxn9JL
+/xrwBh/DuePChu8yxnR1DdqxP5Zzl/1u5/lTwL+nKsR3AfOvCTO+KWaM+X5c2PjHrbWb33ZMP2iM
+iY0xz+OSnf/smzn2N2SfdFID+KO4jOxt4Eef8Fh/COc2/ibwG/r3R3Gx/c8BbwH/EJg+4eP4PuCn
+9fMLuBvhbeB/BOInOO63A7+i8/9fgMnHde7Afwp8BfgS8DdxVBRP5NyBv4XLXZQ4D+mHf7fzxCV7
+/7ruv9/CVUi+2WO/jcsdtPfcf/U17/9Rjf0m8ANP8r77sP+2iMatbW1rH7BPOnzY2ta29pTZdlHY
+2ta29gHbLgpb29rWPmDbRWFrW9vaB2y7KGxta1v7gG0Xha1tbWsfsO2isLWtbe0Dtl0Utra1rX3A
+/m8wDChW554/oAAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.3356... Generator Loss: 0.7553
+Epoch 1/1... Discriminator Loss: 1.4345... Generator Loss: 0.7667
+Epoch 1/1... Discriminator Loss: 1.3151... Generator Loss: 0.8760
+Epoch 1/1... Discriminator Loss: 1.4366... Generator Loss: 0.7039
+Epoch 1/1... Discriminator Loss: 1.3346... Generator Loss: 0.9874
+Epoch 1/1... Discriminator Loss: 1.3435... Generator Loss: 0.7690
+Epoch 1/1... Discriminator Loss: 1.3227... Generator Loss: 0.7796
+Epoch 1/1... Discriminator Loss: 1.4459... Generator Loss: 0.7080
+Epoch 1/1... Discriminator Loss: 1.3379... Generator Loss: 0.6979
+Epoch 1/1... Discriminator Loss: 1.6802... Generator Loss: 0.5041
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsbGl25/X79rxjx3zmc8+dch5q7ipXt4vGNo2h1S8t
+IYRoJNRISOYFCSQesHiBx34BBI+WQOKhpRYSSLRQdxv34LK7bZerKmvOqsy8eed7zxxzxJ73x8Na
+EVkptalM22lfoVhS6kbuE7H3t79hjf+1lrHWsqUtbWlLa3L+sgewpS1t6cWiLVPY0pa29DHaMoUt
+bWlLH6MtU9jSlrb0MdoyhS1taUsfoy1T2NKWtvQx+syYgjHmbxpj3jPG3DPG/OZn9ZwtbWlLf75k
+PgucgjHGBd4Hfh14Cnwb+DvW2nf/3B+2pS1t6c+VPitN4ZeAe9ba+9baAvgHwN/+jJ61pS1t6c+R
+vM/ovjeAJz/3/0+Br/9JXw5830ZhSF1VNLYGoGnAdQ0AjjHUzUcajWPkuusIT/MdF8+Tz8ZAWck9
+yrrBceV60m4RuPK6Uaslzw1C2DyvAb2f4/qAPK+pG+qmlOuOD0BtGtyykGu+wRDJd6kpqwyA6eUI
+6xgdsAtAK4zxPU9/51OttbTaUOr7NY2MJy0KmrKSv1uDcRp5P9fBbeSzbaDWz6X+zlbNZg4t8Opr
+r8sjipyL8+dy71VGUem9kTH6rruZTxyoCvl70TT6DTDGYMxHa7J+SKNzhbWsX6mxFru5vnnM5oPB
+bi793De5cdDHrmQO87oiz/X99BvG/tyzgXp9Vwu1Prz6Oe3XfPRgmo9d/4jWVz3H4OuaraVlo2PV
+G2B/7j1uvvqSfMpTueRUNJWsb1EWWCtzaKpqM291bTd7eb1P67ohigK5hWPQy3iOg/FlJEVWYHzZ
+f6HuoSDy8XRPu0GE48o9rG2wzc/va/ndD3/87pW1do9fQJ8VU/iFZIz5DeA3AKIw4Otf+iLTywsW
+2QyArGhot0MAWp7LciUH03oeoR6yQbsNwFHcZbAbAxB4lovRBIBno4xWTxjAX/vGVzje2QHg7S9+
+CYDjk5dxrD5vlUEghzvo3QBXFjofpUzzcwDi6ACAsb+kdyoHLLnhEvIWAMtmwvnVewD837/198lD
+ZQDJEIAvvfZ5joa7ALSP9rnWg1fPHS6X8nmezgH44cNHZBdjmavSwYlWMrZuTHcpDKnKLfPVAoCz
+dCrXrlJmudzDOoZ//E//OQDTR/f4n/77/w6AH3/vfR5dXcnYHBnjYbdPN5H5NHHNxdMRAM9nCzzd
+0EHoYXSDtXSDGgurWhlMVVMqs8mLirzRQ2EbjJHnWGX0rmnwaznwNSWlHsP/9j/7d8m//zMA7k/P
+ufdA1uECuZdTOiSBfLepDcs1HysNU332OC03h9fT+1pjSQs5KI5r8JUt1NZSKWMdJgHHkYyzpXss
+byp8fTZpTe4pQ3Z8/sd/8D8D4N//kfw9vCKbyJl7cvqYspA5NhfnBKEc2Om8ZDmXvXw6kTW7nuZ8
+7vWbMseBy2QhTKPfahHtJwA8eu8pweE+AHd35d+T14/YG8re6t18jah7W8bWLCnmSwCW+QwnPALg
+6NUvPeIT0GfFFJ4BN3/u/0/02oastb8F/BZAtxVb5iPq1YK1cDVViS1l40X7Qz73imzYxAbsxHJ4
+uzsnANzcj3FVCtSF5d7TxwCEPGe+ksNkL67wQ5nMwY4sUGhrPF8W2XX7BIlKowaaUn5X5yUDVxam
+0o00LFNOa/nurdRghsKQ/MtHPH/3AwBuh4amlI11VcnGTmqHQSCHuMURUS2bY+61yGbibnFrWZJb
+TskykvccZ1eYVO5RzCZMK3l2EFiSUMY0sDJXs05DW7Uj1/PxVaPJxqck5w/k/SYXdFUruLEnm2p/
+f0Bt5dr0aklayvN8GgJX3sNzXFqhMGpfGaixJTaV8VjP4Krk6kY1ZamH0DQ0em9fGcykqHGMMjcD
+6IHtVgvSMJd3KgzzTLQGcnlGnEQMfVm/0mtwC1k/P3RJVKrGBtJSvh+GMi8YhyqU59W1wVHmlJUV
+ZSnrYPIKK6+KF8q9hqEhXep3gViZhXEsUXYJwOORHP7EVARdGU9ocp6fycFMz+csroRRXy8BXZPT
+St7TK2qe6jiTxKXWsXuNj1PIvq8vrpjNRNidP5F1jKs9wuOvyThvnmCUd7lBjgk7cg/PoXJFMH5S
++qyYwreBV40xdxFm8B8C/9Gf9OXGNizqnMKp8SPZdCvP5caRqL6/9re+ztdvHQNwPHyVuCWbwnR0
++Mtr5lNZoLLyWHZkw9puyPNnwouWc8MslQMZrGSiXbfAt3Lgjcnwo4F8riscXw5LEzzDGPn+tJHF
+3G/1yYP7ALQv/yZNWzSJ6/m7pM9Fwn7933+daPV5AHaHd+V5t1oEVharDpc4uTz7+voJ7UyeN/Ll
+98fdXS7mciBWpxFn84fyu6uap4FoN6bq0yDvtB/JvRwf4kykldNyqZXJXnznhzy4J5Lpsqm51Ret
+6d/42lcB6A8jfnRPNvdknlMp09jttgjU3Gr5HcJeF4BeSxhhXhcUCxln4VQbRuiGNUku91g5FVYP
+73RyLfPWMjwRvktgDAsr73GjH/JMD/d0Ck+VWZyopL15a4fdgTzjbFTxlgqIPAkJPTlYV4uMtpF7
+LHoy3yZ3yCtZv4GxjPUAXY2umM1kPlt5hRPKvUN9/6s058tD2WdPcBi48oxHbkmnkMNbTE/lZs++
+wvUNYe7z7CnzZzL503DBdCyMh6rG7ch1b66mW+ySFzK2dMmGKYybMd5cGMH90xW1lTl8rIIgnFns
+mz+U+Xn7FbwD2TueO8CJ5HmeGeI0yuk+IX0mTMFaWxlj/nPgtwEX+F+ttT/5LJ61pS1t6c+XPjOf
+grX2HwH/6JN9F6qiITQeTiYcuhuFfPXf+RwA/9Y3fpnXdsWed/0Ix1EHjZoXRZaTqepfXy+5tfca
+AOG84vpapFhV5izmohJfP/wQgFVnj8OXVDvwE2wu37X4uN21unqDohJNwJTCqZv929zpiObSZBPe
+/ZFw63T+lLf/6lcAOLn7EoEZ6huKb8BphhDLM9x6SK0SP+rt0L6r9ncqEr/ZyYgmwuGzwYToVKTD
+qHOf8lQ0l4VZkasQcCKZi127w4UnY3cqh8VIpMfp5DHGk3lrOSEvv3kLgC+8Lf7ffLLiw66MJ7ho
+eOPwEIDBoEu1seEjEvU7tAPVFExBs2N0HRt8RNNz4gpTqKpNwfWpaCFhT02RvKJ1Le/6sBjBXOa2
+/+areAuRju+/e0m59iQORFP40p1DKjWfuknGbrsn42lF2EY0mht+TtgSjWbtn3aqDk5bVBPXDUiX
+8nnxbI80VAk7z8jncr2ZiWp/5ToMh/LsW3OHayN7qJxYVkPRtm59/lcASAcP+Ob/I/6Qq+sxSVvG
+kOQ73LgjktvPaowjn3eM/FvFDXWm2oMfkFl59rJyWZ7L51FeMMnk89o38uphwl4lVvpsdk7RyNz3
+705xg1f0xQuM8+mO+V+ao/HnyXUcOkmLy7wgV6dO0ov462+JA+/1G68Sd2UD0eTgyMEyS7UFD7r0
+JjJRQezQVkfM4GSHqi0LmtU53Y5cL+q1s2xBmor6GfoRbigbzAuaTcTAbXtQqGf4UuzCZR7SUSfS
+Mv8hNpfD1E267PaEebW6L2NyYQa1ld83s2ucWDaKcRuqQv0WsysiXxbU9WVjp4Wl1VdG0d/n8ECu
+n583NMU9APKrnK7+Ti0ivFWG56sNXNRMzmc6yW1uviUbqF+7/Npf/6vyuSumxty7wvtAVNi9/R77
+fbne3x2yUvMgdyrQgx6quVKUOSaQbTSIu/QGoq67psJVNbeeNJhX5F2eXIm/5/HFhE4hJt+DbIw3
+Us/63teoAvHLPG0quur4+8YXxEHbiXziQOairlZ4HWFOsYnxYhlTkrs0+uxAx9s6Tmgq9f20AHWU
+Ogc71LW8qzEr6jM1JdSMmIzHVF1ZJ/NgwWBXufDPVlycyZ6Ln4vJ8OjiHR69L0G3kpp2JM97+c23
+udlWf0fQ0KQyz52e7ItZXjGbiPlkU5+kI+8xyWe8e18c2q0HV3yoDGu+kH8vreHyQkzCRz+Y0O3J
+ngy8l0l21APb3dv4xz4pbWHOW9rSlj5GL4imAIPYZWacjYf/pDXg9ZdfBsB3PRwrapt1uthCNYVG
+uL1blwQq5Z0mJE9EFY1zn+PboiZ3Aw/PEc7sdUTS5KlDcS2cdmVL+qrWWWdImIuqZmyAp2HoOlWp
+Mx+RpiIlJs+veHou43nrrdu0fFG7m9kzinTNc4VTO/4+jqp+dQVNJVLViUIaVUuNxtj8ZpfKyHc9
+Z0KndQeA8KBF/opoB/PVH5CN1emo81Z5MaVKhiZtM108BCAyDjsasjru+ez7IsWW1+IkK1cjrJpH
+u1GX3dZao2nRGahnfJljA3m2F6lXvC457IuZNBh2iDQMWzYVZiHvVO25VCuNUGTikS8fVTyPRbIV
+zwtqddqFyymPTmXM41nFSVfGud/S9bUGB1mbIIzpxrKWYdTGqvMzyUOMhhFMImvuJx1Qs8PNK5yO
+RqA6Po7p6bNz6rfFlHArmYte4jH/UMY5LhbMz2VNXJvgPRI32aP7ohH+7h+dcW8ke+HVvS53ujIv
+L98c0FGNp186mBMZR9TVa/OCNBLtYZqtcB3Rijwn5o2xrKW3TAl8+f5PcvluPqr5l3PRTB4G32E3
+lr9Pl894+VWZ++5Lh9hAHN2flF4IpmCMixsmrOwYP5JD8dJbB7RDCTl6SRsJCInajW5oY2WDWRy8
+WjaBTSzNlR4Ur2KnIz6DdqeHRu1odFLLFlRGNr8f1aBAGZOUmEB9DY6LWQNSNHph/QMm16IGX/+o
+JO3LpogHb+MPdPPPlriqutapqtGDcxYP1SvsG6yjtn8UUCgzqNQL7bgpcSRMI89iLGJnt9oNJ8fC
+eCZX+7ynh951NLznZfiNhuyClCqTd0qOXuUlZabj0YxoV+5x8aFEZ37y6CFW/RL9TpewLRu6NiW1
+hjA8NyJIRLX19LvdpM2uznG328YoI2vKCk8BObmFlTK9qiP3bR085/ljOdxNXVMh85I1S/74e2JW
+jJcV/VuKHUnlXovQbABerXaN2+jYvJBaD73x/A2OognWZk6J1ehD4Dk4RiNYlcFoiDDoVFhH9kNp
+JErS4BId6IGdwTiVMT/NM6bBBQC/8863AfjRvRFZIGbS7kt9Xv0VwcMM+xGx+r+SuKKO5bNVM9g2
+HcJDeYY/sqyWstZNE3P8JRGMVbfH4r33AZg9kWc8yFKMOpWe/eAn9Hf7Mi9VyG4gvg2322DDT2cQ
+bM2HLW1pSx+jF0JTsAZqz6XlORyFIhlecg9x2yp1a7ClSpXGgCuf61K93kVDo6YBmWUNXHXTBl+v
+N3VGtdTPuUilZW2wXZH+JuuShap5zD08xTp4XhdXvbfJkTikruZPGanZsexdMlTPcS9u41hRr/PJ
+KTQiQfNMohdVnjCZiBR0Cxevp4jHqMv0SqSDOxfJ5h7kmJmMs2zm+DM1fdrneIFI227SIVB1fa4w
+6HRcbhB6ro2ZrkRrOu66uIo3WF0tKeYiCZeZSvD5krgnkmZ4fANfQUjNZE6h+Ay3FdFWHEk3kns5
+xidWZ5/rBHgK7c1qQ6N4A1M6+IG8S3ggHnvzbECrpc7AElw1f+aT+zyYiUO3nTi8qeCqZSjaQRIZ
+2qoFxCQ4bX125BBUco/GDzCKhvVQjWA5IysUtNH1NvB319Ybs6msalxf1t1t5N8izBkpcGpUWUyp
+iEdT8qP7gl59eLl25kK/K7976+23OO6KGeDnl+S6lrFf4jgCj3YQUyWoY0pP1iOOBxRXske8YYJR
+UN7JyQ2yyzMAvqmQ/nRZ4Hvy/tM653IspuAbex3unMres/vXxL1PJ/tfCKbgGIfICekPu+yfyMFr
+HR/iaDzJFkusvjymoGpUjdecAzyLUwujyMtrVIPFdQMczUWw5QJHVTi/Kxu6KVZY3UhVVeM4ciBt
+eEG0UEx5xwXFmvsK282vlsSlqHJLf06QymFq6jHpqajjXp1Qp7KIl1cCdErPl+j5wsGj6GjEZOWS
+Nhr2000cNjVOJqqhE4WUetBNU9OXjxx0AwJXATS1qrvLBdaR96tMyeiBRkbaEZkv9/vw9AnNI3mX
+yzM1W2KPkxOJnAxu9Fk+EQZJ5MJKGc58REeZRdDv6Ry7NDr3dZbjxXoITUO6kMPtFQ6K+cEv5Lt+
+UBEbjfDE3sbUuj5d0FHbeLffoqVjNvqMKG/RGYiK30k6mEhh03lF3JExVRisZkW4lRxGt9XBUdW+
+KnJqKwvRBB6NRrzKsqJSJOcaJ+26DelK7vVwtCIf6Po1Ppc/FsGQxHKv7is+kZqjJ3sh9VLm3vHb
+oHD6xjTYXPaFUT+J9We4ChUPHJf+TWEmjdvBUdBWNQ3Ye118A8kfy967zAr2dT0sDanO7fTS4hwJ
+w0mfdig2cd1PRlvzYUtb2tLH6IXQFDwHdhJDNnHZaWlSTlOwWjwFwHJAqDkKtPdBs8E2PM1a6kql
+qufhrJNaWjmRqoHkAbNGcfTFWhqA1wh3LY0l1/gxpUs4E4nvmASrTk7rydiq+j0ahSg/e3RGRyXe
+6tElppLrQaekUVhqWIp4mZmGShOKiuU5zaVI+VkIJhK1OqvUaTkyWFeToNr7NDrOqIoo9jVicjrB
+TlT66b+u8bCOAoiqCK+rQJhlRnkhJsrz0RXlD+Xe7b44c29/7i6v3hJYtq0n5G2VLllGiLxfUwY4
+6yxHNRMa15LP15mahkK99qVtqNWxV/s+mWobhUrEuInZHyi44qmLr7Bku7zitb5GlYY9nheqYqiG
+7t01GwBUHMV4sfzOq2LcRDXApsFTc8rq2mAamkY1PlvjqvZXkVGvkyCXNZXmI6xDTjaO6HiiCe76
+E352LVqqa31u39S8C40WXBuH0NWI0qTkIhQzINnr4t8QjAxuhdVEMOMrzqadwEJxE46lFWvyV9Si
+Uu027zZEjzRqobko1apirLk0URywUFNjVS841eU7MnOcSjSST0pbTWFLW9rSx+iF0BT8IOLo5E0+
+PP1Dnl2Lbbx/eUp1obntu/km3uwkOU0lkrBQxGA1nuGqdkDjEkaaZWbaWM11L6I5zVK46to+xwtY
+rIQvZsspdqFoyuOEKhPObuMFtfmoVgFAXWW8+2MJ+fz0+yPuvqqJP+XrDAINQ86mrFZiO45XGma0
+Lgsj0trWHnTUj5DWpIqQdNVp2Ry5mKW857Ip6en7e0FIoXUWitCh05NxvtSV309XLuPZ2k9QMAxF
+Axlf3ufyTCTXs+WUm29JqOu1Q7FTD4cJ+y2RiAssk7H4FJq8wleEpNcklLVoGFWhCMzGpdIMSFou
+pWY1lk5BvEZy1jVlvs5iFUmcdAzPFWNRlCWVUU1iluN0RdoWWcnjK9GKXt9RNKrvg6cOw8jgKqLT
+ROFHfgIsRpOArPoL3BCM+oSCvRCrY1tVOaUiBd3AklqR3mtlsryccXxT5sV73+Pdd+S73XbD59ta
+XyMSZ+juoqFWKW+jmM6uaIhe1OD48t5VNmeuCFh7Le/c2WnjeEN9j+UGT2NWKWYo85Z+AJnugW5P
+/GurszkLTaPfDVxqXafziSVpRDto+UNM7wVIiPq0FAYuL9/uc/FBiKsZa7NnlyxL2Zix2aeYyvXq
+icWJhBnMLuSQF6PnJD0F0Nw9wtNNatOc5WwN8SypEXV1rtj6B0/OyVK517xp6Khad5h2aXfFYeSY
+nEAZjvFETXz85AnvPhXn4QenI6apbKQv3Don2hEVdrx8xPNHclBnymyeTaesclWfMfQUdh2EFU0t
+Zk66kkOwf95iqjkeZZPy/EIWv93zcTQt1tg2Tle9+opzKL0JtW5y67XoH8l7FKcNT54LZJa05IZC
+bPdOJNeeOdyfSE5I5s0pS02NdiOMArX8JKSXyAFBsR7TacqObv7SC8lKGf/oPCXWiEFVOth1KnMi
+951nDs+nsg71ssDRrMRgP+D4Sg7b/ccz1E/MSndqTJtMY/OrdIlx5HmBPyBQ87DOHQY3ZT+02zI2
+h5xU8Q2lrVnl8nl8PWJ+Jfso7oEfyvqVodxrkYVUivu489oA/kjAQufjnHJX9tPn10Cvq4Ynal7U
+RcHQyPo6rRFOLWtmbUp9Lof3w/clZ6ZaGE4+J8Cy3d3XUF8lxeI5ZSbjuSxypiIvSUJdA3NGpoIq
+dALamg5eBgWLSJlsK6JX/cK6Kh+jrfmwpS1t6WP0YmgKUYtXXv8y3//Wv+L9a5Gk32h3qGqRbJf3
+DGMN79x/55tYT75zfCiZYMMkpHWgnDhd4vUltNZ4NdNvfx+A56NTPnwu0u1cKzNdzCaMMpW0xqOt
+MNmLgx6Oxg5fee0m9kikqfHlud995zGTh3KP0qzwfWXtJwGLiWgH7797yo9/KsUwfjYSFn85Wm7K
+oPWjiOGuvN/dQZdKpdj5pUi+rC6wWpAkHkREiszr9GNaGt873u2R1orODGV+BtOQ5Z7G1YuS/vCG
+3K//lEtXpEfXb/PSy2I29IxI7tNn3+OPP5BQ1/1RTqLw4Vu3DxloTYJOq0OiDrp1xunZ2WMenEpB
+HzdvcBU3YRuXM6MVhMIYR5OYWrHcazW9YjxVqeo79Foyjqrs4dayDl6wwnfl/bxYHZxFQ6PYCpqG
+K03WauopiYYyfSKiqZbeU1XcliXpSrTC5XVOrijTlTNnpQ7mwnNpa+jT9dRZm8+xa1xEnRD1VbXP
+DPUaO6Io1Gi3xFyIlhYNLL5qcVUecf5EzE3Pq+n0tfpWS9bmwcX7nP/+jwH46pcrQldU/3l2wdMn
+8n6TLCSPZI+EmrUa+yF1qdqRY1BFFzdzcC/kejZqcNuCvPyk9EIwBdeBTlKTOA2ZbpSgFdKcyYvN
+VqcUtWz6PC9YaUbk+EKw57f6x5RqC8Y3GsJIILzVbMb3vicAk6dPn+P4shjxgUz6neOAw7Hca5YW
+LKayuOPzCU9cyUQkr3klkY0ct2RzeKsVPbXTrOnS19j8/X/yHs/H4kd4+HjM9VQ2SKH62H4Us1Cv
+fdcx2JVswCzJKWr1WxgFaVUlgcKEO70djg8Vju05+LVcd+OAQD3nnUrezRw6LMYKCpo59Ieain33
+hEgZ3a1+RKAmzaxRLEGc0Chwp9P9CAh0fb7Aai7FsNMi9EQlHq+EERTjMYGmUUdJi0rj5gQ1iWYl
+Rp02xtMMRvWcBxUkCgo7HiTsKuza1hMmUz28i4pIQWTMZOwrb0wUyly4iUek9jXTnLmW7HPNhNZI
+sQOeQr6rijTVojXTOYX6lWaV3dS59IuCUuHWtRZv6fS7qOuKrK7x1QxyQ58dBYYFCi+ePsnwjIxn
+/njEOw+/I8++Lnj8VARAMWpwXRlHmsm8lvOcfc1qPP/wAtrC9E6vZlyM5PM48EkaYSLGlTMSei5a
+FIq6LEEL2Szqkg9yzeK9dBm6d/g0tDUftrSlLX2MXghNwfECkr07xL19fE+wCcOdHo4nEiEzp8yv
+hV1Hxzuc/USk20NH1KLESXlJsyT94Q3cHTEfMmfM2UwkdxW3SA9ESl0+lXtl5xlJph7ksKTWGgoH
++31aWhfB65Q06sl2VB2esqK9J179v3IzpNZsx6jbo90IZz94qaZfaxw+j/TvrU09w9PlGb1KpNjt
+V/ZBnYqPn6voX9SMA9Vipivqlkj2fbdDd0dvS4ijOIwkUWRm3tBRL7TpQaujGsSxzxs3pbzds8lT
+ei+9CsCHWlPyJ+/e40IjH5eXJbWaGu04A03MapkW7UjWpK0Yg73DNlEl1+qDIe/fE5Npms/Zj+TZ
++8MePYVHt9RMGB62CBO5tt8LsAp5nj6b8c2fipTrt0M6Wv4t0hoYywyKtTNvlpFrZMeNOrQU2pzm
+NU2u8X1FNOJ9hEFI4gBPax1cPLtikat26kc0GgUJtRZlk4MZyNw/OpsTavZlHLvkirm4/0y1rbml
+6GtpumaAUVPqrLzAajGYIHF4ppmpx4HMT71f843X3wSg+4UTTu/Lnu10XaZWzK7RYszVhWgbdb1O
+OrMoQpunkyWl/k/iB6zORAsZdwrcPUE3flJ6IZiCMeB5ls+/cUQ4FxXpcOcOrX3xHHdsRtTIJrzz
+yl1uvyIv+ZrmC/QpGNyRAiL+/h4YhTM7Hb78a78OwKIouDjTDMVCmEKThLiNTF4+m7JUzH0zW5FG
+8owH9zxuvqQqvQJ3gr5hV238t978CsVC7juZneH11RZ3W8y1sMbkqWY4+jGZZlr6kcVV3d8NamJX
+NunOQE78whnTTFXFn4/JNYRW9xwWWmg06HgMNVck1kNlvAhHfR+xAUfrFrYPOrzytni4oycN8Y6M
+v6sRlRv7R9zQKMJPHs1xdF5aZUqsWXZ+v0Wg2ZGhI+sR9Q5wNQ/CMy1ODmQddhpLd1ciAC41sY7T
+0YKvrt/l7bvyvNj6jNX2L+OMeSrjvzVscaim3kgzNd3A2xS3jYc7OFpMJe56tDXcucxKKs0PqNRc
+Cwhw1DyqHYOv891PYqnjDljPpdRIUtJTkyLucK5u/2VQcvdIGORw2GHSU4ZUytgPdxMaDRse3T6m
+pfvwlYNDip74pcqriqtHcui7qUTXos+HHAwkxd9Yl6M7si9223c4fEM/fzDi6lKEYJnJO0VVxgNl
+FO9dLFgqs5gWOQ+Xcu8sT/Ev1uX8PxltzYctbWlLH6MXQlOw1lKXFUmyz+tf/TIAO3sO3QPh7L2d
+L5Er1LhxAzqaPbnXE2k3bPdIDtbqfneTRel1+9x5Veo1zrIJO0fiELq7xj/4EZlmMC4fX3J5IdrB
+aPyMfDnVZ8TgryHEct+i6RGuPdLtiomCUPLI0lf4s22VlJly+TtaYvs0Jxhoncdlj6PbIimHxsE4
+8ozVUhOx9iJGMzFFem2Db/W609AORBrtJG1qTdzJM5Fathxvmn80xnwkmZ2Q4cuSnffo8QcUGs2J
+E63teHKTTKMdJ8OMviOOv15c4WuCVUREptWYfWddPm6Aq+CuvMro90UCd61HEGo/BTci1MSmopC5
+DyOPr78/8jSQAAAgAElEQVQtsOpFveBffFvMjv7u23Q74uT1opBGHYXOOrGpdtg90V4Hh/uU6sX1
+VzVoCXeKipbCnH2tkWHdArPGrFmwmiS024lJ0PnyQZUeIn3nNM0377cf9Hjt67JmWWa4r9GsuK9V
+ou8c4bkyLzdObjFMZE8SWJZa87HqP+RQa0okXdGKW53b+LdEY1s8/RC0xL3vD2j1ZP3aXp/poexf
+u1QzNympvinz+WNnsTEfoCbQ9zOTHHNLTahPSC8EU2jqkmx6Sj2+4vRCVKvZbMDRbc2Q833ailJs
+ggQ0c6xUXH8rrnHWuHdb4WjxUBqXsKcFSJM9gpYiC2uxU0O/AUUNLlp9Wq4AkqbX53iFmBKV1wFV
+1/K51taLStbGXJO5TH0NG51l7PdFvQzcPjuRXPfRLMq+pdDw1e5yzK6GpJara1xVd/de1sIqVz3C
+V2TzzPMlfrnuXxDjddfRkACreQmOhtjSvKFYd70qXdbNlIzTYJcKiJ+7ZFq70dPKM53A4Gnvgd2p
+y25HzIMwqGk8Vam9alMEtFmjCl1L0BZGGNkAPfv41JvOS74fUup7Z+sMViru7oo58/70x2Ra6WrY
+8bipEYfA8yk1E7atzK2X+MT6OawagnV2oedvejX4TkJLzS1Xc2bKuqbWJAffqzHaYMQEDn20dmPg
+gjZ7CdT3EdUR12MZ22HSp3NHri+vHHIe69zKc9vJDtZoyvI8p9B08aCcE2v7ALf3Bk1XC6posSC/
+e4Rdd6+pHOxKPheRA2bti+kQdbX3idbxPBgaLh6ID+4fP3wi3aCAZW651tB3QrFhnJ+UtubDlra0
+pY/RC6EpFHnBo3tP+MF3nzHWHIDDOwVF9W8C0Om28Ne9Iv0Eq5LJ0VwGx6uwirnPGwdnImZA2VTM
+JorFt4aOViAOOyoNvBC6ig94bvG0Bl7OikA5+603djfjXCrU+qh1QK1x4OvRlHIqpoYtMhqFBy9W
+V7h2Xd5MNIXar6m0fFjo+AxvyjuFpwErf91LUq4dHBgiLX3WW7Y2fQfjpE2t0QC3Dmi0HLjeltKt
+0f4w5E6NVci38QJ6d8UJWDsT7LqfZqpgpOWzjVq6M4gJ17kWtqAptBCNV216MKauOE9X1RR3qu/U
+jzbl2ELTEKgkzOY5+Urb8CH/tozBdjQa9MGYh6fiROsMDkm0KvPFLCMJ5dl76+YzjkNaybXExrQ1
+SzJ02/iqCdbPZ4TDdS9QNefyCY1iEHzHp1E8RVXWWC21HvgOlao6jRZkaYqSRM3HVd/SaN3JbF7i
+xgoy0vyD1SQjiGRfZE2XRDFtZb3CK9XcCrtsZLFq9fZ8TtWTdxp9/4c815wZ2z6i0pqfh/0Dhoey
+7r5mfjp3b3J9KfO9qOtND82KhoFGfjqmg9v5dJ3l/9SagjHmpjHmXxhj3jXG/MQY81/o9aEx5neM
+MR/ov4M/7TO2tKUt/cXTn0VTqID/ylr7jjGmA3zXGPM7wH8C/DNr7d8zxvwm8JvAf/3/daPFfMkf
+/P4fcP74g02JrrP3EzKtVtPhBmgjE5oAR0Wh7wunbrIR6UKdhPdOOX9X4LqL6zEL7Tt4+MbrhC9J
+ZmCk1XryscU14qip8op8LKHKi6eXdPfVUXN9zNRK2OdqRyvrDlu458K1r8dT5mtD2XcptEScZ5xN
+KbhKq0SnhUsxVzG+CDi7WIcyl3gaQ79W5+Te7i6+9qxIPEOuYc846FKWwstzW1KrIw0NzTWEWNUe
+XOtTaU0Hr67pHUoDm5feeJtgrtWSFEacLXIWOl7fg3VHPs+FdP1OnTZG6xukH4g0uz6raG6LrTsk
+IlDkaZpZUl/m1jQJmYaBV9pAuPSh0pZoTz8cMdVmu3iWZE8evvqgAoXxdlRzazke7jrCtjKgLeTi
+Xg83lnu7Fx712jGrLQaD5qMO42VkaGmNhKhZUiiytLINptAO4rpmqywj1Z6fLc+X2nFA1Im50OZB
+ru6xq8mUWDN4wzsR7ak6o1sV+VI0jKa+BrQRrFbeamxJ8VTG/uOf/oAf/lD8BJmX0L8hfqc7L73B
+3UzCmkP1KYzu/YD3xpKgtfYnyIDAGch8nTsNrfOUT0N/aqZgrT0FTvXz3BjzU6QF/d8GflW/9r8B
+v8svYAqrVcZ33/mAKptxpCW1BtESV8uxOdQ4KFzXjqgutRuzIwvreBB6srn7Ow3Jl0VNnpcDJjPx
+lgcDiAeqGGkzjTI9Z74Sh2I5yTjVTszPR1NePZJJrZol7z8ThpOf6ka7cxOMXJtlU/KVVuf1G3KN
+bLTbCY7WlZxMFf+wyFlqlGE2a3jwx+LYfP/pGVaVNqctm/J4d8Rrt8R7nUQtAq38bN0Gt/qoWetC
+vfmFphMW82qTJ2AdB8O6t6OHuy8FVQ6/8leYnEqGXpwLo3A9Q6OMKZvlzM26UWyAo/foDDo0E3XW
+dUWtba9GRJoPEXmQNFppmwXXWo7N5BMcdfJ5CgpyVktmU4XwGkulMfa66eEEClprpnj6uxzN8cgD
+YvXeh70Eq6XhLSl1Guo6+BiFgqdXmhvgu1Trxq3zEvdAC53ULQIFalUYHFXBA32n2LHU6pS8PJ0z
+vKV7qMrwmqGuibYDiDxqBT2s0pTFUt7fyYeUlQCW0rNzen21G8aa7epesbyS/fTs0YrvjWVeOklN
+NdfuVOmQ7LGswziQd/pX3/pj3j/XFHdg7VWOwoChHu1FljNd/CWAl4wxd4AvA98CDpRhAJwBB3/C
+bzat6GMN721pS1v6y6c/M1Mw0pL5/wD+S2vtzKxjYIC11hpj/rVejp9vRd+LQ7sqxhRpwSwVKbEo
+amaaXJRExzjqBGxyQ6XFMoyGucK+v+kYHeweYdTB15teY7/3I3mg06bSsmFG4cxlHVNrUsv1aMTF
+uXD2w06LvobnHn/whHe1iu5EJdhXTm7ia1OUVRGz0rCYXxpW/rpn4Iqg0Fbz2bqGQoiJdZzRkiYX
+9OLxbr1JbFmr+6UT4mg25HKZb+oNtKp007MgZ8W6fIan2kNqK5baUzB0A3DX029wNXFp/41fIvuJ
+ZI+yKyr8Ttljel9NMH9KMJd7ZGlKpXPvP7wkVI2lWCkS1K4YuDIvXlpuiuGY3T4DLTd3nV4x1sIw
+62K8nrV4Wlvi+dmUTOsbxK0B7Y7O7aom0CQfL9P6DianUI2mKUpqNf/qRQcbaQHWJsHV9YkVNZpV
+KXFPrgW2pF6ohlVL4WBANDudr7pS7S+sMBN1TJeGpdaRsG5CqJpHpb1K6jyiUlxFkhaMFJrf7C4w
+63J0y5Kpaqrtm2IG2DwhfSbv1B/4HI20InhVowoUq8wFDbVPFrIfv//+JZda8g4MWjwbx3P42eqJ
+zrND6WrLxU9IfyamYIzxEYbw9621/6dePjfGHFlrT40xR8AvzNtsLGSVw2mec/FUlIwbx5bKqo8y
+NJsGLrgeTrgunKEzFjU4Gs81vrvpndf4DZ56pAuT09Rawl3x61GnhVUV0Lu+xA5k8vabffZuCejp
+9/7oj/j2M1nc1rGc3NeWJdNziTiUeUam44hNjdUoQZ15tBX73lGoMZGPJh+S+oaTN8XMebv9MkZT
+i6cKkGrllVRnAtLZJWmpEQAqMmUyaVWRqs0d9+TGrSggm2sdyABijQC4vovufaK9Q/Z/5WsAXH3n
+uwCcnl5wnSs2I43INE8k8iyFpoNfrBa0W5rCvatNe3ZuEKJeca+h0aIfvl9SaCqzVwesdL5Qr/5h
+EnL/Q1nrh5djpto8Newfc/j62/Lsdx5QaSn2eh0BqEOWypDc2TU7igsp45piKus7Pj8jLrSUvDJC
+67gbIJTrupt8lqJcwrrSNIZGQT+OYjoW8wVJT5vP3G4x16zS5fUER6tBVesK1cZSaiZq1NnB00In
++SzHrCNfvROCPe1qpQLCq11af0PxKYMJiRbGubgcEbUlxf349hFtxYZUylQekZEpk3Udg689PYPG
+odI+q7kLXYXef1L6s0QfDPC/AD+11v4PP/enfwj8Xf38d4H/60/7jC1taUt/8fRn0RS+AfzHwI+M
+MaqL8t8Afw/4340x/ynwCPgPftGNjAHjWkxVkWpCShoWrFRVXS4rDNqHoDPE1YYrlfYUzD88I9O4
+szM65dG3fgDA5bsP+N5cJFT78Ijha5Il+MZt8f6G+0NsKupzGoX0FMXnOT6FQoWXVUWx9hyPZAzX
+y5KFqoN1XlGsy4BFFZ4WLfFjF6NVpxtHpVXgkV6LubKcpixSjWD0phjN9rOK5nNdD0/rC7YDB6vq
+bJ64m4zK1aggV8+4X2pdgaCFq/0Nmspg1hBDDOsmOY4bEGn2oTa+pno+oVJzJe23sKrltDyoNlpY
+hLZAoK2YBidwWFYyh8twiFFkojdqKAMtZFI6lDoHlS5jHSREt+Qe81VOpRVC/G6X3k1xQ33x+JAP
+V+uCAVq8xcvJtHWb1/OJKpG2fl5TqpS37Q6pxvLnI5HsPhkdhWDX3XDTbs4WDcVU0an9CEczTAvV
+NpeVi1E0beizaShUJYa5zrN2f8NpPDyV3NPVCruOPhmHQKt8t5KAet0wSKHbtdfGaj/S5VnKQntT
+ni6XtPYEht++7hHvi1Y79rWP6SzH6ppGfshQcS1l4LNQczPxoVSI9SelP0v04V8C5k/489/4VDcz
+Bjfw8V2fqRbfvFpNybSbkhkcgqdZdk3F4kKuWwXNmMmYYiaT6sQL4kQWa36Y4uom/eDD9+ka2SC1
+AoTepMbT9ORsVKA1OIhoiFXl2vNiXA0NrkOZVVmT6au3WiHO2rYsHIxWhYqtR6WLPtWqSuNHS0qN
+ktRlwGkgZeSvfzTGUQDN9brAqa24uyub+GDvkEhB+fUiw1kXlHFcXM2DWBuUk9GUKtTOUqa3KclO
+04CzXi4Hvy2RiECbnyzCHH+tnqYN2VzDibtd9nY0EjPJNx2pSs3LGFUjcnWI2GCM1fCcT4PxVWX2
+XdqBqvmOHLa9kz7BT+XwF4gJCRC023Raovr/8q9/jeoHYhvPUy3oWzk068K1Y4+FMovAdfGVqQ3a
+A5pNbop2slrO0HajNIuCSgucrEYLxisxzdq06WpKdael8OLAsrbar+9NiW8rrLp2qFUwrKt0BVGN
+8iuypiRYl93vD2iMPs9tQEtlLlriw/GKM6qVmFLjh6e881QYwdlyTqBp2elohv/q52T8WrAys9Wm
+sXAS+kStNTzc3TRLNkayUD8NbWHOW9rSlj5GLwTMmaahTjMOEpeVNsWwTc3770i23P7um7RvrJOj
+2kRacy7NNS57+wZGsQR+1cF9Q1TjX+reoOe+C8Dg4Zhn1yKxB19QL3Q3xC60+vDyimePxaH22lGb
+bKwquG8INaJSLNal41v0tIKvu1xSKljIcQzrYIvxLC1N0soUHFO6BatGpOfxYRfji/S8ffwGu9pX
+Mu7Key4vUx48UBDW9JLAlYq8tg7wtJiIT7mBEucar/fchrnWHggcn1qjGaZqcLTcvfFDjCfP7r0u
+4JjeD9+lGws4xr11yFxh48vFBVeP5HehrUlKLWGvnvyymDJfatl3Y1mop7vbDukpvDaIu1jdavtD
+UYE7rkehCPKyblibNrZe4Ov49+++zNc9ee8f/P5vAxAlJQdtMfNs1KJy1bFbzTCFzEUxrlh7VSut
+udhqexhVFarlnLClIr1aEeh3+4lPrNGHUDVIt79HsdAyyv2Kcy1jRx1gCv2OniI3cIm00nLspzia
+5FTYDK/SDMe0YamVopfnEkWon1gWjwSwtJzNNliJTuPxPFXT9fEFvY6oGLmv/T8biFS7i1yPYbgG
+tRkyLcUfuuGmEc8npReCKVgsVVMxrl2SWHsKlit++2fCFL72K0tMrAfBneMb2VhxKAfai306fVE5
+q2yKs1p7mce8fPgrABwuM1a68doD2Y2tfsxIAejzxZTLSpjNXu7T1YV7b5Ex1ozIyhOVOmiFJK6M
+wWn7XM21PHk6ZqUNA+qzCv9YG9OqnbrTDdhVYNHOjQ67LfFee0mXUmvxFWtgVTahp7UhBzsJ3a6G
+lRrDUkFBdVbCOgKjLoV6UbHUjVt2luQKEHLyK1z9rnEjGqP1KC/k7384uuKO9h64SZdBVzaxmzu4
+kaI+ZxnXS723wgqXeUOhvp92p8O15qNM85RG/RkrW2A1u7JjtTX8zS5/8HtaScjCOpTt12AGwkxj
+v0eiRW/RArVXs4pXNHu0Lgx1I0IkCtskQ5mEsldiFdFYLjQ0ueOQzeQ9vJaLidbmzJLyap3N2lAo
+U3c0TOTOnxOqDX9Z5sweiPC5njn0viD7KFOUp7+yOK4iOosFY/UJRbVD7a9zcLp0tb7nrdcllb3Z
+LVi9JHts8t4u0RP5+09HS1LdD7bj8OFchNZDFYCTugYNr69Mybnml7RjB42Gk0Q+bvwXlPuwpS1t
+6f+f9EJoCo1xSP2YvM42zi4nh/NLKbzx3Xd/wN7tXwJgGEdouz48T6IIbjvB9LQXHxeYsXqyd3fw
+18CTxVOCmdy7dhTOOw24mIi2cTpNNxmFz2cLnn3nIQAfXE1ZaOt6NOfAMT4dLXte5Yak0i4+qzlp
+Jt8pbMZIq0PXmsmXtHysolGyR1Me18L58yksSvE41xqF6PVatLWFeLQzxGowPStWrLQ5y8qpMdpD
+s9EGKddFyUQb6sRFzZU2OjHZmLCWdzVNTKVOx+8+lfe8f5WD9j7srIbseloq7aCNV4lEm58+Y+pI
+pGGmJkPZjiRBAkgdaLRU3CStyTXfwYsCBl3tdtURs+v771/wR4+e6HwaXB1P5Rh8X8yYJrrEqJa1
+zqiss5CxahtRUZI62s25DvG1TXy9cqhrLcGsTXZsnqPBKkq3wKhqVZUl45lm2waWgTbosX3t0ZmF
+LFRCX10UXEy0bLvnsD4+fqhRDa+hVMxCWWWk2j7gumoYh1pj02no7gi0PFIsjH/sEl2LSeRHl8S3
+5O/x8/v4p+KAfDSb88FzGefPtBhQWjagmo3NSgKdw9L4m7JwfmCImj8pHvCvp62msKUtbeljZKz9
+dPbGZ0Ff+sqX7T/75j/HpgtsLlzQLB3e+d1/CMD33/kuB13h+DkNl5XYToHacsn+Lm2tStM0KaMn
+co/3Hz/m/FSkY4XdIBlrzchbpjm5Sm7Xden1NebtelQawM8nSx5oe7OiEc7/1z7/Jut+Zk7dsNJ4
+2lu3Dnj5UKTcyi15PhJpNcpkDEnY49a+SCLTafHSLfnuQXefQPmz6yn6zC5YaGZk2dR4mqCze7JL
+4K0r9gzxYnXcqR3u1R5OuC4Ie8CXbknV5qVTcUvDlm4rwA1dfVd5XOiHOFoLobKWoYa3bt882HSY
+fnw62vRt6O6KpjRb5WQLrYacwywTu/2nz8dMFf69G/q4qums9Bm9tsueukO63ZLrXNbvn35wRUuf
+cXTYIlEk4Ne+JO9xsLdDV98/6XdpdB06QZdI3ymzFb5mM1aKw3j65DmnihG5Lifkin9oRwGDrvZd
+HPqbgrQ21+pe7WSD3jymi6/h7mT4MvcvpK9D9KFoPKNsRGrEtzW8c0AnkHv1mwraoiE9vb5PWCtS
+V52gi1VNqRBtv0nIJ9oWMW2Ih1qrwrRpUtF65wrz7t5I+NLNtwCpoHWeqsPXP8OXaDftnSWdQ9Gy
+f/nf+43vWmu/yi+gF8J8MLbBrwqwLlrijnxyTUcbjrxkUyaal3B1veKZZiKuOxftdC3dRFNrWyXB
+rhyQ4sJnpVHm8aLGKAy0WWfWFSlFtm7CYlGEMsNejGvWm8Yl0ia06UK+ENUFK8Wch37EjtFcjKZm
+pQzLrkqmz0Xt9DVm3Ds54O3PSdXe2ThlTzP5ErdDX5uBNMt1ZmhMbOT9aueaxZXErhmXOI5gDML+
+Ancu72o87ctYHeIONP8gNMRqujwtMvqau/Fay8EdyOeephbvtANiVdWbyuIplmO316OqZe7zkdnE
+7AcK9OomDXNdtKJq8BdyYL+YlFyfag1DYwiU++Sqyo6XLmeKzciagMNd+Xtyz9KP5PPJjWMS7ePZ
+UUdlknS5taeVpIOYQGHqGI8glvVZrpagpsR83aWqG5Br5MS7CtGsZvYPYvra6LfrtKlqZRCHcgDT
+qsHM5Hdn9YpEAWmtBo61U9M9xSl4YZtdza7tmBBXGZ3TnrCYihPbnlqWobYBONKM0hCaNajL9end
+EAdmNEnx1PypHI/kQB2+Z8LcotOUOhaBc/DqEd6VjH2xSrm30v3SbbMbf7pjvjUftrSlLX2MXghN
+wTEOgRdj0yvMQoZUnP4ephQJFRYF9UBDXRcZN0JVmQ9EQr128jKDG5Jc1EpCDhR7cBC7DPeEez49
+nTNRMbdQdXe+8BlpEs0ozyk17DVeNvRi4fJuEOFrKbB1z0DHg2Gg/RSG8SZrcbe7Q67qxun1HFQF
+v3sgjrrXvvo5bh2IE6nq+sSKmnPbMQHrdmTa4brjYBSems4nNOq0yk+vqawmdrVv4CvEuuVKARkT
+THGszAV1zUqRe68aaO7KvDyhx98ayjiGd7SobJ2yUmTmjtew1AQlW+Ys9Hpj7NrXuun1UGWGvbaM
+eeLUDBWH0GoivvGKzNHDWcV0JPd7ei6q8cvHhh+oBtYP9xlpsZROa8TuiYzptbt/hUqhxHsDWcfb
+r99mV7srmzqgra38CrfBaOZj7RmClox5pcjLdu+ASvtD7u1aJlqnYa/TxlNswmG4x0xxD5lm4qan
+I3b2ZS8sViVBJp+LumGoazZWE7SfvUVHy+odDHfxXXGE584jri9En293+qx0bKF2QXcdj86hvP/C
+DWlpElg3qeno3lvlDb724gg1DOlMC2rkmmm5m6zh4c6QSKuRJ94Nqk+JaHwhmILFyH+OTz76FgDv
+/pPvMFuImlQPO1zckwUvS0vTF9VuxxX7PGz1MOt0aqClvQaPT94gGsjmP7hxzdmVMJnRldxrni45
+fSZqnTcZc61Q6TwryBS/0GrHGyhAs077jUN6aor02z0CZSCL5VI8wsDJSZf9E8lwe+MLkvW36x2R
+WDkURTfG08Yqlg6uVplqFC/vlBajZca9pVRUAnAiSJXxND95j1Eu4wh/Va614zfAk/ewJiHVMe+1
+Al5KFBdh2oQ7aroohLtOIUtlfoqoRVebtzQ2Z0cxC61utC5exF5f6042bdxI7Oj92mAUdjyzHbTF
+Jq+UHvd/IpGkeSzv3NqL+dVTWafqlmEyUxPreJe3XxImervV2sT3d07kcOy2h/Q1p6Ja1bQH6xbv
+EdZK9CSMmg2i22pNyNqfkfT0EPoBB9pwpm9KSgWAdTpd2lq6/vyh9CD9MJ9w1BGT4E5yQh0onqSp
+mGqfzs+/Lm0JPC9gpdm8pm2Ita29N+/S1cYxN28d0dW1DGrBYNi4j1XA1mzq4VZidnZ6Ca2umnmV
+S1DKXg3b2ndzOMNq8SFnlFMmMoftYcDbjrgOCnNN9SndhlvzYUtb2tLH6MXQFJqGIi9oTp/ywR98
+E4APnp/TqFrnLCMaR3sYtj26il4MlYs2ZUyh8f0ag6+oQi/u0FbHVrVX4zXaxVnV+skkwFd1r3se
+8kxbxZ3NM3z14A/jgEtNqpkoktB3HQKVpGEUkmuN/bpqSNTJ9/rrX+W1t18BoN/XIiR5jKf5/W4+
+x2pEwVLjGsX8ao3D2kivBoDk+IQTbelWVyMadbrmYQ+3Eanywbc+BOCNL0aYjkha10bkGjeft2IS
+zaQcmJhORxO61JM9r1JSLR/mtpyN59yPYtQfSNzqiu0E7PZF6/AbS+2JxMsnFndPxhwVQ/yWdnZ+
+uuTLX5C5uH0psOX7LDAtfd6xw4mja3LrFgf7WgvhZkl7JZJwV9GdXdchGaxxDFP8REvkFTHesThx
+W/mSRmHKYbKGQe8Rar0IywpfcRPtJKRWZ7MZlJRTdXK+ItGOVdlwPhYn7lG/vX59Ys/h/EqbEUWy
+3/pDn2wq69SNQyrFaTA44WWtqbF7OCQpdD+0ROKX6YrMihnguD6OI++BV28K7ZgcIm0t11FYfWmv
+GGu1cmdwxA01iebuJTXyrtHQ4FefDqfwQjCFpq5YTq45++4f8s7vScPTUWZp6YTYzpJYTYLK7zLo
+KFNQ0EjlVpTaDCUol6Qa/gqsIV8XHa1j4s66BLZulE5FWWiDDWMo1VO9Ss9YKpx3FuYUqq4b+xFG
+vt2WZ7eTDh1V+U17SaI1Jl968zbJ+mCpStmULsbT3AinTaNhz6qoMYrFr7SVudc0m85UASVGsw9N
+uk+mnZCyVcrkQtOWF1JhKn8+5ot/R+4R7+8QKWMJbUM7Fub2+qs7lJpyvdQs0roqOdIwY1wHm8Yw
+ZVURqS0eVh5eS6MONxTLv7LomcA5cGkSeafpqqHWwipeu4+6YAjWYKT3a84URPbs4Yp4IfP29nGP
+oz3toNTp4EXrZ2u6/NjQdGabufLV9+FYF0dDp47XwSv0sGhVpMYu2aDe8gZP1yaMHDL1I5hRgaPr
+4+u87e13Wd1TWPHqCn+ojLx/xH6lNRgDmYvC9sl13upxQa3AstBZYbTYatCAowq6WaeGel1CBZ+Z
+0OBrRMgS4GkY3Il8fAVA1UbNIBvgafl96y1Y6X3r0ym5Ni92QgvaPOeT0tZ82NKWtvQxejE0hcaS
+ZRnjckKnr7Fk75Bci2YUxqVWb0nca1EqzmCldQqy8xGutpLrBAGOJrA0WU6jv4u8AkeTZ9YNzWxp
+0QAAdRkw1KSjxbTDshQJPJ8VNPqc2qwzEQMydSiuakM8VBCVPeLwlmAIWq2I0JH7aWgb0/Jo1IOM
+42GMSEcTNpQqsa1Zt2Xz8DVG72JhocCjXZdIPeCPz54wj0SSNLmoraPyfU4fyRhuRCckmgE4TALc
+WLWR1ZxBW77/UIEypqoItLRZpx3iaP0Ka5xNfchuv0Otji1f1eEgSokSMQnybEGtqq2f1iQqVRd5
+QaqAsVwdfEevujz/qczh1dWMUiX+v/32G4Sh1mHIXRpH8SDavMYMLLlqWFGQ4GrxFjdysYoHcXNw
+tRKLWh0AACAASURBVOCIKdaVmlMiBbuR7FBqY8minlPN1dyMWijkBFdNn6QT0tmV+T67mjJ+LM84
+bFqcaKSlabRs3nXOUiNDXi+hqsXxOSgDdkJR/Z3cg5Zma5q1Q9jF+X/Ze5NY3bb8Pui39lq73/tr
+T3/PbV5bVa/sKlM2MSkSsGKFQYjCJEIIBjSRmEVIDCAwYhCkMAIjIRiAUASIgDKKGCQgcCSkhLJT
+dpVdrlevf7c/zdd/u2/WYrB++3t+kknd8jP2tXSW9HTPO+f7dr/X+je/hhGYJ3041Jxweg+K2BKh
+HDidjZAMO0pyPBjeAbv9S5j9EDn7MCS8LTZrRNSrfNXxmkwKPcpyB9F5uP/A5sObPMfNxp581fdw
+Ge4GTgA9nCNfIGkMNEP8OgQUT0vUDfqhpiCdgwqRy985Uh3+3sAcfBrSWQJFRl3Vtwewk+LLUeoO
+EZWQ/K5DBPu92b0pZtGAy++gB0megOFbF0DwPDTEAbcuhAfDFKOjqpKX+BDch6M0jEeWZL2Bl9iO
+ill/Hx9+YgFejx7ZmsRyUePFB7a+MLr8BaQju41Z5GFERGeaTlAy1/Yz+ibAgU+koD+bI+K1QNuC
+miZIkhiis+cSVfZFavsYDSeCrtMQPdmsjoFi+2EiRnCMBdNsCUxqVIh+Rk+Ga6Dli+mPXbhcDNyi
+gkt3LUVEq9Opg19E7kVQJ4MNkwKG2k5lEPBeC3YT3CaCpq+k8ABB/gjyFTJOyFVTIxzZeoVL5KUX
+pnDIAq2QH/w4d7dr9EPtg9ew3T9HQ0BaFykE7DLE2kA5X1DqDRcLsIXYlhq+XR8AISHZqjWOBIJB
+0deDGAxkGyph1T18iuYG/XNkFHJpVY6utMfk1QYq4bm+4rhLH+7G3bgbXxqvRaQAA+jW4PT8DM49
+O4tu/tGP4CU2tDWLHnFsV4rUdyG5AnlcVeNoZAX0AAgl4FFf0RsHGOQlOr2DoZtSxJW7Mz3qivBa
+7YCCymj6+ADLdXWP9aBST+hzV3UAWZnJ/TPE7FefpCnm7IiE6REGtTrDgprRBiC+QQp9cHJC2cMb
+M2TmCiVNCaGpReinEJpdCdXAZ7gaXUyhf/IT+3uG1JUscL20BbCLJ5/BpdCHhAs3pnx8sYfZk4PA
+/Gl+PLPyzwDGkYLL1cqJk4N7tFMUcLjaer6VUnP7DmVP0Y+mgsf7YAKFltyNQAUYEZJ+ndui3bqs
+sXlhjzmau/AJqy5XO2jetfjeBUJlr6dLVzBdaYSJTVekH6Ihv0L1BVyCgdxRi56CMkPxUSCE8Igh
+MBUUC54iPUMS22u7uH4Oh4IqekIZ/aJERa9IdBop4dhSGegtV2DPRi5OmkDsqH+gb5HENmWYHh3D
+G2TW/Rq6ZorhUqgnKmEoQyhbBTmYUHryUPgUfQPp0dHaswXcXlQQ1HsMp28gdqxoS7a8xpZ8ndOz
+GIkakoxXG3eRwt24G3fjS+P1iBTQwyBDEEfQazsLLssca/rrNaE8KAF5o+TAnHMId5WuA+ENEmQK
+LgVDlQv0w0pZt9D0oyzkoNMvkTJX1yOBdj8YgWQIhkvjCVAHFTVzcngdFPO70PXgDa0nrKF7m9tL
+pa1XHYBe23xaNB5UxHaU4x1EVTUUhKT9F2d+xwV0R32AZgfDdppjNCBpKzaO8d57Fkp7HtvV4OpK
+I6vsavf5y8/gUSsscEuEPE7R1BCEZsfBoPXgw2OrN3QMRGi353nAwKRt6xVUzlrKEfNhx4Wz5QXS
+Ag21J4wQkIxC+qZHQTh5ZGyUlm8qTHlsWedCc31qNhsYro5ddwmkdn+akU3bd0gY8YjQw1Be0tqD
+JPkLOoIDsm25Qsugg2mpyLUpULJQorwSkp8JXRd1y9rOdihQSgyQ1tgJEEztc1i3ARriHiQteYQR
+6KhjMBLBge0ZJD4kn0lTOugZycqQuh86gmJBWJjwYPunHQ8OZfH6VsN4LFKzJSlMBcF2o68qpLCF
+iXalkHF7Rnhogj+FOAUYAVMrbG5vUD99AgD47PGnMBRcidwZfFZn55MJ5GBwQpaaUjUcSWl1I9FR
+Dt70CoLmJI7SkMM7zYfROCHCmB2JrQIl9yB3GcSgjN67kIy+HL5UfSuxZ9FxXzaQBEPVmylcmqho
+s4PgCzCYndb6Ct2GxbwwgTZDQakDDHveFCnpyxZVZtOnts1hWKjqchxUiZWc4o1HludQbu1D7t/3
+gIWFw9bZ8iDq4nc+1o0NKStH4ZjFyt6xk00QhIdJKhxH0ITdus0GFcE9o9kpuoohOFMbp9ghGrFw
+24RoGQbnZYZIDuddYUs7Q9YnMZ+7+GRjr/32ZYcl2aVvdQ32TOlOmw4VsQAegVfhDPATnmuo4XOB
+MEJADCIybQTh2QlOE28gIaAGHMPZCA65JFAtJJ8Rbabobmya0hLyLbUDjxiRvqvRF4PnZYEdFwbR
+2Gs4Cz2oiJ2RZISEE4jr5FCB/RnJBNoM3A27L9mFh85YhxqCRXNXaQhK/iutrPIQAMGUqe+nMIT3
+m17Ao0vYaHyOkN2V9SZDKihp94rjK6cPQggphPhtIcT/xv9/QwjxPSHEx0KI/0UMS9LduBt340/F
++KOIFP49AO8DYHUE/xmA/9wY83eEEP8NgL8G4L/+p21A6w5ltsDtJ0+we2xbbNm2QUTvBQMP6zXD
+0nILJ7ErXsDowfc7BCxURTMPhmF3WW2QMWwNAh87snlqrsC6chAwnO8hIYmgbLRAxZWkR496Twg1
+W151rPFQ2313uca+oVjnQx8KdoXqq1uUK4awVN/NFxLVxhJtRDvD9F22L8MLoBrCVur1BxXqnV0l
+ym6Lqmb6oLboDq7TFSaRJV0JbSOsYpUjTu1nG0g0xFiU+wrl0u6v8AS8gC7djLC066IkQ9XpDGJF
+Tv9JivKFTX9M4EC2NkQtKGPX1QrBOVdBrZBVlvi02rTIWTSFA2xZjCxZwB2pKeaEQirHoCMitWoq
+gCup6Qvsn9DGbWRXyfToHpAy5dsLODN6TDYhHMLG28KBiCgYkxHZ6DoQJUP0Ex8eI4keAtWVXd17
+rdEQ9t4THl5XKZSkzZunsdvS/k550GzrHhl7TcYTB7il3sKRQJRSgdtbIX/JSDBx0LSMIEiO6/cd
+Agq5CJXAoOL5uXB8SgiaCLq1z1n2fCDP3cD1eJ/cCpqO314kMS4sDL3o1wcjoVcdX9VL8hLAvwzg
+PwXw79NK7i8A+Nf5kb8N4D/BT5kU+rbH5maD3/vRb+Pxla2gmlbgkqrLQABD/8Bl1+PmGS3Omeul
+nkRCZZtkkmDMeH+9WeD6ioYysULXM29lyOW7LkIKUARaQvKBEF2Pmh6NXgc0tI8fKt0XeYgX7An/
+AjQS4u+TKD3kgwJTbG+tT6Nifh6kl0Bp6wytv8HyY/uyjc/3SGOyDh3LZWhrDcHURmVHULH9rO4k
+EoJRRKdRkBkohuMtK0TsjCjRYzKcXxTg8j4zcD3CxLfXtidefrMucbu2eoD6gxrjI/ugn40mcAWV
+rtwGIVWQO068WXGN7RP7kG6XJXYE72xvMnhMq2YTD2pwuCrsi7D3BNot/SNDDY80gWrf4pT7ns1j
+OCXhuoPyVrXE+rH93svrzzD73P59OrlAKu05ibmC3tjPBIO7lfSgHTJttyWya6skvV0v8JNPraOY
+43oYnxL2LvnvSB7Ed26f7fB8a881jsaYEZMRn9A6IE2RDtB0P4DO7L2p1zVudxa+v/iogmHwfCKH
+iTdHsyRGZqwPTNPOPEP9CV3S9h0cGtxkJdmSMsD4nFiYfgTH2HdHBxVAZ7Rw5hyo1q86vmr68F8A
++A+AQ+dvDmBjDJM34BmAe3/QF4UQ/64Q4p8IIf7Jdp99xcO4G3fjbvxRjT90pCCE+MsAbowx3xdC
+/MrP+v3fb0X/1uW52S+e4+XtGj/4zM6urhtA+HZVmkQGp2fs0bbNwayuIiptt93AW9vv4RMX945t
+1PD4eo3Nyq680TRBTN2DcMYOgS+Q7+3MXyn/4BOonC9Qj9u6Qk4R/QE1qfwQK7oPr3bXSD0LK3b6
+HPvlUOzaYsUKtvO5PYZs9xjKsO+eGExHg8PxDDUlz54+seeRPVnhurAYBK0CdOzTRwqYU49ydnp6
+0HQM+f1Sy4NNfNY5mLIaDlVjouwK3PsxCmXPG/Q47JcGYWrRfFerZ3j623bf38tzvH3Pfu8bb93H
+vQt2FAiPzrY5sp1duTZlgZcbQtCbGlN2RGIRomyJ1SCcOXuioVk4q2s9gBFxEQFHc+oSehFAMReH
+OpdCnkITgTg7u4f+2qZoH/3gQ8jGaibOzi8xGVMH8YjmLGmEnue6+uSHeELr96vFElvf3qepkci4
+vXZsr+F4OsckJFHM97C/sfdy61a4T9MhhwAXpwWUZ69hvf8IYmtX8SCK4R/bZ27sZ2hv7fnl0qZw
+2TrHjIzgCB4Y3KDrgXpvI8S8VCgLu+LvShvxTLwN3Cu7Xf+BgmFqJ9dLSEmNyqVGf/SzIRq/qsHs
+XxFC/CUAAWxN4dcATIQQitHCJYDnP21DvdHYtzl0ECBnDqy6CjlBQ52ukd/aB6+DQDa49FCwI3Dk
+wV+w61ukxxZYMzUtPntpc9zsZYO9a2sR3mN7oaNJgFPmp+OkRUlQkFs5GNOU5sn2C5bkwJnYagfn
+xFrfTpYIx/bGTF4W6BtrYJNla2StDXkrVpZ/d3WFR7F9UB7EJxh/3YqvhEcnaDe2JmB6e8M3aQjv
+nn3YirKAV9EApdR4urXV5LIS8Ei1vv8GU63QwYpiMjf1AkQVo2oN9h49E70xZnyAboeXNdCQ1dAN
+CdBS89IRAW74It8vfOzrgalHE51thpq1GhOOsHhuJ7XbPoemgpDaewjoeble25cuTiuUg2lsJbGn
+86zru/ACu28pKuulDmBMfkWR5GhW9jyaIkU6tfdaxQGizubRUeBDMIXUrDN03QbV0u7bJAFGj+z9
+u3JKbG/thLPdZgBFVL7GZ2umHSQzTiyTFEvWl4K4Qlba+/vsuX2uprM1fIKiAp1iLcjmvL1F+Zzn
+v9HY0w3snOnAKAIEz8+ZRYBhq+baQJNeuli/xGeDEQ39P6M6xbvv2MnrmxOBBKwPJQEkjVHXVzmE
++Kmv4JfGHzp9MMb8R8aYS2PMIwD/GoD/yxjzbwD4dQB/lR+7s6K/G3fjT9n4/wOn8B8C+DtCiL8J
+4LcB/Hc/7Qt9b7DZ1JgmIQLOjI70cO+B7cF3vUDHBrf0PVv9A+CwqCMKcwAvKaUwHWzHzCU+HH1q
+P6Nd9AQiRCP25kMXDtl0m65GS4zAPImRjGx1Ot7sUFAqrKHG180ywyiyl+476iHSzq7i5jg9yPKe
+pinuv/ltAMCOugLSSYDMRjzpNEAyYegXnKBLKaX1yF6T42ULReJL5hQo6B9ZbhYYZfY4bp+8j4aS
+6j1DysyT+PAzW3D6eL3DgzO7eixuGgSV3cb0ZITbHa/hAJDaO1AsbDomwdRmKIjjHdwhCpMCPS3j
+vcQeux5XaBt7PKkb4+2HTPnaPZKRvQ9zRyNgZHFzbI9hUdRIqBsQTkP41FeMHiY4odJ0oiU0TWng
+kT2bAYKwY3mSItI24tFCwCVgqd93MGRoDoQoRyi0oV3RdZ3C9+2zcOHPUI/tcVTJFgGJRBE9OMtt
+hqDh78bhoUu06xzEAyaFxjhu3cJnBGkiwNVkmhYtRucWZKZRIOD11GsbHVaOj+4bJLkZB0rYc87D
+LZolLflkiiPKEDq1jYjCiQ+liXVocvRkjNarW1TdQAUwaNWfgJekMeYfAviH/PlTAH/mj2K7d+Nu
+3I0//vFaIBrbrsOLxQq+4yNO2JvXDs4u7WqUBGOgGeirPfalnR39mrm+aaBYcJOix4ztSYxizL5v
+l7xcdoc2Wj2o8migHoRBwxjulESjOEZT23zwYpWiIXoxJ6X3avUS18QQfDf8LqZUCjpKPFycfN0e
+h2/QsUYRsv9ydPounNKqLqfHc3gUEkXTwZ1aaa7p1tYDuqKCE9lVsLlt0Cu7ukRTgwf3qRJ8z8eH
+P/wd+7Pk924rPCMZZrXO8U2aru6VjxELlF7TIOGtd+jfEDyKIQga1okLU9rvjb1TVDQ5dZBBsIUZ
+jG0kFYglWhZdUzWGe2av56zyIEmeOglcBDFrMA0NDroOEdGIql5hTJm6/rqGMxncqn3kq1vuz17j
+yXgK9569VjAuPBZr0+0MsrL3vTEdakrLaVKZRTyFqO15aLOGqmyd4PztC4Q0mG3al5BsVyfESiy3
+FTAncvG2QUetDgcGR9pGRduQ7eBwgoBFSxUKuMoWHdW9d9CTTHd+MUXDukS7t9+vqwYOlcT7toIg
+IUrJHOSDIdIznJ3ZZzwhfdv3HZzP7P1LZw8PVnnFvkP2wj4Dcu5gFtAb4xXHazEpdG2Hxc0CU9nA
+ZSW36zuUBPIkyoEizyFWHkJ6F3ozcvMzDaXomuOF8Fhx13mLM5pzXN3soAMCeXq6BAcSKcVERrMA
+Dq3hQwGcUNxCH0+wvaVTE/vqfdsgIPQXvQYpFVDFHh27s4EwcFy7ba+2k1ujegRj8i6SMQR1+foO
+X0CwR7ZwNs6eoyWM+8Q/R2fsRBj0CaJwMFk5x/m5dScqPrUpw+1ihXxtH+xNZvD2hX3xHkiBMXED
+OhghIkw5pNlI6iQH+LQpKqQUEAkShYICL+vHC7i0g/EpRZ8mMZrMhu0qbHFMr0hVdRA0SfEiD6az
+1/A0thOd23SHdCW88eFH9phnaYmAx+Y6I3icTHqmFwgc+GSa9qGA2hBzIkdQx+RoaAVZkrlIBynd
+5QdWYlrXMDFh3EogJEy7WwB7por+yO5jdhZCMl1r1RZjnnfWAydn9vejG/vM6r7AvrGTzRQGIqC0
+W2fgsOiqdxUUQXIO9SQ6OUVzZQvU/a04OKx7oykSgqjkSEByMonY8Y+Uc1CflrGGru35mWcKVcWJ
+enKMaGKfqVcddyzJu3E37saXxmsRKbR9h5ebFW4qfSAowXwhc+UGMSS1B5QboCcy0VDFKIlcONTR
+GscSHltFbtrj9IQIvBaYUIp3Nfg3uBJjqgR70kFIJScPAjO2A5U4xid7uxI+W5FXbzQWbJ3WJoNL
+xKLZzxGkEY+5h2LLDdRYUK2B7AaiTQ/NwlD+7H0EJ3Zl9tmm2lYNYp9Fq2OgIUnIa2t4VD7uoh1G
+RzaMT0/ttm5+1GL93LZcS9VhGtl9bxMPO646ntkhoiWdJPrRRY1QE3Z9GR0Yk2huUe7tKu0HCmDP
+vh90CvwGNdOHtq0wMbwWToMx23rGjZBRSqwdCDyixrOVXcV3RYWjKVuIOw9XNVvHyQI1i8Nub1dg
+fyphiNnw+wlcpkSOG0MYiyFA7UAmhKFz2WuqDVz6bmIcAJrpprOF41IDYZqgJIFKM5z3pYd2an++
+/bSApCzcLArgGXtMe7Ju880GiuKqVXoPPluPjruDQySrOFXQDbUViP4UfQszYsu5M+gHopTjQ7CY
+LrsY0ZwsydoeQxRMIUka7LcNOkMouReho5+HgobL+/6q47WYFLpeY7nZwas7vDO2QKAYPTo66XhR
+jEhQ0kw7cMlXqHjHVWuQMAwOxwnIvIXTSKRkp4VejgUBSTFZlr704DHEN12Pivj8um3RUxswjCR0
+OUioMZ/2/QPjzt/fYP+x3Ub70OC8/hoAQKQSoCagEJQWF2OA++766iDH1uUVWsKxnZktQHixc1Bc
+dgsBbIjYSuuD7bwbNAhYqW9oe59vBDKa2pR9h35mJxbVaNQ5lYFHDpqEWouUxnfcFB5z2WAMKFJ9
+2/opisIe/95pEJNRKCif5jpjaDWAakpcXto8WhYx4NuHWykJwdDX9emw5G2wXNpOzMnYx8WR/Z45
++YJrsr9do6HRqySUONw7kISSS51AEcbtyAn0jizQ9Qqak89gMNvna0gK9QSnMVTPiVoZdATJQe4x
+JvW7qewkJscXuP3cdrA+eLbAvTNb+Xe8MRrJhYgWAL3oURCwFhwBEU1sI+VDUEPUkf5B2Vm00WFf
+vUvWZqtgSvsS634MwWdWOS6UGPGe0PogKuGQPar1Ek1FIFq7QwQ7CcUqQRQPWm+vNu7Sh7txN+7G
+l8ZrESlobZCXLdZlhnFlZ/v59ATnLFpNPQOpaTOuM+QkezjstfeBi4oWXdUnS/i0IW9RYU9Wm+5b
+xJwCWxbAEqWgSKqKhAPFlbtEe9ANuF1t8HIQDuHxGg2AYdtCm0ORU+1d678GoF8n6EtbBDTk4MMF
+DFdmY1YHVl889zCIPLc1GYnVDh0r/QYKmk7b3VogmjO0F1P4nb1e8SO7gs1+6310XEnHwgeol+CP
+JFKWsrs+Rl7Y70Wu3ZYTT6CCYTUfo6FlX7Z2oA2dqdMpkhGt+pjuRPuP4FMYpq5K1ISET1MFENFp
+8goitKuVU9gQP+8qvCRz8FvvjfC1h7RCq0qU7Fr0Ex+m4CNKdWIVAdKzn3WCwQPbFvl69ubbKoOb
+2mNKKaQr53No3ncpHMBldyIHjL7i9nxEYxZCWWjelms831vylK8aPHrLRnLBaI79xiJLBeHasTeH
+w+i23i7Rj4bo9gSGz0hbNRB8lg1dsPOqgGGIn8RHcFyqRIv1IZpsli/QDfoc1AtxNmPIkORAdQYl
+7fPmj0L4e6aK8R7uIAjyiuO1mBQcRyANXCxyjZr01vC4x54Kx3mdH9Sc8+0euz1vKOm2QegfdO98
+U0PRLCUr+oO7T9loKGo6NiRPtKKGmzGE9wUqhmp1V6IilPbpzRoVgfkOFXamQYCOL3/2coPHBNPo
+YorFYxsSn73VoTcMKwsLUmmKGoqgGN3t4R/bEM91jwCfmPsFQ8dWoVvb7dZ6hYrt0JEv0fj2GgVB
+iPjMPqQ6pxpy6OBNhrOVDNDxJfU6QBCo1XbpwUWrJ2ArqysoaV8ItdpgQ8DV6uVHaFlrwb3RgeLc
+FDZFEaNTTI/sOT/54Al2rD8EXgCwA6DhHqTdFc8jylzEZJ3OjicICeTC+hYOwTZ5prEll+KcE2iz
+q2FO7d+NGKHfWKq9rlO0uX25+8pAsfKvCd12ogoNQ21Hr4EtJ1zRwLC+4JkGeqAJ0Li32Ge4+ci2
++k7NGLPUVvIXW4U1DXjmOztJuV8LkQl7jb1dDkOFLH2UQ3RU4do/hya0XLAjYXYSgrLunSlRKztx
+VjpEu7P73j7ZwZ9TcWwAcmGFrh/qZwKuZ48tySUyj76arQszINFecdylD3fjbtyNL43XIlJwXRen
+F5coTQNQVCIdjZCyOFPtK+xbu5LmVY2+t7O4onnLZlegY192lsRoKSXmhBJJTU1BP0BPwgi4OgrR
+QJBr3nU9NhTk2FUlpLGfeV7XaGnF7pBddHx5BMnZ+ugsxVDbnaYX0IQr56s9nMF4kMxIGUu4PD+9
+bYHORgJl+QJED6NaUlggdCGmQ9+9hVsNHhETCOopGCWhaFmmx9Sz3Gvsma6c/vwI2Q3FRnYN3I7i
+MqHFc9gNUrW4XKOixmHfttjvKQWX13hW2GufFApLGsqMVzYtKfoOFUPVynVQEI5elTuAWI4iL7AT
+9jMvKHojkxpHZFyqXqDObIjuRMfQBKUV+wWawF47P7aALTlT6Jj6yBDQNKfRokJNSbu+N8hu6cUg
+bDqmljW0SXh+Gj6h8n5qIGgzKMoIHTsfdW0jxdsnz3A6t8/NdHaOLZ+t6+UW24/s6v559QwA8DXp
+IzqhQ/WDNyGntN7bX0OQaNU2gCEOQ9PvUYsWIbtgRZND7yjkguRgRFT6LXrqQcRmeGbH0DFTkR5A
+afcRnBwhdezx51mOkO/Gq47XYlIIwhjv/dwvwY86JDkl170ENXPAcrdHltkL1UoH6YBnH1B5oYBP
+YdPO9fDy+nMAQKRdNHwxtRaQPsMuOQiiikMqITobQgPArq1YOAA6pRFyAhi0GE/OzvCA1fuzMxfL
+HUFNqsPVZ1aopB4l8FOLZw8YtnvGOeSOTTNDt7Mv8vLD56huLZPtln6P01EKpvJwqhqGL6xzLBE2
+9FqcTiFSmz60bFk6JxHendg0IAimWBAbPyo97HZDVXsLh0jHIS1vZY/VhgAhvYckQGj04BKPKGa6
+7xQ2S7I4P7Rhe3D6BhI6Op086mCoppRVLgSVl/pQIlvY760LIv46Byl9Iotsi6e13d/Zm2fwB2Rl
+vTg4PGm6ZfW5QoehAJMAyk7CZdsA5Ch4SQzNVFEQ0CP2T1GxcFN3BituIi2bgzlLOtrBJ8DJOIMD
+zhgeTYk+//QF/u/PbN6+aRPcZvZev7u0E3OJBb5NDk79AKhZO1AaaNkpMyqA4wzdE3tOfuhDJqz9
+VBqua59108SHFq6fJgi4GBqaD7X+GnVvJ7pIBQCZrUKsD/WV3mi06md7ze/Sh7txN+7Gl8brESkk
+Eb723V/A0cUUaO1M7OwC9GA45DfIWRBsUKKiIEWb2dUgcT34dBF+snqMLLczaoUGhuFeqTUMQyqf
+zs6O6GAIsNFdh0bbVamqG4zY/0arIQltPaKhSdc3KLPBwusIhsW8291zHN+j7Vg/gyIQJmdIur+q
+UF/ZivXTj36E9cvPAQCx76M0Qw+dwh3lDRShuNAnmLMqjpWGTMiWS46gJvRMJGDr7fOH6P88K8/n
+l8h/1+pJvDQV3mCHpnQESsoru3TJdlSCnAAj1Duoxm4vOT6Cm9nwU9V7zI8ph067e3HiDUkZzuUb
+2O1pS98vrU49AN30GM/tanpB4NGq0Mg/s8e53C4wSbgKTs+QlOzQBCkkZdmrW4b1M4FWESsQ5igW
+tii3Xm0Ra7tShhcKgpLwASMJ6V6ga+x5VPktFKXRFysXJMSiLQWEIraC13s8LrF+YgVn/p/f+Am+
+R57E+OgM313ZNfUfMIr95/KXSBy7sXa/QMEVOoxncNwvug/NoJRd8Tn0a+QfsJMxctDthijlGXoC
+4GRTwiHeQFL2vnlRww2pPxLeh8OOS5WtDt0jjHbww6/jZxl3kcLduBt340vj9YgUpMTb8zneUrRQ
+EgAAIABJREFU8lPsr+ws+qy/Qb+gCGaiMaNTb4QYTUYMwcbO6teNwWhMyOlNC8GVvZc4CLP2hUZB
+8441STLTMIJmoc1IjYYyX0e+Qsj6gTQ9HGISEhaDstUaBRmO2aY4eAtcX10dzGTT6SWkoSdBN5jV
+SuSNbZu1O+CaK6XADgFJXB1VfOJWwJnbfTz8TgQ3tNoScaJhmHMLtzu0tSQt08Znj/Bt6gOsvVN8
+yN614+8wohydcuXBDMUl5G8czjEnmnL74hkWn9vv3QoXkvJgpp6hX9prbhiZuc0YAaG4OlsCK3t+
+wdk5FO3KTLA+YC66if3d9ac71Gt7P3ZHLgwVnV78xo/x8MhGS8cX34DLourgPp0vt5iTSevIh5CB
+LYg2uxbhnLgVHQE7FuiIQ5mEEukRUX7mCC9f2DaxlDeAYxWxDSpUNVfxW3ueXfOFc7kxgMMcP+g1
+3NTWdhalLZLeNjmeXtn27ORsDvmSkcA3xvBie21F+wTrp/SWqFiXwRFC2JXdLSMEY/u9sgmwooHw
+ZrnF/Os2Cokca0cXTgBX2GNwvBymJYlvWcAwugujERz3Z0M0vhaTgpQuZukJRNDjmMW1x5+/xIIK
+xSeNRJgQ0tx48Al5nU0Y94UxpLIP6WY+gctCYtsU6GDDR5UVSDv7gL2kLqNxHeTsAGzWKxTERbij
+CMS8oBUaI1IYJxN7DLvNBqBzT5oqLNc29PvxR1cAYdX338xRNqx8M9OI/Gs8+ov/LADgW788xl8Y
+gE66hiDOoiFYp681doK6hPsdxuekzQYzKEWMgKmAmmEnq9tuVMCJbL+6rRXW1GsMcuDmLfuAteYC
+5pbyZ8TIm2kGwW7B6bGHlpKXrtlgdGSh5+l3vnGQwZeKpjClRP3UTiC5yTG/pOR6UB9YrFefV6hL
+e82fP7EvRF3lqMlE7NoOz0iH/+DDK/ymtMf0Z39J4N5759yPfbAvQheC1010TxByATg51Qhjhv4A
+5NcobxYM4fcemvDgarpHSJh2miQYzYgn8X3sFvYFzzhZLq5vcV3ZiaLwQkiPOIuTOV5u7QT4y4rC
+MZMFvk9OycWihZPai/hAH8Fz+CxMAoj7AwTbftZsKjiDTyQ6SMLi3apGOibP4TLG5Ny+9F5Ed7LR
+tyBJ5RayQk/F6F46UEwxXSeCyH+27sNd+nA37sbd+NJ4LSIFI4BWSkzCAI20M6Y3naK9tm26hUkx
+Jjtt7PkADUXiA4w0wPULu0oU+z3awTpP6YNM2S7PD+i+iqFz1rUouJLuywyK8NowDHBNYZE48eGR
+rThmoS6vchQkFz1/ssVtZkP+TV3i/SeWPDO/PMY7D+32khELYOMLeLN3AADOxIVp6B5cmsOK5t0O
+xc4XCG4ZNcwFarbF/PMIhkVJmBaKCEMTkzF6+h2Unl0Zok2H6xUxFmWJ9ilXebeEJslp8KjMmzXk
+jilM0CJObAjbZFuUz2xk4nfP4LCQZiRX1J+sUSytFsA4jTH+l2yao41E0VKGDgo7thY3FMG9RY+c
+ojf7rD74Ry72HZrWnqv64ftoKWQb37PRyuSNADtiS8QqQJBQR+PoHJJIT7ONILjSm4VFSrZxhfoj
+to7lGpFDstnRCRRb1T1qQNrVOGvsM3JVC3ywslHF2mkBmg5lyyXAlR6RTRku9QTBwkas63CJMLYR
+275fQ1FQxfFKhDH1F8io7TwHfUuRnKxHl/D8nBlGM7YvpQ/vhCjN/KC8AkFTolrvUdX22NoASJP3
+7PZOAjjB4NP0akMM5qF/kiOOffNz37jArNZIHhJAU8SIKYvtiR7lglLsWqIkf0Dx4fBjDw3zvrJp
+MPEH85UUFScCxxVIBtPNwQBlt4PPiWXiqYOfXy06RBM7ERy7CSYnxBkwLP8v/4f/HQ7TDqk0Wio5
+ARJjApym4zneubAh7+aZfWg+ffoUn+2oqdj3SPxBE9FAUt3JIcOi6DV2FeHV2sDn73s4KKniE8JB
+6NrjnJFyPU0CJEcWH/Hme+9g2dleerfcoeELprXCeE4aMaHdu+0eRW1f3Kws0BR23+six57KwL0x
+UEylRnywA6mgifVwTH/4uarL4f3BKAnxF//KXwIA/Lk//6sAgIf3jxDzniXnI8SpVaT6R//jv4Pk
+o88BANG8R7ag4MgbVIgqNX79H/weAOBm3SEg5fo79x4gSO2xfXSzxJOFnZA8mtCUbYcfLexEcX4v
+xqOJTfMeHU8R0K/x9L1TjEn97jkpFPsMHZWV1DKEnlP92ryB/+rv/T0AwBFrXJMzg2xn9+fNRhiR
+bLNf10DIbsfGQNJbtKBu5aassGXqFhuBmve3Q4uCHJxq32JOGYDQpcHPWYRv8IXPNz1eEGTmjR1M
+C3ttR1MDh7Lzf/N//vXvG2N+CT9l3KUPd+Nu3I0vjdcifXAdgdPYhfBaOKWdwaPTFBFdMbK6RDto
+IfgCknBVxxmkwTyk1MbLeoGIitCh52JC7YW2dqGIb3AH442khy7JpvMl/OFqCIm64szeNNCF3d8F
+5bM8VIcV3fQ9JMlaEDVGHpWiZYcwG6zt7Qo1kh1SukfXGvCJi4jdL+TmBvvysu3hEVXZ1hqD8dce
+DooBKSgkPJp+KGohLFAgZyjur24RU4OyMhXqwYPSc9Az7cg7mrOUzQHFqbQHzWvkOwotEXidBiJG
+NylNWkLHoGJE05v+YLEm2hpDENppg/bGFiPlC+IYuhUqQ2TmbAykdh+XaYfHtKmTOkZwaZ+HgPqK
+Tz/K8QbRlkcjfbAmG/fA6UP72flE4A1GkXvqcHhBictb4iZMANex93Ry4hwKt+p6h5okreScOg6J
+C8MIapnv4LK7FM2Adycu98G0pAsxOqeuhy9xuxpk8RrEdKh2kKAksnJDkphoa0gWQTsH8FjMPQkF
+MmLoi8jA5fMilf2+WApUU0amiQuHae5+2+NFaT+zdlx8862fbe1/LSaFQAl8Y6bwg7KCW7wNAHD6
+LRyfYXkZIKGH6TZvcTm24fxOE9xkItSk1vquRhTav0+DFDmNM86mHlqKEAZscxWdg4QtywIKwcDE
+qzIc8WK/LDsYiqWsB6afAeIZn/hdiFVq932/i/AuUw1dAhD0heyHN8XgXaYru67HsWK6kjqYEs9e
+8YVOOoEb4t5l6UDwxft+XeKysy9QFnWQhHwPOiHHlYvHuQUsbacLhKxq75saypCVGTrQNHARhIFP
+j0eQpNj6QsIwhM3LHFnBlCc3CNnWCwnhzbsCu5VN7Yo6h+bDaKSDGbddmC+cr95PrL/m9N1fgjOx
+aZXX/BzM8DI2LVjMR/3Zm1DfJPs1YZ1k5GP2l1kneObj8oFNO5xH9+GwDdd/Drz9qzbVW7Hw3ucl
+xJF9ns7fnCMvbGoX6T1WW3ufnHqLntc/I58jW/c4Pycc+dg5XO9et7if2M/+BrFGev0WvJHtMhyj
+x4JwZCescF3aF3rSlqip1CQI7TZKQDBNKLTA6WwQdC3wgOC6de1Cc6J7nNln+u3EQcUJ+2yW4OaZ
+3e7FKZDt+axXI9zKocj2auMrpQ9CiIkQ4u8KIX4ihHhfCPFnhRAzIcT/IYT4iP9Ov8o+7sbduBt/
+vOOrRgq/BuDvG2P+qhDCAxAB+I8B/J/GmL8lhPgbAP4GrEHM//fwPHQPHuC9wkFL7YJs1SBjJBCP
+XPRMJc5kgpTAIZ+97X2WwWOBKzma45R6d37oIqR3bTSaQDEFKam/p1uFimQn14QQ7PmazsCwAnzm
+naCk7NaWxU4ZxIgZ5i9EDdDjb3ykkBIg1CoNjxX+jqvygyRAS33JoC0h2EW5iFwEDuG8g2V74CBl
+2Or6EeDY34+uFXz+PBsfY8RbOApsV8CNcgQkLfXPY2wuqHHoekjZUVCuhMcw+JRCNvPJCClNckLj
+o2zYASh7lNGQBzgHGfiBtblTOZa04bte3ODzp7ZjtAOQUP5spATanb0W3/tdq0Hwzs/N8W7yi/Z6
+Bzt0vb1n7c+P8cCxknbOOwZPCCIyNJw5fWOG47dtBwdvtZBTAlvyOXpqD6QPjiBmdi063dq/N5tr
+jC5YHJ6MMS5s6CnqCh1xESrUUB31CRh57r092glNbUZnaCnNJzwPu7ftPh4u7LacowYvb+1q/QQC
+gtJ7rYhwMhS5tYGkRHtLfm1ggJaS8tIxMEyFR9qDIQvym5MQT6lh8ckTigwdC9ybW+CVHBuEpY3G
++kmHd1wbKa3dBmL7x6TRKIQYA/gXAPxbAGCMaQA0Qoh/BcCv8GN/G9Yk5p86KbRa4GUdIFmssDcW
+YOOWI/i8WXmW43huTzJWgEdJbpQ2tDo6PTpYncMTmEZE96UJCua+0u1QsY0oKFPuQqHIecF0jXBq
+b+4oSFExx6urNbJgmEQoQjJTB9lv7TZwW3vDL0+O8O7cfma1yAboP4IT5rWVj0/osO31AiMiJacN
+EFk4P7YZ7c1FC8Hq/Frk6Bt7LY6TBiMaxXpxCkfY8zsasYVYOviYXYYXxR7HWcTr5sBQfCb0pzij
+wMs5r+vF6SWYygKiRr1nzn00gWKHw/V7CMq9d2QRbvMS2aUVGXn+6XP4rDm8//kTnLBaPvJCRKEF
+E63IjfjwozUuYpr/lqdIfTvRrT4IcELQV/CGhyMyOz1SvUfHI/iPOCnsnhxUofrVC0h6Q4SmhKa4
+rajtC+Qfn6Ap7f40HPS17cp4J3OcEwzUmw1yiutItpEvI2GRXwC67QYRBXJRZvjgffty36eqkoy3
+uEeh2cZVeDGoiCUhJOn3N06Hjj8nbEM7LeAYek90BhHTxtNxcpDSlz0wYl71rYk9tpN5iAK2NXwa
+XMLhwvB0t0K2tL8XDx1M1M8WrH+V9OENALcA/nshxG8LIf5bIUQM4NQY85KfuQLwB4rO/34r+qpq
+/qCP3I27cTf+BMZXSR8UgO8A+OvGmO8JIX4NNlU4DGOMEUL8gUCI329FfzJJzfTFGoWrkOd2JpZF
+i7qicrCUyMjTh68g7WKDgICmYBLBpYefLDQmiV2VwiRAOQiuFPmhst8wNAxdBc3+sONIdMbOkSKQ
+UAyPOy9AxbCtrQY79QZ5SYiuSXB5bPdxL7oH37XHOXYNajLcjlj1r/0WasOilXbQDSirWEARhDIN
+2C3YAIO0YysF2JpGWodw2fmoG4GQoKVyY69VGodw6JPY5S1SipD4vovUn/IzCY4Su6qO2ecOfAcV
+r4VqC7iE60bJCAk1BpRToaFOQc/PmsBDyDTIediiLW2aULa7g6GOjCUkj7nZ2PN78fEH+E26UH33
+3gncyIKTzv1baEaIzdpDqyxnYD4aCsmXEI0tUPZ7D+gpmR+mcAZTl00IueV9jYnHUA0M9S3MIkNH
+BWpflpCsYouFgBA2BNc+5dGEC3nNZ2G7g4hZbBb3MWeatiQ2qGkTjKmPuVwUeM6oIdkXACXYWvgI
+ybdpWQCciA4lb3DjGICp5Ko0iAnkcsMxRuykKcryBzuBtrXfy8MtcuIezMagZRTSLDuwmfPK46tE
+Cs8APDPGfI///3dhJ4lrIcQ5APDfm6+wj7txN+7GH/P4Q0cKxpgrIcRTIcTXjDEfAPhVAD/mf/8m
+gL+FV7SiNw7QRgLZIke+sbNdr2qkPtWAhQdDb0dHJTgjmk6xdnB0eoREDUUyAS0og9W34CYQRTM0
+jV1t4npwXC6QcLZelRWywcug7XFMRKOuDAxzwB0l4fqixRpky+kIR+S5P3xwhNizxa71p7fwR4Ov
+A1Wc6hYB/SKMIxEP+aKvULGomAb22E/nPV5QTWmfV9jz2Ee+QklEnN6vMCrsvO4FdtWtdmvMNVl2
+skPrMhrpHAjN3rsnobyBwejy/LfIaZmWQiOZkn0pBRShicIJ0Zc2R+8kI4W+/SLCMgkmp3bVPV5M
+D+fiCA95P6gx2/3elhrJ/nMAwPPr+/BmFh6dqgSGyLzqaYbbOYVb6SehwxJdYdcZkyVQsS3G9BMJ
+Q0akTHzIYxZIBqHZzUu0OQvFvgEkzWK6CvULu7340oFX2e/1dA/PS4ER28h9t0F9bVduT36Ikm3g
+jIQp5AofOpYk1SiNJXEq17saAZ+hSSSgYqp4U7FqbACHEOYnmww3gwO38hGe2uiuTkLE9M0cEU9y
+79EY+7W9Zy92O+yJsj050bi9Zks10zAJJf5ecXzV7sNfB/A/sfPwKYB/Gzb6+F+FEH8NwGMA/+pP
+20gPYK81HNkf+udSCMR0VgqkA5dsx8C4kAGBTIS1nrkOHOLIvd5HvrYh6kT0UKyut62A7skYZCfD
+iRw47HZ4KsDztS1ENWWH7piSZsqB29iCkc/i4q6pofZ2G6NZiHeIce+dCtstm9aqREZHqccbG+Lm
+pkHEwlgYKnSsRD7JagQ8jg3lvDahwZYP+c26xRXrLrELtN4AepHY87qsiHJ5sw2wpBmtJwRKij+m
+roJk90VrH83gbUj0T/5ih5cbOymezAIIyr+NlnvUPKbAcw+cAEXMRiMFOv5OCIkpQUFv3z9FS9xA
+3asDUOtmoCGrGpvBIep5hs09G7ZHlwtUHxDoc1phtrD79n1Kq8M/cGJ0O4EYTFT6FIJeoWhm0HLD
++5Dy2K4hmc6JuQe34c99gQFGAmMQjGwaU9eWz7H7JEPHtNFRU/SDwazssabeZsYJfVMuka8pR9cA
+FU1d8rZHIpi6dhon1P/02F3b+xKaOBXXVdDsTmz9Cjkl66bpBEfUihRje11HEpgd2d9trp7hUzJf
+owchQjp8VckWweKP0YreGPMDAH8QlvpXv8p278bduBt/cuO1QDQabdDWHUzXQhByikbB81icanuc
+z1nscj1MGRoGRPlFfgSXxa4sXyEkzHk0nUBQOGVXPEfIUDKkiMW+svsFgAgKOUPtm10NzZVQ9y1S
+brshoqxZFqgoonni+WiknaGPJqfoSttOS946x/Pnti1088JGKGXW45hheeTJg5+E1wLCJQOOkeiu
+btDs7QxfqBY9C3Wl7JBS8DNyzaHgNxlSIq87rIJbKGgWw6KZgzRg6Ft6kHS8LnIWLQ0O/fHWBRpN
+5ufaQ3LGiGW5wPDIuEzRtCdgGAX4HoDYXtupmiCjuIyzb1EzVVKMUGAcOPRdXLU1LikfVn+s4Ps0
+X7l4BLr6wQ1o6nPjQDjUSnArtMRSO3sNx1jxkb5fQpDEZXIbOqskOXg36voKhq1fGQWIWLjTbQVJ
+yLLLFuo8qUCXPtwsrg9q49ANqiVFeBnxXJc1ylu7+neOQUdMihsIKAxo2h6CQi0p26ZaaCgWh+PW
+RcV9CKnh0CdjcjTFCdvd3YYK5W6JRBCCPxnjJ9c27do7HbqdPY7zqYtkSojoK47XYlKANtD7GpEr
+UFHNWKOHz4c/DBzMLmxudTY/xvFQa8jsv8qTwKD6m9fQvb0I0UkCzRDd7BPI2eAJyX/dEVRCee+u
+x7OFvQG6yGFy+9KEcYgFXxyHACnZC6z5gO2KCvulxfW//2Mf52PSunWHvh1eIPtwrNAdYKmBAiaE
+OSP2MGVNwGOu7pQtysg+bHEb4EXAkNPzoYn99zsXpuDLQtrz76DEhuFuo3NElX2QmraF09tQunUE
+Clq4rx378hsRwh3baxxMAvjKvhTpTMHzCNf1PDRMf3z3C3/NQVWpgsRAeJjKEcZTpgzOCqvnzLU5
+mZ5Pp4dcfLu9xnJjX+gH8QZCs5zvBHB81hKWtJSvFOSgbD26BCjLb9wIhosIvBEEO1dgl0GoFpLu
+VqoK0DElhMjhzSwACPUzVLdsbdE/xTkdwX1JQRYVH2DoZesif07PTuJeio1GyQmybwSEok6kDnAx
+ts/qTPoHzc+I93yc+FjlFh8ReD3eObZ/r1sH99jBmRoXIanh/ZHtqLjVHpKmN96iQUJotnoBTHjP
+tr2DiB2oVx13LMm7cTfuxpfGaxEpaK1RNgU2ixqSvPPW7eCx1+pGEXz2dj3pYURE11DFTTwNsCg3
+fvM+Smo3wmsR0gcyee8EDSu1WyoZS51DkiTVFwY9C0a7ssApq+VNHcKhn0DLnnglOtxniuJO1rgi
+8eeoXaFjdXq7bfHZ0q46z5nmNMrBlitpGPp4Y2JX7jiOoag6vKNjtAw7uNRBjDYtphELY8LFzjC6
+6V24FCrJxvb7v5xL/P0xYbB7g0bYc9rXLTQravVIoujsOQ1Esne+FuLy/F0AwMX0yNrOA/Cb5iDS
+UU/G2NMKzuV5qB7APTpf5xqGeIu2bWE6W+zKfixx/SObEtTUyXzSOwdDna4oUVCuTbcOisGroitQ
+SSJcN1RwDiQkC62mWUHGNqJpCol6+YzXpYaMh+4CgXH7PUxriWJ+fASZUP4sctBTc8KRHvZXhHez
+o9IriZDaE0VcYv0xi86ig2D6E7KrE3k9dGBX6641EISIPjhK8O1Hthj9xvERupAFcvpzIAxgaICz
+aAq8c++R3XfbY0SCnahHaDr73Cpv0BEZwymYHpcdtsRFnAUNOhbTqysH21P6ebzieD0mBWNQNh2k
+7uETA+47HhyXFfBwCpfiqKubDoq4/IBajDo2aMlkvPfwHMGZfVD2N1dYUn57pEdY0AFkw1y3yRoI
+eip6oodhFb3UejA3gld3AMP/KBgeeKAltfjEOYPii/nR4wV2ZBRuswJrqj4JhrC2QcLgzFFITu3D
+dno6w55CLC9a+0I4ZYt2M0CJO+x4p+pawzD87KIAY7I1Ry3zzDPg7Aknt6BCyWuo4GDMBF1qCd+3
+35uNbbX9eHICSdn62iywpfx4qkoME256kiKKUm6DtPCqwtAAKCWw29iX+2axQsj7tN9sEDHdWhIS
+7kt9oCl7iYdwsKg/a6CfsgWY9Ch/wlThwm53OvlnADIRsZ2ij0jbFhE6x048zbMOwUMeFOHqenWL
+/XP7Ak3eCiEj+4xUfYHqOUPwoz00jWHYREHQT1Fp2+2A8DBkKFGcoAxYMyBALhASFbsMI9/Howd2
+svyVP/PzeOvc3utZeoRyMCze2Zf8x4sbnE/tpHd/coqHF7Y9K3QFzRQz8xS2S3sfVtd2Yp34HSIu
+FugBPeJkEUxgMj4PcwPTDyL8rzbu0oe7cTfuxpfGaxEpALbHXTY9PMJ2U2+MhDOc3+QImkHvboUf
+fciVorGrx1vTI8Rn9rPjXYwxsQkCFZY3Nvxs+hH21zb0G0QsKpToh351V0GwSFTva2z39rPHqYRi
+RXpgYrYo0YIMOC1QcPXLmxwt9QSUq3DKELYlVsCXDgz1AI1REAHJPF2P0bkNtR8ybdnvehiG+Bdv
+uFizc5K1BjVlyHupMQ1J4upsxDP37+FFbq/Px7drVDzXQEk4sT2P8+gCc1ak65397O7jCh8vLVag
+qzQkuyETGWJ03x7bA7yLIzIpffp87vsM68ZGN88/2qDY2m08vn6KJLH34f7kAsm3SG77odVTWDUt
+piMSu5wQIVfg/WcTuCQSuV8/Q/wmu1EpvRbzx2ifMeJb/R4yArzGUQJJ6T2YDO3HBAixX6/SU4wZ
+PYhYoOU+PvvwGT74x9bsZZz6OLmc8prbAqcJr2E0Jf0WBSYX9r6fHr8BNBbLcMYuQzMtDyzYX3z7
+CP/8e5bt+d6334ND5+5wGsFhMfPlrb3Xy8U1JFPMX/zWN+GxO9FVDTI+409fbHC1psJ0aSMM0Wr8
+0j17ftN4DuS24N2nBiJnEbfc4yQdJHpebbwWk4ISAhMp8aytEDFum8c+WmUf9Kk/Q0QPgenZKZKx
+DbX9DfUJpyFcIgVXLz9B0tiQuPz0KXafW33/JHwHlw+ZG2/sBJIvNIrWXvRxmCJMbegYSIntLdt3
+joeSNYOeeaaBgRRDayrC1pCyWrSImO+dnswxZbVYMuY0bQufohmR56Et7MNWtAYuJ4tRal+eWXQC
+n1wNFY+woZ/juuqxpDOWcpyDYMyMYiqJqOF+bF90t9OgZSSUbGHYRfGmLhzi5E9SK1gync4wu7A/
+d6ZHdT14E+ToK/vz9voKE4+sU0FKcwu4DDhPH5zi5WO73esf/gCfrclReNTg3pnlHfjdYLC7Q+fR
+w9EboWPdJpEVOqaK+w9ukAt2DB7bF6HzHbTFF2jDxVObHt7Wz7GkF6irNR6NyVx9i6pXX78HSbr4
++rd+C88+tJ/9jU8zPF0StDXOkRH4NmWdxJ+lcJh2qsDFxRt2Eg7jSzxUvL/kQ8yyEAl1GU/iBAMm
+an21hYnsfXWLDD1RqAXrSFHnAkylunyHlrL7Wk2wem5Tl6zJAIdcH5emwKbBNRcvV/Y49Sgq+7SA
+YIuzL4Fc33lJ3o27cTe+wngtIgVHOggnCbLHzxFSDfnhw/sYEyAzPhmhJt98lWk8+dDCkX1Wf3/5
+fI6YeIP+en/QFzSnGsuP7Ip2UvfofZuC/M5n1jTjw+UGMUFB1alCQnMZ6XsHsZdmb7Do6RbEFcxA
+wKGD1K6q0VN4Q5oOgqt73WncsKcdk1NQ6w5n1NEbhSE8Kgwax8BLecyF/ft5mmBDLcb3P78FqQ8I
+IglDcEusJkgJqLpHQ5PqCshzu3pu2wanFH1Zlw0c8jziicHY2NC9MHbD3//oB7gm07JddcCJ3d7D
+UYRHcxtBqHAM/3jC47T3oNwsMWjlFUbjdz+z6doPn1xhs6alvBvg4m1rJR8d2TTg9maDRyN7bHEg
+sG0syKjTCluihbxsjZr8kYu3LY7BG52gY4SlP7tGPLMFvBc3azTXdhvPN0/x9oX1T2xP7f5UEKK6
+tlHhelvjhjqJ8sLFxRs2inlXBpBjXgN6WNZXLWbsdhVyhZCS+D52EGSMZuS2hErh7E1yP6IEOSXr
+nuw2WFzRgxIBRsf2ORtP7DldvhthwlTZTeeoS7vdql1B0k+02kl8eG2xHjeEtHeLJT5jwfi9d2ZY
+MgpzO405nc5T10Ce/Gzpw12kcDfuxt340ngtIgUlHRwnoUX50Q34YjTCviM6rFfYP7Yr0zNPYPHS
+zvgxNRbKeYJxb2df7TnwaNpyOnkIf2RzsifbG2x/z86eVTbkxTlKrp5ZtkEaUl14FKMMcpAnAAAg
+AElEQVQgUq5Gg4ZRQ0cyD4ywaqQAcr/FSLGX7FbQ7N8XWYGYOWDB76cjD9OQykqOQBjZ6CZRLjy2
+Qw39J/e6w5Yw5+aqgCbH3nQuFFtgdVgicO3nGxa4/JMAMcF8wjEIB6KYp3BMGLeSc/Tk/RM2gHYX
+YnafjtlnCdrO1gMCreESe+CNRlAssBoi7bbqGoMuaF8rPDi1btTf/XNjlPTDOBceHp3Z2kD7bVt8
++9Hvfg6XuIm5d4InNN9xfs5Fem1XYzdrgc7uO6eRTYmXaFd25duMfYA2e/fm7yA1nwAAflOvUNPL
+wWvIqE0v4KT2GG7/8e/hmjXJ2J3g/Mju4/zhGJMxoymSla6zGkVuaxhtY9CNmNdHEdQpyW2MBI9U
+jHvnNhqVoxOExFCIbYWAjF6pAoSUaUsa+6zIownCgbgFg4C+JXmoITIbeSRjgQeEZqesl1SnCc4c
+GzXcjxM8Obe1reerHOXWHn9yfooQP1tL8vWYFHwfp+88wugnHyFlGOwlIZyVnQhE4+Lia5YldwSB
+6Jv25gYUKTmfhmhy+1LViy06Um+j6QxH7KsXbo0pUcX+m5w0ikvsKvvZkRtiTmPTz25GGIAKI8fH
+C7IrS764utXYd6xktzu4qb3hx+kUhp6Q4yTFlJLwIV/c8zjBKZWoRZEjJLVYGI2WfX9B3T7VA/cI
+mnn0L05RkXG5ccwhXBVGgOQ6sBmAsPdwzILhqQA0q94i0vh/2XuTGF2z8zzsOeebp3+uuepOPTe7
+OYikRIly4Egy4gQGZASGkyCbBAa8SRAgiyDeeZOFFgECAwGSTQZ4Y1kxgkCIHCBGYkmWBFES2RTZ
+3ezp9p3q1lz//M3DyeJ9/iJbsMOmGqGbQR2gcatr+L/pfOe8wzPkXJASN8T2nvzhhNDmg/svICOQ
+K/A1VCH3eOhoBJSPr/MCxqLOIbsejhXAoihKb6uHMJJjHFUvwVL022wU9u5JqGx4DrsHf4RBJ591
+uvgA51dy3YvQwgElz/y7O8jfkpTA2pIJ7676CF6T8xw5Y6gNN/66QRBJgfnf6CXYeVNUwT12g3R0
+CG9FM92DCbwV4e/jGPFIPmM4jtFj6hnEcr6qe4KS3Bar82CHBMY1EUoipUfsIoXjEGYDZNvy4G/0
+M3fv45DMXs830MvNsyYArAlgazmG7Thw+gS1pRnqnsyzXjZA8LI89y2meem6xA67S+N7W1DHAt7K
+zByekUUhnT7DYCTp0acdt+nD7bgdt+MT43MRKXi2jfuTCd442L6xxCqaHAl32MRy0GOBDkUNPyVa
+iyKhEQATys+NFaFZsl0WBBhvEe75vEB4Tz7icCxFr7pYoCKpqnUUFPEI+5MB8qkU66A69OhoXZBx
+p6GQs/A51AksmtJoHWCHLsGTSYSEbUaHEYbTNdj4xsSjEAGNY0y1QEMXlZYpU91q9Cz5OowTcNOF
+WRdQFPC0XP+HltYUsV3lM8T0SdzyAjjORjfAgDVJNLqC10qY69BeO+x5oOocHBgEI5qweBkK4iXy
+comGaLwNpqNeNegojjq4N0E/lgJmVxfwsNFZqOEwKkq2Jc17EPmYMiw37x3jnQvZ2f7a/RVaSvJV
+/jYMI5KE6EEdDhFQzbvTHvS17P7d2INbSNQzProDnyF2TbYk1tfQ/Kyju68gbQl53j1Ajx6io1DB
+9eVrt0dvBmcP0UKeb5mnsAkxbyobu3w+E7Jux3tDeB0h+EWAIKTcmmOB3WwkrgubkZDh/cnTBtlM
+5myVrgDC24O4DyOZMvw9DY+tyI7SdPmzGiGjybq6Qp/oTutYIWVLcpxE6H7C1/xzsSjYrovJnXvY
+u3cP60tJGdLrDJM+DTv6EUL2dK3EuukFNwUnfF3CIoZ/+NIINsOzVi/gM652rBYW3wpdSBXXbjr0
+e/JSpKVCS3Vl37KxJvOtrYObBaBhN6AyDQ42DyhZYm1LeH1H2SBFA25jIaTV+oCQ2XRRQxFvYJcG
+brKxnwd86u+tyO6zcoMp88VqmYOwdsSTHhSVq7O8Bi8bHnvlaZnjzh47BGGDH1TUh8w7vMyn7aoG
+bUFMxg49GkMF390oWmlo0nptZWDWrHdECUA8SEOhEC9xkDdyf3SjEQyoidn24VKcpVnn0jAHYEr5
+dzAY4PlCOAcfnn6Ah6eEdD9P4FFZqucsUTK9U+bLcj6RD6RSJ3KsfVgHGwp3go6LjNKiGAUAbY9i
+MvUC4MbibNkIV7I4KdVCMWA2WQYVykvWNRu6+xl0x+u766FJN6bABTT9P3MCheqygxPIMwtaA82F
+0Lg5PDpVBb6BZW0MZVi3WlfQBIuZxkHrMUXpDHRCXdB1DJfvQFPJPTl8oQfjyvleLM+wYAoGq4PP
+ed1VLlLItXzacZs+3I7bcTs+MT4XkYLSNix3gLuv3MMjSpddz1cYj6Ua3q4rYIsqz1UCj+YkIXux
+6C6hrqmHWAp7EAAsbaM3lJXUWpXgpomQFeLmZH1j4e45wPKCiLjQg+o23n4tNCMMs3F07ICc8mhb
+9QSDDYtQFTC27LYq0KhJlCqIaAxcDa25O3Qx6qnsbLPrNTqi4+Yk8NieDTrXwUk0wI6DaVy4tC4r
+0aIlLHpG/Yf1fI2IHYdXhiG+/bbsxhMvwA4LhuXaYN2XuNRPWfV2bGCjleBrKEMBwnKJlkrEjV3C
+U/IcHH6vF0WwaBBTNyUsChFYOoSy6Alp+WipX1BSBzPcinCQSTHz43c+xBVFQZov92Gu6RHoxUBJ
+kxyi8mzdwuqLz4TVxlAxi67LCDajrS51YFyBEv/Qmq1CVzH60T2MGUGsz8/Q8rPLezOEWuaLZsE3
+P+3g35e/83t7qNZyP80gQpvws+VsUa3LG3+LJvQQtJyztYbNVBjKgyKDsVvL3FutZihpODQaDREk
+tBOcxtCBPF/fsRCRXVnzOtzAgjOQ61heuCgDomHtEFuhFErXvRRu9TPYfeiUQeo2GAUhnFekgvz8
+uMGMOXyUJNCN3FQnyKFqudmaIT7sEcIdeSmWjzIsV8xx3QcoLuUhts9XWBGQ0h2KqEaw26CmFHiX
+Fjcw2May0DDfc4oGoPbfRmHI6A4NmZOdj5sqc+L6GLMSH9ohPOr5FRtYMjysN+F8scTyOdusqwKa
+lPGIbUrHdtHvSRdB2zEsQr5rtDdGsFbboGJPse2oI5gWNyaoeb7EKSG6/kTDJzgr6kKgkHukCMbR
+2INyaHpSaBjyNdrpEtekmq+LJe4fbVqS8m/n5GgzOUbbLtEQQm71DTr6J6Jp0DXyIkynAsF2ax9H
+u/IZz959DxXzI/t4F9EWw3lfo6OBTafZO50mAEPjun0f6jE1PZMHAF9ItMcAwUAbuftW91DP3pev
+3RY2+RqR76EmVdteaJiQkHUjG4c7PgPYwekqF4Y+kPW1jdVSrm9nR56ZPQigqIBlqRyKKZPl7kOT
+3NG2JTrWjcpOFu+8nqFdkuH4YATbkYUpt49RLzcpwzk6R+aDOyDLsm3QnRPotRshZPcoj2z0SOFe
+Zyls+xbmfDtux+34DONzESmYzqApagy8BP5EwC8ni2M4tO9uiwplxF5xGyMlT98pqW5rNTCEROde
+g9iV8NLYCrovoWj/YARF27iMuo3wE4jrIYDYQ51yV1o3iF35u8ZtcTmTXWpFQQtLaZB7gr7Xg0X4
+c900yCmyWJQlOob8NQWeLW3DGpEZWLkY35XzHDkFCl6rTTm2tq6ASo63WtuwSXxyrArORmJMqZsC
+VUMcA6oOl5TlWi01rI1ycFkh7ks6ls7Tm4Jf5bMCnq8BFjDN1RmKjTBhmmLOAl56meGj9cdyu/oy
+dS5TBY+RS2116IXEBeQeVEpClLFREQzWEVsSBBMsKKyCxtxoVkx6BQpCvZ2LECgkuqmuJapqPKAG
+tRZbC7qT7+PyGTCievQ0g47l+lQlkVDXL5ER4GWmORTTLU9V8GK5d66uYZjSNd4HvJ8+WupcLlcf
+wqE+qB7u3zAbRwz3/b09NFNGFbVGtRG1aVJYdPzGegFTM/1jgbZOFZpC7sXV8RVqSrNdVgYrys8H
+KkeP6YPPtKU5z1H5EtnMjmcYBvKs54WFZyuJshddiyr4GWRJtm2D1WyOFhk8vlRVvUZO7HhhPKSZ
+XGSrGhgl4WPG8Cy9noHgMPi+j/iA7Uttw+PbO2oOsNISBmeZ/J12a2jmd806RQ35/jxbozeRG3w2
+a5CndG0i2EZbQECQDiqDNXPnUAW4XMkEKmsDj4zJ7Zg54MCFx/ZV19oYsN7h2AFqhtrLTHJhs1qg
+ZojbKtzo+qHSyCkBjrJDR/epHoFZy1Dhgyu5ztNlifFYgCtXRYH3H8pEPzw4wMCS8Lhz5TrT/Bxg
+1d+yHGh2DhAabPckLI/feBmaua+x5NwGK4WWLcuqBrRLj4vqGiDzsS1XWK3l+a1Jre6GDd75WL6O
+4h62CcN0wl3MUukOtafHWHVsZVrkH+QW2o5KSYVBQAn0cqRQfCiIxuXpFYym98WE4fXxCBb9Nu3I
+hmJLyFQrVFM5XrIXoqaPo4FcX2lpxLEsptjO4DEtsZIG1/QhPaba1ji5RMuaUr/eQcVrytPFTeoZ
+KgX+Cmy2WQdtAoc1h6JJ0ZBrkwQtwi15TplpsOKmVS4EpBT5AeaP5djT7hIpORNbcYfza/qZrBTg
+c/H9lOOzWtH/50qpd5RSbyul/pFSyldK3VdKfUsp9ZFS6h/TE+J23I7b8TMyPovr9AGA/wzA68aY
+XCn1WwD+fQD/DoD/xhjzm0qp/x7A3wHw3/2/fVanLKy9COp5hfyMOIWygeEu7+VLBD7ls/IWcAi1
+JR+/CxLMa8queT4encpu25wtoOiYbPkJau4qaxYwDQY3vd/Ws7C8oqS8NmhWLNrUOUpW2ruN3b3r
+IiQaZZWmsLmrznSFkLVoXVfoKH9WMGR+b3qN5iO5voXpsB9J0c3EDvyaEvWa4bfpsOB57kYNJnRK
+imIPhoAlr7XQMeTXI+ksBFGJ1XsSrTxZlXjj3xK47/F33kP7XHZme3KEcswUpNioD5foWDyMhtEN
+38NfNLCsjXDMEIb8D4+hWdN42DRlFICGO2KRGWiCuqAUoHn+7Pw8fust/P73fyD3czLGq68IJ8L+
+QgL9LQmZraJEDdlWrwgg6ncpwlQOaEURFGHTFsZoxlR2tn3U1UZPgAY+dQq/J5GZXVWINdmTQY6a
+0nPrZoVwIGmjZbFoWTlot/hMFwNoqo07vV2smTY+OpG0xQlWCMmjWAxbTGgNUMMB6BDlDgIYclB0
+I8cN+w3iPUklsVrcyO+3XR95KJ/trjI0rbB7C7Jyc9TIRnLN2XMLNhmseZ6hazeRl0GT/XRZkjaA
+QCllAwgBnAL4FYivJCBW9H/zMx7jdtyO2/FTHJ/FS/K5Uuq/BvAUQA7g/wTwbQBzYwh7ExPag3/Z
+3yul/i6AvwsAo8kQMC1MdwUzkp3UWrooN7hbN4DNYlZTFyiWVLzhmhbbIbbY/vOaCDVFQusmwWpO
+5Zoig3XIQg2RbY3KoGmxhtbAow5/Vte4pOjq6ioDaGCyQxu760WOlkWkZbOAz1Zn4rQwZCi1lgO/
+kh3ho1ORyfr4fIaC+IdhlGBF8ldiuXDDje+kXPN6nmJNvv6Zt8LrteS1/SjGcEB0XBLAo5isT9Ra
+8djCMWsfJ6sC/94bfwUA8P67x/iIcOSfn4wROBQ8ZbHMCWJo9rnXixnml4Ir8BcVHNYzFh9dIBlI
+dNajTsM0f47VNR2hjwaouBtHkQvD56MsINohtPzyzwAAf/ruB0hJUHrtG1/Cr/+7/yEAQK9+Fxbr
+Mv6ei2gmkVDF5++6DXRCyHNbw7A2sD5fQ7Hu4E8An5FcDEreZRYMtQnqq2soS6I0HXvQFlvRTxco
+Keob7JNQFU9h6E/RrWuUnih54RqgSBM2cJl6ADjEsfe9FiDE2gt7gE0LuQbwUtaYqJhdtDm6jySK
+8ywbhhFraj6+wbpY9gKabEfN0Gx28ggN70s0itGbydeLqkVbS52rM4BOfkowZ6XUEMCvA7gPYA7g
+fwHw1z/t3/+oFf39+0cmrmvYDhC3Up1PJwGyKxHsUFaFZGPDbQ+RslK7YFcgUy4CTuje2MDiTd06
+GiFdyYs3W06RPZMCnH6RPeFYwRBDoF0b9VoKPEHXoGThr0IJTah0HBCM0hqEFEVJKwuGYWTXWMjJ
+suspg15P/m5J8Mgw9bF3T7orr79xiBHNSbQdYtujqvTGHWhd4tGJqBM/vThGxO6Da9mAIhjKsjDa
+kt51u6lSj2w0ZHiq1mDkySQd2Bq44gIYO3Aoe1csJUXpRTUsT0LqZj5Desk+/rCH4R0pVgaNc8OI
+dH35+ZY3Qi+SyX9xdYJWy3no4V3YNOhRgYOOi8/lU3kGT0+vEFF27K/94i/jwc49eSara3gEWTne
+ENuHvyTXVwrGAMESEU1WtN5DOyOG4voJSuo1Kgyg2c1xOcX9nTF0Kp9r2gbpmRQXw4ELhzRrtZOA
+8ohQmuYs/hGWT/4cALAqKiQ1X8wtB05BHctE/mh7uIPEEZyNZXdoNpB1K4Xvyj1sVYrpgryaa5nH
+brMFZdG8uCxgET4dB2Mcn8siVF59gMkD8iooVKO0h95Qzqd0NXox50VqI4op/b+y4JN+/mnHZ0kf
+fg3AI2PMpTGmBvC/AvgmgAHTCQA4BPD8MxzjdtyO2/FTHp+lJfkUwDeUUiEkffhVAH8G4J8D+FsA
+fhOf0opetS30eoXVssQgofFGMcNFzV0O2wioQ+ApDdeR3V8v6bEwPcbymG6/3y2xYut61G+RE7qb
+2T5Ge1I8muxQlNPqAGoerNMSK8iueTG7xvVSwuerqoF94yVJKG5b4zKj9JXbId3Y0isfB4Gs4sOJ
+B5AQdb8vEYEKWgwT2eXi0QA+kWbJUQ8jwmsdMu9Uu4biTur73Y0FXYUOYUz7OqdBtZCdeXBf7tW7
+H0wxZVFu3bY4odjGIgXWSq7p6ZOHuL/3knw2hV09t4a2ZAfbHYUYvsnd2g5h+8SI+Ls3MnSgXbpn
++ShjmrdoG2GPmg12CbB/X6UtVo7c22+99xYA4HwFfPMl8Te4/+LriBniRtUeurv0vwz2sEhlp2wC
+STVMq+HQkl2vVgBFUfx6By7JZrr0sQEfmwV71e0cLT1AQk+hPqSQi5uiphK2YyIUjNgM74WqM/QP
+ON8u1zDEqpSXT+EwKnw4k393p2sUuzRsqVz4TLu0yWGzLRhqBzYhCyal+3m2hEcClz4HrFiOXVwd
+3xSHna0AyT2Jokv6bk7GQ2Tt5gFe4vqC83ea3ojr9HUAJ/zJGoCfpabwLaXUPwHwHQANgLcg6cDv
+APhNpdR/xe/9Dz/us1qtsXRd+ONDXJGpuDo/uYGJtspBQbqwXdqoDfNdClf072+hV0vOak1WMM9J
+2fUzVMzDimqFlKG5zR50Z2J0m+NVFZ4cy019eLHGKW3EW8sCkbKwWdFWlosV4awqX8He+AvaFhzm
+3EEcIU7kIapSJvRWGEHlXLzeu4R1QNnvtMN6i599TRyAWqLK1zxGhZxKxcZzYZiTYmzQWXK8x8eS
+I//+wxnOmT50MHj3T7m4ZafQZHl+93vPMKDf5mQk6UfttvAzekY6M8Q96lXqFu0pvR23lyjlFt24
+ZVUXczQWKeUasPcZqhof4ALXKI1HDyVcf+vpBoTUYXwki4LbhbCYErW9GEnwK/IZ0SWCZ1yI2eGx
+8i20ZIR2TXOD5bBUAEWGJuYh6pmcE1NrWK6BzdqPGlsI6VFp7GdYfiT3NtXnMCWZouQ+hPEhFFPF
+KLmHPJLPLTIboApTOpffffL+UwSkrVfjMQJLNoPSAZwlxXPCHCF9Hu0hoftpH6akWnW2gkUZAMcG
+tmPiJmIFi7e2zemiNslREcb+6LhAuoFPlxUCQqWDUQ9e91NaFADAGPP3Afz9v/DtjwH8/Gf53Ntx
+O27Hv76hjDE//rf+Px5xGJgvvnoP3TJHSlShrVx88U0JcSejCUaerKqx56Ckc7HPXdKyNPJGVmI/
+Vghpzx1GwY19fDAwiDvKfLFo6VhD5EZ22OuzDHNar/kPvgDHk53wjt6DcyQr99bgBQDA/t5dtJRj
+Wz1/hI/fkULUP/nN38Ll29JLfuPFPRQnsiv+78fSfZgWFSJ2J2pjUPDed20Hy2wk1uSeeLYDTW8J
+xxh07PMrY0FRXKbvD+D1JBZNSBxS6xzvET7c6g5fGkoksECGI1at607hrJD7XBDCXFXdDerOsjz4
+dLbeUwoWGZxZY0GxsJU4EgUVSYARjWVG2kLZkoizvsLpXMLguKxR0jDnWUHEp2lhsZhrJR1ipl3u
+MsZDyPN92bfRn1BJOiHGZDbHMa3psrKER0/PUdTDDmXxmrZB0W6IVPSrrC3Ma9lVdyY99Cjjtqw0
+mmbDXC3hE/ui2KEKxwPcuSsEusPRFppQ5tBo6wB//deks9NciGBLmr6LZi7P4w9/7/dwei5z4erR
+M8yWcj7P1wVA1eU1n3lh2h96fNQdMqbNddthwvvtKhsvHEnksUc8Qm/QQ4/4h8V1gacZYeVOhaiU
++31038POgezRf/O/+AffNsZ8DT9mfC5gzsp08KoShSkxDNmmSyJsswW4O9rGDnNVx3RoGeJZpBD7
+PefGuNQyNjRFLIJhgPVKwkt3rQDDkJCtwMBOsU0AiW9ZeGmb/IOgw/yS9QpzjjsTevtRzcdSCjkn
+P5ZLxM8eyzFwgfvkO2fX1/hDauadLuSzcnRIydHwtIFhPXatarhcLDy1SR9sXDN98oyLkGYhreMj
+reRaQ9fFSzQm9em2ZJUWIgKapsX6Rv3n/aLAuJMJP9AdiMDGkrmsCwsdw33XtzHVcrwYCVqyCMug
+h2fs/EzYkpykF+ixDdkZB96mch5bGLBlZ7XAYkP95s8frxoUXIR0ZWPAlqy7M8bjK3FeAkJ8nUpP
+abjBBjsYUtw3thW2GMIfTPbx4ra8NFWRIVttzpmsTtvBI7qFRcqGofGu72gsyIPYstUNzHmHtaas
+XsOfC9/DcR30eB6h3QOoBZrznph8diPYEmCN4lJSjdm8xpKLYZ4ZQG04ONwIOoPW2bBjO/h8JtuW
+QUxvVY0Oigt5Q7HazlZouQGOEw1LycL6/HKGpzRZbkILgzvMoT7luGVJ3o7bcTs+MT4fkYJWsHwL
+baNwd0v6uUcvH+LVXUkfDo/2ELpkr606+IQub+Su6qZEuWK4FwVYE47c1hU0d9ggiFAxPcBiI+HV
+ItoVeOkgPYa9K5GJXiocbkxGrq7R4w5rmDK0XQc3k93hOvu/8fDxnwAA9pcR2JrH7508x4fcrfKN
+l6StoAMah1jujdJy1Cqs2cHw6G25Qo0eyWGpXaLm140uMDAbvcIFUkqH19yttRXAZSqVKAuGjNJv
+dEB1T4qOs1Ufk6Xcr7sMy6cWELDvvkSLCTUf10mFgPd24WV4lfdw0RcMRd3GGE3kexM3hr8p9q9L
+HFL78P2TGa5IFZ1y578bKXzI5zTUPrIBTXQu5/hmLl+fPbjCn7fygV/3JQpwJzvYJy5k2B/jVWI9
+dl97GUMyPlt4qMkUXdN1++L0HOYphWymBRpChT2vQcXOzoODQ6R0Lz+kpN0MBsNanv924sMidD0e
+ObDJusweSfqo3+5hcfAtAEC+/BDpqTz/VOWYZXJuPWNjynukN8/RNhTOB6pGIWYh0lIt9gnjXtct
+CnY+zusN5NtBSyBbMh7AECrff1Vh8ZSM3jrAcfozaEVvoNBYDia9BC8fSFV4e7SFyb48jNEwxsCX
+XK2KS3ixvAiKohJmvULep9iG5cFn2Npl55hTwjwIPPic0BbbY2XjoKESkndwCIcuRUuTYvtQFoi7
+7msAgTpFTTp1WaO8pC3491d49lg+49n8HCsyJh/N5mgZHg/oUfnCTojtHQF4+rGNdEmVpm6KdIOa
+K+VYTy4aIBAOx0C7ABekwsRIqMYzTMYwfISGngBhYEGxu9JUGtFEjv3ANpgcygv07iVQsZ12NJa/
+f9XbR0nh2rO5h7aSboFn9+BacuzxKkGSSJ2kN5Bc37geXppQvCUAkoKckImDaCHn9EKjkVZyLQ8p
+XNv4Ll4pSXcfWlDMs0/rAPsTOY8H23uoKmkjj/p0VRp6eIEOS3vbu9japfBIGyJhJ8lEIcBFtsrl
+OndgsE/p++nFCSoqZw0WU3xMAZ8H268ipZ9HyFqF0RXGo5j3YgyHKETdAhkBUmYk6WV2+Ba++0di
+Vvv48QnWTJXWa6C3Edv1LAy5SawJ/HU8B4pp5VArDAl6cxwFb9N+rzI8Jsr0PJNzTy3gS6wpDV0X
+7RY3HN/Hlxo5p6t6hjU7U5923KYPt+N23I5PjM9FpKAU4CgL/V6MwR3uCFEfkbUpvlUIEmHX+X4G
+ze6BKqi51+vBpTNyp2sEBNjoQYCIO2/dpWDTAp4WTEOLJRpi1VXhIrgjisGYXaHkztzUUywK+TpW
+7Ncbg5RKvHXRxzGZnafrEgvyNSrdYYeYha++JtXrX3jjTWwz3DWuQUPd9mpZIaslxMuoOvbk+gxO
+fU9+XhR4RhDSrEixH0g05QYRzolliMnNjx0HcSy70rLUmJB1GXcG44JQ8NU1AnIGtoheigY1Gouq
+1IcRaoqbeLWFmsW1V9wWfk0pO0Kiu1DfqFW7dYdgRO3K0qDd9fn8NL62Q0br2zLlHqoSIGfC9hT6
+Rs75mZ7hkjoau6tDuGpjNS/X9/JogsMDOYetvQnimDL6nQ+PxVaDCsom54H14HAYIynoWHU4gSJm
+wbE1ko8ey+/sKqyPyaE5kgilW6RAKc93ECrk1EjQXYN33/keACD5+I8BAGeL93B+LB2A47MMK/IW
+hn6ELQpeqECjrgjEY4fHClwUpdyLABZeuiNzJAwcgPN3denArBkhcD6urtaYD0Rb4rWDEUaUsK+d
+FpkrUUUbVvCXt4XG23E7bsdnGJ+LSMFWCkPPwn4SYTKW1S60rRuZL13asMgY7IgYxakAACAASURB
+VFzvxv9k01ezPQWbfod2V6NjtcvSGg57+qqwUVoMFWjzVVQJOpJTyhKw5iSlaAfWUr6eXlbQbFOV
+Y/l3Vs1R/kDajdcfrHHBfHCdFag62fGPhn28tiU77y+9LgXTOztjhIlcX+nVsNkOreIMRkskVLFt
++lp1CI/YhDJt8Hglhb31h1O0dBpetz40+9/NirtHXaMmq8dWCl94gVDq4wIRLd9eeTDAFo1oBmzp
+eQd3QP4OnNxD5xM34PVROPLZOitRamIkmMsq34ZHx5JOuYg3hjMqRf2IjNejCvalFEKPtgW2+97b
+KS648/mug41TTd1l6JdUp9IutvfkeLssqAaBhd5Ifu4qFw4jCe36sDd1ANWD6jYMWxLMyuoG0x1F
+GVBLVOGE3g0zNX/6NqaXcu9eost1FwHLQu59azuI6IdhbBdv/cHvy2d8LK3OD2Y53n0ukdlq1WLA
+LmoXAobPJKhtDCjS25/IOfTCADQgRxiF2N2WSCjvVlCnMg8HhyGsmkLAZMFa2oNN4eHZPENvInPL
+nmdQ9CVZn1bIpj/Z3v+5WBQs20JvNIRyEiAj4zApYJMPoCILFQE52ooAgmnsaOMK1cAlztxyHNwY
+LJoFFCexcQK0NO0oCyl61WkLN2Bnoc0xuxScvWONEfKFhvMIU6r2Rp5M6GQxuZnQH6U/QJHKpJk3
+FTpWgO/tBnjjS8KInLBI6rQ2OiP5gclL2OxBKw04IR90IzMp2vVh2eySqDmGxzJJs77C03PCv6sG
+NjEJZzQvma5LKC5ijTZ4qS+LzcPHc8wohZZs9TAMWSikGYzllvBymVSD/W0YMhUtq0RBRy6d9FFb
+hGZHLOyqEHrDf7MMVENl5NqD9aosLOXDEVaphLlEYANujZNKrjlcAknCDaBucUHw0l69wD2Lpi0M
+94PxFmyG3a4f3SwEKnaxcbrVyhKYNcQoiCcEvfkyPoTmZ+gY6HFhNHdfgDl+GwCwWgiPzw/uQHmS
+SvS0BnyG6JGLi8ciEpOeEx6/zHBG2ndkK/S2KOTSWdDeRoNTQ4c0CSKGYifuYzCU56C9AOGWzJe8
+tBHelefX5iX62/RZJacExsLao9lsa2Dom3n3KMKjE1lMzk5yLDaml59y3KYPt+N23I5PjM9FpACl
+YfsuEl/Bp9KyEw6BZINcDG6YLXlVwqbgaUtSj1WX6DbKX7YFTSdeq7BgKEbawoWlSarZiLesDQpa
+wtnOANladvzC9eGbTQsphE1PhY6CLOfXK2TvCXR5dd7A6ah1EAJgEejueIi9HWkLUYIAVuwiNJQJ
+KwFDlV3Hs+Gyj62CjZiIB4vXYVYBSB4FXBvBJWGwALa3ZUefs/dt1R0Ur88C4DN9UH/SYERR1X4v
+wRX9VsaFnLvX+HC3ZEf0RwFsh4W22TVapm5uWCPUbJGxpVehRkcGo8pcqIYX69iwKHUXjXdu7ACd
+CUPnUx+hzfQBGrraeCG0AMVpZhcFin1qWXxBiqt7W3vob/LHJr+BI6OIYRwyHBsDtZEQoD6Crloo
+EsIUCmiHfhcGcMeCVenbS4zou2HOJDKtDxrYE84FewQVyTPrOoOHc0YFVKo+nxY3JkLD/hChlnOv
+2hoZFaoRNTggInNrLD+PIgc1C6nxKkR1teI5B+jijY5GAjeUiCTwKFeXAx3hzBc4h0+INRyFjnPS
+8SxE9c9g+oCuQ5dVKF0baoc23H4Cd6Mc3ObQ1GgMNNAxN9a029Y6QOOS1VguYagNqOABm3zYNLDY
+C3d7ApBy1BRNs6HkZmg1TUaKNVZz+Yxleg0MCKfmA48ft/jeNbsaeokccgxfOYip1nywu4seFX3C
+cAPnXcP0ZLY6lgNFi3Oni29SIcXPqlZnUISz2r4BWkqSNxn6IW3bLQsW2Zqm4mflBSomqNqyUEjG
+gEVp4O5IDrsOHIRchPyJvOT+g/uwbeLsRyEUlYH1zhghw8+umqOjRqNJZAJa9RqKhI3OVNDxRkm7
+gKGOo9vvYfs1ER95QKOeb0dnSAksmxcGB2QGFnkLj+Adb1xD9+Qz9rZkVYxdDwFXyFalAC3cdQAo
+0LDXKdGRPQkurCqKYHPnMOkVDBWrEPnQ5D7YowncbZkDc4KmxmWBgBwU11qgiwI+pwIB6fMbaeI4
+Uuho6jJJPHRMD/uRg5aYmsHARbhxHCOOo1yskGdyXzK/xIJ8BmVb2OLqFhgbOVMhny98Yefw2VHy
+0OGqlIfdXNSoV3KtL9ydYPp04yX5DJ9m3KYPt+N23I5PjM9HpAAD1RboKoXOcCX2rBvmYF22UB5t
+0XoxbOIXHFrEW56BxQKfrkuAUGHlNwBDOGtV3EhtgRGGCkKoteyCVW3QuoQaJ304Fe3DZrghFSn6
+D/oHW3jel2Ll6kmJOVdl5bi4y0jn4M07cN2NTyOjkRqoKnpIqBo9SsjZ2r6JaFruAqYqoUiOUm2J
+tqLrNgKEI+o3zOsbw5w+K+tt3CIhYg5dhyBmEa1qsWSnIjsuEL4iO6L25L5F4xCw6NFozQGeh/Iv
+Ydjz1/4I7Xzz0ZIOtHb0w8JmqqBbQc81zQCOzx3Rs2HRwn2wJdHIm8rBu9yBLzoDTTs9rWyUm0iw
+seFUfGhUOF4vl4h536zeNnTIiKYIAZ85pA6gcNMGkX+tHKqlbWCbosmoVWHVP4wa6nN4ZEmalVxH
+3a1RNlK065DAI7Mzx130evJ8NyZBKjXQNnUvSoNgJOfQ2x1gQPm7uDMbjx8ckyjnGgtXJxIprOMr
+NJSVi+MWS7pjRz0bCZ+xvU1m7CpEkch1OOcVnELu0fnDAj7h0UVnw6cC9acdn4tFwUCh0Q4WpX0j
+nAkE6CizHu15cEm5U74D1yHzjZBi05ob6qnpBmionoOqhccURMUeFFt8HQU87XmNmjI4Zb6CTUhs
+P7HhDqRzYI6/h5LW9m5M/kXg4pcP5e/+p+OnmD6RyXhgDwHqMap1jjqkmQtx/7bjwKJ4h+XYcPmy
+KUfdKO8oXod2bKjBZoGIgBW1DysHKPnCWsCAzM3xkkKyyxzRiIavSwU7kS6DNfLw7kze6MnAwf1G
+aOCaKYXldTfwaB0N0DpT3sMVHKZVauijqaWLsLFDb5c+1EgKFN7Eg00IutNZ0GyztqslwBZZv5Pz
++fIv7uP/eCzn89G0gt/jomgbbG90IO8VUAP6hmakRe8rNErud20tYU3p3RitYC8pWtK3ANLqFbs6
+prLAkhJgHqBh3SWv1qgWNBrygJQ1rdWJLAp3VIiONHH9/DHqr35dPqK5wIr1Ic00YloukNGRq/QN
+HG4K270BjnZkwY1tG00n359PufFk1Y0ke6wr2FSXT7SPnfvyP8lgC5HD15W1mnKWoqLWZGtObwBb
+tl8h5SYyPamRtz9FM5jbcTtux///xuciUrAsjXgQILI1Rqw8m0WGigU6B0dQGxBKnkCxat9kEha1
+2TNUa9lRgrGDlgUuy27QUWLM6ssOCABgCD+7enoDTCnXFcZ9AnlMiIBh6dbOCFmxaW3IZ9VdASuS
+SOEL9QuoA+ro2SUQy/dPnuUoLdkJbeoPHk52MGYlu13kqCJW1qMROno7Ls5k13KiFP61YBPgzGFW
+m+q1AzeRz/WuGixIuuo6sv4cgz4jHh24SPZlt/7CywmyP5bw8oOPlvjlX5BjD+I35F6aKzSncl/c
+wwqqpYZECFTPJFVw7RB2IEXa7KloHjx7VmL/5wSc5Xi7KIhHqM58hLvUOWwV7ICFwi8y+nlvgcMd
+sbF7b1oBVHt2LR+71Lv4xs4XEBO6u2CU4+ABrjw5n/XqGrqRnXJ23cCpJXqZHP08Dr8uxDOH4i3a
+atGwuGqaEgWLebPvHuMZpeJ2DhVs1uS6FVPXYonUCMlpsNjB9z/8rpxnu4eMOgw1O2P1GrAJpvKS
+CNsDSs47IU6n0lEYuSE8Fiv9WM7tCgskvpzvOAFGIz73do5tEr465W4SIlxfSeqa5RUuaKvXuAEK
+6mHM1xZUQXfsdYGrzfz9lONzsSg4toP94S52Rj62iftWpQ0v2SDzCuhGWm+r/CmsC4JpKLJSPlvC
+xDJRsicKQ9KeLbgwLh9YDsyeyO98+Fg8B//F238Cn74Au7sDhIb0473mZjIlgyHqXB4CDabg7zjA
+cwnrtga72OJ5LuslMJfjfX/9AxTkT0zZpnzj7hivv/YqAGAYOYgJpqrMCh1jtnVJL8kciH0JYb04
+wjKXr1WjYcj2tJ0OAwZ7OWnDx+kMC9ZXojhBF0nou/dzX8Yr2bcBAI/fy6BcWQCaUhbF5vEVrh8L
+Ms96uIXxaxR48XqoOx57rWFoKV9s8PfzUzz+XUltHP9trEDeRV1g8ETaiEf7AwxjOU+XYJve6x4e
+sFPRG9RIIgrT/ojH5k5470YNynflBewPCuy8KByVKj9HnlNg9vlzPP5jeblT+w9Q/4GkR4dvygsW
+jXZhkV3a5jU0OzvT2QzfeUeQibtXEQ7JeGSqjvyt9zFklyR1z3H+fTn/h3EPKSnMixXrRHUFn3YA
+L052sU9QlFtrXNDB7O2Hl4ARpaZkTLavZ2PUI9+jv4+E6k75Yo0Faw2L6zM49CSdphRbWaXwCABD
+L8TlgnWXxQw7bENWA4PmZIMY+3TjNn24HbfjdnxifC4iBc9ReOHAx/72NgaUXeusIZqp8AsW2Qrz
+Y4GdHq/P0F7JLnWzi/gBIha4+rFCt/GJD4dAS4fqp2/j0TMJ8z9+IiFu7PnY2WYxKAzhxQThWA5Q
+0qjDijEZkINAZ+A5nuKIkm96/BpOXpCV+/qdc0wpFz5KEjhL9qkpBnN5leLPvvMuAOC1F7YRE/oa
+OS7qmlVr4gPm6xU+fCLXPK1qGMrMb9sG4Vh2jL29XYyGxDJwN/v+uyfI5hKqJnYIImmh7/aw/R05
+j/3IgZ9LqF09lV2ryhfIWcw7+cH7OPvf/hAAkKsaD16S3fjnv/EAtkuAzJVEXX67wvyYOo+rBVYs
+XKr+AKNE7me6ctHnDuoSPt0t+nj9vlTW/83CYFbLia7gYzvi82tz9Jg2OsQYVGdXSClucj7/GCBX
+YV1bmEzEj3JtTjGdyXW5H8hcufOLI9iuFPtMUSCtJU1bnWZYTWnhfnmM4lCO9zIdrc4vjrG6lGfz
+flDhe408nycPT2AT6LTpKEEDA8KnBx7gkKPiaQ87TLscd4XpmaRCJZW9YwsYh3IvkthDXdBPdTlH
+QcGcRZrDJ5xcU9/C7gVwW/neSBsE7NDNnlxhzfm0hgMNwqI/5biNFG7H7bgdnxg/NlJQSv2PAP4G
+gAtjzBv83gjAPwZwD8BjAH/bGDNTSikA/wDiPJ0B+I+MMd/5ccewLBtJPIGrh7BvFHNmCNh6vOge
+4/RMct90pdAQahtTVSmKXeRcGbeGd2ANNrZaFQyVcduexjyTFfhkKjnySb6GrmkKcmhhsHHnbVtY
+PclFkwio2WbUFYtkZyUuWBhsJwbzP5Fc3OgQQxrA2LDRO5JdsyHK7elqijWLU8M8w10WibJKo2Lz
+OqN0lrvqENyR65s/vcLQ32hHhGiZn2vt3tQXBttEvg00fO6kft8Wg0MAV++fo0iJX4gMbFfODWxf
+lk8KKLJOJ2++hifOOwCAD94+RfFcdtVfGH0ZlSXRW3hHCmD7owROLs/mw36HD2nKE8UW4pn8zs69
+EHZ/wnOm/8HQQ3wg57Z7nWE5lUjg3mAfo1CiCXvSIrbJbCQpK4/nePvxh7xXQFux+Hu0i1cHUudR
+lQeTUyqNqln5dIGYRVfjj3A5k7rS+1ePkLwof/fR9zWGthT8ohelJV1VwLNaaibfuC7wnW0psJ4t
+l0hIXluySDoIHRw8kGvaHuwhY9T3cPYcS0rPnT1f45Kq0iNKxe3vxXiJeJPeaIzLk8cAgNnVOQx3
++ed5iucfyX2+JjnQzVskxEK8eecAPi3tgBTX9Dr1tUZJL4pPOz5N+vA/A/hvAfzDH/ne3wPwfxlj
+fkMp9ff4//8lgH8bwEv87xcgFvS/8OMOoG2NeMuDpwGPzWRlEqBHXbv5BBhS7faLDwCaYTR0KSpO
+P0RRUfZ7mCGiWUoYj9CyKl/MSzgMvw62pAAWmRZDht9bfgNvJWnF/DhA7w7l4+0jWKTt6k5+9613
+fhdb35dzX6/X0EperMGoxtdekkp8UbTY2pUX73wq1zQqB/D50jte8kNnUsdFxwXJI6sx3O9hQOmv
+7b0XoGhqEycOOjqyjFBBb7wE13Kv7h2Nca8nE1Pt7KOJaJMeBbj7SwI1ro+X6GioYzLChLMQvbv0
+O9QxvoYvAgB2VIuI6YpSCprajt6eFPKsrof5/I/kPC8MLrV8bnFSo/oKad1nJTpXFuQCcn2NMtg7
+lK+/vDDQfPG8/TH6NlMlZwBvA0nfYnei9OFMWFx88wg+TWWNMVAX8vy052HF+5zRE9N64AFM+Uy9
+gkdNyxfvvwSvL1/fv/sQD7bk+W3HslA8nbyP5om8YI/v1Jg+IdBplcOQp6KJTdjpDfHG3j25voEG
+NgZGyxgDOpzBGiNmatpjwXDih8CGlep5sDeplO0hptRdfxajNTKffJq+qDRDp6QwXdQdkkPiXk4i
+jJbknTgRztRP1n34semDMeb3AUz/wrd/HWIzD3zSbv7XAfxDI+OPIb6Sez/RGd2O23E7/rWOv2yh
+cccYc8qvzwAu/2I7/6Osi40V/Sn+wvhRK/rdUQL7egXEFey+FIuMKVBzlx9vHaC/K2Fd2Shon6G7
+Q/t27wg1iUG+76HboHxbF6bcQJotDBLZKZ2x/O5+YTCJ5eshYijawj2fTnFIBqY3imAIXbU7tvf+
+WYaHFD1xi8cwLPCERmOLOgXugQcwdfFHG4/DIcIHch15191AeL2gg80dwaFkmGf5MPQCCIY2Np49
+cVTCN5RjiypUtKDX9KX8yquvIr4vBbXs2kLGnV3FDvov/FUAwIPDY6w/Eu2IKGT69KKDnGIpzXSJ
+iCjMr3/1ywhCaRN7ez7MCQVOtsSqXacrjLflvthFh/6vUEPATTDalWLdIDTwHdnx05V4KBi7xYhR
+kf/NFU4uJZpaXs/gbMluPNg6RMz2nGdvRE7HsHtybBM42ID8VGGjGcn9cq+v4JE92YFQ+PgQmuG6
+7fewHcv5jP7KARoKvr5U7iJhZMLLx+Ct+0h25PntDI7gPRScQmP5sOkdseTvhr4Dlw7WSA0CFhp3
+7uyjIGR/0GsRKpkj44AaIG4EZ0PuMyVsoji95xFG1FnwEh/jbfk8p95Y2KdwWolotiMfqUXIc2sh
+5LthHXUIaHL9acdn7j4YY4za0OR+sr+7saJ/9e7EtPoK7fUQZULnIvTgKblI4ysEG+ckzwJSVmFH
+TCN6LrqCgiTZMzTU0O7CBPAkLPPtCMM9mRReSaafU6BH6rTGCmv6+c1XDSxFsUSdwKYeYcse/GK3
+xW9/LGvfS2GJy6Wc57oCHp1JfeGrL34FVrnRcSQ2vvARxfLAQ1XCIYAm8OwbiP46pTz7TgQ33qgD
+OVBc9BwVwGyg4FUOZREGzG5C5LkIWRVHNrrxH1TPEujoC3Ls3X0oWtcTwYzYGSLQ5J2MK3TkKgSj
+V6DIj6gv3kdHtSitNu5cwPCe4AbiF1pwjUJbNvCYHlnagWIXxAXTjqKBcYiVWA8QuPILWQ1o1lea
+JgN4PI/mMzoMEVKW3ujgpkZhXOeGX4HJPvy1PIfVdNMZyGAU8Stlhoj1GlUr5Lm8NW3Woc5JO2dq
+MPpqApzIOZfzNRLeMN9xEJPCrVgHK7oWHVVkZlmGbW4sQz9BR7amVkCfoKXIkt/Nuwx6zlSktRBy
+M2zbFaxSNoDd3hAuNzCHP1d1CGMTg6BSGMrWv+AlqLlRLTpzI0X/acdftvtwvkkL+O8Fv/8cwNGP
+/N6tFf3tuB0/Y+MvGyn8NsRm/jfwSbv53wbwnyqlfhNSYFz8SJrxrx6tgrm2kaGFsxQsQdWcwu1L
+KGdZCqqT0NbJhrBZHGypimtMho7Epw4Rlpl8hreawSNPf50VaFntD0i+adY2IiOhcV7WuH4mu//g
+S3vwtsRyz+5FNwaPupE19ET5+BtDSQPeG/0ugjkJMecGlyx2Zc934Gz6yeyW2EGAitz99fUMW/ss
+mDkRGq6rm51PGQVF3wDdtrBYzLJtByXJLnXWoIYU9ixGPOHhEJqFrOz0DMOv/y0AQHE0RUdpOsu3
+4d97idfHUOLiGg7ZhyoaoLMID3YWUPTxbKsSOXUk7P1dPrwaYBjseNvQPZJvqgiaQjWmmcNQrRi2
+XJ+7O75hHy7+dI3rKwkxnqQdWsrwHebn6DPa6BoSu1QnxwRgWQkUob1tXgPUZVS1BTuUHdah3oAp
+1rCo02CcBI61gYcvABb8jIrQKloDUgBmz5+gfEOedfMnF9jZled7NMnQPJHzD5nmzaollmTd9q0A
+jd74UXbwPZkDTuzBYmFaER5dzhcYjulW7kVQe3I/x49tEOKCYJAgIWvW5bMO4j7W1Jecn5SoruTz
+dvZbPFnI715PV3iy+Mnk2D5NS/IfAfirACZKqWOIy/RvAPgtpdTfAfAEwN/mr/9TSDvyI0gg+R9/
+mpPoVIc0yOAtDVLe1LJJ4NO41CoauGxN2VEPFgVVoCR9aAuFOieDsc1R04XItAaG7cSublCvZVKE
+nBAqt4GRVPJ9pTAgh0E19+CGmyDKxWYSgpqJ33vyHG9Snnx76yvYuic/LrPvYHEuL0U6O0FE05Jl
+Kef2wtFdKL4Iy6sK/p7UOLyoh+pMcvxyLg+5O3DgUljGqs7RMLxswwsompe0eobZM6k+h7sMd+0J
+ylSCszRwYPXZiem+go4y8k06gDORkNjQsh3pAsaS80FUwfIZ8HmXqM84eVeXKJ6Tlail/aL7L8Bl
+RUnbfaCS3+1WDZTLFCxz0Wo6SqWy0JsgQzdjR2kNPHhNOibf/6MVNMVsfAWYmgsLN4Ku9tFlfB5W
+A4t0FmWFUPS8NE0BTbq7S3Zm15Ro+Xc6MrDoyaTyBSwu9vakhmFKp2dk0h4m6C4lFbkyK9TcIIZL
+HyvqY/o3vByDBVPJrWGJkSsnZykNhY3rkwWH9Oymo1boKof3ktTS/MEYeiXf73kJuj45LXaAeJui
+sdcUMVbzm6lZmRprUqdTCwiY6hbLGdQNPfTTjR+7KBhj/oN/xY9+9V/yuwbAf/ITncHtuB2343M1
+PhcwZ3SAWgFlnSE2su1YkYOMxbPY8290AZS7hmFYblKKe+QK1ZqV19DG7r6En72DOwALMb2dCNmS
+UcgpVX+TAhV3viQaIJl8U3737h60x0IUOhiGlJo8tZemNr6rZYf9hknQ2ds8j3cwZX/86VmBl3bI
+hiPv3u66GzXgrD2BBRaJOoWSkuO1oX5aXsKekCWqAmjqG6TLORTVji3XhxVTU3BT7Gwf4nQu53Z5
+nuJNLdGKnQCWI8c2bgnKOsCiBL4e34PhNZnWherkPEzpoEuFSBXv3Uc04L0g47RTKRSLhG1+Bs2d
+1izfQbvZKeMhLC2y5V5fooeuBbqp7IjJy3fwYCqRyfnv/FP82tfkWY/2XwaWjJzIgDQmAYxEUMZe
+SNoAQDkjGG3f/K5eUuNhY9K4ilEvHwMAXHdHJhwAuA5MJ/NCXS/g7cj90iwGqmcrYCU79MC+wpBz
+4XKQIGT/v9fKM++aEjO6lRc7a7ha5oVubHRMC6u0Q5vKdTe8V204RDknqzGcYTGXefbxx+ewaTvQ
+e3OMxuM70EhhtDIVqimZuFUJwy5KWdtIOR+urAahteFXfrrx+VgU0KLDGn4XwGE7pmhCeGShdWWK
+dqPIVIbQm7oD6a35bA7DSn8HBUNkV3n5ESq279K0xey55O0x9fbVVQU/pJ14VsHZJ3/iwRtQfPim
+VlBcWDY26vPxLr5zLYi44UWFNybyAEa9GLNCHtJwN4bHJoEid8KJIiwv5HirBjdipiZuUJBpVxFb
+X80uYegmpQca9o58HZwUyNYUBWlDeOw05Exbrp8t8HAq1zldOrBYa5E2BIFKagjdko1Jy3kV5lCV
+hPZdfYruTM6zyY5x+gdCHe6/0cFRfGkoIFNWLpyYvghpCJdhe50G8G1S269XUDub3J/hednAoniq
+N+xB2bKQJ9bvYJfPIZ7sYrUU9GlxKdfURBpuxNpI2YftbzoxBUzOVKizYRTBWRVZt2GJdEYRGVfB
+Usw7zBVotQGta2hyCSx2ONRghfkPHgMA3p2e4uxcjrcqCiQ9uaZDqhxd5BrXFPmtOxslxWhzq4NL
+hOFyvkJDBSiLPJlZbaM+F6SovVwjXchCd1ln8C9lzj39+F0YIy3egHTyZjpDvnEqaxp4TJudqQMC
+JHF8kqMrfrJF4Zb7cDtux+34xPhcRApNa3C1aBBnKdoTKZJlGCDsy45hwgolQz/bGgAMn2uGi63O
+YYhi0UmIxYXsmtOPrlFn0lHIrAoFK99D9uZLs4ZWkq7ksxwRzTac0RHAQo1SKQwdoBQ1BWfnOb7J
+ndL+6glU/YsAgPvXD3DxRIpS6bJDvc2+8kp2hrTooAJZh4ehDatguOsZ+Nuyk9wZy249iHfg0mJI
+VTWUkl1Qx+amqr8sp1jT/bqcyjX/8aNTXLIQ1boKaLkjWmcw7JsrVwGeRFBqI5c+ywH6D6rwJVQ2
+beu/8xjP6LV4lnbQd6Rj0vflX9UEGGwzeuhlAAVu7LEPNaCRi5oBTJWsDZiotw2LqJouPYWi+cpl
+UWNOq6oyfYScXoo9Fl3ruEKTyt/pqEPHYp9yOpiIhcTcR0mP0MtL4SrouoEDiTxd24fHwqV2AW9C
+c5n4K7CSewAAQyBQ8+QxzmcSNXnvLbFUrOzt2LiiKU/B871YraHp5r1Ka6SbtHG1giFwyqo1egM5
+nu/L/QmzDpMR50pVod2Vv3uhiTFk+tf5IeYLRmc1fSLzEiX5FXVR4pxzIDha4/g9+d3FskXn3uop
+3I7bcTs+w/hcRApV0+J4NoV1oUEgFiI/R/xFgbMGlYbakECa7KYl2bDdKzZS2AAAIABJREFUhDCG
+71DFRuGmHz19+m28fy0FJWvdwhiKh7K41sDgKpWd3XctjCavy+dpD5udrS1a6JD9bbZ2fnByjISt
+qV/Jv45BKD37LesJ1uS/Ty8vcPiitP0uGKEEeQGH0UY/3oUhG9CoCEnEAuvGHzNWgE3tAfsSLUVs
+q5VBQWJXm1tYEC3ZeVIn2R2tMZvKTpqXQOfwvq0jaHokdPBgbez0yD40cwfqntzvejFDV8r9dHeO
+MPyaHKPsfOQ0NZlPuUsmLQqa04yLAVy2kVt7ABUSO7KOYOix2VUsbG6VUPSMxNSg4TWFA4059S7K
+x2cAreyyTY8+dZAT6dqULgKaxehaA5Tk6yIbbcpaCr0WrSKDvSPF48bVcGbEZMRXMGTKKreE4rNG
+KuFkNbBu6kS2ZcFlD7SXAh/PhK2ZrmSOFU0Lm5Hguu6woMp327pwaLPnOh4Gm2k7lnsRWoA3JAz8
+/EPYM4mEd/wE7j7rB12Igr4OSxYiYRtUNOqZ1yka6lA8Pdb48FzOSdmA1f0MFhqVMfDyBk+zDEes
+wnsHLtpUbmp+WcKndFUbrVHSkadaECZseWhp6qLsDj7Tg8PXv4TwUibY+cdPUBG8NHsuN+/+F0aw
+TuQlXcyvoLXQZbVvw3AR0q4CWNXl+wzPf4weU4qJM8Ed92UAQPTCCs4/++cAgD9/doGvfJPpSiFp
+QD5do+aLErhAdSnFPi9xUHHyImGPft5DG8uCZfsxOhq8ZPUaFWXcLNuFcci468lFH/beRKOkCPqt
+9y/QUjbNri6hhwT0mAQd8QT2llDETZiha8gy9HvoSrIrt/fRn8m9bRwFw8XHpc267ms4NKP14hwm
+Z0pUTlF8KJNYhy68kdxbmw60qjFoUxrqhClWP5DrKDIg3cjg9xQUId3KYbE37aAo16a8Bi2Fu41O
+b8xn8vwMTSHnP9yThaCdB4iZmmGpUEZSuHTTCM6ehOjQLTbEGUOq8/o7H+DoEaXk7jjY/R45Gnsx
+Lj6QY7RcmHWo0fLvV/M5rG25376rELFDU6PFdCnXZDO9KJctTp++L/c7M7CZolxXBgkXURWm6Ijf
+6FrS7Ksadib3bagdbBDdPzg/R06V617fRUIZtw+esRD7Y8Zt+nA7bsft+MT4XEQK2ih4xkIcafgT
+iQg6O8BGB6SpsxtzD9+/h9WV9M1P3hfJreHui/Bjicku33+K67WEzwejCYYHsgu89KVfxdn3BIVn
++izI9F4B6HaMH3TwdjYGIgagsKlSCW4oimxTJncO8HMstG3bPqLX6XXwKMJrFOD80/MpsmeCUnz9
+V4RZvlie4PqJFL7+n/beNNay7DzPe9aezzzc+VZ1TT2zm81BUkRKgmTLMiQZjo0gASzDQOxYgBDA
+gJ0ggGNCf5If/mE4cOIAjhMhjgMksq3EdmJBsCNLsqKAkklTTbKbTbKbPdR0q27d8czDHld+rPdc
+dimi2E11dVfg8wGFuvfcc87ee+211/qG93vf81czWomaeRYHZKtEk3QRpstTkn3xKXzfSwQNiUkG
+HbzmSk/Cst8W/8IKu9APaB67c4isj1XIYIsNLgQ3/SZe5HYNs1JnNjdgonLi2RssBs5LqU4tpYg+
+vInPXEm31hUXMplol1Bu69nLbxF03PH88AmCPSUj/QQTrYRaHGTYju9SnKvsWVWYprtP7cDSirST
+etsUTVdSLDOXMF7YJS1PXk54g3LsxnM6DrAqX84GHpmo/BA8PLEVy1fE9TB9m5kwBE/9+y8ScM2d
+ZzynEnagWCisvLJDIcg3Wci1qQh6lk0ydbTK+cHPI3I1OZ2MlgykjfHkznNsNN39G2YjDg6cB3V+
+4I5RnoS0A3fcti0ZCGK9nHocvuXmbL0dUd8VWbDGPi7B60uhejqiqdJoOTVcFWv49EqT6PT/h+FD
+6cEkCfAmdQbqW/CbTZYiFjE2A2Hn46aFrpts/YZ7bz0ekkjUpfGZG1xXF5mdDig1F6cP7jO2brF4
+dsu5su3NhHziJsf2XkEknkdT5phKLbCBxajKYXE37lPXd/l+5Tv2Ch8rifey1+ZcQ/r1xYR/9kXn
+EjZfcItYrbPL5cvOXU8Hh3Raru4cLC3+rvtuIZGJih2Sq+rOzEfkCo8Kr6SUa4tJLwRsmk+6956f
+DFgoC72cTWAgJqjGFC92x7PlHCtX1IpshXyCFWmN37f4Z8ICtIf0V4vCbofGsSj4Tx09e3Z2m0wP
+Eq2MrU+7UMo3m3hNUWnYIVQremS3kHtRSGXdg54eDahStwP0Cstrb7nzuHJ4E9t0IU1NilWNRpNA
+C7YfHONF7p4FwQHRriDdoU++oWtN3fkmWxmMlMNo/lFMV9yPrTaeXYG2NihTd7zF1IV2zcZLFLuC
+kh8s2b7q8giT7A7pb0iwVvD3wK+Yy/WvFlNeP3ThxfMfL6kHbiGrlwVbLbeIhjW3oFcty6ZyKtWk
+YrOjKtdkTpK6a+pvbmC04fjioQ9Dw0LcnMtyyt0jV8E5Xkwo6i6cbI3h5uj30qH8wbYOH9a2trU9
+ZI+Fp1BVPvN5g3k4xGrny+YpxcittEF9m6U27njuYeUGtySU4SUJKCseNipC69wr71KbxTfkPp/f
+pSk4qi/4saksQVfEHL2rVMLEVoDAixj8i+hhtUE/ffkG/U0JdthzFkcuDHj5d97ktyVPPs8rvnDk
+dphnX7kFwAsvRbRXuhalZXhX9eZyQXzkdvqq71b4aKtOVZekXfGA4W0hD6MJ6Vz8e+GIXtPtiCNV
+HCanOUK4UgQWK/SfV78OwcqNDLDWXWA11u7p1y/UuvP5+bel2rdDvEKhiz2kgRqanpdL/SDBiCrN
+qzeJRApSzTpUmVz4bAOvtRpDNQPF56Sp2ymzRkXQc95fbbfBfOoG/Gu//Vtcuu7gz96uG7dm9Ayp
+LsNMM4jdOftJF39DB2nUMNLqqCo10u10MGrgtPUKo2qVjTMqTa6qlTEeuXu5OBfTdhJQHLifB+0p
+2dKNd8MryIVMXKrz1fdLKiEXq8WAN3w3xt+82SO0SrRWYxDhitdQhaTqMZOXVoYlnpCQeejjywux
+QYyRh5BNNTm7FlL3CN8dFHzxwHkE92YlLYUjflZQ89/f3m/sxYz/6Gy73rJ/5ulP8aXyiGeVhO/2
+OyRt93C3gm8TSHjpjFclkDERTHSjMISKHQ/mlpEYWTpRh0ZfhBZxTLASrNXDvZwNGS7cjblar6hJ
+w3BmFgznbrIF84JjLUILOVZ/ZN9nU1j1VlBxMHEnfWMr5hl1JS4pQNqVJ1N3wL1GTKqb9erxglzd
+dU18Iv08qtz/cS/EF2FJZCusMtXb2zF7ar0dHM94VVj7I0nEB6lhLNYd4yec33D9HKPz+8yVa1nk
+sNLaXTEXpVl1UV5J6hFWqkLD6YxCDFIbmx06orlfijVqOBtd6C/Gvn/RRhwGPoWYibw8Z6ES4ViZ
+8zCwNDVZX7iccP1JF479F//r/02lrtJlek5QurBicO5CjeHZHd78178OwBd/6xW+fuIEYEwR4Pnu
+Yoo8Z6T28mpVRQoiAtGv+7U6sdz13EQsRaxStGNaVqFi1dR9nEHLjUt7XpC01T0ZtYnuuUX9OHer
+zW5l8cUmUzbqWMWCbx0fMRZhbxxFJHrQV7IESeTh6dzi3LDUfTg/O0NiaMReRCjyXqtxNb7HnqpO
+tZ1LTK1g7PmIyW03LmeLEb5CiX/+xZsvW2sdJ8AfYOvwYW1rW9tD9liED34c0bhxhdffvntBS/bD
+aU4hwMdoNr9gsF0sZxdNN8upe21qQkrtfXk94b51/vOEgt2lcyO9ZpfOqp9epCd136cp+fKozC56
+09uhvWhmiSKoSYbNlG4H/sRGRaG/95sBpZJ2T7cMDe1WT1xNqOSKbsqljMMQ30U8RPmS+3JRyyRi
+64qyxaILbwUhM7EBX7pSUYqibNOL8HLpQMYFvrgLD+5JwiwwbAhAkxrDmbpHh9NTWPGc2JJcu1Wp
+841rPg295ltDqe+dlRmpPIHpwKPed++viROy8L2Lfv1a6NNouZ2yH8cYeWZgWYpXczF353Y8y8gl
+YX975tHJtMuVBaU09GLP4ClxFy9FmXbwLdJbLix58/ZN7g/cuTVrMZ5AaZQWYZ3wlIgriopoRdfm
+hRBrouU5jYaSvJmhJq4NXzJ1m3HA2UL4luWUumr+N7Y2KXV/75255KRPwhWphw+Nx3kqd78oiBR7
+WstFRWQ6ch7Gg8WcSh57vZZQF98C2ZIodHOgFhfMhM/JM5cYnVceDNx4dzKDnFDGDDmVhFwRRXT8
+98ensPYU1ra2tT1kj4WnkOUZB4e3+fFRwcENlyx5PYQfmLp4sh7n3Dpyq2Pke6Cmk48pfk+jkn7m
+4KCvLOZ8QuKh9+snTJUMakwqwpV0mTawoF7REXz4qCool0LElRkbiuuKjuGK5NQOY7fb7Wc5xxKb
+zcYRvWvueNcvNWm85PIgUSNnfuxW9u1wJfpSUB26D57mS17ccse+FYYkgjzfsS65evVah7bEXLf3
+6iyF0NvpxReNTbMHZ8RC8f3QZbdj3EstuXQx7tqM31RsSbqkiFSGxKdVqtlKqrlhEFNTIrKeGAYP
+3LkntZilPJ1W0mKj55KfT/Zc4m9qZlih56IrMdFYsOOtkh3lA2ZVRnbmdq57D24BsNlNeeXM3dMi
+83l7sWJ0CqhKaTXMe+Sle/988BoAx194g5cfOMTmreMZrUBtyGFETbuqSTwC7bzpysPMKypB0xsW
+PCFEa42IVND0tu+RJkKUarfOFgM2BESYBTlN8ULMGz3KuSvLviDPJr0y5Shz45qMSgp5ClUALZHw
+Yn3SzL1+UU70Lbmuf28v4eq2wyNcf+LThGp4Sss5b910eZWDE3fus2nKN4QK3fcCjO7Tfju4yFdt
+1duUl97fY/5YLAp5Ybk/qAiTimdVS4+WDcbFKssS09x2GfCNwNLMVJuOBJO1EV3xBuze79Pfcwmg
+q40G06G7AfUgpKOHsyzdd1X5ANpu8l8q2pi+u7mDyYi5koSdymcqGsP9TPj8HzE88aq7Mc3YJ91z
+D2H4g30SxQd+rUZr3/nrRvXz1p2QrHJ1+utXr7Bou/N5aRSzOHYPvdd3N7PcrbNp3INXb1YYcUuU
+1w3xxE2wbiPmKQGuyrb7+zP3IqYDNzn2whZ//5tyu7OSUA+NH/gYQbdbUtcuwpC6FkK7WDjWbCCc
+VsSBGxeTGPY2Hfbg4884sRhvy6Oh/or0LMeIF2LWLjGZ+9y2sZx7jjvC9FYKYBmlwrEzpszvCy/h
+GQLBpvHGjF+/5e7Jy25RWPgTUq1zvVqNqOOuabPVxqgXxnjlBT5hFUcMphl2BVOvxbRExZ77PuXS
+fUfUqNESl+JcFYXSWJrbboHYPduh6ijUOJmzFKCu3XQbUjfqkBXueA8m5xehYC1ssyc+jCIveXDs
+LqCm8+l0G2xedcf90R98gZ946cfdvXlum+rYhVXnoyNOfsfxWrw8covi1750kzcnblyPjs8olWD3
+gh5NaX6aDY9cWpjv1dbhw9rWtraH7LHwFKwpKKNjMAn7pYPB+nlMXfDZpFpgBCkt/BqB2GxDUd0G
+S0tZrBqCJmxKkMWPYoaRtBLzjJ1YzEm43dOmGZ2G2zHivKShZNCgEzLULlZbFmxLaHk8kPjHqxVX
+lFzrPB8RSdKu2fTwlKDyrnwSAmEkKueZ2O4Z4Y+qDDXJqKSVmZ0Nqa5eA2Dj1DXqLKoC33PeQ9zb
+p7Xii6hmVGMXYoU7m2zK1SwK9950nNK8Idf3xJKZFV1ZRE3j5eUQdN24NGvSy7BQlnKp6yEjEcWm
+RDS0G//Ip67xYy99AoCP/eBnAKh368SJEr73jvAEty6qkOHIhQfZYES1785zeu52zHuDc6b3XRL4
+1nhCXS54WVo8cSGUdkx26pJxd+851/mNt2fMlDjr1kLqTXetzTAiamg6lwGlhFjyuQhcN0Iq6R+E
+3Ro14RSiekBp3Q0O2j6pMAAtSQTOhjBRGdWaCUs9MoXt4+HCyZnKyMFZwdmZu6ZxNqYZubm12e9z
+ddd5WEGUU8ydVxRfckS5P/AjL/JDz7qS7JPb1+lcc8+AF4bYfTdXt867XHtSodv959z/V17mt/8f
+p/n5+uEJM+POc5lYaqWbh6OppVLT2Hu1x2JRSMuMt87v8Qlvm7kwQZt7Ac2ZMqvlnFPJds/TGctE
+rq+IUOY2ZEcqJLU4oNtYUafXCEWlNj1fglh7C2Xv617BYkVhVVWcq9afT0seqLqwUTdYVR88taBe
+219Ql4Zj4+NX8BbuZnkxmJa7AK9ZYCcuFDKVc/Ho7oMvxSq/wo4EvOnUQKy9UUtCsQf3qQSrrvWg
+WkoMZZ5dYBa8usGU6kE4U+26VaeqCWPRuUxUuUx9Ehhqohz32i6EAAh0jDS1WC2Ei2xBJsGcIPB4
+su/Clc98/FmuPunawfst5zJ7fkIcKqew1cIU6i/ILPFCsfhmk2rsrrstSvkNf5PjvoMMf/7gHuei
+FaMqqKTUlQ8KHoj6/c47cpNPJgzH7u9LCzVBrIMkpBIcPfbBEx+ZTbTYGINVX0JYRjQ74s/s1CFc
+0buF2I4757lo1dJiQnSmeREmLFQBCFODFdlJY+GuaZQvWYr80q8CtgXk2tvZpt8WfuV8QV14g6ee
+d8CsH3r6RZ569po7dy/GU+unLUJWiBJDQSIG6v6Ou48//NIPsq9qyG9+/lt87ZYLTRcjj4U4Ieep
+Zb5ck6ysbW1r+0PY9ypF/zeBfxdX+X4b+I+sOmuMMZ8DfhbHUvKXrbW/+l2PUUB8XvCmf8LVO87V
+Lu7dwmuKFKMzhYUaWLrFxc49lejJYuA4CQCaQY1UmXpMTqREYz/IORdOYSCuAJOldFSvXuQwrNyq
+vJgveFIsyceJT19JqxNBDJMoIbmspB0V1Y6+r/sU9NSzbiMI1IkYuXDGGh/Gzh00FsyllWKyTzVz
+5KcrSbj4xiaFqizWG1NN1BnYqUNLDV9xQXXqvi+8oWPMxhRK2tnRXRLhEMJaQEO7ZlpWdAKJrGgG
+jGZTQhG8WM+SyYPYrtd56Qnn/Ty58RybbWXXE+dtha0YL5TYTbLEqmLkN3NiFc7DxYSFGppy4Q2i
+Zw3FV9TVV1RM5SnYsqISoCIbDxjdc57OQuQn92dz7qoG32s1qJlvE+Cs+BSq2LBSMrTGXVNVFZTK
+ztvAEur84zAmXnlNtYRcc8DLRGqTtLDqyhyNZ2RzEfzUjvGnwikI/eovIyL56vW4wdaeCxm2tvqE
+ypmfWEt/y933T91w4cN2f5NIXqhHfqFPUZULzEoxOvfxNcdrappLXtwi2nFJycF8wQMxPw9txd2B
+O49JljI3789T+F6l6H8N+Jy1tjDG/A3gc8B/boz5GPAzwAvAPvDrxphn7Mrf/Q6WW8v9oqCXWf7J
+bZdZfR7L01fdDXpiaTgQkcX+sM2JWJGsXKv9JCBXPDXDEkmP8eTBmHBFmpGkTEo38IdiDRqmiwt4
+8Z4f8Zag1ItFxalGZjbOGdREVJLruJ8M8FBbcMfiS0be7jfwCxcz22qEnYliR6428wgrKKo1XWwg
+NudZB5bK8KtUWMWWUBTp5BnZQtWHvRne0uHobTohPXULQLUh5T6/j5F+prkU0ei772snIU0xORXe
+Ahuu2rbd3ykMK8h7nqV4eqiu7va5uitSk2YEeggR6Uk5NUgvFdIaGIFmBobSXz3chlxutdUCMp/k
+dOUG9z2fpbL2RZVR6iHLlnOERqap8Q5CQ03ufiuuYZUnCoKAIHD3KSu/TYgTaaErU0tmdQwgTzUv
+ahXFCmMVlNQ15qZyC/28EzKVlEBSejTUm1NODedLCR0rD5GTUVcuo91t0tTCWw7nzGrOzQ/ynKfV
++vzUnusj8WxBqTJlVcRY0c+XaUjQ0aJgNi7YslbYdFMek7TceF554gpPP+3m0+v3zkm0KIyWS8oL
+ENl7s+9Jit5a+y+ttSv4/BdwmpHgpOj/kbU2tdbexClF/Tvv64zWtra1faT2QSQa/yLwS/r5Em6R
+WNlKiv4PNB/oFJYzCpi4kOCrnk92IM3HZpNK2fIHecpAoI9Irl7UDmjKD15MU45nbrUekjqwE1AM
+S5apVnTxLaRZidC8XIkDOnJ3H1QFRkzLSV4wVoE7kjvvDZt4m0pw7f4Q1aHjTfBqGchTMM2PYccu
+M2znLiPNqAmeWz9tdYCU36kGp5CoQlGos3BeUWknor6DERdEtaxj05X2oaGQNJs9Fsx7fHDhZrI0
+dGsrwFZIpDr2aFhRFqpKKKFGWHA+V1099mh57nNXe31qkduNpvffwT9XeJMo8bk4wpeX42WAMAvZ
+JGOpRJwpfAR1YDZzNfqsmHI1cTvlp168zPQNBxXOFwVk4oMIxniZ2yknuNcmo4p2op3Sg0ps1Ivp
+hL6a3zwgVbOcr4pDnIQs5I2Evkeeu505nVZUgmyPF3P6TTdGcUOUd/OKSNBnOxvS2hDOYhKSLt17
+zhSWeHF2gZXY36lxacd5N3E95kHmMDXXdvd5/oa4R2vOxQqxLDXvfdPE1JyHYepzCkHMPUZUgudb
+eTG2muOLJKd/dYf4S+LW2Iroi8fxMDN46fuDOf+hFgVjzM/jvLFf/B4++3PAz4HcFeOR2wqFspRl
+ydfVQjtMR/QFWNoIUnJVBlYAo1G/Tl9dZvNpxalc9JN5RqWMelL5LFRCyhS/lZQEmRuwAzPldDW5
+i5Jg6m5+I+KCKDVRdWI5TQkElgruvA2Zm4z+WQkTkYh0bmOD7wPADvRAnx/j9aSsFHYuhE2NXWJC
+VQy2P6bPvE56+I77e3NJ7qls9s4I03K5Ct9rYGLXflwO3HHJEkxPqML6FkjDMSoCPBHHmGrOaKye
+AoGXgqhJqK6+pF6xXXNMSK3dPnnDjfedm4fUBPaq1Ldgy4wgVvfeYkZNHI5lEhHoYaNekp+7h3Ap
+otHlICTsuvf2N7fo3tQYkbNap9IHOWnNPQgTgbviyCNQGTX2LYFawIMoxsQr4eEZqRCCS3UU5kFG
+pjjh/GzCWMK8jUaTesNl9cNGi7lK36kqGcYYQoHWklrCSJ546NXwfS2WCgPjPCBRDqAZxZTqSo96
+TS6pLH11v0an4a476YrYNq+DyuxVkWOrVZhj8SSoU1kPW6pCo4WAMsX6brDKk5wNjcv4NCTV2Hfj
+OmdCVr5X+54XBWPMX8AlIP+Y/Xb/9XuWorfW/gLwCwDhKiu0trWt7SO372lRMMb8FPBXgR+z1r57
+Gfpl4B8YY/4WLtH4NPBvvtv3VcDMNyQFyCPDwgUstQg8fKU/rK0oVqrRAuNkpoatryC8Hk3t6LPE
+spwL7173iBduBx0KmxAFjmgFoAwC/JXQx9yjJ0osP/GJlDAsNtXddntBJaXl6firF5iF9oN92HKr
+eTzuQ0dwXSWq8sQDEasYr8CLXWRVdRKMyFmqydc0yDlly+3W4faQQHX3wDYpYiXUBg9IJ25c2hJe
+yTsVDLS7ZCOisdiH8+lFP8Dg+JyFuCNi3x13NrM0tNNc2ujSXqk1TywjJXS79R5NiZoU0lrzWUJd
+PAVtCD3BvzdrFxT1pbHMzp37vBB9edLxKH2XcKvdqjNR/4HNUoQFIqzFjBaSi9vQ7jpOOVYl5mSZ
+cUVUcUG/zkKJ4Mk8Zyr+gpUOaNgMKQWbz5iRKnE9Gi8IJb3X72TkLXFzTt0YRl5JKDGViApS8UKY
+JTN5PXvyKo7jBXN5D3VaF2rWu+02G/tuwvS77VWOFn8ulWyTY2rC1jQ8LrAJhSWXd1tkKZWIQIzS
+eTat8BMlbi3EYgYqwoyl2o1NVbJiFnyv9r1K0X8OJ6H0a1JN+oK19j+21n7dGPO/Ad/Qlf2l71Z5
+WNva1vZ42fcqRf/3/oD3/3Xgr7+/0zBgAzAlq9Zvzxoi1ZhrYUzsi5YqrKhrmSlUd5otpryjElst
+rlNruPzD840eS0FKazZiIkfAnEgBOM1RzordWkRf8l/RcoRV+WdWFISKbmaCOWdbPqPbLna89JTH
+4KtK/O0P6TcdDNj/5POYlkhuApHRjroU9rcBSE9LrEqd5WCJmbu4tFCeIdiuk3RUsopaFx6Bt7Ek
+mLtdM/Uy5qrZx9tC2i0vka6EZBtb3Fm8AkB3GnBb3ANFtsQThBzpN+CllMqvRHGTTtPtQLV2SCrp
+tuala/Q3lcQUrdr4IGN/2yVXc1OQKa6fHFb4NSUHC8tcor9TEZi2ypBCdX7vYI4qw8yqnFh5guUi
+YzhwY9TJnCdYJDVuHqkev0yprRiNGk0qNT+djWZ4nps7T4gBObOGgXHHDsvQ4bqBMocVW1lpCyrF
+8ysS27zpU/NccqDVjBjNBDFfZlBKvVzzcN+v4zfcuHhBSqvhcApx3GZyKPj7aECl7lejHE7pR2xc
+3dN3dSnFpzccDjCiEDycLrh75OZIpbnQa9Rp77q53shzyoZLXKbLc3IpsydE+Gqme6/2WMCcPQON
+yLLMA1RWpk7IhrQUP9XqcvWSu/hBmhHHK2Vqd7GH52OGRy5R1e8UPN12rvRerc5S9f245rGrm9sQ
+9HmRG3K5as/Wa4Qd5zIHB0ccjtxi8sTcY5SIDGSsCZPX2Lvk3rv7o59l8Y7DVjSfvEK88wPumrY+
+iV26RCECEJlmHW/74+7n6hXymcRSCovXcomoMFa3ZMtSqUIwnabc/MrvAnDpqW26Ow6nEPf3qU8d
+jXw5FDHj2dv44oEs0hFPSZzlq5M5nlqni8IjylYgHSk9jVJS+bVvff0Od2L38/F2lx0pgW9eHbAx
+dW7wsHAVldde+zLFK18CYDxe0Gm5kMlWbfwNdzMvb3Vpt93DGSr8SKcTpIvD5ec2ad0XVHiwZEvY
+kribkKmd3VOy8zmvx7HAZzePJrQEQsKmlIX7uR3F1Hpu7uxJOOZ0OuPw1NGzT5cZTYUdvV6NUBRz
+QTMm1qLWiF1COPUWzEXak89Lah0RndiQSsnhpRaHmIBN9bP0ujVY3ANZAAAfD0lEQVQKLfRvf/mM
+k5GrrkxOzwnFsbbRdiFD+1KXT9bd52q73oUQzeDOHR6Itv7B0YzjmcpVCo8Pu3Ve9ByUvt3vsb29
+EslJsRPhdmzGInt/i8Ia5ry2ta3tIXs8PAXfo95oYeYT6qIEqweGy5FKT4nlZKyVr8iYDd3KV4/c
+mjbzS6ZT91p/EUGq1dwEBC2VqYY5ixWxZeBW1HbNUqjGPPVn7IqQ5YVeTH7mvrvhlZRa/ftKcG50
+DC01w0cNH9Nw3AKBD9WpNATOf4uFeuFnX/oGAMPTMYE8An9rg3ZPu2BthrehW9F2JB7FyTc5+Ybz
+Dm6+M+BYbq3PMbnk5lpbNaoVY4x2j6odwdjtKNPTGmXoSpl3/Cmxmpz8YrlSwqM0gl1HLTZVKqu1
+Ewq55ZPJkqag4GZmqXrOExgcuK7FcVkQCF683d2kf9WFPHvX2wSB8yo8K61HwGjXLWyJp4TZRm+X
+Fy4776fCYoUJjqs2V6WT8eXXHJ/Ct9IhR2fu/o4WKeZMSszLkrjjvINePeCZJ1wSs+HHuo4pWwpB
+awTkoXbjWYUVzXNxtmRz013fFWlu1JMGkWT/TurnIBKVepxQihw2Vxmz0eyy0XafS1qwvem8o3aj
+xhO57sPtm7zzphu7L9xxJC31m5brkpjb3tklzd09GQ7PGd1xJeXpcMnSuBBsrmYne3dBqNJwr/lZ
+6uL92PATjnR9W7bOcfEhlSQ/SPOAhl+x2UiwNXeRfepsqKIwqZyUODih0ancyyh2MdSG8TkZOJdy
+kJfYhrtxraRDqYUgC0fcF/DmUA9YEjcoJQ5aLac82V3FnAXeaiBrAR0hb1IBZarBkty6iZvfPaQU
+qKS832Jx6rQkbWLIztwDUlxz1xFs72AlcDOZntCSHHweNfEVR+alA0Jly1NOh26BmTSXLMQTeOuN
+BUcdNymeOAkvhGEuXZMQaT5leOAm+fDUY6aqxPdP27wWuXAlX3gXOIPhXLFu3aMhWnebeZhQsNwk
+ZKnrL0pDuyNCkR333mfia9Q8KWRttJkIRPWtB2c0VPmp79QI1Rq9LaaniIJci2y3FdJRl2g6Syna
+qqhc3uDplz4NwGuvOEzcO187ZZSt6O4DjPApJ3nJpsBpZr/D9W21H6viUgJJWxWqNABRyr9595RZ
+LlhxYQgVKiWCpjc9g2258T69G1BTrmLWLCgLN4an0uvsG0vkvpZsHDAO3D09nZ6zlAt//+aUr5+7
+0PSOFLKaecibahF/ttGkbtw8bfY9Tu65uZVv+dy55RaLpcLfcGzIFG4+Pz1nVxiRjX58wVxtwoLs
+bAU+fm+2Dh/Wtra1PWSPhacABmNDEh/2lCzpxY1VcpeyCigmqn83axe6hJe23Hvz8wVN7YL4FrRy
+d3odip5bPc/KnOUKVSceg6phMHIvGx0PL15pL1RMlaALMp+xNAsGE6EYn43xJtqByjGmcB7BvFhS
+CW0WBpvUPqbGJe20rcU+6V0nIZc1FizlsTRNjaDjPIHF3Hkr4yOLn7hda5sWdSVBa7Ule5F+Dk4Z
+nLhdYy5m4CCpM5MwTLksWRTiudw64Ubm3NmJl7FYCOknUEDDhmx1nFeRZhWXN13y7blOwmChHa0Z
+01QCb2cp9HrcvmBJThc5JwcOq3aaVkRNYRnOzrkk4Z66av6mWxAKYVgsCrbrQnomHrn4LgwNuqIx
+8yVUk5mSp4SEtPWQaaUOzdCwJQ2ErU6fSFBh5BHVmz06W+K38A2pKkrjKUzUhOYHNWrifIykH1nG
+MyqplXc7m0yEU6hXsCKP9tVw1KugG7sxfuXgPubWscYFrlx1mL6wu8lVecN1weq7rRqh7qlfeCRb
+7j7sfuqPUgTfBGB65zbXpTPa2HJj1evUaQmyf+2pLrWOG6ve15pcEvnMvVun5OkH3yX5IZjF2pTC
+BLTUItur9y7OLk9z6n1Bk6OYhcQ7umpcmGz63Mjdzbi2X+fGvusvaPd7yjGDmQ7pn6w4zgXG6dco
+MvcQP9mAp9pukTn2C37lS191ZzZb8pbi4Ptqmx19JaPxpFiMkhuUqYsR41YXf+N593pnF+mKXDDt
+GG+K/7ybmPujbcINxeqTEV7szjmeOdHRKAzY7LvXgie3sSPh2jcLQnUimmwPL7jlxiVzC0Etitl6
+QSxH9wpqh8qjTPs0RQo7Kgoyle8SEX7sXd7l+ecdgUo3iXhOMe7lSz3e+NLvALBTjwgkEx+rTFSP
+I6zKZuHScv1pt5BtTXxKiZ3Us5BNdW76IoCxS49IJdk5KQ8mYiPyIjyJq+Rehc3dIC6FZPNz2Lzk
+HuLUTy6IW7dbW+xcdvev06lR6LvzkYBqvQaRwqMogfLYjdf+Tpe5FfArWtJV1aHddO+djwvGYtMq
+0hnRirC3COkpFDr03PwYUFHTa5disJ4ASf0+N7Qo1qIapG6B6HzGweDLwLsgXom7CaHyTv1Zgf+C
+23CCXp/9uRvnDbFtbfe7dBRKd7Y2yBUq+Zsl7TfFN9pckp6sw4e1rW1tfwh7LDwFg8HzQlKb46nb
+MbUL6oGSPc2EjfrKHSxACaNEbvnxcQrSTIwaTRaC6Eabm3TEk9dN2tT3nTs3PZOG4zxnJpqwfj+h
+LvzD5aTgx0TJvcwr3jgTyEgZfs9bEgZK9SYVxYHgyDWfItEO3D+FpuuG85WIgyWldr6oto/NnKtd
+TSvMhtvFw5bbGVrnZ8yVhY5mKULPEuNhilVX3pSG4L9G+HBrPBrKdvn9gBUKvR4bipWuYjKkGgm6
+K0rzvd1NdkXtdbnXYmfHnXNcZMR1eWmlJR0vdf7uu/zRFF8NUfV+TK9yWfY8meKLLs/b3iRU9SGP
+VvwNx3iVu09n0wWFYNfN2OKj8ZoU4LndsS7gWdgKaTTcLt6MIqzub61ZY0uUZ1G7hlWoV64wD57H
+TLiWah6RdJ2rfXk3pxIWovKjC04JT1wQ08CjOFcC06uxFI+lySKu9NycvH3swqvRbMHNIzfHLm9f
+YmfH7ejP3niSSFiVRhQRaJwbaqpLyymje+7+51VwQYxjaNJpi58wWrKjTrFE0nT12BDX3Xd5xgPf
+3aetYJPj2h2NYQXvj07h8VgULBZrStphxFCZ117cYLPmJkceZQRCjYVRgKdSECumHWbM1da84wf0
+9aBEXkS9625cuOXhDaXzd8VN3OnZiIbUlGp+Qly6hWV6eMonPu0m3r9+dcz8VLGvgFVvvjqn0RL7
+z/YhS5Wk6rufJdhWSJCeXMjAm2sSXc0DvFKsUJWPkSZkEc8olKm3vh7cvk8h8lRm7+CdCSDTbGBE
+FGv9nFIS73VRlld9H3PuxsfvhgwGbpKOivICKZjOywtikYYw99e2+uyLi3F3q0NnNfbBGKvsfGVb
+nN13C1kg2vqqMgRCgibd4IJkJOr0qEkbIs1D5ufqYZiJjSr2OV26xXk4m2GMG8NOvUlQc2Prz0py
+sWgZtTTP05R7ypnEcZ2tLWlXJh6Z8kNJZRkqD1LmUlhqZ+TSUGjWG3haDL0yINbcKkOPqborpyJh
+yaZLisIdb5TNGaotP/ACtjdUdRHpy2RR8ECow07jEk+KOWx3t82G2qSjVkimnodYXKODQUXe0uJV
+WjwtnEk7ZjFx7028iKDp5kbka05HPp5yCrbKsKpgNW5scPAV931nRYZ9n/HAOnxY29rW9pA9Fp4C
+gMUjwFBWK+x4g1IraRI2L1R8vBQSAUQyMTyPJzOG4mKcbi9BUFrjL5kfi7m5Ob7Qgoxjt4L7uzXa
+ki83pyXpuSM4mRUJA3WZzZcebY1SV40ZxxsVwyO3s/U8i1Hj/HJ+Tm10zb15cwtqKwl37Y5pA5s5
+2Go5iTAtEVrZBqWUrVfS6TQ6tC/p3KIZ+ZEg1v1T/FL8isNTyiN3TdnTbpcIeZFg0/0ctFvMFq7r
+spplBOoYnJfphRrzqmc9jGKMXHwvAqPOx+XBlMEs1mVkFAOXVDX33fmmzQ12LotR2oS0xcVo8MRG
+DIH18MSDmAtANSlm3D9y38VgwY7CNRsYKCXUYs+YTR0py6o5P00tlRJucbvGQiHkjVaNTl+cj3lO
+TeQyccv9vUrnzNTtGl5KiEMpNs3HLESFZpc5pQBsC1HCjfM5ovUgL5ILJm2fOhubLry9seXO963j
+BScDlyR9YjSknEitfLYkE07BxCW2kLLZxN3/6ekDSlGsLSZTwrHo9U9ycvWxpEVIYJ1XZ3WfqmiO
+TUXRXw6ZioZwNs4IlOXuVT7jZOXJyMP+Lrb2FNa2trU9ZI+FpxB5HpeSOkm7xZ4Shg3fv0Cj2el8
+xVWJ50E5V4w/keDFvORYDSDPLA3bKzSav8ds6rQFivmQYsW62xSqzi8pI7fLxal3gWKcL+7z9sDt
+tseDOVY9Nyv5tOmBh9FG421cwh45KKoxE+zUQZBt4ymwDp1YiVTWzjeoFqp/F4d4AyXUvIJq6F7P
+jThprMEYF1t6VQ/Pd81VPjWMqMT85hXi8FX39rFIV4dv4ItNiWzAyheIAqg8Mf2UFivWqm2NRa8W
+E9ZW35Hy4NQ18Nx5803u3L6j69umu+nwCdKjofALzg8cRqT9pIf1VrwXOanyJNWiIFNHZCY253Qx
+o5q6XXfnSg37QJwTaYm/cJ7VbHJOdi4OCCXZhnnJlkRf2s02kQLm2AQkq902L+huiglc+JZRmlKK
+Ku785AFPX3ZjVOs1mOmch9WM4lQ0bsIxZFnFqSjRlrMcKzk9E1Y0lY95VuXGSXqGEf1bu5URiylp
+dH9IWjgPMX8nIxE9Wpk5L+j+3RP2QvcdwZ+5QiVv0wZ3mRy6XExmEzzRyXlt501WwxJPSFGbepQL
+Ny9mpmIubyLejOiM3TnfH7w3uPNjsShYz2AbPkVcstVy9fEogTwXu3KeX5BGtD2oVAWYqM14Op/D
+Utni3JKKyMPuZaxwpzWbMpYY5/2vugcsNLDTcbgAD0M2FFVYBogfsu8H3DdaqLRIXetDphtepjVs
+4ijU8vFtqszdRBPlmJZLtHHoHoTsm2/AVfdatQiIlKzzbQ/TcBOv8p5xnxkdkd5zi03RPMIqG14N
+aniBcApM8Npu8mcn7rj5W3eJPyGFrPp1Wiqse6FPLsBSyYRCodRk4dzTKEoo5NrfHpwyGLpzfv1b
+B0xGLtnX2nuahtzrqCNwV1nQ1UJer+oU+r7Ka2DmbuXIC5hrAfd0T5NRwYYg2veGC05HaoM/P2Em
++O/pvQPGUpnKlWiNopBex93TVtLAE+TXDxrMdX0HRzNq4kRMc3efvHqdgYRjeoWl6qqKshViBXlO
+71cYycRnolUzXkGoZGVar1MIn+GVdXY7ShrvugXm3umMe2OFDMM5R4fuoc/z/Nu8mmVKU3D5Uh2q
+5WxE9ydcm33Q7oOS38y7BOLmnBzNKQTKK9VHEVZL7AV+oyLVosBRSU9iynuNOul8jVNY29rW9oew
+x8JTKCrL6SxjLwv4RtMRkPYXXS6r4Sm1MwqRtcbtxkXdvEh1+iZgrOXtwXDJq286FzxdGuLErcbR
+bkaoZpZm133XZDxjPpFISTpm+sC9976peOpp93p3c5t33lAfvghCemHIUOXQu1/7CkXgYomr3W0W
+EnMJzhaYsdCEIg+tbrSxShzZMqdautU8W84uuBOWauwK4iXL+oqCbUGssp+t5xTadcxizlyJxkZP
+A3CtohRU2nCEp128HTcIVrJwccnoVASlgujevXfIoi+l4qpkMnC79RObdapLDjvx4rPP8dxz1wBI
+Orqmso4vHoZ8lrI4dsnDgiVe5NxcazJyJYUzCfj0eiWL0t2/V998h2/ccq//8fsneHOX8A1TSyUV
+6LLjvKpZkTMQxLzdKOhZd5/GyxlWJDjlckJdPAvb22qOu9LB5h2NRY+opnLwOIVQEPG2xZvJO5Vi
+9GxaEClkSCKfcuzOuaobKiEkJ4E7tzIvWOTuOs8WE4KJCyVHdkInXIU8XZoi1o2kMTG5m2EGrjw7
+O7krdAcUWUa04byYxA8p5WUVur8EAZXQpHaRM1X5uepm+H15yPOE2SrWe4/2WCwKZVVxPp0TUTKp
+u4nZaBsGgVy/siIXR+HZLGU5X00ETczKsCvW3ziIyWbOhbv74E26W4KE2gbehghXPDfJT9J7nG0I
+h37cZbHnhqOWWsbKEE+KMfc1UWaKBS/96R6j1wXtLef4c1dlGNdKPC0yVZ4RaDI1pBQUbm1hPIU2
+rTbZwk3+yam9cCUrxfiNbkSCCzXC1hg7dAtTFs0x6omIbEFx6D6Xtty5FcuIYijB036Cpyx8r92+
+ADjteRWRau9LLbbHxwNa26tFAeQx80SeECWr1nHLYuDc+ZWuYRZUF4Kw42VGLqbsZTaj1tzQOWUs
+pfqUqlJjmwHNWKI9//KM+4rlbx3epzhzk9uvCtLchQHzE/fgpllFueI4TFpk8eoRsiykMpUQ0Fbo
+tiHykl7UAMHYbVJQSOeyCGZkU/3sc9FdmB4qfvc9wtAdo+W1sZHuXwZWdFE90Xc90ahxV/yR80HK
+YeWu43TUYVOL05XEY0sgOX/mvmswy/itf+WIam7+0q+y4btc0+6z13jx+xxpT7XpEayU0SQW04wb
+hAoPbX1GmrpF6vDlOxyrvX4+toQCib1XW4cPa1vb2h6yx8JT8K2hbwPO/JBnxAbc8MOLkKHpQSLs
+QYVhuJImy91Os3dli7EYk/ubCX5DvAhpG4RvuP36Mf1ELvNcibFal7lYhpv+kk3Bp4+qGgvBgydf
+H1KI7y5VV2MY7dHuup27ub/F6ND93QuKC87HbKNHtNIOeMLhCpZDSyb9guLBAeOhQ7/5W/tkuXtP
+PZNK8hlEe4IuD/pU6gy1pxPKidutva09Eu229r6YjA8LkituZyjPx0wWK3m4IVbov07WxEi/4VAh
+Thqm7DbcTnrn9lucvOWqD34joTdwrujt+ZfZ2FFnat95YCfDEedjNTNtxaCGokYtoMjdbuVXlnrL
+fUd9w93TdiPm9GVXtbi1yEnlCX7zla/RlRs8Tgpq6ladLd15DmczonP3vc8/9TSJMLzFNMMXOUsj
+NizEAn2aul25GMZMIpe4bfTaNMTgjDWUmkeTPKMQv8SGEnXpPGEh9uzJ6R1EG4pJNmhpLMI7bqwe
+XA4o77n7cJYPeULeVBRn1CR7dzY7YvplJZAnzlu7c+uMsZCuvmc5FKJzcgz2NXl3T+5Rr7vjdbdd
+GJTl9oJputO6zGTq5uQ7Z7c4uicBou2AZPH+FBQei0XBGA8vrLMIlpjCPRxpOaHKlPWvRxddZM1W
+wu077oKPpKpjlyGX9tznbNbmvjLIYbvi2a4rOba8XUbqQByfOcabztYluuqpCIqUJHLltu16nS98
+3nVJJn6Ip+qDJ/c0znKippsdYdCle+UF9x2cUMqVbsYefsd1HS7uuJju7FsHHB27CWQGMed39dDU
+BhRyc+N8Fesek9TcInT9+S6NjzkWo1p/A0/S4mHXx9v/IQBKlQ3bb90kUt+CyTssz9w1D3s10nvu
+OrbbIR1BcCdqC9+MfFoic93c2yDTYrH9xA1eeNqxQbW3+vhavLo9t5hutAPsufuO+/dvceum46uM
+vG0S9TOE9QAj+HYsUFSUV3jq7JwtC1YaRufnD8hUMTh/MKDbVU+LKk5+ZRmcuM952Zwr1xwJyyy/
+w3TgHorlMagdg3ikMHBzSV0CN9NbRyStFQHMFpU4Pc0yp1l3D1ylTtpeHUJVTuZxxkxl6WZRwxPD
+ldd0Y7U8LQhVDZjNciZNdx3PPfkUDYVgZ6MR56rQLGZy1NuWrdTN051WwjP7Kqn397isMMhrdGhu
+u7HwWxLvnI+JVJ4tMkNWuo3jzhuHRMpj3c8issUHrCW5trWt7d8ueyw8hcpUpCblGS+iuu5W3+O8
+y2WpOY8nU5IVnXvg0e9LJn7mXms1CjauONfq4596kT3xEGxstKlJ8s3ee4fagXMp9y49DUC8u0M+
+crvL2e0jhmOHXzi6FRL57jy+PE85FIvzsFphbWE6UkY6P2Ym1z5uJFRt8R6EIQzc9yW7bhdoXm+y
+84kfcd8x96git1uXaQqqi5fnyqKFc4rFLQBMNSYUF2NkI/KdlbzdDHvmAFKBGobonFO96c69LLfI
+pm5Hf3sxIfKdC7uwBXV5Jn4iyLRvGU/d567sd2k3VxLoBdGWe0+ju0W8pc/NXeLMmIzMumpPoxGw
+udvRPWlTrbjTlzMyyasbdYn6lyyfv+W8mHJekitsLO2EZSCg0nzBiUKTpS8MCTAWzPkL37pD96oL
+Y6p8SUNkL8lmwDMiIvFn6ii8Umepnf34eESohG7mV8Q6XhR6LFcJX3VD+mmGJ2k233qob4tZNKEv
+bMkrkuAbLFNOFYrMMo9AokOL2vyiYW2/0b8AOO1uOg+lF37igjtj56UbtAX59pM2ZrnCr8yolsJs
+CE2XZglLwftH3jmv/fbn3XlkKQ80HUZ3zjjTeL1XezwWBWuY5RGxt6Q1VOdZFDOVSKitSnzp4bUD
+n9lo9bq7cePzc976opBoN4+ZXHKMR/t7PtVbzkUPWNDvu8vd+MkX3efTkKrhXPsk2WcwcWCTg2zJ
+5++47/vGgyknI+UidLxRUVEdu5s83DmnVBmRTov2lgsZ7NU2/tRlg6tU5KieAaEUCx8QFt2aEL8p
+XLs0FmyWEuZyHZsZhUs/kPXP8c7kMu6M8KZuwSlLd+4mj8jE52j2LJVc+PP5AmNV9srnbIv6vKGQ
+YZYuyFSejG1CK3L3Yf7glDu/7th/Fpfu0Aj1ELbduGa35piOtBdqHp2uOkJ9jzwVW9KkxBcdulVn
+6Ftv3OfVE5XKAnNB2FtVHioeMa1yxnM39qvuxEVVXrRD3zk/4o4W+s1ei2bHXUtExOBUVPnK1B99
+fXrR+Rj2m3grZqWoIhVoC+tTSGVqrhAmsymZXiuimHzk7mW4yDlS2+xSuo0T45ErfCjLkqkW5Nu3
+z2lfcYuTwWKEcM1F9DKNCnZUGanOUqrIzT1vEkDLPdB+VsMotCnnWmzDGbm0Ju++ecTL911o+vok
+ZaQFKctzCvP+mJfW4cPa1ra2h8x8Wxv2IzwJY06AGXD6EZ3C5vrY62P/W3Dsq9bare/2psdiUQAw
+xvyutfb718deH3t97I/W1uHD2ta2todsvSisbW1re8gep0XhF9bHXh97feyP3h6bnMLa1ra2x8Me
+J09hbWtb22NgH/miYIz5KWPMG8aYt4wxf+0RH+sJY8xvGmO+YYz5ujHmr+j1vjHm14wxb+r/3iM8
+B98Y8xVjzK/o9+vGmC/q+n/JGPP++lzf37G7xph/bIx53RjzTWPMZz+sazfG/Kca89eMMf/QGJM8
+qms3xvxPxphjY8xr73rt971O4+y/1Tm8aoz59CM49t/UmL9qjPk/jDHdd/3tczr2G8aYn/zDHPuD
+so90UTDG+MDfAX4a+BjwZ40xH3uEhyyA/8xa+zHgM8Bf0vH+GvAb1tqngd/Q74/K/grwzXf9/jeA
+/9pa+xQwAH72ER77bwP/l7X2OeATOo9Hfu3GmEvAXwa+31r7IuADP8Oju/b/Gfip3/Pad7rOnwae
+1r+fA/7uIzj2rwEvWmtfAr4FfA5Ac+9ngBf0mf9Oz8RHa9baj+wf8FngV9/1++eAz32Ix/9nwB8H
+3gD29Noe8MYjOt5l3IT8ceBXAIMDsgS/33h8wMfuADdRHuldrz/yawcuAXeBPg5a/yvATz7Kaweu
+Aa99t+sE/gfgz/5+7/ugjv17/vbvAb+onx+a78CvAp99FPf//fz7qMOH1WRZ2YFee+RmjLkGfAr4
+IrBjrT3Unx4AO4/osP8N8Ff5tpDXBjC01q6YNR/l9V8HToC/r/DlfzTGNPgQrt1aew/4r4A7wCEw
+Al7mw7t2+M7X+WHPwb8I/IuP6NjvyT7qReEjMWNME/gnwH9irR2/+2/WLdkfeEnGGPMngWNr7csf
+9He/RwuATwN/11r7KRys/KFQ4RFeew/407iFaR9o8P91sT80e1TX+d3MGPPzuBD2Fz/sY78f+6gX
+hXvAE+/6/bJee2RmjAlxC8IvWmv/qV4+Msbs6e97wPEjOPQPA3/KGHML+Ee4EOJvA11jzKpb9VFe
+/wFwYK39on7/x7hF4sO49p8AblprT6y1OfBPcePxYV07fOfr/FDmoDHmLwB/EvhzWpQ+tGO/X/uo
+F4UvAU8rCx3hki6//KgOZowxwN8Dvmmt/Vvv+tMvA39eP/95XK7hAzVr7eestZettddw1/mvrLV/
+DvhN4D94lMfW8R8Ad40xz+qlPwZ8gw/h2nFhw2eMMXXdg9WxP5Rrl32n6/xl4D9UFeIzwOhdYcYH
+YsaYn8KFjX/KrmTAv33snzHGxMaY67hk57/5II/9PdlHndQA/gQuI/s28POP+Fg/gnMbXwW+qn9/
+Ahfb/wbwJvDrQP8Rn8cfAX5FP9/ATYS3gP8diB/hcT8J/K6u//8Eeh/WtQP/JfA68BrwvwDxo7p2
+4B/ichc5zkP62e90nbhk79/R/PsarkLyQR/7LVzuYDXn/vt3vf/ndew3gJ9+lPPuvf5bIxrXtra1
+PWQfdfiwtrWt7TGz9aKwtrWt7SFbLwprW9vaHrL1orC2ta3tIVsvCmtb29oesvWisLa1re0hWy8K
+a1vb2h6y9aKwtrWt7SH7fwErqDr3TXcUMwAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.2873... Generator Loss: 0.8386
+Epoch 1/1... Discriminator Loss: 1.3842... Generator Loss: 0.7583
+Epoch 1/1... Discriminator Loss: 1.4189... Generator Loss: 0.7991
+Epoch 1/1... Discriminator Loss: 1.3378... Generator Loss: 0.8856
+Epoch 1/1... Discriminator Loss: 1.3755... Generator Loss: 0.8002
+Epoch 1/1... Discriminator Loss: 1.3075... Generator Loss: 0.8532
+Epoch 1/1... Discriminator Loss: 1.3158... Generator Loss: 0.9599
+Epoch 1/1... Discriminator Loss: 1.4263... Generator Loss: 0.6772
+Epoch 1/1... Discriminator Loss: 1.3762... Generator Loss: 0.8854
+Epoch 1/1... Discriminator Loss: 1.4065... Generator Loss: 0.7769
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsZel13/f79nzmc+5Y996au6p6bjbZpEhRlCjJCi2Z
+jmwJghzHMSTEhhMDRhzHiS0o74bykrwkRqLAAeQ4gGBFsh0oki3KEiWTEtkcmkPP1TXfulV3PuOe
+9/7ysNY53Y1EZFFkyx3grIe+p0/ts/e3v2HN67+MtZYlLWlJS5qT8+97AEta0pLeX7RkCkta0pLe
+RUumsKQlLeldtGQKS1rSkt5FS6awpCUt6V20ZApLWtKS3kXvGVMwxvy4MeYNY8xbxphfeK+es6Ql
+Lel7S+a9yFMwxrjAm8B/AOwCXwL+qrX21e/5w5a0pCV9T+m90hS+D3jLWnvTWpsDvwr8pffoWUta
+0pK+h+S9R/fdAe694/93gY/+SRe3Ww270u/geQ62lu8cxyWKGgC4vs9cocmznDxNAAiiQP7dOFRV
+AYDvedTGAFDXNZ7r6/0M1pHvbS0PsRaMjsEaQ5ZlACRZThLPAKg8F2NlmlxHeOjk+Ah9BI5jFpzV
+8xzmd5Txymejv3Mdh1pfpCjLxbPDwCcMQ71Gvi2LgiwvAciKgkonxmAw84cD6LwY12H+RKsjchyH
+opR5cYzhnT+Tscr99A44iwtq3qlAzq8xZv4fmdv53/odFy/GVtt3iByDLitBEAHQavk0mk15Zzek
+LvQergFbAZDMZiSJrHWWFjqymkjnynM9SqvvYSxJLNfmZSHPB8LQm78SxpWxNRotvED2he8FeN78
+Gktd5vpsuVeczahKWYeqtos58lyHwpXxG31WXSaLPVZXtTyU//decD1X5sKT93A8g2vkOwcwOm9p
+lmJc3RdAlcvYKr1vnmeLea+txXFcfV69WGu7+A8URXFkrV3n29B7xRS+LRlj/hbwtwAGvTb/4G//
+DCsrbapcNkTY6PPE488B0NvepJb54NbNO9x/5XUAzj99HoCWFzIePQTgzMoaqW6EOJmx0tsBoN0I
+qCKZ7SJL5W8Kjm7ouuHy1vW7ALxy4xZf+8qXAZiut3HrNQC6YQeAz/7TXyYM5F7NpkNoZTHOrLao
+dHHLwlC78rkRyOZptlqkuWzu/aPjBQO4fH6LKxcvANBpyyY4vf+A6w8OALix+5CpbgDX8wl08cGh
+rmS+Gh1loNZQOi2Zl0aLvcP7AERBhBfItaY2rK+35Xq9l2cCImVe1uSUpcyLtRDoLnU8Fkw2zvTw
+TGLiKtVFdTF6P5OV2FAZmfHIdKttXbgCwMc+doZnPvgRmdfWZeJ9eT9n4GOTEQCvvfgVvv7qywDc
+fmNPnmtiHr8o9xgMepxmbX10yesvvQLA3dNdqljGd/kxWTt35hD0RYg8/eSHWb10RtZscImVzYHM
+QVwxPpQ98PKLXwfg69e/yunRIQCzLCX05R7r/Sb7recB8HXPTg6/SRXJe84mKUbnJS9rHFe+95pN
++mt9AM6uXZR16jdoh7pmgB/KHL5x5zW87mMA9K1ldF/GdlrKXN3bvblgoGle0uj0AKjKBOMpQ6oc
+ylKuebD34A6PQO8VU7gPnHvH/5/V7xZkrf1l4JcBLp3dtD3fp1371A15gdXNLTp9OSANQmpHDlO/
+6dC5JrfevvQkAEE1wa4KA3T9NpN8HwBnuk+kE+j5Pj6yoIERLcC2Wzi+HgS3y/ntWJ7XClhfkc1/
+UhjevCHXB23ZYC0XGrr5ndrQb8rG7HhNHN0UJQ7U8jlqy4J7rkdg5Z0elEcEIoBoOiFXz58FYHNl
+CwD/3GO0Xv4CAOPTmCw9BaDOIEYll60Bma/alUPslC6OUSmeZlDJ57LMVHpBq9WBWqWUSp0sy3Aa
+Mt5WGBL5wrDyrMSbSznfXUhgVxlhM2rg1zKvFkuizwjbPqg2UpYZ46msw+T+AwBe+0bFxqowwuNW
+RdjYkDk63ufe7W8C8Md3v8F0KhJ72JR3jrKQg2ICwO6dI06P5fAPT/eYTGSPYCu6nozpXFsY8sVL
+LR6mcg83jjnduwHAaHfGY5UwmcubW0xTmc9RJdfGWU5dy2evqilV80q9kmZLztiNI2FYrttkxZG9
+EIcWq/dynALHkf3kOU1akRzeldVVANZWVuj6wtRLf49yJNceHTzEeXgMwMvH+/QGOkeqPWEs06nM
+RV4aylLm3m/6BKpBlUUp3Pw7oPeKKXwJuGqMuYQwg/8I+I//pIsd4xAEbabjgvZZmZDQb+Cp5KrL
+lNjIwoTdiM1rwgxa4QoAhhG1qlZgOH5ZDvFwOCIei1YRTbo4hXDo1iWV3BiCSA4sucPGBZEeK9V5
+Ll97CoBb9w7xV28B4LnCeF4MI3xVAd2Og61UpWwZ2g3l+IMBgaqlppK/aVYybco4O0mLc33ZHB96
+9imuPHMVgLWeMoUio/Z0g47GjF+TjXB0HJPpwSuqEqPahinKxd+ikmc0nQbOQpuv8KK5WWFwQ+VI
+mWoKIbTaMvfdVpOWI2p+4ZaUqhXUtaXM5Hf9FZnD0HpUnuqn04oskENT5oUwRmC4P6I/UM0jHwLw
+1vUCuy9+560/P+C53g/L3N57i9e/8CIAt2/dY3VD1iw7UTPCqdl7WTSorJhyeqJmo18TdGTuP3L1
+PFe2ZC2felr+hsc5TVXLv/qHr3IzFgEwmtV8qvxz8k7fv8Y0kMPZactzrQnphHJ4T4pjKtXYCpNx
+767sM1cFmQldrDJntw6oatV+XDERZaAF3a4w5I1rmwA8cXkHzwgTi2ZtvvxV0VJLO+PmjRMZh82Y
+5bLua01lMJ7BKpMubQm6B9yyQ6p7hKDC1bV8VHpPmIK1tjTG/B3g3yDm0P9mrX3lvXjWkpa0pO8t
+vWc+BWvtbwG/9SjXVnXFJBmD8fFzkcYzcvJUOLEJmqDqnF/VkIzlh55Ihiq3lOqIyqevkSeicsWZ
+y1dfF6mTTCrC9W0Anik+CMDq2pTmRCTD6sYZQrcrt3V83FC4/Nb2BvREoh+fCtc2kUuN2vVlSB0I
+l4/DBgP1H7T6AzZ6osmkqu6NRh6UUwCevrbCjz8v2sGlxy7TWRXzodVRWz9LeOKZ75fnNT1m1WcA
+ePGrr/NgJGOrjEOlkht1/Hl+C9XssRisepnqGgwiMaIgWpg2ri+/a4QRZ9X/0lkNGASqQYUO2URt
+4yKGQsSt35Z7dfwQPFW1hzNmjpojo4xj5F3TcUCrLe91ciD+gnsnhyT1lwDoPfjz7BvxCX3zxZe4
+e1c+H8UxI9HMSSqR7NWsIIvl/Y2tMFZUoUG/z+PPiznyMz/yUdZ78t5roWgK9XMV50/lPQ5u3uKN
+12Rss9OcP3pJzLRm1SSWpWQ0FD/CuCzxK9mHGTWFUU0gTsgCkfihkWeUgSV15f3T4Yw6lueVdbVw
+57Y8D1/3zmQkWlNdbvD4muy9lc0PcGlHfBwf+tgVXvrNzwPw8t4uI33Xuf82fpgwmvuB8hw1nnBm
+M6z6fjw3IlBf0qPSvzdH4zuppiYuJ6yff4zOmhzA7soWrb56d9029UQ2Smd1i6itE1HKpBtTQCqb
+zQ1W6a7LhDy1dYXVhmzeb95+hdu6mW68IUrLcLbGyoYucqdJX73hodehGQqzCCKHtjomVyLZ2F5g
+CHxZ2Nxx8SL5vL7SZuecqP/b/W0aDblHZeX357wBuTrwrl7Y4epjwhSCVhejTM+dRzVWu3jqlHx+
+68do6IFew+fXXxKbezTLSVRzL/V3DbfCYe5Z9/DUzKmtodWU8TTaPp2WLH1g5O9av83VC2Kz9vsh
+oaM+B9elaAlTM8EKjjoxjR4Osgw/lM9Zd8BMvffDaES7lme3cBaORj+W706nY/JMfvf666/iN2Ru
+D+9ex3HlHsYapiPxpaTz6IOtcHSPO4FDtydr9vGfeJwPr14C4CMvfJiVVWHIzVDXrLOJW8m8rJkV
+PnfjH8v6lRNmqdjlX7r7BoFVH1QlYwt8B6N+INJ0HlCgqi22kIEUnqxvfZRzUshcVUmCM48YOSwi
+JmfOd3n2BRF8Z/pionzg4haPnX9a5qrX5KIR0/W50vKXf0Ss7pfufJVbnxVz6ytvqHnl7TFUn0KS
+GgoVDJVrCdWf41iovzOesExzXtKSlvRuel9oCtY65GUTNxwQaYC1F4SEnoQA3UaH0Iiq5fkRrmrM
+Vj84eYrrye8Kp4EVgUjo16yfEel3Jawp74rZ4XtHcu3pCTNfVObZGWjM5N/D/gpoaLEddIhckdKh
+SknPb+BFou5VdcpqU6TSTuscj21eBGBltUXgiBpYa5x7cGYLz4gafGbrcaKO3Nc4DrWaStYTaVYm
+JUbDX1EU8PhHPiTX2oQ7sYz/5t6Qw6H8bu6E8hyPYm4yFJawI/dzbYCvqx06Dg3NvTijauv22iqb
+oWhpYV3hagjN1iUNleJB4OE2ROJZDYU6YQurERDbcQgnE32GRzNVc8X02Z3K+kXrsk5r5SZxLuOc
+HI65f2MXgFFRYDJ5b8exogUCjjrwvNLg+PI5CkOuXpaI0H947QXOnpexrYSruPMxR7K+hhJHvfBm
+O6Kl8jDwajZ8GWfXsJg7p1bHoLWcxLJ+tXFpqsnQDB1mqsUsogGwyAlwMfQiubbdjfjE8xK+fPaF
+S3zs6Q/L96vy3PWta4Rqjnlha6E1mijANmW+nq+f4cInZV42uzInX4k8DvZlz87SgjpVjaU0GNUK
+qSylfUeCyiPQ+4IpYBzcsEFVWlodOWCNoIerC0BZIhFccIyLLWURyrGoeraRUluxEfO0ppzKyuTN
+DMeTyVkfbDHal0kdq1ffVClVKYdq/+4e+UXZYKYRM/BlHK414poHmqEeMCcATbbZ2Oix3ZdnDFa6
+dBpyyJrdLraQxWuqV7wd+vTWJO4chAGOHqZynFBZjZ6o+l3ZEldtAn99A089yI899Sw/cUUiKv86
+vE6wK2bTw4fi75hMZxSaH1EFHqv6O88N8fS9MSWOL++youGxja0VnFzm1TVArKpv3wU1nwwhfqqm
+yUBNoyTH2nnClqGtUQlb1xTKWJNpTL8r8zXT0OTm5grDU5n73ckh0wOx4e00xemIMMgfJBTq7a/m
+iSo1VJqo1QgKXD2w+fAUd+WcPm+XZinrkHY0ock6RJG8/3R3l3YmY06TgtlU3u/SlZC6L+bfVO19
+p+wSjOTZJSmrffFLDbbgG38gzLlqKBNOLUFDo1Kew86WRC2efvIxPvH84wCcf+IcGrWmG4rQMFWJ
+0YgR8RQTBrpMb4cTQ69Je0XMjZUd+btzr8OmXnsS+SS6TlVRUmWyfm7kUVffGVNYmg9LWtKS3kXv
+D03B1lRFSrP2CNUx5LrJIisN1wWrEmFygqtS1Wpst8wr8pm8SlIckliRVnnsESaaeVdlOG25puMK
+Bz84eUiuqbGVP8Qei0TvNSY0NcXaDxrU9dzJpSmspsLXvAm/MLQ94fjNbojVBBJTGrxC07DVo+0Y
+g1UpZ6uMXDWJ2nMpNQGo1shKXqQ0NRrixDPqRDWhZMLWNfGyn5kcsPuGaAilOrWmRUGl8eqgAuuI
+1DU2w8Yi0UwUMFKVd54O2zXu26nLWY43z2LEXWgCpgSr0YVCn1FXZpFu61QOlQYtfOvi681bbkiq
+uQytYJ7z4BGoQ9FLLaUjZkfdcKlVcqf5lCKdX69JWAYclXzWM0wfyHx94cVXme5LYtS1/fNsnhEN
+qLElkn/v4IDGSDSFr+++SqoZfyU1Dx7I73YuXKaj6+6oaed5HlVPnbVJxEwzD/s+5CpSXZ0LW1vm
+iSGr631eeOoFAJ67PGClJWvZmOZ46ihPZxpdcwJcjU84nYBaE+5MXVHV8n61aZGoYzN9IGs+i0c0
+Namv3QphpHNoK4kUAYE3ADOPSzwavS+YgmOgHUB31aOtdhhFTlqLalwmbVwrL+ljKQNJXsFKGK82
+OVUmh4bIwWhiilO6TNTuKwKLb2VyjNYUuEFJXsq/N2lRTVQFP1qj2ZinFbugISSr6nyWZgRNDek1
+GjRXhZE1A5dGa76ZmotMOHeexBS2KHVsdVbidOS+ReYv1ON8qjUXJseLhLnV4xMq3UBus43blA22
+2d0g0qy6ViHPmMSQlJpqjIZwAYtLSzd8nVVkej9P/TJlNsNo7YfpG2rdGm5eLWofKjvF12SvQuew
+KCrm4QBrfYqZ3KMsC1xNwQ06Lq1Cnt0O1PQzOc66fJfmm1Sa4FXldnEorDXMzeH5X2Perq/wPY/Y
+yLVHJyfcD2UOH9teZaTvN7wtB+XLr79J41TmIl5t8JEPSwTg+v1DElXd7x7c5umehlfHwqQTO6NO
+lHlnM5JczJw4GuBr1qBfavq8gVAjWFurW3zfh56Qz+sN+oGsWacX4WqIe57m7lY+VSH7t0hDqGWO
+HMfDRJp4FFY0O3KPs89J8l6eWSaJ7F/eGPHwVBjI4TSlVBPbSQK88DtLXlqaD0ta0pLeRe8PTcHz
+aQ62wTZITsXBE/sGKtUUiikDjbsGUZtyIipx1dJc77TEc+fxcwerRQWOHeG3xWGY2JisI/eIHwpX
+bpYG3xfHn2v7OJoIUk2GjIdzPXiKp/F2M4/9Vi6u1lFgawKVFG1aaM4IaZXheGoKqcppXANarFXg
+k6npUjo1NtH3nqpZ4jq4rkjBlufgeCKBsjTFXdN8g2bA+Q0xXVqqdTil4VjTkm1t8NVTX2Y5aFVf
+WSVksUi/yUjmOO1GBCr9q0aPcCAaWx1nuJqzUNmQcaEJOeowrCoHq/aRjWaLRKcqjRemUuV6pJVI
+7EClVrNwcFSN9jo5nkZP0mxGPS9hwOKq09SdawfG4Gmk6UyrwSBSE2VqWVNNbtUb0NXqwgNNf19z
+G9TqfDt/5TIffkacklPr8rufvQ3A+mqXpzSF/s5QE85Odkk0GmAnOdZX1f2wJPA1T0HnzQl9+j15
+v+eeuMwFLbTqrg3oduSzG0QEDdEUcGSu3HBA7agG6QXMS4WdqINRR7njQGNVnY590XIa2+e58Akx
+UZ698XtM/+c/BOAzX7rFPI3EdcGZh0QekZaawpKWtKR30ftCU/Bcl7VOB5Ip98ZSTGnxMGrvdiar
+1Nt6cRkTx+p02xXu2rsSEcTzenWPyM4zzQqs+hGKpGZ8XWvktTinsAWur1pDEZCJGcpROmNUipRP
+T6eMVOKdXREHX9jwaDdFImyvdfEDxWGIx7QPhLN76yzi97k6RsdJSqDZf8lsSlLJ78JgBZrqzFLf
+gp/VuJmqHX0XtDLSxDGtsVYzbrXYOBapsVaI9MmTmlKLhAoKXHVgupGD0XTk0SQmTuRlH94TyXfx
+aodCy/NbbQcqtZfXemSHGv+uS4qR2K12oEZ+HBDP7eGjjLIpczwdJkT6Tk5aUaqDMVW/hWN8yqF8
+1w4NqWY3xqMEt6XvXVm8OR6COnADz6GvoeGo4SxSuqeOZRRoybwpaKmi130o69GKAl4uxKE4vXFC
+d0WuPU1Cmj3N7uw7rF0RP9WtV97QcVYkp5I2X2UpSa736xpy1UyclmqSVZu2K45N0z5DUci9/MIl
+CrR4L3Tx1CcUOPLXBAG2Ul+aG2CYa5bBAovDcRwczZCdo1P4jS6NhoRFW/0NfupH5drPvbRLotmw
+pa0INGz5qPS+YAq2qkinExJb8+BYmEKWTNnQ1NDm9jVGD2WoJ/EJVtW17rpsJP++x8xopOJ0Qq7q
+/FE8Yv9UNuz9g12ymUxm9FAO/IWVkKZu/iKNGWu043R8ilPLgt9veThN9eBvSEl25PusNuS79ZXB
+IpnKKxOcTGP9QYOGpsqGwdzBacmRhY3zGbE6iUbxAa0NKd/t6tjD9YCm1sHT6ePNHYa9NsX4usxR
+bMDOIxyK3dBeoZPLQc8n9bzEgU7QXGAvRK6Lo6XRmf7u5MDjWCMfL3/zdZpqamxtnaOzSGnOsep0
+9WvZ5A5wog7h/fQUavldUMNE8x7y4xlTR1OXdUB105JqjoHjGTpa6pxS4ydqxlDR1PHNvfpeDYU6
+Pu+PUtx5abgz49UH4gRMphV/8Qckhby/KeZhtZvzRy9Jtev13/kGw0J+N0srWspMIidgUyNGG+tS
+z/Bm7iycwElRYhAG+eDkCDs/sJWuQcNn7ZrMS9kK+JJWgW5WOwzuyz3WLzUpW+IonwsZrx7jO5J7
+EI+PcDSa1chr8GUuZnVOGosJ5mlEzXFLQOY4PU2ZqSPZM1DpWpOlZI7WCj0iLc2HJS1pSe+i94Wm
+UFcV8eQEsDStIvsc1VSrwu2aQYtCNYF0BhXilEtPRIWvbEKs8Fnj8gA/F00g9yxHN6WQ6jh7yIW+
+cP/tC6JyR36Ir9WQw1FGpTFfZ5pyYkX6OU6HUKV/qWpY6HusnhEVb6W5yoa+R9SGQjXf+F7GjVRx
+GLQQa2frCuFApMBsOOSwlLH1qz6eVnYGoUjBOA6hJVJnfP0e19+SGnsbHWNnonYOx2OO1WG4tqNI
+UI2c1Y46FAOPWgueWq2AXCVMZHy21+Uely/LM+KTI/YPRYLtDcd0NQszSWZsaYy924mIFH0q11Dv
+6PgBL6sJsj9JMYpINQibtCMNydaW4VTG6c6r9ypDrZEyv2rhqhPXK+wCJMZaA3bu5FQHbOBz/txl
+AC6eaZGpI/F0OOK2pkr/qy+9yUOVqj/+g/qMSUigaeMnSUGsMX/juDz3hGhpz3z6eaaVaBsfvyga
+xuljj3HzNUE8ist0kXmazCyhpqQ7c2eo6+Nl8lLf+Pwf8vv7otqPD4dkakomaUqnLRJ9pSXr9PSz
+F9i+KHvyw6tXOUjk2sO0we+8+jsAHDzcY7o703WQ3w02mnz/0zLOp3fO8MZN0WK8wMPO1MFcZmTZ
+/w/TnC1gawfrGhqaA3r+/Dqhel5fvP0qR98URnA73aVjJPkoceXQnQ836K7J79bPrlC4GjcvHEpP
+1daiw5fvSR1ubyKH1C98el15RtjxaBr19JomG105FN1GB+zc066HymnhamJRKzCcacl4bFQzqWVz
+T/Oae4fi89i7Lofm9IuvsWZkkw8GAb01+V3aKgl99WGo2fJvPv9Zrh+LuXJ485TcFTPI8wPOXdWa
+grS/ABaZnQqz6fdWyKbCYEwnZC9RDzcunh62nu/x2LrUBKw1ZTOmZkhPy9bDxhr7M7nH3bsHVC25
+x+XzZ+nofE0Uw3Jvf8rxkfz7qZNi5SxSNCsyRaRaW/MW+QuFMrG6jBZJPNMkpaeoT5iKShOVTODh
+qh+k1ENsa0say7zcP16jHWjpdGsFuyNjundwzD0dU5rKvDz/w5f4z7QOZu+f/gve3JM1afUCPvGj
+PwTAuSDidFeYwsa6RCc+8tx5/vgrcggn4wm1gprYoiRqCJMpMrXf4yEnuzflGQ/2mIxk/T3HSLki
+Uo9yeizq/MmRCJ47uw/wNILz2bUXyRTV6+HRETNN3jKAp8UrqVpUhw8T/mAk++Ib4R38uRnjBQss
+ydpazHdYJrk0H5a0pCW9i94XmoJjDEHo4hcVgcJWtYImsWIkvPS7L3L9VETQ1rlz+C2F+bqrXvH1
+h7RTeZULW+vMfY6HDInUU+2dpkyOhKMf1aIxtN02qS8OnivOGv6WXGvjlIFmpvllQaXwFVGuWWkd
+l75mXm5sbuLVCkIymXB0VxylN+KUoWa8jTx5DzP0SLe05j0oWd8WKb/VXCVUSXl/V1Tg4/yYyy1x
+RG1+6iKzuwKOffvmffZeFkmzdr7JwV3RoB6bZyimGWtaIVe6HjcfiMQfhzFNdUKvrzV4/PGLAASp
+SLA3iyHTqUi2uKw4fCD3aw58wrZImiu+S9eKlpIGohr7fs2qzpUtI/Y8ed7901NmWvkZVV2amkcR
+F1rwVdbMbutcNEvcWO5RVBA2VFaVLkbHV2hKuCkdbtwTTaG+t0/Dn1d79jkT6pp1mxRa5Xn5KcFY
+2PmBv8CZD8k6/Ddv7vE3f+O3ZV6GBW/uiXQPiwFtldL7N27oOCc0tJrTw2gMSDIsPcVVbHS1QheX
+jk7yoOEuMh1HaY2v6eGhZ8g147RWk6Ky9SKV/uzZBsOJ/G5vL1+A51gDpTrYJyOZV2cIEyPn4qE/
+ZF2L0bpOxPE8nb6sF+bNo9L7ginYuqaME0Ljo2eUMK7Zm8gBmyZD1rUS8QPbG0QNqWY8QFTD3E3p
+NWVDh9ZhMpZDc/30kG5LQVsaLjtduXmq96rHB0zui13/CmMGmfgcNvwmrUDjk9bDNmXBEk0wmo2T
+RR1BZSqcYJ6kVKIaHEViWFPzZ/sxscPDq1sodgsNJ+dDTypadSfCaqrwZCyH6kzgs7YtF197+jm+
+rKXDdTymraXFGxev8e+uC+DKW3eE0VVFQW9FnrcS+sSaeDOZTOgP5PvNbo+OllSHkfwdFNvYNVFF
+j+oZRuvP/bU2l+abv78i4K3Ayb6GSJ2I1o6GVo86tDWZKhkNqbQ+xJQWY+ZVrvKMuIpxNHe57Yeg
+yUsYg6vJYgQOqHlQadVm7bqU6lk32AV4yXajy6UdCQe28yOshqg3z0qqcdTexKyKSfDD/+C/5vGv
+/5HM2+0puy9J+PE/+dt/lUtPSIn6cCLz+eAPSloaqjUuOHM49xoCrea8vCnPrTsFZwcKoNJz2dM1
+ee3+CblGO7zApdPS0nAdu29zdrZkb/63P//XODkVH9bf/x9/mcNj2ddlXeOpD6NWM9B1XRyNUAUt
+hw1PGHYdNPGGmkJfVtTFd1b7sDQflrSkJb2L3h+aAhV1OcV2A/KJJr+kkCXC7TcurtJwROI//8wF
+anU6bSoO3ygOiHxVnc43mTKXXA6rChxy8alLXFEP//hEC2NWVjkYiqaQVz5dRTMOa4NV7IHaGkJ1
+Ds6h1B4eHHJzXdT8C8k5Lp6Zo0RvsvmUwGo9Nc3Ij0eL9wMwrXVCxfTvdiMGWyJVAqeBUU3hzH2R
+pCsrd0FVQCcpeOFJSb/9wJU1MtHcuX84w0/kXQ5UVae0VGMZZ6PfYUPnZTiMWZs3UWkGZIVc31+V
+rLArjXMcRKJGnwtWSC7LPY5GBesLGDdDNq9snBdMBQ3Ob23o+3uUWiZZZDUxouZ7pSWdyXvvaRJS
+miR42yqycKaIAAAgAElEQVSTcn9RlRk4Lm01R/BcynmVpKb+1qam0VRos5UuP/2pTwDwiQ88S88T
+TeDO0avsPZBJGlyVyIKJPByNPqw98Tz/0z/6RQD+7n/1S+yIA5/muXXOPS/rd7Z+BoAL5+/yWy99
+FoCXHhwuEoYN4Kox0VJY/p3zHld2BKH5bO9ZBrlorF+88wYvfU7MkaC3wgfOaG+PXAvemobHv08G
+8dynf5Iqln34X9y5wb/8/a8CcDg64EixXFqaY9LaaPORazL3Z7cucF7h3X7786/wimJbuq6Dp20M
+eESF4f3BFKx4l+uixYlWKhbVhGP1nEftDhuKxbe+uQ2x/jCShc/vT+nqPqpmEV5bvbCFxdcJ7Pod
+Wm3xCfRaEk6bjSY0IzkUJ+MEXxUn360xCk5SG5+Jql+uVq8lVYOjoSx4cncIa9dkOF4XX9XATllQ
+nFXVV21St+UTGA1jNZr482q4MAAtP956Qg7/k7s3OVA7c33QYWVLxhkfrPLNXWmQcv/gmHzuldek
+oobv4GuPgbg0bGgu/uF4n8NETQLrU2vozFEMy26ZEa1Ic50icMm0RmG1NSZU5Kk8qajFUsLVhCYv
+DPG0PLu/0sNVxknlkuVqa2c5B5WsVb+vUZaixFMmPBkWCwCYVsOhaea9IyI03wxhr4C1rGtV6k9/
+6qP87F/+Mblve5Op4jg2CkNo5XDOIfCtfUejmsDwwU/9HAD/wz+rqKcy/qef+yGaLWHUjiZIta6t
+8J/+3F8E4I+/eZP76ndxHHC0IrYIFIB11uHcpjz38cc26ClM/NVPfJQf/yF5/2EwwT+R8c8UNLfr
+1Kytntd33pa6bOBn/94vcvmFXwNg78XXeFPN10s7ahI7Pa5ekX3R3jyP05Bo1hu7Q7qvS/WsMyto
+KEjQJH4HQtS3oKX5sKQlLeld9P7QFOqaPE6p6oTZHJ59OKOY90ecWhQ5nXw4pbchnHZQiKoaXo2I
+ZqobJTnjO1rTHxqsFTW5SGaYA62l2JpXIvaI+upPNoaYeX/EilodOKG1pIrWG6Idq9yC/X2RXUXL
+xTryvHR0vKgo9Fc7+CqFrdY7lFWF4sJgKgerKdY2dTDaFzNsyxgunDtL8rpEHMp6SnaiPQXrhMmJ
+xNJvnN4k0YIFbwEibqjnTXQcw9UrAgNmHUOqUnM8mzIba6RlT6RZ43yXSJ2IuA5FIv/eGDTJp5oS
+XY7I91SOKJ5EejokVIg2Z9QkVNxBZyaVngDHwxjfnXeT1GrAPMU9lPs2OzXBHJDGeFTqXIzzEtTB
+Osd0KKoKLdSku7KDrxDuXjXAq8X0On09p6dNeZLXZJ2aH97EC+aNNw2eI897/qmfZaqwfo322gIy
+fj6djhPw1Ic+DcDTl36LSqNAicmYPFQoPI0+VPsVw6dEmt+MU7abMtDGTkQ7EtMmj0/YvyXpz7kz
+j77ssLGlGk3q4SpeY3ewwwc//EkAnr36PD+iDuQ57oWT5JSHooF4fmvRyi90V+mG4oxvuinj7M/I
+0WiMOWeM+X1jzKvGmFeMMX9Xv18xxnzGGHNd/w7+tM9Y0pKW9GdP342mUAJ/31r7VWNMB/iKMeYz
+wM8D/9Za+0vGmF8AfgH4h9/qRlVVcTKZcDyccDSTVNvxMF50T24FbYaxcMnD0zt4tUJsKRTXvpdz
+rhDtofKmGE1d3u70COddi8sTQi0emmkVYZpWjHKRXNMJnKo0c5Mco1KlqINFCCjXlNqsKskRDeRg
+OCPXfod106dWP0iZNPGceSs7TXGdFtiJdj7uZIuiK9OLQLUQR30ETT+ktyL3jajxFcCzwpCrQD+6
+P2F/ItJxnh5duB6dOVKx77K+pjgM5RnuacHQQTwmGGnIriHSrp2E1Om8si4j1rS5SdUkjUc69wf0
+XY0Za1VfepRTV+IHWvV7uA2Fwpuli/Z1ZV5Bc46oJeOdktFzZU17zRbJTN7b9z28UjSPhGoRm1/0
+w64tB4oB8eobL/MjL0gI0TEpL+19A4Bf/r3fptIx/8EbnwPgkz/6U/zgTzwLwMbGE9TqaD2+e4tZ
+JqHvkOeJtF+o0X4ZVZpwcCghy966pXGgjX/ijJnmEJzui/Ywcyyv3JDfPd5v0OlrI2NnjdVI5rYb
+VCSepJa/ces1AHa9u1zoiC+j3B5hFC8iGR1y/LKkt49Hx2w883EAAvXxeLhYDUMm6ZgHt8VX9sbd
+N2iqU/U0r8l1zh+V/tRMwVr7AHignyfGmNeQFvR/CfhhvexXgM/ybZhCWVYcHx9jXMvhicaxxxnd
+gXq1QsONu7cBCJIEPiAbZThVCDPPo3VOFjMwLvFDSX92jy3TeI5we5PtDXEIegrfXdQzHH+eA1/j
+K3yaH9YUvixihUs0h91SJ2Gv4bO6KYvY6joknjChyPoY3SjhrKTQRqjM0cKzBm5bFjzIWviamu3Y
+EKsdiqt9iWqcHt1hoqm6/YGl1tTtulvjaEeqSVWRqfkQqKliTMU8VyXyI5yGPHx1dZ17tyUa8Nbd
+I0pF/j3f0zqQh5ZKGZr1fKZqBhWZJdY6AjfPWLukil8yb1bbULRtqKxPpqottsQowIvX9YmqOUiK
+3LfMMhrX5P0bjR6+OhpDP1hURNrCYrUKUi0ibP02c969fYcvfuH/BmCl2eQ3f18O0NfefHOBZ/i1
+XYku/bPf/nd88FfF0fx3fvav8LHvk+5be69/k9mxMMvs2gHbV2SPNJpidpXxjOSeCKo7dw4ZxfNO
+0pbmvKmvekFLUu7sybXb/gZ5X0274wlDR76fmprTWEIDxydSU3Hj1oQ3vvhLAPyV21+krmScv3vj
+S9y7eVvf3/DM0+Jg/olnvg+A/jOXsEYToHKfe6/JM+Ikxfcz/TxelKs/Kn1PfArGmIvAB4EvApvK
+MAAeApt/wm8Wreg7je+s3ntJS1rSe0ffNVMwxrSBXwf+S2vteA6qCWCttWbRX+zd9M5W9Gvdpj2c
+JKR5wWisAK1OuACtjMua+9r0os4sm1sXAThVhOPUOhwdKB8KI0Zas++ZGR01CYqyzYFi+Vd78ozb
+pxm+qynBzRI0U67d7tNQeDcvCDGVaA1zTP+djW3Wzkn4pxE4lArsmUc1VlGJx8MRlfaGMBoSojVh
+a1Uks+cWiw7FdRWgvVkoVCuZFC3u7IoTyR37JNvaLMZf4fappgoXucRzkYpPgF4YLhyGkevx8kPp
+EbEedLgfyxyejmNcxXq4NdBn+CV+IfM9zjLKtuYjZDGFguY6SYNC8QSCOTZDkZPpmIvJkDrQXgZ1
+iCkVO8G6zFRrKrU4p+msgsKSlbVDEL7dYq1QKV9Xb/eKDBTzoCgqKs1ZOD3NGD8Q7adMWxzOoewc
+h1SdeB2tbH12vcU//GHpLv3CRz9N45KGkfOEvYnMS3xwzKQv79rcFpPIb6zxQGHxHk5rThXgx1JD
+c26GyndO7bF3JOtwshoxUhDeNHlAMNXvnQpHO363Na8gLu/yR3dE9f/q//pr5Gr+xVVGNxAT64Nn
+O3hHsiYHNyTcOJke0l4VUzrpRKSa/l+GTWZzLIu6WuR4PCp9V0zBGOMjDOH/sNb+hn69b4zZstY+
+MMZsAQff7j62qihPJ2RlzVgry5qdnDqWTepWPqn26JvlHkmpB29fFysfEatHvt/tMdA06I0nt4lG
+8wacY/Z3ZfFG2hvRdypio/6Ao5r2hjAC6yY0K20UmxjSrhyyTihq3daVbTbX5HD3moY0lwWN4opa
+k21SL2d6KpvGU2zI9WydqqsNaUa3yEtNnx7Gb6MS3/66TOJxytltUbIOiozR3m0A8u4tDhVj0g1D
+HK0l6DUVerwVEOrnqBWx+/tvATB84SwnDxWX0akoMjkIW7eFKfRWI1wF7Aj8gs5I3s+sedy/J++3
+6Xs4d9WH0df8j8CnUvvbSUrSSkFmymTRaCeZxhBqRylfxttsuZR31WeyXWEK9VHkFYUy4Rqz6Pzl
+2nlExeKrh8F1Lde1nLgZTTl7Xubr/sNjChVO//mPCIbhz/yNT7P1/E/KGBprixbugyc/yfFQfAqr
+G5dpqTCwWhlrnSNaWjk5Ho9I5p58B+xQmZDm0NhZwfRE1zF2qKYicGw7J/Hkd77bIDBiFl89KyXg
+DA2eUb+FNRypqdhOcz72ETFzLp7dIFT0qsk9MTuSgynTubnda3E4kXWqM8NoKt+XdbFoMvyo9N1E
+HwzwT4DXrLX//Tv+6f8Cfk4//xzwr/60z1jSkpb0Z0/fjabwA8BfB75pjPmafveLwC8B/9wY8zeA
+O8DPfts7OQYin16aU7e1U7H1SbRHQlqEuPNGiBUMJ5LLYFWdN+mMG9oncrUzZNhSNbkecKajKcgm
+o7Xx7kIUp8xJRqJ+HkxjZorrt1ND0hNOG7ZCPAX4sBrJ6DZ8+qqWFnlKpVInTqY4mv14mpcMZ6KC
+ntd+lb2LK3haRZdkLg/fFFi126+9xVdui0OsoZGOK1vrbF3VDMPklLGaEte/cZ876hibZSWBZi82
+tPN14EeLlnDGcfm9Ut5j/aW3SOdx7DWH8VDMgKHmWNRJSJlpv85OgGlpZWgrYkf7GZ65tI6rYCkn
+RhFSOhG5NkOZxTPcEwV7CRxKxaC0ecFUtYm80L+N2SIrtDzo0GzNTamCItcsTQOeVjt6CsLi1jWu
+Arl0AkPXmac/u2SnMt8NL+Sps/Kca9ui3fU3rmK0IQ31KVj53voBvrZ9q9wYNCswTlUzvX2Lf30o
+Dr5ZXYrZANK+bt6TQrEiqGASyrzGzQlWsRuL2OdEQWm6azWmEpPAahu7jasdfvoxSXO2Y5fNnuzZ
+g8mE9cta+Tly8TfE3Bpryvh+mWJ3Zc0eZB3u7cqzDw/3maZydoqy4m2D/tHou4k+fA7+xOf9ue/k
+XpHn8vhalzpJWUnkZJamZqr4fH4rxD2QR4WBz/UHoj6dHQgw5iDosrEli3zpwoCmem8b51vEWn+Q
+Tcd4Te3kpKrqiZvQUdDO2m0skmY21/vUgSz+2CtoKgjmHKsizScUtSxck3DRuNTtBkSKwVgkAXGt
+/pGOMIKq7VCqP6CYxfR2hFk8t7HNztNyrdWNvf78WYJSmELj3i6//pb4Br56dJ/7J/pORUk0BycJ
+51VxMa42hrFhwPZI1dY8o9bMqVW7yt1EPOBTVS2HzGh1FAex4SzAZu1xwbrWR3i4eNpMt6kqvi2m
+FBrJyJw2rppEVZLSHmh0od/G0YOep3KgD3dPWTkn9x25FZn6OPKiXoCs4BgKtYcb6idxjCXUpKim
+F3Dkagp6FfDWfW22m5TcuyXj/8cPPg/AzT+8y4//NdmW25/+URwNAebVkKAvJkawZhch5VQjW6/v
+fpG3XheG3PEtqTIp33OZqKnIPPJswCqA7tGbBYHWVKyu9PEj9RVVE76y96Zc84bCz6cBphahcHpc
+cFYjSadUtPdkTSIv4rwR3Eknl2dE/YzZuiJTndbcORTG82A6wtOEuchtUSt4cZrPB/qtaZnmvKQl
+Leld9L5IczaOwHW7vse8i0XiwUSlRzaG1a5I/6jl8JW3tElMLT7MD525SqOpFY702dXOzYObBbl6
++Lv9a9zNJULR0XKzLLL4jkhrxzlhoCZBQJvjVJyZLbdapMQa1R5OTl1Qzr7Z7NJZE8eRn0QE2tq9
+33KwrlTojWaiBbTvHeIPhWuPJrtY9faX7hGHN4TLz0FmqsmIVl8rGa9eJdgQL/P1r315EXUJceg2
+RWOZV5TO0pJAnYjjOKZWMbZXVDTV4el3p3Ai73RbAUu2woCPPv0BAFZaAbG2bpuOC+7dEhWVuKTV
+F4k91tqa46qed9WDYLzQFLYGXdrFXPUPGLsigW8rWM7BxOIq2EgDh1gLwsRVJd9neb6IyjQUS6Cw
+lkjzGO4eTfHUDMqCEff3xayss5qpwpsNFYn5c/F9tn9LnLh7Dwqu/ZQUUvnrlobC4pnQJVcHnWnK
+HDrNFdafVDCcN0PQfIqG9Zll847YCgDjOODK76bROocqmC9u9jGeSPe733iVL3xdzL/hWOZis9Fg
+NZS1uRgNFnBsHWN4+VXRUhI35emZRB0+fFVyKFwMJwpCc/toyJ2HWuiXObR7qi2bYJHXMRwuysq+
+Jb0vmIK1hqxyadYeHd10ncIy0zLqLM25fF5DXa7lhnqih/cV6y64zbUN2dDd1QG+dv60yYioVPut
+06J7TybK7ckkXa59ClU/6/sul9X+7DVqoplWwJ0W2DOabdeQw9+ID7i3K4ux8kJEqUCyD+4dsDGR
+HPf++bUFQKenIJru2KGzqehN3nNkodp9D1fx+6JShpqt2b2yRRDIZnSbAfu74raZjIeLZrJe5BGt
+yGFxPblXXiRMNRSaeT7Hiv5jOjVGsw3P0aNck3k+HsoheOvgmI8/L+vR7rYJpnLfoJ3hDUTNb5wJ
+CPTAtj2NVDwcM8vV032SMjgj873T6TFoi2o7mw0x2tuwUDStIskoFQnLnLXkCloTOTXz5ltJWpFp
+Qlmsh9FzDYka82maMlbwkkYrpDfHZvGcRdPbSJXha3XJebVKGp2AMJAIVZ4UlBp18WlQash1eE+y
+FKvRPvltGfOgG4AmX632A64r1qK6dcBt0jyRWoz25QFHipB0NyroaPh1a3WVTz4rEz3RQ7qa1WQz
+2UMDN2JlQ/esrWkqIFDL22RwVfafg2aF7sXc1eSm+6dTUm0ZUOU5Za1YoBstylz28t4uj0RL82FJ
+S1rSu+h9oSnU1pCVLiavF1BbgeMQqCe/33LZ3BE1OU8zeqvqzMs0TlyFeOrJrl3LilXYtchnpk7J
+8QOoFRSjEwk3bzYgUW5+7YzHxcsiEZ3A4KoaefzgkFo1j9mpprX6FShc2/B4RGBFupjSw1fosjDv
+09bOUfMoce6NqV15Nqsprjro6hWfraclddXRCEcyy5moWnqwV/LSfVHzk6pY3M/xI7rq1TfzBBWn
+JlXdPnNdmhppCcMG4VntSLTVoXugVY7znIC85EQTXi47AX6g6+CXDOb4gtYumrl4gUYf8hmVpmPn
+QUa7JVKu1bLQ0k5dMYy1vfxENYZG0+CodmOnPTToRBi5VPECCRFt9kWpfSAD35AXWpVYVQuYs0FY
+8cnH5dkfCvtM7ivk/1jW8ePnVlj/gDhJe3/hOdx1NVGSgkK9+QUpmULd3T8S1f72NyfM9mWcbmFo
+KgbE+W4Pow5mX52PZqXP4IdEow2rPgepajF3DtkYyOd+2+WT3y/5Ca1AcQ52j7mv+AcPj/aI9Z06
+3T6PaSSivTHA7cpz9ndlr989PuDNh2Je3DlNmKmTlBo8dY57BKyqxvYaj0ZLTWFJS1rSu8hY+51l
+O70X5AaRbZ25QOvxF2g+8xQAVbrO058SaKyyzIlORGQcXX+L9DVxGB3f+hIAJk7IYnEyeU5NU9Oj
+NzdW8VTbGOcVhw+18EWzGA+GCXU154uPkgqqVY3GWaQXQ42n0tE3NecGIo1yCjyFWGt2xZ60qcFV
+CTydjZnNNPZelRQaNso07mncgKYRCWYBgrnzrVyEzXKvYDadt2iejw2MVl96wVP8n/9IKut6fgHq
+PCyPY6gUUdid4yMYyn31nXg+DUUC+sL9O9zQZiif+tHnuPy0gM2Wc7i6JKI0ipHQWKNQvITx3kOi
+loyjdHx274hE02xlts4HRG1NJS+bnF4Xif7Rn/+bCxt9VsIbL4lD948/888B+JXf+BVOHojtnNdv
+IxW3WxG+NprxAxerGY3zArWqenuFa2vJFzBvFbWdg9B6WM25CHS+cypC9e00mx7BVOz63vkf4Nd/
+9e/JDVXDeu2PP8+Nr0mlZljEnI5l7OPJkNGxap5xgau1Ps2e7AtT1hSqxTq+T6yQdVkKV54UH9Xa
+2W1WNDS8qghi/a11tG6NWWIJPdEq1tZqvLb4jyKnT/xQ7n3lYx//irX2w3wbel+YD8aNcLvXqFsh
+0+sykf5T57h/V9NZnQPqN7VJ697rVGoSWE059W1BS51oLd/hiqYHP/nYNj3FDBwlp3wxksP0qmLg
+cZpiFt2IHmWk6jA0FeU7rp9j9YXdFr5GIhpFiqMr1lbP+awagSI8m8qjqT0m8yyl3dPfaRenwA04
+UVRqr9cmmD+w65NqC3MT+ph55yVNBy6KelElaavXWPGFKdRJTaY9JlvrXRw9sOldOdyzYUqpHXZn
+I8PXDrUZ680pLY12mP0RxY6YMQ31kFedLRqBbO5Zw6U+kfE31tdpt+Rd9492GVfiVOtX8l1VRlQj
+VZ+rB0R97WpVxXhaGnw0PObVoThg37ov6jVptmi66jsOxp9Xmr4NLpPnFXhzmPg56ralSOc9Ki2l
+OiLL2mJ08R2vWLRtrzTJzMss3Z58Hh2fEpxRcJrZFzCJMMBkIsLmaPd1oo7WiRQF/UTerxU12NmQ
++05xcDQSUSuUvY/HzMyh7C0HU5mr1DqL/qfTwzGZmlBm55z+rkVTS9ynk5iGAvUkFfQLEU4ze4Kr
+qNOPSkvzYUlLWtK76H2hKbhOQLd1jmRcUa+KamhuJUx9KeapTndJbkpOQpjcwCha8+pAASybHTY0
+x+DymR6XNsWZd+lCn1Dj9CezJhtbInmjG3Lf/Vswe7Qkr3dRWfN2LqcxKGwAwWaEv6It5HhIGIsU
+jmeqGoYFtWh+9MKaiWosm22X5o5w9lABW+7ulfhq2qRlusgFKI1HVzMk0yDDqsSf++ZMDbXyegNU
+6hhM9kekvmgCYenjaSWiZjaTNGr6Z2QMN798n6+9Lhrbx5+7yuV1Mem2Vi8xeVXj9E+I9AmKziJU
+W85q+pdFS6uqcgHHFg9L2iva+VhTnw8mOc07ImGrM2N6vqxZmQWMbohm8pk3fo3f+d8FJOXOiWgK
+4zjH074J7TDAuvOy+5J0njWZl3jz3hHzf62qBdZDaVnA7VmqBdSbazxarbnzcJ5NWzPTtQ4HcKi+
+PK9ZMZ2pSfAN2U+7d+6xMZD1r4tsoYV6qy6DQBb+TKsHqi3VmkFqE0uiZuP+bkoy0FC2Y4kTBTK+
+m+Ouyhy2tMGRacJA29nnayWJwtHVcQ9uy1pXj43ZNDt8J/S+YAq1A3nkkicj8qHYstFWwcm+MAJz
+95T6VCbecz18VbsHXVmAQc9wSXHtLm567GgPQxJL5eqCZzVhLKmtfUc22EovIjnRVS7tI3kVAIxr
+8HTD102f1eelk9Ps2CxsXH/SoaWmQjSQg/7U2WepPFmsjZUWLcX222rvkHXmlZjyjIPbe9y7JWP7
+7JuvcPBQ7nGSJYRGmGGanuBrHYCn0OsELqX2s/T9kLRSWPvyFKORkaNRTK6bMDlQlbTX4uhQqgXf
+HN5j3ZdD//EfvEKnqepuWNEoxW4NWxrnT2+RlbIOTm8DT6sMa6ZMc3l2GtQMzgjq0ehIAHCG9f5C
+Rb+0eZVSI0l5kvKVE8nJ+M1/8VVe+4Y0u8lqhe13Da6mPHu+T6I+ozQumWnkxtY18xJ+152Xb9eL
+kuyyKnnbl2ZxnLm9ZSnV1zLv7uQGLWrtA9lauYBR2Kuo3Wf6UHIZHjyUvXlsY9J78ruqMaWtJkqj
+MEw9TYAjpafmkdH8AayD58jYO/2IwJf5nrpTRppDMTtNqDJ59p07Mq+3smPO70gHrNXWDvuq90ez
+A1Z1zw3cC0zsHP780WhpPixpSUt6F70vNAWCCnZO8cbnKJVP5ckBjUydL+UuvqbEBlHBRk/bwKuq
+1/F8NtSptXOmy8BXKVZPiNXx5Ww0SRS6ym1LIVX0eoKnKmdVlKBxZb5Nk14vdPHmUHFui8mBSKum
+W+CNxMtuqNnclpj1k1fleZ/40McZqI9zpdugsyXZi8a0YV6AlGn04YcsU3Uy/dhXvsh/97/8SwCO
+3poyihSwxFhKM08lVlW1HZCP55V8HrFGXJqrHt01mZd4ryauNO9hS36/4bkUscbY+zk/+UkBITnj
+BXj6HibzqFWDmHvQy9kZZgoyMrIpRSzv3xys0lNTisaEJFEsyJas3eQ0otxUjabZpJrI+9/afZ0b
+118E4M0vf42p3rvZFanqBy5ddSobx8HRRIayLgWrDbDWYtVxZzQjsq7f0VDG1rwTYqBWrcFUFWmq
+EGp6MvLCYnQftlbNohjL1D7Hd6RILakVT2NiiNFU62mKq/cNmwFRNM88dXCjOWK0PjcCz5X9FLU9
+XDU16rWYU82/GUY58VSun+Sy/sO3hhwxb77j8dgF6RkSklE0pElM5bgLR+qj0vuCKRgiHO8p3A0X
+90iq98phRKbQ4W7h4IYaFvIMLT0IvnqbW46lob0Ky0mDREFRygn4jrZOP4xhLBu6HYvt/PELF5g5
+olrN7sBEU55t9a1hKTzAP9XN0y5YC7SdO1CpGry5GfF8V0JIH1EwjafPPkakaD1huMG8+hjToZ6J
+t99pyQGsebhIELp27Tx//UNSR3H9xi4nIzUVIp+OpvyONSnISQrQGg2ngMJTez9vYA7Vs54ZSsXM
+N1oz0vUt+UgO2LOPX2LnaYlaNAYVJtDagPWzuGozU4utO751k/G+3OOkPOSiqyG9/gZuqD0tBwFj
+rUCdOuLEiNwteh25VxgfkztiVn3t3n1+998qnuFkjK9r3dWwb9f3F122isIu6g98x6NUlC1DJbFZ
+WDRorWtLPa++fMfiGmMWbduNlYasAKn6g3xjF0lv9d2YznlhrJ3QJ9O1nuzL70+zko5GgVb8DlZN
+utBAS8OajhfS7sk9PK2cdMoGKACr12+j+Wg4xmXtVCNCRUY8E+Zze1f7S8ZdMm0gXPgR49N9fUaX
+Da3XKVKwznfmOFuaD0ta0pLeRe8LTYHS4u6nFNbgaXGRSRq4WlfuhR0CTfn1ejUrZ4Tr9sby3eqg
+RRFp4YzNqbRazO/UGE33bAYuZqz164p5cGx9zvkimW9Ex/iK8VcaS/WtVIWiBgXmsJOazMjzMiAJ
+5B5XLz/F9jVxyq2vyXj9AnyFNacqIVX05GyKVTi2WqHd6nRCfSAqd2O9wZWrEps+1/Y51fh4WLgY
+hXQjtocAACAASURBVO5CY9iZzbDqkKp8h8gXTcHMItym3M9zDK2RwtZrQZT7xBZPPicaVCe9REc1
+FrcucByFZmsOmENuVqlWlFbOAqZu/7Xb7D0tUvDaWfAUvyGsHZqarutOZbw7/U0auYyn6gX0FfL6
+5md/l4O3FLPH1IvmOm1XNJuOB9m8MUwak6ujMcsLSSoDGaMqCvN1rLGPlIwy1yZKvW/tsNA2jkcn
+tEYyjvbmNtSixg+a8tx+qyKbqPlnwJkpnuXFFp4CqgSBoaHHrrkqc1xl4KhWGIRN3K4iXs9SvBV1
+eI6g1u7mfW0MdBwdUCpMYZm3OJ5IxVPLv0hpRZMbnXfZsv+f2Ml/Ir0vmIKta/I0AeeUQqvzvLZL
+WQiD8NM+3sb/w96bxeiWnddha5/5/PNU4617q+7Q9/ZI9sBZHCTSsmRKiJVAtuMoyAABCfLgxPBD
+4gQIkIcIcJ4SIQ8JgiSOHSSQFU8SBYk2JVOiKIrNqZs9d9+xqm7N9c/DmffOw7fO32RAmU21Ql0b
+tYHGrf7rrzPss8+3v2F9a5GL0M0wpSqOT7d222vCYv/B8WS67Izz8gqqdP3yeYQklJ/HJGzBIluq
+NAWOi4S9Cnn8Q+oQCtBl7O9YKNivEac5QJd5BSE6imXGgvmHAjAZqxb5HDAlH38Dmm63Ic9etjiF
+oRx8PraWIJyu58Jn67CCQlrmXcqXVRtoJkVsE0EzPCoKB+cjAotOF1hUGH8P5ffJLIRVk3AnuKQA
+Q8Sjo6XOCcAMD4GUbqlfkpH0MYmkfLmYDzAZiAG8kqzD99iWXmhERGfO6OLnaoxNiv/6xQx5ISHD
+W6+8ingoz91SNlz+XQm8TI0FTS2IxSJDTnRnlhXLMMAY8274UMYKxffkEb7HOBjI+gNERascBasz
+Rr+rR1noHBFzHHo2QUNLGdUl52Kt4SEaSA5gHhp0CN9sWBUUCWXkfbNEWWZpuc58WAS4Ke1B81mb
+3IVi9cxxKvDYExEwp9Kt+5jNSuafHDnzIQXmKJwScGZhHH5/efaHjYvw4WJcjIvxfeOR8BSgEsC5
+B9hrcFZZa0YDNglHjHIR2uLCJvYhWnSvgkBc1cL1kSTsH2/ZGM6obnQa44jWs+Gm8Nhdp0hndjqL
+YFdI294OUFBAI8kKFPmf7GoGlr1U4IEnGV4AyNJ0yeF3Yp2gWpPavKI6UOEpFKQ6V1CwWLvWGsgX
+pG0vPYXcQUGevXQ4QsJE4pWrq7hD3cyZZaAmhFjz3jKjUZBvwtIeNCsYljOHTSq0xWSIs4Xc60pd
+lkAwcOCGZMe+/y10euLlhJkPj+rZeZZDk605GotHVMzewYh0ZaqnkZFirHhuA9GJPBOn2UEjpSgN
+FaaOB29iRgWsburAMrLbespBdUu+e37HWuIQCvIrRnEMl17MPFogphCNNhq67P/A94QBFJMxWLaH
+QEEtGY6NeRefooxaOhjfyzNYeiAWFObsmXBcByk7UL1rco3V76ziNBfPpVJNEFJ0yHFc+ESf2VUH
+hpWULC9Fb2IYYm+8RhM5Yx6jU9hk//aDOmolcApSWQjrLqKCCuNWFSlf59H5LqbE7XT8JgqGqe91
+XHgKF+NiXIzvG4+Ep6CUA9tZgdOowWZsldohihFrsPM+4obEmelsBndBq0vF6FuOB5+WseJ18M5U
+EIvRYIwxE3e9egCVSrx3EDEOs31UmKCE66B6mXXjVwxOJ9SE/EGCvYGBw7hdeR4i2tZCa8RZuXNH
+sM6lnGSxIcX0FtDc+ZTdgKHWYlZMkPUlLk9J7Jp4MUAE3mwwx7BPWbncRrdsHjIJLC15h0Em91Zk
+gCb/gTIJprHsRqPJBKNT2dHzwRgZ98LKhsT1SV7AYvJ0dDjC7FCuZ8Py0balpJosHOhVTgFl9ey1
+FRS+7P6TwQH6xw/kGN95E8GnBW1nLBdgbscpqFOQrCJmPDxbxAgc8aBuPfc09OuSgLv/9lehuKO7
+U4rBOAliegq5pWBTyg+OWfZAaW2QlGS7+nvrj/zHUv+f/AKfuwIcIlJL7ILWGi4TmLay4BMh2Wh0
+kJGVeXgonufd0wXmSs67NQ5RNEveBw2rKs9MTwPMS1ZpI/fsWQ1ovopuLV3SRNthHUjZrBUqVJgg
+D9YF3xLUu8jJ6RDnGaYLalCO0iV8vW9NoDolP8V7G4+EUYA2sBYZjK0RdrmgtQfdIkuyV4PpSw02
+yQ6gmpKJ16ScO98ELrvMZGcOrIm8jAfzY2TkwTvfjzGcyEzZ5MMzSYSCL16RF+hwAXW2Q+R7MpH9
+YY6iFIItHasYyNulCGiy7NTTGogIbbVNA32KobRTwV6E53VYlxkSnS1gMRmUDSJkdfm7jDDbPAYS
+YiHiVoABuyfTIkVMl3hDuQCFbShhCA0DzUWlHBfKKevuEUbE6ruIEKXsmdgt5ziHb0u40lzxcbov
+1z6pJQhIg6+utOHPidtnh5/OMngthnNv+pgPBW9xcvoa4tck5OsOr2DgyHwOHsp91BMfZ2/Jccet
+EzzRFsPyqac+iJ6S5/uVP/oyEj6f1KIxTXJM2c2ZZzEsJiId3152bsZFgpiJ4CVACOp7ko9YwtGN
+0svEo2UpOAQJ5DxvZsyy4uI5Fmqh3H/Nr0CTiCejyNDJaHdJ2hNETawMZY7CZ2sI2NEbhgqKTNFe
+rxQVXsCGGA1McjhN+VzpHODas6YOrDpFZ1j58esKmsnHKFggT8grmmrM9qUSkdVztHADP8p43+GD
+UspWSr2klPot/v9VpdSLSqk7Sql/oJS6EIq8GBfjX6HxZ+Ep/GcQpif2/+G/A/DfG2N+TSn1PwP4
+ZQD/07/0CEahyGx4zhxxLAmn+mUX8weC1krOJpiy6cQphrh9LK5tnY1Pp9Mn8PwtcYMfD7uoED3X
+Pz/D0bHsXCZPMKUYiib6sWYlsOlqdz0LGcuFPbWC/qbs0tPUlHCCJXnJzDHLBF6mNTy7JOkwYH8S
+xmcZ3q5L6BJNxYLfeCFB60Dgw+nRPQSl5U8LZKlM3/65hD53j+/iIWm3EjvA7n1pvtk7HsJliHJW
+ACNSxS1YNiyAd2XCTIzpuczh7CyBV5Ud6GY1wPqWzN06tTD8boDBoXg00e0B7k0k1JifGkwoPnM5
+uQTtSpg2Zfik+1NEhO1+52QfX7gtLsvv3D3GpY5IoV17fAtzktgGWvaIS2tdHJPZ2rEy6IZc8/bP
+/BVYjjTFWcpdwr8VqeviOAE9ZnQ8D2tdmcOtbhuqK8c+3TvHvSPxCsfU+VTKWipYGygoehW5LlCw
+Y9KxVOlYwFKl9wA49DAC10GLc1gxBRxiS4ZT8RSOhlMsKHvouwa2xSa9ooKgKvPtrLhQxJGEdflM
+Z2Ypq52nMTQbpexcQ3viCTo6RGGIX2Dy3EK8bODKjEZEHVYLCxTUDVW6ipPkPTK2crxfLcktAD8H
+4FcA/C1KyX0WwL/Dr/w9AP8NfphRsArY9TEsZwshY8usH8Kps/1zNkfz5k8AADbsKS6vkeV4xM6y
+9gaut2VBPPXMJl5+hUCeP5pjxJBBKwudloA4ntwSRqeqO0ajJXFheraHSch4N8qxGouRmVbPwcQ/
+NPnGa0UOb5khtgDWoJUx2N6Uisj1zTa218Ul3n31JQDAO/cd9Fp3AQCXwik6N8RNrvnbGA7FGHzj
+JcH9/+4bD6AZBN/c3kKTArKNTh3DI5mj1F0s24FrJQ4AObIyXi5s6SsB8Nh2Fa1VOV8zTeGvSnXB
+IdzZnRgcL8TY3m8s0PLF9Z+NT+CTWWlvfA/VQObLSmRBH5yf4Q+ptfjGJAFRvlitV3FAINpaX2Ny
+KPNiNeTZ3Y52sc9wpqtWUbssRuPWxjpurrGnJWgjSuXcvU253igaoaHlGvJ4hI2WGLV2zcXOE9Li
+nW9NMMnFGJ6R8Wg+LnByLNdzb7TAaF5m5C2osoyvzBKfUIYajm3D9eV62q0aFKmmJ9kcXoth7MoD
+OdcfxsjJ4mSH1lKoZzqd4B7zR/l5hFZXEjMdbkiBV4VHksrQq8GkZNZKI0xH8qyTucZkIZ9PhqQX
+sCO0axQsrlagyazl+AoNPtdxliMjwOm9jvcbPvwPAP5zvMt01QUwMsaUmY2HAH5gM7dS6j9SSn1L
+KfUtnSXv8zIuxsW4GH9W40/tKSilfh7AqTHm20qpn/xR//57pej99rrxaltwwxAJpdZUEcN1dgAA
+4XofK0zKFHuHODxnI8pcdoPn3Qp6N4VLf/VSB5X7YiXXNhrIC9mt641NGHICtmgKbx+dYK2QXS6e
+pujx95daHt6qyw5TvWNj5DI8iFmKcDQiZvW1Z8OmorClLdgWPY+Fwhu74kofsZ39m2++hZDCHH/l
+M8/hqa7oii0WClNCtk/OKWteeFjQ3d09n0KzYjKazzHkjpAqwCcvYeYTJh1jmVwDclxpSZIpzlNo
+ZrLnWbzcEbOxuJne2jPIIlKG9WOk7NZaW8kxnZcCKD0oV1CkddKLOVEB7ctO1AptbO3I76uuj+qM
+dGUTH6srRCHS7RpMVpAy4RapGKeUmJvPC9g26//dGmqZPJPPPSvckOt1g6N9OcZLu3fxjQcSViW6
+wJN9+fyTn7mFz3/i5+U6ydfZP9/HG6+IN/aPvvpNvPWgJFl5FxKdpRqac15yLNiWQuCXtGoVGHbV
+Hp6PEGvqKRzJfQyTGC02ObXcKlzyUCRhG3kgnlWR2njIePR0T9ZvrxJhdUU6ae2ehk2PYDwZY0Bq
+trP5GIsZk8Jcv45uIWUoUSlyOJ54W2HYhe3LtS1G5zD0vt/reL8Cs/+GUurzAAJITuFXAbSUUg69
+hS0ABz/sQMoAVmahSHKosUxUHiZwe/K0qoWD9C3JKUyGe1ARXWVCdL8xnOE6uwg/FVzG4yS2HN3c
+QHFVQobD3Soesrfhi3f/mfz9YoHOMZWlXBueI6WeS48VaB+yzKg0kLPsxacRJREckqGYwoFXlrEC
+g3ZH3NyhyjE9lwfWtOVhpZGBIY+eu72G9rXn5NyHB3jlNSGjBYlHNsMmBux0u9M/QsrqQztwlp3d
+VaNQFZuH0TFXtoXvQenYaFXl2vv3Yzx8KCTf4+EUx6ScatI1fvz6Oc5q8tnLe300SQJ6hgUURWgb
+1jpufUDUibyuzHEvfRHB/XLR5TC5TMwkt7B7LAv6SlBFnsmxS5Ym1wmRHlOvsz3D5TPyMp6ewkvE
+EDx/8zHcJrPUrac+CQBYiQcYDr8mt1pLEdFALrICe2xb//YrGh/5gLQRb1Iz0t7sYT6Q69ypV3AQ
+iPGdxPESbpykBWzG9o2K3IeGAsoSb2FDKxqA4RQ2y91rY7nGpqvhkwCmqaqwqVQ2LBaIKRmvlYNa
+JGvEXpfvLvIIk7nE/XV3a8mfmc+GcEgKq04SJBT9HYzFaLhGwaWRWt1poeLKzy3bQsvhOVoWzKRM
+97238acOH4wx/6UxZssYswPg3wbwL4wxvwTgywB+kV+7kKK/GBfjX7Hx/wdO4b8A8GtKqf8WwEsA
+/rcf9gdGKxSRB1udLqmwPT+CNZVdYHP1A1BdSRJVs6qIsQDIQmlIyWrXYCgxtzhVqPg7cly3gYDS
+6KNiHxUmBDcrsnsM8mNcZt3Zgoucbut8uobTeanKrFBYJWlJKXuuoHIKgPgWVMhQYlzgUlM8k48+
++wSmJMgoJdb8wsaoK+d4avUWvIDgrLCJI2Ia1kKx8GtPhrCZjulN3sDRXXF9C8dGSgbnKCtwNpLj
+RdzNjVLv9vuoApZh9roSLDU2j87n+G5Jf0ZXVe1P0KJoSKQUrlGOLvI0PrQiHtQzz38KvSclTCtc
++bvaw31oLYnUOI9gXMrEuxmepq5mx3ExI2S9lFKDNUV/Jj/rWYbFsXT1fXg0wpgCLpevXUGV6anx
+UBi8m/UqLl8WMFUcRwifkPueT3y01+RZtn0f9pDMzdxd+5MFilTme3t7BYaZ/N3pBJMzmaNZFsFm
+MrneFY9mNJxjwbBxGs/RZMUgSxOEbJSqE0xm+RYs4htO4xHsiICruUIQks6+YpVq92j5kiSt+wUU
+QU+ureFZ/K7jwndJq9YKUWlLsrZFwJ1fKZCeiTdWDzuok5PBtQzAMLyi3pXQe6/jz8QoGGN+H8Dv
+8+d7AD7yZ3Hci3ExLsaPfzwSiEajUuTOPpRpwKMVLAoNVRcr2bw2gbsrFjp2HOyss698RQhTdX4F
+H3tMdo/2dh2mJhb8evo8qlfFcm/vbuN8Krv/1Tdlx38ljHDtkuyC9XoL4WWx0PHoGP4Zk0s2sCBC
+UBsyQdkaFsVClOciY9IxTSP0Wd9f3bqEa4QQu0xJbD9+GZpKJ1u9LgwpzZJ2dSn0USeis3v5CpxA
+zrH9lsY7TWpHeAYP92VX3eufYDElHHtMejQrhikRmMZGXMh89lY1PqIlWTeafgOKEngZE2oDBNhk
+DT5VPl5Yle1sLazg07/wWZnbj30SDmnFHGIhth7/CH7hs/Kcvv6tr8GmFNqljo+YybXmeohxREEc
+eiYvn9/FKvMa94cJJN0LNDe7cG7JsfPXLXQ++DwAYOenJCmbPNzHOr2xam2BDz8hayAKXcyZG2iH
+Xaw8Ll7WlMK1UWYhYyLo+Y9+Bk88J3M4WxhMyF40ikaYsRV7NhGP55uv30WGktEpx5yNa27Vg8XG
+vMvPUZX6pTr6FKhtrFWxVpd7bnevoEevsHF5HSYv+SCYHI4mMESe+p02fMKqm60erJqULzd3QhTU
+i7CJ6ASA7IZ4GFmhkC74czCGYrOdO3cR1H+0LMEjYRRgbOi8icKewGMFILcM8ikTMbMC22vsRFu5
+gsconKJr4n6t2h2sbkooobSFsClu5M61K6gFhOB+uIrJWHKeH3pdXNFP7G2gT9n22FYAqcO1WsdL
+R+Kum8jA4gNTeVm7BpyiXCgauoRKa40hASQNN0d7VbD/DqsBCxXDZyG/3r2EglBcK7Sx0ZF7KbsF
+m16AelfcwUsfuoYPPCGgp3tnd/CKkvvIWzHqU1kof3hPsvDDAQCr5Ccs4OQEJ4UNXGkKrLi9tYXO
+ilznOeXiz50uWmSUrnobeOGzkgTtVrdQf0Luw7IKGKfsE6DK0VM38BTJQtafW0c8kuNWOz5AxSnT
+qUBrcbELktA0v3MZ7deFTMW58wAZFad6GxtLQM7as9fxzONiFDbITRCvA7VIntmVnc/AprLW+XiI
+8yPBWbQ6K7AymTuLwKo09wBfzrF2aRO+KwCpbHKKdE3c8tjMMSbf5mtHMscPjo9h+iWlmwXHZV+C
+ZyMhVmUcyXqbuQ5cdkNu+Q1cuiJrslvvoeJJ4jZwLIBrMmWlYmJSZAk7JgsXLsF3jdbGUubea7VA
+tQJYNDA6WyAjpmE6T+A05DlM5gZgorQYFAhrP1r4cNEleTEuxsX4vvFIeAomT5GfPUAe5HCNWDVV
+byObkZRyfg1uV6x5LRxhoyWJwohdlOhY8GjBA6uK2ZCcBVEdTos1di+ATlhj/4xgCTaH1zFjs8/d
+N+/jdCAexGByimJISTfLQEGsrk0L7+gZFM+Xp/lSWATG4PhUQpSFdtGh++/YsqPUkwZsNg/B8VD0
+JXl69tY+OqviJupErseeJahwNw4ut5D5ssNeyWu4Rzd5I3UAYgRCNjgZGwA9Gtg2/DrLbcMFlCs7
+bKcZolqXuVsfcw6bV9Ba2ZE/a3ioFZIkDFtdgC51MjyEiWXJeJuC5rNrIfyOeCud9RtIVqnp6bTh
+XmPcNC2gSRs3n4oncfXqDRzclvm+2ukjYFNZr76KiGW4rVYbVzeon0jFneniFA6p4pxiAUWvAosc
+il6Mi+oyrHJseeaxpXHyUOZ7dX0TDsMYxw9gK0Kw7QaS8SGfA7kssmJZis6KfLlzV902EpZAg2O5
+t8s1jbvnMt8HBxEqhZQs6880UeOzVIUDl0nOsqtzqhcw7Oq0hxmsbZl7u8jh+myImqUASXZBzIql
+cxgmjAMnw2gh3k0Ra2iWw9GMYM+45t7jeCSMArSBXmQwizOkfPEajQgZNQxHe2MchzJptZnGdMYO
+sEwWo7W6vhRdzfwRLMbRMzOAvU8js5HDYZyVcMF4ykbBzKylM/QJm/7G/RHustMyUUDBl9qhi+tp
+Bx4B+JHJseD5jDboj2Uh3P3Oq2hT2cVjj4albYAvdDE/QR7JC1Q1fTBUx/jbstCSTgR7iyFFNIM+
+F9/x8PYu0kS+M50Bh5SU75PKHPa7bcFKaUz60vsR7QUINuUXNy430Ftb47URNLN9DY4i8Go2xPye
+iLbMvvYHGPliTLIHA6x+gFDp4MMyJ0MHmm70YrSH5EzmyG/mcBlWWIs5crqzaV8M9mJ+D3VCnm+1
+r0DtC9BLFxFQ6lS2cwRUA7P4Uumwjpgv46R/G1XG+EleIKFbfRYPUfTl8wqrKKOjGb77puhSVh1g
+5zrbj1NbODcBJHaK2VjuZcjqxXQRISH9mwsfdikck7/br5CQVm9rpYkDVs/2F2doz4k5metl5cq4
+AMrOVf57MoyRzgc8xg5Mxtb4pA+TsHJlSwUJEFUrmasCoD5m7gLpWH4epaeos8Sh3ACzOduJ3+O4
+CB8uxsW4GN83HglPwfYVWjcCWOoxRCSeqHtbQENcuNg5RHIoiaHWqo3LK2Jhj/ZYKx/FqLBz0EYC
+O6MgyfkQDhF7o9tvw87ELVtZlX/z+R6KQo4xj8cYJbLLH/QPwQ0KDdfBOCH/AsOISsOFV7ZD5hES
+Kv9aSmNBVpZ//M1vYWNHMuCNZyRzbrsOLFp5SwF+jda8do75gwcAgMFMkmXxdIBmlRWJyx0UupT+
+spfup3ZGmBjxNjzScoVOAcNqiWU5CFmnX/toC8Wy+04h7Aky0aU2hdu5CkxIR7d/B7tf/o5cj++i
+3RD3ejwbwz4T7yz9prjZZ8ffwte+TaRkofH0NakGfOSFZ1Fh16IVjZCSvi0+lOuNAsAn7Vq7uIyy
+Pek0GmC1LaGU47QQMlTwfSb7fCAjt0I2ixERgp66DnzKsS2mMywielP0IN9+5Q28tXsPAFDtxGiv
+y/XUV7pLhGiRTTH3ZadPS+yzsdFpyf3bvoPRVK5jEUcIymRzT3bz0bcMJpz7jqWhCX+HiUHNFrjN
+Kgz34jFd/Nk0pUApkFgOZmfsbJ0dLb2KRs1FyOqCrUhY06wDHsOS0RjTcwmPongAZ4co2pMYG7UO
+fpTxSBgF1wtwaecxBFUfQ0VBzZmBn4sh0IsBMrYhO2YFYy6wIxKrrCeHOH6dqknNZFlms+d96D6B
+Po0ewJJkSPafJB7ghHmLh7MYc1qCqlVBStBPvNAorLK3QRZYvVpHSAkhqzBIyCCU5gVi5hfeuHMH
+JxRXeZZVEse2wC5raE8hJfT1pP8QgzfEtZ3PpDh3dhaj4YoC0dXqY1CeZLIT28GQWKpU2+hVxMCd
+RfJ37kRBMf52C6DW2JG5qK1AnYvBUa4N6LL1U3IY+X2NIpa5v/Obr+K37r0CAHj81lW4hPH22xr5
+G/J3a09K/Lp7PMErI5nvSTKD4dyuZnvAQi60WrMwSyUci9jtmXmX0GrLNfv9At0dMU5vH+zDC0iN
+315BsyautMPwqN5sIyWwLFy5ipygqHQ2R8K5c9ICKz12D9YkTGi13kCXavZOP0BGJis0M9gUDFKZ
+hTmp8iPmtpqVAH5DDGGUJpiNJMyJFxHqaxJ6zQ/kOb86fgcJe0rChS0QeQDT8RT1iryYykyQ8EXv
+D+R6k/kY1UB+n6YZEoars7lZ8m1qe4qMeSOXuSo3iZGSE3I0PEffiFE4mtuoDeVZepMqOhvvVSVV
+xkX4cDEuxsX4vvFIeAq2clB1uoA2aAfiEZylDta2xOca78aIyEG4aBQYQLyCwa5YzFHwEt48k0y2
+Sgo8vS1ApiIwWN0k6CmykM3Eep6fS3OVnp1jxF3+4GSK4UJ2Od9PUCEAaG4ZIOduRVg11AI53csA
+ATzqRbiLBDk1IM6TCHdOxF39C5lAg11dhWqSyXc4QbJHDcLzBHO6wb1Lcr3dThUFe/pHoxSuJzgE
+N4tgUz/R0UBIbIFH6TLLU/AzSpmHDbh0/Z04QwzZKudRBeaAish3CBQqMmQTme870RvIlXggThGg
+n0uyslrUsNoRLy1hT3/NquEJalSej5uorsl1vD7Yg3Usy+tys4kRaeNiysCF6Q0s9rjLNw7QyOV8
+r/3+iwh57katA2VKgRfiA1IDxSanMBwtcR1JcYKjM3bYJikuPS4NUfWqPLPP/KUcQUPOvX1pG92a
+3MfCKPjUGHVshcpUzuMxFKl3Gggo1HJ2MkRE/orQtdBkN5q7Irt105+jHzMYSQ0O7otXUWtV0ZzS
+nU+AYiH3vcS/oEAQUpynfwpng9WuPIZNCH0xsZH05P6yKT1XN13CqnNES/7Phmdh9Lp4wOGlBZxC
+PO73Oh4Jo5AXGqNRDKsywdmBvBxh1caEOPTINYiZTf5GluKOJ5NdsSQTXi1SzMmXpx2N4OgBAJFt
+t+8x/nb3kPFlGs9kQS/mI7gEkEyjDEPGbLFlY1bqUWobeUFOvDarFrYCGC5GSODR4XIthYJx5nwW
+4f47rwMAsgPRZQy2dwDyQFoRkCh50UMPuPr4jnyXxCPj/hCLibxIWf8EFbZIn6Ux4oLdcpmDgvkM
+Q5IOlboAs9RhqJHMxUXNzpuYM4PvjQbon0kooWWqMIqBB32Jwxeuj5956nEAwLW//EFg/z7vv46c
+Qqj3bwurkgpa+OSHxUUf9h3YVZnjb732x/j2roRE524XKTteLQrjuFtvIyIyT+9l0BUxPN89O1yG
+f1ee3MatDXmpayEz7spBruX36XAGK5NnOUsL7O+KsV+MpzAEUV2ljqcT2vDaFJ8JNAoacs+yodlf
+kdkpHKJhS17GLM5wTqWu4WyOjDkjL7XRYO5Dt2Rd+LbGNJLj3p33cdMlGVBuYxHLMXSuYJcMSj57
+NdoeeissG6oCWcR8RsVF4nJdRzZsIhoV+S6TaAKPlabcsqHIimW8GRbnJflOAKPEQLzXcRE+QwWS
+fgAAIABJREFUXIyLcTG+bzwSnkKhFxhHL6O9+hQcl735VhN2nZWIcAV4mx9vNmHbYvnSXEKKanYF
+WGOX3eUWDKG7Lb+C6qnsGFNf45u3ZXcccBeZpTFqxOqbOMOA2oazLF0ClXpOjjEBO2Yotf1k5RDE
+2sBDAE3QTOA7yFnTTvMcL771AACwpyWMuGU1lhxVTq+H5kjcuoNvPEBOQpI7+5Q3b/kIAvYLoICe
+yzkGUYqzAfUTswQzdhoGRnaJaxWNQcRKhL6MRSbzYq1FsMeS8Mx8A0MMReHITttSFXxsLGHAm+cL
+LDqStBp8+x5ibkernQUs1gl61MlUZzk67MvoNj188Y/lXgdxgK3HifEvCgSe3Euo5Bpm8wHOYzlH
+ZaEwpC7jw70TbH5AvvPyb72OzrrM0aduSlJvoYZwPZkrN6hiNBUv5v7xQwSO/N2lrcvorknIl5KT
+Il6keHhHnofZqaFTEQ9k1bWwSuq5oGHhgOrXw5nstEfjOeaUistMAptsz7Waj4gdG6O6eDmTRYCC
+fQuZbZCy87HWyNBsSEiUKYN5RrwMpeWblTpycn46QRM54e+T4dmyMuJ1HaiJrHeQQ8KrOihKQaGp
+hXKPf3gwxs6TMhejCdBoP4YfZVx4ChfjYlyM7xuPhKegtYXJzAcKC4ox8jB00Kw8DQBQiz30nmKT
+zGYPmiXJsz1BNs4fvoWMQi/3ChfRSHY/k3qwWBYamwXePJFcRIe13V5Yg+aW/2Y8xpy7PBRQkLNh
+3i+Q+7KLmYhlw8iHz+TTNJ4iJyLO9arQpSiLNpjQso9eld06uRohPRXugcVLcxy+JF7B/jffxD53
+o5hw4If7Q3gd+eyT9evIa7IzP7j7EK/2edw0glcypVKHIZ8AflvyL+Z8Hw+PPggAiKIZekZ2wY6/
+Ck1y1NaW7MA72zewOJMd/9f/73+At35X5vaTTzyNZ39OsAev7Q7Qr8m5fSYle1d8ZJnE7f/Pd38D
+L35NWJH+wvUb2HZENs8JMvgbsnPfeSB5km+/OsFsKjvtugGm5JxY2B7eflOe08npFH+8J6XRJ9bI
+oGRlaAZMnq6FePngNQDA7379JVwNZHfUlTFmx2SAyuQ+js/P8N23pZS5e+Dh1k2Zo51LG2h25dhn
+swRvRfKsH+zJNYxmC8wXFOy1sRSC1bDx8ETm8/d+41sAgKMkQosyb67l44yMsG6RIaBgjh1qTIn6
+PCec++RggdWKrOnVKxoVUt5FSN8lHk4GWKNA0foacyNOZSkVFxcT7NPJPnUC9NpyT09tP43m9R9I
+k/onjkfDKMQx4rffhj0PYK/KDXtuB24gk+O2KwiO5IGuBTs4jWUxhVReCurv4IYlLtLOToDpO5Iw
+m83vwSE9mGM7uEFKsIB9C9WaheMzmcnZdIaUjLtBxUFMjca0opFEZcs03fZxjMRhd2VWQDMm0LaN
+UrjYUoAmXuCLB5Jw7O4/jXUyYzleiMvX5WXqnNu4pmUR3juVRZzMM+SkeRtlC2TnYlj2d48xIY34
+PEthSN9WsIat122kEZN5tRgV3tPwYYHwo3IfHcfHjEIlNmXtTX2M+qZk7P/TX/pP8H8O/i4AIErP
+YWcvyNx/5hZudEgAoslFODcYn7Gmf9fHL94S+PNf/4ufh9MSozZ4sItkwe5J4vPNwR4WQ/m9WXGQ
+UJ3K6ceI6ww7hgu02Itw50QqJ5X8HAFDik61B02NyvPjCLWeGOqnn3gMQU7RmkM5b60yx3XCxrO8
+gBsx2WeH0KG83PH+GQZvSvgzH8t8o8hhl7BibVCn9mi75aFD7kOLXKFX/BzP7sjL3x96eLiQzWnv
+cIztS3KvdXcNTqtUkqbm6TxFzZeFURkD7R1WF041aqR3U0UTTRK/1Ilj0BGgY7l217JQqVCl/aGP
+dE+ez9bPrGBtXZ7rex0X4cPFuBgX4/vGI+EpKLARLbqDYiJEIL31HKvsSNvudRERqplpIJuJJ+Aw
+AXj5mSfxWC5ew5XLCnavLJFVsH9Hyn5RdAZ6drCYZPMBTEuBEKWlmQiA0S4chg9KV5CQrFOVs2WA
+hBRdSaFhlXqFrvArAIDn2QjZ1XZywJ0omMJZE8izbvUx+kNBE+7bB3jIEuEDcgVM/ByhL+c9W6RY
+0K09WUQYs+yVFhoWEXQBS6hdv4MBS3p+VcEhf8HmFQdeWbJCBp/cAu2W7Pxq3Yfjyz11n7+Cn/uF
+TwEA/ujLt3H7638AANjYX4V3XeDRwdNE4L3yNvbv3AYAPFXdw/Of/xl5fs9uIpnIrlvPPGiLCEpq
+EITNBB3OoaMBjy5WpepBUVyl3bEQ3pV5OQ6/CgC42rqBeZO4AjXF3JX5vnK5h50NKRGu1Dpwiadw
+enL/q1ET1dfk/ndP9uAQs5BnBnMmnu/1h3g4ZGcu1ayR5EtZOVcBDp+1G+fIHNmZL5GJ+pmnPTxH
+oaG3Ow5qRLQiV4j64nkEQQjfk+9s9kjtZhcAE6LVJEEIuc7gcoAOy8+LNEHYoZo4cRVJMsV0Jusl
+b4cI53Lcna0Yn3lGjn158yYcn91273EosyT0+/MbjXrTfPiFj8PKE0zP5CWOUSyFTlxbIWDt3bcc
+DPlS5ARrJEmBmD/neY6QjMmOslCwrmzZNhIy37qUka8EHiqcMMe2oH35/PlPfBq//EufAwBsXd6G
+y8x5PpGF+1//0l+Cz56JdcsgG5BBaJHjjCpU54WBRyj01ZYs1s89fR0xX9h3Huzj/kS+2wkLFHTH
+3xqIy3ke5Uj5wvu+hT5l6WGqy34NY3JEhFWbUn7eUkBB3ET1p/HaV/8WAEDlE0SkQ99/6S28+HUh
+OPnOnrzQh6MIOddCEPjo1ai2FOeYLIiX0NaScWnOuVzk+VL0o2JbSzZj1wKaPvEGoQWP/JeWVRbW
+U5yW3ZVZAUPOy//jq1+D4YvQ7y/wxotvAAD++A/+sczP3jtocA5dW2FtTdzurUsb8CmAYhuFBisi
+FmnPJ+MI44FUlyoNB2FDjFrFD2Gzb8ZrKBR86Q8HbK0Omnj6cTnWzlMfQrsmYaqae3h4JMI9NQLI
+bBUgHZQdlQUSh1DqtEA8pYZmDbDJptQfkRjnjV3c+IDkbVy3hsM9+fyVb7yFYI1szetX4ZEr1CJ+
+JbxUQ86el8E0W3I7djYNnKrMi29XMX1H5vNj//Hf/LYx5kP4IeMifLgYF+NifN94JMIHozNks0Mc
+jc5hMlKC2Q4ME22wHfjEDVjGRkCvbEEL7zoesqzsPrTRCuW77UoNScS++FmMeVp2kbEHP3ZhqmJd
+TZFhzp3rja98A69dlx1mrbmCnF1pdk3q+GtWCpfNPvNFigk5EhxtULBvfs2ysUKP5DEmiLqWxnpX
+qM0asUHPkoTpWiPA1ba4uyMmtV6/e4oXB1L/3o9ynGWl8vECEb0fFTgwuXxuM/mU5wUskoLo5EtA
+9O/JvU6P8eZb4h0MRkeY2cxq6zKzbiHkdFu+hUXpeSU55uzZDywbzNEuF07JpQAAmSmQlYhNpWA4
+F0VqlxQJ6NKTqIUWKrzO0aKAT5SepTMkc9kJT88e4t5cZPaiQrzD1TBcCuBsdZtY3ZDk4Up3FcqS
+e6m4dQR1mfuUWptZegqXyta258Oh5xIXMSpsqlJBiGJMRCkxFO0kx8M98VI3V2cYJoKabNR34MSl
++jXVritn8HsSHirHICCNQRJN4QYkpwkrKLX1PCqjdZo9VJSsrcV0inlfQpjxYgaXJDgLa4iiThhz
+jSjPeg/nx/LdaTLCGpnJCytEEInXNDNHS42O9zoeCaOQZjn2jkYo7HhJOGkpA9dmJ6LlICeRSYEc
+BcpuMU60H8KaysOv2g422SF3fbsH2g08uLeH+R5bXLnokiJbQp8tAxTsxLt/tIvf+MKrAAD/9Aa2
+f1Ym9QpkATYSb6nYdJrEOI5l8dsaqDCjfN0L8fOPC6+iycUodJptrD4nZdbV7cfxCbrJrgKaH5fv
+OqTx/tzoHK/9zu8AAP733/4Shsw1nOf50gAYaFT4lpZqgUoDmmIwGhZmJGdZvDbGt74k5cJBtMBD
+9gmcsrW40BZaLGsmSY4J52IUFe8qThmNwCkth/wT5wp5SRRrKWiGINoYzBP5OVEarlsqMskz006I
+MKfwSmjBJRFuljgY7ss13dt/FW999esAgBxyvWvtJnqXpCq13W1ihUQugRXAYv+Aqz34VXkp5gzt
+dK+JiBUl7SoYAoecegCH68nkAGL5vFqX60yHAxyfyfP79q/fRvtn5cX70PoOUjJcje5S1euxBTrc
+cHQ9RTZhiOXaqJKUp1A2/KkYmU5XQoaiYkHn1Lx8MIZNPsu1q1vLknM69rB6mW3k7DRFpmBTFNkK
+LSSKREMTF8ltAvxuTLGh5Tzvdbyv8EEp1VJK/UOl1FtKqTeVUh9XSnWUUl9SSt3mv+33c46LcTEu
+xo93vF9P4VcBfNEY84tK6H0rAP4rAL9njPk7Sqm/DeBvQwRi/sShjUaULWAX/pKJOHOs5Y6nTIGE
+TUmu48Aw++q7tDdGI2yJ9d1uhnh2WzLk20/soNURVys6uYMv/tGLAIA/eFlAQ+M4RbXk37MUVkic
+cjRd4BuvfBsA4DQy/FtP/zUAQBgKpLbZMrjL7spjbaFPF7xWc/FYIVb86pUW2jtk3aXtzfop8sED
++e7aZdRuElQyjaHY8KNs8QgqTeAGE1KfuV7Dq0O5P5UUyJkZn7kKQchOw5ISLrVQGJK+uAFcalc+
+HL+FVzPZje7eG2FEL4VUEYAC0lz+LlEGOUOiuNBwuZPOVAGfnkDCaoGjgJQJMKOVUF0DsLVBZsvn
+WWZgOLclA/LEyWBRi7HTq8MnkCdNY5yCmI3XjnFImrawRXm7nQ2sdcQTrDkhXCPnCGHD2FTE1ga6
+lKljuFIP1uGSI0PbKRI2QVmhh4Ry9YtkhqQM/5rC77A76OPBuXTgzqsjfHz6FwEAp61jDOZybTPK
+xXf1VZyXHKPjDA6JeuzEQ0Qq5lolQoXHdnsCmy+mE+TnkmDu9drorMnO3jk+gyL8e97z0GVzWBn+
+pnoPkS0nCaweJnO+Lxhj1mTVxd7EQJcE+u9tvB+B2SaATwP4DwDAGJMCSJVSfxnAT/Jrfw8iEvMv
+NQrGGORZBu0BTgnC0RnsCl25sL6M+yZZuuTIb3bFzQpdH5cvCzLvpz78BG5uiSteaVThUmjTdZ5A
+5zGJ9y594csAgN9++etohxIaREmCQLm8Ho1Dqjt956XbyPBPAAC/9O/+AgAg0QY7DbnOaeQhZjx8
+PaziJuuez7ZCBDX5/LvfktKca2WY/KYYm1s/9QL8SEIJ71ID5lUxOEnAmPzydaRbErOuHHVwrcWS
+llK4P5B411E5tEUeSLZtmppGOqI7bBQGMzm348SYHcl3JmmKiEw/ii+5pyzEpQSlBSga55oGLlXZ
+SZoDFYYuFkMGy3Vxjy9VYcyyYhLa9rK9PFUGFo3B2goz5HUXfkWW3053FQ0iQeejEwz6Alg6vv/O
+Mp+zvSFhVXMlRINkpk3foNZk5ycqUvLgdbgViq02yI/pVlBQKxNOgRlzQmmWYMoXz7N9BC6/T/3P
+K11gRp3HhdXAg6GUw5tb25iwjBysiXH3wxDTCfNL4QKG5V430qhyY2hfvQKfpLgWLbL2MwSXJdek
+15xlCF3zPOSFJCYSXYddIWlsuUbql2G78tn++RkKniM1E+RsDddWgVlaWv73Nt5P+HAVwBmAv6uU
+ekkp9b8qpaoA1owxR/zOMYC1H/TH3ytFb7T5QV+5GBfjYvw5jPcTPjgAngfwN4wxLyqlfhUSKiyH
+McYopX7gG/+9UvS2bZm0yODnNgy7zGzbX8qIt4MK7JC6fBNnqQL8oesC3+xdquL5x0S85MrOGuqs
+EuTpHDZ7FBwfePJpAUa1ySzc6hRYkMF5OM2XHXX1aoB+IjvsZDLHOy9Lz8NXtkUZ+gONFqYL2Umq
+zQUeT+V4a8026rTyXmiQjomT5xRsVGrohYQEu104dMHt5iVo0oB5lD1fPHgbaSw7zVNXP4RfufYU
+AOALv/nP8b+Q/gu2hTrT+g81uQnGRuTVAai8wCG9im+/dIr9acl8vCQXhscQLbCBKolMcgu4xrCk
+YhusE0MxqGqE9KaYv8VcK5yUoihJAUXvoFAKitUj1ygw54hjKn+PUMP1tuwXCwDrm7J7ns6n2L0r
+u+PBcR8troGb3I3XvCpC3l9VVZZwZrdegyadnmsFcN0KP6fOIwDDtZDnBrpGNee5hqZSlzEhDAWB
+ZgV/X62i3RY4emS7GE/IZdEfIHVIgw/BPCxmKQitgW+FCGw5d83rowOpfHiLClSdSdc5cQyZWgrE
+2M0QjI7hVqswQ4ZjXgR3xj281DQtAjSaskYq8zmmY5m3eVJDxZVr6i9mMAwh3+t4P57CQwAPjTEv
+8v//IcRInCilNgCA/56+j3NcjItxMX7M40/tKRhjjpVS+0qpW8aYtwF8DsAb/O/fB/B38F6l6A2A
+rICq+sgpbqGqGha9BqgcKVF6cRGjETKXYIuVfPrms9hoy05SdQJkI7LcBAY5PQF06nDZHHVpR1iF
+PvbBCfoLiV8Pb59hQPbdSTLFapUMQ2fnOCW56/23pGb+VJTjfMBGqjxHldJld09P0erITnNf97A5
+egAAeGxT4rvLrS6az30MAOA99wLgiEdjZiewqzfkVlckZvXxJKqpxLL+Yzuwq7I7fn6W4u+/9ety
+nVGMmS3XoYkwzLRZJmhhWRi+wXyGyhDFTLQVGg6/w7AegaMQ0gNrei6eJgy4ays0GN6ltguLu/+U
+KL5sUmB/LNvjyAA5d/GiKLAkA9AGCXMXJ1SUri+wZENuX6+hTc2Jyek5HrwpiWA4GRp12WFrVHuu
+aoOgZEdCvERTFnMXdke8BpMrKIvriPkOq+GjKMqkSQpVUpr5VpnbRqoS2OyUrfD+R7MF1i9JYnA0
+HGLIyFhPrsIlgtIs2DxXz5FznRbKhXXC3M9lFxWXGiW+QvGQTWE9Kpdn+RLHYc4mQK2EJeewq/zO
+7hwJSZltigjpdgPeRH7fqBS4ywYs1Ffgjsh4vZmjGv1or/n7rT78DQD/FysP9wD8hxDv49eVUr8M
+YBfAX/1hBzEWkPk2XGgoZpOVay1VmOLUQsbFpPMcsSFW/VCSc72XfeA6s+9RDVaLbmvfw2IqyaDa
+tLLUnnRCeUDVno2Tb0pmeTrow+1Kz0QcGdg8t+sIJRcAPLgtkOCDVQtHpG777mQIwxes6Sh0FiS9
+SAtcvSru5dVtqSLUP/wRWBuSUIIzhyp7KgIPmhyGZiCGQBdnqF2R1mO3uw4FMUyt9TY+2JH7++Kh
+xoRhR/lyGFMGD4A2CWxWZfx7xTJ5CAPY/Lm8dkcDLisxqdbos7oSBEDOenxRNfAIBooJFts7SzEt
+sR4wS0zDIjXLsAn63XCjFF5JvRmsAcV0nQi7qTzfytouohMm8FwPbQJyCGOAtjTchIzQUIghCcpG
+pQLF5WxgL7EsDuHfIp3FbkcU0Kz/W7BRtrbmeYaCYDeLYYRjBSD7G8LQxpTaruPhGUJWHXKGndF5
+gSKSLweZQUjQl5NegqkQIzE6hy4zuqWql5UuxYxsVQe0rAsrMACh0mG7AU0MUpaIUUiTEZK+XJDq
+NGGRBn+cjxAqMVjeWGMS/2jhw/syCsaYlwH8ICz1597PcS/GxbgYf37jkUA0KqPg5xbcOZBZpftl
+gWheaF8hZWlKmxwJIbh759I4cvy1I3zoXNy6j91cQWVFElg6nwCsTXexiRoRkj7JL5zMR0rE20sP
+9hGMI557Dosl0ErFx4LuoeF+98oggcNQY+7YUET/rXsOLrVk1/jU9W1c+ajYS/+ZHQCAfe0mVMHG
+psSFiWVHNNOHS+VqBGSOVgGsDX6WHcKw/u3fbOEz18WPfHke437ZuMO5tKCQlghD2LAS8RROi/FS
+d1GrAppJRZ9+Rejay8TnrNA4ott9MtfoEHuQxgYFNS3PxrIr1QsFzV1Z2RpZXnYUGpQIaKUAlJiT
+khC10MiZGNwfO7i5ShIdT8HpynOw9nKgRG9asjsWRQhllR5BDliUc3dU2cwIy3bgUm3ar7AL1LGQ
+JyzbqgIlXtvkgEvf3TEpwBJmzs01VBbmljyz+kodNj2IPMlgiLLMm/QqiwoKIk9jd45FIOFhNugj
+VnKdJh3ACa/zfEwuhsES0WhMBsUmJ+gEjsNE6bqGYUhU0g3Odx9AU4FdWQ6aLKOOslNMSOXXK+rI
+S4bp9zgeCaMAGBjkmBcTKEsmxFVNeODk2A4I/YdXqaBBWXLFTHjT83B1jcQba5uwO8R9754ibEtI
+0F5/fNkyXQ0lfvVqDtZOn5RjvHMPoxkhqsZFtymhRpHl6FO1qsqqxWE/gqcJsUaO64TU/s3nb+Dp
+58XHq9/6SXiPCzmJ6pX6R2tQhYQHqOzA0BCYPIKCfG6tkK3o1hUsO1izOYyhO2i/gad/WohMPrz4
+EuJXWRevUXVomCKia2wyBycjAa7cPpi9q3kJBa+EitOTRaGRsMqQ5Aa7zHDHlsFm2QGsgbou/4Bh
+gm2hx0qFUygU5Bd0oZbX7xsLs7IPgvmJqHj3xYusHEd0nz+x3kDFkYW+KCYohZqcCXH9NXtpeAwK
+wAjkOWnasMhX6VVDOGxhBteTyRJoysTnmQdt8ZmYYqnaFXo95MQ6JMzYe04VeSkOa1eQ5xLGWdaU
+IQmgF8yvmAUiSsPX5gq5K9czjzLU2A6dLRz4VwjfpwFxrRUo9uUbS0FFYhRtvw3FqoRybeiR5LS8
+itxzY62CiBudGY9Ro1CuNewgq8t9T10NS11I0V+Mi3Ex3sd4JDwFY4A0N3B9CzaTNoGykVusq09z
+6EwscGoVsAvZBWrkQri1tYOtLdldNq9uwQnEEzC99pLTv9HahMXvuyFRjp6H608LvuEj+/v4ta+K
+fuL5aIY6+9FrQRUeMRL1UDLh+ydnyJmU+8BaF//mY1LHfuLGFuo9yVR7m1ehqDOAiKzM8RSGRCfK
+PXxXHXpRgfEEmr10iVIHitBu7UYA5Ge7eQ3OmfASXg8beCWQOeoR3blYT7F3Jj8XeYw7t/d52BSa
+SVxbARbdeYthRGgDLbr2LduCTw1OHwXq3N0HGsvGpRpbKidxvkwidqsBHLIguzDIeA7bshGU3Aml
+SrIuUDIxWEWBkB7IansTV2oS/t2xdxHN5f5Oh9yBzzXymyUhiUGFNG44d6CoMG0WE9QoouIyfNIh
+4GUl0tNFRk4CN3Cg4zKLaWCI5SA5Noxn4BG6Hp3N4VboTQ4U7BqT4hETg34Gh+GMSgMUu0R67gBZ
+PuDzq0APxIMwa6SE6w/hNCh6E7lQLbpm6l0FbjNZAFy/5B6CWy2Qz+gptEM0UlmfGwkwYAKy6Gbw
+8h9v9eHPdFiFDZedekHThUsxGF8D69uyUC5VmvjgEwJp7rKKcOnGBrqtUjPQX8bnmVdDfiwwiax+
+gCCneIddioc6CDbk72585KPYIEhpd7ePHkuAbhigogh2Ykv2uWtjhUIff/XTz+CTT30cAFB/sgpr
+QPdz8QDJF4SXfvidPwIA3B/Nsd4V12/t2RqaH/2sXI81RrorMTMO5XqtJ49wdi6u6le+8l3MyMu4
+erOK6YF89zTysN5lSzlfOmWvoEJb5CxcKLrGlcJbuuLGYEloW2V+ZTuw0GKm/krXRi2QBdhqKgyo
+x3lSFIhT9hI0xVXNTIQhzxcnFo5YqVjMDFy37J60ENAALJaW0IBoZVzvhOgwB6B0gWZPFrexHERz
+irCyDzT3DFo5290zIOrSsKQxlJZrCgxgSkYt9ngksbU0SEk+RMqciTbeUkw2mg4Qs3MzpkJUMdMI
+aqVKlYFPT3w6GaEgiY5NtJFXqcEqDU9whnCV7E5RFW+8wvJybMH0JDS9MpMeh3qnQuliwLZcZKdi
+ZLLxPqaRVNiOdwc4OadQEvtkavU2Vq7L+i28KjLSxLt+CgNyTEYh4uRCS/JiXIyL8T7GI+EpKAh1
+mu3aCEjfHcJHrSaX12xY6FK3LyumOD4W8Ie3Id89v30Ab4fWc8WCvxC7GzSrSOlen+29hhmBTPH8
+3Sx8Y4t8eE4T6ySjcC2N2JHjVW1vSW/WaMhx656DWz35efvSTVQeF88lPXkFg9dIjPL6Q3zloewO
+v3cmVnunUkGleAAA+Osnq7jKTr76pR7UlBqN7Bb8p7/yP+JLpEH7w705LFK/Oy+6aBEXsOV7mFj0
+c0tMRxwjZba5W6/DISGJqUxQYTIrsd7FE4Se/N1G4KJhl7+3kPGeDydAwP79Xs0CHQyUCW0n8PDd
+M3FVD9MUZ5F8IYCNiCI5dV0seQJJr4goT1BjQrQRVNBdlWcZRzNUWqxALTKczQWQs00t9yQFbt+T
+UCLONPSh/PzEE0+gomUOC9tCwu5BTU7IODWYU4l5oRN4hqFZnCIaiecV5zPAMPSkJ2EbBa2oURk6
+8FkxiIIQRss5lo17no2I4K7ZoIDLhDayCIcjqTTd3x/jcl929wXXRXfDB7FZCFFHTk6Hd+7exj9/
+VbxNxwnQWxUP48ObIpeoK3MsbrOLdGMHNkmEbEQY9KlTqpecLu95PBJGAUqYg2xTIOCq22q0UKO+
+3uX6BtpV9gREKY7OxK3+ztvy0hXzGV7gBGexj+2bUvLxtQUrlIn8xhf+Kf7J7wnf39lUFspTN3fw
+wmMCJnryuR08syM/JydTjJhxbvV6GB4LyrDOctS1VhetKrUn7t7Gdl1Cm/z8BIMHdNsCD1Xi7x8j
+Am+ROtipyAuUpT5G74hRaLSeRfBxaYvVL4suxL3RlzEhccwnmiFivvTnKHA+kUVzpgHyzOBgUbbT
+xgiJ8be1wmkk155N06Uh8BWwRijjCnMjCRRy/l0fCm+PS/KVDD0SjjzjN9FhVrs6khfiADEmRArO
+Y40mX/61ZhVzhgzRLIFHd361zp4R34WXyvU0HAcug/hJqhExFzFY6GWpbnYkL1K1VoHzG63iAAAg
+AElEQVQiS1XYcJDH8kyOFj4WJF1tdWOsxWKonTrLhqMI00VpFArEE6koRalCNmffiOPDrlG1ipTr
+9VoDFvUzMyfHiESpVi2DV5AIl+FMnChYdO2dsYM9Ss33nFWcDmQOB6mF4ZEc43Qs520fOrhxk/cX
+JEv2pwcPj3A0kuca5TFGRPs+2KOEwSJFi70PP/txoNJhVaK7haAp3xmNFvCtH1/vw8W4GBfjX8Px
+aHgKAAALjusgYMeaXVNYqbCi0KnB5W4b9xcAe8gnDAe6XRdjKgzdH+1ifS5Wt9bsQBdidS+1mniM
+ljRry3d7W204xDzoXeCFJwVsZOwYX/wXknS0py6ubAsI5YzY8hu9NgwTcQvbQjaV76bv3FmyPJ+g
+Brstu2k9Ey9nnCncukY155/8AFrbgq0IP/ATUA25NutQRE+e7WxiQS3C43SObkN+DvrZklClohQs
+9l0kzIAPixwdQpFr6w4e7EkfiOubJWdiK7Bxgzt6m9ntkcpxTpWqk4XBIeHIWmmcsQKQRwZ/7Ulx
+XSuPyS45+O4MNe7451qhT7d7lE2WvJlpplElyYrVkuPWCsAnJuB0co7wQOZt5ewczbE8917N4AGF
+ZhSVmXrIYbM34HxwjvsHsvs7DzvYvk6ey8zHlPyJl8hnmDrJkv274vmw6AlOwxxJLscr8in6pDHr
+XpU1FDZ8uASvFf0FgpWyulJFgwlIh7iJAhFQ8klUgXMmF8/Cc+yOqOhtHCgmdB1yO1qBjTaVuq4+
++UGkZO5210PsnQmF3isnZ3iTHgaxS0hmKZxj8Wy8+nfw6Q89CwC4trGKdYZSkTpDEf34+BQuxsW4
+GP8ajkfCU1BQcGwbge3DYWktnkYwbACpWHUkI7HgcT9G3RFbtnpDOiNv7WxDkXnIQYbauuzsbr2B
+nImm7ZsfxGcJeX3BSNIy8BVuduQknWADtacEb/BcscAf/6YQhlbW23h2W7j+X73Npp3Qwa0twRXU
+1+bQRDdWtruwDkoGoTGCmXg3n/+Y7AKdtY/h2k+LyEqw0YZD70B5FSCX3cGqyD099+zT6J7J7nJv
+muDNe9I5eGUjxE9NZGeLQhe/fSDfSan/kOocQyIwT/b3MGYuohEbOMw4tTwLV9qyU+5UJOdyMBni
+YEzMQ8XCFchuXQQaObEHPdvFJYuJYGpkTBtzkAUMlUGOU5b8Z3ONhBJrRZLDZhm4wpLm1baDZCHP
+8Z3pFJrkt0/oHA1fntPV7S1MKAwzj2RHPE89BNwx51GMEqZwZTXA9SuSS0r7A8TTkg9BPJCqV8OE
+aEvHzuExs1e3FFxyJ7i5hUkh50kMpQIzF16DaMyWDa3LEmiGCvNKIbUoR/EBpmfiTVYdBzs94lP8
+6vJNK4IqaqGUxpvMB9zYXsHVD35EPtu8ijSVtb7hB/iJviQSe681UVBSsd8XEaFW5mFIb/nm1R1s
++HJPrWr4/7L3ZjG3ZOd12Nq75qozD/94/zv27ebtgWxSskhKlB1IFmBHTmQkgZEEMZDEgRFkAvIU
+v/klD3oIEAQI4ABBBhhCLCsDYiFSDEUDJEukBk5is+fuO/7zcMY6p+baefhWnSYF27xUB9J18G+A
+6J/nnlO1a+9de3/D+tbCeF/69PF3P8b08s+wIOr/q6aVgmfbcD13s7ifnkw22YB238f2SF7MvYMS
+3bFMRtiSB4+CAKuFTMbk4j24kWwKuhXAqWXRDz/zKtxteTnTxjxTBQY02/xgBDvid2/dg80ahtGN
+NrpDmbwHnpjOf/yPvor9L8s9bv7IX0bEPLUeOFDc1H62LFE8lSyJ3RMotftgDzqQ36l2BDSZA62A
+XO7nvSQUbQf/wTZGH/8aAGD8m4/wciSLY+/LX8Totlwj+SiH/7/8MgDgI1KGaSjkVbNB5LjFWgo3
+L5A1+AzfxiXL0t/Yp/7ijW28ynJbpxVtCG5WuQt7LS9mqTIcvEleQbKmvNl3cZ+UcEeTI5xckudx
+NsVhLsvLj0p4hKTvRrKprLMUJ2fywr6TpbDJpWh1XWy/Ivd+6fAmjkcyr51dmfNOGGDQkz58PrqJ
+LkvmW8MOSmIEnsQ5arBsOWJ2ybNgCAbzWy5sXw6GcQ3YowYsVGO8JW5DxkxMFFgoCpYkOzZcTSq1
+VglN0aEWK1FXxxbWDBLufOYm7j6Qcngr7OG1p6z8HO3B6ci9Fbk2W/0+Orflu9qP4BHrMbYcfOXf
++Y8AAD8aP8X8AwGiZRkV0rCLwRuyJr2qC4vZkCpdwlA5q6w8RN61+3Ddrtt1+xTthbAUlAbcUMNG
+hYzoK10Cl8diJipbY58Mvs7NAQKmyFRGSXIbKHPZibXpbOrjVVHAbfN3rT68AeXWEtn5LeXCohS9
+ttwN7NjqtXH7rqDNbnT2cXCTpxFTT++3HsKmaRyGA9jUHlCjNsDiKB+A/2ZjCVDE0nI2aUGlNUxT
+vKaN6JwD0Dtk+u23YN8VFulXXv0QRv0r8u9eC4qmrf9yjZ8iwewv/4KQyyazJXRTSFWrjR7l1aqA
+odtV1gpXxBNMaJl9aa+PvVfFGnPGbRQMZk4+PNpIxM2PNWpK6EVbcnK3Wh6GtHh2gjckEAzAZEtc
+kQwEebWxXjwiQc+PJlixSnR0vsCYqU7Hi7CiLmj77g7e7Ej/dwk7H4xCBIWM51Z/jJD0ddp2kJP3
+IrUVopwWEis8PShYhKlro+FRqCewNIj0Rg0L2qaEfQPXdn3kxM7YFpAuxTULggRz4hts6pxenSyQ
+01LsaoX+WNae7TgY7/0EAECVCjVJgowlz2bpAIqoWWVrGOIb3d4Ybleu3TZ3sHVT7td81xQlmle4
+np7BOGJBrmZTtHfEsuwGHjyfPt1zthdiU4ABTG5gIgt5U6noVThdSOT87EmM2/uNSEcN3+bkssTU
+WCUWZ4Q2ZwnqC0bOb2komsnatmERtFQ1lXO1vSnpNasUiORFSLNz3DiQmEHQHQA9Qn5LyRZ0HIPk
+Y3kZq7unsPkCIZ1DhQSsDPcBmsobZdqsBPhiGqOoPgJhIGHJoDE0VUsNUGbd6u/gkzrkCvWpuAqm
+cNHuyEv6r90QM/I4eYQ1QVGua+FkzRUfWSjp/PdtGwUp3I/fI7lLbxsRBXG9dgvxB8LYZMVXUMyV
+Ly+n0Ify4gU3ic1ITqAJ/3atAdp3JNqPaoXBUL47ffIEZ+tzDtGcfS8wXUofDnohPrctY9UK2qj4
+Qu7ut6BswY5sE6fiLjK4ZEm2XXvDaVmsK2Q08+s8QbstG7WWaYK95cFn3QlUAaypqBUYgAeRFeVw
+uDaCnsxNsl6hzrhhx2tEY7709QAfP3xXLpfLHMwPr1AxE1HGIYp3CSB6ZRtOyDU7DlEuiJ2gi5If
+rlD1xTVwPB+5Q8Wqqtg8n61d6P4Wx5ZzGhiYBas9XaBkfZDKLcQP5Zlu3byFNq7dh+t23a7bp2gv
+hKVgFFA6CstVgnVOcQvLwnQp2/zh2RnunYhZHUYhHCIdFQlZ0lmC9z+WXVufHyN7XXgQ/fQmjMOo
+fp1IBQ0ArJtimTmM1egX9FDxNF6ez+E7cu/R3stwjZhwJWQn7voaLoNE+eGHcFpyD2sYw1QSqMK9
+AqpNq6EjZiQyJdpyAEyebKwGGA/1SiLO2aNfl2vFBTRPF1PmsFgBiDICrsQqSk//CN/6NSm2+s5E
+ItKRrlEx/69MjZhWil9Ym3uvbQ2LFF3nlLifHl/gstHEfPQIJ8+kP4frGM9mEjw7mcd46YlkQaKX
+5JnWi8eoG4MoCeEciKVgWxGKgidinIFThfxMxvjtp8dIeFp3Rw6isqGCq6CZJajbXQQDmZ9GGt5p
+t+FS0yGZLWExiBYnFR6v5Fkm6wQBg59jqlLbQQSLfAppMgcoROOw4hYAsCo3WIaaHBHlOkfuynO4
+vgWLpruGjd9/KJiSe2di/QRVhrAlGYDp7Bm65FBomwiGyMxqOsfqQtyAmND1i/N3MflNCs5YNRYV
+1bOtFb54V1y64e4Y3uiAz0KrQ1cwa1qsLpBTg/Li8AJncwlGpoGCLn64gqgXYlOwlcaWHWBdFMi5
+elStQEIjxNUKT0/EnO10PfS2ZSBcT8y2Cgne/6ZAmE8unuEv/DVZHOFyhiyTWgS/G0Jrebltgl9U
+aUG5TbrGoKbgp5e72L4lE6A7NmqPIq7Ey9++1cbeS+JeOE6Cek2Zi0UOTXC/ehgAu7RdL4QcFqMh
+oAhyj1MU3/qO9L98gj/+e/8XAOA9SzIWP/XXX8PWy5K+tFQXOJFrmFEX6VLw8ItHS2SZgHdmrCa8
+LHKsG/PSNhgzTbXOc2i+eLqykBEyO6EL88vfeYy+ljE+rTUaasNxpw2HgpxPrnIcn4tJXP2hyLBf
+LK9w1Aj2LspN9eQr2x14OwM+n0FOPsIViUXemc+Rslx6d+YiHcgNB5GHuk0w1PEKa8KYR1tMka4q
+OBRsSdUaqiUvdVlnmB/LGAWmg9GOvDg2YxW6KhGv5UVZzKYIenLI2LDgsgzc9jTKOYFKDYFwlcFl
+5W7U7kE35eeJxoxw4z8y8jLejkZ4801Zk8v5FTJyfs7feQcmILT58AhXp3LtK9LPF5MZ/vHvfE0G
+vGdjksgzH4wUDv9YRIFvd9rYGsgh8+ANIeIJDm7BJgtViTbij2VjmaoJEl/AW2FtISkapdHna9fu
+w3W7btft+9oLYSlobRCEFaZxiopFNLZRcBw5debrNc7IM3D/xjbqlZhrFRV38+VTDFno3t4dwW+C
+RZ0IvpJdtVrPN9F3nTEgY9tAIuZg7RaAIf347jbsWoI6pZWhOBVLYGGJaTl+6XXYY+lb/egSJYEy
+2i7gtMV8VtlHwKWAoUxDHxdkwJ5kMlS3g3ouJ8WT/+mr+G0yU2tCm+vHA9g/LZgFneeoPmAl39vv
+YcqKOwsrHNwgpDuTPgSPk43yc0u7aLnUboyLhk4BVl3AYfT9MpbfHRY5GpJDrSwMGbFepykWfL5M
+K/zOpVhhN0mtcpFmOGUdf6VrOFeUkT+eYzyUE7TdC2CRuuwZrY5H0xqG4+mXDmIChAajfRwTsPNs
+coxBX8x/VYt10OrkKGgOFzUwuZK5vKxWOKZFFlYKr9yXeXBoHVqeQk6gz7rwkU3k5M6XFRSh3sO+
+h7wh81k3FHpATWmAStnIG2ZkK8eStGkTPlPb87GKJQCrCo2PSFXvxDksppoOcxspK3e3BlQxdxS+
+8nlZK1sHbXgk1FGTAmVHMgqLqyVOCYyzF2IVj17N0R3RXQsWyJnBuqrbUOTSnM+7WM0m+GHaC7Ep
+GFgoTBdBF0gW8nLnVQlWqaLOa6hQXujVIsOcxCkRpberwkDzy7de2oK9JvehX0Bzg9C9DmouXkPf
+0qQpFOXLVaxgefKSemEKxxK04eXbH+Oth4JuXK5luH50/w5KcvE5O1MgocvTq2Dm5PRfWsCWmHOm
+R8CSGQAE8UA7cEcCWIlWfRwwon6HePrBG29Au5QQb62Rnf82AGCxvMLVV+Ueez91F8MbspF9+SNZ
+SO9CYcrcamIZ9OmudEcGS7I6mdpBRC3JC7IqzUwNWsk4cBUedMUfTmqDlCi+rASOlhxz+sO72sb2
+gBtr7eJZSj2MPMXyjC/9WYxhn5WEzUaQZ7C4TRXBCh/O5Ro/297F6YdSS3I+fRdaS61ITPar6ukC
+1oFsFFEnQEHT+PytQ1wdSVzlrFpi+0P5TnVT/t0rWpiRVam2argcl5U1gW7mbxXC4oZTksOxVgaa
+KMzs2QzFUK7hmwGWjcoWN1D3lgsELLnOKqwvZS2nWKMi/6Xd6+CgLeshZPZhcnEkeg8AnI6N/S/L
+BrG789qGar6MbeRa1v36PXHzjFOhWMt4F9oGItZ2PCuRfks2gg+tt3Fx/mfI0aiU+s+VUm8rpb6r
+lPoHSilfKXVHKfUHSqmPlFL/kJoQ1+26Xbd/QdqnUZ3eB/CfAXjVGJMopX4JwL8J4F8G8F8bY35R
+KfXfAfhbAP7eP+9a2tJo9V14aQ/zuez2VVVDNzBQv4WIGH3lVsinNKNoAhdmigErIMfwUE9kl6zP
+dqFGjC7XCUALAQ1FuE5Rsz5eIUJFkFFdWkgmYs6/8+Rb+O9/QU7pRk788//h30RFq6GyPKAjp0px
+9QHsh3I/a9eHnZL5d/AVDloJsxBGXsQG9UK4EzqvDvBXPyfgluxIBGeyZ78D/evSX3Wjj+OH8vnj
+D56BokFYxTmuHsnpoWh+3/BdnDWBvyxHuycn8BbamCpxQYppBp/VfB5dqrBSYDwVN0IbQxo0U6vG
+NjkMHww+0a4c9+W6N3oBqm3Ci9+vEQ3FwohPSxxx/sZZCbffRP7JqOwDK2aBkoWBxYrKKsmwmMuJ
+d/V4iuz8LelfcVv6mxiMZ9K5OF/i9EQwGx+fXSGBBNe2PA9Zsub9ZG5myQqa2o79UMNyG1r6EOuM
+rsR8BYfYl4rQZRQKhZJ/N7WGHbM6Vs9hka8yCmXdjLttaFq0SbqEIsagqm0s6YINlA1QCX2+krXw
+eLXA2VQCuPO3Vihp0bVe9dB+jZBo6yV4pG1f0LqdX54iTWX+6+0hyszls8Y4qsXFfPv9I1wuGPB+
+zvZpA402gEAJP3UI4ATAT0F0JQGRov/rn/Ie1+26Xbc/w/ZptCSPlFL/FYCnABIAvwbgGwBmxjRQ
+PRwC2P+n/V4p9bcB/G0ACHwPUauLrM5h17LzLbIS50Q0qrBCZyw+oBe0UfYYDKLUWPzWBbpxo5I8
+QbGS268/eAZ9+JH87vVdKDXkU1OLL68B5rzrvILJ5PTI5xmOTiTt98GTR5gTpcbaIjz6zjdRb4nf
+tzcYwyLDEsJbUPssjtruQbFKri6pbXk+Q/brEgwrLzOsJ7Kbrz96C/aa8QwKi3zj8Vuod8Q6CKNd
+hC1JR703W+Eeb6cPH+EjoimPyGwUjWyEJ0R0ujV8+rudoQX7XPzsmZvDsDKwTeduy7cRkMS17jv4
+Fism54XBkLBxXRukFCTJJhSOqYGESMlFmiPhSatcBz1aFftjjc/syNifT9m3yQov7clJ+tGkgM9T
+d20fwX3AZfm4A4cFZJ2bMk+n0yn2iDBVqxZMJdbBfDXF/pYEh7/y2Vdw4zUpdBu15b9eFGA9kXko
+4gVYP4eL8yvEqZykN2/3UTegi+9BkOaMgzjdAVSD5dAeFOMES1pE57MMI8LAt/Y78JiGDYII/jPx
+/Y8WR7jM5ebLmfR9rIGv/Li8Jm5ioaBm5Dvf+TrK71IkJuxh60dYWFe0OCZ9VDOmy8MQBQui7E6N
+vbuMYz2+Qq3k3s/bPo370AfwcwDuAJgB+F8B/JXn/f33StH3O5HxqhzZMkPVwIBhMJ2ICfiNj87x
+5paUxVaOhXrOAJUlm8N3vvE1fOc9AZKMCx8v/4EM9u5wgPBVljjPEvSEgwJBizXZXom8kEHP0xK2
+Ercjt128+1Qm+qxaYdxv5OxlMT6eAMlKXu7WzKC1LxPq+AfALXlj1cBGRTM3fVvMRO1vAyTWgNMG
+iItwnDsIt+W53RF1Gx9PcTGVyX95N8SjY5nYZ7lGTsKS12YlrhJxV5w9Wfz3WxfY75PoBCmeckNb
+P5ttOBrv3+ri4kI2L4ub8JZSKGg4PpuVeETTXikFn9WAO2G44VVsBFngRDimaXyeGWTTnP2wsT+W
+vh14ATKa0t/N5TnOV2ZDF397J8DtvtwvdltYl7Kgv/DjP4ltVqhGFKCN9YcY0XxujbZwur4t/Xhv
+inZXIvFeZx/qjIK17GYrs6BdysEPtjA1suHOPjjGTk82k340xpJaks0LlpYVslr67oSA13BeqgJd
+Qq+v+HLH5yv0OP+h58JwfxnvbMF4spZ/5Vd+G0kiLrJh9mxoUqTcZA+GHeRXBCQ5Ed455AHnLHE/
+k8D1j31eMmpesI2C4K26AkD9SKtowRvK9W7dm8CQju3tx3II/aD2adyHvwzgkTHmwhhTAPg/APwE
+gJ5q5G6AGwCOPsU9rtt1u25/xu3TpCSfAviSUiqEuA8/DeDrAH4LwL8B4BfxnFL0Vm3QzhIc5ZcI
+mCdeokLK/O/HT97HB18UhOGW2wZoQdzgyfAjn72Pu7mcDMXahcvA2NbWNsIuq+9GQ3i+mLCG+oTF
+5RVSBnWKOEYZMvVWnePZWxIE/Oi9h7h3ICbh7VdJjvE0R5cCG+64gpPJaWulgDFC1lktJlCu2IHm
+gmmqcQYrkJNPqyUc5pXr0R6qleSeFa2g1w7GwL3XZICCBY6eiBtUL1JEHblGHpZ4QEGRJU+dWdvD
+j2+J1aDutvFLvy+oyRQ1uoQ5/6VXRohJMPpbC7GOgrqEptqxA2DMKtChY+FOi9oQAw+vNxWj5LRw
+8hoPSJh6cpRiUYppPK4KBFxeVZGjIjakSaHVaYGEMm+vjBU+S7Kb/egOEsrA749a2IpuAwDOJ3Ld
+o/IEsSXPvBuO8Pk7DwAAj8ePccmg4+TgJtpvyly3umJ1uGGEqKB0m1li+225RufObfQD+W6SJjA8
+pdFqBGJWsIomHVqh2qf2gtfGT9+TNfkeWbuVbRCw6rTOCxRk5nb2hvBJF/hKfwTw+fY4d+NFiSKX
+sYi6Q+y1pZ9Rb4jPfpHyhasQLuXmOtSXrHOzKYIqlI08E6tX5zbyE7nfQaeNIcWKfuvrgpv4Qe3T
+xBT+QCn1vwH4JqTO71sQd+BXAPyiUuq/5Gf/ww+8ljIonBJOUaGwGiF1BUUwTbyc4eG7YnB8/tYQ
+2bn4hj1G3Pt/4QAOX/78SY3TQ6k4y3cCdN8Uk9G704Fqkzq8EtNwpXKcz+WFNssEgRYg0OziBI+u
+xB2ZJzPYtuD8dwmr7vVj7NIUdfo2HNDJL0+AM9mw8ukJrFo2CIty6nbXg6Z4h8q2YDnyTNXXDlHU
+siicSr7b/YtjOG8QJntZw/ojWaTdnkLE/La/18XdrmxUl4ksiJcnA7S/IHh5y1H47E15jk7ehrMn
+4/XSsIWTUBbTvUtxUd56PEExJ77DMvgSF+Z220aLlaT1IsHqiJBuAsj6to+uKy/6aNtD5VKp6jhB
+3mXF4Bo4TQiMYnJ/O1R4o3GDQg8v35R+OFaOWzflRS4xgE3fv2rL+Bz0RnCa6lPLB3qyOUXjHo4e
+S9++/rWvAUo2uPAVbsLtFBVZuZdX50gIkGq9dAA3kuebXS4wo8y7Zcl1y6JEyZoZ1ytgsyKytgrc
+viubfseVZ7rK16D4GILI3pRq57YFl7GYmy+1seVK/2/flEMtiIHVUjaWbGkQkFDGcRyE24yf2D70
+gPEvckYmKJBMiAupNVIC0Va2RstnrUi4gzqnqPFztk8rRf93AfzdP/HxQwA/9mmue92u23X782tq
+o2z859i6nbb58hd/FGWd4HwqO/X8ZIaU6Li0qOA1JCRabZSLFaPitm0hoHR8FPpwmmi5tmCYj18l
+KRpWk5SS5EVRNdwm0Lb+pIour5HlDOBYGkFfTq4vvSF73Y/++/8q3nggZuugs4WYsmphucRHM8E3
+tGfAR999DADYZ8XdbB3j//xVoVi7vDzEf/pvS1z29c+8jPVcrJcnDwWt9vDwA1wwd60qteFXHI1a
+8EiVFgYeNIlFak9OkcUyxYo0aJXy8Qu/9Zvy3XaAoMcgaG7Bd8iRQO0BVRpYHDfP0QjYZ8+y4JKF
+JHSBVou8B+RxaDsOpnQfFnmJJaXZirTaBBJrS6OmBGDdSLdpg9lU5vdimiEnwvLBa3fgd4gXqQ1y
+8kGELTG1s6rChJbiqqpRsajK8kI4IRmmz06Q52IhVAXZrHMF/6ZYXm6lQeMN85MzxLQWk/Vqo7ep
+GsIdW2/GxfIcDImHGfQ6+Cf/5HcBAJqVtrVRKEleU61jnE7F5VPpGrPHsq737+1jxczPP/q//zEA
+4PLJKX7mL0pAvL/fh0e0pc5qVDG5KbMYWpN8hlbc/u1X4FBHQ9vRhl8EaoF4zUrMxRVWYjjjC//6
+3/yGMUYoy/857YWAOdcKyKwS6VWO6SknPElhMeUTWBqqAdnYDvjxhmGohoJqZMFzoFGwNzWg+KIH
+ngObBKuNylGgLSjOvqU1agqjlGUJzi2yokB6Km7AW7bAb7/sdnBF3PvZ6mP4pBEPrRRdKkvl8xzb
+JC2Jz8RPfe/ibSTnYiZu+xZuZeKWhJmGzX4MWxRE7e9Cs9pxnS3Qpx/dcnx4JE/1tYHT4QIiNb6d
+GSi6YIt0BYvPpKCRkJDDddvIY3Ii0jXICwOfIJzQtTGkia4VMOJG0G156ES8H83kgd/GmJvNIslx
+PJHFWKYGKSsN09pgxZfN5phYUEioD+nqHElJKK5lUK5lo1sVObRNPko+c17OAWZDXNioWFfSHY7g
+DWQeijjG2mYG5pTEK34b9pxMSdvbqJX0zW534JCzM89z6JKisA0NV73h4YFVGqwokuNanY36lOG6
+yqoUhsxNK2eGupK1vJhMYAismq59PHso6+j9b0ul6bDTh9FU7Fp3YbUoptvdRcHMl54bXGWydmzm
+xlf5OVorcR+NnSAg81Lt1bAJJK7yFFarQQg8X7uukrxu1+26fV97ISwFVAqIHcymyw2QJM8qhDz9
+fNeBR/GVwNWwGxorCqAUyiAjj2AJA7+m6dvS0I0Z6DhwyIkYDcjbVyso09BuGZSkQ7csjYJkG8u8
+QG3kFJsuJM/bMTt452uUqXv5Q+w9pkJ1dAb7UIJddr+LuyRZuQDxCG/X+OIbEgT8sdu3cPCaVNRl
+sxWWrPWvyZw82opQl2KqriwPLskltDJwGoiA6yBkZKsie68fVciXzUMbBDy5lFHIDOvqZxUyl9ej
+2R60HHQjKkp3AozINdnq2ugT4NXthQgVT66Ioie5Qosybq3zGjbFXuLzAhd0D/JlBc2qStMApFAL
+ESeAyrJhcX5buodJQtCap2G55CO0muIiCz4rB3c++xKcWP7e+rF99Mh38d52gW//quT07Z5YeWVi
+oYRYBEk8g0Om8NadA/hdsX6i6RWWlzLHZcqCqCrfWKFZUcNYco04WaKgJZRd0NOR6RAAACAASURB
+VA3yn0Kn4rYcLh5h8ba4kuf2HA8s+fzySYzf/bU/AgB4lO773L2XMPAkaJlPMwTMfLieDdcjvV2e
+IPdkbYAZs6OP3kYEBtWHLm4OhbrO2C0UBLAl4RqBJQHP520vxqaAGlWVY57HyBK+uVWNiu6B5zgI
+aDL2XBeG8uoqY+l0lkMzfVkWgArl5Y78CCMuaD8ycFntVjNZYOcV1gTTlACcsSzMk8MSM0ZysRY3
+BABWRFhe5Esc+gJeSg57OC0lDtA+n2GPoJ99t8QFU0TvvSV+/d3uEK//zM8AAPY8Gz5Ltat6DZvo
+vw7N9qDvI7Dkmdf2Nhas55gsLpFlYopWZQeK6kwNlsgEXWji/qu83DBL1ahQkbAEqoRhrUGbQZWW
+76JLvQU/dDGkier7HnxuLHVZQZOAteLmkFUVam6sSlcIa1mAqVlCOTKGdZ6haOq2WcG6UhlWLBGu
+VCbsvQDWVYIV5eOr0kVtZLPUfsNG5MCwnNrtjhDclPRrUW7h0Vw25JN5H5Unm0LQk3/PohQVEbDZ
+5QVK3sOvHHiUlPdHHZSaG9mcNQzzElWztmqD5kGs9QKrlWw4F5m8/JcL4Mm7klp+GB8jmMjz9yON
+j1N5jm++/4dImVF57UuSKuz2RliSQcnzK7hKYh+rooRyG47NCKG6DQBYTmXtLesEGcFWvj/EFVij
+YV/CDZjNMWPM0z/b2ofrdt2u2//P2gthKRgYVDpFXaQwzCzYroXhSMzS7V6IUSSnWN934TCvvFqK
+SXlxlWAeyw5vTIWb2wQb3djD3VsSiGm5ChXrAxLi15HESI2AQ5ZxArclp1xkT1AEsgMvs3MsV8S+
+kw5+WX4XfiWnp1VkOCIVmqtSzCPp/yiZom/L56+/IizQd8e3sU1GXtdNoenm6FYXHVKfK3L5QWks
+qIeYlwqnSoJTpi4wVTGfI9vwPLbarM40PnxSil0lyw27PIwNSxe8hkFEy2vQ9jiuHna7jdK2h7HX
+0OdbqMgdoa0ChpqOdUnilayCzaCd7/moaFWk0Rpd8iQWSY6UepRr8jDUqoZLF0aZChUtFysssNOT
+ebioNEpaJm2fZCvbbbRdCdB27B5yI2P0bPI+Lr8tPJ3zPIHNqszugbho8benSHUjVPMMq7lYU9l8
+jt4toWbr7EbwOmSpPpdnXrgWkrmctMk6R82aiLwscfiu6DzWA9LDrYGTmLUt6znmHHp3mmASi1Ux
+PZnDowUYPxG48+PpGsFQ1tZg8AAV5yYpckQMMAetPUSktnf7pI4v7qBoylkzA5v8FJWqUHCelKlg
+dKMl8Hzt2lK4btftun1feyEshbKqcTVJkBVmk/Mdd3p47bacsPuDFpiaRd9vIyALj8e69NniEuWS
+3Au2wc4emZ+HEfqh7PyWTpEztxTH4tR51RB5KSfNLC5wzrTYwa2bqNpyWl1lCg/JWRDyfmnZgs/a
+djP5EK1YdvD94Q7u0gfc92fYyuQECe/Jzj66cR9+w8GfZygI/W3vbEFTHbtOZIfP1iU8YgVUMkd/
+JAG1XGnEZ0TQnS9QE5NgM6La7npwG2KEqoQid4TjBahKSclpYyOiJbDTkxM48GqMqY0YWTZc/i6r
+M2gGB03tICdeoqROhaMdVIVcK/NsLJdyPlq1RkRLoewaLJtKUcrNrdMaSBjv0BUUU4Cu00c0ljhA
+jRWGA84fqyxD42N2Kifs+ekhTmkt5pMZFqRjiwZ91EzVDrVAkYPPnSKnuFAaX4IAUijXhsO5drb3
+ETLwWm3J8/VmS1w+kpjB8uIK64XMk1IaV7Rqq3Ox0q5WxwiI6IyMg+lC+rmIV0hOxZL1jIeIMOWi
+kjO5WCoUkDFUUYWKY9ipFNzWHn8HKIvcH8TZ2FYPmvNehwtYJD3OCld4PgDk2QSV+uGIW1+ITaEu
+S6xmV6jyAm1Wnm2Nu+jaMlADrdEeNIOt0CJFVcBc7K1XXkK1IolFtwXPlcVdRzVUStCMATQXpLfN
+moR4jYCRXMRLxMzvx1WM/b7cbzfsYdmVhdBwRtqnCa5i2Sjy4AxBLoGhnVsublTijvTaDgZUyA0p
+xqqSCmT9RrVawe5QKWidfaJqpeUzp10gXDCo1etvzOtBVmFGLMdVfoVZJmZpwYXk7NfogJH1QKPO
+P+Z11Ub63bEU+sTf99m3btfAo3thWRo1MfV23yBbkMasACy6JppVeHlawWrYo5ceIprB9aIAYroK
+C4MuF2+p5CXWWY6cEukmrSSIB6DT3saNuxJFPzAKfZrz50bG+/Ljt/HOtyWwC73CYk7cQ5WgZrTV
+9mLYBcl1Gno7tweHL/r5uwaZz76VGl5N8pknOWxCjG3T5tg7sAayIVupgoMGk5AhbbIrBLrNzQVU
+LPftdDysJvKsT+NzKIKQIsfCmNR7LjEb2gGgxJ2pLy9xxsrdVf8S8VrW9e3BCM5I1oYdy3cVlvAS
+wspRYGokc5IsNVQtfVr0YnRJZ/+87dp9uG7X7bp9X3shLAVjDIo8gzb1Jj205ffgebJnWX4Ej0E5
+q6xQ0nxyqC/ouz4Uj+AwdOFqaivoaoOay9MFTEXdyJwY1wSomZJTlg2XUNww0ZgwSOh6FjyXeAma
+1McnHyOhZmLl1djZZSBuuUCZS5+G4zY8Whbl0WPpb+f2hmnZuD6qlKlVH6iJyVCEARurAnjf0sqR
+E85b2wbtnjzf1qqPtz6Sa7+vpT93dYmoe1vGaqeAIVJSVwVM1ehfOnCJ9fC4BNqegxah4pnONkhQ
+t/ZgkfYu0xlA4hCXFg0soGLFpZ1r2JynylhYsTLQwMBxmvmjKxIpFHx+gxqcdqioxqAlJ7PTr7BY
+SSrv6AMpHnv79/8Qxx+K1eA4gO/R5Tnw0a54Il5cwmtMZkssqUUVIjmSv1WyhqZSOOwac0rkYTTA
+ZUwZwYBCPmfpBtJoWRV8ul3lqsDlkWAEXIvBTGsNaynXnZUl4kR8FLuokDeZ9jJFRUtAJWJVGg8o
+yeI9mZ1iNiNV3qlBpy1p1rrVR++GjEuUE41pucgWxHp0QsQJ4eZZAU1BmdV5iZISes/bXohNQQGw
+lYLrWbCa6LZdw3bJYqRKBAR/qCqH4gBaFPGo8myjE6gcA4e+cVaskNdi4mWZEUp3AMVcPrOrGh4F
+SJ1+BHvG6LRZIuPG4/samgzMTc1BsXwIe4sir1Zrw1jk11OEWwI2aVdjOCFfcLIpVcspnF3ChE0J
+Q1YkU2ooMk0Xa1lIRblEQfcoX1UoKzF9g14HI25eripxeSRVkDEhzGHhwZ0QijvYQk23o9AFmkSE
+rRScRi+GiC63cGB7zMqoCg4r+XShNr8LfB9BT1yvhjBeexVqMhMFfgc5x22tS3iOuILtrgXTxD5o
+aruwMGFm4ai0EJO9aBFfYcaagcsLjclTKfd9Rv3Mq5NL0YLkfHQpvvXyQQuzmfTfcytMc4qxTgjn
+DktE3Dh9t0bzqKs63zxffHGOki5bSSl7t1TIGO9wAKhaNn3LtXB5yM1pLDGqy6nC8lTmr93PkbGS
+1M4VjENuSitB2QxzW+7VclzMcvmu61nweHg56wpzlqJHtsH6ifR/m1mwIrDQbQSUHR8+KQMm9QIr
+xq7itYZH8pXnbdfuw3W7btft+9qLYSkoBceyYNveJoLqKAstmqhh5G/y7WWt4FUS+VeNFKOpETbZ
+gKREGonpr4ocFk1wV22jbNGVmBExV6UAGXCrKMKaQicIXJgFuQQrC4YnhaIwQr8bwIFYI0alUJDd
+vFPeRqdBW3Y0yibiTiSh7gEmZvZa+6gYfa6QwhBWWzEbklUeMkeumyxBvi3AaA/+DvkG7Bx7t8Uy
+Saj9OFnmGB7IiRJZlzA8MVRlo6YrYdcWQHRnU3GalznSlVg/rutvUKFplsBntFzXPiqe/mXaoBVr
+WIYuQwhkjXp2XkOT+MUNI3iB3GfPFZxGUMfwUhnjmV2gupD7nT9boYrFVTieHmJ2RZgyi5asssK4
+J3P9+oNdvH5HMhV37txASUm6sd3HtOR4XDDNECqwrguXj87w+FIQolfrCpprLkkqZKXcZ7VsXIYa
+57xEZUoUpcyT7zq4angNBDyJp4dXMJy/6XGOkmvBMzZGrPz06g4cYm7atlie/cEWBkQxLusMLapu
+OyMbnY5kXUaDDizSqvl0NVH40LSWTaFR++ImuAsgbSyh1RxzjxDK52wvxKaglYLveVAa0MTwL9IY
+8xX93jxBQrKJbJrD5STGK1kcN+4PoPiybfWGcMmK5G1tw2t8/5sFyjX1CDtMFT1dIPNkks0yQ83a
+gHWeodvAg+NkUx+hCX6qLiysa/luZ5hjQrPVfmMHOpMX9uToGC1b/OGSEWR96SPpcHFnBsbnKi1s
+lCwz1ozuL9NLJKfiey47Cj53RQ0bFolpw0EPg33ZFFLyCD6dn+CCZaJlUW7iFmXLQkGyjcq1kPFZ
+mmrJouVjsZT72aGLTkITtV1jSWhyaCqEjCmYDiHjZYmEcZB0kaIMWY+SFSi4QahVgWiH5dyME3Vm
+GulIxn6QRCgIxZ2cnuLiTFKLqzhBydxhSfeiZwUbgR/PdeGQ0DaPe7h7X3z7QTTC/ZaM1/y0Keue
+I6ZUfWEMbpIQNohnyCu592yVYLyUF3a2EZhdoaAYThxXAIFAygDLC5nfOeMQZ1dnAGHnngZadI+C
+3Q62SInfCULYAWHanvxuWUzR4mctpYEb8ky+48NuqlzrDDbz8jUzRnBq6JRuXMeHYj9HUYjykhWo
+TgKTXmcfrtt1u26for0QloLSCr7rwILCgpqI63WKNf8+yW3scLfz7Basoexlc+7K1txgi4IsHSuD
+oYVR+xFs5uZzJ8eslpNywUjPk3WGipyBZlGj9hl9SwusbXI3WtkndN8MIS8nl1gwaDmrt/AmdQuD
+toNFIafjk3c+wiNGtau1BMmsrocHfZGKG/cytEK6IKXGkn1OyJk4W57gyan8rUf7aPFU6XYipAzW
+RZctOIRHd/fkvm2vB5uBqshxkTcuQ1WibjQYdY2ybqpKOQfKQaU/oflq1MudxAEpBpBVazQwGIfB
+K50YTAnNdisFm2zVdWWQ0XVxCwVDERg3palul1gT9OQWBbrEnhxdXCBJxHyu6xI8KOHTOvSCCj4z
+Q0hzVCwU829tY2yJKxG4EaIWuTP6zAY8nuPqSCyF2enVhsezZdnoemJh+N0C85TYgqlYKKssQ+bK
+ZwuTIyNcO7U1TqeNy0Y6t/kaNeemEzioOZ5mAXzwsVyj13YRdsUyGY7EOjjwxuiTF8IPe8hZBlsm
+Oc4uBDj1nYdzHBJ7sEtW8d3xCPtblKcvPVR0ZyyrgO9JP/trg3reAK6fr70QmwIgPI2OqzcPNp8v
+cNFnShJA9oTEE56NyTN5uR0OZP3B+7izLRNblbdwe+c2AGB9OMf8QvzTdw7P8GRBc28q10rXGahq
+jmEYomKJbLJKEfVkwXZCb0OwukVy0YW9RpsAobKaYD0jrn/xDO+9L2Ch3/3f/wDnpfjD51dT3sPF
+7IFsEG/ev4lb90Ursh/sYlnJAjt9XxzU3/v4Mc4JjimKR3CZLgjaDnbZj+2dbeyOBY3X6coGs7d3
+G/MreUmD0t6UT1rQUAQZJWkNj4xTiu7a5HKBjJTzizjZsPvYoQOf8fmw7aEmz+WSyD7EOR6dS989
+34NHkJLvOWizBDhsO0jYj5Whe1HWiFxZ5NOVhmL8IUsTmIb9yNbokE2pP5T56Ps+AoLajG3h9Erm
+NHc/gMMKx7u7BzBKxihfyws0Go6wPJEX3b2hEVE7YXxvd1NeP3fP8fRdliIzWzBd5ZgmTbynQk63
+S2sL55z3lLUhcbzeEP9ETg3NcnDPOHB4iMR5AYcZmobdyfdCDPYlixB5HdhKXI34+Bxpw6I1HGGL
+qeiXCM7r3BnCAhmpaiBuyu8DwCuYxbvUiNwf7jW/dh+u23W7bt/XXghLoTZAUhgYXaFgXl3ZBiyA
+Q6/XRpeh435rCK9DF4PuwOzkAhcnclp9qwgR/ZhEuJ24wFuP5GT+9sfHWBHQEigxue/tjBASaqzb
+wAXDzOt1iSsyGxfKQtSSXbdHJeayc4CKmIWL944wXf4BAODq+BaeHn0XABApYDeS3f/e7m257nKN
+iAAitbJhx4RNh210GcDSWvp2d28bw5ZEnjt+gJp4gtpyEJJg0NM2bJquPpmRB70u1n2xtuqVjYTM
+1V6RomY0fF2UCEh3HvNEqXSJisFKL/CRNqQmcYKOZiDVWHAJD07oX8yWMRJWkS6SBBYrAA8GNmq6
+MSvkWDGirnJmgxyDmtTptY4BuoqWsmHZZOke9fDGPeEcuHWL8687cAlayzIHVSwWS1i58AgxtlYh
+7LWMfUBYdSt0oe6Je3E28XF8yIj80wmqkvye4y72SS13Z48u6u4lnHfF+ptOP0CSrdl/DwvCtDNy
+LOR5taHVC+027mxJP3vdAayGD8ItoQkM06ylcbWDiHIAvcE+bCpeh54Ld1+sgmdHzzbzc0qehvnx
+OQKCt/qDAcIOqdm8GhY5OFdFhpb7w53915bCdbtu1+372g+0FJRS/yOAvwbg3BjzOj8bAPiHAG4D
+eAzgbxhjpkopBeC/gShPrwH8u8aYb/6ge5i6RpEkKBMg8mWfqiuDlifde2kQwRrJLjmbTjCjTFdK
+P3RnPEC2En/y8uoEei4FNcHQg0Mmp1eGLSSl7MYnTCdeFQn6xEJ4C0CNmLrRBpfHEpw5u4rh0r/c
+35W4xfQ8w5rQ1+nJMVYzQb+lo6fo2gzWqRCdXSLWepI/TlcaeiAnpfE92GSQHW63EKc88YjifMVs
+Yca89NNyhidnpGlzPdQ8BdROhIQBw8rI+PhVjXYkqdrStpDyFDcDHymLcowpETFll+dykmbIMFvL
+dT1bIZ/w754NkjnDtRTaUxkjR24BVRcIaOWkqwJXJCjNizWGS+bmlYbqyVz1HDk9o8LgikFCu9Ib
+olTXVrAJid7fGWKLMZOYEOWL+ROURFsWLYUeMRQ38QDuSxHHO4BFLQqHlkmOHDlhx6uzczwkl8EV
+zlHEMg+37m3jtV2RJ+zfEKtCw8duTwKDgX4MF3KKQ+uNmA2IMLUd3XAGYxS2UceyFi7UFZYzFq5l
+FboUBPIIx5zlS6yoUD7SN+B0GliyDedMxujs/UN8/URStRMW9jk6x8stEed59Yufwe42ZfFyFxUN
+oS0vRNtj4Ow52/O4D/8zgP8WwN//ns/+DoDfMMb8vFLq7/D//xcA/iqA+/zfFyES9F/8QTdQCrBt
+Yct1yIzrWRV6FHgZhBGqJvduW7B6fNlsMZ1u3dlBRm6988Ucmiy6+awPi9Lge3sdmJksyC26A4vV
+ClYoA5xeLuD2ZaUPXAsXAUe1LrE1JGnLUMz53/juE0QdUpTZHViQCa1WS3RtmfCLeonslNkFUn+9
+eXAf9+5LtPhOK8eQ1wvaA2Qs526FsiBKM8NoxXu0dtGyZJH3em3YrAjtbA1QMb9f8oVO6wk0Kzxt
+P0LBoN1iukDFXL/XshASiDUKqUXptdBryQZR1jkSYgGswEWQ0XXRChmrJ/MVWbKVhuk3lagKNt2H
+0HbgslARawPU8rlDUzsJ0qaIEsskhaJrY1saI2p99nshPKZH3jsWSPHDZyeYERex1QnxylBerN79
+FmYnMnZ2q41Wl3UuPRnjq8sPkfAldfM1Dug2qskIJ1cSjD5dn6I7aejxuUmpLpwGb+AqgJtXWeTQ
+3NR1I5aT1xum8cyqANaXOHBxo8txbgH9trzI+0M5ZNpKI2e1p9mzURIXksQKK861G4ywe1fGZXQl
+cz0aunAWzOo4IbyF/HtizqV2BgCyFJVqtqrnaz/QfTDG/A6AyZ/4+OcgMvPA98vN/xyAv2+k/T5E
+V3L3h+rRdbtu1+3Ptf1pA43bxhjqh+EUwDb/3gdILyutkaI/wZ9o3ytF79oWuu0Ag8DCnAE+2wZ2
+9yjTth0ipMT3wbZGeyB/uyyMQRmhoI3bDm3YLfn31XQKwxOqNehjeFd26/WSNGF5BxmJK1aBQtqI
+adgxOkpM1FE/R5eWxf2XxaS8/I3vIqBJ1o7aiGshwvjcgx5+YiQmXFiMEQXSv1uf+woAwPENXIeS
+YGqFgCessiw4Sn63vyeBNXfbB1IxOQ9iH3UDrx22kSTyOZIQCxbwFEQQ6DBoENGoyxJNMWBV1Bu8
+RVlqKKYIt3dlfCI7gDZyD+U7WFF74XxZwHYoGIMaboeErTkht5GDfignlNmqETdq1aYA6Er5bReK
+ufWQrlhRFuiSVm+S6M1pa2DgtmnyOw58nuivf1HEUjrDFtYU5fjZB5/FGz/5WQBAEI7Qb6pmywKa
+3BiGlk0YDLG3TWTqnR28WZIFu05x/oGkgS+LDOSFgR2J1VFUCXojsTZu7/bxhFD4VZ7B4bM0FZdZ
+VW5g9a9+4SZe25drhPY2AhLftPoDBJG4sY2bVKY5DLUeSs+DzXGDU2J0X57vC7dexp2Z3Lsi8tSL
+Qnh0RQLlAJUEQdPTFc5n8nexNuiIcfrc7VNnH4wxRjWKKj/c7zZS9L1WaHYHA3QiD+tKFryvLdic
+uMjvY0TNwE7Uh8cFhobrbrlAnBGwkw9QN0pPVY2CEe7T0xRRm1kJp2F79lAyLhFaHRwuxSBywj6C
+vnznZjfAHUKJu31WCGZtHB+LyRmYAAf7Apn96Tc+j7/040LhHiQVNGRRKF/6XlkOwGq4OoigUzIr
+6RQ6lJewNZbNxnZ9BEPZbMaqREXgVLFYIiD70TxfICKNfAMbqC0LBW1YA4WKZnmSLGGRo7ClXBCb
+BBfNplCh6/XYT42Ayfv1+gKlzThIYdCQMtcEgHl+CE0IdhR4cJi1sKsKa3JMuoGDlBu4a9G1MTUG
+LfnsopjDmpKjUTloU7NzEHXw8r3PAABu3+Li/ys+3IqQ58GNzfMX5Se1BGU8Q5bJpoUOs1m5QjiQ
++Yu6bTiMXbnKRofs0OOry81mV9KtSrIMFkFtr1zexbuHEpdZXU1QGhrarJKtTA2XY/GF7VfwudcE
+h9JuhfAc0qxbGjVdsySV9Xaer1ExVoM7ChYh9n53Cw4ZtiPXxaAv3zcp2ZaKDIrGfl2sURI7U4U7
+mFvvAwC0pVEwm/G87U+bfThr3AL+95yfHwH43n3pWor+ul23f8Han9ZS+GWIzPzP4/vl5n8ZwH+i
+lPpFSIBx/j1uxj+7E5aFfr+LgR8ipXx3kZRQ7F4+TeDQLHdMAYeEIyArs1uvURFebIcW7BWrGTtj
+HF59CAA4P7nA2BYzsH+TkvSmREATMI+LjTblJFk0omFoOwH29ihrTuKNYnWOcsG8erRCPZMTyEli
+2B+TAKOnoUk+osmnoKdr5Dy580dLBHss5nm8RsVMQ8ootdcJ0GJk3dnvwJDay9sqoE94mrUcVAsW
+WBFpV88SoFEnrjQM3YTaMxtuBVQ1Kp5GJQk9orsRLDJDe5YFi6i7XtdGfEmthnKN7EK+4w9pOqPc
+6EL4lQ3dpvuwNHAoZ28XGlaLVYdUcNZ1BU1I8HbQxhnn3dIpkkaTou2jTchply5KO+giYJAXpYYh
+9NdNPOgx710FqKh9kT0k92W7hE1aMlOskZ7IeJreAiol3DqwUFo8scmz4VYebArStDpDvEz5+csi
+w4SIU8N1alkWfFYtRk4bPrUjWuMuHGZdlKdQZOSVJJq0zFOEHvEyawVFaT6rrKDJVWEKCxbdDkP2
+bATZZh1WqkK6lrWTXFygfkYo/AMfHrEMz9ueJyX5DwD8SwBGSqlDiMr0zwP4JaXU3wLwBMDf4Nd/
+FZKO/AiSkvz3nqcT2tJoRR482Mi5iI2qkZJGHPUAiow9ZZHB0JTWJQEmWYbpTEwky63RCyk/3+/C
+PBTCjidHZ9hqyaBu3xE3IltXyOkPlkWx4VJ0iwCwZTHd3x9jh3Lvhow5dZYhyygiU7oYEIRydnKJ
+pd0IowDeUiYpv5RU0mxpcEZLrk7nuJGKWxJ1BljMJYPxzbeEyn2AHPv3xFzcy1+Gw0i2QQmLfVaO
+wWrZaBSKWdvSAVzyCxaoNzqIdfmJKGJmFzAEQ5UWgU6WjZz1trGVYc3FtihrJFTiik2GNsls2kwt
+lEWNqontoEJAwFKuK9RNbXsJtCgeY1g7sECMtNnbUW0g62WZ4+pCxuLJOx3MtuQFKhiRrx2DLGY9
+B6qN6pfthRu4+epqhtmlnEVr1lFUaw9BTVGU2IXH51hfejCsj9HGhU1lsIwAqjRf44JEsZeLKRy6
+P/12hMuJXK/ib2yl0CVJkK0T1E1WonBg6LBVaon5XL7/9ExcyeOrKXY70nfdsmCzniWNL1Bz0zdZ
+DcX1qyjkadYL1CtZUIUDLKZyjaP5McDN2SojFBQvft72AzcFY8y/9c/4p5/+p3zXAPiPf6geXLfr
+dt1eqPaCwJxrpEWG2ilQMI+tahtOxB28hQ23YWkBtdVAggkZLhxEpOkuVQpnyJyw39qcMCeTGIcL
+Od1vUKuw4/sbbcPUOFjxpCnzGBaj11u728gKUoObMftj0CZsNZ4nOKWVclVkWGspSnE/7CF3Japt
+yCh8+igF9gVYNRztwr8pfdaIcPh7chr9+ldFduwzt3wkBCQFfojhiCQygYskkedYr3KsKVTS4AeC
+YQ8T0s0BPizVnIJ6Q7KiKgflgpDnfflsvsyhCDUulcGCrtkiKZGT8VrnBq0+8+k8ocuihopl3DLf
+h9W4dsrAJqelDq0NR6FRnzBDOzyhTW6gaQllWY6KVtrJ7AxzyvfVtCTyvIZNGn3b2HBIOGKpCCUL
+3lRdwCI/JqcR9sqGZbPArAYCV1xCt+3DooleeRbUUk5m0LVZLqYAA5tW20V/RUp8N9xYAhmp8lRd
+wwllznTfQsF1mqfFJ3L3aYInx2IJfdDoVlo+rFT+Tldf2iiBZ9kpaqqYl7kNmyCrwKWep+fCkJey
+Xi0Qn0hF5Xw+Q5cFVvFsipSB9+dtL8amUAHLuEJtpZizUm/cDeHVYganklUexAAAIABJREFUNTDl
+4o8cDz7NdYtqTLWlUFEEtswspJsFGwPUiIjcFqbkzJtOyNHYtTfpn6xQiJNGCaqDdiSLvz3chsuX
+E0PZCAZbY/j0jaEusCLjz29/K8MedQXfsF34NlN5joBV1q0SViwv73uXHyH9qvT/zJvg//m9twEA
+51OJy+52XoMnVdZIz5dYMktgBVMkNPPXSQrTUFIxnXA+jZEw9eh3+wABNnVt0EATjaqQcYOYs3Iy
+WmWoif6sqwo5N+FZvIAh6tHJNAima4YVQWkhJ6lNEi+xbqoE3QhBQ2Ee2JvUIMg7icqBKbn4sYYi
+aE1pGyV3kMnlHJczeYHsXJCGTltLuhOAqRxURaNJEQN0hYIogmZFYZvut4njTS2NUQEMYxx6XUAR
+GOdYCjU1GHNWuGqnApjVaGuDKWMGYdRBRWBYVTWHlMJeT2A5HWu4+ff55QXi1YLzk+LDuRwAa5Zp
+d0yO4fiB9C2tYZQA2arzDDWJYhf5CjoVFzGnixJ0AxiWsFfZeuNe5PUEE9ZH9JQL7+C69uG6Xbfr
+9inaC2EpVFWJ5fQKq3WCWjcstMCM9QwDGKyZ6AwtCxXx5/WUQSgUyKnu5Lod1FNqLbZKuMwjvLbf
+wxEr9R6eiyln7Bo+5cnrpYLDuoMyynGLFY44nsF5g/nm9svyu5UFkECkhwqnjJjNO0f4YEIK9K7G
+fR5TLebHw6WNp3MxEx8+eYLJ5YwDYIGYIOzvSFDzR1+6hW7JAgOvwupMAmdOV230FZULwGH1IWsc
+Lj6aIB+KZbPTHYIMXbADjZqHtUmxgb4WczF9J1YGwwxGVxXwl3JeBKrAWZPhMMDRU8Kjd8RF04WC
+phtgKx8JcQplXiHl8vJiBYsS9YoVe1GpsaT+p7YCxOSyCB0Lc0bnE73Co2Opcn12QbWwpI/ujcbt
+yjakJmZVwrgbHwWaNS0tnvL5IENK4ZRSrzB9RFr+fR+eJkNzCTg0hQgFge8pjAKZB3vi4NyRtZNW
+BfjYyEkAoyyNOqBlVlRY0LrNB8CaY7g2Cca0tixm1CbnR1A2s08Xx8A26f4LC06bJ37tIm/TTSPF
+XJ76G/mBwjYAVcy3WgbZkYxn/3Me3OCHo3i/thSu23W7bt/XXghLwZgaRZkhXieoGRNx6wRrpgAL
+K4RR9FvzDDkJVjWVn3MHmzRdbwBYZEnOa4XOluzGa8SwPyIRKgN1edHZyJgVab0JxJmFwrRNKrR1
+juBMYhuqJfRqSTzFfC3XMiXgeHJaHV6d4huER+9ENxFcyEkRunLarWoXC6a0DqIFfvLH5fTLnA5Q
+yrM8OxZLYpLPcHUlMZA9bwcBmaidzNo8X+G0kdKfTyHPlKocmug5r23DEHtRl2rzfOhbKH2xYgr+
+e2A0FC0FO7RR+nIqWWsDEghhMGih38QE+F/d/kQXIY4zrBnQcWuDgqzapnbhZU26l8rfbo0JU8or
+ncMnFsCzpGoWkJTx40eSUn56dFvut12gjmSMA7+ExeDoNMuxPmeBklpjm4hF6D77VqAgw3Mdr+C0
+mTrMbqLtS7DBy9WGFDWl4EpealiOzG/aAg4/kDlJkxVcBhUzxonqusaUataT5QW6tFKyuUGeyT0c
+r0TIQDjanI9FDjfmvM9SWBOJa/R3xxvWJMdrwSV6c5KJhbk4OYKXSx+cG1uwQP3McQuTROIWTtWD
+lf5wgOMXYlPQlkLUdWCcAPOlTEDtKlSMvOaeAS00VFohJVDJMOBWrmsEpKUqZhkUA5FOodFbNy+L
+hy3qBJ6vZIJOLi7Q7nLClUaylB1piBozgoWqIsHl16nT2JWXLTeXqEkrlmkX7UDwBvXqBCu+TKlr
+Y/QZgeiWmfz+6398Co/Bx1vRDtLHBKxsFyiPCGOeiHn61cdPMatlYb5yfom7N8UF2dlpb/LU6cUK
+U2YaMgYRtw9GWDfkHYW3qSmwageKZClOGaE4YwRuIM8Zpw66tIfjNMMVXa0k1xhQGn7YbWFn2AC/
+ZOks0zWyRF7MWVFhPpO/bQsYBA1M3UVZNZTxct/S1FCNq5E4sAj9LZSBJgw9TUpMm5L4XDIL7SxE
+i6zUjg6xJD5jNnNQEkI+toawxCNA2Je+t70S8zMJHj5+dA6zR9KT0oW1I66iNfBhmvoXUqLVlYWi
+EdX1Sizp2hhjo0c4dkxx47JMcEKofOykGHflJfUjF+GVfGeVz7AmMYxvpA/7nTto7cpGN967D4eg
+t9JkyLl+y2WKKpX1O6Ur4o0jaG56ynb+3/beNNbWLD/r+6133vNw9pnuVPfemqvngXYb07GhIZ4Y
+FCmKjIgCwZIVCQkSRSK0/ClS+ICICEQiJCgkSIkDTrABY5Q2drcVD7S73W33UMOtqlt3Hs685+md
+Vj6sZ+/qS9x0VdO36krZf6lU5+5z9n7ftd611/oPz/95iFRq8f2Qix9wa2867pMX7w6nsAkfNrax
+jT1iT4SnYDyfMGlQ90IQZVqURHie2z0X+CDoa5xUWSzc6Z6pKadTa1FVPb5drUO44rmPKLXLT/rH
+a/m3uqS4KkWJlzl3cbwsGQ3dqTOeB4gpjeHNMZlOt7gjXERQxaphqGINnhq08vkeh8dyL7t9ytg1
+R2UiBTn1jviRKx8BoHtum4Zo1W7cPOTCU27Hf+klV4eMX3zIF778TQB8m1IVG7JJq8xyNxeHwz4n
+cldroorzuo11w5eToBMrdbHAEzVytphjKu4kXJG3XGjEBHWRceQBpcqwJvQYqmlscQSnUyVHVUIM
+rMFqjrMC5iopt4MKRqSjgzRnadQFuFRoQMqh4BQzb04gF9wLKvhKNnvGsBT8eXxL5KntGoUarTJT
+dzx6QDrvc/U5zW0zoqWS5HIhtfJGgJFM37mLO3zrG68B0Bgt8dSrlM8ylqvmJs1V7sF0tUa8Kamv
+ECvxqVRW49bYipL52N3v2T2Prub4XCuiuXvZjenkiMld57GEz7hn3diuYaRBubRdPIUufmxIhZy9
+l08YveyanKrqruw099ddq4v5kqnCh9k0JY9X1HpNvOqq/e2d2ROxKYTGsF0JGGcxO5fcICs2JlIX
+mh0uiSQm0m5FBIrPBvoC5rMhSy3M2bhDPXEubrjlkcgdr4yOeU5dgNlTclVHJ/RP3AKcDhcEotae
+ehNO7rlrHI1nRKoLR4p126UhFT6g3YxoCQZNt87BfbfYrh2M+Ky6FbfbrtvxM/sTjl53X/Tpm018
+bRZpFtPfcnmHxcBtWH7D8JktV/Vonjeci4RN8C1TYe6X4yGnquMbrexup04gtuPcpg6fgNS0BBDy
+S4OJ3Ov9oQRg7Hwdv9ZjgzdbgT1SMj2Hkoya51zmald9DdNiLVhSNxF+1V276vtoD+VgOGSkcGuu
+DT3PcqbCG0wmhlQ1+2fbTa6JLSmsQahn8tahm58Xpy9SGLf4a0lAJXHt7OZowb3f+U0AzoqIpOnC
+rVhz3+qkFMrR2LJgay5q/F5nDXxbpHMWczcfqXpDTAjTe+61o1vHbFcV7+/UuPX7CgMEpsoLsII8
+v356Y80a1e3Fa+HkS/vPUptLlUsiNEnYw6iaU989T62t8C+vYM+7Z1174z6Tmvui+zPhc0pDdug2
+6SKYsxg4jEucVSjFiu7tp/jZRktyYxvb2L+DPRGegh94bHUqNGsxcU16A5OciZhzYz/CyoUvpgtQ
+f3hNp4hfpCRqmFosjkEngjkxZKI5C7IZza47/cKGO+36qVmTUUSmoCXatckgZaLMeS/3KbV3Fvfc
+zn5up0FtJZbSa9BbrkgMLbWZ5NZMwUiSdeevuETWS3tXOTLOc6kfhCTPu5ChMe+xqLmmqZmITmbJ
+HLOlE7OXYORGz7P5ms8RuySRJsWq4SYJoaLHmgVmzX9grGGFdrVtMEuRnag7M535pEoGFnjE/kob
+vqSpsKvRqnFOjMFV6T9MhwNGEzcvs3xGIgGUILTkSn6WFGs16uki1TVyCsGnx+P52qNpXazSE31d
+WsYECl08QYmn6UPsQrRrWbpm9j7X6ZCMndcUnniUiT5PQihh6LHiSguCBbjCD3ERsBBWZZYuGUmz
+MpeL3p+m3DxxXsrtoztEIqfs+DWQQE9DrnpWgq+u27u3jrm14yoK2+OInpLUUT2jLo2OsxvumZ+e
+fZ3OZQd/z5oBmee8HC89pChEejY9pdIVMUzqnv/CLJn0xf3oRTBzayesF7Ql7BHFHaJVV+U7NGPt
+u+ZH+b7bBz/0AfuL//TnybIJX/pdh/3/7X/xRY4PnKt9MBuyq9bZWqVKXdyNnR1HblKvNPFWIB1T
+Mld+IcsssXgJL1zYotV1fx+pUpGPRnzjm18B4DdeucX9vltgH/vYp/nEJ91DrJ/v0LTuIb36JVeS
+vPqJT3N419F+/4vP/yuOb7uHG/pLVDnlhfPbnO+qk1CucRT4apyGpMjXpbdK7W19RAQpzgrLUl/p
+cVqueweKqMrkxC2Kfn3I177mQE15sfrGlxj1HwTJR/jQFbeolsslc4mWUPBtXXta2ElCTTmOejOh
+VXMu5yK1lCtgmJfx3LNu8T5zybnljRLShfviHU1TJnJtj46OeP2+7i0zzFU+euryZQA++HTChefd
+N7NX3WN0013jv/mXv05F+pDWs+Sn6leYus10MBxRVfmyVq+y03PPptFsgMhgssmUUoCkQuS/y/mM
+QmHQ6WiIr41+PJ4yVQiW5jMSidf2nv0AAJ/5j/5j/swfciHKIvSoIMDRgwEfvaI1UndzZdOc2FdZ
+sNlYca/gGY9UFZrRw4fcO3Jr5/C12wDcOrrFZ3/0TwAQJwnXX7sFwO23rnPwpoO//9ZrrzPVOhqt
+8jpUOTkRJLrMOVM3qy39Vec3XuFR634GgJOTL37NWvtJvottwoeNbWxjj9gTET6ApbQZ/aMzbr7+
+DQCOOSFqud3+mUodRNixVa/R2harbdu5siYs8SXwEno+M/WYDyZ9Gh33N/VGj0bDnSqRwEZJJ6Kx
+5XbRN+4e8cYdt+ueHdwHI33Ifotj4zyBqO1OorPhGdfecLv80d2H5Eqitf2Yq6qLX97p8fyeo66s
+CX5bT2LA7fatqLZWMw6jClZVF1+PZDJKWUrt+ujojNHc/Xx3WdK4rERiZIm/rkSqKg7DabkmSzHF
+y6RytefzjKWqNZUkRnlGYmXIq9UKDbEo29xjrjms+xHnd91J+VQr4fmnXePO1lPuRAxpkU7dSds8
+6XOoprNaErBUQ9CNu0dMRm7c04dujtO9C0R9VWWmD/H1rIu8YKJkazadrcWBAvWfVaOIQN2xzaRC
+RbLulUpMsYI8ByVLcVquk7K5D4KHLxdLCv1+MV6QZVLjXmYs++7ap9dcpv/gjZe5/bRz2+v1Kg/F
+mu3HMWGxIvmRgrc/py4uD+MVeHblveWUCgvzYERL4Kt+5F7baRj8obgsFsdrbftWvcS76D7v0nGV
+u2cKq+YrIskSK6KWIvDWDV+Bb9eVuSC0lLN/zbuxjaewsY1t7BF7IjyFsoD5sOD1L9/m2r1vAbA4
+mrF91eUAWl5lLWzaavi01axUT1z8V2mD8QUsKC3B2MV4W0/vcq6rxFhk1iSllcpKFyFjt3Q//8in
+7vHK8ecBGGRn3JDi8/ROTPaH3Clex52Yb73yBt/8yu8DMJ4NSVSy3Nlu8tzTrnX26gstzgsy21Fu
+oRN3CCWaazIPKwSatwzxpFOQT4TBKJakp6Iru9Lk/l13Gs/vHNBU7Ly7PWF2yV37985WTDtQlhIX
+BZbSSMiyHJSYNdYSqTW8rlbh7Qtb+BJ1OTocEgtv/uEfuMTz4oC4urdNuyWFZnlN5cIjF2O2F4ZU
+CzfWo614LbA6G2ekgfMgssCdygeDKdtfl7r0h0Zs51ICWBiWmVCoyz75VJoLUpFuJ1UaLfes93ca
+hCJVTbw5pWAWrXrMsTgefNG1ndgZSyXiinlKtiJoLZb40t4uKMjSFRu1i9sn9+/y+uvu3lr5NvZp
+975L3nPEoorLxWAdJiHkK3btGawwFvkUq3g/qXdpt9RgJ5boyeiIqHBj+sZXvoonZu4LT52nUDPd
+YJiyjFyObVm6+XkwKOiKq2NmLIVKo6PSrvMZBT52Rb/1Du2J2BTyPOOkf8C3pm/x4LYQLRNDthIQ
+qXeImqIIDxIadefOne+4zSHsVMhnEhlZDqjp9e3z2/QS55ZWayWeEoxGWWOTFwSSNf/ED3+GP37m
+EmO/8Zuv8saXrwHQfyrluaVzmSee88/euvYaJ6Jay3JLQ1mdsFmnI3x6I61QWaX7F24xBmVJoUy1
+V4D1RSGXxmSCWOejlSrzDLJVtrxGbFQTDyOGBysKtpLeebdRbTcdDPjsIGA+l9qxn5CtWiONxeiL
+XniGTMnYiqDIjVqbTLTuJj/hD3/YwWT/vU/9EXYrbmPt7lQw4jAsV51+YbniIKHYheVY+pHzgm5T
+XXu9GrYqIJM6W2/dO6Imyv0P2ReY+OpWLVOWqgCkkwIjNhdjJTJTrVCTrmgtjEB4gpqN8CorVukQ
+E7k5WiU4x6Mh5Yo5fZ5hxTNRxCGl+Ce8oKRccSMoKTkc9nnjrtMH3dp+motzlxwdlKeUhRKioajy
+yojRUiJCwwF26sZxfHiXqrRHdy9dItJ8NoML6zEPH7jD8Oq5PeIdtwlFgcf41K3JeqsJ91aUdQJk
+VVKSFRdEsMTb1oY0ydZQ8dxL1tWO/uvvjEN5Ez5sbGMbe8SeDE8hXXB4+xqL5RmFiD3jXofzL7id
+NMphR/iFlqmwp376dkOeQuDRV0dlWKtQq+tkq9aoCXlYb9fxdMqtmqvsYk4osZEXWs/yM1Xnzo1v
+/W2+fuQYlIJ0yf1jt1tXG87rGA1P1izCoTEkDaHV9qr02s4TaIYF9XiljyhXtsa6dFoulngt3UhU
+ASWGrMp/JjZ4qm9GhUdTT2qrGfOWEpujs4AjwZjrW25O/HrC8Q3nfto5FKrB+5G/LskVpqTecPfZ
+3ZPoTStmIOaip/Y7/MSP/zAAu3sXqCbqcOzUMOh0zFdCPDOsGrSIGhixFofzlGbFMU7ly5CKOAAe
+5g7TcGt0n0wov2FeUGm690VRjhGxKWWOr2t3t12ouNVO2GrqnqtVKltilw6aVOSFFUVGFLvXRyqz
+Bs2A5dBd47TZ5/REJcJFQaqybGwDJgo7PLNKwPbwx+pK5JQ7gmCPki6e55LRge/C3HQx42wqD3Jw
+QCnEZiVZsrPjxGwq9S62VIOSEpTe0tDccSJAnfM1kkjdXHlOreHW53OmYCxv0QauNH44Ga9xLd2i
+ghUHxuTwmHhP5L3DEN+utCTfmafwRGwK8zznteMzTu/mLBX/vLh/mcsNF3tlswFN0WA1wzpRpAFr
+7SyzIUtlghOvTVOuWpJERGjBWh9Psb+nrLhXa+GtyDQqVc6/5EhUfvInf4C3/nfx/S1yjh64/EK7
+6yb9bDLHKrMcxx4X6i7D/2J9h11lw5NgTKKKyKqlNc1Zg1GCsE25lHhJ1cOIEdkXvXcxC8lXmPrl
+kKTmxtxazulJOOW+XXJFbbE3BVcOFz6DREpP0wgvXN1nhUUmurIgoC3Ny+2a2wgb1YSaNo2nn77A
++QvP6X05YeC+kF7h42l8K0o0bHVNh28qS7xIoVRwjsqR2wjyyzGnijeORIVXmbTw1WU4yoo1LDdL
+32ad9kOPbW3El3cdLqJXDWmIu3Or06GiULIS1fH8FTM3eKV6QtSf0G438EUBfzRscdp0c3Q47pNJ
+wn4wX4AVLkK9LUE4wg/VGWpiZrfcmJaVFOOrBVoM5Gn/jFxgudCvE6jlessPqCr/ZZY+uZiiC/U1
+eJ5PEbqDzItDPIUzJohBa6e78wIvfMLd85bm5PcPbvDwVREKJUta0r+ZeAP8e6Kb6xbUFu+u92ET
+PmxsYxt7xL5XKfq/CfwpIAXeAv5Ta1261hjzOeCngQL4y9baX/lu18jTjJM7DxgeD+hdlGBLq7WW
+ag/zgkIdbqnt8/DAZbL9mbr3OtE64UIcrCnGsjjGaOdfjmbE6kMPpAAcBwEISegtUxLpQz77iU9x
+4YuuTv3ajTvYnnMJIytqr3SBrxjkwrkal/blukeGik7ETjcCodgW0j6MBgWelIPn09laHzKeNonO
+qzkqVaLVZCB9g6Qbs1DzzFYUkClhOKuF3BBsOBq5+xnMliRKNM49n0CoyMh4hDWxK5dmTb6yqt1X
+Yo9QTWVXruzjKYzxaiFWnhXGA2XtqYpsJcuw5UqarSRM3BxGUZVqVZiFg5xd4Uy+Ks6DmbWUY/e5
+2d4CP3Pvy2bTdWWgXotoicikoUpNnZyWEo2VMFvzOvhpCg0l2pY50Sr5q6x+tW4pVk1eniUQAjRK
+SmYad6NmiFU9WHFunN66RahUfuiVTFoiW51ZbKoOVAEEjidnGHWzti9WSMaJps1ihTkps4LZfUeA
+MpI6urFTQjVwFZOIvsK4kJCFoP7mbEws7Yyqulk7eZXrovebzaaEWnvFIGUiusA4rVLuvruA4HuV
+ov9V4HPW2twY8zeAzwH/lTHmJeCngA8A54BfM8Y8Z+2KEPwPtixNObh/nyj3sPkKqpoxExCmUYHa
+iqp7AQsRT0yXq/JdFUTp3QwaFL5w8uMJE7liaTqhIdx6bVUKjON1XGeLJZ7Cks72efBUkpo9xL/t
+punh0GHgy6IgWrVqxw166r70g4y1omsWkYpheraQik9hSJYixfArGN1nUZYUakX2cOPIwgXBiuLe
+i/HtSj8TopZbeK1hlbYo0x9oTLN8yVK8fV5eYJSdt9aSrkpngcdUnaaB5nUrTOhtudi4094lV3+F
+9atEisvtYk6gcq5ZxW55SZEJNZMailXbcxFgVOqrRD7bAnX5Kyn6+YS58gvNaYyJV/qIxZpSPbAx
+zVC8irli/aSCWVFKewG5vmxzP8VOVF1ZWIzCzUh9GwEBJhagJ81o16SPGRi6qmwMJ1PCnnvucxGT
+zA5OONMzaW5ZMlHOT8qcTC3cntrM8/GUQsAqrx/gSTAnDNtkE7cRpJMzxtr3j8eCgfcPae+6sm9Z
+DRiJtCXIUhYSjV2mcx6oTH6qjtEHb95jOHQH1uHZnLmEb6ZpTiYY+zwuYfLuSpLfkxS9tfZf2dXW
+B78DKuA7Kfp/bK1dWmtv4pSiPvWu7mhjG9vY+2rfj0TjXwR+Xj+fx20SK1tJ0f9brSxK5v0JXi3A
+ipjjuH+HZ1Tmb9Q6NATHDeOAFTltoaaXIEyoKFPvh45jEMD3oFRx2vdTykSJKPHnF7YKZqr3NQjl
+NbTqLT7xktu5f/vr32IuaKtXcSebZ836iA0ij2ZTas6VklBueTErsdKR8MS47HX2samSi/MUb0t4
+gp1dCrmJKNkXVxprDcNiOqZU+BA2KmuexLlfcKKTq7kUoUdkuKWPKvApVi6lZ7HqqMwKi8rYXNwV
+sKoVs3fOZdOjSoYaGLE2x1trNoBZFfsVtpAuIVGdPjJYzVWZz9ecgq0LVayETF7cd+Cnl2/dZ1KI
+qRhLtbFKytq1VoXFYhDoR68lpaHhOfe5Ehqq4mK0JiIvVzwDOZ46A+O6PCwvINWY6+06WV1e0zwg
+bLjnc85vc3C0Ot3d2npwdsZJXx6il1ORLqNXDSnF9em3lQSvt5lOVqHrEH9FzlMGLK1O/HJGLpGc
+pajrYkoOHzjN08DWyaU0nRYz5qIeXI77nBw7720gj2FSpHgK/zAlVs/GYomUrDVFSZy+h2Iwxpif
+BXLg576H9/4M8DMAcRyRYciHGaZYuZwBjVStxdQI5ZbtNPcIBLhJtl1JcjEZEIqBJy9SchFmmmxK
+ILn6eu0pQoFMAnE/en60FgLxwiqrKMcEPlef+4T7vPyfs1SYUhYrEVQPf0WCmpVUhCCMRhGztvub
+abZcu7mVQiIdjZJiKnBLEWNGAlO1YyKhDVfCIsW8IA/V1ly8rUtYpBG5ynRp65ig4+Yo8Vcx5IyK
+AFnzsevQA/BMsKbBL7OMSuYWfUcsVYHvEczd/JQjj3JLf7ssKOQ+g4+NFOPOVmVD1tqeBJYidZus
+LS2lujX9RoOwdAv63MXLbk6+/CqDsZuLK9nFdb9G4HmEytfkWU6mLtcglwhsZCjV7l1kEJRuboMI
+FnpOOcUazOZV9H48Cm3YtqwhLhgC36Mm4hQbLPHFZNTbFxL2fpXZN1yfy/TohLkAZ2bPENaVmypW
+TF+GqOU2m+qyDZ5a3Auzfq525pHsuLzZMy/o2cyfY6qS+jIDoxxNQYO5NpYjL6N+KiUq9U7YvXOc
+T9y6vzE+4p4qI4fTJflSPSMVn1r8HhG3GmP+Ai4B+Vn7dv/1O5ait9b+feDvAzQa9fe/f3tjG9sY
+8D1uCsaYHwP+KvDD1sovcvZLwP9hjPlbuETjs8BXvusHWiC3jAYDQrmiraLLSm2jagPqOhEb1Spx
+19Wsg0C0XLU684HChNDHSn3XbweEyk4HsSHyVzJm6n8vgVAJp1lGIYyH9UNeetbx/VXDKhPjduBV
+FcEYi69Tp9oIKTwln9IlHIuS+1KxVvvt61TtpYbOc+6UWIwyakpKBtOMqKcEYyoBkbRkIXdxXlsw
+lgL3fJlSKumWJDFSJ2dH8OJRDOHcJfXmQQa5S9Z6lPjauxd5ztSu5OQE/gkNiMgEr4CR5MubOYtT
+5z5HSQ2zcPdvVvv4wq6fk7cM8OTFlMsET8nT/GxGIBDO/kV3Aj+7s89tjS99cQryCOI4oVinq3Im
+onCfF+79ZWpB2INyVjIXt2PgW8pYzNTjAl99J9MT57lUt318eXRBYPCUdC39gIU4LBK/RkMMy90L
+6gKt9pgcuXv4xmu3WOhZ1/sBvjwoHewMJmOGZ+7r0LoUELk8ItNz90nUY1Pb6ZBJPCisNDSm/O3w
+NwjJJWEQ2Sqkbu11vF3sJTfnc2XdL3g5Vqzb7bOIQLJ/+dGIVIzQcy+h9L/P1YfvIEX/OSAGftU4
+NNjvWGv/M2vtK8aY/xN4FRdW/KXvVnnY2MY29mTZ9ypF/w/+LX+4hdHhAAAgAElEQVT/14G//q7u
+oiwoFhPKZYEvzYLz3R4txZn3lycUb0pdeTIn04kXtVwJbX9vl92OiC/9CE/SZEUek4mHYN6/zt1j
+lxg6UOxZSWdsN91uvb29R7PnPq9b26a65XIYn/jwB/n8F34DgP6ZQzluNULUkOZOdHXWTSpLpkIY
+1gatt8udHffaw9EUM1ZZtLqNrakJKp9BJgyE4um+6TMQo9FsHjAT4Wlpczwh6CYjKCdufGPlJEZe
+TCkMQZAXBMpFlFmJQHqEnmGm5OHtY3cSxe0WZc3Nz8iU1CLlKEwDFO82TLYekzHK9i4Ksoq7z3QG
+qRLFw4f3WCqHwcIQWOdhFBJMPXelwcmrQjyeZWRVCbL43tp7G80zVhCJOHKn/MT4BPnbv+/PXKlv
+OMvX76slERWd4kkpstPc0BAKdV5MOZvKC5svGfXdfRTzObW2y7Xsly4KbkYxV6664tqdwyMGUhhf
+TCbYkbwYPbPbr96kP3Sf9WzyITxN+Oww5I1bjtC3VveJpaPR3nKJXd/MWCp3kM0CrFCtkSlA+YBK
+29LDJWkXvnJRwYyD+25N9tp1dqXJcZoWHAsqHhISrdnN35k9ETBnzxgqUYDZCwjVFvyxD34Ar+pc
+sRtv9BkN1Mo8nvLyvbsAhEoiPn2hwadeegmA5z50mXoiyfh5yPF9R1jxrVtvcuvILbzR0rnJl4Mq
+w6vKTmdPYY9doSTcmdN4xlUfPvvZz/D5L/wWAAt9kXxCQsF5rc1AIKThPKUihahiUpCrbjyTC9ts
+N8juuUXcrcxp7rovlt2qUd5299k/ckmtg8mcUq7sYnHKVLqEla2Eqmrvy+mcmXomIilPna9bTk+l
+kFUkRKsKRmkJFD7t16q01HJs1X/wYDDkwX03r3feuku94RbS/l6XnR13n5cu9djfdRh+MzrS/d7n
+9TsOi/9wMGHeX7UkZ1Sl0r3bbBMdKizS/T596RKzxUrxO+V46BZ3RkYsSetgCYFA1GXi3nc2m3J4
+zW1es8GC2yJkKWy55qDs1JtcesrFVWoiJRsZjhT+vXbrTe4eu/U07s/Wib28XLKz575YIymJf/JD
+H+fFj7u+hIezKa9945b7vDxnLvzJgxOXNvtn//ILfPiSg+abH1oiXRhe/39+h3993bXaD8spHzzv
+1tmP/MQfBaBZ7RF1VrRxS2Zjt56Oj894UEppejDH6np3D9yYo84cBsJeVEISdUP6xwNy9VdkWc5C
+QKZ3ahuY88Y2trFH7InwFIIopHfuHCY2WJUez1/apSnU3fXoPqG63vyx4eq+SzSeyuUaLTNuveVO
+uU63S007cRAUZCOh7RYe5didYqF23HvpkKmINjvVHdoX3UnqJx1ylSp7V17EC1eQXnUcBj4NJbKS
+0mOuZGZ1EXKijsjQLPGV4LGx81z62ZKTvtv5+6MpiTQnLlzICRR23HjgTp2DQYnXcb8vRglGDMc+
+IY22c32nxZRcXYtBx52MdloSNd0RZY5KfEF4je+BwrFndndpV93PS9W2o2XJ2VziO7Uud/uOgu7m
+6ZgXj909+2VCW0ragajPDm8dcXzqTtW7wxF+6cK4yA9BhCtneUEuoR0vVemx3WZXz+nenQc8PJbu
+RWaJ5IUFvo8RxNibqlxq/XX5NunF+MJCnEzGjMZDPaeM4Ym7z15XVGk24/4Dh/47O5wyFwTd5AVV
+PUvPS6gJQ7Aqb85Ly27due2XX8w4OBDS9WREXyQqn/9V50m+ev1VPiEJulbeoz902IMMD+O5uTg4
+m/Pafff60YnzCH7g0x/mxQ9+2D0/v8bg1IUgv3/zLd6872DMt+4e0g7dZ3TrzvsLsgp1NYF1lyHt
+ijAn0TEPFXrmeYGncOOd2hOxKVSShA++9DSVSpPGvlR8Wg1CLaDadMm1Y+deHx0OuX/ffdFX8Ntz
+T9W5p5r4peUZH9akRc2QC5nLds/TEWe3ncv7DU36KJ2zIyx+c+lxUTGZV7+Cpy+p97BkXwy/N5Zu
+8XfjkB1VMHa3EwK1r4a+R6YHfS88ZSDXrlAHZyOIefqyy1vMypzl0m0AuwcGf8eNJRRYZatmyAZu
+Y7obzTiQ8E1zusDcdONvXWrQ3nHjbgr0dOJlxGKf7vVi8lMXy5aFYUsVjp1OyLY2uhs33T28Zm5Q
+KASZzaZUVJXYvdRgWVEvxXJKol4KKwh2Ncg5r8rCyd0JXx+75/RwMKYiR7QRBFx+wRGHdDtuLuuL
+BRVhompRxmAg/sHFnEzdh71ulZbYm/a23eZdzeGhWJFOjmZMVOGZLnJqaq8PPYP4VuhpY07qlih3
+m3o38VhM3b0d5ymlGKd6rQaxWu1zdYGO0yV54DaYdq1L75w7kDzrIxZ57l5zY7b5El+98dk8xd51
+6+0gfUDcVnLkIGMgFanfO3UHWe3NKh2JwTx38RPcw43v7P4hDXWS7tUDBiKOuXniNrfqyCNWpSZ8
+9hJ7TZerOLfV4fWbp7rPnOLdYZc24cPGNraxR+2J8BSiKOCp83vs7exxf+J2w8nhkLjtTuZar8F5
+YaLqpk133/1NXT3o53b2aAlKvNd4ipqEN4IwYiGxkO29E5553mWdep9yiSOmhqu77uQOhwU755w7
+20w6TEVkMp1MUHmbUGQc7UbMtshUvBR8uXCVns/WTLgHP6ZVFwox1O9tTEu98K3CrjkaozClEEFG
+KPh0s2UoI3c6NnvnqPkrbYIJo/sufPC9Fm31+k+tKhVxxLLu7r2WxwylO2lzKFZJSWr0Lrr78JVQ
+DCdzfBGL5HtdGsbN4d5ei12dpJ1mQlRZJcQEZ67UqIne7tyOJTvnvJT28ZC6whV/OGOvo2YrQXgX
+viFsasxnAXZFG2dLCjVbhV6TlkKJOHInYjWs0hIas9Kus7/rxrSYGarb0k+cLSlV5egJ/Tef++QK
+Tf2KxwVJxdU7beoiZ+nsNYmkzHGcCz6desyETM0yQyLPZafVwaiTshG4e49aFUanzjuYjo4pBM2v
+VFtcERdmpVXnAy4ioLHjPKxLJqKKG5PnWw5vOU/h4WTO8xecZ/JUc4+pvKxrkh4cHtxjKk/paDhj
+T41yflylKZ6J0XxIJm7Kd2pPxKbgez6dRo241WZ4w2Xf34yHPLPlJvLCs3s0LrryzWx4QCVx2fBQ
+hB+FF63BH71zDRJx3Hl2QQMX41V7f5jaJRcnj9UvEAc+++dctrhe7RIIEmz8CF8EJ9FeQRXFydJa
+3G1V2O+5h5gXOZFKT92kyeUPuxh35jUczhiIJWRTmoC5+Brj+QStO6qtLjMp/XhKobfiLs19Ua6n
+bXp76i4Ma3hX3AaZmZBcXZInAm/ly2gN6On1evTVDzCnAI07tVPaTRcnP/2sEzl9OofhyKFtWpUe
+cdXNcVSNqacr8pITMpUGV5yJld4Wne2n3fuujrk8UhiQDYlqEvCJYkp1/h0eu2/E8eSUTD0cjWYD
+ywqCHqzzIF6ZY1ROqwuXvN2q0rDu3tLFklbPfVHm0xlZofJjuiDVRtUWca/XH9LWerl0pbPuDh0O
+MkJ1ksZNqLXcZ9f6KyCXjxEtv18p1y3zvQt7VLbdOtu64tZQ+q1XuTuTTID/kPicG9MzO88zksrU
+XnGO9ifcZtDsKh+UxcRikMptQaKuyyBMeOaKez69mmU4cDe9/Yw71Ma7rTV0vdqoo/2aZi3mvKDU
+g0nOYPnuNoVN+LCxjW3sEXsyPIUwprbzNEeTE46VUNu/vEW0pnDfpiVXLeldIhOiJarplMhHWJ1c
+vaBJUnGuoectCWO3Y5a5Jam5E3Y2kbhL0CSSrFhcbWKFX6CSrLs1k16H8Jy7TiMVVLXeXPfK182S
+rcSdYo1Gg1pbXsyiJAgECVZyLmk0SOXOTsYB/tTt8qGXEIg4xTfu9JjPQ2oC3iSBpdcRZiGrkqti
+MGCEtaJUF51ZakrabfcZzaRFXyesTTNm6mA8Gk6x8xXFu/vbWlhwpftRzWtCmLgwoDQj0pHzeMb3
+6+SZqgHCiETRNoFR0vXCJXbm4hgoPRAXgGcChta51Xnm5me6yJmIliyoVumugEe1DkZUaosyx/NW
+itfufpPaNrE6IJPGNtGqctA6x2S20ttcMhtoboMVECilKyKTwPPoCLQW+mMSeSxLJvieSFmqIl5p
+tkgFsS6CObWOe1+9u42Ri34ycfc7WswQ6phk2SJWKBUOpnT3tzXPdZQ7pKrqBb0mqQiA/EZMKPBZ
+NfIpRaXW3t2nJTKXXbUCzLMmy4ELsedhueZyGKYLLl+6DMCts5TThy6h+U7tydgU/IBWa5uTgzmB
+4vA8Mxzec65YIzdsPSvdSL+JJ8BNsUqr+lMmWgTVvQqe/HKv3gLlBrygIFF2Pag7YJKdl/iK6+08
+p1QbR740lEIYDu+OuNJ1T3H4UC5z4mFn7nq1vZBSJKbT8QxfaLtkt0q8VIlTfcpx4aEIhMFytg5H
+ktJSNKTVMHb5AmMLjPoPgr0AMxSVN3OGY6H/EsOpvoTzkSojdLmw62jIy0qNtubKywy5cht3zsZc
+V3mupfzL7nMXafjquOzW8TPdT+RxIjy/CXKWh27TTi66v53lx6Sn4rBMWgRbLiwx0xm+iHLzszGJ
+WTEPiZgXw9mx+9xqKyHUM3v6/DkmAmqV4Yi5XN+B+iT65SmdKxKYxSdaEcpkUFvJcS4K178B2L6b
++97WPsdTt7nZ+RI/0oFTrWCE0uyFbWwuYJi6KPN8hicB4cX9Gabu3teq75JEWk/pSt0qp68+itkg
+JlGo1drfoiZOzMAvyCe6t4ryMpMZkVrDA1Olo4OlLFMmK93I/hZb++6QSfsqz0Y1TlSqns8mpDrU
+wjxcad8SlCmLFVvWO7RN+LCxjW3sEXsiPAVrS/LccRZGiTu5FmdzRoJqWuvRmDlXLPMygorcNdFv
+DbIli6E7BdLOLmxrJ05zciX28sURubLvZbhSkwopV0mYWcZyxV8wmXC0EmUZG2iuGJpFF+55axKP
+chmSq2Ny4eXUlm9TiYWCFVslItNhykzgpnxmIXDuY1DtEIoqLi8d3BdjHIckUI+bLNZUveDLvQ5z
+S6TKx0ThQ2s/pHvendajxQIbraTcAwLBrvujEdcOXFLx0q44Mce70BCfRLbEqsMxXSwZSNTk5PAB
+1YvqTE1dpWZ6NmAxc6FBu9qlEbrkm5mnlDgvplwsKcWFWYrX8Gx0ykRw3qS6RahT9+oLLzI4cXX/
+629NGSvh2xfzce0swohj0wvnTOWlFWPLXNyG48GcM52wrXPufqr1DoFCnwcnp2uNyk6jsXa7Z8WC
+THObiHI+peSWWKnHYUEeuFP8aDzk5NBhPG6ObwGQmYK5vLjc7+OLDCbJG4TyLK13xmTornFn5Lyu
+rShgu+lC27gd0xYQLU4Lvv5lpwO5/0OGrQsfA8BXMnQ5PmGkbs/TdEk6dp97Npsyds4W/cF0HW6+
+U9t4Chvb2MYesSfCUzDGEMU+zasdBjfFlutXKZrudFxkObli52F5hl24nf1UEmRHo/FaQONkfI0t
+62TeWCx4cOtVAApvTDZXXVx16VbUIZBGpQ2gWLjTY3o6o2y63bXzkk/9627HDxJ3mnlFTi7MQn+8
+QOxvlPig0pLv+QTKJYRVd7KVscdSZDxxtYkfKrlUDwlOxLq8YliaLQjOKybPS+KVzFm3Sk36FWma
+kl93p1UkfYftzhVu3nblr34OOhCxQblmQjKeZZELDyKC0uFwQCiPpoi7rNoTh4tTXn/LwXIPX3+D
+REw/U7FYHZYHzEbuJO2O+hixbldKMMGKQShnJsTiQklLrGWp6837QxpbYjz6wFWiu+5Z3r3zkLkG
+sJA3Ng+WLIQI7IcZ0uRhsYDJSsdxDKk0LkrlUUbzjJv3XY7qpH/ITGXbQWtIT12LYR0CJV5zEcwu
+xlOOhq7hq3f5AqMz6T7s1Xnz5VcAmB27/Eye5uQqX9Z2qwSSv/MWNZYD1wi3WE44PZKit/Q4z139
+ANV9aWtEBZ0D5yksBgU3TpxHd+P8Abv77joVzfHSW5JKLyS0wdqjK4uChVTMB4sZnnl3kMYnYlPw
+w4j23iUmRycg7Pzu0112RUIyzU85O3FudZDDWICcUy3s/ngJ6tpb3m6SR78HQDQq+c2v/zYA5688
+RbfjFt629CXTSh+EdU/TkEnhFmxRrZBJVScIFmRN98DWAp6zYk2dPkknnFN40YoboGrGYj7FE76h
+mImirOK0LsFRu5UzCbDWlpjEbYCBvtzZ1GM6UEtvbohjZf1Ti9VCSEkZdNx99qeuHr+cDhiont33
+WWevsyInXSXUkoQEt8FNZhJ8vXNEpgRYdZBSqK17kuacDd2XaWwDHozcc1jo2VQCg+8ptMsKCnE3
+piUYCaWmmWVerrQdcWOe1RksV63THm2xNmO3CLeVaGzUiVR18pQATG3EgxP3rBMb4YnKPY89hlOF
+D+MxhaorhXWHwjK9yTfvuS93lGVEiaj18oSpyEkqUUDmr2jxFI5WQuZD9/yzJKRSdeFI2Gxz/b6T
+ARh/G4dlU4CzreAyUVvrad4nVy/FYF7luOnWwL7WUzp5iDdQu39oGIonufSGay7Na69epy1ofb3j
+wsO4tli3wA+nM8QSwGTicaIQpTAlsSo0iua+q23Ch41tbGOP2BPhKQRBQLu9RZGGvPBRwZmLNq3M
+nVCnd6+xuOBc/rpvyTJ34jVFmFnbr3NWuBMs6dWYX5dARnXJFUnUn+82aDXFs6A84+z4AUt13/ml
+z1B147jTwhi349/90hmzA4ey7KuENgmWBOqMnPkpU2EMJtMxoftTomc7aybl5czFDPE4oSIas8PR
+CbnIX8PbUzx1Lc6kJZAHGfO+JNqqPvNVAbyoUqw6+Co5SyE5d6IVZqNGsCsk4SLkUInWsvRJFP5U
+WjFb8n7SFUlLY0Y5dydKJ0hpFy6Z1e40uCAV74+/8AI9ibMEK0ht3yO8pFPZhKiPinQyXEuwFQuw
+vhKecn2TZkH/FWlLxJZ7p6r5376BL3xDs1dlKY68Uk1lw+MzlqJd6/oxceGunQ0NEzUxnQ1nRIEb
+61vXnTfSX0wYKhF3YbtLIhIWG/mMpXFRxjE1lWLHbYV+7QofqLmTmTIkfN51M3aaV3nlS19099l2
+Xprv3SAThsAkJWYgSrTKjHnhwoBZFNA8cfN8NHfhwJ2Xv/V2aTWqcxqIgDYt2BGc3mfOW0cOb7Ct
+hLkZzLCi+hssM+aCik+nJQdHEg+qbxHX3Wcfnj2i1PAd7YnYFAACz6fSjDk+dV/MWVHS7ArEkk1o
+zdwiPc7uk4oZt2mc29rsNtkXpr7b6RFLw9E2W+z1nLsXt32MYk6rhTlizKKvGvzYMupK5HRUpT96
+C4DfeuV3+a3fdqz1I7EPHzd3iFeLPy9oya1bNO2a6MOzVXJlxmcShUltQSaVqVoSYGor1ukYCQtR
+bbkvYOkvyJSxHk0XGBGrTBZnBLl7bLdGJX2510VP3I99H9tzi7h9Lub2F1R9ySFXE8dWtY5XFe6h
+uWqrjSlLVUP8iKAiBqmdCNTP0e2xFokxhdtUgqYlEJ2jt/TJ1VGZTTNyYe6zUcpYLc5WbbzT5ZSF
+Kifj0ZJSm/qt+U3SuZvnmkmJRbpzJor7auYTKqcyTReMY/FxZsFao7GVGCKRyIwkBlTMC5rijwyS
+gMlKWStL6OoLOVzM1l+guXAhplnBXHTP93RUo9lw67BTL3n1VP0mAqGZ0MNKP3I2HeH5bn2Ob9/n
+N77q+hWmRcoLH3VENd2u1kJeEijuLyh4cOLCi3Z7e42jGSxmPFS797jv8hNxpcATvH2WBKSKH16+
+fZMHR24DSKptnutdBuD3eJl3YpvwYWMb29gj9mR4CsZlx/1Kwutj7YJUeP6j7vTjoU9j1+2Y/mmV
+oXgNqEtLclTipc7NNN59Og0XglTyhEwaCHmZMj+Tp7ASnDma07soZNteh2ThXv/aqzf43RO3s1cv
+lMyUKCyUkT88HWKUEKyZkExSabYsCBvupPHDci1k4mnvXdqIUu5ud69HXHHXrlZ7eMrAzSNXTZgV
+GSsNjwcP50xT52o2z8e0xE2ZP7jP2aG7N6OE49Zek3t3nau6nPbWaLa8KFkK/dZvTZnr3nY6Ctfa
+NXzVsxfenLtngk3feYBR59/NfECr5rr24sjdT+lnhPuCfHtb1M+5TD6LgkgNP37FJ1FS9eWbjt/h
+jcNDdrbd+4Z3J0xS51Uc3H2FQjoM7DfoXHKe3uK2e19ULqnFK25L8NXVOCgLQmkt1psVaioJVdVl
+uVVrEMopmvoFysOR5FDoaIzjHCOI8aLvxj9cplztOgTsD3ysxysPVQ0JWjy85rzJlWxcI6yQ9NyY
+75gHPCeOhMJE1M47b+PhzT5nnrv4XuGeY9x9lkpHVbDc58WOW/fV6l0eKrF7+KbHsO/CsYkqJ+cv
+t0mkU1EvKnxT5C33jk7W4ZYtSup1UX6/Q3siNgULpNbjxqTPl3/j6wD8wL9/lU7wIQCaWzu02yLb
+qD/Pm3dc78LtOxLQ4ICW1KKSrKD5jHj7buTrjrvR8Yx5032B2soXPHP1Iltt92CsiejnbtK/9tZb
+HAnm/MzFffaefQOAk4cuVzGYvV16WiRVLmqPClp1Um0A2XJKVXG+J3j1qCiZKiSY9S1J7u6/txti
+fJXCBNJ6eNbnvno07MkZnRVxzLBGkbgFe3o64u5YMNjbEojJl2T+ijq8ZKXIkRclpXIxo9Mx9++4
++xic15fGBkSC4p70z7h+w117sczYFfFIIzhHLh7A6Uicif05Z6/dBODy5QlXle/Y3u+tBYKLIGK0
+cO7stUP3bK7fWtLccssvCxLSVGxSiy7Z1M1zTI5/ybW7N/dct+Bw/hrb4iGteR1GcqkLckIlNMKw
+QkNgqLjurnE6mJJJYHZ4PGIsmvy48DjfU0+EadIfur+5fseNKV8Ydv/UTwJQbe3zIYUapalxOnH3
+nKXuGVQqdWqlW1sv35pwbt991rmLTWrdHwLg3he/yrWvuk17/yOXAZhnY7zXRMJSmzOfrvgzq9RS
+F04G/hxPUPa9tpuT3aSOWbhx3hlPeeWe+9zBeEFNbdulybkniv53apvwYWMb29gj9kR4CliwWcnN
+20NmKw6CuMd9SbHn85CxJMUb1QZXzj8HgFkoo2sKqpFzucJKjR312MfPetTHckVfspgzAWd6EkCJ
+oFTFYXh6j+t3XXb3wb1XSUXg8pYX8NJHnceyeM41Gj38td+ilC5jWPOpqnJQrwaYvtz5eoCJ3ElR
+V2OQH+RU1LufVQ1VdXN2gzbVC87TmbwpLyezmDOJfrz0DK2qmI+nBW++7urto3t3KaYrDkZ33Vkd
+Rg+dF1CpL6hJfdh6ds3pW7LgrSP3GRffct7BuQsfXDGk011UuNJw8+Z1Q3z511FZoeK7+9x93o1/
+77TJeO7+9nz3Ms1dQcHNlEIu+Hw64OzQhYWHB87FnY9nLPVMO92QVKAtv0yx4s0ski6tcy7bf37u
+TruX79xEzZDs7VYJ9RyCLKPecKdjs16lW1EFJnGftd9LmYkTs9eskitBF29HxApdlvOcB4fOS3kg
+3slaZ8LZqejuL+3QUOfq+UbE7Mx5CpGSk9XdNpeectwK1+4erAli/thLl2hufwCAP/rxGl+bfQM9
+IACuXniW2g+5uTCLkiIT1+TIMFKFwp+HnKpT9ryo4YMo597AeXS37t5hOHaVFmsLPEH5rclYlO+s
+6rCyJ2JTWBY5t2dnvHH0KjsfcW7i/I7lSwsXs+3V2kQi6eiFJWHNfdkaXcXvM8OicC5czfdYit+/
+7W8Tt4QajHxK9Qd4Eh318pypyli3Bid841vXATi6d4K/LTKNnYgLH/lBAJ4q3fV+4UtfIRXxSCX0
+qGthkqVupwGytKRQDG8VwsSBR7JSp4pzfOnk+PmCULj8StV9eerlHYqqWxytCzu0YnfPJ/MB9++J
+gDRbUgnd6yvdyWBQkJ4pnpkHJIq/k6VlBWyzhWWuoPrGQ9dn8OHDi+xfcqHBMpjRbMkV9wM85UG8
+yIfAbaxhIHz+xZhg4Da9pT0hLVRlKHJmivfvD054QyHf8Ez9ECalpn6IZtgiFErn5rSJX1l92Vp0
+PbWrV11FZXfrJcqlXPtlQEPdpc3Y4Gmew8IyU1VmKfLfyCsppUIVzgeEYlCq1XxKrYFBWXJ/4mL4
+UmFCI0zYX7jxPYVHUZEAsIVIPROxNs3tXoLnubm/d23EvtCfk09u0RRS8tzzzzPS3zfVRZuVE2Z9
+la3ru/i4jWdwcp1b16+5Rzk5Zu+i+wydeUw9n6HCtaPhaM0FGvge9Xh1UFWpx+8O0bgJHza2sY09
+YuZtbdj38SaMOcaBME/ep1voba69ufb/D679lLV2+7v90ROxKQAYY75qrf3k5tqba2+u/f7aJnzY
+2MY29ohtNoWNbWxjj9iTtCn8/c21N9feXPv9tycmp7CxjW3sybAnyVPY2MY29gTY+74pGGN+zBjz
+ujHmujHmrz3ma100xvy6MeZVY8wrxpi/ote7xphfNca8qf93HuM9+MaY3zfG/LL+fcUY82WN/+eN
+kQjB47l22xjzT4wx14wxrxljfvC9Grsx5r/QnL9sjPlHxpjkcY3dGPO/GGOOjDEvf9trf+A4jbP/
+XvfwTWPMxx/Dtf+m5vybxph/aow0+dzvPqdrv26M+dF/l2t/v+x93RSM0xn/u8CPAy8Bf9YY89Jj
+vGQO/JfW2peATwN/Sdf7a8AXrLXPAl/Qvx+X/RXgtW/7998A/jtr7TNAH/jpx3jtvwN83lr7AvAR
+3cdjH7sx5jzwl4FPWms/iGOd+Cke39j/IfBj/8Zr32mcPw48q/9+Bvh7j+Havwp80Fr7YeAN4HMA
+Wns/BXxA7/kf9J14f81a+779B/wg8Cvf9u/PAZ97D6//z4E/AbwO7Ou1feD1x3S9C7gF+ceAXwYM
+DsgS/EHz8X2+dgu4ifJI3/b6Yx87cB64C3Rx0PpfBn70ceksRcQAAALBSURBVI4duAy8/N3GCfxP
+wJ/9g/7u+3Xtf+N3/wHwc/r5kfUO/Arwg4/j+b+b/97v8GG1WFZ2T689djPGXAY+BnwZ2LXWPtSv
+DoDdx3TZvw38VVCXEmwBA2vtipj/cY7/CnAM/K8KX/5nY0yN92Ds1tr7wH8L3AEeAkPga7x3Y4fv
+PM73eg3+ReD/fp+u/Y7s/d4U3hczjoDxF4D/3Fo7+vbfWbdlf99LMsaYPwkcWWu/9v3+7HdoAfBx
+4O9Zaz+Gg5U/Eio8xrF3gD+D25jOATX+vy72e2aPa5zfzYwxP4sLYX/uvb72u7H3e1O4D1z8tn9f
+0GuPzYwjO/wFnAv3i3r50Bizr9/vA0eP4dI/BPxpY8wt4B/jQoi/A7SNMatu1cc5/nvAPWvtl/Xv
+f4LbJN6Lsf9x4Ka19thamwG/iJuP92rs8J3H+Z6sQWPMXwD+JPDntCm9Z9d+t/Z+bwq/CzyrLHSE
+S7r80uO6mDHGAP8AeM1a+7e+7Ve/BPx5/fzncbmG76tZaz9nrb1grb2MG+cXrbV/Dvh14D98nNfW
+9Q+Au8aY5/XSZ4FXeQ/GjgsbPm2MqeoZrK79noxd9p3G+UvAf6IqxKeB4beFGd8XM8b8GC5s/NPW
+SsX47Wv/lDEmNsZcwSU7v/L9vPb3ZO93UgP4CVxG9i3gZx/ztf4Izm38JvB1/fcTuNj+C8CbwK8B
+3cd8Hz8C/LJ+vopbCNeB/wuIH+N1Pwp8VeP/Z0DnvRo78F8D14CXgf8NiB/X2IF/hMtdZDgP6ae/
+0zhxyd6/q/X3LVyF5Pt97eu43MFqzf2P3/b3P6trvw78+ONcd+/0vw2icWMb29gj9n6HDxvb2Mae
+MNtsChvb2MYesc2msLGNbewR22wKG9vYxh6xzaawsY1t7BHbbAob29jGHrHNprCxjW3sEdtsChvb
+2MYesf8XWgrDLYVpkJAAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Epoch 1/1... Discriminator Loss: 1.4026... Generator Loss: 0.5969
+Epoch 1/1... Discriminator Loss: 1.2856... Generator Loss: 0.8660
+Epoch 1/1... Discriminator Loss: 1.3118... Generator Loss: 0.7520
+Epoch 1/1... Discriminator Loss: 1.2754... Generator Loss: 0.7607
+Epoch 1/1... Discriminator Loss: 1.3532... Generator Loss: 1.0381
+Epoch 1/1... Discriminator Loss: 1.3217... Generator Loss: 0.8141
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="&#25552;&#20132;&#39033;&#30446;">&#25552;&#20132;&#39033;&#30446;<a class="anchor-link" href="#&#25552;&#20132;&#39033;&#30446;">&#182;</a></h3><p>提交本项目前，确保运行所有 cells 后保存该文件。</p>
+<p>保存该文件为 "dlnd_face_generation.ipynb"， 并另存为 HTML 格式 "File" -&gt; "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。</p>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+
+ 
+
+
+</html>
diff --git a/face-generation/dlnd_face_generation.ipynb b/face-generation/dlnd_face_generation.ipynb
new file mode 100644
index 0000000..4028440
--- /dev/null
+++ b/face-generation/dlnd_face_generation.ipynb
@@ -0,0 +1,2032 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 人脸生成（Face Generation）\n",
+    "在该项目中，你将使用生成式对抗网络（Generative Adversarial Nets）来生成新的人脸图像。\n",
+    "### 获取数据\n",
+    "该项目将使用以下数据集：\n",
+    "- MNIST\n",
+    "- CelebA\n",
+    "\n",
+    "由于 CelebA 数据集比较复杂，而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型，以让你更快的评估所建立模型的性能。\n",
+    "\n",
+    "如果你在使用 [FloydHub](https://www.floydhub.com/), 请将 `data_dir` 设置为 \"/input\" 并使用 [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) \"R5KrjnANiKVhLWAkpXhNBe\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found mnist Data\n",
+      "Found celeba Data\n"
+     ]
+    }
+   ],
+   "source": [
+    "#data_dir = './data'\n",
+    "\n",
+    "# FloydHub - Use with data ID \"R5KrjnANiKVhLWAkpXhNBe\"\n",
+    "data_dir = '/input'\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import helper\n",
+    "\n",
+    "helper.download_extract('mnist', data_dir)\n",
+    "helper.download_extract('celeba', data_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 探索数据（Explore the Data）\n",
+    "### MNIST\n",
+    "[MNIST](http://yann.lecun.com/exdb/mnist/) 是一个手写数字的图像数据集。你可以更改 `show_n_images` 探索此数据集。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f56d19e85f8>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFmBaZwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56d44e20b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_n_images = 25\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "%matplotlib inline\n",
+    "import os\n",
+    "from glob import glob\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')\n",
+    "pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CelebA\n",
+    "[CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸，不会用不到相关说明。你可以更改 `show_n_images` 探索此数据集。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f56d19266d8>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP7E6eb8E6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56d199b2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_n_images = 25\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')\n",
+    "pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 预处理数据（Preprocess the Data）\n",
+    "由于该项目的重点是建立 GANs 模型，我们将为你预处理数据。\n",
+    "\n",
+    "经过数据预处理，MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分，然后调整到 28x28 维度。\n",
+    "\n",
+    "MNIST 数据集中的图像是单[通道](https://en.wikipedia.org/wiki/Channel_(digital_image%29)的黑白图像，CelebA 数据集中的图像是 [三通道的 RGB 彩色图像](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images)。\n",
+    "\n",
+    "## 建立神经网络（Build the Neural Network）\n",
+    "你将通过部署以下函数来建立 GANs 的主要组成部分:\n",
+    "- `model_inputs`\n",
+    "- `discriminator`\n",
+    "- `generator`\n",
+    "- `model_loss`\n",
+    "- `model_opt`\n",
+    "- `train`\n",
+    "\n",
+    "### 检查 TensorFlow 版本并获取 GPU 型号\n",
+    "检查你是否使用正确的 TensorFlow 版本，并获取 GPU 型号"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TensorFlow Version: 1.0.1\n",
+      "Default GPU Device: /gpu:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "from distutils.version import LooseVersion\n",
+    "import warnings\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Check TensorFlow Version\n",
+    "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)\n",
+    "print('TensorFlow Version: {}'.format(tf.__version__))\n",
+    "\n",
+    "# Check for a GPU\n",
+    "if not tf.test.gpu_device_name():\n",
+    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
+    "else:\n",
+    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 输入（Input）\n",
+    "部署 `model_inputs` 函数以创建用于神经网络的 [占位符 (TF Placeholders)](https://www.tensorflow.org/versions/r0.11/api_docs/python/io_ops/placeholders)。请创建以下占位符：\n",
+    "- 输入图像占位符: 使用 `image_width`，`image_height` 和 `image_channels` 设置为 rank 4。\n",
+    "- 输入 Z 占位符: 设置为 rank 2，并命名为 `z_dim`。\n",
+    "- 学习速率占位符: 设置为 rank 0。\n",
+    "\n",
+    "返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "import problem_unittests as tests\n",
+    "\n",
+    "def model_inputs(image_width, image_height, image_channels, z_dim):\n",
+    "    \"\"\"\n",
+    "    Create the model inputs\n",
+    "    :param image_width: The input image width\n",
+    "    :param image_height: The input image height\n",
+    "    :param image_channels: The number of image channels\n",
+    "    :param z_dim: The dimension of Z\n",
+    "    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')\n",
+    "    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n",
+    "    learning_rate = tf.placeholder(tf.float32, name='learning_rate')\n",
+    "\n",
+    "    return input_real, input_z, learning_rate\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_model_inputs(model_inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 辨别器（Discriminator）\n",
+    "部署 `discriminator` 函数创建辨别器神经网络以辨别 `images`。该函数应能够重复使用神经网络中的各种变量。 在 [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 中使用 \"discriminator\" 的变量空间名来重复使用该函数中的变量。 \n",
+    "\n",
+    "该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def discriminator(images, reuse=False):\n",
+    "    \"\"\"\n",
+    "    Create the discriminator network\n",
+    "    :param image: Tensor of input image(s)\n",
+    "    :param reuse: Boolean if the weights should be reused\n",
+    "    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
+    "        # alpha is the param for leaky relu\n",
+    "        alpha = 0.2\n",
+    "        \n",
+    "        # Input layer is 28x28x3\n",
+    "        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')\n",
+    "        relu1 = tf.maximum(alpha * x1, x1)\n",
+    "        # 14x14x64 now\n",
+    "        \n",
+    "        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')\n",
+    "        bn2 = tf.layers.batch_normalization(x2, training=True)\n",
+    "        relu2 = tf.maximum(alpha * bn2, bn2)\n",
+    "        # 7x7x128 now\n",
+    "        \n",
+    "        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')\n",
+    "        bn3 = tf.layers.batch_normalization(x3, training=True)\n",
+    "        relu3 = tf.maximum(alpha * bn3, bn3)\n",
+    "        # 4x4x256 now\n",
+    "\n",
+    "        # Flatten it\n",
+    "        flat = tf.reshape(relu3, (-1, 4*4*256))\n",
+    "        logits = tf.layers.dense(flat, 1)\n",
+    "        out = tf.sigmoid(logits)\n",
+    "        \n",
+    "        return out, logits\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_discriminator(discriminator, tf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 生成器（Generator）\n",
+    "部署 `generator` 函数以使用 `z` 生成图像。该函数应能够重复使用神经网络中的各种变量。\n",
+    "在 [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 中使用 \"generator\" 的变量空间名来重复使用该函数中的变量。 \n",
+    "\n",
+    "该函数应返回所生成的 28 x 28 x `out_channel_dim` 维度图像。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def generator(z, out_channel_dim, is_train=True):\n",
+    "    \"\"\"\n",
+    "    Create the generator network\n",
+    "    :param z: Input z\n",
+    "    :param out_channel_dim: The number of channels in the output image\n",
+    "    :param is_train: Boolean if generator is being used for training\n",
+    "    :return: The tensor output of the generator\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    with tf.variable_scope('generator', reuse=not is_train):\n",
+    "        # alpha is the param for leaky relu\n",
+    "        alpha = 0.2\n",
+    "        \n",
+    "        # First fully connected layer\n",
+    "        x1 = tf.layers.dense(z, 7 * 7 * 512, activation=None)\n",
+    "        x1 = tf.reshape(x1, (-1, 7, 7, 512)) # Reshape it to start the convolutional stack\n",
+    "        x1 = tf.layers.batch_normalization(x1, training=is_train)\n",
+    "        x1 = tf.maximum(alpha * x1, x1)\n",
+    "        # 7x7x512 now\n",
+    "        \n",
+    "        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')\n",
+    "        x2 = tf.layers.batch_normalization(x2, training=is_train)\n",
+    "        x2 = tf.maximum(alpha * x2, x2)\n",
+    "        # 14x14x256 now\n",
+    "        \n",
+    "        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')\n",
+    "        x3 = tf.layers.batch_normalization(x3, training=is_train)\n",
+    "        x3 = tf.maximum(alpha * x3, x3)\n",
+    "        # 28x28x128 now\n",
+    "        \n",
+    "        # Output layer\n",
+    "        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')\n",
+    "        out = tf.tanh(logits)\n",
+    "        # 28x28x3 now\n",
+    "        \n",
+    "        return out\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_generator(generator, tf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 损失函数（Loss）\n",
+    "部署 `model_loss` 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。\n",
+    "\n",
+    "使用你已实现的函数：\n",
+    "- `discriminator(images, reuse=False)`\n",
+    "- `generator(z, out_channel_dim, is_train=True)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def model_loss(input_real, input_z, out_channel_dim):\n",
+    "    \"\"\"\n",
+    "    Get the loss for the discriminator and generator\n",
+    "    :param input_real: Images from the real dataset\n",
+    "    :param input_z: Z input\n",
+    "    :param out_channel_dim: The number of channels in the output image\n",
+    "    :return: A tuple of (discriminator loss, generator loss)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    # Generator network here\n",
+    "    g_model = generator(input_z, out_channel_dim)\n",
+    "\n",
+    "    # Disriminator network here\n",
+    "    d_model_real, d_logits_real = discriminator(input_real)\n",
+    "    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)\n",
+    "    \n",
+    "    # Calculate losses\n",
+    "    smooth = 0.1\n",
+    "    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * (1 - smooth)))\n",
+    "\n",
+    "    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))\n",
+    "\n",
+    "    d_loss = d_loss_real + d_loss_fake\n",
+    "\n",
+    "    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))\n",
+    "\n",
+    "    return d_loss, g_loss\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_model_loss(model_loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 优化（Optimization）\n",
+    "部署 `model_opt` 函数实现对 GANs 的优化。使用 [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) 获取可训练的所有变量。通过变量空间名 `discriminator` 和 `generator` 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
+    "    \"\"\"\n",
+    "    Get optimization operations\n",
+    "    :param d_loss: Discriminator loss Tensor\n",
+    "    :param g_loss: Generator loss Tensor\n",
+    "    :param learning_rate: Learning Rate Placeholder\n",
+    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
+    "    :return: A tuple of (discriminator training operation, generator training operation)\n",
+    "    \"\"\"\n",
+    "    # TODO: Implement Function\n",
+    "    # Get weights and bias to update\n",
+    "    t_vars = tf.trainable_variables()\n",
+    "    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n",
+    "    g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
+    "\n",
+    "    # Optimize\n",
+    "    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
+    "        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n",
+    "        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n",
+    "\n",
+    "    return d_train_opt, g_train_opt\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "tests.test_model_opt(model_opt, tf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 训练神经网络（Neural Network Training）\n",
+    "### 输出显示\n",
+    "使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出，这会帮你评估 GANs 模型的训练程度。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL\n",
+    "\"\"\"\n",
+    "import numpy as np\n",
+    "\n",
+    "def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):\n",
+    "    \"\"\"\n",
+    "    Show example output for the generator\n",
+    "    :param sess: TensorFlow session\n",
+    "    :param n_images: Number of Images to display\n",
+    "    :param input_z: Input Z Tensor\n",
+    "    :param out_channel_dim: The number of channels in the output image\n",
+    "    :param image_mode: The mode to use for images (\"RGB\" or \"L\")\n",
+    "    \"\"\"\n",
+    "    cmap = None if image_mode == 'RGB' else 'gray'\n",
+    "    z_dim = input_z.get_shape().as_list()[-1]\n",
+    "    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])\n",
+    "\n",
+    "    samples = sess.run(\n",
+    "        generator(input_z, out_channel_dim, False),\n",
+    "        feed_dict={input_z: example_z})\n",
+    "\n",
+    "    images_grid = helper.images_square_grid(samples, image_mode)\n",
+    "    pyplot.imshow(images_grid, cmap=cmap)\n",
+    "    pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 训练\n",
+    "部署 `train` 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数：\n",
+    "- `model_inputs(image_width, image_height, image_channels, z_dim)`\n",
+    "- `model_loss(input_real, input_z, out_channel_dim)`\n",
+    "- `model_opt(d_loss, g_loss, learning_rate, beta1)`\n",
+    "\n",
+    "使用 `show_generator_output` 函数显示 `generator` 在训练过程中的输出。\n",
+    "\n",
+    "**注意**：在每个批次 (batch) 中运行 `show_generator_output` 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 `generator` 的输出。 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):\n",
+    "    \"\"\"\n",
+    "    Train the GAN\n",
+    "    :param epoch_count: Number of epochs\n",
+    "    :param batch_size: Batch Size\n",
+    "    :param z_dim: Z dimension\n",
+    "    :param learning_rate: Learning Rate\n",
+    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
+    "    :param get_batches: Function to get batches\n",
+    "    :param data_shape: Shape of the data\n",
+    "    :param data_image_mode: The image mode to use for images (\"RGB\" or \"L\")\n",
+    "    \"\"\"\n",
+    "    # TODO: Build Model\n",
+    "    _, image_width, image_height, image_channels = data_shape\n",
+    "    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)\n",
+    "    d_loss, g_loss = model_loss(input_real, input_z, image_channels)\n",
+    "    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)\n",
+    "    \n",
+    "    steps = 0\n",
+    "    with tf.Session() as sess:\n",
+    "        sess.run(tf.global_variables_initializer())\n",
+    "        for epoch_i in range(epoch_count):\n",
+    "            for batch_images in get_batches(batch_size):\n",
+    "                # TODO: Train Model\n",
+    "                steps += 1\n",
+    "                batch_images *= 2\n",
+    "                \n",
+    "                # Sample random noise for G\n",
+    "                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))\n",
+    "\n",
+    "                # Run optimizers\n",
+    "                _ = sess.run(d_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})\n",
+    "                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})\n",
+    "\n",
+    "                if steps % 10 == 0:\n",
+    "                    # At the end of each epoch, get the losses and print them out\n",
+    "                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})\n",
+    "                    train_loss_g = g_loss.eval({input_z: batch_z})\n",
+    "\n",
+    "                    print(\"Epoch {}/{}...\".format(epoch_i + 1, epoch_count), \n",
+    "                          \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n",
+    "                          \"Generator Loss: {:.4f}\".format(train_loss_g))\n",
+    "                \n",
+    "                if steps % 100 == 0:\n",
+    "                    gen_samples = sess.run(generator(input_z, image_channels, is_train=False), feed_dict={input_z: batch_z})\n",
+    "                    _ = show_generator_output(sess, 25, input_z, image_channels, data_image_mode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MNIST\n",
+    "在 MNIST 上测试你的 GANs 模型。经过 2 次迭代，GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失，或接近 0。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2... Discriminator Loss: 0.4204... Generator Loss: 3.1223\n",
+      "Epoch 1/2... Discriminator Loss: 0.4827... Generator Loss: 2.2592\n",
+      "Epoch 1/2... Discriminator Loss: 0.4310... Generator Loss: 4.2485\n",
+      "Epoch 1/2... Discriminator Loss: 1.2898... Generator Loss: 2.9953\n",
+      "Epoch 1/2... Discriminator Loss: 1.3641... Generator Loss: 1.3200\n",
+      "Epoch 1/2... Discriminator Loss: 1.3577... Generator Loss: 0.6340\n",
+      "Epoch 1/2... Discriminator Loss: 1.5244... Generator Loss: 0.5115\n",
+      "Epoch 1/2... Discriminator Loss: 1.2379... Generator Loss: 0.8513\n",
+      "Epoch 1/2... Discriminator Loss: 1.3270... Generator Loss: 0.8379\n",
+      "Epoch 1/2... Discriminator Loss: 1.6648... Generator Loss: 0.4169\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTAoBAQEx/P/QKIxOKpbFdgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f570073f128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2... Discriminator Loss: 1.2459... Generator Loss: 0.9999\n",
+      "Epoch 1/2... Discriminator Loss: 1.4056... Generator Loss: 1.7060\n",
+      "Epoch 1/2... Discriminator Loss: 1.3388... Generator Loss: 0.7322\n",
+      "Epoch 1/2... Discriminator Loss: 1.1195... Generator Loss: 1.1036\n",
+      "Epoch 1/2... Discriminator Loss: 1.1910... Generator Loss: 0.7477\n",
+      "Epoch 1/2... Discriminator Loss: 1.2023... Generator Loss: 1.2572\n",
+      "Epoch 1/2... Discriminator Loss: 1.2410... Generator Loss: 1.6392\n",
+      "Epoch 1/2... Discriminator Loss: 1.2184... Generator Loss: 0.7372\n",
+      "Epoch 1/2... Discriminator Loss: 1.2382... Generator Loss: 0.6883\n",
+      "Epoch 1/2... Discriminator Loss: 1.3832... Generator Loss: 0.4813\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEphSMAwjgSkFwzASmFIwDCPB/wfZOLg8L5jSKgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56987909b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2... Discriminator Loss: 1.0094... Generator Loss: 0.9240\n",
+      "Epoch 1/2... Discriminator Loss: 1.1167... Generator Loss: 1.6473\n",
+      "Epoch 1/2... Discriminator Loss: 1.4142... Generator Loss: 0.5206\n",
+      "Epoch 1/2... Discriminator Loss: 0.9967... Generator Loss: 1.5393\n",
+      "Epoch 1/2... Discriminator Loss: 1.3248... Generator Loss: 0.5312\n",
+      "Epoch 1/2... Discriminator Loss: 1.0156... Generator Loss: 1.1135\n",
+      "Epoch 1/2... Discriminator Loss: 0.9628... Generator Loss: 1.9009\n",
+      "Epoch 1/2... Discriminator Loss: 1.0585... Generator Loss: 1.5717\n",
+      "Epoch 1/2... Discriminator Loss: 1.0929... Generator Loss: 0.7814\n",
+      "Epoch 1/2... Discriminator Loss: 1.0840... Generator Loss: 1.0624\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4IWCh4dHBP8HGZVdkTHSHmEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56d1a19828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2... Discriminator Loss: 1.0431... Generator Loss: 1.1230\n",
+      "Epoch 1/2... Discriminator Loss: 1.0236... Generator Loss: 1.0033\n",
+      "Epoch 1/2... Discriminator Loss: 1.1009... Generator Loss: 0.8963\n",
+      "Epoch 1/2... Discriminator Loss: 1.0246... Generator Loss: 1.5232\n",
+      "Epoch 1/2... Discriminator Loss: 1.2980... Generator Loss: 0.6295\n",
+      "Epoch 1/2... Discriminator Loss: 1.0272... Generator Loss: 1.3369\n",
+      "Epoch 1/2... Discriminator Loss: 1.1724... Generator Loss: 0.7514\n",
+      "Epoch 1/2... Discriminator Loss: 1.0532... Generator Loss: 1.2344\n",
+      "Epoch 1/2... Discriminator Loss: 1.1428... Generator Loss: 0.8638\n",
+      "Epoch 1/2... Discriminator Loss: 1.1750... Generator Loss: 1.1432\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659f89710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2... Discriminator Loss: 1.2411... Generator Loss: 1.8333\n",
+      "Epoch 1/2... Discriminator Loss: 1.0503... Generator Loss: 1.0242\n",
+      "Epoch 1/2... Discriminator Loss: 1.1395... Generator Loss: 1.0762\n",
+      "Epoch 1/2... Discriminator Loss: 1.3215... Generator Loss: 0.5722\n",
+      "Epoch 1/2... Discriminator Loss: 1.1445... Generator Loss: 0.7874\n",
+      "Epoch 1/2... Discriminator Loss: 1.2529... Generator Loss: 0.6156\n",
+      "Epoch 1/2... Discriminator Loss: 1.0842... Generator Loss: 0.9782\n",
+      "Epoch 1/2... Discriminator Loss: 1.1459... Generator Loss: 0.7799\n",
+      "Epoch 1/2... Discriminator Loss: 1.1442... Generator Loss: 1.4652\n",
+      "Epoch 1/2... Discriminator Loss: 1.0300... Generator Loss: 1.2469\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\neHh4ROCZgoeHRwT/B7t3mfbAx/b3AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659e917f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2... Discriminator Loss: 1.2653... Generator Loss: 0.6031\n",
+      "Epoch 1/2... Discriminator Loss: 1.0865... Generator Loss: 1.2273\n",
+      "Epoch 1/2... Discriminator Loss: 1.2002... Generator Loss: 0.6592\n",
+      "Epoch 1/2... Discriminator Loss: 1.2886... Generator Loss: 0.6356\n",
+      "Epoch 1/2... Discriminator Loss: 1.4369... Generator Loss: 0.4941\n",
+      "Epoch 1/2... Discriminator Loss: 1.1444... Generator Loss: 0.9019\n",
+      "Epoch 1/2... Discriminator Loss: 1.1936... Generator Loss: 0.6823\n",
+      "Epoch 1/2... Discriminator Loss: 1.1784... Generator Loss: 1.4153\n",
+      "Epoch 1/2... Discriminator Loss: 1.5582... Generator Loss: 0.4257\n",
+      "Epoch 1/2... Discriminator Loss: 1.0860... Generator Loss: 1.3010\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659e4ae48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2... Discriminator Loss: 1.0424... Generator Loss: 1.1105\n",
+      "Epoch 1/2... Discriminator Loss: 1.0549... Generator Loss: 0.9376\n",
+      "Epoch 1/2... Discriminator Loss: 1.1714... Generator Loss: 1.1036\n",
+      "Epoch 1/2... Discriminator Loss: 1.1158... Generator Loss: 1.0531\n",
+      "Epoch 1/2... Discriminator Loss: 1.0498... Generator Loss: 0.9878\n",
+      "Epoch 1/2... Discriminator Loss: 1.2111... Generator Loss: 0.8901\n",
+      "Epoch 1/2... Discriminator Loss: 1.4104... Generator Loss: 0.4942\n",
+      "Epoch 1/2... Discriminator Loss: 1.0527... Generator Loss: 1.3645\n",
+      "Epoch 1/2... Discriminator Loss: 1.0832... Generator Loss: 0.8888\n",
+      "Epoch 1/2... Discriminator Loss: 1.1123... Generator Loss: 0.8392\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLgAAABJJREFUeKPg8XgieKPg8Xgi/A+7RzdpakO0cAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56d18d8c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2... Discriminator Loss: 0.9938... Generator Loss: 1.1336\n",
+      "Epoch 1/2... Discriminator Loss: 1.2817... Generator Loss: 1.7325\n",
+      "Epoch 1/2... Discriminator Loss: 1.0349... Generator Loss: 0.8968\n",
+      "Epoch 1/2... Discriminator Loss: 1.0611... Generator Loss: 1.3221\n",
+      "Epoch 1/2... Discriminator Loss: 1.0041... Generator Loss: 1.2203\n",
+      "Epoch 1/2... Discriminator Loss: 1.0210... Generator Loss: 1.1737\n",
+      "Epoch 1/2... Discriminator Loss: 1.0761... Generator Loss: 1.2211\n",
+      "Epoch 1/2... Discriminator Loss: 0.9741... Generator Loss: 1.1375\n",
+      "Epoch 1/2... Discriminator Loss: 1.0377... Generator Loss: 0.8915\n",
+      "Epoch 1/2... Discriminator Loss: 1.0852... Generator Loss: 0.9029\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhZ0ULBZLFP8PkM5IigprKEEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56598589e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2... Discriminator Loss: 1.3093... Generator Loss: 2.5877\n",
+      "Epoch 1/2... Discriminator Loss: 0.9853... Generator Loss: 1.1730\n",
+      "Epoch 1/2... Discriminator Loss: 1.0564... Generator Loss: 0.8760\n",
+      "Epoch 1/2... Discriminator Loss: 1.0396... Generator Loss: 1.4255\n",
+      "Epoch 1/2... Discriminator Loss: 1.0599... Generator Loss: 0.9275\n",
+      "Epoch 1/2... Discriminator Loss: 0.9641... Generator Loss: 1.3584\n",
+      "Epoch 1/2... Discriminator Loss: 1.1231... Generator Loss: 0.8175\n",
+      "Epoch 1/2... Discriminator Loss: 1.0010... Generator Loss: 1.1018\n",
+      "Epoch 1/2... Discriminator Loss: 1.3737... Generator Loss: 0.5330\n",
+      "Epoch 1/2... Discriminator Loss: 1.0614... Generator Loss: 0.9055\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nk4LjODl+Ay5UJ4xtuL2QAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659cd9828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2... Discriminator Loss: 1.0209... Generator Loss: 1.0810\n",
+      "Epoch 1/2... Discriminator Loss: 0.9956... Generator Loss: 0.9358\n",
+      "Epoch 1/2... Discriminator Loss: 1.2294... Generator Loss: 0.6367\n",
+      "Epoch 2/2... Discriminator Loss: 1.0491... Generator Loss: 0.8156\n",
+      "Epoch 2/2... Discriminator Loss: 1.9660... Generator Loss: 0.3475\n",
+      "Epoch 2/2... Discriminator Loss: 1.0618... Generator Loss: 1.2255\n",
+      "Epoch 2/2... Discriminator Loss: 0.9658... Generator Loss: 1.0057\n",
+      "Epoch 2/2... Discriminator Loss: 0.8519... Generator Loss: 1.6786\n",
+      "Epoch 2/2... Discriminator Loss: 1.0465... Generator Loss: 0.9361\n",
+      "Epoch 2/2... Discriminator Loss: 1.0060... Generator Loss: 1.0238\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\no0MLCo1Go0MLCo1Go8M/s2DwvwtwYfMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659eb65f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/2... Discriminator Loss: 1.2068... Generator Loss: 0.6641\n",
+      "Epoch 2/2... Discriminator Loss: 0.8739... Generator Loss: 1.2788\n",
+      "Epoch 2/2... Discriminator Loss: 1.2420... Generator Loss: 0.5994\n",
+      "Epoch 2/2... Discriminator Loss: 1.0455... Generator Loss: 1.3188\n",
+      "Epoch 2/2... Discriminator Loss: 1.5271... Generator Loss: 0.4801\n",
+      "Epoch 2/2... Discriminator Loss: 0.9442... Generator Loss: 0.9758\n",
+      "Epoch 2/2... Discriminator Loss: 1.1125... Generator Loss: 0.8582\n",
+      "Epoch 2/2... Discriminator Loss: 0.9858... Generator Loss: 0.8610\n",
+      "Epoch 2/2... Discriminator Loss: 1.7708... Generator Loss: 0.3445\n",
+      "Epoch 2/2... Discriminator Loss: 1.0218... Generator Loss: 0.9825\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSGUYY7XTZEwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5669705ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/2... Discriminator Loss: 0.9482... Generator Loss: 1.3030\n",
+      "Epoch 2/2... Discriminator Loss: 1.3452... Generator Loss: 1.9974\n",
+      "Epoch 2/2... Discriminator Loss: 1.0928... Generator Loss: 0.7811\n",
+      "Epoch 2/2... Discriminator Loss: 0.8769... Generator Loss: 1.3478\n",
+      "Epoch 2/2... Discriminator Loss: 1.2244... Generator Loss: 0.6451\n",
+      "Epoch 2/2... Discriminator Loss: 1.0969... Generator Loss: 2.4311\n",
+      "Epoch 2/2... Discriminator Loss: 1.0368... Generator Loss: 0.8528\n",
+      "Epoch 2/2... Discriminator Loss: 0.9872... Generator Loss: 1.3598\n",
+      "Epoch 2/2... Discriminator Loss: 1.1426... Generator Loss: 1.1906\n",
+      "Epoch 2/2... Discriminator Loss: 0.9564... Generator Loss: 1.2773\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBcMwyjCjYBhGGWYUDMMo43+f1ceyZSl1bgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659af8e48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/2... Discriminator Loss: 0.9182... Generator Loss: 1.0495\n",
+      "Epoch 2/2... Discriminator Loss: 1.1554... Generator Loss: 0.6664\n",
+      "Epoch 2/2... Discriminator Loss: 1.0098... Generator Loss: 0.9703\n",
+      "Epoch 2/2... Discriminator Loss: 1.0875... Generator Loss: 0.7549\n",
+      "Epoch 2/2... Discriminator Loss: 0.8910... Generator Loss: 1.1223\n",
+      "Epoch 2/2... Discriminator Loss: 0.9773... Generator Loss: 2.6783\n",
+      "Epoch 2/2... Discriminator Loss: 1.0080... Generator Loss: 0.8345\n",
+      "Epoch 2/2... Discriminator Loss: 0.9571... Generator Loss: 1.3360\n",
+      "Epoch 2/2... Discriminator Loss: 0.9197... Generator Loss: 1.3654\n",
+      "Epoch 2/2... Discriminator Loss: 0.8306... Generator Loss: 1.2653\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nx8nhTsFxnBzuFBzHyeFOwXGcHD8CPaLG+4G8bqUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56595b2eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/2... Discriminator Loss: 0.9596... Generator Loss: 1.1294\n",
+      "Epoch 2/2... Discriminator Loss: 0.9953... Generator Loss: 0.8804\n",
+      "Epoch 2/2... Discriminator Loss: 0.9510... Generator Loss: 1.1515\n",
+      "Epoch 2/2... Discriminator Loss: 1.3993... Generator Loss: 2.3715\n",
+      "Epoch 2/2... Discriminator Loss: 0.9080... Generator Loss: 1.4359\n",
+      "Epoch 2/2... Discriminator Loss: 0.8572... Generator Loss: 1.2590\n",
+      "Epoch 2/2... Discriminator Loss: 0.8892... Generator Loss: 1.4629\n",
+      "Epoch 2/2... Discriminator Loss: 1.5488... Generator Loss: 2.5772\n",
+      "Epoch 2/2... Discriminator Loss: 0.9225... Generator Loss: 1.1753\n",
+      "Epoch 2/2... Discriminator Loss: 1.0175... Generator Loss: 1.2177\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njuPkcKfgOE6OfwEnoesT8vs/JQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56592765c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/2... Discriminator Loss: 1.1387... Generator Loss: 2.3912\n",
+      "Epoch 2/2... Discriminator Loss: 1.1287... Generator Loss: 0.7842\n",
+      "Epoch 2/2... Discriminator Loss: 1.0247... Generator Loss: 1.9585\n",
+      "Epoch 2/2... Discriminator Loss: 0.9418... Generator Loss: 1.0972\n",
+      "Epoch 2/2... Discriminator Loss: 0.8407... Generator Loss: 1.2381\n",
+      "Epoch 2/2... Discriminator Loss: 0.8819... Generator Loss: 1.0197\n",
+      "Epoch 2/2... Discriminator Loss: 1.0199... Generator Loss: 0.8055\n",
+      "Epoch 2/2... Discriminator Loss: 0.9692... Generator Loss: 1.6409\n",
+      "Epoch 2/2... Discriminator Loss: 1.2472... Generator Loss: 0.7956\n",
+      "Epoch 2/2... Discriminator Loss: 0.9436... Generator Loss: 0.9713\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f565b29e2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/2... Discriminator Loss: 0.9250... Generator Loss: 1.0925\n",
+      "Epoch 2/2... Discriminator Loss: 0.9684... Generator Loss: 0.9028\n",
+      "Epoch 2/2... Discriminator Loss: 0.9477... Generator Loss: 0.9569\n",
+      "Epoch 2/2... Discriminator Loss: 1.2281... Generator Loss: 0.6713\n",
+      "Epoch 2/2... Discriminator Loss: 0.8281... Generator Loss: 1.1973\n",
+      "Epoch 2/2... Discriminator Loss: 1.0442... Generator Loss: 0.7927\n",
+      "Epoch 2/2... Discriminator Loss: 0.9155... Generator Loss: 1.0041\n",
+      "Epoch 2/2... Discriminator Loss: 0.8651... Generator Loss: 1.6100\n",
+      "Epoch 2/2... Discriminator Loss: 1.1178... Generator Loss: 0.7747\n",
+      "Epoch 2/2... Discriminator Loss: 1.6798... Generator Loss: 3.0678\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNgVJGZuCpIxNQVLmAYKx84FNOBtDAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f565b23ccc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/2... Discriminator Loss: 0.8941... Generator Loss: 1.0698\n",
+      "Epoch 2/2... Discriminator Loss: 1.0571... Generator Loss: 0.8698\n",
+      "Epoch 2/2... Discriminator Loss: 0.8889... Generator Loss: 1.0698\n",
+      "Epoch 2/2... Discriminator Loss: 1.2752... Generator Loss: 2.7747\n",
+      "Epoch 2/2... Discriminator Loss: 1.1641... Generator Loss: 0.7411\n",
+      "Epoch 2/2... Discriminator Loss: 0.8820... Generator Loss: 1.0839\n",
+      "Epoch 2/2... Discriminator Loss: 0.8707... Generator Loss: 1.0716\n",
+      "Epoch 2/2... Discriminator Loss: 0.8608... Generator Loss: 1.2287\n",
+      "Epoch 2/2... Discriminator Loss: 0.9702... Generator Loss: 1.0217\n",
+      "Epoch 2/2... Discriminator Loss: 1.1194... Generator Loss: 0.8550\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4VVXWxn/7lnQCATRgkCIiDqAiCDZAsHcdZeyKgL2h\nY0FHUew4FtSxfGNHZQRGbCgqiDgiKg5jQaVIEUTpHQmpd39/nKyVc8NNcpNbcoPnfZ48hJt7ztln\nn33WXuVdaxlrLR48ePAg8DX0ADx48JBa8ISCBw8ewuAJBQ8ePITBEwoePHgIgycUPHjwEAZPKHjw\n4CEMCRMKxphjjTELjDGLjDE3J+o6Hjx4iC9MIngKxhg/8BNwFPAr8F/gbGvt3LhfzIMHD3FFojSF\n3sAia+0Sa20JMA44JUHX8uDBQxwRSNB5C4Dlrv//ChxY3ZeNMXVSV4wxYf+GQqE6D7AxIxgMAlBe\nXv6Hu/e6QtZIqjN3AwHnVTTGUFZWBiRkzOustbvUOpZ4XzVaGGMuAS6pz7HyUshEbt++PWwCfT5H\nAUqVF8YtxGSc1tpqP696nPszv99Pfn4+AFu2bGHr1q07fCde463red33Ec3n8YCM1efzhT1vv98f\n9vfy8vKIY0gFYWGMoWXLloBzH2vXrgVQ4SCoaazGmGg2yWXRjCdRQuE3YHfX/9tUfKaw1j4DPAN1\n1xRycnIAyMjIAByh4EaqCIOq8Pl8YS+/e5zyuVsQRFoEfr9fj/v9998TMs76vijVHZfIF0/OHQqF\ndphDgBYtWgCwYcMGSktLEzaOWBAMBtlzzz0BWLp0ab3GGc85TpRQ+C/QyRjTAUcYnAWcE6+Tb9u2\nDYhuIlJBfZRrl5eXqxYT6e9Vf4+E8vJytmzZAkDz5s31++vXr4/XcBslqs6h7LKrVq1qqCHVClmb\nU6ZMoW3btgDss88+9T5fvNZ4QoSCtbbMGHMV8CHgB16w1v6YiGt58OAhvkiYT8FaOxmYnIhzFxcX\nA1BSUqKfRfIjZGRkcMwxxwDw0Ucf6XHl5eXucerxYocmyoFnrdVru21AY0zU1+vcuTN77LEHAAce\neCBvvvkmkNqaQiJ9Co0ZmZmZAPTu3ZulS5cCUFRU1IAjcpAQnkKdB1FHn0IkyASXlJTQrFkzAN5/\n/3169uwJOC/khAkTALjpppsAx84sLCyUMYTZoon2S+y222506NABgOXLl9O1a1cAVq5cCcDixYt1\nbIFAIKIwcQuvhnqOxhjS0tIAxzYWQe0WsqFQSNX5VLXrkwl5fosXLwagTZs2auZ07tx5Bx9ZHPE/\na+0BtX3Jozl78OAhDA0Wkow3xDRo0qQJs2fPBqBdu3ZhjsYmTZoAsHHjRgDdieXvydht09PTATj0\n0EO5/fbbAWjatKmGGd3xarmnSZMmceGFF+4w5oZEx44dAZg1axbNmzff4e+lpaX07t0bgPnz56ds\nRKgh0KVLF8DRFsGZqxUrVgCwxx570KNHDwBWr17NlClTkj6+nUYoyMs0cuRI9eRCpRlw880389BD\nDwENG4nYfXcnUnv66adrbHrcuHFs2rQJgBNPPBFwFo7cU15eXkqo3T6fjwEDBgDw+OOPA5Cbm1tt\n/P/KK68E4LLLLvOEQgWMMbzxxhtApU/s3HPP5cMPPwTgggsu4IEHHgAcoSACJJnwzAcPHjyEYadx\nNIppMHv2bCWCrFixQlVYceA1JHw+H8888wwARx99NPfffz8AL7zwgpoK7u9OmzYNcHaXO++8E4Dp\n06cnccTh6Ny5MzNmzAAqiVNdu3ZVx5gxRlXiN954g9zcXMC5199+c7hrydAYcnNzdT5lDN26deO0\n004DHO7Chg0bAHj55ZfVJEvGuzBq1ChuuOEGoHJNtm/fXh3J7dq1Y968eYBjamZnZwNxi0pE5Wjc\nacyHE044AXDsNFHFjzjiiJQQBoLbb7+dQYMGAeEU1qoCAZyX5+uvvwbg6quvVvZmQ0CiOV9++aWa\nNBItcXvKrbX6grVv317No6lTp6pZtGjRooSPNysri/feew9AN4VgMKgv3rx58/jqq68ARyAnAxIy\nF4EAcNdddwHhIfLVq1fr2khLS6Nbt24A6idLBjzzwYMHD2HYaTSFiy++GHCkqxBBlixZ0oAjqoTE\n8a+66ipVA59++mmefvrpGo87/vjjAWf3kCSZhsBf//pXwOGCPPjggwARx3PggQfy7rvvApXaBcDm\nzZtVe0sGVq9ereaKaDZQuVsHAgG+/PJLwNFoRHtx79jxhlx727Zt/PrrrwC8+OKLO3zP7/eH8WXW\nrFmTsDFVB09T8ODBQxh2Gk1BHDLBYJA//elPgONf+OWXXxpyWADsv//+gMNHEBt8+PDhNR7j8/k0\nGxTgxx8bLnVEOBI+n48PPvgg7G+BQEDHJvRrcHbEfffdFyDpz8BaywUXXADAbbfdBsAjjzzCscce\nCziZk6IdLF68OKEagkAcrO+99x7//e9/AcJYqsL+XLRoEVlZWYDDSRGtIpnYaYTCvffeCzheb1HV\nmjVrlhJCoU2bNjt8lpaWFtHBKGMfMGCA5jM8/PDDSVm4kZCRkUHTpk0BxyH6888/A5VU3b59+yoh\na9WqVTz33HMA3HPPPQ02ZqjkAMjL/+OPP6qzs7CwkDlz5gA71ixIFEQoXHjhhRp1EtJXWVkZeXl5\ngDPfEgWZNWtWg/A7PPPBgwcPYdhpNAUJQW3YsEGdXGPHjlXVPVk7QiS4HUcSWjzppJNUUzj11FNZ\nuHAhUKmCr1y5ku+//x6Ad999NywjNJk44IADwjJJr7vuOgBGjx4NOKaBsO6qVsBKBYiW5g4FvvPO\nO8pTSDZKSkq44447gMpQdHp6uo5nzpw5HHLIIUBlwlS0iFfFsZ1GKMhEnHLKKUye7GRsd+rUSSd7\nzJgxXHPNNUDyac4iFAoLC9X+7tWrF/379wfgiy++UJ+IYO7cuaqKS65GQ6B///6agRoMBjUScf75\n5wNw3XXXMW7cOCA1SpuBM98yZuEj+P1+jfzceOONDTY22LFilpvCPnz4cD7++GPAIYsJKU/K7tUE\nIWrFGunxzAcPHjyEYaehObshKvpRRx3FxIkTAcdLLmXcpOSV8BkSBeEnPP/88wD069ePa6+9FoAP\nPvhA1b3CwkJ11p133nmAQ8sVTeGHH35I6DgjwZ0FKbuVFMx1w1qrDrwPPvigQbUF0cj2228/RowY\nAThmGjiawr/+9S+gUstJRaSnp2ty1J/+9CfVdE4++eR4zO0fi+bshqiJkyZN0sKdv/76qy5usdUO\nO+wwPvvss4SNQ669yy5OVe3t27ern6BqIQ0RHKeeeirgqItiq7v9D3VFfWpU9u/fn+uvv16Pu/rq\nqwF49dVXdQ6/+OILwCH/CGHphhtuUF9DQ+Dwww8HnDmUuXfXxJQXLJVRUlLCSy+9BMDQoUN1zPvs\ns49GTBINz3zw4MFDGHZK86E6SMmrXXfdFXA8/OLtr+9OXBMkDv3TTz8Bjmq49957A/Dbb7/pLnbF\nFVfw2GOPAYSVbxfn47///W81OxJJd5brffLJJ0qmGTJkCN9++221x5x77rlK1928eXNY4ZBkIjMz\nk1NOcZqQFRUVKb15/PjxgKPxyLNOBe5KTZC5P/XUUxk2bBjgEPEOPvhgIKY18Mc1H6qDZChOmjQJ\ngJYtW9K5c2eAOqtm0YR/hLwj5B+fz6decWOMnqNbt26MHDkSgKeeekrPK/z8AQMGaMj1tNNOqxPL\nTfwroVCoxrCmz+fjnHOcKvxr1qxRRmBtBWHHjh3LVVddBUD37t1VhRe7ONEQP8duu+3G559/Djim\nl0QYxHwqKSlpkDyC+kDWzbJly9Qvtfvuu+vGcOuttyb0+p754MGDhzD8oTQFceKJelZWVlbvTMpo\nzC7RBNzfPfvsswGHBiyEqssuuyzi8X369AGc6MN+++0HOJ5zocnWBndpdbnn6tC7d2/uuecewCkJ\nFm3JeGNMWFTCnR2ZDMgcL1++XDWhQCCglbIFmzdvTony6dFAntXixYuVn+D3+7n88suBFNYUjDG7\nG2OmG2PmGmN+NMYMq/i8uTFmqjFmYcW/efEbrgcPHhKNWDSFMuB6a+3XxpgmwP+MMVOBC4Fp1tpR\nxpibgZuBmlMC6wixE6vrJRnpuwUFBepTkM+2bt1a7+rI0WgK4myTHSo7O1ubt0RzvLSHmzVrlja1\nadmype4kTZs21Z0kkmPP3XymtuSkSZMm6XwKnyManHfeeeqX8fl8bN68Oepj4wHRDtzz6XY6CiRb\nMtXh8/l0fRYXFzNq1CjAqSIm2ZMJb4Uopc1j/QHeBo4CFgCtKz5rDSyI4lhbl59gMGiDwaAdNGiQ\nHTRokB0wYIANBAI2EAhYwBpjrDHGZmVl2bvvvtvefffddvPmzTYUCtlQKGSLiopsUVGRPeCAA+p0\n3br+5OXl2by8PLt27Vq7du1aW1RUZEePHm1Hjx5tKyIuNf60a9fOtmvXzm7ZssUWFxfb4uJie+CB\nB9ZpDDIXtc3l5s2bbUlJiS0pKbFjxoyp9XytW7e2rVu3ths2bNDj5s+fb30+n/X5fAmd19p+3nzz\nTVteXm7Ly8ttYWGhLSwstNnZ2Q06pmh//H5/2P932WUXu8suu9jy8nJdv9nZ2fW9n9nRvMtx8SkY\nY9oD+wOzgHxrrRRGXAXkV3NMvVvRe/DgIXGIWSgYY3KAicC11tot7oxAa63sUjsgllb0ouaeeeaZ\ngJNcJByEDz74QCnDQ4cODSt4KlwEyZycP39+XS5bZ4j6L87M/fffX9mBgwcPZuzYsYDDQ5g5cyZQ\nmc0ZCAQ0xp6VlaVJNMJ5iBbu0Gl1/RkAJkyYwFlnnQVAjx491CRIT0/XMXXt2lVZlpdeeikAOTk5\narrceuutDdrfoXXr1gAceeSR+tl3330H1M0kakhUNfPcIWzBoYceCpCwRjExkZeMMUHgXeBDa+0j\nFZ8tAPpba1caY1oDn1hrO9dynjoNQkg2jzzyCOC8/K5z6cJ0/z5//nz15svLmiyIYDruuON47bXX\ngMq8CHAWgmRCvvzyy4BDNZYKUmlpacydOxdwIhLRZMwJZDGlpaXpy928eXO1/SVyMHjwYCXKtGnT\nRn0taWlp6hPJyMjYgZ9RXFzMySefDKA8gWRCxnPMMcdobkNubq7eX/fu3YHUJyxVB1nra9as0XX0\n6aefAvDnP/+5rhmRie0laZzV9jwwTwRCBd4BBlX8PgjH1+DBg4dGgnprCsaYPsAM4HtAdMa/4fgV\nJgBtgWXAGdbaGita1JfmLN7Yd955R39v166dVvItLy+nX79+QPxpt/X1AIu07927t1Zz7ty58w6c\nhuLiYv1s8eLFnH766UDdTR6h+7Zo0UJb1uXk5GhPCVGrL7/8cmVeXnbZZapB5ObmqmnWqlUrrQch\nu9WNN96YdJNB5j4QCGji1siRI3XM1lrat28PoGshFej89YHc6/PPP681JoWtKeZnHRCVpvCHyn1I\nZUg+hjS1adq0qTYAmTlzZoMVhkmF9eGG27aGyo3hrbfeoqCgAHCa1lx00UVAcjpSNSJ4reg9ePBQ\nd3iagodGBTd120Od4WkKHnY+eAIh8fCEggcPHsLwh8qS9NAwqEJoq9NxEj1JdtGWPzI8TcGDBw9h\naJSagmQJRgo3VbcTGWN0x6otTBVvZ1Y8w3vp6elKhU1Ugxt3pl519Oi6oL7HW2uj1hDi8cz8fr9y\nQ2SOkxXSrK82JfD5fHpcrPPQ6ISCMUYr9QoVd/v27VG96FKJuLb03ng7s2J+SIGANvrYfffdlfKc\nSAgRKBQK6YtZ1/uIdaHXBfE4fzAYVHOlasOWRMLn89Vb+IgQc1etjnWz8MwHDx48hKFR8hTEfGjI\nrsbJRIsWLbQ46qOPPpr0Qia1QbJSMzIylDbt8/mUNr1hw4Y/zLNKcey81Zz/iAtMqkslO8OzNgwa\nNEjL0xtjtIpzcXGxft5QzVzdMMZoZmogENCKTV5UY0d45oMHDx7C0CjNhz8KxHkUCATUkVZaWqrO\nMEhcBKI6iPPw8ccfB5zsShlnaWkpK1asAJxWd5JR2RBrTMYpRUpuvfVWBg8erJ/tu+++gFMF+g+E\nndd8qC9k8R522GGAU2Zd+iCOGjUqpSi0wWCQTp06AbBgwYIwk0nSgkeNGqUVkKItyR4LfD4fN910\nE4CWG/f5fCqYli1bxkMPPQTAokWLGmw+jTGaMv/GG28AkJcXXlRcqjNJdysPlfDMBw8ePIRhp9cU\nRNUOBAIccICjOUlX39atW2u5rilTpmjhkYbUGESbOfnkk1UFro6XcNRRR/HXv/4VSGyDEHfJM7mO\njG3ZsmVaMzAQCGi/xvT09KQ3X5HaCueeey533303ELk5TSgUCmshuG7duriNwV0sJ5U0z7ogpXwK\n8WKltWzZEoDhw4drcctp06YpIeeJJ54A4MILL1TSyFNPPaWqcUN0EpLFO3DgQAAWLlyoNQ+resil\nwOrYsWO1XmNtnZkCgUC9/Q9irnz++edaDEZ6b/bs2VOfmTFGa0xOnTpVf08GmjZtqj02s7KydM4W\nLlwIwC233KIFdK+99lrOP/98wHnWe+21F1D/xq0iILOysrRe5ZAhQ7jyyisBp3KWbE5XXXUVZ5xx\nBgDz5s0D4L777tOxb9++PZHCxEud9uDBQ92RMuZD1TJbdYXsZpMmTdJS3zfeeKPGza21ulMKqcat\n4n388ccJaUcfDXw+Hw888ADg1G4ER2OoLobu7iUp3nW5p+qITfXldvh8Pl555RUA8vPz9fy9evUC\nwk0tay0TJ04E0OrUiYao66+99ppWPg6FQqpNvfXWWzscc+mll+punZmZqeZPpO9GA5mDUCikFbF7\n9+6tmsCvv/6qGunuu++u3xcNpXPnznz11VeAU/Fb1u/tt9+udRg3btyYNE6Fpyl48OAhDCmlKfj9\n/jpJQ9EuWrRowX/+8x/AqT588MEHAztWPpadRCQ0oJWKp0yZ0mDFUbt3766VliWEtmbNmrAeC/Ld\nU089lTZt2ujx8nm7du2ASls/Xmjbtq06aK213HXXXUD1mse0adMAOP7447VydTJ8NNOmTVPtZd68\nebz9dvWdBay1PPzwwwBceeWVqiHG4ncBxx/g1jbdXAnRWA877DD1afXs2RNwnq90I2/WrJked8cd\nd3DOOecA8OqrrzJgwAAAXevPPvtsQhK3UkYoQHimVzRo1aoVAJ999plW8v3Pf/5TbRn0bt26AWiT\nFYBvvvkGaBjqtIzjn//8J7vssgsAl1zidNJzP2yfz6edmYYMGaKfh0IhZsyYATjt6mtCfTMc//a3\nv6mDdvv27bWWFZdx5+fnJ0XIilr+8MMP64uel5dXY7q6MUYde2+99ZZ23YrHGhCh3LdvXx3bmWee\nySeffKLjkbHJen/kkUc0ArJ9+3ZycnIAh5gmJKt77rlHc36ke9ehhx7KeeedB8RX8MZsPhhj/MaY\nb4wx71b8v4MxZpYxZpExZrwxJq22c3jw4CF1EHNI0hjzV+AAINdae6IxZgLwhrV2nDHm/4DvrLVP\n13IOa4whLy9PHVQ1jUsk7fTp0wHo16+fhubE4VYVfr+fxYsXA6iqDmic/9lnn613W/q6wB3zl103\nKyuL4447Dqi8JzcyMjL44IMPADjooIN05y4pKVEH6+rVq+M6TnFgLlmyRMOQn3/+ubJBq8v/l+O+\n+uorNeOSWZsAnFZ3Mk4Zw7Rp09RJesghh3DDDTcAsHTpUi6++GLA4VzEClHxP/roI77//nvA6c1Z\nU72E9PR0nn32WcAxhYWN6e6D6vP51LR2axoSRh04cGA0bfsST3M2xrQBTgDuBf5a0UrucOCciq+M\nAUYCNQoFcITAxo0bo1I5Jebbt29f/ey0006r8Zj9999fhYFManFxsfbikyzERMLdNPaUU07Rcdxy\nyy0RhYHguOOO45BDDtFzyOI47bTT4i4MBPn5TrPwnJwcfSbr1q2rcXEbY7jtttsApzNTsvMyBKWl\npZqhudtuuwFwww036Evm9/t17lu2bKn5GvHAokWLACczVEy72gqoFBcXc8EFFwCOT+zcc88FYM89\n91QyWI8ePZSSLQ2SBwwYoKZGnz594tbLM1bz4VHgJirbxrUANllrZTX8ChREOtAYc4kxZrYxZnaM\nY/DgwUMcUW9NwRhzIrDGWvs/Y0z/uh5ftRV9XXgK4mgRbN++PeJOa4zR3eGWW27Zwfn0008/8frr\nr4d9lgiIZvPAAw9w9NFH6/VGjBgBwN///veIx11zzTWA44hy02dXrlwJoDtRIiCRGnd37I8//niH\n7wWDQVXFr7rqKtUwQqGQanJTp05N2DgjwVqrDtl7770XcEwGd3ap+7viwKsrDyCSM1NMLZ/Px5gx\nY+o89i1btmiPUfc18vLydJxXXHEF4HAhxERZsGCB3l+sGlos5sOhwMnGmOOBDCAXeAxoZowJVGgL\nbYDfojlZXV5K8cRL+GfJkiW6eNPS0sK4+KeccgpQ2aMRKm3cYcOGaaWgRED8G0I2Gjx4sPoDnnzy\nyTASkkCE2JgxY7SprDsq8/vvv3PLLbcAiTV5xK71+/16nenTp+vCvO+++4DKZqcQTgbz+XxaS7Mh\nIGQgtx3uji6ISv/LL7/oM6mrBz/SmpVoQElJib6wsUCusWHDBqWyi1DYtm2bCuqZM2fGLYJWb/PB\nWnuLtbaNtbY9cBbwsbX2XGA6MLDia14reg8eGhkSwVMYDowzxtwDfAM8H8+T+3w+TWaRXaBly5b8\n/PPPADRv3lxLls2bN0+zIN2FSt555x0Avvjii3gOLQwZGRmajXn88ccDzq4r17z22mv1u8YYPvro\nIwD69++vn7kh9/r2229rG/hEmTx+v1+Tw6AyqWj9+vXqzZfxW2tVY3ObRwceeGBCxhYtZG723HNP\n/X/Hjh0BR8UXk+eggw6iR48eQCUpqL7Iz89XEtKKFSu05Fu8IBEVqQ3hrmzepUuXuJmTcREK1tpP\ngE8qfl8C9I7HeT148JB8pBSjMRo0a9ZMd3+xvyWPHhzpKX6C5cuXh+1YwmUQmzyRCVDXXnstJ554\nIlDpE1i/fr2m1rp3+WeeeUZ3AbeGIDbizz//zGeffQbAddddp5pQojSFYDCobFFjjPpg+vXrx8iR\nIwH47rvvAIdVJ1qMz+fT0JoxRud+3LhxCRlnTRC+hPgODj74YH755RfAWRfCgM3Pz9fkqBkzZtSr\n/4I8s8LCQvW1fPzxx3F/PsJJkfNmZGQo4/Hmm2+O2/UanVCAypdZFmtGRoZ+9v7773PZZZcBDqFD\nHjg4zhhAKa71RTAYrNZTLd7nIUOG6GIR0+bQQw9VcpYxRh1GgwcP1u/KeX/++WdV4f/73/9qubV4\nq6SR4BZMoVCIDh06AI6Z9uijjwIo2cb9EvXq1UtzMNzfaQjIxiG8ELfTLy8vT3kf7tJt9W3KIs7X\n4uJinnvuOSD+TmC/3685KLLJZGRkKOEunp2svCxJDx48hKHRaQobNmzQgqaiIrolvJuqvHTp0rC+\nk+L4ixXVaQl+v58777wTgD322EPVf1Gp165dq+N59tlnNXzlprBOnjwZgHPOOUdDZMnO3szPz9dx\nunsUvvHGG2q6uHcmCb1OnjxZd7ElS5ZoPYFkIy8vTx1+UpvgjDPOUKfjiBEjVBsqLy9Xnkhd4/uR\nnMGJ0uTatGmjTmhBWVmZshzjuUYanVCAygmoLVdBOArgPPxE5zb4fL6wByc27Lfffgs46t5vvzm0\njaZNm+p9LFq0SFXDVGj2snXr1jA/gZhp5eXlaqZJX87HHntMKeY5OTn69z59+iRVmPl8Po0i9O3b\nl/322w+ozIx1p3JDZfXr6667jn/9618xXxsSU25fBM/TTz+tpqlg48aNMRcnigTPfPDgwUMYGqWm\nEC3EgSQQLkCiUFpaqglWUCnlpQJy27Zt1aRYt26d0rU3bdqUUpV/N27cyIIFCwDYd9991dE4c+ZM\n1RCaN28OVDrZwHGCSj0IKV6TaMj1+/btq07FzMzMMNMSwhmNr7zyitZekIY1sSCRzl/RePr376/3\nKhrvypUrtW9JPLFTCgVZCBJWA6cKUzKy9sS+NsZo4ZS2bdsCTv1IoQWvW7euwbIIo8Hs2U6eWpcu\nXZRCLuQfN0KhkL5Yp59+uqan1xfualK1edSNMRppuueee8jNzdXPBSIIlixZoubkggUL4iKEEy3I\nfT6f5kEEg0G9l1mzZgGO6ZOI4kCe+eDBg4cw7JSagsSgW7RoodJ81qxZcY3lRoLf79f6iVAZpfj3\nv/8NwHvvvdcoOmaHQiFtR3fZZZfpfB5xxBF8+eWXQCX1efny5XGtMuwuV1YbzjzzTDXNcnNz9VmX\nlZVpp2tx4K5YsSKlTLRo0L17dzUfysvLtV6C9AaprnJ3rNjphIIxhgsvvBBwXlJ3Db9E49BDD1WT\noKioiFNPPRWoJE015KKsa6Md+a61VtmU8m+iIUKhujGLeTh//nzefPNNwNkIpOntyy+/3CiEb3WQ\nZkbPPvuspkNv375d17AwcxMFz3zw4MFDGHY6TcHv92uhj5KSEpWq+fn5WrU33pCd7bTTTlN667XX\nXpu0nTUaNCbVubZdXu5lzpw52pqtsUO0nz322IPnn3cSi92m6F133aVl4hNtBnuaggcPHsKQUg1m\n44FgMKiFL9euXatU261bt9a7gWhtkNJlb731lrZNe+GFF5KSvORh50CfPn0AJ7Qq2ZD5+fmqhXbp\n0kUb5MaAqKo573RCoSEgcfz27dsraScV6MoeGg+EDHbUUUdp1fEBAwYo4e7RRx+NhwnodZ324MFD\n3bHTaQp1Cb3VN38+0nnASQiSmH0y+kh4aFjU1JquLscbY5SWXV5erpqnz+eLmJUaAxLfDCYVUd0D\ncguAaGm00ULOU53JkJ6eDjikGvc1ZazVCTI3iSdFhLf+7i4573453OOU7/j9fo23i58lXhTv6uZI\nPpd8AXdEw1qrGZPu6lvWWs1EFKG+bdu2sHXjvkZ9nkkkYlYoFArL4HXPjYyzrKxshw5R7vL07rHF\nOree+eD1MpikAAAgAElEQVTBg4cwNEpNwa12wY4SO5IEj7RDJwtSr7+wsFB3oKo7VyQ0lHbQpEkT\nHafP5wvbjWTcsmsFg8GwKIt7zPLdYDCY8Ni6e6d0a4UyBmttWLMU0RCqzrFEqCLNfTyex6677qrF\nc6SWqHvszZs3V6djq1attHRgdna2RtLk3kKhkLaN27Ztmz6nWDWFRu1TkKy433//PYyWm2pwV39K\nxfEJRMjm5+fry7Rx48awF8tDbIiXH6ueSHz0wRjTzBjzujFmvjFmnjHmYGNMc2PMVGPMwop/82K5\nhgcPHpKLmDQFY8wYYIa19jljTBqQBfwN2GCtHWWMuRnIs9YOr+U8dRqEeGqlIcmDDz6Y9NoEsXqe\nUxnuRLJk358xRh2UjTmpKUWR2OiDMaYp0A+4EMBaWwKUGGNOAfpXfG0MTpOYGoVCXSFlxMU2a4jF\nk6iXJRWETUO+jFV7Ptb1WAgvwR9ruHBnFPq1IRbzoQOwFnjRGPONMeY5Y0w2kG+tXVnxnVVAfqSD\nvVb0HjykJuptPhhjDgC+BA611s4yxjwGbAGuttY2c31vo7W2Rr9CXc0H6Qgl8X9psNJYEQgEOOig\ngwCnnTvAN998w6JFiwCn27P0aPT7/WE1+r755hsgsd2ukom61n0QZGRkaOmyli1bqjkpEYd99tmH\n3XbbTb8vRVjKy8s1d2Xq1Kk88cQTADr3a9asiWsRmQZGwh2NvwK/WmtnVfz/daAHsNoY0xqg4t81\nMVzDgwcPSUa9fQrW2lXGmOXGmM7W2gXAEcDcip9BwCgS0IreGKMhvsZOJZaOweeddx7Dhg0DKp2o\nJ510kt6n0F4FsnOtXr2arl27Ag2nKVQNsbmZjvXZ8euruXbv3p0jjzwScHgWUnVa/E4yrwIpqutm\nGJ588smqhUqB3TVrUmdPk7kVJCq0GSt56WpgbEXkYQkwGEf7mGCMGQosA86o4fgwuCvWVnfDrVq1\n4plnngEqG3pcc801jS4rsaCggKlTpwJOdqW8+CLoQqGQmkdV4aZHx6uNejQIBoP6EskCbdasmTZr\nPfLII7n99tsBR5AJOefyyy/XexXh5ff7tQCO27Ho7kgVjYCQ0nv333+/kn6++eYbFZZSqr5du3aM\nGDECcASFzO2oUaNUgLj7SkoP0nvvvTequakJfr8/jFgka1vmsG3btjqHCxYs4IYbbgCcFgXS4Qoq\n+5TK2NevX0/v3k6D92XLlsU8TkFMQsFa+y0QyUY5IpbzevDgoeGQMoxGYwxpaWm6E4naVxUDBw7k\n1VdfBSp3ksMOO4z//e9/QGzhtFhV32gg1YWnTJmiPSKstbqDSn/JDz74QFu5L1q0SOm3Pp9Pd4pu\n3bppdeVE8TT8fr+O48Ybb9QWcmL65OXl6a4cCAQ0gafq/Mn4pC/EXXfdxYQJE4BwrbBFixbaUKe2\nZ9m2bVt1tDZp0kTnaNKkSTovQjF/7rnnVHNxJ3F16tRJ28ZJ5WSA1157DYBBgwbFvBYKCgo466yz\nAKe4r3Qhl4rYRx99tK6LFi1a6ByWlpZqYdrs7GxtOfiXv/wFcLJy5Z579uwZjanT+LIky8vLa13c\nW7ZsCePigyMopCHJ7rvvrhOVkZGhvQTbtm3L8uXLgcpquD/99FNYmWwRCtUJpFjRrVs33n//fcBp\nGiOLdNCgQfrw3fjkk08inkdMjETWgJSX/tFHH9Wq1JmZmfqiup+BvNQvvfSSvujTp0/XGoMzZ85U\n1Vds+y+++CKiidi0aVONrlTnMzruuOMAmDhxIsFgEIC5c+dy2GGHAc4aqc3elhd97dq1WtPTGKP+\nGjE74rE5rFq1SoXURRddpA2Su3TpAjjCQQTEvffeq3PrjqoZY8jLc4J4RxzhKOI5OTmaG9GyZcu4\n+T+8LEkPHjyEIWU0BWttVCrwwIEDdUeXWPMzzzyju8v8+fN19wgEArzzzjuAI2mrZsaVl5fz6aef\nAo4ET1QNR6m598knn6gzrLy8nCeffBKAt9+Oa4AmZuTm5jJlyhTAUalFIystLWXy5MkAfP3114DD\nlRBzrmoEZOnSpQD84x//4K677gLQOoOitVXFxo0bd/CyQ6VW2KJFC5577jnAeb7yeVpamq6faLzy\nshtfcsklYY5Gubao6vGAu5FL79691Ykp1Zk3bNhQa0f0YDDIDz/8AFT2hSguLtb2ftXNZ32QMkIh\nWojqCJUt3oVoInCH7OSB77XXXuyzzz4APPTQQ4CTDSgqZ+fOneMuFGTBXn755QCq/oGT6ioCqbaO\nSJmZmZqebK1NWChKxjFx4kS1r40xvPzyywAMHTq0TtcW2/icc87RcwtJKxgMRgyjpqWl6TOZPXu2\nCnDZAAoLC7WX4iGHHKLEo1122YVBgwYBThOVmmjOe+21F4MHDwYcn4NbmMjLOW7cuKjvMxrIOH7/\n/XfGjBkD1M0PtGLFCl0/Mpfjx4/nn//8J0CtQqUu8MwHDx48hCFlog+1fUd2nU2bNql5ILTV1atX\nR3Ud8dpLTLdZs2bMmTMHgGOPPVZ3o3hB6j2Ih7x9+/a6086bN0/VwO3bt6vDSCIS33//ve4kEyZM\nUHU+UclKxhhOPPFEAF5//XWd41WrVmkCWjR0X1G/r776ajp37gw4JojscqKNXXrppcyfP3+H47Oy\nspR78dNPP2m0Q5yOaWlpaoJ99tlndOjQYYdzuE1Rcb7l5OToDpuZmRnW1l2o0Onp6cqBkSYzDVj7\nAKjsMN2rVy/9TO7ttNNOU3MuynE2vuhDTZDQUjAYVK9sXdV9IabIy7pq1SrNNYh3/oQxRr3L4nnf\ntGmTCohXXnmFRx55BHBsRPE7yMLdd999NTohHYPqOw6o3YvetGlTbrnlFsCx1SUCI2SkaK/18ccf\nA445JtGThx56SEk2Er4Utb8qmjdvrmPdvHnzDnU1S0pKNGI0YcIEhg/fMQHXGKNCTaIoZWVlylZ0\nv0CZmZlhNTsfe+yxHb4TTxhj9N5F4FWFCKzDDz9cBSRUbgivv/46AO+++25CxuiZDx48eAhDo9AU\njDG89dZb+n9p7V4XaZ6dna2qoewi69atU9JTIswo0W7kGl9++SUjR44EHC6EOJx8Pp/W2hMP8667\n7qr316RJE/17XduPR3tfubm5ynuYPXs2999/PxBezbh58+bK8YhkSuy66648+OCDgMNTEG0jIyND\nu3ZJjD6S6QCOui8NdapzxIlT7e9//zt77rkn4JDC5PMVK1aEcUCqnisnJ0e99q1bt1aTp6ysTDWa\n6sYXK4wxagqLZrZy5Up1ui5cuJDu3bsDTvEg0RoA7YUqBLdEwdMUPHjwEIZG4WjMzs5Wh1FGRgaX\nXXYZUGlrh0KhsKo7bg1C7MglS5aog0ruecKECVrSbcuWLepQy8nJ0XBneXl5vbSIYDCoTDmxo7/9\n9tuonaK77bYbc+fOBRwH2Lp16wBnp40n49JdrFWSykpLSyNqAh07duTRRx8FUCpyeno6Dz/8MODc\nX6Qw4wEHHKBaiPAbDjnkkIjjSUtLU9s5kRWgZLeeN2+ePndrrdrrotkkgj4ucy7+kNNPP12dnVDJ\ndAwGg/qs//e//6lPLIZ3dudxNPp8Pr744gvAmbBzzjkHgL333huAIUOG6Mu/detWJc3MmDFD1cGm\nTZvqIpNiHI8//riq+EOGDOHggw8GHO78999/D9R/YRpj1GMuY5g5c2bUx69YsYI77rgDgNGjR2u2\n3JNPPslFF10ExMfkkezMbdu21RrrXrJkiQpc4d+Xlpbqiy5mkkCiPddddx3fffcd4PRHrAllZWVJ\nKYEmL9srr7zCrbfeCjjPTHgUiYw6yP2JMJ0/f77mlwwbNkwFRHl5uTp6hVuTDHjmgwcPHsLQKMwH\nn8+nO+X111+vrEYJ9eXm5oaFlUaPHg04TqRTTjkFcHYGiT1/8MEHgBP7Fq5AUVGR7uzRtJCvLdS3\n6667ctRRRwFoWGnUqFF1CqMKD2PZsmXqDJszZw49e/bUe60Nclx1361LT4r09HTVwmTe/H4/K1c6\nJTm7du2qZgWgCVHbt29XDkht18jIyNDvJKNwzJ577qnJT8YYXRsnnHACkNjCrRKafPHFF3VNZ2dn\nq8ly5513xqWegwtRmQ+aJtyQP4CN9sfv99tAIGADgYANBoM2GAxan8+nP+7vtmzZ0hYVFdmioiK7\natUqm5OTY3NycqK+VnU/xpiI13P/7LfffnblypV25cqVdtGiRXbRokW2VatWdbpOQUGBLSgosCUl\nJba0tNSWlpbaq666ylakmkc91pq+G825unTpYrt06WLHjBljP/30U/vpp5/aNWvW2DVr1thQKGSL\ni4ttcXGx/fOf/6zzYoyxfr/f+v3+Ot1zVlaWbdGihW3RokW145LzytijnYtIP7fffrstLy+35eXl\nNhQK2VmzZtlZs2bZ7Oxsm52dHfNaieb5rly5Up9veXm5feWVV+wrr7ySiGvOjuZ99MwHDx48hCGl\nHI3RdFmui+OvqKhInTbbt2+Pa03Hql2Uq8IYo9EOYVCeddZZ6r2vDX6/n/feew9wTABRyz/66KMw\nlb82EyLaztUZGRl6L6FQSM2OtLQ0Dj30UADeeOMNJk2aBFRyMH7++Wd1KF5++eV89NFHgOPwrY+T\ntnnz5urse/fdd3eItBhjlIXao0cP7rvvPsCJ4ddF1Zd5Ef4HhN93bUlqsUDOPX36dMAxNWXsRUVF\nWmKuoZBSQiHe9tu2bdtUEDRv3jyunYdqC1UtW7ZMXzJ5ac444wwef/xxoHobX4qQ/Otf/9JQZklJ\niVYCWrFiRViD0WhR3dzKnNx6661K2Hn99dfD5uiFF14AwudNfAS//vqrRoHS09NjztZLS0vTeo6R\nQq+HH364pmG76djDhg2rU6hW6M+S7wHOHAlBSMhPiUD//v0BJ8QLjpCQ9XTWWWc1eGcsz3zw4MFD\nGFJKU4g3rLWadNK8eXMt671ixYqYzusuM1/dbl1YWKiFOsTLXFRUpISmVatW6e7t9/s17i/58VlZ\nWbp7TJw4UdXkwsLCOmlUwkOobhcVVfb0009n9913B5wd+JVXXqnxOKEXi5YAMHny5Jjj+8XFxRGv\nKePs3bu3ajfBYFDj+y1bttRM0+og5wgEAlqzIT8/X8dcVlam9RoTBWOMmpDu5yjcm0QlOdUFjSIk\nGQuEodalSxe1VeNRDl6EQnWqXmZmpnYbEvKS+7tffPGFhha3bNmixCkxNVavXs3NN9+s9yDhubq+\ndLWFJAVjx45VwfTDDz/oXLnJRD6fj+uuuw5AVfj09HTNRTn77LNjNgGDwaAKw0jn8vl8WgeyX79+\n6tt49dVX1cT6z3/+o6abCILjjz9ev3vxxRdruLegoEC/O2fOHP785z8DlYzNeJOYmjZtqpuSsCrX\nrFmjWbIJDsMmpRX9dcaYH40xPxhjXjPGZBhjOhhjZhljFhljxlf0hPDgwUMjQSxdpwuAa4Au1trt\nxpgJwFnA8cBoa+04Y8z/AUOBp+My2joiEAhoTcD33nuv2vz1usIYU6t3um3btlqhV7j1PXv2VFOi\na9euYYQryT4UGvD//ve/uLaDr61H40033cTJJ58MOFpVpGzM0tJSdYRKLsrZZ5+tZeXiAXceS6Tx\nhkIhzXwcOHCgVjbec889eeONNwD45ZdflOwmuSadO3cO0x6kfsbcuXP529/+BjgFTUSLjHf0Qc53\nxRVXqIYgGtGNN96YUr1AY3U0BoBMY0wAyAJWAofj9JUEpxX9qTFew4MHD0lETD4FY8ww4F5gOzAF\nGAZ8aa3ds+LvuwPvW2u7RTj2EuCSiv/2rPcgaoDf79fdJtlltdw7jewM+++/vzrGxo4dqz6G4uLi\nsEYlDQXpbP3aa6+p/e3u62CtVXtY+mmIhtNQEJ9J69atNeGsuLhYfQbiaPX7/eonGD58uGobDz/8\nsFb8TuTcS1u4CRMmqL9G5rJjx45RUevjgKh8CrG0os8DJgJnApuAf+NoCCOjEQpVzpWQpxEtccdD\nOA466CDld8ybN69OpdM9RIZktv7f//2fCrILLrgAQEvkJwEJdzQeCfxsrV1rrS0F3gAOBZpVmBMA\nbYD4FdD34MFDwhELT+EX4CBjTBaO+XAEMBuYDgwExpGAVvR1gacd1A/S4sxDfGCM4fDDDwccZ630\nkpRweaohVp/CnTjmQxnwDXARUIAjEJpXfHaetbZG12o8zQc3sSgjI0Pj/hs2bIibkKjNk/9Hgs/n\n04hKYWFhg1N0UxHBYFB9HJmZmUqljtUcq8c6THzlJWvtHcAdVT5eAvSO5bwePHhoODRqRqO7dbzr\nXPp7IlvKNxT8fn9c+QuREAgEdMevOrepPJ+R+A2RPvP5fPr/VLkfd/9M+d3n82n0xN0LVZ5NKBQK\nK90WxT0lntGYaNRGEnK/9PJdSSeOppJQddeL1OA0muPcxTfjATmvmER+v5999tkHn89X5zHWBZmZ\nmeTk5JCTk0MwGCQrK4usrKyoCD3uMScT7utVNzcyb2lpadWO0T3+WO6juuPS0tJ2eH4+ny/seuXl\n5ZSXl7PrrrvqZwUFBXqcfCadsMrKyuIaGUppoeDBg4fkI6WzJOuy08dDUtZXpZTvJ6IcODiJP9Ji\n7tVXX9Xy84lCSUlJWJfraHpIglN4RbIne/TowUsvvQQQ950sEtzPrLrfZQw11V1wU8/B2dnrQyyq\nbg1FOld1c+PO+qyuOU119xoLUsqn4PP56tXqXFSonQ2iYn7xxRcccIBjCvbs2TPhQqGukI5bvXr1\n4tRTHVZ7cXGxZv5t376dBx54AKjs2RnvZr4eokLj9yl48OAh+Ugp8yFaLUF2Jqkd+Pnnn++UmoJk\nJEpvQahsVZ8q8Pl8Wrxm0KBBmn8wbdo0LQO/atUq5TLIdz1NIXXhaQoePHgIQ8poCnWJgbdt2xaA\nSy5xkiw///zzhI2rIXHkkUcChIU6ays5lmykp6drA5W0tDT1HaxYsUKzAK21YaXQkgG5nsT5y8vL\nlelaUlKSMvyEukA0ZNGoE8UeTRmhUJeHJAU0pANRY3zAtcHn8zFixAggPH1ZHHUNDXnZZs2apVWn\nly9fzi+//ALs+Ezk/9FGMuoDEQR/+ctfaNKkCeAUVwEYP348vXr1AmDBggU6jpkzZ6bs+mnSpImm\nWd95551avk+KBV166aW8+eabQHwFhGc+ePDgIQwpoylEaz4Eg0EtrinVh3fbbTctu7azIDMzU3c5\nqNxhmzdvrsVChP2WTIgKK6ZB8+bNVYt54IEHGszhm5mZyfXXXw84beQvv/xyAN566y0gfCe98MIL\ntZbB1Vdfzcsvv1zteWsKk4tm4mYn1ud55OXlaeOg8vJy/vOf/wDO3Aq1OTs7W02vvLw8wOmYvWjR\nIr2nql2/64tGIxTEe/3xxx+z3377AZUVlR944AHOPfdcoObmsFXt2uzs7LCGqPEaayyQMY4bN46s\nrCzAsSHvuecewKl0JPed7BfwgAMO0DqIEgUpLS3l+eefB5wCIg2FjIwMnY8rrrhCXyz3c5KXZt68\nefrZDz/8oPfSoUMH7c4kJde//fZb3YRKS0vD1ldVGz89PV0raFW3RowxKuyluEqnTp3UHNu6dauu\nz6eeekrLwXft2pVWrVoBlc197733Xrp06QLAjBkz9PNYG/J45oMHDx7CkDKaQk1o1qwZt956K+DU\nBhSpKpK4c+fOyllo06aN7gQlJSXqBHvggQc07i87Q3FxsbbwipfqJZAxQqXqH4120aFDBwBtYw+w\nfv167WmwZcuWiBmMiUKTJk34+OOPAafKs2gpUhPg//7v/1RDaEiH3datW5k9ezbgVHOuaSxFRUUa\nMTn99NPp06cPAH379tXvyPEtWrRQFb7qOav2/nC3mjv22GO13d5PP/2kGuABBxygGo18tmrVKtW2\n/vGPfygN222KrFy5Un+X43Jzc7njDqdyQSAQ4E9/+hMAX3/9dUzPImWEQiS7TWy1Jk2a0KlTJ6CS\n2gywbt06AG644QYNSx522GEMHz4cgJNPPllVPGutFhmVlzQ7O5tPPvkEcARFtOSpaCbcrUpG830R\nItIhyOfzqRr48MMPq1c/WSaDqKJLlixRM2bDhg06t+PHjwecuUxkRCFapKenc+ONNwLQvn17fUEi\nPdOCggJVy3fZZRctxFNWVqYv3zXXXAPAp59+Wu2cRxIWst7GjBmj/Sp33XVXfdH9fj/ffPMNAA89\n9BAAH374YZ1UfhEKmZmZas6UlJRoOftY4ZkPHjx4CEPKaAqRIFJ+1apV6p11F5s4/fTTAfjss8/0\nmClTpmgk4qCDDtLsspdffplly5YBaHu0K6+8UnfBQYMG8eKLL8Zt7HXdPcWkWbx4MeBI/nHjxgHw\n/PPPJ9WpaIxhxowZgKNNSaShQ4cOO5guya6bUB2Kiop0Dlu1ahVxXOKsnjNnjj73xYsXc8UVVwDw\n/fffKw8kGq0xUlat/D5//nxtBWiM4auvvgKcaMeCBQuAuq8RcUDKuu/Tp4+aytu2bVP+QqxmXEoL\nBUEoFFJ1Vv4P1TMZJUwjIUuBhHLkZYPKRR2rx7YqiouL6/RwZBwi0Jo2baq9FzIyMmrsmhRv5Ofn\nq7lWWlqqPR4iCaZUIf6Ul5crA/Tss88mNzcXqOwb2rFjxzDmpXSFeuSRR+p9D5GOkzk64YQTWLt2\nrV5PIgeLFy+uVRiI2dyhQwf23XdfwDFnJC1diFn33XefRoNWr16tazjW6JhnPnjw4CEMKaEp+P1+\ncnNzCYVCEXsYQmWHHah0MNZ1JxbvsqiOUOnhFYnbEDDGKPfivPPOAxxN4ZBDDgGcmLl4mUtKSlSd\nf/LJJ7VFfTwbitxxxx2qmdx0003VPhMZu+xsDV3JWRy7c+fO5ZlnngGcrlzgrB9xAr7++us8/PDD\nMV9v1113BSrNADcFfa+99gpzcovWUFBQoJqswBijTsmXX36ZY489Vv/mXuNiNk+bNg2AyZMn89tv\nTluVQCCg5tOWLVtiMjc9TcGDBw9hqFVTMMa8AJwIrJH2b8aY5sB4oD2wFDjDWrvRONvLYzidpwuB\nC621X9d2jVAoVGNHaGut5t9nZ2erTyEnJwfYsZ+hm3Yq0jw/P3+H/oKAStpEFEKtrpKw/Cuf9+3b\nV/sZSnjMWqvhppYtW+rO52YQvvPOOwnRcNq0aaNzLDtc1XsS+P1+TUxbtWpVjaXOEg2Zz++++44p\nU6YATik7cMYpO/oTTzwRl+utX79ezy0Q7WDAgAFq42/fvl1p1a1atWLw4MEAXHfddUBlMVf3PQjc\n/gcJl4ozul27durv6d69u2p0EuqsL6IxH14CngDcBPGbgWnW2lHGmJsr/j8cOA7oVPFzIE4L+gNr\nu4C1NqxEdVWEQiF9EYYNG6bEmRNPPBGobPUOjkotE5mZmalp1lu2bOGUU07R6wmk6Effvn2VGlub\nIygalfmCCy5g4MCBAOr0KigoULUuLS1NzZiWLVvqyyYEnAsuuEAF5Q033KD0W7/fr6bCwoULo3aQ\n1qWv5pQpU7TZ7IgRIzSunp6ermQvEV7dunXT706aNEnT2WNxdMm8lJSU1EsNLiwsZOrUqQAaWdiw\nYYPWjFy3bt0OxKNY4CYjyTyXlJRw8sknAw7XQa6zdu1aHnvsMaByDktLS1XAdO3aVanLc+fOVZP3\niSee0IxIiV5cfPHF2nnK7/drhOPBBx+M6X5q3R6ttZ8CVcvknILTZh7C282fArxsHXyJ01eydUwj\n9ODBQ1IRVeFWY0x74F2X+bDJWtus4ncDbLTWNjPGvAuMstZ+VvG3acBwa+3sCOesUyt6dzKTOB1F\n+h555JFKxRXmnxzjbiEnPRIl3BYIBFTKX3zxxZotF48w2+LFi7VwaaRdurS0VFX0YDCoXIsBAwbs\ncM9NmjRRGvdhhx2mTkeodD7FE8FgkDlz5gBOKE+0oo0bNyqjVMy53Nxc3fGKior02UjX6vrArUrX\n51n06dOH0aNHA5Xmz2mnnaamTU5Ojo4vFk2hpjBxvJLmxOT99ttvuf/++wFU4znmmGP45z//CThz\ndu+99wIwatSo6k6X+LZxANZaW58OT9baZ4BnILoOUe6XaenSpVQcBziTFOkBWWvDXjzxL4igCIVC\n3H333YDj9Y32Idb0wGVBL1++XE0TybnYtm1bWOxaPv/kk080K0+Ov/TSSzWWXl5erj6HwYMHa15E\nkyZNtHry3Llzoxp7NCgtLVW69fnnn6+e8R9++EFVX+GNnHDCCXTs2BFwclQkAzCWitP1LQcv5J5J\nkyZpLP/9998Hwsu6FxYWhnVWSgTiIRCMMWp6rly5UqMWwr1Yvny5mro+n095GLGivt611WIWVPy7\npuLz3wA3Y8hrRe/BQyNDfTWFd3DazI8ivN38O8BVxphxOA7GzdbalZFPETuiaYQh3uDXXntNqdKC\n8ePHq6ZQ3+tWhexyRx11lGbfSRShT58+WhLsiCOOUK0gLy+PHj16APDRRx8BlZEVOecZZ5wBOOaC\nRFseeuihhBWXufnmmwG46667dDeKFFmYOHGiaitDhgxJyFiihTjdZHcFp4xZVYRCoXo1eKmKRNfU\nSE9PVwfktddeq5WyxYTr16+fOmXdXIhYEU1I8jWgP9DSGPMrTpfpUcAEY8xQYBlwRsXXJ+OEIxfh\nhCQHx2WUMWCPPfYAwm11IbkMHTo0YdctLS1l+vTpQKVguvrqqznuuOMA58F/9913gEM8kpde/B77\n7LOPHl9YWMhtt90GOJ7zZORBiFpdNdwrEIFWUFCg9+QmmCUbPp+PiRMnAs7cShTnxx9/TNg1q1Ze\nipcpIpTotWvXqu/ms88+07T6s88+G3CiUuLP+f333+MmpGoVCtbas6v50xERvmuBK2MdlAcPHhoO\nKUFzThQCgYAmP2VnZ6unXlqwJYtoI+r38uXLNcowYsQILezS0PTgumDvvfcGKglAu+yyizpMR44c\nqZfMxTMAABZGSURBVNpPsnH00UerKr1161blU8QSBakN9S2LV1vUwl1QReDuafnXv/4VCCfy/e1v\nf4t4XH2wUwuF1q1ba7ENQMNUCxcubJDxSIESiE9D3GRj7733VkEmpKn169drmvX48eMbLGvy6quv\n1jkdMmRI3LNe44ma5qjq38Q0ad26NWPGONQg8SlYa5Wk9dRTT8Vt7r3cBw8ePIRhp9YUrr76alXV\ntm3bxsiRI4GGqwHQGLUDN4qKijRGLhTsuXPnaqQiEUSqaLH//vvrriqaS6IRL7PP5/OpKeIu25+W\nlqY1Qfr168d7770HVM7zkiVLuPTSS4H4rmlPU/DgwUMYdkpNQRxOV155pUrgDz74ICUKjDZmLFu2\nTHtFiiPvxRdfjFt8vD6Q59usWTN1+DWW5yyajTuRqlOnTppIdemllyrdfNKkScpYlLKBa9euTYjW\nu1MKBYn5p6enq8oeazqpB0dFffbZZwG0KMyHH37YkENSVbtNmzZa5CRZ5mGs13Gbk+6msVKVOhgM\nKm38vPPOU9Mt0fDMBw8ePIRhp9MUWrRowUknnaT/F7VMmGEeYoNwO6SNXTzowvFAY3++onX88ssv\nyrht2bKllh6sjlmaCOw0QkEqNXfs2FFV3N69e2uvyMZiZ6Y6RF3f2Rr6pgoKCws16/SMM87QbNVk\nRsw888GDBw9hiKrISsIHUY96DBXHAQ7VVuoEzp07V1XaffbZRx01U6dODau2u7Mg0f0gjDGadOMu\nWiMoLCxs9PyLVIM7S1LWcpzmOKoiK41aKNRwPqD2hqBuSCYjOFz2eM5LpIIeMTUArTifMUbDcPEc\nrztE5hYC2dnZmksg5lgoFNLvuovPJHtdVS18U58MxnhVS0phRCUUPPPBgwcPYWjUjkZ3Lb9oqhVH\n+lx2wvz8fPX0+ny+uFJ2pTRYWVmZjnn79u11alHvHq+UQisqKlKvdDwzLY0x2ncRKouWlJeX65jd\n89NQ2oEbNVUCjxZpaWmqeSUqczVSj0ufz6dzvHXr1ojVoetbr7I+8DQFDx48hKHR+RR8Pp+2ApNC\nrN9++60m6NS3Ao0xhgMPdFpUFBYWKr00HhAfQCgUitlhZIzR86WlpanWIB21EwHRbkKhUNjvOxuS\n4VPIzc2lZ0+neLmkoWdkZLBmzZqaDosXklPNOVmQF6Fjx47acOT7778HnJx+yZ9fvny5EmzqYgJY\na7VtvXT6jRfqWz7N3bVKXkJrbZiTT4guiUQkOm4qw92b8fjjjwecLMrXXnsNQJumVEUiBYKs3+nT\np+tmJrU+kiQQooZnPnjw4CEMjdJ8EKfMwQcfDMDAgQPp2rUr4FBDpWrzypUrtYmKMBtTHdLnsn//\n/srMnDdvniZ5VU2KkR0oGcVcUz1kJ0zAJ598ksMOOwyobM3mxm+//cbYsWMBuO2225Iyd6L1vfHG\nG6xatQqAyy+/HEiqg3bnMh8EbpVZWm+XlZVpBdxmzZqp57xp06aa1vvEE09obbtUXNgSBbnmmmsA\nuP322/WzQCAQ0Wudlpam/IpkLOxgMJgyuQ4CEZZ33XWXVpPOyMiI2LBV5rCgoEDn+Z577qmxuXG8\nIGbXRRddpGZtKq5D8MwHDx48VEGt5kM1regfBE4CSoDFwGBr7aaKv90CDAXKgWustbUm3NeX0ehu\n6y7FLMvLy2nWrBngVL2Vdt/GGG644QagsoBrKuGss84C4L777gOcnV96A44bNy6scrConW3bttV+\nEMmoWpyVlaW7airscrm5udqWPS8vLyymL07o888/H4Cff/5Z29NLlAlgwoQJ2rIvWajKcI1mLt21\nQcTErIeTOT40Z2NMP+B3nG7SIhSOBj621pYZYx4AsNYON8Z0AV4DegO7AR8Be1lra2SCxJvm7IbU\ntTv22GPVay9hvGSojdEgNzeXn376CUD9CCNGjAj7jiz4Cy64gMcffxxwMhWlEYvYqfFGfn4+n3/+\nuV5DrvPCCy8AMG3atHjz82uFzMUjjzyiZoAbI0eOjNj5S0rRb968WV/MNWvW0K5dO6D+NSbdL3lt\n71NGRoam9kuv0A8//FAbJBcXFzNw4EDA6SgmLQp69uyp/iTJ5znttNPqWvUqPjTnSK3orbVTrLVi\nxH6J0zMSnFb046y1xdban3E6RfWuy6g9ePDQsIiHo3EIML7i9wIcISH4teKzBsNdd90FOG27xSnX\ntm1bIL6dmusD2fE+/fRTrSsZqfchVHrR77jjDv3up59+Wq8Yt5s+W93u3q1bNwC+/vprdWJmZ2er\nyipzGAgE1OG77777ah+CRKJ169aAYxq4HbCSBSsFYKpCam64k7zmzZtXbw1HzFcxVwGlyldF+/bt\nAfjHP/6hPBg5vrS0VOtTDBw4UOfwoYceUlJadnY2nTp1AuBf//oX4PTZOOGEE4D4mo8xCQVjzK1A\nGTC2HsdeAlwSy/WjwZFHHqm/y8NPlSo98mK1bdtWH3ikKEKTJk20J2JBQYF+59///ne9FnRubq72\nWhTz6vrrr9eXe8mSJdrg1t2W/vHHH1fVdcGCBQDstttujB/v7An77LMPn3zyCRA+7/GGFDZdu3at\n5pVs3ryZ22+/Hai+85J053KXUX/sscfqnecg1xG1vrrITGZmppoBf/rTn/j555+ByjlyC5JRo0ZF\nPMe2bdt0E1u9ejUAvXr10nu+++6749YAp95CwRhzIY4D8ghb+RSibkVvrX0GeKbiXA3vtfLgwQNQ\nT6FgjDkWuAk4zFrrFk/vAP8yxjyC42jsBETmlMYRgUAg4g4bDAa19bsxRnfVZPWQrAnGGFVz33zz\nzYhmwJlnngnAc889p6bPli1bNKLyxRdf1OvaoVBIHVTSefv8889XTcHn8zFv3jwAunfvHlbKTvj6\n4pRLT09XraNbt25KL/b7/QnLNBRn7GuvvaaRgxkzZrB06dJqj7n44ou1EA9UqtuxmA+yF9bG3ejZ\ns6dWvzbG6JxXZ2pUB3Foirny448/8vbbbwPhvSZj5azUtxX9LUA6MLXCpvvSWnuZtfZHY8wEYC6O\nWXFlbZEHDx48pBbq24r++Rq+fy9wbyyDqit69+4dtoOJI+7444/XjLSKsQHhTTgaKt7eunVrDYWN\nHj1axyRszF9++UXp3NZapWkfeuihas/Xd+y///47Rx99NFCpKVx00UVqn2/atIm+ffsCOxa8rerQ\n2r59u2oKZ511FjfddBOQ2E7acu7Nmzfz9NNP1/hdWQtDhw7VnRYqae8yl4nEQQcdpNyCyZMn16sz\nd3p6umpFoumeeeaZ/PrrrwB06dJFGycnXFNoDFi4cKH23MvJyeEf//gH4KhZkgdhjFH1KhVopmee\neaZ68ktKSjR2LZl8bs7+jBkzOPbYY4H4mD7WWuVoPPXUU4Cjkgv/Yb/99tP53Lx5c40vuDFGnV3B\nYLBB+0lGwsSJEwHo0aOHflZWVqYdrhIJEfT333+/fnbnnXfW6aWV6Mo555yjRDXplbl8+XJdwz/8\n8ENcxgwezdmDBw9VsFNoCps2bdIMub///e/ssccegFN8RUJ91lp1niWjBkF1EMm/fv16DfX9+OOP\nTJ48GajUEEpKSlQVlx08FgSDwYi9L+Sz0tJShg8fDsC7777LrFmzAMcp+eijjwJORqE45eQ+Tj75\nZK0PsGHDhqR1fK4Np5xyCuB0awZn15Zd9f77749L34qqSVdVNc8OHTro76Jtff3117WeV+b24IMP\nVkr2TTfdpFqvsFj9fn9Y6TYxj2LtcbJTCIXS0lKN/X722WesWLECcOLx7irNQittSOTn5wNOTH/v\nvfcGHJtTFpC8dP369au2GEh9EM1CEf7G7Nmz6d69O+DQcqW34cEHH6yFaCQy4vf79WVIhagOOF3C\nXnrpJSDcDJN18eCDD8blOvKsImVkgvNc5e+bN28OO6Y6GGPUFOjUqRPPPfcc4Pg+ZI3IGurWrZt+\nt3379px66qmAw72IRTB45oMHDx7CsFNoClBJcX3wwQdVpRo/fryqYuXl5eolbygYYxg8eDAAQ4YM\n4b///S8AhxxyiFJvhbUWTy0hWshOd/311+vvQ4cO1bEdeOCBqo4LZs6cyS677AI46rJEVGriDCQa\n8+bN08iNm849YMAAILpEuGia7FTlDfj9ftavX7/DcWVlZZrF26xZM418yDWCwaCyW0eMGMFee+0F\nOMVghMtirVWtR7S0jh07anTt0UcfVY3lyy+/VPZmfbBTCAW/36+hp02bNukD+fLLL9VrX1pamrRW\n3tWhf//+mttQXl6uD65jx45aqv2qq65qsPEJSkpKlCA1bNiwsMYqMs8yXmutEnO++eYbjZ5IVaxk\nQijbkgULler6SSedpGnWUNn8Jz8/X++pVatWmqG4cuVKwKGYS0Rl9OjRYfct566O5jxz5kzAmStZ\nkwMHDuTbb78F4PTTTwfg7LPP1o2ssLCQW2+9FXBedDmn3+/X6z300EM6XokeZWVl6d/PP/983VTq\nUxTHMx88ePAQhkatKYj69pe//EUjCyKFAU3qAYdkE6/y5D6fL8wsqQ1CCpowYYLuGDfeeKPWJOjV\nq5eWrReHVEMjUou7UCgUUdtavny5/i5U4kTSnCPB7/cr4Qoqxzxy5EgA3n//ff1bTk6OmpKBQIDn\nn3e4eIMHD1Y1X5x5bkf18OHD1SwpKyvTNVCdU09MqI4dOzJ9+nQArrzySm0fIFps06ZN1ewYOnSo\nmpXuXT43N1cTnqQuxNSpU9V0Kysr05ocTz/9dEyOxkYtFCQd9bbbbuPcc8/Vz+XlHzJkiH4Wz0ao\nbtUxGkj6digU0jGNHTtWF1X//v1VhU0Gwy7ekMVrrdWwWWZmZlKL2LRo0SIs0lCVRBUMBlU4L1y4\nUJmjgUCAO+64A3CKyNQUQQkEAiokQqFQ1EJv8+bNWvRl33331axSqRlaUlKiYfJAIKDzGQgE1J9z\n++236yYnIfcpU6Zwyy236P3Gq1OXZz548OAhDI1SUxBNQGoYNmvWLGxXkt1KVD1wyn5HqohcH0Qr\nieV6YhoEAgFef/11/bt4rXNyclR9TJVYf10gO2YoFNKdtHPnzpqPkgwMGTJEd9VQKKQOQSlo8tJL\nL2kcPzMzUzUJY4zmJUhFcKgsxFJaWqom6csvv6waontHjwZvvvkmAG+//XaNO7r7nO7eo/vtt59S\ntSX/5IQTTkjIevE0BQ8ePIShUWoKUt9fHDWBQEA/W7ZsGRdffDEQzrYrLi5OquNLrg9OFic4kr/q\nTgAO7VqSuBojZAd2z2+yHabfffed7v4ZGRnqgJMQoxuhUEifjVt7DAaDOu6//OUvgFOY1g35fqQm\nM9Ggrn4tWS9t27ZVzVc0hUQlnzVKoSAPRLyw5eXlYaqVO9YvauQ///nPJI+ycgHIg636oojjaN26\ndbzyyivJHVyc4Pf7NaV3+/btvPrqq0BiG95GwrRp09Sr36tXr4imoltdlyhKXl6eeupXrFhBnz59\n9PdIcG8yyYREQyDcsZsIeOaDBw8ewtAoNQXZhSQO3KFDB/7+978D4e3Si4uLNZknXkUt6wLRFKTP\nZa9evdS5mJWVpTtbMBhUR2NjQ7NmzVRLe/XVV5WxGWumXl1RUlLC4YcfDjjchGOOOQaoTPJav369\nZijOnz9fd9633npLS+HVRbVPdi2OSCXxEoVGKRRkgiSF9Kuvvgrjnwu+/vprJaY0JH77zaldu2nT\nJs19eOSRR8JKfKdaj8ZoMWHCBDXjNm3apGp1WlqavmTJ6HMJlbb28OHDNQ28sUPWiLtDlKSnJ6py\nmGc+ePDgIQyNUlMQSIJLixYttBLxCy+8oDvTeeedl7RWZtFg27ZtPPHEEwC8+OKLmrW3devWlCtj\nFi0KCgrC6krKzhZNCzUPtUM4FL/++qsWu6mtLmXMsNY2+A9g4/VT0UPC+0nSz80332yXLl1qly5d\nas8++2ybnZ1ts7OzG3xcO8tPfn6+zc/Pt5dcckk8zjc7mvfRMx88ePAQhlq7TidlEF6HqEaL3Nxc\nrZ0wa9Ys5WKkwrraGeDmW8RhTuPTij4ZqKtQqEpMcacyJ8vT/UeHzLfYvOBEUeKVqRcLGrKfR7wh\n81tWVhYP/1h8WtF78ODhj4VU0RTWAtuAujXXix9aetf2rv0HuHY7a+0utX0pJYQCgDFmdjSqjXdt\n79retRMLz3zw4MFDGDyh4MGDhzCkklB4xru2d23v2g2PlPEpePDgITWQSpqCBw8eUgANLhSMMcca\nYxYYYxYZY25O8LV2N8ZMN8bMNcb8aIwZVvF5c2PMVGPMwop/8xI4Br8x5htjzLsV/+9gjJlVcf/j\njTFptZ0jhms3M8a8boyZb4yZZ4w5OFn3boy5rmLOfzDGvGaMyUjUvRtjXjDGrDHG/OD6LOJ9GgeP\nV4xhjjGmRwKu/WDFnM8xxrxpjGnm+tstFddeYIw5JpZrxwsNKhSMMX7gSeA4oAtwtjGmSwIvWQZc\nb63tAhwEXFlxvZuBadbaTsC0iv8nCsOAea7/PwCMttbuCWwEhibw2o8BH1hr9wb2qxhHwu/dGFMA\nXAMcYK3tBviBs0jcvb8EHFvls+ru8zigU8XPJUCsKYiRrj0V6Gat3Rf4CbgFoGLtnQV0rTjmqYp3\nomHRwNmRBwMfuv5/C3BLEq//NnAUsABoXfFZa2BBgq7XBmdBHg68+//t3b2rHkUUx/HPgegFI2hi\nEaIRbgSxNVYRLQIKaggROyVgRP8BK0FuZS+ihaigWISLghrkkkbwpY4vIBp8wUjE3JCYNKZOcSxm\nFp7VPMarz+xjMV9Y2J3Zh/P8dg+HOWdndxDKRJZtV7seC7Z9E86odaSZ9ubacRvOYqfyuv4JPNRS\nO1Zx6lo68QaeuNp5i7L9p77HsF73R/6Oj3Bvi/u/lW3Z6cPgLAObta05EbGKfTiJXZl5vnZdwK45\nP/uvvIznMExivwW/Z+bwwkZL/XtxCW/X9OXNiNhuAu2ZeQ4v4lecx2V8ZTrtzNc5tQ8+jWENu6X5\n/9+x7KCwFCLiRnyAZzNztDhilpC98EcyEXEIFzNzuhVSxmzDPXgtM/cp08pHqUJD7TvwqBKYbsV2\nfx1iT0YrndciItaUFHZ9attbYdlB4RxunzneU9uaERHXKQFhPTOP1+bfImJ37d+Niw1M34fDEfEL\n3lVSiFdwc0QMX8BqqX8Tm5l5sh6/rwSJKbQ/iDOZeSkzr+C4cj2m0s58nZP4YEQ8hUM4UoPSZLa3\nyrKDwhe4s1ahr1eKLhutjEV53/ctfJ+ZL810beBo3T+q1BoWSmY+n5l7MnNV0flpZh7BZxhWLGli\nu9q/gLMRcVdtegDfmUC7kjbsj4gb6j0YbE+ivTJP5waerE8h9uPyTJqxECLiYSVtPJyZs58V38Dj\nEbESEXuVYufni7T9r1h2UQMHlYrsz1hrbOt+Zdj4Db6u20Elt/8EP+Fj7Gz8Pw7gRN2/Q3GE03gP\nKw3t3o0vq/4PsWMq7XgBP+AUjmGllXa8o9QurigjpGfm6VSKva9W//tWeUKyaNunldrB4HOvz5y/\nVm3/iEda+t0/3fqMxk6nM2LZ6UOn0/mf0YNCp9MZ0YNCp9MZ0YNCp9MZ0YNCp9MZ0YNCp9MZ0YNC\np9MZ0YNCp9MZ8QdFKCLkC5rYQwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5658d6b780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/2... Discriminator Loss: 1.1537... Generator Loss: 0.7651\n",
+      "Epoch 2/2... Discriminator Loss: 1.0387... Generator Loss: 1.4510\n",
+      "Epoch 2/2... Discriminator Loss: 0.8105... Generator Loss: 1.4478\n",
+      "Epoch 2/2... Discriminator Loss: 0.8504... Generator Loss: 1.3711\n",
+      "Epoch 2/2... Discriminator Loss: 0.8881... Generator Loss: 1.4892\n",
+      "Epoch 2/2... Discriminator Loss: 1.0879... Generator Loss: 0.7779\n",
+      "Epoch 2/2... Discriminator Loss: 0.8741... Generator Loss: 1.2485\n",
+      "Epoch 2/2... Discriminator Loss: 0.9110... Generator Loss: 1.7616\n",
+      "Epoch 2/2... Discriminator Loss: 0.8936... Generator Loss: 1.5694\n",
+      "Epoch 2/2... Discriminator Loss: 0.8861... Generator Loss: 1.0268\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncXJ4UnAcJ4cnBcdxcnhScBwnx98T/qNalT9MKAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f565b247400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/2... Discriminator Loss: 0.9276... Generator Loss: 0.9687\n",
+      "Epoch 2/2... Discriminator Loss: 0.9666... Generator Loss: 1.0320\n",
+      "Epoch 2/2... Discriminator Loss: 0.9749... Generator Loss: 0.8926\n",
+      "Epoch 2/2... Discriminator Loss: 0.7613... Generator Loss: 1.6372\n",
+      "Epoch 2/2... Discriminator Loss: 1.5822... Generator Loss: 0.4344\n",
+      "Epoch 2/2... Discriminator Loss: 1.1882... Generator Loss: 0.8438\n",
+      "Epoch 2/2... Discriminator Loss: 0.9924... Generator Loss: 1.1633\n"
+     ]
+    }
+   ],
+   "source": [
+    "batch_size = 64\n",
+    "z_dim = 100\n",
+    "learning_rate = 0.0002\n",
+    "beta1 = 0.5\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "epochs = 2\n",
+    "\n",
+    "mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))\n",
+    "with tf.Graph().as_default():\n",
+    "    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,\n",
+    "          mnist_dataset.shape, mnist_dataset.image_mode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CelebA\n",
+    "在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代，或者当 GANs 开始产生真实人脸图像时停止它。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 0.6635... Generator Loss: 2.6208\n",
+      "Epoch 1/1... Discriminator Loss: 0.5171... Generator Loss: 5.9576\n",
+      "Epoch 1/1... Discriminator Loss: 0.9434... Generator Loss: 4.5532\n",
+      "Epoch 1/1... Discriminator Loss: 0.9883... Generator Loss: 1.3031\n",
+      "Epoch 1/1... Discriminator Loss: 0.4266... Generator Loss: 3.6278\n",
+      "Epoch 1/1... Discriminator Loss: 0.6535... Generator Loss: 1.7355\n",
+      "Epoch 1/1... Discriminator Loss: 0.6394... Generator Loss: 3.2757\n",
+      "Epoch 1/1... Discriminator Loss: 0.5272... Generator Loss: 2.5773\n",
+      "Epoch 1/1... Discriminator Loss: 0.4938... Generator Loss: 2.2639\n",
+      "Epoch 1/1... Discriminator Loss: 0.6107... Generator Loss: 2.0199\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56c82d7eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 0.4906... Generator Loss: 4.7177\n",
+      "Epoch 1/1... Discriminator Loss: 6.6824... Generator Loss: 12.1958\n",
+      "Epoch 1/1... Discriminator Loss: 1.2869... Generator Loss: 1.1015\n",
+      "Epoch 1/1... Discriminator Loss: 0.8712... Generator Loss: 1.2599\n",
+      "Epoch 1/1... Discriminator Loss: 0.9868... Generator Loss: 2.0520\n",
+      "Epoch 1/1... Discriminator Loss: 1.0734... Generator Loss: 1.9953\n",
+      "Epoch 1/1... Discriminator Loss: 1.7425... Generator Loss: 0.3826\n",
+      "Epoch 1/1... Discriminator Loss: 1.1829... Generator Loss: 1.2268\n",
+      "Epoch 1/1... Discriminator Loss: 1.4086... Generator Loss: 0.8518\n",
+      "Epoch 1/1... Discriminator Loss: 1.2163... Generator Loss: 0.7233\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5658c9d7b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.2762... Generator Loss: 0.8202\n",
+      "Epoch 1/1... Discriminator Loss: 1.0826... Generator Loss: 1.4488\n",
+      "Epoch 1/1... Discriminator Loss: 1.5480... Generator Loss: 1.4554\n",
+      "Epoch 1/1... Discriminator Loss: 1.1184... Generator Loss: 0.7442\n",
+      "Epoch 1/1... Discriminator Loss: 1.3038... Generator Loss: 0.8651\n",
+      "Epoch 1/1... Discriminator Loss: 1.2226... Generator Loss: 1.0445\n",
+      "Epoch 1/1... Discriminator Loss: 1.2978... Generator Loss: 0.7549\n",
+      "Epoch 1/1... Discriminator Loss: 1.2956... Generator Loss: 0.8022\n",
+      "Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.6897\n",
+      "Epoch 1/1... Discriminator Loss: 1.0502... Generator Loss: 1.4712\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n18hMKcxkJjO5Rv4fYYSrPFJzp2sAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5699d05e48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 0.9124... Generator Loss: 1.4509\n",
+      "Epoch 1/1... Discriminator Loss: 1.0586... Generator Loss: 0.9160\n",
+      "Epoch 1/1... Discriminator Loss: 1.4511... Generator Loss: 0.4657\n",
+      "Epoch 1/1... Discriminator Loss: 1.2195... Generator Loss: 1.5226\n",
+      "Epoch 1/1... Discriminator Loss: 1.1125... Generator Loss: 1.1325\n",
+      "Epoch 1/1... Discriminator Loss: 1.2902... Generator Loss: 0.7191\n",
+      "Epoch 1/1... Discriminator Loss: 1.1498... Generator Loss: 0.8416\n",
+      "Epoch 1/1... Discriminator Loss: 1.2848... Generator Loss: 0.7776\n",
+      "Epoch 1/1... Discriminator Loss: 1.9145... Generator Loss: 0.2802\n",
+      "Epoch 1/1... Discriminator Loss: 1.1082... Generator Loss: 1.2230\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5699b497f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.4833... Generator Loss: 2.0466\n",
+      "Epoch 1/1... Discriminator Loss: 0.8034... Generator Loss: 1.5415\n",
+      "Epoch 1/1... Discriminator Loss: 1.1546... Generator Loss: 0.6480\n",
+      "Epoch 1/1... Discriminator Loss: 1.1812... Generator Loss: 0.8394\n",
+      "Epoch 1/1... Discriminator Loss: 0.9518... Generator Loss: 1.0477\n",
+      "Epoch 1/1... Discriminator Loss: 1.2184... Generator Loss: 0.9924\n",
+      "Epoch 1/1... Discriminator Loss: 1.2908... Generator Loss: 0.8527\n",
+      "Epoch 1/1... Discriminator Loss: 1.2357... Generator Loss: 0.9712\n",
+      "Epoch 1/1... Discriminator Loss: 1.3955... Generator Loss: 0.6073\n",
+      "Epoch 1/1... Discriminator Loss: 1.0635... Generator Loss: 1.1717\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5699a45550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.5523... Generator Loss: 0.5931\n",
+      "Epoch 1/1... Discriminator Loss: 1.5956... Generator Loss: 0.4272\n",
+      "Epoch 1/1... Discriminator Loss: 1.2407... Generator Loss: 0.6906\n",
+      "Epoch 1/1... Discriminator Loss: 1.5402... Generator Loss: 0.6955\n",
+      "Epoch 1/1... Discriminator Loss: 1.0282... Generator Loss: 1.7090\n",
+      "Epoch 1/1... Discriminator Loss: 2.0579... Generator Loss: 0.2622\n",
+      "Epoch 1/1... Discriminator Loss: 1.1844... Generator Loss: 1.0045\n",
+      "Epoch 1/1... Discriminator Loss: 1.5009... Generator Loss: 0.7875\n",
+      "Epoch 1/1... Discriminator Loss: 1.2890... Generator Loss: 0.6643\n",
+      "Epoch 1/1... Discriminator Loss: 1.2547... Generator Loss: 1.2426\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsred5mPf887DmtfY8nLPPyEEiKYoaLKmxVdcG3KBt\n0gZN2gJBWhTwVdEUaIEavepNgFwVuWpRIy2QFkXTNk2QBnERG64t25JsiaYsiuMhz3z2vNe81j8P\nvXjffUTakklZYsKL9QIE/7P2P3zf+33fOw9GXdesYAUrWMElmP+qB7CCFazg0wUrorCCFazgQ7Ai\nCitYwQo+BCuisIIVrOBDsCIKK1jBCj4EK6KwghWs4EPwiREFwzB+xTCMdw3DeN8wjF/7pL6zghWs\n4GcLxicRp2AYhgXcAX4ZeAJ8F/gP67p+62f+sRWsYAU/U/ikJIUvAe/XdX2vrusM+IfAX/mEvrWC\nFazgZwj2J/TeXeDxB/79BPjyj7t5sLZW71+9ggFkuQHAeRKTxAkATWeKVTQA8CjJklIeNCsAyqrG\nVvJW1xaWI++oMbAs+d0obSxHpKJCHsOiIiv0VWZJWcjfDSryXL5RZAlpKb/blqArbLZxLPl7FueY\nZAAkZYWN/J4XNdYlyTXkwqyhrPTjhiH/AUZtUlPrOOReyzQx9QU1PH1XiYmt/yhLcBz5Pc7kvSY5\nSabzcw2KVN9b1WRFLr8bkFPpt+XevKqgqp9+rygvx15DbXxgGoZ+p9Z3mVQGT58r9YVlXvMXkUK3\n1zewFLdFWuD6Ms6qdgFwPShTmb8bQFUIAgy7pkhlMU2jIk7kuq70XXmBbgWyqsK8nHddYSvOKwMc\nxe3lMnmOiW4FDKMiyRSfrsnEjeUP5x89L8OUr9uYGMblWsvfzBpKxWdd1VSXi1JBpb9X9dPtwlO0\nGsCPuDYaJrURAPDi9Wd5/fIPr792Udf1+keN9ZMiCh8JhmH8KvCrAHv7+/zWN38P17J4eCxD+h/v\nvM6d778NwNe2fpPm6BUAblVLHr4/BKD2IwAWSUE/FAznWYfutixAXrp0O/K7sejR2ZbVHc9lDC17\nweGJXAeNOeORHBqfBUeHCwBGR+9wZyynbK0j+Hz5L/0i280ZAI9/cEbTEPr39mTBmikvPzzLGbR0\noV1fvpHXjOepjMd1MW2Zq1l5VLp5g1AWs+OHNDotABKzouPLPOZmwKAr75tPYVOX+K0nQkC96oR3\njuRA9HZ9zu7JnMI45cnwCIC2Y3BWyjisUu49WaYYeqhiCobTy3UqqWoZp+uCZcrh9EwZb88KWTry\nXGpazG3B1ewoIy3l23Xx8YnDr/61v0GnJ7g9fW/I1c/IGkfJAQB7t2qiu4KjnRchPtuUsfVLzu/L\nvgi8JW++dQFAmU4AODse0lbifLiMcZUznMYRa4rzyDXYbgvzSSIZ8/XdJoe6Xzwj4q0jwXNrr8E/\n3fs+AMX/8BGTsgy8sA3AWuXjKKHzBJW0cphZsh5xnJM6gtsqMogvGU5WYyolK0vZ34YjxE4HB7n8\n3f9cg8T4LAC/+X98k11L1qHc8h5+xEiBT44oHAL7H/j3nv72FOq6/nXg1wFe/vzLtZcXFFhslrIJ\n/vPpjL/XvA7As9//Au9dF0SdPLBxNgURZxddeVkzY5LLwnb3DGaRLKzbgCjpATC4CsXCA6DRFwof\nX4S090YAzKc9gi2VCMZr7OzIcN+8FzKrZLMNj+X5r+1ZROcb+u0jvvu2LJx9s+Td95SLhz4XYxnn\nlefkcC+GYG3LppqN4FpHxnaSpXQashRlITult7lOVQsXbLYgK2Wug42cYib3dq/ZLEbyvY1bMo/j\nP24T5U8AOHqzpKfDPIkclkEIwOk8oTEQfB2PlJi4JcO5vGtgwaEh+O64Lo5KB71Wl0JFMkd3dF1V\nbPgyzmle0ymEbd6vC7Lih5LHx4XyuYon78j6pd4Fv/09wfmL/77g+LUnn+VgTd54OOyw84LgYn5k\nEdwQYjm9V7L5WXnHd7+n7x04DIeytzq7De7fHwNQuSF3l3LP/nqTJ6mteG4CcC/pMbgqfx/ePWZZ\nC4V4fGcJ/+xjTsow2FJxo7me0uSGfLsl47GSGYNcDu5FEeBbsibTOqepyEtrcFQkq2t5l1WYGPpb\nkdRPJYjsm3Nq59vyjddOGX5uDYDuxxzuJ0UUvgvcMgzjGkIM/gPgP/pxNw+rMf8g/0c89w9+iV87\nEur7t5+F9j/+AwDe+fIG7XeEyy3W1ijP5bDZTdmAycxhKfsd+yLA9XWj1w3MDdmwjSzA66rIXAgi\nJ4OC7ESuSyvFmAm1nnk5x6kgPnSWXIxlwVxLpIfk/gS7vC9ju3+H48kpANmbHk3lwM6hwySU5164\nJ2NYbjfZvhAqf7cNWy3ZuEHhM9DrM18OwdWNAbFSfieGkUo8rcQjs2Uc/h2DowOZa/xYDoTdm2I8\nlnfQXzB8X+YRJ2NOp7IJE7NikHUAMB3ByVrkE4dCLDt1gyuB/P5yuc7RTXn3X6q+yvv+mwAclELz\n32ieszaRsd+L7tIxZDz37y5UQfnJYPK7E0pfJMQ33j9hZgvu2t/8HACNvXfZunsFgPkvtNg+EoIb\nrFV478m2v3ttSvK2ENz1tjDHeGowC2VtinED15G51qcZc1P20+R+CU3ZD9WRHP7yM1PiQ6GsrU5F\nfVfWwe7FFB9zTmZR0egKEd6oQxq7Ir3sBSI9HI07OKUwHjdY0EPW71G2ZFypilIWKjOI+idQs7xU\nOz5AeUsA2Xrs/Nvb/MLXxh9zpAKfCFGo67owDOM/A/4FYAH/c13Xb34S31rBClbws4VPzKZQ1/Vv\nAL/xsW6+8DH//jNY34KvjEVsD8vv4n5G5LqTac49tbLsLk/wUqGkliuUNmVBv1axNmwR2sK5Mgxa\niXBV+m1qNaQtSnnXcj4kdwQFRpkya8hz60XGRSQ6aZq5pIZQ63klIvOFYzM7F47/aLbgfKZGraKm\ncIUjuFZKjHDYWsXrbh3irMvfB0ZBqVxwze/gBzL+3VBEHsPy6aiRcBmAtVSOH7QgFo5/2p1RDuUb\np57q3rHDWS5SzGTUJjQTnbNFrgbDooDUlTF7TREtS6Nm25GxvdLaZX1rR/4eNmhuyjdCq0s/E5Xu\nohJJ40a2wFsTHhZFO+RqXn6+ec53Z5muw8eH2QCyR7IOR6MUW0XlN56IevTS1pc5Qwwe3iRmuitj\n82oTqy9r2V4sqfdUsqpFqqiKI/aXaoy1Z5zH8o1FWJCngpckT1nGcp0Gal088kl3BId5BRNkHGfn\n/kfO5alrzzFYXlV7QNIncAS3x3254/pOE3spxqEDt+BiJDaVn9+Y8eShSMOZVfODUiSMG7Z8+0G6\n5FQNqvOy+rFq2sb9/+ojx/pB+FdmaPwg1GVGNjpk6lxh7+QWAPfMEbO77wIwSg6x9mWo6WJK4YqS\n117KYR1s7NAK5NpIXUJLRapeQBjKgpp5TaiichrKgd7G4mIpCDaKDPPSPm2cEepBDkuDKlfdREXj\n0btvsBjeA+DsbMI0EeK1YbQJLi3gFmSpHFQ3kIN0o9nFdOVdzXZMP5DNUZUpptMHwG/KM123ieOJ\nuFtmLkEoKkpiQKHGwSwJmDnvA7A2uQbAaVTSdwcy3DBjfijvS6OUtFCB16kYuKIz7zmyGcsbHTa3\n5EB89Zkvc7MhY7M2+tiOqG5JZnJtJPg6LISA1MtzhkcypxvOIyabNwH4neGSUIlTFVcfW9Q+f+8u\nF1PBbVZnzEtZ15upEqa3K8IvyPqbazkDV9QEtyypN4Vo++2Qw2NRJVJTDpK3s4VxLgxiWG+y5zwC\n4KHnstB1WuQGy6WQsFxP9Kw5ZSNTQ13DxY9k3qaffuRcLg/py7bD35gJjt55bo2vflYI8dK5DcD1\ncsjIkD3Ujk3mz8ja+NWE4fA9AIInNrfV6zZXQt8+e8LhXZnf42TG0Y+x4fzG8NsfOdYPwirMeQUr\nWMGH4NMhKVgOWXeDR194m2vPiLn4wfWIC0/UhOh+hpeJSDWJ92jdFmrebe4BkIYOobkFgLOT0c9F\ntB1bMYVa7ZNeQjMTo5TryPNTo8HSF4NSy1zHawuntBZt3OwdAMwtC+NCue1CuJXfKIhtMT75jyYY\nhkgg/p7DIBXOe5bOaRWC3nhTXUiDEF/dYoXZpFIppiwcHEs4ol0JJyr8hCpVacVbMi+F+1tOQelv\nAxAkc/qRuGoXt+X5/cUmJ7lwj+MjhzQQLmdaPtZCxlE5BvVnBBcDNa0bjZDbvuBw+7MBTVMkF7cN\n2fJAFqr/DllDJJK9Wr4xf/gK5YtvCI5PP8NmU/D59a+/xOj/Ewnje3cz6lK43GX4w4+D3k6BuSbW\n+dl3DumpGJ9/Vdb0mXqXqCGc9Nq4gXFT5mfPfIpKuHeSbVBtHgPQLNQJVo2wnGcAqLcOscpdGf/s\nkNyUfVZUS1RooNR4GbNwSXdE1fBDiC6EDyfTj+anPVV5l7/oU9vPCd4OYsa2qL07V2T9g+I2m8Uf\ny9jqDQxP9meYXsXtioy12G7yOQTnE0fW7r13DfL1zwDQ+P43uH4he+D36w+beLcCkdjufrRwA3xK\niELgWbxwq0M9LnhnKqLV/ffG9B6LGDk8TYhcGeoyvcdXn8gGyQ9kE68bFnTUUr8MyFS/bps+eSkI\ncsY2sSGEpQhUzEot/JkgsjQLjKm6C8s5RiTPtWyHriEiau6JiLecJoQjET9bZcpU3Xefi1q01mWc\n+w/bPFBr97ORjDPc6RKcqdV7c0ZHXSZZaNJCFtrUQxxYIYYt48ksn/alZm66FEsZxzgxOc9F5+wv\nhUhF6ZCbmRz05f4JKsFT5jNaSmTmDYfnJkIsd67L3A4qh9azQnh6XCPsyr2OCf6mXOejLRzVqVO1\nbjtbKdYT+faT9BG+oS7VnV1euiZqk3Fyh5O54OjeR/gns6WDeSrr3vRMFr4cyGcO5RDHL6S8kgiu\nlls15SO5LtpLTLUZ+YZJ51jGkVsy3qBymLkyD/O+x2wu62ecJZSRBiIUDtVS1qxS+0PizCnvqcqw\nVXN9IgRk2ayZ/Tnz6ACfsYUIf/V+gPusHOiDvEnsiqfhet5TJF7g1LKnF8mSgSXfKK2S9obMezJa\nEDhiH9npCE62ypKt8q7g5fkX+Mev/kDGuVw+VQFKDB5MfzI/0Ep9WMEKVvAh+FRICrUDyaaBtQ51\nJOKS/96IwhBu5oZzlkPhlIZXEM2FcrciMRzNm92nQU+e08OohEJHVYWlxsii2SMy1bKsgU5pdMqy\nlm/YxZgE9YlbawTVmYyj61D3hXtk4pDADsC7Itw/uYgIVNRMnSWtTaH+3dJmXa3EUU/G2cgqUl/D\ndQlIA/m7tayo28J6DTUW5ZaDqZbwqmVRlhr9SAPv0intLdkwRSQ2NMql1ZhycUNEZ/vUJUZ9865P\nrM85tg3qUbiyI5JZw1tjXUN8jWaJpUbXqm7AXAN9rA5GoZZ449JAW0BLxtlNR8xqkRoGvcf0rovk\nsX1yDfeOxAuksYznQ5FsH4SgYOOKqEf3J4f0moKvyUCe+Pn2K9ihBnXlGUVfOHAVzckqGdNiOafQ\n57y5iOpLq8JQQ3FUX3CqRtdlw8CuhfPWiyWGBmdlvopCeExq2Vv2oEF+oF6g8x8t8lyGEDiWwebz\nYqz9Hn2+qOY/b3aTzrpINycLuXuzn1HEsg6uG5OppBAECVks+6lnWdQNkSZqR43nW2165ecBsIoT\nfuUFwfHDV2Mi3UcmNY6GrZTJjxzyn4FPBVGw65r1rMCpMu6qvtSoUkYq5rtHKbtrMtTAahCsHQBw\ndUPDgNsenVCu6yKkqWG3I6um5WsIblbTdHWjGxoG3PO4cyGYsoqcNBOxLjXmRC1ZgOZhm+1KNnoR\nyuHomDn+XJ7b77k8KITw7Pb32CzkOXdg0XFEdO3aIkYPBiXlROZRrOV0kA1LY0F2GZnWlE1nRil1\nRzZmUpgEOg+rtLH0UARFQKcj36g80S3LCWz7Quie33/A6xoAZSYZiWMpDku8VDZs3RHbge12cdZE\nLXHLAFMDekq7xrKEiDpZht2Q61wDbLzQwTBEDeqUY3x1wwZlj4MNIeTurSWPS1mf+xr9aZT1j3Sh\n1eOci1MRzI22T9KW641ztR+9EEElBNluT8mngs/cKok1rNotbObxpXFA5lGZGfZY1ncxd6liwXOd\ngGcIoVpaDnYi65pqjkNcLzGWgu/yvZr2TOYRtiSyGMDTiUSArYznxW6X3URCsLe+4nGwJfhq9bbY\nVluS15SxN/rrmA0NyKscZsrU3MAkdNWmYgZEapCxVNsJ5ga9Uuw2xQaUqRCIrcY3cBcy/3drA9O4\nzPr4eLBSH1awghV8CD4VkkJlQOJCli54MRQZ/Rtdn+kDIYmTqqJbCJfur9/CvCEk027J/7tOTKBT\nicMSQw1qHSeiqjTT0J2Sl0Ktu6759L1GSzjX0bGB6Wn2XdQh0pgGWgbmpkgC+UOloUEBTZEewlnG\npjBPyn0IXOHSgZkyT8TybV2XORnVLsG6cAnb8sg0UcVJGjjq97Y0qKayc6q5qB2h51JoIFDR9jHj\ny6zNjDIVzpV1JaDHNSxsWzhi8b5NZF3iyEHDN+gYId7L8u2DQMVsp8CZCMcvnzmnjESKsZyUqpZ7\nUyfB0WCvhhpzy2kHsyNrY9W79C1RXeKqzXouUsxp+Aw3NRCteSb//1/OYyY/QlRwrxi0Q8Hb0cNT\n9izBR/G8zG93MSDcUwNs7VH1Za6zo4xCA6uzaso81cQzVSlMI2OhUlHZOKY3kOsmS/JU5tpaxtSa\ngzCZqFejgLly8e2DTWZqHE5nEYFKnpfa3KCseXZNxnvw7C5OX9SnlwdTurGqec9dYGrSmBuKVBGa\nLRJfvU9xQLejokDUZKmZmKYdEbRk/w018jlKS8aZqB3O2iH76u26vdfmW3diXZMKx15JCitYwQp+\nCvhUSAqWYdCxXWqzzamhhirrgsQVw0nkRAxy0YHPM/hsoTUHVJftxTa1RgoOrAzXUC5WBSxK4bbd\nyqTS0OXcF+NNmcwp1O3pJRXRSJ477OR0WyJtuFWDa9WBjKMjMbztNGSrI1yp297kjZ5w22fKW1Sa\nreKeBtgDMYK1z4XzOQNoxcI9ylZJR/XvqhVT5RLfUIWaGFM2SBGuWjh9vJbo1laVQ0MovzcbsOzL\nPWuX6Ue1QT0Tn/jR9ru0tfZA16zxTXXfDfp8efysvEPtEwOngdHS92YutkoYZuGAJ+NsREuMQO6v\n1YVWbiZYZzLPRWtOcSK4WDhzeKQRmb0F6T3B+euJuALtEFzNTvxgGPRLxg3Ykve18s8w/KxId1/v\n/uuCl22fRiFrPc1naKY61SwjjgVH8ySjUYlkYarebhQW06W8t7+IOVqIFGMmJhcakdp3vKe1Dtpq\nPE6qmmtTmfN2XPKvzWV/5vsptRob12sRFfc8B29NJMWXDjwiS9ZyLXwO76aMec/xiDI1MJ6LrWm5\nHdMzBVd14IDaX7IwYl3TpaO+jxGLXaVoiRE8fzwBdZOHwzWySKS0zk6LLx/L/N7P66fh9lPmfBz4\nVBCFyjCIXQdv6VKpUaS2MmINwmhUHksNANrfDGg3FWmxHPh30pwdDUV1tttUWgxlFsegWWanVkr+\nSO6fbsiGSccly1zuPZ8cM7QFaTvTHlkuG2HhDBm3BcFojHzRC2mqsRNzyu2hbNyqPcYsRSwt9mzC\nUqzocVfG7scOsRoS7bxNtaai+8QnUzHeV9Vm7kD6huZc7A9hLt/oNVuYsdwz6s5wh0JMihtifMM/\nIVrekffOQZ0EWE6DgS//2Oh0md2Q+XmaDbrEwNKU7LKTUup15c9wIxmb5XexMy1aoji24opEc00W\n7484sQVXD0YRmSO/E9tUz8n4/vqhENaNt2P+yY8wNVqfb9Iq5bBY9mP2myLa566sjWM3mbly+KPz\nEqOS359Mz4kjOVhm6dJtyVzXDN0rZkEzlLWh5ZCdCz6T/py5hm5bZUqigVF5R/aKubQ5u4xPabvc\n2paDXmYO7X0hEFe3JS5kdrLFCwcSN+Lc6uDdkwC4aDglk4hm6sOSI8QAOTmSLOB2OaD2hGg23ZTY\nVQ9VELAstLDFPZjuSEzCo0z3W+5wnqg647nsbosqcWW0xbkjYfFRviCzoj+D5z8PVurDClawgg/B\np0JSMMoaZxZjlQmzTKi9l/OUYs7iEemJlEjqTw3+5ESo+MGOcIGzxRD/poht1WSXzZ5WYapy0qVQ\nzEfDCef63Ewr9Jye58xS4caN1OQwFbHspNPg5oEkZvWCgF0NlS13hAuYSclyKtwjCCz6feF4WbXJ\nZKJiYDYl0px9L9Hw6b11zjWyr789w1gK93R7NWi2W1rLey+eLHjzkXCG8+89YaZxA1e3r7A7EE4z\nSg4JtoSTXj1TN2XDwCxEzGw0HxO4wlWc0iTVsNtFtOTR2yLGc0tjKOI3KBLBd2nX7Dc1oShIGLii\nurXXW/QbyrlLMS6m6YKzU8HLnffucjQSHEbunGUm8+s4Bg9yWYcfnMr8vtM0cNRgehkRAHCj2GPp\nqRTz0hdxfVkr2xYOPl9E1FoIhWTG8YU83TRdhpngu+tckESq3nTUyJvU1HPhutESopFIGGmeE9fy\nXN9p0ssEH+NEY12ShPNSIzq/OeGBxgrMb1n0C40n0BBua/uCJ5bGOY5Peef7opr62VvwO/KOb5Ey\nqeU5Q13Eg4vr/PyzLwJwsLdNcS7S1KiEXFXIi7fHFK/K+N+MRTpYJjGZGjYrw6DwRY1ZuDUD9ZdW\nC5PyJ2T9nwqigG1SDppwHGOo3/VtO+bu+6IDDkczOip2P/Db7KPlhFQbLZoejy8EUeZmjbuQjRvY\nUx4ngqiz4zFvXMjG9Baykc4ikxRBamwmmKp7nc1t1lJZjEa3hdlR/32mqdp1TG1cVh7ysNTvvExm\nLNQU7fomdSkiYbImm2AauGQLVV3uBxQ7cl0uuoR9mcs4kjHcfXSHbx1JBaXxYoydyqFIXDhVUdqd\nZYyH6on4mmzmbbrQk0CfYtTE78u3rxk9IleJU2hQ2XKoHw9ls26WAcfHWiwm9DkbaMmw44zjplxv\nFj2qNVmHhi9rcz4xeXAoB+9dOybRIiVlcQO/L7i1Og7XJzLmqx3B4fjBjO/wZ6H38j7PTITYPz55\nzIWmkqeJENvHo5hQD+lJnpCjaeTTmHksBGk58mjvyO/hSLNSGzlHKhjPCou0I3guK4OOBi817Cam\nqh2HkerqccbM1hgC2+F1X36PpiZLXfdDzUrdd21iLXXVfev7PB7K3ztFzDSQd/SHAWVP94uqyouz\nh9zRakyBn9BvCCE8mVQcfVfW8o4xxp4JwXkd2QvupMvAVxvI7QGxlgRo9W5z9LoQlsRfkJUflXHy\nYVipDytYwQo+BJ8KScGuC9byIQ/qkOxMjHOP3voT7KfVhS2qWMQ2Kw1pCSHlXKnkQRUy3ddEltSn\nDtX7sHQoK61gHOcEY6GB5yOhnMN8yK4jxhmjETNQCrzsxBwstOaj79CvZEzmQCh1dZxj18IdA7dJ\nsCmcJlx2KdMHABw+mnGmsQeBMFXK2xnriXxj2BnTeqIZnOszzGPhUHEmGUyNkU0rFk4yPwk50+jI\naycLKlPm18i6eLeEOxzoe5uhQToWw9coOGVHDViOEbBuy/cW2002E1Ex0LiQo8dzDjNR0TqjDZZa\n2LTd6JFqVJ0zjjDUh56osXM+PSHUOo9rixbvPBRO+mr6Oi/mwkEP9wyuaH7kvan8f7oD2yJd86T6\nIXe6FW4TNeW5W94VGh2Rlk4eyXNH90e880CyMt8/SjBr2Qyma7GmXhKj2WNwJhJC1dcM10lMciFz\nbZQZG6Xsp8PjYx6MBbdxJ8JTo9yGVomOLYt1jXVZSy36aoD1dkrKh7K+k1hUsRcO9pnlskdanoOr\ncQNnCSy11N+wyuiMZZxf25IEpxMzZn9D1rrRyCGW31m+QaWqZ7ps8yQSqe9Uv3c7S3gg6OFG2yCd\nyV6/+WzC91VF+RJdvnFZspyPF+f8qSAKpWUzb61DecjyLREBZ+E9hlobcRCadLqSLfby/hV2t0Tf\nb/UFkbERsK7Ze+3uGm3dxLGf4pxrjca+y+DS6hvKhr/ibPGcp3Hv5BSR6JZDb0muRS+mayVhrLkS\nnmq/vgm+jKeyTbzL0Ni1AjPRjdkrOWiJqDzYEpvE7toWVaUqz/yciWZXtpZL5qFW7U21lP2uz059\nAMC8OsJzZJNvDwIItb5iy6GtwT11Ww9BK6dM5XCXxRy3Jwes39rAUlfEIM1ZO9DS6F0hIPMs4XYm\nKpHpdznoKSFs+kQzwW3etik0d2GpatJi5sC62A6CTkGQi+i/P7XxLSFCrWBOyxeidfPzgtf02xNG\nlqpMZE+rQtkbzzDIZR1mR4f01I0cJzJnzzOpAjk0vfVH1JFcB40avyU4cGrwAtkbaSSHfFhkLPSg\nh0EH41kZj12nNCz53TJSKvV2dANRx0q/SaSuzkPbYk/dge9O2vS0xUC3I+96/+3bZK9IjdFx1OT5\nrhyvh1MbWwOIBrnJ2r4GNX1F1Ydonaa6hjtxRtXRys6nAVNHiJ6zvo41l+/taCySs3C4qhW0dsJN\n6hvy3OLNIRdXhSjsXGQYWiuUj+mEWKkPK1jBCj4EnwpJYba44F98+9e5+08O+cM3hAOlwZReICJu\nc61i60DCRLcPutzS4iq9PaHqtRsQNEVGd2IfxxED3sWFRWtfuMdW8zbPrIkh7dmWcMG+dU7PlXd5\n0ZTjsYjuD94658JQCeOtCLst3K8zOJDxTn5AFAg3tvshblM4Xqvoc72lPRKeu47fFmPeda2D2HIb\nTFUs3ygM0kKNXXVEoZwy0KrNgdHgi5fW9ME6ppYP2/auY6rretEpCFRVMizhwNbcglo4V8922VsX\nHHWCDsNYxtZcn+JqyOxuV77R7OziFCp5xet0NKJo7s9paIGbomiTaZ2J3FEvkVHS0GCw9UZKtys4\n+kUjJLWVo4/OOVQvUHym2YAv7nL+jrz3yXJCrPXPoskhtY5zPG7jaF0HSyWN5/cDWm15r1NVPHgs\n3w7yc2igmQjoAAAgAElEQVSIpNP1JpiVGpt1HsPDJ0RnMub97S7rfcH95LkvMduTtUyNDONYJJOR\njuH09BRDa2m605TvagLWqJewVG9GtRBJsRvamKWoVy/evkbhidfmuSaYpjx30zNIpiI5NtfUc5I0\n6bkSQxEbc8xMxrazsY6rxu+DxZKJL0bMs5lIrl4dcj4XQ+Tr8THTf/QAgOP2KeVbMqa7Vpv+ukhs\nhxcfr2vjp4IoGBHYrxq8f/QIcyY1B23ToXBEhBvUXQ625PrG3ufpt+UADLqqL68BC9koRW9ErsFE\nnbWKi0rFRCdiLRGRP9AowM3qFkFP4/qPungdQfbFZMA8kwM9WeaYW0IUZrVs4jyo8D0hNq7hY6j4\nWXCB35RF3HZzWoaI5g21Ngd1gzKTjXQ2MplY8g07LTBUlPbsy2KvS7x12QTXzSc4udQ+9Jo5SXjZ\nOSrF0r4W6HerZYqtB30+Smhq1aAtu4u/lA1bVG1KrV1oB6JS7Ycm1lxVieY5xVzm3LaWJNoMpjAT\nlpoHUcY/7MA0qrRk+WCbW03BrVPcJtVS9JHh0uhokNT0Mv33Ku6aHMC1H9TEqgMH7gbJUvBimn/E\nbK7ViyrZ2E2rzw2tWJQuP4PrC7EsZj089QaEyT6Zegwm54LXlCWRVot6NzZ4xpJx7Ny22ZnK3kln\nDzn2tDGM2kk2xm2GqprWicWJLfNfRBW2RpGWifw2Su8TakGeN48f8nPrsvee+0KT7c4vyzrd+w5l\noDkY1zQSNIqpFjLOoB5idGVskV9RD2U8YT8g6Mr+29sVRlYXp0weCfE7ab2KrW7iw8c5iWaHPr+e\n8bY2Gvqx6ep/ClbqwwpWsIIPwadCUpiVY35n+n9hP/a5UEOVM4u5opSx7nyRPUOoeZi36XdFTGy0\nVFJodTCEuJINc3JPA0/mNkFDOKEXP8ZU8XLDEEOP3THxNOvP612QnQpV3nZq3KlQ4HvxiJta2/B4\nT7junZOcMhAOZXrbaCc1+ssu6Le7lkvmCWceNHUMhkkjEpHS7R/T0jJZeVxQq6W6Unm3iYXr6nWy\ng9kWzuW4bZaZvNfO1zHWRX0YZJq30EiIR1qYIzhjkYiqFF5x8DRmI+rYbDjye0ML2bTNEHWy0Ix2\nqW2RaOKow1SzIBtzIL9snqNdnOyULW1o6ZLTrTTMe82hmKh/35syv6tzVZG7vd3GOZNvv3Jwhcca\nOJYZQ3xLxp8tPs+kISpduxJuXdZ9GiraO25AoyHqCI0EV8vOJ2sWS42dcLQ0vp345JHgKPQimAje\n9s3u06Y7p7YLZ3K/f6FZlmnMTlM3V7gkVWNfKzQ5PNfSeoW863rL5bXXRU36yivXKbRMfqfaxde4\nEO+XPw/3NGy8KwiP7O+RFzK/5fsmYSV1MEeDt+l1RdoqJh6NWO4vtZao63YxA+H/2bzLqydy/X4Q\nE6un4nHc5crBXwbgB+9KdfSPgr+wpGAYxr5hGL9jGMZbhmG8aRjG39bf+4Zh/JZhGO/p/3t/0W+s\nYAUr+JcPP42kUAD/ZV3XrxmG0QL+2DCM3wL+Y+C367r+u4Zh/Brwa8B//ee9yIwqvO/FPFyWmNoQ\ndNMJSLVfYW/+PlEhbq/CPcVYqI/d09JgpYOlXKDKSqKZRkJacypNlLLtDSz1yWQa7tvMoVIOy9xi\ncZlU1dvEr4Xq3his424IJ2lc9vBrQtAQw2fS8Alj4R5xw6RxIpwiD3s0m6JTWrnaNVybJBTpJ70o\nSAINUY2WJForK7BlzsvUpMpkHmbYJ6i0+k9cYidCy5fGlDV11RnKravMI1I9ez7J8Pa0n0S9xawj\n76uTEVFH7rfVRVoZHXzNqCw9m6pWo6MVYaotgq6FrU14E22MY4UOuSXzM/KKoim2liCNSVWEMiZz\nRto0Nepo9+XSw97TMdsXFEOtNpR2iZcqNc0esxUsdXlkzf1szqItc2pkObaKiEXtEGnl42Bhkvua\nHanVs/OwhbOn9qOoYKq1LJJ+n3IhhkbjpGSuVZiObFkne9DA127dZW+PoCGh5ydzD/V8c6qZld5i\nwdpU5j+cbvNKJe3vqvU2llbBNo8s6oFKHm0NmzcOKFQyiY0InO8KulufhVgTm4wxuRqHW46sR70I\n8AKRrK0Lk22tiuWepvS03frLPYftn5fx/fPf52PBX5go1HV9DBzr9dwwjLeRFvR/Bfi63vYPgN/l\nI4hCVJR8/2KKk9jE2gP8pEi5NtR04CsNRpFs3vnpkoe2hELvaIppWriEtVqT4ymcyb3nxZDwXBC4\nNA021GBYmhor4LqU2vyDqiCoRajZNGf012UTmkc1Y21ke7wu0TanFzMe9CWte9sPmGtz2MayT6Vl\nzIaTMU0t+9bsia/ZokN0qhbraoGteRDLPOZkKpvQvFAvQjigp+O1kzHrrm6kHCw9sFadUzfkfctU\nxFN3ueTsWA8gMwpNMy52U0otINJ2Eig1z8GU32Ijw9GQ4iqKMEytcl3E+C0N8y66OOrHT7RjthtN\nmWmmaa/lk2jWpRtYlFo3rOGFNDVduHEuhMld79LVzM+TeAfXENy+/da3CRfCAB6c1ew25bCkGhfR\n6kJ8We/Qz5hqdy6nnlPNNNitSEFjCPpdjTtYjKg1vP3eNMPf0MYwd4/pbqjBMI9pxbofTjTYKI1o\nDjTfI14QqVpZ2TkjTfL0NcjusVEQar/Oh4sz3n9B63UOAzQFhWbvGONUS+gdy9ino5j4/AEA528V\ntLS1/fKtNznVM9C66uFMtdxcQ6uAZwlKw2ga2+xuyD9e8R2+GH5Vxvb83+TWrwjR5u/8PT4O/Exs\nCoZhHAAvA38EbCrBADgBjRj6s888bUVv/WSFYVawghV8gvBTEwXDMJrA/w38F3VdzwzjaUtc6rqu\njcuqFX8KPtiK3ret2k1czqsMV3PmunWN1dcEniiltiWe83TmM0nEiLTUiruN9V3uqQtqeq9kESs3\nNmLqRCi726mxlAv76qMeTg3slrabK0oKDZl1sib1rlD8IMg5NP8EgIciHJD4GWjLeNfZxNHKwFE1\no5pqX0Vvgamk8UJrDLTbM8qFcMrppOBkKSpDkaXM1b9dq5rgd1v0VJIwtzrMtBBnbXl4U434CyIa\nWsAl1ai1cTrjKBLj3JsXFs99RdywadbEVLHzIu1S1SIGd9SV2/QLPDWyRf4Me6Gif5FSaIm4uZlj\nXgpWmrg2HQ+YtQTf/jBmoCrfLPep8lTHBHND1iwzxFjm2CmjXa11Mb3LQ+1PUVdN3jsXVhnU32au\nzXVGqooZRYhXyt9nU5fjpYj+ZurT7er4K49Ysx2HC5Xx/RGxFputnZSzQ/n72s0FsfZywKwYlTLB\nQy2xlxchrqpa0a7P9J6swzyynhZsvdzgARWBVldNF8c0/0Dbw/3KQ8yxcPzZa0+w17SlvCf3Phka\nTP5QVL4/GU/pRtpjtH+FmSdS09rjhNY17Ro+lueNRcI8lrHP/SGBlpgbOA2imxoB/Fcfsbbxi/wk\n8FMRBcMwHIQg/G91Xf9j/fnUMIztuq6PDcPYBs4++kU1OAX7lslI/dUTw+HnlqojbnkszuT6UfUm\nG9ogxF6TDXY4vWCmIlx58QS0SrJrVlRdWeSBAa6Kz7MTLZphzwnP1JLrLMi0fuBFfMH+c1qd6cxh\n91X53jKRU744hAtbevz1nIxUxbmNRUiqwUnDsyVJLSL9nmbkWeEuvqY19xoP6amvvDBiCv39oieH\np1M2cXWDjh7HDPVQ9RIX2pqJeegT7Uo2nK3x9OnojKWcd6pgzvANIXTJjsHmsRKkVpdd7XCUaFFy\np5wzbWqdwHFKoVl9RAamI+92l2Pm2jxnqYTpSfaYnUw9Ec0m+VjjAtop9oXMP6umlKIa80ADxK5d\nHZC8I/Pfu+/zrang9o2jQ/ZVrTi9PyC15Ns327KN6nCdWOtVzjITL9YOWHaGqwFARmhTRTLXfKFF\nUcYe0+IStwWRpqov7g05U8+ON81ZqMrnj7SQTT7HtuSAbRl7/Fwhc/3d4BghR6CdRkkxsLTy1ou5\nR31L7n38ako/kHwNL8oJ1T5yGfrdsGsa12VOjbN1sr7Ep8zXa57RvTwuYtKxitThmb6rZhYLgawr\nh5EGn4UvNeieaFzL9Gs8evdj1nZX+Gm8DwbwPwFv13X9333gT/8P8Lf0+m8B//Qv+o0VrGAF//Lh\np5EUvgb8TeAHhmH8if723wB/F/g/DcP4T4GHwF//qBeZmLQMn7FbU2jRkytmxdlAS1RlNZpkhluZ\nmA2h3JbWHogCi2ktlHazu4+fagORps1A69I5psX5VETGx6WIu2GR01ALeauTMFPu0Q1CkpNLY86U\nN2NJcnnnVL7Rt2xiU8TaC3ODgY5j2rDgfS2QEefYWucxr4Xal6VDrZ2k63mPYKCW+FGP+ZZQ862J\nljBzSu5OtI28mRBrM5jc9Bho2bFhkLBbaUs7W0TRWWZwpFWU3y9ygoZw3XTWYXQuc3qQHWNuSPTi\nrha1mbKL4wp+srCBO9b6ikGT5VLEVtO18R2J8ZgF8t0N5wmmLxw6KCoWtfAZezYn0+rZcVI+LSxC\nR8Zgz6+wvS3fe/P7J0zU61TNcx7o+p37UwKtIXDefU5xbzFbyLfN4pi6UoNnMadU74OVGNTaR6Oq\ntR5D02ZdozHz4TFnmvz1wM7Z1BDkkppKK5Lk2nBlahgcai3J4nmD9D3t51hnXDajvywS0wKeUwNZ\nfG2L8p4m2A1KMg3X3p9tEpuyH8xa1IsiXNI40PkFDmdaPXqrCZnGUEzHFYlm5nqG7MN4bjxt+jIt\nfVyNWUnnX2P5oozjPDnnov+TtY37abwPf8APG+L8afg3fqKXmVAGsBbBUIuXnJU2+49l09zfDfDH\nsjKTRotdLYASv6TLssgJtKZi4Lvc3BOFcB6n2FrFp4gtSrVBBCr6ut4YUxvFJgmUXZnOo+MpDLSM\n+Hce8f13BfEz3VSNsmQyF3F3bd7GvSlBKn7mcbEhC23ePeRIRe0XctWn64BzVZ5nSUF/rkE4ZcDo\nvuiUdzRk9sAPqLSSTn6+JN+WQ+XaDdI1EbvDOGaiLeptDfoaRyW16pmdIOK73/wNAG6vfw7DPpB3\n32jjIEQt25G5BXaFoxmjdlQzy7VK1WJEoGnPRu0Qahl8P9ew7GaPutRCL65BnsnG9Ns1tdaUb6YN\nOhsy5kqJ5l57wc2J4Ps7kxB7KWLw+Mn4ad/M2mjgDWT9MvWW2E0IfBlbqwo5Hwq+jkYRTiR605rV\nwba0MIz2XWSWUqrX5q0JeKmoFd7SZ6n9Rm82Gzx8rMRQvbDzLKFW9/Rfblr8wVALsvQXLFTO3ta8\nFQubVDM1X4o6nO/KOqydLlhoKLxXpaQaZr88lfE8zn32tD6oGzTwtKvZee2xqWN23RhTVWhTmwQZ\npctUvTKROWeqhXjK+j4vWF8CoL91Ddf/iAaefwpWYc4rWMEKPgSfijBnwzAJnJCFkxCgzS+8krGv\nYb7DGcZVtbi2S0pNGgpMobiH4SHpUqjk1iLhzNa6hOYJp8OBfuWMxNBuvrWGDM+3sfa1L6Hn49XC\ndfdHmyzvyvU5EWlP1I1EM0ouWiaf1bb2Xu2Tai/FKjp+yqW9LZNGJd7YI+1gPBs94UkpHGp8lrN7\nqEbJfkl6LJzy7VR+ey8xaR5oYRls3KVIDcbVirY2B6zdkroQNSAuhaMkRQ0HWup8CIfK8rqjnKYj\n1YXL+Cqd5x4A0KhFbA0GS6xIa//ZIx6riJpVF3S0oYrr2NgqHq9p7EJSNYltEanHqU1Dk9Wqqk3t\naEh036LrirT08rn6+edjThIxRF7sPaD4gXDBpF1hqIrVHJ1jWBq01lRXjnGDPRHMWAxtUlvjApYz\nAo25KBopa6nElpRaA6PlzXhX8TYzFox0D4Qdj/11ke5qy6G3o8FlI41/qCouDE1iOz3iyTPqaTpK\n6GjfyVJxf92ueF4d8MPuGc1IXRjPLelq45eT+xVdTVZ64kpsxqTZo3lffpsFHhueSEJx8wrnJ2JU\nPIwN1gfaF1Sl6SqMCR0ZQ2cWE0/Uq9bdYLGUee/sjllsi7r9ceFTQRRs22RtLeBKbjHW7k6TKmNT\nm32P6pLTc5mw9/aUB196DYDr1fMAPHv932VdrbCNyiDUgqmRv8vmkdqI521ydZclWvgUu8Kba3BT\nEREt5EDeyx6x1LTn5bHNZCRoCi8DcBIYjkXcv7rfZnkoG3O9sUFXrcj+ewbLWtyoa6b0WMA1eUbT\njJdrZ/R8cSfeMCJOdMP2NLBl4IcsNUCotbXHoiXfuOG0MZRYLv0OlmZzaoAhgZewcy4EcmxE/NGZ\nbOi7ye+ztSHfe/TqKV9IvyAoeEUOhJ928EPBhRfF3Nbs0cjYxtQgpL5t4oQaydiTCD2rzKk1HyAv\nXFjXuoxGSTIVApjHcyLdpHcvBG/B9oy9b2hXr4sKN1IvSJ1QTzTnBYfzc80MVMO72xvjqJuyE5xw\nTWsmhtv9p/0NvGpCX7YRzmUk6FqH53ONlu33ONKqVldvb9DZ1H2xMPn+64LPqQY6LeY5P9DKRQ8v\nUhpaw7GZQrsp73gl1uI17Q6Wfu8Fw2AS6j5cuFj3tD7oesBl79pbz34FAGP0gMZnxDVcFzHzNc3B\nGaXke6LetpIFsSvzzpU7FVnJ46G65/Meh7Fc7z075dkvq2u0ex0r+3j9Hi5hpT6sYAUr+BB8KiQF\nqzZoFT5RJ2Q5FooaWDYP1Oc9cw2SkXCdLDPo/KGQ2vtdEXefb+zDuoYun/ssN4Wrzs49HPMyJHgP\nY1vrURXCHS1zSKFdkxbREbNS3mfZLWbfke+d5QmRVpKutC5h6NtUrnocqprtQOswLH0MU8bfafnY\nM+GgtYrieRnQsrWYSrdFrKXNhm5FlWi8v+Yw5KFHqeG1RjDAb8jvS9PCNoQDFXOoTBnzYioSRmok\nTLV6NPMKL9KgpqoiTkQUfbZq8Z5WMB4kMl7X7hCopdt1GjStH/IL09SuUGkLtPxXoWXn7GJGZYv3\nwfRT6vKyF2ZKnmgV68XbnB6qaK+hyPf+1wXfKAQvZ0ZOogYzr4oxtYzbxCjwtc7EQjMn+4XJItEK\nxrVLQ7uDbzYKxhrs5bubFE+0TmdDJcFhRar9SGvfIdzRish2iDp2OHv9Dm8NRbIaqieqriHW3JbE\nzGlqt+qsMnjGF9xP2pqt65Xc3hRc3Y8bOIgaMB9FnKs68sxhxPgzgrvraDxzOyE5kjVNezHVExlb\nas+oCy1t38jJU9nLQ+2MZmRjZpHWHS0SOiq5bGUNmqbk5pjxGKPzk/H+laSwghWs4ENg1PVP5q74\nJGDQb9X/5i99HmNi4S40WywoQQ04PbfkZCxUNzALzpTD5rHok1legXK52oJCYw96nkGofmrbLsnU\ngxoXqocS4DQ086y/Q6k9Gm996QW+el16F+6+sos3F85VaH0Ec32HlqdRd+/X/PFbvwnAa3/8Btup\n2DuOhiUbPdEBl9qzwG9WzLWbccNIGU7Vr24VT5OVHDU0zuKYmZb5KqscR5uzbPR6DK5KtJrf2eL2\n1w8AePnZvwrA5l4Hx1C/e1Xw3/+3f0ee6w45uSP6+aB3woOh4CIPJXb7JEq4d0ddp8OCV+8Jh3Zq\ng1j3iGNbmCqFXL8qNoVn3AGDTCQlvz0j0a7bswfH/MHoAQDHxyPqS/bzwc4vPwKSpMBSA2WaQzqX\ntfr2GyLRnDz4h/y/74ldYvh7r/HGIxn/5HCOoX0Xi9LAVCOgqdGvvZ09Niwp+Nt3MnbVbe03A662\nZcz99hY7t4TrF662YHuuyV7vswC0d8OnpdJKYr76ZbEDjI/VRVjUTDWicWc95MqeSKGbZoCnsSVH\n8ZJUQ+HNy/B4y2ajp31Rd9bxNYpzIwhIbfndb1g81PvnD8RO9npywQPt71DaoH1jJFBAj7VleJSG\nxilU+R/Xdf2FP38FPiXqg0GNU2dMjA12e2Jt3nEWvO9q04xoSa2++dloQUerOJ+cy8I6YUSih60T\nOiy0ycag28bUDe07DY40J6KO5LeHWcWexgI8mY0YaJGO+E+mvPKSGKVGyyts9jQwaK4eDsukQsTE\ndmvGbiSL9aoDb74pYuC4bbI4FSKyeSBEap5uYGktyeGFR6ht6Z9cJHRsbVqjfv6W5/BIG94GhUGk\nomjQ22CmQVhNZ0b9+7ITbt0S9aFZNOlq2rCNhXVdy5I96pK5YsH/1psp/vPybeOReGc69R2m7woO\nsyjlsn+IWf9QnAwrn69UWjF5XULMX84tplvaaNW6Sabeh+m4ZlMNsFFtMFOi/ZFhNLaBqcTbtSum\nGkMQtYVgfeu7MSdDifU4/ME9sljwWZZgaMCSWddYOs6GpoP71ha7qko2r13FMbTgjBlxmMvv7S2L\nhxM9sB1Ny7/XZ/0XdB8WLZqhdrVKPMwLDfnWwjNN0+S6Kdd+c4Nf2RaD9hPPwNBy8EbjCQ+yS49X\nqXgt2FuTe6/s7ZFqubZOaVG3ZWymY7OWiYHx0RNN1T8bM9Fvj9P6h7j9AJ+v6xQNF+HjtoRZqQ8r\nWMEKPgSfCkkhzjPeOHtM5+iCb1dCGZ+71qY3E1Fu0TdJNE89MFIsdVu2WsIF24nHmWay7bhNso5M\na9fb5LKu6WbVwFRpYvhAMyqrGelIo8uSjOREDD/V6ZR//k255+e+0GTthhhtHFfG9lryiN4dES9/\n884bbEQPZB5/+B3OtV9EfS9iqmGn4SNRI4K9Ib5G8S1bKTPta5EvpyRaCNbQLtkZFV6lqlRekFw2\nFjl+wPJCONegeI+T9+T+e5+Te//a+X/Clz6npdksg+hVrbNgvcVr78o4TyYRHe0vkag00qpjTrUP\n5jmgDb9J+SHnqOqMQMvXvVKIVNV5aY22usWcIqHdlgdfizs4tnK0qv5oCUEhrWsqzcUapSWJKXO9\n93tS0Pf88X2WkagPy2jBvPwhW6xUKjQwoJS52LaI8M1RSeNr0vp5K29hbco6lYvl0/4bj548IddW\nf9biTQBOh79EfVsLlmy1CEyRFguz4l6ihs3LitEGeFoF/MUZlA359tUY0nWRUouJzzPaItBfyl44\n7azxuU1pGxf2e7Qt7RW647AbX/ZFjai0fd9FJGpgHJYsqj9fAvvJApwFPhVEwSwM/DOXTr7NIpRN\nHJs5yb6G0uYelaEpq5XHplrou6EE7oT9iDVtthFi09HajUbTxteuQUE1Y5KJyDtU78TOtEc112y6\nMmektoqiPOUb3xZRzev/Eb1SA4dua3DPb4c0juUwrh8/JDK+B8BF44i5ZgPO6pwN7Uc4cJQoLK2n\nh9sxZ+S64Q2nSaLVfZrqB/cXS1quzCNl9LSSThpHZGq1vv+DJlb4bQDu/X1JZb75hZc46H9R8LPn\nchrKnJJ7BneONSgmzjjRbE63JXOr3p+yrmnv86J+2kvIAkIV59u+zVc0Lj+6quK1s8TRUPLYdJiq\nV6Lx3Dn7Gqh0zxySPe329efDcFoy0Hsn4ynLsaTVpOo5OXUzpve0m5JrMY6Kp89W+vbQtKi0LPvn\netof9EbAYEvmsU5OFsq8HzkeHXVKpcMZk0Rw9PCOqH5r67/Pe4HEE6x/fYOGvrfwoVGoumJcqig1\nVwLtMfpsiKnXUdCgmsjYdpxzDG3Qk26KOtf3dwg2ZDyd5gBD16RX5bga9+HlOeOxEItnvig2hfm3\nN7lnCoF8XP1Fjv+PhpX6sIIVrOBD8KmQFIzawKlNFuRY2mtw1DJYG6vKsG5ieVp1ucrobErY5i1f\nrfutDi2VJAh9GsoFy2aTRqWiFiF7S+Hc46Vw4Fl0l2Ol8iQpWSrcuqpzlk8eAPDWG11e0KYmwZpw\n4Mmj90iHMobj104Y9kS6WRymT1vUl6FFpFmHibZy72QOhrYIt8oayxJLfitfElkaKajRc7kf0G1p\nKbJ5j1kpnDJeuBQLea/hpAQap2BF8vxjHvLekeTrb9ptHrwmefzD8X1GE/XWFAU3bOFAfa33uHtl\nk7cSkUC2shhbozdLTEKtu/hLa5tEm4LzaxrmvRwFZFqJuJ+6pL72n7BusnVL1Iqvjecs1av028rQ\nfpzEcPhoRqFr8s6bY2LzjwC4eyjJTvbjY5o92QvZsEO7Elx0SofEEsmk3Q1Y034W21dEZbj52V22\nWxr6bFe01OwWFh3SuVQ5Hp8ZxBrXsbS1vsHoPta5eB9GSckyl+c80yZUdbJdXBZpMUm3JAJxP1ln\nbyDX03mNv6Zq5fazrOtaLbXZkdWuWOuIFNsMHVINY/b9BYEaT8OWg6sVtLuheH7mZcq/c1fW7Huz\nJd/6GQkLnwqiUJsGhe+QBQuKWA63nXfJDkSMSrwmXqEhs/M1NFYGsyX6Xb9lYZoikpnmlPqyTLyf\ncVlwJTQKFpm6mTTN+Am3cCxRE1IzJTrVwp6FjdvWpiVFytiRcZgjDX0m5qIrjdRbzz3hgRbdnHcN\nSrVk13WJWwgxqHrq/hps4Wus+mzk47gaTFOZ2Br7WmoR1LZlYGrmXKtTcmeqodnunFIVfjPK8NRl\nlQQyhrPlGYkG6aRJiNcQMd8y1vDO5Ru+VcANzdEoheAlfsVf0jTsd8eP8dX1tp6P6L4kuDU6XZoD\nUdnyPSWmiwnZuYznzE5paEMSZ71Bpk16y5e+hP39PwTg39ImqP+MHw2TbEE6FyKzsT3mtQs5FAu1\n1BvrfezLxilXnqGYyvo90zjAVo/BwQu/SK7Nfdc6Mj+7ZdCxNYvQSXC1ruStbMqpK+HyVmNOVcsh\nKx7LfhvYVyl21a2dJVh6YgzTxAkFR722vDfPa3otbVj81TXSrny7354QPxJ8W+szSk2lD7XJjuM4\nVK3LuqEDPLUrFdMO6Z4ygHwNX1se4AsBsXYfYP3ivwfA1d/+39lRF/c/+jG4/biwUh9WsIIVfAg+\nFZICVY2ZlBTxklkqFH4UR/QtoXydKy4dzXBcrlns5EKNGxpj0IlNlk3NaluGTwtkuLFJacn7XMPD\nQ7KfTJYAACAASURBVIyKO45S6lswHEkg0Dz+HjNT6zCQYGuSj708hXti2Dm1hFsFzMmHwlHee/SQ\nx09ETCZbPg1CCk2HuBaR8Zq2L3fXTNoakGUHBrH2BJySYOdqzFSD4iBoMFGjnm32MTrCuc5PW8wm\nErAT0eFc/fS9TMu8LR7jaCXi+8kRM+Wq1ZMjzOKycjPsnqiIKjlSfD3pEd8Wlehz1TMkarhdjzze\n35F5XM8L7plqVJuJKDubTjAWYl2dJyaBBgLN/n/23jNWt+w8D3vW7u3r3+nnnnNum84ZFhVSokhV\nJ5YsKQkkI0gAJ4ZS/tgw4MAljhEEQQI4yY9ESP0RI5ADQ1JcIUuRJUeiFNCySA41JIdT79xy7j39\nfL3svvfKj/f5Locq5KVGUq6As4DBnHvK/tZee+31tud9niDFXs0QxS2wfv0GAOCX3hWMgZ/kj+vm\nTN4DANwqgZ/L2r7+8F2cHsq1109IW69cNE3xBHVvC997TcKD3jMvoE9sQr7ZhlERyUMQU6181GxW\nCpISoNCO7XcQ7YsndDIZYsyQtcmmubPFEW5Q57KNCjZJTR7GCTrsxjVTWcvSrLE05Xl4Rznam+QE\nvTSALmHcyw2Y9CwCwrLjoEBzQTmAZgar5JxDDW8mn1FWl7Ag+0FRYfx5+zqiltACjm+8gL/3umhF\nrkBRwJ/i6oM2amRuCmU6KDRJLkoTpcl4ud8G+EJGAMwewTnsDCw9HxZf+KLpwSKYPa0dWOTvz7QD\nA3QfCbbZrXvwIAt53Oohj8h3mBhokFq8v34dCwJycoYitZshZlw7tEyUPLwKAFbA0AUOooh9AKG8\nKG5dIlXycwMzYKWsFPgwAnn4Hg+02nDh+jLfes3CTchL1XYe4B0lB8DYHMBiW3YUyWZOtlqII3n5\n3YYNcD399QhpTE1EZWFIqfXrWwT52G001nhQqAhtzse+1DggA1Y+Abo2F51kK+sOMKcQrlYGSpYs\nI3uGUYt08KaJKfUJ/kwoB+jsCyN8tfi9mYVYp6j4uyMzwWJK8VcCj/xtB/Yl+zZ8A1tdia+71x04\nSl7krpogzcl2xW7JqXbgs0vS8iIEgazbxAqwV64AbilmtqzzkSFtzdadAB71I+cesICsZ+A6SHiN\nDjtHC0vDXso8G1aAiqFdGVbICMJCS6OnZP6FuRLpDQGClELdQO7L2prpELkre8CrHCREepql7Onp\n7josm30X1SZ+HNKVe/LlMYas4KQADAKqyvrJ0MtX4cPVuBpX4+vGU+Ep1LVGtsxQxgss2Z1mOhYM\nYvhDBHCb5P5za0SmJFp8R04+o9YIqLZk1C5sJm3mhQlnRUhR2UBLLFBNxl7oCTxaF686h03X3jAz\nKAJTouUY2aEkz9KG/L03vkA9ogjLYoyYwKpmEMJkTT/o9rG1J9aqSz7E3TWNObPXUd3FNLcez/mU\nFZM14vdNZSBlgnNR21BUTM4yF0YmFt9Ic4DiM6OH4nJ/8toQzYn8brmzDZ9WcH6ePE5y5qrCM025\n732qLVnNdaAjVqmnNmE0BPcwr7rwmLTztkwsqVpV1HKtrG7DdGQ+7dyEomeizRYKWijfnsNw5f6O\nzwXGvrGRYfNUPLBfqTRWDq87GsGqxJsw4glsJpiDtnhb7VkI/wbXtbWGnVCSeTcaHibsefGMNcQJ\nMQvcIw8rAz12xKa1iY4n83FtE61c9sv5EMjHdP+H4v3YvSEGD6Ty07z9PFICiOpIoyLb+IQYisLz\nMCNP5ERNsDekAlY5R01CGb94Dpr6kIocluFahYBdoK4qURD0FVgaFZtFDK1RFzInn2FpI2miqchy\n/SJg2t8DAPiL8VfxHZz/307nSD1WcwZTPMm48hSuxtW4Gl83ngpPQUMjUTkM24LD0Ms2HYRUeW61\nNsFwF8uFgWCLdGrUaFR2CZdNQKWh4TMR5YcLIKYSsbNEzXg3YBdaVtqIqXHYPT5EdCkIs2xiQ/fJ\nADXSCHbFCp+NJEZ0TI0hCTVRpVDWStuxwn5LLPDORoDWmkBXe4KSRqP0ESpqW6YZioLJ0VkIp8GG\np1Isd2Xa8OZiadsBsKCKsN+30FrxTJQx8pEsmM0S47vTKb69LdcolMIyZPluo4mADVpNG3Busac/\nkKTt5qaJKdfTacwQL9jY01wAnqyRziyY5QpiLvPNRoBmvf7UjXGN35/7DlrMwTyKazQNsWjdlngS\n4dDBL3hkiU70SuUNmZ9gSOGbLW3gjDHzspS/azobCDx5/s0ygrVLHokqRIOK3RoOTD5rh/mlvV4K\nsERqGAsoaiRsROqxwI3bdZGzyc7VZH8atTHdlOf7228d4Xt2BHuQmwoZuSU6EfUcVetxV257fx++\nQdo8v4FswbxT4xShIwldg7mfyGqgbq5EiZpoFCsItoMslPWslAePjOULepu6lSKDzCcw3oPfloRp\n+OkXMaDw0U/mD3HWk8bIt3/mZ/Ek46k4FIxaIcws2KjgGbJZ066H25a43Vv9AC2Sd2zdqOCBtWAm\nfcLMRuHLrvLSJZTFVl97AwhkIVXqYVFJBttkok7752jO2QOw3cUzX5YN+AVzhH1upts7LnZJ526y\nQ848O4Ii63KYOXjIvuAX/QDtTTmEnt96Ac3bktrf4kZxdYmCiUFtedg0VkCnClByTxZp10LDwcim\nMInahGrJC91RFaJ9upEPryHlPXW4yW+YNp7x5GW8oxS22aPgOkvAkZf+og8clFJ1WTCkUqM+Kn52\nlTUeZ9md2gU8JrisCVyHHYyQje33zuEYsm7BWQk3EOo5yxtAXcr9XdYTHFMBKjuS7zWfTfEXpvIC\n/pMqxVf5Yka5xXQwcFQlCDxu9ExeDtMrsaFZp1/zsZFIWBF4JgxiD2rLQ2DScLBCUCY95D6FgvMC\nNkmeddmEHcpLtns2g+7JehRjwS48apxhgyQzfcvHGj/j7rRGwL6EkIKOQaNGusWW7YHCbIfAuYcJ\n4kgOxXJ8gCqUvdOpKDwctBEy6eoFxWPMTbGbIkrlgNfuABXJgYK2PNPiPEVCDkpvfIDRuuyXD338\nJrYpgFzhJn6bTOH/4xMeCh84fFBKmUqp15RSv8h/X1dKfU4p9Z5S6ueVYlB/Na7G1fhTMf4oPIW/\nAuAtrKCDwH8D4L/XWv+cUup/A/BTAP7Xb3gFQ0N7NZpBG0vyFLR9B+U6EzVZBjTlBJ+kQCuUE7hc\nUuijymAzsWQFAeg0oFQRAsJgF36O4i05mc9uSRLNTq/BtykF51zD8PrrAID+sQ8jkM9OXQcXfUkS\nzahF6KUF5qwlnw9KKMq5T8wSz/vkG+g1UGSkLmMX3jwpsKSqcUNnqJtyXgYGkBOu3KKXE1cG6jOx\nysPNczjEULheCdOT5FqxcQ+3ibkYMGm5WHcxIuS7GzZRbcs8I2cHbUvuI4gcLJv0PHLyH7QaKFlC\nm1c19Mqj8QoYcxLRtHsw2Zxfu2Ktmobx2PupTBuzSBKDZdlCQuTpfJpg5ksCz2C49uw7Dcw2uBZZ\nCsVux9SFiIAASIoGWkykDQyWVtFFsko6pinSjqxhNk8wof5C3xmj0ZCEJtHcSCsNO5VE26IoEJOu\nLe1kaFKTwTZNWCTWne3IPG9dWjgK5CInPRsnTPatrUXgcmFrW27Ubm+gog3M7RkWbILK/RJpJvNR\nez1ESlz+uiHeXTlTMEn6YlUadVOepV8EyMjlYRx5WHaJaGQYpHe7UJfiEY16czQM6mE0vwf2dfE2\nqrRA1OciPOH4oFqSuwB+BMB/DeCvUkru+wH8O/yVnwHwX+CbHAoKCq6yUKcaPmu08ExMRhIvvr1I\n0KPAbFXVsB3mDNp0F5WNrb64VnXpw6YKka7HWGSy8Q4nD/Hm2w8AAKPX5FqjzMUza7J4g2mMnOQV\ni6LGLJMNNkgKJO9RDKYnG3R+cR/VPbLl5ksYWr424WPEl7p5MUKX2feTM3khen0L46m8mG63gkHX\n0AgymHwUubVik9IYV1RHemeIMXsRPOVjxnzGQivogGpJfNk6hY17EzmM3M0YB03ZgFaiEFwnzsIp\n4QdysMSaPQyXJTJHXrw8c+E1Zd1OL4dYZ/u5nvWxuc4Mvys/Nw0PMQ+Krz68j/JNgR2fmAo5Oxjn\noyneZFt6ygPE2PHxk8RvnDQq3GHG3Z7mMAln6mwGaBL2PZ0RSrw0cTrgod4K8NYdmc+tNYUTHoZB\nUEOVq0OWfQaFBU0BmHuTKZJjORQn8zF2DckDpTp6LIjjOXItdV0jWcra7k1zHCfyHLqLDDbb/Adz\nOSg2/AXiSAzHYJFhQI5KdzpBwvChpz1ELzLXMJf16beBPBdIfNQMRFsV0tuyGMs856enOD4R0NqS\n+piB9Sxc5hneS87ga1mj3YNjmI193oeDG+a39pp/0PDhfwDw1/E14FQPwERrvaowHQH4fUnnlVL/\nkVLqVaXUq0X1h8FdXY2rcTX+OMYf2lNQSv05ABda6y8qpb73W/3790vRh4Gt87DGtu1DEwVotqPH\nnZGLKkNUyOk6Mky4ZNHtMsE3L6bILuV3t9c1jAWtRKvEiO76/TeP8Llz6bQbE1dQ1wGWZGGxOwp+\ng0misQtjg2Qac41LWzyWR9Qw3FpWmBNPMU5mjxFjcRJjSrXj2HWQnMjvRCsqucRAYkti6OjSR6tN\nQe6qg36bOgup/O7F5AJ3Sbs2KAvM5LJwHAt9up1rVY2SmgOLQFzj06/O8ex3PpDfjTswurJuu0kP\nqMTVvAhteCG5I8bijeSNLnJb7jkOU0xisX6T+hST+/L923sFnLG4wdd25Fpp1sTsbKVdOYFDzcSm\nuYOI83zdewRNWbicHJQZ1jCSplPAsWCQCi51lqj5zLpuhPO2fJ5bincwGQI2valHEwMBiWzerBVi\n6kw8Outh63nycE7Yfdpa4Ig0fCdnI7w9pOc5AQYOkbORwjUqgTc4tcX5BpQja/ulL3v4s8+LRff9\nTRTELOxvSuKz2d3DlMlaIwpgMhFu6TYCWuvsto+M3k+SMPF94cNqUUl6uAGPCcN4aeBiLJ7XG/EE\n4wGl5cgZ2rpuIKCO5+LiKzg8FnzHi5/41xCQ3Ttoh0gZjj3p+KACsz+mlPphAB4kp/DTANpKKYve\nwi6A4286CW1gIwth2xobSl68eM3FJiXO708G+NK5uE6DKkZkSqgQrckDfGF9G9UaX+hhgXyNuoQn\nNc4u5ePnZyVMKuhkFE9N9ATXqalYqhzPegcAAHtzgk9GshmzPMIGexT+QS4PsRydYjmiMG2q8Ygl\nu/3KwWgp1747OkVqSL6ifZ9KT5sBXmhIuGJ2K7RrdkT6gElxkiqXh9lYAD5FWc2pC81uzp20h6Uv\nIUhbtWBQnWq9IS5w0LIwjmWjOCdj3LblnpxGBWe2yl5r2Ims830KiJyfnuLUYUVlaWHRZQw/ylFF\nFBw530LTJaR5IfcEc46Q9YL9qo/DI5n/l+u7uNWTzy7SMfYSWcPXCNH1o1MkrwuR6g+4e/g1V9Sr\n7MSAZo+Gmk4Q8WVao/Duop7h7B25RhpMsXdHvh7sBWgRAHa+kWAj/hoJDACUJwmmS3mpirtnKBZy\nH2fjBU582S/fFt3G/X35/R1fXrxe4MFlmdkPbXiKIdisQJtCuK2FrI8TdLFFspQoAeYL+bzDd+7g\nmF2wn8gq2B9mCMJnnrRrZPfEANS7JirCuHPMYJ8ShDXMcXhH/u60LwfaD56to9qUz7g+u42T3oDX\nHcGzPyZzMnqPw6YnHX/o8EFr/Z9qrXe11gcA/m0Av661/ncBfAbAT/DXrqTor8bV+FM2/jhwCn8D\nwM8ppf4rAK8B+Lvf9C9MAA0NvxfCt+SEtq0SDumsOpaLy4Wc0Nf9LsK2pClu9gVvEHVclCv4reui\nzsVKzGAhYaeeud5Gg25+wxZ3MTIL9NviKFo9H9tsbOlbU2xDLOHrawaCL4uX4l0jwcbrCtNgxa9Y\no0kKbccxcIPEKSdWgIDgltAXC73T3YDryf011RylwzN5mSL2Cb4iXVsRuWjSAl/awBYp5b3Mgsds\neTmz4HXFaxjO2HMYfBTdWFxtvROg2hKPp5HZUCBFvVsgp3cTuLKuOlDYINmgm0fYXvEk1jFySFPS\nWBnYYLViUYibnBcKoNK2W9Zw78t632is40UmhNeqjyC1X5VrvCPPJjt3cP6DYq13v3IHqkNOCqXh\nUiY+yR3AJDy4v9IPdWAoeTZr5QLdfUmYdiIfl0tZz36dI+M+qmqx3ENXYXksLrq5tYVuSpVuU6G7\nuu/gAIHLJB8rBG5YwKU3NSpuYGvOCs5GBzqk1F2PMnabNkAt0fWNHHOCuqyhi2uWeGadZ7ZxsCZN\nV1Uu3sHw8hTuptzfdlohbXLxswjWLrEVpYlgR577M12Z2+Z6Fw1C77P+OT4+kHuG9QqsHvd9XqJL\ngZonHX8kh4LW+jcA/Aa/vgfgO/4orns1rsbV+JMfTwWi0dIabV3CziuMGAPu2SZusBb2vPLx7L7E\nSP1wAlORuspYEWMCDstfWWkjJELNLGs4TTm5I38bfVrmu7RsTXOCrbac2m5hYdmiLsThAK85QtFV\n/vIE91w5gTfJ4/9bRQqbjECxNsDyOFynh52X5cTfy17EBkVq/VrKQ52oAeZLEdYpkpSSdmaCBdmg\nLE/uadNto898QXPLg002HizXYZgStz9s5NALmZtDgtaod4H5Qj6kvrjElkGL0ciREy3qqAkc5jNC\nJnarbh9+JNfFeANVKW3kZieAS4i1pT24DiXkCrH4VtlC35e1/+RmB7c25NmE5gydNfFSFl96D585\nZUv5QvIs52tL3PiMxMa/kvoguA/2ooBtUeynCViZeCRZJR6N708QkaF51xvBSyRHExkxsi2ZZ7+h\n4ZI7waAXkM8KRMbqurtoBDKP3q0d3CTq1Y730Gby8GEln+ssM2Qsl66b72GSiWfSGyg0iVXJStk3\nxtKH2Ze/6wXPok825xNjH0NHvK39dvQYLzEiojW0TpAnsm6ZZ4HVZdRIpK0awHXXgP0h2X/9lnjF\nu+vPwL6UffGgrrDPXJO/PYPLsiYiF2H9NX6FJxlPxaEAS8HoOTgwPJyn4qol6AKmuFcb3R66wYqK\n/CZKZoOREIdvTTAm+KX2E9RUMokaPmLWvI1ZDG9TDpl9JYmltnmA9Q5rxqmBlLwIc7gYzeTv7mYp\nrl8Tt7ucycZcmxk4ZDdjaBSPD6fW1hS36xdlHls1dk3pfYAn92HrAEZTNuZioLEk3VoeZ6h4GGhW\nVBZGjGbEsMMdQceSlLM25picUfXInuCMh+Fic0X4YWKNX8/tEHs86Jy5BY9AoLPUQUm58wZPtEYj\nRkhYub52hnJGLsbkEAmo7WgUqKnwtCTxCMzhirIBbmhgv8MOPmMPVs2+hP0SB7Gs+cXrchBOxyXe\nYna+UinqC2JOmiXSpXztGR6mPJCzJp9p3MKWyXAtfRlbz8j9N8oGAouw8Gwb9Q5FZHjwrkVNXGwz\nJLCBXvI8AGB7rcJaJs/JeaFEdUwFab6Zw9gCqH5+eZrg5vWVHHwFk8QpO+E1fq+BNCYVXiNHg8RA\n6zDRXZIYqL+ATeEf/5LdrEugJi6kNAtY7C8pCgeVkjVUmzb61N7csCRsdqNL6Ioq2LMvo8xlrerZ\nEAWxE27PhsEejCcdV12SV+NqXI2vG0+Fp2BoDb8socs5ArKyLqY51pggsYMN9ByZqgpdWOyhN0ny\nWqIHJxD3rLyYIl25jmmwYuNC1y1QE2NlaUlUOn0XrZAsONUMiSmuaK4fIKbSsJUD82NxA298u7hv\nn/MrPJPKZ4xsE92uzPm71TYc/2tIukZXTmvLoQuf1SgpV1a6KXpkDIsTH17JxBjxCr0SAFGDUbWF\nukuNiMJHQkyC8dDGgLoVO654QdX+LbxsiBX8oqq/1kgUjTAnStYsHDgkDc3pKXTKCIqcFWbZgdmh\n222tY+bIRLtxgZyhhEMqvKyI0QJxH4aDTk0EZWigtGl172nczoQh6Q39FQDAOz3g1hHFdQC4pKHT\nI8DxCEeexTBI2taluvJEGVhnExt6Hnpsxoo6JmwydSnPRUX8XEX17LQwYBiCMbiWjFAyNNsouqjY\ngmtNLMQumbrYuJUnGQwSre7uzBFyHlayQDVi+OcJS9N83gFYGm8PbHR3GdotXAzYKNW3t+ASNtBk\nB+jRWoY1WnxTebAIp4dbolnR+lslzFqembkh9+8bayiU7PvWbA+nOZPHTQVVUTW7slFqMp0/4Xgq\nDgXbMLDleajmIUZk8lXzBi5XUNX0GEUgL3ID3mOcvKpJgxVOUJKaOzUb0JVsqtTIYHry8lZJAIOU\nbXWP6kYoYVGLr6wqTKk1WTcdqEdsz/U24W7KtX2KdPRTGzk3YMup8f099nzttxEQdpo6baiUTMoE\nqzhmhQXFRGrlIuPfqeUCNTv4LGcl+uLA4X3GTo12JXOYlg7KTDbQaV3B8mUDpWtCQ/7sx15GEK6q\nAXOUA1nPctjCYib16uP4Aiaz4VuRhAmzysIGkaWO1UJdy9yTCLBz+X7pePBIg29mK97JEMaQqkrF\nCWqS3VgqRT2STZqUMQ4dOSA3+7IWrwwqeIF83TcnWPJZpn4NL5VrTJYxApOdm6UcNqFZPaa365sZ\nfHJJ+trEjBYgSGNkVGrKGe/MlAGrlhc91gFM9qucOwb2qSZVWi5MtqsParYnVzPAIRzd3UJJ5RjP\nacLr0OKQtRlBgrRgz0Howwnl2RiFRrtk9aRlwGZYaJeS13AfmNDE3LgGYNLgqEYTBjtljfkAqk1R\nIXZqGl4FA+SHDCzQNsGpd2BScQx1Atu4kqK/GlfjanyA8VR4CtpUyJouOtrFdEzG5PEY5yOpt2/d\n2EGVyinvaBuqkGQOyPw8HhcoZ+Ima3gISbs1M2zYA/ndSV7CI71bzIx21bCQUyZMoYI1FetozCx0\nXCIagwrOXBJwF1yuMrJwQG0J2AEueUQ3zhViJp/i8QV668xO03sLjRzpdOUJLJGy6SqbLjBaEMn4\nSCybjtrYcMmxUCyQmiu9iAnOHhLSvKgQa7m/HYgl/vi1W3CpWty2CxzeFQ1Gw8xwWcm9YjbGYEWw\n6glSMqobyLR4Qnm8hK1XmpYFPIedemYAlxYotsSyhfM5xrV4YL7nYXlJ3Y62RsxmNDM1YZ3RZyYh\nycsbbfSX4hHMzxt43ZK1H98ZI+2x4tDJ0CFJjGbYElQVzhhSOE6OEQlJykxjWAnzc7ewMCC1njeT\n/59bJRqnMs9DpXCwLp6HvZehVoQd5wVcl8hSNrY1zBg5qdI60RiWwVaeKgaLB5hDPrc5N5FRe8LL\nnn9clTLNAHUk6+lUFcAkoKY3snPNQuhQGMjrIKZlt6slciI6Xc9BsmLSZtdtXSsYDGcqow1vk0TA\nfvnYQ9awUVIH5EnHU3Eo+GGEV77940jfWKJ5/FkAwKTwMJiKezV8CPSZtR9VGTJmtSu6hsuiwmBO\nlwopOoYset0uUNAVXSJBNmfnG9uzz5eAYsbamAHjmNUADaTcjKmqUEZyOCWpbCS/00Ftigsb+iki\ncuANunPYkmSH1ygxmlHa3pSKSstvoaBzlqcljs/l5T5ZzgG6sw5BN0GQwjyXTdxf62HM8GiRTXFG\nF/4cYyiyQS0/KdCQ0g/AhD2K2kXZkp2ZjjU8lgnem6YoI4lFzwijLexHqJcE8fRzOMsVscocy4Jd\np+YY4Zg8l30p2U50H4ucOYDTGYKDVSzuoGBfxcl0ineYJ/A9+Xt3rQXjO+RlXHzhHLMzmUeyMYBp\nygESFCGCkiQ4W3xOhz3YgTyn6dSHbrHCEZuIU9kD02SCy6W465p5p0k2RbngwRoV8Nl1GjZcnLF/\nxGkp5Bnl6um2x9YB1EssuSy2UG/JgTvMmkCfnamhEMvAzBCS2XqwPoFNQR3HsFE+lHmO2jWqC+5D\nVteKRQHzFkPiWYLKk0Nmma1Du7L3soUBxdJvxv1b2ikqguUS6xh1KXmb9uY+tE3tTjNFXn5rvQ9X\n4cPVuBpX4+vGU+EpWJaJfr+F8XofN9fEuvxm8gDGUE7G4fYCNenGGk4Fm+5XRU58nZWPAS9OlaJM\neLLDxdIVCxUtC2jSZqUjsRKTcALnTM5FZY8wv5CfDy7GOKWK78l5hs0Gm38MgTu3rAZsUpt5KsQR\nmY0/9LBEsSWnf1wqxEpc83XCdudrDpokRTEbFp5VYoEPio3HGgFZixRfiQVVyn3GcYKSBCnLoxij\nMd2RyTnuL+Xa3z0QWbbD4NvxccLDfzWu0JjS45ke4959mf/56SWsgIlCNkE5SuOR5LGwNdFQTMbm\nqYJind4xYixG8nVVkG8hPkWPQJ+4clHeFSs26w5hXMicqzKGdyL3dbgQrMSz3+WgPxZKuB8b38Dr\n2btyf1+Zo7EjHmKSLzHYOgAAtCHgnkV5ifqY3Z6ZRuuMieS2iaqiW27ZsNlWWpINOSo1lpGEAQd5\nDpO0ctVogfkaOztPDZhsXHJO5J7GQQt7icyzu7GL5xia/qrKkbL6kIVCzlNXbVRkEm+cHyDryl4I\nDueYrlGf46j5GCNSnxKzEtUwz0TUZTrcRpkKcGzkTHCDVHja82GSdTqnknows5FT/7Od9ZDtyf15\nngnDXPHNGRhOxdt40vF0HAp2E72dH4LjXuLwVdncfSR4aMgGDA9tLDYkxu+05tjssKTIjsSqasJW\n8rumW4C6rrjUAUwqBdWewog6hrNa3PlorjCnmo5RWjgl0uzcKDFeiGs4VhoF8xlFIdn7fujAZNa7\njC3EmWzG42YXYczOt1wjJE8gInmI/VghI4IwMgyAbDvNYYA4YixLSvPSqHHKoHSRT9FiNeBU1Zgz\nXj4NNlDGMs/PJz8CAHjxQKNYhTD9DDVLfeO5jYcTcbtPxwPkC4KlyMqTjwx0qW3Zaz2LZMReEuXB\nZLyfu+5jcZnpjO65nWFuymkymU2BHglAUhc2s+QncY5jV14yvaIvv9hE8SF5ZodfvIsRUYEjEyhq\nygAAIABJREFUN0Y0llzKybTEs4a43eOmlBNdbWKesIyMI8SWfL9dGcjIBtUwcjj7zOaTa3JZurCW\nciBlVQMVn98oc9Ec8iVtt6HJnHRIId3gErBvSGXHaJs4X8gBsujksBhiaFv2Y2EVmBUSXkz0HP1Y\n7nVa+wBzXstWAxOGNqT8xOXpEoYl+7tMxygpOe8aA0wODuSeJksseuQIJfeltdlCVZK2fsdFKyQj\nk90HKByDuIL2iYZ9wnEVPlyNq3E1vm48FZ6CYSqEkQs338H17R8HACTLv4diISfmRTPB9UwsiYdN\ntAkswjUyP98rkNFNtOEhZP24uDxDSXo3pSz4lP+qp2R+DpYoyFlwulhgyTLBneEEBanT+44Fi4ka\nTcx9tTx6rOt3MVFIeRDvzUt4L8vvtrMChisub8BOvTpUWNLVnswjdHKxzE5VIT4RS5IwQeRrGwV/\nt7HMsDTFylcXwOfviqt9oWroVOY5+KV/AAD4zuKv4+aPkrrNCjA8YSLuYoDZRCaaxjkyLRbvlBa8\n9i7heowfzBzmptTbk6MlAvZMqKJGuMMuznMmc2sTBuXPOg0fk0fi+uvmOc5HTPzNYmh6NPfGsm5H\nB3fx4m/Iz+dHBUAuhOEbxyh70hNhH/iYbsk8b61EfbSPiF6hkbrIV55AVSJl9cFJG2iRKq0J0qtl\nY8TEYZimDcWKCsICfmtF12/ikuQyqc8u0I0TPCg+InOodtHpy7P8mBngt0h8U3QYojZ7iK5JGLhd\n3kTOa/jNEMNS3Dd34OHkkszOrHyt9zw41C5tdLdxPJT76Gx6qIhVyVoVhtQpjSLZNyoJMWcX6bhs\noGCncKt4GZ6WbLO2bISmeCFPOq48hatxNa7G142nwlMALMDsQzWnePajbwAA3nyvg+kZeQymJRJK\nmoVdD0YgJ5/JhEvR/wKmUyZWhjUuqe4RqDM8WIh1d+ZLZKFYndBlkimLUARUdnZdmItV8s3DlPJf\n3SRCQ3qRMCBNdGpptAqxXK4zQErauKo7QVgw97FtISVaMmeJVCPENBNLai5CJCOx/kluwfXklD+3\nJSaN9BxLdgBe27dB2ABGjVNsyW3j4vQc3kgsV9wRVqhX31viPyBfROAq/DMmHeNWhPWAaD3fRWGx\n/EguCN+z0VDiKVg3fCxH9LCapzhnjsZbprgcyz+cRDyQZd5FNZfy5nGtsLEu1uxR5QBMlE4bJUwm\nJl+O5RmY92e4w5j8cmuK8l2x3OnGErc2pUHJTkJs2eTX2OZ8HzUQ7ayEejooSvm7QptISIW3Xdto\nkFqvrMWKt60J0CIJrD8FZrIvut0FQlKauc0KXizPslFLQnh+3MCruayxd6zxZ18Ryx1ZBkYtisE0\npSQ5iSsELKcmW+/AvHxF1rOj0YeoY4+yY5wRdmyVZM0yAxhd8YqXkYGtbSZuow6SQvbLpEgR57K2\nFUuoxbUBXL4DGPwKwi98Wn7XPYW7LWvUaTjwFT2hJxxPxaFQ6hqDcgE/U4iN7wQAXOsN0HokbtRb\nSYnBGbECk3N8/3cTeEP68hudH8T1mbwURhYipZs8srsI3pZr2JGJRUS16nVKmdsJ8lPZVO+cD3DO\nOOB4OEPLZiKxaaBviLCGT3jxfV1Dp/JSmIaNdCgbdr2VwjTZcqz30Pfl2ja7+kqVot2UTLZVTuCW\nshE0DCzastm2hvRJZ8A5cfjHdQcrksZwOsXhxSWvUWPARKqx/gAA8LFPxfhFtoh/GhoVW66D7BEq\nsi7nqsBuuUpsykseZimGY5KzXEZo9FjZma8hZFepZ9qwUjmoYs63MShgedREnDkoV0zE9QgG28EH\nwxQ+D9FDaipWfeDaq/K9WW1grZIXZK9uwq8lE7/V+wgMks+sLWWTx2GBnIzKOl+iQ+q2zAuw0bzJ\n56Cx0ZHD2ScoqPI/ii3eXzVdIr6+6spcgyLF3GI5xmQo83tDSz/D26/FmASiZHO+/ufw7p683J/w\nKzTYBz84/h0AQNRYw733JJT6mH4FdkOAY9G4gWVH1rk/6+C5XA5Ri12UxfwUPS0t/Ko0Yd2WfaG0\nBYNQ8aUCihX1O3tUVFLj9I7wjiIy8Mtf+GUAQPN77uDT7/wUAGD+0ouwFE/1JxxX4cPVuBpX4+vG\nU+EpVFmJybtj6KiFkSEW0ay6GLCWPhkZmJ7Jad5o2/jIq9RdXGlG9isoUAthUmDK8GFyYiKnKrPj\n9KA22PgPdkPWQyzYBDUoKhzNiHWwDOTEOuTNELNA5jRls0+QasSEn86yGBYhsQ8WGWIShlpLhcSW\neTbZDKMDBTdaiawAo2P5WjVqzElva7CZq84bGLapTXA4xYiCLOnZFBWbo5KshE+KueqzD+RaBxE+\nfJPh076JmgnaLOhg3iDhzCRCsSJBYCl0rt3HdGzlDDADJhItCzWbv+K6Rko0oRGTNKV4iDQVV3vS\nXCAg/V3iRagHUp6c1RlqQro9dmeOfivFERGpU69AothFuGFj0xLJtjJYf9xINFl1Peopljnd4VrB\npifgKQ9BwCY1s0a1IF0eZeOS6TEMIi8rrWFEMp9xWcAn50I2KTGi53Xn12Tu8Z1LDL7yWa7tDl7i\nGpofaqPBcDKmVoSDDI2m7IWzdAwSgkOpCh65P8rdCVosKSOXe661BcpDwgwqWFxvK8tRrlNcqDRg\n0rszuafT8hJTwtwfvlGgTW2N5f9s4sGLcu3b7Qv4m98aolFprb+lP/jjGGvtlv63Pv1JtPQE948k\nhtpo1hjyzFoPC5wfyssWdUwUesU5J4u0nMwfb45E16gKYgXSxWMp+hwVHDpGMckqItdHygbHbtCA\nQdd352ATILCo07Jwyix675p8xj/99V/C4dvESBTAqgrcaDlYf0F2wkfXbuI5xuuXiWy0k9MBMsbW\n75wniPlSXE4qrBrZqDkLA0DNR6MUoClxbzsGDPIVHnzkFTwTkgewoCiM4+Nv/Jt/CwDw9oGNya/9\nAgCg36pxecRQoTVGmcvBuCdeK6bjFrb6VNMadNDscLMdVWjxoD6+0HAD4gZ4SK13bPhkRep2DIwp\npNrpVDgj1f5mR+H8HXkhrz8v3/vNfzWB25Mw4f/9/BDBbbm/Tz7zl7Au3jPmhyk8XzL1dx+xgcS/\nwEOK3UTTc7zBcOvkzTnmjNWXsUbN0MUhwUi/62LPkgtbXo12r8/10vBoXGpd44ujzwMA7hHUlRxO\nsfVdknO4/xmNn/5J0WP8K9VbaA/+EgBg8PdZDfvdrxK3qbHdeRz+LMsC6YL9NgZD1LKCYRFXYCj4\nWwJX7nRfwO1rIg77sR9qI7j5QwCAF8aCs/n5BzU+8+vCkTx7bhv1/y1t6e3/7Psw/l9k3X7hZ1/F\nj71GUqI/3/ui1vrb8E3GVfhwNa7G1fi68VSED7CBaqvG4F6ElOy0XxrE2P6EuGVvv1UhWJMs9L15\nhp1bdFepo9fdjzAeyMne6ru4f0aL6Lk4W4hFX2+6OB6z7k8OgkdpibWWuFb3khxbVGC+fwncfl6s\n8XRmoSKy8vW3Od27KbKcytYaYM8ROkYLf80Xqqw7+02sEYLrrzgB/Bb6jnhCo6rGkhJqEw3QI0S5\n8g7etzyGBmqaIbtU+FRK8ZJGis6/krmdNb8MAFh660gbEot8yngBv7Yr534ya8F0xfofnTaw92F2\nmE4EEdho20gncv/N/RizE7GOXn+EywfihYQbMxyeUTaOjWTD+RpefkbWajQu4VAd+3iyhfUXZW3v\nv72A+pA8q88fSVOZ8akxHv5z+fmj8RzT/0es5g/+GWA6lWdttA7xxn02bDXENX50bMNM5DOOjhS8\nlF5MXKJpEyJeFrAc2SMuv+dYa3A8mWej14fJUMP3DKSERzcK4GAm3sTpWNYzfVgjeSDPrFYK1mf/\nNgDg7177YfzVf/QHeAgcTVYXKp3DpgCRpXLoVajALkmz1lB8/i5MECmNHx0t4PwbwtXwE/5LuMNG\nt865vLbX01/Bgy+Kx/P6578ITYeg/g9/9bG5N3/6n+K1D38SAPCR33+av2c8FYdCERcYvHYOS5/j\n3ik7x1QJ8/OyGa2wRPAuW0jXS7z0jhwWd/ZlI71wCHx1Q57M/iLEzBV3vT0ysbAp257Z8Mi71yYD\n0dzMsZREMBaokY9IM76T4PBt+btmJ3jczm305XtfSWuYfLBLABHj+r28wrQj8NpXZuvAlvy+wVDk\nxhmQguWtLMUhH5xRA6tsx2pUv+vrlUtX6QpHFDl96e4E57HczOfokjrTCn/tv/3vAAD/+U/9l3Am\ncjBZ9gCjWHZNEubIz67x/sjhmHaAHrP6IxMhKyfzkxhlRKj40kFokJGKDFJFt4DSsjHb7SG8Kbsy\n/UsUPESN2Rz+L8lLffeHJCOf/JMm0Gci5UKj2GGG/MxE6kspenT0AOeJvPTZOfU8h5doubIay/EA\npyVFazRQZfJ9t9nABmTvOGtyEDwTHaC+KQddHz5qU+5bqQIm27LjfIj3KGazdAiK0hrzVSsNNP7j\nz/1LAMDz72xh+E0i7zl/3jrLUK70P2sN0nuieF/ozkgTrgLcqVTMps9+D17+slQ+lr2Xce1SQF3v\njuV0OHztM3h3ImtYvM/nn1Z4vIF+5H/6CbjeV77xRH/X+EDhg1KqrZT6h0qpt5VSbymlPqGU6iql\n/oVS6g7/3/kgn3E1rsbV+JMdH9RT+GkA/1xr/RNKKQdAAOBvAfg1rfXfUUr9TQB/EyIQ8wcObQCJ\nW6KY2FiSibnKFc49sYih14Nms87YcLHcEBe21xUL7KJE1xaLYbdNBEtKs9kLtHJJ/EVmjYDNKjVp\n5O1CQcdMGNY1TpjtU+P0cUflEhkmdP1mubjXYQnkdPAtaByQAKV54CJoyfwfBh7WKRbSPhYXMOin\nGI/EQn1b20fJRpxTpTHgWhDVive3sGh87UFZBnCLFYwlFMwuSTburi5QoHxdrMt7J78Cf4099iMT\n86VYFa+eYOKLG99OxcpP6wptutEDhHDzlVZm8Dj8yaMeOoQ8MyrDvpnBYk+/lfYQu/zBYIwFu0uP\ncIbRp+Tah+/IDV5snCI2xAQ/zEZIH8l152ETCSHfZ6MCy6ngTy4SdpE2Q2Tsnt3oeqguyXjdW8d5\nLZ/9kes25pbsgbU+hWX8NTRYcWhBI5nJ9Y7SBHYmn5EnM5R7xLJQos0D8H6GwxUMaLf8TbyFbzw6\njCvqhoWgZqJ4omBw72R0D0IorEjYP2raOGf373cfnMPZFL7NZfAuTi+k8arZEwzFrY0Po9n8qvx8\n8gdGMdhX/wkA4N1vMt/V+CACsy0AnwLw7wOA1joHkCulfhzA9/LXfgYiEvMND4WyKDA5uUSSLZCx\n3JbWNXYKWRx7ptAJJUZcVBmudwRMNKNg6lpPY+kJMKVVbGNrLi9FZXjIqIfQdLu4nVFLkIuOZIjz\nhTzmfJE+jtsrJGgMSZxSu/BIOOI25NE1HQOdgmAaAP66XO/jag87IVWrXIVttsGZHxZ3zztJUc4E\nnLVr3cetgbzJX7qfYUmWorsFKxZaY9XiEQPwyT+44zlII9nwP9DdxKkvG2U8elNuKdGoKBT7xc89\nwA9+mt134QwueQCd+j049GFL8hYi8pAV4qonMVCR3apoWVi3ZZ0d3YDTJXqzJejHrjNAkxyGKo4x\nglwvK7+Kd4cSi58+eIgWS2cjU1xj9VvPwqQBaGobFasF87NTjCgU+3A5gbmUA8Kt5Z5bU8Dblns6\naDyDlxZyH892biJty+8YwQNoVqDcVbOg20SDSlejeY4uaZMWcQGfVaDJVOElljuvb8i1Xo2HSDlP\nE0DNF/odzbLVNxgJqwvfYXrwWcKuAwMbfO1GrKL1XIWQocQrjT1EL0j0767voUOq+rWlRtlb3Yw8\nR+2t4cO29NdU7gSXZCL73RruhwW+pfFBwofrAC4B/B9KqdeUUv+7UioEsKG1PuXvnAHY+P3++P1S\n9OWVFP3VuBpPzfgg4YMF4KMA/rLW+nNKqZ+GhAqPh9ZaK6V+X6/m/VL0UWBrO6xgB2uYnLEDzKhh\nflQs7Hp6C3Ym1mpDm4hfohtYiWtcqhnWY7GOqjlHwxPrqZMljIW4pc11A9VI3P+Ibmt+2kBuiocx\nO81BY4UENnzSc8MzkGNFVMLPbTawQcx5mSRY3xVLmXxnhIxF9qZ9ifqSitZarF0KHx71YayNF+B+\nVlzq3lqJ/qXM41mDHAR3FxiRvGVWFdA9+X6qbDRJ8Z2t2XimewAAeCMjRd35EuiIpb2057AJBW+j\nByOl3H32IWT78v1oJFZnbvvIMqpF7czgLxheVDE8LZbLWcvRM8QTKiJ5HmF5AB2tOBwb8G0CkrKP\nIBpJrTy4FcBcSEL0BpN66XfdweGY3tbUREqJemujIzyGANplgBl7Qco+5enjDSy7YsU9fwebH5PP\nW7c34PeoNK0+hmUlGIhqLn8fekM4C+6L7l1Mjqgy1aoxqMmunN0BbLm2cyxz+NG1EP/nQMKLqjLg\nsUywf7PGw2+Qv1MK2GGVS3+kgfWh7IvL+TECT77eZOfrzedeRHosydX1Zz6MsCN7q319FxOKILU8\nBbNBWxuJl5Jt1Zh934/KWnzmCC/Tu/1S/bXgUwHY3hUSmPsP/uD5vn98EE/hCMCR1vpz/Pc/hBwS\n50qJrhv/f/EBPuNqXI2r8Sc8/tCegtb6TCn1SCn1rNb6HQA/AOBN/vfvAfg7eEIpel0DRaqB+RnY\ntIjYMPDcfSl7WTdr3BzI6Xj3wML2O9KUkn1IYuBrZ5sYNCWJ5p7uIrMkpWJNGzhzqLF42sZZJiWd\nFsRKrPcK2LHEZHP7PWRULc7cBOqctFxbClul5DOKrlxre7GG0BDPJA4XqEJJWrUvLLS2xEpbsQ+z\nK9bRZRya2z7qE7EChn6Ea69IWdC6F+NaQyzwGaXkPvb8OR69yqRdZeO3NsVShktgkRKOvXBwtJQS\n3/W+zL27qPDwXObz+uDLCH9YGJkCf4qK5dnUc9Aby9qmpcwxv0wwreWz16cO2CSIsEigIupdqDU4\nrMV2nBanZgAFy5P1FBUbm/SkQLsr1r/5VoJL5kQGA8n37C8P8PZQrPn+zMa9dSaYBwkWAyGFnWUz\nzBJ57tsXcq2qqXDAvEvnmoVdR3AWnU4DliHenbKAIJXnM9Nyfzq2kdekR5sYWNqS2q3TBE2SAm9e\nNFHQyuZN8R6GoYGPOrTcySnuFXLfy4kHYBUl/96xBYVtanzsnToIyej3vY01WJF4LK2WeDFOtIs9\n6ptazT2oFr23WYYNgg+yPILRlIcyJg3cnp6g8OTrzWc+gX/xBUFjqjKFxcRmhTYeHG5zVmd/4Hzf\nPz5o9eEvA/j7rDzcA/AXId7H/6WU+ikAhwD+/De7iIZGUVcIWj5yEotUMPAuH9wzG+twmNhxfQeX\nL8vDOKBoRtlKkNpULLo2RziiJuJ2Bp9Z9Ek6RUYVnoRdZhveJqxYEl9nyyZGWg4Zq/YRcUMHNzdh\nDynaUok7OOsu0fLlWvGyRiOhCEmjiZpwY10ZyNg6XNjEClgaKV3tRrQJUDOxc2sBh+3VN1ukR7sT\nIPooFaaOHuI2+6XfeKCRk2LtRC+ws0ao7LlsqkfNEvOZHJCJAWhK2EdqF2MlyTVvcow4IDM1qwlp\nVSDLWXHRLbgkAImLEOuOHEhFFiFQ7CgkuYurC1QGRV+KEtOlHEiX9hQe121jz8fYlmvcxPfJtYyf\nxTWCt97cyVGwpTrvlMBYnt9s+RC6lOez8OSF3/YtLCO5pxYSKB42Wb1EDrkXt4iR5rKOMSHmBVyA\nlHZF3Yav5euRCmBYsufs9QjLAasuDCU71hIOexQusQF7Jj9vPbsB9VAOr/fHx6sXyowsdF+UNX6Y\n+tgiEqWJLTgHYgBsLT+/3k3gF2LoVOSiYv9I31/AN6UPxHVKzDM5FFpsB4/NZ5E2ZM8u3Vdw0/sC\nAOArSxM126VNzKDbghF5Uqb3D3QoaK2/BOD3w1L/wAe57tW4Glfj/7/xVCAaDQ04lcJimmHVAJdm\nGTxKqhdnOTIlrnbtnaBzXzgJztbEFW2XPnISfwboQoGS8o6FBpN18aKBmoQVScxSoZNCkUXZLwdg\nLhNFvXgM+d1NFW5Hkh30D8Ql++rPv4d5wvJPbWLSp8cSFvBXcl16AZ8q1kEkJTQ3SjAdkjauqdHc\nYpl17kBR2r2ibmHvpsL4rrjzS99DTm+kXxZ4q5D72xxr3LN5bWph7KczHC9YzXE85BfUEGh40J7c\nt5EU8IinthekDPMtNImOqHwbnklJvn4EpyP3Fxg1bIOyd+w+NKopKmoYeo4Fn8Sst1WI05y0ae+G\n2N+Vz3OifwYA+Ozdl3Hv3q/Lz1MbTkt+fvjVN7C8LybtYjhFa0V+SzGLNHLQa7NU3VMw+Hxdt0BB\n2LBl2rAt8RB65HqYlBaajjybsQawYBNXncIiMUo9cRFpsaox2xZzpVF1xHLfilOkTYqs1OZjKPpK\n07kAYBDdumG1sD6Qz7CuKexQMKi1dhvPr9ObbMp9BN4N+IpydNrEjKm+oogQ17Jnm0kTC3pyBpOd\nh/crqEPhcnioKwxt8TyUnWGPIdZDbUJR5h54hCcZT8WhUBsauVdiu9HF9Ew2vGVWWF6XBd7dfA6K\n8aKrXSxvys15lN6eVwUCtpUWfgGfi2A5E5Rk+3U3H2J7JNWARihuZt3KoenWbl02cFqIyzybWyjk\nDIJet+Huibu3WLK2bcTwAkKwywgduq3ReoRqKS99o7PAhAAZvymbvHY03G35jMjdRJXLS2/lBcJ9\nkqXM+AALG71r3BA2MGBuRMUh9AWxAEjRjyVUMI0DAMDUn8G8JhuivkhReaS1txTCgWy2sREiYfKm\npLuPYohUSeZdbTyElQjgqmWlMGO2QzcGcCmEazI/kWQOErIfmVUCzfbrARwM2fKZBwViHlRm8SGZ\nZ/+L2O3Ktd5YjJDx+SVVgoR5hLBYwmDMXO7LGrarm7jGMlGQ9eBcp1bm2IRHXMiiLrAgVZVDRulW\nL4GzlJdUhcdIS6otWQssphJ6Zf4DDCp56ZNackNJbKBNxqq3kyWWZIuqp22wrQIV43dTaeyz3b9/\nI0IeElbdukS7kOfa7SRwfbnvBkWDXTVHSrBcNQuwsS7vQDm1cUGpgTdnUzT3JSc2W1L1qjvG/KHk\nH7r9rwBthrephcNc9pltlnB6lLYnqvybjasuyatxNa7G142nwlOwtIFu4cKJF+hzSkMDuP5ITvDF\n1hjfMRfTnT2/RPdS8AmLttT212dtxKwJN5Yu0Bar0kn24W9IRXTn7ksY7AmC0KrJbttZwrPFCt6/\noZDSmo2SU/ROZB6qb+D7t5i4fElO6DfHFTparPxWI8D8gM1YaRPhpszDjrfR2BSPpunJfA3XQk+L\n12GEFkyyIJfBEpYpIYrqyd8YawaSt8UKGNnLeKUhmeU3p0305yRvWTZR0iKsfZeECc8db2Ljvlio\nXwyWsBKyGjdr1ER3FlYXIbUqBgxFppcT3J3Jen5ksIbxrrjRGzqC7ohlblUWbBLHlEzkaUyR0/Wd\nDOfIDLHyb33+HViUSU8mXQQU5XlIdOituIffITVbf+igpFiKtQyw15e/C85vYLYtlvnbS3n+W9sG\nNtpiadfWTLj0YiyzgjkUr6cyF2gVkqQuDVlDc6mwLCUp56VTDAuZs3FhIq5l7fQ4R5YzGTuVZzrN\n5yAlA/Z8ExUrLWdmhZDaEftabKtpOgi3xdv61/vbOGfoU+s+7D1Zt/3mPhoNCRt6hGIbZoiuSah1\nmQOp3F+GFNVEusoWJwnGA/He7j8gwnb4RbybMZF+UeD4TO6vaiTYLGUPxI1byOefkBvAv8STjKfi\nUKgNjSSoEbTb8C/kZQsLH8eRADo6SQfjG/LgArePfEdi1c6lPPBxWCKv+BL3EvQgzCHOroNNuvaT\n5y9hvCehRLnBjLSxBtNhlcF8BVEq191/VKIM5XrZCz7SZ6g7uCYP07QqBA260Wu7+AjzEnkvgku+\nxqqxRBZLiFFRit4wQlQmf144UJ5saNcwAIclKV/uqRw+ghlTO3D7CzimSpG1uEDOjX4UzbDFeHjt\nKxIaOa9sYMcRyLPtOtCP5dAj5HTn9XSIhSP3p9k5Osr8x1n4y7IBj0I7o7qBPtV1EqcHl7FqurqW\naWNGFapH9y5wxB7wr46G2IgdPocMCaHgPVZJJvYDVA26+3mKgm47/BkCWzLuzt4RXqR462RDPndz\nWuOCsvbwQpipvPzpeITJuczZajXRNuV6rbbc03zmIvDkJT0eznHvTTEWc3+JC1al+oMJDgmbTrgu\nDaWwuJDvDbDAQkl4ODJjNB35DJdqYYHq4PuuMz9xax+adPeTKWBukqVpFMBm5Wo4lLl1oxKKbGDa\nNbCYkUDYGuL0LQkZ3r6T4BFJfy9ZUXPSBHOyP81T53FlqJyHyCy5hpk9QLgm+3DJ7uBvNq7Ch6tx\nNa7G142nwlMwYaBpOKjOE5iUAs9nOaYXYkl/e/oO8tep7ddx8AyBPuFzRITMcigmD3XVQLgjbmkX\nHRTk/lsMc9y7FKuSM5mZuEd4OaJVcmvYnQMAgH82hlWJlQtenSKhV9DclkSVLoCEMmCNnXMUPYGt\nxnqOs3NmkR8eYkwMQO6Jaxju7iFmc/7GfoSuKdlipxHCsFZdK5Sid4GT+2LN4rMMX2qzJ+9YI47l\nM6YnS7xLyLZ+nnRmag99wpJffN7E9L4Auc67fVSrVplqBr8Qi+3SN76218VNYj3Mcgs7XbGCw6oG\n2K1a5xUQiFdgsOJQzkcoB9TgnCwwnIs17nkujFAqONe7O2jVEhKoAwnhTr94gOJSvlcuLWgy9Lvz\nBNqR0MVRBRJWMyYPxGKeW0B/+UCezWGAciHPsrVQeHcgptBrtXF9W/bIS7coapOe4uFUntnJyQMc\nvSt/Z1cLzB3Zc2mmYLq8r5Hc8wOjRoMYkipJcEFsxaK7xFpL9si+kv3hRxEGhEmfXl7O50cyAAAg\nAElEQVRiTNo0VWq4d+W6F7cewSak+3hELEi3gwYrKpFhQNEbWSQ1YkO8WwdD9Dqyzvtr8r2TBz3s\nEKb/ejCFWYg3WTsO/oI+AAD8XDWB4wpY6kmhxU/FoaANjdKvsbu2jfMxN1Vn8riN2i5N3DdJqZ4p\njHbkIRSsPsAsoaYUFHWX8MYHco2gRJyI63daLHBEktY5CTydrIE7obxgtxpbuMb0bIEmHnYkjJlp\nB8dKSp9IJXOeeDU2ydxzPdwHWvJ1uqzgueSS3NiDSmR+i+vyEK1Q4+yhXGt6OsJ1ltOU5yHcJ5dk\nLgfd9I0zHNFdPOkUiGvJXjstAw37kPdUISNR7KNCOkfLtQrOLapUnVao2IuQ5Etc3KdaVD+Gza69\nfij5lV67hMmwq25lMNiJ5zvnmI5kbmlrACslcSlbxOemhTHLpeNyCcOXdWs76zAa8oKEjo3+bVm7\ne8cS5iw7v4HGmhyyp+UFajYlV6GJgsjSaM1EzPWoqCl5MVFwazlsBpYBpxTX+M3BAKOpbHtzNkNh\nyHr15WxA26pwOpY98uDOBPfZBRqqDItM7q8d96CcVQs+Q7u4QhExzImBEfUysnmNjkv9CR5ozXUP\ndps19TjDmCS1+w2N5o68mEZvC3FJLQ4etndjYJ3qT+1uC1028S5ME4q6HdOtAmsM76YOCYRbFpQl\neYYt5234zdXh9Bx+55S9IvUD4KboUuAJuVauwoercTWuxteNp8JTsLWB9cxHZKYo1Yp/0MBuQgXf\naogoEasSN1N0RmKBlj264nkbMSnYrIGL0hE3spyFyAlntU/qx1TeM3InXrhneK4WF35mnKMREcew\nXuBT7HMwFwcwc1H6GVOqvlU46FYrCfcUvXWZ2/qsgwu6s/c+ew9fKMXl/fDb4ta9/eIOvr8Sa3xx\n/QStXJbf7jWAKfvzF+JSO54FVwv0tXrvAV5bCrHGx7x1nLFf4YU4wh1Clz8aiYXuGx9C13tZfrd3\nBO/RP5ZrFHvYUBJivGHO8RKt7WQuLu70ZIwj0qnvdDwsyLXYdz0siQsIZ/8fe28aY1ma5vX9zn7u\nufsSe0RmRC5VWUt3Vy9DM/T0eGYYFiPEWNhG2PIHo7GxEBaSJVvmG5LlDyPkRciWMJaNEBJiANuS\nB2EYPAzMwvT0WvRSa+6ZkbHefTn74g/Pc7Mrx8101bQEiRSvVMqoiHvP8p73vM/2f/7/EquhAJpY\nrKeXhHRVLGZn5fG1c/FuHp4+4UBJcE46bf6ELcrN1/tyn43eFyjsXwEgyuqc1SR51gk7lL5iOSYW\nzoYSuJzLMx8vn/HhufRMDBcrbM05Oq7PpiWW8tRz2Ao0yakgZCttEo8FlpxWK4qhgnuKguFK5mDf\nC59rWt5Sla2oiugZCo/PCq4vxY7ecwra605ZrT54jkeh4K6tMuU0UUKZVcZFKMlfFjHT1yXcHKhW\narS1JEiV1KXM8Qp5NhYZbV/mJTq3eS8W6/8w+6Zcb2Ey64intB3UuNR7un37Ic3wSwBsWH+Uv/pd\nSdZ+3HHlKVyNq3E1XhgvhadQmZDVK/L9Pvva5NQzXCJtZrGra3ja4bbZvIGzI7v47W3BD7gJDFU2\nbl4uaKVyjHGeYSrDUKNr0Uglrptqk03fapE3xaLYnQYNtQh3jioiSzUf999gxxELk3cES+A2LK7t\nyLG6B20GC0na1Taaz7UZrZ7Jp3zxEHa35HtfvPU5+h0VgIktVlMte41PyDfWXYsqEOLv0fiCWAEj\nWnBNE26DZcJnY/FuMu8Uo5RHeHpXPJfTny3YtiX5NNnLuKG8CFZScvyeJvZGI87acv3NffE6RnmL\nlpa3SuuAzvrZWDVIVU/BqGiN5ZmkLS2Xzi0sTa5ZOzFmKNav7fo0lKbNMytSSyxhX1F+lvOrLHe0\ne3Qekep9h4MAbyWWMmlWuIi3tRZ66TV2iY7FYt5qL2mG4t0N9nx81Y+8NbXYO5T73lehnkkeMl83\nxE0MjB2VxZsb1Kzvc2fXtJRZV2nBV1o1eCy5iH9hFZSmeJ4pBrkrn/F9+U6x2qFtiKc4zmq0Wmsh\nG49rG3I9/d5NDlTdu+xpkrg0iSs51pZVkiunhlWl1BT9eHAn4b0H8vM1R9ZvLwzYbEl+wdnu0e8q\nZH1xk+UfFK+483e/Q/3HxcvkbT7WeCk2BbOCWgmdeUSnr7RbTkCh7bmzcMbClYRYZedokyN2Jgva\n8Vz2NOkzzBsUuojDcIihSSTDDLiuEFxnX4AwDX/JvlYcejWfrCaLIx+H+Jqxfet6jWLjxwDY6MmL\nsOs5hE3VMLSh6qp0eK9BrybhwUHnVVLNdrevaVZ45FGqrmTDirFUACRZrjA0YVhqa27nwOGLeqNH\nP/uH+AO+9AlsO0c8+OVfA+Bb3/Z5eygL9mlTruFzw++yqDSxuT/g3kg4/PiGzW+dyIKd22O+dEc+\ns/OmhD77nev0tIPTynt09UVZmSV9VCQmNKma8vtllOq8prR8eWHfvObjbf5+mQuGoJiL/XlO/VBh\nvGf/AoAPRgG1Z0q5H/msunKv1WhIiBzvPIMNrQxs7MiL9EYBr9yQa99tZdRX8lK0yoDLlmxYxnhK\nsyGJRqMmm8rsUUFrrv0eOxu8XpOfx90dIkuNT+iwWPNR6gYT3n/GsdLR2aOYCyXRLCzIEvlerh28\nD7L3efJYXqkbzS12tB36sL3P/nXJeN7c2KQq5JqnhUCp/YsER1vmsb3nTN61KmDQkM3ky2VCvyP3\nPWjpRp/dZrKQas637ZjFPbnX7958ny//dXmuX6l/EWd8yCcZV+HD1bgaV+OF8VJ4CoUBCweCehe7\nr+wetZhyKta41azjamOIYzRpb4pX0FSuAM9JCLUpxXRW2Ik2tRQVjidWrkptXBX329ZEXdfaobOj\nSaICikLpw7yc2S0tITnX6B3JMYqGuL7OwOR6U3Zrb1ay1B78qB/SXGlIsO1jIJ+hlNAm29jERH62\n8in5udxr4h6Tueop2OJGMnqF+nWxSls7d+m74q04F4+J3xRr9hsXY8JQodlrbonsgrAu2IRs5XBL\nsRDfLM44H4slmTomx3MJj75QSrmqv1XQzOWzsbfEXKlwijtlrHTGs2qOMZNnslItSsfxWWiprOVt\n80ZXPJdmdUjpqMJ0WpBrAu68lDlM2+/htFX2rzJYadfm4qCF9Y54IePsjJkCGOKGWNXP1a4R3JLn\nfmi9hlMqp0HqsTmQ66iSPWLlQLAmypjcvqTdlfs/uaxzfUOt+A0L1xVPLlkdMzyTZzJvyrG81QDr\nWDyCMwtSJVitiopYPb1CcQeT5PI5VZpjW+xuyRra2a9zc6BMzO6MTJOKlaPCQM4KI5DnbpcxtjaB\nJY6HHUomtbG3zxdburaakrRNspDLE/EeiuUvMY5Us+LrGf9gLOQ7PxW9j+t8XBkYGS/FpmCUYC8q\ncjskULfOebVLK5WYOjSWpIbGWW7ORl1iq0CBJkXZYKnsvN2ZTaiYdJaw1JhyCzBr4pZu2gpXbtdp\nonx3WYSxkp9P45g3ffm5dvOIwJEFZCzlATkTF/dA4vMs38RThytYOjh7kufwk5KsJw/Jbt6W381X\nVLwFwDz6NZrIRrA47+BvyT3NMtmY+hsXFBcSMgXuTTxDjlW92sb+x3J/P3bi88uXGqJsiPt5//6C\nm10Jj/zmGcu6hBU38Hjfkvk8MxN8S1W2DJnD/dTH6Mli7WYBrtbj47xJ4Mu5zVFEqaImppJ4zLKc\nLX1RAiejVmis7oUYSr6S2DPmChaKnklvRy1qU/elavHluMH3dmVuveMRx5dyL2mWMlHo8qG2JE/u\nBNzWjEerbtGoS7jm5DWMuty3FaVMNce0ZikqxruMZnK+fe+CmsKqb1R3KHXDmptL5qZsPkfaM3I+\nG5EM5KX3Hxr4iv/KTZNKgVoTVQvrBm0exnIfX+o3cQK5zh2nQ7Mm89nudtCWF5aZrIt645hKW//z\nVRe3L2uorBI2tJOyMgJ8S3t2FLuQVynjXLA1zmWd7zwVLsZ3dme4Y9k4f6NeZ/i8ufvjjavw4Wpc\njavxwngpPIWKisKsqLW2KbbEmg1o4F5XVN2oJMpUp8AxqSkdmamNSHmZ4qi8d9RuYisBSloYVNq0\nkpsujbWAi4qUNKsco9T++KriqbrEXr1N5kqtv7vVwg2UekwRiM6ez5qWOeqlWKo/WN7Yx6rEvTYs\nEzfT8EGlxat+QG5KTTwYH1Bo92FzMcRTy1u9Lh2VPEkwAvms1b6BaSmy7/0TLOX3PzGWog4DBDP5\n92Y7wK1JcrFwmrxiipjI+zsLrHtipQ+dimag2ArNb60GHfqxdt85TSjWUPCEQis4abeLMRaLt6jW\nz6NgotRsdjXB6omb3MgL8kLuu7wc8yRUTQlf5iS1zjDUq4ivnZFmYs2WgU2onZb3np7htdQDupR1\ncWv3hEkiFrNTzHFU2dpwC4pCvKZkmVNqGLdwtMnLGbFzJEns4TMolfdgUQPfVDj5ImI11UYwDR/a\n16+x9513APjAMHFVQzMtK5xKE8U1pWuLUvrK35AZNfoL5Xowj3DUw3CyAEOb0Sx/qL9rkI80fPAz\n0pWcux74kIvXYGWXZFtyTzVF5hqVQ03h/ZFp86be0937BpFqWVwPj6m68qwkePrh4+XYFCqRYD8P\nl7TmsgjKHQft0qXRq5iPtSOtjEhU1KWpmWKzcthQpaR6XMNW9+q08qnF4g5mNHC1vyDK5cE2vZRU\nH2IeRpjqMjqBTb8nkx206+QaYvSUuCMbpTzcEve0Ok0Z1mVRvTLbJtBSn5lDrn0FjrZZlxOHylIe\nwaZNTctUZWuHVDP4TRVCCa1THFM2m+TCweloqFQPMIeqjzl0uOPJA39zIC/Yzp1X8CMBrqT2u3wl\nknJU77xNVinduTckQzaZUDc8Oy8x23K9aTLD1opDOEspCwnNmlULe1e+l0xl7svlOROlhvd2UkLl\npXS9AMOQ3E2rkbKnuY8ol40pzAZcKKR9OhlQ25FzfO3rv01+b82rGeFpr8GliuMOnVfwVUA4yJuk\nTfl9I6qIlsq8lNgsY6WgVyDQVpozGUvIN5tFGB0pAdeeTOm2ZO6nywRXjY9zruedRuQDOUbn8Yqn\na9FYw+C4kOvc1I7SyoRoKZvmRZQxUfHichbRcGRt2d4cN9Z+HO1zyeYRpRLievngOb9kORsz15Jz\nvx+Q6vlqKjKTLFc0lI2pZbR55Gm/Tt/gtgpZfjW3iafKXvUxx1X4cDWuxtV4YbwUnkJpQhxAo1sj\n64hbW1ZNko5m/ccFpibawrFDbCuBxlKsY73lYmgm2Gwk6OaJ5TxlFYnb6TgjFpFYaSMQ92uyrDAV\nNzCMKi5tsfhb2etER5IEKi2TSjkQJrprW5s5dV8aUaLFkCSUvfWCGbVL7YWv1cgvVXzlXBJcRtrG\nbqtVSZtkygOYXYSUe78un40lsxydlMSmksL0ljgjsaTFhxOmSznu282MqJR76msytNaouGj9BgDT\npwmfrX8egG949xmotF7m1ki64kHkc2FXTrfHpGMl5sjGlCq7ViUL0kIs3srJcBbaEOVLOHAxHTBW\nApVqVrJpatNO5uLVNSFmWCyUt8KtpCvV3rpHR3Eh9++PeKpNV0atxSKR5GiRz8m0qSjVBOziZMLo\nUDkizmImc5nPXsNGGdVZJSbxqdKpXcpni9pTFkqRbkUQKW/Hoj8m1EThajHnQpPJpTYl7Q/2sM7l\ns+2mRTWXuS/zioZWvNaUmHUrpq0kLF5/RDEWD2TmnTCcaPNbbtBoyTUlpYLCFiWpdp0mC5fimpK+\nOBssk/U74FDbkc+MYpmroppxph6fVf86bynn4+RkzndNuWavTIjSj2qY//DxUmwKVQHZCJ65T7HU\nfauCgqO5uG1ZZRJNxHUK4xWNM5UOl/nHddtE+nKbUch0od1r4wmPUlm8b7ZrxNppvSYKWTVMAtV3\nmFYRLeVMPG7E/JRuPHHYJ1Bg1K8cKy/hhY3Vlri9nLrM2rI4aqOK4pq8TOUwIjXFXfVUgcioNTAM\nCQly9wyjrei4tzPymTzQe1OJX/eCiolmkINHDRaqwRgUIx5M5fqvXbq8q9WDvZ/Q0mp4xF4leYn3\nl7/KaUdepl1/k6mS3y4cjzcvtdSnhDPuccFEQm6cYUSlaLwir5GtQ4KsZKpake5EeS6tkG3NRSS2\nQTiW57T0zxkoSUyZLSgvZHHfW34DgK63y4WCu5zjGrWmbLxns0tQfUujNEkqzQ1oXfSsiOhfSsjw\noIzZLhVEtbFBw5frmJYhpZbnWOi8VD1sRZtuNZtMJspCNclZaHfs5GLEWD1ttRt8EA+p3daN8KFF\nq5DjTTFINNfSs5SU1nShKxWHm6sulpbXF2cxw7byioYdLASIFSsvZbrKKR3Vd0gqeCzfm3UWDBTK\ntHIzyqFyZeqGbJ0OmUQSopWXBv/8VOZz8OmYo2fyat8rDJ6tPtmm8KNK0f8XhmG8YxjG9wzD+NuG\nYfiGYRwZhvFVwzDuGYbxd1QT4mpcjavxb8j4UVSn94C/ALxeVVVkGMbfBf408MeA/7Gqql80DON/\nAX4e+Ks/7HiVUbDC48IT72C1cNgcyI5am58wXsrv215MtqbRXTMAJyWRpz34VpNcE4KrrENNBWWW\n2QaNUKG7be3Ii+HSl100vogZbgsUtZ01uB+JVb3VLHGeKbS1IRaq2Tax/UO5hs4cU7Ps4yChG8n5\nsqwuGVSg3FawyijGrrRikjewz8TK5X7ISv3VgVKIz5MaWUvuY+zcJWjI/U8eOjxy5Ljv2SlzpWIf\nP5JqyfZP9hlH0kV4XpS8ci4dnsfNMTOl6OrWlix3pAOzb3T1sy1cTc65QRdnqYlGt4UViEvm2y6D\nhfYrDOTfXv6IItK+jUnJk0Tms+9VTM+U6i5JOavkXi2tPiwnC44NOd+DZMiHpSb48jaxUs3nZcia\nR325plyfFpxuC+CqNZ+RBfL75SJkmaskQKMkMFTBS+HTYcOjg1hYoyh5rHR7GRP6NVlH09hmqJUr\nTzEWbs8mvC/X/hiThRLVpICby7OKDQ0ZzIq3GnK+/FqNREOboVVRKcCpYzXwIjl2WMr3zKrEd2Xt\nVY0ai754YT0jpnQ0PEjnXGiC3bPl2ZRuk8lYwqSV22JrVykE33V5YsvcRlkK5vd7Oz7O+FETjTZQ\nMwzDBgJER+tnEF1JECn6f+dHPMfVuBpX41/h+FG0JJ8ZhvHfAU+ACPjHwDeBaVVVqgjJMbD3g75v\nGMafBf4sgGeZ5POEMpgSObKb77+6jxdpj/nuJvsKpS3CJakmDJdKtBqmCfW+6k66Aa/cFAxBo5rj\naFdentYpa4oOU0anIBiyOpVjfePsESPWdeMLvvddsbz/Se/zTHbk2I7qIvhLC0MTan63hhI40842\nKLQP37EMSOV8ZizTHMURkwux4k7lYnT1uF2b4gPZ/Y2B/K7pjKkUF1HcN0lbwrZkZiHBQpt2FgYj\n1TqYG4/k380vEDyQJGjXOmOVyGe3zAEXLRHz6raeYCylYSg+VMGaZkF7V5W7L22ytb5BntIw1Qvz\nXGoN8SwmY8VbxA6Xmfw9rsYYing0Yp/GkVybPdzgCDnev6gEgn1n/CpvhWL5pmGLzo78PBnOMDPx\nFCp47m2Fc1kXd6dTjj6UJGl281PYam2LjQfE5ypvN3Ko1SVXtKPQdK+KSZWCbpUOaVQyL50lRIpo\nrPv58/KdF8nvvns55wtKEbgfpjxcr2wDXMUn7GfaJVnVuVS05eGjipGWkavLBe4NJe8tDYyOeBCL\nZ3KfppljK9fFxqCOoxgD1/EplZoNz6Gbrd8BXf/VmLnOy7PpnONHcrxTO2KZyPdOKtAOgY89fpTw\noQv8HHAETIG/B/zRj/v9j0rR+75VXdZTblU1bF0EcRniHkjyxYv22NyVCYnO9vkwl4X1bKauXlEx\n0KRk0D2kOBU3360fcxquMeX3iCLtqFOiD6d0yT1JxG0EJud3BSa69eQWF4lk/v/Ga0f85Z+TF+HT\nTZmuDwc5zaaAgszLC0JtAV5YH1IfC+2Ys+HiKM32dCkv9IOnc5KhLLogsOk/kQU06c7pevJSzELB\nslcXT/hQCT/eeKPEVums1dMxqQKkHnj5mhOETPtEeuOc5IZg6pO0xApkTx5ZI26tZN6GDZ9oU+6P\nUsKLTmuFkcj1loMRsxNZ0PNqiDuS6/R7JSvtGwl8+fdpZLFYyks6S1aUnlzQ9fw6blPupWHPMQ/k\nGHcuZWOaBffpn6qAz6tTolMFfYWNNZMkBtVzncZS6dK9x6eMviCdmHu3Kq6j9Gi5z/1TIR9Znq9I\nFC/wpC3HfbUJj2x5jvNn95mWcu7dN1r4ih0pVk16DfkeSjl/rYDTp5L4HOc5uSYVqxJsT4V19TWq\ne0saDWX23osxJ9qVuztj4YpBsXshpobIl7bwbrqjgFIT6cvOGQOlyrt0BhiqbzqjxjXdWCqtguUT\nBwLZmHaMp2xohSc+K/jwea9QxXof+7jjRwkffhZ4WFXVZVVVGfB/AV8COhpOAOwDH1OX5mpcjavx\nMowfpST5BPj9hmEESPjwB4FvAP8U+PeAX+RjStGbJXirivPkCUeaqHn8TsyXcqmxewdnoIivxtaS\nKFHFZyW8MAqb81PxKlr2BXXVRTA7Bj3FGHTKGjNFlVUNcffcoqCWyd+fPbmgUPqsXxu9S+L/LQA+\nlb/O/6B6jX9C99x20SCdi1WqWx3m6gZvj+/g70np0Fv4GF2xpo1H4pnsByVLTWol5YK29sXXqw7x\nntz3RqiWLws4eiohRTq0iN+X4w5rE375WK14YpCqRVj1xJqRPKNn/hEA/sBgwW8+FQ/LmlVcJGK5\nj6MzPq3177kpe/bG4pC4K3iKbpRhtTVkiHfwFP5d73qgpKppUyzia8mIZU0s33YvoLMj1rjRbuJp\ncjE3E+7P5Pq+976Uco0w4r37ikY8KUC1DIpuhDn8qI6zDtWZeDK9T/VNYQvxPxwy/MM/AcCP3WzQ\ndP99AJy9d6lOFL8dq5XfS3j1iZwvano8Ug6E5tYRrVDuO9nK8c5lTT2ayxwPn8yIVYHczCscxSaU\nFQyaKkCk4YNnupxp8njPvWShb1dymTF/IKXm4ksmQV2O9/md1/UaKuqGUrAZNtnuWjDHJU80iVuz\nKTqSeLeX4t1msyckFxLGnRslzxZyz+/0CjZP5BjPKnCVFDf/mD7Dj5JT+KphGP8H8C0gR3hd/lfg\nHwC/aBjGf6u/+99/2LFMAwIbEsNirC9p0095rACam+UFvnIpppmJry5Ty5fN4SLMKRUIc/E0o7Yn\nLlcwaeOqJmBht6gUR29MZUFM7BXRWlWWnOF0LRdesvj74oIXP1nnJ9+VY+x+Wc7XbppEqm3p1Cw6\nNXHr5r6Bm6j76VjPgRTV5trHr9MYaKyelkw9BWrVR9iJbGTVSh54ktWYdCVGDC4SHms2/Mk3l4wU\nZ5FR4Wssap9rFcV2KdJHcu1Wk/RUEO+W2eVY2/Mso+CpJe7strIIn40rbFM2sabVwlFCmtRuYHYV\nojy1MbTrNB4pqKiMSfsSdvQaJraGDJ4bkg1VOWo+5vEzua/5XO757OFT3p2pqG4DEm1xNs5Tio8k\ny9c/5pW6xnnI5f3fAuA3NnZ57dflpY+2juh2lCSntAhVN3Os0O3ZhcdYGYtWxSZhT9m6L0OeeTIH\nw7spj1XodzKRuZ9lkDySuYjK6vm1lRV42m+TdTRnlCTUlZzm+LJgqaI1qzCh0Oc0fbCgvSfz1WzJ\nvyYzShUWtn0DI9b2eWcIlsyn4WdEoeIaNNd0eWEwVQGY6UlBrrPlnIMKXGGaYBr6Px8zt/CjStH/\nJeAv/Y5fPwB+349y3KtxNa7Gv75hVNUPcNX+FY/97Z3qL/xHf4befoGdiVt6+LmIjeqnAOjupcyV\nkKQ9OMOypDLgNWVXj9MBhuoWLp6ZGEgCZ1hep18Tl7FtvIG7lu4yZIcOzEuShVr8zpIsFtfXtU44\neSgu4WL6W3xPe/qbquH4l0//G97+K2uBFKjUimGAUZddvL/V4kttwTpEKt5SmDmWWvmaYxGuM8uG\nRb7urlRCk0kWstQGn4qcrNSQxzc5K+RDeRPSc/E8VMeFeFLxal066z6oxfz8j0lCtJvknM5kjprA\nUJGAFPLvKCmoqxeAB+lEqxpZTKjVBcstydYM1MplEc4NMm3sWaUVTUUVVkaNoCGW7Xg5J1ZthEyh\n397AZCWXw5/73J/jr12T+/jbf/LTmIkc48NvXHA6kzCtzMRLuzGImcsjZbNV8nSiVaB0xt1L8XR6\nJkTKm7inmhzTDLY0UexbDdpacRhNpwRaJXpiVAy0Q/PhSMLRW6/tMM1l7e3dKHl2X74X3Cw4/S+/\nDsD/tH2qD81hLeYDfN/NGdT41HWxkwdTB1MxHp4qTftZxjRTBXIjJ1zzisawyFVhfFWQKf6mzL7/\nDHra8VvaFc26eH95ldPUZ7m06uzuCor2r/zi//3Nqqq+wA8ZLwfM2S3JDlcspw1aNeUDfHDI3k/L\n0w8vr1PWJYP/8NslR5+VbPezUyW/aCY0lSK91iiJT1Ve3btPqmKrxltnlKEyJzXlHGbcwR1o2bDw\nMbqyMO1wh+5NedC//H9mxKHEsMMHMune31qxfqf4yJ5qVAavJApeaTcwda+o9VQ0N2rS6itePqto\nachzlhT0NVa9SLWz0Ko4XyqHoeVQKtzVLS1uqJjIyXCFernoegEgCaWD80ZRAwX3zMqAUt38x7MY\nS2HM0VIFVRsVifqc+02HJ1rS2ncMzi7kZrdcjzNV3NpU8M/JPKFUcIxrGTTWbFhWia1dortmk+/M\nBHY8ChUI9uT70Nvy7t/kfyv+MACNwOMyEve5YT2gk8jGcqG6k0napBzoHMclG9flHBePajS25Jom\nlwbXr8saWGtUbvo25ULm7cZhwN0nMmHdLZdnY7nXwTWXe+9KuLXqyXG/9jjn0z9VjjIAACAASURB\nVP+WHCOcNki1zfrJV0u+pGtEMVG8sCEAa6/dTA3aqhxWf7XCVKKdtCnPOouHNJQpapyXGBrSLSrj\nueDMo2JFXXeZSMvFNb+LoVySuHVybRFotwPGCm22A4NHi09WT7jqkrwaV+NqvDBeCk+BsKT8ZkLv\nlTOiSCz35bUlh4ufAcApHhHnmjlOzrj3SHbB8p64RbWd91j0xE32L44ZPVJAS/ceWaZakbMx3kQw\nBN6uZJvn9evULrTzpbmJq4QlpWeSTzQD7MI/+q4Sa0TiYfzzkBc8hO+PiqknU3r72MV9U3zmdCW7\nfauTsF1IBvmkCQ0NlXrBmNs17eZU9uIw9Nh6zvBcgCZdaxQ8XaklcXieT/5oXvmRQoatKGaqVRkz\nO+dkzYHg5WwmKueu7nV37nDRl7/faPUZKPDmKMp52xOL1ndh80I+4yq82uxW0tGGWG5PgU6559Nw\nZQ6/cx4+bx5a29KPTt9fG835zaWwPP/xcZ/6b4pH85sffIfNunxyb6VJ2VcC2lNVXd4LsRWHsre9\nxPiezNG4H9NPJIQKDuR6dstNFgrUOujvEzQk6Wg+mzPpy3oYzFzejzXUe0fm7dHuJe4vi5t/9GN1\nOsfiQSzuZPxnv8TvOtb3aMwSVq/K9bvzknRfyW5KWQt3kw32TDlHZC54S+Xsv5vaDLQRzjw/xVhJ\nQnusTXBb+EwVJ7i1Krl05Bjnc4MqV4BUbNIdPHdlPtZ4KTaF3KkYbacMLvq8PRaAzZfjEY99mRDf\nsLj8upTOUvcJfKiErorDL59V5HdkUU1DhzQSYM501uRgR46X3D2iLITkc7YUZJvbeId0S0g7W96M\nRAEmrplTq3y9uq3nC3qh7bRG+eKiXoeOlgWfV9KT0+sOk1IeXktVr+rWDdKaLLpt36JvK2a+bGH4\nsthutDWrf3pBLdN+h+gUWxl/ojCmnq9JNr7/kv2gPjibklAVsozEIFlXDiKbnV15qf2OVHVMJ+HQ\nlXl5bfcAs61EH+GQz63E9V+ej9jQORpXskG+agekQ4mHndDikS9XdNDtUCopSM/Omd6VzyQ6c6vf\nMX+ektu6XxuQnctn6hkYylQ1HshL1ctbTFuy4DsrG0c1EvLiGmVX0KK3C/e5bsOuKigFiUdbxXgd\ny6Fryb3OOjbb2j5rZ1O6SnZzTzeK+Qje1m7X4WUXRvKCxY+3fsCMvzjW66IKYNaRCtZisU/DFV7J\nka6FLb9OX9vTt2wTV/sWbm1E7A3ls5fdPuM1oYzGink6olWqDIKVUiuVvWq8ZBEroZC94nT6yV7z\nq/DhalyNq/HCeCk8BdKS6vGK4eFdmntiwZ7t3Kc2FiXl0anNsBQX1j1bYDTE9TUdsT5Vr0lgyM+F\n2SRXwQ5z0yKxlZwkj3GUrirqy217ZkGpHApG0cZYi8zYBqa6vrv7LdrHEnY0hgKDNkDhIOK2r3fW\no8ol98T1fdOw2VB+wNmWWOPDDRPTkg6/ZjAmjcR93GGJ4cuO307Ewp0GAXtNsS7vhw2qUqzEw/M5\nO4lYq89YJb82Uk7BHzCtGRCp3Hm+yllmKsjiGfiZdOV9dlssv2P0aSuUfH/3dXrhmn59n/25JHnH\n13YoT8SKLdULSssV+Z642o25S0N1FWu1glkuCcqfthx+Sifp739XLPDbRfnckpbAhUKNo9kxeSi9\nG/aii6malr2ZXE/9KAbtTqx7TwncQzn3IoGahAxO7ZRbLfm52VM258Y2gaGWttHCMOV8tV6T+qUk\nki8il031pq7pmzFyZvBEtUeTMa8W8ofxegH8LmPtTR5UFl98Vzye02s1NuaKrbAEnLc9WeJ2ZQ63\nyjrLQNb9G7UZxpvy/F6b1PggFsj6YvK+HGscPae/s2ODUIlVEgMsBW2dJzlm9nFF6GVceQpX42pc\njRfGS+EpmH5J882QO/Wf4GQiFui6HeK/IXV+8+wZo1xJLhOXlZIkNzVeTqdjfE+6/lbBCKclt2WH\nDzA4BGDZnZHlcmxnItZq2eijXK34uYVvSxxdWDUq5RAIuybNVyTevaslTc80yTWZ51Gx6SmSbNuj\nsS+x5qjj0MrF+vfEKBO4R/T25RxVfIATSMJpNevhqkpyqX33jaVJokIurzBnkkpCI8rf5cwWlJs/\nv+A/TcRS/PdL9ZQ+Mq91E5aW6hU2PVLNqTqeh6mCKvVdubiWH9DMNXHbyJ4LndhlwtxQZcmwZLYj\nc2uW4nXYeYCnZdgsMBhor39UNqmpmnO8d0BDcQN/3pJz/FffeYdMWYxcI+fghljuKuix2JGyoJ/c\nhbp4TtGhNpI5XbquWEFn+DnsV4Rizq3v0NM5CMxt3CP5vF870Juu8EJlxK5XmLk8h1q5JDTEE+zP\nnrDxWcHAHGrOZPykwWRfG5uCGmOlWFstXiw//qCxdiZW1w1iT75n2iGrSrU3+5LvagY9WnPxCut9\n6N+W3++0GgwVmVlttBlYktuo2jKHdnmJjawLJzKfK7Pb7oxHmjAtyxWL5Sez/S/FpmAXNr3pBn79\nK9xyhRsxb5f4j+VmFstLkvdV3Sg6Yz+Tl7OmJCzu1KOo5MXttg0yRxa8m/cwA9kAmrVtDNUSzG2Z\nvHiWE6hbXpUDMtUitL0clEZ7x21zaym8ifcrCR8+3azjKCPveZGT+fLzH6razysDDa8i35CFeZAp\nZn0zY9vSzrqOg6cZ8lFtQkuhq4kpizXI97mw5J6cwieQfBmJswmake5tbPC41FZzBeA4WcZTdcxz\nDJgrbj+b467XcWDy6lSFSrZlge2HFd5NeXF9owXaWhxOQ+pKj2YVBpHSy1sSzZE3I5oq0hunGYFy\nIsZbIbNUk5nLMfX2IQCXCjX/dw2L+Fxe6LdXLivFfdTciEo3r8uyzVR7QT4zUjbkrRW+9hq4OxH9\nRObWr9vUFOBEt8aGKRunYyktv+FAR1/T3KQsZzq3CU3ttEz7ffbP5JrSQOblYfCMzXPZnO+2HnP7\nXM5xfEexMP+SYQC2Poejc+s5Yc7rdgWB0qYpKKr7LKDYkxe+MfbwzmXTWEwMPJ3oxE5p5vLMKkOu\nd2VsMimmOm8m01S1Ke06dUsrNEuLxE9+12v9neMqfLgaV+NqvDBeCk8B18A8cNjyfoInjngEnUnA\novHbAIzMIWfazbdol3R9cSnzXDkW6hF15cJ3ix7pSkk66jOceC0TXmPdzTJZKNorjcnqkvhbEpKO\ntSzkg+vLbl0Gj3Fur2XL5byt3S65ah86pyv8hljjJ9sd/A0heFmGIw5T7a03JLTpmm1i1UZsOSkZ\ncr46bRK1KoWmDEPfws7lfKZdo+Wpe3nNwSvEYqRlwKuvKRxZE0v35hF+qt2JrkWuHY6V65NpSc4z\nahQ7qu5dk/lxGn08bUZLBwmeZi5LOyDH1/vo0tImr3xTCVHdExJtB7TmPLeIrlVhGKoE7lWc53L9\nm1viSXzG3+CualUYD5/i7Oq9BlDvrBGbDl2VYcsDcf27TkCaynxbeUWoOm6NrCR3Zb47eY1Kcd9r\n76hwG5ipHssxKdM1bqJHrEnqPFzRaYvn0bopn/2c2+XJ4lsAXItcRsromuW175cc+cFjEMiaPHVd\nCk1gbsxr1G/I84sWco2D7jnpB8qh0BnSOZFn2ekXFHXxnFsNk8iR55Co3klzcxNCmc85MbeUCf38\n9BmXqlWSBnNWPzzSeWG8FJuCmRvURibjwTPMpsJy4+8RT+VlOnnnMU/PZZXOsogt5MWatLRLbZWT\n3pAFUfMaBNqLkJsrIs0NuPMLfJW2rxz5fh6UnFxIZnY/C4h8cblatQaW8oX32rvcWchkV5+SB/v3\neg0yhVifuSmrWK95XGC3xbXv7GzQO5QXr6uuaLsJjaY8WD8ICLQFvKo5hNqD0lAY0rgwiAPFvcc+\nvZpqP1odqpYqD9keiXZXmgP57OtxRqzVl4VjE6l6U6OKKBRYFPoxdVMZolrSFl6lFWVNzu2FLfJK\n8gFlWeHrC+b7NqlSis8Ugl1Y0OnJy504OfGlzr2XUldimCycMVF482MlGNmsb/LpQ/nd2WKbEwWO\n5ctTUOGfKA1ptGSjGmjlqBMElCq06uQLPFuu37JjzJo2gNRcbFXcQnscSgBXIV5xhRHoGogimva6\n69QmLbTPoSf3uVu/z+VvviFz/+hbDFSdzNiboY2PKO/KCwAy1zDoq+pVYxDw6oGEM27t8/yMYiiq\nA/272ya7KWFsL99jXhvpZ0vqnkLr+21SPeFOX0KKe1VJMFIw2aSg8mTt2Zt1gruyqRkzA9P5ZP1N\nV+HD1bgaV+OF8VJ4CoZT4mylNHiV8lLZl50VM82lL3MLQ+viidEmaWhlQBuKtt2SSmWy5m5OV42E\na2YYz2TfG/ULGspVYKby/XFRJ1Sk2YPxFNuUbJ5dBewqGYpnZnQP5XgNRxKDywLOlYk4NSIMS/Uq\n/ZK6YigO6wGNpWS+G6+LFW8se9Tq2jxkemTK1efNA2rapFWs5PvWxgnBhSS4mv2IUqnk6lsZ2alS\ne9VDasgx9nbF6/itocVE1XDyoiBUFe+6VcNVd75r92l9TixiS3Pk9U5BqiFDvjUn1kRq4UW4z6HX\nOZVqJ3S35cNFtEeSK0uyW7GhjJzzpctC+Q3mk4qRoaQuGrptmHuc5HqOnRmcalXGLClUoXnXWtHW\nDKt7IHbYNEoatljBZNEmO5Bws1ZsYCpO0sJjpTwa1rpyYlZUyTqcW2Bow5cdZM8FZTK3hqEISaOU\n77nVTSa3pFOzNexxphqTi3mFp5Y7/kin8drKOraJ0REP6tY+3O4Ie7azn+BsyD11bqn3U7WZKKzc\nWBZsbug12H0ynfsqz1CqChZDedZ2d87lSBnDOyVBKB5pwQm2JtvD5oLso/DRjzFeik3BLhw6iy28\nzQe4F0pCYmbsKf3404bJzXvqwjdCXivEtes35bNHj/tEb8rk+J5Joj0D2bMGYV1yEf3IJ9KwIlAS\nC7cZ4Worr11vUS1k0YT1GUWpvQjlJqUCXYi1izJd0GIdk3qMCzlGvzBR2j76Gzt09lTBSpWsXNvG\nT5UP0DJoqL9p+gmkSheuTFHdYcG5q0Kikw6FEteWT12WjmSqr80Mjk/0Mz15UQYpHCqs+v8tcnwN\nH7puQVtLrtmBxa2ZCMb4WnHwcgf7uixMa9HF7shxrWybROPopjUh16mwDAHYFIML7FiqQctGRHQs\nJc6k/QBzqVWgwMD/QF6XY1WTav3EhFePJJ/zyv1DvurKy93yumxtycsWFi2WO/qMA600tQ2qVBWW\nvCnBSPMddkrbURy679FRRkANwzGMFmVLNiY7c0l0My3DPoYlhsgeTon194ES0A4nGW89kbXw2+UT\n9rWv5uQL0GrIOXaXshZOyuI5mcqP1x3aTQVZ5XtkWhp+zQtoaq6hl0vo49ba7G1om6sdEOtzWqxi\nPEuASqPVFs37As1+dCJgsvNHD1hdam/LMuOilGPU7zTY6si5d888TrQSMebjJReuwoercTWuxgvj\npfAUKtci229RZV1ypQAvMpeVLYmTnZbP+ZuSXPni9jVuac9+oYmc8dGEhmb4E7tJpVWGZVBi98Rq\njqZL0qVmZ/sCGrKiBnZNLObpLGUZicu5jU9H6dftjRWlApUMdf2r0kHb/FkmCTXVGZ/YJp/SBN4s\n9EiVl7BUGvZFVodQ7mkrqFOqtmGtLEiUdh5TPIVLK2d+rLyFzQsm+vcLKybUenzYduk78vnhM/ns\no2ZMU0ldSscAFc5pdevYbfGw9pwd4j159OVUricOTMIzbdzqQqwydqRDqpUyGDsb1JR3sVJQUeAP\nCLVpKb87Z7Yllu3yyTbWRGnZxxbHLeUOiOS802GD/p54dGfNCx5rB+p+7YCGgrby9pjramEj9QKc\nVU6kYiqLsmSpfBCelZE6yqnhVLhduRdPtSjLysZUXEBoLVk+VLnAg2O+cyHf21uZeJqsXCm+pZzs\nU20/lGuPXRJH7sNctdn1ZD7PU5lvI07ZVPlCf6/FQVPc+SdhjTSVOepfDkgHcr7lUK59v20RWHKs\nzM4YX8ocm37IyQdi/YeXd5nUvgPAty/k2tv3Yk4UZ7P0U94yJaxsPAiZKz5j2UiJF58s0fhSbApG\nUeJPQxIvJN6UxdGazXAP5EUP4xmW6g4eP3xG95ry+2uW+uTiHGNX3M9Pda/RPhJ3r0oSLkPF/o9h\noZC+ciETfddtYcdK6lJWlFvaM7GdcaTusVVcY6w8jnWl1j4LxyBrhqwqiJRnfRUX3NP4+pXpGYUl\ni/DhscTfg0FAuVBQyaBDpqR5RmCyUjalciwHfnb2lIcP5YU1LEi0zDh8GnOmtN8ny4jZQymnXZZK\nEf8s51LXQFXZeIFc8yyGg/6aRnzOxVPpGFXPF3v5jOKpzEujW8fckoU7zX22mpIb6W4W+J5WTAqZ\nt3ixJEpkXt+5f8LTX5Hn8DXrfW6n4vJn6YzzB3J/54qU/OzmE7ZH4iYbj2CxBtuYCaGyEGVBSmat\ndG7lZSzKklLnYrlMqEbyggR7A8ZKW7WXPMOai6ttd7TbswjJ9XuX4yG/9XUhkL381RFjFZB82G9y\nZ0/yRn4mHZeLZsZ797RleXzONhIqRW2LpRKw7s5UmcosWKvcLuItpi150bc2DHqbkhCYpjY3Mt0U\nMpm3RT3GyOUcvr0kU57P0fkZ55cS/r7zQYRRidGq6nK++/MYv66b2OMLvlqTdfG5neuUM9U2WXqY\n5jpsuAofrsbVuBq/h/FyeAomOHWokh6NC7EI40ZGoRRWxaog2NcM/5MNwkSswKguVumkXTJfqcJO\nYGHPZIfe3gi5HIqf/yy75OJMFXlqSo9mFVSa3R0n5zQvZbd+767PoZ6vYYxR0mVWaz6CtCKrVAAm\nT7HV+iVlzEoxAmnNJl1q0rQnx5okBrFSl2UXFZub6ooabQKUJ1C9kscXC44juV7PtJgrVCZ0HWqq\nXZlnMdvqpQxViTquKvRyMPOCSvtA3uh06W+K57UMAgpNSY9ysfidecKpVtr9vI4fKZnIdMIqFn6K\nHW8P01Xm5i211mmHc6Uv/x5jHirWIUr6lJXKfPdLyvORXrPyTbR7PKnJ/b/DOfeeihucv/oGlkqn\nD8yK1JJ7LQw57mUYs+Frn4jXZH5dPJd60Gau3YDJmcG20qnZuXbEtgqmWnH48NtnfPPikVznbMTK\nkoRfp+hxTa140BPQ0LvjIec7cn+NfMB8XeGwHVztO7irmI6CHEcrUTEhT125j5u1EjsT77XYgvvK\nz+G68qyTyyZ9tfIt08HXbsdJVHD2vnzmPBhyzdE+lkC8ma2FQV4XL7RTdpjo/V0OSvxKzjebjsgX\nn0wi6spTuBpX42q8MH6op2AYxl8H/jhwUVXVm/q7HvB3gEPgEfCnqqqaGIZhAH8FUZ4Ogf+4qqpv\n/bBzWJVJIw/oBSMm2ojTjRK2BpKoyTsxd56K1ZlfG7PtS1fbt1ZiDW7dH/HrmxJ/7xHQUV3JQXTA\nrCaxoR1WjCNtJIoVxXewYE+bo94rRtRUo9IzHrGcy2fDuP/cCzkfaUJtuSJVvoF6aTBWhtYSi0tN\nLt6b3SNeS3d9INYu2PA4akjJbr5h0ptottKZEmlf/FxZhL3LEEPhw+dpzsVUGnU2cpeZI+e43Q14\noGjKSJOZ9cgkVk+h49TpaTK2TowC99jsVWxo1+XDk0cA/NZv3+crqg+5E3Rp3pHr/NOHP81yX8p0\nW8sAe0vvdaGl1eVDTNVzbEwHmA9k3t6ZHhNo7f0iHPNmLJ8/V0t7a3xJcSwJxZ952uefjCV2Hpgt\nGhsyL+bMgracW7lKiaZLvv1APIK3pyO6riQ87+8ccKuhVHeNkN1ELHZ2pJMxrrEIVRF8taBxKbmG\nu+cjni4lOfqF7oxvL+R4/+Hv+3EAvvSaj/8VAV+81/0K7TNZO+/vFZxow9qaQSsqbWbKNuUZGauh\nPNNH84Rv2/8cgAPfJdmSHM1P78ixjpsrflw1KGm6oGJGxeoSL1CF9NEmb48kQf5o+RUArnkeibI3\nvUVAGMhcfa7c5aF6W39sXucXtcNsrXDyw8bHCR/+BvA/A3/zI7/7i8A/qarqFwzD+Iv6//818G8D\nt/W/LyIS9F/84VdhY/T75Jc+hYJOSB1U44fNVkn9TZn4tnubciwu06ZqNT69NWW/JRPtzHr0C1mA\n1k7JxqXSh3UvcKfa+2CI27pxbZsNrUvHpz3eG8kZ++Me8yeyeBcHJpdK7Z6tvbAE5pplTvMKU6sT\nq0XCW44sxnFep9vSLkEVXtnb2qSlsGvsHKU5xCgqojX9WyV/Lzs1fIW42ss5TUfbwZMRHnKOyi44\n6msFRpWgpoZFw5EF4dcNzK58b2tvF29P+wfmTbauyTWPXK1kvBfwmi+Lf3CwyU++JnqN27fbpKly\nQg5MgljmKPbXUu41Ak1mNj9rUE108wpyOppIDQYDtrfle59Zygs/81Peq8uzHk+fMdfEJZvbeCt9\nJuaSdKWc921VOWq1GTbks9ebHjWtOh3tXsPtiONrLucsHdnsi5m2lDcLypXCtTsDNj4tayF4F3ZT\nMT5HmwP2O9qBq3O1u+WRHv1DAD6c5XymJffXs+s067IJXTaVrm5UgNL2P5s7vPqmsi8nhxz2labO\nfosbt+XaOn35/rZ9RuBqz0U5Z6pt6fPEAFOeSfszHsZjeVbdiWxuAxJsTcRuvNLhUNnIq8enfK2t\nrePOjLT4ZGqSPzR8qKrq1/n/E/v8HCIzDy/Kzf8c8DcrGb+N6ErufKIruhpX42r8ax2/10TjVlVV\nSnrPGbBmsdwDnn7kc2sp+lN+x/ioFP32oIuTRNitEEPps7ok1LWem140MbQbLimnNLYkibIdKmNt\n1sbXZp+91zo0dw8BcF2TgSO7tZXfJD2VMtxJIEkyI3pIqyHErX6t4ECl2O+8adPbk918y/cYnou1\nKUOxEk+THG3BJ8VACXVJcIkEpMdPtr/MZl2+VwslMdpo1si0bc/NJuSZQnuLJbnW/5uGWDNjsEdv\nJRbxvLvPGoNsFTsYao0Te0kxkjm6eaBdeGnAsbrz5d6AbiqfjeoFgRKd9F4J2VHG4P2xLIFX/+Sb\nrFbiwjcHr2Aqc7DluVihnLvKlyRr93iuSdBxg9qOuL5/ZByx9/vEMVwUH7KxISHIo3efcBnKEhh+\nIGi8D/aecfdviDX7amoQuXr9doGpScLx4hIrllBBK6EcNgwanxV+i5YX46diSVtmj2VLjlc5JUYp\n86LRIXFm4edy7XuDPgNVZts9fJ2W1vpv3HyVnaUKtXxKXO7l29/inwqNBsbjx1RbYtE7X9zjA+2e\nDIfima3KEhRBOmRBU5mmX//0HX72s/KKvFoesTEQb4QNFdnJa0RLWQtOPGL+SO5/4Fv0XhePpbHV\n5PWbn9KbuQdAzfEpjyVcS/uPePefydz+Rppw9k3FL5RQfgzquI+OH7n6UFVVZRjGJ0NH8KIU/eu3\nr1Vey8BY1RgozHcW72Cq1HyRntFWIRNr3qJQundUDr3eGD/vW1g0EgYaO9q9LqkCfVJriaNY/MpX\n9mFnD39DlYdK0KQum/U7XFP30Zkv2QkkM/zkUhaV8f09gZIKbQegMkZ8KfkyAIdHKYeWQoFtWdhW\nVCNurJWVPEJVZyqX5RpjRNwWd3A5rfBVUHTHm9Mspc5tmlPGriygobck0/r2VijZ8tubDXY8Oca0\nu8vwWCo019tbWAqxJjTJTXnRPUO+v3tkQSgqRnltSlLoTeWPyPSlyTKPXMlJbGWfLtwR2TMFFvUr\nbr+2huu+AioH3/l0m/fOBJT20w80T/Iw5h3tbZmXJSqERPdwn/hCuwSTJzxUtajqXD7QvNZmtyHn\naOf7lB3ZCOxkjoWEGuMqo1T261DJYErfJtK27qQwaercvm6O2fCEMdnvDPGQPIelrcfnrsO9pogP\n4VpMDyU/VOZLqrFc20rVvSqq533UUTnnoJCQ9otvWPz+Q3mhm80FtVyOYR/o9cyvY/gagjycUjYE\nxm7UpyxVY3Kv3uezA31mprBDGfmHFG1xxC9YsWzJxvudbw6Za+vmquLjwhOej99r9eF8HRbov2tm\nyGfAwUc+dyVFfzWuxr9h4/fqKfwSIjP/C7woN/9LwH9uGMYvIgnG2UfCjH/pMCsLL27Tap+RKgFj\nsDXGq2RHjUoHcy4/G+UxXlM+YyuBSjveZ6zc/PtOHT+Qz7pWH0exANVkk3FP+fvVsnnejP38EID0\n9S6Hc61KHDlsI5bQaFdEF8oerbz7cfXibrp2k+qGw3BbrumnjR5tRQWaqHJwmlNUYqEmpkFwJlZu\nkTuYCs1O1LPZLBJyxTS0qj6Olvwb2DQ3xXOpvudRhWIG9rT5ym7d5M2OQLcfODOSr0tmvb55gaWI\nTMf08TVJa7fl31YWkdXl/uzePsaaqCV6BaeQCk5nFFEpn0JgiNteNEd4LbmebNamVhOSGZoL8gO1\npL8eclvVkx+OxH6cDAp81ZIcFsI/ADDodlh44gn5T9+C428A0O/LvJpVg01DQh82bYKmJmNjjyxV\nwcpyDJF4DVUlLnwSpSSKLdlwcgJL5qLmHUAgx675A0xfvJR4LHNhvj/kzvti535l9T0+P5a1F/X7\nzAvlLFgjSPn+sMuCEyWnuWN2qbflOuqtGo6yURu28jZaS5JYxY4uNohOxEs7fhLz5jUJCYy8wWYi\nIUjpagXINFgl4kFtlivePpa5LTxjne/ELcFT4Mok/XgO/ccpSf5t4KeAgWEYx4jK9C8Af9cwjJ8H\nHgN/Sj/+/yDlyHtISfLPfJyLqCyLvNemmroUlrj27qLGQklUnCgnb2nIYG+RKy4/V666uD7FsWXS\ns9jDt8XFLWYBhYpuFtUpC4kqiBry93atTxnL31t4DBWQszXdx+hrWcxwKTe1EjGXl9iqXiTUeM7A\n4/q8rqQt4U2DjjIE2QpoKguPLFBa+oVNIvsO+TgkrJRYRGGyllOAskmVo2FjCgAAIABJREFUnSa9\nNZNHMUIRvxwHU3LtCQiUB7Lx6gGJLSsiLW0ah5JfMO2bzJS23py0CTe1O9JRvHZRx9M2Y9PuPF/p\nZeMSJ5LQLN1r4T+e6md0Bnb2sB7q87DOYE9bv6s68YlMuOnNWSp8+05PjjU/GzLRtu2WVTBRLc3G\nzk3codiRIloxU3Leh8qv+Qc6c5KmvEwbdgtTX7zKzinr2l6dVOTZQO9Lrre0PHzta8gLC9OUZ2r4\nbTxlpzJGJqUSpc5VAGcabBMfSuizd9pjuSObRiMdrNtUKH4A91Jewo1I0mvLDColsTXzgFLXZ6Z5\nlHyckU7koZ6tnvBEK2KbR/3nLdy+bVP4Ekr4WnHIJzbVXK5ztUho35KNevtX7/Jw3RJfK1h+suLD\nD98Uqqr6D/4lf/qDP+CzFfDnP9klXI2rcTVepvFSwJyroiSfLCmaEYupcvylj8iWqhhdhGTrBiQ/\nZKAS9IUq9VZJifJ1UPlzYkO77CYj8gt1y+YFhhKOTI5lF42NJdf3NdmTdnAcsUDVzgxX6/e24WFH\nksz5dF0sWA4v8POt7US+KnhHuyt3VhlRsE77aq08iolVV9JIEtxMKyNBh6nSGfdW8tmT1GCzKRYh\nDh2KmnJEmBm2uvbOeUGihCO9IznvrZsH+EOxkl5nxFf+4T8C4GxrTKKAFqMYkiykMpDtKGQ4dLHW\nzMfZEEMnNLqYkkWauDV9cg1z4pVYtvyfTRn6Msd+o0M1VBVvL2UZyfWH4xRnJs/yXGdr57DJa3Nx\n8Y8jC9NSL6YscJpy/Un7gDes3wQgVWhz7luEitNYlQl+LFbVTFLSlYQ2rmHg1ZQbQkPNdGnR0ArO\nqqqoFPexKiNsS/kh/SWOJoKbWu64sZhhNCQkWniPcEwpL82timjdPYuMj/oLBRUf6HWenZ5z1pcQ\nJGmF1NWdXypBSpXAbKrdrEaPHUsrO/MF5554m23LxVppUlX1SiM7xJupzOAIGto9+amdLnN1J+/n\nJkuluiP5ePCll2JTwKqoOiVRWBEoUcRo0Wehi9E8XVHfVL2By02G53LDlqIAzfaKXMt+09wjWUoZ\nrvILJkoz7sYZp+dSvnmsx7XOjzgaygt23Tx/3p7rnfwYSVMmOKlCCt0M3ntPFyDK+ceayluG3YvZ\neagdk7cXjJUOvMzk+2XoUipbVGQY5Np/vZyvmCDXHM/W2hNzzjV92+pnDJfruHfGqYKpjr0FkSkb\n1uc/8zkAnHafsit/v5zYOEdyf43giEp1EUYL4znZiTtVkI9Z4mp4Va5yTO0ZKMMhU+UBLLMh0zNl\niKpLQmBVbOCt5Jo3Vo8p2hIe5MOU8RO5gWezGeOGdjNeVxWnuzb9DblnRgllojoanX3yuRz789vv\n87VzKT8mF3LeUQ4DQyxEnPhEvrjodlnD9DWcNBpUWop2Q103jRnxRJ5v6K+YajRa38lYRBJiuAa4\net+FalkQDDBfkVL2DWvAsiPnu1x0sTVHkf2A8MGyKgah9ms4Ix6O5J4Wk4SGr23yNZm3cRYxf1fW\n5mJpQVOQkmX7CLMma3x5Xqe8KXOwUjLb9HjM2VI+OzSXZNrh6Uwe8vlbck3vX04plUXrBQHP32Vc\n9T5cjatxNV4YL4WnUGaQnFbU3VMmI4UUZ2c0lEsxsR3m72qfuvkBRwPxCsKmZOpqD7e4DGQnPuhm\nLBeScEnunvHdUPrmv9jvEDTEKm4/VLc1G7KhMFjjWpftUJOS2YhIRUbiZytO72kFQ0FMlQH+Rxh8\nba0SfCltUXlircZ3J5RtuRdzrJBo18KYyN8v7BJrJuZqvKqYK9nJUC3mlu/gaKLVG5fPKdHi04RQ\n9S/rpxanDbFyGyiuP/kJbmno893hI4ZPlXMi/SbpuXZ2bvx/7b1nrGVZeh229snn3JxeflWvYsfp\nnuEMh8NgySQlJo1pUCBsigIsWYQJAwREB0DmgH/sH4JByJBMA7Js2qRt2LQkW6LsAWExiMkSOZxh\nNyf0dKjuCq9eDjeHk8/Z/vGt96aL5Ki7h13dJet+QKFu3br37nj2/uJaAW4/ELV6tk6v/uwcEV3W\nlePaZbTD1G10mT5bags+8SBKPAsAOKzPsMP6ijkMGMfiGOvbryDl3A6HKbK3ZFwPUgEsuf3JCjY/\nJ9Ge6zjEmxfJHihRWmRQMn8IdvX/AQBcIRBKaEeYM+Ki3DO0yMJl2E2okqaZW8LNxVRImZsejOpI\nmtKHldiH4ZLs5qSEdskxOUsB1pWkkWiFaXgO35bISbx6BP9QXs/d7JICyuTF/3Z/ngsLaUU0gelu\njAcT0TYSy8Za5yo/T624DGETw9ItXXQtwXMMO1WsMJ/EyMeYn5HkJxMNazbdw/RYfqM4XeBIS983\nv3UD6oFEKp4f7uFVR8YyYB7HO8lSU1jKUpbyiDwZmoKpENYcGP0VnJ/JiRqHMeaBnIzNcXCJEFTd\nWkOZMQR2QoQaN0ZYiuPs9MjCjDx6D6enKFkyuOv5OJjKafyAbNXDsoqzvmgjV0LgoBCNYHvUQjRl\nhd/0AG8d7wIAXpqLNuLga04lVwFPufQN7NhAIr99PDER0sZtZax6K0okRBTu53N0CNRgxRo57dOW\nL3Z4pEwYE7nlTrsLmGT+UnGEw5G0vueliG1x4J21Pg0AeH6ngbAvv/Vge4wKHXijqIUzyI14PjKw\nzRyCeiaaRppWEJA/MVsPYCVyM5sVF8Zc5sJdUzAX1MKq0ofrmMBldWlojJDQN5IrFyesGN1NIkyJ\npuQ25Pac3K3BIv9BOtlHxJp/ZVXhVsQh5vYKrB7RP7Ii42zeDzFuEWn5WoHSlt8w+zPEDeaZBFWY\nINBrQaTmpoFKKne56Tjo9+X9vnN2qf0kmQPFe3JO9KPMr2HOEOJoZmJ//CUAwBu7VVj8bHyBoAX9\nNaejqVDwNx6WDvKQ0HtrHXhjZl56RARv27gSS39Nr4KQRW71agE3k/2yOxxiRDTyFkPLi0MbI/qX\nDjGDJjzc/KFG1hNt62z3KzhL3xuewpNxKCQZZrsnyKwpjgpCcS1SrDZEVSuvtNFcyMINcILtUiZt\nxCSO9MEUu2sywS+oJmpV2dCLL03xRkM2fXe2AUU8w+iUKaCNCfqapKTeDrZqJOm4EqMkNmASrWLI\nh3o1FhW/4Shcu8D+sxw0t+V73+FuYhjJhl1Jd7GeSppzjXDbg0Bjxtj2atNFk6nZzSsGTh6KGmjT\ns3x0lGGSSjJoNsyQWeJcy+HhfMHqwn6O7KY8eGeDL0vf3B9CY0MOuh/Cn8XPH//nMiZf42hEYtaO\nifguuSs3emw3BFrywJqwEHJe4rOHSAhXVs1NpMQPjN4kSW/WwNYW1dK5DYfOX7udo0mINb+cYUqU\n7jsT+ezNW3V4rAytxgE8Quab0DA4X+2tb8XzW2JuHN7dAQCc+7+DaiaHlG9qOBnJULoVTBjBcfoa\nNvMw2utiai7mQ5SMrowXC9gGAXxmcyiq7na3BoP5ItaI6dijPdin8r1GmWH/lIlxawWY3waPUaRI\n68vLokw19vnwb765i+Q56b8xcYDrMhf5A/l7vKewsSaH1JV2AzFJg6FaiGMxC51ZBGt+4aCU/eb2\nEsR7RKU+tvHbDTn0f8Kv4UYq6/Ob5wGUxcSWdylL82EpS1nKI/JEaAowAdQ1Il1F05Ob9mylxAGd\nZ9uzBoIbcgZHWQsHD0WNGmZyMharEZw1spCcVtCnmlXdiVBlFlt/OsI05a3qEgYrqWLRkVN5zU4Q\nsLrSLxsweIuPG6+juilhqDFzCHoNH7d86eeqrdH6qDjtVCeBF0objW4NHrH8FXkRHHsEqxRtwzBq\nqPPEV26AG7fEoXRA9TzQX8bRW3LTWDULSEX9zNIYa+RafG04gXcm81KvSyzdhY1qXd7rlAGOG3J7\nvqAr8GJp4yhfYN6W1OUikSrRygoQRHILTu0jTFktOBoP4BMDYf5KjD1XNK+1TLSV0o0xvCfzOapp\nPP08cR+MEqM35DNRlOGgLTea49LZObNhd0lhf0VDMzoJKFxUmAVbCxTpd3IOf0vW4PUhmpvMpyjr\nSOqibYzODvDV3dek7WEPN10xMUcrso5Kn+NhKms2On0T40qV/VeoKPIvXO3BZG6B68k4i0UXp/br\nAIDWxMH5c7IHds80HGo6kSITty5BKxClVUCT3/O4EmLjqsz9zdoGTF8+/zn9BzKvxynMt+gQvmVg\nhXwmY30V+ZnsvTPHx8eeb7LPornGwzoiYjn0by7wjMExx3NUaYIdr0+RHr63lMYn4lDQpUIZGah7\nDxBVZcFXUWIlF3U2cZNL1cmbniEgOi05X6B1DYs/lMlLbvgIjmQzzuwa2g8Jo327gZw+hY83JJFk\naCT4JkvqBPyaRmDIw1b1hjinErU4mSF8Q9oj8DO+5/Y2ng3EtLlyfRPTjjyE7WyEs4uU2dTC4o6U\nuFbEikDH9dBtf7P81vAEdZKF6LJA2ZR8gxXCRLsDoGgTidlMkBJ4wypK3D2RMXVzBw9mcnAcF2Jq\nDIpb8AwxCSo6RDKVDXF3+AZmZGc6Hp7Bc4gW1JSNuVpuwCaqUm8SoMGakRVswl9ltEPfxEcqMtaC\nf1ujMxRMlJk86COO5PCa7n4ZoxGRh04XMMhOZbK/3e+pYiuXPnzP7BaMkfQ/Kgr4nHtdVuHR+36F\nsfnxjS7KXannuD84wM01mTcVmNhsywEXhoAZMPks5Dqu17Hdlz6suR1MCXYTbNfQYLm+iTm0lrHs\n70tuyRdevYMVIkafVVKssaz5q2WMgLkz1QM5CYZ5hJyEQ3ZZIqIZsLe7B+9Ixt34YRef3pLfeHbr\nBwEAG9VT+AvWyTgRFpuyZlfOp5gRGKbh+zCrcsB5iVyGM28Og7iM0wHwYMLq0mdKnN6TPbJ9ChCk\nm7GOd5al+bCUpSzlEXkiNAVAo1QZyvImysouAMArTPTnRPjNFqixrty2bCTnoiKck4TDDw1MmAmZ\nf34Xu2us+e8byMgNOB0+RMzU1SpxFWqBj8S/CDI3MGcGmkqqiBeCQ9A3R4ivilpW3SeD8+o1VLbE\nZEgqHfikd0unTSQzOa2L6RAGSWu6r7P68GPrSBO5iTxfY3JC82Alw+ycWAcEL1noCmYNEqhMcUlH\npsb7yBLpxzFS9EyS1nxO8jGsT30XDMbaTS+HRTizk8LE/IKK3jNxh2Ag3VhuxLVsA17I1OC2CTeW\nW8ltaygiMGungsJgFulCvl/kCyyIK3BcNRDdk/X7g2GBk75oGP00R80jLgLXoP/VMYbPiooeWwlS\n5kWMT0OYVVY7OjkszUgToxbpS0fYP5G94OoR4rtcs6cMeKQZdDeHSKZisuUbMqZi1EZky5ri6i00\nO7zRnTV4rIyMzqeYjkRTu3cka7YIXZwS7GEQZrAb1DzGBTzS4oVMWlGLCBbXKS01ciVtZ8MRXiZy\n883Pfg67zR0AwKbJcVZylHRcxx0fER2b8/IUhS85DWVrjJC4kyPmkyR7OU6J+B3lKcpMvnf/5TP8\nC4MmCIpHuC7fjSj9Hr/wOMS7Yeitn7Fw79/NvpY//HYxAKMmk/ZMYxUuQ0uKGIdlmsO1SBEeyMMO\nAHmRocIPzdIUKUtHF5lMpE5sTOn1Ph7PkRF6u4wEdh4AdAk0CMw5cWVR/s5/8lPY6MrmefiFU9Q7\nUt66O/dw8worFI8DXP2ELLoVi4ni1hUGh2KDVJspZlPZ3I16iiHRexoEKp1OPLRZ73Ay0eitSYfO\n5hWs0SY9vJdim76WP3hDvl+tHeBLX6Vf46aDf+cv/JfyesNFMpG5sINjlFMxMYKeHGjjEx8e85zP\nHxQAac2ncwsB04qHxwrjMwENnTHkp/QQHyET0qTVQ9CV0LCuz+DnYqZtbxgg5ig2npUH6e7nC1g3\nvwgA+Of/9Bi9Z+Vw+sV/8jr2XpbDMJmkGHFDtAjGe2urgjrNjp7ysBeS3LbIEMXiPzFzhVHOWhFG\nBjKdwitIE2AWUJr+jDJDTXMjuSWqTPyaEZmkU62jxnqW7VYTxwzVtnsBJodMeiqkrcFijI0eDwrb\nxyZBV+8+jFBz5XXqOVhnaf8skffqngOTJKTNoIqQ9SUN34FL31Wz28SAiVgtJmE55TWsX+P+LrvY\nuCK/Z5cvoLZOsKIjBya5R5/+xA+8rLX+BN5BlubDUpaylEfkiTAfjAcalR/9OloCAK8E4JCkI1dw\n1+S1zkWFL90cRSYn+1rPRUKVejMIEM3l/Z5Z4s65OK16JZ0zUQQvukhAiTDkyZ1CNIRLYex9nfBh\n7vMGRvfldgwrB/i9LzM1+S/E+NL9HQDAjZU1zMdya2x+VNTa8thH44q42dMjE6uCAobRfoJqR347\nPpNbpHtNY7wrfav2NKJE9Ovt5z1M7kh7wQtDHO7LWFe+WW7543/uY5TJLX/v9yz86H/AuLt2UZB1\neTiuwu+RGp59RCXD+Ei2Q9gb4fgBHYmNCMGh3J6x8Spe2pV2klAwBuqhj499kkVQ5RtYsPjGc0wY\nH5d1urOvYFyTG32+J3kD9rd9Gf0vyC24t/hdvPS/MAHobIARuQ8rRYFtQ+ZFu2IOfMRZwf4q4dvH\nJdq8NcdTG3VCKISTOdp0zB7M5BZ3UIGmhtnxbJxHLNCyfMS53MydmodZSfxOOp2nqYXrHVIN2B48\nAqXfP1XwqtJGPmQUolrDAatgr35LF4OH0je/O8HuuWhvV27WMY1lzeqrMvflAtjaIAfp3ECDVHBW\nUcH6NdEm09gFoUkRR2JSdZ/xkI8Z2dq0UYSikbZulshC+bCzMsPZQyKIv0tZagpLWcpSHpEnQlOI\nNPCVfwm4ZAKgw1JXdbuCIJJTvE77vZWt4mFN3nvB2ETUk1P5GfcqTmty8vszC6YvKaq1UE7wO9Mh\nTEe0h/tHA3y9aO6Efy+IBrz4/Sk8U0J5v/fmGzhmWW/026u4QjDO6w9s5N8nJ/fmEWHl1hyou3KT\nDK4AVVKzObUYUSQ35ZQksF4YoNdhsdZQYdSWMRkPC/gm0YR+6xwPnpL3i98lQnDjBOaX5KxXmwtU\nTHlflwWKjChM1eQyrRhMCZ8aGUJmk86PTnBM3wf6U0y/KuHCqXMHD7/CzEP2bRoVeCkSyLTG6y0c\nlgwNr6yh8eD3AQBp8DQ2/geBRB78xSsAAPdXFHqfEI2m+gA4elY0kJfvzGCRfOcEGmvEUXhmQgyC\npxysjC32QcEdS0jSWQlRH8v4Djo+/IX4mDSRpdZVDadEk9r2W9gopG0v9tB3ZM57uoZRSnTkvvg1\nxpUY0zmdsisGMJW2W60Qg1dIehvIms/CEinDjeX9DhobdGa+rOA1ROO5OW7D3hFf0/ZM/t7d0bju\nSFpyWNGo0Q923vGw7rO0PUgQ0wl9VqX2M24jY1ZvZRBhvi17oRW24djEZIhqqPTebTBS5Ik4FN5J\nOgCylQt6eYVeIeqT70qlntEa4xqxENbbNbgdySFoGA34vjz0Vj1FCvHkjull/8gVG/lQNsqbp31E\n5/L4f72yc4/2zaBrAGQiPjgJETJ3fncww/qWICLPPI0Ws0vTq7L4zixDRtg4czpASAeWkTWRFdJP\nm06rwnFgEIor9kMYPDTiVgc6k4Pu8FaJ2T2Zl72OPFRlrHEvEhNleljD2GAEJBthOmc9fniOkEu/\nGMtoh4MJDFP6cPzgBCMSsiRHMTThwfRbORzi2bdHMofXfBPXvyy/NS9yxKzUG8aHONIy7rH7/+LX\nqGqr/00OmPQTt7FJh+9b+6+izOWhaE4dFIpEM0aCHY+QdDdoXhh1TNsyb44xRCeQ+fT8TZi+fGbH\nmKFmELItkkMIRoBVQx74XqWHLBKTYACNLrEW7bSP7FTmazdg5CBXGE9IS+/mGDAXoB91ELC2ReVi\nPiRWDGMg38+bCcJMEtHS6gQlIedVTaPbkPftQPq75mooT36jXSqYhOLfRg6TcHNuYSAi72mVhDxh\nTaESy/49ST2sW7K+/bqBNc7hvBxh4b83g2BpPixlKUt5RP6V0BQSw8C3M5zUnk+RPyWn/zffEvPB\nqn4SFV9ugZXaDXQvGNiCNpxUvpfkJTYIhLpHNmQ7U7hjy63z6e457tKh+BvD9JKy7u2SK/n+4LVX\nMR6SCKRIMM3lFvwWrOPGRNS9a9+5hXRdztzAkjRa2OcwmKXYtiJoYjYUwQClJ05Tm1wQXrkK5cjJ\nX9YrsEtWH2YzJKw43FAuBldFLa998UUAwGF0iLqSWzJ1UxgzmaPC8TDPCTVmv4pdQtLduycFR+dW\nBOeOpAkfT1JExwwLVutYu0AGznxsb4gDq8I08N4VAw2C444WJepnxKpIX8IDFkGdJQv0qjQJavL/\ntd/3cT6Quf94r4G9bfkNr3kKfyG3eJQvYF6Rm3c1Ea2pXmaImAa9akdo9YTvYrseIKrJPNeSfYwK\nMZtuXqS22000c8LwGQHWiafw5tCCORPt5X5WolISZo5FV/NsBusic/YwxYT5HWVziHgg/5FznXLP\nwNSTybLLHdTmNOMqPcxLCbmutG5gk6aCTU2x6/XhM8Tr5TPEDEP61ggeEaQUHGSsqm1G0kdlWDB5\nr1dv+qhTK7YNDZulWUFQx6b+/6H50Ahc7D8lG72x2EF7nYdBR9z3za4DM3kOABBsDOGTOAXVAohl\ngjPjCKYlcfN1n4Al0TPYssXujT7xCaz84e8BAH40neMXCF2m8TUMvuttWYDelgVnTQg52m++hCbx\n/hrf08TTWvpRmgGaYzJcPSMqpx5aKMhOUuRNZB2CsCxasBJRUX0tfU+66pJZKsiHWKRiO+eeAXfl\nGQCAnU+w0Ze8gPT7JRYdPLyKL5SSBpwOFBJXNneSRlgh6Mv+sIGoKps7GvLBzcYwQpLAzmdoGDR5\nijlaG2J2FTMXW56YTT3+1urHnka6IL5gWMD1ZfMfHa3Bq3NMFQ/qRA7OupYHd/1HfOys78jvvrQO\nYyRztLK1A7fCfIJ+Aacp66euSbtJXkedh3cZ3oL5EeZ3VDZQbbFuIX4WrkefQSp9d40xnIm81s0R\nzIHM53pngsXpxwAAN2qv4aEh/VshcI5zGsCnuyZPNTLSAA2GJroVRrZIbDuKDDikone+fRv1AWsR\nhja2hmLmlNdi2CTFNZhMpooeMs6ni8ZlpalCC2kgh5pntRGQdCbLuR7dHIGS/RI4GiYuzGlAkU/V\n9YDFeyuSXJoPS1nKUh6Vb5SK/m8B+LcgIf17AP59rfWY//cZAD8GoADw17XWv/qNdo5WAEwNfORc\nzi/3aoEXTfL5Mb10x7QQtQgakq7DpArnFRYyR4ao+gEWhTjjakN6ZlsWUMqNYdh3sH1lBwBwxwvx\n0XuirlvRGGOS0pg0NdKoRPdYVO1OpcSZLRrBx4+fgvNn5LdXB03Et+QWM96SqyarD6EZ+dDVHN7Z\nBfXaCHVGBnJWcFamFnLS4xXzHIuEKd1BBRG5JKMix3Qu1Y6dIzE/TuMTPD+S+dG3JjCZ0VekEc6Z\n8pyPE8QjAbOZjSRaEJ2dYnRf1M+qOUZMSLT10kN+IvN5tWWhQsTrWkNQjbOpgVZLxp+lxxjXZT57\n5xMs1mRM/sMzHCvRPE6ZRvzsr21gcEPm8IcPnsevfK9oPCuTDAs6M/uRwqQk4MoBcwIqp6i4JAba\nAFYn8tpuaPSY5qs6NoxQTMyMgDqNJEBav8DI6CDV5M2MS4DO0dCqoklnZMk8hYU7gTmT9ZsnfXgE\nsHGaC5isJA0j0dJqThUZHZztuxpBVcbROMtw0pT+e/c7mF1jlIBo5Kqp0QxlfowghENntFFTMGmm\nlcXisojNouZioYacUZRm1kBSp9YU+vDoBM1DBW2+N964b5SK/tcBfEZrnSulfgbAZwD8Z0qpZwH8\nCIDnAGwA+GdKqdta6+KdGnk7QvKFXOT7X9/qoO+JKv0sKghJ9LEK2bgzw0NAD3mmPPj0I8SmDZXT\n3nPasGKWTHu0X9UCsy7TgxfrOCTy807vDDPm3N8LXWDMBf0mUS3tpoFenaWPD15Gm2B90+6baPkf\nBQCYWxnyUEY0WxHV2Do8R0RSl2xhwa1T1VzUkZDYBkzcSWoLFCfSt6EqMWKFZxTZsAn7PVYlmt7H\nAQBlT1KtW/MWXiGLn33iY84ceMdwERnygETTY2iaDZEtc3K2mCOl93419uE16ZH3PWySgzFc2Kg2\nmODFlGhVVjBgUlCoGxgY5J30V7E9uifrsNHGPZadNwayyYfOHXzLqszbw60hmob4EZIrDjqcisNo\nDmdIkl6bxDrVDjKC7NTiEsUmyXtdAwXp5xvFHEXAWplYHtKxacMmclZiBghcMUumSQ3Nhpg8ce7A\nYnpwxMPk480Ag3OZz3lsYFaV98e5iYB9WmXacuQYcArp29Q6QqUjB6c9VQgYZgxXM6xnRFziHrOj\nDMmKXIH1UKFk9awKNUruERXXkJDf0ksIw9Wow2Mg/SB3sHoqfdctB4pJXzAWMFMmqL1L+Yao6LXW\nv6b1Bdcyfh/CGQkIFf0/0FonWusHEKaoT76nHi1lKUv5UOX9cDT+NQD/kK83IYfEhVxQ0b+jaCVM\nZY+WZ8mZdRLneP6m3CS1a3VYHWIgUAGxjBLKlNPQhAGDrsGkNOAyPqzLFHZFbg/mIKHhe9BV0cW8\nziFWZ3Liz6cKXltU8J34GCUr+zQ5ANuxAYcmyDMvrmJ3KDdQy76G0UgSi5peBQty6+Z3xRlU5DOU\nMU2btgsVM8EmS5G6LNJyyMt4WkFBr/E4OoeRi7fodB5fetGd5giOK5gNUfkd8v0wx1NMcR1fOQAO\n5ffCcoHjPvEfB3voh6LGFw/FFHGLGWzmP/gbT+PPPcW58lvwCW+HNECVeQMXOQGupeCSnn6hZrCZ\nJtzshHjIhDNjP0bbI2xYU5y8B4cbmBKa7dNbQ5TfSQyI3ESpeAfZOVIm4USBrFMriYCeRI96LYUG\nAUdqdReGTw2i4kFHTHBizD/PNGqevOfGNZgNws0lNjqcTytYRzqJDnUXAAAgAElEQVSR9oKA4CzT\nryK0aWI+fA1D4noYHmDZ8r0xo09lQyPmvJ7vrqE+J99HbCNpiXbQDNswmaBmeoxO1Ty4vJ7dhg92\nHYYZI6f5ZyEVryGAgrksGJ/BYEq/37ThNeigLBJYpB/UqKL6bgkfKH+qQ0Ep9dMQZOtf/Aa+++MA\nfvzy3/qPmw+E1MMnmju48Yxs0k7+FIIN2eieSShwo4BXygIVwQw5wVEdI0We0mPrzmDQbm925Idz\nJ4BLD3A2uob2mpQfl0kN3Q3ZNA+PS0wD4vkNZJe7mwnqKxICDL74ZXwzPc7urQL2UNTSSTTDGQdg\ntWRzZINA6IAAGFGKkIAeTasKk/2PMxlTrIpLYNBGWeKUCVJRx4UVkvRk7KJL2zd8UUhBnqvu4NWW\nhCmT+wZiV9qbT8ZY0fK911SM/luy2Q4vwGuiCdprEp3Z2A7Q3SQ35QbgELFoHNuwmNTkENHJTmtI\nWWWYJX3UyXPYjwosGAIuDIXGgByNAfu70kdvKn6Q/fUmBqxReL5podTyQG4OYkwWzOHvye6oJTU4\nLtGmok3UtggsYq3DtmWdVOwiJomKNxcVvVkNkcxY2dnuwwrlkNlwUuiC+8ieAvMdAED3OkOnx9+G\ne5PfBQDkrokFw7ppqlGw3Hu9Tn/OokCbZc3Zegq7Ir6BIPFglPSD9AbwGqz/yGSuKoWB3CcDVm7C\n5pqVCwuRRYzGwoE5l/djAsKWdQcWwVkSdYhJKOsUNIewaTKYVRdpfuGde3fyDR8KSqm/CnFAfrf+\nWv31u6ai11r/HICf4299+PXbS1nKUgB8g4eCUur7APwNAH9Wa/12honPAvjflVJ/G+JovAXgC+/m\nNzX+uLOxQ69+ZcNDdSan8cqLNqxQTmCbZDBtswnlXpgPEVxbTutSByhYIOZGPgziEKhcbgz4cxhj\nOh8rc1gncvSn6TEKnlM1L7skIjkiaEZvtoL1mtz+aLTxsCae46uT5xAGoj4Gr9i468jtvbMvnvBF\nZYittMbveVhNL/ANUnia+RQtufmMqYWEWAHDkQUzkRtj5SiEU8qtW5vYyNakT8+xwrG1WsWNfTFX\njq6kcFibb+kI98bye/aZjfWKaCnxivShmG1j2xA4s29b20SXcOm1RYa8Kr/tlUPEEZ1k4QXzdx+a\nN950dIp4Rvbs81OscTXPdIo2Vd83CtkuLx76iJvSnysvh3B2qNqPN2CT5dureXAcWasab9pqQ8Fn\nFMhaM9AIZV+4XgDXIOSbV4FHhGaDEYA096ACYilODKQ5c0ecAOOxfEbPZ8j4SDTmvIHzCJ0NyYfp\n+A/RGUgbUxuoRXQClmIS9dwmFjSvbgx30FmXPl+braB8VtpeM2/Bb8q8OKQNNPziEmncjBOUJXMa\nWlPYIWsprBglcxL0GlG5cwslK36bZQORIfuiGWWwWVWMKIZS7zNG49ehov8MBOD615VECH5fa/0f\naq1fVUr9HwBeg5gVP/FuIg9LWcpSnhz5Rqnof/5f8vm/CeBvvteO/FEtwQHQpSawWbcQ1eU23j/X\n2PQYWovkZj81p2gSz8tvmygZskpy+9KBEzdtlLviaAuvyt/pkQ/HkdM8jjWGNbldbqzfgHoo8f/d\nrIL7lnx+QGKReM1DEIjjsz5+Hdu03yLnVaSpBGJOmhOUJcFRHfocMhMZCWbdrArdkddBWUNZ4e1v\nEOatmyB5XRxc0ytv4exUxtcNTMRE6Zm3+9ATsYcjV+YnqJ0gvckCnvsREtLYpYsAJKvGIJ5iwvBs\nvSRJSdPA2joJcaslUsJ89q0GPFZSlmYFihl9Ientms06opFoHee7JYaezNWBrbDBOH681sVJLn2y\nU9Ew0nyOtEmOi/UJ7ETGXbRs1C0Zd2YW6EDWgaBRaKgqUku0qmikseAc2rMEitgYXS+E3RJfhEmf\nUQqNfCaayXlhID/k+Co5BgNmr8Y2UJP9EDJL9fnG0wgjQZJ+qVZF1mLIda6Q1KhNknhFuwEqvAOz\n9T24KQufrnpQzOS0dny456IhZD4rX2MXKiB9X2Ihoy/GRRsL5lYUuwYWXXHAFgMZf6NZQ8mCsFEe\noToj/0QRwOY6GXkG/UH5FN5v0UqSlC4ZBZVCnJIT8WGOjUi87KpiYMwc8FtXOWHTCN1AHopq2kan\nLg9NYQHzuWz+vckQp/dlw569tAsAcEoLzTVRyVprTcR92TRfjo5hBaK2NvQBDNJ9g5p/a2RgnjAt\ndbMB51jMhGJxCyGZh4LSQZ1Va4owcEHHwpSsxj3nDOkFSEyQXnpV80JU0WkY496xxPlfOXwDf/hA\nklRW6zVskoVkNOnjbFMSgJJUWKc3Rjb8fZm3RTuEPmaexnSMcCGbsJPbUD3ZpEGVh6LOkTL99jw2\n0ekQZCQeo97hgzVx4BPbsCTxShaNL9Noh94CSGVDr/R8eJaM1Q4M+KzBGBDo5fyoiclQDhvTdzBy\n6Dk/7aNklCDUCi5zAeJc+nO2CJHTTdWzYvzBl+SBvr1dQVKXOSyNKhpzeSBrPmnr5zNkY0nUOhzP\ncHpPHrBDPUJOkpy82sP1ROblAkDl0Iwx5gUQzP8QVkjmLAOwQnkIj85kzYJOhlkha91/ReON7HcA\nAFdLYMqkr4/eXsVTL0guy2IhkZ9et45qLodlq+ZD0ekc6QIL0ssPRkNM+2LyjJkLsdl+Cisr8tnc\nD6HpQJ9NZvBjOZxyN0NavDsOyQtZpjkvZSlLeUSeGE3BwKPFRzVDobYqp7K14cMkGKsqPUQNOf0n\nzPyahSEGfeLqpxnSQk77dqBBxjPs7k/xyr7c6KOxqLKm1cbzAZ1oqx4qDVb79V3MyIloOhXMGvL5\nmLdg0NVo+XJ7DO+WsKpyyp+lE6QR6//tBjyyXOt16buZBihY+HQ2MhFZ4qz0xy34hNqakJDl5OgU\nr54RWbg/g5VIPwdugIAI1ZPMwKuM7RgNuUX+fOc2Tpk3cT5IoGmuzI/naOZyqx74OXRBDYHhVBhT\nFLloCnHdwd6UXA+VCMdnNM3MMSISw9RqcrOfRBUcp6IqjBYTBBXS6aUNLLqyfrUTGzZ5E/cJUhKv\nnKNLVOawLjwZMr4F8ki0t0mSocb8k8iS9qygc+lQPM8qUCtyrx3ABcjo7Y1tJNdY7chwqqFzHMTS\nxv5piDeZQp5OhtDnomWGVokbHjMhidkwm5RIcgGRMVdXgVi0AjMBiiqrTnusdtV11C3RQEaxeYnm\nrIsSJR2J97SDPKGjkLgeJ2+FWN2U+bk2W0GLtH/zeYCjvmg3Xzm5j+FCNNm8lPam+QiZL3vWTROc\nMCRpXZ+CeDmoOBUs3uNj/kQcCgqAoQAP6hJGWbkuXmDed72oY3omExnZh1DEpXNZyVZPahhUxDsf\nHNZRuPSsJzUMUnlAyuMpbMZ0+3sk9yheRZ3lr0ZexW2SqLTrMzR7OwCAh9McT5/IYrzFxJweWqj7\nhEtf9VClH6Ayr+BzfdrA+w9w15cH6NqObKTTTh1rMQ+yGtDcEzXfuJpAzS5yK6S/fhrjBg+h3WML\nu0MxfV6AB6su/Xj+tILjLen/9/blILxhA4sTGce9rQRrDuezXcHgLXnoe75GzZZ+nJCsdPLQx7kl\nD8rqaIzpqvRtx23C7xKBOjJg8ZCJE0Z7ZufolIzpx3WcDIkDubiPGw1Zv1M1wjfVRZ2vb8na+f01\n9FcEcn3ntIVzkvS++qX7OGKq9I3Sxx5Lp2+GcjCpmsLgiKxIZoH1I+I8bqe40pQH+mErQXNIE7Il\n+yaeTDAOpW/6OIQ1Eo/8/bMpDln/8ZHxGr7EPIO/aDFFudvEpkUCn16O5ECetlfrUzRnTH8nz6dp\nO0jJXfmpaB1nxOyaHqbIWF7dODSgrpKbk+XdcXsEc8ykh40cxUj2aYRDGCMxn/S5hYO3ZCz3Ddnr\nqwMX92huPn/1GTg9OdzW4x5MIpMHiYfzcskluZSlLOVPIU+EpgAAhlLQyrgEjdhwDHhbcvt1rrQx\n4Q3UKVvwK3I73FgRgI3MnyK7Ty+7raHI7bfwPJhJlb+hsULn2inDtmpeh9uVW9UtC2SkDwuwCnsk\nt9ii3kCxQh7LMbP/dtZhZ6zaKx5iymKf4ayPtMG8h3GJLd5cLh14bbcHm/wVVjhBRGozHRWIaoRh\nm8mNoesZxvRqF+1jtGy5aTe6gNcT9dru7qFBDEanJo7Iozpg3hJTIu9HUG3S3U9OsGCqca6GmNUk\nSlIJWFRWNLCayQ3sFT488iJEqg6b0Ye5UUV1Qoo4aiBxGiCnGRdsOsiPpB9+1wcvXQTOOiJS0rU3\nZA1MdR+vKdECGuUx0pRrk8SYkhglGVZQJ1eianH8zSZeg2ggV8wYq6uCLXCt10RuUPPKJ4hteZ1H\nsmZT1JCdMhN2tYvtnqzJw6KD9UBMxXZrE9foeK21qZZ3AxSRYHtO1gsE5zKHttYw6BYvVmQN1lQF\nCSt3G60UqEp+iucPUDFFg2g9exXPbbHCNJTfOh7vYe7KZBlZgajG18kG3IZoju3VHqwLROgK06Q3\n1rG5Kmas5VXQY5p3sFFDyPyFPIlg4L05Gp+MQ4FWQ1AAKc2HkXKRkPvQsjWeWpcwlecA3aosaJOo\nNMMiQiMjIahuYkRmoqANqJzIPa0OGjQr2p8Q72+mDnC1S1jsWY60FJ/D6f4D6IocFutJH+uutH3W\nlIPJTI8wmsjiT89mmI8lAhD4t/ECAWSTjz4N3ZGHpcHyj8AwMWeCSSsIYV/QnteAiElBOanVPZXi\nNlmF9M1VPD0T/8MzL1xB9WU5AMcJ0JjJpjnYkgfl+v0z1IaysZttdYntWKRVDDVz/GchTJ+btyvt\nVTbaCBgB0Mdd1FjCnXoG2gwNZ3MLFslrkxmjBW6CliEPxYtbGtUuQ7XuCA2WEfuqQLspB9W8ENPg\nftGBx2iA7VVhEdTljfu7sJkUdGwcoMGQcduVtPKNnoenNuUA2fRGWHFl/VYDFzNiEXpZioR1KpbN\n6tmsRCuQB6VqrMEiQYr//bfRySQJdyW4ih2bIaamHEaz13exy1Cn+9o+uooXUtvF4FjAbOy+7Iuj\nmo0qk7S66y1cD+RCOrzVhWIk6sZqC3Xm+008UgfYESxGJ5TpwuK+rhQRKi2Zz2qcw/qU8LhUumI+\nXG+8iFpDPmusdhEwfJnmIfyBmDRZsIW29T5XSS5lKUv510ueDE0BAKCQGkDBOnBbJYjbBI0ImvDb\ncgOvxQ2UNXGuuGRsseIYESm6ImOKJuO40SyFZ5KGzQzQXhMV7qlcnHZWehPVDhOSzBwjQotPchc5\n6eSCVQMui2B8IvUGjRUkMTWT0SGOefs7oxwbN+TWbNgKFUvaywmpZYYBPENujGSeIOHNFi1MJAQT\nGVNTSML5JQGO1zGw6QjV/IYxR3b1gkLOwoJELReJMJ6/jeiWaASjg1OUzBVIk3PULcFjvKsruErC\nkSq91/WmiWIqaq158xTGmGZXLUdJSLuk2sdsIPNVkBSlTExoErL4zjpuWxIOaeEW6qsyR3pXwWhJ\nn4YHcsst1g9QHxMFedXC+YKFRlEBFpKiNCdoEufQIfP1du0plEwa2jJuo7JKiry0iqDOtR72UHoX\nmglNG1uh3BAtQLkp1hsCm7dpDFCPBd5OdUsYh4x/BdLfU3OE44kQ35xZi68hcDsGCJSNJvEUprYL\nw5ZxjK0UvZ60fVv5UDH5SBtTOB5NlEy0pvPUQOKTVzMPYEYyL6ZSKIjf4F9fx3ORaLJ+VdbJby5Q\nsZiyX0nhFKJtlU0fAxLUVIxDHJBj893KE3EoKAAmNHytkGtmZeXAxpmoye2Kg22f3IfrGTJCn7us\nFkyiKsZk+dkoHBSs2mtaNsDsv1bhoMKaAY8PaxkoWCanQPfh0F5ENEGLWXWJU8NTC1Kc++JnyKHR\n8uRASpu30c7uAwDW/CbGzGK8GtUw60r/Oq48eI6xgGbfh/kQNYKDWlYVurwooxYTx5zncGhKXU/q\nsJryW7XNG8inBKl9zcd11m48G4gaPY6GeGogwCs7qzE8yMZstjZwqsVsajZmWFXST49q66puIGEI\nWC9MGC3ZpBbUJaKPf1RgQWp3TdLZONfYLFgOXnPhlYTWsDNUEtmMce0YFnEca6as04uH13G3KclZ\nV/td+OREjPICTsEEKW3iNJTxrd6XwzL7WANPO4T4X2uiQcAc13RQQMaCVgz7nElLTBYr4xgRo0Rb\nRYw2fQ6BvYOSSL8qX6Coy6F1QmCV9M5DRORyj6YGvsUTUyO9VsXvvCL9Xy3JvVA6QEsOntv2FrpM\ngKuqKnLWc9QqXQSs8pzPZB9GnWNUmFlq5jYMmj4qAJo0wVQVaCm5GIqejL/i1JETVdYyXJSFtOGk\nJkKC05RvVfFFf5m8tJSlLOVPIU+EpgDI6ZQY6pL5uWYUiOl9t60CKVNfK4lGwLh4TG8znBAWwSZQ\nbSAgLqOJGhSr1qBcGDQ3FKMXTp7A5OmqJynOEnJBddaREjgkdkzEO8x3P2fdguqh4I1vtx5gdcgo\nSK+FNX4vcwzUCJDhsJ+mXSKypA9G5kDTiVYmGTSdQRm97PlsgZgsyisdD3WTJDKnYxTkazx0Z9CE\nA1+0xLHUqK9i7u8CAILYRVKlw81xkVHdtRITKdOqA2Inpg0fjVJuxNxykBPpY+gU0CNxus0tAymB\nWE6pMdQq+hJWrAoFw5Hf81WIlPUVauzgyBGzIrSJj9A+R8A4/VSNoWNxqKlCI6W2CF2iRfbns5rM\n4TenZ1jQk18vUhj5hYNOIzKk/3E0QWqR5j2XvbLwbTgM15e1HgwC2BR2BxX2M81NJExTnxzImp9U\nK5jqPc5tE4stolLbTXRa0kanJ+p8paGQK0Z7Oh3UfSbG+T4sPmq26cK0CNtOxcY8d5DYMhcVd4ac\n7NKBUwfIgm1ls0stxKZmAsO5rJhN5hqeI5GrxbyP80PRYmrju1hzSLL5LmWpKSxlKUt5RJ4ITUEp\nwLAVahlANjKEEfDgnmQHPvPiVfi5hOTMxgrqPGnBuLq5KLBCzP+gWoNtys0VwoBdimOvMAOUdALF\nzDGomTlK/pRveegu6OFa7COzJRfAyCzkfULBdeS2m4xPYM6IQzB0UKZSrGX2G9DbzAWI2rBZPJQx\nDGfkMVKm81pughqx+e1eB4Mz+V6LFGVjuw7TlLDZaGTCqsjNVqn5UDO58jYyHyXBT29e/Yj8brWH\nYCS3zm7v83CnF0wuVVjNHQBAL/88Sp84BMQb8F0bgUUnWTQFyguw1ggB0ZTM2EOd6b8+qygdjBFD\nbiLlm8jodK3ZDkrmCGTJBF7KlF9mbE7SGiYz0Tpc3UO4xuQRQ6HDu2puWEjpxCtZ4hmVLkLG3bOZ\ng8ggWGmkMWb6szWPkeXyGxVqQmM7Q425v+dzGw2G8lIzuvS75PkCAX1QOpI2OveH2KWNnxYKWIjP\nZHHbgF0TraBPZ2Y2y2DSCZ6FU5jUXrM8gEFYOKjwEobvAhWs3RCfGgBUjHXMWcRl6BlKYpobjo+I\n2qRvEnkpSQFHvpcqB/FYfF6H6RT5Lwkq4s/GETprj0CsvqM8EYeCoRSqtoWKVohZeqpVgZD4fAf9\nIerMTchOB0jpWTZYZ5AgxpxqlF1foKATTAdzJAvGnasTzJj3UG3JpE4iDU1K8rPxAR5qeXhnSQ9V\n5iSMzQzHxE28f8bFzD2EsfRBuX3EESHUvAfIR6IeB40TZAKwjFGVqcYjC5U2E6EWLjJ67Y29BSJf\nEmgGpCkPrclllpXhmzhnWayqF9CWHAq7DYXAYYLMNVnKaq2Kkftlmbc3J3j+2+WAzA5HWO+IGnxn\nv4vAkgekGNMzXc+QzuUBTMxjjFI50PwSCOkEm9szqLH0I9ckYp21YbT5kIZVeC2aXREwZwQniVMM\nTI7FlgN23B7ANS+Qlsc4ZQm46xjw6By0bY3NCh2wHTks5+cjVGsyn2O0YYTyEHq6RERo9PNEI2Od\nR5uOOjgjDIiGPPEGGA2kH81GjpB7oCwCDMYyroWW371vKvQbcmA32zaOOkzOmm/DastDelGVOwsL\nzKeypqO1M5xN5HCuNUxkh0xvb2bwE5q6JVOwYUNxT5pJgTyXNubZGjyXF0pso+Tnpxffry5gZ6zb\nMEcgADVG+7v4x3sC4R9V9vHGvdt4L7I0H5aylKU8Ik+EpmAaCs2qi3YCaHL1jVQJJ+GZNTtFeiCn\nbrg1hT+gJlAX1So8nyFiavN600XB2ns/biGlv8mJiktH47xP4NNyCIuZeWESIj2U6ehnu8jrYj7M\nwwzZidyOPmvsx+MhHHJBnB2bCDO5Vdq6CnTk1ozu5Dh3RduoTOW2hl/DnBWHE3+B6li0g3DcBRK5\nSR8kkuW3aQEJb+vZ6QgTT1TAwOjCuQBIOVKIusSZIPkJekM4XxR1vmONUFfStq6sIg8lJdhwzlAl\nFX1Os6QcZ1jQqVccpDAN4hjMCoSsgsyiEBnzCZrEuRtVC2wS98L0TWAi4xjrCGpMjUynmB/K65ne\nle+3uphqmZ/N2Sry6/K7K7aFHpGIA8dGvyG3/8qQJmH3DOeET0vdBL0xTYbmGhKak+PSRGUhIcUJ\nU6LrrodRRTbDRhjB8Jjxt9+55HLIDs4Qh2KmHr4lZs7RwT6CGvENHAvPZRJmPLEBg5pASKKXxThE\n0hDtwZibSBvSXpynUARjrS8cwLgIpcvvLjDFCp3jUeSjZCh+1BziekjzqWIBdJTGLMarnMYIqxfm\nk8JwX8aUdk6wxVyIyX4Dh38UEfkd5Ik4FCzDQLNSQa/loRiIqh4VOeZE5z2MM/iFLG41C8GSAeR9\niSmfDqeImW/QacSwWF2YVHJokqvomocpK+NCYjX6MaAIbmFkMyxasjGdfgVZQiIWJHgwkXb2S9J7\nT0yYc1KAFxkGfUYcVkz0iAA1VB5MksVmBIAxJjGSlqjiaerBqstrxywvPepVwpvnqsBkIofirjNE\nj6fbtJ/A58G5505QUURnmpMbcjbHyURy9X/zNMSfqcv4rDhG5sjm74/uQq/IAdFasLzbrMGzZdfp\nah3FvrwewEJhyZiqbgbNFOScdQtNzJEQcjyaFLDITOQbGnOmVeexhSlZihYgvqJRYGLyYctPcbKQ\nftZqATyiSDWzGq4yme2swjLs4QgFczLstomkRQr7NELk0stuF2gFfJ0y2aoONHPpQ8OpYVbI+Ibm\nOdYZaQmVhdMT2XP3UzmwXoLG9YIAKJubGJ3JBXGQjNBh2faMB8/cVtg7l+/3ugZWiY85RQ9rkH2t\nmhXEIZmqSC1fpA2Ac+xWcswSeR3EU2RMkTcnKcYk85kMeQgVVbhMiQ7TOqxVmXtruo6pov8reg1p\nVcbybmVpPixlKUt5RJ4ITcF2LGxtN2GfKWT0emdxgZRq/qQ+RTMW51nWvYEiE1VhQtNg+Eofh4wo\nWGGORkNO2mJ/AKPLOvW+j0qD5B0HcrOZnQQWvdv5tESVt+Bd+wA3+nJe7tglTmbMiuRJ3D86g5XI\nab97OoNF8+H0fgOVj8hrpYtL3AObaNDTIkJC4BTfbaBCH9jqles4eEOyIutMbV6EChZZqffeLDC3\ndwEA0Y0VgGpnNbHxZSVq8o0Hos00VBfhoUxMoz1FpWAWZ70OryHsylc3z5GTmTvrkkOhmqJiSduj\n2EHM/I5F/wgOOSfMiosrF4Ayp2L6GKaN6IQ4BW0FKyMuRM9BnWA4o2ENAVPF51q+l489rDD3JMob\n6IjCgutX2tiqiBNQx6uwG+JouyiYOo1TbI1krjz/Kjw6gVW9AZxLP7a1jx5Tmte3ZJzDaAIzkv8f\nTObwQKezAzhK2ijrIWIt8zhjRuDkrI/Ekc9+8sVr6FyRYqzqRxXu/MJvS6frMj97hykM5lPEuz6M\nDdEgfGUiaZPzMvcuzc0x92xQACY1r3r3FjQ5T+1qicKS9SuKBENGa0wlYzaUDzAvIm7ksI9kLYvp\nHO2ROCsPdQvJ4l/JKkkDuV1Ha8tEO5UJGyqFzLmwvUYIdyQ/3W3YqDKMGI4ZburGiGSe0B9PMfZp\nyxcJxgSpUPkQFlGNDJdgn9Mq6qxkRMVFcFUehN7pOnKqwepMo7hObLwpEX9QwLMYpkvG6F+UGRv7\n6DHFuNYq4CZi5x+ZYqc+jGLktMk3ggx1EIx2dgfVVCru7mUXpdUHOEjl8Jq4KToheSw9F81N6ees\nPsV1mlUhcRSrtof0Finn34xQksUot0o897Rs/r28gbkS38XGXEKTzpU6nAvmrLUF9njihrME4Gac\nz0wsILZ2UcjDfTCoYMYQYdVtYuUi2BNWLgldrZqBSo2w5vEOAGCaL7A9Zxsf6WMay4HUu7aBpxqS\nVt4rYxyzhHkQSX8XexqaiVemtwqnIeZTPz7Bm0MBWA3f7OK5OunhrxC9Kz/A/an81iTZR2VF+nH9\n6TZsJaZGZgHWtsynfUf2oX2q0CcRzWpjA9XvYjJRauKU6cObpXzHKvoYMrSoVw+RJEINH3RjeDRT\np9lD3B/RNxXLXG6XLXQ6/P+T+7ANOejPJwGQCEPCxHXQD+WwqObiy9AbczTJNlvtzlCwTuTBF/r4\njUwiTbY3gmksqySXspSl/CnkidAUXNvGrbUesuEEvbqcuidhhHoqN3o2nuN0LEH/4KUjLD7+bwAA\n1jbkRvzYYhPnVPvaXhu5RbwBS8EOxRzZKDwU9kWEgryFqybcgniHeYo7E7nF7w/2UbvgsTydw5nL\nFVqSM7AIh4giUc9m6QJHhDP+Dt2C6kh6aStaR0YosGcJrb7arCOvS3tKWVhZl9thbZZjekNusVt3\nZRy5cuDk7HvVQtIRraFjrABVcUbWhj7uMFphP5Db4OH8c6i9wuhLkmCcMT8gSzGay9z2PBe1M5nP\nUU3Mq7g/upwfZz7DjTUmIY23kW5Jn9ZUBUXMqkyabv7REGpMzWWRYOdp+Y+aZ6Mk6/aZFeKcUZ77\nu7syDmXhARN6VvY6GNH5+/yNp3CFGAJrZg/dikRXBm+IlpeSsVoAAAc7SURBVPMvygWO9uQW3H/9\nBN/z3TIvK9cCbK78IACgac7QI6y+V5CO71oNHztncdTxAmlNVBq/3Ybniqkw2N3Dg9fETH39PqHP\nTiLcZmToK9nv4TuznwQA7OhjZEQKPzx/HQCQJjmOT8XBVxo58qrQEFY6m6hViJEw92Gx+nc4k/Uw\npiEiash6kiFfEw3LHqcImBavC416ZYu/LXPZiGPkNCuHIwvTN6Xvrza/AJtEM2FkonyPrNNPxKGg\nlUbhaJStCvRAVPWmbSOMZWEGroXeqTyESgW48opMdnxVNlIlcDDz5QFTRophkx7yiYLNRB/VaqDW\nYf1EjTDeXoz4jJiC2Qx9vlaxhz22d1RkcFl+HDLnXJkxYmaaTc0KXIthOMNBwEy6JPFQY/QBTF6q\nWgVADH5DATmBWqLeDOWI5KGGbLSR42FGHope3oZRJYBGNIU1lc/EeYReLOrjyBZfxf6oQMBxZqcG\nzo7lgW5ZDvqpbKaa4WJiS83EjGHDPSTYqMnrSqjhs/qu3bZRptJGfjRGFDDrccBwqQ5hkdY9WLdg\naVHFtRMhPCdB7mSC4wEzS5nFmaQLpLQDB+0S6QUortqA60pkJKloGIX83nxG2PrhHIehHIquZSH8\nApm6Vm5igyxhfmcCM+UeoKmVPNyAtuQA1Ve+Be4NhiEXDmYMxSb3TrB/R+bxfp/oR6VGzEjUw/sh\nRp8kBmdWwJzLmBbMiJyWBUxmGO4lHq6ZDBFG28hN2YdWxYJHQts6OK+WgYRl5JEfIT9niX4SImG5\nv+UWKMgi5pA/NIGDeS59mDzMMWZ49vCXJ9idybOTOjmU9d4IZpfmw1KWspRHRH2NG/ZD7IRS5wAW\nAPofUhe6y7aXbf9r0PZVrXXvnT70RBwKAKCUeklr/Yll28u2l21/uLI0H5aylKU8IstDYSlLWcoj\n8iQdCj+3bHvZ9rLtD1+eGJ/CUpaylCdDniRNYSlLWcoTIB/6oaCU+j6l1B2l1F2l1E895ra2lVK/\npZR6TSn1qlLqJ/l+Wyn160qpt/j3ewPKf299MJVSX1RK/TL/fU0p9XmO/x8qpZzH2HZTKfWPlFJv\nKKVeV0p96wc1dqXUf8w5/6pS6u8rpbzHNXal1C8opc6UUl9923t/4jiVyH/DPnxFKfVNj6Htv8U5\n/4pS6p8oRZop+b/PsO07Sqnv/dO0/X7Jh3ooKKVMAH8XwPcDeBbAX1JKPfsYm8wB/Kda62cBfArA\nT7C9nwLwG1rrWwB+g/9+XPKTAF5/279/BsDf0VrfBDAC8GOPse2fBfArWuunAbzIfjz2sSulNgH8\ndQCf0Fo/D8AE8CN4fGP/nwF83x957+uN8/sB3OKfHwfw9x5D278O4Hmt9QsA3gTwGQDg3vsRAM/x\nO/8tn4kPV7TWH9ofAN8K4Fff9u/PAPjMB9j+/w3gzwO4A2Cd760DuPOY2tuCbMjvAvDLEB6cPgDr\nT5qP97ntBoAHoB/pbe8/9rED2ASwD6ANSa3/ZQDf+zjHDmAHwFffaZwA/nsAf+lP+tz71fYf+b8f\nAvCLfP3IfgfwqwC+9XGs/3v582GbDxeb5UIO+N5jF6XUDoCPAfg8gFWtNZPocQJg9TE1+18D+BsA\nLgCyOgDGWmsWLT/W8V8DcA7gf6L58j8qpSr4AMautT4E8F8B2ANwDGAC4GV8cGMHvv44P+g9+NcA\n/NMPqe13JR/2ofChiFKqCuAfA/iPtNbTt/+fliP7fQ/JKKU+DeBMa/3y+/3b71IsAN8E4O9prT8G\nSSt/xFR4jGNvAfi3IQfTBoAK/riK/YHJ4xrnO4lS6qchJuwvftBtvxf5sA+FQwDbb/v3Ft97bKKU\nsiEHwi9qrX+Jb58qJWCH/PvsMTT97QB+UCm1C+AfQEyInwXQVEpdVKs+zvEfADjQWn+e//5HkEPi\ngxj7nwPwQGt9rrXOAPwSZD4+qLEDX3+cH8geVEr9VQCfBvCXeSh9YG2/V/mwD4U/AHCLXmgH4nT5\n7ONqTCmlAPw8gNe11n/7bf/1WQB/ha//CsTX8L6K1vozWustrfUOZJy/qbX+ywB+C8APP8622f4J\ngH2l1FN867sBvIYPYOwQs+FTSqmAa3DR9gcydsrXG+dnAfx7jEJ8CsDkbWbG+yJKqe+DmI0/qLV+\nOzbaZwH8iFLKVUpdgzg7v/B+tv0NyYft1ADwAxCP7D0AP/2Y2/oOiNr4FQBf4p8fgNj2vwHgLQD/\nDED7Mffj3wTwy3x9HbIR7gL4PwG4j7HdjwJ4ieP/vwC0PqixA/gvALwB4KsA/lcA7uMaO4C/D/Fd\nZBAN6ce+3jghzt6/y/33CiRC8n63fRfiO7jYc//d2z7/02z7DoDvf5z77t3+WWY0LmUpS3lEPmzz\nYSlLWcoTJstDYSlLWcojsjwUlrKUpTwiy0NhKUtZyiOyPBSWspSlPCLLQ2EpS1nKI7I8FJaylKU8\nIstDYSlLWcoj8v8BCWsq1ZxHLwQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5658f1fef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3409... Generator Loss: 0.6901\n",
+      "Epoch 1/1... Discriminator Loss: 1.3804... Generator Loss: 0.5383\n",
+      "Epoch 1/1... Discriminator Loss: 1.4042... Generator Loss: 0.7307\n",
+      "Epoch 1/1... Discriminator Loss: 1.5044... Generator Loss: 1.7383\n",
+      "Epoch 1/1... Discriminator Loss: 1.4054... Generator Loss: 0.5633\n",
+      "Epoch 1/1... Discriminator Loss: 0.8011... Generator Loss: 2.2206\n",
+      "Epoch 1/1... Discriminator Loss: 1.2483... Generator Loss: 0.5992\n",
+      "Epoch 1/1... Discriminator Loss: 1.3313... Generator Loss: 0.5899\n",
+      "Epoch 1/1... Discriminator Loss: 1.2300... Generator Loss: 0.9554\n",
+      "Epoch 1/1... Discriminator Loss: 1.3818... Generator Loss: 1.0496\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56997f8160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3802... Generator Loss: 0.7881\n",
+      "Epoch 1/1... Discriminator Loss: 1.3059... Generator Loss: 1.2800\n",
+      "Epoch 1/1... Discriminator Loss: 1.2707... Generator Loss: 0.8262\n",
+      "Epoch 1/1... Discriminator Loss: 1.3435... Generator Loss: 0.7216\n",
+      "Epoch 1/1... Discriminator Loss: 1.1423... Generator Loss: 1.5386\n",
+      "Epoch 1/1... Discriminator Loss: 1.3705... Generator Loss: 1.4615\n",
+      "Epoch 1/1... Discriminator Loss: 1.3879... Generator Loss: 0.5866\n",
+      "Epoch 1/1... Discriminator Loss: 1.2942... Generator Loss: 1.0528\n",
+      "Epoch 1/1... Discriminator Loss: 1.2455... Generator Loss: 0.7173\n",
+      "Epoch 1/1... Discriminator Loss: 1.4368... Generator Loss: 0.5770\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56995d10b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3614... Generator Loss: 1.5139\n",
+      "Epoch 1/1... Discriminator Loss: 1.3375... Generator Loss: 1.1554\n",
+      "Epoch 1/1... Discriminator Loss: 1.5048... Generator Loss: 0.8155\n",
+      "Epoch 1/1... Discriminator Loss: 1.3531... Generator Loss: 0.7889\n",
+      "Epoch 1/1... Discriminator Loss: 1.2752... Generator Loss: 0.8632\n",
+      "Epoch 1/1... Discriminator Loss: 1.3345... Generator Loss: 0.6996\n",
+      "Epoch 1/1... Discriminator Loss: 1.5458... Generator Loss: 1.1624\n",
+      "Epoch 1/1... Discriminator Loss: 1.8949... Generator Loss: 2.2906\n",
+      "Epoch 1/1... Discriminator Loss: 1.5799... Generator Loss: 0.3830\n",
+      "Epoch 1/1... Discriminator Loss: 1.3848... Generator Loss: 0.7840\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndpf9fxjdHT2o6ioaAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f569939a6a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.1885... Generator Loss: 0.7337\n",
+      "Epoch 1/1... Discriminator Loss: 1.4297... Generator Loss: 0.9953\n",
+      "Epoch 1/1... Discriminator Loss: 1.1458... Generator Loss: 0.9505\n",
+      "Epoch 1/1... Discriminator Loss: 1.3446... Generator Loss: 0.7885\n",
+      "Epoch 1/1... Discriminator Loss: 1.2624... Generator Loss: 0.8597\n",
+      "Epoch 1/1... Discriminator Loss: 1.4662... Generator Loss: 0.5536\n",
+      "Epoch 1/1... Discriminator Loss: 1.1388... Generator Loss: 0.7493\n",
+      "Epoch 1/1... Discriminator Loss: 1.4432... Generator Loss: 0.4454\n",
+      "Epoch 1/1... Discriminator Loss: 1.4186... Generator Loss: 1.0688\n",
+      "Epoch 1/1... Discriminator Loss: 1.4904... Generator Loss: 0.4468\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nc7lN5pvCXOYyl9vk/wXydv6uFCRQigAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5699678860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3577... Generator Loss: 0.9579\n",
+      "Epoch 1/1... Discriminator Loss: 1.4756... Generator Loss: 0.6143\n",
+      "Epoch 1/1... Discriminator Loss: 1.2961... Generator Loss: 0.8042\n",
+      "Epoch 1/1... Discriminator Loss: 0.9653... Generator Loss: 1.3168\n",
+      "Epoch 1/1... Discriminator Loss: 1.4968... Generator Loss: 0.7849\n",
+      "Epoch 1/1... Discriminator Loss: 1.3341... Generator Loss: 0.8891\n",
+      "Epoch 1/1... Discriminator Loss: 1.3685... Generator Loss: 0.7253\n",
+      "Epoch 1/1... Discriminator Loss: 1.3404... Generator Loss: 0.9025\n",
+      "Epoch 1/1... Discriminator Loss: 1.2645... Generator Loss: 0.8479\n",
+      "Epoch 1/1... Discriminator Loss: 1.6065... Generator Loss: 0.4065\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f565a450a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.6008... Generator Loss: 0.3544\n",
+      "Epoch 1/1... Discriminator Loss: 1.2181... Generator Loss: 1.1289\n",
+      "Epoch 1/1... Discriminator Loss: 1.2560... Generator Loss: 0.9682\n",
+      "Epoch 1/1... Discriminator Loss: 1.3160... Generator Loss: 1.1959\n",
+      "Epoch 1/1... Discriminator Loss: 1.2201... Generator Loss: 0.7981\n",
+      "Epoch 1/1... Discriminator Loss: 1.2491... Generator Loss: 1.1460\n",
+      "Epoch 1/1... Discriminator Loss: 1.2200... Generator Loss: 0.7130\n",
+      "Epoch 1/1... Discriminator Loss: 1.4682... Generator Loss: 0.8232\n",
+      "Epoch 1/1... Discriminator Loss: 1.4792... Generator Loss: 0.9793\n",
+      "Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.6182\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f566963f7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.2531... Generator Loss: 0.8170\n",
+      "Epoch 1/1... Discriminator Loss: 1.4623... Generator Loss: 1.2401\n",
+      "Epoch 1/1... Discriminator Loss: 1.2366... Generator Loss: 0.9501\n",
+      "Epoch 1/1... Discriminator Loss: 1.3075... Generator Loss: 1.3609\n",
+      "Epoch 1/1... Discriminator Loss: 1.4270... Generator Loss: 0.9645\n",
+      "Epoch 1/1... Discriminator Loss: 1.4111... Generator Loss: 0.8603\n",
+      "Epoch 1/1... Discriminator Loss: 1.2284... Generator Loss: 0.7865\n",
+      "Epoch 1/1... Discriminator Loss: 1.4061... Generator Loss: 0.6807\n",
+      "Epoch 1/1... Discriminator Loss: 1.2659... Generator Loss: 1.0759\n",
+      "Epoch 1/1... Discriminator Loss: 1.4578... Generator Loss: 0.7154\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659ef0908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3840... Generator Loss: 0.7229\n",
+      "Epoch 1/1... Discriminator Loss: 1.3608... Generator Loss: 0.7311\n",
+      "Epoch 1/1... Discriminator Loss: 1.4321... Generator Loss: 0.7518\n",
+      "Epoch 1/1... Discriminator Loss: 1.2556... Generator Loss: 0.9692\n",
+      "Epoch 1/1... Discriminator Loss: 1.2830... Generator Loss: 0.7561\n",
+      "Epoch 1/1... Discriminator Loss: 1.3029... Generator Loss: 0.9397\n",
+      "Epoch 1/1... Discriminator Loss: 1.5198... Generator Loss: 0.4745\n",
+      "Epoch 1/1... Discriminator Loss: 1.2650... Generator Loss: 1.0075\n",
+      "Epoch 1/1... Discriminator Loss: 1.3682... Generator Loss: 0.6288\n",
+      "Epoch 1/1... Discriminator Loss: 1.3223... Generator Loss: 0.6662\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyUIpLGQhC3lI/h/qu6kx5N24XAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659c1f908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3569... Generator Loss: 0.8252\n",
+      "Epoch 1/1... Discriminator Loss: 1.3871... Generator Loss: 0.7830\n",
+      "Epoch 1/1... Discriminator Loss: 1.8022... Generator Loss: 0.2896\n",
+      "Epoch 1/1... Discriminator Loss: 1.3923... Generator Loss: 0.6645\n",
+      "Epoch 1/1... Discriminator Loss: 1.4469... Generator Loss: 1.4170\n",
+      "Epoch 1/1... Discriminator Loss: 1.3721... Generator Loss: 0.7974\n",
+      "Epoch 1/1... Discriminator Loss: 1.1933... Generator Loss: 1.0238\n",
+      "Epoch 1/1... Discriminator Loss: 1.2738... Generator Loss: 0.9327\n",
+      "Epoch 1/1... Discriminator Loss: 1.4144... Generator Loss: 0.9993\n",
+      "Epoch 1/1... Discriminator Loss: 1.3502... Generator Loss: 0.6667\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJf8PYsKe8eIq/vcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659a2db00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.4132... Generator Loss: 0.5582\n",
+      "Epoch 1/1... Discriminator Loss: 1.3437... Generator Loss: 0.7727\n",
+      "Epoch 1/1... Discriminator Loss: 1.3675... Generator Loss: 0.7321\n",
+      "Epoch 1/1... Discriminator Loss: 1.3730... Generator Loss: 0.9555\n",
+      "Epoch 1/1... Discriminator Loss: 1.4001... Generator Loss: 0.6890\n",
+      "Epoch 1/1... Discriminator Loss: 1.4468... Generator Loss: 0.4802\n",
+      "Epoch 1/1... Discriminator Loss: 1.4452... Generator Loss: 0.6017\n",
+      "Epoch 1/1... Discriminator Loss: 1.2895... Generator Loss: 0.9879\n",
+      "Epoch 1/1... Discriminator Loss: 1.3213... Generator Loss: 0.7545\n",
+      "Epoch 1/1... Discriminator Loss: 1.4087... Generator Loss: 0.7632\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/onzFIOhAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659993860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.4724... Generator Loss: 0.4925\n",
+      "Epoch 1/1... Discriminator Loss: 1.3801... Generator Loss: 0.7973\n",
+      "Epoch 1/1... Discriminator Loss: 1.5132... Generator Loss: 0.9511\n",
+      "Epoch 1/1... Discriminator Loss: 1.3006... Generator Loss: 0.9248\n",
+      "Epoch 1/1... Discriminator Loss: 1.4002... Generator Loss: 0.7541\n",
+      "Epoch 1/1... Discriminator Loss: 1.3821... Generator Loss: 0.6088\n",
+      "Epoch 1/1... Discriminator Loss: 1.6036... Generator Loss: 1.3762\n",
+      "Epoch 1/1... Discriminator Loss: 1.2994... Generator Loss: 1.0744\n",
+      "Epoch 1/1... Discriminator Loss: 1.4678... Generator Loss: 0.6439\n",
+      "Epoch 1/1... Discriminator Loss: 1.5260... Generator Loss: 0.4740\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56986f3518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3717... Generator Loss: 0.6639\n",
+      "Epoch 1/1... Discriminator Loss: 1.3463... Generator Loss: 0.7220\n",
+      "Epoch 1/1... Discriminator Loss: 1.3487... Generator Loss: 0.9054\n",
+      "Epoch 1/1... Discriminator Loss: 1.3769... Generator Loss: 0.7837\n",
+      "Epoch 1/1... Discriminator Loss: 1.5438... Generator Loss: 0.5937\n",
+      "Epoch 1/1... Discriminator Loss: 1.1925... Generator Loss: 0.8585\n",
+      "Epoch 1/1... Discriminator Loss: 1.5108... Generator Loss: 0.8107\n",
+      "Epoch 1/1... Discriminator Loss: 1.3479... Generator Loss: 0.8114\n",
+      "Epoch 1/1... Discriminator Loss: 1.2299... Generator Loss: 0.8803\n",
+      "Epoch 1/1... Discriminator Loss: 1.2651... Generator Loss: 0.7570\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nspU9Yv8vXgo3LqcZTikAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f565955d4a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.4447... Generator Loss: 0.6179\n",
+      "Epoch 1/1... Discriminator Loss: 1.3410... Generator Loss: 0.9721\n",
+      "Epoch 1/1... Discriminator Loss: 1.2921... Generator Loss: 1.2052\n",
+      "Epoch 1/1... Discriminator Loss: 1.3283... Generator Loss: 0.9427\n",
+      "Epoch 1/1... Discriminator Loss: 1.3041... Generator Loss: 0.7806\n",
+      "Epoch 1/1... Discriminator Loss: 1.4502... Generator Loss: 0.6852\n",
+      "Epoch 1/1... Discriminator Loss: 1.3364... Generator Loss: 1.0475\n",
+      "Epoch 1/1... Discriminator Loss: 1.3068... Generator Loss: 0.9476\n",
+      "Epoch 1/1... Discriminator Loss: 1.2938... Generator Loss: 0.9685\n",
+      "Epoch 1/1... Discriminator Loss: 1.4735... Generator Loss: 0.5542\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56995bb860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3093... Generator Loss: 0.8099\n",
+      "Epoch 1/1... Discriminator Loss: 1.3734... Generator Loss: 0.6992\n",
+      "Epoch 1/1... Discriminator Loss: 1.4837... Generator Loss: 0.5886\n",
+      "Epoch 1/1... Discriminator Loss: 1.3110... Generator Loss: 0.9667\n",
+      "Epoch 1/1... Discriminator Loss: 1.4147... Generator Loss: 0.9200\n",
+      "Epoch 1/1... Discriminator Loss: 1.3674... Generator Loss: 0.6883\n",
+      "Epoch 1/1... Discriminator Loss: 1.2852... Generator Loss: 1.0330\n",
+      "Epoch 1/1... Discriminator Loss: 1.2894... Generator Loss: 0.9344\n",
+      "Epoch 1/1... Discriminator Loss: 1.1895... Generator Loss: 0.8798\n",
+      "Epoch 1/1... Discriminator Loss: 1.4130... Generator Loss: 0.7914\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5658e986a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3060... Generator Loss: 0.7511\n",
+      "Epoch 1/1... Discriminator Loss: 1.4302... Generator Loss: 0.6394\n",
+      "Epoch 1/1... Discriminator Loss: 1.3354... Generator Loss: 0.7412\n",
+      "Epoch 1/1... Discriminator Loss: 1.5316... Generator Loss: 0.4699\n",
+      "Epoch 1/1... Discriminator Loss: 1.3360... Generator Loss: 0.7564\n",
+      "Epoch 1/1... Discriminator Loss: 1.3748... Generator Loss: 0.9328\n",
+      "Epoch 1/1... Discriminator Loss: 1.3011... Generator Loss: 0.7149\n",
+      "Epoch 1/1... Discriminator Loss: 1.3411... Generator Loss: 0.7330\n",
+      "Epoch 1/1... Discriminator Loss: 1.3431... Generator Loss: 0.7667\n",
+      "Epoch 1/1... Discriminator Loss: 1.5367... Generator Loss: 0.6442\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCmtb29rusf8Xp4rS61SqtBEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f56586897b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3587... Generator Loss: 0.7105\n",
+      "Epoch 1/1... Discriminator Loss: 1.4629... Generator Loss: 0.6467\n",
+      "Epoch 1/1... Discriminator Loss: 1.5333... Generator Loss: 0.8289\n",
+      "Epoch 1/1... Discriminator Loss: 1.4835... Generator Loss: 0.4824\n",
+      "Epoch 1/1... Discriminator Loss: 1.3330... Generator Loss: 0.9264\n",
+      "Epoch 1/1... Discriminator Loss: 1.4501... Generator Loss: 0.7557\n",
+      "Epoch 1/1... Discriminator Loss: 1.3579... Generator Loss: 0.8731\n",
+      "Epoch 1/1... Discriminator Loss: 1.4515... Generator Loss: 0.9340\n",
+      "Epoch 1/1... Discriminator Loss: 1.3060... Generator Loss: 0.8987\n",
+      "Epoch 1/1... Discriminator Loss: 1.3188... Generator Loss: 0.6393\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5658155c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.4152... Generator Loss: 0.6163\n",
+      "Epoch 1/1... Discriminator Loss: 1.4929... Generator Loss: 0.6064\n",
+      "Epoch 1/1... Discriminator Loss: 1.5524... Generator Loss: 0.4964\n",
+      "Epoch 1/1... Discriminator Loss: 1.4786... Generator Loss: 0.7433\n",
+      "Epoch 1/1... Discriminator Loss: 1.4437... Generator Loss: 0.6628\n",
+      "Epoch 1/1... Discriminator Loss: 1.4178... Generator Loss: 0.7201\n",
+      "Epoch 1/1... Discriminator Loss: 1.2742... Generator Loss: 0.8131\n",
+      "Epoch 1/1... Discriminator Loss: 1.3555... Generator Loss: 0.7472\n",
+      "Epoch 1/1... Discriminator Loss: 1.2990... Generator Loss: 0.8521\n",
+      "Epoch 1/1... Discriminator Loss: 0.8481... Generator Loss: 1.3975\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659301a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3439... Generator Loss: 0.6907\n",
+      "Epoch 1/1... Discriminator Loss: 1.3787... Generator Loss: 0.7232\n",
+      "Epoch 1/1... Discriminator Loss: 1.3393... Generator Loss: 1.2362\n",
+      "Epoch 1/1... Discriminator Loss: 1.4625... Generator Loss: 0.6389\n",
+      "Epoch 1/1... Discriminator Loss: 1.2682... Generator Loss: 0.9903\n",
+      "Epoch 1/1... Discriminator Loss: 1.3393... Generator Loss: 0.6646\n",
+      "Epoch 1/1... Discriminator Loss: 1.5688... Generator Loss: 0.5454\n",
+      "Epoch 1/1... Discriminator Loss: 1.1573... Generator Loss: 1.0297\n",
+      "Epoch 1/1... Discriminator Loss: 1.1199... Generator Loss: 1.0091\n",
+      "Epoch 1/1... Discriminator Loss: 1.0635... Generator Loss: 1.0411\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZSl3yHJTWMpSlnKHLDeFpSxlKXfI/w/9ljCbs+pmggAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5659d48588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.9337... Generator Loss: 0.2622\n",
+      "Epoch 1/1... Discriminator Loss: 1.2560... Generator Loss: 0.8398\n",
+      "Epoch 1/1... Discriminator Loss: 1.2232... Generator Loss: 0.7815\n",
+      "Epoch 1/1... Discriminator Loss: 1.4801... Generator Loss: 0.6008\n",
+      "Epoch 1/1... Discriminator Loss: 1.3070... Generator Loss: 0.6860\n",
+      "Epoch 1/1... Discriminator Loss: 1.3470... Generator Loss: 0.7634\n",
+      "Epoch 1/1... Discriminator Loss: 1.5236... Generator Loss: 0.6591\n",
+      "Epoch 1/1... Discriminator Loss: 1.3827... Generator Loss: 0.9191\n",
+      "Epoch 1/1... Discriminator Loss: 1.2797... Generator Loss: 0.8605\n",
+      "Epoch 1/1... Discriminator Loss: 1.3069... Generator Loss: 1.0828\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f564bec70f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.4493... Generator Loss: 0.9787\n",
+      "Epoch 1/1... Discriminator Loss: 1.3226... Generator Loss: 0.8376\n",
+      "Epoch 1/1... Discriminator Loss: 1.3657... Generator Loss: 0.7043\n",
+      "Epoch 1/1... Discriminator Loss: 2.0819... Generator Loss: 0.2141\n",
+      "Epoch 1/1... Discriminator Loss: 1.4158... Generator Loss: 0.5562\n",
+      "Epoch 1/1... Discriminator Loss: 1.2893... Generator Loss: 0.7726\n",
+      "Epoch 1/1... Discriminator Loss: 1.3175... Generator Loss: 0.7764\n",
+      "Epoch 1/1... Discriminator Loss: 1.3040... Generator Loss: 0.9346\n",
+      "Epoch 1/1... Discriminator Loss: 1.5212... Generator Loss: 0.6124\n",
+      "Epoch 1/1... Discriminator Loss: 1.3328... Generator Loss: 1.0946\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9v8C6JUakGWNzJUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f564ab3c748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3074... Generator Loss: 0.8244\n",
+      "Epoch 1/1... Discriminator Loss: 1.5227... Generator Loss: 0.6513\n",
+      "Epoch 1/1... Discriminator Loss: 1.3824... Generator Loss: 0.5856\n",
+      "Epoch 1/1... Discriminator Loss: 1.5399... Generator Loss: 0.5881\n",
+      "Epoch 1/1... Discriminator Loss: 1.3308... Generator Loss: 0.8962\n",
+      "Epoch 1/1... Discriminator Loss: 1.4280... Generator Loss: 0.7422\n",
+      "Epoch 1/1... Discriminator Loss: 1.2792... Generator Loss: 0.9444\n",
+      "Epoch 1/1... Discriminator Loss: 1.3914... Generator Loss: 0.9070\n",
+      "Epoch 1/1... Discriminator Loss: 1.3808... Generator Loss: 0.7781\n",
+      "Epoch 1/1... Discriminator Loss: 1.4229... Generator Loss: 0.7619\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f564a5d92b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3483... Generator Loss: 1.0495\n",
+      "Epoch 1/1... Discriminator Loss: 1.3219... Generator Loss: 0.8450\n",
+      "Epoch 1/1... Discriminator Loss: 1.3759... Generator Loss: 0.8250\n",
+      "Epoch 1/1... Discriminator Loss: 1.4419... Generator Loss: 0.6523\n",
+      "Epoch 1/1... Discriminator Loss: 1.2803... Generator Loss: 0.8917\n",
+      "Epoch 1/1... Discriminator Loss: 1.3013... Generator Loss: 0.7574\n",
+      "Epoch 1/1... Discriminator Loss: 1.3968... Generator Loss: 0.7108\n",
+      "Epoch 1/1... Discriminator Loss: 1.3129... Generator Loss: 0.9058\n",
+      "Epoch 1/1... Discriminator Loss: 1.4153... Generator Loss: 0.8783\n",
+      "Epoch 1/1... Discriminator Loss: 1.3831... Generator Loss: 0.7992\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f564a43f2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.4230... Generator Loss: 0.5748\n",
+      "Epoch 1/1... Discriminator Loss: 1.3313... Generator Loss: 0.9232\n",
+      "Epoch 1/1... Discriminator Loss: 1.2939... Generator Loss: 0.8525\n",
+      "Epoch 1/1... Discriminator Loss: 1.3392... Generator Loss: 1.0006\n",
+      "Epoch 1/1... Discriminator Loss: 1.4827... Generator Loss: 0.5120\n",
+      "Epoch 1/1... Discriminator Loss: 1.2983... Generator Loss: 0.8530\n",
+      "Epoch 1/1... Discriminator Loss: 1.4825... Generator Loss: 0.6011\n",
+      "Epoch 1/1... Discriminator Loss: 1.3184... Generator Loss: 0.8947\n",
+      "Epoch 1/1... Discriminator Loss: 1.2802... Generator Loss: 0.8693\n",
+      "Epoch 1/1... Discriminator Loss: 1.4412... Generator Loss: 0.7739\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/m8wDChW554/oAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f564a1a89b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.3356... Generator Loss: 0.7553\n",
+      "Epoch 1/1... Discriminator Loss: 1.4345... Generator Loss: 0.7667\n",
+      "Epoch 1/1... Discriminator Loss: 1.3151... Generator Loss: 0.8760\n",
+      "Epoch 1/1... Discriminator Loss: 1.4366... Generator Loss: 0.7039\n",
+      "Epoch 1/1... Discriminator Loss: 1.3346... Generator Loss: 0.9874\n",
+      "Epoch 1/1... Discriminator Loss: 1.3435... Generator Loss: 0.7690\n",
+      "Epoch 1/1... Discriminator Loss: 1.3227... Generator Loss: 0.7796\n",
+      "Epoch 1/1... Discriminator Loss: 1.4459... Generator Loss: 0.7080\n",
+      "Epoch 1/1... Discriminator Loss: 1.3379... Generator Loss: 0.6979\n",
+      "Epoch 1/1... Discriminator Loss: 1.6802... Generator Loss: 0.5041\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\na1vb2h6y9aKwtrWt7SH7fwErqDr3TXcUMwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f564a43f0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.2873... Generator Loss: 0.8386\n",
+      "Epoch 1/1... Discriminator Loss: 1.3842... Generator Loss: 0.7583\n",
+      "Epoch 1/1... Discriminator Loss: 1.4189... Generator Loss: 0.7991\n",
+      "Epoch 1/1... Discriminator Loss: 1.3378... Generator Loss: 0.8856\n",
+      "Epoch 1/1... Discriminator Loss: 1.3755... Generator Loss: 0.8002\n",
+      "Epoch 1/1... Discriminator Loss: 1.3075... Generator Loss: 0.8532\n",
+      "Epoch 1/1... Discriminator Loss: 1.3158... Generator Loss: 0.9599\n",
+      "Epoch 1/1... Discriminator Loss: 1.4263... Generator Loss: 0.6772\n",
+      "Epoch 1/1... Discriminator Loss: 1.3762... Generator Loss: 0.8854\n",
+      "Epoch 1/1... Discriminator Loss: 1.4065... Generator Loss: 0.7769\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2MYesf8XWgrDLYVpkJAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f564bd165f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1... Discriminator Loss: 1.4026... Generator Loss: 0.5969\n",
+      "Epoch 1/1... Discriminator Loss: 1.2856... Generator Loss: 0.8660\n",
+      "Epoch 1/1... Discriminator Loss: 1.3118... Generator Loss: 0.7520\n",
+      "Epoch 1/1... Discriminator Loss: 1.2754... Generator Loss: 0.7607\n",
+      "Epoch 1/1... Discriminator Loss: 1.3532... Generator Loss: 1.0381\n",
+      "Epoch 1/1... Discriminator Loss: 1.3217... Generator Loss: 0.8141\n"
+     ]
+    }
+   ],
+   "source": [
+    "batch_size = 64\n",
+    "z_dim = 100\n",
+    "learning_rate = 0.001\n",
+    "beta1 = 0.5\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
+    "\"\"\"\n",
+    "epochs = 1\n",
+    "\n",
+    "celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))\n",
+    "with tf.Graph().as_default():\n",
+    "    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,\n",
+    "          celeba_dataset.shape, celeba_dataset.image_mode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 提交项目\n",
+    "提交本项目前，确保运行所有 cells 后保存该文件。\n",
+    "\n",
+    "保存该文件为 \"dlnd_face_generation.ipynb\"， 并另存为 HTML 格式 \"File\" -> \"Download as\"。提交项目时请附带 \"helper.py\" 和 \"problem_unittests.py\" 文件。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/face-generation/dlnd_face_generation.py b/face-generation/dlnd_face_generation.py
new file mode 100644
index 0000000..55660c5
--- /dev/null
+++ b/face-generation/dlnd_face_generation.py
@@ -0,0 +1,459 @@
+
+# coding: utf-8
+
+# # 人脸生成（Face Generation）
+# 在该项目中，你将使用生成式对抗网络（Generative Adversarial Nets）来生成新的人脸图像。
+# ### 获取数据
+# 该项目将使用以下数据集：
+# - MNIST
+# - CelebA
+# 
+# 由于 CelebA 数据集比较复杂，而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型，以让你更快的评估所建立模型的性能。
+# 
+# 如果你在使用 [FloydHub](https://www.floydhub.com/), 请将 `data_dir` 设置为 "/input" 并使用 [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) "R5KrjnANiKVhLWAkpXhNBe".
+
+# In[1]:
+
+#data_dir = './data'
+
+# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
+data_dir = '/input'
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import helper
+
+helper.download_extract('mnist', data_dir)
+helper.download_extract('celeba', data_dir)
+
+
+# ## 探索数据（Explore the Data）
+# ### MNIST
+# [MNIST](http://yann.lecun.com/exdb/mnist/) 是一个手写数字的图像数据集。你可以更改 `show_n_images` 探索此数据集。
+
+# In[2]:
+
+show_n_images = 25
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+get_ipython().magic('matplotlib inline')
+import os
+from glob import glob
+from matplotlib import pyplot
+
+mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
+pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
+
+
+# ### CelebA
+# [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸，不会用不到相关说明。你可以更改 `show_n_images` 探索此数据集。
+
+# In[3]:
+
+show_n_images = 25
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
+pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
+
+
+# ## 预处理数据（Preprocess the Data）
+# 由于该项目的重点是建立 GANs 模型，我们将为你预处理数据。
+# 
+# 经过数据预处理，MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分，然后调整到 28x28 维度。
+# 
+# MNIST 数据集中的图像是单[通道](https://en.wikipedia.org/wiki/Channel_(digital_image%29)的黑白图像，CelebA 数据集中的图像是 [三通道的 RGB 彩色图像](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images)。
+# 
+# ## 建立神经网络（Build the Neural Network）
+# 你将通过部署以下函数来建立 GANs 的主要组成部分:
+# - `model_inputs`
+# - `discriminator`
+# - `generator`
+# - `model_loss`
+# - `model_opt`
+# - `train`
+# 
+# ### 检查 TensorFlow 版本并获取 GPU 型号
+# 检查你是否使用正确的 TensorFlow 版本，并获取 GPU 型号
+
+# In[4]:
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+from distutils.version import LooseVersion
+import warnings
+import tensorflow as tf
+
+# Check TensorFlow Version
+assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
+print('TensorFlow Version: {}'.format(tf.__version__))
+
+# Check for a GPU
+if not tf.test.gpu_device_name():
+    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
+else:
+    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
+
+
+# ### 输入（Input）
+# 部署 `model_inputs` 函数以创建用于神经网络的 [占位符 (TF Placeholders)](https://www.tensorflow.org/versions/r0.11/api_docs/python/io_ops/placeholders)。请创建以下占位符：
+# - 输入图像占位符: 使用 `image_width`，`image_height` 和 `image_channels` 设置为 rank 4。
+# - 输入 Z 占位符: 设置为 rank 2，并命名为 `z_dim`。
+# - 学习速率占位符: 设置为 rank 0。
+# 
+# 返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。
+# 
+
+# In[5]:
+
+import problem_unittests as tests
+
+def model_inputs(image_width, image_height, image_channels, z_dim):
+    """
+    Create the model inputs
+    :param image_width: The input image width
+    :param image_height: The input image height
+    :param image_channels: The number of image channels
+    :param z_dim: The dimension of Z
+    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
+    """
+    # TODO: Implement Function
+    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
+    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
+    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
+
+    return input_real, input_z, learning_rate
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_model_inputs(model_inputs)
+
+
+# ### 辨别器（Discriminator）
+# 部署 `discriminator` 函数创建辨别器神经网络以辨别 `images`。该函数应能够重复使用神经网络中的各种变量。 在 [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。 
+# 
+# 该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。
+
+# In[6]:
+
+def discriminator(images, reuse=False):
+    """
+    Create the discriminator network
+    :param image: Tensor of input image(s)
+    :param reuse: Boolean if the weights should be reused
+    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
+    """
+    # TODO: Implement Function
+    with tf.variable_scope('discriminator', reuse=reuse):
+        # alpha is the param for leaky relu
+        alpha = 0.2
+        
+        # Input layer is 28x28x3
+        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
+        relu1 = tf.maximum(alpha * x1, x1)
+        # 14x14x64 now
+        
+        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
+        bn2 = tf.layers.batch_normalization(x2, training=True)
+        relu2 = tf.maximum(alpha * bn2, bn2)
+        # 7x7x128 now
+        
+        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
+        bn3 = tf.layers.batch_normalization(x3, training=True)
+        relu3 = tf.maximum(alpha * bn3, bn3)
+        # 4x4x256 now
+
+        # Flatten it
+        flat = tf.reshape(relu3, (-1, 4*4*256))
+        logits = tf.layers.dense(flat, 1)
+        out = tf.sigmoid(logits)
+        
+        return out, logits
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_discriminator(discriminator, tf)
+
+
+# ### 生成器（Generator）
+# 部署 `generator` 函数以使用 `z` 生成图像。该函数应能够重复使用神经网络中的各种变量。
+# 在 [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 中使用 "generator" 的变量空间名来重复使用该函数中的变量。 
+# 
+# 该函数应返回所生成的 28 x 28 x `out_channel_dim` 维度图像。
+
+# In[7]:
+
+def generator(z, out_channel_dim, is_train=True):
+    """
+    Create the generator network
+    :param z: Input z
+    :param out_channel_dim: The number of channels in the output image
+    :param is_train: Boolean if generator is being used for training
+    :return: The tensor output of the generator
+    """
+    # TODO: Implement Function
+    with tf.variable_scope('generator', reuse=not is_train):
+        # alpha is the param for leaky relu
+        alpha = 0.2
+        
+        # First fully connected layer
+        x1 = tf.layers.dense(z, 7 * 7 * 512, activation=None)
+        x1 = tf.reshape(x1, (-1, 7, 7, 512)) # Reshape it to start the convolutional stack
+        x1 = tf.layers.batch_normalization(x1, training=is_train)
+        x1 = tf.maximum(alpha * x1, x1)
+        # 7x7x512 now
+        
+        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
+        x2 = tf.layers.batch_normalization(x2, training=is_train)
+        x2 = tf.maximum(alpha * x2, x2)
+        # 14x14x256 now
+        
+        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
+        x3 = tf.layers.batch_normalization(x3, training=is_train)
+        x3 = tf.maximum(alpha * x3, x3)
+        # 28x28x128 now
+        
+        # Output layer
+        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')
+        out = tf.tanh(logits)
+        # 28x28x3 now
+        
+        return out
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_generator(generator, tf)
+
+
+# ### 损失函数（Loss）
+# 部署 `model_loss` 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。
+# 
+# 使用你已实现的函数：
+# - `discriminator(images, reuse=False)`
+# - `generator(z, out_channel_dim, is_train=True)`
+
+# In[8]:
+
+def model_loss(input_real, input_z, out_channel_dim):
+    """
+    Get the loss for the discriminator and generator
+    :param input_real: Images from the real dataset
+    :param input_z: Z input
+    :param out_channel_dim: The number of channels in the output image
+    :return: A tuple of (discriminator loss, generator loss)
+    """
+    # TODO: Implement Function
+    # Generator network here
+    g_model = generator(input_z, out_channel_dim)
+
+    # Disriminator network here
+    d_model_real, d_logits_real = discriminator(input_real)
+    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
+    
+    # Calculate losses
+    smooth = 0.1
+    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * (1 - smooth)))
+
+    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
+
+    d_loss = d_loss_real + d_loss_fake
+
+    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
+
+    return d_loss, g_loss
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_model_loss(model_loss)
+
+
+# ### 优化（Optimization）
+# 部署 `model_opt` 函数实现对 GANs 的优化。使用 [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) 获取可训练的所有变量。通过变量空间名 `discriminator` 和 `generator` 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。
+
+# In[9]:
+
+def model_opt(d_loss, g_loss, learning_rate, beta1):
+    """
+    Get optimization operations
+    :param d_loss: Discriminator loss Tensor
+    :param g_loss: Generator loss Tensor
+    :param learning_rate: Learning Rate Placeholder
+    :param beta1: The exponential decay rate for the 1st moment in the optimizer
+    :return: A tuple of (discriminator training operation, generator training operation)
+    """
+    # TODO: Implement Function
+    # Get weights and bias to update
+    t_vars = tf.trainable_variables()
+    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
+    g_vars = [var for var in t_vars if var.name.startswith('generator')]
+
+    # Optimize
+    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
+        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
+        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
+
+    return d_train_opt, g_train_opt
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_model_opt(model_opt, tf)
+
+
+# ## 训练神经网络（Neural Network Training）
+# ### 输出显示
+# 使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出，这会帮你评估 GANs 模型的训练程度。
+
+# In[10]:
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import numpy as np
+
+def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
+    """
+    Show example output for the generator
+    :param sess: TensorFlow session
+    :param n_images: Number of Images to display
+    :param input_z: Input Z Tensor
+    :param out_channel_dim: The number of channels in the output image
+    :param image_mode: The mode to use for images ("RGB" or "L")
+    """
+    cmap = None if image_mode == 'RGB' else 'gray'
+    z_dim = input_z.get_shape().as_list()[-1]
+    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
+
+    samples = sess.run(
+        generator(input_z, out_channel_dim, False),
+        feed_dict={input_z: example_z})
+
+    images_grid = helper.images_square_grid(samples, image_mode)
+    pyplot.imshow(images_grid, cmap=cmap)
+    pyplot.show()
+
+
+# ### 训练
+# 部署 `train` 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数：
+# - `model_inputs(image_width, image_height, image_channels, z_dim)`
+# - `model_loss(input_real, input_z, out_channel_dim)`
+# - `model_opt(d_loss, g_loss, learning_rate, beta1)`
+# 
+# 使用 `show_generator_output` 函数显示 `generator` 在训练过程中的输出。
+# 
+# **注意**：在每个批次 (batch) 中运行 `show_generator_output` 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 `generator` 的输出。 
+
+# In[11]:
+
+def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
+    """
+    Train the GAN
+    :param epoch_count: Number of epochs
+    :param batch_size: Batch Size
+    :param z_dim: Z dimension
+    :param learning_rate: Learning Rate
+    :param beta1: The exponential decay rate for the 1st moment in the optimizer
+    :param get_batches: Function to get batches
+    :param data_shape: Shape of the data
+    :param data_image_mode: The image mode to use for images ("RGB" or "L")
+    """
+    # TODO: Build Model
+    _, image_width, image_height, image_channels = data_shape
+    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
+    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
+    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
+    
+    steps = 0
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        for epoch_i in range(epoch_count):
+            for batch_images in get_batches(batch_size):
+                # TODO: Train Model
+                steps += 1
+                batch_images *= 2
+                
+                # Sample random noise for G
+                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
+
+                # Run optimizers
+                _ = sess.run(d_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
+                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
+
+                if steps % 10 == 0:
+                    # At the end of each epoch, get the losses and print them out
+                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
+                    train_loss_g = g_loss.eval({input_z: batch_z})
+
+                    print("Epoch {}/{}...".format(epoch_i + 1, epoch_count), 
+                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
+                          "Generator Loss: {:.4f}".format(train_loss_g))
+                
+                if steps % 100 == 0:
+                    gen_samples = sess.run(generator(input_z, image_channels, is_train=False), feed_dict={input_z: batch_z})
+                    _ = show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
+
+
+# ### MNIST
+# 在 MNIST 上测试你的 GANs 模型。经过 2 次迭代，GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失，或接近 0。
+
+# In[12]:
+
+batch_size = 64
+z_dim = 100
+learning_rate = 0.0002
+beta1 = 0.5
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+epochs = 2
+
+mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
+with tf.Graph().as_default():
+    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
+          mnist_dataset.shape, mnist_dataset.image_mode)
+
+
+# ### CelebA
+# 在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代，或者当 GANs 开始产生真实人脸图像时停止它。
+
+# In[13]:
+
+batch_size = 64
+z_dim = 100
+learning_rate = 0.001
+beta1 = 0.5
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+epochs = 1
+
+celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
+with tf.Graph().as_default():
+    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
+          celeba_dataset.shape, celeba_dataset.image_mode)
+
+
+# ### 提交项目
+# 提交本项目前，确保运行所有 cells 后保存该文件。
+# 
+# 保存该文件为 "dlnd_face_generation.ipynb"， 并另存为 HTML 格式 "File" -> "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。